takotab/nice working seq to fastai tab with rand data.ipynb

## nice working seq to fastai tab with rand data.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "n, m, timesteps = 10000, 10, 60 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f64e1380828>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# own fake data\n",
    "org_dct = {str(m_i):\n",
    "            (np.sin(np.arange(n*timesteps)*(np.random.randn())-np.random.randn()*100)+(np.random.randn(n*timesteps))*.1)\n",
    "           for m_i in range(m)\n",
    "          }\n",
    "df = pd.DataFrame().from_dict(org_dct)\n",
    "del org_dct\n",
    "df.iloc[:150,0].plot(marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.114446</td>\n",
       "      <td>-0.592898</td>\n",
       "      <td>0.204955</td>\n",
       "      <td>0.505713</td>\n",
       "      <td>-0.961224</td>\n",
       "      <td>-0.122878</td>\n",
       "      <td>-0.314546</td>\n",
       "      <td>-0.187950</td>\n",
       "      <td>-0.015137</td>\n",
       "      <td>-0.296673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.600313</td>\n",
       "      <td>-0.946403</td>\n",
       "      <td>0.589096</td>\n",
       "      <td>0.917003</td>\n",
       "      <td>-0.942033</td>\n",
       "      <td>0.877854</td>\n",
       "      <td>-0.380102</td>\n",
       "      <td>0.508302</td>\n",
       "      <td>1.012249</td>\n",
       "      <td>-0.883460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.006571</td>\n",
       "      <td>-0.334670</td>\n",
       "      <td>0.626638</td>\n",
       "      <td>0.778315</td>\n",
       "      <td>-0.877141</td>\n",
       "      <td>0.191343</td>\n",
       "      <td>-0.550421</td>\n",
       "      <td>0.564890</td>\n",
       "      <td>0.149244</td>\n",
       "      <td>-0.139091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.585057</td>\n",
       "      <td>0.583154</td>\n",
       "      <td>0.871155</td>\n",
       "      <td>0.226498</td>\n",
       "      <td>-0.518058</td>\n",
       "      <td>-1.120305</td>\n",
       "      <td>-0.699760</td>\n",
       "      <td>0.879472</td>\n",
       "      <td>-1.013508</td>\n",
       "      <td>0.733990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.034596</td>\n",
       "      <td>1.084056</td>\n",
       "      <td>0.882731</td>\n",
       "      <td>-0.612957</td>\n",
       "      <td>-0.114770</td>\n",
       "      <td>-0.379697</td>\n",
       "      <td>-0.904492</td>\n",
       "      <td>1.114850</td>\n",
       "      <td>0.155097</td>\n",
       "      <td>0.436419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  1.114446 -0.592898  0.204955  0.505713 -0.961224 -0.122878 -0.314546   \n",
       "1  0.600313 -0.946403  0.589096  0.917003 -0.942033  0.877854 -0.380102   \n",
       "2 -0.006571 -0.334670  0.626638  0.778315 -0.877141  0.191343 -0.550421   \n",
       "3 -0.585057  0.583154  0.871155  0.226498 -0.518058 -1.120305 -0.699760   \n",
       "4 -1.034596  1.084056  0.882731 -0.612957 -0.114770 -0.379697 -0.904492   \n",
       "\n",
       "          7         8         9  \n",
       "0 -0.187950 -0.015137 -0.296673  \n",
       "1  0.508302  1.012249 -0.883460  \n",
       "2  0.564890  0.149244 -0.139091  \n",
       "3  0.879472 -1.013508  0.733990  \n",
       "4  1.114850  0.155097  0.436419  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 600)\n"
     ]
    },
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <td>-0.337760</td>\n",
       "      <td>-0.897698</td>\n",
       "      <td>0.060898</td>\n",
       "      <td>0.842003</td>\n",
       "      <td>0.325851</td>\n",
       "      <td>-0.902178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.913026</td>\n",
       "      <td>-0.734200</td>\n",
       "      <td>-0.041426</td>\n",
       "      <td>0.777901</td>\n",
       "      <td>0.973380</td>\n",
       "      <td>0.653314</td>\n",
       "      <td>-0.116416</td>\n",
       "      <td>-0.720925</td>\n",
       "      <td>-0.874417</td>\n",
       "      <td>-0.497024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192062</td>\n",
       "      <td>0.950734</td>\n",
       "      <td>0.104042</td>\n",
       "      <td>-0.722278</td>\n",
       "      <td>-0.538008</td>\n",
       "      <td>0.831013</td>\n",
       "      <td>0.640009</td>\n",
       "      <td>-0.489455</td>\n",
       "      <td>-0.882031</td>\n",
       "      <td>0.216450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.982462</td>\n",
       "      <td>0.591904</td>\n",
       "      <td>-0.104168</td>\n",
       "      <td>-0.842713</td>\n",
       "      <td>-1.074895</td>\n",
       "      <td>-0.503035</td>\n",
       "      <td>0.368854</td>\n",
       "      <td>0.853609</td>\n",
       "      <td>1.108681</td>\n",
       "      <td>0.703296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.607822</td>\n",
       "      <td>-0.565465</td>\n",
       "      <td>-0.901573</td>\n",
       "      <td>0.236676</td>\n",
       "      <td>1.222013</td>\n",
       "      <td>-0.014732</td>\n",
       "      <td>-1.200742</td>\n",
       "      <td>-0.438795</td>\n",
       "      <td>0.982933</td>\n",
       "      <td>0.500335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.861132</td>\n",
       "      <td>-0.329573</td>\n",
       "      <td>0.455247</td>\n",
       "      <td>0.938038</td>\n",
       "      <td>0.862129</td>\n",
       "      <td>0.305723</td>\n",
       "      <td>-0.474433</td>\n",
       "      <td>-1.015828</td>\n",
       "      <td>-0.733837</td>\n",
       "      <td>-0.287970</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.136329</td>\n",
       "      <td>-0.452612</td>\n",
       "      <td>1.028219</td>\n",
       "      <td>0.602593</td>\n",
       "      <td>-0.572294</td>\n",
       "      <td>-1.055045</td>\n",
       "      <td>0.516135</td>\n",
       "      <td>0.942395</td>\n",
       "      <td>0.032582</td>\n",
       "      <td>-1.071227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.772389</td>\n",
       "      <td>0.299902</td>\n",
       "      <td>-0.304926</td>\n",
       "      <td>-0.963405</td>\n",
       "      <td>-1.023088</td>\n",
       "      <td>-0.239489</td>\n",
       "      <td>0.552001</td>\n",
       "      <td>0.980856</td>\n",
       "      <td>0.772081</td>\n",
       "      <td>0.217311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381584</td>\n",
       "      <td>0.750149</td>\n",
       "      <td>-0.043188</td>\n",
       "      <td>-1.003008</td>\n",
       "      <td>-0.143461</td>\n",
       "      <td>1.131107</td>\n",
       "      <td>0.706158</td>\n",
       "      <td>-0.818526</td>\n",
       "      <td>-0.768612</td>\n",
       "      <td>0.616678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0.0       0.1       0.2       0.3       0.4       0.5       0.6  \\\n",
