plot_treasuries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from  bs4 import BeautifulSoup\n",
    "import matplotlib.pyplot as plt\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.treasury.gov/resource-center/data-chart-center/interest-rates/pages/TextView.aspx?data=yieldYear&year=2020'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_data = urllib.request.urlopen(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "html_data = r_data.read().decode('utf-8')\n",
    "html_data = BeautifulSoup(html_data, 'html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = html_data.find('table',{'class':'t-chart'})\n",
    "table_rows = table.find_all('tr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def table_data():\n",
    "    table = html_data.find('table',{'class':'t-chart'})\n",
    "    table_rows = table.find_all('tr')\n",
    "    list = []\n",
    "    col = ['date','1mo','2mo','3mo','6mo','1yr','2yr','3yr','5yr','7yr','10yr','20yr','30yr']\n",
    "    for tr in table_rows:\n",
    "        td = tr.find_all('td')\n",
    "        row = [i.text for i in td]\n",
    "        list.append(row)\n",
    "    df = pd.DataFrame(list,columns=col)\n",
    "    df['date'] = df['date'].astype('datetime64[ns]')\n",
    "    df = df.drop(0)\n",
    "    for i in df:\n",
    "        if df[i].dtypes == 'object':\n",
    "            df[i] = df[i].astype('float')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_curve():\n",
    "    #x = datetime.datetime.today()\n",
    "    #x = str(x.date())\n",
    "    df = table_data()\n",
    "    y = str(df['date'][len(df)])\n",
    "    df2 = df.drop(columns='date')\n",
    "    data = df2.iloc[len(df2)-1]\n",
    "    plot = data.plot()\n",
    "    plot.legend(['Yeild Curve as of: ' + y ])\n",
    "    plt.grid(True)\n",
    "    plt.yscale('linear')\n",
    "    plt.gca()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = table_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>date</th>\n",
       "      <th>1mo</th>\n",
       "      <th>2mo</th>\n",
       "      <th>3mo</th>\n",
       "      <th>6mo</th>\n",
       "      <th>1yr</th>\n",
       "      <th>2yr</th>\n",
       "      <th>3yr</th>\n",
       "      <th>5yr</th>\n",
       "      <th>7yr</th>\n",
       "      <th>10yr</th>\n",
       "      <th>20yr</th>\n",
       "      <th>30yr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.19</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.13</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   1mo   2mo   3mo   6mo   1yr   2yr   3yr   5yr   7yr  10yr  \\\n",
       "1 2020-01-02  1.53  1.55  1.54  1.57  1.56  1.58  1.59  1.67  1.79  1.88   \n",
       "2 2020-01-03  1.52  1.55  1.52  1.55  1.55  1.53  1.54  1.59  1.71  1.80   \n",
       "3 2020-01-06  1.54  1.54  1.56  1.56  1.54  1.54  1.56  1.61  1.72  1.81   \n",
       "4 2020-01-07  1.52  1.53  1.54  1.56  1.53  1.54  1.55  1.62  1.74  1.83   \n",
       "5 2020-01-08  1.50  1.53  1.54  1.56  1.55  1.58  1.61  1.67  1.78  1.87   \n",
       "\n",
       "   20yr  30yr  \n",
       "1  2.19  2.33  \n",
       "2  2.11  2.26  \n",
       "3  2.13  2.28  \n",
       "4  2.16  2.31  \n",
       "5  2.21  2.35  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>1mo</th>\n",
       "      <th>2mo</th>\n",
       "      <th>3mo</th>\n",
       "      <th>6mo</th>\n",
       "      <th>1yr</th>\n",
       "      <th>2yr</th>\n",
       "      <th>3yr</th>\n",
       "      <th>5yr</th>\n",
       "      <th>7yr</th>\n",
       "      <th>10yr</th>\n",
       "      <th>20yr</th>\n",
