{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "febcb23a",
   "metadata": {},
   "source": [
    "# Tweaking plots\n",
    "\n",
    "In this section we cover a some points about adjusting the appearance of plots\n",
    "\n",
    "## <tt>Matplotlib</tt>\n",
    "\n",
    "<tt>Seaborn</tt> is designed to produce nice looking plots without us having to manually set many options\n",
    "\n",
    "If we want to manually set something like the axis labels or axis range, many of the functions for doing this are from the <tt>Matplotlib</tt> library which contains a lot of lower level plotting functions (things that produce or edit bits of plots, rather than producing a whole nice figure in one step). \n",
    "\n",
    "In the olden days, people made plots just with <tt>Matplotlib</tt> and had to write a lot more code to achieve a nice looking plot.\n",
    "\n",
    "### Set up Python libraries\n",
    "\n",
    "As usual, run the code cell below to import the relevant Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d8ecdb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up Python libraries - you need to run this but you don't need to change it\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas \n",
    "import seaborn as sns\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a768c21",
   "metadata": {},
   "source": [
    "### Import the data\n",
    "\n",
    "We'll use the Titanic data again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a3741143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                                               Name  \\\n",
       "0           0       3                            Braund, Mr. Owen Harris   \n",
       "1           1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2           1       3                             Heikkinen, Miss. Laina   \n",
       "3           1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4           0       3                           Allen, Mr. William Henry   \n",
       "..        ...     ...                                                ...   \n",
       "886         0       2                              Montvila, Rev. Juozas   \n",
       "887         1       1                       Graham, Miss. Margaret Edith   \n",
       "888         0       3           Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889         1       1                              Behr, Mr. Karl Howell   \n",
       "890         0       3                                Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1    female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2    female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3    female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4      male  35.0      0      0            373450   8.0500   NaN        S  \n",
       "..      ...   ...    ...    ...               ...      ...   ...      ...  \n",
       "886    male  27.0      0      0            211536  13.0000   NaN        S  \n",
       "887  female  19.0      0      0            112053  30.0000   B42        S  \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "889    male  26.0      0      0            111369  30.0000  C148        C  \n",
       "890    male  32.0      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic = pandas.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/data/titanic_2.csv')\n",
    "display(titanic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dce1064",
   "metadata": {},
   "source": [
    "### Subplot\n",
    "\n",
    "Say for some reason we want to plot a histogram of age separately for men and women.\n",
    "\n",
    "We might like to show these plots next to eachother as panels of a larger figure\n",
    "\n",
    "This is achieved using the function <tt>plt.subplot</tt> which creates... a figure with multiple panels or subplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "43499bf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='male'], x='Age', color='b', bins=range(0,80,5))\n",
    "plt.legend(['men'])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='female'], x='Age', color='r', bins=range(0,80,5))\n",
    "plt.legend(['women'])\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.5) # shift the plots sideways so they don't overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb90592",
   "metadata": {},
   "source": [
    "The arguments of <tt>plt.subplot</tt> are the number of rows and columns to be created in the figure, and then the location in which to place the next plot.\n",
    "\n",
    "In the example above we have one row and two columns, hence we call <tt>plt.subplot(1 [rows],2 [columns],1 [location for next plot])</tt> for the first plot.\n",
    "\n",
    "Can you change the code in the block below to organize the panels one above the other, rather than next to each other?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1b0a73a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1) # edit this line!\n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='male'], x='Age', color='b', bins=range(0,80,5))\n",
    "plt.legend(['men'])\n",
    "\n",
    "plt.subplot(1,2,2) # edit this line!\n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='female'], x='Age', color='r', bins=range(0,80,5))\n",
    "plt.legend(['women'])\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.5) # shift the plots sideways so they don't overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04fc80d",
   "metadata": {},
   "source": [
    "Ah, it's actually a bit easier to compare the distributions when they are arranged vertically! Good choice!\n",
    "\n",
