{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5505d2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2025-11-30 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e63c07",
   "metadata": {},
   "source": [
    "## Histplot - määrällisen muuttujan luokiteltu jakauma\n",
    "\n",
    "Määrällisen muuttujan jakauman graafiseen esittämiseen voidaan käyttää\n",
    "\n",
    "- Ruutu- ja janakaaviota (boxplot), joka perustuu tilastollisiin tunnuslukuihin. Katso https://github.com/taanila/kaaviot/blob/master/sns_box.ipynb.\n",
    "- Histogrammia, joka esittää luokitellun muuttujan lukumäärä- tai prosenttiyhteenvedon.\n",
    "\n",
    "Tässä muistiossa esitetään esimerkkejä histogrammin käytöstä.\n",
    "\n",
    "### Tuonnit ja alkuvalmistelut\n",
    "\n",
    "- Grafiikan tyylimäärittelyssä voit käyttää **'whitegrid'** sijasta **'white'**, **'dark'**, **'darkgrid'** tai **'ticks'**. Voit myös jättää tyylimäärittelyn tekemättä.\n",
    "- **PercentFormatter** auttaa muotoilemaan akselin prosenttiasteikon.\n",
    "- **MultipleLocator** auttaa muuttamaan akselin asteikon jaotusta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6cf278ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "from matplotlib.ticker import PercentFormatter, MultipleLocator\n",
    "ticks = PercentFormatter(xmax=100, decimals=0, symbol=' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986f4c2d-1807-4e5a-9d47-d0ea804e568c",
   "metadata": {},
   "source": [
    "### Datan avaaminen\n",
    "\n",
    "Seaborn-paketin esimerkkidata **tips** sisältää tietoja ravintolassa asioineista seurueista."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "595ef5a4-f318-4fb4-90f7-3cc2cea5692d",
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226fd4d7",
   "metadata": {},
   "source": [
    "### Luokiteltu jakauma\n",
    "\n",
    "Seabornin **histplot** esittää määrällisen muuttujan luokitellun jakauman histogrammina. Lisätietoa https://seaborn.pydata.org/generated/seaborn.histplot.html.\n",
    "\n",
    "Seuraavassa esitetään laskun loppusumman jakauma käyttäen luokitelua kuuteen luokkaan (`bins=6`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb3f7f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Lukumäärä')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(tips, x='total_bill', bins=6)\n",
    "plt.xlabel('Laskun loppusumma')\n",
    "plt.ylabel('Lukumäärä')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17b4ccd",
   "metadata": {},
   "source": [
    "**bins**-parametrilla voit määrittää luokkien lukumäärien sijasta myös täsmälliset luokkarajat listana. Luokkarajoja tulkitaan seuraavasti:\n",
    "- Luokkien alarajat sisältyvät luokkaan, mutta ylärajat eivät.\n",
    "- Poikkeuksena viimeisen luokan yläraja sisältyy luokkaan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9dbf5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Lukumäärä')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0, 10, 20, 30, 40, 60]\n",
    "sns.histplot(tips, x='total_bill', bins=bins)\n",
    "plt.xlabel('Laskun loppusumma')\n",
    "plt.ylabel('Lukumäärä')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd4727de-7f2f-47cd-9547-20e4b68cbac3",
   "metadata": {},
   "source": [
    "Seuraavassa esitetään lukumäärien sijasta prosentit (`stat='percent'`). Pystyakselin asteikko muotoillaan prosenttiasteikoksi käyttäen aiemmin määriteltyä **ticks**-muotoilua. Asteikon jaotus säädetään kymmenen välein."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef231148-034e-4dcf-988d-6fb63edb4316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0, 10, 20, 30, 40, 60]\n",
    "ax = sns.histplot(tips, x='total_bill', bins=bins, stat='percent')\n",
    "plt.xlabel('Laskun loppusumma')\n",
    "plt.ylabel(f'Prosenttia, n={tips['total_bill'].count()}')\n",
    "ax.yaxis.set_major_formatter(ticks)\n",
    "ax.yaxis.set_major_locator(MultipleLocator(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08085de5",
   "metadata": {},
   "source": [
    "### Usean histogrammin yhdistelmä\n",
    "\n",
    "Usean kaavion yhdistelmä voidaan luoda **plt.subplots**-funktiolla, joka palauttaa kuvion (**figure**) ja kuvion sisään sijoitettujen kaavioiden listan. Seuraavassa nämä on sijoitettu muuttujien **fig** ja **axs** arvoiksi. Parametrin `ncols=len(muuttujat)` mukaisesti luodaan kuvio, joka sisältää kaksi kaaviota vierekkäin. Kaavioilla on yhteinen y-akseli (`sharey=True`) ja kuvion koko on **12.8 x 4.8**. Oletuskuvion koko olisi ollut **6.4 x 4.8**.\n",
    "\n",
    "Kuvion sisällä sijaitseviin kaavioihin voit viitata **axs[0]** ja **axs[1]**. Kaaviot luodaan normaaliin tapaan, mutta ylimääräisenä parametrina **ax** annetaan viittaus kaavion sijaintiin kuvion sisällä, esimerkiksi `ax=axs[0]`.\n",
    "\n",
