johnfelios/LSTMREG.ipynb

## LSTMREG.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf017596",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "df = pd.read_csv(\"dataframe.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61c8f9a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=e70d0340-0a5e-43c8-b13a-c238c89b39b7 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('e70d0340-0a5e-43c8-b13a-c238c89b39b7').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>total</th>\n",
       "      <th>need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:00</td>\n",
       "      <td>6532.0</td>\n",
       "      <td>15684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:05:00</td>\n",
       "      <td>6529.0</td>\n",
       "      <td>15577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:10:00</td>\n",
       "      <td>6566.0</td>\n",
       "      <td>15564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:15:00</td>\n",
       "      <td>6544.0</td>\n",
       "      <td>15496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:20:00</td>\n",
       "      <td>6540.0</td>\n",
       "      <td>15423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "              datetime   total     need\n",
       "0  2019-01-01 00:00:00  6532.0  15684.0\n",
       "1  2019-01-01 00:05:00  6529.0  15577.0\n",
       "2  2019-01-01 00:10:00  6566.0  15564.0\n",
       "3  2019-01-01 00:15:00  6544.0  15496.0\n",
       "4  2019-01-01 00:20:00  6540.0  15423.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0dc8fd6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime     object\n",
       "total       float64\n",
       "need        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c139f351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=addf4af9-ebf5-442d-9786-39a6ac62bfe9 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('addf4af9-ebf5-442d-9786-39a6ac62bfe9').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>312659.000000</td>\n",
       "      <td>312659.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9191.756888</td>\n",
       "      <td>15662.322393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4334.798228</td>\n",
       "      <td>5582.455253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1830.000000</td>\n",
       "      <td>302.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5531.000000</td>\n",
       "      <td>12020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8327.000000</td>\n",
       "      <td>16059.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12737.000000</td>\n",
       "      <td>19207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21386.000000</td>\n",
       "      <td>37416.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "               total           need\n",
       "count  312659.000000  312659.000000\n",
       "mean     9191.756888   15662.322393\n",
       "std      4334.798228    5582.455253\n",
       "min      1830.000000     302.000000\n",
       "25%      5531.000000   12020.000000\n",
       "50%      8327.000000   16059.000000\n",
       "75%     12737.000000   19207.000000\n",
       "max     21386.000000   37416.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4c224c71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df['datetime'] = pd.to_datetime(df['datetime'], infer_datetime_format=True, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29ad9dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9+9C8eXMkJCQgO1v8hoBkGVs8c1+ycCA8FgCQJQa2rWPxZ/+TcgqDF+7B/UpKTb/ZdrbCMg7RoTzW9q7Td31SgWNI2ZqkwdSTTz6J3r1749FHH0X9+vXx0UcfwdfXF7t27UJubi5++OEHzJ49G127dkVMTAwWLVqEnTt3Yteu0qLSDRs24OjRo/j555/RokUL9OrVC9OnT8e8efNQVFQ6h9GCBQsQGRmJWbNmoVGjRkhMTMRzzz2HOXPmaPIxe/ZsjBgxAkOHDkV0dDQWLFgAHx8fLFy4UJLvhezv9r37SLtwi1UlZDNuLsZvkuXvo2WvZyefxLaT17DucKbedKauj+TPFtcgAYaH2dBNx+ufpWTTZqqkpATLly9HQUEB4uLikJaWhvv37yM+Pl6TpmHDhqhTpw5SU0u7JaempqJp06YIDg7WpElISEBeXp6mdCs1NVVnHWVpytZRVFSEtLQ0nTQuLi6Ij4/XpCHH1/s/29Fv/k78efCq0bS8QZElLDluyt9XTZ1uhpRryh9HrPq8vuNs28lr+CXN+JyifJa0nOTB1KFDh+Dr6wtPT0+MGjUKv/32G6Kjo5GZmQkPDw8EBATopA8ODkZmZunTWWZmpk4gVfZ+2XuVpcnLy8Pdu3dx/fp1lJSU6E1Ttg59CgsLkZeXp/NHyqJ94bh4s/SpbY2VU8OsO1wajK3456JV6yHHY48bVWXVgCQNc0fKz7lz36rt6TvO9M3SQOKSPJhq0KAB0tPTsXv3bowePRpDhgzB0aOmD4QnlRkzZsDf31/zFx4eLnWWHJpSqt9++Lt0ROytnCuNbMDYecB5IuWHjbqdg+TBlIeHB6KiohATE4MZM2agefPmmDt3LkJCQlBUVIScnByd9FlZWQgJCQEAhISEVOjdV/baWBo/Pz94e3ujRo0acHV11ZumbB36TJw4Ebm5uZq/ixdZEuEITGmw+2Oq+XOsEdmjeji/kHP5yU1ZabW9WHOcKeSZVZYkD6bKU6vVKCwsRExMDNzd3ZGSkqJ578SJE8jIyEBcXBwAIC4uDocOHdLpdZecnAw/Pz9ER0dr0mivoyxN2To8PDwQExOjk0atViMlJUWTRh9PT0/NkA5lf2Q7tpg/Sl+ViCmbybhpuBcgL0ZkyIUblvUe1cZ51MgU5pTk84gSh5uUG584cSJ69eqFOnXq4Pbt21i2bBm2bNmC9evXw9/fH8OGDUNSUhICAwPh5+eH119/HXFxcWjXrh0AoEePHoiOjsagQYMwc+ZMZGZmYtKkSRgzZgw8PT0BAKNGjcJXX32FCRMm4NVXX8WmTZuwcuVKrFmzRpOPpKQkDBkyBK1bt0bbtm3xxRdfoKCgAEOHDpXke6GKbFHNd/7GHWTn3UOQn5dmGe9VJIbC4hIcuZKHFmEBcHnQi8/SyWq3aVUZm3se8HiWnhTDU6SevWFSun0Zt1DNx8PGuXEOkgZT2dnZGDx4MK5evQp/f380a9YM69evR/fu3QEAc+bMgYuLC/r164fCwkIkJCTg66+/1nze1dUVq1evxujRoxEXF4cqVapgyJAhmDZtmiZNZGQk1qxZg3HjxmHu3LkICwvD999/j4SEBE2a/v3749q1a5g8eTIyMzPRokULrFu3rkKjdHI8q9IuYUyXKM1rjstDYhizdB82HsvG+IQGOseXJQbrGRndEB69BADZefpndyjv2a93YvPbnW2bGSchaTD1ww8/VPq+l5cX5s2bh3nz5hlMExERgbVr11a6ns6dO2P//v2VpklMTERiYmKlacjx8UmexLDxWGnTg0U7zlkVTJUviVKzHplIlmTXZopISmyTQta6U/RwOqvcu9Z1c9+fkaPz+p3/s2wKJJKQnS8p5sbb2tlTSq9pOWIwRYoj9ul+s6BI5DWSMyvSGljzfomZbZzK3XkLDYwbZSjov1du7jdWW0tPil+Az4T2x2CKnN6BSzma//MaRFIydToPQyUIH645pvOaN1Xno1KxV7EUGEyR4tjy/sCbDxEpmTWBFGMwyzGYIsWx5QnPWIrMVX68MnuUCrBtn3JY8lNpt7uz1zbL+3nXBaQcs2w4D2fEYIpIi7U3KT7ZOZ9rt3W7oVtzDLCNEwFA9OT1Fn9WjEDqROZtTPr9MIYt2Wv9ypwEgylSHJtW81m5MfaGIWuUbzP19ynr5nhkAZb07B0gm3sJyi98WApW9tns2/dEzJFzYDBFimPTcKX8dY+xkcO7d78EO09f1zu9kNTWH9FfzcIYicRy6HJupe9bO7yHs2AwRaQVMFn7FMm2LPJ1I78QO89cr1B6mLhsPwZ+vxsz1x23aL1ixttil2Kw2lDeNh3Pwmfrj4sayP992vLSzLK2WtqnyOwNJ6zNklNgMEWkxdpYiNV88tX5sy0Y+N1urD+SqbN844NGtkt2XjBpPcYClIJC6xoPk3PIyruHVxfvxbzNZ/BTqmnHninWHso0nsiAvHsVj92ruazyMwWDKSIt+tpMHc/MM/nzDKXk6/aDIGfT8Wyr1pN24Val709bfVTzfxZUkiHaE1+fu14gWT70Pf/xuDUfgykirQtHhYuIAPT8Yrtds0Pylnr2RqXvb9YK1nhPIkOBSbrWVEHFavm11yvDB0TTMJgixRG9Jk1rfbf1FHMTyY2ppwBLGOTr//Zd0vz/v3suSpYPfccIWyuYj8EUkZa/Dlve3gCw7iJUWFyC7Dy2T7CXErVt7hjWrPXMtXyT0t2+xx5WJA7G2+JgMEWKY9enbTtu67FPNqHtxyk4nW3aDZUst/3UNURPXodf0i4ZT2wFc3t3Zty8Y1K6/VpVRJVu36ytkzPSF/xfzy/Us5Qqw2CKFMeeRdAnMm/bZTsZN+7gen4RAFTobUbiG7Z4LwqL1Xh71QGbbofBDMm9qlXf9TRppW3PC0fEYIqoEqN+SrPLdn5Pv2yX7ZB9mRv3F8tw4FBSHnNKROUe7CkFgylSBFuVRq09dLXS9wuKSsxaH9ttkjX2mVh9ZyreKJ2TOePdGWt/x8bopmEwRYojZuPbI1fy8KmFI1/rxSuP0yl/41JrvXaxQTAz4RdWwZB4Pl4r4vXPiTGYIsXRHuxODMft1C6qMixAcExlo6WLOTL+yr3mNJrnkSU1Kab0EXNaK5ZumobBFCkCT2hSIuFBxe/XW85InBOSC0435JgYTJEiKKX2TCHZdGrnb9zRBDk6LAzYy5cCaL+6X1K6nc/WSzNZ7P6Myqe+Ifubk3zS5tu4VVAk2rqUcu2VGoMpIhlYmSbdCMjOZs+5m/rfsPCmUb4KT073nvG/HJQ6C1SOqQOzWmPKH0dsvg3SxWCKSGLX8wtx8eZdzWsx29eQfrZsx8Kfj7SxiYJzYDBFJCJLbqT5nA+QbOi7bWelzgJpYaztmBhMkSLobeNCJCYLSxDkXhL10dpjUmeByuHgrI6HwRQROZ0iPTezomLe4Mg+im00yTZJh8EUKYIUY7VYQoAg6qCiRORY0s7Lu4dlVt69cksY+JmCwRQpgqXVfIIg4NKtOyLnprLtATP+Mm9EYV6q5Cf3zn0kH83CfROqY/6TcqrS939JM2eQTXJ0twuLZTFQsCFPffW31FlQJAZT5NAGfLcLHT7djCU7z9ttm4cv55qVvkLXekZXknv+m50Y8eNejFuRbjTtKiPB0turOP0L6TL3GmFPWXmF5ZYoo1ZAagymyGGVqAXsOls6phDHXSFznMwqHQto9cHKJ8ImMmb7qetSZ8FKfLozBYMpcljFamkaFB+8ZN1TJ8elIbFdzrlrPBFZ7U5RsUlVw2kX5N1uSgxnr+Xjh7/P4d79ErM+d6ugCHeKinHhRoFdm2hYy03qDBDZy3g7VLdYUkVX/iOs5pOX+yVquLsq+7lz99kbeLZVmOZ12oVb2HA0E2O71Ye3h6uEOXMc+YXFaDJlPWoHeGPHu10rTfvb/st2ypV0us7aCqA0OHo7oYFJn8m9ex8tpyfrLDvzcW+4usj/CVPZVwiiSpQPSoy1bZEKgyd5W7nX8ab66Td/J77ZehZfbzlt8TrOXMvH5hPZIuZK2Q5czAHAUsDy9l4wMH2THseu5lVYppQhSxhMEUmO0ZScXbtdvkFuRSdk3DsLMBywWzNPXLdZWzF00T/Yx8mUnZpaLeBOkeFZHKx9WFTKgM2SBlMzZsxAmzZtULVqVQQFBaFv3744cUJ3dvXOnTtDpVLp/I0aNUonTUZGBvr06QMfHx8EBQVh/PjxKC7W/XG3bNmCVq1awdPTE1FRUVi8eHGF/MybNw9169aFl5cXYmNjsWfPHtH3mexHKW2PFmzldB9yZkoj9IQvttkhJ+Kz9Ean1hp08siViqUJzu6nXRekzoLdvPT9bkRPXo+rudaXyOm7ZCul5F7SYGrr1q0YM2YMdu3aheTkZNy/fx89evRAQUGBTroRI0bg6tWrmr+ZM2dq3ispKUGfPn1QVFSEnTt3YsmSJVi8eDEmT56sSXPu3Dn06dMHXbp0QXp6OsaOHYvhw4dj/fr1mjQrVqxAUlISpkyZgn379qF58+ZISEhAdjaLseVAKSfUveKKjS21hz7QN4kxxyGSt9PZ+bieb7x0CoAiqr3MHVT2TlEx+vxnO2ZtePig+8eBKw8TKOXktKP3fz8sdRZEs/FY5cd06tkbAIDVB/Q/dJhzdKiU8gSsh6TB1Lp16/DKK6+gcePGaN68ORYvXoyMjAykpaXppPPx8UFISIjmz8/PT/Pehg0bcPToUfz8889o0aIFevXqhenTp2PevHkoKioCACxYsACRkZGYNWsWGjVqhMTERDz33HOYM2eOZj2zZ8/GiBEjMHToUERHR2PBggXw8fHBwoUL7fNlkEM4e63A4Hu5d++jw6ebMdXIMA28NcnP7QeTUU/6/VCl6YYu+sce2bHK32Z21V/5z0UcuZKHLzc9bF+lPU4Sj1cCzK+OW/FPBib+elCnlFPJZNVmKje39AQNDAzUWb506VLUqFEDTZo0wcSJE3HnzsPukqmpqWjatCmCg4M1yxISEpCXl4cjR45o0sTHx+usMyEhAampqQCAoqIipKWl6aRxcXFBfHy8Jk15hYWFyMvL0/kjqsx/92Tgcs5dLDZjANHC4hKkHMtCQaHhNglkPz/vypA6C3Z3v+ThzW7ryWuY8MsBFBSZ192dqLx3/u8Q/rvnIjYczdQs01cwpZRQSzZDI6jVaowdOxaPPfYYmjRpolk+cOBAREREoFatWjh48CDeeecdnDhxAr/++isAIDMzUyeQAqB5nZmZWWmavLw83L17F7du3UJJSYneNMeP658aZMaMGfjggw+s22kymYJLfzUsqQ2Zvvooft6Vgcfr18SSV9sCAL7ffhbXbhdiYu9GIueQHJX2oad9LplyTGqnH7KwYjtS1vKRNXLuPKx21t9mShkHmGyCqTFjxuDw4cP4+2/deYFGjhyp+X/Tpk0RGhqKbt264cyZM3jkkUfsnU2NiRMnIikpSfM6Ly8P4eHhkuXH0SnkfNJLLQCuZgSD2vu6bHdpScjWk9c0yz5ccwwA8FxMGB4NripKHs0xb/Np/H3qOhYNbQMvd45R5CjuFpXAy92lQruVsuONKvf9dufuSGLwGq1n+cWbpg/GKdHYy2aTRTVfYmIiVq9ejc2bNyMsLKzStLGxsQCA06dL6+9DQkKQlZWlk6bsdUhISKVp/Pz84O3tjRo1asDV1VVvmrJ1lOfp6Qk/Pz+dPyJ9bDVA3x0Tq1ru3S9B2oWbKBGpbcJn608g9ewNpxh40BFdulWx11V23j00mrwOL/+wW2f5puNZFdKW90vaJYz+Oc3ska4dzeYT14wnckL62lIt2HpG6/2H9NVALNh2puJCGZI0mBIEAYmJifjtt9+wadMmREZGGv1Meno6ACA0NBQAEBcXh0OHDun0uktOToafnx+io6M1aVJSUnTWk5ycjLi4OACAh4cHYmJidNKo1WqkpKRo0hBZauPR0huSqVWV2ukq691iamg06uc09JufivlWDNCoj7PfPMsrNmEaETnQLmkqu9GV9c7bcfqGTtrhS/YaXd+hy7n463AmfjYwHIAp06vIVe7d+4ifvVWnJyPpZ+mjmiCUnjvDl/yDrzZVvEbN38JgyqgxY8bg559/xrJly1C1alVkZmYiMzMTd++WPjmdOXMG06dPR1paGs6fP48//vgDgwcPRqdOndCsWTMAQI8ePRAdHY1BgwbhwIEDWL9+PSZNmoQxY8bA09MTADBq1CicPXsWEyZMwPHjx/H1119j5cqVGDdunCYvSUlJ+O6777BkyRIcO3YMo0ePRkFBAYYOHWr/L4ZEceyqvAZSNLWqUuwqzS0PnpiXpDrP2De2kG+kE4AjzrdmTmGmdtuXMhN+OYDGU9Zj49EszNt8WnGB1U+p53E6O1+nJyOZR9/1TPsZMb/wPp748m9sPJat6NI9SdtMzZ8/H0DpwJzaFi1ahFdeeQUeHh7YuHEjvvjiCxQUFCA8PBz9+vXDpEmTNGldXV2xevVqjB49GnFxcahSpQqGDBmCadOmadJERkZizZo1GDduHObOnYuwsDB8//33SEhI0KTp378/rl27hsmTJyMzMxMtWrTAunXrKjRKJ+V4c/l+qbNglWu3C0WrmgPED9KU3I7NEvM2V35D/VrGT9CWNuJVqaz7nVfuLR1DbfiPpSVcHq4uGNGpnuUrtLNiB+m2LzfpD6beAYCP1+rv5KU0kgZTxk7w8PBwbN261eh6IiIisHbt2krTdO7cGfv3V35zTUxMRGJiotHtkTIoZU4nQ9p8tFHqLDiMGyYOulmZrLx7lb6v3UlASYqK1bh1p0jveyqYXn3z1ebTeKldHeTdLUaDEP0dI45lcggZJSsqVsPdtXQmErVagIuFExAfvux4x4EsGqATObKydinWDu9QvpSq/MPIt9vOoPfc7cgxcGM0dRRvUwkAZq47jgm/HJB992UlVx+IQUDp/IHlq9nuFJWg/qS/MG+z/lI1c0ekjpuxCQlfbDM82a+8DxOqRM6dIjSduh7Dl+zFvM2n0XJ6ss7cjtqXAH3zWaZduIXBC/fgdLa8ml+IhcEUKYIlgYjM7+9ma/1hMm4V6A+UgNLi8qNX8zB/q32qmwRBwNdbzmDl3ks4lW35hLlke0t3ZyDhi23419J9Osu3GxkN3dL4/4SDlECpLP4GHM+aQ1dRWKxGyvFsfLb+BHLv3sdHBobNuHDj4UwQZZfhfvN3YtvJa3h1sfFODeUVFavx3z0ZyLhh+pAK9sZgihTBEQKj8vuw4/R1rNx70eTP37pzX2ceP0NfyTdbz+LMtXzsPGPetCHWKCpWY/fZGxi7fL/oJWBkvQMP2qgkHzU+1AFQOuaUNb+jIABzkk9WXG7xGqVh7hQpjmrPuZt6l286/rAXvanflcFSy0p8t/0sJv56CJ0+22z2Z+1FNoN2knly79zH/ou30CGqBtxcHT8m3qbQ9iiA4UDwpe93611e2UXJ1AtWt1mlbQ3Xj+1kUnox9P92F4DSRrtfDWxlt+2awtryBWcrn2j78UbNfISWEARgbsopEXNEUrp0y7wSId1R9q0PSHedvWE8kcQc/y7sYDYfz8aRK7l4bsFOvLLoH/zw9zmps2QXv6dfMZ6oHLk9VUoxJc6aQ/pnchfD1Vz9DbIv6hkU0h4u3ryDOcknRWlsXp68jiTbsyaQAoASRyhKJg1BMF7lafrQL455bLBkSkFOZd3G0MW6s9L/ceAKXntcuml1yDgxgyhzr0P/saJ04N79EqhUgKeb/iljtAP53LtaYwwJAopL1Nh0PBut6waisLgEQVW94Gphzx9TPb8gFZl597Av4xZ+GhZr021R5ab+cUTvcnNLOKTGNlOmO3e9AI9/thljOkfhkSBfUddtbkcIKbBkSkHOXCuosOzIFcdo6GkLcrkQmhsAfbHxFA5fztX7nvbvbenEyVdzjZcc3S9Ro9kHG9BqWjLUJoy182PqeZ3X324/i5E/paHV9GTEzdiEV8s9BNhC5oOhC4w1qraEPI4k5TBUavnP+VvIvl35EBNysuVktvFETkAAUFRc+YwHv6RdwoUbdzDh/w7qLN+XkYO6767RWZcjYjClEPszblXacK/AyOjMzkhu1XzmeGWR/uCjbNoPoLR3TNmTvqlTu/zw9zm89lOa0XTXbheiqFiNgqISFBQZP7bWH3nYsFkAsLZc9aJSx2Aqo9wjyXyv/WR+bytzyG1mgsrsz8jReZ2Ze0/UgXSVQhAETP3zqMnpFVCQJDoGUwpwOjsfz3y9E9NX6z+Yd565jsZT1mOaGQe7M1By1fztexWn5tBnxl+lowebM+nwwUv6S7206TQgNXnNDxWXyOvLd8aLu6W0A2NbUGqbmW0nr6HdjBQMW2L7UlbSpYTTl8GUAhy5UvnN75MHN9SFO5yjMbqp5HYD/Wy96ZOlmnq7KZtcV8w5z+6XqHUC0bL/H7qUq5m0uTLXbxfieGbF0ofC4hKknrmB1DPy75lT3lsr0/HrPtMDVnI8ix5cX7eYMQDscQcZb0tMlsTScruW68MG6E7i3PUC7Dp7A8/HhDnFUApAuUbRMqBdPbDucKao67bmWrPm4FWkHM/Cx880hUoFtJ+xSW+6J7/626T1XTHQXmbTsWyMfjBo5NFpCfDxUM7lZ1+56h6ynPa99OLNOygsViNK5AbLcmFKKTA5BuVczZyYsZ4MptxIu3y+BUBp25qhj0VanykFuHdfvnPzjfrZeLslU2ie8sx8dNt5+jqW7snAB081xphlpQFOg+CqaFevOm5UMsq6NbS72xcUlsg2mDI0HQ+Jr+PM0kEYD0zpAX9vd4lzU7l8J26Xam5hkgIKkkTnHEUUpLH3wi2psyCqCzcK8OzXO0we2VkKt+8VY9AP+gfoNMjEq9cGC/d74Pe7sebgVfzr54fTi9gqiCrz/v8Oi77Ow5dzkWdi+zJTHLmSixbTkkVbH+mh59jerYBBGf85b961U6ltw8gyDKYcgTmlEg52fo9fdRD7MnIw4kfb9kCyRurZGzbpri+GPecfThMhCILeQ+nc9YpDcliisPhhSeGlW3dwQk+7KnPsOH0dT3z5N7o+KHUVw0+pF0RbF5lupAk9TLUt3X0BH+rpkJNzpwhT/ncYBy/liJQzy6WeveE4JTRm3jfEHhdKe21yna6KwZQDMOewVeJwAZVdGG85aJVMkRkNyjMNtFEyRTWfh1Ur323X34Gh77wdSLugf24uSz3z9U4kfLHNqtHKy9qdXc83fgwooQErVe5/6Zfx4eqjKC5R473fDuP7v89h5rrjUKsF/L7/Ms5fL8AHfx7FktQLeOqrHVJnF1dy7inwaqtfupnBqdinm3ZwZu1DmK3Is9ECmSX9wSSmADDjr2M4e60A4+LrI7qWn970xzPzMHZ5Ot7q0QDdo4PtlEvL7M+4hWe+3il1NmSt3YwUdHy0hkWfvXVHt4rM0ECnfx0St8F8mYybd1Dd19Mm6yblKS5RG+wg8+bydADA91oj73+95Qz8vN01PZqjQ/Vf805n30b87G2Y/UJzPNsqTNxMG+BI1XzLdmdIun3te5xcv1aWTDmYb7aeRfLRLPT+z3akXbiJI1dysbjckAn/WroPxzNvy7pqrMyO0/KsHpMbW1cj3nTQEkCyv/Kj5Ze5ePMOmkw1f7y8skAKMFwCGT97GwAgaeUBs9ZtLWctEH2n3Cjo1rqp1Z5TrrUrLJlyYO//fgRHr1Yc58TaSUzlhNU34jJ0obLVGEvWXBb52yvT5hPX9I6IP/WPI7h3X42FO85h8pPRFq27qFicHrzLdmfgm21nTEorCIJsS0ukom+cObHI9btmMKUAlt4zpDrmjmfmoaCwGDERgTbfllxPLKXq/80uqbNgMv72yjVk4Z4Ky1KOWz8P3qnsfKvXAQD//u2QSekEQcDA73YbbLuphAl6SRwMphTgSiVz8plLEGxf9Nzzi+0AgD3vdUNQVS8bb43EdNfEOf6UxtRJr3nvI3MIQmmvPSK2mVKAGVptAsyhrwHkXyKPvF2ZqznKmR2elMe8EUFYjEXkCOR6JjOYIkVjSYKyidXGhYicg1x7STKYckL2OhSL1WrsPnsDhcWOWXVE1nvx213IvWP7ORRNreYjeRJzIm8xGevlqlbL88avZHL9RhlMObBLt/S3tbp22z4jyH605hj6f7sLE34Rt5usKe45aNsfR9R82gazP1OiFlBog7kXL7NqWnb+PHAFj773l9TZ0GvSb4anSBIAvPe7aQ3ZSfkYTDkwUybmtOWkrvsycgAA/0u/YrNtGLJ8j7SDzJFtPfHl31ix96Lo692mp8s+Sev1/+6XOgsGncyqfAiA+yVyLUchsTGYcnIlCi+Gzrh5R+/yOyyZcmjH9IyfVhm2rSNyEDK9ZXFoBCcn0+NSY+cZ/d2Oc+/cx9W8u7hnoKpHpm0UiYjICnLtmctgyskdvJQDb3c3xD1SXeqs6GUomLKknQ0REZEtMJhycq8uLp2f75/34lGzKiecJSIylTzLSBybXGsd2GaKANivh58tCYKAizfvYOofR/DZ+hNSZ4eILJRx4w5mJ5/ErQJ5T7At1zGPyP5YMkUA5FsPbY4vN53G7OSTUmeDiKzU58vtuH2vGEev5EqdFb0EQYBKpap87j3lX1JlSa7xK0umyGEwkCJyDLfvlQ7rsufcTYlzot+BS6VBHkum7E+u3ziDKSJSPN7UyBBbjLB/+54J6+RwHE6FwRRpcOoDchS5d20/RQ0pQ0GR8cGLzWVK7P7VptOib5fk++AkaTA1Y8YMtGnTBlWrVkVQUBD69u2LEyd0Gw7fu3cPY8aMQfXq1eHr64t+/fohKytLJ01GRgb69OkDHx8fBAUFYfz48Sgu1j2BtmzZglatWsHT0xNRUVFYvHhxhfzMmzcPdevWhZeXF2JjY7Fnzx7R99lc9jpwtpy4hsZT1uPPA/YfrZxIbF9sZJUvlZJqwFZDAwqTYzK5Afoff/xh8kqfeuopk9Jt3boVY8aMQZs2bVBcXIx///vf6NGjB44ePYoqVaoAAMaNG4c1a9Zg1apV8Pf3R2JiIp599lns2LEDAFBSUoI+ffogJCQEO3fuxNWrVzF48GC4u7vj448/BgCcO3cOffr0wahRo7B06VKkpKRg+PDhCA0NRUJCAgBgxYoVSEpKwoIFCxAbG4svvvgCCQkJOHHiBIKCgkzed6Uq6/32+n/348nmtSTODZGusqoafx93k9LfLNcLrNKGwuSQrubeRai/t9TZIJHJs1zKjGCqb9++Oq9VKpVOqYn2xaqkxLSpPNatW6fzevHixQgKCkJaWho6deqE3Nxc/PDDD1i2bBm6du0KAFi0aBEaNWqEXbt2oV27dtiwYQOOHj2KjRs3Ijg4GC1atMD06dPxzjvvYOrUqfDw8MCCBQsQGRmJWbNmAQAaNWqEv//+G3PmzNEEU7Nnz8aIESMwdOhQAMCCBQuwZs0aLFy4EO+++66pX5PTUKsFlAgC3F1ZU0zWu3a70OA4Z/dL1JpBWk991EvvMVe+ALfia7legqkyefcsr6LLu1uMUH9AZWLjpeISNdxMvJ4J5f4lMvlOqFarNX8bNmxAixYt8NdffyEnJwc5OTlYu3YtWrVqVSFAMkdubmkPicDAQABAWloa7t+/j/j4eE2ahg0bok6dOkhNTQUApKamomnTpggODtakSUhIQF5eHo4cOaJJo72OsjRl6ygqKkJaWppOGhcXF8THx2vSlFdYWIi8vDydP2fS58u/0fajjSgs5hx4ZL3Kem3laDUgzmNbKDKTKYWSaRduocH76/DttjO2zxBZRa7PRRYVK4wdOxZz585FQkIC/Pz84Ofnh4SEBMyePRtvvPGGRRlRq9UYO3YsHnvsMTRp0gQAkJmZCQ8PDwQEBOikDQ4ORmZmpiaNdiBV9n7Ze5WlycvLw927d3H9+nWUlJToTVO2jvJmzJgBf39/zV94eLhF+22MFAfOh6uPGk1z7Goebt25j+NXK581ncgUth7njNV8zsfUY2r5ngz0m78TJWoBH689buNckaOyKJg6c+ZMhQAHAPz9/XH+/HmLMjJmzBgcPnwYy5cvt+jz9jZx4kTk5uZq/i5evCh1lkTz/d/nNP/fffYGJv/vMPILTStuPyLTQfZI3kx9aJDpQynJUMmD3snGwuh3fz1k+8yQiOR5FbAomGrTpg2SkpJ0etVlZWVh/PjxaNu2rdnrS0xMxOrVq7F582aEhYVploeEhKCoqAg5OTk66bOyshASEqJJU753X9lrY2n8/Pzg7e2NGjVqwNXVVW+asnWU5+npqSmVK/tzRP2/3YUfUy/gCwMDYpY/rP/922HbZ4qcQnbePZSoBZOqaeR5eSUprfznIi7cKLDJuj/96zie+upv3LvPZg725lDVfAsXLsTVq1dRp04dREVFISoqCnXq1MHly5fxww8/mLweQRCQmJiI3377DZs2bUJkZKTO+zExMXB3d0dKSopm2YkTJ5CRkYG4uDgAQFxcHA4dOoTs7GxNmuTkZPj5+SE6OlqTRnsdZWnK1uHh4YGYmBidNGq1GikpKZo0zib9Yo7OuFPnb7CbL9lP6pkbaPtxCoYu/kdnOUuwyFRLUi/g8c+2YMvJa6Kv++jVPBy8lIusPOXPaao0206J/3uKwaK5+aKionDw4EEkJyfj+PHSOuZGjRohPj7erLYJY8aMwbJly/C///0PVatW1bRP8vf3h7e3N/z9/TFs2DAkJSUhMDAQfn5+eP311xEXF4d27doBAHr06IHo6GgMGjQIM2fORGZmJiZNmoQxY8bA07O0d9CoUaPw1VdfYcKECXj11VexadMmrFy5EmvWrNHkJSkpCUOGDEHr1q3Rtm1bfPHFFygoKND07pOKVDeFvvN2lFsiIPXMDRy8lIORnepJkidyHkt2ngcAbDt5jQNJk1Um/HJQ6iyQiP675yJmPNtM6mxUYPFExyqVCj169ECnTp3g6elpUQPP+fPnAwA6d+6ss3zRokV45ZVXAABz5syBi4sL+vXrh8LCQiQkJODrr7/WpHV1dcXq1asxevRoxMXFoUqVKhgyZAimTZumSRMZGYk1a9Zg3LhxmDt3LsLCwvD9999rhkUAgP79++PatWuYPHkyMjMz0aJFC6xbt65Co3RnNuC7XQCAyBpVDKYx9yjIvn0P1Xw8OMSCkzP1ocFQo+Ib+ZWXEDAgIyJbsiiYUqvV+Oijj7BgwQJkZWXh5MmTqFevHt5//33UrVsXw4YNM2k9poz94uXlhXnz5mHevHkG00RERGDt2rWVrqdz587Yv39/pWkSExORmJhoNE/OrrKRfc0pSTt6JQ+9/7MdzcL88UdiB+szRopV2bVg8ELjMxFM+p1t9YhIOhYVB3z44YdYvHgxZs6cCQ8PD83yJk2a4PvvvxctcyQftmj09+u+SwCAg5fYA9DZXc29Z/C9I1cejuFmaADGc9d1GxpzkE4isieLgqkff/wR3377LV566SW4urpqljdv3lzThorEobSbAqtTyBKf/HUcaw5e1bw21GrAUDXfqez8StevVth5RNLJzjMc2BMZYlEwdfnyZURFRVVYrlarcf8+Ryh2dOXvS4cv5+LMtcpvZkTGzN962ibr3ZdxC28uT7fJuknZ9M3gwAmKyRIWBVPR0dHYvn17heW//PILWrZsaXWmSDluFhTiiS//RrdZWwFUUqKgp2SAg1KTNpMKjywoYBor00AqvhE7t0jl3v0S5N65j55fVLyPEVnCogbokydPxpAhQ3D58mWo1Wr8+uuvOHHiBH788UesXr1a7DySDGjfw5btydD8/9XFe3XTGbjZpV24hdZ1A01KS1RZoP33qetmrYslDaTtp10X8H4lHRb4kEeWsKhk6umnn8aff/6JjRs3okqVKpg8eTKOHTuGP//8E927dxc7j05NjvFG+ca+pphuwnx/5NxK1MaPdgHAj6nnTUpHpE9lgVQpRlNkPovHmerYsSOSk5PFzAs5AMMNh01PCwB3iopxPJOTKDsTZ/u9WQIiT5uOZxlPRFSOxcFUTk4OfvnlF5w9exZvv/02AgMDsW/fPgQHB6N27dpi5pGc0IDvduPAxRyps0ESMTQEgjnxx9yNp7DjjHlVgkTzNp+ROgukQBYFUwcPHkR8fDz8/f1x/vx5DB8+HIGBgfj111+RkZGBH3/8Uex8kgIIgoD9GTkmpX3nl4NYsfeiwfcZSJE+Akwr0Vlz8CrW4KrxhEREIrCozVRSUhJeeeUVnDp1Cl5eXprlvXv3xrZt20TLHMmnkbYp412tP2K4ePzgpVyM/jkNFx80Bq4skCLac/6m1FkgIjKZRcHUP//8g9dee63C8tq1a2smKybnk5l7t9L3/zqciY4zN9spN6RkOXeK9C4XBMNVgErjGHtBRICFwZSnpyfy8vIqLD958iRq1qxpdaZImU6bOHDnvoxbFZaVqAXcyC/Ej6nnkXuHA78SEZFyWNRm6qmnnsK0adOwcuVKAIBKpUJGRgbeeecd9OvXT9QMknL8vCvDeCIAaw9WbMuy9tBVfP/3ORy4mIPNx7PFzho5CEPTyRARScmikqlZs2YhPz8fQUFBuHv3Lh5//HFERUXB19cXH330kdh5dGpyuXlsPnHNpuu/WVCkaXRu622RfFkyhhkRkdQsKpny9/dHcnIyduzYgQMHDiA/Px+tWrVCfHy82PkjBySP8JDk6Nd9l3Dhxh3cL9F/lKigcpjxmRxlP4jIinGmUlJSkJKSguzsbKjVahw/fhzLli0DACxcuFC0DJJz4I2FAODLTZVPdryKvUCJSIYsqub74IMP0KNHD6SkpOD69eu4deuWzh+RuRhLkSlmJZ+UOgtERBVYVDK1YMECLF68GIMGDRI7P1SOXMaZEtP6I3qGz2DRFBERKZRFJVNFRUVo37692HkhJ3HpVsXxqBhKkTMZ0TFS6iwQkYgsCqaGDx+uaR9FJAYWTJGplH6sPBZVHe/1iZY6G0QkIouq+e7du4dvv/0WGzduRLNmzeDu7q7z/uzZs0XJHBFRedtPKnvyYjeX0mdYRxnJnYismOi4RYsWAIDDhw/rvKdS+mMjScKFxw2Z6HZhsdRZsEqrOtWkzgIRicyiYGrzZs6vRuJiKEXOwoUHO5HDsajNFBERERGVYjAlc444NII+rOUjIiKlYjBFssDGuEREpFQMpoiI7KisFNZYaWxQVU/bZ4aIRMFgimThl7RLUmeBSFYGtYuQOgtEZCIGUzInwDkaTe05f1PqLBDJinOc+USOgcEUERERkRUYTBER2ZGpAxs7S09eIkfAYErmtpy4JnUWiEgCzlLFT+QIGEzJXN7d+1JngYhswFgBFUumiJSDwZTMcTBLIsfUs0lope8zliJSDkmDqW3btuHJJ59ErVq1oFKp8Pvvv+u8/8orr0ClUun89ezZUyfNzZs38dJLL8HPzw8BAQEYNmwY8vPzddIcPHgQHTt2hJeXF8LDwzFz5swKeVm1ahUaNmwILy8vNG3aFGvXrhV9fy2RduGW1FkgIht4slkoVr4Wh/6tw6XOChFZSdJgqqCgAM2bN8e8efMMpunZsyeuXr2q+fvvf/+r8/5LL72EI0eOIDk5GatXr8a2bdswcuRIzft5eXno0aMHIiIikJaWhs8++wxTp07Ft99+q0mzc+dODBgwAMOGDcP+/fvRt29f9O3bF4cPHxZ/p820PyNH6iwQkQ2oVCq0jQyEv4+73vcF1vMRKYablBvv1asXevXqVWkaT09PhISE6H3v2LFjWLduHf755x+0bt0aAPDll1+id+/e+Pzzz1GrVi0sXboURUVFWLhwITw8PNC4cWOkp6dj9uzZmqBr7ty56NmzJ8aPHw8AmD59OpKTk/HVV19hwYIFIu4xEZEuQ0GTmsEUkWLIvs3Uli1bEBQUhAYNGmD06NG4ceOG5r3U1FQEBARoAikAiI+Ph4uLC3bv3q1J06lTJ3h4eGjSJCQk4MSJE7h165YmTXx8vM52ExISkJqaajBfhYWFyMvL0/kjIhILYyki5ZB1MNWzZ0/8+OOPSElJwaeffoqtW7eiV69eKCkpAQBkZmYiKChI5zNubm4IDAxEZmamJk1wcLBOmrLXxtKUva/PjBkz4O/vr/kLD7dNuwc2QCdyTlFBvlJngYhMJGk1nzEvvvii5v9NmzZFs2bN8Mgjj2DLli3o1q2bhDkDJk6ciKSkJM3rvLw8mwRUKjCaInIkpj4gebu72jYjRCQaWZdMlVevXj3UqFEDp0+fBgCEhIQgOztbJ01xcTFu3rypaWcVEhKCrKwsnTRlr42lMdRWCyhty+Xn56fzR0RkTPnqO0PVeV4eDKaIlEJRwdSlS5dw48YNhIaWjs8SFxeHnJwcpKWladJs2rQJarUasbGxmjTbtm3D/fsPB79MTk5GgwYNUK1aNU2alJQUnW0lJycjLi7O1rtERKRXVE1W8xGVJ9emL5IGU/n5+UhPT0d6ejoA4Ny5c0hPT0dGRgby8/Mxfvx47Nq1C+fPn0dKSgqefvppREVFISEhAQDQqFEj9OzZEyNGjMCePXuwY8cOJCYm4sUXX0StWrUAAAMHDoSHhweGDRuGI0eOYMWKFZg7d65OFd2bb76JdevWYdasWTh+/DimTp2KvXv3IjEx0e7fSXlyPXCIyDLlz+nQAO8KaQbHRbABOpEecr0lShpM7d27Fy1btkTLli0BAElJSWjZsiUmT54MV1dXHDx4EE899RTq16+PYcOGISYmBtu3b4enp6dmHUuXLkXDhg3RrVs39O7dGx06dNAZQ8rf3x8bNmzAuXPnEBMTg7feeguTJ0/WGYuqffv2WLZsGb799ls0b94cv/zyC37//Xc0adLEfl+GAa+0ryt1FojIhga1i5A6C0RkJUkboHfu3LnSgenWr19vdB2BgYFYtmxZpWmaNWuG7du3V5rm+eefx/PPP290e/ZWu1rFp1YichwebvqfaVkqTVSRXAtsFdVmiojIWbCajwDg5XZ1pM6CrMj1vGAwRURkR6YMd8JCKSrTrHaA1FkgEzCYkjmOM0XkfGT68E1EBjCYkjmBl1UiMiCyRhWps0BEYDBFRCQ7ppZHt6wTYMtskAy4ubJ2QgkYTMkcq/mInI/J5dEsuHZ40bU4u4YSMJgiIrIjMYc8UMu1axOJhg/UutxlWlLHYIrIgTzTsrbUWSARuKhUJrWXZChFzsbf20PqLOjFYIrIgUx5MlrqLJAITC29UjOaIpIFBlNEDiTAR55PbWSelnWqmZSushkkiByTPI95BlNERHZkSqHTk81CTVqXPG8rRM6HwRQRkR35eBqfElVlaj0foykiWWAwRURkR8/HhJmUzpQaPPbmc3wcuFkZGEwREdmRl7uraOtiMEUkDwymiIhkyJSaPsZS5GzkeswzmCIikiFTbhoyva+QiOQaPJAuBlNERDLywVONTU7LoRGI5IHBFBGRjAxpXxeAadV8HLSTACDAx13qLNiNXA95BlMyJ+Y8XuQcXutUD7UDvKXOBtkBG6A7PlN+4naR1W2fEaoUgymZ47XSNrpHBwMAWtYJkDYjNjCxdyP8/U4XNAypKnVWyAqmTHDL6wMBwCf9mqJejSpSZ8Mu5Fq+wGCKnNKXA1pi1ag4vNntUamzYhMqlQpuMp1dnUwT5OdpNA1jKQJKp5Ea272+1NmwC7ke8wymZI7VfLbh5e6KNnUD4erCL5jkyZTxqNgA3fFx0E5lYDClQJP6NJI6C0QkA/mFxVJngeyoUaifwfecJbCW634ymCJyUKa0uSH7erdXQ1HX5y3iaOokT9qxQ79WtQ2me6Smrx1yQ4YwmFIgkydBJdEldomSOgsm42EiP4PaRYi6vsAqHni2ZW08W+4m26tJiKjbIftqXKu0BOrx+jV1lrtUclI3qe2PuS+2sGW2qBIMpsipNQ8PMCt9DV8P