    "Can you edit the code block below to produce three stacked subplots showing the age distribution in each class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "af37d19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Edit this code block!\n",
    "\n",
    "plt.subplot(1,2,1) \n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='male'], x='Age', color='b', bins=range(0,80,5))\n",
    "plt.legend(['men'])\n",
    "\n",
    "plt.subplot(1,2,2) \n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='female'], x='Age', color='r', bins=range(0,80,5))\n",
    "plt.legend(['women'])\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.5) # shift the plots sideways so they don't overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384fc90e",
   "metadata": {},
   "source": [
    "### Adjust axes\n",
    "\n",
    "It is often easier to compare across plots if the axis ranges are the same. \n",
    "\n",
    "Seaborn will automatically adjust the axes to fit the range of the data in each plot, which normally means the axis ranges don't match across subplots.\n",
    "\n",
    "We can set the axis range using the functions <tt>plt.ylim</tt> (to set the limits in y) and <tt>plt.xlim</tt> (to set the limits in x)\n",
    "\n",
    "Let's remake our two side-by-side subbplots of age of men and women, and this time set the y axis to have the same range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "472f4581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W+pPiP3oulQCcKNp9JdqSrU9F80MNBBsXMk1DWVnp/IcGAEg6BBsXMgxDHo+pF/60URVVzR/9n9c9W1cO7yXF944bAACaRbBxsYqqw9q979RXCA7IzU5vhWoAAIg93qsAAACu4egem7Vr1+qmm2466bpu3bpp9erV2rRpk2bNmqXPPvtMWVlZGjt2rMaPH9/KlQJIVvQpILE4GmzOO+88ffDBB0HLtmzZojvuuEN33nmnqqqqNG7cOF166aWaOXOmNmzYoJkzZyorK0uFhYUOVQ0gmdCngMTiaLDx+XzKzc1t+vnIkSN69NFHNWrUKF1zzTV65pln5PP5VFRUJK/Xq169emnnzp1auHAhDQNAq6BPAYklro6xefHFF7V7927dd999kqSysjLl5+fL6z2ev4YNG6bt27ersrLSqTIBJDH6FBDf4uZTUfX19VqwYIFuvvlmderUSZK0Z88e5eXlBY0LrNu1a5dyciK/7o3Xe+pMFzj/S6KeByZQt2GEeJI+4/iXUMYHxni9puL4MiunlOjbN1zJNt9Yas0+5eYeFa5km6+UfHOO5nzjJti8/vrrqq+v19ixY5uW1dXVyefzBY1LSUmRdLTBRMo0DWVnt212XGZmYl9Xx+Mx5fU2f2ZNz7EzPppmiOOPPfES/fFJ9PrDlWzzjYXW6lPJ0qPClWzzlZJvztGYb9wEm5UrV2rUqFHKzs5uWpaamqqGhoagcYFGkZ4e+blXLMtWdfWpT1zn8ZjKzExTdXWt/P7EO5V1oH6/31JjY/PXQvEfu8iaZYU4/thjkuiPT6LWH65Q5puZmZY0rwxborX6lNt7VLiSbb5S8s05mn0qLoLN/v379fHHH2vChAlBy7t06aLy8vKgZYGfO3fu3KL7DOXaG0eDQeI9oWw78NWWbYdwMTH7+JdQxgfGNDZaCf0Hl6jbN1LJNt9oa+0+5eYeFalkm6+UfHOOxnzj4iXaRx99JMMwdOGFFwYtz8/P1/r164OuwFpaWqqePXu26PgaAAgXfQpIDHERbDZv3qwzzjhDaWnB760VFhaqpqZGM2bM0NatW1VSUqLFixef8IoJAGKNPgUkhrgINvv27VNWVtYJy3NycrRo0SJt375dBQUFmj9/vqZNm6aCgoLWLxJAUqNPAYkhLo6xKSoqOuW6gQMHatmyZa1XDACcBH0KSAxxsccGAAAgGgg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANQg2AADANbxOF4DE5fGEnott25Zl2TGsBgAAgg0ikJHWRpZlq337tJB/x++3dODAYcINACCmCDYIW2qKV6Zp6IU3N6pi/+Fmx+dmp2vs6P4yDEMSwQYAEDsEG0SsoqpWu/cdcroMAACacPAwAABwjbgINitXrtTo0aM1YMAAXXHFFXrzzTeb1m3atEljxozR4MGDNWLECBUXFztYKYBkRZ8CEoPjweb111/X/fffr2uvvVarVq3S6NGjNXXqVH388ceqqqrSuHHj1KNHD61YsUKTJ0/WvHnztGLFCqfLBpBE6FNA4nD0GBvbtjVv3jzdfPPNuvnmmyVJkyZN0kcffaS///3v+vvf/y6fz6eioiJ5vV716tVLO3fu1MKFC1VYWOhk6QCSBH0KSCyO7rH54osv9PXXX+uqq64KWl5cXKwJEyaorKxM+fn58nqP569hw4Zp+/btqqysbO1yASQh+hSQWBzdY7Njxw5J0uHDhzV+/Hht3LhR3bp101133aWRI0dqz549ysvLC/qdTp06SZJ27dqlnJyciO/b6z11pguceC6cE9DFk0DdhmEc+4h1M4zjX0IZHxhjGOGN93pNhVJOrCX69g1Xss032pzqU27uUeFKtvlKyTfnaM7X0WBTU1MjSZo+fbruvvtu3XvvvXrrrbc0ceJEPffcc6qrq5PP5wv6nZSUFElSfX19xPdrmoays9s2Oy4zM/QT0MUjj8eU1+tpfpx59IlkmiGODzwBwxwfb49nvNUTa8k232hxok8lS48KV7LNV0q+OUdjvo4GmzZt2kiSxo8fr4KCAklSv379tHHjRj333HNKTU1VQ0ND0O8EGkV6enrE92tZtqqrT31iOY/HVGZmmqqra+X3WxHfj1MC9fv9lhob/c2O91tH52hZIY4/9pj4wxwfL49nom/fcIUy38zMtKR5ZRguJ/qU23tUuJJtvlLyzTmafcrRYNOlSxdJOmE3bu/evbVmzRp17dpV5eXlQesCP3fu3LlF993Y2PwT5WgwSLwnlG0Hvtqy7RDO9Gsf/xLK+MAY2w5vfGOjFVd/oIm6fSOVbPONFqf6lJt7VKSSbb5S8s05GvN19CVa//791bZtW33yySdBy7ds2aIzzzxT+fn5Wr9+vfz+43sFSktL1bNnzxYdXwMAoaJPAYnF0WCTmpqq2267Tb/73e+0atUqffnll3r66af14Ycfaty4cSosLFRNTY1mzJihrVu3qqSkRIsXL9aECROcLBtAEqFPAYnF8WtFTZw4UWlpaZozZ4727t2rXr166cknn9TQoUMlSYsWLdKsWbNUUFCg3NxcTZs2rel9bgBoDfQpIHE4Hmwkady4cRo3btxJ1w0cOFDLli1r5YoAIBh9CkgMfAwCAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4huPB5uuvv1afPn1O+Ld8+XJJ0qZNmzRmzBgNHjxYI0aMUHFxscMVA0g29CkgcXidLuDzzz9XSkqK3nnnHRmG0bS8Xbt2qqqq0rhx43TppZdq5syZ2rBhg2bOnKmsrCwVFhY6WDWAZEKfAhKH48Fmy5Yt6tmzpzp16nTCusWLF8vn86moqEher1e9evXSzp07tXDhQhoGgFZDnwISh+PB5vPPP1fv3r1Puq6srEz5+fnyeo+XOWzYMD3zzDOqrKxUTk5OxPfr9Z76XTiPxwz6Gg8Mw5BpGs0PlJrGGYYR9Ory1Dd+/Eso4wNjDCO88V6vqVDKibV43L6xlGzzjQUn+lSi9ahYSrb5Ssk352jO1/Fgs2XLFuXm5uqGG27Qjh071L17d02cOFEXXXSR9uzZo7y8vKDxgVdMu3btirhhmKah7Oy2zY7LzEyL6PZjwbLskINNgMdjyuv1ND/OPPpEMs0QxweegGGOj6fHU4q/emIt2eYbTa3dpxKxR7WGZJuvlHxzjsZ8HQ02DQ0N2rFjh9LS0jRt2jSlp6frD3/4g26//XY999xzqqurk8/nC/qdlJQUSVJ9fX3E92tZtqqrD59yvcdjKjMzTdXVtfL7rYjvJ1oC9Sx5c6MqqmqbHX/2mVm6cngv+S1LjY3+Zsf7raNztEIdf+wxCfn2j42Pt8czXuqJtVDmm5mZljSvDMPlRJ9KtB4Va8k2Xyn55hzNPuVosPH5fFq3bp28Xm9TYzj33HO1bds2FRcXKzU1VQ0NDUG/E2gU6enpLbrvxsbmnyh+vxXSuFiz7aNfy/cf1u59h5odn9M+ten37MAvf+sdHP8SyvjAmFBvPzCmsdGKqz/QeNm+rSXZ5hstTvWpROpRrSXZ5isl35yjMV/HX6Klp6ef8GonLy9Pe/fuVZcuXVReXh60LvBz586dW61GAMmNPgUkDkeDzebNm3XeeeeprKwsaPlnn32m3r17Kz8/X+vXr5fff/ztjtLSUvXs2bNFBw4DQKjoU0BicTTY5OXl6eyzz9bMmTNVVlambdu26dFHH9WGDRt05513qrCwUDU1NZoxY4a2bt2qkpISLV68WBMmTHCybABJhD4FJBZHj7ExTVMLFizQ448/rilTpqi6ulr9+/fXc889pz59+kiSFi1apFmzZqmgoEC5ubmaNm2aCgoKnCwbQBKhTwGJxfGPe3fo0EGPPPLIKdcPHDhQy5Yta8WKACAYfQpIHI4fPAwAABAtBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaBBsAAOAaXqcLQPLweELP0bZty7LsGFYDAHAjgg1iLiOtjSzLVvv2aSH/jt9v6cCBw4QbAEBYCDaIudQUr0zT0AtvblTF/sPNjs/NTtfY0f1lGIYkgg0AIHQEG7Saiqpa7d53yOkyAAAuxsHDAADANSIKNuvWrdOhQyd/5V1dXa033nijRUUBQEvQo4DkFVGwuemmm7Rt27aTrtu4caPuu+++FhUFAC1BjwKSV8jH2EyfPl27d++WdPSjuEVFRcrIyDhh3I4dO9SxY8foVQgAIaBHAZDC2GPzgx/8QLZty7aPf0ol8HPgn2maGjx4sB599NGYFAsAp0KPAiCFscdm5MiRGjlypCRp7NixKioqUq9evWJWGACEgx4FQIrw494vvPBCtOsAgKihRwHJK6JgU1tbqwULFujdd99VbW2tLMsKWm8Yht55552oFAgA4aJHAckromAza9YsrVixQhdeeKH69esn0+R0OADiBz0KSF4RBZs///nP+ulPf6o77rgj2vUAQIvRo4DkFVGwaWxs1MCBA6NdCwBEBT0q/pmmcex6cM3zeNjjhtBFFGyGDx+u9957T8OGDYt2PQDQYvSo+GaahrKz0mR6PCH/jm1ZIQchJLeIgs3o0aP1y1/+Uvv379egQYOUlpZ2wpif/OQnLa0NACJCj4pvhmHI9Hj0xZKXVF+xr9nxqZ1y1fPG62SaBBs0L6JgM2XKFEnSypUrtXLlyhPWG4ZB0wDgGHpUYqiv2Kfa3XuaHceeGoQjomCzevXqaNcBAFFDjwKSV0TBpmvXrtGuAwCihh4FJK+Igs38+fObHXP33XeHfbvbt2/X1VdfrQcffFBXX321JGnTpk2aNWuWPvvsM2VlZWns2LEaP3582LcNIHnQo4DkFfVgk5GRoU6dOoXdNI4cOaJ7771Xhw8fblpWVVWlcePG6dJLL9XMmTO1YcMGzZw5U1lZWSosLIykdABJgB4FJK+Igs3mzZtPWHb48GGtX79eRUVFevDBB8O+zSeffFJt27YNWvbKK6/I5/OpqKhIXq9XvXr10s6dO7Vw4UKaBoBTokcBySuiYHMy6enpuuiiizRp0iT9+te/1muvvRby765bt07Lli3TypUrNWLEiKblZWVlys/Pl9d7vMxhw4bpmWeeUWVlpXJyciKu1+s99QmfAieDipeTQgXqMIzQTmgVGGMYIX6awDj+JRa3H+l4r9dULD4MEW/bN9aSbb6nQo+KH+H2tECPMk3jWx8XN0n0bRyuaM43asEm4LTTTtO2bdtCHl9dXa1p06bpgQce0GmnnRa0bs+ePcrLywta1qlTJ0nSrl27Im4apmkoO7tts+MyM08894WTPB5TXm/zJ7RqeoKYIY4/dh0dM9Tx4d5+hONj/fjH2/aNtWSb76nQo+KHxxNaUPEcO39NRkZqrEuKO4m+jcMVjflGLdjYtq3du3dr4cKFYX0ioaioSIMHD9ZVV111wrq6ujr5fL6gZSkpKZKk+vr6iGu1LFvV1YdPud7jMZWZmabq6lr5/dYpx7WWQD1+v6XGRn+z4wM1+60Qxx+78rEV6vhwbz/C8bF6/ONt+8ZaKPPNzExz/StDelT8ON7TbDU2Nl+/37IlSTU1dTpypPke4gaJvo3DFc0+FVGw6du37yl3H9q2rV//+tch3c7KlStVVlamP/7xjyddn5qaqoaGhqBlgWaRnp4eRsUnCumPyW+FNC7WbDvw1ZYd+OFbx9tNvxfKeNnHv8Ti9iMd39hoxfQPOl62b2tJpvnSo+JbuD0t0KMsK7Qg5CaJuo0jFY35RhRsJk2adNKmkZGRoREjRqhHjx4h3c6KFStUWVkZ9J61JP3yl79UcXGxTj/9dJWXlwetC/zcuXPnSEoHkAToUUDyiijYTJ48OSp3/vjjj6uuri5o2ahRo3TPPfdo9OjReuONN/Tyyy/L7/fLc+xiaaWlperZs2eLDsoD4G70KCB5RXyMTUNDg0pKSrR27VpVV1crOztbQ4YMUUFBQdN7zM051SuanJwcde3aVYWFhVq0aJFmzJih2267TZ9++qkWL16smTNnRlo2gCRBjwKSU0TBprq6WjfddJM2b96s008/Xbm5udq+fbtWrVqlF198UUuXLlW7du1aXFxOTo4WLVqkWbNmqaCgQLm5uZo2bZoKCgpafNsA3IseBSSviILNb3/7W+3Zs0dLlizRkCFDmpaXlZXpnnvu0bx58/TAAw9EVNDnn38e9PPAgQO1bNmyiG4LQHKiRwHJK6LPd65evVpTpkwJahiSNGTIEN1zzz3685//HJXiACAS9CggeUUUbA4dOqQzzjjjpOvOOOMMHThwoCU1AUCL0KOA5BVRsDnrrLP07rvvnnTd6tWr1b179xYVBQAtQY8CkldEx9iMHz9eU6dOVUNDg6666ip17NhR+/bt0x//+EctX75cRUVFUS4TAEJHjwKSV0TBZvTo0dqxY4cWLFig5cuXNy1v06aNJk2apGuvvTZqBQJAuOhRQPKKKNgcPnxYEydO1JgxY7RhwwYdPHhQu3fv1rXXXqv27dtHu0YACAs9CkheYR1js2nTJv3kJz/R888/L0nKzMzUxRdfrIsvvlhz587VDTfcENZVcwEgmuhRAEIONl999ZVuueUWHTx4UL179w5a5/P5dP/99+vQoUO64YYbtGfPnqgXCgDfhh4FQAoj2Dz77LPKzs7Wa6+9plGjRgWtS0tL05gxY7RixQqlp6drwYIFUS8UAL4NPQqAFEawKS0t1W233aasrKxTjsnJydG4ceNUWloajdoAIGT0KABSGAcPV1RUhHTuh7y8PHbzAmh19ChnmaYhwzBCGuvxRHQKNSAkIQebDh06qLy8vNlx+/fv/9ZXTAAQC/Qo55imoeysNJkeT1i/F2IOAsIScrDJz89XSUmJrrjiim8dt3LlSvXr16/FhQFAOOhRzjEMQ6bHoy+WvKT6in3Njm93dm91u/JySSQbRF/I+wPHjh2rtWvX6rHHHlN9ff0J6xsaGvRf//Vfev/993XjjTdGtUgAaA49ynn1FftUu3tPs/8aqg44XSpcLOQ9NgMGDNB9992nRx55RK+//rq+853vqFu3bvL7/dq1a5fWrl2rqqoq/cd//IcuuuiiWNYMACegRwGQwjzz8I033qi+ffuquLhYq1evbnpV1LZtWw0fPly33nqrBg0aFJNCAaA59CgAYV9S4YILLtAFF1wgSaqqqpJpmpyiHEDcoEcByS2ia0UFZGdnR6sOAIg6ehSQfDiZAAAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcI0WfdwbAIB4Fc4VxyXJtm1Zlh3DitAaCDYAANeJ5Irjlt+vqgO1hJsER7ABALhOuFccT8ntqLPGXH9sDw/BJpERbAAArhW44jiSBwcPAwAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA13A82FRWVurnP/+5hg0bpvPOO0933HGHtm7d2rR+06ZNGjNmjAYPHqwRI0aouLjYwWoBJCP6FJA4HA82d911l7766istXLhQr776qlJTU3XLLbeotrZWVVVVGjdunHr06KEVK1Zo8uTJmjdvnlasWOF02QCSCH0KSByOnqCvqqpK3bp101133aWzzz5bkjRx4kT9+Mc/1j//+U+VlpbK5/OpqKhIXq9XvXr10s6dO7Vw4UIVFhY6WTqAJEGfAhKLo3tssrOzNXv27KZmsW/fPhUXF6tLly7q3bu3ysrKlJ+fL6/3eP4aNmyYtm/frsrKSqfKBpBE6FNAYombSyo8+OCDeuWVV+Tz+fT0008rPT1de/bsUV5eXtC4Tp06SZJ27dqlnJyciO/P6z11pvN4zKCvTgvUYRihXak2MMYwFNqVbY3jX2Jx+5GO9/k8sqzQtoFl2bLt0K7vEm/bN9aSbb6x1Jp9yt09KvBNeD3q6GMS2n9bphnoO+H1Ta/XVBgXBI+ZeNvGsRbN+cZNsLn55pt17bXX6qWXXtKkSZO0dOlS1dXVyefzBY1LSUmRJNXX10d8X6ZpKDu7bbPjMjPTIr6PWPB4THm9zV+ptukJYoY43jw63gx1fLi3H+b49hkpsixbGRmpzY4NsCy7qZGFKt62b6wl23xjobX6VOL2KONbA1mA6Tn6t+oxQxuf0i5DtmUpPT0lZjV5jtUUb49pvNUTa9GYb9wEm969e0uSHnroIW3YsEFLlixRamqqGhoagsYFGkV6enrE92VZtqqrD59yvcdjKjMzTdXVtfL7rYjvJ1oC9fj9lhob/c2OD9Tst0Icbx0db4U6PtzbD3N8G68p0zS05M2NqqiqbXZ8bnaaxlzeP+TtFW/bN9ZCmW9mZlrSvDJsidbqU4nbo2w1NjZfj+U/unfVb4U2XikpMkxTO158WXUhXKlbktqd3Vtdr/hhyPfhP1ZTvD2m8VJPrEWzTzkabCorK1VaWqrLL79cHs/RV/KmaapXr14qLy9Xly5dVF5eHvQ7gZ87d+7covsO7YluhfZHF2OBd1hsO7S3WwJjbFuhvT1jH/8Si9uPdHx5Va12V9SEPL6x0QqrAcTL9m0tyTbfaHGqT7m7RwW+Ca9H1e3bp8O7dodUky/wFmCYfSfcPhJr8bKNW0s05uvoS7Ty8nL97Gc/09///vemZUeOHNHGjRvVq1cv5efna/369fL7j7/KLy0tVc+ePVt0fA0AhIo+BSQWR4NN3759NXz4cM2cOVNlZWXasmWLpk+frurqat1yyy0qLCxUTU2NZsyYoa1bt6qkpESLFy/WhAkTnCwbQBKhTwGJxdFgYxiG5s6dq2HDhmnKlCm65pprdPDgQb344os6/fTTlZOTo0WLFmn79u0qKCjQ/PnzNW3aNBUUFDhZNoAkQp8CEovjBw+3a9dORUVFKioqOun6gQMHatmyZa1bFAD8G/oUkDj4GAQAAHANgg0AAHANgg0AAHANgg0AAHANgg0AAHANgg0AAHANxz/uDURLqNc64ppIAE4lnP5g27YsK4RLQqBVEWyQ8DLS2siybLVvH/pVYS3LlmGEdzVwAO7lzWgr27LC6yN+v6oO1BJu4gzBBgkvNcUr0zT0wpsbVbH/1FdEDujUIV1jLu8v0yTYADjKk5omwzS1/cWXVVde0ez4lNyOOmvM9cdeIBFs4gnBBq5RUVWr3fsONTuOPTUATqW+Yp9qd+9xugy0AAcbAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1/A6XUA883hM2XZoY23blmWFOBgAAMQEweYkDMOQZdnKzEwL+Xf8fksHDhwm3AAA4CCCzUmYpiHTNLTkzY0q33+42fG52ekaO7q/DMOQRLABAMApBJtvUVFVq937DjldBgAACBEHDwMAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANdwPNgcOHBAv/jFL3TxxRfr/PPP1/XXX6+ysrKm9Zs2bdKYMWM0ePBgjRgxQsXFxQ5WCyAZ0aeAxOF4sJk6dao++eQTzZ49W6+++qrOOeccjR8/Xtu2bVNVVZXGjRunHj16aMWKFZo8ebLmzZunFStWOF02gCRCnwISh6Mn6Nu5c6c+/PBDvfTSSzr//PMlSTNmzNB7772nVatWKTU1VT6fT0VFRfJ6verVq5d27typhQsXqrCw0MnSASQJ+hSQWBzdY5Odna1nn31W5557btMywzBk27YOHjyosrIy5efny+s9nr+GDRum7du3q7Ky0omSASQZ+hSQWBzdY5OZmanvf//7QcvefPNNffnllxo+fLjmzJmjvLy8oPWdOnWSJO3atUs5OTkR37fXe+pMZ5rG0W8MHbv+07cLjPF6TYUwPGwej9l0P+HUY4RYv45PNya3H2/jA/M9+hwI7U/AsmzZoV7qPc4Enj+BrwiPU33q23pUvG3T8HtU4Jvw/mYD9xGKcO8j/PGt0/fjZRvHWjTnG1fXilq/fr3uv/9+XXLJJRo5cqQeffRR+Xy+oDEpKSmSpPr6+ojvxzQNZWe3bXacxzTl9XqaH3dsQ4RzNfBIeDzh1RNy/ebR8WaY8w338YmX8e3bpsiybKWnpzQ7NsCy7OOBN0HF+vmZLFqjT4Xao+Jtm3o8xrcGsgDTc/RvyWOGON4Mb3xE9xHmeM+x8bHeBvG2jWMtGvONm2Dzzjvv6N5779WgQYM0e/ZsSVJqaqoaGhqCxgUaRXp6esT3ZVm2qqtPfdXuNm08yshIld+y1Njob/b2/H5LklRdXdv0fTR5PKYyM9Pk94dXT8j1W0fHW2HON9zHJ17Gt2ljyjQNvfjmRpVX1TY7Pjc7TWMu7x+z7RtrgefPt9WfmZmWNK8MW6K1+lRzPSqUbdqajvcoW42Nzddj+Y/u/fRbIY63whsf0X2EOd5/bHys+368bONYi2afiotgs2TJEs2aNUuXXXaZHn/88aZXP126dFF5eXnQ2MDPnTt3btF9ftsTt+mBsxXS2w+BMY2NVkyegIESbDu0t0MCY+wQ65d9/Essbj/exgfmW36gVrsqakK+/Vht39ZyNBgnbv1Oa+0+Fdp/rvGxTcPvUYFvwvubDdxHODWF3sfDHd86fSFetnFricZ8HX+JtnTpUj300EO68cYbNXfu3KBduvn5+Vq/fr38/uOvwktLS9WzZ88WHV8DAOGgTwGJw9Fgs337dj3yyCO67LLLNGHCBFVWVqqiokIVFRX65ptvVFhYqJqaGs2YMUNbt25VSUmJFi9erAkTJjhZNoAkQp8CEoujb0W99dZbOnLkiN5++229/fbbQesKCgr02GOPadGiRZo1a5YKCgqUm5uradOmqaCgwKGKASQb+hSQWBwNNnfeeafuvPPObx0zcOBALVu2rJUqAoBg9CkgsTh+jA0AAEC0xMWnogAASEThnCbBtu2mj64jdgg2AACEyZvRVrZlqX370E8oZ/n9qjpQS7iJMYINAABh8qSmyTBNbX/xZdWVVzQ7PiW3o84ac/2xSzEQbGKJYAMAQITqK/apdvcep8vAv+HgYQAA4BrssQGABOXxmArxCgMcuIqkQbABgARjGIZsywrrSsgcuIpkQbABgARjmgYHrgKnQLABgATFgavAiTh4GAAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAaXVAAAoJV4PKHtTwh1HE5EsAEAIMa8GW1lW5batw/9iuy2ZR27cCnCQbABACDGPKlpYV2RPbVTrnreeJ1Mk2ATLoINAACtJNQrsrOnJnIEGwBIEuEct2HbtizLjmE1QGwQbADA5SI5vsPy+1V1oJZwg4RDsAEAlwv3+I6U3I46a8z1x94OIdggsRBsACBJhHp8B5DI+KA8AABwDfbYAAAQp0zTCPmgbw74PopgAwBAnAkc8J2RkRry73DA91EEGwAA4ownNVWGaWrHiy+rlgO+w0KwAQAgTtXt44DvcBFsgBgwTSOsM4fy3jgAREdcfSrqqaee0tixY4OWbdq0SWPGjNHgwYM1YsQIFRcXO1QdEBrTNJSVla4OHdqG/C8rK51rwiQI+hQQ3+Jmj83zzz+vJ554Qvn5+U3LqqqqNG7cOF166aWaOXOmNmzYoJkzZyorK0uFhYUOVgucmmEc/RTDC3/aqIqqw82Oz81O19jR/XlvPAHQp4D453iw2bt3r2bMmKH169erZ8+eQeteeeUV+Xw+FRUVyev1qlevXtq5c6cWLlxIw0Dcq6g6rN37DjldBqKAPgUkDsffivrf//1ftW/fXn/4wx80aNCgoHVlZWXKz8+X13s8fw0bNkzbt29XZWVla5cKIEnRp4DE4fgem5EjR2rkyJEnXbdnzx7l5eUFLevUqZMkadeuXcrJyYn4fr3eU2e6pmMdjNAuHR8Y4/WaisWV5gMnZzKM0A5IDYwxQqxfx6cbk9uPt/GRztfn88iymn8tEHj+hLu9Yv38CefKzgjmRJ+Kbo8KfBObnhZ+jwqvHv3bkFAPyg9/zvE1Ptw5x7qPxFo0+5Tjwebb1NXVyefzBS1LSUmRJNXX10d8u6ZpKDu7bbPjPKYpr9fT/LhjGyIzM/Qr50bC4wmvnpDrN4+ON8Ocb7iPT9yMD3O+7TNSZFl2WCfKCtQVT8+fWN9+sopFnwq9RxnfGoCabs9jhDXec2x8uM8Zjyc29QSCXKjjI7qPeBsf5pwj3WbxJhr1x3WwSU1NVUNDQ9CyQKNIT0+P+HYty1Z19akP6mzTxqOMjFT5LUuNjf5mb8/vtyRJ1dW1Td9Hk8djKjMzTX5/ePWEXL91dLwV5nzDfXziZnyY823jNWWahpa8uVEVVbXNjj/7zCxdObxX3D1/vu32MzPT2KMToVj0qdB7lK3GxuafM5b/6EHpoY73Hxsf6nPyeI+KTT2BUyGEOj6i+4i38WHOOdxtFm+i2afiOth06dJF5eXlQcsCP3fu3LlFt/1tT5SmB84+en6R5gTGNDZaMXlCBUqwbTuseuwQ6w98EMdWePMN9fbjbXyk8y2vqtXuippmx+e0Tw2rnlg/fwKOBuPEa3jxLlZ9Kro9KvBNbJ6T4feo8Or59w8LhjQ+gvuIt/Hhzrm1+kisRaNPxfVLtPz8fK1fv15+//FXvaWlperZs2eLjq8BgGihTwHxJa6DTWFhoWpqajRjxgxt3bpVJSUlWrx4sSZMmOB0aSfl8Zgh/+NkbIA7JFqfCkc4PQ2IF3H9VlROTo4WLVqkWbNmqaCgQLm5uZo2bZoKCgqcLi1IRlobWZat9u1DP+jJ77d04MBhTqMPJLhE6VPhCFxZOpyeJikhP40D94mrYPPYY4+dsGzgwIFatmyZA9WELjXFK9M09MKbG1WxnzPNAm6WqH0qHJ7UNBmmqe0vvqy6EK4s3e7s3up25eUK+owy4JC4CjaJrqKqljPNAnCN+orQriyd0rFjK1QDhIY3RgEAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGvwqSgHhXpSK05+BQBAaAg2DojkhH4AAKB5BBsHhHtCv7wzs3XlRb04rScAAM0g2Dgo1BP6dcxizw4AAKHg4A0AAOAa7LEBEpBpGseuNdY8Dj4Hkkc4f++2bbvyQswEGyDBmKahrKz0sBqYZdkhByEAiSeSK7Jbfr+qDtS6LtwQbIAEYxiGPB5TL/xpoyqqmj/4vFOHdI25vL9Mk2ADuFW4V2RPye2os8Zcf+wFD8EGQByoqDoc0sHn7KkBkkeoV2R3M958BwAArkGwAQAArkGwAQAArkGwAQAArkGwAQAArsGnooA4wdXeAbQ2N57Qj2ADOIyrvQNobW4+oR/BBnAYV3sH0NrcfEI/gg0QJ7jaO4DW5sYT+vFmPQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcA2CDQAAcI2ECDaWZemJJ57QRRddpEGDBunWW2/Vzp07nS4LACTRo4B4khDB5qmnntLLL7+shx9+WMuWLZNhGLr99tvV0NDgdGkAQI8C4kjcB5uGhgb993//tyZPnqzvf//76tu3r+bMmaO9e/fq7bffdro8AEmOHgXEF8O2bdvpIr7Np59+qmuuuUb/7//9P/Xs2bNp+fXXX68+ffqoqKgo7Nu0bVuWdeppG4Zkmqa+OdzwreMCvF5TbVPbMJ7xcTneNA21S/fJsiyd6q/dNA0ZhtHsbeFETvaoI9/UyLb8zd6e2aaNvOnpSTM+HmtK9PGG6VGbdhmyLKvZsZEyTTMqfcob5bqibs+ePZKk0047LWh5p06dtHv37ohu0zAMeTzNPzjt0n1h3S7jGR/P400z7nfQJiQne1Sbdhlh3W6yjW+N+0i28bHuI9G4/bjvdLW1tZIkny+4iaekpKi+vt6JkgCgCT0KiC9xH2xSU1Ml6YSD8Orr65WWluZESQDQhB4FxJe4DzaB3bvl5eVBy8vLy9WlSxcnSgKAJvQoIL7EfbDp27evMjIytHbt2qZl1dXV2rhxo4YMGeJgZQBAjwLiTdwfPOzz+TRmzBg9/vjj6tChg7p27arf/OY36tKliy677DKnywOQ5OhRQHyJ+2AjSffcc48aGxv1wAMPqK6uTvn5+SouLj7hYD0AcAI9CogfcX8eGwAAgFDF/TE2AAAAoSLYAAAA1yDYAAAA1yDYAAAA1yDYAAAA1yDYAAAA10iI89i0FsuyNH/+fC1fvlzV1dW64IIL9Mtf/lLdu3d3urSoOHDggGbPnq01a9aopqZGffr00c9+9rOms6Ped999KikpCfqdzp0767333nOi3Bb7+uuvNXLkyBOWP/zww7rmmmu0adMmzZo1S5999pmysrI0duxYjR8/3oFKW27t2rW66aabTrquW7duWr16teu2b7Jyc59Kth4l0acCotqnbDR58skn7e985zv2mjVr7E2bNtm33nqrfdlll9n19fVOlxYV48aNs3/0ox/Z69ats7dt22Y/9NBD9sCBA+2tW7fatm3bBQUF9uzZs+3y8vKmf5WVlQ5XHbnVq1fbAwYMsPfu3Rs0p9raWnv//v320KFD7RkzZthbt261X331VXvAgAH2q6++6nTZEamvrw+aY3l5uf3BBx/Y/fv3t1955RXbtt23fZOVm/tUsvUo26ZPxaJPEWyOqa+vt8877zx76dKlTcsOHjxoDxw40F61apWDlUXHjh077Ly8PHv9+vVNyyzLsi+77DJ77ty5dmNjoz1gwAD77bffdrDK6Hr66aftH/3oRyddt2DBAvuiiy6yjxw50rTst7/9rf2DH/ygtcqLqYaGBvuKK66wp0yZYtu27crtm4zc3KeSsUfZNn0qFn2KY2yO2bx5sw4dOqRhw4Y1LcvMzFT//v21bt06ByuLjuzsbD377LM699xzm5YZhiHbtnXw4EHt2LFD9fX16tWrl4NVRtfnn3+u3r17n3RdWVmZ8vPz5fUefzd22LBh2r59uyorK1urxJh58cUXtXv3bt13332S5Mrtm4zc3KeSsUdJ9KlY9CmCzTF79uyRJJ122mlByzt16qTdu3c7UVJUZWZm6vvf/37QtWvefPNNffnllxo+fLi2bNkiwzC0ePFijRw5UpdeeqkeeughffPNNw5W3TJbtmxRZWWlbrjhBn33u9/V9ddfr/fff1/S0e3dpUuXoPGdOnWSJO3atavVa42m+vp6LViwQDfffHPTnNy4fZORm/tUMvYoiT4Viz5FsDmmtrZWkk64aF1KSorq6+udKCmm1q9fr/vvv1+XXHKJRo4cqX/+858yTVNdu3bVggULNH36dP3P//yPJk6cKMuynC43bA0NDdqxY4dqamo0ZcoUPfvssxowYIBuv/12lZaWqq6u7qTbWlLCb+/XX39d9fX1Gjt2bNMyt23fZJVMfcrtPUqiT8WqT/GpqGNSU1MlHX2iBb6Xjj550tLSnCorJt555x3de++9GjRokGbPni1Jmjx5sm655RZlZmZKkvLy8pSbm6trr71W//jHPzRo0CAnSw6bz+fTunXr5PV6mxrDueeeq23btqm4uFipqalqaGgI+p1Ao0hPT2/1eqNp5cqVGjVqlLKzs5uWuW37Jqtk6VPJ0KMk+lSs+hR7bI4J7NotLy8PWl5eXn7CrsBEtmTJEk2ePFkXX3yxFi5c2NQcDcNoejIF5OXlSTq++zvRpKenn/BqJy8vT3v37lWXLl1Ouq2lox8tTFT79+/Xxx9/rNGjRwctd+P2TUbJ0KeSqUdJ9Kl/F61tTLA5pm/fvsrIyNDatWubllVXV2vjxo1N51BIdEuXLtVDDz2kG2+8UXPnzg36Y/rZz352wrkR/vGPf0jSKQ9si2ebN2/Weeedp7KysqDln332mXr37q38/HytX79efr+/aV1paal69uypnJyc1i43aj766CMZhqELL7wwaLnbtm+ycnufSqYeJdGnYtanWv6BLfeYPXu2feGFF9rvvPNO0/khRo0a5YrzQ3zxxRf2OeecY0+aNOmE8whUV1fbf/nLX+w+ffrYTz31lL1z5057zZo19siRI+2pU6c6XXpE/H6/fc0119hXXnmlvW7dOnvr1q32I488Yp977rn25s2b7X379tn5+fn29OnT7X/+85/2ihUr7AEDBtglJSVOl94iTz75pD1q1KgTlrtt+yYzt/apZOtRtk2f+r+itY0N27btFscvl/D7/Zo9e7ZKSkpUV1en/Px8/eIXv1C3bt2cLq3FFixYoDlz5px0XUFBgR577DG99dZbWrBggb744gu1a9dOV111laZMmdJ0sFqi2b9/vx5//HG99957qq6uVv/+/XXvvfc2vbL99NNPNWvWLG3cuFG5ubm69dZbNWbMGIerbpmioiJt2rRJy5YtO2Gd27ZvsnJrn0rGHiXRp/6vaGxjgg0AAHANjrEBAACuQbABAACuQbABAACuQbABAACuQbABAACuQbABAACuQbABAACuQbABAACuQbBBTEybNk19+vTRs88+63QpAHBS9Cl34szDiLqamhoNHz5cZ5xxhmpra/X222/LMAynywKAJvQp92KPDaLujTfekN/v1wMPPKCvvvpKH3zwgdMlAUAQ+pR7EWwQdStWrNDQoUM1dOhQ9ezZUy+//PIJY4qLi3XJJZdo4MCBuu666/SXv/xFffr00dq1a5vGbNmyRRMmTND555+v888/X5MmTdJXX33VmlMB4FL0Kfci2CCqtm3bpk8++UQFBQWSpKuvvlrvvvuu9u7d2zRm/vz5evzxx3X55Zfrqaee0qBBg/TTn/406Ha2b9+u6667TpWVlXrsscc0a9YsffXVV7r++utVWVnZqnMC4C70KXcj2CCqXn31VWVmZurSSy+VJP3kJz+RJC1fvlySdPjwYS1cuFA33nij7r33Xg0fPlz33XeffvzjHwfdzvz585Wamqrnn39eo0aN0uWXX67f//73qqur06JFi1p1TgDchT7lbgQbRE1jY6P+8Ic/6NJLL1V9fb2qq6uVmpqqoUOHavny5fL7/dqwYYPq6ur0wx/+MOh3r7zyyqCf//a3v2no0KFKTU1VY2OjGhsblZGRoSFDhuivf/1ra04LgIvQp9zP63QBcI81a9Zo3759KikpUUlJyQnr3333XdXV1UmSOnToELSuY8eOQT8fOHBAf/rTn/SnP/3phNv5v78LAKGiT7kfwQZR8+qrr6pr16569NFHT1h3zz336OWXX9add94pSdq/f7/OOuuspvX79+8PGt+uXTt997vf1bhx4064La+Xpy2AyNCn3I9HHlGxb98+vf/++7r11ls1dOjQE9aPHj1aL7/8sn7+85+rXbt2+vOf/6whQ4Y0rX/rrbeCxl944YXaunWr+vXr19QgbNvWvffeq+7du6tfv36xnRAA16FPJQeOsUFUvPbaa2psbNQVV1xx0vUFBQWyLEurVq3SbbfdpiVLlmjOnDn68MMPNWfOHL300kuSJNM8+pScOHGivvzyS02YMEHvvPOO3n//fU2ePFlvvPGG+vbt22rzAuAe9KnkwJmHERWjR4+WaZpatWrVKcdcfvnlOnjwoNasWaNFixZp2bJlqqys1KBBg3TZZZfp0UcfVUlJic455xxJ0v/+7/9qzpw5+uijj2TbtvLy8nTHHXfokksuaa1pAXAR+lRyINigVTU2NmrVqlUaOnSoTjvttKblL774oh5++GGtXbtWmZmZDlYIINnRpxIbwQat7oorrpDP59Ndd92l7Oxsbd68WfPmzWt6NQQATqNPJS6CDVrdV199pdmzZ2vt2rWqrq7W6aefrh/96EeaMGGC2rRp43R5AECfSmAEGwAA4Bp8KgoAALgGwQYAALgGwQYAALgGwQYAALgGwQYAALgGwQYAALgGwQYAALgGwQYAALjG/w/ipSxkejgYJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1) \n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='male'], x='Age', color='b', bins=range(0,80,5))\n",
    "plt.ylim([0,80])\n",
    "plt.legend(['men'])\n",
    "\n",
    "plt.subplot(1,2,2) \n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='female'], x='Age', color='r', bins=range(0,80,5))\n",
    "plt.ylim([0,80])\n",
    "plt.legend(['women'])\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.5) # shift the plots sideways so they don't overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f49fd6ca",
   "metadata": {},
   "source": [
    "Ooh, suddenly we can see that there were a lot more men than women on the Titanic!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5813143d",
   "metadata": {},
   "source": [
    "### Set axis labels\n",
    "\n",
    "Your axis labels should always convey what is plotted. If you are using <tt>Seaborn</tt> with a <tt>Pandas</tt> dataframe, the axis labels will often be the column labels from your dataframe, which are (usually) meaningful although sometimes they are odd codes that wouldn't mean much to a naive reader (The titanic dataset is a culprit here - what is Pclass? SibSp? Parch?!).\n",
    "\n",
    "You can always edit the labels on the axes and unless their meaning is clear, you must do so.\n",
    "\n",
    "Just for fun let's label the x axis \"bananas\" and the y axis \"fruitbats\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a57548bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hL07GB8Z4vaYS5JIrIRJ9+4Yr2eYbTe3Zp9zco8KVbPOVkm/OkZxv3ASb3/zmN6qvr9fo0aODy+rq6uTz+ULGpaSkSGpuMK1lmoayszu2OC4zM7Gvq+PxmPJ6Wz7LpufQGR9N0+H4Q0+8RH98Er3+cCXbfKOhvfpUsvSocCXbfKXkm3Mk5hs3wWbVqlW66KKLlJ2dHVyWmpqqhoaGkHGBRpGe3vpzr1iWrerqY5+4zuMxlZmZpurqWvn9iXcq60D9fr+lpqaWr4viP3SRNctyOP7QY5Loj0+i1h8uJ/PNzExLmleGbdFefcrtPSpcyTZfKfnmHMk+FRfBZt++fXr33Xc1fvz4kOU9evRQRUVFyLLAz927d2/TfTq59kZzMEi8J5RtB77asm0HFxOzD39xMj4wpqnJSug/uETdvq2VbPONtPbuU27uUa2VbPOVkm/OkZhvXLxEe+edd2QYhoYOHRqyvLCwUOvWrQu5GuvatWvVp0+fNh1fAwDhok8BiSEugs2mTZv0la98RWlpoe+tlZSUqKamRtOmTVN5eblWrlypxYsXH/GKCQCijT4FJIa4CDZ79+5VVlbWEctzcnK0cOFCbd26VcXFxZo3b54mT56s4uLi9i8SQFKjTwGJIS6OsZk+ffoxbysoKNDy5cvbrxgAOAr6FJAY4mKPDQAAQCQQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGsQbAAAgGt4Y10AEpfH4zwX27Yty7KjWA0AAAQbtEJGWgdZlq3OndMc/47fb2n//oOEGwBAVBFsELbUFK9M09Bzqzdoz76DLY7PzU7X6EsGyDAMSQQbAED0EGzQanuqarVz75exLgMAgCAOHgYAAK4RF8Fm1apVuuSSSzRo0CB9+9vf1urVq4O3bdy4UaNGjdLgwYM1fPhwLVq0KIaVAkhW9CkgMcQ82PzmN7/RnXfeqSuvvFIvvfSSLrnkEk2aNEnvvvuuqqqqNHbsWPXu3VulpaWaOHGiHnnkEZWWlsa6bABJhD4FJI6YHmNj27YeeeQRjRkzRmPGjJEkTZgwQe+8847eeustvfXWW/L5fJo+fbq8Xq/y8vK0fft2LViwQCUlJbEsHUCSoE8BiSWme2y2bNmizz//XJdddlnI8kWLFmn8+PEqKytTYWGhvN7D+WvYsGHaunWrKisr27tcAEmIPgUklpjusdm2bZsk6eDBg7r++uu1YcMG9ezZUzfddJOKioq0a9cu5efnh/xOt27dJEk7duxQTk5Oq+/b6z12pguceC6cE9DFk0DdhmEc+oh1C4zDX5yMD4wxjPDGe72mnJQTbYm+fcOVbPONtFj1KTf3qHAl23yl5JtzJOcb02BTU1MjSZoyZYpuueUW3X777frTn/6km2++WU8//bTq6urk8/lCficlJUWSVF9f3+r7NU1D2dkdWxyXmen8BHTxyOMx5fV6Wh5nNj+RTNPh+MATMMzx8fZ4xls90ZZs842UWPSpZOlR4Uq2+UrJN+dIzDemwaZDhw6SpOuvv17FxcWSpNNOO00bNmzQ008/rdTUVDU0NIT8TqBRpKent/p+LctWdfWxTyzn8ZjKzExTdXWt/H6r1fcTK4H6/X5LTU3+Fsf7reY5WpbD8YceE3+Y4+Pl8Uz07RsuJ/PNzExLmleG4YpFn3J7jwpXss1XSr45R7JPxTTY9OjRQ5KO2I3bt29fvfbaazrppJNUUVERclvg5+7du7fpvpuaWn6iNAeDxHtC2Xbgqy3bdnCmX/vwFyfjA2NsO7zxTU1WXP2BJur2ba1km2+kxKpPublHtVayzVdKvjlHYr4xfYk2YMAAdezYUe+9917I8s2bN+vkk09WYWGh1q1bJ7//8F6BtWvXqk+fPm06vgYAnKJPAYklpsEmNTVVP/zhD/XYY4/ppZde0qeffqonnnhCf//73zV27FiVlJSopqZG06ZNU3l5uVauXKnFixdr/PjxsSwbQBKhTwGJJebXirr55puVlpamhx9+WLt371ZeXp4effRRnXnmmZKkhQsXaubMmSouLlZubq4mT54cfJ8bANoDfQpIHDEPNpI0duxYjR079qi3FRQUaPny5e1cEQCEok8BiYGPQQAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANeIebD5/PPP1b9//yP+vfDCC5KkjRs3atSoURo