TakeshiHMM-Gazebo-NOODOM-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosave disabled\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.cluster import  MiniBatchKMeans\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%autosave 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "class HMM (object):\n",
    "             def __init__(self,A,B,PI):\n",
    "                 self.A=A\n",
    "                 self.B=B\n",
    "                 self.PI=PI   \n",
    "def viterbi(obs,Modelo1,PI):\n",
    "    \n",
    "    delta=np.zeros((len(obs)+1,len(Modelo1.A)))\n",
    "    phi=np.zeros((len(obs)+1,len(A)))+666\n",
    "    path =np.zeros(len(obs)+1)\n",
    "    T=len(obs)\n",
    "    Modelo1.PI = PI\n",
    "    delta[0,:]= Modelo1.PI * Modelo1.B[:,obs[0]]\n",
    "    phi[0,:]=666\n",
    "    for t in range(len(obs)):\n",
    "        for j in range(delta.shape[1]):\n",
    "\n",
    "            delta [t+1,j]=np.max(delta[t] * A[:,j]) * B[j,obs[t]]\n",
    "            phi[t+1,j]= np.argmax(delta[t] * A[:,j])\n",
    "    path[T]=int(np.argmax(delta[T,:]))\n",
    "    for i in np.arange(T-1,0,-1):\n",
    "        #print (i,phi[i+1,int(path[i+1])])\n",
    "        path[i]=phi[i+1,int(path[i+1])]\n",
    "    return(path)\n",
    "def cuantizar_xy(xy, cc):\n",
    "    xycuant=cc\n",
    "    out=np.power(xycuant-xy,2).sum(axis=1).argmin()\n",
    "    return out\n",
    "    \n",
    "def path_to_xy(path,ccxy):\n",
    "    estimated= pd.DataFrame(path.astype(int).T)\n",
    "    estimated.columns=[['Path_vit']]\n",
    "    estimated['xcuant'] = estimated['Path_vit'].apply(lambda x: ccxy[x,0])\n",
    "    estimated['ycuant'] = estimated['Path_vit'].apply(lambda x: ccxy[x,1])                                \n",
    "    return (estimated)\n",
    "def quantized(xyth,ccxyth):\n",
    "    xythcuant=np.argmin(np.linalg.norm(xyth-ccxyth,axis=1))\n",
    "    x,y=ccxyth[xythcuant,:2]\n",
    "    return ((x,y),(xythcuant))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Markov_A_2_grafo(A,ccxyth):\n",
    "    dists=np.zeros(A.shape)\n",
    "    for i in range(A.shape[0]):\n",
    "        for j in range (A.shape[1]):\n",
    "            if A[i,j]!=0 :\n",
    "                dists[i,j]= np.linalg.norm(ccxyth[i]-ccxyth[j])    \n",
    "    \n",
    "    \n",
    "    con = np.where(dists==0,np.inf,dists)\n",
    "    graphe2=grafo(ccxyth,con)\n",
    "    return graphe2\n",
    "\n",
    "\n",
    "class node(object):\n",
    "    def __init__(self,x,y):\n",
    "        self.x=x\n",
    "        self.y=y\n",
    "        \n",
    "        \n",
    "class grafo (object):\n",
    "             def __init__(self,nodos,conec):\n",
    "                 self.nodos=nodos\n",
    "                 self.conec=conec        \n",
    "\n",
    "def dijkstra(nodoinicial,nodofinal,graphe):\n",
    "    \n",
    "\n",
    "    numnodos= len(graphe.nodos)\n",
    "    con = graphe.conec\n",
    "    D= np.ones(numnodos)*np.inf\n",
    "    Prv= np.ones(numnodos)*np.inf\n",
    "    V= np.zeros(numnodos)\n",
    "    a = nodoinicial\n",
    "    D[a]=0\n",
    "    Prv[a]=0\n",
    "    Prv[np.where(con[a]!=np.inf)]=a\n",
    "    V[a]=1\n",
    "    Dacc=D[a]\n",
    "    ########\n",
    "    D=np.minimum(D,con[a]+D[a])\n",
    "    cont=0\n",
    "    sucess=False\n",
    "    while(sucess==False):\n",
    "        a = np.argmin(D+np.where (V==1,np.inf, V))\n",
    "        Dacc=D[a]\n",
    "        Prv[np.where(D>(con[a]+Dacc) )]=a\n",
    "        V[a]=1\n",
    "        D=np.minimum(D,con[a]+Dacc)\n",
    "        if (a== nodofinal):\n",
    "            print(\"RUTA CALCULADA \")\n",
    "            sucess=True\n",
    "    rutainv=[]\n",
    "    rutainv.append(nodofinal)\n",
    "    while(rutainv[-1]!=nodoinicial):\n",
    "        prv=Prv[int(rutainv[-1])]\n",
    "        rutainv.append(prv)\n",
    "\n",
    "    ruta=[]\n",
    "    for n in reversed(rutainv):\n",
    "        ruta.append(n)\n",
    "    return(ruta)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R0</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>R9</th>\n",
       "      <th>...</th>\n",
       "      <th>R714</th>\n",
       "      <th>R715</th>\n",
       "      <th>R716</th>\n",
       "      <th>R717</th>\n",
       "      <th>R718</th>\n",
       "      <th>R719</th>\n",
       "      <th>R720</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.359770</td>\n",
       "      <td>3.361873</td>\n",
       "      <td>3.359525</td>\n",
       "      <td>3.365355</td>\n",
       "      <td>3.372117</td>\n",
       "      <td>3.377027</td>\n",
       "      <td>3.376309</td>\n",
       "      <td>3.379349</td>\n",
       "      <td>3.389191</td>\n",
       "      <td>3.393477</td>\n",
       "      <td>...</td>\n",
       "      <td>3.629227</td>\n",
       "      <td>3.628952</td>\n",
       "      <td>3.636077</td>\n",
       "      <td>3.629775</td>\n",
       "      <td>3.635788</td>\n",
       "      <td>3.642282</td>\n",
       "      <td>3.643780</td>\n",
       "      <td>0.474876</td>\n",
       "      <td>0.457221</td>\n",
       "      <td>-0.113440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.099933</td>\n",
       "      <td>2.103343</td>\n",
       "      <td>2.103897</td>\n",
       "      <td>2.109904</td>\n",
       "      <td>2.117552</td>\n",
       "      <td>2.119504</td>\n",
       "      <td>2.120335</td>\n",
       "      <td>2.125302</td>\n",
       "      <td>2.130151</td>\n",
       "      <td>2.132400</td>\n",
       "      <td>...</td>\n",
       "      <td>2.331853</td>\n",
       "      <td>2.331921</td>\n",
       "      <td>2.337438</td>\n",
       "      <td>2.333186</td>\n",
       "      <td>2.335148</td>\n",
       "      <td>2.340472</td>\n",
       "      <td>2.336713</td>\n",
       "      <td>2.009028</td>\n",
       "      <td>0.497757</td>\n",
       "      <td>1.982905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.435912</td>\n",
       "      <td>0.434510</td>\n",
       "      <td>0.432887</td>\n",
       "      <td>0.431534</td>\n",
       "      <td>0.430204</td>\n",
       "      <td>0.428897</td>\n",
       "      <td>0.428586</td>\n",
       "      <td>0.426076</td>\n",
       "      <td>0.424837</td>\n",
       "      <td>0.423619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251262</td>\n",
       "      <td>0.252581</td>\n",
       "      <td>0.253922</td>\n",
       "      <td>0.255287</td>\n",
       "      <td>0.256599</td>\n",
       "      <td>0.257972</td>\n",
       "      <td>0.259369</td>\n",
       "      <td>-3.587735</td>\n",
       "      <td>-0.130961</td>\n",
       "      <td>-3.141290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.675508</td>\n",
       "      <td>1.671137</td>\n",
       "      <td>1.666525</td>\n",
       "      <td>1.663512</td>\n",
       "      <td>1.660460</td>\n",
       "      <td>1.658057</td>\n",
       "      <td>1.654113</td>\n",
       "      <td>1.649741</td>\n",
       "      <td>1.648988</td>\n",
       "      <td>1.645001</td>\n",
       "      <td>...</td>\n",
       "      <td>1.649592</td>\n",
       "      <td>1.651109</td>\n",
       "      <td>1.651642</td>\n",
       "      <td>1.653445</td>\n",
       "      <td>1.656365</td>\n",
       "      <td>1.658857</td>\n",
       "      <td>1.660059</td>\n",
       "      <td>-0.921340</td>\n",
       "      <td>0.151473</td>\n",
       "      <td>-2.594343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.637022</td>\n",
       "      <td>2.638902</td>\n",
       "      <td>2.632085</td>\n",
       "      <td>2.643567</td>\n",
       "      <td>2.651316</td>\n",
       "      <td>2.655378</td>\n",
       "      <td>2.655087</td>\n",
       "      <td>2.664712</td>\n",
       "      <td>2.673134</td>\n",
       "      <td>2.682876</td>\n",
       "      <td>...</td>\n",
       "      <td>2.765880</td>\n",
       "      <td>2.769057</td>\n",
       "      <td>2.775006</td>\n",
       "      <td>2.769103</td>\n",
       "      <td>2.788009</td>\n",
       "      <td>2.784558</td>\n",
       "      <td>2.801169</td>\n",
       "      <td>0.838595</td>\n",
       "      <td>0.301476</td>\n",
       "      <td>0.122279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.755706</td>\n",
       "      <td>4.753299</td>\n",
       "      <td>4.765337</td>\n",
       "      <td>4.777165</td>\n",
       "      <td>4.785247</td>\n",
       "      <td>4.806156</td>\n",
       "      <td>4.811645</td>\n",
       "      <td>4.795866</td>\n",
       "      <td>4.833402</td>\n",
       "      <td>4.875470</td>\n",
       "      <td>...</td>\n",
       "      <td>5.605645</td>\n",
       "      <td>5.609724</td>\n",
       "      <td>5.616324</td>\n",
       "      <td>5.602028</td>\n",
       "      <td>5.605524</td>\n",
       "      <td>5.615700</td>\n",
       "      <td>5.632298</td>\n",
       "      <td>2.026029</td>\n",
       "      <td>0.682428</td>\n",
       "      <td>1.451075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.018440</td>\n",
       "      <td>9.031508</td>\n",
       "      <td>9.040556</td>\n",
       "      <td>9.012540</td>\n",
       "      <td>9.025507</td>\n",
       "      <td>9.035414</td>\n",
       "      <td>9.009221</td>\n",
       "      <td>9.022154</td>\n",
       "      <td>9.030491</td>\n",
       "      <td>9.004526</td>\n",
       "      <td>...</td>\n",
       "      <td>9.310540</td>\n",
       "      <td>9.332047</td>\n",
       "      <td>9.348527</td>\n",
       "      <td>9.365735</td>\n",
       "      <td>9.387755</td>\n",
       "      <td>9.413424</td>\n",
       "      <td>9.436586</td>\n",
       "      <td>3.749774</td>\n",
       "      <td>3.865329</td>\n",
       "      <td>3.141516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 724 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R0            R1            R2            R3            R4  \\\n",
       "count  16701.000000  16701.000000  16701.000000  16701.000000  16701.000000   \n",
       "mean       3.359770      3.361873      3.359525      3.365355      3.372117   \n",
       "std        2.099933      2.103343      2.103897      2.109904      2.117552   \n",
       "min        0.435912      0.434510      0.432887      0.431534      0.430204   \n",
       "25%        1.675508      1.671137      1.666525      1.663512      1.660460   \n",
       "50%        2.637022      2.638902      2.632085      2.643567      2.651316   \n",
       "75%        4.755706      4.753299      4.765337      4.777165      4.785247   \n",
       "max        9.018440      9.031508      9.040556      9.012540      9.025507   \n",
       "\n",
       "                 R5            R6            R7            R8            R9  \\\n",
       "count  16701.000000  16701.000000  16701.000000  16701.000000  16701.000000   \n",
       "mean       3.377027      3.376309      3.379349      3.389191      3.393477   \n",
       "std        2.119504      2.120335      2.125302      2.130151      2.132400   \n",
       "min        0.428897      0.428586      0.426076      0.424837      0.423619   \n",
       "25%        1.658057      1.654113      1.649741      1.648988      1.645001   \n",
       "50%        2.655378      2.655087      2.664712      2.673134      2.682876   \n",
       "75%        4.806156      4.811645      4.795866      4.833402      4.875470   \n",
       "max        9.035414      9.009221      9.022154      9.030491      9.004526   \n",
       "\n",
       "       ...          R714          R715          R716          R717  \\\n",
       "count  ...  16701.000000  16701.000000  16701.000000  16701.000000   \n",
       "mean   ...      3.629227      3.628952      3.636077      3.629775   \n",
       "std    ...      2.331853      2.331921      2.337438      2.333186   \n",
       "min    ...      0.251262      0.252581      0.253922      0.255287   \n",
       "25%    ...      1.649592      1.651109      1.651642      1.653445   \n",
       "50%    ...      2.765880      2.769057      2.775006      2.769103   \n",
       "75%    ...      5.605645      5.609724      5.616324      5.602028   \n",
       "max    ...      9.310540      9.332047      9.348527      9.365735   \n",
       "\n",
       "               R718          R719          R720             x             y  \\\n",
       "count  16701.000000  16701.000000  16701.000000  16701.000000  16701.000000   \n",
       "mean       3.635788      3.642282      3.643780      0.474876      0.457221   \n",
       "std        2.335148      2.340472      2.336713      2.009028      0.497757   \n",
       "min        0.256599      0.257972      0.259369     -3.587735     -0.130961   \n",
       "25%        1.656365      1.658857      1.660059     -0.921340      0.151473   \n",
       "50%        2.788009      2.784558      2.801169      0.838595      0.301476   \n",
       "75%        5.605524      5.615700      5.632298      2.026029      0.682428   \n",
       "max        9.387755      9.413424      9.436586      3.749774      3.865329   \n",
       "\n",
       "              theta  \n",
       "count  16701.000000  \n",
       "mean      -0.113440  \n",
       "std        1.982905  \n",
       "min       -3.141290  \n",
       "25%       -2.594343  \n",
       "50%        0.122279  \n",
       "75%        1.451075  \n",
       "max        3.141516  \n",
       "\n",
       "[8 rows x 724 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_nice=True\n",
    "data=pd.read_csv('lecturasconodometria.txt')\n",
    "\n",
    "\n",
    "cols=['x','y','theta']\n",
    "n_reads=data.shape[1]-3\n",
    "n_reads\n",
    "etiquetas=[]\n",
    "for i in range (n_reads):\n",
    "    etiquetas.append('R'+str(i))\n",
    "\n",
    "        \n",
    "for lab in cols:\n",
    "    etiquetas.append(lab)\n",
    "\n",
    "\n",
    "data.columns=etiquetas\n",
    "\n",
    "if (only_nice!=True):\n",
    "    print(\"NOT ONLY NICE\")\n",
    "    data2=pd.read_csv('lecturasconodometria.txt')\n",
    "    data2.columns=etiquetas\n",
    "    data3= pd.concat((data,data2))\n",
    "    data=data3\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "####\n",
    "#index = data[data.y<-1].index\n",
    "#data=data.drop(index)\n",
    "data.describe()\n",
    "\n",
    "\n",
    "\n",
    "#####\n",
    "\n",
    "xyth=data[['x','y','theta']]\n",
    "\n",
    "lecs=data.iloc[:,0:n_reads]\n",
    "\n",
    "\n",
    "data.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    .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>R0</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>R9</th>\n",
       "      <th>...</th>\n",
       "      <th>R716</th>\n",
       "      <th>R717</th>\n",
       "      <th>R718</th>\n",
       "      <th>R719</th>\n",
       "      <th>R720</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>theta</th>\n",
       "      <th>Y</th>\n",
       "      <th>Vk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.387413</td>\n",
       "      <td>3.388261</td>\n",
       "      <td>3.383989</td>\n",
       "      <td>3.386556</td>\n",
       "      <td>3.394074</td>\n",
       "      <td>3.397028</td>\n",
       "      <td>3.393739</td>\n",
       "      <td>3.397158</td>\n",
       "      <td>3.404876</td>\n",
       "      <td>3.408355</td>\n",
       "      <td>...</td>\n",
       "      <td>3.659081</td>\n",
       "      <td>3.653163</td>\n",
       "      <td>3.657762</td>\n",
       "      <td>3.666290</td>\n",
       "      <td>3.665806</td>\n",
       "      <td>0.772975</td>\n",
       "      <td>0.443180</td>\n",
       "      <td>-0.149974</td>\n",
       "      <td>3.310231</td>\n",
       "      <td>659.262692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.136605</td>\n",
       "      <td>2.139789</td>\n",
       "      <td>2.140846</td>\n",
       "      <td>2.145031</td>\n",
       "      <td>2.154263</td>\n",
       "      <td>2.154085</td>\n",
       "      <td>2.153782</td>\n",
       "      <td>2.159520</td>\n",
       "      <td>2.162855</td>\n",
       "      <td>2.165611</td>\n",
       "      <td>...</td>\n",
       "      <td>2.320935</td>\n",
       "      <td>2.317031</td>\n",
       "      <td>2.319089</td>\n",
       "      <td>2.325015</td>\n",
       "      <td>2.320092</td>\n",
       "      <td>1.974190</td>\n",
       "      <td>0.360502</td>\n",
       "      <td>1.973691</td>\n",
       "      <td>2.200047</td>\n",
       "      <td>398.524301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.435912</td>\n",
       "      <td>0.434510</td>\n",
       "      <td>0.432887</td>\n",
       "      <td>0.431534</td>\n",
       "      <td>0.430204</td>\n",
       "      <td>0.428897</td>\n",
       "      <td>0.428586</td>\n",
       "      <td>0.426076</td>\n",
       "      <td>0.424837</td>\n",
       "      <td>0.423619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.433910</td>\n",
       "      <td>0.435507</td>\n",
       "      <td>0.439195</td>\n",
       "      <td>0.438088</td>\n",
       "      <td>0.439763</td>\n",
       "      <td>-3.587735</td>\n",
       "      <td>-0.130961</td>\n",
       "      <td>-3.141290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.662427</td>\n",
       "      <td>1.658761</td>\n",
       "      <td>1.651282</td>\n",
       "      <td>1.649301</td>\n",
       "      <td>1.645320</td>\n",
       "      <td>1.643757</td>\n",
       "      <td>1.638794</td>\n",
       "      <td>1.634365</td>\n",
       "      <td>1.633089</td>\n",
       "      <td>1.627513</td>\n",
       "      <td>...</td>\n",
       "      <td>1.681693</td>\n",
       "      <td>1.683174</td>\n",
       "      <td>1.684239</td>\n",
       "      <td>1.686629</td>\n",
       "      <td>1.687770</td>\n",
       "      <td>-0.192108</td>\n",
       "      <td>0.177132</td>\n",
       "      <td>-2.595014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.582494</td>\n",
       "      <td>2.581457</td>\n",
       "      <td>2.573797</td>\n",
       "      <td>2.581656</td>\n",
       "      <td>2.582773</td>\n",
       "      <td>2.594208</td>\n",
       "      <td>2.592848</td>\n",
       "      <td>2.600070</td>\n",
       "      <td>2.611988</td>\n",
       "      <td>2.622249</td>\n",
       "      <td>...</td>\n",
       "      <td>2.877362</td>\n",
       "      <td>2.871153</td>\n",
       "      <td>2.881315</td>\n",
       "      <td>2.884924</td>\n",
       "      <td>2.892054</td>\n",
       "      <td>1.153960</td>\n",
       "      <td>0.335831</td>\n",
       "      <td>0.125133</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.821970</td>\n",
       "      <td>4.807822</td>\n",
       "      <td>4.806420</td>\n",
       "      <td>4.816926</td>\n",
       "      <td>4.833722</td>\n",
       "      <td>4.850451</td>\n",
       "      <td>4.850525</td>\n",
       "      <td>4.860300</td>\n",
       "      <td>4.889861</td>\n",
       "      <td>4.930525</td>\n",
       "      <td>...</td>\n",
