2site.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "539900ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import linalg\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74099c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w=0\n",
    "g=0.01#(-2,2)\n",
    "J=1 #(J=1)\n",
    "#plt.figure()\n",
    "for i in range(50):\n",
    "    H=np.array([[w+1j*g,J],[J,w-1j*g]])\n",
    "    Evals,LEvecs,REvecs=linalg.eig(H,left=True, right=True)\n",
    "    rig1=(np.dot((REvecs[:,0]),REvecs[:,0]))/(np.dot(np.conjugate(REvecs[:,0]),REvecs[:,0]))\n",
    "    rig2=(np.dot(np.conjugate(LEvecs[:,1]),REvecs[:,1]))/(np.dot(np.conjugate(REvecs[:,1]),REvecs[:,1]))\n",
    "    #print(J,g,np.abs(rig1))\n",
    "    plt.scatter(g,np.abs(rig2))\n",
    "    g=g+0.05\n",
    "plt.xlabel(r'$\\gamma$')\n",
    "plt.ylabel(\"absolute rigidity\")\n",
    "plt.title(\"Abosulte rigidity with \" r'$\\gamma$' \" fixed J=1.0\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98188662",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=np.array([[0,0,0,0,0,0]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28586dc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.594 \t 0.595 \t 0.057952738317872524 \t 0.05795273831787251 \t 2.8481274572002153 \t 2.8481274572002153\n"
     ]
    }
   ],
   "source": [
    "### Checking & and Storing  the training data \n",
    "w=0\n",
    "g=0.595 #(-2,2)\n",
    "J=0.594 #(J=1)\n",
    "\n",
    "# for i in range(5):\n",
    "H=np.array([[w+1j*g,J],[J,w-1j*g]])\n",
    "Evals,LEvecs,REvecs=linalg.eig(H,left=True, right=True)\n",
    "rig1=(np.dot((REvecs[:,0]),REvecs[:,0]))/(np.dot(np.conjugate(REvecs[:,0]),REvecs[:,0]))\n",
    "rig2=(np.dot(np.conjugate(LEvecs[:,1]),REvecs[:,1]))/(np.dot(np.conjugate(REvecs[:,1]),REvecs[:,1]))\n",
    "#     arr=np.array([[g,J,np.abs(rig1),np.abs(rig1),-np.log(np.abs(rig1)),-np.log(np.abs(rig1))]])\n",
    "#     data=np.append(data,arr_x,axis=0)\n",
    "print(J,'\\t',g,'\\t',np.abs(rig1),'\\t',np.abs(rig2),'\\t',-np.log(np.abs(rig1)),'\\t',-np.log(np.abs(rig2)))\n",
    "    \n",
    "#     g=round(np.random.uniform(0,1), 5)     #1*np.random.random()\n",
    "#     J=round(np.random.uniform(0,1), 5)    #1*np.random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "193a5651",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4752dd8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#                             *********************** EP-0 data generation********************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7a2cee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "                     \n",
    "\n",
    "X_data=np.array([[0,0]])\n",
    "Y_data=np.array([np.zeros(1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e8c7262",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Checking & and Storing  the training data \n",
    "w=0\n",
    "g=0 #(-2,2)\n",
    "J=0 #(J=1)\n",
    "\n",
    "for i in range(40000):\n",
    "    H=np.array([[w+1j*g,J],[J,w-1j*g]])\n",
    "    Evals,LEvecs,REvecs=linalg.eig(H,left=True, right=True)\n",
    "    rig1=(np.dot((REvecs[:,0]),REvecs[:,0]))/(np.dot(np.conjugate(REvecs[:,0]),REvecs[:,0]))\n",
    "    #rig2=(np.dot(np.conjugate(LEvecs[:,1]),REvecs[:,1]))/(np.dot(np.conjugate(REvecs[:,1]),REvecs[:,1]))\n",
    "    #print(J,'\\t',g,'\\t',np.abs(rig1),'\\t',np.abs(rig2),'\\t',np.abs(rig3))\n",
    "    arr_x=np.array([[g,J]])\n",
    "    arr_y= np.array([[-np.log(np.abs(rig1))]])\n",
    "    #arr_y=-np.log(np.abs(rig1)\n",
    "    X_data=np.append(X_data,arr_x,axis=0)\n",
    "    Y_data=np.append(Y_data,arr_y,axis=0)\n",
    "#     g=1*np.random.random() #round(np.random.uniform(0,1), 3)                 #1*np.random.random()\n",
    "#     J=1*np.random.random() # round(np.random.uniform(0,1), 3)                 #1*np.random.random()\n",
    "    g=round(np.random.uniform(0,1), 3)     #1*np.random.random()\n",
    "    J=round(np.random.uniform(0,1), 3)    #1*np.random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "92f325fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.593</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.251799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.647</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.414335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.978</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39996</th>\n",
       "      <td>0.708</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.721973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39997</th>\n",
       "      <td>0.911</td>\n",
       "      <td>0.399</td>\n",
       "      <td>0.106489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>0.445</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.129370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39999</th>\n",
       "      <td>0.593</td>\n",
       "      <td>0.384</td>\n",
       "      <td>0.271784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40000</th>\n",
       "      <td>0.170</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.785573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1         2\n",
       "0      0.000  0.000  0.000000\n",
       "1      0.000  0.000 -0.000000\n",
       "2      0.593  0.373  0.251799\n",
       "3      0.647  0.862  0.414335\n",
       "4      0.978  0.036  0.000678\n",
       "...      ...    ...       ...\n",
       "39996  0.708  0.810  0.721973\n",
       "39997  0.911  0.399  0.106489\n",
       "39998  0.445  0.932  0.129370\n",
       "39999  0.593  0.384  0.271784\n",
       "40000  0.170  0.191  0.785573\n",
       "\n",
       "[40001 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data=np.concatenate((X_data,Y_data),axis=1)\n",
    "df = pd.DataFrame(Data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b285a338",
   "metadata": {},
   "outputs": [],
   "source": [
    "#                             *********************** EP-2 data generation********************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75ea097b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data=np.array([[0,0]])\n",
    "Y_data=np.array([np.zeros(1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f28a37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Mixing Data with grid points\n",
    "\n",
    "w=0\n",
    "g=0 #(-2,2)\n",
    "J=0 #(J=1)\n",
    "for i in range(25000):\n",
    "    H=np.array([[w+1j*g,J],[J,w-1j*g]])\n",
    "    Evals,LEvecs,REvecs=linalg.eig(H,left=True, right=True)\n",
    "    rig1=(np.dot((REvecs[:,0]),REvecs[:,0]))/(np.dot(np.conjugate(REvecs[:,0]),REvecs[:,0]))\n",
    "    #rig2=(np.dot(np.conjugate(LEvecs[:,1]),REvecs[:,1]))/(np.dot(np.conjugate(REvecs[:,1]),REvecs[:,1]))\n",
    "    #rig3=(np.dot(np.conjugate(LEvecs[:,2]),REvecs[:,2]))/(np.dot(np.conjugate(REvecs[:,2]),REvecs[:,2]))\n",
    "    #print(J,'\\t',g,'\\t',np.abs(rig1),'\\t',np.abs(rig2),'\\t',np.abs(rig3))\n",
    "    arr_x=np.array([[g,J]])\n",
    "    arr_y=np.array([[-np.log(np.abs(rig1))]])\n",
    "    X_data=np.append(X_data,arr_x,axis=0)\n",
    "    Y_data=np.append(Y_data,arr_y,axis=0)\n",
    "    g=round(np.random.uniform(0,1), 3)\n",
    "    #g=g+0.0001\n",
    "    J=g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7378dd1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.910</td>\n",
       "      <td>0.910</td>\n",
       "      <td>36.736793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.777</td>\n",
       "      <td>0.777</td>\n",
       "      <td>26.981987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.332</td>\n",
       "      <td>0.332</td>\n",
       "      <td>27.577531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>0.126</td>\n",
       "      <td>0.126</td>\n",
       "      <td>26.688549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>0.857</td>\n",
       "      <td>0.857</td>\n",
       "      <td>27.895578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>0.524</td>\n",
       "      <td>0.524</td>\n",
       "      <td>27.053559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.320</td>\n",
       "      <td>0.320</td>\n",
       "      <td>36.002527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000</th>\n",
       "      <td>0.328</td>\n",
       "      <td>0.328</td>\n",
       "      <td>36.507555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1          2\n",
       "0      0.000  0.000   0.000000\n",
       "1      0.000  0.000  -0.000000\n",
       "2      0.910  0.910  36.736793\n",
       "3      0.777  0.777  26.981987\n",
       "4      0.332  0.332  27.577531\n",
       "...      ...    ...        ...\n",
       "24996  0.126  0.126  26.688549\n",
       "24997  0.857  0.857  27.895578\n",
       "24998  0.524  0.524  27.053559\n",
       "24999  0.320  0.320  36.002527\n",
       "25000  0.328  0.328  36.507555\n",
       "\n",
       "[25001 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data=np.concatenate((X_data,Y_data),axis=1)\n",
    "dfmix = pd.DataFrame(Data)\n",
    "dfmix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c013dab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "dfmix.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ecbd7e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfmix=dfmix.drop(dfmix[(dfmix[2]<35) | (dfmix[2]>37)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f5151abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "dfmix.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c68c0e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.910</td>\n",
       "      <td>0.910</td>\n",
       "      <td>36.736793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.633</td>\n",
       "      <td>0.633</td>\n",
       "      <td>36.453822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.765</td>\n",
       "      <td>0.765</td>\n",
       "      <td>35.652950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.456</td>\n",
       "      <td>0.456</td>\n",
       "      <td>36.215537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.858</td>\n",
       "      <td>0.858</td>\n",
       "      <td>35.937958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>0.660</td>\n",
       "      <td>0.660</td>\n",
       "      <td>35.901597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>0.042</td>\n",
       "      <td>0.042</td>\n",
       "      <td>35.617036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>0.138</td>\n",
       "      <td>0.138</td>\n",
       "      <td>35.520429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>0.320</td>\n",
       "      <td>0.320</td>\n",
       "      <td>36.002527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000</th>\n",
       "      <td>0.328</td>\n",
       "      <td>0.328</td>\n",
       "      <td>36.507555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12314 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0      1          2\n",
       "2      0.910  0.910  36.736793\n",
       "6      0.633  0.633  36.453822\n",
       "7      0.765  0.765  35.652950\n",
       "12     0.456  0.456  36.215537\n",
       "13     0.858  0.858  35.937958\n",
       "...      ...    ...        ...\n",
       "24988  0.660  0.660  35.901597\n",
       "24992  0.042  0.042  35.617036\n",
       "24995  0.138  0.138  35.520429\n",
       "24999  0.320  0.320  36.002527\n",
       "25000  0.328  0.328  36.507555\n",
       "\n",
       "[12314 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7b19db3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>36.301732</td>\n",
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       "    <tr>\n",
       "      <th>9997</th>\n",
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       "      <td>0.798</td>\n",
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       "    <tr>\n",
       "      <th>9998</th>\n",
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       "    <tr>\n",
       "      <th>9999</th>\n",
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       "      <td>0.849</td>\n",
       "      <td>35.632971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1          2\n",
       "0     0.910  0.910  36.736793\n",
       "1     0.633  0.633  36.453822\n",
       "2     0.765  0.765  35.652950\n",
       "3     0.456  0.456  36.215537\n",
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       "...     ...    ...        ...\n",
       "9995  0.276  0.276  35.520429\n",
       "9996  0.561  0.561  36.301732\n",
       "9997  0.798  0.798  36.535034\n",
       "9998  0.469  0.469  36.596156\n",
       "9999  0.849  0.849  35.632971\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmix = dfmix.drop(dfmix.index[10000:])\n",
    "dfmix=dfmix.reset_index(drop=True)\n",
    "dfmix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e39d940a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.append(dfmix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1cc99890",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.862</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1          2\n",
       "0     0.000  0.000   0.000000\n",
       "1     0.000  0.000  -0.000000\n",
       "2     0.593  0.373   0.251799\n",
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       "4     0.978  0.036   0.000678\n",
       "...     ...    ...        ...\n",
