Stock Prediction.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96d9b5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a70fc2e8",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001-01-02</th>\n",
       "      <td>0.272321</td>\n",
       "      <td>0.260045</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>452312000.0</td>\n",
       "      <td>0.227448</td>\n",
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       "    <tr>\n",
       "      <th>2001-01-03</th>\n",
       "      <td>0.297991</td>\n",
       "      <td>0.257813</td>\n",
       "      <td>0.258929</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>817073600.0</td>\n",
       "      <td>0.250384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-04</th>\n",
       "      <td>0.330357</td>\n",
       "      <td>0.300223</td>\n",
       "      <td>0.323940</td>\n",
       "      <td>0.304688</td>\n",
       "      <td>739396000.0</td>\n",
       "      <td>0.260897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-05</th>\n",
       "      <td>0.310268</td>\n",
       "      <td>0.286830</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>412356000.0</td>\n",
       "      <td>0.250384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-08</th>\n",
       "      <td>0.303292</td>\n",
       "      <td>0.284598</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.295759</td>\n",
       "      <td>373699200.0</td>\n",
       "      <td>0.253251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                High       Low      Open     Close       Volume  Adj Close\n",
       "Date                                                                      \n",
       "2001-01-02  0.272321  0.260045  0.265625  0.265625  452312000.0   0.227448\n",
       "2001-01-03  0.297991  0.257813  0.258929  0.292411  817073600.0   0.250384\n",
       "2001-01-04  0.330357  0.300223  0.323940  0.304688  739396000.0   0.260897\n",
       "2001-01-05  0.310268  0.286830  0.302455  0.292411  412356000.0   0.250384\n",
       "2001-01-08  0.303292  0.284598  0.302455  0.295759  373699200.0   0.253251"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = '2001-01-01'\n",
    "end = '2022-02-18'\n",
    "\n",
    "df = data.DataReader('AAPL', 'yahoo', start, end)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84945048",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-14</th>\n",
       "      <td>169.580002</td>\n",
       "      <td>166.559998</td>\n",
       "      <td>167.369995</td>\n",
       "      <td>168.880005</td>\n",
       "      <td>86185500.0</td>\n",
       "      <td>168.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-15</th>\n",
       "      <td>172.949997</td>\n",
       "      <td>170.250000</td>\n",
       "      <td>170.970001</td>\n",
       "      <td>172.789993</td>\n",
       "      <td>64286300.0</td>\n",
       "      <td>172.789993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-16</th>\n",
       "      <td>173.339996</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>171.850006</td>\n",
       "      <td>172.550003</td>\n",
       "      <td>61177400.0</td>\n",
       "      <td>172.550003</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-17</th>\n",
       "      <td>171.910004</td>\n",
       "      <td>168.470001</td>\n",
       "      <td>171.029999</td>\n",
       "      <td>168.880005</td>\n",
       "      <td>69589300.0</td>\n",
       "      <td>168.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-18</th>\n",
       "      <td>170.539993</td>\n",
       "      <td>166.190002</td>\n",
       "      <td>169.820007</td>\n",
       "      <td>167.300003</td>\n",
       "      <td>82614200.0</td>\n",
       "      <td>167.300003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2022-02-14  169.580002  166.559998  167.369995  168.880005  86185500.0   \n",
       "2022-02-15  172.949997  170.250000  170.970001  172.789993  64286300.0   \n",
       "2022-02-16  173.339996  170.050003  171.850006  172.550003  61177400.0   \n",
       "2022-02-17  171.910004  168.470001  171.029999  168.880005  69589300.0   \n",
       "2022-02-18  170.539993  166.190002  169.820007  167.300003  82614200.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2022-02-14  168.880005  \n",
       "2022-02-15  172.789993  \n",
       "2022-02-16  172.550003  \n",
       "2022-02-17  168.880005  \n",
       "2022-02-18  167.300003  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5d2d1f2",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
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       "      <th>0</th>\n",
       "      <td>2001-01-02</td>\n",
       "      <td>0.272321</td>\n",
       "      <td>0.260045</td>\n",
       "      <td>0.265625</td>\n",
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       "      <td>452312000.0</td>\n",
       "      <td>0.227448</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>0.297991</td>\n",
       "      <td>0.257813</td>\n",
       "      <td>0.258929</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>817073600.0</td>\n",
       "      <td>0.250384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-01-04</td>\n",
       "      <td>0.330357</td>\n",
       "      <td>0.300223</td>\n",
       "      <td>0.323940</td>\n",
       "      <td>0.304688</td>\n",
       "      <td>739396000.0</td>\n",
       "      <td>0.260897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-01-05</td>\n",
       "      <td>0.310268</td>\n",
       "      <td>0.286830</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>412356000.0</td>\n",