       "      <th>30yr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.116349</td>\n",
       "      <td>1.113333</td>\n",
       "      <td>1.087937</td>\n",
       "      <td>1.082381</td>\n",
       "      <td>1.053333</td>\n",
       "      <td>1.067937</td>\n",
       "      <td>1.080476</td>\n",
       "      <td>1.132540</td>\n",
       "      <td>1.266349</td>\n",
       "      <td>1.353175</td>\n",
       "      <td>1.689524</td>\n",
       "      <td>1.861270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.634780</td>\n",
       "      <td>0.636827</td>\n",
       "      <td>0.642144</td>\n",
       "      <td>0.628441</td>\n",
       "      <td>0.577240</td>\n",
       "      <td>0.504640</td>\n",
       "      <td>0.475494</td>\n",
       "      <td>0.451535</td>\n",
       "      <td>0.416325</td>\n",
       "      <td>0.415280</td>\n",
       "      <td>0.382894</td>\n",
       "      <td>0.364194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.495000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.855000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.345000</td>\n",
       "      <td>1.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.530000</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.550000</td>\n",
       "      <td>1.460000</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.560000</td>\n",
       "      <td>1.570000</td>\n",
       "      <td>1.570000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>1.530000</td>\n",
       "      <td>1.470000</td>\n",
       "      <td>1.465000</td>\n",
       "      <td>1.490000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>1.985000</td>\n",
       "      <td>2.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.610000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>1.790000</td>\n",
       "      <td>1.880000</td>\n",
       "      <td>2.210000</td>\n",
       "      <td>2.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1mo        2mo        3mo        6mo        1yr        2yr  \\\n",
       "count  63.000000  63.000000  63.000000  63.000000  63.000000  63.000000   \n",
       "mean    1.116349   1.113333   1.087937   1.082381   1.053333   1.067937   \n",
       "std     0.634780   0.636827   0.642144   0.628441   0.577240   0.504640   \n",
       "min     0.000000   0.000000   0.000000   0.020000   0.110000   0.230000   \n",
       "25%     0.495000   0.470000   0.375000   0.390000   0.390000   0.500000   \n",
       "50%     1.530000   1.550000   1.540000   1.550000   1.460000   1.370000   \n",
       "75%     1.560000   1.570000   1.570000   1.560000   1.530000   1.470000   \n",
       "max     1.610000   1.610000   1.590000   1.580000   1.560000   1.580000   \n",
       "\n",
       "             3yr        5yr        7yr       10yr       20yr       30yr  \n",
       "count  63.000000  63.000000  63.000000  63.000000  63.000000  63.000000  \n",
       "mean    1.080476   1.132540   1.266349   1.353175   1.689524   1.861270  \n",
       "std     0.475494   0.451535   0.416325   0.415280   0.382894   0.364194  \n",
       "min     0.280000   0.370000   0.510000   0.540000   0.870000   0.990000  \n",
       "25%     0.580000   0.660000   0.855000   0.930000   1.345000   1.560000  \n",
       "50%     1.350000   1.370000   1.450000   1.540000   1.840000   2.000000  \n",
       "75%     1.465000   1.490000   1.590000   1.680000   1.985000   2.140000  \n",
       "max     1.610000   1.670000   1.790000   1.880000   2.210000   2.380000  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tennor(x):\n",
    "    '''\n",
    "    possible values for x:\n",