       "9995  0.276  0.276  35.520429\n",
       "9996  0.561  0.561  36.301732\n",
       "9997  0.798  0.798  36.535034\n",
       "9998  0.469  0.469  36.596156\n",
       "9999  0.849  0.849  35.632971\n",
       "\n",
       "[50001 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fefa0a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "df.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c9afd5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.593</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.006820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.647</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.011223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.978</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>0.276</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0.962117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>0.561</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.983280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0.798</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.989599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0.469</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.991255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>0.849</td>\n",
       "      <td>0.849</td>\n",
       "      <td>0.965166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1         2\n",
       "0     0.000  0.000  0.000000\n",
       "1     0.000  0.000 -0.000000\n",
       "2     0.593  0.373  0.006820\n",
       "3     0.647  0.862  0.011223\n",
       "4     0.978  0.036  0.000018\n",
       "...     ...    ...       ...\n",
       "9995  0.276  0.276  0.962117\n",
       "9996  0.561  0.561  0.983280\n",
       "9997  0.798  0.798  0.989599\n",
       "9998  0.469  0.469  0.991255\n",
       "9999  0.849  0.849  0.965166\n",
       "\n",
       "[50001 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2] = df[2] / df[2].max()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b68c8265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "df.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4c8acd99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv(r'\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\NN_data\\data_2site')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9aad801",
   "metadata": {},
   "outputs": [],
   "source": [
    "#************************************************* Caution: NETWORK  at WORK *************************************************#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8be0a38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# keras:\n",
    "from tensorflow.keras import Sequential # Sequential is the neural-network class\n",
    "from tensorflow.keras.layers import Dense # Dense is the standard network layer\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e96fb73d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ab5f86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>9997</td>\n",
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       "      <td>0.798</td>\n",
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       "    <tr>\n",
       "      <th>49999</th>\n",
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       "      <td>0.469</td>\n",
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       "    <tr>\n",
       "      <th>50000</th>\n",
       "      <td>9999</td>\n",
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       "      <td>0.849</td>\n",
       "      <td>0.965166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50001 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0      0      1         2\n",
       "0               0  0.000  0.000  0.000000\n",
       "1               1  0.000  0.000 -0.000000\n",
       "2               2  0.593  0.373  0.006820\n",
       "3               3  0.647  0.862  0.011223\n",
       "4               4  0.978  0.036  0.000018\n",
       "...           ...    ...    ...       ...\n",
       "49996        9995  0.276  0.276  0.962117\n",
       "49997        9996  0.561  0.561  0.983280\n",
       "49998        9997  0.798  0.798  0.989599\n",
       "49999        9998  0.469  0.469  0.991255\n",
       "50000        9999  0.849  0.849  0.965166\n",
       "\n",
       "[50001 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\NN_data\\data_2site')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8b4acdd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>39911 rows × 4 columns</p>\n",
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      "text/plain": [
       "       Unnamed: 0      0      1         2\n",
       "0            6529  0.657  0.518  0.013162\n",
       "1           28438  0.587  0.177  0.001291\n",
       "3            3148  0.586  0.586  0.974653\n",
       "4           17852  0.011  0.675  0.000004\n",
       "5            1496  0.533  0.141  0.000983\n",
       "...           ...    ...    ...       ...\n",
       "49995         345  0.369  0.374  0.049140\n",
       "49996       39968  0.342  0.667  0.004131\n",
       "49997       21479  0.332  0.036  0.000160\n",
       "49998       23010  0.334  0.321  0.034842\n",
       "50000        3132  0.240  0.240  0.997546\n",
       "\n",
       "[39911 rows x 4 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.iloc[:,2]<0.8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89aae465",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>49998</th>\n",
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       "      <td>0.034842</td>\n",
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       "<p>50001 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0      0      1         2\n",
       "0            6529  0.657  0.518  0.013162\n",
       "1           28438  0.587  0.177  0.001291\n",
       "2            1153  0.286  0.899  0.001445\n",
       "3            3148  0.586  0.586  0.974653\n",
       "4           17852  0.011  0.675  0.000004\n",
       "...           ...    ...    ...       ...\n",
       "49996       39968  0.342  0.667  0.004131\n",
       "49997       21479  0.332  0.036  0.000160\n",
       "49998       23010  0.334  0.321  0.034842\n",
       "49999        9985  0.916  0.916  0.976290\n",
       "50000        3132  0.240  0.240  0.997546\n",
       "\n",
       "[50001 rows x 4 columns]"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sample(frac=1).reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eecf9148",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split( df, test_size=0.1, random_state=4)\n",
    "\n",
    "X_train = train[['0','1']].values\n",
    "Y_train = train[['2']].values\n",
    "\n",
    "X_test = test[['0','1']].values\n",
    "Y_test = test[['2']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f7dae57",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88a34dc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "1407/1407 [==============================] - 1s 755us/step - loss: 0.0983 - val_loss: 0.0800\n",
      "Epoch 2/200\n",
      "1407/1407 [==============================] - 1s 680us/step - loss: 0.0738 - val_loss: 0.0636\n",
      "Epoch 3/200\n",
      "1407/1407 [==============================] - 1s 690us/step - loss: 0.0527 - val_loss: 0.0395\n",
      "Epoch 4/200\n",
      "1407/1407 [==============================] - 1s 683us/step - loss: 0.0335 - val_loss: 0.0281\n",
      "Epoch 5/200\n",
      "1407/1407 [==============================] - 1s 699us/step - loss: 0.0259 - val_loss: 0.0223\n",
      "Epoch 6/200\n",
      "1407/1407 [==============================] - 1s 734us/step - loss: 0.0211 - val_loss: 0.0182\n",
      "Epoch 7/200\n",
      "1407/1407 [==============================] - 1s 715us/step - loss: 0.0183 - val_loss: 0.0161\n",
      "Epoch 8/200\n",
      "1407/1407 [==============================] - 1s 735us/step - loss: 0.0159 - val_loss: 0.0134\n",
      "Epoch 9/200\n",
      "1407/1407 [==============================] - 1s 765us/step - loss: 0.0136 - val_loss: 0.0122\n",
      "Epoch 10/200\n",
      "1407/1407 [==============================] - 1s 765us/step - loss: 0.0129 - val_loss: 0.0136\n",
      "Epoch 11/200\n",
      "1407/1407 [==============================] - 1s 803us/step - loss: 0.0124 - val_loss: 0.0091\n",
      "Epoch 12/200\n",
      "1407/1407 [==============================] - 1s 778us/step - loss: 0.0112 - val_loss: 0.0097\n",
      "Epoch 13/200\n",
      "1407/1407 [==============================] - 1s 736us/step - loss: 0.0110 - val_loss: 0.0160\n",
      "Epoch 14/200\n",
      "1407/1407 [==============================] - 1s 704us/step - loss: 0.0108 - val_loss: 0.0102\n",
      "Epoch 15/200\n",
      "1407/1407 [==============================] - 1s 815us/step - loss: 0.0102 - val_loss: 0.0088\n",
      "Epoch 16/200\n",
      "1407/1407 [==============================] - 1s 763us/step - loss: 0.0098 - val_loss: 0.0118\n",
      "Epoch 17/200\n",
      "1407/1407 [==============================] - 1s 696us/step - loss: 0.0091 - val_loss: 0.0100\n",
      "Epoch 18/200\n",
      "1407/1407 [==============================] - 1s 730us/step - loss: 0.0096 - val_loss: 0.0085\n",
      "Epoch 19/200\n",
      "1407/1407 [==============================] - 1s 774us/step - loss: 0.0090 - val_loss: 0.0070\n",
      "Epoch 20/200\n",
      "1407/1407 [==============================] - 1s 679us/step - loss: 0.0088 - val_loss: 0.0130\n",
      "Epoch 21/200\n",
      "1407/1407 [==============================] - 1s 690us/step - loss: 0.0082 - val_loss: 0.0107\n",
      "Epoch 22/200\n",
      "1407/1407 [==============================] - 1s 719us/step - loss: 0.0092 - val_loss: 0.0070\n",
      "Epoch 23/200\n",
      "1407/1407 [==============================] - 1s 702us/step - loss: 0.0084 - val_loss: 0.0072\n",
      "Epoch 24/200\n",
      "1407/1407 [==============================] - 1s 706us/step - loss: 0.0088 - val_loss: 0.0111\n",
      "Epoch 25/200\n",
      "1407/1407 [==============================] - 1s 702us/step - loss: 0.0085 - val_loss: 0.0077\n",
      "Epoch 26/200\n",
      "1407/1407 [==============================] - 1s 697us/step - loss: 0.0078 - val_loss: 0.0094\n",
      "Epoch 27/200\n",
      "1407/1407 [==============================] - 1s 704us/step - loss: 0.0074 - val_loss: 0.0052\n",
      "Epoch 28/200\n",
      "1407/1407 [==============================] - 1s 733us/step - loss: 0.0083 - val_loss: 0.0052\n",
      "Epoch 29/200\n",
      "1407/1407 [==============================] - 1s 703us/step - loss: 0.0076 - val_loss: 0.0090\n",
      "Epoch 30/200\n",
      "1407/1407 [==============================] - 1s 712us/step - loss: 0.0076 - val_loss: 0.0052\n",
      "Epoch 31/200\n",
      "1407/1407 [==============================] - 1s 711us/step - loss: 0.0074 - val_loss: 0.0079\n",
      "Epoch 32/200\n",
      "1407/1407 [==============================] - 1s 765us/step - loss: 0.0073 - val_loss: 0.0074\n",
      "Epoch 33/200\n",
      "1407/1407 [==============================] - 1s 715us/step - loss: 0.0081 - val_loss: 0.0119\n",
      "Epoch 34/200\n",
      "1407/1407 [==============================] - 1s 721us/step - loss: 0.0079 - val_loss: 0.0067\n",
      "Epoch 35/200\n",
      "1407/1407 [==============================] - 1s 717us/step - loss: 0.0073 - val_loss: 0.0081\n",
      "Epoch 36/200\n",
      "1407/1407 [==============================] - 1s 751us/step - loss: 0.0074 - val_loss: 0.0049\n",
      "Epoch 37/200\n",
      "1407/1407 [==============================] - 1s 845us/step - loss: 0.0072 - val_loss: 0.0049\n",
      "Epoch 38/200\n",
      "1407/1407 [==============================] - 1s 892us/step - loss: 0.0082 - val_loss: 0.0083\n",
      "Epoch 39/200\n",
      "1407/1407 [==============================] - 1s 875us/step - loss: 0.0078 - val_loss: 0.0052\n",
      "Epoch 40/200\n",
      "1407/1407 [==============================] - 1s 884us/step - loss: 0.0076 - val_loss: 0.0162\n",
      "Epoch 41/200\n",
      "1407/1407 [==============================] - 1s 825us/step - loss: 0.0084 - val_loss: 0.0037\n",
      "Epoch 42/200\n",
      "1407/1407 [==============================] - 1s 887us/step - loss: 0.0068 - val_loss: 0.0045\n",
      "Epoch 43/200\n",
      "1407/1407 [==============================] - 1s 824us/step - loss: 0.0074 - val_loss: 0.0080\n",
      "Epoch 44/200\n",
      "1407/1407 [==============================] - 1s 871us/step - loss: 0.0073 - val_loss: 0.0075\n",
      "Epoch 45/200\n",
      "1407/1407 [==============================] - 1s 869us/step - loss: 0.0067 - val_loss: 0.0057\n",
      "Epoch 46/200\n",
      "1407/1407 [==============================] - 1s 856us/step - loss: 0.0071 - val_loss: 0.0123\n",
      "Epoch 47/200\n",
      "1407/1407 [==============================] - 1s 862us/step - loss: 0.0064 - val_loss: 0.0052\n",
      "Epoch 48/200\n",
      "1407/1407 [==============================] - 1s 906us/step - loss: 0.0071 - val_loss: 0.0093\n",
      "Epoch 49/200\n",
      "1407/1407 [==============================] - 1s 858us/step - loss: 0.0076 - val_loss: 0.0079\n",
      "Epoch 50/200\n",
      "1407/1407 [==============================] - 1s 862us/step - loss: 0.0071 - val_loss: 0.0041\n",
      "Epoch 51/200\n",
      "1407/1407 [==============================] - 1s 916us/step - loss: 0.0076 - val_loss: 0.0035\n",
