6 CDF matching correction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "from scipy.interpolate import InterpolatedUnivariateSpline\n",
    "\n",
    "plt.style.use(\"bmh\")\n",
    "from IPython.core.debugger import set_trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"DATA/eventos.csv\",parse_dates=[\"start\",\"duration\"])\n",
    "\n",
    "df.dropna(how=\"any\", axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.RADAR > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.duration = df.duration.apply(pd.to_timedelta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>start</th>\n",
       "      <th>station</th>\n",
       "      <th>duration</th>\n",
       "      <th>Pluviometro</th>\n",
       "      <th>RADAR</th>\n",
       "      <th>ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13 08:30:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.214636</td>\n",
       "      <td>0.035364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-14 07:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.594438</td>\n",
       "      <td>-0.344438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-14 08:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.569860</td>\n",
       "      <td>1.680140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-14 09:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.367961</td>\n",
       "      <td>2.632039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-14 11:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.274750</td>\n",
       "      <td>-0.024750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                start   station duration  Pluviometro     RADAR     ERROR\n",
       "1 2016-10-13 08:30:00  MB_ANHA1 00:10:00         0.25  0.214636  0.035364\n",
       "2 2016-10-14 07:20:00  MB_ANHA1 00:10:00         0.25  0.594438 -0.344438\n",
       "3 2016-10-14 08:10:00  MB_ANHA1 00:50:00         3.25  1.569860  1.680140\n",
       "4 2016-10-14 09:10:00  MB_ANHA1 01:40:00         5.00  2.367961  2.632039\n",
       "6 2016-10-14 11:40:00  MB_ANHA1 00:10:00         0.25  0.274750 -0.024750"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8544 entries, 1 to 21017\n",
      "Data columns (total 6 columns):\n",
      "start          8544 non-null datetime64[ns]\n",
      "station        8544 non-null object\n",
      "duration       8544 non-null timedelta64[ns]\n",
      "Pluviometro    8544 non-null float64\n",
      "RADAR          8544 non-null float64\n",
      "ERROR          8544 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(3), object(1), timedelta64[ns](1)\n",
      "memory usage: 467.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>start</th>\n",
       "      <th>station</th>\n",
       "      <th>duration</th>\n",
       "      <th>Pluviometro</th>\n",
       "      <th>RADAR</th>\n",
       "      <th>ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>2017-03-01 14:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>2.3750</td>\n",
       "      <td>15.645028</td>\n",
       "      <td>-13.270028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>2017-03-03 21:30:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.010298</td>\n",
       "      <td>0.552202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>2017-03-03 22:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.023374</td>\n",
       "      <td>0.039126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2017-03-05 14:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:20:00</td>\n",
       "      <td>17.1250</td>\n",
       "      <td>61.118928</td>\n",
       "      <td>-43.993928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>2017-03-05 16:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>1.243220</td>\n",
       "      <td>-1.180720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  start   station duration  Pluviometro      RADAR      ERROR\n",
       "214 2017-03-01 14:50:00  MB_ANHA1 00:40:00       2.3750  15.645028 -13.270028\n",
       "215 2017-03-03 21:30:00  MB_ANHA1 00:10:00       0.5625   0.010298   0.552202\n",
       "216 2017-03-03 22:00:00  MB_ANHA1 00:10:00       0.0625   0.023374   0.039126\n",
       "218 2017-03-05 14:50:00  MB_ANHA1 01:20:00      17.1250  61.118928 -43.993928\n",
       "219 2017-03-05 16:20:00  MB_ANHA1 00:10:00       0.0625   1.243220  -1.180720"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MARCO = 3\n",
    "df_marco = df[df.start.dt.month == MARCO]\n",
    "df_marco.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1dfaecd9e8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "radar_hist, bin_edges = np.histogram(df_marco.RADAR.values,bins=\"auto\",normed=True)\n",
    "pluvi_hist, bin_edges2 = np.histogram(df_marco.Pluviometro.values,bins=bin_edges,normed=True)\n",
    "dx = bin_edges[1] - bin_edges[0]\n",
    "radar_cdf = np.cumsum(radar_hist) * dx\n",
    "pluvi_cdf = np.cumsum(pluvi_hist) * dx\n",
    "plt.figure()\n",
    "plt.plot(bin_edges[1:], radar_cdf)\n",
    "plt.plot(bin_edges[1:],pluvi_cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1df72a59b0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bin_edges[1:], radar_cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1df720de48>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pluvi_cdf,bin_edges[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para o ajuste dos valores do radar, primeiro encontramos o valor do CDF correspondente ao do radar. E depois com esse valor do CDF encontramos um valor na curva do Pluviometro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "itp_radar_cdf = np.poly1d(np.polyfit(bin_edges[1:], radar_cdf, 5))\n",
    "itp_pluvi_ppf = np.poly1d(np.polyfit(pluvi_cdf, bin_edges[1:], 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9641054333550713\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1df6d79780>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,70)\n",
    "radar = 20\n",
    "cdf_radar = itp_radar_cdf(20)\n",
    "print(cdf_radar)\n",
    "plt.plot(x,itp_radar_cdf(x))\n",
    "plt.scatter(radar,cdf_radar)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ajustar o gráfico ppf do pluviometro com algum polinomio é mais complicado, veja abaixo como a curva vai ter um comportamento estranho."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31.42334539771832\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "valor_corrigido = itp_pluvi_ppf(cdf_radar)\n",
    "\n",
    "eixo_cdf = np.linspace(0,1)\n",
    "print(valor_corrigido)\n",
    "plt.plot(eixo_cdf,itp_pluvi_ppf(eixo_cdf))\n",
    "#plt.scatter(valor_corrigido,valor_corrigido)\n",
    "plt.ylim((0,100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para encontrar uma curva satisfatoria para ambos os casos, vou usar uma Spline\n",
    "\n",
    "* https://en.wikipedia.org/wiki/Spline_(mathematics)\n",
    "* https://en.wikipedia.org/wiki/Spline_interpolation\n",
    "\n",
    "Eu acredito que com uma sigmoid tambem seria possivel ajustar o ppf.\n",
    "\n",
    "https://en.wikipedia.org/wiki/Sigmoid_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,80,50)\n",
    "\n",
    "radar_dist = stats.rv_histogram(np.histogram(df_marco.RADAR.values,bins=\"auto\",normed=True))\n",
    "pluvi_dist = stats.rv_histogram(np.histogram(df_marco.Pluviometro.values,bins=\"auto\",normed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "itp_radar_cdf = UnivariateSpline(x, radar_dist.cdf(x),k=3,s=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos ajustar o um valor de radar de 20mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9560556564988376\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1df71c0860>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD1CAYAAABJE67gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJztnXl0W9d95z8/YiEgbuICU4olS7brRHZWt67j1HOyKZ3YmdbuUZ02mU5POqO0M62StpMuxz7tSdv0zHRJO+ks6XImaZLO6SRNM2nGk7p1UzdOOx3Fcaws9RInimJKskQSXAQQJEBsd/4ASEEUKOAn4PFdBL/POTjEw3sEPg8P/PLivt+7V5xzGIZhGL3HQNgChmEYxtVhAW4YhtGjWIAbhmH0KBbghmEYPYoFuGEYRo9iAW4YhtGjRHfyxR599FE3ODi4ky9pGIbR06ytrS0cPnw41Wzdjgb44OAghw4duqrfnZmZ4cCBA1026hzz0mFeOsxLx7ej14kTJ2a2W9czXSixWCxshaaYlw7z0mFeOvrNq2cCfGxsLGyFppiXDvPSYV46+s2rZwJ8YWEhbIWmmJcO89JhXjr6zatnArzf/rN2innpMC8d5qUjtBa4iPyxiMyLyJPbrBcR+S8iclJEvioi39l9TSgWi0E8bceYlw7z0mFeOvrNq50W+IeBu66w/m7gpvrtJ4A/6FzrcvL5fBBP2zHmpcO8dJiXjn7zallG6Jz7exE5eIVN7gX+xNXGpf28iOwWkb3OufNdcgRgz5493Xy6rmFeOsxLh3np6NSrXHXkSxUK5SqFUpVy1VF1jnLVUalCxTkqVYd2EO6SG+GaUoVkLNKR31a6UQd+LXCmYfls/bGuBvjs7KyX9Z3mpcO8dHTDy9UDqFhxFCtVqsr0ce7i7xYrjlK5yplzs+yeSrFWrJArVlit33LFCqvrFcrKF6k6KFaqlBpeZ2NZQ7lcJhrVxVrFOdbrgV3SvjkKPnDfzVy3278AlyaPNX0X5ufnOXr0KNFolEqlwpEjRzh27Bizs7MMDQ0RiUTIZrOkUimWlpZwzpFKpZibm6NUKrG4uEgul2N6epp0Oo2IMDExQTqdZnR0lEqlwurqKnv27GF2dpZYLMbY2BgLCwuMjY1RLBbJ5/Ob6+PxOCMjIywuLjI+Pk4+n6dQKGyuTyQSJJNJlpeXmZycZGVlhWKxuLk+mUxSqVSYmZlhamqKTCZDqVTaXN9qn4aHhwEC2adcLkc2m72qfYrH42QymUD2qVwus7CwsOPHqdU+lctlZmZm2tqn+fl5qghDo7s5N79APDlEoVQmk8szvHucufQiVYkQHUyynFkhOpigUCqTXy+S3DXMcnaFgYEI8cE4+XyeRCJBuVSmXCkzNDTM6mqOaCRKNBYlm80y9ryjuF6kUq1cXB+NEolEKBTWcbFBLqyus1qsUJYoF9aKFCpshmC5us0fZMesBPKsnXP1/c0DAoMRIRmLEKVKNDJAPDqAq1QYjEfBOXBVEvE468UiAwMDRCIDlEplYrEYlUqFarXKYH19JDKAyADr6+tUiwXOnVu6qr+n7ZB2ZuSpd6F82jn3kibr/gh41Dn30frys8Brm3WhHD9+3F3tlZi5XK7lzoSBeekIw6u60YIsN7bwqqzXH1svV8nk1pDYIMVKldVihUyhTKZQJlsoc6FQJpMvkytWai21sr4VGzbRASEeEWKRASLNmlxXQiAeGajfhHhkgAGqJOMxhuIDDMUjDMcjDA1GGI5HGYoPEBvQFbjJ5msI8ejApmtsQJo3EbdhbXWNXUO7VK89gJCMDZCIDhCLCCLaN6g1nXzuT5w48cThw4dva7auGy3wB4F3iMjHgFcCmW73fwMsLi56GUjmpaPRq1J1rKyXWc6XWVor1X7mS1zIl8mXKk1/f+OrdrHiKG35ql0sVy/9ql//qf063w4RgcFo7Y8+Hh1gMDrAYGSAeFTqPwcY3AihiBAb2PgpxCLCQJshceHCBXbv3r3tehHYFYvUQnSwHqT124ZbbECIDHQ3lLy9ZH1plr17xsPWuIyg/h5bBriIfBR4LTAlImeBXwFiAM65PwQeAt4EnATWgH/ddUtgfNy/gwL97VV1jrVihZX12i27XmY5X2J5rf4zXwvnTKHMerne6i1XKFcvBBas27HRcow3hGoiWmtVDkaFCFWGBuPEowPsig0wloheektGGRmMkqiHdbTLgbgd2ewuRkdHd+S1NPTz5/5qCMqrnSqUt7ZY74BjXTPahnw+7+UHuR+8soUyz6bX+Fp6lWfTa5zNrLOyXma1WOm4K2FkMMJEMsb4rijjyRgTySjju2IkowNNv8rWvmrL5lf6WP1+LFJr+cYagnqj9dnO1+K5uTmmp6c725kA6IfPVzfpN68dHY2wEwqFQtgKTfHN63x2nY88cZ6pyjILT+d523ftZe9o+0P4ZgtlvrmU59Rinq8vrPFsepVz2e1PCu2KDTAyGGVkMMLIYJTx5MYtxu5ktH6rBXI8MsD8+ee54eB1m90JQfQ3Xg2+HccNzEtHv3n1TIB/u9addpPz2XXu/6uTnF8pMhytkisv88z8Kr9593ewd3SQYrnKSrFCbr12Qi5X7/Y4fWGdU4t5Ti3lWVwrXfa8gxHhpqldvCi1ixelhrh+IsFYIsrwYFTdlbA7/gIG490tpeoGPh3HRsxLR7959UyAfzvX6XZK1TkWVku87x9Oc36l1loeiTpyZTi/UuTffvJrOOdYb6OmNhEd4PqJBDdMJLlxchc3X7OLA+PJrvX5+vB+NcO8dJiXjqC8eibAE4lE2ApN2UmvYqXK85l1Tl8ocOZCgTOZ9c2f6+XqJdueL1xs5Rbq66IDwnBDtcJwvdvjBaOD3DCR5IaJBHtHB9uukLga7DjqMC8d/ebVMwGeTCbDVmhKt72K5Srp1SLzuRJzuSLPZwqcvlAL7fMr69ueNNydqB3KC4VybTlW5UKpVot7x3Wj3P/agyRjzU8M7iT9chy7hXnp6Devngnw5eVlL88ud+K1sl7msdNZHj+b5Vx2nbmV4mYAN2NA4NrRQa7bnWD/7o2fCfaNDTIyGL2kD/zlY2U+txBn70icn7xjH7s86Xf+djyOQWJeOvrNq2cCfHJyMmyFpmi90qtFjs9k+MfnLvDV8zm2dktHBKaG4kwPx7lmOMbeemBftzvBtaODxKPbX+G2d3SQ37z7O/jIE+cpF9Z4/Y271FUoQfPtchx3CvPS0W9ePRPgKysrXl5Z2Mqr6hwnF/J84UyGx85keTa9trluQOAVLxjmew7s5qapJNPDccaTsY6umts7Osj9rzvI+fPn2bt371U/T1D06nEMC/PS0W9ePRPgvTRQ+2qxwhPPZ/lCvXtkOX+xW2QwIty2b5Q7D+7m9v2jjCaCOQS99H75gHnpMC8dQXn1TID7XN/pnOPMhXUeO5PhC2eyPDl7addIaijGK/eP8d37R7n12hESV+gG6aaXj5iXDvPS0W9ePRPgvtV3FstVvnI+x98+fZZnLjhmVy7+hx0QeOmeYW7fP8rt+0c5OJ7Y8eoP396vDcxLh3np6DevnglwX8qDltZK/J9nFvj0MwtkGipGxhJRvnvfCLfvH+O79o0wMhjuW+vL+7UV89JhXjr6zatnAjwej4f6+qcW83zyyXk++83lzVk7rh9PcOv0IK++aZoXpXZ1fcjOTgj7/doO89JhXjr6zatnAjyTyVxxXOQgcM7xxbMrfOKf5vjSuRxQG1v+zgNj/OBLr+HF00OcPn2aA9NDO+rVDmG8X+1gXjrMS0e/efVMgE9NTe3o6z01l+ODXzjHk3OrQG2MkLteNMkPvDjFCxrqqnfaq13MS4d56TAvHUF59UyAZzIZhoaCb+k+t5znQ4+f5/jpDACjgxF+6GXTvOnQJMNN+rV3ykuLeekwLx3mpSMor54J8FLp8mFOu8l8rsifPHGevz25RNXVpsu676XXcN9Lr2HoCpehB+11tZiXDvPSYV46gvLqmQAPsr7zM99Y5Pf+7xlKFUdE4PtvnuJHbt3DxK5YqF6dYF46zEuHeekIyiv4K0q6xOzsbNefs+ocH/riOd77udOUKo7XXL+bD9x3C++8c39b4R2UVzcwLx3mpcO8dATl1TMt8G73H62Xq/zO52b43LcuMCDwU6/axz23pEL36hbmpcO8dJiXjqC8eibAI5HuDYe6vFbiVz5ziq+l19gVG+CXXn89373/6oZ67KZXNzEvHealw7x0BOXVM10o2Wy2K8/zraU8P/3g1/laeo3p4Tjv+/4XXnV4d9Or25iXDvPSYV46gvLqmRZ4KqXv3tjKk7M5fvnhb7JWqnIotYtf+94bGG+zrztIryAwLx3mpcO8dATl1TMt8KWlpY5+f3mtxK8/8i3WSlVeff1u3vsvbuo4vLvhFRTmpcO8dJiXjqC8eqYF7lzrGdW3o+ocv/W5GZbzZV6+d5gHXnewa+OWdOIVJOalw7x0mJeOoLx6pgXeyVeQP/vKHCeeX2EsEeX+13YvvDv1ChLz0mFeOsxLR993oczNzV3V7z05m+MjT5wH4Bdfc4DJoc67TRq5Wq+gMS8d5qXDvHQE5dUzAX4188llC2X+42efo+rgh192TUfVJt302gnMS4d56TAvHUF59UyAa3HO8d7PzbCwWuKWa4Z4220vCFvJMAyjq/RMgOdyOdX2f/FUmsfOZBmOR3jgdQeJBjTZgtZrpzAvHealw7x0BOXVMwE+PT3d9rbPplf5wBfOAfBzr76O6ZHgZunQeO0k5qXDvHSYl46gvNoKcBG5S0SeFZGTInJ/k/XXichnReRLIvJVEXlTt0XT6XRb25UqVX770RnKVccPvDjFnQeDnZ2jXa+dxrx0mJcO89IRlFfLABeRCPB+4G7gFuCtInLLls1+Gfi4c+5W4C3A73dbtN1Z3f/iqTRnMuvsGxvk7bcH3++907PNt4t56TAvHealIyivdlrgtwMnnXOnnHNF4GPAvVu2ccBGiccYcK57ijUmJiZabrO4WuJPv1QbtvGnXrWPeCT4HqJ2vMLAvHSYlw7z0hGUVztXYl4LnGlYPgu8css2vwr8jYi8ExgC3tDsiebn5zl69CjRaJRKpcKRI0c4duwYs7OzDA0NEYlEyGazpFIplpaWcM6RSqWYm5sjn8+TSqXI5XJMT0+TTqcRESYmJkin04yOjvLfHpsnX6pyx/4RUpVlzp3LMTY2xsLCAmNjYxSLRfL5PHv27GF2dpZ4PM7IyAiLi4uMj4+Tz+cpFAqb6xOJBMlkkuXlZSYnJ1lZWaFYLG6u31iXSCSYmpoik8lQKpU217fap43SoivtU6VSYXV1dfM5Y7FYW/t06tQpDh48eFX7FI/HyWQygexTPp9namrqqvapk+PUap8WFhZIJpM7fpxa7dNzzz3HDTfcsOPHqdU+FQqFTe+dPE6t9imdTpNMJnf8OLXap9OnT3PjjTde9XHaDml1iaeIvBl4o3Pu7fXlHwVud869s2Gbd9Wf63dF5FXAB4GXOOeqjc91/Phxd+jQoSu+3nYsLy8zPj6+7fqvns/x83/5DeIR4b/fdzN7Rwa33babtPIKC/PSYV46zEtHJ14nTpx44vDhw7c1W9dOH8NZYH/D8j4u7yI5CnwcwDl3HEgAXZ2GuVKpbL+u6vj947UvCT/0sukdC2+4sleYmJcO89JhXjqC8monwB8HbhKR60UkTu0k5YNbtjkNHAYQkZupBXhXT7uurq5uu+4vv7bAqaUC08NxfvjlO1tGdCWvMDEvHealw7x0BOXVMsCdc2XgHcDDwDPUqk2eEpH3iMg99c1+DvhxEfkK8FHgx1yXh9/ablLQC/kSH/5ibayTf3fHtQxGd7a0vd8mUe0U89JhXjr6zauttHPOPeSce6Fz7kbn3H+oP/Zu59yD9ftPO+fudM693Dn3Cufc33RbdLtJQT/0xfPkihVu2zfC9xwY6/bLtqTfJlHtFPPSYV46+s2rZ67EjMUuH0Xw2fQqf/3sItEB4Sfv2BdKDWgzLx8wLx3mpcO8dATl1TMBPjZ2eev6D44/jwN+8CUp9u9O7LwUzb18wLx0mJcO89IRlFfPBPjCwsIly5Wq4+n5VQYE/uWt4fV7bfXyBfPSYV46zEtHUF49E+Bb/4MVyrUS80R0gGQsEoYS0H//8TvFvHSYl45+8+qZAC8Wi5csbwT4TledbGWrly+Ylw7z0mFeOoLy6pkAz+fzlywXShdb4GGy1csXzEuHeekwLx1BefVMgG+toyyUa1c2hd0C77e6004xLx3mpaPfvHomwLfWUa6Xa9cJhd0C77e6004xLx3mpaPfvHomwOPxS2fV2WiBJ2Lh7sJWL18wLx3mpcO8dATl1TMBPjIycsnyRgt8cAfG/L4SW718wbx0mJcO89IRlFfPBPji4uIly760wLd6+YJ56TAvHealIyivngnwrWPpFjzpA/dx7GEwLy3mpcO8dATl1TMBfnkZYb0FbmWETTEvHealw7x09H0ZYaFQuHTZkwt5tnr5gnnpMC8d5qUjKK+eCfCtdZTrZT8u5Om3utNOMS8d5qWj37x6JsC31lEWPAnwfqs77RTz0mFeOvrNq2cCPJG4dLjYdU+6ULZ6+YJ56TAvHealIyivngnwZDJ5yfJmCzzkMsKtXr5gXjrMS4d56QjKq2cCfHl5+ZJlX1rgW718wbx0mJcO89IRlFfPBPjk5OQly770gW/18gXz0mFeOsxLR1BePRPgKysrlyxfHE42vMkc4HIvXzAvHealw7x0BOXVMwG+3YQOYbfA+20A+U4xLx3mpaPfvHomwC8fD9yPAO+3utNOMS8d5qWj37x6JsAvHw/cj5OY/VZ32inmpcO8dPSbV88EuJUR6jAvHealw7x09H0Z4dYB0X1pgffbAPKdYl46zEtHv3n1TIBnMpnN+5Wqo1hxCDAYkfCkuNTLJ8xLh3npMC8dQXn1TIBPTU1t3m9sfYuEG+CNXj5hXjrMS4d56QjKq2cCvPE/mC/dJ9B///E7xbx0mJeOfvMKPwHbpFQqbd73pYQQLvXyCfPSYV46zEtHUF5tJaCI3CUiz4rISRG5f5ttfkhEnhaRp0Tkf3ZX89I6Sp8CvN/qTjvFvHSYl45+82qZgCISAd4P3A3cArxVRG7Zss1NwAPAnc65FwM/223RxjpKX0oIof/qTjvFvHSYl45+82onAW8HTjrnTjnnisDHgHu3bPPjwPudc8sAzrn57mrC0NDQ5n1fZuOBS718wrx0mJcO89IRlFc7CXgtcKZh+Wz9sUZeCLxQRP5RRD4vInd1S3CDSOTioFW+zIcJl3r5hHnpMC8d5qUjKK9oG9s0q9NzTZ7nJuC1wD7gH0TkJc65C40bzc/Pc/ToUaLRKJVKhSNHjnDs2DFmZ2cZGhoiEomQzWZJpVIsLS3hnCOVSjE3N0c+n6darZLL5VjJ165qqpbWWVtbI51OMzo6SqVSYXV1lT179jA7O0ssFmNsbIyFhQXGxsYoFovk8/nN9fF4nJGRERYXFxkfHyefz1MoFDbXJxIJkskky8vLTE5OsrKyQrFY3Fy/sS6bzTI1NUUmk6FUKm2ub7VPw8PDAORyOaanp0mn04gIExMTHe/TmTNniEQiV7VP8XicTCYTyD7l8/lQjlOrfVpYWAjlOLXapzNnzhCLxXb8OLXap0KhQKlU2vHj1Gqf0uk02Wx2x49Tq306c+bM5r5dzXHaNpyd25rFWzYQeRXwq865N9aXHwBwzv1GwzZ/CHzeOffh+vIjwP3Ouccbn+v48ePu0KFDV3y97VhbW2PXrl0APPz1RX7370/zvTdN8AuvOXBVz9ctGr18wrx0mJcO89LRideJEyeeOHz48G3N1rXTB/E4cJOIXC8iceAtwINbtvkU8DoAEZmi1qVy6qpst2FpaWnz/sWxwMPvQmn08gnz0mFeOsxLR1BeLRPQOVcG3gE8DDwDfNw595SIvEdE7qlv9jCwKCJPA58FfsE5t9hN0cZvCj5dyNPqG0xYmJcO89JhXjqC8mqnDxzn3EPAQ1see3fDfQe8q34LhFQqtXnfpzrwRi+fMC8d5qXDvHQE5RV+ArbJ3Nzc5n2f6sAbvXzCvHSYlw7z0hGUV/gJ2CaNZ2N9aoG3OkscFualw7x0mJeOoLzCT8CrwKcANwzDCIueScBcLrd536eTmI1ePmFeOsxLh3npCMor/ARsk+np6c37PpURNnr5hHnpMC8d5qUjKK/wE7BN0un05n2fWuCNXj5hXjrMS4d56QjKK/wEbJPGmXd86gMPe0ag7TAvHealw7x0BOUVfgK2ycTExOZ9n8oIG718wrx0mJcO89IRlFf4Cdgm1oWiw7x0mJcO89LR910oo6Ojm/d96kJp9PIJ89JhXjrMS0dQXuEnYJtUKpXN+z4FeKOXT5iXDvPSYV46gvIKPwHbZHV1FYCqc151oWx4+YZ56TAvHealIyiv8BOwTTYmBS1WaqN6DUaEAQ/OOPfbJKqdYl46zEtHv3n1TIBvTApaKNW+ivjQ+ob+m0S1U8xLh3np6DcvP1KwDWKxGOBXCSFc9PIN89JhXjrMS0dQXn6kYBuMjY0BDSWEET/UN7x8w7x0mJcO89IRlJcfKdgGCwsLgH8t8A0v3zAvHealw7x0BOXlRwq2wWUtcE/6wPvtP36nmJcO89LRb15+pGAbFItFwK8acLjo5RvmpcO8dJiXjqC8/EjBNsjn80BjgEfC1Nlkw8s3zEuHeekwLx1BefVMgG/UUV4cCzz8GnDov7rTTjEvHealo9+8eibAN+vAPWuB91vdaaeYlw7z0tFvXj0T4PF4HGg8ielHC3zDyzfMS4d56TAvHUF59UyAj4yMAI1lhH60wDe8fMO8dJiXDvPSEZRXzwT44uIi4F8LfMPLN8xLh3npMC8dQXn1TICPj48D/vWBb3j5hnnpMC8d5qUjKK+eCfCNMpx1z+rA+61sqVPMS4d56eg3Lz9SsA0KhULtZ8mvKzE3vHzDvHSYlw7z0hGUlx8p2AabdeCetcD7re60U8xLh3np6DcvP1KwDS6vA/dDvd/qTjvFvHSYl45+8/IjBdsgkUgA/o1GuOHlG+alw7x0mJeOoLzaSkERuUtEnhWRkyJy/xW2u09EnIjc1j3FGslkEvDvJOaGl2+Ylw7z0mFeOoLyapmCIhIB3g/cDdwCvFVEbmmy3Qjw08Bj3ZYEWF5eBi62wH05ibnh5RvmpcO8dJiXjqC82knB24GTzrlTzrki8DHg3ibb/Trw20Agp1snJycB/1rgG16+YV46zEuHeekIyivaxjbXAmcals8Cr2zcQERuBfY75z4tIj+/3RPNz89z9OhRotEolUqFI0eOcOzYMWZnZxkaGiISiZDNZkmlUiwtLeGcI5VKMTc3R6lUYnx8nLViGYCFuXOsxSJMTEyQTqcZHR2lUqmwurrKnj17mJ2dJRaLMTY2xsLCAmNjYxSLRfL5/Ob6eDzOyMgIi4uLjI+Pk8/nKRQKm+sTiQTJZJLl5WUmJydZWVmhWCxurk8mk+RyORYXF5mamiKTyVAqlTbXt9qn4eFhAHK5HNPT06TTaUSkK/s0MzPD/v37r2qf4vE4mUwmkH0ql8sUCoUdP06t9imTybC4uLjjx6nVPp05c4YDBw7s+HFqtU+VSoW1tbUdP06t9ml5eZlYLLbjx6nVPj3//PMcPHjwqo/Tdohz7sobiLwZeKNz7u315R8FbnfOvbO+PAD8HfBjzrnnRORR4Oedc1/c+lzHjx93hw4duuLrbcfMzAzXXXcdd33wyzjgr/7NK4gMhH85/czMDAcOHAhb4zLMS4d56TAvHZ14nThx4onDhw83Pa/YTj/EWWB/w/I+4FzD8gjwEuBREXkOuAN4sNsnMvfs2UOp4nBAbEC8CG/ov7rTTjEvHealo9+82gnwx4GbROR6EYkDbwEe3FjpnMs456accwedcweBzwP3NGuBd8Ls7Kx3JYTQf3WnnWJeOsxLR795tUxC51wZeAfwMPAM8HHn3FMi8h4RuScQqyYkk0nvKlCg/8qWOsW8dJiXjn7zauckJs65h4CHtjz27m22fW3nWpcTj8fJelaBAv03gHynmJcO89LRb17+JGELMpmMd5fRQ83LR8xLh3npMC8dQXn5k4QtmJqaapjMwR/tqampsBWaYl46zEuHeekIysufJGxBJpNpmJHeH+1++4/fKealw7x09JtXW33gPlAqlViv+hfgpVIpbIWmmJcO89JhXjqC8vInCVuwZ88eL6tQ+q3utFPMS4d56eg3L3+SsAVWB67DvHSYlw7z0tH344EPDQ152QIfGhoKW6Ep5qXDvHSYl46gvPxJwhZEIhEvywgjkUjYCk0xLx3mpcO8dATl5U8StiCbzXo3lCzUvHzEvHSYlw7z0hGUlz9J2IJUKuVlGWEqlQpboSnmpcO8dJiXjqC8/EnCFiwtLXnZAl9aWgpboSnmpcO8dJiXjqC8/EnCFjjnKJQrgF8nMVuNpx4W5qXDvHSYl46gvPxJwhakUinWy7U3wacywn77ytYp5qXDvHT0m5c/SdiCubm5iy3wiD/ac3NzYSs0xbx0mJcO89IRlJc/SdiC4eFhLy/kaTVnXViYlw7z0mFeOoLy8icJ28DHk5iGYRhh0TNJmMvlvLyQJ5fLha3QFPPSYV46zEtHUF7+JGELpqenGwLcn6utpqenw1ZoinnpMC8d5qUjKK+eCfB0Or15Ic9g1I8Z6aHm5SPmpcO8dJiXjqC8eibAReRiH3jMnxa4iD//TBoxLx3mpcO8dATl1TMBPjI2TsVBRCA64M9BmpiYCFuhKealw7x0mJeOoLx6JsCfn5sH/Gp9Q/99ZesU89JhXjr6zatnAnxwV62O0qcKFIDR0dGwFZpiXjrMS4d56QjKy680vAJrxTLg1zgoAJVKJWyFppiXDvPSYV46gvLyKw2vwIXcGuBfC3x1dTVshaaYlw7z0mFeOoLy8isNr8DwWO0kgG8B3m+TqHaKeekwLx395uVXGl6B2fQi4F8XSr9Notop5qXDvHT0m5dfaXgFynVV31rgsVgsbIWmmJcO89JhXjqC8vIrDa9AZDAJ+DXwPCaNAAANn0lEQVQSIcDY2FjYCk0xLx3mpcO8dATl5VcaXoHFC7VJQX1rgS8sLISt0BTz0mFeOsxLR1BebaWhiNwlIs+KyEkRub/J+neJyNMi8lUReUREDnRdNJ4A/OsD77f/+J1iXjrMS0e/ebVMQxGJAO8H7gZuAd4qIrds2exLwG3OuZcBnwB+u9uia+u1OnDfWuDFYjFshaaYlw7z0mFeOoLyaicNbwdOOudOOeeKwMeAexs3cM591jm3Vl/8PLCvu5qwul57A3xrgefz+bAVmmJeOsxLh3npCMqrnTS8FjjTsHy2/th2HAX+qhOpZkQHdwH+tcD7re60U8xLh3np6DevaBvbNBv6zzXdUORfAbcBr2m2fn5+nqNHjxKNRqlUKhw5coRjx44xOzvL0NAQkUiEbDZLKpViaWkJ5xypVIq5uTkWljMArGaXKRRGSafTiAgTExOk02lGR0epVCqsrq6yZ88eZmdnicVijI2NsbCwwNjYGMVikXw+v7k+Ho8zMjLC4uIi4+Pj5PN5CoXC5vpEIkEymWR5eZnJyUlWVlYoFoub6zfWJRIJpqamyGQylEqlzfWt9mljnrxcLsf09HRX9+nUqVMcPHjwqvYpHo+TyWQC2ad8Ps/U1NSOH6dW+7SwsEAymdzx49Rqn5577jluuOGGHT9OrfapUChseu/kcWq1T+l0mmQyuePHqdU+nT59mhtvvPGqj9O24exc0yxuDOVXAb/qnHtjffkBAOfcb2zZ7g3AfwVe45ybb/Zcx48fd4cOHbri623Hrzz0DMfPFfjF1xzgDTf5M2Tk+fPn2bt3b9gal2FeOsxLh3np6MTrxIkTTxw+fPi2Zuva6Y94HLhJRK4XkTjwFuDBxg1E5Fbgj4B7tgvvTqlIbRhZ3+rAR0ZGwlZoinnpMC8d5qUjKK+WaeicKwPvAB4GngE+7px7SkTeIyL31Dd7LzAM/LmIfFlEHtzm6a6aXH4d8K8PfHFxMWyFppiXDvPSYV46gvJqpw8c59xDwENbHnt3w/03dNnrMqoDUaDsXYCPj4+HrdAU89JhXjrMS0dQXn6l4RXIezoeeL+VLXWKeekwLx395uVXGl6B/MaExp4FeKFQCFuhKealw7x0mJeOoLz8SsMrUNkYjdCzk5j9VnfaKealw7x09JuXX2l4BfLF2pREgxG/lPtt/OFOMS8d5qWj37z8SsMrUKzW6tV9a4EnEomwFZpiXjrMS4d56QjKy6803IZK1VGuwoBAbKDZhaHhkUwmw1ZoinnpMC8d5qUjKK+eCPBCwwlMEb8CfHl5OWyFppiXDvPSYV46gvLqqQD3rYQQYHJyMmyFppiXDvPSYV46gvLyLxGbUCj5WUIIsLKyErZCU8xLh3npMC8dQXn5l4hNWPe0Bhz6bwD5TjEvHealo9+8/EvEJvjchdJvdaedYl46zEtHv3n5l4hN2GyBe1ZCCP1Xd9op5qXDvHT0m5d/idiEzRa4ZxfxQP+VLXWKeekwLx395uVfIjah4HELPB6Ph63QFPPSYV46zEtHUF7+JWITCh6fxMxkMmErNMW8dJiXDvPSEZSXf4nYhEKpNg6KjwE+NTUVtkJTzEuHeekwLx1BefmXiE1Yr1gLXIt56TAvHealo89b4P6WEZZKpbAVmmJeOsxLh3npCMrLv0Rsgs8X8vRb3WmnmJcO89LRb17+JWITfL6Qp9/qTjvFvHSYl45+8/IvEZvg84U8Q0NDYSs0xbx0mJcO89IRlJd/idiEi2WEkZBNLicS8c8JzEuLeekwLx1BefVUgA9G/RoLHCCbzYat0BTz0mFeOsxLR1BePRXgPrbAU6lU2ApNMS8d5qXDvHQE5dUbAe7xeOBLS0thKzTFvHSYlw7z0hGUl3+J2ASfywidc2ErNMW8dJiXDvPSEZSXf4nYBJ/LCPvtK1unmJcO89LRb17+JWITfB6NcG5uLmyFppiXDvPSYV46gvLyLxGb4HMXyvDwcNgKTTEvHealw7x0BOXlXyJuoVJ1FCsOAeIR/8oIDcMwwsL7AC/WRyKMR0DEvwDP5XJhKzTFvHSYlw7z0hGUV1sBLiJ3icizInJSRO5vsn5QRP6svv4xETnYLcGLJYT+1YADTE9Ph63QFPPSYV46zEtHUF4tA1xEIsD7gbuBW4C3isgtWzY7Ciw7574DeB/wW90SLNRb4LEBP8uD0ul02ApNMS8d5qXDvHQE5dVOC/x24KRz7pRzrgh8DLh3yzb3Ah+p3/8EcFi61N+xORa4p/3fPnbrgHlpMS8d5qUjKK9oG9tcC5xpWD4LvHK7bZxzZRHJAJPAQuNG8/PzHD16lGg0SqVS4ciRIxw7dozZ2VmGhoaIRCJks1lSqRRLS0s458hK7extPCIsLi6Sy+WYnp4mnU4jIkxMTJBOpxkdHaVSqbC6usqePXuYnZ0lFosxNjbGwsICY2NjFItF8vn85vp4PM7IyAiLi4uMj4+Tz+cpFAqb6xOJBMlkkuXlZSYnJ1lZWaFYLG6uTyaTxGIxZmZmmJqaIpPJUCqVNtdvt0+pVIq5ubnNM9NB7FOhUCCbzV7VPsXjcTKZTCD7NDg4yMLCwo4fp1b7FI1GmZmZ2fHj1GqfCoUCuVxux49Tq31KJBLMz8/v+HFqtU8DAwPMzMx4lRHxeJxCocDq6upVH6ftkFZXCInIm4E3OufeXl/+UeB259w7G7Z5qr7N2fryN+vbLDY+1/Hjx92hQ4eu+HpbWVor8f9mMhSyS9z3yheqfncnmJmZ4cCBA2FrXIZ56TAvHealoxOvEydOPHH48OHbmq1rpwV+FtjfsLwPOLfNNmdFJAqMAV25+H9iV4zvu3mK5WU/T2KOjo6GrdAU89JhXjrMS0dQXu30gT8O3CQi14tIHHgL8OCWbR4E3la/fx/wd67LF/9XKpVuPl3XMC8d5qXDvHT0m1fLAHfOlYF3AA8DzwAfd849JSLvEZF76pt9EJgUkZPAu4DLSg07ZXV1tdtP2RXMS4d56TAvHf3m1U4XCs65h4CHtjz27ob7BeDN3VW7lH6brLRTzEuHeekwLx19Pakx9N9kpZ1iXjrMS4d56ejrSY0BPvWpT4Wt0BTz0mFeOsxLR7959UyAf/KTnwxboSnmpcO8dJiXjn7z6pkAL5fLYSs0xbx0mJcO89LRb14tL+TpJo888kgamLma311aWpqamJhYaL3lzmJeOsxLh3np+Db1OnD48OGmU/rsaIAbhmEY3aNnulAMwzCMS7EANwzD6FG8D/BWk0nssMsfi8i8iDzZ8NiEiHxGRL5R/zm+w077ReSzIvKMiDwlIj/jiVdCRL4gIl+pe/1a/fHr65N+fKM+CUh8J70a/CIi8iUR+bRnXs+JyD+JyJdF5Iv1x0I9lnWH3SLyCRH5Wv2z9qqwvUTkRfX3aeOWFZGfDdur7vbv65/7J0Xko/W/h65/xrwO8DYnk9hJPgzcteWx+4FHnHM3AY8QwDACLSgDP+ecuxm4AzhWf4/C9loHXu+ceznwCuAuEbmD2mQf76t7LVObDCQMfoba0BAb+OIF8Drn3Cuccxsj0IV9LAH+M/DXzrlDwMupvXehejnnnq2/T68AvgtYA/4ibC8RuRb4aeA259xLgAi1MaS6/xlzznl7A14FPNyw/ADwQMhOB4EnG5afBfbW7+8Fng3Z738D3+uTF7ALOEFtHPkFINrs+O6gzz5qf9ivBz4NiA9e9dd+Dpja8lioxxIYBb5FvejBF68tLv8c+EcfvLg4P8IEteFKPg28MYjPmNctcJpPJnFtSC7bMe2cOw9Q/3lNWCL1uUhvBR7zwaveTfFlYB74DPBN4IKrDZAG4R3P3wN+EajWlyc98QJwwN+IyBMi8hP1x8I+ljcAaeBD9W6nD4jIkAdejbwF+Gj9fqhezrnngd8BTgPngQzwBAF8xnwP8GbzEFndYxNEZBj4X8DPOueyYfsAOOcqrvb1dh+1qflubrbZTjqJyPcB8865JxofbrJpWJ+zO51z30mt2/CYiLw6JI9GosB3An/gnLsVWCWcbpym1PuS7wH+PGwXgHqf+73A9cALgCFqx3MrHX/GfA/wdiaTCJs5EdkLUP85v9MCIhKjFt5/6pzbuGY3dK8NnHMXgEep9dHvrk/6AeEczzuBe0TkOWrzu76eWos8bC8AnHPn6j/nqfXn3k74x/IscNY591h9+RPUAj1srw3uBk445+bqy2F7vQH4lnMu7ZwrAZ8EvocAPmO+B3g7k0mETeNkFm+j1ge9Y4iIUBuP/Rnn3H/yyCslIrvr95PUPtTPAJ+lNulHKF7OuQecc/uccwepfZ7+zjn3I2F7AYjIkIiMbNyn1q/7JCEfS+fcLHBGRF5Uf+gw8HTYXg28lYvdJxC+12ngDhHZVf/73Hi/uv8ZC+ukg+KEwJuAr1PrP/2lkF0+Sq1Pq0StVXKUWv/pI8A36j8ndtjpn1H7KvZV4Mv125s88HoZ8KW615PAu+uP3wB8AThJ7SvvYIjH87XAp33xqjt8pX57auPzHvaxrDu8Avhi/Xh+Chj3xGsXsAiMNTzmg9evAV+rf/b/BzAYxGfMLqU3DMPoUXzvQjEMwzC2wQLcMAyjR7EANwzD6FEswA3DMHoUC3DDMIwexQLcMAyjR7EANwzD6FEswA3DMHqU/w/dpKOR8KfG4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itp_radar = itp_radar_cdf(radar)\n",
