{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "954d4333",
   "metadata": {},
   "source": [
    "# Lets-Plot Cheatbook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0644352",
   "metadata": {},
   "source": [
    "## Contents\n",
    "\n",
    "- [Preparation](#preparation)\n",
    "  - [Imports](#preparation-imports)\n",
    "  - [Data](#preparation-data)\n",
    "- [Basics](#basics)\n",
    "- [Features](#features)\n",
    "  - [Interactive Maps](#features-interactive_maps)\n",
    "  - [Customizable Tooltips](#features-customizable_tooltips)\n",
    "  - [Formatting](#features-formatting)\n",
    "  - [Sampling](#features-sampling)\n",
    "  - [Images](#features-images)\n",
    "  - [Correlation Plot](#features-correlation_plot)\n",
    "  - [Joint Plot](#features-joint_plot)\n",
    "  - [Residual Plot](#features-residual_plot)\n",
    "- [Geoms](#geoms)\n",
    "  - [Graphical Primitives](#geoms-graphical_primitives)\n",
    "    - [Line Segments](#geoms-graphical_primitives-line_segments)\n",
    "  - [One Variable](#geoms-one_variable)\n",
    "    - [Continuous](#geoms-one_variable-continuous)\n",
    "    - [Discrete](#geoms-one_variable-discrete)\n",
    "  - [Two Variables](#geoms-two_variables)\n",
    "    - [Both Continuous](#geoms-two_variables-both_continuous)\n",
    "    - [One Discrete, One Continuous](#geoms-two_variables-one_discrete_one_continuous)\n",
    "    - [Both Discrete](#geoms-two_variables-both_discrete)\n",
    "    - [Continuous Bivariate Distribution](#geoms-two_variables-continuous_bivariate_distribution)\n",
    "    - [Continuous Function](#geoms-two_variables-continuous_function)\n",
    "    - [Visualizing Error](#geoms-two_variables-visualizing_error)\n",
    "    - [Maps](#geoms-two_variables-maps)\n",
    "  - [Three Variables](#geoms-three_variables)\n",
    "- [Stats](#stats)\n",
    "  - [Identity](#stats-identity)\n",
    "  - [One Variable](#stats-one_variable)\n",
    "    - [Continuous](#stats-one_variable-continuous)\n",
    "    - [Discrete](#stats-one_variable-discrete)\n",
    "  - [Two Variables](#stats-two_variables)\n",
    "    - [Both Continuous](#stats-two_variables-both_continuous)\n",
    "    - [One Discrete, One Continuous](#stats-two_variables-one_discrete_one_continuous)\n",
    "    - [Both Discrete](#stats-two_variables-both_discrete)\n",
    "    - [Continuous Bivariate Distribution](#stats-two_variables-continuous_bivariate_distribution)\n",
    "  - [Three Variables](#stats-three_variables)\n",
    "- [Scales](#scales)\n",
    "  - [General Purpose Scales](#scales-general_purpose_scales)\n",
    "  - [X & Y Location Scales](#scales-x_and_y_location_scales)\n",
    "  - [Color & Fill Scales](#scales-color_and_fill_scales)\n",
    "    - [Continuous](#scales-color_and_fill_scales-continuous)\n",
    "    - [Discrete](#scales-color_and_fill_scales-discrete)\n",
    "  - [Size & Shape Scales](#scales-size_and_shape_scales)\n",
    "- [Coordinate Systems](#coordinate_systems)\n",
    "- [Position Adjustments](#position_adjustments)\n",
    "- [Themes](#themes)\n",
    "- [Flavors](#flavors)\n",
    "- [Faceting](#faceting)\n",
    "- [Labels & Legends](#labels_and_legends)\n",
    "- [Zooming](#zooming)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aee91a3",
   "metadata": {},
   "source": [
    "## Preparation <a class=\"anchor\" id=\"preparation\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2068059d",
   "metadata": {},
   "source": [
    "### Imports <a class=\"anchor\" id=\"preparation-imports\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9cc7f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:57.734999Z",
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     "shell.execute_reply": "2024-04-17T07:29:58.223668Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <div id=\"yzE4pb\"></div>\n",
       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "                if(!window.letsPlotCallQueue) {\n",
       "                    window.letsPlotCallQueue = [];\n",
       "                }; \n",
       "                window.letsPlotCall = function(f) {\n",
       "                    window.letsPlotCallQueue.push(f);\n",
       "                };\n",
       "                (function() {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    script.type = \"text/javascript\";\n",
       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.3.1/js-package/distr/lets-plot.min.js\";\n",
       "                    script.onload = function() {\n",
       "                        window.letsPlotCall = function(f) {f();};\n",
       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        \n",
       "                    };\n",
       "                    script.onerror = function(event) {\n",
       "                        window.letsPlotCall = function(f) {};    // noop\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
       "                        document.getElementById(\"yzE4pb\").appendChild(div);\n",
       "                    };\n",
       "                    var e = document.getElementById(\"yzE4pb\");\n",
       "                    e.appendChild(script);\n",
       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from io import BytesIO\n",
    "import requests\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "from scipy.stats import multivariate_normal\n",
    "\n",
    "from lets_plot import *\n",
    "from lets_plot.mapping import as_discrete\n",
    "from lets_plot.bistro import *\n",
    "from lets_plot.geo_data import *\n",
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629e4fde",
   "metadata": {},
   "source": [
    "### Data <a class=\"anchor\" id=\"preparation-data\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2eede300",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-04-17T07:29:58.369288Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>cty</th>\n",
       "      <th>hwy</th>\n",
       "      <th>fl</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 manufacturer model  displ  year  cyl       trans drv  cty  hwy  \\\n",
       "0           1         audi    a4    1.8  1999    4    auto(l5)   f   18   29   \n",
       "1           2         audi    a4    1.8  1999    4  manual(m5)   f   21   29   \n",
       "2           3         audi    a4    2.0  2008    4  manual(m6)   f   20   31   \n",
       "\n",
       "  fl    class  \n",
       "0  p  compact  \n",
       "1  p  compact  \n",
       "2  p  compact  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')\n",
    "mpg_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7bcdb4ff",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-04-17T07:29:58.374653Z"
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   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>min</th>\n",
       "      <th>median</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2seater</td>\n",
       "      <td>23</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>compact</td>\n",
       "      <td>23</td>\n",
       "      <td>27.0</td>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>midsize</td>\n",
       "      <td>23</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32</td>\n",
       "      <td>41</td>\n",
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      ],
      "text/plain": [
       "     class  min  median  max  count\n",
       "0  2seater   23    25.0   26      5\n",
       "1  compact   23    27.0   44     47\n",
       "2  midsize   23    27.0   32     41"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "class_df = mpg_df.groupby('class').hwy.agg(['min', 'median', 'max', 'count']).reset_index()\n",
    "class_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "64238e52",
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     "shell.execute_reply": "2024-04-17T07:29:58.382166Z"
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   "outputs": [
    {
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       "      <th>cty</th>\n",
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       "      <td>9</td>\n",
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       "      <td>e</td>\n",
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       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>[e, p]</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>r</td>\n",
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      "text/plain": [
       "   cty  hwy      fl\n",
       "0    9   12       e\n",
       "1   11   14  [e, p]\n",
       "2   11   15       r"
      ]
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   ],
   "source": [
    "fl_df = mpg_df.groupby(['cty', 'hwy']).fl.agg(pd.Series.mode).to_frame('fl').reset_index()\n",
    "fl_df.head(3)"
   ]
  },
  {
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       "      <td>506.7</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1967-09-01</td>\n",
       "      <td>515.6</td>\n",
       "      <td>199113.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2958</td>\n",
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      "text/plain": [
       "   Unnamed: 0       date    pce       pop  psavert  uempmed  unemploy\n",
       "0           1 1967-07-01  506.7  198712.0     12.6      4.5      2944\n",
       "1           2 1967-08-01  509.8  198911.0     12.6      4.7      2945\n",
       "2           3 1967-09-01  515.6  199113.0     11.9      4.6      2958"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "economics_df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/economics.csv', parse_dates=['date'])\n",
    "economics_df.head(3)"
   ]
  },
  {
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   "execution_count": 6,
   "id": "fcc102ee",
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       "      <th>PID</th>\n",
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       "      <th>state</th>\n",
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       "      <th>poptotal</th>\n",
       "      <th>popdensity</th>\n",
       "      <th>popwhite</th>\n",
       "      <th>popblack</th>\n",
       "      <th>popamerindian</th>\n",
       "      <th>...</th>\n",
       "      <th>percollege</th>\n",
       "      <th>percprof</th>\n",
       "      <th>poppovertyknown</th>\n",
       "      <th>percpovertyknown</th>\n",
       "      <th>percbelowpoverty</th>\n",
       "      <th>percchildbelowpovert</th>\n",
       "      <th>percadultpoverty</th>\n",
       "      <th>percelderlypoverty</th>\n",
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       "      <td>1</td>\n",
       "      <td>561</td>\n",
       "      <td>ADAMS</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.052</td>\n",
       "      <td>66090</td>\n",
       "      <td>1270.961540</td>\n",
       "      <td>63917</td>\n",
       "      <td>1702</td>\n",
       "      <td>98</td>\n",
       "      <td>...</td>\n",
       "      <td>19.631392</td>\n",
       "      <td>4.355859</td>\n",
       "      <td>63628</td>\n",
       "      <td>96.274777</td>\n",
       "      <td>13.151443</td>\n",
       "      <td>18.011717</td>\n",
       "      <td>11.009776</td>\n",
       "      <td>12.443812</td>\n",
       "      <td>0</td>\n",
       "      <td>AAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>562</td>\n",
       "      <td>ALEXANDER</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.014</td>\n",
       "      <td>10626</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>7054</td>\n",
       "      <td>3496</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>11.243308</td>\n",
       "      <td>2.870315</td>\n",
       "      <td>10529</td>\n",
       "      <td>99.087145</td>\n",
       "      <td>32.244278</td>\n",
       "      <td>45.826514</td>\n",
       "      <td>27.385647</td>\n",
       "      <td>25.228976</td>\n",
       "      <td>0</td>\n",
       "      <td>LHR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>563</td>\n",
       "      <td>BOND</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.022</td>\n",
       "      <td>14991</td>\n",
       "      <td>681.409091</td>\n",
       "      <td>14477</td>\n",
       "      <td>429</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>17.033819</td>\n",
       "      <td>4.488572</td>\n",
       "      <td>14235</td>\n",
       "      <td>94.956974</td>\n",
       "      <td>12.068844</td>\n",
       "      <td>14.036061</td>\n",
       "      <td>10.852090</td>\n",
       "      <td>12.697410</td>\n",
       "      <td>0</td>\n",
       "      <td>AAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>564</td>\n",
       "      <td>BOONE</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.017</td>\n",
       "      <td>30806</td>\n",
       "      <td>1812.117650</td>\n",
       "      <td>29344</td>\n",
       "      <td>127</td>\n",
       "      <td>46</td>\n",
       "      <td>...</td>\n",
       "      <td>17.278954</td>\n",
       "      <td>4.197800</td>\n",
       "      <td>30337</td>\n",
       "      <td>98.477569</td>\n",
       "      <td>7.209019</td>\n",
       "      <td>11.179536</td>\n",
       "      <td>5.536013</td>\n",
       "      <td>6.217047</td>\n",
       "      <td>1</td>\n",
       "      <td>ALU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>565</td>\n",
       "      <td>BROWN</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.018</td>\n",
       "      <td>5836</td>\n",
       "      <td>324.222222</td>\n",
       "      <td>5264</td>\n",
       "      <td>547</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>14.475999</td>\n",
       "      <td>3.367680</td>\n",
       "      <td>4815</td>\n",
       "      <td>82.505140</td>\n",
       "      <td>13.520249</td>\n",
       "      <td>13.022889</td>\n",
       "      <td>11.143211</td>\n",
       "      <td>19.200000</td>\n",
       "      <td>0</td>\n",
       "      <td>AAR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  PID     county state   area  poptotal   popdensity  popwhite  \\\n",
       "0           1  561      ADAMS    IL  0.052     66090  1270.961540     63917   \n",
       "1           2  562  ALEXANDER    IL  0.014     10626   759.000000      7054   \n",
       "2           3  563       BOND    IL  0.022     14991   681.409091     14477   \n",
       "3           4  564      BOONE    IL  0.017     30806  1812.117650     29344   \n",
       "4           5  565      BROWN    IL  0.018      5836   324.222222      5264   \n",
       "\n",
       "   popblack  popamerindian  ...  percollege  percprof  poppovertyknown  \\\n",
       "0      1702             98  ...   19.631392  4.355859            63628   \n",
       "1      3496             19  ...   11.243308  2.870315            10529   \n",
       "2       429             35  ...   17.033819  4.488572            14235   \n",
       "3       127             46  ...   17.278954  4.197800            30337   \n",
       "4       547             14  ...   14.475999  3.367680             4815   \n",
       "\n",
       "   percpovertyknown  percbelowpoverty  percchildbelowpovert  percadultpoverty  \\\n",
       "0         96.274777         13.151443             18.011717         11.009776   \n",
       "1         99.087145         32.244278             45.826514         27.385647   \n",
       "2         94.956974         12.068844             14.036061         10.852090   \n",
       "3         98.477569          7.209019             11.179536          5.536013   \n",
       "4         82.505140         13.520249             13.022889         11.143211   \n",
       "\n",
       "   percelderlypoverty  inmetro  category  \n",
       "0           12.443812        0       AAR  \n",
       "1           25.228976        0       LHR  \n",
       "2           12.697410        0       AAR  \n",
       "3            6.217047        1       ALU  \n",
       "4           19.200000        0       AAR  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midwest_df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/midwest.csv')\n",
    "midwest_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24beaa42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:58.721190Z",
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     "iopub.status.idle": "2024-04-17T07:29:58.732595Z",
     "shell.execute_reply": "2024-04-17T07:29:58.732394Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>population</th>\n",
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       "      <td>IL</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IN</td>\n",
       "      <td>5544159</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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      ],
      "text/plain": [
       "  state  population\n",
       "0    IL    11430602\n",
       "1    IN     5544159\n",
       "2    MI     9295297"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_df = midwest_df.groupby('state').poptotal.sum().to_frame('population').reset_index()\n",
    "pop_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd6372fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:58.733626Z",
     "iopub.status.busy": "2024-04-17T07:29:58.733532Z",
     "iopub.status.idle": "2024-04-17T07:29:59.254581Z",
     "shell.execute_reply": "2024-04-17T07:29:59.254391Z"
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   "outputs": [
    {
     "data": {
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       "      <th>state</th>\n",
       "      <th>found name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IL</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>MULTIPOLYGON (((-89.13301 36.98200, -89.16777 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IN</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>MULTIPOLYGON (((-84.81993 39.10544, -84.83405 ...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MI</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>MULTIPOLYGON (((-90.41862 46.56636, -90.00014 ...</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  state found name                                           geometry\n",
       "0    IL   Illinois  MULTIPOLYGON (((-89.13301 36.98200, -89.16777 ...\n",
       "1    IN    Indiana  MULTIPOLYGON (((-84.81993 39.10544, -84.83405 ...\n",
       "2    MI   Michigan  MULTIPOLYGON (((-90.41862 46.56636, -90.00014 ..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states_df = geocode('state', pop_df.state, scope='US').get_boundaries(9)\n",
    "states_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5cad4bb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.255620Z",
     "iopub.status.busy": "2024-04-17T07:29:59.255538Z",
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     "shell.execute_reply": "2024-04-17T07:29:59.268060Z"
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   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.959184</td>\n",
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       "      <td>0.096849</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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      "text/plain": [
       "          x    y         z\n",
       "0 -1.000000 -1.0  0.094354\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "def generate_random_data(size=50, mean=[0, 0], cov=[[1, .5], [.5, 1]], seed=42):\n",
    "    np.random.seed(seed)\n",
    "    x = np.linspace(-1, 1, size)\n",
    "    y = np.linspace(-1, 1, size)\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    Z = multivariate_normal(mean, cov).pdf(np.dstack((X, Y)))\n",
    "    return pd.DataFrame({'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()})\n",
    "\n",
    "random_df = generate_random_data()\n",
    "random_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c334c86f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.269302Z",
     "iopub.status.busy": "2024-04-17T07:29:59.269207Z",
     "iopub.status.idle": "2024-04-17T07:29:59.442048Z",
     "shell.execute_reply": "2024-04-17T07:29:59.441841Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=225x225>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = requests.get('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/source/examples/cookbook/images/fisher_boat.png')\n",
    "img = Image.open(BytesIO(response.content))\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb76152",
   "metadata": {},
   "source": [
    "## Basics <a class=\"anchor\" id=\"basics\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cfb48a68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.443215Z",
     "iopub.status.busy": "2024-04-17T07:29:59.443133Z",
     "iopub.status.idle": "2024-04-17T07:29:59.450635Z",
     "shell.execute_reply": "2024-04-17T07:29:59.450444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"BG6ubV\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "\"cyl\":[4.0,4.0,4.0,4.0,6.0,6.0,6.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,6.0,6.0,6.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,4.0,4.0,6.0,6.0,6.0,4.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,8.0,8.0,8.0,8.0,8.0,6.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,6.0,6.0,6.0,6.0,8.0,8.0,6.0,6.0,8.0,8.0,8.0,8.0,8.0,6.0,6.0,6.0,6.0,8.0,8.0,8.0,8.0,8.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,6.0,6.0,6.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,6.0,6.0,8.0,8.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,8.0,6.0,6.0,6.0,6.0,8.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,8.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0,4.0,4.0,4.0,4.0,4.0,8.0,8.0,4.0,4.0,4.0,6.0,6.0,6.0,6.0,4.0,4.0,4.0,4.0,6.0,4.0,4.0,4.0,4.0,4.0,5.0,5.0,6.0,6.0,4.0,4.0,4.0,4.0,5.0,5.0,4.0,4.0,4.0,4.0,6.0,6.0,6.0],\n",
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       "\"data\":{\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"BG6ubV\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a65808bb0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(color='cyl')) + \\\n",
    "    geom_smooth(method='lm') + \\\n",
    "    scale_color_brewer(type='div', palette='Spectral') + \\\n",
    "    theme_classic() + \\\n",
    "    ggtitle(\"Simple linear smoothing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311dedee",
   "metadata": {},
   "source": [
    "## Features <a class=\"anchor\" id=\"features\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be46cd3a",
   "metadata": {},
   "source": [
    "### Interactive Maps <a class=\"anchor\" id=\"features-interactive_maps\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f1e80df4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.451829Z",
     "iopub.status.busy": "2024-04-17T07:29:59.451706Z",
     "iopub.status.idle": "2024-04-17T07:29:59.466693Z",
     "shell.execute_reply": "2024-04-17T07:29:59.466473Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "},\n",
       "\"map_join\":[[\"state\"],[\"state\"]],\n",
       "\"size\":1.0,\n",
       "\"alpha\":0.3,\n",
       "\"map_data_meta\":{\n",
       "\"geodataframe\":{\n",
       "\"geometry\":\"geometry\"\n",
       "}\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"oD5eVq\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a658a7c40>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot() + \\\n",
    "    geom_livemap() + \\\n",
    "    geom_map(aes(color='population', fill='population'), \\\n",
    "             data=pop_df, map=states_df, map_join='state', size=1, alpha=.3) + \\\n",
    "    scale_color_gradient(low='#1a9641', high='#d7191c') + \\\n",
    "    scale_fill_gradient(low='#1a9641', high='#d7191c')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cefe9a2",
   "metadata": {},
   "source": [
    "### Customizable Tooltips <a class=\"anchor\" id=\"features-customizable_tooltips\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec1a040e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.467868Z",
     "iopub.status.busy": "2024-04-17T07:29:59.467744Z",
     "iopub.status.idle": "2024-04-17T07:29:59.472300Z",
     "shell.execute_reply": "2024-04-17T07:29:59.472115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"rX4nEo\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"fl\",\n",
       "\"fill\":\"year\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"mapping_annotations\":[{\n",
       "\"aes\":\"fill\",\n",
       "\"annotation\":\"as_discrete\",\n",
       "\"parameters\":{\n",
       "\"label\":\"year\"\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"tooltips\":{\n",
       "\"formats\":[],\n",
       "\"lines\":[\"fl|^x\",\"@|@year\",\"count|@..count..\"]\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[20.0,93.0,4.0,32.0,75.0,8.0,1.0,1.0],\n",
       "\"fl\":[\"p\",\"r\",\"d\",\"p\",\"r\",\"e\",\"c\",\"d\"],\n",
       "\"year\":[1999.0,1999.0,1999.0,2008.0,2008.0,2008.0,2008.0,2008.0]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"rX4nEo\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a6571aec0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes(x='fl', fill=as_discrete('year'))) + \\\n",
    "    geom_bar(tooltips=layer_tooltips().line('fl|^x')\n",
    "                                      .line('@|@year')\n",
    "                                      .line('count|@..count..'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376ca51f",
   "metadata": {},
   "source": [
    "### Formatting <a class=\"anchor\" id=\"features-formatting\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c21a1c5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.473487Z",
     "iopub.status.busy": "2024-04-17T07:29:59.473367Z",
     "iopub.status.idle": "2024-04-17T07:29:59.479501Z",
     "shell.execute_reply": "2024-04-17T07:29:59.479314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"o8OMTJ\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"unemploy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"format\":\"%e %b %Y\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"area\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"#253494\",\n",
       "\"fill\":\"#41b6c4\",\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"o8OMTJ\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a658ee290>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(economics_df, aes('date', 'unemploy')) + \\\n",
    "    geom_area(color='#253494', fill='#41b6c4') + \\\n",
    "    scale_x_datetime(format='%e %b %Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb3e656",
   "metadata": {},
   "source": [
    "### Sampling <a class=\"anchor\" id=\"features-sampling\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e94cfbff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.480660Z",
     "iopub.status.busy": "2024-04-17T07:29:59.480545Z",
     "iopub.status.idle": "2024-04-17T07:29:59.484968Z",
     "shell.execute_reply": "2024-04-17T07:29:59.484781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"FpPQqd\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"color\":\"cyl\"\n",
       "},\n",
       "\"sampling\":{\n",
       "\"name\":\"group_random\",\n",
       "\"n\":2.0,\n",
       "\"seed\":42.0\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"mapping_annotations\":[{\n",
       "\"aes\":\"color\",\n",
       "\"annotation\":\"as_discrete\",\n",
       "\"parameters\":{\n",
       "\"label\":\"cyl\"\n",
       "}\n",
       "}]\n",
       "},\n",
       "\"data\":{\n",
       "\"cty\":[16.0,14.0,11.0,14.0,13.0,12.0,16.0,15.0,16.0,15.0,15.0,14.0,11.0,11.0,14.0,14.0,14.0,9.0,11.0,11.0,13.0,9.0,13.0,11.0,13.0,11.0,12.0,9.0,13.0,13.0,12.0,9.0,11.0,11.0,13.0,11.0,11.0,11.0,12.0,13.0,13.0,13.0,13.0,13.0,11.0,13.0,15.0,15.0,15.0,15.0,14.0,14.0,9.0,14.0,13.0,11.0,11.0,12.0,12.0,11.0,11.0,11.0,12.0,13.0,13.0,12.0,16.0,14.0,11.0,13.0,16.0,18.0,18.0,15.0,17.0,17.0,15.0,15.0,17.0,18.0,18.0,17.0,17.0,16.0,16.0,17.0,17.0,11.0,15.0,15.0,16.0,16.0,15.0,14.0,13.0,14.0,13.0,14.0,15.0,14.0,13.0,14.0,14.0,18.0,18.0,17.0,16.0,18.0,18.0,19.0,17.0,16.0,17.0,17.0,15.0,15.0,14.0,13.0,19.0,19.0,18.0,19.0,19.0,14.0,15.0,14.0,18.0,16.0,17.0,18.0,15.0,15.0,16.0,18.0,18.0,19.0,18.0,18.0,18.0,15.0,15.0,15.0,16.0,17.0,16.0,17.0,16.0,18.0,17.0],\n",
       "\"hwy\":[23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,19.0,17.0,16.0,16.0,17.0,15.0,17.0,21.0,22.0,23.0,22.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,19.0,17.0,18.0,25.0,17.0,15.0,18.0,26.0,26.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,26.0,29.0,26.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,17.0,17.0,19.0,17.0,19.0,17.0,17.0,26.0,25.0,26.0,24.0,26.0,26.0,28.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,19.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,26.0,26.0,27.0,28.0,19.0,17.0,20.0,26.0,26.0,28.0,26.0,26.0,27.0,17.0,19.0,18.0,20.0,24.0,23.0,24.0,26.0,26.0,26.0],\n",
       "\"cyl\":[8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,8.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0,6.0]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[],\n",
       "\"computation_messages\":[\"sampling_group_random(n=2, seed=42) was applied to [point/identity stat] layer\"]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"FpPQqd\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a658a7940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(color=as_discrete('cyl')), sampling=sampling_group_random(2, seed=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd3240d3",
