SDSS_galzoo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import scipy.stats as sps\n",
    "%matplotlib inline\n",
    "from matplotlib import pylab\n",
    "pylab.rcParams['figure.figsize']=(12,12)\n",
    "\n",
    "def shuffler(filename):\n",
    "    \"\"\"\n",
    "    Read into dataframe and shuffle\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(filename, header=0)\n",
    "    # return the pandas dataframe\n",
    "    return df.reindex(np.random.permutation(df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88943, 370)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phot_df_raw = shuffler('phot_ext_rgilmore.csv')  # main data file retrieved from SkyServer\n",
    "phot_df_raw.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(85365, 371)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply some cuts on the data\n",
    "#Nonphysical model outputs\n",
    "phot_df = phot_df_raw[phot_df_raw['deVRad_r']>=0.0]\n",
    "phot_df = phot_df[phot_df['expRad_g']>=0.0]\n",
    "phot_df = phot_df[phot_df['expAB_g']>=0.]\n",
    "phot_df = phot_df[phot_df['deVAB_z']>=0.]\n",
    "\n",
    "\n",
    "# Redshift limits\n",
    "phot_df = phot_df[(phot_df.z<0.5) & (phot_df.z>0.0)] \n",
    "\n",
    "\n",
    "\n",
    "# Remove high extinction cases\n",
    "phot_df = phot_df[phot_df.extinction_u<=0.5]\n",
    "\n",
    "# Nonphysical flux params within models\n",
    "phot_df = phot_df[(phot_df.aperFlux7_g>0)\\\n",
    "                  &(phot_df.cModelFlux_g>0)\\\n",
    "                  &(phot_df.expFlux_u>0)\\\n",
    "                  &(phot_df.modelFlux_u>0)] #check that no negative linear flux params appear\n",
    "phot_df.reset_index(inplace=True)\n",
    "phot_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extinction corrections to flux\n",
    "\n",
    "flux_col_lst = [col_name for col_name in phot_df.columns if 'Flux' in col_name]\n",
    "#print flux_col_list\n",
    "#print phot_df[flux_col_list][0:10]\n",
    "             \n",
    "for flux_col in flux_col_lst:\n",
    "    band = flux_col[-1]\n",
    "    phot_df[flux_col+'_ext_cor']=phot_df[flux_col]*2.5**phot_df['extinction_'+band]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "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>psfFlux_u</th>\n",
       "      <th>psfFlux_u_ext_cor</th>\n",
       "      <th>extinction_u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.314991</td>\n",
       "      <td>4.885679</td>\n",
       "      <td>0.135561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.485687</td>\n",
       "      <td>0.526596</td>\n",
       "      <td>0.088257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.807525</td>\n",
       "      <td>2.080155</td>\n",
       "      <td>0.153318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   psfFlux_u  psfFlux_u_ext_cor  extinction_u\n",
       "0   4.314991           4.885679      0.135561\n",
       "1   0.485687           0.526596      0.088257\n",
       "2   1.807525           2.080155      0.153318"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phot_df[['psfFlux_u','psfFlux_u_ext_cor','extinction_u']][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extinction corrections to magnitude measurements\n",
    "\n",
    "mag_col_lst = [col_name for col_name in phot_df.columns if 'Mag' in col_name and 'Err' not in col_name]\n",
    "\n",
    "             \n",
    "for mag_col in mag_col_lst:\n",
    "    band = mag_col[-1]\n",
    "    phot_df[mag_col+'_ext_cor']=phot_df[mag_col]-phot_df['extinction_'+band]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "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>deVMag_u</th>\n",
       "      <th>deVMag_u_ext_cor</th>\n",
       "      <th>extinction_u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.08213</td>\n",
       "      <td>18.946569</td>\n",
       "      <td>0.135561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.06981</td>\n",
       "      <td>22.981553</td>\n",
       "      <td>0.088257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.26883</td>\n",
       "      <td>20.115512</td>\n",
       "      <td>0.153318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deVMag_u  deVMag_u_ext_cor  extinction_u\n",
       "0  19.08213         18.946569      0.135561\n",
       "1  23.06981         22.981553      0.088257\n",
       "2  20.26883         20.115512      0.153318"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phot_df[['deVMag_u','deVMag_u_ext_cor','extinction_u']][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "501\n",
      "371\n"
     ]
    }
   ],
   "source": [
    "# remove unextincted columns\n",
    "print len(phot_df.columns)\n",
    "for flux_col in flux_col_lst:\n",
    "    phot_df.drop(flux_col,inplace=True,axis=1)\n",
    "    \n",
    "for mag_col in mag_col_lst:\n",
    "    phot_df.drop(mag_col,inplace=True,axis=1)\n",
    "print len(phot_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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11WxXln8myTVJ/uVo8Y3Pt9buaq2draoHkpxN8mySu8ZKyu9O8qtJXpjkE621h2f8uQAA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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c4e090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#redshift (distance) distro of galaxy targets\n",
    "xx=plt.hist(phot_df.z,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SA/U2Mrtm3A9chpV1+KvK87zvJxEE6jex/8yt9H4ux2qnW4GX/DsjKNOoJD/f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\no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Eqb+9wrihfn9TfbV3nntoWHFR55tkS6TwSnm3UM7YZ4W5I+MVKusr2WQuLcgyJXmNhd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bRjkkTo34inuz0KP8kNAfLvHNEiMfK/huB2mZQp0b89N4TfBa5FhhSYag8Zs380y37TSUxHgi\nIpMr6L1oiiVM1iLyvS/Y9yXy2vKXUdJjO3md84nkFwRWVXAjxQVhwHslZwvT2DWwAijXINI7C78g\nCC0K1+PLnJ+I9PQGu7/2ovkxbXLYnyakC4fiRUM30pWDtNzlyK7PFjsH3M8/AmNIkq/Sp88XqKiY\nT1nZQBobr2bDhgfIO6JsKzhXGtGJIyIiAiJxjog4ppGPKl/N/PmL2bz5kYOFHkz3DLBtmxHlFryG\nNyR4F+MJzBYUQWwkTYKy7gjgPYFD3+DQfm45ecu4ZhSJLMFXwitxfbDj2hEZ+po77xcz5806ZZxJ\nXjfdF6/ZfQVPchsRCbUI5Rx3rhaUwJgtQ92PfIRzIYpgNiGy+oobh+3Xhkjgg6Tt4sym7w9BW/NR\n8hrBPvsRwZ6DIrpdeM3xPLTgmU668Mjdrt2bSLuQdKOkuixssbC14LvJwI8otuIrd+cL3S5a0GKi\nA/huL8f1wZdeH4CPbIckey7F/37OCNprJ22PaNerP5rzV1BkeQRKAqxCTwhMS18LzKKj4xS0WPsY\nS5d+nQULrubee+cdle9wLCsdERFxEEer7XijX0SNc0REIbymOdSYet3z+PGh3rc1yeuZTaM6PPH6\n4sTpTauCfXvTMzdltp0fvB+aQH+nHZ3ivmsNdK+mJz3B7RfqnA913lDjavrr8Pvhme/7ub4MT6Sx\n7Zd4TXBFpv0mt69pw/PzqvMPcXN0gpuzEYnXZJvGvOjYae7ngOBazHTvpwRzU5FI91uk1x1VsO38\nXs5nOvBwzk5IpPed5ubDrs+URPrvqUmxTnxaMM7sOUa5Nov6YNtM29zkrndRW5MSr7O2eyh7fSuC\n/g4J2sxq8Wf2co4R7jp5nX5NzflJa2trUlNz/sH3h8Ohcg0iIiLeuiBqnCMi3to41ONgbz+Xtyx7\n6qmnePLJbZmtpQVn2Ioivt9FpZnbUETyjGCf0EXix8iRoQw5TwzDW7FZJNMigQORZVoYrV6EJCP2\nGRRFvA5FJQ8E2y8DLgw+m8+zClhIomDlq0FR4ksLO/+aqAAAIABJREFU2u5LWj5wP5KRDCRd6noy\nioh+Cj2q709eprAfOXO0u58/QzZ89cg72GzNimzkRrifffGe0DtIyyWsAMkWir2ZXyZd/c405G0F\nfa1B1/QKNL+voIj7LhSFrURSCpOizEE64m2kI7smd7EqgIYmpJF/Hj9fWX2xSXBw49qGdOO3Fozt\nTHSdFqH7ch9yBrHrW4eKooSSCmvTrPvsuxZ0r2QxCa+59sj6DkcZRkRExJEiEueIiGMER/44OG1Z\nVl4+j2efLUVWZEZ0WhBBnhscV48nqnvx5O8zbv+QJFmZ6P6k5QdLEHFa7fZZiIjOI6St1wxFWuIS\n4J/wutkr3PZ2RJ7CIhuliGzi+pIgstSBpARGsgz9SMsH2l1fL0Ua31uA3yGC2YSkKqGEoB1PBsuQ\nRd8aPPk+AHzYfb8mOG+42ACvN/4FXo6xF5HZLCzhbir5Yh+Xuz5bQRM77z8Av8Ffw9n4ctvfRNfF\npBt9EXHeTz5Rr8W1uy84pgOR7So33jokv9jvxr4RXVfz2LYEx8/hSbPhOeC/UDJlbyR7F14edAW6\nFmei4iXZ69sbHiN9/0Pax1sLjrKyuezefWbKE7233zsgkEi9+6jlHREREW9PlChSfeygpKQkOdb6\nFBHx58C73/0+Nm3qRkTqMmAXNTXr+PnPfwCExHoWFik+8cShjBlzIps3P0RPTwmeIJoOdzUiOL5N\nEZ39QDWwGRGkXZljrTpfSEKNgAzAV4KbhnyfByO/4EVu30WunbORO4WVkDYf4vtRRHYnIt1DUCLc\nOzJ9NULVjMid+QifiRIS15PWXVcikmja31+7vlp1QfCk9k94fXQ4RvMz7kLEqw2Rd3O6sMId68mX\nwT7Xze0Lrh9NiFSegoj31ExfGpAvsbVV5uYxe7160KLBzjXXtTsxs+86lLzYgMiqLU5mo2ubrewX\n7m9a+Dq0uClB3s/t6Dpm+x4mDs5x/fmm23+N63OFm/9tyAP7T24uOzP7ngIMQvfqCygq3+j2DRdu\nXcDn3VzZuetRQZYNrg+WvPpOYC+DB+9kwoSTgb48/PBmOjuV1FpePo8f/nAt8+cvzv3eVVevZsuW\nJw6S6fLyeSxYYImFMSodEfF2QUlJCUmSlBzVQUer7XijX0SNc8RxiNbW1qS0NNSmjk6gKamunprS\nYtbW1iZ5/+GZidfamhey+RQX6VDf5fShFYHmtUjbO6Zg+5gkrW0enKQ9cStd26MT73d8epLWPZv2\nNDuG/PjT58p6L5+QSBs8wrVvHs3mH1zkO2zaZvMEHp/kxzjcvWwcYZ+s/+Pd96Ncv21/Oz70r7a+\nDU68R/U0974y8Xrj/u7YUPdbmcjn2MYdjiGcd5sv0wJPTdJjsrmvTIrHM6VgDgYlXpvdm646vD6T\n3Diyvs3Z+2OQ6+vEzHdDE+9nPcR9P8nN9dnuvc1Pv2DubK5nFpx75kEtcpFOubp6qvM/t2PkfT58\neP6+iJrmiIi3H4ga54iItyZWrlzlXDJqg61X8T//U0aSdANT2bDhIg4c6CKvGb4aOAFFh01DOwNp\nS7eT18J+HEX5qpAu9I8FPToJeLzg2C4UlQNFC/uR9y+eC9yOIqD3A3tQFPFyfKW4rM2cRTzDbY2o\nqqDh+YKxN6Ao5UkowgyKXi9z+41BsoYWFF19N2lpxhWk9b3mPPJtZCPnSzMLq9DcjkRVFfuiaOkL\naE77kHYNAUk0FpO2ujPHkc/jo8LVwBTXbmiv9m1UpS9Ekf+2SXHKXDuhNd5m977WzZm5m+xy48+2\n34WkEs+4z5chLfihMAJFmLOOLFkv7Xqkx95GsR3dbOQJvQNplCuQa4nN1Tw0X7egKLjN9X9TVNK9\nqSm8hwxylnnwwSdJkrRFX0lJA8OGVbFnj90zlxUcHxERcbwiEueIiGMS7UAfkuQG93kenZ2fRmQu\nxI8RCbIS2lmbuIWIiKxBRGQsIiUXo4Sta8l795qc4gVkKdbktnciucAud57V6BF+Fl146cEQROTH\nAFciGcMwjky72o23lqujONnRkhFD2cAvSCcB9sMT1qKEwjp8qW2TYJSihUUWO9H8XE261HcDItS/\nxSfChRKGd5In/StIW73NQwudbiRzOQtfUKSCfHnrLAajBYTpukOJQw26pqZx/ywirtuAvyKddGlJ\nkU+7NutcW++n2OPZip104nXYh0IVuhdApL4Nb6k3Ei0KdiJJULjQuA8/XwvRnLS4frdTpB/v06fP\nQUmF90T31njd3Vm7QxgwoC9/+MMuvI/5LMrKumhq+t4RjO3YQ0x8jIh4nXG0Ieo3+kWUakQch8iW\nzy4tLbLWstLOtp/ZqWX3G59ICjAx8bZzJtuw8tvZx+5T3fYqt39l4u3UwrbPdG2ZDVxoyRbKLkzK\nYBKGJFEpZbNECyUDIxNv0xba1J3i2q5K4MQkL50IS3o3uXNaGWxrq7fy0KGUYmiSfsRfZDcXWruZ\nHKZI4hHuW+HGe1sv+2flESMSLyOxn+G8htdwYJKXdAxOfLnuIkmFyXTGZea6v2t7VOJlPFMSb0E4\nyM1DZSJrvUluv/6Z/gws6PfwzNyOzHzOlmk3iUiRTGh8kpaF2LyMdu2Mc8dPSkzKUlo6OFVOu7W1\nNSPDaE3SFn1DksGDT86du7p62pv3x+E1IPt3pbx89BHZ70VEHC8gSjUiIt6ayBY62b17Eps2pfcp\nKdlKv349dHZeiiJtD1FswTUSRZSbUYJYC3KgsOjdVaSLYbThkwnBRyiTgrZ3u+3mBGFWX4uQ5MOS\n0M5FkcBR6JH6j9z3l+Mfw1+DkgFN1mHJgI/hS1pbQtpprr2rkezhTygxcbrrfyi/aEbRzwYkW7Ax\n1uIT22a5z6tREltft/9ZSB6QHdsOFJm1yng7C+YmW5xlnusHZJ1QJEPpIV0+2oqRTEDzX+PO/Qi6\npgdQRLrDjasWb4NX68b4rWDMRfi9O74SL3Ppj0/evAIlaf7J/VyLrtlGFOWuQMVXBiMJiY1pLXp6\nsQ3N4Qp0r5hMYhGawxF4eUYbMJx8GfLFFEf7y/EJqmcFx1yOJDIvI9kQ2D3c03MqCxeu4KabvsOY\nMSOprBzNuHFj2LMnbNcqN+q4UaOG8tJL6TNv374j5cTxVoG3sNRcdXRo21tpDBERxxoicY6IOEYQ\nesu2tbUxY8ZFdDreVVJSz6c//fc8+OATbN/+I/r37+aZZ0qRY8GhvHTXIWlESE7akSezPXZvIV8B\nrhFPvg11iDCba4VVwWtHemgjnFvx1mr98KW5mxEZXIEIUD1eZ2vlnKvc9/cjkvgoIqz3IjlAQlqC\n0IxIXbb/KxC5+pnbZwJ5vXIj3qfZPpuWNhz3k4gUP4ZkKI2IxIVWf+ZiEWI/Is6NiNDNcu+78O4Q\nLW7uJqPqdzZXtW6OpiLifF0wh53A35J3uJgMnI6u/bygHyZPmQd8AElZFqPrdgveV7oNOZZcF7R5\nFiLDY5FWuxkR4BvIoxPJVayflfj7cJc7p0lFbCFzWkE7I4FT0Zy2uDm4Fc3ZGnfcNrdvO7oWWf9w\n0KLoXuBGnnkGnnmmGZhCWdn/oays3v1utZDV6A8Zsobycm89B3Xs2TOb9esnx4qBERERkThHRLwZ\nODLd4QEsSaykpJPvfGcdSXIW8DFEICwRyiKTFqnNttUHnxx3GV7LGhblyMKin5V4ffRsRFjaXVtn\nojLSL5Mmsx2IGJ2LiHFIZlYF/evv2h7h2v4JIolhmed/QFFei2RvdX2xIizXoMhtFv+LoqbfxOur\ns0hIR01x513jvrOy02cionojvhx2g/u+BUXSm9z3oU54PCLPv3PtvoDIZR+UULgREdJF7pVOUvPX\n1K5zG1oA7EAkey1eG1zpjtnp+hB+142u5dVojk1rPdPNzXI8qa3N9KHeHY/rSx3StX+A4gIstZlt\nYUR9dvD+bET6s77fc5Dt4CPk7ylbHITa6lsRaS7CLvKLpXV0dv4b1dVrqKxcx29/+8dM9FmYOHEC\n27cv5sCBTl56yd+PRxqxPVZ0xV7Xrc/Rfzoi4rUjEueIiD8zigouZD1iV65c5fxmRZh6embhyWQ9\nIl+G6YhcPoov8PE0erSeuNcX3b6z0CP4gcBoRF4rkXzDontrUTRzFYoIbsNH+N4NfBkRl52I5BQV\n1Xikl9FbtUGrgHcLcDLyW97rXi2IVO4FnkKR2az38Xnu/emuzyGJs7bXBPNzE+kCJfNQ4mIWHfg5\n2wusdNstahyOczm+UMjzaF6bEJEvQ3N3GYrMP433c34cSSZ2u743u++Xk07sfAK/KMhKTerQNT8N\n+KXr725ExJvRvTLD7WeLJfPCtvb+L1pcDCQtc6kJ+mD2phb5HobuhcuRC8hitHA6gIrKhOiDkvje\ngxY4d6AFTYmbA0jLYR5DSYtZv2nQ/P8UXdOh6L6ZiJIhp7rP2acjp9AbXnzxTyxbNp/58xfzwgsN\n9LhpLiuby8MPHzjo9Vxa2tBrG73hyAsZvfHISsCammK0PCLiNeNoRdFv9IuYHBjxNkeRn6xPBlQC\nT3X11GCfcH/z4p3pEqGqEiV2DXPJU1MTJX5NSXpPHhye+ATArP/ykERJX7e5trK+uJb0ZwlHRcl3\nI1x7ZZnjhydKmAs9l7OJggOTvE/1xIIxmAdymBxY4eZjmtsejt/aGuq+X+L2D8891J0/3H+Ja6sq\nSXsGZ32UT0iUnJdN5gvPX+na75MoyS7reVzpPocJgrZPkYdyNvGu0l0z87+2a5VNQLTEwiKP6/Ca\nWl9uS9I+0UOTfCJkUbJiU9BGdr4sKXBKZr/s/R72u8Kde4A7bkni73fzhR6eKLGwNpjPbJ8qk/Ly\nkUHSXFNSWjoiqa6elvm9K/7dPFxyXdHvd/SAjog4NkFMDoyIOLbR1tbGfffdj9eCCj09ZxAm8Lz4\n4o1OZ9mOorFbkWZzF0qKuxc9Lv8Z6YhhaMs2j+Ikq7HAPeQjqOtQ9HgFikD3Ix9NbkARQXsEPgZF\ncn/s2jwTlfBeix5oleGt3u5EkcXLUaTxfSjqG+qvrVJgLYqKnu3GHtrLgSLRtaRlKaV4W76LkASk\nv2vvJBQhPc/1/3tI520VBnci6co3M+O9Hvg6+aTCWwrmJutXXORN3eLGY17RZ+MlNCvc93eh6Cxo\nHmvJ2xCCJBTZ4//VHTsL3TdZ278z0JOIFeh+2u7OGUa6/4giyaFkxvreH0lKluOTKUe4eTJJzEi8\nzn4yihbj2luHynD3JZ3INyToa7Z8eTO6p25BFQJ/ga7nl9E1aEeSjXHod2Ijuh8vRlFqs2g0vXYt\nBw7cTleXl3H09EymsnIdRTj33EkHv4sR24iIiEicIyL+TEiXzA4fK1vylsdTT/2eMWMq6Oi4Ge94\nEEoQbkDk5p3kH2uvwxOe5a59Q1iquTeU4N06soR1NP4xO4gcTSNd+nqe649pkkFkyCQlda7dh5Cr\nRhGKpAngJQfTSHsP90Zkr3XHfiw4FrwEYjpeLtBIHq+QTzxsQGQx7KtpxdvIa8xD7HU/7yJdEKUW\nTxarXBszg3PXkCaTNh/Z4/8XLVJWu2OKit+A5n8roVexdxmxAiQbXR+sAMhWvGb7RVQi+yS3vQUt\nisYgQr8M3Z/dSNddRdr55GHSZH0F6YTGTvf5NNIkvAnJOb5P+npb6e2nSN+HE1DZ8PD+rGPcuCqe\nfJIcijTBy5YdGVk2XfPu3c8FyYdRVxwR8XZDJM4REX8mpK2hLKHvjyiKdjOeBM4jSS7mmWfWki+a\nYUl5h8JziPDsRNZh5Ygwmi7aCpKE7Ropt0Ij5u4QElaLZm8mTch+STFpxfX1cuCTbpzrUURyjRvb\nDkREDVsQgT2TfGJXA7JD24ecLiy5sTeMDY4PFxNzUJQ+JMqNrt0s0SxKPDwJJfrZAiAkhEZA1yLS\nnXU86UCksIfiaP4B93ovmht7MjHd7bsYJV2egr9mhia0AFvj2l6Hj/Li3t+GCpmEbhrh+WtQoZFd\naNFhY9rnPj+PSP9ZwP+QJqT/hO4xszW0/m1FxUyyC5AwUfRpvHXiFnQPHsA/nTCcgQj9PvLoW3CO\nNcAGN66FwCvU1n6cCy64wC1itZeR21erCc7qmsvK5lJdvZrKytExSh0R8TZDJM4REW8KpmO+xeXl\n9zJ27Ck8+aRJGsz9oDdYMlwN3m7NYMQvdCSw0s5rUQTOzm9JWb/DP8YeBpyIf/z/NUSY9rnzrUUk\nqAZPcE8q6ONWRGz/A098rNTzdYggtaNoohGmx9xYnsdX3wsx0u2zFpGgUFbRTJqAmy1fERJE8Pbh\nifc+4Asomt6IiLXNb0im5wKfRsRzNnKryJI1S4RrdmMPvZb/E8lF7u6lX134innZio4WDQ7lDyEm\nue/CxNHJbhyrEOEciSQ1I3JH6zrejZ5GXJrp992u39kk1bAKYyizMazDPx3JwhJF56B71CpNjkH3\n5Gjy5dBnITI8kHxCaCV57KesrItzztlNZeV7Ug4XvRHk0BbyUAidM3bvfi7ll9zZCZWV6/j5z39w\n2HYiIiLeWojEOSLiz4TsY+CSknoGDSpn9OhRQB9KSx+np+ckFAlcQZ44WQRvNYpabsBbhZm/7yD0\nmDwb/d3m2sxGmh9BZG22O98v8A4c9vi/ExGkFUiK8E/Af+M10m3kZQSdSO8aEhCzDTNt9IWkddZr\n8QS6T2bsc5DcwiLpL5DGZES07PgDpMt1m31ZM1qcnI2iudb/RYiQ3YQI2K2unc+7bfWIUH7atVPm\nzrmNPN7hxl1POhrdiCKpv8T7KmfnbAJpsl2D1xJfAzyACDCZ48MnBkPdd+9D16A86EMDkk38G3oK\nYDBS+lMkoQnLiZsbh7l1FD1ZOBz6kV7gNSHZRwuSXqxx51iCLxn+Rfe+3vV5FlqwmMFyB37xZouk\n9H04fnwVF1/cfNCx5v7772f+/MVs376LceOqWLZs/lFHg9va2pg/fxmbNz9ET89ngMmUljYd7rCI\niIi3C442m/CNfhFdNSLeQLS2tiY1NecnNTXnvymlZ+381dXTkrIycwPIllYOnRCshPLJicpOT3I/\nqwLHgNB9oLJg25Qk78wxJWjDHCPGFxxrJbPNwaI1KS7ZbSWvw3LQoaNB6Mpgx0wqOF9V4l0WTgnO\nNTPJO3xkHTsq3TiXJOkyygMTOWmc79oZk6ic9MzElxrPOl/MTHyZ8CrXvrk5tCbecWRiknfuMCcK\nu77nu/Nbf23cVrp7irumtyVy5si6UxRd52GJd5Gwe2O863c4X0VzPNadd1ziy3jbnFckxeWuJyXF\npbxDd42BiXdNmRLMxQnBvNr5QqeUbCn0ont1mGtjYiLXl2luvAMz+44P9p2Z9O17QlJWNjJJO4LY\nMU1JScnwpLp62hH/LciWsNZ9LQeX0lJ/H8XS1hERbw3wKlw13nSinOtQJM4RbxCy//TezH9u1dXT\nXD+KrLemBETAyNGkxFvQmSWXkZaQgIWEOiSYWYJu//BvSzxhLurLtEy/pgTnDAmEWX0NCs5vdnCT\nEk/MQru3gUmadA5LvKVcZTC+pJe+TQoI1ER3bFOSt46zvoeWaPa+yI7tBPf92QVzGRLq7Djf5b4f\n7/o71Y3RrostiKYmeXI8NTOnYX/OTPJWbtl+GVk3yzZb8BTNW3bOxyWe5IdkOHtPDs2cd1Aikj3G\nnbd/Qb8GJ95KzhYkI9x8FN1Ho91c9PbdkiS/0Bzo+h6S+ErX34lJ2j7Rxr8kte1I/xYUWc3ZHFdX\nT31TF+URERFHj1dDnKNUI+K4QTo578irgL3eaGtrY/Pmhw6xh2l9VyNJgNl2XYEKfoAkAp3INs4S\nr0AaXNPAliKJgmlcK932UUh2YAVOBlNcYjurE37M9QfSGul2lLRnlmlZt4MG9Ch+LPBhvAzh88iS\nzR63/7vbvggliB3K+QNURGQHPpHxCpRk+VXXp08h3e95br9bg77NxCfQZZGguR9IPoHP5Bc/KRjn\nYlQq+mkkAzFLP3O+sOIio4EppDXE30fSiSL7wElIm2zXugLZ7YX9akA6YbOLs/PVU1x+OzzWqh7a\ntnbS2uJmJH1J0P3UAvwWSUCWu33qKLYvNN1zs+vfw66vP3PneAf5JNAVpKsLht8tpri8+l3o/g3n\ndCPSSof2iWE76b8F8+cvO2y1v927n8ttg51H5b4RERHx1kYkzhERrxGHKq9b9N3KlaucNtJ0pXWI\ngOxF1edOQuTsbNKexgPxxLYZ6V4fIk8k6tGvduKOqwF+gLdc24l3TKhDCXsHEMGoRMQmQc4PphO2\n8w4jTa4fcecy27g55C3s+iHCN5Q8JiG98hPuXLj3fVESpJG+08jrvbsQ+baxG0kb446zqn91yOO3\nCJeRn7se5PE8t2D/ErdPacF3I/EJbgvdGIrKaE91+1kCZ2gzuI30/M5ByZrbkX69twp/Z6GFTZFv\n9Cx03cciTXzW2znrHDIZXf9mt/9ot/3Drn/70L2YdXxZSB6hs4npoW9EdoL3o2udhXlqF5WC/1PB\ntjOBZ0nryS1ZcB/eEjHEfnSfznSfT3OaZd0zvVf76yJrJVlRUc5//mckzRERxwsicY44blDk0fpa\n/VWXLl3K9dd/xRUwmcqGDZ/knHPOpbJyBNOmvZulS7+eK70rTEb/6Oeif8a7SRcxKUufiFUUuzf0\n5oPchY88fgJFlfciEpstfFIP/C0iSxYVvgv4S7wbwjSgFfkEh5G9Ilu0ejw5s+IWPcAmRLSNMF6B\nL1c9jXS08Dv44h9WNGQovpjKHZgrSR6rEJG34iZnI4I2DU++jYh/DU8se1A09keIVL1E3p6uBiVl\n1pImUPNcf61PYxEBzOJpNLclKII8ibRPsfkhL3N9+Cw+ap+9/ovc+ezciwrO9zjef7kC+X6bjd5G\nFNk/B10zm8sHETGegPyahyDivgb/9KPI1WO/62c73jM762zyRzeGb6Po+BjXVnjMAeSYsgNFw60U\nvHlTFz0VWVYwP3MoLe2hp+dDpB1X6ujXr4cDB8LFSx09PTWEEWh7GhUufoVa/L16Ke9977ZImiMi\njiccrbbjjX4RNc4RbyBez+TA1tbWVEIQjExpPH2pXq+HrK6emlRXT3PfmWa5SGc7KUmXoy7ax3Sz\noX6ztxLVQw/RzphEetVQA5pNvhvoXmOSw+thT06k87VkNdPchsmHVjq8SKdb6V6TEiUIViS965FH\nJOk+2n5FutahwZyfkEA/t09VIn3uTNevUCM8KElrlHtLsgz11CMSX/I7vD+yJbJtHrPXvUjTXTR2\nK5sdlgEPy5VXJkoanOLmcJw7f1a7PTzxWmxLpsxqpkclaT39oZIihyRerx72xTTOlYk0+tly56aT\nHuHaH5lpf2riEy3te+tDURLk+KSi4sTE6++nJDA8GT/+7F60ylNSn+1vRJgXUVY20iX1Hp02OiIi\n4tgEMTkwIuLPh8P/8807T6SJ9pDEO1Gk9xNhPCVDKEKyM9IRICNf5jzQW0LY8MS7YoSEx0iFuWbY\n/sOCNlqTNIm3BLrKRElWIcEZluQTxM4OyJG1Oa2gj6McOZqZpAngsEQE1n6GbZuLhLklNCVpUpe9\nNuErdICwY23upmWui20vmtvsNZ0ZzO0Sd8ykxBPJ3o41ctmUFN8T4ZwMd3MRjmFQIhI/LFGi4jj3\n3twubOFQNDfZxDpLVrTtRYuRQW5cdt9lF2oD3PfjXXvh/Ba5vwxLDr1QzPZxTKLfkSIirm3jx78r\nKSkxB5IpSVnZ0KS1tTWprp6aeLcZJcn6hW5TUlo6IqmunhYk8fp+jB//rpgEGBHxNsGrIc5RqhER\n8bqgqOzyVEpLG+hxEtLS0tvo6clqUK8CnsE/fjYP21D/+jJKxLLErC3Iw/bTyHf3FvTofzi+4MUF\nwTnmIXnFMlT4ogLvDRzKBMJKboOC403ukC1u0YBkFp8O2gsT/J5CJZ4fc9/3RZrdH7txtpAuu3y6\n2/dh8vpZ0y/PBb4M7HHbW9Ej/yq8ZGQFeR2vYSvwAXeOvvjEvXokHxiKEvvmo+RCw2X4qoChVKAR\n70cMksg87MbxCl6Kcg+6Ri344jIgeUxWfmIJnKHG+mZ8wZaxwJ2oat/qYAyNSJazmnTpaUvMK0f+\n20VJl2eST55bhpf1FPlu2znN0zpEBdK1hzKetXjf65KCPpQD/0paChFiIH7uat04w+TEacFx04D1\nPPmkys2XljZw7rkjWLbsewA8/HC63HhZWRfXX9/MD36wms2bH6Gn5yts2kShP/O2bX/gppuWR3lG\nRMRxikicIyKOAKHOcdq0d7NhwwPs3v0cZWX1dHYWl10uL7+DBQua2LBhHbt3P8cTTwzkpZeyLZ+K\nSE4lSq56mbwzQbbwxFqU3HcH0vKaBvQ+RABvxBcDGYvX3q5BxGcGIrLZcsZWyc2KZ9QH24twEiIv\n3UgzHLa3C5GY21yb9Ygs/RXpMs0XIlJ/KyLdu5B2OwtLMmvHl4sGEaaLyRcimU7eGaITLT6+hche\nNnGvBel7zdFhImmNcwfSP+9HBO9F12ZYwXCta/d0pJGegxIJP4jIs5HcWYhkjyG9IFmLyPS1eI32\nTneeYW6fy9z4FpG/V9bhFzl2/ASUgPmc63uYdAm+QEyIclTW3DAdXYMszkQJqlndcSVaMNRnttvC\nI6sfN425wRYq2T5Odm0Mo7hk+SVun/rU91q8rmH+/MU8+OBWurvHo/kBmED//js577zz2LDhAXp6\nZgfHZR1G5tHT85k3xY0nIiLi2EAkzhERh0FbWxsf/3itS/JrZ/36L2PEr6xsLhUVd7J3bzoxafjw\nxXz0o3/DDTes4cCBTjo6XqSr67PkSwgPQ84QRvyKHACK0ImP/q3Gk+IfIxKSBG0uQoQwdKDIViVs\ncm2aC8MtwIdcuw8hUhMmhJmrxWyUZLYIT2wt6exDeCePAygJ7ZfkCc9cRPbM4u5l8g4almS2kWLC\nuyiz7Q7X7gpEPEfhyXZYejxEHxTVtAXQHEQC5Ri5AAAgAElEQVQ0F6LI8Q+CPjai+bbS4d6dQQl5\n+5ADxFfddiN1YR/XIKs8G6stCgagxMlvkra1s+iqRb4fKxiDoR3dX+Z2Uo+u13R86W57etHltoUJ\nnd1ooRMS4t+Tv3/XooXQt/BR90405z0ogn8duqaj0OKuCi2QXkBzPQKfmBmeb58732DX3xXBd2Gy\nn+EsFJnvQtchjU2bNrkx2TW5CN2XN/LSS/Dxj9cycaLdF23YomXAgD7s25ddhGYXahEREccLInGO\niDgM0v7P5v8rAtTZCRUVi3PHDBs2mLVrf0j6cXkN8q+tR+SoG/gD6Wh1HXnSWOTuMCT4/DIicWOA\ne917k3xYhDMkPCCSNBY9kh8JfNdtX+T6YyW9JyBrth3o0XZvrhbTgavdOMJo8N1IJnAyctToTx5d\niARORq4T3WhBYRZq+/C2eFsKjn/cfT8LLxlZ6/pU5bZd6z7PpLj0+DwUYc46Myx0fZ7qPlvJ71Fu\nvx1IOhI6hfRHhPFa8gQ/xH5ElHvwbhJG6hoRUf5f9PTgOvKLDTvOYBHdVoqt6VYA/wksdX3pRIuZ\nh5B7h7mXdKEoeQ+Ksof93ufGdSq63hYRH46uTX90zX4PVKNr2Y4WJbuRbdxXgv7+HhHnSxGpLnft\ndyHCvAwtMLJSkHLSvxOhq4ndA9moejcqn569Jvrc0QG7dv2re4rUF/ud7OpqoqxsC52dlwO7jsqN\n51BWlREREW9NROIccVzhjfhHNm5cFR0d81I2d88+W4rI1BhETgegqNkHgR/io4f15MnaP7ttT6NI\n4xQUvTTCfA2KVNqj7B2IAPwRH41ME3yhEU9ALAI6DBGZRYgcmi9zKSJJFtVcADyAIqChrGCr28+K\ngmSjwSsQObrOfb6KPNkbhBYHtt1Ifg8iWj148taBouOGOjemhYign4oI3Hp8IZV++AilyU7CAi6P\nubnYSB5VaPFR5/pwk9veiAjoGtLzbFrsIo3uVndOG2M3ui/OoVjXvQ6RyOsL2upy++9wY7MFybeR\nTOLFgmMq3M8FblwWcQ7PuxY/12YBuBfN+yB8QZ1/QBZ/YeGdS5HV3m5kb3gvkvxsQfeFzU32/ngY\nWRWWkV5AHkCkezPpe8au+a/xxXNsobQWkeZfomvagq5vQu+6d49nnvlfTjxxFM8884WD/ezqgurq\nNVRW6po2NR2ZZ3P6SdWhvKEjIiLeSojEOeK4wav9R5b2f04X4igtbWDmzCbOO28+8+cvY/v2HYwb\nN4HHH38K/dO3YiQhITANZRuezIT4CyQLsOqBFwfHViACe55r4ypEcrPV6YowFhHM9yDy+WVEVsJC\nIZMRkfmG23YFcCU+8fEXQXt1KPI3DEUf+xWcs4M0sWwn/Vi/C5GzJeRJfhHZWuvOtQZFbIeiiKlF\nauchInpzsM38l5tQdD1MunvQzcGtrr0iiYjdHwtJJzMaaTO04Yt2ZAur1Ln2V6D7YR+KyIJI4wh6\nR1Yr3ICf6zsRwRyECPPvEHH9POmE01vwRU0mu/6MRcVVstiLZBQDkS76ejfuK9HiogZd88Xko7dV\n6Hr8t9vnHvxiajX55EjTb59K3g98BV7PHuq9x7ox9UNPWx7FL5QeR08OytA9MgoR6FvJa7vnIOmG\nLWbmAZfyxz/enpuRysoR/PznPyiYK4/sovxYqVQaERHx+iIS54jjBkf7jyz8R7hgwdVs2KCI09ix\nH+f22///9s4/uq6ruvPfK8uyZevnk2xZjmyHPGKEkRqrK9NRR2uN0pJg6AxpVzRrSlmZEYHhR/nh\nxFHAgEmaIS8rtBBaaGetNJTYHlKmP8jAuO1C5lfTLphmOnRM6gYIbUhpQ0iIMcwk4MQE7fljn61z\n7rnnPl3LlpVI389aXtZ7795zzzn3vHe/Z5999p7B3NzFmJt7LW699bdx4MDb8MADX8Xp0+/HyZPH\noeLzLuhDPLay3QEVWdNQC3EocGxZ+qj7F1uNb4GKh1+BitIuqAAOj7kZKhJCEWg+rnNQqzGggjuV\ndbAPXty8AipK3gS/CdGsqebfbCmVX4SiS0mcQnoUKqgehLogDEEt63G2QYvyEFtujwPohm5cewrA\nJdCJRSy6Y+vtb7m226a7W6AW03XwG8psOf8G1zfTyG+eHHLvmdDaibxvtyVssQ2aV0PF+qC7dqcr\n+13ub5vs7IVa3PdBfdS/DR/94zroffhj164N0FWLz0InNXDvNYK2b4be1yvc+d+Bn0RcBxXaA+7/\np5B347ne1cWyBT7trhP6fl+N9KPj6/ARYBrQ8WLZIo+6NvlIFjoeXwf9nqQ2oIaTkj3wFmWzxN8B\nXSGxTao+mYnfSPh2qAvIRhR9u0egqzm2EqOuHuvXr8FPfuJXkNra3o4TJ3biZS+bKl2lSk3Kh4cX\nShlPCHk+QuFMSILig3D/vHX6ZS+bcql51Wp86tSXcOONH4TIB6EWx4NQwfwI0mmDH4QKLhPxJnAe\nhX/4/zJUxMV8H97t4VqosIn5OtTq1wd163gKGuUB0E1N10OF2/rEuVlQ/n+AivM3woc4ewr5tMbf\ndZ9NQyNOvB55YX0URTH9DIA6VCCGVmHAR0R4nXsdRleIQ/XtQ7p/JfHet6CC09KcPxi0w/xjPwxv\n4d4LjQgyGh3zGPLuHSbmroafhNwH7wv+++6c97h22yTJjj2KoovC610dbHMioK4Xv+X64BA0Y+Pj\n0H6MJ2aW+noP0r7WJq4noGO1G9qXlnp9LbwQDy3lZvXd4tofWsGvg24GvRe6gmGTngw+Ssn7o3qE\nvsvXIG8Nvg7AH0AnCGUbEr8NnTh+G+noItand7j2vAl6P34MffQ9AP2e/Nhd/zEAe/HOd74Dl156\nKW6//U6cOPE9PPDAj3HsmEYdSa1SHT16FK9+9Vtw6tQLYCsSKro/gvb2/ec0UykhZPmhcCarhjjl\ndkvLPkxOFuO0Ainr9HG8+tVvwY4dQ/j6178KtZ79BtSa91qIfAMqau6C3/xkLgKhf6aFJzsOfbAf\nAfDTUEEaP/w/gKLrgG1KOwpNt/13yEcYuAEqdtcD+M/wYvOG4HNbNm+L6maC1QRS3ZVvx5oQ+RWo\n2DB3jhvcdTZChZAddxgq8q5A0Uod+9YCftPZaeRF1Y9c3ToSffRWFC2ma1AUW6919fk3UIEfW9rv\nhFritwbvvy+oU+hD+49QIfhR15YJqPXUuBEqpHUzmdblcqjfbQu078LrplKpXwzdsPd78G4nW1xb\nzLXmWui4ifl/0L77QcnnO13d9kPH0P3QR0G4aS90SZlBMUrHXqhQtg2FWwG80rXReAM03ODroX0e\nE7rGmMXX3DG2wG8I1cgXupH2NfDxq20CdR2KKxYhT7jy7oD29U6oK9TPun54BLoC8Qzq9SEcOHAA\nAOYnyadPe799W6UCEAjr+90xQLgi0d8/gAMHXoFf//UbcerUM9i61az4hJDnMxTOZNWwZ88eHDjw\nNtx0U97N4tJLLy0sv5448b3g1VEAh3Hy5Adw8iSgwmIPVNB0AvgYVBAdRDGSwRGon+zN8JuUTMSZ\naN0LtfrF7HTXsXBoT0OFn7l5mIh5K7wgsGuZFTK1SfCIe28f1Aprotbi2oZl74UK4tuiMu5IvH4C\nRevyDqjwDMN3/QDpBBhPQ62AHVCxbG16N3SSkmIEulxvYed+CJ00WJSIv3ftHIJGCPkk0j97j0JF\n98fd6+NQa/qPoD60JuL2Qe/VceSF2y8i7zv9LHQ8fA8q5O9F3iWkWYxsSwRz3JXT5t6/E36Vwog3\nW4Zh574EnfiU+W3bpGoERat1mAxnDj7cYTyO/graL++Bt06Hlug5+KQ8sc93p/t8CDoJ+BB08vJ2\n6GrJLchHb3kNdGKaoeg3fxB+EmMuGfYdm4Z3i3oEwKuQ5wB0z8DN+P73n8DRo0eb+iGfOPF4bjVK\nr/EIdO/BCwBch/b272Ny8m1473s/hNOn1T3loYduwJVXvgpHjvwB/ZwJeT5zpqkGl/ofmHKbLCGp\nNNlXXHHV/OeWjjfLOiSfbjhO/2vl1ERTCdcln6Y6PC5MGTwu6XTTHZJPGTwglgpY0yhPiKZRromm\nD66a6juVIvqq4Jy+4Jo9QZtTaaHLrheW1e3+t/TOU1JMwW1pwsN00wPiU013CdAeXXNGgMHEOdZH\nYSrnfCplfR0e2yHFNNmdoqmpD7lrWersmmjaaEvjPSU+7XTcp7PuXo2LptqekHx687AOlvK5P+qf\n8BhLJT7h6lGWprrDnWdpvVOpsXvFj9V4nJSljre+GRJgW0mb43rscNfb5uqzNmjfjGtPrwCXR/dg\nnXttqb3jFPD9QR+PBHWZlfz3ZoOrw2Z3z2aDc3qD61td+oLr+XTy09PT878H7e2+L9vbBxJpuOOx\n3C2NRiP5WwOM535vyNIxOzvL1OhkQcCU24QsHu/X3Au1glmYt5QfLaCWrhb4nf9xUhGLXPCzUKso\noBaxQ3ZF+KXp9VCr2EGolfA1MJ9LteI9jXxijnhpOgx1ZtnvgDgKiLfAma/vP8KnjX450iHZLkDe\nHeRa6PK5Xc9SOrfCW2Gvh1qPv4uixfudUCvkLhSzGz4MtX7OuHrHCVUuRDFWs9XjuKuXbbqbhrcy\nhsf2QK3e+93xtpFsL9RVpQtq5f0c8tE6Xgfgj6CuJMcBXObKtggXj0FTgZ+Ebjx8IYrh+W6B+q4/\nAeCXXNtf4Np7caJNT8Nbt1Pjy+puluY1rr9iy7RFFDkM7wJjoQbjCCD7XL/sg46LRpNrH4Z3SXol\n9L6/CRo5pR1qVf45+A15L3XHvRVq6bZNqL8LdY15EOqPfgreyr/Xte1K9943g7qY+8U09Pu0Aeqi\nBOi4/DL0Hr4/eO8U1M3oAqif+B8idpc5fPg6XHzxrThw4AA++cnDQbQM/7fnS4hXmmwjcRmM77y0\nMBQgWVLOVGkv9T/Q4kyWkJQFyawRaiEy61FotY2tWv3iLamxNXrGWfa2iLf+HRJgk/vbrrEhKtOs\ntGaZ6wusZCNStFyFlsxuZ9kbEbVOmhUttH5eJWoV7wisel1B/Ta4cmLrm1mCJ1wZ466MKVEL+GZ3\n/PpEP/S7dqes67WSa4XW1g2St8b2CjAqap0M3+8P+j2+Vk28JTm0vo4v0K+pz8Zdn8ZW9H7xls6N\nwbmxxXFG8vetS4DpoIy4zI2uf+My+sRbTcP6bXL1SPVDj7tWh2vDiOtH63/rv23BPYhXGmxMXeXK\nshWUGXdtO2+y5L7aysyka5t9vknUOrxditbpPtffG1ydu8SP23iVJ2XhHSq5j7Zy0y/pFaBxqdXq\npb8hbW02rm2M5c+v1erSaDSi4/qlra1HGo1G7jeorW2TjI1NJC2jtJoujoVWFgkxQIszIc3Zs2dP\nwYKUt0KY9Si0GO0BMI329nfj1KkfQX2B/xxqBbTjzHr8Vah17IPufYsCEGbdG4XfbDcdXOcgvK+w\nRWOYQTrW8xDUyjgEv/HMsKgGV0AtrkPu319CozFshVrZwjTGYbzkK6B+0o+4881n1Kyya6AZEOP0\n1MddP9wCtTanrPCWtvvnoBvG1kI30z0Jteg+FvTZzSj63t4CtU7ahq6noNbuu+B9gUO2wmeTG0B+\nE1xqY2gf9P5+J/HZg1BLbCqKgyXaaIdas4+jaMm9C0UL9Fuh/ZC5+lsyk7Xu35qoDqNQP/kXoZjQ\n41lo/ObPu3KOw9+zUehYtQ2d5lf/NPIxtT8Cb/FuSVz7S9CNdZch7/M8CvWDvwZ6b+KxfQvykUzu\nd8cdcK/fB71XTwXnj7p2fAE61r4Ff/+moasTL4CPxpHyF/9h4r2noH34BDRU4BPIr6jYasyn5i3D\nJ048Du8b/yza2lpx+rR9n69HnNTn5Mlp3Hrrb+Omm67FPfccdPHdX4TbbruxsPH49Gng2LE7AFyZ\ns4yejdWUFm1ClpAzVdpL/Q+0OJOzZLFWmtnZWWlpMZ/fhqjfq1rnWlu7Jcs6AyuGWalmnWXMLGxV\n/KG7nLUsZR3bIN4K3CNqFewuseAdEmCXs8qZ5c8snuYPar7Bdfe++QD3RNdP1dv8YqcCK51ZNFN+\nvj1B+SlLZeizG/s9rw3ONd/WlNU3vO5sUK+1kre2miU+9Km2Pp91/TMiectnv6hFdlKKfsLm8xxa\njWMr5oh4n2i7j7aCMSJFy2Zsge4TtdxvdOXtkKJ1vcu9NxHVr1uANsmPkx7XnkbJ/bXxOOnqGF6n\n5urRE5W3QbxvdlyeWcntXod+5inLb5/7fMrVP1xZmA2uaSs0tsJjKzfx/RyOyrF7FvZJTfKWdjvO\n7r1fUbj88svd78FQVG6PFL+/M9Laulny4zdt5Uz7Pl9VOGexVtNmq2qrBfbBmbGaVzawCIvzsgvl\nQoUonMlZcLY/mNPT08FD2j9cs6zbPZjtob1LvGtDr3u4TkrZsq+WFy6xz4p3YwjdDUwkm2tHvyu7\n3z2wbaPgbHB8uOQdL/evk7yY7HXt6JD8xsDYLWIgaq+5mNg5qXZae8qEgQndlOiy/rb2pNxZut17\nJkrjzzYG/RRuMotFVyyILwraZ+4YJsgm3Oue4P5uTPRzt+RFpr03IjpxMfeCWCinJgY2Fqx9U+6+\nbRUVwSbebNNg7HqTmmikNnj2BOeHAtTccda5+g+5Ntv1Dol3cwjbOxz0tfWlCdXURMsmG2UTkfXi\nJ29r3fsNybtzxJOK7eInicOSn2DZPUlNIuxzLbNWG5Ass82g8ffukOsf/91LbxpMC934NyreCHm2\nwvlMzlupgsk2eddqdRkbm1xRbTvXrPZJBoUzWfUs9qFhu+A7OgZF/UpTFjKzuJqoMd/LWMDF1jMT\nGUOivs+hxdSsc2EEgXFXhkVe6JCisA1FZiwAwtexj6wdc0i8dbpXvBXO/LOHJB99wnxgzQI4IUXh\n1CFegMXidDp4r8y6vUVUZA5IXryamDOR1xNcKyxjODgm7k/r/zKRZiJzRrwosvsci3S7973i/XMn\nEuWaVbJHNIqE3UcT9imLut2v5lZJb91e6P7btRqSj/xg43JI0qsfNgmx42tRnzfEC0qLONIQ7z8d\n+3KnJmY2psqs4bEoNj/7cDJn92Gz6BgOj59K9M3mkutdFf0/JMWJjk3KbIzYpKJDGo1GQYBU8V0e\nG5uU1tbu3Dl27GIFTdXfwJUqmFZqu5aK1e4PTuFMVj3+R8ALrrGxidwxao2YdMuwtpxuVrJmLhcm\n3ibd68mS42y5epvoZrZx994O97+5PwxJWvxYG0bcub2Stk5WEU5xCLJQgJlw6RcVJaHgN3FoG/li\nIbRNitbo3cFxZonfFrQ1nDCkXArMRaVDigI3FjEmYMLPy0LVjbv7UJf8xCUU7aHVdpf738prFtJv\nWNKbRO3+mavIJlFhNy5eaA5LftWhW/zqxebEfbNrbGpyzXB1wcSmiYhhVycb96HAtPKsPanNh+Fk\nJpwY2cbDciuqjoPNUV93i99oGovkeLzYd89WX8LrhC4l8aQhtnxvkGLox3Bjo93XbSX9axPZsJ/7\npV4fnf9tMUHc1ua/Cy0tfUnLp240tMnNuLS19eSOqWIRjo+pKhxXqmBaqe1aKlZ7f1E4k1WPfxD5\nB2O5FcdcD7ZHD82UuAsfrtslHX0jfmDHlmizOK4JhEJKNNhyvAkcE8/xdboS54YRJPzSsxdKJmhS\n4jUWSibWyyYHqf6xfqlF1+yKypgRFbGhD7XF9zXBGbZlnXjraEPy1srQhSQ10VkveUEf9pmNlV7x\notWs2tbuZsK5X3TCMBjVt1tUgJswngrKtLqHrhAd4mMe2z03dwxrR7t4P/VDkndbsPs/LXkLuwnd\nuniLfNyW0MKeF4R58TspKo5T4yG1QhNayLe461hUFxOgdu/iSVjovxyvXsTj1O5BLLQ7ov6xMWnu\nPrWg3eH3pUeAC6U8fnnx/dbWzbnfofwEvlzEnq1oaTQazgCg48rKryK4V6pgWqntWipWu4WewpkQ\nkaa+hvkHmomaMoFkD/WRSECES7Y94q114+JdOcqSSsQuDeaPGS5Dm2iaCuqY8ucMN6uZ0LSl69hd\nYci9b+KuzJqWel3WlnBTlgkqs0bHx/dJUbB2Strn2IRMzfXLLmk+QegStebH17T2xm21zYomqodF\nhVJo/TWLa+hrHk9IbPUgFMvh2AjHWmwNjydM5vIxFR0b9oe1Z9i1qS5FwWluEjtFrewmFm38mHtI\n6AJj/V92n+MJV0okN3MJsjbErin2efH72lykx+O0X3QSEd+rZi4ZFkZx2P29TXQ82sSmR/ITXLuO\nhW3Ml9vZuS33G+R/Z2LXkpmciDsbkaebmYtJhM7k/JUomFZqu5aSlerrXoXFCGeGoyMrjv7+vgpH\n3QbgP0JDdK2DT+gQhpU6DGAYPjXx1dDkFT+Bhgkbdcd/AD7pxrVNrrnVHXeDK3M/fGrr/dCwbI8B\n+GNoaLAHoWHspoMyLITaFQA+4f4ehYbEsvB4L3LXCUNQWUKX18KHhIs5Dk3WAmgSle3Q8HJvcG03\n9rlyrPyr4cOM7QXwfxNlXwwN03YQmuDlJdDQfalkHUeC8o5A+yQO//ae6PV+5FM9W6rwOLQgoP11\nGMCLAbwCwJ9CE5aEIdSOQ1OqDwL4M2iYuZugYePWQkOh1aBh1sJ63AFNRnMHfLi4O1FMxf6eRJv2\nQ8McptK2W398AJq0453BNeyzw/Ah7wAdZ9e4dsy5NvwzgDcjH0qwbDwAOmYsEcpj7r2TyIcY3A/t\nR3vPUmBvdO1cB+2vOK38BPSevQ75750lVNkEn6Ql5OvIJ9+Zdm1+EbT91n/NkpD8GPo9fqd7fR2A\n30YYXq+z80Y8+aS49gl0LLwM9fqn8dBD+WQw+/e/I1f6zMwb8MUvWjKlv4Tv6xvwzW8OJo4DgONo\naTmEEydGFkz7DQC3334n5ubisWIh8hZm4dCcz09WaruWkj179rCPzoQzVdpL/Q+0OJOzpJnFwX/W\nL3krovn6jjrr04B438kJKW4C7BK1VpZtIoytlKHPbV3KN6mZZW+XpJeKzZWgbKncLFxxxI4NolY5\nO36HFP1hU8vaoXXVXA1Ca2mqLgPil8BD/1JbYreNZaFfcmwVDPsk1cdD0WtzRdgk3vJvYyBe7g99\na5tZJ80XNrY22wa4srqbRX2HLOyLHFvIy6ykoXXVrP3mbhO689jKRWjpPCTqyhBH9ggt8PZZ7Kph\n92lYfMrqKVF3pW7x48QstYNS7hZj/W7HWtSXLvH+1zUBxiQfXSUuoz8oJ1xVqZK4yMqLx1Sx372f\nsu/jLKtJo9GQRqMhtVp9PtFJymI3Ozsra9YUXYhi67RFgAitx1WspClrdUtLX2VfaELI4izOyy6U\nCxWicCbngGYPibIHmoqvAckLEXs/tdzfbFNY6GNpy/6hiC0LD2YuCOaLGT/4TRzFwrghKm5t45kt\njfe69+MIA33iNy5eJenl8jDCwIR4F4ft4gV8KiJDTbywCoX5hqAMO2daim4Y1lfd7tj1iWPCCAph\n5AsLGTYu3q/W+tP8mMONccOunQ0pToxmSvqlmb+0TY7M59YiqsRxgNcmzrX+KXNdCf+2iVjKRz4c\naxbtwyYTNrFIiXaLa23CclS8j/Ww64twI6FNSq4K7mnYX81cP2ziuS5xTDypMLFv4/qQlPvkx/23\nQXSMbhUvuncl6mabFfU8i4gxNjYptdomCTc1xqI2NVG3KD0a2zlfz1RGwiouGwttAmxp6ZVGo1H6\ne1hWRwppspqhcCYrjqWykqT8oL31zMSziYGUcA6FQyhiLR7xeCBadon6Udbc/xZHOfaftNi5A5IP\nQRf6T4dCz6y5M+ITsZigq4mPZRsLPGtfKHqbbYCza2wS79cbtj+OplBmSTY/Z7M4mmV0wtW9N/gX\n+po2xIc6sz5siIrz2D88TgxSE7X89kja99r8bs3v2CYEC/mqh4LLxodtdovHkb22iYxFdrDoDBbm\nMLU5cL3k/ZnjeqRWLuKwdWE4OOvX1D2z+rZLeoVl2p2/SdQfOBTOqegiC4XUM5/4uF2pFYbUeIpX\nTOIY0mbhjjeq2uQqFWKwRwYHd+YiYhQnOCNSq9Xnf4+Koncm8j3OT35S4nYh4Vy2ipYS06nfy4Xq\nSD/g5YcrAssDhTNZUZzrTR7hD9P09LRLcBA+EG2zUCgibONZLCTsoWOC1cStRcWwjU2hiIwforbc\nfJXkQ6cNiRfj4SauOMvdBvFZ+MrcARqiVsCuxLVM7KQEQpiQxcRbKFbDqBrWxnDpvJl7QhwNwizw\nNlmJl+BjsWUW9nz0FC0zZQG/SnxUi5SYt/6oRddNudykwrTZBrsZUctm6I4QCvJByYekS1nR481+\noQtOqj/L2hseM+T6y2Jvd0l5X4+LJoSJhWreIuvrG/bNBtFJTbMxFd/XQ+IzJdqEZUqKwj4V89qS\nEll87il3vk0Y48mi3Wcra4O0tpoF3tdrzZpNwbXKIu3MSJbVpLNzm4v9PhXct2JdW1s3z7t1LOa3\nrqpFuqyM4vnF7ycjTywf3NC4fFA4kxXFuQwrVMzWZUu4FjN4JHjQD4sXrWFUiw732qxTZcveJk7N\nL7RM2KTONfeCVHa9OJqARfvYJWmf2bqUR+UwH9wwJNp68UvaFrlhg3g/1Dj83C7xoiXODBf60tr1\nTPinHuKxFTSOwzwjKnJ3i3d/mBKdFNSD98r6OzWJMbFr9RmJzrN43FauTR7iGMZhRjlbDbD0zaHF\ndzDow9S9N/EXCudOSSdhCRPRxDG2Y3cG81W2VQoT9inRbefE97NsrKbEfCj+zR/aJm/x/Y0FuY2T\nKckns4ktxL1SFNi90bVT40AnS62tm2V6elpqtVRkktDifVXi79Qqi61QWBvO/HermcWxym9hs2OK\nbh1FVzUK5+WDIfSWj8UIZ0bVIKuC22+/E6dOWfSGo9CoAr8E3Z1vO973QyNr3OVe3wmNarEFukN/\nBBoZ4jEAn4buyk/t+n/UHfMdaBSGMlrAvqQAACAASURBVCyqgHE9NArCYQCTyEcusKgfMdPQqBZ/\nljh+DsDvoBjN4YeuTT8A8LvB54ehu/KPQSM3PO7efyuAu1GMfnEHgO9CI4BsidpyN4CXujbthEZO\nuMPVa1eiHanoGjdDI1Q8C42kEEaLmATwWWhkCrhrPwvgjdD7aFwL4HJXzpvctS3ywGHoffoSfN9Z\ntIa3AFgPoOFevxnA11wZV0MjTayBRmd4vSvH7tH3AbQE5+4FcKE75oMoj/gwFLXpzQC6AJyG3qcM\nOlZ3QqOSjEKjVWyBRlu5GBqN4i4A4vrpgCvrbgC3Q6OhTECjxMRRZCwSyWF3zfCzbyTquzHxnqAY\n6UMA3OPKewf0Pl3j+uMQgA8hf99vhN6TnfDRau4E0A+9z+ug9+bbSI/JsqgSj6Kt7e04cuT35yMI\nvPCFL8HJkx9Bvs+HoOP2OIAXwI+NR4O6pK77KWj/vw4tLfswN6eftLfvx8zMYSxEs8gG+egb1csM\nyw4jTUxO7sOtt+5fdHmErGrOVGkv9T/Q4kwc52r5anZ21lmWzHoVxlgtWnUHB3c6/7+UJbTbWbps\n41rqc0s0sVHUyhf7Yk6J33y4XdR6G8eKNmud+fReHlncul354fXVotrePuj+Tvn0jkt7+1ZR621s\nVTQr9Yzb5d8p+aXw+Ngh8Uvr5pNdEx8hwSyo4TlmhQ8tv2WbJTdLOoug1TNVn0Ouz/uC+2eZ7VIx\nmS2Dn/mPD7m+LbPGhmmmLcmJZYk0i24qprGtaoTL/qGleED8Sod9HvvgmlUz/Hwm+N/G1Pqg3+Ny\nwk2F1vYR8eMpHKfmVjIiRbcMcwMKN9Oa9T32+Z8JzjHffnNvmkz01bj4jX6xO074vWuWLTBeYdHv\n5djYxHwUi1qt7r4LcZ+ba02vDA5un99ENzY2IW1tZatM5mqi/WGpts+lv+pCPrBn+ntJn9rnDnTV\nWD5AVw2y0ljMj3vsyxxm1vJ+zGnhnGW1+U029fpoycO5X3yCBlu6Nd9G24w0JF4Izzh/aouIEQom\nC4MXX2MyIVTWufcnRUXUluRDXCcJJiBTCRxMyGyQop+tZi20/q7Xd0tr62YnootpsrWPDrl22YQh\nTt0d+9CaoB4Qv7QfR4cIXTXi5f94Y2PcZ83cZ+weDbjjw2uYa4BNWOIybLNZKLCngvPtvJQLg03a\nQlG5TvLZBMOxlprU2UQlnHBYaMWRoI2HxLtjlGX0s2NqouOybKIQunSk/O3j+9YrurnQ3FTWiRe7\nw5IPP5dyQekS76pR5tvc6+7dhBTFsU3C+sVvZrRsk+PS2bldWltD//x4M2PcnvxmPhPd+f0R5gqj\nfd8sJNxSQzH8/IX3bnmgcCarirLYqfmU2sVoEp2d26S11Sxa/qGdCuc0OLgz8eCuu4d2vEHOstSF\nURn8w3d2dlY6O7cnyrP4teGDeDhxXBzbdkZSAm9sbDLoA7Msmm92uLEtjm6hlsNUqCr1wcsfOzY2\nKbOzs06IDIgPaZYSjaHA0HqrZTwUZGaNjjeshRvOTFzFoin0mS3zDzeBG6cLDzeW2YY5y0QXHtMm\nRV/vHin66qasnaGl2ASthfgL/YFNkJVZNbukVtviwimG6avDY2cqleP92NdJ2uI/6Y5LTQR2iFrZ\ny9JSm8C1Nlk8bBP+4XiaEL9CsUHU2twhra02XpqNp/Xu3O3iM1HGEy8T6eH9MF9ya6OVW5xopMLH\nqYCedPfBJtJenFP4EPL8gMKZrBrKlrbymyzSrhihGLRYrbH4NtHYaDScyDZx3SN+CXtz8AAON37p\nQ72lpbuQIMFbg8M6mXiwpesJSYm/9vatQRkmOnZLuMQexp8dG5twO/7NGjkZlZm2VjcajWTM13BC\n0tLSJ2NjkzI9PS0obDqM22dW9UExi/aaNZvcJCLv6pJlZjUMRc7WRJkWKWJS8uHGTKzFyVxMlM84\nQTUh3mq8TfLpq+1/E25mhS6bGJgVNhavNgnYJUXhNiP5ONqhyFNrfn4yFQrBPmlv7xcNX2fuI3F7\nrS6xq4ZZdFPp21ObP22CFruNDIu3vKf6I7z3w8F55toTu9OYYA6vYe/Hk8pwUhVupLXxNykWuaS1\ntU86O1MuS2GZNenoGJSxsQlJTURbWzeXukao60bYjzoOmy21x8lTCCHLB4UzWTWU7UJeSDgvtIwa\nC/Is65GWFhPK46LL0LEQCUVH/uHc2blNBgcvkizrFB+9IuUeEVt0dxTESr0+KiIi9bpF0TDRUZO2\nNr1WlnXMn9PWtsm5WFgZ+djRbW09uQd/S0vvvOhO9W0qw9nCYcv6JC+EQ3eRMj9V66dh8eIqPiZM\nZBKKpSHxsZJHxE9ExqW11RJghH0fu5WEEwGz6FsZOyTtwmG+xak6WlvDeNzrSq7d5e6RifNZ8TGi\nQ7HYJ0WxbOV1B+WG98RcaMraYOLd2hKH9rPPbIIRhrVLWeDDvpmMXlss8LDuZXU6JD5cnfk2h+2y\n8ZGKvNEtjUajJGZ7PkLN2NikiIiMjY0l+nWqySpM+aQhFRWh0Yjdp9JxnQkh54fFCGdG1SArivzu\n83BHPNDSsg/vfe9M6c51II6+YY/E66ERHvYA2AaNljDtzjgO4PcAvDhR2k48+eSb8OST10MjIuwB\n8IfQXf8HAfRBIxvcDY3O8AVoJIpvQyNePAPdrQ8Ap9HV1YWjR4/im998FD7CxH4A1+D06Y/i9OkL\nANwyX7fTp+HOt7oCg4PvxcjIEddXfwAAeNe7bsP99/8d5uZei2PHRtHSMlNoyd/8zf1uR34r5uZ+\nMyjzjujIUbS0AJdcchBf+cpxiDwL4KegUSR+GsALAfwjNPrAHmiEk5hTrh/+BBpZ4d8iHynjOmiU\njo9AI4e8ERrJ4c0A1kKjNAB67/cAGEVb23X48Y/XIB/RYjs00oPvH4068mFopIr90CgYgEbRqEGj\nQYR12Qu9nz+PfFST/dAIFh+Dj1LxDQCtro6pa+93/fMT93oPgP8CjeLxmCtnrytrNzTiRHj+da7f\njkCjiRiHAfwcNGLHfwbwPmjUi1tcO8ehEVTudMc/DeASAH8flLHH1eEOAP8VGhnjw+76V8BHQDkV\n1HW/q/vDyNOL/HcI0KggZXRCI8E8Cf2ujUK/d2E0jFdBI5C80LVJv+P33HMQDzxwP/IRQq4F8J9y\nV+jv78Ott96KY8cecu2x6DM/A+ATOHXqBtx00+1u7ANf/OI0hoeHm9Q5zQc/eBC+3+y9W3DgwIHS\ncwghzy0onMnzkrLwTMWwS+/AX/yFCcX/1lQ0l7MTKir2AOiJPvsSVKhtQV4I3AAVxHa9D8CHdANU\nVByGipnvuDJiMXAtNOTZADSE2Jdx++13QuS3UAyF1eKObc4zz2T4zGfuAQAcPXoUt99+J771rUcw\nN3f7fJlzc8eRF157cfLk6/HZz44iy94GFfRHALzB1SsfBq+npx39/X246KIteOihR+BDilm4NhN8\nh13/hGHsLNTcJ4N+2A/gba6dj0BDsV0B7fsHoaHaLoRORPzEAQA6O2/C+PjDOHHiEhw7diny92Bf\nood+5P5PhRzbBxVth93nj0JF4Nfc5y9GMdTdRQD+Fhq67XboPf4odHIU8wx0HH02qtvT0D5aD50o\nvAUa+ixmCMDnALRDheF73Ht3u/rahOAJVxdA+/uYK3e3ey8DcIE77jpX53Bishs6+TFMVB+E3hcL\nQXg1fNi7w+5az7o6xWTIi1ubVNj/d0PDQY5Dx9798AL0KPRR9l53ro0t4B/+4WGcPv06APdBx0a7\na9vdrk0AcC0mJ9+eELWH3TkA8KXchFF/dz6C9nYf0s2P78MM70bISuZMTdRL/Q901SAVCcNK2Ua1\n1DHxBsLLL7/cLfH2ydjYWCFlbd4VIQy3dUjyodNS/qTj4n1uwyXclC/oeHR+2ifblvItxFXxGNvo\nOCnF5Bz5qBpjYxPz/eJdUlJ+uzsky3pdFrVwY1vsb2rZCwdF/WI3iU89nSq3LvkMbrZRKxUuMDzP\nInb0iGa2i6MaTEtqud/uq/q5Don67Zpbh7kb+KV93Wxmfs2pOsS+vl3i3QR2ROWZX7KVFSbOiDcQ\nmm+2hUPrS9S3Q7y/dByGriZ+c6rVO3RRaBaC0Tbmhe4J8evYtcTcSsK+8FFktO59opv2LHKHuVrE\n36EB0TFk7R4RHx0ldPvYIn5zaRwNI9UmczmJI8tMiA8Tqe5O7e0DCV/omaAeHYVrhC4bth9ioagI\nKVeN6enp52U0BUaBICsB0MeZrCYWin2Z+jztw5jf0NNoNHIh7GzDnd+UF/qexpnkBiTvJ2viuvjg\nbW3dLHlxmRIAm921puYfUPkMiL1iG+7y2fFGxKelVt/atrae+Ye736Q4K6mNjcCFgWhoVr9t7ppx\nBIq1kvb7HRE/GemVwcELpa3NYiOXCedYZIaC1co14Rn6fqsoyW/gsnPXu5BiofBcJ17Udkgxo50J\nLjve+t1EvZ1rAn9UfAi8lGg1gRluDozFfBgHuUt8KvcJ0QlE7CcdRurYFpRl0S1iP+HUpC41aUhN\n/HrFT5bCGNDWV5cn2rJDfFr77ZL3T646ibQY093R+3HdUqLexmDxHA2tWJYuvDe3f2CxcXZVOPus\nki0tnW78+3JDAV5FjJf9Ni6lqGXcYbJSoHAmq4qF0pSmrbNlm5Dy56cePPnywo1ItsktTGKi1rM1\na/qkVrtA1q/Pxze2B6Q+jMNyykWTbSKyUFgq7vVzE/f2sE1FwMi/b+LSEm8Miwr0ScmHuQutpCmB\nsrukT2tB+bGgOiRmJdeNk6HYNYtrWZSIUESF0RuGo2tp2Lh8FBMfCq6tLZWkZDx6bffGRPm4q/dU\ncP2y+lmot5roJKIsvnS8Sa4sake4cW+qSXm7E323S/JCMhxX/VKc1KXKTUWDCVdhtoi38HaICuTU\nxMlWHHxYQj/27fsxK8VoKbFFuyY64RuU4mZD2zia/o6raB0M7qFOWuy7o9FeinWv13eXitEqQrX5\napa/jk+Hnb9XoThtdr3zIWrPVYpoWq3JcrNswhnAy6E5cb8BYH/JMR+G7jb5CoDdTcpasg4iK4tz\nJ5zrYlEtmv34xw+ktrYeGRubdOJsSorCbSb624tYe0hoCKxhUetgTbzl1azb5Q+mhR6eCwt/Eyax\nRTaV5W7ciY3Ykhcu64d9ahZKsyCPu3ZaHUyghxZTExI+YUXRKh+KNhNu3SXH1Fw4vngCEFokmwnn\ncVFBGE8AwuyPPaJiNY7HHPZTj2tPnPxmk+Qt2FZGXK/JRN3Ljk1ZhlPvbXf9PCxFYZ2PvqL3OV5Z\nMSGbCndnE7GUAB8UP7HsFRXYFu3EJqBx6L4OKc+EaMK/Jro6M+KukRb5usowJfGYb2vryX2HyiLL\nlP0uhKsabW2bkiIw/Xtk47jZOMxP7BcSxudK1DbjXFyDVmvyXGBZhDN0V9I/ANgB3TnyFQDD0TGv\nAPBn7u9/CeC+JuUtYRetHp4rM/kz9QE807LLfnhnZ2dlcNB8YdXHsrV1Y1NXDU0uMeBirO6QlpZu\naW3dLGNjY/NxV6enp6VeH5XW1s3S2bnNhbuyGMwToiLHlsNnBdjprt8p6sIxI7Vafb4PNA5yr3jR\nbBbZ4oMpy3qks3NbTnib9blWq0u9Piq12hZ3rQ5pbd0knZ3bpF7fJfX67shfWZxoCV0k7EG9SXyW\numHxvqWdoj7AZqlT0afW2zj2siWzCF0h1ot3awgTmMQTmw4BLhAfzi3OsNcramW0eMTdC5TVEvTx\n+uA8s3pandeJD4XXKXnXj7js3qBe9aj9NqbC40fFJ3npdn06IWbt1AmCCfzYOpxKhmP3LBSzFvM4\nvp+pSUJsgbVkKmaBNTeSgaBtZundJj5+eZkl2izj8aTM4jj3S72+K1gRmIz+j8tbKKHLFvGhHkNL\ndjgOLGFKsT/ipCVnIupSItvC2+WPs9+J0DWnJuHvka5AxVkg88J5MQYDW42y+O5n+xtcxU1uod/6\n8yHwCVmI5RLO4wA+Hbx+Z2x1hm41/+Xg9dcADJSUt2QdtFp4rszkm2XxO1d1KsseqDF7ixvZGo1G\nsDkw3sQXxhW2c6zeofV4XXBMl7S05OMUZ5lZBGPr7Lpc+ZpxLxbxG8QLutiy5rMRtrVtkkajkfDf\nXSf5pBBl7h/x+2GCjZ5EOfaQ3yh+UhBbYXdIfnnd2pOapIR9EYugMEZwGOPXylgrxYQdqfThUyXv\nh5vaul2bYmtwuPExVc+43FRq8XBclU3W1NqZTyRj1lqbnKQsxqGLg00kYn9ns5SnfI3De2TW89Q1\nNkittsVtnLMJQuhKkzovdGEJ2+L9uM3C6zNS2gQ3tXIwIrXaDudC0Zf4fDz4Z5+lRKpZs9PiezHi\nT0SSSY3ibIPFZCn97p41xFZ0OjoGc8mW4u98OslTUXCmVsaK154569/gsv6p+vyhcCbPBZZLOE8B\nuDN4fTWAD0fH/AmAfxW8/hyAny4pb8k6aLXwXPlBWigZyVLVSa+bfjiGD7RiPzUTAbGFTkpf1+uj\ngZ9ifFz4XtmyuqXg3iTNrE/pLIRmQS7vd13SLovyYe4S5QKjPClJmfUx7k9re+wCEYp3Ez1lG9VS\n1zJXEBNjVzVpZ/y6rO7mehFPMsp8ke3vUAA1t/iaddIyynV0DMqaNeGkKxbpFqFim5jgKhe+4UTF\nJou7Ev1UdLloa8v71asYCicskjgvnoAVsz6GmfjU79f6yjJmFl0xwuPTmz3jTaXN3FjiSZ+v82J+\nk9SSnK+zRa8p/6055MZQXmzHx6T8qqsI01DUphPAXLXo9i5E1efPc8XAQ1Y3ixHOz8k4zjfffPP8\n35dddhkuu+yyZasLWbnMzLwBn//8r2Buzt75xjkp96KLLkZXVw3Hji22hMegiTYeBnAl8nFlzwWd\nADYl3n8CPv5wM0YAPHQO6mExkd/i6mNxnRfbzh4A97i/D6OYeGOxvBItLZ/D3FwYo/nmxHGPwscr\n3geNN/wogI6mpff39wEADhw4MJ8Iw2JsA8Dk5Ltx8ODH8dBD+wC8CJpY5W7YvWpruyVVLMbGRtHf\n/zBOnPgygEvR3z+AEye+h2PHrkF+TD0M7fdpAO9Ba+tp3HzzO3JJOcL46PfdtwFPPjn/iTvvOmgs\n5tdCx89eaEznCxHH+b755nfMx1Pfs2cPLrlkxH1XBqDJXybgk9RMY2zsy7njjxz5GN71rtvwrW89\nglOnMpw69Xr4eOlXu//zyY80LvlgUOfD0FjXQzjbcXfbbTfiyitfhdOnNRlQW9uzuO22Gxc8r6Xl\nO5ib02u2t+/Hjh3DOHkyf8xFF100H3fdiGPVW/z6+Bh772Uvm1pUu5aaKu0g5Fxz77334t577z27\nQs5Uacf/oK4as8HrKq4aXwddNZaM58pM/ny4apRdt5mrRogPPTciatmLrV0Lu2qoy0W+XbOzs5HV\n0NwofB+UuWq0tm50VrXYXcPX4+xcNaak6IaRamOXFF01mrlQxO4TC7lqlIX+WshVY12Fa9m9W8hV\noybe/SAeLxqPur19IHKlSFmBu6ReH5V6fXeiHrHFOh0pocq4jqOpWHQWDWnm+6Nsg1oxnGHRbWSh\n+hTL0A13tlk23s8wPT09v0cglV7al2f3e+F2lNUlVYexsUmXpn5jNGY25FwjzuY3aSG3jtRvcrzn\nY6l+t4v369y4alS9Hi3J5LkMlslVYw385sA26ObAF0fH/AL85sBxcHPgkrMaNgcudF11magJUJP2\n9k3Jh3ZYx7GxCbfBrnxzYKPRmF9St9fNfP0GB/3mwMHB7YU+sPBXa9ZskvXrazI2NjH/EL3iiquk\nXh+V9vZBWbNmk6xd23FWmwM7O7fL+vU1996o1GoXiG2c7Ojols7O7dLevlXWr9doFIODF0lHx6C0\ntQ3ImjV90t7eL/X6qHR2bnWiskM6O7fK2NikDA5ud5sPO8T8VWu1ARkc3C6trZulrW2TtLSsF+/L\nut790+ur33KXWLzcjo7N7t5tcIKnR4BeaWnZKGvW2HkW/cF8si12dI8A7e7vcHNgr/jNgbqBsaWl\nWy6//HIZG5uU9vb+oH4dMjh4Ue6exvc9fm3oPd0m3ve4Vzo6+pKicjHfgzK//oWSAaXO182tk+68\niTMS8ed6s1n8HVyoHan2LNTueGPv+fydrHKtparPub5fVa+33M8fQhZiMcI50/POjizLXg7NGdwC\n4KMi8r4sy97oKnSnO+Z3oGHrfgjgGhH5PyVlybmoEyGEEEIIIWVkWQYRyc7onOeaSKVwJoQQQggh\nS81ihHPLUlWGEEIIIYSQlQSFMyGEEEIIIRWgcCaEEEIIIaQCFM6EEEIIIYRUgMKZEEIIIYSQClA4\nE0IIIYQQUgEKZ0IIIYQQQipA4UwIIYQQQkgFKJwJIYQQQgipAIUzIYQQQgghFaBwJoQQQgghpAIU\nzoQQQgghhFSAwpkQQgghhJAKUDgTQgghhBBSAQpnQgghhBBCKkDhTAghhBBCSAUonAkhhBBCCKkA\nhTMhhBBCCCEVoHAmhBBCCCGkAhTOhBBCCCGEVIDCmRBCCCGEkApQOBNCCCGEEFIBCmdCCCGEEEIq\nQOFMCCGEEEJIBSicCSGEEEIIqQCFMyGEEEIIIRWgcCaEEEIIIaQCFM6EEEIIIYRUgMKZEEIIIYSQ\nClA4E0IIIYQQUgEKZ0IIIYQQQipA4UwIIYQQQkgFKJwJIYQQQgipAIUzIYQQQgghFaBwJoQQQggh\npAIUzoQQQgghhFSAwpkQQgghhJAKUDgTQgghhBBSAQpnQgghhBBCKkDhTAghhBBCSAUonAkhhBBC\nCKkAhTMhhBBCCCEVoHAmhBBCCCGkAhTOhBBCCCGEVIDCmRBCCCGEkApQOBNCCCGEEFIBCmdCCCGE\nEEIqQOFMCCGEEEJIBSicCSGEEEIIqQCFMyGEEEIIIRWgcCaEEEIIIaQCFM6EEEIIIYRUgMKZEEII\nIYSQClA4E0IIIYQQUgEKZ0IIIYQQQipA4UwIIYQQQkgFKJwJIYQQQgipAIUzIYQQQgghFaBwJoQQ\nQgghpAIUzoQQQgghhFSAwpkQQgghhJAKUDgTQgghhBBSAQpnQgghhBBCKkDhTAghhBBCSAUonAkh\nhBBCCKkAhTMhhBBCCCEVoHAmhBBCCCGkAhTOhBBCCCGEVIDCmRBCCCGEkApQOBNCCCGEEFIBCmdC\nCCGEEEIqQOFMCCGEEEJIBSicCSGEEEIIqQCFMyGEEEIIIRWgcCaEEEIIIaQCFM6EEEIIIYRUgMKZ\nEEIIIYSQClA4E0IIIYQQUgEKZ0IIIYQQQipA4UwIIYQQQkgFKJwJIYQQQgipAIUzIYQQQgghFaBw\nJoQQQgghpAIUzoQQQgghhFSAwpkQQgghhJAKUDgTQgghhBBSAQpnQgghhBBCKnBWwjnLst4syz6T\nZdmDWZYdzbKsu+S4j2ZZ9niWZX97NtcjS8u999673FVY1bD/lxf2//LBvl9e2P/LC/v/+cXZWpzf\nCeBzIvIiAF8A8K6S4w4C2HOW1yJLDL+8ywv7f3lh/y8f7Pvlhf2/vLD/n1+crXD+RQCH3d+HAfxS\n6iAR+SKA75/ltQghhBBCCFk2zlY4bxaRxwFARB4DsPnsq0QIIYQQQshzj0xEmh+QZZ8FMBC+BUAA\nvAfAIRGpBcd+T0T6SsrZAeBPROSnFrhe8woRQgghhBByDhCR7EyOb61Q4BVln7kNfwMi8niWZVsA\nfPdMLl5yvTNqACGEEEIIIeeDs3XVOALgNe7vaQD/o8mxmftHCCGEEELI846zFc6/DuCKLMseBPBS\nAO8DgCzLBrMs+1M7KMuyjwP4nwB2Zln2T1mWXXOW1yWEEEIIIeS8sqCPMyGEEEIIIWQZMwemkqJk\nWfYbWZZ9Lcuyr2RZdk+WZV3LVb+VTrOkNFmWzWRZNpdlWS11Ljl7yvo/y7K3ue/A8SzL3rdc9VvJ\nlPz2XJJl2V9lWXYsy7K/zrLs0uWs40omy7KhLMu+kGXZA26c73XvV0qoRRZPou/f5t7ns/c8UDb2\ng8/57F1CmvX/mTx7lzPldiopymcAvEREdgP4e5QnVCFnTzIpTZZlQwCuAPCt816j1UWh/7MsuwzA\nKwGMisgogA8sQ71WA6mx/xsAfk1ExgD8GoD3n/darR6eBXC9iLwEwM8CeEuWZcOonlCLLJ6479/q\n+p7P3vND2djns/f8kOz/M332LptwTiVFEZHPicice3kfgKHzXrFVQpOkNL8J4O3nuTqrjpL+/1UA\n7xORZ90xJ857xVYBJX0/B8AsnD0Avn1eK7WKEJHHROQr7u+nAHwN+ltfKaEWWTwlfX8Bn73nh7L+\ndx/z2bvENOn/M3r2LqfFeSFeC+DTy12J1USWZVcC+GcROb7cdVml7ATwr7Msuy/Lsj+nu8B5ZR+A\nD2RZ9k9Q6zMtbueBLMsuBLAbKtYGmFDr/BH0/f+KPuKz9zwQ9j+fveefaPyf0bN3wTjOy0GWZQcA\n/FhEPr7cdVktZFnWDuDd0KWi+beXqTqrlVYAvSIynmXZvwDwRwAuWuY6rRZ+FcC1IvKpLMv+HYC7\nkP8ukHNMlmUdAD4B7fenEsmvuHN9iYj7Pnifz97zQNj/AH4CPnvPK4nfnjN69j7nLM5Zlr0GwC8A\nePUyV2W1UQdwIYD7syx7GLpU9zdZltHqc/74ZwD/HQBE5H8DmMuyLJmJk5xzpkXkUwAgIp8A8DPL\nXJ8VjXtQfQLAx0TE4v8/nmXZgPv8nCTUIkVK+p7P3vNEov/57D2PlIz/M3r2LrdwziVFybLs5VAf\nnytF5Jllq9XqYb7/ReTvRGSLiFwkIi8A8AiAMRHhw2vpiJMCfQrAzwNAlmU7AawVke8tR8VWAXHf\nfzvLskkAyLLspQC+sSy1Wj3cBeCrIvKh4L0zSahFFk+h7/nsPa/k+p/P3vNO6rfnjJ69yxbH2SVF\nuQxAH4DHoTvZ3w2gDYBV+D4Rc+hnzgAAANdJREFUefOyVHCFk+p/ETkYfP5NAJeKyMnlqeHKpmT8\nfwwa8WE3gGcAzIjIXyxXHVcqJX3/IIAPA1gD4GkAbxaRY8tVx5VMlmUTAP4SwHGoO4ZAf/v/GrpE\nug0aWeDfi8gPlqueK5GSvj8AHft89i4xZWNfRGaDY/jsXSKa/PZ8HiqoKz17mQCFEEIIIYSQCiy3\nqwYhhBBCCCHPCyicCSGEEEIIqQCFMyGEEEIIIRWgcCaEEEIIIaQCFM6EEEIIIYRUgMKZEEIIIYSQ\nClA4E0IIIYQQUoH/D7OCiRwAgfQzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc39190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#xx=plt.hist(phot_df.petroMag_r,100)\n",
    "xx = plt.scatter(phot_df.psfMag_r_ext_cor,phot_df.z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xk/zX3f2eqvr5JH/U3Q9W1YeT3Nrd9y0+MPhwkrdlb7eMx5O8qbu7qr6Q5N4kl5N8OslH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\nBgCAIfEMAABD4hkAAIbEMwAADP3/NqJ/AkJK/NMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107b6dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#radius fit distro\n",
    "xx=plt.hist(phot_df.deVRad_g,100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deVRad_r</th>\n",
       "      <th>p_el</th>\n",
       "      <th>p_cw</th>\n",
       "      <th>p_acw</th>\n",
       "      <th>p_edge</th>\n",
       "      <th>p_dk</th>\n",
       "      <th>p_mg</th>\n",
       "      <th>p_cs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.548579</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.614075</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.480558</td>\n",
       "      <td>0.699</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.713980</td>\n",
       "      <td>0.965</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.863721</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    deVRad_r   p_el   p_cw  p_acw  p_edge   p_dk   p_mg   p_cs\n",
       "0   2.548579  0.930  0.012  0.002   0.017  0.039  0.000  0.031\n",
       "1   1.614075  0.805  0.003  0.018   0.035  0.128  0.013  0.055\n",
       "2   1.480558  0.699  0.000  0.010   0.086  0.139  0.065  0.097\n",
       "3  11.713980  0.965  0.002  0.000   0.005  0.025  0.002  0.007\n",
       "4   2.863721  0.915  0.011  0.008   0.033  0.030  0.003  0.052"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our target variables - fraction of votes for various galaxy classifications\n",
    "# p_el: elliptical\n",
    "# p_cw: clockwise spiral\n",
    "# p_acw: counter-clockwise spiral\n",
    "# p_dk: unknown\n",
    "# p_mg: merger\n",
    "# p_edge: edge-on spiral \n",
    "phot_df[['deVRad_r','p_el', 'p_cw', 'p_acw', 'p_edge', 'p_dk', 'p_mg', 'p_cs']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.35639567665324895, 0.0)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10eb0a6d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hNc7a+bVac6iskNynVB9aNj6jIc5F62fO8XNQnweR4twY3E1pxagxWKoxPPl9\nuY7PUZL+sjFxzwYt8+vYSOo2FGHE2RChaNmSXv1MDtKX9wUK5JfTT7M1kAntLgVnO97+n6PY8W+G\n4sgUsr7UOU9GxEgfGEwmpS5Ui+TAOmb+zCHz2JJedLZw/1KLK8f0nRP18f+zSTkyxjOfOyuO2yKd\njPMCIx37BYrTj98iXT7C14MXOtKZ8Qw58rm8n95bt9KvZNowTcHBMr0Wa8nxsVbXtWNKLbde52u2\nQyFySEq8dQnR+PhcNmZscb53SXMkLUbtKNYVQsOFhVuZI9thSIFm1eWF2WGJ7/h40Uo4MbGitlGz\nDAOr+7HHDxPFoWx8XGhClketVwpTp+vEi3XmrO3DarhfrqTvoHDJeOKQerzz82oaB8ZR9LuMkIff\nTON8nDDibIgQW7aYrDLJYb2zZnHkF9MCBblESjzTcyUp3qFYh8wkjAle2MoPTolsiWUZwTw5KyeT\nmDTNdU6PnH5mTbWM3zxJzuKeC9PHFj+WKJymWFognRGlTGSBQnQSHndNwqFLIkL7gm42xKfm31ir\nvOq/T+NrBzJRq8162YKM+MHkkkliLiGNRmLlXOIxkHPhlm+PJsVJk92wrnoQcV4muTAYnHwltyCM\n5TC12jQ1GmGhNzFxjs6caZEmnziqezNOOMI7Ds5COoxUI01convlr2WduUKUlHBOvT5fmSwMaz3W\n2pwLs6idm4vwUOZ0mBKWUUQNeSXBvSuKjtEc2cYwGpTdK+E3jTjP3uWWv3JhxNlARPGLo9VaI2nN\nKt8qZ2fA1CKsr4CLpKdLMZFbomLCDWmdZcJ8jhy5O03O2skWWykZYC1wn/IkKyXOnJSgTSEjobRM\na2WsUnCQO+vbNEtB250S5x1xLlu4Was7Q3mCnFq7eVEjrfgcbUK+0FhzzW0oJ56OoGjxuZkkyqgk\n0sFRI/wbVEy/rkkO4hB6ecnJFSpa8FtJ2XIXoTw+rnv5a5kT54nnodzaTwmWZgXm7ImD7rODkK+Q\nEIbvnZCtT5M+5OoqJt/QFkpu/mpj5+Q8xes4nGa7+rk5p7phNKRaGRwC7jBpq0dpZTxOS+1h69Kv\nSevIFo6vVlQjzml2Wpex1nA0MOJsULYzJynW/KYki+PKrvobVrOS5ix4kpxIIpiSI+38opUrpIae\nEedosg4tLN4MhRjQvAjQLHGp5boshNwlijMkpqSRYzh3k3PZYrxL8eKEifFF347TFCzhPBaT5GQU\nbJHViDdH8iZcAAAgAElEQVRbJ1nywYRJG+dZMU5pGWsU5DS7FLIWamPDxD6NHpJbwLDT6Y74Oz2O\nFygcTWOHwgKHdzqKC5uyBBou+x8vXnih1CFeHJSdOwwBPKwGMhfFQwtjNrxeOM7M6P7ezvan39dD\n8R2VE9goiHOwlMfzU8v+2Ol0DuzsVyXr4ijG5DAYRV36NZlV+/9qlW6MAtWkGpZy+zhhxNmQPAAv\ne/JwgXQrnPyOrbpaQozUIZDPldbXWQoRJuTDt0UuEYdm2d1RX6CONPFv2gOdk4OwZpbbw/ISSerl\neYtUjArCsge20qX1MCFuUxxzmq18rJ+eI0e0WWJxVoxHnxwZTq2wzlvatfUsxclPyiy/PEZaPO3U\nOs0h/LSFT7oIWKNYFsJZIznCx4wyrnJuSLLM48Bt1eQDLEMpbhHX6y0KsbWl5XSLGo3Zgua53+/T\n5uY2NZsXKaRb3/BjUnSY1F78zvKaRtVwGRalk+Lm5k4msccatVptajYv0srKRWq12tRuX1bbGsc4\nDvcRE3tJTnJxmXu9HjWbF2hsbIHiReMZ32+e16cJ2N3X+3Y6HR/lZIFarTWv92UH1kAaNze398cq\npFV3qdXb7cvUbF6g6ekVarev+O8uUbN5gZrNi/vnbm5u0/T0iv9ux3+34y3tYWfFXVNZ/pX9xDZp\njGtu08QEx07niC1nM3N9Lgnb6J519fosTUysREk+0nO1RCZ8rGxjp9PZj3PO/chd2/T6phrsYQnp\noEXBoHL53gmSHScJO3Xq1EDJS0rSTwKprtKGo2rnYet2u2aWAOU4YcTZIB6ifSpm9NuhkBb7EgUn\nqtSqm5KZWXLEiTW/u1QkaEy8NHJ0nookT3PiYoK6Rc4qKy3PKeljcp8jlkUHMQ4LV7RUX/Ev3XWK\nsxyyFlxr+6JSFhPVVQryEybj2iKhTUWLs4yNfIOKcbDZ8qvps2V8YElMTil9SKNMcH/1xC3u74sE\n3KuMH1v5iu1sNM4KK/AcBSv5JDmyp1nUW16brV2rDqVa3Hb7ko/aEaystdostduXaXycQysGhycZ\nGUQ6lYXdGamFD86fjcaU0EVr8zKNlR3HFOa6wj3aI7eoDISViX2sAS5GL3GyklQzPu0d7tLQf+63\n8fE5euCBB0iXaEkrPRPpdMHcEnOiPNV6WMhpuzRdf43mPPG+7Ml7Wt7ifhsaDS2jKO+ozYjziqnb\ngaV9cuyiqxRjjzvNs76QT0loMcFHOk8XqVabphDGMowzLwK0EIIHtRqXEWenJ8/H9I7bot1v3czc\n1eu62+HnqrThqNo5qnL1nVvTOB8VjDgbFGsWkXtBpw/EXSonnzMUImewvEFamM+QI6esi+6SI0Iy\nfi6/DKVDGzsHkvgtfSFy/asUolTIF9W9/rxcaDu2FGtZAHcoWEanKA6vxsSVLfG7og5JqKWDnUY+\nOYQft3+3pJ1p+3jcZNs1SYf2km9RkDrI3/i8GxS2/rSFBc+DneQ72V4mM1v+8zblFwbzhXBW4Rwu\nQwuHxpavnLxD6/cCpVkpJyaWRIbEdIzDcc5KuENu4Zemar8g6pGLlT7FjpVcthyv4vzY3Nz2Eo0N\ncU3jY5zVXNYTJyaamDjnFxba3OP5o801XqxW8Q9gK7923A1xjDaH5HhpdYVj2PJWXh6XRckxmjNo\nGvnG3dMcHSWMbbGuYpKfoqZelzRoc5JlQvH39fqCJ+/xYq5KJJWcpbKMsA1KCBP3p/x6xteq2M5h\nte5HgSptOKp2jqpcPSyrSTWOCgchzg0YXlG4fv06PvWpp/G2t70H3/oWf/vvAfwcgI448n0AvgPg\nRwG0AHwVwDKA6/73OoCPAHgBwP/qj/8ZAG8B8AiA/xbA/w3g9wF8EsBrAJwBMAHgDwC8HsAPAfhl\nAD8M4BqAfwXg3aKOywBqvrwxpY1P+uMfAfBxAAv+948BWAfw9wD8AoAfE+e8F8BL/u9xX8Z5AE8D\n+DqAWwDeBIB8nR9R6vzfANwH4NO+TfDHPO3//jMA/9Yfk+LbvuxvAPiw/+4RAKsAukk7f0ipGwAa\n/txlAE8B2ATwO3Dj/TEAPwg3nvLcxwC8U5yXwyqAPQA/AuBx8f3jCGP0hwB24ebEmDim5vv8ZQDv\n98c+iTxej9/4jX8Hd/3Tfj4J4LUA/ghufBiPgOia78dvKWV+R/nuPIBpAJ+BG5ffArCO27dPA/jn\nSd2Pwo3fnv/+Q/jWt4C/+It/BOB7ACYRrtv7/HeM/8+3+xkAD8ONczq/vpwZCwB4Ac8//wXcufMR\n//kxAKcLR92+/Tf+r9BGdx8+is3NN+CDH3T3t45vw12nFK/3bXxJ+Y2xBzff+LqfKzn2uPE3cHPy\nebh5k8MMgL8Pd40AN36/grW1Zezt7eHFF7+dPXNx8Rze8Y778PTT74ObB2/GxMSvodt9OntOGZrN\nabz4YvzdnTtL+OIX/wTAz4r2vX1gWXt7e3jrWzu4fftDAIBnn+3gU596GtevX99/5j/xxFMAgG7X\nfQ8AX/nK1+Hmc7gHvvKVnz5Qf1zZD+PZZzu4fdt9nph4/MDjc7QIc/kb3xgbdPAJw18D+CWEd88j\n0J97hruGYZn2Uf+DWZxHgjjkUy56xBQVrbkcAYM1zNvktLoc/YIte9KKOEXx1jBnK+Tt7m0KFjlp\n/ZBRNzQrmbRe8RY/R+JgqzA78KXb8Rz1Ig2VxhrihjgvlWfMUj65hwwlpzku8lZ/ei7rdef8eE4r\nx7AVfJ2KEUpmKISQk1pNrQ/S2i63s2VZXXLW6QUqpjCXY8a/uSD8wXrtrqfTqt4ip+FOnQnltZD9\nXFLas0Nx0ps+6c6bq1SUIaQyg7KoKVdIzwK5kTmeo17IkIR8rTXv99S5NLQ/jJUsn++NYC100hZ9\nN4X10kUnuEWq16cpyDDkGIVr4aLsTCbt4iyWabSUdOt+tFINtoyGVOipUyPXkZu/mlRDkxvUhMxg\nl+I+uONYIjPIObCqVGN8fM73K7Vg65k5B0k1tHjbMhNjDoMSwhSdyculGnzOsJbv40KQpsTX9jja\nOTqphubMPXPo9hl0wKQaBokQpoqjNPDDsUWOvGnkkJ1t2FkofZlqpKN8Ozauh6M/cLxkorBFnhIT\nKemQTmucdW/XtzW3MFjyZa6K8jdI1/xKjXGO/F70bZY6Su7PPDnS286cy/reVKYiZQ+nyJFZKQOQ\nZaQLGiaRRafGVmuZarUm5XXivKXdFcfIa5DOiV3fP+n1Pe8JHGchlBp1SeSlpIHHt2y+cL9295Of\nhHTAcsw58ohcTHB9OeLNhIvvB5YflaVql2MfX9NGY5KABU/8tv0/J8Wo153O2jnSXaSwBZuW36V6\nfWFfh+siRshsj3y8XCh1qdGYoomJFa8jvywc0jhbZCqNcqRJOgeurLxu3+Ex7Ts75mnOgSsr9/g+\nc8KcLQKmaGXlXmo2L3rt8iVaWbmHgu5/gxqNqchZMiUbtdo0tdtXIsc7LUwgy2va7Us0Pb1CExNL\nNDFx3s/R2DHy1KlpIqJEW36BgBmq11ulzoFlIevKnAOZMIVFQbjOOSJb5jQW+65UJ2exAUVPv871\nuvkTZ84cNtb1SXAOzDnTHkc7R1GuLlFrjayNhhhGnA37iMNdMRFbp2KaZ+1FziRDWlZZT3gPFUmd\nJBxMajgNNhMvjdiukrPK8cOa25dqfLUELOsUR62Q+tUpKlpOTif9Lj6YHOnh9ufIGFGcjZHb0yRn\nkZ0mPW7yRQpRPXqiztSix3Vw39IxY6Kcs/Q5bXajcTbJhKf1V14zts5r5PGCb3PsUDYxsehf6OwE\nWdxVcBa3Xd+GK5TLLBjPO54bM7Sycu8+SSmew06HZQs3RzYdGQsRMpwDnlwY8uIsva7pbkTaZkdO\ngy67aLGMLZTSOW42ihwh4V7+uxTujQ0aFNM6LJSXfR2c+TP0JxeO7zCh+KRFu1abj4iaW7zFu0GD\nNMOH0aS6DJGxPrRen90n/2UZA49K+5pGBKnivJaPZlGc64PaWJXMVUlXfpB6jptMnwSt9WFQ3KVx\nO5mGo4ERZwMRyYesJDFsrUoJxkJyg0qiIAkRH8NSCWmZOE06oeMwZJskrU5BapEjxTIMGnv8ayRS\n9qcnjtOIvOybRrQWxcs9tQa3KMS45jFInTfYEr4mxoJ/11KJs8NfjkDmFgs7pDslrvq6UoK7SC4S\nRVr/ZFIG16vJT9hKHC+yVlZe58mazKrIzkitfStl+G0hU8cShW36lLy6sXRRCjT5wfaAcVzy5zfJ\nzb1VqtWa1Ov1BBnvkyP1sxRIu2Ydzjlz8nwp3l+xox+X4yzSrk9lJE6TLsREPo6ewIRVShfYuu76\nM4q41LqDnHTik/de0YEyJvuDydowbZuYkJkreSzC3C/LGHhcUoNB4cjK4vwOk459WGhll6Wcr9Lu\nuyHfOAmSkcMAqFNq+Qfqd7tZr1gYcTYQka6Hc8RHI1xXBFlIiYKWOIVJnXyhz5Kz/p1Xjm2SHv6q\n549JX7QsW5BaSc1iHeQCRbKsb2sGsiUXEfxy3RX1sQyE9a1M0DjiwmUqkjiOZsGyENleLXQcRyxJ\nvz/ny75AjlxLecoMhZTm3D/eCWASlpOtpEQ/1TJrchq5a8DxqrcphIRr+WNYbxsvnGq1OVpZuegj\nW3A/0jo4EoiUELGFmtvK15MXDgvE82N8fIlqNSm94XLOkZt7XLbs6yyFxZm2MNymVLfKfZFSCdfX\nnjhfC62na+WDJXbb96kY51e/92IJFBOC+J7XzpuL5CAaqloGdeLML3gZhaZ4nIyL3O9z4hWpsdet\n4lXbtrLyuqTO4Sy0w1pHR21NrRKTOUdQD9sOF+0jXnS325cP1e67Zf09CZKRg8KiahwvDkKcLarG\nKx7X4aJD/B6Av4M0ggHwXbgIGj8C4NfgIl3wb28E8Lv+s/S4n0QxWsGTAL6m1H8KwPfDRceQx3fh\nolvMifI7cBEEgBDl4cMA/pHS7kUA9wC45Ov+A/H7w3Ce6vf58mS974eLfND15/0RXCSG/0Mc82G4\nMZsD8AW4aAnLcJEN2NP5R/2/CbhoHx8G8FH/23v9b4y/go4fhIuswPhxAH8rynkULprGGQDzAP7C\nt+ubAN4DF5HhZ8SYvAdjY5P4ngwEEdX/Sf//03CRO077+mfhoi3Itvwx3Hy47o9fhbuG74OLWtEU\ndT8KN29uAXgCPN5EwNe+9j4Afxcu4sUPIY7k8QW4sf+or196kb/Ll/kYXEQIoF7/c9y58xMAXkC9\nfgtvfOMGPvjBX8U73/lufO1rf+nPqyFELHjM9+HP/HdyHrwPLgpJ3GaHJwF8zrfpxwF8F//wH/73\nuHXrFvb29vDEE0/hN3/zX+LFFzsA/g1clIdnANxJxvAxAFOo1x/FnTvwfXgUr33tCr70pTEA2whz\nHHj++Uext7e3HylhZWURXyvcUl/15zyClZXF9EcP7bFOuHPnCTz3HPCWt7wDzzzzq/uRFxhc7yCk\nkRXc9V+Bm6cckeTt0KK7TE1NAShGiXBlXALQweLilwvnaW3ja8Ftun79Or7zne/B3ae7/qh8FA0N\nVcdA64OMdHFU0CJoABhRO74HF9GBI+X8NeKoMi8fDHMdTyYuI9xLJzFqyascwzLto/4HszgfGkVP\nabZ63VBWsixjkPGLF8VxHEM2jd+bWpx2KLYOs7WtTNPKWc1y2lq2VnBEinkK3v5b5CzDLA1IPdsn\nM+1MI0isKcdwpsWWKD+VA8hIINo2OuuX2wQ8oLSt7fvXoWDN1ZKSsPX7BjnLrkxqo41p0cM/6Hc5\ncoRMFS41wj0KGQ8nKd83zYK+oW71ujo50QunPF+kEGs754h5IyqDox5wHGTpSBfGJE1B7to+MaHF\nPOY+7GTqvkVO5uHmS2rZ29zc8Q58qdU+tZZv0fT0SuQQVxa3uJi4Iuxs1OtzPlte2JGRkSnCdZdR\nYbhds1E9w2hX02cLO5I5a3Heujw9vZIkclncH8tcspGq2+o5y2urtUzFe03K0WZHZoEchTU1tYwe\nRGbg2hE/14dpB7fB+UTE8/flKNV4uaPovD5DwKm73axXLGBSDQPDecpzQH8mJrktVt5yP0WO4Mit\nfXZMSsOgyRt7ieIEHfzgZdKWRudgB562OEYjY0zqOfqG5rTH2eR6FHTU7BzVI12SseVfph0qkn2u\n7xY5Esn9TNunkb2dpO0b4vddX+c0xWRLOvbp3tThxa/JUdL6pfaWZRD9pI+sJW5T0Ahz+3iMl8gR\neb4+GumU380r4bc4k6JcqEhNcjreOeK8SrXaNHU6nUK4sFinrGmQZ33kjzSrIct8Ul2+DKPHZJ/J\n5nYSAUKPtBH3bWa/HZ1Oh4ioEnGOoyi4+5C1uek5cVKVbXJ6/FQjORmdw4Ro0JZ2mWPb+PiScLbT\nJRFBl8sEO9SvEe00PXkOOdLq0o+nc2l7fwxXVu459BY+j4lLQpN3fKxSzihkF4OSnJT1IXWYTBfJ\nL0fnwLuJ0UTVmCb3LuZ30yky58CjgxFnQ4RiCCMZhosfkmx5TMkwkxy23KYvxi45gsphzdbJ6aU5\n06DU3fYp6IKZhHMkCi4zJXfz5CysbLFkTWwu6oN8+EsCys5yC/64aV+mdBRMrfAc3YMdE3cojcmb\nJ7k8jlJ/LQnioAgQkuAx8byHgoU9N15z5MIOamVrJHuGHDlepmIUEL6G61TMrtjyx8fXamJiiYjI\nWz7ZUU5G+uhTftx2KCy8mOjxuayHLxvz3KJw3re1SyG8Il9v3g1o0fg4Z8mUKbOlc22e7KWRNiYm\nlr1WdIGC9j1o9zudTsZ5N7bGVdeMdpVYwVq4P74e7hkwNrZA7falyCKcWgPThZD7O14oMNHd3Nwu\nlMWh7JyDZHredkLC50rbkn+2xW3RM2K6e67RmB2qjhQcts+FlGMfiHB/p+msqz+fiwunYTBsNIxi\nVJQ09KeLFFOvN4cmf4McH1/JBHpU1nUXnSld+I4dQYsNREacDQl0J8EVCjGcpTVRIx6zVCSB8vd7\nKJCslJAzOeEtf/684NvQo9iZqk/FcHRpchaZhEK2Q0ZruEFx5A4ZYYAJIzs9rlGICMK/p8kWuC9M\nwjg+dEoepRWdFyi75BYHFylYAwfJEjjRjCSeswSMU9HJkqUCHGpQc07LLTZmRfkaId3yx3QohCZc\n9WPXIGfVbPl+n6GJiZX9OL95p7ZbpFur0/nD12iN4jB3+rnxror8bYPiHQsXps9JN6aSa8x9ix32\n+LtabT4j+Yjnfbt9KSElvBvDn2f3pRWtVtvHO75SOZlE+r0WF1i/R9pUTHhUvE5SKqKXvaUez+dw\nLOOUlMudFY4lLIlULo50mSOjbv0uOv5OT5/N1lGVqBbJZnFshpW/jIo4D1tOPipKuhifKYRJLEMZ\ncUzjWb8SJRujup5hAS+NOXNH0GIDkRFnQ4JY/8nWqI54scgbXXuYSslGut3NpDpYdHSiNJccy0Rl\nlgIRZIK7QcFqq0Vg4CQhWkINTcKR08+y1bHly+pQSOJwIdMXaZHnMmVUi22K+73jf5fEisl32n5J\nkCdJb3NLqZOjdzBxTqOdzJBbpEyLsWCN8zoFwq5ZUqWcgC2jbJ1O5Q0ygQzvNmhjPunHl6NisIwn\nR9xTSzVrd7fEuRte9zuVjKs8l3csymI0nyU9HF6aDTCM7/j4EjUarOHf2k/WMohsptbWHImosvWt\n18cLT3m9tUVb8Tox+XMkoPi7Iz/u/uSMd7JdoV/aHHD3mCYjyEXqKCNY8TjI5C9xApRGI02AMjyx\nKZ47XLSOXPtHYaEctpx8VJRin6pqnHPl8twt7ooMp8M+LhzGKh7iqB9MusMIxqq2/+f8VgxHAyPO\nhgKcDo9DnHHMXUn8+IGbOpWdo+C0JknEgi+Dz8uRbmlhzMXZZd2jlq5Zxv9NtxV7FMjjZYoTu8g6\nljIv8B2K9drz5AjdLjkCFesxQ11l/WUpBRPyHjlLs0aAu+L4OoWX/BTFY5cuYrQxlvUvirJYv7xA\nwRlPLnrYGZG16JJwp+my01TKGlnjhccsuQXQgjiHZTeyDl4klJMs1we2Nkv9fSohYZ32ReX65dJp\np+PHc1w7jhdvFwhYp0bj7H4iFemwWIU4jzIWr2Z5Le6itPy11uRWsb6VSa3rR7wYrdXm6IEHHiC5\neJFpqWPilH8maH0tWnTDHKwyNqHu9Fnikkfk5CRVyZEmkZFjVxYfetD1G4V8YZhytDmzubntnQMH\nE+dcXcOEpAO2ThxxPsxCxjnzxo6ww0p3GO6dkMomLY7zUcGIs6EApzll5yBpreObnEkIb92zUxlr\nQtMH3iyFJB9MbDWHry7FMocccSbSLZ47FGLClhGenZK2TlLRGW/ev1y3yJFbOQ65NNxSG06kp3I+\n69sgZRyadGIj+Sx1tXJM5UN4lvQkKhwLu0uO0LHcYJeqReBgQsxzgjXq0gLO6ddTUquVl47hLIU0\n47nFRm482XLNc1cSZC6Xy+uQI+otf64k1AsU0pNr9fO17lO8QyCPu0LFxZurv9GY9XGqmYTEWt2x\nMU4YFMin5sB2GBJRtLxq98I5ajRmqdFYSPoxSe4eiqMxBIez8ExYWbmoWg45NnNRGlbclRhkQdYc\nCatIIALpycV8D9enqgOiXn6Y2ysr9+wvlsoyEp5EaOQ3jszi7sNUqlFGLnO/acRZxvM+KRjtjsTB\nI9dYyu3jhRFnQwFum4wjM6xTsCLJ0F1aOuMWBUc6tl7yS3SFghaYX1Ssg16gEHaMX36pNZtf2JwG\nO6fNlXKLsP0VCA9H69CkGlKrzNah81QkYWUa73lyi4QtClY8SRCZcDBZ4d94EVL0dnfjIutYJ0e6\neYxnRflbou5ctkKN8Kda8LIdAR7XNOQgW3AlsZRt1mQPuTEsu8b8uUvOWpymW9fGbIuCpvw8xYuP\nGXLONbxI7Ivy5QJqyY/3MsVzQC4quf4pcYxcsBHpcoftyArtrNI7Pi13MVGMtNpWuZ9TwlMkzvqL\nt9GY2m+L0yFfzhI+LcRZ3jlya789MbnkEIgbVKs1fdrzS1E4Qa1/B7Xc9ft9GhvT+r4cfT7oIiWn\n0y0jXC83hzjW3mtp4ImqJWjR5mcaDWcY7fQwOMx4j5o4H3Se6Qtf0zgfFYw4G/YRe4BLi+YUBaep\nMtI4TcWoEJMU4nxy+Cu2kMoXJpPuHQpb3GzRbFEg8C0CJihISST5YdmBRrjvpVgKwGSkRyGkmpSS\ncJ+0zIlM3rQx4C3+JqWxYIMVVtORM/ksko+ivCDVJPPOQErUtIfpMunW/FUqasRzIdfYeq8RW2nZ\n54XWvC+fHehS4pmWwdbhNHKHi00az5k0jbrsT3rNppPyZJ8WfJi6uL56/TS121eSe0JaqHkMilrF\nVG4RxkbTAbOlPhDSoH/k+bBNweGyW8kSqln0tBBx+lavW8Sl+uIyHXValy5DcWOZEkVHsuUc5rTp\nYUHYaMzuR96Q9R9GKxqngOeFW7zwOiihGUaKoC0kTool+m6Qy+NYQBx2vA8r1RjVta7XU4f2RarX\nTeN8VDDibCCicr1g7CTGkSdSi/A85S2EbIWW5CC3DS8tdtpxqbVUhkHLEbEZCsSQt9dZXiLbrFnH\n0i1yJu9byhiwPOJGZiwGWVK1GMFL5GQFXGfuXB43aS3laCNM+Ob8uGr9ZB2wnAN8bGqdTWUiGjlM\nrbVSZ84RTG7549IwSkwUeYx552ODQkKbK1Qub0nrniF9IcHzpUXXrt3w8ZtZ7+1C2jlrcLC4umOm\nKOj5c/KgnEMnO3tK63+xDy5ZiFwgSsfIWZLxrjnqRAqNtGhW4HqdEwJJ56Ib+8cP8xyRZEeTKwDb\nUeg5PjYOhck6+kVKF8hS5lLFgjsIQZsdFqu1WqtQx0EwLEEelbPYKDGI4A0iuCd5MXDYuNpczkEJ\n/qgWB26+Fuew4WhgxNlARGXhhorOQMGZpkuxQ5ZGHjaomJVrJnPsDgXCNEV6tAjtPHYuYwlJarHl\n9jPp5YgSWxSTqaJez5GcKXG+/H3O/77j65fhyjRSydEh9G3xQLzZcTGVDZwl3bqaOqyl7WRrxDqF\nCBCaVT7VrEs9tBu3Wo2vyQ0K8a41cpjLwCgXPBw+Lm1rl0JoN+5PGv2Edxe6VNR3y6Q1bO2eo7zO\neoaAMxkCFsc8DlZU1ohzjPHUaikXUTcI2N0n3sXFViCpsm2SvLn2a1rjeT9WXVUfqd3XY2NLJXUV\nFxVcbr/fp3b7MjUaZ6nZvLC/dT7o5d/r9ahWa/rrsKEmpmEi7WQg0leA+8sL+eL9P8hSO6h97prE\n1rpRJD3huqu0i+UwxVjmgyNJHLVldpCsxF0zd6/mJDKjaOMo+xnanUbVOZmROwbBhR5Nn6Pjd7tZ\nr1gYcTYQUVm4IY0AMWFg6xBLDeLQW+Hlnp7Poc7S+LAhTFcgzjITn7TiyfLkw++M8gBZpWJGQBnS\nbjY5P7Y4BlKuES/+LiUeKXltibFI9b7zoi4m8+lDnUn8ZDLGLYq1xAuZa8YElJN2XKIQcYIJ9Zgv\nr+V/v0GOlK0Tp0IfH+drLfs5T83mBVpZuejHY0b0PW2D/JxGWpF9WKcQOzvnqMjXUe4isBRFSiY4\ntJy2Q+D03ZxkxMk1Uit9XLeTVbBERl4PXqTN+mNiB8mgJ9YkHSn5TzMMamMQz+fp6ZXCfe1Ia5ro\nJx2HOdJT9u5So7GwbzluNHi8wjE5AiwJTjFL3Zwfn1ha5MZnhyRpLxL5PHHmXYHNze2BzmcSzgIf\nW9uHsbIPwrAWWbnbN8gh7jisuWXE+SAZCA8CTfPcbl86kNNm6JMmmTo+B8RRLgTCAlzuGM2OqKWG\nFEacDUSke4CHzH7pC6xFsTMZE7J1ciGs2LIqtaDp+Uy6d/yLIk0OwhZS/n+H2HKnp5/m8rVICDky\nyS8ElpIwUcy1N0deJPmWhJIXAqxP5d/ZsU7KR2RdTNrYGW2LwrY1k7NU2sBjkYvpzOPM4zZJToct\nndWxDwsAACAASURBVAlT4rRGIdEIk60NCk6Ari0TE8v7ZMmd26MQrSJHBJnQapbOHf/bOpVLLFIS\nHUejiLXjvJCRjoyTxDsUaZzkcE3T68rlSmt4ej12RRnubyaUWoSJQNSlc2fqXJhbPITPzeaF/XuZ\nSboju7LcWXLzjR0rWbvNshj54m0ljn/FNmjhyKRD7MTEOZ8FUGu7dJbt7juY5Ykzj288TzXyHrdb\nJ30MJ72Jy2y3Lx3bsze/2zc4ysIoHcxyKCPn2rVtNi+OtH6i3BgdXE6Tm88HjWoxLEa94HHvsGJI\nRcPRwIizYR/uZj5PQXrB5La4neUswdP+xTut3LhTFIheSkhkWDBJbtPvpMOa1FwzmWNyI8lnLpxb\njqAxiZKOe2l/dynEtU0t6kx4UueqNDNdTqtdtPLpBIPJjUagzpMjO2sUQsul5V5KzqsigeFxa5Oz\nxKfJbKQ0ZZLGxzkhiCOL9Tprec9RMUkIXz9th2CXgsSHiWgqF5KLDdY9MwmMFzbT0/MZghfIdz76\ng9wp4PZdEddCGze2Jrvz2QKqO8q5BD1axr3wHS900vGP+8pRJ9wWNDvzavcaz0W5yCxaDsfHg0bS\ntb0qcZb3bNdrtbV7L5X57IprnUq85ig4l8ZSrOBUGL5j8lgMd7dVyC7odhni9rXbV45cuxo7RRbH\nsAqZOg7iXNYf7dryAm6UyO+IDu53LmqHtoNyVJE7qvTnMNfNomocL4w4GyI4Z4lUY8h64B1yBGyV\nYnkGPzzZqWeNYocn/n6LQsi51LqtvTzmKCZIWxQItCaF4Ex4kmQtiePjtLrB6ZHL4n6mzlvScnma\nHEHjdNiso2YL/VkKpEUm9Ujbu+THLdVjnxXlMmFf82O/Q4EYS8ugJFgsxeBydygsNBbJORreoFgf\nzJ81UsfkcFr5fZVyWufpaZYrtCjoNuU84IXZBjnyvOj7wXIUacHdoRBRgrXRbGWfTT6npLrty1j2\n5clEOGvkCOo8TUwsK/3jlxvvoJz157DMZZJCKLwlchbcbXILGb7ut6jRWPA6Wm3x9jrKEdDpadbD\nLxJwiWq1pl/YzlDI+Bh2GzY3t731lL9nqVVxMTY+vuTvdZ4n533bw7xqNBb2nwtuSz5dHM/Qyso9\niRU9Hf+tQhKROFmRbNc2FRfZTQphBIk0S7Qjb3FUjFptkiYmFmli4rwP6cfhH8O4nDnTos3NHTUt\n+sTESiWLYK/Xo+npFWo0zu6T7dSaqEU/iY8pSoja7csDSXOv1/MW37BYHTaj5CAMOq/VWiJ3DwbJ\nV6Mxu+9QOyrddRiv/LzWiGfOshscA9f9fHcLsrIweXIn57B9Gz1x1nbGTKpxVDDibIgQW2+YrJyh\nYgxmJn9M7lbJWWQvUSCk25SXVbBFzqW4DXrW1AKVEjUmPkzqUq3oLd/eljieKCwEVinOQBcsY+H8\nJgXCskJBf8xOdVpM4tj6GpN3jmbBMYdbSX0xqYkt2btJXcWtajfOsgwmgTLUXs7CzddknYoWvvRz\nas3lhZEWCi9N5Z0SKn5xtCnIUG4lf8s5w4sj3oWY99dZxhnvUZhTfL1borx08TLjndaYuMj2yh2O\n1NotHSw16/6uUsaWUr8M15g6bGrSGW0BxhKpteRapAR0V4zbBPV6Pep0Opk6+O8z+88F96LvkrvH\nz1I8511CE0dgixb/Xq+nhLrU5n6Z8+YMaddwYuIctVqvUc6T1kQph5Hjwt+VhT3MExs3frzIduc2\nGgsilTdL0cICpxg9Y4diQu9I8CBrs5Z4pN2+NNKoFlWiaejz+RLJe3hUuuuiXn+mMK4pNILqFkss\nt9Kvsbb4cYu/7kj6Viz/YFkkGfocPj10OYZqMOJsiFB8IJ8jTkigv+xScnSaYrLM+uEFKm7XszWY\nX3RrFCdESaM8sA6aX4Qp8WUSnZLalNxolr+tkvNZMsD92lHOl9n9crIHJjlM4MviCktieWtA2WnM\nYraupWOkEZMNilOJ83nl2dTc79sUj1/aJm2M+Vxp5ZfWXk0PK/XMaaQJJkO7lFodQzp0Li+33cvf\n80IxWIuLGno+h/uXcxhNFwjch0UqLvjmyVl85e5DzgKe68MOhfmhzZH42nGylfKxnhdWNs50J+/H\neFxcvOtiSvNGY4qazYvUarWp0+l4aUTqHKyH5Av9Xfdzw+3EtFrtfZJRtNZ3Keis5S5HTlbDixq5\n81N8RqTWyHixH44Lzoapnt1d783N7QFWehepqMzJTrt2OYfGQdbNnFV50Hl5+URRdjQK+YhWX7N5\nsZRsas64Yezkzqdb/LHcqVgXz53RWYqL99bBybg5Bx4vDkKcGzC84rC3t4cnnngKADA+Drz00pMA\nzgN4GsD7S858HYCO+PyY//yM//wjAH4FQAPALwJYBvAUgK/6c6/7OpoA/twf9xFx7i8D+Fn/+cd8\n2V8G8NGk3vcB+IQv+8PKbz/nv9sDMAngSd+W6/6Y3wdwGsDD/jh5/pMAvgBg3H9eUMZh2v+/B+BP\nAbwAYNd/91rf18cA/DMAL/k2AsB7APwEgHv8OPB4DIO/Euc8BuDXAHwQwDtFP14A8DHl3L8E8CFx\n3GUAjwKoKce2AHQBbCBcB8YfijY8DmBJOf9PAXwcwOsBfB7AHbi+f1gc01LOk0jn2zO+/T8BN2/S\n6z4/oDwA+Kb//xyAdyHM0c/DjcPl5Pg/h5srVfBVhGsCAGcAvFu0k8fsr3w9PBYXKpYPuLE/DTff\nXgPgM8oxr4ccm6985acrlPsS3vrWDm7f/pD//B7Uar8MIr4fHwNwDXwPffnL/xF37vwg3P3yh/53\n4Lvf/TRefPEDAICnn/5R1GqnAPw83JzsAlhBu72KmZlZPP/8o7hzh+t/HG58vg5gDsAlAJ8E8DR+\n4AcIv/7rnwQA1OvfgbvWT/r+fxrufofv830AtgHcKumrHPunAdRQr4e2TEw8jm433JfuWTkHYLVQ\n0vx8E9/61mdQfEY9BeAt+MpXvo47d9K5+hTCs2gJwLvx/POPYm9vD9evX4+ez93uwyX9GA57e3vR\nNX722Q4+9amncf369QFnloFG07gE3/jGNwvfbW29aX8eAPF77Pz5Jp577gsApuCec38f4+N1rK19\nP771LSA8a98L4Gu4c+dn8dxzwFvf2sH6+n1H0geJ69ev4/r163jwwV289FKYK7dvu/k13DX4Hty7\nZ8N/3httYw2Hx7BM+6j/wSzOh0K6bVSrpVIG1nOmUg3NGpdutS+Skz5ozgts6ZmjWD7Bx+SSfWiW\njlnxe/obW6RSbTXroucppPLWLEBsYeQ2xtt1IZoDf69t31+m2Aol2zdFxTHLSTWmqagB5u3itqhX\nc/7TtqqbSns2SNcan6GiJZataalMguUqXG8aZYOtl31y1lWWYWiSHbbQn6bifOO5kIvekqZRD20Y\nG2O9NNeZXtdFirf8+btp31+28uSkGhwRRP6uSW34GvN457Im5qQaLMPg45Yy54axGRtboAceeCBz\nHM+zdExz0qJb3oIq6+iSm4+pNVjfMWHrKks6QkIHHvMQPUZa5Io7ZNo8YEfDVDrG91m68zNDwHgU\nVo//3tzcEdZ6juEdPzuDlbM4VvlsinI3Jjx7cjGqNZlNzrGtTHIxKEZzWajB4tjz7pyUR41GquFk\nGrHGPk36E7dXk0XN0MrKvYXsuJpjbpBzcF2jlWpIjELvHPxEZH/PDD7RcCDgABbnu06UCw0y4nwo\n6FtuGxSSm/Qo1nVyCDbWEsubVQtfx+SHE49wSDMtaLt8+eYc1lKCIzWcHOlD/tahEEKs2gsrfM+L\nBqmz5RjD7Ij3Ggr65VUKzpJpPTKiiNxCXiP3Ur9IgZjN+c/L/nObnCOZDAPI7Vvzf8+SS0fOBF8j\nFKmjpkb8OGKKvLbspMjb2axt5+1w7ueK/5dGgJglN19Y8sMOfdJpTJLpG34c04gWp6l43fn/VNfL\n8hpOTsLXTS4GmIzO+/Fe9aSNX6z8spTb+Dy/eHFx2l/3VQrxkHkBdUM5f5LSWOHOOZHnP89tN8bO\nwe0UxTIa1vjz8fIatJI6U8LIv8/QAw884KUOnMyHHX+rJh86R/U6Oz/yb1LuVKWMLZUEybjMOond\nVpK5FMtvNi9Sp9MR5N5pvUMyn56/brzoP0WdTidqSxyqkK8tx/AOZYawjEWdOWuQc/pW5+gXL3C4\nzxqx6vV6+yH8BkWDOKgcI03SkhJp5wDM74ptCveSe87X6wv7Gvf0+lXV8/b7fUFuw/MrlbLEfcnJ\nSMKzs16f33f008ZAcw7c3Nyh6ekVajYv7Ms6DotRhKbLJdUyHA2MOBsyxHmVHOFcoOBkJV+8/JJd\noaBN5pd1WhZbdDTCywRih2JtIZ+XrqJbFMjrhqiX6+v6NrFFSKZ25rLT6A7cTtbBbZEjj+vkLLI5\nKyc/cFOSqj3EpGZZaoj5+B4Fre4qcZiymOQxgU7LviL+bvtzZynEadYiXyz467ZAsWV0yf+taWxT\nXWZKuplIaOTIZUOs1eZ99jnpPJi+3MoWTssUk1C2GhcXI8FqyS9dbW5eKfSr3b7sNYe5cHmcsW+X\ndG92npNzFBZtKdlPo5BwhAterMZWR/dyl4sV3r3gPrIj2qporySxUpvc2y+btcL6rgfPSbmwSRfK\nzhGu3b7kyQ0vyDQS7eZIoyEXtkH7m4uKkJKXEF2Bw+1JDWuX5CKhLCV30LreU7jG4+MzA56P7vly\n5kzLk/tAokIb5fXqDsxmmCNQRxlyzsUWX1DbWOU9MTGxRMV3w3p0TIiR3qVgNCl36ivWm3fk09uX\nI84bhTKqEteyCB2HTWRy2DIsHN3xwoizocSiktv2Z+czGSlgnvKOeyy50B5mkrTw1jqTxVQuwOGp\ncham9CU9Qy4b3joFGUBKEKSDYhotY4acBTdHgjnMVbo9rI2BdARbpqKldpV066/crmbHMu0lHh6W\nwYqaOmNOUrD4cqSC1Mo+49unyWSWqUjspCMWW9rT68wEN7xwnPWqGAasmJY7/T11hGSLcExOW61z\nPsqDdArTsvYVyXu7fcVv5zYpvOiDXCSQ3FwKdCknalEguTIyCS/QmIDzjoxOKN32v/xdnpdGBOH7\nSLt3+foE8ugc3XIJgridLMGZIZm0JE5KFCyvcUScLjUaZ/cto/1+35PW4gKh7LnkZA7bom3pjoWT\ndHQ6nQIJKY+uoC3g5iOro36NndUyR7I0B75BhFezzKbh/LToFgcNNZdm4yuzXGvj4MIfppKu3eiY\nEKlJz444aEw0C7427oOlGmlSoViWMmgM9Tm0fWhrcYqDXM+xsWLmz7GxU4dqhyEPI84GIiLlZcYP\niT7FVs5+coMycZEW5xWKCQq/vHMvIPmZM53xyzcNO5cSAfmA1EjCHBVTGafHcKIP7QW6QI7AM+Hk\nFwNbUNJEJ3zeOsWh5+TDWyPUS6RbWGWUg2VyFlAmUExaOSzgAsXxpfn63aBi3GaWiMi6dvz/rKNO\ntcY90snnvG+DtOjLl2SRlLXbV7zVOdWcTlKwmo9RcSGRWuFZItAjt6haJd5pGBs7Q2FeckzptK61\nQtuC/jElqiz1YIvqMumpquX4s5WXrW3yPtry48SL0OIioVZrUa/Xy+hiWxT8A7Q5vUp5q75rKxOl\nYAXWjlsiYJ2azYuR939YYGuh3spDhVWx3gWSnJIwvi+KY9ZuX8k+33L16eM3t39cKlHQdMgaites\nG0UDGfQ8HhQLuqxfVZCzwufO1xcyO5RKkWTinnBMbgFXbTEhYziz/CN3rFx4bG7ueCnSOqU7KAfL\nNqjNx7j/h9kNOOj1dH3kZ7B7JzQaZw/cDkM5jDgb9lHc6uKXoZRYyKQm2/7lvCT+Z0InrRBTFMhd\nqrtNZRBs3eIXfqox5Re1fImx1Vezmp1LvtMe4POibo2ApDKMFQqkcdXXrxG8bSpaY7T+so5as1y3\nxbXgBUWXinGX01i9l6nYb43cLIqyWKO4SiF29yKFlOi89ZyST14YyBTVTOrzFr2gc2bJDctRlsnp\njScp6IGlfli2f42Cdlxz4mOLmLaV2RJjIBdWOckRE+Al0VZ5PF8nSSTZCtujIK3QxlEjxm4+ljuU\nzZPuV7C1b+FNHX8nJpYiXWy/36fx8VYyDvw5LJCYtLEet9m8oFj1QxuazQulJDEvw3BtCJrksNhr\nty8LElWcV2WkJSePKC5w5/y1df1mHWyr1fbShPge1siwG1MpyYotoIMIUVV5Ro7MVSHnOUt6WdvS\nMXSLmxkxB2ao3b5cuK6aA55WV87aehgpQ2qJlmHnDl6Ou4ZO2x8/d8rCCA7CQWU57v6Nr4PM/GkY\nLYw4G/bhHvZMfiVBJf8S4QgUTA5SwtEiR4KaVHQOu4eC/u0iBTKh6T9nyaWwnqZ8coJlpQxtey6N\nR5wek26fyz7lMpyxrIJfuEwAU/LUSr7X0h/PUUjhLC2saTtZbysXNmlZUjuaRkbRoiTcopCAhPXk\nabrmnaRP6fkXkn6vijJyfZGEksed65wmJ4/h6zto0cPyEi2z4VkK8YG1c3m8gj7Yee5zNrH0+CsU\nLJ2cKVIbU+m4x5E30hjT6Tlp1jxp1ex6eYi8p6RmtDjvx8fnosgTQce6GxGH8BvPaXkt4zjLLLOI\niXiqIw99qtcXsjKGqo5qLutfHEmBJQzt9qUoIUYuiYSMl9tuX9rXJBctyRxlhgn0OT9e81EdsdU9\nWNalg2Poi9zxqU6IDkOc5YKrjBwWJXqxfKIKiZ2e5ucczx/nI5AeF5wzw6KIU9Hn2nOQLIgHId7D\nkPLiwqG4oN3c3Ckto6zegxNndtiXxHm+UjsMw8OIsyGCsyAwIZBbyz0KlpicAxi/HKQFjKUdkpBK\n+QS/dHcoSB9OUbA0aiTjdaIuTU/LzobcD9a2bfm6HhBlp5pp1q6mkpWUGKXh+jRLsmZBlgSH00Vv\nid9O+bZpCUhkaK/iA7soe2FCxNEbNN2yS1KhW7BTspeGC5R95795sZMSBSbVNygOXdejeC6w/IBl\nQ4OIs9QSp7+1xDGptZ2jfDC5KdP4c1kpYeK5lVq6p6Lt4qK1LbcA0DLwycUcL14cWZZEkbelOQpF\np9PZj7gQIl7E/Rwfn8tmYgu66XgLuvhil4uDIrGvolsuIw0u0kSRmBR1wDte+uOIAy8ciuRQ7szk\nZCzaHArftdtXqNVq+4geu4W26X0pXvNB1vFBJLLf73uNcT7CShUZhKY3T8OxSWlLXkc8Q8AlajYv\nqu0fFE2jCmkMlvz4Olcds/JxLlqj2aqeOoAO0+bB9ebGt7pUw0U3iZ9D9bolQDkqGHE2RAgezF0K\nEohU7zmrvggcmWMnKv5N1yLG5LxP8YuLLYhySzt9mZVFcGDrJbc3jXrBDlUc2i2VfpRZsWVUBNlu\nDrXHhJytjCmpklbdVO8srbAaYedID23S4x2n5D3tV6oZ5jqXqUh0c3rxXMbGdOw1+QCPC+88bIjv\nUvI6RUXnxlmK9epSI36OipnoZLSNYpSDen3Op9xm/XdKyHdpbGzJkzfNSfQchUWelOOcil52mtY1\nbqvTEDcaZ6nX63myXSYfWs2G+XKOZCxbktZoTX6ilX2WQuaxxeg3nTjf8uN8wfcj1r+XRz6IiUa5\nM2A4Xqbt5nFwxxW3zPX28kImt4ApJ+5Sw5s+Bzh7n6ZRLnPw0zDIShpLQfj+YCLvNPQsySkjrFXH\nvXj99WdUvT55IEJZ5Zzcda56fr7O+