       "0  1.114446  0.600313 -0.006571 -0.585057 -1.034596 -0.941631 -0.077671   \n",
       "1 -0.913026 -0.734200 -0.041426  0.777901  0.973380  0.653314 -0.116416   \n",
       "2  0.982462  0.591904 -0.104168 -0.842713 -1.074895 -0.503035  0.368854   \n",
       "3 -0.861132 -0.329573  0.455247  0.938038  0.862129  0.305723 -0.474433   \n",
       "4  0.772389  0.299902 -0.304926 -0.963405 -1.023088 -0.239489  0.552001   \n",
       "\n",
       "        0.7       0.8       0.9  ...      9.50      9.51      9.52      9.53  \\\n",
       "0  0.587005  0.975617  0.677658  ... -1.036854 -0.643083  0.848557  0.826092   \n",
       "1 -0.720925 -0.874417 -0.497024  ...  0.192062  0.950734  0.104042 -0.722278   \n",
       "2  0.853609  1.108681  0.703296  ...  0.607822 -0.565465 -0.901573  0.236676   \n",
       "3 -1.015828 -0.733837 -0.287970  ... -1.136329 -0.452612  1.028219  0.602593   \n",
       "4  0.980856  0.772081  0.217311  ...  0.381584  0.750149 -0.043188 -1.003008   \n",
       "\n",
       "       9.54      9.55      9.56      9.57      9.58      9.59  \n",
       "0 -0.337760 -0.897698  0.060898  0.842003  0.325851 -0.902178  \n",
       "1 -0.538008  0.831013  0.640009 -0.489455 -0.882031  0.216450  \n",
       "2  1.222013 -0.014732 -1.200742 -0.438795  0.982933  0.500335  \n",
       "3 -0.572294 -1.055045  0.516135  0.942395  0.032582 -1.071227  \n",
       "4 -0.143461  1.131107  0.706158 -0.818526 -0.768612  0.616678  \n",
       "\n",
       "[5 rows x 600 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def to_tabular_sequences(df, seq_len=60, feature_name=[]):\n",
    "    new_df = [np.array([]) for _ in range(int(df.shape[0]/seq_len))]\n",
    "    i = 0\n",
    "    if not np.floor(df.shape[0]/seq_len) == (df.shape[0]/seq_len):\n",
    "        print(np.floor(df.shape[0]/seq_len), (df.shape[0]/seq_len))\n",
    "        Warning('You will lose the last part of your dataset.')\n",
    "        \n",
    "    for j in range(df.shape[1]): # 10 \n",
    "        current_start_timestep = 0\n",
    "        for i_n in range(int(np.floor(df.shape[0]/seq_len))): # 1000           \n",
    "#             print(current_start_timestep,current_start_timestep+seq_len)\n",
    "            new_df[i_n] = np.concatenate([new_df[i_n],                                          \n",
    "                                          df.iloc[current_start_timestep:current_start_timestep+seq_len, j],                                          \n",
    "                                         ])\n",
    "            current_start_timestep += seq_len\n",
    "        \n",
    "            \n",
    "    df_new= pd.DataFrame().from_dict(new_df)\n",
    "    if feature_name == []:\n",
    "        feature_name = [str(k) for k in range(df.shape[1])]\n",
    "        \n",
    "    df_new.columns = [f+'.'+str(i) for f in feature_name for i in range(seq_len)]\n",
    "    \n",
    "    return df_new\n",
    "\n",
    "tab_df = to_tabular_sequences(df,seq_len=timesteps)\n",
    "print(tab_df.shape)\n",
    "tab_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l3z4KuPAe/wHv2DtEOl/kyWPnjwNkJl/kP/zDixw+Z2Gz3AFO1lH6ABVzN0fKPrMn4O8/DIEe+OV7IdS/7JDJeIa13fX3GuS5d673KvHA6DPq19iAY1MJvG5t2Z2SJBLwsJBpr+tuJl/knZ97gj956Ghb1yGZiGX4zv6zfGTfCEPlC/pA2M9cKr+4ZBx9w/BrXlAbviMjI5w9exYrjp/RRJZiSedwbHFQXsjkiaUL5GcCuBTNMw0EAoyMjKx4jMz8QdyNfOEXr+Z9f/kkv/b1/dz/yVtYuzRjS8/BwXvhTXeBv37GpIoz2QCHXLu54vgjXP+W/0bE7+HHhyZ5+97zo9v34HiM7x84h8el8fl/fVW7l8PpqNhY3TK4OJj1hXxomgNln4VJYc9dysPdD0DP8nNN13Um4hne3qBDW557Z71bxAVk9Gm48i6162zAsckEWwfDy+SnkkjA03ZDvO++NMZEPMOLo3NtXYfkr584QVHX+fW3VPfypLJsNpVjbaQcL372F4Zf84IK/l6vl61bt1p67jv/7Ak29of48r/ds+jxZ09E+dVvPsOX/82+hh8U1WQLRdL5YuUDCKIb9P/9m3184ItP8WcPv84ff3CJ+uLAt6CQEdO3HCaayHI4dA1XnPt7fLl5btu9lkeOTJ43A78PjouM/wevTSy6iLaLkzNJwj43a5YoetwuYfCm1N8nE4NvfAgSU/Bv74c1u+oeFk8XyORLDPc0KPuUM/94pggbb2hpp++xqQUuW9/T8PvdQS8nWt0cV0OppPOVJ4Vh4rHJBUolXVliaIWZRJZ/eO40d165YZECqlJWTJSDfyYm4oRBLqiyjx3G59OM9C2XPr5pYy9et8bzp1aaRaMWOT5vadDas66bfZv7eXUstvgJcqN3/VWw/krH1zeTyDHacy2gw8knePuetcwkcrx85vzIgg6Nx/G4NLKFEg8cGG/3cjhdR+YpkVp/ZTz0GZg6DP/q6zCyr+FhkwuiT2OoQc0/4vfgdmliju+mG2DmdUg6X9rL5IuMzqa4ZO3yzd7K2tpc9nn89WmOTyW4bms/yVyx7aZzX33yJNlCiU/cvljBJwUFFbnnK98Uli8GuSiCfyydZyFbYH3v8g9CwOvmipHelgb/uAz+dbT9u4YjHJtMVBuD5k4Jd8bpw7DPoEePTaKJLAuDV4AvAicf57Zda/G4NH50nqh+Dp2Lc93WfnYNRfj2/iab5C3gdDS1rOQjUervUyqJmQuXfRguefuKh07ERPBvlPlrmkZ3wMN8OiekwdCSuv8b0wl0vb7SRxIpb/i2q9P3y0+eYLg7wH9+x04AjkwstGUdkvsPjPPWXWvZvmbxe7ZIUKDr8PyXYb1xFeBFEfzHy1fuDb31P6D7tvTx6lisZYqWRpk/iOCfLZQYnZiGR/8A/vI6OP4IvPW/wZW/6PjaCsUSc6k8/ZEwbLkFTvyEnqCXG7YNnBeSz0KxxJGJBS5d381H9o3w8pl5jk+178NZKJYYnU0t2+yVDHb51c3xnToE6VnYemvTQyfi5eDfIPMH6A35iKUL4o7S7W9J6ef4VAKAnUON960iAQ/5ol6xGGklB8djPHU8yt03b2HvetEcd9RKF74iSiWdiViGncPL3y9Z859J5MQI1pmjcO2vGH7tiyr418v8Aa7b0k++qK+stFHISsF/91AX73P9jOGvv7k8hOV98Mn9cOt/ER4+DiNLFANdfth2m1CVzJ3mHXuHODGd5I3phONrWIkTM0lyhRJ713dz51Ub8Li0tmb/4/OZZW6etQx0KSz7nHxCfN365qaHTpYz/0ZqHxDn33wqJ2zBN1zTOPjnM6aX2ohjkwncLq3h+wVC6gm0Re75lSdPEvK5uevaTUQCXjb0Bjk62b5zPprMkS/qrKtzB9cT9OJxaWJP6fkvQ7APLjNuNHlRBH9Zs9tQp+YPcM1m0Wa+v0Wln/lU4+C/95n/wp/7/pK4qxc+9kP40JehZ9m4Y8eQKovBsA+2vUU8ePLxitLn4TZn/4fKm72Xru9hsMvP7bvXcu9LY8Y7pBVzMirn9jYu+8yn8mq6yE/9VGj6ezc1PXQinqEv5F3k4b+UnqC3koiw6QbRN5Ar14znR+HJP4O/uhn+eATOqZnwdmxqgS0DIXyexqEnEhA6lHiL6/6T8Qz3vzLOR/dtrGyI7x6OtDXzr5Tv6tzBaZrGQJeP3Nw4HH4ArvolU5YuF03w97ldDIYbaZ59rOsJVCR7TiM/cL1Lg3+phOfIffzIcxu/P/QX4gPZYmRD0mDED2t2C4+gEz9hQ2+QS9d3t730c3A8hs/jYlu5oerD14wwvZDl8dfNy39VcLoc/BvZXsu67Jzd7L9UhFNPGcr6QQSyRpu9kt5QbfC/EUoFePh/wFffBZ+7HB7+ffCGwBMQFwIFHJtKsKNBc5ekXZn/1352imJJ52M3VxWFO4cjnJgWd5vt4FxMJK7reuoH9YGwnysm7wW9ZHpP8OII/nNp1veurOMf6PLZ8skwg/zAdS8N/vExKGaZ6ruaI1OtuRAtRb4HA2GfsArYdhuceBxKJd6xd4gXRuda9j7V49C5OLuHIxU3yLfuXstA2Ne20s/JmSQhn5s1kfqJxaBUZNgN/udegWwMtr7F0OGT8WzDzV6JKPuUA+zG60Bzw3P/V/SUvPV34VMvw6/8GK79OBz6F1ECtEG2UOR0NLXiZi+0J/NP5Qp849lR3nnpMJtq7uJ2D0eEb/5Me0o/lb2bBr/LtWEXt8S/LwQA/dtMvfZFEfzH59Os7135dqiVI/di6XxFareI8ocrsPYSTkWTpNvgOSSVKRUXym23QWoGpg7ytt1D6Do8/Yb16UF20HWdQ+PxRS6VXreLO6/awCNHJtsyLP10NNVQ5glU3FBtK35kvX/LLYYOl74+K9Eb9BLP5CmVdAj2wt3fh19/Ej7xDNz6/0F/OQO+4d+Dy2OqgageJ2eSFEv6ijJPqCZFrcz8v/PCWWLpPL/y5sUBdFd5o/VomxQ/52IZvG6tIutcym368/SXZk1t9EouiuA/Np9mQ5PgPxD2t2zkXiyVX571QyX4D2zaja5XPVBayUwih88tho8D1UzzxE/YtkaUNs7OtUf3PBHPMJfKV1QYko/sGyFf1PmXl8ZavqZTM0m2NKj3g0J/n1M/hcFdzWcxI0wMZxLZpmWfnpAPXaeqqd98o7ADX3ohiwzDlb8AL31DdBdb5Fh547RZ2Udm/q3S+hfLTV1Xbeqt7P9Jtg124XFpbZN7TsRE+a5R1eK2+PcY09fAjneYfu1VH/xzhRJTC1kDmb8o+7RCW9ywK3X2BLj9bN66A2hPtjGTyDLQ5atmsj0bYHAnnPgJYb9HWAHPt6ckVd3sXRz8dw93c/mGHr7dzBhPMYViiTNzqWWGbrVIOZ6txKKYh9NPG673Ty9k0fXGpQKJPAfn0wbWdtOnoJiDZ//K0BrqcWwqgUujkkQ0ItLimv/Dhyc5HU3xq29eXjbxeVxsWxNuW+Y/Pp+uq/QBYOoIWxZe5OuFt5EqmI9bqz74T8Qy6DrNM/8uH9lCqSX2zrWmbouYPQH9W9k8GMHvcbXlhIuWg/8itt0Gp38GhRzre4KMz6uT/pnh4HhcDBsbXj6c5CP7Rjh8Ls5rS7ujHUTK8FZKLLoDXtwuzV7