22TEBhhLOT5BAGb3b4HZL7TQWV6LQ2MoWu+modg2vgt+GNJaZ7mxB6TeTUNtmCuqDIMpcmp+XuYNdsdSQbKGpYePv7f+45SNkx1Xneo+cHPVvUUbay7Eq5N0GEzJHE8OeVFyLFWvpnOMQ+OIDJWIyrQtLsnIo0FsS2UPDKZkjtdK6TUIVujgl+Uiv4TGbEfjaAyNgK7gmJ8qYexhrnzJeYeoGjbMjTTkek9kMEVkxMDYOpr/921ZG7UDvNG/dbiEOTJN+etuqL+XJPmghyztYWnoBmJobj4ll6CSLmtKH73ceYu3F37TMqd9TZzQswFm9mvGp0470x7XxNfDDdsndMGnzzWTMEeWGdC2jvFEpCiGbrSs/nN8+jrP6Ls3OFpgLddjm8GUgvyrcxReaCP/EhFHo33uqlSAi4FR0we0tc1vExsZKMp63F3NO93b1K0mynbpIYtvbAZuILUCWNro6MID9ffMbBDMtlBywmCKSCQznrVNaZWlN2BrZ8pZNaq9dSsg8Rj4LTs3qKl3uaOVRjizAB8PbEx6HDve7aqz3NQSmvJVy2HVOGyGLTCYIjJC+6KlpKERlJRXZxdZo/KeloZ+SVNuqFW93MzPEMlKVJAvagd4G23iYcop31PhHVE4nQyRQkl96sr02kEiMn6T1J/ClGOjZR1W1yqNoaDI3EuBoQ4KSibXh0RJg6lt27bhySefRK1ataBSqfD777/rvC8IAiZPnozQ0FB4e3sjPj4ep06d0klz8+ZNvPTSS/Dz80NAQACGDRuG/Px8nTQHDx5Ex44d4eXlhfDwcMycObNCXlatWoWGDRvCy8sLTZs2xdq1a0XfX1KmqCBfVPNxrzBOk73OaQe8HpJIDB0bBUUlds0HyUP5QGPT8WxW+dqJpMFUQUEBmjdvjnnz5ul9f+bMmfjPf/6DBQsWYPfu3ahSpQoSEhJw7949TZqXXnoJR44cQXJyMlavXo1t27Zh5MiRmvfz8vLQo0cPREREIC0tDZ999hmmTp2Kb7/9VpNm586dGDBgAIYNG4b9+/ejb9++6Nu3Lw4fPmy7nSfFcHdVYc978Uge97jO8mdbhkmUI9OIcQ2t4sGJdKU0qU8jeLi64NN++tvjGaryOJl5W/N/3kvlgZNSi4PVfHr06tULH374IZ555pkK7wmCgC+++AKTJk3C008/jWbNmuHHH3/ElStXNCVYx44dw7p16/D9998jNjYWHTp0wJdffonly5fjypUrAIClS5eiqKgICxcuROPGjfHiiy/ijTfewOzZszXbmjt3Lnr27Inx48ejUaNGmD59Olq1aoWvvvrKLt9DpXgltDmfBwFDZWOyuLu6wLVci25jT3xjujxidd6sIcYT6ZcDW1q/EtIw9zcZ3rEejk5LQEyE/qo6Q7cV7W7z8rz1OB8pSohC/Njb015k22bq3LlzyMzMRHx8vGaZv78/YmNjkZqaCgBITU1FQEAAWrd+OBlkfHw8XFxcsHv3bk2aTp06wcPj4XQMCQkJOHHiBG7duqVJo72dsjRl29GnsLAQeXl5On82oedKyGJbcaW89Tjm9G+O4R3q6U9g4d0oorpI07dIeDcMqsqLsdTKz8+mTaYP6WQHpvz0/0t8rMIypd8/5HrIyzaYyszMBAAEBwfrLA8ODta8l5mZiaCgIJ333dzcEBgYqJNG3zq0t2EoTdn7+syYMQP+/v6av/Bwjv+kVKH+3nimZRjcXMW9ykh9zdLuEj3r+eYS5oRsR/+tRacHqshb1DdYJBknxfUg2M+rQjsqBuC2IdtgSu4mTpyI3Nxczd/FixdtsyGp78hkMcl7nWhtnmPLyIOl08kYIsWN8bfRHH/MEub8VO4uxm/NznprkOt+yzaYCgkpHQsjKytLZ3lWVpbmvZCQEGRnZ+u8X1xcjJs3b+qk0bcO7W0YSlP2vj6enp7w8/PT+SPHZOgiaLQ7u2jbt/6OaekapI4H5eqnYW3RrWEQpj/dWNJ8mPK78jdUjsQuUWgQXBUDYo1P/eQI53TNqp5mf0auBWuyDaYiIyMREhKClJQUzbK8vDzs3r0bcXFxAIC4uDjk5OQgLS1Nk2bTpk1Qq9WIjY3VpNm2bRvu37+vSZOcnIwGDRqgWrVqmjTa2ylLU7YdIn20T+rXOhlobyUhGV0zHU7HR2vih1faICzQR5wVWvhjscpGOUz5id9OaID14zrB11O8gVblfB3oEFVD6iyIRtJgKj8/H+np6UhPTwdQ2ug8PT0dGRkZUKlUGDt2LD788EP88ccfOHToEAYPHoxatWqhb9++AIBGjRqhZ8+eGDFiBPbs2YMdO3YgMTERL774ImrVqgUAGDhwIDw8PDBs2DAcOXIEK1aswNy5c5GUlKTJx5tvvol169Zh1qxZOH78OKZOnYq9e/ciMTHR3l8JKdTE3o0qLLP3E6Cnm+7prL193nTlQYxjQnvCakOllmKUZhqiUpUO2dAguKrNtkH6Odp53K6eOPOOyoGkwdTevXvRsmVLtGxZ2v06KSkJLVu2xOTJkwEAEyZMwOuvv46RI0eiTZs2yM/Px7p16+Dl9bCH0dKlS9GwYUN069YNvXv3RocOHXTGkPL398eGDRtw7tw5xMTE4K233sLkyZN1xqJq3749li1bhm+//RbNmzfHL7/8gt9//x1NmjSx0zdhHh+O/WMT5l6oxJjawRSm5qt8Mn3tc74f3LrCMpIBM4696lUe9kw2dGxoL7fFDXh4x3pYP66T+Csm0Rmav1EOno8Jx38GtMT2CV2kzorVJJ20qXPnzpUOwKVSqTBt2jRMmzbNYJrAwEAsW7as0u00a9YM27dvrzTN888/j+eff77yDMtE35a18eeBq2gfVR0z152QOjsOz5yb0XeDW2PEj3sBAI1r+dsoRwZUks+ywC4+OthwIjJb7QD7Neyf9nRj/JF+BSMfr4evNp8GIN/2IyQfb8Y/iu2nruPQ5Vyps1KBi4sKTzWvVWF5/9bhWLHXRp26bES2baaolL7SBU83V/w8PBb/6hwlQY6oMt2jg3F0WgL2vd8d/t7uoqxTjBumo1UPyEV9A1VdvZvq77xiTWHl4Li6+GV0e/h5PTyupBgNWvJeqmQWTzdXPBcj79kayvvwmSb49V8Geo3K9FrGYIroAbHOUR8PNwRW8RCt4WexibOVlm8nY0LvaoMCH1Qlid2V31k0CGbvXrItS+NolUqFJa+21akulpPRnR+Bu6sLWilsgm4GUzJny4akpGvz8WzjibQYfUA3Mw6JqK6/Z9iBiznmrUizecsDoe8Gx1j8WbJf5wNLupaTcxMEAY/Xr4kX2shzoOlghR7TDKaIHjh69eGUQP/q/HBePXsFtGMMVNtW9dLftPGlcmPRiFnjU1esqXBIh6EqshZmjir+3eDWGJ/QAHH1qouQKyL5MHYZk2vxAoMpmbO0dOHTfk1Fzolz8XSzvsek2b+dgeSNa+mvMprQsyFq+D58ihPzIuP3oL0Xm8fYx5SnGuP1rlHYmGRaD7nu0cEY0yXKYHAmRVsqUraJvRpKnQUAym3fyWDKQfVvUwcJjdlzS0rmBiKGkn/8TFPUre6Dmf2a6Sz393bHnn9307wufwO1JhByr2RyXWs5c4BmaNf9vd3xVo8GiAqS59hNsZGOMx6QVKRsuK9v0/Vq6JY+V5NpG6ry5Hr5YDDlwHw8JB35QnGkfpo3dLGtV9MXW8Z30dvGwcXFtEuLGFWVjULZqFopXnv8Efh6uuGV9nVFWd8TzUJFWQ/pMje+ahDyMNi29Jwuu84820qePfxYzUc2Yc3DjIszFwGIyFCMZawaz9xv39pgztYXGbGOJkc9Kr3d5TOYbq0AbxyY0gNTn2osznHBa4lNeJnZnOCxqBqY/UJz/JnYweJtlk167uqiQoDPw2E2+Atbh8GUzFlzf32rR33xMqJgXRrUxDeDjPdOqyXyAIzmFuub8lN7uJl+ympv39K2d9q7wPtp5WIijHfltuV3+Hy5sYRcKym1fKs7rw32JlbJ97OtwtA0zN/ic1p7OiI5MvY9SV2DYAiDKQdWK8Ab28Yrf5h+ay0a2hYJjfUPoqitj8RVGaZcGpePbIfoUD8sH9lOs6ysJ1jvprr5N7S+ejVN76kX6m+/Eb7JOq1MCObKhNpx5HYqZeJwcSYzp5qv7PrXPDxApz2kdlzCwVitw0Y1Mmft8c3zw3SGqkUtvQZa89UPiYvAktQLFZa3qlMNa9/sqLNs0SttsOFoJvo0052WoXt0MLaevFZhHf8d0Q5/pF/BR2uPGc2HWKO4k+2Zc7yZ+7tGBOofA42sE1HdB8czb9t8O8F+Xjj8QUKFqmg53h9kWvBkFEumFK6KkUmPTW2g7Exe76p/PKf2j4g7Zo81FyofT9Ofc6pV8UD/NnXgW+4z2sX52nkJ9vPCiE71zM6TWBc5c56Ay9p3SKVvi4rzhpnC0B7a8unflFXPeLYpBsdFoOOjNcxat6+XG1IndkXapHgLc0faJUn/G/MYejYOMan5gVh8Pd0qrfrVx9C0SFQRgymFS3mrc6Xv1/L3sk9GFMRQ26iOjz6cXV37xmSvOnrtG60YmzT3wmkv5uRK6idnJVV9mNKGZkDbOpj2dBOLvtdQf29U99UdnVrfJLWkn/Y53Tw8AAsGxSDCisFxA32sH8pAp5pPz/tfDWhl9TbMpdRZP1jNp3AhRoIlJd0M5IrfoXQ4N6AZzPiqDH2vbesGYs/5myavx82Vv4+pxA4RXu/2KM5eL8DTFpaeGjMuvr4kNRus5iNSCHMvD4arbIx9zrwtaV+3xH46k9Mtb8qT0QCAwXERRtNKHceKsfnpfZuIsBbjzBkKxVBSbyPNBqzh9OOUiRwk+Hu7Y+ErbfB0i9rirviBN+Mftcl6jeE4U0QKIfUN2hCdfElwxQi1U5XwoLi62P3vbnivTyOjaWX6U8mSWdWnNsuFYS+0lucgkfYi9+oruV4XlYLVfDLH41t85pYYRVS3T08m7XzJ+7JrvWA/x27Lp31jCrPTMATm3AxZdW1/cqy+0m4Pau5k27bSMESeUyoZw2CKnI+J95G1b3TEzYIiqxqJyoVSb55KGsXfUFb9vN2wbXwXswZcFXP7+jpQiNU54YXW4fh132U0re2PQ5dzK7zfv3U4Vuy9KMq2lE6GsZROntxdXfBK+7pYvPO8aOvf8143lKgFxM3YZDTtX292xPHMPDxev6bRtHLEaj5yOtduF5qULrqWHzqY2YVcLEMfqws/LzcMame8XZFDU04spSNAq6eVIAB1qvsY7SxiLVs21jdUqtKuXnVsn9AF/ze6vd73h3WMtFmelObRIF8A8u1lC4hf1RdU1cvkgX8bhfrhmZZhyn3wkzoDZHufPdcMNat6YngHXtgA8caTEvuc115fTV9P7J/cw6aNl3/7V3sEas0U//4TpQ3DR1owBpXDMuM3TrtwC7NfaI5nW9bGC60rTkpta2Icj5asIzzQx2CpW2SNKqhZ1ROP1Kyi1LhYNN8Nbo1nWtauMK9e2Xk3+cG/9FCHqIoPs3KsLgVYzecUnm8djudiwvDHgStSZ0VSh6b2QFbePUQFGa+Tl+LCX/6JTMwnWH03yZZ1quHx+jXx2/7LAEqnozkwuQf8feQz6rmSbsDVfDzwbKswPNtKt6G11E/a5rRPE/tG5e7qgp3vdoWLSoWfUs+Lu3KFCQ/0wZz+LSosH9YhEsOketC1YWDyRreHvQGbhfnj4KWK1cDGzH2xBWI+3ChmtmyGJVNOQuoLuhxU9XI3KZCSihx+IVMDqWdb2aY7dnlKOG4/7NsEYdW8deZL1OZp47ZSZQx9V//u3QgJjYOx8JXWdslHee6uLrKu2qJSYgfS2lPXTH2qsUXrKD9IrJyxZMoBtKoTgH0ZOfDzckPevWKps0MKcvZ6gUWfM7dheLt6gdh11vTBIJXk5XYReFlP27Z/dX4EV3PvoXEt+4yvZOgXCazigW8GmRZI2TJ2bV030HYrJ9nRHgrCy0288ct6NZHnFDcMphzAL6Pao6hEjR5ztlkUTE3q0wgfrjE+6S3ZlvaNTOwSGUNrO3AxR9TtPBZVHTtO3xBtfUouz5jQs6Fdtyf3no9NavtLnQVSsGZh/ng5NgJ9moVKnRW9WM3nAFxcVPByt93IxWQfjjB1Snyj4ArLgqp6WrxvMo8PZEWs7+q7wa0R7Gf76pU/Eztg4Sut0a4eS6ykYq+23GIcm4FVPPBCm3BUMWMSeHtiMEWKaJdiqbYSVi34ell+0svpF3F/MP9a20jj36W+fFvTXkbqAFPq7ZtDrJx2jw7G+ATblKpNfTCVUJu61dA0zB9dGwbjPwNaStcA28nZchJ3JZ07YmAwJXPmBDph1SwbadmRD/k6Fo5e7m5So+HKvzk5Nbo1dBi90r6u0c9uTHoc7/VuhAk9G4ibKRNIHefLfQoQbaIMjfDgX1vdZAfH1cX/jW6PH1+N1SwLquqlGR6A7KtBudHGxQyAHHl+UX0YTDmQz59vjl5NQrDytTijaZ+PcY55snLuFJmVfmz8o4gO9dPboNjWdNtM2Webbyc0wLMta1fa0yuiehWM6FQPPh6WlbTJ/SLoOOT/Tbu4qBATUc2mEyo7u2UjYhFR3QdLh8caTftpv2Z2yJFzYDAlc9V9PYwneqBWgDfmvxxjUnWM9jPD3fslFuRMXv435jG9y09m5Zu1nrHx9bH2zY7wtUG9/EuxddAgWF5DM/h6umF2/xbo2rBiWydrtawTAAB4zooBLKWuglZSVYWYX5XU3ztZrv0jNbB1fBc8pmfAy/Jq2GnoAXEGlJX3MclgSuYeqemLT55tiu8G226MmKNX82y2bnsxVKBsSmAp/lb1e6JZLawb2xGjOz9iNK3YF44QE6d0sJb2N/Ljq22x5NW2eKNrlMXrk+ryOaBtHcx9sYVEW7eMmBPE2rItjSHLTChJIXGJcZn5tF9T61diAnmHUhwaQRFebFvHpuuPjQzEmoNXbboNKqVSqfBOz4YYHBeBzNx7eOf/DmpKz2xxsfi/0e2Rd/c+agfYJ5jSVtXL3epJS6V6GJ3xbOkNYtvJ69JkwAIR1atUmCJISdqbUJJCthUt4phoYpfqyrxgisEUyT/iN4WhJ2lbPmBbs+5Qf2+E+nvbvBopJqKaTddfntjft9wvoHLTso75v3dQVU9kl5v8W7tkNKK6D5pyjCiHJ0DAsy1ro6CwWJTrhpI6b4iB1XzkEHesqAczstsTA4dSr2tV4xnqwKjUfVNqvs1RW6sXcFkQpf1wsumtzgYnMiZlK/8w5+KiwpD2dUUfYFWMh8aI6lVEyInt8AxxUtpTXMioB7/Fqnq5Y//73e3WoBIw/8lL341Zd5kyf4ggrYl0xR+5Xdz1hfo/zGuPaPEb3SvJytfiEN8oCP95sWWl6Uwd4uOt7vXROqIaImtYftObyd5lkrH0XLP0ofKf9+JNSrdiZDsMaheBcd3rW7YhO5F1MDV16lSoVCqdv4YNHw4md+/ePYwZMwbVq1eHr68v+vXrh6ysLJ11ZGRkoE+fPvDx8UFQUBDGjx+P4mLdKVe2bNmCVq1awdPTE1FRUVi8eLE9dk9S2l3/ldRjqTLVqnigiqdul2u5FzV/onXzKBscUwnMrfaJjaxu0XbELhkKD/TBoak9cPbj3k5R6lSZtpGB+H5IG4QHVhyLzZKg+PVuj+KX0e3RyoKqxjKe7rK+JTkerZ/Z0muluYfKwNg6WDe2I2pWNe3BN7ZedUzv28QmPazFJO/cAWjcuDE2btyoee3m9jDL48aNw5o1a7Bq1Sr4+/sjMTERzz77LHbs2AEAKCkpQZ8+fRASEoKdO3fi6tWrGDx4MNzd3fHxxx8DAM6dO4c+ffpg1KhRWLp0KVJSUjB8+HCEhoYiISHBvjtrR+6uDy9aSr2p1KtZBWevFUhWwiBGNV+L8AC80TUKxWoB1RTQcHj92E7YdfYGXoqtg60nr+HgpVx01Go4bOhYGtW5HgKruKPjo+Y1SBf70CxRC6jq5S7yWknb5CejUcPXA0+3qC11VkhkAT7uyLlzX2eZuRNYf9S3ieyHObCE7IMpNzc3hIRUnCU6NzcXP/zwA5YtW4auXbsCABYtWoRGjRph165daNeuHTZs2ICjR49i48aNCA4ORosWLTB9+nS88847mDp1Kjw8PLBgwQJERkZi1qxZAIBGjRrh77//xpw5cxw6mHIEfZqG4oXW4ail1VOt/CnaPCzAZts3N5YydPlI6lE6svi+jFtW5cceGoRU1Yya/MOQ1ihRC7hR8HBg1JNZt/V+ztPNFYPi6pq/QZEvum5aVVaGguHNb3fWu7xN3Wr45/wtnapCRyPGt+3v7Y6JvRuJsCZSAlcTzlEHjJ0qkH2Z6qlTp1CrVi3Uq1cPL730EjIyMgAAaWlpuH//PuLjH9a7NmzYEHXq1EFqaioAIDU1FU2bNkVw8MOSi4SEBOTl5eHIkSOaNNrrKEtTtg5DCgsLkZeXp/NH9qVCabVNZW06Xoq13bASTzyYvTw80P7DDsiBSqWCm6uLTlDiLfKE22Jfg025qBtq8zPvpVYY3fkRk2YYUBq/B/NIdm4YJGk+HLHEQs7s9XXXq1EFUUG+aG3n3sX2JOuSqdjYWCxevBgNGjTA1atX8cEHH6Bjx444fPgwMjMz4eHhgYCAAJ3PBAcHIzMzEwCQmZmpE0iVvV/2XmVp8vLycPfuXXh7679RzpgxAx988IEYu0kmqhPog4ybdx4u0HMlKH8xdnO13fPC4/VrYu0bHRFh4fx/jijYz7xSm4TGwVh/JMt4QpFotw/UdyPxqWSak6CqXninp20mAJbaxrceR3pGDro1krZRfqdHS6uMfTxccadI+TMzKIkt2866ubpgw9hOUKkcN2CWdTDVq1cvzf+bNWuG2NhYREREYOXKlQaDHHuZOHEikpKSNK/z8vIQHm75tBn2YKjRsBIO7TMf94aLCvi/fZfx9qoDUmcHQOlFQcxB7iQYdFoU2g1X1WbuxJcDWuFE5m08+dXfet8X+7qrvT7trL7/RDSOX83DyE71xN2gQgRV9UKPxhWbU9hbgI8Hjk3rics5dxE/e6vU2XF42qeXsQboT7eoha0nr1m8LRdH6DZeCdlX82kLCAhA/fr1cfr0aYSEhKCoqAg5OTk6abKysjRtrEJCQir07it7bSyNn59fpQGbp6cn/Pz8dP7krl5NX/yR+Bh2vttV6qzoOPyB8bZpri6lvTmf05qgWWmnpqM+kWlrY2ZjVA83FzQNs9+AkIZ+gjqBPvjs+eZ4VGZzJzojbw9Xk4djIPsxVGqr0GdA0SkqmMrPz8eZM2cQGhqKmJgYuLu7IyUlRfP+iRMnkJGRgbi40jYNcXFxOHToELKzszVpkpOT4efnh+joaE0a7XWUpSlbh6NpFhag02BbDsTs8spLsP1pl/BEBfli3diOSJtk2hgyxtjy99SeRNxQiRqPJ7JWv1ZhWDaC8w46OlkHU2+//Ta2bt2K8+fPY+fOnXjmmWfg6uqKAQMGwN/fH8OGDUNSUhI2b96MtLQ0DB06FHFxcWjXrh0AoEePHoiOjsagQYNw4MABrF+/HpMmTcKYMWPg6Vk6xsWoUaNw9uxZTJgwAcePH8fXX3+NlStXYty4cVLuul050ujGcn5KcoKCqdKx4EL8UF2kwVNtOQjo+ISH7Z+sqb5wRFIXDLlJnQErvNntUZ3Xs15ojvaPyHPeQfPOL0cZkdA2ZN1m6tKlSxgwYABu3LiBmjVrokOHDti1axdq1iwdq2bOnDlwcXFBv379UFhYiISEBHz99deaz7u6umL16tUYPXo04uLiUKVKFQwZMgTTpk3TpImMjMSaNWswbtw4zJ07F2FhYfj++++dalgEHw9XzOnfHMUlAsb/ctAu24wK8sV7fRqh3oOeUwem9MDdohK0m5FSIa2nGcGe9jQYi4a2sT6jdlTNR5njH8k5gC1Pu12I9oTAfhx7SkfvpqH4essZxEaaV20rFn0DiSpF+YGDla6qpxtuFxaja8MgrDt8VersyJasg6nly5dX+r6XlxfmzZuHefPmGUwTERGBtWvXVrqezp07Y//+/Rbl0VE807K0LZK9gim1IKBLg4fdsP293eHvbd4NTd9DlfaNXXv9Unksqjp2nL4BwHiVUb2avpj6ZDRqmDgysFyoDPxfSTpEybPkQCpe7q5IHtfJKdr5OTNTft2tE7rgdHY+2tTVP6yBdjON2MhA7D53E4ByO9RYStbBFNlH41oSzAgvwommr9B59OOP4N1fD6FP01DrNyCCBS/HoOnUDSanf+WxSBvmxjZC/b0Q3ygYXu4uqCLzKR/KT3XyYptwnMrOR7t6+ktgnDmWYCBlPWvmKZSLwCoeaKunhHLewFa4U1SsMy2Muw2HopE7eV/5yKZSJ3bFrYL7khSp2+qhpX+bcLSJDERdmcww7gxTl6hUKnw/pLVt1i3Sel5uVwd7z9/CmC5ROss/4cS6JDLth7zfxzwmYU7MY25JUp9mDx9Y3+vdCPO3nsHUpxprhrRwtlicwZQTC/X3Rqi//p59A9qG4797Ltps2wFmtA+qYaAxc4MQ3wrLVCoVHqlZcTkpzxPNQpGZe0+UdX3Yt6ko6yEyRjuIMLfpgr1p59Wa4GdEp3oY3jHSqUsznbdMjiolVnuj57XGhdL2qQklAl/0b4E2dath4Su6Dcn/TOyAD/s2QYIMBhkk25