    "    '1mo','2mo','3mo','6mo','1yr','2yr','3yr','5yr','7yr','10yr','20yr','30yr'\n",
    "    '''\n",
    "    df = table_data()\n",
    "    y = str(df['date'][len(df)])\n",
    "    df2 = df.drop(columns='date')\n",
    "    data = df2[x]\n",
    "    plot = data.plot()\n",
    "    plot.legend(['Chart for ' + x + y ])\n",
    "    plt.grid(True)\n",
    "    plt.yscale('linear')\n",
    "    plt.gca()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tennor('10yr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3dd3zV1f348dfJImRBNoEA2WGGESAsNagooqIoipTiKNaF1tbdb+sotT+11lbrtqKotbgVC7iKRBCQvXdIQMLIJIvs5P37I0Mg6ya5Nzf3+n4+HnlA7ufzOZ/3SW7eOTmfM4yIoJRSyvG52DsApZRS1qEJXSmlnIQmdKWUchKa0JVSykloQldKKSfhZq8bBwUFSUREhL1ubxWnTp3C29vb3mFYndbLsWi9HEtH67Vp06YcEQlu6pjdEnpERAQbN2601+2tIiUlheTkZHuHYXVaL8ei9XIsHa2XMeZwc8e0y0UppZyEJnSllHISmtCVUspJ2K0PXTmvyspKMjIyKCsrs3coZ+jRowd79uyxdxhWp/VyLJbWy9PTk/DwcNzd3S0uWxO6srqMjAx8fX2JiIjAGGPvcBoUFRXh6+tr7zCsTuvlWCypl4iQm5tLRkYGkZGRFpetXS7K6srKyggMDOxSyVwpR2KMITAwsM1/5WpCVzahyVypjmnPz1CrCd0Y09cYs8IYs9sYs8sYc3cT58w2xmw3xuwwxqwxxgxrcyRW8MWO4xzMLrbHrZVSyu4saaFXAfeKyCBgLDDPGDPorHPSgfNEZCjwZ+A164bZui92HOf2dzcz89W1HMo51dm3V13MiRMnuO6664iOjiYxMZGpU6dy4MABUlJSuOyyyzpU9meffcbu3bubPJadnU1SUhIjRoxg1apV7Sr//vvvZ8CAASQkJDB9+nTy8/Mbjj3xxBPExMQQHx/PV1991aZyjxw5wqRJkxg0aBCDBw/mueeeaziWl5fH5MmTiY2NZfLkyZw8eRKAd999l4SEBIYOHcr48ePZtm1bwzVffvkl8fHxxMTE8OSTTzZ737feeovY2FhiY2N56623Gh2fNm0aQ4YMafb65u7zwgsvEBMTgzGGnJycZq9v7mtmSfzl5eXMnDmTmJgYkpKSOHToUKvlni49PZ2kpCRiYmKYOXMmFRUVrZbbISLSpg9gMTC5heP+wNHWyklMTBRrOZJ3SoY++qVMeXaljJj/tUx8arlkFpRarfzmrFixwub3sIeO1mv37t3WCaSdampqZOzYsfLyyy83vLZ161b58ssvZcWKFXLppZe2u+zKykq54YYb5MMPP2zy+KJFi2Tu3LltKrOqquqMz7/66iuprKwUEZEHHnhAHnjgARER2bVrlyQkJEhZWZmkpaVJVFSUVFVVSWFhoUVxHzt2TDZt2iQiIoWFhRIbGyu7du0SEZH7779fnnjiCREReeKJJxruuXr1asnLyxMRkWXLlsmYMWMaYo6KipKDBw9KeXm5JCQkNJR1utzcXImMjJTc3FzJy8uTyMjIhvJERD7++GOZNWuWDB48uNG1hYWFLd5n8+bNkp6eLv3795fs7Owm693c18zS+F988UW59dZbRaT2e3vttde2WO7ZrrnmGlm0aJGIiNx6663y0ksvSWFhYbPlnq2pnyVgozSTV9s0ysUYEwGMANa1cNpc4Itmrr8FuAXAL7QfKSkpbbl9k6prhCfWl1FZVcNNsZUUV7rw1PpSrvrnt/w+qTve7rbryy0uLrZKHbqajtarR48eFBUVWS+gNvruu+9wcXFh9uzZDXFERUXRv39/1qxZQ0FBAVdeeSW7d+9m+PDhvP766xhjePLJJ/niiy8oKysjKSmJ5557DmMMU6dOZejQofzwww9cdtllLF68mJSUFObPn88777xDVFQUANu3b+f++++ntLSU9evX87///Y8lS5bwzDPPICJcfPHFzJ8/H4CwsDBuuukmUlJSeOaZZxg3blxD/OPGjaO0tBSAYcOG8dlnn1FUVMQHH3zA9OnTqaioICgoiIiICFasWMHXX39NQEAA8+bNA2D+/PkEBQUxdOhQHn/8cXr27Mn+/fvZsmULsbGxDV+T2NhYDhw4QN++ffn0009ZtmwZRUVFXH311UydOpU//vGPDB06FKgdmTF48GCOHDlCUVER69atIyIiguDgYMrLy5k+fToffPAB99577xnfi88++4zk5OSGoXfJycl8+umnXHPNNRQXF/P000/zz3/+kxtuuKHRe6a6upoVK1Y0e5+YmBigtlFaXFxMt27dGr0XmvuaARbF//HHH/P73/+eoqIiLr74YubNm0dhYWGz5SYlJTVcKyIsX76cV199laKiImbMmMETTzzBrFmzmi337H7zsrKyNv0sWpzQjTE+wMfAb0WksJlzJlGb0Cc2dVxEXqOuO6ZbWKz0HphIXGjHhiX97at9pOan8s9ZI5g2rDcAcYNyuGnhehYe7Mbbv0qiu4drh+7RHF1roml79uxpGJb1p//uYvexJt8u7Taotx+PXj642eNpaWmMGTOm0dCwoqIivLy82L59O7t27aJ3795MmDCB7du3M3HiRO69917+8pe/ADBnzhy+++47Lr/8clxda98/mzdvBuDHH3/ksssuY8aMGWeUP2HCBP785z+zceNGXnjhBY4dO8Zjjz3Gpk2b8Pf356KLLmL58uVceeWVnDp1inPOOYfnn3++xbouWrSImTNn4uvrS05ODmPHjm2oV0REBPn5+Vx//fVcf/31PPTQQ9TU1PDJJ5+wfv16duzYwbZt29i5c2ejoW+HDh1ix44dTJo0CV9fX7Kzs4mNjQXAx8eH7OzsRl+/V199lalTp+Lr60t+fj6RkZEN50RHR7Nu3bpG1+Tl5REVFdXwemRkJHl5efj6+vLII4/wwAMPEBwcjIuLS5PfL0vuY4zBx8enyaGAzX3N6mNpqtxHHnmEUaNGMW3aNDIzMxkwYEDDeT179qSioqLZcn19fZk6dSqvv/46Hh4e+Pv74+/vD0B8fDyZmZm4uro2W25QUNAZ8Xt6ejJixIhG9WqORaNcjDHu1Cbzd0Xkk2bOSQBeB64QkdxWb2wMr61MszjQpqxJzeHFlFSuHRXekMwBJsYG8Y+Zw9l4+CR3/mczldU1HbqPci5jxowhPDwcFxcXhg8f3tB/Wd/CGjp0KN9++y27du1quGbmzJltvs+GDRtITk4mODgYNzc3Zs+ezcqVKwFwdXXl6quvbvH6v/zlLw3XtaR///4EBgayZcsWvv76a0aMGEFgYGBDXc9O5sXFxVx99dU8++yz+Pn5