    "Usean kaavion yhdistelmissä kannattaa yksittäisen kaavion muotoiluun käyttää **plt**:n sijasta suoraa viittausta muotoiltavaan kaavioon (esimerkiksi `axs[0]`). Lisätietoa https://matplotlib.org/stable/api/axes_api.html.\n",
    "\n",
    "Lisätietoa usean kaavion yhdistelmistä https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d28d375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Juomaraha dollareina')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "muuttujat = ['total_bill', 'tip']\n",
    "fig, axs = plt.subplots(nrows=1, ncols=len(muuttujat), sharey=True, figsize=(12.8, 4.8))\n",
    "\n",
    "# Käydään muuttujat-lista läpi: i=järjestysnumero, muuttuja=muuttujan nimi\n",
    "for i, muuttuja in enumerate(muuttujat):\n",
    "    sns.histplot(tips, x=muuttuja, ax=axs[i])\n",
    "    \n",
    "axs[0].set_xlabel('Laskun loppusumma dollareina')\n",
    "axs[0].set_ylabel('Lukumäärä')\n",
    "axs[1].set_xlabel('Juomaraha dollareina')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2283cbc2",
   "metadata": {},
   "source": [
    "### Displot\n",
    "\n",
    "Seabornin **displot**-funktio on oikotie usean kaavion histogrammeihin kategoristen muuttujien määräämissä ryhmissä. Funktiota voidaan käyttää myös useiden muiden kaaviolajien kanssa. Lisätietoa https://seaborn.pydata.org/generated/seaborn.displot.html.\n",
    "\n",
    "Seuraavassa tarkastelen tipin (**tip**) suuruutta sukupuolen (**sex**) ja tupakoinnin (**smoker**) määrittämissä ryhmissä. Kaaviolaji määritetään parametrina `kind='hist`. Kuvion kokoa säädetään **height**-parametrilla.\n",
    "\n",
    "**displot** palauttaa **Facetgrid**-luokan olion, jolla on omat funktionsa otsikointien ym. ominaisuuksien muotoiluun. Esimerkissä olio sijoitetaan **g**-nimiseen muuttujaan. Lisätietoa https://seaborn.pydata.org/generated/seaborn.FacetGrid.html.\n",
    "\n",
    "Yksittäisiin kaavioihin voidaan viitata rivi-ja sarakenumeroiden avulla, esimerkiksi `g.axes[0, 0]`. Rivinumero on pakollinen vaikka kuvia olisi vain yhdellä rivillä kuten tässä esimerkissä."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c6e28ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tupakoimaton nainen')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkyaaNm2a8vLy9Mwzz+ipp55STk6OZs+erSuuuMLHHfgHk51fHUQFJSUlKSEhQS+//LKvSwHgZWz/qIrY0wQAAGAAoQkAAMAADs8BAAAYwJ4mAAAAAwhNAAAABhCaAAAADAi40GS322Wz2c7522EA8FfMHQDOJeBCU1lZmX744QeP/wyGK8rKyrRlyxa/qqky6Me/BVI/3uyFucPz6Me/BVI/nuol4EKTP7Lb7SopKQmYT7D0498CqZ9A6qUiAq1/+vFvgdSPp3ohNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBgB+zWq2+LgHA/xCaAKCCyso8e+Vks9msmJgYmc1mr40J4MyCfV0AAJyvgoJMmrR6l/ZlF3psjIKCAoWFhUmSGtSyql9SI4+NBeDsCE0AUAn7sgu150i+R9Ztt0t5eXmKiLDLZPLIEABcwOE5AAAAAwhNAAAABhCaAAAADCA0AQAAGEBoAgAAMIDQBAAAYAChCQAAwACfhKa1a9eqc+fOatGihVq3bq3Ro0erqKhIkrR582Z17txZcXFxSkpK0qJFi3xRIgAAgBOvh6ajR4+qT58++vvf/67vvvtOS5Ys0YYNGzRjxgzl5uaqd+/eSk5O1saNGzV27FilpqZqy5Yt3i4TAADAidevCB4VFaU1a9YoIiJCdrtdOTk5+vPPPxUVFaXPPvtMkZGReuihhyRJrVq1Uvv27ZWWlqbmzZt7u1QAAAAHn/yMSkREhCTppptu0sGDB3XttdeqY8eOmjBhgho3buz02IYNGyo9Pd3lMWw2m1tqdYfyWvyppsqgH/8WSP1UpJe//rhtZcZ0ZSy7p35Dt3zFdrvs+v/fUTlf/7aB9N6U6MefeWruMNntHtvcz6moqEi5ubkaOHCgqlWrprp166qkpESvvvqq4zGLFi3SjBkz9Pnnnxtap81m0w8//OChigH4u/j4+Ao9z9W5w2q1KiYmRv3nrdNPh45XaExXXXlhdU3s1lL//e9/VVjouR8JBqoiI3OHT3+w12KxyGKxaNCgQercubO6deum48edJ5+ioiKFh4e7vO7Y2NhKf+J0F5vNpszMTL+qqTLox78FUj++6MXVscLCwhQR4aHPnna78vLzFREeLplMCgsLkyRFR0d7ZjwPC6T3pkQ//sxTvXg9NH3//fcaNmyYPvroI4WGhkqSiouLFRISooYNG+rbb791evzu3bvVqFEjl8cxm81+90f3x5oqg378WyD1481eKjKWyXTux1SE45CcyeQ0xvn+dw2k96ZEP/7M3b14/dtz0dHRKioq0r/+9S8VFxdr3759euWVV3T//ferXbt2ysrK0pw5c1RSUqJ169Zp6dKl6tSpk7fLBAAAcOL1PU3h4eGaOXOmXnrpJbVu3VrVq1dX+/btlZKSotDQUM2ePVtjx47VxIkTFRUVpREjRqhly5beLhMAAMCJT85patiwoWbPnn3a+2JjY7Vw4UIvVwQAAHB2/IwKAACAAYQmADhP1LSGqKzM+1eJ8cWYgD/y6SUHAADGhVcLVlCQSZNW79K+bO9cp6lBLav6Jbn+DWYgEBGaAOA8sy+7UHuO5Pu6DKDK4fAcAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQAAGEBoAgAAMIDQBAAAYAChCQAAwABCEwAAgAGEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAY4JPQtH37dvXo0UMJCQlq3bq1Bg8erKNHj0qSRo0apWbNmikuLs7x79133/VFmQAAAA5eD01FRUXq2bOn4uLi9M0332jZsmXKycnRsGHDJEmZmZkaPXq0Nm3a5PjXpUsXb5cJAADgxOuhaf/+/WrSpIlSUlIUGhqqWrVqqUuXLtq4caOKi4u1c+dONWvWzNtlAQAAnFWwtwe84oorNHPmTKdln376qZo2bart27ertLRUEydOVEZGhqpXr65OnTqpZ8+eCgpyLd/ZbDZ3ll0p5bX4U02VQT/+LZD6qUgvZrPZLWO6MpbdXqkhz6x8xXa77DKd9i5vccf7KZDemxL9+DNPzR1eD01/ZbfbNWHCBH3xxReaP3++srKylJCQoG7dumn8+PHatm2bUlJSFBQUpJ49e7q07szMTA9VXXH+WFNl0I9/C6R+XOklPj7eK2NZrVbFxMSooKBAeXl5lRrzXPLy8yVJRUXh//tvkcfHLFcQdiKs7dixQ4WFhW5ZZyC9NyX68Wfunjt8Fpry8vI0dOhQbd26VfPnz1d0dLSio6PVunVrx2OaN2+u7t27a/ny5S6HptjY2Ep/4nQXm82mzMxMv6qpMujHvwVSP77oxdWxwsLCFBHhod0+drvy8vMVER4umUyyWKySJIvFoogI7+wNCAsLkyRFR0dXel2B9N6U6MefeaoXn4SmvXv3qlevXqpfv77S09MVFRUlSVq5cqWysrLUtWtXx2OLi4tlsVhcHsNsNvvdH90fa6oM+vFvgdSPN3upyFgm07kfUxGOQ3Im0yljeGrMM3Hn6x9I702JfvyZu3vx+ongubm56t69u1q0aKFZs