n1QnNRSjArG9WciEqZ0levsmCp/HtsM0UEVNq1XaUy/USpXU1/yVeYgeXa4h6pjr4tK8483zTM364DPYrFiR/aLCIIQNoF+U/+TNKb2KshZvx1XOpsAFBuWzNTsq3MPbMPlkyR2cw5oax5OuGJS0SVCfX3Qqi/l9lzQtqSkq5b5l6fvxkUA39vd8x6vrltMqRgLJkis6nMKZqyakO234R9OdwO2Yy7qwoeTtwziEyzfUIXAMCaQ/IZ/0ihBVMmaV03EOmTu5tU+ubI34M+vFqR2cwrmbI86OJAis7j1QdDQox6/BEc+SABh6YmWDQxqtjto1jNKG9uri5wkzjobq7AJgdlLLk6K7Ua09YYTJHZzDmXDJ2sxmKsYR0i4eXuWA2HeQ0ybFKfRlg/thMmJDRAFU83i397c0bLN4VYvQnJcoY6schFy3Jjlyn1NBf7+uRsDdAZTJFelQ0299GDbuYvt6tj8fqNnWd1qyt3Ogkyn4uLCg1CqlpUGmVLTzSrJXUWnNaGcZ3wetcovP9ktNRZqdQLrcN1Xiup5MaamgPSxTZTpGP3v7uhoLBYZ96y8l5oE4746GCUqAX8vCvDJvngKU6WEPtGVq2S84Bsq35wVbzVo4HU2TAqupafzmsFxVI611klBYFyxJIp0hHs54V6Jgx6GVjFw6SnGrWBNIY+27tpCKp4uOJJlggQgObhAWal//ZBb6PPtXob1bFihP8xXR5BlwY1Mac/ey8pxYiOypuSSSranTx8RZ4KytliM5ZMkVlq+D58UlebUHxkykBw2uYNbIViteCQczw52bVFEtq9jR6pWQVfbzmD93o3snh9Vb3csWhoWxFzSLYm9bVDSee5l7srfny1LUrUguijtTtbDaLj3bHIpjpE1dD831Cpk7Yqnm54snktJDQONmn9KpVK8oshyUc7PROsAsCTzQ2XXJZVV7SsUw3fDW6Nug4w2SyZbkTHetJmQGFFMp3q10SXhuLMeOHMeNcii5n64PHlgJb4ZpDuRLhO9tACgG0SLDGue329y2sFyGeQRpIXMdq5mVpVyFOayjCYIrNoBwRqE+r5BKcMm0gsXu6u+GpgS7z/hG6PrrBq7O1Jhrlq9Qqd+mQ0Zjxr3kTX5lZ5ebk/vJUyvnJODKZIEs5Wn06We6JZLQzroFtS0L91OF7rVA9LXmV7Jqpo6/jOmv8/2bwWBrS1fBiXynBgYSrDYIrM0rJOgOb/YdW80atJCJ5tVRs73+1q3oqcMJjiE6t1XmlfV/N/d1cVJvZuhMfr15QuQyQb5R/Oavl7o16NKqhXswqq+Yg3vMWjQbo9nX9kME8PsDcfmWRj0uPYfe4G+msNUKdSqTD/5RgJc6UMHq4uKCpRIyrI+JATZNiAtnWweOd5qbNBMvFmt0cNvufiokJy0uOa/wOl7ZvKB12BVTxws6DI5G36aVX/De8QafbQHeS4GEyRQd2jg5F8NAufPdcMUUG+RoOBjo/WwPZT1xEd6oejV/MAGK7O83R3nkLRA1N6oKhEjSoij+PizNiYnxqFPhwsU9/h4GqD0fS1x8crNtBmlIemc+LVnQya/1IrXLh5B4+YMIgnACx6pQ2u5t7DL2mXNMGUIY42715lvD1c4Q3n2V9b8TIQgE/q0wg/pl7Af0e2s3OOyF7qB/viZFa+6Os1FPcYCtZdtJbvPndT9PyQcjGYIoPcXF1MDqTK0ocH+hh8Mpv8RDSmrT4qUu7I2URUr4JX2tfVqWoBgOEd62G41GMLkaTE7tBiaIYG7WCqXT39Y6DVrc5xzZwRgymym6Zh/lJngRRu6lONpc4CyYZ5EZTKjE8YCs6ign2x53xpiZShwYXbP1IdH/ZtgvrBVc3KHymb8zRcIbsxNIUMmxIQkSVsNZSKqe2b/m90ewyOi8A7PRs+/KzBdarwcrsItDUwej85JpZMkei0L1DaxeVsmElEYtGucjP32lLFwxUFRSVoVacaNhzNqvB++bblMRHVEBNRzeD6zJ2DlBwPS6ZIdIYvK7zgEJH5mtSu2ERAu7eeKSVX2klWv9ERiV2i8Gm/ZvBwq3gbdHPltcpakU42JyaDKRKdbsmU/uVERKaa+mRjjOxUD3+92VGzzJqhDyJrVMHbCQ1QrYoH4upVr/C+9ro9yrWNquFbOgho9+iHk7e/16cRAODVx0yb08+RrRoVh0l9GqFXkxCps2JXrOYj0QVW8dT8XzuAYixFRJbw93HHv3s30lnm5mJeWYA57a60r1Xl5xfd/HZnXMm5hwYhDxuYv9wuAt0aBSHEjxNwt6kbiDZ1na+9GEumSHTPxYRp/q/SadfAcIqIxGHujAIjO5UOn9GnWajRtJXFXVW93HUCqTKh/t68xjkxlkyR6PS1QQBYMkVE1ts+oQtu3ytGiL95pUATEhogvlEwmofrtr8a2aketp68ppnxgcgSLJkim+gQVQMA0Ldlbc0yPrQRkbXCA30QXcvPeMJy3Fxd0DYyEJ5uurMRPBZVA/+8F49vtOYZFQRgRMfS9k/lqxeJ9GHJFNnEj6+2xd37JTrz0bH7MBHJUc2qnhWW/bt3Iwx9LBK1ArwlyBEpDUumyCZcXFQVJvZlyRQRKYVKpWIgRSZjMEVERIpmbmN0Y8r34CMyhsFUOfPmzUPdunXh5eWF2NhY7NmzR+osOQyWTBGRLTSp7Y/vBrfG2jc6Gk9sgtQzN0RZDzkPBlNaVqxYgaSkJEyZMgX79u1D8+bNkZCQgOzsbKmz5hCignzh6+mGOoE+UmeFiBxM9+hgixqm69OqjuGpY4j0UQmCraaQVJ7Y2Fi0adMGX331FQBArVYjPDwcr7/+Ot59991KP5uXlwd/f3/k5ubCz0+cE9oRFRWr4aIq7VlDRCQnf5+6jo3HsjDlyWiOGeVExLh/szffA0VFRUhLS8PEiRM1y1xcXBAfH4/U1NQK6QsLC1FYWKh5nZeXZ5d8Kp2hMaiIiKTW4dEa6PBoDamzQQrEO9sD169fR0lJCYKDg3WWBwcHIzMzs0L6GTNmwN/fX/MXHh5ur6wSERGRjDCYstDEiRORm5ur+bt48aLUWSIiIiIJsJrvgRo1asDV1RVZWbrTCWRlZSEkpOLs156envD0rDjQGxERETkXlkw94OHhgZiYGKSkpGiWqdVqpKSkIC4uTsKcERERkZyxZEpLUlIShgwZgtatW6Nt27b44osvUFBQgKFDh0qdNSIiIpIpBlNa+vfvj2vXrmHy5MnIzMxEixYtsG7dugqN0omIiIjKcJwpkXCcKSIiIuUR4/7NNlNEREREVmAwRURERGQFBlNEREREVmAwRURERGQFBlNEREREVmAwRURERGQFjjMlkrIRJvLy8iTOCREREZmq7L5tzUhRDKZEcvv2bQBAeHi4xDkhIiIic92+fRv+/v4WfZaDdopErVbjypUrqFq1KlQqlajrzsvLQ3h4OC5evKjYAUEdYR/KOMq+cD/khfshL9wP+bHVvgiCgNu3b6NWrVpwcbGs9RNLpkTi4uKCsLAwm27Dz89P8SeDI+xDGUfZF+6HvHA/5IX7IT+22BdLS6TKsAE6ERERkRUYTBERERFZgcGUAnh6emLKlCnw9PSUOisWc4R9KOMo+8L9kBfuh7xwP+RHzvvCBuhEREREVmDJFBEREZEVGEwRERERWYHBFBEREZEVGEwRERERWYHBlIVmzJiBNm3aoGrVqggKCkLfvn1x4sQJnTT37t3DmDFjUL16dfj6+qJfv37IysrSSfPGG28gJiYGnp6eaNGihd5trVy5Ei1atICPjw8iIiLw2WefmZTHVatWoWHDhvDy8kLTpk2xdu1anfdffvll+Pv7Q6VSQaVSoUuXLorbh1deeUWT/7I/T09PRf4eWVlZiImJgYeHh2Y/4uPjzd6PAwcOYMCAAQgPD4e3tzcaNWqEuXPnVsjPli1b0KpVK3h6eiIqKgqLFy82ug+CIGDy5MkIDQ2Ft7c34uPjcerUKZ00H330ESIiIuDi4gKVSmXx+SH1ftStW7fCsdW4cWPF7ce+ffvQvXt3eHl5wc3NDR4eHqhZs6bNfpOrV69i4MCBqF+/PlxcXDB27Fij+1Fm3rx5qFu3Lry8vBAbG4s9e/bovP/MM8/Az89P83v06dNHcfvQuXPnCseVt7e3In+PM2fOoHHjxnB3d4dKpYKXlxd69eplk/349ddf0b17d9SsWRN+fn6Ii4vD+vXrje6Dqdes9u3bw8fHBwEBASZ/P+U3RBZISEgQFi1aJBw+fFhIT08XevfuLdSpU0fIz8/XpBk1apQQHh4upKSkCHv37hXatWsntG/fXmc9r7/+uvDVV18JgwYNEpo3b15hO2vXrhXc3NyE+fPnC2fOnBFWr14thIaGCl9++WWl+duxY4fg6uoqzJw5Uzh69KgwadIkwd3dXTh06JAmTdOmTYW+ffsKU6dOFQAIHTp0UNw+DBkyROjZs6fQuXNn4YsvvhC2bNkibNu2TXG/h1qtFtq1aydUq1ZNmDx5svDnn38K/fr1E7y8vITw8HCz9uOHH34Q3njjDWHLli3CmTNnhJ9++knw9vbWyePZs2cFHx8fISkpSTh69Kjw5ZdfCq6ursK6desq3Y9PPvlE8Pf3F37//XfhwIEDwlNPPSVERkYKd+/e1aSZPHmy0KBBAyEhIUHw9fW1+PyQej8iIiKEqKgozXG1ceNGISEhQVH7cfnyZaFatWrCqFGjhA4dOgiTJ08WWrRoIXTr1s1mv8m5c+eEN954Q1iyZInQokUL4c0336x0H8osX75c8PDwEBYuXCgcOXJEGDFihBAQECBkZWVp0jRs2FB47rnnhLFjxwoAhO7duytuHx5//HFhxIgROtesv//+W3G/R35+vlCvXj0hKChImD59uvDrr78KnTt3Fvz9/W2yH2+++abw6aefCnv27BFOnjwpTJw4UXB3dxf27dtX6X6Yes2aPXu2kJSUJPj7+5v0/ZTHYEok2dnZAgBh69atgiAIQk5OjuDu7i6sWrVKk+bYsWMCACE1NbXC56dMmaL35j1gwADhueee01n2n//8RwgLCxPUarXB/LzwwgtCnz59dJbFxsYKr732WoW0586dEwAIKSkpituHIUOGCE8//XSFzyrt9zhx4oQAQDh8+LDm/ZKSEqF69epW7UeZf/3rX0KXLl00rydMmCA0btxYJ03//v2FhIQEg+tQq9VCSEiI8Nlnn2mW5eTkCJ6ensJ///vfCukXLVqkuTBZ+3tIsR8RERHCnDlzdD6rtP345ptvhKCgIKGkpEST5uDBgwIAYdeuXTbZF22PP/64yTfvtm3bCmPGjNG8LikpEWrVqiXMmDGjQtrNmzcLAIRTp04pbh8Mrc9Wx5at9mX9+vWCi4uLkJubq0mTk5MjALD5fpSJjo4WPvjgA4PvW3PNMher+USSm5sLAAgMDAQApKWl4f79+4iPj9ekadiwIerUqYPU1FST11tYWAgvLy+dZd7e3rh06RIuXLhg8HOpqak62waAhISESredn5+vyH3YsmULgoKC0KBBA4wePRo3btxQ3O9RWFgIADrrdnFxgbu7uyj7kZubq1mHKfnR59y5c8jMzNT5nL+/P2JjY41+h2L9Hvbej08++QTVq1dHy5Yt8dlnn+HGjRuK2o/CwkJ4eHjoTN7q7e0NoPS8scW+WKKoqAhpaWk623ZxcUF8fHyl287Ly1PkPixduhQ1atRAkyZNMHHiRNy5c8dm54it9qWwsFDTHKGMl5eX5liz9X6o1Wrcvn270jTWXLPMxWBKBGq1GmPHjsVjjz2GJk2aAAAyMzPh4eFRof41ODgYmZmZJq87ISEBv/76K1JSUqBWq3Hy5EnMmjULQGlduCGZmZkIDg42a9uff/654vahZ8+e+PHHH5GSkoJPP/0UW7duRc+ePfHmm28qal/KLi4TJ07ErVu3UFRUhBkzZiAzMxMBAQFW7cfOnTuxYsUKjBw50mh+8vLycPfuXYP7UJbO1G0D4p0f9t6PN954A8uXL8fmzZvx2muv4aOPPkLv3r0VtR9du3ZFZmYmPvvsMxQVFeHWrVt49913AQCLFi2yyb5Y4vr16ygpKTH72Jo4caLi9mHgwIH4+eefsXnzZkycOBE//fQTXnrpJZudI7bal3bt2qFKlSp45513cOfOHRQUFOCtt96CWq1GcHCwzffj888/R35+Pl544QWDaSy9ZlmCwZQIxowZg8OHD2P58uWir3vEiBFITEzEE088AQ8PD7Rr1w4vvvgigNInhYyMDPj6+mr+Pv74Y4u3dfr0acXtw4svvoinnnoKTZs2Rd++fbF69Wrs3bsXaWlpitoXd3d3/Prrrzh58iQCAwPh4+OD+fPnw9vb22BDeFMcPnwYTz/9NKZMmYIePXqY/LmlS5fq7Mf27dstzoMY54cU+5GUlITOnTujWbNmGDVqFFq2bIlz585hyZIlluwCAPvvR+PGjbFkyRLMmjULPj4+CAkJQWRkJLy9vZGVlSXJb7J9+3adfVm6dKnFeTh27Jji9mHkyJFISEhA06ZN8dJLL+HHH3/E77//jvT0dEXtS82aNbFq1Sr8+eef8PX1hb+/PzZs2AAPDw90797d0t0waT+WLVuGDz74ACtXrkRQUBAAca9ZlnCz69YcUGJiIlavXo1t27YhLCxMszwkJARFRUXIycnRicizsrIQEhJi8vpVKhU+/fRTfPzxx8jMzETNmjWRkpICAKhXrx6qVauG9PR0TfqyIs+QkJAKPdUMbXvy5MkAgO+++06x+1Bm9uzZcHFxweuvv664fYmJiUF6ejpyc3ORlJSEDRs2oH79+oiOjrZoP44ePYpu3bph5MiRmDRpks57hvLj5+cHb29vPPXUU4iNjdW8V7t2bU3JW1ZWFkJDQ3U+Zyjgu3v3rtXnhxz2IzExEceOHQMAFBcXK2o/Bg4ciIEDByIrKwtVqlTBW2+9hbt37+Kzzz6zyW9iTOvWrXXOkeDgYHh6esLV1dXk872sp9eff/6p2H0os2LFCgClPcqUti89evTAmTNncP36dUycOBHr1q2Dn58fmjVrZrP9WL58OYYPH45Vq1bpVN+Jda5bzKKWViSo1WphzJgxQq1atYSTJ09WeL+s0d0vv/yiWXb8+HGzGzzrM2jQICEuLq7SNC+88ILwxBNP6CyLi4vTabxdtg/BwcECAGH//v2K2wdD+/K///1PkfuifVxt2LBBcHFxEdavX2/2fhw+fFgICgoSxo8frzc/EyZMEJo0aaKzbMCAASY1eP788881y3Jzc/U25lSr1ULXrl0FlUpl1fkhh/0o+z0+//xzwcXFRbh586bi9kN7XwICAgQvLy/h1q1bOu+LtS/azG3wnJiYqHldUlIi1K5dW6fxdtk+1KhRQwCgyH0wtC8HDhxQ5L5onyNLliwRVCqVcPz4cZvsx7JlywQvLy/h999/N2kfzD1HrGmAzmDKQqNHjxb8/f2FLVu2CFevXtX83blzR5Nm1KhRQp06dYRNmzYJe/fuFeLi4ircdE+dOiXs379feO2114T69esL+/fvF/bv3y8UFhYKgiAI165dE+bPny8cO3ZM2L9/v/DGG28IXl5ewu7duyvN344dOwQ3Nzfh888/F44dOyZMmTKlwrACQ4cOFXx9fYVPPvlEACAsWLBASE5OFs6ePauIfbh9+7bw9ttvC6mpqcLLL78sVKlSRXj00UeFyMhI4fz584r7PVauXCk89dRTQtWqVYUPP/xQCAsLE3r37m32fhw6dEioWbOm8PLLL+scm9nZ2Zo0ZV3xx48fLxw7dkyYN2+eyV3xAwIChP/973/CwYMHhaeffrpCN+MLFy4Izz//vODl5SV4e3sLycnJQnJysnD69GnF7MfOnTuFOXPmCM8//7xQtWpV4b333hOqV68uPP/884r7Pb788kshLS1NGDBggODt7S14eHgI06dPt+iaZcq+CIKgOW9iYmKEgQMHCvv37xeOHDlS6b4sX75c8PT0FBYvXiwcPXpUGDlypBAQECBkZmZq0gwZMkTw9fUV3n77bQGA8NtvvwnJycnCpUuXFLEPp0+fFqZNmybs3btXc80KDQ0V2rVrp8jfY+HChcKzzz4rVK1aVfj3v/8tBAQECK+99ppN9mPp0qWCm5ubMG/ePJ00OTk5le6Hqdes/fv3Cx988IHg6+ur+b5u375d6bq1MZiyEB50/yz/t2jRIk2au3fvCv/617+EatWqCT4+PsIzzzwjXL16VWc9jz/+uN71nDt3ThCE0pt3u3bthCpVqgg+Pj5Ct27dhF27dpmUx5UrVwr169cXPDw8hMaNGwtr1qwxaR+0hxqQ8z7cuXNH6NGjh1CzZk2H+D3mzp0ryn5MmTJF7zoiIiJ0trd582ahRYsWgoeHh1CvXj2dbRiiVquF999/XwgODhY8PT2Fbt26CSdOnNBJM2TIEMXvR1pamhAbG6v4/RCE0pLTwMBAu+6LKWn0+fLLL4U6deoIHh4eQtu2bSucW4b2YdiwYYrYh4yMDKFTp04O83u88847dtsPQ9fmIUOGVLoP1lyzNm/ebPQ7KqMSBEEAEREREVmEvfmIiIiIrMBgioiIiMgKDKaIiIiIrMBgioiIiMgKDKaIiIiIrMBgioiIiMgKDKaIiIiIrMBgioiIiMgKDKaIiIiIrMBgioiIiMgKDKaIiIiIrMBgioiIiMgK/w/HekRnQhvABAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df['datetime'],df['need'])\n",
    "plt.ylabel('need')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e81d4bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=b83b6756-de4e-4330-a8b0-315e3042c391 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('b83b6756-de4e-4330-a8b0-315e3042c391').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>total</th>\n",
       "      <th>need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 00:00:00</td>\n",
       "      <td>6532.0</td>\n",
       "      <td>15684.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 00:05:00</td>\n",
       "      <td>6529.0</td>\n",
       "      <td>15577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-01 00:10:00</td>\n",
       "      <td>6566.0</td>\n",
       "      <td>15564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 00:15:00</td>\n",
       "      <td>6544.0</td>\n",
       "      <td>15496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 00:20:00</td>\n",
       "      <td>6540.0</td>\n",
       "      <td>15423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "             datetime   total     need\n",
       "0 2019-01-01 00:00:00  6532.0  15684.0\n",
       "1 2019-01-01 00:05:00  6529.0  15577.0\n",
       "2 2019-01-01 00:10:00  6566.0  15564.0\n",
       "3 2019-01-01 00:15:00  6544.0  15496.0\n",
       "4 2019-01-01 00:20:00  6540.0  15423.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840d7925",
   "metadata": {},
   "source": [
    "## krataw mia ana 4 eggrafes na meiwthei o thorivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "016a47df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[5::4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2509dc1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78164"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6979425a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=b4c77007-0b8b-4139-8600-5a252ac351b0 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('b4c77007-0b8b-4139-8600-5a252ac351b0').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>total</th>\n",