   "metadata": {},
   "source": [
    "### Images <a class=\"anchor\" id=\"features-images\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b9846fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.486119Z",
     "iopub.status.busy": "2024-04-17T07:29:59.486008Z",
     "iopub.status.idle": "2024-04-17T07:29:59.498044Z",
     "shell.execute_reply": "2024-04-17T07:29:59.497835Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"L2YJRc\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"name\":\"classic\",\n",
       "\"line\":\"blank\",\n",
       "\"axis\":\"blank\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"image\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"show_legend\":true,\n",
       "\"data_meta\":{\n",
       "},\n",
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9BC0UnXBj4B5nAAXYI4iuCFULn5LoPcJ2o5XjRHlYEeyp2mBk7zAodZgUOwzRjAMHdQ0lo4EifjxBcStxXeFjI8GP4ihiuo0xnOctVxXM39vvbJKEuebRXmWjieSrptlLaTGm3oxiVSq5SZqhZHFMOpQenMzOeJZtn0eGac3tyAVjS/ZlwMBOP5dKJhVRyATYikUw0konFctFotgD7zBSgNokZCEIXnzGJxKqvhhlEAwnrKWrzhJ7CDgBbx/sOM12VXFcZc+rtEmkp21HZHwBrCznBI7ww6HKAHdnPeQq2B5bmIIIbAA5xnoLv2XyWEzzAhTYMe472h46w3EUsd6UwXCOKlvMCJWx/OS9Uxg3WilJHISXodhWxQ0VcNK+K+OlDrAyCHT/EDmOn9R3iAMFD7OghVhICMAI2WPEjrOgRdvgIM0QJSkIDW/I9z3Y7i5iBYlbwKDsEH6eU46sU+OsloXZVolefYZtzfOuCwLrINeZosnCbMNAuCjfxg5UMZwXT3SoKgKOWifToTG50Oj0+k7A5Mx7vnN+XAYLBbDg8FwrlfL6Uy5MA3N6UwxUvOMz0UeAb6iiBnWiYMTBNN4Mf2FMr8B2hDf9gX28Fz13B9x+g24u4IYijuJFbgqKgiO/BTrLl5ZaRT+EG8sCRRzi+BlmyiusthuWvd6q0bxrmA045c7aC4yzjeCr7Q3XidJUgXcRKFLLSR9iLCE7mCDcOU+UID/RFBiMpmalCRg4jUwjbzGQhaMqM7L2jvp0c6fPiG0VMfzErAHaWcEIlnEAJ11MtCrYo473GOc7AkmB4WTi0BCWRavy4dHBBMjAvG1oUmOe6pcEmvrONb+vXBOQGv8Yc1Jr9poHg2ER8cjoxNR2dmY3P2mOztsTUbGxiOjI2GRqeDA5PhIdHQwVHWH5KUFprDjERhC4+E+12RL7dg8IPhJxNwnmeHoBJBisvUMTx1osCnzQb/+7ntZV8TynX/Wn3TBEPAq0PRUFO+ACENwRshLE9QfyFOIe4oefh5Z/1F3L9Rzneo7TJyt7Jqr7JSvpIm2BWYI3pZhf1juO9am85fbCMPl7D85Qy3IW9nhJm7CgzVcxZQHAzxbzYUV74KC+I4EaOstCzR5nZIuZcEWqzRawUpnWYEhTjbgAUfDG4oHlHi1jO8n5fgzzSoUvSTRmWKcM0JNi6qNCclFrTyuGsdnReNZTt14a6BbPNzGEaf4IhmuJKZ4QKm0LnMlj8JotXZ7CbB9wIq8dgdQM6i0dr9egsPjigoJAdoAR8jN2DpVAkbm3Fuf8gUJI/Dx4XsUALsRMXFOJTIT9Y0u9rkYf31Wv/4vvFtaJAGc/zWc9siQCVLIe4EeAg7ym4hfDCI7wA1oYO88LAIT4C38b3g+VFPF8x11fGcdczZ5SDuang+dnIOV/u0rEvHn/+cAPwLZznm139g2GtfZWmjFexvQ3CVCkzVsbJlnCzJbx4CT9UxveV8b3lAM9Xyg6VsuKlrHQJK4ORLmYniznxInYEpQq7ppAVpAQouHuOYktTCdddKfDWSwPNskC7LNAh83RJnN1SB18XlJmjclNIbgrI9D62ZLKlx9DaZ+niDDOFE+Co2ug1Dgb1Zp9CO6vS25U6m1xrl+nscp1LZvBKDB6p3qPQe/9kHcVqeR/uKCZouFgQLhX4u7TJjxp1f/7vh5sU4SohpCK2Cgkssnhc3OEoL/iVjoKgYCcseUAFx87XBJOLN29vbNx8snH13sbNjY1bGxuX7t6DjcDC6srlh1c3NsInN5RTp9iGJSh7K7iRcoDnq+A7K/i2Kv4MgmevYLsrWIEyVhRMLWEjijnRYk6YkqCUHWW/QEcCfYuYXvjsxbAy8DzlfGc131EnsDcJbG2CaZrEJjT45daQROcG5yAiSlVTNIa+pVdPYw9yJNMgn2UkNjqdHRqLg6Yag1upd8u0TrHWJda4xTqfQOvv13qEKmdBEcdPkeDuKcTYvlzmwUediDAlCnmxp/AjeYr6I1DIg5ql4th+pvvTPmelPEUbWKnpd75Z3P8XPyisEaGIdYhphyoHXQ8/Ahzuj2HABnp4pD8M54EDoAUK+6NHBEAcKJWkioVxmACwxJfyvEVMO6RlwpHlxNLN9QsPv7y3cffJxr3HG/c2Ht3feHAP8fDmxmPw9cLdjesbG4uXNtoE9kaeo47rqhe6q3kzJXRzOcvIMnsm0xezn2/0D89Xsu1FPbM1glhVfxyts5wghPxibqiEHykTxKCFbaC0P1ohSmB5AgH4MRRA8vl2glJPIiB92nLUXc131fDt9bzZBt5ku2CKLplWWoPT7kUo4UenYuNTUbtnftyes46lDINREHRgLGEdjkBWarQGdJYAhFW5DknJVbo5Ki9X5edp/BzZzJ+qo1iEQ2KhJR6AjQpZstUwVyd0/6qI+5c/Koa1Cbr1CNtZIkAHgI47HYXzgKC4o4WCZxyFoAsP4YVlglC1OAyFLXd01bVwN7ty9fzFW3cfbDx+svFo496jjVuPNm4+2rjxcOPWnY3b9zY2Lt9HwXXx3IMO3kgjY6iGMdClsElGIgZnejy6GDx+cfHyg5O3N+KnHgqGMpXMaajAqoWhcmEYXSdUY9wA2AleQot6GCIfZu3Lc5RA0Bc4uhVHvZA4Vfa7IATU99sa+6ebOGNctWtgJueLrSWy59K5M+nsqWTmdCixbveuQFEP4dM6EtJbPLDKKzR2mcYmUdsFKjtP4WDLnUyFh6nwQcuFOAqfmSIUPjBWohK7CMNPQoQSRf3RHaCYB28BgRPUPMDylEoixUIow92VklCD2PuzQ8y/er0U1npwFPWvMARpAFJQEMEU3AQewnkgdKHoJYgWCaNFglihMHFYmDosShziR0vESQjS5aJgRb+vWuCTTJ1Mrm/kVi5d+vza/QePnmw8eLxx6/HGtUcblx8+uXTnwaUv70Fe+uDW40dg6snP74l1Mxz5uNWWdiTX5s5cP3Pr4dXHG19ubFzZ2Lh4f+Piw40Bz3EY7ErOLFxnmdBfIvQd4bggbQVBy4Xx7aEU15QQMndJYfsJKeUGCcGUgFQHZTuwLlUJ3PVCV5PI3qfya8azM6FTwcz57OKlY6tXl49dys6tz69cDidO2dy5yZn40EhQb3Qo1VMy9bRIMSVUzPDkMyy5jSFz0qXOHqm7R+Zkym0Fm4UkBSh84GIMQhdxk4jY6dyLKBbEdhBFSvWHqpUZSCUPsr3lstiHPdNv1psLOc4mqf9HH9P++ielLapwGc9V0u/FHUUKCqOFwlgeeIhSWGEEDiiF04pixcJkkSh5RJQGYKNclimTxCHHLeE4ytk2/uBCaOVuemn93PmLd+7cevT47uPHNx8/uf7o8RcPHl66dfvcjZvnHz65fefh3ftPNtYv3Rye9BsGnZdubtxEKQHi/sbjuxuPbj15fO3Bk5OX7w8657vljgaBvYxrL+K6SkX+EjFa7sFOcBTWd9xUUPYFkLlLQogsXpbxQoSAEii0cwJHOd4yrhfdKxX5miRe8fC8dmpx1HvcFV0Lp07HU2uJ1CqwdOxSKrseii77gvMud3ZqNjY6ER4ZC2uMTrneIVQ72Ap7n9QBdoKjvXJXn9wOjvopQkHoYj6ikIgitIDu5D/oaHQbYRDoENdzVBSqkMd/3Wh9rVQJRjbL/f/yu6a/eaOsTRM9yraXCnywhj7vqCgKfKWjEEfLRNFKYbBG6G8QeYTDC4GFG8m5tVOnz9y4cePevTsPH9x+8vDLJ4+uP7x/BdoHd68/enj33r17N2/dW1u/5PIkpmYCN249fPBk48nGY0gMbt+7cuvu5TsPb91+8nj1wo1BR7ZHBuumHcopuM5yWaRMGoXEAwulYGqkXBjFgW3YQwjSjgrIPH5gt2CalnLD+H2oUp6/sj9QKwo0SnwsY5KlDSmGkzOBk57IGgROj3culVldWDqbnVsD5hZOz8+fSqSOR2Ir4djxsem4dTSsskCd5GIpnGAnXe6lK31MtbeghB+gCIUPvJXMEVCMFlBCdsbFF3FUGN9GLA/+Lkf4PhAU4uhvW4ffbhqoU8ZbFIG/f6vyOz+r6NBBITJdLgqUCPzgMVJQFCsSxRHiGAAPjwojZaIw5ILg4lFx/KgoUShJFYozRySZYnEKrfXCSJUo1CgJNIs8XFN6JnoOHF07dfbq1eu3bt16ePfOkwd3Nx7ceXzv1saTBxuw/fDhxmMooB5fuXzr1NqFYyun10+duXH96sZjCKM3Hz+8fP/B5fsPr995cP+LLx+Nu3Id/eOVjPFyrqtaFq1UJGBZgHdEHYuFANzUF0PmLhnl/UEK8CFfigJlvAhGqAKyc1GwXhTokIdaBPZ+fWjMc3zWf2JgPDQ2GYnEllKp5VRqMZddWVw8ubiwmk6vxONL0fiy27cw6UibxuJSc4Cn8fYp3X1KDzgKphaU9gcpQuEDbyVzBMDwkxCnRIkogRHfRhTA36VEHIZktLDf/xnTATG1Rh5tV4f/n1+UfveX1TRjChytkkKkDMAxSEFxvFjyFHgI56kQRYBycaxEkgApi6TpIkm2UJo9KkmXSdPgLjhaJ/TWcKd7lIFh1/Hc8vn1MxevXrl588t7D24/fHIflHyyATUUcOchLOdQSYGmj7CNB3efnFs7ffOLixt3rm08uLbx8IuNR1ce3rt2+/bNKzcezXoXYdVr4NqrBYEGVa5Ot1gmS9Up56qlSZAPxbytULo9pj7HV0r8HCAZBfojFf2xiv44RgwjUtkfqur3N4r8zUInU+1TDSc0QxG53mYZ9gdCC9FIJhJOxCPpZCIHbcAfd7ujbm/S5c1NuzJDU0k4WGjwM1QeWO67JM4OkbMA0inEloJbU8SfZ9sB1GZkXlOKjuKBML7LFhe0TPiMoDjl0gQE0cM87yd9NqiZIA6V8JzdmuDf/fTI996spJvTxSxbjTxWho5MwXkgTG4zNYppGq0Qh6pEwXJxqBTTFNQERwEo7UHZCkm8RhKBDKyGb2fo49OR84urV86fv37t2v1bNx/eu/3o0YMNcPTJvUeP7jx4ePs+8hVzFOyE9sn9jctnzl8/f+7GxTNfXlq/c+3cw7tXH927fef2g/XzX7rDJzWjGb4x3aVOdBnnGcNr9OE1mnG5TTNXL01UoxAeqZHEgEox6BjG+KM4GqsUxDGiaA8/WMHzVXFc9Xxnq9DRIZyhCSY5StvAVDaUOhOMrwSj88HoAuANzNldyWlncsadnXTmRh0563RWPRoXmcPgaKfY1txvaxNBHBX5gTIBmpTYJEDzADLfUq6zThqoFnlL+Y4KcbBKEgMPKsRJ7JOEKCCKUgIiFgIKgt21FZgoleI4tBhRnANsV406XafNgqP/8CETlntY98v5Tr458N0fvff9d2vblYEaMUo0G7QnDrHjlbKlClGuVIBEL5XGS2TRQmn0iMhXIfbXSP3VslClNFIpTVTKM+WyXKkkUyRIlIqTENUaVJkqoa+y38EaXphKXo7PXVw5fvHMuRuXL9+6dv32lzdv378PPj549Pge8OTxQ3RT6skG4jHi7o2b1y9cvHBy7eTy8vGl5eMrJyEHOHv+5sKxK/7U2WHnsnosI7QmBANZ1czJAf9l5dQ6x7zUKg7X8r11QvRjjmoYPiioRUGI6GAtQhjDSGDECAGfCKkioXLTjefBNMWc2SRQhfDX9gdq+r21fHezyEtThLmWjHz8mMG2Ouw5CavNgH3ZMrtonJozTOZ0k3PaiZxuYkExmhVa4ixtoFfhpUld7RJHm8TTIvaBo17cUfRO/Bi8axUfxWrI06HfKwT28n7AB3G0pD92lB/9Azm6a8DOPHlBgVpNBvfyrQbLGxUaeAhxtFrsFlh8//Dae69/1EjTolQSPlSD9mSxYK5CNF8uzEKxDCJWKZMVquRRVbwI1JQFq+WBKnmkShatkiXB0QpZplyarlXMVUlS1eIoiF7Os1Xyprv1Yb1tCULF3MLZ1bXL5y5eu3j52pUb12/f/fLB4ztPNmt3iKsPNzY2nmr6aOPJ7Xs3Ll45u3r22MKJueyJXHYtO38umFib8a9YZ+e040n5UEI8kJCP5nQzq8qJNb55sUsebRL4GsT+emmgVuKvEvt3OlotSAB77igWUEHNPCBPHtAUJlKTNNyuStB0Gbohy9SnGPooQxuja8N0daRHHexVhaHlGFNMQwy2O+XeNpG7WWhvETib+l11/Z6CMrGvTBwoFwXR9fXH4Wqq+8MoAZL50VdzImedzFch8MM6UilJw4iiBQXWvl0CB1N0bisc7pYqSQIjBlRKtxOpUcQbtZlPeqYKue4yUbBEHKyRepla11/927uvfdTSqY1CDlPID1erFksluUpJtlKSglNVS+Pwwgpl4qgiUSiLlSsT5Yo4UKFIVShA0DQ4WgWOSuOVfF8Vz9kq9fSqvTxDQD2WGHHP+8LHUsnj6A/H1i6cO//5F1c+v3nr2v0Htza2O7oVRBFg7N17d6/e+PzsxbVjpxfn1zK5tXj2tCeyOuFZNs3k1GMp8VCSb032D2al4yuS0VWuZblHnW6RRholIYij9bJwnSxaLQ5VSsKw6APVojhQg1FNApm71SSQugtiCMNVgmAekAeo4Hmhf2oFPhTsRQFoYRvCaqPA1SCwN/KdeNsodDQL3E0iZ5vYBz3ZKvLCNqiJP1vPd9TwXAXlEn8FmoVoCuJXUyOI1ID7imA5b7ZG5GpUBiuFAXi2VpYrEyZQDSEO7R7oMkrOofWUClBAYMSBKlmeaBHPAy3YBoLWqZKwVhTzva3aKE3h/L++9/aPP2qDuh5yGKiWqhTpMkm8VpmqVcQb5JE6eRhKqwp5tFSeKJYly1TZEkX6qDxVrshWyLOQucKb1suTDdJoq8Tfpw1qxrNjnmP20DFf/EQ0veYPzseiS7ncyZWV06dOn7l06cKXN6/eu38TTMR4sPEEHH2MNH2Ecffexu27969dv3z+83UIpctnsnOnoqnT4fTZSf+qfionGU7xLAm2KcEfmhNNnOAMrDDNiz26uTZlHK6hThoBQetVySppBIHN1RpxAqjFqCGBzN0aEkjdxSJ3tTCUpwbdjIOU1ItWfIEPBM07Ws1HX/zWC12Ngs22QYSSgSaJt6HfhW7+Cz2NYg/sgf3wbJ3AVSvwFFRIvNsdBUHrBFCoQpR2Vgpma8SOarGrrN9dIQhUomQUv6bQLsEdpQTey7sHt/MZIKhgw1Yq8L1ZrX2/bQhcbNImaxWxHmu2R+X5z3//9hufdXebYL2O1KogEw1WSH0QYmulrkapu17mrpf76+EpZaJanS1XzxUr0kXyVJkqV6HMlaNZEW9RJjmWOdnI3Ij7RDB9cW7x0lzuVCqxlIjOh4O5WGQhnVxemD+2urp28cK5G9cv371zY+PR7Y3HdzYdffwYi6CPN+4/2rh7F3hw/cYX6+dOLK1m08cCkXmoIZyRkwOORdlwkmeJsYzxXkOyz5zlDB3r0c8Dnbq5ZlWyXgoFUwTKPphg+Oqx6agkViuO1YlQixdVO4EshZAaETFQohFC5mitCG2ASHlwWaG+rANNBd5aAVrHoYVt2APb0DaK/I0SX5M40CD2Ysd44CUFlVJftcQHK0WNKFwrjAL1wjCcqJI322OMd+kg57BXCT3V4jBKBrAMDLZ3z0tw7oXUyBLPII9hRBs1KWhB0JJ+d60sVKsMNapiXYYkTeH53/7mlz/f39ttiNVKPXUKX4XI1qhy1UumWuWTncoZmtrRqfS0KoNNyliDOlWtzUEQLZWnqjRzteocxNomZaJHnxQO5ozTy67ImczCpWMrF1bm1tKRdNgTiQbnouF59Ac66YWVpWNnTq9f/uLClzcuP3l4B90EfbLl6AMo8+/ev37j/pUrD65duXnh0unl4/PphWR8EardSUfKOp1WDkd5pjDLGGUYIZlLtKmT7ZpMmzrTrsm2qjNNqlS9HE1IWDeq5Ql8AcH7pE6CqBdvbkBashMyd0FrQkjd3TQ7nAfsBCDG16M7HgEIn5CVovpJ4MPDao0IbVcJ3NV89BU/3qLDROjXvXUSf/4YPJ0tgPgBdWutJFwnjsDMaxBFGkVh0LmaZ1O7TwsmFiGaNsoDMDBlvFCDPIMmriS8ayL/Uee+ilp5Mg+M2RYxNOukQQiHHcZ0lzFTzJnu0Kea5f52wfR/+m8/+fXBnj49dN9Es8LWrp6V2xI6e9zsTo54s4OunHoyxTdFaaCpNNiiz1YqYtXyWLMu067PtWni3boEfyDDN0aUg7ExWzYYOT6fO7Uyt7qYWEgFkuAohNJQIB2NZHKZhRPHVs+dXf/80rkHd24+vg+aYo7ev3/n+vUrFy5dXDt15fTp6+fOfH7y1PHcQi45D3E0ljzhDi0P2+fUo3FwtM8Q7dEnOtTRZkUMRqFZmcJpVCYblek6Raoa5o8sCS2A9wnEV6ABa+uliToZAYTi1m75vRNSdzfNjuSpE4NLYSQo5ii+yuOaIuegKhAHIMRWCX2QtkIL29Xg27bt/J5qLJ0tqEVrHHolnBcJKsQcFXurOVND8SsK+2o5Z7JJ6m+URQr7nLWSJEVHw7XS6B/IUcWmnQhFpEEZ3U8bKWJOFzInei3ZUt6MePZUi8TZzh3/s7987aMShtAa6pCMMY12xaQ/fPzs8cs316/dPn/tzonzX4Zz562z83xdsF3sAj8gNW+RB3uMyT5TulcXhZVXNpIVGIMSk98wFJyyp2OJ40sL60vZ1XR0IRxEf53j8yQDvmQiPrcwv3Ly5Kmz62du3vjy3q2bj+7dBe58efPz8xfWVlaXsnNXz567eeHCF6fXj+eW5lIL87nVdO50MHHSlzo75FqGaomhD3drwm3KcIsq2q5JQZoBpiJUqWZ1ukGVqVGkUV6+zdE6WaxeFkWRTBaF7T+Ko2AYqAaONoiDjeinPEGgFsIq5ABi9F0UDmxDfASqoOYRBrbvx59Ca32nPg5hEn3HKo60SOLNkmirONwmDVQxJ4Yil3oMoUaps1bobJCGG6B8FoQb5YkGeQyAz486AttuVMRR8r6DWpTRxwjZFvN2RYMCujUKQPxAaxx0hCzWosmAnY3qLOzBHzYqE/DBGhXRcr77xwfY8NEgV2mUemXOUwrHepfCwxRO/MVfv/bDXxwcc80H5s57s6cSx87MnVw/f+3qhS8+v3jpi4uXrp4+cx1W8Bn3cc1IUjmc6TdFmFof1xiSjqQBrj5Ik8zwtR6Rwau0Bs2jkQlbxuVf8gWXvT70Owm7M+Gwx92uRCiUSyTm05mFTHr+0sUrVy/f+PL6zWtXrp8/e+nY8mo2kUuE4xdOnr567uIX6xdOLp1cWVxbXb2UmVuf9eTsoRNDjiXIRxm6QJfC16kMdKjCEE3bVVCpeFqUcehz6BP47OXiWKN2vlaZASCsArC/QQEHRAGq/Vy/GX13y5bZ0TybERQjbyeejOYd3Qlu6k6Qox26WJMCZPc3S8KtsnirONoi9LeJfTXsiZHoRboh0CJ3QpHVIg+3ypOQDEBKQHguQkcB+BgvxdFWbRbshN4HEfFt2AltkyaHVjd5HPY3qZJQ5Dar4rC4l3Jsh3tH6eZ0CwywNiycWOQM5mAF5whH//y//vO7+8rt/kVf6kR8YW3p1Llzn1+5cuWLSxfPnl9fO3fq7JnTl4+vXk7NXQrEzg5MZfWjcaUlpBgIw+ILCE0+pnIW4Kjt/Xq3zBI0jieHbPPjzsUp10IovuYNLM3YU+NT4YnpkM0V8wUyoWhueXl9be3C+fNXz5z54vix9bncsWxmeT67fPrEOlT0Vy9dO7d+afX4+YWF04Hw4uhMTKabFekcfJ2Lr/dz9CGmPtyrCXWp/D3aSLsyBPUTZB0QTaErIIlvUOUwR5GgdUokKCwjjYow5ijFrt4bR3FNv6ajUBW1KALNkmCrLNoujbYKQ80CX6vQU8+ZAkchaWtXOJqlHpi+HapMkzzRJI9DYgTzuFWVAGAD5UnyKAq0O6iXhfFAu5NGNNEp0KJO4ZEbNtq0GWhR8JbF2o1LFZCiKJOdxgX8gC59tlOfOtAx0KWLwUrdKvc1SZ092gBdHzHZVyzDwb//nz+m9UqzSxdCydX5Y+dX1y9evXbz4tkzp44vr2TTc8n0fPbY0uLZheUv5pavjs+khidi1tGoZSxqHk/oJ+KqkajEGuyTT/cqZvuUTrbWKzKHFcNJ/cSCeXo+nP08kDht9y+P21Mj07FxWwKCoju0GIgdS+ROzS+dz8yfCseXoHiPpY7NL66vnjgLdn5+6cb5s9dWVs7FEivoR2v+RZXRprV6DGMx6+zciGtlxH3C6jimn1nuH0gIhjKKmVW6MdWqiHTqMij1VKTqlelNVJCnJqBAbFJGUNmHwi0VSMaLGFkczxy2Wws5xmbBhDm6XVNwFNIAMh1JHYUY06r0t8qDnfJopzQGQbSJ52rudzTyp0bC53u13g65o13u61RHu9SZNkWqTZ3u0KbBA5ohB8AGPISSExJWYkic20yqdg2eXUAL2yAo0KpJg6wdhkVwtE2X6zItwnRsUSdoUCExJz9rN9ONiRaZt13uKWcMdcrdkrF5Z+pz06Dnv//lP/aLzPHs6WlHOpY5CVosLa2tHTu1mMklA+GgKxjwJoLhxWDsZCixPjgWGRqPDk8mh6dTQzNZqy1rtc9bHYtcg5ehcdNVbobGC3EOIrRkIKMZm7dFzrnj5zyJ867Yuj14cjZwzBY87ggfB2tdoRV3aMXum4eyfcqZdAUXwsnjy8fOr6ycgXZh6Uw0vuzx5xLpkyfWv4wkT4VTp6OZM/G5i+mlq5nla+nF6/GlGxb70oD35FDwHNeSapP6aboURI0mRRLSU8hNG9SpBjVsJ5vU8SZ1FEC+UoJi7KhHpWoSZM3TKIsBuKm4o89EU0moThrC1tivbuuxFxZ0agJtCn+HPNSliHVIohBEwdG2fmcLf3ooeJamcrXLbDDMHapIhzIFjuJxFCIo5O8AbOBxdK8dbVYlwch2XRbsxB/CNs200KLJQR+BoO36OXCUZppr1ybeLpNUcWe7dVGaOsAxxysZwyxDyOpbtyfOC+TD/+l//x88kdUdPjZuz0Sz5wPR1URybT6zloktRnwprzPucKFfOYy7F8Zci/rhmHUM7Jwbti8MORahjhnxHh8JnpKPJXmmIAjK0vm5hjDPGBGYEtKhtHF6ecB5fDywPhlan/CfGoWDPcdGvcfgteC3eTJpGIsYxkOW6diwIz3hzkIs90eXfJFFT2h+xpWYdcShZvr8yqPTZ2+fOnPr5Okv104hTq7dOH7i6uLxa+7YKUfi3GzsvMAaQyWdJgbRFAIHVPpIU8xRjHiD5o/vaJM0/Jym6IsxaWC3Le5oh9rfpoRgGexUhNsloWa+t5Xv7hS72oUzI8GzXQpbm3S2TeaGA9rksVZY6+FdZRGQEtTM2wnLOmwQQ+4cVUBKAI+peDSFUNqoTIOmNPNSiyYNgbbHMgep81ulwk51pFsThGDDtSRahNN8a0w1vageS3Ak1j/787/vl49M+5bH3Su++EVn4JTbf9LjWnQ7sk5bcmY2OW5LDsykdFMpxVhcNZwyjM9ZZ5YH7Mcghhnti0bHksF1TDGVYxn9NNkshFKooji6AE8bFBgi/aZovzkmsMQBoSUGiAdi0qGEfCgmtoQEBo/A4JZY/MrhkHo0oh0LD0xHh6YjY/YEBNex2ej4bDQYWTp24guo286euXHx/M0vLt354sLti+tXTx27uLRwJpI+7Yqdmg6tisxQutl7oZCSh2i6TKsq1aJKQ6XfqElhJBq1UEvFUEylBNX1DZVoKaik86C4Lo/jmoIt2zVFpsqClICXgKNecBT9WbQ81CoKQBBtF3i6Zd4uiX0kCIWwbTOOylHCCo62KeMtRODKEkAiHL5e757tL+kyztMHViCIIlM1uTbdPKz1zZiy7bpkoyRQRB/t1sWhwmCbElXMkU6Zg2uOdMsdAqO3jaX6z3/1LzSeWWYJSC1RqSkm0UXk2rDOGDEYgwajX2fxqQYCkoEA2+JnGAJ8U1w0kJWPzqsmlmTjc+KRtHAoKRiKAe3S6VqmtZU/xlQ6WQonQ2Lrkzq6Zc52iaNFONssmGkX2tBfmsvcsJOucAGQvzLUDrbaydU6eXpnv94hNNiVA27TeHBkJj42G5+0x13eTCiysLSwfnz5zJnVCxfXL39+5osLq+fW5lYXUytQUU260iP2rNjoZSgcfSp/u8Tfo4N0K92qzkDhCHO1SQskm3RxALqFGhQDB8QIACUbWzQrYGlN4NEUd3S7ps0yILCTJqmfiE1HIRn1QcYJmkKp1MRHvyqlK309CudY+CzmqL1T7sUcDUMobdsqkvLgJRS5o3FCIHEkB1KI50h0alKdmkSXJsYwo4rB6D2vmDrGMCagKurSxlsVoQ51tNuQaJb7myTuXkO8Vxft0YagFv5dKY+mcIGjDZwR1USyoUf2F//zR60cE1PjZGlDnSIPXR5kKyMcuZ8n9/AULo7KyVS7ezSeDq2nXe3vUPh7VFGmMc2y5ljWTJ8Zzhzs1nr7tL4qpqWwVVHda6KJprpFsx38yWbeVBN/tpY3U8uZrufNNgudrWJ3i8jV2G9v7rd1QOmm8nUr3TSpo0M80ymZ7ZbbmBq70OxVj8aMU8lhe3bKuwSZq8O/6AsfC0ZWovFjyfSJbOZkNn0iGV0KRZZmPHO68Yh2PCE0BRhqH10dahF7IStFjmpgSUljjiafOqpJUHc0vusWq8+Uz0RffNzzyR7EQnAU1m7MUQI7X+ioH15SAIlmlybaqQg1Ch1Qy0MAgB4EQTsl01bPapfcQVO52yQuFGiVsRZppBWSUSLA3Z0gd1VRQlrUMULaNHGowDpwVOlORJKmjIMoXEMcVlKJOQSJnT+1npi/EMysz0TWDLaceDDE1nsYem+zcPzHHzbxBuLdGn+n0sexplqlLro22CKyMXQhqMGP1nP+9l9+2SOZAAuhXmxWhhpgAYH3VYW6VEHIX2lqX5c22KGPtBvj7YYEeN+li0GO26qB8Y5B266NwgHNomkIpf/0y6I61lCX3N2h8NYIHO2qSJ3AVcN31HBs1exZADbqeI6GflezyNss8jQJ3QBsgFitEvRLny6Fp1vtoev8LEOQZ4n2D8TFQ0nJcEI2mlKMplUTac1UVjczZ5yZB/S2JbY5wrAk6KZYjzFO08c7dHA96KratKlWXZ7EUzRJQl4YI2I7W+xVceyFaA82iM8MLnYbIYLd8EJAr26iCDRutegLSxIXX+BuQbsiiuohebBZ7EDLE5rcji7pDGRaUD92yZ3dKl+HDK31UNpDKKXkaJsy2qIKN6sJaNFEdtKqjkBQ7NImIWSiwIkLKk90y2MMVURgiElMQaXZNzIdDcZX0tnVaHLZE10e9+dM0zGOdpqlnt5XyTjUKheOpDqU6H5TFXscslKIpu1SN9+a5Or9xXXc7/zLW72Kaboh0GlMQKSph343ZiB1a9GE2zSRdl24XR8DO1uNKaDLkOzUJ9q0iRYUnFCIgu0uXaRH7WfqA3/5/75XQbewzAn20HyDMgpBq0UebZGGmsQB9PMIkR824CH0G/RzmzySB3W7MgZd2q4MwFIG0nfrw73GKMgHoRroMUTwh2Ak05oEWAMphjXdbcl0mbOdpkyHMd1uSLXpk88YuRPKjhLHDkxQRH7Ppp1bA9qkCuE0KoMASLmTr+uoMgy0wUFiB7YYuSGIdoin6CrnoG8NHIXBQN9zKEKwzrYpQqAdIe2qGCGgHSFtmigh4GinJoY5mgBBtxyNcHQpERTOpojSHBiYiDr9c8HwvCeQcYYWxnxZw2SEb7Cx1LNvHmzp03mYxiDDFOofzhR2mcXji/2DWUhMVdMrULscrOj7m3/6FUMx06cPdEFuoE80QjJnzoGsrdoIxMjtjqLv+jFHIc2F+NQMpQmsqpijTEME5vOfffdXlb1mjjXBGV5oVMXA0Q5tGq68Qx3HPz5soBRFm9yaeJvAQ1igu/XJdnW4QxOCiA7nzAMP8ztp+mgeiOidxmSnIQ106FMAXNhXoE0QgtarrwssOzgtKhSD8sCihNOkCIKOhJC5+CJHYSq3KYKw9LSIHDSFh6Hz96ldsNCzdN4h/ylY6+maQLcqAOtgtzoGEZfMRRgMQshchBWKkA5NBKGOooFUJ2iaZI862auOC8xzYmtWbkkoLSHTSHhsNjFjT0AVbAstDrlSqlG/fjpOV07XMEyyiQzUeYKRLHcg2Si0qWyr6tlV0ci8fGJRMzm370jrf//eG7ijnXqUsTWCf6YshJw2XRSWeKQIrKGGBAiKOwoqgw1t+nSLPgstDDwcwzKF20XT//f33mnkjjJNYE+kHqaxea4T1NEhHWEskalapCPshBa2YT/ewjHgKPodlj7eaYBMOtZljOEtuAiXgZZyU7zXlOg2J6CFbfQzNEOiXR+HC8AF3RVwMBFk7r6YnZriEYdQU0Ja5NQATQuQefBA6GkW2ruVXrYREjtfj3yGb/IPB053yux92mCPOog7itYmpCkBYBUhFB2NQvCgaSPdOvRjH7ox3WdC/+wqx5TjW+agvpZZkoqBqGEkPjSVHJtJjc7EZ0NLY17I1RKj/pU6pkFvW+BZw3Sdj2GMVHPG2JYkpKTy6WXwtVcTUIxnfvdZ/V/+w09YKhvU7CAHxNEmfQochXWzw4B0QeHKkIBwteVoAj2EuGXMthnn2g0ZSAHhCvnWeJtw6i/+6XdQ/XDMcdCrRRvvti6AQG34cG5r23EtMDNgTwcmWReEQwiKaD4g+bpMyW5TimZO0YxJ2Mb34NvQQloCR6IA/6yjeEzNh1UiR+OEkMUI6ppGCDV9mY52KVEyCuUnFEYQMvnmCCRtdKVNaA0N+051SW19Wj/kczRlkKaK0FTRThIXsQWaGOJ4SdRx6NvLfB5mTkEpzR6Y5w8u8gcXmIYEz5gQm+PygaR+JGWdzIzO5Ebs2SFbZswLoXRBMhD4rJZr8hxrEk7yh5PNYtvBNq1wdB4qJ/5QGkJdq9gOhchvPqn9q3/8Gd/g4lgiEJxAzVbQzjyH6wiqdRsiEMzAibyjmCgpSAE7TTkwFZZ+cJRnidWxBv7Pv/tVS/8EOMoeyIIoTWp0QkJXtpuEb3cZMzRzug0KMn2sA80KkDKF7MRafE8n5mgn5mgHFkTza/124FQ7d3ZuzoEEIWTuksXd7aF327qPws1OTbcH1OdoVVADNC2AGhOS0U65H2ojtjEssMbA0V7FrGggDI5CkMAd7VIEupRhGnYTgBCaNrYTcI4MWOOIiPYaw32GMN0UQdWDKckwZ5iWLNuc7VGH6eoQRxsWGqNSS1w5GNMNx00TKfmA3zgVt9pzh+p5pZ0q+WQWHGVboy0SexVvHOoMqEXghGxLnKb0Qr386w+r/vp//Uxk8vKtqDTGHW03g38ogMGeHmMU3EVmmDKgC9T1PUaIcGmoVGApRzZgjrIMoQbO0H/753c7RVOQNsCMAifqFRE4YTvKE5J54OH2Pfg2vF2XGc6ZbtPH2zfziiRWCSWxYiiBTQk4JkuzZGiWHA0rlcBpAC4GoBlTuwFS3m7s7sRzkPR/nKZLEILVsvF83HkmQkGhqQ7jtKlCOK3KICHtyhAhkHAS0ioPFNTx7C0Sd7cqxDJE0FcjA1GO3kOTTokHI0O+1Q7hJLoJp0E/D6MpKDv6Ak0JOwiW+F5dsFfvh5KcpgOgUAtvvoUqCLG8TxVka0JcTYCn8Qu0frExINS75YMBw0zmR++W80xB+cQc2xzp1nrbVZ4+M/SgnzOU6VR6wNE+XYhvDv3qvbL/8Q+vC40engUt6y1atNy3GFD0wnI+mCRRlAsa8XiW6sHERVqYc7ijKInUQXcF+5TOH75TJjCHe7XodlWPGdXa4NyWdptG5vfgG9sP6MTCM2b/JvmCHcVjY/q5ZwF4Fxzc1K+SlVjQr+1oXtO8nXn20NEGngNCVKfU1ylziQYTqMel033yadVoXDGWZKhcfWpPL1baM2GdkgcxaQiA0EJIvlbFK1NsMSUFFvo+Y7DPGECtAd2LwY3pNaAUsEcb6dVEelVBujLQp/Ax5G6m3NUlGOnXuxqYhhb+IGTSkE9zrbE6/mQZa7RJ5uqzJBhWqKn9bUo3qC8cCP/st4Xf/edf8HVOriUERoIrvSNLEEdRfELVSQx/RzwlhRUW6hVUuEAcxZd7bNTp+gjPFK6hm//+x5+wdW6IqRD1YYHD0tZkhymxk05zkgAsUhKCxU4CMEHTO4FclpBeBPoI2BIRz3c17MHHBTboZijdsEoADdBXOI2HmE5UOSAwQUM7aVcHCelQEdOuDJBR0CENgKMw8GxdWGSN84w+umwKUI7EgD6VnaHyMNQBmhz9K1MQybopOoprmr97shtHGRCinnUU17RHH6PrY326GAOWWkAd5qj9bKVDORztEo4qx1MsnZ+u80MkrhNOt6l8oCBrMM0eyrAGEgxLDBIAcPS1tw780/ffEZncbFOAbob0LtEzMN+5OZzo3jgkG/hyj6WDeUdRTEVBFFv96foQxxAsp2m+8+/vMTVOhi7QZ0B3YSC5pOaoOYmv+JiXz7T4+v5c220hFvSFjia2O5rXNL9/+wHYU7tyNK8poaAv2VEYcjCPY0ygv38wRxlqB0MxQ5dPKkejiuEoFE9MtZep8Xcr/OAo2AyOdmuCO+nRhnYCayJepOOAZF8BOGoIM7AMcpujOEhToM8Qh/qJpUdwdRHpQKSDP3S4XigajPWo3JLxHAjUIndD+AQFIS5CKAVHMU0jTIPnX994/4c/36ccCjK1HpStWjadyDsKqXA+JYVoSjcielHFjVU5Rlj94/AWkLhXdKr/7vvv9xt8HFOYY8Xu9hs365udYMUQAaiQR6Sfa0FHuLadLehIJ6J3M2Q+D7p+9KFi2PSLbnapAaX7bJjAg2n8IXQF05rEb6cQ0q2L53mayGmghg4RQuZiFwmdygAZBWwIDzKv0JrRTi/zDKEe+QxLZetTTIGjssFwn3yWpfHh/7wJx5iia6OELqJvxnVhQnYKiqtGCEQjpiHGNIBMMYZxq2cx8mEYzgCaMowJZKohIhuM7yvpK2pRsPUB9L38QPJoz0AxfRAcBdW6YJLoUQXWBzmMMUjXOv/pR79949efaceifWoXpKq9A1mol/H4BE4zjBFg85tGI7rpA3Ub+o4exaQ0zZTFHYVZJB6IlbYpvvuDDyTWMKS2/KE0w4pqmk3zjM+3+H2lnS0KgZh5z7W9cEnWzHMt0EcCobiwiOPfWhFqyhvJ8UfnYAPKStxXiCnYQCR38pypW5pGyHI/MndpJJC5CxQwdXHcUfXkIlvrpctmuBoHSzmjHo3hjsJOjh7c9bANSQi6mJGBnaBahwj4/AB0BE6+jwjZchRIbHe02xzrMkYA/FY2CnjgqCnJMsYraMZ/e7OEbwxi/1YCXF7gSKepXeXrs6R6rakeSxxe22uO0C1hiKNciw8c/elb4Gi4T+3gDKXAUZRibjnKNEVxR9Ftc3DUFN8cYyzQ0vBwa0COQuJ+qE7wnX/7vXQArfsscwyOQbdOsVfhd+C3t/m78c+1oFGPJbmzxa//ufYFwKsIwa4/CmC3ShCQzADd+iB/NNs/loM9sI0vNVD5kYWPfIjJB53NRA6M1AZ30qkJEIL9IoKALpWfjAK6OtKnCQssSaE1wdF4mMpZcJStmtWOJ+SDIXCUq/fzDOFumZuljzF1Ubo2TOgiLH8kRHYKmp/WzwFeQkYBkwFaUBCgIz/iSDUrAgYvP1roBqo50cgd/e0hmnIsB6KIR3LdGj8IKrOdQHLAeA/A8bEeU7jHFGRYw/yBwL+9/t4P3nhfMehjal0wKnC2bjxKwWkxR5lmFHexiYEcYlgSmKPJns37Pmj1hEgvtEQO1HC/+4P31WMJyIP7dCGIo5Ab9G5LAbdD6hCJcwxrmhIk58GvP/qcpriX3OG0YHyOPQgeRyFfh5amC+C5zU4INUWm6kKEEIqLfpZAlCgCZO4CBb2qsHAwJ7Sm0N88aDw8nYujtoOjhqm0YgjFUZ4hADkATeqCSgVA/5SUNrgTGKSd4I7iQIzEARHJgeU7zTakod1yNIk72jOQeIplM94wTXHZaEZkDvebInD9bGO0lD6AOkiH7sODYVgcjYCj3cZArynAMrp//IuP//Hf3hIZHRyDF4yEY/qG5nFH0a83MEdhtPKOsjBgxceW4EzeUZE1Co7+r9c/Ns3kmFpfrybAHMh151PAHaCcgQgGptFO8N+R7ORrOAoxHn0uDDyZASDd5w2mxOPzkomF/uEMHINuS+tDMASE9GGVAB0rW4FedJcwiv8AkhAyF3vUxHSrAmRAHA2ppo6LB1PdUjtX6xUY/WyVDRw1TmegXmYobAJjkG8Md0nsEG4xRwkEJXN0u6lYGYT3UYy8RXEUBM3HUSyGxekWkCmCQwPhIIXCsiiI3x/VCBXjmVbRtGw8xzZHPqqXQZoFjsKw9VohesX7rLE+a6TPGgZH+3SO19858J1/fIOrmeWbA/CmkOCyRxa70dqKan8W1FXI0XAvei8U/2DwAPRzOGwJ7kXBPsIy+gUmz2cV9O/9+D3oqB7FbLvMDgd36GKbhZfx+RbPa3e21B3FST/X4kbubPHrR5qaw8hRUwiHZQrzBuLSiTnV7LIE67o+fYBpDOUH4jnyIQaPOPl64w/hqGp6RTScRn8dq0ZxlKt1Y2u9zWpfkFgCHI2LrXb3Kd1QNrGN4R7I80DHF8KAVU+H/tYWYOAYgjtbVBgZQ8+10EEQGrfYnPrYOhXuhZRjKEGHoD6UEAwne7Ve1ezSP79dXs4wMc0BzkC0W+vhDsabZQ7pzDH8l2zgE9MaZw1AoYAWero52CIZ/d7rvy9uYgpMrn4LmlpQj3OH5qC6gmlAt4T6LAGMELwjHctE4eXdBl+vOcQaindoPY3SaeV0Orl+98LdjZ5+7Q9++WFdr6RTPNqhtNEH4h1G9CrwG94aJRiQ2GDb6OTYTNvZYlOIAMZA/EWAmrtrkaCWCCjINoe3WoRgMNGr9nAtIbrW3T8YbZdOs/XoIQvTF0AjAsoags8BKgN07DYfgiRmwcJCjNq/e3rUfuSobHxONJDk6AK4ozydB3d0wLEoNvvZaifsBEfZWvSXZb1qLyVHmfrnP2EevAueA+b3lpQINPW3oOt80LNMI/QRXLoTurVZOP7ax419Rh9nOModgZgXKKabG8TTkGNBiAVNoTJgWqMgGdjJHIiAfK3Ssb/94TuFzUyRxQeO9mn9HHOSN5hBKZolTLcG6FYfgDu6VS2B38EuvbcdputQTDKV6tPMlncJjzb2irXD7Sxpr8RMV02yBwI91jBkzKAd8hJLMAB8G5sAxHxNR3cHmqKWCOZoOA+6U2aGuOXo1bgE1hD0g2gwwtK5uQY35D9sI64pDEcQ/cTREHgO6H8AhgODVNOX4mgv7igIyoPFS4P+AwdY6zcdVcwO2p86ylB54ACItX0qcNT/Yhi6AAB24mBGPv85AbwLniPv6GYQxezEpzX6KZMhIBiCZAA6xa13rhS1KWvYA7zBUK/eA3G0V+97t5JH03iEoznIAVChrQ/B8HAGYgxTEA5gWYJt8rH/+s8/A0clAwEYG7hOtinGtkBSCKEu9JyjeDiHE8IEYA7GWlUOCKiiiQRTZy/t6C+q621lSOu6+ALDZL/Fzbb4O3VeyJWReaYQAMEb3e3CtjHjiWFYIoTA7Hop4I5umfpUVprSDqZCf8ons2CqcCDMNfq4lgCyE+t2PCXAvvZ7BljKAFjWcJ4G1GfphRydEMqO+grAPFjH0Y16tRs5qnHz1A6O0jbkWBIbfWylA1Z/7Fl0B4qh9lF1lKknEJTMUQRWsux0FJ/BwuE49CwEUabB92ElVzya5FgDNK2dNxRtV9iP9prEEzkoBSD3hZcjU2FgrFHM0TCEug75+H/5nz862sKWDQX6zQG2LghlFhNSUku87xlHA3lHYTyYsNAPxEDTHkOApnHyzF7NRGTMnXlrX9HP3z2sGHLx9E6G0ccaSrZpA1j5/E12dEtTY4g/EIHO1NqXuMYACApZNQoBpvCeOkrXBCgBmhaAdgiVBxxFaz3mKFdlH3YuiwxelsIOkRWPsjCiTI3/KwHjUfIKw6/bdJQQliG4E9zRvJc46CetGPAq6E2WzttvCb9XynznSFf/YLh/OAKhlD8ULe+zcCxRqABgoYGUlwUppjYIQwI74bTcgTDYDI7+H9/51/IOvmIkxDOilYED1YwuBLG2bzMZBUF9kOCiazBGgf6hZJfa1aF280ezwol5iNYwlv1Gt2bY96M3P37no3LtWJChnIbzCybnm2GdMUe3jy6+vT1peY7tDj3jkzX6UsjXTHhimo+mPEuUATJpvGbvqngoztR62kWTEAKQo9vuAOBf+G0Hv6X49J6jjjhU0TU+QjZ92x1wPKSXBWj5VnuQghBH1S5Y6MFRYMS1ItR7mHJbv86LL/dsTD5KjrJJBCVzFGEidpSD/iVOP8fg55tDPFPwzQMdJZ0q8RBo5GWavM3iiU8aRZLxrGA4BasDz5qERRxSexgPLoQTk587EETLsXTsz/72X2u6hcrhEFfr5BvD/eYETFauFcreEBLU6nnOUVAc0hU0HsYwXvgzNF66bIotG/vFu8UfHGpUD/qFJh9ceavCwxxI5Wvnb7ajEXAUaibo2AH/usl5TGCN9KldktEMJUfR/RyS5ZTM0d2Eue2AqQX4Os5WIUc5KidHbedrnEKtc8y1LNC5mIoZgd4HOUCf0gnRFConptb3Ylg6PwB2bsO3E7BtJ7ijmJc4yE7uJgE4oN8Ukg7HuiSTlT06WKzlo4leja1HPVvao61kmKHfsQkQ7B9IcCFh0AehhZ0gN2RabJOvQzL8X/7u+40MGTjKUtkE5qjQmnc0wAQ7LR5oWWaUlkEaAMA85lvj3IFktwZZCMMDDyXWsGki9cNf7n/z/XLtcFhkRgGpWezgDmeZWGGXrzbwbbwKIWR7NfNMZQOZ9Etg01GUdgOYnRwTgg9lmQ4mfEQ0mFBN5sBRAHayTJuTE/tGOsLI35zZYvN+Yv6eI4kSMJMJQV+tUwH9P3f4FkTK/4+5twBu81q3hn3KJ+U2KSVt0jTkOGZmZmZmkkmWZYFtSbbMLGZmMzOzHaa2SVMONKWUg01a/Y+kntzMgf+7d+bOfF9nzZ6trVfy+7577fWstaWoEI8gzkOV5yjmearFsaVL0FJEUxyNbkOKoueoLv7/jzn6bwj6nzgK0PPyXzmqIyhVsczqXuP0bhTVCDols+LB4+1S3f+xmNW/XknvE46dAq3tUK7CBIAh1v1Et2aTAYLRtwXmld6zRu1aauAP7rf1raNKZUPrVOkMv+e4AMyrbE13sJ6j1J5lPVYNHGVojhkqA8yWLlr1nNbtEcKsyJfUY6fNHCITc6qG5t5vYo9y+09IJj9ukKzod23+3+GoXjX/haBwo3R0VK0BaTCtmib+VIsYVuxWq2QObrteI8Ag6b5SrNuBMewV/qPVfaEbOKor8f+RoP8nji7/N1s9LZeM2sQLUMf1n4LOtQnGO4WTIKLSvg1o27kjUO7Z6iXdN0tkiy3COd1G6X/gO0jsvwI4TVOuGEBXAcVXGeo1A/5pxNAHr/mQ3HTNBgAIqv+ga71dMq+ePM/tXq9s0zgF58LKaeaOSIaOiUeOo5okFZ3dvUsft0oW4O6ANAKxWN3HAPA++l9lWoLoKh47UUVVHbT3a2GrRd2Lwp51unyV03Nc94tifTCL60BNWveyHvrvoWq2dD/kBERX6zbUdHvXuk9cQH424Q4Kutc8grJT8kl9E6dhJbO6dPELqMDsPwGv7dT/e3ndPo4GXgs5cu2hq/4nPDQ5Dy2NAbC6/lfAGzoDtINFCPIPixaEkz94itm9BYvqr5uvWdFMvT9x7FrX9AdwFbobOKj7YFx3CRABlTpfqKtFynXDyKOt7kNgvVE06Jd+utcM4md4aBgxDP4F2SJVNv+wNWwcgQga+lCxDVvyhj5kIZZy2YgNq0q1CkOQliAkcdVLDMk0sLOTP1bb2cUQz4A3heTE6VpjQawGvflv6PMjNF35J47+Eyn1XwbQUfDhiO6qHuGo4VkAnHoDewROI6WkFVHBkA0fh8XD7wV/PJeG6ahhDdE1YKwX2b3HDOzU0QsmW7MBAYuiXIQwzu5bQ5I5pk5B7Vy1pGdB2LNGly1xgVhK4PEWSLWOnT1LOnTrfhFXT9Attv7cOnUF7mjnf3F0VTpw1CssL6u40cBROEw3oyodyQzMA9cBEwww/LjD/y2OwoUA7Qw1HdYb3EkgIix4YEYLfwLuqnrizODCx/KRE+DrQKfATekcv47Nm4IB3e+uwYxw+4/pbZ4+PetbWPYGl6hP2yuPTvpfCfuRh4YRQ+YB2umgWAJeGlpgIYzQpHBKizACLXhOAyFhiunSWaN20WwLSD13vEM4DXSEQk8TTLQy+9nS6SZGL0s2C4OQnFhqXXJi67iy/H+Efh38BVgNus+u9Pjr/GBxqFcedtiaVYBhxMBRPf5iMOsvrMiGTsJp9M5+UERgNzIHQeBB8lmqRap8prCaw+vfaAGidMNtPQF3DcIQ3B2DBHKh5KmXGZqlDtlMCrLO0j2EIugGjoqAiFLg6FGKfFUv2KuM7mVG95IOXav6hfEXR+GvG/j31weDKt2sS4eOeYbmFpR39I6foouBo7rqpssKsiWoBrqp1egmFZYNPNRv7vx7GBzOoyPwWgBQ5H8FcA/BmRjuDLd7k9+7KRo4BksdYoZk8Ci3a4UmmQaDx1LMs5UL/K4VOED3rW3JPLNrlQeRTrkIDAFPxdIrrv4He2HiVmkqw8zqY4x+0g0PH87+vyUDiKWOlwrdxhEkH+AftNA3jEALWejhs9DnqJbAcBp16pcUcBmeg3vNlM4I1YvKgU1F/wZdOC7QLMOpg04AteFc4eBH/+Sj+NezMUC3LBSL/0rTf3r4yMjqQ44y/4LuWd0umGIJ3AiUeDgfxfAJtnJRMXqyjtXfwNP9kBNNtcDr3wJGGj4VM3AU3gT8K129yOxabhaORWbh7Lwi6MIuade8sGsJOCroOc6AzATpSrNC71o0AAitF/tNlmoD5pKlXtJZN/DiGgNHwZQvC/s2XAIySqtoXaNHYSXrPu9Q6owKCAzwDMRbx49/sBCO//+h6b+Fgan/ffxbgrL1pISVzNWs8WBNQthQLYn7NgXdq3z1Etg5kKSxpYvTq59QeCMNlC5R97Kod0P349HKZaAvXDu8Cl6rY4xy2TDOVC78xTP5IuBRdj5Kyn8rXrqXyHSk1LFTqgs/oIDARZhKQwdg2PoEwCCcJFDRSP/55yobjKBsFuo7RTDOkc1IulfkfetU/ihcBlM+RxVPwwFAOMO6+U8cffQUH3LUcCUA3c6rHrqrVS4D6R8d1F22XOdImP9Fzb9gON5wAVmodiAonLp65ISkd72NN5qD6WSowNnMCvs39WK8ytFsAbd09NJs6bV/lQGT0bXSIhgNTkE5+kYzRN1i9SwsRY58RTJwGg6DuQSOMjSL/4COo/94E3Dki8A/cGCGbxQYOAqkdPBJwZJZqqE1thzi1ypIDhwmHj7N79f921r9TsgyrBmdkKtWH5qW/yZYuq/y/A8Al/CvAPsO58lR6cw3eBtYS2z5HOgOVzkn0iwNTJ0FJZpYvrB6/LK0a5HGG2JKxlm63DwLZQraNt4YeD9wNdDXs2rmX9pZHWsVKwCGHOZ06SFYYDD+DUDyFkH1AHBWPA3Y+lU4MVHvOksnojowdcoK1RuW0yKcqrp/06hDPAe+BMSpQzABjIQKCJPHlc/C2QNH4ZJAWf+qqjKd4j4k3z/BsJgeqv1/6aiei/9EU53t+HcchcFH2Wl4Q8OzsPqBndh6CU0yI+vfbOMMA0crmmTAUdHgZjNvVDZyAs4Q8r5o4ASEIbZ6kwM5Rkd63RcOeT1rTbxh35gCj8AEtrjXwFGeck0xdI7ffYyv9wN09cI/sKhjlXIdbitMko6jqqVO9YrhGwU6siqXBb1rdl4JpBaRvH+RJZvkdUOynIKaKB8/D6Rs5E1WdvTWMIaBppCXH9ru/8TFR0cMfv1/yun/xFGWbpdmvmvk5NDMebDOwMux2Q+Gpk7Prl7aOHVF3rXUP3p0efNTZe+CXDPfNbCmGtwCNVUNHpP0rlJ4YxzFrKx3gyIY5crngdk8xQK8m0C1JNAsCtXLwCGOagXuEnD0UYI+ylG2au0hdEVJpzXzBmsB9xbchYGmBuIaOAoLwMBRqHVC5awRTCE4Zd0+qGpJNXx8cPaD0fkPR+bPq4Y2QP8hP1GFE63cUap0pk041S6a/k8cBafykFiPqqmOi3pJN8AghwbAkoI/+hA6wVcsPUpQA3ENgHVSXMWmi6ZZ0jlgJ6Q6ONtm9lAju182fAzMtaB3XafE0nlx/4lO0RxHvc6F1CVbMBQpkJM6Rp9zUKp/RDpfPgi1HoREoN5QjbwP5R6M16McBU7rE6Weo5oFjnoejMQ/cZSjWbJ0iyS3i8Xdsyz5OL97oUMyCRw1MPJ/ytGHNH247/G/xVGoOaKuhd6RE6Oz5wYnT43NnV9c/3Rx/ePpxQ96R9YbWqXTC+fe/+gGndPTSVUNTxwbn39foJ4D9RpdeB9aae9y1+BRvmpWqJiHcbFqUdy9KOtakfWtKHvXFWCu1KtsxSpLvgLGiS5ZBEAHACMGwLMAjlL3b82B0GzZPEj1Q4AIgt8wbHcCoKOLQNIZGIczF3ctc8UTRnTpPMw6UzILUYkmGOMr53vGj0+ufCjsnmtidLfxh5u5w02cYZCoNvFcp0xHAvq/A5wrnIHuJHR1eYmpcx7AzgXD5wI6puodjMHNQAvmAcooeB3wPYYWFhkM/ieOQsVJL2mBK4RThZUDet/BG0VWsaBMMJXz4oEtsKqGhCgZOgFqCnUf3lNnpFSLIISwWGsoahvPmNDYLIlqVNEzD9VNqFrRjJwRdG/ACcBhTGCnek4PHU0NHgO4aNBRiD5/ZXDVms5kS2dM7ENr2iQC9RRbMSHsXaLIpmkKXaEEbeCBc9A7KunAcfnQSX63ztf+M3S/6bwMJsQAtmbZkB0NMERJCC7/FoanHm2Bjuye9X9qwU0qh7YkPUsAECQGb5ArHtP0LfUOrbV1yjaOftJOkecVEJtaRQr19OjEURa/n8btRaDqK8hMae+8qGuOIR7hKmegUDAkk0zpFLgaUFO+egEUTtyzIu5e56rWOcpNlnydIV2hS5YB0AEwZasGsORrALZiQ49VvmoZlBjeBBQaiMiSTRvICgTlKxdBp9ngHyQzHNkcqLVUsyqSzRmpu7e4wmkGZ4wrnmIJRjtYPXzV5NjyOYpkkKWeahEM49oU7bI5Wvd6NXuc03ccKMIRTxnAEk4w+GNs0aRAPseTzgC4kmkAPPVXRzZn2E0AQdWptXTB4Dj1c7AO6dKQNKHUCvq2BH3HAJKhU2T6EL97S9h7rFM0o3Ou0nlJ/7EcdCe+UaYaOkEVTBqWXQ6yFV8nEfZsAsMMkQuIpbOSek9JVc10SidYqjm2ZpYhn2TLpzA1bAeP6JCYzK6BGY6oX6gYV4AkaBZFqjmGaIItnVYMbnYIh9mqGWE/1JYxTvc8VT4FYatdMs/rOwbVv0O0ANoMC48mnILaZ+4YRROOgLrAm3PUs5zuRYp4lMUfUKln2Jy+phYpXziu7lmXKpelqlVV31GWcFrRs8EUTApUMBPTHMkEaBIwQNK7LB9YE/cswdwDoIzyYP1ACJPNsOD0BjfZPau66+pepqoXqPIZ8N+wcjjdEGuWoYW+YYSuhGis+8VdYd86v28N3Ih4YBPeanj+fAdvUNq72DW0yhEODo+ti6TDAlFfWET64NAKsrR2ZHRdIh2RysZ5wv42luToh58rhqYpoi6abEg8uNjMH6Appmlg+pXznfJZ3T9nl0B6noUJpUkXGZJNpvQYQ3KUJlqniVYZknWmbI0hBe3cAOIypRswQhdv0ESbeqzSRJN08RiAIRlniidZMEeSKQBYL5BqPljhf/CHL5sXyhbF4iWjlHR0CaqWVM+kcTRC5TDkCZakVzO6yFWPAk0Zyok6Vi+J3gvVTfc5tWQRKELlj8ONBrLzpHPAerZomi6cFCp0i0AgX4DJg3c3tBy5Tkc75fMQIODaDL+0TQEPoFyEiYfK+GgLOg0A+QQqgwEHpekQToNs87vWWzgjCBwdnCjoKCw+AFjn1Py6ds6ooOsYV30U6jJDsQavhXeGP9EunWb3rMAE83uWYZJg4mHRE+pEzp7xfsFpPUNLYsWETD2j6VuRKOZYgnG2cIIlnBJpVpiQDpULkoF1jmaBDSrSNU/TLDaLpljdazT5HFh2XeCQzwLDqNxBG5fIZoaGIuxnyka4XVOcril+1/jQ2NLAwAyLKW/v1IlTb/+KXL2o7lmFxcCTTYnV8yLVjGZwRdY9J1RNiNTj0u4p+cCcfGBB2jcHUAwugsGV9Mzx1NNczQy/d56hnmwR9lOU47yhFXbvgnhgHbyNDOpvj84r87qhdi0Cm6GeACmhXMCIbgupZ1XXdkEohHI/h8C2cORjDH6fqnf22vVbUkk/przWxsodWVxVVko6cfSSWjXe37cAxMUSG+Y2T85tnOaqBhiKoXZRP105xe1b61TOdSoWOuSLgHbZgm4qweNJl4F2QEFgKl28RpesGEQUaj1UdrYC+LoKfNU9pcOG7gDJtI6d0imdKsM06QExXaRcAgrxJXM88SxXNAOtQKrjqFyxYmTv5RIUG+4XGewXGZqQk5VdWlrT1jG+BGZU3saSUfh99RRVG2sIVJ0hnmtkjEIc6ZAugaJAC7Sgq9bhRFuE4Gh1X9fvFOt/9V200CZe0B0jntPL54yh1a+/aUOrr2hLj7aGPaZWwaSw/ygoqI7cohkwypBJy2oEmDqxuA/M+zgACj25Q5VV3ChUrdH4q3T+Fl28xZKf4GlOCXpP83tO8bqPw/nQZDojTxHOQX4XajaxBKGVfaxfUO7wyCm1ZlWhXOnqPSaWrVCZkxLFllR9TKjcEig2qaJFuL+8Lt2/N4T6y+1foirGhBCMlJNUEdyHWYFmEujV0C509o2sbKRXNtNaONIOsaqRJ2GoumvaKORWCra6kdBIoYrUbRxFdQe/ha1u4qhF/XA5fXVMcZtQyekaokg12KZOJKEeTWwsJzVhqpsr6zuqW+iEFhquvp3YxmziSinSrmo6r6yhnUTntUu7G7lqfKO0jjbQwhxpZA5T+FNQRpSDJ0W9W2wZyMoqF0KteJYugmC+KtSscxUQOxbYstH80rqJ+dNy1RhP0HXhwhWFrM/O2s3G0iUiJAlVXDXcP1tDaFVIBkSCLjZbOjK+MD69xuBpVAOLTMmooGe5htoNMwjFEHj5X1/wkOm2emgSnVEE7QDV0G9mzXIU01BYeKpZKBH/KOiz4CdZ0gVd+4gZhTL7EELFkkC+yAPTKJnjimZ54jm+ZB5oypdOGe23f9c51N7G18rC09Ij1Nsl0CM5N6d7dCw1pzApsyghrTglC93YIpTKJzm8ITDLkIfaFavNksV6wSygSbwA/UbRfJt85VG0ypZbpEtt0kXdlq/O5y2AqkELVQkAValTOvUowMxBFQOtAogHjxqCjsGxQdIqJXGp4indJoXOhg73TJxCYNoaKF2qgeNC2VG+7DRXdoInP8lRnGTItiiilTYBWGSIO1scJaxdsPlHFX0ngaM2jokBgYjuni2JeIHNmZDL1/iipaa2IZFsgwt6wF+Qdp1gSVZ4ap3TqKUNtAvHaYrhWoagni5CEhtTCtDpBeiYtPyIxIzQuOSd+w96hoTZensHJMQHpiQ4hQaHZ6UFJcYl5eXEZ2dHZWTGZOV6hEZbevi7h8V6RsRH5xQ5hUQ4BIV4REX5xMV5REc6BgY4+/t7hob6RkYa4BcV5Rke7hIUZOXh4Rwc5BcX6xERbu3n4xwW4hUbDS93CYoPTSiOz8Il5VYiypvBELeyuhuoSr5qWt6/DAUdWEUXDYOVlPUtgW0Ta6ZG507iqykixbBKM9bfP7m2cnJ0aJrexliaXl6f3zqxdvqLi1eWpla/+uL7b6/+9PXlH25c//Wryz9NT23JFZM1DTyRUlcbgfc0MVTFFYZ8lSlfp8vX6HoJ0EecKeCloGsWMgxPPclVjfPUEwZwVRMcpc5o6TGji0Q6sv7FV450kSOd17eLAtkyX7rEkyxyRPNcPXjiBaAsSzRiZOV32CvOyS3K0TPGIyg52NHPJSk3c3xhPik9y83TLywyITExMzYmKS+3qBJHqmtiNLAGmiULHcrVFuligxA4Og8dAAwCoKP7JWjZkmGkVTIDabdTPE6RTDwEPOwQjTHBwitm6PJpA6APIwCIHS2CUZpsVscPGRwwi2uQoGt5ULIhwMF1gpVhSifSi2t4yhmwLzzREk+0CgzjS9f48g2+ah2MOWQskBN9cNFt+vKgoEum8kqaLewi4hPRvb3rGvCI0rn+gRM9/Sc6GcMS5YpUvcGTLyr6joJONzD68U3i0JSysLSS6Kx8v5iY4LgEJz+/I3b2Vi7OJrY2xlbmEYkxFs7W3mF+1h523tEBXjF+R9ysoOMT7h+XkhARF+MfGgpwdPM0t7F39vZ19fX3DAy293D3CPQLig6zdLK1cLIKS4qAN3H2c4P38Qj2tvN2MnexNnEwO2RrYuvlYOpqZePj6BTkbufnDHAMdIOHh2zNbDydHP3c7bxdnAM8A2LDguIjvCMC47JT0otzkxGZIYlR/jGh4SmxUekJIXGxTZ3cjDxMVh5aKO6pwNV6uPqUFpbd+vGW9vYD7X2t9net9pa+YwA8/PUP7S2t9p72wS/aM1ufSoTDCkj0khkw00wRcMsQ2NeYijV9HV+CDK5z87JJoWYWAI6CJRvmKsf4wFHVOFcxDiMc+QRHDg5+Vs9RXZLWvY90gSmBFTXPEi8CONJlAFe8xIE6pgdXuMgRgwxPGXnHu7jFOlv5Wlr72joHezr4eOSWIufXN/OLUR4+/rV1TRQa1d3D2dXVNjk50svfG9/AauANs7qXwfDRQRc1oJQLFMVsO8ihfIaqnANzbdjB0XWUMwzllAFM1TSApZ4xgK6YpMknIJp0SkY7xCMA6MBDXu8ycBQSABAUAjXQFwjazBkQ9q5AIunkD3WNbxFbRaQ2McgGLDKpcl4kmxFIpqAoCBQzAvUMLGhe9zQDYkHXFF3a38pVMCS9zSxxekG5m19kQlIBl6Ph83q5vF6JdEymnq5rFjZT5DReX0OnpJTQHp2JDIjLdg9NMHX2sfHyPWRjtcdkv6md5SHLIwctTBy9nd183Vz93QKjA+y9bLxC3a09zN1CnN3CXfbZ7rXxtfYM9Q6ICvIJ8wuJCktISwoMC3LxdA2OCPH090bh0DVNNc5eTi7ezvbuNhYOJo4+dnuPvGvhYuES4OLk52DpannEycTMxdTc9YiDn+1+6717LXYfcTxo6nTIzNnYyt3UwtXkkO2+I46HTJ2MTRwOQsfcxQRg6Nh5WwHgKWO7/fAQTszKxdrO2T0qJmNwZJbDkdjbOJseMkuLTeqsa2K3tjdXEBow+NZKIrOhuYNErkFhWyuqO3FkWkUjp4Yq6RCxm/gNBAqb1sWk9wiE4BEhGc+DqWDLlpl6QJ8lnoV8QhOMAQW5EK2EQ1TBIAvmEVysYhKoqYMMcvoMiDHkk39wdEHP0Xk9R3VgSxYAIKIGsMGhCefYIt0mlJGJs4WJi9VhRxsLNzc7H39rDz+/iMQiDMk3LNYnJLymsZHcXBMQ6REQ4RiV7OHsb51eWIysaiG2cACEZnZ1G4/UysXX0+soogaapJkpb+Oo2rlqA1o5mk7xQIdw0IBO0RBVMkKXjTHk4wZA3/CQrZqCiMDrWYAICcymyaao0kmQ1UZ2P6FNTpOOs5TTBkeoHF6rbpfwNVOyvgW6oEfVPSaSajoZPFJDG5pALsRW5qCxGShURFp6XE5OcFK8X2wEKIpvVOARe/P95gfdPd1ycnJy8xCpmVlxSak5xcjo5LTY9Ez/6GgotSb2tnstjxg7WJq6WrsAr2KCzB0tD5odtHK0OmxpvP/IPmtnC2tHcysnU58Qd0fgcKirvY+Vc4CdW6jDfpvdxk4H3cI8QfMsPe28I/z9Y4Lhj1q62GSV5CIwhVNrMx9d+8jW03qf1Xu23jamzoffs9yzz/xdc6cjdp7Wlm6mpo4mZs6HrdzNbL0sbDzND9rsfc/inQNW7xy03m3ufNDG9fBh673G1of2mx/YZ7b/kJXxETtTU3sza1cbJx9nMwdzS2cr6EMLfRiHjq2b4wFjM1tb16bGdmRxmZuDy3tvvX3grV3bjIxeeeyx56E1Mnr98cd2/M3oRSOjt599dv+LL+95/Bm33fv9ja13GD3zitFz7766t4VIGx9ak0kmhdJpvn7rRrc9pN/a1NlQMKa8eRp/FlQQSEbhjndyxgybRwC2ZE5PviUAR7IKwYAjXzJ84KnbLDfUff2GKLzkISDXs0WTLH2QpYvmjUwdbEwd7Y/YOx62cTWx9Txs53fIwnuvqbtXcEomEh+bkWPsYG3v7+QR5WLssscxyN4lIMAvIikgKsUrJM4nDKpJRlhCFjwEBMWkQT8yOTc6NT8mDQGITC1IyMXF5uBjsnGAuNyKRAQhpag6rYQcnFQCCElGhqeVwVNJBcR0ZG0muiG+oKa0lptcVJde2pSFbg1ORiciSDnlLQh8G6qGga1jF+Baiypaia0CQhMPga4uLEVn5WdHJ8b6hgW7B/g6+3k5+Xs4+HtYeznb+7tCuQRZcg50sPYwO2D9rrWLiZunXWRUUEh4gKOb/f4j+90DPd2DfPyjgg7amDj4Oh+yM7bxsHLytztsD1J06IDlHlMrk/2H91nZW5pZmx6xNHHxdHL1cnb1dvQKcnXxsfcJd3cNcHDytfEIdtpv9c5+q3d9I32t3GyO2Jl5hXj7hfraudqW4Uo/v/qpolvy4SfnHmjv+oZ6HLE5uNd09x6Tt138HUNjg8LigoOifN0CnUGY7bxs7L1tQQ6BrKCa1m5mZqCRDgddfK1cvK33HXnb0sHiPdP9bx/c/a7Je9DZc3jvQUtjGzc7Z19XJx8XkHl7T0dTe/N9ZgfgWRNLc0dXj7z8YmonIz0pLdQ/8M0XX7I3PuRqZrL7uWcOvvL8ey88u+vpx3Y98Rh0Qh1sHd552+WtnUX+gXXp2dav7fQ1s/O1cRNRhCpRn1I2JpGPC6RjXNALqc5usWRgNGfEmi2WaJkpXOLJV7myJSpvAgCSqWfnHKisXiYX2eIV4CgIMEe1yFbNAVjKWX2iMljVKSaEfcmobitKOsYWj7LFw1AkmcJRumDGyNjUzNrB2dTGycrBx9jCy9w+bOd+D3uvtNhsUmgqxswz4pC7/2Ef7z1udpbh3gc8Haz9QkKS8q08Qm28wu19o8xdg/xiMr0iUj3DU7wj06ANjM9xDU6AA9xCEh394mx9Y218490jsvziCzyjcryic31i811C052CUx2DUgDQsfNPtPSKsfaJg4d2AWmW3ok2PsmOgRn2/mnm7nGWHrGOAameEdm2PvGuQalhycjghAI77xg7z+jA6HQnD4/AsICgyCBXP09nH0+v0ECPkABwbBbuDpae9vssD1p7WoFEeYY4W7ub7Nz3amiEb2IS+LUgL3/PfSb7gEx2no5BsaFRaTHW7taH7Q69tX/HAfNd+0zfcPU1c/e2cnV3cnXzMD58xNPby9TSwsbR1sHV0cLW3DfE5+U3XnL2dnD1dTpovjcgwts72M3U2jjA3zsqLDguIgJdiHC1tkqJiqzDY9tJxCZC5WRfj/bB7zGh/gfe3Rnk72FhesDby3Vza/VP7f27f9wRyAXAKgcfJ2O9GQVLamx/+IiDmbmjmZWTuY2Lua2zpY2LpaWDJViOPcb79pocMLYyNXewNrO3OmxtBq2Dp4ubv5e9hzPYEjjggPlhCzsbBxd3Fltw5tT5+Kg4J0urt154Yd+OV9598dm3tz1hs+fNd//+5Bt/M7J8Y/u+557Z88wTdm+95vjaK+5vbXd6/VX7N14LNLdyMzYrTM3BoyrKy6rLysnZBfjolKK4LFRRRUtVM7eyiU9skte0qBspfe3sYQpvpIM70ETvqu2QtbJ6G6jqmjZZTauitl1T195NbtWQWpXEdhmhQwKoahcT2oXVFEkjQwn1FkAR9XTwujv5XVzlCE81zJQO8BVjIsW80b79e+wd7Xbv2bf3gPnLrx/ctdcpMgmfmNeUVNDmEFy0wzTwgFfCQb+EZ00dX7F3f9vN/4BHpE1gyn6ncIfQDO+EEtugVMewbDPvOOj7JpW4RuV7JxQ5R+Ra+iW6xxR4ROW5ReQ4BqU5h2S4R+S4hmUBoOMSmukZlecRmQuAjlt4tkMgUDYNOs6h2Y7Bmc7BWW5hua6hOU5BmU6BGa4hmS7BGQ7+Se4h6QGxCP+YfCf/BEsXSB6BXoHhAWFh3gHBtq4elk7udp6+Dt7+Nu7e75pbBcTF2Xq6O/t5+EUEOHnZv3NgJ6igr7+ft48fqEtgWCSEmICoaLeAQK+QYPcgP/+I4Ij4iNjkyPik8PyClPSMKB8fJ3d3z8OHrXx8Qrx8QW9DLO0cnd29bJ2d95sY7z20z83Hw9nDCZ4JjwyxtDbz9gLDZNOIq0wJjeggksx2vu1xxDQtICjGzSM3MqoBjR6Ry/MTE7Lj4yP9/ZysLEoQuQ/u3rt///6tO7d///OP5Mx0EzurA1ZmflFhFi4Opo62xraWh6zNIaKZ21raODnYuzp6BntDxoJ1BRYCYO1mZ+/l5OjjAtnL1MHisK2psc0REzszeAqOcfJ2O3TE3N7BNS422dXZzd3O/uDbu8x279q17SnLd97wMjvoZbLfz/RQkOURm52vme14yc/4vQxna7c3Xgw5/G6A8b5IB5sQB0dUTl5MaIyDvbuTm7+jR5C1qz9YHOeQOJeQRHv/GPeQVP+Y3LDEoqD4fK+wFI+wxOD47NhsJLRBCdlBcTn+sVm+kZmeYemeIRkeoRlOQUmOejiFJLmGpnhEpPpEpftGp0WmF8KrwpPzQxKzYrOKEvOQaUWYImxNQWmNka+3jZOzqbW1ib2jg6tnUHhsblWtMB9NO+yUZuqBeNch86B30R7X3LdcsvYHIE1CUQd98y1Ci+xjMJHIjpRKfiyallolyCAJoV/Uoilq6Ua292TVSOPK6cWtPVXsUSKtp6yeX0CgFxIZxdUsaItITCSZg24QwHh5oxDfKsU2i6EPwDSJSmv5gGIiOx9PQ1TQS0gsVA0HSWLlYtrzsG0ITBsC21JU0V6Ib8soqolJKc0rqclEVCRnYaAfmYgMT0JFpZbHpGP9o/LKiJSsImJaXnl2ISYmMf3gYUtHF9+gkGQTM8/Hn3rz8Wd2Gj395o53zHfttzGz9YWI4egaANSNj0uJCo0uzkNkxMWH+QZFhCXaO/olJiO8/aO8/CIDwuMTUnIT03L8giPhfkHhB6FKTEpLSUmzBNVxci7JzvGxs/Oysm6vrAx3duli0DV0Gio5SdTSSquuLsvMQuXk4IpLrI4cMd5/gEQgXjh3UfuHVvun9sEfWit75z2Hj5g6OD2/c5ettx8kNmtPH1h1zj66pRgYkRAaG+8e4u0e6uES5GrjZWvqbAaw9bZzDXYLiAt08HME6QW7YuluBYNmLuYHLI0d3TwtrJ28fQJTkjOSY+PTYuMWhgY/O31sfaRvoVspb2vg1xFHePQTo33HhvtODvWc6JF21eNPDyqvH1u58eGZK2dPH19eCvINdHH1snfzs3OHGhVk7RNmHxjtHBrvGhFv4Rlg4xfkGBBq5eNn6upq4uZi6+vjHBbkFRXumxATnJzkn5joHRXrEhbhGhLpEhrrGJxoH5BgHxgPHefQZOfQRIcAqLTh5uCZ/MIBjgHhHqGxrkERDuCKgiOtHVyMuKx6SjtRKmJGx4SnpGZSGGK2aIgpns5GMWLyKOY+pQddi991KTIPJrzjgvz7ofi97vmv2ybudEzd65FzyK/QIgxtGV5uFYHZ750PraFvGlzqndnY1n1y9uKdJuE0id6LrhejGsQVrcryZll5g6SiQ11KFpTUCmAc16asaldjWuS4Znllm4rQoSF0qsvrxUDQgkoGtlFCaFXim8TEdkVVqwxfL8TWcgnN4jqqorZDUdUobGf0N7R3QR1ppAzUUwaJTT1VDV01zX1V9aqaRnVNg4LcIC3HdhCItJDg1H177WvIInKDHF/Fr6oRZeTXRyViLR2i9pv6mFkH7nzb6s3XjG1MXU32WJjtMbV9z9zb1sv0sPNhU7f9hxzf3G36/KvvhsVl5pZUFJeTnL1CXt7xzmtv7j10yMLD3T8jNSclPi0mLOrAO7tYnc0T/V20xpo2En5CI1UwKcKO5sWRvo3ZyeYaYk5Gam5uNqmWXFFLJtY2iHnSfvXA5Nj8sWPnXDz97dz9gmKTXYKjvCISvaKTA+MzwlLyY9OL4jNKEzNQidklQQmJwckJQUmJPjGRbmGhbmHBPjHRISkwmOQZGeYUFOgRERqamhKZmeYfH+cSFOwTGhMYGhcYEp2alo3HVmBLS3+4ekV777b299sPvr06IuKqKS1XT25qv7ny+cay9tpnM0Lasox9akit/fUb7e83wYbcuHotNDTcLzDCPSDSOQB4Fu8anuwWleoTnxuSURCWmeuXlOgZHe0VF+OfFOceE2ru5fK2lbGxq425j6tTqL9HbIRPfKxPQpxfQoJfYqp/Yj6YPb3ly/WNy/OMznQMTLDwCDVz8bf2DLFw8zd39bP1CjJ18oIZePeIxSGzI0aff3zmq8sffXv9skatXF/fvHr9h/MXrhw/d+3S1QdLx78R9pxkyI/mVyjjC7j+ia0pZZLw3A6bEKR7fKVzNNY7mZSC4YTltXglEeOQtMQyZjqen0OUBGbWwwHwMBFFyyMICmtE2ThWJo5ZVC3KI/AyMQwESVBcI4bxErIE3azCNKtRjYrSOllZg6KkWoCqFSJJglwsNQ9HQ1bzUNX8okpmOQwSOcgqFrqag6sT4Ov5hEYJuU1JaFKgSUIkQYitU+Hquksq5YU4WVmVmlDXj8IK8QRZTZ0SXUarwjNiIxHBAdloLIcIRxJllTBOELUyRpppA620/oYWZUFBLaqoFldYjUxHx3nGFERmViEqiguIhWUN4XEFEYmFYfEIPJla28ava+N6BcSZW7uDxDrYeVsccXK39/F3CfJx84pPiDj1wfrwuHrr+Myp04uzcz3ffHvp5KmF1fXJi5+d+fjapcG5kQISht4to/WpWoR8jbqP2kgTsuU9XaPk+g5ifSe6poVM4ZfUtCPJHaW11FIyDUWiFFd2FGJbEeWtBRVURAUVikwerjMHA2iHNhfbkVrSlFJcD21mWUseDm5dZ3Z5R0ZpQ2JWWQaiPCoxMyA4ikphIguLrn/2ufbmr9pbP2vYlA9W5rS//TguFRwbH7p8bGNBI/v+7Jb2+yurPQrtvZvaX3/94eq1r7683thCLSojFeHry2ppFe0CbJsQ2cguqmOVNDDKmykIEjmjHJ+Fr0AQSSlolEdM1D4n+8NuLmY+no6hIX7JKTGIgmRUeVoZPrWsqrSWDeUUamlJDbOUzCoiUiAoJxRUxuVhw9JKIMlYe4Y5BUQ7+kdZeQTawKL1cDfKSs0uQSArMVWnTpz99voP3V1D589/el+rvXtfe+9P7d0/dVu806uf9U98QBPOaUbO0YQztR3dQ7MXNCOnOPKF3olz0t5NaBUDx9AkfnphUzGemVXSiqsVC9QrVa2agGRCBp6Tjeclo+n5BFFWJS+5lJZZwU1DA4OpSUgqjEM/oZQSX9QRX9yeUU4vJHDLyOK/UCsEjpYQOEgiF5iKIgJBRYAyEru0kgEPyZRuTIOkhMhHVktR1fLiKmlRhQJF1BRhxNgqGRonrKgQ4HCMnIzKAO8kkWCUVCcjNmqQeF55taiMKGig9uHIQiSWiqtilpbUt9Zy2qtpvCYOvbJ1jKueUY2RiJQSbBuGSC3Gt7Jkw+QOUX55bTG2ISalAFvRXEVsxWHqGms66yta8EWVdVjioJy3Odfz/uak9qcvWvD5onbCL1+evXRs5trFrY9Or97+6auzZ4+2UlpGF6YEPSpsNalL1r08vvz+iQtCnrKukULnKSqbqOX1tBqGrIomwbcJyxv5ZXUcdC0PQ+aV14qKiWIEQYSoEORXCYsIYmhzsdx0NNMwAm0WlpOGpCUjqZllzHwcM6OkOjI5HyTQ2smLL5Dm5yK+uPSJ9sF9EMgBCR+TlYbPSWvFoe5e/fzmFx+fmBxpKSvqZ1MLYiJ/+uwz7f0/frj+w0cXvxBIejGkDmwju7JdWtGpwLTJS5ulyCYJsllc0SnDdworO4SVVBGRKsa2c4rJrZkV5HxCQw6hvoDUUtrIqOoUEhkyIlVW0SbBNskwjXJskwKKKhFqZrsKSiWKzK2ja4jtEiSBgsA2wZosJXbkYxryseRcFM4oK6UiP5PQVMu7+5tWJurbvetgRzsT7NHt23f//BMW0s1vvv1+c+vEJ59eVih6Ozu5ZGJ7YmxOSwMTW0YO9o8LDUzw8QgvzMOiiglmhx3NTZwSYrLjojLx5XUchjwmruiAfXRkYWtKOTssvykeSYtFdobmNMWUdGRWCJIxzKQyZgqWlY7jGfpJZfTInLp4RFM6sjOnnJaNpmaVUQrwLDRZiMDRCyuYhVh6bll7fnlnIY6WVdwck1GBqGzPxDSmIOuSkE1ppa1ppe3JRR0Q+AowrNIKLgrLQKHb8dg2dCk5KS5naHC+k9VV2SjMQ7fgGgWYel5li7CoohVZ1YJAErOykNiSisaK2roSTFlCCj4hFZuRX46uyytr7OT15mMau8Y3xpbPFlY0trKVCRklpHp6cyuHUNmMLSEis8vzEwvzguI6khJPU1u0KwtrdUTE/t3SpNhyi8MZu3fGvbY96Z13MoyP+G5/gxidqP3tvvbqj/X55a1YspIjG+8dU8p68FX1JZjqAlxNZTs3l9CSVdmciWtOxzRnlreCKOZiKVBYUlDticj2xOI2aFNKO5NRnUkl7fHFrVG59dGIxlhEU0xBE7Qwkoqi5GCpFc0C/+hMJ58wr+AYhbq/prr+wrkPL3906epHFz45dfz00uywRCBoadD+8O3vX1/96vzpWA9XXFZmblTM2vjMnR9uffbxV+OTa7nF1amFJJ1mV9BTyjoSUB2pUBireBmVvCQkSAw7G8sHGcrGcXIqOQVEQTGsJbKwoFoIfQRJBIsqr0qYVyHKwQmKibISkhRFVqDrlLhGDa5RhWtU4Jpk2HqdqQM7V9UqqaOoiK0idA0DU8tqoKuMkuNwaYl4pXjmj7taEa8HWYRbXlz74fsbsM70H43dvX/3l8W58eNbywuzY7/c+ObDYyfwecVNWAKxqCw/IR2ViciITGipqGuvbvZ18Az3Dq0swecmZpMx1TKWJDw02cIlJp/AwbdrcvBsOGmo7xkVLESNBKQUFBSkFK4WxBUuGNocIq+QJERUcgEgjcUEHoosaGAMiHvWiisY5SROAaotPh2XkkNAYtryiutCovNKCa2lpJaymg5MLb2yiU9oEpRXs4ox7YjixvhEZF5OJSIXV5KPFTIlTcTGzmY6k63Ckag5JdXEFh6S2JqGJMRklRRV1Mal5ickZDTUNF65+MnPX17+7sLFL48e/fnLq1vH3z/6wZUrN+52cBTDs5t3tNpWhnBx41QsFNGySoVIw+/kJgWAMUgTkSmUHKSzkZGbkZEwNHguN2cmMWkrLWsjOX0pJb07Ib43Oa3fP5Zh6s7xitqop880MwmxGaTMEkpFQ20pQcaSoVGEUnxdWX1nXnUboo5aVMfQKygHDVdEYOTiOzMwHdloejaamVMOIZKdh+MiKvgFRBGYqCwsMxNLSy+nZJS1pyJb4gtqwzMrg5NKY9LLbF1CHD3DjM2dvXwiPD0D11eO/vDNjUF1N6eTQm9uqqvA+zs75yQkuFtbh7q7e9nYZEXHFaZlxYfGlhVh21rYLI46NqXUzDHcI6IgPIMYnEEKzCCH5jYHZja6xRFDstqDM9uC01oCUxoCkuv9k8l6VAel1YZmNkTktALCs9oiczriCpgpSFZcblNCfkNyQVNKYSMgrbgpA9mSg2pJL6qHQJxX3pRVUpNTWgttdmlNaVUH2Buj3AxMcT5BwOnSPtCmJuW6OnnyuaKFuXmtLm3e//3ezTt3fjp+fLWnR7q4OL4wOUgh4SIdbasL8prRpZW5OcVJSYl+AVIqg1bbhCsqq0DiKtFVJFx1S11HPam5Alcfk4TwDUuNSi6uqGfDyqhoFXbNnJJNHJdPnOpUzlJUcy3yqdJGQW41A9riOn5Jnbi8SYVt0UA5gCIONrSkkg4niiPRyI285jZxcUmNq0sQsaolMjwF8m16Sn5CbAZ0CPjGsaGFkb4ZHk0aExifFp2JzEJmxqRVIMpL0xBtuJoz86tSCq9L2NPewMLjGqrIHShSU3ltG4nCyUZXllXV1tS1oktxX1/+Cm7F5vzCZH/f+dOnzrz/wdc//fLBpU+3Tpw+eerMd999B0Xmg7NnXOxtyhF5zVhsazGyPCSyKT613Nknce9Bs8eNYo338LOSC3fvKn/uReqrO8dNbM+lZFGi/BWpcZt2odd98yZdohFv7I82t3Q3ORJm4eC267DPPus4tzDrQ45YYge6g4/hKJAUMZGpbqFoGuuFdWQOuZmP6eAV17OxVQocRl2KkhWViItR4hKcvLhCVogTIUmSMrI0uaABgaGUVtE8QzIOW/k6uEb4+Ma7uoQcNnbc/up7zzy948Xnd76zy/jwIevntu145ukXn37qhb8/88K2v7/47LaXdmx/6909+82MzX09/GIj4gL9Qt1dfIMD4wKDk6xsA2ydI/0iCgLj0cEpVeHZDe6xVXZhGP+0Zp+UOp8Usm8y2Tel2ieZ5JVY6RGPd4vF+iZWecdX+KcQQrNqA1NInjFY/yRCQkFjcEJBQg4qDYGJyyiOTi2AFJiSh0rKLgZkFKDTEaVJmQUp2UX5SEwBCpddiC4oJRhFhib7+4RrlIPAyZXFjS5179HNExc//Oj+vd/v/37X8EWDmzdvXLhw8tKlM8fXZnq5NEYlWkNtZZHwxPwsCgFfhUCwGhrba2op9c0itkjEldAp3G714GD/FLm63dbG682dh9/eY5lfXNPB6qpuFbfyBqjSyQbOYGWnupY9UMcbQoOzaeBjWiUltXx0k7yOPdYunVWOn+6ZOd8zfUbav9Qzsl7bzM3Jx6FKSVUVjQ21He0t9KzUnPCgyADvoMigKC9n78LsIkpDJ62JImYIatCVU70jZ1c2Pz11/rer12d6+iXt1B8/+eL+Nz/2sOW8FraAKhYLNSQgaFOnSN3fSuN1dLAmRmc00q7hrv4fr3492tPLp1M/ufhBT1/36tba2sZqQ30tEY85s7WuvXdH++DuWLeim89Y1CiWZOJ1gVBaVNISHJF26NA7RkbRtqZtSXHRzzyXZ/TkjJ3PWe/ocf+QjvzYtoTQFWOvX93zJu3CM994LzMmDIUvbSOQE91Dgsw90gJTclPRpBZRUYughKYqo3aVNyuIRAm5SlRPFpNaJKVUUXGnrLxGTSANVBD7kDhVLkqcVsJLLmGDPmXjuHl4dhKivhAiVFGtrUPwO3vM9+62cLYPsDXz2LPL9M0dB2wsvaCy2dl6P/3U9tfe2P/qjndf3rHn1df36rHnzT2H9h+2MLew9XD3DQ2J8vUJcnby8vEJC/CPtbEPcPGMzSwgg9eKya4LTa+PLWLEFbPD86hRhZTook4wb4DY4o6Yovbowtaogpao/Kaw7LqQzGqfRJyZZ/pL73kavWr5xHbTmAxkHqoSTWhAVdaV4EhIfHURhpBTVJ6eV5xbjAZq5hWVFZaWI8vxOQUlsUkZaRnFRkDQ8JDYs6cu/Hlfa9io+6v9U/vDje+++PzT77796srlzz7/7KPvvr32+aX3v3r/zI2L57+9cP7auVNnF+fvf//9T5cvf/f5l99+fuWLC5988sEn31z97trl70CKvvvmt7wc9GNGO4yMXt+2bV9sHLq2SVWIoqUVtOZjOIWVgnw8v7RGXlqnhE4mhpVaQglNIyYVNaej2iKzCWW1vCZ2P1c500RX8uSjfNkwjtiWnF50xMwpJCQuNCQmJTkzOiYxOT2rEFmWnV9IJNfT6Gw+Xzg3tzA/OzczNX3q2NGPL3z48/ffDPd11xMq1+ZmPjp5ekrWe352a0jc0y3SbCwcnRlbGO4dX51bG5D1nJxbOza+IGps//O7G1dOn5kf6P3ms0+++/brjY2NK5e/OLa2NjU0sD473SuTbM1P/3L9yy/fP/H1hTOnp0ZWFNL+pvp+ck1LRsbbjz8RcsQy44BVzsvvhhgZTbvETHvGTOQUMjsqqZXFC+7xHzil8S0D0q0cQ7JirVLCEhCIsLAEqAxm5t4ZiBpSZxeG0oNq6cW0DGBr+4ikPnLtELFxsLS5J4/aV8AdKursLmiW5zbI81vVxdShYtpwfntfVqM6p16WWy0qIPEqG8WpWRUmh13efu3Aq9t2Whg7Gu+3eXPHvu0v7TY74hToH2Nn5/X882/ueP29V1/b/cqOd3Tta7tffm3Xjrf27Np9wNrG0d3NGzgaHBTh5urr6RkMHHX3inT2iIHTq2yQFVRw4ws6syqkiOqu+FJ2CoaXiuWm4TnpFdzMCh4AIrIuJVdwAblV0LIhY9gHIQ45Jxo7xfjH5MZnl+WUEAHZxYSsoqrU/PL49OLY1EKw+AkZRYnpRWk5qPTcsqjEbA+/qPcO2BodPmgBBe7OzT9+/uHm73f+vHfnwU8//Hrrt9u//vzb1sZmf29fb0+XQi5VqxQD/d0KsUBMpXTzOQoG49za6vsbG9q7d3W7z/e1ZDwRVuKenXtHBicMdL99U1tNbIsNL4iPQiXGYeNisWFhqLgkEgonwNeq89A8AAInLKqUIInyEoIkpbDTLxYTl1OdjW4GI5WQX5WNqsfVMvPQdcRGVnUTU9k3Ob10TN0zunX8XN/ghEzZXVldm11clIsqjk1LTc7JTM/NTUxPzS1ApGSmNrU0iqUCtUY+MTksFHFYbOrqyvz02PDywMj1MxcXe8fmhyZvf//bD1e///jMxVtf/aT9/uY35y6dm1yYlaq0d37X3r71541vtHdvXvn00/bWNqVYvLmweGJldaynm1pfvzA6PDvUN6KR9Yg4YyqxnNKyoJENcxn0CgwxD1EQEplr5UywdMNvPzDkFqGx9hEFhWVFulfEBa25x132zB53ja539Y8KcH9550svbdsW4OyTEpESF57eUMelM/trG5S19T2Q+Jvqh9tbpuqbxsrJvbk1itQGZXqHspAqRbRxspq5mW2irA5FarsyqUWR1CTP6+zNa5SXt6tJ7erAsKw3tx/a+dKeF5/YfmCP2Zs73nvx2beee+a1F55785WXdm7fvue11/Y88+z2p7e9/PS2V6F96tmXn9z2wpPbXnrm2Zeefua5HdvfPGJiaW5m8/aufTt27H77beO395jv2e9obh+WnFcHKQdRJUnD8HMJisxKaWalOLNSmEUQAHIIQj2gI9ATlF9EFpc1KUvrZBCeIGmg66VgrLG13IoGAZbMwdSwcbVcfB0HV82srOUAcNV0DIFSUcMg1nMqaugofGsJpsmotrrx9MlzQKn79/787ZfbP9745dZvd0FEf797/7NPPv/y88s/3gA9/f77b7+7/MWXZ06cvHXjh5+vf31+68S9n3/75osr2tv37t+6d//m/bnJ+eJ8ZGpS5vGt0zqO/gmz/EdTHS3AI976sK+TTcTe3S5/M9ptYx9XimFlIdqwJDmqSpxfxgIgK/n5aEZYYqWjT0ZMOhZ4iSUzykidOSgyEt+EQFfja9oWt86e+eiLE+c/Pnvxs+9/vv31jZ9//O3Oz3d/vwexTvvgh99++fHmz9Be/farz699ee6jc8sbSxc//fCDi2e3jq9196nkKuHM7KhcJmS2tnSQ6xjN7SIOr62puaOpRSWUMJraejgCWVtnbnhkH4et/fXHz88dO39y+eoXF04f2xzo6lFJZH0KpZIvENPpXULhzGD/WE+XSsgRMTsGu6T0tjpWZz2X0qgW0jmN1bTK8iWFYLK1/lxby1hE9IZf+KSXr4+RUdazj62/eeSBbdT5fc4MoxdLjB6LMjKKe/o5lKlNqZ1rW3KurKqZgaolp+ElJFEzoq21jF1fzkUjqfmozvxKemYVLRnbkI6uyMZX5JMb8xupqbX02GpGcrMMwRrKp/TlNiux1F58s8zRLerl59/Z/+bBPdv3vLV9D1jPJx578eknX3r279vBgz722HNGRk8/+eTzTz393ONPPf/4U88+9uS2x5/eZvTk340ef+rFl7bve++Ql6e/n2/wERPrXbsO7N1rtv+Q/fOv7H/5TTPP0PzSamEJWZ6OFeQRlQW1XXnVilySNLdanEMS5RINEAIKayR6iICmQNA8yMEEfilZgqoRocliFLxJBacIB1PPRRG4ZVVcVBUbTdS1SCy9BENB4YGsbCyRU05gGB3bOn718rXPP/3i+rWvL39x5cIHl65evv7N9e/h4eb61umTZ86f/XBr4/jG2tHJ8Zk+Tf/81Nz06KRcpJgcmervGthaPQr+FaLi3PTS5Njs8MA4dAb6x8dGZ5cWN1saKe72Pk8YPfu40XNP/u0VI6OXPDxjK6qo6bmEovI2BKo5PY+cVViLxHVmF5HD44sDI3PykdWF6JoSbF0esrKgjFhW2VBR01pWUUfnyaYW1zeOn13dOjE5t3Tq/IfnLlz64quvPrv85bXrV7+78e1Pv/z4681fbt25ef+Pu3/+11d2de2D+7dv3/4ROn88uKP98/69W7/eAZHX/nHjxndQyh/cvv3VJ59eOXMO6nuIncOEUqp9cLNfIygsSqZS64f6ujeXV5dm5k5uHj26tLw2M/vJubPLkxOr05NQ94+tzk+M9hw/vry+Nb92bGFirGtCJTg60XXlw3Xt/eva6x9+3Fk/5uC04eszFue/HBNwwzNI6xTyo633hcN2p80cznr6L3kFjAWF051dVGkZQ/iqbjxJXIQXI8ncktrOkroaJBmHriWQ2uqaGLVkChFHri/HVCAQpQVFZRgSsqotDd0Wimj0zKqNw/Oiymgp5YzkooZ9Jm5PPP7q7tf37Xnz3WefefmVl9984bnXH39s21NPPv/qK2+9+OLrRn976okntz319LNA0L89+cxjT/39yb9vewxo+tiTzz73EiQnN1dvdzeffe+Z7Njx9ltvHXhzl8neg052btGxmYR8HDMPfBpJgahWP9TRjEoe1Pp0HEcPNtT6PIJu1wkICgAFza/iAU2hNWxjA0qrpUiiFEkQF1cKiyp4+RidVKFJYhSBX4xlFZYzijDMEhyzGEc3IlZUkiqr8OWYKhy+EovDo3GN5AZkQUlHcyeyoLSmitza0N7RTBHzpBCGhDwppZ3R1Nje2cEQCmR8nqSne7BL069R9w30g0ppursGVle2ZqYX4dnBgbEvP7861NuVFhcdFugHF21jZZ2Tm9/cRq1t6ijFETGE2vKqOkJdS30bDV1FKkCiq2rqqRxJPhKHwtcUoatwVfUl6IrmDkZVTWNsUhqNzfvw0qeAhZX1i598euLM2TNnznz+0UfXPvn0x6+u/3DtqxtXr/367Xf3fv5Fe/+BzoHcvX3nx5+0D37X/nFf9+nfg9//hFZ7Hxh8597t2/fvgOuG//64c097+4723v3fr3/tany4i03X/nFTpWAj0ZliOVPH8j+1v/3yq96j/3H/1i14tysfXaxAFjeTCERMGa68+NKn79/X3l07t44oTuM1EFjVqLQU36+/PqG9eUl77fRKUvT51LjZzPDl9JBlW4uJt944Zml20dPjAxfnLwKDTjp7rPr4LybHreCRiy3V3cTy4frq1ow0AQ5HwWFayYTmBhK6JD8+wDfa2i7XxiH1jZ1pr+8sNHNoTERwq2hUsriiQpRfzg9Kq/dMqHKLRjuH5L36jpWR0Qvbtm2HwP7Uk8++8uobr7668/En/m4EUrntxZdefu35F14x+tuTjz3+5N8ee8rI6DGjJ5584pmnH3/6GaO/Pfb4E0+9+uqOA/sP799nDEkfXggcffX190wsvKOTUQgMJRPVmVHOyqmSpmNFsSXMBBQrAcWIR1HjSikJpdREFC2pjJaMpkcjmuOL21PKKOkYejaOZdDRAqIoE8POLufmYHgQQvLxwnwsLwfNzkYzs8oYuWgWAsstwHEKMKxcFC0bScks7chBU42+vnrlwvkzW2urjbVkYgW+r6v7xjfffvnJFx+c+XBhevGTC59d++L6xXOfnNw826McYNEFy0tbIyMzFy9+cQck6U8dbt+GRnvn9gND0vru2x//1Keue3f/gEn99fvLn13Y/Pm7zz+7dGppfmx1dXZieqR/pK93qK9/ZEDRpeQIuXXNtdEJUZ4+rnHJseSG5pT0nJZ2Wm1Da1ExytHJLS+/CIlE1dTUlpagert75qdnpsbG15aWZybGPzhx8sNjJy5sHf/i7PtfnvvgqwuXfvry6s+Xr9369vsfr1y7e+PH+z/9ov3tlvbmbe3Nm9r7fwC9HuioCb7m9xu//LCxtTk4OLg4PbswMnFmfnmxuw90dG1kQHv/lkxEI9WUMbmt90F64WX34cUP7t8HIgPX733y/rnVqYn3j26KGTR2Z9va+vytP37d+vDo8GT/9x99cOfKZ/OzA6fOL2q1N7T3rt8Z7P6Fz/5Kyfy+i/U7r1MrYGh7FbfriR9mJN6tq9aKxV/Xkn7rk/6yOvr50kA3vea3S8f/vHbxp0snb3x+7tavl298f6lPQklwsgh8+SXUu/uI23eKrVyWslEnyKyRSlZbbjM+txOPkRaghUnFjNCM2oAUrIlT+AtvHdr+9j5IQmAxH3v8GSDoc/8fW+8B3cSZvvGSTUJIz6b3RgcXDDbNGPeq3nsdlVEb9d67ZEnuFeMKGNN7CRBCQkgICdm0zab9Q3ollSRL2vq+Y2U5e8+9nOd855tPsqzym+d93pmxuPn2m2+54+pr5syadc2M/jbrqqvxEfS3q2YYnT3rqlmwOXv2nL/fdtett9xx4w233XPPI489tvTu+xfcdteimhaJQB3moXGRqZNv7qajOaa+g2XoZBna8aYey3GwNmAUABXZuiUzAka5hlauLi3AsmJzu8TSJbf3Scy9YlMPDrqtT2nvB1g17g2oc0Bh7ZkhtQu194LAWVV2XLOeOLhvoLu9tyM3uqG/vTUZ9nqhtPgcLjFXaDWY+Qx+7brapurmuor61cvKd23dd+nS79BR/DGN67c/8VOm4Fl/4kT+5/ff8FbpP39Alv3z3z9fvvzLr39c/nn614s/XXx7bDB95qk9v/z42c+XPv/hh89//+On73/4+sJH7/7w0zegWNwvlfF6+7MwsjhsvkDU3dsTjUZ1Gi2XzWHTaSwKRchglpcsMykUx3buPL5jx5GtW0/v27d/bOzQxOQTk9uhE3r+yLHXnjnz7osvf/jqGx+/8a+3z53/4aNPv/nw0/979Z/ff/rlz19f/PL9j6Yv//rTt9/OHLn4c+fO7e+89fbv/768b3J72hUIqA1RLcYqX//9W29O//DNjvF+g0Y0NtYD8QDC+W/T8OJ+nXmtv1/68duP33/n3FNPvv3CCyNtbT7M8Nbr5yEVnz7/zLGnnnj12Zf/9cyrG3L9Fz+9CO/LNx99MP3T5Wmw9t9+mP71u+nvL05//un0yZNnU4n9fvuugO1YJv75meM/fvlWpid47KWDmeG4zavx+rTT0z9c/vXz/0x/B6D/8MErGyxoN4X8nFr3hcn9tdHzgS32ijN31Nqx0dAZRTstmh6dZQR1jindG1naeHEd76aHC2bf+dAdD8+7avZN11x30zVz8JoOrvm3q6+b0bV/0XkVgApb11wz+1oYYQ6zv8Fwzeyr/3bdNVdff/vt90PbdPV1dzz4+PIWupIhcdbQ9U0CLw1tpaoyVDTLMXZzjJ3gowJbNzT1iGcDfqLb3iP9S10gub0brFTh7FW6+uTOPpmjFwRNlco9CP4Ki3Cr3Nops7SBEEu7wtqhtHWq7F34uVxz+yydSq6Wi/VqRCUTMSkEDp0iZLPJTU00AonU2Cxg8chNxJrymvWr1j9z/PTlS7+BQf7nr2NTeDn97Xf808PphM3f/gQuf7v865V7/OdXqIyXLn31rlUj4tDqgy5sx9aR06eeeO7Mqbf+9fqln7576fzZby5+8flnH9EpLSVFiwcHuvfs2fXQQw/ccstNcx9/eNHcR5ctWrC6oOCOa6+5f87sW2fNunPWrNqFCxoWLSIsLRCVr2OVrWSsWceoqGRVVjOrqunrK0GsmjpeQ5NJKhNTqShPGLI5cuHoaG/ffkgiwxtPnzz+9JPHjhw9sP/gnhdeeP7okUPbNm06vmfP7qHhHb39HS7Xxy+/8MnrL3bEXAJW/T9efGr6P5dndkYA9Ndf8Zf7G95P/vztv14698Wbb5w7fOTFJ4788vXnwOi3P3zxzntvf37h2+8//OWHDy5BGP5P/n34+c/pH3+BHPzHpW+nL12a/udbx1O5S8+fnf7ik7P7tvW5rYeH+yYG2jrbIxObujcNtzctX7jw+qumv/10+sKb0x++8dPLT39/bO9zXscrOuNXzsBFZ/Arb+x9X+a0OTki9wU5bowXUiIZtjzLVLcz9W1EZbSkUXzb/JWz7513ywNzr7nx73i3fuMtgCmYaJ7Rq/52DdR0vNZf/beZ6bVXGAVwYem6664HTMFub7zp77ff/uA1c+4sLqvnyWyIMclA/Ex1EvySqcUPjlLRVoo6TUGTdF1r3ke5xja2ISswtYssnVDoZY5uYBQ/DWbrklo75U786iIQ4urGE6q3T+XpVbgA0OxfsmbklgxizSpsOcTWJjRkZlkxFOKO3aQ1ahUKCQ94BXEZVAaJxGMweAwWm0qvWVc1PgSdxPT3X//w4/eXvvnmuy+//Pqzz7745JNPPv74448++BBa/nfffuedN//5+j9efuns8y+fe+HV8y+99Nxzp48/8fyJoy+cPIZwmcUL5y165NEVhcWlxSsKly6rKK+SS1RigdxudenVhiVzl6wrWxdw+9OpBI/P4nGZLfXV1IY6ZkMtrWLdyocf5K5d3Th/HnnBAmlpmbR4uXhJgWTRUua8edTCpdTSEsaqUnJZSVPx0vrCxU0rlpHXrGwqK2koXUZcu5qwbg29rvqJ3dt//+ar9kR4y8TQVxc//O7nr3+dvvzJ95+8+K8XX3nr/N5929755z++uvD2p2+98ucPn07/8e355w4adOz33zo7ffn76X//9J9fL/32508QOuGnfpv+5ffLEHMvT0Pd//FHSL0w+fOnb6anfwGbxpH+ZaZb+23665//DS3bH99B0vgVh/zy5emff/n1pX9MGR0/PvHUry+ev3jixCttXX/sO/rHwaPTz5//bmBsesfBT8yBi5bwdGbwciD9nSfyrkp/QaH5EtFOY55/Y67XTfYnzLZNKizJUeqJMiEZ5XCdLHm8QRitlcQaFIlGRWQVTftIafMtj5TcfN/C2TffNfvGW6+dczO4KfTvs64BQGfPGCqE0Wuuuurqq3BO8X9XXYVXevh39dVX33jjzYApMAq9/6233jf7hrtWVRDEKrfB3Sm3ZAE1sEyhpR9MlGf+y0e5eMfWne+ZQHxjm9DcASaKuHrBQfEL32xdEgusdEhs7SCZs13p7lF5upXuLsTZLrfhdEotrRJzUmr6ryytfF161qbRvnjYZdarMA2CIiIRl85jknlMKotKpBKaUEQa9rkNanU6GnWarb3t7T1tbd3t2e52fOzpyPV2tvV3tQ90d2zs75kcH9m+ZXzz6NDE8ODUptHd27bs2zZ5ZOf2PZs2aaUSMYstYQtUEpXN6GhPd0dDmd6u4aGBTYf3Hhvs3Niwvrlubb1Zaw6HAiazzuO0uC36mMOKCTgusajbbMoqFP1q9bhGu8dsOR2OvpRInQtGTwdDm426Cbtxq8c25XdM+Z2TfufWkHtbzHe4O7crG9/Vlj4+MrCzs/WTF56Z/vwDq5D55pnj03/+8POPH1/6+fPvfvnsj+lLB45MXX/DrKHuxPSlz6Z//2b6t4vTv3z8x1dvbO7xT//44fQvP+BHevEY+sNvf/742/SlP3AGYfMXiATTl36EJA6T37/9YobNy9N/4keLf/0W77Eu/v7Tj9O//PnzJTwTf//dN6++PH3pu1/fe2ub1//pgYOfPXn8ws5t00ePf93a+YHd/7U9/B5X9a0I+01mn0YcvwgMP4n0f5rcl82unwzW38yu33Tm5yiMASbLSiRxy6uay6rwS0d5RrYyQtWkCJpcvbq1Ec0QtOl6sW9ZvfShwoY7Hi25/vYH5tz8d2D0CqZ/dfTXQOnHU+lVeS+9+uo8pjCBSg+MXn/9jVfNuva6OTffeus91914990PLllTw6CLbeCjNEWErklT1ekWeYKNdeTzKIhtyF3pmcTWLsijM36Jl3hQnlSRJSe0tIqsGbEtK3e0I84OGKW2rMLRhtgzMmtaaklJjAmJKSbG4mIsKTZmZoXcRrNWKhcwZHy6QsSS8mgwmrUKq14lYFClXBaL1HJs/55vPv0I9N3nn3zy7ptfffjOd198dOniJz998+nP3352+Ycvf//p4qWLn/37+69+/fHiL999+dM3n//87ReXf/gaNn/77uuXnn4qaLeFnK72RHpkYOPm0ckTR05uHt8+PrJ1fGhLV7ZPzlc217QI6QIhmx+NBSVSvhlTOwwoJuG7pMIht9PHoA2h6t0m815Utx9RnTU73/SG/unwve50HdMoD+uRJ4yqE1btk07jMYfhgAndqUfGZPz9Dmyv3fhkzH86G//55JHp11/slLB/Ovvkl2eOvLxv/J3TBz587anfvnlnaii19P452zvC0//+YvrDV6a/fmf6/Zemv3jjuYEw9DHT51+avvDR9Ndf/fHlR998+f63P3783aVPv/nmw5+//ujrt16b/vzjH95648cLb//+7WdvvfHCpx+++e7LZ75895Wv/++1b75+7+z7z79w4ez5F0/834tPvXZk97fvnn/n7TOHj078efE92BM+evXp6e8+mL7w+ptuy0kq5U2e6Hs1dgnRTzuDP2jNP9pcH+gNbxt0/8DQg3z6bj51nNbgqSrj4n/8UrO+oraijkzgolwsKnB0MmzdDGc/ydxNxDqpWAcU31q2o7hSOr+k+a6HFt1yx33X3XT77Bv/Pvv6266ejR9vmn39LWCQ11x7/UzonHPdtTfMmX399dfh4+zZswHT6667bs6cG+Cm62+4FRi99vo7Z990//yiykaGhoH48GustK1sQztZ1QqMsmcuZwMBozPlHr/eEhIn5FGo9Xi5t+GRFBiF4g5WKrbkQFJbu9zRiWNqgxiaVTo6cEytWZm5VWpK45gaksAoJNRZfod+fEPH7q2jowMdna2RkNusUwiBV4sOUYrZLrPu3Onjn114+8nDu99+7dylrz7+8YsLv3z5wS8XPwL99NUH33/23jcfv/31h/+CnuD7z9//6O1XPn3v9S8uvHXhzX+88vyp508eOrBtdOtwd9xnzcb9XbnklrGNAz3dg719Q4MbN41vHurbsHNq24FdeyBoNFSs9zltwZBnYLB76+bhvlxic3d2V2duxGEj3n8fMn+Bv2x1R/n68fqmZ0TIuybHJ1b3J2bbBaPuXUz9rgm94MQ+8Vk/8tvftulfNqheNCi/6Ui/7jbv5zGeVEkub9o4fWT/kygyfXTfr9vGpp95YvrVM9PPH5t+7dmfnz3086m906cPgya5xENy5lMq7sdJ13M63hs+y3M21/fb90+/d2H6q08vf/3Rxa/+77U3njt0eOrE/qknpsbfOnXi2MTImf07X3/h1NBg7uD+LScObH7j2UPTX7//zvkntx8dP//h2SdP7frgX2ffOHXo/JN7z5w7fPTUjtfPHT+yfYPdJo/51NMfvfzpRNfzBuV7VtM/ZYrn6Kxn+IIDXM5uqbCHRvBUrLZVrBYULOCtWMJYVUCpXl1RUUliiyR6u9wSFFtjYnebwNvDdvezXAM0ax/F1E3HuiEpEqXxSoq1tEby6KKyO+97ZM7Nd+Ank+bcOuvqG6665gqjN/ztqtmga6+eM/ua66+7FsbrgFFw02uvvXbOnDnXXnN9nlGo9bfeNXdRSQ1VaAGMoEMX23vl7iGeuZdv6RFYe/FLK61dfDP0+51Caxf0TOCjkEcFphwPy+SbeiA1b6X5bJpvpBBnF2LrABCVji6FvR2x5mTmjMSYFmMpiSElMiSl5sys1qj7xKEdb7929r1/vvjxe6+9+8a5MycPPrF/6tiBbSeP7No6NvB/b770j7On3nr1+VfPnXry0Pa3zz/9+pmj55/aD3rl2aMvP3P4uWO7T+6f3Ld5qKc1lArYhjqTUyO9Q13pTMSdDFomNsT3b+/es617x2TXYF90akvP2Ejn5NbBkZGuoaH2vbsntm4ZbM+Etox2H927ZWKka9fuyR27t3Z3pMcHOt8689S+rvYEn4csLdAtXBQoKulauWayouYEgfYaT/qOQP42k/elgHdRwP5WwrukkvyOqX+36H81aS9hmoso8r6I+56I8ymq+FSrvKCUvieX/JPFvKw3fi2U/mK0fq7TXcC075q0r2sVb2iQlwXcC4jsXRbzF1R1SST8ns38nkX7iME63sT6eXjb9OffTv/4Hd6b/+f7d959cef2oSN7Jo5uHX31ycMHxwa3DXUd27/1wMHJ9z96/bmzx9577ez0Bx9++/zZZ584cOHTd8//66XPL376w+effv/JJ2+98epLzz4zmW6DJu+qm6658cGbzJggbRT7CHU5EqGbREm1EFwUkqyljtpYA30ktK1SvpjPErJYojUNRPw7eZrZUpXDFep2RPtQTwekOqG9m2PrYVl6mKZepqmfaehlaDpI0lQty7+OoC1cWf/g3KW33vHgzX+/H3TDrXfdevt999z/2E033w78zb72RmjegVHQdX+bc83frs376I033njLLbfMue4muA+09vDjt9z5+ONL1xE4BmhouLo0S5/lm7uhYaJpMtCosY1t4KNMXQYyAEMLt2agZwLxjdk8oyJTGzgooIk4+uTAt6UbhFh7YJQaO8SGrMLahVg65cZ2KZaRGFrF+jRIqMevCZ61oTsNcLUl/e2pwMhAtrc9Gg9aswlPZzoQcOlTIbsJFWVj7kTAajfIc1FnT8Ld6tFFLAiMG3L+Tb2p4fZId9y1eaA1E7QmPNhAW2SkM5X0W71mdcSt7c05NvR4RodC27ZkhwYjk5vbhzYktk72TG3t3bK5a9NEx9hwdnwo05l1Z2KW7Zt7WjP+7u7kQF9rVzqUDdiDqLQd00hXrdCsXKEvKnYUlURXrOxfX/cEX/qmxfO+3f0vmeR9RPyhSvaZTvWVUfudzfiT2/Zvn/NLo/b/EPFnRt0HGuUbYv4Fvfo9jeqfPN75msbXapvf4whfpNDOcbmHW5qeZNKOU4gvCTjnqKRvdJoLXObHDPqHVNKfGvVbdMZJnuTXnfunv/pq+rsvp3/8ZPo/31788q1TJ/e8du7J00d2PLNv24Z0WM4iKoUMeM4vv/LM6GTfgV3juzPpV7ZtP7p1bGy4Z+Pmwe7B9pG+7q5EojOZGMrlGheXSAlULiK6t+ixRSWPRv2GLe2xHKbpMOg0VKJaLmRJOGRESNUo1xLJdTReHY3fzFOVtnDqBSiBqZSpPAZ7q8bWipdFG15YcUqwLq6ln2cd4Bh76Wg7UZKsYfoqiIa19ZzFJeseeLwA9Oi84kfnFS5aurxsZfn99z1y9133337bXbfc9Pdbb7jllutvvnnOTTdddyNe8efMufP2O+656+6bbrj5phvxQ6T3PbTonoeLFpTU06ROjacPN0hjB7RHQCrP0im09+TPM4lsPQJLFzwfsFLomYBRoblNZGkXWztAwKjE0iWz9cAoMXZKAE2sQ2RoE2gzXHVKbu6SmTolWA54FWlbhbpWoTYp0CZxRoMOg00ndxoVDgxJhaw2gyjoQruzno6koyfnHelNbNmYmxjIbuiI9WaCA9nA1FBmojc23hOdHExtGUgOd4Q2tgcneuM7R9vHuxMb28JjPYmJnvRQe3QwFx7ujHdl3OmYuSvnaUs7cklnZ8abTth7u6LxiDXkx1qTrlzS3Z72wudk1PACLjTgVoW8qNsm06kYZp0gm3BkY069nCsgNwhJDTIKgV9fR1m1il9RrSPTjRRqWMCN8FgJHrNVwOoS8QbE/BEJf0TE2ywTTCHibXLhFjEXNCnj4xJxtgvY++SibXJxP5cxiirHMR17/qOG8tJI8/oRhDUqIh00S7fLGRN8wqSUedil22LXbQpax6Ouwai9M2Zti9sycXsqYsnFXd2ZgAHhKTiUJY/eUzzvYUwpjgUs/X2RkcHoloH45t7Ixk7/hp7A+MbUts2dh3dtPDi14czxPbvG+qW0FsL61YODnRabRsQjtUbs8F5FHFqHRmrRyWQIl8Ijk/i0ZjalhUWjcTh0Dp/K4JLoXDJdQGUr5Bq3zpqQYWGxIaZydqL+jcAH9NdixwaZe1hk72fp28jyaJPA18SzF5RTC1Y3LVxW+cjC0kfmlyxaWlpSsqasdO3SxQUP3vPAYw88dOv1N95+0y1Fi5Y8fO/9ML/rjrsfeejhxx9+5EFok+6+d+HjC5YsLnl8wfJHFq5dUcOjyEN5EFmaVpoiRlPF2YZs/mI88M58t8TBcrBSL/TCJqw3SQKwqPBuAGQ5+gx+GN/aifvozCg1t0vAYk1t0L8LDRmYwIrU2CY25oBX2BQbWmede/rom+effeXsk+OD2f72SCps7m519+Y8m4ZSo/2x4Z7oUFcY0OxKuDrj7p6UZ6Qz1N/q7E05N+Q8g1lvV9wGGsh4t23MjvfERrsiW/pbQWNd8ZGO6ERf62hvGh4WHnwUFgcyW0e7Ng217Z0aghVopWFxQ1ccJp1pn9eqivsMaZ8u7dOkvNqQSxnxajpyvkw2YLGpqYwWKosskgulShmTyyHTGRQGs4VMkSikqFZpMeu9DlPYYUo7TJ0ua7/HltUpcjp5DpXmUEmvST3iwSb8tnGvaVfcfSgX3hxzHxzsUNAI1Oq1Lo2csHYZo6pkJGXd0xvY0enqdSNbMrZDG1PHNnUeGM8d3dp9bMfAUdCuoaN7hw/vGT6wc+OhPeOnju1sqVmlFrNcBrVOyt82NLBlqPPpE1NPn9r87Kktz5yYOHlk+MShwVOHh08/MfbqmT3Pn5h6+Zn9PRkfpuLJeGS1lMsk1Qad+p5ssKctFAvZ/H6z042pDTKJWsgUMEhMUhOpua6pcX1VZUnJioXzF819fNH6GhJHotfbEzpHSmlLQ6sBZZSiSjB0OeivOcZOrrkDGhemJgU8EUXO1U2SwgrS3KKqhxeufHTB8oVLVixfvraivIpKoJQsLSxesPiRu++599bbHrv3vntuufXu225/5P6H4RctW1JQvGBhwdx5S+cvXLxo2bxFpfOKa0vrJQR5GAKo2N4vMneJzZ1Sayd4KtBJVSdAeUzxawYsnUAnTGCxQeQDjiGhwk0QUvEkCnTOSAbV39KBQ2mGzgnyaLfS0QMjbIqwLCALEukzs4Z7MjGvBRFQyQ1rwm79cF9ifCDZGjZ1tTpzcUsmYsYNNeMDEx1sj4OtDneFetLW9pipI27Mhg1RlyLiRFJ+Xdih8Jklfos0bFcGLHIbyrWqOS692KRgI3yCRkqTcZtFzHoJu5FHrVYIiFJOk5BRl9+Em2CTWFvKbFjTWDy3bvHDLcULyKsLW1YXNZcvry9fvmrZotr1q2sq17KYVI1GDf+kUqlEIuELBSKVTGPTeyPeWDqSSoUyyUB3OjTUFh/ORoYygcGkdyDhGusI7xjKgNPv2pg9sLF1a0dgQ8oDlZpKrC0snBsKOUiE9UzKuu0TmacODB7a3jbW5Tq0rfOZI5tOHhh95sDImYNjzx6aOHVg7OSB8ScPTRzdN7p/xxAACvbPJtdKOERMIYi6TWmfAxjdv2PgwJ6+Q3v6D+/tO7yn9+Cu7gNTXfu3dh6Y6tmzuePw9g0WDT8dtg10xAVMokxEjwatAQ9m0IikYjqfT24mVJWuKipcvvTBxx6496H77rrv3rvuu/+2O++ac/3Ns666dtasaytrCHyxBrNFDI6kyppQ2tug5+BhOcSzQeIcBBMVOXqhoYHmmq6OESSuOqaupJq2cHndkhU1RStrl5VWlpfXElsocpFs3eo1q5eXgsBdC+YuWPT4vFUrSu/4+5333/vA4rnzVxQWli4tKFy4GBhdUrS2eB1lLUHZIgvBbgCMyux9Cid+jQgwCgjS0CRAiR+6nzkRCgJAQbBOUkTBWeH5wAidPt4qzWCapxNGmMMK0Kl29YGAUVi8wqjM1D7rulm4Hrj9+spVBSZUoBSTgRg5r4lNWkdvXs1oWQMMiRiNPEo9s6WK3rSuqWJpZenDa4vvK192/+rCe0oW3Fay4PbVhfetKri/bMm9a4sfqiqbW1HyWOmie1csvGdt0aNLH/n73HuuL3zsLpgUPX73yiUPli1+qHbV4rVFj8OtVaWL1i17vLG8mFi1fHXBo5VF88TV5exVpZKqCj2VpCK3iAmNAjI8maaatatKi5dWrFlJIbfQaRQqlczlckUSIUvAkaEyk93o9tk9HovXZY54rcmAPeGzpAKWTNCciVhhN+tr9w90Bgdy3p2QhruCw7kAJObq9WXVNWuralbNnXuPWsHobXf352wTfYHBnH1jp7cn6+6IW7uipp6osTNizAR0rX59IqALu9Qus8RhkrIo1WuXL9TI2CULH57/wO0WpcSkElp0XLOBaTfyPDZhyCWLeJCoSx6yS3xmUdAmD9gVNasLwETdFrSkcN661YVMegOZWFlZvmzVikXrVhevWLZw3mMPPvTgPQsXzJs797F58+YtXlJQUFiyqKB44ZLixQUla1ZXMekiVOtAMZ9CH1SYUtALg5XKXPi1xmByXFMX25ADA2sW++u41lWN4seW1d63YNWikipgdGnx2rKyiqZGEplIKylYBvRVrFqzvmz16uLlAGtVeWVJcWlRQfGaspWE+nomgUiob25sILeQhOWNomq6niCPgFsLrb2ifKy0dHDxw6JZ4A9sMk8nTPLGyYVGasZZYZ4PAFDukRmnvGKfMMkDCmU9zyuMeIk35vJ1HxqpWY/cfetdN109/8E7VhbNbaxcPu+hm4sW3kOsL6tcvahi5YLqtUtaasoojesINWvq162oWVtMbSprri5orFxCrCsm1S9rqSkk1S9nk8s55AqQhF2vlVF1coZKREYlVJOaqxZQpMwmnZilFdMxGdepk0DqirsNMMI8YEZdeqnfpPYaETWfZpJwtFSKvL5RS6K4JHKHHLHI5VZUZUKVS+Y/fustN1w35+obb54ze87V18y+6ta/33LvfXc+eO9dCx5+sHjh/JUFS9YULakoLqheXthQVkQsX8GuL5dRapWsJi2fYJBSMBnVKCZqiGujCDOEChvLlpYufMyMIuT69VDwwML1AoKUsNomIlgEzWZ+C0Kq4jWsRqjVSmaNkl0rZ9Ug7Ho5vxFeI49epZZSCXWlFWWLtHLWisWPFDx+rx2VujCFScPBULoJZdkNXL9FHHbIwzZZwCzOBrHOhC0ZMIiZTQxClYxPrVlfVr6qQC5mqBGOmENitFRzSY0gYnVl3ZpV65YtX1lQCDUXSCouWgGYLi5cVli0fHnhCjqBgekcLk/S6Ws3+zo0TvxMtxg/4tMrdgwIbX1cYzsAAeWepYmRpY6V9dyCNS1ra5mVDex1VcTGFoZMqmIxuDUV1atKyirXVNSsq6pbX9NU07B+TQX88hUlK0uLV9RXVlMbW2CxuqqpupFVRZTXs01kZRx8FJokobVbgLVBgw8WDizmSzwAms+jTF2rzNWfZ1fiAKC7AVacV2Nb3kfzIOa5zDMKxglc5sHN0wm84jSDj9532423XTfrwTtuWrNsoVUntRukkJYQAVklpiEiCiplmbRilxF1W7Q+uzHkNgacaqdJbDMI3BYp2IlVz7djYp9d6TTK4WfdZkXEY4h6TT6bxmtFw27MZ9VbtYjXonMYVDadwompTWopbBpVEpDbpIFFWFGJ2CxiPSriSaE7aCQJiDQFV6iRSE1ardmgRxBZ6coVj8975IFH73/o8QfvfuAu0CPzHl6w8LHF8x5dvmjB2uKlVWUljWtLoRdhVFdwGirp1WsEzZUIrV7NatTxW4BOTNRs4NZm9Nw0ynIJqVo2Yc2ix2rKimvXLHvw1tlSclWbG00bxRmTJK7l9bp13S5D0ohkHWiHX98VNrYF9eCjrUEDcBb1aXMJh5TXXFteVL2mgFy3FlPwETYp6TWCbafDWDKoz4Swtqi5PWLMBvWQsDNeHbRYYbtaL2eXr1hUvXZZU/Wa0sK5XGq9gksUkuthp2opW9ZYvHTd44+VPfBgU1FJ5eKCtQuXrllSWFZQvGxpcUFBUXHhslUFy4jVjVKeTKuyaQ0+aJ60DvzgotzZB+Ve4RuRujbwzZ34KR8sJzTmGnnm4vWMRwurHi9aP79gHSTLlfj5Ir4K0XIZvKo1VXlGiQ0tPAanvqqB0ERZV169blU5ob6RQ6bRSQwikU2gSSoJsjqumYamYB+QzFw1IjDluIZW8Uz6zJdyzgyv+WP40CTlGQV2gU6gNn/ySfrfKp8H8Uqt/99JntQ8o0pb9yyrRm1QSGR8JqlhPSoDDxA6zUqLXuqDNtOidhjVNpPaqkMxjdKkQ+0mfcBpcJgUNkwOd7NjCoNKqFcKoBv12Q12TGXHUK8N81gNFp0SU8uMWpVOrVLK5BbMaDZgVqPJbXfAGPT6YMWkN/hcbljBtDoRj8+i0e1WB6JARVIlX4JwRTKRUo253KjF3MxiLFheNLdo0dKywmVrlxeVFUJEXV2xcuWaZavWFNXUrKSQqqFosumNPFazQkjTIWyIfRYV2yinWRCqV8uOWoUREzeAUjtswiBCMPObeuN2VnNF4fz7gbPSpQ82rlqYtMtabZKYjuUQNeccir6Qpc2lDWOiVqeqza9LuVUhqzxglfmsclDUp48HsMWP/n354vshq1BqVxkkTL2UmfDok15dawDLhYy4/Ia0WxO3K8fag/AbswFzyIbWl5c0ri9rqly9umg+7EUaep2qpVJZV47WVkhWlvIKi6QrVpLmLmx8dH7dYwvrFxVULy1as3jJihmtXLB4feGKulVVjZUthBYunaPlKjwCXZxvxIFgYTm6rpWqToLhURRRKhImih3rCNKS9bTitcTC0oalJZXrq4hcjkQuwa20YvX6yrVVQGpdRR2bxqmtblhZVlG6onzdqorGmgZKM4nYSGpspNW18OFB6nkWhrZV6OgHRvHGaOb4PG8GzSsCRv+6Ns/RC1zmAwCYOvAKK/iFJqa2K8LPBeAX7OGHpXB2Zy46gRXYtUAwkc5gOstu0LlMBq/VpFVK/A7MpEesRhWMOrVYh8qMerXVqAcnM2hQg05vNVtcNjOs2EyAl93vdrntNo/DHvJ5XTYA2GDSGd02l9/lc1mddpPNYXObTC611opfNG726I0uEKIywmgwuVGdzWIPwEQgVtc2UKrrSFqzW+MKKl0BkclBRTQsVK8NBAUGQ0l11WPLChcuL1oBQWn1iqLCRSVFi5dDnn/8/nXLFxGqy4TUOkgUQnKtmFSLsolWCdPMI6lJVZLK5fLqZU56VRqhtiHUpKgxI27caBEOWGUDLo2KUEVcubR8/r3Fd81pWHxfREKMiQkxQaOduDYmImZVvJSKn7bIu4LYhrSzP2Xvilg6Y9aelKsv52sNW/wOlVHFoTdDtL6TQ6h0asRtIXt72NaddG9sD27uiU/2Jbb0xMY7QqNZ/9aeeF/c3p909SXcmJTjxVQ6MVvDpbbqZTExzUttDNNbwsQmzfJi+YIF5rJVropq89pKfXklur5GvK6CAmFx6ZLyhYuA1+bSNbSqJg6JLeSjEqVdog0JDDijTEOGoc+wjW0QFvHL5Fz9UFib+TZom2poqjqKrLKJt66G2kzgCARKI+YAK6WTmAK2kE1hE+uJQo6ITKDX15JJRCaPLZIJ5RKuhM3gU6nCFir+1YUNfDvsAHwbflaJbcxx9GmOHm+JoDECNPO1Hj8yasZP2TPxXSUBdguGCuwCuFJnH9wzf0gfELzCKAjmeS7xb7XAsjx9Kx86+pnD/vh1T06z2W40WvR6k15j1qMmA4rpVDqtEtxPqZBCkUUQRKFQKRUaNWpANZjZ5LBaXECm0+Fzu0JXZDF59Vq7TuMwG30OW9hmCZowr87gUWMBhSkEUprDIJUlAlJboxp7HLXFQIgxSBdjdVQpiacRGXxck49t9TKNLpreysFsLK2xgsKYv6x0/pLCwkLI80Wrli5dNX/+6sfn1SxaTCkplqxZiVavNTXX2YlN9uYGe1O9q6nB39IcbW4KVa2PV1Zkq9fFS5dtoTaPNqwfbVo/Rq7qJ1T0UuqnUEkrheCrqVIXLlYsnptqWh8uL+5oWDNArEysKc5WrhokNycJtREpc1NH+N1zx145tfeZ/RNPH9y0ZUNqA3RdPdHuVjd0Y1D67RqeRy/Z0OrfO9E70hHdMdxxYufwzo25p3aP7B/tOLqp6+Bo26HRzv0b27b3p6d60l0hR8Zjscr5nW7jwdbAgIIdaV4fbayIVZfbiwosCxZ5ipbH1qy3F5UqFy6RLylUlq6Sr13LLVtBLChsXFLUULiCVtEgZYqVMpNMYVeYEsbwoDa4UQZdtqMXTA7v8R19kEebhZ4GjnlVo7S8RVJNljVRkZombm0jQ6938XkyAJFN45Kbqc11BEoLTcyToko9NGQgIU+OIhqQkCdlsWQ0LlreIqthm4nKGN3YzjK1sbAsFHq+EQcx39TjCVjXCn6Z906gE4o+rFxpmwBfPAbMJATgGyYiS3v+Qn2Fuy9/nD+/AiMsSu1dMke3GL9+VKPXqLVqpUqlUOKjEpiUyWQSuQKRyhCRTAGVV4polRqIkC7M4jfbwxZ7zApyxK22hMkaM5oiemMIM0bVWr9K7dPqw5gxBiPMpSqfBIuLjKCk2JQQm1ISc1JiTuPXX81chQVzvj5CENnXU1WQnDj6MM8W5zliHGsQYBWZAgK9o5kmLCmtKFiwrGReYfm8ooaFxZQly3lFq5DS9ZrV6/Xr1lprK33EphCVHKZQI2RqkkLP0RgbWPx+InVjC3GSSByvrDrBZOyuqdpeXbGpvny8pWozk/QEptkkEg0wGcHyta4VRRMc2iSbMEGonqLWHRIxttGbJ1oatsoFAxb1E5t6P3v92TfPHHrx+I7Xzhw8uK1/pDu8ZWNmQ2eoN+vtTbu7E47BrG9Td2xTd2IwE9jclz482b97OPfUzuH9w9nDo+3Hxjqf2tL37Pbh4+M927sSXR5Tr8/uVQg7TepnO6I79dIBFnEjmzxMJ3XXVbVXVPbXNw61UPsJlG4iNUehpqgMXwtBX1UpWb1G1UCQ1JPFLSwZSyYXG8SIQ6wNiS0ZrikL3DAw/Hp46Jm4+hxTnaTIQ008++pmpIKI1JAVjRR5dSOnqpaOojZM71QrDEK2FOhsrGkGTKkEOosuaKyncziIVKTis4QguQQVirVEBrKepKhmmxvloRZ1kqCKtSBhAhIkIwGiIkREIqAWWahJEmgU+5ulQYI8DOCSlTGYgCiqOGyCYIJnEn2arUuBocIceMW/FMjcBsji3ws0c+4URpjnYQVbnaVW6VVKnVKhVSnBPLUKpUYsVwqliEiuEkgVHDFIxZPppagdNfl1tqjBnjLYW432DIwGa1pjTqBYQmmIqQ1JGRqRqkIKXVytTyPahFQZEagiAmMr1wTK8swZnjmXH/kW2Mzl1xm6eKPEW8Ey1ovcLENSYE6JLEmRMSrSB+WGMKJy08mS6tL6qsXl9fPKSI+XCRevM5U1hqpo6WpWtJbmIhJcLEpQyI1IRBGxOCoUJYTijEjSxhG205i9FNomFmsTiXhCLJ6sqRqrLt8tpO3TiPZhqqci/n1O+w67tVPITVObR6SsHRpBH7lyjNNw1qffKqa01a057DEe6Uue3b/541eefuv5I689s/+fZ488dXBiz+auyZHMtvG2HWNt20dzeyY6D0317xvvBEx3j3Uf2rrh9IHNp/dPnD+y7chYxxPD7acn+17YufGfh6bObR/e153aGLBtTYc7LNoRB/ZCe+ywSTUpYm8Tc3eJ+VuYtAkqfYrNHadzJgWSHQp0qxodkipa2VzYA50kipHM0lD4cjJfRJVJhQaVLqiytIotObwvMeZYRvxKOZY+y9FlubqMQN9KR4I1DEMdXV9HResoCMTO8vUUqcQAhU6lNDBp/KZ6cn0NAUZCE43QzFxXDiGUz6JD3WfSCCwRXykU66gcTRVVXcu15hkloQmyOk7TRNm6hNiBX0Fy5ZBT3jjZ+PdB4H0S/mUQ/7XY/HqeyxkPxucAK0ubBGoBzby/5gnOT/J/ZzJLoTblpdJYVBoTgmJipY4vU/Oh75Np899pz5AYOXIbX+0Saf0SXVSqj8mxBGJMgmAiMySl+oREmwAiheqo3JBWmtoQLCvTt4pnnJ9r/f8Xx9LONrfRDa3NinCt2NuijHAtObY2ztfGeOqwQBmQKP1SsYPRLGssbWGtJgtLiYoVzaaV5GgNt5cg30hUdhPFMZYgJBbF5EgUUYVlqoAYCYmQqACJskVJOjdLYY9wBFM84UlUs7kJrLHpgBY56jTvMOl3Oe3jRtOYxZbkcyI04oCcO6Hm9bLrt6vZz0Ut/cy6WE3p09nAK7snXjo89cbT+984feCVp/aAlZ4+vOX00cnNg8lj+0effWLrsT3Dx3cPnzowcXL3yJGt/Qe39h/ZvuGZfeMvHt36j2Pbjo7kDvQlzkz2PD3W+cKW/pMbsnvSgRGXcVvU04upp9zWZ+OBgwb1FjF3SizYIRVN8vmbePwtIumUTLkd1W/XGzeh+l4xkmYLEixelCtBmxgoVYzQZDyyRMjDUCymcXZKbPiVJQJHD4RFvqUH+noB1ibU52YYDdcwjNU0XRVRUUOSVTfxaxs4ao3DaPRKxRoqiUtspgOpYgHUTY1MouWwFUyGjMOSC3kKEUeGl342wpNaCAILQeolquNkXYZugP0hCxYjsuQ4xgwX/2u7Nrxjm+HyLxZnzovmW/58s5+/HgrcMe+U+coOFAKReTfNs5tn9IpwRqUKU14SpVmqNovVJh6iZ0s1LLmOJTMwZBhNZqLLbDS5gy53MxAfU+Fnq4JcNMzXRgW62Ixwqvga/LumeGhcguHnBvIXB0AExo9KWNrzOP6v8oyyTDmaPk1QRZuQEEWb5FnbWJoYF41wVcBoSKwICIUOJlFBrGCK6vhog9jSIHLXC2NNsi6aZoih66OrW5nyJE+REmoSIl1EqAvydQGeNsDRZKXmLgnWxVWPS3S7FMaTmHOshbGJzj1qsZ2KxHda3dsc/mHMNWRwhhjcMJWxCdOOKoTbdNJzKd/TQWtr07o2Qs1LfdkXtg69cmzna0/tfen4jheOTp07tv2FEzvASif6oycPjJ46MLZ3U8eRqd5T+0ZO7RkGTHeNte/d1PXE1MA/Tux4/cT2Z7f1nRjOvLp76PmJrmeGskfbQgeSnk1O/Ra3qVsh3umxHnTbp1DVRiF/o1A4JpWOyuRjCtUYqjvgC+/1R3Z6g2NmZ4dSHxcqQhyJjy0z0aR2sckstUjYGg7XIFQFRViKrk3w7d1sazfT3Mkxd+UZ5apTdFmojmVd1aRY3SRf1SCqJsqbKHIKU+10p6yWkBLBOEwJSCHTmzG3wxoAczWbQlqNW620alRmpVwn4Ck4PJQjsbCUPgYapelbyYYsWZemaONUNAIiKcNQ0/PnQmECLVR+M++gICjx+cyaT6vANBtrhVFgbRfaOqCQwnymqGbzgs38fViGNAjYnSVW2EQKG4xilVWitonUVq7CxEIwutSA04lY6UoHQ+mlqwJ0VRDE1cd4WJSPJQSmuNCUEhiTPEOcq49zdDGWJoK7IJYGwYSFJnAnN6SZWJphSF0RXZ8EsU0ZlrEVJmQ02qIIgqjaOCziDq9P8nVJQF+kiQrlHhbLQCXIOC1iNV1pZaA2MuIlIwmWFvbBNg7+HYdpoSktsqWkrjjijSi9IYU3iHhSaDCr8UMK7pCaBhHTmALzra2LN5B3Wj3HWzt2+JM7Y+0TobZBd8LLR+Jy1bjT1S4XTjkM/xhq2+8zuWpWp2gtZ/rajvRn337m4Ltnjpw7MnX2yNY3nzv8xvOHd493jPdF9mzuGOsO9SRtu0ezx3cO7hnJbh2IbxtM7xrJwPzs4YkXD42d3tp1sDv07Gj27Ejbk12RvRHrLr9xSCPuUwhChNpBhXhMpewTiDIMZiuD3S4S9ynRQb1x0GSbDMYmw6nJaHosmOizB9IaW1CidfM1LhEW0Pjc2qBa4uAL4cMKwF5NVEYZpjYKlqPos0zjzMXwhhwHUqA02Mh1rCGh64iq1Y3iKoKsgSQlUBGzNWLEfIgM4zBkTJpQwEUQqU4pN8AKl6OWSowSkY5Nl/I5cpXCKJGbSSyUInEQFQGiJknBMjQ8+Kbp+jhdG8MJM7WDa+YPkeaP1YMgeuaRzQs286QydEmaBn4wARTiGXoGxCuAzqTBXB7ZPKk4ozLUI0HdEpVLrHZIUKdI4xRoHBy1gyI1kWUWisxORlxUhZ+mCjPQOFMT4xkTfHNUYE4ILDG+Kc4zxjiGCFsfZWhCNHWAoQlzDHG2PkZHQ3R1BO4PcTMveD0gCDF5QfRk6hMwgVfeLPMSED/slPCyqViKiiVATCzONESpSg9JaCawUQpLCW+cWoJpeCoDT+mRGoKIOSDColJHVOoKyX1BZciHRt26uE2XMOujeoXXrg8bpBab3ORVYF6RkrFsFVLVkNIYB4OpjD3cGW7LhNr89oiCj3iM1qzH5VaK2+3oseH2ndmgmdngEdKOj/ac3NwPjL56fNfLx3e+9vQ+YPT15w6BWU4Np3dvah/MuoHRo9t6nto7NNkb3pBxDGXd413BjRn3kYn2IyOtBweioz50MqTbn3TsDZsnzPJBJTdFb8gyWixrlgWbajtFkhRHEKAy3BRGkCtKoYZ2i6PN7m61uju8kYFodiDS1uFJxI2+AAptls0td3iVPqsyoJK5RDKPAMqOoRXsjaTPgMMBo1RdmqFOsZQJliLKRCIMRaRR4Gjh2yCSNtC1jRRFfYtIqXaoVTapWAeM0kgCOgUPoGymjM2U19exSUQxmcgnNLO5LCmqskoRSwtdWUXXVvPsDUiYDNXZ2sm3d/BteHeBozZTyvN/avfXF5LNXAaVb5uATtgEE/0rnmoTFHUUBJM8rzDCgzD1uJ3lLfYKoLh0qVlijVeodvNVDpBA7RSgbi7qYqudVMQGgBKk9haxo0XqIsn9VGVkBikfSeUhq/0U1EdBAzAhqXxEpZek9MNNVDTEMsSBvzyXHGMaQARBuIZNlhYnFcCFx+EaUvjd0CjQCUGHpAzCHBglaKLNmkiLNkzSRclosEnmrOcb6tkoXWIUqewKlV0uxVC5yaZx2jVOo8xilTktUg8m9ejlfhQJK5VhiSIsRIICuQ+ymkLt0eu8VpPXrDY1rKnhERkBsycXzXrt4TAAGuo0OWISBIvFW6PBgBtDoxZ073D7E5MDEaMsqJM+t3/rG08fOH9s5/GpDa+d2v/uiyfOQsU/sfPY7pFD2wZPHdq0e6Jr10TH+VN7zj25c/twdlNffKjNv6UvOtrmOz7ZfWpL15nJrl0pOwB6NO1+Ohc8FLROGuT9QuYWrTxLI7RzWd1SZVqI+Jl8O5Xj5kujWlOr3Zd2hZLOcC7U2h3vag+3xW0xr8btUjk8ak8ADdoRv07skovdInlQiDccGWAUWhmirpWkbYWmu0Hka+R7SGIfhFEqEm4ReWnyAFnsokocFL6JyFCpDX6VxoUoLdA8CflqKOgigUou1Skg8okxGkNOo4rFYq1GY1eqbVwxRubp1pFV0NfWSXwkbQo8G9IalESKJsY0ZNgziY5r/n/5KMAKgEKPD/0+OCiOIyCrigCLVDRGU4FPgeUlYA4jsAgOiJvrTOHFQyr2VzCF9VkAJTRDIJ7Gzdd6uBovW+2mK1w0xElD3BTECyY6oyBNGaIqQ/BrSOoQURUmKAP5Gg0iKPEVqNpQr/OCZw8jTQd2mGbhrX2WM/P9v1DN8Z0GA3ZTcCs8RfipmceM4LsXlC1VOP+AFHWYAg8udjZxzS08E1Pq4EhsfIlVKLZKRBax0CwWmIR8I4ujZ3AxOtdE51lpfCdV4KYIfRSRBxItQ+LmK71KfdBgClNo4uLCtXU1JJvZg+mdOoPP5EhYPDmDPaU1hQxGr91kUwl4AYNqW292rDVolzBDWsmB0b6zR3c8c3Dri0/uf+PsidefP/7Kc8fOnTp4bO/mqNsQ8xktWknUi504uPXsqQOTI939uXB/xp/zY9v6Uq8e2/mvYztf2TlyZih3KO6etKifSvhPxYPbjZoNIn4Hg97OYPRLpBNGW48ayyKaHGpsN7laTe6owe3XOXP+1qQ7lXS3pjyZpCMV0AWccrtZbLPJfWZFSK+KoGgMQRMC/FubU1RNEk+KujQABD4K3ODn0GfULPY3CFwggsRDlrjJIhtVaIF8CZVTpfcpNXbM5PN4YmqlicOSKpVGuz0M7IJszoTNk4IcCCZaT1c2cY31PEsN31En8jbIgtDgw1gvDTQh4WZFhASVXZOkQvrUpqn4BIpkCrgEAXkznXtyplVqBZ8CQRQEq4KUAiMTjYI9wQoYGUeXyI/43WZu4mhTs8A1OWonCCYAKAf1MFWePKC0GUBpSjyJMtSh/GOBYzP0fwneCxALy8Ke1IKEIRWRoe9Tx2FCUERAMIF1/FjazDyv/E1wN5Iqlt+EEeYUNIG/15o4vpOporB7MdQRqsyPG4DYxZR6mGInW+hg860snoXNNYKYPCNf5eOoAxx1iKMOs9VRliqGlzllAtIYSxkUQvuFuBkCbNV68oKCtSSaCD4SINJgjZpcGcydQ61JjSlisgSNWpOQSjUIOF0BZ8amR2nNXoVw73Dfpr7c+GDb7i0bD+/avGdyeMfmDVOjfRt7M6N97blEQIsIbCb1vu0TJ4/t29jbngq62iKO7qhj50Du9I7RJ4e7piLOMZtuDFOOorJhhShHJ2co5DGFYlKr7WRzffVNWYHE3ECSrak2tNDianObMxw3+50qR1uoM+1rS3qyaU8uac+EDGEo8Q4FVPkgpoxoVFGlKiaFJlUdY6gSUE+h425SRuvlIVCzPIS/7bIQ2Fi90FvNdVaxLFUsUzUdq6ZpG9kGqshEF+H/UQlbqBXJDKjWIRAoaRS+RKJJpnsNZj9kUJnaLlY6wUEJXB1D7iQKoaI6oeLhroREAM16eQQEtp0XPIG/5vmPXhEC4R2VKgJBMR/8gDyeIQWClgOEEznDK+CYn8BK/qb8rSCoA7OATrbKwVJBfXcDoCCG0k2Vu0gSJ4ggcRGlbqLUi0vmAzXCfin5/1GjNABvTR44GK9wmX/GV5TH8QqgecEm0JmHHvY8MFfwVGAU9go6EqTLfWxFAPonMRqWqsNyNKTURTWGhN6cNFjTiDEuNUFrlIL2VmjAr+UW6LIgIQQgJMRXhURoQGkIUdiq0rVNFIZEq3fpMS9mjwOjWntaaYqh5ojZHnZa3JhckXbaD41v2N3fGQWnwtRT/R3bxzdsHu7fsXlk/44tuybHtoxtGO7rSMcCPrvRiqEKicBpM24Z2zgy1GfUqpikRo9BEdBL+yLOk5MbTo319hoVETZpEJVts2GTRm23kJehUfol4lE12iOSJlnctFAWE8mjUlUE0QUURh9qjdsjbZGOlB9obw1YEiAfFneifpvcY0Z8RmVIqworFCExEuLJg/AaKQr8Lb3CaJ0MAlKoaeaIepPIB2Mt3w2MllN1awnqcoICGGUjDrbMKpDjdVwsx4BRuRKTSrV6vcPhiqm0DkRl1xj9KkOQITaR+AZgtJ6FNQpsLRJPk9xfL/bXiLyVQl+VCJ/kVSv21Un8uLPKgkBCvsrnMQXHoatjgCku4PW/wiuzIkiW+0kyH2yCJcEnfoXXvHj61v8voz7o4q8wCrtOs8jRLHI1CZ0NAked0IWfS5AGAErw/Fqhp5rvquQ61nPsNQI3rDTOPMW88pheMcv/9UuaNvW/ngqT/CJdl4aEAK8KXhvsf5CA4QVAlmKrQky5n4P4+Qp//qv2VPoYiiU05pRQFxHoInx9jKcFJbiaJBdNg/gQz2UBhszLkbskag+RgSxf3UCkCqGKqVC7GguozFGlOakwRtWmMGYJaFQGjVie8bmPT00cGBkM6JQGPjtsNQ33dmdT8Uwy0dvZtqGvt7+7Ix4O6dQAJ18mEgoFPLVK4XE7TUaDgM8VsBm0unXClio5odYlYvZYtTlUEmSSUjyatWpdr1y802nd6XbsdLu2WG0btbpBHZZVasNiBSRRj1TjV1u9OodT57YbfFFfm8+WdhpjDixq10Wtar9Z6TepAgYNVPmITBmCzM1WBJlK/HQ5VZeiQNsE76omCYKCS4ddFDoVTQpAaZwp9zUcy3q6oYapp8lccnMCdk544Uq9T6X3aDEPfkEqaoMdmMlVMHhqOeqw+7I2f5tE66OJrNDU17IMwAAZCcGHBTsAEFkjCdbJwgAloIlv/pfUvwzrv4zmP0cQJDcyEgAcrwiMr0XshoeFrg5gzefJK4JNChLAfRTQBEDzjLJRH/u/jEISpSA+uBMInlle+RNfeZuEfQWeSr3YB6TmAYUnl6cTJvX/fa55oGEzrzzE+QAAjwDz/H2uMA2eChPYJCAzgnZK5mMqg0xFgIX42UofV+HjyDxssZ0ptFIFZrrMBW86xBKa3Ie/TrxLiNIUMYmpTW5uQ8xZmsBW0yIpXtlUXFYvU5olMkwsMwkRO3SKUn1EYUqosLBa65aKVDwqw4wgg8l4TySkFXBlNJpdp3eY7VqtHjNYXC6Pxx1wu70wFwhENCqLwxYgcrXd5vZ5Q2aTXYMabCazhEZ2KkTC+sqVD9zJXFGQkPHalJIwk7zFbt4b9G6yGnMiQZTFCrPYcR6/Vaa0MXlmhsDClbkQvVdjd+qcNr3XagwFfe1Oe6vVnLCZE1ZjwqyPGLVhkz6KasOIJixUAaABeFtwf4JGHsuSdfi3P0DnBKKgeDRkoEn6zJ9q4J+F2NMkcjXw7QSRnacJaVw5lTmuNkZQY1ipc0PnDm4qFOtYHAWJKiExEL7UpDVHNeYYF3FCfqVKnc0CK/gFJEuIdrADQPqE30g14Ge2GIY2qjZDVCUhmDbiVMBnigNKkIfzPkpRhHF4ZAGC9K/G5oqPUmaohaz8v856ZREIbhZ6ZgGdTKUTxFJ5WGovU+WjKzwUmRsPH3ih98Izg98Bdg0dFoTFK+6Yj56k/+aPv9KkNgUT2MzD97845ols/m9ayvtrHscrmOI3KSMNskCeb/BseHObxd68mzIQP34SAfGw5R6m1EUXO2AXJ4lsRLGDJHaRJV6SNECWBcmyMKiGbm1kW5s5lvJGyfIK2tyCiofnlzWSBEKpXiCDKGbhyB0CbVCOxaDGSpRWldKAfw2bWjOYad3YlgtYLC6DOZtodToCTk80mmgHWR1BHWbXYU6VxtREYDa20IQSVSCcyrT1+kNJk8mF6c0xhx2AMrBotLISakmBvHKNldgYYlH0leuyEuG43bLZ694aDI663F06fVqjj2uNaYunwxfP+ZMhW8hu8DissUCwU6cPKdU+ROlRoUEQgnilUpdU7ubL3SyZmyJyEoSORihuYg+8XVDia+XhamkQ6u96gbdGAK7hqeO68STKwatcNc8ONRAYhXr9/9T1nk1yJGnSGD6QvL1bMTPQaK2qu7TWWmuttdZaV2uFBgYYjNqZ27u9l+8djTTjB/JX0iOjUYudW5q5pUVlZldlRnj44x6ZmIk1rkqj94nKCj1Q7pyT/5NlZYLi3mivUtlOMF4PxJvJwqjcvsg1z/3poTXStkZ79sQIhRQ8C9RRAB+8qIGN9772h0CLAAQlHxnLgfwEhBiQtf02qjwJ8lRNKUFpTQdQ3ylT6Ufi7pp3oOmao0RHEY/C1SWAkAR2AgGGoPb0xJGZO7Io8eT/DoF4SPxN6ZokOPwwKikJT5+wZaIcavcDPYQtPDWdTAC9Ygqc46uDl/ee6h1toGo4SxBjYnGcxEWQqGjKn5tzZ9bCORTanJ4D6B1TbGiJ9gFbrA9vhKSPiBopr8LlM8yoUPkcPP5M0HtsY7WHWPUO1RAjWmhfhjNdpSHgCRUKtUmpuSw2zzLNs3TrMt+7LXWvi/V5tdwrpIuL7vA///zr//XX/3h3eXM+O393/+ny+tP57Y/3H/7t+uGX7vi60pphOKE6mXzP5oq5fKlqc4qQ0RmcdXqno/7iP3/66afz1b9dX/7fH9//ZTm7KWY+VEv/x+ns22L+/zxd/j93N/91uvrLbPrzePLzdPpvlzf/71/+x4ez20G5M26O7i6+fXv343jyNlua5iuLdHGeLq/y9YtC4zJTWiVyMwhupLzwF+eu3MyWmRiTY31qqk3PNemFIX+hy55pUytNcmlIrIyppTm+MMTn1swpOGpMTk2pmTU5sSZH3vw8Wj0L5SaR/BjFpNQ+w4xtD69Hi3foh1CyHUi04/kRCJptnIGj5lDTEGgaQx1DdABfC95bsmeW3CWFKXNpzl5Z81eO0q2rcu+pkSEG/LW39F0TgLzJ2nhgLOkDhgkUBDXXpKTaiQYJ5YxhpSaVOgFf8fJJsDxnsAxVVqTEMwR15xeYc47swp5Z2tILa2ppSS6MibkxsdAn5ga0U0tTegWgrYvPNNGJNjbFIbqTwpw5xc3YC5d2Um4IC9HA/MYh/DkOmUn3LfENf/edmXP0si6xNKTJIU10rI2M9fExrgRdbI4M0FmWSM+VGgcKS1R5qAIQaVyTGlG9/czR+0iFLF/DIXgSA0e4LjcE99lqnS1cbs6bg6vG+B4imoBzaF2CwcXGolYbZZKFbr39w8O3P334aTFetZuT2eJ+tPwAoQVQGWv9KwBpN5zq+KJ1gy3mj1Y7o5vu+DZfGifSnUyqnnIH6uHQZb1+12qe57Pfthu/jod3xfz/mM/+6+Ls5+HgspC7LJfedrrfDiffn57/cP12WO+ng6lMrNBtL+bLt+BKe3Rf6VwXOzflLkzzx+b0u3L/Ide4zjSuku3rSOsyUDt3lVbW4tKQW+kzK21mCY7qc+f6zJk5e2HPo/7Ahl4DqE5wYiAooWl8gqkORQRNE/ULSGmhe5WpL4PJDvJTrXsZTLRSpWkwM/Alu4TEKFP5GRI9JACCBSbY0itb9txRuMH324s3tsK1o3TvLL91lu8A7AFr6bCaUytTEtslOO3IniG9odAHqjeUo3FmsYnm+rV2UhBvyRCU6qi/dPUEk9Kbn7qzBJidqAXO7AwX5MqfMbhw5C+chStyt6UbT/nemr20ZC5MqTNMVn2cmbLJUwD7adeQq89f23JXAG3gkDl9jnPwV/hbuh9/C+hiC210rg5PVaGJMjgGNLGlJjbXxhfaxAzs18WnerA/PkYHYc7gooPVixi8aesm1bkDguVT32O2O6UX7MpduvPQ1Dt3ZhXIn8XL52ZfhS22STW+cKqVLAxy9UW+Q9gJjgKp2lmqOC6WB8VCY9Kffffu08f3P02nZO2wM76HloAfw7OfRuc/Nyff1oYP+fqZO9wwu3PhVDdTnhXqSyCcaHuClXSqUY5lUZ4fhhPi8hKpy2LxY6f706B/Vy0vkvH7ev3fr65/uby+7Q+mpUozle3X2jfnt7/++Jcfv//31fKh3lrWe1fj00+D0x87ix9q04/V8Xe1ySegMvpYGDxE21eR7nWsfx/p3Qe79972g7f1DjXXXnlrLd1ZctcQNjoiJgJowSn6UBedoNxbU3NooSMzjTdhnM592RFoivAEb1rv36C2xHLDYucKt9mafsBdA/Xp96XRx1idlGMUevLv+HIXGE2Muy5xqo2vlOEZoIrM1dEFRs2QOsVRjLI+NoOiWdKnOJ8sLzDAACG32VJTDBa+kBpQykhgrabrsE/4Wrl54kiNnOkxJhYUNNrESWQJINy4RYlHobdlz1AsrJkzzB47uJW7cpXuAMpFCrrnS45inqFNZxvlKOU0ZSQF5TdlLf1Dd/neU3mrT54BhtQ5DgFm5tdxDQHYg/KVt3CBGOjOLFzpKUFmQh6DMQR1F85JR+SvPIVbwJ279ObOIuWraPnc5KvwVR69I5mvzSrt82r3qtC5SbdvMr235fG3zemH9uQejGw0RuPB8ury4fz8fau3AvNgA1zJgTs5hv+jDhhbb3wgNcRsvmqudtrs31faF6XmWa66SOaGhXy/ka1OitVFubEs1y5rzZtG8zSb7/r9s2T6qlz9YTYHQT+dnj/MV1fDyajeHnUGzXqnXGrVaoNWZ9nsX5ZbMJ2r+uhdof8207pLIh4xSJBacemrn/qaF/7mlbd+5axcWsuXNGJ7mh/cjW89tfcElXfoSV/1AUCNUgT7Cn+PlKPIUBce2NMzDDQykDXa8aRH8fISqShSnCAepeoro6fkiHQCmQluGV0XqVygtw2RPrIL5j8IByZgyACUeIAyFRxVhKZS/0Ds7YndXUDu6wEKf18VxI+OjLEpSjEpyPEpLgDjRSlIfSc+kjWH5AQBHx+xk6SgGqmNhKOQJeT3NTtxGFXVGBtDnxmCwneTmcEACnoFzdNEZsCabQD9CNpRscSW6iva9ASwE6DaiUMApTIYTCUWR/El+HJb7obCnr2xZa5t2UtH5hwX4yvfgIWO9NKSGKNgAdbYyBYf2lJjwJKcWJIz9AJKjCV9bkudWxNLX+4SHPWkJnJzki1z6OyJZGlMXuEpT0NFlLAZNBgDn2ycYyeCVLHcR8VvNCYI/vHcAFbMn5uGSmduTIbUxJue+rPzcGmJIeQofEpLKlGeV7s36fICapqvLcm2OEhHMu1McdXsvx3PP8yXH6bzj6Ppt4NhJxR96A1B0PezxUVveD1dXoznsBYgaIv8o69hvT6s1MaV1qrRv26P31UHb3Pdu0TtCoabrgMyS4FLZ2nhqq7c1TNX5dxROmdc+wVqLlNw37kq712VB3eZgGoHjoKm2tgErhTOHmrqr1xmh9+iFsXrV5XJd+3Vj/ihWHUFTQ2X5mAn5iQ6LVQ4jZSgtStXem5PLRxQRJi3NJEMEBS6Q8Yof+trfOepfcCPoujbi3fW/A0toXroEQY0NAJHtcGRMQo3TIo+uE4qOJPFqViS8F44RemGvYQAkedhDEHp4hTJ9YZI1xwn+o8UT0kN+YQ1Nn02oPDdn4kF8lGmXtDrWNdxcFERGKFeU7JSEgNgHia0t/rgr78PNj+Q/+Bv62O4/R2w9gn4c3wP+IoORc/acne2zK01fWNOXhhip4bIUhea4ialzpbE2ZA5m3JXQ+Vt60JdS3Rog09NTxiOznDNuFqAqXGn6BFv4cpfOIPf5yr9h2KbwhB2hsqhbB+aEa2cot5le+QfCGTaV9HCKJxspnKtXKGbznWjmR48mRvfHB/68qf4FSvJao+C6or1BJqIzpmPFafF5gX4nSyA08NIuodcPGhNxo3erDlYYluuDzK5USa/LFfnxcr3y7MfL66uBuMzhKzp6bQ/rhZqnVb/4vzm44efb2+/6w8vUOvJ2sDVz9DRYvceHA0WyboPKoYjM7FnJ5poW5/oIS2ZUhMj4+NNpA8vDYkLQ+rSmL4CTKkLjBS1UvBLytBQHugpQwN1uA/AlSKDkpobH8ZqV9nuWzINMGMLc8gqHEWqcZuoXWe7D6nmWxDUEB5C/IgTJasEBKS+kYJ+BWBiACDoGsw4Iiq981bewiVCeqEyziwK4JUXmbt8/Zv1Ubr6vm5TjgJoQFadmRXhqDHaM8dHUGCwE4Agm2AEozAx089KibuF+4QLgS4u6UcADXV4rgxOFYEJ2vo4bNA57SBKaLR/c6bMNxK7+yJXDw38lSoETkN9yQ3TeWlOXlnil6bouSF8qg3ONL6JyjNQerqA2tczBJDux/bEDF4TpRwEgoF25GY021GfvuaoM3NG3p50VfjqMFflVZmiFm/OE296M/1QaQlHWxp/V1/+WB69zzQRg1r50rDaIK8ppqrzWBXZ8QwGDmPJzIc+ogZYG0XZKS4Mnoov2Y+XZijKsHGF+ioQb8KPIuzPJ+fNUrOUKAzKrYfF+Z/vHv5y++7Xm7tPy4s/X9+/X57dz05/evjux4fvz+bno8H04f7bb99/+vb9j+AoPMbyFFHqodFFSrsvtK8T5P9LO7HFuqZIyxBtGWNtmaes8FVV/hag9JGSqvQOlP6JOrhQhzE0pxgm1F9aguHswVF1ZKyOjGj6VAa7Kn8Hf4sR1wZb5kiPOr1c76F7/iuQ672LVW/AzsqU/He+namF0tMBTDF0xQzqSBBHnYQTQyy5sOZhgm/N2RtT5tqYvgDofuo0QFNkGF/p1lu88RSInUVOp2GIMpKwMEsCOgnE8REAtwrK0qNEWXNnT/zlMygoXevHSEOQ9JGxJjigM4a6b8o2Uwp1+Ro0WgOssmavQS/MYxBxfd1o4DbonVC+ok2ZqomgpqM35/SE9U7sAV/VwZk1cUMQv/yMcwanjtSFM3PuTqPynrnSS0dybotPrbEJCKQNddUBdH2P8UADuXcEqP0jjb+v8bbl9qLQEBfqgkpLzOTN+9I9ZAVfDklxHqickzWBymkwNwRH08V+vjrOVmbR0hRH0RWMh+vDjYGggCXSdSZHtmhHaowb3HmjJ+cIlGP5AUJxIF73RSql2mjYX3XqvVq+3szXx7XustVfVNujfBkE/eHq9mo4e7u8+q9f//e//vmv91cPt1cP//Hv//Nff/n39+8+nZ/dXZy9v7r8fr74ttm+qiHUt2/S1QvImzM5sMa7gCXZs6WgJhMIijUxM0YmqDBa/1gTmCo9E4VvqgwigC6Z+LJSJ4AFYj6zwHKKdE/WoRJzWFLQFP0mdVbkjpLKXdX5m0jModKpA5E/szQFyVNTc2RgjoyULlK70JMqH4ES1TI4xmDR4SZqnbxYAykCHAUfwArIBEyXI4+ofQeOPtL0M0f9zKMZKqU0wpN1g9QUTMWWyGVmji3Zk5g9oeS1pqbQf2+JLBCQlfPCJbGhJOicMTQ61URWFBLP4EtIvUMootw/pqKIqwfQxk66H5IJQDtxMvZASsFIKq5Ud3FLoLur9BZO31d5b45cArbolS16YY9dOuIXzsSlE0QPTPT+odbbU7s7SkddbqvKrBWJtazw1OWeusTTkHraUk9H5u2DoArfGBxV+/r6QE/na0jMKeioWBfQOdMJhJvGOSIIDBlMT6R6GSwuQNxEfhjPdoFItudK9lBeoD2wcbpwTx/uWGMYsJ7WWwOklvQ2xyQ3xVTWuN6e8kZqmeIkme8n0h1wNJEo1artcW867U1X/end4uzD6urb5eW7+fn3F3dXw8XlZPWXT7/+8P7H0+nl5dntLz//BRz9/uPP4Oh0fLVcvF2dfgcT2+je1ru3pe5tpnERKa/8xXmgNAtVVvSJWqR6HSheYrrCdlujS3P0VOWdgqMyH3p+IvUTSIJjSXAoD44UwQFiE2q9JjxSo+772yJXzVM8s2fmjGKRpUZsHcmx1FbQeJsic5GtSXG0aY4+zzUURNYaipiCAeleqMAXwy1w9YXuASDyDCW+sQyzJTilcqMNw3qtrBmy0vKIPIRmRU0nzUyPDzKZyk7JSuWT0hQNU3T8xJVl9DZHcrErj+teGuIzTXiM6YJJAwqCYXx7l2tts81NgFZqSjscxbXiQkG7NVOxBR3pOQDlq9Q7Zgg9Zs4nwomSxBiAJWMAJsyfDESOvtI9VrumDMafMQQskYU5PDcGxyj3xuAIU9wem0NNoXam1IxZhzslASt36yrce0oPzuwNbJA7dwFHpXKUjqVursKjsqcsgQYCAXxYonmbaN7HGzfQD8QjpB9k8wR5IWiC8mqFXuZW5MFvmXRfrM6cXL8GuWHdlNa0N9nHmdHsAOa12Fgh62RKI3jZarWPANSu9tuVzrI/f3d+ez87H1eaN+PFL28/QERX/fn7y3dXy+tObTDqL+5uHlDur87vRsPloLecz+4m04d277rauqi2r3FV+TZS3WWsdp5onifb1670lBTHzMqWnJvgx4IjjQ+YwB3pIqfQUXQyGMN39jjONkA5Sv2oLkosrD421Yb6qDyY3jp/2xDsGkM9b26BdGiLjizhgSM+C+Qv4o23icZ7X/HaHJtr/ANGR4fgKEZK7B5SgJea2Kk6ulKE5pgV+AjKChwdvr0NmSAOJDTRRYlJgFUwxxdmqHiwTyM8TUhgJAUkE3tom+4HiSONW8S1J/rQCFSFB3XmmMWFDFlU08eQyCbgqMyHC+quSfklQak0rsEQEdfdFzq7uEoKtHEnhJr+hSqEoHeui53pojCpZ3LfnFDTPSHUdI4knpHMOyPVygMDSqD2TjW+mS4wM4QWgL9474XcZi/sqVNbcmVPrdzpc/IxfWaIL1WBKa6KZ2thLvFs5KfRR3J3D51rCg/VztqxPLQvcLEkPq2jove2rGQkJq7kzJ2au9Ff8ZErNvClRqHcLFY6i5TOAshG6ak9MTHHJqh0MGTYan0dua0sNmdllpzSmje6K/ZQwxNp+uOdcKobSpIHiYVcO5coxQKpZCjVLbeRjc6Gc7AY9hQKejk561e7g+bwenl3tXqLgPX9x18+vv9pMb8sFdqlQnc0vBxP7+qt03rnsgSn2zjPty6yzct47TRRv0i371LdD+HGg7twicygCQyVXsbe+Aa4X6oXqPJM2b3UAelzGc4JDWFJVeGRws8sQoWGGHE4JTh7nbejdtWd8Um8euNLzhXmoj06csTG7sTCnVq6kitbbK4PjdXeoSYwVnpHEBH0MNfS5dn7Ys9U5p8fGRrHxjrH0oKQCZ0Y/b7I0aUQO3sKTx8FjRiS4EhHMIS4OFOIEwtHcoowSvJobIRJog90EIIhKN7cypNd4gQADXwkHDWEx7hhcJxEJeZOZN4uCEoQHCOtQ7rXQHX+hxYT8klpysyz/t+cgHcm9UwJF50jvq3PNrVZ+sahtoY2hcBOOIrTVMGVNnymCy51gQWg9c+1/ukahtBMH2Rkwz9AEVd5eyhAcndHaG0JbG10DVgu908hzFBoY/LSGD/DNNMGhtBdlbMh0KXFupTGXtZ5miZ/HxwFMDa+zNKTmltCfTAV8KSmAG0jxTuTY3t8YYxMTdGZI33qzV4gQ4Dc2G+GY/PXHaF2MDNKFmep0jyWG0bT7WZ92qmNWpVes9TqVLvT9njRHc/aw3/9+NPFdIWPby/f/vrp3z7cf78YXXSak35nPp9c3l5/fLj/abm8b7Tmzc7pePl+MH3XHN5VOtfgKMp9orKCfpPfhRRFpyoUcV8Pw0TWIJkVFY6lwbU2+c6O0N1DzSW1PjCVBydkTZB5/gdXSjJljKgvQXBgj02toZHG2VTbKiZv2xkaOoIDraMGKKxVsaHA1+UFhqLc3gZHVYwuKv0LZWApZyDxr6S+OdQH4g1Ag2iDOW0i8wwBkucIehgpuasNa4t5Di6CoJj/ICj4CqANpqIBUgJoUPoCOPOJOTYDcNEw4CAoSR7ujtTZwtQER9XhqS62ACPXpKT5BgUapKROgAonPlKarp0opjU5jbkN8E8TPiM6ChFlgLY6dIobXpMYkHmmGkJNAogoSpjaO6aQO7uAgrlVqaMJk8Q3lbnGktTZEzPAxIWCnpgah7rKnrrINTdY+iJblxcaSwJ9jqtOyIxZo6dhCnRBTVdiCo5iGymRl/apeICpem9D7awobEVA5ShDY8zhCSaGITyxJZeu1Kk9MUNBtMUGFn9bZcupLFlbsB7JDOP5CXxCtjxOpxqFbKNR7U0H89X4dD6YTzvDSXtwvTi/O716d/H24erh5uztcnY5GZ6huIOdt9efQNBv3/1yffVpOL7pDi770/tq67zSuYSOpqqnmcYVXYdyp6cqyIeXsJMSVBkY0CU/mlb1yTMm0Z99xkqK6uztwKnjZNRrNZnhPXyJJTxxxBcOjLuvp7bVVNaq0dUETSP5y1j5Nl65C2TPcQ7cv8rVA0cFlia0QOQYYphATUDmX2CLEQQvFYEZBOJx6JnMqsX0WCMwhKwQU+tu6/xda2SICuZMTLH1ZcmzQGzxETXNk4Z+z6EC6GSchq0x0H2CrrcmFpb4HEw1RAlNIcsoItrITMesvdOV0S9pul5IojSlBuAfpigmIa3ARXAUZAUXIZkQTp61d2xoUpwYWxxzB3uIoDqGAnNbZOmIrV2JrSe1d9eQ2FoE9obYVgdBBeYKOMozlo61hWN9hWOq8yxNcBSVCHUHvpnsMVWFlqrcgZRTFupTMn1GbS9q7FVbZAhGGnxto78DEfWmF2An2vAAGlcV1FTaS4Sgzgo4ao3OQFD0Mqn1EG9XA+Veas2rbSWeKsSW+qSGmMmVdwZr8Kbl1lmtMc1nG6ViczW/+uHDT798+uWH9z98evdxNZx99/ABcf77b3/Eodn0/P720y8//+d3H/7yw6e/vr37aTy66g0up7N3nf5VMjfsjN8OFt81xu8R7ROVs3ybcBQDifGyJJZ03ZosDkYmdCkaJQ4dLvWPxN4BDTFMjulDZWT+nsjZBEBuXWgMPcJYoybA0NsjE29qFcqee0AATxsKqrLXDd4uuOtKLpyJpS22MEeXltjKnr6yJIjlxWiqQ2ea6IUheWNK31L5BEcZMIuJ4YUusoQ3AEBQXWgKt4A+ZDA2+HtgHm4ERATWvLTHxnQPDlFQJ2YO9Z9APn+L6IwA1IwvQVNNeEpXQLElvGRmCWRc6h5IXP21hgntnbULwX4cpRA7SRIS2nt8a4drbnFMTbaxcWKof7mfZ2njEAVbV+Pq6zxDQ2BqCUwNCr6xLjDV+MYq11Dk6AsA2AmagoICS11obeB38VvoJkwJWuuRK+GEYAnAUb4uy5IFj0V+jiIkM+YhpRBOrbsht5ZQ+sFOtClx3ckZKAsDQD0AxgnaaYkv0MtkmdDZUjjrCnsFHIVz4KvDAlVYZoyrzQmtNeUMVhL5YX98WSy2k8liszE4X11/ePvxz9//8tdf/+N6dd2qtpuV9sXpzcX5fbM57HRm9/c/joeXIOiHb//94vy70fh+OHrbaF/EMwM3kedxIDU2Idm4KvboACYEA+nKkvVwxEFb6jE50DHCANHAShqhuQosiZFVUrg1lESJuyv1EB2lvhB+CXNP7x8YA31v5hSqGS5cOSMTg7utdjSkphJPm+NqskJ9SWZrKN196Kg2OEO5x6hBBSArVFzg3GCxSHiiKcXZwyggVyn8Q4QBANqJIcDP4UcZpo417hakEcnMFOyBf+AreIn7ItUpOqJMJS9VphfezJLiCeSXLtJC/1EFoBYoB4wZH+CuhI4Wz1o/MdWOjVVsgQN14VBTBI60JYClK1OAo2uaUsqiNPCtLVBQYMNHEHco905Ugbk2vNJHz9TBBdpK/ww7cQh8xWk4GbkeoLFJ6R7KnX2IqNgKcQVZa6AmaIot2ImKj7pvjC704Rk1QFI3yV6IaGhj+mJIpNYaV5M+EAcOBJ4TiV+gilrDPbAQ9R1bMJVSE0DRj5KH+1doBHKn8APmwACRAgOJLkZfS+x1ub2h9rQ07obSWeYqIlJjUucoGtxFnS2tsSR1tqTZlUlmm+lsM19q12q9VrPfbQ3Hw9np8mI+WpSLtXym3O9Nlsvr7mBZrg5LlWE204lHqrFoNZftpzMDX6BqsCbVxrjJUXD4m1Z/S2kpiAxplaMKsYeDxDBDCwEaRGBM6QoMrWbwXcjUPEeXa+8AKCxkUJxtDCV4gw6RudroOkxyrjYHFyS31dWuJoTTEhyCo674PFV/QKi3RqYG/4BZ7EP/t6EOIKgmMIWOGuMXxsS1IX5FDFv0dM1R/DTlADgDkLU//2N4wBaFCDQ1gqk+QlBKSmyhlCj0ANoU6yxLtRaNJ9AGlatNAb7KXS2ZsyVxNFEoucbKibF8bCBcpLwE2MYayijcHmorn+YVhprQTgoqq9iPE3AmKAjqgIIQS6qgx/oaS1d9LUxsStK7ijzaOEQZDMrCiSIw6YNLPQlPMzhRylSZo4POAi8BXCHYSeeoxNEW21siW1Ngb+JK8OsgKJSePkGFnZKYS3xtSqRNqix5vbPiTc+oDYpVb5KNe2wj5StsE/W30cqtP3fuiM+gLohZUktVhFG0N2GCAfh9dBHslNbTVDhK4KjanrcFmvCjKPSI9oFE2x2qxLPNSmPcG64mk7PJdDWfnc2mq8l4MZssry7vbm/enZ3fXF29e/fhz3fvfp6fPjy8/XU4uCkWRpXSvNm6qDcv8pVVurgczH5ojz7lG/fBzArCD3nDJWncHcbV4WJaYntDZEMNQfpsAJrQHKAVn1nLG4lcA0RJdAjyOC27Gt8IoshklxZbncUEVjlbuE2ZuWr0DTzJpT9z5ojOiQeILpzJU3f6HFukVZVnwDFU+eYGdFThm0JfNKElVRk0UNxpfYeiwyJCMtD5qOwQTvAS7MQ8pyuGxtAA1ERB8GXmNJj6s9CL02B+hUJhi/QBR2zoSowBtE2BNmbmEz2ZLgQ64mrJSMDwYaqBoyijGHvMib/Xxb/xcl3WKS3WNgD7cQ54DI6CgkfayqGmTIGPoCnIKvOMwV2AFn3qBHjGppDxozCjMsdA5uh9FtEWCAqayslE6igeQ2ILfKXzVeUjwk8dCIGnJ3WS4cQMFJvyfFWMIwtyxF5Iqc5Tl5gL8JRgaqJ+B3aGoaCV61DxClUPQgWC6n19qIg5NLZEpsbgGLXellzBstsweIkZihR0VKJPm3w1V6TrCDej2VFr9DBcfmyObqvteak5zhY7mXyj253e3L779v33d7fvyqV6PJaOxtLlcrvVnTW7szb5T7U+DIa3F+ffn5/+AIIWy4ve4GGy+HE4/zGeW3khNkFilHXutsJeg+bhdjDqUCbcO8aIrS+wNNlDVXZPlXkjim9KktuKzL6mcKSvsgwofXV0NbjFoEyMuzrP1Zfh6fENhsAI/IPjNAVH9sgsWrpLVO+hoG7kwtgcR1WuDgo9pihoDXLrMAcCU4waxotWPFohT/DlpjpYQQTC1YGpkLm66Pz12gt5iO3uwMrrmEcqyKkgKOCMo4j1yapfAnVsCDoC5mAHZMUe7EfFM/ia5mDvichU5usLgMRSxXcB4ChqgdBMfB6xeram2N6RMIxkVryG/xDI1+QccNfWpufLXH05PCsklgFOWAOHcA62tIIA+AbswUxla6snmgpLVTpSFlmqwommxNVXeYYaEVE74eWXoExlhL8NYDpBUbiWGseMSV8jTsjTlVlL8I4skZct8ijNOXQHUjl4hpKtctYMiE1paOodRgs5l9Y4tbMjw/w0oGeKAkOJrcufwJ8ZChAemb0KigsMKfAeflRmSJp9FeT6TGWZqcyTpXGhRv6zWZ3B6Xh2OZtdzBfnN9cP33386d3Dd+/ffYKC9vvzYqWbK3XzVfJvMivVebW8qNXPWu2rWuMyV5incvNc5cLuaxlddbIkFCexBrwxMbWSWPDPeRHgGIood8CX7otyFABHeSCQcY0qIDLVAJAPFAQUjpbW0zUQbzoEBLoiV5s/UWVYitSRMktpjb+iA0fHl6z3IeYzoPWd9j+oCYJSHcEWNKXPBfWM/bXFp8ZQDxph9DfdSZirs3Dx1JUAU0FcMLKLhiMGgo5wFA1ToIWTERVIrUfXkwUaQxFbni5/pErvSKJ70iSwK0vuyVOHqvyJrgxdFDDX9w8BegE8E9MpZpC7RZlK1tW8IwAsBGXBS3q3+EiZivvkGmv4/mP0LzpaWWTYWQJTQU1GQbuo9XJnFzoKDSAabyS8OdZkWeoM9hAwfQTAe4GpqP4YRXJfhjxHFWfLQntcx96JhacIvj42H0j9EFdkdgCCCjMAoOSRqmetya3IUg2FrQlgCB8XuYgJBl+LXF3mUBra4juPJYFjqZevDKltGXe4ASmN50fRXD+UqCXz7WZ3sTx/d3Pz8fz8fjY9HY8WD28/np/dTKfn08X12cW31/c/XD/8fPv2l7Pz76Gg8UQ3GG74gw27p6Q1p+W6hNnbUNtKSktJaa8oHXVcm9hckdnqdFqu1zfQG1RKZJ+9FsmynhEFcVmWltDcBEE5ugoId6jIHMpSB9IkKHgoSwBH8uSxInmiTGF7JE+IzVWJhTBY6WwrXD3a7VAHjNQaVHEwmhSkkLq61HSRi2FmEQAHr3DAFzVBUxOKUnQMaTD6W3pv3RzE3BsC4KU92vekJiAuSAmmYqc3Da1FeJ2CrERHEd846gwukSVPHEhj+2CnmIDOOQrKPICS6R+Cra/85uPnKvOI9QngIoCvAtZHKbPBV7EVxb2Lco+ij85dR/sTDTS1QHmJWXSoTB0oksCuNLoni5G2EnOJFL4jdQ5bkBix4EAS3hV4D4XeXY59h2U6kXj3hO5dsRfYErjecJ0bPNeuOHgoj27xA8C2ILQriu2J4/sS9EYSw7kni+PLoaMiK6kzWl8H6iuzFaDNXGXwSOhiiZ0STdDsLvjjrVh+EE03Q4lKMtto9ZYXF+/evfsBIvrD97+8vf+wXFyMRqvTi4f7h58vbz+1BmeZfK/duchk+4FgNRipJ9ODdGGWyM2imRk4SlZzvcSQWSNIGBNX6jRQuAZ1QCDQSGh8lFIyRuYaVTXeI/62TiIwtURGoCE2NR9hrgPHyixLQcTyWJmGN6XCeSCNH8nTkE90NUeHClYG0GBrizxDhQI/hxqLCEuWAj/LJ1VQBWOXUb5ITrBUETGhgEpXg8nyfXMIOg2x7FsCLbO/aQt1ADRMvga2ngR51Ock/1EqAkekR4+6Y5MnQkNRbCpDP0hEMJUB3D/0A1eDS8F1YA5B3sGnI3VhV5amDPvvAAUpCx/lUF2gwJ8Ae/LMviJ7oAR78nQ/1WbwEpPycToybkHuREIaSu190BSzn60tHykx+3O7kgQAxgD7mE4MQcFUEJRy9PAzRxmkcc6hLL7N922z7YcC9wHHdsixCFRhvjYuNGWktoLMXhSachxt8kgRA0dPVGmME1udBziaAldT5mkrfOQ5Q4UueKHWo8iwNSno6I7Q7U/NHJGO3llSmJNqc8LoJO9A+aL1YLzq8CSM1qDdGY1G88Viu9edIjll0sVsplLINev14WhyNVvd19vzcKzabK9a7fN277rVvSo3yPv82fIyVzsPZ2eh3CKQW8I3m4IDsi5rbzCy1CLqbqrwmScUACXTusP3Vfl9VXFPWUAe3ZfjaI2vqwsNdbBTZu0oHX2yJu/uS62oEi3QHb4T4R0gBtRJhltsbZIlFIaRaIADkFJK1jXwkZ5woMxQXTiBSTAUUXOgoxB4SlCVu0ngbKhdTTAVhRsTzwgd9TfBQjekNNoHR7XOss5VAQyeGqWvHaU/1AGbXdHhE7GhpLDWERhJZkRqtlQpRzHVJMxcYcJKT2LrUBeC+vsPAddIjeNvgJsE6MzDzQN0NZ7upHvQwD0zlSh3IMe2QMs9/CgAjgIsVQ7zm8on+Aem7kpjwAEDStxdWXxHGtuWRIE3/MCeOEI5ChHdOjTsHRtRmvdFHpYyzDekJNa81FYSWwpCE7HjPF2BcZ8VoRGOsyW39ZUO8i4LEgMNKBx9/liZ2peGdwTeTZ6DJfbzVCGFKW30lFyhui/WDqW6kXS32Tnv9E4Ho8vJ5KrbnYOU8Ug2HEjWyp1Bf95qjCORnNebDEdLXn9OpXHLFHadwW9zpjz+gi9cCyU70fQwlpvmGtfZJnnX2JeDSx4agl3yDldwqPH0QCYqpWAq8Ll7H00X8U6MgnJMTWRQrr4OjvK0NY66wlYVjxV5ljwHkMFlxhfVnCYbuAhYCPAPKgM1gbLsSFMQF6gJRIdqKmUqbVBN5VJPzOBx6ZqpYMS7I+Q56vQlNamlrLAxj0XsRa2zaPBUbKGWNzkMZCbYYrbj45dAHsVpSmveGug8YStSAm0eOgqCIjlSjkqtdVNopvONQFBm8byOsktN5Lr4/gawO+AozkRngdAwkWA2QD0NgK9Cj4CRICuAO0QFAe2gjlvC6KYggu22KPaGF9vgxzcF2JncEcMTp/dlmQN5FhwlUGd+g11RGNgWR7ZE4Q1h6DU/8JLre8nxbnB94NO+MLjLdYKjr3c1r7YVBwLbntizJ8Eh/6E8eKyKcXUpuExUD9gyAPX9QJI+lOaOZPljRfFEWTiQp6lmH6lSbHWGb8jBy0qteYO7bvRWje6K1pGDjiqNMY0lbnSktIZgMFwaDlDm//zp47+9vfv++uIBmI5OP7z78ecf/np7/fF0+XB1+d3q7EO7e9bpnZerk3iqHY7VY+lesjAC123BqtQQExkTPE3sSA4rEubp02JbReasMw8nO1RNMUyk6CPdmuuMZe9Tu4/oTSGx98k6ibEl0DdAU3AU7DyUZnCPtNbDnqJX4aAAzH/M83WcWMcM6tMwUhTkfFQbbZHSlAQ4xn3SRypCxjcD6FJsFfYaFBQ6ioYGETDQZup4xxpsovfICw+RjjtO/qOTvtQIZKVte7iNQzpXSWUrQHefSE0VoqPOts7d1Xl7BL4BIEeJN9VwNeDQpiC2JYzvSlLgCrj4D3GsLtIMzjwT+ht314YGX0WEUJHZh8lDGpMkwEhQ8w0vtMEPo42dOAod3Zfl9qRZBo8Ehb7S3gEp0ZtwhwCtdJiyXNLGnkerSoUWxvpYlTiWxU6kQY4suM+2buypdzgmliLI1selzpIm0NQEYKfKLHV6WxzGF+LPmcvL4KdxDQeyIhQdF4YECWyJQtuC4JbAt833oNaThwIyn1AdkZtIoffHO8Xm2WD+fr76cHH1/ft3v3734S93V59m44tRZzbpLc7n12Dq3fXH89O3o8FFtTKKJeqQ0ni67g/nLfaY0RJ2BQqxbD+WG3oTHYu/agzUNS4MdhqqD3/CNeLukGkqEhPREQD5Bq70cw+XgccwoKsBx3qyFZvaYkMLEOmbYCoAAwPsi1N7oiS5O/GjfaKlaUMY2xTFtyVJ6tCwRXtLnKAZmgJJixpZ9NgWIxAkZDPGHUSHa6KTWWDMQz5BUMQ+maWoclTBUV9qEkxPPLG+yVPVOYqY5GZvDW21lbz8gC3sky3Q9MYHocw0kltavK0nSlsDHAVTRfoiH+YG+UmRZq4APjp3wkwaWnCZQpyl9ff/D2AqgAZOxmCD1mDkmpToDnQKBVQTH3HokXy0lCsyoDv6+lhdXpMeRGfCaZ0+ZwIv0TXoDtqnuFSWAik1+RsbwNHkSNFRpRBu1NaCWBVh8WyHQrvQmGJpojvSwBuB+w3fuyn0I3Xty+ObwhCBILItwnggNxAnB6/C15fBA5RUobGEUM9WJVnyCEsW/NOGfJNZKABHlcaETBcGFPqQSuvTGwMOVzIaKaVTjVKh220vFrPb5fyuXOzHY5VctlssTbK5Qam66I7ui5VpsT7PFUeBWM3hL7pDFU+k7g43NPas0VtG1DUGgbbB35FYi/DNzPJLmabvNUchAdSvozpTbtEAABBXra3wNGWOipQF6OiRLItgRDgKdgLSJNhGFte0ZM7TwUIFwxhh+DAizCiUSWBV5wkfiACnaNkB4JFQ3+nyHJO9oNBkRE6UCYCrSYv0eb42daKIctUJhTlPnusGO65I3+pvmTx1i68JgKPYY/HVtfYC7BMAshrsRYOr6kCulxiLUlMJW7GhgKKPgH8IeycK74ui+xLE/BjNeqgLlEyIuiT6ydOUxI8JQ1tkMzfAYpY2cOc7QiTlMDSS6bhH7MlT1FxzDGX6HIutR9iCoSmj/fjknbGtzHpT98u1DwwD7aYviw46ZYcf2hWQq0WZpldFulKLrkxDX/mazIk0fMBzH/AdLL7zgO/iqBMMI4MA2HmgwMcEZABmH4bMFJ0505eAMTzH78KectXkyTW6ZV8c2hMFD8VkoWCDZTkUeTiKkEgTVVpSrmg7UV6G0yOTIydReiyuVCTVag2urt7+urz8vlBdhBNtgzXJFTm2DtT7JyaexCNShsWqmFQTk2vDalMURw32FGB2ZR2BKtytNVBT2zJCbVygBpIcZZKtSLBV6W1BaEMQfM3zA5hUmJCYsbSkkHqtyKwLFAUZL2mKhbArz3BUeYGuLDbWpOaayFDlawvQI4jigZgM9JE0wUx4igwhLhEpmiOzj35dV8RfoU/YygxLBmsU3xVFGITh/g8kUZDnCJckj53I47hgUBP9fywJARw4FmWUr0pIDTm1raKDb7GV5Kac3JCVGzNWT9MZ6AB2XwtbV7BrcdbkcGLy8BN8F+6fp04LtFmhrgA1RdIX6AsiQxkQGEq4OFwlhp8K4eeZlMEe3P8+sjYzHWkiXgPE5TCrRdDLLXGMEpQGQJYmf6wt0C3lKwgKylKQpa7Pyxxrkw7Q5/XE0TOLfHS6k18XRg5AUHECI4ELI1NFUwCh0aEo97t8/7E8IlDGjsW+vRPLJsuMFAWVxYgq3B0w0ppYMu+LdMW2+vqNKggDBp5Isji6j64XRY4kEdpLLFl489j+dFuzy3PsC1y7HCsg0oa9iV6xeVVuX7UHt+3hdaY8DiYa3kgNDlWk8guVPrU5BS1nSwNceUioios1SaEmhYYn3PWG24DTXzXYMjj5gGt6daB8vqt4tq18ta/f4TgOhUGWOMxTQJOKkE9MJ0xCZt0tRussgAYF2thPljWInqU22L43J14AjU2Of5sXxJRGp/E0eYDMwM/AR/BPbm3ILHXYCfII4zMdQeVjeWoNsPNQEocu4Ht2hGECQRCAtIGj8FdkOikSYJTSUgIdsRVp01xZ+Fjk3+V6yb0ocfsZ9AD2S3VphTGnt5Vt3haoafM2ra6609f2BHvuQFdtyjwhZJfH8b0saRQjAeBnyGzArJIl6awCHQ+I/jOVWhCil4XZQ1dSqdxu8gLAFj+4PoSdmOXEO2KWI/EZitgea3NkYUiRRPRGDD9AP8KwQ/ZgBzVgcIZ53QEUzwGYvrSBPSgrZMZL4+RrxdE10H0cZQb9SDpaW+TpSqjO0D/UQdR6FAe5tXQiDnz1Rv6HZwIMOXmXVIvvhDCQV0vpYwtUKBK8BEEkrVcnbuANx7vFD+wIQzxcMO5FGARH4Rz2RX5w9M2ReZtjE2hicFQApBQRhyfz7nOMOnPM5EhozWGVIWD15ELJdrF5Mbv4uTl8N1r+PL/+j/H5X5qj71PlK1d4oHdUZdq4XI+8lTa7SzZf1egsyHTQHveR0EWeEaijIm2Sr0odSTBACa7qsZgeyqAL4EcIF7kJQeX5ccFrvGZ7cAsvWE7gNW7n2EWB9hu2Z5Pro9ji+UGsXYgxVBDRUxjCR6ggJQBEEcAUpUfp+TgHO3EOXfaHrmNQMCL4Q5yA7yc/cezcOHFtst07PB9LHOTAn6iTRDstRdyv0gLKVmTGPNgJTQVNlaY89hsdVZ21JMPNykIieURtyFpcFYevYfc1nqjsdbmlgrGESICs4Ci9uJdHrlcsjJbnDce3wQ2icNPc/ZrtZfb4KR0pF0GdL3nzJeCpN0XhLeRuBmi/QanC8Etj4Oj+49p7GiANJTE664VlUBMqToGPa1DWApjoh+IYcCSJY2ZDSj9bkSSuEP2LWi82ZqBe2ydmlsAu1SeEBjLMkFgMMIb5SJ44Zmw+XTGB2V8zgCQkfoAljW9xfFscD3pmTxB4umf4elPzct/wzy9Eb1gm+FGlOYN8E85NCo3LavcmGGtW2+RF+vn5d4hQ8dxAY0pAHffYJpBYrI3xlRGePALxwMDAijn8bQyDw990h1u+WBd67I517OEmkq8l0LL42wZfW2aunsiT+8IIdAuzCGAeN6D/Q3RZA8Csw8XjRuAFD6Cvn+fbmo7ANrr9MynpKK/pSLmIQ88ObM8P7S+OHC9ZzjWtKbPpN6z/nP4txgJ+AJSlFpEIKs+3zfWCo2Dq60PLiz3jsx39y33TFsu6x3GyZRGIKFjLU8ZhAA4EPjixQ75Hoc/IdWmlIWNx14OxcSg+sbqrcm2c6CicLEwDBoAs0/D9BJ8nFn4PdHx5jBnpwvYlx/uK66P+A+yEoIKm0E6wgRCCUVOQcn2UnoA/AWCe4KJQiaCsiN50sePLJ0aEnSqSEynvMTsZP5Qh968knKPPQmibTgki5/wQOHQsQ2VJIe2hWgn1JRpxnPGVKTiC1T6Wh5gKGzjgWr7ekB3jZuWJNTCcVI0eHRUJYUmKI+YpwD6uRxCCiMrMZTB+k+OEjh5LAmp7UWbKcGT+rWPT60PNNtvIFrv4co/RltRZIjyxbftAAbzZk77ckm7jBJb+ROTkywJskYes1x5bKP74SvrNpvzVvnaHbWGJ3EJtVOMomP11rjJ8KPEdiH3wKiAoLOMOP4L7xezCReLCcHl/d51f7F/vwR1tcLy/AeUcRhkA5yh9vyQxgAY9RCmOv6L6ClZgPz6CuyAxqPzs0AFuYA+t9Yw1CoFLmM8gFVcRg1hCSiGo4CI4ust146PKWjZ6WnpXDSkKtoeviLJFvkOuc+/EdizwiJVhtTGtNWd1lhx67MkW17XBdrxmOV4e2oBXR3bMHtwDJgruARSElBKCfuYoJd9/B631a1DiAnTVDQ6J2ia6QnHEPAeidooGasgtPfo5pT3GRnwE6X/z/fhayDkuDJcKAh0JI2AqR5mGrMJFMc8g6mSKqxEXQpvHVtRNrTWDQIOqKtClITlUbDCEAB1dCM8a2IkxhiaRtITLIHU2CjtEprHACw0wehokzWggiiGwH6X5UGiHUoKLHKGFJ7GwuIZjnlGq8gJciZ0ncR1xrYc8KwgqVISgvocCN8wxvMeJxM+RBTGLkOresAzPdlXAywPtK5bpm13t1zs6DArRMG5gg02Asv6liNJbAJ4fOVDZX0L