2Y5ApN04nBdamqq1q5dK7vdrk2bNmnu3Ll8ew4AAPic1/c0LV68WPv379cnn3yiFStWON23adMmDR06VM8//7wOHjyo2rVrq1+/furQoYO3ywQAAHDi9dDUo0cP9ejR44z3d+3a1enwHAAAgD/gZ1S8xGq1+roEAABQCVU+NJWVef5iJ2azWTExMU4no3ljXAAA4D4+vU6TPwgKMmnS6l3al+2e64+cSUFBgeOruw1qWdUvqZFHxwMAAO5V5UOTJO3LLtSeI/keW7/dfuK6VBERdq9/TRgAALhHlT88BwAAYAShCQAAwABCEwAAgAGEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwILgiT1q3bp0OHjwou90uSSopKdGOHTs0YsQItxYHAADgLwyHpmXLlumee+7RmDFjtHDhQoWHh0uSbDab8vPzlZSU5LEiAQAAfM3w4blp06ZJkpYvX6758+drwoQJSkpK0saNG9W9e3fVrFnTY0UCAAD4mkt7miSpqKhI11xzjQ4fPqytW7fKZDLpqaee0p133umxIgEAAHzN8J6m3r17S5Lq1aunI0eOqE6dOvrjjz9UUlIii8Wi/Px8jxUJAADga4b3NMXHx0uSbrrpJj366KN65513dN1112nYsGGqVq2aLr/8co8VCQAA4GuG9zT16dNHkvTMM8+oQ4cOCgkJ0ciRI5WTk6Pdu3dr9OjRhgfdvn27evTooYSEBLVu3VqDBw/W0aNHJUmbN29W586dFRcXp6SkJC1atMjFlgAAANzP5es0vfzyy+ratauqV6+uunXr6q233tLChQsVExNj6PlFRUXq2bOn4uLi9M0332jZsmXKycnRsGHDlJubq969eys5OVkbN27U2LFjlZqaqi1btrjcGAAAgDu5fJ2mpUuXatiwYRUecP/+/WrSpIlSUlJkNpsVGhqqLl26aPDgwfrss88UGRmphx56SJLUqlUrtW/fXmlpaWrevLlL49hsNkOPM5vNkqT/XXLKM8pXbrfLLpNjsdEa/U153edr/SejH/9VkV7Kt+nKjunKWB6bP84wd3h0zDNwx/uprKxMVqtVZWVlbqjI9wJpW5MCqx9PzR0uh6ZOnTrphRdeUMeOHVWnTh2ZTP+/IdevX/+cz7/iiis0c+ZMp2WffvqpmjZtql27dqlx48ZO9zVs2FDp6emulqnMzMxzPsZqtSomJkYFBQXKy8tzeQxX5f3vZPmCsBOv2Y4dO1RYWOjxcT3FyGt8PqEf/+VKL+XnX3p6LG/OH+VzR1FR+P/+W+SVOUuSqtWuprIye6XDqHTi/5SMHpUotdn0361bVVJSUulxPS2QtjUpsPpx99zhcmh6++23JUnvvfeeIzDZ7XaZTCZt27bNpXXZ7XZNmDBBX3zxhebPn6+5c+fKarU6PcZisaigoMDVMhUbG2t4Iw8LC1NEhAc/ttntysvPV0R4uGQyKSwsTJIUHR3tuTE9yGazKTMz06XX2J/Rj//yRS+ujuXR+eOkucNiOTE/WiwWRUR4Z2/ABZHVFRRk0qRVu7Qvp5If8ux2FRQWKsxqlUymMz6sQaRV/W5tpKZNm1ZuPA8LpG1NCqx+PNWLy6Fp1apVbhk4Ly9PQ4cO1datWzV//nxFR0fLarXq+PHjTo8rKipyXH3cFWaz2aUX6izbb6U5dqubTE7jnO9vSldfY39HP/7Lm71UZCxPzR9nmjs8OeaZ7Msp1J4jlbu0jN1+Yu6PiLAbqv98ef8G0rYmBVY/7u7F5dDUoEGDU5aVlpZq586dp73vdPbu3atevXqpfv36Sk9PV1RUlCSpcePG+vbbb50eu3v3bjVq1MjVMgEAANzK5W/Pffnll7rlllsUExOjq666SldddZViY2P14IMPGnp+bm6uunfvrhYtWmjWrFmOwCRJbdu2VVZWlubMmaOSkhKtW7dOS5cuVadOnVwtEwAAwK0M72lq0aKFvv/+e40bN0633367atSooR07duiee+7Rm2++qfvvv9/QehYvXqz9+/frk08+0YoVK5zu27Rpk2bPnq2xY8dq4sSJioqK0ogRI9SyZUvXugIAAHAzw6FpxowZkqTff/9dgwYN0u+//65169bp9ttv1xVXXKF//OMf6tat2znX06NHD/Xo0eOM98fGxmrhwoVGywIAAPAKw4fn/vjjD0lSVFSUgoKCVL9+ff3000+STlwW4ODBg56pEAAAwA8YDk3Tpk2TdOJr8m+88YYk6YILLtBXX32l9evXn3KpAAAAgEBiODQtW7ZMkjRo0CCtXLlShw8fVv/+/dW3b189+uijeuyxxzxWJAAAgK+5fMmBK6+8Uh9//LGkE5cf+OKLL5Sfn6/LL7/c7cUBAAD4C5dDkyT99ttvOnjwoOz/+/GjkpISffXVV3r00UfdWRsAAIDfMByaMjIyFB8fr+nTp+v1118/5SdUrr76akITAAAIWIbPaerZs6ckKS0tTRMnTtTUqVPVuXNnrVu3TnfddZfi4uI8ViQAAICvGQ5NmzZtkiQdP35ct99+u5o0aaIff/xRkZGRGj58+CkXqgQAAAgkhkPT888/L0m68MILlZeXp7p16+r333+X3W5XVFSUjh075qkaAQAAfM7wOU3lJ31fd9116t+/vyZMmKCYmBiNHz9e1apVU926dT1WJAAAgK8Z3tP0wgsvSJKGDBmiv/3tbyotLdWwYcO0cuVKvfvuuxo2bJjHigQAAPA1ly85EBERoVGjRkk68ZMqn3zyiduLAgAA8Dcuh6acnBwtWLBA+/btU1lZmdN9qampbisMAADAn7gcmgYMGKADBw7ommuuUVCQ4aN7AAAA5zWXQ9PmzZv1xRdfKDIy0gPlAAAA+CeXdxVdeumlKikp8UQtAAAAfsvlPU0jR45U7969lZycrJo1azrdl5yc7K66AAAA/IrLoSk9PV07d+7U22+/7XROk8lkIjQBAICA5XJoWrFihT788EM1bNjQE/UAAAD4JZfPaapVq5YuvfRST9QCAADgt1ze09S/f38NHTpUjz/+uGrWrCmTyeS4r379+m4tDgAAwF+4HJqGDBkiSfr4448dgclut8tkMmnbtm3urQ4AAMBPuByaVq1a5Yk6qpSa1hCVldkVFGQ694PdyBdjAgAQKFwOTQ0aNPBEHVVKeLVgBQWZNGn1Lu3LLvTKmA1qWdUvqZFXxgIAIBC5HJqaNGnidB7TX3F4zjX7sgu150i+r8sAAAAGuBya5s6d63T76NGjmjdvnjp06ODy4EePHlWXLl00ZswYJSYmSpJGjRql999/XyEhIY7HDRkyRF26dHF5/QAAAO7icmhKSEg4ZVl8fLweffRRPfDAA4bXk5GRoSFDhmjv3r1OyzMzMzV69Gjdd999rpYGAADgMS5fp+l0atSooYMHDxp+/JIlSzRw4EA9/fTTTsuLi4u1c+dONWvWzB1lAQAAuI3Le5o++OADp9slJSVatWqVrrrqKsPraNOmjdq3b6/g4GCn4LR9+3aVlpZq4sSJysjIUPXq1dWpUyf17NnT6SdbjLDZbIYeZzabJUl2u0urd035yu122WU67V3eYvR1MbIOd6zLH9CP/6pIL+XbdGXHdGUsj23HfjR3uGXMs/RzOv7+Hg6kbU0KrH48NXe4HJomTpx4yiBXXnmlRo0aZXgdderUOe3y48ePKyEhQd26ddP48eO1bds2paSkKCgoSD179nSpzszMzHM+xmq1KiYmRgUFBcrLy3Np/RWRl3/ipO+iovD//bfIK+NKUkHYiQlqx44dKix0zzf2jLzG5xP68V+u9BIfH++Vsbw5f/hy7vDEmOX9nIkn5itPCqRtTQqsftw9d7gcmlavXn3a5UVFRa6u6hStW7dW69atHbebN2+u7t27a/ny5S6HptjYWMOfOMPCwhQR4cGPbXa78vLzFREeLplMsliskiSLxaKICO8k+rCwMElSdHR0pddls9mUmZnp0mvsz+jHf/miF1fH8uj84Qdzh1vHPKmfM3HnfOVJgbStSYHVj6d6qdC35x555BGnZT/88IOee+45ffrpp5UqZuXKlcrKylLXrl0dy4qLi2WxWFxel9lsdumFOsv2W2mO3dAm0ynjeHLc03Hnm8fV19jf0Y//8mYvFRnLU9uxP80d7hjzbP2czvny/g2kbU0KrH7c3YvLJ4JPnTpVixcvliSVlpZq/Pjxevjhh3X99ddXuhi73a7U1FStXbtWdrtdmzZt0ty5c7ncAAAA8DmX9zTNmjVLjz/+uLKzs7Vs2TIdO3ZMM2fOVMuWLStdTNu2bTV06FA9//zzOnjwoGrXrq1+/fpV6BpQAAAA7uRyaIqJidHMmTPVo0cPNW3aVAsWLJDVaq1wATt27HC63bVrV6fDcwAAAP7AcGiaPHmy0+0WLVpo3bp1mj59uoKDT6zmqaeecm91AAAAfsJwaFq/fv0py2JjY5WRkSFJZ/w9OgAAgEBgODTNmzfPk3UAAAD4tUpfEfyvkpOTK1EKAACA/6r0FcFzc3NVWFio+Ph4QhMAAAhYlb4iuN1u11tvvaWcnBx31QQAAOB3XL645clMJpMef/xxffjhh+6oBwAAwC9VOjRJ0i+//MK356qYylybCwCA85HLh+e6devmFJBKSkq0Y8cO3XvvvW4tDO5V0xqisjK7goIqH27NZrNiYmIMPdZdYwIA4Gsuh6bExESn20FBQXr00Ud12223ua0ouF94tWAFBZk0afUu7csurPT6CgoKHL9EfiYNalnVL6lRpccCAMAfuByaTnfVb5vNpr179+ryyy93S1HwnH3ZhdpzJL