PnDRD8d4/LY0dWJ7qD7YFhZioWjO14YcTZE6Pf7nkxIK1d6\nU7KGWYtO0aXYCqfrH8PLWTq6ScvzGoUXviSICxTkHh0KRFRaygbHdnUaYNlHdnA7Lb7jsGzpFjY/\n9CRhTEPnMXFLJRzSuYkf2Ly44NBa/OLTyuOFzIb/vCL+Th3t0qgZ/BLQnPtkCDOivHU6Df+VjgOT\nmhUqLhpkGvX0hc+RKlJCxgskXmCxUx2Pg1w0SIs1yJE5GWVE7gBwOUya04UQv6C7oj52+uNredGP\nxykKTjljBExHTlbunpILjiVy90g6jltCCsD1ai9EV3+zeSFK5BBC5aX3Jfe9qvRFXq+YEDi9qoyH\nvEAhhGKcSCQlGUTDp3/Wdc+7lEZYcFKWuNwQai9HjtN7rBW1X3MMLCZ6icdM6lvTvgwTd1l7Lsuy\ngoEj7luQzqTPjm50HauVfxDi7O6Vg5DYKiS0LNX44YizrhWvslNwEKJ7UEt1DhaO7nhhxNlQwMrK\nvRS/1DUSxREGWhSTGN6uZ73VOhVf5kxI2Lq7QjGBZvLT9fWHrTlXlpa4g4kNSwIGEQRNw5oSpFTn\nzYsGfqCyZpl1tGl5bPlbpyAVYEusJh9gHTOPpbSqM5HvUjEEFzvHDSK1G+TIXnF7NSbP2kJgkULE\nE/2lHCzQaxQcAy+LvubkNTz2c37sue1NUb6sky357MCWEhkeE7koalGjwQuL1Lotrf18fpoVTt+W\nDslFVinM9aB5dY5l2xQkOXxtNGlDINByvsWke57CglE7V4vMskXj462CTrrRmPVZQuU1losHt8ga\nG1uK9KhOHpDuZvC9EEIeptvoTlJRHlqNj5Xb49JiGrLydcktstsErHunvbjfzeYFbzWU90u6M8PP\nG/aXYIfn+ULbQ/3p+K5G/c05IR40+kXeIlx8rjWbF1RyKZ8DbIkvI3rVpATpPRGcNg/a30EktExT\nfDipRnEsqxDngzotHmY+aNAjEI0duDxDOYw4GyIEixLfgFMUMtnJm5KjInAaZHbm2hEvthv+82kK\nEo7X+fPk9vgOxdIQ+ULWXlRs6dJeDkz60vbyC5MJmpbdT7Mkr1JM6lON8rofo1x6cO7Had9maTXO\naSyZCMuXRJ9CdAntnB3SCekqhQUHL2akFZwJHFux58ktZLSMeWsUS1I0C3ZO2sJJVnLEmb/bokB8\n5TVmC3GLgjWbrb6xPtN9pxFkngc5SyvLGziUoDxOa/OFpK/s3HhZtHeZ4oWTPLZHWsKTVC4wPs56\n43QBpY2fNj/iaCcu098VYdlmTX1a5lnRnvBy1yN87CRtCSRDky1oodX4+VO2PR52xOQulNOVh4XR\nLWLH3PHxOf89L7xPk1w8OFLNPhDxXKkia0gdEnOE6DAWRu1cl5Y8Xdy2SuQpfE260fN9mAga/J1c\n1DjClsb0P5M9/7Bw82OJtLlR1uZBUpXNze3CuAySahyW/I5yfMIzQT6vzeJ8VDDibCCicBMHa0Xf\nvwyZ5LGUQkuDzKHAmETJlxBbQWW8y5TUrItzNeKnvQRy5Ictgaw7Zc3xAsWa4lT3LK12TILZAsdW\n91ySCCbr8mU+T3GSELn9zrKLNFReSiC1scr1m4lrzhqc07mmllUmWqllmh0ZeXEjpROp5CUlgi1/\nLoeTSy3IcuxviPrOUpHkz5ObS6tUtIjzHF1OykoJXlG3GrTu2/6fvJ454pxLfsLWdjnXzhFwnoKV\nXGpR42vmiB7LH3jstHq0uX9GmQNFeUFsleyTbrHi3YVwLdkRL0/MisR5GNKYkyDI49ttPfweO0fG\n869YFpPLzc0dT4zkQjsmp2XPSo3wVJM4HJ44s6Y9aPQ3BjhEuueAFhauajs02YwLg3aGwqL+NJ05\nM3ptbarlHyYKRlVyW2WhIM8dtdziMMhF9zEcDQ5CnBswvKKwt7eHt761g9u3PwTgqwBeAPA4gA/5\nIx4H8DSAywD+GYBfBNDxv70A4F8CmATwnwL4OoCPAfivALwbwDiAn/DHPgJgHsBbAFz3Zb7fn/NR\nAM8orasBeFR85rZ82pfHeBTAOQDnfR/+FsBzAE4D+Dl/zGMAln3dLwD4FwC6AL4H4Id8Ox7zx3YB\nPOSP3fO/vV5pH2MBwJ8DeNJ//p747fO+/F+DG9MXAHwcwFkAP570oev//kMA27499wF4O9z4nIYb\nV67nCwC+64/9OoC/BvA+uOvxLgAfFuU/CTfe/4P//AKAP/Xt+xDCNeX+z8Bdu5cAPADgv0MY88fg\nxgb+vP8JQB1hrDtw1wkAaqjXx3DnzmkAFwH8K9/3nwbwTYSx52v7dV/W3wHwS77MF8TfAPBjAH44\nafN7AdwGcMr/vY4ifhfu2rxXfPcIgGu+f48BWATw835seO69FsX5NqOUz+Dx3IO7FnwvcV2f9nVd\nhhvfj8PNoWuo1/8tgM/4cbnsz3khKf8PEMb3UQCX4K73v8D4+Pfw0kvvhRvDd/kyHvP1AsCT+Na3\n/hLAv/ZtvO7rfgfCPfguuPuYf3f40pd+H1/60tfg5tgLon3v8u0Jf09MPI5u92k88cRTJeM0PO69\n91588YvF7xcXF7C4uIBPf/ot/ptduGuYHncOv/7rn8SDD+7ipZc+CjfHFwvHvfDC7+HBB3fR7T6M\n69fdGOzt7e33p9t9GADw4IO70WcN3e7DePbZDm7fdp95bKogd+7169fxpje9SbTnw/vt/NSnwrjv\n7PxjfO5z/x7Al/GNb2zguecqVVvAE0885d8RywCewp07r8dLL3Fhfw3gdQD+Af72bz+Gvb29/bYc\nFuH99HYAn8dnPvM5fOADj+Kf/tN/OkSb3b14+/Zr8ba3vQef+MQvFNp3/fr1St8dJdL5VVa3PBb4\nfxE/nx4B8FdH1UzDQTAs0z7qfzCL86EQr5z7lHdIklY2tkizMwpbVTfIWcu0hB5sSZThk+bIyTdS\ny6q0lLClkR2D2LK5TMHJisva8Z+nMvVrltkWBV0zWzil8yBbrdL2Sa22ln1sS5SfK2OW4q1Olil0\nyFlG2SKmJ1Jw5z9Azjq7SiHxieZ8yJZLjmjBFl1N9sLt0qzCyyQdMl3WuZz1lbMlpvIZvpac1jp1\ncmMNa5nFN7W6tiiW1aTxxKXzF88XTfaQWmN554KdAOW2dJroZ5LiEIdau135zeZFn/GrK+pKtdys\n218Q38/QyspFNYwYsEXXrt1IokFI2Ygmo7lFmsUVmEu2xdPwdU6/LxNecIQLaaEb1uqXbo/XGsin\n7AAAIABJREFUanORxjrdrpdxq4vnx/eLHv5OT9ueOtRViZSROhPKMGybmztqnOkqGNW2/mHkBW68\ntN2p4VOLDwOt3lyqc/3c4nN3VKmxR6lTHvY+iXcV+Flx1v9zO4OGowFMqmEIDybeEs85zs2S29JN\nY9fKtL/8mZ3iUmc1KWfgJB0y6xtrf2fJ6RFZbypfaJIsy+x38mU6RzohZJKTI7mzFJyETlO8tX7D\nf88puu/1Y7VGOnHmeNWnqVzfu0ox+eHQXrIvZUQsjbcsnSrT6BVr5LZWpda3l5QhIypoiyfWTU9R\ncIg7qxy/TIEwa7KUOQoJbljGwnG8uxTr0LV+pw5q2jVYpxCXO+fYlX63Qylxc3FSU118uohp+bG4\nSMVFV5E4h4gYZcfy4oOdLN0CKI5wIRc33UROoS32UhnNDXJzOZa+1GqT1GxepImJZZqeXvFkPF1k\nrO7H7SUqvtBrtTmanl6hdvtSVtfM56Xku9m8IKJExNroNG61LLNqquvQVl6Y8hxs+XkRE8Hi1rwu\nKUn7MkpyNQocxqFNj0xTNLSMnjiXy3fK2uwWNwc7v0r5o9IpDy9pksfKZF98n08fqj2GPA5CnE2q\n8QrDzs5VfPrT/wuAN/hv/i/oMojvAJiC225/DPE2+eNwW3iA2yL/DwC+hiDTeDvcNvq7k/N+DG5r\n/ofgtqv/FG4b/z8C+HsAPufLY4kF4CQH7/Z1vgtuW/uLAL4fbqv5YTjpxyNJP34cQVKR4vfgZA1z\n/twpAGcA/Iz//UcB3AtgGsCX4Lb73wm3Vf1eONnA46K8R+C2yuu+nL8LN46a3GMVsbTh2wA+Isbp\nSeUciSnEY/o+365VFK/To749H4Ub0w6cjOAH/XkXfRv/QKnnqwhb8bz9vwB3jR9Hcavwu3CyHq43\nxbrv69fhrv0knMTnc3AymjO+TUBRKvEInIznSTh5zq/ByVBS/D8A/hPfZk0KNIf4ur0Xbi7+O7j5\nMA2ivwHRabitTymZ+DycdESO75MA/th/99NwY5C2exzAe3HnziyAL/s6n0Sj8SV897tp+87DzfUP\nA3gL6vVH8YEP/MT+Fu4HPtDFT/5kF3fufB+AN6Nev4VvfGMDb3jDPXjuuUfgJBypDOcphHvpO3Cy\noL8GsCPGaAdEn8GLL37Af34MwNvg5ug1cf4qXnzx3XjLW96BZ5751WRr3L3Sv/3tJ/Htb/8xxse/\nimee+d8L2883b97ET/7kE7hz5yMAgM985lG88Y2XcN999+K5594J3mZ/6aWG/ww8++zj+NSnnla3\nshcXF5JvLuMHfuDL2Nm5ire97T0AgPe9751405vehPX1dTz33G8D+Bu46wn/9zsL5VaF3N5/8MHd\naDxu33bygePc/mcMIwPQcP36dbTba14mswfXp7fDycUeE0c+gp2dfzyyNnW7D+Mzn/kfcefOwc53\nsr2iZGcUOG4pRx4TcM8beZ8/kjnWcFcwLNM+6n8wi/OhUAwqzw52qbc0W9c06x9b2zhihrZ1r1kd\n2eqchjmbpnyyC+mIxBY5LTEHJwxJM2uloezYwrpNIdtemm48Xc2vk7PcLlGIZavJIrgvHPniEpWH\nIdOcwbgvbJFP276uXIsrpO8cLFNsfelTsOovUYg9vUF6VkBN1tClEJmDrcYsYchF2+BrtKJ+76yb\nLH1gx6NTFNJnT1MxkokW0YOjvkhZjfw9tXZzmDvN+fEWyS18Xd7AjrDSCZIdKuUclHNxgYANarcv\nZVMgNxpnh4oKEOId53aPuP1yB0j2rUyuxecXHQf1iA48R7YKodCc1VzfGYq/1yNLaNY+bcu70+kk\nc2PSS4zCZzfePM9mo/OrSjWqOgseN0YhK+CIKm4XgMMP8m7ltr/OLipNWR+HDU147VoxvXpOFpMf\n/9FLNUaNw0s10nvIpBpHBZhUw1D0sF/3L/M2xdrfJfEQKqbXDTIIqemVN7L2omZJwE5yfNm2NRMa\nritN3sEPkl0KoeDSaA+ajESSU47esEJBC5yWn5K0bfEbyyV2KOjAZZ9Z7iC3zZmwp8R+htxWfdv/\nOy3GepLiKCdMxlr+WPkbp5mWmmKNbC4l57AcQxtnJtgpgdXmxxQVF2NTSplbfrt9gooLlm1x3OWk\n7Tx2MiTTeT8OrH+eE23YpiA72qA4YyS3O52XTCr5OqRh0TjySJfqdV4ELStl8Audr8MW1Wot6nQ6\nPmpEnNRnUMIMnbBySvNUu7tOIVRkeg5rJHOxgG95XXYx2yIn9ohf6HKREEu6OJSbfq/foDh0WvEY\nmcEvJRnpFrp7xkk52rpSZ1hQrKzckyXlKfEfFOqsjAwNu9V/EGlA6H/8vKkqdXDE9XKib+eFcjGT\nXxlxjsejXD4xKIxhlUVJ0X9nK0pOdNIwzPWVx+q5EiaPqdWvPhhxNiR6yFQXKzMCyptzjYrEkz8z\nMZIv0DlRBpMYGSprsH4wkNXUciitY9IRSoaAq5IUhV8suRd/qg3VFgZnSY/by9bDnJNganlep5Ba\nO3VwO0Uy/JP7PO8/d6ioj2YHPB5v+fJLdcHauPDiY5ti5zp29OT06ExKtJjWC+TmjCQvXdJJ5bwn\nVPPK8W1x3ArFznmaRZz7M0OdTmff0hVCl0kSmyOf2ncynfcaxSH3tpNztXL5u3VKna02N7ep0+kU\nssyVvVT1EHGc/nyX2O+g1VoWcY3TvnUpLOi0RYFLxtJuX6ZGI/VzcL+zJdDtGEiixovRmLw1mxco\nOP+m90J3P6FHu30psk46Elck7jnC4ZK/pH4Z6VwJO1kTE8sje77mrtuwVuCy46vWIZ8zqR5bs/aW\nkVy3cCne67Va3nHv/2fvbWMkza7zsFPVNTVd3VX9Ud090zXu6V5uRct2o9uesoikg/5RjDzLcX7E\nwE4FCCIzLok2GcUMyV3W0ssFaXkRFkET5qxkKgI2w1iatRkk/iHQoASkRwwdMqAA/yDIMGspFC2G\nIERvSHBjybStoZbLPvlx79P33HPPfau6p3tmxX0vMJiuqve93+97n3vuc54TA9li4DsJGE8DnM/a\nge+80v1ypZ1BRL4L5xh62mU6+1QC5zIpB4ocoJRqGrDYyAcVAS4OOVhMpbPNWOS3pxZKePdrqoZF\np5DgTy74FqBe4gAEdHCV3DF17qgZYBaLj0U72edgVbOss9rJRYJ8Dcqvi/rrfCwrJ8CsRR9AUA8A\nl7Goi74+58w24BBiet4Ym4scaBSWFRkOlfp7DRxhhR74/7UzKUCfRWnBuOL6OXaUFQe6Ya2KgTNO\nBuzoYfY8lNSUIacWeyiSoL8POI32iLGyA+dINYZud48bjXWWETr172lfwcILUCpDj8vNj/U35tis\nB7bS0bfJRNtcqbS504EqSGrJjEHNmNMALn0m2ud6Hc8K3hWoc1yfanWZu90d7nav+TrZJxU5cGRr\nP2un4z5LvXDL8XCad6mcY0VAqAj4WUAqd30ROMydRFSrKzwcDgtB5SSQ2+v1/VjE33e717L9k1qA\nV6csP27vJKrHJN3ns3Tqu990FuDebYYlrW2Oa7XFc6pxmUrgXCZm1sd5+kW7zCno0cfxUtoKx89Q\n6NDH5xa4A1cX3FxEsLPoFBbY2DLqiLpfYLd4ywV8wOEYfYkdUC2KKoc2IiS0plNc5hC0YoPzC/sC\n2xuCkSpjUZWt84kXwgD+LKC9wjZwAriUgEsDRfAZAWgk4MiB+TbHwWbAmR+zHezD4lRLykgO7OSk\n5KB+ooHkLNsqLegXqI3I3y5ysNhuiPFH+WiPfC4smgw2lgh+s8sh1Llu4zUONAW9GQwWw0BVwBwB\n7UGr2MgNbFpfZx22pSNt4IXnb4EbDR1xcHQsRRciE+ox05uheAxbrc1M2GicmsDir6kDh1EdJoHO\nSsVF2gugY9+XEVOfZOjwSe9QB4DiMcsBoWmBcLW6fAzEreuLALg9fvtq/qT3MetTjBjkQg7Obdri\nk4dud2+KPnLXT4oiaUn7Tbp/GnrMeVmhTwPIz4IH32pdYfc+WfH/drjVunKaJpRpilQC5zIdp8PD\nQ7+IaGcoHIFra25qyUi5k2NOwYhlKYRzoUXBgJUbFi9NGUAIbcvKu8+Bp4vytcVU6kVraoSmaACk\nLHLsQKmBNCyi0vGs7esOgC0BDjYtCK9sL1ihrhpQ4OVrcw4D6LIk2eC8CGsxgJTkL+O6PufDfsvP\nV8WYweFuzIFu4cYSsmLOcpkD4+gH0AsOxWdpDcUGhNk+Ochx79scOO1wqMPfsKZazqfaiq7Hqq3q\nusJEA65UJEjdUXmvcqDW3Mm0A9/tqrZa16ZUED1/nDXWdi6yKSArHEfZlJuP8AzU62s8M6PB2aSN\n4IhrtUsZ4CwdUfHsgy6waoyBCy/e6/V5PB5npfvqdWkYkOMW6ldkCUYKAMi2zlrv22nDczvr/JLS\nr148tsLnykupGvJ9lvp5yHZqPW03trtcra4c04fcxmmDHUVtk4kG3GpdnbjOnIbHezpec3rNeTls\nnhaQn0V97MifM6dtSpkmpBI4l+k4wTvfhVEFcJJONAAARQ550noIgKiv22XbYUnzXbc5Dv6BhR+A\nVNMvdBhtONVhgQW4So8XYyALEGxZNNE3j7EDgrDY5igVGoRjA7LFsRUeAP8ix7zjQw4AbZ6dVVQu\n8kscrGXYZADMA4hLEGCFm77CQYsaIEg7MAKQjPy4aKc83U998TfqBufAXf/36FhlwdYIbqk+Q3lr\nnIJNgDaAZ8v5EiBBz+d1f4+ea3KcrLk+4KBDLdup+xZ61xtMtCh4xrhH87jxjOXAMCgNTXZzUI6N\nflYsALrCso0ubLPV9qYHT5aj502Rl7PwWsf2MzP61MDqI0sxRp+MYNNnA++wGZHPQQxiut0dtoCi\nvWmI5y+Am+sP92xpS3QRcK5WVybqVxfTK9x3Tl1C0l5wkhKfrvR6B0kZgZqEuR/fI9sTq1E4v4Vm\ns5NYdlNjx8K5hNxO+/j1B5xPm+9ZWMBLVY0Hm0rgXCZmthxBAEwvcSzNte8XJMt5SFqCg4XCXvjD\nCzkc6+tF8YDztAAs3tpCuO0XPdBFAggIEZUsSgeOgOFwh3u0xeWi+Azr3SHnpd80KLop7pX5IsIe\nLOgAo7r8Boejfljg9SYCdAFsZDRISZ26Ys6wpIpI0DLiAEbg7AUKg+asy76XwFv2HUC/vG8g/t7k\nAOCtOYTP0ord4DwneeDrgblhbcg02AZQ0uV3OczNIbu5Zc0ri54j+YhyE4I+vczt9mXz2N/d2xL9\nK+kQu0zU5G73mrdCXmD7+en7eoGzDgm2i5w6nd5hmzKF94AD+r3egQd0uiy9ycpRlVAPeb+jkYRA\nMTbNq1JpH/NYU4fPcF2v1xd9un9sObXHbeO4fgCUqWxnClAdsIbhABZdN0bTSLSBk5tz6LMt8fsc\nlFJuZsua5OwnreLTA9Q0n1Zr88Rrz7S88GlA5sOiatwPIL9/50A78meZzieVwLlMzKwfegBgGc1v\nmQPlAWBgzG7xlUBYP7wHHFuXVzjWONaRqHSo3NzR7gI7sKIXZViO9T27HIPSprpPS7zBUjbmYK0G\niDlU+eq6A9zvcApSQZWQC92AA1Vg7P/fYHtBb4vrEfVM9t8+B2UPC7BJoK9BIugSllUaqh36Hulw\n2PHX7LDj6WKDgzllgS/dnxibAw6SZ1Z9MM6aN52jEGE+gY6TOyHA5sDV0Z2+wNEPYBW/Y1OIub/N\nxRrdeg6D8oANYszxvn79uqdKYKMEoG3Ve8wOtFwVwMsKJY1nWHLZRxxOA2Q/YkOrAeMSS663cxR8\nLFPWAacKPDgRCHMBCiIW8JCgYjgcJnrVUqovgKL0vQFQKu9311pSXu4UqdnsHAOZVNLOWdpl2ToU\nuLvejcN0Em2By9vr9cWmwX1vU2f2C6X5dFmuHen8L5KCs/J0dUnHrNN5bOp156S8cNxT5Pinr5nW\n0n+/KTeOD8IJ0ZbubJxbeW/0VALnMjGzBs59sfDER57xkfAOx9bfnPYrAF1XLJxtjvmRGkQuZ8pf\n5gBGLGAJIGdZ2fSiDiUCHXxllQMAyh2bSqAHsDPHAeAM2KaEwPkoZ6GVf1shpAG4ZH/kLKyzHBxG\n9jnmN+foB3Ns6zLrY3OMAUApaCnyuguiPRZHWB5ty/aBtiI57praoy2uqKs19jJ/gGbrOkgAwoI3\nx5WKPhnY4qBtPuAUdCH8uEUT0eOIMbGd/4jaCihJ51Z5AgRA7cbeAUN5snLItp74NgfQbzli4lRA\nbmJj57lKZckHF9GqO+hD9PkuB1140FFSgDyNJVCDHuuzFRAm52Dn6id5/u4UR5dvOcI1GmuF/FvM\n8RxVI333xiBWt81ZyGPZsVptPgFok2TmNH+5Xl8zr5ucR0rxaTY7E9ebmD5iv2cnWWtfj1JzRScH\n51k/W7qzpGqcVyqBc5mY2TrG28++0ILzHyyoOQc56TS0yI66AFWDETsQctG4B+AZ5QIgwLImgZ4F\n/iwJMcspDkACQFQu+gB9OYu3BIDrHDYS1zhYeq1722yDFAnuoAwx4HygGXnvki8XtAkEUoEiBI7s\nYWlMAVBsnRypcnMUBLRZcqrn/L0rHCzcOZ6ttMaifaDeaIpDEf1E0za0VjUALOgs4K9LK3JRcB7d\n1/KalnGNnn/a8g8rNiyvNtgi2hDAQm8eQPeQgB3Se5g30oqXm8eg4awbv6N/9n07g6506rClrc2Q\npjzkdC7NeR3oFFCc1FJYBJ4cgI6ttpaShIsgCJrKOhNd5FotBbo26HZztVpdyUjeubHUzoGyHTm1\nDCs5ukjsyKhVLKalM/R6B9xqbXKrdfXEsnsB7F9hDdiq1dXCvGzKyMmBs7XhKNLyftDpvLjUVgrr\nlXznXDiXsspUAucyiSS5ZsGSZi24WIikAgUA6JZfQFfYgSoLxLbE/Tn+Ko5LJVCYVZ+tlwUi6WHB\nB4C02nHZl3OV04UfAN7iaIJmAeAHR0iAm5y1fkHVWW9GNDBfZUdTkFQRyxGvw24B3+Y06p+UWNOW\nY1ja+6ItAGp7HI7XLcv5qr//EY4l/rAx2Bbjl6POAJzNspsHloSbDvZibZakJXWVY51q8MaxqbN4\n49tsc/EtMClpSbBOWuAa82+FiQ642exwo3GFwwbGBRNJVSdQhpMldBrOOY3pNtv8cTlH5KYzB9Cx\nibJOXcI8z1lNU0eyfXbvAWYH5tOxt8JlW9ZVGQTGAoSTQGcKXkYJVWN2tp203bKa2hblkFel0sxQ\nNRYydBJ33TShu5EsOguc9k6iPIF6yHK11Xk6BZEdThWOuoUSfnH9DkU/TUfVKB6P/anvP+/0YIEz\nnJWlv09pcT6vVALnMiXp8PCQq9U5dqBAUwFgkYRjWA4gXuIQrMJ6uUESLgcIwLuENXeeHSDa50Ap\nQFkAXWP/3YbKD5ZYDRoRSvsq54EFi5c6LNeaOoL+QFvkYn7IAUReZQcSLQWDbfG3POLf4yAB1/T3\na/oAnLgAruVxnQR/uYUf1uIFdo5usOyijtuc1lfXRYPyNofTCAmiJVgF8NQhw9F+TfGA5VbSY6Si\nxqrKC3O1wwHcWycPXbZBslZ/gYOhnutNdU1qEddH77HOsaZ6tBmbiGp1jofDoaFOgfHGWFu/yc95\n/rWTAsSGMwRQcOA39KMlqYY2pYARm1etG+2eTw0gNPfYWaTj59Wy6OZ40UgWeIGSC47Trb61HNzs\ndsaqNb3ega/TLgdKT8xvDmHGw6ZE1qkI8KV9MIroRG4zkc5x3d+Hh4dGdMe8jF3ewQ7PVEuNV+w4\nKZO1mUGwmUnOgbJ8TcfR787zAqnTpgdJJcmp4pTpfFIJnMuUpNRbWlofAV5BB5CWKw3Icr8BOOOl\nq0NES4ktCcSkkyLy7Bv57xqfJRcboLLJAdBN4sZKgCItaweiDNRXciE1oIRz1sjfi0V2jYMKBxwE\n9aYFgHPAQUVBOixaGs7SIpobJ8nRlrJeI9+/kCsDwMYmZndC3uhzAOSrHE4rYiAVW/DR73JM9HyQ\nHF1ZHuopHdEk+LaoHpgPElhCZWPAwWK7yin/+47vD2kV19beZe5297jX63O3uyOk2+R1oDBp6/mW\nBwc6cAs2BShX10nL9Y05cI11GSNOA+GgL/G8u0iBCG6S0hhSMNhud7la1brwbk5qEJZSKNI2WVrf\nvd6BCU7SDUoKXgKwwXMU3g25TYLjOcftlO8JgN8ccHVttfWki1IAioieKcdJ90l/StCbbqLg7DiN\ntfTw8JA7nUc4ZwCRjpP6vkn1m0TXCfc7rW5LzlI7Oz4IJz2rrg/GOdCijLXOrbw3eiqBc5mSlAJn\n8BS1dQzOdRboxMJ3yGm4YfBgJViWC3oRyLvI8aJ6yCmlQx/3Dzm1Au+r8ixgJsGFdSQurZ0HHMIv\nw2HuJtv8XshVaZkxSIIV6QKDfgJnK7l4o40S2Hc4lpDTVuGRH5+x0a9oyz7H9AcpYVcEnLucWmwt\naoPkOI/E58UJ+WurKsC+bGOO2qHHcJXT8NS4H0FrVjnlp2tgLmUFc3MFeS+yvUmUDoL4HtxlPG8y\nL4uCM+Aw/zAX9fyXwWSsDcEux46ooe76WN+iTHS7e1yttlg7s3U6mxPeNzZwbjTWEppFzjkwpkKs\nHVt0ZZ0DOEyDNHW7O6ZSg8WR1vQAi4oRwr2n/VTkOIhyY2c+PHt9TpVd9o/5ypNpFroNwUo8yVKP\nPMN11vX9wjbl1DAmWWlzdSviur/enAhPk4rGNKfAVKbzSSVwLlOSDg8PuVKRRz+wSFkgpMnxMTCs\nwuscdsGw5q1w0Fm2wIw8ts9ROJqcyrwtcOAWQ9cY1qMBO0rGNQ6cam2xkSoJsGjPsgNJCxyHj17k\nwOGGZBwW+csceN1FTllttqkBcFgrAouWCsOIg66z5moDnN1kKEXEluM+B+cvvVlqq3IA5vZ9PhLk\nW5b1WTUWqGcukpkDqvV62y+O8xP6UQNWSfEA4LcA4SI76zeoMxvs5gesplJtAnzdXDtzOuPWQoa+\nlhueNtv86psqf4BbWPewOQsA3lnd2pwGLdK+ChI4j/14WPXFnNL1cn+D6uCs56ib6xcHmLU+9ToT\nbXGrtWkAWD1v0/Dnjr7hNlYyep1+d02ib8Tl3mGbh+3ykNJwzuK9xXLeVSpLJiiX/iKS3mEB707n\nsYjLbdfT3kS7QDryObEVMux2y7w2Il7ytDzsmN9+eeo6nKRueuxy1+SA5YPkGp9XmgT+U6UmR2cs\n0/mkEjiX6TjJF487goODEICmXuBH7EApvNIlwACgBW9yh4MebxFlYI0DQLa41bvsPLllqGXJMyw6\n3ke+tsUoWFihMIFFbsd/f5Vj0A2HDEnpWGK36IOKYjkXSqtqvHilVt9VVZ616QDnu8l5y6EGeTpv\nUGTyARKCCgO0kwHUoYIx5NiREScSEtxKUKrBn6vH7CyAMzY10KWWJxfQ3oaetUW/wBy1lEkwF6zI\njlKSD2XB+ROqBuscAHgOcOrvpfU2WNdt/vIuEy1zp/OYoBpscaAJQNYtbEra7a6nEsQgtFJp+tDI\n1qkRxudRTi3kAKp64+aAXK2mn9E1xuYjdXqMLfPV6jIPh0MFLmNQLJ0DXdS/YvAT824nA6VwvcXD\nXjmud6WiN2iLx+3UPF4N3iw+rzNKBAu8PiHT4DkHnFutTRFwJf5tkrVXgjBI9RXJ0Vn8crRR0yYk\nleekVAXLIm8pkpzEgvyTAJwntcGdVsYnO6WqxvmlEjiXiZlT3phNF4AVcsQpIIQ6hV6AVtX9+pgf\nwA+OfXKR0nzVO+o+yIEBvB74OgBUW5a8nPxXDFgC+JHH40XBJ8BrhkrEIxzL9EmLu0X7gBLFgGNL\n3SI74DjPgW+r6wDrdc7C2uKgiyyBqwY2XXbAXEoGSmtxm4OzotwA5Y5rrbrqEOkaWLc5dTjE/9DI\nhmMiKA/6VEBbsa15tM75uZA7JdChjaFbjTkN+g8Av7xWK12AmoQgPoFr7py9Yq1kp/wgn7cVTilJ\nA67X14TTYQCh7hQJIcxlX6CtGGM53mscW4H1eFmOkG4OpHzk3IkBqB9LhY5hOYcymzYQWz8nBdNw\nvGE9Dy9ycMq1NpD7U0WkS4GgBuKY3yF/zQ0OVI2F47JrtUXhBJiCqmnoH9MC2hxFBcBNq58U9UdR\nQBDnsAhH7UCTs5wMT1r/P+tUjcnAuYwc+CBTCZzLxMxWAJQcRxcg0FoIU+cdZ2W2AkQgWIh8SepF\nRFsLLSm2NgcPfr34WbJvmnerQRaAGyTmNF82tZDGToYayB1yGnwCtAdJKYAFFVHZ2hxzbbG4WxrB\nQw4LqFbA0BrC6Bc4KIIbnWqAxt/N+b4DgLzDgRsKK6aeE9YcucYhMqLUu0YfyAVAAiFpbZbcbABh\nzEmtT73EjudtOU3mdHdz1naLzrDBaYhyyWeG8xlOOnRbdDCXJrsoYFY5Vp1S0Nrt7plAxlmjtYPh\ngPPh3XcY4LrVusqNhkUvSnnmjcZlHxRFUzX2OQ0KE1M/cim/sbdoA3xc73a7OxEkubrOi/rIaJT2\nXLDyzXFvHejFHLcATtyHAM4SHA6HQw+43YaoUmkLK/whxxvS6RwOp00WlaZaXT7mc8v2SbrHNFKA\nOWpI8C04m3Y8LOfAs0qTqRolcH6QqQTOZWJm/ZKTQENzOmF5sOgWW5wCsLHPY50DtQF8WixS4H0C\nRB5w4IM2OLY0WosOtIj1b1JSDfXB0eYWp5HuABihPgAptSKL94BtULOSLDYpTxjWaoA1WJU1uME9\nABky6AwAD6gWXZ8PLOZW9MENjiX9rA1ELhQ6QOqcKP8mO0v0PMcAcs7oX+2cOOCgPXrAgeohreIS\nQKOvYG3DuAM4yzm1wkHFRAP0bU6twhLwSnCLMchtBKxFC3Xuc9gE5uQXJa0EvHm9QbOcTDVodfqt\nOmJet7vDvV7fc6B1HpiDfQ7zXm5iQgQ9SzlDPw/d7t7xgu5k9FY43gCibTJ65GTgzGywQSqYAAAg\nAElEQVRFm+Po3tNaFt27L6cQo+f+Ktdqi8ebE0lxyANnvelLAY7sw05nM4k6F4K4SArOkqDxgLYW\nxuYsAOd4PPanBxvsNuiur6Ad7cB7fOLS6WxmxilPock5LD4s6/DDANrTqInknQNLOboHmUrgXCZm\nljtaye3FSwxgDhY9gBi9IMDyCie3wfFi4F66GxxLet0R121ykP+Sv8O5DcDC1qK1gQUWadA4LNAr\nwdgWp+AfiwL6Bt7tcA5cMxcE1y5rkcQCAaqA1gS2HDBxD8q0rHc68h0sNjnAZYFlSVmZ5KTY5nTD\nIDnqu75/50WfzavrNS1F8r9BdwDY1JsQWP/dnKpUUmeyWLpP0xAkFxDSf3BclRbYDQ4BYmKebtD1\nzoFh3aerog36Wg3QpCVa8srl86afk22jj2WdrXpCutEa4w2uVpePqR4uwp5sOxxEXf91Oo8eL+ha\nlzl+VvEsSZWOYqqGTJPk3k4KeFKLqu4n53gJDrlzyMOcbXOlsnyCAC2wvqNfcLIVz81AjZD+ADaX\n2XZCXDYdDU+SXHhv/UwdqO/A98aGV45rbHQpontY3OZW6+pDA80Pmtpxv2WG9UwGQFk4xxq/sVMJ\nnMt0nNyLMkfDgPe2pAoAyII7C+cyaYm8yXEI7ZzmLECUBTaX2XGXAbwt4GgFeJDALMdbRR4ASrDs\nWCBomZ1FWnOeY05loGpYzpSwKII7reuUOw635MwkGLH6ta/6dBJYloB6wMGqbDnxrXEabQ4bJ1in\ndAhmzRGXY5IeB4eFGqGyrXmDkNp3uNN51HMkpTY1lE6w8cMGChrZA65WV7jR6Ij8D9nm8INOs8vO\n0gy6ka435rPW2u6z25hZANman9gkSSoH6E0D35ZNji26ms4k843BeeBDY+zhiHmJw6lPXpuYaN2D\noVmWR/Xj8ZgrlaIN4CUm2udud88EfZNAw7TObdMmtymAMoWjIs3M2JSCHI8a1AXN97X7zW22Go2O\n51frdxk28ta8soEn3t9uPNy99wv4LMt+alTBe1G+d+VJQuCiayu6rJ87zYgtprkAKuedptWwPkuL\n9P06MFoqNESz912vMtnpNMC5RmX6iUxf/OJXiOgfENE6Ef1X4penieg6EX2OiD5JRL9JRE/533aJ\n6P8mog8S0aeI6E1E9H3/22/4//9jIvoln2/FKHmbiF4hop2C2n3E//9eIqoT0R8Q0XeJ6EUiej8R\n/SkRXSCiDxPRlv/+u0T0RSIaEVHDyPMxInrO1/vzvh4H/t7HieiGuv7I5/O8//wUEb1EREN/z5NE\n9D3/2w+J6Pd9fclf9yly/Yd2zBt1+vfk+pvEdVeI6NO+PntE9Fki+oT//Tki+hYRdYy8vunL+w4R\n/R2f98+rdr1MoQ+rRPSCL4uI6J8R0S+IepAv/71E9E7/+X/w9zIRzRDRJSJaIqJ/Sm4uDUVZn6Aw\nb4iI/qWq7+8Q0cfVPe8mN6eqRvuIiH7kr1mn7373j8ntoy+TGz8iomXf/k+SPQa/RZcvd+j73/8j\nCv3+Erlx/KSqy5Pk5liT3Jj8mIi+Ta7/huT67hvk+vl/8+0nInofueenR0R/n4j+JrkxJF/Pf5xp\n2wYR/Ssi+ofk5ubf8PXfoPAskq+3nK9PkRsnoriPbxDRkFqtX6T9/bfQaOTKvXXrNn3uc18m91z9\nBX/tV4nozZl6IT1CR0cHvk5unrz66tM0Hj9PzNZz/rKv6yrV61+nhYW/ePzLq6/+fUJf37vn6nTj\nhmvP3bt36dat20RENBq9i27cuEGf+cyL9OyzH6Ovfe1f0NHRO+irX92jJ54Y0mc+8+Lxfbl7dfry\nl79Mr702Q+GZepqY/4R6vV+n1dUVGo1eNO67TXKuHh0RPfvsx+jrX/863bv3cSIi+uhHn6EPfeg9\n9KUvPUP37tFx3kRDqtf/If34xxfo6Ahz5O0+r0+Tm0+fI6JfI/fexBx8idxccqnReIZGoxePP3/x\ni1+ho6Nboh/3on48i1SrzdBrr+lv30zxc/ICueeDiGiPfvqnv0W//dtuLXjLW94ixiP06+rqZSLa\np/BcDGl19VtnVu+zTHfv3qUnnhgej/OXvpTOuwefZonolyl9X5XpdZNOirTP+x+VFuczSbE1BWGq\nYQ3pq9/kUfdFjiW8cDyM4AugDeA43JKZg6QZjvullUVbO1c45mNeZJteAYoIrBlWuWtsy5WhLqAd\nyNDK2mp9R5Qnd/xNDnQJy3HyMsfH7WirpI9YUll98XvTt1EGo5EnAdJqpesIes2KHxtpte4b5SJE\n77b4XjtQWsFR5LhJLqakH0hKgbTQ50J662ApOKpEHS5yqpdtWf+cbnSY25I/rq/VcwgW6AXftiuc\nt47jlMQKX67VVPD9HDcaq0K2re+pAtZcCicHjcY693p9b32UJxsuXygywAronnubIwk9XucEpgMZ\nSR66HmdtLQWVZI673Z2EVqCfcdSvKPLfJCvdtMffjcYVow37psXPWdMtmpIM/x2eXxmMBOMXIkda\n7xJQV9ZMCbii0NxnLbtmUTWGw2HUp8FhUZ8EjQr7XKfXk/LFpLqch7zdpDInWbhL58AHm6ikapQJ\nKXhIr3PsgMUcuLJWWGcZDtiiLWgnMYAa6dwGqgUWWOkoqF8IGyLvbbZ5vEvsnBClmoVVLo6ZpcMT\nOLY6UIoV1lQen6eLb/FR/C4HagycA/UitMUpWLO0srUmMwCTdg4EXWSZHYUBmxZscjB2FvUD/bzF\ngYZjaRCDdqGB4ICDNJ6sKyQANznlf1p11xETJfA44Hhzpukg1hhJuTrMN8xjjB0UL+T9oLNAqxwh\nty0ZRFCNNI0p8Gfd930OfP8AoobDoTiGt/JHOxe52905fp4ff/wmN5sdtuc45k9uo9OOjstdsJNN\ndsB4wLkNVr2OORRvACGNZwGPwH9Ng21oelHsUBb3pQQwVjlawo6Z2d7opMB5krKHoxvEGwZQONL7\n0/eBo7fElBdLzq3X6yeOiWn+Nvg8qYxbt7vDtdolbrU2jznTsb6zDka1xJ3OI6eiMbyelC+K6nJe\nutC5MqcZ15Kq8WBTCZzLxMx87DXvNGTx8K1xAC+wlOUAFRaCnAVKAyDIP8FqDX3hMcfgVlrw8EIY\ninxy1tycYxJUMvocJNHkdQARc0aeMhgMFvRFzgMP6SBmOX9JyTppQZZgHVJ7+xw0onPKBrJsABpY\n86TzEeoFwL/KbrME7izqIS2V4LFr4F6kKDHHDjC2fVuhrb3EAXyjDbDqH3AA9pYiCOZMCqjCbzle\nr2W11pZeaUEbc3wioOUSAZb1grXJ8eaxzU51pMWpZbfN9fqSsi5az5A+mZBjg1Dmjk+to7bZPNub\n6m9rDi1GTnexXBgs/NCxDnWpVi8m383OtrnbvXbMadZlwckNltmi+kr9YN330iEup9SgwYeLcijf\nF+65A8jv9Q6OwWo8/rGW9CS941QuL/SRZXXHiQA408PhMNlQSPk3jFORTvK0Vt1pr520eflJTA/a\nOj4NULcdoEuL83mlhwacieivENHXyZECnzF+XyBHePo/yZG7fq4gr/ProTdACsewllV03S/KW+yA\nkKUK0OdwrJ5TmNDAWUbPgn4xwPI8p+Gcl9g5ZOHlUASI0qACsTIFrM1WXZc4H14cdAYNXOfZgSId\n0ERbRQGwr3LYEFgOcU0OIFnWAQ5n0nGoiBYBC60lEShBtQXoQb+Rm5s5DhZmgH2tmwyqBvJG6G2t\ndQzAtcUhIuJAXYN+kHnP+b7uqnLRF31OTyDQDzeZ6DoH2sSI806HAWTlASw2HfqaSxzk61ZUPed8\n5Dit1jEpvPi++rzty9GOkE6KT+oMp4oY0ikW81OPjwPjAI/FjmJy44cNNiTusBkLc6xSWfLKFG5D\nqMNnTwt4T0rVyFmuO51HOZYrnOdmc0WoDOkTtjQPJEtFA+A63TAU/eY2E6kkXdovk/Svw4YkLSN3\n77RW1bMGkQ/L6nzSch9kPacDzlbMglJV47zSQwHO5Dx9/oCcF9cFD4631TXPEtHH/N+rRPT/EVEt\nk9959tFPfAov+9yCBTA3w+mRkARfFzjV8tVWPYAVcKe1JRZ6xHoBkvWD5rNctEH1gGVU3wuL5nUO\nFmqrvbAqa0ALQLjIqXyU5MhKACFBvMxDAl4JegG2oGKxIMrSwEbK40HyzfrNOta/7Megy3YQkHbm\nPrRrm1NLbIvDCYVWCZDWaz0mcvOD8NkHHPjPUK6AjvcFdicOAPNyI9HmfDAPnDSMfX4I421zA901\nFliE3J7k7etr1lW58e8xFxbgctWPxSOcPkNW4J0VTp8RuREIYa3d8y19AnCKIecnaCdho9RuXy6k\nFuQjL+J/PB9WPzajCHoyqpxW2XB83x1PT+gXAmdNxYi1n1OLbvz+C78FDvJ0IB6piM4Rq5gUBwAJ\n1A9Ztr2hyoFfS32kyGIuU06TOkclOAsQ+bB4zq8HfvX9nhSkdD337izT+aSHBZz3ieh/FZ8/qK3O\n/rv/3v/9JiL6RkF+59ZBb4QUL+QSLEqrKY7P4VAGXdFZDgFTAFosTuWcv08DPIvDDKc4ncc2B94u\nwKV2QBoZ7UCkOPB+sQBZ181zTKEAN/VA1EPyksGb1m1A2O4DUaYEk5D2y3HDJfjNARdwbyH1pvur\nyzYwlMf8uUhmFth5jINea25jcpHTI8MFDrx52Qe5ebfs+8eysIH2IUExgK6MzgfKB0DoPqe0DMcD\nTBccAG/IK8rrJeBcY0fLsBwGMT8tANL3Vue2Ua7UwMVGYMgp39rSj55kIdfyhV0O+urpHI4DpugN\nrqUtrQP8wPo8HejWPGfQN9JgIFo3WfbLgC1woZ0bpdOjFRgmfDcdT1qmokAtkpIyiX+cgteRotKl\nVA2ZirnkKUe7iFereefy+lzI7ZOm8+IOv17LRToLbrpzWI3lJCuV9oNqwhsuPSzgPCCi2+Lz24no\nk+qaJjk9rJeJ6AdE9J8W5Hd+PfQGSHEo3hE7wALQxxyOuq1wxpoHawGhIiuyFUQDIa+lpfYip05l\nlrMejrRhvW1zoCYA7I45WOYAUHZFmQDnmjqgqRES6GgAAdCUAzRr/pprHNRGrLagPTnu+Igd3UE7\nPmLTo9uglTsk4ARoBbdcAztcZ9UFIGGNYyCpHUPhJAhtbhtchk1SUZ/IfsDGbpsDnURTeCwgjv4A\nT19zpuXzsKru3fH573CwQi/6/y+JfpYAZM2DNUtVQVprdXtj0F+tLnOnsxlZbXNObnYZbXageVlc\nF98bgBKeJ4yJ7KeR18CWz+sCuzkpnQT1pjB3ohFObeCYmAOA0E3OBVrRtA0dGjoAcv38unDW1m8n\nUTuwLNknAWSWkkmtNs/d7jXTOVAn22rcz4L6Iq3iXFsm8cxPkt6owPksyncncWkU3DKdTzoNcH5Q\nOs43iOirzPwzlUqlS0Sfq1Qqf4GZ/5118XPPPXf891vf+lZ661vf+kAq+ZOQFhYWiOg1Cvqb/xM5\nDeQXyOmOSu3bp8npMd/wv/8CxTqjT5HTHn07OX3dOjnd2bcQ0VeM0n9EQZtU6+w+Q07TFPq/UtOU\niOgXjfx+399D5PSNf96Xu+F/+xki+hUieoev/9fJ7dHuERF0ZWv+8+cp1SJ+kpye7pDc/g9av4+T\n01T+PhG9h4h+y//9UXI6wk/58g6I6H/07UbbyPfBS0ZbfsaX8z1yeskkrp8hon9CTjd4noK+9PuI\n6APkxuhz5NhQ7/fXr/trhuR0aHH9s/73eQq6xF+jMCc+7dtHRPQu1SdPU9D8/THF+sd6jhCFsSQK\nOtVv8u0k//ePiOjPU9CPJnLz4e3k9J5lahLRH/lyXyB3WKXHB3XR6QI5vdx5X0+pxfoN///PkeuT\nVSK6S07D92Vy7LHHKWgqY/7KtpGv84iIOnT16prXsbV0jnPp+/5faMvREdF3v/seYt4iog9TpfJv\n6K//9Zv0j/7Rk+RsCUSun9+h8nqZKpUnqVK5R0dHv0nhmVr39UR6kl599YfkxvYlCnMVbXQa6rXa\nHdre3qavfnWf3PP2LXIa3/+ciP6QXJ8/5+9/jjCn6/XP09HR6FgTuFJ5HzEvEdFfI9fff5W+9rWn\n6O7du2aPHB016NlnP0JENTo60u+F20T0V6Prb926Ta++GnRuX331RXr++Y94LV75/H6HiF6jhYVl\n+tVffZ5u3bpNr7zyZiIKms5ERG9724BeeeV79Lu/+w2vQR3r+d69e5d+93e/RlKTvV7/wLF29jTp\nxo0bdPXqGn3zmx8mootE9CS99toGPfroZ491kYvSaPQu+tKXhsca0o3GM/Sxj7n6/+zPvnuq8qFN\n/La3Dcxrnn/+10nrnT///EfoQx/60MT8Zbp79y698sr3qFp9io6OQn2lTrW+fpI+97TJ6qdcua/f\n1KR0fXwqc22ZTpq+8IUv0Be+8IX7y+SkSFv/I0fVOBSfLarGbxHRgfj8eSJ6Sya/c9pXvDGS2/HC\nChh2rM6BJxcBLGdJxRGypb4gw7VKS94Fb9WxHK2wG7ecEndVGYscnHwkZWHf/2Y5/VnH91AFyEU5\nxHeWpRTRx2R+oBaAetEWZcp7tXrIDqfW23XRFtm3lvXOcpqc5TgUOegUGEvQYJaMfCV1RFrqF0RZ\nus+mcXTDuMl67vnrdjjQXrb93xfVtVIm7mbmb9Rf61ijH62+usjB4ovImdIyucyTdaLhlOicAIOF\nWJcHus0cB2WTNgfqi+5HnMyEtjQaoEvB8Ta1UsNK6SyIOk+8A+TcGbPtbOks9M1mx6QUpKHJMV+c\n1fr69evCAqxPsmKrsVOr0E6iri/tCIX7U2nv2g6Pjgvd6/XNd2UcnS+1mqcKGqkFfdqUtttZwE9q\ntZYW8ZjeEkeRBIfZsmLn6ATWyUm73Z26PmneI65WVyIu+7R1OUmZp7nmvNJZcKxLHecHm+ghUTVm\nKDgH1sk5B/55dc2vEtHf9X9fJme+aGfyO8cu+slPmitYqSx70BwWkhRkYeGWXv2SH2odRS8zUY/d\nwq4VFixwAJAFPrCl/hDC/ToALgN/AJQ1OShU6MUux9fNOZmhjBHbYZkBBjRw18fVFg3hGgeZvDnR\nR8yBerLMqZ5wjgqSO7rXAHCVAy1HAkpNj1ljqDa4vDvseM/YLCBMc4tjRzStzSypIuCm6nquc3BE\nHXAMxFAW5iPyl8524AnrjZUEw7ofR75c54h4/fp1jqkJfTUeXdXHFnC2qEyyPMj1Yb6C3x3mXLe7\nw73egaGMYY2tBOFwhoxVNpiljFhxSOfw3Fvf3znON1avyI0nePgbHM/tfP6x9JzlK7DN2s/BojBY\n4Kzb3TP0ot0z3+3ume9Jx03H/EmpYgBeDpTHdW02O8dUiWmAkSX1BorKaVMqibfPrdZm1mlRt1+D\n8GpVU7FckBQr5QDiSakKJ7n+9eD4N026X+Be6jg/2PRQgLMrl/4KuXO7f0lEH/Tf/ddE9C7/d4fc\nuej/5f/9lwV5nWcfvSFSns82NB5IgEe5UEKnF9Zm5CEBkuRIWguldkBaZAfOHvWfYf3a8C+Kkf9/\n3S9iWj5tjwN4XeCgIjFJh1haoiRgReQ8GUEOlvM9DoE8AIKk49I0VjnwnJd8/4HDrEG3ltuzrJfz\nnFc9sPilffH3BjuwijEF0BmKsZS8aGxiAAD1ZgOOaOD+znGwVhdxmdc5r8ogecBw6tRKH01f3rov\no+7zQ99a/SDBKXjuWllCjofs+9w4QKGjb5SXOqZpizwseE5lAXPJykv2SRpMQy7MLgIeNjFwDrT6\nGXPBig65kAmKkVNqaXK6EcoB5/1Ejzp+L+G53GA3L917AcFfcu+4Xu8gsuLW60vegrzNISLniFut\nqwmYcf0fc6HlJgfKIKkEHuZFO7m2CCw535N4g2sB+pMkyxLuAuRMD1zjvGBECeC+2702oey4HAvM\n643eNPmc9NqzVAN5UNZqa/Pi2oiAVsE5kGj5XOvyRk4PDTif5b8SOJ9til82ucXUApvSgfCAU4kc\nHNXmLEwSUEoAuswxgJGgBgDeCmV8oPK8I/KDt/8Mp6DAUvoIurOpPJjOX6oLwEJsbRZWWS7WISS1\ntIItsA3w5JH/PKebhpzTIgCHBdZwzZhjCbkRB9CjN1Db7KymCGiTyn4F/esOp3QAOAvqMTjkoNZi\n1ReqFaCU5K6RDpOgW1gAGH2q6y4d1rY92LRoE5d8eaAcYaOFeYCNoXa4zNFsUuAcWyD7nAbLkZZY\nWc4i12qISilPlLDRPfD9ooOZuDkEWTuA4mazU6ig4CyzljyiDNMtrd16w+GcPCW14fDw0APyNsfR\nNON7pVqGBWZsR0Otwb7K9fpyYqm0HTqXIiuylX+QGtTlrkT5a6dDi4YyM7NyKvUKKF+0WptJgBor\nbPbJgLPezNhW8ZyzYggPLxWIRoWW72mtyDngfFaW6Adp0S5WOhlxugbMnEs9ylQC5zIZKfZUt0By\njvsLq9WAHZCwLLeX2FlnYU0NC3RYZC2gaNEOJFfXih64Ia7Tv13jYOEGIIYFCYBcAuUDTqXrAFRy\nFnQJuDWtA5Y3aZHKBV6xuN9X2QHLXNAZAD7UG5bPfY6Dy6Au8+Ie5qD60ecA6K3NBEJMa8rOobpu\n1ShXqo9IyycWcYANDXBln+E0wrLo5xRRJADCvLRCamNeuwiBtdo8D4dDrtWsubbP8QmDrCe0qPc5\njfyYcpVjvu+CAK17xrgB/Gpt5oGqn5aC1BvEyxwieOKEYe34fgkMtbXLVpSQ1nFJWWIxVvI0KvVN\nkJbBlFKxIO5PwVgOzNjAOX0/WN9ZsnVES1MGb1nJfG+D1RSUynvcGE8Dnt2Gw5o3MVAuAvFFeQfj\nSLxJz0mqSWWTWm1RUET05qmYzz2tlfes6CG59CAVOdKy5PzQpyHuFK5M55NK4FymKKXaqA3jpTvL\nNiiRgTs2xINdBHzAP5aLprVgWJa5BfG9dU+bg2OZ9Vvf+H6dgzV7GqmvXN0ABrRlustp6GtYaUFP\n0HlpTjgAPq61FutdTi16coNw3Y/RCsfWW1gstL4xNgBF9dI6vhLkbHMeCKD+cIZDuXLcMC/W2bas\nHqr6F+lTYxOD/ljhoGes53oMYh2nU58woH3SqprrIyuAzpb6DJ30Da5WLwrQmNugyb7Yz9TD2uxe\n46DLjfGV9YAV2wW/kADEOea1GGAdgASOh81mR2kOS365tYk8EHVxVm4AIjsE9z7bUSJtgAvHt16v\nn/B5reutzVGvd6CcOx3dqFJpZRzd5DMRb5AmWXnzHPTDqI4WcIR1udnseF587rmTbetzr3eQBJlB\nm3Igtd1e45zmeqC4uHDl3e5OxNOP+8DecJwFCLXq/5MHnK2T4ZKqcV6pBM5lipLlkNLpbHKgTwBI\nWAE3EEr6KgdVgtwxOl4Cu5zyRLVT2qLPV1MY5kW+sB5LcAmFgiX1G6ypFkjFwlj0ksK1AMGgH8i6\nddgBoiKggzIADNY5pUTAAgurZZ/TzcKYJegIDpEo21qEYSGG7jKoInKRzvUNAIG1IUGbmuyoCn0O\nG4bc5mbk6wPwCg1x7RAKHrjeIEjVhqL6g2fb5HZ7i20+OpQ72hxb+gHcERhFPhMHvn3QbpZlWjrl\nlnqH++xOeiSwlpb+IopTCoZi+k1u8yjrMRBtgVrKKlerrQxNAVZ29/x3uzs+EAPm4RwHR9SrHCuY\nLDHROlcqLe52u0mfXL9+/RjoOoBulW1x6lcNzu5I8ZpjBYler5eU3+12TUuls6RjjoS5pmkllnNg\n4PPvcrU65+kybqOnA5mkBgzMC2mld8YJGbI81lXG+FvzJtTf4mbjO80J15Zk184NtgwLbpMi39k5\nXwZ7buccIc+CU/yTR9XAOio3vqWqxnmlEjiXKUqpA8yqf8HjSG6fQzATLFx4EY85BreQDstJ2gE0\nAlA0OSy0WDCWODgCavkqi2sq5b/wYj5kByYvswM8B/7aFscAHU5m0hos85b0jbZvL9qzxSGUNaym\nSxyHjAYNBGVh0d8Wec9yzFWDlQlt0Y40mpcq2ykBlwVaUVeErpa/W0Br17d7k4O6hrUQaiUNOEal\nHviBQrKj7kHAlXQxiK859P8mAVQ46g04tsxKK/kKB4AM3vIdtp0zAfDhlHrHKHOVneqIrJfVZyvH\nvGEnKRfzkwMY15tDSXGyTgS0tNxaJl/mQE+SzzQs6Rij3DOMsdGnD/rUxorGiM0M+llyyeU1+jmF\n5V9uaiAlF1vHAw0h1D3mJC9zLIl5kavVxax0mvU+0xJsKb1EzjPXX7KNtdpiorghy3cKL9pnJIw/\nLPSxxJ6kxMTzt9PZjNpmU1hsa7W0qDreuR3FdZqQ4blIhogOqdNpgWpuLH9ynANT+l21WgZAOa9U\nAucyRSk9Fh1xcByywAGcjACQN9iBYWmF1g+1BI0SGOYcwXToaDiR6Jd1yt0LVI0ZDsAbbXhElCnB\n9oqon1y8L3AKBAAYLnDcLvSVBME4+r/k+wV88CY74L3JgWss64NFzVrIco6DA1WPnHpI2+e7qMrd\n5tTCrPm7DU5lBbfEWOfAHuT+oOttbYIk4IayAwATKDhyrHP0HoBkbJZSXdzQv5ADXPFjssOB42xt\nojSAZ1HGuq+zdjyzrG5tYdXLWeVxf9Pzb9scNNNdFMEYqIGjvMGt1lVPT3DjV6ksc6OhowLmnFtB\n49hR7YivscBp3onYuk5ujPFZXtNnSDU2GqtZK7SkjNgqQe7amAdtbXha2XekpRpi6T474AjKmrQ8\nW051Nj8YVAfXv8XqMo8/LiMCHnIqzdhmPHeas+76SG9S9UY9bBICjeZAzJ1gDe90HjGs7vHGVyuL\nDIfDiaG7T0ON0Nb7anXl2G/grMHuw9KDDo7NpcX5QaQSOJcpSjFVQ3o5ayuGXtS05XOFHfha5zi0\nct9/3+V4cb7MzjqXAw5yoQcnWII5ADFYe1fZgVksGpZTmgygIsuDhVhKVG2zDaYZz9oAACAASURB\nVFLloq8t1ZbDhubRSitsrp7onzGn/FALsOyzo4rAKrzCgTaD+9bEZws4NNkBP0kBsQAi+mWJw8Zi\nktQY5tglDpxX6zrNI77AwaFNq6gAUMs2gF89x+AM232FTY2W2NNH3kUgXVImMMbSso0FDZZdWU9p\nPc3x1W+yA/bN4zxlQBNmt2h3Oo9xHP56jjudx445rL3egQfYcoNXRKXSPHxsLIKl2+lMp+DU3iRY\nYwDLvX5WJNh0dWw0LguQl1o4LYk3y0oZW0PTPq/XLx/fa+UnNylaNi99l6bKLY7Ski8f4DHmS1vP\negycHVVDW6bxbnTvW1A74rwtHWBQQ1L/lNjJ7oD1Oy3WHA8GhVpt3tSyntaSbAHnIum6eBw01W9U\nWNZJ06Q2nBeodqcgFq2teWZllClOJXAuU5Qmc+SshX2T7cUXoMFaQCXQWPcv6D3jBbDAQeoLFqkt\n/5vk+4FTq1/+m2xLpMGSZh1/bqvyQCOx2tFXecr+sSxEWiFDAiurnlB7AFABDQPayEO2HTWb7IDt\nZQ4W7ZEYq2LgEADnFoeTgQHbVmSpWLHNbhEuctrsc7qw6yN8LGx6jCWgG3PQaF715QIgY0Mi6UM5\nusoWB1UHgDU5Ln0u1v4GRQg0laHqFw2ctDMs6pb2baWyKKzF6QkALJ1YlGOnttSSGQNGUBxy3NM1\nUT/JJd/y/bbGRIOMfrE8hcF3OD0IfTkzs+yDiqxn+9TRGCQnGZb2PsPqjg1EDrho0BIDsHT+dzqP\nZfNDHaQjXQ5ga57y7Gybe72+6q/cu1MG9sHm3QocFUeFtHW0w/sWwDV9D+l75MYaJ3dhM4O2Omu4\n3BymBoZa7VI2MM3jj980HTQtS3LqfDnPRN2I562Ta2uxdn2urJMA3Uma0efFhXblLhrtWziT/MuU\nphI4lylK9oIirQ7aOgmOYg4ggeuoNSYl1xeWPljpYGHuc3DY0jxeWOmwwOS41Kvif2nBkqGJxxzA\n2bzPX4KUJQ4OhRrczXMAF9JZacQODOhjWl1HS39aW3W7HMDvNY55qLCsSz4rKAoyzzkOfGsA3CLg\nvMb2JiZneccRO4A2xkY7O66qsuX9I3ZWtT4HDq7ub1jxh2qMwKGW1+5wKomo59Gm+g5cfT0ubp43\nGmue84+FSj8PsO7d8U5qWtZwJ9N/ckwRAGefG401v+hqYAkwu8yNRseDT2lFxrjGx7dWmOlUrgwn\nCW6cKhUHXLvdHU6jTjqqFkCR5l+6iIc4WQjzGqAzbAouc26cpEX38PAwcqojmudKpXVsadZtA+C2\nLJyxBm684a7XF7K6w5YTXS5kdBG3ttu95iknMggQNkXY1OhnwL2fut1r3nmvL4CrtMhbz1eRHJv1\n/t7ldrvrqRyptj1AYQrU07wmg+Dp7mGGcQdzNPSbVGLR5dibw262rNMA3SLgfJ7qGy5v25m7TOeT\nSuBcpijFD7hcwADi2hykymDtA5dXUxDW/AvxEY65pcv+QZfHv5YUmJRMk5ZYWDe1drAFnC8li0ew\nhoEmAGv1Locw4jkALi2gUBaB1W6WQ3Q8fRyPxVnrQVuWgn22FvMYGEqO6TWOudHWwom2SWrLRf8Z\nHPKRKEtTdGTdrP6B17+1QC2IMQeFwrpf8jEltURfdyFTzj7rhSNtA7jS674MbIg0iLXGZUuUi+fB\num6Fiea5273GjQbUVSS9YT4T6hn9EzZalcqydwyzuO9644BTAcybDXXNKnc6j7KeVw6IWPm7OQDF\niDxn16kqTOalhmcHgDZ+16Bv03kHy7rlvEy0bVjT3XsipwgRawo3OWyiQdtpZWkB+e9iWkNOyzjm\nVrfZPa9jo28lJz+e50WAzJIOdOG+Y1pPGiY9PdGYZDEN/RjGxOkzryXXTr/W5EFwfN/0YNSKwoiN\noVW/++NSp+2eJr/TUjkCVUNuvtxGv0znk0rgXCZmDg9tr9f3Fh0J2CC7NVAvnyUmuuJf9uuccqPl\ndelu2IG5Fb9oWGDKcsCyaAJY6C3VhuscgG6HA2iUYFZbVS3es9SMDguLe/mucOBLg+ah74dlU1q4\nAWb1teuZ/tDAEBuPA47VOCznKjjYyQ3MnOpjSKvhuhz3VYN6GZp6K9N3oNtIgIj7AV7hkAi6gWVl\nxQYpp/qhnZuKTkukM6fm02JTJvOzAmBYAH6J03k4YGywut0dYQXTIFSOB/pFL4pO+SG0V9M98Dzl\nJPkgOeecUx3g1JZkd78ESGk0QFluscVPgytIwmmgC+cwXW9Ik1m/4VRBW4NzahrMGsisZcsr5kZr\n4DwZHMWbCK33PFB9nBvDIuAcrPnd7l5ijdZW99hiPWBJT8txdB1Xvq8s0fGmCHm7uqQUjVzdIVsp\n5f3y900PbuONUkr/0dda82waC3EO/E7Df74fKkdw4A+bv2q1BM7nlUrgXKbkoXUvbzhsHXIAKfLo\nV4NjaRnRlgQLSOJ6AANrgZBgGYCwSBar7+u1zA7Mao4lgMiyuM8Ch2ucgogtdoAEYAcgzAJIlhOP\n5SwDC6ulrwxQrIGgBooApQuc4z8GoKVBXq4vJeVD9x/AkrTQSWWUVJPXtV2rWkjLPRZfAGvUc2jk\ntcnBQVJveDRVQ3Kyc1xezB3MgznfhnmjbG39w9zVwWmsCIRXj8cRC/bMTM7aDefZPsebHVluzolU\njm3uxEDODZyS2F75qKvNm0WfBuDvLJv9Y0c1gIicEkVOOzjd0G0fA7a0Hu47yV/u9Q48bzbemNiA\n87Gk7eA4a/BnAZxA1Uj7G1QH3BuUJtJ2VKsr3jIqLf/pZq9SaWYsx/G1ztoez69JXF7LubJorUh1\nx0fH904CilojWjseTl6vJDVpMticxqJ72rynSUXln9TCrfOq1dJ3W63Wvq/6limfSuBcJvOhjRdh\nLAjymFQvEvKljd9gVYlfQrEMHRYcy4kKFkitDW3VdUUsNE6Gy7bmtjkETskB50V2wKPLAfDJI3pw\niHNUggHH1uk0SlhcPwkit3xbHuEUuIFessvB+c+yDgMogj8OdQl9TY7ugLqP2VnpV3iyZjDm0BJr\ny0dwarrIwTp/wEFhBTQdeUQN69CApXXU0VIA+GW/5cJlS9BvWfFRb4x1ni4Qc8flfMf8gL74rHEv\nNgUaaMt+XWCiNBhHOo+tusngROg/a1OGDQ6AGfrI4tpLv4QcPUdTqLQSykhcmwID6WjX7V4TUf22\nOJxIuTyg7ZsG1XBjAapISkNwgEwGPol5ySlNrNPZnNrREN+lQDCEWq/VND0nffZsp707fs44pR/w\nuWVCfWxrfLzZbrWumtEB72+tiMf8+vXrZgAYbCIsTni3ey2yAE8CuZb1+yyc7VL6yP5E1Y6zSCcB\nztamJCdzWabzSSVwLpP50Aa5pDtcqSBIxrp4GVovUPCDlzjoAmswt+EXAilBlnIgnTzVIdsAXYNS\nWCAlv1O+0GUdIY0Ha5+2XGq6B5wIL3FsjYPTns5/zdcdYWivsg3qwNGWVns4PU7amAQwEAJ76M2K\nBEuL7ACwBFLWZmWBHeiDMyJACYDTrP9OR2ZDfrDKSuvdwPedltGTNAkAT0lZ2GNbomyVcx7kKUcX\n9BpY5uUGSNdBz2lrfu+KvrZoJHLzENc7OBSi32X/QP88d1qByGxwzMS4aBCLum2KscX4tTkO2IPy\nZXu1xVkC/FSurFZrKmqBBa4hOxk/t5ICkoLcMYdgLDbYdsAMG7Bg6cy9z1qtzSTUNqyrMzNpn7vT\ngOmAjEwAdM4xVNPV9CZjkSf1h9Y6LgJwVrsDXQXPQxhDOF2eBIRONrLoeWNTeoos4SehLZyGF3yW\nlt+zSidpc35zFRssKpWSqnFeqQTOZUoe2np9jWu1eQ5WqCVOHdNAW7CsS0vsjj+thRTAWQa9OOTY\nqgx5MmbbutZiW7FCWxTlYg3QBOAMoASrdpvTQCvIA05WA9FWgCPtkLGgvoOkW84S1+QQDGVLlK1f\njjm+MfKTkR31dXCEkWoXqKPerMgTBQtYj9gBZ811h+7zI5zSWVBmrv74H5bkVQ6UG33PIgcAL8vI\nRR2c5Zge0ucQxRFA1NqsSIs88kSAl5t+zHLtaXMa+Ab9s+Z/H4i6y+dK02cARPSpjeRNA3g3Oabk\nYGwB+PU4FrVXnyqkIX1BZ3CqGVLhQIMqAHgXrtpJk/Uz6g53OHDJ84FGLMsgJM8sOkeOs+q45jku\nuF32NGka62+R4sdpncRyoNvVJ1WzCY6E01ET0o0O5lLRewqnX4fZvpDgdFrwOh6PC8OBT9tHZ8k1\nvp807bjbdCUrcuDsA6n3GzGVwLlMzBw/tI5jCKusBA7yxYdogRv+N8knlUoc2okMoAULqeQ6L3LK\nVbUswDIwyzTAWVop8bKHIgas3wdGHrItuA98bABq9AG0g/X9sALsqns1MN/nAG6ssovClmNj0+S0\nDySwRejkfXa0D00JkBSDHI1ll50FeYsD4Ef0MBk6HIDukr/G4shiAUA74JyI+y1AM8/piYOVN75D\n3+pNwCI7DjWiJ274sqWFDjxuacXe9WWAtiQ3IgP/3Z4YV3DVEdZcgv0do9463DT+nnREDmCvZekC\nxQF83W53xyt+4B48f1oychIgWuLhcMit1lXO8/XRbyscOwG7ejmJO50vrkkVNBqNVWWZTWlgmhaR\nc+oLwWD0qdMqu6iY8XdFDms62eAmD/JOC5aL3uMyHwdG09OOIBW4l9S3iCqAMi5ckEYQ/Q5z+bgy\nYguztITrvphWgaLI+TOXivKWDvI5x8HXQ7KVZSyH9lKO7rxSCZzLxMzupdHt7vkjypwk203xtxWp\nyKJwwNKLl7YEEIcc83FxlAgwssKxp/dVDsfWslws2EOOgYDmXMu24Ls1DoAJlkLUwXJqlIEutPMh\nKCpYJKwXHNpykzWQiPnX2DRc4UCT0GVtc9BnRptw3I/+xnXr/n8ErpBKGODHatBvAVfwvwGUALpH\nnAJUUFp22QIisRUc+Tr9aEcVssC9RdOwrHuwsrbYduzTwBPXAJhjs5MDP7vsgC/avut/W2aiR8V4\nYK5YznwAFDd9XuvsgmTMc9Azh2V70hE5+qHNbs4MGUFLqtXFiBfrrMSLvv8vcXxiIPnk4TmrVtGP\nsg3Y+Mq+OvTfb/i69zio8sDKj82uezdUKkFWsl5f43p9kcPpCfqnz8GqPcvj8diD5ytGv2xwp7N5\nrNAxHA4N9Yi2f9cNOGx6dNtjC7umE2h6g9ycdDqbom8cuM8pOcSbgH2uVFrc7V47M0s0M7SPtaa6\n5ten9IlJZbqNj84zvMNqtUWvQR7GOEc/gTNmq3XV65LbwJpZbgT0ZqhfWN8ccJ7G0nw//V90b5ES\nR34TpGlVpY7zg0wlcC6TWEg1iMuBzVXOh3rOgVSAMQ2eAHBn2ZYygzVv5Be1nNQajuHxQpFgygId\n2AQMjBd/lVNnOqsPLO4yjuY18MYGQWpPW318WVwH4I1+OWQHrrTm8oG/X0Z9xG8NDiAWdBRY/DUX\nFxZLXG9FVdTgCRZFgI4c1xpAXb7sO76PwPdGmzAvNHjZZntMdAAJOCJeFHnZsl6pLnfb90Gf7Y3N\nRX8dgs7Ikxn0McoHGLfAPsKUL3EA4LKfQQua4+AgV0SlsDYCscrIcDjkxx+/yY2GZWVNj3qJ6oxn\n2r0fZMTDIcenOKA76XpqdRecuui+XeDZ2TYPh0NPE0N+mKt6HjZ4OBwWjKsFCtFObVm31HFmou/q\n9bVjsBcCl+yKa0aZv7HZGjOAnU4BCOUpE/dDIQj35uhS+DtYgXFKEVP4lhIOdLudvrNbrSu+n3Rg\nnQXudB7Nbh6082Sl0so6McZ95u6pVJYSHrsFUq1+nGTlPpv+T+/N/WbRJ23H1ju+zSnHudRxPr9U\nAucyZXbvEmxgMQFwkfq/8iW8yQ7EaXoGFngLwMJBrsN5z/11laemjeCFD6B6yAHEwaqtATu0lC2n\nslWRj3ZckuVaTlqSdnA5k4+MDKbbe9V/DzAsLfaHbGnOBse6nFVSlw8rtFV+3+djycZZG4Wbohw5\nZ6y6aM4wACNe9lLWzpqTOcrFDgduL65pi3E84GBt1+DUkgJEudDWbXOtdslbQpFfm8MmxxpjCdas\nDZLk3lvgryv+BrCb5yDJKLnMsjy9obT6H6cmssycZKSc77BOD9huEzTNi8AZxtoqz1mT03ZZx9Cr\nggJgPVu6XPwNB1M5D3MBj7YZp1yzs+1M0JppHUvdd9CHzr9/03thxS4CdpPf77lnUvbRNYZxotvd\nUZbNWH4UAC+8A+WG2Kk52DzvmK4Sc7DTd9dkZQn3vDpljpT2UyS/JykZFrXmNLzr4v5P77Wsx7Ay\n2+/y1GLv6q6dUdtM1JiqfmU6eToNcK5Rmd4AaY+IGkT0fiKaI6INIlojoj+ldnuB/vW//gERvVdc\n/34i+s+I6J8R0RERjYioQ0SvEdGLPr+XjXLeTES/QERPE9HPiDxfIqIXiGieiJiI3klE60R0m4j+\nAyJ6kog+6K//D4now0T0x/5znYg+4fN5OxENiehPiOhZ35YfEtHzRPSfEFHVl0/+urf78oiIbvi6\nP0dEf+B/vyHq/hgRfUr01zP++u8SUZOIlnx9dojo4/5+pNtEdKD68ANE9I/9/Z8lors+v+f97/8F\nEf046UHXpueM72VZuvz3+/rptEJEv+H/J3LtRT+837j+ZXJj9yQRfYVc36PNOi0T0Uf8331yc6VO\nof+f9r+/038+8Pki/Z7/905yc+Mb5PrsK0S04PNBG/d8ff8euf78ZXJzCvNylog+77+X/fICEV0R\n372fmC/Ra699kKrVp3we3yU39v8vEf2+/+4jRPQmcnMUc+Sz5ObhS0T0PlHG+4job/oyBkS0bfQV\n0kvknqE6EX3Sf/cMEf23RPTPfRkvk3vm9gryIXLzZJ3cnP/OhGuJiGbIzR2051FyffxeIto0rv8+\nued1Uvpjcs/jCxT312O+XmNK56pOVf//Hrm5+W5y76cXKczXXPrEFPnX/L9/R0S/RD/84QsUzy+i\nML63M+Xc9b+9TK7Nv0NHRz9Ht27dphs3wntkNHoXff7zf42Ojuxcvva1f0FHRz9V0J5p07sorv97\nyT1LL1J4D7k59od/+AH/+f8g18YXyL2H3P337hHdunWb3Dh+isLcfC8RvUpERD/60atGHf6cyOMl\n+sVfvEVHR79E9tqQTzdu3KDPfOZFX4crNBo9R7du3aZvfnO6e4mInnhiSPfufZyIiOr1J6le/wC9\n6qvcaDxDo1HRHLLT3bt3fZ3cuMpxttIrr3yPQh8TET1Nr7zyZlpdvWxc7d5L9+4RffGLn6Xf/u3f\nICKit71tQO75D+Pj0pNpFmV6eOmkSPu8/1Fpcb6vZFM1YGGWVIYRxzw5WMsQBllahZscLFtjDrSD\nnOUYVIxtdrxUTROAzJ20NOoodzklBjhoab1WaR2TVIoqx21sc3r0LR3ZZAAJ9B0i5c2xbZ2Dk+SY\nnaUH96NPQX3Q98GBUjrQwRrU4dTan6OcgLKhxxxtsPjF1pE2lCYghbbPIVy2vlYHKLEsrXA2Be9Z\nq1OALtAX9xdRhKyTFFBD1o17YL3HZ0Tnk/0PhYkVdqcsOfqKvE+ebEg5v/TIOaZqgG9ttU3mv2vk\nAd1vUAdkuHdNJ7CoGgeibMs52Kqz1oPWVA1djvRRyPkVWEF1QNXI0SNyVA3Leq2fmwVfT3nCkrci\nx32Muuj3F+gpkxQiYqpG+C4+0ZiWKpDqSzvdZOfQCd+OfbZoci6ATL79zipqnTwuMnNRmGtcK+d1\neipYry/dt6rISSTdcion0+Y9Le1C3mtZukFPSWUaw/OnaSQlx/nBJjqFxfmhA+WkQiVwvu8E58AU\nBLrj6kZjzb98rePzOxxTCHCEh4cezk5wXoPTEzivVojuIq40PmvnO4B9fd9jbB/JFh2zL3IaPQ5S\nYtIxbpWDwyKc7MCtlQB2heOXIAA3jnlHHAN/9IPVBzH/MoAkOE+CHzrn85NOdcgXi9geB5AOesMS\n21zQeQ6cUAmMNWgBL1uGALfG5bLxHZw1MS/Ql9h0IU9QcPY5qKZoPrI+QteLM/pHAybZT02O9Y/h\nqImN27Qc2zlxnZy3I3Gt3LhhAwGpOYvPPFJ/S1rNFof5vcjXr183VAjkpm+BiYgDdcptCBCcRPPT\nnfMmnNnaXK1eFOXKeXORg2+CFb3yjmifO162dYAHHBRaBtztXmNm9pzoS1yrXVKBN4aiXGy2tFa2\nHJsLHJ7tC0w0qyI7WnQvbO62uFJZ9nr3eM/Z1INpNInhcNjr9b0etJy/0wfkiMGXA8wAZZrqYI1L\nTJ+I2492WM6ZjcYVZs45sYX3vDUfW63NwjDd07T5tJrXk6gXk/KeRrHDdvQrvqfX60/kbtsb34tT\n91uZTpZK4FymKOW4ZuF7/aBjcb4kvtdgRVqK8bKGlQdWJoAh5GkB5w0OahQATPqaPqcguCioSA6k\nr2S+3zTysKTuAMgBZObYgULZTtQJ5fQn5F1klQPfFvJQmieqQTC8/jV4Q58CzNzkoPjQ5iC5JkFv\nEZ8Zv1t9uc2x5KG2Sjc5qLdIC/uWqMsiB0UIBG1Z4aDeIBd8qw5NloFq6nU4UFrOihJogG9u5YnT\nCAkYpHV/xDEne8DBURCnDvkIg5XK0rFShAMuFtjE2IXF2JZI2+de74BrtVRxod2+zMysLLvuNzga\nAgiMx2NvxVzmANrls9riNCiSnCuXOXZMhRqLflbceNTrS4mTFLifwblQbkYawoIqn0tIKGp+KN5Z\nod/hHBcAbfws93oH3Ok8ovIL7azVLp3Cocx2FpwmTePwFpz4pHNncIac5BxoqWp0uzvH+WteeK02\nH6mQnKeSRVE6D73mk4Lx9DSgWD+7qB+KuOZlOvtUAucyRcleXBe5XsdLvM8hitk+h4hi0ilQLo4W\nqIKcGZQF5AI35BRoyAVslCkHeXc5LNYrHB+3aqsRFgvLoanNeTk6a+Ogr7Gc2KwQ27AGW4Er0L/S\n6W2BbZk05L/MAegV1RGOWNI6JscKQU4kVQfjMGAHvAf+3kscdLyRv6Vnra3w0AoGIN7hYPVbYgfW\nQYtIQygHqggc/wC49HUIfWxZO9E3aFdTRM3U8woUDES9XDHqJh0M0XbMbQmmLZpIX4y7ZY1vc7d7\nLVEjcLquMxxHIFxgaSnHghuHhHbPtHvm80Bvkl5ueqxsPU/ScVePZc4xWG/+0O9uHKxNfq/XF9bw\nWHEhBYJr3pKXK5uPy5nWUhjqlCrc6FDZRckK8HLS0M9pHUdJW9I54d7t0KzG/JJhuiWI63QeYw3Y\ncBoQ7u9nrciTZNpyFvOzSGcNyk8Cxs+6bcEJXwLnxftpTpkKUgmcy8TM8TFhpaKP9ZucqmVI8LLu\nF0toNEu5Ogu0SQt0W30Gj7XGsYoA+MBFR+/S0x2BIDQggBUXIXu32aY+7HCsIQ3rtOb55rSOrXIv\nc6CtSMB4Qbz4ADDlESosyAu+X7RFeZnDBkGOWVH0RWmxv6n+Zw4BRKwTCESpk/0wx0HJBNrSFhce\n/O153x8IEjLkdKNU57Bp0HW4Ka7b4PwJQduXdcg2sJYWTXfy4SynOYCtOf6SZrHC4LGGDaG1+cO8\nstrk/rbCQEtAgmc2WCXlM7vMIapgsB46C7XUEh+J4CCpWkuttpbVyy1WHLD6uS+uwWYCG67cqY/c\nSKSnSxZwDlQNu84aLDl9Y1DQ5Fxos9XWtO9jkBTXacyg3MAKO206ifWySAdYgjP5vBbJsEk1C0sW\nTdIGchSPs0ihbmOW2vQPMprfSdN5UkWKUknVeLCpBM5lSl6OTnweTlFwgLOsSNjZSpAgAWGHHWix\njpw1KNHAE85mwcLk6iAXYLlI64VviR0Y0xZTgPs7oqw5fx00hQ84dqRCfhD2R5tXmei68cKa4VhC\nTtMFtJVS9knf+B2gGsfgOxxTL1rG2OxziBSINsryBhwoD9iYSKAH4GEBZ22l1xuYNgfnMej2ShCF\nKG7SsW5f5SettEVRE3Gk32XbUUlqQd/x+SFYSczbxdg3mx0VvQ/tWTTK2OGwKRiI/kWUTF0fXJub\nAw7UWI5VOnJdscwYThK2RQCKEC1TauS6fCxn0Jqhl+vmYa/XV1Jiui+3uVJpC56wZWkGmBtkfpfP\ns3Z+XOThcJiA1xD1FPMznN7o/rMdsPAMzB/nOYmTLEGSA+LaWXRUCIqsfKa1Xk66Dnnn5OwsACcl\n8/KyaPisHa4XTrxJyKUw99JTkvsBma+HdPbAGe+fOIhPmc4nlcC5TOZD7AT+4VCDMM36BQqrrQTR\nFr+4xg5IwBJa9CK+wym/U3JLt5MXdQpmwAOG4gScrPbZpjno8iV9YdJ1lgOMBorSMQ4WNH3Pvvrd\n6mssIrq91vVWSOgdUV6TY34xuMI4LQBXust2wBVpAbQWV/DWcyon6L8FJmpxtZrTzp5jtwGT88E6\nWdjndIMg582+uA+LjKUiIXn3Kxz0psecWr8PVb0WOYTbzlFDpIZyAHX1+nIEnnL6rkiOy4twyXoz\neYetENdxv21HVsXAJ0dfuc+xVXvL6DNYtqHv7L7vdB7xXGMZZRKbE73JlbSWbZ6ZWUuiCbq84MC6\nzbA86gh0gXowYE33qtfXJtItwmlKM2s11E584OuiHtevX2cZNXD6I/s0OMYk6+W0POYccE6BvuOv\nF/eRfA/usgZsrdbmqekPss1OacQy2OT1nV9vabrTgPvnWJcW5webSuBcpsxRKxY9LCQDti1GMuCF\nBeAQXQvgOweu5WfrZQk+aZuDgkSXA09WqxTI/CbJSu2qz0tsc0w1cM7RA/R1mu9rhSsHaMkd4wP4\nWcoEus2X2bZcQ8UDf+sy2iKfXD/CEm9Zp2Veyxxb7TVwBgcX9+PFr4GpHFfUL1hO03mZcmGDpR4n\nKTpwCeYurJXg3GKzgPHBJhJA0NqEyXkqx/KOp0CtcuD2x5uW8XjM4/GY1ilkwgAAIABJREFU2+0u\nt1pXvURkvLAG9Rs5NpYjYaBqxPXrM+aStMDaIN85FwUetZ4z2hoY5pdznov9CZrNjulD4UByuimu\nVpe51zvg8XisnMyC5J8FoFxd9zmEK7dBpQ0K3Qa1Wm1PsPDm3zm5kNIyjxC84qCwjlaadD94zDGn\nO32eUUe9QZNhq/O8cMzpFqfvsu1ozk6bLDDZ7V5L2mcFkTmL9KA5z6cpL3dPKUf3YFMJnMtkUDVa\nHHNoVzmmM0iLkaRq5JyqNLCWYAXgB7Juw0w+ACyQiNtWL4tDf791PA71g5scLIe6bdKiOjAWGovu\ncMBO0UBfp6ko15JFywZcoExoT3WpDbzOKSAG93qXHW0DG54ibrMF+iQwskCFZXHFGGrLq6S1yLa0\n/XiMOOgEb3Kw8k6yZIPqsM/Oupvj0MPSjE3KMjsghd+anEYT1NrTst9BeZHXWlEcYX0dc9g0LnKj\nAdqS7COcisBSO8t6nlSri9zt7ikJMes52hBl74i8rXnm8q/VVo7ztedkWHhtrrPV9zdVOeG3ZrPj\nQX/oR2dNxuc0P4C4MF9wyuU2OZbjXtpPIT/tuBZTNRBRc5aJdrlaXUkc+mx6zMn4yLLMoNOctjkH\nuh1wdScDtdqi4hwHyo8tP9jlSRHqwn32JkCCNwvYus3ZdJsA2S7b2fMg6S89JmcBeF8PKhv3U8dc\n1N0ynU8qgXOZmDl++cTC93jhgv9rgVrIiunjossc+Kw41tbOUlsc6+3mKB97HIMYBBiR1pKcrq6m\nTkBNQlol0T4pY4ZgDrvsQPMGB4clWJyw8MGKiE2ALA+WTNQzRxcBsDwQ+etjbalOoC2l0mlQngaw\nul/yrkccACRAZQ60Xs7kCWthm93CDPqEHJegwZuntBxwbGXOqYeAV7nAdn1AZwFo1/xr6WxktcUC\ngtaGTEoq7vs8YWUOXN+8DBvmC76zynB0lm53R1hrrWdEniZgDuhgJHJ83aan1dr0kmIpaCeaVVxX\na8M27cZsJJyO3Sa7Uln2QDoPQEMo5Zwqiw10Dg8PeXZWnqC4OVapNJPrHPiTUoDhnkolDsJxUuCs\nQd0koKoturpdzsIcv18ajVV/SoEN6KSNTaijRdXQQL4I7KUWa9A2UnpR0dqT2+hYDp32vfcHeE8K\ncs+CRnOWdcw9v2U6n1QC5zIlKT5O1Y5fWlWi7RcwqcEqNUHlQrTt/0FDGFbeNXbAGI5qGhjKaFMo\nE4DR4qlKQCbBAu5fNxYUHVBC6wvPGvlI4CBfarDErnFw4tMbEa1QIsExLC/QJZbX6c2JpASgz1GO\n5vEuctgY4Hcd4cyS/cP3YyNPHFsjcAZ4zUXUmQHbFpI9DhsLWK2tY+CuuF870S35sQJIyPHFNfjJ\nAWcAQWuzlVNkmVN/F3HmZXkWrQV1XOVYrtCiM/U5boMENX0O81XzyBfZPZOoK8B+95jOESLbDdg9\nP8scHCNlPlDQGEa/2RJ/+0aQjVTz3QHCyZscze119QWtJ4ybBi9xHdI50WptGk57xVSN+IQAoHhJ\nGSXC5gVR6yZRN2ydfbfhsizMsdQcnj2tqiGBbxqoKLcJwLywgbf7exoJvgAI5bycTqLNApMnle1L\n6+DeG9qZFOmsHDdPmoqBc5Xd2oBT14tMVD11WWUqTiVwLtNxkk4vttoBQJ8MrwzJOHnNAQf1ClhQ\nEV1PAxaATFiPpYwZFmNNkcCLOXcUCW4twLVeaBbZBnO4poirDXqBPqpPuYLh2DwNLuF42njJaUCO\nsi5xCDayyUHtQ0dmKwLhoLTAAovyoSyR4+m2majCQc9aRs/rcCzTJ/sFIBqWcgucyuArcuw03QOb\nGdmnAObSKXXAUq4q1AfzTlvtsXHSAHJN9CXaIeUCZR+D6mEpeSyxA49bnLNkhg3HiihPK5BIqog8\n+cAc0eViEyutyhLcYZNkzW9swGJHTByNOwBwYIzRLHe717x+spREDDzranXF6/2mwFcfxQen5AMG\nHzsEG5keOBcpjmjgPCm0NNGu6bQnnQOHwyG3211ut7vHYDEGOvKEJw2tXSQPJ+tr6+z32QLK9foS\ndzqPcjBIuA1rq3U1U8c0j0lho4vrM52FNe0n+VwVc8Zzzoun4Vc7S3/embSozCJqzlnxpkuqxusn\nlcC5TMzMwoN537885zgATGmxyVnwRv6Fucmx0oG0Lo5EfhZwkZq02mq9xMHxygK42jIuwxDr42WA\n2TV2/ONtlY8Fti5xAC5om+TEakus82p2nOMrHMAWZPrQfou+ssmB063rpi13OUAhgReoNuB4a+qC\nvh99Y1FPwN8dcgDVsHgPjfG0Ni6YL9Kqn6uHVHqABXeWA10DQETfi+hxEuwCqLY5BpRtdnMXfbXK\nKfdZb26u+vZaZa+oMmXYaLnhaHNwWpQ0Bljv5UZnwFgMU9Aenh3nRBfPJ+doiMieh2xbv+WzHv/m\nrJwS5MeAG9bS9Df5ztD8cQe6x+NxBC6Gw6EHz2HsarUV/z6K22U5TyLZVsxUtcIBP0ubW84bN/7T\n8pZtEIy/xxxoXyn4msahLHaUXGS5qZWW6/i6+BStCBBbIDUHFm0LeDe6ZlJKed/2PLNUSlKeerHj\naFGyNgFWHmdNwdB9UQS0886BJXB+kKkEzmXiw8NDpVvrnLeqVc2bwiKiH1Dt5b/gH+RHOQAFSQ9o\niu91XuDbSp3dPge+pgQwWOQADNMXR1gMoZTxKNtOflbACkTOa3MA9Quc0ji2M+UvcQpSpcoHjqa3\nOFiFZJvuiOsB1qdx0MIiI7nMqINclA45WJ5Rnrb4X+YQiRFgDhbVXQ7ANgesdjmdG+grWKkxxvpe\nS2LpgInaXK+3uVK5wHEAHXlvbj5sMeTMQrm6jGnoQqsc5rmeM2viOm35nOdGY5273WvCQlv0HEBS\nUf4GS6vsPwcWer1+5DxWry8ZjlfxnHDAA3VJx8EFhNln28q9e+ygl/4mA6ug/2FRdhuHWm3xGOwF\ny3baH64OoOn0mWh0fN9k7mt67J8CLvgHrPh5p2k5eQm0HJCKHflwspSnCRVRImTSRg68FyWgTOtk\n84enKa+ojZbeuHvGTkZNiJVCrHeBNAbEm5gwv2Njx0naV9RGq65n7Uho5Vuvrx3P8Un52zrsF+67\nTmWyUwmcy5R5UeUCSrTUAwpQqRdlDUJhCZDWZm2tsPiwAHQyYIIEleBZWuCjz4EzK8GypjNI8Xhd\nH23VSzmAwaKsv89J2gFMos45a528BwAayhsA6xrogo+dUyrIWfvhqGmBI1gcNzkF4nC2u8k2WIVD\nYHAYCsoY8vqx6mvJpdf9csdbUSXNQYN9qx0AL9CsxvXQCF7mXq/HkwKUuL/xG2hN0ip+wLq+QQbP\nWZQn0wO0HJ6crwDO8VjgaHk6Luocd7vXjq8JHOTU+azb3fNATTsbOs45AGnKp7WskVr6EZsSuTFL\n30c5HeKipPthkoxbfBIQg7AiCbRi4Kw32TZN6CTgaxpe77TAeVpQmadqgDoG2tkcN5udM1W4sOa/\nHvtc/U4Cck967VlRMJBytJNp5kfYYF/y/5wxqEznk0rgXCZDRQMLtwZCWFQus6Mb/Dn/wMrr8tzC\ncGSrQfYdUabmtEoACQAH2sQOh8VZA8G2eNlaoFrmu8yBt1282KRKHuiTDU55sBc5RGAMliv3T/JN\nrUX8pvpbLuz62HqOnWVvmYMTI1QutGSapsvoMnPjtsAuSmJq2Yl5uvqEAhsoef2Suh7/SwAqNwSy\nLtfY8Wb16QNODWQf6bDPAMOgiSz58YkBkgtgkVPrgKUZjq6QmZP11pu9VX8d5gCAWe45sKghoMS0\no+AgwfpvR8bD4j4ej7nb3eFa7RK3WpuJ09aFCzhdQruCZTeoL1jPp1PGaLe73Ok8JjjKkqKl26E/\ny+emzVbgkuFwGFlZ71cj2JKBc+1NteotCbSivGNQGVuuY6UPt4lsNDrc6x1MDdKmsYzaFvVYBlBr\nNE/qUwssujbGgXO63b3s9dOOVa93kFEKyW9irPLOQy3jvJLFN59WocR2pJ5/gLV/Y6USOJfJDFZQ\nqcxxbGHSoEiCTcnvLbKiwvooF8o5DuDSOvaT1jsA7zlf9oBTAIJjUWlRtD36gxVo4OugoxJiQZDA\nV6uKQCYLig+4XnKJAa5R94uqD5aNPMHtXuEQzASApYjXLKkLdzjoXuPzZQ70Ews4axAnHdTame8B\npGHd77ADuFCCkNZ+qXMtqSJWm7SqSFuNqwbkG+wAMRzdRiJvbbGV0Qvjfnz88Zvcal0x6gN9c1it\n0U87HHjJVXZWRWgNS6dGuTnUG72LHCQW54yysXnBHMPzGdrV6/WPn+mYJhADeusIOHD/Q8Q7otVj\nqgezs1o7egnynE+4xzMzywI8b3H6PGsHwpykXaDJ1OvLkTW7CMhajns5sJlurOxgItMAKASugXNg\naoEPFINJQW50eywO8rSqDoGHPIr6NMdLPylX16ZqNE8MymWdY53q+chqP2kTo9N58pFPmqbhL+cD\n1qwmG2OZygAoDzaVwLlM4rgtOGBUKot+UYQVcN14MCXvbOD/X+QAjuQLdY0D0Nri4IAnedQ2hxIv\ngRD1DM48c+y4XXKxh0URO3WoLqBOAN84Skb0ui4H57A2B41euSCgTG2pgsXecgayKBGWJW6RwwZi\nzHZo8TEHpYgiy7wEgyO2NwBW/pKKAKu+Bn8WmMPYh41XsPoucuC2ypDrsl8AxHSb0OeSBqG5xjH3\nMdwrA4JYgGnH/y6tty4PpxBhRURrsnsOVkX+141+nOE0WArApKT6IBgPTgdw/RwXb1IsEBpA7uHh\noecESwdDaxMaAI3LS1rOl477gjk40jlKh8vT+UVYz+w6u+cJTrguz3p9icfjMfd6fW63ux4Q5TaB\nerziZ66Yexp4v+50YsUM1BG4230O6kDxJsuy5GrwY4FbiwqiLaXTgLoiGsi0ltFcHu77+Jk7ad72\naeXGqag1zOw1u2On3m5359RW4PPiI580TVsP9L3Vr3JjrFPpHPhgUwmcy+Q5jumD12ptcrO55BcV\n68HUYA30CAACcHIBegDiltmBgSYH2bp1tjnVKxyCkwBcIcKXVhZY5OBoBy1fXJ+ToAOokKB7nx1w\n1HXpi7YecgCxKxxbESVws2gYlrVXgwMLUEBpQ1r4Jai2gAf6TFu/sElAPS9ykPA7ZAd6pOwdIkfq\nOq9wPuiM5ryOOChLSHrPIdtc7TRkcsqRjS2k4X/wrmWEScwROBXKspzDYAA9FpAHV17mZy9YwbqH\nTQ4+y/ro05lgEXRtgAVaji02P3jeNlg7zOXCU0u1Ax0II6eZ3W53hcOhNX8sbr8E93Pcal1NQI+z\nyOK50ZrS2nkXJ0thM2hFDAwA8WaSb6XSjPolttxisyzbntJBplPPcPVzKiYxCNfA57TAucgpMvd+\nt+od897dKUq9ftlvGkfRtbnkImKmz+hpOenW89Rudye2cVK+D4t+gXRSy/dJr3cnQfHpXKXSPI+m\nlIlL4Fwmn/KezFAZAC9ULi7y+Bs0BCymW5xaOQE6AEo0WNIUhlVOvYWx0EGuzjqGx4Irw2FbQBTq\nHfJ+LOaWYxNeZrB4W4FDNKi1+KpabQM0Dxkq2gJkeDFaXLh1DgB3TpV/h/OaxprrjXxlwA2UrWkS\nq6oMWVfLUbDPsZVZ/j4pBLj+DnWARbov2qgt4HD+A23EXpzjY31rswGahLw3p94hx0aDTqiUYHOm\ngV4RV3zM6QlGsCo3GtbJ0E1RNp7p2KpavJmznnc4EcbW3eAsGp7HRmM1ok0w60iE8iRIR53EbzaP\n3laSsOlMFuA8PDz0IDcus1a7lICsHJiJucxa7jGAcCt09SQrZKq0sJRsAKYFz3n+7yGn7/YlnkbW\nLY3O6GQ4T0LViCkl6XugyNL6ZyWdhmt9Ekt5vQ66Xzidq9dLi/N5pRI4l4mZi/QwsTDCGW+NHQiR\nYABKAgAlFugYcGzBhNUYL5NDtQCvcKwhLBd0LXEmrWo5IGhxHBGUBRHrAECxCbCk0FY5gFSd3xVO\nnfE02APgBkcVdRiw22zk9GRlf8m2oN9w3SI7feEux6obI07BPmg1HQ5gE3MgRyeRFujrHICcBFmw\n4Or7pQV5zPEcwpE+ADDKSCWWAiccVpYBxxzrXFASuQmQQGnA1eqij+DW5/F47MEJArVIrWo97pb1\n/wIHK2nb99Mmx9zrthhvafG05+rMzBpXqy2j/DscguNssw2A5YnPHd+vl5log6vVi37TbDliXlWf\n5bOlFVFygVt2o99Sx7kw3oE6oeuBfltmizImZcdSC3IeqACwOWtdDHZrtcVjabSiqH6phT8tt9W6\n6pVJ7MAiFifbqmdOqm0aCkQxcLbejfvH3xfl32jAuNH2/y4cA7acpVc7rcbrTiz3qUOe/1lNp6GM\nTMOJxpxwFKpNMQ6b3GhcOa/mvOFTCZzLdJwOD7UepgZlUhIOwACgjzlo+1qLX7o4TQa29kJpgzoc\nb0urmCWTJ1/Q0pom22WBK2wglpjosUwb25nv+xzrAYNvajkZyvpKCwKcIXW4X6s8Sb8ADxXWvC2f\nhwwxDiA/xwGA5qy9AI/YCOk2oJ5LnAYgmfdljtltjOCQqSMrYjwWOWh5S7oEyhqIv6U1PDc/cmA3\n5hRXKouchtJe5rA51Mf6F9gt9pCBwsZO1i+AVpe/u79aneNWa5NnZmBRzT0LjiPcaFhOi/scJPa2\nVP0w7nhG5XMgN2cYB8w3BFnR5TgHLQdy8XzkwomvsuN2TwKbjp/tQKGmbiywAwSgPaXzXQI7gIlu\ndyfSpi+mXVibXX3y5YLLBMqK04ZOuajTvxdSwI85sZxV2TiNs9tkqbYcz3xyBL5aLX0H1GpzU9fF\nDnbiTnaq1ZWJjoAPm4YxbflSKWRSGPFpy403HPp9tcCVSqnjfF6pBM5lYuZ49xoWB+uFihf3Jjvw\nIy2LNzleuCeBXakmkXt5a8urDDiRywsWp0scAypQRWAB1851aB9zcQANLBYWENd6uRI0zft+A2db\n56+ju2nnPFh1N9gBkl3Ogxbdb6hHHpjFlnFb3ijUL7fgIo8xB4c4SdfREf1y44k67hvf42/J2Y05\nsMWR/3S9rXbkuLvg1et2SV4vylvhQMuA86ksS56y7Ir79NhJC67egErqB04YLGdQ9I8ce/m3CywT\n8m1zAL13fFtWuVa7xOPxWPFjc/Ngg91pTixvl3N8cr4Wkq6EUyeA8NGJqApFoCYGoNazqLn0N4/r\n2esdCOuxbrum5siw8Tbgzen3TquykbPmTiPJBkAXB8Ba5UqlWSiRh2T3XTt7fVoXa+5c5lrt0kSA\n+bAd/07i8HfW9Uz70TL0lFSN80qnAc41KtNPVLp79y498cSQ7t37OBER1esfoF7vU/TSS/8Pvfaa\nvvp7RPQCEV0goqeI6NNE9D7/25uI6H8noh3/2wtEdEBELxJR0yj5zUT0DSL6FBH9QORDRPS0z/uG\n/zwiop8iov+ZiL5MRO8V176XiJaJ6D/3ef0eEf2S/+39RPR3iejfENF/Q0R7RPS3fD2fF2W9WdXt\nP1Jl/C3f5h/7fB4noo8Q0YeJ6EdEdOTz/iwRvdP/T/66XyeiOSKaIaL/TtT5N4loKMp41X9fJ6JP\niLrtk+vDH/k++bHP72kiWld5oN8+SESfVL99ROSLdJfcOP0BEd3z9f49/9uf+N/w95H/+zYRfVy0\nUSYmonf7ti4S0S+IOrzo2/ZLql4fNvJB+oa/T7ZtUtojNwc/QUT/ioj+sr9vz//+9SnyYOO73yei\ndxDRP6W0Xd8h1x9PEtFvEdE/IaIKuTn2877sIbl5inTbf/dpcv1J5J6BXSL62+T6ZYNC+18goiVf\n9t8mokeJ6D1E9Cvi/qd9ubp+X/f/v5xpb83XVY7LLxPR3yOi75Obc3+DXnttjz760Wdoe3ubwvxa\nJ6K3i/ueIaJ3UKXya8T8x0Q0S2HevZf+7b89Ip2+/e3v0K1bt2l2tkb37v0OEV0houeI6LtE9BR1\nuxv06KPfotHofyEiolu3bhMR0Wj0It24cSPJj4joxo0b2d/idI/S98mC+PwSEX2NiF6mH/zg39Oj\nj/4UHR2902x7tfprdHT0lyk8G+vkxv5xkn3baDxDo9GLlE9X6N69A/rZn303/fRP/0Xq9/8SffGL\nX6FXXvkeXbnSoT/6o4/Q1tY6DQbvoVu3btOtW7ep3/9L9NGP/srxe/xLXxrSZz5TVEboo7t379Kz\nz36Mvv3t79DW1pvpYx/7O0nf3b17V/T7u+jGjRs0MzNDP/5xnOfMzExhmXE6oPi9/wEi+lN67bVP\n0le/SvTEE64N1jjeunXbt9X167177rvpxvz+07TlP5h6vkru+Zbrxo/OMP8y3Xc6KdI+739UWpzv\nK+UsEmm0sQVOj6kvsuNCLrJTstC6wYvsuNEIUGFZ0eC4tc1xCOicRfmOLwtWLTjX5fSAtVXWugbH\n65K+cJGdhbjD6dGtdoy85MuXzi2wMLZ5sqMbnM8sJ7m+qCOsf+AVgjbR5uDYyGyrgsgQ5GirtpoP\nOVBE0Kd9cY8MngLLs+wXUFGaIi+Zv3UsDs1nmQ/K2/C/dzg4PY7YzTNI5mnFlFVxzS7HlstlTh1O\nNf0CPGXdN1CEAQdeW/N1f8AKJOf6EseW2txcRL7WOF1m90ytZu7XWuMuqtj/z97bx0iWXfdhp6pr\nuqe7q/qjqnuma9UzvdqSN61WjzktKUgbDaRIa5YjwwphTyUQojBoSw4ZKRS/tpZaEvoIQ7Yh0fFQ\nlGLHxEoie2AZcIAIDOgAqvEmFhWQkQObWdMrwhQthqBD0ytzRQvYDYdaknPyx72/uueee+6rV/0x\nQ5HvAoPpqnrvvvv13vudc3/nd+r1Dne7j4nf5DqwdljysQSpagLqCF5u51nO9c8OcEzVAdbGCTXO\nssReQNw3sbcuHqPwPHOSaXHfEVyqObtxf9xOWKt1JfI4pim0sYug198g+i6mjti0B3iec1SNsjSH\nXB2Hh+k9fnh4OFU9cXKbNDYiR0V52BrNZa9/Hu1MqRopJYqodaprVCVfqKJqVMWWPOqLrFAAL5Y8\nmA58s4Dfvvgd2q0aeB6L+qxsYgArAEmgChxxAGMyKGjEQWUBoBwv9Ry4xpY3FCQAQvvG8agDBgPa\nusi2jnVOJUNuqQ8zx8kXuzXuK+xA/iEHQLLGKRhtsUyNa2+zWlx0BEUt+b42xfyAkgBwLSkWV9ht\n1V/21zpkO23zgeh7jx0dZpWDTrc0EBY5zZwIYAxeNbSyZcDiKgeaBSg1Vzhw9BFYd50DVUAGHM4Z\n87noz0HiEAt8SgoQ+rjBzhDAedY9BYNzwDMzlrEBabwULNVq7XGSDX2vyQQc7XaPb9y4IbLoaaBm\nXReUhQMFEFeSZB4OYFpjIscWcn2y/aD47EcJWM66hOBAa5v7ilfbSGMsarV2BFi1Yob+Wyb00P1J\nAZCMH7GeUdZY4nMxj1q2ydEzmkm7cmC6SE+6Xse9scb1+lzpoDcZEIkxm0bGbhoKxHlwoR8mVQP1\nhoBRGJ9yDS+d+hpVsUsFnKtiSB6tc6NhcVxzoHjSw10Cvi3jBa29y1BU2BV/L/hzhxxLJ2ngMGSb\ng4yXIwLhtIfRChQEyMpxtjts80kPOPX4WuoLOxyC/eDd1cE2UiVAGht63Lc4ZKxDdkKtw3yBg8yU\nBdJzwB1efaimQGWizwHUo62SUwvQJMGABVJg3CDzH7jU2tu3zrYkHOZjjlMAqDMfwjt/xc8TgKk2\n5jS4s9Y+6ivi+vY5SM9JLzU44xc4NhLleoAW9qJRrzNeGo2OqrfNjcayUq4IaxPpsZHhLgZEMCph\nWNk8bygdaDCiwZnjIqeAXAJsy0sKMPWgAr729vY4vTcPPJfZCl7bj9o4SXqtPNfa1Y/5mR44DxPl\nDi2BF7zbqWHd613L9iMHnIOsYLify6p8FAcslgOZZQDxeXKhpwkOPM81bT/v5878OlVxpQLOVWHm\nkDK22ex6LVjLU9TlGLTK9Mk45oDT7XsJSK56UADpKTzoAVL2OWR1Q0AVXhrwfst2WWAll5EMD38o\nJAAEgkaCv3HNFgevpbW1rMEwvJcSNMrrL/j+I/U4PKXaSJFpyNGWZeP6EvBqr/1jvu6euJ6cC3hw\n9ZZ+LhOcvgbagO+1Sgfk++QLHglvrL5IowXt7Gfaktv+X8ucg3l/XM0jPPVynmE8gJqB7XPLeOqo\neUawnzTWUspEbFzsc1CLkDsPeqwlvUJ6/LE2tn09Dqh3u48lyhUhE2io18qol2bolO3fZqIFvnHj\nRpJiWgIDG5A/wkTb3GpdGUu8WaBTg73zLKPRKDO37THQtAwbCQ5PsxWf2+3Le6L1+ro4XkfIzJgD\n9LOz697LfMzp8wk7R+kOBcbJAp/OOIv1g4tSQ5cZs7MGow+b0vEgir1TWCVAOa9SAeeq8Gg0MrxD\nua1CvNQ3/EPb8twCaMQargE4HHPsUZPeLe1lhAcOW+8a2A3Viw1ZDou2zTV304n2p9eUAFxvLY/Y\n2iZ3fZpn52HU1xhwrI99lQNPN31xxw9BJHxB8hQNaPT5VipcWadUv4DmM4BrRxwHmTN4c2V94Pwu\n+993fT04XibCQPuHHDjtLaNOSW2wPGyg7MiXhEznnuPetv286N90VkGknca9AKNpwCnPXSqEyDrB\ng84pc4A2Au+55kZb66rPwcCbhjccDNJ6PQWIzlCW561zCtqRDh6AKt09cTtU7v6t1zteNk/PbVhX\n0usnt5yd3nHYxThvpQQHqtJnRat1dXzM0dGRz6wKmtMKt1pXxlJppwFm4dkb1pVFmwjUm22/Fvqs\nKW212kok35ZT63B/SwNTG3xhl8aS+pNAtde7xpbnuty4nxzMTuNFnnSthy1pdxYlGP6VqsaDKBVw\nrooQ1ddeIm3BggKAl+aA0+2hNgfKgAScCPzY5jSAcEkcm/IyA9DtoW6uAAAgAElEQVQYsG1ZH/rz\n1jjm3spryEAtKzlHmwPQk+BnwLGXHYATfbIADGgtG749m6puDXxzcljyISi95QByPXYAOZfwQ4Nv\njBNAsgZMAOYIUpQ0jElebr2zgHHAcdc4GCPgEEsNcA2ctzlwo6VHGtxzBF0hkArnb3LsndXrU/bB\nmj98B+NJcrYtWs6icQ0YRJNk9qB7jbZiznY5fQne4tioyBlFRWA6NbDa7Z73umLXIAf2JSXAMko3\nOTUA5G5CmkFTgxc7AVPg0ubAzWmAjwNVqSEgveh7eweeEpPSTiDNJ4P7iriuVjstSo0FIsMYoR3p\n+q3XO4X0ijCP0tNuP8cs40a33YH5eK2WSZGtDYbZ2fWp5m4a4F0Esi2aInZD/iyB6NSZ4N7XVTmf\nUgHnqgg+nX4YwVt3i+PAObwArQfzKjsxdulhxI0MHpZFt9B0CK1EcEk86LGNf5njoC39AoDnW2+7\nW/QQcIyPxEMIAHeOY2AEHV+Lt3yJY8+kBAwwRqx2aqqF5K1qHV9tPGhKANoOQ2ad48x36MeB/22V\nHQ0Hqhg5NQRsycIrbYE7CRAx1tCjtubZompoSkGgCDgaj+TiSs8wgP+AA3DNaVEfs+3ZlX0AmMG4\nW559rQLRZqJHRb9yfOuw+1Gvd7jXu+6DyGDwWbsVcvdDjuEqB71yfS/qdZYCRGbmkEAhZwzKYFQL\npOM5ob+/xM5oSgH5JLpDLhCxCPjktI+LQLcbd5n8ZUEF9eF5lQLcVutKkrzEStpR1E6L1gKDxqon\n/7x2azZHr9AJXIhcQh2XzCZdO+hHUdudSku8/rvdxya+c8K4B5rJeQFnXK9s0CPugQetC32aYjtP\nlh92s75tSwWcq+Kj3/WLHmBui53SgPQ+tv1LxNpK3+LY4yeVDgD4ioCjfoBJqga2Sq9yAEAyO2Du\nIbjm27XPgS6igwcBuJY4td6XOPZqdjkAbQ0eLVABoA2g1+cUuENeDSBxwMHrrb2+ltpBnwMvXF5j\nnx3wt4I9tVIEDJ2lzHygvyvsONS6Pnjylzn2/qNugBNdLwBum4Oyhj6m7dt7g8Oa2OfAEwcfflOM\noeU97bDbCUCGQ92HkbomAAZ43hr0Wy8sCchHHCc3iQG0lCaLA7fSOmdn2xzvqKz4a1lBkUse1Oh6\ntrnRuDTmJjOD54v51qDcGWC12gU/tj0/BzEAd/zpnAd/jRuNReVhXIlSTOcAzPz8ZZFRMA1AK7MN\nPykrX7u9ldTRaLTFeGqDG3M49MobxdcPahG2xz31JAeAenh4GHHJ4z7ruUr5146H3PcZ6w7Gihoy\niHB2dkUlQInrKRrjMKfht9nZ9YnvnAdF1Zi0G5E32KZv08Ms9vN65WE369u2nAQ4VwlQvs3K0tI6\nEf0lIvo8EV0il7zkm+QSedwhov/OH/m0//x+cskY/grFiQPeQU50/VfJJYQ4JpfA4RvkEhl8k5xA\nu07a8SEKyTVk+X98/ZvkBN6/6esm//0Xiaju2/JRIvp+csLvKGjvC/77zxLR/0cuGUrdt0W240n/\n/Z+nOIEEEdHQ//+/+v78ayLqkktuANH5O0Q0Z/Sj7uuT13ib7zP681ZySUM+QHHiig8Q0SfIJVF5\ng7/WPzSu0SGi1/mx+CAR/aD//9+RS9wyZ/R3KL67S0SPk0vu8QYi+jsUJyZwSS3c79vkEqbo+t5G\nIfnJ91A6hm8lO/nINygk4CByY6TL4+QSs/xT359fJ6JfIZec4kNExET0o+SSf/wQEf0f5BKw6PLn\nfLueJKLfJaI1364L5JK8vEBu3N/q6/wEubV3kdxc3yGXuORL/vs534aBr/+7KV7LNymsv1/3Y/Pj\n4+9efvlr9MM//J9RvT5Lc3MX6P79v0YuwY8uXWo279GTT/44/Y2/8S66d+8eEf1136anyCXYeIJc\n0pAvUr1eoze96SfoF37h7XR/3Jyniej19JrXfJ7+0T/6LSJySS1+7MfeRC5B0fPk5uuOr+fL5JKz\nvEDMTxLRka/nKarV/pSazV+gCxcu0JNP/gz91m/9Nj333B+Te3bI67n779q1D9Mv/uK76PbtZ+jF\nF/+IPv3pC/Tccz9ORM/Ts8/+59TtrtPs7D+mV15xZ9brb6dXvWqHBoM30y/90t8hok+TTO7w4os6\nYVFckKzjn/yTf0b37r2esA7v3yd67rkPRok1vvKVl5PzXeInrO8NIvoxShP3vI22tq7Q5z5H5O4f\nty5efHFm3IaQWOp1Yp5c4osXX/wjeu1r3br52Z99M73//R+mr3wl3FP37hHdufM2cs8Aop/7Ofes\nHQ7fSB//+KHv16of8zYRvZ7m538zSazymc98hu7dex995StEn/mMS15z/37oyyuvEDWb76KXX/4g\nucQzeGZ+vnCM3bnpc9v67qzLzZs36SMfuTNeT0Tb4+QsSCyiE3t9/OOH9LM/+2b63d/9v4nIjWMY\nS9RcNsnSt1r5BsXvvqf8d1X5linTIu3z/keVx/lUJfVgwHOU3z51Hs1b3kMivcGWd0ZKvMHrcsRB\nn1dyVvU5knKgvbtdjgPZVjhIrm36NmJrvMPOC3mNAz3E8o5tZH5D0NuQg+avlE479p/RL+mVHKh2\no37p4ZS8Ren17IljMUcWVQMeyDk19vCgW32CB7nP8Y7CGrutewTKSU7vrjjXqg/zkaPxXOWUhqA5\n55q6YiWEWOFYt1qumWUO1BQ9TnJsL3EIsGpzLJkHvWpI72n6xDIHVQ0r0BRrHbschwyPb9it2fXX\n0frQWsmhzUQL3G5f9h4+ay4lxaTNRNtGconhmEoARQTpeUxpM3Ks9qPr7e31C54htn50/nnjrt9o\ndCJ+qfPAWwHB4fqW51GrSaR9uRW1qV7HDok8XlNf0jb0etcF5SBOTFLkRYeXV6cOD7Em+vjwGfzh\nMDZ5T3o81tj92De95Ht7/RPRYebn15Oxm5+f7HE+K4m48hQY9uOUBqjK4FStz/1nh6qBHVOpolR5\nnM+rUEXVqEp4+GjAnHvwSy6pjsDfYhtUSc4rOMXymG0OQMUCjxZf1aJFLHIMHKFyAX5i19e9zbZ6\nxxbbgRYAZJrysMwxpUIHpF1kLdcUy7QBlFrXQ+Y8ZLLTWftA88CL/4AdrcaaM72tu86BsiLHAAAZ\nShlaSULqQMvfIPGHF5MVKDbnf7/Ivd51/6LKZXsEpxgSgRaosOYfa3aT03HaVsduFfRffz9Q84pE\nNxtGG1Y4cIb1+tJGD6TkZBzBJge1lR4jQUx46ecBmbv24rgfUGRw5277PuP+tdRcNjnmTEtAnQfO\no9FIALmYS615vwHQ5LfrXX35/hapIhQBVksxot3e4DTAs8NxIGuTpZFer6+OQZUFeIPGcfw9Mgxa\n52gutyX3CeCc1h2yF0qwF+QG5f0Yz0+ZTIK535xcXzx2Fsc79945TcBnzPdO10ZxcGQYz7Ka29O0\n/0GXNBuqe2dU5XxKBZyrwszuARg8EXjIat7kKjuPLTyvEiDLgC2L99lXdemXsRXwgjp77DR4ywAn\nK+Jf8kuRxOOY44A58JOhvCBTeEsQbwElcG0PjOtvcQqKr6lzLeB4SYzxJXagE2nGrf4N/GfpzQNg\nBPjs+rrQVgs44XyMbRoQFY5Z5GDsIPGK5A3Dcwx+JzJLtrnRWBQ6udpQgY7yJOURay5uiTqscZKg\nxAqKkqoXk9aV1FOW38ODba1XrEW9A5EG7rnvIBcIzvi+/07L18GQhJZ14AI7AIG50B546/6B8bfq\nj++xNqBqtSb3ejsRT9mpQ2z74x9jeW9pD6YD2em6ByB3bbaCIV0/4O21ig2YrrIMVpbtceALSYsu\ncdgROFJrZoHdmmtHaaXLajGXSSgiwZiVztqWwIsNb1wnvrfSuBILaE9bRqORlyJ0O464r4uOLwOI\nywR8xs6etF965yFkyMSY7fq13CoN9qdp42nLNMA8rBO55qvMgedVKuBclUxwCoAkvKkSQO5yoCvo\nFxte+lqqLCcBJ0FrEWi3FBB0IojLnM9uiK3s9EEbPJs6KMsCtEVeTv2yB5VlqI4HCIOnOBfsh7ZD\n3QNeT8uTKaPlreBGS5ovF5CJ+RqyHai37eejo+qF1xsGj5Sb0171FW42uyLwS9IXmhwHfcp5ksC3\nKLhxh9O1MeBY09nSuoYHX9NrrHlfZadFbSl3WGunwyl4xZhZYA/3kbzXcM7Af/cYBwMPlJIYaAUQ\nqtcmUpCHOXGfL/nx0xrsMm29vBccKLlxQwcNgiLh1oP07jlQuMgakCOJhwOzUgd+6Md68lZ6Km3n\n7kEdkIgSgqPl/YKdr2JwiuvFWswrYzCmwY+UuNNUDasvMj26BHdxH9O1FgISMd/lU1lPU4JRFu6V\nIkWQSdJvwYucp/qE61pGVexJtxPCyPs53LPnqeoxbZk2+DFnqFflfEoFnKti8uCcZ6XJgUKh+aUA\nReB5anDY5QCEcgoccnt5Q70k9YNAe4cBjuY4BuAaWKKdOeCMdjDbcnwarA04NhJAXUA9liqH5lgC\nMIO/rdMuAwBK6oCUBNvi1Avc5gBscnQNDfY1Fx20gRE7b5slqabHV/etJ/6WXlprjVlc0mN2nmDL\nk7vDIbsZ2nXEwWAB3x4qE5a3/CrH6yzmLdfrLU4pLEvcbC5zrSbXGegMduKBVIED2+6WsdJnGzhj\nbnI7EnMcr0/pUQsv89FoxLVai8POgDxn0V//Oseyi9b46zWkP1sv7340DgAzARwhSyjmaKh418sc\nKEp9UX8KxmWRvNWcLi+OseTY5uc3FA3Apl0EugaecbgPHZVJ8o5T4GgD+bIlVuxIQVyrdVWNV7iP\nQZ+xPJrTeDpdv/Wzbo3r9U7ixZ0k/ZZy0+1ELGldbi1MUjhB36zjiPYT+lFROW/gXKb+eD1hd0jO\nQ5U58LxKBZyrkuXiNZtIsY0Askv+pWBtr2sAtcvxC18HPOnjpZbqLeMlqT2oknIx8scjwAuAQoLs\nAQfgJ70Nls6yvI70PG6L60nerATDi+xSC1svCAAngKhjtgPJZnwb2xyCyiRn1/JaDlU9ZYDzsW8/\nvGsH4hrQcobHENSVHD0Cf8OjIwP6hhyCLjXNAJrVaHebL15sG9vUoCDkQCd4qFLTWYP+Jd8OmVp9\nyK3WFW63e9zr7XC3+zgHAIp1paX1sC52OTUqYQzg+vsckuuM2KZ8WFQNuS7t+9O1tXieAX5mZnSw\nrrz3Lvs+w1OOdZgD8rp9+D2XGEV+p/upwb61dpGdUiZVOl2a7hh0pLtUtVpbATn7nopTT6eSbvKY\n8wJbuQBJR6GQY73AzkDa517vmunRnJaCYAc0uvGUPPDce0Y+OywDQI6f7O/eXj/JMJnjmpdr8z43\nm92p+M3nSdUos1biY+BEQpD+HBPNnVl7qhKXCjhXJfsQcFuYCH7D9vCCfynorXT5ckFCjW0OwBvb\n//scOK/6XJnCWL5gc5QJ+XK36BL6Bd7x/bmoHjCax7jr/wHUAkwscJxFENvXq+ySXtzimOMrry81\nf6U3xQIbWungAgdeudyitLIsdvyxmpqxoD5jfNc58KktUK4VLXKgXNa7wkH3WtMHLK9kqljhvJJI\nztLjvDJIm9N+gesLj3CX7SQx4IbDkNG0D8yXdd1b7IwagBOsEZcN0Hl4kYJ8wA6orxrXgarIPrv1\nJr2WuGZq0AUKhm5XSmWwgYI0dnA93a7lqC73PIBhq1VOHBUm1gde98a3vHbxHIaAQH3vdv1Yt806\n3Hi7OSiTTCPe6sduF4xZB3rBU7aoFSEYdpgN9gvZP/PBghawO0nQWT5AEhx5PO/kvKR0iGnB/aS1\npYM4i9ROLOBsBe/ldLmnoTekTpyFSMu6DBA+aXDgWfG8U+CcqvtU5XxKBZyrwsyxUL7zvD3KKdBY\n4ADAcuAUW+XwovU5BWaaWykpCQiwg3cSPGELXMpARLTnFud5zlucJqOAygXkxaSXb8gxUIbnUHqb\npeKGDEDSXsjcCybngZSfASYAsgCgr7JtgMhMiWEL3M1DjwM3XIJpeFEtr49sowZxq+JaemwsoJzz\nKFrfXeQgLwgQqiXhdP/xMgEvHHNhXRf0F00DkGMJA8WaO6Q8jz099fqF8cux24X8nrw+qCoArNLw\nucJuPeodHVBkHmeAtZRbu8zt9rpPdtEfv2jjLXtt7ADMW310nvNW62oGqA85cMYHkecXFIkUcFrA\n+DK3WleEAkiRlKUVgKspVWu8t3dQ+LwL3FxNu8JORJy+Gs9IR+sIQYZEbe71djLeUhiQwfM/Kcvg\nWXkyc7uIWnJNPntBGwkGhbuXi8bS5oeHZFEWvSAn/aapGpPBog3Oy4DSRgM7GJAZTXdYyuwGTKsM\nMg24l4lrcseEe8XaCVyd2P6qnKxUwLkqzGwFCOYA3ab/rKkXEvzOc/DEyRcgzgGwslQr4NmVdVvb\n4fDSNTlE/S9w8E5Kqga8zLI96xwAJbzXEnAXea7xItegx1IZAX0kx++DV1u+eDQY7Ij6LMALMC3n\nAJ5+yb3d51gXW7cV2/W6nRr8gsd7mZ0nEAZJ2DoNmRm3OAXwmvZgBY7CANAcXgCnPrtgMQvQSrB/\nS/1v9SsH6HbZDlRFm+HJl+mamzw7uzq+p8pJyGkZQOzugLPd4pBNssNEu7y3d+DpLEhtvyXGdhC9\nkJ13Ubd/kUP8wTGnhoMdaJXjoErPoMXlnZmB/vQRWxn4AFLCTlfu2ZADvClInPS8s73bsUGhwZNN\nj5E7LXG/NNUgSAO6Z5MGTmdF58gBtDzPOARm5nSpreIMCes5MzmgzeKfTwK+ZzE+Vh2WcTlpN6AI\n6E8z/sXc5WLjyVGEctSqSsf5vEoFnKvCzJa2ai6Yb5Nj5QvJ4ZQvXAt0XubgQcLNLvVi4Y2GvBG2\n6i9wqrMKMCoDAq1gNmgvbxntua6ORXrwYy5WnVji4EW/xUHazVLQwPEDTsELjIkjDp67C5waCRsc\ne76t5BSSHnHMNiDf4QCSMT8xXzSmwOBamm4BQwTjlNNCfoxT4wlGEdow5+cBnk8JOqydhjYHoGoF\n4A04aFTL9az7qTm6GvgAXGrj4hLHhovNcX7iiVsesG6wze225hAKK7pfSxxSjbvvarV5Y36POBgB\nQ+71rnO73fMJPnQQLeYC63LIsQGQGlAACy64apPlPS+lzazALZeyHOM2YEuqbhLvOHBa9TMkDe6D\nB7vICxg0juX6wn0YPMU4z9ESLKoRdojSYFRtUExSjDhLHrQFQm3g3Bu3N0c7yYFIFzAbHzs/380C\n39N61M/CI2+NgdbQLpbAOxbr0R4na3cG6iiTxnaaNRDalCbxmZ+vVDXOq1TAuSrMbAFnqCpo0AMa\nRs5Ts6Lq0aDHAgXYStdAAiDC0r5tc+AIw+uWuy48tRrUWsAMnrEDdtviGtBJj64F1Jcyx4PCsuHb\nfNG4NoCA7qu1DXdL/Y2XPsYrp9HM/joDDnQXfVyfHRiJg+gCp1ryVnMBP+iz/k32RdM+IAO2yzEn\nWZ4PHWqM8yEH7yQ8rkeibjk/WDdXMnO/xDG4tAwn7Hb0fT+1+gS88buc3j8Y81T9wvUbiTes+0sD\nFGve8FIG+NWKMOl6w1awywBXTIPp9a4rjzOoDbFn2nnBQKsI3uVGo8PNZtdfc48bjUvcaFwaayKn\ngDuWfws0kIPxNnaggEnDMtyDRV5Anb0vGO7ufBvQay44ePIwoIPxIbnWKb/XVow476CzSTzjaXjO\nge4iDeGlQo75WRgGJ+UW58agTAIYu+3pfRrrkFtrOd3JmZaOYvXHyqw7MzM5g2NVTlYq4FwVZoau\nqn7hg/sLHuuAHXCxQAc4vhviZaYB8hbbgM6qDw+PfVGPBgnSE7qvzrPq0h43C0xc5gDmZds1LUNT\nAXR/sA1+wCGbIl6y8NLPiWvgRQoPuvSu56TBNDiHd/tRtsEXlE5k8GDuOL3tDP4zeOzwOOa3fu12\nSwBYHNyWKqBIowSGnCVntyvO77AzUtoce0ktL7pem5YXWstvSWqNTpghQYkE7fMcp4pHIN6G/74M\nXSanYCF1vQHo8rx/bJXnE7kEgDg/L+9TSe8ZROd1u0h+IpVg+ky0yfX6sqH3nAt2HPpzOnx4eBht\nkQePPgLwXNZF5w3H8yfm6IZt7eARdt/huXeN3Rp3EpwANLEHdsRxMKjM2BaDIklxyFEkcp7NaXiz\n05YinvE0QXYxcMZc2hSX+JzTAWerL3o8Jo3hJP5wubYPo4BC0F3SMdTP2OLkM0UBkLn+hV0lyJzO\ncb2+fMJRrcqkUgHnqvgMU3rbd5HDC1y+ALEdLr18UGfQQHPA7oWPLVYJLOQLRHu7JIhscgqgAJr0\nVrr2MFrAEuBsjh3A1AkgAHpzwAXH7XAeOEN7FmMjvQ3wBuM3bPUfcgALGqzNqetrDWGMoeTJWtn4\nIPtWFOy3zrFHWfZJgk208wrH1AUZYGX1RXqSczsX8jO0qXNKLLlMkV3fLhlAirWDfkiq0SHbPOct\njg2uXEZCAGP9G0Ca9lJCKWWT09TfucBNWYdFxbko+jYpCY57uTYa6LPVLwQvgqcv5R8tLXOsXxmw\nO+SUZpQaBqA0hDiLfT8fR0yktZ2t8Vgb6yLrXS0E4YXMcah7wE88ccsrcqTPFwCgdEsec73PMYe/\nz7pf+a37VDFiUjkPb3RZ4JkLdLN3hXolqAUn60MZjnHRNU5zfevcXu8aa2NM0nueeCKvsz3pWpbk\nXq5/RHXjeTBTelyrMl2pgHNVzAQAwWsIb9ExO0CEoD750sxxgQG2cL4V2LPEDuQg6AtbTporbSWz\nWFLfgU6AZARSPcPSmT7mEEi4y0Gz2AIR1zlOvrHOAZilMkChvzkusQQIOO+A8xzKFT+O8BgPOPZM\ny0QsMmgNgXTXxNinHhDXH4x9jioAGkufY0CMoD25dY6+73DQ2MZcQPJvjWPAYtFpsPVtJdnZ5xDA\nJ9sijRJrPK0djj6nHuM2x0bEKFPfKudk0qQMWDqeqMs6r+f/wYOK64PCtMBBVhHpoQHsH2fL+HFK\nAkg2tM3xfSuVBeT6lBQuuS1vrVENwHP3UjoejcYlfuIJpJnWQcdamm9fjYuro9e7JnjI8W+B1hE/\ney5elJz+uD+BtmA9s7DO5bMwz3k9L27uaby1ZUsRtSGn913Ut5N6zS1PrJ7nSVST046hbvtZB/vJ\nkqvb3r2wd4yqcj7lJMC5QVX5tir37v2p8e0jRPSTRPQMEb2LiP5TImoQ0TYRvZOIDsWx7zTOnyGi\n7yWif0xEf5GIniSix4nodUR0h4h+kYieJ6I3ENE1InoLEd0jog4R/X0iepmI/jYRfdTXd9mfi+s+\nRUQ130YiotcT0VeJ6D/0dez769whoov+vJuifR1R1y8R0Zd8m95FRItE9DZx7FNE9B8Q0W1xzjV/\nzCYRHYh2voGIPmyMxzNE9D6Kx+2D5Mb50I/FrxHRjnEu5uKjfgx+noj+DRH9FBF93h9zQETvJqLP\nEdGPE9ExEf01IvpN//nniOgTvg0bqh2/Rm4sV8iN19fJzQfK00T0GiJ6lsJ4P+XrwZh+kIj+ChH9\nXdWPLxDRV4jo/eqaHya3Hr7b9+uPiYiJ6DfIjS0R0U8TUV1cE2265q//p0T0TT8OHySif0VEQ3Jz\nMktE30N2+abx3fNEtOb7/iQRXSA3bv+MiP4hEb2XiF4it6bfLs57C7k5/wS5OXg685suC0R0RG5M\nPmr8vk5Ev0dEAwrjcZOIXiDX9z9PRH9Cbs4e8b/fIaJfFtd+Pqpxfn6ZXnrpPeTm7Rly9xjKDBF9\njdy9fN+PBcrzRPQpIvoSdbvL9G//7R8Y7f0yEX2QWq0mvfQSvrtJbh50maN4fb2FvvGNN9Czz16j\nen1IRL9C8Vp50v9/13//Pv/5KSJ6wl/nefr8579I9+8Pjd9QsP5D3V/72geN9ulyjdzYPkNEX6Jm\nc5Fefvma//zLor7no37V62+nF1/cobt379LNmzfpIx+5Q7dvP0NERP3+m+n27Wfo9u1naDh8I928\nKdv5rVVu336G7t0L43bvHo37QfTXKV7zb6N6nQv7c/PmzRP1V7fj/n0id9+frrz44h/Ta187ICKa\nOBdW2z/+8UO6d8/9PT//NA2Hd5Jz5NwPh3fOYb7rFJ5TRETX/XdV+ZYp0yLt8/5Hlcf5VMVtN0nP\nn0VvgEVrWbtNTikPcnu2yYG/WaQDrbdjQe8Aj1JumafBECktYMCBM6y3YuGhhZdObtvPcfDm7oq/\nLS+bNR67HAIbZfCj5XUE57bIMy3nAsFplpqDpGtgLJEAQWZalJ67JsfjjnFFn2+JY3X7ZYDiOjtP\n4qLRLms79woXB/BBYtBaI8gkN/RztcyxRxtexG0OVAtJj5G0A+ld1dQXUHf0jsKjnHo9ZV8091dT\nNS5zUP2wfm9z4G4Pk+s7zzEoFPKei71vTvFArut5jr2nsm7dT9Ch4uC/2dl1g6Ps+j07u25kfNSU\nEldvr+cUBgInGe1O11kISsx7uouk5RBYaB+D9Z2nalhZ+fLt2eJW64q5xY6SBugt8sxMZ6yXjWNO\nqgFctpT1/E72fModpkW+cePGidtUVKx2hDk9GVVjdnY94nlb2tqTylnwznP1lqWixFx7PKcunFlb\nqhIXqqgaVRmNIAjfYwkeZmfXeW/vwFM58HDRwA5KCNvifMkR1qAA/Gdru1zzWwFcBhyD4mWOgxRx\n7Co7oNjnkG0M4BiZxSTVY5+DRu6qqv8ChyA4gCjN5c0FQYIjvu/rnuN0K1wCRgn6JaiVYBBjAek9\n0EYs+onUtpbKHwNOt8Et2cHr4jqyvzYwcXUC6OUyIVocdQBfi/6Qoz5AnQTjO+SQJAW60Wj7orru\nEhPN+nO0asixn6uO7/82h4Q2ug2XOQ5+w28Y51Wu1RaM39AnUILk+l30195kJz2HtbfNjpIRaBsh\noK34/nEScJp6ooMpoYeeA55p/8EvbbWucK3W5np9WSVJgbEWqi4AACAASURBVEELmUadjCfIxQUK\nBeqXPHo3Z0dHR/4ZdSlpy8zMelZCTQdgpTxnabhiTbjgwGazGz0fLRDr5kEHxm17+ofNe2XWQYo6\nA+MSHx4eFgK/siBtUoBcWRBeBOBi7rkzFov4zWephlGvr3p6zkFSZ9kASzcX6XN3WvB8XqUsBz2X\nRKoq51Mq4FwVZmaf4rjHzWaXe73r0Y2a8gPx4u6w47DOcfAsDTmWHMupXOiARIvf2hbHWwBKf7fN\nqXcLAHfDOH6F85zNS+ZLPIB2CaoAitAHWZ/0Zo3YTpONLHn6WgOOPeoAjVJvV3KboZOtJbYkF3yN\n45d6jn8LHjUAuh6LdQ5ezzl2oOMWO9Cp60O2Qm24YCdgw5j73PhLbzHGV+805Hc1nPxY6i1Nd0nw\nvbXOOhySBFlBteAC698OOTayFtgB/a7RBxhM8IZvsgTOVlxC0NR14CZNd33sE5HIsUP2xBSU5oIv\nkWFOZwV0UnQAsNKgLLu7JLWaw7qH/JxTDoi97L3eDjOXB4JHR0eJEkLwvA/H7SkCThK0HB4ecq0G\nw1DGZqD+NMFLDDZTgyUYCMGTOykTotXGovE4ieSZBeAspZKc5rOWAsQ6KiMFJ+vJBc2dpMRe8xiU\nn6X3GG0/D880M3MFnB9sqYBzVbIPWYBp98LaZweYNzgFBE2OvcwSFFsvzb7/+xIHL5wGSQAm+2x7\nRS0gnEvasurr1t43ZJ6zgHmbbVAJkNfhQAOAd00aC3j599TY2LJUAUghSM9KZbvBcRAfxgkGg0ww\ng/r1i7wpzgdVw8rKiLGXbUXw5r6od6DO1571ZT82CE4EILJAsewTguc0HWef492KHFUGYx8D8nrd\n2unY9O3TwN5uZ6+3w3t7B1yrIfgVQHpHnLvLs7MAelClAFgFIAUNYhINZl8cA6qGpLfEOsd4MVte\n2EB7iM+PAykxB03WHtF4azttdwDT2njMSfixb7/Td04NAi35JRPoDCPJtzLAxAKMrdZVvnHjRqIr\nXfZZ6ahu+fUoU3eX0QJ2xk0ckDgtkJsEjM8q0NApMsXymZbRkdc1TukSk4DwWQZJBkMmnYezDLw8\nD0UUWWyVnbkzq78qcamAc1XMB5F7GQAYSXBrW7YBsB1w8PICmGkPD1QVZFS09H5tsQMhMoufBtWH\n4gWMrWbL27nPYWsdXk/NW7boFnXOUwhy2r7wJFvyfvDMWX2R3mdJf1jjoOahqRM6uYqWXAMdwAL/\nc2pstTrHkIOWswa7OHZT/KbrX2QH8iVnWHtgLepHjwNA7mXGf4UdlQHtsAwo25sZ5NX08S01p7Gi\nxOzs0jjrlwQFLjV00x8vjRlcUyu3wODA/EjFiiLg3OZ0jQQQnpM1G41GkUcYIHM0GhkSWQcc7ktw\n1Q85GF+bY93bIpAI0Do//4j6Dd570JeseU09tnnu8skAjvWsK5M1ruj8eCyt+vsF52N3Ity79fqC\nmZGvTD9jCTT5bN0vTIt+UiDn1oM0uJa52ewmdcX9zjkPyvX1rIGzyyB5svGeVLdNCUnr1zJ703im\nHVUDRjzUeBwtryrnUyrgXBXzQRSCdjSHzQIqaxyoBpYG8qL/vscOEGvPlk5HDB60fsFoL2QcuGRr\n1S7670YcOJfYCj3i2CjQGstW/QP1kLeAsAUMwCVd8WMFDqv2DkvwCJ41guUscCWBor5eLihRg1bJ\nv8WL/ALbXowL7IAmrpl7CeoXkW6H1a59cZ0djrWIsRaX/W+r4nsNyC3Kj5Qb1B52y+DCOnMGFwAz\ndmFarSvcaDTVtVc5Dtw8MK6HNSQBhGW4SYMDx08PMnJ6vCm3GB7wS/4ftuCDIZFKYY04eMz3o+QP\nzoMo+y2pWJpeIw0Vtx7BT7a85lb/p9nq18ZEUYrpMvJje3t9AULj9WgFBsZUjcscdnfCbsfMTGow\nTAJygcOt19qa2Z6zoA5YGsVEaWKXGKjn7v3ya/okmf+seyE3b44/vRPRSYraMymQMyedlx5bvH6s\n4takdqw4XfeqnE+pgHNVzAeR2zK1QLDF65SpZteNc9ri4W09NIvSB8vvwGtDRLvVvgV24B7eRclf\nBHgFaNBc4AWOPXu32IE0qZWrH/JF/E09BjI4C95beJs0ANRJMCwPLdI0W9nz8OK1QJmlVtHnEBmP\nYEk721zQbU4pAzEntwgoA5TK/rb8OB+KOmHw6GPlSwg7IdvsuNaaQ6553Zh7zIfVzw6H9eZefKli\nhOSuY+wWfd0DdtxlZATsc9ghsHZS0Id1JtoZA8c4ycPQeyKDJ7/RWB6/3HVKavfvIAIKcYppGKj7\n7Iw5nbHyIsOQtCP64zULj3bMG4U+O8YSY4zrampMCiriNusdn+VsIozcs86B+gD2nccxBcPWc7Eo\n6UZZj2FQ+HCGqu1Vj1N7TwJQDpAX8aXDd2USr5QF1dbYwSC09Iwtbvy0VA2rfZM86NbvqRd4KBRR\n8gaHbod13bIKIMyTvfG4v3Nz4c6HEY/7d4lrtWbhGFbl5KUCzlVh5vRB5ECCpfQA0ITUngOOgQ2+\nx4sVL7oBB96pftBaYLvPKeCDV7TPLjjPApMb4jh4GOEBneMAuHNprOUWOV7q8oG0wAHQDtW1cN4K\np9QJgIwLHKf1HrADFlZfdqOHuh3UBsNFjhXSR8OL1+cQyAdjIufhBLCW2dT0GMFwAP91lR1g3OSg\nogAPPq6hJQHlmugbfZPjMXlrN6w7UHfAK9aeGAmW5zlw3+OXThxQCbANz/UBhzTWeixBsUHmTZ2V\nERQO7DjA+ImN0W73qgDCfe52r3Iq+SiN2KG/nqbtOM/57OwKz85irUqwCqPBkhFs8sWLbU/R6I8B\nCtIVz8/r+3bovfFXOQBne5259mgQ737TYHRv74C73cc5GJOY45VxKm69PnIeSweWsPt1wM4okM8s\n1/de71q23rPw1GqgHcvTSapVnooji2trasSX8QhPC0LT8ZT3daCQnWQn5KRjehJOtzU24btyayp3\n3dzOhNXHScA5B7jlWAZjOsSD1GqdqcexKuVKBZyrYhZ3M8Mzp72K4KsC3MgH0I5/CcvMZABOu5wq\nX7TZTg8MvmOHgyoD9Gu3OHgYO+I8KXMGoCN5thIgaq8wvKSgT+DYAWtQE2TCJF0AoAqgS1I/Dn0f\nUD/k5AC6cL42UjT/GRrGGqhvcuz1RLppi2aCLfgDDpkIL/qxXeW47RYohGqEBEUa1ENdYo5dFrtb\nHECt5EvLILP4oe/6kA8wDVH1AMlXjPZKhQ0NutGfAcdGwjEH740EqU2O1xn6nEvBjQBL/ZtsN77P\n0Z+s4El5DO47UB9y1JyOmGMJcEANYrblITssvdCNxnLkGYzbJCkpGJu0La3WVRMsaS9tfqsbuuSb\nY0WNssB5NBoZihpLIhgvrD94/MsCcuta0/JUbQpNuWuG53V8v/Z6O8pAgWSn5KNrT2y/dL/ddQcc\nVIYG7Ayj9RMbFScpJwHOMcVG9/2kwHk4jgXQijPaA24bTillpMx6yBm+VTmfUgHnqjBzjsuH7XQL\nANxix4WEdxMvcLyE5DkWz7PLDlQ1xbkAfjscKBWpzmkKcqCiAC+a9sDKQDocJ4MTLeBX5zg5iOz7\nBud1reVLWBoGwUsWdIcBLG5x2JrVRooEaZfY9kxL4wBtRjvkcZc5NmZgaFggbdGYF3iSdfKHIp5z\nix1A13zxy35uc8lc1th5hAHyw0so0CYkEMypU1jzJ3cV4CWXx0zmYIY+W7sl8LpvcGrkwOgD5xoJ\nfnQdOXk46xjpbSzLOcfaPPCfU3k7N756vWyrYzriGjGAsPpQ1gtpBVPleLBlPaQWeHLeV6QwDzsA\ne3sHJw6gK3teGW7s9NeUzxkHYhsN7ETAQM1x1t2YWJ7Y3Lw5VQ1tZG6P+e5lyll58Kelalie7ngc\ni6kabvflQPDKNXVpxeRI5yhAlhE5KagQpUq5/WBLBZyrkr2RwxaR9cLpc/DMya1tC0xZ5wNU5IAN\nXpT6hVwG1Fjbk/DkaFAtg5g0qLCuj3MAtnJARfZBqmoA5El6yyQjBZ7mHtuehaH/3Qq81J5pXfcV\ntkHVqroOKA54Icu6rDG4Ja6paSraYLE4xpc5fhkEqtDR0ZHyio04TnaiQWyRioklRTUNcJZeZ4BN\nyPbpOZKcdhwLo0obhxZ3VO6uYB4kxeSY85knrTEGV/yYUzk6K0AX86mfA32276EDngQ+cs+g1NOG\nXZlthtHb6+1MFeiWA87NJnZaQt+R/e4koC4HRssC5JMCybzXOl3PtVo7y8VtNruF3lJZ7OBNm+Ms\n2wm6T693bWp+s9XnMtzysuMqKUI6fiAXWFivdwRFqRjkTrOTUdaQsuNcKo/zeZUKOFcleyPbyQwA\nBPACO2ZHRQDHFcdLHeJcEB/4xguq7iEHWoYGwTkgK1/4FnDeLfgtp/9sbX9iGx6eFqs9bXHeKHMM\nghFBFwDVwwIgkt8NCoWVSEVKxKHtuDZUHooAstUHcJBlCmicI7neOam8fQ60AIBPyTsvMj6g7AFP\noMsY6FI+A0QecaySYtERtjjI2y2K39f8NTRVSAPcXNIUGEA6qM4yBjfZ9uqCZiPpOUucjqlUdGlz\nnPEO1BwdW3BZ1L3EcR+hr421ArCl/7bWi6Q+bXHYord3SpAlsAisFAdTSepUfI2cZzPnzbWoGjY/\ntG228zQAXQKf86KBjEYjH9gts2JawHnVpMRg3lzQ5LWI324VCywWAec02BNzMDrROJy1NnLZXQDL\nE1zWUz/t3JdZc/auXQWcz6tUwLkqhUEwseoD+MZdjrfrwXODBxpcS7yM2pwmztABTF12XjY8SAEW\n9QtZUzUgXQYv3wyn3lIJGi1Q01fHy3TaoDwAsEAlQHqwtQQUghDxMsgB502OtbEBaBZUfQvq98VM\nnQhMk/1GMhHNS5bgBpJ98poXxThLKo48v82B8406JLCSAHrT/9bnAOrbmXpXmegxdqDWAo/4H7/L\nsZASg0PhuZQBNPvs1jKA6CYTNXlmZt3rD4NL2xnXA/WFVuvKOLtmoNxY82ABp6IEPQfjtrVaV3yC\nlXlfP+4DrEFNwbnl23vRz+kGh7TkAbRJ2k23+5gCTE1//iaHoNXcekF6e7kuQb2BoQRQPswCqJQa\nFo8NvH1BGSLlk0Jfusy2PLMdHFgWdExDwUjB6NF47KF0UgY8ae/n3l4/66HNgdJGY1EZDGk2Qwf6\n5DNrGEnb5frqslOmBtO0dBk3H6PsOFjlNMaHVdz4xYorkJ/UTgoLJJfVAz8PwG8Zf1XmwPMrFXCu\nSuGN7BKhwPs6Gr+sgscUwOmYY7CBrWYAFgmKesZNfoUDDUFzVzfZAalVDoATCTquqnqQrU3Xj4es\nlZYbYGCXAycZdcit8Cbb6ZxjsBY4wvhsJS8ZMtEjnKcXACTlgNmWuAbaZnk0d9X14TEGxxbHbXPQ\nlAUlA55tvBgt4KLnqi3mJ4AnLa9FtMDz8zCodhmC/XEiEWts8MJ6nFOeOUAxxmKF9/b2/Nq2dj2g\nbBFARSzpFTzjVjY0B8LgCY1fWOkaW+CwsyDXgfTML3G9viCApEUVGhpavTme/iV2hkr6kq/XO3x4\neDhO7EI0a5w/KFgvnaROrI+cCkAAgTH4g2Zujh5gB2yNovWUl/eKAZX1rMvxQzWYKZteWvbVjS3i\nAkJ/y0jo5fi2mm6ENuRAKWQJddsB4G0ubWqYW31158nEUk1uta5kaRN54Ox2W6bhRp8lcB6NRoYR\nsGQYHBi7NLDQ4kwXXe+0vG5ZqsyBD7ZUwLkqzBwSO8jsaOCiOe1Y6+UMpQt831a/2alMbQ9Pi0OA\njgUULSqABm6QAdP0ijXxHc5rc6CDhJdJUOIARcACovrFbWnSot9a51oe08xcQwav5ZKbLHMKWpeM\nY6UaQYtDgKH2Wu5y6vme42CgWLQUa24vsa1E8pg41qIOIGDUAka6foBjbbxYSTXaPpDQGsddTvug\n277ORNumJJgLjJrjlKqBTIFX2Bl22zw/v8btds+n4baUURwIQJY5Bwpk6vNwLABPkGkDLUP3z87G\nB53pGIDngk7RDv2bnBsYGJtjLx0AMtqaSq4tcbz7sB9pUmsPsgPVEkDmgd0kgKtBS62GcQgeeaJm\ndI6b65QGNAmojUa2vrL0tOfAUwCGuRiCcsAZ9cfjv+hTxrvPjUaHa7UWh50au83We8PtvGB36wIf\nHh4W8rdTr/gKS0BatpyV5zbUM0l73o3ntCB52racpM4Q8yAzB66cSZuqkpYKOFclGxwov6vVVvyD\nNfdgsYKZttkBB/29pUQAeTtZp+TCWtvT1oNNbs3f4qBwoIEKPCS59MxSUUDW32a5nZkqUgCwwdu6\nz2FLWB4HHm2xN8kZAlq/F95t2TZsOWvAKjV1LSkxXFMHl1gezE1VR05HOyfRhu9yc6f1fy0ALvm1\n0pNnv+SIVv1W/y6nbYcknwbrMIT60Tky+n1v78DzO7UnXY5lWA8AkEH6zPZkAZwEtQJJExpwo9HJ\ncFOtMXdgtlZb9YB9k0MiFmns5ZQ9YAhL3jb6iLmJ12+j0RnTCmKvsvbk21SqHHCSlIVe77qZ2ANg\nQwOzWq05VsmwipuvNDhyb+9gfG0LSNZqq6Uyy00j7yb7GoL8JnOmcZ7uu/TghoBveS+kdUK1pgwo\ndWnn43vU6WLn+wuHjJvDbT4Jv1mP1WnAazA49Dylz5QymtonLacxBNI4DbfLVZXzKRVwrko2Cjx9\nsBZZ5BZAhsdLvpBWMg9tgGZIj00ClBJQy21yUC0kz1YDPrmtjwxybXZcadmmLbZBMfq9wrYG7wrH\nKhrwbD/KAVjK8YWBcJXj4DVc7xoHCoyl5iGBCMYAvNkNDtnsYDzgeh0OtAw9JxYQ7XHQr+5y4NRK\n8AbvqzUvknObM3rWxW8jDrsQAGvW+trnPFjXUlnf5fsM+gbUOEBB2ueQAj5tY9CPBo/fmn+tPAHg\nuckhkBFcW9QXtvDjLf7AsyZa4G738czWut59WeWQYEbzLy2jyFJrWRTzB4UMaYDKezsFYCEmAAas\nNJz0OQ60tNu9ic8rB7wOTA6u7XXdjY7RYCvsnMXn9XrXxfMxXQtuJ24yyNGpsKdRFwkGYpgblyb8\nwASLUrFCGwuxB9u6/2IvdhlQahkwFo0nx98+a67vSUoYlxHL57bTLS+nLnK27ShnYMlScZwfbKmA\nc1WmAM59jikQCAjLBdhs+pt4md22NW5ut40Uc4Kb4nMOkGJrHTznQ84rD6yq+hHsti+uD3qIPP9Q\nXA/eZJ1yG8lXLM+vNAI0daPDYdu7nxnfXCpigF25rTzJ2wpAKA2XdU49z3P+nwZe+kGMNu+KOuX2\nNs7X2q4yqcq6GHcrqx6CCPfZGTJFxhP416uZYzXFAGtL86gvcLoNf8T2GsTcoJ8WP7Yo4yPWIsC3\nA/7gHAcgoRMIod3us5UYIQDAALQdF3yTm82uAuW6zW0O9yCoMN8lftfgVxoJeoy7fm6Q8AjGQz6h\nDTiuAHs5wBaDUEcpkKoPNnAOgNAK4IoVgeJnYKhT07902vdUco451Veu1zsmX37SsziXRr1siQMA\nD4z+2LzpSWV2NqUINRprU6VAP2u6w7QlBvDxmnqQ7TsdcMb9hfvXBZVX5XxKBZyrUoqqER6sIw4U\nB6Qcxpbiirhx4fUdsaNxSAqF9qjMic94yViZ/SQYXmMbfOL68CRusgM4I3YeZK3UoMEYQBUCAdvq\n2AUOWbKkd1ICTq02ILdbAYausQ30JFcZ4wOQh4ej5K5K77Y1DrltXvkZyh2Sh/2o0Q5kPVzkYhBl\nXXObQ+IYqWYg6QOW19RquzWuMC4kJzgfTBYH5uUCNGNw7wKF9DWHnO4uwLOOPmpDaIOhTDMzsx6B\nxdAOvbZzHnq9zjC3OmA1bNvb4LLNqfTfHEuA7xJpxHXGxivAulaoOWJpJDjDqaOO2R7zo4v4sZO4\nt7aiRbi/baMBhnB8P0qqhga/LmjaXptlAxWtMm1gYxkwl45JmyUNqFZb5UZjeep6mdlTgOLnxOzs\nshkzc1blYXGLzxtEn46qYSkQXTjzNlbFlYcGnInoh4noM0T0WSJ6OnPMq4noOSL6fSL6nYK6zmt8\nvmNKLjgQQT71uk45PMcBMMqtLngfAcQ08MyBXf25K+rXXju8xCyPYJtjKTUJkiRlQ54Dj9TQv9BX\nOB8cqJOZXGYHHiCHZ9Xf4QCakL7ZoplA5guBch3fjhan/OVlfwyAi05WAsPFGu9dToHDrri2BMHy\nuEscwI/0DANE5SgOev5W2QbKGsDmwCKuKcEvKAHQstZgObc1nQPomJ8DdrslG57j3+R0tyGkgQ7R\n7TJIUK/b2HsMqTu37Y22awCVtt/p9Vo7GyPOgWo7UAw7PqkhB2CvUwgH/XGs3VsctMX13HdEu6Qh\ntcq93vXIg1oEHHOUCQ1E8Sxrta56sB+AiJ2wA/c7xqDHRAuJ51iCpkkAvShgbxK/OQeeTuqRtM5z\nkof9qD8nAYVuzQ5YBqU5WcfzoWCcxng4Deh9ULSS0wUHpuu6KudTHgpwJqI6Ef0hEW0R0QUi+udE\ntK2OWSaiTxPRd/nPawX1neMQffuXSQ8FF3ne4lT/9CLHtAUJgKD8oG9mC5Bp4AzZuSL+KraTNe1g\nlu1ARUnfsH6zttRzXlUJeEFBkN4zfQ5eyMtizPRWqeVpnufAs5btBm1jgwPYkUYLEnsAyGvlEW0E\nNdV1QRPQwH6LAy9cU1zAPwZYlWOS084G7WaL4zTmOcAtaSVtf85AfJbzCL1tyCZqUA5aSMcfG8bI\nBcJKSULtQT1g51W2KCdLTDTD9XoRjWeB47nE+QM/VhucpiiPk7KADx0rYyxyoDkB1GLu3A4EDGNH\nWXiEA2i2uKqb4y1rS8s2vZ80sC3y8m+OPbryOWNdJwah8X0jeeGWekettsLNZjfaepe/O2MASXDk\nbtls4fMSPHPwiTVtA6BUGxxlAFfOW5ujcUySPZsmhfY0cmqOZ97hYLg7w/HixZTG1Wx2z8STW8Z4\nmGTk5FJhn/a6ReW8vdW2otLSmV+nKq48LOC8T0S/LT6/U3udieiniOg9Jes7p+H5zijpQ2EY8fVs\nT4/2FjbZeUARsZ0LHOpyqmShE3wMOAaCOS+uDpzDNS35rKscPKYasFoZCo85Tc+9ziFQTfZbev00\n2NN0DwQKarBrpUzFy+iYY6++zmSoua5aqxpGQMeP7S6HlOeLxvktzumCxkocEkRpAwb9hGdaX6Md\nHd/rXfOycToltQaAmgqjNbIxR7rtM6JuPUcY5x4H6UOsB4vWgMC7Y7aNQ4BmCzivsR0oZ/HCLzD0\nqAM1p8NEu2PQeXR05OXUrJTZcq6gHDFQ18G9aCvdHB4eKnAuwW+Xwy4J2icNiZyR6pJy2FxgOSfD\nhKqhKRNa/SFnZBfrSZfPulaG1jY7u+7rDVSIXu9aKdDk7gFQzAZKySVWKpmUbCQeL1vzelLfcse5\nduiAZEdv6nYf5fQ5u2JqNE/rybV2DKQSi1VfHEg7SsZiUqpu5tMB5wfhrXbPt4qq8aDKwwLOAyJ6\nRnx+PRH9qjrml4nobxPR7xDRPyWi/7KgvvMboe+AEjw5t9h5ApeMB49+EFovKETdw3OmvXXgyUKZ\noc8BgEN1QHvaLFmyVQ6BepYHW6sprPsHJuqBB26FHTjJASQZlIctSQmURmxvxcNji6Qqul5kSJTn\n5bRyAeAAmC0wIvmiAK1WXwDqpMqD9lQAVGIuNQ3glqpTjjm0XAccS5xpIIe/Q/vgOavXIZu2zQ6Y\naS8nPJI41xoPa02AQw7lDv17l8PLf8hBLzp9UYfdC0mJ0MAaQFirjqANTY6VPCxVGqnCgfXmrn3x\nYnvsRXV820l64NgFyCW6QPIZtLXNRIsmpzgEH2ljaZvd7gEMUatPzjurZefKelO1NzY9L0/J0UAn\nnGvPYf5ZmQIoCcZdIo3UM17G05yCn0tREo7Z2XVfv+2Vz7fVrR1rTNH26VJGY5y1Ib/G8/Pr2XWT\nn4NygNS9i+J3ikzpPjnQPf09l7BHj9FJwe9pvdWTSipdCQdJRdU4r3IS4NygB1MaRPT9RPQXiWiR\niH6vVqv9HjP/oXXwu9/97vHfr371q+nVr371A2jit0fp97+fnn32bxLRr/pvPk1EG0R0k+7dIyL6\nMM3P/ybdu/d6IvogEf0+1Wqz5GwWWTbI0dE/S87uOSSiVSJ6kogeJ6I3ENEdIvpRIvp1f87/TETs\njyciegsRXfTXecQf/wIR/a7/bYaIfoKINonoHUTmctwmogN/3W/4et5Nji4/Q0QfIscWeoWIXkdE\nT5Oz3Z4UdbzFt/cFclT8e0T0vxHR94pjniGiD/h+ogyJ6O/7895ORE2jff8vOfr+U+QYSdtEtEZE\nbxPHPElEryGi3/Z/v9+38UNGfbvkxuvLFMY4V+rkxvf9op//jfj9N3wdv0FEP+m/O/TX1uWzRPQX\niOhfEdEPEdG/8ee8hYg+TmENoLyTiL5GRPeJ6FpU05/8yZ/Qj/zIj9L9+10i+qI/7m/5X1/v6/kQ\nEV0u6BvKK0T0vPH9jm/Dk8ZvW77tP01u/HDtNxHRvxDHPU1hTT5DRC+T6y8K/v4fiOgJajb/T3r5\n5SfJ3QdMRH+ZiD5BRP+SiP4uEf2P/nirTbLcJTcG7yMioq997Sl67rkfpB/5kR+lb36zRm4NFZX/\nmNxaf6/6/oDcuquRW2/4/TVE9L/T/ft/zqjry0R0ldz6lfP7JBEtkBvjQ99muW7eQUR/j4heoLW1\nz09ob1ru3r1L73nPr9Arr/z3RET0nve8g77v+x7P9AdFzldc1yc/+Ski+hIRfdO42n167WsHNBy+\nkW7evDmxbTjmr/7VQ7p377vJzfH7CONz/z7R7dvPFNb1/vd/mNwzWI7pzxHz3xx/98orRM3me8mt\n1cntEi0kohfoB37go1Eb7t6969v8PnJjEZdPfvJTn2hpbQAAIABJREFUdPfu3YJ2P0Oyn0REX//6\nz9CrXrVLzz03RfNKlrW1y+Q2rD9KRH9ERA166aX30rPPEn3844e0vf09yTlbW5t0797T/l2W9tGt\ncdf+e/fsebp58yZ95CN36PbtZ4iIaDi8Mz7m7t279K53vZe+8IUXaGtrk37xF99Vas2cVQnrZoOI\ncF99mtw9XZWzKB/72MfoYx/72OkqmRZp63/kVv5IfLaoGk8T0X8rPv86EQ0y9Z2TXfGdUSZJOMGa\nd1kEW2xzP7F1f115fay64dVGkJnexr9ieF4G/ho7/hq5rGiSQmBxZEFVaLOT20IQ3RanPMfHfPu6\nHDzC0itk0VeQAhzXzgWIWclKLrDzMCJl9FWOpfv2OaVQgJoCSbZtttVAMF+WBxxe3l0Ocmb6GBlw\niPYuqmvscKCeWJnsoNygt8blepIZALX3FdfU/Go9/xh72dY5lvza/Jqxxgdceu393vfjdcBx0GKg\nKjk+cV/8ptUNENSod1pc+u3wfc6TmvP8yf5LJZPd6DqNRocd776ImmNRgywa1gYHDj/6C81snDcp\nvXQ8j3bq7XDNXu+6SovssiK2WleF5naqzhFrWusEL0vj/lnxHpOD90ZcVstYFlvxI/0ul+5ZlzKx\nKzGnXHPSbSpITNVI12W3+7inENmqLvk2pvKCxX1Kr12UCtslLboiMuHaso7Tcpdj2orbFSi7Zs6i\nBI59qlZVlfMp9JCoGjMUggNnyQUHfq86ZpuInvXHLpBzIe1k6jvXQfp2LzZw3k8eAmGbDFv2hxw4\nrOAlX1E3sfXC7xoPaB10tM0xLxdpdC9xUMjY9Nd7lEMgmHwIWsBWcwR11jAN9HAsdFsBiqG2IPmd\nGnhc5QBINO1hw7geUn3L6+Y4zKALLKpjARwB9JA5ESocVhprvEhAi7GA62UOqXWv+jHXYyv5yUi6\noo0WrAvJ7z4Q18lt8UuqyTU1nqAGWcFry77dEkSivxjDA86vGazhZgTQXLriFufpOM7QaTSWude7\nzoEGYt9nRGtcr1/kVuvqmIYAY/XChdz6tILx9jmk3JVBtm4Out1H+fDwMKI7jEajTP2rEae4Vmtz\nr7fDe3t9nzFOp3wfcHrPDErzSCcFB1rAstW64qXUYFgujQFaLiArft5JsIvxXPRrzAZS5eqNU8Bb\n4FPXkVI1oCee8pOnCeKzjksNFXDXh14po5gKEoIDl1kr4/R6O6ruZe71drL9Pzw89PSTcpJ4k6gl\nVp91f+v1Vd7bOzDlD8us1XjO02dG2TVzFsWtm/SZ3Wh0zvQ6VQnloQBnd136YSL6A3L7vO/03/3X\nRPRGccxT5PYc/gURvbmgrnMcom//kj5E4cV0D0EcU6tZKhbSqwdPotRsXeQUrFreyH1Rz4BjrhaS\nXMgAwAUOCVjw0myyA574nMuGJ1+S4CNbvOB9cSxUQjY5BoBoswU2kWxEZy7sZI6HB976XgOmHMjb\nzHwPybmcBxyAZZudcaI55dojqhNAWKAQABfKGRanWvYHfdVGSKzGkPYvF4jnrt1uYx5gSGjALzXJ\ndbChGx9wQ9MgrZzEYYc16CmWQnQpnGUgGGTajo6OEq8WwL/OboZ7z3nSrAQwsQEAgJKLyi964btA\nNqzvIVt88FbrylRAwTLiG41LPtV2qn3uAFd8fC5tt30Ny2kA9SAbBOVKSGnt1nBOvaHIAxnGFAa5\ny2A3rQrEpJJzluQk+3JjEMfH3BrfK0Xnp+8bW8Fo0rhP48kt4hnLNV6kI56vdzJwPu/ido7iNszP\ndx9oG76TykMDzmf5rwLOpy+j0cj0NMTZs3LbxXgpwwu841/saxzAI+gHCMKygAboBjpDl5VQpOeP\n18AEQVfH/m+tPWttPSOLoK5rIK63purR7bc8uRgveI5kcg6ZdU73S9ejDQ/MjzUf/cz4SqNjnd2L\nSgf+4SUGDWIEEMqgSlnnIsdGiwXim5wGL2rVEWSEXPLjAkMFHl1pnF3glOaCY/S62WaiJne7V730\nWkekSWZOt6ZhtEl6jAPxwQN8wDMzGL8Rh+Q/GgigHxq45igix0y0rWgHEgRj18Vl5KvXl5PsZhJs\nu90h7Y22t9bdva1l9Vyyn1wCi9g7jIDDdO2WSaGt67UoG0Rr3GgsRt5lANMyoCUPjqw5QaBjSvHI\nGRGWRzOX+KMIxMV0D/fM1bJ9Z1GKgjGnAaS2ikV+TsK60c+K8pJ5+vplPLllA/SmDeQrQ9WYppzU\nM91opNS/RsNWhqnK6UsFnKsyLkVSP+6BUpQOWkoTAWRpni04shatAeAoB/r059WCY6WHEpSPRc5T\nMsBplJ4TbHWjjxKAWHVoT6WVHlrWecyBloHUxB0OCU0k1/oGOxC6yGlKZTnG6HfKl3X1auoExly3\ndZ0tr2s8D5qaYXGNcxkG0Qctm6cNnQV2nFkYZAvswDQMoB4HAwcAS8ohWobXgqdZSGMhZwiu8uzs\npTFoTr2+KxyrbwCsLhTUD/nCXU4NQmus4NGLAfUkMBXau6LaYgMDO8U61tZSBAJTcAvjOJ99b5qS\no2wQ7XOvt5PQTCyObJG28/z85YiucuPGDQ67ZZe5VptP0lpPApMW4Mp5vouOPW8FBjnGk/jPZQGc\nPjZXd/jeui9i6c+z5gGXNQZOMv4wqF1q9H5ChSpb9I5FmTEISiIAzoGylJNUrMrpSwWcqzIu7mUb\ngIu0nFP+nQTC8kGIB0+ONy2BCWToinjJFnAGtzcHNOT/bSZ6RNSbGgfupX9V9W+dAzDUW7uWh/GA\nnbf8EgeNad0H8J4vi37HXMhAQ8Fn0EMsPWjwyvscgP8xp0YAghZ1v1eN8T/mPF1Eeo4t40EGMS5x\n8Bjr49BHLQOm5x7AOn4ZuGNGHAdggd9rSYttsjT2HC8T3nR9bI9jo2t17Mm1OfPWPQG6UGwYpIFI\n4ATDq2/tZPQ5GErBqJuff6QUv9VxkWVfR2wBFFvLeHncDuk5trf5YcQ0GTsISKEtAex03FF9jd1I\nU1oCMkfj0IYcAsD0NvYwqsd5sWNDUwOeSYDKoizU6x0TjO7tHYitdTfWjcaiX2exVnNZj/dJymnr\nm+SB17/F3nT9/BxwoAdeP3Me8KT2ymNOE8hnSQqWAc+j0cjQSx9O9HaHtqZUjZykYlVOXyrgXJWo\n5B4uLsBJ35gIjpMABMB0EnAGoJkERrT3E9v12L7W6a8l9QCawm2Og9J0UIykIgB8bfrr4Hud1EHz\npAHuRxyULSSNQepOg1ebo2ZoQwGAxAKE8MgNOSSCsYwDi0oCgK+pK9b5bQ4Uhh7bwBn0D4AIeF8t\nZROLF67XAvjyEswh5bUEsnIuctzx8DIi2vSpmC8WtE3z+WXiGgns9Q6MS11eq60mns+iRAxEK1yr\nzRv93eYY1Mb3R5ksaG4nSc6z82BLgDI7C4oO1uwcuwDWwFtFsUFteOnX66vc6+14xQtwrbEWhr7d\nxdvZ2ognWvOKPil4daDDAg65WAD9OTXANT90EnC2NZgHIoNgDIhdX7B2Yi787Oz6RI/3SbLfnWXR\nnn6iVW61rhSCxHgMnaHbbveSJDun8X6fVd9Oej2LhlKGrpTjnBcB5/gc6/28PFXbq1K+VMC5KhNL\nLmrX9mIhAMyiasxx4LNCjcDipl5ULxWk9L7CQU5OAh29PQ+O4hHHHGX0YSjqg6fSAlvzvj0bHIDe\nAseeVwBgAHkt1wXQKVOIX+ZimTGLmpKjpcC7LJNtaOOgzYFzrkHokFMjYMQxpWSZnYdfeooHqj5N\n1UDSmWNfpwTUAL3au65TZ1veYyTqAB9e0ycmUWZW2FFfjnlmBoaDbhvaYF3bBrAxncVWdXBxBFg7\naf31Ogw92Z5Vjo0rDT7KZYQL3m7sHAyil7ILSpvjkBkzBoGHh4dRfZrPG7d7oK4Ho2uNXcCoW2u9\n3rXC547bAu/7LfCDLHe2TPyFBGYpyLaClVeTtujsgBLc5oBPfB/l4hMm0wNS0Dl53s+zFHmPc+A5\n582dFLh33ln3zrKcJXDWOxbF5+hn9hLXavNn2bWqiFIB56pMLEEnUkfvN9WNK4HcLQ7BTPAaa/3h\nOY4BJb6b48Bj1UoCSxwDIWTX2+KQWQ+eLU2rgFe2L64JMKVfegeseaUBLEngBw/lLNucaP0SBZjM\necDh+dOATOrv6n6A57ujjllhosc5GBjwKILz3WPpBQyKG/A4Lvvra71mjOkRByWQRT8HMEi6HHjd\nEjhrnjfA6BqnMnyW0oNWdblkHANDy6LMgIIw4Pn5DePcKwVzeIlD1j+ZfVCu/Tmu1y+YL/ygjrHL\n+WBSGSy5wAGQWiDL9uBZL9tANYExGh/rXsIDP2fpmOp6ZQa/bvcqp/epNogBpGO60TQgqBh4WfSp\nYTT2kjISb4svsjYqa7WUH2qrquQD4mwpSQvMpzsnmhtuK4Hkj580jqf14Ib2pPdJEVC0rh3WZp/x\nbEV/plH4+FYop6FqlA0uzZ3jdkjhJLnKtVolR3depQLOVWFmO8ADn4PkE/i0UEuYS144DqjJBx0A\nnOURAjja9C9s0Bn22XmmLA7qrqpbbi8vcwCmq+L7+CUTgBWSTwD8SrBqvQhvibpBnwDY74lrWaAL\netdtxna+DbwGnHpe4b0dckohgAGzLK6z5v8/EuMEwHvAqRcMWtYWbcGikuiX5qa4xg1Vj0XVkGAC\nXtU2pyBjccK5AGm6fX0OVB0rSNIZMe32BqfrF2DaMmiGHMDfSua4ZW40tNqDm1+nWjMU3+m061q+\nDh7562zrhe+bbShOMBIbozi2233UqDcG9WmQl6vDKYHoOIXcTgru32KgX+Y5JaX7HJB1xnC93uHD\nw8NCcBgbEhiX4Omfn38k24b4XPe82ds7SLSL87ECcYCiS8oBJ4I7vziJBp4L06X0Tus5uQe3KNBv\nGiUVi5JDNOTZ2XURLPetCZxzBohOC3/a+sqcYycwakzdp6qUKxVwrorJn4uTCkAzWXomN/yDHnxj\neD1HHHumAdLSl03qhZbb3Xr7FeAy6JumEnLwJm6yAx3zbEuVwWssX2gA0zi+KPBwX9Sz5f9fFdfR\nnnKdxQ0AOJX/CglLtjgEEV4TY2Z5x6GesOrPuWhcf8u32wLCOa55zuCR4wCPcY6zXURF0eBR0yr2\nOXio19kGyZoDD4UHfIZRscYuE6TmLcNou84hmE/SMbAmBuyALXjkMLas/q2KF74G1jqzpZSA1AYX\n5kC2d9v3Y5/dGl4322AB0pwUmPtO7hT1Ob0v3foL1IjcnEpjSB+zzG5996N+nQS8WeC92ezy3t5B\nlq6SV37APYzn3VKWRjIaQc9eGuvbY5AU89nj506ttjKmdzher1xL6X2tAaIMLoxlFcsDyklc7WnV\nNFxQ5vQe1qL24D6wMuKVNRDOuzwoCknZ+cgl8KrK+ZQKOFfFeHilslIhAchFDt5NzfOMo6NjmbeO\nqk9yheObPYBLeCs1EF1hR0sAaJBgC14eUB6wvZ6CG9dWAAMANHiGYw+Q1JMNGr37HF660qu7z0E6\nDnQB6fHGS1XqIC9xmgVQjhm8j7iW5MFanlUAwj7H8nQW0Otzuu0PMA2euB4HyLwNxedVTjMi5nYa\ncm3ZF9fRINriL0NdBFuUW5m5LgLnAHUX/VjFmejCOteJY3Bdaw3vCoBlGSqS7oJ7ROp858YOfH4N\n7tNxtF7medCL3R/0G7sQ8f0FD6+uIxgKGOND1pScWk1TfuJ6dSkCDbl+WIFle3t9M/026r94Ec+q\ncP/Mz6+Z1w3ZU/WzMVXACLScTa7VWmNAmSooIAFPeSB8UgrDSfnEublwfcHO4xrX63NTAcjJwBnG\n5/SUlPMsD0I2cBpwnnvHVeV8SgWcq2I8BKwtxisctr+xLWs99KQkGbba5ba2BJwWcEbAYIcdT9by\nYgLkyi1LeGT7HLzTHY4BsAYLAKtbHLIlQgVAguA2By/ugNNgRozHETvgKEG55fGGd1YHzUmvXU7/\n2KI+pAAnprRoz72mA4zE95qHKrnMK+JaAI3XOQaUessQwYRDUYfkiOs+XuIALDGmm/57ydOWOxf7\nftwlDUXPtX4xS6NBUmUusFt3OoV3DuRLbXCMp+t/rbaS4VFj7WGN5Iy0nMGn7yWd6XGJkbVRA42U\nFwkA2+YUEC74bfSYAmElfIC3NXgJMXegHGlFkngetObxJNBQBLhyQFB7zlHm59Psg5hveIlx7Xzy\njhSE5qQ97bZv8zSe1dz4TPJQFo2r1S540mXq9Xq9MzYCLENiWr61lfAGvPSz9OqepTrHgwDO01wj\nKEDJZ8CFM21PVUKpgHNVhHcEgDenvatfttYLAN4z1LPIgb6gZc80VQPb5HrrWntDrZTR8HJLIAdP\nofZYS6Atk2EArOboDHLrXIN9yUOV18idI4PfNACX4FaPre1dTK8p00hrAwW/dzgEIqKdufplMCjo\nB9KwOBLHXxL1g+KgNUplO+W6Qgp0nKvXB7yWGkwCsOdoMtZ4rXGaqVLKEsrr5DznFzjm2S+wA94A\n43McZ2CUaw+g0gpwzCmXWP0HN77HzsBtjs+xABgARKt1hYPxZhmxq9xoLEbZDGMKSuoFTLm4qDPn\n6XbzMDu7YgDjeGdFgusyoLgMuGZm0T95XF/8vTsGbXYQ4PVkLIpAj92umKNdhu6gubRlPZQ5AJnz\n4gfVlBTYn1RFwmqPzHw5ydN9krpz+tgnKaehapTt03TAeZWd4+CS/+ecCFU5n1IB56ok3hEXqJID\ndfKlp6ka2DbXiTAgG2Z5ahY4cJJBadDpcCUYym1fWxJk8sEDzyWoFhKgwOOKNkgvIQDQI/5vyxOs\ngbz0fheBbek1vMYBjML7bcnHWUkyrou/dTa+ZfFPGxttDklQirLpIagN42KpGGAcUa+k3Fgv5L6/\nVpexne3WAiTykElRn6cpEyuccrQxZ/BU6wQzMih0zTgPgapy/NMkGQ405wC1Bv5YB49zCCjrCwBr\ngcrQJoAjl9AkzOX8/GUf2Je/P3I0iDgJh6VgYnG4i+uXEnJxv+KdDgdWAdpTL7DlyazVWqVpGMzl\n6Rx2vMEB6/UPoBOelZjTIIVZq4WkL7lxigM195lolW/cuDEVQLSAW6wTXgy0ytQZP8vSXchA28kb\nOGdVioLXc+OV312ZfmzKtKnsOadJZ56naiwYa3jhxH2rSnGpgHNV1EMecjraa6x5tR3/20UOnt4D\njgGfBGeWF3edg0daejoBYAAUL/uX1zLbHlFrSxtthXdMPkAhxQbecM8fCzk3SdXQnmqdzjonOQWQ\nBiCYo3fIc6Qnt88u+FHqXg/Zfsm3OLzgAHI1BQQv+eucvgwlsNdUDniBQS1AJH3OgNEP8CW2jY22\nOuYiE81xr7fjX8YdtilDlnzhNXZraZdj/jlSdrd9XdZuBPqh1wg4zJA77HPQeO4x0aPsDAlrHNC3\nXqbuduRhdRrK2tjZ4gDwhwk4lS/s2Atqb7frl3vqOd3mlHICT7o8bphNVqEBYa226oOM3bEyacck\nkGd7dnen4kKnDoFAu5Dn2Jk20W+st0BBsIPiAsiEx7gI9GhJvGk9oDlaxWmAM/pmpzu3gxGL5NfO\nihqRBq+vl/Ie53cczgY4n6RMS/EoHxxo72pW5XxKBZyrom5mC+Ba/CkkM5Ccy754wIJSIbewpdoG\nlA42/ItJy6RpeTQoG1htmeQRL+L4SsULGXhncaulBxxgNkdtAICAl1Z6l6GAYbUBwHjAcaCi3N63\nXvLw7m9xsfdMGgT4PgVH7trSC3yVHZiFd9h6UOfAbpdjr69Fn3BACS+RkJBjkhydNiTkWEKFQlIz\nsM7kurVltWw5vBk/BuAEW228yDFwTmkHvd510U+0WSfyCdrhvd5O9sUZ37/59V2vr46BY6ulU7Dv\nJ20kWuXZ2ZUIfGpNZJteEStJ9HrXxklMcHyQGnPXLcdhDpnUynocJQUNxkrqibQySILLj3vESdQV\nJWMJkpTb43blKAin5cha5zs6gs2rnlYtQ4P+XHY/axz29vpnqjqRD2jNj12ORoL7/GElUjkvbnSg\nCcb3b1XOp1TAuSrqIaeBTxFHd0f8jpc1vItW9Dm21FfYAY5jDuoSHf/iwXF6mxceRiSFuMxptkD8\nLbfjZSa4HC9bBovhOEvmK7ctL2kQCH4bic8WkBlw8MYTO4ANioPF7cV3luEAz6gEehqUyv4t+3r6\nHOgVmuoiaRP6d2RQlP2Gd90aI+weXOF88g83L/BIuhfMIjsPesf/s/pmAfVb6nfsKIBTDcNhn0NC\nEKueDeM77SmX6hPoJxRVMGcp7abZ7PJoBOMS17DAGNbrfha0ahAKz24cqBfmwoErnd57QfXNUYOO\njo6i7H2TOZnp/EvFDSvoy0r2oL3FkgddFpTllCdSHeY1dvfDGoeA4rafuxFbuzAulbgNyoja47HK\nUUlOC6CsMQgJdooNhUn0gGn4xhbNqNW6cqYAcVrgHO98xEGsuk8PupylQSGLM+hTI78q51Mq4FwV\nZg4PRZcFTIOkHBiCp06D3DbboOOSf1khwh7AQb6UVjm/hS7pBhbI6HGgC+B4DQhz+swSCB/7NkJl\n41j9Js/VihEy6x/0opFBEZ7KPgeZOgmmh5xPnABgjPOkZ2FFnJ9TEDnmNDANL5Yldh5lZO9rcgiG\nvOXbLgGYvCbAItaJlq9b9r8PGHxOdy0dFDjkRqPjJdzkXK9wUD2RnGSM26T5xG4EaEOgB0mwu8Qh\nUyLqvpype199tsYbL+0OO0pHeszs7CUPbKVKTVGyGalEMvR1rHCvdz0CZhKE5reqsROyysGAuMg6\nyLHRWCglTyYTkdRqLeOauyzXa5HMmK7XAu1lQNloNDKTZzggqwNVJQUsrMd8oCieD8vqnEBt6PWu\nG3zhkLHxLBQjNJC1vdAHJvUiR3mZtk0uOVZqFJ4lcJ6WqpHuwEyfaOc8y1mqe6BUcnQPtlTAuSrj\nYgWtBCqFJacmQaO8aQdsB7Fd8y9KgLIDTrVr8ZLZ4mIOqkWRwANzkfMKFGuceg2H4rqgSEjKCTxp\n8ELJcwdG/VC0gELEgAMobqvrAchKIGy1G9xla0uuyQHgDjgGpescPJ59cc6mH38YHDgHgFVnKFzm\n4EkH/QHeWisD3j67tbPIzpunQQY87o7yMDu7LkCXNpbaHGgMNzhIxQ3Y5lRLg+QqpzQgHejnjJJu\n91GfWAK0hbJZD/VcXRV/w8Ovj7GoK1ZAKOZDJxXa4VQ20K0lgAQ72EvPF4yGBQ6Buo6iMzubqn3g\nZR/qjY3T2PhBEKAEoG0v0Rc/UwD4JwE3RzNJvZw6IM2ijYCqYgGM/LNEPgu0AYX7APdk+D1to1wr\nxZSXk5YUrA4NI2E0nsfJ508Gu7ngwLP2rE4THHhedIhv5RIHYgeHSlXOp1TAuSrjYnuomjwzIwEy\nPH8AgHgZy+QJuInly15vBeuEHxJEdjjIceHlnb4UUvCD9gx83RYo3+QQ7NZhBxDhEZMgFsAJfWpx\nAMrg+LZU/QCx8OYNxXc6kAjgEt5cPPCO2SWP0IANlBOLqiG93ACjG+y4xV3fXqs+eBxByZCAdZdj\neaMdDqnWZV1W6nCMD2ggALoycA8AZkMdI+daG0vIUKnXQZMDb3nAqRFieQx3k+NGoxEfHh761Ngt\nDrshXXa7KjLRjPRIarC7LsYLxpY0HMC9lmtujQOFaZ9DBkkk0dDtz1FUHKir1Zrc7T7G8/OPePCK\ndW55tTd8fTDssM404B+O5c/CLopOI+6eEbXaAs/MrHvlji2Od1ig7R23wxkta+wUbC6LeV3hdnud\ne70dfwyUVWDgBC9n6pUeMQz0VuuKCZxdnRp0uPMbjUvc611TCiRYB7jvbSpOfJ10DUoDpwxfu+gY\nixZiedxB4QAlahKvfJJChlZnaTQ6hbsSD4ImcVag3Y3pgd/x6JdWO3lQ/ZQlGL9hx4ho7oFd/zut\nVMC5KuOS45Lt7R14Dwq2NSGlpoELvJh44cJbKyP247rTz3iJ4XiASAv8AGxJaSt41HKeO4uzqL3O\nI87zjNk4Hx5U7f3scJ7q0jHqlsYAPMcbfgxghFjSZbucjqPstzX28vqgU8jjrKA3JCexAIEGuSu+\n3UWBezBCOhxn05MAWgOanNLGoriWBpjWutZb812vAIE2Yh1iTYLK0ONgcFkGwwKnyiuYX4vOI3d4\ncCxoJEOenV3PyNXlAp9WORiyuXvAqqvO6Yu3JurQhqqcS0g82l7oOKGLTkWt5xHnWzsJyFSpvweN\nJU4EkuO56m3+CxestY77P6bFtNs9D4qlhxkAPva6xt5+mxM9O7sSAU8Z0IcyCQjq3+FZtzjezWY3\nCfaUQDfHK88VR4kpzhx4XrzeonJaABsCS9cK50af86D7iRI//3DPLT6Qa38nlgo4V2Vc3ENQb+0N\nudW6Il7eEoQccR5g6ReuBo8AQdLrtsExIAfo3GKbb3wgjpVBXzmO8Lb/bAEpqEYADFgvus2C8y8Z\nfQSY0cFIltEAz+iAg5cRHvOrHF7UlhdpU32+pf7OeVz1Z4DZWwX9t+YxZ9jk6DQaKAFIX+SwG2AH\n1QWlEQt45owdC9THW+tBD1oCds3VXphQB3YPclxlaWjocZK7IvHasKXPjtT4IeMlgmKt66Bf+j46\nYlvmsMnB+C0ThInfc9xq9G2N3ZrOGaa59u9n6o7HD17SnLKCDEB1AZrWOoXhFL5LdZgBqFYSNYte\nb8c/N6/y3t6BV6XocADZMNDT3QSbdnJstqXod02pqdc7Xk3F7hczG4GTxTQHpysez2OvtzNV+78V\nSy7QtajdD7OfFcf5wZaTAOcGVeXbsty8eZPe854h/fzPP0nMjxPR64noDr300teI6D4RPUVEh/7/\n54now0S0TkRH/nsUHPdRIvoUEf0qEW2IY54nol/z35O/zleJ6C8R0U3/3TUi+hoRfZaIHiein/R1\nPENEXyKibxDRz4tjl4noN4ioSUR/6q8hyze/celSAAAgAElEQVSI6AXfti8Zvf9BInojEb2biP4n\nInOZf5WI7vg2PU9EA//9dxPRReN4IqIvEtHLRPQB1defUsddJaIvE9EfEtEPEdEbiOi/INdvIqIn\niegJ34+nxHlPEdE93y58/k1V9wERvVWd8woR3aUw3l8iohtE9Ky/9u8YfamTG5/Xi+8+5Nv1z43j\nF4zvZNkmtybuEtH3EtEf+O+XiOg/IqKPEdFtitfWW4moS0S/RERf93UQET3i2/1/EdFPE9EFcmvi\nLhH9J77NbyOiTSL6PnJrRpam/x/r5hNE9D517Q+qz28X9TxNbg5eIKL/ZUK/dXmEwjyk5d//+6+S\n69sHiOgLRPQTRLRJjcbX6ZvfxL36E+Tuqb/s254r14hoh9y9+SVyY7hJRLNE9Lco7t+Tvl03iegv\nTOjDVzPf3yU3bl8md588Qm5N/4Lv00f9cW+Y0O6iMkty/D71qd+nu3fv0s2bN+kHfuBV9Oyz8dFr\nax0aDt9It28/Q7dvP0NEnKlXrxFXbt68SR/5yB1/LtFw+A+IiOj27WfoxRf/mJ5//qv0uc/9O3Lj\nSfT882+lT3/6s3T//m1fw9vI3YM3iei/Sup//vl/OW7/pHL37l365Cc/RUSvi7533xF95CN36E1v\negd97nP/mu7f/xV66SXy136CrDW3tnbZ14V1cCc5RpYvfOFFcs/xQ/Hdz0xs98Mod+/eFXP2xlLj\n+2enWGs4t66r8lDKtEj7vP9R5XE+02J5HQKXt8/OMwjvSW7r3Mpkhi1tK8Uwsv9JHiS8PnobapmD\nnN0xp15vyKnJIDFJEbiUeEnC7212njZru32GQyIWrZ6Ba2qJr9zWuK5bt/96ZowOON1Sh3Ren2Mv\npNRsXszMqfTawmt2i1MuMwLt4GWWAZDHvk6tSmF5gOU8aF61bvPjxhggmE3Xeyj6obfyEcTV5Px2\nv1wr8PprmojeJcA8tv01UG9Rv/Vcr3K8lkB7cp7JENAGLyXasTpOsxy4rQeiLZYHecjpHEBazVLD\naKl2FfVJeh5z/O8ldt5t7aHWOxA5qsZips6L4rMbo5x32JLDq9VSve5erzeVhBvUPxyNQ3uRc97z\nYx8Qm86TnVgmbkuOjoKAZPQ1x3VGX6UU4LR0A+fBHrC7F1xwcqt1NRmfh0VhOGkbHHcbQeaBNlW8\nBg5OldTmNIWIjPuzwkXnVaiialRFl2LdzFH0MLGTRFzw369mfs9JiAG4bnMM9A45SKQhaAo6wjlw\nCm7qLjuqhwY8A/87QHiHA2je8W2Y89fc4DQoL5afcoB6IPq9yyHrn24buOAI4msax+QUQfbZAfhV\nDnrVRxwMmYH/+7rvN+bAGiNNlcFD/xYHoGXxi0HpAAd7WdQn18YaBx1mHRyIdZGjFKC/GlQAqFp9\nOea8TCH6sM5EG1yvL3t1B9Bz5PFNtsG5BLjQ/tXHtJlohRsNBIlCTs8FVrbb6zw/v+4D59bYgT5w\nmnW69CU+PDzMSqtZ2QTBbd3bO+Bu9zEOmQ4d1Wlvb88HP276+QBdwOrLnB+3PgeFERhs0Dpuc7u9\n7qUsH+NYbcUCx/tcr69yr7ej+LSX2d3nWEPyGQJe+CJ3u49zr3eN5+c3xoF7ji6QpxdobWI7MUYs\nTxh0mIuDwyxecHqv2YGBCJpzXNo0kNnqQz6JiuTZByMrSNHl7wlLyq8sP7jb7SbrptfrmeNUps7z\nCq47ScY+nXVSa43LYzUlpmww4VkVx5OXwdwH3Gh0Htj1v9NKBZyrkpT0ZSA9vDIYDLxiqCisckgA\nET/AQ+DSVXYqBZqzKb2gOqmIlInbFr8jnXLOo4O02jICHr/Bs4gXpgSQOxwAGzyi1jX64m94zAHw\nmxwy7Wkur9Y6znFuNWhEpsU5Ue+cql8GGuL6S8ZxliTbJQ78aktCDQD4UdV+SN4BXAKAQIpvWY3x\nkN06QNusl7rkrUvwvpGZCwD9Iu8aMge6l6bjDlv9XM1coy/6Bc+wnrc2Oyk3HCPnsM0p+N4W18m/\n3HPJPKaV5YqTBq2I8ZLKEn0OMQW4d/NeU3g2U0BqA0bpSXXaz+1xm5AWO9SXgslpdX1xreCdzant\nhGcA2jnJS2mPMZ5R7rxGYzkbkIe2FaXLzs1xXqc7zE9IgqN3dWyAPu17wubWtk9cXxmv8EnA9bTA\neZrjvxU43FVw4IMtFXCuCjPz+EUFcX7nxZHeQ4BigIpb7AAmgt866qZd4BgUyJctgoOg0oGtdKhe\nWC9pqHboVNWQUtNeH2yJw/sLb+2CuOY1jsHkZQ4UDbThlvpfg6QhBxCP7WVo10rABGWRTba9olLl\nYYmD/J2kZADoy8DLXAIOPU4HYv52OQSEoX5sS+KcnNLAGtsvS4BkAC549bUMoQaPeqwkpeAyp1vQ\n6xyoFPIceN01SEDwGzzdW+MX8uwstv/jrf9e7zrbaxDgvWjnAetYBuHp9a+NhDxwdhJqfZ9OOQaH\nMeUgeLog/ZXLHGgHKgIkW5rXOUMC9UAvOKYM1GpNL4VXDGitbHfMOXk0O6GHDvjTxdJ1tukrDlyj\nvkmAKNQbU3qazW7kqS4jJ1dMx0jHMNXpjtdivd4Ra8S1q1Zb9eoxxeC0THF9twMrT14f6nLvAZ24\n5KS0j2nP+7MAnOWash0QS+fehu/UUgHnqvDR0RFrkOS2sfWNuMUpIFzglHoBr46MTAcgxsv8Csce\nr2N2oAjH6ZcRvJYWYMOLD5n5rnEKrgCWAZQ2OehIa4B+zCGrIbxTRxwDCryMHuFU/iu3NYp+WC8b\n9K/jxxmc5QMO6hJtDjxPAK2ckWF912IH8DGHAEkA2toLt+TnBEYLPHIW1QZyhRJoF2Wd1GPTNq6P\nMetzmmlxhpE8JagxwCAZ+b9haMXrANzgeK77DNpMt/uoBxfaWzzMzN0+hzkEhWU/M066732ODSF9\nf4XPOmVwrFUspeA0Bz4GhSlwHon5stoMqoQ2pI5UPSngmQQYc+AY1AppLGj1ikkJPdLrWGuxI9aP\n+65Wa6vxzQMi69nZaCyeWAKtmI6RtkHSUORYyQySut6zokO4tq0l/W+3L5+iPr2eY5AbH+PmFFkn\nJ5Vp6CLT8JUfltzeZLnDSlXjvEoFnKsiPFMBqM7MrBs3oqWTCw8kQM+ItQcvTZWMbWyA8/AQdCDR\neklLD6i8PlJs66x8FsDR8mYAu1c5zbo34tSLDUAraR3glsmXsuWdBjd7lR3IRPIU6fFiTmW61jlw\noNEOUE8sLxo87bqdSDW96ftqcbZRl6TNWNv0W2oc19imPKwZ51rgUdJSNBjP8aC1R32oxlK3L96a\ndtQHCRzjua7X53h+fp0bjUvcbm9xvQ7Dpm+0BUYiggKJgwdX1tthO3gTgBV17HMwAG3QFLb3ZZtG\n7GhQlqycXmsOiNbrmk5iUV3QltSz6kBaMf+6qKTAMM52J6Xd7GfSfimgkuOJhx2wsD56vevjc05C\n1ZiUNKRMAcizdg1ynGRwuB9U8o2wWxB0nIlmCwFmOa+7vt+HY054HCgbjISz6u9J+crnxc3OlXTd\naUPf3fdVOZ9SAeeq+EhwvMTdS/vChXib1d2YmhYAoCBvVoueYVnDAIAW8NOqGzhmkWPAtCquD1oA\nPIxWggUrKEjzgwFcLbCmlQVkOmvpqRtxTE8AlcNSOcBvONdKdqGB5j7HGRAxbyvs6DM6vfUCO6AM\nr670xst6b4n6ASb+f/beP0ay7DoPO1VdU9PVXTXdXd0907XsmV5tyatOqxuatpigjRZQtDLLcQCL\nAadh2BEoVWyJtJ1E1FK19EpY2hCsBijJXIoi/MdiY4kziANEEQw6AoL0SJFMIStAQSQrDMOIlkxS\nAqQNCVFSINEcaZeckz/u/eqee+65r151V83sku8Cg+mqeu/dH+++d79z7ne+o8E5gJ4GURZPW3r4\n5T2zEoBYnO42x9kj9RhIgwse+W1/D62+ubnklAAAUq2FWnqNncJByDgXe8PCgvUEhwQxbU91wpjA\ngIBhiZ2FZXbzRvK5i+erxfENBmPOALnBoA/0+ze53e5xq7XBnc4NbrWeUPVKiom8P9azs831+joP\nh8MkAcdwOCzt3YvPTcGt40Djnse/TZN9zylM2CmoJQ++0ViJ6BDTeswnBZ1Namd6f4Oxo8H76elp\n9J6elKSjTJkGBJYJoLT6lMtemPK9Y0dMs7nplUjOZ6hNKm8EvjJK0X1IYx72WO801euNx9Lub4RS\nAeeqeE5nDGBrtRUVrX7MsURbjl9bpHiggU9OEUOCMO3BXvLt3Gbn6Rj5Y/ocAhJznjf5sgGA2eZU\nHeOm0Q8AihVf/3WOAeqS+Gxtl1tb4GjPFgdOeC7xiObRHol6Bzw5s55sK5Jl5IAzQNwpO9DV5eBV\nB0jT24QyMA9jNeCQevqqvxZ2H7RH3AoEBA0nDrYKuxgazMM7Z81BSOdZvG05ZmeqLgcc+/0DD+Ck\n4QGqSLo7EtQSAOr1XAQQxzWt+7ETtQWgyfLSBu57jkZzInjIV4z2yPmF8QZ1CiBeysWt+/s7GqtD\nhAQfsUFdRvoLwZq2VxlZQaFEE0CUUxE5Lgy+03U5w0nTguLgQIDAaUEu5AOt4zVwz7XTAm/wuGqQ\n6aQE42NzHu/z9GdWtIOUv7yRrSNuQ2osufs3G3Crx+S8wHnWHudJ98EZ8tohccJSFrBer1Q15lUq\n4FyVLP8vThuLBReAJRcwtMUpkNDZpaSnJyclt8q5F2fYdu76xV1vf8OzB88x6Bzw/umtfNkegCiA\n+hQI5D3r2BK3vHNbxnfg7XY5cI1zAUzyf60SIiPpLeBtbVHvF9TT5bwcG4CZ9PYCBA783/J+rHOq\neX2JYyAPbrBsz7L/J42RLocgRAAo7QGGAssKp2235hKk2uDVsxQX9gXgcUbBwsIaBxnD1NBxQX1B\nGq5eBzXnyJ/ngmMbjRXudvuJPJs7HhSO3XGgV3hetZGBdmjaCfSycU9yXN8jcRzGEB7xLrtdERjO\n8bMAekC45vTgI3CF9bOpg0td0Ginc11wetP+5OoL9AJLxzuc32ptTq3j7AwH2zOcUkUCFxzBnEXg\nTQfJhXmQGsrdri0HV6Y/llFmgXZ97Umgcdr5EVNVital84N7a0y0xndZGtCsjY1JAD59B6xz+k44\nX5BmVSaXCjhXJcv/ky9yeJXwd0jdqrcUEbyG7Wh45+T2twQl8OJK4AMKAzzCepEfcAr6tEcWi+4K\nBwk3ePZyagcIeNJe7lwSE/0dPKQWMJFayrKfUh5MgsFUOSDQF/SWPsDsGtveTYt/jHuy4dtyIF64\n8DLrc7qc0lW0MkEZTjOCShH8hjGD+sWu334GmJOLwTV/nQEHIGhxb/d8P9bYeW5xrNW2DXbzQ89D\nGFM5kA4gnwJnzNtYHQH3Qt5zN37N5ub4+To8PFY0qTipx3A45NSokfx/7KZscUhLj/uSA85anhAe\ndXwGxQYKOLF39qLAOd2eP+KgzhJfq1ZbU1vV51FACIafM1Kkgg3UeFLAlgOI5YCO5NRrh4T7u15f\n537/wNCGHiWAzF0zTUtvBcuV9aRO4xnG2uEMkV0m2uZabc3UO57kRS4ydHJqIxf18ObGZNprz4Pe\nYclPyp2ElM6zwPqdUK9fvlAbqpIvFXCuCjNb/D9H1dBeE7xQut23cABN2MK3gvewGDG7BAdW0B+2\nYKXXK0fVkNvcGshK7xuyuq2J83G8BRyw1WUtxNbxA6NdALUHHANJXNvKyLjPYRHf56D0YdW5yiGo\nTy7yixySw1xmnczBeSA1xQZJZCwADDqMVT+u0WVHaXF9qtc7fpvdAtwaOIObrr2LaAPmod6OBPCU\n8yIFDikIlt5YLVV3zHkJRDd+bq7ngLcVyHqJA2ANi3GeX+uu2+/fzHrZiI7GxqsF6hyVRPYZzyMM\nLxijFlVDxi9gLloG6z5b/GfQGtLdqZGfG+vZ5BEouT5bY54C9ckAD8UCOc6g0fcw5YoH50JaR3kP\noebUSyAfc4AtSolW1HCgNbwPJD97Ur8tcDcJ4GrjwVEG4vmEZCEafIYgxvLUGr3uzDLwblaAdz7A\nOX33aYNISsha792KqjG/UgHnqoyLCzSRAHZjrKsaXtLw2upMYjm+sgx6OuMA8sBRHnLYLh8wPI7x\ndRCcNOAACq1FFoveDY4zFsrjzzjluq5wrI+srw/QIUEJeMzgVrc5BPnd4ZQuAK1h7aHXFAW0RXv8\nnMxTPM44XgJgUCN2fbugw3vCjvet2yQ92qBo3GUH/iUAb3OcgRG0hl1/TUl5sCge8jM85BYQ3eZm\nE8GAA+N3eMqPfL0WVcIKLpVUiy4Hj3+RcYT+tTi/kwBjJQQTumOH42OCioc+9476H21M6wH4DDzi\nFNCcnZ1xr/c0E61zvd7xnlQ57sccMlX2GImL6nXMqRMOSXOsIEMLTHe5399LVB2Gw2GijlGk+GDJ\nuoV4hcn6xrlgM10sD6aL8dD9iilggR9ug6NJntEgF6fjEPQcCNcvSooi65XBeRZgLWpfboxyBpw2\nHlxgOd7nfXbvuPjdl/NUy3ZOG5A4CyA9K4rF/Kga8U5a0XyzYkQq4Dy/UgHnqoyLZTnD0xVbwFi4\nZapngEt4teBprfvvkF1Qewd3OYDQHQ5exY64TpeDZuwJ23QBTf/QYFlzm2VAGRKXABxpYA0vJlJo\n4/gFjkEsFD1yqbYxTgAnT7EN/FY4BGPu88LCplAuyAW+yc96fCQdwjJIJMUBYEfL4sGrlBtPgHZQ\ndSR1AuPVFb/lgPMaBw60NYZyZwN16mOeML4DRecmh+yFOuBQGxVtDqmjR5xPDmIF5AHUr/KtW7cm\nSKFJb/AZO4MBY+WuBZWKMIdiz/nh4bGncEiKyxL3+zc9ANIGWqwosbgIugJ+1+N8hYMhFdOHrCQn\nuXcJQGiv99Q4SQi80dKDFit12LJg5wVQ+jxn1JywDKwKY7g2Ngj0vIYhkAN/Kbi3ghlhaFnG0PFU\ngGwSgJt2vKzAR208uH7oeZI6UIo8sNPQMWYNUmcJwh9lcGD6fGl5Uad1X5X5lAo4V2VcioBzHMlc\nBHy2jQf4RCzGFlACbUAH7GgqwYm4NjyHABjymtdU28446ChrqgQ8S5c4JDyBp3abw1Y++itBgMWV\nfprjgCY9NrLuI84nnAj9brU2hJ6rHYwWf94y72M6Jqnetkv8AVk/q10jnuQxjuvAOMnz4EXUwBvg\nXFIw5FhIdRAJ/jXgvcxpQJlO846AN3jU4XG9ygE0X/XjLVVATjmkO9fcdA3wNSjXz8UyO2MRc3OX\n48A+KIpISTx4z2UcwTGHTJMSGI/G4NT2FuP+wTjVvw/E/bNoPXaQLACEXZ82GlwMRK/3dOKRntcW\nvS5hJ0fung3HbYUnV9+/SRn4irbwpae43z/gfv/AVNyYZgzmQRmQ9QdJxnB9WwVFz7U4wLCMkkUI\nAIzpPvPo4xu1FN37dByqBCiPslTAuSrj4ugYcVAKqBqdjtQXBuiSwS7g6K6q46TH2AKJWxy2xCcB\nzZwetFbVgKcQQSsAOdo7DM8SQMqI42AdABANnBFAB0+kNgSO2C28Vnpw+XmHU0CXRvgHT+ESx5QS\n9HdZfe4Z4wQudZ6/6MZnkYs95quiXxY4usk2KF5jF7CHcxE0usuBO7vNQcf6jEOk+ED8bbXLMoiO\nOQTCrTGRSzXswI6U25M7JjLYD2MpjTV8t8gxkAZdBccgS6VsD+ZK8Fh2Otd9YhWca/UtgM0AJk44\n1uQuopEUXduBV0chsGhWAw4AV8/JLrs5boOedCtZ7wphN2JNHZMGwc3zfecAiPVM9cZthbfbHr88\ngEt52HFGRUvR4SKGQgqmJitiTDteus2OqqHHZZft4Mc0+6MzCNOxDY4CLfWXHjsr4PyoDLVZFJuq\nUQHnR1Uq4FyVqGDr0nHmjscvefeC1LrFVzn1FOcUCABUtUwYvHZWAg0s9hqIhYUhgO5VdqALQGWX\nA7jIgbxVDhQQgC0ruh/gFB4/C+jib4DxdXbA4oidFxHb8ABz4JViXAAerXHYZOfJvsyOHqK9Ywg6\nbPsxQIZCCQQgxwcDwvIU9Tnm6locui3xmwbHK/66oDdYhlCba7UuNxpWUhOZgW/AQQ1Fe7Elt16m\nRZdAVXs3MQ7g+kqpwba/X+iTvE9WQho5tpDoA3jucJypL59ApN/f43p9w7cfiiJ54Cw9dJC6CzsQ\nOdoLwLqWhLzCi4vd8TPu5rf0kmuqhqVcs+bbbQcxpfxeHANjNGcoxGBIUjhyaaTP854LwANjKO97\nR7R1VBC8mAdwoY74OYkT6pQDgGX6awdohjpnBZ5lO5wnHs+oi1txxqmk2cl+lqOkuPGZjr6SG6Pp\nx+7RpM2+aJH9slQ1KqrG/EoFnKtSWOKXPzRddziAA+1FPuHgPbS8VJf87xbFQnNOT9W5mqqhF/Ye\nx17uSXxagC2AcMmN1sAf4DO3LY/kHlq1AVq/mhsKgIy2Sq+xBuPyb3heJYcYgFHr3+5zDCYln3mN\nUy8bDJAlUS/ad+K/7/nvgnSc6zO8lqDqWBJ4GFvwwHNGkMXPBrg84xjgrXOakhxGAurSgG9dHL9s\n1Cd3XXLShQDx2mi0siJixyXMjUbDqneorhd2QXKpf4MBoufrCne7Wxy8xanHWwK1wIvHM40xzRmy\nkm50hWUSnpxag9Y6tp+lGDhbFAmdqfA8ICf2zsKw1YG7gaalAVuzuRkZA7nEJ2dnOhOe62eZwL/0\nHTy5v0WBfTq4bBbe1dz9CfSOgWpHCoalQSjpHGUkUidxn8uO3ZudAhI/v3gmq5Tb8yoVcK5KYXEv\nFIDhsCj3+9I7iReOBhKWagO8pZbcGYCeBIT4bcABmG+wnaLYAoOnYmG0dJRDEFAAhFYaZoAES20A\nqcBzxoJsDwC/BtkrHBsKyLZn9VHXD9CSA3gSlMjraAAOYwNpwKXCh1bvkEFtXY7nR5edwVA0FyDZ\np42g3LgDzEz2/AUNbMjNyT7CM4a26Pr0Z22gyR2BMmOOdrh+AgBb2c+C0QGOfkoD0Vv6t27dUuO4\nyu32Ve50rnv+6THn5rOUFmu19A6AdR8QCBnuo/OYB773JAUFtL3fv8nNpuZ9b/gxWhtfwwKAFq92\nWpATA6Ue5w2kPN/YMgYsykUxh3cyGD4PqAse22A4S371rLyrMYXPta3TuT7+XddVq7XNYFKrlM20\nWDRGZcfuzQ+cK6rGoyznAc4Nqso3TPnSl75IRL9NRD9FRMPx95/97LNE9H1E9N8S0XP+25eI6EPR\ncUQ/SkRfIKL3EdGXiajjv/8OInqvOO69RPRufz4R0T0i+jl/7XtE9C+J6D4R/ao45jkieoaIbvvP\nDSL6Z6r+DxHRXxLRrxFRm4ieJaJdInoXEX2MiJZ8Hz7qj3+eiH7A98UqbLS7QW58rDY9rdrzj4no\nCSL6QyL6CfXbiIjeSkSfIqKvENEl8dt936bXKYz3p3wfLhHRGhG9Q7X1M+TGDu18t//8PBF9JxH9\nCyL6af/7s/66LxDRgT/2dXLjI8d7S/TtA0TU8/1/TvXlJXL3/oO+nX9XnEe+f+8mol8gok+SG/+h\n/6zLH/nv301EP2v8/gfkxgfX/xMi+g9EdMXX/W4xBs8Q0XcR0d8mon1yc7KoHFC/v0NEH6Hf+70/\npIcPXyOif0HMB0T06oRziYh+h/r9J4joJ+n3fu+PiKhOf/Znf0Rf/epXjWO/QvX6V6le/zJ99auv\nEtGrVKt9kZh/mjC2Dx4QfeAD7yPMt1brebp16z+hT3ziHxER0c7OOn32s39MRE8R0TG5uf2MH4Nw\nf5rN99OnP/06vfbaR/w3n1BtOSY5z+v199F3fue306/8ym/Sw4dfIDeP3ksPH76b3Hx5lmq1r9EL\nLzxPt2+H+3z//n165zuH9ODBTxAR0SuvPE8f/7ibk+9855Dcc/gSubm6QvX6Xfqn/3QUXUOXr33N\nGrvpymj0HnrllSE9eEBEtG4e0+3+EX37t/8CjUb36Pbt23T//v3o99u3b9OLL75MDx++SOH+fIr+\nyT95kR4+dPfnlVeG9MILP0CvvPK8r8vdsw9+0I3Biy++7Ntzr7DP05T79+/Tpz/9O+TehURE76Ja\n7S9oMPjhcZ3ufoQ59eKLL5+r/gcP/rLwu9u3b9PHP36PfuRHPkif/OT/TQ8f/n0iIqrXR/Rt37ZP\nH/xgvt8vvPACvfWtb53LGOkSzwd3j0aje8UnvaHKw5LfVeWxlWmR9rz/UeVxnltxW205LyC8zPDY\nWFYv+KdaLucap1zT1PsUEkocsR1AiLbZWcYCzxleshE7DzS2o3OeJhkEh/aAwgBZsn22tYZ1wKH8\nbZ1jD74+D7J98ITDOw3vLYJmtDSaDj7c5BCA1/XHg0M89Md0OHCrc0GOuo2aOzzgvNIHvF2a267V\nGCapfYzEeYtcnJJcHm+NsWw/UrHLeafVKa7w4eGhuidLPsBJy/5pqobjr/f7Kb/Y5iSecKOxHqk1\n2IGPsVdbBpzZWTihuhIC1FKObaqG0+1umlvigQqQxiQUB8jlPIGuXY3G1SRRiq3vjHnttqVrtfa5\nec7PPHPHB2jqAMErCU/W8tKm/StHRZi2ndN4iG1Fk/2x1nWOOnKetvX72DkKFIF+/6BUm7Sc30XK\nRakauAboJUWa42/Eksa1OLWqqsynUEXVqIpV4gVS0wokIIR6BUCLTlUN+kEuWEx+N2JHT9B84C7H\noEhfA9xcHeAklTLW2YE8GaiFLX1rS/pEXHfd9wvb2dB/lnxYff4+p8YAgAwCByXI2eQUvEiOLGgV\nWOSt8bzJISHLnjhXqp2g/ZJjjYQ2MuAwV4cMtLvMLoAPWthy3AFwoFRxyoGis8sx6NR0iCWu17ts\nq1NAJhCqLnKe4Ps8iAk87xPGXHOUgSM/fkj0At52TEWIwehddlrcuxwS9EhQN+Jmc9VTC6y2Lon7\nLmk60ijVVB/wt+N7klMnCEF8ATgg2JBghYIAACAASURBVC5uU4/TjJNLU22NTwOcA5XgJjtjxY2X\nBWxkcKADajB+XZ9qtdULARxHm4Ex2meifW61Nif2o9O5zv3+TU/VQFBwGrtRtOVvBT5apWyAW96o\nGXCsYW8HOE9L27AMG6sfbvwkNeykNA2jbCkbHFg0lm/GIEFmZudQ0O/wxcfdrK/bUgHnqiQllboJ\nahH1+rrgVQIYHXFIg60lvq6xA4lWIJD2CMJzqEEGPG7yHAAJnY2v7Rdj8I110NQVdqASwMzS19XJ\nPwCWehySU8ALHEfMuwUdQOrM/609hgPfN+m5zkmKAfhokJkD/LIfSEaCsZJjCiOiyynwXeYAFrVq\nxpMcwLnOpgg+MQA55ggWcgB253Gu10OCjl7vSW61tjh4v/Xifk18t8G2tCGAeVicbYCgrw1jBHXj\n2K6qH+B/m4PkH3SfY3ALcBjLO8qgOhhvOeOEOeb6428ol2CMMd+PuNWypMHA83YqM73eU4nkZOD4\np+2AN9t6R+SkK9Pjgne42XTpmO02IIX3cZQNT4OgXNDYeUuZ9Ma2oYD5cT4gWhZ0lin2O1sae3qH\nYSTSNccgW6fVLipW1sV+/2apvk5jYMyqTALGRRKC82zTRT3vdjD2yoxbWhWUCjhXJSnWIqG38nq9\nJzkGTjLYzFq8NQiDQgV+04tPvJiG63b8b/DyWPWtc3GSDr2lpcGJDJRyL0933LI6D/Uf+/qucxqA\nqAMrASb3/fX6oq+5vgAksTjGMghST6StQw3AJukHElgfiToOOHitR+J6k2gcd0Q9Mn0xjK0bTHQc\neZ1i40r2ExJhyxynBNfzZYlTz/cxOyNB0lFyQX0S3FpBgrKt6xwMjHS+59QUgkFx6j+3Wc6ZRmNd\nqDUgCYzsI1KpI2ELDNcRx6ovsv9yrqT3zSlz5FRQ9rMaw5Z0pS4aYCN7nj137jLRPtdqMW2i2dyM\nrj1rLV8L3E/yRE6aT2WoDznKxHmAlPXOdjsd8IDbFBLrPJ1Wu6gNbu7E78+Fhc3E8MntUDxq4Dwp\nCDD8Hr9f5+V5npWHO/ceqsp8SgWcq8LMOkOUvTDJYyxPQ6ybbL0gAUg0N3SN7aQd8BpLz66Wbct5\nHtucT9sMj/ZAXFv+npP4gvfujIO3VgJPSykEahtddp7BoW+XBj2W9xw6uXIMU4+Iu47F/76j/kdd\nevylccLiuHyKZpvXjLFe8mMLHjkW7xxflUW9EjjrNkguKvrc9/erx45ikZt7m+raRaDfyrRnzSMk\nU0nvuVsMdzjdbYDBJVMYB+MAAPSZZ+6YigVuTGFM6QyCoBcBxCCd/Q2O57w1Rpc5NUSuMAD6ebfV\nc0Z4fq7a3Hqt6zwrT631/stt5wdjSO6w2fOpDAi0xqHTuTE1kIrbFXaVgjcZxn03ua4Gbrk5mSup\nMsoVdrSB2PCx1pRpAPqsSg44S35zMKJmZ5xN256yJRgly5wa2cszb29VXKmAc1USj0ujsZxkdzo9\nPVUvWAs4Xec0Ex7E8DUg1udaiyloDJoCoMGLBQIBinc4Tbpywmlq4x3frmUOVI0c6EbAna7TBlEB\niIIWob2XWhoP8m6SNpDjAzsuqgNJ+poS6F4R183160hc0/JgSHADqgrqA5CVXli8wEExsObMBsdB\nhGjfCdttAI95idN5lBpRgX/aEdezdjc0NUgfY93ba5wGvSKYVdNE3EI2HA7H3jgHjPPb1baXDhrY\nlnGzyrVaZ/y5XtdgGLKCmloEStESBz7/HsdZD8+3uNvc4BtKzzkEdna7b+FJoCXly+a1o8t6brVk\nXlGSjTSpSTxXyoJdK2jUfVd+rLVkm8y+qPuB4MAiDnBI6x6D6Vx/wjoh52hKy7MSl0yTKXFWutOW\nhzcdp03TaH2jAee4LzIOBp7/7szbWxVXKuBcFZPj1+/vTdAiHXGtpoPb4AGVvN6hWhw22fbM7rPt\n8ZIBD21OQTj4xpJXDfrHIgcKAwDvEhcn5wAou2wuAIE2YAG6Nqdb5SccPIJSRcSqf4tjbjB42G3/\n+QYHmgOC1loc011k4B/asCt+kwBYAzLJL7dAq2wb7sd1jr1M1kKK+oq81NI4gEHUMY4fiPOkbjeo\nCrEyglPEkEYHaCdILY7xtUA0vPPb7HYLgkHQbG7y4iIMkT47gxGBtNhlSOd4s7mpVDPi+IHT01Ph\n/TrmRmOd4zE54ZCOPJ1H/f7N8bm1GuYivIeS9495fOzP3VBjJw2v9Dk43+KO/mJurnC93maZabHZ\nXPXjE+5rrdaNuM6W9/LwcFBYb5G+dJrZMDWSZH/l/QEQnRYEhra557zTuTG+RpmxdjSZY7OtkpM7\nLeCclkNuZwBNte4voiwy64A93Y6c4sc865xF3+J2P8naCOv1njx3e6tSXCrgXBVzy7DRuBo95OnL\n5YRDqtUtDkAEIE+CFWQLBDgGgJGLaYdTzvA1TuW9LPWGbXYA7kDUsSWO1UAtB4j1MZc55Szn+L2S\nprDv23TCsUdwiUNaa33+Gsdezyc49vhZFIcc0IFxoBOySO6yDs7qcLz9rOkcMthQthseXYDBHPcZ\nRlPO02udp3cvYJzJe7To/8diL0HhjrGwY+4NxLUA3PR4wVMu698dKyDEoHbVt3eXW60uF2XFS3dN\nwq5Es7mqAucwn8DxPuEQoJoaNwByYetee6Ut77oNFh2YXztXpj7twc2rPaRSiAgObLd7SbIMF1gI\nIw996ka85Bx1QYPfGNAXz8VZehtz9JWy8mnhmKO5tHUaDrlTOZHv8i5raT/NUZ+2XJTOcN7rz9PL\nXaT6cb5299m9v6/6f25XqyrzKRVwrkqBjFWeC+cAS25LHh7POxwoGGfq70ANgXRO2G4qyjgHObGc\naoIE1jLLmwQvGhBZYGLDLwAIsJEKFdjSzp2/6b+TQOyMc1u7IYhLg7ub7LzFlhdXjqX0HqL9FjVC\n1rvDKf/2priPCD7rc+xJPWIH3CAnJgPYdD/lvEBKdOxu4F5rUC/7eJVjT/uJuOYSO4MMdVnnS0qN\n1TZsufe5Xl/nfv9AgWHbwEFQXPobDJwlldFPGzYaOB+JYyzOPu4RvOzgMseGXRxYKGk/9rNN1OV2\nu+f7Ynn310Smvz3udG5MlE1jnkbzeMRpJscAiixAE4LR0nkP4BG/p8K4T6bBSKnCydntZgN4wj3J\nZSjMn5/GARTRKsqWabygjm8OB8o2E12O6EhWmnh4y3O/5/s7H+A8bwm6adpvGZyaZ29TilLnSr+/\nN7M+VCUuFXCuShL5HoCgW6w7nRvRCz23RZhKxjHHtICBOk6eu8/F9Ai56BeBLcubZ4GXDXZeau3F\ntqgaAGGSa42/rTTb2qDAIq9BA7xiHdEGi3+rA/9G7EBlkT4zvP86IQbunZU6HBxvgFoEV8K40ZQG\nADNNvQGHGzrMoHigb5fZAXed6ESPW1cds8rBm7XIMSVI6onDGw3N4yJwDaNonzud69xoLIs+p5Qe\nSG3lFTPc/XrmmTs8HA55YWGdJV/deVA17xgBp7gH+roDMcagpGz7e7zHMG5cIo9tf71Tzsv2uaRE\n0kvruNHac4hYh9iYPU8CDiz0YUyteR7rOOeVGLShemdcxyRQWtTGmDZ0XKiMcRGwVRbc63NiHvJd\nDsHLbs6D6jOLUtYocB5nTfNLE6DI6+odg0ke6XkDW9QxC++yVaah34R+prx5i3sPcB1ok6GOZnN9\npv2oSigVcK4KMwcvgPPo7LNWeMDLLWyDWoARL4hdDkGBAHcjdmAJ1Ip4G9Ut1EUBeTimmItoBw/u\ncwCDWBjxN9o2YAdA4EG1AMdNDl6uXXH9Yn3SQN3IGRoAertsGw4DznurLY/8Osfa09qzDBCsgSpU\nS3DvdADlSab96eLp+gKQru81wBh2HSDNt8pB0g+0Fj0WV7jV2vAgUQJlGEBaWzoYgDbH2mq75DiH\nMarVVng4HHK3C5Cao8ps8+HhwOSxDodDDxxA2QEAdpSXWq2jjFhw1NHuNB7BDkR0Owbtdi8KHoMc\nnAYIztMeBxeF52s6j5/NGQWPeJtzHHAY6PKdlAOYcUzCqMCrHdMgAI6Gw6ERVCfnaLHnNtdH2fYi\nIFaGTpIbB8eTh4G3z7Vap5Tndh7FkqNrNK5mj3fjNj29ZJ7Adt6lLPCP55Q9j3PjZgUHElXBgfMq\nFXCuCjNbi5Te4j7idrtnLOqj8UPqgDGCpRY49i7BW6h1e3c58GSxiGgJsiV2nOW2OG6NnbdNg1bt\nFV0WC/UWhy1/vHw0EO1ybmFPednLHALv8MKyZOHanIJ3qVMNMJALPBxw8DJP8sjLBCJdJnoLB8/x\niMP2/hGHAETwwi+zA9rImqfr2Wc5H4JCCF7UVznQOu74dtsUCud5lcaUnlOghuhzkSlyS4xfkdEy\nEH9r0HmNbXk2OZ4H/hhw5uXOAOalNgxWDK9gWDSHwyEH4yT2ug+HwzFIcJKPixzPRcuYsL3f9fp6\nqa1/Zvb3HoGQ2K2A9ON0wNkCCk4tQhp/5QIOz87OfGa/eIwXFta507kxDmJOVS9ikGIHKcp3STxv\ner2nC9+XMcjB89AdB3eWAUrnA1TunSW53xflEF+ktFoI4sYujTNsc+W8wPnNXso8gxcDzlXmwEdZ\nKuBcFWa2Xs54ucmF3wJBeHHGvMAAYnLnY7GRWs276rcbfkGXXsQVv6Afi3qhkwwZMMmBtgCK9i4B\n8APYnrKti4n2BYBkpw2XQFAnpYABATB7ygEwH4hxBNDF+AKoW4ogWxxUPXB+usXuztcSawCoXY49\nzBZoBe9ZUyzg8ZQe/VtiPKXcnjNKWq2e2OHAPJOcVRgh+h7gXmGBSAFjCrSlobLEnc4THOtAW+MJ\negnmljV39tgOorykeKjx9R0QtH9DJsVO54b3Ko58O1GHtRNiA2etNJErjkKRKpK4+5A+P5O2ysEB\nb7d73OlcH/+dPicryTUtgGGp/sBArdfXkuQr1jVsagYMpHSeST1tSwnBtSkdG7SnLDCcHlDJd/PF\ngOcsvLi93lPJ2PV6TxXWOS1V4+u55DnL5agaGDenrhQbMEStx9y7r99SAeeqMLPt1Uil06zFB94a\naxv8SJ2Dv7VXWaZTxnegblge1pC2OaUhaJBqA5Q4IAseMAny2pxa8NqbaXnIASSP2Slj5LymAMRy\n0VniFNgiAcqAA/jVAEd6sdscEoFMXnCJul4STHuY9f0AELfuBwC4vke7HMB2GkjabK56DrDmamM3\nAbrQcjHAjsIZ23NOg1xkfESQ5w0OWsi7nO6CyPHEHLQAl0zCscfB+NhjohWRVtoCunkKRGp8Dv1x\n+z6QT0vU2VQNK/tdrgSPrjVP23x4OIik16xgr3TxjwFl2GGQRsJBBNyKgwr1NrTcFdjlen29kK5Q\nDJyP1PVPEq1p23ONLI7xdYs8g7pY4FV/l3rLi+XyypRZ8YZz2Q8n1T1NcOA05c1E6bDugQwInBQc\nKPsX1isEaTo6XlXmUyrgXBVmTh9itzi3xOLHnKoSrPnFw15AUq1ZnJ/zFK6x87reZAd2wIXWiyaO\nta6zzyGwDGAiB/iR6EECcQd8gtUPKoOV6AJAQHrIQd2A99ZqY5/toDrr2DvinBEHisId8Z0exy7b\n/c6N11UO2ejkbzJaflnUb9077elFG3Jg2/IiSm6xvAdrHBQyJMA/5RRoXhbtWRH3N2dojHzfBpnx\nzPHoMd+HbCm7dDo3uFZb49Q4cmorIUOb/E0nC4q9slBMcEkvYHAA/D/N0Pdut3tTBarFGSxjjmRR\noJf13ggawOkzF3SlbS9jkSxYXjpOcrHL0yLkPHDa0Wi3NhgDlQqGQ/w+S+d2zG+fjqqR8yrCix8S\n+pTfAbBK6EfY6SnKEJgrZTS1H1V5FEGEsyyzVAupqBqPtlTAuSrjUhy5jYUE1ANsqctteu0J3eGg\nUiHPfyJ5YQTgBh3Ku+zAjPamXeHgjdQ0CARFhSCtfNvAA9ZBVTuZtp1wkMyDRjNUI7Y5JKeQSVAs\nYwOcbQtU688jDlQBjIPMagiaizYqdvw49Fkml0hpBVpXWYLKJU756JAglFxteKKlF1QDQAuU5dIu\n5wA+PM51jqk7S75N6+xA7BkHnm4u2cwd9bcE//Gxvd7TEZ/UjQnmQS7gcE0dL+lDjnrRbve42+1z\nv3/gPboDH3Aor5V6q23wltIMrOcanuOUwnDCacrtK0y0MJGSkQa4YQzt4LlyMmspkJB9iBPI2AlA\nynjG5d8hc2o+iNd5tTUNA++BFOhOT8Eo9lbHYPeYQXk6DzgMXvzwbq/Vyu9SBB5+GmcyjbLHLD3E\nswSij6LMFjjH2tnYfazKfEoFnKtilvihPuXYKwWpNrnFjkX8yH+3LB7irn/RI7FJj1M+LSgJRxwo\nGDnZOyzOCNYCmLvmr4Pj4FG8I+oG6GNzcbdl2gD+dzgExZ1wygtdYVtSDwB4jZ0XXy5+EpRJQ8Dy\nfI382GFMLKNCy+hJXeZFdkYHdgKsQEYAdQ3idHtAxQA/9JSxmAf6DrxZfU4T1+Tk3HBtG7zEcnsy\nQ2LOC2nd4z7HAZno9zHrxDD9/p7PIrbJ9fo61+sdlTEzR8WxgDTuA+grLoAvJBiR91N6m8O1XBY9\nCd5sgCqBTQz8cyoUN1nrdkN6zyp5xQsAyel40dY1c+e4/u+zk3G0djOOStcZ3nXou5UJ74jx/Fne\n5GmyBqb1xnUF/nt8P8PxMdidBFSLeNphByCtq6jE9+komTdlwV/R/T4PoH6zAedZeshzmvNVmU+p\ngHNVzJK+HCU4niQJZ323wbGnEAFvQePW/Q0OKtQTchxT/L2rFhLo4nbZphXIxSLHMbUCkRCcB1CT\no4q4JACpJxvJWUDluKauhW13gE3L83XEbmGXVA19jNVn9FMGP+aUQ8C/XuPYS2xxdfc5eK21V9/a\nDZABmW0jZTtA+AmnHHgA3bWCe+cMm3p9nbvdt/jvzjjNkoi2Sm1pGYjXZQDJlFJh7QoUJbPBuC9y\nXsNYeuhP/T1Y58ARj42nGLzZXukAxGHwyrF3YxfTIK4m/ShK2WtzhmVm0COu1TrjFODTejLLeWoH\nnAJJ7Dg5Iz6XflqC3V7vSY4NlrzONtp1US8peL5SEi9VH3HzFTsIjlaD4wPFot3uZeuIaTRrEcju\ndK4n97CIn4x+xxkgU8pVWcpHWWpOWUA5SyD6qLjSs6rHXievzLClVZGlAs5VyZb4RYmHcs8v7H2/\nyFt0B8tjaHmG9qMFIAYXuJa1hbznF02dtU6CX2sR1NezQA8C2bB1v+r7iW1cABAoL1jgdoUdJ1oa\nAFIv2trehwcX17eAKhRFJiX00N/dFMeDQgOPvgUq0X4NICwDaeh/04BdG1vw1Ae+qFNzCOmmHQCC\nF/zMXxNUB3C3L7ObL9ozZ91LCYwt7fDrHOgTBxyD1A2fFEQaHkWAXWZY1Elp9nlhAZSW3DVijnig\nQKQBsPAoHx4ec60Wb9HWaisefI1UOzY5ZHsMmfaYWdyHuE0LC5vZd4MFetL05vPx+AWAhDkm3yF4\nhoq4wxibfY53J8JcqtXWhfa0u06zuZoESJ4H+MQAbxQFNlqBkHIMHdjVxsJKaeoLJAqZbbWSHOjN\n88wHSR1FOxWT2oexPO88mpVR82biSjMzu7VTv/8qjvO8SgWcqzKxhBeJRQ0A3UAu7lreCYuUBZzl\nZ8sDCoqC5vHi5W3RDaTe8DWOs6kBxHbZ8cIOxaIL0H7GIavfkAO1AQsWaBZSHu6uPw6eUWwlx6DH\nXfepzPcAh+BKa883aCkSkFq8cslBRmIT0Fg0xQH3Jk57bIGJFBAi+G1PHQ/Qe8Q6kQ760eu59M2O\nQ4xxGolALQQV3uDgHcf82+F0LlrGyE0OW+85VQWAehgNaD88z/scUplbY6753OgjAKoMOlvznxFU\nK+8/7tkR12pdkShFBxd2udlc5dPTU2HQSuDYZreI5igH8TWCYZyO38LCZhaEWOAijosI3tDzUhkm\nvZO0x7bZ3BRc5bgv8Vjp3Yx8avXArT6ONOwnZXKb7DG3wfEk0OaMnNSItYClvStwFNUVxuuoUIkl\ndy0rfmGSqoa8h3FiF2eYxB7t+RlguZLjnr+RlTrCO12uk6uPu1lft6UCzlUpLFigWq0e56XhLC1j\nyIhBlQHZ0SRgk4Fr1zh4RuX1rTolVWPAttav5EfD4ybTSQOkLnIALzJYbsQxUJUgfodjb6b0TuNa\nAI2aX6oBHyTkpOwauNPyRYhtdw2cYaRscOoh7onzDjj2Lmv6gwZ/FpgAmJTAu8shEQzaLCkaqQyZ\nqzsEIjYa61HQ2unpqffQQqtbGgbw4MMgggEyMNrbF99rwITdBT13huwMHk1H0u0YiPsio9kRJHqZ\n4211bTyg/8iSOGQ5X+v1Ne73D7jf3/NBg1u+zjN2XsoiVZnJQZH9/oHyIFqqMQvjzxYotGTTtEav\nDkidxGMt4uSW+T4ELOaAswUALS3udgQwi68ZvguqIpM42nlP7ySvqUuMMxk4n52dqeyI7v0ljy3r\noc2BycXFbjJ2/f5e9jpWGy3DRL4fHqXHN3evJ/HmHxW1I1ecgk/c5lqt4jjPq1TAuSrZki6EORWH\n1eTlGbaF9RbSKrsAuT6HxB1I2NHmOPMfeMGxxy3la0KlIVjaef6xxS++xg7ogGcM7rVFAzni4sAz\n0CGg+6xpArmARA3g5LhLGgIAfS6NuAVQAagsIHXX3wfpOb/GQb1D3ovLHHje0ssJgwFZCa0x0YBW\nArkRdzo3xioTwQtmeZFB3xhxyOanxwhjuMvp2EFt5JRtjrikjoR53ulc52YT6eh1QKccu1V2gFNy\nvHPzRHLPLeB/pDy5+E1SFLT3dDfzW8wxdwlJ5K7HEesgL/dM4j7vsFaPsLyrqTxZ+hzmeKxFUmzT\nbJ0XX9t6L8B4kRq4G6qdRV7s/Fy3AO1FJdymGY8gXejm9HlBaK7OXu/ppC+Tsi5a5Y3g5Q191PEa\n8bOj7+kbgdrRaCxxLB96mRuNpUfahm+kUgHnqmSLe5nJBWPIFie42+37NMISdCyz45BaChO5ICks\nYAfsQBEAigRpUq1jQ1xDerIBeizgnAMd8hohwMkGVgChVh34/YRtgG2dI8cIIAZ8Xnjt5e/Soys9\nxxqg4rgzzieoQfCTpNtARUUbSQhas4yPm2wrish2YGwPxLU19QHeWJt3GygIR36c13z7B5zqmDp+\ncL1+mVstJKOBUTLy19K8Z1AzUo9gLAfm5mNI7CHHSfO2c55hXO+Ii4I6tZJD8GrK+QKj0JpL0oC6\ny43GilLakIBbA8pc1tBREtgWq3QUA+eyHOmwdZ9eo6hYnujDw2Nut3uq76tM9CRr4FyrdVR/Ut70\ncDgUoBTPO+I28oA4BxKn4U1P4+GclTfUuo51zxqNq1Nf+42giBG3wT07rn/FtJE3QttrNWQODO/Z\nWq3KHDivUgHnqmSLeyFIT6UF+lb58PDQWLABajUgOJpwvS4Hb7UFOG7673v+OHCWwWO29KflljhA\nTZoWN9X31SBVBpxZnokrHIK5EOxntUUrKjxlHCfHQgKHHFdXA9QuO8oBftcBRVfYAUfweCXoBe1C\ng0roYktAbl1T01MWOVBl9jkAWwT9WQbOHucDQ9E/gFak6HbJb1qtJ7jfP/Ae7D0+PBz4oCpcy6LL\n4F7usuX9RfCW9iy5MU71U2MDTcsO4pgwjxwdA+2At90F3yLDGsD7cDhU4M/x9Tud69zv70Vb3rXa\nKrdaqM+NvbWtGzIfWm28w2lymLwnOd1yT6kaNmc2jXEIwPT8wER7XVMu7SnreTYcDsU7MAZT3W6/\nMD2ynE8Wb9gOtBtdiDf9uIpF1Vhc7E59nTeC19aak2WS2bgdIbl7Mzv1lbLFdjJMfx+qUq5UwLkq\n2XJ2duYDt4pUHMAHxaJ35hdZGYSmAwVxjRSghEA1bMenC4x7UcNLprWOTzmmOCCAz2qHpjZYwFly\nmJc5bMGD1tFhB+Z1MpENdiASQP2EtYavuw48u6hHj0efg7xe7h6APrKnXuCatiI996DISIBz2fdJ\nK5uMxO8SBOYoCPB8IvBKJsOR10WGR4v+Aw7yIsdb6PDoQvdbS7ZJT/0+x/rR2HK12g0QhXtrAzW9\nGFpayyHJjTaqBqKPuC8jz2feY51Mw/UztB+8Z0dlSdU2cm0s4wUOUnKyjQDzsg8j0bb8GAGYHh4e\nZ1MHp+BRB5KucIhXiAPzplGwyPF8Qxv0/XbUIcjW5dOAFxmzQUEmxz9Ok8ecnzdtXX8arvh5i+Nb\nxxSfsqoaZdv8KIoVbFommU1qKG6MA28flSHg3kO26lFV5lMq4FwVs2Dxc0FaJxySiEiv1CbHHFN4\nLQGeAFRkAhXp7dPqG/D4rYtrIRAMW+hlQOSyb9sGhxTVmtMpF38NCiyqxhk76smqOg4pwtsct0V7\noa4wUc1fc8DBq95mBwqHnEqsAdDAQwsDRYIrgDQkH9Fe4o4fD21gWJznNdWPW36s15iIxNiv+H96\nfI44Ti6y5sdM3jNZ3xV/HT2mQw6BdjXRhgXRP9SnAxs1FQj8YbRp4NtkgXUYCe1ojGu1FW61Nsee\nRjwfDjxZnh7QX5xh0O3uqGx3qxx0k12wY73eEfdIz2c9H7ocslmGPmgvFwCrS6ox4pivHOaD8163\nxVh32WVoxHOgaSir3GyuiDTV03lGoQjiPOQHfjcAEpcHvo9bHM9Zp/7SbHa507k+Pne6oLZAa+n3\n93xQGt5x8pjwToKxcnh47P8NxucVA+ebvj8b3Gptme3MUTZSI3cnOmaSN1ODOaQ3v4hXN1ef87bG\nO13Tpu6eNWAOAYdppkyrvnhcYnnASSXnpc59X9SGojYXjZWrC7uBcr2p5OjmVSrgXJWk2NutADYI\nTAOQlguODjCTi64Gldga1gvzKhM1OQbUuTTVOY4xQDw8nVfYVscAKO2yUxWQ6bNl+46NvkleMdpY\nvJWdZroDuMbis2C8/GS9yxxv0xPx1wAAIABJREFU53c5GCI57vdJ5pxcRkb045bRFhKfl8S4am/+\nNY4Njytsc3j1PcGYAhQCvGnjw9o9QMZIGG1aVQVtaou2y+tqOT4oiCDrIfqzyrdu3RILrfbKwpO+\nPeathiBbKIIAvMs2wDiYBJzXOW53MDjidslnFTsz3ej4AMJbflytsbYMW1xnaZzgpKyHzcmpaS7m\nIqeebVByZN+1+kWcBdEqOY5yPHbyubON8SB1BzC6Kt6RlpGMOZPPnpgLYnRGlh6PkBHVqRzlxzoX\nfHheLm4R4LYUPiZlnMyD1ot7ZotoMLn6rPEqSwXKjan1PTS0dRviuZSf17mxcnVhZ/Kq/+fWrKrM\np1TAuSpJsfmHWxySUVjUjdy2veYqy2CmJzkFQDIATQLqa+LFIL2OeoHZ8OeAX3yd7cWwK46/LOqQ\n14IcmnX+HfG/BQRzwXiaDoLzUJfkuGrOZ5EyhgZY0ApOFwU3hlqDWgJXeB3leTB2pMf+hv/b0t/u\ncgxEVzj2TFn6x7IvfaMNrl8u0M+qD8AY/Oz03GAsTQKoMMBA4bBAF7Pz6IPTjB2EXSPRBHj5mkcs\nx0irq4DHbdUrx8z9HQdq4dmwjJozTsfbGmvrO8wnNy7Q7C0LymwlCmv+TJcVMwd0AlDPPcPMsQFv\n1Ws/Y/AMSu/+M8/c4VZrU52XHxPLg2hl9MMYlQF6Oem88wLnovNydVnFBq1prElZ0Jr3vubnSLGX\n/3xtyCnB5ChC5ahT6bzO3QdH1agSoDzKch7g3KCqfAOWBhH9OBENieg+Ef0oEf17Ivoh//sniegd\n6pzPENFrRFTzx32KiO4R0Yf87+8noj8nopfEOX/bf77n6/o1IvqEP+7fiXPf5X//imjD1/x3B0T0\nK76+ZqY/TxBRm4j+ARH9IBH9z0T0E/58lB8joo8S0S+I7+779v0BEf2vRPQ/it9q/rcniOh9/roo\nzxPRdxDR/0ZEbyOit4rfdn3fPirqv0dEH1Btbos2vExErxLRQqZ/3+L7NjJ+axLRTxLRj/g6tn19\nt/3/T/hryz7fI6Kf8p+H5Mb/kj9n26jjCf8bymUi+h4KY3mJ3H2S5VVyY/b9RPSvM/0iajSsPj9N\nRD9Hbjw+RPE9c6XZvESvvdZOvnfld8j1kYjoWVpcrNNf/MWBb8evUTo3MGe/5v/9gf/8OhF9nr7n\ne/4O3b59m+7fv0+f/OT/Q27sXiJ3T+R1XqYwThtE9AUK8+vvEtEz5Obh7xNRPdN2q7xMYRw+ZNQp\nn9UFImJyz8uJ/+6bMtdd9/9vEtE9eu21S1O0aZrSIqLnxOcfJPd8FZf79+/Tiy++TEREo9F76Fd/\n9d8S0bspfk5R/phCf1G2iei94vPz5ObFF1ADyWfvF3/xX42PfOEF9//6+jfTgwcTm0pERLdv36bb\nt29H3126lL6zGo3X6K//9V+g0ejeuH+5srOzRX/yJ3LsnqOdnW/x4/F36LXX3NxtNj9Dg8Fz9Pa3\nuzEYjd6TtGVSWVtbSupaW+uZx7744sv04EF4jh48IPr93/+xqepDuX//Pr3znUN/PaJXXhnSxz9+\nb8JZdtnZ2aIHD54f37Nm8/30pS89TW9/+8nEMbl9+zZ9/OP3xJy7Nz7+275tj37rt7AeYA59/lxt\nLCquvkUi+gjFz/mzM6+rKhco0yLtef+jyuN84aK5kXrrKKQf1lYxAnpWlMW7wiGo6y47L5fF4e2z\nDJRy10B64pyXT56LoDvLo4ZAPEsL+grH3j7pXTvl4Pk+4bDNa6loSOrFnhoDpN3W3G6ceyKua23T\ng0uMc5YyfQH9wPJkgv4iPeHgMZ8a10NAmuxLjnZyIvrSVdfQetx76j5bihngcS9zoGpoiTlK0kyH\n+dL1iXow3/SW7SIHmoa+jwsMb3Qc3CPnV+h/CNiCfB7a6IIoO53rnrMoPWqSNgD+6q5oA1Qs4LnG\n/MrNufhvl20QY4HnLrcTJL3RA06DW0HN0XNtxEHfOWzLT9pyx/ul3z9I6rGpGkiqgyDjS3zrlqYP\nldmGP+bwDOg6cC1Ls7wl+jviWg3PWZjn4A7r92iv95R578rSECw6C7j1ZcfaygioKXiNxnpEPylK\n7pGrz41vPP9zHOfzqlaUvVYc8Fl+jmiOcZkxKVOK6polVYOZ2Q4OrDIHzqtQRdWoSo5rpyPj4xfS\nFXYyahLYIdsbAMERx9uregsSwVvgZ4FiIfWW5Ytgm2OqwJ3McQP/P7jYSAmNoCpIc0ngDACfA7g5\ncAvViBV2QG2BA1+17ceky/Z2NLR3ASz1+Db8Ndb8PwBxfR2k95Y8Vvl7m0NKb0kLuev/bvtxwdhc\n9v1BX4oC+9A+gN5VDtQX1GdxlZfYAdktDsGgAIGb4jr6PIBLyPSBw7vKjcYyn56eCqm2NDjQyb6B\n8xsbbIuLLrAJixGC2C5dirm1zeamSHmtQThk3NznoPMMgyxV22g2r4gMgTKANRdU6e5ls7nCnc71\ncdDi2dkZNxrLHPPOU+DdaLhgx07nuufTajqVNCaP2QHLDT/H4udGbiUXKTnId0ejEdodUovjuV/l\n4XDIvd6THAIGT8d16cDCSdv0h4cDYeQEg8WNtTZownmNRtdQWkjHCNzh1OGAubfMvd7TUwe+oZ8y\nIFW/s4sC6srTGGL6SY6ikKvPXTNW1Si6RhFonWaMiugj0wYHlr3ueUpZdZOLBAcyMweHgDTgqwQo\n8yoVcK5K6ZdF6jUq4v522QEfZFbTnl8dUAPQvc95gIj6ZMpiW7/Seay02kJPHI+FDZJly36htnm1\nDtxZHGPwQwEU19gBQqg7BM+Pfd1Njr3yMAbabAfoWdkMJRg+4VjZJJdOG0Bfq4Fg/PY4eHu17N86\nhxd1HFEfvHunqr6c0aAz352J3y2upxWM1+FarcPD4dDPT6icpEZE4I9aux/b3GxujhUUpGpECLQL\nHjz33KQJIFzbw2fnrcRcseYQgC7mCEBzUersXCIGK56gx0T7YxAWex1XuNt9C9vP23X1OZ4HF/EO\nAnAGLeX4N0trelJ2vdx7zOIEx3xwO5CrjHqGlIpzAFs/a0eFwOs8oPG8KhQXAc65dsQ7CO7Z7/dv\nZts1KwWNiyhhFJVZA+dHVdyaqdeKS4+7WV+3pQLOVSllvduei9yL+Ip6kE/YAdMjdnJTa2x7YBG4\ntcapl1IHUkFCS3vwsFUND7IE6fCCDzimLyAQ0ArIQ90W5QNAFZJ52qMKcAivrlYKAVXj1LjuJQ5B\nZ9LLrgPM4IW9yWEbXdZhqRPI8y09a9AldJtWOEjp7bIdrAWgsc4xCNbHwQOeM7xkGnfdNqvOOJNd\ns7nJvd4NttJE5zMcIkgoB3AHLJ8PF5Rj6TjvR59B2wi6vdYzg3sM40iCaJ0UxwUfak9kCpzD9QFy\nLRDppPBkunQ5B4Ph4Ly8N8ceMbcrdTxO0CJBi/SYOo3qPODUz7Ybp1SqcpLMWdmtcWT9i99P1o6I\nlaUxlqqzgLIFnK336HkUJS6iQpFSBDbPTUsI17Ln6aNIYJLuCMxeleNxJGKZpgQD3t4Nrcp8SgWc\nqzLVghMeVGtRX2Un/7akXqg9TpNqpDJGsTd41Z+HJBp6gZL8X+mpBae0y0ETVnpCc0Acdaf8wiDf\nBm3ePgOsBaUGrQICegb4m5vswEmaljdOOAH6ATz1aAe40AD9sl93xXd6TLXsmaVmoSXOIM+Wuz9I\nOmKBNAk+QdWAgSDH/pTzyiNQb9HGBnimVh/T9lqAJfBHcY9kv5FUJaeu0B//HVJwW56ey1FfAfgC\n99Qy9KDwYKVzd55jB+KC7KDFgdRSZrXaakQ/sVUt8Fyc+j4iNmGTQZ+SmQphgOi+NBrr3O/vmdnk\nHIVkEuB013ZAe5uDdrt7PspKp5XZGmdmQbdBe+Tzu+Lbpg2yVaHlnCpChN0Fd+/Bl7c4x+fxbl7U\nI3peikC+HXknyqPw1M7DQzwrr/i8S7w+W46AiuM8r1IB56owc7DepffIeim12z3u92/6BWLEATxC\nIk6+SLEVbwGsHqdb7rtsB3VpACX5lhbwkkFTXb8I5qTcWFwTv8vgwD2OwfCOuoaVJW7Dn29xdC9z\n2g5kbZPA3g72cO0AHUP+XsTBHjLSUQeOsDxmwERHXKutswP6axwSx+i+7oi2WfcK3vcROxoDvHmg\nIqxwvQ7wa3n6dhmBMQsL6xwDml0OBhCAOZK72Nn+NDBwyS5Gvu3yOhiXK+woMjCSdN90MA92FSTo\nAp9+P0m5HLa3QRNCnW6+xnzc6aS7AjB3fOFabS3xSrtr6J0EzCttBHdUm0K9LqlKjhedGjHIxJcD\nnJBLswKTiUZmIN6sSl6KLzaSJnG3rdgQvFct7/njAM5WOY+XNUgsDjhnRL8ZgXORgZXjm8+ijvOU\nuO+psVrJ0c2vVMC5Ksyc09gcqJeS9trCMym1lQEeBhx7cjS4sAAHtqwt8KePBZDR9AdwlaWqxAbn\nk0vcZQcYttW1ljn1SML7fSLOk2Mir73FeX1jTUGRCTlwrAVKtth50XP8cgSGabB3YNy3kThmxAsL\nOvjwLkteb6CeaI1k8Gg3/Pi7TJO1mgwcvMTBuBpxs7nqDa8jdkaK2y1otTbHwMrNPQTlaXArgRVA\nXwxOAGKsCPugJKHHD+OFTIxB0aPRWBmDnfi5sLjSTmcaCVCsZy2Xkno4HAoPZ8zdDGAlUHckpcpK\n4axBRADX4K6vcUxrig2lHEc48OitZ6pY2ze8a9wzL2knFhDqdG7M1fPnVDCsJDaTQWQZEGR5+Tud\n63x6ejo1zWAeVIJpwaebQ/IZRFzHykzbVabMcjwmU3rcu/Mi4HmW7Y3v203WCVBqtYqqMa9SAeeq\nMHMugOdYAY80Oj2WHdvgWLEB36UvnzR7G+SwrMV4Xx27woH2gCQfCCxEdjbNHd5RiyNUL7Y58Jel\n9JkEsmeceukOOE4tnaNq6L5c823R3GXNF8Z2sQbYkC/T3l6MJwwQGSCZC3h0ihS1WpebzSvquDNO\nDQcoH2jvpJWJ7xKHHYlVDqm40beUQ316eqrAxA6nyUVyQaNu3JvNa9xoXB3vjKTHYp5b90YHn55w\nrdbhTudGxOGNnxUtQQiFlBG3Wk9Enkdmm2cqOcM5QJmClQ1uNJbHnvT4txBkaQHnw8Njbrd7Ef0i\nt9WL7fz4WMQC6J0W0ICK5dSYOQsaz+tBvIgXz9F3LnEAzJe4VmtH9+0iddrzsOsNmPRez7OvVpl2\nzPNBhrML0pumzMeD6/oVB5GmRuC0bZilhzx+l+CdFZ65drsCzvMqFXCuCjMX62IGT9aA7S1dyde1\nVB9yfFkE6wFAYtvaymiHgD8EGGogK0EegKys95RDgCI4xLIP2xxzPIvlqgI1AJ5WDZ4W2KZqgAYi\nAeoROzCivcWQ5ZMAe8049wbbPGsYHBZNZZdToC+NloFxDtQyQgpv5zm2gvi2/fEWoEM9Yby73S3v\nUZX9szLp5VRAYGiACqMpRBo494zxlsetJ8oaoAvEC9YRazmuVIUkcF1tjrEbD0eJiO8hFlXr+YzV\nOtJrSm8WvNyhDn2ORZs5HgPzVE/bGUUwLPr9m57HjPvl6ESdzg0TEOay380zYC4XoFevW4ZfLKtm\ncYPL1ukAshWvgEBC90zkdijmXaYdcxs434nu45uxnBc4TzN+86KW5OhGVZlPqYBzVZi5+OGPt1at\nBxTADlv2+vccmLZAODyUW5yCniO/AGmOZG7LGPUiEG2XnRfTAlTauytVPazrQwIOXF79e59Dwg3p\nMUXQm9TaRb2bHKgQki+u69V6nVC5sLir+xyS1OC3JY4NjQ0O3mkcZxk7SHwBo2SVa7WcdGBOQWKf\ng7db00d21TnWuIKrLc9b5CC/h8DDlCeb/g/9arQnGChOPSIFd+DqOu/wE+wAuAZdevfAtdt5WHMB\nlWfqHrpnrdO5LtQwLGqTbdhJEKaf7ZC+Xj9D1q4JPP55oIS6rDiJ3LvG4k0XKfkUlTJgpDjQ2dZY\nl+05T6ro2Omwx8H5ILXuUy774wLPZcc8nU9XfL/KB3G+Ect5qRrTgOF5qXZUCVAebamAc1XGpejl\nicXbBWwVgZkRa29bCmQkjUJSC6T2r/baQSINi84k4Nxlx/uSvF/dNqkbDJ7nNX/ukgANSGIi+wTl\ngZwXdCPzMgN4sgL88Du+O+VYk3lF1Im2dDlwVXGeppZgvHf98QiQ0+ANVJBttgMyAeywyyBBqV5I\nTzP3ZU1dUxovV30dmCuWd1Yaaetse7st/ed1diAXKiNr7MBMSi2q1ZYF6JHXgcHglBGc3B2C/IL6\ng91uqdVt8a6vifNSMOXqivVyA+CO77dejC1udCrJCK+9BuarbO8+yMDao6kAgAMa8Zyp19dmxPW0\ngUvumDxwDu2xzrV2DrSHOgaXm2xLPO5ObPsbsWCtsDJBziKArmz9s1a+OE9w4Hk44rNuezq3nDOk\nKvMpFXCuCjOXA83OS3RivPy1li0AxhYHgHbMgXogua5bHDL5yYXdpdl14OAqO9CDa2xwAI8AlFj4\n0SapqoG25bh5AIPwRi75RX3fX3vX/4MM3QnHoAOBcPi8xoEWYtV3V1zfAvxdc5wbjUW2KRlaii5H\nLSnSxZYBYnc5JK+R2/OQa7vJgeOO8ZW89x3/nbUFrgF7DMDCcfscFDNybV5jGzgX0YXk3LgiUnSH\nY5FoI1ZDkEYXvNU5I2yNU1AKIwxAF88CgiOlBz69fyGroAa8UJ0IMmla+UFyiQNNYJ9rtY64pk1X\nCBKP2jDCGK6xzOxXpgSgEebMJI3mSe+uSV68Iiqao1XFfe/1eoXnTkoVbdMZ0p0Iiwowa1WIeYFM\nZptyMylRzUXLvLy2b+b2LCzAAA/rwsLCxiNtwzdSqYBzVSbSNNIo/AMOMnN7bAMVgGD8BsCnF2CZ\njAJAYMkvZjkJOQBG7e1Apj4JziR4tRYzeO0Arq5ynIp5mVMaR04qbosD7eEu25rQ2qu+on4HWLG0\nblfZlr5b9u0uopZYOwUyGE57za2te4yfvF9LXK9rkBi8r06TF9rCFnCWbQiguNO54akQqPcmx7z0\nK77fi8YYa285NMElVx6LC7zbKXCJ5z6uB29wET0I6h+gFsl2Y4GDoScl8UCh0de2AkXDWBVxY20A\n1+Varc23bt3yxjC895YRh36EwC+3fR1UUgDGpfxfEUg7L48ZHk6ZZhylTIrqXJ1BojDM9Ubj6sRz\nc321dysAkuN6YgB+seA6q51aWWbWoM49o3E/W60nZnZ9q+T48Y+zzNM4KVPs92AlRzevUgHnqhiL\n62isq+qAj+SGSpAqObsazEmvstQ6ltSMnQwAQeawAQfvq6RznHHYbtfnQrNZBhwCdFg0EunB1PSQ\na2xtpebTfMu+YYG0wKe8Dry6CCzT2/gWxxvXgZLIOjvNXfm9BLfgY1vjPOBgEOUMjlC/k5nT4HeZ\n+/2bZvpkKxlG8FaucK/3lHneM8/cMUDtCQeN5BPRt0vc79/06bSRdGaZ0yQxm6zvf63WKpQEw4Lo\nri0DVNPF2/028veDOVYm0cedsL29usdBEu/Ij7k138I8KvLw2cDZtT3wjAHg9XFul6LfP4gAgeUx\nrtU6U4G083Fq9TvI0QIuEhzIzKbxtLCwGZ0DicRJ7c21tYgvC274RTPgnYdSctHidkLiOVyvr4zH\nYlZgMtyHgd8psderx+l5nmeZNJbBYRTvRFZlPqUCzlVRL1x4iQECpdSaBaRWmajJznMMfjACzQBs\n2hwAjNSaxda+vq7cBtdgF4A7l7lOe68vc1A8OGYHWHocb/MDJFtgyOI/6kA8aDtrbzoSdmje6Mif\ns8nx9vwkagcoAtqQwfVkRkLQZdb8GKSgw6bdIEDvMsdeTmcoNZs2F7bb7Xtg0GVI/LkkJqk3T3or\n6/U17/mM02UfHh5zrbbk2wE+uVQpie95v3/gr4F7g/5b4yk/l5MECxkHpTFiBSCCunSHw87MNru5\nh52Nfe/JzoHq2MMc1DOseZGmopaLbKplLSklMrhwpPpzhZvNbkYRA8Goa6KNq6UC5nQpA64CJ9rW\nh76oUkHMH3d97/VunMsznr5Lg1pGUTtnobaQV4WQ9LjZAudmE+/REKzcam3MlL6Q8sW7nItbedy0\njXmUMmPp1uH4PVurVcB5XqUCzlVRD6bcfpWgwAKVeFFvckohgJ7tgGMFDQkO4TnWyhKT+LpH4pqS\nv6m9ptYWNyLA4TFDBrSzTF0rxjXAU8XW/AbbtBKLN6ozBC5x4ChDHUJyveWWuOSTS6+fFWwGEOmU\nP5x3EVzyIw70jhwg07rWS759OpjOqXo4z7IMLlzhW7ducasFpRDZvl1VZwCvtdqaTxsNOoOuq2m2\nOXBFJbjKzZ385xzdwPbcAjxe5xB4CMqLtbvh6CW1Wttr+1pzRnrJsQiecGo82qmo9SJbr6/xcDhU\nUnQ67bXkyof73evdMN8VTjM6NfKmBWllwVXgmqd15oBzLtufVdz5e+x2CtaZaE8EDk4HZov42/MG\nzrbaBRwY7r2Q21GZ1lsLL3l4x8l58+RMZdfyuyZ2YOfjpm1Y5SLe9zJj2e/3Wb8r+/1Ub7oqsykV\ncK4KM4cHO8gnWSBBAj7puQIvU1MIAHywvd7lALIlcFz217shzi0CP6hjg4M6BTjY8lirH9JTfeY/\n5xRBwJfVFAwLaFpb3Rb3W3oPJQfcWugAXuFllsk2MC6WQQPwDEUEydFFvfJ+6rHNjV2XbbC9xo0G\ndgni/vR6T4uxlsGDGiwCJMJLD09tWlcA2piHJ4Kr+9SE8ZVzL6WdOM81rhWAhkVnciDrJrsgVszv\nHV+vlfTiaPx9oKfEXt7AWdfGxrYfnwEXedisRbZeX084uXECkhxVI1W6CNfPGSV5kKZLWXAVOK3a\nSLSpGrVaO9LfntQOt5sQG33D4TAbFFgEgAIvPtw/6H/rpC928ptg8OQSsEwK5A7v8Xhug/+d240o\nqlOPl3tG4LyQz/buTHYBZLGB89F4br/RgfNFve9lxjKm8MEQXpl1V6riSwWcqxIVF4VvBQpts/Oq\ngVML4ILFXm6RF0nTjTh4WQHEtEdtiYMnWV9jhYMnGx5RgMBljj3Q1hb3wPdFZimUL34EB0JiT3rQ\nJMAAWMYWvG7nNbZl0TAGRUaBBPkYT7QBABX1WX2EIomkrUAp5DqH9NlIUiLvD+qxggNlUhbNvbap\nKZ3ODbaDQuX8WeZ4TGE4Fcm6oT49d66oz8gwCToNAO8+E7VYApxGY0VlyIuD3kJ2PnmvrTkudyn0\nYu/uecjKOeKgLY4AWsxhbWzg8z632z0TOOVAhg1IpUydBZy7CX0l9qjm+fgapFlAoSy4sugPRF0e\nDofjYwKYO2LLk18EpoLnNNz7Wm01AZbN5qYHxcUAKKc0IbNC1mpdnzAmXOv09DTZGdCBiIeHg1Jt\nKKMEEu86hLkyiZseq7SkcRgIbpwlVUNmxmw01rMGxyxSpM+6XNSIKDOWdpxRe9ZdqYovFXCuSlK0\nZyS8YM84ABDwZ4/9P0tKy1rEb3IM1hDMp4+TqZYBAgHmJJ8UwAjgvcUOHF/lGDABCEE/+kScM+AA\nDkFnkMaB9K4jnfea+h2c2qscshAO2TYcABwtr64EzgFohe9xjYFoowQwaNeqOF+mHge4PBHXOuKg\n2IFjLnFqzKQyaPHYbLB+eddqSxmOLnY2irzyk1RJwF3X51/349Ph2JBCAhcAcNwnB05DAGDM38Yi\n5zRrjzilg1g8dnig5Xi5jHsSDDnQprPKSa+7bRDAi6mLLT93Mk5K0u/vjf8eDocCbFrZL91zIzWW\n40XcUYCCcZQHadZiP01QX60m3wfOoJTgIwYn0wGVdrvHlgGqaTtlOdy5xDm2IRVfK0c7iSkYMVi1\n2mCNrdX+lLp0x+w7jKCizJeY4zJN/CxAaqxu4zTUz0M3mSWYn6bMioZTHBxoGeqVx3le5bEBZyL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cbLH8GnfYbX3GkHDzj2sPQ5KIFYgBYBgjCEQLOAhq8cq+3os3wWnCcVxqHu0woTLXK9jj6k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\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UT976WIu7QM87pmThuMjW3zZeHKyzWyD428za1rzkuo1zKXer85Py03JMec8ip4GPh9gF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\n6CXZhUJL9xo7wast1EIXvTgrR9jWk5w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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd5cbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = phot_df['deVRad_g'],phot_df['deVAB_i']\n",
    "print sps.pearsonr(x,y)\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['deVRad_g', 0.72697382033133828, 0.0]\n",
      "['deVRad_r', 0.69263060921868835, 0.0]\n",
      "['deVRad_i', 0.67852878609926204, 0.0]\n",
      "['deVRad_z', 0.67444338949538996, 0.0]\n",
      "['fracDeV_g', -0.66555127811401193, 0.0]\n",
      "['fracDeV_r', -0.65412898164934241, 0.0]\n",
      "['fracDeV_i', -0.63377377936897417, 0.0]\n",
      "['expRad_g', 0.62432872211784962, 0.0]\n",
      "['expRad_r', 0.58603216465984997, 0.0]\n",
      "['fracDeV_z', -0.580696247501992, 0.0]\n",
      "['expRad_i', 0.57649779553643021, 0.0]\n",
      "['expRad_z', 0.57515337988697679, 0.0]\n",
      "['fracDeV_u', -0.55719004524555305, 0.0]\n",
      "['expAB_i', -0.54583176683460988, 0.0]\n",
      "['expAB_z', -0.54525159464598594, 0.0]\n",
      "['expAB_r', -0.54340837935255548, 0.0]\n",
      "['expAB_g', -0.52923958432893337, 0.0]\n",
      "['deVAB_z', -0.52550665088868098, 0.0]\n",
      "['deVAB_i', -0.52391789795700761, 0.0]\n",
      "['deVAB_r', -0.52107628170249332, 0.0]\n",
      "['deVRad_u', 0.50941131249218641, 0.0]\n",
      "['deVAB_g', -0.50393881864144918, 0.0]\n",
      "['deVMag_u_ext_cor', -0.49334667573966723, 0.0]\n",
      "['expMag_u_ext_cor', -0.48821692671485956, 0.0]\n",
      "['expFluxIvar_z_ext_cor', -0.43582949578179808, 0.0]\n",
      "['lnLDeV_g', -0.42254675967283883, 0.0]\n",
      "['deVMag_g_ext_cor', -0.41906574254905821, 0.0]\n",
      "['modelMag_u_ext_cor', -0.41808792920373616, 0.0]\n",
      "['expFluxIvar_g_ext_cor', -0.41129880774368349, 0.0]\n",
      "['lnLDeV_r', -0.41049902916883857, 0.0]\n",
      "['cModelMag_u_ext_cor', -0.40948194535679749, 0.0]\n",
      "['expMag_g_ext_cor', -0.40622134555539979, 0.0]\n",
      "['petroR50_r', 0.40328732646322685, 0.0]\n",
      "['lnLDeV_i', -0.38762342218442847, 0.0]\n",
      "['expFluxIvar_r_ext_cor', -0.37760799075882379, 0.0]\n",
      "['aperFlux7Ivar_r_ext_cor', -0.37616096301761498, 0.0]\n",
      "['deVFluxIvar_z_ext_cor', -0.37360086003281179, 0.0]\n",
      "['petroMag_u_ext_cor', -0.37199799506779435, 0.0]\n",
      "['z', -0.36994226911818873, 0.0]\n",
      "['modelFluxIvar_u_ext_cor', -0.36343666715910794, 0.0]\n",
      "['expFluxIvar_i_ext_cor', -0.36250758735921879, 0.0]\n",
      "['deVFluxIvar_g_ext_cor', -0.36008028801624015, 0.0]\n",
      "['expFluxIvar_u_ext_cor', -0.35823532982961048, 0.0]\n",
      "['deVFluxIvar_r_ext_cor', -0.35442140299518338, 0.0]\n",
      "['petroRad_r', 0.35165251935360753, 0.0]\n",
      "['petroRad_g', 0.33584388416723571, 0.0]\n",
      "['cModelMag_g_ext_cor', -0.33288042030205783, 0.0]\n",
      "['petroMag_g_ext_cor', -0.328166017002882, 0.0]\n",
      "['deVFlux_u_ext_cor', 0.32372827292215084, 0.0]\n",
      "['cModelFluxIvar_z_ext_cor', -0.32055769628828895, 0.0]\n"
     ]
    }
   ],
   "source": [
    "# For the first modeling attempt, let's identify a subset of parameters which are highly \n",
    "# correlated with the target variables, and take the top N as the starting set\n",
    "\n",
    "# Based on experimentation, I find that this method reaches peak accuracy at around N=50 features\n",
    "# For higher N, accuracy decreases slightly, likely due to overfitting\n",
    "\n",
    "res=[]\n",
    "for col in phot_df.columns:\n",
    "    if 'p_' not in col: res.append([col]+list(sps.pearsonr(phot_df.p_cs,phot_df[col]))) #all independent vars\n",
    "\n",
    "out = sorted(res,key=lambda x:np.absolute(x[1]),reverse=True)\n",
    "for item in out[0:50]:\n",
    "    print item  #variables sorted by declining correlation\n",
    "    #if 'p_' not in item[0]: print item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# First attempt at a 2-category classifier: can we distinguish elliptical from non-ellipticals?\n",
    "\n",
    "# Elliptical galaxies compose roughly half of the training set, so I am not worrying about \n",
    "# rebalancing at the moment\n",
    "\n",
    "input_set = [item[0] for item in out[0:50] if 'p_' not in item[0]]\n",
    "test_size = 10000\n",
    "\n",
    "lm = LogisticRegression()\n",
    "X = np.array(phot_df[input_set][test_size:])\n",
    "y = np.array(phot_df['p_el'].apply(lambda x:int(x>0.8)))[test_size:]\n",
    "X_pred = np.array(phot_df[input_set][:test_size])\n",
    "y_pred = np.array(phot_df['p_el'].apply(lambda x:int(x>0.8)))[:test_size]\n",
    "lm.fit(X,y)\n",
    "res_df = phot_df[input_set][0:test_size-1] \n",
    "res_df['actual'] = pd.Series(y_pred)\n",
    "res_df['pred'] = pd.Series(lm.predict(phot_df[input_set][0:test_size]))\n",
    "res_df['cor'] = res_df.apply(lambda row:int(row['actual']==row['pred']),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4436 4735 8852\n",
      "Overall correct fraction =  0.8852\n"
     ]
    },
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      "text/plain": [
       "    deVRad_g   deVRad_r   deVRad_i   deVRad_z  fracDeV_g  fracDeV_r  \\\n",
       "0   2.818519   2.548579   2.346887   2.028795    1.00000          1   \n",
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       "4   3.155905   2.863721   2.783529   2.480793    1.00000          1   \n",
       "\n",
       "   fracDeV_i  expRad_g  expRad_r  fracDeV_z ...   deVFluxIvar_r_ext_cor  \\\n",
       "0          1  1.576391  1.543333          1 ...                0.992972   \n",
       "1          1  0.814247  1.031178          1 ...                4.754089   \n",
       "2          1  1.026478  1.025192          1 ...                3.733170   \n",
       "3          1  5.874155  6.853486          1 ...                0.062441   \n",
       "4          1  1.656565  1.574843          1 ...                0.908091   \n",
       "\n",
       "   petroRad_r  petroRad_g  cModelMag_g_ext_cor  petroMag_g_ext_cor  \\\n",
       "0    5.877106    7.126864            17.262856           17.317126   \n",
       "1    4.142321    2.837454            20.450511           20.418391   \n",
       "2    4.399189    4.493791            18.652910           18.673220   \n",
       "3   22.476040   23.527080            15.076435           15.207505   \n",
       "4    5.421303    5.804179            17.284528           17.376608   \n",
       "\n",
       "   deVFlux_u_ext_cor  cModelFluxIvar_z_ext_cor  actual  pred  cor  \n",
       "0          26.367979                  0.058698       1     1    1  \n",
       "1           0.605586                  0.222342       1     0    0  \n",
       "2           8.981298                  0.217645       0     0    1  \n",
       "3         157.180861                  0.005094       1     1    1  \n",
       "4          18.202865                  0.049865       1     1    1  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results here are encouraging, nearly 90% of the 10000-case sample classified correctly\n",
    "print sum(res_df['actual']),sum(res_df['pred']),sum(res_df['cor'])\n",
    "print 'Overall correct fraction = ',sum(res_df['cor'])/float(test_size)\n",
    "res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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   ],
   "source": [
    "# We can code up a summary confusion matrix to look at counts across predicted and \n",
    "# actual categories\n",
    "\n",
    "imax = max(res_df.actual)\n",
    "rep = np.zeros((imax+1,imax+1),dtype=np.int32)\n",
    "for i in range(len(res_df)):\n",
    "    rep[res_df.actual[i]][res_df.pred[i]]+=1\n",
    "pd.DataFrame(rep) \n",
    "\n",
    "# rows are actual class, columns are predicted\n",
    "# 0 = non-elliptical\n",
    "# 1 = elliptical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multinomial classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65787\n",
      "3219.0 2447.0 8900\n",
      "Overall correct fraction =  0.89\n"
     ]
    },
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       "      <td>19.188529</td>\n",
       "      <td>-64.035310</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.721340</td>\n",
       "      <td>11.713980</td>\n",
       "      <td>11.647900</td>\n",
       "      <td>10.733570</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.874155</td>\n",
       "      <td>6.853486</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>17.801437</td>\n",
       "      <td>0.017452</td>\n",
       "      <td>-634.619900</td>\n",
       "      <td>15.076435</td>\n",
       "      <td>17.049387</td>\n",
       "      <td>0.516520</td>\n",
       "      <td>-998.352800</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.155905</td>\n",
       "      <td>2.863721</td>\n",
       "      <td>2.783529</td>\n",
       "      <td>2.480793</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.656565</td>\n",
       "      <td>1.574843</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>19.605081</td>\n",
       "      <td>0.109985</td>\n",
       "      <td>-41.882240</td>\n",
       "      <td>17.284528</td>\n",
       "      <td>19.216241</td>\n",
       "      <td>6.340792</td>\n",
       "      <td>-93.268810</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    deVRad_g   deVRad_r   deVRad_i   deVRad_z  fracDeV_g  fracDeV_r  \\\n",
       "0   2.818519   2.548579   2.346887   2.028795    1.00000          1   \n",
       "1   1.232881   1.614075   1.580743   1.427668    0.88109          1   \n",
       "2   1.584836   1.480558   1.301109   0.924960    1.00000          1   \n",
       "3  12.721340  11.713980  11.647900  10.733570    1.00000          1   \n",
       "4   3.155905   2.863721   2.783529   2.480793    1.00000          1   \n",
       "\n",
       "   fracDeV_i  expRad_g  expRad_r  fracDeV_z ...   expMag_u_ext_cor  \\\n",
       "0          1  1.576391  1.543333          1 ...          19.619429   \n",
       "1          1  0.814247  1.031178          1 ...          22.989233   \n",
       "2          1  1.026478  1.025192          1 ...          20.639182   \n",
       "3          1  5.874155  6.853486          1 ...          17.801437   \n",
       "4          1  1.656565  1.574843          1 ...          19.605081   \n",
       "\n",
       "   expFluxIvar_z_ext_cor    lnLDeV_g  deVMag_g_ext_cor  modelMag_u_ext_cor  \\\n",
       "0               0.114804  -84.794900         17.262856           19.383819   \n",
       "1               0.423759   -0.029159         20.422681           22.569883   \n",
       "2               0.329174  -37.330250         18.652910           20.836492   \n",
       "3               0.017452 -634.619900         15.076435           17.049387   \n",
       "4               0.109985  -41.882240         17.284528           19.216241   \n",
       "\n",
       "   expFluxIvar_g_ext_cor    lnLDeV_r  actual  pred  cor  \n",
       "0               6.026212 -199.305900       0     0    1  \n",
       "1              27.065754   -0.289762       0     0    1  \n",
       "2              19.188529  -64.035310       0     0    1  \n",
       "3               0.516520 -998.352800       0     2    0  \n",
       "4               6.340792  -93.268810       0     0    1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our attempt at binary classification looks encouraging.\n",
    "\n",
    "# Now, let's attempt to distinguish elliptical, face on- and edge on-spirals, and mergers in a  \n",
    "# single model, using the softmax regression feature in sklearn's Logistic Regression package.  \n",
    "# Our target variables will now be an integer representations of the possible classes:\n",
    "# \n",
    "# 0 - Ellipticals\n",
    "# 1 - Spirals (face-on)\n",
    "# 2 - Spirals (edge-on)\n",
    "# 3 - Mergers\n",
    "\n",
    "# We will use a majority of votes method to determine the training class for a galaxy, \n",
    "# discarding ambiguous cases\n",
    "\n",
    "input_set = [item[0] for item in out[0:30] if 'p_' not in item[0][:2]]\n",
    "test_size = 10000\n",
    "\n",
    "ycols = [col for col in phot_df.columns if 'p_' in col[:2]]\n",
    "def gal_class(row):\n",
    "    gc_dict = {'p_el':0, 'p_cw':1, 'p_acw':1, 'p_edge':2, 'p_dk':None, 'p_mg':3} #dictionary for our classification\n",
    "    for item in gc_dict:\n",
    "        if row[item]>0.65: return gc_dict[item] # We will set a bar of 65% for deciding vote on \"correct\" class\n",
    "    return None\n",
    "\n",
    "phot_df['gal_class'] = phot_df.apply(gal_class,axis=1)\n",
    "mod_df = phot_df[phot_df.gal_class.notnull()].reset_index()\n",
    "print len(mod_df)\n",
    "#print mod_df[ycols+['gal_class']]\n",
    "lm = LogisticRegression(multi_class='multinomial',solver='lbfgs')\n",
    "X = np.array(mod_df[input_set][test_size:])\n",
    "y = np.array(mod_df['gal_class'])[test_size:]\n",
    "X_pred = np.array(mod_df[input_set][:test_size])\n",
    "y_pred = np.array(mod_df['gal_class'])[:test_size]\n",
    "\n",
    "\n",
    "lm.fit(X,y)\n",
    "res_df = mod_df[input_set][0:test_size-1]\n",
    "\n",
    "res_df['actual'] = pd.Series(y_pred)\n",
    "res_df['pred'] = pd.Series(lm.predict(mod_df[input_set][0:test_size]))\n",
    "res_df['cor'] = res_df.apply(lambda row:int(row['actual']==row['pred']),axis=1)\n",
    "print sum(res_df['actual']),sum(res_df['pred']),sum(res_df['cor'])\n",
    "print 'Overall correct fraction = ',sum(res_df['cor'])/float(test_size)\n",
    "res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9999\n",
      "Class count in test set:\n",
      "0    7752\n",
      "1    1307\n",
      "2     908\n",
      "3      32\n",
      "Name: actual, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deVRad_g</th>\n",
       "      <th>deVRad_r</th>\n",
       "      <th>deVRad_i</th>\n",
       "      <th>deVRad_z</th>\n",
       "      <th>fracDeV_g</th>\n",
       "      <th>fracDeV_r</th>\n",
       "      <th>fracDeV_i</th>\n",
       "      <th>expRad_g</th>\n",
       "      <th>expRad_r</th>\n",
       "      <th>fracDeV_z</th>\n",
       "      <th>...</th>\n",
       "      <th>expMag_u_ext_cor</th>\n",
       "      <th>expFluxIvar_z_ext_cor</th>\n",
       "      <th>lnLDeV_g</th>\n",
       "      <th>deVMag_g_ext_cor</th>\n",
       "      <th>modelMag_u_ext_cor</th>\n",
       "      <th>expFluxIvar_g_ext_cor</th>\n",
       "      <th>lnLDeV_r</th>\n",
       "      <th>actual</th>\n",
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       "      <th>263</th>\n",
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       "      <th>363</th>\n",
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       "      <td>0.337970</td>\n",
       "      <td>3.099596</td>\n",
       "      <td>3.102630</td>\n",
       "      <td>0.410543</td>\n",
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       "      <td>0.069210</td>\n",
       "      <td>-321.077500</td>\n",
       "      <td>17.321305</td>\n",
       "      <td>20.181045</td>\n",
       "      <td>4.831824</td>\n",
       "      <td>-1101.52100</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>867</th>\n",
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       "      <td>8.468210</td>\n",
       "      <td>9.799077</td>\n",
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       "      <td>0.160053</td>\n",
       "      <td>0.166368</td>\n",
       "      <td>3.834180</td>\n",
       "      <td>3.858619</td>\n",
       "      <td>0.109853</td>\n",
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       "      <td>-2359.941000</td>\n",
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       "      <td>17.846954</td>\n",
       "      <td>1.681714</td>\n",
       "      <td>-2911.94700</td>\n",
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       "    <tr>\n",
       "      <th>1011</th>\n",
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       "      <td>5.867348</td>\n",
       "      <td>6.373719</td>\n",
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       "      <td>0.158520</td>\n",
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       "      <td>3.219748</td>\n",
       "      <td>3.158849</td>\n",
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       "      <th>1241</th>\n",
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       "      <td>4.275159</td>\n",
       "      <td>4.175169</td>\n",
       "      <td>4.869287</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>5.514565</td>\n",
       "      <td>4.401240</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>17.991412</td>\n",
       "      <td>0.020087</td>\n",
       "      <td>-2962.168000</td>\n",
       "      <td>15.274217</td>\n",
       "      <td>17.671792</td>\n",
       "      <td>0.617538</td>\n",
       "      <td>-3793.00500</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>1.793868</td>\n",
       "      <td>2.174252</td>\n",
       "      <td>1.926275</td>\n",
       "      <td>1.891646</td>\n",
       "      <td>0.718259</td>\n",
       "      <td>0.636582</td>\n",
       "      <td>0.675949</td>\n",
       "      <td>1.295261</td>\n",
       "      <td>1.336613</td>\n",
       "      <td>0.333105</td>\n",
       "      <td>...</td>\n",
       "      <td>19.544053</td>\n",
       "      <td>0.143158</td>\n",
       "      <td>-16.152310</td>\n",
       "      <td>17.998647</td>\n",
       "      <td>19.136583</td>\n",
       "      <td>9.356932</td>\n",
       "      <td>-67.67579</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>0.317915</td>\n",
       "      <td>45.485770</td>\n",
       "      <td>36.802390</td>\n",
       "      <td>29.663210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.393044</td>\n",
       "      <td>41.520960</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.344738</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.012960</td>\n",
       "      <td>17.344838</td>\n",
       "      <td>0.081378</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>1.251884</td>\n",
       "      <td>1.590851</td>\n",
       "      <td>1.430534</td>\n",
       "      <td>1.579295</td>\n",
       "      <td>0.818113</td>\n",
       "      <td>0.851795</td>\n",
       "      <td>0.812504</td>\n",
       "      <td>0.973154</td>\n",
       "      <td>1.027773</td>\n",
       "      <td>0.898401</td>\n",
       "      <td>...</td>\n",
       "      <td>18.254377</td>\n",
       "      <td>0.189075</td>\n",
       "      <td>-276.256100</td>\n",
       "      <td>17.378194</td>\n",
       "      <td>17.907297</td>\n",
       "      <td>6.922881</td>\n",
       "      <td>-299.79800</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>9.892220</td>\n",
       "      <td>9.891839</td>\n",
       "      <td>9.404440</td>\n",
       "      <td>7.437802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017478</td>\n",
       "      <td>3.715469</td>\n",
       "      <td>3.555017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.922049</td>\n",
       "      <td>0.104034</td>\n",
       "      <td>-762.004200</td>\n",
       "      <td>17.344834</td>\n",
       "      <td>19.003339</td>\n",
       "      <td>3.454149</td>\n",
       "      <td>-685.26050</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3843</th>\n",
       "      <td>4.551131</td>\n",
       "      <td>3.757606</td>\n",
       "      <td>3.296361</td>\n",
       "      <td>3.261423</td>\n",
       "      <td>0.134420</td>\n",
       "      <td>0.157837</td>\n",
       "      <td>0.308316</td>\n",
       "      <td>2.030733</td>\n",
       "      <td>1.877421</td>\n",
       "      <td>0.485454</td>\n",
       "      <td>...</td>\n",
       "      <td>19.986092</td>\n",
       "      <td>0.112867</td>\n",
       "      <td>-172.265100</td>\n",
       "      <td>17.868546</td>\n",
       "      <td>20.238252</td>\n",
       "      <td>5.983251</td>\n",
       "      <td>-360.57230</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>1.322646</td>\n",
       "      <td>1.134757</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.223105</td>\n",
       "      <td>1.008508</td>\n",
       "      <td>0.973031</td>\n",
       "      <td>...</td>\n",
       "      <td>21.309200</td>\n",
       "      <td>0.447822</td>\n",
       "      <td>-17.368590</td>\n",
       "      <td>18.708678</td>\n",
       "      <td>20.932570</td>\n",
       "      <td>17.890372</td>\n",
       "      <td>-52.75722</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3913</th>\n",
       "      <td>3.702849</td>\n",
       "      <td>3.313958</td>\n",
       "      <td>2.714463</td>\n",
       "      <td>2.677120</td>\n",
       "      <td>0.759702</td>\n",
       "      <td>0.850769</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.831421</td>\n",
       "      <td>1.784248</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.929500</td>\n",
       "      <td>0.232422</td>\n",
       "      <td>-8.378350</td>\n",
       "      <td>18.516852</td>\n",
       "      <td>20.511070</td>\n",
       "      <td>14.449121</td>\n",
       "      <td>-17.87963</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425</th>\n",
       "      <td>10.824650</td>\n",
       "      <td>9.710116</td>\n",
       "      <td>9.610291</td>\n",
       "      <td>8.379803</td>\n",
       "      <td>0.986614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.824222</td>\n",
       "      <td>6.937904</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16.488840</td>\n",
       "      <td>0.005035</td>\n",
       "      <td>-436.636000</td>\n",
       "      <td>13.933219</td>\n",
       "      <td>15.960110</td>\n",
       "      <td>0.162175</td>\n",
       "      <td>-956.20000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5424</th>\n",
       "      <td>4.146514</td>\n",
       "      <td>3.810590</td>\n",
       "      <td>3.537755</td>\n",
       "      <td>3.687052</td>\n",
       "      <td>0.192063</td>\n",
       "      <td>0.309130</td>\n",
       "      <td>0.381631</td>\n",
       "      <td>1.864851</td>\n",
       "      <td>1.691979</td>\n",
       "      <td>0.475525</td>\n",
       "      <td>...</td>\n",
       "      <td>19.308578</td>\n",
       "      <td>0.173936</td>\n",
       "      <td>-336.345900</td>\n",
       "      <td>17.773972</td>\n",
       "      <td>19.316519</td>\n",
       "      <td>7.854721</td>\n",
       "      <td>-523.39810</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5545</th>\n",
       "      <td>7.723771</td>\n",
       "      <td>7.822509</td>\n",
       "      <td>7.834815</td>\n",
       "      <td>7.938914</td>\n",
       "      <td>0.217403</td>\n",
       "      <td>0.078103</td>\n",
       "      <td>0.000820</td>\n",
       "      <td>3.166507</td>\n",
       "      <td>3.011765</td>\n",
       "      <td>0.091825</td>\n",
       "      <td>...</td>\n",
       "      <td>19.041738</td>\n",
       "      <td>0.087654</td>\n",
       "      <td>-363.404100</td>\n",
       "      <td>17.534383</td>\n",
       "      <td>19.008848</td>\n",
       "      <td>4.963046</td>\n",
       "      <td>-305.51440</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>1.503903</td>\n",
       "      <td>1.264844</td>\n",
       "      <td>1.191962</td>\n",
       "      <td>1.044162</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971332</td>\n",
       "      <td>0.803468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.549646</td>\n",
       "      <td>0.419795</td>\n",
       "      <td>-39.128350</td>\n",
       "      <td>18.377695</td>\n",
       "      <td>20.245466</td>\n",
       "      <td>18.239233</td>\n",
       "      <td>-158.63530</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6229</th>\n",
       "      <td>10.535660</td>\n",
       "      <td>8.540431</td>\n",
       "      <td>7.603132</td>\n",
       "      <td>7.091725</td>\n",
       "      <td>0.138442</td>\n",
       "      <td>0.290854</td>\n",
       "      <td>0.441251</td>\n",
       "      <td>5.626434</td>\n",
       "      <td>4.824051</td>\n",
       "      <td>0.655178</td>\n",
       "      <td>...</td>\n",
       "      <td>17.923274</td>\n",
       "      <td>0.059013</td>\n",
       "      <td>-3923.075000</td>\n",
       "      <td>16.234027</td>\n",
       "      <td>18.011254</td>\n",
       "      <td>1.372644</td>\n",
       "      <td>-3125.08600</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>7.107142</td>\n",
       "      <td>5.717090</td>\n",
       "      <td>5.375099</td>\n",
       "      <td>4.932603</td>\n",
       "      <td>0.739806</td>\n",
       "      <td>0.946085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.775403</td>\n",
       "      <td>3.542833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18.222153</td>\n",
       "      <td>0.057340</td>\n",
       "      <td>-1210.234000</td>\n",
       "      <td>16.316914</td>\n",
       "      <td>18.287313</td>\n",
       "      <td>1.809905</td>\n",
       "      <td>-1359.46200</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6470</th>\n",
       "      <td>29.663750</td>\n",
       "      <td>29.664970</td>\n",
       "      <td>29.675830</td>\n",
       "      <td>29.674260</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066469</td>\n",
       "      <td>16.192280</td>\n",
       "      <td>14.462140</td>\n",
       "      <td>0.278509</td>\n",
       "      <td>...</td>\n",
       "      <td>16.082146</td>\n",
       "      <td>0.003035</td>\n",
       "      <td>-21054.110000</td>\n",
       "      <td>14.424058</td>\n",
       "      <td>16.132786</td>\n",
       "      <td>0.078901</td>\n",
       "      <td>-18107.05000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6913</th>\n",
       "      <td>29.652660</td>\n",
       "      <td>23.821850</td>\n",
       "      <td>21.231730</td>\n",
       "      <td>20.097950</td>\n",
       "      <td>0.029681</td>\n",
       "      <td>0.085500</td>\n",
       "      <td>0.131906</td>\n",
       "      <td>9.977024</td>\n",
       "      <td>8.900132</td>\n",
       "      <td>0.230062</td>\n",
       "      <td>...</td>\n",
       "      <td>17.419046</td>\n",
       "      <td>0.014808</td>\n",
       "      <td>-4595.304000</td>\n",
       "      <td>15.186530</td>\n",
       "      <td>17.512606</td>\n",
       "      <td>0.393385</td>\n",
       "      <td>-5759.72300</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7137</th>\n",
       "      <td>11.715330</td>\n",
       "      <td>11.114830</td>\n",
       "      <td>10.191660</td>\n",
       "      <td>9.580692</td>\n",
       "      <td>0.166037</td>\n",
       "      <td>0.362132</td>\n",
       "      <td>0.448338</td>\n",
       "      <td>5.805045</td>\n",
       "      <td>5.065201</td>\n",
       "      <td>0.586617</td>\n",
       "      <td>...</td>\n",
       "      <td>17.435274</td>\n",
       "      <td>0.058728</td>\n",
       "      <td>-3383.784000</td>\n",
       "      <td>16.140826</td>\n",
       "      <td>17.553204</td>\n",
       "      <td>1.246206</td>\n",
       "      <td>-2624.05600</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>6.213326</td>\n",
       "      <td>6.231277</td>\n",
       "      <td>5.381876</td>\n",
       "      <td>5.772491</td>\n",
       "      <td>0.593132</td>\n",
       "      <td>0.714777</td>\n",
       "      <td>0.829988</td>\n",
       "      <td>3.792691</td>\n",
       "      <td>3.656508</td>\n",
       "      <td>0.801062</td>\n",
       "      <td>...</td>\n",
       "      <td>18.189124</td>\n",
       "      <td>0.066096</td>\n",
       "      <td>-1296.307000</td>\n",
       "      <td>16.661261</td>\n",
       "      <td>17.762844</td>\n",
       "      <td>2.499569</td>\n",
       "      <td>-1327.18100</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7431</th>\n",
       "      <td>2.088420</td>\n",
       "      <td>2.005379</td>\n",
       "      <td>1.913405</td>\n",
       "      <td>1.910509</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.266264</td>\n",
       "      <td>1.258656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.484002</td>\n",
       "      <td>0.160370</td>\n",
       "      <td>-9.002486</td>\n",
       "      <td>17.812144</td>\n",
       "      <td>19.845812</td>\n",
       "      <td>9.342389</td>\n",
       "      <td>-10.84767</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8227</th>\n",
       "      <td>29.674930</td>\n",
       "      <td>29.536900</td>\n",
       "      <td>29.672770</td>\n",
       "      <td>29.676710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029263</td>\n",
       "      <td>10.651210</td>\n",
       "      <td>10.284020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.937495</td>\n",
       "      <td>0.016134</td>\n",
       "      <td>-3031.941000</td>\n",
       "      <td>16.110618</td>\n",
       "      <td>17.627765</td>\n",
       "      <td>0.910575</td>\n",
       "      <td>-3262.45600</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>3.666743</td>\n",
       "      <td>3.367726</td>\n",
       "      <td>3.187799</td>\n",
       "      <td>2.962282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.940744</td>\n",
       "      <td>1.912672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.051221</td>\n",
       "      <td>0.099301</td>\n",
       "      <td>-285.561200</td>\n",
       "      <td>16.745011</td>\n",
       "      <td>18.682841</td>\n",
       "      <td>5.514487</td>\n",
       "      <td>-508.46920</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8293</th>\n",
       "      <td>1.568569</td>\n",
       "      <td>1.481326</td>\n",
       "      <td>1.486624</td>\n",
       "      <td>1.304126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997497</td>\n",
       "      <td>0.973218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>20.423604</td>\n",
       "      <td>0.215305</td>\n",
       "      <td>-8.897234</td>\n",
       "      <td>18.323159</td>\n",
       "      <td>20.192004</td>\n",
       "      <td>16.495636</td>\n",
       "      <td>-75.45387</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8340</th>\n",
       "      <td>9.890799</td>\n",
       "      <td>8.415833</td>\n",
       "      <td>7.108675</td>\n",
       "      <td>6.747465</td>\n",
       "      <td>0.297359</td>\n",
       "      <td>0.389718</td>\n",
       "      <td>0.494101</td>\n",
       "      <td>4.200860</td>\n",
       "      <td>3.820804</td>\n",
       "      <td>0.561581</td>\n",
       "      <td>...</td>\n",
       "      <td>19.467405</td>\n",
       "      <td>0.121792</td>\n",
       "      <td>-610.634500</td>\n",
       "      <td>17.416280</td>\n",
       "      <td>19.396025</td>\n",
       "      <td>4.018406</td>\n",
       "      <td>-757.37110</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9105</th>\n",
       "      <td>2.646686</td>\n",
       "      <td>2.776070</td>\n",
       "      <td>2.993659</td>\n",
       "      <td>3.297217</td>\n",
       "      <td>0.609048</td>\n",
       "      <td>0.470560</td>\n",
       "      <td>0.294007</td>\n",
       "      <td>2.026266</td>\n",
       "      <td>1.931791</td>\n",
       "      <td>0.104702</td>\n",
       "      <td>...</td>\n",
       "      <td>17.837898</td>\n",
       "      <td>0.148672</td>\n",
       "      <td>-464.684700</td>\n",
       "      <td>16.984613</td>\n",
       "      <td>17.779168</td>\n",
       "      <td>3.834294</td>\n",
       "      <td>-444.16040</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9438</th>\n",
       "      <td>2.979961</td>\n",
       "      <td>3.123724</td>\n",
       "      <td>3.136822</td>\n",
       "      <td>3.073474</td>\n",
       "      <td>0.177655</td>\n",
       "      <td>0.199433</td>\n",
       "      <td>0.215913</td>\n",
       "      <td>2.037685</td>\n",
       "      <td>1.949461</td>\n",
       "      <td>0.420762</td>\n",
       "      <td>...</td>\n",
       "      <td>18.041017</td>\n",
       "      <td>0.169706</td>\n",
       "      <td>-1962.840000</td>\n",
       "      <td>17.038452</td>\n",
       "      <td>18.043337</td>\n",
       "      <td>3.971400</td>\n",
       "      <td>-1452.97700</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9517</th>\n",
       "      <td>4.655257</td>\n",
       "      <td>3.998634</td>\n",
       "      <td>3.699898</td>\n",
       "      <td>3.654509</td>\n",
       "      <td>0.693411</td>\n",
       "      <td>0.737756</td>\n",
       "      <td>0.883926</td>\n",
       "      <td>2.433285</td>\n",
       "      <td>2.373379</td>\n",
       "      <td>0.933747</td>\n",
       "      <td>...</td>\n",
       "      <td>19.321190</td>\n",
       "      <td>0.066213</td>\n",
       "      <td>-255.771200</td>\n",
       "      <td>16.919015</td>\n",
       "      <td>18.885390</td>\n",
       "      <td>3.820954</td>\n",
       "      <td>-341.09360</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       deVRad_g   deVRad_r   deVRad_i   deVRad_z  fracDeV_g  fracDeV_r  \\\n",
       "166   12.560650  10.270730   9.567334   9.169669   0.573141   0.751087   \n",
       "263    7.312759   7.939163   8.098401   9.893099   0.000000   0.031091   \n",
       "363    6.731761   6.699277   6.011037   5.554335   0.159050   0.264422   \n",
       "867    7.941263   8.019423   8.468210   9.799077   0.112714   0.160053   \n",
       "1011   6.190080   6.373897   5.867348   6.373719   0.040067   0.158520   \n",
       "1241   4.958196   4.275159   4.175169   4.869287   0.065312   0.166176   \n",
       "1262   7.859071   6.257401   5.729597   4.650141   1.000000   1.000000   \n",
       "1838   1.793868   2.174252   1.926275   1.891646   0.718259   0.636582   \n",
       "2271   0.317915  45.485770  36.802390  29.663210   1.000000   1.000000   \n",
       "2815   1.251884   1.590851   1.430534   1.579295   0.818113   0.851795   \n",
       "3556   9.892220   9.891839   9.404440   7.437802   0.000000   0.000000   \n",
       "3843   4.551131   3.757606   3.296361   3.261423   0.134420   0.157837   \n",
       "3849   2.077105   1.679277   1.322646   1.134757   1.000000   1.000000   \n",
       "3913   3.702849   3.313958   2.714463   2.677120   0.759702   0.850769   \n",
       "4425  10.824650   9.710116   9.610291   8.379803   0.986614   1.000000   \n",
       "5424   4.146514   3.810590   3.537755   3.687052   0.192063   0.309130   \n",
       "5545   7.723771   7.822509   7.834815   7.938914   0.217403   0.078103   \n",
       "5680   1.503903   1.264844   1.191962   1.044162   1.000000   1.000000   \n",
       "6229  10.535660   8.540431   7.603132   7.091725   0.138442   0.290854   \n",
       "6415   7.107142   5.717090   5.375099   4.932603   0.739806   0.946085   \n",
       "6470  29.663750  29.664970  29.675830  29.674260   0.000000   0.000000   \n",
       "6913  29.652660  23.821850  21.231730  20.097950   0.029681   0.085500   \n",
       "7137  11.715330  11.114830  10.191660   9.580692   0.166037   0.362132   \n",
       "7304   6.213326   6.231277   5.381876   5.772491   0.593132   0.714777   \n",
       "7431   2.088420   2.005379   1.913405   1.910509   1.000000   1.000000   \n",
       "8227  29.674930  29.536900  29.672770  29.676710   0.000000   0.000000   \n",
       "8260   3.666743   3.367726   3.187799   2.962282   1.000000   1.000000   \n",
       "8293   1.568569   1.481326   1.486624   1.304126   1.000000   1.000000   \n",
       "8340   9.890799   8.415833   7.108675   6.747465   0.297359   0.389718   \n",
       "9105   2.646686   2.776070   2.993659   3.297217   0.609048   0.470560   \n",
       "9438   2.979961   3.123724   3.136822   3.073474   0.177655   0.199433   \n",
       "9517   4.655257   3.998634   3.699898   3.654509   0.693411   0.737756   \n",
       "\n",
       "      fracDeV_i   expRad_g   expRad_r  fracDeV_z ...   expMag_u_ext_cor  \\\n",
       "166    0.840145   6.117905   5.782674   1.000000 ...          17.521175   \n",
       "263    0.031117   4.072709   4.166236   0.004904 ...          16.703542   \n",
       "363    0.337970   3.099596   3.102630   0.410543 ...          20.390075   \n",
       "867    0.166368   3.834180   3.858619   0.109853 ...          17.979244   \n",
       "1011   0.189875   3.219748   3.158849   0.235991 ...          20.070308   \n",
       "1241   0.219599   2.595887   2.490453   0.343211 ...          17.857309   \n",
       "1262   1.000000   5.514565   4.401240   1.000000 ...          17.991412   \n",
       "1838   0.675949   1.295261   1.336613   0.333105 ...          19.544053   \n",
       "2271   0.000000   1.393044  41.520960   1.000000 ...          17.344738   \n",
       "2815   0.812504   0.973154   1.027773   0.898401 ...          18.254377   \n",
       "3556   0.017478   3.715469   3.555017   0.000000 ...          18.922049   \n",
       "3843   0.308316   2.030733   1.877421   0.485454 ...          19.986092   \n",
       "3849   1.000000   1.223105   1.008508   0.973031 ...          21.309200   \n",
       "3913   1.000000   1.831421   1.784248   1.000000 ...          20.929500   \n",
       "4425   1.000000   6.824222   6.937904   1.000000 ...          16.488840   \n",
       "5424   0.381631   1.864851   1.691979   0.475525 ...          19.308578   \n",
       "5545   0.000820   3.166507   3.011765   0.091825 ...          19.041738   \n",
       "5680   1.000000   0.971332   0.803468   1.000000 ...          20.549646   \n",
       "6229   0.441251   5.626434   4.824051   0.655178 ...          17.923274   \n",
       "6415   1.000000   3.775403   3.542833   1.000000 ...          18.222153   \n",
       "6470   0.066469  16.192280  14.462140   0.278509 ...          16.082146   \n",
       "6913   0.131906   9.977024   8.900132   0.230062 ...          17.419046   \n",
       "7137   0.448338   5.805045   5.065201   0.586617 ...          17.435274   \n",
       "7304   0.829988   3.792691   3.656508   0.801062 ...          18.189124   \n",
       "7431   1.000000   1.266264   1.258656   1.000000 ...          20.484002   \n",
       "8227   0.029263  10.651210  10.284020   0.000000 ...          17.937495   \n",
       "8260   1.000000   1.940744   1.912672   1.000000 ...          19.051221   \n",
       "8293   1.000000   0.997497   0.973218   1.000000 ...          20.423604   \n",
       "8340   0.494101   4.200860   3.820804   0.561581 ...          19.467405   \n",
       "9105   0.294007   2.026266   1.931791   0.104702 ...          17.837898   \n",
       "9438   0.215913   2.037685   1.949461   0.420762 ...          18.041017   \n",
       "9517   0.883926   2.433285   2.373379   0.933747 ...          19.321190   \n",
       "\n",
       "      expFluxIvar_z_ext_cor      lnLDeV_g  deVMag_g_ext_cor  \\\n",
       "166                0.025341  -3821.456000         15.502786   \n",
       "263                0.024411  -9038.763000         15.456436   \n",
       "363                0.069210   -321.077500         17.321305   \n",
       "867                0.042080  -2359.941000         16.194050   \n",
       "1011               0.106908   -369.199400         17.863200   \n",
       "1241               0.061937  -3861.732000         16.339850   \n",
       "1262               0.020087  -2962.168000         15.274217   \n",
       "1838               0.143158    -16.152310         17.998647   \n",
       "2271               0.000003      0.000000         25.012960   \n",
       "2815               0.189075   -276.256100         17.378194   \n",
       "3556               0.104034   -762.004200         17.344834   \n",
       "3843               0.112867   -172.265100         17.868546   \n",
       "3849               0.447822    -17.368590         18.708678   \n",
       "3913               0.232422     -8.378350         18.516852   \n",
       "4425               0.005035   -436.636000         13.933219   \n",
       "5424               0.173936   -336.345900         17.773972   \n",
       "5545               0.087654   -363.404100         17.534383   \n",
       "5680               0.419795    -39.128350         18.377695   \n",
       "6229               0.059013  -3923.075000         16.234027   \n",
       "6415               0.057340  -1210.234000         16.316914   \n",
       "6470               0.003035 -21054.110000         14.424058   \n",
       "6913               0.014808  -4595.304000         15.186530   \n",
       "7137               0.058728  -3383.784000         16.140826   \n",
       "7304               0.066096  -1296.307000         16.661261   \n",
       "7431               0.160370     -9.002486         17.812144   \n",
       "8227               0.016134  -3031.941000         16.110618   \n",
       "8260               0.099301   -285.561200         16.745011   \n",
       "8293               0.215305     -8.897234         18.323159   \n",
       "8340               0.121792   -610.634500         17.416280   \n",
       "9105               0.148672   -464.684700         16.984613   \n",
       "9438               0.169706  -1962.840000         17.038452   \n",
       "9517               0.066213   -255.771200         16.919015   \n",
       "\n",
       "      modelMag_u_ext_cor  expFluxIvar_g_ext_cor     lnLDeV_r  actual  pred  \\\n",
       "166            17.417055               0.691834  -5040.66600       3     1   \n",
       "263            16.643562               0.594435  -8390.90800       3     1   \n",
       "363            20.181045               4.831824  -1101.52100       3     0   \n",
       "867            17.846954               1.681714  -2911.94700       3     1   \n",
       "1011           20.000868               7.021210   -727.83010       3     0   \n",
       "1241           17.908059               2.267866  -4391.81200       3     1   \n",
       "1262           17.671792               0.617538  -3793.00500       3     1   \n",
       "1838           19.136583               9.356932    -67.67579       3     0   \n",
       "2271           17.344838               0.081378      0.00000       3     2   \n",
       "2815           17.907297               6.922881   -299.79800       3     0   \n",
       "3556           19.003339               3.454149   -685.26050       3     0   \n",
       "3843           20.238252               5.983251   -360.57230       3     0   \n",
       "3849           20.932570              17.890372    -52.75722       3     0   \n",
       "3913           20.511070              14.449121    -17.87963       3     0   \n",
       "4425           15.960110               0.162175   -956.20000       3     2   \n",
       "5424           19.316519               7.854721   -523.39810       3     0   \n",
       "5545           19.008848               4.963046   -305.51440       3     0   \n",
       "5680           20.245466              18.239233   -158.63530       3     0   \n",
       "6229           18.011254               1.372644  -3125.08600       3     1   \n",
       "6415           18.287313               1.809905  -1359.46200       3     0   \n",
       "6470           16.132786               0.078901 -18107.05000       3     1   \n",
       "6913           17.512606               0.393385  -5759.72300       3     1   \n",
       "7137           17.553204               1.246206  -2624.05600       3     1   \n",
       "7304           17.762844               2.499569  -1327.18100       3     0   \n",
       "7431           19.845812               9.342389    -10.84767       3     0   \n",
       "8227           17.627765               0.910575  -3262.45600       3     2   \n",
       "8260           18.682841               5.514487   -508.46920       3     0   \n",
       "8293           20.192004              16.495636    -75.45387       3     0   \n",
       "8340           19.396025               4.018406   -757.37110       3     0   \n",