       "      <td>5.579594</td>\n",
       "      <td>5.559503</td>\n",
       "      <td>5.572615</td>\n",
       "      <td>5.582180</td>\n",
       "      <td>5.597586</td>\n",
       "      <td>2.314745</td>\n",
       "      <td>0.703569</td>\n",
       "      <td>1.371344</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1056.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.018440</td>\n",
       "      <td>9.031508</td>\n",
       "      <td>9.040556</td>\n",
       "      <td>9.012540</td>\n",
       "      <td>9.025507</td>\n",
       "      <td>9.035414</td>\n",
       "      <td>9.009221</td>\n",
       "      <td>9.022154</td>\n",
       "      <td>9.030491</td>\n",
       "      <td>9.004526</td>\n",
       "      <td>...</td>\n",
       "      <td>9.348527</td>\n",
       "      <td>9.365735</td>\n",
       "      <td>9.387755</td>\n",
       "      <td>9.413424</td>\n",
       "      <td>9.436586</td>\n",
       "      <td>3.749774</td>\n",
       "      <td>1.344648</td>\n",
       "      <td>3.141516</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1479.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 726 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R0            R1            R2            R3            R4  \\\n",
       "count  13000.000000  13000.000000  13000.000000  13000.000000  13000.000000   \n",
       "mean       3.387413      3.388261      3.383989      3.386556      3.394074   \n",
       "std        2.136605      2.139789      2.140846      2.145031      2.154263   \n",
       "min        0.435912      0.434510      0.432887      0.431534      0.430204   \n",
       "25%        1.662427      1.658761      1.651282      1.649301      1.645320   \n",
       "50%        2.582494      2.581457      2.573797      2.581656      2.582773   \n",
       "75%        4.821970      4.807822      4.806420      4.816926      4.833722   \n",
       "max        9.018440      9.031508      9.040556      9.012540      9.025507   \n",
       "\n",
       "                 R5            R6            R7            R8            R9  \\\n",
       "count  13000.000000  13000.000000  13000.000000  13000.000000  13000.000000   \n",
       "mean       3.397028      3.393739      3.397158      3.404876      3.408355   \n",
       "std        2.154085      2.153782      2.159520      2.162855      2.165611   \n",
       "min        0.428897      0.428586      0.426076      0.424837      0.423619   \n",
       "25%        1.643757      1.638794      1.634365      1.633089      1.627513   \n",
       "50%        2.594208      2.592848      2.600070      2.611988      2.622249   \n",
       "75%        4.850451      4.850525      4.860300      4.889861      4.930525   \n",
       "max        9.035414      9.009221      9.022154      9.030491      9.004526   \n",
       "\n",
       "       ...          R716          R717          R718          R719  \\\n",
       "count  ...  13000.000000  13000.000000  13000.000000  13000.000000   \n",
       "mean   ...      3.659081      3.653163      3.657762      3.666290   \n",
       "std    ...      2.320935      2.317031      2.319089      2.325015   \n",
       "min    ...      0.433910      0.435507      0.439195      0.438088   \n",
       "25%    ...      1.681693      1.683174      1.684239      1.686629   \n",
       "50%    ...      2.877362      2.871153      2.881315      2.884924   \n",
       "75%    ...      5.579594      5.559503      5.572615      5.582180   \n",
       "max    ...      9.348527      9.365735      9.387755      9.413424   \n",
       "\n",
       "               R720             x             y         theta             Y  \\\n",
       "count  13000.000000  13000.000000  13000.000000  13000.000000  13000.000000   \n",
       "mean       3.665806      0.772975      0.443180     -0.149974      3.310231   \n",
       "std        2.320092      1.974190      0.360502      1.973691      2.200047   \n",
       "min        0.439763     -3.587735     -0.130961     -3.141290      0.000000   \n",
       "25%        1.687770     -0.192108      0.177132     -2.595014      1.000000   \n",
       "50%        2.892054      1.153960      0.335831      0.125133      3.000000   \n",
       "75%        5.597586      2.314745      0.703569      1.371344      5.000000   \n",
       "max        9.436586      3.749774      1.344648      3.141516      7.000000   \n",
       "\n",
       "                 Vk  \n",
       "count  13000.000000  \n",
       "mean     659.262692  \n",
       "std      398.524301  \n",
       "min       24.000000  \n",
       "25%      303.000000  \n",
       "50%      577.000000  \n",
       "75%     1056.000000  \n",
       "max     1479.000000  \n",
       "\n",
       "[8 rows x 726 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fast_load=True\n",
    "get_new_ccs= False\n",
    "\n",
    "\n",
    "if fast_load:\n",
    "    datamugroso=pd.read_csv('pddata.csv')\n",
    "    data= datamugroso.iloc[:13000,1:]\n",
    "    \n",
    "else:\n",
    "    if get_new_ccs:\n",
    "\n",
    "        K_corpus=1500\n",
    "        N=100\n",
    "\n",
    "        kmedias=MiniBatchKMeans(init='k-means++', n_clusters=K_corpus, batch_size=1000,\n",
    "                          n_init=10, max_no_improvement=10, verbose=0)\n",
    "        kmedias.fit(lecs)\n",
    "        ccvk=kmedias.cluster_centers_\n",
    "        mbk = MiniBatchKMeans(init='k-means++', n_clusters=N, batch_size=1000,\n",
    "                          n_init=10, max_no_improvement=10, verbose=0)\n",
    "        mbk.fit(xyth)\n",
    "        ccxyth=mbk.cluster_centers_\n",
    "    else:\n",
    "        ccxyth,ccvk=np.load('ccxyth.npy'),np.load('ccvk.npy')\n",
    "        print(\"CUANTIZANDO SIN RECALCULAR CC\")\n",
    "    aus=[]\n",
    "    auvks=[]\n",
    "    for i,lec in zip(xyth.values, lecs.values):\n",
    "        _, au= quantized(i , ccxyth)\n",
    "        aus.append(au)\n",
    "\n",
    "        auvk= np.power(lec.T-ccvk,2).sum(axis=1,keepdims=True).argmax()\n",
    "        auvks.append(auvk)\n",
    "    print(i ,au)\n",
    "    data['Y']=aus\n",
    "    data['Vk']=auvks\n",
    "    obs,est= data['Vk'].values,data['Y'].values\n",
    "    np.save('obs.npy',obs)\n",
    "    np.save('est.npy',est)\n",
    "    np.save ('ccxyth.npy',ccxyth)\n",
    "    np.save ('ccvk.npy',ccvk)\n",
    "    data.to_csv('pddata.csv')    \n",
    "    print('new obs/ est almacenados')\n",
    "    data['Y'].describe()   \n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff8826adf60>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_s=data#.iloc[1001:2000,:]\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.suptitle('Training Set Color=Vk', fontsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(x= data_s.x, y=data_s.y,marker='+',c=data_s.Vk)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff882674be0>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tlLoJwGcA/HYYDfkEAHcqpW4EcC6AZ4jIAaHz/TPCbYQ4hQIYIWRstCnfZ8rmi4RXqqSh+AiACwA8RESuE5HfLTj88wCuArAFwLsB/CEAKKW2A/hbAN8J//1NuI2QxqT9IJiIlbikzMDIKEjSDUHH43Q3bErXglRKvaBk/6bUZwXgFYbjzgJwlmX7CLGCSxERVzAKkvQJ8SDtsx/7hZnwyfChDxghZMTkfQxjHzAXIlOqCGrU/EIBjIyK+bzrFpAxwbUgCSFtQQGMDJ70S9sKJTDiCCttADUHxCNtCf5MxOoXCmBkVNAJnxAyNkyzmovpLi10cfr0CwUwMnjSk8bKKmcQ4o4yx2cfjtGEAFlzeFv9jrOnXyiAkVHBpYiIK2zMMTTdEEJsoQBGBk8mDxhdwAghY8NTIla+v/qFAhgZFXTCJy4pTcTqpRWEAJLqbfFi3M6XIqIE5hMKYGTwpCchOuETV9h0JXY7MlSU8Qtpm9JM+IQMibvuW8Ed9+6qfPyea5ewbnkJd+3cDTUH9lu/psXWkcFRlgeMKjDigbxc5DQRK+kMCmBk8KSnoD/52Petzj1kn3V4/TMfij/+aHDeGaceh99/8s85bB0hhDTHh7BPcc4vFMAmRqTpacLa5Rn2XLvkpkEOeeETHoSjN+xV+fiv/WQbvnLFNlx3+30AgKWZ4IY77mureWRgWOVh5ZOLeCTWgLnIA6aU9jNpHwpgI+fPP34JPn7xdbjmjc/EBy/8Gf7qP3/YuMw1S4Jz/+QkHL1hbwctbE40aTzn0Rvx6CMPqHzevbtW8ZUrtsXrR65dmvENkGSQEhtk2X5CXOBLMKL85RcKYCPn4xdfF3++7vZ7sTQTvP60h9Yu79rt9+J937oGN965szcCWFMiAWxpJnTiJwk2TvjttYIQAFl3xEjwZ78bNhTAJsbSTPA7Tzyq9vnfu/Z2vO9b12DXSn/SPUSTkK0uIlHjq/g75S9CSN9oc1pShs+kfZiGYkqo5nmL1i4HXeb+HglgdWeNWSiBRdnzl2bCCYhkKHN8ZhQk6YTcy6Mr+ALqFwpgE0Kh+QNjXSiA7VrtkQAWYrs+WnR09FOWROiESmKsliJivyEt4ycKkv3YJxTAJoSLh8TapSD6sV8myHq/a9EEKXwDJIT0jvy85FIW45zXHRTAJoRSzaO2IhNknwSwCNtfFpsgYyd8TkYkCy2MpM+4nq44//mFAtjEaKrGTgSwVQetcUPTSSPyAZsJoyBJgtVSRO01gxAA9i4WpP9QAJsQCu6c8PvpA2Z7fBjKHT49BXyQEkL6R97NIj93uSqbvox+oQA2IZRq/ha1dimMgtzdHwGs7pwxCy/F6lxhJvQBI4swCpIQ0hYUwCaEiwiXNUvBE6eXGjBL/V4cBakURCTMA0YJjARwKSLSJ0T72XEaCqelkTIogE0I5SAPmIhg7fKsV074dSeNRI2vIAgTsTprFSGEuKFVAT9VNl8k/EIBbGo4MJmsW5r1KxFriK05KGuCFMyYB4zk4FqQpI+4XIw7DfOA+YUC2MRw8bhYt2bWKxNkbaEpTkMBQIJrM+f8Q0Ls+hU7DmkZyvqjgwLYhFChr1NT1i71ywRZl+hKJCZILkVECOkf+XnJ5WLcmbUgOQF6hQLYhHCxFBGA0fiApdeCnNEJn2hgFCTpCz7M3Zz9/EIBbEK4ki36JoBF2OcBC/6uzhUkNEFS/iIRjIIkfaU1HzD2Y69QAJsQCsrJO9Ta5b75gNU7LzFBImWC5AxECOkZLU5LFLq6gwLYxHDlA3bJ1jvw0vdvxsc3b3XQqoCrb92BP/zQxXjp+zfjG1fean2+rYo+vRZkEAXJyYhkKetRNEESX6T7Wtp/1S2cAH1CAWxCuMgDBgCnHn8YDt13D1zw01vx4W9f66DEgG9cuQ2fv/Qm/PePb8ZnLrne4sxmKrDV8MIIuBYkSeBakGRqcPrzCwWwCeHKCf/3Tjoan//jJ+Exmw5sZcCuXZ7VKtfaByz8m0nEygmIpCnpVMwDRnzQpmtEdi3I1qohGiiATYg2Bldb47WOA7R9ItaUCXImTENBCOktovnC+WrYUACbFAous/m5fvePJpOZJ6VCHAUZOeGDaShIPdhtyFBJ910GIfmFAtjEcOk0LC3lbRBILf8b68W441Du9GLcVkWQkUMnfNIH8vNSnIiVaSgGDQWwCeHKCT9TpuPyknLbnwmyUZDBd84/BLDXhFJzQNrGh7DPXuwXCmATQSkVCGAuNWDuigKQvH3ZmiDr+oBFrM4D06wIGAVJCOk9sfbegcjEGa87KIBNBKWiRKwOfcDEzlRoU67NrFB3EopyokWCKTPhkzylSxH5aQaZOL6mJc5/fqEANhEU2oqCbGfE1inV9mE4i53wAxMkoyBJhO1Y4YOLtE365Tn+5NoHjDOgVyiATYTIp6XfJsh6bay/FFHiAybgYtyEkOmhsmGQxCMUwCaE2yQU7SUurZsOou5i3EEUJE2QZJEykz2jIIkP8vNh7D7RRWOIMyiATYTIBOliLchMuS35gFklYq1ZT9YEKWEUJKc0Yt+n2GtI2zAKcnxQAJsIkRO+WxwLc1GptYu1PTEyQSb1zud16yaEkHbIz9yJ9t5B2WkLJE0AXqEANhEUlPPXG8tgxerlwnIh5JqTxqIJkhowkqX8ZYA2SDIeOPv5hQLYRGiaK0tHW0v31I1GbLQWJDPhkxTWiVjZcUjL6KY3vjAOGwpgE0Kh307DsZDoqb6ontVIA0YBjBDSQxaXIvJTD2kXCmATQinXiVidFbVQbq0oyBr1AMkSTTRBkjxcC5JMCc5+fqEANhGUcq8Bs10026ZkqyjImm3ImyBnM74BkgB2A9I7UpO3Syf8NDSl+4UC2ERQUK0sxu2SplGQ1ik2ojQU8+DCCIRrQRJCJgWnvO6gADYRIg2YS4IoyBac8AE/a0FG5ysVmCBbiuokw4VrQZJ+0k4iVs5/fqEANhES7ZJbH7B2ErHWE6rs14IMTZAqioJsy6RKhgbXgiR9w4uwz37sleWuG0D8EWl6nJbpsqxoLUhL37Laa0HGJkikliLiDDRGlFL4hy9egdvuuR+veuoxOPKg9V03iZBK6OakdA7DxuWnZnEGIfmFAthEUCocWo6d8Nugvg+Y5fGRGl+lFuOuVzXpOdvuuR//9j8/BQAcv3E/vOgXN1U6r0xj7HppL0JMsKuND5ogJ4IK/+d0DNdMF1Gh2FoaMFuBML0WpEhgkqQCbKTYmhQtT6DmgPgk9l91XC7nP79QAJsI7awF2dJSRL4WxU5FQYoE4hujIMdJ+q7SzEyGRNvdNbsWZLt1kSwUwKZCvBSRQyd8ZyU1o276isQEiSQKkhMQsaAvY4CMn7SGP57HXecBoybXKxTAJoTrPGDSktOUL0FoltKAzWb116Ak/Sfzlm95vG35hAwJdt3uKBXAROQsEblFRH6Y2vYmEblcRH4gIueIyP6pfWeIyBYRuUJETk5tPyXctkVEXuf+p5Ai4kSsjl/Z3UZBBn9t5bq6JiVJpaGQ8P2S5imSpjQPGFVgpGV0M1LiA+Z2vuL055cqGrD3ATglt+08AMcrpR4J4CcAzgAAEXkYgOcDeHh4zjtEZElElgD8C4BTATwMwAvCY4knIh8wp2tBOispX66fp1o6lJuLcY+bTKg97zEZID6EfQ4Nv5QKYEqprwHYntv2JaXUSvj1QgCHh59PB/BRpdT9SqmrAWwB8Ljw3xal1FVKqV0APhoeSzyhAOcasLqLZlcrt/rxdX3A0iZIEQmiIDkFkRpQqCNd4KLfpedw9mO/uPAB+x0AXwg/bwSwNbXvunCbafsCIvIyEdksIpu3bdvmoHkEaDFdhMPyIuHHepGN2o0IapqnnPDnnIBGia0PWESZNtaXtpZMl6JErGTYNBLAROT1AFYAfMhNcwCl1LuUUicqpU7csGGDq2Inj8Jw1Mt1IzVtz0sfLhJl4B/KVSJtYu2E304zCInxI3OxJ/ukdiZ8EXkxgGcBeJpKnlrXAzgiddjh4TYUbCeeCEyQLteCbCdxqXUi1pqTxix1LYK1IDn9jBXeVzIm0il0mpLNkde8PFKdWhowETkFwGsA/KpS6t7Urs8AeL6IrBORowAcA+DbAL4D4BgROUpE1iJw1P9Ms6YTG1SYCt9pGgq4jcKJB39NQcj2t0nuMxfjngY2Wk5GQZKu8TklcfrzS6kGTEQ+AuApAA4WkesAnIkg6nEdgPNCjcqFSqmXK6V+JCJnA7gMgWnyFUqp1bCcVwI4F8ASgLOUUj9q4fcMjq9fuQ1/9JHvYWW1edc/+fgH4B+fe4J2X1tpKNrAtolNF+OOPjMNBYmwXoqI/Ya0TH6+AigwDZ1SAUwp9QLN5n8vOP4NAN6g2f55AJ+3at0EuOKmu3H7vbvxW084EmuXlmqX8+XLb8YlW+8wH9DKmkHtqKwDTZR9wfZRkNnM0jOaIEcLBSRC9GSXIuI48UltHzDilteechz22WNN7fNvvmsnrrj5buP+yAnfaRoKuDXZpSyQdolYHdQdmSC5FiRJMwCFMRk5RVOSa4GJs59fuBTRhFDKbSLWtqibENX2t+lNkPb1kv5ju+AwoyBJ33AZQGWC859fKIB1TLL8TsPBVZIUNciE7z4RaxvYClJ1J43FKEg64RNC+o9bHzBOel1BE2THZJOPtluP88W44VYFXnctyLg9lj9uQQPWUmZ/0i9sHOwZBUm6xufqHL5qumTrHTh781YoAGtmgt876WgcfsB6T7X3BwpgI6HMbyrSgLmmrQFrIwjVnaDSmrZ4Me5aJZGxYd0P2HGIR1zmAUvj6wX0wxddi7Mv3ooD16/FbTt2YdPBe+El/+soL3X3CZogOyat9Wm1nuiD00SszorKlVszE77l8bOcBmxGE+RosfUBI6QvtN1fuxgPK3OFB+63J7786qd01oY+QAGsY1z1Oymx2ymlQid8d7iPgqxnjk0ncLUha4KUcC3Iic4EREvpWpC0QZJOGeZ8tTqfY3mJY4cCWE/wFZ3o+nnRhn9C3SjIGjVlPnEpovGS7qdV7rGtKcannw6ZJrpErK7x9f65qoCl1I+Y6uihANYxrkyQlXzAXDvht2aCtDs+UYDZnZg2Qc6ESxERQoaF87UgPYlCq/M5ZjOZfKI9CmATQkE5Xozb7RtTLIxCvEwE6WshIlyKiCxQGgXppxmEZIOGhq4Bmyssp96ApzrvUgDrGFeCRlkKhXb6t7SzwpGtYBcebJ2GIveZJsjxYp2ItUH5hJBiVucqk4dxqlAA6xh/UZDu84C1ha82Lq4FWW8NSkIIaQvdlBSnoXBcvlcN2JJMPo8eBbCRUCWHlVLuM+G3MmAtfbFqBkFqlyKaU/4aJW35uUz9AUL84aOvefMBU6AGDBTAekPbUZBBIla3a0G6ewfLMhP/UWXRYtzUgBGAa0GSfhMvReTECT8VIexNAzbH0owCGAWwjnH1wC+L4FMIB1eP+3x0LermAbMNMJilJoBZmAeMD9Jxkh5nNkOurE8NYXF7MmzGmOJkda6wNOPooQDWE1r3AWvh1abNfF0+3sQyTvjiPrEsIYS4Ih80BLgXznxNf6tzlc0DNtF5lwJYxySpF5oR+IAVREGG/5xnwndYXsRM7MqtqznjYtzToe21HdltyFDJ9F3PTvhThwLYRFChBOY8E77DJ08mIrSGE74ti1GQNEGSLGXDhX7EpG20UZAOfcAydXlLxKpCt49pDyAKYB0TR/A5SIVfNhidO+H3bOw0zwMmXAtypGRD7cvvse2DaIx+OqRf9G2+bcKqUhkn/KmOHwpgHePKBFmhJvdpKNCOxkjELhN+bZkpvxh3k7IIIcQzw80DBkZBggLYaChzIG8r4avTpYjCv3UFIfu1INNRkJHgR8ZJvTvLpYhI1+h7bjs9z58T/hxLwihICmAdE2l62s+E735w9cV+70ABlixFNCIV2Jh+i0sqLUVEJ3zSM3TiylDH+OpcYSnlhD/Qn9EYCmAd46rjlclCSgWD1fU7RxsTgG0UZIytD5h2Me46FRNCiD9cvvtmE7H6c8Jf6skLfJdQAOsJPrRJCi2YIF2WlTKT2kwEdSeNWcYHrIHg11MoTCZk/FwszisdLnyGkJb0NhrlAAAgAElEQVTxqeXyZoIMnfCnLoNRAOsYVx0+0N4U5QFTzh/IfRs89lGQkvksAkZBEgD245K9hrRNJm9hS3V4c8JfzUdBThMKYBMhWAvSraZN0E7iLG+aKM1i3JS/xkkm1yTvMSExXYyHVUUTJEABrHuUcqJJKlvHsLUlg5yWlQQk1IuCtCNtgoyiIIHhOrbmGcev6BauBUm6RjeOk7mq/braYHUOLC3J5MfPctcNmDq+OryCCoQ9h2X24QVGKZXyHbNrUMYJH4k/gut8aaR7sj5gFRKxthAGec73rsObz/uJ9tBjDtkb733J4+zqNPDur12F919wTfz9qccdgr85/XgnZZOR480Jf861IEEBrBe4eNZXWUjatRN+md9Z7XJLTJCu6tQtxg2MR3MUXCdKkn3h21ffjlvuuh/PfORhme2X3XAXvnLFNmf1fOunt+Ke+1fw1OMOwbev3o5vXHmrs7JJPxjFYtxMxEoBrGsCbYuHCEgV1uWwzDKzpy2ZVQEqClmBb1u9xbh1a0EGZVJwGRvZUPvq55UmYrXqJgr77bkGb37ez2e2vuW8n+Dym+62KaiQVQU86KC98Obn/Tz+6CPfww+vv9NZ2cQ/Y9QORQLY1C0N9AHrGFdvMIEwVFyWgupN8tQiquQ0c12PSPJ9PsAJbz5X+PBF1+L+ldWumzIKuoiCdKXZVUohleNyNBrdqZPNWxj8de4D5isKkmtBAqAGrBf4EInaWHNSpNzsWZeqxSq4WWYpWow7KHN4k8FnL70Rf3HOpbj+jnvjbcP7Fe3RBy2CybfQ9TvR6lzF2t0BvG+RjrH1j3QBTZAB1IB1jCuH77LIwVbygMHtgK26FqSrGrNrQUprb5U+uHvnbgDA9h27O25J/3GZiNXlI8RVv5srhVlauzDEDk0SCl6eB6sBYyZ8ABTAJkPsAzaAPm9jJlVKpQQ32yjI7OfYCX+Az6shttknba/tWHV9SR9h93OVTbFCxseQ0zcopYI+mnlJ6LBBHUIBrGMCd283cZBlfbieq3phlS2aIAuy+rcRBYmUX8VIjHdTndRK6dmFcR19O0+Zd8RhuaQbCudCh+XXzb9oy2roZLtMJ3wKYF3j61kQ+Eq5SfoaIQVC34133ofLbrgLd9y7q3qBYftsMtI38QEzR0HaldMHpj6RtUZZIlYbbS3cjj8Tc6UyfZuMg7zG3nn58COsr4YTLH3A6ITfD3z4gCnn+q+w4MVNt+/YhSf+w1ewOld48CF747//7MlWRZb7s7nBZILs83qQW7ffi5f/x8W4b1c22vGqW3csHDsWTZ4LMmkoLI+vVH6DPpP4HrpJf7KaSm3TZqAM6R4X1oB0Imsf/oKRBqxMAHvFh76LH994FyDAa04+Dqcc/4DW2+YbasA6RsFtdnpzPe4xvYXdc/8KVucKyzPBnfe16xSezgNmizETvouGtcSVt9yNH91wFzYesCcevnE/PHzjfth4wJ5dN2u0uHTCd52Hz1xPNg0FGTa+BGhfCqlYAJPipYjO/dFNmM0E1952L75zzXY/jfMMNWBd4yqnVUlhbTjhm6IgowljZjmik/f/skz4VsUWEmnbsmtBuiu/Lf785IfgkYfvDwD4/tY78HVmOy8kE2rfs/vr+rmXSUPhuGzSHdooSKfl++ktkQBW9nxQAE5++KH4wLd+5qFV3UANWA/w5a4RaNvcVqZ7mEVC2aymU2cgFBU5nqrM5yZ5wKJTIt8zoN9h+7qmmX52j39G/7GNgqx4TJHPmDMn/HyEWa91uqQOLp8Z6d7h2wk/qbe44rHOZdSAdYxLf6biTqrca8AMZcUaME9LLDVhHgtviQnyHV/9KfZYs2Q856nHHYKfP2L/ZhU3ZMhh6F1jI5C4XYqovTLSKKUScxK7yeDxJXtUWU3FBZET/qwkClKpUGEw4j5MAawH+HiYusgWn8cUBZlOqGozfUTrYpY10SR01bmODztsX1x241148Ia9cci+67BmSfCur11VeM4Pr78TZ734sdZ1uUCrAbOIjjr7O1tx01078fzHHoFD9t3DbeNGhO1jqGoesKZlVCGfZXys2oOpkV2KyL3Dqotnw87dq3j/t67Bjl3mJdHu2bkCIK8BWzxOpdo0Vi0uBbCOcZUaoiglBBCma2hezWK5mpETRRHW1YDZ5qNpYjL8/B8/KfP9yjecVnj8c97xTexendeurw3SgmfRpbjn/hW85pM/AACsX7uElz7p6Lab1hv67APmmrkaxpqvpB8kEfLNI2a/v/UO/P0XLi89bu3yDJsO2qtSmWPuyRTAOsZbHjCVUuk6oswEWTdRa902+njmzEQ6TVMRX9oaOYFWV5N2rwxxxfEOKOuLNn3VlAfM9RqkQSb8yAmfaSiGju4FM/ZXdfhaHZggmzEP55Wzf/8X8LijDiw9fqXgZTYdNTzWPkwn/B7gJA9+meN6tGSP8yhIbW0A7DVg6ZQcVSeWKLrTF0simPdLAZYhfcnz1yUtOI51QjNhn9fLtvxqB/lKxMo0FOOj7b4TJMBuNjHUPXti01EMBbCOKYuMcl2Z05oMSR6b+puVJ5U1nFevOitEEifSLmgyQWainSY75fV3snfpAxZrwCiIjZIkeW/zsqIiXARNOfc1Fhm1OZ0CWMe4mnTLlpGIfMB8dOa0E36dn2fTROVZlFia+ckWbYNJ65W/MmrKGrCav7e0L1r1VX8BN7Y5+Eh/8TZUHZggras0DLDWVm7pGRTAeoCf7Nju6yrLP2VtgkyZaIqFSf1eH8LlTCTOY9MFWh+wind1YjKXkUoRix0sReSKeSoNxdgfYFOi7USsLgqMF/a2Pa+g3jL3miFDAaxjXOlvytZ8CxKWuu3E2TXssnUlbbKvM3ActvcB8/Gwmc0EffNfr+qQn40E7NmPmACuIp7LWExDwXs9NlxqUtMvzH3pKW2kTeojFMD6gBcVWDavSqtVxQO6ZgEl53X5PJlJPxbrrjIB55uZWUGg+5/glbr+b6UWSEcpZFwyV4k2eOwPsClQxx+2Di76iq3gZLSixPultivLEKAA1jEuF+itspyDWxOkxOWmiQQU2wGd9pGpOuAC37Z69dVhqes0FJqqq/7urH8YKaKNWxz07/brTJsgo3rJCMgkYm2heHSnLZ1qUBAFsIkQCSou/aRM/lpNVdqiKzRdfo0yXSEi6FkeVqMGxXRf8p+ngJqQ9BmkoaDqawq4EVxSLiNOSgKavuqr1Et8mXvNkKEA1gNcCEVlSfTa9JMyvTXVtkBaTARKpRfjbv+hszTr2qdmUdtXWQM2dsmjBUrXgrQoK1pqy1SHq/uzOk9etJiIdfjo+kUbM12gAWuh4KI6Tcm8/TajMyiAdYxTx9xSJ3xH9YSURUHavrmoMhtNfFx3w7PrKEgdVbtPUYqKsaMMn6scX6n8Hl1OpZBxwifjQBsF2TsfsJruJ4bfIRh3JC8FsJFQ5sgbRwu6zIRvMkE29Mmy8UVQmvrbJIiC7N4HzHRt05sXo1MXyyH+MA0/1w+YTBoKmZ6wPQkcdppkLpDe9JX8PNeXdrmGAljHuJSJCk2Q4d82EkEuRNs1DCEuNafWK9YJgRN+hw3QkEnEWnBcJhFre83pJbUTsZatBenQ7O00Ez41YOPBU0qGshVIqpA8Z6rWWX7kmN0ZKYB1jLNM+BVNdy47szGLcfh3ZpkHLFoL0tavBp7yKwFBGopeJGLNXCV7J/wpq8Cq9ElbM3eVN3RlCEN23XeVApciGjm20eLVynQwLdT0xzVbUfSR9mOBAlgPcPUWXbYYN9CSE75hyZsma4v5yn1jS9cmSB3ZrPhmJhQIqKH/v9hVC1fzaSj6/9NJh6RfmPvCVPpsqQAmImeJyC0i8sPUtgNF5DwRuTL8e0C4XUTkbSKyRUR+ICKPTp3zovD4K0XkRe38nOERaX2aUlZG5CvVxhhbTPiZtKlWGgop8UXIeVQ7dm0rZCaCuUYDtrI6x9bt98b/br5rZ7xvPldYcZy7os59nHIi1jRWv91lFKThePeJWJmGYkzotN7x7XU4kJ044ddciqiY/mTod81yhWPeB+CfAXwgte11AM5XSr1RRF4Xfn8tgFMBHBP+ezyAfwXweBE5EMCZAE5E0J8uFpHPKKVud/VDhorLB2FhUcr9Q9cYQlwzEgYqzPvSrFmtYvIBe/05P8THNm/NbPvA7zwOJx27Ab/3gc04//JbcM0bn9m4fm0i1vT+gmOnHAVpi+1YcTG2XET3RmlZEq36eB9exA29WKLMlMqozw8DB5RqwJRSXwOwPbf5dADvDz+/H8CzU9s/oAIuBLC/iBwG4GQA5ymltodC13kATnHxA8aAk+VMSspQ4X8u37aNCUDTfgA1x3PZupbpz6b8Sm0wmwUmnjy33nM/Nu6/J/7xuSfgdaceF28DgPMvv8V5OzIeYBV/+5RdwOqaX8uurFW389BPo5cDpqGYBi6HsYu1IOsEYOmOTfJWipPggL5S1wfsUKXUjeHnmwAcGn7eCCCtBrgu3GbavoCIvExENovI5m3bttVs3nBw2a/KOqlSfk2QTdaC7OuAMwUWKAAH7rUWv/GYw3Hq8Q8ItrXwG8qSMhZd8rTvWk8v7yQxpXOpQ3SPM2koeLMHjU6ooXg9Dho74avgaeRsiCul3qWUOlEpdeKGDRtcFdtzXGTCr5AHDG4FMLMJMtxv+btUeE6VnGbpz6786KpgSsSajjBtI0KpCOM9LTJBTuyh3Lb2r0qRPvpp1DeZhmIauDR9O0lD0WDuz35P2uR3NvVLXQHs5tC0iPBvZGO5HsARqeMOD7eZtk8el1qpIr+edLI9V8R+oIYoyAYWyMoo+BUmlmblecBijUYLDWuWYy1ruiXllL3YtJFXrwnpdViBcT+8poJW692CKaOrpL39GkF+qSuAfQZAFMn4IgCfTm3/7TAa8gkA7gxNlecCeIaIHBBGTD4j3EYcdfhKUZAtOeEboyBrThLBm1iBMFnQlrYRgTYKUhfh5k0DZswDlheMMzsnhW0AQhtO+GUvWy7G52rOBEnGg+6WunzJc7FuaDL3Nywn1qSN24xeGgUpIh8B8BQAB4vIdQiiGd8I4GwR+V0APwPwvPDwzwM4DcAWAPcCeAkAKKW2i8jfAvhOeNzfKKXyjv2TxdVcWZw7K7AU+xBUkrdwuwkiMuPZJWL1+84WREHqTJDJ5yRE3H39SZGakPTK505O/uoFxvXuHA7KxAeMiVjHjMvb6kpoatQG40u896Z4pVQAU0q9wLDraZpjFYBXGMo5C8BZVq2bAM5MkKVRkGFdDqpKqtQbOCKRqFEi1qJ9mieZL1PQbCbaKMigEdFDL7ou/RJzxvoWWYW6mgKnUZAo6acO7k+knU2PvSnf9zFQHBHujrp5G9PUGWdVzPxj7cPMhN8x3vKAoT0n/IVB18hPyU7l7NsHbGbIA5Y2QbaQIzGpxyLHWr7+TBTkWGe0ClQyF1o+iiqZNQ0aaJevDkxDMV4yUZAt3F4RdybIpiQrt4y7H1MA6wEuOlnVyEEfHXoe12U3ICMNXelv0W30NE6XZnofsExTHKYVqEJlEySjIHuNC43pQhoKJmIdNW6iIIO/wTziyCfZcj5e8FdNlTNmMyQFsI5xaqYqLEo5X4zbVG1+IVVbbKJxOklDofUB06ShaPHJVyURq+m+6PYRPS7Hiw+hd840FKNDG3TUwoznosQ6L/pVjuybO4crKID1AC+Z8JX7h65ppfr0G1Xdh05xQEG9Ml0wC9X0ej+08G+sAfPT0KrdZ9IasBaiGpscn8YUTVyHSDmbdsKfsrl5TLRtvXBhgqxL0bJpY4YCWMe4dIwvzAPmuK6FwjVfbZ3wAx81scofphBIlr7U1NFvKrJCtukDpq2vxm8f6xtlFboSSKL+3SZMQzERHLo5pBfQbl5edR/VqgjGK5BRAOsYV/2qkhpXKacPAFNJiQOl65LD8jsUHpbCEZNPRZFZj7JFH7