mP/ai6Nw0oO+Hap14aAWZJ1QFB5VN35UY2A573w/Pf0XYCFjg+NQCm/pDBLwrS5bDPjcuTVhUtIKfHJ2mJ+jlnZfWv6vaNdzdtuA/Ec8w3GCzl/1foejy8q3ibZaSi1Uf/JvJPCUDYemT4fxm5oqZf/823C6NHUNdHJ1sQ/BP5hhcOnVq221igMzZ51nfG6z0TbSaQ+NxtgyE6fIvt6R635vW43O7+E4Ls/9orSy2AS6XZr/Lt1LvN6j0ia1s7SCRCUhl07cZt/ymGPu4/2+MHb+EY5OJhjbOtWiaVunybQXRRJbh7kBD36rdwxHG5tMtVx/pus65WKZ+5p+Jwcv/yPTGO5il25II46IJ/s3KPgOVbjnng/98veBfKsHsycqO/a6h7rbUGWcWspULYYUtt4DmghM/YaQv2Davk0Pn4nVHEoKQ677j0iH+5eWxlk1mkxvMzUZcDoRtDic5+TgMXV7Xtrkekwa6e6G27GMwqK2/SiQCz3zRdONXvlji5EySnU2UPpJW+vs0G1O6q9yN/HqLk7G5VJ5coVT/9/jD34F8kviV9wDWyoqrPvjLLLVh3ayMzHZbYSVbN/NPTEAhXVFY7B6OML2QbamCRdd1ZpK5RUPIAWH5u+EaOPET1vcGWMgUiLc4C4pn8ozOppZt9tZy0/YB5lP5lklRZSlnWZlsCbaGbuQzcOY5wyUfgIl4Fp/b1fSiJBuZDJV9JDf/JiQm4cA/GX8Ooh+iUNKbyjwlkYC3ZVLP2WSO/hV+h1XFT2vlnlWN/5LY9foP4aWvw82/SWiLmNdspay46oP/2FyawS5/0zrjYI09qpNk8kVyhVLlg1dBaqhl5l8+4Y60sLswkS2QK5SWl31AZHxjL7ApLD6QrS79HDknsq6Vgr+Uw7XqglnN/FeurQ+EbciIzz4HxazhzV6oDnFpJD+VyAQkljKxtm23wbor4anPi8Yzg7xuUOkj6Q54WpZgRJM5+uuYLEo29AYJ+9wt7/Sta86XmoX7/iMMXQa3faZyl24laV31wX88lm5a74daPbazWWNDX59K8Be2rbvboC+eqWj863wQtt0GepEd6VeA1gf/g+Nik/HSBmUfEMNTwEQN2yazyRwurU6n9hIGunzMWk0qTv5UlNyk86YBVhrfWIvf4ybodZvL/DVN1P5nT8Dh+w0/7dhkAk1jmTNlI0TN3/nMP18sEUvnV7xLcrk0dg5HWl6GPVcO/otK1t//z+IC8IEvgcdP0Ocm7HN3yj71GJtLs6GBoVstPo/Qtjvd6LVi8Hd5K1OZ1kT89IW8LQ3+8sI3UC/zH7kWPEHWzQjb37EWa/0PjccZ7PI17KSF6gW8VZl/NJmjL+RrOuhjIOxjIVuw5hp78glRaw80Hn6yFCPWDpLekNf8xXLP+8Qd6tNfMPyUY1MLjPQFCfqMmRN2Bzwt2WCdS62Q8NSwezjC65MLLbWZnohlcLu06p34a/8spvnd9luiH6PMQJe/U/ZZiq7rhhq8JINdfsfrxQ1N3aJvQN+WinOnpmnsanG2Ucn862VBHj9svong2SfxujXGWiz3PHQuzp513SuWMnrLmf+cmTKGDWYTOfqa1NWhejE1fVHKJWFsv2GVD1THNxoN/j1Br/ENX4nLDZd/VKzNoOzzuAFPn1pateFrdNN+51CEuVSe6YXWWZuci2UYiviFCmlhQmT9G66Bm//TouPEnlIn81/EbDJHtlBqqvSRWH0TzdA48z+5zJtj93B3ZYxcK5AXvobZ9bbb0GaOclkk3dKyT65Q4thkYsV6P1Sli62s+TcLGmBjL2L0aWG2ZmKzN5EtkMoVGe5ZeR9CssjZ0wybbxRmYmeea3pooVjixHSSHU1sHWrpDrZmoIssxzX7PVb34FqXjE3E06Ler+tw/29APg13fgnci6XOA2FrSeuqDv4Vjb/R4G/xTTRD1dGz5mTT9YrGv5adQ5GWjpGTF76+Rptf228H4L3+F1oa/N+YTpArlhrKPCXesi2FbQdNg0ST2YaeK7VYlhGf/KkoBZpwd52MG9P4S3pDXmJW9khGrhV+P6d/1vTQ0dkUuWKpqadPLZGAh5KO402Xssy7TN68hN3DcrBL64K/0PgH4eVviAlub/s9WLNz2XGDXdakxKs6+I8b1PhLBiy+iWaom/knpkQH58DiGZ2tzjaiySw9QW9jr/Why2D9VbwnfT/jc60bsF3r4d+M/rCPuRZu+BrL/C0qyU4+Ieb0+la2Q6hlIiYuMEY2fEGWfSyc874wrHuToelfp8q219sMbvZC6ywejJZ9+sNiv6lVjZe6LiZ4DfcE4PH/DRuvh+t/ve6xcmCQ2QrBqg7+0oCs3uD2egx2+ZlL5RwdDBJL5dC0qnkVUKP02bro2Kq+uDUSs5lEdrnGvxZNgxv+A2tzo+xOPtuyZqqD43ECXldDz/xa+sK+ltT8iyWd+XTeVOZvquyTicG5l01+xYF0AAAgAElEQVSVfKC5BfBSxChHiwF2040w9kLThi+5z2XkvZJIT3+nLR5kstdXz25lCbuGIi3L/OMZUb7bHMrB/GnYdQe46ofrgbCfYkk3/Xtc1cF/fD5DyOeub6VQh8Eu4XLoZOYo7ZwXKURm3xBfl5R9uvweRvqCLcv8ZxK5+kqfWi69k5R/LXe7flApMTjNoXMxdg93N2y/r6UvpHBs4grMpXLoevOMEcTv0ed2mZvle+pJUVM3sdkL5ss+PUEvmXzJmhJp883C7G38xRUPazi/YgWqzp5OZ/5ZekPeynyIldhVVvwUW7AHJzX+l+inxAM16p6lyOTCrOJnVQf/sfkU63uDTZtdJMrsd1dAmLotCRizJ0T9tGf5eD4xRq5FZZ9mmT+A28vk7l/mVverzJ542fE1VTz8m2z2SvpCvpbU/CvlgmYXS6rj9kyVfQ7+CwR6xe2+CSZiGXqC3qZNjRKZGMWtZP9yL6JJ3V9m792B5Z5MjWiVrfNccmWNfy27hiNkCyVGZ513tZXdvRuz5cRwqHHwt+pOoCT4a5r2Lk3TjmqadlzTtM/U+f7dmqZNa5r2cvmP+ckDFhifzxje7IXqbamTip+61g6zJ8RcVvfyD8eu4QgnZpJkC867jc4k6pi61WPf3aR1H90Hvuz4msbm08QzhaabvZL+sJfZFpR9oivJYutgapB7LglHvg+X3gke46USMDbEpZaKuZuV4B/qhzV7mgf/TJ6wz20ou5bImr/TXb5GN+2h6vHTijKszPz7E69DeA1EhhoeO2DRNtx28Nc0zQ18AbgD2AvcpWna3jqHflPX9SvLf5yPGojAYXSzF6qZv5OKn7qmbrMnKp29S9k13E2xpPPGlLMbrLmC6HRspnoAGB7awL3FNzMyej8kZxxdVyMP/0b0hX1k8iXHp6AZ3SiU9If9xrvHjz4kBACXf8T0uqbiGYYM1vuhxtzNaqlz841C7rmC1UMs3WB40Qp0B1szytHopj0I9Z2mtUaAcS6WQdMgNHtYCC1WoCIoaEPZ5zrguK7rJ3RdzwH/BLxfwevaIp0rMpvMGd7shergDSfN3ZZl/rpeV+Mvqdg8TDqbbRjtdAQI+tzc63svHj0H+7/q6LoOjsdxaVWpXTP6W9ToNStN3QwGjsGwz/h5deBb0L0BNhm3dJCIzN+Yxh+qkmPrm743QW4BJl5teEg8na9s4BqlVUPcRfA39n4FfW4294daUoadiGUYDrvRpo+sWO8HsVmtae0p+2wAztT8+2z5saV8SNO0A5qmfUfTtI31XkjTtHs0Tduvadr+6elpW4uqWjkbz4K6A148Ls1Rf594Or/Y1C0VFR7pDYL/1sEwXrfmuKOg7Fw0VPYBcn07OBC4Fp7/MhSce78OnYuzdTBs2BZA7qc4velbUYmoLvsko/DGI3DZhxqqOxpRKJaYXshaK/tYvVhuvlF8XUHyGc80mF+xAn6PC69bc7TmXyrpzKWMKbYku4YjLZF7jsfS7OuaEaZ+TYK/x+2iL2TeObZVG773A1t0Xb8C+DHwtXoH6br+17qu79N1fd+aNWts/UC5YbK+0RScOrhcFjbmTKDr+vLMP1pf6SPxul1sX9PleJ1RBrOmG75l1vcG+AfXe4S972v3Orau0WjKsBkYVMswTmf+c8kc3QEPXoN17P6wn3S+SCrXJJgd+hfR1XvFR02vaSaRo6RjquzTbWaaVz16RsR+1Qp1/1i6UCnjGKUVA11i6TzFkm647AOi7n9qJmlNHWWCiViGK33lwURNyj5QnhnRhsx/DKjN5EfKj1XQdT2q67q8LH0ZuEbBz12Rqbj4cUYlbxJhv+tMJpvOF8kX9cXBf4mVcz12tUDxs6KpWx3W9wa5P7ELfc1uMdzDoTb86UR2RTO3pfSHW2PxEE0akMXWYHhT7tVvw5rdhj7wS6mMb4wYP+cjfg8uzUbwB1H6GX264TlgpewDZVtnB3X+le5egwkPiD24ki68ipxkIpZhN6Pg9sHgjqbHiwbV1mf+zwM7NE3bqmmaD/jXwH21B2iatq7mn+8DDiv4uSsiN20HTQQOEG+iUzX/uqZusyeEZW/vcpmnZNdwhPFYxt4HtAmV98vgB2FDb5BkrkT66ntg4gCcfkr5mgrFEnMpgwqkMq2ydTazUQg1SrKVLkrzoyKIXv4R0VBnkrr+701wuTS6gxacPWvZfCMkp6t3sUuIZ8xv+AKOZ/5mN+0BNvWHAGctzRcyeRayBTbl34C1e8Dd/L0b6PK3PvPXdb0AfBL4ISKof0vX9YOapn1W07T3lQ/7lKZpBzVNewX4FHC33Z/bjOmFLEGv8Lo2w6BFe1Qj1LV2kDLPFSR9ctPXyTFy0UQOn8dVdz5uPaSE9tSG90CwH57+ovI1zSZFI5WZC3hPUGx+OZ35mw7+lWFBK5xbr35HfL38w5bWNLVgrsFL0mvV3E0iN6brJADFks5CpmAx+Dvr7Ck37c38HnvslskMIBv11qSOrajvr0UICtpQ89d1/UFd13fqur5d1/U/LD/233Vdv6/899/Wdf1SXdffpOv67bquH1Hxc1diOpFlMOIz3OAlGQj7mFlwJnBUTd2WBP8VSj4AG3pFtiFLWU4wk8gxGDb+fkkJ7VgCuOqX4OiDwnVQIdPSZdTEbbnH7aI74HW85h9N5kxtFBrqIXn1OzBynbD2tsBELIPXrZlaF0BPyGdN5y8Z3AGhwbqbvomM+QYvidPB36ipWy2tCP7nYhnWME8gG4VhY+W/gS4/8YyYxGeUVdvhO5PIssZEuUAy0GVwY84Cy9rcdV1YOzQJ/q044WYSWVMZtpyONjaXgqFLAR1iYys/yfSaxIfTTM0fRCbnZOav6zpzpjP/JmWfyYMwddDSRq9kIp5hbSTQdLjMUnqCXnOjHJeiaaL0U2fTVzZpmVX7gFDfOdnkJe2c+8LG19YVcL7/4Fwswx7XafEPg3s/gxZmRqza4D+9kDVVK5ZY7ZYzQmxpzT89Jwy8zoPgb6bTEUQm6/O4GI9lKtPHiJ1Z+UkmMSs/lfSFnM384+kCBZMqkZDPQ9Drblz2efXboLlh752W1yUmeJk/522XfUCUfuZPQ3x80cNWfH0kTo9yjCZzRPwe/B7jpWG3SyMS8FizwzDIRCzDXq0c/A1n/uZtw1dt8J9J5ExnjEDlbsGJLt9KzV/q/A0ofQACXqF5djILihoxdatB0zQ29AZFP0VPWewVO6t0TdVNaPOZ/1zS2QslmFOJyOPrZv6lkij5bH8rdFmXOE/EjE/wqqU3ZGGa11Kk3n9J9i+DpBW1TyTgIZEtOGakNpvM0W/ydwgiGXMy+J+TMs+ejRDsM/ScwWZ3lnVYlcE/Xywxm7QW/B3N/NN5kTnITdUlQ9sboWma9YlLBtB1nahRX58a1vcGhOqhez2gqQ/+ctPe4Ca0pC/krK1zVSViUkkWbhD8zzwr7pos2DnUMhnPWgr+8tyyNTFu6HLwdS2r+9sq+5Sfk3Ao+ze7aS/pDjj3WQSYiKXZ4xo1Jfetzoy4yDP/2UrDkrWaPzjj7BlL5+kOeKqbqrMnAA36Njd9bnfAuWwjnimQK5YMyzwlG3qDYpC72wuRdY5k/oMR8x/OvnLN36kRgNWNQnNrE3sRdc6rV78NniDsfrflNSWyBRLZgimZp6Qn6EXXYSFrI8i6PbDxujqZf3nD12STF1SdPZ264zW7aS9xMhEDiM7H2Fg8a7jkA9aS1lUZ/GWt2FLmH3bO36dud2/PRjEcvQndDp5wMwlrZYz1vUGmFrLCcbRnBGKjiteVs7Rp3xfykS2USDvUhTlr0tpBUreGXczDwe/C7p8Hv/FO5qVIeaAZaweJtMSwNM6xlk03wdQhSM1WHrJT8+922NZ5Npm1lvkHPY6WYLvix3FRamrrsOg5fg8+j7mZEasz+FusFQMEvG66/B5Hav7LHD1nTyyb3tUIJ+uMMlswX/YRip/JWLYc/NVm/lY37Z3u8p21mPnXlS6eeBzSs3CZNW2/ZDJmTeMPCgUFm8t6/zPPVh6KZ/K4NOjyWcn8nTN303XdlKlbLU5m/ulckZGc9PA3nvlrmsagSYuH1Rn8y5n/WguZP+CYv88ya1sDGn9Jd9DrmLysYu1g8oMgG73OzqfKwX9MbF4qwqz8VOJ0l280kSPkcxsemCKRHauLylEHvwv+brjkbbbWZHZ8Yy1VT3+b5/yGa8DtF4Pny8TTeSIBr2n5KdSMcnTgvF/IFsgX9fOu7DMRz7BHGyXvDkGfscRQIrp8L/bM36JEUCI25tRn/vHaKV7pOZHxDay82SvpCXocL/tYqfmDGJpDz0bhQJhS4+9fKJaYNWntIJG38s5l/tbKBZGAh3xRJysbcQo5OHK/qPUbKP2tRMXXx4LU07anv8QbgG1vEQ1/5QucSHjMZ/3g7ChHqfG3uuGbyZccGbB0LpZmr+s06f5dpl1dG6rJGrAqg/9MIkuX32PYBngpgxZ8Mowgav5S6XNSfDWY+ctsw4lNzBmLHwSZZY7Pp5Vr/WfLM3LNdPdKeh329Le6Udi9dAPzxGOiz+PSD9pe02QsQyTgIWShvNKrso9k97th7qSo/SOydisyT3B2lKMMkpakniHnhstPzKfZo43C0BWmnzsQNhe3VmXwF7Vi879UyUCXX/mG7zI7Z4Maf0l3wEuxpJNyYEJVNJmlz+AQ61oCXjeDXX6h+KkEfzV1f2mxYWXT3vnM35pEsFrDLgeN1+6FQA9su832msyOb6zFtq1zLTvvADQxhpLy/AoLm73gbM3f6r4NONt0mZg6SbeWIjDyJtPPHezyVfY7jbAqg/+MSRvgpQx2CUmeLd3zEmSzyrLgb9DHxckTbmbBXINXLRv6gozH1Ad/O5v20tzNqUHuVjcKF2Wy+Ywoj+x+r+k5vfWYjGct1ftBXMQDXpf1gS61RIaE5PPIA4CUN1sL/j6Pi4DX5UjN34qpm6TbwfnC7qmDAPg2WMj8u3wdb5/pBXvBfyDso6RbHGrdgKqpW/lkO/s8DFwCXmPDZmR25sQJF01av1Pa0BsQXb7BPtHkoyzztx783S6N3qCXOQc2fHVdL3v528n88/DGo2KC22UfULIuYe1gLfiDOC+VJRa73w3nXoH5M2U7Z2s1f3DO1tmKqZtE6Z3SErrmDlNCg6F6Y9BXxuz/ZVUG/xkL3aq1ODHIfZHeuZCDU0+Zut2vZP4OBDSz1g61rO8JMj6fRoey4kdNzd/qPAZJX9jHrAM1/2SuSK5QsrzhC+XM/+C94oK59S2211Qq6UwtZC1t9kp6QzY9/WvZ/R7x9eiDxNMFy2UfEO+ZI5l/IkfQ67a0Lyj/P05Ir9ckX2fSswF8YdPPNZuQrLrgny0UiaXzlpqDJFZMkpqxyNRtbD/kk9aCvwMn3HQiy6CFYAZC65/JCzsNekZgXl3wD3hdpucxSPpCPkfKPnZUIjL4p5ILcPQh2PNeQ4M6mrGQKZTHEVo/55U2EQ5shzW7KR1+gHS+aLnsA6LE4sSGr9V9G6h2KzsR/DflTzAdbj65qx5mE95VF/zlRq3VjBGqb6JKxc+iQS5vPCamd215s+HnO6V5zhaKLGQKlu+UpLWzkHuqa/SSd29m5zFI+kLO2DpXTN1sbPj2jj0OuYQSlQ80GBJkEiXOnrXsfjfa6afoIWGpu1filIOm1dIdVD+LqhOxTGKOESZJ9O629PyLPvOXtWI7mX81+Ksv+/SEvHDiJ7D+agj2Gn6+U5l/RfVgNfjLoS5S7pmaUTLUxe6+TX/YGVtnK6P/JHJK2si5H4rhJyYu/ishm7PsBP8eu6Mcl7L73Wh6kbe6XrK1rm6Hav52Mv+A143fo34jOnbyZQCKa83Pbwbz5+SqC/6VBi8bgaM36MWlmbNHbUZlw1dLw9gLsP12U8+PBDxodgdt10FKKq1mQesXBX9p7Wx/qMtMwpq1g6Qv7GMupb4vYtbGRqHbpbHGX2Db7E9h7/uEGZoCKudWyEbmH1Kc+a+7ilxomJ9z77e54evMNC87wR/kABy1n8X0GRH8fSPmlT4Afo+7Ulo0wqoL/rJObydrdLk0+sNqtf6xdB6PSyM0/jPQi6a13S6XRpdf/S2wNIKyGmj7Ql6CXrfyRi+7wb8/JGRvqvsiZm00BwH8nPcAvlIGLlWj8gFFZZ+Qj3S+SEaVGZ7LxfSGt/EW1wF6PNZfszvoTM3f7PCipThh8eAffYIJvY+B4S2WX8PMZ2bVBf+qtYM97fRgl/mByCshTd20k4+DNwQj15p+DSfM3aqmbtbeL03Tqr7+irT+xZIw3bLS3SuR/j6q6/6zSTHo3upG9Dv5GTF3H2y+WdmaVAT/bgcULKNrbyekZVkXfcbya0T8HtL5IvmiOs+oVK5AJl+yvUGuVHadibF28qc8WLye4fLMbiuYuaCtuuA/k8jSHTA3mq0ewtxNbc2/stm7+WZLXi5OzDSt2jlb/yCs7xVyTyJqhrrMJnOUdHulO2m3rLruL60dLG1EZxNcX3iBZwO3gMve+VmLqg3f2tdSwamuq4jrIfpGf2T5NZyweJAJz3mV+R99CLee5xH3LZW9ISuYKd+uuuA/bbO7VzIQ9iut+cfTebb55yF6zHI7vxO3mtFEFr+NTBaojnP0+JQMdZlWsGnvlK2zrVrx6z/AT5ZH3bcoXVMsnS93w1r/HVadPdWdX3NZeLR0JYETP4KStdKPExYPdjbtJT2qM//X7mXWM8S5LmubvRIzSdzqC/4WPeCXotrWOZbOcwMHxD+23WbpNRwJ/kl7kkqAdT1BZhI50VquoNHLboMXOGfrHLUT/A/fT8w9wLMFazruRtjxz5E40UQYTxd4VL8OLR2FUWuln2o5Sl3mb3ffBoRJn7L3Kj0HbzzKU/5bGLTRpQ2Y6tdZdcHf6uD2pQx2+UlkC8o2wOZTea7MvwLhNTB0qaXX6A56lDsJiu5ee/sjfeGakoECrb/Vwe21OGXuNmtno3D8JU53vYl4Vl39GsS5ZTf4S9sRlZl/PJPnZf814PZVjN7M4oSts9UxnLX0BL0sZAtq/L8OPwClPN8v3Wg7dn3w6hHDx6664K8q85cboKpKP7FUjt2pF0TWbzHLdibzt6d6gCU9CDL42xjqYnW+QC3dASHXVV3zn01YM3UjuwDzp5mLXKJcvRJL5ys1e6tUPf0VK9yC3eKcP/JAxePfDNU5viozf+umbpJuFXOPJQfvhb4tPJnaaHkAlWTLoHFbiFUV/NO5IolsQVnNH9Q0epVKOutzJ+gqzME2c/r+WnqCXtL5oinnvmbY8fWRLDK6UjDUZSaRw+9x2dr4crk0ehV3+WbyRZK5orU7pakjACR7dpErltRJKqkzG9oCkYAHl+I+kng6TyToFUZv86dh+qjp1+h2oOYfTebwue2dX8rUUckZOPE4+d3vJ5EtKoldRllVwV+Fxl+i0t9nIVvgJu1V8Y9t1o28VDt7VhwqbWb+VaVITonWX3b32tmHANGDoLLmXxncHrIS/IVVb6ZftO6rzP5VBH+XS1Pr74PI1nuCXtHNDjB9xPRrOGFrIu7eLCq2yijruD98P+hFpje/G4C1EXs1fzOsquAvPeDtqEQkgxVnT/uZYyyV582u14iHt1aDowVUWzwksgVyhZLtmv+ysg/YqvvbbfCS9IfVZv62VCKTB8HXhda3CVCbyS6bDW2RXsUWD/F0Xkwv6y/Pop19w/RrdDlQ87fb3Qs1FyW7n8WD98LAJYz7LwHUJK5GWV3Bf0F95q9C8RNPJLjOdYT5dfYae5SdcGWqemd779cipYiC4K9q36Yv5FNa869sFFq5WE4egrV7iATE/0tV5l8s6Sxk7NkmS3qCXrUbvvKi5I9A1xBET5h+DbdLI+xzq9X52zB1kyhJxBJTcOpJuPQDTJU/iyoSV6OsyuCvInCEfB5CPreamv+Z5wlpWTIj9oy8VA+RkA6VdiRvULuugvCo94ZtZv451kTsT7dS7expeaNQ10XZZ+1e5U1LMiNWEvxD6ga66LpOPFNTjurfXp1eZxJh8XB+Zf5yjq+t9+vQ90AvwaUfrMSutTZmMphlVQX/areq/cAhX0eF2id45gkKugttq73gr7rsU7F2sJn5e92iSSyWzgslkw2tv7B2UJT5h0Xmr8rczXJn6MI5oeUeukx505KK7l5Jb9BLTNGdkrBk0Kte/v3bLJV9QNo6q9X52y/7SBWSjd/ja/fCmt0wtJfphSxul0a/lf0ki6yq4D+9IAaRe00OIm/EQNivZMO3b+JnvKJvJ9I7YOt1KkMkFGWNtsoYS+gN+SrWwvRutJz5V6wdlNT8veSLOklF5m6zyRxul2Z+OMnkIfF1SH3mP59SGPxD6so+MlhXHD0HtkFiUkheTRIJeFnIqllXtiAUgXZFDl1+m+qo+DiMPl2Z6TC1kGGwy4fLZU/kYIZVFfztDm5fyqCKLt/0HP2x13iydJmyLkx1NX/7emdJd63pnI1GL5WKLanKUTXRazaZoy9k4QNaVvqwdq/y4d8q7JwlcqCLisal+NJyVP928dVC6UelrXN1097e+aVpWvmct7iuQ98D9Iq7q935FVZQEvw1TXuXpmlHNU07rmnaZ+p8369p2jfL339W07QtKn7uUlRtFEqUZP6jz+CixHNcTsBr7+32e9wEvC6FNf8cXX6PLU8YSU/QU11Xzwgkpy0NdVHR3StR3eVrWRY7eUiY3oX6a9QraoKZyrKPysalysxqeZc0YD34qxzlGLUxhnMptpouX7sXhi6DNTuBsidZCzd7QUHw1zTNDXwBuAPYC9yladrS0fMfB+Z0Xb8E+DPgT+z+3HqosnaQDHSJDUNbmdDo0xQ0D6cDu23r1qHs7Kmw5q9qf2TRB8HGUBcV3b2SXmnrrKiObblWPHkQhsRHQrV6RWnNv/x+qfCskedoRYLaV5Z7Rs3X/VWOcpTqLxXnveXgHzsLZ59bNNNhKp5tqcYf1GT+1wHHdV0/oet6Dvgn4P1Ljnk/8LXy378DvE1TEQlr0HVd3DqpzPy7/BRKur1b9NFnGPXvJBDqUrImlRYPKqwdJIvGANpo9JKTxeyYuklkoFZZ9jGtjCrmYeYorK3mQxGFowljS4OsDWSzXmXvxgbLyj7+Lugatlj2UZf5q3D0lFi2WJcmdzveAQiRQzSpNnE1gorgvwGo/ZSfLT9W9xhd1wtADLC3+7mEZK5IOl9UEjQkg5UuX4sfhnwaxl7koOdSJZkZqB0iocLaQdJbKxO0ofWfTmTxeVxEbLTeS6RyYk5R41I0YeFiGX0DirlFZn4qa9jxdB6/TTtnScXWWUnmX97wrR0rOLDdcuavyhJDhZe/xHIiNvYieAKVhGA2maNY0i/I4K8MTdPu0TRtv6Zp+6enp009V8Xg9qXY9vcZfwlKeV5ml7Lgrzbzt2/tIOkJeskWyh9QG0NdZsp3bypuDCMBD26XpiTzzxdLxDMF8xmj3OxdGvwVqVdUWDtIKuZuCs6vunck/dus1fyDUh5r/4JpWbFVh0UiBzOMvwjDV4BbrKGi8b8Ag/8YsLHm3yPlx+oeo2maB+gBoktfSNf1v9Z1fZ+u6/vWrFljahHTCjzgl2JbWlm+vXu2cMl5F/xL5VGJqmr+i4yuPD6IDFvO/FX9Dl0ujd6gV0nNX15ATF8sJw+C5obBnZWHVJYxVNg5S5Q0LpWJp/OEfO7Fsuv+bZCcgkzc1Gsp0dSXiSZz9IW8SiSV0mLdVB9JsQDjL8OGqysPTStUuJlBRfB/HtihadpWTdN8wL8G7ltyzH3Avy3//cPAo7qqzpsyTmT+thtyRp+BwZ2czoTUlX0UNbzE0nmKJd22tYNkWQOaxUavmYS92b1L6Qv7lGT+8gJiWiI4eQgGdywa26my7ONE5q+i0SueyS/Pri0qflT2Rswms0rq/SDeL1GOMuGyO30ECumq2R0wFc8ArTV1AwXBv1zD/yTwQ+Aw8C1d1w9qmvZZTdPeVz7sK8CApmnHgU8Dy+Sgdqlm/uoCh62TrlSCM89Q2niDMu8VqI6Ps6vFltYOKtU+UFMysKj1V2XqJulXZPEwa1UiOHVw2fAe1Ru+KjT+IKTEIZ9bSc1fmM0t2bexqPVXaeusortXUum7MbOu8RfF1w3XVB5yInYZQUnNX9f1B3Vd36nr+nZd1/+w/Nh/13X9vvLfM7quf0TX9Ut0Xb9O13VrJh8rMLOQxaXZNymrxdYUoekjkImRHt4HqJHigTottipTN0nF1rmi+Cl3+Zq4wSuWdKKKg39fWI1TpaVu6Ewc5kcXKX2gfPemMPNXofSR9Coyd4unC8szf4vunpGKoaH990zsc6k5v+T/z1SZbOxF8PeIEliZ6YUsXX4PIZ99kYMZzqsNXztMJ7L0h/24FbZH+z1ufB6Xtcz/jKj3zw2IK7zK4A/2u3xVWjtAvbJPeahL0vhQl7mUsHZQWfvsD/uU1PwtSQSnDouvyzJ/D7lCiWzBvnpFxfzeWroV2TovMnWT+MIQWWfa3VPlKEcnMn9zwf8FWH8luKqhd2oh2/LNXlhNwX8hq6QxaCmWs7TRZyC8lqhPqF6V12XtBn/FJnh1a/4AsVHDr6Gyu1fSGxI1f7tbTNFkDk0zOcilxtahlupekr1MtljSWciqKymCkHuqaKhqeEfSv91C5q+m5l8olphP5ZUFf9OJWD4DU4cWlXygHLs6wd8604q7eyWW67OjT8OmG5grnxhyyLldVHnDyMxflYvgMrtpC1r/SoOXwot4f8hHoRwk7TCbzNIb9Jq7s5w8BL4I9G5a9LCqYCaDjtLgH/SpafKSg1yWMmBe7hn2CRM1u5m/7PdwLOFpxsSrUCosUvqACP6dzN8GM4q7eyWWlBnxcVHr3XRD5Rba0ui/Oqgyd4smcvSGvHgUOaC6XRoRv8de8HdArtunqMvXUrlg6pCwdVjSs6DK1nneieCvYPRlac/EsEwAACAASURBVKU7kv5twvfJhNzT5dLo8tvfJ1HZ3QsWgv/YC+Lr+uXBv9UyT1glwV/XdWGM5Ejm7zH/IZXt25tuUH/CKdJiq7R2kPSEanoQLAx1UTmMR9JfvuOy2+UbTZgM/roOk68tK/mAusxfpa+PRMU0r4VsAV1vYDlRUfyY3/S1fber0MUWqr9HwxvR4y8Ki4vu9ZWHUrkCiWyhE/ytEs+IWbQqg4Yk4rfQkDP6DHhDMHwFc6kcLg0lHYVQ0/BiU/kwo9DaQbKoAU3TRLnj3Csm1iSsHeqWCyyiytbZtAQ1Pg6Z2LLNXlC3ganSzlnSE/KSK9izUqiYutU7521o/e1eLKcqnbRq9PSLhhgZYexFUfKpuROcVrwmM6yK4K/SA34plk660adhZB+4vcylcvRa8YBvQJdfWBbYzfxnkznlG+TLuo+v+iU4/RQce9jQ86WtrUrPPxn87Wr9ZxI5c8F/Sg5wWR78q/s251/m3xsU75ed0o/M0Otm/hV3T5NafwWjHKcWys1UCkclyr6bpqTnIXqsbskHWt/dC6sk+DtRLpCY3vDNxMXt/qYbAZhLqmvCgfIQiYBHidpH1e2vZFnwv+4eUeP90e+ItvYmiACrdk2Vmr8NuWeuUCKWzps7vyal0mfPsm+pLvso1flLczcbm77VddW5g/OFhPeTybKPis72qXiWgFeNaaCk26jdyrmXxdcNVy1eU5t8fWCVBH+nM/9krkjRaEft2efFUOaN1wMi6Kiey2nX2bNQLDGXyittiIMlts4gPH7e8VnR8Pbi3zZ9/oziYTwggobbpdnK/OVzTXVgTh6E7g1i72MJXX41ZR9n1D72nT2rjp4N1mXB3VPFKEehpw8ovbM0HPzHyp29ncxfLU6+gTJLSxjN0s48C5oLRq4FyqP/nM6wTSKbnpSXfcoa8UWa+t3vgc23wGN/JG59V2BacXcviDulvpDP1oavTC5MXSynDtUt+QB43C5CCga6xNJ5Al4Xfo99O2dJt4rgv9TLfykW3D27FdT8nZBUGh6uNP6iKHmF+petqdWD2yWrJvi7yw6OqjGtqx99WoxnC3QDIvPvU1j2kWuyI/VUNcd0KXWNrjQN3vmHkJqFn/5pw+dKl1En/E36w15bG75V10WDayvmYfpoXaWPxJKKbAkxhY6eEln2sXN+LZvitZSB7ZCaERviBpFOqHaa9aYWMkrr/VCu+RvN/Jfo++WaWj24XbIqgr9QYjjzBpqqzxbzcHZ/pd6v6zpzqfx5l/lXfH0c2PCFOjLU9VfClb8Az34JZk/Wfe5cqjzQwoF9mz6b5m4zZveUosehlG+Y+YMaW+f5dM6B4O+rvLZV4uk8mkbj2rr0tTFR+okEPBRLOqmcdRWSLPuoxNBncWES4mPLOnuhfRp/WCXBX/Xg9lpMNeRMHIB8CjaJen8qVyRXKClr8JKIOqP1wKFyTm4tKza9vPV3weWFH//3BmtSN75xKXb9fWQ3tOFzbLK+rUMtKqSLKu2cJWGfG49Ls1n2KRDxexonYxbcPe1aYmTyRRYy6vX03UGxJ1gormDrPF6/3g/tGdwuWRXBX/Xg9lpMZf6jz4qvG28AasoryoO/x9aGr2pHT0nF1rleoO1eB7f8Jhy+D07/bNm3nfD1kfSH7Wf+Aa+o0xti4gC4fbBmV8NDVNg6x9IFeoJqzy1N02w3esWbOY1W3D2NB3+pHLL6nk3FnVHVVG2dV4gPYy+IfcB1V9RdVzs0/rBKgr+zmX/5pDOiNBh9WjQ29Qgzt4q1gwNlHzuNOHKUnfKSQTkQNbwNvvGTQgHzg98W8w5qcFKu2x/2MV8uK1lBNngZVomcOyAknu7G76+KzF+1o6dkUae2BWLpOoNcavEGoXvEVNlHvp7Vi1JV46++7ANNOu7HXhR3gb7woofbNbhdcsEHf13XiSZbUfZp8kFNzsDJx2HTTZWHZKlB9YavXWfPaDJLn8LGM0nTdflC8LbfE5rnQ99d9K2KXNeh4F/Srb9fphq8dF1k/sPLs7xaVHj6O1H2ASH3jNlU+9TV+NfSv9WU1l8GSJnBm8UpPX1FENLo3NJ1UfZZf9Wyb8nB7ao3oY1ywQf/hWyBfFFX7lMjMVz2eei3IJeCm3+j8pAsf6jO/JuecE1wopkKDF6ULv+IqPk+86VFD4/Npwn53M2DhgVkM5vV0o8pa4f4OKSiTYO/3bJPoVgiodjOWdIbsufsGU8bWNfAdlNln3U9ImOfKI88NEt1VKLisk8zr625k5Ceq6v0mXZg9KwZLvjgLyV8qgOsJOB143O7Vq6xH/k+vPYduPW/CBfHMjLYqN7wtZv5qxzcXksk4EHTmlyUXC647lfh7HMw/lLl4bG5NBt6g0obcCT2g7+Ji+XEAfG1Tn23lojfQ7ZQIlcwMf+1BnnX0OPAxbLX5kCXpmUfEAlAKtq090PSE/Ti97iYtBr8F7J4XJpjn8WG8aFBcxe0b3C75IIP/lXNuvoMSLJifTY9Dw98Wmj7b/lPi741Vx4Aojo76252wjUhWp56phpX2da5aV32yl8Qjp/P/nXlobH5NCN9QeVrAnv+PsWSzqyZsuK5A4AmzocVsGvuJu8qexSXFKGsJrNd9mkW/MtyT4OlH03TWNcT4FzMevBfE/ErL3U2HeU4/hK4/XVlv+0a3C654IP/XMqZ7LqWFYP/j35H+JO//y+FncGiteXNDwAxgO2afyLnWJmsN+Rrvq5AD1x5l7hbSkwDIvhvcCj4y7scK8FfjpY0lfkPbAd/14qH2ZUuOmHqJukNeVnIFlaWLzYgXyyRyhWNlX3AlMHbUHeASRvB3wn/nKafxbEXxV1gnc3/TuZvE6dki7U0rM8efwRe+nu4+VP1N3RSOUcuSpUTzkJ2li0UWcgWHKn5g4kGtOvugWIOXvxbEtkC86k8G3pDjqypmvmb3yyMmu0/ONd8sxfsm7s5GvyNyBcbULVzblKO6tsKaKbq/sM9AVs1/zUOZNgBrwuvW6tvOjd3Sszy3nprgzVlifg9BI1KiBVzwQf/Subf6rJPdgHu/w0Y2AFv+Uzd582n1Pv6yPWAtQ+nzH5Ve/lLDAf/Nbtg2+3w/FcZmxFTnZzK/ANeN2Gfm9mk+YulKV+f9JyYWdyk3g/2p3lVg7/686vS5WuhMU6ek03LPt6AmPZmQvEjg78Vi4fphawjqhrZF1H3nH/mS0Lfv+/j9dfk0AAqo1zwwX82mcfr1ipOiU5Q14fl4d8XU6re/wVxIjdYmxOZv+khEjXITFa1nbPElPXE9b8GC+PkDt4H4FjNH6C/y2cp858x4+sz8ar4aiLztyr3dMLRU1Jp1rNwfplaV/9WU1r/4e4AuULJtElfvlgimsw5ZpvcXc/fJz0HL/4dXPbhSt/PUto1uF1ywQf/ufJsVSdUIpJlPiynnoLnvww3/PuKlUOjtanW+EsMG0otwSlrB4mpzcIdPwe9m1l7+GsAjPQ6GPxDPmYtlMlMNZ+dk0qfNzU9tFtZ5u9MkxdYKyuamjGw7krR82Gw9DNcbtA6F0ubWpM8553aWO2uN2Lyhb+FfBJu+mTD57VrcLvkgg/+0aQzdfValpV9fvbnEFkHb/1vDZ8jTN0sDP02iGEf8SU4vUfSW+4ONXRr7nLDdfcwNPcib3KPOtaoB9LiwULNP5nDY7QbeuKAGFQSHmx6qIqaf9DrxudR/xHutSEoqEzxMjK29IZPCL+nx/7Y0GsPl7X+ZuWeTlk7SJbd7RZyouSz7TYYvrzh89pp6garIPg7GWAlkYCXRLYg7AGyC/DGY7D3zmXt2rWk80WyhVKlfqoaq8G/WvN3ruxTMOO+eNUvkdUC/FrwYUdtbfvCPuas1PwXsgx0GbyzPHdgxQ97LV0Kgr8TWT/YrPmnZf+BgbV1r4Mbfh1e/Xa1ZLYCMvhPxMxdxCvdvQ510i67C3/tO5CYgJv+Y8PnyMHt7ZJ5wmoI/g4MS1mKVC4ksgU4/jAUs7DnPSuvq3zL7FT/gbjVNB84ZpJZfG6XY3skpmWowV4e89/O2wtPCM9/hxgI+4harPkbuiPJp2HmdUObvSD2bYJetw2dv3PBX57vVmr+K45wrMfNvyFmXzzy2aaHruny49JgwmTZp+Lr41TZJ1gzVlXX4Wd/Kbx8tr+t4XPaOcFLcsEH/9mUc5p1yaKGnMMPQGig4tnfiDmHunslVmv+0UTOeCZrASs9CH+T/zl85ODFrzmyJhCZfyZfIpUzd8E07OszeQj0oqHNXokdczcnM3+PW8y5tdLlG8/k8bg0gl6D8sVgH9zyaTj2I7GX1mRdayJ+03LPqXgWTXNun0sMcS8PmnnjUZg6KEwMV/iMdYK/TQrFEvMpZxQ1tVRkecmUOEl33SHq1Ssw67DthNWBLk5ZO0iqts7G1pbJF3k2OcSZnn3w3JeXuX2qYsCixUPUaOY/8Yr4ajDzh3LwtziXNtbMNtkmciSnWaTTqKnk4rp7xB7aw78vMucVGO4OMGHS3G1qIctA2IfH7Uy46w54KZZ0krkiPP2X0DUEl3+46ZqgPYPbJRd08Je3pc7X/EXm7zr1U8jGYfd7mz7H6c7j7qCHhIUuzGgi62hDnNnMf3xe3MJPb3kPxM/C/GlH1iXtLMwEf13Xjfv6nDsgOpd7Nxt+fTvTvOLpfGXkohP0hqx5+sczBfMXJV8I3vJbwu/p6EMrHjrcEzBd9plecKbBSyLP+eToKyLzv/7XwLPyZ6yT+dtkLumsZl0iM/+uEw+Br0vs4jehWvZxTuoJ5jcMZxy0doAaoyuDgWOsHPz968reJzOvO7IuufdiJvjHMwVyxZLBzL/c2Wsi443YsHV2suwDYjaDlQ1fYepmYT/pql+GgUtE7b/UWCww3B1gwqTFg1PWDhL5e/A//0XwhuCaf9f0Oe0c3C65oIP/bMuCvwcXJQbGHoZL3t6wqWvR2lJ5R0zdJE3dBBsQTWadLfs0s7hdwtk5Efx7N5eN0KaPOLIuK5l/pSeiWYNXsSBGN5qo94MoF1jZ8M0XSySN+OfYwOo0r6ZTvBrh9gjp9PRhOPDNhocN9QSIZwqm9m7EtCzngn930Mta5ug+/j1xEQv1N19TGwe3S1ZF8G+Fzv8q7RiBbBT2NC/5gJDJ9QS9jtYZwdzGaipXIJMvOWbtANDl8+DSjA8AH5tL43ZpDK0dhq5hmD7qyLr6LTh7Vnx9mr1f0WNQyJiq94P1DV8nu3slPSFrzp6GHD0bsef9ovHrsT+CQv26fsXX32D2XyrpzCScsXaQ9AS9/Cv3Y2ilomj8NEC7Nf5woQf/VGsy/+6Al3e691PUPLDjHYaeM+tw85nZDBuct3YAYetspgdhbD7NcHdAXCTX7HIs8+8OenC7NEuZf9M9EtnZa1DjL6lrG2KA+RYE/96giWa9GuJGvPwb4XLB238fYmeENUIdhrrNDXWZTeUolHRH9fQ9QS9XuE4Qj2yrziduwnSifbN7JbaCv6Zp/Zqm/VjTtGPlr30NjitqmvZy+c99dn5mLdVBLs59CAD8bo13uZ/nVPe1YlPPAEKF5Ny6qtO8jGeOTls7SETgMLaus3OpqqHbml0w/XpTxYcVNE0M8nCk7DNxQHi2D+40taZIwEsmXyJvctPeSWsHSW9INOsljTbrIcpR8ymbG9HbbxdmiccfqfttafFgNPN3ursXxGdxp3aWaHCb4edMxbNtm+AlsZv5fwZ4RNf1HcAj5X/XI63r+pXlP++z+TMrzCbzdPk9+D3OWqJqU4fYpE3xauQWw89xPPO3oKdvhf01mJOhjs2lq54+a3ZBbkGMQnSAgbDJ4L8g9OFNN+UmDogJbisMbK+HVYsHU/45FukNmu/yPTaZoFDS2T0csffDR66Fsf11k4Bhk+Mcq4PbHbR8d+fYqE0z4d9i6Ph2D26X2A3+7wdkZ87XgDttvp4pZpNZx7N+AI48QAmN5/0rN3bVMueQnbPESvB32tpBYrTsky+WmIhnqm6ea3aLr45t+poM/skc/aEm+nBdN+zhvxSrts6y5u+k1LPbZL8GwGvjMQAu22Ds7rghG64WA5LmR5d9K+Tz0B3wGB7qUtXTO1dicUVfx6XpnPEYk/m2e3C7xG7wH9J1/Vz57xPAUIPjApqm7dc07RlN05RdIGZT+dZIpQ4/wGHPHsYLxjMapz2HKkMkTASOmaQJb3ob9AS9xAxkjBOxDCW9xse/Evwd2vQN+yr7REaYWTDQ4BU7A5l505u9YD/zd7rsA8YluwCvjcUI+9xsHWjseWWIkX3i69gLdb89bGKcY0v09FMiWTmhbTR2ePlupN1ln6aCXE3THgaG63zrd2r/oeu6rmlao2LtZl3XxzRN2wY8qmnaq7quLzPy1jTtHuAegE2bNjVd/FzSxGBtq8ydgslXean7Vw1/SNO5Ipl8ydHMbMUhEg2IJnKEfG7HJwcZXZeUeVYmeIUHIdgPMw4Gf5M1/6Z3SZXN3uY2zkupevqby/ylCqcVwd+M3PPVsRiXru+xL18cukzsoYy9AJd9cNm3h3uChp09p+IZugMeAkbtJqzw/7d35vFtnWW+/77avci74zWO4zRLk7RJidOmpemalrSFlr0FeltmyjIMwwWGgQHuwjCfMsO9/dwLXC5cKA3Qy2UvdLrQ0mmhC9CFhDaJkyZpEmezbMf7JtuSLL33j/ccWba1HNk6kmyf7+eTj60jWX5PLD16zvP+nt/Te4QQDk6GE+W+MzndNw5AU6U5k+uMkjLzl1LuklJujvPvEeC8EKIOQPvak+A5fNrXduA5YO7MQ3X//VLKVilla3V1dcrFD2TB1I0jjwPweulVhoO/3t1r9lVJ3CESSeg3EswyQFlhjNdJEvQGrxlDXKo3mJb5lxe5GBoPGe6KNuTr031QTWuKM6A7FSXznOM7PBGi0GXHaZKMGGJr/sZeX1PhCEe6RtjUULLwX253qpkIHfvi3l1bYtzfp2c0wIoSk1U1PUfpdKxkaNLY66q9dwyA1VULvEJaIAt99TwK3K19fzfwyOwHCCHKhRBu7fsq4M3A6wv8vYAK/qaXfY4+DjWbCXhXGa7Nmu3ro1PiSTPz9wdNL/mAykjDEalcUJPg0zL/urKYN2f1eug5YoriR+9sNprNGvL16Tqo1Cmu