{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2023-12-10 / Aki Taanila\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now().date()} / Aki Taanila')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib - osa 7\n",
    "## Usean kaavion kuviot\n",
    "\n",
    "Tämä on jatkoa sarjan edellisille osille\n",
    "\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib1.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib2.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib3.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib4.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib5.ipynb\n",
    "* https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/matplotlib6.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>nro</th>\n",
       "      <th>sukup</th>\n",
       "      <th>ikä</th>\n",
       "      <th>perhe</th>\n",
       "      <th>koulutus</th>\n",
       "      <th>palveluv</th>\n",
       "      <th>palkka</th>\n",
       "      <th>johto</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "      <th>työterv</th>\n",
       "      <th>lomaosa</th>\n",
       "      <th>kuntosa</th>\n",
       "      <th>hieroja</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3587</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2963</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1989</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2144</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2183</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nro  sukup  ikä  perhe  koulutus  palveluv  palkka  johto  työtov  työymp  \\\n",
       "0    1      1   38      1       1.0      22.0    3587      3     3.0       3   \n",
       "1    2      1   29      2       2.0      10.0    2963      1     5.0       2   \n",
       "2    3      1   30      1       1.0       7.0    1989      3     4.0       1   \n",
       "3    4      1   36      2       1.0      14.0    2144      3     3.0       3   \n",
       "4    5      1   24      1       2.0       4.0    2183      2     3.0       2   \n",
       "\n",
       "   palkkat  työteht  työterv  lomaosa  kuntosa  hieroja  \n",
       "0        3        3      NaN      NaN      NaN      NaN  \n",
       "1        1        3      NaN      NaN      NaN      NaN  \n",
       "2        1        3      1.0      NaN      NaN      NaN  \n",
       "3        3        3      1.0      NaN      NaN      NaN  \n",
       "4        1        2      1.0      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('https://taanila.fi/data1.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kaavio on **Axes**-luokan olio. Kaavio sijaitsee **Figure**-luokan olion (kuvion) sisällä. Saman kuvion sisälle voin sijoittaa useita kaavioita. Voit ajatella, että **Figure** on kehys, jonka sisällä on yksi tai useampia kaavioita.\n",
    "\n",
    "Seuraavassa luon **subplots**-funktiolla kuvion, jonka sisällä on neljä kaaviota. **subplots** palauttaa kuvion (annan tässä sille nimeksi **fig**) ja kokoelman kaavioita listana (annan kokoelmalla nimeksi **axs**). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seuraavassa lisään kuvion sisään neljä kaavioita kuten edellä ja määritän kuvion kooksi 8 x 5.\n",
    "\n",
    "**wspace** ja **hspace** määrittävät kaavioiden välisen tyhjän tilan (w = weigth eli leveys, h = heigth eli korkeus).\n",
    "\n",
    "Lisään kuhunkin kaavioon sisältöä yksi kerrallaan. **axs[0, 0]** viittaa ensimmäisen rivin ensimmäiseen kaavioon, **axs[0, 1]** viittaa ensimmäisen rivin toiseen kaavioon jne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(8, 5))\n",
    "\n",
    "fig.subplots_adjust(wspace=0.4, hspace=0.2)\n",
    "\n",
    "# Kuvion otsikko\n",
    "fig.suptitle('Vastaajien taustatietoja')\n",
    "\n",
    "sns.countplot(data=df, y='sukup', ax=axs[0, 0])\n",
    "axs[0, 0].set_ylabel('')\n",
    "axs[0, 0].set_yticks([0, 1], ['Mies', 'Nainen'])\n",
    "axs[0, 0].set_xlabel('')\n",
    "axs[0, 0].grid(axis='y')\n",
    "\n",
    "sns.countplot(data=df, y='perhe', ax=axs[0, 1])\n",
    "axs[0, 1].set_ylabel('')\n",
    "axs[0, 1].set_yticks([0, 1], ['Perheetön', 'Perheellinen'])\n",
    "axs[0, 1].set_xlabel('')\n",
    "axs[0, 1].grid(axis='y')\n",
    "\n",
    "sns.countplot(data=df, y='koulutus', ax=axs[1, 0])\n",
    "axs[1, 0].set_yticks([0, 1, 2, 3], ['Peruskoulu', '2. aste', 'Korkeakoulu', 'Ylempi korkeakoulu'])\n",
    "axs[1, 0].set_xlabel('')\n",
    "axs[1, 0].grid(axis='y')\n",
    "\n",
    "sns.histplot(data=df, x='palveluv', bins=6, ax=axs[1, 1])\n",
    "axs[1, 1].set_ylabel('')\n",
    "axs[1, 1].grid(axis='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jos teen kaavion suoraan dataframesta, niin **subplots**-lisäparametrilla kustakin dataframen sarakkeesta tehdään oma kaavio.\n",
    "\n",
    "**sharex** ja **sharey** -lisäparametreilla voin pakottaa kaavioille yhteisen x-akselin ja y-akselin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>proportion</th>\n",
       "      <th>työtov</th>\n",
       "      <th>työymp</th>\n",
       "      <th>palkkat</th>\n",
       "      <th>työteht</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Erittäin tyytymätön</th>\n",