NSrPGNOopbhixQoWLFjAU0891Wq9LbF161YOHjzI9OnTrVKeNc2fP59p06a1+/ply5bRu3fv1k+0gVZb6Kb2O7sA2CMif2/mnH7AJ8AcEdlvyY39vdxZvPUo910UT68enm2JGYDc4nJ++/5WooK8eWxa49baZQm9OVlSycOf7eSBj7bz1xkJuLvqKM3O1lJL2lYGDx7MRx991Ozx0/80d3V1paqqirKyMu644w42btxI3759eeyxx84YA2ztZVw9PT0bWv5NWbhwIUuWLGH58uUNybVPnz4cOXKk4ZyMjAz69OkDwM0338zChQs5ceIEv/rVr5qNu7KykquvvprZs2dz1VVXNbweGhrK8ePHCQsL4/jx44SEhDQc2759OzfffDNffPFFwy+K5mJZt24dt956K1CbGPv06XNGl0FGRgbJycmsXbuWjRs3EhERQVVVFVlZWSQnJ/POO+9w+eWXA3DjjTeSlJTUbJ0t0dLXzJJy668PDw+nqqqKgoICAgMDWyy3XmBgIPn5+VRVVeHm5nbGOc2V22HNda7LTw85JwICbAe21n1MBW4Dbqs753Xg5GnHm+20r/8YOmyERD60RP7f0rY/QKuurpEb31gnsX9YJruPFbR47vPL90v/B5fIzFfXSG5xeYvnllVWSVll4wcbzbH04WFpheVldgXO8FB0zJgx8uqrrza8tm3btiYfis6bN0/efPNNOXnypISEhEhJSYkUFRXJ4MGD5dFHHxURkfPOO082bNjQcM2dd94pb7zxRpP3fvPNN2XevHkiInLs2DHp16+fZGdnS1VVlVxwwQXy2WefiYiIt7d3s/F/8cUXMnDgQMnKyjrj9Z07d57xIC4yMrLhoWh5ebnExcU1vCYijepaU1Mjc+bMkbvvvrvRPe+7774zHoref//9IiJy+PBhiY6OltWrV59xfmVlpURGRkpaWlrDQ8WdO3c2Kjc3N1ciIiIkLy9P8vLyJCIiQnJzc884Jz09vdmHopbcp6WHos19zSyN/4UXXjjj4eU111zTYrlnmzFjxhkPRV988UUpLCxsttyztfWhaJtHuVjrIzExUea9u0mGPPKlFJZWNFmZppRVVsl9H2yV/g8ukbfXpFt0zcebjkjsH5bJxKeWy57jjX8BlFdWy8LV6TJy/tdy4TMpktdK4q9nSeJbnZotcX9YJu+tP2xRmV2Boyd0EZGjR4/KNddcI1FRUTJo0CCZOnWqbN68udmELiLyhz/8QaKiomT8+PFy4403NpvQv//+exk4cKAMHz5cUlNTz7jv6QldROQ///mPDBkyRAYPHtwwckSk5YQeHR0t4eHhMmzYMBk2bFjDD76IyOOPPy5RUVESFxcny5YtExFpGOVy6623yoMPPthw7tl1XbVqlQAydOjQhrKXLl0qIiI5OTly/vnnS0xMjFxwwQUNSXfu3LnSs2fPhvNPH522dOlSiY2NlaioKHn88cebrc+CBQskOjpaoqOjm/xF2FJCb+k+zz33nPTp00dcXV0lLCys2dFFTX3NWir34YcflsWLF4uISGlpqcyYMUOio6Nl9OjRcvDgwVbLveSSS+To0aMiInLw4EEZPXq0REdHy4wZM6SsrEwKCwtbLPd0bU3opvZ45xs1apS8+dlyLn/he/5v6gBuOTe61Wuyisq4/d+b2XT4JL+5IJbfXRhr8WyqrUfyueXtjRSXV/GPmcO5eHAvRIQvdp7gr1/u5VBuCYn9/dlxtIBBYX68e3MS3t1a7pFq7eFhbnE5lzy3iqyicvw83fj2vmSCfBo/ie9qrPFQdODAgdYLyEqceW0Qb29vRo4cyYcfftjwcNPROfP3y9J6NfWzZIzZJCKjmjrfrp3KQ8N7MC4qkDe+P0RFVcsPLnceLeCKF1az+1ghL80eyT2T49o0NXZ43578966JxIb4cOs7m/jzkt1c9fIa7nh3Mx5uLrx542g+um0cz88awfaMfG5/d3OrMbVERLjvw23kl1by/KwRlFZW88Syve0uT6nm7N27l5iYGC644AKnSeaqfez+lPCW86I4UVjGf7cda/ac/247xoxX1uBiDB/dPo6pQ8Pada9QP0/ev3Uc00f0YcH36RzLL+WvVyfwxd3nMmlACMYYLh7ciyeuGsrK/dnc9+E2amra9xfMgu/TWbEvmz9eOpDLh/Xm1+dE8fHmDNaltToASKk2GTBgAGlpaTzzzDP2DkXZmd2Xz02OCyY+1Jd/rUrjqpF9zmh1F5RU8uzy/by5+hCj+vvzypzEDndZeLq78vdrh/GLpH4M6d2jyTHqM0f3I/dUBX/9ch8B3h48evmgNv01sCOjgKe+3MtFg0KZM7Y/AHedH8vircd4ePFOlv7mHKcfcSMiukCXUh3Qnu5wu2cVYwy/PjeKvSeK+G5/NgBlldX8a2Ua5z69goVrDjFnbH/+8+uxVut/NsYwOiKgxQlHt58Xzc0TI1m45hDPf5tqcdnF5VXctWgzwT7d+OuMhIak1t3DlT9NG8z+zGLeXJ3e4Tp0ZZ6enuTm5rbrDamU+mk9dE/Ptg3ptnsLHWDasN787at9vPpdGvkllTz91T6O5pdyXlwwD10ygIFhjSc/2Joxhv+bOpC8UxX8/Zv9FJZW8tAlA3BrpWX98Gc7+TGvhPduGUdPL48zjl04KJQLB4by7P8OcFlCb3r37G7LKthNeHg4GRkZZGdn2zuUM5SVlbX5B8QRaL0ci6X1qt+xqC26REL3cHPhpgkRPPHFXtam5TK4tx9PXZ3AxNig1i+2IRcXw19nJODX3Z3Xv09nf1Yxz88aQY/ujbeEKq2o5sUVqXy65Sj3TI5jTGRAk2U+evkgJv/jO+b/dzevzEm0dRXswt3dvU27rHSWlJSUNk2jdhRaL8diy3p1iYQOMHtsf/ZlFnFubDDThvXGxaVr9L+6ubrw2LTBxPfy5ZHFO5n+4mr+dcMoooN9gNrFwT7adIS/f7OfzMJyrhjem3mTYpotr2+AF3edH8vTX+1jxd4sJg0IafZcpZRqC7v3odfz6ebG368dzpUj+nSZZH66WWP68e7NYykoreTKF1eTsi+LrVlVXPLcSh78eAe9e3bnw9vG8dx1I3BtJf5fnxNFTIgPv3lvCyn7sjqpBkopZ9dlErojGBMZwOI7JxDu78WNb27g2c3lVFTV8PLskXxy+3hGRzTdzXI2DzcXFt40mnB/L361cAP/WpmmDxCVUh2mCb2Nwv29+Pj2ccydGMkvB3rwzT3nccnQsDYP0asvZ8qQXvxl2R7u/XAbZZXVNopaKfVzoAm9Hbw83Hj4skFc2N+9Q+PJvTzcePEXtbNeP9l8lOte+4HMwrbt8q2UUvU0oduZMYbfXBDLK79MZH9mEdNe+J503RNVKdUOmtC7iClDevHx7eOprBbmLFinLXWlVJtpQu9CBob5sfCm0Zw8VcH1C9ZTUFJp75CUUg5EE3oXkxDek1fnjCI95xRz39pAaYU+KFVKWUYTehdUvyfqph91T1SllOU0oXdRlyaEMf+KISzfm8WDH29v9zK+Sqmfjy4z9V81Nmdsf/KKK/jH//bj7uLCn68cgoeb/g5WSjVNE3oX95sLYqiqqeH5b1NJyynm5V92fE14pZRzarW5Z4zpa4