      "Epoch 52/200\n",
      "1407/1407 [==============================] - 1s 840us/step - loss: 0.0065 - val_loss: 0.0102\n",
      "Epoch 53/200\n",
      "1407/1407 [==============================] - 1s 877us/step - loss: 0.0064 - val_loss: 0.0038\n",
      "Epoch 54/200\n",
      "1407/1407 [==============================] - 1s 801us/step - loss: 0.0077 - val_loss: 0.0071\n",
      "Epoch 55/200\n",
      "1407/1407 [==============================] - 1s 855us/step - loss: 0.0074 - val_loss: 0.0050\n",
      "Epoch 56/200\n",
      "1407/1407 [==============================] - 1s 820us/step - loss: 0.0069 - val_loss: 0.0074\n",
      "Epoch 57/200\n",
      "1407/1407 [==============================] - 1s 893us/step - loss: 0.0071 - val_loss: 0.0040\n",
      "Epoch 58/200\n",
      "1407/1407 [==============================] - 1s 816us/step - loss: 0.0066 - val_loss: 0.0054\n",
      "Epoch 59/200\n",
      "1407/1407 [==============================] - 1s 872us/step - loss: 0.0069 - val_loss: 0.0067\n",
      "Epoch 60/200\n",
      "1407/1407 [==============================] - 1s 1ms/step - loss: 0.0070 - val_loss: 0.0141\n",
      "Epoch 61/200\n",
      "1407/1407 [==============================] - 1s 836us/step - loss: 0.0060 - val_loss: 0.0055\n",
      "Epoch 62/200\n",
      "1407/1407 [==============================] - 1s 879us/step - loss: 0.0077 - val_loss: 0.0030\n",
      "Epoch 63/200\n",
      "1407/1407 [==============================] - 1s 826us/step - loss: 0.0072 - val_loss: 0.0066\n",
      "Epoch 64/200\n",
      "1407/1407 [==============================] - 1s 920us/step - loss: 0.0093 - val_loss: 0.0112\n",
      "Epoch 65/200\n",
      "1407/1407 [==============================] - 1s 965us/step - loss: 0.0067 - val_loss: 0.0056\n",
      "Epoch 66/200\n",
      "1407/1407 [==============================] - 1s 963us/step - loss: 0.0086 - val_loss: 0.0070\n",
      "Epoch 67/200\n",
      "1407/1407 [==============================] - 1s 941us/step - loss: 0.0072 - val_loss: 0.0094\n",
      "Epoch 68/200\n",
      "1407/1407 [==============================] - 1s 952us/step - loss: 0.0075 - val_loss: 0.0095\n",
      "Epoch 69/200\n",
      "1407/1407 [==============================] - 1s 881us/step - loss: 0.0074 - val_loss: 0.0100\n",
      "Epoch 70/200\n",
      "1407/1407 [==============================] - 1s 869us/step - loss: 0.0067 - val_loss: 0.0051\n",
      "Epoch 71/200\n",
      "1407/1407 [==============================] - 1s 845us/step - loss: 0.0067 - val_loss: 0.0045\n",
      "Epoch 72/200\n",
      "1407/1407 [==============================] - 1s 904us/step - loss: 0.0081 - val_loss: 0.0052\n",
      "Epoch 73/200\n",
      "1407/1407 [==============================] - 1s 919us/step - loss: 0.0080 - val_loss: 0.0180\n",
      "Epoch 74/200\n",
      "1407/1407 [==============================] - 1s 852us/step - loss: 0.0071 - val_loss: 0.0032\n",
      "Epoch 75/200\n",
      "1407/1407 [==============================] - 1s 945us/step - loss: 0.0068 - val_loss: 0.0071\n",
      "Epoch 76/200\n",
      "1407/1407 [==============================] - 1s 826us/step - loss: 0.0074 - val_loss: 0.0059\n",
      "Epoch 77/200\n",
      "1407/1407 [==============================] - 1s 848us/step - loss: 0.0071 - val_loss: 0.0148\n",
      "Epoch 78/200\n",
      "1407/1407 [==============================] - 1s 866us/step - loss: 0.0072 - val_loss: 0.0114\n",
      "Epoch 79/200\n",
      "1407/1407 [==============================] - 1s 875us/step - loss: 0.0064 - val_loss: 0.0052\n",
      "Epoch 80/200\n",
      "1407/1407 [==============================] - 1s 853us/step - loss: 0.0082 - val_loss: 0.0049\n",
      "Epoch 81/200\n",
      "1407/1407 [==============================] - 1s 830us/step - loss: 0.0075 - val_loss: 0.0108\n",
      "Epoch 82/200\n",
      "1407/1407 [==============================] - 1s 843us/step - loss: 0.0084 - val_loss: 0.0045\n",
      "Epoch 83/200\n",
      "1407/1407 [==============================] - 1s 851us/step - loss: 0.0090 - val_loss: 0.0208\n",
      "Epoch 84/200\n",
      "1407/1407 [==============================] - 1s 845us/step - loss: 0.0090 - val_loss: 0.0057\n",
      "Epoch 85/200\n",
      "1407/1407 [==============================] - 1s 842us/step - loss: 0.0071 - val_loss: 0.0035\n",
      "Epoch 86/200\n",
      "1407/1407 [==============================] - 1s 874us/step - loss: 0.0120 - val_loss: 0.0140\n",
      "Epoch 87/200\n",
      "1407/1407 [==============================] - 1s 858us/step - loss: 0.0077 - val_loss: 0.0028\n",
      "Epoch 88/200\n",
      "1407/1407 [==============================] - 1s 856us/step - loss: 0.0076 - val_loss: 0.0026\n",
      "Epoch 89/200\n",
      "1407/1407 [==============================] - 1s 871us/step - loss: 0.0072 - val_loss: 0.0129\n",
      "Epoch 90/200\n",
      "1407/1407 [==============================] - 1s 864us/step - loss: 0.0070 - val_loss: 0.0059\n",
      "Epoch 91/200\n",
      "1407/1407 [==============================] - 1s 843us/step - loss: 0.0088 - val_loss: 0.0127\n",
      "Epoch 92/200\n",
      "1407/1407 [==============================] - 1s 889us/step - loss: 0.0074 - val_loss: 0.0080\n",
      "Epoch 93/200\n",
      "1407/1407 [==============================] - 1s 826us/step - loss: 0.0079 - val_loss: 0.0087\n",
      "Epoch 94/200\n",
      "1407/1407 [==============================] - 1s 863us/step - loss: 0.0075 - val_loss: 0.0088\n",
      "Epoch 95/200\n",
      "1407/1407 [==============================] - 1s 881us/step - loss: 0.0064 - val_loss: 0.0083\n",
      "Epoch 96/200\n",
      "1407/1407 [==============================] - 1s 890us/step - loss: 0.0083 - val_loss: 0.0064\n",
      "Epoch 97/200\n",
      "1407/1407 [==============================] - 1s 1ms/step - loss: 0.0071 - val_loss: 0.0028\n",
      "Epoch 98/200\n",
      "1407/1407 [==============================] - 1s 988us/step - loss: 0.0087 - val_loss: 0.0106\n",
      "Epoch 99/200\n",
      "1407/1407 [==============================] - 1s 876us/step - loss: 0.0064 - val_loss: 0.0172\n",
      "Epoch 100/200\n",
      "1407/1407 [==============================] - 1s 906us/step - loss: 0.0071 - val_loss: 0.0104\n",
      "Epoch 101/200\n",
      "1407/1407 [==============================] - 1s 879us/step - loss: 0.0072 - val_loss: 0.0114\n",
      "Epoch 102/200\n",
      "1407/1407 [==============================] - 1s 926us/step - loss: 0.0062 - val_loss: 0.0061\n",
      "Epoch 103/200\n",
      "1407/1407 [==============================] - 1s 931us/step - loss: 0.0056 - val_loss: 0.0057\n",
      "Epoch 104/200\n",
      "1407/1407 [==============================] - 1s 978us/step - loss: 0.0087 - val_loss: 0.0164\n",
      "Epoch 105/200\n",
      "1407/1407 [==============================] - 1s 1ms/step - loss: 0.0092 - val_loss: 0.0061\n",
      "Epoch 106/200\n",
      "1407/1407 [==============================] - 1s 922us/step - loss: 0.0081 - val_loss: 0.0045\n",
      "Epoch 107/200\n",
      "1407/1407 [==============================] - 1s 877us/step - loss: 0.0070 - val_loss: 0.0048\n",
      "Epoch 108/200\n",
      "1407/1407 [==============================] - 1s 876us/step - loss: 0.0065 - val_loss: 0.0180\n",
      "Epoch 109/200\n",
      "1407/1407 [==============================] - 1s 880us/step - loss: 0.0076 - val_loss: 0.0098\n",
      "Epoch 110/200\n",
      "1407/1407 [==============================] - 1s 882us/step - loss: 0.0085 - val_loss: 0.0040\n",
      "Epoch 111/200\n",
      "1407/1407 [==============================] - 1s 934us/step - loss: 0.0079 - val_loss: 0.0123\n",
      "Epoch 112/200\n",
      "1407/1407 [==============================] - 1s 895us/step - loss: 0.0087 - val_loss: 0.0061\n",
      "Epoch 113/200\n",
      "1407/1407 [==============================] - 1s 897us/step - loss: 0.0081 - val_loss: 0.0135\n",
      "Epoch 114/200\n",
      "1407/1407 [==============================] - 1s 907us/step - loss: 0.0069 - val_loss: 0.0048\n",
      "Epoch 115/200\n",
      "1407/1407 [==============================] - 1s 908us/step - loss: 0.0100 - val_loss: 0.0178\n",
      "Epoch 116/200\n",
      "1407/1407 [==============================] - 1s 933us/step - loss: 0.0071 - val_loss: 0.0144\n",
      "Epoch 117/200\n",
      "1407/1407 [==============================] - 1s 960us/step - loss: 0.0077 - val_loss: 0.0036\n",
      "Epoch 118/200\n",
      "1407/1407 [==============================] - 1s 955us/step - loss: 0.0077 - val_loss: 0.0029\n",
      "Epoch 119/200\n",
      "1407/1407 [==============================] - 1s 973us/step - loss: 0.0076 - val_loss: 0.0204\n",
      "Epoch 120/200\n",
      "1407/1407 [==============================] - 1s 919us/step - loss: 0.0078 - val_loss: 0.0080\n",
      "Epoch 121/200\n",
      "1407/1407 [==============================] - 1s 910us/step - loss: 0.0060 - val_loss: 0.0026\n",
      "Epoch 122/200\n",
      "1407/1407 [==============================] - 1s 899us/step - loss: 0.0077 - val_loss: 0.0036\n",
      "Epoch 123/200\n",
      "1407/1407 [==============================] - 1s 902us/step - loss: 0.0077 - val_loss: 0.0067\n",
      "Epoch 124/200\n",
      "1407/1407 [==============================] - 1s 927us/step - loss: 0.0072 - val_loss: 0.0116\n",
      "Epoch 125/200\n",
      "1407/1407 [==============================] - 1s 934us/step - loss: 0.0078 - val_loss: 0.0094\n",
      "Epoch 126/200\n",
      "1407/1407 [==============================] - 1s 885us/step - loss: 0.0082 - val_loss: 0.0048\n",
      "Epoch 127/200\n",
      "1407/1407 [==============================] - 1s 901us/step - loss: 0.0069 - val_loss: 0.0050\n",
      "Epoch 128/200\n",
      "1407/1407 [==============================] - 1s 1ms/step - loss: 0.0081 - val_loss: 0.0034\n",
      "Epoch 129/200\n",
      "1407/1407 [==============================] - 1s 1ms/step - loss: 0.0069 - val_loss: 0.0095\n",
      "Epoch 130/200\n",
      "1407/1407 [==============================] - 1s 968us/step - loss: 0.0063 - val_loss: 0.0057\n",
      "Epoch 131/200\n",
      "1407/1407 [==============================] - 1s 959us/step - loss: 0.0079 - val_loss: 0.0081\n",
      "Epoch 132/200\n",
      "1407/1407 [==============================] - 1s 878us/step - loss: 0.0073 - val_loss: 0.0070\n",
      "Epoch 133/200\n",
      "1407/1407 [==============================] - 1s 927us/step - loss: 0.0097 - val_loss: 0.0117\n",
      "Epoch 134/200\n",
      "1407/1407 [==============================] - 1s 933us/step - loss: 0.0071 - val_loss: 0.0127\n",
      "Epoch 135/200\n",
      "1407/1407 [==============================] - 1s 940us/step - loss: 0.0064 - val_loss: 0.0036\n",
      "Epoch 136/200\n",
      "1407/1407 [==============================] - 1s 880us/step - loss: 0.0064 - val_loss: 0.0053\n",
      "Epoch 137/200\n",
      "1407/1407 [==============================] - 1s 925us/step - loss: 0.0068 - val_loss: 0.0055\n",
      "Epoch 138/200\n",
      "1407/1407 [==============================] - 1s 927us/step - loss: 0.0076 - val_loss: 0.0059\n",
      "Epoch 139/200\n",
      "1407/1407 [==============================] - 1s 896us/step - loss: 0.0087 - val_loss: 0.0066\n",
      "Epoch 140/200\n",
      "1407/1407 [==============================] - 1s 912us/step - loss: 0.0070 - val_loss: 0.0165\n",
      "Epoch 141/200\n",
      "1407/1407 [==============================] - 1s 919us/step - loss: 0.0085 - val_loss: 0.0041\n",
      "Epoch 142/200\n",
      "1407/1407 [==============================] - 1s 962us/step - loss: 0.0061 - val_loss: 0.0025\n",
      "Epoch 143/200\n",
      "1407/1407 [==============================] - 1s 924us/step - loss: 0.0082 - val_loss: 0.0057\n",
      "Epoch 144/200\n",
      "1407/1407 [==============================] - 1s 913us/step - loss: 0.0079 - val_loss: 0.0055\n",
      "Epoch 145/200\n",
      "1407/1407 [==============================] - 1s 916us/step - loss: 0.0078 - val_loss: 0.0048\n",
      "Epoch 146/200\n",
      "1407/1407 [==============================] - 1s 916us/step - loss: 0.0071 - val_loss: 0.0066\n",
      "Epoch 147/200\n",
      "1407/1407 [==============================] - 1s 934us/step - loss: 0.0072 - val_loss: 0.0033\n",
      "Epoch 148/200\n",
      "1407/1407 [==============================] - 1s 915us/step - loss: 0.0112 - val_loss: 0.0092\n",
      "Epoch 149/200\n",
      "1407/1407 [==============================] - 1s 904us/step - loss: 0.0072 - val_loss: 0.0092\n",
      "Epoch 150/200\n",
      "1407/1407 [==============================] - 1s 949us/step - loss: 0.0070 - val_loss: 0.0098\n",
      "Epoch 151/200\n",
      "1407/1407 [==============================] - 1s 918us/step - loss: 0.0063 - val_loss: 0.0088\n",
      "Epoch 152/200\n",
      "1407/1407 [==============================] - 1s 936us/step - loss: 0.0078 - val_loss: 0.0088\n",
      "Epoch 153/200\n",
      "1407/1407 [==============================] - 1s 948us/step - loss: 0.0064 - val_loss: 0.0071\n",
      "Epoch 154/200\n",
      "1407/1407 [==============================] - 1s 894us/step - loss: 0.0069 - val_loss: 0.0046\n",
      "Epoch 155/200\n",
      "1407/1407 [==============================] - 1s 879us/step - loss: 0.0081 - val_loss: 0.0221\n",
      "Epoch 156/200\n",
      "1407/1407 [==============================] - 1s 950us/step - loss: 0.0060 - val_loss: 0.0031\n",
      "Epoch 157/200\n",
      "1407/1407 [==============================] - 1s 903us/step - loss: 0.0068 - val_loss: 0.0043\n",
      "Epoch 158/200\n",
      "1407/1407 [==============================] - 1s 939us/step - loss: 0.0060 - val_loss: 0.0070\n",
      "Epoch 159/200\n",
      "1407/1407 [==============================] - 1s 909us/step - loss: 0.0066 - val_loss: 0.0057\n",
      "Epoch 160/200\n",
      "1407/1407 [==============================] - 1s 925us/step - loss: 0.0090 - val_loss: 0.0042\n",
      "Epoch 161/200\n",
      "1407/1407 [==============================] - 1s 876us/step - loss: 0.0086 - val_loss: 0.0041\n",
      "Epoch 162/200\n",
      "1407/1407 [==============================] - 1s 883us/step - loss: 0.0078 - val_loss: 0.0054\n",
      "Epoch 163/200\n",
      "1407/1407 [==============================] - 1s 926us/step - loss: 0.0059 - val_loss: 0.0171\n",
      "Epoch 164/200\n",
      "1407/1407 [==============================] - 1s 889us/step - loss: 0.0077 - val_loss: 0.0062\n",
      "Epoch 165/200\n",
      "1407/1407 [==============================] - 1s 1ms/step - loss: 0.0084 - val_loss: 0.0109\n",
      "Epoch 166/200\n",
      "1407/1407 [==============================] - 1s 896us/step - loss: 0.0077 - val_loss: 0.0041\n",
      "Epoch 167/200\n",
      "1407/1407 [==============================] - 1s 927us/step - loss: 0.0067 - val_loss: 0.0031\n",
      "Epoch 168/200\n",
      "1407/1407 [==============================] - 1s 896us/step - loss: 0.0078 - val_loss: 0.0051\n",
      "Epoch 169/200\n",
      "1407/1407 [==============================] - 1s 881us/step - loss: 0.0057 - val_loss: 0.0036\n",
      "Epoch 170/200\n",
      "1407/1407 [==============================] - 1s 888us/step - loss: 0.0070 - val_loss: 0.0121\n",
      "Epoch 171/200\n",
      "1407/1407 [==============================] - 1s 947us/step - loss: 0.0065 - val_loss: 0.0083\n",
      "Epoch 172/200\n",
      "1407/1407 [==============================] - 1s 902us/step - loss: 0.0060 - val_loss: 0.0105\n",
      "Epoch 173/200\n",
      "1407/1407 [==============================] - 1s 892us/step - loss: 0.0085 - val_loss: 0.0043\n",
      "Epoch 174/200\n",
      "1407/1407 [==============================] - 1s 911us/step - loss: 0.0062 - val_loss: 0.0046\n",
      "Epoch 175/200\n",
      "1407/1407 [==============================] - 1s 887us/step - loss: 0.0064 - val_loss: 0.0041\n",
      "Epoch 176/200\n",
      "1407/1407 [==============================] - 1s 892us/step - loss: 0.0063 - val_loss: 0.0048\n",
      "Epoch 177/200\n",
      "1407/1407 [==============================] - 1s 893us/step - loss: 0.0079 - val_loss: 0.0045\n",
      "Epoch 178/200\n",
      "1407/1407 [==============================] - 1s 896us/step - loss: 0.0066 - val_loss: 0.0041\n",
      "Epoch 179/200\n",
      "1407/1407 [==============================] - 1s 894us/step - loss: 0.0063 - val_loss: 0.0061\n",
      "Epoch 180/200\n",
      "1407/1407 [==============================] - 1s 893us/step - loss: 0.0058 - val_loss: 0.0041\n",
      "Epoch 181/200\n",
      "1407/1407 [==============================] - 1s 972us/step - loss: 0.0065 - val_loss: 0.0064\n",
      "Epoch 182/200\n",
      "1407/1407 [==============================] - 1s 974us/step - loss: 0.0057 - val_loss: 0.0116\n",
      "Epoch 183/200\n",
      "1407/1407 [==============================] - 1s 889us/step - loss: 0.0067 - val_loss: 0.0058\n",
      "Epoch 184/200\n",
      "1407/1407 [==============================] - 1s 890us/step - loss: 0.0069 - val_loss: 0.0040\n",
      "Epoch 185/200\n",
      "1407/1407 [==============================] - 1s 917us/step - loss: 0.0060 - val_loss: 0.0028\n",
      "Epoch 186/200\n",
      "1407/1407 [==============================] - 1s 887us/step - loss: 0.0049 - val_loss: 0.0041\n",
      "Epoch 187/200\n",
      "1407/1407 [==============================] - 1s 896us/step - loss: 0.0069 - val_loss: 0.0046\n",
      "Epoch 188/200\n",
      "1407/1407 [==============================] - 1s 923us/step - loss: 0.0063 - val_loss: 0.0090\n",
      "Epoch 189/200\n",
      "1407/1407 [==============================] - 1s 953us/step - loss: 0.0061 - val_loss: 0.0069\n",
      "Epoch 190/200\n",
      "1407/1407 [==============================] - 1s 899us/step - loss: 0.0056 - val_loss: 0.0041\n",
      "Epoch 191/200\n",
      "1407/1407 [==============================] - 1s 891us/step - loss: 0.0063 - val_loss: 0.0048\n",
      "Epoch 192/200\n",
      "1407/1407 [==============================] - 1s 925us/step - loss: 0.0064 - val_loss: 0.0053\n",
      "Epoch 193/200\n",
      "1407/1407 [==============================] - 1s 917us/step - loss: 0.0061 - val_loss: 0.0224\n",
      "Epoch 194/200\n",
      "1407/1407 [==============================] - 1s 872us/step - loss: 0.0079 - val_loss: 0.0033\n",
      "Epoch 195/200\n",
      "1407/1407 [==============================] - 1s 916us/step - loss: 0.0056 - val_loss: 0.0112\n",
      "Epoch 196/200\n",
      "1407/1407 [==============================] - 1s 892us/step - loss: 0.0053 - val_loss: 0.0027\n",
      "Epoch 197/200\n",
      "1407/1407 [==============================] - 1s 949us/step - loss: 0.0058 - val_loss: 0.0035\n",
      "Epoch 198/200\n",
      "1407/1407 [==============================] - 1s 870us/step - loss: 0.0056 - val_loss: 0.0020\n",
      "Epoch 199/200\n",
      "1407/1407 [==============================] - 1s 893us/step - loss: 0.0063 - val_loss: 0.0095\n",
      "Epoch 200/200\n",
      "1407/1407 [==============================] - 1s 906us/step - loss: 0.0057 - val_loss: 0.0047\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28965224eb0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the Neural_Network\n",
    "\n",
    "Net=Sequential() # creating a neural network!\n",
    "Net.add(Dense(16,input_shape=(2,),activation=\"relu\")) # first hidden layer: 20 neurons (and 1 input neuron!)\n",
    "#Net.add(Dense(4,activation=\"relu\")) # second hidden layer: 10 neurons\n",
    "Net.add(Dense(4,activation=\"relu\")) # second hidden layer: 10 neurons\n",
    "Net.add(Dense(1,activation=\"linear\")) # output layer: 1 neuron\n",
    "# Compile network: (randomly initialize weights, choose advanced optimizer, set up everything!)\n",
    "Net.compile(loss='mean_squared_error',\n",
    "              optimizer='adam') # adam is adaptive and works better than normal gradient descent\n",
    "\n",
    "# Does the training\n",
    "Net.fit(X_train, Y_train, epochs=200, batch_size=32, validation_data=(X_test, Y_test),shuffle=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e078a036",
   "metadata": {},
   "outputs": [],
   "source": [
    "Net.save(r\"\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\EP_ML\\Model_2site.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e8a90c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "History=Net.history.history\n",
    "#History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "643b07f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ANwHjAQGSgSnAHcBnItKzlX1PBpYD1wOjgTogE5gKPAS8JSLJzbTtCXwCPAwcDfQGKoEUZ5w/BZaLyFmtGVt7kKBhzYqixDk7duwgMTEREeGVV14JW/d3v/sdIsLIkSMbPtu0aRP33XcfJ5xwAqNHjyY9PZ2MjAz23Xdfrr32WjZt2tTRmxAxxcXF3HzzzRx44IFkZWWRmprKqFGjuPrqq1m3bl2z7SorK7njjjs47LDD6NmzJ4mJifTu3Zt9992XSy65hBdeeGGPNnV1dTz44INMnz6dXr16kZiYSG5uLmPGjOGcc87hX//6V0duausxxsTkA/BjxYoB8oGjnc99wDlAiVP2Riv6zga2Ou1XAlOcz5OAHwE1Ttn9zbR/3Ck3WDGV6xnzNGCZU1YBDIxkTJMnTzbtyX3Pv2eG/vI1s+C7ne3ab1fw4YcfdvUQ2g3dltiko7ZlxYoVHdJvOEpKSjr9OzuKkpISc8IJJxjAnHnmmc3WCwQCZvjw4QYwc+bMafh82rRpxnOuNtnZ2cbn8zV6P2/evJB9XnLJJQYwl1xySZu3Y/369Q3fuX79+j3Kly1bZgYNGtRQJyUlxWRmZja8T05ONs8///we7UpKSsykSZMa6omI6dGjh0lISGj4bOjQoY3a1NXVmWOOOWaP/ZKcnNzos3A09xuL9vcOLDRRXLtj2cJyKTDBeX2GMeY9AGNMwBjzDPBDp+x4EZkZZd83AP2wVpHZxpiFTt81xpj7sCIE4EoRGe1t6FhdznHePm6M+YMxZpfTvt4Y8xFwilOeCpwY5djaBY0SUhSlO3DxxRcD8Oqrr1JUVBSyzvz581m/fj0AF110UcPn48eP569//SsrVqygoqKCoqIiqqur+fzzzznuuOMoLi7mnHPOobKyssO3ozlKS0s56aSTyMvLY+DAgbz++uuUl5dTUlLCN998w6GHHkp1dTUXXHABixcvbtT27rvvZvHixeTk5PDCCy9QWVnJ7t27qa6uZsuWLTzxxBPMmjWrUZunnnqKd999l5SUFB5++GFKS0spKiqisrKSgoICXnzxRc4444zO3AURE8uC5RLn+UNjzIIQ5U8D653XF0fZt1v/aWPM+hDl9wBlWIvJBU3KemKnpQAWhurcGPMdUOi8zYhybO2C+rAoitIdOPnkk8nOzqa6uppnn302ZJ1///vfAEydOpV99tmn4fN7772XX/7yl4wbN47U1FQAEhISOPjgg3nttdeYOHEi+fn5IadNOov777+f9evXk5iYyFtvvcXs2bPx+ewJfNKkSbzzzjsMGzaM6upqfvvb3zZq++mnnwJwww03cPrpp5OcbC9NPp+PAQMGcNFFF/Hggw+GbHPxxRdz+eWXk5FhL1EiQp8+fTjttNN4/vnnO3SbW0tCVw8gFCKSBhzhvH0zVB1jjBGRt4CrgVmh6jTT9xhgSAt9l4nIPOB4p++bPMUFQDmQjvWlCfUdI4Ac521IUdPRaKZbRYkT3vwVbFvabt2l1teBv4tO7f0mwPF/bdcuU1JSOOuss3j44Yf597//zZVXXtmo3CtkvNaVlvD7/Rx33HEsWbKETz75hAsvvLBdxx0pzzzzDABnnnkm48eP36M8MzOTG2+8kWuuuYY333yT4uJisrOzAejRowcAW7dujfj73Dbbtm1r28C7gFi1sIwjOLZlYeq5Zf1EJCdMPS/eX0Qkfe/r/dCZd3vAeXuJiNwkIrkAIuIXkWnAy075884UUaejU0KKonQX3Gkh79SPy2uvvUZRURHJycmcffbZe7SdN28el156KWPHjiUjIwMRaXjcdtttAOTl5XX8RoSgpqaGJUuWAHD00Uc3W++YY44BIBAI8PXXXzd8fuKJ1uPg3nvv5bzzzuOll15i586dYb9z9uzZDU7Mxx9/PE899RT5+flt3ZROISYtLMAAz+stYep5ywYQnIZpz76zRCTDGFPmKfst0As7tTQHmCMiJUAadp+uA34J/C2C8bQ/79/MtDVzgZ+rYFGUWKedLRKVpaVkZma2a59dzdSpUxk+fDjr16/nP//5D7/73e8aytzpoJNPPrnBeuDyy1/+skGUgLWq9OzZk6QkmxGjrKyM8vJyysvLO34jQlBYWEh9fT0AAwcObLbeoEGDGl5v37694fX555/PF198wT333MPTTz/N008/DcDIkSOZNWsWl112GZMnT27U19SpU7n11lv5f//v//HWW2/x1ltvNXzH0UcfzcUXX8yMGTPabRvbk1gVLN5/W0WYet6ySP+hre27QbAYY6pE5ApgKfBnIBHI8tRPw04JJYf7DhG5ErgSoG/fvsydOzeyLWiBMWsWk11uw+CWrVxFn/Lv2qXfrqKsrKzd9k1Xo9sSm3TUtmRnZ1NaWtru/Yajvr6+07+zo/Buy1lnncVtt93G448/zrXXXgvArl27eOONNwA7peLd7g8++KBBrFxxxRVcccUVjBkzBr/f31Dnlltu4fbbb6eurm6PfVZbW9vw3Nb9WVZW1ui125/388rKyma/p7q6uuF1VVVVo3q33HILl156KS+99BKffvopX375JWvXrmXt2rXcf//9XH311dx6662N+rvqqqs45ZRTePHFF5k/fz6ff/45eXl5PPbYYzz22GOceuqpPPLIIyQmJoYcT3O/saqqqo49J0QTUtRZD+B8guFVI8PUO8ZT77AI+/6Np01CmHo/8NTr36RsOMGQ66eAyVjn2sFYZ+F8p2whkBHJuNo1rPn1G0z1LQPM0F++Zh6et679+u0iNHw2NtFtaRkNa24b3m1Zs2ZNQ8jtggULjDHG3HfffQYwvXv3NrW1tY3aXnbZZQYwxx57bLP9X3HFFQYw06ZN26OsM8Kaq6urjd/vN4B5+OGHm23v3fYPPvgg7HfV19ebBQsWmFNPPbWhzcsvv9ziGJcsWdKwPwBz9913N1tXw5ob45VuaWHqecsilcBt6ltE/FgflQnAE8aY84wxXxljyowxm40xjwMzgWqskPllhONqPxJT8ddbRa5TQoqidAdGjhzJYYcdBgSngdzn8847j4SExhMGmzdvBuCAAw4I2Z8xhg8++KCjhhsRSUlJTJw4EYD333+/2XrvvfceYKN/DjzwwLB9+nw+Dj30UJ5//nmGDLHxJe+++26LY5kwYQIPPfQQRxxxRMRtOptYFSxeD6DmJ/Yal0XqNRRt3yWmsf/KLIL5Ye4I1dAYsxJ43Xnb+QHtien4TS0+AipYFEXpNrjOt8888wwrVqzgs88+a/S5FzeSpmnuEpd//vOfYTPIdhbnnnsuAM8//zzLlu0ZB1JWVtYwtTV79uyG7YLGU0VN8fv9Db463mmwcG2AhvBvb5tYIVYFy0rAvdLuGecVxC3bZoyJxOEWGkcGRdL3iiafe6OGwjmHrHGeh0c4rvYj0f7gMnw11DgOXYqiKPHOOeecQ1JSErt27eKSS2yqrnHjxu3hWApw3HHHAfDmm29yyy23NDjWFhUV8ec//5mf/OQn5Obmdt7gm+Hqq69m+PDh1NbWcvzxx/Pmm28SCNjL39KlSzn22GNZv349SUlJ/PGPf2zU9pBDDuGnP/0pc+fObeQ4nJ+fz09+8hPWrl0LWKHjcuqpp3LZZZfx5ptvNkrEV1hYyB//+McGS4+3TawQk4LFGFMBzHfeHheqjrM44bHO23ei6PtbwF1Aorm+04Ejm+nba7IYGuar+jrPne/95giWLH+tWlgURek29OzZsyGUd+FCm+IqlHXF/fzII+1p/Pe//z2ZmZnk5OSQm5vLb3/7W4477jiuvvrqzhl4GDIzM3nllVcYOHAgeXl5zJ49m/T0dLKzs5k4cSKffvopycnJPPnkk0yaNKlR26KiIu655x5mzJhBZmYmPXv2JCMjg4EDB3LvvfcCcN111zXKdltZWcmjjz7K7Nmz6dmzJ9nZ2WRnZ5Obm8vvfvc7jDGceeaZXHHFFZ26HyIhJgWLw+PO8wwROSRE+VmAm9LwiSj7duufKyLDQpT/COtEWw882aTsa8/rkL92EekHnOa8DZWlt2NJSgdUsCiK0v3wChSfz9dswrfExETeeecdbrrpJkaPHk1iYiLGGA4++GD+8Y9/8Morr8TMtMf48eNZvnw5c+bMYf/99ychIYHq6mpGjBjBVVddxfLlyznzzDP3aPf000/zhz/8gZkzZzJ8+HBqamqora1l6NChnHPOObz//vvceeedjdrcc8893HrrrcyePZtRo0ZhjKGyspIBAwZw8skn88ILL/Dcc881ZNuNKaLx0O3MBzbk2o3EyQNmOp/7sGKlmGYWP8TmRnG9nYeFKPcufrgcmOx8noQVIdU0s/ih8/3fOOUB4E5ggFOWgrXarPaUT4tke9s1SmjZi8bclGXOvvkR86sXFrdfv12ERqPEJrotLaNRQm1DtyU26aoooVjNw4Ixpk5ETgY+BIYB74lIBVYwpDjVFrHnWj+R9F0sIicCb2N9UhaKSKnTrxt4/g5wXYi2ARE5wynfx6lznYiUYSOLXFlaD1xvuiLTbaINcMr011CtFhZFURSlGxCDNp8gxpgNwETgZqyzrAFqga+wKy4faozZ3cq+vwL2A+7COsgmYtcI+gSbg+V4Y0xId2pjFzeciBUrc4GdWLFTBazCpu4/0Bjz99aMrc24gsVXQ2296ZIhKIqiKEp7ErMWFhdjTCl28cGbWqrraTMHOy3UUr0C4HrnEe24yoH/cx6xhSNYMny1bK/TKCFFURQl/ol5waK0AidKKN1Xo063iqIo7cCnn37K6aefHlWbww8/nBdffLGDRrT3oYKlO5LkWlhqqKlXwaIoitJWampqKCgoiKpNYWGk6cGUSFDB0h1xpoTSRC0siqIo7cH06dPdSFGli4hpp1ullThTQipYFEVRlO6CCpbuSIOFpVrDmhVFUZRugQqW7ojPT0ASSaFafVgURVGUboEKlm5KvT+FVKp1SkhRFEXpFqhg6abU+5NUsChKDKCOmsreQGf8zlWwdFMCvmSSjU4JKUpX4vf7qa/X5I1K96e+vr7DF5NUwdJNqfenkEw11bUqWBSlq0hLS6OsrKyrh6EoHU5ZWRlpaWkd+h0qWLop1sJSRXVdvZqkFaWLyMrKorCwUK0sSremvr6ewsJCsrKyOvR7NHFcN6Xen0xyXTUBA3UBQ6JfunpIirLXkZmZSWVlJRs3biQnJ4eMjAz8fj8i+n9U4htjDPX19ZSVlVFYWEh6ejqZmZkd+p0qWLop9f5kEmttWujqugCJfjWmKUpnIyL06dOH0tJSSkpK2L59e4dbW6qqqkhJSenQ7+gsdFtiE3db/H4/aWlp9OrVi8zMzA4X4ipYuikBXzLJ9VUAVNfWk5Gsh1pRugIRISsrq8PN5S5z587lgAMO6JTv6mh0W2KTrtoWve3uptT7k0kMOIJFQ5sVRVGUOEcFSzcl4Eshob4SUMGiKIqixD8qWLop9f5k/O6UUJ1GKCiKoijxjQqWbkrAl4zP1JFAneZiURRFUeIeFSzdlHp/MgBp6IrNiqIoSvyjgqWb4gqWFGp0SkhRFEWJe1SwdFMCPitYUkXT8yuKoijxjwqWbkq93yYo0ikhRVEUpTuggqWb0mBhoVqnhBRFUZS4RwVLN6XBh0Vq1MKiKIqixD3tLlhEpKeIdE4OaqVZGkUJ1aqFRVEURYlvohIsIjJARC4WkeNClO0nIguBncBuEZknIqPba6BKdDSeElILi6IoihLfRGthuQx4FJju/VBEUoE3gAMAcR5HAO+ptaVr0CkhRVEUpTsRrWA52nl+psnnlwCDgULgB8CFQB4wEPhRWwaotI6Az0YJZYg63SqKoijxT7SCZZjzvKrJ56cDBviNMeYRY8x/scJFgJPbNEKlVbgWlkxfreZhURRFUeKeaAVLL6DEGFPpfiAiPuBwrGB53lP3XSAAjGnrIJXoCfgSAcjw1+qUkKIoihL3RCtY/EByk88mAGnAcmPMbvdDY0wA2A2kt2mESusQHySmkeHTKSFFURQl/olWsGwFkkVkuOezY53nT0PUz8D6tShdQWIqaep0qyiKonQDohUsC5znm0TEJyK9gaux00Fveys6oiYZK3KUriAx3QoW9WFRFEVR4pxoBcvdzvNFQBGwGRgKrAdea1L3GOf569YOTmkjiamka5