       "      <td>0.250384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-01-08</td>\n",
       "      <td>0.303292</td>\n",
       "      <td>0.284598</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.295759</td>\n",
       "      <td>373699200.0</td>\n",
       "      <td>0.253251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        Date      High       Low      Open     Close       Volume  Adj Close\n",
       "0 2001-01-02  0.272321  0.260045  0.265625  0.265625  452312000.0   0.227448\n",
       "1 2001-01-03  0.297991  0.257813  0.258929  0.292411  817073600.0   0.250384\n",
       "2 2001-01-04  0.330357  0.300223  0.323940  0.304688  739396000.0   0.260897\n",
       "3 2001-01-05  0.310268  0.286830  0.302455  0.292411  412356000.0   0.250384\n",
       "4 2001-01-08  0.303292  0.284598  0.302455  0.295759  373699200.0   0.253251"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27fdfefa",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.272321</td>\n",
       "      <td>0.260045</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>452312000.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.297991</td>\n",
       "      <td>0.257813</td>\n",
       "      <td>0.258929</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>817073600.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.330357</td>\n",
       "      <td>0.300223</td>\n",
       "      <td>0.323940</td>\n",
       "      <td>0.304688</td>\n",
       "      <td>739396000.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.310268</td>\n",
       "      <td>0.286830</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>412356000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.303292</td>\n",
       "      <td>0.284598</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.295759</td>\n",
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       "       High       Low      Open     Close       Volume\n",
       "0  0.272321  0.260045  0.265625  0.265625  452312000.0\n",
       "1  0.297991  0.257813  0.258929  0.292411  817073600.0\n",
       "2  0.330357  0.300223  0.323940  0.304688  739396000.0\n",
       "3  0.310268  0.286830  0.302455  0.292411  412356000.0\n",
       "4  0.303292  0.284598  0.302455  0.295759  373699200.0"
      ]
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df = df.drop(['Date', 'Adj Close'], axis = 1)\n",
    "df.head()"
   ]
  },
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   "id": "79cf9c65",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ddf0b571",
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.272321</td>\n",
       "      <td>0.260045</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>0.265625</td>\n",
       "      <td>452312000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.297991</td>\n",
       "      <td>0.257813</td>\n",
       "      <td>0.258929</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>817073600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.330357</td>\n",
       "      <td>0.300223</td>\n",
       "      <td>0.323940</td>\n",
       "      <td>0.304688</td>\n",
       "      <td>739396000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.310268</td>\n",
       "      <td>0.286830</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.292411</td>\n",
       "      <td>412356000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.303292</td>\n",
       "      <td>0.284598</td>\n",
       "      <td>0.302455</td>\n",
       "      <td>0.295759</td>\n",
       "      <td>373699200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5313</th>\n",
       "      <td>169.580002</td>\n",
       "      <td>166.559998</td>\n",
       "      <td>167.369995</td>\n",
       "      <td>168.880005</td>\n",
       "      <td>86185500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5314</th>\n",
       "      <td>172.949997</td>\n",
       "      <td>170.250000</td>\n",
       "      <td>170.970001</td>\n",
       "      <td>172.789993</td>\n",
       "      <td>64286300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5315</th>\n",
       "      <td>173.339996</td>\n",
       "      <td>170.050003</td>\n",
       "      <td>171.850006</td>\n",
       "      <td>172.550003</td>\n",
       "      <td>61177400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5316</th>\n",
       "      <td>171.910004</td>\n",
       "      <td>168.470001</td>\n",
       "      <td>171.029999</td>\n",
       "      <td>168.880005</td>\n",
       "      <td>69589300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5317</th>\n",
       "      <td>170.539993</td>\n",
       "      <td>166.190002</td>\n",
       "      <td>169.820007</td>\n",
       "      <td>167.300003</td>\n",
       "      <td>82614200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5318 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            High         Low        Open       Close       Volume\n",
       "0       0.272321    0.260045    0.265625    0.265625  452312000.0\n",
       "1       0.297991    0.257813    0.258929    0.292411  817073600.0\n",
       "2       0.330357    0.300223    0.323940    0.304688  739396000.0\n",
       "3       0.310268    0.286830    0.302455    0.292411  412356000.0\n",
       "4       0.303292    0.284598    0.302455    0.295759  373699200.0\n",
       "...          ...         ...         ...         ...          ...\n",
       "5313  169.580002  166.559998  167.369995  168.880005   86185500.0\n",
       "5314  172.949997  170.250000  170.970001  172.789993   64286300.0\n",
       "5315  173.339996  170.050003  171.850006  172.550003   61177400.0\n",