       "      <td>8.536585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.975610</td>\n",
       "      <td>40.243902</td>\n",
       "      <td>6.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jokseenkin tyytymätön</th>\n",
       "      <td>19.512195</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>10.975610</td>\n",
       "      <td>23.170732</td>\n",
       "      <td>18.292683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ei tyytymätön eikä tyytyväinen</th>\n",
       "      <td>36.585366</td>\n",
       "      <td>19.753086</td>\n",
       "      <td>36.585366</td>\n",
       "      <td>23.170732</td>\n",
       "      <td>35.365854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jokseenkin tyytyväinen</th>\n",
       "      <td>28.048780</td>\n",
       "      <td>43.209877</td>\n",
       "      <td>28.048780</td>\n",
       "      <td>12.195122</td>\n",
       "      <td>30.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Erittäin tyytyväinen</th>\n",
       "      <td>7.317073</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>13.414634</td>\n",
       "      <td>1.219512</td>\n",
       "      <td>9.756098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                proportion     työtov     työymp    palkkat  \\\n",
       "Erittäin tyytymätön               8.536585        NaN  10.975610  40.243902   \n",
       "Jokseenkin tyytymätön            19.512195   3.703704  10.975610  23.170732   \n",
       "Ei tyytymätön eikä tyytyväinen   36.585366  19.753086  36.585366  23.170732   \n",
       "Jokseenkin tyytyväinen           28.048780  43.209877  28.048780  12.195122   \n",
       "Erittäin tyytyväinen              7.317073  33.333333  13.414634   1.219512   \n",
       "\n",
       "                                  työteht  \n",
       "Erittäin tyytymätön              6.097561  \n",
       "Jokseenkin tyytymätön           18.292683  \n",
       "Ei tyytymätön eikä tyytyväinen  35.365854  \n",
       "Jokseenkin tyytyväinen          30.487805  \n",
       "Erittäin tyytyväinen             9.756098  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframen laskeminen\n",
    "df1 = df['johto'].value_counts(normalize=True).sort_index().to_frame()\n",
    "df1['työtov'] = df['työtov'].value_counts(sort=False, normalize=True)\n",
    "df1['työymp'] = df['työymp'].value_counts(sort=False, normalize=True)\n",
    "df1['palkkat'] = df['palkkat'].value_counts(sort=False, normalize=True)\n",
    "df1['työteht'] = df['työteht'].value_counts(sort=False, normalize=True)\n",
    "\n",
    "df1.index = ['Erittäin tyytymätön', 'Jokseenkin tyytymätön', 'Ei tyytymätön eikä tyytyväinen', \n",
    "                'Jokseenkin tyytyväinen', 'Erittäin tyytyväinen']\n",
    "df1 = df1*100\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '%')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PTVc7duwA8L9naB61vybz0GPfGxQUBAC4fv06/P39rX9u3LiBadOm5XqwPL/bqxp+Ds19/v77b/Tr1w9vvvkmLl26hClTpqBx48aoX78+Vq1alePrX3jhBdSvXx+jR4/GlStX4Ofnh71792L+/Plo166dzfnG79y5g169eqFfv35ISUnB1KlT4ePjg9atWwMABg8ejJ49e+Ktt95C586dkZ6ejnnz5iEtLQ0DBgx46HZnr+DXrl2LZ599Nsd7Zbp3747o6Gh0794dAwYMQJkyZRAVFYXdu3fjk08+UT5y1ZUsWRIxMTH47bff4Ofnh1KlSqF9+/bo2LEjihcvbvM/htWqVUO7du0wY8YMpKamon79+jhx4gRmzJiB+vXr4/nnn7feJ/C/puxpvXHjxvD29sby5cvRrVs39qUYLXoD7r107JVXXsF3332Hf/3rX7m+f8FRX3/9NYoWLYo6depg48aN2Lp1KyZPnizt/kl/ee2vW7Vqhf/85z9YunQpKlasiJIlS2Lnzp1YtGgRAOTrPaEWiwUff/wxwsPD8emnn2L8+PE5vqZjx45YtWoVxo0bh+eeew41a9ZEoUKFMGnSJPTo0QNpaWn473//i23btgEAbt++DeDR+2syDz32vT4+Pnj11Vfx0UcfIT4+HrVq1cIff/yBqVOn4oknnsDTTz9t1/YCwKZNm/DCCy/gmWeekffDcAH8P437vPLKK6hSpQreffddfPHFF2jXrh1mzpyZ59dbLBbMnTsXr732GhYvXozevXtjw4YNeO+993I8AAYGBqJp06b48MMP8cknn6Bhw4ZYvHix9fW1DRs2xFdffYW0tDS8//77+Oijj1ChQgV8//33qF69+kO3u3nz5vD398fw4cOxYMGCHNeXK1cO3377LWrVqoXx48fjnXfewaVLlzBr1ixEREQ48JMiV9KpUycULlwYvXr1sh6FrFOnDsqUKYNXXnklx/8Yjh8/HgMGDMC6devQu3dvLF26FF26dMH8+fOti48Hm7KndXd3d7zyyivIzMy0njSA1KFFb9myX54j8+VmAPDBBx/gl19+Qb9+/XD48GFMnz7dejCJzOlh++tZs2ahQoUKGD58ON59910cOnQIs2fPRtWqVa0n+XkUX19fvPnmm1i5ciV+++23HNdnL3pu3ryJCRMm4KmnnsLkyZNx+fJl9OvXD6NGjQIAfPPNN7BYLNa5j9pfk3nose8FgAkTJqB79+5Yvnw53nrrLcyZMwetWrXCwoULbU4k9Sj169fHc889h8mTJ+PTTz+V9FNwHRbBd6oBuHeGlODgYEycOFH6fXfp0gUAcv0gJCItHDlyBB06dMAPP/yAWrVqGb05pDhZvUVGRuLAgQM5XpbhqD179uDNN9/E4sWLUb9+fSn3ScbTcn9N5Azue43Dl5wRKWTPnj3Ys2eP9SxRfEAlLcnqbfHixTh79iy+++47TJgwQfJWEhFpi/te4/ElZ0QKuX79Or766is89thj/B9D0pys3vbv34/Vq1ejS5cuOT57hojI1XHfazy+5IyIiIiIiEyLz9AQEREREZFpcUFDRERERESmxQUNERERERGZlsuc5SwrKwsZGRlwc3ODxWIxenPIRQghkJWVhUKFCmn+AY1skB7E/shoejXI/ig3fAwkI9nTn8ssaDIyMhAbG2v0ZpCL8vf3t34IqVbYIOWF/ZHRtG6Q/dHD8DGQjJSf/lxmQZO98vL397frk08zMzMRGxtr9+3spdccVWc5Oif7dlofGQIca5C/K7VnsT+1Z5nhe9KrQe6DOetht+FjIFs3YpY9/bnMgib76UV3d3eHfqiO3s5V56g6y9E5ejz97EyD/F2pPYv9qT3LDN+T1g1yH8xZD8PHQP1nqfg9OTorP/3xpABERERERGRaXNAQEREREZFpcUFDRERERESmxQUNERnK09PT6E0gIiIiE+OChoi0l5WZ68Xu7u7w8/PL+w2CedyOiIiIKJvLnOWMiBTm5g788BZw7VT+b1PWB4j4UrttIiIiIiVwQUNE+rh2Crh02OitICIiIsXwJWdERERERGRaXNAQEREREZFpcUFDRERERESmxQUNERERERGZlkMLmszMTHTp0gXDhw+3Xnb48GF06NABAQEBCA0NxYoVK6RtJBERERERUW4cWtDMmDED+/fvt/795s2b6N27N8LCwrBv3z6MHz8eEyZMwJEjR6RtKBERERER0YPsXtD89ttv2LhxI5o3b269bOPGjShdujQ6deqEQoUKoWHDhmjTpg2WLl0qdWOJiIjItXh6ehq9CURUwNn1OTQJCQn48MMPMWvWLHz99dfWy0+fPg0fHx+br61WrRpWrlxp9wZlZtr3yeDZX2/v7eyl1xxVZzk6R4+fgTMz+bvKH3d3d4dvK2s7HPm+2J/as8zwPendYF7z3ABYcvl37O7uDj8/v1xvIzIzkSV5u1z5d6XiLD4G6j9Lxe/J0Vn2fG2+FzRZWVkYOnQounfvjho1athcl5KSkuMITdGiRXH79u18b0i22NhYu2/jzO1cdY6qs/T8nhzlyDbyd5U3T0/PPP+HJz9OnjyJO3fuSNkWwPUbZH/6z1Lxe3JUbtuX/W84fshQpJ09m6/78ahaFZX/Mwknjx837b9fzjIGHwP1naPKrHwvaObOnQsPDw906dIlx3Wenp5ISkqyuSw1NRVeXl52b5C/v79dR3MzMzMRGxtr9+3spdccVWc5Oif7dnqyZxv5u9Ker6+vlPtx5Ptif2rPMsP3pHeDD9u+tLNnkXr8uF33Z+S/X85yfhYfA/WfpeL35Ogse/rL94Jm9erVuHLlCgIDAwHcW7AAwM8//4x///vf2LVrl83Xx8XFoXr16vm9eyt3d3eHfqiO3s5V56g6S8/vyVGObCN/V9puh+z7c4XvKy/sT/9ZKn5PjpK9fWb+98tZxuBjoL5zVJmV75MCbNiwAQcPHsT+/fuxf/9+tG7dGq1bt8b+/fvRrFkzXLt2DV9//TXS09Oxe/durFmzBhEREdI3mIiIiIiIKJuUD9YsU6YMFi5ciA0bNqB+/foYOXIkRo4ciQYNGsi4eyIiIiIiolzZdZaz+02cONHm7/7+/li+fLnTG0RERERERJRfUp6hISIiIiIiMgIXNEREREREZFpc0BARERERkWlxQUNERERERKbFBQ0REREREZkWFzQuytPT0+hNICIyDB8DiYgov7igMVBmlsj1cnd3d/j5+eX5Sap53Y6IyEwyszJzvfxhj4F53YaIiAouhz+Hhpzn7mbBO8tjEHclOd+3qVa+OD5/LUDDrSIi0oe7mzuGbx+OszfP5uvrq5aqiokvTHz0FxIRUYHCBY3B4q4k49jFW0ZvBhGRIc7ePIsTiSeM3gwiIjIxvuSMiIiIiMhF8D2E9uOChoiIiIhIZ1m5vCf6Ue+jzu02xJecERERERHpzs3Ngk0LjyHxUkq+vt77cS8061FT460yJy5oiIiIiIgMkHgpBdfO5//kUJQ7vuSMiIiIiIhMiwsaIiIiIiIyLS5oiIiIiIjItLigIaICg6fCJCIiUg8XNESklqzMXC9+1Kkw87odERERuTae5YyI1OLmDvzwFnDtVP5vU9YHiPhSu20iIiIizXBBQ0TquXYKuHTY6K0gIiIiHfAlZ0REREREZFpc0BARERERkWlxQUNERERERKbFBQ0REREREZmW3Qua3377DR06dEDdunXRqFEjjB07FqmpqQCAw4cPo0OHDggICEBoaChWrFghfYOJiIiIiIiy2bWgSUxMRJ8+ffD6669j//79WLVqFfbu3Yt58+bh5s2b6N27N8LCwrBv3z6MHz8eEyZMwJEjR7TadiIiIiIicpAqHzht12mbvb298euvv6J48eIQQuDGjRu4e/cuvL29sXHjRpQuXRqdOnUCADRs2BBt2rTB0qVLUbt2bU02noiIiIiI8paVJeDmZslxefYHTtt7O1dk9+fQFC9eHAAQEhKCy5cvIzAwEOHh4Zg2bRp8fHxsvrZatWpYuXKlXfefmWnfp3Vnf729t7OXFnPy/MTyfJC1Ha7+89N6u5ydqdfPT89ZZm9d5iz2p+0sR39XZnv8c2aW3g3mNY+/q4I5i4+B2s7S69+Vu7s7Ni08hsRLKfm+jffjXmjWo6ah/4bt+VqHP1hz48aNuHnzJoYMGYJBgwahQoUKOZ62Klq0KG7fvm3X/cbGxjq0PY7ezqg5np6eD10VP8rJkydx584dKdsCmO/npyVHtlHP78tsvys9W3e1f1eOKCj9OfO7Muvjn96zHJHb9vF3xVl64mPgozmyX0y8lIJr55M1nZUfWv2uHF7QFC1aFEWLFsXQoUPRoUMHdOnSBUlJSTZfk5qaCi8vL7vu19/f364Va2ZmJmJjY+2+nb30mpNfvr6+Uu7H1X9+2bfTkz3bqGcXrv670oqs1h2Zxf6MmfUoZnv8c2aW3g3K/lkUpN+VirP4GGjMrEcxcr/oKK37s2tBc/DgQXzwwQeIjo6Gh4cHACAtLQ2FCxdGtWrVsGvXLpuvj4uLQ/Xq1e0ZAXd3d4dCcfR2rjonP9sh+/4K0s/vYRzZRj2/r4L2u9JzG1zl+2V/j94G2fdXkH5+DyN7+/i7KtizHMHHwPxtg1lnafXzs+ssZ76+vkhNTcXkyZORlpaG+Ph4fPrpp2jfvj1atGiBa9eu4euvv0Z6ejp2796NNWvWICIiQvpGExERERERAXY+Q+Pl5YUvv/wSn3zyCRo1aoQSJUqgTZs2ePvtt+Hh4YGFCxdi/PjxmD59Ory9vTFy5Eg0aNBAq20nIiIiIqICzu730FSrVg0LFy7M9Tp/f38sX77c6Y0iIiIiIiLKD7teckZqUuVDlYiI7MXHPyIi8+OCpoDIzBK5Xp79oUq5vUErr9sQEZlNZlbOzzN42ONfXrchIiLX4/Bpm8lc3N0seGd5DOKu5O8c5NXKF8fnrwVovFVERPpwd3PH8O3Dcfbm2Xx9fdVSVTHxhYkabxUREcnABU0BEnclGccu3jJ6M4iIDHH25lmcSDxh9GYQEZFkfMkZERERERGZFhc0RERERERkWlzQEBERERGRaXFBQ0REREREpsUFDRERERERmRYXNEREREREZFpc0BARERERkWlxQUNERERERKbFBQ0REREREZkWFzRERERERGRaXNAQEREREZFpcUFDRERERESmxQUNERERuTxPT08lZxGR87igISIiIpchMjNzXObu7g4/Pz+4u7vn+zaOzNFqFhFpq5DRG0BERESUzeLujvghQ5F29my+vt6jalVU/s8kzec4M4uItMUFDREREbmUtLNnkXr8uDJziEhbfMkZERERERGZFhc0RERERERkWlzQEBERERGRaXFBQ0REREREpmXXgub3339H9+7dERwcjEaNGuHf//43EhMTAQCHDx9Ghw4dEBAQgNDQUKxYsUKTDSYiIiIiIsqW7wVNamoq3nrrLQQEBGDnzp1Yu3Ytbty4gQ8++AA3b95E7969ERYWhn379mH8+PGYMGECjhw5ouW2ExERERFRAZfvBc3FixdRo0YNvP322/Dw8ECZMmXQsWNH7Nu3Dxs3bkTp0qXRqVMnFCpUCA0bNkSbNm2wdOlSLbediIiIiIgKuHx/Dk3VqlXx5Zdf2lz2008/oWbNmjh9+jR8fHxsrqtWrRpWrlxp9wZl2vkJvNlfb+/t7KXFnLw+hTg/7N0OR2fJ+n4d/flp/Xt1dqZe/ek5q6C2ntss9qftLD0fl/gY6Nw8FX9Xej4uPep+XPXfMB8DtZ3F1uXNduiDNYUQmDZtGrZu3YolS5Zg8eLF8PT0tPmaokWL4vbt23bfd2xsrCOb5PDtjJrj6ekJPz8/h29/8uRJ3LlzR/NZ9szJD71+T85wZBv1/L5kzipcuDAKFcr5MODp6Ym4uLhcb5ORkYH09PR8zzBL6/bO0kpB6U/PxyU+BuZfbtun4u/K1R4rzPhvWCt8DHw0tp6T3Qua5ORkjBgxAseOHcOSJUvg6+sLT09PJCUl2XxdamoqvLy87N4gf39/u1aSmZmZiI2Ntft29srKysLp06dRvXp1uLkZf3I4X19fU81x9PeUfTs92bONevWn1Sw3C2Bxs+++RFYmsoSU8fmiV+u5zWJ/xsx6FCObcJRZHgNl/37N+LvSc5ar/xvmY6Axsx6loLRuT392LWjOnTuHXr16oVKlSli5ciW8vb0BAD4+Pti1a5fN18bFxaF69er23D2Ae0+LORKKo7fLTWaWgLubJcf9P2yFm9tttKTXPybZc2T+nrTiyDbq+X1Jn/XDW8C1U/n72rI+sER8CT1/g3r24gptFrj+HNwGs85yhZ/fw8jePjP/rvScVdD+DT8MHwPztw1mnaXVzy/fC5qbN2+ia9euaNCgAcaPH2/zLEWzZs0wadIkfP311+jUqRMOHDiANWvWYNasWdI3WA/ubha8szwGcVeS8/X11coXx+evBWi8VUQauXYKuHTY6K0gIiIicki+FzT//e9/cfHiRfz444/YsGGDzXUxMTFYuHAhxo8fj+nTp8Pb2xsjR45EgwYNpG+wXuKuJOPYxVtGbwYRERERET1Evhc03bt3R/fu3fO83t/fH8uXL5eyUUREREREruTBE2CR6zD+3e1ERERERC4gK4+z3mS/lzq393/kdRvSj0OnbSYiIiIiUo2bmwWbFh5D4qWUfH299+NeaNajpsZbRY/CBQ0RERER0f9LvJSCa+fzd2Iocg18yRkREREREZkWFzRERERERGRaXNAQEREREZFpcUFDRERERESmxQUNERERERGZFhc0RERERERkWlzQEBERERGRaXFBQ0REREREpsUFDRERERERmRYXNEREREREZFpc0BARERERkWlxQUNERERERKbFBQ0REZEOPD09jd4EIiIlcUFDREQkSWZWZq6Xu7u7w8/PD+7u7nbdjoiIHq2Q0RtARESkCnc3dwzfPhxnb57N922qlqqKiS9M1HCriIjUxgUNERGRRGdvnsWJxBNGbwYRUYHBl5wREREREZFpcUFDREREZBCeLILIeVzQEBEREWlIZNp/soi8bkNEOfE9NEREREQasri7I37IUKSdzd/JIjyqVkXl/0zSeKuI1MEFDREREZHG0s6eRerx40ZvBpGSHH7JWWJiIpo1a4Y9e/ZYLzt8+DA6dOiAgIAAhIaGYsWKFVI2koiIcqfn6+/5Wn8iInJFDi1oDhw4gI4dO+LcuXPWy27evInevXsjLCwM+/btw/jx4zFhwgQcOXJE2sYSERVUuX3wohYf1sgPhiQiIrOx+yVnq1atwvTp0zF06FC899571ss3btyI0qVLo1OnTgCAhg0bok2bNli6dClq164tb4uJiAogez+w0dEPa+QHQxIRkdnYvaBp3Lgx2rRpg0KFCtksaE6fPg0fHx+br61WrRpWrlxp1/1n2nlWj+yvt/d2D5PXEcj8bovWc/ScJevn6ujvSebvVYuZWvSn5yy2/vBZrtSfu7u7Qx/Y6MjPz9EPhnTlxyWztq53gw/rT+b9PYxZf1dazJL5+3dkH+