8eLCGDx+uRYsWxbhiAMmGPgUkDm+sC/joo4+UkpKiV155RYZhBJd36tRJVVVVGjt2rC644ALNmDFD69ev14wZM5SVlaWSkpIYVg0gmdCngMQR82CzefNm9enTR926dTvitsWLF8vn82n69Onyer3Ky8vT9u3btWDBAhoGgHZDnwISR8yDzUcffaS+ffse9baysjIVFhbK6z1c5rBhw/Tkk0+qsrJSOTk5rb5fr/fY78J5PGbI13hgGIZM02h5oBQcZxhGyKvLY6/88Bcn4wNjDCO88V6vKSflRFs8bt9oSrb5RkMs+lSi9ahoSrb5Ssk350jON+bBZvPmzcrNzdXVV1+tbdu2qVevXrr55pt17rnnateuXcrPzw8ZH3jFtGPHjlY3DNM0lJ3dscVxmZlprVp/NFiW7TjYBHg8prxeT8vjzOYnkmk6HB94AoY5Pp4eTyn+6om2ZJtvJLV3n0rEHtUekm2+UvLNORLzjWmwaWho0LZt25SWlqbJkycrPT1dv/3tb3XDDTfo6aefVl1dnXw+X8jvpKSkSJLq6+tbfb+WZau6+uAxb/d4TGVmpqm6ulZ+v9Xq+4mUQD1LVm/QnqraFsf3OzlLl56TJ79lqanJ3+J4v9U8R8vp+EOPieP1Hxofb49nvNQTbU7mm5mZljSvDMMViz6VaD0q2pJtvlLyzTmSfSqmwcbn8+ntt9+W1+sNNobTTz9dn3zyiRYtWqTU1FQ1NDSE/E6gUaSnp7fpvpuaWn6i+P2Wo3HRZtvNXyv2HdTOvV+2OD6nc2rw9+zALx/3Dg5/cTI+MMbp+gNjmpqsuPoDjZft216Sbb6REqs+lUg9qr0k23yl5JtzJOYb85do6enpR7zayc/P1+7du9WjRw9VVFSE3Bb4uXv37u1WI4DkRp8CEkdMg82mTZs0ZMgQlZWVhSz/8MMP1bdvXxUWFmrdunXy+w+/3bF27Vr16dOnTQcOA4BT9CkgscQ02OTn56tfv36aMWOGysrK9Mknn+jnP/+51q9frxtvvFElJSWqqanRtGnTVF5erpUrV2rx4sUaP358LMsGkEToU0BiiekxNqZpav78+Zo1a5ZuvfVWVVdXa8CAAXr66afVv39/SdLChQs1c+ZMFRcXKzc3V5MnT1ZxcXEsywaQROhTQGKJ+ce9u3TpogceeOCYtxcUFGj58uXtWBEAhKJPAYkj5gcPAwAARArBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuAbBBgAAuEarLoL52Wefqb6+Xn379tWBAwc0Z84c7dy5U//93/+t7373uxEuEQDCR58CklPYe2zWrFmjiy++WKWlpZKk6dOna8WKFdq9e7emTp2qF154IeJFAkA46FNA8go72Dz++OM655xzNGHCBH3xxRd6+eWXNW7cOP3617/WuHHj9Oyzz0ajTriAx2M6/meaRqzLRQKjTwHJK+xgs2nTJo0ZM0YZGRn629/+Jr/fr//6r/+SJJ199tnavn17xItEYstI6yDLstW5c5q6dOno6F9WVjrhBq1GnwKSV9jH2KSkpKipqUmS9Le//U05OTk69dRTJUl79+5VZmZmZCtEwktN8co0DT23eoP27DvY4vjc7HSNvmSADMOQZEe/QLgOfQpIXmEHm6997Wv65S9/qQMHDmj16tUaMWKEJOnDDz/UvHnz9NWvfjXiRcId9lTVaufeL2NdBpIAfQpIXmG/FTV16lTt3r1bt99+u3r27KmbbrpJkjR+/HjV19fr9ttvj3iRABAO+hSQvMLeY/OVr3xFv//971VZWamuXbsGlz/22GMaMGCAfD5fRAsEgHDRp4DkFfYemx/84AfasmVLSLOQpMGDB2vLli267LLLIlYcALQGfQpIXo722JSVlcm2mw/ifOutt/T2229r3759R4z7y1/+os8++yyyFQKAA/QpAJLDYPPiiy9q1apVMgxDhmFoxowZR4wJNJRLL700shUCgAP0KQCSw2Azbdo0jRgxQrZta8yYMbr77rvVt2/fkDGmaSozM1P9+vWLSqEAcDz0KQCSw2DTqVMnDR06VJL07LPPauDAgerYsWNUCwOAcNCnAEit+FTU0KFDtWvXLv31r39VQ0NDcLllWaqtrVVZWZkefvjhiBYJAOGgTwHJK+xgs3r1at1xxx1qamo6dGbY5vetA9+fcsopka0QAMJEnwKSV9gf937yySc1YMAArVy5UiNGjNDll1+u3//+97rjjjvk9Xp15513RqNOAHCMPgUkr7D32GzdulWzZs3SgAEDdNZZZ2nhwoXKy8tTXl6eKisrNX/+fJ199tnRqBUAHKFPxT/TNIJ70Fri8YT9GhxJLOxgY5qmsrKyJEm9e/fWli1bZFmWTNPUueeeq5UrV0a6RgAIC30qvpmmoeysNJkej+PfsS3LcRBCcgs72Jxyyilat26dCgsL1bt3bzU2Nmrjxo0aOHCgqqurQw7UA4BYoE/FN8MwZHo82rLkedXv2dvi+NRuuepzzfdlmgQbtCzsYPP9739f99xzjw4ePKhJkybpzDPP1J133qkrrrhCS5Ys0cCBA6NRJwA4Rp9KDPV79qp2564Wx7GnBuEI+43LkSNHatq0aWpsbJQk3Xvvvaqvr9fMmTPV1NSkadOmRbxIAAgHfQpIXmHvsZGka665Jvj9ySefrNWrV6uqqkpdunSJWGEA0Bb0KSA5tfpQ8zfeeEOzZs3S3Xffrblz52rz5s1tLmbr1q0aMmRIyIF9Gzdu1KhRozR48GANHz5cixYtavP9AEgOke5T9Cgg/oW9x2bfvn265ZZb9M4778jr9SorK0v79+8Pfnxy3rx5Sk1NDbuQxsZG3X777Tp48GBwWVVVlcaOHasLLrhAM2bM0Pr16zVjxgxlZWWppKQk7PsAkByi0afoUUBiCHuPzf/8z/9oy5Yteuyxx/TBBx/o9ddf1/vvv6+HHnpI7733nmbNmtWqQh599NEjruuyYsUK+Xw+TZ8+XXl5eSopKdG1116rBQsWtOo+ACSHaPQpehSQGMLeY/PnP/9ZkydP1vnnnx9cZpqmLrnkEu3fv19z587VXXfdFdY63377bS1fvlyrVq3S8OHDg8vLyspUWFgor/dwmcOGDdOTTz6pyspK5eTkhFt+kNd77EwXOBlUvJwUKlCHYTg7oVVgjGE4/DSBcfhLNNbf2vFer6lofBgi3rZvtCXbfKXI9yl6VGSF29MCPco0jeM+Lm6S6Ns4XJGcb6sOHj7WH2ufPn3CPj9EdXW1Jk+erLvuuksnnHBCyG27du1Sfn5+yLJu3bpJknbs2NHqpmGahrKzW77qb2ZmWqvWHy0ejymvt+UTWgWfIKbD8WbzeNPp+HDX38rx0X784237RluyzTdSfYoeFT0ej7Og4jl0/pqMjPAPc0h0ib6NwxWJ+YYdbC6//HI99dRTGjZsmNLSDhdgWZaWLFmiSy+9NKz1TZ8+XYMHD9Zll112xG11dXXy+Xwhy1JSUiRJ9fX14Zb+b7Xaqq4+eMzbPR5TmZlpqq6uld9vtfp+IiVQj99vqanJ3+L4QM1+y+F4q3m85XR8uOtv5fhoPf7xtn2jzcl8MzPTXPXKMJJ9ih4VeYd7mq2mppbr91u2JKmmpk6NjS33EDdI9G0crkj2KUfBZurUqcHvm5qa9P777+v888/Xeeedp65du+rAgQNau3at9u7dq+9973sOpyGtWrVKZWVl+t3vfnfU21NTU494ZRVoFunp6Y7v52gc/TH5LUfjos22A19t2YEfjjveDv6ek/GyD3+JxvpbO76pyYrqH3S8bN/24vb5RqNP0aOiI9yeFuhRluUsCLlJom7j1orEfB0FmzfffDPk5+7dux91eXZ2tl555ZWQBnM8paWlqqysDHnPWpLuueceLVq0SCeeeKIqKipCbgv8HKgBAKTo9Cl6FJB4HAWbP//5z1G581mzZqmuri5k2UUXXaQf/ehHuuSSS/T73/9ev/rVr+T3++U5dLG0tWvXqk+fPm06KA+A+0SjT9GjgMQT9pvqU6dO1WeffXbU27Zs2aIbb7zR8bq6d++uXr16hfyTmg/6O+mkk1RSUqKamhpNmzZN5eXlWrlypRYvXqzx48eHWzaAJBKpPkWPAhKPoz02O3bskNT8fuivf/1rXXDBBcFXJ/9uzZo1euONNyJWXE5OjhYuXKiZM2equLhYubm5mjx5soqLiyN2HwDcIRZ9ih4FxB9Hwebee+/VX//6V0nN5x245ZZbjjrOtm2dffbZbSroo48+Cvm5oKBAy5cvb9M6Abhfe/UpehQQ3xwFmxkzZuiNN96Qbdu68847ddNNN+nkk08OGWOapjIzM3XmmWdGpVAAOB76FADJYbDp3r17cNeqYRgaPny4srOzo1oYAISDPgVAchhs3n77bQ0YMEAdO3ZUz549VV5eftzxhYWFESkOAJyiTwGQHAab0aNHa8WKFSooKNDo0aNlGMYRJ1UKLDMMQxs3boxKsQBwLPQpAJLDYPPss88qLy8v+D0AxBv6FADJYbAZOnToUb8HgHhBnwIgteIimKtWrWpxzHe/+91WlAIAkUGfApJX2MHmpz/96VGXG4Yhj8cjj8dDwwAQU/QpIHmFHWxeffXVI5YdPHhQ69at01NPPaXHHnssIoUBQGvRp4DkFXawOemkk466vF+/fmpsbNR9992nZcuWtbkwAGgt+hSQvMK+CObx5Ofn65///GckVwkAEUWfig7TNOTxmI7/AdES9h6bY2loaNCKFSuUk5MTqVUCQETRp6LDNA1lZ6XJPMpFR4/HMKJUEJJa2MGmqKhIxn88Gy3LUlVVlerr6zVlypSIFQcArUGfal+GYcj0eLRlyfOq37O3xfGd+vVVz0svlkSyQeSFHWyOdfG4jIwMfetb39I3vvGNNhcFAG1Bn4qN+j17VbtzV4vjUrp2bYdqkKzCDjaXXXaZBg8erPT09GjUAwBtRp8CklfYR3BNnjz5qB+lBIB4QZ8CklfYwcbn8yklJSUatQBARNCngOQV9ltR48eP1913361NmzapX79+6nqU90oLCwsjUhwAtAZ9CkhejoJNfX198NXPPffcI0l6/PHHJSnkkwe2bcswDG3cuDHSdQLAcdGnAEgOg01RUZHmzZunIUOGqLCwUCNHjlSPHj2iXRsAOEafAiA5DDZffPGFKioqJEllZWW64447VFBQENXCACAc9CkAksNgU1BQoNtuu02/+MUvZNu2JkyYIJ/Pd9SxhmHolVdeiWiRANAS+hQAyWGweeihh/TMM89o//79+vWvf60BAwaoS5cu0a4NAByjTwGQHAab7t27B09B/uabb+onP/mJTj311KgWBgDhoE8BkFrxce8///nP0agDACKGPgWp+eKc/3nNsOOxbVuWZUexIrSHiF3dGwCAeNGaK45bfr+q9tcSbhIcwQYA4DrhXnE8JberThl11aE9PASbREawAQC4ltMrjsM9wr5WFAAAQLwi2AAAANcg2AAAANcg2AAAANcg2AAAANcg2AAAANeIebCprKzUHXfcoWHDhmnIkCEaN26cysvLg7dv3LhRo0aN0uDBgzV8+HAtWrQohtUCSEb0KSBxxDzY3HTTTfrss8+0YMECvfjii0pNTdW1116r2tpaVVVVaezYserdu7dKS0s1ceJEPfLIIyotLY112QCSCH0KSBwxPUFfVVWVevbsqZtuukn9+vWTJN188836zne+o48//lhr166Vz+fT9OnT5fV6lZeXp+3bt2vBggUqKSmJZekAkgR9CkgsMd1jk52drdmzZwebxd69e7Vo0SL16NFDffv2VVlZmQoLC+X1Hs5fw4YN09atW1VZWRmrsgEkEfoUkFji5pIKP/vZz7RixQr5fD498cQTSk9P165du5Sfnx8yrlu3bpKkHTt2KCcnp9X35/UeO9N5PGbI11gL1GEYzq5UGxhjGHJ2ZVvj8JdorL+1430+jyzL2TawLFu27ez6LvG2faMt2eYbTe3Zp9zdowLfhNejmh8TZ/9tmWag74TXN71eU2FcEDxq4m0bR1sk5xs3wWbMmDG68sor9fzzz2vChAlatmyZ6urq5PP5QsalpKRIkurr61t9X6ZpKDu7Y4vjMjPTWn0f0eDxmPJ6W75SbfAJYjocbzaPN52OD3f9YY7vnJEiy7KVkZHa4tgAy7KDjcypeNu+0ZZs842G9upTidujjOMGsgDT0/y36jGdjU/plCHbspSenhK1mjyHaoq3xzTe6om2SMw3boJN3759JUn33Xef1q9fryVLlig1NVUNDQ0h4wKNIj09vdX3ZVm2qqsPHvN2j8dUZmaaqqtr5fdbrb6fSAnU4/dbamrytzg+ULPfcjjeah5vOR0f7vrDHN/Ba8o0DS1ZvUF7qmpbHJ+bnaZRFw9wvL3ibftGm5P5ZmamJc0rw7Zorz6VuD3KVlNTy/VY/ua9q37L2XilpMgwTW1b+ivVObhStyR16tdXJ337vx3fh/9QTfH2mMZLPdEWyT4V02BTWVmptWvX6uKLL5bH0/xK3jRN5eXlqaKiQj169FBFRUXI7wR+7t69e5vu29kT3XL2RxdlgXdYbNvZ2y2BMbYtZ2/P2Ie/RGP9rR1fUVWrnXtqHI9varLCagDxsn3bS7LNN1Ji1afc3aMC34TXo+r27tXBHTsd1eQLvAUYZt8Jt49EW7xs4/YSifnG9CVaRUWFbrvtNr311lvBZY2NjdqwYYPy8vJUWFiodevWye8//Cp/7dq16tOnT5uOrwEAp+hTQGKJabA59dRTdc4552jGjBkqKyvT5s2bNWXKFFVXV+vaa69VSUmJampqNG3aNJWXl2vlypVavHixxo8fH8uyASQR+hSQWGIabAzD0Jw5czRs2DDdeuutGjlypA4cOKClS5fqxBNPVE5OjhYuXKitW7equLhY8+bN0+TJk1VcXBzLsgEkEfoUkFhifvBwp06dNH36dE2fPv2otxcUFGj58uXtWxQA/Bv6FJA4+BgEAABwDYINAABwDYINAABwDYINAABwDYINAABwDYINAABwjZh/3BuIFKfXOuKaSACOJZz+YNu2LMvBJSHQrgg2SHgZaR1kWbY6d3Z+VVjLsmUY4V0NHIB7eTM6yras8PqI36+q/bWEmzhDsEHCS03xyjQNPbd6g/bsO/YVkQO6dUnXqIsHyDQJNgCaeVLTZJimti79leoq9rQ4PiW3q04ZddWhF0gEm3hCsIFr7Kmq1c69X7Y4jj01AI6lfs9e1e7cFesy0AYcbAAAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFyDYAMAAFzDG+sC4pnHY8q2nY21bVuW5XAwAACICoLNURiGIcuylZmZ5vh3/H5L+/cfJNwAABBDBJujME1DpmloyeoNqth3sMXxudnpGn3JABmGIYlgAwBArBBsjmNPVa127v0y1mUAAACHOHgYAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4RsyDzf79+3X33Xfrm9/8pr761a/qqquuUllZWfD2jRs3atSoURo8eLCGDx+uRYsWxbBaAMmIPgUkjpgHm0mTJum9997T7Nmz9eKLL2rgwIG6/vrr9cknn6iqqkpjx45V7969VVpaqokTJ+qRRx5RaWlprMsGkEToU0DiiOkJ+rZv366///3vev755/XVr35VkjRt2jStWbNGL730klJTU+Xz+TR9+nR5vV7l5eVp+/btWrBggUpKSmJZOoAkQZ8CEktM99hkZ2frqaee0umnnx5cZhiGbNvWgQMHVFZWpsLCQnm9h/PXsGHDtHXrVlVWVsaiZABJhj4FJJaY7rHJzMzUeeedF7Js9erV+vTTT3XOOefo4YcfVn5+fsjt3bp1kyTt2LFDOTk5rb5vr/fYmc40jeZvDB26/tPxBcZ4vaYcDA+bx2MG7yecegyH9evwdKOy/ngbH5hv83PA2Z+AZdmynV7qPc4Enj+BrwhPrPrU8XpUvG3T8HtU4Jvw/mYD9+FEuPcR/vj26fvxso2jLZLzjatrRa1bt0533nmnzj//fBUVFennP/+5fD5fyJiUlBRJUn19favvxzQNZWd3bHGcxzTl9XpaHndoQ4RzNfDW8HjCq8dx/WbzeDPM+Yb7+MTL+M4dU2RZttLTU1ocG2BZ9uHAm6Ci/fxMFu3Rp5z2qHjbph6PcdxAFmB6mv+WPKbD8WZ441t1H2GO9xwaH+1tEG/bONoiMd+4CTavvPKKbr/9dp1xxhmaPXu2JCk1NVUNDQ0h4wKNIj09vdX3ZVm2qquPfdXuDh08yshIld+y1NTkb3F9fr8lSaqurg1+H0kej6nMzDT5/eHV47h+q3m8FeZ8w3184mV8hw6mTNPQ0tUbVFFV2+L43Ow0jbp4QNS2b7QFnj/Hqz8zMy1pXhm2RXv1qZZ6lJNt2p4O9yhbTU0t12P5m/d++i2H463wxrfqPsIc7z80Ptp9P162cbRFsk/FRbBZsmSJZs6cqQsvvFCzZs0Kvvrp0aOHKioqQsYGfu7evXub7vN4T9zgA2fL0dsPgTFNTVZUnoCBEmzb2dshgTG2w/plH/4SjfXH2/jAfCv212rHnhrH64/W9m0vzcE4ceuPtfbuU87+c42PbRp+jwp8E97fbOA+wqnJeR8Pd3z79IV42cbtJRLzjflLtGXLlum+++7TNddcozlz5oTs0i0sLNS6devk9x9+Fb527Vr16dOnTcfXAEA46FNA4ohpsNm6daseeOABXXjhhRo/frwqKyu1Z88e7dmzR1988YVKSkpUU1OjadOmqby8XCtXrtTixYs1fvz4WJYNIInQp4DEEtO3ov70pz+psbFRL7/8sl5++eWQ24qLi/Xggw9q4cKFmjlzpoqLi5Wbm6vJkyeruLg4RhUDSDb0KSCxxDTY3HjjjbrxxhuPO6agoEDLly9vp4oAIBR9CkgsMT/GBgAAIFLi4lNRAAAkonBOk2DbdvCj64gegg0AAGHyZnSUbVnq3Nn5CeUsv19V+2sJN1FGsAEAIEye1DQZpqmtS3+luoo9LY5Pye2qU0ZddehSDASbaCLYAADQSvV79qp2565Yl4F/w8HDAADANdhjAwAJyuMx5fAKAxy4iqRBsAGABGMYhmzLCutKyBy4imRBsAGABGOaBgeuAsdAsAGABMWBq8CROHgYAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BsEGAAC4BpdUAACgnXg8zvYnOB2HIxFsAACIMm9GR9mWpc6dnV+R3basQxcuRTgINgAARJknNS2sK7KndstVn2u+L9Mk2ISLYAMAQDtxekV29tS0HsEGAJJEOMdt2LYty7KjWA0QHQQbAHC51hzfYfn9qtpfS7hBwiHYAIDLhXt8R0puV50y6qpDb4cQbJBYCDYAkCScHt8BJDI+KA8AAFyDPTYAAMQp0zQcH/TNAd/NCDYAAMSZwAHfGRmpjn+HA76bEWwAAIgzntRUGaapbUt/pVoO+A4LwQYAgDhVt5cDvsNFsAGiwDSNsM4cynvjABAZcfWpqMcff1yjR48OWbZx40aNGjVKgwcP1vDhw7Vo0aIYVQc4Y5qGsrLS1aVLR8f/srLSuSZMgqBPAfEtbvbYPPPMM5o7d64KCwuDy6qqqjR27FhdcMEFmjFjhtavX68ZM2YoKytLJSUlMawWODbDaP4Uw3N/2KA9VQdbHJ+bna7RlwzgvfEEQJ8C4l/Mg83u3bs1bdo0rVu3Tn369Am5bcWKFfL5fJo+fbq8Xq/y8vK0fft2LViwgIaBuLen6qB27v0y1mUgAuhTQOKI+VtR//znP9W5c2f99re/1RlnnBFyW1lZmQoLC+X1Hs5fw4YN09atW1VZWdnepQJIUvQpIHHEfI9NUVGRioqKjnrbrl27lJ+fH7KsW7dukqQdO3YoJyen1ffr9R470wWPdTCcXTo+MMbrNRWNK80HTs5kGM4OSA2MMRzWr8PTjcr64218a+fr83lkWS2/Fgg8f8LdXtF+/oRzZWeEikWfimyPCnwTnZ4Wfo8Krx792xCnB+WHP+f4Gh/unKPdR6Itkn0q5sHmeOrq6uTz+UKWpaSkSJLq6+tbvV7TNJSd3bHFcR7TlNfraXncoQ2Rmen8yrmt4fGEV4/j+s3m8WaY8w338Ymb8WHOt3NGiizLDutEWYG64un5E+31J6to9CnnPco4bgAKrs9jhDXec2h8uM8Zjyc69QSCnNPxrbqPeBsf5pxbu83iTSTqj+tgk5qaqoaGhpBlgUaRnp7e6vValq3q6mMf1Nmhg0cZGanyW5aamvwtrs/vtyRJ1dW1we8jyeMxlZmZJr8/vHoc1281j7fCnG+4j0/cjA9zvh28pkzT0JLVG7SnqrbF8f1OztKl5+TF3fPneOvPzExjj04rRaNPOe9RtpqaWn7OWP7mg9KdjvcfGu/0OXm4R0WnnsCpEJyOb9V9xNv4MOcc7jaLN5HsU3EdbHr06KGKioqQZYGfu3fv3qZ1H++JEnzg7Obzi7QkMKapyYrKEypQgm3bYdVjO6w/8EEcW+HN1+n64218a+dbUVWrnXtqWhyf0zk1rHqi/fwJaA7Gidfw4l20+lRke1Tgm+g8J8PvUeHV8+8fFnQ0vhX3EW/jw51ze/WRaItEn4rrl2iFhYVat26d/P7Dr3rXrl2rPn36tOn4GgCIFPoUEF/iOtiUlJSopqZG06ZNU3l5uVauXKnFixdr/PjxsS7tqDwe0/E/TsYGuEOi9alwhNPTgHgR129F5eTkaOHChZo5c6aKi4uVm5uryZMnq7i4ONalhchI6yDLstW5s/ODnvx+S/v3H+Q0+kCCS5Q+FY7AlaXD6WmSEvLTOHCfuAo2Dz744BHLCgoKtHz58hhU41xqilemaei51Ru0Zx9nmgXcLFH7VDg8qWkyTFNbl/5KdQ6uLN2pX1/1vPRihXxGGYiRuAo2iW5PVS1nmgXgGvV7nF1ZOqVr13aoBnCGN0YBAIBrEGwAAIBrEGwAAIBrEGwAAIBrEGwAAIBr8KmoGHJ6UitOfgUAgDMEmxhozQn9AABAywg2MRDuCf3yT87WpefmcVpPAABaQLCJIacn9OuaxZ4dAACc4OANAADgGuyxARKQaRqHrjXWMg4+B5JHOH/vtm278kLMBBsgwZimoays9LAamGXZjoMQgMTTmiuyW36/qvbXui7cEGyABGMYhjweU8/9YYP2VLV88Hm3LukadfEAmSbBBnCrcK/InpLbVaeMuurQCx6CDYA4sKfqoKODz9lTAyQPp1dkdzPefAcAAK5BsAEAAK5BsAEAAK5BsAEAAK5BsAEAAK7Bp6KAOMHV3gG0Nzee0I9gA8QYV3sH0N7cfEI/gg0QY1ztHUB7c/MJ/Qg2QJzgau8A2psbT+jHm/UAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1CDYAAMA1EiLYWJaluXPn6txzz9UZZ5yh6667Ttu3b491WQAgiR4FxJOECDaPP/64fvWrX+n+++/X8uXLZRiGbrjhBjU0NMS6NACgRwFxJO6DTUNDg375y19q4sSJOu+883Tqqafq4Ycf1u7du/Xyyy/HujwASY4eBcQXw7ZtO9ZFHM/777+vkSNH6o9//KP69OkTXH7VVVepf//+mj59etjrtG1blnXsaRuGZJqmvjjYcNxxAV6vqY6pHRjP+Lgcb5qGOqX7ZFmWjvXXbpqGDMNocV04Uix7VOMXNbItf4vrMzt0kDc9PWnGx2NNiT7eMD3q0ClDlmW1OLa1TNOMSJ/yRriuiNu1a5ck6YQTTghZ3q1bN+3cubNV6zQMQx5Pyw9Op3RfWOtlPOPjebxpxv0O2oQUyx7VoVNGWOtNtvHtcR/JNj7afSQS64/7TldbWytJ8vlCm3hKSorq6+tjURIABNGjgPgS98EmNTVVko44CK++vl5paWmxKAkAguhRQHyJ+2AT2L1bUVERsryiokI9evSIRUkAEESPAuJL3AebU089VRkZGXrzzTeDy6qrq7VhwwZ9/etfj2FlAECPAuJN3B887PP5NGrUKM2aNUtdunTRSSedpP/93/9Vjx49dOGFF8a6PABJjh4FxJe4DzaS9KMf/UhNTU266667VFdXp8LCQi1atOiIg/UAIBboUUD8iPvz2AAAADgV98fYAAAAOEWwAQAArkGwAQAArkGwAQAArkGwAQAArkGwAQAArpEQ57FpL5Zlad68eXrhhRdUXV2tr33ta7rnnnvUq1evWJcWEfv379fs2bP12muvqaamRv3799dtt90WPDvq1KlTtXLlypDf6d69u9asWROLctvs888/V1FR0RHL77//fo0cOVIbN27UzJkz9eGHHyorK0ujR4/W9ddfH4NK2+7NN9/UD37wg6Pe1rNnT7366quu277Jys19Ktl6lESfCohon7IR9Oijj9pnnXWW/dprr9kbN260r7vuOvvCCy+06+vrY11aRIwdO9a+/PLL7bffftv+5JNP7Pvuu88uKCiwy8vLbdu27eLiYnv27Nl2RUVF8F9lZWWMq269V1991R40aJC9e/fukDnV1tba+/bts88880x72rRpdnl5uf3iiy/agwYNsl988cVYl90q9fX1IXOsqKiwX3/9dXvAgAH2ihUrbNt23/ZNVm7uU8nWo2ybPhWNPkWwOaS+vt4eMmSIvWzZsuCyAwcO2AUFBfZLL70Uw8oiY9u2bXZ+fr69bt264DLLsuwLL7zQnjNnjt3U1GQPGjTIfvnll2NYZWQ98cQT9uWXX37U2+bPn2+fe+65dmNjY3DZQw89ZP/Xf/1Xe5UXVQ0NDfa3v/1t+9Zbb7Vt23bl9k1Gbu5TydijbJs+FY0+xTE2h2zatElffvmlhg0bFlyWmZmpAQMG6O23345hZZGRnZ2tp556SqeffnpwmWEYsm1bBw4c0LZt21RfX6+8vLwYVhlZH330kfr27XvU28rKylRYWCiv9/C7scOGDdPWrVtVWVnZXiVGzdKlS7Vz505NnTpVkly5fZORm/tUMvYoiT4VjT5FsDlk165dkqQTTjghZHm3bt20c+fOWJQUUZmZmTrvvPNCrl2zevVqffrppzrnnHO0efNmGYahxYsXq6ioSBdccIHuu+8+ffHFFzGsum02b96syspKXX311frGN76hq666Sn/7298kNW/vHj16hIzv1q2bJGnHjh3tXmsk1dfXa/78+RozZkxwTm7cvsnIzX0qGXuURJ+KRp8i2BxSW1srSUdctC4lJUX19fWxKCmq1q1bpzvvvFPnn3++ioqK9PHHH8s0TZ100kmaP3++pkyZor/+9a+6+eabZVlWrMsNW0NDg7Zt26aamhrdeuuteuqppzRo0CDdcMMNWrt2rerq6o66rSUl/Pb+zW9+o/r6eo0ePTq4zG3bN1klU59ye4+S6FPR6lN8KuqQ1NRUSc1PtMD3UvOTJy0tLVZlRcUrr7yi22+/XWeccYZmz54tSZo4caKuvfZaZWZmSpLy8/OVm5urK6+8Uh988IHOOOOMWJYcNp/Pp7ffflterzfYGE4//XR98sknWrRokVJTU9XQ0BDyO4FGkZ6e3u71RtKqVat00UUXKTs7O7jMbds3WSVLn0qGHiXRp6LVp9hjc0hg125FRUXI8oqKiiN2BSayJUuWaOLEifrmN7+pBQsWBJujYRjBJ1NAfn6+pMO7vxNNenr6Ea928vPztXv3bvXo0eOo21pq/mhhotq3b5/effddXXLJJSHL3bh9k1Ey9Klk6lESferfRWobE2wOOfXUU5WRkaE333wzuKy6ulobNmwInkMh0S1btkz33XefrrnmGs2ZMyfkj+m222474twIH3zwgSQd88C2eLZp0yYNGTJEZWVlIcs//PBD9e3bV4WFhVq3bp38fn/wtrVr16pPnz7Kyclp73Ij5p133pFhGBo6dGjIcrdt32Tl9j6VTD1Kok9FrU+1/QNb7jF79mx76NCh9iuvvBI8P8RFF13kivNDbNmyxR44cKA9YcKEI84jUF1dbf/5z3+2+/fvbz/++OP29u3b7ddee80uKiqyJ02aFOvSW8Xv99sjR460L730Uvvtt9+2y8vL7QceeMA+/fTT7U2bNtl79+61CwsL7SlTptgff/yxXVpaag8aNMheuXJlrEtvk0cffdS+6KKLjljutu2bzNzap5KtR9k2feo/RWobG7Zt222OXy7h9/s1e/ZsrVy5UnV1dSosLNTdd9+tnj17xrq0Nps/f74efvjho95WXFysBx98UH/60580f/58bdmyRZ06ddJll12mW2+9NXiwWqLZt2+fZs2apTVr1qi6uloDBgzQ7bffHnxl+/7772vmzJnasGGDcnNzdd1112nUqFExrrptpk+fro0bN2r58uVH3Oa27Zus3NqnkrFHSfSp/xSJbUywAQAArsExNgAAwDUINgAAwDUINgAAwDUINgAAwDUINgAAwDUINgAAwDUINgAAwDUINgAAwDUINlBRUZF++tOfxroMADgm+hScItgAAADXINgAAADXINhAktTY2Kj7779fhYWFKiws1JQpU7Rv377g7S+88IJGjBihwYMHq6CgQN/5znf0hz/8IXj7ypUrNWDAAL333nu68sorNWjQIA0fPlwLFiwIuZ9//etfmjx5ss455xwNHDhQZ511liZPnqyqqqrgmKKiIs2dO1e/+MUv9I1vfEMFBQW6/vrrtXXr1pB1tVSTZVl65JFHVFRUpNNPP11FRUWaPXu2GhsbI/3wAWgH9Ck4EolLkCOxfetb37JPO+00+8orr7RfeeUVe8WKFfbQoUPtK6+80rZt216yZIl96qmn2vPmzbP/8Y9/2H/84x/tkpISe8CAAfbnn39u27Ztl5aW2v3797eHDx9uP/PMM/Ybb7xhT5o0yc7Pz7fXrFlj27ZtHzx40P7Wt75ljxgxwv6///s/e+3atfa8efPs0047zb7rrrtC6vna175mjxs3zn7ttdfs3/zmN/bQoUPt733ve8ExTmqaP3++XVhYaL/44ov2m2++aT/11FP2aaedZs+dO7e9HloAEUKfglPeWAcrxIfMzEwtXLhQGRkZkqTs7GxNmDBBr7/+uj777DNdd911mjBhQnB8z549NWLECL3zzjs68cQTJUm2bevmm2/WyJEjJUlf+9rX9PLLL+u1117Tueeeq23btqlHjx568MEHdfLJJ0uShg0bpg8++EBvvfXWEfU8/vjj8ng8kqRPP/1Ujz76qKqqqpSdne2oprfeeksDBw5USUmJJGno0KFKS0sLzhFAYqFPwQmCDSRJ5513XsgfUlFRkTp06KA33ngj+EmEL774Qtu2bdO2bdu0du1aSTpid+mQIUOC3/t8PnXp0kUHDx6UJJ122mlatmyZLMvSZ599pm3btunjjz/Wli1b1NTUFLKeQYMGBZuFJPXo0UOSVFtbq+zsbEc1nXnmmXrooYd09dVX68ILL9Q3v/lNjRo1qu0PFoCYoE/BCYINJEldu3YN+dk0TWVlZam6ulqffvqp7r77bv3jH/+Q1+vVKaecov79+0tqfvXz71JTU49Yz7+Pefrpp/Xkk0+qqqpKXbt21cCBA5WWlqYvvvgi5PfS0tKOWI/U/H60JEc1/fCHP1THjh1VWlqqX/ziF3rwwQeVn5+vO++8U2eddVarHicAsUOfghMEG0iSqqurQ372+/3B3anjxo1Thw4dtGLFCg0YMEBer1fl5eX67W9/G9Z9/O53v9ODDz6o2267TVdccYW6dOkiSfrxj3+sDz74wPF6LMtyVJNpmrrmmmt0zTXXqLKyUn/96181f/58TZw4UW+88YZ8Pl9Y9QOILfoUnOBTUZAkvfHGGyG7Wf/0pz+pqalJp512mrZu3aorrrhCBQUF8nqbs/CaNWskHX5l4sS6devUqVMnjRs3LtgsvvzyS61bty6s9VRVVTmq6fvf/77uv/9+SVJOTo5GjBiha665Rl988YVqamoc3x+A+ECfghPssYEkae/evZo4caJGjx6tbdu2afbs2Tr77LN18cUXa9asWVq6dKl69OihzMxMvf7661q8eLGk5veSnSooKNDzzz+vBx98UN/61rdUUVGhRYsWae/evercubPj9eTk5Oikk05qsabCwkL98pe/VNeuXTVkyBDt3r1bTz/9tIYOHRpsWAASB30KThBsIEn63ve+p7q6Ok2YMEE+n0+XXXaZ7rjjDhmGoccff1wzZ87UT3/6U/l8PvXt21dPPPGEHnjgAZWVlWn06NGO7qO4uFj/+te/VFpaqmXLlql79+4677zzdPXVV+tnP/uZysvL1bdvX0frclLTj3/8Y/l8PpWWluqxxx5Tp06dVFRUpNtuu60tDxWAGKFPwQnD/s+jqgAAABIUx9gAAADXINgAAADXINgAAADXINgAAADXINgAAADXINgAAADXINgAAADXINgAAADXINgAAADXINgAAADXINgAAADX+H/sxOC3ETIbQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1) \n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='male'], x='Age', color='b', bins=range(0,80,5))\n",
    "plt.ylim([0,80])\n",
    "plt.xlabel('bananas')\n",
    "plt.ylabel('fruitbats')\n",
    "plt.legend(['mad'])\n",
    "\n",
    "plt.subplot(1,2,2) \n",
    "sns.histplot(data=titanic.loc[titanic.Sex=='female'], x='Age', color='r', bins=range(0,80,5))\n",
    "plt.ylim([0,80])\n",
    "plt.xlabel('bananas')\n",
    "plt.ylabel('fruitbats')\n",
    "plt.legend(['madder'])\n",
    "\n",
    "plt.subplots_adjust(wspace = 0.5) # shift the plots sideways so they don't overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1114fe23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}