Lomb-Scargle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lomb-Scargle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Lomb-Scargle Periodogram \n",
    " * after Lomb (1976) and Scargle (1982) \n",
    "* statistical tool designed to detect periodic signals in unevenly-spaced observations\n",
    "* least squares fit of sinusoids to data samples\n",
    "* also called Least-squares spectral analysis (LSSA)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T11:00:27.207483",
     "start_time": "2016-09-27T11:00:25.258695"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNMAAAIPCAYAAABZvZgmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAJBITAMAAACARGIaAAAAACT6BVvsWMOhRy11AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa770e68c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=1, num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "def sin_func(t,ph=0):\n",
    "  return np.sin( (t/350.) * 2. * np.pi  + ph)*1. + 100.\n",
    "\n",
    "T1 = np.linspace(0,2000,1000)\n",
    "T2 = np.linspace(0,2000,1000)\n",
    "T3 = np.linspace(0,2000,1000)\n",
    "Y1 = sin_func(T1,np.pi/16.)\n",
    "Y2 = sin_func(T2, 0)\n",
    "Y3 = sin_func(T3,-np.pi/16.)\n",
    "\n",
    "f = np.logspace(np.log10(1./500), np.log10(1./100.), 1000)\n",
    "\n",
    "\n",
    "axs[0].plot(T1, Y1, lw=0, marker=\".\", ms=1.0, label=str(len(T1)))\n",
    "axs[0].plot(T2, Y2, lw=0, marker=\".\", ms=1.0, label=str(len(T2)))\n",
    "axs[0].plot(T3, Y3, lw=0, marker=\".\", ms=1.0, label=str(len(T3)))\n",
    "# axs[0].set_xlim([np.min(T1),np.max(T1)])\n",
    "\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T1, (Y1)-np.mean(Y1), f*2.*np.pi) / T1.shape[0] )), lw=0, marker='.')\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T2, (Y2)-np.mean(Y2), f*2.*np.pi) / T2.shape[0] )), lw=0, marker='.')\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T3, (Y3)-np.mean(Y3), f*2.*np.pi) / T3.shape[0] )), lw=0, marker='.')\n",
    "# axs[1].plot(f, npgram)\n",
    "# axs[1].set_xscale(\"log\")\n",
    "axs[1].set_xlim([200,500])\n",
    "axs[1].set_xlabel(\"Period in d\")\n",
    "\n",
    "\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T11:01:16.383585",
     "start_time": "2016-09-27T11:01:12.424996"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNMAAAIPCAYAAABZvZgmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xt0VeWdP/53EgyFJJRQLtHq0DJGbseOxUvKWl5KVvsT\nLzXqVC2mM4i2ddGLcrQ2TJ1h1pd2+IqtHhxvM6uG1o5Aqb0QOiijjkJbtVTg12pI+YnDlI5yES0M\nSbgkJOf3x8OTc07O/uyzzzn78uy936+1uqgBws4559nPsz/P5/N5KtLpdBpERERERERERERUUGXQ\nF0BERERERERERBQWDKYRERERERERERE5xGAaERERERERERGRQwymEREREREREREROcRgGhERERER\nERERkUMMphERERERERERETnEYBoREREREREREZFDDKYRERERERERERE5xGAaERERERERERGRQ0UH\n0w4ePIizzz4bmzdvHvrali1b0NTUhNraWkyZMgUrV660/LsPPvgg5syZY/v9e3t7sWDBAowfPx5j\nx47F/Pnz0dvbW+xlEhERERERERERua6oYNrLL7+M2bNnY/fu3aioqAAAHDp0CFdeeSVuueUWHDly\nBO3t7UgmkznBtt7eXtx99934+te/PvT3JF/96lfxzjvv4K233sKuXbvwpz/9CV/72tdK+NGIiIiI\niIiIiIjc5TiY9oMf/ACtra1YtmxZztd/+tOfYsKECVi4cCEqKysxZ84ctLa2or29fejPnHfeeThw\n4AAWLlyIdDot/htHjx7F6tWrsXTpUowdOxYTJkzA8uXLsXr1ahw/fryEH4+IiIiIiIiIiMg9joNp\nV1xxBXbv3o0bb7wx5+s7duzAueeem/O16dOn44033hj6782bN+Opp57ChAkTbP+NXbt2ob+/P+f7\nTZ8+HX19fXjzzTedXioREREREREREZEnRjj9g5MmTbL8end3N0aPHp3ztdGjR6Onp2fov8844wxH\n/0Z3dzcAoKamJud7Acj5fkREREREREREREFwHEyT1NbW4p133sn52tGjR1FXV1f099JBtKNHjw4F\n0Y4ePQoA4vfbt28f9u3bV/S/RURERERERERE0XP66afj9NNP9+z7lx1MSyQSeO6553K+1tXVlVf6\n6cTUqVNx2mmnobOzExdddNHQ96qursY555yT9+f37duHCy64AHv37i3t4omIiIiIiIiIKFLOOOMM\nbN261bOAWtnBtOuvvx7f+MY38NBDD+HLX/4yfv3rX2P16tVYv3590d9r9OjRuOmmm7B48WL8+Mc/\nRjqdxuLFi3HzzTdj5MiReX9+37592Lt3L5566ilMnz693B/Fc6teX5Xz360fa1X/58ILgcHBzG+M\nGwc8/zxWvb4KW/duBQBccMYFmT9/8cXAsWOZPz9iBLBli5eXTuQr8bN/ww3A7t25f3jbNvnvrFoF\nPPhg7p+/6y6gtdX1a160aBFWrFjh+vclyrbq1DSS/REWx8uFF2Lw1NxSCQBTpgBPP50zF23duzXz\nd5qagJMnM9941Cjg17/28Kcht/E+ZE+PlQvOuGDoa60fawU+/Wngz3/O/MHKSuC11/L+jpO5iCgq\nHK+rpkzBqn+4dug/t+7direffhtPtz+tvjD8OefUXEQUVo7XYqee2fWnv/LUM7u4brOZi6h4f/jD\nH/D5z38e+/btMzeYNm7cODz//PO48847sWTJEkycOBEPP/wwLrvssrw/W1FRgYqKipyvLVu2DKtX\nr0ZnZycA4LHHHsPdd9+Nc889F319fbj22mvxyCOP2F7D9OnTMWvWrHJ/FE+lXk3hrZFv4ZMf+SSS\ns5OZ36isBPQJp7W1wKm+cQCGfqbUqykAwOYTmwEAyVOlrxg5EujrUw8/F1yQO1ERhZQeK2M/OjZ3\nvDQ0AAcOZP7ggw8CycxYmjVrFlKvprDpj5vwFt7C5hObkXzgAeCBB4CWFkAH+B98EPiP/wBO3XPc\nMnbsWOPvQxR+2R8xPTecOfVMnDn1zMxYSaVU0BjAIIC+6lp84IT13PLWyLcy46W/X/2BESOAgQG1\naTN7NnDihOc/F7mD9yGZ/rxfO+fa3HWYXksBQEVF3loqe27ZfGKz+rv/9V/qN7PnpfPPB665Bujo\n8OGnIfKW5XjJXksBwKRJwP79AIBZw/7u8p8uz4yXgQH1G/oZcPduzi0Uao7WYnotBeBkRRUefuDk\n0GOL+Iz//vvqD9TVAT09aj46//y8Zx4yR0U6rSM54bN9+3acf/752LZtW+CLx5QaC5afc70Iywuk\nZQcWHSzALL9PIgHs2JH5Q+F9O4nksZL9sFNdXXABJn6frIkNM2e6GlC75pprSsrIJSqF+Bkf/rDj\nYAGmF3M53yc7SFBVlZuxRsaK+31IWos5mluGbWjmfe9T40QTN0YZUKOQsxwv2XOLRdB5uMQlCfzl\nl/8yf8zpIAHgaD1HZDK3njcsv0/WxigABtRK4EesqNKT70pDHAXS0mlHC6/k7CQ++ZFPYtMfN2UW\ndZ2duQG0Sr6lFG6WCy/9sDNpkuXCK5XKPEQBmbGS5+RJtXgDVBA6kXDvwol8Is4rqVTmYaeqSs0N\nDhZe+nvkBAv271dBAUAtCEeUnchOFAhHgbSZM20DaYAaJ/rv56zDABVYqKpS/3/9ehV4IAqZ1Kup\noc+1GEirqrIMpA1fh02pn5L/zAKocTZzpvr/fX1qHBKFkKNA2jXXONq4t3zGTybVOk7HDO66K3eQ\nkRG4OnZJURlp2Q8lRWaS5XyfbHqwpdPq+zOLgELEMjMGUMEuvYNZZCbZ8ADB0Pc+cSLzELVjh1ok\nMouAQiZvXgEyO5glZpJt+uMmAFljpaMj8xA1MKDGDbMIyGCOM9KyN2mKzCTLDqhl/zdOnszMLevX\nq4ceZhFQSDjapCkyk0wcK52dmbmlr0+NxwLBbCKT2G7SZAfS3JhbBgczAbq77uK8YhimMXmk4CDT\nga8SWGYRAJmGoPqhhygE9FjJk13CPGmSbSAtmbSfW/J2Rk+cUCU9QOahp0zz5s0r+3sQFSIGnvUm\nTUVFSYE0y11RQC0E9dzS16fKP8lYvA/ly1uHNTTkbtKUsJkijpcTJzIZanffXcZVE/nPcpNGf46r\nqmwDacPXYfpeJFYKdHRkMtR6elgpQKEhPuM3NGQ2aR58sKy5Jc/Jk5kMNVYKGIXBNA/ZDrIHHij7\n++ct4pLJ3IceTkxkiOHp/0NflyYkIBNIq60danBbCvGhp7s789Bzzz0lf3+ND7HkNTHwXFeX2aQp\n4yAacRGXTGYeeg4cYAmbwXgfUnS5WnZpJgD12dW9AMvsm1nwoSed5sYmGcVuLQYImzTpdEnZztn3\nIj0Os8tIAajxN2mS+v+6UoAoBPKeWxKJ3LmljOwxMWlmcFDNLUyaMQqDaS5zvIArM0XT0UMPJyYK\nActAmt51qa52JfXf9qGnqooTE4WG5QJOZ9m4sElj+9Cjszl/8Yuy/x0ir4hBZyDz2a2tdeUAGvGh\nR49FZnOS4cTx0tCQKVdzsXVM3sbm/v2cWyg0xMCzw0qaYuSNFSB3bmHSjBEYTHOR7QJO9xsocZBZ\n7SaJizg+9JBhrMowxQkpO8vGxf5M4nj5znfUr5yYyGCW4yWVyl3AudxHwzKbs7qaGTdkPMtNmrq6\nTJZNiZs0UmZPnmQyc4DHgQNsGk1GkFpiWPZJ0wkA+nPsxr8vbWzqSoF0Wo1TojDR66Hq6rIqabI5\nTpqhwDGY5jJxAQeoicKlQTZcXhYBJyYKo5YWV7NsHMmemLq6/Pk3iRzKu7dn0+XJZZZCWxEXcjrA\nzeAzGcjRYTZ6A8Ul4kZNR0dmY9OFVgJEbhPHS/bc4vIBTQU3Ntk/jQwkVp4lEpkWTi4f0GRbKVBd\nrf4/NzYDx2CaS8QJKTs4UEaatLSblDeos2VPTNwVJYOI42XDBvWrB1k22f+eZTanzrhh8JkMZLmA\n0xmcHp2CJo6X7F1Rzi0UBnqjpNxeNjaH3ViW5OiNzYEBzi0UDro6oIwMzpIw44YMJVaeZVcH6M+u\nX06cUOs/bmwGjsE0r2UHBzxk+dCTTGb+Xe6Kkul0fw4PMzg1y4ee++5Tv/b0sNcgGUEMOgOZBdxn\nPuPjFZ2S3Upg8WL//30iC7atA9JptWHiUi+b4cRMToAbm2QksXWARxmc2QpubAIMPpOvCpXwW1ae\n6fWPSz04JeJ40es/Bp8DxWCaCwr2fnKxhrpo+/dndkXZBJcMIC7gdH+Oq67y9N931IeAvQbJR477\nMGn6IaO62vUSnOHERVx3d2ZXlMFnMlV2dYDeMPGIWJKTvbHJ4DOZTH8+PaoOGM5yY1OXyjH4TAaw\nrTzr6/O0OqCgjg4Gnw3AYJpXsnd3PF7AaQX7EOhgBZFpshdwHgcHgAJ9CHSvQQafKUAFWwe4fEBH\nSWbMUL8y+EwBK9g6wIVT1Muyfz+Dz2SEgr2ffKgOAApkczL4TD6zK+G3pOcWvQ7ymPiMr8s9WVUT\nGAbTyiQu4Hze3bGVTGZKchi5poCIC7js3Z2gMjizZQefuStKPihqEefxAq7ok6N18Jk9O8g0uq9g\nVZWnJTjDieNFj1k9holMovsKelwdkE0cK7qqhv2gKEDiM75uS+Nh64CiKhY4twSKwTQv6OCAT7s7\n2QqW5DByTabZuFH96tPuTraCvQaXLPH9mohsTyQMIDhgSwefeRIuBUQcLwEEB2zpflBsu0EBsQ0O\n6L6CPlQHOKLHLecWMo2u9PKp8kwruLE5MMDgcwAYTCtDwbICUxZwmg5W6OAFkU9sgwN9fZ7u7pRE\nl+SwZweZZOdO9auHc4vdydGAxSJOZz7zJFwySSIRaHBAHC/64YttN8gkAQUHAIuTqjXdD4ptNygA\ntv3QAbXu8bDyrOiyU70u1OtE8g2DaW7LzhwIaHenYOSaPTvIFHrHce7cwC5BXMhNnKh+Zc8O8lHB\nsoLaWnMyBzTdfFf3CSXygdg6AMjMLQEEB2xlt91ggIB8ZDu3AJ4HBwrJ62ELZMbvu+/6f0FEVvQ6\nJ6BDB8RnfB18Znaa7xhMK4HtAs6HzIGyTJumfmV2GvnENivNkLICy0VcdsNoZqdR0PTDxNKlgV2C\neHAHkCmNZnYauajok26BTMlawMGBgm032JeTgqZPUg/yREI7uu0GM5/JRwWz0vR6xzQ6MYHZab5i\nMM1G0Yu4lhZgcNCI4ICYbcPsNDKFqZkD2WbMUItM9k4jn1jeuw0JDthiaTT5SHzYATIlayYGBzSd\n+cy5hXwgjhf9+dOfxwDZHkYAMPOZgpVKZU5SD/iwNNvstNpa9uX0GYNpRbJdwG3YoB54AixZy2aZ\nPQAwO4184ygrzYDggG1pdGUlD+4gX4j3bAOy0rIVPK2QpdHkkqL7xmSXrBlA3NjUwefeXv8vikjr\n7TUiOFDQpEnqOhkgIB9Y3reXLFGfwQAOSyuKXieyNNo3DKbZKGoRp3ulGZCVVpA+Uaq/nwECCsbO\nnWpSCiArreiMUwafKUi694XJWWlaZydLo8kXYpDq3XeNK1kTg+QTJ7J8jTxXsFeaAVlpWsHsNAYI\nyEPivVpnpQHGHJYmzoHZfTnZO80XDKYVSfzwBhgckNj2t9HZcwwQkEdss9IGB4GaGqOCAwUP7ujv\nZ4CAPOGoD6chWWmaOBeyNJo8Jj7wGBgcsMXyNQqK7pUGBJaVVvTGZk2N+pUBAvKbQeXQw1nOh3q9\nyN5pvmAwrQjiAk73SjvtNKOCA7Y6OlT5GgME5LeAgwNFlw0Bmew0BgjIT4YGnjXLOVHv2rJ8jfym\ns1YMK1kTN2oAlq+R58SSNcDI4IC4UbN0qcrk1P12iVwkJgCkUmo9U1Vl3NwiSibVunFwkMFnHzCY\n5gad3WVIr7ThxIUcAwTkIcsFkeGBZ9uDOyor1YTK4DO5zDbjGTAuK60g/YDGAAG5zDbjGchkr4QF\ny9fIQ2ISgAG90uw2Ni2vW5evpdOcW8g/+hlZPzMbRnzGZ3aabxhMc0hcwLW0qOyu004zv1facAwQ\nkEfEBZzhgWfAwcEdDD6Ti8Kc8cz+NmQMwwPPYsAcYPCZ/BW2cuhs3d0qQ+i994K+EooY8R6ts+wN\n6ZXmWDKp1o+Dg+yP7rERQV9A6IUgOAAIp48CKkDQ1aUCBIY+sFFEhDnwDOQGn4m8FpK5RTRxogqm\nNTSEpzSCjGe5lglB4Fmz3Jjdvx8YMYIBAiqa3gcf/rEXEwAA9TmrrDT6vpzd7znvZxg1Cjh2TI37\nMK4lyThhOqTDiviMP3cu8ItfsD+6xxhMc8jyg5pKqeBAZWUobuiWNwsGCKgE0gJOsxwvIQkODM+2\nyflZGCAgF0Uh41lcxDFAQC4Tx0tI5hZbDBCQH3SGyqhRwV5HOZqbGSAgf4Qg8KxZzo8dHWodpvuj\nG77ZFFYs83RALMNZvlwNMkPrqB1jiQG5RBwrADAwEJrAs2j/fvUzMEBAXgpZcEAc9/qBjSUG5JUQ\nBZ4Bm9Lo5maVXccAARVB6jsmlqxt3Kg+Z83N3l9cmcSfoaNDjff+fs4tVDbbTU0g3IFngC1qfMBg\nWjn0A3VI6qjFiYkBAipS0Sdi6kBtiALP4nhhgIBcIn7GohB4BhggINeIDzwvvqjGSkgCzyIGCMgl\ntn04QxR4Bmx+Fj3eObeQV0IUeAZsNmp0BdqxYwFcVTywzNMBy4edREINMkNPjiq6jwJLDKhMtj06\ndCPykASeNcufiSUG5IKCPTpCFni21NEBjByZCRBwbiG36QcEAz9b0jrMtr/Nhg0qQEjktqgEnoFM\n+drAQNBXQiEntnEaGAhV4NnWtGnqkB62qPEEg2kFiA88u3apScnQk6OKxgABeaWlRY2VsKdKazpA\nMDDAHgTkvigFngEGCMgVlg88DQ1qU9Pw5tBWbPvbMIOAyiAGaw0OPEvEnwVggIDKJq5bdElkyALP\n4njR2Wk8Yd0TLPMsRUuLepAeNcrYB2m7PgqARRqoLjHQAQKiItn26ABCGXgWfyY9wbIHAZXI8rOl\nA8+GZjyXRD+4MUBAJbDtw2l4c+ii2yEAwPjxKkDIHrZUAnG8JBLq1/Hj/b0gl1j+XHrDiS1qyG29\nveo+HKLAsybeA2pq1HzJNgKuYzCtAMsHHr3DHpI6ascYIKASiTdvnSpdVWVs4LkQy5+NAQIqgzhe\nQh54Biw2aoDMA5x+oCMqV8iDA+xhS77auVP9amjguWTsYUtlsLwPJxLqHhzCjGdbel3JCjTXMZhm\nQ3zgCWGqdDbbE3IABgjIPSFNlXaEAQJyUwQCzyL9AKcf6IgcEtcru3apX9va/L0gF/EUXHJTlE8l\ntD0FF2AbASqaeP8NeeBZnDOTSbW+ZAWa6xhMK1bId0M18SbCAAGVQLx5hzzwDNj8bPohTj/UETkQ\ntR4d2cSxAqhFHMBFHDkW5YxnWwwQkJtCnPFcUEeHug8cO8a5hcoXgcAz4OAUXFaguYoHEFiwPZUw\n5BHrgtragHvuYYCAHBPHS0QCz4DwMyaTakLiKbg0jHSKn60IBJ41y/GiDyJYsiSaARDyz/Ll6tfG\nxmCvo0wFT8Ht6/P3gijUxM+TPvEy5Pdd8edrbFTPZsuXh/5nJHfZrcWi1uPZER5y4wkG04oRkYg1\nYDMp6QBBXx9PKqTyRKAMp6DmZp5USEWxvPe2tKjd9fp6/y/IL1zEUZHEdYruJxayE2+tiJtROkDA\nkwrJAfFzpA+ymDbN5yvyhuXP2dmp5hb2GSQHbBNmoh54BlSCw6FDTAJwEYNpFsQP4YsvqgeeCEWs\nLW8qOkDADAJyQAwOGH7ibTGYQUDFsPrI2y7gNm5U4yUigWdxvEybpoLsiUQkAiHknSj3f3KEAQJy\nQ4QCz7ZGjWKVAOUp6vEjkVDP+CHPeM5mOY/qCjQeROAaBtOGsX3g0TvqEQgO2GIGATkkjpcInngr\n/qz19WrBygABlSKi/Z9sMwh4EAGVKmKbmrYZBKNGsUqAHLH8HKVSaqxUV/t/QR4Rx8vSpcDixawS\noILEz5Bel0R9HZ9MqmCazsKjsvEAAqci1P8pm9gwevx4NQnzNCkqRV+f+vzEYYeQBxGQA+K9dvly\nNVZCevBAKlVk32ceREAOWI6XVCqSm5pis+jmZvXAw2bRZEP8/CxZoj4/EdrUBISfV98PeBAB2bA9\n1AbIrE8iQlx3TpumflYeNugKBtOGsT2Gvaoqkr0rLG8ubW1qEmYaKNmwHC86ABux/k8Fj5smEogL\nOEBlNQ4MRC7wLI6XuXPVeNFN5ImGEcdLRA4ecEzfE1glQKWI0KE2juj7AucWKlaENzUt59POTrXu\nZJWAK1jmSdaYBkoFiGWPEev/lI3NoslVEej/ZJcgZDledJ/BQ4c8vjKKnEOH1ANPxMpwbEs92WeQ\nCrD8/ESw/5MmjpfOTs4tZEv87OjPTFwCzwD7DLqIwbQstqfhDAxE5jSc4dgsmlwVkdNwisJm0WQj\nTofaOMI+g2RD7P8ERKr/03Bin8GRI9lGgPLY9njWn5eI3l/Fn726mn0GyZL4mUkk1HNLiNs42X3U\n2WfQeyzzdCIGp+GIaaAAF3FkybKMS++GRjjwLE5M7DNIxdKnwEZ00S+OF/YZJEHBEs+I9X9ybGCA\nvaDIGX2oTRzp+wNLPckpvQ6JYDWNZttnUK9DqWTMTMtiuehvaVEPyBHr/+QYd3nIgrjDE/HdUE08\nbpq7PDSMOFYi2lvQkWRSjRUip/SmZoTLUcSNGt1GYPlyrsNoiPh5iUFvQfFn7+hglQBZsi2nj9hp\n6o7V16sSV5Z6lqXozLSDBw/i7LPPxubNm4e+tmXLFjQ1NaG2thZTpkzBypUrc/7Ok08+icbGRtTU\n1ODCCy/Eb37zG/H7b9myBZWVlairqxv63yc/+cliL7No4m6ofjCOcMQasMkg4C4PORWDMhxb3OWh\nYkS4t2A2cW7VD3o8TSp27JolixnPQKjLcJwSqwSqqtgLipyJaG9Bx3QPUlYJ0CniOkTPLREOPAMF\nqgR42GDZigqmvfzyy5g9ezZ2796NiooKAMChQ4dw5ZVX4pZbbsGRI0fQ3t6OZDI5FGzbtGkT7rjj\nDvzwhz/EkSNH0NraiquvvhqHDx+2/Ddee+01zJkzB93d3UP/27RpU3k/5Sl2CzhRxMtwCuroUAON\nuzyUxfLGHKMyHHFi0llGXMTRKZafFV2GE9fdUIBtBCiP+MAT4dPUHdObVCz1JNiMlRhlPIuvwdKl\n6n7BKgEqJCbVNIBNqWdVFdsIlMlxMO0HP/gBWltbsWzZspyv//SnP8WECROwcOFCVFZWYs6cOWht\nbUV7ezsA4IknnsC8efMwe/ZsVFVVYdGiRZg4cSJ+9rOfWf47r732Gs4///wyfqTSWD7wxGhSAmwm\nJu7y0CniZwRQAdeBgdikClu+FjrLiIs4QoH+TyE+hr0Ytn0GKZaSyfjGkAthlQCVJSbVNLZYJUDD\niJuaQHyrabS5c9V6lHNLyRwH06644grs3r0bN954Y87Xd+zYgXPPPTfna9OnT8cbb7wh/v6MGTOG\nfn+41157Ddu2bcM555yDhoYGfO5zn8M777zj9DJtSQu4uJd4FsRdHipEB1p14DWu9A3m2DHu8pAs\nZsews9STnLAt8Yx4GU42y/Gi7xUs9STYBF1jVE1ju1HDuYVO4aE2ijheOLeUzXEwbdKkSaiszP/j\n3d3dGD16dM7XRo8ejZ6eHgBAT08PampqxN/PNjg4iA9/+MOYO3cutm3bhh07dqCiogJXXXUVBgcH\nnV6qe2I0KQE2A427PHSK+Bl58UUVcF261P+L8phUHi6+FnoRx12e2ONuaAEs9SQUyHiOURlOQWwj\nQGCJ53Bin0GAcwvJYrapCdjcO9hGoCxln+ZZW1ublzl29OhRjBkzBgBQU1ODo0eP5vx+b28vJkyY\nkPe9Kisr8fzzz+d87eGHH8bEiROxc+dOzJgxw/IaFi1ahLFjx+Z8bd68eZg3b56jn8HygbihQZWs\nTZvm6HtEhXjyXGOjmpQSCS5qKV/MAs+a5Xjp7ARGjuQuT8yJ99KY7YYCBU7R4onRRDnE8cITo8lO\nxKtp9HM+pwkqhuX9lJuauZqb1SEEIT8xes2aNVizZk3O16Qe/W4qO5iWSCTw3HPP5Xytq6sLiVOp\ntYlEAp3Dgi9dXV24+uqr877X22+/jQcffBDf+ta3hrLZjh8/DgAYZVM+tmLFCsyaNauk6xcfeHTD\nfQaOFB0g4C5PbIljJeJlOCXNKwwQkCSGu6GAzf0jIos4Kp0YPIr43FK0ZFIF01glEGvieInppqZt\nqSeTAMhKDDc1AZux0tERiSQAq0Sq7du3e96Lv6jTPK1cf/312L9/Px566CH09/fjpZdewurVq3Hr\nrbcCAG699VasWrUKmzZtQn9/P1asWIGDBw/iuuuuy/teH/rQh/CjH/0I9957L06cOIH33nsPX/nK\nV/DpT38aH/3oR8u9VOdSKVWyFsP+T2wWTUWLcRkOm0WThIfaOMR+HSSJ8dwiluOw1DPW4lziaXdw\nCUs9yYo4XmK6qWmLpZ4lKzuYNm7cODz//PN4+umnMX78eHzpS1/Cww8/jMsuuwwA0NzcjMceewwL\nFy7EuHHjsHbtWjz77LNDZZnLli0bymIbNWoU/uM//gNdXV04/fTTMXXqVNTX12Pt2rXlXqbI8oGn\nvV19qGIWsdbYLJqsMNBaBAYIYo2H2uSzvX9wERdb4lhhGY41nhhNVmI8t9ji3ELDxXxuEedcJgGU\nrCKdTqeDvohS6dS9bdu2lVzmaamuTv3a3e3e9wwRsRwHUGmgAHDihI9XREGzLfHctUsFWmOYPQDY\nvDZ1dars4r77Yld2EXfiZ4L3T+vXpqVFlXrW1wP79wd0ZRQEcaw0NKjNiLlzmT0wHO8jNBw/E9Y4\nt9BwMZ9b4vaM71msKEvZPdPCyvbDFPN+FMw+IsdiXIZTEHtBxZblPTQGZTgli0i/DiqeuN5gGQ4P\nhCJn2FtQxrkllmyf8WM+t9g+49fXq9enpSW2r08pyi7zjBxOSvZY6hlLLPGUia9NR4dKI+/t9f+i\nyDwswwGSIRqqAAAgAElEQVRgM17YCyp2WOJZIvaCiqxUSq5IFMcLNzUB2Lw+LPUkjZuaQyzHC9sI\nlCS2wTRxQc9JaQgbesaLtIizbXhbXc3AM2xeo1OnEnMRFw/i5wCI7UlrjnERR1pMT1objptYRC5g\nL6jYEe+d3NS0p9enMa/QK1YsyzzF9E/uhjrD14eAzKTEwLOsqYmlnqTmlurqTHA15izn4GQSWLyY\ni7gYYYlnYSz1jBe7ZYLleGE1zRDxfsJSz1hx1MaJ63F5vLDUs2ixDKaJuBuaQxxozc0qkMJFXKRI\nc4v4OeBD7xAu4giw+RzouYW7ofa4iCOW4TjT2anmFlYJxIIYIGA1TQ7xdaquVmvWVIqBlLji3OJM\nWxuwZAmwZUvQVxIasQymcTc0l07Is5pfLCcmHSDgIi6+uBvqHBdxseCo4S3ffwA2c3Bbm8pOY6ln\n5InjZcsWdc9k4BkAD4QiG6ymcU4nAbS3cx6OOPGeyRLPPGKVwJIl7PdchNj1TGPDWyLn2PDWOfG1\nYr+OeONuqCXL8cJ+HaQX8DF84LVrPm+JB0LFhmUPqPZ29czCapohtgdCAcCePf5eEJmDJZ7O1dSo\n14sHQjkSy8w0SzEu8Sy6RwPAfh1ETrHUMxa4G+oSlnrGguV4SaXUAp6bmnksMwg6O4G6OgYI4kq/\n77xP5ijYM4tVApElvvf6wLTJkwO4KnOxSsAdsQumiR+c3l410DgpOcN+HbHAhrfO2ZbjMEAQX9wN\ntWS7iGO/jkgTH3hivKkJ8BZB+Rw1Uydnmpt5IFRc8cA0EQ+EKl/syjwt6bx6nrRmSSxf07vHRdUl\nUFiI7/uePeq956SUR3zN2trUa8YAQSSJ7ztLPIunH3LYryN+Ytq31gmxfG3yZPXAw1LPeOGmpqhg\nqSerBCJLfO/7+hgYKpZet7LUs6BYBdPEB57ly9Uga2ry/6LCrLlZBQja24O+EiKzMUAQT2ymbkuc\nk9mvI9IsH3jYt7Ygy/GiN7VYJRBJYnCAfWtLwySAyOKmZmnEe4xet7LUs6DYlXla4m6oLbEcp6OD\n/ToiTCzx7Ovjbqig4Mlr7NcRSbbtAwC+38Viv474YTN1ohy2JZ5kS3zteKpn/LBvbWn0qZ5UUKyC\naWLDW4C7oQWIExPTZuOFu6GlY7+OSLJteNvXx91QG+JDIvt1RJY4XthMvSDbA6H27GFPzrhgM/XS\nMQkgssT7I/vWFiTOyzU1alOYSQC2YlXmaYm7oeXh0eyRI6ZKk2OWryH7dcQLd0MdEe839fVqbmY5\nTvTpUzypNHpzi5mckSKWX7GZekHia6fpKgEKlVSqyLctlVLrCG5qlqapSY0VfTgQWYpNZhp3Q8sj\nTko81TM+uBtaPmbAhpJevFltzHE31CP6VE9mckaK5XiJ+SmexWCVAPG9LhOrBCKn4AnR3NS0ZdvO\naeRIJgEUEJtgmoiTUvkYIIgU8abK3VDHxNdQ9+tIJPg6RpneDeUJ0QWx1DMebPs/sW9t+Rob1aYm\n55ZIEMcLT/EsiuXryABBaBUd+9TvMYOmBYn3nPp6lnoWEJtgmthMHeCk5JA40CZPZr+OOOBDbVFs\nF3HM5AwVaf3A3VCP1derxTDnlmjjhpxjrBKIuT171HhhwLQ8vOdEiuV9kad4uoNVAgXFIpgmPvCw\nmbo7bruNJ69FhG0zdYCTkhuqq3mqZ9RxN7Qo4n1HL+K2bAngqshNYhBIb2qyxLN8DBBEBk/wdAer\nBGJsyxZ1T+SmpiOsEihdvA8gqK7m4qMIYkNP/cDIgRZdnJSKJo6X5mb1Wra3+39R5CrL95gnRIuK\nbh6s55beXk+uhwywa5daOzDz0DHx0A7dz1RvflEoie+vPiGa7QOKIh4I1dfHTM4IEMeLXjdwU7N8\nej3LQzssxSKYZvnAw93Qkog3rcZGNdi4iAs1MQDESck9+qGRR7NHE0+ILhpPXos2cd3AwLO7brtN\njRVWCUQTT4gmcoYnRNuSNjbFuZpJALZiUeZpSe9GcDfUHZ2dQF0dF3EhZlvi2dfHEs8S8OS16OIJ\n0cUrKRbPk9eii4HnktiW4yxZ4u/FkOtsNxaqq3kfLJL4ejY2st9zBPCEaB90dKhnfCYBWIp0MM32\n9CjuhJakYPYARQ93Q93Hk9eiSe+Gcn4piXhoR10dSz1DTFw3MPBcMnF9W1PDk9eiSFd96FJeKh+T\nAELN9hm/t1etwzi3WJKmhoK9Gtnv2VIsyjzzcFLyRn29unmxHCeUbEutuBtaEvE17exUryl3eULL\n8r3lbiiRc3193IRzW1OTek31vYhCRSyzevFF9b7edpv/FxUB4uvKe1D06GdQ9hZ0l17Xcm7JE+nM\nNDEwwEmpLAVPXmM5TnRwUiLKI94DuRtaFnHOnjyZmZwhZds+oLqam5olEsdKRwcwcmTmRGGKBh3w\n4draXSz1DC3xHrh8uRov3NQsGasEihPPzDROSt5IJtVry0Vc6Ii7dnpSamry/6IiQnxta2rUa8tD\nO4iGWI4XHUDjyWvRoUurGBx1H6sEQks8Ibq6mn1ry2BbJQCw1DNKuKlJPotsZhp3Q71jW1NdX6+C\nadzliQZOSt7RmZxbtgR9JVQky3tgIqGCo42N/l9QHLAPXSiJ6wWWVpWNVQIxocuq2LfWG7wXhY5t\nvzQqG6sEihO/zDTuhnqrrU099DBAECribijAEs8yiTui+iGHKdPRsGePuvdxbimLOF50yUYi4e8F\nUUGplHUSlJiVy0wbb7FKIJTE8XLokHo/GRgti/j66vsQqwTCT29qMmGmbGKVAPs954lsZhp3Q71n\nuTOgj2ZngCD82HfANbYnrzGTMzS4Gxog3QuKpZ7hx0wbV9jeh5jJGR18L73FKoHQEe99en3ATU3y\nUSSDaeIDj94NZaaN93h8bmiI40XvajPI4x0u4qKBJZ6uEu9JfKg0kjTNiw88em7h+sA7zc2qEoPl\nOKEhtg8AuKnpAvF+xCSA6OAawTXieGESQJ54lXlyN9RVtuU41dVAe7v/F0Xuqa7mxOQSlnpGg/g+\nssTTH7p0g+U44aXfO5Z4usayHKejQwX4mclpPLH8EFDvX18fH1pdwgOhwk98D/V7xxJPb7GdU55I\nBtPEBx72HXCduIgDWFMdEpbjhbuhJZP6B9nSmZxEJM/ht92mxgpPXjOe+MCzZYtaiHNT03vcEAs/\nvof+YIAg/F58Ua0Pbrst6CuJDMt5nEkAeSIZTBNxUvJPXx/704WA+MDD3VDXia81MzlDwXY3tK+P\n7QP8kExyDg87vQDnpqZreGhHuInvHzc1SyZtarJKIPzE9xBQ6wPOLd5jJmeOyPVME3utcFLyhHhD\na2xUmWmsqQ4nPrCWrOh5vKMDqKtjJmdYMdPGE+JcPnky55YQsFwbpFJqAc75xXWW44WHdoSbft94\nn/MP+z2HE0s8PSE+47Pfc47IBdNEnJT81dmpAgQsxzGa5Y2Sk5InCp4AyUWc0cT3j5k2/rrtNmDx\nYs4tYdTergJp3NT0DwOXRrM9IZrvXcnspmPxNdeHdrS3cz43lPjescTTXzy0I0fkyjzF9E+WeHpG\nLH9iqWc46Z0GTkr+Yamn0cR7nM60IdfZluNwLjeaOF509i03NV0njhce2hFO3NT0H/s9hxtLPD1h\ne0AK+z0DiGAwzRJLPIPR2KhublzEGcf25shMG0/x0I4IYaZNMHR/Oi7iiOzx0A6jiUFQZtp4xrbn\nFpMAjGb53ul1APvW+otJAEMiVeYppn+yxNNT4qTEUs/w0c3U6+uDvpL44QLOWOI9jpk2nhLn9KYm\nYONGYPlyBv0NxGbqwbAcL7och8KHmTb+Y79nY4nrAR3MaWry+YriQVz/st/zkHhkprHEMzjc5TGS\nuDPHZuqeE197ZnKGC0s8g9PRocYK+3WEB0+IDg4zOY1ke0I0wBJPj1m+/p2d6lcmAYQHNzWDxVLP\naGWmsZl6cMQdg/p69cDDxurhwBLP4DCT00i2u6Es8fRUwUM7yChsph4scbwwkzMw+hmzqJedJZ5E\neXhCdHB4aIe96GemcVIKls5wWr482OuIoVTKerOAzdSDx0M7IoC7ob4Qxwobq4cHNzWDxUxOI9n2\n7mKJp+d4aEcEcFMzWOz3DCBCmWkFd0Q5KXlKXBDw+Nzw4KQUPGZyGse2UTEFh5mcxhHHCjc1fSOu\nhWtqOLcEoOiXms3Ug3fbbcDixZxbDCLe17ip6QvbKgGuhSOemcbdUDOwX0cgkknrhZy4G8dJyTfi\ne8BMznBoaVGB58bGoK8k8njyWjjYnhANcFMzaE1NaqxwbjGCOF6WL1fvE5up+8LyfUgmWTYYFlwD\n+Ea8Z7Hfc3SCaZYL7i1b1K/cDfWFONC4iDMfSzx9Jy7i+vqAQ4eCuSjKId7T9I61blZMweAiznzM\ntPGVGHxmqWc49Paq94mbmsFiEoBRLO9r3NQ0Aw/tKD6YdvDgQZx99tnYvHnz0Ne2bNmCpqYm1NbW\nYsqUKVi5cmXO33nyySfR2NiImpoaXHjhhfjNb34jfv/e3l4sWLAA48ePx9ixYzF//nz0Fpj8V72+\nSvpm6lfuhrpK6sUl4iLOGGJwgCWe5qivV+8FF3FEQ3jymtnEIA4zbTxT9FqMjGGbcUu+Ed8HJgGY\nj5uavmKVgKyoYNrLL7+M2bNnY/fu3aioqAAAHDp0CFdeeSVuueUWHDlyBO3t7Ugmk0PBtk2bNuGO\nO+7AD3/4Qxw5cgStra24+uqrcfjwYct/46tf/SreeecdvPXWW9i1axf+9Kc/4Wtf+1rxPxkzbXzH\nxUGIscTTdyz1NJ+4GwqwfYApYr6IMx4zbXxne2hHXx+QSPh/UVQY55ZAWI4XJgEYwbZ9AOd+c8Q8\nCcBxMO0HP/gBWltbsWzZspyv//SnP8WECROwcOFCVFZWYs6cOWhtbUV7ezsA4IknnsC8efMwe/Zs\nVFVVYdGiRZg4cSJ+9rOf5f0bR48exerVq7F06VKMHTsWEyZMwPLly7F69WocP35cvLbWj7Xmf5GZ\nNp6RenHZ4iLOCGIAh5OSOfTg4iLOTGymHgjx3hXzRZwJbE+IBlji6ZGi12KdnWqsxPzktaCxfUBI\nsNTTXKmUupfV1wd9JbFjef+KeRKA42DaFVdcgd27d+PGG2/M+fqOHTtw7rnn5nxt+vTpeOONN8Tf\nnzFjxtDvZ9u1axf6+/tz/vz06dPR19eHN9980+mlKsy0CYS4SOAizlzsOxCYgk27KTC27w2bqZsj\n5os4o7HEMxCsEggpbmoGgqWe5rJtHwBk5n8KVsyTAEY4/YOTJk2y/Hp3dzdGjx6d87XRo0ejp6cH\nANDT04OaYbuS2b8//HsByPnz+ntb/XlbnJCIcohHS3M31DyTJwO7dqlMTr4v5mCmTaAs72HJJLB4\nMef8AImBG5Z4mmfyZLWp2dLC9yUgluNFb2qyxNMcHR1AXV1sAwRG04d0cVPTd+J8X1OjxkoqFbv3\nxXEwTVJbW4t33nkn52tHjx7FmDFjAKjA2NGjR3N+v7e3FxMmTMj7XjqIdvTo0aEgmv67dXV14jUs\nWrQIY8eOzXzht7/FvIoKzDv77OJ/ICqL7W5oTY26AXIRRwTAZrx0dqpFHDM5A2PbTJ3tA8xSXx/b\nRRyRFXHzTM8tPLQjENzUNJP4vlBgxPdEl3hyU9MsTU3Axo1qnRzQOmzNmjVYs2ZNztekHv1uKjuY\nlkgk8Nxzz+V8raurC4lTvbESiQQ6h00OXV1duPrqq/O+19SpU3Haaaehs7MTF1100dCfra6uxjnn\nnCNew4oVKzBr1qzMF+rqgNNO46QUIMubYFsbsGQJsGVLQFcVb5aLBGbaEDnHTJtAiQ86em4JcBEX\nV+IDD5upm4tZnObhe2ImVgmYhyWegbOc9w3I5Jw3bx7mzZuX87Xt27fj/PPP9/TfLeo0TyvXX389\n9u/fj4ceegj9/f146aWXsHr1atx6660AgFtvvRWrVq3Cpk2b0N/fjxUrVuDgwYO47rrr8r7X6NGj\ncdNNN2Hx4sV47733cPDgQSxevBg333wzRo4c6fyi2HfATDGvqQ6S2P+JPW0Cx5PXzMI+diHEucU8\nzLQJlG3fNB7aERjL94XN1AMnjhf2e/ZUKiXfhsT3RM/z3DgjQ5QdTBs3bhyef/55PP300xg/fjy+\n9KUv4eGHH8Zll10GAGhubsZjjz2GhQsXYty4cVi7di2effbZobLMZcuWDWWxAcBjjz2GxsZGnHvu\nuZg2bRqmTJmCRx991PkFsZm6EcSbIE/HMQszbczFRZxn7BZwokRCBTeZaRMoBjvNwhOizcaT10KA\nmTZEOTjPm02c92OaBFCRTqfTQV9EqXTq3rZt2zJlnrq32qnDDMgwqZQqx6mpAfbvD/pq4o3vhfl4\nP/OEDqQVtbHJ98IIYllhIqHKcRobmQ0VtJYWlZk2eTLfi4CJ40VXe5w44fMVxZNtTy6+F0YQ36OG\nBrXxvHQps6F8wnk+xAxbK1vGilxWds80IglPXjMcm6mbj4d2eEJaD7MJsfl4aIc52EzdfOJ4qa72\n90LIGpupm489OX0n3rf27FHjhXNL4Lhezii7zNMobKYeDtXVXMj5SEyXZomnMcT3qK1NvUc8tCNY\nbKZO5BxLPM2nN9FiVo4TFLEsiiWexhDfo2RS3c8OHfL/oojCJoalntHKTGOmjVHEaHVzs9q55uk4\nRPaSSbUjysbqvhDvWcy0MYq4I8pMTl+JJ0Qz08Yo4slrI0eqkikKDpuph0N9vXqvUim+Vx6zLfHs\n62NPdEOwSiAjWplpzLQJh44OdUPkIs4XlrttbKZuFNuT1wD1XvHQjuAw0yYQRR8UwUzO4DHTJjxY\nJeALNlMPD9sqAYCHdgSJJZ5kqOgE01jiaSRxYuIiLliclMKjuVm9V+3tQV9JpIn3Kp1pU1/v/0WR\nJdtyHICZnD4Qx8uhQyrwzOwNY4jjhaWeweKmZniw1NM3BTeXyXfSxqa4DtCxmJgkAUQnmKZLPJua\ngr4ScoKLOM9xNzR8LN8znWkbo5RpozDTJjDJpP1hEeIirq8v0+eO/MWNsvDg3OILMTjATU3j2AZy\neG8LTkuLmteZMBMOMcvkjE7PNJZ4GkmclNivIzjsO0CUR7xX6Z1oZtqEgz55jaWenrIcL3pzjH1r\njcOT14jKxH7PnhPvU1u2qGd8bmoGQlr+ivNJMgksXhyb9ijRCaZR+HCHx1M8Wjp8eGiHYdhM3Vi2\nizge2uEp8YFHb45xUzM8eGiHp8SxojNt2D7ASDy0wzA8qMNY4j0uRs/40SjzvOEG9h0wmFiOo98v\nluMQDRFLPXloh2fEexRLPMOLh3b4j2VQxhLL13hoRzCYaRNOvMd5yvI+lUrFJsMpUmLUzikamWn7\n9jHTJoxuu02lgb74YtBXEjncDY0gLuD8xxJPo4n3OZ3J2d7O984DLPGMEGZyekrMoGWmjdHE923y\nZFXdwUxO/7S3q/Uv5xYjsZ1TVDLTyGi2J68xQOAv7oYaTxwvzOT0jPiacxc6nNhY3X+7dqmNGj5g\nGouHdvhLfL3ZTD0ULN+/225T7x2TAFwnjhc9j3NuCZ+YrKGjEUzr7+ekFFYxOz7XL2JwgLuh4cVF\nnL+YaWM825PXAJZ6liiVsn7ZbE+IjsmiOZJY6ukvbmqGF5MA/MUSz1AQ1wYxKfWMRjDttNM4KRlO\nHGhNTepGGZPjc/0gvtaclELD8j3kIs4T4nhhpk24NTer8dLeHvSVRJ/OaGLfWqPZVgkALPV0GTc1\nw41VAv6yfL1Z4hluMen3HI2eaQAnpbDq6ADq6riI8wMnpfBjvw7/MHAZGuLJa3V1LPUsgbScErMA\nX3xRLZhvu827iyJXiH0GgUwmJ9fT3tGbmpxfwov9nv3DEs9QsK0QiMG9LhqZaaNGBX0FVIBtOQ5L\nPV0lvtaclEJDfA9Z6uk6y9eamTbR0NfHbFy/VFczCBNmzOR0lZjxzE3NUGGVgPds2wdwDg+/GGRy\nRiOYdsstQV8BOcBSzwCxxNNYUn8iS1zEuUq8JzHTJlTE4HNjoxovEV7E+cW2fQDAvrUhIY4VHtrh\nD25qRgOTALzX0qLm78bGoK+EHIrroR3RCKa1tgZ9BVSOjg51w2SpZ9m4GxodtievAVzEeY2ZNuHX\n2al+jfAiLnDLl6uFclNT0FdC5WIWiGssg5bc1DSWtKkpBp+ZBOAa8TXW87aexymcYpAEEJ2eaWQ8\n25pq8hZ3Q41VdLymqQnYuFEt4hjsKYvlPYklnqEk9oLiw6srxPm7t1ctlDm3hIY4VhobVaPoRIIP\nsF7gpmZ0sN+zKwr2cKRQEdcJEe/3HI3MNAq/yZPVjTPix+d6Tdzh4Y5z6BQsxzl0yN8LiguWeEZL\nfb16gGUmp/tY4hktnZ1qrLDUsyxiVjk3NY2VTMp7k6wSCABLPKMl4qWeDKaR7ywnJi7ivMNJKXqq\nqyOfNu2lgg1vAWb9hYwYfG5rU7+yHKdk4nhhiWco2R4IRd7hpma0sNSzbCzxjB7x0A4gsvc/BtOI\nIsK2mTrASSmExPdUl4kwk9NdqZQKUtbXB30lVAJxEdfXx0xOL7DEM3pYJVA2ywCBfj25qRk6tlUC\n7PfsDQaeoyfCVQLsmUa+s62pZr8O93FCip6ODmDkSDVeqGjiPUjvMOtsJoqG+noVTItovw6vMYsp\nmiz7FXV2ql5QrBJw15496kGSa1siADb90nTfWm5qhpK4XmhrA5YsiWS/ZwbTyBydnQwQlMHyBsbd\n0FCzfYhlmaf7dPZSxCb6uCi4iNuyxd8LigDxgSeRUBs1nFuIABRopk6hJr63TAJw35Ytan3LTc3Q\nshwvySSweHEkEzxY5kmBEMvX2AvKXdwNDT2WerpLfD1Z4hldOjjKchz36E0vzi2hJZav1dSoBx6d\nIUI5UqkiK5VaWtTryYM6okff/5gEUDTx/qPnaW5qRo9+vo9YqScz08gsET8+txz63mM1v3BHNIZY\n6ukulnhGgu29sK9P3Ui5SHdMnFO46RVdbW0qgyCiJ695RRwr+nXk3BJarBLwSSql5mm+pqEmjpfm\nZnU/bG+P1DqMmWkUCHFHIuLH5/qKu6GRwJPX3CW+nocOqfESoQmesuhMTp68Vj6dDatfUwot8dAO\nPsyKksn8acL2hGhAvZ6cW0JNfI8nT1a/MpPTMfG1bG9XY4VzSzTpJJmI9eRkMI0CE8fjc8thtYAb\n+j2rAAF3Q6OvsVEtPLiIK49O++QDZOjx5DX3iA88u3apOZrZ49HFAIE79OunX0+KHiYBuEcHWTi3\nhJ7tBoOuEogIBtPIc0X3l4jw8blu425ofFi+17pfBxdxjnA3NOZ0li7nFiIArBJwi/g6vviieh1v\nu83/iyJXie8xkwCKZvla6hJPirYIVgkwmEaBEScmnUkVoYHmO/2wyBLP6Ovr4wKkXNwNjRQxaNrU\npMYK5xZHLOfolhYVeOYpnqEjbWyySsBD+vXjpma0sUrAMXF+1vMyNzUjIU5VAjyAgDxX9Boiwsfn\nuk3spbV8uXr9OClFhvheNzbyaHaHxNeQ95p40Id2HDoU9JUYTzzIQWcq8V4TffX16oGHh3bYEseK\nPiGam5qRYvl+d3YCdXXM5CyHnpe5qRkZcTkcj8E0CpQ40Orr1Y2Vp3qWhpNSfHR28lRPB8R7jW6m\nzkybyODJax5i4Dm07HquWmprA5YsUZtzDKYVjydExwvvjY7whGjC5MmRSgJgMI3MxKPZCxKDAzrN\nvL7e5ysir4nvORchpdNByAhM6JQhjhV9NHtEFnFesXzg4dwSL6wScEQMDuhNTQYiI0V8v5kEUDqe\nEB1J4liJWBIAe6ZRoGwbejJAUJotW9Rrx93Q+ODJawWJ95rqat5r4qSjQwUHIrKI8xVPiI4ssY+R\nvjfy0A5L4uvGwHOkWb7v+r7IJAART4imIRGaWxhMo8CJN1eevGZLDA7opo7cDY0cnrzmMu6GRpY4\nVshWwePsAc4tcRLBk9d8wcBz/PDQjtLoZzxuakaSuKZoblbveXu7/xflMpZ5krmamoCNG9mvw4Jt\nw9u+Pk5KEWb53rMcx5Y4XnR2EndD44WHdhRPn+Kps2ApUsTAMw/tsFXwUBuuXSPJ9kCoPXtY6imw\nfN14imc8dXSoQzv27An6SsrGYBoFjos4F3FSii/26yDKI568FqF+HW4S52Oe4hl5tgdC8VRP53iK\nZ3zF/FRPnWQ2/DZhe6ojD0yLNNsKgYgkALDMk8ym+02wF1QOsYSJk1Lkie99zPt1pFJyRbjla8ZT\nPOONvfKK09cXmYUvFUnPLSz1zCGWL/EUz1gQ33/eK4vDuTjyxLGi1996PR5SDKaREcSBFvMAQVHY\n8Dbe2K/Dkm3DW4CZNhEnBp919m7IF3FuYjP1eLM9EKqvj1UCTh06pF4vZvHFU4yTAJJJ64+9eG9h\n39p40+vvkFcJsMyTzMZeUHnEdGk2vI0N8TMQ434dRT23sOFtrFiOF91GIOSLOF9wbqHqarUOY6nn\nEMvgAOeW2BDL19ra1HMLkwAKY9/aWIj6YVDMTCMj2J68FuNdnqJVV3OhG2d6l4eLuCGW9xb2FiTK\nI87DbKYeG7YnrwEs9TylYIkn55ZYsPwcsEogh+0J0UQRKPVkZhoZxTKDoK0NWLIE2LIloKsyi+1u\nKBvexkIcGnp6ir0FI0lqfmx78hpP9QRgk+3K3oIE8EAopzi3EMADoZzg3BI7UT0QiplpZD79ZNTb\nG+x1GMB2N7SvD2hq8v+iKBDiZ4GZnAAK7Iay4S0BkenX4Sn2FowVVgk4Y/k6sbdg7PBAqMLE14hz\nS2TZHQhmSa/Hi/pL5mAwjYxiu5DT/TooH3dDSeMizh4b3kaW1PwYsAmuhnwR5xbbuZdix3K8cG6x\nx96CpLHUE4DNvMvegrFkeyBUdTXQ3u7/RbnAtWDazp07cfnll6O+vh6TJ0/GsmXLkE6nAQDPPPMM\nPgSJQGMAACAASURBVP7xj2PMmDE477zzsG7dOvH7DA4Ooq6uDrW1tairq0NdXR3GjBmDY8eOuXWp\nFEbs1wFAuBFxUool25PXgNgv4mx3Q/v6GHgmhXOL/MDDMhzKxrkFgM14YW/BWBI/DxHoBeUZ9haM\ntKI3NvV6fM8eby/MI670TOvp6cHll1+OuXPnYt26dTh48CA+85nP4OTJk7j66qtx3XXX4V/+5V8w\nf/58vPLKK7jqqquwfv16XHbZZXnfq6urC/39/ejt7UVVVZUbl0chY3vyGvt15OOkFFu2p3rGuBeU\n+LpQbImfBc4tMpbhxJZtqSd7QeVjiScNF4FeUOUS7yOspqHhQrxJ40ow7de//jUOHjyIRx99FCNG\njMBf/MVfoK2tDXfffTeOHj2KSy65BAsWLAAAXHzxxbjpppvw+OOPWwbTXnvtNXzsYx9jII3yxXwR\nJwYIOCnRcFzEWWOmTayJ99Dq6kwbgRhmlTDoTI61tQGLF8e61NNyvLDEM7Z4/ywSq2liLYoHQrlS\n5jkwMIDq6mqMGJGJzVVWVuLAgQP485//jNGjR+f+o5WV2Llzp+X3eu2113Ds2DFcdNFFmDhxIi67\n7DK8+uqrblwmhQQbehaBu6Gxxj5H1tjwlorCUs98DDzHnmU5ToxLPW0PtWGJZ6yx1DOf7YFpAKtp\nKFeID4RyJZh28cUXY9SoUfjmN7+J48ePY8+ePXjggQdQUVGBBQsW4LnnnsPPf/5znDx5Ei+//DKe\nfvppHD9+3PJ7jR49Gp/4xCfQ0dGBP/3pT7jmmmtw+eWX449//KMbl0ohwUVcPssAAXdDSRLTRRwb\n3pJEDLLqrN6IlnranawljhcGnknCUz1zMfBMkhAHCDzDaprYi9qBUK6UeX7wgx/Es88+i7vuugtn\nnnkmGhsb8bd/+7fYtm0bZs2ahX/7t3/DP/7jP+KLX/wiLr30Utxyyy145ZVXLL/Xd7/73Zz/vvvu\nu/H9738fGzZswFe+8hXLv7No0SKMHTs252vz5s3DvHnz3PjxyCQxLPW07f/E3VCC8BlhqWeuJUuA\ngQHuhsYcSz0dYOCZYFOOE9NST/H1YOA59mwrBGI6t1i+JqymITvNzcCGDWq9XsJYWbNmDdasWZPz\ntcOHD7t1dSJXgmn9/f3o7+/Hi1kT6yOPPIKPfexjOHr0KGbMmIHXX3996Pc++9nPoqmpyfJ73Xvv\nvbjhhhtw3nnnDX3txIkTeaWi2VasWIFZs2a58JOQKWwXcffcA2zc6O8FmYiTElEe8d6hT4SOSRCe\nitTcrOaV5csj98Bj9+NYjheW4dAplsHnZFIF02JUJcBDbcgJy89JhOcWiTheWE1DKHAg1IgRmfV6\nkawSqbZv347zzz+/pO/nlCtlnoODg5g7dy5WrlyJdDqNbdu2Yfny5Ugmk3jzzTfxiU98Aq+//jpO\nnjyJH/3oR9i4cSO+/OUvW36vHTt24M4778SBAwdw4sQJLF26FN3d3bjuuuvcuFQKEbHUs6pKZZiE\nLA20VGJp0saN6nXgpBR74mckpqWeeVpa1H1j/Pigr4QCFtdSTytiqQXLcKgQzi0KSzypkBjOLSJW\n09Ap4vpj/Hi1Xg9RGwFXgmkjR47EunXr8Nhjj+GDH/wgPve5z+Hee+/F/Pnz0dTUhO9+97u49tpr\nMW7cODz00EN45plncM455wAAfvWrX6Gurg5vv/02AOD73/8+/vIv/xJ/9Vd/hfHjx+OXv/wlXnjh\nhbwyToqxuXPVQItBs2jb/k8DA+p14KQUG0X3PursVJ8T4cCXqBHHC3dDKYv4OWEvKJZ4Ug4x+Byz\nXlA81IaySWsx8XMS0l5QpbJ8HVhNQ06E8LBBV8o8AeCSSy7B1q1bLX/v9ttvx+233y7+ve7u7qH/\nrq+vx8qVK926LAox2zTQkSPjvcuzfLkKpM2dG/SVkOlGjVIp0zHqM5iHu6HkRMzaCLDEk5xgiSNR\nmWJU6skST3JCnE9C2EagIp1Op4O+iFLpOlh90AHFSF2dGmj33Rf5icnSyJHq1xMngr0OMl8qpSam\n6moga+MiNhIJlT3Q2MjsASpsxKk9xpMng70OD9kGRzi30DDi5yWRUFnP48cD+/cHcGX+sP35ObfQ\nMOLnJSb3VvHnj8HcSsURPysNDSphZu7cspMA/IgVuVLmSeQVsRxH75pHuNTTtsQTYBkO5RH7DAKh\n2uUphTheWIZDxZg2TWX9xrEXVCKhysJZhkNZCpZ6vveevxdkip071Xjh3EJOVFerz0vE2whY3i/0\nfMq+teREyEo9XSvzJPJVnEs9WYZDxaqvV2MlbqWe2b0FiYax3BXt7FRzS4R7QYnlFfpnZhkODSNm\nEIwfH/m5xXK86IDIqFH+XgwZT7y/NjcDGzaEJkBQCvE+sWuXWodFOIOVilew1PPYMbWON7wCjZlp\nZDRxRxSI/C6P+LO/9576uSO6cKXSiZ+Ztjb1mYlwLyjLn10HnnnSGhUjws2ixQxOjYfaUDFClkFQ\nLNtDbaqqgKVL/b8oMp7l56ajQ31mIl4lkIfVNGRDvMfqdXsIKtAYTCPjFSz1jOAiTvyZuRtKBYil\nnlVVKqAWpwDBoUPq52YZDlkQg8/NzWqsLFni/0UFRZfhMPBMFsSxEoE2AnanZIt4qA2VQt9fI9pG\nwPI+sWSJmk9ZTUPF6OxU6/cQVKCxzJPCS5d6hnQRpxdvRa3FuBtKpZo7NzanSQHgbig5YlmW0tGh\nmiUfOxbQVXlHzPTeuVP9ysAzCcQSrvp6lTGfSETu82M5XnRvQfZ/IoF4n+3sVHOLvt9GhO2hNnoe\nZTUNWbA9Jbq6Wj3jG17qyWAaGc92oDU2qkmpoSFStfiWP3MqpSYlluGQDXG8RLjPoOXPzN1QKkcE\ne0GJDzw68MzeglSKtjbgnntC22dQWk7Z9n8C2FuQbImfn+wqgaiv5Vta1M/LQ22oAMvxovsMLlli\n9FhhMI1CQ2wWPWJEKE+TKnoBx/5P5JD4GaquVgHZiAQIuBtK5RKDzzpAEOE+g0OWL1cPPHPnBn0l\nZLCCzaLjQAeeR40y+uGODBbBKgHx3rBxowocMvBMpQhJlQCDaWScossfR40KRRpo2dj/icoVg9Ok\nAKgynKoqBp7JEcugbDKpgmkDAwFdlfvEBx69GcXAM51itw6zHC8RrBJgxjOVw7ZKIKRJAFZsM571\naepRfjYjV4jjZdo04+cWBtMoNAoeN214GqhT4jHsAwM8eIAcKbiIM3yXxyn2fyJPhWAR55T4wKMb\nYbP/E5UjxFUCVsTxwoxnKoL4ORo/PrJ9Bocw45mKFNYKNAbTyDh28bCoN4sWJ16dScTdUHIo6os4\n9n8itxRsFm3wIq5sOvAc8mAhuctuHSaOl4jMLSIGnskt+/dH5iACZjyT5wyvQKsM+gKIXDFtmvq1\noSHY6/BC9sEDnJSoXPqhOQKLOEtLlqhfuRtKRUi9mhoK0OYYP17de1ta/L8oFyVnJxl4JtdYjhfd\nFymkBxFksxwvPHiAimT5OdL0fTdlMe+EhDhv6vmS1TRUBHG8NDer6iy9vjcMg2kUKnqQ5d289S5o\nyDMILG8kPHiASmC7iNMLnBAHCMSfj2U45Ka2NrWIC/FBBOIDDwPP5KZkMvekwhASxwr7P1GJxM+U\nvu8aGiAoi54vly4N9joodCzHS0cHMDgI9PYGc1EFMJhG0RHiAIE42QKZAGEUyybIc5afLb3ACWmA\nQBwvOjNVZ6oSOSRu1OgH5/7+0AYIRAw8U4nEzQy96ac3AaOCm5rkNn3fDXGLGjHjub//1B9g4Jlc\nMnEiUFmZKbc3CINpFDq2aaCDg6ENEFhKJNTPxFRpcpNe4Bh8UmEqVULs4t131Xhh4JncNG2aWsSF\nNEBgOWcy8ExlstzUCHGVgNiHE+CmJpVM3KgBMv33DAwQaNJaTNzUXL5czZecW6gE4jO+wS1qeAAB\nhZLtQQQGBwgkYjnerl1qUmKqNJUozMdNWxEfeFpa1Fhh4JlKFMWDCMTx8u676lcGB8ht48erz1fI\n5hZRQ4PapJk4MegroahpawPuvtvIAEHJGHimMonrllGjVCZnS4tRGfUMplG0hDBAYHsqIXt0kAvE\n46YrKzMP1YYp+iPPHh3kAttTcKMSIGDgmVwgBp/37zd6bpHYnkpYWRn+cU+BET9byaTqmWZggECT\n1mKWPxMDz+SlpUuBe+4xrgKNwTQKJdsMghAu4iwtWaImJaZKk1cmTgxdgMBy7OseHZWVDDyTN/bv\nD2V2muV4YeCZXCIGn2tqjA4QDGeb8Qww8EyusPycGRogsCOOFwaeyQW2wed77sn0sDVkvc+eaRRa\nYr2+3hHRPWEMZpuV1turJiWmSlOZxJ4dOoMgJAGCgqcSMvBMRbDqBROVU3BtTyVkc2jyWsgPuRmy\ncaPa1GxuDvpKKKqicsgNA89UgqJ78ul1vkGn4DKYRtGjd0TCnJ3GBp7klxAFCCwx8EwesFzIReGQ\nGwaeyUVROQXXMoje0qKu/7TTQpFdR+YTN2sMDBBIxCQABp7JD7oCrbfXmLmFwTQKLdsTcmpq1K8G\nn5AD2EysbOBJLovCKbiWPwODA1SiZLLI5KyODrWIMzxAwIxnMkJIAgRiBoSeE+fO9feCKNLEU3AN\nCxAUhYFnKpG0DrN9xjdsbmEwjaJJlxgYfEKOuIBLJFRwg6nS5DLLz1yIAgSW46W3V/3K4AC5SFzI\nGbaIKwoDz+QBcaMmzAGC7D6cDA6QH0Iyt1iOdwaeyU96va/X/wFjMI1CTVzEJZMqO21w0PjstDxd\nXUA6zebQ5J+QLOLy6L6IPDmK/BKCAIE4LzLwTB4JU3+bbGIWJwPP5BFxo8awAMFwBftwMvBMLrPt\nYWtQf3QG0ygSLG/yBmeniQu4hgYVSKutZXNocp3tIq6yEujpMa53mh7blpOq7ovIk6PIAyb3tym6\naS8DzxQEwwMEIgaeKQgGBQgcY+CZPGa5rjGoPzqDaRRdyaSq3x8cNC5AIHr3XaCiAujuDvpKKG70\nQigEvdMAMDhAvhD72wAq+GxodloeBp7JQ7b9bQwOEFgGzDm3kMfEjRqDAgTZbPtw9vSo/8/AM/nN\nkP7oDKZRJIgLOV2/b1iAwHIi1TcDfXMg8oBtf5uqKqN6p4kLOIDBAQqWftAOKDvNqmmvOF7q6lTG\nM4MDFAR9jz5wwJi5BRAC5amUuk6Acwt5SswirqlR92sDg8959PzHuYU8JD7jG1KBxmAaRZturt7X\nZ0x2mjiBslcauazoUrBp09RncPFi7y+uHLocmoFn8pi4iNu/XwWfDe6dNkRnDjA4QB7SGzWW80vA\nwWfHGBwgD0hrMUv6GcCQ7LSCp0NXVXFuoWDo/ugDA4EGnxlMo8goePqaYdlpORIJFRyormavNAqO\nzk4zJPgsZtHpRSYDzxQkg4LPtllpADBpks9XRJRl/37VwsKA0mixD6cuWauoYHCAPCc+syST6n6d\nTmfu3yZavFhdI3ulkQ8KZqcFGHweEdi/TOSXzk5g5EgVIEgkAqvrty1Z6+pSv953n49XRFEnxWWH\nT0o5n8mrrgLWrwc2bPD68mzxkA4yRXa2Tc7nUR/coYPPJp5kxqw08pnt6WsHDqjsLxPv3cxKI48U\n/XHPDj4HzHI8p1Jq3quoYK80ClYyqe7dPT3q+SCAtQ4z0yhSxEwW3TtNB61MovvZMDhAJujoUNlp\nAwNGZKflyO5nw6w0MsGMGerXADOfmZVGphFPXws4O00cKy0t6rpYskY+sj24Q9+3A8xOE1uC6Gxs\nPf8R+aBgdlpAfTkZTKPIsbz5d3SoEsqAmnraZqXpnSee4Ek+Eyemq65SvwaUnSaOF72AmzSJgWfy\nlThWDCuNHqKDAyxZI5PorC8PS6OL6k+l6bmOJWtkiuzgs0lzSyKh5ruqKmalkRl0aTQQSNsNBtMo\nPnQJpYeR66IXccwcIBNlZ6cFfOT0kJaWTFkBgwNkEv0A7nPwWez9lH0tzBygABQ8uEO33fCRuEmT\nSKi5jsEBCoDtwR36/h3AxqY4XnSFj950JfKR7dxSURHIxiaDaRQ5BZt6Ar5Grm0XcMwcoIAVzE7z\nuTRaHC8MDlDAxIeezk6V+RzwiVJDGBwgk3k8tySTRSYuMzhAptKZz6ZsbOqetdXVZvYIpXgLKPjM\nYBqFWtGZYB5HrotaxOkFHIMDZKKODtXHL51WB3gEqaFBLSarqxkcIDP5kPmczdGBNgwOkE+s1mLi\nRk0AbTcKHmjD4AAFzKSNTcvxkt2zloelUYAKtt3wOfjMYBpFkm1TTx8j144WcAwOUMDE8aL7+PmU\nNs0FHIWB5XhJJlXwGQikZ8eQkSMzB9owOECmyg4+B9UPqqWFcwuZLzv4HOBhBEPzGg9LI5Pp4POO\nHb4dRsBgGoWak0wwy5IcHbkOoiSHwQEKk5kz1a+/+IVn/4R4YhTABRyFR3d3ph+Uh3OL7YmEfX2Z\nayHyibQWc9R2w8O5Jfvfzhsv+t/lgTZkCHG86GeFnh5PM27EPpwNDZlDBzi3kAHEthu6qgbwbWOT\nwTSKLMumzJqOXHu4Kyou4O65R/3K4AAZxDZtWu+KelzumTdm9alRFRVcwJFRCpbkBHFEuw4O6AA4\nkSE2/XGTfBhBEBk3dXXq362qYs9aMl8ymbmvl1nuKbXHSb2awqY/brL+CzoBgK0DKAy6uzMtnW64\nwfN/jsE0ijzLh56ODk93RcVJqa4u0xiawQEykOVDz4kT6tcyTmCzW8ABQpbNjh3q/7OvIIVF9q6o\n3jhxkThedHknDx0gwyRnJ/HJj3zS+je/8x31a0+PJxubluNFH/6U/e8TGSLIjc1PfuST9gkAbB1A\nhhHHi35u2L3b82tgMI3iK3tX1IOJKW9S4gKODGf70KN3Rf3qQ6CD3LW1DA6QkWx7DVZUqI0TFzNu\nxE0aXYIDcG4hI4klOR6Ve9q2DtCbNDNnsjqAjGW5sanLPctoJWBVkm27ScMEAAqBvPHS2ZnZ2PQY\ng2kUC+JDj37w6Otz5aFH7DeQnWXD/hxkMPGhR++KAsDddxf/fYtdwOksGy7gyAB2J0dbPvR85jPq\n154eV/un5W3SZDdRv+Yazi1kvLzxsn9/JuNmxAhX/628tZj+/jz8iQwmbmwmk+o+D7jWpoabNBQm\n0snRluOluxu46y7Pr4nBNIqVvEVc9sRUZmNPcUICgPXr1a+1tezPQaGRN15OnMhkc5b50GNbCs0F\nHIWEXsTljZWOjkw2pwsPPZaB55aW3LmFJThkOPGhR7cSGBgou1KgYJZN9r9HZCgxCSC7lcD69a5U\nCthu0jCDk0JAHC+trZ7/2wymUWzYPvToMoMdO8p66LHsN1B5apgxy4ZCRBwvJ0+qX8t46NGBtLzx\nUleXKYVmlg0ZxO7kaDFAkF1msH697dxi11PQslwtlcoE0qqrObdQaIgPPXpjs6+v7Lklz8iRmU0a\n/e8QhUTePKBPjgZU5k2JATVHmzTM4CSD2K3FAKFSwGOuBdN27tyJyy+/HPX19Zg8eTKWLVuGdDoN\nAHjmmWfw8Y9/HGPGjMF5552HdevW2X6v+++/H2eddRZqa2sxZ84cvPnmm25dJsWcGCDQZQaAmkSK\nzFATd0IrK1UWD5AJQlAkrVmzJuhLcJ0YJMh+6CkyQ00MpDU0ZAJpkyYxy4ZCxbZ/WvbcUsRmTXZg\nIK9cTZcuVFXlZNlE8T5E0WS5sVlGQE2cW4YH0ji3+IL3ovJl3/ctNzbLCKhZBp6zA2ncpKGQEZ/x\nPeZKMK2npweXX345PvKRj2Dv3r341a9+hbVr1+Jb3/oWtm/fjuuuuw533HEHDh8+jEceeQTz58/H\n5s2bLb/Xk08+iYcffhjPP/883n//fZx//vm4+uqrMaBTs4nKlB0gyNntOXEi89CzY4fjPjeWE1Iq\npRpQ60Dagw+Wf+FktCgsHKVeBIDFabj6oWdgIJN9afe9s8aa5cOOLimYNIml0BRqeVkEujwaEDdr\nhu+2ioGBlhY1twDq12GbNFG4D1H0OSqPLmKzRhwvlZWZQNrMmQyk+Yj3otLY9YSyDKjp+eCuuxxt\n1ohrsYaGTCBt2CYNUVgMf8Zf9foqz/9NV4Jpv/71r3Hw4EE8+uijGDVqFP7iL/4CbW1tePzxx/Hj\nH/8Yl1xyCRYsWIDKykpcfPHFuOmmm/D4449bfq/vfe97+MpXvoJp06Zh5MiRuO+++3DgwAEx+EZU\nCnG3JzugduBAwYWc5QKuoSG34WE6zXI1Cr1Nf9yEljUtmbGSHVBLp9WCTljIiRk2OuisH3YYSKMQ\nc5xFsGOHbdaNbRm0ftipqAAGB93+EYh8IwYIOjszAbWBAfVZt8m6sRwvwzc0r7mG5WoUauJ4GRzM\n3ayxOUxNXItlb2hWV7OShkIt+7O9de9Wz/89V47NGRgYQHV1NUZkBR4qKytx4MAB/PnPf8bo0aNz\n/nxlZSV27txp+b26urrwd3/3d5kLHDECjY2NeOONN9Dc3OzG5VKM6fWYjm1lP/gM/feJE0O9mwZP\nLeQqhz3kZ09IQwu4VCo3iFZVxQmJQsWuJxSQGSdDZc0dHbmf+/XrVSZA1kO+o9IbQD088WGHIiB7\nvOR89k+ezPQF7OtTD/vDPveW4yW79Abg3EKRkT0n5Hz2Oztz5pbBu+7C4N33YMTgyZw/r+WMlxEj\nMgcNAKoygBuaFBJ2H1XLZxZAzQf6c9/To+aWrJJmy2cWQGVJ79iR+Qdqa1naSZGRnJ3E2//f2/gl\nfunpv+NKZtrFF1+MUaNG4Zvf/CaOHz+OPXv24IEHHkBFRQUWLFiA5557Dj//+c9x8uRJvPzyy3j6\n6adx/Phxy+/V3d2NmpqanK+NHj0avb29blwqUZ7s3Z6hzJvu7tzSzAMHMFhRgZMVFfkLuP9niZq4\nsgNptbV82KFISc5OomNeR85irmVNC1KfgNr91zujp7LUBisq8N9n1eUGBnSJWnY2ms4eYCCNIiS7\n1CBvbtFZNwCwY4eaWyor8wNpI0ao8ZEdSJs5k3MLhV52KdvwfoDS3FKZHsDgsLll6O9/sT0zt+hA\nmj55moE0ipDhGWot96WQ+HYLUr/6TuYwNQBYvx6DFRXor6zID6RVVqqxkh1ImzmTgTSKnNaPeX+a\nZ0VanxJQpt/97ne466678Prrr6OxsRE333wz7rzzThw7dgzr16/Ht771LezduxeXXnopPvrRj+KV\nV17Bq6++mvd9xo4di9WrV+PKK68c+toFF1yA+fPn42tf+1rOn92+fTvOP/98PPXUU5g+fbobPwbF\n2KrXVw2lg15wxgUAVHroP/7fVzCmJ/fhRYxCV1YCr73m4VWSqRYtWoQVK1YEfRm+WfX6Kqz7rRov\nZ56Z+foD9+bvAInjZcoU4Omn3b84ooCtOtWmo7U1M7dkzysAsHzJLzHCoh2s5XhxOLfE7T5E4ZQ9\nPvJ+L2stBqj12KX/vA4f/n935/1ZcW659NKSTzgkd/Be5C3dC2rrNuDtga0567Dv3PvLvLEhjpUR\nI4AtW7y4RKLAPfjgH7Bq1eexbds2zJo1y5N/w5VgWn9/P373u9/hwgsvHPraI488gieeeAIvvfQS\n9u7di5lZO7Gf/exnceaZZ1reZC+++GJcc801+MY3vjH0vcePH49169Zhzpw5OX923759uOCCC7B3\n795yfwQiIiIiIiIiIoqAMWPOwM6dW3H66ad78v1dCaadOHECZ5xxBr7zne9gwYIF2L59O6699lp8\n+9vfxrRp0/CpT30KL7/8MmbMmIGf/OQn+MIXvoDt27fjnHPOyfteK1euxJIlS7BhwwZMnToV9957\nL/793/8dXV1dqNJlRFn27duHffv2lfsjEBERERERERFRBJx++umeBdIAlw4gGDlyJNatW4dkMolF\nixZh0qRJuPfeezF//nwAwHe/+11ce+21eO+99zBz5kw888wzQ4G0X/3qV7jyyivxhz/8AWeeeSZu\nvfVWHD58GNdffz0OHjyIiy66CBs2bLAMpAHev0BERERERERERESaaz3TiIiIiIiIiIiIos6V0zyJ\niIiIiIiIiIjigME0IiIiIiIiIiIih0IbTHv33Xdx7bXXor6+HhMmTEAymcTAgMUZ80REZVi7di1G\njBiBurq6of/pfpBbtmxBU1MTamtrMWXKFKxcuTLn7z755JNobGxETU0NLrzwQvzmN78J4kcgopA6\nePAgzj77bGzevHnoa+XcdwYGBvD1r38dDQ0NGDNmDK699lrs37/ft5+HiMLJ6l60cOFCfOADH8hZ\nHz3xxBNDv897ERG55fe//z0+/elP40Mf+hAaGhowf/58vP/++wCCXReFNph20003YcyYMdi3bx9+\n+9vf4oUXXsA//dM/BX1ZRBQxr732GubPn4/u7u6h/z355JM4dOgQrrzyStxyyy04cuQI2tvbkUwm\nhxaamzZtwh133IEf/vCHOHLkCFpbW3H11Vfj8OHDAf9ERBQGL7/8MmbPno3du3ejoqICAMq+73z7\n29/GCy+8gG3btuGdd97BqFGjcPPNNwf2MxKR+azuRQCwdetWfO9738tZH33hC18AwHsREbnn2LFj\nuOKKK3DxxRfjwIED6Orqwvvvv48FCxbg8OHDwa6L0iG0a9eudEVFRXrfvn1DX1u7dm36rLPOCvCq\niCiKLr300vSjjz6a9/Xvfe976alTp+Z8beHChem/+Zu/SafT6XRra2v69ttvz/n96dOnp9vb2727\nWCKKhO9///vpyZMnp9euXZuuqKhIb968OZ1Ol37fWblyZTqdTqc//OEPp9esWTP0ewcOHEhXVFSk\nd+/e7eWPQ0QhJd2Ljh8/nq6urk7v2LHD8u/xXkREbtm5c2f6yiuvTA8ODg59raOjI11TU5N+4okn\n0uecc07On/dzXRTKzLQdO3Zg3LhxaGhoGPra9OnT8fbbb+PIkSMBXhkRRcng4CC2b9+ODRs21+NG\nxgAAIABJREFU4CMf+QjOOuss3H777Th8+DB27NiBc889N+fPT58+HW+88QYAWP7+jBkzhn6fiEhy\nxRVXYPfu3bjxxhtzvl7Ofed///d/sXfv3pzfnzhxIsaNG8f7EhFZku5Fv//973Hy5EksWbIEDQ0N\nmDp1Ku6//36k02kAQFdXF+9FROSKqVOnYsOGDTmZsT/5yU8wa9aswNdFoQymdXd3o6amJudro0eP\nBgD09PQEcUlEFEHvvfceZs2ahRtuuAE7d+7EK6+8gl27duHzn/88enp6hu472ujRo4fuQT09PZb3\nKd6jiKiQSZMmobIyf4nW3d1d8n1H/xnel4jIKeledOTIEcyZMwd33nkn3nnnHTz11FP453/+Zzzw\nwAMA5Gc13ouIqFx///d/jw0bNuDxxx+3vdcA3q+LQhlMq6mpwdGjR3O+pv+7rq4uiEsiogiaOHEi\nNm/ejFtuuQUf+MAHcNZZZ+H+++/Hs88+i3Q6bXkfGjNmDADr+1Rvb+/Q7xMRFau2trak+05dXd3Q\nYtHq73PtRETF+NSnPoUXXngBl1xyCaqqqnDhhRdi0aJFWLt2LQDei4jIfUeOHMFf//VfY/Xq1fjl\nL3+JmTNninEhv9ZFoQymJRIJvP/++3j33XeHvtbV1YWzzjqLN2Eics3rr7+OxYsX53zt+PHjqKys\nxEUXXYQdO3bk/F5XVxcSiQQAdZ/q7OwUf5+IqFiJRKLk+87YsWNxxhln5Pz+/v378ec//5n3JSIq\nyrp16/Cv//qvOV87fvz4UOYs70VE5Kb/+q//woUXXoienh5s3boVM2fOBGDAuqiEHnBGuOSSS9Lz\n5s1Ld3d3p3fv3p1OJBLp//N//k/Ql0VEEfI///M/6dra2vT999+f7u/vT+/Zsyf9iU98Iv3FL34x\n/f7776fr6+vTK1asSPf19aVffPHF9JgxY9KbNm1Kp9Pp9H/+53+mx4wZk37ppZfSfX196VQqlf7Q\nhz6UPnToUMA/FRGFSXbT73LvO//wD/+QPvfcc9P//d//nT5y5Ej6pptuSs+Z8/+zd+/xURd2/u/f\nM5PL5DYBagIolyK5AEU9D/BYu0GtW7ViES9IbRCUjtFzantc/K3ttrsH3d1f7W912x631bVFdgSl\nSWuJysWu7doqhbSuLeyvXpCQiAtYCYkomYTcJjPf88d3vpO5JhOSTGaS1/Px4MFk+Cb5ZshkZt75\nXK4ct68NQOYI/1n03HPPGfn5+cavf/1rIxAIGL/73e+MkpIS4yc/+YlhGPwsAjB6PvroI2POnDmG\n2+2OWEJgGOP/vChjw7STJ08aq1evNs455xyjtLTU+PrXvx5z4wLASO3Zs8f4i7/4C8PlchmlpaXG\nX/3VXxm9vb2GYRjGH//4R6OqqspwuVxGWVmZsXXr1oj33bZtm7FgwQKjsLDQuPTSS43XX399PL4E\nABks/AWsYYzs547P5zO++c1vGrNmzTKKi4uNm266yWhra0vZ1wIgc0X/LPrxj39sVFZWGgUFBcb8\n+fONJ554IuJ4fhYBGA3f+973DJvNZhQUFBiFhYWhP0VFRYZhjO/zIpthBNeuAAAAAAAAABhURs5M\nAwAAAAAAAMYDYRoAAAAAAACQJMI0AAAAAAAAIEmEaQAAAAAAAECSCNMAAAAAAACAJBGmAQAAAAAA\nAEkiTAMAAAAAAACSRJgGAAAAAAAAJIkwDQAAAAAAAEgSYRoAAAAAAACQJMI0AAAAAAAAIEmEaQAA\nAAAAAECSCNMAAAAAAACAJBGmAQAAAAAAAEkiTAMAAAAAAACSRJgGAAAAAAAAJIkwDQAAAAAAAEgS\nYRoAAAAAAACQJMI0AAAAAAAAIEmEaQAAAAAAAECSCNMAAAAAAACAJKU8TGtra1NZWZn27NmT8Jgf\n/ehHWrBggVwulyorK/XEE0+k8AwBAAAAAACA+LJS+ckaGhp0xx136MiRI7LZbHGPeeGFF/S3f/u3\neumll3TJJZfotdde03XXXafp06fr5ptvTuXpAgAAAAAAABFSVpm2ZcsW3XbbbfrOd74z6HEnTpzQ\nt771LV1yySWSpEsvvVRXXnmlfvOb36TiNAEAAAAAAICEbIZhGKn4RCdPnlRJSYnsdrvsdrteffVV\nXX755UO+X2trqz71qU/p4YcfltvtTsGZAgAAAAAAAPGlrM1z+vTpw36fkydP6rrrrtPFF1+s9evX\nxz3mxIkTOnHixAjPDgAAAAAAABPBzJkzNXPmzDH7+CmdmTYcr732mlavXq0rrrhCTz31lOz22I7U\nEydO6OKLL9YHH3wwDmcIAAAAAACAdHPuuefqj3/845gFamkZpnk8Ht1777369re/rQ0bNiQ87sSJ\nE/rggw+0bds2LVy4MIVnCKSXDRs26NFHHx3v0wDGFfcDTHbcBwDuB4DE/QB45513tHbtWp04cWLy\nhGn19fW65557tGvXLl199dVJvc/ChQu1ZMmSMT4zIH1NmTKF+wAmPe4HmOy4DwDcDwCJ+wGQCinb\n5jmYoqIi1dXVSZL+8R//UX6/XzfffLOKiopCf+65555xPksAAAAAAABMduNSmRYIBCLe7ujoCF3+\n05/+lOrTAQAAAAAAAJKSFpVpAAAAAAAAQCYgTAMmgOrq6vE+BWDccT/AZMd9AOB+AEjcD4BUsBmG\nYYz3SZytAwcOaOnSpdq/fz8DFgEAAAAAACa5VGRFVKYBAAAAAAAASSJMAwAAAAAAAJJEmAYAAAAA\nAAAkiTANAAAAAAAASBJhGgAAAAAAAJAkwjQAAAAAAAAgSYRpAAAAAAAAQJII0wAAAAAAAIAkEaYB\nAAAAAAAASSJMAwAAAAAAAJJEmAYAAAAAAAAkiTANAAAAAAAASBJhGgAAAAAAAJAkwjQAAAAAAAAg\nSYRpAAAAAAAAQJII0wAAAAAAAIAkEaYBAAAAAAAASSJMAwAAAAAAAJJEmAYAAAAAAAAkiTANAAAA\nAAAASBJhGgAAAAAAAJAkwjQAAAAAAAAgSSkN09ra2lRWVqY9e/YkPOYXv/iFLrjgAhUUFGjRokV6\n8cUXU3iGAAAAAAAAQGIpC9MaGhr0mc98RkeOHJHNZot7TFNTk2655RY99NBD6ujo0D/8wz9o9erV\nam5uTtVpAgAAAAAAAAmlJEzbsmWLbrvtNn3nO98Z9LitW7fq8ssv18qVK2W327V69Wp99rOf1dNP\nP52K0wQAAAAAAAAGlZWKT7J8+XLdfvvtstvt+tKXvpTwuLffflsXXHBBxHWLFi3Sm2++OdanCAAA\nkNbcbqmhQQoEJLs98d9VVebx8Y6tqpI8nvH9OgAAADJdSsK06dOnJ3VcZ2enCgoKIq7Ly8tTZ2fn\nWJwWAABAWhksMGtvl9raJK10S3MaJAVkNhlE/t38ZzNNC1wTdsyxKmmnR8eOSfX1UmlpbPBG0AYA\nAJCclIRpySooKNCZM2ciruvq6pLL5Rr0/TZs2KApU6ZEXFddXa3q6upRP0cAAICRiheatbZKnZ9z\nR4Zg1t/HqyRD0qJ6bf5Fp5btCyT82PtmmbNml+0JRFxX88169XSWqkd2eeMEb81/rlJ9sScUtFHN\nBgAA0l1dXZ3q6uoirjt9+vSYf960CtMWL16sAwcORFx38OBBXXLJJYO+36OPPqolS5aM5akBAACc\nFSs4s6rA6uul3l7zT0SVWWGrGZTtiQ3KrIDslpcCyvFLeX7JL8mh2L/ntJvvH37MnPaAbjnkVUuh\nN+7HXbYnYAZu99TL21mqeNVsq1aZ70MVGwAASBfxCqkOHDigpUuXjunnTaswbd26dfr+97+vn//8\n57rpppv03HPPac+ePfrhD3843qcGAAAwLG53ZHB27JjUt9ytwO1R4VmwymxGpxIGZeEBmcWR4O94\nx+QFP27hR4k/bnTgFl3NtvXPVcr+hSf0tVgBG6EaAACYbMY9TCsqKtKmTZtUXV2tyspKvfDCC/qb\nv/kb3XnnnfrkJz+p5557TmVlZeN9mgAAAIMKb90MtWzePtCy2aOANr96XMv29EqKH55ZBgvILPEn\npkl92eay9hxfIHRdMh83OnCLCdfOPaqau/aGvhYrYAtvDaViDQAATAYpD9MCgcjWhY6Ojoi3r7nm\nGl1zzTWpPCUAAICzFl2BppVu6Zb6uC2bU3uk0q7E4ZllsICsL9uunLxC2YMJlj043Mz6Oydsq4A9\nmOz1dXdGhGvxPm70uUSHa1N7enXog+aI84xuDT3aWqW95R6CNQAAMKGNe2UaAABAJrJCtIgKtGDr\nZrzZZv02Kcsw3zdReJYoKAsPyHLOIqXKCZbNJQze6utjAjeLda6lXdK0bvNriJ7Ftn2RVzU3Sr15\nH6u5tFzWcgOq1gAAwEREmAYAADAMESHaPZEVaOGtmxYrjMoyJL9NchiJw7OzCcqSMtTH9HgiA7c4\n1WzxwkCrem3Nm9ItB6Xti9oktWnZsbCqtYOrYhYZEKoBAIBMRpgGAAAwhPB5aMeXuNUbDNFueTG2\nAi2aFUZ5XU65SmfFVoalS7IUdR7R1WxZgYC8re/L5e2JqV4LD9Wst0NVawu2RiwyqKUVFAAAZDjC\nNAAAgAQiqtCuMVs5N796XLe80JuwAs0SXXXmyrTkKM65uhJUr1niLTRY8/bQraBUqwEAgExCmAYA\nABBlsFbOqT1ScV/s+0S3buZMxIQoXvVa+PYFp1N9/r6YgC26FbRm5SnJHlCg+Ji8lfXa2riKUA0A\nAGQM+9CHAAAATA5ut1RcLG097Zb3nmJtenerTn/fqzVvB1T5kTT/Y3MQf7i+bLvkcsleViZVVChn\n7R1Se/vkSIU8HvNrXbNGqqiQqqvNr9/lCm0KteT5zRDy+sPSoccCOvQDafOLPZLTq8CiOnnvKdbW\n024VF5v/DwAAAOmKyjQAADDpxatEC5+HZglfIjChK9CGK/rrtxYahG0JtYRvBZ3TblartRT2SOrR\nvllbVXNPvWqPrGKuGgAASFtUpgEAgElrsEq04r7IIE0ylwg45k/CCrSzEaxasyrVlJtrXu9whLaC\nWtVqZR9JlR+Z89VOf9+rxw/Wqvnach2+ppJqNQAAkHYI0wAAwKQz3BDNauV0raqWmpqkxkZCtGRF\nt4LOmyeVlEQcYi1vCLWCHunV5n3N0jmHaQEFAABphzANAABMGmcbolGFNgo8HjOEbGqSVqyIqFYL\n2CIPLe0y2z+ZqwYAANIRYRoAAJgU3G5p927J+1m3AmW7tfklr65vJkQbF1HVavb5ZRFLC3qybMrx\nB1s/35ROfycsVCvbLe9n3dq9WyovlyorCdYAAEBqsYAAAABMeG63tG2b5Fvuli7aps27fFr3hpQT\niDyOpQIpFnUb57jdUkODnC0tktcryQw68/xmqHbLQWn7ojbVXL9NbZJO7fYoEJA+/tj8P+a/DAAA\npAJhGgAAmJCCuYwCAen4Erd8fx3c0vmSuaUzJyD5Zc7rIkRLE9Ztb5URtrWF/ikyVPNpe0Wtau7Z\nK8mutmNV2rbNE/EhAAAAxgptngAAYEIJn4t2+JpKNV9brscP1sadjeaQpOxs2jnTjccTM1fNEr6k\n4FBtsw7VHtbm/94q318zTw0AAKQGYRoAAJgw3G6p9szAcoFDtYd1qLZZ1x/pjTsbTSUl0tq1hGjp\nKHyuWpxQrbRLmv9xcK7a2wGd/r5Xm97dKu89xao942aeGgAAGDOEaQAAYEJwu6UtH7vVO3dguUDl\nR2bgUtoVeay1YEArVhCkpbuoUM1aUtBvk7IM8xCrWs0K1R4/WKvma8t1+JpK1Z5xE6gBAIBRRZgG\nAAAyWnhbp3HBNm3+jzate2MgQMsyJL/NvMyWzgwWDNVy1t4hVVQoa36ZvC5nxCHhLaCb9zVL5xxW\n79zd2vIxgRoAABg9hGkAACBjRbd1nn7YpzVvDiwXkCSvyynH/DKpooIQbSLweKTGRqmpSa5V1RHV\napbSLnPz56EfSJv/o03GBduYpwYAAEYNYRoAAMhI8do64y0XcK2qlpqazACGEG1iCa9WCwvVerJs\nyvEH56m9KZ1+2Beap0aoBgAARoowDQAAZJSh2jpDWC4weUS1gDrzi0KhavQ8tfAlBQRqAADgbBCm\nAQCAjJFMWyfLBSYxqwV01SozTA0TmqfWHNDml7zqnbtb2zrcbPwEAADDRpgGAAAyQrJtncxFgzwe\nM0xNME9t3RvmLDVfWb0OX1NJ6ycAABgWwjQAAJD2rIo02jqRtATz1PwyKxmvPywd+rFXh2oP0/oJ\nAACGhTANAACkNasi7fGDtbR1YvjCQ7XsbLOCUWYYO/90cElBcJ7a4wdrteVjAjUAADA4wjQAAJCW\nwhcNPPneFl1/pJe2Tpw9j8esXAzOUvPbbcoyzH8KzVM70qsn39ui2jNulZeLeWoAACCulIVpra2t\nuvHGGzV16lSVlJTovvvuk9/vjzkuEAho48aNmjNnjoqLi3XRRRfpueeeS9VpAgCANBC9aGDdmwZt\nnRg5a5ZaRYUc58+X1+WM+OfSLmndm4YeP1ir5mvLdfiaSlo/AQBAjJSFabfeeqtcLpdOnDih119/\nXS+//LIeeuihmOOefvppPfnkk/r1r3+t9vZ2Pfjgg/riF7+oY8eOpepUAQDAOIq3aIC2Towaa+Nn\nU5Ncq6rjz1M70qtDtc06VHuY1k8AABAjJWFac3Oz9uzZo0ceeUROp1Pz5s3Txo0btXnz5phjXS6X\nDMOQ3++XYRiy2+3Kzc1VVlZWKk4VAACMo8EWDTgk9TtstHVi9ITNU+t32CLnqX1szlOzWj/Z+AkA\nACwpSajefvttTZs2TTNmzAhdt3DhQr3//vvyer1yuVyh62+++Wa9+uqrWrRokRwOh2w2m37yk5/o\n3HPPTcWpAgCAcWJVpD3ZXKtbXvApxz+waMAhyetympVEhGgYbR6PsiR56+vk8vao36bQPDWr9VPG\nVtXcU6/aI6skt4dvQwAAJrGUVKZ1dHSooKAg4rr8/HxJUmdnZ8T1jz32mLZv364//OEP6u7u1qZN\nm+R2u/XWW2+l4lQBAMA4sCrSBls0QJCGMeXxmN9jFRXKml8WmqdmtX6Gb/zc1uFmOQEAAJNYSsK0\ngoICdXVFTg223i4qKoq4/t/+7d+0YcMGLV26VFlZWVq/fr0+85nPaMuWLak4VQAAkGLl97v11PRi\nPX6IRQMYZ1Hz1MJbP8M3fj7RuE2Hr6mk9RMAgEkqJW2eixcv1qlTp9Ta2qrS0lJJ0sGDBzV79uyY\nMC0vLy/2JLOylJubm/Djb9iwQVOmTIm4rrq6WtXV1aNw9gAAYKyU3+/Wuw5r0UBkW2dftl05eYUs\nGsD4iGr9tJR2Sbc0+rTsz4e1b1YzrZ8AAIyjuro61dXVRVx3+vTpMf+8NsMwjDH/LJIuv/xyzZo1\nS5s2bVJbW5tWrlyp1atX64EHHog47oknntCDDz6oX/7yl7rooov03HPP6fbbb9drr72mCy+8MOLY\nAwcOaOnSpdq/f7+WLFmSii8DAACMkvL73WrO36bNu3xa94YZpFn6HTZl3b6eEA3jz+2W6uvV192p\nHF9A3cFStTy/1O2Q+hzS9opc3VW2RuunEqgBADDeUpEVpaTNU5K2b9+u/v5+zZs3T5deeqmWL1+u\njRs3SjJbPa0k8Stf+Yr+7u/+TrfeequmTZumRx55RDt27IgJ0gAAQOYKVaTt8un6wwMVaZK5aIAg\nDWkjbOOnKipk5GSH5vmFt34++d4W1Z5x0/IJAMAkkLLKtLFAZRoAAJlnsIo0ZWczHw3pze2Oaf2U\npD679MyiXN31l7OVlWXXXFuVmr7L9zEAAKmWiqwoJTPTAAAAJDNI+9Yva7XqsE85/sgZaSopYT4a\n0p/HI5cU0fppbfy8/kiv3vmgWZK079yjyjkqrS2i9RMAgIkmZW2eAABgciu/361vvrxFK470qrhP\noVY5h2RWpBGkIVOEtX6Gb/ws7ZLmfyxVfhTc+tlE6ycAABMRlWkAAGDMWRVpaw8akW2dEhVpyFxR\nGz/7bVJWcIBKaZe07k1D8tfqrjKx7RMAgAmEMA0AAIypgYo0I6Kts8dhl7OgkCANmc1q+2xoUFYg\noFN/fl+f6O6JaP180rFFX80RgRoAABMEbZ4AAGDMWBVp6940VNplXueQ1Ge3yXn7HVJ7O0EaMp/H\nIzU2Sk1N+sSXqmNaP9e9aejxg7XaUlSunL+uVPn99H0CAJDJqEwDAACjrvx+t44aDXriP97TiiO+\niIq0U3lOfeJL1YRomJiiWj/jLSdoOPeoyiW2fQIAkKEI0wAAwKiyqtGqPujVjE6puM+83qpII0jD\nhBds/ex/eouy/OYQtdIuaVq3OVNtak+vjJe3qPx+AjUAADIRYRoAABg14fPRSrukbof5J89PRRom\nmSSXE+QcldYWMUsNAIBMQpgGAABGRbyNnXl+qSMnW5o/T5+oqiJIw+SSxHICObboq5ViOQEAABmE\nBQQAAGDEBirSekPz0SSzGq3otrXmcHaSAkxGUcsJ+uzxlxNs63CrslJys5sAAIC0R5gGAABGZLCN\nnbR1AmE8HuXcsV6n8pySFFGh9ubLW7TzZI4uf6WcQA0AgDRHmAYAAM7aYBVpOXesJ0gDonk8+sSX\nqtXviKxQm99uqLLdp+tam1X1xxyV30+iBgBAumJmGgAAOCvxZqRRkQYkYajlBG/7ZDNqVS62fQIA\nkI4I0wAAwLCFb+20KtIcYmMnkLSw5QQfdpyQo71TJV1GqPVzxZFeGf+xTTl/3aC5tipCNQAA0ghh\nGgAAGBYq0oBREryvzJC096pyFb/SHLpPlXZJtxzyadn7h9Vw7lGq1AAASCPMTAMAAEljRhowNi57\nuUk5d3w5tJyg2yHl+KXKj6Tqd3r1xx8+pZ8vLh/nswQAABJhGgAASBJbO4ExFlxOoIoK+RzZygum\n1Xl+qbhPuuLIuwRqAACkAcI0AAAwJPcOKtKAlPB4pMZGuW5bG6pSs5R2G7rhnWYCNQAAxhlhGgAA\nGJR7h1tXPEhFGpBSwSq1jpxsdTvMq6zlBFcceVdPX+RS5Q8r5d7hHtfTBABgMiJMAwAACbl3uLXs\n77doeRMVaUDKeTwq6u3T7gVl6rObIbYUrFA71KGdf39YVzxYS6AGAECKEaYBAIC4rIq0tW9EVaTZ\nqEgDUmn1W03asbBMrXk2SZHLCZY39arqwS0qv59ADQCAVMka7xMAAADp6coH6vX55oGKNIektlyn\nDs2u1mUEaUBKrX6rST9fXK6L/nxU07v6VdxnSJJKu6R1bxqy+WtVLqnpu9w3AQAYa1SmAQCAGD9f\nXK5b3/LGVKQdml2ty5p4sQ6Mh9VvNani4z69MWe92nLN5QTWHLXqd3r1xx8+xXICAABSgDANAABE\n+Pnicl1x5N2IGWltuU795/z1BGlAGrisyaNDs6vVZ7OF5qjl+aXiPnM5AYEaAABjizANAACElN/v\n1oUnmlXabbaQmVs7RUUakGYua/IoZ/16ncpzRlxf2m3omqZmHZ6aQ6gGAMAYSVmY1traqhtvvFFT\np05VSUmJ7rvvPvn9/rjH7tmzR5/+9KdVVFSkOXPm6JFHHknVaQIAMGmV3+/Wt35Zqxmd5oBzSWrN\ns2nHwjKCNCAdeTz6xJeq1ZGTHbrPWssJKk77qFIDAGCMpCxMu/XWW+VyuXTixAm9/vrrevnll/XQ\nQw/FHHfo0CF94Qtf0Ne+9jV1dHToxRdf1D//8z+rvr4+VacKAMCk497h1jdf3qIVR3pV3Gde154j\n7Tl/vla/1TS+JwcgMY9HRb192r2gTIenZKvPYbZ8SmaV2g3vNBOoAQAwylISpjU3N2vPnj165JFH\n5HQ6NW/ePG3cuFGbN2+OOfbxxx/XTTfdpHXr1kmSLrjgAv3ud7/TsmXLUnGqAABMOu4dbl3xYK3W\nvWmEFg7k+aWPClwEaUCGsJYT/Kq8TK15NkkDywmoUAMAYHSlJEx7++23NW3aNM2YMSN03cKFC/X+\n++/L6/VGHPuHP/xBc+fOVXV1tUpKSrRo0SK9+uqrmj59eipOFQCAScW9w61lf79Fy5t6IxYOnMpz\nat6Nq8b13AAM3+q3mrTn/Pnqsyu0nIA5agAAjK6UhGkdHR0qKCiIuC4/P1+S1NnZGXH9qVOn9IMf\n/EC33367Tp48qR//+Me6//77afMEAGCUWRVpa98YqEhzSOqz2fSJL1VLHuakAZlo9VtN2rFwoEIt\nfI7aikPN8ubmSG73OJ8lAACZKyVhWkFBgbq6uiKus94uKiqKuN7pdOrGG2/U8uXLZbfbddlll2nd\nunV69tlnU3GqAABMGlc+UB9TkdaW69R/zl9PkAZkOKtCLXqOWp5fcvX5dOqndQRqAACcpaxUfJLF\nixfr1KlTam1tVWlpqSTp4MGDmj17dkyYtmjRIvX09ERc19/fP+jH37Bhg6ZMmRJxXXV1taqrq0fh\n7AEAmHh+vrhct77jVU7AfNuqSDs0u5rNncAEYc08/Pnicl1x5F2Vdhuhf/tEd4/6tj6l/zy2V5e9\nzGxEAEBmqqurU11dXcR1p0+fHvPPazMMwxj6sJG7/PLLNWvWLG3atEltbW1auXKlVq9erQceeCDi\nuFdeeUWf//zntWXLFlVXV2vv3r1asWKFamtrtWLFiohjDxw4oKVLl2r//v1asmRJKr4MAAAyXvgL\na7/MIK0t10mQBkxgP19crmuampXjN6vTQvf9fJsOfWY+gRoAYMJIRVaUkjZPSdq+fbv6+/s1b948\nXXrppVq+fLk2btwoyWz1tJLEK6+8Ujt37tSjjz6qKVOmyO1263vf+15MkAYAAIZv71XluuGd5lCF\nikNSn10EacAEt/qtJl38/3xZdQtzI5YTlHQZunBvs94rzdHeq1hOAABAMlLS5ilJpaWlCeeedXR0\nRLx97bXX6tprr03FaQEAMGnsvapcC37/bmhGmkNSa55Ne86fr9VvEaQBE13Tdz0ql2S8gtLIAAAg\nAElEQVTYtun6d30q7RpYTjCvzafC37+rvVeVU6UGAMAQUlaZBgAAxo97h1ul/9Wskq7IijQzSOOF\nMzBZNH3Xo4aL+/SL0jI1FkcuJyjpMvTpV5qpUAMAYAiEaQAATHDuHW5d8WCtZnSaVSiSWZH2s3ll\nBGnAJOTxSL+9skkrp/fp+Zllas2zSTIrVnMC0oJghRoAAIiPMA0AgAnuygfqtbypV8V95tvtOdIv\nps/XK5cTpAGTlccjNTZKv/3LJv1i+nzmqAEAMAyEaQAATGB7ryrXrW95Vdplvp3nl1qyXfrtlU3y\nMCYNmPSsKrVtswYq1MLnqFGlBgBALMI0AAAmqOiFA5LUlutU68xVBGkAQjwead/nzAo15qgBADA0\nwjQAACagvVeV69OvxC4cODS7Wpc1kaQBiJTMHLWWc11SZaXkdo/vyQIAMM6yxvsEAADA6IquSHPI\nXDjwy3Pnax1BGoAErIpVt7tJeqVca441KydgXlfSZai/u0M60SFvyzG5wt8BAIBJhso0AAAmkEQV\naSwcAJCseHPU+m1SlvljRS5vj/qf3kKFGgBg0iJMAwBggth7VbkWvhY5I601z6Zts8pYOABgWKLn\nqL1bbFNrvvlvfklZfkPe+joCNQDApESYBgDABDHrjaM650xsRdq+zxGkARi+8DlqF1y1XrvOz1Wf\n3fz5IlGhBgCYvAjTAACYAPZeVa5p7T51B1/ltubZVDubijQAI+PxSI2N0toij766aI2euYAKNQAA\nCNMAAMhw1sKB4j7z7fYcadc58/W/cgnSAIwOj0daU+DRXfPiV6h1/XQbmz4BAJMG2zwBAMhg1sIB\na+Nenl86Vpitb/U0acVV43tuACYWj0eS26OvLpLk2KLrmwyVdkndDsnW55MOH2bTJwBgUqAyDQCA\nDJVo4UBD8VytWMFrWQCjL7pCrXGa1Ocwg3yJOWoAgMmBMA0AgAxkVaSxcABAqnk80vqp5gy1BWsq\ntL0ymzlqAIBJhTANAIAMk6gijYUDAFLFqlCr+FWjvlK5Nu4ctY66LXqvNEd7ryof13MFAGC0EaYB\nAJBhZr1xNKYijYUDAFJtsE2f3Q4py2doXptP/+eeZnXl51CpBgCYMAjTAADIIHuvKte0dp+6g+Uf\nrXk2PXNemb7V06SqqvE9NwCT01Bz1Jz9Un63j9ZPAMCEQZgGAECGsNo7i/vMt9tzzIq0r7Q0sXAA\nwLiKmaM2v0itebaIY1zeHnX9dJtUWUmoBgDIaFnjfQIAAGBo1sKBnID5dp5fOlaYrW/1NGntWoI0\nAOPP45Hk9qjhV9L/aJF0TrluOdmsHL/5M6vbIdl8PunwYXlbjskVeicAADILlWkAAKS5RAsHfj9l\nLhVpANKKNUdt1SrpWz1NmnLtl1W7MKz1s988zuXtUf/TW6hQAwBkJMI0AADSmdutT7/SzMIBABnF\n45FWrJCy/92jmvODrZ+V2aEFBX5JWX6DOWoAgIxEmAYAQLpyu+Wtr4upSGPhAIBM4PFIa9dKJb/3\nSI816u6ytdp1fq767OYvBiTmqAEAMhNhGgAAaarll/VyeXskRVaksXAAQKawKtQqKqTCX5tVas9c\nYAtVqEXMUaNKDQCQIQjTAABIUx09HeoPLsNrzZeeqXSxcABAxgmfo1bye49q5q7XrvOZowYAyFyE\naQAApKG9V5WruMdQliH126SPc6WvfGoVFWkAMhZz1AAAE0XKwrTW1lbdeOONmjp1qkpKSnTffffJ\n7/cP+j5vvfWW8vPz9dvf/jZFZwkAwPjbe1W5Pv1Ks0q7zLezDEmyad4bHoI0ABmNOWoAgIkgZWHa\nrbfeKpfLpRMnTuj111/Xyy+/rIceeijh8WfOnFF1dbV6e3tTdYoAAIy7vVeVa+Fr78YsHWgoms/C\nAQATwnDmqPVt2yoVFxOqAQDSSkrCtObmZu3Zs0ePPPKInE6n5s2bp40bN2rz5s0J3+erX/2qbr75\nZhmGkYpTBABg/Lnd+vQrzTrnjPnYF7504L7TTVSlAZgwkp2jluMLSF6vtHs3gRoAIG2kJEx7++23\nNW3aNM2YMSN03cKFC/X+++/L6/XGHL9161a99957evDBB1NxegAAjD+3W976upiKtGfOK9O3epq0\natW4nh0AjImh5qiFtLVJ9fW0fgIA0kJKwrSOjg4VFBREXJefbz5CdnZ2Rlx/6NAhbdy4UbW1tbLb\n2Y8AAJgcWn5ZL5e3R9JARdrukvn6H+1NLB0AMKElmqPWnmO2fEpST5bU190pHT5MlRoAYNxlpeKT\nFBQUqKurK+I66+2ioqLQdd3d3friF7+of/mXf9F5550XavEcqtVzw4YNmjJlSsR11dXVqq6uHo3T\nBwBgTO29qlwXnvKq2yHl+aXWfGnXXJdq3mlSRQVBGoCJz+Mx87GGBqnl1x7VfFaquWmbNu/yadkx\naUanVNwXMA9ua5O2bRt4RwDApFVXV6e6urqI606fPj3mn9dmpGAoWVNTkyorK9XS0qLS0lJJ0s9+\n9jN94xvf0NGjR0PH7d27V5///OeVm5sbuq69vV0FBQW644479Nhjj0V83AMHDmjp0qXav3+/lixZ\nMtZfBgAAo85aOHDOGUPdDnNW0PaKXNWcv0Ylv/dQlQZg0nG7zeKzts+4pcrdUkGbNj9v1/VNAZV2\nSQEF22tKSsQPSQBAtFRkRSnpoywvL9eyZcu0YcMGdXZ26r333tO3v/1t3XnnnRHHXXbZZerq6tLH\nH38c+iNJL774YkyQBgBAxotaOJDnl1oKpZrz1yj73wnSAExO4XPU1LhC+rBCNcsLtavCbIEPvYBh\njhoAYJykbCjZ9u3b1d/fr3nz5unSSy/V8uXLtXHjRklmq2d0WR4AABNagoUD+wrKVPJ7j9auJUgD\nMHlFz1GzN65SzdUleuZChZYTMEcNADBeUjIzTZJKS0v17LPPxv23jo6OhO8XCATG6pQAABgfbrf6\nn94il9+sSItYOHDa3NxJkAZgsoueo+b1u1VzPXPUAADjj3WZAACkUrAiLctvDFSk5UvPVLp057Em\nzZjB60AAsHg8UmOjtGpVsErtT2tVc3WJFtwrbV9oD1Wp+SXJ55O3vo4KNQDAmCNMAwAghVp+WS+X\nt0fSQEXarvNzVVO+SiUlUlXV+J4fAKSjoeaoOYLHubw96vrpNuaoAQDGFGEaAACp4nar4KMOdQdf\n9bXmS89cYGPhAAAkIZk5at0OyebzSYcPq2/bVqm4mFANADDqCNMAAEiFYHtnUY85J609J1iRNnc9\nCwcAIElWhVpFhVT4a7NKreZal3aV2dU4TepzSHn95rE5voDk9bKcAAAw6gjTAAAYa9bCgWB7Z55f\naimw6e7yNXK9SkUaAAxHzBy1f2pXzSfv0II1Fdq+IDtUpRbS1ibV19P6CQAYNYRpAACMpQQLB/bN\nKFLgeQ8LBwDgLIXmqGVL2mm2ftZ8cq12n5+r9hyFWup7sqS+7k7p8GGq1AAAo4IwDQCAsRJVkcbC\nAQAYXaE5aiXm285feXRX+RpN+Zts1V4gNU6Teu3Blk/JrFLbto1ADQAwIoRpAACMhQQVaSwcAIDR\nFT5HLSdHCjzvkf60VjVXl2jBvVL9Irvagq2fAUny+ahQAwCMCGEaAABjoOWX9fEr0lg4AACjLmKO\nWonMts/GFdKHFbpzeaF2Vpg/h0MvfpijBgAYAcI0AABGm9utgo86QvN6rIo0Fg4AwNiKN0dNb6/S\nXdeU6JkLFVpOwBw1AMBIEKYBADCagu2dRT2GJKk9Z6AijYUDADD24s1RszWtUM312dpVwRw1AMDI\nEaYBADBaohYO5PmllgJzRpp2elg4AAApMpw5an5J8vnkra+Tystp/QQADIkwDQCA0ZBg4cC+GUXS\nTo+ys0V7JwCkULJz1IId+XJ5e+R/t5nWTwDAkAjTAAAYBQkXDpSvUkmJWDgAAOMk2Tlq/TbJYQTf\nidZPAMAgCNMAABgpFg4AQFpLOEftWpd2ldnVOE16b6pCrZ8BSfL5qFADAMRFmAYAwEiwcAAAMkLc\nOWr/1K6aT96hBbdV6FNfdYVaP0MvktrapPp65qgBACIQpgEAcLZYOAAAGSVmjpok2y6P9MNG2RtX\n6WvXR7Z+9mRJfd2dzFEDAEQgTAMA4GywcAAAMlb4HDUjOCet91mP/O+sUM312dpVITVOk3rtUo4v\nYB7AHDUAQFDWeJ8AAAAZx6pI85uvwCIWDpy/SiUfEqQBQLqzfkbv3m3mZE6nZOzySH3SXdfslpHf\npi077bruUEAlXeYcNbs1R628XLLbzfJjftgDwKRDZRoAAMORoCKNhQMAkHmi56j19kra6ZFxaIV0\nqkJ3faEw/hy1I0do/QSASYwwDQCAZEXNSIuoSGPhAABkpOHOUeu3SQrQ+gkAkxlhGgAAyXC7pd27\n41aksXAAADJfsnPU3p06EKwFJMlq/SRQA4BJg5lpAAAko6HBrEBQdEXaGhYOAMAEMdgctZqrd0sF\nbbIZDj25w691b0g5wQI1tbVJ9fXmYwVz1ABgwqMyDQCAobjd6jr+nnqCv4JqzZee+VS27i4fqEhb\nu5bXTgAwESSao6bGFdKHFbKdnhfT+tmTJfV1dzJHDQAmCcI0AAAGE1w4kN/tk2FI7TnSrgqp5op5\nyvl3j1wuKtIAYKKJN0dNOz3SY40K/EtTTOtnr13K8TFHDQAmi5S1eba2turuu+/Wnj17lJWVpbVr\n1+q73/2uHA5HzLE/+tGP9Oijj+qDDz7QzJkztWHDBn3lK19J1akCQEq43WY3yIlL3OqYXS8ZUlZv\nqWw2uwwjIJvNLmdrlWw7zZSmtFSy282Zx+F/000ytrp+uk2ubp8kKc8vHSuWaq4ukRqr1NMjzZnD\n7Q8AE5XHYz5e19ebFWq9vZGtn3dds1tGfpu27LTrukMBlXSZc9Ts1hy18nIerDFpWc91rXmyZ3M5\n/Pnu2X6MZC9zF8Vw2AzDGq85tq688krNnj1bmzZt0okTJ7Ry5UqtXr1aDzzwQMRxL7zwgtxut156\n6SVdcskleu2113Tddddp8+bNuvnmmyOOPXDggJYuXar9+/dryZIlqfgyAGBY3DvcajjWoBMtAf1/\nu49q2fF+2QyHDNnM6cZ2vyRDM86Yx7cURn0AQ5JN2ndetmqumCuzoDhg/n2sStrpkcMh5edLM2cS\nsI06t1s9z2yRs998qOx2SLWLbaqZuz7U3klVGgBMDlYw0NIieb3BK1e6pbkNyp7Soid2eSPnqEkD\nv/3iAQMZbKhQLF7gFQhI7e1hsweNsDA6weW+5W4FzmuQ/c/mBwqc1yDreW/4ddGXn9y/V1XtR9VQ\nPFeSzury76fOld0hXXrqqP50nnn9RX9OfNl6kh7976svuYx0Lg2kIitKSZjW3NysiooKffDBB5ox\nY4Yk6dlnn9X999+vY8eORRz7xBNPqLOzU1//+tdD161atUozZ87UY489FnEsYRqAdOLe4daV/7hN\nlx7tlwIOBWQGZbJJMzqlAp+UZUh+mQPsrb+D0ZgUdjn6mH6bdCY7MmzbN8uumpUOqT9P6ixVeMDm\ndEp+v5SXZ7ao8Fh+Ftxus03H5wtd1Z4jTbn2y6GFA8xJA4DJJ7jcWW1tki34u7HcL7plq9ytx3a1\n6frDUmmX+didFf5KiwcOpKFkgrKhQjHrfhAeiOl4lZk3zWkwn58q/uXN/7VXy7xHte9cp+To07IP\nerVvlvnMeNn7A8l0+HUR/25IU3vN+1x3sOktzz/2l63n6In+/VSeU97cbPnUM2g4N2OGNOuNo3r/\nQvN66/Jlcy6jfG4EJkyYtmPHDt1555368MMPQ9e9+eabuuiii3T69Gm5XK6E79va2qpPfepTevjh\nh+WOmjtAmAZgPO29qlyz3jB/M9Uf8Ku0w0gYmAULzEbE+hjhD972gNQTFrLtm2VXzXWFEeGafbdH\nhYUDbaI8Dich7JWSdXu35ku75hap5h0vBQYAMMnF+X2Lsm9xy7dwmzbv8mlZsF5gao/5Ij/0izMe\nQJBig4VlMe3LcYIyh0Pyf8E9eCgW+g4PaPOrx2MCsZEEYUOFVjGhdRqxzm0koV2Pwy57cCaj1+VU\nV0G2uvt7kg/fwi9Pop87qciKUjIzraOjQwUFBRHX5eebq286OzsThmknT57Uddddp4svvljr168f\n69MEgKEFn5F4jzbpM31GRHAWHpg5ov4OD9KiQ7HBKtPCP2b0x87zm3/n9klFH5nvN6c9oFsOebV9\nkVc1N0qa9q4Ci7bL2zlT3mDV2rFj5pMnKtYG0dBg/gpW5u3dZw8uHVg2U9nNvA4CgMnOegywKtTi\nzVFz2Bz68fP+yNbPtjbzQXgSvrjF2IoXmoVXlR07NhCQhV+29PTIbFue06CesKDMr4CU1y4VtEnF\nx7R5V5+W7Qto36xmSdKyfYGI87AC5Dnt5vV5/sSXw4Mw63lt9OXo573Rl7OM0QmtRrsyLZmvLZnL\nTn/A/GSSXN4e5Xf0KMuQZrzaHDp2xqvN6rE1y9kv9b1nXp/jC0Rc9rYcU9dL20NBHOHbyKWkMu35\n55/X3XffrbbgCxNpoDKtvb1dRUVFMe/z2muvafXq1briiiv01FNPKTs7O+YYK2287LLLNGXKlIh/\nq66uVnV19eh/MQAml+Azk5aOEyr4uFPZ/Yac/UNXmkUHZpHtmjadzM+SzWYoK8umvCynZpwJfrSo\nLQNdx48op6dfWUb47/yGXsUcXrW2fZHMYM3nlPw50sFVtIIOxu1W10+3ye7zydkfrEirMJcO5B5d\noTUFHm4rAICkweeoFRUF5JzSrv9VO9D62ZMl2W12c/NnSYlUXEzZOIYtOjhLVGHmcJjP9UJWxqkw\ns55Z2gLa/EqcqrI55qHR1ZbxgqWzqRKz3qcnWObj7Jf6ss3Pn+MLhJ73hl8Xfnmwaq3hXm5pGXxO\n2lAz0xrOc8qw9anqg94xaT8daRXeULd19O3qKp0V+fMpUZnjOP/sqqurU11dXcR1p0+f1t69ezO/\nzbOpqUmVlZVqaWlRaWmpJOlnP/uZvvGNb+jo0aMxx3s8Ht1777369re/rQ0bNiT8uLR5AhhLLee6\ndE5LR0T1WTzRwVm/TTqTZVNLQZbsfqeyemYqzxnQjJln+WTZeuAKX+PZ2ip/h1cOI/Ic4umXdCYn\ncagmmR+2sJBQTW63vPV1cnl71O2Q+hzB2+36bOmNtSp726OmpvE+SQBAuok3R62kROr5vFsd8wZa\nP2d0SsV9Ye/IggIMYqhqs/DgLEJ4aGaTNLtBsgUkZ7DCzOc0K8zCZpLZJE2JE5QNN8zx2ySHkTik\nGSwIi1stlcw6zzS635Tf79ZRo0FzbVX6zkt7Bw3kXvvEXAX80mc+jlyGkHB+3AgCurMJ4qz/SytY\n87a+L5e3Z/DQTUqL/6MJMzNNki6//HLNmjVLmzZtUltbW8JtnvX19brtttu0a9cuXX311YN+TMI0\nAKMqrAot76MOFfUmrgCLWQyQly1bv1M6M1P/mV2lV9amoHrJ7TZ/FSmZFW2trerr7jR/2x2HFQ5F\nzFezFhiEVavl5EzSUM3tVv/TW5TlH3hYbJwmLbizRGpcIe30qKJCamwcx3MEAKSteHPU7HYpsMIt\n2wKz9XPLTruuOxRQCQsKECXe707jhWYJq83CKsxCoVl/MDQ7HlDDHPOXr9EVZsOtKosXllnVY0kH\nK+GXJ/n3e6Jir73TzFDO2Vqlvj6ptzTx5tJE4dtIgrjo74c+e9Sm4jCJAtQce7b5TRtdhRv+hYZf\nHsXvhQkVprW2tuprX/uaXnnlFdntdt1xxx16+OGHZbPZVFRUpE2bNqm6uloXXXSRDh48KKfTGfH+\n69at07/+679GXEeYBmDUlJfL/26zHAmq0KzKr1AgVSBJNu0rnK/7Tjdp5sw0eT5gBWydneazsDjC\nZzwMVF7ZJV/h5A3VwirSIhYOlNtUM3e9tNND0QAAYEjhFWoRoUew9TN7Soue2OVlQcEkNlS1mVXZ\nOGiLZpxqs80vmEFZotAsmSqlsw5FwqvH+N4ddclsXD2+xB0K3CQpcF7kZR2v0uYDg29Ojd6ian3v\ntOZLp53xKxdHEsKOZeA2ocK0sUCYBmCk9l5Vrv+j4V0V9BgJq9BC1WcFNu2bUaiaz82U/c9VKvy1\nJ33DJitU6+42H5Ri6v8H9Nukd6dKC+5V3LlqEz5Ui1OR1meXnlmUq5rz10g7PRQLAACSFl5hFB2Q\n5H7RLVvlbvU4EiwokCSXS5oxg2BiAon3PZGw2myoFs041Wa3HJRy/MNr7bPCjWFXlfE9mZaSCdzC\nv/f6lrtDIZsMxZ2lF9rMem6u7vrL2dr8m+Oqim49VfzAdqSBWyhkC7+jxJszGf6Fh31vEqYNgTAN\nwFkJDph39PoUsEVuzJFiq9C2V2arZt5auV71hPYDZNRzCStYC06mDdgke9RP/oCkjkHmqjkc0rx5\nmnjzwsJKCKhIAwCMtnitn9m3mGGJ0znEgoJJ8RutiWewds2YarNwN7ilhfWSo1fK7k2qRTM8sEgk\nbkARHkpQVTZpDGcGX3jglttapdyXPPJ+Nv4Ci1DoNszFFSMJ3ELhr3XyUUHbgQ8/1NJduwjTEiFM\nAzAswVCpv8Ob8Id1RBVa4XzVHG+aOEFS+LO71lYFvN6YaryI1s84oZrLNYGe08d5hUNFGgBgtIW3\nfkYXWQy5oECi9TMDDFaNGBOg3eCWZke14zkCUl67As6Bdk1peC2alqRb5/h+QtBQVW3W5Xg/xxwO\nyf+FBFti82IXXuybHQzckgyJu7MkGfG/9/sdtojOkvDg+C05tNTnI0xLhDANQFKCIVoyw/m3L5Jq\nlpVJjzVN/F8Il5dLR47Ena0WE6oFHNJH8ybO7RL2ysZqb2jNl3bNz9bdZWsVeJ6KNADA6LFerLa0\nSF7vwPWDLSiIQOtn2okXoCWsPLMCtLDQzB5w6smdfao6Fr9dM5kWzZh2OOsJmkRohlE3nMq2hCHb\nMNqXlx1Twgq31nyp3WnT1F7pnDNGzP3lgKSlEmFaIoRpAIYUtlggnn5JZ3Kk7fOLVPO5mdKxKtl3\ne1RYmOFhUbKiWkCjRYRqK+3S/75D2mneKBkbqiWqSLtQqllWIefmxsz8ugAAaS/ZBQXhoQqtn+Nv\nWK2bweDMmkYWHqDZDIee3OFPul1zyBZNqs2QBoYdsg1jsYb6nZIMKas3IoB+bXau7r01V99/Nnah\nS7dD+i9DqgoQpiVEmAYgoWBIlKiV0R6QerKDIdo7Xjmd5g/2vLxJ+vx0iFAttKTgq9nSn9aGAjUp\nw7pPElWkVUg1V5dIjSuknR5VVEiNjeN9sgCAiSjZBQVWux+tn+PjrFo347RrJmplG3JGFKEZMtxo\nh2zhrdG5rVWaPVt6P9/8eRld6fnfXdL/PEyYlhBhGoAYg7R0hobsL7Cr5rrCiTkHbKQGCdUS3X6S\nMme2WGWldPhw6M1QRdr1AyEhr08AAKmSaEFBf0W9bFm9Cth7af1MoZG2blqVZ8lu1xyyXZP/V0xA\nIwrZwrhcUu+1bvWWxs4gNNo/lLHlNGFaIoRpSDfuHW41HGvQt39yQtf8V4ck6WR+lqZ39YcuGza/\n9s3K0v991Vz1O1uDVauloctF76/SzNc91ox4ScrMDZLjYZCWzlBl1d2uib+hcjRYoVr4YJegUOtn\nVKiW9rdncIur3eeTsz9+RVrGhIIAgAkj0YIC+01uGbMblFU8ROsnvwUasejfJUa/gLdaO61qmWRa\nNxOyPhiVZ0CE4YRsJSXm33FeqpiuWCntYZtnQoRpGBfBF+T9gX6dcjk0zWsFZTZNP2PI6TN/w5QV\nPNyQWZkqKdRWZm2MtLQUmiX8krR9oVSz3CV1lkqFwTQtLKzIzzdLzCV+YRUyREvnpFssMJoGWVIQ\nCqO+kAEbP91ueevr5PL2RH5PUJEGAEgDiRYUSEm2flKlNmzxqtAiBCvPclurlJMjdc6pl2HvlbJ6\nEwZo8Vo3B513xv8VMKTBto0m3DDqN1cQEKYlQJiGMRe857Z0nFDeRx0DQVnwXhMelIVfTkai97UG\n4scEbNdnS/15Zsgme2hQvsPBnC9aOsfYILdzqE3yRklnBiq80iqwdLvV//SWiNXZjdOkBXdSkQYA\nSC/hVWrhBmv9ZEHB4IaqdolZBDEnduvmv+3u02f+O/I5UMIAzao8Y7smMOYS3b8//PCATp8mTEuI\nMA1jIniP7Dp+RDk9/coyBirKzsbZBm7hn7Nf5kyFnuxgNc2NkgIOqS9f6ozcQDlpWkJp6Uy9qFDN\nL8mhqOq/a13SP7WH3mXcq73CKtKs823Nl3aV21Qzdz0VaQCAtBPdcjhY6ycLChIb7HaMu0BgYb3k\n6JWyE1eeWc8lEgZoVJ4BaWHlygPatYswLSHCNIyaOAFaMsHXYNVlVvh1Mi9L07v7g/9iU4HPiAno\nhhOyJROshbeEzpw5wR7Laekcf/GmJSsYYk6RFvxfrrRZTtCVn6P87oHz7LNLzyzKVc35a6hIAwCk\ntWRbP1lQMGDI1s1wg2zglJLYukmABqStVGRFhGmYvIYRoCUKylrybZrRZd6FWvKyNKPLLxlZ+lXe\nWv2/Mz3xH0/DH+XDNwy0tkrd3eqTXzm+QFIBW9xgzRc5v0rSxAjWaOlMLwn6UPpt0taLYueojUuo\n6Xar+5mnlBfMsrsdUu1im+4+f70Cz1ORBgDIDIO1fmpOg1QwxIKCCf6bxWQ3cDqdUt9yd2jb32Ab\nOBNt3QzYJLshAjQgzRGmDYEwzWRtkAwooKNtrfKpW/Llqej9VbLt9Ki725ypZeU10oR9LB3aWQRo\n0csCts8vUs3nZkrHq1T0G8/oLwOIDtu6u+Xv98VtZwwXPmtNkvbNkWquLYqoVpOUme2OtHSmpwRV\ngonmqEkp7DqJMyetPUeacu2X5fyVZyK/pgAATECDtSzmftEt3/m7IyqrJkPrZyerHXcAABt+SURB\nVLIbOK3W2MLCgHyOdvU4ktzAGa/yLBAgQAMyAGHaECZ8mBbcGuno9aknW/rIlaVp3n45+4Ltg/lZ\nmt7VL6cvWCUVHFgfertAmnHG/FDbK7NVc8Xcge2QnaWSzS5bMBCa0EGbFU6dOCF/Z4ccwwjQWgql\nfedlq2be2vFtnbSeLXR3m88S4mxWtITPcnh3qrTgXpltoB/Nkx4bSJdcrgyYr0ZLZ2YICzvjzlEL\n25gppaDtM9GctPnZqnnTfGVRUSE1No7R5wcAYIwkbP2Mmvk1UVs/h2rjDM+/ej7vVuecgaUNDptD\nP34+uQ2cVJ4BmY0wbQgTLkwLC8+kyK2RUmwANJy3rTY4S0T10ops6fRcWRsitdOjoiKNfsXVeAh7\nkZ9xAVoiwwjWApJ84W2gK4IbQYMVXDab+VvNtKvkoqUz8ySYo9aaL+2qiG37HLPvuTgVadFz0ibY\nL+YBAJNQeOtnzDbKuQ3KnhK/9VOSnP2S1+WUq3RWRoRFybZxWgFaT2mDnM6AnFPa1daV5By0eBs4\n0/g2ATA4wrQhZHyYNkR4NtqiwzW7BsKW/55qXh8K194YqCKx280HLMlsF037x5awsCng88Xdwplx\nAVoi4cGa3S719iYMDa02ULNSyC75CiPmqklKj0CKls7MlWCoS6K2T7tduuOOUfx+S2JzJwsHAAAT\nRbyQyfpFaTKtn9YcsHQMkJIJ0OK1cVoBmlWFNtgcNBYIABMXYdoQMi5MCwYfvjMdMgxD9mGGZyOp\nTBtKdLi27cJga1Z/ntkSGla1ZoVraResBW9ff4c3bhAzVIAmmY+hhYVp9nUlK047azwR7Z/ptKyA\nls6JIWqAifWzJVHb56iFW0lUpOXmSmvW8P0CAJh44haIR7V+enZIX2gcorVxnEu3k52DNpw2zggE\naMCkQJg2BOsGysrar/z8Jen5ojqsciiZIfLhrE2NUnAGWl6WZnQPzEg7mZel6dbbjmx9pLk6x98q\np7qVXZSnlgJD0052KCtgvpgND9eGCtribok0bJI/K27V2rgGa8FnD4mq0KSoAG3OQCAjpcnXMNqs\n77vOzrhtoFarZFosKziLlk6qizLAUG2fKwfm+I04GE2iIk0yKy/b20f6hQEAkJ6Gav0sKgroR786\nrqVHemWXNKVHaTFTbbhz0M6qjVMiQAMmEcK0IVg3kC6/XvrtzvRpRwyrEDI6OpKuDosOz0JbI0c6\nxyx6O6QkGUbEuQ0WrsUEawlaBCWprCyF7XZDVKJJUa2NUWFRdvYEC9DiGaLldTjLCsbkdhpmS6ek\n0W8NxNgZqu1zpV3633eMbNNngoq0bYtydef5a2Tb5ZFhpMUv2wEAGHODtX6Gh1EqGN+ZaiOdgzas\nNk7aGYBJhzBtCNYNtLFC+p9fjJzzZSkqSmG7WlSFVLLVX9Ioh2fDOd/6evNyVLiWSHhFU6JqpjEN\nqZJoZYwYur9QqlnuimljnJQztsrLpSNHEi4sCFWBLZJqbpDUG3m7jerzkGG2dFom7f9dJovq14gJ\ncL86gk2fSVakUckIAJiM4hWJW/nScGaq9WXblZM3OnNQkm3jHGwOGm2cAIZCmDYE6wb6o6TF4XO+\npIiNhZIZSmVljdELqiQqpKQ44VlllmquOD99ZpEl2ZIaPnssVM2UihbQsDAoUVAZkNQ0TVpwT3bM\n90BGz0MbLVHPYOLdjkNVqY0o0DqLlk6JXyhOCHGe0ffbpK0XxW76TOr/e4gZaVSkAQAwSOunFDNT\n7d9ekFYcTtwmebbVakO1ceoGtzS7QbmtVcrJUVJz0GjjBDAYwrQhhG4gSUsUpx3xBkm9kZVToxrw\nBF/M2f3GoBsj4w71T1XQd7bCt0T6/YNWM/lsQ7eAjijIGqSKKe45RFUzTYp2zuEaosJvqCo1yWz9\nLC0dxvOVs2jptD4P/3cThNstPf10xDP5RJs+pQRBWFQgS0UaAACDG7KlcqVbmtMguyOgJ39zXFV/\n7pVN5ky16Movq1rN63LKtao64YPscD6n8toVcLbJHnAqO9tQr783uTlotHECSIAwbQjRYVo4Q1Kf\nTdp2UeLh+ZL5Ymvu3GEEAmEhU3+/L+E2zph2yBWxbagpbUEdiaiKkiGrwqyKpr78mBbQpIPDIcKe\nmOq4OJVotAQmyfqe9npj/iliccMsuzk0Pux2tmZwDHpb09KJcGEVpkNt+pSiArGwts5w0Vs7JSrS\nAACIJ9kqMStYW9XYm3AmWZ9d+s8ry/TUnCY1NJivaaTB2zijq+FshkOGbeCAzS8ouTloVKEBGARh\n2hAGC9MscYfnG46Y4EUaIugJBkqGz5dwrlhMhdS1Y1gVNx6GO6/M2gIaJ1gb9LYIq2BK1IaYaDOn\nlEEhZbpJMFPN+j+IDDziVyBGV5G1nOvSOS0dMaEzLZ2TXIJNn6HvsajvC6dTemJ6udYcb1ZOVJFs\ndEUa3z8AACRnqPllVvC1+RedWvZ+IKJaLVQZnmfT6RyHDNnUUDxXklTVflT7znUOzIK2KRTQWVVo\nVuXZvjnma7Cqo0q8YZQADcAwEaYNIXQDFRRoSV/foHO+pDjVYjdI8mdLp+dKskdUUBUVSY/3uLXS\n2Can3yeH4vTlR33sJmuYdia0cY6U9ejb2ZmwBTQmyAyvEAy7ze27PXpyVrlWtTTL6ZOyDSVs5wy1\nHsYJc6hkGgWDVKlZImaq+ZySzW9+z3eWavOeo1r2fr9sAYemd/erqC/2/5KWTkhKuOlTig3VDv2o\nQ/PbDWUZA0/eQ8dUDFSk0dYJAMDwJduSKVtAm185rnUHeyN+uWVVj3U7zLfz/GGP0wvNZ4LLjgfU\nMMf8JW2iDaJ+u02OQNQLLgI0AGchFWGajBQ5efKkccMNNxhTpkwxzjnnHGPDhg1Gf39/3GNffPFF\nY/HixUZ+fr6xcOFCY/fu3XGP279/vyHJ2L9/v3nFl79sGNnZhmF2nsX9Ewj+7ZeMfsk4nSPj0DTz\n7x6b+ed0jozT2TbDP8jHMSTDp/+/vXsPjqq++zj+2U025LpxkxIJkhCSEAgkYLWJGZggjpU6pTLT\nQkS5JdGoY5FitQO2AurTxwoZLx0HpVyaabiOrcVqoa0IRNopXsrQQUgqE8IjU2KGYNCQJYRN2N/z\nx5J1NzdWCCHJvl8zO9mc8ztnf/s7+93v2e+ePefr9hsmWoxmFPs1iYkxJiPD061BK4Ax9x2r9vFq\nH/P2W3dj3b69Wi2eZbTMZvSU3W+srVZj7PZBPs6XsXXr1t5dYXq6Z2Alc9HSebtcVNfbsfVS27Yu\ntmX7MhtultFj6UYyxmLxzA4J8TwkgkxxsSd4hwzpMv6bQzzvGR3fHy5YZTZMsPq9Fwwdasztt/dy\nHAADTK/nAmAAIg6uTnGx5/NLeront3ZKzzOKzYZsmzkV+fU+ek+fAZpDPLeO97u9Xdr/NOHh7OBf\nBeIAwa5Trega6OoAoGti9uzZstvtqqur08cff6zdu3fr+eef79Suurpas2bN0vPPP6+mpiY999xz\nKigo0LFjxy7/IGVlksslFRd7DnGx2TzfYvho/8mgVZ6jG2JdUsYZz98hxnOLdUn21q4vKiB5vlG5\ncOkqdOFL7brh7mKVHHL7HSGVnu45uOfo0UH+BUrHMbd2PWqh8h/f9jGPdUn2Lo5ekjzfcp0Nk47G\necZ67Jx06X9d0spGWXd4jkKx26XCQs+3aIN6nC9j27ZtvbvC6mrPwGZkyJqWLtnt8j3+0CrPthvd\nYTu2H70Z0mF1bRbPkZs3PGFXSXKxtLpadruUliZlZEgLFnBEYVAqK/ME75w5kt0ul83/nSDiouc9\nw3dqm0XaNN6mkpRCaWWjwneVyW73nB/Nbu/lOAAGmF7PBcAARBxcnbIyz+eX6ur23CoNGeKZFx4u\nDXm3TCWHXfpzfLqOxtp03B6q+gjPJ6zzIV8fndYu4tIRaB3ve4WHf/0AQ4dKqamencP772cH/yoQ\nB8C11yfFtGPHjmnfvn0qLS1VeHi4Ro0apeXLl2vDhg2d2paXl2vKlCmaMWOGrFarCgoKNHXqVG3c\nuDHwB2z/gOZyeQoCNpt3Vle/1OzqHGidztMlTwGtMUwqu1kKf9qmkpHF0spGWf7sKeykpwdxYaB9\nzAsLuy1k+rJ0c1/yjHVjmKeAdsMTMRo7J0MlycWyvl7tV0Bzucix15Tv3lRjo6zp6Z22qe9/vtux\nPc7Oh1zallk2jc33xEvIzjKlp3u2XXV1EBSccXmX3j/C5hV2WVRr12aRauyhKjns8hbR2NcGAODa\n8P3Oq72+1X5/5ZBqzbjRpZU/atXRSWn6v6E2HZiargNT09UUbvH+fNNls3rzustm9a/MtSfy9pX+\n4AfsHAIYMEL74kEqKysVFxenYcOGeadlZmbq5MmTOnv2rOx2u1/b7Oxsv+XHjRunw4cPX9mDl5V5\nbpdOBmCpq5OamvyadHWS+/arzLWfUH9zVqhKpkd6ZlbNlG1VmecqoBn8fN9P+3hLflc+1cWLfudW\n8x3zjmO9dVyMHrwzUaGfT1b6m2Wqr/e0m1nIOF9X1dWdzlTrux19r7B6zmbRqchQfRg/UotOV0sn\npPTzxAsu49L7R9il15nrvFNhrW61hFp0wSq9k5Sm/7FUK+NGXkcAAPSVy+dbz1EEo3wnXfrsFdZ+\nic8O90nkAAa6PimmNTU1KSoqym9aZKSnMOV0Ov2KaU6ns1PbiIgIOZ3Oq+uE75u1b5EnIkKWhAQ1\n155QyAXPleVabNIZe6ja3Bf1zxGhWjxhnhI+KJP9dc/iM2dKZa6r605Q6K6w5jPmbe42NdhDJFl0\ncsJI5e+uVrGk4uvZb3SvY3Ha7VZ7tdOSkCBZrQqdPFmxZWWKlZQhacF17TAGJN+i2j//qfDJkxVe\nVqb5kuZf774BAIDLo1AGYJDrk2JaVFSUmpv9r3Hc/n9MTEyntufOnevU1rfg1tF//vOfb9ahxx7z\n3AIwQVKFJD1+0G/6wYNdtUa3AhjzKHmuuoFv7quvvurbsbvc9mQ7ojf4vs4CeE31eRwA/QwxABAH\ngEQcAN+4RnQF+qSYlpWVpYaGBtXX1yshIUGSVFVVpaSkpE7FtKysrE6BX1VVpdzc3E7rTUxM1PDh\nwzVv3rxr13lggLj11luvdxeA6444QLAjBgDiAJCIA2D48OFKTEy8Zuu3GGO6Oid/r5syZYpGjBih\ndevW6fTp05oxY4YKCgq0YsUKv3ZHjx7Vt7/9bZWXl+uHP/yhtm/frqKiIn3yySdKT0/vtN66ujrV\n1dX1xVMAAAAAAABAP5eYmDg4imn19fV67LHHVFFRIavVqsLCQq1atUoWi0UxMTFat26d7r//fknS\nrl27tHTpUtXU1CglJUWlpaW6++67+6KbAAAAAAAAQLf6rJgGAAAAAAAADHTW690BAAAAAAAAYKCg\nmAYAAAAAAAAEqN8W0w4dOqS77rpL8fHxGjZsmAoLC9XQ0CBJ+uijj3TbbbcpOjpaqampKisr81u2\nvLxco0ePVlRUlHJycvThhx9ej6cAXLWe4uDRRx9VeHi4YmJivLcNGzZ4lyUOMFjs3btXt912m2Jj\nY5WYmKif/OQnamlpkUQ+QHDoKQbIBQg2Fy9e1NSpU1VcXOydRi5AsOkqDsgHCBZvvPGGQkND/V7r\nhYWFkvo4H5h+qLm52SQmJppnn33WtLa2moaGBjN9+nRzzz33mC+//NLExcWZ119/3Vy8eNHs3bvX\n2O128/777xtjjKmoqDB2u93s37/ftLW1mVdeecXEx8ebL7/88jo/K+Cb6SkOjDHm1ltvNRs3buxy\nWeIAg0V9fb2JiIgw5eXlxhhj6urqTHZ2tnnmmWfMmTNnyAcY9HqKAWOM+c53vkMuQFBZvny5CQkJ\nMcXFxcYYQy5AUOoYB8aQDxA8nnzySfPAAw90mt7X+aBfFtM+/fRT8/3vf9+43W7vtLfffttERUWZ\nDRs2mIyMDL/2jz76qJk/f74xxpi5c+eaRx55xG9+Zmam+e1vf3vtOw70op7i4MKFCyYsLMxUVlZ2\nuSxxgMHE6XQaY4xxu93m8OHDZvTo0ea1114z69evJx8gKHQXAy0tLeQCBJU9e/aYrKwsc++995qi\noiJjjDHr1683Y8aM8WtHLsBg1lUckA8QTKZMmWJee+21TtP7Oh/0y595jhkzRjt37pTFYvFOe/PN\nN3XLLbeosrJS2dnZfu0zMzN1+PBhSepy/rhx47zzgYGipzg4dOiQ2tratGLFCg0bNkxjxoxRaWmp\nzKWL81ZVVREHGDSioqIkSUlJSZowYYKGDx+uoqIiVVZWasKECX5tyQcYjLqLAXIBgsmpU6dUUlKi\nrVu3KiIiwrt/xGcDBBPfOIiMjPROJx8gWLjdbh08eFA7d+5USkqKkpKS9Mgjj+irr77q83zQL4tp\nHS1btkw7d+7UmjVr1NTU5N2pbBcZGSmn0ylJcjqdPc4HBirfOGhsbNQdd9yhxYsXq7a2Vps3b9ar\nr76ql156SZIuGyfAQFRTU6Pa2lpZrVbNmjVLTqfTb0dSIh9gcOsYA2fPniUXICi43W7Nnz9fTz75\npLKzs/2+aGxqaiIXICh0jANJ3lggHyBYfPHFF7rllltUUFCgTz/9VPv371d1dbXmzZvX558N+nUx\n7ezZs5o5c6a2bt2qv//97xo/fryioqLU3Nzs1665uVl2u12Supx/7tw573xgoOkqDr773e9q9+7d\nys/PV0hIiHJycvT444/rjTfekEQcYHAaMmSIEhMTtWrVKv3tb38jHyDodIyBnJwccgGCwgsvvKDI\nyEgtXLhQkmQ8p6qR1PXrnFyAwainOOCzAYJFQkKC9u3bp6KiIoWHhyspKUmlpaX661//KmNMn+aD\nfltMq6mpUU5OjpxOpw4cOKDx48dLkrKyslRZWenXtqqqSllZWd75R44c6XY+MJB0Fwd/+tOftHbt\nWr+2LS0t3ko8cYDBYv/+/crMzFRra6t3WktLi8LCwjRu3DjyAQa9nmJg165d5AIEhc2bN6uiokIO\nh0MOh0Pbtm3T1q1bFRcXp+zsbHIBgkJ3ceBwOPT222+TDxAUPvnkEz311FN+01paWmS1WpWbm9u3\n+eCKzrR2jZ05c8YkJyebBx54wO/k68YY09DQYBwOh/n1r39tXC5Xpys07Nmzx9jtdlNRUWFcLhdX\nKsGA1VMcvPXWWyYyMtLs2bPHuN1us3//fjN06FCzZcsWYwxxgMHD6XSa5ORk88QTTxiXy2U+++wz\nk5ubaxYuXEg+QFDoKQbIBQhWRUVF3qsYfvHFF+QCBCXfONi+fTv5AEHhv//9r4mOjjalpaWmtbXV\nnDhxwuTl5ZmHHnqozz8b9Mti2ksvvWQsFouJiooy0dHR3ltMTIwxxpgDBw6YyZMnG7vdbtLT072X\ni2+3efNmM3bsWBMdHW3y8vLMxx9/fD2eBnBVLhcHa9euNWPGjDFRUVEmLS3NrFmzxm954gCDRVVV\nlZk2bZpxOBwmJSXFLF++3LhcLmMM+QDBoacYIBcgGPkWEYwhFyA4dYwD8gGCxb59+8ykSZOM3W43\nCQkJZvHixebChQvGmL7NBxZjLv3QGgAAAAAAAECP+u050wAAAAAAAID+hmIaAAAAAAAAECCKaQAA\nAAAAAECAKKYBAAAAAAAAAaKYBgAAAAAAAASIYhoAAAAAAAAQIIppAAAAAAAAQIAopgEAAAAAAAAB\nopgGAADQS1JSUhQREaGYmBjZ7XZFR0frpptu0pIlS+R2u69onVu2bFFWVtYV92ns2LEqLy/vcl5W\nVpa2bdt2xev2tXLlSt1xxx29si4AAID+LPR6dwAAAGCwsFgsWrt2rRYsWOCdduTIEd15552KjIzU\ns88++43XOXfuXM2dO/eq+mSxWLqcd+TIkSteLwAAQLDiyDQAAIBrKCsrS/n5+fr3v/8tSXK5XFqx\nYoXS0tIUHx+v6dOnq6amxtvearVq8eLFSkhI0IwZM/S73/1Oo0aN8s7/xz/+oSlTpsjhcCg1NVUr\nVqyQy+Xyzl+5cqVGjBihG264QT/96U/V1tbWbd9SUlK0ceNGSdLUqVP185//XLfffrtiYmI0btw4\n/eEPf+h22Q8++EC5ubmKjo7WpEmTdOzYsSseIwAAgIGEYhoAAEAvMsZ477e2tur9999XRUWFpk2b\nJkl6+umn9Ze//EV79+5VXV2d8vLyNG3aNL+C2PHjx1VXV6dNmzb5rfvo0aO66667VFBQoNOnT+u9\n997TO++8oyVLlkiSysrK9Morr2jHjh06ffq07Ha7X6Guo45HrK1fv16vvvqqzpw5o5kzZ+rhhx/2\n61e7hoYGTZ8+XbNmzVJjY6Nefvllbd++vdsj4AAAAAYTimkAAAC9xBijH//4x3I4HHI4HEpISNCi\nRYv0s5/9TAsXLpQxRr/5zW/0q1/9SiNHjlRYWJiWLVsml8ulnTt3etczZ84chYSEKDY21m/9W7Zs\n0cSJE7Vo0SKFhoYqLS1NL7zwgtavXy9jjDZt2qSHH35YN998s2w2m5555hkNGzYs4P7fe++9mjhx\nomw2mxYsWKDGxkbV19d3ardjxw7FxMRoyZIlCgkJUV5enkpKSvwKiQAAAIMV50wDAADoJRaLRWvW\nrPE7Z5qv06dP69y5cyooKJDV+vV3mq2trfrss8+8/w8fPrzL5U+dOqXU1FS/aSkpKTp//rzq6+v1\n+eefKzk52TvParVq5MiRAffdt/Bms9kkqcsLJ9TW1uqmm27ym5aamqp//etfAT0WAADAQEYxDQAA\noI9861vfUnh4uN577z3l5uZ6p9fU1CgxMdH7f3c/lxw1apTeeustv2k1NTUKDw9XXFyckpKSdPz4\nce88Y4xqa2t7+VlIycnJOnHihIwx3r6ePHmy1x8HAACgP+JnngAAAH3EarXqwQcf1NKlS1VbWyu3\n263NmzcrMzNT1dXVl13+vvvuU2VlpVavXi2Xy6Wamhr94he/0Ny5c2Wz2fTQQw9p3bp1+uijj9Ta\n2qpVq1YFXOQyxgT8M8177rlHbrdbzz33nFwulw4ePKi1a9dyzjQAABAUKKYBAAD0oRdffFF5eXnK\nz8+Xw+HQyy+/rD/+8Y+aOHFil+0tFou3SJWSkqJ3331Xv//973XjjTcqPz9f3/ve97R69WpJ0uzZ\ns/XLX/5Ss2fPVnx8vKqqqpSTkxNQv3wfx3daV2JjY7Vr1y7t3btXcXFxKi4u1n333RfoEAAAAAxo\nFsOZYgEAAAAAAICAcGQaAAAAAAAAECCKaQAAAAAAAECAKKYBAAAAAAAAAaKYBgAAAAAAAASIYhoA\nAAAAAAAQIIppAAAAAAAAQIAopgEAAAAAAAABopgGAAAAAAAABIhiGgAAAAAAABAgimkAAAAAAABA\ngCimAQAAAAAAAAGimAYAAAAAAAAE6P8BnDbhrkmOOD8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa792d96c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=1, num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "def sin_func(t,ph=0):\n",
    "  return np.sin( (t/350.) * 2. * np.pi  + ph)*1. + 100.\n",
    "\n",
    "T1 = np.linspace(0,2000,100)\n",
    "T2 = np.linspace(0,2000,1000)\n",
    "T3 = np.linspace(0,2000,10000)\n",
    "Y1 = sin_func(T1,np.pi/16.)\n",
    "Y2 = sin_func(T2, 0)\n",
    "Y3 = sin_func(T3,-np.pi/16.)\n",
    "\n",
    "f = np.logspace(np.log10(1./500), np.log10(1./100.), 1000)\n",
    "\n",
    "\n",
    "axs[0].plot(T1, Y1, lw=0, marker=\".\", ms=1.0, label=str(len(T1)))\n",
    "axs[0].plot(T2, Y2, lw=0, marker=\".\", ms=1.0, label=str(len(T2)))\n",
    "axs[0].plot(T3, Y3, lw=0, marker=\".\", ms=1.0, label=str(len(T3)))\n",
    "# axs[0].set_xlim([np.min(T1),np.max(T1)])\n",
    "\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T1, (Y1)-np.mean(Y1), f*2.*np.pi) / T1.shape[0] )), lw=0, marker='.')\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T2, (Y2)-np.mean(Y2), f*2.*np.pi) / T2.shape[0] )), lw=0, marker='.')\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T3, (Y3)-np.mean(Y3), f*2.*np.pi) / T3.shape[0] )), lw=0, marker='.')\n",
    "# axs[1].plot(f, npgram)\n",
    "# axs[1].set_xscale(\"log\")\n",
    "axs[1].set_xlim([200,500])\n",
    "axs[1].set_xlabel(\"Period in d\")\n",
    "\n",
    "\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T11:03:15.504601",
     "start_time": "2016-09-27T11:02:24.168332"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNcAAAIPCAYAAABQVjhcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa78a1eac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=1, num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "def sin_func(t,ph=0):\n",
    "  return np.sin( (t/350.) * 2. * np.pi  + ph)*1. + 100.\n",
    "\n",
    "T1 = np.linspace(0,200,10*200)\n",
    "T2 = np.linspace(0,2000,10*2000)\n",
    "T3 = np.linspace(0,20000,10*20000)\n",
    "\n",
    "Y1 = sin_func(T1,np.pi/16.)\n",
    "Y2 = sin_func(T2, 0)\n",
    "Y3 = sin_func(T3,-np.pi/16.)\n",
    "\n",
    "f = np.logspace(np.log10(1./500), np.log10(1./100.), 1000)\n",
    "\n",
    "\n",
    "axs[0].plot(T1, Y1, lw=0, marker=\".\", ms=1.0, label=str(len(T1)))\n",
    "axs[0].plot(T2, Y2, lw=0, marker=\".\", ms=1.0, label=str(len(T2)))\n",
    "axs[0].plot(T3, Y3, lw=0, marker=\".\", ms=1.0, label=str(len(T3)))\n",
    "# axs[0].set_xlim([np.min(T1),np.max(T1)])\n",
    "\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T1, (Y1)-np.mean(Y1), f*2.*np.pi) / T1.shape[0] )), lw=0, marker='.')\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T2, (Y2)-np.mean(Y2), f*2.*np.pi) / T2.shape[0] )), lw=0, marker='.')\n",
    "axs[1].plot(1./f, np.sqrt(4.*( scipy.signal.lombscargle(T3, (Y3)-np.mean(Y3), f*2.*np.pi) / T3.shape[0] )), lw=0, marker='.')\n",
    "# axs[1].plot(f, npgram)\n",
    "# axs[1].set_xscale(\"log\")\n",
    "axs[1].set_xlim([200,500])\n",
    "axs[1].set_xlabel(\"Period in d\")\n",
    "\n",
    "\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read EBFS data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T09:08:29.873871",
     "start_time": "2016-09-27T09:08:25.194053"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('max_columns', 50)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T10:11:20.676191",
     "start_time": "2016-09-27T10:11:20.390051"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113458, 2)\n",
      "(106485, 2)\n",
      "(106482, 2)\n"
     ]
    }
   ],
   "source": [
    "EBFS_11_16 = pd.read_pickle(\"../muon/EBFS.pkl\")\n",
    "# EBFS_11_16['Days'] =  (EBFS_11_16.index-1302710448)/(24*3600)\n",
    "EBFS_11_16['Days'] =  (EBFS_11_16.index-1291074900)/(24*3600.)\n",
    "\n",
    "EBFS_11_16.sort_index(inplace=True)\n",
    "print EBFS_11_16.shape\n",
    "\n",
    "EBFS_11_16 = EBFS_11_16.reset_index().drop_duplicates(subset='Start', keep='last').set_index('Start')\n",
    "print EBFS_11_16.shape\n",
    "\n",
    "EBFS_11_16 = EBFS_11_16[ (EBFS_11_16['Temp']>250) & (EBFS_11_16['Temp']<400) ]\n",
    "print EBFS_11_16.shape\n",
    "\n",
    "EBFS_11_16.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T09:23:00.708629",
     "start_time": "2016-09-27T09:23:00.599835"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T10:33:35.146641",
     "start_time": "2016-09-27T10:33:12.989396"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000,) 0.001 1.0\n",
      "CPU times: user 21.9 s, sys: 8 ms, total: 21.9 s\n",
      "Wall time: 22.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from scipy import signal\n",
    "\n",
    "T = EBFS_11_16['Days'].values\n",
    "Y = EBFS_11_16['Temp'].values #/ EBFS_11_16['Temp'].mean()\n",
    "\n",
    "f = np.logspace(np.log10(1./1000), np.log10(1./1.), 1000)\n",
    "print f.shape, f.min(), f.max()\n",
    "\n",
    "npgram = np.sqrt(4.*( scipy.signal.lombscargle(T, (Y)-np.mean(Y), f*2.*np.pi) / T.shape[0] ))\n",
    "# npgram = np.sqrt(4.*( scipy.signal.lombscargle(T, Y, f) / T.shape[0] ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T10:33:36.200464",
     "start_time": "2016-09-27T10:33:35.152467"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 400)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNMAAAH8CAYAAAD7U+kMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xd4lFX2B/DvAAFJAoQSqoAgXXCBUG0k9JqZBEILSLGi\nrIK66rr+3FV3WV1XxUVwVUBADSUJmQlNqiAgIF06KAjSJEAgkEAI5P39cXYwQAKTTLlv+X6eh8fH\ngDMHYXLve+6559g0TdNAREREREREREREd1RMdQBERERERERERERGwWQaERERERERERGRh5hMIyIi\nIiIiIiIi8hCTaURERERERERERB5iMo2IiIiIiIiIiMhDTKYRERERERERERF5iMk0IiIiIiIiIiIi\nDzGZRkRERERERERE5CEm04iIiIiIiIiIiDxU6GRaWloa6tWrh1WrVl3/2oYNG9C2bVuEhoaibt26\nmDp1ar7/7QcffICoqKjbvn5mZiZGjBiBSpUqISwsDMOGDUNmZmZhwyQiIiIiIiIiIvK5QiXT1q5d\ni/bt2+PgwYOw2WwAgPT0dPTs2RPDhw9HRkYGpkyZgrFjx96QbMvMzMSLL76Il1566fp/V5DRo0fj\n2LFj+Omnn3DgwAEcOXIEf/zjH4vwWyMiIiIiIiIiIvItj5Np06ZNQ3x8PMaNG3fD15OTkxEeHo5R\no0ahWLFiiIqKQnx8PKZMmXL91zRv3hy//fYbRo0aBU3TCnyPrKwsJCQk4K233kJYWBjCw8Px7rvv\nIiEhAZcvXy7Cb4+IiIiIiIiIiMh3PE6m9ejRAwcPHkT//v1v+PquXbvQrFmzG77WuHFj7Nix4/q/\nr1q1Cl999RXCw8Nv+x4HDhxATk7ODa/XuHFjXLlyBfv37/c0VCIiIiIiIiIiIr8o4ekvrFKlSr5f\nv3DhAoKDg2/4WnBwMC5evHj936tXr+7Re1y4cAEAEBIScsNrAbjh9fI6ceIETpw44dHrExERERER\nERGRuVWrVg3VqlXz2+t7nEwrSGhoKI4dO3bD17KyslCmTJlCv5Y7iZaVlXU9iZaVlQUA+b7eiRMn\n0KpVKxw/frzQ70VEREREREREROZTvXp1bNq0yW8JNa+TaU2bNsWSJUtu+Nru3btvufrpiYYNGyIo\nKAg7d+5EmzZtrr9WyZIl0aBBg1t+/YkTJ3D8+HF89dVXaNy4cdF+A4G2fz/wr38BW7cCd98NdOwI\ntGgBVK0K2GzAmTPA7t3At9/KP2vXBsaOBR5+WHXkRIGVnQ1MmQJ8/TWQmyufgQceAO69FwgJATIz\ngV9+ATZskM/L1avAgAHAk08CoaEBD3fMmDEYP358wN+XCACwaRPwwQfAvn1AvXqytjRrBrjbK5w6\nBezYASxdKp+bpk1lbWneXGnY5Fv8PuSBjAzgv/8FEhOBu+4COnUC2rQBatUCgoOBixeBn34Cvv8e\nWLMGKFkSePRRYNgwIChIdfREgaNpwPLlwIQJwNGjsqZERQGNGwMVKgDXrgEnTgDbtsnacvIk0K4d\nMHYsxnz8Mb8XkbWcOgV8+CGwZAkQFgZ07gxERAA1awKlSgHp6ZIHWLsWWLdOfs0zzwAOB1C8uOro\nTWnPnj0YMmQITpw44b/qNK0IbDabtmrVKk3TNO3MmTNa+fLltfHjx2tXrlzRVqxYoZUtW1ZbuXLl\nLf/dX//6Vy0yMvK2rz106FAtKipKS0tL006dOqVFRkZqI0aMyPfXbt68WQOgbd68uSi/jcC6elXT\n/vY3TStWTNMaNdI0l0vTrl27/X+zfr2mde6saYCmDRqkaRkZgYmVSLWNGzWtfn1NK1lS0155RdN+\n++32vz49XdPeflvTgoM1rUYNTfvuu8DEmUefPn0C/p5E2qVLmjZ6tKwTbdtq2qpVmpabW/Cvz83V\ntCVLNK1VK02z2TRt7FhNy84OXLzkV/w+dAdLlmha5cqaVqaMpr377p33VSdOaNoLL2haiRKadt99\nmrZtW2DiJFLt9GlNi4uTtaVXL03buvX2v/7qVU1LTNS0evU0LShI69O48Z2fc4jMYsYMTStXTtOq\nVtW0Tz7RtMuXb//rDx7UtGHD5PP10EOadvhwQMK0mkDkijweQFCQChUqYOnSpUhMTESlSpXw5JNP\nYsKECejQocMtv9Zms8Fms93wtXHjxqFp06bX/33SpEmoX78+mjVrhkaNGqFu3bqYOHGit2Gqdf48\n0KMH8OabwBtvANu3A9HRQLE7/O9v21ay219+CcyfL/9+4EBgYiZSZcYM4KGH5MRm2zbgnXeAypVv\n/9+EhQGvvw7s2SOVa1FRwEcfBSZeIlVOnJBqzc8/Bz7+WE46H3lEqpwLYrMBXboA69cD//43MHGi\nnJ6mpQUubqJA0zRg3DigWzepxty3D3j5ZeBOLUmqVgXefx/YvBkoUUI+b4mJgYmZSJW9e6Vac/ly\nYPZseQa5UxVz8eJAv37Arl1S9bxnDxAbC/yvXQ+RKV29CowZI9XL0dFyq+zpp6US7Xbq1AGmTQNW\nrwaOHJHP13ffBSRk8jG/pekCwBCVaadOaVrLlpoWFqZpy5YV/XX27NG0hg01LTxc07Zv9118RHry\nr3/JKc2IEVJxUxQ5OZr20kvyOq++evsqHR9iRQgF1L59mla7tlRielMts26dplWpomn33qtpR474\nLDxSg9+H8nHtmqY995ysCW+8UfRqmcxMTRs8WF7nk098GyORXqxfL88sTZpI9UwR9WnTRtNCQjSt\nTRtNO3PGhwES6UR2tqbZ7ZpWvLimTZxY9Nc5e1bToqI0rVQpTUtO9l18ZIzKNLqNM2eAyEjg2DFg\n1Srpy1FUjRrJHeuaNaXqZts2n4VJpAv//KdUCrz+uvRKu+uuor1OiRLAe+9J/6h33pHXJDKTX36R\ndaB0aenr9Ic/FP212rWTKrVr12S9OnLEV1ESqadpwLPPSs+nTz6RGwJ3uhVQkOBg4KuvpAph1Ch5\nTSIz2bRJqjebNpW1pU6dor9WlSpSaXPwINC9u/QqJDKLK1eA/v2BRYuA1FTpfVZU5cvL6zgc8prz\n5vkuTvI7JtP8JSsL6N1bmhF+9x1w//3ev2bFilJyXacO0LMnH3rIPKZOBV57DfjrX4G33779NTVP\njR0rVz3//W/g3Xe9f707GDRokN/fgwgnT8o1zdKlgRUrpGm6t+65B1i5UgZ9dOsGnDvn/WuSEvw+\ndJM335RhA5Mny9Ubb9lsclDz0kvAc88Bc+Z4/5pEerB3L9C1qwwXWLgQKFfOq5cbNGgQ0LKltKs5\ncECeiS5f9lGwRAppGvDYY5IAczrlmdxbpUrJYY3DAcTFyWA1MgQm0/whNxeIj5fJaQsXAvlMIi2y\nsDBgwQKp2unRQ/qxERnZsmXAU0/JFM6//tW3r/3cc8D//R/w6qt+73PDh1jyu+xsICZGJtkuXQr4\ncjJR7drA4sXAb79J35ucHN+9NgUMvw/l8cUXkkz75z+BkSN997o2m0xlj48Hhg6ViZ9ERnb2LNCn\nj/QIXLTozr0EPXD9e1GLFvIs9MMPUtGpaV6/NpFS48ZJ4mvGDHkW95USJYCvv5bet337Aj//7LvX\nJr9hMs0f3n1XMtWzZgGtW/v+9atUkcXu2DFgxAguTGRchw/LCUynTtII3RcVaTd7800pmx4xQhrj\nEhnVc88BW7bI+uLN9ZuCNGgApKRINfWLL/r+9YkCZcsWeXB//HHglVd8//o2m7QjaN9e1rCTJ33/\nHkSBcPUqMGCAJNTmzZNDe19r314+L9OmAePH+/71iQLF6ZR2NH/7m3xufK1UKRn6UbGiVKldvOj7\n9yCfYjLN15Yvlw/Z669LSbO/NGwITJ8uDz5cmMiIcnKAgQNl4zZzppzI+IPNJtdI69SRqh4uTGRE\nU6cCn30mfZ/atPHf+3ToAHz4ofSDSknx3/sQ+cvZs3Kq37Sp/D32xyEN8PtDDwAMHix9B4mMZtw4\naRmQlCTT0