BEu1h+dfOCxpQ1YHUpu8xOtNfNi4jJR0G6eaiSLvH1wtCVtlQ7hizmuSFDAawHuOpkvs12JlNbWgNWV1iq3F4VRSD6GajRvcovR5T+nXFbfE2cFX+7Ln/ZVLD1f2tLeC66U060GTkTJBkRmlvqOhFrU+qu1GH0I0ZoGm3csn5CAaxjXI2fUh+wsEv7WIy7bhqKOAqyLBqnw9EYhffr7tuiD5h70hNTXK8hPN1wW4LPExbG+obLt/zIBBn107FrEKaK09ua9tl1WCwphwJYx7g0pxWVFCbCd4rRBJnyA6jznLeZWwLfNj8Z/oHEtyYfCZlOqOvdB6zicWmha3ITbU3za5lWwE5rUNxPXQjFkZYzI4hP7maPC5/3z9lSRFZRkIvH5jVpY+3DFMB6gBs/kvLcWa5NdUnG9yzzefC3USb8firAUk745lYk0aHuW6pV8VfNA6Yph+ixvXdN7rXLl4fIB4yJWMdHjSFvV75IdwEqhkCusfdiCmBd47C/lw2e1vKA5bVB4d9ayfikXBWedyZvLbpTQyyA5X3AVCLcxhowT20yURTaPbUoyIzw2dFvL1t2zEWr8mkognqnda+nhJNErOFfFzJ7XR+wImzyQg4NCmA9wE8esKaRidXrjOqa1XSe7PPyE5FmIZ+GIhIeAT9q8/Q1qnq9MibIcc5nzvERBemS1TgKsuOGEOek/fna8O1z4QNWS1AqCC4Yuw8jBbCOcWkWLOv6rp3wTZoeN9Fc5lKyEW1RcIGfgRplGM9HQaYxLVLuAq3zv0kQNkSnThHrxKotlG8a6y59Bueplx+AjtVjx7lmyFVQmHW1RS4dGG0nXu66AVPHlXlAm0olp/FwrvUozYRvN/unoyD7Ot4izYI2D1j0JdaA+fkV1Z3wU59backwGLr2b+fuVfzXJTdg58p8Yd8Nd9wHoJ7/5e07duELP7wJCgpPf9ihOGSfPTL779u1iv/6wQ24X1NvESccvh8eefj+1u0hZpwGQabM1o01YI7GVl03lqFBAawHOOlkmmUk8j4/Cm4TsZppZgYpO61LH7AlgxN+oN0IaPMSN5mYpmyCbMuHxCoG0uSDaXkzv7nlVvz5J35gbpMAh+67R9i+6m8zH9u8FW/8wuUAAkHuz08+LrP//MtvxmsK6jVx3AP2wRf/5CTr80hAsr6ieZ8LfLinVK03/Zv77JLSFApgHdPmczAf9eZaUElMkIvaIKB+ItYu1yQrI9Is6EyQZWkoXK9EkNRrtEEWfO3pBfZAlV/etslSX0a1UnavBhqoD7/08Tjm0H0W9q9dnmG/PddY17871GwtzwS7VxfbEtX7yT/4RRx54PpKZZ7xqUtx9a33WLeFFNPKS56DKMjkbOsIrCa7BwsFsI5xJRSVmSCBsBM7jk5JCs7Vg3oasCopHFTuc9oBvm1mpkSsKml7kp5DY6Zs2M6iRcDLz9V/ngJ1f2+ZwGwjUKe1pJky7JoU/5YD9lqLDfusKzzWpr/F43amfxBHmw6qUG/EujWz0T48+4BLwb9Prlbx3DlyGySd8HuAD7NgotL1V9cYs7jEiVgLlyIKtxVqoNxRUQE2aRNkmq5+e2m9lu2qOm3Y/tyyYkf+TOwd6cTWEW3M43UTZ6dJohft6l0sKNwXt2ucExYFsI5x1a2S1AfpCMFcPQ40MJk6DdF+6WzctTLhl+YBU5nPXn3A4jQUBSbI6F7k9ruYRHQ+YJXTUGQ+j3NCc4fd9al8azUDsOm6ea5I53DS/Z7aq1qwq/WaIh8zb23osO4uoQDWA9rq+FmTk3L+0DXlu6prgsw4KVdsqu8Xo0hbqY2CjH3ADNGhrbeumEmbIAu+FVGqDardBvv98XEW2mxBdcE/MUXpo+Fi4X+Emu2h0UauQanps6stq+H56RfNPplGXUMBrGOUI/WN7qGfzZfVXrTgoq9T8UReRtl5+lxYfh4KSRSk+ZhEA6YPTmhErKXIqMBSH5MviwKgXjtKFrF3wq92whBEF7dZzJunNpg6bWSX1yHo5sVsygI9BbCOabO/6zQwbSRiNTGr2busTTIeZw2zD9jiRFIkALkks/ByQR2T1oD1wP/N1E9NGlNjORqfIBM2OfXSpii9CdLev4e0i4s5Ja1talqa0r0gVjpP/7IqkF5HxTeFAlgPcJQGDEDBAGrBCd9ogqxZVyDERGXYjThfD4WZwQcs3V7zEk3ttKlyFGTm80hnNMe4XopoCMKLS22yjQmUdEtXy/7oqrV5yRgyFMC6xtPcpKDMiSAbl71YF1A/esXG5q9UVnBrm2Qx7sV9iz5g7m9uOmQ8qTf5ljFB5q5iRmic2DPR9udaH1/xBG0aCmuNr7msxbKraw8SHy+9gD6xLtMbinzvnCdibViebn5qyph9wJgHrGNcZafPao40/mDKfSc2abjSiVhrlVtyWpcv1EvhK8uXLrsJP77xLjz6QQfgwYfsnbm2ZZrBzpiu/JWh8/tgYIxayT4vKzZkXL5Ip03Lq0rh7O9sLT3nwL3W4pcfdqimrLpt0H8fuQKMAlgfaC0Ksm0nfE3qCyDrNGozHoNIQnt/mPR5bbNh72CJl7d/eQsA4IkPPhj/8dLHZ9prCuR04q+hcchN//LMPS9ywu+rFNIWNX9umRndKhGroZ/a9ty0z04ZVmWnnnra7uHJGZxk8TVWD9lnD6zOFV7zyWrLTV1wxlNx2H57avc1XoooXc6I84BRAOsYV/1K5wOmdbpuYfbM/4bI1FVfA1YcDt2lpuARh++Hi//yl7FzZY5Xffi7uH9lNd63kAnfkwasckLOcc5h1lTpP50sRdTS/elyvHQVWTdG2k7E+qJf3IQzTjuuMMIbAL5w6Y34u8/9GPfvXvTD8JnwewxQAOsBPt8oXdvmdaiS/XXL1dalQjNuzbrqcNDewTIs65aXsBI6g6X90BIN2GKkZFN0/iCmzwvnTtgE2Qfznqmf1o/6ddvrFZK8S/r9kamKD9e+4Dq5s0mjlebAvdY2rtPUhvh7KpXRmH3A6ITfMe40YLo8YOnP7qNKyhaBtg0fjh9QJhNIdFxPRqMpY7jZB8xvw/O1pZ3w+3INu8Dmt/uUNWxvSaW2lYwlzeHl619WL47CmgN0t6+Ny1q1yKKI+zqGlin3EQpgHRMIHe10wPQDP1IrO01DEdeTq9eBsFf0zFC5z+ks9J2RijBNFuPOHeKmmrCOZFudNQEnLH9VwlpjVkHK6UU/LSC/ckbRfqty2dtao8sXqTZfKFtS8vYOCmA9wGlES87xPv7scaQ2jWAZiv9AOshAe3UNwQm+MAVH6PaNnT6bX63z5VmML5vVKKKIbFPwTC3tRvVDiQVOr6vlgCh2c6g3ukynxRrZvg1aR1AA65g2n4PpsufzNkyQYT0GXydbJ/xIQyAlRv+FxbiD1ljV5QKBZBOwpvfp5gwH91qfB6z62Q6bMgmq5dqqVlYQhez+4dV3RvqzvFHojuGwnqqmwNKk37b1OipniFAA6xhnndjgd5Svx6kTfkm+q2S//a8cmtkin2JAF/3V2lJEFe9qpj3DuryNsV2GyWsUpK0TvoUzfNnLTKZcFWocoP/9tSLcpvx0dUxmfumBLVvbR8K/TZuXXtIo6MLjnLAogPWA1gaT5qHjY9w2jZYqO6svPmBZE6TK7VtMpeFDE5BZF3JBAEx/HueE1mcUVGHnHqumaKy/ayz0YS5YtKJ0ZdfwCwWwjnEWBalbpiLVqaMIOLfrvBmczcMNM+s3+7BUy8itviC5z0UCUF10zqlFQleaKUdB1v25zpOdOjjf1sey6gNWIXIB0Ofhm8r6fP3DfP+cL0VU5bi4A5hVYFbPmZJDh/o8qAIFsB7Q1nxma3axxWRiTC9tUbfuolMWf5ffPGC6tuS1cDofsLZ8fCo/iFOC8VgntCq0koi1olnTdz/tWlYaSkDNEMi/4HWFKfodqK9NK3NjGSsUwDrHzZNQJ+ykS563+MRdEDTCv7Uz4eec2/tKoCkw7MNiDjQnGrBU+el2WJ1bstLAGOlDfzL2lRrBKsF5zdqjKzdOfFnoA0b6gsvlzVzi1NdYM5eOBQpgHdOm/9LKarJUxEoLUZAm8hN11bGTiYIsPjLzuQ/5lYLfnFaBafwaPPuA5YmEkKlrwOyeWVUc3at3vuL7U7kYq/q6vNdB/VPubM1pkvvPhqplFiZirXGrddW6cubvO1yKqAe46GSJsBN03Xf+z0/x91+4PN7/pnOvAAAs2zpmFdVpXPOwub/ZEKZsAeIfn2+vaDY60TqpRUFaFncvfM60rUBzN1Zsf6/tvap0vIL2aWPtAxaf5/bpFAUJlOUBowqsP3Qh28a+vwV1N4+CjD2Cw/44zhmLAljHtNWtfrrtHuy9bhmvfOqDsWZphvt2rWBpNsOzH7WxhdryESwBdQehyQQSl58TMsryK7VF/kFV5gPW1s22XYx76hqwLn+605UoKgUI2Anbkvp/UZk25U24q7WGy2AqlysctPVyMFYogPUAF51V5wO297plvPzJP9e4bGOdhu35fEHB20yV3xitBTm8watUNhBA58fm1gcsVVcmP1D62GyNkR/g0gCvb1PqPmRcRkEqkwoss79COW1Jz2Xa0ylL7R1SJNS4vCNVn0Oupw+dMJl/iR9r16MPWMcopVqRN3z4RRkTsYZ/HVo7teVHn4MlVNqpq4giTZ3O96brSSTRgE1bL1FFgGkjChLQj8k2+66ND5ZC2gezQLthk2Fg4trWIWB7ewqjIGsGiBQtmzZmKIB1jKt+ls/JVVXn5IKFKMjI2Xsm2v1lJAO82oOyi/B+HXm/rIXr0rHQk36ATmWCa0qVfmVrAq67Pz6u2mFkCnQ4kH1WPdY+TwGsB7QhPOSXxmmDMpW1/VtQtSjIvggP6XQOeT80EU0aCnc++JXurSk4YjabnhN+egrv1AfMhQbMUstQ9fcGZnS3aSgCH7Tp9TaXmLRK7k2BzY+rk8FeX14SyNWHZZfaggrH1OsAACAASURBVAJYxzjLhB+bA6OovPYnPZMJMs66X1O0rBJlE6Eiz5oOBmn6QbWwFJFmW9ePocQ0PIw8a0OiytUsO6aNOzLeRxcBHPmVWs8FkWXDXY+d6mxEAaxjFNCOE4hX9bDefj8zCGjVy61WX1/I3EatD1jzdifh2RWONWyYGdIMjJm6q0JUWvDaKipw8VjbF5Wma60ay01poPUaMPt6ae5uj66E6yLH+Hhb4zQUSTFlUfFDhgJYD3A5kDI+YG074Ze0oZYJMsz7YnNOV4Mz/XDJt0H3E7qeRCLN5ExjHiVZ2lmKqDhYpBWtJO/14Cky6/Xt3tZpjvZ3RftGrsKlANYxbZmC2oquTGOMgozfXuqpqsuc8Ps06aSbks0D1o6Zr8nElDFBumrQQOjz763zogK414BEgTsmv606yo2xP0DHQN0gqeJjLLW6BY0wReWOAQpgPcBJJnxNVvr2k+HpfbWiybtpGgqjCTJnTuoqDUV6KgoeXtl8XIvRoX5aldSnNw1PfXkYm19eqVs5doZ3SR/kn+n2tHbpyjndtAKKeaM9+Zf4sUIBbKT4MEEa666ppokEqaG8NacFmbxAo/NbcLlwbp2JKRGMB3KBHWJtUrS8V07ureVxrSzGLVLgA1an3un1NdcUXXeX/rAu+5NdrrjFgxM/x3H7EVIA6xhXOaziMlI+Sa3rvyITpGESqOuEX/iGlatPQQGqJ0Jb3gRpCE7whUkDN+YJrQpdaf9cp4apIoTno6PLyyyfN1yal0h9XPUk6/k5Oq/ATG3dhonqSSmA9YA2VMmqpXLTJL5aubpTzt5N6PugTP86lfuu14A1p0kZ0X1Zmk0vN1Pd3+tyKaIyKidibenWZV9sdPvt6cWL0Yhx2RdslyIq9NuyqneRbBTkeH1WKYB1jKsHYV4b1aWPz7ymk3BVrd2iD1g3vgIZTVI+ClLrA+Z0trQm44Q/1hmtAlV+uvX1qRIFCf1tq/uiZBstXOkYiQJIdPvrev9PuLM5ILkX2QvfR+HWJlE0oQDWOa5NhUliUB8mSH0NcR6wml74Q12ANXs9NJnwfbQhXd+CZjI8ZoJz4zD6UrVG2ry0de3EPMGu5hVHLu9WRycv++4omqvGHDREAawHOImCzG/wIIEZTZCefZ98pNzQkQ7Xz/9EXei0m6WIEudU7f4K505dA2bz5Khkgqza9wz9tG7XbaPLF/n3xMfUTJtB6mG6F66F68pLEcXR7xofMItE0UX1Jr953CI8BbCOcT05pYvz1XXNmfBtoyDDKCyLpYj6RJkPWNemmMQE2X//uq5pwQJZekwbPmBtaCtIN2gFlYnc2LH+TApgPcDFm0zeHBikdGjZCb9EC1O3epvFuBMfMP9kM+FntRu6SEMfLmD5XqCrf4oasHSf7FL41PuA2ZVhcAlqTNCHw5UotD5g9tXqfCGJIxzdf+u5oECorzv3L/rLJuWMWQdGAaxjXD8MkrxUHtNQmEZP9NXWx6Dmeb5JP1wWTJCabOJd/5rMUkQdt6UrbMdEpVQPFUvtQuhN3ASmeseHT9Gt6yIPmMn1xLSteol2dY2BRgKYiPypiPxIRH4oIh8RkT1E5CgRuUhEtojIx0RkbXjsuvD7lnD/Jhc/YOhEkUdNyQ+eIOdQ83KrsCB/oV4W/Koao3weMNf5leqSMUG2pAGziTIy1TeboXtp0DO2Akhbxxfdt8o1xn42bvt84AJg1lrVWQRc0M6SXFMkf9W7n/HMOO2bPZjb26K2ACYiGwH8EYATlVLHA1gC8HwA/wDgLUqpBwO4HcDvhqf8LoDbw+1vCY8jcBwFGf9tf9IzDbI4o3bqu1W5Axlv6YdLXgjUWXG61uhlTJBTk8BCTCkWfODLmTpTNn3Axo2LlzrL45N5TuOEX7cNxijIgTwMatLUBLkMYE8RWQawHsCNAJ4K4BPh/vcDeHb4+fTwO8L9T5OxX90KuJoY8xcyMEH6CYNcWHMQqlHNySLeevriAwYxC7y6B70TDViDHpPWYExVKWHdT1xGQZYUV9kJv0a9lcoN3RZMWiv6gHVL+4m1q5Vf6cXaZkyUHGtwSRwFtQUwpdT1AP4RwLUIBK87AVwM4A6l1Ep42HUANoafNwLYGp67Eh5/UL5cEXmZiGwWkc3btm2r27zh4NhUmMkD1pEJcq4SU4ZdOVlH9qGZLhaE4BbTcVS5tM96+ze09Y95QitDZxq+4qa78eXLb8ZXrrgFO+4Ppi7rKMgKJ5iOqR2sYnFs1b4XO+EXHmNRMWmNroTbIq2qqzk7fll0Ulp/Wa57oogcgECrdRSAOwB8HMApTRuklHoXgHcBwIknnjjV54Q9HcyKphrz2jd7FXfxfpX7rHxEHGiQVGPy844uksy32e/+lXmu/oCZDE+4bUoifObiRJXC6f/yDezcHVyrP3v6sfijpx3TWjuK+nbV/mGXhqL6wGhlKSKM14F6LLRxf+wfR/qXVSZiNfPLAK5WSm1TSu0G8CkA/wvA/qFJEgAOB3B9+Pl6AEcAQLh/PwC3Nah/FATmOnfSQ7IUkQeVtWHRbIVAIGr6u2yGXNdvSvlgCv1SRI7qQT15O4qCDNaCnCi5NBRKATt3z/GCxx2BtUsz7Ni1kj+8SpGVMGrAKp6/cJ7j8Z2YIIsj3LrOrj81TKbf7hOxOqrXTTGDpIkAdi2AJ4jI+tCX62kALgPwFQC/ER7zIgCfDj9/JvyOcP+X1VjFWktayYTf0A/Lrs4FCSy3JE5155asCdJwmFKZz111Ismlc0hPiH2M/ko7tvasaa1jMmlEl+Gw/fbMaC1tr0/1wwuiIKsOkxo3r6tREvS1iXU2j7i4ttZpggy+v5ljbNuw+AiJ6xqzgNbEB+wiBM703wVwaVjWuwC8FsCficgWBD5e/x6e8u8ADgq3/xmA1zVo92hwPjelHiBd+Wo09T+zM5sgFNy6McGaJqHWNGBx3fV/72zCjtF5H7BIKyjQ37Mq/apq3zNdcx8+YFWIxm3+xSLZHwVxOK6Y1KKP98FV9GKypFEPf6RDavuAAYBS6kwAZ+Y2XwXgcZpjdwJ4bpP6xogrZ/mFPGCOyq1S52K0X2BWbfxgMWnA6hXbOplM+DCYZlumqIb0WpBTU4GZfcDC7eJHa+liTFqZ5g1jlAyHIsHXd2BP+jhXVZcHfYxXY89M+D3ArQ9Y+NdDjzWli1Aqm4i1aksUstfCJLAspqFo39yqI5MJX2XboNMi+PABK8yaHe6zXaNzTOS1XOnUHNnLYnezqkVBFh9U3QQZ/G0jDUU0AovSUFiX26RRxIir2299XwuE+rrRiyYT5MgVYBTAusaVoLTwZq/Z5otA+1a/7qHIB2kt14LpCov31seDqEjLNo8EsAk74S/4gGU0YIsPApdO+OZj2+vwXZtwhjKWh0rfxrHr9/5E29a3X+oGCmAd49pUGAsEHnzATOaNeU4bZJ0Jv/S8nA6jQ3+3NJk2aH3A/DvM6s6dTc8CWRDQEfwNTOaS0mhall/xvjhJQxGV1YIXmIRhkEUtsR5rE+trrjFpPF37vVpHQRbcWLvkxJoQMpXeP14ogI0EXYf3FQW5oOlR9SpXSsVOwED/521JLemj1ZyY1OotUtUEOdY3yjLy/iRp/xpT+oX229R+HV0J3F1r4MaOy8CeqhSsRFR7VlmcjxLXAGC8L4wUwDrGdceKBQLAm1pI9xOCqDIxH1ClXMPFWfABU91P9Pl8bqIRclz6gJnbUb6vK0GjS9K/PbNdZbcvLilVofCKXa+sn1qnoahQr80UkM4Dpn241oxMm1hX80afRVuX83EfrBttQQGsB7hQJS8+WDw4phsdwdXkfMCA8ihIl4+iWtcoeoBO0AQZk5Muok+zUAVm8ukro8n1rNvd2xgnaQ20i3qHMpb7TJHJ2a0mu9rNKoqCrDMOiqI7x959KIB1jOvnoEkgaANTRuTIr62Oq0j8Bg7ztVG5z/k1JL2RfmDnTZAaIceH0FNogkQUnTo9J/xEe5NlnhJK2+5CXfRTGydmnWnWtN8GJmJtCUd9yfb+VDELWq8DbHDXSNLD2JU3FCiAdY0jTdVCHjDl7+1BZ2qTgv2Vyx3YoMtqwDQmSJd1Ge9uURSkitMtTPWhuOADpvL7cibICqOo6jgzXfK62mL3Lvhh/j7o22oy4xYxdg1G1/RtGE/Vt7QuFMB6gNMoyPhv+wPBmIsKqlmuqZJzdT5gXbC44HjaB6wbDVgRPoXyvpF+o9btiAXTaHNL98pJIlaLtvUhESsfyc2w8flrQuUoyNi1182d1VWbSZws4xXsKIB1jKtupcvw3fpi3HFl2e1RWgjbyT9qs43ZpEvK1q1ciOtxkYYiZTLT7y84F91G+/UBQd6EHXybeTFBluy3vCetLMYtZh9BlRJWSfd0dReS6PfFfXWTBJu6/ti7GgWwjnGtlUg/5P2ZIHXfXbzqGzanfmO0GHdXD4XM9U6bIDXLZ/iQecrSUERLRPVduHWOQXhIKxd096xKt7LpezqTpr2vpP296+puTzrgY2BU7Yd+0qZkn2Nj7UMUwMaCzgesdZW1voI4n1ftcuu3ySdZbUred2hxm8s0FGYPMHMlCoG0P2an1jKCyTz58YkTfuj7FOd1s7tAVY4vXYrIUkyqMkxs0gEkQTD6PHF1l5khzTD53nWtiSzqrTb9Tvc76vgbDhEKYB0TREa562ZJGL2yGgR1MC/GnXfCr0b6AVB0nsp97oNvU74NWh+wrrVOKjS1SfV7MjZ0i9ant7cumGo6atOIsWrndHPHdfnwiDvcuDXYnhFFQWqE9JrtKYuoHGsPogDWA5xEQea+181GXwdtFKRnZ+MuyAtZWRNkO3U28SMKloiaZhSkSr1TZwT4jAly8fpW0jJVvNeufMCMAQUarBOxxlGy+v22ZZL2cH0fqioCquTXbto2lQzXUUMBrGPaeg76kL+KzGDR2nqA/cO+bPAuphHoJg9Y2lSz+ODW5NpyeK+N5t+Cc2In67HPagUsasASEyRSplnbW1X1eN2VH7NAM2b/HV8UBT/07dK6uteJubuCtDdgKIB1jCtt0cLg9OIDFlalM0HWqDufLHNIpgul8ksRLQqeLn5NkwkuFspHrNI3Ybpuaa1O0GdbvDKuHk6x1s7Ov6v8GJNHY3Zb175HJMDVXajte1hkNnTYhjG/MFIAGxlpH7D20ftqxb5cdcaNmAW7pHyV+ewj4EBHoQkSmuviUgNm2F4aBRml+ZiaBBaS18ikhRmttqaKmc+mfk1HLfN5NJdleULFMp0KWBMU9t1jDn7wMacsHBdZNrSBGjXq1f6w7L4hvYzbQAGsY1zFFWlNGx29OeRzkPkRBXsWEaRNQ9H+lSiLgowesOOczsyY/KYSE2R2n+2Drcrxrq65TT+qsmxMquDko1YFNrVe02/6rIl01bbaL/IDYbnrBkydKGWDs/LiMPr2O+4sLP9VH/4u1i4vxdvv3rkbh+y7roYaOiAavM96+ze0GfV3razGn1/+wYuxc/ccxx22j2Vtzck7bUvucxsasLTAYMPu1Tne+81rsOeapYVUDFMi75sXXYZZ6BvX9mUpum1d3xOFyEcw+qbfb4NQBdYqLl7qrBMAF5xXuwsvvKw6KrfnUAAbCbrw+rYFsIceti9+/8lH4+6dKwv7HrvpANwTbrdaOgXAU47dgBf/4ibsWp0bj1sOpb+VeVD4ScdsqF6JM1JO27nf2JYPWCmGSqJ79JAH7DPqsG4Tpgi+OA+YIUFtpbUgKw40o4DlIQ3F5G74iDD1XdfTu/VSRI58wHT1pgMPqAEjreKyfyUCQft5wPZYs4QzTn2ocf8HL7imVrmH7LsH/u9ffXi9RnVEoMlMOeEbjmleT1i+ZRRkVPdzHrUR37v29tG+UZaR1/6lw93TPmC2moWqxxc9TNrwAbOZAaI5oygNhe2MMuaHZx9wmdzZBWP11WoL+oD1ADdRkNnvPjRgpbhaEKynFEXNdbcUkb6WvA/U1CbKbLqJRZLI29x2h0KO6Yr7GKZd3u+p9bWxUyVopOnUn/HN1KX0GQkUwDrGtSYiKq5PGo46TsNDIKsx0ewrc2yoQW0XizjaTy8cTpXEB0y8XJdiHzDbsqqYR6uXF/uAGUzUdVbtGM5o7i+JkjZ7NV27ElS1mCQmSI2fYImGvmq9ZUuujQUKYB3jasmgfBl9eL6OffBkyAU9dLUUUWkNYki3MHJKfcDymsE2oiBNLmCWQk3Xzvq2DKy5k8Pl7WnjVo955Q4KYCMj7qjK7RqTjag4doZmqliMgpTMZ12C2saULehs2J25tj3pFl2QF4zTplnRhK5WuVQ2w8zFmLRZEqgoYk1XriDqu3rtBn3A+kS3mmyngpshYGns/YcCWMe4y4Sv2da8WO8Msc2AZjLSRNS5mixr9Ze0CdLwgJ0CC5rihSjIcLtluVWOD7TdujbZlDI8xvmr/OFrDU4nUZA15pXiesv9zYYMBbCOce0sn36AdP32UGXR1iGTF2QWMuF34YRfsr3Ix2fMVLouLYv/7tbJC6imnav+AFNIwv71PmA184CRVnA1v1uv1VtyT2stQ5f/PpEXRApgI6WOuaBrhjbmMhqTXON1DzEnaShQ5shdXIdM/JGYz/WVzze0eB8riTkWDTDvsnbCb+ENS1D8a+r0nqk8TLuhy2urMVM7LrlKzrEhQwGsY1zn60rnMeraB6zO7+paa1eXvGCkywDexRwSPfzya1aOdUIzklvoPdmcbE+7gLWyFJHRCd99XYvnlJ+kzY/WsN6hjuc+oQx9t6tLWyYUWfsJltY33k5EAWwk5DtpnzRgY33Y582MC1GQLfiA5dfZXNivOT7YnkT7BXl1RnpTSsinmsiaZn2koah+78rLqnCMZRqKaPE9vQmy3qQyzZ7mBzfLm9lRmEy4ZnsWzkv5rKa+jg4KYB0TT3puS/SyFqRrhiaoBQ/sRQ1TsE/3ezz8QIPWLZsHbHjXuikmv6nkuuh9pSpFGjYcZ7aa4jrC88Ru9yToen439Sknkb6pxMkDe4xZwaWIGnLnfbvxv995AZ5w9EH1ls9xpKlaeLA4KLMpdbKuD3W45U2+uuzNbt5W7QoJBMRUuybqhB+zkIYi+BIsxp0y2bYSB+lWe+B6KaJoLkpfB91+G3TBKMSO+PJpLr7La1s5CjJ6WdF2kTpRkBUqNvzQMz/9Q3xs81bsu8carFlK9El7r1vG+37nsThsvz2t2+MTasAact3t9+Lym+7G+751TddNAZDvp8MUZoZE+nJnfMBCh+6MX42jOgud8PNmT23d08uEb8qsPQ/Xe5dQ8mjzspj8sHz4gJHx4exltabgbxK2GmTJSb7nNPYm3n/Bz7Bz9xy33H0/HvbAffGEow/Czx2yN664+W5cfeuOGi3xCzVgDWk6GUah301ZWAtSqc5V1LbVD9kvyaAwyGpbHPmA2exP8vJGjrxSuIbl2BHJ+r8ln8PHWd6UXKVMm/otji0ty8I+WilIINTimopVsBcWO5+ERo7LOdNlkmBX2KQy+ovTHoqjDt4LF111G772k22DmOKoAWuIiw7ncooyaWS6xOYaDWm+XsicnnHCD7RM6d2uJksbM1ZUp0rkDO1xY8cUSZZOcpkWzlqJgjRsjx2NK1Y6sVs3efJ+il2T9FfDfstm6g7PBDdVLGft8iysPzhjPoCBQgGsIfOGTzJXOXIWM3x3L8x0XX/bpP28Fpy3EWnAUtqWDiKWFoIDkE23MDVMAQgzT86+vsdEEkVWfseTpYgMGt0aKXNshUtSHVfBNLYvhkVaqcDj1L6TG5cisihrzVJw7Cw8pemz2QcUwBrS9BbXUusXlReZnOA2v1gTql6jAYwXMyo7WUj4FMtqwNxQnMogN5FpKh3z4rYmTBP6PKUZ0z3QXEZBlpqPqxUz8IFC+oqLp0UbXbNqeph1S0vx8cAwXjIpgDWkL1L2og/Y+DVQXZMXZBaWIkLeB6z9vlJmggzCuvV5nqaAbpxE29MLqDfVNBrr1z3mbJ3wUWNsVzGRqkVTrG6/DWPPZO6DdA6/NN1psiO/QpMNslZxGWKXAYuy1ixL5py+PJuLoBN+Qxo74SvXPmDhAxfdC2B90cC1RfrX5R9YiQ+Y20kgl1Wi2vHpdsGd6aIOV227B5/87nWZ+o/esDd+4zGHe2uDTiuZpOdo78KUlW3lK1n1OGt/nOJRO+4RPU1cpj+pO37yZ+Vda6uUuzZMQzEb0CLEFMAa0lSr4WrJoD5PjGN2Lo59wHKCdDRpuI6CrNqe/PdsJvzuTJD/ceG1OOubV8f+GqvzoGW//uiNrS45Yvq5yZu2KVqwvE02Lxq6n2idiLXGratyim6NTNsy8oz9Jcwn+SvpeuWG6nnAmu1vi6UZfcAmR98iLdJpBzqf/GpUP6R1v/KapIWliAzmwCYEvn0F+/N1ajL1i2GpGR/sWl3FQXutxZVvOA1XvuE0/NHTjgk0hZ4aJLkstIlvWPa7rYBa1cm9aRkRVceJzRyQmCBNTvj1x2fPpslh0dOLp+3PNdpaFAVZZsJem0q+GvXNqM/37dmsgwJYQxprwByZIBd8W4DeqMUG8CLSmMWfKAuCRRfXQeX+di3frs5V/Kaapu1Lkxe04u0pDZiXtSB1GjBrH7DhDKiu+xspx7Y3JU7u+jNr3XOjO5k5Lx0ALC8t7kyEtv6PEwpgDXEiZbcQBQnHvmU+GMB4yZDNG5XVOEaKljKzjjWlZWQPWPQBk8X8ZR5ZWVVYTglgybImfhqU9/PKZNwOtiwcX6XMKrj8hbY+YDZ5ykyLtZdpXwvLHtrg7iE67aPTRKwV726hBr5OvdoBVDyPRaSXH0rKC/5SAzYBXGjA3JDtxIETfrciWJ3ahyQ0hpkmku/5KEiV8wFzVW+RE6zBCSwdVdRlFOTKXGE5YzYI/rauATNEVS044avs9urlVz1S88aeb4yzurpnSOO5r5hut6vpvW5/MgdBNm9Y1gRpLm+NRgMWOeEPQeinE35D3CjA3E1TWY0M8UW+H+gEi7oTglIKd+1cwX57rintb/n9kSNqenvTPGA/u20H7tu9Gn/fa+0yjjhwfaVzV+Z5DZhf0qkmgGSinxmW4KnSvqq/oQuH6bjuij5qEkZpmHzA6t6w/j8KB0oHF7ZIq1p7jiuLEDZsX54t6pBiAaxWS/xCAawhfYm00L3Zd+1/Ya+B68e1rIzkBJvM58gHLGXuqlnN2Zu34rWfvBTn/elJcdlVSZzKs+2s25bvXLMdz/23Cxa2f/ZVT8TxG/crPX9ldZ7x2/CdJyrfJTOJWFvXDBavz1q1bps2dv0S1vUcNAbyDukRrq9t9SjI4gOdLEWUKquovCKfyr48m4ugANaQ5nnA3C6anURBdj/5RljlN+pLoyuQnoh0GdRV/r2uZl/58uW3AAC23HJP6bELS3rkvkeLLdftt7fv2AUAOOPU43Dkgetx1a078KZzr8Ct99xf6fyVucLSbDFyyZdjeV74jK9DbIKsZ4Ns0vo6rgLtpK5QsS+c0exlVatt/cQWN26ldbVWmm2OnjuJb6a9OTFJQ+GgIS1DH7CGNF4LEo6iIB2UQWqQt+2lPi5EQbpIQ1EisBtcwBb21G1LNKk98ZiDceojDsNJx2wAAOxerVbeyupc67fR9gM6EbTyvpKRBky0gkcV4aiqAFX2cKp6DYb0QtO1HypxT1GUYd1hXNSnbcdMktOv/xIYNWAN6es9dpXgtQlR7VUf9n29libyEXULJkggMyM1/X11bmdaIxq0K5igdq8qvOW8n+DoDXvh9J/faFNiWE7QmGj5j5XVeaWzVwxpKHTcce8ufOiia7FrJSn75Ic/AA974L4W7c0SBUfExD5g4ddYAWZ3s6reWxdpKIKT7A6v0rxMHjDN7QyE/3pzypBSZ/SN5CUhS+Dm4O66unpa2PYR3eGm5ZdMx2XKi/YNoMtRAGtI04klmvSaku/0fTJB2jDEF2btJBias5RRB2VTflKorRYlvTQVEFzfB2/YG3Ol8Nbzr8RMYCWAxU7roe48CgPfVVUAW1VYM9NEQWouzpcuuxlvOveKzLafbrsH//ybj67c3gjThB5p9CSUPNqcs12VbZMOwtZUGUXJmlo7xPFJiqkdBemwLBPBC2NBGzT1JU74/ZfAKICluHvnbtx8107j/iMOXI91y0uZbfNqzx1vZDQenZsfuq2/bQJtit5JNnqEuUjEarCeFR5r2iAQPO+xR+B5jz0CbznvJ3jr+VdaaTZigSXSgIXCVFUTZD4Ra5GAsBpWdsEZT8Vh++2JZ7zlf+JtdcnXVrZEk+surPu9tpritp4rGd84bR6wBmX3/1lYGaUUduxaxd7run185hZ18FovAGPldcbMwotjg6EQO+H37NmsgwJYiuf+2wW4/Ka7jftP//kH4q3Pf1RmW3MfMDdLBulK6HwpopAmg6nP5CfAbCLWYGfZQ60KTXKJRccXvilaaGHzmqTIBLm7ogZs93yOdWuSaSdJ16F54OeEvXwKibronPDzb9q29VRL8+DQXFRdBVa57iiZcOFSRNWbOFqiqOQvv/rJOHrD3q3XZ4qCdE7VKMgCDVOdOU73nEq/dBaN+6J5jVGQA2P7jl34haMPwm8+/siFff/0pSuwPYwAS9P0FrsyQcblRSYnx9GVvuiL0GiD9i0MkQYsZYJs6gMW/S24sYtRkOFfjQkuHa49q3jdIwVUpMSKTJBVfcBWDXnAtA/8XJtF6k+qyUMs6zeTTPR+el6RD9gYX1SGOAeVcd5lSVSyDwHMRKSBb4rz/uT1niucdOwG/ONzH5lUH7/U9R8KYCkUgAcdtB6/csIDF/ad9c2r9ef0RMrW5gHrpCUJQxoIdcg7weaFm8AHLMHFdbDtbnEi1pSmJ2JW4/4kvzdrgtxV0QS5ezWfhsLchnybixYRP+sbV+PszVvj709/2KF49TMesnBc/qEV5wGTSDjLIigAHgAAIABJREFUHV9hEHWzFJHd6K7SbxRSTvja/d0H9pDuKXxpqu1mkX9xzM4zRZq1Iw7YE4fss0fSvgFFQTINRYoibZRp2mmaaySa9FwxgD5nZAgDRoeu3TofsPo3p7pf0qITfq5dGSGxvqo+1oBZmiBX56Y0FAU+R5LUaeoj519+M266aycedNB6bN+xC1/60c3ashbGWtoEibQGufSnGBpbTOG9q1qVReO6FpeSPE4dN2TApF7xMtuLXkjqUHktyJIXN9s+V5ZotcwJP79/SHnAqAHLYNYb6d6OgeYTS3C+Ax+w3KBwbdqsQx2jTtdttiHSFGTf1aJ9ghvuuA8vePeF8bZ//soWfOiia+PvSzPB6097KH7xwQdXrE+/SHIRSpkFijpZ6BONUagBC02Qu1eqR0HqnPC1TYjqinzADL5J0aHHHLI33vnCE/HyD16Mq27VJ63NLzSdmDmbJaitgrnsfoyTyMfLlN6APmAjxbLTd+EmYhz3mm1p39a+QwEsRZnQonv49dXRz5VzvwuGqtkqI2/OSved5zxqI3bcvwIAeMih+2DHrhWsXcoqnL902c248OrtpQLYwuWz6KOLea00wo/F7YnTUITFRP5cuyu+bq7MVSy0AWXjLXvMTMQ43uZKFQpqavHnZ7bn84DF7aswhmyEIRv/PfNxburzQVGQBWlG17O7qR/W6XOm8ZoPjlk8b/E5F33r67M5DQWwHKZ7bdre/Ba7cZbPL9nQCw2YZf39Hy56dOP8pGM34KRjNxSed9QZn7N6mla5nGXRQul7MqvxcMynoRARrFmSyibIldW5NhFr0WWQ1F/TYen+XhSeL7nK0pGWae1YGxZIk4BVZ5zanlLVBwyRJtCwv+s5ZYpE/UabsLSDyNriyGW3s3gScalH1yfLzukTjXzARGR/EfmEiFwuIj8WkV8QkQNF5DwRuTL8e0B4rIjI20Rki4j8QETssym2TNEEYw7N7udt7tNkaXOFetLkauTCsa0zQKOan0LejcymlkWNTupzDV8J3cNgzdKsuglyrjI+YJLM5pq6sseIiLGtGQFMY0IzJ2JNbW/ZBOmKIY2nrusfNY4urm2XL/NjrPPcybchu8JIkdZ4sT2JNrv/g7mpBuytAL6olPoNEVkLYD2AvwBwvlLqjSLyOgCvA/BaAKcCOCb893gA/xr+7Q06dWaEKRfJPPM2bR8l5MyvQuMDxumvXZpe3VlFn66iN+DFY3PfC8pPfCWqT1R5syAQCmA1lyKKPhW9TccasAIn/LlSWJZZXKhZA5Z1XFaZfcVBCyaqmvoLtXIF+7XntPB2Fcx/0WfdfqBurx/As3CQLFzWu28Gdu+wKmPve6/HkXIzlu64Gti1tvT4pXt24Ui5GXvt2A/YvprZd8D91+EIdQuw/arK9R+2eiM27N4zc876e27CkXIz1tx5DQ7edT3uXL1HW+ZGdSMO3KWA7euT9t27O2jfPfsA23Pz0mwNsP8RldvWNrUFMBHZD8BJAF4MAEqpXQB2icjpAJ4SHvZ+AF9FIICdDuADKphBLwy1Z4cppW6s3XrHlEnvRckio891zG7taKqGlwdsqJN03XYHea3sji9ti8kHLKdNSlNPA5aUs2ZpVt0HbHWO5dmi4l2fBwxhXcHfmSEQJjo2EihnGhuazgSb3y6CeA1E27fnqse7GJM2L222PlhROo6y8qoytDmojxjcFxf7wI0/AN75JOvyfw3Ar60D8J5qxx8E4GvrAHwt/JfiT8N/eFv1+t8LAHdnzzkZwMnrAHwQ+Otoo6bMzwPAj8J/IftH7ftm+C/NwQ8BXvnt6o1rmSYasKMAbAPwXhE5AcDFAP4YwKEpoeomAIeGnzcC2Jo6/7pwW0YAE5GXAXgZABx55GJC1DYpnNgM5on0c8cmoaVr8m+ufYpYsnqWDWjGzkcR2j+czE7laRa1MtUrivOARSa41L5Z8nSuTN5pHUDgA2ZhgswkYi1ogs58ahIk5ip54SjyFVu8dMGRMxEUrYHoBEPRRWZYfTHtv6noa6hf70DfrfpP+sLuCBLE4smvBQ48unIRX79yGz713evxN6c/HPvssab0+Lt27saZn/4Rfv0xh+OJuQCiT1x8HS7Zegf+9tnHV67/TedegQPWr8VLn3RUvO07V2/Hh799LV7/zIfic5feiBvuuA9nnPrQhXPPOOdSPG7TAXjOow6Pt927axWvP+dSPPtRG/HkvB/uun0rt8sHTQSwZQCPBvAqpdRFIvJWBObGGKWUEhGrsaeUeheAdwHAiSee6HXcFr3Fmh55GRNkzTr7Eq3omq4jsHxR94E4s3zem9IDZNqyYILMlSGLn22ihfJO+EANE2TaByxqZ0EesKiumYhxfbdA+xz5ihX/pvSuZDHuyHnf/l5aRUG6Guu2mvYqvobhS1s+UCG/34axzm1dUDqfzkNz4LEnAxsfU7ncq3Zcg3M2/wh/dfzTgb3KTZD3330/zjnnv/HoBz4cTzxhU2bf96++FJ+//ib87QlPr1z/l7/6dWzcZ0+89IQT423XrlyHcy68BH963C/hOz+7HJftuAtnnPCUhXM/+6kDsMdBh+M5Jzw83rayczfO+eT+ePgDHoonn1BdEO2CJgLYdQCuU0pdFH7/BAIB7ObItCgihwEIxXJcDyBtfD083NYbAnOgwQfMZC7KacDq1Wl92gJJu1X8//7IP9Wuy9DekvNpHGwfNoJqGrCF8wrN5LnvORNkmtgHzKJunTP7miWxMkGuyWTCL3KwjS9s/NckIKX9lwRmgSN/jzJRkCktt+1dqXJ82TFVhT+7NBTVj22TIThE95YCzemWW+7Bv371pwCATbdeh1MBfPy7N+HWK38aH7fHmhme/9gjsefaJSfNcZ6IVbOtam9RWBzTk8gDppS6SUS2ishDlFJXAHgagMvCfy8C8Mbw76fDUz4D4JUi8lEEzvd39sn/C0CVGXKBrBO+2+Y0YaiatSG1OK9Bsn3YzSpG3WX8DO2qiM/I+1OlP9sIgTpza9UoyPlcYa6QdcKvYH1LfMDM/moKiVm0KGFtXsuVFijzDvptoOsjdfq87TnVBESVJKTV7Vf0AesTmw5aj//+8S34hy9eDgA4ZXYtTl0LvOdb1+KK3A084oD1+OWHHaopxV44Lrql9Z+B2RPTgUdSkFdGt+Zx