       "5316  171.910004  168.470001  171.029999  168.880005   69589300.0\n",
       "5317  170.539993  166.190002  169.820007  167.300003   82614200.0\n",
       "\n",
       "[5318 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7510390e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2            NaN\n",
       "3            NaN\n",
       "4            NaN\n",
       "          ...   \n",
       "5313    161.0511\n",
       "5314    161.3107\n",
       "5315    161.5670\n",
       "5316    161.8021\n",
       "5317    162.0560\n",
       "Name: Close, Length: 5318, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma100 = df.Close.rolling(100).mean()\n",
    "ma100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b9feec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc9bd2fdb20>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(ma100, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d53698b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             NaN\n",
       "1             NaN\n",
       "2             NaN\n",
       "3             NaN\n",
       "4             NaN\n",
       "          ...    \n",
       "5313    150.28395\n",
       "5314    150.48520\n",
       "5315    150.70870\n",
       "5316    150.91260\n",
       "5317    151.10040\n",
       "Name: Close, Length: 5318, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma200 = df.Close.rolling(200).mean()\n",
    "ma200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0898c775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc9c30548e0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiEgNsC0157Dn+DlKD6VwFxsy4S423jgrv+icf05YDsD2tMLvmDwZAgNh4EBe+HI4cZFxXNblsuOq/0SgHmMRERGRWsK/jJXrcooF4HEzN5Y6DjCiYwMARnVr7G2YPBkGDuTP3YuZs20O9/S7Bz9H9e9vVTAWERERqSUSUjJLteW5PL6ZKdbuzChxbP8DeKnZ+QD0bB4F27bBqlVw+um8OOdFooOjubrb1RVceeVQMBYRERGpJWauSymxv398cZ7Lu0T0gRO37R9aMW9T0bzHTJoEwIaBnfhh7Q/c0usW6gTUqZiCK5mCsYiIiEgtddvQVkDRLBWntKpb4rirrDmOf/4ZWrTg7dTfcDqc3Nz75gqvs7IoGIuIiIjUIAfOP1xcs+iQEvvB/t5ZJvY/jNc82tvze9sQb2Besm0vKxKLZqogNxemTiXzrOF8sORDzmt3Ho3CGpVn+VVKwVhERESkBjlELqZBeFCJ/f2zTOwfKpHn8gbk9Jx8Tl87h6/u/zdb92QVXTB9OmRn835PQ1puGnf3u7tca69q1f/xQRERERHx8ViLA1PmsVU70kvs929djzemJRDk7+0rzXN5MAaGfvY6g797F4A9+WsIav0PHrukN3zyIq6QIF5J+4UBzQbQt0nfiv0xlUzBWERERKQGOViHcUZuAZl5rhJt9UK9D9/lu71X5Ra4aZadxoBvP+TbDoNJbNSSW3/6kCnhf5Id/y8YP56vL+7IlvSFvH7mGxX5M6qEhlKIiIiI1CCeg4ylWJmUXqot0M87xnjsF4sByC3wcMXin3B43Px7wBhe63MBK977knynH21vvQqbkswLnfbRrl47zmpzVsX9iCqiHmMRERGRGuRgY4z3T8lWXFDhw3f7mX1pXDz/JwrOOZdtkQ3AbTl7XR2iLnuBn+puZU0HB0tW/ZP3hryHw9S8/tXD/iJjzAfGmGRjzIpibS8aY9YYY5YZY741xkQWtscZY3KMMUsKX+9UYO0iIiIiQsmlnA8WjD0HTL324dW9fWOLATwuN+e/+TgheTkEPPFYiXPTQiLIuulWXnfPISYkhjFdxpRf8SeQI4n6HwEjDmibAnSy1nYB1gEPFju2wVrbrfB1U/mUKSIiIiIHs2z7Xt/2wYZS7A/P1w9owbjLezKkbf0SPcYZD/4f3eb/wQejboGuXUtdvy9/Jz+u+5Fru19LoF9g+f6AE8Rhh1JYa2caY+IOaPut2O7fwIXlXJeIiIiIHKFXf1/v2z5oMC5sH9WtMZ0aRwDg73TQIjWR1394gYhdGxjf+VTmjrqc68u4/otVb2Ot5YaeN5R7/SeK8hgccg3wS7H9FsaYxcaYGcaYAQe7yBhzgzFmgTFmQUpKysFOExEREZHDqBNY1PNrgXyXh3W7Mkqcsys9FwCno9hUbnl5vPXdc3TatYEvu5zGI6fdSlBAyX5TN+mkO7/jjXlvcE33a2gR1aLCfkdVO65gbIx5GHABnxU27QCaWWu7A3cDnxtjwsu61lo7zlrby1rbKyYm5njKEBEREanVhneI9W1bDzzx40pOe2UmO/fl+tof/X4lcEAw/uEH2qds5sbzHuKBM8aS7+dfYnhFrmMZiUHXkxbwHh1iOvDcsOcq/sdUoWMOxsaYK4GRwGW2cO1Ba22etXZP4fZCYAPQpjwKFREREZGyudxFwyc81rJwSxoAadn5QMlloh2mZDBODQ5nSquTfE37H8hzk0ZywJP42bq05m0W37iYmDo1uzPzmKZrM8aMAP4JDLLWZhdrjwFSrbVuY0xLoDWwsVwqFREREZEyuYrPSkFR+N3/wN38zWm+474eY5cLJk1iWnwvPI6iXuKgwrmN9/l/gyWfl4d9ytkdemBM2avp1SSHDcbGmC+AwUA9Y8x24DG8s1AEAlMK/0h/F85AMRB40hjjAtzATdba1AqqXUREREQoGYw91vrC7/4H8eZt2lPiOABz5kBqKlMGFvUWg3du452ZO8kN+JUe0edw+8CDPjJW4xzJrBSjy2h+/yDnTgAmHG9RIiIiInLkXO6ixTs81uJwlOwxbhgR7DvuG1bx6acQGMisuO4lPivQz8F/F/wXlyefr0a/WMGVn1hq3pIlIiIiIrVM8THGmbku8grcQNEy0PtXvbu0d1Na1Q+DpCT4+GO45hqyAkNKfNaU1bv4ZNknDGkxhFbRrSrpF5wYFIxFREREqrkCT1GPcXJGHp0L5ynOyHWxekc6//trMwAPnNEO8vLgkku8Y4zvvZfW9UNLfFZawQo2pG3g8i6XV1r9JwoFYxEREZFqzl2sx9jpMNQL865M57GWM16bxZqd3jmNg/yd3p7i2bPhnXegZUuePrdTic/KcP5BsF8wF7S/oPJ+wAlCwVhERESkmiso/vCdx+JfOMY4LSu/xHmBfg54/33o0gWuuw6Azk0ifMevG9CUrTlTObfduYQFhlVC5ScWBWMRERGRaq7kw3dA4dRq783eVOI8k5AA8+bBmDG+c4ov+NEpfiNpuam1chgFKBiLiIiIVHsFxYKxtRZPsR7kEr76yhuIRxdNOuYsNj/xJ8s+IbZOLMPjh1dYrScyBWMRERGRai4zz+XbXpmUXjRX8YF+/x169IAmTXxN+3uM3WTy07qfGN1pNH6OY1oDrtqrnb9aREREpAZJzy0Kxs9MWl3mOQGuApg7F266qUT7/hXtsp2zyHfnc3nX2jmMAhSMRURERKq9jGLB+GA67UyA3FwYUPZKdqbOLDpEdqB7g+5lHq8NNJRCREREpJrLzC047Dl9tq/0bvTvX+rYVze3YU/BMkZ3Gu3rQa6NFIxFREREqrHsfBeLtu497Hl9tq2Atm2hfv1Sx/7eMQmAizpcVN7lVSsKxiIiIiLVWGJazmHPCXAV0Hfbchg2rMzj41eNp0tsF9rWa1ve5VUrCsYiIiIi1djwV2YCcFqH2IOe0zNxFSEFeTBiRKlj2/ZtY862OVzc4eIKq7G6UDAWERERqQGCA5wHPfZqxE6svz8MGVLq2Pdrvwfgwg4XVlht1YWCsYiIiEgN0Ca25BLOL1zYxbcd+9cMzCmnQGhoqetmbJlBs4hmtX4YBSgYi4iIiNQITaNDSuwPbed9yC46ex8sWwannVbqGmstM7fMZGDzgZVS44lOwVhERESkBnAYGNmlYan2vluXezeGDi11bH3qepKzkhnYTMEYFIxFREREagSHMbRvGO7br1snAICTtyyFsDDo2bPUNTO3eB/cU4+xl4KxiIiISA1w4LIc+xfq6Ld1GQwaBH6lFzyeuWUm9evUp03dNpVQ4YlPwVhERESkBnA4DNbaEm2xGbuJT00sczYK8AbjAc0G1OrV7opTMBYRERGpplxuj297WLv6HJCLOWnbCu/G4MGlrt26bytb9m3RMIpiFIxFREREqqkCtzcJ/3NEO/ycDjwHBOO7A3aQHxoOXbuWunbWllmAxhcXV3qwiYiIiIhUC+7CLmLnQbo641YvhkEDwFl68Y+ZW2YSHhhO5/qdK7LEakU9xiIiIiLVlLuwi9hROEbYUqzLOCsL1qyBXr3KvHbW1ln0b9Yfp+PgK+bVNgrGIiIiItWUx7O/x9gbjC87qTnhQX78cscA76IeHg90717qun25+1i9ezUnNzm5Uus90WkohYiIiEg15bEle4xjwgJZ9vjp3oPfLva+lxGMF+1YBECvRmX3JtdW6jEWERERqab2jzF2OMqYbm3xYoiOhqZNSx1auGMhAD0blV70ozZTMBYRERGppjyFs7U5y5qHePFib29xGccWJC2geURz6oXUq+AKqxcFYxEREZFq6qCzUhQUwPLlZQ6jAG+PsXqLS1MwFhEREammPAfMSuGzejXk55cZjPfm7iUhNYGeDRWMD6RgLCIiIlJNHfjwnc/iwgfvevQodY0evDs4BWMRERGRasp9wHRtPosWQUgItG5d6pqFSYUP3qnHuJTDBmNjzAfGmGRjzIpibdHGmCnGmPWF71HFjj1ojEkwxqw1xpxeUYWLiIiI1Haeg81KsXixdxnoMla8W7hjIXGRcdQNqVsZJVYrR9Jj/BEw4oC2B4Cp1trWwNTCfYwxHYBLgY6F17xljNFyKiIiIiIVwF3WrBQeDyxZctAH7xYkLVBv8UEcNhhba2cCqQc0jwI+Ltz+GDi3WPuX1to8a+0mIAHoUz6lioiIiEhxRUMpijVu3AgZGWUG49ScVDakbdD44oM41jHGsdbaHQCF7/UL2xsD24qdt72wrRRjzA3GmAXGmAUpKSnHWIaIiIhI7bV/KIUp3mO8+OAr3u0fX9y7Ue8Kr606Ku+H78qYXRpb1onW2nHW2l7W2l4xMTHlXIaIiIhIzbc/GDsPDMZ+ftCpU6nz5yfNB7Ti3cEcazDeZYxpCFD4nlzYvh0ovu5gEyDp2MsTERERkbJk5BZw0Tt/AQfMSrF4MXToAIGBpa6ZnzSf1tGtiQyKrKQqq5djDcY/AFcWbl8JfF+s/VJjTKAxpgXQGph3fCWKiIiIyIFe+HUteS7v03clZqVYtsw7I8UBPNbDn1v/pG+TvpVVYrXjd7gTjDFfAIOBesaY7cBjwPPAeGPMtcBW4CIAa+1KY8x4YBXgAm611rorqHYRERGRWuuTv7f4tn1DKVJTISkJOncudf6K5BWkZKcwtMXQyiqx2jlsMLbWjj7IoWEHOf8Z4JnjKUpEREREjpyvw3jlSu97GcF46sapAAxrUWaEE7TynYiIiEi15xtjvHy5972MB++mbppK6+jWNI1oWuqYeCkYi4iIiFQzHk/JSb/qBBYOAlixAiIioHHJ2XL35u7l942/M6LVgWu2SXEKxiIiIiLVTHZByUe4QgIKFxpescI7jMKUnEH3m1XfkOfOY0yXMZVVYrWkYCwiIiJSzWTluUrs1wn0A2u9wbiMYRSfLPuENnXbaGGPw1AwFhEREalmVu1IL7EfEuCElBRIS4P27Usc27x3MzO3zOTyLpeXXCFPSlEwFhEREalm/t64p8R+SIAfbNzo3WnZssSxz5Z9BqBhFEfgsNO1iYiIiMiJpU39MACaRAVTJ8DPOyvFpk3eg8WCsbWWT5Z9woBmA4iLjKuCSqsXBWMRERGRaiQ5PZd7vl4KwNc39aNhRLD3wP5gHBfnO3dB0gLW7lnLPf3uqeQqqycNpRARERGpRr5euN23HejnLDqwaRPExkJIiK/pk2WfEOgM5KKOF1VmidWWgrGIiIhINZKTXzRVW1SIf9GBjRuhRQvfboG7gC9XfMnZbc8mMiiyEiusvhSMRURERKqRsKCikbAlZpnYvLnEMIqZW2aSkp3C6E6jK6+4ak7BWERERKQaiQ0PKt3o8cD27dCsma9p4uqJBPsFa7W7o6BgLCIiIlKNrEzaV7oxORny86FpUwA81sN3a79jRKsRhPiHlD5fyqRgLCIiIlJN/LA0iXdneWefuHlwfNGBbdu874U9xvMS55GUkcT57c+v7BKrNQVjERERkWpi2ba9vu3bhrQqOrA/GBf2GH+7+lv8HH6c1fqsSqyu+lMwFhEREakmXB7r264TWGw5imLB2FrLxDUTGdpiKFHBUZVcYfWmYCwiIiJSTeS7PWUf2LYNgoKgbl1WpqwkITWB89qdV7nF1QAKxiIiIiLVhKswGAc4D4hwW7Z4xxcbw8TVEzEYRrUdVQUVVm8KxiIiIiLVRJ7LG4xjwgJLHtiwAVq2BODbNd/Sr2k/GoY1rOzyqj0FYxEREZFqYndmHgD1w4sFY2u9wTg+nk1pm1iycwnnt9NsFMdCwVhERESkmqhbxxuIR/cpWsiDPXsgPR3i4/l2zbcAnNde44uPhYKxiIiISDVRNzSAAD8HF/dqWtS4YYP3PT6eiasn0jW2Ky2jWlZNgdWcgrGIiIhINZFb4CE8yL9kY2Ew3tc8lr+2/8XZbc6ugspqBgVjERERkRPAt4u389wvqw95Tl6BmyD/A+JbYTCe7UzEYz0MaTGkokqs8fwOf4qIiIiIVKT//bWZR79fCcBdp7YhyN9Z5nkTFyeWbtywARo3ZnrSXwQ4A+jbpG9FllqjqcdYREREpIrtD8UA//p1TZnnWGvLbN8/I8WMLTM4qfFJhPiHVESJtYKCsYiIiEgV2p6WXWJ//ubUMs97/Y8EALo1jSx5ICGB9FZNWbhjIYOaD6qIEmsNBWMRERGRKmKtpf+/ppVoKxV8C/17yjoALu/bvKgxNRV27uTPln54rIfBcYMrqNLaQcFYREREpIqkFC7YUVz9sKBSbe/N2ujbPr1Tg6IDf/wBwIwmbvwd/vRr2q/8i6xFFIxFREREqkh2nrtUW05B6banfy6arSI0sNjcCb//DmFhzChYT+/GvTW++DgpGIuIiIhUkfTcglJtBS7PQc//v5EdSjZMmULmsAHMT1rA4OaDy7m62kfBWERERKSKZOS6SuwHOB0UuA8ejDemZBbb2QgbNzJ9QFPc1q3xxeXgmOcxNsa0Bb4q1tQSeBSIBK4HUgrbH7LWTjrW7xERERGpqdJzvD3G3916Cg3Cgzj7jdnkuw8yLRvw4Jnti3YmTADg2/p7CM8PZ1CcZqQ4XsfcY2ytXWut7Wat7Qb0BLKBbwsPv7L/mEKxiIiISNn2D6WoFxpAg4ggnMbwxbytzEnYXeK8AD8HNw5qWTS+2OOBjz7CdXJfvk+axtltzibAGVDZ5dc45TWUYhiwwVq7pZw+T0RERKTG++eE5QCEBfkDRUF57JdLfOfkFrjJd3mICPYvuvCrr2DVKmZdOZg9OXs4r915lVZzTVZewfhS4Iti+7cZY5YZYz4wxkSVdYEx5gZjzAJjzIKUlJSyThERERGpFfb3BGfne2ek2F1sGrd9hcMtwgvDM2lpcPfd0L073zbJJMgviBGtRlRuwTXUcQdjY0wAcA7wdWHT20A80A3YAbxc1nXW2nHW2l7W2l4xMTHHW4aIiIhIteV0mIMeS83KB/D2GO/eDSNGwO7deN57l4lrvmVEqxHUCahTWaXWaOXRY3wGsMhauwvAWrvLWuu21nqAd4E+5fAdIiIiIjWKx2PxdxpuGhR/yPPuGb8UgPDdO6F/f1i2DL75hgWxbhIzEjWMohyVRzAeTbFhFMaYhsWOnQesKIfvEBEREalR1idnUuC2NIkKPuR5q3ak0zA9hd5XXwBJSTB5MowaxcTVE/Fz+DGyzchKqrjmO+bp2gCMMSHAcODGYs0vGGO6ARbYfMAxEREREQHW7EwHIK7uwYdBzFqfQvO0JP43/lEC3dnw22/Qty8e6+GrlV8xtMVQooOjK6vkGu+4grG1Nhuoe0Db5cdVkYiIiEgtsH/scIdG4SXaW+3eytO/vQV/v8jeXR4mJ8wl2z8I5/Qp0Mc7QnXmlpls3ruZp4c8Xdll12ha+U5ERESkCqRm5eMwlJiGLTrEn5d/foVuO9ZBdjZDNi7g1zb9OPvKV32hGOCjJR8RFhDGee01vrg8HVePsYiIiIgcPWstr/+RAJSckaJ/wny67lzPvWfeyUs/v0KnB34udW1mfibfrPqG0Z1GE+IfUmk11wbqMRYRERGpZFmF8xUf6Pz5k9gVGs33HQb5Fvs40DerviGrIIurul1VgRXWTgrGIiIiIpUsJcO7gMdz53cuaszMpF/CfCa1PYUCpz9vT99Q6jprLW/Of5O2ddtyctOTK6vcWkPBWERERKSS7Q/GTaOKDYX45RcCXQX80vYUADKK9Rj3au5dSHj21tksSFrAnX3vxJiDLwoix0bBWERERKSSPTtpNQAxYYFFjb/8gjsyigWN2wOQnVc03GJY+1gA/v33v4kOjuaKrldUXrG1iIKxiIiISCWx1vL9kkSWbNsLQPO6IfsPwG+/4Tx1GB6HE4DMPJfvugA/BwmpCXy/5ntu7nWzHrqrIArGIiIiIpXkq/nbuOPLJb79IH9vCGbNGkhMhNNO8x3LyvcG42bRIVx2UjNe+/s1/Bx+3Nr71sosuVZRMBYRERGpJA9MXO7bblU/tOjAb79534cP9zUlp3vHIT85qiO7c3bw3uL3uKzLZTQMa1gptdZGCsYiIiIiVaBBeFDRzm+/QZs2EBfnW/BjfXImADv35fLYtMfwWA+PDXqsKkqtNRSMRURERCpB4t4cAOqFBgDQp0W090BeHkyf7htGUXzBD4CUvNV8tPQjbu9zO3GRcZVVbq2kle9EREREKsGX87YC8Mol3agXGkjb2DDvgTlzIDvbN4yieC72kMvrSx6iYWhDHh7wcGWXXOsoGIuIiIhUolPi6+Eonn4nTwY/PxgyBMA3P7HFkur/Btmp65l6xVSigqOqotxaRUMpRERERCpBRq6LsCC/kqEYvMH4lFMgzNuD7PZYANL9vibLbzqPDnycIS2GVHa5tZKCsYiIiEgF83gsH83ZTEauq+SBXbtgyZIS07S53B6ynDPY6/8/QlyDNISiEikYi4iIiFSw3Zl5ZR/YP03b6af7mkb2ymC3/ysEujtRr+AO/JyKa5VFf2kRERGRCrYtLafsA5MnQ0wMdO8OFK6Mt/k5nDaKmPxHMAT4xhxLxVMwFhEREalgf2/cA8BZXYotzuHxeHuMhw8HhzeS/ZrwK6v2LCLCdQlOQsv6KKlACsYiIiIiFeyD2ZsAePHCLkWNS5ZASopvGIW1lsemP0bziOaEuocBEKBhFJVKf20RERGRCpSWlc+erHwAQgKKzZQ7YQIY43vwbtL6ScxPms8jAx/B4F39bs1TIyq93tpMwVhERESkAs3bnFq6MTcXxo2Ds8+GBg2w1vL4jMeJi4zjyq5X+k4rNbWbVCgFYxEREak1bvxkASNenVmp31ng9gDw9mU9ihq/+AJ274Y77gDg5/U/syBpAY8MeAR/p3+l1idFFIxFRESkRrPW8s3C7ezNzmfyyl2s2ZlRqd+fWTh3cZemkfsLgv/8Bzp1giFDvL3F0x+nZVRLruh6RaXWJiVpSWgRERGp0RKSM7n366Ul2jamZNIypnJmfXhg4nIA6tYJ8DbMmuV98G7cODCGn9b+yMIdC/ngnA/UW1zF1GMsIiIiNdrmPdml2p6dtNq39HJFmb42mVemrPPtB/k7vRv/+Q9ER8Nll/nGFreMasmYLmMqtB45PPUYi4iISI22aXdmqbbfVycT/9Ak/rhnUIX1HF/14fzSjVu3wrffwn33QUgIP6z5nkU7FvHhqA9L9BZPvnMgKxL3VUhdcnAKxiIiIlJjWWvZmJJ10ON/bdxTaUMqAHjzTe8Ubbfcgsvj4sGpD9I6unWp3uK2DcJo2yCs8uoSQMFYREREarBJy3fy5fxtBz0e5OeslDq+uakfZGfDu+/CeedBs2a8v+C/rN69mokXT8TPoUh2ItAYYxEREamx5mzYDUC7BmFMvOVkfrytf4njwQHlG4w3787C7bG43B6C/B20axDG73cPoldcNHz6KaSlwdixZORl8Oj0R+nfrD/ntju3XGuQY6d/PBEREZEaKyrEOxPEF9f3JapwVohHzmrP0z+vBsBhjm4BDZfbw3X/W8D/jexA/AFDMLalZjP4pem0rFeHjbu9wzeuOjmOVvVDwe2Gl16Cnj2hf39enP4YyVnJ/HDpD5ijrEEqjnqMRUREpEZK3JvDG9MSqBPg9IVigOsGtOT1Cztx34yP6d2nLfTuDePHH9FnvvjbWqavTWHYyzNKHXth8loAXygGaF63jndj4kRYvx4eeIDEjCRemvMSl3a6lJOanHQcv1DK23EFY2PMZmPMcmPMEmPMgsK2aGPMFGPM+sL3qPIpVUREROTIXfnBPACy8t0lD6SnM/y20dz699csbtTWuzzzJZfAU08d8vM8Hst/Z2wEvEMzDvTj0qRSbQ0jgrwLejz/PLRuDeedx8N/PIzbunl26LPH+MukopRHj/EQa203a22vwv0HgKnW2tbA1MJ9ERERkUoVV9hb+9PtxcYVFxTAxRcTuHA+Y8++l+sufAwWL4bLL4dHH4UFCw76ebuz8nzbIcXGJu/OzCPugZ/LvKZBRJB3+edFi+Cf/2TujgV8vPRj7jzpTlpEtTjOXyjlrSKGUowCPi7c/hg4twK+Q0REROSQ/J2GNrGhdGocUdT41FMweTKZr77ODx0Ge9v8/OCNNyAyEp555qCf99eGPb7ttOwC3/b4BWXPeuHvLiDorTfgxhuhb188V17BHb/eQYPQBjwy8JHj+WlSQY43GFvgN2PMQmPMDYVtsdbaHQCF7/WP8ztEREREjlqey0Ng8enYNm6EF16Af/yD4JtuKHlyeDjccQd89x0sX17qs9Ky8rnjyyW+/YxcbzBOSM7ghV/Xlji3Tl42t835knXj74A774QePeCbb/hs5ZfMTZzL88OeJyxQcxSfiI43GJ9ire0BnAHcaowZeKQXGmNuMMYsMMYsSElJOc4yRERERErKLXAT5F8s6tx7r7d3+IUX8HM6uGd4GwAK3B7v8bFjISwMni099ndDStHqeUPaxpBX4L2m+Op28x4exvD1fzP93Ru4d9anmDZt4JdfYMYMMmMieGDqA/Rp3IfLu15eAb9WysNxBWNrbVLhezLwLdAH2GWMaQhQ+J58kGvHWWt7WWt7xcTEHE8ZIiIiUsu5PZYv5m0lK88FwNY92czZsId9OYVDHqZM8S7F/PDD0LgxAIH+kOmcxtXfXceF4y/kxdXvs+PWK+Grr2DdOt9nfzZ3Cxe+8xcA0+8dTOcmkWTkucgtcLM9LQeAYH8n9WdO5a3vnmNHWD34+2/vd44YAcCj0x4lKSOJV09/FYfRpGAnKmOtPbYLjakDOKy1GYXbU4AngWHAHmvt88aYB4Boa+39h/qsXr162QWHGOwuIiIicihTV+/i2o+9WSLhmTNo9fAvvmObnzoNunaFvDxYuRKCgliQtIBzPhvNjuwEAkw4zaLqk5CagNM46b/FQ6+oToSffSH1Qurx5LfpOGwEAX75PDgqiI8WTGHhjsWc0bYP85cPIMA2Z8WpIYSefSbLIxozevRzrHjlIt/3z9oyi0EfDeKW3rfwxplvVPrfRkoyxiwsNmlEyWPHEYxb4u0lBu9CIZ9ba58xxtQFxgPNgK3ARdba1EN9loKxiIiIHIsd+3K4+sP5uDyWhGTvcIf4mDpsSCmaS3hzwwTvWN/vv4dzzuG1v1/j3in3EupfD//0awn29GXL8yNZv2c97y9+n2nTP2SJSSb/IMugBTkjIb85Bc61uMmlV3BfXv54Of3djUn/9Q+oH0Nk4cIiS3Yu4dT/nUpkUCRLblpCaEBo2R8qleZQwfiYV76z1m4EupbRvgdvr7GIiIhIhfpp6Q7W7Mwo0bY/FA9qE8NdXSLg1MvgtNPwjDyLe369i1fnvsq57c6ld8RDjJteNOKzdd3WPH/q89DyBtxtWmHvvJOdj95Nz+c/wGMyiYuO4surL+axiSnMTtiDm3TyzEQ25E1k0MUeGgSncd7KZxjlGkWziGb8tf0vbpt0G9HB0UweM1mhuBrQktAiIiJSbaVm5/u2G0cGk7g3x7f/8VW9vAt3ZGWR+/K/uGLCaL5e9TVj+4zl36f/m22pub5gPHX1Loa1j/Ve2LIlzssuhzfeouENNxPs6QnAuxefQnx0JPExOcxO2IPThvG/bxM5ZZvhp08eZ6JnBR8v/Zi3F7ztq2FAswGMv2g8DUIbVMJfQ46XgrGIiIhUGz8tS+KU+Hq+JZ6T9ubQJCqY3+8eRJC/07fQxq2tAuG002DqVNKef4xzF9zBzC0zeWn4S9zd726MMcTVq0Pb2DDW7srg2o8XsPn5s4q+6Lnn4Ntvyb/1NuhxO89d0IVuTSMBePDM9nz81xbGzvmSEev+wr74IqMvuJfRQE5BDrO3ziYlO4WIwAhOb3U6fg7FrepCd0pEREROSB6P5ZuF2zm7ayOC/B1MWJTIvV8vpV2DMH69cyAut4e92QVE1wkgyN87X/Gz53Um/I/JnHXf/VBQwNa3nuMM+wkJ2xP44oIvuLTTpSW+Y9PurLK+Gho1gieeIPjuuzktrDfxMSf7DgX5O/nX2h+5ZPZnTOp2Kmfec4/vWLB/MMPjh5f/H0MqheYLERERkSqXledie1o2ANn5LlYlpfPRnM3cP2EZj/2wgmXb93Hv10sBWLMzg22p2bR6+BdmrEshItjf+yG5ufxjyv8Y+X83Y1q0YO7vH9Ev53US0xOZPGZyqVAMkL9/DuOy3HYbaS3b8OjUcTQNKjZZwRtvcMl3/2VixyHcNvx2MKbc/g5StdRjLCIiIlXuyR9X8dWCbQxuG4Ofw8Hvq3f5jq3dmcGu9NwS509fW/TQXGSgEz77zDtH8ZYtFFx8Ic9c25qnf7uUJuFNmH3NbDrV73RU9fy6Yid1Ap283vcaxn/+AK4n/g/efhN++AHuvBPX2edwX7tr8Tich/8wqTYUjEVERKTKfbVgGwDT15ZeDXdFUjo3fLIQgHpZadw4dwJmch7PWkt6UB3O+2IpbE0gv0dXPn/2Yp5L/Z51f33D5V0u5/UzXiciKOKw3x/g58BaizGG1Kx8bvrU+3007cT7vUZx7X/fhuVLYd486NULv88+xf3MzPL7A8gJQcFYREREqtSh1lTwcxhcHu/xgRsX8t4fb0BaGulBdXBYS3ROOslxrfjmnbHcm/M9W9a/SNfYrvxw6Q+c3fbsw373O2N6cNOni8h3efjk7y1c0S+OPZl5Jc5Je/I5WNYP/vMfGDUK3n8fwsL45Y4BBPppVGpNomAsIiIiVeqHpUkHPfbQSfXZ8NaHdE9ay4UrppLXrgNnj3qMdTFxABj3CoKafM+anf+hS2wXJp01iRGtRmCOcNzviE4NfeH7i3nbuKJfHCkZRcF44i0n06NZFJzRHv75zxLXtm8YfvQ/Vk5oCsYiIiJSqTamZBITFkhYkPehubQs71zENw5syf0j2pGWnU9ddy489xyey97EmZWJyzhYecGV1HvzFda+MoNs5wwynD+T51xFaFYEb575Jjf0vOGYpkb768Fh9H7md1bvSAdgVeH7z2P707HR4YdhSM2hYCwiIiKVIrfAzbxNqVzxwTz6tIhm/I39APBzeocjXNu/BU6Hod6f0+G66yAxEecll3BG8MmsrdecB85pyJYVL5Nc521yPan4eRoSlX89Lw8fy9W9j+7huuJiwgJL7O+fqSI+RivV1TYKxiIiIlLhrLW0+79fffvzNqXy6d9b6NY0kke+WwFARLAfPPYYPPkkdOwI33yDq3dPFvzfI2Q5P+HmPxZhrYez2pzFbb1vY2dKGx77fhVndWp13PUNahPD0u17AcjOc+MwaPxwLaRgLCIiIuXG47EYQ6kxvhtSMkud+8h3K+japHCogrUE3nkHvPUWXH01vPkmkxNncsdbndgdsBanJ4bLO97KY0PvpEVUC+81reDKfi3Kpe64uiHMWJfC61PX88a0BKD0b5CaT8FYREREys2p/56Bxbui3CuXdOW87k0AuP5/C33njB3Wmv9MXQ/A0m17aZixm9vnfAVLf4X77mPzgzdz1w//4Ls139E6ujUxeQ8R7OnLIwOG0CKqYoY3bE31Li7y8pR1FfL5Uj0oGIuIiEi5WJG4j43Flli+66ulnNe9Cdn5Lt/Sy0+f24kxXWIY9PGrRE/6joYZewhyeR++c917Ny+fE80Tb3XEGMOzQ5/l7n538/uqVD6bu4W4uiEVVnudQEUiUTAWERGRcnLgcIn9wyTu+mqJr21M+lroPJSeGzcyNb43v7Xux41jBpN+Si8uWf0kv079lfPancdrI16jaURTAM7q0pCzujSs0NoD/UquYPf2ZT0q9PvkxKRgLCIiIuVi4qJEAE5tXx9jDCsS9+HxWCav3EWj9GTun/Ex/GsGtGlDzq9T+D0rln+OaMvqrA1c9PVFrN2zlnEjx3F9z+srvfYg/6IH7Xo2j+KMzhUbxOXEpGAsIiIix8xay68rdtK1aSQz1nmXc373il60eHASABdf8SJvLviBM9b/DX5O76wTDzxAcFAQz1rLh0s+5PZfbifEP4RfL/uVYS2HVcnv6NuyLp/N3QrAp9eeVCU1SNVTMBYREZFj9vXC7dz/zTLffremkRhjGNG2Lv3/8yRjlvzC3qBQsm4dS9i9d0KzZgBk5mdy00838dnyzxjaYiifnPcJjcIaVdGvgLO7NuL2LxYDEBzgPMzZUlMpGIuIiMhRW7ptL6Pe/LNU+5uFY3NfXPA5YUt+4Z0+5/PZiKuZ9cRI3zlb923lzM/OZFXKKp4c/CQPDXgIp6Pqw+jpHWOZvHJXVZchVUjBWERERI6KtbZEKI4K8Sctu4AnR3WkcWQwfPQRYW+9jr3tNm56/XVuKnbt8l3LOfPzM0nPS2fymMkMjx9e+T/gIN6+rCcea6u6DKlCCsYiIiJyVJ77ZY1v+9NrT6J/63pFB+fMgRtvhFNPxbzySonrfln/C5dOuJTQgFBmXjWTrg26VlbJR8ThMDjQoh61mdY6FBERKUcfzN7Emp3pJdpmrEshcW8Oi7em+R5QA2/P65Y9WQd+xAnN7bGMm7kRgEX/N7xkKN62Dc4/3zuO+KuvwM/b/7Yvdx83/XQTZ35+JnGRcfx97d8nXCgWAfUYi4iIlJs8l5snf1oFwObnzwK8QfLKD+YRXSeA1CzvQhYTbj6Zns2j+HZxInePX8qXN/Slb8u6VVb30fh5+Q4AnhrVkeg6AUUHsrNh1Cjv+7RpEB2NtZaJqydy+y+3sytrF/f2u5enhj5FkF9QFVUvcmgKxiIiIuVk4ZY033aPp6Zw/+ltWbfLu+jF/lAMcMFbf9IzcTVnhOfTPSOIzdviq00w/r/vVgDwj5OaFzVaC1dfDUuWwE8/Qfv2ZOZncs331/D1qq/p3qA7P47+kZ6NelZN0SJHSMFYRESknPy+Ktm3nZqVzwMTl5c653xXEtf/71nap2wG4DrA8/n90KkT3HKLN2AGBJS67kSxL6eAeqEBOB3FxuI+8wyMHw8vvABnnsmmtE2M+nIUK1NW8tyw57j35HvxcyhyyIlP/18qIiJSThJSMunUOJwViemljvm5Xdy7cAI3zvycrOgY7j3zTpY0bEOLtCQ67UzgjoINcNNN8K9/wRNPwD/+AU4nm3dnkp6fSXSoi9g6sfg7/avgl5V0XvfGRTvvvgv/939w+eVw771M3zydC8dfiNu6+fWyX0+oWSdEDkfBWEREpBxYa5m5LoVzujZiY0oW2fluAD69vBvd5k2lznNPY9avh8suI/SNN+izPoOvJyxlef1dfN5hB89FJJJ1djCOvC34r7gC/0euwRMeRkpeNtbkARDoDKR/s/5c3uVyLu54McH+wZX2+zwey71fLwUgt8DjHT7xxBPe1xlnwLhxvL3gHcb+OpbW0a35/tLvaV23daXVJ1IeFIxFRESOUo+nppCalc+m587EGO+Qgg6PTgbA5fEw