P0lPh7405/kx4YN/nsfIn85ckQO5mNjgTfe8N/7lC8PuFxScPDUU/57H/IJJtN86cwZ\nWSw6dpSMtb/Z7dK34+WXpWkokZG8/rr8vZ01SxYOfwoJAebOBY4fZ8UNGc/PP0tV2siRvr2uVpBn\nnpHN4siRMuyAyEieeUZaYCQlFX2QjaeqVJHDoFWrgL//3b/vReRr338PvPWW7Meiovz/fv/4B9Cq\nlSSfL1zw//sR+crVq/L3tmxZ6cHpr0MatyZN5PA0IUF+kG4xmeZLf/yjTPeYPh0oXjww7zlunAw3\nGD5c+ukQGcGaNdJzZtw4oG3bwLxn/frSOPqzz4D58wPznkTeunYNGDYMqFw5cFXI7itsZcvKNTm2\nEiCjmD1bfkyaJIM1AiEyUpIRf/87sHVrYN6TyFsZGVIA0KaN9JYNhKAg6Ql16pQ8MxEZxTvvAOvW\nSWLL3wUAbvHxwKBBckB0+HBg3pMKjck0X0lOltPJCROA6tUD975BQdKDYP9+6Q1FpHeXL8sUnPbt\ngRdeCOx7P/EE0KuXvP/Zs4F9b6KieP99qR6YMcMnTaE9FhYGfP65tC6YMiVw70tUVCdPykNHXJx/\netnczl/+IpUEI0bIoSqR3v3lL0BamiS3/NVmIz/33gv85z9SeLB4ceDel6io9uwB3n5b+m8++GBg\n33vSJDnYfPJJHmzqFJNpvpCeLo1uY2KkBDTQmjWTu9vvvitNd4n07M035erY5MmBq+B0s9kkQXD5\nMvDnPwf2vYkK69AhmXD7wgvAQw8F/v27dpXkwIsvAkePBv79iQrjueckKTBpkv+v4NysZEk52Ny5\nUyoYiPRswwYZ+vT22/4ZZnMnw4dLS5ynn5bp1ER6lZsrB/G1a/u3T1pBwsJkTVuy5PcenaQrTKb5\nwhtvAJcu+W8aoSdeeUVORUePlg8+kR7t2AG8955cKWjSRE0M1apJ347PPpOSbSK9ev55oFKlwPTg\nLMj77wPBwcDYsepiILqTZcuAxET5+1qpkpoYWrSQ5urjxkkinEiPcnKkyqVFC3VXLW024NNPpZqU\nt2pIzz77DFi7Vv7p7x6cBendW4bqjBkjBTykK0ymeWv7dskY/+1v8pCuSlAQ8PHHkhz48kt1cRAV\nRNNkIahbV4ZmqDRqFBARIaeiV6+qjYUoP6mpwLx50ictNFRdHOXLS3/DpCTg22/VxUFUkCtXJCnw\n8MPSY0al118HwsMD38KAyFMffywVlJ99FtjrnTerV08OVj/4QK7REenN2bPAa69JhX5kpNpYPvoI\nyMqS69mkK0ymeUPTpBKsYUO5XqBahw7SqPDll2WSFZGeuFwyfv3DD+VKjErFiwP//a9Uyn3+udpY\niG52+bJUpXXvLlM1VYuPB9q1k5iYfCa9GT8eOHBAkgSqbge4hYQA//434HSyHxTpz5kzMr3zySfl\nQFG1F1+U63MvvaQ6EqJbvfmm7Hn++U/VkQA1akjhzqefArt3q46G8mAyzRtz5shUwgkTpDJMD957\nT/oP/OMfqiMh+l12tmyaunUDevZUHY1o1QoYOlQWJ45oJz2ZOBH49VdJEqhODgBAsWLSMHrHDqlm\nINKLtDSZovnMMzLZXA/695cqhjFjmHwmfXn7bZkQrZerlaVKSeXzwoXSE4pIL/btk5tnr70GVKmi\nOhrx7LMypfpPf1IdCeXBZFpR5eRIOX+vXkCnTqqj+V2NGpK0mDCBDaNJPyZMkLHOH36oj+SA29//\nLuPh//Uv1ZEQifR0OQx5/HGpetaL1q2laTSTz6Qn48bJP1U0hi6IzSa92/bulYmFRHqwf78c1Pz5\nz0Dlyqqj+V1srFzRfuEFJp9JP/70J+Duu+VQRC9KlZJhgwsXSp9Q0gUm04pq6lTg559/38jpyYsv\nSo8dlU2ridwyMmS62eOPA40bq47mRjVrykL5/vvAsWOqoyGSz0p2tkzx1Js335QWAh99pDoSIjmg\nmTRJHnpUDR0oSMuWwIABsg+7dEl1NESSRKteXV/JAUCSzx98AOzaxZ7PpA+rV0vP2nfeUTd0oCB9\n+wIPPihXozlwUBeYTCuKrCx5qBg8WD/XCvIqW1aq5r74Qk5GiVT66CPg4kX5O6lHr74q0wrfflt1\nJGR1v/4qn5cXX1Q70KYgtWrJ8I733pPGvEQq/fWvMiBDr5Nm334bOHFCEn5EKm3dCsydK/3SSpdW\nHc2tWrWSCrW33pKbP0QqvfEG0Lw5EBenOpJb2Wxym2b7diA5WXU0BCbTimbCBOnT8dZbqiMp2NNP\nS9WNXhMYZA1nz0oz5lGjpFxaj8qVk8qGqVOBI0dUR0NW9s9/SlWxnvth/PnP0nOHV6NJpd27gRkz\nZBqgymm3t1O/vlRkjxvHoVCk1t/+Jn8fVU+7vZ0335Rq0y++UB0JWdm33wIrV8rfx2I6TZM88ADQ\ntavEyOo05XT6t0THLl6UU/nHHwfq1lUdTcFKlZLMenKylE4TqfDvf0sPjFdfVR3J7T37rFR0vvOO\n6kjIqo4dA6ZMkaq0MmVUR1OwKlXkmtB//gP89pvqaMiq/vEPOTB84gnVkdzeG2/IUKiJE1VHQla1\neTOQmiqJ5xIlVEdTsKZNZXjH3/8urQ6IAk3T5Ht2RATQp4/qaG7vzTfl+T4xUXUklsdkWmF9/rmc\nMOo9OQAAQ4bItRw99nUj8ztzRh64//hH/UzCKUhoqPQfmDxZrtoRBdp77wEhIZLY1bsXXwSKF2fv\nNFLjp5+AWbOAl18GSpZUHc3tVa8OjBwpw3cyM1VHQ1b05ptAgwbAoEGqI7mzv/1NDpYmT1YdCVnR\nsmXAmjVy80xPw9Ly064d0L27fL6vXVMdjaUxmVYY2dlSaTNkCFC7tupo7qxkSdlszpolm0+iQPr4\nYyk/fvFF1ZF4htVppMrJk8CnnwLPPy9/B/WufHm5uj1xInDunOpoyGreeUemEY4cqToSz7z8skzp\n/ewz1ZGQ1WzeLI3U33hD31Vpbo0aAQMHShsB9k6jQHv7baBNG6BHD9WReObNN4E9e4CkJNWRWBqT\naYUxbZo0kzVCVZrbyJGy6WSCgAIpM1Oq0h57DAgPVx2NZ8qUkcTf5MmS3CAKlPffB4KCgOeeUx2J\n58aOlQMmNlenQDpyBJg+Xb5X67GRen7uuQcYOlQOY3l9jQLp3XeBevUkQWUUr7win/M5c1RHQlay\nfr1M8fzzn/VflebWpg3QpYsknzVNdTSWxWSap65elUWpXz+gYUPV0XiudGm5vjZ9OpurU+BMmSLX\noY1SleY2apRUdH78sepIyCrS04FPPpHr0OXLq47Gc9Wq/X59LStLdTRkFe+9J9WbTz+tOpLCefVV\nOYxlc3UKlIMHpW+y+1q+Udx/v1xfY4KAAum99+Q6dHS06kgK509/ArZskcEJpASTaZ6aMwc4dAh4\n7TXVkRTeU09J1c1//qM6ErKCnByptBk4UE7kjSQsTBpaT5rE/jYUGJ9/Lp8ZI1Wluf3pT5IMZH8b\nCoTTp+Xv2vPP63eCZ0EaNpTD2Pff5/Q1CowPPwQqVACGDVMdSeG98grw44/AN9+ojoSsYP9+ICVF\n9jR6neBZkM6dgebNOWFdIYP9jVFE02RR6tJF/sIaTWgo8OST8tB24YLqaMjsZs2SKsiXX1YdSdE8\n/zyQkcEKAvK/nBw55BgyRP9DOvJTpw4wYAAwfjwb4JL/uXuOPfOM2jiK6oUXpH/t/PmqIyGzO3MG\nmDoVGD3aONeh8+rQAWjdmgkCCoz335c92JAhqiMpPJtNkoCLF0sCmgKOyTRPrFsHbNoEjBmjOpKi\nGz1aruIwQUD+pGmyKPXoIaX6RlS7toxn/+ADJgjIv5KSZHKZkdeW55+Xqm0mCMifrlyRgRdDhwKV\nKqmOpmjatZMf48erjoTM7pNPpALSqIlnm02q01auBDZuVB0NmdnJk9IK6fnngbvuUh1N0cTFAbVq\nSV9OCjgm0zwxfrzco+7eXXUkRXf33fJh++gjJgjIf9asAbZvN3ZyAJAeI4cOAXPnqo6EzErTJGHb\npQvQrJnqaIquTRugfXtZW4j8JSkJOH5cHniMbOxY6W2zbZvqSMisLl8GJkwARowwzgCo/Dgc0ipk\nwgTVkZCZ/fe/MunWaH048woKkrVl5kxZJymgmEy7k8OHpYHn888b7x71zcaOlYak8+apjoTMasIE\n6Q3TubPqSLwTESHXDNhnkPxl7VqpeB47VnUk3nv+eUkQ8IoB+UPeVhv33ac6Gu/ExgI1a7I6jfxn\nzhzg1CnjH2oWLy6VdbNny++HyNeuXAE+/RR49FHpmWxkI0YApUr93g6BAsbg2aEAmDhRJkc9+qjq\nSLzXujXw4IOyKSXytaNHpZJr9GjjJ54B+X2sWcMEAfnHhx8CjRsD3bqpjsR7sbFS/czqNPIHd6sN\nMySeS5SQyb0zZ8r1IiJfmzgR6NpVbtQY3WOPSVLt889VR0JmlJIi34effVZ1JN4rV05yFZ9+KklC\nChgTPPH6UWamfAN/4gnjTY4qyJgxwHffATt2qI6EzOa//wWCg405OSo/djtQvbpM9iTypaNHAZdL\nHqrNkHgOCpLN6NdfA2lpqqMhsxk/XiqezZB4BmRPWaIEKwjI9zZtAn74wRzJAUCmkcbHSw+4nBzV\n0ZDZfPwxEBVl/Ipnt2efleRgSorqSCzFBLt4P5o5Ezh/3jyLEiAJgqpVJXNN5CuXL8uDwfDhQJky\nqqPxjaAgmYL71VfyfYDIVyZPlglrRpwcVZAnnpCm0VOmqI6EzMT9YPDss+ZIPANynWjwYDmsvXpV\ndTRkJpMmSSPyXr1UR+I7o0fLoB6nU3UkZCbbt8vtEzM94993HxAZKUlCChiT7Ez85NNPZSph7dqq\nI/GdoCDg8ceBGTOAixdVR0NmMWeOVKSMHq06Et968kkgO1sm/RD5wtWr8hAdH2+exDMAVKwoU3A/\n/1ymyBH5wtSpsm8ZOlR1JL719NNSobpokepIyCzOnJEigKeflquRZvGHPwAPP8xBBORbEydKewq7\nXXUkvuVuUbN9u+pILIPJtIJs2SLl0k89pToS33viCbnCOnOm6kjILD75xDw9OvKqVk36QU2aJE2w\nibw1f75MWzLj2vLUUzLkZvly1ZGQGeTmSnJ2wADjN4e+WUQE0KqVtEcg8oVp0+Qz89hjqiPxvdGj\ngdWrgV27VEdCZpCeLrdOnn5artybid0uSUJWpwUMk2kF+ewzoEYNoGdP1ZH4Xq1a8vv65BMmCMh7\nO3cC69dLFZcZPfMMsG8fsGKF6kjIDD75BGjbFmjRQnUkvte+vVwzYBsB8oUlS4BffjFn4hmQB7lF\ni+T3SOSN3FxZW+LigMqVVUfjew4HUKkS2wiQb3z5pdwSePxx1ZH4XokS8jw2cyZw4YLqaCyBybT8\nXLggjZQfe8x8GWu3p58Gtm6V6jsib0yZAoSHA336qI7EPx55RKYuTp6sOhIyup9/lgTBqFGqI/EP\nm00SHy4XJxWS9z79FLj/fkk+m9HAgXLVm2sLeWvFCllfzLq2lCwpw61mzJDWG0RFpWnyPTc6GqhS\nRXU0/jF8OHDpEjB7tupILIHJtPzMmgVkZZmzVNqte3epUOMVA/LG5cuyuRk2TDY7ZmSzASNHShPs\ns2dVR0NG9tlncl2tf3/VkfjP0KHS42rqVNWRkJEdPw7MmyfJWZtNdTT+ERIin5fJkzmpkLwzdSrQ\nqBHwwAOqI/Gfxx+XvnCcVEje2LQJ2LHD3M/4NWvK9Gse1AQEk2n5cQ8eqFVLdST+U7y49E6bORPI\nyFAdDRmV0ykJJjOWSuf16KPAtWtSsUpUFDk5wBdfSOK5dGnV0fhPWJj0uOIgAvLGlClAqVIyqMPM\nnn4a+O03SRwSFUV6OjB3riQHzJp4BiRZ+NBDTBCQd6ZMkZ5iXbuqjsS/Hn8c2LBBEofkV0ym3WzL\nFmDzZvP26Mhr2DCpLEpMVB0JGdXkyTJlqWFD1ZH4V+XKco11yhT2GaSiWbhQJt6OHKk6Ev976inp\nA7VsmepIyIhyc+V77cCBQLlyqqPxr6ZNgTZtJNFOVBQJCdL/yWwTb/PzxBMy4Obnn1VHQkaUlSVF\nJMOHm2vibX5695ZnF/YZ9Dsm0272xRcywa9HD9WR+F/NmkCXLjIBiKiw3FP7nnhCdSSB8dhjMmp6\nyxbVkZARTZsGtGwpPaDMrm1boEkTri1UNCtXAocPm/saTl7Dh8sgAvYZpKKYOlUenM3a/ymvfv0k\nwc4EARVFUpLcxhoxQnUk/ufuM/jll+wz6GdMpuV15YpkrIcMMe/ggZsNHw6sWQMcOKA6EjKaKVNk\nU9O3r+pIAqNbN6B6dfaCosI7dQqYP1++31qBzSabuJQU4Px51dGQ0UyfDtSvL9NhrWDgQNlzfvWV\n6kjIaLZtkwM+qySeg4Pl6vcXX0g1HlFhTJ4MdOoE1K2rOpLAGDlSWvE4naojMTUm0/JasECaWw4b\npjqSwHE4JCHCCgIqjGvX5IFn8GDZ3FhBiRLyveHrr2VKDpGnEhIkwTR4sOpIAmfIEDmgmjNHdSRk\nJBcuSPXAsGHm7v+UV/nyshebNo1tBKhwpk4Fqla1xm0at8cekyrOpUtVR0JGsn8/sHq1dRLPAPsM\nBgiTaXlNmwZERAD33ac6ksApXRoYNEgmMl67pjoaMopvvwWOHbNW4hmQU57z56XZL5EnNE1O0aOj\ngYoVVUcTONWrSxuB6dNVR0JGkpwshxVW6P+U1/DhwK5d0rOXyBOXL0s147Bh1rlNAwAtWkgbgRkz\nVEdCRvLFFzIgKSZGdSSBNWKEtOQ5elR1JKbFZJpbWpo0iLZacgCQTdzRo/JhI/LEl1/KNZw2bVRH\nElj16snAhS+/VB0JGcW2bcCPP1qjR8fNhg8H1q5lGwHy3PTpQFSUuaep56dLF0lAcxABeSo1VSZ5\nWm1tsdlkwrrTKf2viO4kN1dulQwcCNx1l+poAqtfP5mMnZCgOhLTYjLNzX0NZ9Ag1ZEEXps2QOPG\n3MSRZzIzpXrg0Uetcw0nr6FD5XoBm0WTJ6ZNk8bQ3bqpjiTw7HZpI8AKAvLEoUMyfMAqvQXzKl5c\n1tSZM6VKmnsXAAAgAElEQVTiiOhOvvoKaNfO/NPU8xMfL03Vk5JUR0JG8N13wK+/Wq/iGQDKlpW9\n2Jdfso2AnxQ6mZaWloZ69eph1apV17+2YcMGtG3bFqGhoahbty6m3tSge/r06ahfvz5CQkLQunVr\nrF+/vsDX37BhA4oVK4YyZcpc/xEZGVnYMAtv+nSgVy+gUiX/v5fe2GxyspWSApw7pzoa0junUxJq\nQ4aojkSNfv3kSsXMmaojIb27ckVOQ4cOtdY1HLfSpYH+/SWZlpurOhrSuy+/BEJDgdhY1ZGoMXy4\nVBqlpqqOhPTu9GmZABsfrzoSNe6+G+jYkQc15JmvvgLq1LHOUJubDR0K7NwJbN+uOhJTKlQybe3a\ntWjfvj0OHjwI2/8qUtLT09GzZ08MHz4cGRkZmDJlCsaOHXs92bZy5Uo899xzmDFjBjIyMhAfH4/e\nvXvjXAFJm40bNyIqKgoXLly4/mPlypXe/S7vZMcOYOtWa17xdBs8WB78UlJUR0J6N2OGXHW85x7V\nkahRvryMoefkNbqTRYtkqI0VK23chg8HjhyRiiOigmiarC1xcUBIiOpo1GjYEGjblmsL3dmcOfKZ\nGTBAdSTqPPoosGoVcPiw6khIzy5fBhITpQDAirdpAKBrVyA8nC1q/MTjZNq0adMQHx+PcePG3fD1\n5ORkhIeHY9SoUShWrBiioqIQHx+PKVOmAAAmT56MQYMGoX379ihevDjGjBmDypUrY24BDbw3btyI\niIgIL35LRTB9ulSk9ewZ2PfVkxo1gMhIqaIgKsiJE8CyZdYslc5ryBAZR797t+pISM8SEoA//MFa\nQ21u1r699BpkBQHdztq1wM8/W/tQE5BKI3cSnqggX38trQPCw1VHok5srEyTZ/KZbmfePOmtZ9Uq\nTgAICpI2VgkJwNWrqqMxHY+TaT169MDBgwfRv3//G76+a9cuNGvW7IavNW7cGDt27Cjw55s0aXL9\n52+2ceNGbN68GQ0aNEDVqlUxcOBAHDt2zNMwC+/aNfnLNXAgULKk/97HCOLjgRUrJGFClJ+EBPmm\nHBenOhK1evaUCjUmn6kgFy7Ida3Bg1VHopbNJv8PUlLYC4oKlpAA1KwpVc9W1r+/XIlOTlYdCenV\nwYPA999bt9WGW2go0LevHNSwFxQV5KuvgNatrdlbMK+hQ6XXM4cN+pzHybQqVaqgWLFbf/mFCxcQ\nHBx8w9eCg4Nx8eJFAMDFixcRclPJft6fzys3Nxc1atRA9+7dsXnzZuzatQs2mw29evVCrr/6raxe\nLckjqz/wAHLKExQEzJqlOhLSqy+/BPr0kfHSVlaqlDz0fP01e0FR/pxOSR4NHKg6EvUGDZKT4YUL\nVUdCepSTI9dwBg4E8tlnWkqVKkDnzpy8RgVLSJCr0NHRqiNRb+hQYP9+4IcfVEdCenT6tOw7rH6b\nBgAiIoBGjXjV0w+87ogcGhp6S+VYVlYWypYtCwAICQlBVlbWDT+fmZmJ8HxKk4sVK4alS5fe8LUJ\nEyagcuXK2Lt3L5o0aZJvDGPGjEHYTQ/3gwYNwiBPJnPOnCm9n9q1u/OvNbvy5aXiJiEBGDtWdTSk\nNzt2SPPKt95SHYk+DBkCfPqpXE+yejUF3SohQf5e1KqlOhL1GjUCWrSQ/ydWbS5PBVu+XB56rDhN\nPT/x8XLd9ddfpVqPyE3TpNImNta6vQXz6tgRqFZNnuXatlUdDelNYiJ7C7rZbJJU/Mc/5OZEmTKq\nI/K5mTNnYuZNw+EK6tHvS14fATZt2hS7du264Wu7d+9G06ZNr//8zp07C/z5vI4ePYoXXngBmZmZ\n1792+X/XQkqXLl1gDOPHj0dqauoNPzxKpF25ImOVBw60blPCm8XHA5s2yUkPUV4JCUCFCkD37qoj\n0YcHHpBEPE956GZpacDSpUwO5DV4MDB/vlSoEeU1c6ZcwWneXHUk+uBwAHfdxVsCdKstW4B9+6zd\n/ymv4sXllsCcOdK2hyivL7+U3oKVK6uORB/i44GsLNNOjB40aNAt+aDx48f7/X29TqbFxsbi5MmT\n+Oijj5CTk4Nvv/0WCQkJGDlyJABg5MiR+Prrr7Fy5Urk5ORg/PjxSEtLQ0xMzC2vVbFiRcyaNQt/\n+ctfkJ2djdOnT+PZZ59Fly5dUKdOHW9DvdXSpcDZs7yGk1evXpKt5hUDykvTgNmzpT+F1XsLuhUr\nJgtTYqIk5oncEhPlgMbqvQXzGjAAyM6W669EbpcuST+9QYN4qOlWtqy0U+A+jG721VdyFbhTJ9WR\n6MfAgdKuZ/Vq1ZGQnhw8CKxbx8RzXrVry1Co2bNVR2IqXifTKlSogKVLlyIxMRGVKlXCk08+iQkT\nJqBDhw4AgI4dO2LSpEkYNWoUKlSogNmzZ2PRokXXr2WOGzfuepVa6dKlsXjxYuzevRvVqlVDw4YN\nUb58ecz21x/6rFlA48bA/ff75/WNqHRpKR9PSGBDT/rdpk3AoUMslb7ZwIHAuXOSmCdyS0iQUeSV\nKqmORD9q1gQeeYQJArrRwoVy5YRVnDcaPBjYto0To+l3167Jc8vAgUAJr7v0mEfbtpIkYCUn5TVn\njkx7tdtVR6IvAwYA33wDpKerjsQ0bJpm3IzJli1bEBERgc2bN6Nly5aF+4+zsuR05+WXgf/7P/8E\naFRLl8qD4A8/yAQUohdflGb7x45JWT397r77pLHnjBmqIyE9+OUXoE4dqSDgieiN/vtfYPRoqSLI\np28qWVC/fnJQs3mz6kj0JTsbqFpVPi9vv606GtKDFSukIm39evYHu9krrwBTpsjaEhSkOhrSgxYt\ngAYNWIV1s+PHgbvvls/LiBGqo/E7r3JFHrLu2KQFC4CLF3kamp+oKEk0soKAAJlWOWeOPPQwkXar\n/v1/n9xINGuWVPjyNPRW/frJVb7ERNWRkB5kZEgfPe7DblWqlHxeeEuA3BITpQKrTRvVkejPwIHA\nmTMyzIRo/36p7O3fX3Uk+lO9utwSYJLRZ6ybTJs5E2jVCqhXT3Uk+lOihGziEhMlkULWtm4dcPQo\nr3gWZMAAuab0zTeqIyE9mDkTiI4GQkNVR6I/lSpJ1TMPagiQQ4jsbK4tBRk0SPr+bNqkOhJS7epV\nIDlZkgPsLXir5s2lColXPQmQAoCQEKBnT9WR6NOAAcCyZTJFm7xmzWTa+fPSp4ODBwrWv79c6Vu/\nXnUkpNrs2UCNGsCDD6qORJ8aNZK+i3PmqI6EVNuzB/jxR64ttzN4MLB2LXDkiOpISLWZM4GHH5Z+\nenSrRx6R69Cs5KRVq2RKNCtt8mezybqbksJbAiT78ehouSVAt+rbV/6ZnKw2DpOwZjLN6ZTpezwN\nLdiDD0q/Dm7irO3aNfk7EBcn0yspfwMGyKjprCzVkZBKSUkyDbl7d9WR6Fd0tFxhS0pSHQmpdPq0\n9GflFc+ClSghA6ESE3nV0+rmzJFenBERqiPRrwED5Oo4bwlY2549wI4dfMa/ncqVgY4dedXTR6z5\ndDx7NvDQQ9KAj/JXvLhkrpOSeNXTylavBk6e5KJ0J/37A5mZUvFK1pWYCPTpA9x1l+pI9MudbGQy\nzdqcTkkQ9eunOhJ9i4uToSYc0GBdvOLpmSZN5JYAr3pa25w5QNmyQLduqiPRtwEDgJUrZWgHecV6\nybT0dLknzFLpO+vfX3pl8aqndc2aJQ1vOTnq9urVA1q25CmPle3bJ6ehTA7cWb9+0ovx119VR0Kq\nJCUBkZGc6nonHTpIr0G2EbCub7+V5vpxcaoj0b+BA+WWwMWLqiMhFTRN9uF2Ow817yQmRqqfebDp\nNesl0+bNA3JypHSebo9XPa2Np6GFM2DA71OCyXqSkqThLa943lmfPkDJksDcuaojIRXOnpWpe0w8\n3xmvelJiIlC3rhzY0e317w9cugQsWqQ6ElJh1y655smCmTurUEEGQrEIwGvWS6YlJQEPPCCjYen2\neNXT2laskL42bKbuGfcmbv581ZGQCklJQO/ebHjriXLlZBPHgxprSk2VfpwxMaojMQZe9bSunBw5\ndOChpmfuvRf4wx/YWN2qZs/+fX9BdzZggAyEOnpUdSSGZq1kWkYGsGQJT0MLw33Vc8MG1ZFQoCUl\nycakRQvVkRjDPfcAbdrwlMeKfvoJ2LaN13AKIy5ONnHHjqmOhAItKUn61latqjoSY4iMlKueTD5b\nj/uKJyttPNevn9wS4FRPa9E0uQ4fEyOV73RnffoAQUEyBZeKzFrJtAULgOxsXvEsDPdVT/brsJbc\nXKkeiInhaWhhxMXJJCle9bSWpCQgOBjo0UN1JMbh3sTxqqe1nD/PQ83CKlFC1mJe9bSeOXOkJ2vz\n5qojMY6+fWUPtmSJ6kgokH78Edi/nwPTCiMsDOjUiZWcXrJWMi05GWjdWhqqk2d41dOaNmwAfvsN\ncDhUR2IssbFyGsrR7NaSmAj06iUJNfJM+fJA586strEa9q0tmv79gUOHgC1bVEdCgcIrnkXTuLH8\nYGN1a0lOln1Fp06qIzGWvn2B1auBU6dUR2JY1kmmZWYCCxfyNLQoeNXTepxOoHJloF071ZEYS926\ncoLMUx7rOHhQHnC5thReXBywZg1Hs1tJUhLQvj1w992qIzEWXvW0nhUrgPR0tg8oin795HbFlSuq\nI6FAcTqlb21QkOpIjMVul3+6XGrjMDDrJNMWLZLm4H37qo7EeDjV01o0Te7PR0dLZSIVTt++MoSA\n/TqsISlJhg707Kk6EuOx2+V7DK96WsOFC1K1y8Rz4fGqp/WkpMgB3R/+oDoS4+nbV66Ur1ihOhIK\nhJ9/Bnbs4G2aoggPBzp0YBGAF6yTTEtOloqRe+9VHYnxFC8uVzLmzuUmzgr27gUOHOCiVFSxsdKv\nY9ky1ZFQICQmSq+00FDVkRhPhQpyJYPXcayBfWu9ExcnlbDbtqmOhPwtN1cqRRwOXvEsivvvl+c9\nri3W4HIBpUpximdR9e0LLF8ulbBUaNZIpl26JJUirEorupgY4PBhYOtW1ZGQvzmdQEgI+w4UVePG\nQMOGrLaxgl9+ATZt4jUcb/TrB3z3nfRoJHNLTgZatZLJx1R4kZHSMJqT18xv40bg5EkeahaVzSZr\ni9MJXL2qOhryN6cT6NKFh5pFFRMjn5N581RHYkjWSKYtWSKVIrxaUHQdOkhjR27izM/plEqbu+5S\nHYkx2WySuHe5uIkzu6QkOQ3t1Ut1JMblrrzg2mJu7FvrvaAgmYLLgxrzczqlR94DD6iOxLj69gXO\nnJHDGjKvtDRg7Vomnr1Rvbp8r+FVzyKxRjItORm47z6gUSPVkRiXexPHBx5zO3YM+OEHLkreio0F\nzp4FVq1SHQn5U0oK0K0bUKaM6kiMq1Il4JFH5OGRzOubb4CsLN4Q8FZMDLBrl7RiIPNyOmXPzb61\nRdeqFVCrFhMEZjd/vrQg6tNHdSTGFhsLLF4svU2pUMyfTLtyRSa6cAPnPfcmbv9+1ZGQv6SmSqNj\nNlP3TsuWQO3arCAws5MngXXr5PsiecfhkEbR58+rjoT8JSlJ+tbWq6c6EmPr1k0GnvBg07z27ZPe\ntTzU9I77lsDcudKDjszJ6ZSqqsqVVUdibLGx0tN04ULVkRiO+ZNpy5fLBp1XC7zXtSs3cWbndEpf\nlvLlVUdibDabLEwpKdzEmVVqqvw59+6tOhLjcziAnBxu4swqO1uqBzh4wHvBwZJQ4z7MvFwu2Wt3\n7qw6EuPr21cOvr7/XnUk5A+ZmdLKiYln79WpI4UArOQsNPMn01JS5CS0aVPVkRhfcLD00uImzpzO\nnZPqELtddSTmEBsLnDgBrF+vOhLyB6cTePhhuaZI3qlVC4iI4NpiVitWSN9aVnH6RkyMrCvHj6uO\nhPzB6ZSEaXCw6kiMr317oFo1JgjMaulS4PJlPrf4St++cqh56ZLqSAzF3Mm03FypHuBoad+JiQE2\nbJDeWmQuixZJw3wuSr7Rvj1QpQqveppRRoZUPTM54DsxMfI96PJl1ZGQr7lcQN260ruWvNe7t7Rj\nYJ9B8zl5UhKlrLTxjWLFZE/rcklfLTIXp1PWlfr1VUdiDn37SrXf4sWqIzEUcyfTNmwAfvuNyQFf\n6tWLmzizcjqlOqRmTdWRmEPx4pIgSE7mJs5svvlG+nFybfEdh0Oql5YvVx0J+VJurjzI2u081PSV\nChWkHQMrOc1n3jz5nHBCtO84HMChQ8COHaojIV+6elU+L0w8+07DhpKcZBFAoZg7meZ0AuHhUiFC\nvlG+PBAVxU2c2bibTnJR8q3YWOCXX4Bt21RHQr6UkiLN1O+5R3Uk5tGkiZwuc20xl40bpdqGa4tv\nxcQAK1cC6emqIyFfYvsA34uKAsqWZRGA2axZA5w9y0NNX3M4gAULJFlJHjF3Ms3l4mhpf4iNlU3c\n2bOqIyFfcfe04QOPb0VGAmFhPOUxk+xs2Wjwiqdv2Wzy/zQ1Fbh2TXU05CsuF1CxokxbI9+x2+Vh\nZ/581ZGQr1y4IJW53If5VsmSMqGeyTRzcTqBGjXkRg35jt0uz/dr1qiOxDDMm0zbu1fGS3NR8j27\nXa5ucBNnHi4XcO+97Gnja0FB0t/G5VIdCfnKypXy0MO1xfccDiAtjZPXzMTp/L3HF/lOjRpA27as\n5DSTxYvlsIaVNr7ncABbtwKHD6uOhHxB035vH1DMvKkMJSIiZH1h8tlj5v0b6HLJJByOlva9atWA\ndu1YbWMW7p42HNThH3a79Oo4dEh1JOQLKSkyQrxZM9WRmE/btrK+MEFgDgcOAHv2MPHsLzEx0r8x\nK0t1JOQLTidw//2yvpBv9eghh5s82DSHH3+UFipcW3yPQzsKzbzJNPdo6dKlVUdiTrGxcoqWmak6\nEvLWDz+wp40/desm1wy4iTM+Jp79y72Jczq5iTMDlwu46y6gSxfVkZhTTAxw6RInr5lBTo60D+A+\nzD/KlgU6dWK1jVk4nfJn2qGD6kjMyW6XZOWPP6qOxBDMmUw7cUImebJU2n9iYoDLl7mJMwMO6vCv\nMmW4iTMLd+KZ/dL8JyZGqji5iTM+p1MSaSEhqiMxpwYNpDUDKzmN77vvgHPn+NziTw6H/H9mv2fj\nczpl4m3JkqojMafISElWsgjAI+ZMprlHS/furToS87r3XrnmxKuexud0AtHRHNThTw4HsHo1cOaM\n6kjIGykpMmWNzdT9JzISKFeOCQKjO3VKet+x0sa/YmJkz5uTozoS8obTCdSsCbRooToS84qOluE2\nCxaojoS8cfgwsG0b1xZ/4tCOQjFnMs3lktHSFSuqjsTc3ONzuYkzLg7qCIw+feSKIDdxxqVpkuBh\n4tm/SpaUE2du4ozNPaCIh5r+FRMjFU2rVqmOhIrK3Uyd7QP8q1o16cvJtcXYXC7ZJ3TvrjoSc3MP\n7ThyRHUkume+ZBpHSweO3S6bOI7PNS6nU67gdOqkOhJzc2/iWDJtXHv3SkN1XvH0v5gYYPt2Du0w\nMqcTePBBoHJl1ZGYW4sWUtGUmqo6EiqqrVuBX3/lFc9AcDhkaMelS6ojoaJyOuWZpWxZ1ZGYG4d2\neMx8yTSOlg6cli1lfC4/aMbldMrpDgd1+B83ccbGxHPgdO8OlCrFq55GlZkJLF3KfVgg2GxSLcvJ\na8bldAJhYcAjj6iOxPwcDpl+u2yZ6kioKM6ckb53XFv8r2xZoGNHVnJ6wHzJNJdLenlxtLT/cRNn\nbMePy6AOVnEGht0um7jly1VHQkWRksLEc6CEhkrjeibTjGnpUhlQxAeewLDb5SrO9u2qI6GicDrl\nOnRQkOpIzK9RI6BhQyYIjGrBAul7Fx2tOhJrcDikhUB6uupIdM1cybScHOnTweRA4LjH5+7YoToS\nKqzUVOn91LOn6kisoVEjoH59VnIa0dGjwMaNXFsCyeGQBvZpaaojocJyOoEmTeT7Hflfhw6cvGZU\nBw/K/pmJ58BxOGT/e+2a6kiosFwuoF07aZ1C/sehHR4xVzJt9WqOlg60yEigTBn26zAip1M24RUq\nqI7EGmw2+d7ETZzxpKYCJUpIY3wKjD59pOLZ3ciejOHqVfkz4z4scEqWlP423IcZj8slV9q7dVMd\niXU4HMDp03JYQ8Zx6ZK0SuGhZuBUrw60acODmjswVzLN6QTuvlt6eVFglColmzh+0Izl/HlgxQou\nSoHmcACnTsn1WjKOlBQ5OChfXnUk1lG5sjSw53UcY/n+e+lrw2RaYNntwJYt0siejMPpBDp3lkNp\nCow2bYCqVbm2GM2yZdIqhWtLYNntwKJF0rqB8mWeZJp7tLTdztHSgRYdDWzaBBw7pjoS8tSiRXIt\nmotSYLVrB4SHM/lsJOnpwMqVTDyr4HAAS5ZIQ3syBqdTruC0bq06Emvp0UOqZ1mdZhynTwNr1nAf\nFmjFisn/c6eT/Z6NxOmUfneNGqmOxFocDtmDrVihOhLdMk8ybds2acDKB57A69lTem9xE2ccLpdU\ncNaqpToSayleXK6vMZlmHIsWydU1PvAEnt0up6FLlqiOhDzhPtSMjpYHVgqcsDCpnuXaYhzz58tn\npk8f1ZFYj8Mh/ep27lQdCXni2jVg3jw+46vQuLH0P2UlZ4HMs9txuYBy5aQHFAVW+fLy/53JNGPI\nzpZmklyU1HA4gH37gL17VUdCnnC5gIgIaSFAgVWvHtC0KTdxRrFzpzygMvGsRnS0VNGeP686EvKE\n0wm0by9XDimwoqLkai3XFmNYt06GEfG5JfDy9nvOzVUdjS6ZK5nWqxdHS6tit0sJ6IULqiOhO1m5\nUv6cuCip0bkzEBzMCgIjyM6WyjSOYVfHbpcT6atXVUdCd+JyAaGhQMeOqiOxpuhoad/wzTeqI6E7\nycqSilsmntVgv2djcTol6dymjepIrMnhAH77jf2eC2COZNrx43LNk4uSOtHRwJUr3MQZgdMJ1K0r\nFR8UeKVLA127chNnBKtWSeKZa4s6Dof0rVu9WnUkdCculzygliqlOhJrql0baN6ca4sRLF0q0wl5\nqKmO3Q5s3syhHXqnafLcwvYB6rDf822Z42/lqlUyGrx7d9WRWNc99wD3388Pmt7l5sqfkcPBQR0q\n2e3A+vXAyZOqI6HbcbnkAfX++1VHYl0REUCNGlxb9O7oURlExMSzWnY7sHChVKiRfjmd0ouoQQPV\nkVhXz54c2mEEu3YBP//MxLNKxYtLMpPXovNljmTaypVyraBsWdWRWJvdLr24uInTr40bgRMnuCip\n1ru3JDPnzVMdCRVE02STzQnRatls8v2Kk9f0LTVVHkx79lQdibXZ7dIzbdUq1ZFQQa5elbWfiWe1\n3EM7mCDQN7YP0Ae7nf2eC1DoZFpaWhrq1auHVXkW6g0bNqBt27YIDQ1F3bp1MXXq1Bv+m+nTp6N+\n/foICQlB69atsX79+gJfPzMzEyNGjEClSpUQFhaGYcOGITMz8/ZBbdnC5IAe2O3AuXMy6pv0yekE\nKlUCHnhAdSTWVqkS8NBDrLbRsy1bpNqGDzzqORzA4cPA9u2qI6GCuFwyiKh8edWRWFvz5kDNmqy2\n0bPvvwfOnOFzix44HFKQce6c6kioIE6nHNKwfYBa7PdcoEIl09auXYv27dvj4MGDsP3vpD49PR09\ne/bE8OHDkZGRgSlTpmDs2LHXk20rV67Ec889hxkzZiAjIwPx8fHo3bs3zhXwjWv06NE4duwYfvrp\nJxw4cABHjhzBH//4x9sHlpvL0dJ60LIlr+PonbvvQPHiqiMhux1Ytgy4eFF1JJSf1FQ5uX74YdWR\nUIcOMq2bFQT6dP488O23TDzrgc0ma7zLxUpOvXI6gWrVgNatVUdC0dFSKbhwoepIKD+//sr2AXpR\nujTQrRuf8fPhcTJt2rRpiI+Px7hx4274enJyMsLDwzFq1CgUK1YMUVFRiI+Px5QpUwAAkydPxqBB\ng9C+fXsUL14cY8aMQeXKlTF37txb3iMrKwsJCQl46623EBYWhvDwcLz77rtISEjA5cuXCw7uvvuA\n6tU9/a2Qv3ATp29798oPnobqg90u0yIXL1YdCeXH5ZLTUE6IVi8oSK5GM5mmT4sWSXsHPvDog90O\nHDnCSk49YjN1falZU/pyMkGgT2wfoC/s95wvj7+T9+jRAwcPHkT//v1v+PquXbvQrFmzG77WuHFj\n7Nixo8Cfb9KkyfWfz+vAgQPIycm54dc3btwYV65cwf79+wsOLjLS098G+ZvdDvzyC5DPny8p5nJJ\niW7nzqojIQC4916ZqMpNnP788os8iDI5oB92u/yZHDqkOhK6mcsFtGgB1KqlOhICpJKzbFmuLXq0\nc6d8D+Ohpn7Y7XIgkJ2tOhK6mcsFREXJLQFSj/2e8+VxMq1KlSools8pyoULFxAcHHzD14KDg3Hx\nf1eXLl68iJCQkAJ//ubXAnDDr3e/dn6//jom0/QjMhIoU4b9OvTI6ZQS3dKlVUdCbnY7MH8+h3bo\nTWqqVENxQrR+dO8uU7uZINCXK1fkihSTA/pRsiTQowf3YXrkdMoeOSpKdSTkZrcDFy7IVXXSj3Pn\n5M+Ea4t+VKworU+4D7tBCW9fIDQ0FMeOHbvha1lZWSj7v8maISEhyMrKuuHnMzMzER4efstruZNo\nWVlZ15No7v+2TJkyBcYw5j//QdiMGTd8bdCgQRg0aFAhfzfktVKl5KHH5QJef111NOR24oSU5k6f\nrjoSysvhAP7xDxnawc21fqSmyp8HJ0TrR5kyUlXrcgFjxqiOhtxWrgQyMljFqTd2OzB4sPQcqllT\ndTTkxmbq+tOsGVCnjqwtPEDTj4ULpZ9ddLTqSCgvux3485+l33NoqOpobjBz5kzMnDnzhq8V1KPf\nl7xOpjVt2hRLliy54Wu7d+9G06ZNr//8zp07b/n53r173/JaDRs2RFBQEHbu3Ik2bdpc/7UlS5ZE\ngwYNCoxh/PjxaNmypbe/FfIVux0YMgQ4dkwGEpB6qakydKBXL9WRUF4REb8P7WAyTR/OnQNWrQI+\n+gByk3kAACAASURBVEh1JHQzhwN4+mmZhFexoupoCJDvXbVrA/ffrzoSyqtHD+k1lJoKPPus6mgI\nkMTmli3ASy+pjoTystnkuWX2bGDiRPay0wunE2jVCrj7btWRUF52O/DCC9LvuW9f1dHcIL9Cqi1b\ntiAiIsKv7+v1d4zY2FicPHkSH330EXJycvDtt98iISEBI0eOBACMHDkSX3/9NVauXImcnByMHz8e\naWlpiImJueW1goODMWDAALz66qs4ffo00tLS8Oqrr2Lw4MEoxVMc4+jZUxI3vGKgH04n8MgjfADV\nG/cmzunk0A694GmofvXpI5+T+fNVR0KA/Fm4XJLk/N+Ed9KJsDBpu8HrOPrhckn7ADZT1x+HQ25w\nbNqkOhICpH/dokW84qlHdetKNScHQl3ndTKtQoUKWLp0KRITE1GpUiU8+eSTmDBhAjp06AAA6Nix\nIyZNmoRRo0ahQoUKmD17NhYtWoSw/zUTHDdu3PUqNgCYNGkS6tevj2bNmqFRo0aoW7cuJk6c6G2Y\nFEjly0sDXG7i9CEjA1ixgouSXtntwOHDwI8/qo6EADkEiIjgaageVa0KtG/PTZxebN4sFei84qlP\ndrtcwz1/XnUkBMj3ragooFw51ZHQzR58EKhQgWuLXqxYIdcI+dyiTw4HsGAB+z3/T5GSabm5uXjk\nkUeu/3tERATWrFmD8+fP48CBA3j00Udv+PXx8fHYs2cPLly4gHXr1qF169bXf+6111674RpoaGgo\nPv30U5w4cQJnzpzBlClTUJoN040nOlq+GWZkqI6EvvlGmkTzgUefIiOlNxc3cepduSKnoaxK0y+H\nQ64X3NSLlRRwueTw7OGHVUdC+enTRx52vvlGdSSUni7tA7gP06cSJeTzwiIAfXA6gXr1gCZNVEdC\n+bHb5XvamjWqI9EFXgwn/7DbZRO3eLHqSMjpBFq0kL42pD8lS8q1D27i1GMzdf2z24FLl4ClS1VH\nQk6n9OEs4XX7XfKH2rWB5s25tugB2wfon90O7N4NHDigOhJry82V71l2O9sH6FXLlnJ7g0UAAJhM\nI3+55x5pSMxNnFpXrkgpLkul9c1uB7ZuBY4cUR2JtbGZuv41aAA0bsxNnGo//wzs3Mm1Re/sdknk\n8DqOWk4n0Lo12wfoWdeuwF138blFtQ0bgN9+49qiZ+5+zy4X+z2DyTTyJ7udd6pVc1facFHStx49\npDExN3HqaJr0S+NpqP45HMC8eVLpQWq4XECpUkC3bqojodux26Vn2nffqY7Eui5flqu2rHjWt5AQ\noEsXHtSo5nIB4eHSH5X0y93veft21ZEox2Qa+U90NHDuHO9Uq+R0AnXqyOQV0q9y5aQxMZNp6mzd\nChw9yms4RuBwAGfOAN9/rzoS63K5gM6dgdBQ1ZHQ7TRvDtSsybVFJTZTNw6HQ9aVU6dUR2JdTqfs\nw4oXVx0J3U6HDtLvmWsLk2nkRxERQI0a/KCp4u474HCw0sYI3JPX0tNVR2JNLhcQFgbkGa5DOtWq\nFVC9OisIVDl9Wg7JWGmjfzabPJjyOo46bKZuHL17yz/nzVMbh1Xt3Qvs28e1xQhKlpSeqXzGZzKN\n/Ih3qtXatAk4fpynoUYRHQ1cuyb9bSjwXC4ZBBEUpDoSupNixWRtcTq5tqgwf778f+/TR3Uk5Am7\nXfpx8jpO4OXmsn2AkVSuDDz4IBMEqjidQHCwVD2T/rn7PR8+rDoSpZhMI/9yOIBffuEmTgWnE6hU\nCXjgAdWRkCfuvlsqbriJCzx33weehhqHwwEcOgTs2KE6EutxuYB27YCqVVVHQp5wX8dJTVUdifWw\nmbrx2O0yLTozU3Uk1uN0At27A6VLq46EPOHu92zxtYXJNPKvDh2kHxSv4wSe0ymVAyVKqI6EPOVw\nAIsWAdnZqiOxltRU2RB07646EvJUZKQkCLi2BFZWFrB4MRPPRlKypFTd8qAm8JxONlM3GrtdhkYs\nWaI6Ems5cUKSz0w8G0fZskDHjpbfhzGZRv7lvlNt8Q9awO3bB+zZw0XJaOx2aVS8YoXqSKzF5ZIB\nEGXLqo6EPOVOEHBtCaxly4BLl7i2GE10NLBlC/Drr6ojsQ5N+/1Qk83UjaN+felvx+RzYKWmyuek\nVy/VkVBh2O3AqlWW7vfMZBr5n8MhV6gOHVIdiXW4XFImzb4DxnLffUDdutzEBdK5c7IRYKWN8Tgc\n7NcRaC4X0LCh/CDjcPeD5NoSOHv3Avv3M/FsRHa7DCG4elV1JNbhdMoAqAoVVEdChcF+z0ymUQB0\n7y5VBNzEBY7TCXTrJo08yThsNtl4u1zSuJj8b9Ei2TBHR6uOhAqL/ToC69o1ecBkcsB4ypWT6zgp\nKaojsQ6nEwgJ4aGmETkcwNmzwNq1qiOxhowMYPlyri1GVKMG0Lq1pW8JMJlG/lemjGwmLPxBC6iT\nJ4H161lpY1R2u/wZbtyoOhJrcLmAiAgZAEHGUrYs0KkT15ZAWbcOSEvj2mJUDodU4Z45ozoSa0hJ\nYTN1o2rVCqhWjWtLoCxcCOTkMJlmVHY78M030mvQgphMo8CIiQFWrwZOn1Ydifm5XECxYtKng4zn\ngQeAihW5iQuEK1ekMo1VacblThCcPas6EvNzuYAqVYC2bVVHQkVht0vF84IFqiMxv6NH5UCMyQFj\nKlZMPi8ul/S+I/9KSQFatgRq1VIdCRWFw2Hpfs9MplFg9OkjC9K8eaojMb+UFOk7ULGi6kioKEqU\nkM8Lr0X736pVcr2AlTbG5e7XwQSBf7mbqUdHy4MmGU+1akC7drzqGQipqbKWs5m6cdnt0ut5xw7V\nkZhbdrZUpsXEqI6EiqpJE+Deey373MIdEQVGlSpSccNqG/86f15OBrgoGZvdLtNY9+9XHYm5OZ1A\n7drA/ferjoSKyp0g4NriX3v2AD/9xMSz0TkcwOLFQFaW6kjMLSUFiIwEypdXHQkVVVSUtKmxaIIg\nYJYvl6omVnEal7vfc2qqJfs9M5lGgeNwAEuWAJmZqiMxL/YdMIeuXaXPCjdx/pObKw88MTGyESDj\ncvfruHRJdSTm5XJJM/VOnVRHQt6IiZHPyZIlqiMxr/R0YOVK7sOMrlQpGXLDgxr/cjqBevVkmj0Z\nl7vf8w8/qI4k4JhMo8BxOKQ5ITdx/pOSIo1Ta9ZUHQl5IzgY6NKFyTR/+uEH4MQJVnGagcMhlTbL\nlqmOxLycTmmmftddqiMhb9SvL1dyeNXTfxYulAnRrOI0PocD2LIF+PVX1ZGY07Vrss91OHioaXQP\nPABUqmTJ5xYm0yhw6tUDmjblKY+/XL4szdR5GmoOdjvw/ffAb7+pjsScUlKA8HDgwQdVR0LeatQI\naNjQkpu4gDh+XJLPTA6YQ0yM9K+9elV1JOaUkgK0bs0J0WbQo4f0vktNVR2JOa1fD5w6xUNNMyhe\nXPo9W/AZn8k0CiyHg5s4f1m2TPoOcFEyhz595KRu/nzVkZiPpskDj90uGwAyPne/jmvXVEdiPqmp\n8jlhM3VzcDjkKuJ336mOxHwuXZIr5zzUNIewMOmdZsEEQUCkpEhP7XbtVEdCvmC3A3v3Wq7fM5Np\nFFjuTdzq1aojMR+nE2jQAGjcWHUk5Avh4VI2zWob39u9GzhwgIlnM3E4gLQ0YN061ZGYj8slE6Ir\nVFAdCflCRIRUTTFB4HvLl0tfYK4t5mG3Sw+8c+dUR2IueQ81OSHaHLp0sWS/Z/7tpcBq2VI2cezX\n4VvXrkn1AJupm4vDASxdyqEdvjZ3rkzpYjN182jTBqhalQkCX8vIkAnRvOJpHu7Ja06nPNCS77gP\nNRs1Uh0J+Up0tNymWbhQdSTmsnMncPAgqzjNJDhYBqhZbB/GZBoFFjdx/rF2rVRl8DTUXOx2Du3w\nh5QUoGdPmdZF5lCsmHxeuLb41oIFwJUrXFvMJiZGmqpv3qw6EvNwH2qymbq51Kwp1ZwWq7bxu5QU\nOdTs2FF1JORLdrvcELBQv2cm0yjwHA7ZxG3dqjoS80hJAapVk6a3ZB716snkNYud8vjVL7/I957Y\nWNWRkK/Z7cDPPwO7dqmOxDySk2VdqVVLdSTkSw8/DJQvz7XFl77/noeaZmW3S2VadrbqSMzD6ZQ+\nnDzUNJfeveUwYd481ZEEDJNpFHiPPMJNnC+5+w44HOw7YEYOhwwh4NAO33A6ZfPWo4fqSMjXOnYE\nQkO5tvhKVpZMiGbi2XyCgmTIDVtu+I7TKYeabdqojoR8zeGQAV/ffqs6EnNwH2ryiqf5hIcDDz5o\nqUpOPnlT4AUFSeaaDzy+sW0bcPgwT0PNym4Hzp4F1qxRHYk5zJ0LdO4s1wvIXEqVkuu7FtrE+dXi\nxZJQYzLNnGJiZBiLxSav+QWbqZtb06ZAnTp8bvEVlwsoWZKHmmZlt0u/54sXVUcSEPyOT2o4HMCO\nHXIlh7zjdMr47shI1ZGQP7RuLT07kpNVR2J8p05JUpKJZ/NyOIBNm6SVAHln7lx5iGzQQHUk5A9d\nu8rkNSYIvLdjB3DoECttzMrd7zk1FcjNVR2N8aWkyACosmVVR0L+YLfLlWiL9HtmMo3U6NYNuOsu\nbuJ8ISVFKv2CglRHQv5gswF9+0oyjZs476Smyv/P6GjVkZC/9Owp3wtTU1VHYmxXrkjPk759VUdC\n/hIcLHsxXvX03ty5khiIilIdCfmL3Q6cOAFs3Kg6EmM7fRpYvZqHmmZWrx7QrJlligCYTCM1QkJk\nE5eUpDoSY/v5ZzkR5WmoufXtK5u4detUR2JsKSnSeDs8XHUk5C/lyskDLQ9qvLNiBXD+PJNpZudw\nAOvXy/pCRZeUJMmWkiVVR0L+8uCDQMWKXFu85XLJtWgeappbv35yIHf5supI/I7JNFInLk42cbyO\nU3TJyVLh17276kjInx54QBobM/lcdBkZwLJlPA21AocDWLkSSE9XHYlxJSfL6XLTpqojIX/q3Rso\nXpwJAm/s2SMThPv1Ux0J+VOJEpIASk6WZBAVTVKSDKKrUkV1JORP/foBFy5I7zSTYzKN1OndW07x\nLFIG6hdJSXKtKSREdSTkT8WKSRNwbuKKbuFCubrGKk7zi46W6bcLF6qOxJiuXZPkSt++ci2azKti\nRankTExUHYlxJSfLFOGuXVVHQv4WFwccOAD8+KPqSIwpPV0ONZl4Nr8mTYDGjS1RBMBkGqlTrpxs\nPizwQfOLX36R3g1xcaojoUDo21eqONmvo2iSk4GICKB2bdWRkL/VqCGDO1htUzSrV0tfG07xtIa4\nOGDVKhnQQoWXlAT06SO3BMjcOnWSgV98bikal0sOa7i2WEO/fvJnfuWK6kj8isk0UisuDli7Fjh2\nTHUkxpOcDJQqBfTqpToSCgR3ry9u4govMxNYsICJZytxOIBFiyzRr8PnkpNlgnDr1qojoUCIiZEK\nxLlzVUdiPAcOANu3s9LGKkqWlN54iYm8JVAUSUnSe656ddWRUCD06ye9V5cvVx2JXzGZRmr16SOT\n17iJK7zERKBHD6BMGdWRUCCUKCEPPbzqWXgLFgCXLjGZZiUOhyRRV6xQHYmx5ObKoI7YWF7xtIrw\ncCAykgc1RZGcLFNR2bfWOuLigH37pE8eee78eWDJEiaeraRZM6B+fdOvLUymkVrlywOdO5v+g+Zz\nR44AGzYwOWA1/foBBw8C27apjsRYEhPlimfduqojoUBp3Fg2cSkpqiMxlh9+kEpxXsOxlrg44Ntv\ngbQ01ZEYS1KS3A4IDlYdCQVKly7SpoZ9Bgtn3jwgJ4cToq3EZpO1xemUP3uTYjKN1IuLkx4tJ0+q\njsQ43Fc8e/dWHQkFUmSkJKCZfPYcr3hak80mlZxOpwwjIM8kJwOVK8tVHLIO95RjJp89d+gQsHkz\nK22sJu9VT/JcYiLQvj1w992qI6FA6tcPOHtWJqybFJNppJ7dLqPZedXTc4mJQLduQNmyqiOhQAoK\nkutrSUm86ukpXvG0rgEDpJE+r3p6RtNkbYmNlTWZrKNyZTmsYYLAc8nJMnSgZ0/VkVCg9esH7NkD\n7N6tOhJjyMgAFi9m4tmKmjeXWyEmLgJgMo3Uq1AB6NjR1B80n/r1V2DdOi5KVtWvH7B/P/t1eIpX\nPK2rRQvg3nuB2bNVR2IMGzYAhw9LEpKsx33V8/Rp1ZEYQ1KS9K0NDVUdCQVa165ymM3ks2cWLACy\ns3nF04psNnluSUkx7S0BJtNIHzia3XNz50qZeXS06khIhU6dZBPH5POd8YqntdlskhiaO9f0o9l9\nYvZsoGpVmRxM1hMbK9WJvOp5Z+6+tTzUtKZSpWQPzmSaZ5KSgDZtgNq1/5+9O4+rsk7/P/4GyQUE\nlwS11HJKUcNytMxCbWyaTKPSEgnLjRazmsSmpsWssbQp07LSzCUSU8kls0ZtfmXlEtW4VaZmZpZl\nkpJJgluK9++P64tKgoKcc+6zvJ6Ph4/DuTmcc1Gc+9z3dV/X9XE7ErihRw+bx7lsmduReAXJNPiH\nbt3sxIeDuJObPduuitWo4XYkcAMHcWVHiydSUqS8PGnRIrcj8W+HD9s+JTmZFs9QFRcnXXYZny1l\nUXRRk7m1oatHD+sQ+OortyPxbwUF0sKFJJ5D2YUXSo0aBe1nC8k0+Ic6dViavSx++knKzuZDKdQl\nJ9usjrVr3Y7Ev9HiiZYtpWbNaPU8mexs+3yhxTO0JSfbjEFaPU9s5kzm1oa6zp2l6GjOW05m4UJp\n/35aPENZUavn3LlSYaHb0XgcyTT4D5ZmP7k33rAh9Ndd53YkcFPnzlLNmtLrr7sdif+ixRPS0VbP\nefPsgB4lmznTVlm75BK3I4Gbune3KsV589yOxH9995306afSjTe6HQncVLWqdM01JNNO5vXXuagJ\nOxbfvt1GOgUZkmnwH9dfb7dBWgbqEa+/bi2eNWu6HQncVKWKXeXLymJVz9IsXEiLJ0zPnkdXE8Px\nCgvthLBnTymcw8KQVq+e1LEjx2En8vrrUrVqzK2FHV+sWSNt2OB2JP4pL8+OxXr1cjsSuO3ii6XG\nje28Jchw1AT/ERtriaIgfKN5xHff2SqefChBklJTpc2bpRUr3I7EP82cydVQmBYtpIQEWj1Ls2SJ\nXTGmxROS/R28/z4LQpUmK8sSaaziiauusvnFnLeU7M03bfEfPlsQFmbnLW+8YSu7BhGPJdM2bNig\nzp07q1atWjrrrLP05JNPyvm/iomFCxfqz3/+s2JiYtSqVSvNO0H5+OHDhxUdHa3q1asrOjpa0dHR\niomJ0b59+zwVKvxZr17SRx9JW7a4HYn/ef11KTKSq6Ewf/mLVLcuB3El+e03af58Es84KiVFevtt\nae9etyPxPzNnSmefLV10kduRwB8kJ9uJD9Vpx1u3TvrySzspBKpWtS6BGTPoEihJVpZVup55ptuR\nwB/06iXt2hV0XQIeSaYVFBSoc+fOOvvss7Vt2zYtW7ZMM2fO1BNPPKHVq1ere/fuuueee5SXl6ex\nY8eqb9++WlJKz+z69et18OBB/fbbb8rPz1d+fr52796tatWqeSJU+LvrrrPyeWZBHW/GDK6G4qhK\nlawta+bMoBzoWSFz53I1FMWlpNgcvYUL3Y7Evxw6ZFeKe/a0BApQp47N5Zw+3e1I/M/rr9uYjauu\ncjsS+ItevaRNm6SVK92OxL9s324VriSeUeS882xRqCArAvBIMu2jjz5Sbm6uxo0bp2rVqqlRo0Z6\n4IEHNH78eM2aNUsdOnRQ//79FR4ervbt2yslJUXjx48v8blWrFih888/X5VYmj00RUfbQM8ge6NV\n2Jdf2sqNVNrgWKmpUk6OtHSp25H4lxkzrHKPq6Eo0qSJ9Oc/0+r5Rx98IO3cSeIZxfXqZWMlNm92\nOxL/4Th2bHr99Ta3FJDsWKNePTvuwFGzZ9sMzh493I4E/iQ11boECgrcjsRjPJJMKywsVOXKlRUR\nEXH0icPDtX37dv3666+KjIws/qLh4dpQyrDGFStWaN++fWrbtq3i4uJ02WWX6ZNPPvFEmAgUvXpJ\nX3xh5fQwWVlSrVp2tRgo0q6dtWeRfD4qJ8cSBCSe8UcpKdb+u3u325H4jxkzjiYagSLXXWdjJegS\nOGrlSunbb6m0QXGVKtnKrq+/TpfAsbKy7Jzl9NPdjgT+5MYbbdzG22+7HYnHeCSZ1r59e1WrVk0P\nP/yw9u/fry1btmj06NEKCwtT//799e677+rNN9/UoUOHlJ2drdmzZ2t/KUvUR0ZGql27dnrrrbf0\nww8/6Nprr1Xnzp31/fffeyJUBIKrrrIyehIEpuhqaI8eUuXKbkcDfxIWZh9Mb7xhbY2QZs2SIiJs\njglwrF69bPDtG2+4HYl/2LvX/lvcfDMtniguKkrq1s1aPZkFZbKypLg4q0QCjtWrl/Tzz9LixW5H\n4h++/176+GMSzzhe48bSJZcE1Tl+mON45lPy888/17333qs1a9aoSZMm6tWrlwYNGqR9+/bp7bff\n1hNPPKFt27apY8eOaty4sT7++OMyV5wlJCRo4MCBuuuuu4ptX716tdq0aaMOHTqoZs2axb6Xmpqq\nVN7EgevWW6UPP7Q5BKF+kP/JJ9Kll9p/Dw7i8Edr1kgXXGAVN1df7XY07mvb1to733zT7Ujgjy6/\n3D5T3n/f7Ujcl5VlJ4HffsuqtzjeggVSUpL0+ef2GRPKCgulRo2sxfPFF92OBv7GcaSmTW3Y/iuv\nuB2N+556Snr8cVsRmDnP+KOxY6XBgy0B7cHKxaysLGX9IUmXl5enZcuWadWqVWrdurXHXutYESd/\nyMkdPHhQBw8e1AcffHBk29ixY3X++edr7969atGihdasWXPkez169NDFF19c4nMNGTJEycnJatWq\n1ZFtBw4cOK5V9Fhjxozx2n8guKRXL/tAWr5cKuVvJWRkZUlnnCF16OB2JPBHLVtKLVrY30moJ9O+\n+UZasUK6/363I4G/6t1buuUWaetWqUEDt6Nx17RpdqGGRBpKcuWVdqIzYwbJtGXLpG3bqLRBycLC\n7LxlzBhp3Dhb5TOUZWWxYBpKl5wsDRpklfG33+6xpy2pkKqo8MqbPNLmefjwYV111VXKyMiQ4zha\ntWqVnn76aQ0ePFgbN25Uu3bttGbNGh06dEivv/66/vvf/+rOO+8s8bnWrVunQYMGafv27Tpw4IAe\nf/xx5efnq3v37p4IFYHissuk+vUZ6HnokA3MvvFGm8sA/FFYmB3gv/lmUA30PCVZWXbwlpTkdiTw\nVzfcYMPDQ32lwu3bbXn63r3djgT+6rTTbJXXrCzp8GG3o3HX1KlH25OAkqSm2jzOUF8xeu1a65gg\n8YzS1K0rXXFF0JzjeySZVqVKFc2bN08vvfSSatSooRtvvFFDhgxR3759dfHFF2vUqFHq1q2bateu\nreeff14LFy5U06ZNJUnLli1TdHS0tm7dKkl69dVXdc455+iCCy5QnTp1tHTpUi1atOi4Nk4EuaKB\nnjNnWkIpVL33npVJM0wdJ9K799H5R6HKcSxBcv31UrVqbkcDfxUTY8PVX3sttGdBzZxpK60lJ7sd\nCfxZr17Sjz9KH33kdiTu2bPHVibs04exIyhds2ZS69ZBkyA4ZZmZVtHapYvbkcCf9eolLV0qbdni\ndiQV5rGZaW4oKt3zZh8sXPT557bCWCjPgrrxRlvVdM0aDuJwYpdfbrfHtNuHlBUrbF7af//Lqrc4\nsaJZUJ99Jh0zUiKkXHSRtbkyWxAncviwtQFfeaU0caLb0bhj+nRbpIPZgjiZ0aOlhx+2WVC1arkd\nje8dOmSfKz17Si+84HY08GcFBVK9etKDD0qPPOK1l/FFrsgjlWmAV7RqZXM6pkxxOxJ37NolzZsn\n9etHIg0n17evLVIRqisfv/qqLTxwxRVuRwJ/d+WVUmysVaeFog0bpJUrLUEAnEh4uFU+z5xp1c+h\nKDPTZtaSSMPJ3HSTLVYRRCsVlsu779oIgb593Y4E/q56dauMnzIl4LsESKbBv/XrJ739trRzp9uR\n+F5Ri+tNN7kdCQLBDTdIUVGhmSDYv98OXvv0YbYgTu6006zqd8YMO/EJNdOmSTVqhG7FN8qnb1+b\nBRWKVYxbt0qLFpEcQNnUqyd17Rq6RQCZmdJ551m7K3AyfftaxW92ttuRVAjJNPi3Xr2szeD1192O\nxPcyM6WrrrIPZ+Bkqle3hNrUqQF/lafc3npLysuz5DtQFr17WyvO+++7HYlvHT5sybTkZFacQ9mc\ne65VZoVigmDaNFuwpEcPtyNBoOjXz8ZOrFvndiS+tWuXHYvRTYOy6thROvvsgP9sIZkG/xYXZ1fP\nA/yNVm4bNkiffkpyAOXTt6+0aZP08cduR+JbU6ZIl14q/d/CNsBJXXihDYwOtc+WDz6wgb/9+7sd\nCQJJ//6WeP7hB7cj8R3HsYtT3btbJSdQFklJUp06NnoilMyaJR08SDcNyi483M5bZs2yhV4CFMk0\n+L9+/Wy+y9q1bkfiO5mZNrz0mmvcjgSB5C9/kRo1sr+fUPHTTzang8QzyiMsTLrlFmnuXOnXX92O\nxndeecWSiJdc4nYkCCTJyVJkZGh9tqxcKX31FS2eKJ/KlS2h9NprllwKFZmZtvhT/fpuR4JA0qeP\nlJ8f0GMESKbB/3Xtald5QqWCoLDQPoRTU629ACir8HD7YJo5U9q3z+1ofOO11+x90rOn25Eg0PTp\nY/vb6dPdjsQ3fv3VDlhvuYU2HJTPscOiDx92OxrfmDLFEgMsaoPy6t9f2rHDVhcPBRs3Sp98QuIZ\n5fenP1m7ZwCf45NMg/8rusozbVpoXOVZtMiqbai0wano08eGRc+d63Yk3uc41kpx/fW04aD84uKk\na6+VJk0KjTmD06db8rBPH7cjQSDq31/avFlatsztSLxv71475uzfn0VtUH4XXCC1ahU6rZ4ZGVLN\nmtJ117kdCQJRv342giJAxwiQTENg6NfPllt+5x23I/G+iROlhASb6QOUV5Mm1u45caLbkXjfJFYp\nzAAAIABJREFUJ5/YFVHmP+FU3Xqr9OWX1tIVzBxHmjzZRgfExbkdDQJRhw7SOeeERoJg1iy7KHXL\nLW5HgkDVv7/0n/9IubluR+Jdv/9u+4Q+fVjUBqemRw8bI5CR4XYkp4RkGgJDq1aWXJowwe1IvCsn\nx1bDGTCANhycugEDpKVLbd5LMJswQWrcWOrUye1IEKiuvFJq0MASTcFs1SppzRqSAzh1YWF2YXP2\nbOm339yOxrsmTZL+9jdrQQJORa9eNnoj2OcMvvWWtbTefrvbkSBQRUdbB9rkydKhQ25HU24k0xA4\n7rjDKtO+/97tSLwnI8PaWm++2e1IEMi6d7c5g8FcnfbrrzYbbsAAO2AFTkWlSlJampSVFdCrSZ3U\nK69IZ5xhA6KBU9W/v3TggM2qDFZr19qK2CQHUBF16ticwQkTgnvO4MSJUmKidN55bkeCQDZggI04\nWrDA7UjKjTMQBI4bb7Ts9aRJbkfiHYWF9rvdeKPNHgBOVZUqdtKTmRm8CxFkZtoBKi2eqKj+/aWC\nAqu4CUZ79kgzZlhVUUSE29EgkJ15ps1Fevnl4J0zOGnS0XmKQEXccYe0aZP0/vtuR+IdmzbZnGcS\nz6io1q2liy4KyA40kmkIHFFR1pP/yivWox9s3n1X2rLFsvNARd1+u7RrlzRnjtuReJ7j2MncDTcw\n/wkVd/bZtmJfAB7Elcn06bb0/G23uR0JgsHAgdK6ddJHH7kdieft2ydNnWoJ9sqV3Y4GgS4x0WYg\njx/vdiTeMXmyXfxPTnY7EgSDAQNsBdwA60AjmYbAMmCALUTw1ltuR+J5EyZI558vtW3rdiQIBuee\nK/31r8GZIFi82BYeuOMOtyNBsLjzTunTT222WDBxHGncOFt44Oyz3Y4GweDyy22hm2BMEMyZI+Xl\n2cIkQEWFhVny+e23rYUtmBQtPNC7t1StmtvRIBgEaAcayTQEloQEqX17q0oJJj/9JM2fz8ID8Kzb\nb5eys62KIJi8/LLUrJnUsaPbkSBYXHON1KiRNHas25F4Vna2LTxw111uR4JgER5uFzLmzLHB48Fk\n3Di7CHXuuW5HgmBx8822ymWwLXIzbx4LD8Czju1AO3jQ7WjKjGQaAs8dd0gffCB9/bXbkXjOyy/b\nlZ2bbnI7EgSTbt2kunXtBCFY/PyzNHeu7QdIPMNTKlWy6rSsLOmXX9yOxnPGjbMqoiuucDsSBJN+\n/ew9k5HhdiSe87//2b977nE7EgSTmBg7tp80KSBXKizV889Ll11mRQ6ApxR1oM2b53YkZUYyDYHn\nhhuk2NjgqSDYv9+Saf37SzVquB0NgknlypYgyMy01S+Dwcsv2+/Vp4/bkSDY3HqrJWiDpYIgJ8eq\nh+68kxVv4Vm1a0spKTZGoLDQ7Wg848UXpcaNpauvdjsSBJuBA60DJVhG1KxcaSveDhrkdiQINgkJ\nlqR9/nm3Iykzjq4QeKpWtQ+mV1+1AeuBbsYMaedO6e9/dzsSBKM77rCrocGQINi/X3rpJauKqFXL\n7WgQbE4/XUpNtVlQwVBBMGmSJZ779XM7EgSju++2QdHBkCDIyZFmzbLjsEqV3I4GwaZVK6lDB2nM\nGLcj8Yznn7cZnKx4C29IT7cRFStWuB1JmZBMQ2C6807rpw6wIYXHcRz7cL36amvFATwtLs5aDF58\nMaBmEJRoxgwpN5erofCeu++WfvhB+s9/3I6kYg4csCrOm26y1dYAT7vwQksQjB7tdiQVV1Tx3L+/\n25EgWN17r62Au3y525FUTE6ONHOmfVaSeIY3XHON9Kc/Sc8953YkZUIyDYGpbl2pV6/ATxAsXix9\n+aVl4QFvGTRI2rpVevNNtyM5dUWJ56QkqWlTt6NBsGrdWrr00sCvIJgxw056Bg92OxIEs3/8w9q9\nPv3U7UhOXVHiuV8/Es/wnmuukc45J2ASBKWaMMESz7fc4nYkCFaVKtl5y+zZdu7i50imIXANHmxv\nsjfecDuSUzdmjPWHX36525EgmF1wgdSpU2AnCN5/3xLPJAfgbffdJy1dGrgJAseRRo2yk7fmzd2O\nBsHsmmts5ctnn3U7klOXlWWrEt59t9uRIJhVqmQXzmfPturnQFQ047lvXxLP8K7+/aXIyICYj04y\nDYHr/PNtCfNnn7WTh0CzYYO1EqWnsyohvG/QIOmTT+xfIHruOXvPd+rkdiQIdtddZ9WPI0e6Hcmp\neecdaf16SwoC3hQebhc43nhD+u47t6Mpv8OHpaefttlPzZq5HQ2CXb9+UnS0ddUEosxMSzwzagPe\nFh1ti0JNnCjt2eN2NCdEMg2B7d57bUDh0qVuR1J+Tz8t1a8v3Xyz25EgFCQl2cnCv//tdiTlt2aN\ntHChtRSReIa3hYdL999vS7N//bXb0ZTfM89IbdvaPCvA24qqVAJo9bUj3nrLLmw++KDbkSAUVK8u\nDRhgCYLdu92OpnwOHbILTD16MGoDvnHPPfY+8fP56CTTENi6dLFqleHD3Y6kfLZskaZNs8qBKlXc\njgahoFIlO2H4z38sORVInnzSVo5KTXU7EoSK3r1tNuczz7gdSfmsXGmzOO+/n8QzfCMqyhaFmjTJ\nFogJFI4jPfWUdNll0iWXuB0NQsXf/27tkuPGuR1J+cyeLW3eLD30kNuRIFScdZYVnDzzjL1n/BTJ\nNAS2sDDpkUekRYsCa77NqFFSjRrSbbe5HQlCSa9elpR68km3Iym7r7+WZs2SHnhAOu00t6NBqKhS\nxVrwX3tN2rbN7WjKbsQIm2HVvbvbkSCUFI2rCKTZaYsX28qKVKXBl84804b3P/usVFDgdjRl4zjW\n1dC5s/TnP7sdDULJQw/ZYkpTprgdSalIpiHw3XCDDVl+4gm3Iymb7dulyZNt5kD16m5Hg1By2mmW\nlJo1S9q40e1oyuapp6wdul8/tyNBqLnjDqlatcCZnfbZZ9aaOnSoVaICvnL66TbAf+xYaedOt6Mp\nm3//W2rVyhIEgC89+KD02282zD8QLFhgC0BRlQZfi4+Xeva0c4GDB92OpkQk0xD4wsOlIUNsptLq\n1W5Hc3KjR0sREawcBXf06yfVq2cfTP7u+++tMuj++6WqVd2OBqGmRg2by/nyywGxPLsef9yq0nr1\ncjsShKJ//MMG+gfCqtHLlknvvWfHjrRDw9caNbJjsWeekfbudTuaE3Mcadgw6dJLpY4d3Y4GoWjI\nEBuPNH2625GUiGQagkNKinTOOXYy4c+2bbMrt+npUq1abkeDUFS1qiWnpk71/+q0J56QatemHRru\nSU+3mVD+3hp9bFVaRITb0SAUxcba7LQXXpB27XI7mtI5jp2ctWolXX+929EgVD34oFVxTpzodiQn\nNm+ezeIcPpzEM9zRsqXUrZv9DfphdRrJNASHiAjpscdsZaZPPnE7mtINH27JjPvuczsShLKBA611\ncuhQtyMp3Vdf2YyERx6xZAbghpgY6Z//tNb8LVvcjqZ0w4ZRlQb33XefneyMGuV2JKV77z2rTBs+\n3DobADf86U9Snz7Wbpyf73Y0JSsstOPEv/5V6tTJ7WgQyoYNswUwJk92O5Lj8CmC4NGrl63s+cAD\nduXR33z7ra129eCD1j4EuKVqVftgmjVLWrXK7WhK9sgj1goxYIDbkSDU3X23VRL761zO7Gy7kPSv\nf1GVBnfVrSsNHiw995z0009uR3M8x7HPlksukbp2dTsahLphw2x2mr8mn7OypHXrbGEbwE3nn28r\new4b5ncLd5BMQ/CoVMnmQC1bZsMy/c1jj1kbBLPS4A/69LGFO/xxoOzy5dLcuda2XaWK29Eg1EVF\nSQ8/LL36qrR2rdvRFOc4NquqdWspNdXtaAC7oBkVJT36qNuRHO+NN6QVK2hZg39o2NAWIxs1ylYs\n9CcHDth5y7XXShdf7HY0gJ0T7Nrld3M5SaYhuFx1lZUiP/iglSf7i+XLbXDiY49JkZFuRwNYBcuT\nT1rLy3vvuR3NUY5jM90SEmhZg/8YONDmcg4e7F+Vz7NnS//7n52M0bIGfxATY8c6U6bYCoD+Yv9+\n+2y5+mrp8svdjgYwDz10tFvAn7zwgo028Pd5oQgdZ59tczlHjpRyc92O5giOvBBcwsKkp5+2suQJ\nE9yOxhw+LN1zj3TBBdKtt7odDXDUdddJHTrY3+fvv7sdjZk5U1q61Fa9rVTJ7WgAU7my/U0uWiTN\nn+92NObAAbtwlJTEPBv4l9tvt5lQ99/vP8nnZ5+1VXlHj3Y7EuComjVtQYzJk/2n8vnnn22swZ13\nSued53Y0wFFDhti5wZAhbkdyBMk0BJ+LLpJuucXeaDt2uB2N9NprVjnwwgskB+BfwsJsddlvvrG/\nT7cVFNgA6+7dpSuvdDsaoLikJOlvf7O2Sn9IPo8cKf34o90C/qRyZauW/H//z1r23bZtm1XY/P3v\nUny829EAxd11ly0gM3CgfySfH37Y3sP/+pfbkQDF1aljbfqTJ9u5tR8gmYbg9NRT1vLyz3+6G8dv\nv1nlQEqK1LGju7EAJTn/fDuQGzbMTjjcNGKELRX/7LPuxgGUJCzM/jY3b3Y/gbVpk71f7r/fZh8C\n/ua666RrrrGZUG6vVnjvvVK1av69gjVCV5Uq0ksvSR99JE2d6m4sn35q80GfeEKqXdvdWICS3HGH\n1KqVnbv4wUgnkmkITnXq2HLTmZm2IIFb7rtP2rPHf1fqASRLpEVG2kmPW9assfabhx6yuQiAP0pI\nsATWE09IX33lTgyOYweR9evbyoSAv3rxRRsY7WaFy3/+Y+MDnnvOVuUF/NHll9uc2Pvuk3791Z0Y\nDhyQ0tKsw+f2292JATiZSpWkceOkVaukl192OxqSaQhit94qtWtnHwx79vj+9RctsjLUUaOkBg18\n//pAWdWsaSc9c+bYSYevHTwo9etn7TcPPOD71wfK49FHpbPOkm67zWZi+tprr0nvvmst2ixoA392\n1ln2fnn+eXdacnbvtrlPXbpIN93k+9cHymP0aDseuuced15/xAires7IYCwN/Nsll0gDBtg5w7ff\nuhoKyTQEr/Bwq0z76Sfft3vm59uJVqdOdgv4u549peRkO/H4+Wffvva//22VaVOmWLsD4M+qVbML\nJdnZvp81+P330t13S71726qEgL+7916pTRv7m/X1hc1//EPKy5PGj7c2bcCf1atnFzanT5dmzfLt\na3/2mR2LDRliFdiAv3vmGSkuzi7Gu9juSTINwa1pU3uzvfSSDcL1Bcexfu5ffrETLg7gECheekmK\niLCqTl9V3HzyibXMPfSQnXABgaBjRyk93a6Krl7tm9csLJT69LE5Ni++6JvXBCrqtNOkadPswuY/\n/uG71505047BxoyxCjkgENx8s9Sjh51H/PSTb14zP99mO7dsacdiQCCIjraL8NnZrq7STDINwW/g\nQKlzZ7sq+uOP3n+9V16RZsyQJk2ypeGBQFGnjg2eXbBAevpp77/eL79YRVzbttYKBASSp56yK/gp\nKb4ZsP7440cHVNeo4f3XAzylSRNbvGPCBN9U3Hz3nc18SkmxUR9AoAgLszlQVatKqanW9ulNjmMd\nCTk5loCuXNm7rwd4UseONsf24YelpUtdCYFkGoJfeLhdFa1WTbrhBmn/fu+91qpVtvT67bdLN97o\nvdcBvKVrVxtq/sgj0vvve+91Cgstwb1vnx3AnXaa914L8IYqVaTXX7e26L59vVvN+dZblkwbPpyV\noRGYbr/dkgP9+1tbv7cUFEjdu0unn27JO7oDEGhOP92Szp984v1qzpdesnOkl1+2pDcQaEaMkDp0\nsIvz27b5/OVJpiE01KkjzZ1rB3DeGhr9ww9SUpJ0wQXWVgAEqn/9S7riCpuhtn6955/fcWzA7nvv\nWRUnC3QgUDVpYvNt5s3zXnvMmjWWeL7hBlpwELjCwqztsmlTqVs3accOz79GYaEtNLB5s/T221Rw\nInC1b28Ld7z4or1vvOG//7VjsfR0FuhA4IqIsAubERH22VJQ4NOXJ5mG0NGmjS1IMH26XelxHM89\n9y+/2DDoqlXtAK5aNc89N+BrlSpZtViDBtYi7en26JEj7Wro+PHSlVd69rkBX7v2WpvXMXKkrbDp\nSZs22XukSRNrwabKBoEsMtISz/v22WdLXp7nnttxbHGO+fPtxIoh6gh0AwfavwEDpDfe8OxzL19u\nlTxdu0qjRnn2uQFfq1vXzr83bLALj7//7rOX9lgybcOGDercubNq1aqls846S08++aSc/0tWLFy4\nUH/+858VExOjVq1aad68eSd8rpEjR6phw4aqXr26OnXqpI0bN3oqTIS6lBRp3DirHBsyxDMJtdxc\n6fLL7SrrwoW2sghCTlZWltsheFbNmnbVMjxc+utfpS1bPPO8Tz8tPfigNHQoK90ieKSn26qFf/+7\nfcZ4wqZN0t/+dvS9GB190h8Juv0Qgs9ZZ1lV8g8/2Im8JxJqRdXOL79s82q7dq34c6JC2Bd5QFiY\nVab17Gkt0v/5j2eed/lyu0jTsqV1B1Sq5JnnBdzUurWNxFi82N4z3hzrdAyPJNMKCgrUuXNnnX32\n2dq2bZuWLVummTNn6oknntDq1avVvXt33XPPPcrLy9PYsWPVt29fLVmypMTnyszM1Isvvqj33ntP\nO3fuVJs2bZSUlKRCF5c8RZAZONCuwvz733Yyf+jQqT/XN9/Y/JodO6QPP5SaN/dcnAgoQXngeMYZ\n0gcfWOtMYqK0du2pP1dhoSXRHnzQFhsYNsxzcQJuCwuzz5V777XqmGHDKjZOYPVqe89VrmyJh9jY\nMv1YUO6HEHwSEixBvGGDtbP98MOpP9f+/daiNnaszUhjwQG/wL7IQypVskVnrrnGWtgmTarY8y1c\naGM8WrSQ3nmnTBdpgIDRqZONdfp//0+66iqfLA7lkWTaRx99pNzcXI0bN07VqlVTo0aN9MADD2j8\n+PGaNWuWOnTooP79+ys8PFzt27dXSkqKxo8fX+JzTZo0SXfddZeaNWumKlWq6KmnntL27dtLTb4B\np+Qf/7APp8xM6bLLTq3q5u23pYsvthOmpUvtgwkINuecYysInn66/b2/9lr5n2PHDjsQfOYZa4cb\nNox2NQSfooTa8OE2dzA5Wdq5s3zP4TjSxImWSDvrLFvyvWFDr4QLuOqii6SPP5b27LGv33mn/M+x\naZMdw735pg1sv/12z8cJuO200+zv+4477G/8jjukvXvL9xyHDtmg9muusYTDu+9KMTHeiRdw09VX\nS4sWSV98YfNmvcwjybTCwkJVrlxZERERR584PFzbt2/Xr7/+qsjIyOIvGh6uDRs2lPhc69evV8uW\nLY/cj4iIUJMmTfTll196IlTgqN69rRR061bp/PNt2fay9Fhv2WJXQa+7zq6o/u9/NlAXCFb169tJ\nT48eUp8+djD2zTcn/7mDB6VXXrGKzeXL7WTp3nu9Hy/glrAwGyEwb55VdbZoYQnoslTXr19vV1IH\nDJD69ZOWLLHFc4Bg1ayZHUO1aWOtmf37Sz/9dPKf27vXZhRecIHNrF2yxJLXQLCqVOlo9eXUqVKr\nVnZRvyzjarKz7QLNo49KDz9syefq1b0fM+CWxERpxQqbZe5lHkmmtW/fXtWqVdPDDz+s/fv3a8uW\nLRo9erTCwsLUv39/vfvuu3rzzTd16NAhZWdna/bs2dpfSh9rfn6+oqKiim2LjIzUnj17PBEqUFxi\novT551KvXtL990uNG9uHzfLllggo8uuvNtT25pttEPR770lTplhvds2aroUP+ExUlFVyzpljqws2\nayZdf73d/+WXowd0Bw/a1aDhw6X4eOnWWy1B8NVXLDaA0HHddZYc69jREtDNmtlogbVrj44WcByr\n2pw50xLUCQlWafP227Y4BwvZIBTExUkLFtjf/Pz50rnn2nvmnXek3347+rjff7fE20MPWcX0kCHS\nLbfY503btu7FD/hKWJhVpq1aJTVqZJ8zbdpYku3rr48ehzmOtU5PmWJVaO3bWzt0drb0xBM2CxcI\ndueeK02b5vWXiTj5Q06uRo0aeuedd3TvvfeqQYMGatKkifr06aNVq1apdevWeu211/TYY4/ptttu\nU8eOHdWvXz99/PHHJT5XVFSU9v6hdHXv3r2KPkFP91dffeWJXwOh7LbbbIZAVpa1oT3xhG2vWdOS\nA0XJ3LPPlu66yyp0qlWTPvvMtZDhX/Ly8rR69Wq3w/C+xo1tYO3ChdZ2UFQNEBlprQh79liyoFo1\n6S9/kZ580io3f/zR86uCAv7uoYdszs3UqdLjj1tVQESEVQX8/vvRVp34eEsOdO0qValiM9NOQcjs\nhxB82ra1izNz5lhlZ9FIgerV7T3z22+WJIiOtgs0N91kLdAsUuaX2Bd52dNPW3J51ixp0CAbORMR\nYe+PvXulAwfscW3a2IWcK66wJBr/TxBCvipLF00FhTlOxZczPHjwoD7//HNddNFFR7aNHTtWkydP\n1ocffqht27bpvPPOO/K9Hj16qEGDBhozZsxxz9W+fXtde+21+uc//3nkuevUqaN58+apU6dOxR6b\nk5OjCy+8UNu2bavorwAAAAAAAIAgcMYZZ2jlypWqX7++V57fI8m0AwcO6IwzztAzzzyj/v37a/Xq\n1erWrZuGDx+uZs2a6YorrlB2drZatGihOXPm6NZbb9Xq1avVtIQ5UxkZGXr00Ue1YMECxcfHa8iQ\nIZo/f77Wr1+vSiUs3ZuTk6OcnJyK/goAAAAAAAAIAvXr1/daIk3yUJtnlSpVNG/ePA0ePFjp6emq\nW7euhgwZor59+0qSRo0apW7duumXX37Reeedp4ULFx5JpC1btkxdu3bVV199pQYNGigtLU15eXm6\n/vrrlZubq7Zt22rBggUlJtIk7/8HAgAAAAAAAIp4pDINAAAAAAAACAUs5wEAAAAAAACUEck0AAAA\nAAAAoIwCNpm2Y8cOdevWTbVq1VJsbKwGDx6swsJCt8MCEGRmzpypiIgIRUdHH/lXNA/yf//7ny6+\n+GJVr15df/rTn5SRkVHsZzMzM9WkSRNFRUXpoosu0qeffurGrwAgQOXm5urcc8/VkiVLjmyryH6n\nsLBQ9913n+rVq6eYmBh169ZNP//8s89+HwCBqaR90cCBA1W1atVix0eTJ08+8n32RQA85YsvvtDf\n/vY3nX766apXr5769u2rnTt3SnL3uChgk2kpKSmKiYlRTk6Oli9frkWLFmnEiBFuhwUgyKxYsUJ9\n+/ZVfn7+kX+ZmZnatWuXunbtqn79+mn37t165ZVXNHjw4CMHmosXL9Y999yjqVOnavfu3brpppuU\nlJSkvLw8l38jAIEgOztbl1xyiTZv3qywsDBJqvB+Z/jw4Vq0aJFWrVqln376SdWqVVOvXr1c+x0B\n+L+S9kWStHLlSk2aNKnY8dGtt94qiX0RAM/Zt2+funTpovbt22v79u1av369du7cqf79+ysvL8/d\n4yInAH3zzTdOWFiYk5OTc2TbzJkznYYNG7oYFYBg1LFjR2fcuHHHbZ80aZITHx9fbNvAgQOd3r17\nO47jODfddJMzYMCAYt9v3ry588orr3gvWABB4dVXX3XOOussZ+bMmU5YWJizZMkSx3FOfb+TkZHh\nOI7jnHnmmU5WVtaR723fvt0JCwtzNm/e7M1fB0CAKm1ftH//fqdy5crOunXrSvw59kUAPGXDhg1O\n165dncOHDx/Z9tZbbzlRUVHO5MmTnaZNmxZ7vC+PiwKyMm3dunWqXbu26tWrd2Rb8+bNtXXrVu3e\nvdvFyAAEk8OHD2v16tVasGCBzj77bDVs2FADBgxQXl6e1q1bp5YtWxZ7fPPmzfXll19KUonfb9Gi\nxZHvA0BpunTpos2bN6tnz57Ftldkv/Pbb79p27Ztxb4fFxen2rVrs18CUKLS9kVffPGFDh06pEcf\nfVT16tVTfHy8Ro4cKcdxJEnr169nXwTAI+Lj47VgwYJilbFz5sxR69atXT8uCshkWn5+vqKioopt\ni4yMlCQVFBS4ERKAIPTLL7+odevWSk5O1oYNG/Txxx/rm2++0c0336yCgoIj+50ikZGRR/ZBBQUF\nJe6n2EcBOJm6desqPPz4Q7T8/PxT3u8UPYb9EoCyKm1ftHv3bnXq1EmDBg3STz/9pGnTpumFF17Q\n6NGjJZV+rsa+CEBFPfLII1qwYIHGjx9/wn2N5P3jooBMpkVFRWnv3r3FthXdj46OdiMkAEEoLi5O\nS5YsUb9+/VS1alU1bNhQI0eO1DvvvCPHcUrcD8XExEgqeT+1Z8+eI98HgPKqXr36Ke13oqOjjxws\nlvTzHDsBKI8rrrhCixYtUocOHVSpUiVddNFFSk9P18yZMyWxLwLgebt379YNN9ygGTNmaOnSpTrv\nvPNKzQv56rgoIJNpCQkJ2rlzp3bs2HFk2/r169WwYUN2wgA8Zs2aNXrwwQeLbdu/f7/Cw8PVtm1b\nrVu3rtj31q9fr4SEBEm2n1q7dm2p3weA8kpISDjl/U7NmjV1xhlnFPv+zz//rF9//ZX9EoBymTdv\nniZMmFBs2/79+49UzrIvAuBJ3377rS666CIVFBRo5cqVOu+88yT5wXHRKcyA8wsdOnRwUlNTnfz8\nfGfz5s1OQkKCM2zYMLfDAhBEfvzxR6d69erOyJEjnYMHDzpbtmxx2rVr59x2223Ozp07nVq1ajlj\nxoxxfv/9d+eDDz5wYmJinMWLFzuO4zjvv/++ExMT43z44YfO77//7jz33HPO6aef7uzatcvl3wpA\nIDl26HdF9ztDhw51WrZs6Xz33XfO7t27nZSUFKdTp06u/W4AAsex+6K5c+c6kZGRzvvvv+8cPnzY\n+fjjj53Y2Fhn+vTpjuOwLwLgOb/++qvTqFEjJy0trdgiBI7j/nFRwCbTtm/f7iQnJzt16tRx4uLi\nnPvvv/+4/7gAUFFLlixxLr30UicmJsaJi4tzBg0a5Bw4cMBxHMdZuXKlk5iY6MTExDhYzmdbAAAg\nAElEQVTnnnuuk5mZWexnp02b5jRr1sypXr26065dO2f58uVu/AoAAtixJ7COU7H9zsGDB50HH3zQ\nadCggVOjRg2ne/fuTm5urs9+FwCB64/7ogkTJjjx8fFOVFSUc8455zjjx48v9nj2RQA8YfTo0U5Y\nWJgTFRXlVK9e/ci/6Ohox3HcPS4Kc5z/W3YFAAAAAAAAwAkF5Mw0AAAAAAAAwA0k0wAAAAAAAIAy\nIpkGAAAAAAAAlBHJNAAAAAAAAKCMSKYBAAAAAAAAZUQyDQAAAAAAACgjkmkAAAAAAABAGZFMAwAA\nAAAAAMqIZBoAAAAAAABQRiTTAAAAAAAAgDIimQYAAAAAAACUEck0AAAAAAAAoIxIpgEAAAAAAABl\nRDINAAAAAAAAKCOSaQAAAAAAAEAZkUwDAAAAAAAAyohkGgAAAAAAAFBGJNMAAAAAAACAMiKZBgAA\nAAAAAJQRyTQAAAAAAACgjEimAQAAAAAAAGXk02Rabm6uzj33XC1ZsqTUxyxcuFAtW7ZUVFSUWrRo\noQULFvgwQgAAAAAAAKB0PkumZWdn65JLLtHmzZsVFhZW4mO++eYb9ejRQyNGjFB+fr6GDRum5ORk\nbdq0yVdhAgAAAAAAAKXySTJtypQpuummm/Tkk0+e8HGZmZnq2LGjrr32WoWHhys5OVl/+ctfNHXq\nVF+ECQAAAAAAAJyQT5JpXbp00ebNm9WzZ88TPm7dunVq2bJlsW0tWrTQl19+6c3wAAAAAAAAgDKJ\n8MWL1K1bt0yPKygoUFRUVLFt1apVU0FBQak/k5OTo5ycnArFBwAAAAAAgOBQv3591a9f32vP75Nk\nWllFRUVpz549xbbt3btXMTExJT4+JydHF154obZt2+aL8AAAAAAAAODnzjjjDK1cudJrCTW/SqYl\nJCRo9erVxbatX79ebdu2LfHxOTk52rZtm6ZNm6bmzZv7IkQA5ZSenq4xY8a4HQaAEvD+9Ix//Uta\ntkzKy5Nq1pQ6dLBt3tK9u/TDD0fvN2okvfmm917P179f0Wt+8YV0wQXefy1/xnsU8F+8PwH/9dVX\nX+nmm29WTk5OaCTTevfurWeffVazZ89W9+7dNXfuXC1ZskQvvvjiCX+uefPmat26tY+iBFAeNWvW\n5P0J+Klgfn+mpUnZ2VJiopSR4d3X+vprSzRJdvv115I3/7P+9a/S/PlSbq4UG2v3vfl6vv790tKk\nTz+132/fPmnsWO//P/RXwfweBQId708gtPlkAYITiY6OVlZWliQpPj5e8+bN05NPPqnatWtr+PDh\nmjt3rs4991yXowQAAIEiLc2STRs32m1amndfLzHRklqS3SYmevf1MjKkpCSpaVO79Xaiyde/X3a2\nJdIku83O9u7rAQAAlJfPK9MOHz5c7H5+fn6x+1deeaWuvPJKX4YEAACCiK+TMRkZvq2EK3pNX/H1\n75eYKO3adbTyztvJuyK+/n8IAAACl1+1eQIAgOAU7MmYYE++BHPyTjpazZiba387aWnB//8UAACc\nOpJpALwqNTXV7RAAlMJX709fJyrcSMbAs3z9/8xfW0v5DAX8F+9PILSRTAPgVRxoAP7LV+9PNxIV\nJNBQHv7aWspnKOC/eH8Coc31BQgAAEBw8/UAe6C8fL2og+T7hTIAAIDnUJkGAECI8lUrJG2XCAS0\nlgIAgLIimQYAQAhyY44ZgKPcai0FAAAVR5snAAAhiKoYwF1utJYCAADPoDINAIAQRFUM4D43Emi0\nXAMAUHFUpgEAEIKoigFCD4seAADgGVSmAQDgJ3xdMUICDQgttHcDAOAZVKYBAOAHqBgB4G2JidbW\nLdHeDQBARZBMAwDAD1AxAsDbaO8GAMAzaPMEAMAPsCAAAF9g0QMAACqOyjQAAPwAFSMAghEt7ACA\nYERlGgAAfoIEGoBgQws7ACAYUZkGAMAJpKVJ8fFUUwDAqWDRAwBAMCKZBgBAKWhPAoCKoYUdABCM\naPMEAKAUtCcBQMWRQAMABBsq0wAAKAXtSQAQmGjRBwB4E8k0AABKQXsSAAQeWvQBAN5GmycAACdA\nAg0AAgst+gAAb6MyDQAAAEDQoEUfAOBtJNMAAAGJeTgAgJLQog8A8DbaPAEAAadoHk5urrRrl93n\nZAkAUITPBACAN1GZBgAIOMzDAQAAAOAWkmkAgIDDPBwAgD9iBAEAhAaSaQCAgMM8HACAvykaQbBx\no92SUAOA4MXMNABAQCKBBgDwJ4wgAIDQQWUaAAAAAFQQIwgAIHSQTAMAAACACmIEAQCEDto8AQAe\nk5ZmbS2JiZxEAABCD599ABAaqEwDAHgEg5cBAAAAhAKfJdN27Nihbt26qVatWoqNjdXgwYNVWFh4\n3OMOHz6soUOHqlGjRqpRo4YuuOACzZ0711dhAgBOEYOXAQBwR1qaFB/PhSwA8BWfJdNSUlIUExOj\nnJwcLV++XIsWLdKIESOOe9zUqVM1adIkvf/++/rtt9/02GOPqWfPnvrhhx98FSoA4BQweBkAAN+j\nMhwAfM8nybRNmzZpyZIlGjlypKpWrarGjRtr6NChmjx58nGPjYmJkeM4KiwslOM4Cg8PV5UqVRQR\nwXg3APBnDF4GAMD3qAwHAN/zSYZq3bp1ql27turVq3dkW/PmzbV161bt3r1bMTExR7Zff/31Wrx4\nsVq0aKFKlSopLCxM06dP1xlnnOGLUAEAFUACDQAA30pMlHbtskQaleEA4Bs+qUzLz89XVFRUsW2R\nkZGSpIKCgmLbx44dqzlz5mjFihXat2+fJk6cqLS0NK1du9YXoQIAAABAwKAyHAB8zyeVaVFRUdq7\nd2+xbUX3o6Oji21/5ZVXlJ6erjZt2kiS+vXrpxkzZmjKlCkaNWpUic+fnp6umjVrFtuWmpqq1NRU\nT/0KAAAAAOCXSKABCFVZWVnKysoqti0vL8/rr+uTZFpCQoJ27typHTt2KC4uTpK0fv16NWzY8Lhk\nWrVq1Y4PMiJCVapUKfX5x4wZo9atW3s2aAAAAAAAAPitkgqpVq9efaRAy1t80ubZpEkTtW/fXunp\n6SooKNB3332n4cOH65Zbbjnusb1799aoUaP02Wef6fDhw5ozZ44WL16slJQUX4QKAEEnLU2Kj2d1\nLwAAAADwBJ8k0yRpzpw5OnTokBo3bqx27dqpS5cuGjp0qCRr9Swqyxs4cKCGDBmilJQU1a5dWyNH\njtRbb72l888/31ehAkDQSEuT5s+XNm60WxJqAADAU7hgByBU+aTNU5Li4uI0a9asEr+Xn59f7P6g\nQYM0aNAgX4QFAEEtO9tW95LsNjvb3XgAAEBwKLpgl5trq4mmpTG7DUDo8FllGgDA9xITpdhY+zo2\n1u4DAABUFBfsAIQykmkAEMQyMqSkJKlpU7vlijEAAPAELtgBCGU+a/MEALiDBBoAAPC0jAxr7czO\ntkQaxxsAQgnJNAAAAABAuZFAAxCqaPMEAAAAAAAAyohkGgAAAAAAAFBGJNMAAAAAAAEjLU2Kj7db\nAHADyTQAcAkHggAAAOWTlibNny9t3Gi3HEcBcAPJNABwAQeCAAAA5ZedLeXm2te5uXYfAHyNZBoA\nuIADQQAAgPJLTJRiY+3r2Fi7DwC+RjINAFzAgSAAAED5ZWRISUlS06Z2m5HhdkQAQlGE2wEAQCjK\nyLDWzuxsS6RxIAgAAFA2HDcBcBvJNABwCQeCAAAAABB4aPMEAAAAAAAAyohkGgAAAAAAAFBGJNMA\nAAAAADiJtDQpPt5uAYQ2kmkAAAAAAJxAWpo0f760caPdklADQhvJNAAAAAAATiA7W8rNta9zc+0+\ngNBFMg0A/oASfgAAABwrMVGKjbWvY2PtPoDQRTINAI5BCT8AAAD+KCNDSkqSmja124wMtyMC4KYI\ntwMAAH9CCT8AAABKQgINQBEq0wDgGJTwAwAAAABOhGQaAByDEn4AAAAAwInQ5gkAf0ACDQAAAABQ\nGirTAAAAAADwY6w2D/gXkmkAAAAAAPgpVpsH/A/JNAAAAAAA/BSrzQP+h2QaAAAAAAB+itXmAf9D\nMg0AAAAAAD/FavOA/2E1TwABIy3NytoTEzmIAAAAQOjg2BfwL1SmAQgIDF4FAAAAAPgDkmkAAgKD\nVwEAAAAA/sBnybQdO3aoW7duqlWrlmJjYzV48GAVFhaW+NglS5bo4osvVnR0tBo1aqSRI0f6KkwA\nforBqwAAAAAAf+CzZFpKSopiYmKUk5Oj5cuXa9GiRRoxYsRxj9uwYYOuvvpq3X333crPz9eCBQv0\nzDPP6I033vBVqAD8EINXAQAAAAD+wCcLEGzatElLlizRtm3bVLVqVTVu3FhDhw7Vfffdp0cffbTY\nY8eNG6fu3burd+/ekqSWLVvq448/VkxMjC9CBeDHSKABAAAAvsUiYMDxfFKZtm7dOtWuXVv16tU7\nsq158+baunWrdu/eXeyxK1as0FlnnaXU1FTFxsaqRYsWWrx4serWreuLUAEAAAAAgFgEDCiNT5Jp\n+fn5ioqKKrYtMjJSklRQUFBs+86dO/XCCy+oT58+2r59uyZMmKD77ruPNk8AAAAAAHyIRcCAkvkk\nmRYVFaW9e/cW21Z0Pzo6utj2qlWrqlu3burSpYvCw8PVoUMH9e7dW7NmzfJFqAAAAAAAQCwCBpTG\nJzPTEhIStHPnTu3YsUNxcXGSpPXr16thw4bHJdNatGih/fv3F9t26NChEz5/enq6atasWWxbamqq\nUlNTPRA9AAAAAAChJyODmWnwb1lZWcrKyiq2LS8vz+uvG+Y4juP1V5HUsWNHNWjQQBMnTlRubq6u\nvfZaJScnH7cAwYcffqjOnTtrypQpSk1N1bJly5SUlKQZM2YoKSmp2GNXr16tNm3aaNWqVWrdurUv\nfg0AAAAAAAD4KV/kinzS5ilJc+bM0aFDh9S4cWO1a9dOXbp00dChQyVZq2dRJrFTp056++23NWbM\nGNWsWVNpaWkaPXr0cYk0AP4jLU2Kj2cgKQAAAAAg+PmkzVOS4uLiSp17lp+fX+z+VVddpauuusoX\nYQGooKIVfnJzpV277D7l3wAAAACAYOWzyjQAwYkVfgAAAAAAoYRkGoAKYYUfAAAAAEAoIZkGoEIy\nMqSkJKlpU7ulxRMAAACAJzCbGf7KZzPTAAQvEmgAAAAAPInZzPBnVKYBAAAAAAC/wmxm+DOSaQAA\nAAAAwK8wmxn+jGQaAAAAAADwK8xmhj9jZhoAAAAAAPA7JNDgr6hMAwAAAAAAAMqIZBoAAAAAAABQ\nRiTTgCCXlibFx9stAAAAAACoGJJpQBBLS5Pmz5c2brRbEmoAAAAAcHIUJeBESKYBQSw7W8rNta9z\nc+0+AAAAAKB0FCXgZEimAUEsMVGKjbWvY2PtPgAAAACgdBQl4GRIpgFBLCNDSkqSmja1W5aWBgAA\nAIAToygBJxPhdgAAvIsEGgAAAACUXUaGtXZmZ1sijXMq/BHJNAAAAAAAgGOQQMOJ0OYJAAAAAAAA\nlBHJNAAAAAAAAKCMSKYBAAAAAAAAZUQyDQAAAAAAACgjkmkAAAAAAAB+Ii1Nio+3W/gnkmkAAAAA\nAAB+IC1Nmj9f2rjRbkmo+SeSaYBLuNoAAAAAADhWdraUm2tf5+baffgfkmmAC7jaAAAAAAD4o8RE\nKTbWvo6NtfvwPyTTABdwtQEAAAAA8EcZGVJSktS0qd1mZLgdEUoS4XYAQChKTJR27bJEGlcbAAAA\nAABFSKD5PyrTABdwtQEAAAAAgMBEZRrgEhJoAAAAAAAEHirTAAAAAAAAgDIimQYAAAAAAACUEck0\nAAAAAAAASJLS0qT4eLtFyXyWTNuxY4e6deumWrVqKTY2VoMHD1ZhYeEJf2bt2rWKjIzU0qVLfRQl\nAAAAAABAaEpLk+bPlzZutFsSaiXzWTItJSVFMTExysnJ0fLly7Vo0SKNGDGi1Mfv2bNHqampOnDg\ngK9CBAAAAAAACFnZ2VJurn2dm2v3cTyfJNM2bdqkJUuWaOTIkapataoaN26soUOHavLkyaX+zF13\n3aXrr79ejuP4IkQAAAAAAICQlpgoxcba17Gxdh/H80kybd26dapdu7bq1at3ZFvz5s21detW7d69\n+7jHZ2Zm6rvvvtNjjz3mi/CA49AjDgAAAAAINRkZUlKS1LSp3WZkuB2Rf4rwxYvk5+crKiqq2LbI\nyEhJUkFBgWJiYo5s37Bhg4YOHapPPvlE4eGsjwDfK+oRz82Vdu2y++xAAAAAAAChgPPfk/NJtioq\nKkp79+4ttq3ofnR09JFt+/btU8+ePfX888/rzDPPPNLiSasnfIkecQAAAAAAUBqfVKYlJCRo586d\n2rFjh+Li4iRJ69evV8OGDYsl01auXKlNmzYpLS1Nacf01yUlJalv374aO3Zsic+fnp6umjVrFtuW\nmpqq1NRUL/w2CHaJiVaRlptLjzgAAAAAAP4qKytLWVlZxbbl5eV5/XXDHB+VfXXs2FENGjTQxIkT\nlZubq2uvvVbJycl69NFHT/hz4eHhWrx4sTp27Hjc91avXq02bdpo1apVat26tbdCRwhKS7OKtMRE\nSlwBAAAAAAgUvsgV+Wwo2Zw5c3To0CE1btxY7dq1U5cuXTR06FBJ1ur5x0wi4KaMDOnrr0mkAQAA\nAACA4nzS5ilJcXFxmjVrVonfy8/PL/XnDh8+7K2QAAAAAAAA4EcCoVOM5TIBAAAAAADgurQ0af58\naeNGuz1mnL5fIZkGAAAAAAAA12Vn22KAkt1mZ7sbT2lIpgEAAAAAAMB1iYlSbKx9HRtr9/0RyTQA\nAAAAAAC4LiNDSkqSmja1W3+dmeazBQgAAAAAAACAE/HXBNqxqEwDAAAAAAAAyohkGgJOWpoUH++/\nq3oAAAAAAIDgRTINASVQlskFAAAAAADBiWQaAkqgLJMLAAAAAACCE8k0BJRAWSYXAAAAAAAEJ5Jp\nCCiBskwuAAAAAAAIThFuBwCUFwk0AAAAAADgFirTAAAAAAAAgDIimQYAAAAAAACUEck0AAAAAAAA\noIxIpgEAAAAAAABlRDINAAAAAAAAKCOSaQAAAAAAAEAZkUyDR6WlSfHxdgsAAAAAABBsSKbBY9LS\npPnzpY0b7ZaEGgAAAAAACDYk0+Ax2dlSbq59nZtr9wEAAAAAAIIJyTR4TGKiFBtrX8fG2n0AAAAA\nAIBgQjINHpORISUlSU2b2m1GhtsRAQAAAAAAeFaE2wEguJBAAwAAAAAAwYzKNAAAAAAAAKCMSKYB\nAAAAAAAAZUQyDQAAAAAAACgjkmkAAAAAAABAGZFMAwAAAAAAAMqIZBoAAAAAAABQRiTTQkhamhQf\nb7cAAAAAAAAoP5JpISItTZo/X9q40W5JqAEAAAAAAJQfybQQkZ0t5eba17m5dh8AAAAAAADl47Nk\n2o4dO9StWzfVqlVLsbGxGjx4sAoLC0t87Msvv6xmzZopJiZG8fHxGj9+vK/CDFqJiVJsrH0dG2v3\nAQAAAAAAUD4+S6alpKQoJiZGOTk5Wr58uRYtWqQRI0Yc97h58+bp4Ycf1tSpU7V7925lZmZqyJAh\nmjt3rq9CDUoZGVJSktS0qd1mZLgdEQAAAAAAQODxSTJt06ZNWrJkiUaOHKmqVauqcePGGjp0qCZP\nnnzcY3NycvTQQw+pbdu2kqR27dqpU6dO+uCDD3wRalDLyJC+/ppEGgAAAAAAwKmK8MWLrFu3TrVr\n11a9evWObGvevLm2bt2q3bt3KyYm5sj2gQMHFvvZHTt2aOnSpXr66ad9ESoAAAAAAABQKp9UpuXn\n5ysqKqrYtsjISElSQUFBqT+3fft2denSRRdeeKH69evnzRABAAAAAACAk/JJZVpUVJT27t1bbFvR\n/ejo6BJ/5tNPP1VycrIuu+wyvfrqqwoPLz3vl56erpo1axbblpqaqtTU1ApGDgAAAAAAAH+UlZWl\nrKysYtvy8vK8/ro+SaYlJCRo586d2rFjh+Li4iRJ69evV8OGDUtMpmVkZOiee+7R8OHDlZ6eftLn\nHzNmjFq3bu3xuAEAAAAAAOCfSiqkWr16tdq0aePV1/VJm2eTJk3Uvn17paenq6CgQN99952GDx+u\nW2655bjHvvH/27v/0KrqP47jL70yt915Zcu7vLaVi+V07ZplucVCFCFwyFjmCslkjv5IJekH4V+z\nWtkPsZICwbDJwjYYbkboyLTNIC1lBGqbaQYWxmhXs9rV1tr2/v5x8eLdj/u9kPfc23w+YJxfn3P3\nuX+87u5enHtPc7PWrVunvXv3xlSkAQAAAAAAAE5xpEyTpD179mhgYEB5eXkqKSnR0qVLVVNTIyn0\nUc9rl+XV1tZqcHBQy5cv15QpU8I/69atc2qqAAAAAAAAwKgc+ZinJGVnZ6upqWnUY729veH1EydO\nODWlpFNdLR05IpWWSnV1iZ4NAAAAAAAAhnPsyjREV10t7dsnnT0bWlZXJ3pGAAAAAAAAGI4yLUkc\nOSIFAqH1QCC0DQAAAAAAgORCmZYkSkslrze07vWGtgEAAAAAAJBcKNOSRF2dtGyZNGtWaMl3pgEA\nAAAAACQfx25AgP+PAg0AAAAAACC5cWUaAAAAAAAAECPKNAAAAAAAACBGlGkAAAAAAABAjCjTAAAA\nAAAAgBhRpgEAAAAAAAAxokyLUXW1VFAQWgIAAAAAAODmRJkWg+pqad8+6ezZ0JJCDQAAAAAA4OZE\nmRaDI0ekQCC0HgiEtgEAAAAAAHDzoUyLQWmp5PWG1r3e0DYAAAAAAABuPpRpMairk5Ytk2bNCi3r\n6hI9IwAAAAAAACTCpERP4L+CAg0AAAAAAABcmQYAAAAAAADEiDINAAAAAAAAiBFlGgAAAAAAABAj\nyjQAAAAAAAAgRpRpAAAAAAAAQIzGTZlWXS0VFISWAAAAAAAAQDyMizLt5Zelffuks2dDSwo1AAAA\nAAAAxMO4KNNOnJACgdB6ICAdOZLY+QAAAAAAAGB8Ghdl2j33SF5vaN3rlUpLEzsfAAAAAAAAjE/j\nokx7+WVp2TJp1qzQsq4u0TMCAAAAAADAeDQp0RO4USjQAAAAAAAAEG/j4so0AAAAAAAAwAmUaQAA\nAAAAAECMKNMAAAAAAACAGFGmAQAAAAAAADGiTAMQV42NjYmeAoAxkE8guZFRIHmRT+Dm5liZ1tPT\no4qKCmVmZsrr9eq5557T4ODgqGNbW1vl9/vldrtVWFio/fv3OzVNADcYbzSA5EU+geRGRoHkRT6B\nm5tjZdrjjz8uj8ej7u5uHT9+XIcOHdLmzZtHjPvhhx+0YsUKbd68Wb29vXrllVdUWVmpc+fOOTVV\nAAAAAAAAYFSOlGnnzp3Tl19+qS1btig1NVV5eXmqqanRzp07R4ytr6/XwoULVV5erokTJ6qyslKL\nFi3SRx995MRUAQAAAAAAgDE5UqZ1dnYqKytL06dPD++bM2eOLly4oD///HPEWL/fH7GvsLBQp06d\ncmKqAAAAAAAAwJgmOfFLent75Xa7I/alp6dLkoLBoDweT3h/MBgcMTYtLU3BYHDMxz99+vQNnC2A\nG+n333/Xt99+m+hpABgF+QSSGxkFkhf5BJKXEx2RI2Wa2+3W1atXI/Zd254yZcqIsVeuXBkx9vrC\n7Rqfz6cZM2Zo1apVN3jGAG6k+fPnJ3oKAMZAPoHkRkaB5EU+geQ1Y8YM+Xy+uD2+I2VaUVGRLl26\npJ6eHmVnZ0uSurq6lJubO6JMKyoqGtHwd3V1acGCBSMe1+fzqaOjQ93d3fGbPAAAAAAAAP4zfD5f\nXMu0CWZmcXv06yxcuFA5OTn64IMPFAgEVF5ersrKSm3atCli3JkzZ3Tvvfeqvr5ejzzyiFpaWlRV\nVaWTJ08qPz/fiakCAAAAAAAAo3LkBgSStGfPHg0MDCgvL08lJSVaunSpampqJIU+6tnY2ChJKigo\n0CeffKLXX39dWVlZeu2119TS0kKRBgAAAAAAgIRz7Mo0AAAAAAAA4L/OsSvTAAAAAAAAgP86yjQA\nAAAAAAAgRklbprW1tam4uFhTp06Vz+fThg0b1NfXJ0k6duyYiouLlZGRoTvvvFN1dXUR59bX1+uu\nu+6S2+3WAw88oG+++SYRTwEY16JltLm5WfPmzdPUqVOVl5en2tpaXf+JcjIKxFe0fF7T3d2tW2+9\nVfX19RH7yScQf9EyevLkSS1ZskQej0fTp0/Xiy++qKGhofC5ZBSIr2j5fO+995Sfny+Px6OCggLt\n2LEj4lzyCThjcHBQixYt0po1a8L7HO+JLAn19PRYWlqa1dfXm5lZd3e3+f1+e+mll+y3336zrKws\n2759uw0ODlpbW5t5PB47fPiwmZm1t7ebx+Oxo0eP2sDAgL377rt2yy232OXLlxP5lIBxJVpGOzo6\nLD093fbv329mZqdPn7a8vDx7++23zYyMAvEWLZ/XDA4O2uLFi83lcoXHmZFPwAnRMhoIBGzatGn2\n5ptv2sDAgJ0/f95mzZplW7duNTMyCsRbtHy2t7dbamqqHTt2zMzMjh49apMnT7avvvrKzMgn4KSa\nmhpzuVy2Zs0aM7OE9ERJeWWa1+tVIBDQ6tWrZWa6ePGi+vr6lJ2drebmZk2bNk1r167VxIkTtXjx\nYj3xxBP68MMPJUk7d+7UypUr9eCDD8rlcunZZ59Vdna2WlpaEvysgPEjWkZ/+uknrV27VmVlZZKk\n2bNnq6KiQm1tbZLIKBBv0fJ5TW1trXJzc5WbmxtxLvkE4i9aRuvr6zV79mxt3LhRLpdLd9xxhw4e\nPKjHHntMEhkF4i1aPjMyMjRhwgQNDg6GP3ExadIkpaamSiKfgFPa2tq0d+9ePfroo+EsNjc3y+v1\nOtoTJWWZJklut1uSlJubq7lz52rGjBmqqqpSZ2en5s6dGzF2zpw5OnXqlCSps7NTfr8/4nhhYWH4\nOIAbY6yMLl++XFu3bg2P++uvv7R//37dd999kqSuri4yCsTZWPmUpPb2djU1NdCwCzsAAARlSURB\nVGn79u0jziOfgDPGyujx48d199136+mnn5bP51N+fr52796tnJwcSWQUcMJY+bz//vtVW1urhx56\nSCkpKSotLdWrr76q+fPnSyKfgBN+/fVXPfXUU2poaFBaWpomTJggafQeKN49UdKWadf8+OOP+uWX\nXzRx4kStWLFCwWBQ6enpEWPS09MVDAYlScFgMPwCONpxADfW8IxeLxgMqqKiQm63Wxs3bpQk9fb2\nklHAIcPz2dPTo+rqajU0NIzIoUQ+AacNz+jly5e1a9culZSU6MKFC2ppadGOHTv0zjvvSCKjgJOG\n5/PTTz9VbW2tPv/8c/X19Wnfvn3atGmTDh48KIl8AvE2NDSkJ598Ui+88IL8fn+4SJNC+XO6J0r6\nMm3y5Mny+Xx666239Nlnn8ntduvq1asRY65evSqPxyNJox6/cuVK+DiAG2t4Rv/44w9J0pkzZ1RS\nUqKhoSEdPnw4/OJFRgHnDM/n6tWrtWHDBs2bNy88xq67OQj5BJw1PKMpKSkqLi5WVVWVXC6X5s6d\nq2eeeUZNTU2SyCjgpOH53LZtm1atWqUlS5bI5XKprKxMK1euDN+EgHwC8fXGG28oPT1d69evlxR6\nD3vtfWwieqKkLNOOHj2qOXPm6J9//gnv6+vrU0pKigoLC9XZ2RkxvqurS0VFRZKkoqIifffdd2Me\nB/DvRcuo2+1Wa2uriouLVVZWpgMHDkS8SJFRIL7GyqckHTp0SLW1tcrMzFRmZqZ+/vlnrVu3TuXl\n5ZLIJ+CEaH9DCwoKRtx5d2BgILxORoH4ipZPn883YvykSZM0efJkSeQTiLfdu3ervb09/D62sbFR\nDQ0NysrKkt/vd74n+hc3UIibYDBot99+uz3//PPW399v58+ftwULFtj69evt0qVLlpmZadu2bbP+\n/v4Rd2n44osvzOPxWHt7u/X393MXFSAOomX066+/tpSUFNu1a9eo55JRIL6i5XO4mTNnRtzNk3wC\n8Rcto99//72lpqbali1bbGBgwE6ePGk5OTn2/vvvmxkZBeItWj5bW1stPT3dDhw4YENDQ3b48GHz\neDzW2tpqZuQTcFpVVVX4bp4XL150vCdKyjLNzKyrq8sefvhhy8zMtJkzZ1pNTY319/ebmVlHR4eV\nlpaax+Ox/Pz8iH8EzMx2795ts2fPtoyMDCspKbHjx48n4ikA49poGf3777+tvLzcXC6XZWRkRPyU\nlZWFzyWjQHxF+xt6veFlmhn5BJwQLaPHjh2zhQsXWmZmpt122222efPmiHPJKBBf0fL58ccfW1FR\nkXk8HvP7/dbQ0BBxLvkEnHN9mWbmfE80wey6L0sBAAAAAAAAMKak/M40AAAAAAAAIBlRpgEAAAAA\nAAAxokwDAAAAAAAAYkSZBgAAAAAAAMSIMg0AAAAAAACIEWUaAAAAAAAAECPKNAAAAAAAACBGlGkA\nAAAAAABAjCjTAAAAAAAAgBhRpgEAAAAAAAAxokwDAAAAAAAAYvQ/6RYFaQJNX7UAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa30cf0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2, ncols=1, num=None, figsize=(15, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "axs[0].plot(T, Y , linewidth=1,color=\"red\", ls='-' ,lw=20 ,label=\"EBFS\")\n",
    "axs[0].set_xlim([np.min(T),np.max(T)])\n",
    "# axs[0].set_xlim([np.min(T),500])\n",
    "# axs[0].set_ylim([-1.1,1.1])\n",
    "\n",
    "axs[1].plot(1./f, npgram, lw=0, marker='.')\n",
    "# axs[1].plot(f, npgram)\n",
    "# axs[1].set_xscale(\"log\")\n",
    "axs[1].set_xlim([300,400])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read LFBD, LFST, LFSR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T11:15:39.091443",
     "start_time": "2016-09-27T11:15:38.781873"
    },
    "code_folding": [],
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-6052b2bf80bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mEBFS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_pickle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../muon/EBFS.pkl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mEBFS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "EBFS = pd.read_pickle(\"../muon/EBFS.pkl\")\n",
    "print EBFS.info(max_cols=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-27T11:15:41.031139",
     "start_time": "2016-09-27T11:15:41.000932"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'EBFS' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-3f785aa62481>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mEBFS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'EBFS' is not defined"
     ]
    }
   ],
   "source": [
    "EBFS.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:17:45.129242",
     "start_time": "2016-09-25T21:17:44.296911"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 99205 entries, 1293865200 to 1474673400\n",
      "Data columns (total 1 columns):\n",
      "Temp    99205 non-null float32\n",
      "dtypes: float32(1)\n",
      "memory usage: 1.1 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "LFBD = pd.read_csv(\"LFBD.txt\", sep='\\t', comment=\"#\", header=1, usecols=[1,2], names=['date','tmpc'], index_col=False, na_values=['M'], dtype={'date':'str', 'tmpc':'float32'} )\n",
    "LFBD = LFBD[~pd.isnull(LFBD['tmpc'])]\n",
    "LFBD['Start'] = pd.to_datetime(LFBD['date']).astype(np.int64) // 10**9 # convert to unix timestamp\n",
    "LFBD['Temp'] = LFBD['tmpc'].astype(np.float32)+273.15\n",
    "LFBD.drop([ 'date','tmpc' ], axis=1, inplace=True)\n",
    "LFBD.set_index('Start', inplace=True)\n",
    "print LFBD.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:17:45.158135",
     "start_time": "2016-09-25T21:17:45.134098"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1293865200</th>\n",
       "      <td>276.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293867000</th>\n",
       "      <td>276.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293868800</th>\n",
       "      <td>275.149994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Temp\n",
       "Start                 \n",
       "1293865200  276.149994\n",
       "1293867000  276.149994\n",
       "1293868800  275.149994"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LFBD.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:17:45.866706",
     "start_time": "2016-09-25T21:17:45.162843"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 99313 entries, 1293865200 to 1474673400\n",
      "Data columns (total 1 columns):\n",
      "Temp    99313 non-null float32\n",
      "dtypes: float32(1)\n",
      "memory usage: 1.1 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "LFST = pd.read_csv(\"LFST.txt\", sep='\\t', comment=\"#\", header=1, usecols=[1,2], names=['date','tmpc'], index_col=False, na_values=['M'], dtype={'date':'str', 'tmpc':'float32'} )\n",
    "LFST = LFST[~pd.isnull(LFST['tmpc'])]\n",
    "LFST['Start'] = pd.to_datetime(LFST['date']).astype(np.int64) // 10**9 # convert to unix timestamp\n",
    "LFST['Temp'] = LFST['tmpc'].astype(np.float32)+273.15\n",
    "LFST.drop([ 'date','tmpc' ], axis=1, inplace=True)\n",
    "LFST.set_index('Start', inplace=True)\n",
    "print LFST.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:17:46.365958",
     "start_time": "2016-09-25T21:17:46.345578"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1293865200</th>\n",