9HUKecBeBeBDYQTkVQBegiC1xdki8rsAfgbgeeGx/397bx5vyVHdef5O3ld7qaQqLUVJJSEJySAh9hIIi80SI9ZBBnsMBtuA8eBPG2Nj8ACmmcbdDDT+tNsYjJsZfQxtmME2NLgxA9g0i9gHsQgjQEJQSKCFEtq32t+7MX/kFhkZERkRuUXee776qO67mZERcTMjIk+cc+LEJwE8E8BeAAeytFFh0xqlg3P9JSM/4pDn3ZWvVs0HDOMPfrYVVYtE6M+zhVXQX1D5cKpMqQHLfcBk4cd/pqjLx9UEuZr92HVaE6SurGoassSJmAtRzHx1GjDTS0b1aVPTua2CdMPXfNwFsUzCFnwIGAT1SV72m3twROp3ydUHgP8OfOwPngxxQroP6rW33IdL//orWDXZ7i35N2HUMg/Y5FINWJV8HFj4rYiEEP8KYI/m1MWatALAK9qU1zfWMBSGRiVL2TFJ3DE54bsyNTOFKjx4q94dfY5KX6jml3RtFWSt0PqfIWEoak74Dptxr2UjorwZtxpLrVpWPgvOfMASGMuZz6U+atEsqluKlX22qvnxbYpOmkzo20hQIFbvcDcO7UzyAdOvSp2mVn3qmMaIJCFsTCSzYvZoNqxbD6ybZX+nfa1LYcS22XVIMdoAs4756rSyvit/x4Qj4UtYNWCGQV1dBeldZkDsMB2qKaerfNsQ9mLpvh59objd+Ztn4GqCrAoidr8p/XddMSE+YPN5tS5AqgG74c4DuOyLPzZdBgA4fHSepXcYcaHXgBkHYqlOOiGhSUhWV7QWxx0EjqH7mddm3JH0p4nNrabJPN36DJJQ5uIP5ftoHHqEZ46a+kljqtXkifp7ztSXY4QFMIkmrZH2ebZdBRlwzZj5hhBTXfogeBVkEuaEr+OxZ+zA16+/s3a82FpHI4AE+YDl10rHzjxxC664/k689ZM/aLyeCDh1Rxk0UZFhjdfkn6a6yk74qW+dxQdMyNeV9Uh9x/rD5EMVsidnH4JVXj+TD12YD1gkEuAC0HgrCwGsfK37+EO5Piqf0DFd4ROhICTA9FiwACaRStP6c6YQALIGTLitxO8F3SDOY1+/5C8XXWgGF2yBRU2YhL0P/e7j8aK/+RoOHa02wppGR2oUpg2oreUXPlNlPm997sPwxmed63T9LCFsXFfO0F1WdMr7TprqKkRZJ53Prk5wTI+nZ9I4YJBuht+DcU3dhUAS00RvOhWYLs79UyeAWYQl7/ydrvMPAK43QZaadusYoalDKXT61WMMWABTsIWh0FGNAxbwxANmla75jk3QzL6nuvRJ6EyL4Kd9IuWzfr4+SZgrAgUp6atpmpnLKqM8HyJs2RA2lNjaiLri0qbZqgZiNWsWVS1X7pucxwGrp2/6BR5O+JaVmel5d/oZMlIfL7MPWICfYwf1ipUIhtgqWgGse21QH36Apto1aciFnCijdMKP7gnVaLUZ96IRYoKsOvQGlOl/iZbS7i2KfNlhdhgK7UqAecbl+etMZvr8zNdqZ4otfCWSjpqWzWFWPUawD8QVDZjBF66uAcuPV+Pg+zvhu13QxW0LXW09JlNwiPZlaBNX43ieB2KVfMAKAcahqs7viwYTpL+QbvbZlIrTI+rXT8kHjAUwCZ00nWMyf3SxCrKXGYUmPsrQ+JY/hQ4jE+JDpV4f4lDtYrYzHa/GAfPXUJYxz7ppXLYXhHosIbNmq6IB05ggi/KUaqvxhvpsg40rWL3agtv993lOIlNxmRYcpT5gPKnLcdz8oTXtTJBZH7cIwKFN3uZn2RZ1vDK6HmhMnn1o/fqCTZAyGmlaOW2/XJPgk9/dh333HDJec2R13kmDVVW1IeaCvvCZ/U5ygA/s5wnBuLVOaFEmzU+O3L4LHzCPHxAacsOEi/mtIlhZfMDKtlMXpISk65IHZnmgJ9TvhVNz9LkZnbycREA2zc+4HDP0mtk22zRN4F3oTXQmLo0AFuLn2YSvT5YLRmEO1LjqW3fWFrQ5JlgAk7A74UPbuqqR8Kvn7z10FL/3gSsbyz3luE0+1XQiZMUS40fhv5R/97zhiWG1mUo9jclPUaPKV1dBVjRg6afPQJUnTTpuXNqYQrl2SnLCN0bCF0KKhC/XtEqt2sV9qWoDfcduJ1NyB3kAi+9TORWGEsCcXRy0qyBzfyiHgvwskJbzfq3O5jpRfDf0DmG43nVsHRsWwCRs9muzE77+bwBYzYJGvv4ZD8GvP/Y0Y77bHHagb0SZ6ejiowyNj/8BMD0/EXV2H+Kg7CP8kEazo6KeLn3A6heGLNcO3XbJhFXjrAiNqXO4yQRZXaTg+pOK3wO7hq0rdL835F72Yt4XACUW03jApK7JiXrKxKYAK5bhJ/VVxrY+Hmqq0zeRbm5KdYswO6Y+xRqwiWGSpgGzA3DFQVpJkTfsTetmOHZTB0KWJ1Oc/U6xzqGDjqvPkSxUp9cZ8pMTF3Wrfuou9dKAaTRprbCYSNQ628J2CAh7GAqDkFzVLtRNb06BWBtTKIW1xCcb1oL3R7QmSJI3u08/u6xpk19ZSJsz1a9pEm/2Q+vXn7Mr2AlfImSvtqoTvpI++xxiECzNYZLJaWJO+FMj/3nzQKHE1QnftpKxll/t2poTWEFpRvTxAauaBdtiy6UU9mTByqABm5fpUkHN/HLQrSpNfU3K3+c9eDum14a6CAjX0kfnlsNQ6M+HtPHFHQSG0rA4a6jmq6n5UbrnPg7pro/Kpc/6oMuvko/N5wymPhWu2RsSFsAkUpnF7F+jjw5t9gHr2mHZh5ianrMJMqZKexDa0RPHMBRFOdm/RjO55lj+kpAFjSJ9iA9Yxxowl5WYsgbMVteKqVI5V2rTSDkusrz776edmWc8zdZp2eMyhZehL/PYbFy5ACbh6wbigynPTvtRQ2ZmJ/zudhnpExbAJGyO68ZZoWY2XXxHx28rCzVlhuhOSxGOf/lTmjC39gEjNzNGqdV015bJV8t5yLjsE6fPrTsn/LLZaiY3qsnVojGcV5zwyfpylMual5JZeCBWj3uhS1le7v4g+ugn+fhnDkPhvxn3lPqzL9G94OdrNQGsDEraXTE2s2ZoMfV3p1Rek++rppElA/hzdgELYApG7YLhgVo34x5VAzZ+HLCcqTnXu6KajkJWQfquQLRqijRa2iK9Zi5QmlDdKyE7rXeJKe4UoJoWzfWShYO6HKoIc+rx7D/V384Vp9WsnXUD/4zGfhkt4ggwmAky+3RaBSk54MvXuLRP1z7dtHDG2+xsWQZpL8s8FtlWTMcEC2AZQmjeULU0mmNSwzZJ8cP4gFXLtK3ojJWxXxKhhDvhe2q1Gpqo1pfCkiYkEGv3JkhLWWpazbEirQCSRMpTk1AXk0ruozYfs67oKoaS88vS4znl/jQEg7uFZ37A9MYgH+LTgGlMkJo2rxL6M4b4+S79xaS5ju75aGABLKPZjKT317FpwHR+N0MRMlh2TdDy+gkN2TUTZMDLyc/8JxrT7z+8Vr2m0OhkZVYcdNNPn4GqjBzfkQnSYs5Qf2xiGVTn0sybNH3VNBnKs0sod8JX6metvXsauQ616z1vZch7ZWwt9ATehd5E59em9QHzccJ3a4g2rVpoOzP1V7k8Y300PbDrOIV9wQKYglG7YDguN+z6KkiD2aMHaoEkA/w1+iK2caprJDciL3yDBTYJ9Ktzgetv369cIyrXquX70rVgb3tBqGXZl5aXixNMmkVVSwzUTaqyBtkH1/Rtdtqo5OP4DHweVT5mmH3AAjRaE3kRhjDcKsj0s3kvyFWATCbIeNFq7hWFiGl8AFgDthCUL1F9I0+1FZpGUDlk0oCNw9hjn2/xY8/SQwmdCRO5bUUkKn+by/q1Pacar9VNBkJ8wITodnZpdehVXvi2QXUu1UtngTS9xGTtZeoD1l8btG234pdP2DWNaSCbYk3nw579VPu2jbXYnIzmc6MTvtUE6flsrGZN0fEEjSxhUSw/ildBTgx5U14fookDpqiF4296BiY0Yc5fRvPAB+08SBRaTXsxZ564pX6pxaSW+0z5jFOps3v3mPwrKyZTy2ooeTPuJNGn02/VVOYNjeDhtgqyOU1eVhdjgd/equ3La8OEurM30b3gdU74+SkXE2RH1Qhpc/UQTsp33TV5eZpzvApyYjSZkVxWQdac8B1WcvTFFJ3wp0uoBswtDpgsVHubxooQFnmZUvnIBUgPDRi61oDpDIPZEaUNJwbTYp62qgFTB3Ahpa3/TUlWVi7s+vwItBvsXRylTdd0iawlNO3NGVzsBF6Gvgz3gndUDgQ64fvSlRm9yM/yu8hy3j4h5VWQk6JJu2BeGSRrwPQmyCEkodJWLh8cVwTzXWU3hRmLjHrPfe+2yaxtwrbsGtA/btUJX5fe57bPe5Ls9Row1QfMXNdK3yO9fw4V/9TLTQf6fvtLV7cuyAl/pL41tgauT+LUgKkCWDYGWy7z/RmlUNeNSV2HyYWgmiYXTHVO+Lpc4oMFsAzbw0yPG66TnrGpQw4xBsn1a3pRx8yU6lyuIqx+dyVJHH1zHLUyuoFPXpSRpyrSFwKyx0DVsfxly0v1dbKabCsaMIukBr1PXe5rUr+s+de2feGUOkAP02IPKQsfL4sPWOhPjf9V6M/QGpZmDVg9EGuTsOSVv4J+oUY3N0X1zXQtP8fVv3ZsWADL8HkRVo8J4/myEQ0nVshmqrFnn1MSptoQHAcMfo6iQthfZFoNmHKFnCYpBmfnKmQR54dxwk8TVNPafMDKSPgaE6RBS1nVgJX92fdF4twGOrh3Ia1tLCf4WFZi98FQGjDnYiw+YN2aIBvOez5yW3J1j2PX8tgJf6LYwlDoHqfVByyfXXdTtQbKUkpdRxyDn+vgH393qVI3Qfo64fv+ZruWVnuFoj2Tryx9wDxqILoV7G0bUQtUfY5s0a1lcyUZ8tO5m+V/JkYN2HD4+YC5PQSvZ5X5eBmtNy20nxN4Fzrjo1UaFI0JslgFaWnZvr/CZtYMvSP1d6dcXkN9NK3Sd5/dsWABLKPpJWpyTK2ugjTMugeUg+Q6jq0BC2GKdQ5+zhaBopK/VI5V7W65VjcaJQEvEoG+wlAYnMDktLqDGbJmTjdZKgRQpe7qs6utGnX4qS5p7NumNF9fzcsvfeg1jJ78Xq4NbOJyigNmMEG6jDNdhDbpzs+xqrI2lWWDNWATolFL4+ADZh70Q2vlTow+YH2swIkJcphd2rCt6tPR6AOmM0HmJjWd9sxjcM7pOgyFzUQia7UA+96Zc1FmZposkUaEm1cmLH6BcSt1Hc4C6YWXAgyZz41Be6CGBXEq3yZgT5b0twxmgnRNOF+rmSCd4oB5/owQn0VrfpY2lcel02ELdO7qXzs2K81JloPmVZAGlatVA5YLQgP7gA1WWsdIL9EpoAqYvlW3bS4tU/EztHuB1a+1pHAxT9Troi2mNSb/SrnvkE1gFeXv0Zl2m55R5ntujZvWBvtztun2NHlNt4e340v/Gbj+S2PXAn+071781rojOO17m4F9m3sv7/z7D+P96+7D7o//n8D6mTnhz78L7HqE9lSXwqJNBk+FeP9eE9KmbX06HVvj7ycsgGXofGSMiSTsPmBZnkNowORyRzB96ggpPxa/NR9C+znBbWCUTZCA3U/RdLHu2hAHXdGTE76+LB8NWOmED5NgqzG5ViZJ1P+suav27foIfP0Fcx8w064f3lZ26drWfOO9wOohYMeZHWQWzgZxEFvoKNatATjS/0t+ZfUIttAhJEf3A7AIYCc8GHjocyuHfLrqEKZwXzRum2X5eRpNvdOxtZ86dQkLYBlNkfBtank1j/J7V7XzQBjMTSMygX4QhKqK9w5D4agByxHpw/UiF/B05SRJs3miXoeuBXuzFk6nbDMGYkUp3JCUVjUTq3XPs0uoKhz59t2m5Fa9pa8vnujnZZmbGE0LjrpegOHNfBV4yDOB5/zViJUA3vzer+MLP7wNv3feg/Dapz+k9/K+etU+vOLvrsT/eN6T8As7j/G6tjRBmhuA/1ZEFif8ABcF7bxRNKWwW5jYCX+i2PaC1CFL2XWJezhBSC4jFs2rt1PnJLqMRHbPizhgvkMPOWrALFosJTvztUUayaSXfUaxF6TWBFlt1zaBVQ1DoctTV+syhhvpZ9IOv9Wnf0cyJxqMxjAjPmiczMcg7y9T0LCUfXyEQj0wRhBw8GHT9inHsXVsWADLaDIXmvxPdOaM8nt2bRcVdEQWYmIZ7H1s8bHU2YdQXwPf/cqaktr2OizT1NP71L4vJ3wd9TAU5roKSTWnc7bXhZ7Iy8jrIZvefCcDTc/R1kZ876es7WvCJ++qCVJXrvCfZHRJdALYsC/4kDvvuxuJF7r3YUA2tjGfLOdtZU3FB4wFsAw3Fb39ulE349Yem6A0MyFq/gkhJkiHIav0AfN1wZcECt2qwOwC370gu9To2l4QqsN/YpjV5r+trgGrps0d7Wtl5OcsAl5XdHHnQl4sCxGIVRPpfQzyCOvzgdRKbZ5dEWrGkkeInGLrK9208doRhzQlU9mMe/zWHAuSKUKHaVYovxDMGrDhBCEhmk1VgxGhU2cfhIb9IAozDRjN5JrDQvmUSQLsQ6lflXv6JlQ/OtN5IPPD1PbB9FPejDvNs6TytwBW1+Y4sjbH0SyYU7oVUT1/l5/qksbuA+avpXB3wnfPM/fxMoXjaOMD1ok2QhPpfQwmZYJU3CQ6y9d0IrAc2wK2prJ072zfXUbGggWwjKao9bZBqczDkOcQGjCpjGEj8Dfj0w1GFxo9aBvnzFdNngrXYaYx2ypIXx+wIR+RPLhSgwaMpHTpcX1+q2tz/OLbPodb7zsMAFg3k/wnIYzX2nG7oIv2HdLcRnsXddlYIjFB5vdycBNkm3vpUFe/FZNmf0zvWHGWRtK0U0aaRleHaQjI47fmSAhxcAZUJ/wIfMDEiIOtwoRkqSBUX6MQ05zTIKExpdnqo1xsTOMSpFFXleGc8Ot111W10IBl9hZTgNz8tx84uoa7DxzFU885CeefvgNnnLClqEtffafrfF2fgJcGLMvZ7AMWTie/PxIBrFxZPJAJsmUx/lueuaFfudxNSfJvNvqAWd7ZvivMx2L81hwJxUvUlqbhJVFTow5oCqwsoc+PRSIBeayunyStNGA+5TQMb1oTZHFB/coQH7B51yZIixVUoPqbTDsHmDYcr2inJS3Z2lr69xPOOgEvufCMynVq7i6/1VdzUDuW19GxNYS0t8krwOZzACIqAWwKGhYgbXNda+tMzzVUQ250QWgYH0x1MS2aiw12ws8o44CZVWBNs8KaBsxJrOsWAfllE4kE5sGU6qw6e4f4gLkMEnmKEI1q7VrZBOnvAgaBjjVghYnB4HMkpzX1wVwDpqyCrJWVHT6aeVHPZurwV86afcfuxlWQHYtAzptxe7SWwr/Pcp9Hiy0o1tLPKHzA8s+BNGDFX2H33tUh3aetdKot1vquyr/aXi9dm+Q4YBOjSWtkagRVJ3wlzyE1YBUfsOHKtVF2jCl0BX9KH6rsu+f9ThxnprJQ4DvoqflXfcDMwo9rfq2xznCrew+a7ld+TF0Fqe2bBKxlD2ymPLD0az9t1XbbfH0JQ2o4ljagM4Ftvpp+RqUBm8a4ljqkm8+Htg1tnxUd+TnW3qW6CVquNKlfb1oxHRssgGU0aRdMjaqyFZHDSq6+EUJMwvatYwoqYx3tNuP2KSfFuFLXYoIs5Q9JoEmqaVwr0csqSJPGRUlrGvSBsl6mPPPjq1mnXUlIe756rPnHjjHR6avIVAFmNDAFl9u6a+cpeIA0AAAgAElEQVQCGMWgAcsEsPnIFXGFetDAdtwCzQ79Fh8wW4ZkFzpjgQWwDKd4TBYpHKh3yPLFMI4qauytiEJKH1tr54Oq4POvu9sgUa7Ma/IBq1dANUFWS0/T+wxUXZsgqznXj1RNpqYwFLkGLHfCr+coC6B5HjNVAKO6wBpeez26W2db6aUty8fRxscJX5QvPKNJ2PPRd+cDFpEGLI8DNpgTvlnT40JimrkoeOVvdMnxD9brkto68dKcM/mLxgYLYDkNdrum2Tdg9gEbVANW/DNsuTYm0A9aEboVke8g0ZRSV7oa2V11apfPudC9E745s7TqJKXNj6v9TMnTYFpVy6oJYIZVlovA2L+rtQZmnvuARSCATc0JvyEmVuj43GdwXxc/5qJ8w8KWKbx3WADLkFxEtBgdgKVGqJ4ezwes3YxpLCbQXyqUYSjCau7qyFqkCTSTV9Jo0ntpwETXTvhlvprSFIFRr7ET8+p5rQZMk79eA2b2mTPhIniXs3XNy8LiB6fNC70owArthXGyiQANWFdNpdCAjW+CLH0ypzFiuY4zngowLaE+YKbqEVnqZdWATSMQKwtgCt5O+JLZ0TQzH1IQCnHU7ouQVXaTQnGe7ssJP0dAeA/66qo+1aSXnvPUgHnVwI51mbnGByw9btA0KxXT+YDJaVQBTK6H/32Ou5WPXb3OfMCi0oANe1ND+10fKwLJ0azpnJeCaUFb5Zjleo4DNjFsM1Qgm0VoWly+lYmcR/m9WY3aFdo4YL2XygDh41DwwOgxSSj8xyzZeC8E6NIEafF/UmfTeaBVVQOmbkWk22JJ13frJsj+BJVOtdLC3b/Txw+06gOmOx/g31NoWQVW1/y91hOi9LlbBLC1uf/EhIgqz9+nbmtZWavzsN/kw0otVIo/BLuwGNrkTVrSLkknTfY2p22TlD4n3fPp4p52BQtgGS4DpNqG3/zxq/HZH9yK9SsJjqzOjZtxDysJlYPR+E74fuVPYcYiY9LI+GTgHYbClp3mduca2rKOZaIiEr7PsNm1CdLg12WvQjWtKQxF7Xcp5ozaKkjy2xqqvM77Ei2uZYeZvMftXBf95y8EXbdz2wZ88bW/hA0aH7ArrrsDz7/sa0H5zhLCe168B0958En44DduwOs+8l3vPD5+1T58/Kp9QeW78pbnnoetG9LfHDyeu5ogPfJPF7PoMw2qp5LVodU1zJJSSDYtCknLq2e3bkb4yt47cNa//efK8bNO2orPvPrJ/vXrCRbAMlzCUKhN4Me33Q8A+HfPPhdv/Oj3Rt2KKMY4YDk+77SxhUYf1E2Ufeue6BqVBsUFzNxGpb+fv+dUfPCbN9Yd1Kn+t89y+s5NkNmnfjZd1biY4mUJpcHnVzT5tiU2E6T9UuN1xvMdjwX9+IDlPjeGfW/Rbkx5xnkPwLm7tjmnv/KGu3D5tbfhwOE1SQArfcA+cuVNxd8vfNxp2LVto1O+B46u4d2f/zFuuPMAAOC62/ZjlhBedfHZznXbtmkd7j141Dl9CO/83I9w/W37cd4px7bKp49Vy8bQEAFyvs7r8N6Dq9i2ccW+SMfS6/63pz0EF5xxW+349i3r/SvYIyyAZTQLLXWbshDAI3YfiwvOPL6Sh5rrkEKFXMfpiDLTJtgJH64asMyMKOAsGeQxvmqBWOU0hQbMna6d8JvK0jnhm0z9pQasPmvWzZbrGjCMoijydsIPqONY2mVZgH7aQx+AX37UKc7X/u1Xrsfl196W3pcGH7AXnH8qHr77OKd877j/MN79+R9XQo6smxFe6SGADcFlX7yuMHe2gRo07WFCk/m6LkaHew4exbGb1hXftRM0y6Tmkaceh0ee6tYexiQeY+jINPlraR0FsxPFcv5YNGCRqMB8zUsTs0DWfKh877bvJrmiSaDXONjnBdgewZh7QaoLGWQEqve01GyJWrr0fKYBMwg0arV1YShq1XP4rS63o+su2UfXzn28zD5g7cYy/xWUkiDdIID5baNTFdBDfNuGIEmokwm1q2+jb/76LMNGcXUSKwtgRm1b9jkho0kNFsAyNC4yulTKNQIJlR3a+GIYsIGkSpJcmJweU6xz6FZEvpvkpj5g5vQVc11Rt+wloxHeksQgqdjq4J7UCVsoj1QDpvNZq1LzAZOuN5UH6LciKi7x/KGuj1Ev5Pk9Bz+TvnvaPtCtunWl0jybBDCPrNVmn2p1vao2CAllCwxa9rqm1dYh+ZueZUgYCl36ew4exTZJA6ar4pT3PM5hAUzB5l+jM0ESpA5t8E0ZehVkLKqk6XYLN0IcyNXrffyvGn3AdOY6NY3mb18hsA8nfO0AqxyUV9SpdQKkehkWF6gvjZWZqgGrP0snocEhTddhKrwXuLimIb2/a3k+/Nl7XylPbBsCsfpUS90BYi4cn/PAzBLqzATZ/cSpX7P2vZIAZmrrbkqTuGEBLKPJkVrXiPPNgk1burTdQiIEeaVcLGOKcz+d2DJIVXHhHwnf8wEJ+z6fcm6JIqzofKDCfMA6joSf56stTK9BUe/BvJzpVPKshKHQ9MVZUh3++nhRqejuXXkPXE317s/Aq00WJkZ9DCXRcgGGt2ZEqlepAdO/srx+pzJxShd7xEeirMoNXgTpGBPLezjqcKGGWr97DzX7gOXE+OxcYSf8jCaznTbGUjZgmTQhQwroOh+w0VWzAcXHIjT60CIKhV8YCp+8FWFFa44zaJSsdUHXGjC9UJWXVRHA8uMNGjCj9k+ptmqClPPyNcs0pR57ahHD3CY0hljXJkg1rRCI8i2eEGUxztrlo9PsygTl3+H9Wp0LfPOnd+H013+icnz7ZrsPWFGVKb40MlgAy7DFFCnT1Ad+otKXxrw8vqNKOiBQ+gzE0i5jGPz7gBT9je/9Do3WbPTB1/lLFbP8LI1DWAcb3e8FaT6nOkc3m/qrecrpdFrh+lZE9fALLj916G7Wlw9YKvBSdo2+kFBH+qBrIY2rXQpg2WfsK8ZnCaGLOK+mRRWt8oRh0hSwoOHQ0dS8/MSzT8CjT9sOIP3tv/KY3ZV862V5FRMlLIBlNJntjCZIqbmZN+Puv3vrSohxULExtf5UapCy7wHXO2nAUJoR/U2Q9TLL9FUhzYW2K+GM+TqYM0yLXQon/Mw6ZTLpqfW2RcL3Hdyb7qH1uQUIwr7EsFWSr6N7ZbP4Bh8wH62sGoBYCKGNCTc2SVJt66HvkUQzsdAxtibpyb9wIn7niWc6p5/yYrMcFsAymgYoncRfaMAafGmGaNf5APL7f/ft4phur7shCRkwptiZgk2QRE4bYZdCgV2gr/h3KRGkde07YBFkoSXpCp02AoZjpvqK4nzmwaQRPnX51/rHID5g7e+dzzPwKS338TJrNwImGcG1UZ6jqAdiDc1ZFXgjtUBilq1e7MIEaRtngiyQplWQCPAlK6xPhnGt8Tq/8mKCBTAF88tN7y+SJHWH5zJBnmf/nHH8Fvy7Z5+Luw8cAQCsX0nw1HN2DlByM22XUcdOqMk3sZh69OXY76U+DEV5bS19rlFykQLzOvRkgjROXjSJTRqw8hq9Zi/9vWWOtUCsGgmso0WQ1sdchuJwI+SFHEMPDDdBio5NkHk7QvYpRtf+6Mh9wNri6oTvl6dZaeF7J100WbqSmqxWU4AFsAyXh1l3shcgJEXLMe0FOUTnThLCbz/hjN7L8SF0JjQ1ynr7z/CdNGBKOW5CgfJS11wbpAETXUfCN5tBVU1PIS8ZfMDKMBTV4+kl9fzrPmDVsruka3NJP2bg9B6Yw1D4Cyq6RRTuF2flOviA+eReCv2laT/Gd3iSVON3ha+C7GcSrG0jLYqxuf9oy2+wCEyB1mEoiGhGRN8moo9n388goiuIaC8RfZCI1mfHN2Tf92bnT29bdpe4NJya6UMxQaqZLIKKtBM8OmWMM1ETJp8kV9Rl5k0INLRT6dbNsp5ti2tlCp9io/O9IC1CoBr2wDXci6l+hGpf1PqAjaAr8vcB8+lQvrXpD2/hTf7SoQYsp2KCjOg+5cyIMJ+3nww0OuEHjF/W2+V5M9VFNLY0HRQXFV3EAftDANdI3/8MwNuFEGcBuAvAy7LjLwNwV3b87Vm6iMgHcYt/jUbDRVQ395Tnp+8kyJhRn6u/icVRA2bx4zKVXwor2bWatkgGIc1el24HvCIrnc9RJYHilC2R38PSByzXqtnL1u0FqV7jMrt2skB2PBnrQ7tchNWBfmIQ5gOm0WA6Utn7swcn/Jy0Tcc3ShOhm0CshufZKk+DWbNNKeZ3r/74RA0mFVqZIIloN4BnAXgLgFdTeqcuAvDCLMn7APwpgHcDuDT7GwA+DOBdREQihuU5aJbCCaTRgAkkRLUl/7U84+vbg2CwGBmZqq9YGyd83+bvqACrhWzQFVM8H18N2FANWnnh65zr02TpgdpWRNLd0vVv3co31dzrXNU2phcAm3AIJ//8cuCa4xvTP/L+vXjgkSPANfc0pt1xy324JLkW22+4FzhyjDXtE1a/iwfdtwWbDq3gfnEncM2Ryvk9B3+Io2tz4Jo7G8vN2fmzu3BJ8mMAwIk33wXAfYPkk2+5A5ck12Pjjw8Cd3wrPdipE37xtKOcJM8S8vLPNOGy52yQBlG3cjlAQ+7ynjRtVZZeF+PTc6OtD9hfAngtgLxnHw/gbiFEpi/GTQBOyf4+BcCNACCEWCWie7L0t8sZEtHLAbwcAE477bSW1XMnxKFPnXmbfMBYB+bOlO5UKRCEaTp94/P4xX+qtkld+1aX4zvVAd0+I7LUQfU5UjdRzim3c0rPJ4VmTy2r+l3nhN/XfLC4/4bzvzH7DJ5y5d8BVzbn9fv5Hx9sTvsQAJetB/CF5rRvBYCb079fqMn/1R7l5uwBsGd99uXL7tcBwOMBPH49gE9mB2gGbNimTRsSB6xwwp/HOUmeJfkqyHZtsmm1dUjuXd6upr5hLmuaE3aZYAGMiJ4N4FYhxLeI6CldVUgIcRmAywBgz549g93hcoZsNkHqIt2TZIOs71FX9U1ZNqY8M3GhEigS/r+3aZPcHDWFi6reJFzpArF6TbKFXnMUik0Lp5q8TGnrGrD8t8tpsnOyAKozQar1c/ipXbTzrXQo/eN3v4im19ubPvZ93HXgCN75gkc15nvVzffg9R+5Cm993sPwyN127dNL/vYbOO/kbdiyfobLr70NH/rdx1fOv+4fr8J8DvynX314Y7k5X9p7O/7jJ1MPlX9/6UNx/gN3OF97+bW34j996lpc9puPwe7tm4HNO9L/Nfg4Ytd2iYB/8NAhICKsdfAGtK1YbJen+VxwpgZsYWrie3LutNGAXQjgOUT0TAAbAWwD8A4AxxHRSqYF241iToWbAZwK4CYiWgFwLIA7WpTfKU2hBDQuYECmbjW9j5ok+2XBte/HYYz2J7TaLqYBuQAB970ga7GONGr+RHkRuTAXAisdCtY2B3TV38xUX1UAVjWTUmmVb3UNWPWl7ENT+nIyprl3BMywhjmtINn1iMaybtxwGLcdOQzsahaEDhy8A1eLe3Fg+7nArhOsafcmt2PHxh04ZuMKrqWttfx/um5/qm10KDfn/tv34WpxEACwf/u5wK6TnK+99+c342pxGIdPPA84cas1bYgGrLIKMsJBekbVEDHBqyDR3Mf9s6675IRSrmY0FqW/Lj8d4bNzJdgJXwjxJ0KI3UKI0wG8AMDnhBAvAnA5gF/Nkr0YwD9lf38s+47s/Odi8f8CHHzANBK/yI6XJki9DXLRNUFdMqVbpfqS+Jsg/TRgLm00J1HqZhMQfPeC7NYJv66tKssSBo2dYoIUVQ1Ycb2o/y3nVw/EOl7jW8EcgtyG465jsZX5AiCzb2J+PpTQcbCPGFZyvl2b1bsi3YqovWesbostmdD766K19sHuA2YuP0btpStdrIJUeR1Sh/y9SH283pMdfw+A47Pjrwbw+h7KDqbJEdAYiJVIejEo55d8FaQab2dR+fr1qVOy9ypIXx8w6V9tfvKKs0TxAdMIbyHxvObZwpOuqDtEl6iHTDtOqH237KuK+VWptroZt+5XufxSlzS22TqBkGCOOekdzNugvxPDoZsUuGJa3KRN65G5GiMv1lWQri4Kzfn0J8R2mqfJ/aehh0X46JzpJBCrEOLzAD6f/X0dgMdq0hwC8L90UV4flEKCzeWvPvNOTZBNppGOKrngxKMPdSN/rP/tWzcBAI7ZuM7zer8I1T7tSQ1DIZ0o/8wnDl6R8J2TdgLp6mvQgBUmyOx4NVm94qZArEL4b//S9r6sYA2C3IbjPjU2do1ku3J9NRXlBM4lb8+6SH5RfWkU25IQYVUKBBYqJBKaBbmwGG2aSVNAsF6ncU2nbVsABQdHws9o1IBBL2DJ6etO+Pm1U24i4YQOF1Mhf/azhPCpVz0JDzpxi9f1CTluxi2ZEa0+YBphRTazAIqWzKBRstYFYZozl3ybjpnie+Vf1b0gdR5g8jNTXxTqooousZmPqfAB60EDFpFk4a0h9nge/nkrJsh4blPBLCEcXm3fGJs07aEWiq76SaggtQgKDhbAFGyOgDXTR3bC9EJaBCfBLpiaZsuXYzau4KyT7E7COhLyc2T104Dl1yg2SE0aLx+wjrUFzU74kg+YVAe1TvJ53YtbzV81P1bqUjloqbxyXRtSHzA3AUwESAwujzh/tkQwSsSObmoS9efnfKWHC4O/dq30i0pjV8U3SKdbEZXfw32rmseZ0Gej0ocPWLo6udlFYYqwAJbRvEpE5wMmMif89LtqyolojcEo+L6Ypne30h+oe5k7Xe2qATP8rctP/lteZambDOQv23d9bi/+ry9cZ63D7z3lQXjlxWcXfo9dUfYr/RtfLilftfjsv/pypQ55tPDcpKiLmF/4a6J+rlaq8NcJND3GIked4Ac/DZhP3aKa/AVoqYDl1YDlGvK2PrTpb+12dLW5T3iP+4GWIhe3odhhAUzBqrLXNDiSrjFvxt1J1SbLZF8YjoTGxTJt52HCdxDVOfHKNSUivO15D8OPb9tvzeczV/8cn/juPrzy4rP72wvSqAErv//iWSfgDy46C4dW57W0m9fP8OjTthvzTBcPANs2pX56J2zdUK9LXq73r2jPCs2dfcCAAHONw6/Kfbx0u37keZDnuq2qE76/lgroRwCTtc9dTyq6YkbpKsi2NJogA4voSitVXGJx/9FexybIxaHJDq1Tg+Yd17RH3bL7gC06hT9RsHNsyms+9B3rIHLrvYeLv23mEvV4qmHLr8uPVdM8//zm3SY2riR41+V78ZoPfQc33XUQu47d2HiNKzZHa1UzceymdXj1JQ92yRVAVbs4z8yZr7zobDz1nJ3a36AT3Nz2gnR4/hYfMMBTA+bxlotp5Ak1c7loib3HWEkoyQXP2KiZIAMr6eLqELKCW0eboLa2q6y+r0GlxQELYBkhTvjzwmdCrwErvMSm3EJa4eunMi0jZP7ravGkHHnkacfh1B2b8LXr7PGIt21awcGja81mctm8CKpo2Nrc20se+gD8v1ftw9euuwNbN6zggjOb9yt0xTZYh/rm6AQpIQSSBFi/kuARp+ojwle2RfK8X65mIm0YCqJsFaS7E36omacpTW6WNm7GHWhGBBqsCw3XNqYNMkGmv3HexnGpRxJK3VraDotNrg4h2eveh6FYgxSj+dnGtNDEFxbAMprMhUT1hirP0HWD1iKoSLtgqjN2V0IFsF968En40msvaky3ujbHWf/2n5vbqOa7eu9DanreKcfi8j9+SsCV7mhNkAjrO7pLfOKXVTRgLpe4K8CMJB6BWB2L9CbXXhDcNJK++GtZPEyQAXUpso1T/koDsXYg5XQpLMlo20iAOtHFk8tYln9xUdFHINZJUq6kcn+c8gw90fjzTEuf0z2LLnjmL4gOt0a0liNEkxN+tSIVP5ciTefVa4VtpVuoYkL34p475DXEvdGNL4R0FeTcwwfMuTyLiXcIdKtYna/NPp1WQXpr18po/yGxq4YgIapuRRRq2tP4gtbSBKwi7RrzxFJ/omn7wCnAGrCMZkfA+vYcQkpP4DhgJhZVEM2fapebU9vK8Y2XQ1SuzI21LRa/zaABa5WnvAoSzY7WuS/fY978aRzN7puL1iyhdDeE8970KWMa03ZJRdlePmD9vACrJkjd+XbhGkL9jHrTgEn+kXH1ipRZQtkqyHY03fdgJ3yT1to7o/TDuv7NYBKfOiyAZTSpM3UmSEirZ3SOjosgoQ/NFO9VqBO+K/KLyNUZNQ1DUdeAxYbVCT/E6Qj6F3fur2nj2Y84GXfuP4LVTPh6wLaN2L65eXeDV150Nj73g1sb062bJbj4nJ3a+vrEAfNj3A7VxgfMuMeuJa1P3kXfEHGugkyoGxOkzjrTBV1tMVeaIP18wGLV6vvAAlhBgyMgDE74+XmNo+Mi2KjbUPzuWN/+LSlWQfatASucw5WCDfWRv9u2IoqD3FyobyQh1dVF+BcOPmCnHLcJf/LMc7zLu/CsE3DhWSd4XyfjFwfMPxSIix9m7uNl2ry5tQ9Y4AVOw4evdg1l33ARzscgNUGW30PrqLPOaBP55GlKLxAQrLchT1gmaIhPq+8D+4BlhAhLdSf8+nlg2hL6kExVpTzY7LnBHFHfWkc2s8SpjW1e4RSSafohCx3zeZxaDiB9gaQasP58wMaisjI3cBVkH3HA1DAUMTJLskCsHayCtMYBC7gDROjsxjVNDiyynj3BBGABLKNRWNKckH0HUjWvqgFzWd+xuIT4qUxpNjOUBkwuC3BvTUlSb5Ox3V1bfYKd8PPrpWOxajlyZuTrA+aXv8u7UmROrTptPwzHfPD309I9yY7ylv7uy6euLQl1FYhVr9FslSdswXr97mX57jVft6hxwFgAy2hyUi5nY7Jjb7l6Jo06rs87wr49KF13/ljI28ogAhjSgco3pIcaiDVW9A69YavTylWQkgYsUj+fnJW+NuPuPEfP8kn/t9O12aebBsxTu1aZMHe7u0NXJEl19WJoHeVxwJamK9qYSn0wBZeeEuwDltFkopEde3V/6+zsy+4D5stUBbVhNGD+jrSJZuYb22BVCX6q0FoDJmWZB2KNEgJmnptxe2uKndpOpiUkg88N2rWfEEd5oFl4SNP61qX8jUIgyraRb0XUei9IAkR9966SgOxnCeHjV/2stvjk/sOruODMHV55NcbLtDmcYdrvVxbAMpqMhfrNuMvjeh8wu2P/ouMzgy2umdKtyuo6gPwFwH9VbeqEr1zbR8VaYGsjgYsgtSsrfQKxjsEMcy8n/KnQRdgKF61vSBwr2QcsRteHhNyEz+Z8CGuwSWD+/PElD8Y3fnKn9tz/dG59pa+NcmzyNF1OpxsYYQFMpaENqCurSid8nQ+YU5bMRJH9/4Yoy0+QpUqbjHVXBlusp9B95fJraoFYY/vxGUTpKkivMBTOQrhZw6iSaxzJqAJrZ6oLNkH2lHf0qyCTaiDW0JtPDoKc7+9/1sN34VkP3xVWIc86yO4/ch9ehEVuESpex6Fxla5mNpbOnFISzZgV60tvKLwdhSc6oxnKCd93SUciaWVjvbWFsKQ5F7KtCaDvq6kGzD+voVjx0YAFmmYb8wXKQKxdhaGQfcBCYkWgn3FB7k/RBmLN4oC1X/xQVw7IjD02hCoqYg0u7QMLYBlNalDdbEw2kaRO+IoGrLh2ug2kCxbWBJkxjBM+NQZi1V2jtsnY2qJv7B+nPDXXxxpsE0jr6xMHzDfvWPDXUrlr7/wfbdUEGePAM0uatxByQRY2Y6RJUWEMxBppaB0fWADLcWwEFcdeyUSiU/MuQgNhzORtYQgBDJJWwtWUVtGARa6N1c7QQ53wi1WQ5bH4NWDuJsgQTZTLezzfashk7g7RFMnp/Z3wy3Kb04bknZvn42wbpAZiDRSnybJCv23e3WKvg6kdxFDzUFgAy/A17wDV1TO6VWoxzzqGwGZe0jG1+7XmsV9gW/L3hasDNqE68MbuuK1fdRfmm1M64VdNkHG8ZOoQEWbU72bcMeCtAdMI0sa0AXWR843oNhXMEqQmyJb5JA0OpD6hbfrE7AOmPyHKBJOFBbCMppgiOmfW9OWWHk+1DXoJLKZBMHZifUnqyPdpizUQK0ltMpIxtkZTrM0wJ/wsy5oTvndWg+EVB0z43xcnDRhkHzDd+bC4bDkhQlJebnNav9xl8/xctPtdfTHTuLWEkC44aF+fvml6AkYf6wm9M1RYAMtwXeJf7Q/SKkiNv83Sh6FY8J+dr1DqezNuIPMBg58gpdsgPrZn0hgHLEgDptGcROwDBvjFAfNh7JeTPPb1GojVL+uKBixWJ/zCqtLSlcUlEv6YXaN01TEpPwzXRa7Vd4EFsIymlRi6RiB3XNnfxjXPZcFVxR2LKtyVXAM2RBBHWZvlmj71S4zcCT/7NLiAdWqCjDHYJpDeg1QD5lbBENOsS8uRw1Do2lp7QcVTS1U8R/e0PjWprIKMq1sAKDXrbbcj0r2bZMYedl3dfxbRwhTpkDQ8RSPweJjyC0Ln6BiS59IzoXs1pA8YgGIVpGtxiRxscuxR1oB1FWSg35beBBl3INakJw3Y2Mh3PGSlIlCfRGhT+pogK4FY4/QPzAWw1WycaWP8HXIrolCa4oCphPhtxwYLYBlNG2frgjsKaVDXxc5ZBBt1F8T56m9P/mIYci9I32vyOsa6CtIaBwxtNWAlsQdiXcEcc3S/GbcuJpo5X5EF8DU8j5aaotBVkH0MIPJ4HasGTLfyPjSfWCdggPvYZPQBi/HhOcKR8DOatFV6s4ZsgtStgmxnu1824h0i9KxlS8SH3gvSVaDXvUhjbYqmsAdh5JOlMoNYQw3kpHHA3IbjKfWTkMUj5bVm/8C2qD5gMZqnV7IG+5ef+SGAcEFjRoQf3Xo/zn/LZ7Tn7z+0OkwoHQOhWxHlTPn9ygJYTpMPWJ5M0YCRpAHjzbirBGkvuq9GbwzrhJ8OVL5hKGUtPFsAACAASURBVIpVkPnxMUar1cPAey4B7ruldupEIXDFhsM45nPrgC9VNUDvPXAkbUN/vt6ruIevzXHFhiM47qPr07X8AN67/whmdxHw5+uCf0ZfbAJAtOoViLUPrbpsAzD55LUyhPXohO+L7OeWmiDjk8Cecd4u3HjnQazOBXYduxHbN4e13ZdceDq2b7H3oXNP3haUd6c0KT9MCo4eq9Q3LIBlhKxYlNuDXgOWMeUW0gERa79bUTjhDyHUyI60zj5gKAI5jvoM9t8O7PtX4LRfBE44u3LqyNE1fPbbN+P8E3fg7JO2Vs594/u3YP3KDL/0Cyd6FXfP/iP47PdvwVN2nYSTj9tY5LVp/QxPPtsvryFYmwv8wzdvxsrOp+MxDunlPWhdcXr8uSnOkHlIubKg6L9hdl6uX5muecfuhH/qjs148y+f1zqfC848HheceXwHNeqH1lsRRfjsXGEBTMGoAdOtyJE6rk4DViwfXlIJzPt3T0xQy53wZ7NhNGBASBiKcpYv5zMo89X081G/ATzqRZVT9913CG/4xmfx5vPOw9kXPLBy7q9v+DK2b16PX3rOY72Ku/nGu/GG73wF7zl/D04+ZycA4B3Xfwm7t2/Ck5+zJ/x39MTq0TW88Wv/gtdvObPzvGN6OYUKb13EwqrnLZkgEdd9WjaatPMmU3T5bboPLz6960g07kel8yuRjuvU9rwKMscnfMJ0blbhhD+ECVKOp+Rxnbr6aZTbmwtgSX2+VwjpprAHbZzwawtm/PMaElc5I0QccQ7EKk2bdI7b/n5c+r99ru1jXibHyJMXUzHj4a8Bm9iMXQMLYBlNz1I3GMiDuqxtUPPkrr2YlGEo+i8rX8nkOuQQkRKGojw+OPO19DOp+zhZw1BABPUd3crK2MNQ9EVM2nf/UBHpZy/vWWll4PRf49PGeRXkAio4WADLKN1r7E/TtL0JSf42ZVp/v7JFwncAndpAWAhgsYahiOUlY9WApZjqF9J3dKEX5iLuSPiA+2o/ERRSwzUMhcXpOUAjSYa/3a5tah3hyBaLmEOULAeBrjoLoOBgASyjFJY8rpH2RtNqwLLPKTeQoZnSvRrSBAlkLwyPl2BC0vZYY6rrCwHMvMrPFIaizZ1VNWD8jh2XYBNkL0740ngtwjStTLc0hYAyXzfdp8cCWEZTHy8ecsWvpHxBaCPhL8AqjTYs+u8u4oAN4YSvEfDt6bMtSJRjoyByE6RGA5Y72PbuAxbvQB2iKXb9JT555/maguMGRYyXfcACV0H2sZE0O+HHQ/AqyAUIQ8ECWEazE36WThqWhHQi7dAmDdiUm0h7XMfPqTlVPuO8BwAAfuXRu3svS35hOLcnaVLg89LunLlFAMs+dU8+dHgtTY1SX52AE/6iE7oKcohArNw0xqN4ug0L4GrXLYCCgwWwArsdWjuTlPxKkkTnM7EARuqBmVJnOv2ELfjJ256FX9h5TO9l5XGLfF5GiewDNqZsazFB2jQ0IXGngLJPvuG/fw8X/fnncfXP7o3aB8zf98X9vni5VGQaR50PnXzeh0ocMM9rE0vbaIsaoiXWtrFM+Phfy9+nrOBgASwjRJqeS74DFX8bhWXt27r9M5lQyLuNVs0sYjwTnEsYCgMhNT7zxC34nSecgcedsQPX3b4f1+y7d+l9wGLogsGBWHuoC1CaNufz5R2jY6DJ/9p0PIY23RYOxJrRtKRVZyqRfQcIdV8FVoD5sQgdqi/SdiachVlCfWHIeCZIswAGy0s2VHBfN0vwxmefi5vvPoh//t4tWJun9y1WLUdIVHtnHzCPp54L6SazcIivVJs4YCgmcH2YIKUQLVJZzPC4Llarh3nyXzgXG6wBy2hSZ+qchYUonVJJCuxXnA/Y3miRWNKf3QvyrfQxP8lbEY32PGwCWIbWCR/ttFb5Zsarc5HFAQvPawhcBY2QBQVOTviZL5Q5DEWAE76E75V9tld5mUGoqZvpFu823VM9hoQFsIxSWNKf1wZilY7LMZeK86wBA+Dnt7Ts98pE7jTsM+iQZBYfdbAKDcQq2vl35Bqvtfl8aQOxjk114uBpgsw+e9mKiKoCJreM8XBeALeAUQZYAMsIEZbkmahtM+4pN5A2eC8rXoQpTU8Q6u3LfgHVw1CM9ZpxCcSqc8JHu76Ta8DW5iLqYJu2e6DDZ4eAcuLYnHm+qtu6916LW+irgcwF5v6c8FHkz8L5eBSmxMDr2Ql/gXCVwtVGk1B9prYIqzS6gAWrbhAQXmagVGiTVkGObYIknQZM/7IH2puG8vhsq3PBYSgiINgJv484YPIK4ZambqYbfKxP8pEpPzsWwDIaXZWVF4Wq/iTUV0E2mTWZOrFqKcZGNZnY0uXIoVFC91XsBIc4YCbaTF5UDVisWo5SCHXDx5/PS7ummHxr1wTEy6r0Z18HfkNA2C6QF03NPe4n0z2hy4TYBLlANC6FLRJWPiQnfLONelnxX9215DfMAiGPA+bz8pW3IuqpYi7YBDCLlqOtyWs2MSf8RSd8K6J+VGClCbLd4gKmJY0+YPrVsIvwtmABTKF5KWz2qQhsNg3FlCX0LliEjjI28rJ5p/TIVkHKjsZjmyB1Tvg2LUeAxkVmJUmHt7W5wHw+Yhy0BsJ8JR3N0B6Z56Y4W9wlb0f6cAVYWa6h3beRywiyCRLshR8BoT7DUxaeWQDLaOrM6rijxi7RBWIt/cSm20Da4f+7l/VOueCiIZTvX8XRGHE64eeYnfDbrIJMP1cjjwOW06fGPIZJkO/9TxKzf2B6vE1dqhnF3jYWmXKxmv4ZmCcE03fxYQEsoylmlxrVPf/MBwn5ZVfkuQA26i7wiW/E6HF1SJbbb8XReMxYR7ZVkJZVekK081sjIswSKsJQxNoPgwKxOl/jnnkRB8ywg0XI85DT+29jVNZLRysNmBKINdKmsRS4roI0vl87r9FwsACW0fQw1ReFqu0i3SrI/FxHdWSWF8qdwOCuFZJDo8SxF6S/BqwtqQCGzAm/gwx7pE8fyBg2uu96K6I2vyl1wi/H8liF82Ui9BlM+dnxVkQZrtoqtc+XPmCaOGCeL8xFI+hnL+etaoRAhRO+U3qqvmTSPEaiTSDWlpVeyTRgi7ThclvNoDFfoOIDposDNuRWRE17ybYRKWWf3Vzzx4yDuqBNJX9//s9/9WXMsr9vu+8wNq6fFVdOFRbAMhobgZpecQBMqD4jK8yaXVVywYlgkh4tsjnR1p6qJh/FzDKWABK4GXcXoTNmCWWrIOOdCJXbnPlc0226tPx0NaDJ9De0oJLX3RQJv50GrNwnddSN6pkC0yO46CEn4fs334Oj2Yqiz//gVtx3eBXHb12PZzz+gXjg8ZsHrGW3sACW4boje+nUrKyChGUz7iXt20v6s3tDAI1vaVXjUGxFNOYs37YKsvBt0/mAtReaUg0YB2Idj6pPYsiVRhNkUH3KzFkDFgdNcvQZJ2zBXzz/kcX357zry7jqpntwyUMfgDc885yea9cvLIBlNHVmNRZJ4YRP+SdpVfZAvDPvofALn7Dc98pEumzeJ322FVGhARtRvSjMccCKJDofsA5ejLMkKTRgsZsgb7//MK695b7GdIdX5x67IaT87O5DjXmrJsgf/vw+bF5fPrMjq/OWq1J9fcDsK0/aaMzTnUvKfJZ9jB4T39WMG1fSidzWDdMXX6b/C7qiSVuljAXzwhxExXXzuZon29SYbiAqfcBsAxUVIVs1k4LRVkE2R8I39pQufMDW4g/EunFdgg9ccQM+cMUNTukfsftYx3zTl9Wf/csP8Gf/8gOn9Pk1z/0vX9XW04c2ccCanPDbbNKdbtOVDtijrhBmCpwn31kyFsAWiKYwFPX0yNLnn7qtiJbX/AhIvi1RRCGaNqkGzO8+kjrL775abhR7QdZf3k3+T201orkPWOxajv/2u7+IG+864Jx+zwO3O6U7+bhN+Pv/9QLcdeBIY9qECBeedTxWkgQ7t23EmupTAeCxZ+xwrqOKdxDX7LN3J3yM2DeYYFedYzZOX3yZ/i/omAYFWIE6KOhm1+xb4E/E78hxofRF0dimKj5gVBHaRnXCT1a0D7fUgBnigLWsciqApZqOmE2QD9t9LB7mqNXy5fEPOt77mqc99AGdlE2Gv52uzZ6XUdPVKg6Y5M/bwWpbJhxVmeHKIghgHAcso0kKr83UlRATps24Y551943vL48hVlGshJhvZL+xUe9tLoBpsIahQPsJzEpCOLqWC2AtM2Na0Xkg1hYSWGqClFZB8lR5dHz9GrduWNdfZQaCBbAM1zAU8tJlQHLCT/QDBXdrdoXrApKm7DahXj6jbo81XiT8NYsAZjZBdqGZmCWEI6tZX2UJbHCqOzP43f9cY2kOxBpaq7oGLOE34XgEmiBnC9Cfg3V4RHQqgPcD2In0Fl4mhHgHEe0A8EEApwP4CYBfE0LcRWnveweAZwI4AOAlQogr21W/Pc/7L1/B/sNruPtg6iPhGohVjZxPRPjRrfdj76334ayTjinSLLECLAi+XXoIwBd/eBuOrM1x4jEb3K6RXzIY2QdME4JCRvcu7cJ3cCZpwLgvTgxLiJL0eIuslRh5PPKMh2+8zM1ZANalFsAArAJ4jRDiSiI6BsC3iOjTAF4C4LNCiLcR0esBvB7A6wA8A8DZ2f+PA/Du7HNUHnj8Fhw8sgZgC07atgEnbtW/3NTBu1gFmZ244Mzj8Ymr9uFfb7ynFMCw3GEVXPcvzGFFmZmXXngGvvjD2wAAz330KY3pCakG4a79R/DOz/4I37npnvF9wCxccd0deOdnf1Q5dv+h1db9Z2UmmyCXty+ORZs73vS42q2ClEMK8SrIGHB9Bm/7lYfjb7/6EzzGcSFKzAQLYEKIfQD2ZX/fR0TXADgFwKUAnpIlex+AzyMVwC4F8H6RtvqvEdFxRLQry2c03i4FeLNRD8RaPf60c3fif//o93Dw6FpxjeDlNcvBz74N/OgzvRbxQgAvPC37cjuAL+jT/Zvkh1idCZy796s4/cgaTjl6O458DngigFOO2wR84V97raeWm68EyKwBO+ukrbji+jtxxfV31s496KQtrYqeEfuATZX8cRkj4bfJW3XCb5EX047y8bo9hZ3bNuJ1T39Ib/UZkk6WERDR6QAeBeAKADsloeoWpCZKIBXObpQuuyk7VhHAiOjlAF4OAKeddhpiwRSINW8y+b5Uh2UBrIOtVKZMiPZikjPRy/8j8KNPjV0LAMCrEqSenT9Mv18g+6nuB3D58HUCAJz+ROOpT//Rk2q7SOS0NTOkJshqzD5mGjSFKGllgpSuX/ZwQWMTugpyEWgtgBHRVgAfAfAqIcS9splDCCGIyKubCCEuA3AZAOzZsyc6q1SpAat6DubReQ9JAhjYBwyA+0x1ss76a4eB3Y8FXvrPY9cE5/3pp3Dw6Bre+svn4fnnxzOBsfmAERFmPfWTlSTBgaOrWTn9lMGYaXPPy4VPJlqYIKUgxfkemMy4LOMTaCWAEdE6pMLXB4QQ/5gd/nluWiSiXQBuzY7fDOBU6fLd2bFJUBtIlK2I1s3SrV8OHZ1Xkixzx16aF958DZitA2bjx6VZwwxrQFqXCOozNrOEcDRfBbk0DXIxSHrUgFW36WLz9JgIxZ96mQhefJutanwPgGuEEH8hnfoYgBdnf78YwD9Jx3+LUi4AcM/Y/l8hlFsRpZ/lVkSETetmig8YO3cCfjGoJimwOqzyGwpub1WqTvgjV2YJadOfS79b/fjRxgkfoGIMn8+XO17j2JQhoJaPNlPkCwH8JoDvElHu2fsGAG8D8CEiehmAnwL4tezcJ5GGoNiLNAzFS1uUPThlB63GAZP77cZ1s4oJkp07/ZjslkXzVWDdprFrAYDbm8osIRzJBTCWwCZJf1sR5WM5EwPLKAO3WQX5ZZjH+4s16QWAV4SWFwumOGBALoApJshlbFXLhkOYhaHg9lZlpRIHjO/N0LTyAeszDlglo+V8+ceCUKxJywTH/3XE4AJW6bgb1iU4tMoaMBWfcXKSA2FEAljOMg5mOhIqV0GyAmxalCvP9ee73YybG8fYTHLsbwkLYI7U4oBpHAc3rZvh0JFqGIpl7te+HWqyqyDna9H4gDFVZgnhzv3pLhfshD8tEmXMVWmzv2mirIJk4ZwZAxbAHFFnY0YTJGvA6kxVsHIlIg0Yt7cqX5eCu+7eHoefHuNGUxywdnlzHLDYWEYfzTjeGhNAXZFTCGBSz924Lqn4gKnnmWYmebvma9ZI76MwxfvYAyceswF37D+C7/7pJThm47rmC5hO6SIOmGm1Y7utiMqN6ueCV0HGwDI+ARbAPClnTfUNRDeuzHD3gaNS2uUOQ1HMYB1VYJNVlEWkAVvKUczC+3/7sbj74FEWviaI6vah0jIKBW9FFBnL+K6M5K0RPzUn/GogfADpdkR7b70fl77rywCAm+46yB17GZivxSOAMRVO2rYRJ23bOHY1lpZ2ccCKZZDa861XQUomSB6ox2cZF0LwW8MRdSzQrYJ83qNOwf7Dq8X37VvW41GnTn/H9lDCutMEO2FMgVjHrgDDdIi8abZKm7iBqRN+kREv0IiAZXwELIA5UzWn5Stw5I578Tk7cfE5O+uXLjmuM9XproKMyASZsYRjGRMhbV+q8qbZKq00YJVArIL7SwQs4zPgVZCeqFsRMUxMAhg7EzOLhLxptkpbE+RcGsu520TAEj4DFsAcqXfQ5d1A1JUmJ1rbNZMiIh+wSd4/ZmHpQgNmmuy2MUHKgp0QYin9j2JjGZ8BC2COGJ3wB6/JIjNRtWJEPmAMs0jI8bpU2mrAOA5YXCzjM2ABzBE1KKDOCZ+psjQzmohMkDmsmWVioO0YQLCYINtlXAmqzb1lfJbxGbAA5okaiJVXzzTjM1Od5N0U8WxFNMn7xzAGiGCUtNpuRSTnwROW8VnGZ8ACmCNq08ijKC9fk+mPSa6CnM8BMY9GA7aMgxgTL619wCxhKNoshEp9y4Q2niMzDsv4CFgAc6QWB4w7biPq9k0Licj2/oxEA5bDzZJZBAiEuUHSahUIPzNBFq4k3GNGZxnfpSyAOaKu6BNS12W6Y3KdcJ4F3o1EA8Ywi4RNA9ZGZZ77lpUmyOCsmI5YRiGYBTBPiuB9rAFrxPfWTFJPFpkAxs2RiYm27dEaiLVNvjUNGDM6S/gQWABzJJfO1U7PTvjNTNK3y5XYBDBujswCkfQViDXbiqhYTJVwxxmbZRy7WABzRWkcHAfMgYCbMzk19Dz3AYtDAMtZxsGMiY/W7dAWB6xVINZcA7bIs8NpsYxDVlxvjYjJG0e5FRH7Drjy/113B9YcliwdOro2QG06ptCAxeWEzzCLQGqC1I8d83n7fNmVJB6WcQU3C2CefPK7+/C9m+/BzXcfBMAd18bWDSs4ZsMKPnHVPnziqn1O1+w6bmPPteqYyEyQyzmPZOKlZSBWIlyz7z6876s/qZ27c/+RFvkCB4+u4f/52k/T79xvRmcZn0Asb43o2bltI2YJ4T1fvr44lhBw0jETExgGZPP6FXzjjU/FgSNumi0CcNzmdf1WqmtyEyTFpQHjiQETA9uz/vywU44Nuv7k4zbh6z+5E1//yZ3GNCF5n3zcJhw4sob/4xPXZN95HB+bZRyzWABz5Jxd2/CdN12CI6ul3nv9SoKtG/gW2ti4boaN6+ISTjolMg3YMg5iTLwcv3UDrvkPT8e6WVjD/KdXXIj7D68az2/fvC7IdPVvnvwg/Pr5p0EAmBHh2KlN/BaQZdRCxvHWmAhbN6wAG8auBRMV87gCsS7fEMbEzqb14X1j/UqCHSvrO6xNChFh+5bu82XCWcbJI6+CZJg2RKYBYxiGYaYBC2AM04ZIBbBlVOczDDNdllEDFtdbg4mTw/cBa0fHrkWcHMycgyMRwJZxEGMYZvos46QxjrcGEy83fh1479MA0SLozjKwEodz4DIOYgzDTJ9lnDyyAMbYuefGVPh64muArTvHrk2crNsMPPDCsWvBMAwzWZZQ/mIBjGkgX+X3yBcBxz9o3LowzizjbJJhmOmyjJHw2QmfscNb7UyKJRzDGIZZAJZx6GIBjLETaaR3Rs8yDmIMw0yfZZw8sgDG2Ik0zALDMAyzOLAJkmFUWABjGIZhmM5hAYyxE9lWO4ydZZxFMgzDTBEWwBg7rAFjGIZhmM5hAYyxwwLYJGFNGMMwTNywAMbYYQFsUrDcxTAMMw1YAGPsFD5gLIAxDMMwTFewAMbYKTRg3FSmBCvCGIZh4obfqowdscbarwnBJkiGYZhpwAIYY2e+ygLYhCDWfTEMw0wCFsAYO3PWgDEMwzBM17AAxtiZr3IQ1gnCpkiGYZi4YQGMscMmyEnBghfDMMw0YAGMscMC2KRg+YthGGYasADG2GEBjGEYhmE6hwUwxs58jX3AJgivhmQYZgps3bC8E/zl/eWMG/NVgFgAmwq8ByTDMFPif/zRk/CTO/aPXY1RYAGMscNhKCbF9s3rcD2AGeu2GYaZACcftwknH7dp7GqMAr9ZGTvsAzYp3vLch+Ere2/HE88+ceyqMAzDMBb4zcrYYQ3YpDhn1zacs2vb2NVgGIZhGmBDBWOHA7EyDMMwTOewAMbYYRMkwzAMw3QOC2CMHRbAGIZhGKZzWABj7LAPGMMwDMN0DgtgjB32AWMYhmGYzmEBjLHDAhjDMAzDdM7gAhgRPZ2IriWivUT0+qHLZzwRbIJkGIZhmK4ZVAAjohmAvwbwDADnAvh1Ijp3yDownrATPsMwDMN0ztBv1scC2CuEuA4AiOgfAFwK4OqB61Hy0d8Djh4YrfjoueunwPbTx64FwzAMwywUQwtgpwC4Ufp+E4DHyQmI6OUAXg4Ap512Wv81uvUa4Mj9/ZczVY55APCgi8auBcMwDMMsFNHZloQQlwG4DAD27Nkjei/w5Zf3XgTDMAzDMIzM0E74NwM4Vfq+OzvGMAzDMAyzNAwtgH0DwNlEdAYRrQfwAgAfG7gODMMwDMMwozKoCVIIsUpEvw/gUwBmAN4rhPj+kHVgGIZhGIYZm8F9wIQQnwTwyaHLZRiGYRiGiQWOhM8wDMMwDDMwLIAxDMMwDMMMDAtgDMMwDMMwA8MCGMMwDMMwzMCwAMYwDMMwDDMwLIAxDMMwDMMMDAtgDMMwDMMwA8MCGMMwDMMwzMCwAMYwDMMwDDMwLIAxDMMwDMMMDAtgDMMwDMMwA8MCGMMwDMMwzMCwAMYwDMMwDDMwLIAxDMMwDMMMDAtgDMMwDMMwA8MCGMMwDMMwzMCwAMYwDMMwDDMwLIAxDMMwDMMMDAtgDMMwDMMwA0NCiLHrYISIbgPw0wGKOgHA7QOUw7jDzyRO+LnEBz+TOOHnEh9DPJMHCiFOdEkYtQA2FET0TSHEnrHrwZTwM4kTfi7xwc8kTvi5xEdsz4RNkAzDMAzDMAPDAhjDMAzDMMzAsACWctnYFWBq8DOJE34u8cHPJE74ucRHVM+EfcAYhmEYhmEGhjVgDMMwDMMwA8MCGMMwDMMwzMAstQBGRE8nomuJaC8RvX7s+iwTRHQqEV1ORFcT0feJ6A+z4zuI6NNE9KPsc3t2nIjondmzuoqIHj3uL1hciGhGRN8moo9n388goiuye/9BIlqfHd+Qfd+bnT99zHovKkR0HBF9mIh+QETXENHjuZ+MDxH9UTZ2fY+I/p6INnJfGR4iei8R3UpE35OOefcPInpxlv5HRPTiIeq+tAIYEc0A/DWAZwA4F8CvE9G549ZqqVgF8BohxLkALgDwiuz+vx7AZ4UQZwP4bPYdSJ/T2dn/Lwfw7uGrvDT8IYBrpO9/BuDtQoizANwF4GXZ8ZcBuCs7/vYsHdM97wDwL0KIhwB4BNJnw/1kRIjoFAB/AGCPEOI8ADMALwD3lTH4WwBPV4559Q8i2gHgTQAeB+CxAN6UC219srQCGNKbvFcIcZ0Q4giAfwBw6ch1WhqEEPuEEFdmf9+H9KVyCtJn8L4s2fsA/HL296UA3i9SvgbgOCLaNXC1Fx4i2g3gWQD+JvtOAC4C8OEsifpM8mf1YQAXZ+mZjiCiYwE8CcB7AEAIcUQIcTe4n8TACoBNRLQCYDOAfeC+MjhCiC8CuFM57Ns/ngbg00KIO4UQdwH4NOpCXecsswB2CoAbpe83ZceYgcnU8Y8CcAWAnUKIfdmpWwDszP7m5zUMfwngtQDm2ffjAdwthFjNvsv3vXgm2fl7svRMd5wB4DYA/zUzC/8NEW0B95NREULcDODPAdyAVPC6B8C3wH0lFnz7xyj9ZpkFMCYCiGgrgI8AeJUQ4l75nEhjpHCclIEgomcDuFUI8a2x68IUrAB4NIB3CyEeBWA/SnMKAO4nY5CZpy5FKiCfDGALBtCYMP7E3D+WWQC7GcCp0vfd2TFmIIhoHVLh6wNCiH/MDv88N5lkn7dmx/l59c+FAJ5DRD9BapK/CKn/0XGZmQWo3vfimWTnjwVwx5AVXgJuAnCTEOKK7PuHkQpk3E/G5akArhdC3CaEOArgH5H2H+4rceDbP0bpN8ssgH0DwNnZqpX1SB0oPzZynZaGzP/hPQCuEUL8hXTqYwDyFSgvBvBP0vHfylaxXADgHknFzHSAEOJPhBC7hRCnI+0PnxNCvAjA5QB+NUumPpP8Wf1qlj7KmeZUEULcAuBGInpwduhiAFeD+8nY3ADgAiLanI1l+XPhvhIHvv3jUwAuIaLtmXbzkuxYryx1JHwieiZSn5cZgPcKId4ycpWWBiJ6AoAvAfguSn+jNyD1A/sQgNMA/BTArwkh7swGuXchVfMfAPBSIcQ3B6/4kkBETwHwx0KIZxPRmUg1YjsAfBvAbwghDhPRRgD/N1L/vTsBvEAIcd1YdV5UiOiRSBdFrAdwHYCXIp08cz8ZESL69wCej3RF97cB/A5SvyHuKwNCRH8P4CkATgDwc6SrGT8Kz/5BRL+N9B0EAG8Rvs/YpQAAAFxJREFUQvzX3uu+zAIYwzAMwzDMGCyzCZJhGIZhGGYUWABjGIZhGIYZGBbAGIZhGIZhBoYFMIZhGIZhmIFhAYxhGIZhGGZgWABjGIZhGIYZGBbAGIZhGIZhBub/B09UcXVHn4X8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_s=obs[:1000]\n",
    "est_s=np.ones(obs_s.shape)\n",
    "for i in range(len(est_s)):\n",
    "    est_s[i]= int(i/(len(est_s)/7            )   )\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.suptitle('obs, vs sample_k', fontsize=16)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.plot(np.linspace(0,len(obs_s),num=len(obs_s)),obs_s       )\n",
    "ax1.plot(np.linspace(0,len(obs_s),num=len(obs_s)),est_s *100      )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R0</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "      <th>R3</th>\n",
       "      <th>R4</th>\n",
       "      <th>R5</th>\n",
       "      <th>R6</th>\n",
       "      <th>R7</th>\n",
       "      <th>R8</th>\n",
       "      <th>R9</th>\n",
       "      <th>...</th>\n",
       "      <th>R716</th>\n",
       "      <th>R717</th>\n",
       "      <th>R718</th>\n",
       "      <th>R719</th>\n",
       "      <th>R720</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>theta</th>\n",
       "      <th>Y</th>\n",
       "      <th>Vk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.208487</td>\n",
       "      <td>2.214837</td>\n",
       "      <td>2.221299</td>\n",
       "      <td>2.227875</td>\n",
       "      <td>2.234565</td>\n",
       "      <td>2.241374</td>\n",
       "      <td>2.248300</td>\n",
       "      <td>2.255346</td>\n",
       "      <td>2.262514</td>\n",
       "      <td>2.269805</td>\n",
       "      <td>...</td>\n",
       "      <td>4.636539</td>\n",
       "      <td>7.834864</td>\n",
       "      <td>7.831544</td>\n",
       "      <td>7.828493</td>\n",
       "      <td>7.825711</td>\n",
       "      <td>3.230296</td>\n",
       "      <td>1.145155</td>\n",
       "      <td>2.554403</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.235402</td>\n",
       "      <td>2.242092</td>\n",
       "      <td>2.248899</td>\n",
       "      <td>2.255825</td>\n",
       "      <td>2.262871</td>\n",
       "      <td>2.270038</td>\n",
       "      <td>2.277329</td>\n",
       "      <td>2.284744</td>\n",
       "      <td>2.292287</td>\n",
       "      <td>2.299958</td>\n",
       "      <td>...</td>\n",
       "      <td>7.831219</td>\n",
       "      <td>7.828393</td>\n",
       "      <td>7.825834</td>\n",
       "      <td>7.823543</td>\n",
       "      <td>7.821520</td>\n",
       "      <td>3.223964</td>\n",
       "      <td>1.149565</td>\n",
       "      <td>2.570661</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.263432</td>\n",
       "      <td>2.270478</td>\n",
       "      <td>2.277646</td>\n",
       "      <td>2.284937</td>\n",
       "      <td>2.292353</td>\n",
       "      <td>2.299896</td>\n",
       "      <td>2.307568</td>\n",
       "      <td>2.315370</td>\n",
       "      <td>2.323304</td>\n",
       "      <td>2.331372</td>\n",
       "      <td>...</td>\n",
       "      <td>7.826231</td>\n",
       "      <td>7.824122</td>\n",
       "      <td>7.822281</td>\n",
       "      <td>7.820706</td>\n",
       "      <td>7.819398</td>\n",
       "      <td>3.216884</td>\n",
       "      <td>1.153924</td>\n",
       "      <td>2.586603</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.294336</td>\n",
       "      <td>2.301775</td>\n",
       "      <td>2.309341</td>\n",
       "      <td>2.317037</td>\n",
       "      <td>2.324863</td>\n",
       "      <td>2.332823</td>\n",
       "      <td>2.340916</td>\n",
       "      <td>2.349146</td>\n",
       "      <td>2.357515</td>\n",
       "      <td>2.366025</td>\n",
       "      <td>...</td>\n",
       "      <td>7.822832</td>\n",
       "      <td>7.821505</td>\n",
       "      <td>7.820443</td>\n",
       "      <td>7.819649</td>\n",
       "      <td>7.819120</td>\n",
       "      <td>3.209675</td>\n",
       "      <td>1.158389</td>\n",
       "      <td>2.603681</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.323826</td>\n",
       "      <td>2.331642</td>\n",
       "      <td>2.339591</td>\n",
       "      <td>2.347674</td>\n",
       "      <td>2.355893</td>\n",
       "      <td>2.364252</td>\n",
       "      <td>2.372751</td>\n",
       "      <td>2.381392</td>\n",
       "      <td>2.390178</td>\n",
       "      <td>2.399111</td>\n",
       "      <td>...</td>\n",
       "      <td>7.821588</td>\n",
       "      <td>7.820976</td>\n",
       "      <td>7.820630</td>\n",
       "      <td>7.820549</td>\n",
       "      <td>4.627016</td>\n",
       "      <td>3.202996</td>\n",
       "      <td>1.162371</td>\n",
       "      <td>2.619348</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.355349</td>\n",
       "      <td>2.363569</td>\n",
       "      <td>2.371929</td>\n",
       "      <td>2.380429</td>\n",
       "      <td>2.389071</td>\n",
       "      <td>2.397858</td>\n",
       "      <td>2.406793</td>\n",
       "      <td>2.415877</td>\n",
       "      <td>2.425112</td>\n",
       "      <td>2.434501</td>\n",
       "      <td>...</td>\n",
       "      <td>7.822191</td>\n",
       "      <td>7.822313</td>\n",
       "      <td>4.629899</td>\n",
       "      <td>5.048106</td>\n",
       "      <td>1.339982</td>\n",
       "      <td>3.196053</td>\n",
       "      <td>1.166367</td>\n",
       "      <td>2.635456</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.388031</td>\n",
       "      <td>2.396673</td>\n",
       "      <td>2.405460</td>\n",
       "      <td>2.414394</td>\n",
       "      <td>2.423478</td>\n",
       "      <td>2.432713</td>\n",
       "      <td>2.442102</td>\n",
       "      <td>2.423785</td>\n",
       "      <td>2.402998</td>\n",
       "      <td>2.382644</td>\n",
       "      <td>...</td>\n",
       "      <td>7.824806</td>\n",
       "      <td>5.048904</td>\n",
       "      <td>7.826826</td>\n",
       "      <td>1.333134</td>\n",
       "      <td>1.330385</td>\n",
       "      <td>3.188999</td>\n",
       "      <td>1.170226</td>\n",
       "      <td>2.651871</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.423049</td>\n",
       "      <td>2.432146</td>\n",
       "      <td>2.441394</td>\n",
       "      <td>2.445996</td>\n",
       "      <td>2.424709</td>\n",
       "      <td>2.403871</td>\n",
       "      <td>2.383468</td>\n",
       "      <td>2.363488</td>\n",
       "      <td>2.343919</td>\n",
       "      <td>2.324750</td>\n",
       "      <td>...</td>\n",
       "      <td>7.829409</td>\n",
       "      <td>1.336569</td>\n",
       "      <td>1.332416</td>\n",
       "      <td>1.333018</td>\n",
       "      <td>1.339445</td>\n",
       "      <td>3.181707</td>\n",
       "      <td>1.174037</td>\n",
       "      <td>2.668428</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.448974</td>\n",
       "      <td>2.427595</td>\n",
       "      <td>2.406668</td>\n",
       "      <td>2.386179</td>\n",
       "      <td>2.366117</td>\n",
       "      <td>2.346467</td>\n",
       "      <td>2.327220</td>\n",
       "      <td>2.308364</td>\n",
       "      <td>2.289888</td>\n",
       "      <td>2.271783</td>\n",
       "      <td>...</td>\n",
       "      <td>1.334811</td>\n",
       "      <td>1.333946</td>\n",
       "      <td>1.337732</td>\n",
       "      <td>1.752277</td>\n",
       "      <td>1.752673</td>\n",
       "      <td>3.174667</td>\n",
       "      <td>1.177781</td>\n",
       "      <td>2.684110</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.389029</td>\n",
       "      <td>2.368883</td>\n",
       "      <td>2.349153</td>\n",
       "      <td>2.329827</td>\n",
       "      <td>2.310895</td>\n",
       "      <td>2.292346</td>\n",
       "      <td>2.274168</td>\n",
       "      <td>2.256353</td>\n",
       "      <td>2.238891</td>\n",
       "      <td>2.221771</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337586</td>\n",
       "      <td>1.754217</td>\n",
       "      <td>1.754119</td>\n",
       "      <td>7.853936</td>\n",
       "      <td>7.857839</td>\n",
       "      <td>3.167475</td>\n",
       "      <td>1.181319</td>\n",
       "      <td>2.700229</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.332752</td>\n",
       "      <td>2.313740</td>\n",
       "      <td>2.295113</td>\n",
       "      <td>2.276860</td>\n",
       "      <td>2.258972</td>\n",
       "      <td>2.241438</td>\n",