       "      <th>need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-01 00:25:00</td>\n",
       "      <td>6513.0</td>\n",
       "      <td>15354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-01-01 00:45:00</td>\n",
       "      <td>6421.0</td>\n",
       "      <td>15203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-01-01 01:05:00</td>\n",
       "      <td>6201.0</td>\n",
       "      <td>15168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-01-01 01:25:00</td>\n",
       "      <td>6261.0</td>\n",
       "      <td>14862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-01-01 01:45:00</td>\n",
       "      <td>6193.0</td>\n",
       "      <td>14704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-01-01 02:05:00</td>\n",
       "      <td>5861.0</td>\n",
       "      <td>14815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-01-01 02:25:00</td>\n",
       "      <td>5745.0</td>\n",
       "      <td>14753.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019-01-01 02:45:00</td>\n",
       "      <td>5747.0</td>\n",
       "      <td>14605.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2019-01-01 03:05:00</td>\n",
       "      <td>5649.0</td>\n",
       "      <td>14608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2019-01-01 03:25:00</td>\n",
       "      <td>5556.0</td>\n",
       "      <td>14592.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "              datetime   total     need\n",
       "5  2019-01-01 00:25:00  6513.0  15354.0\n",
       "9  2019-01-01 00:45:00  6421.0  15203.0\n",
       "13 2019-01-01 01:05:00  6201.0  15168.0\n",
       "17 2019-01-01 01:25:00  6261.0  14862.0\n",
       "21 2019-01-01 01:45:00  6193.0  14704.0\n",
       "25 2019-01-01 02:05:00  5861.0  14815.0\n",
       "29 2019-01-01 02:25:00  5745.0  14753.0\n",
       "33 2019-01-01 02:45:00  5747.0  14605.0\n",
       "37 2019-01-01 03:05:00  5649.0  14608.0\n",
       "41 2019-01-01 03:25:00  5556.0  14592.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93d5e44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['datetime'],df['need'])\n",
    "plt.ylabel('need')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e456f01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df['datetime'], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a21268c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=e7369308-69d1-48e8-89e1-845b3869ff58 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('e7369308-69d1-48e8-89e1-845b3869ff58').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>total</th>\n",
       "      <th>need</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:25:00</th>\n",
       "      <td>2019-01-01 00:25:00</td>\n",
       "      <td>6513.0</td>\n",
       "      <td>15354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:45:00</th>\n",
       "      <td>2019-01-01 00:45:00</td>\n",
       "      <td>6421.0</td>\n",
       "      <td>15203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:05:00</th>\n",
       "      <td>2019-01-01 01:05:00</td>\n",
       "      <td>6201.0</td>\n",
       "      <td>15168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:25:00</th>\n",
       "      <td>2019-01-01 01:25:00</td>\n",
       "      <td>6261.0</td>\n",
       "      <td>14862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:45:00</th>\n",
       "      <td>2019-01-01 01:45:00</td>\n",
       "      <td>6193.0</td>\n",
       "      <td>14704.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "                               datetime   total     need\n",
       "datetime                                                \n",
       "2019-01-01 00:25:00 2019-01-01 00:25:00  6513.0  15354.0\n",
       "2019-01-01 00:45:00 2019-01-01 00:45:00  6421.0  15203.0\n",
       "2019-01-01 01:05:00 2019-01-01 01:05:00  6201.0  15168.0\n",
       "2019-01-01 01:25:00 2019-01-01 01:25:00  6261.0  14862.0\n",
       "2019-01-01 01:45:00 2019-01-01 01:45:00  6193.0  14704.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f76e4ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the min and max values of the column to be normalized\n",
    "min_val = df['need'].min()\n",
    "max_val = df['need'].max()\n",
    "\n",
    "# Normalize the column using Min-Max scaling\n",
    "df['need_normalized'] = (df['need'] - min_val) / (max_val - min_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a9cd6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=563d697a-9aef-4e1e-a96f-7672ce667c70 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('563d697a-9aef-4e1e-a96f-7672ce667c70').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>total</th>\n",
       "      <th>need</th>\n",
       "      <th>need_normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:25:00</th>\n",
       "      <td>2019-01-01 00:25:00</td>\n",
       "      <td>6513.0</td>\n",
       "      <td>15354.0</td>\n",
       "      <td>0.408978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:45:00</th>\n",
       "      <td>2019-01-01 00:45:00</td>\n",
       "      <td>6421.0</td>\n",
       "      <td>15203.0</td>\n",
       "      <td>0.404870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:05:00</th>\n",
       "      <td>2019-01-01 01:05:00</td>\n",
       "      <td>6201.0</td>\n",
       "      <td>15168.0</td>\n",
       "      <td>0.403918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:25:00</th>\n",
       "      <td>2019-01-01 01:25:00</td>\n",
       "      <td>6261.0</td>\n",
       "      <td>14862.0</td>\n",
       "      <td>0.395592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:45:00</th>\n",
       "      <td>2019-01-01 01:45:00</td>\n",
       "      <td>6193.0</td>\n",
       "      <td>14704.0</td>\n",
       "      <td>0.391294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "                               datetime   total     need  need_normalized\n",
       "datetime                                                                 \n",
       "2019-01-01 00:25:00 2019-01-01 00:25:00  6513.0  15354.0         0.408978\n",
       "2019-01-01 00:45:00 2019-01-01 00:45:00  6421.0  15203.0         0.404870\n",
       "2019-01-01 01:05:00 2019-01-01 01:05:00  6201.0  15168.0         0.403918\n",
       "2019-01-01 01:25:00 2019-01-01 01:25:00  6261.0  14862.0         0.395592\n",
       "2019-01-01 01:45:00 2019-01-01 01:45:00  6193.0  14704.0         0.391294"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "133aed41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='datetime'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df['need_normalized']\n",
    "temp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff6bd3d",
   "metadata": {},
   "source": [
    "### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d160fe70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def df_to_x_y(df, window_size=5):\n",
    "    df_as_np = df.to_numpy()\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(df_as_np)-window_size):\n",
    "        row =[[a] for a in  df_as_np[i:i+5]]\n",
    "        X.append(row)\n",
    "        label = df_as_np[i+5]\n",
    "        y.append(label)\n",
    "    return np.array(X), np.array(y)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ecb44515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((78159, 5, 1), (78159,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WINDOW_SIZE = 5\n",
    "x, y = df_to_x_y(temp, WINDOW_SIZE)\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0d2a804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 5, 1), (60000,), (5000, 5, 1), (5000,), (13159, 5, 1), (13159,))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, y_train = x[:60000], y[:60000]\n",
    "x_val, y_val = x[60000:65000], y[60000:65000]\n",
    "x_test, y_test = x[65000:], y[65000:]\n",
    "x_train.shape, y_train.shape, x_val.shape, y_val.shape, x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6bb7784",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-04 11:33:17.223326: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import * \n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eff4bbf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-04 11:33:21.370755: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 64)                16896     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 8)                 520       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17,425\n",
      "Trainable params: 17,425\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1 = Sequential()\n",
    "model1.add(InputLayer((5, 1)))\n",
    "model1.add(LSTM(64))\n",
    "model1.add(Dense(8, 'relu'))\n",
    "model1.add(Dense(1, 'linear'))\n",
    "\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bf5c4435",
   "metadata": {},
   "outputs": [],
   "source": [
    "cp = ModelCheckpoint('model1/', save_best_only=True)\n",
    "model1.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c4fa4806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1866/1875 [============================>.] - ETA: 0s - loss: 0.0104 - root_mean_squared_error: 0.1019"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 16s 7ms/step - loss: 0.0103 - root_mean_squared_error: 0.1017 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0425\n",
      "Epoch 2/10\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 0.0012 - root_mean_squared_error: 0.0346"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 14s 8ms/step - loss: 0.0012 - root_mean_squared_error: 0.0346 - val_loss: 6.3277e-04 - val_root_mean_squared_error: 0.0252\n",
      "Epoch 3/10\n",
      "1860/1875 [============================>.] - ETA: 0s - loss: 3.3365e-04 - root_mean_squared_error: 0.0183"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 14s 7ms/step - loss: 3.3293e-04 - root_mean_squared_error: 0.0182 - val_loss: 2.4280e-04 - val_root_mean_squared_error: 0.0156\n",
      "Epoch 4/10\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 2.4463e-04 - root_mean_squared_error: 0.0156"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 14s 8ms/step - loss: 2.4465e-04 - root_mean_squared_error: 0.0156 - val_loss: 2.1469e-04 - val_root_mean_squared_error: 0.0147\n",
      "Epoch 5/10\n",
      "1857/1875 [============================>.] - ETA: 0s - loss: 2.2207e-04 - root_mean_squared_error: 0.0149"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.2257e-04 - root_mean_squared_error: 0.0149 - val_loss: 1.9194e-04 - val_root_mean_squared_error: 0.0139\n",
      "Epoch 6/10\n",