9LO4+ZZ9hkzu7oXYaV7GrpRO9vqZDEW4aKH1fp3GVug6AOG5f6vaEg+9owFDH+I9o1lQ1fQc4bxnteH3Ynufn/pSD4Uu86YPGmGhwf+rwA1CiOPALu02QohWIcQD2mMuBPYJIQ4AzwJflVIuOPhPBMNMhMJUmJnJjvXA2Zdhw1vTasgx29dHJ11nz36TrR10jG5G+4bGWeF1z1RrVW9QNXR/b8bXVZ6GudtEMIw/GDYm85xHvR/mv+FrtrUDqD0ll8NmuNGrzac2ezMW/Bu2wdQE9MwNFbWlBUSk0sqnomd00tyN1cAYDJ9loHCN8eDfO8bq6txm/bDA4C+l7JdSXi+lXKuVhwa04/uklB/Svn9RSnmRlHKL9nVPJhaeldLKmRcBCWtvpMTjYCw4RSSSOiM128tfp6QgPU9/s60ddIw6e3YMTswd2l69Xn01ofSTjrNnVOOf6EopOA5PfBZGfNDQOq/1LGTD10yZJ6g9pbI09pQO+YYpcNppqS7OzAIatqmvcUo/taXqb5Jq01dKabq1g/469ZeuZXA8mPJqREpJe6+flqoM/T8tgEXb4ZuV0kr3QRB2qNmE1+NEShgz4CmiN5+ZNcVLpzR2iEQKpJSanbP5ZR89MKW6KvENTdBYPqtcEg3+mZd7VqQR/HuTNXj5XoXvXgV/vh92fBxaUxt5xcNpt+Fx2uYl9TQ78wfN2dNg5n/IN8zG+hLsmfKqKW9WczN8r865q7ZEJQyp5J6jgSkCUxFzO2l7jwBQ3LiZUFhyun886cP7xoKMBqZoWeyZfy7Jiqlb10FVhnB60srS9KuSMpPfoBWFynnRSO1zZHKKUFhmpeyjbxYm+2CKRCSdQxM0zB7a7q0Dd4kpmb/+Wuk3EPzj+vqEp+D5+2DPDRAah7segd3/ktK+NxnzsXUengiZ/toCzdnTQM0/HJG83jWSuZIPKHfUhlbV7DULo41e0e5eM8s+PUfA4aGh5UIAjnWPJn24vtmbsSukBbBog/9gNnx9utui9dx06rODfnNN3XTqylTtU29kSUa/7lOTjbKPAd+hntEAobCcW/YRwjSPH30PZjCdso8e/AdOwQ92w7P3wqZ3wMf+ZMjaOxXzMXfLRs0f1BWckcz/VN8Y48Ewm+ozoPSJpWGbSgImR2YcLi904nLYUlo8RPX0ppZ9jkLVWi6oLcUm4Nj5FMG/zw9AS46VPrCIg3808zertDLWo4Ywa2Zd6WT+Ayb7+ujUa1mzPhAlGfobpSYLZlJGNnx9Q+ryuHF25g8q+Jvg6+9yqPGEhmr+2gdqNLl49BPKd+hde+BdD6jxgxnA63GmpfMPhSOMm2znrFNWaKzmH93sbcxg5g/QuA2Q0PnajMNCCG2iV/Lg35uF7l56jsKKjXicdpqrijjWPZL04e29Y7gdtrlXvDkgb4O/lOpyMhED/iA2YaK/SbfevKNn/jFzfFMwZLK1g06DJpH0GQj+Hdpj5mTaJlDksmO3iaSSSr3BqzHeeqrWw9h5Uwa6VxQba/TqGwvg1ZuDwiHo2AuX3JlyPF+6lKSZ+Ue7e7OQXBjd8D3kG8HtsHFBpksZ9W9SX+OVfgwMdTG97DM5rKbPaZ3p62u8Bso+flZXFeXUx18nb4P/oc5hTvSMJbx/wB+krNCVuQ2m2cyy6fWm0ZBjtqmbTl2pnvmnbnjxDU4gxPTPmImR7mNfsg8j3ebBhOzfqLNnnz84rQ8/f0j59TfOT9WTjHRtnYey0N2rU1boZDwYJjCV3NmzzTfMhXUlmS9zFlZAxRromGvzUFOaOvPvGZ3E41RXe6ag70utUPX+9bVezgyMM5HECbW9z58Xm72Qx8EfVC0xEWZ759B9EMqaoKAMIDqazoi0cjBLwb/I7aC0wGmo7OMbmmCF143LkZ0/eaqssWNwgvJCZ/xGFxMVP0adPWf4+uhyw8btGV+P153ehm82HD11jJTvIhHJ650Z3uyNpbE1rsNnXanK/JN1kavxjZ70hsmnQ49S+sRm/lLC8Z742X9wKsLZgfG8kHlCngd/fXMkHqZ393a3zXBqTGvDdzxkusZfp76sgC4DA619g3GUNSaSynrCNxhH5qlTulLNQu3NfOZv1N9nhq9Px14oroHSxoyvJ90N32xM8dIp1d5fyRq9Tvf7GQtMsTkTtg7xaGhVJcAR34zDNSUeAlORpBvS5mv8j6rXaZly81yvWVknKv2cHRgnHJFW5p8Kh01wqjd58DfNzjkwBv0nlb+Ihsdpw2ETKd+okyHVeWy2xl+nocyDz0jZZ2iChkTB1gSMlH0SfhjZbFC11jSt/4A/mNJ3aIavT8c+lfWbkEF6PU4mQmHDA12y4eipU2Yg89c3exds45yIBM1etQYmepne3dtzRA3wsakwuqqyCLfDljD455PME/I4+Lsddk4lzfxD0aHcGef8IUDOGMsnhDBUn82KBDWG+rKClGWfSETSNZzdzL80iUxQSqmuRJJtPptk8FZR5CKoDUBPRHAqwvBESAX/8QEYOGlKvR+mhQRjBrN/PRBnRedfmLpT+3DnCC67jXU1CxzgkojazWB3zbF3NqL118s+ptF7NFrvB7DbBGtrihPKPaMyTyvzT47baUsY/KWUWs3fLKVPm/o6a0CHkYac6aHy2Sv7DE+Ekpqo9Y4l0NSbSLLMf8AfZCIUTv5hVL1eKSkCydUT6RL19xlLXPrR/4ZVXpep9X5I3+IhmzX/qLNnssy/Y5gNdV7zHEYdbvU+TBT8Eyh+JkNhRienzNP4TwzCaNeM4A+wLonip713jKpi9/xnHGeYvA3+LoeNfn8wbr1xZHKKcESat6nadUD5ypfUzzhspD476FfrzcaGL0xr/buSZP9RWWUWM39l6xyK64UU18p5NiYpfqL+PkmGuswY3N6xV1k218d1IV8w+l6SUa3/8ESIIpPtnHWmPZri/19JKTnUOWxeyUenYZvS+oen33srvG6ESBz8TZ/dqw1woXpm8N9Q66VnNBC3kbC9N3+UPpDHwd+tqVJO9c/N/k23dtCdGmfVePOy7FOaWuufVFZpEqUFygtpNM4ViW7lnLLsAxkv/Uw7eybuitZ9faq9LhX8V2wClzlv2pI0B7oMjWenuxfU612IxDX/swPjjE5Osbne5ODf2KrsNGL2gJx2G1XF7oRDXaZn95pU9tE8fVixYcZhvfwVr/TT3udnjRX8U+MdOUk1g3HlnqYG/3BIbeTEmclqpOwT9fXJcuafTOsfDbZZVvtAfHO36SuRJBvQZatUrTfDm77Tzp6Jg23U16fIqYzFTKr3Q3r9I6CcWbP12rLZkvdrZNzGORH6pq9v7qZvImfP6dm9Jmb+rmKlTIthQ61SPb0xK/gPjQcZ8AfzRuYJeRz8xdQE19gPxlX8DJoZ/HuPQTiYIPgbL/uYOcIxlhVeN3abSLrp6xsap6zQSZFZzS5xSGbr7BuawOt2UFKQZD12hxqSkuHMv8JA5q+XfaoDZyEwbFq9H9Kr+UspOaQ1VGWLsiQb922+YZx2wbpakwNaRYuy05it+Cn1JM78teOmBf/eI2pfalZ1oKbETWmBk6Oz6v4ne/NrsxfyOPhjd7Lbcziu1n/AzGEp+mZvnAEdJQZ8WAbHg5R4HFmpyQI47DZqSzx0JtH6+wYnqM9CZ28syRqEdB//lM031evjB//RbvjNP6j5ymlS7HbgtIukzp59owE8ThsF5zU74awE/9RlH9/QBH1jQbauNDnTjqG0wJlww/ewb4R1Nd6Zw3jMQAiV/c/e9E2R+Ttswry9t56jc+r9oFSB8Wwe8k3mCfkc/N0lXCYPHVrqEQAAFwxJREFUcLpnrlGSqWWf7oPgKIDKC+bc5fU4GAskH+iSlbnCs6gv86TI/FPIKk2gLImzZ1KNfyzVG1SAD8WcW+d+uP9a2Ps92PeDtNclhKCiyJXU2bNPG98ofPvAUxr3tZApygpdFLrsnE4ia9bZf24IgK0rM2MqZ4TSQhfDcTZ8pZS0+YbNL/noNLSqcmyM+qu21MPwRIjJ0FzZbs9ogGqv2xwPnfEB8PfMUfrorK/18kb36IxekvY+P067YGWW34fJyOPg76U4Moq3/+CchpxBfxC3w0ahy4SMo+ugGsZtm/vcXo8DKcGfZKDL4Hh2rB1iUVr/+BlQVFOfZRfB5Jn/uLEPo+p1gIS+4+r264/A93cr9U3lWjj9h3mtLZW/T79fa/Dq2KcyTpt5bxO7TXBJUxl7Tw+mfOyBc0O4HDY21JmkqY9DIpuOjsEJhidC5it9dJouAyTsfSB6KNroFSf7Vxp/s+r98Td7ddbVehkNTNEZs6723jGaKgpNt3lPh/xZyWzcXiLY2CFf4/zIzPrsgF/5+mTcs0PKGR7+szGyOaeCf3Z1vLrFQ7wrkuGJEP5gOLms0gQSBf+TvWOMTk4ZW09U8XNUDVH5xV2q6efDv4dNb1dXAZPJLXTjUZnC2bN3NEBDYUTNjzWx5KPTuqqCI90jKUuK+88Nsbm+JGslRdCmecUJ/ofM7uydTcu1sPHt8MyX4fjTwLTWP17pp2dkkmqzGrz0ucJxyj6g5J4Ab8SUfpTMM39KPpDPwd/mYKxqC1fb9tM+S/FjWnY9dEZt8MV09sZiZHNu0B/KQdmngFBYRjcqY+nIgdIHoMBpx2kX0UlQb5wf5dM/38+NX3sBl93GFWuqUj9JxRo1RvO3X1BDVC6+He5+HLw10LwTZBjOvpT22lJl/n1jQbbY2kFGshL8L11dgZTw6pnE2X8oHKHNN5zVkg9MZ/6xicXIZIh/f/08DpuIBjrTEQLe/m314f/QPdB3nBot85+96bvv9ACn+vzUl5kl8zyqps3N6gPSWbdC/Z/om77hiORM/3hebfZCPgd/gAt2cbE4Rafv3IzD/f6gOROpojbOW+LebcTcbXDcZMO5OCTT+udC4w/Tts5tHcN8+P/u48avvcBTh7v5qyuaeeFz1xrLGB0uqFwD4/1w/ZfgHd8Fp/aGXnmpkoKeeiHttVUWuRJu+IYjkgF/gA1hbaNZlxmayNaVZdhtgn1JSj9vnB9lMhRhSxY3e0FJdqVUvQ9PtnXxNz/6C633PsPDr/m4YWONmneQLVxFcMdPwO6En95BrVslO7EWD7891M0HHniFhrICPnJViznr6DmqrkoTVB5KC53UlXqics+OwXGC4Qhr8kjmCZA97d88KN50E7aX78Nx6jm4arrDctAfZKUZJmXdbaqeXLMx7t2pMv/JUJjxYDgnmT8orf8lTTPvy4XGX6e0wMmLJ/spLXDyyevX8sErmtP/v7n1m6r3YvXOmcedBdB46byCf0WRm9HJKULhyJwSytB4kIiE5onD6sqjsCLt50+XIreDTfUl7D2deHiNvtl7SbYzfy2Rufq+Z5kMRagqdvP+S5u4bWs9W1eWZXUtakFNcPuP4MG3UfzYRyh1fyha83/wxdP802OH2bqyjD13bzev0bL3CGy4JelD1tV4o5l/ex7KPCHPg7+t4RKGRAk1PX8EPh09rtf8M073QeXS54wfKFN1Y+p66Fxs+EL8cY6+oQk8TlvWOo5j+cyN6+kenuS921dSPN8eg6Ydie9bfRU8969KfZFGkNY9oQb9wTkdoH1jQUCyYuQQrN81nxXPi+3NFfy/l88QmArHlU4eODdERZGLlRXZ/RC/uLGUVZWFXLa6glu3NHD5mkrzBigZZdUVcPN98Pin+aK7hN8NfZyvPnmU7zx/kl0X1vDN911CgRliEIDR8+pKNEG9X2dDrZeXTvYzFY5wMg9lnpDvZR+bjTeKtrNhfC9ElOVtKBxhZHLKnADbdTBuc5dOqg3fbJu66ZR4HBS7HXG1/rrSx7SBFkm4+aI6/vrK1fMP/KlYvROQcObFtH5Md4ON5+/TNxaggT48gT5TO3tns725nMBUhEO++BvY+88NsaWxNOt/x3U1Xp7/7LX893dv4cq1VbkP/Dqtfw2t93B78NcUHfsV33n+JB+4rInv3PkmcwK/lHDo17BHSwhWXpb04etqvATDEU73+2nv81NW6MxJApaM/A7+QE/NTsrlMFM+NcQ56p2T6Zq/vw9GOxMqfSB12UdfW7bLPkKIhFr/bPv4Z5WGbaonI03Jp5oDIXG89iPoOzHjvr6xAJfYtGNZ2OzV2bZKXbnsi1P6GQtMcbxnjC25KLPkM7u/SnvRFr7i2MM/X+3l3rdvNkdK2bEP9twID/2V2ui96xFtuHxipge7jHGq109LVX6VfGARBH+55joARg/9Fpi2T8j4pmr3zJm98ShwqsHkiTZ8s23qFksirX+n0YaqxYjDrfTfp9IL/pVFblrFMS545Ytw/9XQ9lD0vlfPDLLNfgLpKFD9Hlmi2uumpaoobt3/YMcQUpKbGns+43BRePse3E4Hd/V9nYxfkwydU8qiB65XSsBbvwkffQFarkn5oxesKMYm4Fj3CO19Y3lX8oFFEPzrG5o4EGlBnHwGiCmtZNrLP6r0SZz5Tw90SZD5+3VTt+z7dccb6jIRDNPvD2Zd459VmndCz2F15WaQ8iInt9lfZMrmUQH+V/fA43+P3+/nV6/6uLb4DKJ+q1KVZJHW5nL2nRmc068x3dlrBf/Z1DatxX7DP8HJ30HbLzP3xKFJle0ffRyu+ix84i/wprviNn/Gw+O001xVxKtnhzg/Esi7zV5YBMG/paqI5yMXU9K3HyYGzbN26G5TDn0pNg6T2ToP5mjDF5Tcs98fnNHqHpV5LtXMH9SmL6RV+il3C26xv8yJiqvgg7+BKz4B+/Yw+d1d1ATP0BQ8kdV6v05rcwVD46HoBqHOgXNDNFcWZs3Nc9Gx/R6l/HryH9NKApJy6CFVBr7jx3DdfwZ3+