SQoiiK0g2ISrAYY77A5mIpw073JGEjhk43xtQ1qX6x8/xhWweptJLEVFJ1SkhRFEXpBiRE28AY87iIPAuMx1pZvnMcbBsQkSTgQeAh4PV2GKfSGpLSSaFMBYuiKIoS90QtWACcsOYvw5TXAE+0dlBKO5GYSorZqVNCiqIoStyjqzV3ZxJTSaFanW4VRVGUuCfaxQ97iMhRInJAiLL+IvK8iBSLSKGI/FtE+rTfUJWoSUwn2ejih4qiKEr8E62F5XKsE+1l3g9FJAF4BzgNyAR6AOcD7zv+LEpXkJhCkqnSKSFFURQl7olWsMxynp9q8vk5wH5AFfAn4P8BJcC+wJVtGaDSBvzJJBhNza8oiqLEP9E63Y50npc2+fxsbPK4m4wxdwCIyFrgaeBM4N62DFJpJQnJJARUsCiKoijxT2sWPywzxpQ2+fwo5/lJz2cvYUXMfq0bmtJmEpJJMDVU19VhjOnq0SiKoihKq4lWsKQ0bSMiY4BsYI0xpiENvxPavBvIassARSRTROaIyFIRKXOcer8UkZ+31T9GRPqKyN9E5FsRqXScheeJyBUiIhH2MdLpY5kztnIRWSciL4nINW0ZX5vxJyMYfKaeuoAKFkVRFCV+iXZKaDswQET6GWO2OZ8d7TyHWvwwFShu7eBEZCgwFxjmfFSBXZ9oivO4QERmeleJjqLvydj1j3Kdj8qwDsNTncdZInKyMaY6TB/XAn8luIJ1JVAHDHce04H7ox1bu5Fg9VwSdVTXBUj0axS7oiiKEp9EewVzk8VdDyAiacBV2Kmf970VRWQgVrC0avFDEfEDr2LFylbgGGNMOpAGnAuUAgfQeBoq0r6zsWsf5WKXFjjIGJMJpAM/BmqxDsZ3henjeqc8CbgPGGuMSTPGZAE9nfYPRzu2dsVvdVQStVTXaqSQoiiKEr9EK1geAAT4uYisBFZjfVR2AC82qTvDeW7qoBsplwITnNdnGGPeAzDGBIwxzwA/dMqOF5GZUfZ9A9APaxGZbYxZ6PRdY4y5D7jJqXeliIxu2lhEJmAtKwDXGmN+bIz51i03xhQZY941xtwQ5bjalyYWFkVRFEWJV6Jd/PBtYA7WojIGGADsBC5w0vV7Od95bu3ih5e47Y0xC0KUP41dJRqCCy1Gilv/aWPM+hDl92CniPzABSHKfwMkAl8YY/4e5Xd3Hq6FRTRSSFEURYlvonZqMMbcDOyDzb1yLDDKGNN0OigJWAD8gVYsfuhMNR3hvH2zmXEY4C3n7axQdZrpewwwpIW+y4B5ofoWkXTgDOftvyL93i4hwQqWZGo1eZyiKIoS17R28cNNwKYw5TXALa0dFDCOoJhaFqaeW9ZPRHKMMYUR9D0+RPvm+j4em/zOy8FY6wrARyIyA/g5cCiQAeRjrUp3GmOWRzCejsPvmRLS9YQURVGUOKZVgqUTGOB5vSVMPW/ZACASwRJt31kikuFYXQBcnxaDTYp3M9avpxzrrOtGCF0oIj8wxjS7arWIXImTCbhv377MnTs3guFHRllZGUt2rWYi1un2sy+/Yvd3/nbrvzMpKytr133Tlei2xCa6LbGJbkts0lXb0mrBIiJ9sRfsKUAf7AV8BzaS6AVjTEEbxpXpeV0Rpp63LLPZWu3TtytYeno+vwVribnS9bMRkYOAB4H9gUdEZIXr1NsUY8yDTl2mTJlipk+fHuEmtMzcuXOZOG4yLLUWln0nTOTIUb3brf/OZO7cubTnvulKdFtiE92W2ES3JTbpqm2JWrA44ca3YEOb3akRN8mawTq03ikifwN+b4zpbs4T7lSVANXAicaYjW6hMeZLETkRG0GVhl1X6dTOHiTQ4MOSJLU6JaQoiqLENa2xsDyBzYPiXrAXAnlO2SCsxSUZ+BXWufWiVnyHN/V/Wph63rKmywVE2ndJlH17Xz/nFSsuxpgtIvJf4ArgaBHxd4lwa8jDomHNiqIoSnwTVZSQiJwKnIcVK3cC/Y0xRxpjznMeR2Lzm9zh1DlfRE5uxbjyPa8HhqnnLctvtlbb+i7x+K9AY7+XlWHau2XpBLPpdi5OHpZkaqnSxHGKoihKHBNtWPPl2GmfPxljbjDGFDWtYIwpNsbcCPwJK1p+0IpxrQRck8D4MPXcsm0RRghB48igSPpe0eTzJZ7X4Rbo8a5F1DUL+Xgz3aqFRVEURYljohUsB2GFxB0R1L3DqXtQtIMyxlQA8523x4Wq4yxOeKzz9p0o+v6WYEh2c32nA0eG6tsYsxZY57xtGvLsZZzzXALsinR87YprYRHNw6IoiqLEN9EKlp5AsTGmxQUNnTrFNI6qiYbHnecZInJIiPKzsAnswPrVRINb/1wRGRai/EfYnCr1hF6r6DF3DKHaO+sonee8fcMY0zXmDfVhURRFUboJ0QqW3UC2iGS1VNFZYDDbadMaHseuQyTAC+56QSLiE5GzgIecem+GyLQ7R0SM8xgWou87gG1Yx9rXnZWbEZEkEbmaYNK7B40xq0O0vxPYiHUufkVEDvV89xTswopp2LWKbo5+09uJBO/ihypYFEVRlPilNas1+4DrIqh7nVM3ZA6SljDG1AEnAxuwDrDviUg5NkHbs0AWsIjQa/201HcxcCJ2qmZfYKGIlGBzrdyPXYH5HZrZTmNMOXY6aQt2gcYFIlIqIqXYfbS/09fZxphwjrkdi5PpNkXqdEpIURRFiWuiFSyPYi0evxORW0Qko2kFEckUkT8Cv8M6mz7c2sEZYzYAE7FWimVOf7XAV9gVlw81xrTKgmOM+Qq70vRdwBpsTply4BOso/DxxpjqMO1XOe1vJuiI6we+Bf4OjDfGvNaasbUbjoUlzVevU0KKoihKXBNVHhZjzIsi8ixwNnbF4utF5EuspcEAg7F5WFKwwuYZY8xLbRmgMaYUuMl5RNpmDnZV6ZbqFWAT4F3fyrEVRzu2TsWXAOIj1V9PgVpYFEVRlDimNYnjLsImivspkAocRTBs1w3lrQPuxooapasQAX8yqUYXP1QURVHim6gFizGmFrhBRO4EziC4lhDAdqzPygvGmEgTuSkdSUISKXUaJaQoiqLEN61e/NARJPc0V+7kSZng1F3SXD2lg/EnkxJQp1tFURQlvmm1YImAHOAbbPK4jvweJRwJyWphURRFUeKeaKOEWoO0XEXpMPxJJIv6sCiKoijxTWcIFqUrSUgmGZ0SUhRFUeIbFSzdHX+Ss5aQWlgURVGU+EUFS3cnIVnXElIURVHiHhUs3R1/kl1LSKeEFEVRlDhGBUt3JyGZRNTpVlEURYlvVLB0d/zJJJpaqmrVwqIoiqLEL2Hzo4jIxW3oe4+FEZUuICGJRNTpVlEURYlvWkro9hjBdYKUeMSfTIKpobougDEGm4BYURRFUeKLSKaEpA0PpatJSCLB1AColUVRFEWJW1qysAzvlFEoHYc/mYRALQDVtQFSEv1dPCBFURRFiZ6wgsUYs7GzBqJ0EAnJ+B0LS1VdPdkkdvGAFEVRFCV6NEqou+NPwudYWDRSSFEURYlXVLB0dxKS8Zl6fATUh0VRFEWJW1SwdHf8SQAkoblYFEVRlPhFBUt3JyEFcAWLWlgURVGU+EQFS3cnwVpYkqlTC4uiKIoSt6hg6e74kwGdElIURVHiGxUs3Z0ER7BIHVXqdKsoiqLEKSpYujsep9tqtbAoiqIocYoKlu6Oa2FBLSyKoihK/KKCpbujFhZFURSlGxBWsIjI70Xk+mbKBorIkBbavyAi77dlgEob8fqwqGBRFEVR4pSWFj+cA2wD7gxRthDo3UIfhwN9WjUypX1wooSSRfOwKIqiKPFLW6eEpF1GoXQcTh6WDF9ALSyKoihK3KI+LN0dx8KSnlCnawkpiqIocYsKlu6OY2FJ99erhUVRFEWJW1SwdHdcC4u/XsOaFUVRlLhFBUt3x4kSSvVplJCiKIoSv6hg6e44eVhSfTolpCiKosQvKli6O66FReqo1rBmRVEUJU5pKQ8LQIaI/D7U52CTy4Vr26pRKe2HY2FJ8dVRXacWFkVRFCU+iUSwpAM3hSkPVyaAiWpESvsiAv4kx4dFLSyKoihKfBKJYNHkcPGOP5lkTc2vKIqixDFhBYsxRn1cugMJSSpYFEVRlLhGBcneQEIKyeiUkKIoihK/qGDZG/AnkSS1amFRFEVR4pZ2FSwi0ltEstqzT6UdSEgmydi1hIxRH2hFURQl/mizYBGRJBG5TUR2AtuA3SKyVkSuavvwQEQyRWSOiCwVkTIRKRaRL0Xk5yKS1Ma++4rI30TkWxGpFJFCEZknIleISNTOxiLypogY5zG3LWNrV/xJJFILoAsgKoqiKHFJWKdbETkM+AQoBAYZY6qblAvwCnAMjaOJ9gHuE5EhxpjftHZwIjIUmAsMcz6qAJKBKc7jAhGZaYzZ3Yq+JwNvA7nOR2VAJjDVeZwlIic33eYw/V0KHBftODqFhGSSahzBUhsgJdHfxQNSFEVRlOhoycJyJFaIPNXMhftiYJbzejvwEHAXsNFp9wsRGd+agYmIH3gVK1a2AscYY9KBNOBcoBQ4AHiyFX1nA69hxcoq4CBjTCY258yPgVpnu+6KsL9+wJ1AEbAy2vF0OP5kEowVLFWaPE5RFEWJQ1oSLFOxid9eaab8x87zRmCiMeaHxpifAxOApU7/l7ZybJc6/QCcYYx5D8AYEzDGPAP80Ck7XkRmRtn3DUA/oBKYbYxZ6PRdY4y5j2AyvCtFZHQE/d0P9AR+gRVusUVCUlCwqOOtoiiKEoe0JFj2cZ4/b1ogIr2ByVhB8xdjTMOF2hhTBvwJa2WZ2sqxXeI8f2iMWRCi/GlgvfP64ij7dus/bYxZH6L8HuwUkR+4IFxHInI2cBrwEfBIlOPoHLwWFg1tVhRFUeKQlgRLX6DEGFMaouwwz+uXQ5S/6TyPjHZQIpIGHNGkn0YYG+7ylvN2Vqg6zfQ9BhjSQt9lwLyW+haRXKy4qQauNLEagpOQhN/UAOh6QoqiKEpc0pJgycZaGUIx2Xne5LWuuDgix3VkjZZxnrEtC1PPLesnIjkR9u31qYmk733D1Pk70Ae4xRizOsLv73z8yfgDamFRFEVR4peWBEsxkN5MbpWDnOdFYdoboDW39AM8r7eEqectG9Bsrbb1nSUie6w6LSInAedjhc1tEX5315CQhD9gLSzqw6IoiqLEIy0tfrgKOBw4FXjC/dCZsjkSK0hC+ZcgIplY68rmVozLa5WpCFPPWxapJae1fZe5b5woo38AAeAHxjgOIlEiIlcCVwL07duXuXPntqabkJSVlTX0N6pgF7nV5QAsXLSYQH4ka17GDt5tiXd0W2IT3ZbYRLclNumqbWnpyvUW1pfkJhF53xjjWh1uxoYAh4sgOth5/rbNo4w9/gYMBO41xnzW2k6MMQ8CDwJMmTLFTJ8+vX1GB8ydO5eG/qreIbD9IwBGjt2X6ZMiNUbFBo22Jc7RbYlNdFtiE92W2KSrtqWlKaEHsEnjhgFrRWSBiGwErsOKlXeNMc0JklOcOl+0YlxeJ9+0MPW8ZaEcg9u9bxE5GrgcyANanRSvU0nOxFdXgY+ATgkpiqIocUlYwWKM2QmcA5RjM8weAgzGhitvJZgLpREikgqc57x9txXjyve8Hhimnrcsv9labeu7xIkacnnIeb4RMCKS4X0QdFL2ez7v2tSyKdYFKYMKTc2vKIqixCUtOjMYY94Xkf2w4mR/5+MvgPuMMbuaaTYZm1K/lmB4cDSsxPqH+LBRPSHDjwlG/GwzxhRG2Lc3Mmg8zWemdfte0eTzYc7zf1v4nqkELTOnAS9FNrwOINkKliyppFotLIqiKEocEpH3pTFmM/D/Iu3UGPMJdg2iVmGMqRCR+VjH3uOA25vWcdYxOtZ5+04UfX8rIpuwuViOA54L0Xe6891R9R2zOBaWTCp0SkhRFEWJS9q8WnMH8rjzPENEDglRfhbBTLxPhCgPh1v/XBEZFqL8R0AGNiS70VpFxhgJ98BmvAX4yPP5S1GOr31xLCyZUql5WBRFUZS4JNYFy1Ksv8wL7npBIuITkbMI+pK8aYx539tQROaIiHEew0L0fQewDetY+7qzcjMikiQiVwO3OPUejOmEcJHiCJZcf5VaWBRFUZS4JOyUkIgMCVceKcaYTa1oUyciJwMfYv1G3hORCqzISnGqLaKFtX6a6btYRE4E3sZmsl0oIqVOv4lOtXew0VDxjzMl1DOhSldrVhRFUeKSlnxYNmBDk9uCieB7Qjc0ZoOITMSurnw6MBzryLsceAq4xxhnkZzo+/7KcSb+JXAiNvqpHOuU+zjwL2NM95g/cSwsPf2VbNcpIUVRFCUOiURISIePIgzOmkQ3OY9I28wB5kRQrwC43nm0C8aY6e3VV7vhWFh6+KrYpGHNiqIoShwSiWAxWEvLY8DHHTkYpYNISAFfIlmiUUKKoihKfNKSYHkTmIWdipkDrAMeBR73pOlXYh0RSMkiM6BOt4qiKEp80lKm2xOw+Up+A6wBRmAjaDaIyJsicpaIJHX8MJU2k5xJFhVUqw+LoiiKEoe0GNZsjNlqjPmrMWYsNpnaY0AlNmnb08BWEfm7iBzYoSNV2kZyFhlUaJSQoiiKEpdElYfFGDPfGHM50A+7AOB8oCfwY+BLEVksIj8Vkdz2H6rSJlKySadcLSyKoihKXNKqxHHGmApjzKPGmKOAUcBfgC3ABOAubKiwEkskZ5Fu1MKiKIqixCdtznRrjPkO64j7X6BVOVGUTiAli9RAuTrdKoqiKHFJqxK6AYhIGnA2cBlwhPsxNp3++821U7qI5CxSAhVUBXRKSFEURYk/ohYsIjIVK1LOBNKxImU3NvPso8aYr9p1hEr7kJxJcn05VXV1XT0SRVEURYmaiASLiAwELgEuxYY2CxAA3sVOB/2vtSnylU4iJQsfARLqKqipC5CUEMvrXiqKoihKY1pa/PBs4PvA0Vh/FwG+w4Y2P26MyevoASrthLOeUCYVFJbX0C87pYUGiqIoihI7tGRheRqbmr8CeA475TOvw0eltD/OekKZUsnOsmoVLIqiKEpcEakPSwUwHZguEvVaiMYYMyLaRko7k5wNWAvLzrLqLh6MoiiKokRHpKs193YercG0sp3SnjSysKi7kaIoihJftCRY/tApo1A6Ho8Pyy61sCiKoihxRljBYoxRwdJdSM4EoKe/UqeEFEVRlLhDY1v3FpwpoX7JNezSKSFFURQlzlDBsreQlAHio3diNTvUwqIoiqLEGSpY9hZEIDmT3IQqtbAoiqIocUer1xJS4pDkbHpIlfqwKIqiKHGHWlj2JlKyyJJKCstrCAQ02lxRFEWJH1Sw7E0kZ5JOBXUBQ0lVbVePRlEURVEiRgXL3kRyFmmBcgCdFlIURVHiChUsexMpWSTXu4JFHW8VRVGU+EEFy95EchaJdaWAWlgURVGU+EIFy95ESjb+mlKEgIY2K4qiKHGFCpa9icz+SKCO3lKqFhZFURQlrlDBsjeRPRCAsWnF6sOiKIqixBUqWPYmsqxgGZlcrBYWRVEUJa5QwbI3kT0IgGGJu9mlgkVRFEWJI1Sw7E2k5UJCCgN9hTolpCiKosQVKlj2JkQgayD92KUWFkVRFCWuUMGyt5E9kNz6HZTX1FOq6fkVRVGUOEEFy95G1iCya7cDsLmwsosHoyiKoiiRoYJlbyN7IClV2/FTz6bC8q4ejaIoiqJEhAqWvY3sQYgJ0JfdbNxV0dWjURRFUZSIUMGyt5FlQ5tHpxazsVAFi6IoihIfqGDZ23Cy3Y7PKGWTWlgURVGUOEEFy96Gm+02pYSN6sOiKIqixAkqWPY2UrIgOYsh/kLyi6qorQ909YgURVEUpUVUsOyNZA+ir9lJfcCQX6ShzYqiKErso4JlbyRrID2cXCwaKaQoiqLEAypY9kayB5JSuRVAI4UURVH2FoyBD/8MRZu7eiStIuYFi4hkisgcEVkqImUiUiwiX4rIz0UkqY199xWRv4nItyJSKSKFIjJPRK4QEQnTbqSIXC8ir4rIRhGpFpFyEVktIo+IyOS2jKvDyR6Ev3IX2Qm1bNqljreKosQhu76D1e909Sjii5J8+OhW+PaNrh5Jq0jo6gGEQ0SGAnOBYc5HFUAyMMV5XCAiM40xu1vR92TgbSDX+agMyASmOo+zRORkY0x1k3ZHAJ806a7UGdco53GpiPzJGPP7aMfVKeSOAuCw7EKdElIUJT5ZcB+seBlu/K6rRxI/1FY2fo4zYtbCIiJ+4FWsWNkKHGOMSQfSgHOxIuEA4MlW9J0NvIYVK6uAg4wxmUA68GOgFpgF3BWieSJQD7wEnAX0MsZkOeM6GCtmfMDvROTyaMfWKfQeA8CBqdvZpFNCiqLEIzXl9qFETq1zvq+rDl8vRolZwQJcCkxwXp9hjHkPwBgTMMY8A/zQKTteRGZG2fcNQD+gEphtjFno9F1jjLkPuMmpd6WIjG7Sdi0wzhhzmjHmeWPMLqdtvTHmS2AmsMSp++sox9U55IwA8TM2YSubCiswxnT1iBRFUaKjtgLqKq1fhhIZrmWlTi0s7c0lzvOHxpgFIcqfBtY7ry+Osm+3/tPGmPUhyu/BThH5gQu8BcaYPGPMmuY6NsbUAP9x3o4QkZ5Rjq3jSUiCnOEMNXlU1NSTtzs+f7xKHFK5G5Y+39WjULoDDRff+LQWdAmuhaW2qmvH0UpiUrCISBpwhPP2zVB1jDULvOW8nRVF32OAIS30XQbMi7ZvD95fg78V7TueXqPpV7MJgC83FHbxYJS9hqXPwwuXQ9n2rh5JbLN1CVSVdPUoYps4txZ0CXG+z2JSsADjCI5tWZh6blk/EcmJsO/xIdqH63vfCPv1Mt153grsakX7jqfXaJKK19MzRfh8nQoWpZOoKbPP1aVdO45YJhCAR2bBlw919UhiG/eiG6fWgi4hzvdZrAqWAZ7XW8LU85YNaLZW2/rOEpGMCPtGRA4DTnXePmxi1UGk9xgkUMvsgVV8oRYWpbOocUzS6izZPHWV9lG+s6tHEtvEubWgS4jzfRarYc2Zntfhwli8ZZnN1mqfvsta6lhEegNPYYXgGuC2FupfCVwJ0LdvX+bOndvSV0RMWVlZ2P4yS8qYDIyuWcmTO/fnpbc+oEdKbOrXlrYlntjbt2XEutUMBr7+Yj4l2bEjlGPpuCTWlHAEsHXDar5txZhiaVvaSrhtOaSkkFTgy0/nUZ6xqVPH1Rpi4bgM2LKY0cDOgnyWtWEsXbUtsSpY4g7HCvMKMBQbcn2W4wvTLMaYB4EHAaZMmWKmT5/ebuOZO3cuYfurOhC+/gXHDIWbNkPCgLFMnxipkapzaXFb4oi9flvKXoY8OHC/0TAyyrYdSEwdl6LN8Cn0z0mnfyvGFFPb0kbCbstC+3TQAeNhYGzn6oQYOS7zF8Ma6JWV1qaxdNW2xOYttb3gu6SFqecti3RSvN37FpF04HXgUKwlZrYxZnGE4+kaUrIgcwD9qjeSluTni/Wxc7erdGNck7ROCTWPu4+qWzTq7t3Uxrc/RpfQMCUUn/ssVgVLvuf1wDD1vGX5zdZqW98l4SwlHrFyFFAOnGCMaZoJNzbpPRrfrtVMHtpTBYvSObhCpVYTFjaLu2/UMTk8DUnQ4tMfo0toCGuOz30Wq4JlJRBwXo8PU88t22aMifSK640MiqTvFc1V8IiVaViflxOMMR9HOI6up9cY2LmGg4f2ZNW2UnaX13T1iJTujlpYWsa9+1XB0jz1dRCota/VwhI5amFpf4wxFcB85+1xoeo4ixMe67yNeAUsY8y3gOuh1Vzf6cCR4fp26ryBFSvl2GmgjyIdR0zQbwLUlHF0LxuN8MlajUpQmvDa9fBRWN/x6Gi4w+sGFpaVr8Ft+wQjnyLFGPjykeanfNTC0jJeq0qcXny7BE0c12E87jzPEJFDQpSfBezjvH4iyr7d+ueKyLAQ5T8CMrBrBu2xVpFHrLjTQPEnVgBGHwsIY3Z/RHZqIh+t3tHVI1JijfUfwcZP26+/2m4U1rxjFVTsgrJt0bd7/XpY8nTo8gYrlAqWZqlVwdIq1MLSYTwOLAUEeMFdL0hEfCJyFuBmVXrTGPO+t6GIzBER4zyGhej7DmAb1rH2dWflZkQkSUSuBm5x6j1ojFndpO807MKJR2EdbI+Pq2kgLxl9YMih+Fa9xtRRvfho9Q5dV0hpTHVZ+4qLWMjDsvodWN8Of1k3CV55lLkh3Qy2W5eELm9wui3VdXKaw2uha4s/xu4NbR5KXKGCpWMwxtQBJwMbsA6w74lIOdai8SyQBSyiyVo/EfZdDJyIzUK7L7BQREqwAuR+IAk7FXRdiOZnEsxkmwA8JyLbwjwOj3Z8ncq4k6BgGbMHVrKjtJqVW/WuTvFQU9a+0zcNkR1dOCX03k0w99a29+NO6VREKVhcy8m2FgSLCXSPqbOOoD0sLHkL4e5JULC8fcYUD6jTbcdhjNkATARuxjrLGqAW+Aq74vKhxpjdrez7K2A/4C5skrdErBj6BPgB1nISalUt7z5LAfq28Ehqzfg6jbEnAnBU/ecAfLxGp4UUh0DACpaadgyvrXUsK11pYSndGv00Tijc/VIRpe+XK3QKVkB97Z7l3otJPIQ2f/tW6O3oSLz7qLX+GMWbnee8to8nXnD3W6AWAvVdO5ZWENOCBcAYU2qMuckYM8EYk2GMyTLGTDHG/M1ZGTlUmznGGHEeG8L0XWCMud4YM9oYk2qM6WmMOdIY87AxJtBMm8c8fUfymNs+e6KD6DkU+k0kc92bjO2XyUffqmBhybNQVdzVo+h63AtytE6l4ejqKKHaKrtidGlBy3Xn/x3+FdIv39IwJRSlYHHb1VfDztV7lnutKrHueLt9JTx1Dqx+u3O/t5GFpZXWAnff7k3/9faaSusiYl6wKJ3AuJMg7wuO38fHwo2FlFXXha9fWwUv/xiKwy3FFKcU58GLP4BlL3T1SLqeBsHSTuLCmK6PEipzhEpNacvbtW0p5C9qvry1U0Jeq8m2pXuWe6c4qmN8xWZ31e3KTs7j1B4WFteXaK8SLJ59FYd+LCpYFBjxPQCOS19Hbb1hwXctnIC3LYFF/4Y1EUeTxw+Vzgzj3nQSaw73wlpbYaeH2or3ItOeVptoKN0W+nUoqortSb25C2JNG31Y/EmhHW+9Yq49p+M6gqoi+9zZliDvPmq1hWVvFCyVoV/HCSpYFOg/CRLTGVm5mLQkPx+3FN5csqXxc3fCveuKdVN8Z9AQVmvaJ5toI8HSRRfi0q3B1651oDncC1lzF7S2WFh8iTYPUijH20Y+LDH+O6wsss+dPU6vdaC1FhZ3zLFuxWpPaisgJdu+rgvlohnbqGBRwJ8IQw7Bv+lTDh+Ry9zV28OHN5c4J/3uOCXknrziwdmxo/FehNrDIlLrmYLp6ikhaNnxtiXB0hYfluQM6DfRCpam/7V48mHpagtLUkbrxfReOSVUCak59nUcLmmggkWxDD0Ctq9g1rAENhdWsmFXmAtKqbMcU0k39K53T16xfqHoDLyirTmLyPu3wBOnRtafazlITO/CKSGPhaUlx9uGKYOiZsqd30hrLCxJmdbCUlUMRZsal9dWQXJ24++IVbrKwuL+llJ7tsHC4h7fvcTCEghYkZLmCJY4zHargkWxDJsKwIzU7wD46Nsw5vIGC0t3FCzOySvWs4zWdcK6T16R0pxFZOtimw03EgHiOrmm92psbelMSgsgcwD4EtrBwuJsQ9Q+LI6Fpf8k+77ptFBtJWT0tq9jXbB0mYXFI1jUhyUy3Gm01J7Oe7WwKPHKgAMhIZXeuxYyLDctfJp+9y61JL/7ZeKsjgMLy+Yv4C8DO14wNpoSakZgVOyyCc4KloUu9+JeZNJ779lfVYm11nS0ECvdClkDIKNveB+W+rqgYHOtCF7qqm0ui4RUe+GLZtw1ZXYqo8dQZ0xNhFNthb2o+BJi+3cIXWxhEUjtoT4skdIg8tTCosQ7CUkw+CDY+AnTRvdmwbpdVFWWw6L/7JlgyHW2rauK/u4y1mmYEophH5aC5VBfA4XrOvZ7IhEsbjjr1sUt9+daadJ7QaCu8UV+3Ycw7w7I/7p1Y42U0m2Q2c8KlnBRQt6LWKgpIff30XOYfY7mf1DtWFhSskL3X1sJiamQnBk/UUKdPc7aCruPElLVhyVS3P+fWliUbsHQqbBtGSeOTKKqNsDbj/weXv4RbPgkWMcYOyWU46w72d2mheIhSqjcsX5VdHDuC+9FqFkLixMGnv9Ny/15BQs0nhZqmH7p4LvdMo9gKQvjw+IVEaEEiztl2NOxkoTLdrt7A9w6HHaucdo6FpaEZHvBbXrBrKuExDTr5xLLv0PoWgtLYiokprTdwrLXCBZHoLg+LBolpMQ1+54CIhy0+VF+f+xwDt/xLACBnWuDdSp32wydgw6y76MVLMteDJ+Mq6tx76xj+c7WncroaOtWdXgfFgnUB6fQtn7Tcn+un0u6459RE0KwhDLPFyxvn6lHN8ttZj/IbMHC4r2IhbqgufvGndYJdyy2LbOWKHfNmuoyaz0BG2LatH+vhSXWBUuDD0snT6vUVVlR1xYLy97mdNtgYXGnhNTCosQzfcbCARfBFw9xWc2T9JYSAkZ4Z958iiuctUJKnAghV7BEm4vltevg3Zvab8ztTTxECZW7gqUTLCy+hODrJiTUOfsotadN0d7SnW5tU8HiEUHNObju+g7+cTisede+D9TDO/+vdavsuhaVjH72UbGz+TVwvBexUD4s7v5wLSzhQpvLmwjMmlJrYQE7LRRKsCR0kWDZ8hW8dI314YmEBgtLF0wJJaS03sJSX2f78CdbwdMZTuxdzR4WFvVhUeKdGb+1GTgX3AuDDqI4axTJxes46d5PKCipCjrc9pto/+zRWFhqyu0d2cZPY1cQeKeEIr2r37a0c3PSlDlTQh2dDr261E6dQMgooMRa5xjuMx1Mfcur3jYVLKGmhJreqbsCefd6+1y4Hj69x1rqosW1qGT2txYWCE6vNaVBREhoC0tNUx8Wz7EI1DeeRvUeL2OCPizQgoUlo3P/J9Wl8Nz34ZsnI0tZYExjgd+ZDvjuPkpIbd2F1/2dZQ9s/L4709SHRS0sStyT2RemXmtfT72enoPHcViPIraVVPGXN1YGLSpZA+yfPRoLixsOHaiFdR+167D3IFBvV8ONloYTl4l8DZ2nL4APbon+u1pL0zv2jqK6FDL62Nch9kWDYHGWdmBrC1N9DXd4uU6fQRFUV24v+PUVRY3buBdE1zrihiK3xuHYFduuDws0Py3kfm/WgPBOt9mDAWnsw7LmXXjsBDsVBI0tYnVVVtwleQVLk4tlV00JvfM7KNoYHGtLVJfabUnLtf/pzvSJqK2wU0KJKa278DYIlkH2ORo/FmPghStg7fvRf29X4