       "      <td>2.224249</td>\n",
       "      <td>2.207397</td>\n",
       "      <td>2.190871</td>\n",
       "      <td>2.174665</td>\n",
       "      <td>...</td>\n",
       "      <td>1.755474</td>\n",
       "      <td>7.857858</td>\n",
       "      <td>5.343303</td>\n",
       "      <td>5.026669</td>\n",
       "      <td>4.779808</td>\n",
       "      <td>3.160238</td>\n",
       "      <td>1.184736</td>\n",
       "      <td>2.716319</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.279651</td>\n",
       "      <td>2.261690</td>\n",
       "      <td>2.244085</td>\n",
       "      <td>2.226828</td>\n",
       "      <td>2.209909</td>\n",
       "      <td>2.193319</td>\n",
       "      <td>2.177049</td>\n",
       "      <td>2.161093</td>\n",
       "      <td>2.145441</td>\n",
       "      <td>2.130086</td>\n",
       "      <td>...</td>\n",
       "      <td>5.161067</td>\n",
       "      <td>4.870013</td>\n",
       "      <td>4.783200</td>\n",
       "      <td>7.881194</td>\n",
       "      <td>7.886631</td>\n",
       "      <td>3.152842</td>\n",
       "      <td>1.187967</td>\n",
       "      <td>2.732539</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.229770</td>\n",
       "      <td>2.212780</td>\n",
       "      <td>2.196120</td>\n",
       "      <td>2.179783</td>\n",
       "      <td>2.163760</td>\n",
       "      <td>2.148044</td>\n",
       "      <td>2.132627</td>\n",
       "      <td>2.117501</td>\n",
       "      <td>2.102659</td>\n",
       "      <td>2.088094</td>\n",
       "      <td>...</td>\n",
       "      <td>7.880194</td>\n",
       "      <td>7.885543</td>\n",
       "      <td>7.891169</td>\n",
       "      <td>7.897071</td>\n",
       "      <td>7.903251</td>\n",
       "      <td>3.145599</td>\n",
       "      <td>1.191207</td>\n",
       "      <td>2.748563</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.182707</td>\n",
       "      <td>2.166618</td>\n",
       "      <td>2.150836</td>\n",
       "      <td>2.135355</td>\n",
       "      <td>2.120167</td>\n",
       "      <td>2.105264</td>\n",
       "      <td>2.090641</td>\n",
       "      <td>2.076288</td>\n",
       "      <td>2.062202</td>\n",
       "      <td>2.048375</td>\n",
       "      <td>...</td>\n",
       "      <td>7.896219</td>\n",
       "      <td>7.902333</td>\n",
       "      <td>7.908727</td>\n",
       "      <td>7.915401</td>\n",
       "      <td>7.922355</td>\n",
       "      <td>3.138198</td>\n",
       "      <td>1.194280</td>\n",
       "      <td>2.764677</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.135755</td>\n",
       "      <td>2.120551</td>\n",
       "      <td>2.105633</td>\n",
       "      <td>2.090994</td>\n",
       "      <td>2.076627</td>\n",
       "      <td>2.062526</td>\n",
       "      <td>2.048685</td>\n",
       "      <td>2.035097</td>\n",
       "      <td>2.021756</td>\n",
       "      <td>2.008657</td>\n",
       "      <td>...</td>\n",
       "      <td>7.915361</td>\n",
       "      <td>7.922296</td>\n",
       "      <td>7.929513</td>\n",
       "      <td>7.937013</td>\n",
       "      <td>7.944798</td>\n",
       "      <td>3.130316</td>\n",
       "      <td>1.197380</td>\n",
       "      <td>2.781752</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.092769</td>\n",
       "      <td>2.078364</td>\n",
       "      <td>2.064226</td>\n",
       "      <td>2.050348</td>\n",
       "      <td>2.036724</td>\n",
       "      <td>2.023349</td>\n",
       "      <td>2.010216</td>\n",
       "      <td>1.997320</td>\n",
       "      <td>1.984655</td>\n",
       "      <td>1.972217</td>\n",
       "      <td>...</td>\n",
       "      <td>7.936139</td>\n",
       "      <td>7.943881</td>\n",
       "      <td>7.951908</td>\n",
       "      <td>7.960223</td>\n",
       "      <td>7.968826</td>\n",
       "      <td>3.122601</td>\n",
       "      <td>1.200270</td>\n",
       "      <td>2.798354</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.050009</td>\n",
       "      <td>2.036389</td>\n",
       "      <td>2.023017</td>\n",
       "      <td>2.009887</td>\n",
       "      <td>1.996994</td>\n",
       "      <td>1.984333</td>\n",
       "      <td>1.971898</td>\n",
       "      <td>1.959684</td>\n",
       "      <td>1.947686</td>\n",
       "      <td>1.935899</td>\n",
       "      <td>...</td>\n",
       "      <td>7.960443</td>\n",
       "      <td>7.969049</td>\n",
       "      <td>7.977945</td>\n",
       "      <td>2.161047</td>\n",
       "      <td>2.099944</td>\n",
       "      <td>3.114403</td>\n",
       "      <td>1.203202</td>\n",
       "      <td>2.815892</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.013135</td>\n",
       "      <td>2.000184</td>\n",
       "      <td>1.987465</td>\n",
       "      <td>1.974973</td>\n",
       "      <td>1.962704</td>\n",
       "      <td>1.950653</td>\n",
       "      <td>1.938814</td>\n",
       "      <td>1.927183</td>\n",
       "      <td>1.915756</td>\n",
       "      <td>1.904527</td>\n",
       "      <td>...</td>\n",
       "      <td>5.146470</td>\n",
       "      <td>2.156959</td>\n",
       "      <td>2.097287</td>\n",
       "      <td>2.040896</td>\n",
       "      <td>1.987523</td>\n",
       "      <td>3.106816</td>\n",
       "      <td>1.205764</td>\n",
       "      <td>2.831921</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.977208</td>\n",
       "      <td>1.964901</td>\n",
       "      <td>1.952813</td>\n",
       "      <td>1.940938</td>\n",
       "      <td>1.929272</td>\n",
       "      <td>1.917810</td>\n",
       "      <td>1.906548</td>\n",
       "      <td>1.895482</td>\n",
       "      <td>1.884607</td>\n",
       "      <td>1.873920</td>\n",
       "      <td>...</td>\n",
       "      <td>2.091069</td>\n",
       "      <td>2.036135</td>\n",
       "      <td>1.984079</td>\n",
       "      <td>1.934683</td>\n",
       "      <td>1.887749</td>\n",
       "      <td>3.098967</td>\n",
       "      <td>1.208285</td>\n",
       "      <td>2.848456</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.945138</td>\n",
       "      <td>1.933403</td>\n",
       "      <td>1.921873</td>\n",
       "      <td>1.910544</td>\n",
       "      <td>1.899413</td>\n",
       "      <td>1.888474</td>\n",
       "      <td>1.877724</td>\n",
       "      <td>1.867159</td>\n",
       "      <td>1.856776</td>\n",
       "      <td>1.846569</td>\n",
       "      <td>...</td>\n",
       "      <td>1.986573</td>\n",
       "      <td>1.937976</td>\n",
       "      <td>1.891762</td>\n",
       "      <td>1.847763</td>\n",
       "      <td>1.805824</td>\n",
       "      <td>3.091526</td>\n",
       "      <td>1.210542</td>\n",
       "      <td>2.864060</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.913908</td>\n",
       "      <td>1.902724</td>\n",
       "      <td>1.891734</td>\n",
       "      <td>1.880935</td>\n",
       "      <td>1.870321</td>\n",
       "      <td>1.859889</td>\n",
       "      <td>1.849636</td>\n",
       "      <td>1.839557</td>\n",
       "      <td>1.829649</td>\n",
       "      <td>1.819909</td>\n",
       "      <td>...</td>\n",
       "      <td>1.893303</td>\n",
       "      <td>1.850069</td>\n",
       "      <td>1.808827</td>\n",
       "      <td>1.769441</td>\n",
       "      <td>1.731793</td>\n",
       "      <td>3.083828</td>\n",
       "      <td>1.212742</td>\n",
       "      <td>2.880104</td>\n",
       "      <td>4</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.880929</td>\n",
       "      <td>1.870325</td>\n",
       "      <td>1.859903</td>\n",
       "      <td>1.849658</td>\n",
       "      <td>1.839589</td>\n",
       "      <td>1.829690</td>\n",
       "      <td>1.819959</td>\n",
       "      <td>1.810392</td>\n",
       "      <td>1.800986</td>\n",
       "      <td>1.791738</td>\n",
       "      <td>...</td>\n",
       "      <td>1.802700</td>\n",
       "      <td>1.764412</td>\n",
       "      <td>1.727774</td>\n",
       "      <td>1.692683</td>\n",
       "      <td>1.659044</td>\n",
       "      <td>3.075173</td>\n",
       "      <td>1.215060</td>\n",
       "      <td>2.898060</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.853147</td>\n",
       "      <td>1.843028</td>\n",
       "      <td>1.833082</td>\n",
       "      <td>1.823304</td>\n",
       "      <td>1.813691</td>\n",
       "      <td>1.804241</td>\n",
       "      <td>1.794948</td>\n",
       "      <td>1.785812</td>\n",
       "      <td>1.776828</td>\n",
       "      <td>1.767994</td>\n",
       "      <td>...</td>\n",
       "      <td>1.731873</td>\n",
       "      <td>1.697268</td>\n",
       "      <td>1.664075</td>\n",
       "      <td>1.632210</td>\n",
       "      <td>1.628579</td>\n",
       "      <td>3.067411</td>\n",
       "      <td>1.216994</td>\n",
       "      <td>2.914096</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.826759</td>\n",
       "      <td>1.817100</td>\n",
       "      <td>1.807605</td>\n",
       "      <td>1.798269</td>\n",
       "      <td>1.789090</td>\n",
       "      <td>1.780064</td>\n",
       "      <td>1.771189</td>\n",
       "      <td>1.762461</td>\n",
       "      <td>1.753879</td>\n",
       "      <td>1.745439</td>\n",
       "      <td>...</td>\n",
       "      <td>1.668702</td>\n",
       "      <td>1.637248</td>\n",
       "      <td>1.629984</td>\n",
       "      <td>1.633023</td>\n",
       "      <td>2.875379</td>\n",
       "      <td>3.059604</td>\n",
       "      <td>1.218808</td>\n",
       "      <td>2.930187</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.801792</td>\n",
       "      <td>1.792568</td>\n",
       "      <td>1.783499</td>\n",
       "      <td>1.774581</td>\n",
       "      <td>1.765812</td>\n",
       "      <td>1.757188</td>\n",
       "      <td>1.749328</td>\n",
       "      <td>1.761824</td>\n",
       "      <td>1.774561</td>\n",
       "      <td>1.787544</td>\n",
       "      <td>...</td>\n",
       "      <td>1.631279</td>\n",
       "      <td>1.634367</td>\n",
       "      <td>2.874876</td>\n",
       "      <td>2.866574</td>\n",
       "      <td>2.863949</td>\n",
       "      <td>3.051776</td>\n",
       "      <td>1.220494</td>\n",
       "      <td>2.946272</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.776790</td>\n",
       "      <td>1.768002</td>\n",
       "      <td>1.759360</td>\n",
       "      <td>1.762276</td>\n",
       "      <td>1.774853</td>\n",
       "      <td>1.787671</td>\n",
       "      <td>3.526610</td>\n",
       "      <td>3.552700</td>\n",
       "      <td>3.579301</td>\n",
       "      <td>3.606427</td>\n",
       "      <td>...</td>\n",
       "      <td>2.872785</td>\n",
       "      <td>2.865169</td>\n",
       "      <td>2.866425</td>\n",
       "      <td>2.867781</td>\n",
       "      <td>2.869234</td>\n",
       "      <td>3.043454</td>\n",
       "      <td>1.222140</td>\n",
       "      <td>2.963318</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.770963</td>\n",
       "      <td>1.783554</td>\n",
       "      <td>1.796386</td>\n",
       "      <td>3.531289</td>\n",
       "      <td>3.557313</td>\n",
       "      <td>3.583845</td>\n",
       "      <td>3.610901</td>\n",
       "      <td>3.638492</td>\n",
       "      <td>3.666635</td>\n",
       "      <td>3.695342</td>\n",
       "      <td>...</td>\n",
       "      <td>2.868131</td>\n",
       "      <td>2.869544</td>\n",
       "      <td>2.871058</td>\n",
       "      <td>2.872671</td>\n",
       "      <td>2.874798</td>\n",
       "      <td>3.035811</td>\n",
       "      <td>1.223526</td>\n",
       "      <td>2.978945</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.545822</td>\n",
       "      <td>3.571953</td>\n",
       "      <td>3.598595</td>\n",
       "      <td>3.625762</td>\n",
       "      <td>3.653468</td>\n",
       "      <td>3.681726</td>\n",
       "      <td>3.710552</td>\n",
       "      <td>3.739961</td>\n",
       "      <td>3.769969</td>\n",
       "      <td>3.800593</td>\n",
       "      <td>...</td>\n",
       "      <td>2.873751</td>\n",
       "      <td>2.875473</td>\n",
       "      <td>2.883291</td>\n",
       "      <td>2.895777</td>\n",
       "      <td>2.908471</td>\n",
       "      <td>3.026974</td>\n",
       "      <td>1.224973</td>\n",
       "      <td>2.996925</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.635384</td>\n",
       "      <td>3.663110</td>\n",
       "      <td>3.691388</td>\n",
       "      <td>3.720234</td>\n",
       "      <td>3.749662</td>\n",
       "      <td>3.779690</td>\n",
       "      <td>3.810333</td>\n",
       "      <td>3.841609</td>\n",
       "      <td>3.873535</td>\n",
       "      <td>3.906131</td>\n",
       "      <td>...</td>\n",
       "      <td>2.888284</td>\n",
       "      <td>2.900908</td>\n",
       "      <td>2.913743</td>\n",
       "      <td>2.926792</td>\n",
       "      <td>2.961940</td>\n",
       "      <td>3.019054</td>\n",
       "      <td>1.226133</td>\n",
       "      <td>3.013029</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.724128</td>\n",
       "      <td>3.753469</td>\n",
       "      <td>3.783407</td>\n",
       "      <td>3.813956</td>\n",
       "      <td>3.845136</td>\n",
       "      <td>3.876962</td>\n",
       "      <td>3.909453</td>\n",
       "      <td>3.942629</td>\n",
       "      <td>3.976510</td>\n",
       "      <td>4.011115</td>\n",
       "      <td>...</td>\n",
       "      <td>2.917941</td>\n",
       "      <td>2.931115</td>\n",
       "      <td>2.972667</td>\n",
       "      <td>5.589263</td>\n",
       "      <td>5.515427</td>\n",
       "      <td>3.011351</td>\n",
       "      <td>1.227131</td>\n",
       "      <td>3.028622</td>\n",
       "      <td>4</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12970</th>\n",
       "      <td>3.874779</td>\n",
       "      <td>3.880779</td>\n",
       "      <td>3.886931</td>\n",
       "      <td>3.893235</td>\n",
       "      <td>3.899692</td>\n",
       "      <td>3.906304</td>\n",
       "      <td>3.913072</td>\n",
       "      <td>3.919997</td>\n",
       "      <td>3.927080</td>\n",
       "      <td>3.934323</td>\n",
       "      <td>...</td>\n",
       "      <td>7.309910</td>\n",
       "      <td>7.297823</td>\n",
       "      <td>7.286025</td>\n",
       "      <td>7.274511</td>\n",
       "      <td>7.263280</td>\n",
       "      <td>1.444881</td>\n",
       "      <td>0.333114</td>\n",
       "      <td>2.354856</td>\n",
       "      <td>3</td>\n",
       "      <td>939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12971</th>\n",
       "      <td>3.899983</td>\n",
       "      <td>3.906422</td>\n",
       "      <td>3.913016</td>\n",
       "      <td>3.919766</td>\n",
       "      <td>3.926673</td>\n",
       "      <td>3.933738</td>\n",
       "      <td>3.940963</td>\n",
       "      <td>3.948350</td>\n",
       "      <td>3.955899</td>\n",
       "      <td>3.963612</td>\n",
       "      <td>...</td>\n",
       "      <td>7.280945</td>\n",
       "      <td>7.269650</td>\n",
       "      <td>7.258637</td>\n",
       "      <td>7.247903</td>\n",
       "      <td>7.237447</td>\n",
       "      <td>1.439050</td>\n",
       "      <td>0.339028</td>\n",
       "      <td>2.371161</td>\n",
       "      <td>3</td>\n",
       "      <td>939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12972</th>\n",
       "      <td>3.924973</td>\n",
       "      <td>3.931835</td>\n",
       "      <td>3.938856</td>\n",
       "      <td>3.946037</td>\n",
       "      <td>3.953379</td>\n",
       "      <td>3.960883</td>\n",
       "      <td>3.968551</td>\n",
       "      <td>3.976384</td>\n",
       "      <td>3.984384</td>\n",
       "      <td>3.992553</td>\n",
       "      <td>...</td>\n",
       "      <td>7.255519</td>\n",
       "      <td>7.244956</td>\n",
       "      <td>7.234670</td>\n",
       "      <td>7.224658</td>\n",
       "      <td>7.214919</td>\n",
       "      <td>1.433460</td>\n",
       "      <td>0.344513</td>\n",
       "      <td>2.386530</td>\n",
       "      <td>3</td>\n",
       "      <td>939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12973</th>\n",
       "      <td>3.952013</td>\n",
       "      <td>3.959323</td>\n",
       "      <td>3.966796</td>\n",
       "      <td>3.974433</td>\n",
       "      <td>3.982235</td>\n",
       "      <td>3.990204</td>\n",
       "      <td>3.998341</td>\n",
       "      <td>4.006648</td>\n",
       "      <td>4.015127</td>\n",
       "      <td>4.023778</td>\n",
       "      <td>...</td>\n",
       "      <td>7.231160</td>\n",
       "      <td>7.221339</td>\n",
       "      <td>7.211790</td>\n",
       "      <td>7.202510</td>\n",
       "      <td>7.193500</td>\n",
       "      <td>1.427610</td>\n",
       "      <td>0.350065</td>\n",
       "      <td>2.402376</td>\n",
       "      <td>3</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12974</th>\n",
       "      <td>3.980193</td>\n",
       "      <td>3.987961</td>\n",
       "      <td>3.995895</td>\n",
       "      <td>4.003997</td>\n",
       "      <td>4.012269</td>\n",
       "      <td>4.020713</td>\n",
       "      <td>4.029329</td>\n",
       "      <td>4.038120</td>\n",
       "      <td>4.047088</td>\n",
       "      <td>4.056233</td>\n",
       "      <td>...</td>\n",
       "      <td>7.208888</td>\n",
       "      <td>7.199791</td>\n",
       "      <td>7.190961</td>\n",
       "      <td>7.182397</td>\n",
       "      <td>7.174098</td>\n",
       "      <td>1.421686</td>\n",
       "      <td>0.355539</td>\n",
       "      <td>2.418103</td>\n",
       "      <td>3</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12975</th>\n",
       "      <td>4.009554</td>\n",
       "      <td>4.017788</td>\n",
       "      <td>4.026194</td>\n",
       "      <td>4.034772</td>\n",
       "      <td>4.043525</td>\n",
       "      <td>4.052454</td>\n",
       "      <td>4.061561</td>\n",
       "      <td>4.070848</td>\n",
       "      <td>4.080316</td>\n",
       "      <td>4.089968</td>\n",
       "      <td>...</td>\n",
       "      <td>7.188595</td>\n",
       "      <td>7.180206</td>\n",
       "      <td>7.172081</td>\n",
       "      <td>7.164218</td>\n",
       "      <td>7.156616</td>\n",
       "      <td>1.415690</td>\n",
       "      <td>0.360937</td>\n",
       "      <td>2.433733</td>\n",
       "      <td>3</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12976</th>\n",
       "      <td>4.040388</td>\n",
       "      <td>4.049104</td>\n",
       "      <td>4.057996</td>\n",
       "      <td>4.067067</td>\n",
       "      <td>4.076316</td>\n",
       "      <td>4.085748</td>\n",
       "      <td>4.095363</td>\n",
       "      <td>4.105163</td>\n",
       "      <td>4.115150</td>\n",
       "      <td>4.125328</td>\n",
       "      <td>...</td>\n",