    "print(itp_radar)\n",
    "plt.plot(x,itp_radar_cdf(x))\n",
    "plt.scatter(radar,itp_radar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O problema dessa interpolação é que os valores de x **precisa** estar aumentando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x must be strictly increasing",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-dc3fefd31e00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mitp_pluvi_ppf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInterpolatedUnivariateSpline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpluvi_dist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/ic/lib/python3.6/site-packages/scipy/interpolate/fitpack2.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, w, bbox, k, ext, check_finite)\u001b[0m\n\u001b[1;32m    597\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Input must not contain NaNs or infs.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x must be strictly increasing'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0;31m# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x must be strictly increasing"
     ]
    }
   ],
   "source": [
    "itp_pluvi_ppf = InterpolatedUnivariateSpline(pluvi_dist.cdf(x),x, k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.7189414 , 0.80445737, 0.84854614, 0.88042352,\n",
       "       0.89737301, 0.91036148, 0.92081694, 0.93029679, 0.93684981,\n",
       "       0.94435484, 0.94926829, 0.95538029, 0.96      , 0.96181004,\n",
       "       0.96446062, 0.96682927, 0.96878049, 0.9697561 , 0.97408734,\n",
       "       0.97560976, 0.97560976, 0.97756098, 0.98078723, 0.98441343,\n",
       "       0.98634146, 0.98678776, 0.98829268, 0.98829268, 0.98926829,\n",
       "       0.98926829, 0.98926829, 0.9902439 , 0.99219512, 0.99238267,\n",
       "       0.99317073, 0.99317073, 0.99512195, 0.99512195, 0.99512195,\n",
       "       0.99512195, 0.9963026 , 0.99797757, 0.99804878, 0.99804878,\n",
       "       0.99902439, 0.99902439, 0.99902439, 1.        , 1.        ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pluvi_cdf = pluvi_dist.cdf(x)\n",
    "pluvi_cdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descobrir onde o array não está \"crescendo\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "        True, False,  True, False, False,  True,  True,  True,  True,\n",
       "       False,  True, False, False, False,  True,  True,  True, False,\n",
       "        True, False, False,  True, False])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_increasing = (pluvi_cdf - np.roll(pluvi_cdf,1))[1:] > 0\n",
    "idx_increasing = np.insert(idx_increasing,0,True)\n",
    "idx_increasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pluvi_cdf = pluvi_cdf[idx_increasing]\n",
    "x = x[idx_increasing]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1df6c19e48>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pluvi_cdf,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "itp_pluvi_ppf = UnivariateSpline(pluvi_cdf,x, k=5,s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos agora jogar o valor da interpolação do radar, na curva ppf do pluviometro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valor real de precipitação : 19.62mm, quando o Radar mostrar valor de 20mm\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "itp_pluvi = itp_pluvi_ppf(itp_radar)\n",
    "print(f\"Valor real de precipitação : {round(itp_pluvi.tolist(),2)}mm, quando o Radar mostrar valor de {radar}mm\")\n",
    "plt.plot(itp_radar_cdf(x),itp_pluvi_ppf(itp_radar_cdf(x)));\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "meses = [\"Janeiro\",\n",
    "\"Fevereiro\",\n",
    "\"Março\",\n",
    "\"Abril\",\n",
    "\"Maio\",\n",
    "\"Junho\",\n",
    "\"Julho\",\n",
    "\"Agosto\",\n",
    "\"Setembro\",\n",
    "\"Outubro\",\n",
    "\"Novembro\",\n",
    "\"Dezembro\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_graph(radar_cdf, pluvi_cdf,corrected_quantiles, month):\n",
    "    plt.figure()\n",
    "\n",
    "    n_month = meses[month-1]\n",
    "\n",
    "    plt.title(n_month)\n",
    "    \n",
    "    plt.plot(corrected_quantiles, radar_cdf,label=\"Radar\")\n",
    "    plt.plot(corrected_quantiles, pluvi_cdf,label=\"Pluviometro\")\n",
    "\n",
    "    name = \"IMAGENS/CDFs/{}.png\".format(n_month)\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.savefig(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isIncresing(array):\n",
    "    return all((array - np.roll(array,1))[1:] > 0)\n",
    "\n",
    "def idx_of_Increasing(array):\n",
    "    idx_increasing = (array - np.roll(array,1))[1:] > 0\n",
    "    idx_increasing = np.insert(idx_increasing,0,True)\n",
    "    return idx_increasing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdf(radar,pluviometro,k=3):\n",
    "    \n",
    "\n",
    "    \n",
    "    radar_dist = stats.rv_histogram(np.histogram(radar, bins=\"auto\",normed=True))\n",
    "    pluvi_dist = stats.rv_histogram(np.histogram(pluviometro,bins=\"auto\",normed=True))\n",
    "\n",
    "    max_ = max(np.max(radar),np.max(pluviometro))\n",
    "    \n",
    "    x = np.linspace(0,max_,50)\n",
    "    radar_cdf = radar_dist.cdf(x)\n",
    "    itp_radar_cdf = UnivariateSpline(x, radar_cdf,s=1,k=3)\n",
    "\n",
    "    pluvi_cdf = pluvi_dist.cdf(x)\n",
    "    if not isIncresing(pluvi_cdf):\n",
    "        idx_increasing = idx_of_Increasing(pluvi_cdf)\n",
    "        pluvi_cdf = pluvi_cdf[idx_increasing]\n",
    "        x = x[idx_increasing]\n",
    "    itp_pluvi_ppf = UnivariateSpline(pluvi_cdf,x, s=1,k=3)\n",
    "    \n",
    "    save_graph(radar_dist.cdf(x),pluvi_dist.cdf(x),x,month)\n",
    "    \n",
    "    return lambda x: itp_pluvi_ppf(itp_radar_cdf(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correction(serie,itps):\n",
    "    month = serie[\"start\"].month\n",
    "    \n",
    "    serie[\"CDF_CORR\"] = itps[month](serie[\"RADAR\"])\n",
    "    return serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEGCAYAAAB8Ys7jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXl8XNV58P89s2lG22gba7GEMdjGMYsNIYBNsAEDNlkgGEghb5LSnxPS1pAmaZom6ftL2zQLTd62aQNv0iYNgdAsJIGEJqZACEsCBhwcm83xblmLJY22kWbTbOf9YxaPZI00M3dGMw+e7+ejj2bmnrnzvUdXj47Ofe5zlNaaChUqVKggD1OpBSpUqFChQn5UAniFChUqCKUSwCtUqFBBKJUAXqFChQpCqQTwChUqVBBKJYBXqFChglAqAbxChQoVhFIJ4BVEoZRqU0oFlVIDSilrCT7//Uqpys0TFcqCSgCvII3/D/glMAJcX2KXChVKSiWAVxCDUsoEfBi4L/F1+4ztzUqpHyulfEqpQaXUPyil7lNK/SqtjVUpdZdSqk8pFVJKvaGUet+M/XxIKbU3MdIfUUo9q5TqVEpdDnwv0UYnvr6b7X4rVCg0lQBeQRLXADXAduKB9HKl1Blp2+8FVgPvAq4EOoH3zNjHl4j/EfgYcA7wAPCAUmojgFLqrcA3gS8DZwGXA/cn3vs8cEficXvi6y+y2W+FCsVAVWqhVJCCUuph4KjW+uOJ59uB3VrrzyqllgP7gau01k8mtluBw8A+rfVVSqlqYAz4uNb6/87Yr1NrfaVS6gbgu0CX1npiFof3A9/TWqu01+bdb2F7okKFOJUReAURKKXaiY+s70t7+bvAnyilLMCqxGsvJDdqrcPA79LaLwNswLMzdv8McHbi8RPEg/4RpdQPlVK3K6Va5tHLZr8VKhScSgCvIIWtgAX4nVIqopSKAN8H2oDr0tpl8y/lzDYq+ZrW2gtcCNxAfET/p8DBxNRK3vutUKEYVAJ4hbIncfHyQ8TnmdfM+HqA+MXMNxLN16a9zwKkB96DwBSwYcZHrAdeTz7RWke11s9qrT+XeP9xIHlBMpTYtznX/VaoUGgspRaoUCELNgOnAf+utT6WvkEpdS/xaY8w8N/APUqpjwBu4C+Bek6Mrv1KqX8D/kEp5QZ2AzcTT0e8OrG/64EziE+HuIkH8C5O/IE4kvh+nVLqt0BAa+2db78VKhSDSgCvIIGPAC/ODN4JniEeaD8E/Anw78CjgJd4NskTgD2t/d8AMeBrgIv46Pn9yQufxC9Gvhv4LFAH9ABfAL4DoLXeqZT618S+XcQzVG7LYr8VKhScShZKhTctiWmOPwCPaK3/stQ+FSoUmsoIvMKbBqXUemAR8Hvio+ePA6cTz1apUOFNRyWAV3gzYQb+N/G0vjDwGnCF1vrVklpVqFAkKlMoFSpUqCCUShphhQoVKgilEsArVKhQQSgLOgf+9NNP66qqqoX8yAoVKlQQjd/vH964caNrtm0LGsCrqqpYuXJlXu/t7u5myZIlBTYqHtJ8QZ6zNF+Q5yzNF+Q5z+e7a9eu7kzbxEyhWK0LvviKIaT5gjxnab4gz1maL8hzNuIrJoA7nc5SK+SENF+Q5yzNF+Q5S/MFec5GfMUE8OHh4VIr5IQ0X5DnLM0X5DlL8wV5zkZ8xQTwU+mvaqmQ5izNF+Q5S/MFec5FHYErpb6jlBpSSr2WYbtSSv2bUuqgUuoVpdQFedvMQSgUKsZui4Y0X5DnLM0X5DlL8wV5zkZ8sxmBf5d4Oc9MXAssT3zdDnwjb5s5CAQCxdht0ZDmC/KcpfmCPGdpviDP2YjvvGmEWutnlVKnz9HkeuB+Hb8n/wWlVINSql1rfTxvq1loa2sr5O6KjjRfkOcszRfkOUvzhcI662iUaHAKHYmiozF0NJr4iqGjMYhFiUWiEI2hY4ntkWji8fTnjReei7naftJnGPEtRB74YuI1k5P0Jl4raAAfGBgQldspzRfkOUvzBXnO0nzhhHMsEiE0Mk5kwktk0k9k0ktk0kdkwkfE60u87kt9hU/a7iPq8xfM6+2//QG1y07uSyN9XIgArmZ5bdYKWUNDQ2zduhWLxUI0GmXLli1s27aNgYEBampqMJvNTExM4HK5GB0dRWuNy+VicHCQcDjMyMgIXq+X1tZW3G43Simamppwu93U19cTjUbx+Xy0tbUxMDCA1WrF6XQyPDyM0+kkFAoRCARS2202G3V1dYyMjNDY2EggECAYDKa22+12HA4HY2NjNDc3Mzk5SSgUSm13OBzYbDY8Hg8tLS14PB7C4TBtbW14vV6Gh4fnPKba2lqAsjkmr9fL0NBQxmPK5ue0kMdkMpno7u429HNa6GNKOhfz3CvkMXm9Xqampkr++zTzmFqamxk6eJTwgBtHMMzwvsMwNkFoYBjfseMcHptganAYYsaL9ZkcVWAyYbKYwWRCm0xYrBaigMlixmSxEENjsVqJAZhMWO1VRKJRTFYLJouFqI4xFYsy0d9/0jFFIhG6u7vn/DllIqtqhIkplF9orc+ZZdu/A09rrX+QeL4PuHy2KZQdO3bofO/E9Hq98x5MOSHNF+Q5S/MFec6l8o14ffi7+wn2DRLsGyTQP0Swf5Bg3xDB/iGCA250KDz3TpTC1tyAxVmHpbYaa30tlrqatK/E8/oaLLU1WGZst9bXYq5xoEzFTdabr4937dr18saNGy+cbVshRuCPAHcopX4IXAx4Cj3/DTAyMiLqxJfmC/KcpfmCPOdi+kb9QfxHe/Ed7sF/pAff4V78R3rwH+5lamhk3vdbm5zYOxZh72jF0bEI++L4Y49Zs+T887C3uzDZyv+uTCN9PG8AV0r9ALgcaFFK9QJ/C1gBtNbfBLYD7yC+BqCf+LqEBaexsbEYuy0a0nxBnrM0X5DnbNQ3Gpwi0N2P70gP/kM98e+He/Ed6WHquDvj+0xVNqqXLMa+uDUVmO0di3Asjn+3ty+a9YIgQO3EBNX19Ya8FxIjfZxNFsqt82zXwLa8DbIkEAhQL+iHIs0X5DlL8wV5zvP5Rry++LTG8fjURqBvMD7F0TeI73APwb5ByDBNq6wWqpd0UL20i+ozOqlZ2kXNmadRvbQTe8eivKcu3mx9PBdillQLBoOlVsgJab4gz1maL8hyjgammNh/BMuBnsTcc2IuOvE42D9EZMI75z6U2Yyjq21akK4+s4uaM7qwL27FZCl8CJLUx2DMV0wAl5aPKs0X5DlL84XycY6FwgSPu1OBOHmBMNA/xNTxIQJ9Q4RHxwE4NMd+THYb9vZFqbno5HSHo2MR1Us7cXS1L/g8dLn0cbaUOg98QZCWjyrNF+Q5S/OFhXGORSJMDY4kAvNQWpCOT20E+4eYco9mnNpIoqwWLC2N1C7pSM1B2ztacSxeRFX7Ihwdi7A2N6DUbJnEpaOU50U0pvGFoniTX1ORxPcoV5zZiMNqPuk9pc4DXxDs9tkvWJQr0nxBnrM0X8jNWWsdv8FkbILw+ARhz+Tsj8cnCI9Pxr+PeQi5x9DR6Nw7N5mwt7tOjJzbXfELhmkj6SpXE0NuN62trQaPemEp1HnhnYowMBli2B/GOzU9KPtCUSanoqnv3lAE71QUfziWcX/ntdfS6Tw5gBvxFRPAHQ5HqRVyQpovyHOW4hsLhU8E3L7jDO05eCLwjiWC8czHiaBMLHNAmAubq2l61kZyBJ14XtXanNX8s5Q+TidbZ18oysDkFIPeEIOTIQa8IQYm448HvSF8oXn+CM6CAmpsZmpsZuqqpn+3mWe/KGukj8UE8LGxMVFXlqX5gjznUvpqrZkaHMZ/tA//kV783X2E3KPxkfCMoBz151+syFxbjdVZh7WxHmtD/fTHDfVYG+pOelzlasJUZSvIcUo7J+CEcySm6RkPxoOyNxQP1onHg94Qk1NzB2i7xURrnQ1XjZW6Kgu1NnP8q8pMbfJ5lXna92qrGbMptyklI30sJoA3NzeXWiEnpPmCPOdi++polEDvIP7uRJA+2of/aC/+I70EuvuJBrLLHlBmczy4NtZjqq3B3tyAtbEeizMRfBsTgdlZj7Up8TgRlE3W0v6KSjonojHNwRE/Lw0p9u49yOuDPgJzTGlUmRWtdVW01dlorbXRWmejrdZGW10VrXU26qvMCzK/b6SPxQTwyclJUXewSfMFec6F8I34AgT7EkH6aCJIH+nD391H4Fg/OhzJ+F5rUwPVSxdTffpiqpd0Ym9vmR6QE4/NNdWpQHD8+HHa29sNOS8k5XxORGOaw6MB9vRPsue4l1cHvCfNQXfU2+ior6KtNh6U04N1g91SFhdgjfSxmAB+KhVpLxXSnOfz1VoTGh6L19LoHSDQO5B6nPweHvXMuY+qthaqT++kemlnPFCnPbbW5/5L92br44UkpjVHRgPsOe6NB+zjXrwz5qk76qs4sw4uXd7G6vY6mmvK/1Z6I30sJoCfSrmdpUKa86KmZvzd/TMC80AiWA8S7BsgFpz7l0PZrDgWt+Loak8E58XxAL1kMdVLFme8XTtfpPVxKX211hwdC7LnuJdXjk/yynEvEzPmrdvqbKxur2V1ex3ntdeyqNbG1NQUVVVVJbLOnUoeeBkizRfKy1nHYidGz4nc5UDfQPwuwGQu89DIvLnMFmcdjs42HJ2t2Be34ehsw764FUdX/LGtpbHo1ebSKac+zoZi+Wqt8YaijPjDjPjC8e/+MKP+E4/7J0J4gtOnsFw1VlZ31LEmEbRb606+WHsq9bGYAC4tnUmaLyy8c2hkHM/uvYkAPUiwNy1QH8+iXGgyl3lx64nAnPbd0dmGpa5mYQ4mS6SdF/n4BsJRhtOCcio4z3gtFJ2/lHVztTU+wk4E7bY627zz1qdCHycRE8BttsKkRS0U0nxhYZy11ow+9zI93/s5g9ufmecioXN6HnOiMp1jcRv2jkUE7VYaW+RkSYC88yLdV2uNPxzD7Qsx7Avj9oUZTj2Ofx/2hU+al85EtdVEc7WV5hpr/PuMr5YaG4tqrTlfaJTcx7kiJoB7PB4aGhpKrZE10nyhuM6h4TH6frSdnv96BP/hxAp8JhONa8+n+vTFiUDdir0zUTa0o3Xe+eeh7m5xAbxczwutNRNTUYZ9oURgjgflo4NjBJU7Hpz94TnT8pJYzYqWOQJzc42VJoeVatvJdyUWgnLt40wY8RUTwFtaWkqtkBPSfKHwzvHR9i56HkiMthNTIlXtLjrf92463/duHIvzv0270sfZEdOa8UBkxkj5RKAe9scfhzNOaZy4EGy3mHDVxEfHLTVWWmqsuBKPk68vVP50JqSdF0Z8xQRwj8dDTU15zWfOhTRfKJxzaGScvge30/PAI/gPHYu/qBSuq9bR9YHradm4tiBlRE/lPk4nGtMMeUP0eIL0eaZwz5jSGPaFyGK6mVqb+aSgbAn7Wb7YlXqt2moqi9zpuZB2XhjxFRPAw+F5LmiVGdJ8wZiz1prR539P7wM/Z+CXT58Ybbe10Pm+6+h837twdBY2Je1U6+PJqQi9nil6xoP0eqbo9QTp8UzRPzE1x+g5jtNuSYyQ46Pk1OPqEyPp2SrldXd3s6RT1q300s4LI75iAnglf7b45OOccbS9cS1dH3xPwUbbs/Fm7ONITDMwOUXPeCJAj58I1DNT6tJpqbbS2VBFp9NOW61tWqBurrZis+SXKvlm7ONyo5IHXoZI84XsnGORCP5DPUy8cQD3E88z8Iunpo+2b313fLTdVfzbxaX3cSAc5eBIgP1uPweG/RwcCdDnCWac7qiymOhyVtHpjAfqrkTA7nRWzTp6LrSvFKQ5nxJ54JLmtECeL5zsHB6fYPKNQ0y8cYDJ1w8y+fpBvPsPT7+7USlarlxL1wevx3XVuqKNtrPxLWf8oXiw3n08Rv/hoxwY9tPrmWJmrFZAa61tRpCOP26psWJa4PlnSX2cRJqzEV8xAdxsLs4Io1hI8tWxGP6jfYy/tJux7uNMvH6QydcPxBeknQV7Zxv1Zy/DueYttN+4merTSlOcqVz7OBmsDwz7U1+zBWuLSXF6o53lLdUsb6lmRUs1SxrtVOU53VEMyrWP50KasxFfMQF8YmKCxsbGUmtkTbn66miUyTcOMv7y60y+cZCJ1w/g3Xt41prVJruN2rPOoP7s5dStWkbd2cuoW7UMq7OuBOYnU8o+jsY0o4EwQ94QQ9749yOjAfYP++mbI1i322Ocf/oiVrRUc3qTPWOR/3KhXM/juZDmbMRXTAB3uVylVsiJcvGNTYXw7N7L6Au7GXthD+O/e5XIpO+kdlVtLVSfdQYN551F/dnLqFu1nOozOhd0SiRXitXHWsfXNRzyxtPxhrwhhnzxIO32hhhKpOjFMsxVzzayTgZrv99PdXV1UbyLQbmcx7kgzdmIb/n+ds5gdHRU1IlfKt/IpI+x373K2Au7GXtxD57f7yU2Nb0in6OrnYaLzqX+3LNSo2tbcwO9vb10dnYuuHO+5NrHMa2ZnIriCUQYD0bwBCOMB8J4ghFG/OH4SNoXD9JzrW2YpNFhYVGtDVeNDVetlS6nfd6RdeU8Lj7SnI34igngep6qc+XGQvlOuUcZe3FP6mvitQMnraNYe9ZSGi9ZQ+Mlq2m6eA32jkWz7ktiHwfCUYa88ap148EI44F4YPYEI6lAPZ54PDEVyThqnkmVxcSiGiuLam3xIF1rY1GNNfE9np6XT2qexD6WhjRnI75iAvip9G/RXER8fga3P5MaYfsOHpu2XVnM1K85m8aLV9N0yWoa3nYetiZnVvsu9z4ORWIcGo1fHNzv9rPP7aPH80rWQRnidxs67Zb4l8NCgz3+1VhtpbU2XjzJVWOjrki3g5d7H89Emi/Icz4lplAGBwdF5XYW2ldHo/T9aDsH/vFbTA0Op143OapoeOs5iYC9BucFZ2Opya88ZTn1cTga48hYMJUjvX/Yz9HRwEk50iYFi+uraHScCMrORFBuSL5mt9Bgt1JvN2Mt8UXDcurjbJDmC/KcjfiKCeDlui5fJgrpO/zMS+z7+7uZfOMgAPWrV9J+3UYaL1lN/blnYbIVZtmoUvVxNKbpHguyPxGoDwz7OTwSIDxjaG1SsKQxPs+8oqWaNluYNUvbyirtbj5O5fN4oZDmbMRXTAA/FZn8w2H2ff4ehn+9A4jnX6/4mz+l/fqrFnQVmUITiWneGPSxs8fDqwM+Do34mZrl9sNOZ1U8WLviAfvMZse0Ow5HRkZEBe8KFQqNmADu9XppbpZT+9mI75R7lINf/TY9DzwCsRiWuhrO+OgHWfLh92K2F2+tv2L28Zg/zM7eCV7qmeDlvkl8M4r+t9fZWNFSzXJXNWe1VLOspZqaeepFSzsnQJ6zNF+Q52zEV0wAb23Nv250KcjHNxqY4uh//JDD//Y9oj4/ymym67YtLPvkVmwtxb8xoZB9HNOa/W4/L/XEg/b+Yf+07V3OKi7qqueCxfWc5aqm3p77qSjtnAB5ztJ8QZ6zEd+sfmuUUpuBfwXMwLe11nfN2H4acB/QkGjzaa319rytZsHtdtPV1VXIXRaVXHx1LMbxhx5n/5f/PXX7uuvqSznr/99G7YrTi2g5HaN9PDkV4Xe9k+zs8bCzd3Ja9TybWbG6vY6Luuq5qKue9nrj/0lIOydAnrM0X5DnbMR33gCulDID9wBXA73ATqXUI1rrN9Ka/W/gQa31N5RSq4DtwOl5GWX2KOTuik62vqPP/54//N3XmXjlDwDUnbOclX97J82XXVhMvVnJtY+11hweDfBSzwQ7eyZ4Y8g3LaWvtdaWCtirO+qwF3i+Wto5AfKcpfmCPGcjvtmMwC8CDmqtDyc+7IfA9UB6ANdAsuq7E+jP2ygDTU1Nhd5lUZnP13foGPu+8H8ZevRZIH4r+/JPf4TFN29GlagYT7Z9fGjEz+P7R/nNkXGG/SeK0ZsVrG6v5aKuei7uctLVUFXUXyZp5wTIc5bmC/KcjfhmE8AXAz1pz3uBi2e0+TvgcaXUnUANcNVsOxoaGmLr1q1YLBai0Shbtmxh27ZtDAwMUFNTg9lsZmJiApfLxejoKFprXC4Xg4ODBAIBXC4XXq+X1tZW3G43Simamppwu93U19cTjUbx+Xy0tbUxMDCA1WrF6XQyPDyM0+kkFAoRCARS2202G3V1dYyMjNDY2EggECAYDKa22+12HA4HY2NjNDc3Mzk5SSgUSm13OBzYbDY8Hg8tLS14PB7C4TBtbW0cPHiQjo6Ok44pPDaB979+Sd/3HoFoFJOjiqb3v5u3fPxPGPV56Tt+vGTHdPDgQdra2mY9pppGF/+9p4cdA2GOek4EbWeViXNbrLz9zBY6zH5cDfFCV96JQaYcxf05RaNRzGazoZ9TNudeMs2rEOde0rmY514hj6m/v5+zzjqr5L9PuRxTX18fK1asKPsYkTym4eFhHA7HnMeUCTXfbZxKqZuBTVrrDyWefwC4SGt9Z1qbTyT29U9KqbXAfwLnaK2n3dO9Y8cOvXLlyjk/LxNjY2OiKoyl+2qt8fz+Dfp/+hj9P/4fIhNeUIrOW9/Fsr/+MPbW8liEdWYfR2OaXX2TPLZ/hB3dnlRedq3NzBVnNnL18iZWuKoXvEZ1Jl8JSHOW5gvynOfz3bVr18sbN26cdU41mxF4L5A+w97JyVMkW4HNAFrrHUopO9ACDGWx/6yIRqPzNyojotEovsM99P/0MY4/9Dj+I72pbc0b3sbKv72TulXLSmh4Msk+7vMEeWz/KL86MJqaIlHAhZ11XLO8mXVLnHkv0VVIpJ0TIM9Zmi/Iczbim00A3wksV0otBfqAW4D3zWhzDNgIfFcp9RbADrjztpoFn89HS0t5jFTnYso9ysDPn+TID/6b4OsHU69XLWqm7Yar6Niyifrzziq7Cy3+UJTH9o/y8o5RXhs8UW62o97GNcubuWp5E4tqbSU0PBkp50Q60pyl+YI8ZyO+8wZwrXVEKXUH8BjxFMHvaK1fV0p9Hvid1voR4C+BbymlPk78guZtusAlwcp5odKIL8DQY7/h+E8fY/jpl9CJv6jmmmpa37GBjps20fz2t5bs4mQmtNa8OuDj8f0jPHtknGAkPuNlt5hYv7SBa1Y0c25bTdn9sUlSzudEJqQ5S/MFec5FX9Q4kdO9fcZrn0t7/AZwad4WWVBuC5XGIhFGfvM7jv/0MQa3P5ta0UZZzLiuWod1w1s5+3/dgLnaXmLTkxnzh3l03wiPHxilf2Iq9fqyBgvXndvB+qUNVM9zF2Q5UG7nRDZIc5bmC/KcT4lFja3WwhRsMoLWmok9f4jPa//sV4Tco6ltDReeQ/uWTbRfdyW2lkb6+/vLLngPTob48auD/M++EUKJ2iMt1VauWt7ENSuaMPlG6eiQcwtyOZwTuSLNWZovyHM24ismgDud2dW0LhYTr+5jz5//Pb4DR1OvVZ95Gh1brqHjxmuoPn36Sjal9k3n2HiQB/cM8uTB0VQ51rWnOXnXW1q4YHEdZlN8isRnKR/nbCinPs4Wac7SfEGesxFfMQF8eHiYmpqakny255V9/O69HyU8PomtpZH291xFx42bqF/zlozzw6X0TXJw2M8P9gzy2yPjaOLlWK88s5Fb1rRyeuPJNcPLwTkXpPmCPGdpviDP2YivmABeqr+qE6+eCN6LNl/Gmv/4Qlb1t0s5Cnh9wMv3dw+ys3cCAKtJcc2KJm4+r5WOOWqQnEojl1IhzVmaL8hzPiVG4KFQaP5GBWbitf3sfO9fxIP3prdnHbxh4X211rzcN8kPdg/y6oAXiK/r+K6Vzdx47iJaauZPASxFHxtBmi/Ic5bmC/KcjfiKCeCBQGBBP2/i9QPsvPmjhMcmcF3zdtZ864s5rXyzUL4xrXn+qIcf7BngwHD8M2ttZq4/28V7znbhzKFM60L3sVGk+YI8Z2m+IM/ZiK+YAL6QuZ3TgvfVl3L+t7IfeScptm8kpnn60Bg/3DPIsfEgAA12Czeeu4h3vaVl3sUQZuNUyp8tFdKcpfmCPGcjvqW/HzpLBgYGFuRzJt84GA/eox5cV63j/G9/EVNV7ncgFss3FI3xi73D/MmDb/CVZ7o5Nh5kUa2VO9Z18r1bzuaPVrfmFbxh4fq4UEjzBXnO0nxBnrMRXzEjcJut+LdxT+49xEs3JYL3xrWc/59fyit4Q3F8f983ydef76HXE7/5ptNZxS2rW7nizMaCrLa+EH1cSKT5gjxnab4gz9mIr5gAXldXV9T9T+49xEs33kl4dJyWK9eyxkDwhsL6jvjD/MeLfTx1aAyIB+7b3trOpac3pHK4C0Gx+7jQSPMFec7SfEGesxFfMQF8ZGRk3tq4+TIteF9xCed/50uGFw8uhG80pnnkDTf3vXwcfzhGlVnxvvPbuOncRQUZcc+kmH1cDKT5gjxnab4gz9mIr5gAXqz6vpN7D7HzprTgfe+XC7Lyu1HfvUM+vv5cDwdH4leoLzmtnj9f20lbXfFWpZdUQxnk+YI8Z2m+IM/ZiK+YAB4IBKivr5+/YQ5M/uEwO2+6k9DIOM2XX1Sw4A35+04EI3znd/08+ocRNLCo1sq2tV2sXVL8mxOK0cfFRJovyHOW5gvynI34igngwWCwoPvz7jtyInhveBsX3PuPBQvekLuv1ponDozyrZf68QQjmBXcdF4r71vTisO6MJUBC93HxUaaL8hzluYL8pyN+IoJ4IXM7fTuP8pLN91JaHgsHry/+xXMjsJOTeTie2Q0wNef60ktpLC6vZY71nWyZJZ6JcXkVMqfLRXSnKX5gjznSh54Dnj3H+WlG+8g5B6leX1xgjdk5xsIR/mPF/v4s4f/wGuDPhrsFv768iV85R3LFjx4w6mVP1sqpDlL8wV5zqdEHrjdbry2tvdAWvC+7EIu+O4/FiV4w9y+Wmt+e9TDN17oZdgXRgHXrWrhtre2U1tVuh9JIfp4IZHmC/KcpfmCPGcjvmICuMNhbETqPXCUnTfeScg9StPb38oF932lqAsuZPLtn5jinud7U5UCV7RU89FLu1jhqi6aS7YY7eOFRpovyHOW5gvynI34iplCGRsby/u9oZFxdt54J1NDIzRdegFvvf+rRV8tZzbfl3o8fPg/h0bvAAAgAElEQVSne9nZO0GNzcyd6zr51+tWlEXwBmN9XAqk+YI8Z2m+IM/ZiK+YEXhzc/5LfQ0/8xJTQyPUn7uCCxYgeMPJvuOBMP/nmWOEo5orzmzkTy9eTGN1eS39ZKSPS4E0X5DnLM0X5Dkb8RUzAp+cnMz7vYGe4wA0vf1CLDUL8+9Vuq/Wmn97rpfxYITV7bX89eVLyi54g7E+LgXSfEGeszRfkOdsxFdMADdS9DxwrB+A6tPaC6UzL+m+Tx8e47dHx3FYTfzl+tMwZViGrdScSoXwS4U0Z2m+IM/ZiK+YAG4kVzLQE0/TcXQtXABP+o74w9z9fC8AH7l4cVFvhTfKqZQ/WyqkOUvzBXnOlTzweUiOwB2ndRRKZ14GBgbQWvO13xxjcirKhZ11XHtWec/NnUr5s6VCmrM0X5DnbMRXTADPN9VGR6ME+gbj++hcuL/MDoeDxw+M8mLPBLU2M5+47LSMK9iXC6dS+lWpkOYszRfkOZ8SaYT5Fj0PDgyjwxFsrqYFyT5J4gkrvrEjPnXy52s7s1pUuNScSoXwS4U0Z2m+IM/ZiK+YAO7xePJ6XzIDZSHnv2Nac/eLA/jDMdYtcbJxmYzylvn2camQ5gvynKX5gjxnI75iAnhLS0te70sF8AXMQPnF3mH2jkZw2i38xaVdZT91kiTfPi4V0nxBnrM0X5DnbMRXTADPewR+bGFH4H2eKb71Uvyi6Z2XdpZlvncmTqWRS6mQ5izNF+Q5nxIj8HA4nNf7kiPwhcgBj8Y0//RsN1ORGBe12Vi/VMbUSZJ8+7hUSPMFec7SfEGesxHfrAK4UmqzUmqfUuqgUurTGdq8Vyn1hlLqdaXU9/M2ykC+uZILOQJ/+LUhXhv00eSw8LH1S4v+eYXmVMqfLRXSnKX5gjznouaBK6XMwD3AtcAq4Fal1KoZbZYDnwEu1VqfDXwsb6MM5JsreWIOvLg54N1jAe59Of5ZH7/sNHxj7qJ+XjE4lfJnS4U0Z2m+IM+52HngFwEHtdaHtdYh4IfA9TPafBi4R2s9BqC1HsrbKAM1NTU5vycWiRDsHwKlcCxuLbRSimhM89VEoapNK5q4+DRnXr6lRpqzNF+Q5yzNF+Q5G/HNJoAvBnrSnvcmXktnBbBCKfWcUuoFpdTmvI0yYDbnvi5ksN+NjkapamvBVFW83NAf7hlk/7CfRbVW/vSSTiA/31IjzVmaL8hzluYL8pyN+GZTTna2HDg9y36WA5cDncBvlFLnaK3H0xsNDQ2xdetWLBYL0WiULVu2sG3bNgYGBqipqcFsNjMxMYHL5WJ0dBStNS6Xi8HBQQKBALFYDK/XS2trK263G6UUTU1NuN1u6uvriUaj+Hw+2traGBgYYGr3PgBMribGx8cJhUIEAoHUdpvNRl1dHSMjIzQ2NhIIBAgGg6ntdrsdh8PB2NgYzc3NTE5OEgqFUtsdDgf9fs0Du+JTJ7etqmH4eC+WtjZ6enqIRqNzHlNtbS1ATsdktVpxOp0MDw/jdDoLekw9PT2Ew2FsNhsej4eWlhY8Hg/hcDj1/vl+Tgt5TNFolImJiax+TuVyTEnnQpx7C3FM/f39VFdXF/3cK+Qx9fX14XA4Sv77lO0xDQ8Pz3tMGYOz1jNj8YwGSq0F/k5rvSnx/DMAWusvp7X5JvCC1vq7iedPAp/WWu9M39eOHTv0ypUr5/y8TPj9fqqrc1v4oPcHv+C1j3+Jjps2cd7df5vX585FKBrjoz/fx+HRINetauGOdV2pbfn4lhppztJ8QZ6zNF+Q5zyf765du17euHHjhbNty2YKZSewXCm1VCllA24BHpnR5mfAFQBKqRbiUyqHs9h31oyOjub8nmJnoPzXrgEOjwbpqLex9W3TL5Lm41tqpDlL8wV5ztJ8QZ6zEd95A7jWOgLcATwG7AUe1Fq/rpT6vFLqukSzx4ARpdQbwFPAX2mtR/K2mt0j5/ecuI2+8Bkoe4d8/OiVQRTwV+uX4LBOn8fKx7fUSHOW5gvynKX5gjxnI75ZLammtd4ObJ/x2ufSHmvgE4mvouByuXJ+z4kUwsLmhU5FYnz1mW5iGm46dxFnt508T5WPb6mR5izNF+Q5S/MFec5GfMXciTk4OJjze4o1Av/u7/rp9UxxWoOd2946+/RMPr6lRpqzNF+Q5yzNF+Q5G/EVE8Dnuxo7k9hUiOBxN5hM2DsWFczjleNeHnrNjUnBpzYswWaZvQtz9S0HpDlL8wV5ztJ8QZ6zEV8xATxXAv1DoDX2jkWYrFnNFM2/z3CU//NsNxq4dU0bK1xyrnRXqFDhzYeYAO71enNqn1pGrYAZKN96qZ+ByRBnNjt435q57+zM1bcckOYszRfkOUvzBXnORnzFBPDW1txuhS90FcLXBrz8Yu8wFpPiUxuWYDXP3XW5+pYD0pyl+YI8Z2m+IM/ZiK+YAO5251YcqtA54I+8Ef/8m89dxNKm+dewy9W3HJDmLM0X5DlL8wV5zkZ8xQTwXFe1KWQVQu9UhOe6PSjgnW/JbvUMKavwpCPNWZovyHOW5gvynI34igngTU1NObX3p+bAjeeAP314nHBUs6ajlkW12RXFytW3HJDmLM0X5DlL8wV5zkZ8xQTwXP/NCPbEa+wWYgT++P74TaXXrGjO+j3S/o0Dec7SfEGeszRfkOd8Skyh1NfXZ902GphiamgEZbVgbzO2wOmxsSB/cPuptpq49PSGrN+Xi2+5IM1Zmi/Ic5bmC/KcjfiKCeDRaDTrtoHexPz34laUwdrAjx+Ij743nNGIPcNNO7ORi2+5IM1Zmi/Ic5bmC/KcjfiKCeA+ny/rtoXKQInGNL86EK8Uds2K3OapcvEtF6Q5S/MFec7SfEGesxFfMQE8l4U/T2SgGAvgL/dNMBqI0OmsYtWi3JY9krawKshzluYL8pyl+YI856Iualwu5LLwZ6FG4I/tj4++r17elHOqj7SFVUGeszRfkOcszRfkORd7UeOywGq1Zt32RBXC/AP4RDDCC90eTCoewHMlF99yQZqzNF+Q5yzNF+Q5G/EVE8CdTmfWbVM54AZSCJ86NEY4prlgcR0tNbkviJyLb7kgzVmaL8hzluYL8pyN+IoJ4MPDw1m3DSRzwA3cxJPMPrlmefa53+nk4lsuSHOW5gvynKX5gjxnI75iAni2f6UiPj/h0XFMVTaqFuUXfI+MBjgwHKDWZmbdkvz+OkobBYA8Z2m+IM9Zmi/Icz4lRuChUCirdskLmPbONpQpv8NL3nl5+ZmNGRdsmI9sfcsJac7SfEGeszRfkOdsxFdMAA8EAtm1M1hGNhLTPHlwDIBr8rh4mfLI0reckOYszRfkOUvzBXnORnzFBPBscyWNphDu7JlgPBhhSYOdswysuCMtFxXkOUvzBXnO0nxBnnMlDzwNf4+xlXgeS0yfXL0i99zvdKTlooI8Z2m+IM9Zmi/Icz4l8sBttuxS+VJVCPMI4OOBMC8ei+d+b1xmrCRltr7lhDRnab4gz1maL8hzNuIrJoDX1dVl1c5IDvivD40R1fC2znqaq43dDJCtbzkhzVmaL8hzluYL8pyN+IoJ4CMjI1m1M5ID/vj+ZOGq/NIP08nWt5yQ5izNF+Q5S/MFec5GfMUE8MbGxnnbhD2TRDyTmB12bC3zt0/n4LCfw6MB6qrMXHya8XrC2fiWG9KcpfmCPGdpviDP2YivmACeTapNeg2UXC9APp4oG3vlmU3Y5llxPhukpTKBPGdpviDPWZovyHM+JdIIg8HgvG3yLSMbjsb49cH86n5nIhvfckOaszRfkOcszRfkORvxFRPAs8mVzDcH/MVjE0xMRTmjyc6yZkdefjORlosK8pyl+YI8Z2m+IM+5kgeeIN8ReCr3e3mzodzvdKTlooI8Z2m+IM9Zmi/Icz4l8sDtdvu8bfx5jMBH/WF29k5gVnDlssJd/MjGt9yQ5izNF+Q5S/MFec5GfLMK4EqpzUqpfUqpg0qpT8/R7iallFZKXZi3UQYcjvmnNk6MwLPPAX/y4CgxDRed5qTRUbhC8Nn4lhvSnKX5gjxnab4gz9mI77wBXCllBu4BrgVWAbcqpVbN0q4O+CjwYt42czA2Njbndq11znPgWutU9smmAl28TDKfbzkizVmaL8hzluYL8pyN+GYzAr8IOKi1Pqy1DgE/BK6fpd0/AF8BinIJuLl57ptrwmMTRH1+LHU1WBuyu7Np/7Cf7rEgTruFi7oKW0N4Pt9yRJqzNF+Q5yzNF+Q5G/G1ZNFmMdCT9rwXuDi9gVLqfKBLa/0LpdQnM+1oaGiIrVu3YrFYiEajbNmyhW3btjEwMEBNTQ1ms5mJiQlcLhejo6NorXG5XAwODhIOh2lsbMTr9dLa2orb7UYpRVNTE263G9OxQQDMrc2EQiEGBgawWq04nU6Gh4dxOp2EQiECgQBtbW0MDAzws/3xvzVvW2TG750kEAgQDAZT2+12Ow6Hg7GxMZqbm5mcnCQUCqW2OxwObDYbHo+HlpYWPB4P4XCYtrY2uru7aW1tnfOYamtrATIeU319PdFoFJ/Pl/rM+Y7JZrNRV1fHyMgIjY2NOR1Td3c3Lpcr4zFl83NayGMymUyMjIwY+jkt9DElnY38nBbymAYHB1m2bFnRz71CHtPAwABnnnlmyX+fsj0mj8fDyMjInMeUCaW1nruBUjcDm7TWH0o8/wBwkdb6zsRzE/Br4Dat9VGl1NPAJ7XWv5u5rx07duiVK1fO+XmZ6O7uZsmSJRm3D/z3r9n94f/Nos2XccF3/3He/YUiMW75/mt4Q1G+ecNKzihQ+mCS+XzLEWnO0nxBnrM0X5DnPJ/vrl27Xt64ceOs1xWzmULpBbrSnncC/WnP64BzgKeVUkeBS4BHCn0hc75cyVznv5/v9uANRVnW7Ch48AZ5uaggz1maL8hzluYL8pyLnQe+E1iulFqqlLIBtwCPJDdqrT1a6xat9ela69OBF4DrZhuBG2G+XMlcc8BTixYXoHDVbEjLRQV5ztJ8QZ6zNF+Q51zUPHCtdQS4A3gM2As8qLV+XSn1eaXUdXl/co7Ml2qTSw74sC/Err5JLCbFlWcWp/CNtFQmkOcszRfkOUvzBXnORnyzuYiJ1no7sH3Ga5/L0PbyvG3mYL6i5yfWwpw/B/xXidzvdUuc1Nuz6oKckVZUHuQ5S/MFec7SfEGe8ymxoIPH48m4TWtNoDe7EbjWOlX3u9C53+nM5VuuSHOW5gvynKX5gjxnI75iAnhLS0vGbaHhMWKBKayN9Vjqaubcz94hP72eKZocFi7sNF73OxNz+ZYr0pyl+YI8Z2m+IM/ZiK+YAD7XX6nAsewXMk4Wrtq4rAmzqTCFq2ZD2igA5DlL8wV5ztJ8QZ7zKTECD4fDGbelL+QwF8FIjGcOx29bLVTd70zM5VuuSHOW5gvynKX5gjxnI75iAvhcuZLZZqA8d3QcfzjGWa5qljQW90q1tFxUkOcszRfkOUvzBXnOp3w98GyrEKYWLV5e3NE3yMtFBXnO0nxBnrM0X5DnfErUA6+pyXxx8sQceOa/ZEPeELv7J7GaFZcXKfc7nbl8yxVpztJ8QZ6zNF+Q52zEV0wAN5vNGbcFeuJ/webKAX/iwCiaeO53XVVxcr/Tmcu3XJHmLM0X5DlL8wV5zkZ8xQTwiYmJWV/XsRiB3ngAt3fOPgLXWvNE4tb5TUW6dX4mmXzLGWnO0nxBnrM0X5DnbMRXTAB3uVyzvj41OIIOhbG1NGKpmf3C5BtDPvonQrRUWzm/I7ta4UbJ5FvOSHOW5gvynKX5gjxnI75iAvjo6Oisr2eTA76zJ/4X7u1LG4qa+51OJt9yRpqzNF+Q5yzNF+Q5G/EVE8Az1S3PJgf89/2TAFyweGFG35DZt5yR5izNF+Q5S/MFec5GfMUE8Ez/ZqRywDOUkfWFouxz+zEpOK9t7tUtCom0f+NAnrM0X5DnLM0X5DmfElMog4ODs74+3wh8z/FJYhresqiGatvCXZ3O5FvOSHOW5gvynKX5gjxnI75iAnimteFSc+AZRuC/74tPnyzUxcsk861lV45Ic5bmC/KcpfmCPGcjvmICeCaSOeCZRuC7EgH8rQs4/12hQoUKC4GYAO71ek96LRaJEOyL//vhmCUH3O0L0eOZwmE1cdaihb07azbfckeaszRfkOcszRfkORvxFRPAW1tbT3pt6rgbHY1S1dqC2V510vbk9Ml5bbVYFih9MMlsvuWONGdpviDPWZovyHM24ismgLvd7pNemy8DpRTpg0lm8y13pDlL8wV5ztJ8QZ6zEV8xAVypk0fQc2WgaK1PXMAsQQCfzbfckeYszRfkOUvzBXnORnzFBPCmppNLwAbmGIF3jwcZDURoclhY0mAvut9MZvMtd6Q5S/MFec7SfEGesxFfMQF8tn8z5hqBp4++S/EXWdq/cSDPWZovyHOW5gvynE+JKZT6+pMXIA70xHPAZysju6tE+d9JZvMtd6Q5S/MFec7SfEGesxFfMQE8Go2e9NqJHPDpKYSRmGbP8XhqTinmv2F233JHmrM0X5DnLM0X5Dkb8RUTwH0+37TnsVCY4HE3mEzYO6an4fxhyEcwEqPLWYWrxraQmilm+kpAmrM0X5DnLM0X5Dkb8RUTwGcu/BnsH4RYDHu7C5PNOm1bcvqkFOmDSaQtrArynKX5gjxnab4gz/mUXNR4rlvoT+R/l24uTNrCqiDPWZovyHOW5gvynE+JRY2t1umjbH+GhRx8oSh7h3zx8rHtpStqM9NXAtKcpfmCPGdpviDP2YivmADudDqnPU+lEM7IAX91wEtMw0pXDTULWD52JjN9JSDNWZovyHOW5gvynI34igngw8PD056nbuKZMQIv5d2X6cz0lYA0Z2m+IM9Zmi/Iczbim1UAV0ptVkrtU0odVEp9epbtn1BKvaGUekUp9aRSakneRhnINAKfmQO+q7+0+d9JpI0CQJ6zNF+Q5yzNF+Q5F3UErpQyA/cA1wKrgFuVUqtmNPs9cKHW+jzgJ8BX8jbKQCgUmvb8xAj8xBXcEX+Y7rEgVRYTb1lUXWiFnJjpKwFpztJ8QZ6zNF+Q52zEN5sR+EXAQa31Ya11CPghcH16A631U1prf+LpC0Bn3kYZCAQCqcfR4BRTg8Moi5mq9hPryaWXj7WaSzs7lO4rBWnO0nxBnrM0X5DnbMQ3myi3GOhJe96beC0TW4FH8zbKQHquZKA3nnZj72jFZLGkXk9Nn5TB6jvSclFBnrM0X5DnLM0X5Dkb8bXM34TZKkHpWRsq9X7gQmDDbNuHhobYunUrFouFaDTKli1b2LZtGwMDA9TU1GA2m5mYmMDlcjE6OorWGpfLxeDgIIFAAJfLhdfrxXroGACm1ib8fj9ut5u6ujpe7vEAcI6riu7ubqxWK06nk+HhYZxOJ6FQiEAgQFtbGwMDA9hsNurq6hgZGaGxsZFAIEAwGExtt9vtOBwOxsbGaG5uZnJyklAolNrucDiw2Wx4PB5aWlrweDyEw2Ha2trYt28fHR0dcx5Tci08r9dLa2srbrcbpRRNTU243W7q6+uJRqP4fL7UZxbzmPbt20dbW1vGY8rm57SQxxSNRjGbzYZ+Tgt9TEnnYp57hTym/v5+zjrrrKKfe4U8pr6+PlasWFHy36dsj2l4eBiHwzHnMWUMzlrPGovTg/Ja4O+01psSzz8DoLX+8ox2VwFfBzZorYdm29eOHTv0ypUr5/y8TBw/fpz29njGybH7HuaNv/4qi299F+f+y2fjr40F+dBP99Jgt/DD/3UOphLXBE73lYI0Z2m+IM9Zmi/Ic57Pd9euXS9v3Ljxwtm2ZTOFshNYrpRaqpSyAbcAj6Q3UEqdD/w7cF2m4G2UuroT0yInMlBOHHT69EmpgzdM95WCNGdpviDPWZovyHM24jtvANdaR4A7gMeAvcCDWuvXlVKfV0pdl2j2VaAW+LFSardS6pEMu8ubkZGR1OPZcsB/Xwb1T9JJ95WCNGdpviDPWZovyHM24pvNHDha6+3A9hmvfS7t8VV5G2RJY2Nj6vGJuzDjOeDRmGbP8fLI/06S7isFac7SfEGeszRfkOdsxFfMnZjpqTaBGXVQ9rn9+MMxOp1VLKotTfnYmUhLZQJ5ztJ8QZ6zNF+Q51zsNMKyIBgMAhDxBQiNjKNsVqpam4HyufsynaSvJKQ5S/MFec7SfEGesxFfMQE8mSuZmj7pbEOZ4vrlUv8kHWm5qCDPWZovyHOW5gvynE+peuAzqxAGwifKx64uYfnYmUirSQzynKX5gjxnab4gz/mUqAdut9uBkzNQXh3wEolplrdUU1eV1TXZBSHpKwlpztJ8QZ6zNF+Q52zEV0wAdzgcwMk54Kn0wTKa/4YTvpKQ5izNF+Q5S/MFec5GfMtnyDoPY2Nj1NfXn5hCSYzAd5Xh/Dec8JWENGdpviDDWWuN1+tFay1ugWCQu6ixUora2lpUDjciigngzc3xjJP0AD7qD3NkLEiVWbFqUU0p9U4i6SsJac7SfEGGs9frpaqqCpvNlqo/IglpzknfUCiE1+vN6c5MMVMok5PxkXYqB/y0DnYn0gfPaavFZimvQ0n6SkKaszRfkOGstcZmi99PEY1GS2yTO9Kck742m435alPNpLyi3hyEQiHCE17C45OYHFXYWhrTVp8vr+kTkFdUHuQ5S/MFec65BpRyQJqzEV8xAbytrS0tB3z6/Hc5BnBpuaggz1maL8hzlrbCO8hzPiVWpR8YGJg2/903MYXbF8Zpt7C0qfyuOkvLRQV5ztJ8QZ5zOBwuyee2tLSwfv161q1bx6233orH48n6veFwmLvuuouvf/3rRTQsHEb6WEwAdzgcqRzw6tPaU6PvNR21ZVE+dibSUplAnrM0X5DnbDKVJkQ4HA6effZZnn/+eRobG/n2t7+d9XvzcY5EIjm/p1AY6WMxWSg2m42RtBF4ueZ/J0leBJKENGdpviDPOZeUtmLxtre9jddffx2IZ8i8//3vZ3x8nHA4zN/8zd/wjne8A4B/+qd/4kc/+hHt7e24XC5Wr14NwH333cf9999PKBRi6dKlfPOb36S6uppt27bR0NDAq6++ynnnnccXvvCFkhyfkT4WE8A9Hk8qA6Wqq53dx71A+eV/J/F4PDQ0NJRaIyekOUvzBXnO7/juq0XZ7+MfOj+rdtFolGeeeYb3v//9QPyuxfvvv5/6+npGRka45ppruPbaa9mzZw8PPfQQTz/9NH6/n02bNqUC+Lvf/W7++I//GIAvfvGLPPDAA9x+++0AHDp0iIcffrikaYfRaBSLJb9QLCaAt7S00NsTnz901zXgG4rSUW+jra6qxGaz09LSUmqFnJHmLM0XZDqXgkAgwPr16zl27Bhr1qzhiiuuAOIZG1/4whd4/vnnMZlMHD9+nKGhIXbs2ME73/lOqqurqaqqYvPmzal97d27ly9+8Yt4PB58Ph9XXnllatv1119f8pzxfIM3CArg4+Pj+BMj8L3mOsBbVuVjZ+LxeKipKa+bi+ZDmrM0X5Dn/IsPnl2SaZ/kHPjExAS33HIL3/72t/nIRz7Cj3/8Y4aHh3nqqaewWq2sXr2aqakp4MRUxMw88G3btvHAAw9wzjnn8P3vf5/nnnsuta26unrhDioDyYWu80HMRcypkXGiXj/mmmpenojnTZbr9AmU7uq9EaQ5S/MFec6lzqmur6/nrrvu4u677yYcDqdWpLdarfzmN7+hp6cHgHXr1vHLX/6SQCDA5OQkjz32WGofXq+X1tZWwuEwP/7xj0t1KBkx0sdiRuD1ofhfVXtXG28M+VHAmvbyDeDS8n1BnrM0X5DnXA451eeddx7nnHMODz30EDfffDO33norV155Jeeccw7Lly8HYPXq1dxwww1s2LCBzs5OLrnkktT7P/vZz3L11VfT1dXFqlWr8Hq9pTqUWTHSx2oh/8Lu2LFDr1y5Mq/3vnrvg/R95mvYLruYuzZ9kOUtDu55T377Wgi6u7tZsmRJqTVyQpqzNF+Q4TwxMZEquDU1NUVVVXleZ8qENOd03/S+T7Jr166XN27ceOFs7xUzhaKG44n8I84mAC5YXN4V3STNcyaR5izNF+Q5lyoP3AjSnI34ijnS8HE3AEftTqB887+TlPrKdj5Ic5bmC/KcyyEPPFekORvxFRPAvUd7AThsq8dmVpzdWt4jmYmJiVIr5Iw0Z2m+IM9ZWmU/kOdsxFdMANfuMQAmGps4u7X8ysfOxOVylVohZ6Q5S/MFec5GcpRLhTRnI77lHQUTaK0JJG7imWhoLsvqgzMZHR0ttULOSHOW5gvynKWNZkGe85t+BB4aHkMHp5hyVDPlqC7r/O8kpc6fzQdpztJ8QaZzhfJFRABPjr7HG5qpqzJzZhmWj52JtH+VQZ6zNF+Q51yq6Yj0crK33XYbfr8fgK6urnnfm8n5ve99b05laXPl1Vdf5Yknnsj5fW/6KZRkEauJxibWdNRhNpX/VebBwcFSK+SMNGdpviDPuVR3jqaXk7XZbNx7771ZvzeT84MPPojT6SyU4knMFcDnKlf7pq8HnlzIwdPYXNb1T9Kpra0ttULOSHOW5gvynMsh7XHt2rUcOXJk2mu//e1vueWWW1LPP/WpT/H973+fJ554go985CPT2t16661A/G7NkZERAO655x7WrVvHunXr+MY3vgHAsWPHuPjii/noRz/KunXruP3223n66afZvHkzF154IS+//DIQX0X+jjvuYOPGjWzYsIHt27cTCoX48pe/zMMPP8z69et56KGHuOuuu/jYxz7Gli1b+LM/+zOCwSDbtm3j0ksvZcOGDfzmN78BjPWxiMu1vuQIXMgFzAoV3iz8qnN9Ufa7eeD5rNpFIhF+9atfsXHjxqzaX3HFFXziE5/A5/NRU1PDww8/zA033DCtze7du1PBXmvN1VdfzaWXXkpDQwOHDx/m3nvvZeXKlWzcuDb4rMoAAAn2SURBVJGf/OQnPProozz66KP8y7/8Cw888AD//M//zPr167n77rvxeDxcddVVbNiwgc985jPs3r2br3zlKwDcdddd7Nmzh+3bt+NwOLj77rsBeO6559i/fz833ngjO3fufPNPoYwe7gPA1NFKe52MgvjlVm8hG6Q5S/MFmc6lIFlO9sorr6SzszNVD3w+LBYLl19+Of/zP/9DJBLh8ccf59prr53W5oUXXuCd73wnNTU11NbW8q53vYsdO3YAsGTJElatWoXJZGLlypVs2LABpRSrVq3i2LFjADz11FN87WtfY/369bz73e8mGAzS29s7q8/mzZtTqzC9+OKL/NEf/REAK1asoKuri0OHDhnKQskq9CulNgP/CpiBb2ut75qxvQq4H3grMAL8kdb6aN5WM/B292MGTlu5RMxdVq2traVWyBlpztJ8QZ7zNf2/Lcmt6ck58ExYLBZisVjqeTAYTD2+8cYb+c53vkNjYyPnn38+dXXT/2ufKxMovXSuyWRKPTeZTKl5bK019913X6qQVpLkFEs66eVqM31uURc1VkqZgXuAa4FVwK1KqVUzmm0FxrTWy4B/Af4xb6MZ6FgMBuK30a9avbRQuy06bre71Ao5I81Zmi/Icy7lWpFz0dnZyb59+5iammJiYmJasL/44ovZs2cP999//0nTJxAvPbt9+3b8fj8+n49f/vKXrF27NuvPvvLKK/nWt76VCsivvPIKEL++Mdd/WGvXrk2Vsz148CC9vb0sW7bMUB9n86f1IuCg1vqw1joE/BC4fkab64H7Eo9/AmxUBRoqT/YPY46E8VfXcv6yRYXY5YIg5T+FdKQ5S/MFmc7lSGdnJ+95z3u47LLLuP322znvvPNS28xmM5s2beLJJ59k06ZNJ7139erV3HrrrVx11VVcffXVfOADH5j2/vn45Cc/STgc5u1vfzvr1q3jS1/6EgCXXXYZ+/btS13EnMnWrVuJxWJceumlbN26lXvuucdw1cR5y8kqpW4CNmutP5R4/gHgYq31HWltXku06U08P5RoM5y+r5///Of6M5/5DBaLhWg0ypYtW9i2bRsDAwPU1NRgNptTBdtHR0fRWtO9qxvvRz7F6Gmns+nRr6eKs7vdbpRSNDU14Xa7qa+vJxqN4vP5aGtrY2BgAKvVitPpZHh4GKfTSSgUIhAIpLbbbDbq6uoYGRmhsbGRQCBAMBhMbbfb7TgcDsbGxmhubmZycpJQKJTa7nA4sNlseDweWlpa8Hg8hMNh2tra6OnpoaGhYdZjcrlcDA4OpjISyuWYenp6qK+vz3hMc/2cSnFMTqcTj8dj6Oe00MeUdC7muWf0mDweDy6XC6010WgUm81GOBxGKYXZbCYSiWA2m9FaE4vFsFqtJ21PTnGkbzeZTKmpiGQM0FpP266USq0Rmc12iE+nhMPhVDZHJBLBZrOlRrbZOpfqmABisRgWiwW32004HJ72c+ru7s5YTjabAH4zsGlGAL9Ia31nWpvXE23SA/hFWuuR9H3lUw/cfWyQXT/5Nf5omBv+6oM5vbeUSKj7PBNpztJ8QYZzpR74wmKkHng2FzF7gfTbnzqB/gxtepVSFsAJFKTog+u0VjZ94lbGxsYKsbsFY+YPQQLSnKX5gjzncsgDzxVpzkZ8s5kD3wksV0otVUrZgFuAR2a0eQT448Tjm4Bf6wIXfTiVCtSUCmnO0nxBnrPE2i3SnI34zhvAtdYR4A7gMWAv8KDW+nWl1OeVUtclmv0n0KyUOgh8Avh03kYZ8Pl8hd5lUZHmC/KcpfmCDGelFKFQCGBaqp4UpDknfUOhUM4XubPKA9dabwe2z3jtc2mPg8DNOX1yjkhbDFaaL8hzluYLMpyT6XDBYJBoNMrU1FSplXJCmnPSVymVc6kFEbfSAwwMDJT9xZ90pPmCPGdpviDDWSmVuvlFwkXXmUhzNuIr4lZ6gJ/97GelVsgJab4gz1maL8hzluYL8pyN+IoJ4LMlxpcz0nxBnrM0X5DnLM0X5Dkb8RUTwMv1lt5MSPMFec7SfEGeszRfkOdsxHfeG3kKyZNPPukGuvN57+joaEtTU9Pw/C3LA2m+IM9Zmi/Ic5bmC/Kcs/BdsnHjxlmXclrQAF6hQoUKFQqHmCmUChUqVKgwnUoAr1ChQgWhlF0AV0ptVkrtU0odVEqddEenUqpKKfWjxPYXlVKnL7zlNJ/5fG9TSrmVUrsTXx8qhWeaz3eUUkOJCpKzbVdKqX9LHM8rSqkLFtpxhs98vpcrpTxp/fu52dotJEqpLqXUU0qpvUqp15VSfzFLm7Lp5yx9y6qflVJ2pdRLSqk9Cee/n6VN2cSKLH1zjxVa67L5Ir7izyHgDMAG7AFWzWjz58A3E49vAX5U5r63AXeXum/TfNYDFwCvZdj+DuBRQAGXAC+Wue/lwC9K3a8znNqBCxKP64D9s5wXZdPPWfqWVT8n+q028dgKvAhcMqNNOcWKbHxzjhXlNgIv6eIReZCNb1mhtX6WuStFXg/cr+O8ADQopdoXxu5ksvAtO7TWx7XWuxKPJ4nXEFo8o1nZ9HOWvmVFot+Sy99YE18zMzLKJlZk6Zsz5RbAFwM9ac97OflESrXR8UJbHqB5QexOJhtfgBsT/yb/RCnVNcv2ciLbYyon1ib+NX1UKXV2qWXSSfzbfj7xEVc6ZdnPc/hCmfWzUsqslNoNDAFPaK0z9nEZxIpsfCHHWFFuAXy2v44z/0pl02ahyMblv4HTtdbnAb/ixIigXCmn/s2GXcASrfVq4OtA2dxHrZSqBX4KfExrPTFz8yxvKWk/z+Nbdv2stY5qrdcQX6PgIqXUOTOalFUfZ+Gbc6wotwCey+IRFHrxiDyY11drPaK1TpZG+xbw1gVyy5dsfgZlg9Z6IvmvqY5XzbQqpVpKrIVSyko8GP6X1nq2e6XLqp/n8y3XfgbQWo8DTwObZ2wqp1iRIpNvPrGi3AJ4WSwekQPz+s6Y17yO+PxiOfMI8MFElsQlgEdrfbzUUplQSrUl5zWVUhcRP6dH5n5X0Z0U8Rr5e7XW/5yhWdn0cza+5dbPSimXUqrh/7V3hzoRA1EUhv+bVesxGF4Ei0IjMCQg1/AAGF5jFQlBkCAJj4HDIJBo5GKaXMTtGtSA6Uzyf6qi4mTSnEmm7cx8vQZOgPdft3XTFS15/9MVXW0nm5lTROwPj1gBdzkfHgG8ZuYz9aA9RB0e8UWVZs95r6MOvpjmvJdL5QWIiEfqi4KDiPgEbqkXKmTmltr3/RT4AHbA1TJJS0PeM2ATERPwDZwvOKHvHQMXwNu85glwAxxBl+Pckre3cT4E7iNiRU0mT5n50mtX0Jb3z13hr/SSNKjellAkSY0scEkalAUuSYOywCVpUBa4JA3KApekQVngkjQoC1ySBvUD/qPC8c53ppEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = df.start.dt.month.unique()\n",
    "functions = {}\n",
    "itps = {}\n",
    "for month in months:\n",
    "    df_month = df[df.start.dt.month == month]    \n",
    "    itps[month] = cdf(df_month.RADAR.values,df_month.Pluviometro.values)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>start</th>\n",
       "      <th>station</th>\n",
       "      <th>duration</th>\n",
       "      <th>Pluviometro</th>\n",
       "      <th>RADAR</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>CDF_CORR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13 08:30:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.214636</td>\n",
       "      <td>0.035364</td>\n",
