{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d563033c",
   "metadata": {},
   "source": [
    "# Import tools/models be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc9e6e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "import joblib                                         # to store model\n",
    "import os\n",
    "import errno\n",
    "from sklearn.model_selection import train_test_split  # this is very important"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313e157e",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bdc08ed0",
   "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>$M_{inv}$</th>\n",
       "      <th>$rapidity$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>512.889687</td>\n",
       "      <td>-1.698364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>349.604335</td>\n",
       "      <td>0.649795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.015218</td>\n",
       "      <td>0.935912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>475.693746</td>\n",
       "      <td>1.691695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>422.624040</td>\n",
       "      <td>1.233733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8604</th>\n",
       "      <td>310.392511</td>\n",
       "      <td>1.763102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8605</th>\n",
       "      <td>322.772764</td>\n",
       "      <td>1.343101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8606</th>\n",
       "      <td>373.867518</td>\n",
       "      <td>-0.255696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8607</th>\n",
       "      <td>275.877980</td>\n",
       "      <td>0.169265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8608</th>\n",
       "      <td>440.485176</td>\n",
       "      <td>0.265116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8609 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       $M_{inv}$  $rapidity$\n",
       "0     512.889687   -1.698364\n",
       "1     349.604335    0.649795\n",
       "2     102.015218    0.935912\n",
       "3     475.693746    1.691695\n",
       "4     422.624040    1.233733\n",
       "...          ...         ...\n",
       "8604  310.392511    1.763102\n",
       "8605  322.772764    1.343101\n",
       "8606  373.867518   -0.255696\n",
       "8607  275.877980    0.169265\n",
       "8608  440.485176    0.265116\n",
       "\n",
       "[8609 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "directory = '../data/'\n",
    "filein    = 'checkNLO.txt'\n",
    "file      = directory+filein\n",
    "data = np.genfromtxt(file)\n",
    "\n",
    "m        = data[:,0:1]\n",
    "rap      = data[:,1:2]\n",
    "x2       = data[:,2:3]\n",
    "\n",
    "m = np.concatenate((m),axis=0) \n",
    "rap = np.concatenate((rap),axis=0) \n",
    "x2 = np.concatenate((x2),axis=0) \n",
    "\n",
    "Data_ave = np.stack((m,rap),axis=-1)\n",
    "df = pd.DataFrame(Data_ave)\n",
    "df.columns = ['$M_{inv}$','$rapidity$']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0248b96b",
   "metadata": {},
   "source": [
    "# Split data into training and testing sub-sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "de4cf784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for reproducibility\n",
    "#X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 , random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(Data_ave, x2, test_size=0.4, train_size=0.6 )\n",
    "\n",
    "# rename data for convenience\n",
    "x2_train   = y_train\n",
    "m_train    = X_train[:,0:1]\n",
    "rap_train  = X_train[:,1:2]\n",
    "\n",
    "x2_test   = y_test\n",
    "m_test    = X_test[:,0:1]\n",
    "rap_test  = X_test[:,1:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2624e86c",
   "metadata": {},
   "source": [
    "# Now let's do some training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b8ef444a",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Training and prediction with Linear Model'''\n",
    "#-----------------------------------------------------------------------------#\n",
    "# Input:                                                                      #\n",
    "# X_train  := training data set                                               #\n",
    "# y_train  := targets for training                                            #\n",
    "# X_trest  := testing data set                                                #\n",
    "# y_test   := targets for testing                                             #\n",
    "# filename := name for training storage                                       #\n",
    "# model    := name of the mode we want to use                                 #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output:                                                                     #  \n",
    "# loaded_model := trained model                                               #\n",
    "# R2 := quality of the training on the testing set                            #\n",
    "# y_fit := predicted targets                                                  #\n",
    "# y_est := estimated targets                                                  #\n",
    "# ----------------------------------------------------------------------------#\n",
    "# Output labelling :                                                          #\n",
    "# Training(X,y,filename)[0] -> R2                                             #\n",
    "# Training(X,y,filename)[1] -> y_new                                          #\n",
    "# ----------------------------------------------------------------------------#\n",
    "\n",
    "def training (X_train, y_train, X_test, y_test,filename,model):\n",
    "\n",
    "    # train \n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # save trained model\n",
    "    joblib.dump(model, filename)\n",
    "\n",
    "    # load model \n",
    "    loaded_model = joblib.load(filename)\n",
    "\n",
    "    # R^2\n",
    "    R2   = loaded_model.score(X_test, y_test)\n",
    "    \n",
    "    # predicted targets \n",
    "    y_fit = loaded_model.predict(X_test)\n",
    "    \n",
    "    # estimated targets\n",
    "    y_est = loaded_model.predict(X_train)\n",
    "\n",
    "    \n",
    "    return R2, y_fit,y_est#, model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "06b6bed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x2_train,x2_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ca19e5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "m_train,m_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be16d388",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model for storage\n",
    "filename = 'storage/x2_NLO_KernelRidge_not_thinking.sav'\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,\n",
    "                            KernelRidge(alpha=2, kernel=RBF(), degree=3, coef0=1, kernel_params=None))\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "#coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "3b225d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est,y_train;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1cf2c418",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit,y_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49492be3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8636444315469691"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "93efe92e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "56253913",