5QEFmefhRcT/zD1+CYUWPgVhQyq92TMCfto1/3DJgi/Y3e5an+1bKUSq3FFSwqO6uLQElMRpfcnSfiU3gKACCoiyjlEv0WVR2WFKtrWxw1tCWmwoo/aj1iIPknUlzweiqw4aaXTWdtYicJFKEpOooCn2qsPLHBk8wioeS4IE4AKe1J/QRiPBjIarttEcgh4QlDJlQPtALtNzQcrmewf8QiM8bgsArrvf5ieubI/tXB9Y/7pn/sGt6yfFQ4NBrnu8NH0IbwMkHTOmhU/xL50SrDHrhc3JMAGg827O+2DO/OLBidq39Fs5npn6UJQuiIn/1RvpyR3HASB3udFeIzkW5CCB+iowFradtCY8M/p7G3ZJayjxtBn+Io1s87ybXg0L/6sj5zY7xjxta4OWRBZIg1CQ0jhJ5ZGIvkHWTaDtamJVa163B28H03WT+rje864/uV5c/Xdz+enr154v7v+QqK6M9e8S37XPMUPQdlgk44rnA1I0j0+sj48sD/fM9NfDsQPP8ULvNc2ywba9P7LgG3BF81/MjFDQPwyTzswPLiyPbSxZu2bnBAUXcGDUAl/0l6E6M5oEEaQZdBw6F6QIFeoB2Aj2N9gmALwTesF34TvQAIibdT3+FdAjPi5PxPSS8K1OwYcR3MW99UMA0ouAIdTnYd1RzCOfWiWOH42IJfTA5ICVbFtpm27/eVP3umej3L6Uv93Q7bNsBx4EIb3SUvJEe2KnQJbhi94nAgVn95MWhGf3+imV9c2KHpu7wPfSu6NShdo2+bQDN22CmLxUb+hIGNezAl/vxESSm/p2ycE1BbMFI7Hl27HzBdmP/lii0I4nsSqPk7RDIKsbj2AVXRGc5hgTz/jXjq/67JLw8dBxLIsiJR6IQissO77E80d58fmD604b8Dy9F//KU809fHf3+KeurV8I/bam+2TNhGNDXAAYDI/380AqggY8MJzz4BpyDPSDoNqqBOAxVQNdDhsFRZKYTWYCjCEFHheqIUBNCtFeZkpBq1PpQtJbKDoqVeaV5Fk11dea4XBNAbJdpQkp9PFlc9OefGoOHVGkZzy3U1oJQHTsQujeOza+P9MCLIx1o+s2u5tm+/tUxRNSD23+6Z3t64Hx14mNKsJde3qtjxxqUQ2tugVWUmhhNyidUFb4uKzTkMS0B2qDvfIlNRRw6UWLaY/7DaEbwt3SW4gsf+4H5FUwMCjo9cBT6BUNIxoLxr+h8DATtLmZxOgPJhJQCsOAyfYYskTqrZl/L5G2izZGH97gOeB6UeEzavROLRB01O8smR0mmifAknt1j8xM4nh2+6/Wx5fmBATSFju4KIV2uxyH8HBUJ5xie0cpCGbnmIpT16YHt2aF9XXGo3BKO4uoZrMMyKhSkF9UW7IfNp2afJhUSZZg68plqxDzRtEj9O/asxZW4qxMXGAMciZg6IAjAT5Nn9HwURxfu6MW+bpdjPRY69o71W/uKjQPVi0MjuhjdTbsemvSnbf3vNzR/2NT+cUv31Y4Be+gJj+PN8W2yyQ8hrpLEynZss507HAf8InSULjvLjHGdMx9MDgq1s2JtVamv4qmu0RJX64IWe8pkTyu0QY7Qunei3dxX7bJ0JyK7RBM0OLLOYCOQmibLF+Xuu9rgfaFzl6pdxMunQKpx44yPhPoMwgeZpcee18e+5weOr3fNuEhcM4BLxXVimH6joFQ+KajykRU3YQDMA/9IeZHHaBu8BHfZqiQtHTh5zXJ6PhVgHP0Na9enwXdt8gJ/px1HdvgTjAUqPl0ZPRT60Wmv9gzPtkjcJO5L5IOgKq15eHquPLTPcx5wbPtsK0fshSuzussGW05ngT0raMzZJyDontCzC3vEsYOjyARv2JguFlQTSAulHRhGaMoIIU2RlKNryVwDH6lVoh6ArFB+kUXg8MBIxGfyAhETQo+Yh5aU7pTraz/0pXlfu5/flHt4xDdH1jcHplcHZiTHZ3vmp7umr3fhrnS/eyl5uqdFoHm5r/rmleDZhgAJ5pBj2mBZQE0KKj90/ChrMUhQoM+FLIHBQxpDJkN3I66CLq+PbfjF14em53taFGgEHcyBHY4JflSsDuksqWxpBo62e9eD8dvZ8uPF9Z9v3/3H2w//8+L2X3vjh1h2YHSkBTLPPke/xzYc8S1bhwY4UYE8KNHFAZEuItJERboYS+LZ4TsxKJgVZJ5w/NvcADIT4wgD69JMJySZk5/rMhVOSsRjRRwyCb2ETOJewLY1EenRx3tUJdciSm0AuoWqJq0waNAKsy79lKw4B9r0/IiscIGjj7UXlU0aRV8BKPRI7ghJEFSNtQQ/irjJU0ZlxqzaVtI6y5jnKkteYUqjB44FLhgh4JBjgX3niFwiRUBvKzyB2GyDxWIfaAoDRMDQdIuHX3Vv8r0UWwIfCEqeWWNOMMAesvPzCfQc7N8VB/ckoQNZ5FAePVLEZNYKAJ+HnuJq0ugIOh3Jm53Cx7JFZyTtXOSSNaj7RuEgT3f4frBki+OBmNGZCg8Ko/J0W/NsW/1sR/tiT7/2LWDSK5b5SOo/kXhf7Cl//83J0zd8cHT3WPPq0AAP8Gzf+HTPAKBNz+drU3SJA+4cwQhzFTvxK69ZUFCyjIKfxjXAzp5Iw+Slab4T1RlzAIGMq/ABEk1Ya06yuSbWsRZgcwyHx5qNbfHmjgSNE75JoQ34I/V8jfz3Sm3ePLLU9pHqmG9Hlto7Nm4eal/tKp/tSJ9vyZ5uy/Z4ls0TEyQHTm7rxPXiwL7J8hyIIrgkSMk2MrLQR/4t4ecg8WWogGFDGSFLEBxYGkxCpHjfgSR4rIhy1AmeFkU/fSSDQGLCg+6kcu6JMDOD2MNWxQGy+iGDcw2Ql7yQqtEbJ/YN5tuwB0ADJCHVhtRYP11qpemKjt02lxQfIhw7+qfbuhd7xo1D8zb6WR2VGFIKSw6AiMrNWRQi8l988TUcgbrFU9bZsipDTKmPyrVhsTJI1p62uI59kRccBVNR8cFRXBOAGwMoESmojn5Jyi+xwfMAv6EpsC4xVJnWwMxGlcF+TE1qpGj5+O8cXedEKma4c3D0+b4FwJ1/s6F6uqVCEQFNn+8aQD4AE4+tDB+KXK8O1F+/FnzzmvdqS/RmizRgfTByYCGGE0DjcWXj2EYBBlBgJwCPC6DHwVGiDRwnyhZ5J1XkAd1f7WuhgmyZ50jk4Mm86FyByH7C1u8fKHf35LtQ7mMNX2SVq7w7h8rnb/h/+Oboj09Z37zmvtoR751o2CLLm10FW2i3OIqpwqzWual0LtEIJLpKY0xlTduDnUh25Y1Nxbrc7onvzSHMsRcUxGVji4vBBMP9Iv5/tU0WAdDGLKUkpvcIYgF0TKnu0JEFsAfMQ818fgh7g/JixUfwkiWPgMcSc0FqKYpNebTBWjAYPMZfrb+QoX7g8aV4ZqWSRlWIyGuWg61IcJh1UOT3R6gTQkwAeRA+fg2BJgYRBU0xw/X2nM1XtXorDl/FF2l5w02bpwR9fSI2pgW6BEsW2OQQ7SEc5WFyQOTN5NKP7S9PHK85rjdc92ue5zVDxDV+Q1NK0B1RgHKUvBonDdN0TLF2RdDL9U5qmGiRIlkNlpexlXQpGMyg5Pibdh5Y6RoZ+gJ0Ie8v8x3Ed7OtqOOvDghBkTY2Tyz/9Iz35F/2fv+M/XJb/GJD8OI158WW8PmOBtoJ8mEIITxr+cFgU0Gl7HwUIQwt28vATRelyaFjO+QNP/cMsWZbuckybp0YXh9qUKQkqgCXbxGK7Dy+hc0xKlW+ULieL00pIvGWw1uwe/JWd1ptDHLE5n229ohjgFF++Vr09BVvY1d2IjSr9GHy1N5fkmpDe2zTmz3tqx3dy13j/omHJ0+Qp1yf58+XwE1RoJh8CSI0PDcVQrAQY/rswPR034iPCGQAdgK0TSmLLf6K6i6V1RNljKuBJYhTcQVZP+suTSahL5dUMWoAhglMRa1HVKCP7JHrD/keCKfUmBbrk9iq7Xmjt2r1N2yBJmyo2V0yOnMyXRjdiNmOokS2ltwjR4/lwW2eA1J6KPGfqCLHyjB5u0dO6jW0EIQD+d7wva95f6esf6eyX7B2vRN/BcmEy6GLGvC41O8jnSCmYIuIDV+4NoXE439mA52Oa15+vW2g+GbHCNMJ6wkdhQ0FO2G3yT9X4jrIq6JcJ+4C97LDtX69RermIc94xNG/2hZs7Qp5UvvLPR1K/De7egCqg1EELzHGLNQ+SIUQ9cQNEhAXwZyGX3y6a8EF4HpIuSf/8sQPyw8d3SMvqTiOxR6yOMqzcKUetTHe6tyOxu9a7atYvOPxFgG9KcrmGTl80/6RavdIJZA6wNFoup2rTqvt01R2kM6OUplhLNnxBSsGa0Kq8vLEtoMT3T5bBwMNM8DiunZY1hebut8/l6F00otfGxViWLlkTY1WeSquOEQufkcHOoKXYCH1b7Too6Z/oYWk0IN52A9QpaRsBl/Rxgk4HzQFR8FUAIYB9MVOEsvEzDslzKLp41N7RmWo0ICj0JENFmPiMTGAIyOs/NMdNeLsFtt8IHSyROTBr9qcMjoLJldebU5I1UGh3MuXujkiB0zqkxNFiK+Ng6YcVeREEeZp4kLQ3JyVWUvkX1SS19RzXF3mWJXYk0V2JKG9LwC9XGPtUL80qWAtzYBfrrdRy4/STy08DoG7IO4/v1b9by/klJG0lIOINAYBVFDXskpJDFH56o386RvZ0y3F8x3VywP9G5bpzQkci5WjCLAkLvCGL3cdsnXPN7hvtngsvhEOlZpvmBwUjTcnVlI6OPavdzTf7Gqf7ukgwy+PTLA9zCEH84skLVEfTIzstu6bLTXM6DbHBo4eCpz7fCvAEti5YqdY7Pa4CuXKcjB8O198PD37Yb76Dvkpkx+EojWLK6WzRBQ6n0jhAKRqdzjWjMZaQCRaB+Kpdq44KjUWtfZFrX2ZKc7t3qrGmNMY84BMm+LIw0dS0BFctOM6ccFfbav/tKVCDaRWDVMU5g3nkAdsSuicm2Jb4PoSuyLPl9gTeykOpH6cjEL6gmUCXrOtW3wnTqB/si/xHcoCR/IgTsNHGFPm38A9PgX9UkepuMCPApAS0HQHRoLrOsJPCN17fDsAM3Yi8/FVAaEm9GJbvnWk40ic5BVHXQRSCsCSKg2pJzAESnvR4GuoHGW+lswPisdsqIyfqDF10gBbkwLov/6hoI6T1nSciUP0KN1JeUxXLugaB/0euthB8yPNTFQyKahSrkEJCvxxQ/vVlh6Nx+V6ev6xDeXjgO/aFxDs8Vw7fALoKAIHSvDmofr5puCPz/b/+Y9vfvcvL3/3+w1EE9iAHY4DJ5PzOajd1jdHZkSup0RidczTHQ0d+D9taf60qcPv4kqI6DIp7eW+Afh6Swk/un1i3T4x03SP/PRmX80TOI3mVDo7abavCsWpy501GMNGU6Q3uOz2L1rdi1rnrFBdZIqTUvOsO3o7GD10B7fM/32kF41UwqFSPNnIFobjxYfR4lOucibXJbaP4AocbJH/SOAlEo5ax6iJyJCWm3IqaxEZWaSF+YtzFeTdVnAUTKUzEGwDNrh2WDjwDPyjpATbsAX/wMKXx+bnkDcGz5Apj4zYg/3AqxML2oSsxxZMCbJydwjLCyGw4idIuGQWAakdwmSGQ4OCwjvRVSdS4sVM2RF6D3jufZ7zWOoHWBLPvoDUOjCVLfWJtMSMKgxxiSYoVvrZIusOS33I1gsVbokq9ERmSKpsBUCoJy+iMku45CEhXQyij85eHjqQbXe5Xqj3PvMeBludoS/AU9AXMugLDfQ58rEyRd91BWtpclqvVoBbfyMZy8HIoRPRlYKawnV2oW1qFpEG1pGcCUbEezHFzoYah2JHn5mRMMR3gzSvduXIKP/r7179L//09T//7k9/+Jc//v5fnr7cFL3cVr3e1b3a0X6zqfx6Q4HIBZP6as8AL/v60AJg3iN+fbOl/WpDDXy9qWHeioDptMDygtOU1q8PoRBGkpwODZBw2FN81Yst9astzS7LxBHatYZwOtsZ9s+77UWzPkmnGm5vxmxLaoxxmSYiVkWkSAy6iMWZKZYm08nNqHfWrk/LxX40UtOaEpAQDDBJeKRoelEuUTe3mPQDgSf9Q9bdLK/2TMDrfTPaG4cw5Y/h79FrHlnQS7Tf1j1GuxffjH4DYG8AGsWoTT+WRwD0J/wDXSJgVtwsdI0PW7Q3j2xbLPs2vNkxqrmDOWqlh9A5WyzriTiAkMSRBSEiuxw7Ayu080jm5avDYn1cZkhLdEmxKiZEMQdxeTaexGWyp13+ktEeV+v8Sr1fpvb/f9SnBcy1xU3TAAAAAElFTkSuQmCC\",\n",
       "\"xmin\":-0.5,\n",
       "\"ymin\":-0.5,\n",
       "\"xmax\":224.5,\n",
       "\"ymax\":224.5,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"L2YJRc\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a65772620>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot() + \\\n",
    "    geom_imshow(np.asarray(img)) + \\\n",
    "    theme_void()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "376261b0",
   "metadata": {},
   "source": [
    "### Correlation Plot <a class=\"anchor\" id=\"features-correlation_plot\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b43a5d68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.499263Z",
     "iopub.status.busy": "2024-04-17T07:29:59.499112Z",
     "iopub.status.idle": "2024-04-17T07:29:59.502681Z",
     "shell.execute_reply": "2024-04-17T07:29:59.502493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"3tjNOY\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"ggsize\":{\n",
       "\"width\":400.0,\n",
       "\"height\":400.0\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"scale_mapper_kind\":\"identity\",\n",
       "\"na_value\":0,\n",
       "\"guide\":\"none\"\n",
       "},{\n",
       "\"aesthetic\":\"x\",\n",
       "\"discrete\":true,\n",
       "\"breaks\":[\"displ\",\"cyl\",\"cty\",\"hwy\"],\n",
       "\"limits\":[\"displ\",\"cyl\",\"cty\",\"hwy\"],\n",
       "\"expand\":[0.0,0.0]\n",
       "},{\n",
       "\"aesthetic\":\"y\",\n",
       "\"discrete\":true,\n",
       "\"breaks\":[\"displ\",\"cyl\",\"cty\",\"hwy\"],\n",
       "\"limits\":[\"hwy\",\"cty\",\"cyl\",\"displ\"],\n",
       "\"expand\":[0.0,0.0]\n",
       "},{\n",
       "\"aesthetic\":\"color\",\n",
       "\"name\":\"\",\n",
       "\"breaks\":[-1.0,-0.5,0.0,0.5,1.0],\n",
       "\"limits\":[-1.0,1.0],\n",
       "\"na_value\":\"rgba(0,0,0,0)\",\n",
       "\"scale_mapper_kind\":\"color_gradient2\",\n",
       "\"low\":\"#d7191c\",\n",
       "\"mid\":\"#ffffbf\",\n",
       "\"high\":\"#1a9641\"\n",
       "},{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"name\":\"\",\n",
       "\"breaks\":[-1.0,-0.5,0.0,0.5,1.0],\n",
       "\"limits\":[-1.0,1.0],\n",
       "\"na_value\":\"rgba(0,0,0,0)\",\n",
       "\"scale_mapper_kind\":\"color_gradient2\",\n",
       "\"low\":\"#d7191c\",\n",
       "\"mid\":\"#ffffbf\",\n",
       "\"high\":\"#1a9641\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"na_text\":\"\",\n",
       "\"label_format\":\".2f\",\n",
       "\"show_legend\":true,\n",
       "\"tooltips\":{\n",
       "\"lines\":[\"@corr\"],\n",
       "\"formats\":[{\n",
       "\"field\":\"@corr\",\n",
       "\"format\":\".2f\"\n",
       "}]\n",
       "},\n",
       "\"sampling\":{\n",
       "\"name\":\"none\"\n",
       "},\n",
       "\"geom\":\"point\",\n",
       "\"data\":{\n",
       "\"x\":[\"cyl\",\"cty\",\"cty\",\"hwy\",\"hwy\",\"hwy\"],\n",
       "\"y\":[\"displ\",\"displ\",\"cyl\",\"displ\",\"cyl\",\"cty\"],\n",
       "\"corr\":[0.9302271024198506,-0.7985239689348548,-0.8057714082675277,-0.7660200208864677,-0.7619123538971949,0.955915913649572],\n",
       "\"corr_abs\":[0.9302271024198506,0.7985239689348548,0.8057714082675277,0.7660200208864677,0.7619123538971949,0.955915913649572]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\",\n",
       "\"size\":\"corr_abs\",\n",
       "\"color\":\"corr\"\n",
       "},\n",
       "\"size_unit\":\"x\"\n",
       "},{\n",
       "\"na_text\":\"\",\n",
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       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a658ec280>"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_plot(data=mpg_df.select_dtypes(include=np.number), threshold=.5)\\\n",
    "    .points().labels()\\\n",
    "    .palette_gradient(low='#d7191c', mid='#ffffbf', high='#1a9641')\\\n",
    "    .build() + \\\n",
    "    ggsize(400, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ebfee2",
   "metadata": {},
   "source": [
    "### Joint Plot <a class=\"anchor\" id=\"features-joint_plot\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ebfd20b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.503828Z",
     "iopub.status.busy": "2024-04-17T07:29:59.503676Z",
     "iopub.status.idle": "2024-04-17T07:29:59.509429Z",
     "shell.execute_reply": "2024-04-17T07:29:59.509238Z"
    }
   },
   "outputs": [
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   "source": [
    "joint_plot(data=mpg_df, x='cty', y='hwy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e811b2",
   "metadata": {},
   "source": [
    "### Residual Plot <a class=\"anchor\" id=\"features-residual_plot\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a720f102",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.510472Z",
     "iopub.status.busy": "2024-04-17T07:29:59.510357Z",
     "iopub.status.idle": "2024-04-17T07:29:59.644080Z",
     "shell.execute_reply": "2024-04-17T07:29:59.643823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"g3usf0\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a65808fa0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residual_plot(data=mpg_df, x='cty', y='hwy', size=5, alpha=.5, color_by='drv', marginal=\"dens:tr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56188c8b",
   "metadata": {},
   "source": [
    "## Geoms <a class=\"anchor\" id=\"geoms\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024cf9da",
   "metadata": {},
   "source": [
    "### Graphical Primitives <a class=\"anchor\" id=\"geoms-graphical_primitives\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc68eba5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.645325Z",
     "iopub.status.busy": "2024-04-17T07:29:59.645163Z",
     "iopub.status.idle": "2024-04-17T07:29:59.652138Z",
     "shell.execute_reply": "2024-04-17T07:29:59.651936Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"F3VFTb\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"unemploy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"path\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"F3VFTb\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a5516b850>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(economics_df, aes('date', 'unemploy')) + scale_x_datetime() + \\\n",
    "    geom_path()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e573ff4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.653036Z",
     "iopub.status.busy": "2024-04-17T07:29:59.652958Z",
     "iopub.status.idle": "2024-04-17T07:29:59.666737Z",
     "shell.execute_reply": "2024-04-17T07:29:59.666531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"q75FeO\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "           var plotContainer = document.getElementById(\"q75FeO\");\n",
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     "shell.execute_reply": "2024-04-17T07:29:59.669778Z"
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    "ggplot() + \\\n",
    "    geom_rect(xmin=0, xmax=1, ymin=0, ymax=1)"
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       "           var plotContainer = document.getElementById(\"YY2R6D\");\n",
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(economics_df, aes('date', 'unemploy')) + scale_x_datetime() + \\\n",
    "    geom_ribbon(aes(ymin=economics_df.unemploy - 900, ymax=economics_df.unemploy + 900))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3530c79",
   "metadata": {},
   "source": [
    "#### Line Segments <a class=\"anchor\" id=\"geoms-graphical_primitives-line_segments\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "588c8e04",
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    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.678995Z",
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   "outputs": [
    {
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       "};\n",
       "           var plotContainer = document.getElementById(\"zAnAWs\");\n",
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     "execution_count": 24,
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   ],
   "source": [
    "ggplot() + \\\n",
    "    geom_abline(slope=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c66f12fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.681882Z",
     "iopub.status.busy": "2024-04-17T07:29:59.681806Z",
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     "shell.execute_reply": "2024-04-17T07:29:59.683682Z"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"eHbV9u\"></div>\n",
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     "execution_count": 25,
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   "source": [
    "ggplot() + \\\n",
    "    geom_hline(yintercept=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "677c6ef1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.684660Z",
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   "source": [
    "ggplot() + \\\n",
    "    geom_vline(xintercept=0)"
   ]
  },
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   "id": "5a78f724",
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     "shell.execute_reply": "2024-04-17T07:29:59.689397Z"
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    "ggplot() + \\\n",
    "    geom_segment(x=0, y=0, xend=1, yend=1, arrow=arrow())"
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    "ggplot() + \\\n",
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    "ggplot() + \\\n",
    "    geom_spoke(x=0, y=0, angle=0.64, radius=5)"
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  {
   "cell_type": "markdown",
   "id": "30e38ce1",
   "metadata": {},
   "source": [
    "### One Variable <a class=\"anchor\" id=\"geoms-one_variable\"></a>"
   ]
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  {
   "cell_type": "markdown",
   "id": "fdb419c5",
   "metadata": {},
   "source": [
    "#### Continuous <a class=\"anchor\" id=\"geoms-one_variable-continuous\"></a>"
   ]
  },
  {
   "cell_type": "code",
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    "ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_area(stat='bin')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4205fe07",
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    "execution": {
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       "}\n",
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       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"Fa0voN\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54ee33d0>"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_density()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3b3cf53a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.711794Z",
     "iopub.status.busy": "2024-04-17T07:29:59.711715Z",
     "iopub.status.idle": "2024-04-17T07:29:59.715340Z",
     "shell.execute_reply": "2024-04-17T07:29:59.715152Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"wb2QHZ\"></div>\n",
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       "           var plotContainer = document.getElementById(\"wb2QHZ\");\n",
       "           window.letsPlotCall(function() {{\n",
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       "       })();\n",
       "   </script>"
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       "<lets_plot.plot.core.PlotSpec at 0x702a54f1e320>"
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     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_freqpoly()"
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       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..theoretical..\":[-2.85723417203165,2.8572341720316508],\n",
       "\"..sample..\":[3.4373622499013656,41.56263775009864],\n",
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     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes(sample='hwy')) + \\\n",
    "    geom_qq() + \\\n",
    "    geom_qq_line()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "401e4c58",
   "metadata": {},
   "source": [
    "#### Discrete <a class=\"anchor\" id=\"geoms-one_variable-discrete\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a3690cb3",
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    "ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar()"
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    "ggplot(mpg_df) + \\\n",
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     "execution_count": 38,
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   "source": [
    "ggplot() + \\\n",
    "    geom_function(aes(x='hwy'), data=mpg_df, fun=lambda t: t**.5)"
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  {
   "cell_type": "markdown",
   "id": "379d98a8",
   "metadata": {},
   "source": [
    "### Two Variables <a class=\"anchor\" id=\"geoms-two_variables\"></a>"
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  {
   "cell_type": "markdown",
   "id": "0250a2fd",
   "metadata": {},
   "source": [
    "#### Both Continuous <a class=\"anchor\" id=\"geoms-two_variables-both_continuous\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ca8ade7e",
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   ],
   "source": [
    "ggplot(fl_df, aes('cty', 'hwy')) + \\\n",
    "    geom_text(aes(label='fl'))"
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    "ggplot(fl_df, aes('cty', 'hwy')) + \\\n",
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   "metadata": {},
   "source": [
    "#### One Discrete, One Continuous <a class=\"anchor\" id=\"geoms-two_variables-one_discrete_one_continuous\"></a>"
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    "    geom_boxplot()"
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   "id": "10d5a9e7",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"zJQGlu\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54f035e0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('hwy', 'class')) + \\\n",
    "    geom_area_ridges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "28665911",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.811169Z",
     "iopub.status.busy": "2024-04-17T07:29:59.811034Z",
     "iopub.status.idle": "2024-04-17T07:29:59.848149Z",
     "shell.execute_reply": "2024-04-17T07:29:59.847881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"1ESPS1\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"1ESPS1\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54fc3610>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    geom_violin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4472ee5e",
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     "shell.execute_reply": "2024-04-17T07:29:59.853813Z"
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    "ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    geom_ydotplot(stackratio=.5)"
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    "ggplot(class_df, aes('class', 'count')) + \\\n",
    "    geom_bar(stat='identity')"
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   "cell_type": "markdown",
   "id": "179898ec",
   "metadata": {},
   "source": [
    "#### Both Discrete <a class=\"anchor\" id=\"geoms-two_variables-both_discrete\"></a>"
   ]
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   "cell_type": "code",
   "execution_count": 49,
   "id": "bb605f55-d983-4412-96a7-0e30cf576f93",
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   "source": [
    "ggplot(mpg_df, aes('fl', 'drv')) + \\\n",
    "    geom_count()"
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   "cell_type": "code",
   "execution_count": 50,
   "id": "8ac7c0bc",
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       "   <div id=\"Rzh6dQ\"></div>\n",
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       "\"kind\":\"plot\",\n",
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       "\"layers\":[{\n",
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     "execution_count": 50,
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   "source": [
    "ggplot(mpg_df, aes('fl', 'drv')) + \\\n",
    "    geom_jitter()"
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   "cell_type": "markdown",
   "id": "2afaa759",
   "metadata": {},
   "source": [
    "#### Continuous Bivariate Distribution <a class=\"anchor\" id=\"geoms-two_variables-continuous_bivariate_distribution\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c23ed32b",
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       "           window.letsPlotCall(function() {{\n",
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    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_bin2d()"
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   "execution_count": 52,
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"EzXsl8\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b42cb0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_density2d(aes(color='..group..'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5338c53d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.928023Z",
     "iopub.status.busy": "2024-04-17T07:29:59.927869Z",
     "iopub.status.idle": "2024-04-17T07:29:59.991246Z",
     "shell.execute_reply": "2024-04-17T07:29:59.991045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"1OmaUX\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"density2df\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"..group..\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"1OmaUX\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b42d10>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_density2df(aes(fill='..group..'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525d134a",
   "metadata": {},
   "source": [
    "#### Continuous Function <a class=\"anchor\" id=\"geoms-two_variables-continuous_function\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2645109c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.992319Z",
     "iopub.status.busy": "2024-04-17T07:29:59.992207Z",
     "iopub.status.idle": "2024-04-17T07:29:59.998466Z",
     "shell.execute_reply": "2024-04-17T07:29:59.998285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"cIcmF2\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"unemploy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"area\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"cIcmF2\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b60b80>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(economics_df, aes('date', 'unemploy')) + scale_x_datetime() + \\\n",
    "    geom_area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2c0a18de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:29:59.999401Z",
     "iopub.status.busy": "2024-04-17T07:29:59.999327Z",
     "iopub.status.idle": "2024-04-17T07:30:00.004974Z",
     "shell.execute_reply": "2024-04-17T07:30:00.004794Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"oC4B1G\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"unemploy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"oC4B1G\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b60e80>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(economics_df, aes('date', 'unemploy')) + scale_x_datetime() + \\\n",
    "    geom_line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "95c7a277",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.005882Z",
     "iopub.status.busy": "2024-04-17T07:30:00.005807Z",
     "iopub.status.idle": "2024-04-17T07:30:00.011674Z",
     "shell.execute_reply": "2024-04-17T07:30:00.011504Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"bq1N1d\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"unemploy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"datetime\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"step\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"bq1N1d\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b60d90>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(economics_df, aes('date', 'unemploy')) + scale_x_datetime() + \\\n",
    "    geom_step()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08dc851a",
   "metadata": {},
   "source": [
    "#### Visualizing Error <a class=\"anchor\" id=\"geoms-two_variables-visualizing_error\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bd7bc972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.012580Z",
     "iopub.status.busy": "2024-04-17T07:30:00.012501Z",
     "iopub.status.idle": "2024-04-17T07:30:00.014901Z",
     "shell.execute_reply": "2024-04-17T07:30:00.014731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"lLO9ec\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "\"class\":[\"2seater\",\"compact\",\"midsize\",\"minivan\",\"pickup\",\"subcompact\",\"suv\"],\n",
       "\"min\":[23.0,23.0,23.0,17.0,12.0,20.0,12.0],\n",
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       "\"data_meta\":{\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
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       "\"geom\":\"crossbar\",\n",
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       "\"y\":\"median\",\n",
       "\"ymin\":\"min\",\n",
       "\"ymax\":\"max\"\n",
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       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"lLO9ec\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
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      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54f035b0>"
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     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(class_df, aes(x='class')) + \\\n",
    "    geom_crossbar(aes(ymin='min', y='median', ymax='max'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7326c52b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.015780Z",
     "iopub.status.busy": "2024-04-17T07:30:00.015702Z",
     "iopub.status.idle": "2024-04-17T07:30:00.017997Z",
     "shell.execute_reply": "2024-04-17T07:30:00.017818Z"
    }
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   "outputs": [
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       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"6cU2QW\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
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     "execution_count": 58,
     "metadata": {},
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   ],
   "source": [
    "ggplot(class_df, aes(x='class')) + \\\n",
    "    geom_errorbar(aes(ymin='min', ymax='max'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ecadef77",
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     "shell.execute_reply": "2024-04-17T07:30:00.020794Z"
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       "\"data_meta\":{\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"linerange\",\n",
       "\"mapping\":{\n",
       "\"ymin\":\"min\",\n",
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       "\"data_meta\":{\n",
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       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"pJK5Rk\");\n",