       "      <td>7.169949</td>\n",
       "      <td>7.162262</td>\n",
       "      <td>7.154835</td>\n",
       "      <td>7.147666</td>\n",
       "      <td>7.140755</td>\n",
       "      <td>1.409603</td>\n",
       "      <td>0.366225</td>\n",
       "      <td>2.449426</td>\n",
       "      <td>3</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12977</th>\n",
       "      <td>4.072597</td>\n",
       "      <td>4.081809</td>\n",
       "      <td>4.091202</td>\n",
       "      <td>4.100779</td>\n",
       "      <td>4.110541</td>\n",
       "      <td>4.120490</td>\n",
       "      <td>4.130628</td>\n",
       "      <td>4.140958</td>\n",
       "      <td>4.151481</td>\n",
       "      <td>4.162199</td>\n",
       "      <td>...</td>\n",
       "      <td>7.150283</td>\n",
       "      <td>7.143409</td>\n",
       "      <td>7.136790</td>\n",
       "      <td>7.130426</td>\n",
       "      <td>7.124316</td>\n",
       "      <td>1.404305</td>\n",
       "      <td>0.371111</td>\n",
       "      <td>2.465613</td>\n",
       "      <td>3</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12978</th>\n",
       "      <td>4.107352</td>\n",
       "      <td>4.117092</td>\n",
       "      <td>4.127020</td>\n",
       "      <td>4.137137</td>\n",
       "      <td>4.147445</td>\n",
       "      <td>4.157946</td>\n",
       "      <td>4.168643</td>\n",
       "      <td>4.179538</td>\n",
       "      <td>4.190632</td>\n",
       "      <td>4.201930</td>\n",
       "      <td>...</td>\n",
       "      <td>7.137442</td>\n",
       "      <td>7.131152</td>\n",
       "      <td>7.125115</td>\n",
       "      <td>7.119331</td>\n",
       "      <td>7.113798</td>\n",
       "      <td>1.396990</td>\n",
       "      <td>0.376653</td>\n",
       "      <td>2.481274</td>\n",
       "      <td>3</td>\n",
       "      <td>871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12979</th>\n",
       "      <td>4.142621</td>\n",
       "      <td>4.152891</td>\n",
       "      <td>4.163355</td>\n",
       "      <td>4.174014</td>\n",
       "      <td>4.184870</td>\n",
       "      <td>4.195926</td>\n",
       "      <td>4.207184</td>\n",
       "      <td>4.218647</td>\n",
       "      <td>4.230316</td>\n",
       "      <td>4.242196</td>\n",
       "      <td>...</td>\n",
       "      <td>7.123998</td>\n",
       "      <td>7.118386</td>\n",
       "      <td>7.113024</td>\n",
       "      <td>7.107913</td>\n",
       "      <td>3.890347</td>\n",
       "      <td>1.390660</td>\n",
       "      <td>0.381648</td>\n",
       "      <td>2.496985</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12980</th>\n",
       "      <td>4.180555</td>\n",
       "      <td>4.191390</td>\n",
       "      <td>4.202424</td>\n",
       "      <td>4.213660</td>\n",
       "      <td>4.225101</td>\n",
       "      <td>4.236748</td>\n",
       "      <td>4.248605</td>\n",
       "      <td>4.260674</td>\n",
       "      <td>4.272957</td>\n",
       "      <td>4.285458</td>\n",
       "      <td>...</td>\n",
       "      <td>7.111586</td>\n",
       "      <td>7.106684</td>\n",
       "      <td>3.888706</td>\n",
       "      <td>7.097625</td>\n",
       "      <td>7.093466</td>\n",
       "      <td>1.384189</td>\n",
       "      <td>0.386675</td>\n",
       "      <td>2.513193</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12981</th>\n",
       "      <td>4.219110</td>\n",
       "      <td>4.230513</td>\n",
       "      <td>4.242122</td>\n",
       "      <td>4.253941</td>\n",
       "      <td>4.265971</td>\n",
       "      <td>4.278215</td>\n",
       "      <td>4.290676</td>\n",
       "      <td>4.303357</td>\n",
       "      <td>4.316261</td>\n",
       "      <td>4.329390</td>\n",
       "      <td>...</td>\n",
       "      <td>3.892089</td>\n",
       "      <td>7.097572</td>\n",
       "      <td>7.093564</td>\n",
       "      <td>7.089801</td>\n",
       "      <td>7.086284</td>\n",
       "      <td>1.377561</td>\n",
       "      <td>0.391456</td>\n",
       "      <td>2.528911</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12982</th>\n",
       "      <td>4.259428</td>\n",
       "      <td>4.271421</td>\n",
       "      <td>4.283628</td>\n",
       "      <td>4.296052</td>\n",
       "      <td>4.308696</td>\n",
       "      <td>4.321561</td>\n",
       "      <td>4.334651</td>\n",
       "      <td>4.347970</td>\n",
       "      <td>4.361519</td>\n",
       "      <td>4.375302</td>\n",
       "      <td>...</td>\n",
       "      <td>7.093375</td>\n",
       "      <td>7.089785</td>\n",
       "      <td>7.086440</td>\n",
       "      <td>7.083339</td>\n",
       "      <td>7.080481</td>\n",
       "      <td>1.370994</td>\n",
       "      <td>0.396138</td>\n",
       "      <td>2.544712</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12983</th>\n",
       "      <td>4.301400</td>\n",
       "      <td>4.314005</td>\n",
       "      <td>4.326831</td>\n",
       "      <td>4.339881</td>\n",
       "      <td>4.353160</td>\n",
       "      <td>4.366669</td>\n",
       "      <td>4.380411</td>\n",
       "      <td>4.377586</td>\n",
       "      <td>4.331717</td>\n",
       "      <td>4.286944</td>\n",
       "      <td>...</td>\n",
       "      <td>7.086605</td>\n",
       "      <td>7.083673</td>\n",
       "      <td>7.080986</td>\n",
       "      <td>7.078541</td>\n",
       "      <td>7.076339</td>\n",
       "      <td>1.364355</td>\n",
       "      <td>0.400711</td>\n",
       "      <td>2.560480</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12984</th>\n",
       "      <td>4.345273</td>\n",
       "      <td>4.358512</td>\n",
       "      <td>4.371983</td>\n",
       "      <td>4.385686</td>\n",
       "      <td>4.386418</td>\n",
       "      <td>4.340274</td>\n",
       "      <td>4.295236</td>\n",
       "      <td>4.251266</td>\n",
       "      <td>4.208328</td>\n",
       "      <td>4.166390</td>\n",
       "      <td>...</td>\n",
       "      <td>7.081453</td>\n",
       "      <td>7.079180</td>\n",
       "      <td>7.077149</td>\n",
       "      <td>7.075360</td>\n",
       "      <td>7.073812</td>\n",
       "      <td>1.357638</td>\n",
       "      <td>0.405176</td>\n",
       "      <td>2.576294</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12985</th>\n",
       "      <td>4.391312</td>\n",
       "      <td>4.394668</td>\n",
       "      <td>4.348267</td>\n",
       "      <td>4.302979</td>\n",
       "      <td>4.258770</td>\n",
       "      <td>4.215601</td>\n",
       "      <td>4.173439</td>\n",
       "      <td>4.132252</td>\n",
       "      <td>4.092007</td>\n",
       "      <td>4.052675</td>\n",
       "      <td>...</td>\n",
       "      <td>7.077895</td>\n",
       "      <td>7.076283</td>\n",
       "      <td>7.074911</td>\n",
       "      <td>7.073781</td>\n",
       "      <td>7.072892</td>\n",
       "      <td>1.350838</td>\n",
       "      <td>0.409530</td>\n",
       "      <td>2.592227</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12986</th>\n",
       "      <td>4.310638</td>\n",
       "      <td>4.266194</td>\n",
       "      <td>4.222799</td>\n",
       "      <td>4.180419</td>\n",
       "      <td>4.139020</td>\n",
       "      <td>4.098572</td>\n",
       "      <td>4.059043</td>\n",
       "      <td>4.020405</td>\n",
       "      <td>3.982630</td>\n",
       "      <td>3.945691</td>\n",
       "      <td>...</td>\n",
       "      <td>7.075948</td>\n",
       "      <td>7.075016</td>\n",
       "      <td>7.074324</td>\n",
       "      <td>7.073874</td>\n",
       "      <td>7.073664</td>\n",
       "      <td>1.344041</td>\n",
       "      <td>0.413687</td>\n",
       "      <td>2.608293</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12987</th>\n",
       "      <td>4.187647</td>\n",
       "      <td>4.146034</td>\n",
       "      <td>4.105379</td>\n",
       "      <td>4.065650</td>\n",
       "      <td>4.026819</td>\n",
       "      <td>3.988857</td>\n",
       "      <td>3.951737</td>\n",
       "      <td>3.915434</td>\n",
       "      <td>3.879922</td>\n",
       "      <td>3.845179</td>\n",
       "      <td>...</td>\n",
       "      <td>7.075843</td>\n",
       "      <td>7.075649</td>\n",
       "      <td>3.883037</td>\n",
       "      <td>7.075983</td>\n",
       "      <td>4.298266</td>\n",
       "      <td>1.336515</td>\n",
       "      <td>0.417590</td>\n",
       "      <td>2.626037</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12988</th>\n",
       "      <td>4.072591</td>\n",
       "      <td>4.033562</td>\n",
       "      <td>3.995408</td>\n",
       "      <td>3.958103</td>\n",
       "      <td>3.921620</td>\n",
       "      <td>3.885935</td>\n",
       "      <td>3.851023</td>\n",
       "      <td>3.816862</td>\n",
       "      <td>3.783429</td>\n",
       "      <td>3.750703</td>\n",
       "      <td>...</td>\n",
       "      <td>3.883248</td>\n",
       "      <td>7.078088</td>\n",
       "      <td>4.301313</td>\n",
       "      <td>7.079782</td>\n",
       "      <td>7.080991</td>\n",
       "      <td>1.331337</td>\n",
       "      <td>0.421720</td>\n",
       "      <td>2.640536</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12989</th>\n",
       "      <td>3.955400</td>\n",
       "      <td>3.918937</td>\n",
       "      <td>3.883271</td>\n",
       "      <td>3.848378</td>\n",
       "      <td>3.814235</td>\n",
       "      <td>3.780821</td>\n",
       "      <td>3.748113</td>\n",
       "      <td>3.716091</td>\n",
       "      <td>3.684736</td>\n",
       "      <td>3.654030</td>\n",
       "      <td>...</td>\n",
       "      <td>7.080649</td>\n",
       "      <td>7.081738</td>\n",
       "      <td>7.083070</td>\n",
       "      <td>7.084642</td>\n",
       "      <td>7.086457</td>\n",
       "      <td>1.322349</td>\n",
       "      <td>0.426145</td>\n",
       "      <td>2.657648</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12990</th>\n",
       "      <td>3.854759</td>\n",
       "      <td>3.820448</td>\n",
       "      <td>3.786870</td>\n",
       "      <td>3.754003</td>\n",
       "      <td>3.721828</td>\n",
       "      <td>3.690323</td>\n",
       "      <td>3.659472</td>\n",
       "      <td>3.629254</td>\n",
       "      <td>3.599653</td>\n",
       "      <td>3.570651</td>\n",
       "      <td>...</td>\n",
       "      <td>7.085600</td>\n",
       "      <td>7.087353</td>\n",
       "      <td>7.089347</td>\n",
       "      <td>7.091584</td>\n",
       "      <td>7.094064</td>\n",
       "      <td>1.315221</td>\n",
       "      <td>0.429925</td>\n",
       "      <td>2.673643</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12991</th>\n",
       "      <td>3.760010</td>\n",
       "      <td>3.727681</td>\n",
       "      <td>3.696028</td>\n",
       "      <td>3.665032</td>\n",
       "      <td>3.634674</td>\n",
       "      <td>3.604936</td>\n",
       "      <td>3.575801</td>\n",
       "      <td>3.547254</td>\n",
       "      <td>3.538176</td>\n",
       "      <td>3.553602</td>\n",
       "      <td>...</td>\n",
       "      <td>7.092239</td>\n",
       "      <td>7.094658</td>\n",
       "      <td>7.097321</td>\n",
       "      <td>7.100226</td>\n",
       "      <td>7.103377</td>\n",
       "      <td>1.308033</td>\n",
       "      <td>0.433584</td>\n",
       "      <td>2.689670</td>\n",
       "      <td>3</td>\n",
       "      <td>919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12992</th>\n",
       "      <td>3.670131</td>\n",
       "      <td>3.639646</td>\n",
       "      <td>3.609784</td>\n",
       "      <td>3.580529</td>\n",
       "      <td>3.551865</td>\n",
       "      <td>3.545191</td>\n",
       "      <td>3.560604</td>\n",
       "      <td>4.924805</td>\n",
       "      <td>4.946744</td>\n",
       "      <td>4.969050</td>\n",
       "      <td>...</td>\n",
       "      <td>7.100613</td>\n",
       "      <td>7.103708</td>\n",
       "      <td>7.107048</td>\n",
       "      <td>7.110633</td>\n",
       "      <td>7.114464</td>\n",
       "      <td>1.300777</td>\n",
       "      <td>0.437107</td>\n",
       "      <td>2.705849</td>\n",
       "      <td>3</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12993</th>\n",
       "      <td>3.585621</td>\n",
       "      <td>3.556835</td>\n",
       "      <td>3.552149</td>\n",
       "      <td>3.567549</td>\n",
       "      <td>4.930365</td>\n",
       "      <td>4.952268</td>\n",
       "      <td>4.974536</td>\n",
       "      <td>4.997176</td>\n",
       "      <td>5.020195</td>\n",
       "      <td>5.043599</td>\n",
       "      <td>...</td>\n",
       "      <td>7.110723</td>\n",
       "      <td>7.114498</td>\n",
       "      <td>7.118519</td>\n",
       "      <td>7.122787</td>\n",
       "      <td>7.127303</td>\n",
       "      <td>1.293468</td>\n",
       "      <td>0.440517</td>\n",
       "      <td>2.722004</td>\n",
       "      <td>3</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12994</th>\n",
       "      <td>3.574470</td>\n",
       "      <td>4.935895</td>\n",
       "      <td>4.957761</td>\n",
       "      <td>4.979991</td>\n",
       "      <td>5.002593</td>\n",
       "      <td>5.025572</td>\n",
       "      <td>5.048936</td>\n",
       "      <td>5.072692</td>\n",
       "      <td>5.096847</td>\n",
       "      <td>5.121407</td>\n",
       "      <td>...</td>\n",
       "      <td>7.122583</td>\n",
       "      <td>7.127042</td>\n",
       "      <td>7.131750</td>\n",
       "      <td>7.136706</td>\n",
       "      <td>7.141912</td>\n",
       "      <td>1.286109</td>\n",
       "      <td>0.443815</td>\n",
       "      <td>2.738148</td>\n",
       "      <td>3</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12995</th>\n",
       "      <td>4.985512</td>\n",
       "      <td>5.008076</td>\n",
       "      <td>5.031017</td>\n",
       "      <td>5.054341</td>\n",
       "      <td>5.078057</td>\n",
       "      <td>5.102171</td>\n",
       "      <td>5.126690</td>\n",
       "      <td>5.151622</td>\n",
       "      <td>5.176975</td>\n",
       "      <td>5.202756</td>\n",
       "      <td>...</td>\n",
       "      <td>7.136214</td>\n",
       "      <td>7.141363</td>\n",
       "      <td>7.146763</td>\n",
       "      <td>7.152415</td>\n",
       "      <td>7.158320</td>\n",
       "      <td>1.278698</td>\n",
       "      <td>0.446990</td>\n",
       "      <td>2.754307</td>\n",
       "      <td>3</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12996</th>\n",
       "      <td>5.059692</td>\n",
       "      <td>5.083366</td>\n",
       "      <td>5.107439</td>\n",
       "      <td>5.131915</td>\n",
       "      <td>5.156803</td>\n",
       "      <td>5.182112</td>\n",
       "      <td>5.207848</td>\n",
       "      <td>5.234019</td>\n",
       "      <td>5.260635</td>\n",
       "      <td>5.287704</td>\n",
       "      <td>...</td>\n",
       "      <td>7.151608</td>\n",
       "      <td>7.157454</td>\n",
       "      <td>7.163554</td>\n",
       "      <td>7.169908</td>\n",
       "      <td>7.176518</td>\n",
       "      <td>1.271236</td>\n",
       "      <td>0.450041</td>\n",
       "      <td>2.770448</td>\n",
       "      <td>3</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12997</th>\n",
       "      <td>5.137266</td>\n",
       "      <td>5.162113</td>\n",
       "      <td>5.187378</td>\n",
       "      <td>5.213071</td>\n",
       "      <td>5.239199</td>\n",
       "      <td>5.265769</td>\n",
       "      <td>5.292792</td>\n",
       "      <td>5.320275</td>\n",
       "      <td>5.348229</td>\n",
       "      <td>5.376661</td>\n",
       "      <td>...</td>\n",
       "      <td>7.169360</td>\n",
       "      <td>7.175932</td>\n",
       "      <td>7.182762</td>\n",
       "      <td>7.189849</td>\n",
       "      <td>7.197196</td>\n",
       "      <td>1.263851</td>\n",
       "      <td>0.452955</td>\n",
       "      <td>2.786666</td>\n",
       "      <td>3</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12998</th>\n",
       "      <td>5.218254</td>\n",
       "      <td>5.244336</td>\n",
       "      <td>5.270861</td>\n",
       "      <td>5.297837</td>\n",
       "      <td>5.325272</td>\n",
       "      <td>5.353176</td>\n",
       "      <td>5.381558</td>\n",
       "      <td>5.410428</td>\n",
       "      <td>5.439795</td>\n",
       "      <td>5.469666</td>\n",
       "      <td>...</td>\n",
       "      <td>7.187859</td>\n",
       "      <td>7.195125</td>\n",
       "      <td>7.202651</td>\n",
       "      <td>7.210438</td>\n",
       "      <td>7.218488</td>\n",
       "      <td>1.256171</td>\n",
       "      <td>0.455778</td>\n",
       "      <td>2.802778</td>\n",
       "      <td>3</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12999</th>\n",
       "      <td>5.303035</td>\n",
       "      <td>5.330425</td>\n",
       "      <td>5.358282</td>\n",
       "      <td>5.386617</td>\n",
       "      <td>5.415437</td>\n",
       "      <td>5.444755</td>\n",
       "      <td>5.474575</td>\n",
       "      <td>5.504912</td>\n",
       "      <td>5.535778</td>\n",
       "      <td>5.567182</td>\n",
       "      <td>...</td>\n",
       "      <td>7.208778</td>\n",
       "      <td>7.216769</td>\n",
       "      <td>7.225024</td>\n",
       "      <td>7.233544</td>\n",
       "      <td>7.242332</td>\n",
       "      <td>1.248564</td>\n",
       "      <td>0.458465</td>\n",
       "      <td>2.818969</td>\n",
       "      <td>3</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13000 rows × 726 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R0        R1        R2        R3        R4        R5        R6  \\\n",
       "0      2.208487  2.214837  2.221299  2.227875  2.234565  2.241374  2.248300   \n",
       "1      2.235402  2.242092  2.248899  2.255825  2.262871  2.270038  2.277329   \n",
       "2      2.263432  2.270478  2.277646  2.284937  2.292353  2.299896  2.307568   \n",
       "3      2.294336  2.301775  2.309341  2.317037  2.324863  2.332823  2.340916   \n",
       "4      2.323826  2.331642  2.339591  2.347674  2.355893  2.364252  2.372751   \n",
       "5      2.355349  2.363569  2.371929  2.380429  2.389071  2.397858  2.406793   \n",
       "6      2.388031  2.396673  2.405460  2.414394  2.423478  2.432713  2.442102   \n",
       "7      2.423049  2.432146  2.441394  2.445996  2.424709  2.403871  2.383468   \n",
       "8      2.448974  2.427595  2.406668  2.386179  2.366117  2.346467  2.327220   \n",
       "9      2.389029  2.368883  2.349153  2.329827  2.310895  2.292346  2.274168   \n",
       "10     2.332752  2.313740  2.295113  2.276860  2.258972  2.241438  2.224249   \n",
       "11     2.279651  2.261690  2.244085  2.226828  2.209909  2.193319  2.177049   \n",
       "12     2.229770  2.212780  2.196120  2.179783  2.163760  2.148044  2.132627   \n",