g0FB7WwKN2EqdfDJa/BmOMhZwQpZZu56sghvPRNPvmb1wFiT/pZTUJr62rg4aPh2zdD9+u9a1vTwau7Ln0e/nlE9D42VcWA2NeRXCwCAfsdnel8XNaJTrcpPawlLUTelMRa56Tfb6KdF28pUqim3Frv3NTgHhG0c6fdpu07m1g8XLHgChZXYBSuj2JDaNxHpjMl5P2sKe4FrMfQ8BaWlGx7AfCKR1fEu9NWZR7B4gqd5nxY3AUiE9MiixKqKm6fxSnXz4OvHrUJJCG4plbY7y6yz9mD7XNniqvaqqCFJVC7ZyRjS7hjdccejWCpKYelz3XsAqk15e0fBegKlORMuzSEWliUboE/AYYfCSKQOxICdVw9KZF+WSksXbkKk97bpvPPHtR4KmTrYsj7EhbcF7pf17zfb4K9C+1IE3LBMpsfZNfalut6qSrxTINEMC9fsdPeZba0Lk17UVsVdHTtDKfb5AxISgt5UWzwYUnLsUnQWooUaojsSHPeB/sMOL4QVaVNLpTuhcTNSusKjNZaWHyJVlxleiwsq16n9/ZPGtd1hWv2wNA+LF7hkZbb2IfFPS6uRachqmtXMLooqZkpobpqwFjLQSRRQk+dBy9eGb5OJKx9z+6b4/5q30ciWNw6rpWiM5MteqOE3PfRUNUGC4t7fFuaAm0L8/8OD0yLXoiFw50SSky1D9ciVl3Wvt/TgahgUcKTOxKA1JIN/Hr2WJIqtrGyPJ0bnlvMdzXZmNKtwR973pf2ecM8KNq8Z1/uneeBl9iTeahkY2vehVVvtH3cu2zGXnZvjLxNXY11wItmXtu9KIULkW1PvFMPzfmwlBbAX4bAps/a9l01Tgr5pIzwPiypOdB7bMsiorbcpuVPSnf6D/bpc/Z1bXlR4zauWGhqYSnNjz69f2mBFaM+H6Q7U12bPoPnL2P4+icb160ugeQsu20hLSyu8Ei3Ydpe8ei+dn/vrpit9FpYmggWV7y7ES9eC0tzFxNjrP/Umnfabt3Yvd46EGf2d7YhAuude2x6DLHPrRzD2f9cYKebo8GbhwWin95osLA0ESyRCB/3f7djVcdd6Hd+a3+D7XkjVOv5bSWk2N9afS3cPRG+frz9vqcDUcGihCd3hH3etZaTJw1gSk4V9RkDeGf5Nh5eUouYel6e95Wtk/dFcA2Upc/t2VdJvr0AjDvZvl/zzp513r8Z3vlt28dd6AiWoigEiytQslzBEoGFxb3zryrqnDl89wTWY0jzF5Wti60VZstXbfuu6hJ70UxMC2ltSqwttdNFSemQ1d/WaeqPsWG+zeVjTPCu2BUsnlBpV/yYpiKxwYfF2W6vMIw2Uqh0q50OApsoMTUHljwNdVWkVO2w03ve703JDgoKbxlYi5Mv0eZSScttLFhca0tJKAuLsx9dC0tylp3ScC8mDReV1OC0UXOWvopCe4zqa+C7D6LbF00pXGdzK7n+DZ00JVRdV89Xm3bz2foopzfbamFp6sNSXWKnGf8y2GaSDod7rOuqWjc1GQnujVa0UZjhcP9v3uiqil324eYIinFUsCjhScu1J+3C7xARMmt2MGHsWL763TFcOOtwAB5/az5/f38NZvOXMPJ7MPhQWPLMnlM+JflWDGT2hT772WghL4F62PGtPXm2JZlToD54t990YblwuN/ZcBKL4ATsXUyvM6aF3Itf73HOxSpEWG6DWAth5YoUbzRLUnrIPCyJtSV2OkjErs8DjfcHwMpXbLbkyt3WIpKUFpwS8lyI0wJ2XyfUNBUsRfa5fLs9rqXbgu2jnRYqKwgKFgi+HnQQPlPbOGLIFSypPbAO2E1+C95Inz2mhFzBssVeSN2LY8Xu0D4sEKzjvQt2+29OOO/2XCyb8xuLBGOgcAP0HG6ng5OzIotAcy0sDf+X6EOb1+8spz5gWFtQGn6F+Ka4yfVabWFxBYvHh2XrYiseW5rq8d4obHfqvvhD6zfXXrjnreI2/Ieb4v62ElKC+Wvc322cZCxXwaKERwRyRlhfkNpKeyLL6k+i38d+4/YD4Mzh1Tz57mdISR5lfQ6EiWdbc+m2pY37Ktlioy7A+rE0jeIpXG+T0sGebaOhOM/edUJ0U0JNLSyR+LB4o0wiFSyLnoQ3fxn5uLy439FnrH0OZWVx/XZaOtkZYyMdynbsWeaNZklKb96HxU1CleVMJbh+Si6uM3LpVk9kRyogDVM6VVWVpGKPe0qgnKpaj5ndFZEmYE+qZQUwcLL9LFrBUr4jGFIN1sF03Elw5A32vVfcei0ssKcfiztdBsEpIfeC6/VhcY9X9mBr9XIE2KurSiirrgv23zAl4bkLdkVNc8LZvbvvvz+sfjvy3ClNKd9pBZmbvTq1Z3QWlh6tt7CsLrD/sfKaevKLoxAdtZVBS4H7PhpcS2Bmf2zoeknw99TS9K734l6wAnautZa6b9thKhvsf80Vve2ZLqG2wgoVn89jYVHBonQ3ckdanxD3z+PeTeeOguwhnCfvcstke6K95iM/n6UeZaNBvn6CmrpA8M6pJD8oWPrua/0QGt2teARMJGvTNId7we45PLopoQYLizslFK2FJUI/lpWvwjdPRT4uL26IbG9XsIQ40bj+O+EES30dvP5z+M/p8Mmde5Z7LQHNCJbE2tJgEirX96GphcU1aZfkB/0ORBpZbbZut9tURwKZVLBhl+e7vEKhrMAKxN5jrFCKRrAE6u2+8gqWE+6Ac/4TTP7m/a00FSxNLX7VpY0tLKbeYw1yfVjyPRaxsY2+48/v5fH8ws02Csvbv5sELTHNWjrc7wqFa2E59Gp7I5HXwlRGczj95IljcUrLidyHRfzBY9+KXCZrC4Lbtqag1PqRvXotzP1r840CgcaZbqF1Pizit79DN3mfa5mMRLCIz55fti8PCpWmYr21eIVzu04JOdNooBYWpRuTO8LeKb9wmZ23H3ig/dyfAEdeh2xZyKzC/xLwJ7MrYywXP7WW/MEnUPv1f5j+x5f4wRMLKSsvs38KVwz0sdaZRiJl+0pAIK1XMOKkYAU9di+JbrzuhWzkTHvyifTuy73ryopiSqisIGhlaMgVsj38Cb90q73bbk04atkOe2fvCr9Qpnv3xNs01NoYWHC/9Sl5eCYsfMT6oLgCx4t78UnKcHxYmglrdn0e3PE0PcEWewVLpXW6hUZ+MQXb7X6rTO1HBlWs2+7Z71XFQQfZok1WFGT0s9aAwhDjbo7K3dZK465j5MWdFghpYenhvC9q3KamLOiL05DTpcDu44pdTih4RdA3oPeYRt9RTjKLNhc1b2FJTA36uTQXfVO43t48jD3B3iCEu8Nf/DS8+rNm+rH/l++/vJPtpVWOhSUCwVJVZKfMWvK1CcPqgjJy05MAWL91Jzx9vg2vnvsXWDc3dCNXnLTVhyUly4rn5OygDwsE/dKao2KX/c/3G2+njzpMsEj7pkuorQxOpyYkW587V6iEEyzv32J/PzGAChalZXJHAga2r4Jz/wt9xgXL9r/ATqFs/QbfgP158qojGdc/kyu+PYjE+kouT5vHh9/u4Cf/fB2A+gzXwmIFy+MvvcHPnl5EIGCseMkZbtP+uxaW//2QcSv/b88xle+CvIWhx7trrT3ZDzrYvo/Ul8O9SGf0tinpI7Ww9BtvX7vm/2cuhCfPbD5su2nESzSUb7fjc5OvNT3R1FVjivOoMMmOk6fH92TXWnj719bfwdTDKffBqGNCO6/WNLGwhPRh8VhYElPtha7EY2Gpqw5ahJwpoRpfMs8t3EwFyZSV2v29c4etk5A7DJ8Y8rZ5ptaqioIX+62OcM3sa0V0NA6P7p1kKMGSnEFNYlYTwWIvaDWJ9mL84DuLqK33ON5WlwUFhevDUZJn91t9tbUgQnCNJfc/40xRlpPK15t2e1ZsbhKl4nW6DWdhyRlu6w06GDYuaH77V74KX/87dBr7wvXU42NjoBcLvnMuxhGFNRdZQZeYZi0OrZkS2l7KXzKe5YOUGzlr3nE2vPr42+w55+Wf4K8LEQnm3UcRWFiMMRRXNvH1qi4NWrBcx+qGKaEW/pcVu+z/r89+9je4+XM7PVW6tX1SNbjT2H3HRy5YjGn5/+BaOME+11ZGtpDq14/D4lZahNsZFSxKyww+xObZuOBZGD2rcVlCMhzh3LkNOogeaUn854pDGD95KgU5B3FZ4js8dskBdvoH+OErW62DbkZfqhKySdy5gpe/yefvH6whsH0FG/xDWVAxELNztT0Bb1tCcs2uPU3y8+6w2URDrQGy6zt7B95g6o/Q8db9jpRsJ5Q3Qh+W7CH2BFZWYM3VW5fYCJ2N8/es783Z0po7srLt1uLQIFianGh2b0BMgC8CzhSEc8KrqQvAdx/az654H676BA640F7wdm/YMwrGG34bakrIGBLqyoLWJbB3+94pIe/2leRTX13Oe2vL+MXzS9hYKsxfuYl1O8ooKrRiIqWXPV6uxQWwx6TXaPt6mxUsjy6uZGeSk7SwtspmlP3sn9aCsDpE5Bk0nJjXV6WFLK5K6RP8nQQCUF1CUSCVK56104trNuXx6HzPBaGmPDgl5Po8FecFBWS/CQAUrnVC/RumhDZRTRJG/GwurGRXnXMBcS04dVEIlsL1dloCoNeo8Banknz729v57R5FNTvWstXkUEMi89fudBLhhbewGGMoLdqJSe3hTPFFn5W3uq6ejbsqOKrsTZIT4IvEg+wN0SE/tGK6eDOTv7oBHpkFH90ebNgon0jLFpZXFucz6Q/v8LOnF7G50GlbVdJYsJRuDf52W7SwFFqh3ndfwFjL3fgzrd9ce0ytFG20AmjApMinhDbMg7/vD5s+b75OoymhFCvyXCFfUxZazLpTqR0VDRUlKliUlukxGH74sV0zJhQHXgyTzoNJ5wKQmZLIbWdOou+s65HiPI6s+4x7Z1uzfr9BI7jz3dX86KlFfFMzkEMzCjjjwEHc/94KAjvX8srWbB5Zn41g2PyUx4TtrBhtjLFOmVuX2D9cqMRwu9baO3B3Jd2iDeG3b/dGZ10Ux8KSnIVJzmTT1gJ2l4dZFC3giA831XtZgfUbcS86n96zR5OkmhJ74YBWWlh2WCuBKxSaniCd6Z1PAo7Vp3gzO0qrOfCWd8n76nW79k3O8GD9nsOsRaDpXWVD+K3Hh8V791hdgs9Ny+6S1X8PkdJQffcWKsvL2FXj55FLpjCwTy5pUs3L3+RTXmxPmuIcrx27nJNoXY29OGX1t+NwLCzPr67jhfVJgLHC9YEj4a1fWrP1f8+yJuxAPcaYBv+p6mJ7Efq/T0NfiKtS+gR9fqpLAMOHG2tYXewHYEpfH3e9u4a83RXB/eM63Wb2B/Gxe+t6Hn7bCpRAXytYkncuo9yXQVWq9Z0xxXmUmhRm7Wv9RRbtcISi+9sLaWEJIZxrKuwxyxlm3+eOsFaR5oSGezEOsVxF+bY1bAj0ZWCPVD79bpe9GFcVh80x8tzCPL7bnMf2WnsBDCRl8OGS71iaF3l03/qd5SQHKkmpL2V57xP5WdWVmDHH28Ihh8IJd1Cd7PjTfHRr8OLaNJ8IhLWwvLVsGxnJCby9fBsz7/yIL9YXBqeEwD47kUEV6YMwZQXhLSVeCwtYwTr6WAD+/tJcnvw8Cr+5UBRtsmkLsgfbc0ok6RJc6+PyMGtsuU7v4FhYPE63EHoasGKXFWTFec0vFNqJqGBR2k5iKpz2z4a7ygZGH2vvABfcT3q1vWDcctHR/HjGSN5Yuo0N/qEMr9/An07dl9kDykggwNHTZvDnay4CYHDVt+SlOnemO1djjOHaZ77h8L+8T527Tsv2Jifguhoo2kRp+jDuXFBEwJdEXeEGe/f3+YN7RFLkrV5E4O5JvP3S4wSqiu1FyOensC6ZFRvyue1tzx1paZOTR7mT5Tazn11zp6zAhmUD7DMDVr8VfO+QVOMxtZe2wsJSvsN+V2KK9QdpYrov3rIKgLXpNpKmeNs6Xl+ST1V1FT23f05g+PTG/bmL9zWZFtqQb4/XmiJDfUKq3U7vtrsXxjSvhaV/w4XxzaVbeeDVjwEwOSPYunkdyaaKoycOZ+a4vmRlZdM3uY5XFudTWer05USbFO/eaYVGg8Wrh91mZ2mH7aYHb+Y7J96Pb7fRPtevhF9utKuNz7sDM/cvnPD3T/jT6zYh2fI1Vsh9ulWCK497qE52LCye711V5OPAkYNAfBw/0l6Y57yy3I7N63TrTyCQ0Y/Pv1nC/CX2eN+3IhmAdKlmW10mP/mfvYhJoJZyk8KFhw4l0S8szKu0/id7+LCkBaecQlku3OPlWlhybL6kB196j1e+C4rszYUVLNqwIzgN6Ybhlu2wOXKAxOINbPX35/Kpw8nbXUmhyQDMnlZNhx2l1fzpjZVkU86aErua9+76FCrLinlxkT1GdfUBfvn8Em5/exUrt5aEDFleXVBGf7GCO7XXEEqr6the6vmNHXQFi/f/I5z9hA03XvKM/dy5IdhR5eOrfEeoNGNhqa0P8MmanZw4sT9zb5jBoB6pXPWfr6gpL24cWu5EFb5ZMhwJ1FJd2syimOAIlhwr/FN7wr6nNPjmLVmxgsc/3dB820go2ugIFneqMYLzxC7HV2rlq82LrT0sLJVBB3EIbR1yp4xMfXQpIjoIFSxKx+Hz2wiGvC/sHyklG0nO5IZjx/DPCw9k6hHTkdoKUsryuHOadbzbd9Ih9BkwrCGa4zfFp1CHn/rtq3jqi828/E0+GbW7SKguAqBuq3MCXvka/J+TsdHU84+lhr9/uI71dbm8v2AhG1/5E7z5C1g/t2F49QHDq6/+Dx+G9V+9ywffrKHSn84HqwpYX+ojy1fJK99sseGndTVw30Hw4AyPc567mF7/oIXFNbnPvgOTkELAs0xBIGDwVXlOCo6FJb+okjeWbrXTNuGor7NCwXVCdRKW1QcMbyzdSnl1HdvWLaPQZHD27FnUGyF/4xpeXpzPoUnrSaeShf79G/fpXvA8gqWooobHPrRh5Rf9ZwWPL3TvbD3+BO7dmHdKKGsglG3H1NVw9/trKNpm+1zpH0NO7VYSpZ7+vVyfl3R6p9Szfmc5lSWuYLEZU/01pSzJK2Z3oTN1ltKjIWdKvRG+N3kcO5IGUY+P8v6Hco25kT/NK2JntcAp98KoWdQu/Derthbx6KcbWFNQytr16wkYYZfJ5JO1nrtKh6qUPo6JfEfDhXpDWQKThuRAchZZlHPdMaN4b+V23l62rZHTbSBg+K6mJ9k12/jhFOtE+9zqekoT7LZm9hrAu2tLqfVZa0A5qUwe2pN9B2TzteN4W15SyOfrdrFmi93mvFLDS0t3UCPJ5G0LTrMVVdSwbEsxa7917qgda9knu621YPmyRfxvTS2rtpVQUxfgokc+52cPv23vksFxbIf1z/2awGMnsGPx22TUF+PP3YejRlv/nm8dq1Jz1pqbX1tBZU09fRKrWFeWyOqCUvIr/WRQaaeUgC837OaZhZu578PvOP7ueRz0p/e54vGFfLUxKLDXFpQy0Ge/o2d/ux1rCva0Jr28NZsd2ROsD46bgBC4Z14eVz3j3LA0Y2H5ZnMRpdV1TBvdm37ZKTxy6UHUBwy7du2gzvFPMq5wASr6Wb+32577iDrHZ6k+YLj3gzV2Osl1qk7Ltee3q+bDzN83RE72ZTerC8rYWhxaQP35jZXc+HwLEZBFm6x12J1qjGRaaOdaQGzd/K9D16mtarCw1PmTqagoo2L3tmDkXCjB4k3VsLvrp4VUsCgdy/7n27ni/K+Df0DguPH9GTR2in1TsBzfjpU2AilnhJ0TH3wIJmcfBk0+gfWBfiz4YgF/eHU5R47qxfOn24tCvRE+nDeXSX94hy9fe9Dembxhc2p8XpLDI5dMoceAkezn20TOsscA2LZyAd9sLqK4opZH568nfbc94Z3ZrwB/TSmbyhO47LGFmMR0JvTyU15Tz8vfbIEdK6GqGLN9BeahGdbht9Fien3sn3vHKkjvzWbfAD7hQPIXvcOaglI27CznuLs/5p2VTpvEtIY7p188v5hrnvyao277kH8v2ICpKAydubRiJ2Cs0y1Aml1076VFW7jmya85+4EFVBWsYVvCQI6bOIjtksv2zWtZtKmInw3Pox4ft6/ux8Zd5dz61ir+8Opy7l9UjRFfoznqh+atI7He+qxcNWt/VhU6UwMen561G+3dVp0bRQNOLhbD6u/WsmpbKQflVFBk0nkjP50scU7gSY5lJCmdLH8tiX4hS8qpl4SGBQkzqeCU++bz/X+8Zzfbn96wavROsrno8BGcevh+nFDzZw7aeDXzN1XxyCfrOfLWD3lr2TaYeA5JFduYmvgtaYl+fvr0N9SUbKc6qQfpKUl8vHrPu+eqFG8kkhUsJaSx/+AeNhKmsojvHzGcsf0y+fMr30CgrsEC8tinG1hVkcl+GWUc2tfe3d542hFk9LIWoz79BjN7Qj921DsCJymd1CQ/BwzuwZK8IkpI44NvVnPOg5/x8pfW8XP63Z9x7TPfkFffk03r7d1zXX2AE/7+CSfe8wlPvW2tV58XZfOPud9x+cs7CCD8fEoiKQnwt3dW89in69mwq4LexrkQpeVCwQoC9QHSNn2AD4P8z65D1HvoOEb0zqBPZjKLdjqXhRCOt68uzufVxflcM30f0gJllEo6P/nvInbXJdMvpZbVBWUUlFTx9vJtJCf4mHfjDP5y+gSOGt2LJXlFXPTI53ZKBmthGZ9pf1P9h9glQNZsb2xNqq4z/P7l5dy161D7H8xb2CCcV+yopaTOWnias7B89O0O/D7h8JFWjA3vlc6dZ08iOVDOpnK7netKbR/liTlcdMJMAL797jvu/dBON//3843c8c5qHp63zlq7AnXBqdDsgdZqkdGHevz0F7tt81bvKYpLagyPzd/AswvzrMN1KKpK7H73WlgiycWyay2MmW2DBVa8ErqOx+l23e46EgI1BMp3hk+R4E2mGAN+LCpYlI4lOdP6uEAw9NXF/aMse95GrvQaZVOmA5x8D3LpG/z5jEmQPYhB9XlkpyZy59n706fSmvd39DqEg9MLOG7fvoyoWMzb9VN4o/5gik0aZx83k5nj+pI7cCSDAlvIlEqKTDpLv/iQU++bz6Sb3+FPb6zkiHRrwu5VspLpQxLo37cPVx61D/sOH0SGVDK2Xyb//XwT8+ZZAfH9mhsoqBDWP/NLAk5EzOqKDEoTc+1dXt5CyrJGcNr9n7KkbhADzDbOve99Tr73E7bsriSx2jlR9ZsApdtYuKGQDWtX8fsxWxiSm8bvXl7OR4/8CvPv0/l8+Rr++/kmquscweDc7RRKD77bUeZYWAp57qvN9MpIZuOuCnrX5kHOPvh8Qnlqf5IrrCiaWP01RT3G82VBgGm3z+Whj9fx3MI8bntvPdtMLlXb7T4tLK/hsfkb2L+PPYlfOn08U0bZE+drC7+jLu9rPn7/Nf7xhs35cf5/1nDdM99w9gML+NcSe5f7yddLSPL7OLJPNYHMgaTlDg4ec9cknZSGv7acaaP7kEUF9UlZDT4FPzqsN3edM4mzx1tB8LOXNrC4yE6xFPtz2W9AFpcdMZyijNEcM3EoH/1iOu9eP43hvdL5/cvLKBl2DOWkcGXPr7hmxkhWbi2ht6+UpKw+TB3Zi49W78AYw+bCCu77cC3nPfgZz2zpYcdVtLEhWqxM0pkwKJieP9Hv48+nT6C8rKjht11UYa1Jvh6DyawusKLSn8SJB41G3BD+jD789oR92U2mswvsdh44tCdVtQHWlfoZml7Hk1ccwoWTexPwJfKH0/bnhasPIyFnCGkVW9hSVMlHq3ewpaiSnx8zmovHGspI55x/r+LWt1ZxzMShSPYghpitHD88kXdXFHDnu6uZObYPF+6baDdtwFFQms8389+kr9nFuh6H0wu7LaPHTUJEOGJkLz7f6lhjmvg0LM8v5hfPL2by0J5cc3hfxNTTt08/vi0opT4pgyHp9nc6b81O3l1RwJGjejM4J43zDh7CnWfvz2s/nUr/7BQuffQLfvTk13yydifjUksAIaffUHqkJfLm0m18vm5Xg3Vjfn4dxZW1vFx3KDW+VFj0BDWVVkwP6J1DZrozbea1sNRWwY7VAHy0egcHDO5BdmpiQ/GM0b3JkkpWFtpV2Rdtt9+V1ndkgyXv+KFw7wdr+Wj1Dm53poXfWVGAcS7qu8lsnODQ52eX9GBSdgV9MpP5KIQonr+ljpr6ABnJCfzfe82kwnenXXoM8ThztxDlWFVi/ZkGTYFhR9rs0qGmhWorG5yUl22vIUnqyQiUEMgdZctDWdRcwSJ+FSzKXsIhP7Rhjx4LC2B9AHL2geX/s9aGw34ULEvLaciemtRzMEOlgA+uO4LemcnWrJ3Rl36Tjia7agu3HhEgxxQx9fjz6XXZM6y9+GvOOdJxOnUihQKjj6dq2EyOzNjMwxdP4VfHj+WigwexT/0GO8VSU4ZsXUJWj178ZvY40jOzkeoyLjh0KMvzS1i7ZAFVksIZZ1/CN31OYWjJV3wy16ZDn/2vb7njE0eI7FjFa/mZpCT6OP24Y/BhmNW7iCG5abx17VGMSy1mN5lUZwyG0nzufn8NN6a+zPc3/YpnLhzNz2aOot+OTxEMf3/yBX7zv6Vc8NDnrMgv4dF3rEj4wQubmPm3j9ham0Zt2U4+W1fIpYcP5cUrDmCAFDJopPUlSugxmAHs5JjBkFywiOzxx3D8+H5cNW0E83/1PZb94Vhe/fFUNpo+rF+7nK82FvLXN1dSUVvP4YOTrY+Mz8dph9gonYfeX8Lyh3/I/h//gMN6WCtEz159+WzdLmrqAjy32p74v12zmpnj+pBYnk9O/+Fcc/LU4HFtyMNiQ6XPO3gwPX0V+FJ7NERtjM4OcNoBgzh/orWkpWXl8NYG2yypR39EhNyMZBb8+nvcfe4B9EhLYkTvDG45dTzbS6u58qkVvFU/hUMqP+H7h/RnWG4aozKq8Gf24ajRvdlWUsWrS7Zy0r2fcPvb37KpsIL/bXXyyXgsLLm5fUhLSrBTUk4Uz4FDenLeJDvV8+H6Cu5+fw2lVbVMmTgBqa+2Pktpuc5yBU5CtfQ+DOyRSmZPa8VJzbDbdfCwHJITfCRn5DA+14qFfqkGX2IaFxwylMlDc8gZMJKBspNXF+fz9Jeb6ZWRxFXTRzCUAtL6jeTSw4fz82NG8/dzD0Cc3DSzhiaSm55EfcDw2xPGcfwQe1weLbB+LrLAOoMPuvhBKgcfhcFH3yE2dHzWvn3ZUGkvasZzx71pVwVXPvEVPdOS+OeFk0mqtaJuwkj7/xrQpw/JgQpy0pN4eN46thRVMmu/vt5/O30yU3jqB4eyb/8slucXc+DQnhycUwkZfZCEZM6eMphFm3dzzoOfccY/F7C7vIZ3NtQycVA2x+w/gjfrpxBY8SqvLrSWj+9P25dZ4/tRZRKpq65g465y/rcoD/PpPXD/oezetJylW4qZNrp3o3H4AtUkUM/KQsOGneV8XWD3j+SOaMipc/roBHLSk/j+o19QWVvP1dNHsLW4ijUbrC/SL9/cwin3zmfDTiuethVXkVffk+FJxRw1ujefrN1JfSAoGowxzN1cy5ShPfnx90by8eodjabHGnAFS8+hkJSGSc1h6+bv+P3Ly/jhvxfa6WkPK7eWcP0/X7Bveo2ymZsL11lLb1Mcp9uCkiq+3RX048rzO5ac5qaEfIk2tYBOCSl7BT2GwHlPw9Tr9iw7/WFb9os1Nsw2BBVpg5BAHRnlzp1GwXLos699AHz5CADpo6dx8PAcJo/oH2zcbwL4EvBNu5F+4w4npXI7Rw8KcNW0Edx8RDJSVxn83hpPbobkLKgu5dT9BzCwRyrTs7eRPHACJ+0/mOPOvxafGA4pfY+dJpvLjhpNgclu+MrtKcN54erD6T/KOr7++XDh1R9PZXBOGhMyitke6MH7W3zUF29l3podTE3bjJh6ZM07XHdIJmN9djt/Nama/ztnf5blFzP77/PYvcauvnzRsUcwtl8mH+cFqCvbhQicfuAgRidaC0zWAHvxyRk4gv5SyC/S3wCEhAMu4B8XTuZXx4+lb5a9KE0YlM2QEfvRuzafM/6xgGcX5nHGgYPo6a9qcCpNTLGWgd/NGsoY2UyWVHBG9UsYfDzwg5ks+PVMXvrREcw+3CYUTK/ezhkHDrKm7OyBjS1rDRYWG3k0c2wfjh+ZSkJ6T3v3508O5sNxRMLdl87gytmHAjBoSDDCSUQa/U4mD+3JSZMG8Nm6Qt7xHUVibQkpG97nzZ8dxfCUCkjvxVHOxeunTy0iOcHHBz+fxke/mE7vrHSKyKR650YCTnbdfQY543bzdDj8ZKr9fT2zpIhH52/gnIMG03ewndJg62Kb+NAeCPvsTOENHGAvDAP62Pf9slP44jdHM3bYIHzVHqdbN1QXyOi7D72lmOc/W8MHq7ZzxuRBJPp9sGsNvtx9mHPyfvxk5ih8PnFy06wjJUF48OLJ/OOCyezTO4OUygLqfUk8v2sYAAdULmBH2kiScgaTeu5jyIXPNxzr48b349TDreB944uVfLx6Bw/PW8ex//cxJZW1PHjRFHvT4Pg8jR46iH9ffjAjB/dHqks5fEQuq7aV4hM4elxjwQLQJyOR532/Yu6R3/LEZQfTl50NNzK/mT2ORb+fxa1nTGBlfgnH3z2PbRWGy6cO56rpI/igdgK+qt2UrbXOwgeM6M/x4/tRRRJr83dy/kOfc90zi9m66G0w9Wx79RYApo1pLFjciKwik8bPnvmGwoAzTZkzwgnjzyC1aid/PWMCAQOXT92HHx61D36fsORbxxJpMikoreLkez9h7rfbWbBuJ9tMDr3MLo4a3Zviylq+2bybud9u570VBby/cjsFFYbzDxnCxYcNJSc9iV88t5inv9jUSIRsXGd9jEy29ecqIJcVq1by7MLNvLuigF+/uLSRA/N/P99E3XZrrZlXmA0jj7YFGz5pqFNWXcc3m4uor6mg0iTx0qItVJqkhvJvilKsKG9uSii9t5OoUQWLsrcw+tjG4bQugybDmONtPpdmqEhz7gB2fusskLiqsWBZ+ry1krgrS3sZMRNuWGOz8w5wMvS6TmlucrrxZwRDVN1QRycPS2aSn09unMbw2nVI/0m2rOcwGHoEyVJHdp9B/Gb2OH511rSGr7z4pFlWEPQcBgmpyPaVDRfX7PrdJOcMZGFhMn5Ty77pZeRUOAmrVr0G65xcKf5kJvg3cuoBA3n+qsP50eF9+Fn6ezBqFqdOP4Tbz5xEfk0aqfWlHDWiJwN6pAYjppz9ktV3OIlSz6iNT9n1nULtH2DA8LH0khIev2Acr/90KredMbFxYjTH72RK2nZSTCWk5SK15dQlpFvHQ4cfzT6YWklkdFop0/ZJd9adGhi0NHj6ss8G6qrw15QEM76mZAVDfD15