   "metadata": {},
   "source": [
    "# Let's plot and see what we have"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c97b86ca",
   "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": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "ax1.scatter(m, x2, label='data')\n",
    "ax1.scatter(m_test, y_fit,label='prediction')\n",
    "ax1.scatter(m_train, y_est,label='estimated')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "ax2.scatter(rap, x2, label='data')\n",
    "ax2.scatter(rap_test, y_fit,label='prediction')\n",
    "ax2.scatter(rap_train, y_est,label='estimated')\n",
    "\n",
    "ax2.set_xlabel(xlabel='rapidity',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_KernelRidge_not_thinking_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a651a0f",
   "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": [
    "fig, (ax1) = plt.subplots(1, 1)\n",
    "\n",
    "plt.ylim(-1000,1000)\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_KernelRidge_not_thinking_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97025949",
   "metadata": {},
   "source": [
    "# Let's give it another go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "98a7e102",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = '../storage/x2_NLO_KernelRidge_smarter.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic = m*np.exp(-rap)\n",
    "\n",
    "kinematic_train = m_train*np.exp(-rap_train)\n",
    "kinematic_test = m_test*np.exp(-rap_test)\n",
    "\n",
    "X_train = kinematic_train\n",
    "X_test = kinematic_test\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,\n",
    "                            KernelRidge(alpha=1, kernel=RBF(), degree=3, coef0=1, kernel_params=None))\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "#coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c51df2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2399469293305252"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "6f0e9a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fit,y_test;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "2b8f9e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_est,y_train;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "88d519bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00012255])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b331f9a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "416020bc",
   "metadata": {},
   "source": [
    "# Let's plot again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9e3aa712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6+NQBdWdJ4OGXPoz18043iT780oemRiNdgN4Mr8fFTxefOmjSIXdtfDut0YB4N9kREQPpI5MnTz6iVht9wTEcDsNGpu6pRFJNwYtmljLrmPx+nT8R3XhU1dThT9MG1mwadgnBXZedyj1XzLA0arrSrp6t5RIilk1lnJwHcuK2s5IwFhMeFYWDDilxXFUOQ0JiHw09G6k/jpwvzzmGh176MGm7SxF8+bf/ZsvuZlvnycStJYGKDbvo6M48pVGVMkmssC4QNBU0THTj6ZOzjtV7xvPbbZs6IUU9CmiFkBULT44dm6pw0BFUPDpwDIfDoGM2CSZmI/WFh1760NT4RFRpy2jowWYgo1hLX9V0ja6eVC1Nq2rq+N+125OMmbGOI93EncrwJF536YITU8Y4crPdcccc8YWDDmlxXFUOg47ZE+pAZRv19TxGFxDAqkumxSRGBgO73fj0Cd9qBVQfCNqauDORE1k0s5S7Lj/VckyJ1zviCwcd0uIYDodBp7+xjIFGkCxb/toHzXSHB7Zy3Xi9xIwoPUYxubya45c/xeRorKJiw66UK58Jfq+tibsvqwKrmFHi9VIVDjr08oc//IH6+vrY669//eu88cYb/T7v3r17+fOf/5zxcddcc82Aqek6hsNh0KiqqeOkH/5tyK/r9biYe3yh5fuJz/LBUISHX/pwwIv1dNwJmVTG4DLEG7FUbjBj69l0E3emqwKr9GIBSQbBqnDQiW/Ek2g4HnjgAU466aR+n7evhmMgcQyHw6Cg1wZk0obVjHuumJHSCOgogrhJ7OFvfJKr5hyTMvPKyGCWwYYikuvXbItlP/UlDdlYx6FP3AWGzK5sd/yf8tIFJybJrHsUYbkqsFqJSMyFExfNLI1JtOiV9COR6j3VzF83n+kPTmf+uvlU7+m/XP9DDz3EGWecwYwZM/jWt75FJBLhmmuu4ZRTTmHatGncfffdrFu3jtdee40vf/nLzJgxg2AwyDnnnINejDxq1ChuvPFGTjvtND7zmc/wyiuvcM4553DcccexYcMGQDMQZ511FrNmzWLWrFm8+OKLAJSXl/PCCy8wY8YM7r77biKRCEuXLuX0009n+vTp/OY3vwE0/aprr72Wk046ibKyMvbv39/vz67jBMcdBgW7tQHpuGHNNlu1Dy4huMtQ61BVU8dzbzUhod/ZWwOF7hbL1GhYVYx3GYxyIBhKDn4n3rYUt9Eqs6rU77WdnaVjVANI1QJ3sKneU03FixV0RTR58YaOBiperACg7LiyPp3zzTffZM2aNWzZsgWPx8N3v/tdbr/9durq6mK1HYFAAL/fzy9+8Qt+8pOfMHt2cvF1R0cH55xzDj/+8Y/5whe+wIoVK3j66ad54403uPrqq1m4cCHjxo3j6aefJicnh3fffZcvfvGLvPbaa1RWVvKTn/yEv/71rwDcf//95Ofn8+qrr9Ld3c3cuXOZP38+NTU1vP322+zcuZN9+/Zx0kkn8bWvfa1PnzuRYVlxCCE+K4R4WwjxnhAiqeWVEOLLQogd0f9eFEKcavdYh5HBQGXYSKAnhTihTkiVscBvoitoMIyGLnhoR/nWSDAUQUlzSIHPY+kG0mMj16/ZljL4vfLJXUmijqGItFTXtXJ/nTu1KKOajVRuuKGu9Vi9dXXMaOh0RbpYvXV1n8/5zDPP8Prrr3P66aczY8YMnnnmGZqbm9mzZw/f+973+Pvf/05eXl7a82RlZfHZz34WgGnTpvHpT38aj8fDtGnT2Lt3LwChUIhvfOMbTJs2jcsvv9wyPrJp0yb++Mc/MmPGDM4880wOHjzIu+++y/PPP88Xv/hFXC4XEyZM4LzzBk4uZshXHEIIF/BL4AKgFnhVCLFBSmm8K+8Dn5ZStgghPgfcD5xp81iHEUC62oDBQDdWdl1BAm2cnT3hjNvURqTknitmAJml8oLWPMrjEpZqvVJi+nRuR6KlPtpL3erzWBl0Y42GcWWRac1Gqns/1LUejR3mTZKstttBSsnVV1/NqlWr4rbfcccdbNy4kV/+8pesXbuW3//+9ynP4/F4YvLqiqLEBAkVRYnJnt99992MHz+e7du3o6oqOTk5lmP6+c9/zoIFC+K2P/XUU4Mi4Q7Ds+I4A3hPSrlHStkDPApcbNxBSvmilFLvWvISMNHusQ4jg+HIsJFofT3srHZK/d6Yf/6Wi062HQsxoruG9EBxJuRmuWPHJP5t626nxKdzOwZxgt+bsmdHqpRZs7iFlfFP1cAqFUNZ61GcW5zRdjucf/75rFu3LhYvaG5u5oMPPkBVVS699FJuu+02tm7dCpBSBt0Ora2tlJSUoCgKf/rTn2J9zBPPu2DBAn71q18RCmkPC++88w4dHR2cffbZPProo0QiERoaGmLtZAeC4TAcpYCx+UJtdJsV/w3oqTmZHuswTCyaWUpu1sBKn9vhIRuFhYmZQotmlvbJnRUMRfjftdu5Yc02gIyC8Xr2lABTX5pZzUW6SVf/XKn2y9SgW30eMxddVU0dSpon3KGs9Vgyawk5rvin9BxXDktmLenzOU866SRuv/125s+fz/Tp07ngggvYu3cv55xzDjNmzOCaa66JrUauueYavv3tb8eC45ny3e9+lwcffJA5c+bwzjvvxJpETZ8+Hbfbzamnnsrdd9/N17/+dU466SRmzZrFKaecwre+9S3C4TBf+MIXOOGEE5g2bRrf+c53+PSnP93nz53IkMuqCyEuBxZIKb8eff1fwBlSyu+Z7HsucB8wT0p50O6xQohvAt8EOOaYY0774IMPBvUzOZCRxMdIYG9lb3A0lUT6YGEnYC+A9w3jnFv5bFr3397KMsv9/F4P226Zb3msmSyMmaSL8VrGY9O50QZCFj5TWfXqPdWs3rqaxo5GinOLWTJrSZ8D40cyh4Osei1gbHg7EahP3EkIMR14APiclPJgJsdKKe9Hi4swe/bskZBQc0RzuBmNRLdSKon0wcBulpdZ4V2qyVn/XGb7eT0uKhaebHktK1kYK4z30EoiJXH/oc6qAi17yjEUA89wuKpeBU4QQkwRQmQBVwIbjDsIIY4B1gP/JaV8J5NjHYaew8loQHxfDRh8v7vHJfB7PbEsKbtGqi4QjFO+1es3zKRRjAWAfSnQy1QWJlEp18poCLSVid4A6miVYj/SGPIVh5QyLIS4FtgIuIDfSyl3CSG+HX3/18DNwBjgvmhWQFhKOdvq2KH+DA6HN8+91RT3OpMMsL40iLrrsvheGnZcTjqJ4oT6f+lqK1KJKJqRifEs8HlSKuUa0VdNVTV1LF23PZZJVhcIsnTd9thYM0FKOWjZQkcjfQlXDEsBoJTyKeCphG2/Nvz8deDrdo91cMgEPWVVn3jzvZ6U6bE6uo8+nVvGiHGS1UnnckrETPk2k4I8MxLPke/12FL99Xpc3HJRr8srlcExroKs6kpWPrkro7Hn5ORw8OBBxowZ4xiPAUBKycGDBy1Tfa1wKscdjjqEIE5GPBAM4VEEBT5PynoOfQK3azQ8LhE3yeoYn9btrjz0CTpdnw47BsXsHK50VYlREl1eVqu1xFa3Vvc10/qZiRMnUltbS1NTU/qdHWyRk5PDxIkT0+9owDEcDhmjTTw7CPZTh2q4UKXWVMlISJX4stzcctHJKVcDdYGgZXDb61EozM02nbjNVglbys9jRdXOlJlLOhOi0h9WfTpWPrmLrpBqq/+GmXspYkMeptTvtbV6GojsKSs8Hg9TpkwZ8PM6ZIZjOBwyoqqmju+v2cbhaTJSUx8IsmhmKa990Jyy0ZTV9rAqk4yFHs8wGhvjpJ4YbzHDKP1htdoxe3K3qtTuS0W/lWy6VcV54jX9Fq6wweyB4jB4OIbDwTb6E+9INxp9FTXUA7m6OGKm6FpQZq1fzaTc9cnWCl0SxUr6ww5m588kwG8cg9UKwk4gvmLhyUldBj2KSJki7DBycQyHgy3SpV2OJPoyQuMTdX/SczPRyzKqxyZS6vfGUliBWHW6GV6Pi2y3YvpEb1apbfc7TBxDf7C7MnE4PHAMh4Mt+vrEeziQWJzWH4FGCRy//Cnbk7PZfroRM8ZFFAsDowehIVls0cq9VGrz83V0h6mqqRuwyT3TFGGHkYtjOBxsMZTidENJgc/DlvLzYvGITNJzrejPqqzA54llYhkNgZWBSQxC23mit5sObNrjI4GBSA12OPxwDIeDLXI8ymGbRZUKKZPTU+3UMwwWenMmqxWe7tpyCREnhGgsDkxHYjqw8ZxmGVtWUuipUoMd43Fk4xgOh7SsqNp5RBoNgNZgaES54dIFzSNS4vW4+j1ZmxmZKeXmbVWtxpJprw6HIwen57hDSqpq6mzVGYwkXELg89j71Z7g9444N5zu9jFDX2kYMZNg7wtW17TabnXfRtr9dBh4HMPhkJIf/GVn+p1GGBEpbfc7X7rgxH71iBBotQhWfdHt9EtPRI8VmLVytYqfDMRkbXZNjyLo7AmbChNmamiM6DElR/Dw8MQxHA6WVNXU0dEzMlw4mWInsK3rSC1dcGKfOgDqXQS33TKfuy47NSY1rjc5KvV7yc0y9wYLtMZPZsZBDzCbKdxadRociAZJidf0ez0gtOJCs37jVsYtXbMoY19yO33MHUYeTozDwRS9udGRilGsz061uNnxiV0Ezfz6VnEDCdy+aBqzjy1Map5018a3uWHNNsssJbtpt33B+DnmVj6blChgjGGkqs1IlW3lxEYOfxzD4ZCEXf2kocTv9ZCb7e5zfUUi2e74xfbti7RaCDvGI5OmRFY1IfrKwTgB28lSGspCOjsxjMTxrHxyF8vWbafHsOJL/BxObOTwxzEcDnGM1GC4Lk2x8sldGSuqmmFWo2BHasToSrKDlQigsRFSKpVcsyfxoSqkszJ6RrdYorGz+m6Mn8POefVzOzUiIxMnxuEQx8onR15fLF0Ib/n6nQNiNHQSs5HsPPEaj7ET4E3Vjc/o609FXSA4LEFkOzGMTFKZ9ftr57xOHGRk46w4HOIYyIl5IPB6XFx4aklGzZMywdjnwkrWw+yYTIrfrFYImUy6xsnT7BqDgR23WCbupQkG9xxAxYZdsRhKTkL6tBMHGdk4hsMhxkh7mhMCwpFIv11nqdRy9T4XmQg4TvB7B2Ri64tPf6gnz3RuMbu6XgKSAvjd4d6i0pbOEDes2cb1a7al1NLSV1+O62p4cQyHAzAyA+JSQshiLnfn1ZBdtBHhCSBDfrqbFhBum5m0ny6jYWY8BNpElMlqRp8ArTLO7BqDTFY4fb2GnTH0N4ZgV/fqy3OOSdLUSjzG2K8klbEfjtWXQzxOjMNhxAbErXDn1ZBTsh4lK4AQoGQFyClZjzuvJm4/Y8Gc2SSkb8vEaOgToN9n3oDIaruR/krUD0TNxkDFEBJjOAU+T1zVvt/r4Z4rZsSy1ozNrVIhIW1tzUBVzDtkjrPicDjsqsOzizYilPhYjFBCZBdtjK06hKDf+lN6CrDZE7nVnG/HFvRXGytRcr0vq4X+uNqs2uCmIzEulA6JlkxQHzVuZtRF403OqmNocQzHUc7hWB0uPIG02/sbR/d6XFQsPNlyQmq1UNBN3G5Mt7XTec+qxapOga83w6w/Qod9raXojyJupgbT2Egq1SrFcVkNPY6r6ijncFzqy5A/o+120aVCjJLlVq4bOzpNiem2dlxTqYyGXu2earVgl1Tj191Jk8urOX75U0w2pAL359qZxGYS03PNUngzvb7DwOEYjqOcgarEHkq6mxYg1fhYglQ9dDct6PM5vR4XXzxzUlxcJJXff6BrHKxwCZFU/zEQlddW4z93apGpsdPvhdXvi51r243N6F0NE4se9U6HZgxXrcvRiuOqOor58m//PdxD6BPhtpl0ga2sKjvoEiKZ+P3NahwSdaYGwiirUvJ+ZVncNruV12YY4xN+n4dst0JrMBSLVaQydsFQxNLdluraRnddqmwp0NR4R+W4uWHNNu7a+HZc7GbRzNKUVfZOttXQ4RiOo5Sqmjq27G4e7mH0mXDbzD4bCiMCYn70TFNs0+lMpZsk7WA2IaeTMbHCTB7E63Fx9xUzYp8jnbBlYiOpdNdOvKaeLaUHvs+dWsRzbzXFWvZ29IRjRahmRsBO+q9TKDj4OIbjKGXpY9uGewi2mHt8oW0DJ4R1UNyq7sM4MfdHQylVXUJf0Sfkqpq6uCrrAp+HS08r5a/bGywrr82ws6JKt1Iyrs7sZHRZ3Rdj4FvHSo135ZO7LEUere6xI5g4uDiG4yikqqaOw6UT7Cvvt9jaz+txkeNRTCVT9LoPPYVXROs+QgosXfDV2H52nuStsopSPQFbGbQCnwdfljvO1fXcW02xDKxgKELFhl20dYUw9qVq6Qwl1d20dCaLNiZiJzaS6oneKPA4GNlbVvu2dIbiUm7161fV1FkWbw5ErYuDNY7hOApZvn7HcA/BNlad/BYqm1nmXssEcYAGxvLj0GI2hOaZ7mtV91F0zHMsmrmC6j3VrN66msaORsZ8ooju/Qs40Hiy6dO01VN7qlTb/BwP3WE1ySDdclFyum+iYUqVZZVIOheNnRWV8YnemEJsR0rebCVmdU2JtsIwnjPVaifxc6UqotSD/HMrn3WUdQcJIQdBOG4kMXv2bPnaa68N9zBGDCNRWiRTFiqbOcf/EL8uHEWj20VxOMK3m9v5Z+AqNqjJxmPU1HKESRmyQLDqrFVUvFhBV6Qrtj3HlUPFpyrgw5dYvecvNCpQrMKS477AtX+fg8S+5Il2Hbj7ihm23Dt2qqpTISApmK5TVVPH0se2xxljjyK46/JT+1T0ZzaRJxrHS08rZc2rH6XsyOj3emKy+ddbxFgSP5fVfXIJwRfPnMTjr9cljSUxU8shNUKI16WUs83eG5Z0XCHEZ4UQbwsh3hNClJu8P1UI8W8hRLcQ4v8lvLdXCLFTCLFNCOFYhAx5+OXD22gAzPY/zqqiPBo8bqQQNHjcrCrKY7b/cdP9reo7Ij35LH/ux3FGA6Ar0sWqLSupeP8vNLiEdg2XoHzvXxg1tZxRU8vJmfBYnOSJt2Q9eWPNV3J+n8d2TKC/vvm0LppEA2qjZ64deRKrldhftzekDfYYe6P4LGI1iZ/L6j6pUvLcW039rnNxSM2Qu6qEEC7gl8AFQC3wqhBig5TyDcNuzcB1wCKL05wrpTwwqAM9QhmuBWb2+Co8BS+j59WEWs6ke9+iPp3roUIXXUr8BNOlKDxU6NJ+cxIIt0/FU/BS3KpDSm27KHjJdO5sjXSCkvBO9ATa/xOCREqIvAn/INQ6M27S8rgE7V2pM4WM9CeNN112k1k8IBSRaTOQrIxCxYZdaYPUdl1tehDcbGXicYmkz5XK7eZ0GBx8hmPFcQbwnpRyj5SyB3gUuNi4g5Ryv5TyVWBkNYc4zBmuug3NaLyEEBIhQAiJp+AlssdXpT3WbFJvdJtXEFttd+dtT3JVCaFtlxFf2jHYpTW0H/e49b3XQJuYE+M0qZ5+zQrzPIkGzIQCn8fSFZNOVDHdhGr1fiAYiq1CBoKWzpBpTCs3y530uVIVYNqp6nfoH8MRHC8FPjK8rgXOzOB4CWwSQkjgN1LK+wdycEcqK6p2DlvdhqfgZdOJ21PwcspVh8clTJ9As8I+uj3Jk1lW2NwICJf5xCdcQWTEfDLpaw2GHP1vcj/+GkIJpYx9WPWV0P81tsjNzXanfHK/x1CHYUa6CvZEmRS7Ae6hwkwXLF2Tqb7UuTjYZzgMh9njUyZ/o3OllPVCiHHA00KIt6SUz8ddQIhvAt8EOOaYY/o+0iOE4ZdNT9VZwZrcLDdCJHclDOxfyOiStYSV3uPdqiCwf2HGI7MyKhJBjqomucTSnk+AcBnTfh+jC0yNR6pK5y5DvnQgGLI0ZKV+b9qAb6oVRWL/c7NU41nH5A+J4bASeLRaKVilBdvpXOjQP4bDVVULTDK8ngjU2z1YSlkf/Xc/8Bc011fiPvdLKWdLKWcXFRX1c7iHN1U1dZaZKkOHlasltQsmEAzR3hXG44rf7/PtHfywqYWSUBghJfnhCLkyjHfCGnKPr0zqy5EKq8C5GvIzpXEW40KqFhBJcPOYbDJFKCrZ4zek3CfRdWVVNJd4t+w+RVtNvImaUFaxjJf22KulAWJ9OaxwmaW30ZtZlU7/yy6LZpaypfw83q8sY0v5eY7RGGCGw3C8CpwghJgihMgCrgRS/2VFEULkCiFG6z8D84H/DNpIjwC+P+xGA0ItZyZNslJq29Meq0rI3Uru8ZWMmlpO7vGVzPY/ziWdbWyqrWdV00G6FUGrywUpmjpZkUowcas8kUZZiESgqllIKaIGQxBqmYOwk5KE9arGiHFVYLVC0CuuE0UP03HuVPOHpy+eOclW/3C7DafSxRhK/V5+uvhUU+OgS9gbm0Jl8hkdhpYhd1VJKcNCiGuBjYAL+L2UcpcQ4tvR938thCgGXgPyAFUIcT1wEjAW+IvQnlrcwJ+llH8f6s9wuFBVU5eY+zMs6HGMvmRVufNqyC55DKFon0RkBbi7yMOYAz7KOjpZXeBPciclNnVKhS6Y6B2/CelqicUlgPhqc1cIqXroargkdt4vuF6gOs+NaZFIAuliJsbOgVYxBTOZDjs891aT6fZHXv6I2ccWpi3AsyputGp09doHzaau0XOnFqV1I2VSle4wfAxL5biU8ingqYRtvzb83IjmwkqkDTh1cEd35DCS8ta79y3qU/pt9vgNMaOhE1YUVhUWUNbRaZlJZdXsyYxw20wOJRiZ3BNWpuwy6M6rYasvajCMk6qJERFqbpzRMCselKFej2tfRQytSLWSMMZXzp1aZDrhzzmugK0ftiaNx6rRlZWhqt7REBM0nOD3xokrDiQD0UvdITVOP44jmMOx10YiVm6eVpf2q1scNs8W6k9TJ3deDYrFdRVPIKZ91eCJrjaEIEdKrmg7hCfhydwl3FSe80P8Xk/s3En90iesIZj3WOyYgXbZpEpDNcZXrCb8vQeDGY0nleaUnR7neiOpvvTXGKhe6g6pcQzHEcqKqsOrj/hCZTObs65jT/aX2Jx1HQuVzWmOENSqY/lecyvZCbn//W3qlF200TJuPy6smmpfdSkKz/t8rNjfSnbIi5Sg9vjxNH+RUOuMWLaQqW6WgKyCl6jeUw1YPzH3dUJN1T0Peif6VIVzmQSb7dZLJCYFVNXUMfPWTVy/ZlufJn69yNGpGh98HJHDI5TBTL/NRKfJDguVzVR6HsAnegCYKA5Q6XkAQvBMxIdwdyYdI9UsFkzya2OIeEEVCFenxXgyq8qwdHNJyac7O1ibZ/681eh28c/AVRxo7tXL6iReVNLy3AJWvbyKUOsM05TY1z5ojtNfyqRhkf5+OiXZ/jSIMmKnZ4aObqzMtK507PTX6G+Ro0NmOCuOI5DBXG1YuVq8k36b8bn0VcZqz30xo6HjEz0sc6+le99FSDX+aVlKBUS4dwzuIEIJ0VV/BR27y5OMmFlWl4Ywzb6ydHMJwb98udbaV6GCJJHFCX4vQUNNRioXWmtPq2VK7CMvf9SvJ+lFM0stM5r02Imddrg6qVY/RlcbpE661o1SuiJFo4Exu24mRY4O/ccxHEcgg7nasHK1uHJ325IQ0dFXGROVAwgB1bk+5k+cwPTJk5g/cQLVuT4miINa1lPDZag9fqQErxhLlvAlBcz1wLUZ4f1fQJHJv+pCSLLHP5m0Xe0ZY7lAaXILKlveS3KPeUQ2svlzcdvMJt3upgXW9R8yPi51Rv6jHP+xZeRNvZHJxy/ljPxHkw7J5Ek6XezEbmzFThxBd22V+r2Waz3j/Un3ORQhWFG10/K6doscHQYGx1V1hDHYelRWrhY7EiJGlrnX8twoN6sLxtKgZ0ZFM5IaPG4qxhZyUIagO75NbDspZNItxlYmXuA5EcHs2Ve4kt1g7tz3LR+Ti8MRLursRDkAPyosoC1aP5Kb5eXi0yex6RVvUmzi+2u3xRoxhdtmQvFfwNWTdG414os51c7If5T3i7fGUo33ewRtxVs5A3il9crYMZk+SadLd7WTDptJb/ZUE7rRKKWTNYlIycMvfZhkhPTrpkoldmpBBh5nxXEEMTR9xFM5HuzHEWpGdVIxtjAuM8lIl6Jwb6Ef0NxjxgJAK30pfXuBz4Ogt0p5mXut7U9zzxUzQFhUv0jJkpZA7GWPImInCHQH+Gv9vdy0OJgUQP7SmfGyN12NX0h2v6kuzS0XfX2waKupAvDBoq2x18bWsn3NQuoLmajPpioGNE7m6QL4YP3bVR8IWrrZfro4fa8Rh8xxDMcRxNBkjqQ3Dn6vJ+mPWJ+gS/1erppzDPcUFqbVgepyB8keX0XOhDVxMRXh6kKazO1C6cGdV4OUWtMfNeoTmiBSKfD3fp4Cn4dFM0tRhPW4yjq0FYpZ4WFXpIvVW1cnHXP7omnJV1WzYrIlathHV8NlcbGZJre5gW5yizg3EtDn9NO+GpxM1Gftxk0S3WSZINF+9y89rTR2fIHPQ7Zb4YY124bEmB5tOIbjCGIo6jZSBXd16Y4LTy1h1SXTKDBUQ+d7PVw1R3vyfvilD9nvTv+rJyPepD4aoMUmzGYXoUTILtpIIKj1qNYnsno5Nu21PC7BLRdpXegu//jlJoOReKWkOldT4LWUdu9oNN2uB4pjyQXuYGyhlRgzAigKmxvo8RHiVjSp3Eap6E+9QyZB9EziJsYUZL3uJREro1IXCPL463UsXXAid18xg66QSiAYcmo5BgknxnGEMFR/FN1NC8gpWYdQEkT4VOhuvASA9a/X8tftDXw8608c/NhWmtyCMWHJO2/Ooi7qn1dDfpSsgOV1pAQhIpZqHpbbo3GOuza+HUsLvTO8mPzIE7SaTPb5EZVDaEq8Oqdkf5XH22sJ524BZMyVFhSCirGF2nGqSsCVfL7i3GLTceljUSz6nydKpLjbJ4P/g/gPKiXujslU1dTFJt6+Ni2yMjjXr9kWu3dWLp5M1WfTxU3MVHk9LoFHEXH9OQTwqeML2XswaPqQZDSYqWIwTmV5/3EMxxGA3kt6KNC1nbLHPxkLLMuIl+59C2MTX2dI5RTfn3m3eCthQ3B3f0kN+cXbkEJqDiKJpQUQAqSSHEBOj3a+umjRGsANayL8/OAf+GFRYWw8AG5VpfxgC9fS275Ur5e4IFLAm8eFafTE/4l0KQrlRWNMr6ygsGTWEtP39LH8cHvAfNQJgf3a3DYUkyYmH3pb44LQdmovzCbKVIbFTo3IQGpKmRmxUESSm+Ui3BOJORMlsPXDVlZdMo0b1mwzdZqm+lz1gaCldDykr4dx6MVxVR0BVGzYZdo5bTipHbc1bpIGQAhUBWTMRyOi+uQDeWXtZHpgfNHMUvw+DzPafdx+oDkmxV4SCnP7gWZmtPc2fwqGIjz88ocEQxGWudeyz8IdZRbMB1CjkpLVe6qZv24+0x+czvx186neU031nmru2/1VS19LogvQMnvNE4ibHNO5jaxcUvkWriAd49P7YAffrSb7DoPRSBxXqjhLqvf66tpziCejFYcQYgrwADAFqAJuklJ2Rd97RUqZ1BvDYXCpqqmz3dc5FQuVzSxzr2WCOEC9HMud4cVJxWwQ9dFPeAxhyDwS7mBSw6JDLpshTt14ZBwStSb3+Mqo5MjnAfi4509cMyGb/W4vxeEIq5oOUtbRSafMojy8OO5YvcZigjhAXqTU1L2VispXKukKd9EV6QKgoaOB8ud/gCJAxbxALUdVKezIo/b4ymglvHU7Wxnyx02M6dxGVhOlIjQDk6pori4QZMbKTXG/X1ZP6P1x/2TaYbA+EOTuK2akFIK0eu8GizYDTmV5ZmTqqroPWA+8BHwPeEYI8Vkp5SEg9SOMw6AwEE9KC5XNnON/iK8VjqLRPYnicIRvNz8EAdigzouTGAGhBacTEIpqW8rcDCmTH+JtqJUnj0No0uvekvVU75kJH77EnuKtdCmaAdBrRNplDlsCV5oaR4CHcsfT7sp8QR7oDpgMKmIuby8lJeEIZ3d2UpX/AUq0t7iZxApoyQey+XOmGUlWk7TVhNzRE+GqOcfw3FtNsX0SpWR6mhYQMPk+E2s2+uv+WbrgRJau2x7XJtjjEuRmmbfMnWBI5U1lrMzeu2vj2wMiq3K0k6nhGC+l/GX052uEEMvRjMcFDLDDwcEeA5FJNdv/OLcV5cVcSw0eN7cV5XEDj/NUOJccQz+MVF+z0b3itwgemx9otyVSBighbtp8E6oaAZN6iPsLfew2aEolTpr3uHKImBjIwWDjqFy6ldR3QEpBV8Ml/ORzVwMwt/LZtE/3VTV1KVW6nnuriS3l51FVU8dNmx5EGWfoP5IVILtkPRLztrfGJ/RMCgKtP2Dy65MnjDatS9IbU6UymFbvDbRk/dFKpo9U2cYXUspVwFrgGWD0QA3KYWi5f4w7KR4RVhTuKvKQM2FNkryHFUY//TcOhO31Vh1EVKlaLlv2u3t1qsz0t0I2P3O/EIIGj5uArb7mkvHKp4Dkuo2bNj3IvD+fHxdTAW1CT/UN6JP/opmlFE56xjLbywzjE3pfM7t07tr4dlKMLqRKy5a1VvLvdsnx9N5vv9fjVJb3gUwNxzvR1UUMKeVPgD8Dxw/YqBxsMVBBylYrl4wQtt1Fmox4b7bRa4FLyYukmHyH2aggRKzFrJn+Vp/8ZDpqhn9Wdq4V0VYWiU/37rwalHHraA3tRyJp6Gjgh5tv4fR77ky7GjVO/m0h88nYLEif+ISeSUGgGZm2rO1rPEJ3qbV09n7XrcEQr30w2GoLRx5pf8OFELMML68Enk/cR0r5M2DSAI7LwQZGue7+kWGxhMWurtw9sdcb1HmU7j/N0kDIgXdOZYz+RJ1Jt0Ck1D5SCrsnhECGvQNqG3Oztar2xEnTzOiFZDeducnijUY8LsHSBSfGMsCkxQdKDNQX+JKf0DMpCDTDysC4LH7/+hqPMHOpSbSCVKc4MDPsPBo9J4Q4F0BK2S2l7DbbSUrp3Pkh5Mu//XecXHd/GLj5TcZpSm2VJ/bPQAzBqkSPadjaV1X5RMNMbf8UH0uKCD5P7oCaxs7IISB50kyVtpsSCdtbnqXixQoaOhpS7xhFr65PdOukqw5Pl85rZXjmHFeQNBqPIvocj7BagemSJQ72sRMc/zPwlBDiKinl48Y3hBDzgEoppXlqisOgsKJq54CJGZr1o+gPejW4iPbpsKI/nqABR+lBqpBCogqA0VJTph1VUp72lEH1AIqiaHGWBMwyyNIhwzlaoWc0uBvyvqatNiz2F9FeI1ZZbiFV8vj7v0W6u1Je19i6NxSRlgFvq2C0nYwrswypc6cWseaVj5LO159HJZcQttxfTmV5etIaDinld4QQDcCjQojvSSl/LYSYBvwIKAPeHOxBOsTzyMvJf1CpyB5fhafgZaKl2oRazqR736JoTcbaAZvEByKddiBOoEY8KIoKIn0HOiGi6a82aknaokFsadGVMH4MPhDW+2RsPESElU/uwpflJuR9jZyS9aYaV737S3JK1sfV1iSN0dWSdlWUuBrLNL5gN+Mq0fDMrXzWtKg1olobr3RYGQ3oXck5leX2sJWOK6W8VQhRB/xKCPFFYC7wEfA14I+DOD4HE1L9AejEGwvjJCXxFLyEktWEy/ehaU3GUNGXJ++051SVmGZWLL1W9SCUMEa3S9J1bQxEjU2i6e+Z4urEn+03r+tIcTnLe6L00NIZoqUzRO7xJsF8s2tEYziRtpmmI/ZFvATd1obArHe7IgRVNXV48rexeutqGjsaKc4tZsmsJZQdV5Z0jr5mXKWTDukLpRaFhgItxXdu5bOWGlh2NLyOJmwZDiFEIfBxIAKcBbwInCOlDA/i2BxMsNMWVjMayaqyOnrHvuF0F0kJkY7jUbIOongCA1M4LiWhwBmxJ2yrJ+1RU9O7mpJObZhEje4bSwS0dbfikZJQwo02u+9Sak/3dgL1mQTzFU+AL885Jq5fOWgxhPL2Q6zKU+Ll4aMPJflZ42n+6HzCbafGnS8iJTdtepCckvWEouHOho4GKl6sAEgyHn3tY56qmryvwXGzGg5dODHx/pjhrD56sZNVdQuwB/gf4Kdoq4zZwM8Gd2gOiVTV1NlqC+speHl4jEKGwWy1p4iO3eX0tMwZmEC4ELhHvWX5tt4QyjbRMQkUQoHTYobIbjBdReKWEmHzs3XsLk/bpCqT6wOUjCrh9kXTTIPXl7Q0UJGg31XZdJCde2vZ/KVn+NH8q00zm0Th32JGQ8eqF0lfMq6qauro6DZ/JtWzwfqCWRD/7itmsPdgMK3R0HF0rTTsrDh+gKZPtVJKuQ9ACPEh8BchxHjgKill/8WSHNJSsWGXzT3TT1QDbleklj8lbfqftFazvS6zgbJ0Zk/j7rwassdvQLiCGcYVtJ0lKh7/60SCxxJum0l30wK8E9bYuolBuxeMGoZw26nJq0UJSueM2EtN2j4+xuFWtRxhYyGnR2TH1HpNg9f/nEhZ60ex5lQx8idFRRlX4z2xARny0920IGY4rVY8Zr1IMpVgT4wxGCnweUyzujLB7D5Y6VdZ4eha2TMcn5BS7jZukFI+G03RfQr4O3D+YAzOIZ6BEDPsZWCFBREiKpVuf+UwGC4zdyReMk2vCrcTE0iFsWfG59s7eG6A759wBxn98VtRzeyuADXnTQQXxyRAuoCccRsRnlZE2M+hffMB4mRTXB0XmcYdQJugt3VcyjJ5Hz6hyddX5/pYXVhAg1vAC5o7T9f+MgbaZciPMOmlYtWLJBMJdrNgOmirgy3l59k6R6ZkKrLo6FrZy6rabbF9azQd11yTwGHYkFKJU68dUjK0Aul21+1Q/BO49apmlOgmYHhtWhWe6mIpBiQ8Adx5NSzrWstb4SytX3o6Mrkfrk5L37HwBGKmSgLjlU+x9LSvsmhmKVPKq2NrTGNcxyqnq/ep/gyalR6WuddSMyrIrWML6bLQzDIazvCBBeSWVsW5q3JcOZa9SDKhv/IlfcFKv+rS00pNY0OOrlU/GzlJKd8TQnxqoAbjYM0FP/un/Z2li7QZ7yOqkMIa02GmGHurIsgt2EZW9+kEOkNa4H0Ax5JTsp6apk6WtHRSMTZ93/SBQo9rSJKfvq2emMcW72L+uuTMJ+NT/QZ1Hht65pFbUImiBFKOQXdRqe2zuHjisWxp/lParCodu7URfQ2m94dU7rTZxxY6NR0m9LsDoB73cBg8qmrqeHd/h+39++uWOawRAjHucbr3C+6+6Gru212SpjI67uD0qw4lROXYMWz58CNqsrNYkzd60I2wVF2xjC53Xg2BMRuZ/uD15HmK6N6/gKbAyUkquL6C7UQK19PQkZz5VB8w+Vw2DKxuvEIRyaZXStlSvsnW+DOpjRgu9Vord9pAdjo8knA6AB4GZBq8OzzWEoOHUEKIwr9x18a3WTJrCTnCZqsYgS0j0KYI1vvyeN7n65vRyDiDTNvfqOIrkbSG9hPMfxR3Xk2c0Sj1eymc9Ixl5pPZ03u6TK3Emo5MXEeZdN0zZj6BVu2t72uUKhnsroQOqRmWnuNCiM8CqwEX8ICUsjLh/anA/wGzgB9EFXhtHXukUVVTl5GW1EJlMy9Llc50+hlHOHqL1bL2DjhwkOWFo7WWtQNycvhRYQk9aarHrY9Pv7KJ2z3aJEv7OVn6PGfC2ljgWhHaE/2o4v2mp2/saORWw1N9fJMuc/ReIMb4SSrXUfWealZvXU1DRwOKUIgUq+SOic/Mgl7jY+bGSlx5GFcpgFPdPcwMueEQQriAXwIXALXAq0KIDVLKNwy7NQPXAYv6cOwRhf0UXK3w758F/x4RyrMjgdypy7npnyF+1BaASA8/LBoTV4xnfPDP1KZ0uYOAguizRKRgXEhlv1vEXhveSt7bE7DWpUqQF0mlP1acWxybXO/418ME81NnnHlENh31i+Im/FRCg9V7qql4sSLWOleVqmlmFmjGx8qNleNRUq5S+t04yqFfDMdj6RnAe1LKPVLKHuBR4GLjDlLK/VLKV4HE3+i0xx5p2E3B1avFpRCHTeB7MNFug+TJPBe3F/op6+jktqaDccVuXfVX0P5WZUYFdfHXSNENEYFCtqVXSg352f3enQTrr0TKrKibDEs/44RRJZSMKrG+nqHpUnbRRstfAWNdR8HEf5gaDUUoCAQluSXMLLgAd9HGmOKxO6/GcozVe6q5afNNMaORaox63MLKjWXsmWGkPhDMOPPKcWsNPMNhOErRdK50aqPbBvvYww478iI6w1YtPhhI2ftffxGCx/K05pRlHZ1sqq1nx96P+P2HPbEn3+6mBUg1Pg6SagjpvEw5rhxWnbWKcMhrKS/iav08YC9dWE91XTJrCTmuHOuPGnU3pXI7GTOfzAr2tPFJVp21is5QJ68c/CuKJxDrjJhTsh6ZuzUuPlG9p5qzHj2L8hfKTdWAE8dolF3PNM12gt+bUeMofUVj7Ji4fP1OW8bDMTjWDIfhMPuTsztD2DpWCPFNIcRrQojXmpr612ZyOLEjL6LTd5fJCGUAV04qWnHb/IkTmD55EhdMnMD3vb1Z5JG2mZyW+w1KckuinQz9dNVfoRW6ZTAEKUGEC7hwwnWUtXeguM1bnwKsumQ6H5+9OiZDb3Yu7T/BxR+7mLLjyig7royLP2a9wI6l7FqsoES4IDb5Ve+pRlh8uPzsfCperKC1pzXpL05fNdQHglTV1HH6PXdy479+aCnmmDQGAW0lS7hv91ep3lNtaQT8Xo+lVEkmMiaZBOaN9MfgHA0MR3C8lvhugROB+oE8Vkp5P3A/wOzZs4+wGdUcBYl6pMQ2BnzpJCgvGhM7b6PHTWPxLjyyhnHKp6K5+WXAd+IUUq36iVgq24b8tO2+kcD7W9hQ8BCMyTPdzxPxxMUBUl9D8sQ765k5biZlx5XxfG1SA05tL0ks68lMkkSqHoL75rN8/U62tzxLVe3PLFcH6YyA8ATw+zwsX78T5ZgnUfqQ/q2nB194xnU8+lxRkvDghaeWpK2hsFNf0deCQrty8Ecrw2E4XgVOEEJMAerQ2tF+aQiOPazIxE0FMKWnh91ZWU58wwwR+18vSogpH3+eTZetiNtszOaxktaAZHeVlDCxI4/fZV1HqTjAgvwJljK4uXTRGnElv2dBlwyx+qVVlB1XZuleAuJUgbuIlx/RM5rCRFi39xfg6ruwdZbqQZXaRDqqHwWWXZEutjT/iUtP+ykPv/RhbM0sgcdfr2P2sYWWMiN26yv6WlA4HBXshxND7qqKSrFfiyZV8iawVkq5SwjxbSHEtwGEEMVCiFrg+8AKIUStECLP6tih/gxDQSZuqhOL73OMRh+wEuXT6wh6mhZYOlGl6knKyqr3f8DnjvOyYNIEGtzWhqHNZf5np/UyN79gY08AsNaDSnRPhdtm0rG7nPa3KunYXR6XFSWVPqYR68eLHk7M+pPpdY2kisfoNHY08txbTUm3eaBUaPvaDz2TOMrRyLAk+0spn5JSflxKebyU8o7otl9LKX8d/blRSjlRSpknpfRHf26zOvZoZtKkO6n3f+AYjT5gnISNgVC9Yc+7N63A3TE32XioAqTHvBmUEGk1rKy+KX/WOErC5vLeEjjr0bM4e+LZuEX8+aWqJDVc6sv17RJWBAeKtmo/t081tXX5WflUfKoCJU09UXFu8aA+3afrh25FXw3O0cKwFAA6pObMO55O2nZi8X00+j9ARbP2xYFj2d35SQK5Bx2j0Qek6oqlpqaSxPh4Wwfv+uKbMbmECq40T+1WRX5CaCpiCe/luHJYPuf71Dx9I2vc5scFugM89vZaRNJknEIihXi75/W48Lnz6Iy0pR5/Gva7Bb6C7Sj+15OGesWJV7BijuYCrNlfw5q3zWNFbuEmGA4yamo5qsGdphclKp4A89etTquDlY6+yIZkKgd/tCHkQKQ8jmBmz54tX3vtteEeRkZMLq+Oe31i8X3Jqwq9R/aRYjMGuo9suvNFfOz82ssAsYC4sYpahvz4Oi4ib9SD7Pf0fWEupNQmbpOxKFIihUKxks2SffUg1T4LJ6o9fjp2x3c21BVen3urKW7y8+Rv44dbfkhINQTPM7z9ipSMzhpPa2h/0nsluSVsumxTrCL8QPYjZBW8AgbF5vysfDrDnfFjUD2EAqeR438VVeldeeUIDxXzbuuX8XDIHCHE61LK2WbvOSuOEcaXf/vvpG2NZq6oI22VMZRGA8DVSfWeasqOK6M+ajSMmUgiK0DQ/Se6+jkumcK4q0Kwc8xnqH7zEVYX+LW4SB+vl1i7UZryCVnbtuqlnxHo2Y8M+Qm3TyWn4N+oNq+vAq0h81T3xo7GhFXcIrr3LcLrccXcRPPXzdfSfY2fQQmRVfAyakJhpTE5wGFk4BiOEcSKqp1s2d2ctH2YOmscvtic/HS12Al+L4ExyYV4mkeov4bD+gwluSVUv7VmQOTZ9SC1RxGMynFTHwjGgstmxqPsuDJ+tNZLuyGOsDL0D35a5EvuQW62WgJk2I80q1UJ+7neRJjTmM5qrVisYnbHGnoCMUPvMPwc3Up4I4yHM8ikcug/ulrs0gUn2pIVT4lJmblUPZY9xAFaD9VRPrYgI6OhSIlHjb9Ojqpy0cEc/F4PCGjpDNkqWksMPi/uaE7qQW6FCgT3zU/KnJKqh85oN0Kra1bvqbZ83xIhWLF5BWc9ehbTH5zO/HXz+3YehwHBMRwjiFTKRw6DQ2NHI4tmluLPGmf/oIQvKkdVqWw6SGXTQYpDYYhWn4cCpwHCNOtISEmnoqRfHRm0T/yRCLfvP8htB+I1tyoONFPZ9SqLXFsIReIvliqtNTG1tF6OjZNlWdISsJwgssM+ximfouJTFZTkliAQiHBBkopuImOLd3HT5pssP2oqwjJMoFuTlNcLCM2Mx0iQChkJYxhMHMMxQkj1i3Vkpy8ML/mqChV+lrfsIyeDP4d8z7i4ibuso5Oyjk6erq3nb3s66W5agMf/Ooq707wO0IY7zauMZtr+0/jbniDb36/lGwfC/LzQz/KiMQCsajrIptp6yjo6AcnXex4yPU9dIGg6eZ07tSju9Z3hxXTKLECTaKkYW2ga81BUF50HF7F0wYmUHVfGpss2sePqHRx698aURkNrLrU2pZ5VTthn+V4iXZEuVv/zRqjww92nwI61I0IqZCSMYbBxYhwjhO9bNGs6I/9R3kzpKXfoDwEB0ydPpDgcYUZnFy95c9KuAlwihzlvzeEu8QvTXSeIg5n1OrdAihAnzv4MV7xyNfvVF8kpegwUbdJt8LhZMbYQIGo4YIJy0PJcZj0rnnsrPri9QZ1HY04tB4u2anLvJh9OSkFW65dYOf/LSbETqypt0IL1YtIztIa6Td8HLU7T0rSA3JJ1cVlVqWhQer+/Jf9YyrbOawiGzojbZ6ilQo4GuRJnxTECWFG10zQAfkb+o7xZUnPkZVANEm5VxR+JIKREsZtmLgQyWrRnx2gARGQXL03+C6dOmcT8iROozu19Sq7O9fHZSSUoHmuBQ7vokhxbys9j/JS/x4yGTlhRWFVY0Lu/tzipaM1IMBThjn89zPx185n+4HS6x5Zza+EN7Mn+EpuzruOM/Ed5o3iXln5scR+EgFevX4Ynf1vsPHq8wapo7p4rZrCl/DzaLLKwIL7DYFjNzkD2tPf7qygYxXFZj5juNpRSIUeDXIljOEYAVvIibxc7RsM2En7YpvLCh/XsaIEfTf6C/ZaxOnbvtYBWl6t3whpbSHWuL+beafC4B+x7a+xopHpPdVLqqk6rS2H65EnMn1TKc2deHtd21Yg7r4bcE1YS9P+Jho4GJJJuT5CfF3n42ygfE5UDtBS9lnaVJML+WLMm/Tx6vMGTvy1llbalXEq0wyCgtcZ1dyYtsPOz8vEoqb/PLkXhoUJzwzkUUiF6XMPK5h1JciWO4RjBqEe6zRjA4lM15OenwV9CRQBu+A9lhdOo2L9/QK9hRZeiUF40hvKiMbYzpIQKbhs9R/Ky8vjhlh+mOFH0idvtovz9apb+7f8ATZZcJ9ar3B1MsmddisLqAj8A+1LoawFkq5JP7D+W1VtXJyn7dkW6WPXyKu7b/VUOlVzPCbNXc9PiYJxrZsmsJclyKRJCLWcSbptp6d4ryS1h8xc3c9vc22KBeEtNL7drWKRCjHENM440uRInxjHMHEkBs2FDSm5s2cdrbU8zpTzI2OJd+Ef/kcax/hTHMLBhozQrDOM8JyNewm2n4vG/Hj9RmsiQCCHiqqtTooTIKtpI3e6ZeFwCjyIIqTJtvEUXZCwOR8x1tqSkJBxhSUuAc7v+zpkd5iuH1p7W2MpIX4VAfPMomfA8LgR4/K8QCR5rmRKti1HqPUkAzvjdGQTdyZN0TsTHrZdMG3KpELO4hk7qYszDE8dwDDPL1m033T5p0p0EhnYomTMAMiE5qtrv4jeAn4zLYnxhFR9v30FdfgNdSpqJHMiSLkLCXhC2vxhvU47SSTjvdSKJk7kQmgwJUKzCkrMqWP7C8syuE518QxFJgc+DL8tNa5oaFf3uL2kJcMvYMXQb7l2OqsayxrQLoPVKtyHD0hXpovyFclZv1fSmVm9dTUQm32+hqGQXbbSUsTdzcTXvW5gURFdUF837FqbUptJlUAbaqFjFLwRYSsMfzjiuqmFkRdVOeiLJS+5Jk+4c+eKFA+ACyo+osYKzfp0v6q5p9Lip939gK5tJhvwcqr8MtcevWZF01x9Al1e3Ioi4eswvA+zY+xGbPqyl7Lgyy7iAFUaZ87O6nuNp8V0mhFP33tBD7mXBEDcfDCTViMSMRpTrm1tAtR8/0lcf1tXimsEza+Gb48phbuF/JdVERNpm0tGgfX9618aOhssIt820rJsYzDTZo02G3VlxDCNWQfERbzSg/+OTklaXlhXUk2Z1kBF2xiU1OfCsqKChGvIzsSOP8Ki9lmmoQ/V9FIcj3F7o57G80agPTgNAQUG1ITxjzExaqGym0vMAvmAPS1p8KWVNSsIRGilitOjBJXuNblARrCosoLxoDAqagSkJR7iqOUKw4RJ84zehulpQIgV4syMpFXe7Il0oQrGs4ZAhf1IDqgmjSphb+F/RDoHaE70+2Uu0niNmdSNmqccwuGmyxgZgOkdaXMOIYzhGGO68muEewuCR2PkIaE0TkE17rkwndAlqaHRcfEFkBahzd9DV8EUAckrWEKdc3heXXJpj/KpKlxBxk3mOqnJsqIc1eaPjjk1lNLTJWCIjblDCWrvbCWvxtfXga9ZWNfqKoXJMAYGEanWpetiz/wrmdM3k54Vf51aDgQm4er8bfQQNHjc/LcpGNEDbuzfG3g8XbCenZD0haV2noaoqUroQCTUaUgJKD+68mpgxKPV72VR+HnMrn40ZDR2rWELiPokGYbD7fsDRI8PuGI5hwqw1rDuvBl/JGltVxQOOlHggru/EQCNIEYDN+GRaPCBTAUhVKoDLRNAwRHbRRsLtUwcmaJ7iPuaoKuUHW8A3htXjS2jsaKA4IllysIXlRYWmxwoE2a7suGwmqXrIar2S6ce38ErzXw17S6rz3IzCz4rmAECssr06N5fVU06hob0hrgcGwD2F9sQWVSVCVtFGQoan/c6WU3G7BCWTn7N0SenXyx7/JCLazyTa+wrh7iSnZB1y/AaEK4jIGkf1niD1gbTDsSTRIFgVKEo0af3+TvR96ftxuOL04xgmEntuABSdsJwu9zB9HyargYEmNtEP1PnTrToSs5RUlSmNs3ijZJtVO3BAIISN7yDVtVOtNqTkirZDrDjUDRfdC9MXA1C9p5rVW1enjANUnlUZJ4WuT/qjpi43HbMiJdv3fhS/MX8S3PAfppRX40roP6J4AraNppTQ/lZl0vZ7rpiBp/XXVOz9S5wkvVQ9cTpWucdXolj0c9dxCzfhcDZS6Yh9XiDW5EkN++nev8BS5qTU740LTCc27ErEKPvu4PTjOGzocplLSg8Jg73KkdJ2r4dMmBMM8pLXax2XiE7wejppj/tf3Ey++RBDftsquQJY3HaIJ0aPSpIh90lJp9VnFYLnR42Gc+6KMxoVL1Yk1UYkYiaFHr2o6f7G1Vh1ro97CgrY5xEUr5uPf9JxhHyvxLnrpOz/b9+26vupEL+FLMHqAj+NbhfjwxEKmmbwimGCt3OfwzIMrjAiOr6cknWAREQr6BVPgJyS9XRBkvEwiy8Y3UlmK4++xDsGK0trpOMYjmHArDXsEc9gGCYh+MCTxRVth5LiAsZ9SkJhNtXWAzB/4gTT2VFK6G5aQM6EtdjRu5BC8MToUVx8qJ3nc300ut0Ue/K5tuFDdniE9XiARpcSMxqAaUFdIl6Xlp1j7o9PbBCro1CrjqVmVJCVYwtjabYNHQ0wqiHpNgzEV/T1nodACVIWIi4bq1Z9kXlcCfQ9jpcYG9G2RV2MbTNjdyFV3YTuTppSXm16x9LFO4yGwu/z0N4VJhSVubcKyh+JOOm4w8C+Q/GpmJ84bhmjp95osbdDKhrcLp73RbWiUlQTm/2cSLhtJqGWM22n3nYpCs+PGs2m01ey4+qdbPrSZrYGv8pzvlEpZ2Fjim31nuqU7inQsqpu+dQtgHl6Z6jlzKQhSwk9LWcyr+deyguOj6vNSEdJbgkgUMO+jLOQrYQWJ4je7dlFGwf0OUJfvehGY0v5eWkn7r6kzyam87Z0hmJGQyeVjP2RhGM4hplPHLeM2iw9QjjCU3ATGSHxsZg2lJUwH2h6ThMnaDLqJuj1D937FlHWFtZEEm1IgiSuHh5sP4N9bus/qxxXDktmLQF6XVSp8Gf7GZ01muUvLGf+uvnMP6MuaaXQvW8RoZY5SCmiQxZEAnPo3rcIsOcW0tH7hec13MP5u89D2FQbdOfVkHt8JTMmT0wSfgTo8hXHNLT63TQrAWPtit0MKStBRqN7K7Gnxsond9nK6DqSxAytcFxVQ8yUhKB4zGgcbowQo2GnEZIeW2nwuHGrKkIVSKV3/Hr9gzsaLK72eCgJh+kUIm26cGKB3gS/l4BFBbQiFCo+VRGTzUjlovKIbC75+CKeeO+J2D4NHQ38teteXHkXJ/n0u/ctihkKAJ9HodSfTX0giBIpMG/xmoDRqM1ue5pVngf4WZsvye2m6UvNib029muX9Ao/QtRd5fHi+9ytbJmupdda3Z++YKxdAfsFd+nSZxMD6VYaVGYYx3CkxkAcwzHEGKfbhcpmnhu2kWSAWZbQSDZ2KTLEwoqClNLwRYhopz5ikx9EVzFpjGOOK4ezJ57N/HXzaexopNiTx/96D+JpaWVlUWGce8gjsrlt3so43SZdg8ls+K6WxTxf+w9TMUHvhDWoRRvjUmmNuPNqEEUbactq5YSPFePlWHa3t8TdCinh+FEzCLJPG3tuMUtmLYmNb3nWY/joYUW0FuSxvNGoaC4KpWMe7fsuip3LTAtLF08sc4+B82+OrcrqA0Fc6oK4e903BDKUT5chq0rQ27TKaoKu3lPNqpdXxTS1/JP9/OKMcsqOi5cFSaU9lQrjqsXM+BhjIIezUXEMxxCSGBTfO/kvjPSvoC+1EsCQpPemJMU149+SePyv4M7bnjyRpUip9asqC1qbeOKtR2Nppw2hVlblKVQckKw8cJB7Cvw0ut3kZ41j+ZzvxxkNgDxPEa2h/cmnD/k50Hgy3QXmHf0QoGSZZxQZn/4hGgiXDaZ2//3WD9jx38+bXmI8B6jO9cUyo4qjWWmfaw9y88z/4fHGutiEaOV6qne74QZtotTTjUdN1epHQoHTcI96C+EJaK63TH5FIj5um7keiGZIEYxLD7AKUlfvqWbF5hVatlaUQHcgpj5s/H7sups8LkFulpvWYChp8k9VqQ6kNCojHSfGMYQYg+LXFtzC+1kD17dhsJD08Zck+rkG/RfMLCqc4T0ViopwZeaX9qqSjaN8cbUKYHjSjraRreyQ+LJcLH+hnHn/dwpn/f4kpv/hFM7506doaTo+SZtJd70oQpDniW/tmjxuLaPIiKkSrsXtUF3W7quniibGeosY+448lDuOR17+iGAogiv62WXYb3oOJaI1mTL279CNnsf/Ot1NC2h/q9I6jmKx4lMlsUl2S/l5lPq9SWcwC1Kv3ro6zmjohNQQq7eujttm5fLyez1x/UbuuuxUtt0yn/cry5KC8qkq1dMZlZHOyH7cPYIwCqldW3ALD47vGvFGA6L6f309WAhttTIAKropLhJbFZWEIzGJ8MzPksnOIqUrS8/cqs71UeGTdEWzploVAWjvHVQP4c57ha7A6bEnbxlXyS1p/uj8tDIerqxW/F4PgWC0HiODwLM+sRvR3SfdY310K/ETX5eicG+hn0iz9rkjUuL1uJid9yVe7/htnMGSqofLpnwDMI/l6EZvvPIpcjNUExCuzriaC7tSIlauQdBWZjGXY24x88/QNbLitacqFp5se0VgVak+we897LsEOiuOIeJ6Q0/xteOCw2M0+hLQHohsr8H8rAJUIXBLSbAfYokeoSQ1GQJSW02Lz1Uc1iab1QX+lBIeqhLBPeotOnaX0/5WJR27y+PcTp0tp+JqXhxNjzWnOLeYioUnxzKEjBlGcR8jcWGmepg5+otxWUMrqnbG0k27THpdAEnbg6EI7+05kcuPvQERLtCeEcIFXH7sDdxy3n8B1hO2K6uVLeXncXWLSk5CtluOqjLaRDna+Bn1SdZuam06pWFjR8O/1t/Llec2WXYztEOqzK3DXU3XMRzDQPAwWGkcboQVRRPlS3FvPcJlaTxDMkLErGGSIHVabsL2HFVlSUsASF0zEjt9mhXCgcaT2XTZJirPqiTHlRN/rWgW1KKZpbGWrT1NC5Ikz3NcOZw55sK4iX1y9qd5ve3PtBYvwXd8JfvUF3n4pQ9jT9iWBshke30gyC3n/Re3znqE/MbVHHr3Rja9UhpbZVtN2Pr2j5/4fW5qaouTc1/e1MbE/bOSPosxi0qfZO2k1oJ5B0IrjP3ezdxQdjB+L4nGx+6YRyqOq2oIMBM0HC5cUhIZTMM1qG6pvl9DkZLbzrqD8uetCy0txSVTXCs/oiIEmuosAo8KqhTUqmNRIllE3Kkzh6wmaB19cjSm8JplQfUK7J1H9Z6ZlvsBrHz2Tzz2wd0IT6/cSGKgvbspOfMpMfXVOMZUGURLZi1JklQxpv6evvBbAPxx612Mkw3UyzHcGb6KV9R5uGUN2eM2ItzxrjzjJGtXmVa/B3FZVdl+At0B03ufyrVlFyvhw8NdTXdYDIcQ4rPAajSH7wNSysqE90X0/c8DncA1Usqt0ff2AoeACBC2EuEaSRj7buSduAw5jHpUo8IRWl3KwEzumabpJu7fV2n0DMlRVb7XFOJHa70oxZhnifVhDB4p+WxHB0+M7q0UP+RWWF5UTFf4ErJ5EgVrw5GFG6XjIjohSb4Ckp9Aja1TE6mqqeOOfz1MZ+6TKJ4AQihJbVr1/dbuuR/FY6EOHDUcxt4YiidAid4bY08RYZJ7TqQK9m4pT230qvdUs7rncRqn+CA8nc598+PGEW6bid/rITfbTX1bkFK/l/ln1HHf7q9y847e8+nXSYXZPZy/br5p9X6mTbQy5XBW0x1ywyGEcAG/BC4AaoFXhRAbpJRvGHb7HHBC9L8zgV9F/9U5V0p5YIiGPKDIYa4Qb3MpqbWdMkAAIhPxwgGqBcmPqHQrwl7LWSm58FAnrwWupE4N8gnfZGrz9iYbsD6MxRdRed7nSxqHPgnr0uFmYyrJ8rNkzvK4ScxuXn/ifudOLWL920+ijFuHEl0h6EnUib2/KzbsQhwbMB1WottMn7QLfB423TwfgFMLzMd4gyGGB5A9vgpPwcu0Ijn1jwqXf/xyNl22KemaSQKP7hbTNOPWYIiKhSdz18a32a++yLoP1oMh5disv7ld0q2IHJIZcll1IcQngQop5YLo6+UAUspVhn1+A/xTSvlI9PXbwDlSyoboimO2XcMx3LLqRvn08Scso9M1vIbDH4nQI4Sm3mqj6hqwlA5PqUzbV9JM4tmq5P81dTBadMVqDHKk1OJGFsflhSOctvtiNqjzAPjvCd9lnaGgLUdKOs2MUJqxiOj9MXNxSQlEvAiTILMu69EXzKTBBeCzIVNeklvCnnfO1lYRJvuqPX46dpcnbRfA+5XJE7Jem9HY0Qhhf2yloBmNl5Ju3RUnXsGKOSvitlk97SeOxe/10B1WCYYilpLs/bmvxs9i5t47GhlpsuqlgLFJQC3xqwmrfUqBBrQ8l01Ca0DwGynl/YM41gFluI2GS0raFCUjeXO/qsZ1gtNxA68OtNFIhZSooQICTQt4sb2DSs8DlHXUaymvY82bH+m0uRSWudeyoWceC5XNrDgY4IfRBkdA7ByJ3fg+f6iLx0fnW1Y469lTZqmkQvXh8vSQmBjkFu5+PcmauYQk9tJwGzoatPTewGlxHRBBi1/0mMQvwDzTJ9VKwVPwsunX8dg7jyUZDqs4gvHzeD0uhCBtwWE6schUpHIDDhSHc6V4IsNhOMz+whOXPan2mSulrBdCjAOeFkK8JaWMK38VQnwT+CbAMccc09/xDgjD3hJWSs0zneFEb7UgDffhXLbQe2gYzu2Rkuv393Bzczlej4unXRJCUOH5I6sL8my5rGpGBcktqOSfnhYWhCewpCUQk/3W/02uku7k4favkq13pTN8XEV1cVVzNwW0J8mLoHpQJWBSbDYqa5TpBKVPKnWBIC4hiEhpKg+emOev62vZRSgh3KPeoqvhkrgmTlbyJV6Pi3OnFjG38tm4Ce++3ea1Gb7xm1Ct+oNIlekPTo97oi/OLTad8JVIAQJi1zO6wmQKravqPdUjcqWQTn7kcOOwc1UlnKsCaJdS/sTqesPpqtLdVO68GrwTHh2+1UZfM5362uVuIIheWwEuaevgYOOVvJ53QSxYfNOmBykZ8zD73elXcV5VRUXETe45qkrFgea4nhFmQ1gS+i4b1HmxCdpsojW+R9hP1/4F5ExYY94eBMGOq3fEbUvVmc5XsJ3CSc/QFmqiOLeYltrP0NR4cuy6fdF8Mnbv08eueAKMC6tc39zMrA4fd4YW81reBZw7tYjHX69LKoRzf2yp6bkFAiEEqkwjVKN6uOzYG5g9udA0vmAUgwSttateTOfOq7G8v/1xV6WjPysG4/iNJHYpHEmkclUNRx3Hq8AJQogpQogs4EpgQ8I+G4CvCI05QGs0vpErhBgNIITIBeYD/xnKwfcFb8kwGg3o17Utj0yXPZXJdqvzC4EqBE+MzmXf6NpYLr2n9dfkjvsz+z3ps8NcUtIlRFI/Cl0aJN0QlrnXAlqg1qpQz/he+3vae1ZptsWhENx9CuxYG9tmJajnzqtBGbeO1tD+WGFapHBtbPVqKi8CaUv99bHphkfJCoCA/R6FW4sK2Taqk9W5/8eWzx/gubeaksZ2QeRfFFsIABbnFnP5xy9PPQAAJcRje+4n1DqDik9VUJJbgkBQkluSZDQgvlbDqlUsQH17A3Mrn41TahgIEntx6CsGq+skSrJbqeseLpXiiQy5q0pKGRZCXAtsREvH/b2UcpcQ4tvR938NPIWWivseWjruV6OHjwf+omXr4gb+LKX8+xB/BFvo8uneSb/NUM9iBCEEGa9IU+3fRwMWUgS146Ouvh1rWb37cbpSFddFx+BXVdoVxbI+w1igZyboV9bRGdeASEd3JaXCrA4iVhzY0QlPXqdtnL7YcvIwMwwh2Y13/CYOtc20jmsYP66Mf63XYpT6vRwq2oS0UrWtrYdnbqU+8OOk0y9zr2V7S2dyXEjKuKDyY+88lnrl4Q7E0nXTuZcS6x6spOJlyD8obqBU6caJ16iqqeOmTQ8ixvyN3OIAgZAfj7qAkInB8/s8SdsOB4aljkNK+RSacTBu+7XhZwn8j8lxe4BTB32AA4BEMxru3N1Du9pInNCGsthPSha3HeJfvlz2efqmGWVFmz4/PXMrjQUWC2UpUYDL2w7xg4MBzpryCcKiw/KcAq3BU15EpdOlEDL07dD7Scxo721IdEb+oxws2kqTW1AUloxpmsUrrVfG3k90Z+kKsC5PS5wxAiAUhGdupSoyF8XCEFkaBncAr8eV0tdv/JBSatqxuovNE5zN0ktO5Ic7zEUOYwa1tdZUb2mCOMDE6G2NN7atMQOwYs6KWCB83p/Pt1QBzuSJ21j3UL2HJKVbqSqxAsW+9A9PRSbaUnf862GUcevi+rlnl6xHkrxaau8KU1VTd9jFORzJkUHEnfvewK820q0A9LTUAaoX8VrIbQhVTfrlESg82P4Nrm4OJ2kPDRittbFspuRBaa6t9XmjeWqUjzbarc8TrT+R0WZNIZHsyiovGsPFx+Uz+uO3MnpqOW+W1LDfo61g9nsU3i/eyhn5jwLxbh9hUICVzZ9j+95aNtXWJ8VTZOtHnF11JmXihbjtC5XNbM66jgnh5OA6QEluMasumYav4yJb3j8hIL9xNZ27yxmvfIpVl0zDk7/Ncv/Y/c2fyLlTkxV66+VYQEsq2FRbz469H2mfz11oer7u/QssVYD7o82UVOCY8OueqRso0b1kdEPZ0Zaq3lPN/HXzCfr/lLRSNFMyBgipMkkRN9U4RgqO4RgEJpdXM/6EZYNzcj3raKAxMw5SanXPicVyUiKjk3TcKYQku2gjX+7YR8WBZkpC4QEba35EZUp5NY2MZUlLIKVhCglB5ZgCawNjN7AvBN2EwNUZrXZMNi4Hi7YC5m4loYQonPQMIn+i+emBQnGIn3juZ6GyGdCMRqXnASYqB0w/p1Gf6tXrl1GQ40/7MUpyi5M0lxJlxGNIGdPaoqcD93/WJe1yZ3gxQbLjN3q8cP7NppPegcaT6Wq4BLXHj5Sghr1I1UPOhDWEJ/6QeY/MY/qD05m/bj7Ve6qTrmdG5SuVRGT89yuESvb4J2OvrdKI56+bn3S9dDGMdNpSRul4S+Uaq74lBgOXaSxluHAMxyAxqDUbg5UGm4BEEw9M2i/FakZ4AuwXRbGn0cqmg5mvPhKMjVtVufFgCxcpm/lRz+Vc0N6T1jAFFMV84h3glVCTW2h1BhaTQluoSeuA5zE8meb6mD9xQqwP+tOjsmJB+GXutfiE1relrKMz9jmFlKaB4/Izkgv2jFjVjaTSYYqtjILN3By6hz3ZX2Jz1nUx47ZBnUd5z39D/iRAaP9edC9Vkbmmk16+1xNLIOiqvwKhhFHcnQgBnZE2WntaY8H/ihcr4oxHVU0dp99zJyf/9iym/WEa8/58PtV7qi31pfRqfTPBQOPknni9dP0xUgkWQuo2wLGxmcjYQ7yBO1z6dDgihwPM5PLqgWsJOxSCganoy7XDfj6adS3/fvsOflWQS6PbRb6qkhWO0GZHI0tKPFISpreJ1KWH2rmos5OZ7rXM67mXW+QfKetop6yjk2mTJ1meSp8A9U58JeEwS1oCrC7wZ9T/IRXF4QhVo67nnEgeQZMq8eLc4ljbVNZ/I6nYUI+n3CKbWRjYzAQRL4hQ1tEZ/RwCKnoTCI2Vzl6Xl2DE3C0jrKTfLeonShJWaXoy2kRxgErPAxDSDMdreRfADavi9r2r8lnTSS/Ho+D1uAiGItaZYFG6Il2s3rqasuPKYkFmo5RKa2g/P9x8i+XxoK3kLj0tWQfKbHLXr1cfMC/KNK4GzLSl9BTd1mLrlQZorYMvnvINHt3rSkptNhq4w6VPh7PiGGAWKpt57oTE7OI+ksnELSX+SGRw3FgZjGG2azz7T5nIrUW93eMCLhdtLhcpAz6GmpGQngkVS8cdRXWuL5bhVCB6Yxf5EfMVhL5d78RX835vnCGdq8suepaUL9jALc21yedUPcwt1HpSMH0x5E8y7dGhN0ha7bnPOpPW4O5KfHK2Mhqgdbe7afNNSS6gJbOWJMu0GyThzfCJHpa518Ymu0S3z371RdPjAp2h3qd1OxXu7fWwYy13bXwbUfg308wyK2RE6wb43FtNSe9ZrbIaOhoYfcKPTYt0U8Vg4txKKVSOS3JLuG3eSm45779SrlpSXW+k9elwVhwDyOTyakafuGHAAtOZ8sKHdZx1TKmpREhflWgzWvQIwS75Jm9vriAsEuILekF4imOt0FNETz2kZTjVy7FMjD6ZL29uYcXYwniXmpS0uhTmT+ytEHcZNHGNleKxjoF2P2T0PpYkZEmVdXQA0nBOBSm0WgVAa2p0/s00br3V9LSNbk1Ww4XJPY/GD3QqX6lM6xYxoko1SQTQVKZ9X3IAP5EJ4iDBngg/3LwSRv879oXqciZmmUMT/F4WubawKPtW5tvs9nf7v25kdtuVPFMcsNzHLdzJWVX7FgLmT+hWqywAaSKuaOXuMupzhbzzITTTPP3apJAx1aqlPhAk3+vB4xKEDFo1I7FPh2M4BpqBMhoZuqlKwhGkhPKDLUn59VJ1oRBBZrq+lDLe1RFNd02lddWlKKCadzgUgj673xrdLu4May6fO8OLqfQ8gE/0pDQCDR435UVjKC8aYzLRa//+cOwYQladA6XEJyUeVdLmUigOR/hOczuLOttMDaB+Tu3+R8XzPQHWfXA3s/cUUjZ9McX/uZeGUGvSscZAvhAQRuHvuV7uHVNIo0tQ/M4DLBmVC2Dp309FzAXU3gHP3AqttZTlT6Ts/Jt7XWk71mr1JSHrFUy9HIM7rwY5+t/JX6MSImfcRtoNhsPrcXHPSe/Ck7dAKMiSsI/yojGpfweE4LHRuWw8+Bj/CJWYphyX5JawZNYSlj/3Y1RXS1I1vyIEU8qr4yq8zVRw4y4blUw51DaTCSbS7WdPPJsn3nvCUslXl6EXngATRpXYEkpMVA0IBEN4FEGBz0OgMzRiNa2GXHJkqBkqyZHpt/ydkoKfU+//oP+GI8PVQaJ8hrGYrSgsOSiyUzYUElLikjLpqd3s+vnhCD4paXS7NLdKpnIkZp/NhjEZH4rw3nt3xV4vVDaz2nNf3GHzJ05I+TSbeJ8umDiBRqv9paSy6WBsXylhnyjio1lLOfnNu/EFG0yLBq3iJ7oURpI4oGFc0FsXkRdR6czKIaTGP8Fmu7JjTYgyRQA7apviDYPHCxfdG288nrkV2foRUsLfRvV+xvHhCAVNs9k1dm8KJV5BXsM98bIc/1wArb2apdMmT7IV69qxt5aTvcviaiJAixfcNm9lLA6yfP1OQt7XLCVhvB5XzCWkrxisVh66JIzZ92RFopKvUUYklepu9Z5qS8NnPMdwiSOONHXcI5K27ghyIIwGmn/eJ6WtJb0iZZLmUm9AVZvwTp0SzX4xwWzSKg5Hep/eE2h1KWzeq00CVhN1fkS1bBaVHfZxcee+OFnzM4JBtuXkWIsVSklh02lxmzao81gm18ZcVpC+Vavu8vp8eyfNchSN7tT313hPhRAUV7xHMVCxo57pvt+zamxeUpC7y6pKPepfj7mIXlpFY08gZnCAuJViq9sFCa1suyJdGbmoEikKqcmriWghYsxwTF8M0xdzXHk1p+c/yp6xNTG5lkaPm4biXQhh/RBSklvMpkTtpSdq4/ex6a56qmgiP/rU1dzxL3esQVV+1jiWz/l+XOfD7S3PxvXnSOxoaCwG1FVw0zVvspMlpZOo5JuYoqufx9g3BKDixQqku0vL9E4Ys+5qG6niiE5wfAD48m//PaDna3OZp5ImLg5zVJXL2w6xusAfS+2szvXFHwOW9QxmRkfHm2Ilql/jOot01+XNLVzRdihpwIrqIrB/YdL5jg2FNeNleU0RV6Gtc2d4Md2y11hY1m0Y0GMJQoCapm1r3HGMjdUm/KH9DG4dU2Qa5LbC2E2u7LgyNn1pMztm3cymg12UdXRSOabAXmMqC7wub6ynuBr2IdX4c0nVw/XN2gNCYjpwdbg56XwT/F52jd2bpPGlPflbrSYxl4xPqGOxlZwgBHdnqzz72C8Itc7gjtmPsvOanWz+0jNJ7p8tzX+KGQ3jOI0Fd4kxD9PkAEPzpkzaxupKvnZSdHWXodl7xjHrwfCRmp7rGI4BYMvuZkafmEHBn16NbTFRFocjSTn840IqkY7jkVJEDxVM7VJ5fPSoWPZSg8fNirGFccZDEXBds/kE/6OoK0ZPETWeJ2hZxSRi4oCf6+hiSuMsxoVUrc4gagDKOjpZ0Rygsulgbw1CKMz/NnVyvO/frM0brcVJollTa/JGU5OdxbiwhRy3xQS/QZ1HhfifWCNTOxOSblwKaKey5T3L78CYrRWUWfyo5/JYbYInr4awy+Kp2+S+mXWTq6qpY+5TY5nS8gu+lndJtGd5evzZftMJ75ZP3cKtsx4hsvsuOt69ma6Gy3sL7nr8dDVcwsx2n+l3XVFUmJR1tXTBiSkyoGRyJbiEM8ZcaO7TT6hjKevopKKlnRJPPlrWhPlV9rsVzvE/hLt0BSu2fzZWw5GInZ4eiVlJZceVpRRXtNs2NseVw6pzb4wrrkw3rsaOxpRjNq5aRmp6ruOq6icX/Oyf2g92g+JScnlbOz882EJ1ro+VRckNhHTXhdHl9EffeO4s+hCtfxWAZJvXnfTwF1YUVhUWJLmuhEjuN2EMLCc97ab4LI1uF1LCo/J8bSUQXQ08lXUdExVzl1n0o/PTcVnJ5xaCx/JGc2LDTPYV70pqMNRt0WAI4JGuOXQo4Whjp9TZUjmqytmdncyfOCF2H8y6GOoFh6qE/aKIH4Uuj3UPBMgq2mjbI6lImZRZs6JqJw+/9GFsvnx59HsoNk6Y48qJFfyl6lan+cNn4lc+FaczdafSwZsFVckrJSEof6Gc1VtXx861aGYpP3lznKXGlKv188iCvyFdLSiRAi6b8g0tc8wM3Q0WDcoTDcqXTV/M3MpnCYy5xTRmkhdRWVWUR7cSRKDVcJi1iLXKltJTZK2yklI1b7JqJ3vxxy7m+drnbXUKtBqXbpSs+pDcbli1mGmF6duHEyc43k+0FNxlGRmOQ29piqMLlc3M9T/K7wpzaHS7ov0QWriwM9l1lC7wm3iNnXt7g5ERKXAJ6+/ZVrDSQEkozMaP6pnS/efYtoXKZm5x/5FC0W55qrBUmDml1DKgfu47F/HUqNzeIGfECwiEqzNlsyH9+svca5kgDrJfjKV49kKeeGMtvywcHTMSZ3d28sToUUmG+uJD7Tzv89HgdmlZY2h++KtbVFYc/FnSA/GoqeW2b5eIft96UBPghjXbYudcqGzmnx9/0rL9rIz4EK5OlEgBq869MTZJ2Q2YJkp6j55anrKcxphCahrIN0kx7Q9TyqtxmfQVyVFVcqQ0TS1P7LlhGshWPXQ1XMI45VN9Dib3t51sqvsH2Lq3Zr1ajMH+wcQJjg8SGa82EtigzmND8zyIupjbgBlZ14GSbDjSBX5TESQLRcqYlIURKYlNlnbQV0R1UaE76NVXMp4/MVGqU2aRQ4/ltRQ0uY0NbfcSbpuZ1KQoMXiYyAZ1Hht6tFWBAN6/sIyXXstlfcdvyKUbITTjaxaXeCxvNJe3HYozKg0eN/eMVRnr2RVrnBT7bHZUaaNkhX1x8hvZbiXOaFR6HmBReKzFQ4Gge99FeIKzuf2SaZQdp00UmQRMly44MW5fNeRP2ZvcWLkNkO3K7p3cIrkE6i7kR7VeQgvMFV0zzQDSnqi1VNZjxz1Ck1vEVsTLi8aYHlPf3sDMWzdxy0UnxwLekHoVZofksc9IMlDz1823fQ0740o35kQ5+ZGSnusYjn7w7v6OaGxj4Ir9jDUKOp0yi6ywj26PPb+mHtg2potmhX3c3FzLws6OpPdsrTmlxK+qlB9s4dz2MOXRmgqI11fSScy2fSxyNucr27i87RBr8kYn7XB52yEmiN40UyvRwOyijSkb+UD8Ml5Bxi5lZXz1OIuZiGH+uI14D06Pe+KTzZ/DU7I+roLZo3hQ1TARw91MTAYIhiJx55ntf5xFhWM1t5pJSrIQkoKSR1je9jCLXCsB7Z5n0hti0cxSXvugmfZXH+H/udZQ09LJyqIxSUFvI40djaZPy1L0xBlB/fw6VTV1LF23PVa8VhcIsnTd9qT9jCxdcCLXr9lGuG0mJ0d7yeu/S1apzTLkp6UzlPbcmZDOGKfKkDIzHomrlVVnrYqt4jIxPvr1h9tQJOIYjj4Sy6Qa4CrxDeo8CBFzu9TLMdwZXkxgfy4FJY/EPzFb1D9kS5mkidTtCfKDcYX8gEL8qkqrEMg0AVlFasLV48IqX2kOc1XHfurlGMrDi2M+f0WQpK+UiBBwhetfeAjxg2hfpMcM6biXtx1iRXOAWsMqxiowm06ywujP/h/1z/iUXoNWnCoN1OI7bAs1seqSaWyrvp+v9zzEBOUgXRTz3DGXs/rAy3ETQMWGXYRyn4zVEgSjsZnc4yuT6gvceTX8vMiT9vvsUhR+natyiY3GT7Pbnoa7r4vFETj/Zqoic+na+ig/cv8Wn+hhYicoB2B1YYFlynVxbnHKrJ/EFFedlU/uihmNXtfhARqfGAuuVb2xDgOLZpay8sldHHK/wjNFmznTUxxz2V7VHOGnRdmoisFoG2JeoYgmSe7J35bRpG7Gtur7WT76ER4qdMUetI5rOpE5T/wbnjjA6mMm0uVK+G4SVmc6VkamZn9NXAFhX8Y5UnBiHH0k49hGFF8Y9r1b2adrVhZeyy/GeLUaCbBWqJUy9SRpA2OxnJTExTMS2Zx1HROV1MbDpOdTkiurPPT1mEEa+7GVpiusklCY33/Yw50G46Xj93qoWHhybDJTK/wohhVAojG1Q0luCZs+/vXkiurEwjl6/fXGGI1QehCGiQ/VQ2T/ZYjCv6V0GRkRUrJj70eaCu0N/zFtRbpQ2cyPs36HF4OOk8dLhfwWX+95yPT7qS6aREW+19TPvvyF5cn9LqJjeWpPkDvDi3lSncf7lb0T3uTyatx5NfjHbaDH3RmfhGFyv3RWPvsnHvvg7qSkiMujPcmXPZNseGPjAU6YbV7QZ7v/+I61rP/7/7KqKC8p/qX/DUyfPMk0DmXWQ96qRkQRimlHRLNxDlfRn5GR1nP8sGdyeTVjPp650VBUOLjvij5fV4DmYkhz3eJwpG8xESmT0mqt0JsN7cn+Ej7RFVdPYTr25EQqLVVUCmrVsXFGA6xTiJe0BJioaEqtusw3aEZj2y3z4/64Qp78uJqF1QV+Lj7UjmLZEz3+pZ5GW/3CrcwfXxBfK6MXzhkYW7yLnJLHeps5uYPxRgMg2qNDsSH2pxOrT2nVCun03hDG7+Bnnl/HGw2AUFBbJVmsCMuaai1TUq3SUYvDESYqBzjH/xBFJ6yM62uhx6W6PcHeVF89PdzkfulsaTZvfLSl+U+UHVeG/+BK017voLklU6W92uKZW/l14SjT+Jeeem5VI2R2n6yua9VGN3H/w6Enh+Oq6iM9ik2jISUSEXNdpPPPp+J3hSmqqw3X6490+CqDzIZOC6PiXicGwwtpp0e6OaiOSplVZcZx3Q+bbr+qYx9jhHkfcOhVatUD4q3B5LqKf4zKoiIvPuD9xOhRSYHwGAnjvvhjFwNQ4ZN0Ke7YOfS2smWt8RXRYkwVwobqbluoiZJRJeayFwlLMWN6dqe3mAsqn6U+EOTKnJe42fUAXrTvQImmHCTKoHyvpRUOjY2rsI+RP9EyJXXJrCXc9MIPUOmdMN3RsVTn+ri9KJ+Q0FY9DR0N/HDLD/EWe5IK8WI9zDs6Y4aPHWupfuFWVmdrDziWPeGjE+rSBSfy/TXbTJMqzp1axCthi3RcJPPXzU8fR2itpbHAvNmW/gC2pCWQ3GPdpD4HrNNwrVYceVl5cXGPltrPEAzFJ2QEQxEqNuwaMbEOZ8WRIZPL7XUoA0BKzggGLZ+WMiXtKkJKju/p4aaiMb0B10wQovcJMUqPdFMR+krcbmbB8CwRJkgOS0LfTbv6MLI56zpWun8fe3LWGwY1MDa5NWmCQdNl1sE8r311rtv0KfJ5ny+uuNJK/fH5PX/XfP0WT6J1cgyTDZ3uguohW585z1PEkllLyFbjv58cVeWKtkNxRZP6yi/syuHmjktjT6Gf8K3h4olj41ZBZsV9t44tZNmoeXTKrLhrhV05cYq7iby2t5lExXpd8LJyTEFSq92QGu2UaELs9zZ/omY0/rGUCp+MjdMK/Wl+0cxS8n0e032ee6tJk65Xzd83aw6VRP5E6xVFdHusIDciTQsGjcwt/C/TVrmzCz6fVLzpFm46w51xzaWC+Y/GSby782rIPb6S8LH/a1kEOdQ4MY4MsR3biE7iX/hwGreEvzYg1/74x/6f+SpCSkrCEY4J9fByQjFbnPGwuRRQpORHTQdZ0N7F90PfTool7Mn+EmZJOVKCRNAicxFCq862uSiL20+NvhYILMuKgVp1LPN67rXMa5/+h1PM/dJ6zADNVTZ9yiTTcYrowMxGIKTkn++34KedejmWe7iSjcc9kfYeS9WDt/VKXr1+GU/eNYGfF+SbrqiMqELhodD53Bz+KqBNJImJEjmqSrYqNY2rBMYooznngwt6g/veYnyfu9U03qAz/XdnI90tSdtLQmHtoSQDccuSUJhN+1q0GMcztzJ/dPr4m4ILNZKDVDpQIgV07pvP59s7YgH3ejk2FufyelxxIodmQ0sZ79ixlr9s/H/8aOxoyxgHQJBsvJf8IuV9A612Zp/6YpLo4njlU9y0OBiXbRUMB03VjnXhxMS0dBj4WhornDqOAWJyeTXHTllBs83YxkAaDYBrm1u5rajA8pf7VLNCPiFihuCmojEpJdF11OjK48HwLF7pnpf0vrEfRsKlEEjGiHY6ZRZ/jHyGy13Pp6zv0I8z0muUJMSMR7wRCZLNXeHFXDPqFZZ51uB7ohH+qWUSMX0x7FhLcSRCg4mQofHpsl6OsazLKA5HQHHR4Eq+Z8XhCIXRhlITxQFulffzslpkWrAmpESNuiu7mxbQ0aa5Ic5X87iotj5pfylBil53gCJVLnP9C4nkfGUbXxuXRYMS/7m6FIUuiyLP5kgbFStWAisB8JnskxiMVYtbTJPM7ax6Ex9czursZEnHV3ntqbFs7rJ2CxmJSIlwdWjfuruF3JJ1nNN0kImdbUBvR0IlLKgKzYXQTMJtMxk11byVbsp4x/TF/PPRGr4nH4/LqprSdCKnBl9EFUEaGEP9acs4PY3RAE0ORDIzycNQTzDJNTj9wemm59CzB83S0q2yuYYSx1Vlk6k/eAqA5uyQLUnoC9t6BtRoALS0fZJbmppNXRlgXcSnoi21f5RB/+8uRWHX2L2m790ZXsx6X168UF6CuKJP9HC+so3y0NepVcfGguBgIrKXazaV6Ugtm+iS++N6XP9n1m0U+LJYFroPX7BB26/1I3jyOl7d8Bsa19/E2R2dye46KTk7WpmvyqhQYtMCy0D8koPNtjrl+UQPNx5owZNwPY+U3L6/Oc5dqbvVdn3ihiQXUqfMolmOSvrD9Ike/sv1DyYqBzJOfEgn/mgWjLXqaJfWP2HyVPD3UaN5Qp1HXSBIvRxjS4xSiPjvQ1Ui/LowPtbmEz38xP2rOBen1bjTaU+9lncBd3g+Tb3bg4qgy93F9pwc5vXcyyxlLa8uep7TF34r7bghsy5+VuPS+5NbpZ9nIsI4GDiGwyZdEZsuvajReKT+7gEfwy3hr3Gw7VM89VEj299P9vtbfZn69kThxNFhFY9q/bnMfmkXKpuZ7X+cVUV58UJ5CbERgAnKQZ5U53Gh61ecEPoz83ruNRfZMzk2jtZaqpt3Mj8fpk+eyPmjJXft2szXex5KroYPBZnw+p2Mk0087/OZTmTP+7RrCbS6mXDbTL7XFDI1yKd1+Ah8eDEiXAASxoVUy4yzz3d2cluCsOOt+w/S0vbJ2D4eRbB0wYlU1dTxlVePTTKs5aGvx7XGNaKvxKwmXr8qTQ3g2RFXXIvXRB+5WUFhd9OCpBiMfv8QItkeqwpWhbBtBr/mj0OL+U5LR59a95oZTLdQUQSxTLuTD0xOii9YBbGNFBzzJJ6ClxBCRj+ixFPwEqM/Uc64k3+CJ3+b7XHqWW9GrPSyrFR6L5vyDbweV58N4WDjGA4b6AFxuwq4g2E0dG4Jf42PdT9k+uR3uYmUOVLymTYZ26wHnLe//xGVe/O5vqnHMj018ZdWz6Z6qNCVMnVRR8mfyPuVZeRmu4lEJ6F7C817bicea6QqbwwV7/+FBpdACsF+j8L7xVupGWUejC3hIPVyrOWTudn21wKX8teP9scF4qUEjxrkc+0dtL17I13v/JinP6q1TFNu9Yynq/NszbDv/Yi/1TbScmhu/MozOofqk/UGdR7zeu7luO6HmddzLxvUeTQw1vT8OmYqwDkolB93iaY6azBcFx/q4IksGRd8TQwWmxUUhttmcv6+EkpCYdMkCy2dWsTUd9X91mnmxqM3qPP4Z8uXqegUvckJNkm3UvGJHn4WfBF1/2Xke8YhEOR7xsGBy7n2fmJJDIlU1dSxt+cZSxeqrQC7gUUzS9P2FtcxqvSCQIQLCHx4MZteKeWMae+juHqS2ynYMISDjWM4bGI3IJ5xJlMfaZGjkrataA6wuO2QZgikRJGSUwP53HCgy/SPYp6yC9k2jZX7W0yfVGd2qBz/sWXkTb2R4z+2jLn+R/GJHlsTcrd0xbJ2jBNTJpM5aLfzvrwsuhKi8V2Kws8L/abHtMhcvHSlzZRRETE3x2nKOwji9xcCxijtsZqRkCr586jxpm42CdzRfTn/G/wKH+t+iCldf+b7Pd/mXFET50rRq51TyWLXn7YsToY80bUHxK0cS0JhKvbvp6xwGmWfuYtNh1xRA9jA8z5vsqGO+sh1rFwr3+vcw6ba+hSCOpL2tyrxH1zJj+Zfjdc12nyvSPz5X8u7gNCnNtJx4JcceuvHtvqiKKqLa1s7TN8z3p+vHZPFFadPYvOXnuHW6X/j4JtLaWo8OWU9hNbbIvXfbeI9S8ci1xa2ZF/H+zlfZkv2dSxybbHct+y4Mr57/P8Rfu9O2t69kVDbTPapL/J6x2/B1Rn3t+vP9g9JYDwdTnA8DbH0W5tG49Dbdw7JuKyG8sPmAF894GZez70AbAZKs79kuq8i4HxlG74O8IjmuPz/szs7eSK/tznRfo/gJ0U+Rh3osqxKj++b3TtAozT06LCHNk845bHQG2MVIrWx6ZRZce6qbuliyyjJLwrzTTWg9PiEhJhi8ERxgK+If1jeU71m5KmcXO4a64tJYPTWdAjGdhzP9TzKndm/4KHc8fymMJfn3CHeDGexpMVHWYfmSiEETwbmWcpll/q9mi99cgE8cyvV4eZo/YCIu2bFgWY2JQbWn7kVbviPlhxw9ynQ+pH1vTP4yI1CiLpUSM2oTr5W6KfRPckyt01GfHEtTj98bw7/174xrgWxW1U5Yf+JvBJ97fW4OHdqUZwuVPf+BUmZQ1JVkGpOTBn4kinfYOFp2b3S7EIBGUlSA2jwuPnrBz9m9upbWdjSxGwxhjuVXpUBM6mU+kCQ3OLUGXyJ9ywlib3bo7E3wDIjK9FdaBYUB/C6vcNuNMAxHCmJd1Glz0YaKqMB4MfcDw7x9Q0AERTcFqHz0lh2VHyM4e+5uZYuJdNiqISAcRZhGtffxEuRuXET0779l1JQ8mjcCkI/NiwVFLQMJLchOGplqIrCksciZ/Nl17O4UImg8FSujx8V5SdrQKFJpRtTXs16hlu5oSaIg2QXbYzTTYrdk+JSqt57C5/SQ3Wuj5+P9dClhAERXzDY0cnPPL+m0JPFjAXfjJs8z8h/lINFW2lyC85/QHJx/gKuu+E/rF43n66EYrK4ojojrR9BhV+rl4j2+LY08gYfuT6Jbqu+n2WhB3hulFb/od9Dqd/DRBFGpYv5Z/Q+vX9l11qOz40k3dNPdj3LaVwZk85InCTDbZo6rln66pbENrTG3uhPXmfaS6ZLEazOdVPWImOxD0Kam2yhspllnWuh4mBMz2uCfywHWs6MxjiSbpXpPTOSmJH2tLgZX7oWvQkkrkCtguIN7Q1MKa8edpVcx3CkYaS5qHSsUmK19+LlqJUUoulCJGs4NXjclp+n0e2Ka5iUatIdJw+wfP1OVl0yrVcosOshapo6+Xlh/LHntodjNSN7ElZIVoZq9oEJXO56PmZkNubmUFHkT045Fpo/3fiEbvaZjRN8IvVyjHWGSyQYp+hqZXDLOjpxC5UV8te4XSfDJXO5a+PblMr/4/3irXGruz+1b6Tp/75Lg2gwfWaxzqyKZpdF1wmm9054knzki2aWsuifj0NrD6sLxtpq7CUUVWvbitbAKV8eoqwj+f5J4O4rZsQmuRvWbEs6V7gtPn3V63Fxz+nvxos2njAf3t3U+/rUL9F4wLw2w3h/9BUjYeKVp6MrgXumreQrr14K+VvBldx6AKzjCmaqujnZjebPmQlKA0YSV6BWKeJqyJ9SoXiocGIcFmQqYjiUqw1I7ret0yPd3BmOf6qpl6mDrZl0ADRW0upV3Rs/Sq7q1q47ptc14NpChfgNE5UDXNTZG6Df+FE9cw4pcVpVieM1a6M7pXEW3+vcE5sEdENgVaeSONGmmuATkRJKlQOUWGTWGd1sdmI47kgX8vFvENnwfZYuOJGDRVtNx/Ji5F+W/v/0Ka3aWBPvnZYxdpCy9g7tqf3uU7RVStS1leozmH6u9vre41P8mRhjC8aYilFva0v2dVysbKbU7+WPp3/A6TtviY4pagxf+13c6+BrD5EVNs/GS7w/E8RBU8UDQkFO3/1zVl0yDW/bFUkZWQD5WfmWcQWzjLTEB7feE1nXryRmYnU3LTCtPjd2w0zVe7yqpo65lc8yxaBsMJAMi+EQQnxWCPG2EOI9IURSxY7QuDf6/g4hxCy7xw4EfVW+HUo2qPNYGvoWh9Ts2IInIgWPRM5JqvS+M7w4qV7AiOVEkbDqMKtf0OmR8YtXKcFLFwuVzdQFgnT+7eZ4dVl6b2+BaOc05Z3Y9geyrooLDgOc2x5m6p5FnPPORTz4YRePdm0wuNksjJ+BxIkkkyC9VtgI1zcfJCchRTVblVxnuCfpAvJ6IPfUKZP4TekO3E/Ppslt/jvW5Bbm6aUpvgczkqRb2gLw1+s1v7txYo7O/HbqLOI/l4wZHSuMk9y5U4uA3iy9icoBFKG5TVfn/h9bPn+A03f/POn3xUh1ro+LJ46h251cq2N2f+rlGCYo8S7cGK21LJpZyqvXL+PHn74tTvSx8qxKNn9xs2VcwSxOZfr35vGmlHhZNLOUS0/rXTmE22bS1XBJUt/4pKJCk+tX1dRx06YHCYy5hdyp5QTG3MJNmx4cUOMx5K4qIYQL+CVwAVALvCqE2CClfMOw2+eAE6L/nQn8CjjT5rH9JtNWsEPtpjLiEr2NilxILnc9z+vqx+OMh7HHhz7ZGj+alR/cr6p4VZk2BqClZkraZQ65dMVu3RjRHvMv53RaLN/RgvRfcf2D19WP87Tr08wo+ya4ToZnbkW21lIvx/DjkLaKSmxypZPqKdlsIrET4E9E/+z3FBayzyVM70mq+I+Ze2xlUSH5qmpacV4cjrCmawNrmgr5SWERXW6tMK+ipyWlcrEteswylKTlZ3CjVcCHTBINdFL9tbyf/SUIAhVwG3BbNnTjIkck3O9QEJ74H4iYu4zAQh7fIo6lk1L231sQ+7HsuDJtNfbMrfD+K7D7WuBaCLbEYiJ6nKKqpo4/eu7gLGVX7Pgu6eLG8Le40/NdKnIfj+uNAkRXdrWm5/rE1pW8l/0MLt213A3UQgc53BS6EOhgWdZ1cZIrr+ddkPRx7vjXwyjj1sV1z5Tj1nHHv9wsmmmvpCAdQ65VJYT4JFAhpVwQfb0cQEq5yrDPb4B/Sikfib5+GzgHmJzu2EQy1ap6dcNv+FrzzzMyGkPtptKx6oOhazglYtbiFcz/EHNUlVuamuP6n3dLFwJBlkjOigItCO8yiafoFePpenZ0kM3TF9fE+WyNvSdS9f2w6smuy60kTiRWnzmdnDz0CqBYYRV0txpjfjhCtyJSjkXvV/J63gVs+fwBWP+NlGPsL2afAdLHtTLBQtoqLVb3MTGOZRvFA4vui0nVJPVeMWLoK/JyxVmcIXckfYawhJrT7oyvNDc7r+Fcj6+8gkvUv1vejwgQke64v72gzOI/p92eVNF+8m/PMu31ovb42fWNF1LciHhGWj+OUsC4rq2NbrOzj51j+8WkrXfZ23EAjIarn24wqz4LiVlVOuVZJj5ezP3gtzQ1c3x7QVxV89LQt/h/oW9aLrDMjIY+nnTuMoBcupMCfcaleKpOg6ZFcapqajTAyvef3mhA+vw6K1Vfq1VRm0tJOxaf6OFGz1qt+nj6Ypjy6bTj7A9mnyGdWnGm9PXXP9NaoLSood5eIc/cmtJFZuwrYmY0ANwCzdVmxOy8hnNdrG5KeT9ckPTA5hU9ydcBy14vmfSAScdwZFWZ3Z7EqchqHzvHIoT4JvBNgGOOOSajwY2TTcAk6x30WbOfRqM0mk5nzMiwi94EySqzyiw45/W4KMHCxwuxiUGnR7r4eM9dpjn8y+Ra84wu4QKZ/Fnq5Rg2qPMo9GRxffh35MtDticNY7ZJqkwyu5leicf02+WTAancY3bGMkEc7DWsV2+AivzBGOaQ0pdVR1/cjGnRM55SZD4l7Ztq3InnsTpvdLtLZC7BYnXe/KxxtIb2m24fKIZjxVFL/Mw8EUhcX1rtY+dYpJT3SylnSylnFxUVZTS4/aLIOm6hrzLe+nFaozH3+EL2VpZx1Zxjkn6/dN0aXZrA5zH/GuYeX2iqeXP34hnsrSxj4mWr6BbZce93yizuDC9m7vGFSZIHwiKrI/Gj9kg3/y/0LbweF1+ecwx+b3xw9s7wYoJmwb/TrkkKauvj8XpczCj7Jv+8+GXrMiuRfB+M2SbpVi32nohF0hhTonjAqxX5kT8p+nPfsFoV2Q10J31/+SkecEAbu5J6lXc40t/7aIp+b1NkPiXtm8k+VsdEt0uT331bmJx3+Zzv40mYFzwim+Vzvt+3a5gwHIbjVeAEIcQUIUQWcCWwIWGfDcBXotlVc4BWKWWDzWP7xUezlvL63sZe45HwXzqD4RKCq+Ycw8Pf0ITtbl80jbuvmGGpW7NoZilv3PY5rppzTMx1ZTxHSs2b6YvJ/sIvaPGMj7mTbgp/g7wzvsTD3/gkW8rP4/3KMraUn6cdc/7NSZNm2JXDeuWzmksKQT1jWRr6Jq/nXcCqS6Zx+6JpbLtlPvcYPsPreRfwn9Nuj1Or5aJ74cKfaf/mT0IiaKSI5VG/vD7uRTNLeX/ylebG47SvJm0y6v48qc7jTs936fSW9F539n/3jsOTm/4Lnv01bYyWBkCJNxSL7oMb34eKgFaV/bkfJ91DiQ3VWPrnHjNtvHT+zeCyMAzeQm3si36pGZCU9HMaMJ30FG0Fmu7QoqmZGXL6dx9NMWY8mfyNWO0rpnza+ns3+64Sz2s4l3LaV9P8Donk79oiU6vsuDJum7cyLjvstnkrB7TifFgaOQkhPg/cg+a6+72U8g4hxLcBpJS/FppexS+AzwKdwFellK9ZHZvqWn1p5PTqht9w/Ou3UhCtzm6Wo1gZ/kpSmqtHgbDKsFdxZsSOtb2yDQmZHUPKX78Pr/9Bc20Jl7ZaufBn/T+v8fN5fBAOglTNr7FjLfztRgg2a6+9hZphSHc/zO4hJG975tbUaaqubFDcEIrPcNL/IrulhywRTt14yc5nSBxvYiHd+TfDhy/1fh9GB6V+38DwfpT8Sb2f3WwMidsTmfJpzeWWOL7C42DvC9r3lo78SYbP85HBXWohIeIthJO/kPz5re6XnnFlklUFwIML4f1/9b5WsjRjbfVdpfrbM/5NGMnKhQvv0X4ewr/dVMFxpwOgg8NgUeHHfC0itBWMg8MIZqRlVTk4HB2k8Ws7OByuOIbDwWGwSOPXdnA4XHEMh4PDYDF9cSxZIC6JYDhiSg4OA4ijjuvgMJhMX+wYCocjDmfF4eDg4OCQEY7hcHBwcHDICMdwODg4ODhkhGM4HBwcHBwywjEcDg4ODg4ZccRXjgshmoAP+nj4WCB1E4mjD+eeJOPck2Sce5LM4XZPjpVSmqrEHvGGoz8IIV6zKrk/WnHuSTLOPUnGuSfJHEn3xHFVOTg4ODhkhGM4HBwcHBwywjEcqbl/uAcwAnHuSTLOPUnGuSfJHDH3xIlxODg4ODhkhLPicHBwcHDICMdwODg4ODhkhGM4LBBCfFYI8bYQ4j0hRPlwj2cwEUL8XgixXwjxH8O2QiHE00KId6P/FhjeWx69L28LIRYYtp8mhNgZfe/eaAvgww4hxCQhxHNCiDeFELuEEEui24/me5IjhHhFCLE9ek9WRrcftfdERwjhEkLUCCH+Gn195N8TKaXzX8J/aP3MdwPHAVnAduCk4R7XIH7es4FZwH8M2+4EyqM/lwM/jv58UvR+ZANTovfJFX3vFeCTaA2f/wZ8brg/Wx/vRwkwK/rzaOCd6Oc+mu+JAEZFf/YALwNzjuZ7Yrg33wf+DPw1+vqIvyfOisOcM4D3pJR7pJQ9wKPAxcM8pkFDSvk80Jyw+WLgwejPDwKLDNsflVJ2SynfB94DzhBClAB5Usp/S+0v4Y+GYw4rpJQNUsqt0Z8PAW8CpRzd90RKKdujLz3R/yRH8T0BEEJMBMqABwybj/h74hgOc0qBjwyva6PbjibGSykbQJtIgXHR7Vb3pjT6c+L2wxohxGRgJtoT9lF9T6IumW3AfuBpKeVRf0+Ae4BlgGrYdsTfE8dwmGPmX3TyljWs7s0Rd8+EEKOAx4HrpZRtqXY12XbE3RMpZURKOQOYiPakfEqK3Y/4eyKEuBDYL6V83e4hJtsOy3viGA5zaoFJhtcTgfphGstwsS+6hCb67/7odqt7Uxv9OXH7YYkQwoNmNB6WUq6Pbj6q74mOlDIA/BP4LEf3PZkLLBRC7EVzZ58nhHiIo+CeOIbDnFeBE4QQU4QQWcCVwIZhHtNQswG4Ovrz1cAThu1XCiGyhRBTgBOAV6JL8kNCiDnRjJCvGI45rIiO/3fAm1LKnxneOprvSZEQwh/92Qt8BniLo/ieSCmXSyknSikno80Rz0opr+JouCfDHZ0fqf8Bn0fLptkN/GC4xzPIn/URoAEIoT39/DcwBngGeDf6b6Fh/x9E78vbGLI/gNnAf6Lv/YKoMsHh9h8wD81VsAPYFv3v80f5PZkO1ETvyX+Am6Pbj9p7knB/zqE3q+qIvyeO5IiDg4ODQ0Y4rioHBwcHh4xwDIeDg4ODQ0Y4hsPBwcHBISMcw+Hg4ODgkBGO4XBwcHBwyAjHcDg4DDBCiAohhBRCuC3enxx9/5ohHpqDw4Bg+ovt4OAwqDSgKaHuHu6BODj0BcdwODgMMVLKbuCl4R6Hg0NfcVxVDg5DgNAag7ULIX4hhDgu0VUlhPiDEKJWCDFTCPGCEKIz2gjo28M4bAcHUxzD4eAwyAghvoKmU/RjKeW1xEtwG8lDawj0EFrvhleBXwkhzh2SgTo42MRxVTk4DCJCiGXAHcB3pJQPpNl9NPBdKeVz0WOfB+YDXwSeG9SBOjhkgGM4HBwGj7uBrwOXSSntqJ126kYDtFiIEOJd4JjBGqCDQ19wXFUODoPHF4FdwD9s7t9isq0byBmwETk4DACO4XBwGDzOR2vc87doN0EHhyMCx3A4OAweu9D6NJwA/F0IMXp4h+PgMDA4hsPBYRCRUr6JZjyOwzEeDkcIjuFwcBhkpJRvA58GjgU2oaXdOjgctjgdAB0cHBwcMsJZcTg4ODg4ZIRjOBwcHBwcMsIxHA4ODg4OGeEYDgcHBweHjHAMh4ODg4NDRjiGw8HBwcEhIxzD4eDg4OCQEY7hcHBwcHDIiP8PGL3+JDLKYJUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "\n",
    "ax1.scatter(kinematic, x2, label='data')\n",
    "ax1.scatter(kinematic_test, y_fit,label='prediction')\n",
    "ax1.scatter(kinematic_train, y_est,label='estimated')\n",
    "\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='kin',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_LO_KernelRidge_smarter_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ecdf81e8",
   "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": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_LO_KernelRidge_smarter_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac29f023",
   "metadata": {},
   "source": [
    "# Let's give it another go 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67604fae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name of the model\n",
    "filename = 'storage/x2_NLO_KernelRidge_smarter2.sav'\n",
    "\n",
    "# training data \n",
    "\n",
    "kinematic = np.stack((np.exp(-rap) ,m),axis=-1)[0]\n",
    "kinematic_train = np.stack((np.exp(-rap_train),m_train),axis=-1)\n",
    "kinematic_test = np.stack((np.exp(-rap_test),m_test),axis=-1)\n",
    "n=kinematic.size\n",
    "\n",
    "# reshape to correct dimentions\n",
    "kinematic=np.reshape(kinematic,(-1,n))\n",
    "kinematic_train=np.reshape(kinematic_train,(-1,n))\n",
    "kinematic_test=np.reshape(kinematic_test,(-1,n))\n",
    "\n",
    "X_train = kinematic_train\n",
    "X_test = kinematic_test\n",
    "\n",
    "# train \n",
    "Training_machine_learning = training(X_train,y_train,X_test,y_test,filename,\n",
    "                                     KernelRidge(alpha=1, kernel=RBF(), degree=3, coef0=1, kernel_params=None))\n",
    "\n",
    "# predict\n",
    "R2    = Training_machine_learning[0] \n",
    "y_fit = Training_machine_learning[1]\n",
    "y_est = Training_machine_learning[2]\n",
    "#coefs = Training_machine_learning[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4a01ab5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8785685035737428"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8782b62",
   "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": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "\n",
    "ax1.scatter(m, x2, label='data')\n",
    "ax1.scatter(m_test, y_fit,label='prediction')\n",
    "ax1.scatter(m_train, y_est,label='estimated')\n",
    "\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='kin',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$x_2$',fontsize=16)\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_KernelRidge_smarter2_1.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7968235f",
   "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": [
    "fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "plt.ylim(-100,100)\n",
    "ax1.scatter(m_test, (y_test-y_fit)/y_test*100,label='relative % diff test')\n",
    "ax1.scatter(m_train, (y_train-y_est)/y_train*100,label='relative % diff train')\n",
    "ax1.legend()\n",
    "\n",
    "ax1.set_xlabel(xlabel='m',fontsize=16)\n",
    "ax1.set_ylabel(ylabel=r'$\\% diff$',fontsize=16)\n",
    "\n",
    "\n",
    "plt.savefig('Plots/x2_NLO_KernelRidge_smarter2_2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40d2f421",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