xZYYzZbYzZZYy5u4lzjDHmn8aYVGPMdmPMSNuE+/NjjOHei+L556wRbM+o3YZv17ECe4ellOqCLPn7vQq4V0QGAWOBecaYQWedcwkQW/dxC/CyVaNUTBvWm49uG0+NCDNeXsuyHcftHZJSqotpNaGLyHER2Vz3/yJgD9DnrNOuAN6WWj8APY0x7dv4UzVraHgPFt85gYFhvtzx7mZe+PaAvUNSSnUhpi2r/BljIoCVwBARKTzt9SXAkyLyfd3ny4EHRWTjWdffQm0LntDQ0MT33nuvo/HbVXFxMT4+Pp1+38oa4V/by9lwopqnzu1OiJd1H5Taq162pvVyLFqvpk2aNGmTiIxq8qCIWPQB+ACbgKuaOLYEmHja58uBUS2Vl5iYKI5uxYoVdrv38fxSifr9Unnyiz1WL9ue9bIlrZdj0Xo1DdgozeRVi5p2xhh34GPgXRH5pIlTjgJ9T/s8vO41ZSO9enhywYAQPthwhIoqnXiklLJslIsBFgB7ROTvzZz2OXB93WiXsUCBiOhTOxubPbY/uacq+GrXCXuH0ir9paOU7VnSQp8AzAHON8ZsrfuYaoy5zRhzW905y4A0IBX4F3CHbcJVpzsnJoi+Ad15d91he4fSoo82ZTDk0a94a80h3ZlJKRtqdWKR1D7obHE7nrp+nXnWCkpZxsXF8Isx/Xnqy72kZhUTE9L1HiDlnarg8aW7cXM1PPr5LvYcL2T+FTrjVSlb0J8qB3fNqHDcXU2LrfTK6hq7bW/35Bd7KC6r4pM7xjNvUjTvbTjC7Nd/IKe43C7xKOXMNKE7uCCfblwyJIyPN2U0udRuTnE5Fz+7kpmvrqWqk1dt3Hgojw82ZjD3nEgG9PLj/osH6IxXpWxIE7oTmJ3Uj8KyKpZsP3bG60Vlldz45np+zC1hW0YB7/zQeX3tVdU1/PGznfTu4clvzo9teL1+xmt1jXD1y2tI2ZfVaTEp5ew0oTuBMZEBxIT48O66HxteK6+q5tZ3NrHneBGvXZ/IuXHBPPP1frI6aWu7hWsOsfdEEY9cPhjvbmc+qhka3oPP75pAZJAPd7+3laP5pVa7r4hYtTylHIkmdCdgjGF2Uj+2Hsln59ECqmuE372/lTUHc3l6RgLnDwhl/rTBVFTX8PjSPTaP50RBGf/4Zj/nDwjh4sGhTZ4T4uvJy7NHUlVdw2/f22KV7iAR4bHPdzHhyW+56c317DtR1OEylXIkmtCdxFUjwvF0d+HddT/y8OKdLNtxgj9eOpCrRoYDEBHkze3nRfP5tmOsTs2xaSx/XrqbqhrhscsHUzuNoWkRQd78ZfpQNhw6yT+Xd3xdmue/TeWttYeZFB/MxsMnueS5lTzw0TaOF2iLXf086HroTqKHlzuXJ/TmvQ0/IgK3J0dz8zlRZ5xze3I0n209ysOLd/LF3efQzc3V6nGs3J/N0u3HuXdyHP0CvVo9/8oRfVh1IIfnV6QyNjqQ8dFB7brvv384zN+/2c/VI8N5ekYCBaWVvLgilbfXHmbx1mPMnRjJEFcdA6+cm7bQnciccf0xwMxRfXng4vhGxz3dXXls2mDSsk/xr5VpVr9/dY0wf8luIoO8ueW8qNYvqDP/isFEBnrzu/e3kneqos33Xbr9OA8v3skFA0J48uqhuLgY/L09+ONlg1h+73lMGdKLl1IO8sDKEt5cna6zVpXT0oTuRBLCe/L9g+fzxFVDm+3qmBQfwiVDevH8t6kcySux6v0/3XKU1KxiHpwS36bWv3c3N57/xQhOnqrk/g+3tWk26fcHcvjt+1tI7OfPC78YibvrmW/pvgFePHfdCP5750T6+rrwp//u5sK/f8eS7cd01qpyOprQnUzvnt1xcWlxYi8PXzYIVxfDw4t3UlxeZZX7lldV849v9jO0Tw8uHtyrzdcP7t2D/5s6gOV7s1jwfbpF12w9ks+t72wkKsiHBTeMprtH879Ehob34IHRniy8aTReHq7c+Z8tXPniajYcymtzrEp1VZrQf4Z69+zOPZPjSNmXzfA/fc21r6zl+eUH2Hokn+qa9rVa399whKP5pdx/cXyLD0JbcsP4CCYPCuXxpXuYu3AD+zObHqWSVVTGHz7dwdUvr8Hf24O3546hh5d7q+UbY0iOD2Hpb87h6RkJZBWVM+u1H1ihY+GVk9CHoj9TcydGMrRPD77bn82qAzk8881+nvlmPz26u3NtjCG5DWWVVFTxz+WpJEUGcE5s+x5qQm3CfX7WCN5cfYiXVqQy5dmVXDuqL7+bHEeonyenyqv416o0XluZRkVVDb9M6sdvLoglsI2bZru6GK4Z1ZcpQ3px3Ws/cPu/N/HuzWNJ7O/f7tiV6go0of9MGWNIigokKSqQB6ZAbnE5qw/m8taaQ7y56yTTjxUyqLefRWW9teYwOcXlvPLLke1undfzdHfl9uRoZo7uywvfpvLOD4f4bOtRpo/owze7s8gpLmfq0F7cf/EAIoO8O3QvX093Ft40hmteWcOvFm7gw9vGERfq26EylbIn7XJRAAT6dGPasN68NicRH3fDnYs2U1LRev96QWklr3x3kEnxwYyKCLBaPAHeHjxy+SCW35PM5EG9WLT+CJFBXnxyx3hemp3Y4WReL9i3G+/MTcLDzYXrF6wn46R1HxQr1Zk0oaszBPp049aEbqTnnOKxz3e1ev7rq9IoKK3k3osaD5O0hn6BXjw/awQ7/3QxH9w6jpH9rN8t0jfAi7d/NYZTFVVcv2A9uXUrQVZV17Dp8Eme/d9+rnllDY8s3qkjY1SXpl0uqpGBga7MS47hhRWpTIgJ4orhfZo8L6e4nAXfp3NpQhhD+vSwaUw+3Wz7Vh0Y5scbN47ml6+vY86C9fQL8GL1wRyKyqowBvoHeLHh0GEG9/Zj5uh+No1FqfbShK6a9NsLY/khLZc/fLqT4X170j+wcRfHyykHKaus5p7JcXaI0PpGRwTw0uyR3PbvTeSXVHDp0DDOiQ1mfHQgft3duf6NdTz6+S5G9vMnVvvaVRekXS6qSW6uLjx73XBcDPxm0ZaG2ZXHC0r5YMMR7vzPZt5ee4irR4YTHdz1dkpqrwsGhrLt0YtY/dD5PHl1ApcmhOHv7YGri+Ef1w7H28ONuxZtsduGIUq1RFvoqlnh/l78dUYCt/17M9e/sY6c4gpSs4oBCPHtxpXD+/DQJQPsHKX1eXk0/WMR4ufJ364dxk1vbuAvS/fw5yuHdHJkSrVME7pq0ZQhYdw4PoJF638kKSqQ60b35ZzYYOJCfTo8RNERTYoP4dfnRPKvVelMiAlkypAwe4ekVANN6KpVj14+iEcuG9TqkgI/F/dfPIB16Xk88NF2hvTpQbh/66tKKtUZWu1DN8a8YYzJMsbsbOZ4D2PMf40x24wxu4wxN1k/TGVPxhhN5qfxcHPh+VkjqBG4+72tnb5Xq1LNseSh6EJgSgvH5wG7RWQYkAw8Y4zx6HhoSnVd/QO9efzKIWw6fLJT92pVqiWtJnQRWQm0tCSdAL6mtkPVp+5c6yzhp1QXdsXw3p2+V6tSLTGWzHwzxkQAS0Sk0WN9Y4wv8DkwAPAFZorI0mbKuQW4BSA0NDTxvffea3fgXUFxcTE+Ps4zZK+e1stymadq+MPqUkaFunLbME