      "1871/1875 [============================>.] - ETA: 0s - loss: 2.0205e-04 - root_mean_squared_error: 0.0142"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 13s 7ms/step - loss: 2.0195e-04 - root_mean_squared_error: 0.0142 - val_loss: 1.6994e-04 - val_root_mean_squared_error: 0.0130\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - ETA: 0s - loss: 1.8372e-04 - root_mean_squared_error: 0.0136"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 13s 7ms/step - loss: 1.8372e-04 - root_mean_squared_error: 0.0136 - val_loss: 1.5198e-04 - val_root_mean_squared_error: 0.0123\n",
      "Epoch 8/10\n",
      "1870/1875 [============================>.] - ETA: 0s - loss: 1.6726e-04 - root_mean_squared_error: 0.0129"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 12s 7ms/step - loss: 1.6721e-04 - root_mean_squared_error: 0.0129 - val_loss: 1.3750e-04 - val_root_mean_squared_error: 0.0117\n",
      "Epoch 9/10\n",
      "1864/1875 [============================>.] - ETA: 0s - loss: 1.5291e-04 - root_mean_squared_error: 0.0124"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 13s 7ms/step - loss: 1.5263e-04 - root_mean_squared_error: 0.0124 - val_loss: 1.2776e-04 - val_root_mean_squared_error: 0.0113\n",
      "Epoch 10/10\n",
      "1873/1875 [============================>.] - ETA: 0s - loss: 1.4209e-04 - root_mean_squared_error: 0.0119"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model1/assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 1.4203e-04 - root_mean_squared_error: 0.0119 - val_loss: 1.1815e-04 - val_root_mean_squared_error: 0.0109\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x158a28fd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=10, callbacks=[cp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfdc2dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model1 = load_model('model1/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a9b60b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 3s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "train_predictions = model1.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "82e49cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15cd0ce80>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0fd743ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_predictions, 'r', label='Predictions')\n",
    "plt.plot(y_train, 'b', label='Actual')\n",
    "# Add legend and labels\n",
    "plt.legend()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Enargy')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26828a85",
   "metadata": {},
   "source": [
    "### kathe 1000 xronikes stigmes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2af003d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_predictions[:1000], 'r', label='Predictions')\n",
    "plt.plot(y_train[:1000], 'b', label='Actual')\n",
    "# Add legend and labels\n",
    "plt.legend()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Enargy')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e175d53",
   "metadata": {},
   "source": [
    "### kathe 50 xronikes stigmes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9b93a684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9fv369Zk6dSpt2rQhMTGRxx9/vEC9RCIiUsi+/ZZP4rsBMGioDxUr2tyeYlQ6+qc83MSJE4mOjs4VbMCEm3Xr1lGhQgVmzpzJ999/T4sWLejcuTNr1qxxPe/FF19kz5491K1bl0qVKgHQuHFj3n//fSZMmEDz5s1Zs2YNjz32WK6fffz4cVq1asVdd93Fgw8+SOXKlYv2DYuIyIVZFgdemsR/MQu63j28jM0NKl4O69yiCg+XmJhISEgICQkJOXogAM6ePcvu3bupU6cO/v7+NrVQipv+3EXE48yfz8s3LOEZXubq9qksXen+4eZCn9/nUs+NiIiIh8kY+zqfcjcAw+53/2CTXwo3IiIinmT9euYv9OJvahManE6vXnY3qPgp3IiIiHiS117jE4YBcNdAb0rjHA+FGxEREU+xYwfx3yzjO7oDMGyYze2xicJNHkpZjXWppz9vEfEYb77JFGsAaZQhMhKaNrW7QfbQOjfZlCljiq5Onz6ttVpKEedqyt7e3ja3RETkEsTFYU35jE/ZAsA999jcHhsp3GTj7e1NaGgohw4dAiAgIMC1Mq94poyMDA4fPkxAQAA+nr6TnIh4tnHjWJwSxU7qExRk0bdv6f380v/m53Duiu0MOOL5vLy8qFmzpoKsiLivxET44AM+5gMA+vVzlPitg4qSws05HA4HVapUoXLlyrk2iRTP5Ovri5eXys9ExI199BFHEnyY5bgNrNI9JAUKN+fl7e2tGgwRESn5kpPhP//hcwaQYvnSqhW0amV3o+ylX1dFRETc2RdfYMXG8onPcKD0Tv/OTuFGRETEXaWnw+uvs4Ir2Z5Wn4AA6NfP7kbZT+FGRETEXX33Hfz5Jx+XeQCA22+Hf9hTslRQuBEREXFHlgWvvcZxQplp3QZoSMpJ4UZERMQdrVoFa9bwhc9gzqaVoWlTiIy0u1Elg8KNiIiIO5o2DQv4JPBhwPTaaLkuQ+FGRETE3WRkwDffsIZ2bDlRE39/6N/f7kaVHAo3IiIi7ubXX+HgQVchce/eUL68zW0qQRRuRERE3M3MmSQSxLSMPoAKic+lcCMiIuJOMoekZnErp9P9aNgQrrrK7kaVLAo3IiIi7iRzSGqatymyufNOFRKfS+FGRETEncyYwWHC+F/GtQD07Wtze0oghRsRERF3kTkk9S23kW5506oVNGhgd6NKHoUbERERd7FiBcTGuoakbr/d5vaUUAo3IiIi7mLmTA5ShaXpHQDo08fm9pRQCjciIiLuIHNIaia9sfCiQweoVcvuRpVMCjciIiLuwDUkdSegIakLUbgRERFxBzNmsJvarEpvh5eXWZVY8qZwIyIiUtKlp8O33zIDU2TTqRNERNjbpJLM9nAzYcIEateujb+/P5GRkaxZs+a8z50yZQoOhyPHzd/fvxhbKyIiYgPnkJSXhqQuhq3hZvr06YwaNYrnnnuODRs20Lx5c7p06cKhQ4fO+z3BwcHExsa6bn///XcxtlhERMQGM2awnYZsymiGjw/ceqvdDSrZbA03b7/9NsOGDWPw4ME0adKEDz/8kICAACZNmnTe73E4HERERLhu4eHhxdhiERGRYpY5JDUdsxTxDTdAxYo2t6mEsy3cpKSksH79eqKjo7Ma4+VFdHQ0K1euPO/3nTp1ilq1alGjRg26d+/OH3/8ccGfk5ycTGJiYo6biIiI21i+HCsujmle/QANSV0M28LNkSNHSE9Pz9XzEh4eTlxcXJ7f07BhQyZNmsR3333HF198QUZGBh06dGD//v3n/TljxowhJCTEdatRo0ahvg8REZEiNXMmm2nG9oyG+PlB9+52N6jks72gOD+ioqIYMGAALVq0oGPHjsyaNYtKlSrx0Ucfnfd7Ro8eTUJCguu2b9++YmyxiIjIJUhPh2++YRqmu+ammyA42OY2uQEfu35wWFgY3t7exMfH5zgfHx9PxEXObytTpgwtW7Zk586d532On58ffn5+l9RWERERWyxfjhUfb4akMjQkdbFs67nx9fWldevWLFiwwHUuIyODBQsWEBUVdVGvkZ6ezpYtW6hSpUpRNVNERMQ+M2awhnbsyahFuXKm50b+mW09NwCjRo1i4MCBtGnThnbt2vHOO++QlJTE4MGDARgwYADVqlVjzJgxALz44ou0b9+eevXqceLECd544w3+/vtv7r77bjvfhoiISOHLnCU1jScBU2sTEGBzm9yEreGmb9++HD58mGeffZa4uDhatGjBvHnzXEXGe/fuxcsrq3Pp+PHjDBs2jLi4OMqXL0/r1q359ddfadKkiV1vQUREpGgsW0Z6/GGmO24HS0NS+eGwLMuyuxHFKTExkZCQEBISEghWVZaIiJRUI0aw5P3f6cQSQkMhLg5Kcwlpfj6/3Wq2lIiISKngGpIy3TW33lq6g01+KdyIiIiUNMuWkRp/lG8cZutvDUnlj8KNiIhISTN/PgvpzBErjEqV4Npr7W6Qe1G4ERERKWnWr3cNSfXuDT62Tv9xPwo3IiIiJYllkbx2M7MwW39rSCr/FG5ERERKkr17mXesLYmEUK2axZVX2t0g96NwIyIiUpKsW8ccegDQu7cDL31S55sumYiISAmSvnYD/8Xss3DLLTY3xk0p3IiIiJQgaxae4jCVCSmbzFVX2d0a96RwIyIiUlJYFj9sqQXAjVcnUaaMze1xUwo3IiIiJcWePfxw9noAuvULsrkx7kvhRkREpITY8+NWfqcp3qRx4y3qtikohRsREZES4oc56QBcVWUX5cvb3Bg3pnAjIiJSQvywoSoA3a48bnNL3JvCjYiISAmQmGCx+FgzQPU2l0rhRkREpAT4ZWo8qfjSgD9pcFN9u5vj1hRuRERESoAfZp4FoFv4GvD1tbk17k3hRkRExGbp6fDj2jAAukUesrk17k/hRkRExGarVsGRM4GU5xhXdg2xuzluT+FGRETEZj98bwFwIz/hE9na5ta4P4UbERERm33/bSoA3XzmweWX29wa96dwIyIiYqNdu2DbLl98SOVfzQ6iDaUuncKNiIiIjX74wdxfzTJC2zeytzEeQuFGRETERs5w040foLXqbQqDwo2IiIhNEhJg6VJTTNyNH6BNG5tb5BkUbkRERGwybx6kpTloxDbq+R+AJk3sbpJHULgRERGxSY4hqRYtwMfH1vZ4CoUbERERG6SlwY8/mmPV2xQuhRsREREb/PorHD8OFXwSiGKl6m0KkcKNiIiIDZxDUl35CR/S1XNTiBRuREREbOCqt0mbBWXLQuPG9jbIgyjciIiIFLMdOyAmBny8M+jCzyomLmQKNyIiIsXM2WvTsepOQkhUvU0hU7gREREpZq4hqTLzzIHqbQqVwo2IiEgxOn4cli0zx91iPzYH6rkpVAo3IiIixWjePEhPhyZ1k7nszB8QEACNtGFmYVK4ERERKUY//WTuuzXaYQ5atgRvb/sa5IEUbkRERIrRypXm/toyy82B6m0KncKNiIhIMTl6FHbuNMftYr8zB6q3KXQKNyIiIsVkzRpz36CBRfktS80X6rkpdAo3IiIixWT1anMf2TABTp+GcuWgYUN7G+WBFG5ERESKyapV5j4yNMYcqJi4SCjciIiIFAPLyhqWikzOHJJSvU2RULgREREpBjt2mAX8/P2h2d655qTCTZFQuBERESkGznqbVi0tfH9ba75QMXGRULgREREpBq5i4npH4cwZCAyEBg3sbZSHUrgREREpBq5wU+53c9CqFXjpY7go6KqKiIgUsTNnYNMmcxx5epE5UL1NkVG4ERERKWIbN0JaGlSuDLVifjEnVW9TZBRuREREiphzSKp9ZAaOzb+ZLxRuiozCjYiISBFz1dvUPmTGqIKCoH59exvlwRRuREREipgr3Phn9tqomLhI6cqKiIgUofh42LMHHA5oe2K+OakhqSKlcCMiIlKEnL02jRtD8JYV5guFmyKlcCMiIlKEXENSbTPgNxUTFweFGxERkSLkmilV66CKiYuJwo2IiEgRyciAtZnbSEV6rTMHKiYucrq6IiIiRWT7dkhMhIAAuDx+oTmpIakip3AjIiJSRJxDUm3agM9G7QReXBRuREREikiOYmLn5lLaU6rIKdyIiIgUkVWrzH1k1b1w9qwpJq5Xz95GlQIKNyIiIkUgKQm2bDHH7cnswlExcbHQFRYRESkC69eb2VLVqkG1v5aZk6q3KRY+djdAxJ3FxZk1uZy3HTugYkWoUeOcW+VkapyOoezurbBzJ0REQGQkNGkC3t52vw0RKQKueptITNIB1dsUE4UbkYuQkQFbt5p6wOxh5tChi30FP6AZFalKTRowirfpzzAIDDT/2UVGZt2qVi26NyIixSarmDgdXthkvlDPTbFQuBG5gP37YfJkmDTJbHx3Li8vaNDAonmlgzQ/9D8axXzHcULZR41ct1MEcZQwjhLGAD4nyD+N7qemw+LF5uZUrZoJOUOHQteuxfRORaSwucJN5T0qJi5mCjci50hNhf/+Fz79FH76yfTaAJQrBy1bQvPmmbdqR7hi1acETHkftu/LeoEqVcwOeU2aQOMUaOyL1bgiCX6B7NvvYNw4mDjRi35eX7Ns2ku0OrXU/C+4ejX8/jscOACzZsGcOWaqRdu2tlwHESm4AwfML0deXtA6+VdzUsXExUbhRiTTjh0wcSJMmQLx8VnnO3aEu++G226Dsn4ZsHAhfPghfPcdpKWZJ1WsCIMHwz335LlnjAMIBULLm2/dtw9++cVBt1H1Wb26PtWHDjVPPHXKjM2//jr8+KN5zfXrwc+viN+9iBQmZ69N06YQ+Ee2lfykWCjcSKllWfDnn2ZEaNq0nCNDlSvDoEFmZKhBfQv++gvenwMffWRSkNNVV8F995nk4+9/UT/XxwdmzIArr4Q//oCbb4bly035DYGBJk1dfrm5/fEHvPwyvPRS4b1xESlyOYqJ12XuKaV6m2Jje//YhAkTqF27Nv7+/kRGRrJmzZqL+r5p06bhcDjo0aNH0TZQPIZlmaLgDz6Avn1N3W6jRiabLF5seou7doVZXyezf/oKXqv4Og2e6GFmNtWrB489ZoJNUBCMGAGbN8OyZXDnnRcdbJxCQmDuXBOifvsN7rgD0tOzPSEsDCZMMMdjxsDGjYV1GUSkGLjCTZt0848cFG6KkcOyLMuuHz59+nQGDBjAhx9+SGRkJO+88w4zZ84kJiaGypUrn/f79uzZw1VXXcVll11GhQoVmDNnzkX/zMTEREJCQkhISCA4OLgQ3oWUVBkZpuNjyZKs2+HDOZ/j5wdRrZKJrvUnA8rNosaWH02QSE3N+URfX2jXDgYOhNtvz+xmuXSrV0OnTqbW8KGH4J13znlCnz4wc6Yp8lmzxrRDREq09HTzC0xSEvz+bQyX39bI/FJ04oRqbi5Bfj6/bQ03kZGRtG3blvfeew+AjIwMatSowciRI3nqqafy/J709HSuueYahgwZwrJlyzhx4oTCjQCQnGx6f5ctM8M8K1aY/0uy8/e36NDyDJ3Ct9Px7M+0i5mK/+5tuV8sIgI6dMi6tWpVZHUv33wDvXub4/feM51CLocOmeGpI0fg+efhueeKpA0iUng2bza/jwQFwfH/TMH77sHmt5hFi+xumlvLz+e3bTU3KSkprF+/ntGjR7vOeXl5ER0dzcqVK8/7fS+++CKVK1dm6NChLFu27B9/TnJyMsnJya6vExMTL63hUmKcOAG//mqCzLJlsHatCTjZBQRYdGh2ik5hf9Ax6Ufabvscv5V/53ySlxc0a2aKYJxhplYtcDiK5X306mVGnkaPhgcfhDp1ss0Ar1wZxo8341Yvvww9e5q2ikiJ5RySatcOvDdoJ3A72BZujhw5Qnp6OuHh4TnOh4eHs3379jy/Z/ny5UycOJFNzp1VL8KYMWN44YUXLqWpUoIcPgyzZ5uRmkWLzqlTwWSBq66Cq+oc4OqD02m+4G3KrDqQ80m+vmZ69TXXmFuHDmBzL96TT5ri5smTTT3QihXZMkzfvjB9upkaPniwmR5epoydzRWRC3BtlhkJLMhcmVjhpli5zWypkydPctddd/HJJ58QFhZ20d83evRoRo0a5fo6MTGRGjVqFEUTpYjEx5tlX775xhT+OtedAVPne/XVmYGmwSHqr5qKY+rnMGtz1pPKlTMB5pprzJPbtYOyZYv9fVyIw2GmiO/ZY0LbzTeb3/6qVMl88IMPTNHQhg3wxhvw73/b3WQROY8cxcRvq5jYDraFm7CwMLy9vYnPvqAIEB8fT0RERK7n79q1iz179tCtWzfXuYzMTzkfHx9iYmKoW7duru/z8/PDT2uEuJ3YWBNoZs6EpUvNTCen1q1Njcptt0G9qqfNejOffw6//JKVfHx94ZZbYMAA6NLFLQpxfX3h228hKgpiYsz7W7Eic3QsIgLGjTPv54UXoHt3U4sjIiVKYqKZlQkQGRpjZgsEB2tl4mJmW7jx9fWldevWLFiwwDWdOyMjgwULFvDAAw/ken6jRo3Y4tw7PtPTTz/NyZMnGTdunO29MStXwsiRZv22Bg1y3pcvb2vT3MKZM6Z25n//g/nzc898btfO1Kb06gV1qqea7o0x0036OXky64kdOpgA0KePW1748uXN6sjNm5u/U0uWmDpEAPr3NwvkzJ0LQ4aY5OPjNp2vIqXCunXml7FatSB8d+b4lFYmLna2/s84atQoBg4cSJs2bWjXrh3vvPMOSUlJDB48GIABAwZQrVo1xowZg7+/P1dccUWO7w8NDQXIdd4Of/xhFpJ1bvyaXcWKOQNP+/bmA6s0bwadnm4CzPz5JtCsWJG7GLh9+6wemlpVMwPNqzNNl86xY1lPrF3bBJr+/fNcHdjd1K1r3spHH5mlblzhxjl2dfnlZlr4f/4Djz9uZ1NF5Bx57gSuIaliV6Bw89dff3HZZZdd8g/v27cvhw8f5tlnnyUuLo4WLVowb948V5Hx3r178XKTtHvTTabQdccOc/vzT3N/8CAcPWp+C88+CSwiwnQu3HGH+UdQTBNzbBUfb3olfvzR7GBw/HjOx6tXh+houP566NwZIsLSTKB5eYa5uEePZj25UiWTem6/3dTRuMnfk4s1YoQJN7Nnmz1qqlXLfKBaNRNqhgyBZ54xQ28NG9raVhHJ4gw37dsD0xVu7FKgdW68vLzo2LEjQ4cOpVevXvjnc3VWOxX3OjenTsHOnVmBZ9vmFH76xZtjJ7K6bS67zHxG33EHlIBOqEJjWbB9O3z/vSmLWbUqZ+1McDBce60JM9HRplfLgWXGp6ZONT005waaW281qfCaazx+SOaaa8wU92efNWU2LpYFN94IP/9shuGWLfO4cCfijizL/OJ66BCsWJJGhy5Bpubmzz89olfZbkW+iN+mTZuYPHkyX3/9NSkpKfTt25ehQ4fSrl27Aje6uNiyiJ+V+YH9/vvw7bekpMJ8rucr+vEd3Ukia7XbKwJ306/Wr1zb/Bhe119HRsPGWDjIyMB1syxz73CYST9