       "13     2.182707  2.166618  2.150836  2.135355  2.120167  2.105264  2.090641   \n",
       "14     2.135755  2.120551  2.105633  2.090994  2.076627  2.062526  2.048685   \n",
       "15     2.092769  2.078364  2.064226  2.050348  2.036724  2.023349  2.010216   \n",
       "16     2.050009  2.036389  2.023017  2.009887  1.996994  1.984333  1.971898   \n",
       "17     2.013135  2.000184  1.987465  1.974973  1.962704  1.950653  1.938814   \n",
       "18     1.977208  1.964901  1.952813  1.940938  1.929272  1.917810  1.906548   \n",
       "19     1.945138  1.933403  1.921873  1.910544  1.899413  1.888474  1.877724   \n",
       "20     1.913908  1.902724  1.891734  1.880935  1.870321  1.859889  1.849636   \n",
       "21     1.880929  1.870325  1.859903  1.849658  1.839589  1.829690  1.819959   \n",
       "22     1.853147  1.843028  1.833082  1.823304  1.813691  1.804241  1.794948   \n",
       "23     1.826759  1.817100  1.807605  1.798269  1.789090  1.780064  1.771189   \n",
       "24     1.801792  1.792568  1.783499  1.774581  1.765812  1.757188  1.749328   \n",
       "25     1.776790  1.768002  1.759360  1.762276  1.774853  1.787671  3.526610   \n",
       "26     1.770963  1.783554  1.796386  3.531289  3.557313  3.583845  3.610901   \n",
       "27     3.545822  3.571953  3.598595  3.625762  3.653468  3.681726  3.710552   \n",
       "28     3.635384  3.663110  3.691388  3.720234  3.749662  3.779690  3.810333   \n",
       "29     3.724128  3.753469  3.783407  3.813956  3.845136  3.876962  3.909453   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "12970  3.874779  3.880779  3.886931  3.893235  3.899692  3.906304  3.913072   \n",
       "12971  3.899983  3.906422  3.913016  3.919766  3.926673  3.933738  3.940963   \n",
       "12972  3.924973  3.931835  3.938856  3.946037  3.953379  3.960883  3.968551   \n",
       "12973  3.952013  3.959323  3.966796  3.974433  3.982235  3.990204  3.998341   \n",
       "12974  3.980193  3.987961  3.995895  4.003997  4.012269  4.020713  4.029329   \n",
       "12975  4.009554  4.017788  4.026194  4.034772  4.043525  4.052454  4.061561   \n",
       "12976  4.040388  4.049104  4.057996  4.067067  4.076316  4.085748  4.095363   \n",
       "12977  4.072597  4.081809  4.091202  4.100779  4.110541  4.120490  4.130628   \n",
       "12978  4.107352  4.117092  4.127020  4.137137  4.147445  4.157946  4.168643   \n",
       "12979  4.142621  4.152891  4.163355  4.174014  4.184870  4.195926  4.207184   \n",
       "12980  4.180555  4.191390  4.202424  4.213660  4.225101  4.236748  4.248605   \n",
       "12981  4.219110  4.230513  4.242122  4.253941  4.265971  4.278215  4.290676   \n",
       "12982  4.259428  4.271421  4.283628  4.296052  4.308696  4.321561  4.334651   \n",
       "12983  4.301400  4.314005  4.326831  4.339881  4.353160  4.366669  4.380411   \n",
       "12984  4.345273  4.358512  4.371983  4.385686  4.386418  4.340274  4.295236   \n",
       "12985  4.391312  4.394668  4.348267  4.302979  4.258770  4.215601  4.173439   \n",
       "12986  4.310638  4.266194  4.222799  4.180419  4.139020  4.098572  4.059043   \n",
       "12987  4.187647  4.146034  4.105379  4.065650  4.026819  3.988857  3.951737   \n",
       "12988  4.072591  4.033562  3.995408  3.958103  3.921620  3.885935  3.851023   \n",
       "12989  3.955400  3.918937  3.883271  3.848378  3.814235  3.780821  3.748113   \n",
       "12990  3.854759  3.820448  3.786870  3.754003  3.721828  3.690323  3.659472   \n",
       "12991  3.760010  3.727681  3.696028  3.665032  3.634674  3.604936  3.575801   \n",
       "12992  3.670131  3.639646  3.609784  3.580529  3.551865  3.545191  3.560604   \n",
       "12993  3.585621  3.556835  3.552149  3.567549  4.930365  4.952268  4.974536   \n",
       "12994  3.574470  4.935895  4.957761  4.979991  5.002593  5.025572  5.048936   \n",
       "12995  4.985512  5.008076  5.031017  5.054341  5.078057  5.102171  5.126690   \n",
       "12996  5.059692  5.083366  5.107439  5.131915  5.156803  5.182112  5.207848   \n",
       "12997  5.137266  5.162113  5.187378  5.213071  5.239199  5.265769  5.292792   \n",
       "12998  5.218254  5.244336  5.270861  5.297837  5.325272  5.353176  5.381558   \n",
       "12999  5.303035  5.330425  5.358282  5.386617  5.415437  5.444755  5.474575   \n",
       "\n",
       "             R7        R8        R9  ...      R716      R717      R718  \\\n",
       "0      2.255346  2.262514  2.269805  ...  4.636539  7.834864  7.831544   \n",
       "1      2.284744  2.292287  2.299958  ...  7.831219  7.828393  7.825834   \n",
       "2      2.315370  2.323304  2.331372  ...  7.826231  7.824122  7.822281   \n",
       "3      2.349146  2.357515  2.366025  ...  7.822832  7.821505  7.820443   \n",
       "4      2.381392  2.390178  2.399111  ...  7.821588  7.820976  7.820630   \n",
       "5      2.415877  2.425112  2.434501  ...  7.822191  7.822313  4.629899   \n",
       "6      2.423785  2.402998  2.382644  ...  7.824806  5.048904  7.826826   \n",
       "7      2.363488  2.343919  2.324750  ...  7.829409  1.336569  1.332416   \n",
       "8      2.308364  2.289888  2.271783  ...  1.334811  1.333946  1.337732   \n",
       "9      2.256353  2.238891  2.221771  ...  1.337586  1.754217  1.754119   \n",
       "10     2.207397  2.190871  2.174665  ...  1.755474  7.857858  5.343303   \n",
       "11     2.161093  2.145441  2.130086  ...  5.161067  4.870013  4.783200   \n",
       "12     2.117501  2.102659  2.088094  ...  7.880194  7.885543  7.891169   \n",
       "13     2.076288  2.062202  2.048375  ...  7.896219  7.902333  7.908727   \n",
       "14     2.035097  2.021756  2.008657  ...  7.915361  7.922296  7.929513   \n",
       "15     1.997320  1.984655  1.972217  ...  7.936139  7.943881  7.951908   \n",
       "16     1.959684  1.947686  1.935899  ...  7.960443  7.969049  7.977945   \n",
       "17     1.927183  1.915756  1.904527  ...  5.146470  2.156959  2.097287   \n",
       "18     1.895482  1.884607  1.873920  ...  2.091069  2.036135  1.984079   \n",
       "19     1.867159  1.856776  1.846569  ...  1.986573  1.937976  1.891762   \n",
       "20     1.839557  1.829649  1.819909  ...  1.893303  1.850069  1.808827   \n",
       "21     1.810392  1.800986  1.791738  ...  1.802700  1.764412  1.727774   \n",
       "22     1.785812  1.776828  1.767994  ...  1.731873  1.697268  1.664075   \n",
       "23     1.762461  1.753879  1.745439  ...  1.668702  1.637248  1.629984   \n",
       "24     1.761824  1.774561  1.787544  ...  1.631279  1.634367  2.874876   \n",
       "25     3.552700  3.579301  3.606427  ...  2.872785  2.865169  2.866425   \n",
       "26     3.638492  3.666635  3.695342  ...  2.868131  2.869544  2.871058   \n",
       "27     3.739961  3.769969  3.800593  ...  2.873751  2.875473  2.883291   \n",
       "28     3.841609  3.873535  3.906131  ...  2.888284  2.900908  2.913743   \n",
       "29     3.942629  3.976510  4.011115  ...  2.917941  2.931115  2.972667   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "12970  3.919997  3.927080  3.934323  ...  7.309910  7.297823  7.286025   \n",
       "12971  3.948350  3.955899  3.963612  ...  7.280945  7.269650  7.258637   \n",
       "12972  3.976384  3.984384  3.992553  ...  7.255519  7.244956  7.234670   \n",
       "12973  4.006648  4.015127  4.023778  ...  7.231160  7.221339  7.211790   \n",
       "12974  4.038120  4.047088  4.056233  ...  7.208888  7.199791  7.190961   \n",
       "12975  4.070848  4.080316  4.089968  ...  7.188595  7.180206  7.172081   \n",
       "12976  4.105163  4.115150  4.125328  ...  7.169949  7.162262  7.154835   \n",
       "12977  4.140958  4.151481  4.162199  ...  7.150283  7.143409  7.136790   \n",
       "12978  4.179538  4.190632  4.201930  ...  7.137442  7.131152  7.125115   \n",
       "12979  4.218647  4.230316  4.242196  ...  7.123998  7.118386  7.113024   \n",
       "12980  4.260674  4.272957  4.285458  ...  7.111586  7.106684  3.888706   \n",
       "12981  4.303357  4.316261  4.329390  ...  3.892089  7.097572  7.093564   \n",
       "12982  4.347970  4.361519  4.375302  ...  7.093375  7.089785  7.086440   \n",
       "12983  4.377586  4.331717  4.286944  ...  7.086605  7.083673  7.080986   \n",
       "12984  4.251266  4.208328  4.166390  ...  7.081453  7.079180  7.077149   \n",
       "12985  4.132252  4.092007  4.052675  ...  7.077895  7.076283  7.074911   \n",
       "12986  4.020405  3.982630  3.945691  ...  7.075948  7.075016  7.074324   \n",
       "12987  3.915434  3.879922  3.845179  ...  7.075843  7.075649  3.883037   \n",
       "12988  3.816862  3.783429  3.750703  ...  3.883248  7.078088  4.301313   \n",
       "12989  3.716091  3.684736  3.654030  ...  7.080649  7.081738  7.083070   \n",
       "12990  3.629254  3.599653  3.570651  ...  7.085600  7.087353  7.089347   \n",
       "12991  3.547254  3.538176  3.553602  ...  7.092239  7.094658  7.097321   \n",
       "12992  4.924805  4.946744  4.969050  ...  7.100613  7.103708  7.107048   \n",
       "12993  4.997176  5.020195  5.043599  ...  7.110723  7.114498  7.118519   \n",
       "12994  5.072692  5.096847  5.121407  ...  7.122583  7.127042  7.131750   \n",
       "12995  5.151622  5.176975  5.202756  ...  7.136214  7.141363  7.146763   \n",
       "12996  5.234019  5.260635  5.287704  ...  7.151608  7.157454  7.163554   \n",
       "12997  5.320275  5.348229  5.376661  ...  7.169360  7.175932  7.182762   \n",
       "12998  5.410428  5.439795  5.469666  ...  7.187859  7.195125  7.202651   \n",
       "12999  5.504912  5.535778  5.567182  ...  7.208778  7.216769  7.225024   \n",
       "\n",
       "           R719      R720         x         y     theta  Y   Vk  \n",
       "0      7.828493  7.825711  3.230296  1.145155  2.554403  4  207  \n",
       "1      7.823543  7.821520  3.223964  1.149565  2.570661  4  207  \n",
       "2      7.820706  7.819398  3.216884  1.153924  2.586603  4  207  \n",
       "3      7.819649  7.819120  3.209675  1.158389  2.603681  4  207  \n",
       "4      7.820549  4.627016  3.202996  1.162371  2.619348  4  207  \n",
       "5      5.048106  1.339982  3.196053  1.166367  2.635456  4  207  \n",
       "6      1.333134  1.330385  3.188999  1.170226  2.651871  4  627  \n",
       "7      1.333018  1.339445  3.181707  1.174037  2.668428  4  627  \n",
       "8      1.752277  1.752673  3.174667  1.177781  2.684110  4  627  \n",
       "9      7.853936  7.857839  3.167475  1.181319  2.700229  4  627  \n",
       "10     5.026669  4.779808  3.160238  1.184736  2.716319  4  627  \n",
       "11     7.881194  7.886631  3.152842  1.187967  2.732539  4  627  \n",
       "12     7.897071  7.903251  3.145599  1.191207  2.748563  4  627  \n",
       "13     7.915401  7.922355  3.138198  1.194280  2.764677  4  627  \n",
       "14     7.937013  7.944798  3.130316  1.197380  2.781752  4  627  \n",
       "15     7.960223  7.968826  3.122601  1.200270  2.798354  4  627  \n",
       "16     2.161047  2.099944  3.114403  1.203202  2.815892  4  627  \n",
       "17     2.040896  1.987523  3.106816  1.205764  2.831921  4  627  \n",
       "18     1.934683  1.887749  3.098967  1.208285  2.848456  4  627  \n",
       "19     1.847763  1.805824  3.091526  1.210542  2.864060  4  627  \n",
       "20     1.769441  1.731793  3.083828  1.212742  2.880104  4  627  \n",
       "21     1.692683  1.659044  3.075173  1.215060  2.898060  4  119  \n",
       "22     1.632210  1.628579  3.067411  1.216994  2.914096  4  119  \n",
       "23     1.633023  2.875379  3.059604  1.218808  2.930187  4  119  \n",
       "24     2.866574  2.863949  3.051776  1.220494  2.946272  4  119  \n",
       "25     2.867781  2.869234  3.043454  1.222140  2.963318  4  119  \n",
       "26     2.872671  2.874798  3.035811  1.223526  2.978945  4  119  \n",
       "27     2.895777  2.908471  3.026974  1.224973  2.996925  4  119  \n",
       "28     2.926792  2.961940  3.019054  1.226133  3.013029  4  119  \n",
       "29     5.589263  5.515427  3.011351  1.227131  3.028622  4  119  \n",
       "...         ...       ...       ...       ...       ... ..  ...  \n",
       "12970  7.274511  7.263280  1.444881  0.333114  2.354856  3  939  \n",
       "12971  7.247903  7.237447  1.439050  0.339028  2.371161  3  939  \n",
       "12972  7.224658  7.214919  1.433460  0.344513  2.386530  3  939  \n",
       "12973  7.202510  7.193500  1.427610  0.350065  2.402376  3  871  \n",
       "12974  7.182397  7.174098  1.421686  0.355539  2.418103  3  871  \n",
       "12975  7.164218  7.156616  1.415690  0.360937  2.433733  3  871  \n",
       "12976  7.147666  7.140755  1.409603  0.366225  2.449426  3  871  \n",
       "12977  7.130426  7.124316  1.404305  0.371111  2.465613  3  871  \n",
       "12978  7.119331  7.113798  1.396990  0.376653  2.481274  3  871  \n",
       "12979  7.107913  3.890347  1.390660  0.381648  2.496985  3   24  \n",
       "12980  7.097625  7.093466  1.384189  0.386675  2.513193  3   24  \n",
       "12981  7.089801  7.086284  1.377561  0.391456  2.528911  3   24  \n",
       "12982  7.083339  7.080481  1.370994  0.396138  2.544712  3   24  \n",
       "12983  7.078541  7.076339  1.364355  0.400711  2.560480  3  919  \n",
       "12984  7.075360  7.073812  1.357638  0.405176  2.576294  3  919  \n",
       "12985  7.073781  7.072892  1.350838  0.409530  2.592227  3  919  \n",
       "12986  7.073874  7.073664  1.344041  0.413687  2.608293  3  919  \n",
       "12987  7.075983  4.298266  1.336515  0.417590  2.626037  3  919  \n",
       "12988  7.079782  7.080991  1.331337  0.421720  2.640536  3  919  \n",
       "12989  7.084642  7.086457  1.322349  0.426145  2.657648  3  919  \n",
       "12990  7.091584  7.094064  1.315221  0.429925  2.673643  3  919  \n",
       "12991  7.100226  7.103377  1.308033  0.433584  2.689670  3  919  \n",
       "12992  7.110633  7.114464  1.300777  0.437107  2.705849  3  716  \n",
       "12993  7.122787  7.127303  1.293468  0.440517  2.722004  3  716  \n",
       "12994  7.136706  7.141912  1.286109  0.443815  2.738148  3  716  \n",
       "12995  7.152415  7.158320  1.278698  0.446990  2.754307  3  716  \n",
       "12996  7.169908  7.176518  1.271236  0.450041  2.770448  3  716  \n",
       "12997  7.189849  7.197196  1.263851  0.452955  2.786666  3  369  \n",
       "12998  7.210438  7.218488  1.256171  0.455778  2.802778  3  369  \n",
       "12999  7.233544  7.242332  1.248564  0.458465  2.818969  3  369  \n",
       "\n",
       "[13000 rows x 726 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "obs,est=obs_s.astype(int),est_s.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 7)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####BAUM WELCH APPROX###############???!?!?!\n",
    "\n",
    "\n",
    "N=est.max()+1 \n",
    "K_corpus= obs.max()+1\n",
    "TRANS=np.zeros((N,N))\n",
    "for i in range (N):\n",
    "    fromTO=[]\n",
    "\n",
    "\n",
    "    indices=np.asarray(np.where(est==i)).ravel()\n",
    "   \n",
    "\n",
    "    for indice in indices:\n",
    "\n",
    "        if (int(indice)==len(est)-1):\n",
    "            \n",
    "            indice=indice-1\n",
    "\n",
    "        fromTO.append(est[indice+1])\n",
    "    fromTO.append(0)\n",
    "    fromTO.append(N-1)\n",
    "    aux=np.asarray(np.bincount(fromTO    ))\n",
    "    aux[0]=aux[0]-1\n",
    "    aux[N-1]=aux[N-1]-1\n",
    "    \n",
    "    TRANS[i,:]=aux\n",
    "TRANS=TRANS/TRANS.sum(axis=1)\n",
    "#####################################3\n",
    "EMIS= np.zeros((N,K_corpus))\n",
    "for Vk in range (K_corpus):\n",
    "\n",
    "    indices=np.asarray(np.where(obs==Vk)).ravel()\n",
    "    estconvk= est[indices]\n",
    "\n",
    "    numestenvk=np.bincount(estconvk)\n",
    "    if (N-len(numestenvk)>0):\n",
    "        numestenvk=np.append(numestenvk, np.zeros(N-len(numestenvk)))\n",
    "    EMIS[:,Vk]=(numestenvk/np.bincount(est))\n",
    "\n",
    "A,B=TRANS,EMIS\n",
    "PI=np.ones(N)/N\n",
    "Modelo1=HMM(A,B,PI)\n",
    "Modelo2= HMM(A,B,PI)\n",
    "Modelo2.B=B+.0000001\n",
    "Modelo2.A=A+.0000001\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#np.linalg.norm(Modelo1.A-Modelo2.A)\n",
    "np.save('A.npy',Modelo1.A)\n",
    "np.save('B.npy',Modelo1.B)\n",
    "np.save('PI.npy',Modelo1.PI)\n",
    "\n",
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.99300699, 0.00699301, 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        ],\n",
       "       [0.        , 0.99300699, 0.00699301, 0.        , 0.        ,\n",
       "        0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.99300699, 0.00699301, 0.        ,\n",
       "        0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.99300699, 0.00699301,\n",
       "        0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.99300699,\n",
       "        0.00699301, 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.99300699, 0.00704225],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 1.        ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>theta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "      <td>16701.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.474876</td>\n",
       "      <td>0.457221</td>\n",
       "      <td>-0.113440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.009028</td>\n",
       "      <td>0.497757</td>\n",
       "      <td>1.982905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-3.587735</td>\n",
       "      <td>-0.130961</td>\n",
       "      <td>-3.141290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.921340</td>\n",
       "      <td>0.151473</td>\n",
       "      <td>-2.594343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.838595</td>\n",
       "      <td>0.301476</td>\n",
       "      <td>0.122279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.026029</td>\n",
       "      <td>0.682428</td>\n",
       "      <td>1.451075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.749774</td>\n",
       "      <td>3.865329</td>\n",
       "      <td>3.141516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  x             y         theta\n",
       "count  16701.000000  16701.000000  16701.000000\n",
       "mean       0.474876      0.457221     -0.113440\n",
       "std        2.009028      0.497757      1.982905\n",
       "min       -3.587735     -0.130961     -3.141290\n",
       "25%       -0.921340      0.151473     -2.594343\n",
       "50%        0.838595      0.301476      0.122279\n",
       "75%        2.026029      0.682428      1.451075\n",
       "max        3.749774      3.865329      3.141516"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "xyth.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff8825e8358>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_s=data#.iloc[1001:2000,:]\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "fig.suptitle('Training Set Color=Vk', fontsize=16)\n",
    "\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.scatter(x= data_s.x, y=data_s.y,marker='+',c=data_s.Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}