+rlm0p/X45lo7Wa9KkSZtEZFSTB0Wk1Q8gAtjZzLEZwD8AA8QA6YBfa2UmJiaKo1uxYoW9Q7AJrVfb/OObfdL/wSWyan+2TcpvjX6/HEtH6wVslGbyqjUmFt0EfFJ3r9S6hO58g5OVasZt50XTP9CLRxbvpLxKJxwp+7FGQv8RuADAGBMKxAPW37BSqS7K092VP00bTFqObfZqVcpSlgxbXASsBeKNMRnGmLnGmNuMMbfVnfJnYLwxZgewHHhQRHJsF7JSXU9yfAhTh9pmr1alLNXqxCIRmdXK8WPARVaLSCkH9fBlg/huXzaPfr6LBTeM+lnOpFX2pYtzKWUlYT2687vJcXy7N4tvdmfaOxz1M6QJXSkrumF8BLEhPjz55V6dQao6nSZ0pazI3dWFey+KJy37FJ9uOWrvcNTPjCZ0pazs4sGhJIT34Nn/HdBhjKpTaUJXysqMMdx/cTxH80t5b/0Re4ejfkY0oStlAxNjghgbFcDz36ZSUqFLG6nOoQldKRuob6XnFJezcM0he4ejfiY0oStlI3jUdqEAAB3dSURBVIn9Azh/QAivpBykoLTS3uGonwFN6ErZ0L0XxVFYVqVLAqhOoQldKRsa3LsHlyWE8cbqdHKKy+0djnJymtCVsrF7JsdRXlXDSysO2jsU5eQ0oStlY1HBPswYGc7baw+x7Ui+vcNRTkwTulKd4PdTBxDi2427Fm2hqEwfkCrb0ISuVCfo6eXBP2eN4Gh+KX/4dGf9bl9KWZUmdKU6yaiIAH57QSyfbzvGh5sy7B2OckKa0JXqRHdMimFcVCCPLt5FalaxvcNRTkYTulKdyNXF8Ox1w+nu4cpdi7ZQVnnm4l2FZZV8szuT3ccK7RShcmSt7liklLKuUD9P/nZNAr9auJH/t2wPVwzvw6oD2aw6kMPWI/lU1wiuLobHLh/EnHER9g5XORBN6ErZwfkDQpk7MZIF36fz9trDGAMJ4T25IzmacVGBLPg+nYcX72LPiSIeu3wwHm76x7RqnSZ0pezkgSnxBPp40D/Am/HRgfh7ezQcS4oK5G9f7+PllIOkZhXz8uyRBPp0s2O0yhG0+mvfGPOGMSbLGLOzhXOSjTFbjTG7jDHfWTdEpZxTNzdX7kiO4dKEsDOSOdT2tT84ZQDPXTecbUfymfbCau1XV62y5O+4hcCU5g4aY3oCLwHTRGQwcI11QlNKXTG8Dx/cOo6qmhqueWUNWUVl9g5JdWGtJnQRWQnktXDKL4BPROTHuvOzrBSbUgoY1rcnr80ZxamKatak5to7HIew5ceTbDjUUtpyTsaSGWvGmAhgiYgMaeLYs4A7MBjwBZ4TkbebKecW4BaA0NDQxPfee6/dgXcFxcXF+Pj42DsMq9N6dT01IsxbXkJSLzduHHJmX7oj16slHanXQ6tKyC4R7h7ZjYTgrvWosKPfr0mTJm0SkVFNHbNGTd2AROACoDuw1hjzg4jsP/tEEXkNeA1g1KhRkpycbIXb209KSgqOXoemaL26prGH1nM4r6RRHRy9Xs1pb72yi8o58eX/8HB14aXtlbx7cyKJ/f2tH2A72fL7ZY2xUBnAVyJySkRygJXAMCuUq5Q6TVJUIGnZp8gu0nXVW7I+vbar5cXZI+nl58mvFm5gf2aRnaPqHNZI6IuBicYYN2OMF5AE7LFCuUqp04yJDAB+SliqaevTc/HycCU5Pph35ibRzc2F6xesJ+Nkib1DszlLhi0uAtYC8caYDGPMXGPMbcaY2wBEZA/wJbAdWA+8LiLNDnFUSrXP0D496O7uyvp0fTDaknXpeST298fd1YW+AV68PXcMJRVVXL9gPblOvmuUJaNcZolImIi4i0i4iCwQkVdE5JXTznlaRAaJyBAReda2ISv18+Tu6kJif3/WaQu9WfklFew9UcSYiICG1wb08mPBjaM5ml/KrxZuoKKqxo4R2pbOJ1bKgSRFBrAvs4j8kgp7h9IlbTh0Eqh93nC60REB/PmKIWzLKGDHUefdNUoTulIOZExkACI/JS51pnVpuXi4uZAQ3qPRsXHRtUl+7wnnfUCqCV0pBzKsb0883FxYl6b96E1ZfyiPEX174unu2uhYuH93vD1c2a8JXSnVFXi6uzK8b0/W/wxnQbamqKySnUcLSIoMaPK4MYa4Xr7aQldKdR1jIwPYebRAN5s+y6bDJ6kRGBMZ2Ow5A3r5si+zyGn3dNWErpSDGRMZSI3UJjD1k3Xpebi5GEb279nsOXGhvuSXVDrt5CxN6Eo5mJH9e+LmYnT44lnWp+cxNLwHXh7Nr2gS38sXcN4Ho5rQlXIwXh5uDA3voTNGT1NaUc32jHySWuhuAYgPrU3ozroUgCZ0pRxQUmQg2zPyKa2obv3kn4EtP56kslqafSBaL9CnG0E+3bSFrpTqOpIiA6isFrb8qP3oAD+k5+FiIDGi9VUVB/TyZZ8mdKVUV5EY4Y+LqU1kqnZBrkG9/fDzdG/13LhQXw5kFVFd43wjXTShK+WA/DzdGdTbTxfqAsqrqtnyY+v95/UG9PKlrLKGH/Ocb/VFTehKOagxEYFs+TGfSidsabbF9owCyqtqGpYXbk39SBdn7HbRhK6Ug0qKCqC8qob0AuddPdAS9aN9Tl9hsSWxoT4Y45wJvWtttqeUstjougT2/OYy3jnw7RnHevl5cvcFcUyMDbJHaJ3qh7Rc4kN98ff2sOh8Lw83+gV4sS+z0MaRdT5N6Eo5qABvD35/yQC+25ZKr9AzW6fr0vP45YJ1nBsXzENTBjCot1+j60sqqlifnke4vxcxIY65yXRVdQ2bDp9kRmJ4m66LD3XOkS6a0JVyYLeeF028HCE5efgZr5dVVvPvHw7z/LepXPr8KqaP6MM9k+PIL6lk5YFsVu3PYdPhk1RU1xAb4sM395xnpxp0TFrOKUoqqhnZr22bQMf38mX53izKKqubXJnRUWlCV8oJebq7cvM5UVyT2JeXUlJ5c80hPtl8tOH4wDA/bpoQQXlVDQvXHGJ/ZhFxdbMoHcnxgjIA+vh3b9N18b18qa4RDmYXM7h347XTHZUmdKWcWA8vd34/dSDXj4/ggw1HiAjyYkJMECG+ngBkFZXx1tpDLNl+nHsmO15CzyysTei9/DzbdF39EgD7ThQ5VULXUS5K/Qz06dmd302OY/qI8IZkDhDi68mYiACW7Thux+jaL7OuhR7i161N10UEeePh6uJ0/eia0JX6mbssIYzUrGKHXLDqRGEZAd4edHNrWz+4u6sL0SE+7HPAOrek1YRujHnDGJNljNnZynmjjTFVxpgZ1gtPKWVrFw/phTGwdLvjtdIzC8sI8W1b67xefKjPz7KFvhCY0tIJxhhX4CngayvEpJTqRPXdLksdsNsls7CcXj3a1n9eL76XH8cLyigodZ6dn1pN6CKyEmhtBaC7gI+BLGsEpZTqXJc6aLfLicKyNj8QrRffq3bsvaPVuSUdHuVijOkDTAcmAaNbOfcW4BaA0NBQUlJSOnp7uyouLnb4OjRF6+VYrFEvv/IaDPDi52uZHmvZjEtba61eVTVCTlE5pXknSElp+6qTJ0trl0z478pNnDrU+iqN1mLL96E1hi0+CzwoIjXGmBZPFJHXgNcARo0aJcnJyVa4