       "           window.letsPlotCall(function() {{\n",
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   "source": [
    "ggplot(class_df, aes(x='class')) + \\\n",
    "    geom_linerange(aes(ymin='min', ymax='max'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1de3c522",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.021922Z",
     "iopub.status.busy": "2024-04-17T07:30:00.021792Z",
     "iopub.status.idle": "2024-04-17T07:30:00.024172Z",
     "shell.execute_reply": "2024-04-17T07:30:00.023997Z"
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"data\":{\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"pointrange\",\n",
       "\"mapping\":{\n",
       "\"y\":\"median\",\n",
       "\"ymin\":\"min\",\n",
       "\"ymax\":\"max\"\n",
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       "\"data_meta\":{\n",
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       "\"metainfo_list\":[]\n",
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       "           var plotContainer = document.getElementById(\"PLHHXC\");\n",
       "           window.letsPlotCall(function() {{\n",
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       "       })();\n",
       "   </script>"
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       "<lets_plot.plot.core.PlotSpec at 0x702a54fa92d0>"
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     "execution_count": 60,
     "metadata": {},
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   ],
   "source": [
    "ggplot(class_df, aes(x='class')) + \\\n",
    "    geom_pointrange(aes(ymin='min', y='median', ymax='max'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2811dec6",
   "metadata": {},
   "source": [
    "#### Maps <a class=\"anchor\" id=\"geoms-two_variables-maps\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3c28936d",
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     "shell.execute_reply": "2024-04-17T07:30:00.037654Z"
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       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"geodataframe\":{\n",
       "\"geometry\":\"geometry\"\n",
       "}\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"3bDyfB\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b63cd0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot() + \\\n",
    "    geom_map(data=states_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c1379c",
   "metadata": {},
   "source": [
    "### Three Variables <a class=\"anchor\" id=\"geoms-three_variables\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "489c224a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.038661Z",
     "iopub.status.busy": "2024-04-17T07:30:00.038583Z",
     "iopub.status.idle": "2024-04-17T07:30:00.058819Z",
     "shell.execute_reply": "2024-04-17T07:30:00.058628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"CIMA4G\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"contour\",\n",
       "\"mapping\":{\n",
       "\"z\":\"z\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"CIMA4G\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b7c520>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_contour(aes(z='z'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7ebf3d03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.059703Z",
     "iopub.status.busy": "2024-04-17T07:30:00.059583Z",
     "iopub.status.idle": "2024-04-17T07:30:00.084981Z",
     "shell.execute_reply": "2024-04-17T07:30:00.084789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"BuW2Y4\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"contourf\",\n",
       "\"mapping\":{\n",
       "\"z\":\"z\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"BuW2Y4\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b7c7c0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_contourf(aes(z='z'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fd0ecea3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.086002Z",
     "iopub.status.busy": "2024-04-17T07:30:00.085851Z",
     "iopub.status.idle": "2024-04-17T07:30:00.102266Z",
     "shell.execute_reply": "2024-04-17T07:30:00.102075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"5uvySp\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"raster\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"z\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"5uvySp\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b7d450>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_raster(aes(fill='z'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2eb55077",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.103218Z",
     "iopub.status.busy": "2024-04-17T07:30:00.103138Z",
     "iopub.status.idle": "2024-04-17T07:30:00.118919Z",
     "shell.execute_reply": "2024-04-17T07:30:00.118738Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"DphXjy\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"tile\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"z\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"DphXjy\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54b7d3c0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_tile(aes(fill='z'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3278b79",
   "metadata": {},
   "source": [
    "## Stats <a class=\"anchor\" id=\"stats\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0effab99",
   "metadata": {},
   "source": [
    "### Identity <a class=\"anchor\" id=\"stats-identity\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "03dfa58d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.120001Z",
     "iopub.status.busy": "2024-04-17T07:30:00.119888Z",
     "iopub.status.idle": "2024-04-17T07:30:00.124982Z",
     "shell.execute_reply": "2024-04-17T07:30:00.124805Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"figures\":[{\n",
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       "\"x\":\"class\",\n",
       "\"y\":\"hwy\"\n",
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       "\"text\":\"Bar geom, identity stat\"\n",
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       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"zxS4Bi\");\n",
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       "   </script>"
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     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    geom_bar() + \\\n",
    "    ggtitle(\"Bar geom, default stat\")\n",
    "p_bunch_2 = ggplot(class_df, aes('class', 'count')) + \\\n",
    "    geom_bar(stat='identity') + \\\n",
    "    ggtitle(\"Bar geom, identity stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b3b902",
   "metadata": {},
   "source": [
    "### One Variable <a class=\"anchor\" id=\"stats-one_variable\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e953eb4",
   "metadata": {},
   "source": [
    "#### Continuous <a class=\"anchor\" id=\"stats-one_variable-continuous\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c8e5054b",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2024-04-17T07:30:00.129489Z",
     "shell.execute_reply": "2024-04-17T07:30:00.129319Z"
    }
   },
   "outputs": [
    {
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       "\"pad\":true,\n",
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       "};\n",
       "           var plotContainer = document.getElementById(\"cqxz9O\");\n",
       "           window.letsPlotCall(function() {{\n",
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       "       })();\n",
       "   </script>"
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       "<lets_plot.plot.core.PlotSpec at 0x702a54b7ddb0>"
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     "execution_count": 67,
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   ],
   "source": [
    "ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    stat_ecdf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839dabab",
   "metadata": {},
   "source": [
    "#### Discrete <a class=\"anchor\" id=\"stats-one_variable-discrete\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bd6dd86c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.130430Z",
     "iopub.status.busy": "2024-04-17T07:30:00.130321Z",
     "iopub.status.idle": "2024-04-17T07:30:00.135859Z",
     "shell.execute_reply": "2024-04-17T07:30:00.135688Z"
    }
   },
   "outputs": [
    {
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       "\"layers\":[{\n",
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       "\"..count..\":[52.0,168.0,8.0,5.0,1.0],\n",
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       "\"x\":\"fl\"\n",
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       "\"ggtitle\":{\n",
       "\"text\":\"Lollipop geom, count stat\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"lollipop\",\n",
       "\"stat\":\"count\",\n",
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   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar() + \\\n",
    "    ggtitle(\"Bar geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_lollipop(aes(y='..count..'), stat='count') + \\\n",
    "    ggtitle(\"Lollipop geom, count stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1a80d426",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.136765Z",
     "iopub.status.busy": "2024-04-17T07:30:00.136687Z",
     "iopub.status.idle": "2024-04-17T07:30:00.142190Z",
     "shell.execute_reply": "2024-04-17T07:30:00.142018Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"SYtHXs\"></div>\n",
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       "           var plotContainer = document.getElementById(\"SYtHXs\");\n",
       "           window.letsPlotCall(function() {{\n",
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       "   </script>"
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     "execution_count": 69,
     "metadata": {},
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    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram() + \\\n",
    "    ggtitle(\"Histogram geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_step(aes(y='..count..'), stat='bin') + \\\n",
    "    ggtitle(\"Step geom, bin stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8b2c0119",
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     "shell.execute_reply": "2024-04-17T07:30:00.163577Z"
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   },
   "outputs": [
    {
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"3I5wtL\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54be0d60>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_density() + \\\n",
    "    ggtitle(\"Density geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_point(stat='density') + \\\n",
    "    ggtitle(\"Point geom, density stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9649bada",
   "metadata": {},
   "source": [
    "### Two Variables <a class=\"anchor\" id=\"stats-two_variables\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d83c78",
   "metadata": {},
   "source": [
    "#### Both Continuous <a class=\"anchor\" id=\"stats-two_variables-both_continuous\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "efa46c12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.164811Z",
     "iopub.status.busy": "2024-04-17T07:30:00.164672Z",
     "iopub.status.idle": "2024-04-17T07:30:00.169400Z",
     "shell.execute_reply": "2024-04-17T07:30:00.169222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"FnZ0Hm\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"pointrange\",\n",
       "\"stat\":\"summarybin\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
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       "\"hwy\":[12.0,NaN,15.55,17.375,NaN,17.428571428571427,18.105263157894736,20.875,23.36842105263158,24.5,26.192307692307693,26.45,27.545454545454547,29.130434782608695,NaN,29.75,30.666666666666668,32.6,34.0,34.666666666666664,NaN,35.0,41.0,NaN,NaN,NaN,NaN,44.0,NaN,44.0]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"FnZ0Hm\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a54bad990>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    stat_summary_bin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4a06fa1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.170322Z",
     "iopub.status.busy": "2024-04-17T07:30:00.170248Z",
     "iopub.status.idle": "2024-04-17T07:30:00.177035Z",
     "shell.execute_reply": "2024-04-17T07:30:00.176861Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"xyFxiw\"></div>\n",
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       "\"data\":{\n",
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       "},{\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"xyFxiw\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54c0df30>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_smooth() + \\\n",
    "    ggtitle(\"Smooth geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_crossbar(aes(y='hwy', ymin='..ymin..', ymax='..ymax..'), stat='smooth') + \\\n",
    "    ggtitle(\"Crossbar geom, smooth stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6873ce4",
   "metadata": {},
   "source": [
    "#### One Discrete, One Continuous <a class=\"anchor\" id=\"stats-two_variables-one_discrete_one_continuous\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "63d74d5e",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-04-17T07:30:00.181413Z"
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   "source": [
    "ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    stat_summary()"
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  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "59020839",
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   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    geom_boxplot() + \\\n",
    "    ylim(10, 50) + \\\n",
    "    ggtitle(\"Boxplot geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    geom_linerange(aes(ymin='..ymin..', ymax='..ymax..'), stat='boxplot', color='black') + \\\n",
    "    geom_errorbar(aes(ymin='..lower..', ymax='..upper..'), stat='boxplot', width=.9) + \\\n",
    "    ylim(10, 50) + \\\n",
    "    ggtitle(\"Linerange and errorbar geoms, boxplot stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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   "cell_type": "markdown",
   "id": "9bc5c9af-08e4-4766-bb18-caf5420e32d1",
   "metadata": {},
   "source": [
    "#### Both Discrete <a class=\"anchor\" id=\"stats-two_variables-both_discrete\"></a>"
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  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "05b9e285-3a40-4d64-9f76-94af22303f2c",
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    "ggplot(mpg_df, aes('fl', 'drv')) + \\\n",
    "    stat_sum()"
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   "cell_type": "markdown",
   "id": "6d6d9563",
   "metadata": {},
   "source": [
    "#### Continuous Bivariate Distribution <a class=\"anchor\" id=\"stats-two_variables-continuous_bivariate_distribution\"></a>"
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   "id": "1f2772c7",
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       "\"cty\":[9.014444444444445,10.805555555555555,10.805555555555555,10.805555555555555,10.805555555555555,11.70111111111111,11.70111111111111,12.596666666666668,12.596666666666668,12.596666666666668,14.387777777777778,14.387777777777778,14.387777777777778,15.283333333333333,15.283333333333333,15.283333333333333,15.283333333333333,15.283333333333333,15.283333333333333,15.283333333333333,15.283333333333333,16.17888888888889,16.17888888888889,16.17888888888889,16.17888888888889,16.17888888888889,16.17888888888889,17.074444444444445,17.074444444444445,17.074444444444445,17.074444444444445,17.074444444444445,17.97,17.97,17.97,17.97,17.97,17.97,18.865555555555552,18.865555555555552,18.865555555555552,18.865555555555552,19.761111111111113,19.761111111111113,19.761111111111113,19.761111111111113,19.761111111111113,20.656666666666666,20.656666666666666,20.656666666666666,20.656666666666666,20.656666666666666,22.447777777777777,22.447777777777777,22.447777777777777,23.343333333333334,23.343333333333334,23.343333333333334,24.238888888888887,24.238888888888887,24.238888888888887,24.238888888888887,25.13444444444444,25.13444444444444,26.03,26.03,27.821111111111108,27.821111111111108,28.71666666666666,33.19444444444444,34.98555555555555],\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"fxOqzZ\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56d40c70>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_bin2d() + \\\n",
    "    ggtitle(\"Bin2d geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(color='..count..'), stat='bin2d') + \\\n",
    "    ggtitle(\"Point geom, bin2d stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9946eff6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.201503Z",
     "iopub.status.busy": "2024-04-17T07:30:00.201391Z",
     "iopub.status.idle": "2024-04-17T07:30:00.308002Z",
     "shell.execute_reply": "2024-04-17T07:30:00.307756Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"2CkoC2\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
       "\"data\":{\n",
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       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
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       "\"ggtitle\":{\n",
       "\"text\":\"Density2d geom, default stat\"\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"2CkoC2\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56d56ce0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_density2d(aes(color='..group..')) + \\\n",
    "    ggtitle(\"Density2d geom, default stat\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_tile(aes(color='..group..'), stat='density2d', size=.5) + \\\n",
    "    ggtitle(\"Tile geom, density2d stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d0d9160",
   "metadata": {},
   "source": [
    "### Three Variables <a class=\"anchor\" id=\"stats-three_variables\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dadfb339",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.309327Z",
     "iopub.status.busy": "2024-04-17T07:30:00.309239Z",
     "iopub.status.idle": "2024-04-17T07:30:00.346938Z",
     "shell.execute_reply": "2024-04-17T07:30:00.346736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"9d8Gmb\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Contour geom, default stat\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"contour\",\n",
       "\"mapping\":{\n",
       "\"z\":\"z\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"9d8Gmb\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56d71120>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_contour(aes(z='z')) + \\\n",
    "    ggtitle(\"Contour geom, default stat\")\n",
    "p_bunch_2 = ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_path(aes(z='z'), stat='contour') + \\\n",
    "    ggtitle(\"Path geom, contour stat\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "960c4bdf",
   "metadata": {},
   "source": [
    "## Scales <a class=\"anchor\" id=\"scales\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a27361da",
   "metadata": {},
   "source": [
    "### General Purpose Scales <a class=\"anchor\" id=\"scales-general_purpose_scales\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "4066412f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.348191Z",
     "iopub.status.busy": "2024-04-17T07:30:00.348094Z",
     "iopub.status.idle": "2024-04-17T07:30:00.355052Z",
     "shell.execute_reply": "2024-04-17T07:30:00.354869Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
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      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56d71e10>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='fl'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Bar geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_continuous() + \\\n",
    "    ggtitle(\"Bar geom, continuous fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "898658f7",
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     "iopub.execute_input": "2024-04-17T07:30:00.356183Z",
     "iopub.status.busy": "2024-04-17T07:30:00.356083Z",
     "iopub.status.idle": "2024-04-17T07:30:00.361780Z",
     "shell.execute_reply": "2024-04-17T07:30:00.361580Z"
    }
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   "outputs": [
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       "};\n",
       "           var plotContainer = document.getElementById(\"T4hoxY\");\n",
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     "metadata": {},
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   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram(aes(fill='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_discrete(guide='none') + \\\n",
    "    ggtitle(\"Histogram geom, discrete fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6874ccba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.362939Z",
     "iopub.status.busy": "2024-04-17T07:30:00.362816Z",
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     "shell.execute_reply": "2024-04-17T07:30:00.369401Z"
    }
   },
   "outputs": [
    {
     "data": {
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   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(alpha='fl'), color='#0c2c84', fill='#0c2c84')\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Bar geom, default alpha scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_alpha_manual(values=[.4, .1, .8, .85, .9]) + \\\n",
    "    ggtitle(\"Bar geom, manual alpha scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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  {
   "cell_type": "code",
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   "id": "60066cf0",
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     "iopub.execute_input": "2024-04-17T07:30:00.370780Z",
     "iopub.status.busy": "2024-04-17T07:30:00.370666Z",
     "iopub.status.idle": "2024-04-17T07:30:00.380875Z",
     "shell.execute_reply": "2024-04-17T07:30:00.380698Z"
    }
   },
   "outputs": [
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"unemploy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Point geom, identity size scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"datetime\":true\n",
       "},{\n",
       "\"aesthetic\":\"size\",\n",
       "\"guide\":\"none\",\n",
       "\"scale_mapper_kind\":\"identity\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"size\":\"psavert\"\n",
       "},\n",
       "\"show_legend\":false,\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"alpha\":0.3,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"bYrJrw\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56dc00a0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(economics_df, aes('date', 'unemploy')) + \\\n",
    "    scale_x_datetime() + \\\n",
    "    geom_point(aes(size='psavert'), shape=21, alpha=.3, show_legend=False)\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default size scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_size_identity() + \\\n",
    "    ggtitle(\"Point geom, identity size scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1e3c01",
   "metadata": {},
   "source": [
    "### X & Y Location Scales <a class=\"anchor\" id=\"scales-x_and_y_location_scales\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e2e12ce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.382046Z",
     "iopub.status.busy": "2024-04-17T07:30:00.381933Z",
     "iopub.status.idle": "2024-04-17T07:30:00.396562Z",
     "shell.execute_reply": "2024-04-17T07:30:00.396361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"WXef5P\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":3.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
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       "\"pce\":[506.7,509.8,515.6,512.2,517.4,525.1,530.9,533.6,544.3,544.0,549.8,556.3,563.2,567.0,568.2,571.6,576.7,576.5,583.5,588.7,588.9,593.9,600.3,600.9,602.7,609.9,613.2,618.5,620.5,622.8,628.7,634.0,632.3,636.0,642.4,646.3,648.5,652.9,659.1,658.3,656.6,665.6,676.1,679.4,682.0,688.8,691.1,699.8,698.9,704.9,713.0,715.8,720.9,728.4,731.5,736.2,749.2,752.5,758.0,761.6,769.9,776.3,781.1,794.9,800.5,806.1,816.5,825.8,832.8,835.7,841.6,844.3,854.1,853.3,869.2,868.2,876.9,876.6,884.5,889.7,901.4,910.8,922.4,928.0,937.9,954.8,955.1,959.2,956.2,961.8,975.6,989.4,990.6,995.0,1018.9,1026.8,1039.8,1047.0,1054.8,1060.9,1075.8,1092.1,1107.1,1107.7,1114.9,1125.4,1122.7,1140.5,1149.6,1158.0,1168.8,1176.8,1189.0,1211.5,1215.0,1231.3,1238.3,1247.3,1257.1,1263.6,1280.5,1285.7,1294.5,1311.4,1327.0,1336.0,1329.5,1355.1,1377.5,1396.4,1412.0,1425.8,1426.8,1447.0,1452.9,1466.9,1480.6,1496.5,1502.4,1517.8,1531.2,1538.4,1558.8,1575.7,1586.1,1615.6,1633.9,1641.6,1657.3,1666.3,1697.3,1701.4,1708.2,1695.2,1700.1,1718.8,1747.1,1763.8,1780.5,1817.1,1826.8,1851.7,1870.0,1884.2,1902.9,1904.4,1913.8,1934.5,1942.1,1966.6,1965.5,1963.9,1970.6,1988.8,1997.1,2021.2,2024.1,2026.3,2044.5,2048.1,2072.2,2080.1,2104.6,2125.8,2149.3,2161.6,2174.0,2177.0,2202.8,2226.4,2245.9,2276.0,2304.4,2320.4,2334.9,2357.6,2366.3,2393.6,2419.4,2403.5,2431.6,2457.5,2474.5,2495.6,2494.6,2512.2,2533.8,2531.3,2571.4,2582.6,2618.8,2640.8,2648.5,2659.5,2696.4,2689.4,2715.7,2752.1,2794.7,2755.8,2771.1,2811.3,2827.1,2820.2,2823.6,2835.2,2857.5,2861.7,2881.2,2898.6,2971.8,2932.9,2928.4,2997.1,2935.5,3001.7,3013.3,3038.8,3048.4,3072.8,3094.7,3130.8,3126.5,3134.5,3144.2,3174.1,3213.7,3221.4,3260.5,3263.0,3293.6,3318.5,3342.7,3368.0,3375.0,3413.7,3430.2,3459.7,3483.7,3488.0,3498.8,3543.0,3551.8,3566.6,3585.7,3620.6,3621.9,3633.6,3643.3,3684.2,3730.7,3728.2,3754.9,3770.0,3775.8,3804.5,3821.7,3848.3,3870.1,3870.6,3871.9,3861.3,3841.0,3866.7,3913.0,3907.1,3933.2,3940.5,3966.0,3969.1,3984.7,3976.0,4003.6,4020.5,4084.7,4099.5,4117.0,4131.5,4158.4,4177.1,4204.8,4220.9,4255.3,4284.7,4300.5,4336.4,4340.7,4355.3,4352.5,4393.4,4422.4,4440.0,4468.9,4481.1,4511.5,4532.8,4554.1,4571.1,4585.1,4632.6,4646.0,4671.1,4669.5,4708.9,4720.6,4762.6,4775.0,4812.9,4825.6,4841.6,4851.2,4850.8,4885.4,4890.2,4933.1,4977.5,4970.2,5005.3,5020.5,5013.9,5055.6,5097.5,5085.7,5132.8,5173.3,5208.0,5223.8,5229.8,5251.9,5275.0,5296.6,5328.5,5351.2,5378.6,5411.1,5434.0,5454.2,5459.3,5460.2,5494.2,5548.8,5587.0,5601.7,5637.7,5661.1,5692.1,5689.9,5723.8,5750.3,5788.1,5837.9,5871.7,5890.0,5925.0,5965.6,5998.8,6015.4,6070.5,6072.9,6101.8,6132.9,6196.2,6225.7,6254.0,6281.5,6326.5,6378.8,6402.1,6437.9,6538.7,6535.3,6619.7,6685.8,6671.1,6707.6,6743.9,6764.1,6799.1,6882.9,6888.2,6902.4,6945.7,6977.0,6995.8,6987.9,7001.2,7047.1,7060.7,7072.2,7108.9,7012.8,7208.4,7167.9,7147.7,7174.3,7218.3,7237.2,7305.4,7282.7,7318.2,7380.4,7401.5,7391.0,7430.7,7459.7,7512.8,7533.1,7535.9,7598.4,7621.0,7628.1,7678.6,7738.2,7834.5,7835.0,7845.7,7899.6,7929.2,7987.4,8019.8,8076.0,8088.6,8163.2,8147.2,8218.9,8253.1,8321.1,8374.6,8420.6,8481.5,8470.2,8529.2,8569.5,8645.6,8643.9,8724.8,8829.5,8832.4,8885.8,8926.6,8938.5,8969.6,9059.8,9090.1,9122.1,9174.8,9215.1,9240.8,9322.6,9321.8,9354.7,9373.2,9380.2,9469.0,9516.3,9546.8,9585.1,9615.7,9651.3,9667.3,9709.6,9753.9,9797.9,9827.0,9897.8,9908.4,9930.0,9913.4,9959.4,9996.8,10053.8,10107.9,10104.7,10094.7,10043.5,9960.3,9820.8,9730.7,9783.8,9766.0,9718.5,9724.8,9748.9,9806.9,9841.7,9961.0,9883.4,9931.9,9940.5,9998.9,10001.8,10030.6,10089.1,10112.9,10131.0,10151.4,10184.7,10228.2,10249.0,10304.7,10354.7,10392.1,10435.5,10470.1,10550.5,10587.6,10612.0,10636.8,10677.5,10700.6,10738.1,10753.1,10759.5,10772.2,10862.1,10953.5,10951.8,10979.7,10968.6,10946.3,10977.2,11004.1,11061.5,11099.8,11136.8,11140.5,11202.8,11239.6,11227.1,11205.4,11244.6,11268.8,11296.7,11329.2,11366.9,11419.8,11487.6,11517.9,11512.5,11566.2,11643.0,11702.6,11748.4,11817.0,11860.5,11944.3,11957.4,12023.0,12051.4,12062.0,12046.0,12082.4,12158.3,12193.8]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"pce\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Line geom, time x scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"breaks\":[-7.9056E10,6.743952E11,1.4278464E12],\n",
       "\"labels\":[\"1967\",\"1991\",\"2015\"],\n",
       "\"time\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"WXef5P\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56dc14e0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breaks = [economics_df.date.min(), economics_df.date.median(), economics_df.date.max()]\n",
    "labels = [str(date).split('-')[0] for date in breaks]\n",
    "p_common = ggplot(economics_df, aes('date', 'pce')) + geom_line()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Line geom, default x scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_x_datetime() + \\\n",
    "    ggtitle(\"Line geom, datetime x scale\")\n",
    "p_bunch_3 = p_common + \\\n",
    "    scale_x_time(breaks=breaks, labels=labels) + \\\n",
    "    ggtitle(\"Line geom, time x scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2, p_bunch_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7efb859a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.397729Z",
     "iopub.status.busy": "2024-04-17T07:30:00.397611Z",
     "iopub.status.idle": "2024-04-17T07:30:00.415652Z",