cXr2sdNKCVmevkJw47FjSErw0WO/o61D8LdvkprkRyp2QlovBvZIZWy/THLTk3jyikPZp3cGCX4f3x+fRF6gF2tXr6BwlzWDjxvqCA7Hh8UlOWB9KL43cThDc9O47pjRQeth6VZId/wbXMHiOEn7nc8TnCkhgOy0RMQriOqqgqIOGhyRA0WbqQ8Yzpky2EanFW2CAQc03vgcG9qcUFvK5KE5HL2vIxhK8vFnD+RPF8+i2FgLV+o4J59Seq7NCO0gIlx7wmQC+Phu0yYu/tcX/PH1lRw8PIe3rzvKTpHt3ggvXgmZ/ZGhh3PkqN74U6zAP3Kk3caDhuWQk57EHqyba/93q53FGl1R65CRnMA5Bw3hgYsnU1hRQ89kYfaE/oztl0XSqBkAnJXpRAgmpnHoPrnUSBLfrNvK7ooapo/IImf3N1RJCqO3v8WFI2sYPyC78RicqKv0zB4s3lxEdk/HEdw9P2X0hbJtfG9sX+beMJ0bjx1Dj7QkDhmew4rv7EX70P1G8eqPpzKwZxrff+xL7np3Dbv9vUiuKODIkb0QgfMf+pxLH/2SK55YyBVPLCQ9EWZP6E9aUgJ/O2sSPp9w84tfctrtLzN/7U4+Wr2DTz5bQJlJ4YnFZSzcUMiyskz2TS9h0e9mccOxY3h1cT7/+dxaYeoDhreWb2NGr2ICCJe/Wsixj26gNLE3NeusqHt3RQGTb3mX0++bhz9Qw8Ofb+OBj9fRJ6dHw+5YsE32XHHcoa60gI3V6XyyKxMTKl9TJ5PQpd+uKBFQkeac0HautqsU11XZpE09hlrn1doKK4ia3HED9jM39LbfBDsXu+Vrm8582xIb3td7LAzY3yZfci+cyZmAgdpypHyHtb54w7YnnQcb55OYbS9Kw4bu01DUY4gzHeXz20UK3VBSbFjzsLEH8ZuZM+GFf/PczHLknXqbfG7t+7ZSep9G2X7HD8xm/HcfQfVumPYrwFpG8kcMg41w3kRHbG1bYkNke41yB2J3AQaO+kXzO9gJbZ7WpxL6OttfUxYM+3SFyxYns/AJf4OXrqE2IZNETzc+vw9fj4GcPSABv5vXJXuQ3aeJ6VBb7skD4UwN1ZTbi7XrYJicFbSwVBYFF7jLdC6+mXvetXsZnJPGGz+dSu/MFHj+ALtP6mqstcaJhnj8soPx+4ReGUGRPDTLT0L/fRmx7S0KFm6g1KQyaZhrKRloM/aWbLXTlE5elLMPH8fZQw5xNn5QcBCuQ2b/STYTq7tQpfu5uz9dvILFmysDGkK9B8sOeg8bzz69M2CNk9HWFeAuzuKFqZVNwmBLt0LWAKaP7UvlgP1g6xdkjD++2X0oPj+k9uCSEdkcPuUwfD7hgME9rOjO+wpe/IH1h7jsrWDm4GQbCn3UsHQS/cKJkwbYZQnSezsrXju4GVPzv7F+FiVbYMT39hjDjDF9ePlHR7Bw4UJrUQL+eNFMAv8cS9pOZ7ojMZVEvw9/Yiqp9TXcc94BTE1aS/K/a/lNzcXMSfoPN+e8hc93WuPOHWvWfsMHwyIYdcAMKL8wOI7Mfg3J44b1Sm9odux+/di0oRAS4dzp+zMoJ40Xrj6MX76wlFcX55MycDCyq5Se/ipOmTSA/KIqLjl8GD3TEvlo9Q78xVtISbQCf8bYPkwf05vCJy6iZsMXHPHI3/D7fMxLXsKapP255fWV9M1K4fqkPvTjOyTJz1VHjeDL9YXc8uoKDh+Ry66yGnaUVnNQ30LwDeHGyRP5YNV2Pt49gsmr5vHavHXc+tYq9u2fxc+OHAD/g2H9elGcV8vBkwaAc9+2cIef2uE9SXQEy4r8EmrqAwzITqFk4wbyajJ4rzyFqYmV7Ny6kV4DQ9x4dhJqYVFinvqENHvR2LmmsRXB5ws67g6b2nwHLklptp3XwtJ3vF0TyV0fqWFKyM2BURZMytRvYrCv/U61FxZ3pdqUHnauNzm7wXHPjnO/YLKuQMDmYcnsS0IPK8LSN7xry6bdaPMirHoNRsywAmrXd/ZusKoEFtwLo461FimH7x1ot/2Yoc59x7Zldn/4HRnhrpEzoXnrChAytJlqj2BxL6DuUgj7zIAT7yJv0Il79pU5AH/Z1oacKWQNtKLRtYy4fbnCsGx7cN0eCC5EB40/7z0WDrnaRkK0wMg+mdbJst9461Piiifn4to3K6WRWHEZe/Hf2Tj4ZAaY7RSRxYjezm9g4tk2LPjrJ+x7d22fZI/wSMsNJjFzp4T67gf/b1twFWRXOCeHECx1VTbPjTdXBjSIzusOTmXOyU6isi1fAWJ/I16cY5xaGWIBSsfakzrsYJs7ZPChe2y/F0ntSbYpZcqwHA7sZZClz8G/T4OHv2ctZ+c/23iJDue30j+llrm/mMEF4zPg/sPgjtHw7CX2v1tVYtMPpPa0AnLbEiuMPRYWL+P6ZzE4M3iJSk7w4xsxI1jBScvfIzuLGSOymDmuL8lbrJg7+dyrSDzkcnxLn9tz6QnHwjJ1/HBOmNif0w4dYzPruscno0/I9PzH7NuXXCmlHj+D+tn/eFpSAn8/d3/uv+BAjprinB9Kt/J/5x7As1cdxgkT+3P4yF78evY4Du7f2D4gNeXkbn6X/qaAX44r4rRBpfQLFDBm2lkM6pnKlqJKJu1/MFJVDDtW4/MJt505ieREH3NeWc4bS7eSlOCjf20evl6juOLIffjvDw5l0hHH0Y+dPPL6J4zum8kTlx3C90bY43PSlJEs/8OxHDwyOE1baDL5riyZ2tKd3PzqCmb/fR6n3jefg//8Pum1hYzeZzgnzzgCgHtffDfkseosVLAo8UGvUdYC8dFtgASFijstNPTwyPoZeADkLwqm0O/vnGQGOkIgxePDAvbktm2ptcy43wX2BH3ZWw0WD3w+e6LrPbqxpafvvjZypGw7VBba7LAZ/YKiZt1cO3Ux6dzgxXnE9+zdOcaKkC8ftqGO03/ZaFMSe1pB4ndTshcsa2wF6j0GjrkZjvlD+H3iJo/zzlFXl3imhJy7zMLvbL6J1B4w6VzyB56wZ19Z/a3T32Zn5WD3YpTZRLAMPcw+f/e+syaNK1iyPVNCRcHFB/2JcPxf94w0C0ffCTYhmLvGTnrv8PXTezHyisdYeeobbDvuAfw+5zjm7GOnFr961Gb7dDPPei0lIsFpIW/2Xy/hLCxgt7u2Krg+Dtj95ktg/4wSxvV3fpNbvrK/f0/+EMA5jkJahUewGGMtQ+7+n/EbuyxDQojpmkZjzbHCZNNn8Lcx1qqyfSUc/Qe4dim4liUXz/9lYI9UfNsW2WRvI4+205yPHg8f32ZF+Yzf2rqr7Ppiezjjh8PNtu1Ptv85wJ+USlaC4wey8VPoPY5DJ4xGDvuxPS6f/bNxH47lMqfvEO47/0Bym4rXjH6N848AbFzAgDTD2fumI+m5jf7jInbaqvcAR5hGmk5/zdsN0U0/zPma28Zby1jafifw5A8O5eGLpzBy+gU2YMFJmtc7M5nrjxnNvDU7+e8Xm5g2qpf9/7uRPsCgiXYf/WlyGf+5/BCy0xKDU81JGdbK44hig3DQviNYUpjAju35/Gv+ei49fBj/vPBAfva9kfT1lzFg4BAOPtCeH6+eGJwC7gpUsCjxwZgT7BSLCdioI9cXYvxp1jO+97jw7V0GHGgv/v8+xZqGXb+UIYfZC5qbbtubZXTbEnvx9zhDAratd4riwIuDIdwursgpWO5JNNevIecIdZX2TtmfCKOPs5/tMz1ozdn8OXx2vxUxAyfTiAEH2qmVdXOtCbt8h7UYuYjYdZ68Fp9QpPa001BfP259Iz5/0Iof9w46KWgWb3RXHYqhh9vt/PBP9r17MXKFhnvcegyxY13+kl0aoGEqzjMl5LWwtAZ3UUr3ZB1q4cMQ7HfA4Rx02IzGHx50hd2ub98I5idpKhjcaa30ZgSLu7J103beFZu9C9SB/c1nDQyGthpjBUvT3wJYP7DswfTe8Qn8cyo8d6kNVa2vDh6HpPTgOMPhLoC44D473iveh+tWwNRr9xw/7LmydP4i+3zaP2xb8dulKnJH2v+ILzEoWCIZj8vQw21f3n2U4KSZr6+z/xf35iV7oPVPW/TvoA9SbSV8ei8Mnwa9Rob+jsy+1vLjCtOda+HR42DuX+jlK8XXnCB1RWGozLTlu0hyUxq4LH/J+svse6p9veo16L8/ZPVnYI9U64OU2c9aNJc82+A/ctGhQxnTN5OaugCnj/LZqVavBbXvBEhM53vp6+mZlgjz/mYF56CDYKxjoXSsgZKWwz8vPoSjJ4+jt6+cxy6dwpyT9+O48f257si++AI19tyQPRjET9+6dlqRupWoD4sSHxxypX00ZeTRQc/4SBh1DAycYi8Ow46EkcfYzzP6wC886xI1rONSYi0sw49que/pv9rzM2dVaravgF7OqsOZ/ewdblova33pv7/9fMZvYNQsW26MPVF8cqcd69Tr9+w7IQmGHWEvyGMcsdNv/J71WkIEznwEnr7QXuiqimHsiXY8YP1ifAk2YVZLguWgK+w2LH4GMEFn6qYWFrB+R/P+Zl+7lhSv021lUUPCuFaRO8qOfd1c+74lC0s4Rh9rT9qvXmsv5CnZzQuWtGaEkWMBaZiqc2kkWJpMCYEVd264a9FG6xzpTmE2pd8E0r993Z7Zt/3PCn0ITslFSmpPe/EvWGFvEAZNCV+/6UKN+d9YcZLi5LG59HV45gI49Br7m+i7b9DpPRoLS0qWHYs3TXxiit132xZboTHsiGDZYT+21omvHrNi66vHrT/StMea/w73ZqKswE7frXjJvv/6CXsMWxIsa96FdR9Zq/DU62048GMncFjZDtj9vE3f0H+SrXeA4zuz4iXYUgjTf71nv5POtYJj0wIYdgQJfh+3njmRu95dzYw0xyraK2hhwZ9g99GmBdYiPffPdnHGU+4L3nS5vzHnt5rTuz+Yaqbv47H+uTlY0nvbG6oeg7s8tFkFi7J3kT0IfvB+y/VcP4NP/27vrIceEb5+c2T0sSeFghXBC5OT64Gs/o5g8UQfudMzIna6au17VmA156Mz4nuw5h2beA8aW1iiYfhRcPk78PR51sJz+sNBXxgRa8mpLm48LdYcPYbAtCZOviNnWkuV12Iy+niPYHEtLNmNLSzeE3G0+BPs1ImzynOkFpaQ+PzWWvXRbTY8/+Arg/vHpUGwNHNByx0BP1+1p8WrQbAU7el0C3Z/uittb3H8r0JZWABO+weffvQeh0+dBneOg4/+aj+PRhSAswBikX3dTLqBRuwhWBY1nqbtNRJ+5FlJuP/+VrCIv2ULYFOm3Qi71gXfJ6RCbUEwO7T3v9p/ov1tf3a/tS7N/z8YOrWxqGmKazUt3WaP2YqXgzcXW7+BcSeHbpeYYuuteMlGHS59FtZ/bIMFTIC8QScxeNN8a0kZcpi1ru57Cgw+OOh47Syi2IixJ9j/3+KnGsa9/6BsHh/7Obwyx54zmv4ehhxqF4zcthQmnQ+n3t94qtr1t3L/Ew2rv+8KWlRdweJm1j7sx43XDusCdEpIUULhnoC/+8BeWA+4qPV99Z9kF0Jcb9OpN5ygnfVH9nCe9LYDe4EMFQEF1lwMsOhJyBrUthNKn7Hwk6/tYnNNfRzck1hLFpbmGH4UXPS/RmHQDJwctHp4LSw1Zda87/VhaS2uT48voe19HfwDmy/o6JtCWywcB9mwVqFQF2evhaWuas+pxx5DrGiuq7HTQf7koOUuRF81ybn2QjTu5OBq5pmtsLCAFcuRHHP3wle4zk5PlmzZM+zai/ubz+zf+DcRCSOPbmxtTUyxYmLBfVZsN93H3/s9IHbZjtKtVvCEwxV3W76yfl3bllix6k7TNidIAU66G0570FprT7nP+nKZAFzyGt+NvMz6/xz+E2u9Su9jRV1Cso06zBkRtLZ6SUq3wmbFy0Hr45cPwzv/z6aEuPKjPa19QxwfsdHHwcn37Hn+cC0soQSLi+vH465ddvAP7BRbF6IWFkUJRbJzEckdBac/0ODg1yqO/TM8cTIseYbahHQS3ZNF7gjY2s+GZ4fiwEvsiShcZEzvMcFVklszHdSU5oSR63vSe0zbv8PF57ORT9/8p7HTLVgrS1Vx45DY1uBanNJ6Nb9t7cV+p1th1Gt0dO3cbS7a5EwJNbGwZA8GjI282vKVtRo0te6EYspldtkL8QWtepHiCpZIrCtgfZQGHWwXKHSnPsMJFvfC3EyEUFQkpDrregnM+tOe5YMPspat4jw7HdScdcql12h7IzD3L1aAgRUMaTnw8o/C3xSM80TOHXChI0hS7P5Zsc3+j2b9ESaea4WMK9Zm/QmOntP8b/SQK2Hxf+0U8VG/sJa+oVPh7H+HbjN8mr3xGDXLWhqb4k7VpoUQLLs32N+Ld0ooRlALi6KEIj0Xjr8dLny+bY6fYC0X338TsgZRleK5cEz/tZ2eau4k1XOota6EE0siQStLa6eDIiEp3ZqevQ647cHEs62521lCoSHapCTfntDbuu9dEdcZJ92kNOtvEK0wyuhnHSI//JOdUgnlwwKw8F/WLyFSn62hh1vxkNEv9EUrHMOn2Yv0hDMjbzP5Uti1Bj67D5DGaQCa0nc/63gb7VRVKFyL1IEXNS/aRawPRktixa17yn12fF89asVVz6HWD2Sf6ZGlUHDJ2Sd0ZFu/8cEIRbDHp+lx9zLgACtyFtxvLSvl2+0q0c391nw+e/ya6zOhGQvLsv/BPVPglZ84gkXCW5Q6GRUsitIch1wZ9ClpK7kj4Kp5LBv/2+BnKVnRRUg0h5vwqj0sLM0x/CjY77SW60XLPtPg13nBaRQ3rNydymjrNI4r4triv9LR+Hw2r0mvMVhH5aaCxXHS/fQeGw13xLWR9StifRdm3x79mHqPtnfooSKCmmO/06xlcv3H1hLXNN+Ml4Rka3k86Irox9aUjH72e2f8v7b35ZI9EGbfZl/ve4p9TkyBi18OmeiuU5j5O2stW/gvO9XTNLQ8GlJ7WOumGybuWo2++Y99Xvaiteal5UYvdjuQ2BmJonR30nKoTumAC+e4k+CYW4Jh0R3BrD92XN9eC5J7N/rcpfa5rRaWtBzrG9CzmWm3WCEtx/r4vPKTPR1CswbaC5X47fRkUx+XcLQU3dOeuBamLx4IPx3kEirqrzUc8TOY8v32F6UTz7FC2vUH6WqyB1mL68e3w/faKM58frjg2eB7N/Fl1gA461F4ZJZ1+I/Eyb4TiXnBIiKZwM+BM4DhQD2wGngauMcYU9OGvvsCNwInAkOASmA58DjwiDGh1uhu1H6E034W0B8oARYBDxpjXmjtuBQlKhJT4IifdvUo2oeBk+Hy92DlKzYioz0uuJe+HvTBiWUyesP5T+/5uT/RXjwHTQk6YscqU74PXzxoI186i4QkSOiAGwGRrrOmNMe0G+0+bku4fyh8Pvvb6z0umL9myTMxZ5mMacEiIkOBucAw56MKIBmY4jwuEJGZxpjdITsI3/dk4G3AnaArAzKBqc7jLBE52RhT3Uz72cBzgHsmLHH6mgXMEpFHgctbEj2KojRh8EH20V5Em4MkFjntny3XiQX6jLPhy+5yBEr7ItL+YsXF6xvl5q9J76DvaiUx68MiIn7gVaxY2QocY4xJxwqEc4FS4ADgyVb0nQ28hhUYq4CDjDGZQDrwY6AWKzzuaqb9cOBZZyzzgTHGmGwgG7jZqfZ9IMyKc4qiKN2Q3mMii2JSYpf+E+0SCvuf39UjaUTMChbgUsBdGOUMY8x7AMaYgDHmGeCHTtnxIjIzRPtw3AD0w04BzTbGLHT6rjHG3Afc5NS7UkRCxSjejBU324ATjTGrnfZlxpibgAeder8VkZ5Rjk1RFEVRupZpN9qEjzFELAuWS5znD40xC0KUPw24eYIvDlEeDrf+08aYULmG78FOEfmBC7wFIpKO9acB+IcxpihE+784z1nAqVGOTVEURVGUJsSkYBGRNMB1lX8zVB3HN+Qt5+2sKPoeg3WwDdd3GTCvmb6nAm7cYXPtNwArox2boiiKoiihiUnBAowjOLZlYeq5Zf1EJNKc5N5kFZH03TSuy9t+eQTtm8mhrSiKoihKpMSqYPGmBtwSpp63LEQ6wXbpO0tEvBmQ3Pa7jTEVEbSPdFyKoiiKojRDrIY1e9MrhhMF3rJIUzK2tu+yJu3DtfWWNzsuEbkSuBKgb9++zJ07t4UuI6esrKxd++tKdFtiE92W2ES3JTbRbWk7sSpY9gqMMQ/iRBRNmTLFTJ8+vd36njt3Lu3ZX1ei2xKb6LbEJrotsYluS9uJ1SmhUs/rcCkqvWWlzdZq375LQ5SHax/puBRFURRFaYZYFSz5ntfhlvP0luU3W6ttfZc4UUNN2/d0oplaah/puBRFURRFaYZYFSwrgYDzOtwStG7ZNmNMYYR9eyODIul7RZj24SKA3PbhIokURVEURYmAmBQsTvTNfOdtyCVoRUSAY52370TR97fAphb6TgeObKbvT7AZcsO1H4oNzY5qbIqiKIqihCYmBYvD487zDBE5JET5WYC7wtYTUfbt1j9XRIaFKP8RkIFdGbrRWkXGmHLAXYn5amddoqb80nkuBV6KcmyKoiiKojQh1gXLUkCAF9z1gkTEJyJnAQ859d40xrzvbSgic0TEOI9hIfq+A7sOUBrwurNyMyKSJCJXA7c49R501wlqwu+BcqA/8KqIjHLap4vI74GrnHp/bM1K0oqiKIqiNCZmw5qNMXUicjLwIXbF5vdEpAIrslKcaotostZPhH0Xi8iJwNvYTLYLRaTU6dddZvQd4Lpm2q8XkbOB57BTR6tFpBhrlfE71R4Dbo92bIqiKIqi7InYJXliFxHJxK6ufDowHOuMuxp4CrjHGFMTos0cgisuD3fW9gnVd1/s9M2JwGCgCutU+zjwL2NMIFQ7T/sRTvtjsBltS4GvgQeMMS+Eaxuirx3AxmjatEAvYGc79teV6LbEJrotsYluS2yi27InQ40xvSOtHPOCRWkdIrLQGDOlq8fRHui2xCa6LbGJbktsotvSdmLZh0VRFEVRFAVQwaIoiqIoShyggqX78mBXD6Ad0W2JTXRbYhPdlthEt6WNqA+LoiiKoigxj1pYFEVRFEWJeVSwKIqiKIoS86hg6UaISKaT5XepiJSJSLGIfCkiPxeRpK4eH4CI5IrI90XkPyKyQkTKRaRaRPJE5CUROS1MW28G43CPkZ20LZdGOJ6jw/TRV0T+JiLfikiliBSKyDwRucJZL6sztiOSbXAfH4Zo36nHRUTSROR4Efl/IvKiiGz0fMecCPto034XkREi8oCIrBeRKhHZLiJvi8gZnbUtIjJQRK4RkedEZK2zHZXOmJ4Ske+10P6xCI9bRAlG27gt7fIb6urjIiLDovw/PRqij/Y+Lq0+53r6iIn/S8xmulWiQ+yCi3OxWYEBKoBkYIrzuEBEZsbAUgHbaPy7qwJqgYHO4xQReRM401kEMxS1QLjVuevaY6BREAB2hCmvDvWh2CUh3gZynY/KgExgqvM4S0RONsaEbN+OFLRQngjkOK+/DFOvs47LwcAbrW3c1v0uIrOxWa7TnI9KnL5mAbOci9DlJjIHwVZti4gMxiaa9F4sKpz3w5zHuSLyL+BKY0x9mO6qgOIw5ZE6OrbpuDi0+jcUC8cFu/5cS/+nFMBdgy7c/6m9jkubzrkx9X8xxugjzh/Y5QCWYH/A+cDRzuc+4BznB2KAN2JgrAb4HLga2Mfz+TDgYafcAP8O0XaOUza3q7fDGc+lzng2tKJtNrDVab8SmOJ8noRdfLPGKbs/Brbz557jMqarjwswHXtRew+4DTjXsy/ndOR+x2bbLnPqfAKMdj7PAP7g2U83duS2OP8X47S7GBjgfO7DLjfykmcstzTTx2NO+WMxcFza9BuKleMSYd/3OP1UAD064bi05ZwbW/+X9tgh+ujaB3C558AfFqL8PE/5zC4e64wWyv/pGevgJmVtOql1wLZcSusFyy2ek9bwEOW/dsrr3D95F27nCmcs85op79TjAvhDfLYhwgtjm/Y78G+nfGszF5sHnPJioGdHbYtzITkwTLkAbzr9lAIpIeo8RvteGNtyXNr0G4qV4xJBvylYIRRSIHTQcWnLOTem/i/qw9I9uMR5/tAYsyBE+dPAeuf1xZ0zpNAYYz5socojntfdIo11M7jH4WljzPoQ5fdg70z8tGKBz/ZCRA4HxjlvH+6qcXgx4ac3WqLV+11E0gF3zv0fxpiiEO3/4jxnAae2NJjWbosxptgY83WYcgP8y3mbQfAYdhhtPC6tJpaOSwScDvR0XnfK/6mN59yY+r+oYIlzRCQNOMJ5+2aoOs7J6y3n7azOGFcbqPK89jdbK44RkTHAEOdtc8esDJjnvO3KY3a581yCnYeOW9phv08FUltovwFrOg/VvrPp9v8lh3g6Lu7/aY0x5qMuHIeXkL+TWPy/qGCJf8YRPI7LwtRzy/qJSE6Yel3NdM/rpc3U2U9Eljne6mWO5/pDInJAJ4wvFL1F5CtnLJUiss7xyJ/eTP3xnteRHLN922OQ0SIiGcDZztv/muadoF1i7bg0pa373dt+eQTt94twXB3FdOe5BrvCfXPMFJHVTvRGidgow/8TkVEdP8Q9aM1vKC6Oi4jsA8xw3j4Srq5DZx2X6Z7X3nNuzP1fVLDEPwM8r7eEqectG9BsrS5ERHpg50TB+kt820zVXlih5kZCjQauAL4SkT929DhDkAYciL0w+LCOZhcAH4rIv0KEH0Z7zLIc8dDZnIudToDIzNexdlya0tb97rbf3YJ4c9t32f9MRIYDVzlvnzHGlISpPgjYB3vc0rAXmp8By0Tk6g4d6J605jcUL8flMqxvUR3weAT1O/y4tHDOjbn/iwqW+CfT8zrcj8JbltlsrS5CRHxYB63+2DDgn4Sotga4ERiDdSLMBdKBY4GvsCeD34rIzztl0DYi6w/AJGc8OdgTyxHY6AKA7wN3NWkXL8fsCud5sTHmqzD1Yu24NEdb93tmiPJw7bvkfyYiqQTDSHcRvCA15Wvgx9hokWTn95uF9Tv4DhsJcr+InNnRY6Ztv6GYPy4i4sc66QO8bozZFqZ6pxyXCM65Mfd/UcGixAp3Ayc6r68xxixuWsEY86Qx5nZjzGpjTK3zWY0x5h3sfKmb02COiGR39ICNMe8YY+YYY5YYJweBMabeGPMp9kT7srs9XWRebzUish9wiPM2rHUl1o7L3oxjzfsvMBmba+N8Y0zIu2NjzN+NMfcZYza6TqbGmApjzIvYY7/BqXpHJMnB2sJe8Bs6DpvzBFr+P3XWcWnxnBtrqGCJf0o9r9OardW4rLTZWl2AiNyBvaMAuM4Y869w9UNhjKkCfuO8zQBmttPwWoUxJgDc4Lz1ASd5iuPhmLnWlSrgydZ2EmPHpa37vTREebj2nXrMnLv4/2CjLeqwYuWd1vRljNkF/Ml5OxToMj+kCH5DMX1cHNz/0xaacUCNhPY6LhGec2Pu/6KCJf7J97we2GytxmX5zdbqZETkNmxiMoBfGGP+rw3deUO692lDP+2CMWYtsNN56x1PtMesxPHG7xTELuNwofP2BdP27Mixclzaut/d9j2d6LyW2nfa/8wjVs7BZlu90BjzfBu7jZXjBuHHErPHBUBE+gAnOG8fa4eQ6TYdlyjOuTH3f1HBEv+sxKaGh8Ze2U1xy7YZY8Klvu40ROR24BfO2xuNMXd05Xg6Ea/HfSTHbEUHjiUUp2CdHyFGcq+0E23d79724SIa3PbhIiPaDUesPIl1knbFyjOd8d0xQkweFw+XYJe38ObG6RKiPOfG3P9FBUuc43hfz3feHheqjjPPeazztlUm4vbGMUm6UyY3GmNub4duD/W8DpXkqFMRkREEL/wN43E88Tc5b5s7ZunAkc7bzj5mrvl6LdAeuSJi4ri0w37/BKhsof1QgknaOvy4ecSK17LydDt1HxPHzSHcWGLuuDTBzb3yoTFmXTv016rjEu05Nyb/Ly2lwtVH7D8IpuYPAIeEKD+bGEnN74znDs94fh5hG2mhPBn4zOmzjBBpoNt5G1oajwAvOuOpp8kaPARTXpcDw0K0v5EuSM2PTRRV73z3r+PluBB9av5W7XeCqcbzgewQ5fc75SVEkGq8jdviB55x6tYC57Tj7zcHG5FigM2Ar6O2pT1+Q7F0XJq0meo5153XVceFVpxznXYx9X+J+qDpI/Ye2JU43cUP83BECdaCdhZ2nQZDbCx+eKvnj3NdFO2mYUOFLwQGeT5PxDrhfeHpN6KFtNq4HcOc7/whdh5ZPPv8UGxmYXc8eywMRuNFxZYDk53Pk7CLlFU317aDt2sOwQtg/1g9Ltj05r08j03Od9zW5POM9tzvNF7M7WNglPN5OvB77E1DVNvamm3BipX/eo7VWVHuv4uwgvoMoI/n81Ss0+5qz3GLRgi1Zlva/BuKleMSoo/HnDa7sCHKnX5caOU5Nxb/L+1y8tBH1z+wF9D1nh9mOdYc577/mlbeWbTjGId4xlOPXfY83OMGT9vpnrYGG7u/g+BqoW6ff+rE/e0dT5Uznqomn/8LSGimj8lYp1y3bkmT7Xk7kpNcO26Tj+Bd5MsRtumS4+IZZ0uPx9p7vwOznf+XW78Ie4fpvn+UFu6U27ot/7+9ew2Z46rjOP79aRuMNU0v0rTWUOsFS7EKUiporInVIJYq2Hh5U41arUKxiPrCF9VUhIoGRaqgqRUVBaUtVLxRsa23VGqLRvuiRqhWMBrT2BhSyEWbvy/mLC7rPs+zyZM8O9t8PzDMzpw5Z87s7M7+9+yZs8ClQ+sPTfBeeuvIPjeOlP14e06Gj+MA3e2ux/W8HKvXUB/Oy0j+FUP1+fyE+zym54VFXHP7+H45ZhcQp+lP7Q1yA93wyo+3F9YDdD3Cl/Wgfs+Z8AIwmDYN5T2zHcdtwHa6byz/pms92kb3J1wXLeGxLKe7LfBbdN88drX67KPrCH0L8IoJylkFfJbum9N+YA/df3NczVE2wy/imNYPPfdXTJhnKueFxX+YLOp5B54HbKH7knCwXdB/DFy5FMfC/3/ILzRtHNnneXS3Cn+Prq/SnnbeHqNr0fgUY/6d9zgdyzF7DU37vIzkf+/QNpPW/5ieFxZxze3j+2XQjC1JktRb3iUkSZJ6z4BFkiT1ngGLJEnqPQMWSZLUewYskiSp9wxYJElS7xmwSJKk3jNgkSRJvWfAIknHUZJq09pp10WaZQYskpZUkk1DH+ILTtOur6R+OGnaFZB0QvvHtCsgaTYYsEiamqo6e9p1kDQb/ElIkiT1ngGLpJmR5JHWt2VjkhVJbkyyPcn+JLuT3JHkZQuU8dQk70pyd8tzMMmOJLdO0jE2yeokn06yLcnetu+Hk3w3yduTPG2evCuSfDLJH1q+fyb5/kJ1luRPQpJm0+nA/cALgUPAAeBM4I3AFUneU1VfHc2UZCVwB7C2rXoC2AecA2wANiTZXFUfGbfTJFcBW4BBUHII2A88t01vAH4PbBuT/RzgN8DzW30PA2cAlwPrk1xRVXdO+gRIJxpbWCTNoo8DZwFvAU6pqpXAhcDP6K5rX07y0jH5bqELVg4BHwBOrarTgWcBgwDnw0neN5oxyeuBr9MFK1uBVwLLq+o0YCVwKXBzK3ucL7a0VwOnAM8ALgG2Aye3OntNluaQKu8alLR0kmyiCzhg4buEvlNV1w3lfQQ4ry2+pqruGil7OfA74AXAD6vq8qG0S4D72uI1VbVlTN1uA64EdgOrq+pAW38S8EfgfOCXwGVVNVdgMlrm4CL7KPCiqto1kn4RXasMwJqq2jpJudKJxmhe0jStWmBaOUe+raPBCkBV7Qc+0xZf134CGnhbm/8V+Moc5V7f5s8EXju0fh1dsALwwUmDlRFbRoOVVucHgT+3xRcfRbnSCcGARdLUVFUWmDbOkfXueYodpD0FGP5Z6OI2v6eqDs9Rn4eAHSPbA7y8zXdW1QPz7Hs+982T9rc2P+Moy5ae9AxYJM2iHROmnTXm8Xx5oWuBGc07GC/mLwtXbU775kn7T5ufvIjypSc1AxZJs2i+zncLdcybtOPeuO3s9CdNiQGLpFn07AnTdo15vHrCsh8dWvf3Nj8fSVNhwCJpFq2bIO0w8Nuh9YO+J+vmun04yQXAuW3x/qGke9t8VZKLkbTkDFgkzaI140albaPMfqgt3llV/xpK/nabnwtcPUe5n2jz3cBPhtbfA/ypPf5ckmVHXmVJi2HAImkW7QVuT7KhjZEyaB35AXAB3Qi2HxvOUFW/Bm5vizcluTbJ01ves5PcDLy5pV8/GIOl5X0CuJauD8sa4K4kawYtNUlOTbI2yTeTXHicjlk6oTk0v6SpSbJzgs3eVFX3jqy7AbgGuBU4mOQA/xuzpYD3z3H78bvpxlh5FXATXWvJPuA0IG2bzVX1pdGMVfWjJBvphuZfA/yi7Xt/yz+weYJjknSEDFgkTdOqCbYZ9/PLHrph7T9KNzLtauAxuiHzb6yqX40rqKr2JrkMeAdwFfASuiHyd9L1U/lCVf10ropU1TeS/By4DlhPN+ruMuBh4EG6FpyHJjgmSUfIofklzYyhofnfWVVfm25tJC0l+7BIkqTeM2CRJEm9Z8AiSZJ6z4BFkiT1np1uJUlS79nCIkmSes+ARZIk9Z4BiyRJ6j0DFkmS1HsGLJIkqfcMWCRJUu/9FwQozIPp/3NJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(Net.history.history).plot(figsize=(8, 6))\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=25)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.legend(fontsize=25)\n",