       "      <td>0.9643906593531182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-14 07:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.594438</td>\n",
       "      <td>-0.344438</td>\n",
       "      <td>0.9602440682006892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-14 08:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.569860</td>\n",
       "      <td>1.680140</td>\n",
       "      <td>1.1097719013563594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-14 09:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.367961</td>\n",
       "      <td>2.632039</td>\n",
       "      <td>1.4039279815681456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-14 11:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.274750</td>\n",
       "      <td>-0.024750</td>\n",
       "      <td>0.9614528539858156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                start   station duration  Pluviometro     RADAR     ERROR  \\\n",
       "1 2016-10-13 08:30:00  MB_ANHA1 00:10:00         0.25  0.214636  0.035364   \n",
       "2 2016-10-14 07:20:00  MB_ANHA1 00:10:00         0.25  0.594438 -0.344438   \n",
       "3 2016-10-14 08:10:00  MB_ANHA1 00:50:00         3.25  1.569860  1.680140   \n",
       "4 2016-10-14 09:10:00  MB_ANHA1 01:40:00         5.00  2.367961  2.632039   \n",
       "6 2016-10-14 11:40:00  MB_ANHA1 00:10:00         0.25  0.274750 -0.024750   \n",
       "\n",
       "             CDF_CORR  \n",
       "1  0.9643906593531182  \n",
       "2  0.9602440682006892  \n",
       "3  1.1097719013563594  \n",
       "4  1.4039279815681456  \n",
       "6  0.9614528539858156  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.apply(correction,args=[itps],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>start</th>\n",
       "      <th>station</th>\n",
       "      <th>duration</th>\n",
       "      <th>Pluviometro</th>\n",
       "      <th>RADAR</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>CDF_CORR</th>\n",
       "      <th>CDF_ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13 08:30:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.214636</td>\n",
       "      <td>0.035364</td>\n",
       "      <td>0.9643906593531182</td>\n",
       "      <td>-0.714391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-14 07:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.594438</td>\n",
       "      <td>-0.344438</td>\n",
       "      <td>0.9602440682006892</td>\n",
       "      <td>-0.710244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-14 08:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.569860</td>\n",
       "      <td>1.680140</td>\n",
       "      <td>1.1097719013563594</td>\n",
       "      <td>2.14023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-14 09:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.367961</td>\n",
       "      <td>2.632039</td>\n",
       "      <td>1.4039279815681456</td>\n",
       "      <td>3.59607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-14 11:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.274750</td>\n",
       "      <td>-0.024750</td>\n",
       "      <td>0.9614528539858156</td>\n",
       "      <td>-0.711453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                start   station duration  Pluviometro     RADAR     ERROR  \\\n",
       "1 2016-10-13 08:30:00  MB_ANHA1 00:10:00         0.25  0.214636  0.035364   \n",
       "2 2016-10-14 07:20:00  MB_ANHA1 00:10:00         0.25  0.594438 -0.344438   \n",
       "3 2016-10-14 08:10:00  MB_ANHA1 00:50:00         3.25  1.569860  1.680140   \n",
       "4 2016-10-14 09:10:00  MB_ANHA1 01:40:00         5.00  2.367961  2.632039   \n",
       "6 2016-10-14 11:40:00  MB_ANHA1 00:10:00         0.25  0.274750 -0.024750   \n",
       "\n",
       "             CDF_CORR CDF_ERROR  \n",
       "1  0.9643906593531182 -0.714391  \n",
       "2  0.9602440682006892 -0.710244  \n",
       "3  1.1097719013563594   2.14023  \n",
       "4  1.4039279815681456   3.59607  \n",
       "6  0.9614528539858156 -0.711453  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"CDF_ERROR\"] = df.Pluviometro - df.CDF_CORR\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[[\"CDF_CORR\", \"CDF_ERROR\"]] = df[[\"CDF_CORR\", \"CDF_ERROR\"]].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>duration</th>\n",
       "      <th>Pluviometro</th>\n",
       "      <th>RADAR</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>CDF_CORR</th>\n",
       "      <th>CDF_ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8544</td>\n",
       "      <td>8544.000000</td>\n",
       "      <td>8544.000000</td>\n",
       "      <td>8544.000000</td>\n",
       "      <td>8.544000e+03</td>\n",
       "      <td>8.544000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0 days 00:36:18.230337</td>\n",
       "      <td>2.682317</td>\n",
       "      <td>1.705295</td>\n",
       "      <td>0.977022</td>\n",
       "      <td>-2.509249e+07</td>\n",
       "      <td>2.509249e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0 days 00:47:43.212458</td>\n",
       "      <td>6.951322</td>\n",
       "      <td>5.194622</td>\n",
       "      <td>5.869782</td>\n",
       "      <td>9.025687e+08</td>\n",
       "      <td>9.025687e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0 days 00:10:00</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-53.711793</td>\n",
       "      <td>-4.067672e+10</td>\n",
       "      <td>-1.700031e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0 days 00:10:00</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.037430</td>\n",
       "      <td>-0.030878</td>\n",
       "      <td>1.348555e+00</td>\n",
       "      <td>-1.562064e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0 days 00:20:00</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.162219</td>\n",
       "      <td>0.174438</td>\n",
       "      <td>1.760624e+00</td>\n",
       "      <td>-9.053916e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0 days 00:40:00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.853044</td>\n",
       "      <td>0.820891</td>\n",
       "      <td>2.010928e+00</td>\n",
       "      <td>2.742566e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0 days 17:00:00</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>77.127213</td>\n",
       "      <td>142.990000</td>\n",
       "      <td>1.700085e+06</td>\n",
       "      <td>4.067672e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     duration  Pluviometro        RADAR        ERROR  \\\n",
       "count                    8544  8544.000000  8544.000000  8544.000000   \n",
       "mean   0 days 00:36:18.230337     2.682317     1.705295     0.977022   \n",
       "std    0 days 00:47:43.212458     6.951322     5.194622     5.869782   \n",
       "min           0 days 00:10:00     0.015625     0.010000   -53.711793   \n",
       "25%           0 days 00:10:00     0.187500     0.037430    -0.030878   \n",
       "50%           0 days 00:20:00     0.375000     0.162219     0.174438   \n",
       "75%           0 days 00:40:00     2.000000     0.853044     0.820891   \n",
       "max           0 days 17:00:00   143.000000    77.127213   142.990000   \n",
       "\n",
       "           CDF_CORR     CDF_ERROR  \n",
       "count  8.544000e+03  8.544000e+03  \n",
       "mean  -2.509249e+07  2.509249e+07  \n",
       "std    9.025687e+08  9.025687e+08  \n",
       "min   -4.067672e+10 -1.700031e+06  \n",
       "25%    1.348555e+00 -1.562064e+00  \n",
       "50%    1.760624e+00 -9.053916e-01  \n",
       "75%    2.010928e+00  2.742566e-01  \n",
       "max    1.700085e+06  4.067672e+10  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>start</th>\n",
       "      <th>station</th>\n",
       "      <th>duration</th>\n",
       "      <th>Pluviometro</th>\n",
       "      <th>RADAR</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>CDF_CORR</th>\n",
       "      <th>CDF_ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13 08:30:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.214636</td>\n",
       "      <td>0.035364</td>\n",
       "      <td>0.964391</td>\n",
       "      <td>-0.714391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-14 07:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.594438</td>\n",
       "      <td>-0.344438</td>\n",
       "      <td>0.960244</td>\n",
       "      <td>-0.710244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-14 08:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>3.2500</td>\n",
       "      <td>1.569860</td>\n",
       "      <td>1.680140</td>\n",
       "      <td>1.109772</td>\n",
       "      <td>2.140228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-14 09:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>2.367961</td>\n",
       "      <td>2.632039</td>\n",
       "      <td>1.403928</td>\n",
       "      <td>3.596072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-14 11:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.274750</td>\n",
       "      <td>-0.024750</td>\n",
       "      <td>0.961453</td>\n",
       "      <td>-0.711453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-10-14 12:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.121554</td>\n",
       "      <td>0.128446</td>\n",
       "      <td>0.970610</td>\n",
       "      <td>-0.720610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-10-14 12:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>0.625225</td>\n",
       "      <td>0.624775</td>\n",
       "      <td>0.961421</td>\n",
       "      <td>0.288579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-10-20 16:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>0.880495</td>\n",
       "      <td>10.119505</td>\n",
       "      <td>0.980026</td>\n",
       "      <td>10.019974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2016-10-20 19:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:50:00</td>\n",
       "      <td>8.2500</td>\n",
       "      <td>3.004278</td>\n",
       "      <td>5.245722</td>\n",
       "      <td>1.745019</td>\n",
       "      <td>6.504981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2016-10-20 21:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.011158</td>\n",
       "      <td>0.238842</td>\n",
       "      <td>0.980595</td>\n",
       "      <td>-0.730595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016-10-23 14:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:30:00</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>4.680203</td>\n",
       "      <td>16.319797</td>\n",
       "      <td>3.046420</td>\n",
       "      <td>17.953580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016-10-26 00:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.118274</td>\n",
       "      <td>0.881726</td>\n",
       "      <td>0.970866</td>\n",
       "      <td>0.029134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016-10-26 01:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:30:00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.105403</td>\n",