9S67Dbpby8PEVE2H3yS+sAAPiCW85pysrK0l133eWOVQEAAPglt4Qm6cSlBwAAAAKV20IT354DAACBzG2hCQAAIJAZPhF848aNZ7zv6NGjbikGAADAXxkOTd26dTvr/RyeAwAAgcxwaNq+fbsn6wAAAPBrnNMEAABgAKEJAADAAEITAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQAAGODT0HT06FG1bdtW69evdyzbvHmzOnfurLi4OCUlJWnRokU+rBAAAOAEn4WmjIwMdenSRXv37nUsy83NVe/evZWcnKyNGzdq7NixSk1N1ZYtW3xVJgAAgCQfhaYlS5Zo4MCBevrpp52Wf/bZZ4qMjNRDDz2k4OBgtWrVSu3bt1daWpovygQAAHAw/DMq7tSmTRu1b99ewcHBTsFp165daty4sdNjGzZsqPT0dJfHsNlshh5nNpslSXa7y0MYV75yu112mU57lzdVesyz9HM6Rv8WvlJen7/XaVQg9VORXsq36cqO6cpYHtuOmTsqOaBnBdK2JgVWP56aO3wSmurUqXPa5fn5+bJarU7LLBaLCgoKXB4jMzPznI+xWq2KiYlRQUGB8vLyXB7DVXn5+ZKkoqLw//23yCvjemrM8n7OpCDsxKS4Y8cOFRYWumVMTzLynjmfBFI/rvQSHx/vlbG8OX8wd/i3QNrWpMDqx91zh09C05lYrVYdP37caVlRUZHCw8NdXldsbKzhT5xhYWGKiPDgxza7XXn5+YoID5dMJlksJ4KhxWJRRIR3Er1bxzypnzMJCwuTJEVHR1duPA+z2WzKzMx06T3jzwKpH1/04upYHp0/mDsqN56HBdK2JgVWP57qxa9CU+PGjfXtt986Ldu9e7caNWrk8rrMZrNLL9RZtt9Kc+yGNplOGceT455JZcc8Wz+nc75sfK6+Z/xdIPXjzV4qMpantmPmjvPj/RtI25oUWP24uxe/uk5T27ZtlZWVpTlz5qikpETr1q3T0qVL1alTJ1+XBgAAqji/Ck21atXS7NmztWLFCiUmJmrEiBEaMWKEWrZs6evSAABAFefzw3M7duxwuh0bG6uFCxf6qBoAAIDT86s9TQAAAP6K0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAPiVmtYQlZV58EfUz8AXY+L84vMrggMA8Ffh1YIVFGTSpNW7tC+70CtjNqhlVb8k138cHlULoQkA4Jf2ZRdqz5F8X5cBOHB4DgAAwABCEwAAgAGEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAY4Jehafny5YqJiVFcXJzj36BBg3xdFgAAqMKCfV3A6WRmZqpDhw5KTU31dSkAAACS/HRPU2Zmppo1a+brMgAAABz8bk9TWVmZtm7dKqvVqpkzZ8pms+mmm27SwIEDVbNmTcPrsdlshh5nNpslSXZ7hco1pnzldrvsMp32Lm+q9Jhn6ed0jP4tfKW8Pn+v06hA6qcivZRv05Ud05WxPLYdV/G5wy1jusiVv38gbWtSYPXjqbnD70LT0aNHFRMTo3bt2mnixInKzs7Wc889p0GDBmnGjBmG15OZmXnOx1itVsXExKigoEB5eXmVKduQvPx8SVJRUfj//lvklXE9NWZ5P2dSEHZiUtyxY4cKCwvdMqYnGXnPnE8CqR9XeomPj/fKWN6cP6ra3OGLPiszXwXStiYFVj/unjv8LjTVrl1baWlpjttWq1WDBg3SAw88oLy8PEVERBhaT2xsrOFPnGFhYYqI8ODHGbtdefn5iggPl0wmWSxWSZLFYlFEhHcSvVvHPKmfMwkLC5MkRUdHV248D7PZbMrMzHTpPePPAqkfX/Ti6lgenT+q6Nzhiz4rMl8F0rYmBVY/nurF70LT9u3btWzZMj377LMy/W+jKi4uVlBQkEJDQw2vx2w2u/RCnWX7rTTHbmiT6ZRxPDnumVR2zLP1czrny8bn6nvG3wVSP97spSJjeWo7rupzhzvGdFVF3meBtK1JgdWPu3vxuxPBIyMjlZaWppkzZ6q0tFT79+/XuHHjdN9997kUmgAAANzJ70JTvXr1NH36dK1atUoJCQnq1KmTYmNjNXLkSF+XBgAAqjC/OzwnSQkJCVq4cKGvywAAAHDwuz1NAACcL6xWq69LgBcRmuAxNa0hKivz/sVkfDEmgPNbReYrs9msmJiYSp1ozHx1fvHLw3MIDOHVghUUZNKk1bu0L9s712lqUMuqfkmNvDIWgMBR0fmqoKDAcbkCVzFfnX8ITfC4fdmF2nPk7BezAwB/4Mp8Zbfrf9cPtPvkEhDwPg7PAQAAGEBoAgAAMIDQBAAAYAChCQAAwABCEwAAPsBlWc4/fHsOAAAf4LIs5x9CEwAAPsRlWc4fHJ4DAAAwgNAEAABgAKEJAADAAEITIH6pHABwboQmBBR+qRwA4Cl8ew4BhV8qBwB4CqEJAYlfKgcAuBuH5wAAAAwgNAEAABhAaAIAADCA0ATAr4WEhPi6BADnIU/MHYQmAIZ5+9IKZrNZMU2benVMAO4XKHMH354DYJjXf5E90qp+tzaSzWbzyngAPCNQ5g5CEwCX8IvsACoiEOYOvzw8d+TIEfXt21fXXnutEhMTNXbsWJWWlvq6LAAAUIX5ZWgaMGCAwsLC9J///Efp6elau3at5syZ4+uyAABAFeZ3oenXX3/Vhg0bNGjQIFmtVl1yySXq27ev0tLSfF0aAACowvzunKZdu3YpMjJSdevWdSy78sortX//fh07dkw1atQ46/Pt9hNn6BcXFxv6AVaz2axLIy0KNnnwzH67XYURJlktFslkUr3qIbLZbJ4f9y/cOuZJ/XhlTIMqNKbBfs6kfk2LbDab35ysXFZWJovFopKSErfX5JXt5S/q16wmm83mUi9ms1lBQUEyufi3dHXuKB/Lo68Hc4d/91nJucMXfZ5tvmLuOPfcYbKXzxR+4sMPP9Trr7+uL7/80rFs7969atu2rb766ivVq1fvrM8vLi5WZmamh6sE4M+uueYaw8GnHHMHgHPNHX63pyksLEyFhc5fSSy/HR4efs7nBwcHKzY2tkKfNAEEhqAg1888YO4AcK65w+9CU6NGjZSTk6OsrCzVrl1bkvTTTz+pXr16ql69+jmfHxQUpNDQUE+XCSDAMHcAOBe/OxH8sssuU3x8vF566SXl5eXpt99+05QpU3T//ff7ujQAAFCF+d05TZKUlZWlF198UevXr1dQUJCSk5M1cOBAl89RAAAAcBe/DE0AAAD+xu8OzwEAAPgjQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQBwGna73dclwM8QmgLIkCFDFB0dfdZ/SUlJXq9r8eLFio6O1u+//16p9XTr1k3dunVzU1Wet379ekVHR2v9+vW+LgVVBHOA+2RkZKhPnz5eGcudJk2apOjoaF+XEbCCfV0A3Kdv377q2rWr4/aUKVP03//+V5MnT3YsCw0N9UVpbjFq1Chfl+CSpk2b6t1331XDhg19XQqqCOYA91m0aJF2797ttfHcpXPnzrrhhht8XUbAIjQFkEsvvVSXXnqp43ZUVJRCQ0N1zTXX+K4oNzrfwkdERETAvPY4PzAHoF69eqpXr56vywhYHJ6rgs502OjkXd9JSUl6/fXXlZqaqoSEBCUkJGjQoEHKzs52et6iRYvUsWNHXXPNNWrevLk6dOig5cuXn3H8Y8eOqUOHDkpKSnLsrv/zzz/15ptv6o477lBsbKxuv/12zZgxQ2VlZaet77HHHlNycvIp6x4wYIDuvvvuCtdWPs7w4cM1Y8YM3XzzzYqNjVXXrl21efNmp8etXLlSDz74oOLi4tSsWTPdcccdmj9/vuP+k1/nSZMmqW3btvryyy/Vvn17NWvWTO3atdOSJUuc1puTk6ORI0fq+uuvV2xsrB544AGtXbvW6THR0dFKS0vT8OHDlZCQoLi4OPXv319ZWVln7Q2QAmMOkE5sB//+9781ZMgQxcfHKyEhQWPGjFFRUZFeeeUVtWzZUomJiRo+fLj+/PNPx/OOHj2qF154QbfccouaNWumhIQEpaSkOGoZMmSIlixZon379ik6OlqLFy+WJB0/flypqam67bbbFBsbq3vuuUfp6elOvSUlJWnixIl65ZVXdP3116t58+Z6/PHH9csvv5z1b2Jkm7bZbJoxY4buueceNW/eXNdcc426du3qND+cfHjO6Hy2c+dO9enTRy1atFCLFi2UkpKi3377zXF/+Xtm7dq1euyxx3T11Vfr+uuv1yuvvKLS0tKz9hZICE04qwULFigjI0MvvfSSBg4cqK+//lo9e/Z0TGRpaWkaOXKkbr31Vk2fPl3jxo1TSEiIBg0apP3795+yvvz8fPXq1UvHjh3TO++8o4svvlh2u11PPPGEZs6cqfvvv1/Tpk3THXfcoQkTJpxxd3yHDh20bds2/fzzz07r/uKLL9ShQ4cK1fZXn376qVatWqURI0Zo/PjxysrKUv/+/WWz2SRJX375pVJSUtS0aVNNmTJFkyZNUoMGDTR69Gh9//33Z1zv4cOH9eKLL+qRRx7RjBkzdPHFF2vIkCH66aefJJ34P47u3btr1apVevrppzV58mTVq1dPPXv2PCU4vf766yorK9P48eM1ePBgffnll3rppZfO2hfgKn+dA8q99tprCg0N1eTJk9WhQwfNmzdPycnJOnDggMaNG6euXbsqPT1d8+bNk3Ti5O4+ffro22+/1bPPPqtZs2apb9