     "shell.execute_reply": "2024-04-17T07:30:00.415461Z"
    }
   },
   "outputs": [
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       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56dd43a0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(midwest_df, aes('state', 'poptotal')) + \\\n",
    "    geom_jitter(aes(color='state')) + \\\n",
    "    coord_flip()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Jitter geom, default y scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_y_log10() + \\\n",
    "    ggtitle(\"Jitter geom, log10 y scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5dd0c259",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.416918Z",
     "iopub.status.busy": "2024-04-17T07:30:00.416786Z",
     "iopub.status.idle": "2024-04-17T07:30:00.426473Z",
     "shell.execute_reply": "2024-04-17T07:30:00.426272Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\"pce\":[506.7,509.8,515.6,512.2,517.4,525.1,530.9,533.6,544.3,544.0,549.8,556.3,563.2,567.0,568.2,571.6,576.7,576.5,583.5,588.7,588.9,593.9,600.3,600.9,602.7,609.9,613.2,618.5,620.5,622.8,628.7,634.0,632.3,636.0,642.4,646.3,648.5,652.9,659.1,658.3,656.6,665.6,676.1,679.4,682.0,688.8,691.1,699.8,698.9,704.9,713.0,715.8,720.9,728.4,731.5,736.2,749.2,752.5,758.0,761.6,769.9,776.3,781.1,794.9,800.5,806.1,816.5,825.8,832.8,835.7,841.6,844.3,854.1,853.3,869.2,868.2,876.9,876.6,884.5,889.7,901.4,910.8,922.4,928.0,937.9,954.8,955.1,959.2,956.2,961.8,975.6,989.4,990.6,995.0,1018.9,1026.8,1039.8,1047.0,1054.8,1060.9,1075.8,1092.1,1107.1,1107.7,1114.9,1125.4,1122.7,1140.5,1149.6,1158.0,1168.8,1176.8,1189.0,1211.5,1215.0,1231.3,1238.3,1247.3,1257.1,1263.6,1280.5,1285.7,1294.5,1311.4,1327.0,1336.0,1329.5,1355.1,1377.5,1396.4,1412.0,1425.8,1426.8,1447.0,1452.9,1466.9,1480.6,1496.5,1502.4,1517.8,1531.2,1538.4,1558.8,1575.7,1586.1,1615.6,1633.9,1641.6,1657.3,1666.3,1697.3,1701.4,1708.2,1695.2,1700.1,1718.8,1747.1,1763.8,1780.5,1817.1,1826.8,1851.7,1870.0,1884.2,1902.9,1904.4,1913.8,1934.5,1942.1,1966.6,1965.5,1963.9,1970.6,1988.8,1997.1,2021.2,2024.1,2026.3,2044.5,2048.1,2072.2,2080.1,2104.6,2125.8,2149.3,2161.6,2174.0,2177.0,2202.8,2226.4,2245.9,2276.0,2304.4,2320.4,2334.9,2357.6,2366.3,2393.6,2419.4,2403.5,2431.6,2457.5,2474.5,2495.6,2494.6,2512.2,2533.8,2531.3,2571.4,2582.6,2618.8,2640.8,2648.5,2659.5,2696.4,2689.4,2715.7,2752.1,2794.7,2755.8,2771.1,2811.3,2827.1,2820.2,2823.6,2835.2,2857.5,2861.7,2881.2,2898.6,2971.8,2932.9,2928.4,2997.1,2935.5,3001.7,3013.3,3038.8,3048.4,3072.8,3094.7,3130.8,3126.5,3134.5,3144.2,3174.1,3213.7,3221.4,3260.5,3263.0,3293.6,3318.5,3342.7,3368.0,3375.0,3413.7,3430.2,3459.7,3483.7,3488.0,3498.8,3543.0,3551.8,3566.6,3585.7,3620.6,3621.9,3633.6,3643.3,3684.2,3730.7,3728.2,3754.9,3770.0,3775.8,3804.5,3821.7,3848.3,3870.1,3870.6,3871.9,3861.3,3841.0,3866.7,3913.0,3907.1,3933.2,3940.5,3966.0,3969.1,3984.7,3976.0,4003.6,4020.5,4084.7,4099.5,4117.0,4131.5,4158.4,4177.1,4204.8,4220.9,4255.3,4284.7,4300.5,4336.4,4340.7,4355.3,4352.5,4393.4,4422.4,4440.0,4468.9,4481.1,4511.5,4532.8,4554.1,4571.1,4585.1,4632.6,4646.0,4671.1,4669.5,4708.9,4720.6,4762.6,4775.0,4812.9,4825.6,4841.6,4851.2,4850.8,4885.4,4890.2,4933.1,4977.5,4970.2,5005.3,5020.5,5013.9,5055.6,5097.5,5085.7,5132.8,5173.3,5208.0,5223.8,5229.8,5251.9,5275.0,5296.6,5328.5,5351.2,5378.6,5411.1,5434.0,5454.2,5459.3,5460.2,5494.2,5548.8,5587.0,5601.7,5637.7,5661.1,5692.1,5689.9,5723.8,5750.3,5788.1,5837.9,5871.7,5890.0,5925.0,5965.6,5998.8,6015.4,6070.5,6072.9,6101.8,6132.9,6196.2,6225.7,6254.0,6281.5,6326.5,6378.8,6402.1,6437.9,6538.7,6535.3,6619.7,6685.8,6671.1,6707.6,6743.9,6764.1,6799.1,6882.9,6888.2,6902.4,6945.7,6977.0,6995.8,6987.9,7001.2,7047.1,7060.7,7072.2,7108.9,7012.8,7208.4,7167.9,7147.7,7174.3,7218.3,7237.2,7305.4,7282.7,7318.2,7380.4,7401.5,7391.0,7430.7,7459.7,7512.8,7533.1,7535.9,7598.4,7621.0,7628.1,7678.6,7738.2,7834.5,7835.0,7845.7,7899.6,7929.2,7987.4,8019.8,8076.0,8088.6,8163.2,8147.2,8218.9,8253.1,8321.1,8374.6,8420.6,8481.5,8470.2,8529.2,8569.5,8645.6,8643.9,8724.8,8829.5,8832.4,8885.8,8926.6,8938.5,8969.6,9059.8,9090.1,9122.1,9174.8,9215.1,9240.8,9322.6,9321.8,9354.7,9373.2,9380.2,9469.0,9516.3,9546.8,9585.1,9615.7,9651.3,9667.3,9709.6,9753.9,9797.9,9827.0,9897.8,9908.4,9930.0,9913.4,9959.4,9996.8,10053.8,10107.9,10104.7,10094.7,10043.5,9960.3,9820.8,9730.7,9783.8,9766.0,9718.5,9724.8,9748.9,9806.9,9841.7,9961.0,9883.4,9931.9,9940.5,9998.9,10001.8,10030.6,10089.1,10112.9,10131.0,10151.4,10184.7,10228.2,10249.0,10304.7,10354.7,10392.1,10435.5,10470.1,10550.5,10587.6,10612.0,10636.8,10677.5,10700.6,10738.1,10753.1,10759.5,10772.2,10862.1,10953.5,10951.8,10979.7,10968.6,10946.3,10977.2,11004.1,11061.5,11099.8,11136.8,11140.5,11202.8,11239.6,11227.1,11205.4,11244.6,11268.8,11296.7,11329.2,11366.9,11419.8,11487.6,11517.9,11512.5,11566.2,11643.0,11702.6,11748.4,11817.0,11860.5,11944.3,11957.4,12023.0,12051.4,12062.0,12046.0,12082.4,12158.3,12193.8]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"date\",\n",
       "\"y\":\"pce\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"series_annotations\":[{\n",
       "\"column\":\"date\",\n",
       "\"type\":\"datetime\"\n",
       "}]\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Line geom, reversed x scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"trans\":\"reverse\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"wtCLhb\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e58850>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(economics_df, aes('date', 'pce')) + \\\n",
    "    geom_line()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Line geom, default x scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_x_reverse() + \\\n",
    "    ggtitle(\"Line geom, reversed x scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4751206c",
   "metadata": {},
   "source": [
    "### Color & Fill Scales <a class=\"anchor\" id=\"scales-color_and_fill_scales\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85061d4",
   "metadata": {},
   "source": [
    "#### Continuous <a class=\"anchor\" id=\"scales-color_and_fill_scales-continuous\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "714551e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.427685Z",
     "iopub.status.busy": "2024-04-17T07:30:00.427572Z",
     "iopub.status.idle": "2024-04-17T07:30:00.433088Z",
     "shell.execute_reply": "2024-04-17T07:30:00.432907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "           var plotContainer = document.getElementById(\"CI6hrn\");\n",
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     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram(aes(fill='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_grey() + \\\n",
    "    ggtitle(\"Histogram geom, grey fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
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     "iopub.status.idle": "2024-04-17T07:30:00.439543Z",
     "shell.execute_reply": "2024-04-17T07:30:00.439372Z"
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   },
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram(aes(fill='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_gradient(low='#006d2c', high='#edf8e9') + \\\n",
    "    ggtitle(\"Histogram geom, gradient fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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   "cell_type": "code",
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   "id": "67431865",
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       "   </script>"
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     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram(aes(fill='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_brewer(type='seq', palette='GnBu', direction=-1) + \\\n",
    "    ggtitle(\"Histogram geom, brewer fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c689cb7d",
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     "iopub.status.busy": "2024-04-17T07:30:00.446810Z",
     "iopub.status.idle": "2024-04-17T07:30:00.452264Z",
     "shell.execute_reply": "2024-04-17T07:30:00.452093Z"
    }
   },
   "outputs": [
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       "\"metainfo_list\":[]\n",
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       "};\n",
       "           var plotContainer = document.getElementById(\"GHts9J\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
       "   </script>"
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      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram(aes(fill='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_viridis(option='inferno') + \\\n",
    "    ggtitle(\"Histogram geom, viridis fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b1dd77e9",
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    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.453145Z",
     "iopub.status.busy": "2024-04-17T07:30:00.453026Z",
     "iopub.status.idle": "2024-04-17T07:30:00.458532Z",
     "shell.execute_reply": "2024-04-17T07:30:00.458360Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
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       "\"mapping\":{\n",
       "\"x\":\"hwy\"\n",
       "},\n",
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       "\"ggtitle\":{\n",
       "\"text\":\"Histogram geom, hue fill scale\"\n",
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       "\"aesthetic\":\"fill\",\n",
       "\"c\":150.0,\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"foRK7V\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54df20e0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_histogram(aes(fill='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_hue(l=80, c=150) + \\\n",
    "    ggtitle(\"Histogram geom, hue fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6184d37f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.459423Z",
     "iopub.status.busy": "2024-04-17T07:30:00.459346Z",
     "iopub.status.idle": "2024-04-17T07:30:00.469540Z",
     "shell.execute_reply": "2024-04-17T07:30:00.469368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"34Oqgp\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
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       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
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       "},\n",
       "\"figures\":[{\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"x\",\n",
       "\"y\":\"y\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
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       "\"geom\":\"histogram\",\n",
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       "},\n",
       "\"data_meta\":{\n",
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       "\"bins\":7.0,\n",
       "\"data\":{\n",
       "\"x\":[-1.0285714285714285,-0.6857142857142856,-0.34285714285714275,0.0,0.34285714285714297,0.6857142857142859,1.0285714285714285],\n",
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       "\"y\":\"y\"\n",
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       "\"ggtitle\":{\n",
       "\"text\":\"Histogram geom, gradient2 fill scale\"\n",
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       "\"kind\":\"plot\",\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"34Oqgp\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
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       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e686d0>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(random_df, aes('x', 'y')) + \\\n",
    "    geom_histogram(aes(fill='x'), bins=7)\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Histogram geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_gradient2(low='#4575b4', mid='#ffffbf', high='#d73027') + \\\n",
    "    ggtitle(\"Histogram geom, gradient2 fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23dc5d7",
   "metadata": {},
   "source": [
    "#### Discrete <a class=\"anchor\" id=\"scales-color_and_fill_scales-discrete\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "827e7040",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.470509Z",
     "iopub.status.busy": "2024-04-17T07:30:00.470389Z",
     "iopub.status.idle": "2024-04-17T07:30:00.476702Z",
     "shell.execute_reply": "2024-04-17T07:30:00.476524Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"4oh7C3\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
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       "\"nrow\":1.0,\n",
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       "\"figures\":[{\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"fl\"\n",
       "},\n",
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       "},\n",
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       "},\n",
       "\"data\":{\n",
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       "}],\n",
       "\"metainfo_list\":[]\n",
       "},{\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"fl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Bar geom, brewer fill scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"type\":\"qual\",\n",
       "\"palette\":\"Set1\",\n",
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       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
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       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[52.0,168.0,8.0,5.0,1.0],\n",
       "\"fl\":[\"p\",\"r\",\"e\",\"d\",\"c\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"4oh7C3\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
       "   </script>"
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       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e69270>"
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     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='fl'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Bar geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_brewer(type='qual', palette='Set1') + \\\n",
    "    ggtitle(\"Bar geom, brewer fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "09736ae3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.477590Z",
     "iopub.status.busy": "2024-04-17T07:30:00.477514Z",
     "iopub.status.idle": "2024-04-17T07:30:00.484146Z",
     "shell.execute_reply": "2024-04-17T07:30:00.483972Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"D30zVb\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "},\n",
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       "\"scales\":[],\n",
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       "\"mapping\":{\n",
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       "\"x\":\"fl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Bar geom, manual fill scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"values\":[\"#fbb4ae\",\"#b3cde3\",\"#ccebc5\",\"#decbe4\",\"#fed9a6\"]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
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       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[52.0,168.0,8.0,5.0,1.0],\n",
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       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"D30zVb\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
       "   </script>"
      ],
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       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e9f940>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='fl'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Bar geom, default fill scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_manual(values=['#fbb4ae', '#b3cde3', '#ccebc5', '#decbe4', '#fed9a6']) + \\\n",
    "    ggtitle(\"Bar geom, manual fill scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d89329",
   "metadata": {},
   "source": [
    "### Size & Shape Scales <a class=\"anchor\" id=\"scales-size_and_shape_scales\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "02c92038",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.485076Z",
     "iopub.status.busy": "2024-04-17T07:30:00.485001Z",
     "iopub.status.idle": "2024-04-17T07:30:00.490933Z",
     "shell.execute_reply": "2024-04-17T07:30:00.490758Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   <div id=\"NQYCHn\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
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       "\"geom\":\"point\",\n",
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       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Point geom, area size scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"scale_mapper_kind\":\"size_area\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"size\":\"cyl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"alpha\":0.2,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"NQYCHn\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e759f0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(size='cyl'), shape=21, alpha=.2)\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default size scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_size_area() + \\\n",
    "    ggtitle(\"Point geom, area size scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "23ecb885",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.491841Z",
     "iopub.status.busy": "2024-04-17T07:30:00.491758Z",
     "iopub.status.idle": "2024-04-17T07:30:00.497816Z",
     "shell.execute_reply": "2024-04-17T07:30:00.497646Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"ULLjix\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
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       "\"figures\":[{\n",
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       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
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       "\"data_meta\":{\n",
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       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
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       "\"data_meta\":{\n",
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       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Point geom, size scale in range 3..6\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"size\",\n",
       "\"range\":[3.0,6.0]\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"size\":\"cyl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"shape\":21.0,\n",
       "\"alpha\":0.2,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"ULLjix\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e9f190>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(size='cyl'), shape=21, alpha=.2)\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default size scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_size(range=[3, 6]) + \\\n",
    "    ggtitle(\"Point geom, size scale in range 3..6\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4654598b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.498756Z",
     "iopub.status.busy": "2024-04-17T07:30:00.498638Z",
     "iopub.status.idle": "2024-04-17T07:30:00.502986Z",
     "shell.execute_reply": "2024-04-17T07:30:00.502814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"yvJkZN\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"hwy\",\n",
       "\"y\":\"cty\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"coord\":{\n",
       "\"name\":\"fixed\",\n",
       "\"ratio\":1.0,\n",
       "\"flip\":false\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Lollipop geom, default linewidth scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"lollipop\",\n",
       "\"mapping\":{\n",
       "\"linewidth\":\"cyl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"dir\":\"s\",\n",
       "\"slope\":0.7,\n",
       "\"intercept\":0.8,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
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       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54ed29e0>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df[mpg_df[\"fl\"] == \"p\"], aes('hwy', 'cty')) + \\\n",
    "    geom_lollipop(aes(linewidth='cyl'), slope=.7, intercept=.8, dir='s') + \\\n",
    "    coord_fixed()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Lollipop geom, default linewidth scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_linewidth(range=[.5, 2]) + \\\n",
    "    ggtitle(\"Lollipop geom, scaled linewidth\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ad1238f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.503885Z",
     "iopub.status.busy": "2024-04-17T07:30:00.503808Z",
     "iopub.status.idle": "2024-04-17T07:30:00.509950Z",
     "shell.execute_reply": "2024-04-17T07:30:00.509778Z"
    }
   },
   "outputs": [
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       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a549a4f40>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(stroke='cyl'), shape=1, alpha=.2)\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default stroke scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_stroke(range=[.5, 2]) + \\\n",
    "    ggtitle(\"Point geom, scaled stroke\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e2cd66c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.510903Z",
     "iopub.status.busy": "2024-04-17T07:30:00.510784Z",
     "iopub.status.idle": "2024-04-17T07:30:00.514593Z",
     "shell.execute_reply": "2024-04-17T07:30:00.514412Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   <div id=\"lBE7sT\"></div>\n",
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       "\"figures\":[{\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Point geom, shape scale with solid=False\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"shape\",\n",
       "\"solid\":false\n",
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       "\"shape\":\"fl\"\n",
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       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"lBE7sT\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
       "   </script>"
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      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a549baf20>"
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     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(fl_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(shape='fl'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default shape scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_shape(solid=False) + \\\n",
    "    ggtitle(\"Point geom, shape scale with solid=False\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "69b61ad5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.515501Z",
     "iopub.status.busy": "2024-04-17T07:30:00.515428Z",
     "iopub.status.idle": "2024-04-17T07:30:00.518838Z",
     "shell.execute_reply": "2024-04-17T07:30:00.518661Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "},\n",
       "\"figures\":[{\n",
       "\"data\":{\n",
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       "\"hwy\":[12.0,14.0,15.0,16.0,17.0,16.0,17.0,18.0,16.0,17.0,18.0,19.0,17.0,18.0,19.0,20.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,20.0,22.0,23.0,24.0,25.0,26.0,22.0,24.0,25.0,26.0,27.0,23.0,24.0,25.0,26.0,27.0,28.0,29.0,25.0,26.0,27.0,28.0,29.0,25.0,26.0,27.0,28.0,29.0,31.0,26.0,27.0,29.0,30.0,31.0,29.0,30.0,31.0,29.0,31.0,32.0,30.0,32.0,33.0,36.0,32.0,36.0,34.0,35.0,33.0,37.0,41.0,44.0,44.0],\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
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       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Point geom, manual shape scale\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"shape\",\n",
       "\"values\":[0.0,12.0,1.0,10.0,3.0,13.0,2.0,4.0]\n",
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       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"shape\":\"fl\"\n",
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       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"zpDmzx\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
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      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a56dd4190>"
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     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(fl_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(shape='fl'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default shape scale\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_shape_manual(values=[0, 12, 1, 10, 3, 13, 2, 4]) + \\\n",
    "    ggtitle(\"Point geom, manual shape scale\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ebbaca",
   "metadata": {},
   "source": [
    "## Coordinate Systems <a class=\"anchor\" id=\"coordinate_systems\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "06df1a82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.519750Z",
     "iopub.status.busy": "2024-04-17T07:30:00.519671Z",
     "iopub.status.idle": "2024-04-17T07:30:00.525051Z",
     "shell.execute_reply": "2024-04-17T07:30:00.524878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"yQECiY\"></div>\n",
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       "\"text\":\"Bar geom, cartesian coordinate system\"\n",
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       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"yQECiY\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "   </script>"
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      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Bar geom, default coordinate system\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    coord_cartesian(ylim=[0, 250]) + \\\n",
    "    ggtitle(\"Bar geom, cartesian coordinate system\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
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     "execution_count": 101,
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   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default coordinate system\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    coord_polar() + \\\n",
    "    ggtitle(\"Point geom, polar coordinate system\")\n",
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       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"coord\":{\n",
       "\"name\":\"fixed\",\n",
       "\"ratio\":1.0,\n",
       "\"flip\":false\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Point geom, fixed coordinate system\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"sLs0cD\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a549f4c40>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Point geom, default coordinate system\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    coord_fixed() + \\\n",
    "    ggtitle(\"Point geom, fixed coordinate system\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "228f4f81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.538855Z",
     "iopub.status.busy": "2024-04-17T07:30:00.538734Z",
     "iopub.status.idle": "2024-04-17T07:30:00.563093Z",
     "shell.execute_reply": "2024-04-17T07:30:00.562893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"qE8VzT\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Polygon geom, default coordinate system\"\n",
       "},\n",
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       "\"scales\":[],\n",
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       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"geodataframe\":{\n",
       "\"geometry\":\"geometry\"\n",
       "}\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"qE8VzT\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54a0ef80>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot() + \\\n",
    "    geom_polygon(data=states_df)\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Polygon geom, default coordinate system\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    coord_map() + \\\n",
    "    ggtitle(\"Polygon geom, map coordinate system\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "411e8799",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.564072Z",
     "iopub.status.busy": "2024-04-17T07:30:00.563992Z",
     "iopub.status.idle": "2024-04-17T07:30:00.570406Z",
     "shell.execute_reply": "2024-04-17T07:30:00.570225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"SUo33H\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"ggtitle\":{\n",
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       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"SUo33H\");\n",
       "           window.letsPlotCall(function() {{\n",
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     "execution_count": 104,
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   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Bar geom, default coordinate system\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    coord_flip() + \\\n",
    "    ggtitle(\"Bar geom, flipped coordinates\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f972e481",
   "metadata": {},
   "source": [
    "## Position Adjustments <a class=\"anchor\" id=\"position_adjustments\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4392f94e",
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     "iopub.status.busy": "2024-04-17T07:30:00.571287Z",