    "plt.ylabel('MSE Loss', fontsize=25)\n",
    "plt.xlabel('Epoch', fontsize=25)\n",
    "plt.savefig(r'\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\EP_ML\\loss_2site.png', bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "879619eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.grid(True)\n",
    "plt.xticks(fontsize=25)\n",
    "plt.yticks(fontsize=25)\n",
    "train_loss = History['loss']\n",
    "val_loss = History['val_loss']\n",
    "\n",
    "plt.scatter(range(len(train_loss)), train_loss, label='Training set',s=20)\n",
    "plt.scatter(range(len(val_loss)), val_loss, label='Validation set',s=20)\n",
    "plt.ylabel('MSE Loss', fontsize=25)\n",
    "plt.xlabel('Epoch', fontsize=25)\n",
    "plt.legend(fontsize=25)\n",
    "#plt.show()\n",
    "plt.savefig(r'\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\EP_ML\\loss_2siteS.png', bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9946c828",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14567fd9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2006b748",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model = load_model(r\"\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\EP_ML\\Model_2site.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40d3ed1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 16)                48        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 121\n",
      "Trainable params: 121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6026df8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 16)                48        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 68        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 5         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 121\n",
      "Trainable params: 121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "####################### Totoal No of parameters to tune\n",
    "Net.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a241cd56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 580us/step\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c992ae95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,r2_score,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3830ba31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 :  0.9683683174639232\n",
      "MAE : 0.018116815179168688\n",
      "RMSE: 0.06839965065322763\n"
     ]
    }
   ],
   "source": [
    "print(\"R^2 : \", r2_score(Y_test, Y_pred))\n",
    "print(\"MAE :\", mean_absolute_error(Y_test,Y_pred))\n",
    "print(\"RMSE:\",np.sqrt(mean_squared_error(Y_test, Y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dba3031d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 663us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.01313353],\n",
       "       [-0.00928193],\n",
       "       [ 0.01688224],\n",
       "       ...,\n",
       "       [ 0.00233209],\n",
       "       [ 0.01471078],\n",
       "       [ 0.02155703]], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_predicted=Net.predict(X_test)\n",
    "Y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ca27fbb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 662us/step - loss: 0.0047\n"
     ]
    }
   ],
   "source": [
    "loss=Net.evaluate(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c4f0566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im=plt.scatter(X_test[:,0], X_test[:,1], c=Y_test, cmap='Spectral')\n",
    "#ax1.plt.colorbar()\n",
    "#ax2.set_title('Actual_rigidity on g-J plane')\n",
    "plt.xlabel(r'$\\gamma$',fontsize=25)\n",
    "plt.ylabel(r'J',fontsize=25)\n",
    "\n",
    "cbar=plt.colorbar(im)\n",
    "cbar.ax.tick_params(labelsize=25)\n",
    "\n",
    "plt.xticks(fontsize=25)\n",
    "plt.yticks(fontsize=25)\n",
    "\n",
    "plt.savefig(r'\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\EP_ML\\actual_2site.png', bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c62aaea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "im=plt.scatter(X_test[:,0], X_test[:,1], c=Y_predicted, cmap='Spectral')\n",
    "plt.xlabel(r'$\\gamma$',fontsize=25)\n",
    "plt.ylabel(r'J',fontsize=25)\n",
    "\n",
    "cb=plt.colorbar(im)\n",
    "cb.ax.tick_params(labelsize=25)\n",
    "\n",
    "plt.xticks(fontsize=25)\n",
    "plt.yticks(fontsize=25)\n",
    "plt.savefig(r'\\Users\\iamaf\\OneDrive\\Desktop\\OneDrive - Indian Institute of Science\\reja_awadhesh\\EP_ML\\predict_2site.png', bbox_inches='tight',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0266312",
   "metadata": {},
   "outputs": [],
   "source": [
    "#                             *********************** Accuracy Calculation********************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2dc6771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.175</td>\n",
       "      <td>0.925</td>\n",
       "      <td>-0.013134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.191</td>\n",
       "      <td>0.833</td>\n",
       "      <td>-0.009282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.741</td>\n",
       "      <td>0.208</td>\n",
       "      <td>0.016882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.270</td>\n",
       "      <td>0.977</td>\n",
       "      <td>-0.009915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.891</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.018237</td>\n",
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       "      <th>4999</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1         2\n",
       "0     0.175  0.925 -0.013134\n",
       "1     0.191  0.833 -0.009282\n",
       "2     0.741  0.208  0.016882\n",
       "3     0.270  0.977 -0.009915\n",
       "4     0.891  0.671  0.018237\n",
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       "4999  0.686  0.884  0.014711\n",
       "5000  0.869  0.094  0.021557\n",
       "\n",
       "[5001 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "new_data = np.column_stack((X_test, Y_pred))\n",
    "pred=pd.DataFrame(new_data)\n",
    "B_pred=pred\n",
    "B_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aef54826",
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       "5000  0.869  0.094  0.000159\n",
       "\n",
       "[5001 rows x 3 columns]"
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     "execution_count": 30,
     "metadata": {},
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   "source": [
    "new_data = np.column_stack((X_test, Y_test))\n",
    "A_test=pd.DataFrame(new_data)\n",
    "A_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63a0bebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def SPR(value):\n",
    "    if value < 0.90:\n",
    "        return 0\n",
    "    if 0.90 <= value:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b43645df",
   "metadata": {},
   "outputs": [
    {
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       "<p>5001 rows × 4 columns</p>\n",
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      "text/plain": [
       "          0      1         2  SPR\n",
       "0     0.175  0.925  0.000494    0\n",
       "1     0.191  0.833  0.000731    0\n",
       "2     0.741  0.208  0.001112    0\n",
       "3     0.270  0.977  0.001076    0\n",
       "4     0.891  0.671  0.011340    0\n",
       "...     ...    ...       ...  ...\n",
       "4996  0.294  0.400  0.010523    0\n",
       "4997  0.652  0.154  0.000777    0\n",
       "4998  0.090  0.320  0.001116    0\n",
       "4999  0.686  0.884  0.012484    0\n",
       "5000  0.869  0.094  0.000159    0\n",
       "\n",
       "[5001 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "A_test['SPR'] = A_test[2].map(SPR)\n",
    "A_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2f2cbad4",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1         2  SPR\n",
       "0     0.175  0.925 -0.013134    0\n",
       "1     0.191  0.833 -0.009282    0\n",
       "2     0.741  0.208  0.016882    0\n",
       "3     0.270  0.977 -0.009915    0\n",
       "4     0.891  0.671  0.018237    0\n",
       "...     ...    ...       ...  ...\n",
       "4996  0.294  0.400  0.010283    0\n",
       "4997  0.652  0.154  0.014553    0\n",
       "4998  0.090  0.320  0.002332    0\n",
       "4999  0.686  0.884  0.014711    0\n",
       "5000  0.869  0.094  0.021557    0\n",
       "\n",
       "[5001 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_pred['SPR'] = B_pred[2].map(SPR)\n",
    "B_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ecd7bbe7",
   "metadata": {},
   "outputs": [
    {
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       "<p>5001 rows × 3 columns</p>\n",
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      "text/plain": [
       "          0      1  SPR\n",
       "0     0.175  0.925    0\n",
       "1     0.191  0.833    0\n",
       "2     0.741  0.208    0\n",
       "3     0.270  0.977    0\n",
       "4     0.891  0.671    0\n",
       "...     ...    ...  ...\n",
       "4996  0.294  0.400    0\n",
       "4997  0.652  0.154    0\n",
       "4998  0.090  0.320    0\n",
       "4999  0.686  0.884    0\n",
       "5000  0.869  0.094    0\n",
       "\n",
       "[5001 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C3 = A_test.drop(A_test.columns[2], axis=1)\n",
    "C3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ca9322cc",
   "metadata": {},
   "outputs": [
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       "          0      1  SPR\n",
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       "\n",
       "[5001 rows x 3 columns]"
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     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C4 = B_pred.drop(B_pred.columns[2], axis=1)\n",
    "C4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9ad9c88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "C4 = C4.rename(columns=dict(zip(C4.columns, C3.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7bf5ead4",
   "metadata": {},
   "outputs": [
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       "      SPR      \n",
       "     self other\n",
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       "4237  0.0   1.0\n",
       "4661  0.0   1.0\n",
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "C5=C3.compare(C4)\n",
    "C5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d352c8a5",
   "metadata": {},
   "outputs": [
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       "<p>4932 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1  SPR\n",
       "69    0.391  0.392    1\n",
       "70    0.297  0.488    0\n",
       "71    0.532  0.532    1\n",
       "72    0.933  0.927    0\n",
       "73    0.644  0.786    0\n",
       "...     ...    ...  ...\n",
       "4996  0.294  0.400    0\n",
       "4997  0.652  0.154    0\n",
       "4998  0.090  0.320    0\n",
       "4999  0.686  0.884    0\n",
       "5000  0.869  0.094    0\n",
       "\n",
       "[4932 rows x 3 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f8b49a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
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   "id": "dbc1ded4",
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     "data": {
      "text/plain": [
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   "id": "7672268e",
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       "0.996"
      ]
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     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "(5000-20)/5000"
   ]
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   "execution_count": null,
   "id": "b510d1a1",
   "metadata": {},
   "outputs": [],
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   "execution_count": null,
   "id": "1f241a9b",
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