/+FTycrNp/6EL2FEX9i+HTp2hB9/hJEjyXfns8s9gR2BT1Hg2ILDRsC+blx/Sjes9eBau4aCuXNwZKaxM6wJieFt2RzVmjy/HazbvZyrvr+KuybfxQ09b+Dek++lXki9Q5V7xPblFPDKlHW0jKnDFf3ifO17s/N5YMJyfl25E4AxPRrC9dfD++/D1VdT8NYbjP39Lt5Z+A5ntT6Lz87/jIigiHKpSaQyKRiLiIgchYzcAt+DdCuT0unU2BsAcwq8PcSPnd2ROrlZ1LngApg6Ffr1g//+F0aMwI3l/YXjeHLGkyRmJNIxthNXdxnHf36qj8GPuq4WNK8bwtMpq1n85td8esY1jJ4ymfC8qaQGz+e31n35rsM13PRMP95a+A4v/PkCb85/k7F9xnLPyfcQHRx9TL9p/uZUcgvc/HfGRmYn7AZgdJ9m+DsdLNicyoXv/OU7d+Y/WtPssnNh9mx45BFS/nk7F355BjO3zOSfp/yTZ4Y+c0KsYidyLIw9AVZ46dWrl12wYEFVlyEiInJYl78/l1nrd5do+/3uQZz67xn0jovi6wtaw4gRsHw5vP66t2fV4WDOtjnc/svtLNqxiP7N+vPwgIc5Pf50MvNcdH78t4N+n5/bxaBNCxm5ehanJswlLD8H2rSB++5j1Zm9eXLOc3y18ivCA8O586Q7uavfXYT4hePvNL7e7MOJe+DnUm2Txg6gQ6Nw37HOO9bzQe4CYn6cCA4HvPceiwa14fyvzmdX1i7eO/s9Luty2VH8JUWqhjFmobW2V5nHFIxFRESO3BUfzGNmsUU6imtODjN+eQrWrYMJE+DMM9mZuZN//v5P/rf0fzQOa8zLp73MxR0vLhFaX5+6npenrDvsd5/dOhLHd9/yctJ0/BYthIYN4dlnWX56d56Y9RQTVk/Aj1CCXIO4vuflvDzq4kOGY7fH4nSYEsH4tUu7cceXS7w71jJo0yLG/vkFPZPWQEgIjBmDvfde3kufzu2/3E5MnRgmXjyR3o17H9kfUKSKKRiLiIiUg+cmrea/MzdybrdGNI4K5s1pGwAw1sOgjYt4dto4GmXshp9+omDoYF6f9zqPT3+cPHce9/S7h4cGPERoQGiZn33267NZnrivVPsLF3RhUNsY/J0O/jtjA/+duRGsZd0gBwFPPglz58KgQfDUU3xex8P13z5KjnMu1uTRrl47nhn6DOe2OxeHKbnY7WPfr+Djv7b49k/vGMuVJ8dxcnw94v75E4M2LeLO2Z/Tfcdacho1JfiB+3CPuYzleVv515//4ssVXzK85XA+O/8zYurElONfWaRiHSoYH/MYY2NMU+B/QAPAA4yz1r5mjHkcuB7Y/4/TD1lrJx3r94iIiJwIPB7rDaXA5f3i2JeTj7+7gAuWT+WmuROI27sDV5Om8P0fzG3ux9XvdGX17tWc2fpMXj391cPO0GAp6qgaf2M/xn6xmDcv607P5kXjhv9xUjNvDcbwd6veDJwzBz74AO67DwYOpE90Ex7qcho/tr+EhKgE4FcuGH8BMSExDI4bzOC4wZwWfxq2oH6JUAzQvG4dTo6vB4mJTPvzNVr8+TsbomK495rLiBzTljlJv/Dnfx8hPS+dAGcATw5+kocGPKTxxFKjHM/Ddy7gHmvtImNMGLDQGDOl8Ngr1tqXjr88ERGRqrFuVwaJe3Po2DCc0CA/5iTsAWBM32b0bB6F56efWfTJrYTtSmJpg9b89M+XOOvxW3ll8dvc/+H9NA5rzI+jf2Rkm5FH9H0ej/f9qxv60qdFNH8/NKzUOY0ji6Zn256W4x3re9112Esv5cOx/6LLbxN4ePoHPDz9A1bHxLHv2efYfAFMWj+JaZun8fWqrwHw88QS6N+Js9r1ZvrKQPxsU/LyG5D19mssf+0hpjfM58M7GrGpbioFns9gJrSv155LO17KgOYDGNpiKI3CGh3nX1jkxFNuQymMMd8DbwCnAJlHE4w1lEJEpPpJTs8lcW8O3ZtFVXUp5W7T7iyGvDS9RFtYkB8ZuS7WPDiQoIcfhDff9K5W9+KLcPrppOXu5ervr+b7td9zbrtz+eCcD4gKPvK/zY9Lk7j9i8Us/r/hRNU5+Mp3Ho+l8+OTqRsayIDW9XjmvM78tCyJ2z5fDMDCS5oT8vtkdjz3b1rs3UHeLbeReNrZtGwYSUL2Nian/M3Li35ll2MTOf6ZB/2eTvU7MazFMAY1H0T/Zv01XEJqjAoZSnHAF8QB3YG5eIPxbcaYK4AFeHuV0w5xuYiIVENj3p/Lul2Z/P3gMBpEBFV1OeVi4ZZUPpu7lTyXp9SxjJwC+m5fQVDfe2HNGrjzTnjuOWxgIBNXT2Tsr2NJzkrmldNf4Y6T7jjiGSH2O7trI0Z2aXjY6xwOQ1a+m6zUbD6bu5WRXRrx7aJEAG4c2JK63dvj7tqRM3fGMX7N13R6603i33wdgNaFr9sKPys1GFbXgzX1ILleEPbMM+lw+hj6Nu1Hg9AGR1W/SE1w3D3GxphQYAbwjLV2ojEmFtgNWOApoKG19poyrrsBuAGgWbNmPbds2XLgKSIicgL6YWkSY79YXKKt+EIX1ZHL7eHqj+aXmIbNGO/ibljLqQnzuGv2Z3RM3giNG8OHH8Lw4WzZu4XbfrmNn9b9RLcG3Xj37Hfp1ajMjqhyVdb0agCbnz/Lt932kV84o1MDFs5eRqtdmzDWYrAYCy6Hg73B4Xz3f2d7h2OkpkLbthAeXuG1i1S1CusxNsb4AxOAz6y1EwGstbuKHX8X+Kmsa62144Bx4B1KcTx1iIhI5XC5PaVCMcC0tckMbRdbBRUdn6S9OdwzfikD28SUmpt4/I39aJexi5BbbsA5ezbJDZux4rEX6XT/LdjgYN5dOI67J9+NxfLS8Je4o+8d+DkqZ92sf1/clbvHLy3R1iw6pMR+nsvDd0uSILQeJw3qxjcLt/uO9WtZl/uHtYL4whXz4uMrvGaR6uB4ZqUwwPvAamvtv4u1N7TW7ijcPQ9YcXwliohIRduQkklqVj694w69ctr2tBzf9pi+zdielsP0tSlc89ECfr6+Fx3j6oGzesxSYK3l5Of/AOCvjXt87SO7NKRffF1671oPI0d6n4p7+23qX3st9f392ZGxgxu+vISf1v3EsBbDeP+c92ke2bxSaz+/RxP6t6pHn2enAtC1aSSfX3dSiXMGt41h+lrvBFHXnNKCXs2jeGDicjo1DueLG/pWar0i1cXx/KPtKcDlwHJjzJLCtoeA0caYbniHUmwGbjyO7xARkQqWkVvAsJdnALD88dMIC/IvcdzjsezLKcDlsVz3P++D0t/ecjLdm0Xhysnlnosf4eqFP9LxX+vAzw/i4qBvX7jiChg2zPuv6k9A8zallti/6uQ47jmtjff3jx8PV13lXUDjl1+gTRv25e7jv/P+y9Mznybfnc+rp7/K7SfdXmp+4MpSPzyoxNCJA310dR+6PD6Z9FwXHRqF07ZBGOm5BYzu06wSqxSpXrTAh4hILZbv8tDmkV9KtP08tj8dG0X49k9/ZSZrd2X49sOD/Fh2Q2d491145x3YuZMN0Y35sf1ArhgYy4KkBSRsXoR/RjYtQxrT696XiRh1SaX9piMxfW0yn/69hd9XJ/Pz2P5YC21iwwhw5cO993pnnOjfHyZMYAXJvDHvDT5d9ilZBVmc0eoMXh3xKm3qtqnqn3FYe7PzcXks9UIDq7oUkRNGhc9KISIi1dNb0xNKtZ31n9lsfPZM0nML+OSvLazdlUHdrL2csfZPTtq2gpG52+CJzd6TzzgDO3Ys/WbOYJ//t9zlWAst8L4ASMQsvpRBK/+Pi4ffxfntzyc2tGrHIj/07XI+n7sVgAGt6xX9Q8CSJTBmDKxcievuO/nhqn68/sslTN88nSC/IEZ3Gs0tvW+plIfryktkyMGnfROR0tRjLCJSi/X/1x9sT8vhxQu7cN83y0odD8vL4ta/xnPNgh8IcBewK7I+scMGQL9+MHIkqc1iuG3SbXyx4gv8PE0IdQ8hwNOWFhFt+fqmk1iTtJRZr93DN37rWB0DDuNgUPNBXNThonINya/9vp69OfnUCw3kpkHxOB0HnyHjpGd/Z1d6HgCz7h9C0wAPPPEEvPoqKU2iee+RM3k7fSrb0rfRPKI5t/S+hWu7X0vdkLrlUquIVK1D9RgrGIuI1FIej+Ws12cTFuTH+Bv70ffZqexMz/Ud75G4mv/88AKN0neTesEl/DpiDN3O6E+nxt4e1skJk7nmh2tIzkrm0YGP8t4vXTF4H7yrFxrIr3cOYFd6Lh2jAuDUU1m5ZT7jn72M8Rl/s2b3GhzGwai2oxh70lgGNR90zNO9bd2TzcAXp5Vq/+CqXnRsFEFsuHeO5fTcAsKD/Bn68nTaNwjnzbNawnffwSOPsNCTyOtjWvNl+Fby3HkMazGM2/vczsg2I7XksUgNo2AsIiKl3P/NUsYv2M653Rrx6qXdyc538ceaZO7+cglj5n7Hg9M/wNm8GY4vvoCTimY8yMrP4v4p9/PWgrdoX689n5z3CT0b9Tzo3Lrrnj6DgH1pcPLJkJKC/fNPVkS7+Gz5Z7y36D325OyhS2wXxvYZyz86/4Ng/+AyP+dAExdtZ0DrGD6fu5VXfl9X+gRriU/dzu2BySTPW0KDzD2E5+cQnJdN6/y9BKUlMqE9vDO4DnOis6jjX4cru17JrX1upUNMh2P6m4rIiU/BWERESkjPLaDL478B8Ool3Ti3e2PvAY+HFf+4nk5ffUDmGSMJ/fwTiIwEIM+Vx7iF43hm1jMkZyVzV9+7eHro074ge+eXi73z5h4gLNCPOQ8OJSxpm3cIRkAATJ0KbdqQU5DD58s/57W5r7E8eTl1g+vy+ODHubnXzYfsqf11xU5u+nRhibZZ9w/hh8+n4PriK3okrqbNnq3EZnpnnsj1C2BHWF32BgazoDFM6pzPtLjd7COX1tGtubX3rVzV7SoigiLK+joRqUEUjEVEpIT9vbtv/KM7Z3UuXIY4Lw+uvhq++ILsG28m5K03wOHA5XHx8ZKPeXLmk2zdt5VBzQfx3LDn6Ne0X4nPtNYyaflOTo6vS/enppQ4NrRdfT64qrf3AbfTTvMuK/fNNzBggO/aGVtm8PTMp5m6aSp9m/Rl3MhxdI7tzL+nrOM/U9cD8PS5nRjTtzl3fbWEbxcn+j7/5HAPn2/4Dj74AOtwsLFRPEsim7KwUVumxMfSpLeLNLuImVtnYE0Wgc4gLup4Idd1v46BzQdW61X7ROToKBiLiIjPqqR0zvzPLKDYEsIeD1xyiTesPv883H8/HizjV47nsemPsW7POno36s0zQ5/h1JanHjZIrt6RzvLt+7h/gveBvjaxobx8UTc6N4mANWu8C2ds3OidGu3JJyHIOw7YWstnyz/jrsl3sTd3L/edfB+fTumOg5BS33FFv+bcdEpzoj77iKDHH8VkZmLvvou1157L/837ld83/oEjcC2pud4V7eIi4xjecrj3FT+cyKDIcvqLikh1omAsIiI+4+dv4/4Jy5hw88n0bB7lbbzvPnjpJXjpJezdd/PTup94ZNojLNu1jE71O/H0kKc5p+05R92zOmNdCld+MM+3P/ufQ2gcGcy85Vvp+caz+L07jpy27Ul4/AU6X3KWtycZ2J29m/um3MdHSz7C2CCCPT3w97QkwBNPoKcV9bINs+PTCH7jVValb2DWaW2ZPbQVM9KWkJjh7UmOi4xjUPNBDGw+kIHNBxIfFa+eYRFRMBYREa/9MzjUCw1k3kPDcDgM/Otf8MADcOutTL3rXB6e9ghzE+fSKroVTw5+kks6XXJcq7sd+FDe8A6xTFm1i1uHxHOfZxO7Lh5DbGYqiXUbse30c+n7f7dDu3bkFrhp8egbZPpNIiR0Dcm5W32f4e+GOvmQFWgocHj/d6xBaAMGNh/I0LihnNryVOKj44+5ZhGpubTAh4iIAHDpuL8AeOKcjjiw8MST8Pjj7B5zPjcN2MGET4fTJLwJ7579Lld2vRJ/p/9hPvHoTVm1C4A3p20gtU9TfrzuHc5Y+yfnrJrByV+8A5+/hTu+FZMDGvOqXwBt07bTNXknmcC8Jk6W9GvB7vj6ZHZoSp2GzWkf04EBzQfQIrKFeoRF5Liox1hEpAbYm53PjHUpRIUEcMUH8/j6pn70ah7lC4puj+Ws/8xizU7v0s6bb+4Et94KP//MpBuHcl3r1ezO3s0Tg5/grn53EeQXVG61rUjcx8qkfQxpV58+z0w95LkxmamctWY2fbctp9POBPw8bmK7tsf06wcDB8KQIRAaWm61iUjto6EUIiI1iLWWS8b9zabdWcx7aBiZeS4e/2EVExZtL3XuqidPZ2tqNnd8vpi8NWsZtmE+F+1cSrt1i0mo5+DROzrzRd5COtXvxCfnfUK3Bt0qtPa3p28gwM/BZSc1o88zv5Oe6wJgzVMjCPRzkJyRx0nPFoXnB89ox42DNCRCRMqPhlKIiNQgKxLTmbfJOz9viwcnlTru7y6g97aVDNy0iE1Nx9J0705+yc/BYNkaAdP6N+aOC5oyxbmZIPcqHhnwCI8MfIRAv8AKr/3mwUUh93/XnsS5b/7JyC4NCfL3zlkcGx5E35bRrExMZ8ljpx1yaWcRkfKmHmMRkSr2Z8JuUjLyqBPox9B29Q8ZBq21nPPGnyxP3OdrC3AV0Hr3FkZmbabZ8vl03LmYrZHZLI51MDMuiq2RAewNcZEamU0qWQA0j2jOFV2v4OZeN9MwrGGF/8aDmb85lW5NI/F3Fj3c5/ZY8l0eggO0FLOIlD/1GIuInGD2ZRfwxI8rWZa4j4TkzBLHejSL5IaB8dz/3gwapydzU5wfQTsSGRIfxfzkPOJXp3JxhIEViwhwrSPTP4nV9T18Xx+Wne1gR6in8JM8GJtDw9AoWtVtxJB6belcvzOD4gbRqX6n45pporz0josu1eZ0GIViEakSCsYiIpUot8DN4q17uf5/C8jM846vxVo6Jm9kwKbF9ExcTZN9u2i8L5ll+dm+61JC4I9GsLAhbGsIv0RBwijwFGbbQONPh6g2DG/Sk071O9E5tjOd6neicVhjzdQgInKEFIxFRCpIem4BF7/zF5t2Z5Hn8pQ6Hpyfy3mrpvHYlmkErlkFQEJ0E9LaNufpsAZsjzfsaZbHzKyN5Prt9V3XKKAxfRt1YXSz3nSK7Uzn+p2Jj47Hz6H/ShcROR76b1ERkQowe/1uvpi/1Tc92n7B+bm8GJlMj+Wzafj7JMzevezq24lZ/7mJWY0KmLV7EUt3/YXHeoN0qzqtOK/1GfRo2IMN2+sTGdCG587tWxU/SUSkxlMwFhE5Anuz84kI9vcNS1izM52NKVmc2bn0g2vfL0nkji+XANAmNpQrmvnRdemfhEyeRPyyuewll2kdQphyVWOmNgpjXfYKSF1BcHowfZv05ZEBj3By05Pp3bg30cGlx+CKiEjFUDAWETmMpdv2MurNPwF4clRH5m5M5eflOwBo3zCcxpFBzFyeyLOdg+gT5eD7//3J6MxUumcmMSpjI44VS5jbBL7pEcmU06KY57cLN9mEBuxgUONBXNf8dgY0H0CPhj0IcAZU5U8VEanVNF2biMghbNqdxZCXppdqN9ZD96S1nJowl/6bl9AueTMBHhceA5sjYXl9WN7Yj3ntw5kek0UGeRgMvRr14vT40xkeP5y+TfoqCIuIVDJN1yYicpTmbNjNtR8tIKfADUCj8EDqbFjH4I0L6bN9BSftWAuufcxv5OCr3o3Y2rEdU1x72BO8B5czv/BTXLSKjuYfLS5ieMvhDGkxREMjREROYArGIlJr7M7MY3taDk5j+GL+Vu46tQ3Z+S4GvTjdd87ch4axZEMyT46bSs/URLrsXM8tYXsJXbKQfbsTmd0MJveM4uGW/iwMNHjwANupG5xD59jOtInuQK9G3egc25mOMR0JCwyrst8rIiJHR0MpRASAaWuTefOPBN74Rw+WJ+7j+v95/zM5pm8znj638yGv3ZiSyWM/rKRn8yiC/Z3cMLDlCTV37tqdGczbtIf/+35lyQPWEp+6nW5J6+i2Yy1tUrbQbO9OYjNTcWDZVQf+bgKzukYyvYVhcfBePFgCnAGc1PgkBscN5pSmp9AltgsNQhucUL9ZRETKdqihFArGIsLb0zfwr1/XlHmsbtZeRodlcu9pbSEyEtq2heBg3/EZ61K48oN5B/3s5Q8M5OWXvqZg/kIuaRVKl/hY6N8feveGCgqSeS43e7ML+HvjHn5YksTUNcm+Y6F52QzeuIB/7FhMm5XzqJftXVrZHRrK13GxTIl3sqZZAdvqpbPN7gEgwBlAvyb9GNR8EIPjBtO3SV+C/YPL/G4RETmxKRiLSJlyC9wMfGEayRl5vrawvCz6bFvB7WY7jRf/RcyGkoHZ7ecgu3M79nTvxIeuQBa7XewN8pAZkENBYA65Ng2HZzeWNHL9skgLcuGwEJ1T9IrKhRZ+MXTtdy5dR91Am8Zd8Xf6l8tvmrBwO/d8vbREW4e8PVyXtY4zN84jcOZ0TEEBNqYeCWeewvTuEUwL280fqQvZlbULgMZhjenXtB99G/elb5O+9GjYQ0FYRKSGUDAWqeVSs/K5/5ul/L46mUdHdqBDo3Du+HIxuQUe0rPzaJ+8mf9GJpLz7Q/Eb12Lw3rYWj+Q2UNb8XVjB7P8UjEh2WSQQb51HfR7jIV62RCbCTF5fjhdYbgc9dhdJ4LW3RsyeXMSxu6jQUA6W90pFDi8//3jsIZGdWJpGt2CZhHNaBrelGYRzWgW0YyuDbrSPKJ5qWEK2fkugvycvDZ1PXM37SErz83yRG/vb71gJy3XLeWKHQsZsG4eEYlbAEjs1JxpIzvyRwv4I3slW/Z52xuGNmRoi6EMbTGUIXFDaBHVoiJug4iInAAUjEVqoV9X7OTJH1eStC+3RHuAq4BOuxLotX0Vvbev5OSdawnM3sfyWPizfxyz24XwZ0gK2/NSAAgPDCc/uwVOG4PTRmAIwhCIwwYRnQN39IhnQL1oYk0Y9WwwfsF1oE0baNAAjGFPZh4hAX4EBzh5btJq/jtzIwArHh/Kpj++ZOl3/2XdxnlsC7VsbRHFtphAtnrSyHMX9WI3DG1I78a96d6gB3v3NmbN9mBWbffHEILBG5jrZu3lpG0ruDh1NYPW/kVyVjKLmvmx8JSWLGoRxEL/FLZme+cejg6OZkjcEF8Yblu3rcYHi4jUEgrGIjWIx2NxOMoOcdZapq9N4b8zN/D3xlSM9dA8bQcdUzZxc1AyrJ5HWupqtkW6SYiGdc3C2NgkmKVBe8m23inGGoc1ZkDzAfRv2p/+zfrTqX4nZq7fw9Ufzi/xXed1b8wrl3Q7qtoz81x0emyyb79dgzDW7MxgyfWdCP3kI/jwQ/y2bsFGhJPyj3OZM+gkltbLISF9CdM2/k1ixgZvt3Qhf7c/9XMDaJzjJmpvLnXyYW+okzUNA0jyz/Gd16ZuG3o07EHvRr0Z2mIoXWK74DCOo6pdRERqBgVjkQqQ53IzJ2EP/VvXw99ZviErM8/FvpwC/kzYzYd/bmb1jnTO796YH5cl4e8u4Jk+dcnbuJmcrdsJz9pH+rYd1MlIw9+dyl5XMrkB+8gKymJ3YDabwz0kRMOGKNh7wDDZpuFNiY+Op2tsV/o28Y6nLWvYwn65BW4ycl0EBzgJDTy22R6LryJ3IGM99N26nDGr/+DUVbMJLMgjz+lPUng9jAWXyWVjdC57gnNICoPt4bAj2p+0BhHsjQwmM8SPiMhYWkbH07NhT3o26km3Bt0IDww/plpFRKTmUTAWKQe5BW6mrUnmp2U7KHB7mL85lbTsAiJz0mmXmcwZzYJpEgjtw51k700nY88+XJlZpOMgqkkdYppEUhAZSm5EHfLCgtmek8+S5AwC/S35WRms3rKDlN17CLI57EtPx8+dQ5Ari5D8dEIKMghyZRLgysZjcskIhPTCV2qw97U7BHIOeH7N3zpo6leX1tHxxDftSqt6bWkV3Yr46HhaRLaosgfKvl+SyB1fLgEgMsSfvdkFpc4Jy8vitHV/02b3Fhqlp+ByODm1RxxhUWFQty507AidO0Pr1uBQ76+IiBwZBWORI2CtZWd6Lk6H4a8Ne1iRuI8d+3LJS91L1oZNeHZuJiJnJzEFu4jMSSIsLxn/3BRynLnsCYE9wZT5vi+o/Gr09zgIM4GEOIIJ8QslPCSSBhGx1ItsSN06MTQJb0LT8KY0jWhK0/CmxIbGVoshAwu3pNE0Oph6dQLZk5VPalY+S7fv5aKeTQDIyndT4PIQVUfLJ4uIyPGpkiWhjTEjgNcAJ/Cetfb5ivouqZ1WJO5ja2o2YUF+eCys25nB9pR0dm/YSuKqjTTMSaO1zSZy3x4i3BmEBxeQZ7LZRy57PLnkB7rIdBawz5HPbk8umX755DjzyfErINvf5XulB1r2hYM78tD1hDlDCHdG4G9DcdgwmkY14OTohjQMiWJLYh5pu3PJ2Z1FVL4Lk55D4+AAuseGEmgdRERG0qB+XYJDIwkMjSAgNJLAsEgC6tYnsF4DAvwCCXAG1NgHxHo2j/Jtx4QFEhMWSNsGRSvGhQb6QWBVVCYiIrVJhQRjY4wTeBMYDmwH5htjfrDWrqqI76so+S4PuzPzcLktdQIchAY4cAI5+S5y8lyEBDgJDXAWXWBMmS8LuC24PEW98xm5Lqy1BDgNefku3C43xuPG4fEQgCUvPx88HoINBPo7CQwJwgQGgr8/+PlV2MIIR8Ljsbg8Fo+1uPdvu9y4Clx4XC7cbg8elxu3202By43LlU+BqwCX201BQQEFBQW4rYuC/AI8ebm48vMoyM8lJycHm59HQX4e+fk55BfkFL7nkufKxVWQR0p6Blnpe/HkZuDnysbpycJjsnGbLNwmhzxnPvuCvL20a8Lgu0DYGwS5RzBFbli+g/ACJ+EuPyI8AcQ5wgkLiCA8OIqYqAZERDUgMqYpEbHNiAyrT0RQBBGBEUQHRxMdHH1U8/DuyymgToATv3IemywiIiLHrqJ6jPsACdbajQDGmC+BUcAJE4zzXG4ueHsOqZn5FHgsBW4PLrcl3+XBbS2GkkH29e//xdlrZgEQVvg6mPmNoN91x1bX8QxsMXgf2DeAo/Ada0rsm+LvvnNN4bbF2NLnOgrP9RiwBtzGFr5729yOwvcD98sz8zkLX0FA/dKHg9wOImwgkc66RASEERkURbM60URGxBIRGUtYcBThAZFEBoUTHhhJvTpRRAZFEhEYQURQBGEBYTgdztIfXEEigstnMQsREREpPxUVjBsD24rtbwdOKn6CMeYG4AaAZs2aVVAZBxfo56R