vf2kpKTg6HVoitbLsVirXosOrWV3UQXPJXeNSUat1etYfiny9bckJQwgOalfm8sXER774WvEL4zk5CEdiLRtbPk+tMYol1HAe8aYQ8AM4CVjzJVWKFcp1YkuTQjjgAN1u5yoH4Peo30PRY0xxDnZZhcdTugiEikiESISAXwE3CEin3U4MqVUp5riYKNdsuoSemg7+9ChdsbovswiRJxjCWJLhi0uAtYC8caYDGPMXGPMbcaY22wfnlKqs4T4ejLagSYZnSho3yzR08WH+lJQWklmYbm1wrKrVvvQRWSWpYWJyI0dikYpZVeXJYTxyOJdHMgsIraLr+1yorAcd1dDgIXL5jalfrOLvScK2z38sSvRmaJKqQYN3S4O0EqvnVTkSWuDMVpy+pouzkATulKqQX23iyP0o2cWlnW4Ve3v7UGwbzcOZBVbKSr70oSulDrD1CG9OJBVzJEuvolyRyYVnS4m2IdUTehKKWc0NLx2OdmunuQyC8o6NMKlXkyIDwezi51ipIsmdKXUGSKDaqfEp+WcsnMkzSsqq+RURTWhbVw2tynRwd4UlVWRXeT4I100oSulzuDv5Y6fpxuHunBCb9jYwgojU2JCah+MdvW/SCyhCV0pdQZjDJFB3qTbKaGXV1XzyOKd7Mqpbvac+nHj1upyAUjN1oSulHJC9kro1TXCPe9v4+21h1l1tPllba0xqaheqF83fLq5cVBb6EopZxQR5M2xglLKKptvJVubiPDY57tYuuM4Pbq7k1FU0+y5J6ww7b+eMYboYG9toSulnFNkkDci8GMnDl189n8HeOeHw9x6XhS/SOrH8VNCZXXTST2zsAw/Tze6e7Rt67nmRIc4x9BFTehKqUYig7wBSMvunG6Xt9ce4rnlB7gmMZyHpgwgPtSXaqHZbh9rTCo6XUyID5mF5RSWOfbuRZrQlVKNRNQl9EO5tk/o/912jEc/38WFA0N54qqhGGNOW2Ol6Sn5JwrLrdLdUi8muG6oZif9ArMVTehKqUb8PN0J8vEg3cYJbufRAu75YCuj+wfwwi9G4OZam5Kigr1xMbDvRGGT12UWWGeWaL2GkS4O3u2iCV0p1aTIIG/SbdxCX1K3Zsxr1yfi6f5Tf3g3N1d6eRv2nWicYKtrhOxi67bQ+wV44e5qNKErpZxTRKDthy6uOZjDiL7+9PRqvARuuI8L+zIbt9BzisuprhFCrdiH7ubqQkSgtyZ0pZRzigz2JruonOLyKpuUX1BSyY6jBYyPCWzyeLivC0fyShvdv2GWqBVb6PDTmi6OTBO6UqpJkYF1D0Zt1Epfm5aLCEyICWryeLhPbXo6cNYep9acVHS6mBAffswrobyq88beW5smdKVUkyKDaxO6rbpd1hzMwcvDlWHhPZs8Hu5bm57O3nwis2FSUccX5jpddLAP1TXC4dyuvWxwSzShK6Wa1D/Atgl9dWoOYyID8HBrOg0FdTd4ebg2Grp4orAMVxdDoI91E3p7RrpUVNUw793N3PzWBk6eqrBqPO2hCV0p1aTuHq707uFpky6XEwVlHMw+xfjopvvPAVyMITbUl/2ZZ7fQywnx7YarS/u3nmtKVN1fJJYm9Joa4b4Pt7F0x3G+25/NlS+tbhRrZ2s1oRtj3jDGZBljdjZzfLYxZrsxZocxZo0xZpj1w1RK2UNEkLdN1kVfczAHgPHRTfef14sP9Wmyy8WaQxbreXm40adnd4sejIoI85fs5vNtx3hgSjzv3zqOkopqrnppDf/bnWn12CxlSQt9ITClhePpwHkiMhT4M/CaFeJSSnUBkUHeNpktujo1F38vdwaF+bV4XnwvP3JPVZBT/NPmEyesPKnodJau6fLiilQWrjnE3ImR3H5eNCP7+fP5nROIDPLm1+9s5MUVqXbZAanVhC4iK4G8Fo6vEZGTdZ/+AIRbKTallJ1FBnmTX1Jp1f5hEWHNwRzGRQfi0kq3SXxo7RIAp7fSTxSWWf2BaL2Y4NqhizU1zSfjd9cd5m9f7+eqEX34w9SBGFNbh7Ae3fnwtnFcntCbp7/ax2/f30p1C+XYgpuVy5sLfNHcQWPMLcAtAKGhoaSkpFj59p2ruLjY4evQFK2XY7FlvYqzaseAf/zNKmJ6WmdlwxOnajheUMbk6rwW4y4uLqY6bQcAS77fQmWGO+VVQlFZFadyj5GSkmOVeE5Xk19JWWUNH3+5gmCvxu3dDSeqeGlrOQnBrkwNPsnKld81Omd6L4FidxZvPUa0ay4JwWemWZu+D0Wk1Q8gAtjZyjmTgD1AoCVlJiYmiqNbsWKFvUOwCa2XY7FlvVKziqT/g0vko41HrFbm22sPSf8Hl0hadnGL59XXa+T8r+WBD7eJiEhadrH0f3CJfLzJevGcbl1arvR/cIms2JvZ6NiBzCKJ/b9lctVLq6WkvKrFckorqmTwI1/K/R9ubXSso98vYKM0k1etMsrFGJMAvA5cISK51ihTKWV/ff29cHUxVu1HX5OaQ+8enkQEell0flyoL3vrRo/YalJRvegWRrr845v9uLsaXvllYqvrsHu6u3LhwBC+3p3Z7JruttDhhG6M6Qd8AswRkf0dD0kp1VV4uLkQ7t/daiNdamqEtWm5jI8Jauh7bk18L18OZBZRUyMNk4pCbJTQA3264e/l3miky86jBSzdcZy5EyMJ9rWs/37q0DDySypZc7Dz2rit9qEbYxYByUCQMSYDeBRwBxCRV4BHgEDgpbpvUJWIjLJVwEqpzhUZ5G21sei7jxeSX1LJhGbWb2nKgF6+lFRUk3GytGHrOWtubnG2mCZGujzz9T56dHfn5nOjLC7n3LhgfLq5sWz7cc6LC7Z2mE1qNaGLyKxWjt8M3Gy1iJRSXUpEoDfr0/MQEYta1dU1wr9WpXHhwNCG2Zf1VqdaNv78dHENm10UkllYhk83N3y6WXs8x09iQnz4atdPY8k3HMpjxb5sHrpkAH6e7haXU9/t8tXuEzxePQR3V9vP49SZokqpFkUFe1NSUU12UXnrJwO7jhXw5Bd7mf7ialbszTrj2OqDucSE+LRpYlBc3dDF/ZlFdZOKbDNksV50sA95pyrIO1WBiPD0l/sI9u3GDeMi2lxWR7pdisoq2zyWXRO6UqpFEXWrLlraj34krxQAX083fvXWBl757iAiQkVVDRvS85jQwnT/pvh0cyPcvzt7TxTVTiqyYXcL1E4ugtoHoysP5LD+UB6/OT+mXRtSn97t0hZrUnMYMf8bvtp1ok3XaUJXSrWofsNoS/vRM07Wrlb46bwJTB0SxpNf7OV372/lh7RcSiurGdeG7pZ6A3r5su9EEZmF5YT62jah1+8veiCriL99tY9w/+7MHN2vXWV5urtyQV23i6WjXXKKy7n7/a1U1Qgfbz7apvtpQldKtah3z+54uLpYvOpixslS/DzdCPXz5IVfjOC+i+L4bOsxbnlnIy4GxkW1rYUOtSNd0nJO1Xa52LiF3qdnd7q7u7Lg+3R2HC3gtxfGNbsipCUuret2WWtBt0v9gl8FpZWcFxfMd/uzKSqrtPhemtCVUi1ydTH0C/SyOKEfOVlC34DaMebGGO48P5ZX5yTiagzD+/akh5flDxbrxYX6Ul0jVNWIzcag13NxMUQFe5OWfYqYEB+mj+jTofLqu12WWtDt8sbqdFL2ZfPwpQO56/wYKqpq+Pas5xAt0YSulGpVZJDl+4tmnCwl3L/7Ga9dPLgX396XzCtzEtt1/wG9flrEyxYrLZ4tuq7b5d7JcR1eptfSbpftGfk89eVeLh4cyi/H9mdkP396+Xla9IugniZ0pVSrIoO8OZxX0upiUyJCxskSwv0bzwIN9fMkpJ3935FB3rjVJVZbj3IBuGpkH2Yn9WPKkF5WKW9qK90uRWWV3LVoC8E+3Xjq6gSMMbi4GKYM6UXK/myL93XVhK6UalVkkDcVVTUcyy9t8byc4grKKmvoe1YLvaM83FwaWs22HuUCkBwfwl+mD7V4NmtrzosLxtvDlWU7Gre2RYQ/fraTI3klPDdrBD29PBqOXZoQRkVVDcv3WLbGuiZ0pVSr6ocutramS/0Il6Za6B0V38sXFwPBVt56rjN4urty4aBQvtp1gqq6v3LySypYtuM493ywjcVbj/G7C+MYHRFwxnWJ/fwJ9etmcbeL7aZbKaWcRtRpG0afE9v8NPaMk7Ut+PAA67bQAX6R1I+IIG/cOmHGpS1MHRrG4q3HeHOn8MKe1WzPyKdGwLebG79I6scdk2IaXePiYrhkSBj/Wf8jxeVVrc6Q1YSulGpViG83vDxcSctuuYV+xIYt9LFRgYxtx5DHruK8uGB6ermz5lglI/rBXefHcm5cEMPCe7b4S+rShDAWrjnE8j2ZXDG85RE3mtCVUq0yxhAT4sOBrJY3Qc44WYq/l7tN11pxVJ7urnz9u3PZ8MNaLp08weLr6rtdlu043mpCd8y/XZRSnS42xJcDmS3vt1k7ZNH6rXNnEeLribd72x601ne7pOxrfbSLJnSllEXiQn3IKiqnoKT5mYsZeSX0tUH/+c/d1KFhlFsw2kUTulLKIrGhtcMG9zfT7VJTI2TkawvdFkb19yfEt1uTwx5PpwldKWWR2JDaZWyb63bJKS6noqqm0SxR1XG13S69SNmX3fJ5nRSPUsrB1S9atT+z6Rb6kbohi321hW4T9d0uLdGErpSyiIuLITa08fZs9X6aVKQtdFsYFRFASCv7mWpCV0pZLDbEt9kWev2koj6a0G3C1cVwSyt7mmpCV0pZLLaFkS4ZJ0sI8vHAy0PHoNvKzedoQldKWUlc6E+7+ZztSF4pfbT/3K5aTejGmDeMMVnGmJ3NHDfGmH8aY1KNMduNMSOtH6ZSqiuoH+myv4mRLrXL5mp3iz1Z0kJfCExp4fglQGzdxy3Ayx0PSynVFdWPdDm7hV5TIxzNb7yxhepcrSZ0EVkJ5LVwyhXA21LrB6CnMSbMWgEqpbqO+pEuZ49Fzywqo7JadMiinVnj6UUf4Mhpn2fUvdZoSpMx5hZqW/GEhoaSkpJihdvbT3FxscPXoSlaL8fS2fXyrSln55HqM+65/2Q1AHlHDpBSlm6V++j3q+069XG0iLwGvAYwatQoSU5O7szbW11KSgqOXoemaL0cS2fXa685yOov9jIiaQI9utdu+HxySwas28bU88YSE+Jjlfvo96vtrDHK5SjQ97TPw+teU0o5ofqRLqmn9aMfyavb2EL70O3KGgn9c+D6utEuY4ECEbF8m2qllENpaqRLxskSgn274enuaq+wFBZ0uRhjFgHJQJAxJgN4FHAHEJFXgGXAVCAVKAFuslWwSin7a2pNl9p10LV1bm+tJnQRmdXKcQHmWS0ipVSX5uJSu3vR6Wu6HDlZwoi+/naMSoHOFFVKtUNsqE9DC72quobj+WXaQu8CNKErpdosLtSXzMJyCkorySwqp6pGdGOLLkBX0VFKtVlsyE8jXSqrBUC3nusCNKErpdosLvSnkS7urrV/6GsL3f40oSul2qxhTZfMYvy6u2EM9O7pae+wfvY0oSul2qx+pMuBrCJC/TwJ9fWkm5uOQbc3TehKqXaJDfVhTWqubgzdhegoF6VUu8SG+HKisIx9mUWa0LsITehKqXapX9Mlv6SSvgH6QLQr0ISulGqX+jVdQBfl6io0oSul2iXcv3akS+3/tYXeFWhCV0q1S/1IF9AWelehCV0p1W6xIT64GAjroQm9K9Bhi0qpdrthfASDevvh4aZtw65AE7pSqt2G9e3JsL497R2GqqO/VpVSykloQldKKSehCV0ppZyEJnSllHISmtCVUspJaEJXSiknoQldKaWchCZ0pZRyEkZE7HNjY7KBw3a5ufUEATn2DsIGtF6ORevlWDpar/4iEtzUAbsldGdgjNkoIqPsHYe1ab0ci9bLsdiyXtrlopRSTkITulJKOQlN6B3zmr0DsBGtl2PRejkWm9VL+9CVUspJaAtdKaWchCZ0pZRyEprQLWSMecMYk2WM2XnaawHGmG+MMQfq/vW3Z4xtZYzpa4xZYYzZbYzZZYy5u+51R6+XpzFmvTFmW129/lT3eqQxZp0xJtUY874xxsPesbaHMcbVGLPFGLOk7nOHr5cx5pAxZocxZqsxZmPdaw79PgQwxvQ0xnxkjNlrjNljjBlny3ppQrfcQmDKWa89BCwXkVhged3njqQKuFdEBgFjgXnGmEE4fr3KgfNFZBgwHJhijBkLPAX8Q0RigJPAXDvG2BF3A3tO+9xZ6jVJRIafNkbb0d+HAM8BX4rIAGAYtd8329VLRPTDwg8gAth52uf7gLC6/4cB++wdYwfrtxiY7Ez1AryAzUAStbPz3OpeHwd8Ze/42lGf8LokcD6wBDBOUq9DQNBZrzn0+xDoAaRTN/ikM+qlLfSOCRWR43X/PwGE2jOYjjDGRAAjgHU4Qb3quiW2AlnAN8BBIF9EqupOyQD62Cu+DngWeACoqfs8EOeolwBfG2M2GWNuqXvN0d+HkUA28GZdF9nrxhhvbFgvTehWIrW/bh1yDKgxxgf4GPitiBSefsxR6yUi1SIynNoW7RhggJ1D6jBjzGVAlohssncsNjBRREYCl1Db9Xfu6Qcd9H3oBowEXhaREcApzupesXa9NKF3TKYxJgyg7t8sO8fTZsYYd2qT+bsi8kndyw5fr3oikg+soLYroqcxxq3uUDhw1G6Btc8EYJox5hDwHrXdLs/h+PVCRI7W/ZsFfErtL2FHfx9mABkisq7u84+oTfA2q5cm9I75HLih7v83UNsH7TCMMQZYAOwRkb+fdsjR6xVsjOlZ9//u1D4X2ENtYp9Rd5rD1UtEfi8i4SISAVwHfCsis3HwehljvI0xvvX/By4CduLg70MROQEcMcbE1710AbAbG9ZLZ4payBizCEimdunLTOBR4DPgA6AftUsBXysiefaKsa2MMROBVcAOfuqT/T9q+9EduV4JwFuAK7WNlg9EZL4xJoralm0AsAX4pYiU2y/S9jPGJAP3ichljl6vuvg/rfvUDfiPiPzFGBOIA78PAYwxw4HXAQ8gDbiJuvckNqiXJnSllHIS2uWilFJOQhO6Uko5CU3oSinlJDShK6WUk9CErpRSTkITulJKOQlN6Eop5ST+PyYoJVU9Vw0zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tennor('20yr')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}