JKj4EP+1oz7hf1nKVn64+6H636s3tBU65cuVwvT0lJyfH66qJFi+L27dt23X9sbKy9m+TU7R7k6ekJPz8/h2578uRJ3LlzR/M5es6yZ05+yPo9acmRbdTz+2LrrjFLK7n9fvm7cnyOnrPYX078XbnWPhhw/f2wkftgtu7cLAAoXLgwChXKfXnh6emJuLi4XK/LyMhAenq6Q9sISDzLmaenJ5KSkmwuS01NhZeXl1334+/vb9dKMjMzE7GxsXbfTgu+vr7KzZI1x9HfU/bt9GTPNurZH1vXf5ar95cfKv6uVPye8pqld4Psz7VmyZzjyD7E1R8DuV90vVkWixvc3Cx2z8nKEhAiy+Yye/qTtqDx8fHBrl27bC6Li4tD9erV7bofd3d3h6J09HYy6Tlfr1my57jC7+lRHNlGPb8vV/gZqti63rMetg0yt0PFn5+K35Pesx62DezPdWZpMccV9iEP4+r74IdtA2fds2nhMSReSsn313s/7oVmPWoCcPz7kragadasGSZNmoSvv/4anTp1woEDB7BmzRrMmjVL1ggiIiIiInJhiZdScO18sq4zHf4cmgeVKVMGCxcuxIYNG1C/fn2MHDkSI0eORIMGDWSNyBM/G4GIiIiIqGBy6hmakydP2vzd398fy5cvd2qD8pKZJeCey2vysj8bwZ7bEBERERGRGqS95Exr7m4WvLM8BnFX8vcUVrXyxfH5awEabxURERERERnJNAsaAIi7koxjF28ZvRlEREREROQipL2HhuhR+F4nIiIiIpKNCxqSLjNL5Lgs+71OeZ3+L7fbEBERERE9iqleckbmwPc7EREREZFeuKAhTfD9TkRERESkB77kjIiIiIhMie/PJYALGiIiIiJyYVl5vM/2Ue/Pzet2pB6+5IyIiIiIXJabmwWbFh5D4qWUfN/G+3EvNOtRU8OtIlfCBQ0RERERubTESym4dj5/JxuigocvOSMiIiIiItPigoaIiIiIiEyLCxoiIiIiIjItLmiIiIiIiMi0uKAhMhGeb5+IiIjIFhc0RK4mKzPXix96vv08bkNERESkOp62mcjVuLkDP7wFXDuVv68v6wNEfKntNhERERG5KC5oiFzRtVPApcNGbwURERGRy+NLzoiIiIiIyLS4oCEiIiIiItPigoaIiIiIiEyLCxoiIiIiIjItLmiIiIiIiMi0uKAhIiIiKgD44cykKqkLmoSEBPTv3x+BgYGoX78+xo8fj4yMDJkjiIiIiCgPItP+D2fO6zZEZiH1c2jeffddVKhQATt27MC1a9fQr18/fP3113jrrbdkjiEiIiKiXFjc3RE/ZCjSzp7N19d7VK2Kyv+ZpPFWEWlL2oLmr7/+wt69e7F9+3Z4enriySefRP/+/TFp0iQuaIiIiIh0knb2LFKPHzd6M4h0I21Bc/r0aZQuXRoVKlSwXvbMM8/g4sWLuHXrFkqWLPnQ2wshAABpaWm5Ph3q7u6Of1b0QpGcV+WqajkvZGZmItOBp1H1mmXvHD1nucrPL/uy7D609KgGc5OVlYWiRYsiPT3doZ9Vbtzd3YFyNQG3Ivm7wWPVACd+V3rMsnuOi8xypf7c3d3hU8oHHhaPfN3f0yWfdvixwp45es5ydI6es2T//PRqMD/9FfLxQRGP/H1fhZ527nelxyx75+g5y1V+fq7yGOju7g7vysVgseP/l8pULObw78qeWY7O0XOWK//8HjbLnv4sQlKlq1evxtSpU7Ft2zbrZefOnUOzZs3wyy+/oGLFig+9fVpaGmJjY2VsCinI398fHnbsdBzBBikv7I+MpnWD7I8eho+BZKT89CftGZpixYrhzp07Npdl/93Ly+uRty9UqBD8/f3h5uYGi8Uia7PI5IQQyMrKQqFCUt/ulSs2SA9if2Q0vRpkf5QbPgaSkezpT1qh1atXx40bN3Dt2jWULVsWAHDmzBlUrFgRJUqUeOTt3dzcNF/9Ez0MGyQjsT8yEvsjo7FBcoa00zY//fTTqFevHj755BMkJyfj/PnzmDVrFtq3by9rBBERERERkQ1p76EBgGvXrmHMmDHYs2cP3NzcEBYWhiFDhuT7DdZERERERET2kLqgISIiIiIi0pO0l5wRERERERHpjQsaIiIiIiIyLS5oiIiIiIjItLigISIiIiIi0+KChoiIiIiITEv7j34ll3PlyhWcO3cOD57gLigoSPqstLQ0JCYmIisry+bySpUqSZ9F5sD+yGh6Ncj+KDd8DCQjqdqf6RY0KSkpWLZsGf78888cP6AJEyZInXX58mXMnj0711mLFy823RwA+OabbzBx4kRkZmbaXG6xWHDixAmps3788UeMHj0aSUlJ1suEEJrM0ouK/ek5i/05h/05T68GVewPULNBFfsD1GxQxf70nKVyf6Zb0IwYMQIxMTGoX78+ChcurPmsa9euoWnTpprO0msOACxatAijRo1CREQEChXS9tf/xRdf4I033kC7du00n6UXFfvTcxb7cw77c55eDarYH6Bmgyr2B6jZoIr96TlL6f6EyQQHB4tz587pMiswMFAkJCQoM0cIIQICAkRmZqYus+rUqSPS09N1maUXFfvTcxb7cw77c55eDarYnxBqNqhif0Ko2aCK/ek5S+X+THdSgCJFiqBChQq6zCpRogQ8PDyUmQMAwcHB2LNnjy6zatasibi4OF1m6UXF/vScxf6cw/6cp1eDKvYHqNmgiv0BajaoYn96zlK5P4sQD7wryMXNmTMHV65cwYABA+Dt7a3prJUrV+KXX35Br169ULZsWZvrZL6hSa85ADB69GisWrUK9evXzzFL9utPp0yZgu+//x4tW7bMMWvAgAFSZ+lFxf70nMX+nMP+nKdXgyr2B6jZoIr9AWo2qGJ/es5SuT/TLWhCQ0Nx8eJFWCyWHNfJfpNRjRo1bP5usVg0eUOTXnOAe6/TzIvsmLt06ZLr5RaLRfob6vSiYn96zmJ/zmF/ztOrQRX7A9RsUMX+ADUbVLE/PWep3J/pFjR79+7N87rg4GCps+Lj4/O8rnLlyqabQ85TsT+9Z5Hj2B8ZTcUG2Z95qNif3rNUZbr30AQHByMwMBBFixbFtWvX4ObmhsDAQOkhA/ciKl26NA4fPoz169dj//79KFGihPS4KleujMqVK+PmzZs4duwYypUrh6JFi2oW8a5du9CvXz+Eh4fj6tWr+PTTT5GRkaHJrDNnzmDcuHEYMGAArl+/jiVLlmgyRy8q9pc9S68G2Z/j2J8cejWoWn+Amg2q2h+gXoMq9pc9i/tgJ+l2+gFJrly5IsLDw4Wfn5947rnnxD//+U/x8ssvi0uXLkmf9eeff4omTZqIRo0aiQ4dOohGjRqJhg0bilOnTkmdc+3aNdGxY0dRs2ZNUadOHREXFyfq1KkjDh48KHWOEEJER0eLhg0biilTpoi6deuKK1euiObNm4tPP/1U+qydO3eKgIAA8f7774uAgABx8eJF0aBBAzF37lzps/SiYn9C6Ncg+3MO+3OeXg2q2J8QajaoYn9CqNmgiv0JwX2wDKZZ0LRu3VoIIcTgwYPF+++/L5KTk4UQQty6dUu8//77YtCgQdJmBQQECCGE6NOnj5g4caL1FHeZmZli4sSJokePHtJmCSHE+++/Lz766CNx+/ZtERgYKIQQYtasWeK1116TOkeIez/HmJgYIYSwzvrjjz/E888/L31WeHi42LZtm82sI0eOiNDQUOmztKZyf0Lo1yD7cwz7k0evBlXqTwi1G1SxPyHUalDl/oTgPlgG0yxooqOjhRBCNGrUSCQlJdlcd+vWLREUFCRt1r59+4QQQjRo0EDcvXvX5ro7d+6IevXqSZslhBDPPfecuH37thBCWL+PtLQ0awAyBQYGiqysLJtZWVlZ0r8nIYSoV69ejlnZl5uNyv0JoV+D7M8x7E8evRpUqT8h1G5Qxf6EUKtBlfsTgvtgGUzzHprsM1pkZWXlOLuFxWKR+smqBw8eBAC4u7sjOTnZ5rrk5GR4enpKmwUAhQsXRmpqKgBA/P85GlJSUuDl5SV1DgA8/fTT2Lx5s81lv/76K5566inpsypVqmT9WWaLjY3F448/Ln2W1lTuD9CvQfbnGPYnj14NqtQfoHaDKvYHqNWgyv0B3AfLYJoFzZw5cwDce0NYZGQkbt++DeDeLzwyMlLqG8L27dsHAGjatCkGDx6Ms2fPIi0tDWfOnMHQoUPRtGlTabOAe6chHDp0KP78809YLBYkJCTg448/RkhIiNQ5APDee+9hyJAhGDx4MO7evYvIyEi88847GDRokPRZffr0Qb9+/TB16lSkp6dj/vz5ePvtt9GzZ0/ps7Smcn+Afg2yP8ewP3n0alCl/gC1G1SxP0CtBlXuD+A+WAbTnbb54sWL6N69O+Lj41G6dGncuHED1apVw5w5c1CxYkWps27cuIGBAwdi37591iMCISEh+PTTT1GqVClpc1JSUjBixAhs3LgRwL2jDSEhIZg0aRJKlCghbU6233//Hd999x3i4+NRsWJFtG/fHrVr15Y+BwB++eUXLF261DrrX//6F1q0aKHJLD2o2B+gb4Psz3HsTw69GlStP0DNBlXtD1CvQRX7A7gPlsF0CxoAyMjIwL59+5CYmIjKlSvD398f7u7ums07f/48EhISULlyZZQrVw7JyckoXry49DmJiYm4cOECKlasiPLly0u/f5JD1f4ANmgG7I+MpmqD7M8cVO0PYIPOMN2CJiwsDFFRUTkuDw0NxZYtW6TOCg4OzvVDnAIDA7F//36ps65cuYJz587hwV9HUFCQ1DmXL1/G7Nmz8eeffyIrK8vmOtmf3JqSkoJly5blOkv2J9LqRdX+AH0aZH/OYX/O06tBFfsD1G1Qtf4ANRtUtT+A+2BnFZJ+jxo4d+4cZs+eDQCIi4vDiBEjbK5PTk62vpnKWX/99RdGjRoFIQSSk5Px5ptv5phVsmRJKbOyffPNN5g4cSIyMzNtLrdYLDhx4oTUWSNGjMC1a9fQtGlTqW+iy2tWTEwM6tevr/ksLaneH6Bfg+zPfuzPnI+BqvQHqN+giv1lz1KhQdX7A7gPlsEUC5oqVaqgTJkyuH79eq7Xe3t7Y+rUqVJmPfXUU2jevDmuX7+OgwcP5nijmYeHB0JDQ6XMyrZo0SKMGjUKERERKFRI219JbGwsfvrpJ3h7e2s6BwD27NmDlStX4sknn9R8lpZU7w/Qr0H2Zz