ly0JAgLk5j/387OkNSk83a898950JNdm3YQLzC0y3bqbGt02bbPnkwAFTFDx5cs5vCgszPTS9e5s9lzw80GQ3fboJvRER8Pff59RE79tnhqdOnjRje9HRtrVTRIxdu0zdsK8vJCzbjH9kc/Nb3PHj+gWkEBTbCsVpaWl8//33TJkyhXnz5tGgQQOGDBnCXXfdRaVKlQr6skWqWMPNyZPwxRcm1Pz+e9Z5Hx/XbtJJBDCXm/mKfvzEjaRSOLN6nMEnKMiMWjRtatZNadbM9A5dzO4Bp07B7t1mz8jdu82ieWfP5r4lJ2cdb9mSs7PF19cMMd1yiwk11atn+wHJySYBTZ5seiGcM53KlTO9M/36mYKTUhRosktJMUWJcXFmY8++fc95wj33wCefmPuPPrKljSKS5fPPzQ4tHTrAiqGTzM67Wpm40BT79gvJycm8//77jB49mpSUFHx9fenTpw+vvfYaVapUudSXL1TFEm5+/92sS/L55yYhgOlOufNOGD4cWrQwH+yJiTluxw+c5tuF5Zm2vDp/7i+L15kkvKx0vMjAiwwcfn54hQTiFRqMl28ZMjJMoDh92tzOnMm9JdL51K2bFXYuv9w086+/ct4OHSrY269QwdQg3XKL6aEJCsr2YEaGWZt88mQT/LIXBl99tVmkrnfvQtu7yd099xy8+KJZxyfXgtz/+58Z0wsLM3PnS2kIFCkp7rvP/J7x2GPwxukR5hfbRx+FN9+0u2keodjCzbp165g0aRLTpk2jXLlyDBw4kKFDh7J//35eeOEFEhMTL3qX7+JSZOEmJcXUiLz/fs5PoYYN4f77zWyezNldF+3UKbMq7VdfmTVcnMvxOhymWKVbNzP+lJbmuqUmZ3DmDJw56+D0WS+OJ/my9XgVNh+pyubDVdgcX5nYxIsPDuXLm5qgOnVMyYu/fx4371T8jh7EP3Y3Vc/+RVTQ7/icPG66erLfEhLMLfsqfFWrwqBB5qYx6VwOHjS9N2lpZmPhHDsvpKWZVf6OHNHQlEgJ0LSp+d129mzoMba9qS7++mszviyXrMjDzdtvv83kyZOJiYmha9eu3H333XTt2jXHzKb9+/dTu3Zt0jKHX0qKIgs3L71kKj/BzPHu0cOEmmuvLZzil8OHzRonX31lilou5aUIYwtN2UwzttCUrTQhmEQu4y8uCz3OZZfBZc0CqdM+nNDIhtCokUkxYMacNm0yn7SbNpnbtm2uYbaL4utrFqEbPBhuuKF0z4m/CM6NwfMcfbr3Xvj4Yxg2zNyLiC1OnDC91pYFcftSCa8frGLiQlbk4aZ+/foMGTKEQYMGnXfYKSUlha+//pqBAwfm9+WLVJGFm717zcaLQ4eaDxrX3N0isHu3+W1g7VpTpObjc/6bt7cJHikpZswqr/szZ8yUrtjYvH+el5epkjt9Gvbvz/s55cub4bYGDcxxaKi5hYRkHTtv5csX2Q7bnsi5kF9AgKm7ztEBuGCB6bGpWNEU52hoSsQW8+aZSYx168LOb38z/x+qmLhQFfmu4DvOnQKTB19f3xIXbIpUzZpmSktx/CWuU6do9hY6etSsRvj77zlvx4+b3z6c6tY1/3CbNzf3LVqYSuHSsFiPDa65xtRF/fEHfPYZPPRQtgc7djQ1N0eOmKLF66+3rZ0ipZmzQ/3KK8lavE8rE9umQOFm8+bNeZ53OBz4+/tTs2bN0rmfk7v/Ja5YMWunbCfLMj0Cf/xhhqaaNbN9B+3SxuEwi/rdf78p6Ro5MttfNR8fM1X+o4/MsKXCjYgtnOGmQweywk2bNra1p7Qr8CJ+jgv8ll6mTBn69u3LRx99VOIW+LNlnRuRS3TypBnpPHnS1JbnyDALF8J115lwGhsLZcrY1k6R0igtzQwXJyWZ5TCuuFvFxEUhP5/fBepqmD17NvXr1+fjjz9m06ZNbNq0iY8//piGDRvy1VdfMXHiRBYuXMjTTz9doDcgIjkFBZn1M8DsN5XDNdeYqWxHj2o9DREbbNligk1ICDSpn2omWoC2XbBRgYalXnnlFcaNG0eXLl1c55o2bUr16tV55plnWLNmDeXKlePRRx/lTc3vFykU998P770HP/xg6tdr1sx8wDk09eGHZlrVDTfY2k6R0mbFCnMfFQVe27eadcyCg019otiiQD03W7ZsoVatWrnO16pViy1btgDQokULYs83+0ZE8q1xY7Pac0aGyTE59O5t7mfNuviVHEWkUORZb9O6tfvXYbqxAl35Ro0aMXbsWFJSUlznUlNTGTt2LI0aNQLgwIEDrt29RaRwPPCAuf/0U/PLocs110DlymbF54ULbWmbSGnl7LnJMVNKQ1K2KlC4mTBhAnPnzqV69epER0cTHR1N9erVmTt3Lh988AEAf/31F/fff3+hNlaktOvWDWrUMGs6zpyZ7QHn0BSc84CIFKX9+80wsbc3tGuHwk0JUeDtF06ePMmXX37Jn5nrnzRs2JB+/foRlGMjoZJHs6XE3b3yCjz9NLRvDytXZntg0SIzblWhgpm+r1lTIkVuxgyzqW2rVrB+bYap/j99GrZvN9vvSKEp0kX8UlNTadSoEXPnzuW+++4rcCNFpGDuvhteeAFWrYING8x/qkDW0NShQ2ZoKlvBv4gUjRz1Nn/9ZYKNv79Z1V1sk+9hqTJlynD27NmiaIuIXITw8Kz64RzTwr29oVcvczxjRrG3S6Q0ylFvkzmhhiZNtGeezQpUczNixAhee+21ErcppkhpMWKEuf/qK1ND7OJMPbNna9aUSBFLSoKNG81xhw5khZumTW1rkxgFWudm7dq1LFiwgF9++YWmTZtSrly5HI/PmjWrUBonInmLijI7YWzeDNOmmTVwALj6atO1Ex9vNtX8179sbaeIJ1u7FtLTzdZ6NWuicFOCFKjnJjQ0lNtuu40uXbpQtWpVQkJCctxEpGg5HDBokDn+7LNsD3h7Z82a0tCUSJHKUW8DCjclSIFnS7krzZYSTxEfb/abSk+Hbdsgc4kpWLIEOnWC8uXNrClfXzubKeKxbroJfvwRxo2DB4edgcBAs8rmwYNQpYrdzfM4Rb63lIjYLzwcunY1xzl6b666CiIi4PhxMzQlIoUuIyNrKYYOHYCtW83JihXNvz+xVYHDzTfffEOfPn1o3749rVq1ynETkeLh3Exz6lTTgwPkHJrSgn4iRWL7dvP7Q0AANG9OziEph8PWtkkBw827777L4MGDCQ8PZ+PGjbRr146KFSvy119/ceONNxZ2G0XkPG6+2Yw+HThwzq4LffqY+9mzIds2KSJSOJz1Nu3aZa6XqXqbEqVA4eb999/n448/Zvz48fj6+vLEE08wf/58HnzwQRISEgq7jSJyHn5+cMcd5jjH0NSVV5ox/xMn4H//s6NpIh4tx/o2oHBTwhQo3Ozdu5cOmeXhZcuW5eTJkwDcddddfP3114XXOhH5R86hqVmzIDEx86SGpkSKlGZKlWwFCjcREREcy1w5rGbNmqxatQqA3bt3U8omX4nYrm1bM1PqzBn45ptsDziHpubM0dCUSCE6fBgyt1UkKgo4csTMTAS4/HLb2iVZChRuOnfuzPfffw/A4MGDeeSRR7j++uvp27cvPXv2LNQGisiFORxZvTdTpmR7QENTIkXCOUuqSRNT8+bqtalTx2ycKbYr0ArFH3/8MRkZGYDZiqFixYr8+uuv3HLLLdx7772F2kAR+Wf9+8O//w3Llpm9+y67DPDygltvNRtQzZmTNW9cRC6J6m1KvgL13Hh5eeHjk5WLbr/9dt59911GjhyJrxYMEyl21atDdLQ5/vzzbA/06GHuv//erMEhIpdM9TYlX4F6bgBOnDjBmjVrOHTokKsXx2nAgAGX3DARyZ+BA2H+fBNunn3WdNzQsSOEhJjljFevziwQEJGCSk42e0qBem5KsgKFmx9++IE777yTU6dOERwcjCPbgkUOh0PhRsQGPXua4f7du2H5crjmGswCHF27wtdfw3ffKdyIXKKNG03AqVQJ6tXD9Ij+/rt5UOGmxCjQsNSjjz7KkCFDOHXqFCdOnOD48eOum3MWlYgUr4AA6N3bHOdY86Z7d3M/Z05xN0nE4zjrbTp0yFyIeM8eSEoye7jVr29n0ySbAoWbAwcO8OCDDxIQEFDY7RGRS+CcNTVzJpw+nXnyxhtND05MjLmJSIGdt96mcePMpYqlJChQuOnSpQvr1q0r7LaIyCW66iozG/XkSbPzAgDBwdC5szn+7jvb2ibi7ixLM6XcRYFqbm666SYef/xxtm7dStOmTSlzTlq95ZZbCqVxIpI/Xl4wYAC88IIZmrrzzswHuneHn3824eaJJ2xto4i72r3b1Ob7+kLr1pknFW5KJIdVgCWFvbzO3+HjcDhId21PXPIkJiYSEhJCQkICwcHBdjdHpND99RfUrWvqAfbuNdPEOXDAHDgcEBsL4eF2N1PE7Uydan55iIrKGp6iSRPYtg1+/NEMAUuRyc/nd4GGpTIyMs57K8nBRqQ0uOwyuPpq04X+xReZJ6tVM/s0WBb88IOt7RNxV7nqbZKTs/ZhUM9NiZKvcNO1a9ccu36PHTuWEydOuL4+evQoTZo0KbTGiUjBDBpk7qdMMXkGyJo1pbobkQLJVW+zbRukp0NoqPkFQkqMfIWbn3/+meTkZNfXr776ao6p32lpacRoNoaI7Xr1grJlzeSoNWsyTzrDzfz5cOqUbW0TcUeJiVnL2biWi8peb5NtvTexX77CzbnlOdoBXKRkCg4220pBtjVvLr/cjFklJ8Mvv9jWNhF3tHGj6QWtWRMiIjJPOsNNs2a2tUvyVqCaGxEp+Zxr3kybZvIMDkfWXlMamhLJF+fqJ23aZDupmVIlVr7CjcPhyLHVgvOciJQ8nTubMoDjx81EDiBraGruXEhLs61tIu5m/Xpz75oCDgo3JVi+1rmxLItBgwbh5+cHwNmzZ7nvvvsoV64cQI56HBGxl7e3GZoaP96U2fTsiZnmUbEiHD1qqiM7drS7mSJuIVfPzfHjZokFgCuusKVNcn756rkZOHAglStXJiQkhJCQEPr370/VqlVdX1euXFmbZoqUINddZ+4XLMg84eMDN99sjrXXlMhFSUiAHTvMca7F+2rVMkVuUqLkq+dm8uTJRdUOESkCHTuaVYv//BP2789c0K9HD1Nl/N138PbbmuUh8g82bDD3tWubjk9AQ1IlnAqKRTxYaGjWb5qLFmWevP568Pc3a8k757aKyHk5h6RUb+M+FG5EPJxzz0zX0FS5cibggIamRC6Cs5hYM6Xch8KNiIdzhpuFC7VasUhB5Oq5saysXk+FmxJJ4UbEw111FZQpA/v2wa5dmSe7dTO1NuvXm2IcEcnT8eNZ/25c4WbvXrNkcZky0LChbW2T81O4EfFwAQFZy8UvXJh5snLlrN3/vv/elnaJuANnMfFll0GFCpknnUNSjRqZgCMljsKNSCmQq+4GsoamVHcjcl5avM89KdyIlALO9W4WLYKMjMyTznCzeLFZyENEctG2C+5J4UakFGjXzgxPHT4Mf/yRebJBA2jcGFJT4aefbG2fSEmlnhv3pHAjUgr4+sLVV5vjPIemNGtKJJdjx+Cvv8xxq1aZJ1NSYPt2c6xwU2Ip3IiUEs6hKVdRMWSFmx9/NP9pi4iLs5i4bl0oXz7zZEyM2XQ2JARq1LCtbXJhCjcipYSzqHjJkmwbgrdrBxERZlrr4sV2NU2kRLpgvc0VV2jrkhJM4UaklGjRwmzHkJiYVUeAlxfccos5njvXppaJlEzadsF9KdyIlBLe3nDtteY4x9CUc7xqxYpib5NISaZtF9yXwo1IKZJ9KwYX52J+v/0Gp04Ve5tESqKjR2HPHnPsKiYGhRs3oXAjUoo4w83y5XD2bObJ6tWhZk1IT4c1a2xrm0hJ4uy1qV/f1A4DZj2ovXvN8RVX2NIuuTgKNyKlSOPGpn747FlYtSrbA87em19/taVdIiVNnvU2zs0yq1fPNn1KSiKFG5FSxOE4z9DUlVeae9XdiACqt3F3CjcipcwF625Wrsy2P4NI6aWZUu5N4UaklHGGm9Wrs9UPN2sG5cqZmoKtW21rm0hJcPhwVmmNiondk8KNSClTp465paXBsmWZJ318IDLSHKvuRko555BUgwYQHJx50rIUbtyIwo1IKaS6G5Hzy7PeZtcuOHHCbNTWqJEdzZJ8ULgRKYWc4SbHJpqaMSUCnGfbhSVLzH27duDnV+xtkvxRuBEphZzhZtMms1gZAO3bm+lUO3dCfLxdTROxnbPnJkcxsTPcdOxY7O2R/LM93EyYMIHatWvj7+9PZGQkay6wiNisWbNo06YNoaGhlCtXjhYtWjB16tRibK2IZ4iIgCZNTBmB8/9sQkPh8svN8cqVdjVNxFbx8bBvn8n5LVtmnrSsrI1lO3WyqWWSH7aGm+nTpzNq1Ciee+45NmzYQPPmzenSpQuHDh3K8/kVKlTg//7v/1i5ciWbN29m8ODBDB48mJ9//rmYWy7i/vIcmnLW3WhoSkopZ69Nw4YQFJR5cs8ek3h8fCAqyq6mST7YGm7efvtthg0bxuDBg2nSpAkffvghAQEBTJo0Kc/nd+rUiZ49e9K4cWPq1q3LQw89RLNmzVi+fHkxt1zE/Tn3y8xzvRsVFUsplWcxsbN7s21bs2SClHi2hZuUlBTWr19PdHR0VmO8vIiOjmblRXSJW5bFggULiImJ4Zprrjnv85KTk0lMTMxxExFTOuBwwPbtcPBg5klnuFm3DpKTbWubiF3yXLxP9TZux7Zwc+TIEdLT0wkPD89xPjw8nLi4uPN+X0JCAoGBgfj6+nLTTTcxfvx4rr/++vM+f8yYMYSEhLhuNWrUKLT3IOLOypfPWqDM1XtTty5UrgwpKVm/woqUIhfsuVG4cRu2FxTnV1BQEJs2bWLt2rW88sorjBo1isXOQq88jB49moSEBNdt3759xddYkRIu19CUw6Ep4VJqxcXBgQPmn0GLFpkn9+2D3bvB2zurJk1KPNvCTVhYGN7e3sSfM+U0Pj6eiIiI836fl5cX9erVo0WLFjz66KP06tWLMWPGnPf5fn5+BAcH57iJiJG9qNiyMk9qMT8ppZy9No0bQ2Bg5klnr03r1tkqjKWksy3c+Pr60rp1axZkm6qRkZHBggULiMpHNXpGRgbJqg0QKZCrroIyZcw+Ort3Z57M3nPjSjwini/PehvnyICGpNyKrcNSo0aN4pNPPuGzzz5j27ZtDB8+nKSkJAYPHgzAgAEDGD16tOv5Y8aMYf78+fz1119s27aNt956i6lTp9K/f3+73oKIWytXzqzdB9mmhLdubZaYP3TILDkvUkqo3sZz+Nj5w/v27cvhw4d59tlniYuLo0WLFsybN89VZLx37168vLLyV1JSEvfffz/79++nbNmyNGrUiC+++IK+ffva9RZE3N6115oNNBcvhmHDMEvLt2ljem5+/RXq1bO7iSLFIlfPzcGDZsVuLy/TzSluw2FZpavfOTExkZCQEBISElR/I4LpsYmOhlq1zFplADzxBLzxBtxzD3z0kZ3NEykWBw9CtWomxyQmZi5n8/XX0K+fmVao2YO2y8/nt9vNlhKRwhUZaSaC/P23mRgCaMaUlDrZi4ld6/RpSMptKdyIlHKBgVl76LgmSDnDzR9/wIkTdjRLpFhdsN5G+0m5HYUbEXGVE7h2Mqlc2dTaWBasWmVbu0SKS656m7g4s3y3wwFXX21bu6RgFG5EJHe4AQ1NSalhWVnhxtVzs3SpuW/WzCznLW5F4UZEXOv2bd4MCQnnnNRifuLhDh6E+HhTe9a8eeZJ1du4NYUbESEiImsUyrVvrbPnZvVqSEuzrW0iRc2Z3y+/HAICMk8q3Lg1hRsRAfIYmmrSBEJCICnJdOmIeKiffzb30dGZJ44cMcX0ANdcY0ub5NIo3IgIkEe48fIC51YoqrsRD2VZWeGmS5fMk856myuugLAwW9oll0bhRkSArHCzejWkpGSeVN2NeLitW81O4P7+2SZFaUjK7SnciAgADRqYX1LPnoUNGzJPasaUeDhnr03HjlC2bOZJbZbp9hRuRAQwy3nkGppq185MIdm7F/bvt61tIkUl15DUsWOwZYs5Vr2N21K4EREXZ7hZtizzRGBg1txY9d6IhzlzJqu8xhVuli0zhTiNGkHmJs7ifhRuRMTFGW5WrICMjMyTzrobhRvxMEuXmmHY6tXNnlKA6m08hMKNiLi0bGnqDo4ehZiYzJPOuhsVFYuHyT4k5XBknlS48QgKNyLi4utrdgmHbHU3znCzcaNZ80bEQ+Sqt0lIgE2bzLHCjVtTuBGRHHIVFdesafrt09Nh7Vrb2iVSmPbtM9PAvbyyLd63fLkZj61fH6pWtbV9cmkUbkQkB22iKaXBL7+Y+3btsu2LqSngHkPhRkRyiIoyv83+9ZfZUBDQYn7icXINSYHqbTyIwo2I5BAcDM2amWNXlnFuw7B6tZkmK+LG0tJg/nxz7Ao3J09mrV6pcOP2FG5EJJdcQ1PNm4Ofn5lGtWuXbe0SKQxr18KJExAaCm3bZp5cscLUldWpAzVq2Ng6KQwKNyKSS65w4+sLrVqZ49WrbWmTSGHJvgu4j0/mSeeQVKdOdjRJCpnCjYjk4iyx2bTJ9NYDWXPEV62yo0kihUb1Np5P4UZEcqleHWrXNrNiXVnGGW7UcyNu7PhxWLPGHLvCTVJS1jIHCjceQeFGRPKUa2iqfXtzv2mTWbNexA39738mtDdunK20ZsECU2Vcs6ZJ9eL2FG5EJE+5wk2tWlC5MqSmmtWKRdxQnkNSkyaZ+969i709UjQUbkQkT85ws2qVyTM4HBqaErdmWXmEm7g4mDvXHA8daku7pPAp3IhInho3Niu3nj6dtd2Owo24s23bYP9+s6rBNddknvzsMzMFPCoq29bg4u4UbkQkT15eWbOmctXdaMaUuCFnr80110BAAKYrxzkkpV4bj6JwIyLnlavupm1bMzy1Zw8cOmRXs0QKJNeQ1PLl8OefEBgIffva1i4pfAo3InJe2cONZWH2ZnB23WtoStzImTNZS9m4ws3Eiea+b18TcMRjKNyIyHm1aWPqEw4dgp07M086h6YUbsSNLFtmVjCoVg0uvxxITISZM82DGpLyOAo3InJefn5Ze++4hqa0UrG4IeeQ1A03mJFVpk0z1fKNG2cFdvEYCjcickG56m6c4WbtWrMamogbyFVv4xySGjo0M+2IJ1G4EZELyhVuLr/cTDVJTITt221rl8jF2r8f/vjDZJjoaGDLFrMHg48P3HWX3c2TIqBwIyIX1KGDuf/zz8wJUj4+WWNVGpoSN/DLL+a+bVuoWJGsXptbbjGrbovHUbgRkQsqXx6uuMIcr1iReVKL+YkbyTEklZwMU6eaE3ffbVubpGgp3IjIPzpv3Y3CjZRw6ekwf7457tIF+O47OHYMqlc31cXikRRuROQfOcPNvHmZ6904Z5ds2QKnTtnWLpF/sm4dHD8OISGZmdw5JDVoEHh729k0KUIKNyLyj266ydQQb92auRBa1armN9+MDFi/3u7miZzXtGnm/rrrwOfA31ndOIMH29coKXIKNyLyj0JDsyaVjB+feVJDU1LCHTkCH39sjocNA6ZMMV2PnTvDZZfZ2TQpYgo3InJRHnjA3M+ZA3v3ok00pcQbP96s09eyJXSJTtcmmaWIwo2IXJQrroBrrzUjUR98gHpupEQ7eRLefdcc//vf4Fi4wKTy0FDo2dPWtknRU7gRkYs2cqS5/+QTONOktSnIPHjQrJImUoJ89BGcOAENGmRmGWchcf/+ULasnU2TYqBwIyIXrVs3qFkTjh6Fad8HQNOm5gH13kgJcvYsvPWWOX7qKfA+cdSMp4KGpEoJhRsRuWg+PnD//eZ4/HiwIlV3IyXPlCkQFwc1asCddwJffAEpKdCqFbRoYXPrpDgo3IhIvtx9N/j7w8aN8GvFbuakem6khEhLg9dfN8ePPQa+Zaycm2RKqaBwIyL5UrEi9OtnjsdvvNIcrFtnPlVEbDZ9OuzeDWFhmbsrrFtnFpv098/6iyseT+FGRPLNWVj87fxgDgY1hDNnzAeIiI0yMmDsWHP88MNm4Uk+/9ycuO02M1NKSgWFGxHJtxYtzJYMaWkOPgx72pzU0JTYbO5c+P13CAqCESMwvYnTp5sHnatQSqmgcCMiBeLsvfnoUA+S8VW4EVtZFrz6qjkeMSKzk2bBAjh8GCpVMvsvSKmhcCMiBdKzJ1SrBoeSAplJb82YElstXmzytb+/GZIC4KuvzH3fvmaqn5QaCjciUiBlysB995nj8YyE7dvNqmkiNnD22gwdCuHhmH0XZs0yJ1VIXOoo3IhIgd1zD/j6whoiWUNbWLvW7iZJKbR2Lfzvf6Zz5vHHM0/OnQunTkHt2ln7oEmpoXAjIgVWuTLcfrs5Hs9I1d2ILcaMMff9+kGtWpknnUNS/fqBw2FLu8Q+CjcickmchcXT6Uv8ku32NkZKna1bYfZsk1+eeirz5PHj8OOP5lhDUqWSwo2IXJI2baD9FSdJxZePf73cTFsRKSavvWbue/SAxo0zT377LaSmQrNmcPnldjVNbKRwIyKXbORj/gB8eHoAqX/utrk1Ulrs2QNffmmOR4/O9kD2ISkplRRuROSS9bqjDBFljnCQasx676DdzZFS4u23IT0drr8e2rbNPHnggJkXDlkFYVLqKNyIyCXz9YV7W5qZUuO+qaaRKSlySUnw2Wfm2DVDCsyKxJZlltB2VRdLaaNwIyKF4t4BZ/DjLCvj6vD113a3RjzdtGmQmAh1656z+LCGpASFGxEpJFVubMGzvAjAQyPTOXLE5gaJR/voI3N/zz3g5fwki4mB9evNgje9e9vWNrGfwo2IFI7LLuPxqBU0ZTNHjnlnLYEvUsg2bDAL95UpA4MHZ3vA2WV4ww0QFmZL26RkULgRkUJTZvRjTGQoXqTz5Zfw0092t0g8kbPX5rbbzJ6YgKmz0ZCUZFK4EZHCc9NNtG2cxMO8A5i9p06etLdJ4llOnszKMPfem+2B9ethxw4oWxa6d7elbVJyKNyISOHx8oLHH+dFnqWO99/s3QtPP213o8STfPWV2TKqYUPo2PGcB8AEm8BAW9omJYfCjYgUrjvvpFzVUD5KvxuA8eNh5Uqb2yQewbLgww/N8b33ZtsyKj3dTJ8CDUkJoHAjIoXN1xceeYTr+R8DQ2ZjWXD33ZCSYnfDxN2tXQubNoGfHwwcmO2BJUsgNhbKl4cuXexqnpQgCjciUvjuuQdCQngr4W4qhySzdWvWzs0iBeUsJO7dGypUyPaAc0iqd28TrqXUU7gRkcIXHAzDh1ORY7wb/goAr7xidnAWKYgTJ7Jmet93X7YHkpPhm2/MsYakJJPt4WbChAnUrl0bf39/IiMjWbNmzXmf+8knn3D11VdTvnx5ypcvT3R09AWfLyI2evBB8PWlz58v0e3KY6SmmuGp9HS7Gybu6Isv4MwZs8l3hw7ZHvjpJ0hIgGrV4OqrbWuflCy2hpvp06czatQonnvuOTZs2EDz5s3p0qULhw4dyvP5ixcv5o477mDRokWsXLmSGjVqcMMNN3DgwIFibrmI/KMqVWDAABzA+wGPERRkCovff9/uhom7saysIakchcSQtS34HXdkW6pYSjuHZdm3xV1kZCRt27blvffeAyAjI4MaNWowcuRInnrqqX/8/vT0dMqXL897773HgAED8nxOcnIyycnJrq8TExOpUaMGCQkJBAcHF84bEZG8xcRA48ZgWXzw7EHuf7EK5cqZ4amaNe1unLiLFSvMPphly8LBgxAamvlAYiJUrmyGpjZsgJYt7WymFLHExERCQkIu6vPbtpibkpLC+vXriY6OzmqMlxfR0dGsvMh5o6dPnyY1NZUKOSrLchozZgwhISGuW40aNS657SJykRo2hB49ALh33zNceaXZzfm++9DO4XLRnL02t9+eLdgAzJ5tgk2jRtCihQ0tk5LKtnBz5MgR0tPTCQ8Pz3E+PDycuLi4i3qNJ598kqpVq+YISOcaPXo0CQkJrtu+ffsuqd0ikk9PPAGA1xef8+kr8fj6mjKJV1+1uV3iFo4dgxkzzHGOFYmTk+H1183xHXecM1YlpZ3bDlCOHTuWadOmMXv2bPz9/c/7PD8/P4KDg3PcRKQYtW9vCj1TU2n037d45x1z+umns8olRM7ns89MjmneHNq1y/bAyy+b8c3wcHjgAdvaJyWTbeEmLCwMb29v4uPjc5yPj48nIiLigt/75ptvMnbsWH755ReaNWtWlM0UkcLw5JPm/sMPGd4vgcceM18OHmzWXxPJS/ZC4vvuy9Y5s2lT1sJJEyacs+iNiI3hxtfXl9atW7NgwQLXuYyMDBYsWEBUVNR5v+/111/npZdeYt68ebRp06Y4mioil+rGG80c3pMn4cMPee016NULUlNNSc62bXY3UEqipUtNTXq5ctmWsElNhSFDzJoCt91mbiLnsHVYatSoUXzyySd89tlnbNu2jeHDh5OUlMTgwYMBGDBgAKNHj3Y9/7XXXuOZZ55h0qRJ1K5dm7i4OOLi4jh16pRdb0FELkbmhpoAvPMOXqnJfP45REWZxdm6doVzOnFFXPtI9etn1oUE4M03YeNGs9VC5kxbkXPZGm769u3Lm2++ybPPPkuLFi3YtGkT8+bNcxUZ7927l9jYWNfzP/jgA1JSUujVqxdVqlRx3d5880273oKIXKw77oDq1SEuDr74grJl4bvvoG5d2LMHunWD06ftbqSUFIcPw7ffmmNXIfH27fDCC+b4nXfgH0oYpPSydZ0bO+RnnryIFLK334ZHHzVTxH//HXx82LHD9OAcPQrdu5sPNG9vuxsqdnvjDTPRrk0bs2Em6elwzTXw669mmPO//9UMqVLGLda5EZFSaNgws1BJTIzZ1jktjfr1TQ+On5+5f/RRuxspdjt8GP7zH3Ps6rWZMMEEm6AgU2WsYCMXoHAjIsUnKMjM7fXxMTs5ZwacK6+Ezz83Txk3ztykdEpPh/79ITbWdPD16wfs3g3O+svXXwctxir/QOFGRIrXLbfAzJm5Ak6fPvDaa+YpjzwCc+bY2kqxySuvwC+/mK0WvvkGAspapsfv9Gno2BHuucfuJoobULgRkeLXo0eeAefxx7O2ZujXD158Ec6zj654oAUL4PnnzfEHH8AVVwATJ5oHypaFTz/V5phyUfS3RETskUfAcaSnMX483HwznDkDzz1nNtgcMgQ2b7a7wVKUDh40gdayzJ/3wIHAgQNZRVgvvwz16tnaRnEfCjciYp8ePczGQdkCjo8jndmz4euvzXL7yckwebJZfv+66+CHHyAjw+6GS2FKSzObYh46BM2aZS5fY1kwfLjZ+TsyEh56yO5mihtRuBERe/XsmTPgDBiAjyOd22+H1ath5Uro08dMD1+40JTsNGxoPgC1fqdnePppWLbM1JvPnGlGoPjqK5Nky5QxQ1NaH0DyQeFGROyXR8AhPR0w+25Onw5//WXWPQkNhZ07YeRIsybgv/9tpg6Le5o7N6uQfOJEaHBZmim2GjjQnHzmGbN1h0g+KNyISMnQs6dJMdkDTnKy6+GaNc2H4P79ZsmTBg0gIcHsn1i7tinNyLagubiBPXvMHzOYsNq7zW4zI+q550y47dcPnnrK1jaKe1K4EZGS49Zbcwac+vXNDJnUVNdTypWD++83m23Onm1WsD192ix+XKcOjBgBe/fa+B7koqSkmOHG48ehXTuLN1t+aQqrnAv1TZ0KX3xhhqVE8knhRkRKlltvNamlalXYt8+scdK4sfmgyxyqAjMjuEcPWLMGfvoJOnQwHT3vv2/2q7r7bjN8JSXTY4+ZbRXKh2YwI/xBfIf0N7vGX3kl/PabWclPqxBLASnciEjJc/PNJpn85z9QqRLs2gV33WWm0nz7bY7pUg4H/OtfsHw5LFoEnTub2TcTJ5rC4/79FXJKmpkzYfx4c/y5z1Bq/fCeKRh+8UVYvNh0wYlcAoUbESmZypaFhx82lcSvvmoqibduhV69zFjUjz+a6cKZHA7o1AkWzD3Dim/j6HrlCTIy4Msvoenl6bzzQgIZ6aVqn+ASJzXV1EjddZf5c3iS17j5yBTT1bZihSke9vGxt5HiEbQruIi4hxMnTE/O229nzQFv2xYqVzbTpQ4fNgulJCW5vmU9rXiS11hANADX+KxgcotxXNamoln+9vLLzX1YmA1vqHRZswaGDUph8zZfAHowm5n0xmfIQHjnHVNnI3IB+fn8VrgREfdy5IjZPPG998wyxnnx9TXDWZUqYZWvwMdbr+LR+MdJIpBynOJ1nuA+PsSLzP/+wsPNCoHPPmvGsqTQnNp7jGeGHODdBU3IwJuKHGEcD9Ev9Cccn3xseuJELoLCzQUo3Ih4iNhYmDUL/P1dQYZKlUxPTlBQrmLU3duTGdw/lSXrAwG4LmwTE8uOpNa+5VlP8vaGwYPNVOTq1Yvz3XiWM2dg7lx+ensbw1cN4G9qA9CfqbwdOYNKg282U6XKl7e3neJWFG4uQOFGpPTKyDAdPk89ZT5/g4Lg7VfPMrT5OhxvvG5WxAXw84MHHoDRo6FiRXsb7U4OHIDnn+fw1//j4aSX+Yo7AahV5gAf3bWCLs+1NwsWiRRAfj6/VVAsIqWGlxc8+KCZadyhg5l5PGykP11fvYr9739vilqvvtrMKX/rLbjsMrNho/Z5uLDUVHj7bY43bM+nn1o0TlrLV9yJlyODR+46wu/HqtFlYh8FGyk2CjciUurUrw9Ll8Kbb5pOmnnzTF3xl7s7YC1eYmZitWhhNm185hkzm2f8+BwrJnucAnTiJybCf9/YymNVv6L1ox2pmPQ3w/iUo4TRrJnFqtVevP15GIGBRdBekQvQsJSIlGrbt5stANauNV/36gUffggVy2eY/a6eftqsswNm/ZVPPjHFx57AskyyGzMGVq2Cq64yF6BnT6hSJdfTk5JM59aiRbBofirrNniRbuXc0LJhQ4shQxw88ogWF5bCpZqbC1C4EZFzpaWZz/cXXzTHERFm14ebbsIMuUyaBC+8kLV51X33mRlb7jp9OT0dvvkGxo6FTZtyP+5wZAWdW28lqXx1Xn7ZzMQ/t/OqLju5tuFBrn2kJZ26BVG1arG8AymFFG4uQOFGRM5n/XqzEPK2bebre+4xpTeBgZi6myeegA8+MA/Wrm2WQe7c2a7m5l9ystmz6fXXYccOc65cObj3XrjjDliyxISeVasAsIA59OBh3w/YmxIBQE3fWK5N+ZlrWcS1jeOpOfE5iIqy6Q1JaaJwcwEKNyJyIWfOwP/9n+mlAFNT/PnnZssjABYuhCFD4O+/zdfDh5uwEBhISorZCDIszMwqzzfLMlXOx4+bRQvLljXT20ND873P0qlTJsd88AHExWbQu8Fm7t7xJC0P/2KeUL48PPSQmRV27oywffvY9fECRk5oyE/HTXCpzW7G8RDd+AFHYCC89JL5Xq0oLMVE4eYCFG5E5GIsWgSDBpkdxh0O02nzwgtmN+tdm5PY9eKX7PplJ7uoy07/K9gV0op9h8uSkWE+76tVgxo1zAShmjWhRkQqNTP2UOPYb9SMX0v5U/vg2DETZLLfsm0O6uLjk3Mtn+xr+vj6mh6ZzNuO+GDe3xjF5JgoElLL5XqpVmU2c/cth+g3rj0h1XJX+p49a0arxo41L+nra/FE53WMTnqGgA3Lzb5fb71l3qBIMVK4uQCFGxG5WAkJZnurKVPM12XLnn9R5Pyqxn5asYHWrKcVG2jFBqpyEAeYKVyhoXD6tOnJ+QcZOPiZLoxnJD/R1XW+Pn/yAO/RkBgmB45k9tkbSUnzdr2XPn3M7ulXXmkC3I8/wsiRZjsvgBtuMJPEGjQonPcscikUbi5A4UZE8mvOHFN/c/iw+ToszMwOr1sX6lZPpu7aadRd9An12ElYYDLxp8qxlxrsowZ7qem63+t9Gfu8anE4NTTPn1M5LJ1WLaFVW29atjQdM95pyfgkJeCTeAyfxGN4JxzD58QRfE4cgWPHmLvnCibEXMfOxHAAHGTQtcEuHrh6EzdcEYtXWT+z2vK//sWR49588YWZ8LV1a9bPbdjQlBD9/LP5ulo1MyzXq1e+R8NEiozCzQUo3IhIQSQlmR6NWrUgz/86FiyAoUOzanGqV4eWLaFVq6z76tXB4eDkSbOQ4IYNWbetW/MekbpYISGmFOj++6FevQs/17JMzfCnn8K0aaaDCMzo18MPmy223HUimHguhZsLULgRkSJz+jRs3myqkCtXzte3njkDW7ZkhZ1Nm8yIVFpa1i09PedxaqoZMho+HPr3NxOf8isx0QScHTtg4ECzmKFISaRwcwEKNyIiIu5He0uJiIhIqaVwIyIiIh5F4UZEREQ8isKNiIiIeBSFGxEREfEoCjciIiLiURRuRERExKMo3IiIiIhHUbgRERERj6JwIyIiIh5F4UZEREQ8isKNiIiIeBSFGxEREfEoCjciIiLiUXzsbkBxsywLMFuni4iIiHtwfm47P8cvpNSFm5MnTwJQo0YNm1siIiIi+XXy5ElCQkIu+ByHdTERyINkZGRw8OBBgoKCcDgchfraiYmJ1KhRg3379hEcHFyory256XoXL13v4qXrXbx0vYtXQa63ZVmcPHmSqlWr4uV14aqaUtdz4+XlRfXq1Yv0ZwQHB+sfRzHS9S5eut7FS9e7eOl6F6/8Xu9/6rFxUkGxiIiIeBSFGxEREfEoCjeFyM/Pj+eeew4/Pz+7m1Iq6HoXL13v4qXrXbx0vYtXUV/vUldQLCIiIp5NPTciIiLiURRuRERExKMo3IiIiIhHUbgRERERj6JwU0gmTJhA7dq18ff3JzIykjVr1tjdJI+wdOlSunXrRtWqVXE4HMyZMyfH45Zl8eyzz1KlShXKli1LdHQ0O3bssKexHmDMmDG0bduWoKAgKleuTI8ePYiJicnxnLNnzzJixAgqVqxIYGAgt912G/Hx8Ta12L198MEHNGvWzLWQWVRUFD/99JPrcV3rojV27FgcDgcPP/yw65yueeF5/vnncTgcOW6NGjVyPV6U11rhphBMnz6dUaNG8dxzz7FhwwaaN29Oly5dOHTokN1Nc3tJSUk0b96cCRMm5Pn466+/zrvvvsuHH37I6tWrKVeuHF26dOHs2bPF3FLPsGTJEkaMGMGqVauYP38+qamp3HDDDSQlJbme88gjj/DDDz8wc+ZMlixZwsGDB7n11lttbLX7ql69OmPHjmX9+vWsW7eOzp070717d/744w9A17oorV27lo8++ohmzZrlOK9rXrguv/xyYmNjXbfly5e7HivSa23JJWvXrp01YsQI19fp6elW1apVrTFjxtjYKs8DWLNnz3Z9nZGRYUVERFhvvPGG69yJEycsPz8/6+uvv7ahhZ7n0KFDFmAtWbLEsixzfcuUKWPNnDnT9Zxt27ZZgLVy5Uq7mulRypcvb3366ae61kXo5MmTVv369a358+dbHTt2tB566CHLsvT3u7A999xzVvPmzfN8rKivtXpuLlFKSgrr168nOjradc7Ly4vo6GhWrlxpY8s83+7du4mLi8tx7UNCQoiMjNS1LyQJCQkAVKhQAYD169eTmpqa45o3atSImjVr6ppfovT0dKZNm0ZSUhJRUVG61kVoxIgR3HTTTTmuLejvd1HYsWMHVatW5bLLLuPOO+9k7969QNFf61K3cWZhO3LkCOnp6YSHh+c4Hx4ezvbt221qVekQFxcHkOe1dz4mBZeRkcHDDz/MlVdeyRVXXAGYa+7r60toaGiO5+qaF9yWLVuIiori7NmzBAYGMnv2bJo0acKmTZt0rYvAtGnT2LBhA2vXrs31mP5+F67IyEimTJlCw4YNiY2N5YUXXuDqq6/m999/L/JrrXAjInkaMWIEv//+e44xcil8DRs2ZNOmTSQkJPDNN98wcOBAlixZYnezPNK+fft46KGHmD9/Pv7+/nY3x+PdeOONruNmzZoRGRlJrVq1mDFjBmXLli3Sn61hqUsUFhaGt7d3rgrv+Ph4IiIibGpV6eC8vrr2he+BBx5g7ty5LFq0iOrVq7vOR0REkJKSwokTJ3I8X9e84Hx9falXrx6tW7dmzJgxNG/enHHjxulaF4H169dz6NAhWrVqhY+PDz4+PixZsoR3330XHx8fwsPDdc2LUGhoKA0aNGDnzp1F/vdb4eYS+fr60rp1axYsWOA6l5GRwYIFC4iKirKxZZ6vTp06RERE5Lj2iYmJrF69Wte+gCzL4oEHHmD27NksXLiQOnXq5Hi8devWlClTJsc1j4mJYe/evbrmhSQjI4Pk5GRd6yJw3XXXsWXLFjZt2uS6tWnThjvvvNN1rGtedE6dOsWuXbuoUqVK0f/9vuSSZLGmTZtm+fn5WVOmTLG2bt1q3XPPPVZoaKgVFxdnd9Pc3smTJ62NGzdaGzdutADr7bfftjZu3Gj9/ffflmVZ1tixY63Q0FDru+++szZv3mx1797dqlOnjnXmzBmbW+6ehg8fboWEhFiLFy+2YmNjXbfTp0+7nnPfffdZNWvWtBYuXGitW7fOioqKsqKiomxstft66qmnrCVLlli7d++2Nm/ebD311FOWw+GwfvnlF8uydK2LQ/bZUpala16YHn30UWvx4sXW7t27rRUrVljR0dFWWFiYdejQIcuyivZaK9wUkvHjx1s1a9a0fH19rXbt2lmrVq2yu0keYdGiRRaQ6zZw4EDLssx08GeeecYKDw+3/Pz8rOuuu86KiYmxt9FuLK9rDViTJ092PefMmTPW/fffb5UvX94KCAiwevbsacXGxtrXaDc2ZMgQq1atWpavr69VqVIl67rrrnMFG8vStS4O54YbXfPC07dvX6tKlSqWr6+vVa1aNatv377Wzp07XY8X5bV2WJZlXXr/j4iIiEjJoJobERER8SgKNyIiIuJRFG5ERETEoyjciIiIiEdRuBERERGPonAjIiIiHkXhRkRERDyKwo2IiIh4FIUbEXErgwYNokePHnY3Q0RKMB+7GyAi4uRwOC74+HPPPce4cePQwuoiciEKNyJSYsTGxrqOp0+fzrPPPktMTIzrXGBgIIGBgXY0TUTciIalRKTEiIiIcN1CQkJwOBw5zgUGBuYalurUqRMjR47k4Ycfpnz58oSHh/PJJ5+QlJTE4MGDCQoKol69evz00085ftbvv//OjTfeSGBgIOHh4dx1110cOXKkmN+xiBQFhRsRcXufffYZYWFhrFmzhpEjRzJ8+HB69+5Nhw4d2LBhAzfccAN33XUXp0+fBuDEiRN07tyZli1bsm7dOubNm0d8fDx9+vSx+Z2ISGFQuBERt9e8eXOefvpp6tevz+jRo/H39ycsLIxhw4ZRv359nn32WY4ePcrmzZsBeO+992jZsiWvvvoqjRo1omXLlkyaNIlFixbx559/2vxuRORSqeZGRNxes2bNXMfe3t5UrFiRpk2bus6Fh4cDcOjQIQB+++03Fi1alGf9zq5du2jQoEERt1hEipLCjYi4vTJlyuT42uFw5DjnnIWVkZEBwKlTp+jWrRuvvfZarteqUqVKEbZURIqDwo2IlDqtWrXi22+/pXbt2vj46L9BEU+jmhsRKXVGjBjBsWPHuOOOO1i7di27du3i559/ZvDgwaSnp9vdPBG5RAo3IlLqVK1alRUrVpCens4NN9xA06ZNefjhhwkNDcXLS/8tirg7h6WlPkVERMSD6FcUERER8SgKNyIiIuJRFG5ERETEoyjciIiIiEdRuBERERGPonAjIiIiHkXhRkRERDyKwo2IiIh4FIUbERER8SgKNyIiIuJRFG5ERETEo/w/qMUDm6Xae/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_predictions[:50], 'r', label='Predictions')\n",
    "plt.plot(y_train[:50], 'b', label='Actual')\n",
    "# Add legend and labels\n",
    "plt.legend()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Enargy')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f5cae0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2 score for model is 0.9938258368817008\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(y_train, train_predictions)\n",
    "print('r2 score for model is', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "886e5dc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE score for model is 0.0001367927343491835\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse = mean_squared_error(y_train, train_predictions)\n",
    "print('MSE score for model is', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "95759a98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 0.008223388725517138\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "print(\"MAE\",mean_absolute_error(y_train,train_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "726c4a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE 0.011695842609627726\n"
     ]
    }
   ],
   "source": [
    "import math  \n",
    "import sklearn.metrics  \n",
    "mse = sklearn.metrics.mean_squared_error(y_train, train_predictions)  \n",
    "rmse = math.sqrt(mse)  \n",
    "print(\"RMSE\", rmse)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9b492a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}