     "iopub.status.idle": "2024-04-17T07:30:00.577724Z",
     "shell.execute_reply": "2024-04-17T07:30:00.577549Z"
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   "outputs": [
    {
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       "   <div id=\"1sdCat\"></div>\n",
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       "           var plotContainer = document.getElementById(\"1sdCat\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='drv')) + \\\n",
    "    ggtitle(\"Bar geom, default position\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='drv'), position='dodge') + \\\n",
    "    ggtitle(\"Bar geom, dodge position\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
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  {
   "cell_type": "code",
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   "id": "41fdb119",
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     "iopub.status.idle": "2024-04-17T07:30:00.633224Z",
     "shell.execute_reply": "2024-04-17T07:30:00.632993Z"
    }
   },
   "outputs": [
    {
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       "   <div id=\"S62Men\"></div>\n",
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       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"S62Men\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54a448b0>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_density(aes(fill='drv'), color=\"black\") + \\\n",
    "    ggtitle(\"Density geom, default position\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_density(aes(fill='drv'), color=\"black\", position='stack') + \\\n",
    "    ggtitle(\"Density geom, stack position\")\n",
    "p_bunch_3 = ggplot(mpg_df, aes(x='hwy')) + \\\n",
    "    geom_density(aes(fill='drv'), color=\"black\", position='fill') + \\\n",
    "    ggtitle(\"Density geom, fill position\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2, p_bunch_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c47ff5a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.634408Z",
     "iopub.status.busy": "2024-04-17T07:30:00.634322Z",
     "iopub.status.idle": "2024-04-17T07:30:00.641605Z",
     "shell.execute_reply": "2024-04-17T07:30:00.641321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"ySdA67\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
       "\"data\":{\n",
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       "           var plotContainer = document.getElementById(\"ySdA67\");\n",
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       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "   </script>"
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      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54a2b580>"
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     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_bunch_1 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    ggtitle(\"Point geom, default position\")\n",
    "p_bunch_2 = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(position='jitter') + \\\n",
    "    ggtitle(\"Point geom, jitter position\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2d3c01ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.642688Z",
     "iopub.status.busy": "2024-04-17T07:30:00.642492Z",
     "iopub.status.idle": "2024-04-17T07:30:00.652341Z",
     "shell.execute_reply": "2024-04-17T07:30:00.652159Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\"..upper..\":[31.5,26.0,25.0,29.0,26.0,20.0,18.0,23.0,26.0],\n",
       "\"..ymin..\":[26.0,21.0,25.0,28.0,20.0,17.0,12.0,15.0,24.0],\n",
       "\"..ymax..\":[35.0,29.0,25.0,29.0,28.0,24.0,19.0,26.0,26.0],\n",
       "\"cyl\":[4.0,6.0,8.0,5.0,4.0,6.0,8.0,8.0,6.0],\n",
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       "},{\n",
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       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
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       "\"..ymax..\":[35.0,35.0,35.0,35.0,35.0,35.0,35.0,29.0,25.0,29.0,28.0,24.0,24.0,24.0,24.0,24.0,19.0,26.0,26.0],\n",
       "\"cyl\":[4.0,4.0,4.0,4.0,4.0,4.0,4.0,6.0,8.0,5.0,4.0,6.0,6.0,6.0,6.0,6.0,8.0,8.0,6.0],\n",
       "\"hwy\":[24.0,36.0,36.0,37.0,44.0,44.0,41.0,17.0,NaN,NaN,NaN,25.0,25.0,25.0,25.0,25.0,23.0,NaN,NaN],\n",
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       "}\n",
       "},{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"position\":\"jitterdodge\",\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"color\":\"black\",\n",
       "\"shape\":21.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"xgsiNz\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54a881f0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cyl', 'hwy', fill='drv'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    geom_boxplot() + \\\n",
    "    geom_point(color='black', shape=21) + \\\n",
    "    ggtitle(\"Point geom, default position\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    geom_boxplot() + \\\n",
    "    geom_point(position='jitterdodge', \\\n",
    "               color='black', shape=21) + \\\n",
    "    ggtitle(\"Point geom, jitterdodge position\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "cccf951d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.653311Z",
     "iopub.status.busy": "2024-04-17T07:30:00.653175Z",
     "iopub.status.idle": "2024-04-17T07:30:00.659336Z",
     "shell.execute_reply": "2024-04-17T07:30:00.659161Z"
    }
   },
   "outputs": [
    {
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       "   <div id=\"k0vVXP\"></div>\n",
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       "},\n",
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       "\"class\":[\"compact\",\"midsize\",\"suv\",\"2seater\",\"minivan\",\"pickup\",\"subcompact\"],\n",
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       "};\n",
       "           var plotContainer = document.getElementById(\"k0vVXP\");\n",
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       "   </script>"
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       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a567cb9a0>"
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     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('class', 'hwy')) + \\\n",
    "    geom_bar()\n",
    "p_bunch_1 = p_common + \\\n",
    "    geom_text(aes(label='..count..'), stat='count') + \\\n",
    "    ggtitle(\"Text geom, default position\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    geom_text(aes(label='..count..'), stat='count', color='white', \\\n",
    "              position=position_nudge(y=-2.5)) + \\\n",
    "    ggtitle(\"Text geom, nudge position\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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  },
  {
   "cell_type": "markdown",
   "id": "6efb0aad",
   "metadata": {},
   "source": [
    "## Themes <a class=\"anchor\" id=\"themes\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "939ffac3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.660251Z",
     "iopub.status.busy": "2024-04-17T07:30:00.660172Z",
     "iopub.status.idle": "2024-04-17T07:30:00.677801Z",
     "shell.execute_reply": "2024-04-17T07:30:00.677619Z"
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   "outputs": [
    {
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       "\"ncol\":2.0,\n",
       "\"nrow\":4.0,\n",
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       "},\n",
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       "\"metainfo_list\":[]\n",
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       "           var plotContainer = document.getElementById(\"9foCBq\");\n",
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     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point()\n",
    "p_bunch = []\n",
    "p_bunch.append(p_common + ggtitle(\"Default theme\"))\n",
    "p_bunch.append(p_common + theme_none() + ggtitle(\"Empty theme\"))\n",
    "p_bunch.append(p_common + theme_void() + ggtitle(\"Void theme\"))\n",
    "p_bunch.append(p_common + theme_minimal() + ggtitle(\"Minimalistic theme\"))\n",
    "p_bunch.append(p_common + theme_classic() + ggtitle(\"Classic theme\"))\n",
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    "p_bunch.append(p_common + theme_light() + ggtitle(\"Light theme\"))\n",
    "p_bunch.append(p_common + theme_bw() + ggtitle(\"Dark-on-light theme\"))\n",
    "\n",
    "gggrid(p_bunch, ncol=2)"
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  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a9da20f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.678870Z",
     "iopub.status.busy": "2024-04-17T07:30:00.678749Z",
     "iopub.status.idle": "2024-04-17T07:30:00.683813Z",
     "shell.execute_reply": "2024-04-17T07:30:00.683643Z"
    }
   },
   "outputs": [
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    "\n",
    "custom_theme = theme(\n",
    "    line=element_line(color=main_color_normal, size=2),\n",
    "    rect=element_rect(color=main_color_normal, fill=main_color_light, size=2),\n",
    "    text=element_text(color=main_color_dark, family=\"Courier\", face=\"bold\"),\n",
    "    geom=element_geom(pen=main_color_normal),\n",
    "    axis_ontop=True,\n",
    "    axis_ticks=element_line(color=main_color_normal, size=1),\n",
    "    axis_ticks_length=7,\n",
    "    legend_background=element_rect(size=1),\n",
    "    legend_position='bottom',\n",
    "    panel_grid_major=element_line(color=main_color_normal, size=.5),\n",
    "    panel_grid_minor='blank',\n",
    "    plot_background=element_rect(fill=background_color_light, size=1),\n",
    "    axis_tooltip=element_rect(color=main_color_dark)\n",
    ")\n",
    "\n",
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    theme_none() + \\\n",
    "    custom_theme + \\\n",
    "    ggtitle(\"Custom theme\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b52211",
   "metadata": {},
   "source": [
    "## Flavors <a class=\"anchor\" id=\"flavors\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f7fa6852",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.684952Z",
     "iopub.status.busy": "2024-04-17T07:30:00.684838Z",
     "iopub.status.idle": "2024-04-17T07:30:00.698106Z",
     "shell.execute_reply": "2024-04-17T07:30:00.697834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\"flavor\":\"solarized_light\"\n",
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       "\"text\":\"flavor_solarized_light()\"\n",
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       "\"kind\":\"plot\",\n",
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       "\"mapping\":{\n",
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       "},{\n",
       "\"data\":{\n",
       "\"cty\":[18.0,21.0,20.0,21.0,16.0,18.0,18.0,18.0,16.0,20.0,19.0,15.0,17.0,17.0,15.0,15.0,17.0,16.0,14.0,11.0,14.0,13.0,12.0,16.0,15.0,16.0,15.0,15.0,14.0,11.0,11.0,14.0,19.0,22.0,18.0,18.0,17.0,18.0,17.0,16.0,16.0,17.0,17.0,11.0,15.0,15.0,16.0,16.0,15.0,14.0,13.0,14.0,14.0,14.0,9.0,11.0,11.0,13.0,13.0,9.0,13.0,11.0,13.0,11.0,12.0,9.0,13.0,13.0,12.0,9.0,11.0,11.0,13.0,11.0,11.0,11.0,12.0,14.0,15.0,14.0,13.0,13.0,13.0,14.0,14.0,13.0,13.0,13.0,11.0,13.0,18.0,18.0,17.0,16.0,15.0,15.0,15.0,15.0,14.0,28.0,24.0,25.0,23.0,24.0,26.0,25.0,24.0,21.0,18.0,18.0,21.0,21.0,18.0,18.0,19.0,19.0,19.0,20.0,20.0,17.0,16.0,17.0,17.0,15.0,15.0,14.0,9.0,14.0,13.0,11.0,11.0,12.0,12.0,11.0,11.0,11.0,12.0,14.0,13.0,13.0,13.0,21.0,19.0,23.0,23.0,19.0,19.0,18.0,19.0,19.0,14.0,15.0,14.0,12.0,18.0,16.0,17.0,18.0,16.0,18.0,18.0,20.0,19.0,20.0,18.0,21.0,19.0,19.0,19.0,20.0,20.0,19.0,20.0,15.0,16.0,15.0,15.0,16.0,14.0,21.0,21.0,21.0,21.0,18.0,18.0,19.0,21.0,21.0,21.0,22.0,18.0,18.0,18.0,24.0,24.0,26.0,28.0,26.0,11.0,13.0,15.0,16.0,17.0,15.0,15.0,15.0,16.0,21.0,19.0,21.0,22.0,17.0,33.0,21.0,19.0,22.0,21.0,21.0,21.0,16.0,17.0,35.0,29.0,21.0,19.0,20.0,20.0,21.0,18.0,19.0,21.0,16.0,18.0,17.0],\n",
       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"name\":\"grey\",\n",
       "\"flavor\":\"solarized_dark\"\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"flavor_solarized_dark()\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "},{\n",
       "\"data\":{\n",
       "\"cty\":[18.0,21.0,20.0,21.0,16.0,18.0,18.0,18.0,16.0,20.0,19.0,15.0,17.0,17.0,15.0,15.0,17.0,16.0,14.0,11.0,14.0,13.0,12.0,16.0,15.0,16.0,15.0,15.0,14.0,11.0,11.0,14.0,19.0,22.0,18.0,18.0,17.0,18.0,17.0,16.0,16.0,17.0,17.0,11.0,15.0,15.0,16.0,16.0,15.0,14.0,13.0,14.0,14.0,14.0,9.0,11.0,11.0,13.0,13.0,9.0,13.0,11.0,13.0,11.0,12.0,9.0,13.0,13.0,12.0,9.0,11.0,11.0,13.0,11.0,11.0,11.0,12.0,14.0,15.0,14.0,13.0,13.0,13.0,14.0,14.0,13.0,13.0,13.0,11.0,13.0,18.0,18.0,17.0,16.0,15.0,15.0,15.0,15.0,14.0,28.0,24.0,25.0,23.0,24.0,26.0,25.0,24.0,21.0,18.0,18.0,21.0,21.0,18.0,18.0,19.0,19.0,19.0,20.0,20.0,17.0,16.0,17.0,17.0,15.0,15.0,14.0,9.0,14.0,13.0,11.0,11.0,12.0,12.0,11.0,11.0,11.0,12.0,14.0,13.0,13.0,13.0,21.0,19.0,23.0,23.0,19.0,19.0,18.0,19.0,19.0,14.0,15.0,14.0,12.0,18.0,16.0,17.0,18.0,16.0,18.0,18.0,20.0,19.0,20.0,18.0,21.0,19.0,19.0,19.0,20.0,20.0,19.0,20.0,15.0,16.0,15.0,15.0,16.0,14.0,21.0,21.0,21.0,21.0,18.0,18.0,19.0,21.0,21.0,21.0,22.0,18.0,18.0,18.0,24.0,24.0,26.0,28.0,26.0,11.0,13.0,15.0,16.0,17.0,15.0,15.0,15.0,16.0,21.0,19.0,21.0,22.0,17.0,33.0,21.0,19.0,22.0,21.0,21.0,21.0,16.0,17.0,35.0,29.0,21.0,19.0,20.0,20.0,21.0,18.0,19.0,21.0,16.0,18.0,17.0],\n",
       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"name\":\"grey\",\n",
       "\"flavor\":\"high_contrast_light\"\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"flavor_high_contrast_light()\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "},{\n",
       "\"data\":{\n",
       "\"cty\":[18.0,21.0,20.0,21.0,16.0,18.0,18.0,18.0,16.0,20.0,19.0,15.0,17.0,17.0,15.0,15.0,17.0,16.0,14.0,11.0,14.0,13.0,12.0,16.0,15.0,16.0,15.0,15.0,14.0,11.0,11.0,14.0,19.0,22.0,18.0,18.0,17.0,18.0,17.0,16.0,16.0,17.0,17.0,11.0,15.0,15.0,16.0,16.0,15.0,14.0,13.0,14.0,14.0,14.0,9.0,11.0,11.0,13.0,13.0,9.0,13.0,11.0,13.0,11.0,12.0,9.0,13.0,13.0,12.0,9.0,11.0,11.0,13.0,11.0,11.0,11.0,12.0,14.0,15.0,14.0,13.0,13.0,13.0,14.0,14.0,13.0,13.0,13.0,11.0,13.0,18.0,18.0,17.0,16.0,15.0,15.0,15.0,15.0,14.0,28.0,24.0,25.0,23.0,24.0,26.0,25.0,24.0,21.0,18.0,18.0,21.0,21.0,18.0,18.0,19.0,19.0,19.0,20.0,20.0,17.0,16.0,17.0,17.0,15.0,15.0,14.0,9.0,14.0,13.0,11.0,11.0,12.0,12.0,11.0,11.0,11.0,12.0,14.0,13.0,13.0,13.0,21.0,19.0,23.0,23.0,19.0,19.0,18.0,19.0,19.0,14.0,15.0,14.0,12.0,18.0,16.0,17.0,18.0,16.0,18.0,18.0,20.0,19.0,20.0,18.0,21.0,19.0,19.0,19.0,20.0,20.0,19.0,20.0,15.0,16.0,15.0,15.0,16.0,14.0,21.0,21.0,21.0,21.0,18.0,18.0,19.0,21.0,21.0,21.0,22.0,18.0,18.0,18.0,24.0,24.0,26.0,28.0,26.0,11.0,13.0,15.0,16.0,17.0,15.0,15.0,15.0,16.0,21.0,19.0,21.0,22.0,17.0,33.0,21.0,19.0,22.0,21.0,21.0,21.0,16.0,17.0,35.0,29.0,21.0,19.0,20.0,20.0,21.0,18.0,19.0,21.0,16.0,18.0,17.0],\n",
       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"name\":\"grey\",\n",
       "\"flavor\":\"high_contrast_dark\"\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"flavor_high_contrast_dark()\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"6XoXYg\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a567f94b0>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    theme_grey()\n",
    "p_bunch = []\n",
    "p_bunch.append(p_common + ggtitle(\"Without flavor\"))\n",
    "p_bunch.append(p_common + flavor_darcula() + ggtitle(\"flavor_darcula()\"))\n",
    "p_bunch.append(p_common + flavor_solarized_light() + ggtitle(\"flavor_solarized_light()\"))\n",
    "p_bunch.append(p_common + flavor_solarized_dark() + ggtitle(\"flavor_solarized_dark()\"))\n",
    "p_bunch.append(p_common + flavor_high_contrast_light() + ggtitle(\"flavor_high_contrast_light()\"))\n",
    "p_bunch.append(p_common + flavor_high_contrast_dark() + ggtitle(\"flavor_high_contrast_dark()\"))\n",
    "\n",
    "gggrid(p_bunch, ncol=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "918134ba",
   "metadata": {},
   "source": [
    "## Faceting <a class=\"anchor\" id=\"faceting\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3c0d116a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.699021Z",
     "iopub.status.busy": "2024-04-17T07:30:00.698910Z",
     "iopub.status.idle": "2024-04-17T07:30:00.703964Z",
     "shell.execute_reply": "2024-04-17T07:30:00.703786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"ILqYA1\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
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       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"facet\":{\n",
       "\"name\":\"grid\",\n",
       "\"x\":\"fl\",\n",
       "\"y\":\"year\",\n",
       "\"x_order\":1.0,\n",
       "\"y_order\":1.0\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"ILqYA1\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a567e7730>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    facet_grid(x='fl', y='year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "30eb87af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.704879Z",
     "iopub.status.busy": "2024-04-17T07:30:00.704796Z",
     "iopub.status.idle": "2024-04-17T07:30:00.709104Z",
     "shell.execute_reply": "2024-04-17T07:30:00.708934Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "           var plotContainer = document.getElementById(\"nn9VPD\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x702a568b8d30>"
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     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point() + \\\n",
    "    facet_wrap(facets='fl', ncol=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fba9d79",
   "metadata": {},
   "source": [
    "## Labels & Legends <a class=\"anchor\" id=\"labels_and_legends\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "99f13f41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.710046Z",
     "iopub.status.busy": "2024-04-17T07:30:00.709922Z",
     "iopub.status.idle": "2024-04-17T07:30:00.715615Z",
     "shell.execute_reply": "2024-04-17T07:30:00.715445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"ho1gs9\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
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       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"name\":\"City miles per gallon\",\n",
       "\"aesthetic\":\"x\"\n",
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       "\"name\":\"Highway miles per gallon\",\n",
       "\"aesthetic\":\"y\"\n",
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       "\"layers\":[{\n",
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       "\"metainfo_list\":[]\n",
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       "};\n",
       "           var plotContainer = document.getElementById(\"ho1gs9\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
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       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a543a0280>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Default plot\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    labs(x='City miles per gallon', y='Highway miles per gallon') + \\\n",
    "    ggtitle(\"Use labs()\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "0c33b33c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.716506Z",
     "iopub.status.busy": "2024-04-17T07:30:00.716423Z",
     "iopub.status.idle": "2024-04-17T07:30:00.722121Z",
     "shell.execute_reply": "2024-04-17T07:30:00.721951Z"
    }
   },
   "outputs": [
    {
     "data": {
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    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point()\n",
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    "    ggtitle(\"Default plot\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    xlab('City miles per gallon') + \\\n",
    "    ylab('Highway miles per gallon') + \\\n",
    "    ggtitle(\"Use xlab() and ylab()\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
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    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='fl'))\n",
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    "    ggtitle(\"Default plot\")\n",
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       "\"cty\":[18.0,21.0,20.0,21.0,16.0,18.0,18.0,18.0,16.0,20.0,19.0,15.0,17.0,17.0,15.0,15.0,17.0,16.0,14.0,11.0,14.0,13.0,12.0,16.0,15.0,16.0,15.0,15.0,14.0,11.0,11.0,14.0,19.0,22.0,18.0,18.0,17.0,18.0,17.0,16.0,16.0,17.0,17.0,11.0,15.0,15.0,16.0,16.0,15.0,14.0,13.0,14.0,14.0,14.0,9.0,11.0,11.0,13.0,13.0,9.0,13.0,11.0,13.0,11.0,12.0,9.0,13.0,13.0,12.0,9.0,11.0,11.0,13.0,11.0,11.0,11.0,12.0,14.0,15.0,14.0,13.0,13.0,13.0,14.0,14.0,13.0,13.0,13.0,11.0,13.0,18.0,18.0,17.0,16.0,15.0,15.0,15.0,15.0,14.0,28.0,24.0,25.0,23.0,24.0,26.0,25.0,24.0,21.0,18.0,18.0,21.0,21.0,18.0,18.0,19.0,19.0,19.0,20.0,20.0,17.0,16.0,17.0,17.0,15.0,15.0,14.0,9.0,14.0,13.0,11.0,11.0,12.0,12.0,11.0,11.0,11.0,12.0,14.0,13.0,13.0,13.0,21.0,19.0,23.0,23.0,19.0,19.0,18.0,19.0,19.0,14.0,15.0,14.0,12.0,18.0,16.0,17.0,18.0,16.0,18.0,18.0,20.0,19.0,20.0,18.0,21.0,19.0,19.0,19.0,20.0,20.0,19.0,20.0,15.0,16.0,15.0,15.0,16.0,14.0,21.0,21.0,21.0,21.0,18.0,18.0,19.0,21.0,21.0,21.0,22.0,18.0,18.0,18.0,24.0,24.0,26.0,28.0,26.0,11.0,13.0,15.0,16.0,17.0,15.0,15.0,15.0,16.0,21.0,19.0,21.0,22.0,17.0,33.0,21.0,19.0,22.0,21.0,21.0,21.0,16.0,17.0,35.0,29.0,21.0,19.0,20.0,20.0,21.0,18.0,19.0,21.0,16.0,18.0,17.0],\n",
       "\"hwy\":[29.0,29.0,31.0,30.0,26.0,26.0,27.0,26.0,25.0,28.0,27.0,25.0,25.0,25.0,25.0,24.0,25.0,23.0,20.0,15.0,20.0,17.0,17.0,26.0,23.0,26.0,25.0,24.0,19.0,14.0,15.0,17.0,27.0,30.0,26.0,29.0,26.0,24.0,24.0,22.0,22.0,24.0,24.0,17.0,22.0,21.0,23.0,23.0,19.0,18.0,17.0,17.0,19.0,19.0,12.0,17.0,15.0,17.0,17.0,12.0,17.0,16.0,18.0,15.0,16.0,12.0,17.0,17.0,16.0,12.0,15.0,16.0,17.0,15.0,17.0,17.0,18.0,17.0,19.0,17.0,19.0,19.0,17.0,17.0,17.0,16.0,16.0,17.0,15.0,17.0,26.0,25.0,26.0,24.0,21.0,22.0,23.0,22.0,20.0,33.0,32.0,32.0,29.0,32.0,34.0,36.0,36.0,29.0,26.0,27.0,30.0,31.0,26.0,26.0,28.0,26.0,29.0,28.0,27.0,24.0,24.0,24.0,22.0,19.0,20.0,17.0,12.0,19.0,18.0,14.0,15.0,18.0,18.0,15.0,17.0,16.0,18.0,17.0,19.0,19.0,17.0,29.0,27.0,31.0,32.0,27.0,26.0,26.0,25.0,25.0,17.0,17.0,20.0,18.0,26.0,26.0,27.0,28.0,25.0,25.0,24.0,27.0,25.0,26.0,23.0,26.0,26.0,26.0,26.0,25.0,27.0,25.0,27.0,20.0,20.0,19.0,17.0,20.0,17.0,29.0,27.0,31.0,31.0,26.0,26.0,28.0,27.0,29.0,31.0,31.0,26.0,26.0,27.0,30.0,33.0,35.0,37.0,35.0,15.0,18.0,20.0,20.0,22.0,17.0,19.0,18.0,20.0,29.0,26.0,29.0,29.0,24.0,44.0,29.0,26.0,29.0,29.0,29.0,29.0,23.0,24.0,44.0,41.0,29.0,26.0,28.0,29.0,29.0,29.0,28.0,29.0,26.0,26.0,26.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"cty\",\n",
       "\"y\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Use guide_colorbar()\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"color\",\n",
       "\"guide\":{\n",
       "\"name\":\"colorbar\",\n",
       "\"barwidth\":10.0,\n",
       "\"barheight\":200.0,\n",
       "\"nbin\":40.0\n",
       "},\n",
       "\"scale_mapper_kind\":\"color_gradient\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"point\",\n",
       "\"mapping\":{\n",
       "\"color\":\"hwy\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"aqwChf\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a543eed10>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes('cty', 'hwy')) + \\\n",
    "    geom_point(aes(color='hwy'))\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Default plot\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_color_gradient(\n",
    "        guide=guide_colorbar(nbin=40, barwidth=10, barheight=200)\n",
    "    ) + \\\n",
    "    ggtitle(\"Use guide_colorbar()\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "1c43a537",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.736515Z",
     "iopub.status.busy": "2024-04-17T07:30:00.736428Z",
     "iopub.status.idle": "2024-04-17T07:30:00.743808Z",
     "shell.execute_reply": "2024-04-17T07:30:00.743630Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"kPjWlA\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":2.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"fl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_position\":\"bottom\"\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Default plot\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"manufacturer\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[18.0,11.0,2.0,5.0,1.0,32.0,5.0,24.0,1.0,6.0,2.0,1.0,14.0,1.0,5.0,1.0,1.0,2.0,2.0,2.0,1.0,4.0,8.0,5.0,3.0,2.0,10.0,4.0,34.0,13.0,11.0,3.0],\n",
       "\"fl\":[\"p\",\"r\",\"e\",\"p\",\"d\",\"r\",\"e\",\"r\",\"p\",\"r\",\"p\",\"c\",\"r\",\"d\",\"r\",\"e\",\"p\",\"p\",\"r\",\"r\",\"p\",\"r\",\"r\",\"p\",\"r\",\"p\",\"r\",\"p\",\"r\",\"r\",\"p\",\"d\"],\n",
       "\"manufacturer\":[\"audi\",\"chevrolet\",\"chevrolet\",\"chevrolet\",\"chevrolet\",\"dodge\",\"dodge\",\"ford\",\"ford\",\"honda\",\"honda\",\"honda\",\"hyundai\",\"jeep\",\"jeep\",\"jeep\",\"jeep\",\"land rover\",\"land rover\",\"lincoln\",\"lincoln\",\"mercury\",\"nissan\",\"nissan\",\"pontiac\",\"pontiac\",\"subaru\",\"subaru\",\"toyota\",\"volkswagen\",\"volkswagen\",\"volkswagen\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "},{\n",
       "\"data\":{\n",
       "},\n",
       "\"mapping\":{\n",
       "\"x\":\"fl\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"legend_position\":\"bottom\"\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Use guide_legend()\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"fill\",\n",
       "\"guide\":{\n",
       "\"name\":\"legend\",\n",
       "\"nrow\":3.0\n",
       "},\n",
       "\"discrete\":true\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"bar\",\n",
       "\"mapping\":{\n",
       "\"fill\":\"manufacturer\"\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"data\":{\n",
       "\"..count..\":[18.0,11.0,2.0,5.0,1.0,32.0,5.0,24.0,1.0,6.0,2.0,1.0,14.0,1.0,5.0,1.0,1.0,2.0,2.0,2.0,1.0,4.0,8.0,5.0,3.0,2.0,10.0,4.0,34.0,13.0,11.0,3.0],\n",
       "\"fl\":[\"p\",\"r\",\"e\",\"p\",\"d\",\"r\",\"e\",\"r\",\"p\",\"r\",\"p\",\"c\",\"r\",\"d\",\"r\",\"e\",\"p\",\"p\",\"r\",\"r\",\"p\",\"r\",\"r\",\"p\",\"r\",\"p\",\"r\",\"p\",\"r\",\"r\",\"p\",\"d\"],\n",
       "\"manufacturer\":[\"audi\",\"chevrolet\",\"chevrolet\",\"chevrolet\",\"chevrolet\",\"dodge\",\"dodge\",\"ford\",\"ford\",\"honda\",\"honda\",\"honda\",\"hyundai\",\"jeep\",\"jeep\",\"jeep\",\"jeep\",\"land rover\",\"land rover\",\"lincoln\",\"lincoln\",\"mercury\",\"nissan\",\"nissan\",\"pontiac\",\"pontiac\",\"subaru\",\"subaru\",\"toyota\",\"volkswagen\",\"volkswagen\",\"volkswagen\"]\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"kPjWlA\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54405d50>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot(mpg_df, aes(x='fl')) + \\\n",
    "    geom_bar(aes(fill='manufacturer')) + \\\n",
    "    theme(legend_position='bottom')\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Default plot\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_fill_discrete(guide=guide_legend(nrow=3)) + \\\n",
    "    ggtitle(\"Use guide_legend()\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6bfc21",
   "metadata": {},
   "source": [
    "## Zooming <a class=\"anchor\" id=\"zooming\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b18cb051",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-17T07:30:00.744637Z",
     "iopub.status.busy": "2024-04-17T07:30:00.744560Z",
     "iopub.status.idle": "2024-04-17T07:30:00.780758Z",
     "shell.execute_reply": "2024-04-17T07:30:00.780437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"c60SUH\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"kind\":\"subplots\",\n",
       "\"layout\":{\n",
       "\"ncol\":3.0,\n",
       "\"nrow\":1.0,\n",
       "\"name\":\"grid\"\n",
       "},\n",
       "\"figures\":[{\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"theme\":{\n",
       "\"name\":\"classic\"\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Default plot\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[],\n",
       "\"layers\":[{\n",
       "\"geom\":\"map\",\n",
       "\"data\":{\n",
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       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "\"geodataframe\":{\n",
       "\"geometry\":\"geometry\"\n",
       "}\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "}]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"c60SUH\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.subplots.SupPlotsSpec at 0x702a54e756f0>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_common = ggplot() + \\\n",
    "    geom_map(data=states_df) + \\\n",
    "    theme_classic()\n",
    "p_bunch_1 = p_common + \\\n",
    "    ggtitle(\"Default plot\")\n",
    "p_bunch_2 = p_common + \\\n",
    "    scale_x_continuous(limits=[-92, -82]) + \\\n",
    "    ylim(36, 43) + \\\n",
    "    ggtitle(\"Zoom with clipping\")\n",
    "p_bunch_3 = p_common + \\\n",
    "    coord_map(xlim=[-92, -82], ylim=[36, 43]) + \\\n",
    "    ggtitle(\"Zoom without clipping\")\n",
    "\n",
    "gggrid([p_bunch_1, p_bunch_2, p_bunch_3])"
   ]
  }
 ],
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