       "      <td>1.894597</td>\n",
       "      <td>0.971895</td>\n",
       "      <td>1.028105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-10-26 16:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>03:10:00</td>\n",
       "      <td>28.2500</td>\n",
       "      <td>8.459200</td>\n",
       "      <td>19.790800</td>\n",
       "      <td>7.628728</td>\n",
       "      <td>20.621272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-10-27 00:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.011080</td>\n",
       "      <td>0.238920</td>\n",
       "      <td>0.980603</td>\n",
       "      <td>-0.730603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-10-27 02:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>0.190539</td>\n",
       "      <td>1.059461</td>\n",
       "      <td>0.965807</td>\n",
       "      <td>0.284193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2016-10-31 14:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:30:00</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0.034825</td>\n",
       "      <td>6.965175</td>\n",
       "      <td>0.978218</td>\n",
       "      <td>6.021782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-11-02 10:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:10:00</td>\n",
       "      <td>10.7500</td>\n",
       "      <td>1.616263</td>\n",
       "      <td>9.133737</td>\n",
       "      <td>1.700235</td>\n",
       "      <td>9.049765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016-11-02 11:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>2.093310</td>\n",
       "      <td>-1.593310</td>\n",
       "      <td>1.805346</td>\n",
       "      <td>-1.305346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2016-11-02 12:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>3.286372</td>\n",
       "      <td>7.213628</td>\n",
       "      <td>2.177835</td>\n",
       "      <td>8.322165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2016-11-02 14:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.031870</td>\n",
       "      <td>0.218130</td>\n",
       "      <td>1.538324</td>\n",
       "      <td>-1.288324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2016-11-02 15:00:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.489188</td>\n",
       "      <td>-0.239188</td>\n",
       "      <td>1.555167</td>\n",
       "      <td>-1.305167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2016-11-02 15:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>0.363403</td>\n",
       "      <td>0.886597</td>\n",
       "      <td>1.548104</td>\n",
       "      <td>-0.298104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2016-11-02 16:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.333862</td>\n",
       "      <td>0.166138</td>\n",
       "      <td>1.546712</td>\n",
       "      <td>-1.046712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2016-11-02 16:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.229033</td>\n",
       "      <td>0.020967</td>\n",
       "      <td>1.542596</td>\n",
       "      <td>-1.292596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2016-11-02 17:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.260965</td>\n",
       "      <td>-0.010965</td>\n",
       "      <td>1.543714</td>\n",
       "      <td>-1.293714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2016-11-02 17:40:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.179419</td>\n",
       "      <td>0.820581</td>\n",
       "      <td>1.541094</td>\n",
       "      <td>-0.541094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2016-11-12 12:10:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>1.7500</td>\n",
       "      <td>0.027227</td>\n",
       "      <td>1.722773</td>\n",
       "      <td>1.538278</td>\n",
       "      <td>0.211722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2016-11-13 01:20:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:30:00</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>1.108355</td>\n",
       "      <td>-0.358355</td>\n",
       "      <td>1.616730</td>\n",
       "      <td>-0.866730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2016-11-13 13:50:00</td>\n",
       "      <td>MB_ANHA1</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.071295</td>\n",
       "      <td>0.428705</td>\n",
       "      <td>1.538816</td>\n",
       "      <td>-1.038816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20955</th>\n",
       "      <td>2018-02-01 00:20:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.011566</td>\n",
       "      <td>0.113434</td>\n",
       "      <td>2.016637</td>\n",
       "      <td>-1.891637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20963</th>\n",
       "      <td>2018-02-12 15:20:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>0.039762</td>\n",
       "      <td>1.210238</td>\n",
       "      <td>2.006953</td>\n",
       "      <td>-0.756953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20964</th>\n",
       "      <td>2018-02-12 18:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>3.7500</td>\n",
       "      <td>0.047004</td>\n",
       "      <td>3.702996</td>\n",
       "      <td>2.004485</td>\n",
       "      <td>1.745515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20965</th>\n",
       "      <td>2018-02-12 21:10:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.020111</td>\n",
       "      <td>0.042389</td>\n",
       "      <td>2.013690</td>\n",
       "      <td>-1.951190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20966</th>\n",
       "      <td>2018-02-12 21:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.047157</td>\n",
       "      <td>0.015343</td>\n",
       "      <td>2.004433</td>\n",
       "      <td>-1.941933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20967</th>\n",
       "      <td>2018-02-12 23:20:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.013501</td>\n",
       "      <td>0.048999</td>\n",
       "      <td>2.015969</td>\n",
       "      <td>-1.953469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20969</th>\n",
       "      <td>2018-02-13 18:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>01:40:00</td>\n",
       "      <td>8.6875</td>\n",
       "      <td>0.915875</td>\n",
       "      <td>7.771625</td>\n",
       "      <td>1.764981</td>\n",
       "      <td>6.922519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20972</th>\n",
       "      <td>2018-02-14 00:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.031972</td>\n",
       "      <td>0.280528</td>\n",
       "      <td>2.009617</td>\n",
       "      <td>-1.697117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20973</th>\n",
       "      <td>2018-02-18 01:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>01:30:00</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.337558</td>\n",
       "      <td>0.537442</td>\n",
       "      <td>1.911842</td>\n",
       "      <td>-1.036842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20976</th>\n",
       "      <td>2018-02-18 20:00:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.010613</td>\n",
       "      <td>0.676887</td>\n",
       "      <td>2.016967</td>\n",
       "      <td>-1.329467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20978</th>\n",
       "      <td>2018-02-18 21:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>01:10:00</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.748687</td>\n",
       "      <td>-0.186187</td>\n",
       "      <td>1.802264</td>\n",
       "      <td>-1.239764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20979</th>\n",
       "      <td>2018-02-19 00:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.074832</td>\n",
       "      <td>-0.012332</td>\n",
       "      <td>1.995072</td>\n",
       "      <td>-1.932572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20988</th>\n",
       "      <td>2018-02-19 20:00:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.015757</td>\n",
       "      <td>0.484243</td>\n",
       "      <td>2.015191</td>\n",
       "      <td>-1.515191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20990</th>\n",
       "      <td>2018-02-20 07:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>7.090017</td>\n",
       "      <td>-7.027517</td>\n",
       "      <td>3.214050</td>\n",
       "      <td>-3.151550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20991</th>\n",
       "      <td>2018-02-20 08:00:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>02:50:00</td>\n",
       "      <td>8.1250</td>\n",
       "      <td>19.262949</td>\n",
       "      <td>-11.137949</td>\n",
       "      <td>32.950408</td>\n",
       "      <td>-24.825408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20992</th>\n",
       "      <td>2018-02-20 11:20:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>02:30:00</td>\n",
       "      <td>5.8750</td>\n",
       "      <td>7.265746</td>\n",
       "      <td>-1.390746</td>\n",
       "      <td>3.325756</td>\n",
       "      <td>2.549244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20993</th>\n",
       "      <td>2018-02-20 14:10:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>02:40:00</td>\n",
       "      <td>1.7500</td>\n",
       "      <td>13.832142</td>\n",
       "      <td>-12.082142</td>\n",
       "      <td>9.367721</td>\n",
       "      <td>-7.617721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20995</th>\n",
       "      <td>2018-02-20 17:50:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>02:10:00</td>\n",
       "      <td>1.6250</td>\n",
       "      <td>0.078330</td>\n",
       "      <td>1.546670</td>\n",
       "      <td>1.993897</td>\n",
       "      <td>-0.368897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20999</th>\n",
       "      <td>2018-02-25 18:00:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.275681</td>\n",
       "      <td>0.036819</td>\n",
       "      <td>1.930523</td>\n",
       "      <td>-1.618023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21000</th>\n",
       "      <td>2018-02-25 19:10:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.421042</td>\n",
       "      <td>0.266458</td>\n",
       "      <td>1.887542</td>\n",
       "      <td>-1.200042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21001</th>\n",
       "      <td>2018-02-25 21:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>1.512467</td>\n",
       "      <td>-1.199967</td>\n",
       "      <td>1.666509</td>\n",
       "      <td>-1.354009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21002</th>\n",
       "      <td>2018-02-25 22:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.484492</td>\n",
       "      <td>-0.421992</td>\n",
       "      <td>1.869770</td>\n",
       "      <td>-1.807270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21003</th>\n",
       "      <td>2018-02-25 22:50:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>1.1875</td>\n",