++WrNmjUaOHCnpxGHOm266SXXq1NG7776rm2++WUVFRXrwwQf10Ucf6bHHHtOUKVMUHx+v4cOHa9q0aU41zZ07Vz///LNSU1M1ZswY/fjjjxoyZMg5X+tzbdOvvfaa3nzzTXXp0kUzZ87Uiy++qOzsbP3jH/9QQUHBGdd7rvnsl19+UdeuXXXkyBG9/PLLGjt2rH777Tf9/e9/15EjR5zWNXDgQMXHx2vatGlq3769Zs+efUpwDGQcnsNZmUwmvf3226pevbqkE7v7U1JS9PXXX+vmm2/Wb7/9pscee0wpKSmO51x88cXq2LGjvv/+e9WvX9+x/M8//9STTz6pP/74Q/Pnz9cll1wiSfr666+1Zs0ajRs3Tvfee68kqXXr1rJYLHrjjTfUvXv3U3bLt23bVmFhYVq+fLmeeuopSdLnn3+uP//8U+3bt5ckl2o7WWlpqWbNmqWIiAhJJyb65557Ttu2bVOzZs20e/duJScna/jw4Y7nxMXFKTExURs3blSLFi1Ou97CwkKNHTtWrVq1kiRddtlluuWWW/TVV1/pyiuv1Icffqjt27frvffe09VXXy1JuvHGG9WtWze99tprev/99x3raty4sVJTUx23t2zZohUrVpyxJ6Ai/HUOKHfllVfqxRdflCRdd911Sk9PV0lJiV577TUFBwfrhhtu0OrVqx0fZg4dOiSr1arnnntO1157rSQpMTFRv//+uxYuXCjpxGHOkw9tLliwQDt37tSCBQsUHx8vSbrhhhtUWlqqKVOmqGvXroqMjJQk1ahRQ1OmTJHZbJYk7d27V5MmTVJ2drZq1ap1xtf6XNv0oUOH9PTTTzvtbbNYLOrXr5927NihuLi40673XPPZ5MmTZbFYNGfOHMdjWrVqpdtuu00zZ87Uc88951hX586dHX/rVq1aaeXKlfryyy+dzqULZIQmnNUtt9zimCylE7ueQ0JC9N133+nmm292fHo6fvy49uzZoz179jj2iJSUlDita/Dgwfrxxx81duxYx2QpSRs2bJDZbNZdd93l9Ph7771Xb7zxhtavX3/KhBkWFqa2bds6haaPP/5YCQkJuuiiiyTJpdpO1rBhQ8fkIUl169aVdCL0SFLPnj0lSQUFBdq7d69++eUXZWZmGlr3X88vKT/3oPxT4tq1a1WnTh01bdrUaZf3LbfcoldffVW5ubmqWbPmKespX1d5fYC7+OscUO6vQSE4OFi1atVSs2bNFBz8///3FhkZqePHj0s6sS3PnTtXkrR//379+uuv+umnn/T999+fddvdsGGDGjRo4AhMf60xPT1dmzdv1k033SRJio2NdQQm6f+388LCwrOGpnNt0//6178knTi8+Ouvv+qXX37R6tWrJZ193jnXfLZu3TolJibKYrE45p2IiAhde+21WrNmjdO6Tg5m9erVO+terkBDaMJZXXjhhU63g4KCFBkZqWPHjkk68Qlq5MiRWrdunYKDg3XFFVc4jqeffI2TgwcPqlmzZnrzzTd15513Kjw8XJKUm5urWrVqOU1yklSnTh1Jckx2J0tOTnbsmbnwwgu1Zs0axydOV2s7mdVqPaVvSY5DEkePHtWoUaO0cuVKmUwm/e1vf3NMpq6su3y95c/JycnR4cOH1bRp09M+9/Dhw47QdLoaua4M3M2f5wBJTmGg3Mnbxsk++ugjjR8/XgcOHFBkZKSaNGkii8Vy1ufk5uaqdu3apywvX1b+epxu/JPnjzM51zadmZmpF154QZmZmbJYLGrYsKEaNGgg6ezzzrnqycnJ0fLly097HlpUVJTT7ZNfp6o27xCaqiCTySTp1A04Pz/fMYmVy8nJcbpts9mUnZ2tqKgolZWVqXfv3goJCdF7772nmJgYBQcHa/fu3froo49OGXfy5MmKiIhQcnKyXn/9dY0YMUKSVLNmTWVnZ6u0tNRp0jx06JAknfGTWcuWLVW3bl198sknqlu3roKDg9WuXTtHb67U5qqBAwfqp59+0ttvv60WLVooNDRUhYWFWrRoUaXWW716dV122WV67bXXTnv/xRdfXKn1A1LgzAEV8d133+m5557Tww8/rMcff9yxF+jVV19VRkbGGZ9Xs2ZN/frrr6csP3z4sNtrPJ28vDz17NlT0dHRWrZsma688koFBQXpq6++0qefflqpdVevXl3XX3+9evToccp9JwfZqo4Twaug8k9mBw4ccCzLzc11nIz8V//5z39UXFzsuL1q1SqVlpaqVatWys7O1i+//KL7779fzZs3d2xcX3/9taRTJ+TatWurYcOG6tGjh9LS0rRp0yZJUkJCgmw22ymfcson3ZN3h5cLCgrSPffco1WrVmnFihW69dZbHb25WpurMjIy1K5dO7Vs2dJx3Rt3rDshIUEHDhzQBRdcoNjYWMe/tWvXaubMmU67/IGKCpQ5oCI2bdqksrIy9e/f3xGYbDab4zBUec3le2PKXXfdddq3b98pweqjjz5SSEiImjdv7rYaT+fnn39WTk6OHnnkETVq1MhRn7vmnd27d+uqq65yzDnNmjXTnDlz9Pnnn7ul/kBBhKyCoqOjddFFF2ny5MmqXr26goKCNGPGjNPu0v7jjz/05JNP6pFHHtGBAwc0fvx4tWnTRomJiZKkBg0aKC0tTfXq1VONGjX0zTff6J133pGkM55fk5KSoo8//lgjRozQkiVLdOONNyoxMVGjRo3SoUOHFBMTow0bNuitt97Sfffdd9ZrsyQnJ2vWrFkym82aOnWqY/kFF1xQodqMat68uZYuXaqmTZuqXr162rRpk6ZPny6TyVSpdXfs2FHz589Xjx499MQTT+iiiy7SmjVr9NZbb+nhhx9WSEhIpeoGpMCaA1xVHm5efPFFderUSceOHdP8+fO1fft2SSfOL4yIiFCNGjWUlZWlr776SldddZU6duyoBQsW6KmnnlL//v11ySWXaPXq1Xr//ff11FNPqUaNGm6r8XQuv/xyRUREaNq0aQoODlZwcLA+/fRTxzfXKjPvlF8UtU+fPvr73/+uatWq6d1339XKlSs1ceJEd7UQENjTVAWZzWZNnDhRF154oZ555hmNGTNGd955p26//fZTHnv33Xfr0ksv1YABAzRp0iTdd999evPNNx33T5kyRXXr1tWQIUM0YMAA/fDDD5o6daquuOIKfffdd6cd32KxaOTIkdq9e7emTZsmk8mk6dOnq2vXrpo7d6569+6tFStW6Omnn9bYsWPP2kvjxo111VVXqWbNmmrdurXTfRWpzaiXX35ZV199tUaPHq2UlBStXLlSL7zwgtq0aVOpdYeFhSktLU3x8fEaN26cevXqpc8++0zPPvushg4dWqmagXKBNAe4KjExUSNHjtSmTZvUq1cvpaamqn79+o6rppfvSerYsaMaNGiglJQUffDBB7JarZo3b57jOkxPPvmkMjIyNHbsWPXr18+tNZ5O9erVNWXKFNntdv3jH//Q4MGDtX//fs2fP1/h4eGVmneaNGmitLQ0mUwmDR48WP3799fhw4f15ptvnvY9UZWZ7FXpDC64JCkpSQkJCXr55Zd9XQoAH2AOAJyxpwkAAMAAQhMAAIABHJ4DAAAwgD1NAAAABhCaAAAADCA0AQAAGBBwoclut8tms1Wp38IBUHnMHQDOJeBCU1lZmX744YdK/0yGO5WVlWnLli1+VVNl0I9/C6R+vNkLc4fn0Y9/C6R+PNVLwIUmf2S321VSUhIwn2Dpx78FUj+B1EtFBFr/9OPfAqkfT/VCaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQAAGEBoAgAAMIDQBAAAYAChCQAAwABCEwAAgAE+CU1r165V586d1aJFC7Vu3VqjR49WUVGRJGnz5s3q3Lmz4uLilJSUpEWLFvmiRAAAACdeD01Hjx5Vnz599Pe//13fffedlixZog0bNmjGjBnKzc1V7969lZycrI0bN2rs2LFKTU3Vli1bvF0mAACAk2BvDxgVFaU1a9YoIiJCdrtdOTk5+vPPPxUVFaXPPvtMkZGReuihhyRJrVq1Uvv27ZWWlqbmzZu7NI7NZvNE+RVSXos/1VQZ9OPfAqmfivRiNpvdMqY/CKS/pUQ//i6Q+vHU3GGy2+32CldVSTfeeKMOHjyoa6+9Vm+99ZYmTJigAwcOaNKkSY7HzJs3T+np6frwww8NrdNms+mHH37wUMUA/F18fHyFnsfcAVRtRuYOr+9p+qvPPvtMubm5GjhwoPr376+6devKarU6PcZisaigoMDldcfGxlb6E6e72Gw2ZWZm+lVNlUE//i2Q+vFFL/70ugXS31KiH38XSP14qhefhiaLxSKLxaJBgwapc+fO6tatm44fP+70mKKiIoWHh7u8brPZ7Hd/dH+sqTLox78FUj/e7MUfXzd/rKky6Me/BVI/7u7F6yeCf//997rjjjtUXFzsWFZcXKyQkBA1bNhQu3btcnr87t271ahRI2+XCQAA4MTroSk6OlpFRUX617/+peLiYu3bt0+vvPKK7r//frVr105ZWVmaM2eOSkpKtG7dOi1dulSdOnXydpkAAABOvH54Ljw8XDNnztRLL72k1q1bq3r16mrfvr1SUlIUGhqq2bNna+zYsZo4caKioqI0YsQItWzZ0ttlAgAAOPHJOU0NGzbU7NmzT3tfbGysFi5c6OWKAAAAzo6fUQEAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQAAGEBoAgAAMIDQBAAAYAChCQAAwABCEwAAgAGEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADDAJ6Fp+/bt6tGjhxISEtS6dWsNHjxYR48elSSNGjVKzZo1U1xcnOPfu+++64syAQAAHLwemoqKitSzZ0/FxcXpm2++0bJly5STk6Nhw4ZJkjIzMzV69Ght2rTJ8a9Lly7eLhMAAMCJ10PT/v371aRJE6WkpCg0NFS1atVSly5dtHHjRhUXF2vnzp1q1qyZt8sCAAA4q2BvD3jFFVdo5syZTss+/fRTNW3aVNu3b1dpaakmTpyojIwMVa9eXZ06dVLPnj0VFORavrPZbO4su1LKa/GnmiqDfvxbIPVTkV7MZrNbxvQHgfS3lOjH3wVSP56aO0x2u91e4aoqyW63a8KECVqwYIHmz5+vrKwsTZ8+XU899ZTi4uK0bds2paSkqHv37urZs6ehddpsNv3www+eLRyA34qPj6/Q85g7gKrNyNzhs9CUl5enoUOHauvWrZo6daqio6NP+7iZM2dq+fLlWrx4saH1lk98sbGxlf7E6S42m02ZmZl+VVNl0I9/C6R+KtJLRXtm7vA8+vFvgdSPp+YOrx+ek6S9e/eqV69eql+/vtLT0xUVFSVJWrlypbKystS1a1fHY4uLi2WxWFwew2w2+90f3R9rqgz68W+B1I83e/HH180fa6oM+vFvgdSPu3vx+ongubm56t69u1q0aKFZs2Y5ApN04nBdamqq1q5dK7vdrk2bNmnu3Ll8ew4AAPic1/c0LV68WPv379cnn3yiFStWON23adMmDR06VM8//7wOHjyo2rVrq1+/furQoYO3ywQAAHDi9dDUo0cP9ejR44z3d+3a1enwHAAAgD/gZ1QAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQAAGEBoAgAAMIDQBAAAYAChCQAAwABCEwAAgAGEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGBAcEWetG7dOh08eFB2u12SVFJSoh07dmjEiBFuLQ4AAMBfGA5Ny5Yt0z333KMxY8Zo4cKFCg8PlyTZbDbl5+crKSnJY0UCAAD4muHDc9OmTZMkLV++XPPnz9eECROUlJSkjRs3qnv37qpZs6bHigQAAPA1l/Y0SVJRUZGuueYaHT58WFu3bpXJZNJTTz2lO++802NFAgAA+JrhPU29e/eWJNWrV09HjhxRnTp19Mcff6ikpEQWi0X5+fkeKxIAAMDXDIem+Ph4SdJNN92kRx99VEePHtV1112nYcOG6fnnn9fll19ueNDt27erR48eSkhIUOvWrTV48GAdPXpUkrR582Z17txZcXFxSkpK0qJFi1xsCQAAwP0Mh6Y+ffpIkp555hl16NBBISEhGjlypHJycrR7926NHj3a0HqKiorUs2dPxcXF6ZtvvtGyZcuUk5OjYcOGKTc3V71791ZycrI2btyosWPHKjU1VVu2bKlYdwAAAG7i8iUHXn75ZT399NOKiIhQ9erV9dZbb7n0/P3796tJkyZKSUmR2WxWaGiounTposGDB+uzzz5TZGSkHnroIUlSq1at1L59e6Wlpal58+aulgoAAOA2LoempUuXatiwYRUe8IorrtDMmTOdln366adq2rSpdu3apcaNGzvd17BhQ6Wnp7s8js1mq3CN7lZeiz/VVBn0498CqZ+K9GI2m90ypj8IpL+lRD/+LpD68dTcYbKXX6HSoFdeeUX5+fnq2LGj6tSpI5PJ5Livfv36rqxKdrtdEyZM0IIFCzR//nzNnTtXJSUlevXVVx2PWbRokWbMmKHPP//c0DptNpt++OEHl+oAEDjKz790FXMHULUZmTtc3tP09ttvS5Lee+89R2Cy2+0ymUzatm2b4fXk5eVp6NCh2rp1q+bPn6/o6GhZrVYdP37c6XFFRUWOC2m6IjY2ttKfON3FZrMpMzPTr2qqDPrxb4HUjy968afXLZD+lhL9+LtA6sdTvbgcmlatWlXpQffu3atevXqpfv36Sk9PV1RUlCSpcePG+vbbb50eu3v3bjVq1MjlMcxms9/90f2xpsqgH/8WSP14sxd/fN38sabKoB//Fkj9uLsXl3+wt0GDBqf8q1u3rnJzcw09Pzc3V927d1eLFi00a9YsR2CSpLZt2yorK0tz5sxRSUmJ1q1bp6VLl6pTp06ulgkAAOBWLu9p+vLLL/XCCy84/WCvJFWrVs3Q+QCLFy/W/v379cknn2jFihVO923atEmzZ8/W2LFjNXHiREVFRWnEiBFq2bKlq2UCAAC4leHQ1KJFC33//fcaN26cbr/9dtWoUUM7duzQPffcozfffFP333+/ofX06NFDPXr0OOP9sbGxWrhwodGyAAAAvMLw4bkZM2ZIkn7//XcNGjRId999t7Kzs3X77bfrX//6F0EHADzAarX6ugQA/2M4NP3xxx+SpKioKAUFBal+/fr66aefJJ24ltLBgwc9UyEA+KmyMpeu2OIys9msmJgYpxNZPT3m6fhiTMAfGT48N23aNN1zzz2Kjo7WG2+8oZSUFF1wwQX66quvZLFY+DQEoMoJCjJp0upd2pdd6LExCgoKFBYWJkm65pJIdU241ONj/lWDWlb1S3L9G8xAIDIcmpYtWyZJGjRokPr3768HHnhA/fv3V9++fVVWVqbBgwd7rEgA8Ff7sgu150i+R9Ztt5+4pl1EhF0mk1Q/0urxMQGcmcvfnrvyyiv18ccfSzpx+YEvvvhC+fn5uvzyy91eHAAAgL9wOTRJ0m+//eZ0yYGSkhJ99dVXevTRR91ZGwAAgN8wHJoyMjIUHx+v6dOn6/XXXz/lJ1SuvvpqQhMAAAhYhr8917NnT0lSWlqaJk6cqKlTp6pz585at26d7rrrLsXFxXmsSAAAAF8zHJo2bdokSTp+/Lhuv/12NWnSRD/++KMiIyM1fPjwU67uDQAAEEgMh6bnn39eknThhRcqLy9PdevW1e+//y673a6oqCgdO3bMUzUCAAD4nOFzmspP+r7uuuvUv39/TZgwQTExMRo/fryqVaumunXreqxIAAAAXzO8p+mFF16QJA0ZMkR/+9vfVFpaqmHDhmnlypV69913NWzYMI8VCQAA4GsuX3IgIiJCo0aNknTiJ1U++eQTtxcFAADgb1wOTTk5OVqwYIH27dunsrIyp/tSU1PdVhgAAIA/cTk0DRgwQAcOHNA111yjoCDDR/cAAADOay6Hps2bN+uLL75QZGSkB8oBAADwTy7vKrr00ktVUlLiiVoAAAD8lst7mkaOHKnevXsrOTlZNWvWdLovOTnZXXUBAAD4FZdDU3p6unbu3Km3337b6Zwmk8lEaAIAAAHL5dC0YsUKffjhh2rYsKEn6gEAAPBLLp/TVKtWLV166aWeqAUAAMBvubynqX///ho6dKgef/xx1axZUyaTyXFf/fr13VocAACAv3A5NA0ZMkSS9PHHHzsCk91ul8lk0rZt29xbHQAAgJ9wOTStWrXKE3UAAAD4NZdDU4MGDTxRBwAAgF9zOTQ1adLE6Tymv3L18NzRo0fVpUsXjRkzRomJiZKkUaNG6f3331dISIjjcUOGDFGXLl1cLRUAAMBtXA5Nc+fOdbp99OhRzZs3Tx06dHBpPRkZGRoyZIj27t3rtDwzM1OjR4/Wfffd52ppAAAAHuNyaEpISDhlWXx8vB599FE98MADhtaxZMkSTZw4UYMGDdLTTz/tWF5cXKydO3eqWbNmrpYFAADgUS6HptOpUaOGDh48aPjxbdq0Ufv27RUcHOwUmrZv367S0lJNnDhRGRkZql69ujp16qSePXs6XX3cCJvN5tLjPam8Fn+qqTLox78FUj8V6cVsNrtlTFfGstsrNeSZla/YbpddptPe5S3ueD8F0ntToh9/5qm5w+XQ9MEHHzjdLikp0apVq3TVVVcZXkedOnVOu/z48eNKSEhQt27dNH78eG3btk0pKSkKCgpSz549XaozMzPTpcd7gz/WVBn0498CqR9XeomPj/fKWFarVTExMSooKFBeXl6lxjyXvPx8SVJRUfj//lvk8THLFYSdCGs7duxQYWGhW9YZSO9NiX78mbvnDpdD08SJE51um81mXXnllRo1apSrqzpF69at1bp1a8ft5s2bq3v37lq+fLnLoSk2NrbSnzjdxWazKTMz069qqgz68W+B1I8venF1rLCwMEVEeGi3j92uvPx8RYSHSyaTLBarJMlisSgiwjt7A8LCwiRJ0dHRlV5XIL03JfrxZ57qxeXQtHr16tMuLyoqqnQxK1euVFZWlrp27epYVlxcLIvF4vK6zGaz3/3R/bGmyqAf/xZI/Xizl4qMdYYvFFea45CcyXTKGJ4a80zc+foH0ntToh9/5u5eXP7tuZO/PSdJP/zwg8vfnjsdu92u1NRUrV27Vna7XZs2bdLcuXO53AAAAPA5l/c0TZ06VREREerYsaPjpO3Zs2erc+fOlS6mbdu2Gjp0qJ5//nkdPHhQtWvXVr9+/dwSyAAAACrD5dA0a9YsPf7448rOztayZct07NgxzZw5Uy1btqxQATt27HC63bVrV6fDcwAAAP7A5dAUExOjmTNnqkePHmratKkWLFggq9XqidoAAAD8huHQNHnyZKfbLVq00Lp16zR9+nQFB59YzVNPPeXe6gAAAPyE4dC0fv36U5bFxsYqIyNDks74e3QAAACBwHBomjdvnifrAAAA8GuVviL4XyUnJ1eiFAAAAP9V6SuC5+bmqrCwUPHx8YQmAAAQsCp9RXC73a633npLOTk57qoJAADA77h8RfCTmUwmPf744/rwww/dUQ8AAIBfqnRokqRffvmFb88BAICA5vLhuW7dujkFpJKSEu3YsUP33nuvWwsDAADwJy6HpsTERKfbQUFBevTRR3Xbbbe5rSgAAAB/43JoOt1Vv202m/bu3avLL7/cLUUBAAD4G7ec05SVlaW77rrLHasCAADwS24JTdKJSw8AAAAEKreFJr49BwAAApnbQhNcU1bm/T1zvhgTAIBAYfhE8I0bN57xvqNHj7qlmKokKMikSat3aV92oVfGa1DLqn5JjbwyFgAAgchwaOrWrdtZ7+fwnOv2ZRdqz5F8X5cBAAAMMByatm/f7sk6AAAA/BrnNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADfBqajh49qrZt22r9+vWOZZs3b1bnzp0VFxenpKQkLVq0yIcVAgAAnOCz0JSRkaEuXbpo7969jmW5ubnq3bu3kpOTtXHjRo0dO1apqanasmWLr8oEAACQ5KPQtGTJEg0cOFBPP/200/LPPvtMkZGReuihhxQcHKxWrVqpffv2SktL80WZAAAADoavCO5Obdq0Ufv27RUcHOwUnHbt2qXGjRs7PbZhw4ZKT093eQybzVbpOt2lrKxMVqtVZWVljmVms1mSZPfyb+i643UpX4c/vcaVQT/+qyK9lG9blR3TlbE8th2Xr9hul12m097lLe54P51uLjyfBdK2JgVWP56aO3wSmurUqXPa5fn5+bJarU7LLBaLCgoKXB4jMzPznI8JCQlRTNOmCq7kJHsuZrNZMTExpywvKixUXl6eR8cuVxB2YsLdsWOHCgvd8yPBRl7j8wn9+C9XeomPj/fKWFarVTExMSooKPD4dpyXf+I3KouKwv/33yKvzR3ValdTWZm90mFUOvNceDqlNpv+u3WrSkpKKj2upwXStiYFVj/unjt8EprOxGq16vjx407LioqKFB4e7vK6YmNjDW3kZrNZk1bt0r4c9wSJ07LbVVBYqDCrVTKZdM0lkeqacKksVqsiIrzziSssLEySFB0dXel12Ww2ZWZmGn6N/R39+C9f9OLqWGFhYYqI8NBuH7tdefn5iggPl0wmWSwnPlRaLBZFRHhnb8AFkdUVFGRyzzx50lx4Jg0irep3ayM1bdq0cuN5WCBta1Jg9eOpXvwqNDVu3Fjffvut07Ldu3erUaNGLq/LbDYbfqH25RRqz5F8l8cwym6X8vLyFBFhl8kk1Y/8/71pZ5k3PMKdbx5XXuPzAf34L2/2UpGxPLUdOw7JmUynjOHtucMd8+TJc+G5nC/v30Da1qTA6sfdvfjVdZratm2rrKwszZkzRyUlJVq3bp2WLl2qTp06+bo0AABQxflVaKpVq5Zmz56tFStWKDExUSNGjNCIESPUsmVLX5cGAACqOJ8fntuxY4fT7djYWC1cuNBH1QAAAJyeX+1pAgAA8FeEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGAAoQkVcvIPKwMAEOgITVVETWuIysrc86Oi5b9UbuT3fNw1JgAAvubzK4LDO8KrBZ/4pfLVu7Qvu5K/VC6poKBAYWFhZ31Mg1pW9Uty/ceWAQDwR4SmKmZftvd/qRwAgEDA4TkAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITAACAAYQmAAAAAwhNAAAABhCaAAAADCA0AQD8Sk1riMrK7F4f1xdj4vzCD/YCAPxKeLVgBQWZNGn1Lu3LLvTKmA1qWdUvqZFXxsL5i9AEAPBL+7ILtedIvq/LABw4PAcAAGCAX4am5cuXKyYmRnFxcY5/gwYN8nVZAACgCvPLw3OZmZnq0KGDUlNTfV0KAACAJD/d05SZmalmzZr5ugwAAAAHv9vTVFZWpq1bt8pqtWrmzJmy2Wy66aabNHDgQNWsWdPwemw2m6HHmc1mSZLdk980LV+53S67TKe9y5sqPeZZ+jkdo38LXymvz9/rNCqQ+qlIL+XbdGXHdGUsj23HVXzucMuYLnLl7x9I25oUWP14au7wu9B09OhRxcTEqF27dpo4caKys7P13HPPadCgQZoxY4bh9WRmZp7zMVarVTExMSooKFBeXl5lyjYkL//Et0CKisL/998ir4zrqTHL+zmTgrATk+KOHTtUWOidrw1XhpH3zPkkkPpxpZf4+HivjOXN+aOqzR2+6LMy81UgbWtSYPXj7rnD70JT7dq1lZaW5rhttVo1aNAgPfDAA8rLy1NERISh9cTGxhr+xBkWFqaICA9+nLHblZefr4jwcMlkksVilSRZLBZFRHgn0bt1zJP6OZOwsDBJUnR0dOXG8zCbzabMzEyX3jP+LJD68UUvro7l0fmjis4dvuizIvNVIG1rUmD146le/C40bd++XcuWLdOzzz4r0/82quLiYgUFBSk0NNTwesxms0sv1Fm230pz7IY2mU4Zx5PjnkllxzxbP6dzvmx8rr5n/F0g9ePNXioylqe246o+d7hjTFdV5H0WSNuaFFj9uLsXvzsRPDIyUmlpaZo5c6ZKS0u1f/9+jRs3Tvfdd59LoQkAAMCd/C401atXT9OnT9eqVauUkJCgTp06KTY2ViNHjvR1aQAAoArzu8NzkpSQkKCFCxf6ugwAAAAHv9vTBAAA4I8ITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITPKamNURlZXavj+uLMQEAgS/Y1wUgcIVXC1ZQkEmTVu/SvuxCr4zZoJZV/ZIaeWUsAEDVQmiCx+3LLtSeI/m+LgMAgErh8BwAAIABhCYAAAADCE0AAAAGEJoASVar1dclAAD8HKEJAaUilzkwm82KiYmR2Wyu8Lhc5gAAAh/fnkNAqehlDgoKChQWFlahMbnMAQBUDYQmBCRXLnNgt0t5eXmKiLDLZPJwYQCA85ZfHp47cuSI+vbtq2uvvVaJiYkaO3asSktLfV0WAACowvwyNA0YMEBhYWH6z3/+o/T0dK1du1Zz5szxdVkAAKAK87vQ9Ouvv2rDhg0aNGiQrFarLrnkEvXt21dpaWm+Lg0AAFRhfndO065duxQZGam6des6ll155ZXav3+/jh07pho1apz1+Xb7iW8xFRcXG/o2lNls1qWRFgWbPPjtJ7tdhREmWS0WyWRSveohstlsnh/3L9w65kn9eGVMgyo0psF+zqR+TYtsNptsNpvLz/WEsrIyWSwWlZSU+E1NFVVWViaz2Wx4e5ZObNNBQUEyufi3dHXuKB/Lo+9v5g6v9VmR7TiQtjUpsPrx1NxhspfPFH7iww8/1Ouvv64vv/zSsWzv3r1q27atvvrqK9WrV++szy8uLlZmZqaHqwTgz6655hqXLyHB3AHgXHOH3+1pCgsLU2Gh81fFy2+Hh4ef8/nBwcGKjY2t0CdNAIEhKMj1Mw+YOwCca+7wu9DUqFEj5eTkKCsrS7Vr15Yk/fTTT6pXr56qV69+zucHBQUpNDTU02UCCDDMHQDOxe9OBL/ssssUHx+vl156SXl5efrtt980ZcoU3X///b4uDQAAVGF+d06TJGVlZenFF1/U+vXrFRQUpOTkZA0cOLBSP3MBAABQGX4ZmgAAAPyN3x2eAwAA8EeEJgAAAAMITQAAAAYQmgAAAAwgNHnY9u3b1aNHDyUkJKh169YaPHiwjh496uuyKsVms6lbt24aMmSIr0uplJycHA0ePFiJiYm67rrr1LdvXx06dMjXZVXY1q1b9dBDD+naa69VmzZtNGbMGBUXF/u6LJcdPXpUbdu21fr16x3LNm/erM6dOysuLk5JSUlatGiRDyv0DuYO/xZI8wdzh3GEJg8qKipSz549FRcXp2+++UbLli1TTk6Ohg0b5uvSKmXy5Mn67rvvfF1GpfXr108FBQX6/PPP9cUXX8hsNuuf//ynr8uqkLKyMvXp00ft2rXThg0blJ6erm+++UZvvfWWr0tzSUZGhrp06aK9e/c6luXm5qp3795KTk7Wxo0bNXbsWKWmpmrLli0+rNSzmDv8X6DMH8wdriE0edD+/fvVpEkTpaSkKDQ0VLVq1VKXLl20ceNGX5dWYWvXrtVnn32m22+/3delVMqPP/6ozZs36+WXX1aNGjUUERGh0aNHa+DAgb4urUJyc3N1+PBhlZWVOX54NigoSFar1ceVGbdkyRINHDhQTz/9tNPyzz77TJGRkXrooYcUHBysVq1aqX379kpLS/NRpZ7H3OHfAmn+YO5wDaHJg6644grNnDnT6aKcn376qZo2berDqiruyJEjGj58uP71r3+dVxvU6WzZskUNGzbUe++9p7Zt26pNmzZ65ZVXVKdOHV+XViG1atXSo48+qldeeUWxsbG66aabdNlll+nRRx/1dWmGtWnTRp9//rnuuusup+W7du1S48aNnZY1bNhQ27dv92Z5XsXc4d8Caf5g7nANoclL7Ha7Xn/9dX3xxRcaPny4r8txWVlZmQYNGqQePXqoSZMmvi6n0nJzc7Vjxw7t2bNHS5Ys0QcffKCDBw/queee83VpFVJWViaLxaJ//vOf+uGHH7Rs2TL99NNPmjhxoq9LM6xOnToKDj715zDz8/NP+T9ai8WigoICb5XmU8wd/ieQ5g/mDtcQmrwgLy9P/fv319KlSzV//nxFR0f7uiSXTZ8+XaGhoerWrZuvS3GL8h9mHT58uCIiIlS7dm0NGDBAX331lfLz831cnes+//xzffrpp3rwwQcVGhqqRo0aKSUlRf/+9799XVqlWa1WFRUVOS0rKipSeHi4jyryHuYO/xRI8wdzh2tOjWZwq71796pXr16qX7++0tPTFRUV5euSKuTDDz/UoUOHdO2110qS4424cuXK8/LEzoYNG6qsrEwlJSWqVq2apBOfuCTpfPxloQMHDpzybZfg4GCFhIT4qCL3ady4sb799lunZbt371ajRo18VJF3MHf4r0CaP5g7XMOeJg/Kzc1V9+7d1aJFC82aNeu8nfQkacWKFfr+++/13Xff6bvvvtM999yje+6557yd9K6//npdcsklGjZsmPLz83X06FG9/vrruu222xQREeHr8lzWpk0bHT58WNOmTZPNZtNvv/2mqVOnqn379r4urdLatm2rrKwszZkzRyUlJVq3bp2WLl2qTp06+bo0j2Hu8G+BNH8wd7iG0ORBixcv1v79+/XJJ58oPj5ecXFxjn/wrZCQEM2bN09ms1nt2rVTu3btVK9ePb300ku+Lq1CGjZsqOnTp2v16tVKTEzUI488oqSkpFO+TXI+qlWrlmbPnq0VK1YoMTFRI0aM0IgRI9SyZUtfl+YxzB3+LZDmD+YO15js59u+RAAAAB9gTxMAAIABhCYAAAADCE0AAAAGEJoAAAAMIDQBAAAYQGgCAAAwgNAEAABgAKEJAADAAEITPGLx4sVKSkrydRmVFh0drfXr1/u6DKBKYf6AvyI0AQAAGEBogkf9/vvvio6O1u+//+5YNmnSJHXr1s1xe+XKlerYsaNatGihdu3aac6cOY5fDB8yZIhefPFF9enTR3FxcWrbtq3Wrl2r0aNH67rrrlPr1q21aNEix7pWr16trl27qlWrVrr66qv18MMPa8+ePZJOfHrt2LGjHnvsMV177bVaunSpDh48qAEDBigpKUlXX321br31VqWnpzv18O2336pDhw6Ki4vT/fffr507dzruS09PV8eOHZWYmKi4uDj16dNHR48e9cRLCVQ5zB/wN4Qm+NS6des0YMAA9ezZUxs2bND48eP19ttva+7cuY7HvP/+++rVq5cyMjLUvHlzPf7447rsssu0du1a9enTRy+++KKKi4v1xx9/6B//+Id69+6ttWvX6ssvv5Tdbtebb77pWNfWrVvVvn17rVmzRm3bttWIESMUEhKijz/+WN9//70efvhhjR49Wvn5+Y7nbNiwQbNmzdLatWtVq1YtvfLKK5KkLVu2aMyYMXr++ee1fv16ffLJJ9qzZ49T7QA8h/kD3kZogk8tXrxYt956q+666y4FBweradOm6t27txYuXOh4TMuWLXXttdcqKChILVu2VFhYmLp166bg4GDdcsstKi4uVlZWlqKiovTxxx8rKSlJeXl5+uOPP1SrVi0dPHjQsa6QkBB16NBBoaGhslgsGjNmjEaNGqWQkBDt379f4eHhKioqUm5uruM5PXr0UO3atWWxWHTbbbdp7969kqTGjRtr2bJlat68uXJzc3Xo0CFFRUU5jQfAc5g/4G3Bvi4AVduRI0d01VVXOS27+OKLtW/fPsftyMhIx/82m82qUaOG47bJZJIklZWVKSQkRMuWLdPChQtlMpnUuHFj5eXlKTj4/9/mderUUVDQ/39W+O233/Tqq69qz549uuyyy/S3v/3Nsb7TjR8SEiKbzSZJCgoK0ty5c7V06VKFhYUpOjpaeXl5stvtlXhFABjF/AFvIzTBLT744AMdOnRIvXv3liSVlpbKYrHIbDZLkkpKShyPzc7OdvzvBg0aOD55lfvtt99Up04dx+3yie1cPvnkE82fP1///ve/HZPX6NGjnc4h+Ou6SkpK1KdPHz3zzDN68MEHZTKZ9OOPP+qjjz4yNN6cOXP07bffaunSpapdu7Yk6YknnjD0XAD/j/mD+eN8weE5uEVBQYHmz5+vQ4cOKS8vT59++qmaNWumCy64QDVr1tTHH38su92urVu3asWKFY7nderUSatXr9Ynn3wim82m//73v3rrrbfUqVMnl2s4fvy4goKCZLFYZLfb9fXXX+uDDz5wmnD/qqSkREVFRbJYLDKZTNq/f7/GjRvnuO9cyj+FhoSEqLS0VB9++KH+85//GHougP/H/MH8cb5gTxPconPnztq2bZvuvfde/fnnn0pMTNSgQYMUGhqq0aNHa+LEiZo1a5aaNWumBx54QBkZGZKkq6++Wm+88YbefPNNDRs2TLVq1dLf//539erVy+Ua7rvvPmVkZOjuu++W2WzWFVdcoe7duystLU3FxcWnPD4sLEwvvfSS3njjDY0ZM0YXXHCBHnjgAe3evVs7d+7U5ZdfftbxHnvsMe3cuVO33HKLqlWrppiYGD344INat26dy7UDVRnzB/PH+cJk5wAqAADAOXF4DgAAwABCEwAAgAGEJgAAAAMITQAAAAYQmgAAAAwgNAEAABhAaAIAADCA0AQAAGAAoQkAAMAAQhMAAIABhCYAAAAD/g92bCp0BkQyPgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(tips, x='tip', row='sex', col='smoker', kind='hist', bins=8, height=3)\n",
    "g.set_axis_labels('Juomaraha', 'Lukumäärä')\n",
    "\n",
    "g.axes[0, 0].set_title('Tupakoiva mies')\n",
    "g.axes[0, 1].set_title('Tupakoimaton mies')\n",
    "g.axes[1, 0].set_title('Tupakoiva nainen')\n",
    "g.axes[1, 1].set_title('Tupakoimaton nainen')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294c1860-bf7c-448c-8f5e-3aefed1cb052",
   "metadata": {},
   "source": [
    "### Muuta huomioitavaa\n",
    "\n",
    "#### Fontit\n",
    "\n",
    "Tekstiä lisäävät funktiot (**plt.title**, **plt.xlabel**, **plt.xticks** jne.) tunnistavat tekstiin liittyviä parametreja https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text kuten esimerkiksi **fontsize** ja **fontstyle**.\n",
    "\n",
    "Jos haluat tehdä kerralla koko muistiota koskevia fonttimuutoksia, niin katso [matplotlib7.ipynb](https://github.com/taanila/kaaviot/blob/master/matplotlib7.ipynb).\n",
    "\n",
    "#### Värit\n",
    "\n",
    "Kaavion väripaletin voit vaihtaa antamalla kaavionluonti-komennossa arvo **palette**-parametrille. Esimerkiksi `palette='Set1'` muuttaa paletiksi **Set1** tai `palette=['green', 'red']` muuntaa kahdenvärisiä pylväitä sisältävän kaavion pylväät vihreiksi ja punaisiksi.\n",
    "\n",
    "Jos haluat käyttää kaikissa muistion kaavioissa samaa vaihtoehtoista palettia, niin käytä **sns.set_palette**-funktiota, esimerkiksi `sns.set_palette('Set1')`.\n",
    "\n",
    "- Värejä https://matplotlib.org/stable/gallery/color/named_colors.html.\n",
    "- Paletteja https://matplotlib.org/stable/gallery/color/colormap_reference.html.\n",
    "\n",
    "#### Kuvion koko\n",
    "\n",
    "Kuvion (**Figure**) oletuskoko on **6.4 x 4.8**. Jos haluat vaihtaa koon, niin luo kuvio ennen kaavion luontia, esimerkiksi `plt.figure(figsize=(4, 3))`. Usean kaavion yhdistelmän koon voit vaihtaa **subplots** funktion **figsize**-parametrilla. Funktiolla **displot** luotavan kaavion kokoa voit säätää **height**-parametrilla."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ea30ae-afe3-4ba0-853b-01fb0dc1e855",
   "metadata": {},
   "source": [
    "### Lisätietoa\n",
    "\n",
    "https://tilastoapu.wordpress.com/python"
   ]
  }
 ],
 "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": "3.13.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}