v0M62u8vNTeD5BXbp46eR/8y4tcvOrcho0ItD8X3aTLSPD398PJ38OfvqEkg0lKPjped2Jb5wF/EK87e6ZuscRT/ORK459V6i9R1rpplH6cp5+nQozxl5LrVXZ/473I23+Ma/QMT7q/iC0czGq9X+fSZpV4zLZ62H9uyMr6k2Gzw63/S/n+PPXFhT+flPDSt9UchzXXz/tp1td6CWtXcfnk46+T98EfYLRqK35RDMefiZZW5p1dv/4o/Pi98D8uhPta4EfvgKf/q2oYuvj2lD+etOwznn1TN514vv651PhnDbsTmi5Pb9O37ZeMUMwr9unekX0FV3DT5FcYLV2nXguNl5qw2OSsqiykqtg9o+7fPTzJ+ZGAtdmbihUXws6/h4M/h+PPLOy52p9TpcTLP56wkcsIevezTUBTZf6JLvJa56+zqrqEl/ovZteJZxhY+zG887FMDozCE5+DAz9Rg0Kar1TKntqLVJ3foE7c63HSMTge977B8exbO+g0xM38x7HbRLQNfsmyeic8/bSyevbWJn9scByO/oZXCnbSNz5dW//RS2cYdtdT8DfPwGQvlNSZvOi5CCHY3lw+I/jvP6euAqzM3wA7PwOHH4bHPw1/+xK451lqeelbULQCLnr3gpajT/VqLC803/Z6HiyKzL+lqoinApthrBvPwNHoNCbDnPszfOdKOPiz6c2bd31P1XpbrkmrQagkxYZvtjX+OjUlHoRghtbfNzhBbYknf7TZZtGsdf8aKf288SSE/OwvvSG6f9Q7GuDJQ128Z9tKCgsKoXyViYtNzvbmCjoGJ+jS/o77z6mBKdkc4LJocbiVImf4LDz7L/N7jt5jcOJpuPTD6vkWwKrKItwOW15u9sIiCf6rq4p5Pqz8dpqHXjSeXYen4LmvKhtgGYEPPqE2bxag4ki24Tvgz76vj47LYWOF1z2n5r+k6/06dVvAXQqnDVg9tD0E3nr6K7dF/X1+vvcsobDkzh1NKX7YfLbPqvvvPzfIxrqS7HroLGaadsD2D8Er/2fO5C9DvPxtcHhUE9kCsdsEn9q1jjsvy10ykYxFUfZZXVVED+WMlK7nxpFfs9W9D77nhKlJNehjKqACursYXF61M+8uhv6T0LVf1fJvvk8N5lggXo+TscAUUsoZ3ZZn+v34hiay6rU+m9laf9/gBDtaKnO2nqxhs6uW/1SZ//iAsgO+7KOURQoY9AeZCkf4yStn2bm2Ki+02BfWeSl02dl3eoBbLqqjrWOYd21rzPWyFhfXfwmO/RZ+eodSc1WvN/Zz/n448DMVL4oMuM4a4GPXrMnI85jBgjJ/IcR7hBCHhRARIURCbZwQYrcQ4pgQ4oQQ4vPp/p7mKrVZ8nLN++mmEpdNgqdE1e7rt6rSTeN2KGlUgWCsW3m9B0bhXXvgnfdnJPCDyvwjEvzBmdODHjvQCcAtF8e3ec0G9aXTWv9QOEL3yOTyyPxBST4HT8FwR+LHHHkUIiG46N1UFrmYikj+bX8nncOT3LkjP7Izh93Gm5rK2Xt6kBM9Y/iDYaveny6eEvgPDwMCHnzb9DCgVOz7vkood/ytqcvLFxaa+R8C3gl8N9EDhBB24FvADUAHsFcI8aiU8nWjv6TQ5aCu1MOT9mt4IrSeD25v5gs3JzdWMotYW+dYz5rHDnTRuqo8p8qa+jIPzxw5j5SS7uFJInKJK31iWR1T99/6vviPaXtIjWSs20pFlw+A//3749SXerh+w4osLTQ125sr+Prv3uAPx3sBa7N3XlSvgw8+Dj+8BX74VnUFUJVkHOdUAP58P1xwQ8IJXUuNBWX+UsojUso4E7ZncClwQkrZLqUMAj8Dbkv3d62uKuL1zhECU5GcKWogvr/Pse5Rjp0f5datucv6QZV9AlMRBvzB5aHxj2XFJiioSGzxPNIJp/8IF70HtDm+AKf7x3n/ZU15NV5ve3M5UsIPXzxNicdBc2V+bhjmPdXr4e7HIDIFD75VlYET0faQmst7+fLI+iE7G74NQOw0lg7tWFqsririjR7lj52rTVWIDf7Tm76PHejEJuCmzdmXB8YSq/VfFhr/WGw2aH6zCv5TcQa1HPo1IGGzku/pwd9pF9y+PfcbvbFsbSrDYRN0DE6wZWWZOUPIlwsrLlQfAOGgKgENtM99jJRqo3fFRjUucpmQsuwjhHgGiCee/k9SykcyuRghxEeAjwA0Nc18Q66uKkKf457bzF+VfUa0zF9KyaMHOnnzBVVUmzUw2iB6oPcNTUQz//rlEvwBLrwVjjwG39oO1/0X2PTO6eHrbb9U3cDapb8e/G/aXJfzv9tsCl0ONjWUcsDq7M0MNRvhrkdV8P/BzUoa7C7WhCFeCPrh/CG47VsLaupabKQM/lLKhU608AErY243asfi/a77gfsBWltbZ7hbxWplc+mLXTKr7HOwY5izA+P83XVJ6olZQg/0XcMT+AYnqCp2Ly+J4EXvUaWfZ76kzNr+9A244cvKs6lrP7xlWvvdUFbAR69u4Y48y/p1tq8qt4J/JqndrKyYn/gsdOxVYpDAKIS1udclDdGrwuVCNqSee4G1QojVqKB/B/D+dJ8k1hgpl8F/9hzfRw904rLbeMumFJ2lWaC80InHaaNzaILO4WWi8Y9FCFi7C9ZcpzL939+r7DuKawGhrgQ0bDbBF27KjWjACDdfXMcfT/TRusr8MZLLhrqL4Z6nZh6bCkBgTM2Gdi7xTvhZLFTq+Q4hRAdwOfAbIcRT2vF6IcQTAFLKKeDvgKeAI8AvpJSH0/1djeUFOLTaZ37U/KeIRCSPH+zk6vXVlBbkprM3FiGEJvdUNf/G5VTyicVmgy23wyf2wVv+VdV7196YE8uG+fKmpnJ++6mrKM1Rx/iyweGGoko1HH6ZsaDMX0r5MPBwnOOdwM0xt58AnljI73LYbTRVFnKmf5ySgtz1phW6pge6/Pn0AOdHArxtS25VPrHUlxXQodX8d22syfVycovDrdQb2z8EyJQPt7BYTuSPvs0ALVVFlBc6czKPVkcIQbFb+fs8dqCTAqedXRfmj0a8vszD0S4liV02Sp9UOFwL9mmxsFhqLAp7B50P72zhTH98R81s4vU4GBwP8cfjvezaWEOhK3/+G3WtPywjmaeFhUXa5E/UMsBlLZVclgdeNV6Pk+eO9TA6OcWteVTygZnSzmW34WthYWGYRVX2yRf0gS4lHgdXrcuMAVSmaLCCv4WFhQGs4D8PdK3/7s21eTekQc/8vW4HJR5LKWJhYREfK/jPA13rf+uWtF0qTKeuVGmVrazfwsIiGVbwnwfNlUU0VRSyoyX/GnA8TjuVRS5rs9fCwiIpi2rDN1/4j9dfwEevbskrJ8hY/nH3BhqtzN/CwiIJVvCfB0KIvPbMee/2lakfZGFhsazJz9TVwsLCwsJUrOBvYWFhsQyxgr+FhYXFMsQK/hYWFhbLECv4W1hYWCxDrOBvYWFhsQyxgr+FhYXFMsQK/hYWFhbLECFlfk44EkKMAsdyvQ6TqQL6cr0Ik7HOcfGz1M8PltY5rpJSVqd6UD53+B6TUrbmehFmIoTYZ53j4mepn+NSPz9YHuc4G6vsY2FhYbEMsYK/hYWFxTIkn4P//bleQBawznFpsNTPcamfHyyPc5xB3m74WlhYWFiYRz5n/hYWFhYWJpGXwV8IsVsIcUwIcUII8flcrycTCCG+L4ToEUIcijlWIYR4WghxXPtanss1LgQhxEohxLNCiNeFEIeFEJ/Uji+lc/QIIf4shDigneOXteOrhRCvaK/XnwshXLle60IRQtiFEK8JIR7Xbi+pcxRCnBZCtAkh9gsh9mnHlsxr1Qh5F/yFEHbgW8BNwEbgfUKIjbldVUb4IbB71rHPA7+TUq4FfqfdXqxMAZ+RUm4EdgAf1/5uS+kcA8B1UsotwFZgtxBiB/DfgK9JKS8ABoF7crjGTPFJ4EjM7aV4jtdKKbfGSDyX0ms1JXkX/IFLgRNSynYpZRD4GXBbjte0YKSULwADsw7fBjyoff8g8PasLiqDSCm7pJSvat+PogJHA0vrHKWUcky76dT+SeA64CHt+KI+RwAhRCNwC/CAdluwxM4xAUvmtWqEfAz+DcC5mNsd2rGlSI2Uskv7vhuoyeViMoUQohm4BHiFJXaOWjlkP9ADPA2cBIaklFPaQ5bC6/XrwOeAiHa7kqV3jhL4dyHEX4QQH9GOLanXairyucN3WSGllEKIRS+9EkIUA78CPiWlHFFJo2IpnKOUMgxsFUKUAQ8DG3K8pIwihHgr0COl/IsQ4ppcr8dErpRS+oQQK4CnhRBHY+9cCq/VVORj5u8DYieQN2rHliLnhRB1ANrXnhyvZ0EIIZyowP9jKeWvtcNL6hx1pJRDwLPA5UCZEEJPpBb76/XNwK1CiNOokut1wDdYWueIlNKnfe1BfYhfyhJ9rSYiH4P/XmCtpi5wAXcAj+Z4TWbxKHC39v3dwCM5XMuC0OrCe4AjUsr/GXPXUjrHai3jRwhRANyA2tt4Fni39rBFfY5Syi9IKRullM2o997vpZQfYAmdoxCiSAjh1b8HbgQOsYReq0bIyyYvIcTNqLqjHfi+lPIrOV7SghFC/BS4BuUeeB74EvBvwC+AJuAM8F4p5exN4UWBEOJK4A9AG9O14i+i6v5L5RwvRm0E2lGJ0y+klP8shGhBZckVwGvAnVLKQO5Wmhm0ss8/SCnfupTOUTuXh7WbDuAnUsqvCCEqWSKvVSPkZfC3sLCwsDCXfCz7WFhYWFiYjBX8LSwsLJYhVvC3sLCwWIZYwd/CwsJiGWIFfwsLC4tliBX8LSwsLJYhVvC3sLCwWIZYwd/CwsJiGfL/AWSqo0VhRWoFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_tab(df,row,seq_len=60, features=None):\n",
    "    df_plot = np.array( np.split(df.iloc[row,:],df.shape[1]/seq_len))\n",
    "    \n",
    "    if features is None:\n",
    "        features = range(df_plot.shape[0])\n",
    "    pd.DataFrame(df_plot).T.loc[:,features].plot()\n",
    "    \n",
    "plot_tab(tab_df,1,features=[1,2])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai import *\n",
    "from fastai.tabular import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(8000, 10000) (10000, 600)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0.0</th>\n",
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       "      <th>0.2</th>\n",
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       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "      <th>...</th>\n",
       "      <th>9.21</th>\n",
       "      <th>9.22</th>\n",
       "      <th>9.23</th>\n",
       "      <th>9.24</th>\n",
       "      <th>9.25</th>\n",
       "      <th>9.26</th>\n",
       "      <th>9.27</th>\n",
       "      <th>9.28</th>\n",
       "      <th>9.29</th>\n",
       "      <th>0.59</th>\n",
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       "      <td>0.975617</td>\n",
       "      <td>0.677658</td>\n",
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       "      <td>0.845843</td>\n",
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       "      <td>-0.817732</td>\n",
       "      <td>0.032371</td>\n",
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       "      <td>-0.041426</td>\n",
       "      <td>0.777901</td>\n",
       "      <td>0.973380</td>\n",
       "      <td>0.653314</td>\n",
       "      <td>-0.116416</td>\n",
       "      <td>-0.720925</td>\n",
       "      <td>-0.874417</td>\n",
       "      <td>-0.497024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.507859</td>\n",
       "      <td>-0.937069</td>\n",
       "      <td>-0.008017</td>\n",