5dB3axoYT4OfA32nwdzrwcxqchb2xgX5O6oYGEODnIDTsUuYmdMdicDod+DkdZOa7cVlwe8BjPXjclgK3m30BWVyathJrLQ4DTgPG7J9t1Ru6nQbA4AYcDgfG4cBisMbgwWAcBhwOPBY81mJdbqzbg3W7sG43xu3BWA8Ojxus92l+76d7sNYD1uLB+461gPX+n8Pg8X61L+haAx7j/U7L/n0K07HxhWAHBicOjHF43x2Oon3jxGEcOB2F78aJwxicOHE4HDjN/tf+8/xwOhw4nf44/PxwOv0JCAjE6eeHf0AAQQEhhAQEExQYTHBgCMFBwQQF1iEgIJiAiGgCIqIJDAgh2D+YiMAIAv3079lFRETk+FRUMC7r3/OX6Ay11o4DxoH34bsKquOQ3rysx5Gf3OuWo/rsMUdZi4iIiIhUrYoa4LgdaFpsvwmQVEHfJSIiIiJy3CoqGM8HWhtjWhhjAoBLgR8q6LtERERERI5bhQylsNa6jDG3AZPxPjL1gbV2ZUV8l4iIiIhIeaiweYyttZOASRX1+SIiIiIi5UmTqIqIiIiIoGAsIiIiIgIoGIuIiIiIAArGIiIiIiKAgrGIiIiICKBgLCIiIiICKBiLiIiIiAAKxiIiIiIigIKxiIiIiAgAxlpb1TVgjEkBtlTR19cDdlfRd0v50X2sOXQvawbdx5pD97Jm0H0s0txaG1PWgRMiGFclY8wCa22vqq5Djo/uY82he1kz6D7WHLqXNYPu45HRUAoRERERERSMRUREREQABWOAcVVdgJQL3ceaQ/eyZtB9rDl0L2sG3ccjUOvHGIuIiIiIgHqMRURERESAWhyMjTEjjDFrjTEJxpgHqroeKc0Y84ExJtkYs6JYW7QxZooxZn3he1SxYw8W3s+1xpjTi7X3NMYsLzz2H2OMqezfUpsZY5oaY6YZY1YbY1YaY+4obNe9rEaMMUHGmHnGmKWF9/GJwnbdx2rIGOM0xiw2xvxUuK/7WA0ZYzYX3oMlxpgFhW26l8ehVgZjY4wTeBM4A+gAjDbGdKjaqqQMHwEjDmh7AJhqrW0NTC3cp/D+XQp0LLzmrcL7DPA2cAPQuvB14GdKxXIB91hr2wN9gVsL75fuZfWSBwy11nYFugEjjDF90X2sru4AVhfb132svoZYa7sVm4pN9/I41MpgDPQBEqy1G621+cCXwKgqrkkOYK2dCaQe0DwK+Lhw+2Pg3GLtX1pr86y1m4AEoI8xpiEQbq39y3oH1P+v2DVSCay1O6y1iwq3M/D+j3FjdC+rFeuVWbjrX/iy6D5WO8aYJsBZwHvFmnUfaw7dy+NQW4NxY2Bbsf3thW1y4ou11u4Ab+AC6he2H+yeNi7cPrBdqoAxJg7oDsxF97LaKfzX70uAZGCKtVb3sXp6Fbgf8BRr032snizwmzFmoTHmhsI23cvj4FfVBVSRssbOaHqO6u1g91T3+gRhjAkFJgB3WmvTDzGETffyBGWtdQPdjDGRwLfGmE6HOF338QRkjBkJJFtrFxpjBh/JJWW06T6eOE6x1iYZY+oDU4wxaw5xru7lEaitPcbbgabF9psASVVUixydXYX/2ofC9+TC9oPd0+2F2we2SyUyxvjjDcWfWWsnFjbrXlZT1tq9wHS84xB1H6uXU4BzjDGb8Q4jHGqM+RTdx2rJWptU+J4MfIt3qKju5XGorcF4PtDaGNPCGBOAdzD6D1VckxyZH4ArC7evBL4v1n6pMSbQGNMC78MD8wr/NVKGMaZv4VO2VxS7RipB4d/9fWC1tfbfxQ7pXlYjxpiYwp5ijDHBwKnAGnQfqxVr7YPW2ibW2ji8/9v3h7V2DLqP1Y4xpo4xJmz/NnAasALdy+NSK4dSWGtdxpjbgMmAE/jAWruyisuSAxhjvgAGA/WMMduBx4DngfHGmGuBrcBFANbalcaY8cAqvLMg3Fr4r30BbsY7w0Uw8EvhSyrPKcDlwPLC8akAD6F7Wd00BD4ufIrdAYy31v5kjPkL3ceaQP95rH5i8Q5pAm+e+9xa+6sxZj66l8dMK9+JiIiIiFB7h1KIiIiIiJSgYCwiIiIigoKxiIiIiAigYCwiIiIiAigYi4iIiIgACsYiIiIiIoCCsYiIiIgIoGAsIiIiIgLA/wOGgUhS0JecvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(ma100, 'r')\n",
    "plt.plot(ma200, 'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87fe8c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5318, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1a2f54ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3722, 1)\n",
      "(1596, 1)\n"
     ]
    }
   ],
   "source": [
    "#Splitting Data into Training and Testing\n",
    "\n",
    "data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])\n",
    "data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])\n",
    "\n",
    "print(data_training.shape)\n",
    "print(data_testing.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "96815dfd",
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.265625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.292411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.304688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.292411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.295759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Close\n",
       "0  0.265625\n",
       "1  0.292411\n",
       "2  0.304688\n",
       "3  0.292411\n",
       "4  0.295759"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1da1646b",
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3722</th>\n",
       "      <td>28.442499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>28.440001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3724</th>\n",
       "      <td>28.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3725</th>\n",
       "      <td>29.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3726</th>\n",
       "      <td>28.820000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Close\n",
       "3722  28.442499\n",
       "3723  28.440001\n",
       "3724  28.875000\n",
       "3725  29.770000\n",
       "3726  28.820000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_testing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "95de54ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0a4c0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00094921],\n",
       "       [0.00176053],\n",
       "       [0.00213238],\n",
       "       ...,\n",
       "       [0.83992471],\n",
       "       [0.83371555],\n",
       "       [0.83894036]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training_array = scaler.fit_transform(data_training)\n",
    "data_training_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b512ad8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3722, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9bac22a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for i in range(100, data_training_array.shape[0]):\n",
    "    x_train.append(data_training_array[i-100: i])\n",
    "    y_train.append(data_training_array[i, 0])\n",
    "    \n",
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "04e9794c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3622, 100, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e8816db",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'keras'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/bc/pmm77s2133n1vc42fk2m__4c0000gn/T/ipykernel_19744/1093003452.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLSTM\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'keras'"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e9f24ac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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