       "      <td>0.874432</td>\n",
       "      <td>0.313068</td>\n",
       "      <td>1.773829</td>\n",
       "      <td>-0.586329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21004</th>\n",
       "      <td>2018-02-25 23:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>0.3125</td>\n",
       "      <td>0.013096</td>\n",
       "      <td>0.299404</td>\n",
       "      <td>2.016109</td>\n",
       "      <td>-1.703609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21006</th>\n",
       "      <td>2018-02-26 01:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:50:00</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.420485</td>\n",
       "      <td>0.142015</td>\n",
       "      <td>1.887701</td>\n",
       "      <td>-1.325201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21007</th>\n",
       "      <td>2018-02-26 02:40:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.161500</td>\n",
       "      <td>0.276000</td>\n",
       "      <td>1.966487</td>\n",
       "      <td>-1.528987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21009</th>\n",
       "      <td>2018-02-26 07:00:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.0625</td>\n",
       "      <td>0.023392</td>\n",
       "      <td>0.039108</td>\n",
       "      <td>2.012561</td>\n",
       "      <td>-1.950061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21011</th>\n",
       "      <td>2018-02-26 15:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.012722</td>\n",
       "      <td>0.112278</td>\n",
       "      <td>2.016238</td>\n",
       "      <td>-1.891238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21016</th>\n",
       "      <td>2018-03-10 19:10:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:40:00</td>\n",
       "      <td>3.3125</td>\n",
       "      <td>0.026665</td>\n",
       "      <td>3.285835</td>\n",
       "      <td>1.408804</td>\n",
       "      <td>1.903696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21017</th>\n",
       "      <td>2018-03-10 20:30:00</td>\n",
       "      <td>MB_SEG9</td>\n",
       "      <td>00:20:00</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.017507</td>\n",
       "      <td>0.107493</td>\n",
       "      <td>1.410844</td>\n",
       "      <td>-1.285844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8544 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    start   station duration  Pluviometro      RADAR  \\\n",
       "1     2016-10-13 08:30:00  MB_ANHA1 00:10:00       0.2500   0.214636   \n",
       "2     2016-10-14 07:20:00  MB_ANHA1 00:10:00       0.2500   0.594438   \n",
       "3     2016-10-14 08:10:00  MB_ANHA1 00:50:00       3.2500   1.569860   \n",
       "4     2016-10-14 09:10:00  MB_ANHA1 01:40:00       5.0000   2.367961   \n",
       "6     2016-10-14 11:40:00  MB_ANHA1 00:10:00       0.2500   0.274750   \n",
       "7     2016-10-14 12:00:00  MB_ANHA1 00:10:00       0.2500   0.121554   \n",
       "8     2016-10-14 12:40:00  MB_ANHA1 00:20:00       1.2500   0.625225   \n",
       "9     2016-10-20 16:00:00  MB_ANHA1 00:50:00      11.0000   0.880495   \n",
       "11    2016-10-20 19:00:00  MB_ANHA1 01:50:00       8.2500   3.004278   \n",
       "12    2016-10-20 21:00:00  MB_ANHA1 00:10:00       0.2500   0.011158   \n",
       "14    2016-10-23 14:40:00  MB_ANHA1 01:30:00      21.0000   4.680203   \n",
       "16    2016-10-26 00:40:00  MB_ANHA1 00:10:00       1.0000   0.118274   \n",
       "17    2016-10-26 01:00:00  MB_ANHA1 00:30:00       2.0000   0.105403   \n",
       "19    2016-10-26 16:50:00  MB_ANHA1 03:10:00      28.2500   8.459200   \n",
       "21    2016-10-27 00:50:00  MB_ANHA1 00:10:00       0.2500   0.011080   \n",
       "22    2016-10-27 02:40:00  MB_ANHA1 00:40:00       1.2500   0.190539   \n",
       "25    2016-10-31 14:20:00  MB_ANHA1 00:30:00       7.0000   0.034825   \n",
       "28    2016-11-02 10:20:00  MB_ANHA1 01:10:00      10.7500   1.616263   \n",
       "29    2016-11-02 11:40:00  MB_ANHA1 00:20:00       0.5000   2.093310   \n",
       "30    2016-11-02 12:10:00  MB_ANHA1 01:40:00      10.5000   3.286372   \n",
       "31    2016-11-02 14:10:00  MB_ANHA1 00:10:00       0.2500   0.031870   \n",
       "32    2016-11-02 15:00:00  MB_ANHA1 00:10:00       0.2500   0.489188   \n",
       "33    2016-11-02 15:50:00  MB_ANHA1 00:20:00       1.2500   0.363403   \n",
       "34    2016-11-02 16:20:00  MB_ANHA1 00:20:00       0.5000   0.333862   \n",
       "35    2016-11-02 16:50:00  MB_ANHA1 00:10:00       0.2500   0.229033   \n",
       "36    2016-11-02 17:10:00  MB_ANHA1 00:10:00       0.2500   0.260965   \n",
       "37    2016-11-02 17:40:00  MB_ANHA1 00:40:00       1.0000   0.179419   \n",
       "40    2016-11-12 12:10:00  MB_ANHA1 01:00:00       1.7500   0.027227   \n",
       "42    2016-11-13 01:20:00  MB_ANHA1 00:30:00       0.7500   1.108355   \n",
       "45    2016-11-13 13:50:00  MB_ANHA1 00:20:00       0.5000   0.071295   \n",
       "...                   ...       ...      ...          ...        ...   \n",
       "20955 2018-02-01 00:20:00   MB_SEG9 00:10:00       0.1250   0.011566   \n",
       "20963 2018-02-12 15:20:00   MB_SEG9 00:40:00       1.2500   0.039762   \n",
       "20964 2018-02-12 18:30:00   MB_SEG9 00:50:00       3.7500   0.047004   \n",
       "20965 2018-02-12 21:10:00   MB_SEG9 00:10:00       0.0625   0.020111   \n",
       "20966 2018-02-12 21:30:00   MB_SEG9 00:10:00       0.0625   0.047157   \n",
       "20967 2018-02-12 23:20:00   MB_SEG9 00:10:00       0.0625   0.013501   \n",
       "20969 2018-02-13 18:40:00   MB_SEG9 01:40:00       8.6875   0.915875   \n",
       "20972 2018-02-14 00:40:00   MB_SEG9 00:40:00       0.3125   0.031972   \n",
       "20973 2018-02-18 01:30:00   MB_SEG9 01:30:00       0.8750   0.337558   \n",
       "20976 2018-02-18 20:00:00   MB_SEG9 00:50:00       0.6875   0.010613   \n",
       "20978 2018-02-18 21:30:00   MB_SEG9 01:10:00       0.5625   0.748687   \n",
       "20979 2018-02-19 00:30:00   MB_SEG9 00:10:00       0.0625   0.074832   \n",
       "20988 2018-02-19 20:00:00   MB_SEG9 00:20:00       0.5000   0.015757   \n",
       "20990 2018-02-20 07:40:00   MB_SEG9 00:10:00       0.0625   7.090017   \n",
       "20991 2018-02-20 08:00:00   MB_SEG9 02:50:00       8.1250  19.262949   \n",
       "20992 2018-02-20 11:20:00   MB_SEG9 02:30:00       5.8750   7.265746   \n",
       "20993 2018-02-20 14:10:00   MB_SEG9 02:40:00       1.7500  13.832142   \n",
       "20995 2018-02-20 17:50:00   MB_SEG9 02:10:00       1.6250   0.078330   \n",
       "20999 2018-02-25 18:00:00   MB_SEG9 00:20:00       0.3125   0.275681   \n",
       "21000 2018-02-25 19:10:00   MB_SEG9 00:20:00       0.6875   0.421042   \n",
       "21001 2018-02-25 21:40:00   MB_SEG9 00:20:00       0.3125   1.512467   \n",
       "21002 2018-02-25 22:30:00   MB_SEG9 00:10:00       0.0625   0.484492   \n",
       "21003 2018-02-25 22:50:00   MB_SEG9 00:40:00       1.1875   0.874432   \n",
       "21004 2018-02-25 23:40:00   MB_SEG9 00:40:00       0.3125   0.013096   \n",
       "21006 2018-02-26 01:40:00   MB_SEG9 00:50:00       0.5625   0.420485   \n",
       "21007 2018-02-26 02:40:00   MB_SEG9 01:00:00       0.4375   0.161500   \n",
       "21009 2018-02-26 07:00:00   MB_SEG9 00:10:00       0.0625   0.023392   \n",
       "21011 2018-02-26 15:30:00   MB_SEG9 00:20:00       0.1250   0.012722   \n",
       "21016 2018-03-10 19:10:00   MB_SEG9 00:40:00       3.3125   0.026665   \n",
       "21017 2018-03-10 20:30:00   MB_SEG9 00:20:00       0.1250   0.017507   \n",
       "\n",
       "           ERROR   CDF_CORR  CDF_ERROR  \n",
       "1       0.035364   0.964391  -0.714391  \n",
       "2      -0.344438   0.960244  -0.710244  \n",
       "3       1.680140   1.109772   2.140228  \n",
       "4       2.632039   1.403928   3.596072  \n",
       "6      -0.024750   0.961453  -0.711453  \n",
       "7       0.128446   0.970610  -0.720610  \n",
       "8       0.624775   0.961421   0.288579  \n",
       "9      10.119505   0.980026  10.019974  \n",
       "11      5.245722   1.745019   6.504981  \n",
       "12      0.238842   0.980595  -0.730595  \n",
       "14     16.319797   3.046420  17.953580  \n",
       "16      0.881726   0.970866   0.029134  \n",
       "17      1.894597   0.971895   1.028105  \n",
       "19     19.790800   7.628728  20.621272  \n",
       "21      0.238920   0.980603  -0.730603  \n",
       "22      1.059461   0.965807   0.284193  \n",
       "25      6.965175   0.978218   6.021782  \n",
       "28      9.133737   1.700235   9.049765  \n",
       "29     -1.593310   1.805346  -1.305346  \n",
       "30      7.213628   2.177835   8.322165  \n",
       "31      0.218130   1.538324  -1.288324  \n",
       "32     -0.239188   1.555167  -1.305167  \n",
       "33      0.886597   1.548104  -0.298104  \n",
       "34      0.166138   1.546712  -1.046712  \n",
       "35      0.020967   1.542596  -1.292596  \n",
       "36     -0.010965   1.543714  -1.293714  \n",
       "37      0.820581   1.541094  -0.541094  \n",
       "40      1.722773   1.538278   0.211722  \n",
       "42     -0.358355   1.616730  -0.866730  \n",
       "45      0.428705   1.538816  -1.038816  \n",
       "...          ...        ...        ...  \n",
       "20955   0.113434   2.016637  -1.891637  \n",
       "20963   1.210238   2.006953  -0.756953  \n",
       "20964   3.702996   2.004485   1.745515  \n",
       "20965   0.042389   2.013690  -1.951190  \n",
       "20966   0.015343   2.004433  -1.941933  \n",
       "20967   0.048999   2.015969  -1.953469  \n",
       "20969   7.771625   1.764981   6.922519  \n",
       "20972   0.280528   2.009617  -1.697117  \n",
       "20973   0.537442   1.911842  -1.036842  \n",
       "20976   0.676887   2.016967  -1.329467  \n",
       "20978  -0.186187   1.802264  -1.239764  \n",
       "20979  -0.012332   1.995072  -1.932572  \n",
       "20988   0.484243   2.015191  -1.515191  \n",
       "20990  -7.027517   3.214050  -3.151550  \n",
       "20991 -11.137949  32.950408 -24.825408  \n",
       "20992  -1.390746   3.325756   2.549244  \n",
       "20993 -12.082142   9.367721  -7.617721  \n",
       "20995   1.546670   1.993897  -0.368897  \n",
       "20999   0.036819   1.930523  -1.618023  \n",
       "21000   0.266458   1.887542  -1.200042  \n",
       "21001  -1.199967   1.666509  -1.354009  \n",
       "21002  -0.421992   1.869770  -1.807270  \n",
       "21003   0.313068   1.773829  -0.586329  \n",
       "21004   0.299404   2.016109  -1.703609  \n",
       "21006   0.142015   1.887701  -1.325201  \n",
       "21007   0.276000   1.966487  -1.528987  \n",
       "21009   0.039108   2.012561  -1.950061  \n",
       "21011   0.112278   2.016238  -1.891238  \n",
       "21016   3.285835   1.408804   1.903696  \n",
       "21017   0.107493   1.410844  -1.285844  \n",
       "\n",
       "[8544 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "kernelspec": {
   "display_name": "Python [conda env:ic]",
   "language": "python",
   "name": "conda-env-ic-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}