       "      <td>0.966113</td>\n",
       "      <td>0.237674</td>\n",
       "      <td>-1.110846</td>\n",
       "      <td>-0.684935</td>\n",
       "      <td>0.789810</td>\n",
       "      <td>0.915796</td>\n",
       "      <td>0.703413</td>\n",
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       "      <td>0.591904</td>\n",
       "      <td>-0.104168</td>\n",
       "      <td>-0.842713</td>\n",
       "      <td>-1.074895</td>\n",
       "      <td>-0.503035</td>\n",
       "      <td>0.368854</td>\n",
       "      <td>0.853609</td>\n",
       "      <td>1.108681</td>\n",
       "      <td>0.703296</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.488590</td>\n",
       "      <td>0.684048</td>\n",
       "      <td>0.751033</td>\n",
       "      <td>-0.623372</td>\n",
       "      <td>-0.867582</td>\n",
       "      <td>0.211122</td>\n",
       "      <td>0.928282</td>\n",
       "      <td>0.148698</td>\n",
       "      <td>-0.905295</td>\n",
       "      <td>-0.879144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.861132</td>\n",
       "      <td>-0.329573</td>\n",
       "      <td>0.455247</td>\n",
       "      <td>0.938038</td>\n",
       "      <td>0.862129</td>\n",
       "      <td>0.305723</td>\n",
       "      <td>-0.474433</td>\n",
       "      <td>-1.015828</td>\n",
       "      <td>-0.733837</td>\n",
       "      <td>-0.287970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.889053</td>\n",
       "      <td>0.019115</td>\n",
       "      <td>-0.901737</td>\n",
       "      <td>-0.365437</td>\n",
       "      <td>0.705665</td>\n",
       "      <td>0.598105</td>\n",
       "      <td>-0.735526</td>\n",
       "      <td>-0.910243</td>\n",
       "      <td>0.254413</td>\n",
       "      <td>1.058759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.772389</td>\n",
       "      <td>0.299902</td>\n",
       "      <td>-0.304926</td>\n",
       "      <td>-0.963405</td>\n",
       "      <td>-1.023088</td>\n",
       "      <td>-0.239489</td>\n",
       "      <td>0.552001</td>\n",
       "      <td>0.980856</td>\n",
       "      <td>0.772081</td>\n",
       "      <td>0.217311</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.693414</td>\n",
       "      <td>-0.868973</td>\n",
       "      <td>0.457096</td>\n",
       "      <td>0.999172</td>\n",
       "      <td>-0.128114</td>\n",
       "      <td>-1.007975</td>\n",
       "      <td>-0.298816</td>\n",
       "      <td>1.066459</td>\n",
       "      <td>0.660450</td>\n",
       "      <td>-1.035437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 301 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0.0       0.1       0.2       0.3       0.4       0.5       0.6  \\\n",
       "0  1.114446  0.600313 -0.006571 -0.585057 -1.034596 -0.941631 -0.077671   \n",
       "1 -0.913026 -0.734200 -0.041426  0.777901  0.973380  0.653314 -0.116416   \n",
       "2  0.982462  0.591904 -0.104168 -0.842713 -1.074895 -0.503035  0.368854   \n",
       "3 -0.861132 -0.329573  0.455247  0.938038  0.862129  0.305723 -0.474433   \n",
       "4  0.772389  0.299902 -0.304926 -0.963405 -1.023088 -0.239489  0.552001   \n",
       "\n",
       "        0.7       0.8       0.9  ...      9.21      9.22      9.23      9.24  \\\n",
       "0  0.587005  0.975617  0.677658  ...  1.100737  0.342460 -0.795071 -0.536118   \n",
       "1 -0.720925 -0.874417 -0.497024  ... -0.507859 -0.937069 -0.008017  0.966113   \n",
       "2  0.853609  1.108681  0.703296  ... -0.488590  0.684048  0.751033 -0.623372   \n",
       "3 -1.015828 -0.733837 -0.287970  ...  0.889053  0.019115 -0.901737 -0.365437   \n",
       "4  0.980856  0.772081  0.217311  ... -0.693414 -0.868973  0.457096  0.999172   \n",
       "\n",
       "       9.25      9.26      9.27      9.28      9.29      0.59  \n",
       "0  0.666396  0.845843 -0.447279 -0.817732  0.032371 -0.554713  \n",
       "1  0.237674 -1.110846 -0.684935  0.789810  0.915796  0.703413  \n",
       "2 -0.867582  0.211122  0.928282  0.148698 -0.905295 -0.879144  \n",
       "3  0.705665  0.598105 -0.735526 -0.910243  0.254413  1.058759  \n",
       "4 -0.128114 -1.007975 -0.298816  1.066459  0.660450 -1.035437  \n",
       "\n",
       "[5 rows x 301 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_idx = range(len(tab_df)-2000, len(tab_df))\n",
    "print(valid_idx,tab_df.shape)\n",
    "feature_name = [str(k) for k in range(10)]\n",
    "feature_columns = [f+'.'+str(i) for f in feature_name for i in range(30)]\n",
    "cat_names = []\n",
    "dep_var = '0.59'\n",
    "feature_columns.append(dep_var)\n",
    "tab_df.loc[:,feature_columns].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "procs = [FillMissing, Categorify, Normalize]\n",
    "data = TabularDataBunch.from_df('data', tab_df.loc[:,feature_columns], dep_var, valid_idx=valid_idx, procs=procs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = tabular_learner(data,[16,16],metrics=mean_absolute_error, callback_fns=ShowGraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()\n",
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 00:20 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>mean_absolute_error</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.036876</td>\n",
       "      <td>0.021155</td>\n",
       "      <td>0.118292</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.032165</td>\n",
       "      <td>0.018682</td>\n",
       "      <td>0.111314</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.043541</td>\n",
       "      <td>0.013645</td>\n",
       "      <td>0.093738</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.034051</td>\n",
       "      <td>0.026890</td>\n",
       "      <td>0.133935</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.031514</td>\n",
       "      <td>0.026749</td>\n",
       "      <td>0.138565</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.030528</td>\n",
       "      <td>0.019548</td>\n",
       "      <td>0.111530</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.027726</td>\n",
       "      <td>0.021749</td>\n",
       "      <td>0.120564</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.025895</td>\n",
       "      <td>0.013727</td>\n",
       "      <td>0.093854</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.024393</td>\n",
       "      <td>0.010545</td>\n",
       "      <td>0.081759</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.018428</td>\n",
       "      <td>0.010693</td>\n",
       "      <td>0.082767</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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": [
    "learn.fit_one_cycle(10,1e-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_lan",
   "language": "python",
   "name": "env_lan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}