/2J5deDarSH6B+gyr2B6jToOr9AdwHS6HJyaA1NHPmTN1mrVq1Spc5AQEB1g9u0lrTpk1znMNdK88//3yOc7ibnYr9CaFfg+zPOezPeXo1qGJ/QqjZoIr9CaFmgyr2JwT3wTKY7j00wL03aF2+fNn6OsP09HScOnUK3bp1kz5r9+7dOWadPHkSI0eOlDajb9++6Nq1Kxo2bCjtPvOycuVK/PLLL+jVqxfKli1rc12lSpWkzpozZw6uXLmCAQMG6HI0QC+q9Qfo1yD7cx77c45eDaraH6Begyr2B6jboGr9AdwHy2CaBc2BAwdQr149zJ07F1OnTrWeQk8IAYvFgmeffRbLly+XMmvt2rVo3bo1xo0bh+XLl1s/2CgzMxMpKSkIDQ3FzJkzpcwCgNGjR2PVqlWoX79+jsBkv3GqRo0a1v9+8Gco+7XCoaGhuHjxYo4PwQIgfZbWVO4P0K9B9ucY9iePXg2q1B+gdoMq9geo1aDK/QHcB8tgivfQAMBbb72FmJgYLF26FNOnT4eHhwe2bNmC999/H2PHjpV6ers5c+agdevWWL9+PZYsWYI7d+4gOjoan3zyCT799FPcunVL2iwASEtLwyuvvCL1PvPy4CfEamnixIm6zdKayv0B+jXI/hzD/uTRq0GV+gPUblDF/gC1GlS5P4D7YCl0e3GbJHXq1BFCCHHp0iXRrl07IYQQCQkJokmTJtJnBQQECCGEuHLlimjTpo0QQoikpCTRuHFj6bPIHNgfGYn9kdHYIBmJ/VFeTPMMTWRkJCIjI1G+fHkkJyejQoUKuHDhAoQQ8Pb2lrpi7t27N+bNm4eKFSsiISEB5cqVw99//4309HQULVoUKSkpUubMmzcPvXv3xowZM/L8mgEDBkiZVbduXRw8eBA1atTI9ek/QN5TgG3atMGaNWsQGhqa5yw9jxLIoGJ/gH4Nsj/nsD/n6dWgiv0BajaoYn+Amg2q2B/AfbBMplnQiP9/q09QUBAGDRqEadOmwc/PD1OmTEGRIkVQoUIFabPq1asHAAgJCUG3bt2waNEiBAUF4YMPPkCRIkXwj3/8Q8qcffv2oXfv3tizZ0+u1+cVgiPmzZsH4N6pAWXeb2569+4NABg4cKCmc/SkYn+Afg2yP+ewP+fp1aCK/QFqNqhif4CaDarYH8B9sFRGPTXkqKSkJBEZGSkSEhLEyZMnRcuWLUXjxo3Fjh07pM9KS0sT8+fPF7du3RJ///236Nmzp+jYsaM4evSo9FlGS0hI0G1Wenq6brNkY3/aYH/5w/60o1eDZu5PCDaoFT4G5g/704YK/ZnmLGeq0+vUgEeOHMFnn32Gy5cvIysryzorMTERR48elTrr3LlzmDlzZo5Zf/zxB3bv3i11FjlPjwbZH+VFtcdA9mcuqvUHsEGz4T7YOaZ5yVm2GzduYNmyZYiPj7f+gLLJPr3i+fPnMWfOnFxnLV682On7z8+pAWXJfk3jxx9/jCpVqqB69eo4f/48GjVqhMWLF2Po0KHSZmW//vTDDz+EEAJlypRBQkIC/Pz8EBUVhZ49e0qbpTeV+gP0a5D9ycH+HKdXgyr3B6jVoIr9AWo3qFJ/APfBMrlpcq8aevfdd7F69WpkZGRoPuv999/HH3/8gbp16yI4ONjmjwxz5swBAOupAadNm4bQ0FDs27cPXbt2RalSpaTMAf73msbTp09jwoQJ6NSpEzIzM9G9e3dMnToVq1evljYr+/WnsbGxmDlzJvr3748SJUpg5MiRmDJlCrZv3y5tlt5U6g/Qr0H2Jwf7c5xeDarcH6BWgyr2B6jdoEr9AdwHS6XJC9k0VKdOHXH9+nXdZt25c0fzOXqcGnD//v1CCGG9z9TUVNGoUSPr9UFBQdJmXbp0SQghRMOGDYUQQiQnJ9ucUrF+/frSZulNxf6E0L5B9icH+3OcXg2q3J8QajaoUn9CqN2giv0JwX2wDKZ7hqZKlSpIT0/XZVaNGjXw999/a3b/2StmPU4N2KtXLwBA1apV8e2336JIkSIoVqwYTpw4gTNnzsDNTV4KrVq1AnDvd/XLL7/Ay8sLWVlZOH/+vM1rKc1Ipf4A/Rpkf3KwP8fp1aDK/QFqNahif4DaDarUH8B9sEymew/NqFGj0Lt3b4SFheV4Ki4sLEzqrJEjR6Jbt25o3rw5SpYsaXOdjPOC63lqwIMHDwIA3nnnHfTr1w+NGjVCz5498a9//Qvu7u54/fXXpc1at24dgHv/UAcNGoS1a9eiY8eOeO211+Du7i71dcl6U6k/QL8G2Z8c7M9xejWocn+AWg2q2B+gdoMq9QdwHyyT6c5yNmLECERHR6NcuXI2K0qLxSL9g3r69u2LgwcPonr16jlmyXpDGHDvrA+LFi1Cx44dcfv2bYwcORJJSUkYNWoU/Pz8pM3JdvfuXRQuXBhubm44cuQIkpKS0KhRI+lzAODy5cvw9vZG4cKFsX79eiQnJyMsLAweHh6azNOaiv0B+jbI/hzH/uTQq0HV+gPUbFDV/gD1GlSxP4D7YCk0eSGbhurUqSNOnz6t26yrV69qPic2NlbzGdn69OkjNm3aJDIyMjSfNWbMGHHixAnN5+hJxf6E0K9B9ucc9uc8vRpUsT8h1GxQxf6EULNBFfsTgvtgGUz3HpoyZcqgSpUquswqX748ihQpovmcTp06oW3btliyZAlu3bql6axnnnkGY8aMQUhICCZNmoQ//vhDs1kJCQno2LEjwsPD8e233yIpKUmzWXpRsT9AvwbZn3PYn/P0alDF/gA1G1SxP0DNBlXsD+A+WAbTveQsKioKO3bsQM+ePVGqVClYLBbrdZUqVZI66/vvv8eaNWvw5ptv5pgVFBQkbU5SUhLWrFmDqKgonDx5Ei+99BLat2+Phg0bSptxv6ysLOzYsQNRUVHYsmULatWqhQ4dOkh//Slg+72dOnUKzZs3R4cOHaT+/PSkYn+Avg2yP8exPzn0alC1/gA1G1S1P0C9BlXsD+A+WAbTLWhq1Khh/e/suIQQsFgsOHHihGaz7qfFrGxnzpxBdHQ0Vq9ejcKFC2PTpk2azMn2yy+/4OOPP8alS5c0+56y/fbbb/jwww91maUV1fsD9G2Q/dmH/cmnV4Mq9Aeo36Cq/QFqNKh6fwD3wY4y3VnOZL/p62F+//133WYBwO3bt3HkyBHExsbi5s2bmp0J4ty5c4iKikJ0dDTu3LmDtm3bokOHDprMSklJwYYNGxAVFYUjR46gSZMmGDt2rCaz9KByf4A+DbI/x7E/OfRqULX+ALUbVK0/QL0GVe4P4D7YKbq9W0eSYcOGib179+oyq3PnzmLVqlWaf7DSrl27xJAhQ0SdOnVE69atxddffy1u3LihyayOHTsKPz8/0aNHD7FhwwaRnp6uyRwhhHj//fdFnTp1RMuWLcWCBQtEQkKCZrP0omJ/QujXIPtzDvtznl4NqtifEGo2qGJ/QqjZoIr9CcF9sAyme4amWLFiGDhwIEqUKIF27dohPDwcFStW1GRWkyZNsGDBAowdOxYtW7ZEREQE6tatK33O22+/jVatWuGrr75CnTp1pN///Z577jlMmTJF+mtNc1OoUCHMnz8fgYGBms/Si4r9Afo1yP6cw/6cp1eDKvYHqNmgiv0BajaoYn8A98FSaLpc0khaWpr46aefRN++fYW/v7/o0aOHWLdunbh7964m844ePSrGjh0rGjVqJFq0aCHmzZsnLl++LO3+f/75Z2n39SidOnUSq1atErdv39Z81r///W/djqToSbX+hNCvQfbnPPbnHL0aVLU/IdRrUMX+hFC3QdX6E4L7YBlMd1KABx06dAhjxozB8ePHUapUKYSHh6N///4oUaKE1DmZmZnYuXMnPv/8cxw/fhxFihRBSEgIhg8f7vRKt379+tajDe3atdN05bxgwQJERUXh4sWLmh9xGDt2LNatW6fLkRSjqNAfoF+D7E8u9mc/vRosCP0BajSoYn9AwWhQhf4A7oNlMOWC5urVq1i7di1Wr16NM2fOICQkBOHh4ahUqRKmTZuG5ORkLFmyRMqsI0eOIDo6GuvXrwcAtGnTBuHh4ahQoQImT56Mw4cPIzo62qkZ6enp2LJlC6KiorBz504EBQUhPDwczZs31+zTfI8dO4ZVq1Zhw4YNKF68OCIiItC2bVuUL19e6pz09HRs3boVq1atwq5duxAUFISIiAi89NJLpv2kYtX6A/RvkP05jv3JoUeDKvYHqNegqv0BajaoWn8A98FS6PZckCQ9evQQfn5+onXr1mLhwoU53mR08uRJUadOHSmzWrRoIWrWrCn69Okjfvrppxxvnjp16pSoV6+elFnZEhISxNKlS0W7du1EUFCQiIyM1OyTVjMyMsS2bdtEu3bthK+vr6hdu7YYOHCgiI+P12ReTEyMdVZwcLCYOHGiuHXrliaztKJ6f0Lo1yD7sx/7k0vPBlXoTwj1G1S1PyHUaFD1/oTgPthRpnuGZvTo0YiIiEDt2rVzvT4lJQV///03nnnmGadnzZs3D+3atUO5cuVyvT4tLQ1paWkoXry407OAe5+qunbtWqxbtw6///47QkJCULlyZURFRaFbt27o27evlDl6HXEA9D2SogeV+wP0aZD9OY79mesxULX+ALUbVK0/QL0GVe4P4D7YKdKWRjrp27dvrpd36tRJ+qy2bdvmennTpk2lzlm7dq3o1auXqFmzpmjTpo346quvbI46/PrrryIgIEDKLD2POOh5JEUvKvYnhH4Nsj/nsD/n6dWgiv0JoWaDKvYnhJoNqtifENwHy2CK0zZfuHABUVFRAICdO3dixowZNtcnJyfj5MmTUmadO3cOs2fPBgDExcVhxIgROWalpqZKmZXt448/xiuvvILly5ejVq1aOa7/xz/+gW7dukmZFR4e/tAjDk899RS2bdsmZdYTTzyBb7/9Ns8jKZUrV8bKlSulzNKS6v0B+jXI/uzH/sz5GKhKf4D6DarYH6BOg6r3B3AfLIObtHvSUKVKlXD69Gns2bMHmZmZ2LNnj82fuLg4jB49WsqsKlWqoEyZMnle7+3tjalTp0qZle2DDz7A6NGjc0T83XffAQAqVqyIQYMGSZm1fv36XEPO/jRaDw8PaU+fXrlyJdeQO3fuDADw8vKS8rSw1lTvD9CvQfZnP/ZnzsdAVfoD1G9Qxf4AdRpUvT+A+2AZTPcempEjR2LcuHG6zJo9ezb69eunyX3fuXMH169fBwC88sorWL9+Pe7/VSQlJeG1115DTEyM07PuP+KwZs0atGnTxub65ORkHDhwAL/++qvTs+4/kjJ37lz06dMnx6wffvgB+/btc3qWEVTpD9CvQfYnD/tzjF4Nqt4foE6DKvYHqN+gKv0B3AfLZoqXnN3vzz//RFRUFFq0aAFPT09NZ61YsQKZmZnWNzDJlJycjFdeeQWpqakQQqBp06awWCwAACEELBYLXnrpJSmzso84ZP/DeZDMIw7ZR1ISExOtR1LuV6RIEWlHUoygSn+Afg2yP3nYn2P0alD1/gB1GlSxP0D9BlXpD+A+WDpp78bRyYIFC0Tr1q1F3bp1xQcffCAOHDig2awff/xR9OnTR9SsWVN07dpVREdHi9TUVGn3f+3aNXH+/HlRp04dceHCBZs/V69elTbnfrNmzdLkfnPz4Ycf6jZLLyr1J4T+DbI/57A/5+nVoIr9CaFWgyr3J4SaDarUnxDcB8tkupecZdPrQ4GAe6fRyz7F3V9//YVWrVohIiIC/v7+Uu5/5cqVaN26NYoWLSrl/h4mNDQU4eHhmh1xuF/nzp3Rvn17XY6k6E2l/gD9GmR/crA/x+nVoMr9AWo1qGJ/gNoNqtQfwH2wDKZd0ABAZmYmdu7cic8//xzHjx9HkSJFEBISguHDh0v/RSUmJuKnn37C999/j7i4OJQoUQIVK1bE+PHj8c9//tOp+27Tpg3i4+PRsmVLREREoF69epK2OqcNGzZYP4k2MDAQERERaN68OYoUKSJ91sKFC7Fq1SpcvHjR+r3VrVtX+hyjqNIfoF+D7E8e9ucYvRpUvT9AnQZV7A9Qv0FV+gO4D5bBlAsavT4UKC0tDZs3b8bq1auxc+dOVK9eHe3atUObNm1QqlQpzJgxA6tXr8bmzZudnnX06FFERUVh3bp1KFmyJMLDwxEWFoYKFSo4fd+50eOIQzY9j6ToQcX+AH0bZH+OY39y6NWgav0Bajaoan+Aeg2q2B/AfbDTdH2BmwR6fihQvXr1RHBwsBgzZow4evRojutPnz4t/QOW0tPTxdatW0W7du2En5+f6NWrl9i6davUGdkSEhLEsmXLRFhYmKhVq5Zo2LChaNeunTh+/Lj0WRkZGWLbtm2iXbt2wtfXV9SuXVsMHDhQxMfHS5+lJdX7E0K/Btmf/difXHo1qEp/QqjfoIr9CaFOg6r3JwT3wY4y3TM08+bNe+iHAqWlpSEtLU3KebTXrVuHZs2awcPDw+n7yo9jx45h9erVWL9+PbKysvDqq6+icuXKWLRoERo3bozIyEinZ+h9xEGvIyl6Ubk/QPsG2Z9z2F+k0zP0bFC1/gC1G1StP0C9BlXuD+A+2CnSlkY66dSpk1i1apW4ffu25rOaNm0qvvjiC82PYMydO1e0atXKetRh06ZNNkcdDh8+LOrUqSNllp5HHPQ8kqIXFfsTQr8G2Z9z2J/z9GpQxf6EULNBFfsTQs0GVexPCO6DZTDdgubLL79U6pR9Qgjx8ssvi/nz5+d5ir6rV6+K77//XsqstWvXirt370q5r0eZO3euuHLlSp7X3717VyQlJemyLbKo2J8Q+jXI/pzD/pynV4Mq9ieEmg2q2J8QajaoYn9CcB8sg+lecpZNpVP2/fe//8XLL7+sy2ntVD5ln55U6g/Qr0H2Jwf7cxxP2yyHSg2q2B+gdoMq9QdwHyyDaRc0gDqn7FP1lJELFixAVFQUTxkpAU8ZaT/2J48q/QH6Nah6f4A6DarYH6B+g6r0B3AfLIMpFzQqnrKPp4w0DxX7A3jKSLNgf3LwtM2OU7FBVfsD1GtQxf4A7oOdJu3FazpR/ZR9PGWka1O9PyF4ykhXxv7k4mmb7ad6gyr2J4Q6DarenxDcBzvKdM/Q6HnKvvXr1+Oll17Kccq+5ORkKff/IJ4y0vWp3B/AU0a6OvYX6fQMnrbZOSo3qFp/gHoNqtwfwH2wU6QtjXTStm3bXC/XYpUcFBSU6+WyT3PIU0aah4r9CcFTRpoF+3MeT9vsHBUbVLE/IdRsUMX+hOA+WAZTPENz7tw5zJ49GwCwZs0atGnTxub65ORkHDhwAL/++qvTs/766y+MGjUKQgjs378fgYGBOWbduHEDW7ZscXpWtlatWllfK1m2bNkc11+7dg1bt25Fhw4dnJ6l5xEHPY+kaEn1/gD9GmR/9mN/5nwMVKU/QP0GVewPUKdB1fsDuA+WwU3KvWisSpUqKFOmTJ7Xe3t7Y+rUqVJmPfXUU2jevDmCg4Ph5uaG4OBgmz8tW7bEvHnzpMzKFhERgbfeeitHxNOmTQMAlC1bVsoDKQBERkbm+qm3TZo0kXL/91u/fn2uIYeGhgIAPDw8XP6BFFC/P0C/Btmf/difOR8DVekPUL9BFfsD1GlQ9f4A7oNlKCTtnjT273//GwDw5JNPon///prO6tSpEwDgiSeeQFhYmCYzEhMTcebMGQDAF198gWeffRb3P1mWlJSERYsW4d1333V61v1HHJKTk/Hmm2/aXJ+cnIySJUs6PQewPZISFxeHESNG5JiVmpoqZZaeVOsP0K9B9uc89uccvRpUtT9AvQZV7A9Qt0HV+gO4D5bNNAuabFqHfD8tQ/bw8MCgQYNw/fp1APc+gOjB6zt27ChlVvYRh+vXr+PgwYMIDg7OMSt7xeys7CMp2d/Xg2QeSTGCKv0B+jXI/uRhf47Rq0HV+wPUaVDF/gD1G1SlP4D7YOmkvRuHHNaiRQvdZq1atUq3WTNnztRtFjlHrwbZH+VGxcdA9mceKvYnBBs0E+6DnWeKkwIQERERERHlxhQnBSAiIiIiIsqN6d5DA9z7oJ4//vgDDz65JPv1jpmZmfjpp5/w559/Iisry+a6AQMGSJ1F5sH+yEjsj4zGBslI7I9yY7oFzZQpUzB//nyUK1cOhQr9b/MtFov0mEePHo1169ahRo0aOWZRwcT+yEjsj4zGBslI7I/yYroFTXR0NObMmYOQkBDNZ23duhWLFy+Gv7+/5rMelJGRgVOnTsHPz0/q/ep9xEGvIyl6KSj9Ado0yP6cw/6cp2eDqvUHFJwGVegPUK/BgtIfwH2wvUy3oElJScELL7ygy6ysrCzpD2a52bZtGz7++GNcvnzZ5pdepEgRHDp0SOosPY846HkkRS8q9gfo1yD7cw77c55eDarYH6Bmgyr2B6jZoIr9AdwHy2C6BU2TJk2wZs0avPrqq5rPat26NRYsWIDevXtrcv9169bFwYMHMWnSJDRv3hwlS5bEyZMn0bp1a8ycORPt27eXPlPPIw56HknRi0r9Afo3yP6cw/6cp1eDKvYHqNWgyv0BajaoUn8A98EymW5Bc/fuXQwfPhxz5sxB2bJlba5bvHix1FnHjh3DwYMHMXv2bHh7e9tct3nzZqfvf968eQCACxcuYOjQobhw4QJ2796N5s2bo2rVqnjnnXfQpUsXp+fcT88jDnoeSdGLSv0B+jfI/pzD/pynV4Mq9geo1aDK/QFqNqhSfwD3wTKZbkHj4+MDHx8fXWZ16NABHTp00Oz+//77bwD3PjnVzc0NlSpVwpkzZwAA1apVw+XLl6XP1OOIQzY9j6ToRaX+AP0bZH/OYX/O06tBFfsD1GpQ5f4ANRtUqT+A+2CZ+MGaBmrdujXWrl2Lvn37wtfXF2+//TYiIiIwZMgQFC1aFEOGDMGOHTukznzjjTdw8OBBeHp6anbEIdugQYPw888/4+mnn9b8SAo5Ru8G2R/dT+XHQPbn+lTuD2CDZsB9sDymeYYmMjISkZGRGDFiRJ5fM2HCBCmzevfujXnz5qFLly55vlFKxi9j7dq1AIChQ4di0KBB+Ne//oVBgwahf//+yMrKwtChQ52e8SA9jjhk0/NIitZU7A/Qv0H25xj2J49eDarUH6Bmgyr3B6jVoIr9AdwHy2SaBY2eTyTVq1cPABAcHKzL+cafeeYZrFu3DgBQuXJlbN26FSkpKXj88celz2rXrp30+8yLSh88pXJ/gH4Nsj/HsD959GpQpf4AtRtUsT9ArQZV7g/gPlgGvuTsIVJSUuDl5ZXj8p07d6Jx48bS5ixevBhvvvmmzWWHDh3CsGHD8NNPP0mZoecRBz2PpKhMr/4A7Rtkf+ajUn+Afg2yP3m4D3YMG5RDpcfAgtCfaZ6hyZaWloY1a9bg8uXL1g8FSk9Px6lTpzB79myps/r27YsFCxbAw8MDAJCamoqJEydi5cqVOHr0qLQ5s2fPRvHixREeHo6MjAxMnz4dCxculPq0oJ5HHFReI6vYH6B9g+xPDvbnOL0aVLk/QM0GVeoPULtBFfsDuA+WNdgU5s6dK4QQYvDgwSI4OFi0aNFChIaGitatWwtfX1/x4YcfSp/51ltvid69e4v09HQRExMjmjdvLlq2bCliYmKkzjl27Jho0KCB+PLLL0VYWJgIDQ0Vv/32m9QZ2ZKTk3O9fMeOHZrMU4XK/QmhX4PszzHsTx426BiVG2R/rk/l/oTgPlgG07zkrFevXpg/fz6Cg4OxfPlyJCYm4ttvv8XkyZOxcOFCHDp0CNOnT5c6My0tDf369UNiYiLi4uLQuXNnvPfee9bVukzHjh1D9+7dUbNmTcyaNQuenp7SZwBAly5ddDvioOeRFK2p3h+gT4PszzHsTx69GlSpP0D9BlXrD1CrQdX7A7gPdpZpXnI2f/58APeeyqpatSpKly6NEydOAAA6deqEhQsXSp/p4eGBWbNmoW/fvmjYsCGGDRsm9f5nzJhh8/e6deti9+7dmDt3LgoVuverkf2mqqJFi2LgwIGYOXMmjh49imHDhsHNzQ1LliyRNmPevHno3bs3PvjgA+zYsQNlypRBeno6ihUrhtOnT2vy6ctaU7E/QP8G2Z9j2J88WjeoYn+Amg2q2B+gZoMq9gdwHyyTaRY0bdq0wZo1a1CxYkWcP38eTz75JBISEnD79m24ubnh9u3b0maFhobavMYwLS0NV69eRUhIiDUwGefr3rNnT47L/P39ceDAAQDQ5HWOM2fORL9+/dChQwfNjjjs27cPvXv3xvbt2/M8kmI2KvYH6N8g+3MM+5NH6wZV7A9Qs0EV+wPUbFDF/gDug2UyzUvO1qxZgzZt2mDevHn45ptvsHLlSkyZMgV///03ihQpgjt37uCbb76RMmvVqlWP/Bo9T30n2927d9G3b18ULlwY8+bN02xOUFAQ9u3bh8TERHTu3Bnr16/H3bt38eKLL2Lnzp2azdUC+5OH/dmP/cmlR4Mq9QewQZn4GGg/9iePqv2Z6hka4N6p55588kmUKFECH330ESZNmoTk5GSMGjVK2iy9Ql27di1at26NqKioPL8mLCxMyiw9jzjoeSRFLyr2B+jXIPtzDvtznl4NqtgfoGaDKvYHqNmgiv0B3AfLZJpnaLItWLAAPXv2zHH5tGnT8O6770qddfnyZcyePRt//vmn9Q1N2WScr7t169ZYu3YtQkNDc73eYrFIC0zPIw56HknRm0r9Afo1yP7kYH+O06tBlfsD1GpQxf4AtRtUqT+A+2CZTLGgSUxMxJkzZwDcO9PFl19+aXOe66SkJAwePBgxMTFS5/bo0QPXrl1D06ZNUbhwYZvrVPoEXi39+OOPCAkJQVZWlvVIynvvvYcnnnjC6E3LN/ZnXuzPcezPeSr0B7BBM1OhQfZnXnr2Z4oFTXJyMpo1a4br16/ner2Hhwc6duyIDz/8UOrcoKAg/PTTT/D29pZ6v7lJTExEdHQ0Ll68iEGDBmHfvn1o2rSp9Dl6HHHIpueRFC0VhP4AfRpkf/Zjf3Lp1aAq/QEFo0HV+gPUabAg9AdwH+wsU7yHpnjx4vjtt98AAC1btsSGDRt0mVuiRAnNzjd+v+xzj1etWhUnT55Ely5d8M4772D06NGIiIiQOmvEiBF5HnGQ4f4jKV988QWeffbZHEdSFi1aZKoHU9X7A/RrkP3Zj/2Z5zFQxf4A9RtUpT9AzQZV7w/gPlgK3T7CU5KxY8fmevnQoUOlz1qxYoUYMGCAOHz4sIiPj7f5I1OnTp3EDz/8IIQQIjAwUAghxPbt28XLL78sdU72/SckJEi/32xJSUmiQYMGwtfXN9c//v7+Yty4cZrN15qK/QmhX4Pszznsz3laNqh6f0Ko2aAq/QmhfoMq9icE98EymOIZmsuXL1tX5ytWrECtWrVsrk9KSsKmTZukzx05ciQAWO/bYrFACAGLxWL9QCcZTp06hbZt21pnAMDzzz+vyQpW6yMORh1J0ZLq/QH6Ncj+7Mf+5NKyQRX7A9RvUJX+ADUbVL0/gPtgGUyxoClTpgyWLFmCxMREpKWlYfr06TbXFylSRJM3aMk6u8mjeHt74+zZs6hevbr1srNnz6Js2bLSZ/Xv3x8jRoxAr169ctx/pUqVpM5q3Lhxrpf/+9//xmeffSZ1lpZU7w/Qr0H2Zz/2J5deDarSH6B+gyr2B6jToOr9AdwHy2CKBY2HhwdWrlwJAOjZsycWLFigy9zKlSsDAI4fP44LFy6gSZMmSEpKwmOPPSZ1zhtvvIE+ffqgb9++yMjIwPr16zF79mx07NhR6hxA+yMORh1J0ZLq/QH6Ncj+7Mf+5NKyQRX7A9RvUJX+ADUbVL0/gPtgGUxxljOjJCQk4O2338bRo0dRuHBhrFy5Eu3bt8fChQsREBAgddbSpUuxbNkyxMfHo2LFivjXv/6Fbt26wc3NTeqc+Pj4PK/L/sfrjLS0NLzxxhtITEzEpUuX8Pjjj9tcX6RIEbRv3z7XM1+QLT37A/RpkP2Zh4r9Ado2yP7k4j7YfmxQHhUfA1XuzzQLmrp16+LgwYOoUaOGzaed3k/2axoHDx4MLy8vjBgxAi+88AL27duH2bNnY/v27fj222+lztKbHkcc9DySojX2Jxf7sw/7k0/rBlXqD2CDsvEx0D7sTy4V+zPNgmb//v0IDAzE7t2781ytBgcHS53ZqFEj/Pzzz/D09ERwcDD27t2L9PR0PPfcc9i3b5/UWXrR+4iDKtifHOzPMexPHjboGDYoB/tzDPuTQ+X+TPEeGgAIDAwEAHz22WdYvHgxihcvrvnMwoULIzU1FZ6entbzaKekpMDLy0vz2Vr55JNP4OPjg6+++govvPACnnnmGfTu3RufffaZtCMORhxJ0Rr7k4P9OYb9yaN1gyr2B7BBWfgY6Bj2J4fK/ZlmQZPtypUrus0KDQ3F0KFDMXLkSFgsFiQkJGDcuHEICQnRbRtk2717t/WIQ3Zob731FhYuXChtxrx58wAAX3/9tfTXHxuN/TmH/TmH/TlP6wZV7g9gg87iY6Bz2J9zVO7PdAuaF198EW+++SZatGiB8uXL26z+wsLCpM4aPHgwRowYgZYtWwK4dwq6kJAQjBkzRuocPelxxMGIIyl6YX/OYX/OYX/O07pBlfsD2KCz+BjoHPbnHJX7M92CZseOHQCA7777zuZyi8UiPWYvLy9Mnz4diYmJuHDhAipWrIjy5ctLnQHci2nZsmX4888/kZWVZXPdhAkTpM7S84iDnkdS9KJif4B+DbI/57A/5+nVoIr9AWo2qGJ/gJoNqtgfwH2wDKY5KYBREhMTER0djfj4eLzzzjvYt28fmjZtKnXGoEGDEBMTg/r166Nw4cI218l+ME1JScGIESOwceNGAPceBEJCQjBp0iSUKFFC6qzRo0cjNjZWlyMpqtKjP0C/BtmfuajWH6Bfg+xPDu6DHccGnafaY6DK/ZlmQXPgwAHUq1cvz+sXLFgg/bzWx44dQ/fu3VG1alWcPHkS0dHReOWVVzB69GhERERIm1O/fn2sXLkSTz75pLT7fBQ9jjiEhobmernFYtH1E3hlULk/QP8G2Z992J98WjeoUn+A2g2q2B+gVoMq9wdwHyyFMImAgACbv7/66qsPvV6GTp06iR9++EEIIURgYKAQQojt27eLl19+Weqc559/Xty9e1fqfT5MQkKC+Oqrr8S4ceNEUlKS2LJli26zzUrl/oTQt0H2Zz/2JxcbtJ/KDbI/16dyf0JwHyyDaU5/IR54IunixYsPvV6GU6dOoW3btgBgfars+eefx+XLl6XOeeONNzBx4kQkJiZKvd/cHDt2DC1btsSGDRuwcuVKXL9+He+88w5++OEHaTMOHDjw0OvN+EFfKvcH6Ncg+3MM+5NH6wZV7A9Qu0GV+gPUbFDl/gDug2UwzYLmwXNZP+rvMnh7e+Ps2bM2l509exZly5aVOuf777/HsmXL0KhRI/zzn/+0+SPbhAkTMHz4cCxfvhyFChXCk08+iZkzZ0oNrFevXjZ/z35AyDZz5kxps/Sicn+Afg2yP8ewP3m0blDF/gC1G1SpP0DNBlXuD+A+WAbTneVMT2+88Qb69OmDvn37IiMjA+vXr8fs2bPRsWNHqXMmTpwo9f4eJq8jDu+++660GUYcSVGRXv0B+jXI/sxDxf4A7Rtkf/JwH+wYNiiHio+BKvfHBc1DvPnmm3B3d8eiRYuQlZWFzz//HB07dkS3bt2kzgkODkZWVhaOHj2KCxcuoHz58qhbt64mH0iUfcShevXq1stkH3Ew4kiKivTqD9CvQfZnHir2B2jfIPuTh/tgx7BBOVR8DFS5P9MsaDIyMhAVFWX9e3p6us3fMzMzNZnbqVMndOrUSZP7znb16lX07dsXv//+O0qXLo3r16/j6aefxsKFC1GxYkWps/Q84qASlfsD9GuQ/TmG/cnDBh2jcoPsz/Wp3B/AfbAMpjltc16nf7vfli1bpM68evUq5s+fjw8++AD79+/HoEGD4O3tjWnTpqFatWpO33+bNm2wZs0aDBkyBEIIjBkzBl5eXkhKSkJkZCQyMjLw+eefS/hObC1duhTLli1DfHw8KlSoYD3iIOtIQN26dXHw4EHr34ODg7F37948rzcDFfsDjGmQ/dmP/cmlZYMq9geo2aCK/QFqNqhifwD3wTKZ5hka2aHmx5gxY3D79m0IITB+/Hi0atUKnp6eGDt2LBYtWuT0/ffu3RsAsHv3bmzYsAFeXl4AgBIlSiAyMhIvvvii0zNyo/URB6OOpGhJxf4AYxpkf/Zjf3Jp2aCK/QFqNqhif4CaDarYH8B9sEymWdAYITY2FuvXr8fVq1fx+++/Y+HChShRogTq168v5f6zX0eYlZWV62sMH/y0WBn0OOJQtmxZTJ8+3fr3MmXK2Pz9sccekzJHdVr3B+jfIPszDxX7A7RvkP3Jw32wY9igHCo+BqrcHxc0D3Hnzh0ULVoUmzZtgo+PD8qUKYPk5GQUKiTnxzZnzhy0bt0awcHBiIyMxMcff4xixYohJSUFkZGRCA4OljLnfnoccTDiSIqKtO4P0L9B9mceKvYHaN8g+5OH+2DHsEE5VHwMVLk/07yHxgi9evXC448/jgMHDuDll1/Ga6+9hjFjxiArKwszZsyQNufixYvo3r074uPjUbp0ady4cQPVqlXDnDlzpL8hsUmTJli/fj2Sk5MREhKCX3/91XrE4VEfhkT60qs/QL8G2Z95qNgfwAbNhPtgMpKKj4Eq98dnaB5i/PjxmDJlCgIDA9G3b18cO3YMaWlpGDdunNQ5lSpVwrp167Bv3z4kJiaicuXK8Pf3h7u7u9Q5gD5HHEgOvfoD9GuQ/ZmHiv0BbNBMuA8mI6n4GKhyf3yG5iFSUlKsb9C6386dO9G4cWNpc8LCwmzeMJUtNDRU+lN3eh5xIOfo1R+gX4PszzxU7A9gg2bCfTAZScXHQJX7M/+STEN9+/bFggUL4OHhAQBITU3Fp59+ihUrVuDo0aNO3fe5c+cwe/ZsAEBcXBxGjBhhc31ycjJSU1OdmpEbPY84kHO07A8wpkH2Zx4q9gewQTPhPpiMpOJjoMr9yf8YXIUULVoUAwcOREZGBg4dOoS2bdti9+7dWLJkidP3XaVKFZQpUybP6729vTF16lSn5zzIy8sLEydOxMcff4xChQrh2WefxZw5c/D7779Ln0XO0bI/wJgG2Z95qNgfwAbNhPtgMpKKj4FK9ycoT3fv3hU9evQQYWFholatWmLixIni7t270ufMnDlT+n3mpXPnzjbfw507d0RkZKSoWbOmbttA+aNXf0Lo1yD7Mw8V+xOCDZoJ98FkJBUfA1Xuj++heYS7d++ib9++KFy4MObNm6fZnPPnz+Py5cvI/nWkp6fj1KlT6Natm9Q5vXr1gpubG2bOnImjR49i2LBhcHNzw4QJE1CnTh2ps8h5evUH6NMg+zMX1foD2KDZcB9MRlLtMVDl/rigyUVoaKjNBxylpaXh6tWrqFChgvVMEJs3b3Z6zoEDB1CvXj3MnTsXU6dOtc4UQsBiseDZZ5/F8uXLnZ5zv7S0NPTr1w+JiYmIi4tD586d8d5771lfI0rG06s/QP8G2Z/rU7k/gA2aAffBZCSVHwNV7o8LmlysWrXqkV/Trl07p+cEBAQgJiYGL7zwAkaOHAkPDw9s2bIF77//PsaOHYvy5ctj2LBhTs95kJ5HHMh+evUHGNMg+3NtqvcHsEFXx30wGUn1x0BV++OCJh8SEhIQHx+PcuXK4fHHH5d+/9lB//333+jfvz/++9//IjExEREREdi6dauUGXoecSC5tO4P0L5B9mdeKvQHsEEz4z6YjKTCY2BB6I+nbX6I5ORkDBs2DFu2bLE+/dewYUNMmzYNJUuWdPr+IyMjERkZifLlyyM5ORkVKlTAhQsXIISAt7c3bt26JeG7uGfgwIHS7ov0oXV/gH4Nsj/zUak/gA2aEffBZCSVHgMLQn9c0DzE5MmTkZKSgrVr1+KJJ57AX3/9hU8++QSTJk3C2LFjnb7/7CfHgoKCMGjQIEybNg1+fn6YMmUKihQpggoVKjg9I9uDT4/qccSBnKN1f4B+DbI/81GpP4ANmhH3wWQklR4DC0R/mp9HzcRCQkLEtWvXbC67cuWKaNCggdQ5SUlJIjIyUiQkJIiTJ0+Kli1bisaNG4sdO3ZInZM9q3///qJGjRrC19dX1KhRQ3Tv3l3cvHlT+ixyjl79CaFfg+zPPFTsL3sWGzQH7oPJSCo+BqrcHxc0DxEcHJzjnOOpqakiODjYoC1yXmRkpOjatauIi4sTqamp4uTJk6Jr165i5MiRRm8aPYD9kZFU7E8INmgmKjbI/syD/ZkLTwrwEL1790b16tUxZMgQWCwWCCEwadIknDp1Cl9++aW0OTdu3MCyZcsQHx+PrKwsm+smTJggbQ4ANGnSBD/88AMee+wx62VXr17Fq6++it9++03qLHKOXv0B+jXI/sxDxf4ANmgm3AeTkVR8DFS5P76H5iGGDBmCLl26IDo6GpUrV0Z8fDwsFgu++uorqXPeffddXLp0CXXq1IGbm5vU+37QnTt3UKJECZvLSpYsmeMfEBlPr/4A/Rpkf+ahYn8AGzQT7oPJSCo+BqrcH5+heYQbN27g559/RmJiIipXroyQkBAUL15c6oyAgABs3boVpUuXlnq/udHziAM5T4/+AP0aZH/molp/ABs0G+6DyUiqPQaq3B8XNC6gbdu2+PLLL1GuXDnNZ506dQpdunSBh4dHjiMOzzzzjObzyTXp1SD7o9zwMZCMxP7IaNwHO48Lmlw8+AFE2Tw9PVGrVi0MHTrU5vWHzjpw4ADGjRuHsLAwlCpVyua6sLAwaXOy6XXEgRyjd3+Avg2yP9emen8AG3R13AeTkVR/DFS1Py5ocrFq1apcL8/IyMCGDRtQtGhRzJw5U9q8ESNGIDo6GuXKlbN57aTFYjH9J7eS/fTuD2CD9D/sj4zGfTAZiY+B5sQFjZ2Sk5MREhKCAwcOSLvPgIAArFixAtWqVZN2nw8y4ogDyadFf4D2DbI/NZi1P4ANqoL7YDKSWR8DC0J/PMuZne7cuQMPDw+p91mmTBlUqVJF6n0+aODAgblenn3EYdSoUdKPOJB8WvQHaN8g+1ODWfsD2KAquA8mI5n1MbAg9MdnaOwwf/58LF26FC+88ALGjBkj7X6joqKwY8cO9OzZE6VKlbJZRVeqVEnanLxodcSB5NKqP8DYBtmfOajaH8AGzYL7YDKSqo+BqvTHBY0dVq9ejdu3b6NDhw4oVEjek1s1atSw/nd2xEIIWCwWnDhxQtqcvKjyoUqq06o/wNgG2Z85qNofwAbNgvtgMpKqj4Gq9MeXnNmhbdu2mtyvkW/4yj7i0KxZM8O2gfJHq/4A4xpkf+ahYn8AGzQT7oPJSCo+BqrUH5+hcQHDhw9HREQEgoKCdJ+t5REHMg+jGmR/BPAxkIzF/sho3Ac7z9xbr4hixYph4MCBKFGiBNq1a4fw8HBUrFhRl9laHnEg8zCqQfZHAB8DyVjsj4zGfbDz+AyNi0hPT8fWrVuxatUq7Nq1C0FBQYiIiMBLL72kyRk1iB7EBslI7I+MxP7IaGzQOVzQuKBDhw5hzJgxOH78OEqVKoXw8HD0798fJUqUMHrTqIBgg2Qk9kdGYn9kNDZoPy5oXMTVq1exdu1arF69GmfOnEFISAjCw8NRqVIlTJs2DcnJyViyZInRm0kKY4NkJPZHRmJ/ZDQ26BwuaFxAz549sXv3blStWhXh4eFo27YtvL29rdefOnUKHTt2RExMjIFbSSpjg2Qk9kdGYn9kNDboPJ4UwAU88cQT+Pbbb1G7du1cr69cuTJWrlyp81ZRQcIGyUjsj4zE/shobNB5bkZvAAFXrlzJNeLOnTsDALy8vPDMM8/ovVlUgLBBMhL7IyOxPzIaG3Qen6ExyIULFxAVFQUA2LlzJ2bMmGFzfXJyMk6ePGnAllFBwQbJSOyPjMT+yGhsUC4uaAxSqVIlnD59GomJicjMzMSePXtsri9SpAhGjx5t0NZRQcAGyUjsj4zE/shobFAunhTABYwcORLjxo0zejOoAGODZCT2R0Zif2Q0Nug8vofGBfz555+IiorCnTt3jN4UKqDYIBmJ/ZGR2B8ZjQ06jwsaFxAaGooFCxagcePG+PDDD3Hw4EGjN4kKGDZIRmJ/ZCT2R0Zjg87jS85cyLFjx7Bq1Sps2LABxYsXR0REBNq2bYvy5csbvWlUQLBBMhL7IyOxPzIaG3QcFzQuJjMzEzt37sTnn3+O48ePo0iRIggJCcHw4cNRqVIlozePCgA2SEZif2Qk9kdGY4OO4YLGRRw5cgTR0dFYv349AKBNmzYIDw9HhQoVMHnyZBw+fBjR0dEGbyWpjA2SkdgfGYn9kdHYoHO4oHEBLVu2xIULF9C4cWOEh4cjNDQUhQr974zap0+fxuuvv479+/cbuJWkMjZIRmJ/ZCT2R0Zjg87jgsYFzJs3D+3atUO5cuVyvT4tLQ1paWkoXry4zltGBQUbJCOxPzIS+yOjsUHn8SxnLmD79u3YtWtXnqfr8/DwYMSkKTZIRmJ/ZCT2R0Zjg87jgsYFNG3alKfrI0OxQTIS+yMjsT8yGht0Hl9y5kJ4uj4yGhskI7E/MhL7I6OxQcdxQeNieLo+MhobJCOxPzIS+yOjsUHHcEHjIni6PjIaGyQjsT8yEvsjo7FB53BB4wJ4uj4yGhskI7E/MhL7I6OxQedxQeMCeLo+MhobJCOxPzIS+yOjsUHn8SxnLmD9+vW5RhwaGgqAp+sj7bFBMhL7IyOxPzIaG3ReoUd/CWnh3LlzmD17NgAgLi4OI0aMsLk+OTkZqampRmwaFRBskIzE/shI7I+Mxgbl4jM0BqlSpQrKlCmT5/Xe3t6YOnWqjltEBQ0bJCOxPzIS+yOjsUG5+B4aFzBr1iz079/f6M2gAowNkpHYHxmJ/ZHR2KDzuKAhIiIiIiLT4kvOiIiIiIjItLigISIiIiIi0+KChoiIiIiITIunbTbQ2rVr0bp1a0RFReX5NWFhYbptDxU8bJCMxP7ISOyPjMYG5eFJAQzUunVrrF271vrBSQ+yWCzYvHmzzltFBQkbJCOxPzIS+yOjsUF5uKAhIiIiIiLT4ntoDHTgwIGHXr9gwQKdtoQKKjZIRmJ/ZCT2R0Zjg/JwQWOgXr162fy9bdu2Nn+fOXOmnptDBRAbJCOxPzIS+yOjsUF5uKAx0IOv9rt48eJDryeSjQ2SkdgfGYn9kdHYoDxc0BjIYrHY9Xci2dggGYn9kZHYHxmNDcrDBQ0REREREZkWFzRERERERGRa/GBNA2VkZNh8mFJ6errN3zMzM/XfKCpQ2CAZif2RkdgfGY0NysPPoTFQXh+kdL8tW7bosCVUULFBMhL7IyOxPzIaG5SHCxoiIiIiIjItvoeGiIiIiIhMiwsaIiIiIiIyLS5oiIiIiIjItLigISIiIiIi0+KChoiIDPXZZ58hKCgITZs2xaZNm6yX37p1C61atUJiYqKBW0dERK6On0NDRESGOXPmDL799lts2LABR48exahRo9CsWTMAwNSpU9GtWzd4e3sbvJVEROTK+AwNEREZxs3tf7shIYT178eOHcOJEyfQoUMHozaNiIhMgs/QEBGRYf7xj3+ge/fuCA8PR/HixTFu3DgIITB27FiMGjUKFovF6E0kIiIXxw/WJCIil/Ldd9/h9OnT6NGjBz766CMkJCQgNDQUgwYNMnrTiIjIBfElZ0RE5DKuX7+Ob775Bu+++y4mTpyI4OBgfP/999i0aRO2b99u9OYREZEL4oKGiIhcxn/+8x/07dsXxYsXx+nTp1GzZk14eHjAx8cHp06dMnrziIjIBXFBQ0RELuHQoUO4ePEiWrduDQB46qmnEBMTgzt37uDEiRN46qmnDN5CIiJyRVzQEBGR4TIzM/HJJ5/go48+sl42ZMgQbN68GS+88AICAwPx0ksvGbiFRETkqnhSACIiIiIiMi0+Q0NERERERKbFBQ0REREREZkWFzRERERERGRaXNAQEREREZFpcUFDRERERESmxQUNERERERGZFhc0RERERERkWlzQEBERERGRaXFBQ0REREREpsUFDRERERERmRYXNEREREREZFpc0BARERERkWn9HxcVmmHh5dYWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axs = df1.plot(kind='bar', figsize=(10, 2), legend=False, subplots=True, layout=(1, 5), \n",
    "                           sharex=True, sharey=True)\n",
    "axs[0, 2].set_xlabel('%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lisätietoa\n",
    "\n",
    "Pääsääntö: Jos teen kaavion suoraan alkuperäisestä datasta (ei siis yhteenvetotaulukosta), niin kannattaa käyttää **seaborn**-kirjastoa.\n",
    "\n",
    "Kahdeksanosaisen muistiosarjan viimeinen osa keskittyy seaborn-kirjastoon: https://nbviewer.jupyter.org/github/taanila/kaaviot/blob/master/seaborn.ipynb"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}