       "      <td>272.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293867000</th>\n",
       "      <td>272.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293868800</th>\n",
       "      <td>272.149994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Temp\n",
       "Start                 \n",
       "1293865200  272.149994\n",
       "1293867000  272.149994\n",
       "1293868800  272.149994"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LFST.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:29:05.948981",
     "start_time": "2016-09-25T21:29:05.660193"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21798 entries, 1293865200 to 1334559600\n",
      "Data columns (total 1 columns):\n",
      "Temp    21798 non-null float32\n",
      "dtypes: float32(1)\n",
      "memory usage: 255.4 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "LFSR = pd.read_csv(\"LFSR.txt\", sep='\\t', comment=\"#\", header=1, usecols=[1,2], names=['date','tmpc'], index_col=False, na_values=['M'], dtype={'date':'str', 'tmpc':'float32'} )\n",
    "LFSR = LFSR[~pd.isnull(LFSR['tmpc'])]\n",
    "LFSR['Start'] = pd.to_datetime(LFSR['date']).astype(np.int64) // 10**9 # convert to unix timestamp\n",
    "LFSR['Temp'] = LFSR['tmpc'].astype(np.float32)+273.15\n",
    "LFSR.drop([ 'date','tmpc' ], axis=1, inplace=True)\n",
    "LFSR.set_index('Start', inplace=True)\n",
    "print LFSR.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:29:06.885180",
     "start_time": "2016-09-25T21:29:06.866042"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1293865200</th>\n",
       "      <td>273.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293867000</th>\n",
       "      <td>273.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293868800</th>\n",
       "      <td>273.149994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Temp\n",
       "Start                 \n",
       "1293865200  273.149994\n",
       "1293867000  273.149994\n",
       "1293868800  273.149994"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LFSR.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EBFS vs. LFBD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:31:10.507534",
     "start_time": "2016-09-25T21:31:09.437820"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reindex from a duplicate axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-fe0483cd454a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# select only common x values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mmain_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain_x\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mmain_x\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcomp_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0mmain_x\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcomp_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mmain_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmain_x\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;31m# main_ref_y = scipy.interpolate.interp1d(main_x, main_y, kind='linear', assume_sorted=True, fill_value=(main_y[0],main_y[-1]))(main_x+0.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    622\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    654\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'integer'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 656\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    657\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   2285\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reindex'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2286\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2287\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2289\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fillna'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2227\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         return self._reindex_axes(axes, level, limit, tolerance, method,\n\u001b[0;32m-> 2229\u001b[0;31m                                   fill_value, copy).__finalize__(self)\n\u001b[0m\u001b[1;32m   2230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m     def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   2245\u001b[0m             obj = obj._reindex_with_indexers({axis: [new_index, indexer]},\n\u001b[1;32m   2246\u001b[0m                                              \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2247\u001b[0;31m                                              copy=copy, allow_dups=False)\n\u001b[0m\u001b[1;32m   2248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2249\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[0;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[1;32m   2339\u001b[0m                                                 \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2340\u001b[0m                                                 \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_dups\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2341\u001b[0;31m                                                 copy=copy)\n\u001b[0m\u001b[1;32m   2342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2343\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[1;32m   3584\u001b[0m         \u001b[0;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3585\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3586\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_reindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3588\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36m_can_reindex\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m   2291\u001b[0m         \u001b[0;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2292\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2293\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2295\u001b[0m     def reindex(self, target, method=None, level=None, limit=None,\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy import interpolate\n",
    "\n",
    "main_x = EBFS.index # x-sampling from here\n",
    "main_y = EBFS['Temp']\n",
    "comp_x = LFBD.index # will be resampled\n",
    "comp_y = LFBD['Temp']\n",
    "\n",
    "# select only common x values\n",
    "main_x = main_x[( main_x >= max(main_x.min(),comp_x.min()) ) & ( main_x <= min(main_x.max(),comp_x.max()) )]\n",
    "main_y = main_y[main_x]\n",
    "# main_ref_y = scipy.interpolate.interp1d(main_x, main_y, kind='linear', assume_sorted=True, fill_value=(main_y[0],main_y[-1]))(main_x+0.)\n",
    "\n",
    "print main_x.shape\n",
    "print main_y.shape\n",
    "print comp_x.shape\n",
    "print comp_y.shape\n",
    "print comp_x.values[0:10]\n",
    "print comp_y.values[0:10]\n",
    "print comp_y.iloc[0],comp_y.iloc[-1]\n",
    "\n",
    "comp_ref_y = scipy.interpolate.interp1d(comp_x.values, comp_y.values, kind='nearest', assume_sorted=True, fill_value=(comp_y.iloc[0],comp_y.iloc[-1]))(main_x)\n",
    "\n",
    "print comp_ref_y.shape\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:22:01.601900",
     "start_time": "2016-09-25T21:21:59.903668"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100) 808.0 0.0\n",
      "Mean:  -2.10742 , Std:  1.77288\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAARoCAYAAAC/n8NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabfb6eac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('max_columns', 50)\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, sharex=False, sharey=False, num=None, figsize=(7*1.3, 7*2), dpi=80, facecolor='w', edgecolor='k')\n",
    "H,_,_, h2d = axs[0].hist2d(main_y.values, comp_ref_y, bins=[100,100], range=[[240, 340],[240, 340]])\n",
    "cbar = plt.colorbar(h2d, ax=axs[0])#, extend=\"max\")#, vmin=1, vmax=300) #.set_clim(vmin=0, vmax=15)\n",
    "print H.shape, H.max(), H.min()\n",
    "cbar.set_clim([1,H.max()])\n",
    "h2d.cmap.set_under('w')\n",
    "\n",
    "axs[0].plot(  [200,400]  , [200,400] , linewidth=0.5, marker=\"\", color=\"black\", ls='-' ,label=\"\")\n",
    "\n",
    "# axs.set_xlim([alt_y.min(), alt_y.max()]) #1353885000])\n",
    "# axs.set_ylim([alt_y.min(), alt_y.max()])\n",
    "# axs.set_xlabel(\"EBFS Ground Temp in K\")\n",
    "# axs.set_ylabel(\"AQUA Temp@\"+str(bar_lvls[0])+\"hPa in K\")\n",
    "\n",
    "def linedist(X,Y):\n",
    "#     D = np.sqrt(X**2+Y**2)\n",
    "#     return np.sqrt( (X-D)**2 + (Y-D)**2 )\n",
    "  return (X-Y)/2.\n",
    "\n",
    "dists = linedist(main_y.values, comp_ref_y)\n",
    "\n",
    "axs[1].hist(dists,bins=41, range=[-10,10])\n",
    "# axs[1].hist?\n",
    "axs[1].set_xlim([-10,10]) \n",
    "\n",
    "print \"Mean: \", dists.mean(),\", Std: \", dists.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EBFS vs. LFST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:22:28.612837",
     "start_time": "2016-09-25T21:22:28.508661"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(91979,)\n",
      "(91979,)\n",
      "(99313,)\n",
      "(99313,)\n",
      "[1293865200 1293867000 1293868800 1293870600 1293872400 1293874200\n",
      " 1293876000 1293877800 1293879600 1293881400]\n",
      "[ 272.1499939  272.1499939  272.1499939  272.1499939  272.1499939\n",
      "  273.1499939  273.1499939  273.1499939  273.1499939  274.1499939]\n",
      "272.15 283.15\n",
      "(91979,)\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy import interpolate\n",
    "\n",
    "main_x = EBFS.index # x-sampling from here\n",
    "main_y = EBFS['Temp']\n",
    "comp_x = LFST.index # will be resampled\n",
    "comp_y = LFST['Temp']\n",
    "\n",
    "# select only common x values\n",
    "main_x = main_x[( main_x >= max(main_x.min(),comp_x.min()) ) & ( main_x <= min(main_x.max(),comp_x.max()) )]\n",
    "main_y = main_y[main_x]\n",
    "# main_ref_y = scipy.interpolate.interp1d(main_x, main_y, kind='linear', assume_sorted=True, fill_value=(main_y[0],main_y[-1]))(main_x+0.)\n",
    "\n",
    "print main_x.shape\n",
    "print main_y.shape\n",
    "print comp_x.shape\n",
    "print comp_y.shape\n",
    "print comp_x.values[0:10]\n",
    "print comp_y.values[0:10]\n",
    "print comp_y.iloc[0],comp_y.iloc[-1]\n",
    "\n",
    "comp_ref_y = scipy.interpolate.interp1d(comp_x.values, comp_y.values, kind='nearest', assume_sorted=True, fill_value=(comp_y.iloc[0],comp_y.iloc[-1]))(main_x)\n",
    "\n",
    "print comp_ref_y.shape\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:22:38.343693",
     "start_time": "2016-09-25T21:22:36.533998"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100) 753.0 0.0\n",
      "Mean:  -0.890821 , Std:  1.67418\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAARoCAYAAAC/n8NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab7697ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('max_columns', 50)\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, sharex=False, sharey=False, num=None, figsize=(7*1.3, 7*2), dpi=80, facecolor='w', edgecolor='k')\n",
    "H,_,_, h2d = axs[0].hist2d(main_y.values, comp_ref_y, bins=[100,100], range=[[240, 340],[240, 340]])\n",
    "cbar = plt.colorbar(h2d, ax=axs[0])#, extend=\"max\")#, vmin=1, vmax=300) #.set_clim(vmin=0, vmax=15)\n",
    "print H.shape, H.max(), H.min()\n",
    "cbar.set_clim([1,H.max()])\n",
    "h2d.cmap.set_under('w')\n",
    "\n",
    "axs[0].plot(  [200,400]  , [200,400] , linewidth=0.5, marker=\"\", color=\"black\", ls='-' ,label=\"\")\n",
    "\n",
    "# axs.set_xlim([alt_y.min(), alt_y.max()]) #1353885000])\n",
    "# axs.set_ylim([alt_y.min(), alt_y.max()])\n",
    "# axs.set_xlabel(\"EBFS Ground Temp in K\")\n",
    "# axs.set_ylabel(\"AQUA Temp@\"+str(bar_lvls[0])+\"hPa in K\")\n",
    "\n",
    "def linedist(X,Y):\n",
    "#     D = np.sqrt(X**2+Y**2)\n",
    "#     return np.sqrt( (X-D)**2 + (Y-D)**2 )\n",
    "  return (X-Y)/2.\n",
    "\n",
    "dists = linedist(main_y.values, comp_ref_y)\n",
    "\n",
    "axs[1].hist(dists,bins=41, range=[-10,10])\n",
    "# axs[1].hist?\n",
    "axs[1].set_xlim([-10,10]) \n",
    "\n",
    "print \"Mean: \", dists.mean(),\", Std: \", dists.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EBFS vs. LFSR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:29:38.333068",
     "start_time": "2016-09-25T21:29:37.881720"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reindex from a duplicate axis",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-161ba574b45d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# select only common x values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mmain_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain_x\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mmain_x\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcomp_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0mmain_x\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcomp_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mmain_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmain_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmain_x\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;31m# main_ref_y = scipy.interpolate.interp1d(main_x, main_y, kind='linear', assume_sorted=True, fill_value=(main_y[0],main_y[-1]))(main_x+0.)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    622\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m_get_with\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    654\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'integer'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    655\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 656\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    657\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   2285\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reindex'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2286\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2287\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2289\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fillna'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2227\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2228\u001b[0m         return self._reindex_axes(axes, level, limit, tolerance, method,\n\u001b[0;32m-> 2229\u001b[0;31m                                   fill_value, copy).__finalize__(self)\n\u001b[0m\u001b[1;32m   2230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m     def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   2245\u001b[0m             obj = obj._reindex_with_indexers({axis: [new_index, indexer]},\n\u001b[1;32m   2246\u001b[0m                                              \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2247\u001b[0;31m                                              copy=copy, allow_dups=False)\n\u001b[0m\u001b[1;32m   2248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2249\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[0;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[1;32m   2339\u001b[0m                                                 \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2340\u001b[0m                                                 \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_dups\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2341\u001b[0;31m                                                 copy=copy)\n\u001b[0m\u001b[1;32m   2342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2343\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[1;32m   3584\u001b[0m         \u001b[0;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3585\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3586\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_reindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3588\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36m_can_reindex\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m   2291\u001b[0m         \u001b[0;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2292\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2293\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2295\u001b[0m     def reindex(self, target, method=None, level=None, limit=None,\n",
      "\u001b[0;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy import interpolate\n",
    "\n",
    "main_x = EBFS.index # x-sampling from here\n",
    "main_y = EBFS['Temp']\n",
    "comp_x = LFSR.index # will be resampled\n",
    "comp_y = LFSR['Temp']\n",
    "\n",
    "# select only common x values\n",
    "main_x = main_x[( main_x >= max(main_x.min(),comp_x.min()) ) & ( main_x <= min(main_x.max(),comp_x.max()) )]\n",
    "main_y = main_y[main_x]\n",
    "# main_ref_y = scipy.interpolate.interp1d(main_x, main_y, kind='linear', assume_sorted=True, fill_value=(main_y[0],main_y[-1]))(main_x+0.)\n",
    "\n",
    "print main_x.shape\n",
    "print main_y.shape\n",
    "print comp_x.shape\n",
    "print comp_y.shape\n",
    "print comp_x.values[0:10]\n",
    "print comp_y.values[0:10]\n",
    "print comp_y.iloc[0],comp_y.iloc[-1]\n",
    "\n",
    "comp_ref_y = scipy.interpolate.interp1d(comp_x.values, comp_y.values, kind='nearest', assume_sorted=True, fill_value=(comp_y.iloc[0],comp_y.iloc[-1]))(main_x)\n",
    "\n",
    "print comp_ref_y.shape\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2016-09-25T21:22:01.601900",
     "start_time": "2016-09-25T21:21:59.903668"
    },
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 100) 808.0 0.0\n",
      "Mean:  -2.10742 , Std:  1.77288\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAARoCAYAAAC/n8NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabfb6eac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "pd.set_option('max_columns', 50)\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(nrows=2, ncols=1, sharex=False, sharey=False, num=None, figsize=(7*1.3, 7*2), dpi=80, facecolor='w', edgecolor='k')\n",
    "H,_,_, h2d = axs[0].hist2d(main_y.values, comp_ref_y, bins=[100,100], range=[[240, 340],[240, 340]])\n",
    "cbar = plt.colorbar(h2d, ax=axs[0])#, extend=\"max\")#, vmin=1, vmax=300) #.set_clim(vmin=0, vmax=15)\n",
    "print H.shape, H.max(), H.min()\n",
    "cbar.set_clim([1,H.max()])\n",
    "h2d.cmap.set_under('w')\n",
    "\n",
    "axs[0].plot(  [200,400]  , [200,400] , linewidth=0.5, marker=\"\", color=\"black\", ls='-' ,label=\"\")\n",
    "\n",
    "# axs.set_xlim([alt_y.min(), alt_y.max()]) #1353885000])\n",
    "# axs.set_ylim([alt_y.min(), alt_y.max()])\n",
    "# axs.set_xlabel(\"EBFS Ground Temp in K\")\n",
    "# axs.set_ylabel(\"AQUA Temp@\"+str(bar_lvls[0])+\"hPa in K\")\n",
    "\n",
    "def linedist(X,Y):\n",
    "#     D = np.sqrt(X**2+Y**2)\n",
    "#     return np.sqrt( (X-D)**2 + (Y-D)**2 )\n",
    "  return (X-Y)/2.\n",
    "\n",
    "dists = linedist(main_y.values, comp_ref_y)\n",
    "\n",
    "axs[1].hist(dists,bins=41, range=[-10,10])\n",
    "# axs[1].hist?\n",
    "axs[1].set_xlim([-10,10]) \n",
    "\n",
    "print \"Mean: \", dists.mean(),\", Std: \", dists.std()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "nav_menu": {},
  "notify_time": "5",
  "toc": {
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 6,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "toc_position": {
   "height": "520px",
   "left": "0px",
   "right": "1154px",
   "top": "106px",
   "width": "212px"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}