       "9105           17.779168               3.834294   -444.16040       3     0   \n",
       "9438           18.043337               3.971400  -1452.97700       3     0   \n",
       "9517           18.885390               3.820954   -341.09360       3     0   \n",
       "\n",
       "      cor  \n",
       "166     0  \n",
       "263     0  \n",
       "363     0  \n",
       "867     0  \n",
       "1011    0  \n",
       "1241    0  \n",
       "1262    0  \n",
       "1838    0  \n",
       "2271    0  \n",
       "2815    0  \n",
       "3556    0  \n",
       "3843    0  \n",
       "3849    0  \n",
       "3913    0  \n",
       "4425    0  \n",
       "5424    0  \n",
       "5545    0  \n",
       "5680    0  \n",
       "6229    0  \n",
       "6415    0  \n",
       "6470    0  \n",
       "6913    0  \n",
       "7137    0  \n",
       "7304    0  \n",
       "7431    0  \n",
       "8227    0  \n",
       "8260    0  \n",
       "8293    0  \n",
       "8340    0  \n",
       "9105    0  \n",
       "9438    0  \n",
       "9517    0  \n",
       "\n",
       "[32 rows x 33 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Our success rate in the first-try multinomial model above is fairly high overall.  \n",
    "# However, our classes are not balanced (most galaxies in sample are elliptical).\n",
    "# And we are not correctly identifying any of the small number of merger cases that appear\n",
    "# (class = 3)\n",
    "\n",
    "print len(res_df)\n",
    "print 'Class count in test set:'\n",
    "print res_df.actual.value_counts()\n",
    "res_df[res_df['actual']==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rudygilmore/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:11: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "      0    1    2  3\n",
       "0  7613   63   76  0\n",
       "1   313  811  183  0\n",
       "2   344   88  476  0\n",
       "3    20    9    3  0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As before, let's look at the confusion matrix to look at counts predicted vs actual\n",
    "# Classes:\n",
    "# 0 - Ellipticals\n",
    "# 1 - Spirals (face-on)\n",
    "# 2 - Spirals (edge-on)\n",
    "# 3 - Mergers\n",
    "\n",
    "imax = int(max(res_df.actual))\n",
    "rep = np.zeros((imax+1,imax+1),dtype=np.int32)\n",
    "for i in range(len(res_df)):\n",
    "    rep[res_df.actual[i]][res_df.pred[i]]+=1\n",
    "pd.DataFrame(rep) \n",
    "\n",
    "# rows are actual class, columns are predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# It looks as though a substantial number of spiral galaxies (N=1 and 2) are being  \n",
    "# misclassified as elliptical, which is the more prevalent class.  \n",
    "# All mergers (N=3) are being misclassified.\n",
    "\n",
    "# Maybe we can use resampling to balance out the training cases before building the model..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification with resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fracDeV_g', -0.29058614911398994, 0.0, 0.6921983552762172, 0.0, -0.01985719021125722, 6.5442693420474347e-09]\n",
      "['deVRad_g', 0.35505521853438837, 0.0, -0.68282720730386537, 0.0, -0.036564346794189691, 1.1769497916922989e-26]\n",
      "['fracDeV_r', -0.26944598846008705, 0.0, 0.68102006263014692, 0.0, -0.016257841797036765, 2.0306984005345621e-06]\n",
      "['fracDeV_i', -0.25058523789270398, 0.0, 0.66099113895277273, 0.0, -0.015043956659306258, 1.1045665891642282e-05]\n",
      "['deVRad_r', 0.33242706416627799, 0.0, -0.65707546741538858, 0.0, -0.028405840161803083, 1.0326486536944963e-16]\n",
      "['deVRad_i', 0.32797418929847161, 0.0, -0.64195006975406177, 0.0, -0.027041744210223172, 2.7403664924372629e-15]\n",
      "['deVRad_z', 0.32390783457743794, 0.0, -0.63798034427197292, 0.0, -0.030671938119013122, 3.1457133340157582e-19]\n",
      "['fracDeV_z', -0.21602770064289506, 0.0, 0.61867559314194176, 0.0, -0.026765732595845266, 5.2194180831771675e-15]\n",
      "['expRad_g', 0.30136048533283033, 0.0, -0.59459181027810259, 0.0, -0.035774512022504049, 1.3821475494447468e-25]\n",
      "['fracDeV_u', -0.25971730416284372, 0.0, 0.58074179051417962, 0.0, -0.017256077636410884, 4.6054755903748377e-07]\n",
      "['expRad_r', 0.28374398636222592, 0.0, -0.56242777159727941, 0.0, -0.030974571170466508, 1.4041270642704156e-19]\n",
      "['expAB_z', -0.032098385824203407, 6.5624562889162095e-21, 0.56206446854309799, 0.0, -0.030079496198899512, 1.491961799300381e-18]\n",
      "['expRad_z', 0.27013809510090814, 0.0, -0.55535051595164131, 0.0, -0.027434080733483676, 1.0845955738747924e-15]\n",
      "['expAB_i', -0.025062611130572319, 2.4129997386531164e-13, 0.5528961889850782, 0.0, -0.029509687394512502, 6.4834765659085815e-18]\n",
      "['expAB_r', -0.020441921525655514, 2.3276999121182809e-09, 0.55145973992218489, 0.0, -0.030990362043728542, 1.3459697214144739e-19]\n",
      "['expRad_i', 0.28106093421404271, 0.0, -0.54986477449982885, 0.0, -0.029468709997924055, 7.1983579106485054e-18]\n",
      "['expAB_g', -0.012381649566338599, 0.00029726929011487366, 0.54805211270133014, 0.0, -0.036175420848717617, 3.9849059174904094e-26]\n",
      "['deVAB_z', -0.013924221773964995, 4.7333450833280432e-05, 0.54652230248845823, 0.0, -0.035002137892870731, 1.460303867086221e-24]\n",
      "['deVAB_i', -0.0033532255311065789, 0.32722928090044567, 0.53458113686329911, 0.0, -0.029506800016893757, 6.5314674414394253e-18]\n",
      "['deVAB_r', 0.00048026347922693253, 0.88840880314816451, 0.53272122287278223, 0.0, -0.031678752309742139, 2.085692976537522e-20]\n",
      "['deVAB_g', 0.0088460037757920502, 0.0097499028717040314, 0.52520788872365609, 0.0, -0.036549325878852651, 1.2340077033281157e-26]\n",
      "['deVRad_u', 0.25628733038333312, 0.0, -0.48416209722146575, 0.0, -0.045074316439892008, 1.2062331519209393e-39]\n",
      "['expMag_u_ext_cor', -0.32403229012329887, 0.0, 0.41520359251155137, 0.0, 0.055542267597030424, 2.6188132603211697e-59]\n",
      "['lnLDeV_g', -0.32650020980280459, 0.0, 0.40963067253718088, 0.0, 0.0055868643234265854, 0.10261227047112802]\n",
      "['deVMag_u_ext_cor', -0.32672164774505014, 0.0, 0.40122758113685808, 0.0, 0.071775857909524787, 6.8783791265762144e-98]\n",
      "['lnLDeV_r', -0.31340630189552504, 0.0, 0.39750793686275748, 0.0, 0.0064209316146496727, 0.060653352306088454]\n",
      "['psfMag_z_ext_cor', 0.072550734203768882, 5.6101567389486157e-100, -0.3843317777846294, 0.0, 0.056721215877600442, 8.8706771829535111e-62]\n",
      "['psfMag_i_ext_cor', 0.062332573710530373, 3.018946225940379e-74, -0.37337223234720751, 0.0, 0.052099941211002997, 2.1582051933914053e-52]\n",
      "['petroR50_r', 0.27375912384646084, 0.0, -0.37318346101327488, 0.0, -0.019359340462022531, 1.5429157300082209e-08]\n",
      "['lnLDeV_i', -0.29434056772334671, 0.0, 0.37087504849715203, 0.0, 0.0052504336734267212, 0.12502411499715443]\n",
      "['psfMag_r_ext_cor', 0.044139800994935197, 4.3543026021234214e-38, -0.33962750279311593, 0.0, 0.053938357562519738, 4.9595769741680201e-56]\n",
      "['psfFluxIvar_z_ext_cor', 0.083543888467407318, 4.8324817773821893e-132, -0.33650467005102236, 0.0, -0.037037320640006796, 2.6249007082724576e-27]\n",
      "['fiber2Mag_z_ext_cor', 0.045388490613629255, 3.5525931333745446e-40, -0.33628645363086862, 0.0, 0.048358349916946368, 2.244590808089372e-45]\n",
      "['expFluxIvar_z_ext_cor', -0.2383007573745699, 0.0, 0.32805204142341177, 0.0, 0.055472887578189099, 3.6463284273384849e-59]\n",
      "['aperFlux7Ivar_r_ext_cor', -0.155391690461123, 0.0, 0.3266742031010399, 0.0, -0.037090539286013073, 2.214488035517081e-27]\n",
      "['modelMag_u_ext_cor', -0.28984579450939163, 0.0, 0.32574088567840193, 0.0, 0.05293660792123344, 4.9401634700991611e-54]\n",
      "['skyIvar_u', -0.16634398132217476, 0.0, 0.32417882711628632, 0.0, -0.27593174381144098, 0.0]\n",
      "['petroRad_r', 0.23350563414924791, 0.0, -0.32271514586851696, 0.0, -0.027480754320718993, 9.7052137473355275e-16]\n",
      "['cModelMag_u_ext_cor', -0.2868643223741929, 0.0, 0.31663261886663996, 0.0, 0.071719253403409539, 9.7536488850034655e-98]\n",
      "['fiber2Mag_i_ext_cor', 0.029976515658773701, 1.9496382059545109e-18, -0.31553874367563267, 0.0, 0.043825207867672647, 1.4319976343922509e-37]\n",
      "['psfFluxIvar_i_ext_cor', 0.062082560342021836, 1.149241923861155e-73, -0.31033667385585395, 0.0, -0.044345421961480683, 1.9906930703255879e-38]\n",
      "['expFluxIvar_u_ext_cor', -0.19070624077024806, 0.0, 0.305527544117006, 0.0, 0.039214804572647304, 2.0533875409452551e-30]\n",
      "['fiberMag_z_ext_cor', 0.028535454421504548, 7.5008794325910411e-17, -0.30519485863329998, 0.0, 0.046929394339082907, 7.8292530669337954e-43]\n",
      "['expFluxIvar_g_ext_cor', -0.2201916003779455, 0.0, 0.30460091175083942, 0.0, 0.045041650941491908, 1.3690480444611963e-39]\n",
      "['deVMag_g_ext_cor', -0.29055163290552938, 0.0, 0.30340318987912085, 0.0, 0.058587814353188515, 8.531030395515179e-66]\n",
      "['deVFlux_u_ext_cor', 0.26425098985495205, 0.0, -0.29905768787699849, 0.0, -0.024776030110892109, 4.4904987445083363e-13]\n",
      "['expMag_g_ext_cor', -0.27931943733586662, 0.0, 0.29753832528729335, 0.0, 0.056656628383235992, 1.215131826033689e-61]\n",
      "['petroRad_g', 0.21616065018429978, 0.0, -0.29004276499219567, 0.0, -0.036305680627780113, 2.6524372714913941e-26]\n",
      "['fiber2Mag_r_ext_cor', 0.014184256136599595, 3.4070020618573368e-05, -0.28562570561987033, 0.0, 0.044704120237211147, 5.0382922269306297e-39]\n",
      "['z', -0.15590253013971372, 0.0, 0.28540526819587742, 0.0, 0.047129122964297096, 3.490567760278295e-43]\n"
     ]
    }
   ],
   "source": [
    "# Let's see if we can improve performance by using resampling to increase representation of \n",
    "# less-common galaxy types in the training set\n",
    "\n",
    "# first, lets fix a shortcoming of the first multimode classifier, which was still using a feature  \n",
    "# list derived for the binary classifier, and only looking at correlation with elliptical votes;\n",
    "# let's update to take the max of several correlations\n",
    "\n",
    "res=[]\n",
    "for col in phot_df.columns:\n",
    "    if 'p_' not in col: res.append([col]\\\n",
    "                                   +list(sps.pearsonr(phot_df.p_cw,phot_df[col]))\\\n",
    "                                   +list(sps.pearsonr(phot_df.p_el,phot_df[col]))\\\n",
    "                                   +list(sps.pearsonr(phot_df.p_mg,phot_df[col]))) #all independent vars\n",
    "\n",
    "out = sorted(res,key=lambda x:max([np.absolute(x[1]),np.absolute(x[3]),np.absolute(x[5])]),reverse=True)\n",
    "for item in out[0:50]:\n",
    "    print item  #variables sorted by declining correlation\n",
    "\n",
    "input_set = [item[0] for item in out[0:50] if 'p_' not in item[0]]\n",
    "mod_df = phot_df[phot_df.gal_class.notnull()].reset_index()\n",
    "\n",
    "# next, I'll manually select some weights to place on the different classes, then use Panda's \n",
    "# sample method to construct a new dataframe\n",
    "\n",
    "weights = {0:1,1:6,2:8,3:200}\n",
    "mod_df['rs_weights'] = mod_df.apply(lambda row:weights[row['gal_class']],axis=1)\n",
    "mod_rs_df = mod_df.sample(50000,replace=True,weights=mod_df.rs_weights).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000\n",
      "3219.0 5750.0 8042\n",
      "Overall correct fraction =  0.8042\n"
     ]
    },
    {
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       "      <th>fracDeV_r</th>\n",
       "      <th>fracDeV_i</th>\n",
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      "text/plain": [
       "   fracDeV_g   deVRad_g  fracDeV_r  fracDeV_i  deVRad_r  deVRad_i  deVRad_z  \\\n",
       "0   0.000000  12.317930   0.000000   0.000000  9.889244  9.086150  8.345079   \n",
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       "4   1.000000   2.477686   1.000000   1.000000  2.301013  2.206827  1.954369   \n",
       "\n",
       "   fracDeV_z  expRad_g  fracDeV_u ...   expFluxIvar_g_ext_cor  \\\n",
       "0    0.00000  5.417314          0 ...                1.467655   \n",
       "1    1.00000  1.567726          1 ...                6.598605   \n",
       "2    0.64044  3.617977          0 ...                7.004402   \n",
       "3    0.00000  0.912620          1 ...               12.571660   \n",
       "4    1.00000  1.511336          1 ...               10.587355   \n",
       "\n",
       "   deVMag_g_ext_cor  deVFlux_u_ext_cor  expMag_g_ext_cor  petroRad_g  \\\n",
       "0         16.762501          44.052899         17.118691    7.677796   \n",
       "1         17.459100          20.636265         17.861420    5.696524   \n",
       "2         17.698160           9.837479         18.137090    6.469973   \n",
       "3         18.699361           2.621114         18.831821    2.299999   \n",
       "4         18.441697           9.574482         18.795437    4.684728   \n",
       "\n",
       "   fiber2Mag_r_ext_cor         z  actual  pred  cor  \n",
       "0            18.966580  0.043587       0     0    1  \n",
       "1            18.542143  0.107560       0     3    0  \n",
       "2            19.323809  0.096189       0     0    1  \n",
       "3            18.942378  0.087762       0     0    1  \n",
       "4            19.501390  0.100790       0     0    1  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's rerun the analysis after training on the resampled set\n",
    "\n",
    "#input_set = [item[0] for item in out[0:30] if 'p_' not in item[0][:2]]\n",
    "test_size = 10000\n",
    "\n",
    "print len(mod_rs_df)\n",
    "#print mod_df[ycols+['gal_class']]\n",
    "lm = LogisticRegression(multi_class='multinomial',solver='lbfgs')\n",
    "X = np.array(mod_rs_df[input_set][test_size:])\n",
    "y = np.array(mod_rs_df['gal_class'])[test_size:]\n",
    "X_pred = np.array(mod_df[input_set][:test_size])\n",
    "y_pred = np.array(mod_df['gal_class'])[:test_size]\n",
    "\n",
    "\n",
    "lm.fit(X,y)\n",
    "res_df = mod_rs_df[input_set][0:test_size-1]\n",
    "\n",
    "res_df['actual'] = pd.Series(y_pred)\n",
    "res_df['pred'] = pd.Series(lm.predict(mod_df[input_set][0:test_size]))\n",
    "res_df['cor'] = res_df.apply(lambda row:int(row['actual']==row['pred']),axis=1)\n",
    "print sum(res_df['actual']),sum(res_df['pred']),sum(res_df['cor'])\n",
    "print 'Overall correct fraction = ',sum(res_df['cor'])/float(test_size)\n",
    "res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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       "      <td>16.859049</td>\n",
       "      <td>9.325549</td>\n",
       "      <td>19.698901</td>\n",
       "      <td>0.057433</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.803886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.920506</td>\n",
       "      <td>5.754169</td>\n",
       "      <td>4.392871</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.555619</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.503626</td>\n",
       "      <td>15.088230</td>\n",
       "      <td>152.685630</td>\n",
       "      <td>15.557800</td>\n",
       "      <td>16.871460</td>\n",
       "      <td>16.896179</td>\n",
       "      <td>0.029821</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.349600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027798</td>\n",
       "      <td>15.349750</td>\n",
       "      <td>15.447710</td>\n",
       "      <td>17.452160</td>\n",
       "      <td>0.062294</td>\n",
       "      <td>6.593308</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035562</td>\n",
       "      <td>15.912493</td>\n",
       "      <td>180.938609</td>\n",
       "      <td>16.354383</td>\n",
       "      <td>9.674854</td>\n",
       "      <td>19.166269</td>\n",
       "      <td>0.047730</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1241</th>\n",
       "      <td>0.075056</td>\n",
       "      <td>24.907900</td>\n",
       "      <td>0.123616</td>\n",
       "      <td>0.117243</td>\n",
       "      <td>23.819670</td>\n",
       "      <td>23.815450</td>\n",
       "      <td>24.448400</td>\n",
       "      <td>0.197838</td>\n",
       "      <td>9.387571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317466</td>\n",
       "      <td>15.018878</td>\n",
       "      <td>384.049449</td>\n",
       "      <td>15.541808</td>\n",
       "      <td>17.127680</td>\n",
       "      <td>19.281560</td>\n",
       "      <td>0.024614</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.699520</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.901767</td>\n",
       "      <td>2.700522</td>\n",
       "      <td>2.568494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.589764</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.493425</td>\n",
       "      <td>18.243580</td>\n",
       "      <td>7.374698</td>\n",
       "      <td>18.578990</td>\n",
       "      <td>5.566939</td>\n",
       "      <td>19.068358</td>\n",
       "      <td>0.178933</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.664938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.533591</td>\n",
       "      <td>2.491115</td>\n",
       "      <td>2.013384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.536320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>13.676961</td>\n",
       "      <td>18.623287</td>\n",
       "      <td>22.691908</td>\n",
       "      <td>18.992777</td>\n",
       "      <td>5.260222</td>\n",
       "      <td>19.184969</td>\n",
       "      <td>0.239132</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>0.091871</td>\n",
       "      <td>28.664020</td>\n",
       "      <td>0.050821</td>\n",
       "      <td>0.087072</td>\n",
       "      <td>23.826890</td>\n",
       "      <td>16.677420</td>\n",
       "      <td>22.877510</td>\n",
       "      <td>0.165537</td>\n",
       "      <td>10.702100</td>\n",
       "      <td>0.233626</td>\n",
       "      <td>...</td>\n",
       "      <td>1.881342</td>\n",
       "      <td>16.828328</td>\n",
       "      <td>49.865805</td>\n",
       "      <td>17.397598</td>\n",
       "      <td>15.538140</td>\n",
       "      <td>20.155508</td>\n",
       "      <td>0.035076</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>0.739246</td>\n",
       "      <td>4.405424</td>\n",
       "      <td>0.924393</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.426312</td>\n",
       "      <td>3.323353</td>\n",
       "      <td>2.901511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.483638</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.799768</td>\n",
       "      <td>17.807893</td>\n",
       "      <td>30.693164</td>\n",
       "      <td>18.190853</td>\n",
       "      <td>5.749047</td>\n",
       "      <td>19.231804</td>\n",
       "      <td>0.118640</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>0.297359</td>\n",
       "      <td>9.890799</td>\n",
       "      <td>0.389718</td>\n",
       "      <td>0.494101</td>\n",
       "      <td>8.415833</td>\n",
       "      <td>7.108675</td>\n",
       "      <td>6.747465</td>\n",
       "      <td>0.561581</td>\n",
       "      <td>4.200860</td>\n",
       "      <td>0.374594</td>\n",
       "      <td>...</td>\n",
       "      <td>4.018406</td>\n",
       "      <td>17.416280</td>\n",
       "      <td>28.898746</td>\n",
       "      <td>17.911541</td>\n",
       "      <td>8.347596</td>\n",
       "      <td>19.625023</td>\n",
       "      <td>0.103373</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3843</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.958399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.417218</td>\n",
       "      <td>6.876963</td>\n",
       "      <td>5.021695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.365738</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.323311</td>\n",
       "      <td>14.804058</td>\n",
       "      <td>345.559621</td>\n",
       "      <td>15.141938</td>\n",
       "      <td>15.892600</td>\n",
       "      <td>16.935662</td>\n",
       "      <td>0.027521</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3849</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.113420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>16.001540</td>\n",
       "      <td>15.321790</td>\n",
       "      <td>13.280440</td>\n",
       "      <td>0.017233</td>\n",
       "      <td>8.138582</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.378119</td>\n",
       "      <td>17.012063</td>\n",
       "      <td>37.097363</td>\n",
       "      <td>17.373323</td>\n",
       "      <td>9.946127</td>\n",
       "      <td>19.530310</td>\n",
       "      <td>0.052914</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3913</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.866488</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.610112</td>\n",
       "      <td>3.591066</td>\n",
       "      <td>3.465287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.506204</td>\n",
       "      <td>0.825140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948294</td>\n",
       "      <td>15.568998</td>\n",
       "      <td>124.598971</td>\n",
       "      <td>15.885928</td>\n",
       "      <td>5.890203</td>\n",
       "      <td>17.032371</td>\n",
       "      <td>0.047818</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4425</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.663974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>6.373273</td>\n",
       "      <td>6.467491</td>\n",
       "      <td>6.890406</td>\n",
       "      <td>0.001572</td>\n",
       "      <td>2.738735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.862085</td>\n",
       "      <td>17.282339</td>\n",
       "      <td>29.209637</td>\n",
       "      <td>17.738539</td>\n",
       "      <td>4.971918</td>\n",
       "      <td>19.691489</td>\n",
       "      <td>0.088047</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5424</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.244821</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.184031</td>\n",
       "      <td>2.735376</td>\n",
       "      <td>2.024805</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.858546</td>\n",
       "      <td>0.896192</td>\n",
       "      <td>...</td>\n",
       "      <td>10.468866</td>\n",
       "      <td>18.052987</td>\n",
       "      <td>1.900080</td>\n",
       "      <td>18.651707</td>\n",
       "      <td>14.138590</td>\n",
       "      <td>19.112847</td>\n",
       "      <td>0.102771</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5545</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.684710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.411237</td>\n",
       "      <td>6.747405</td>\n",
       "      <td>6.038580</td>\n",
       "      <td>0.089139</td>\n",
       "      <td>3.388911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.146163</td>\n",
       "      <td>17.614087</td>\n",
       "      <td>41.523225</td>\n",
       "      <td>18.338087</td>\n",
       "      <td>5.823877</td>\n",
       "      <td>20.253351</td>\n",
       "      <td>0.204254</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.508050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.376740</td>\n",
       "      <td>15.353930</td>\n",
       "      <td>16.040930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.736097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.430776</td>\n",
       "      <td>16.974855</td>\n",
       "      <td>82.974073</td>\n",
       "      <td>17.499105</td>\n",
       "      <td>8.966158</td>\n",
       "      <td>20.038627</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6229</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.131450</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033541</td>\n",
       "      <td>23.454620</td>\n",
       "      <td>22.975660</td>\n",
       "      <td>24.344390</td>\n",
       "      <td>0.087027</td>\n",
       "      <td>10.556900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479044</td>\n",
       "      <td>15.547171</td>\n",
       "      <td>122.811229</td>\n",
       "      <td>15.868681</td>\n",
       "      <td>14.536830</td>\n",
       "      <td>18.805255</td>\n",
       "      <td>0.033542</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>0.739806</td>\n",
       "      <td>7.107142</td>\n",
       "      <td>0.946085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.717090</td>\n",
       "      <td>5.375099</td>\n",
       "      <td>4.932603</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.775403</td>\n",
       "      <td>0.086426</td>\n",
       "      <td>...</td>\n",
       "      <td>1.809905</td>\n",
       "      <td>16.316914</td>\n",
       "      <td>85.246305</td>\n",
       "      <td>16.747544</td>\n",
       "      <td>8.420620</td>\n",
       "      <td>18.331618</td>\n",
       "      <td>0.073760</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6470</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.844259</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.868090</td>\n",
       "      <td>4.026062</td>\n",
       "      <td>3.188996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.895784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.398773</td>\n",
       "      <td>17.841480</td>\n",
       "      <td>2.891788</td>\n",
       "      <td>18.318500</td>\n",
       "      <td>7.851704</td>\n",
       "      <td>18.925867</td>\n",
       "      <td>0.177715</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6913</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.911883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971544</td>\n",
       "      <td>4.380702</td>\n",
       "      <td>4.792568</td>\n",
       "      <td>4.859884</td>\n",
       "      <td>0.814038</td>\n",
       "      <td>2.902982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.255989</td>\n",
       "      <td>14.592833</td>\n",
       "      <td>626.918215</td>\n",
       "      <td>14.966333</td>\n",
       "      <td>7.883327</td>\n",
       "      <td>16.490880</td>\n",
       "      <td>0.029443</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7137</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.674070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013757</td>\n",
       "      <td>29.671590</td>\n",
       "      <td>29.662300</td>\n",
       "      <td>29.664730</td>\n",
       "      <td>0.066381</td>\n",
       "      <td>11.216200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479685</td>\n",
       "      <td>15.626874</td>\n",
       "      <td>176.617171</td>\n",
       "      <td>16.109924</td>\n",
       "      <td>15.275850</td>\n",
       "      <td>19.597371</td>\n",
       "      <td>0.017060</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.556330</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.955840</td>\n",
       "      <td>15.348480</td>\n",
       "      <td>16.120200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.512290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.303372</td>\n",
       "      <td>17.277314</td>\n",
       "      <td>48.021836</td>\n",
       "      <td>17.699154</td>\n",
       "      <td>8.538476</td>\n",
       "      <td>20.075040</td>\n",
       "      <td>0.072409</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7431</th>\n",
       "      <td>0.461522</td>\n",
       "      <td>8.836692</td>\n",
       "      <td>0.724658</td>\n",
       "      <td>0.935505</td>\n",
       "      <td>5.898221</td>\n",
       "      <td>4.374843</td>\n",
       "      <td>4.005365</td>\n",
       "      <td>0.890636</td>\n",
       "      <td>4.077615</td>\n",
       "      <td>0.626466</td>\n",
       "      <td>...</td>\n",
       "      <td>6.007095</td>\n",
       "      <td>18.260596</td>\n",
       "      <td>11.297445</td>\n",
       "      <td>18.687086</td>\n",
       "      <td>6.693216</td>\n",
       "      <td>19.829158</td>\n",
       "      <td>0.155356</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8227</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.856993</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.913469</td>\n",
       "      <td>5.300271</td>\n",
       "      <td>4.315418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.959027</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.304686</td>\n",
       "      <td>16.475748</td>\n",
       "      <td>81.481517</td>\n",
       "      <td>16.919258</td>\n",
       "      <td>9.245286</td>\n",
       "      <td>18.174258</td>\n",
       "      <td>0.066802</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>0.064067</td>\n",
       "      <td>4.025874</td>\n",
       "      <td>0.356862</td>\n",
       "      <td>0.378442</td>\n",
       "      <td>3.687500</td>\n",
       "      <td>3.753201</td>\n",
       "      <td>4.094726</td>\n",
       "      <td>0.569483</td>\n",
       "      <td>2.348300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.335356</td>\n",
       "      <td>16.540108</td>\n",
       "      <td>113.097889</td>\n",
       "      <td>16.803058</td>\n",
       "      <td>4.857699</td>\n",
       "      <td>18.612504</td>\n",
       "      <td>0.074825</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8293</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.942748</td>\n",
       "      <td>0.071109</td>\n",
       "      <td>0.121650</td>\n",
       "      <td>7.941637</td>\n",
       "      <td>8.109632</td>\n",
       "      <td>7.700018</td>\n",
       "      <td>0.128764</td>\n",
       "      <td>3.610440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.485943</td>\n",
       "      <td>16.693414</td>\n",
       "      <td>64.789528</td>\n",
       "      <td>17.099524</td>\n",
       "      <td>6.101304</td>\n",
       "      <td>19.265089</td>\n",
       "      <td>0.075858</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8340</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.895770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026620</td>\n",
       "      <td>23.813960</td>\n",
       "      <td>24.005180</td>\n",
       "      <td>23.815390</td>\n",
       "      <td>0.022968</td>\n",
       "      <td>8.214314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.651777</td>\n",
       "      <td>15.563293</td>\n",
       "      <td>228.696281</td>\n",
       "      <td>16.093653</td>\n",
       "      <td>12.176750</td>\n",
       "      <td>19.495780</td>\n",
       "      <td>0.034164</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9105</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.087050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.042571</td>\n",
       "      <td>2.958752</td>\n",
       "      <td>2.164743</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.771134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.146166</td>\n",
       "      <td>18.595325</td>\n",
       "      <td>9.174294</td>\n",
       "      <td>19.001715</td>\n",
       "      <td>8.277800</td>\n",
       "      <td>19.285158</td>\n",
       "      <td>0.219191</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9438</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.075045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.559542</td>\n",
       "      <td>1.420120</td>\n",
       "      <td>1.503109</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.286409</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.931437</td>\n",
       "      <td>18.403246</td>\n",
       "      <td>10.958748</td>\n",
       "      <td>18.721816</td>\n",
       "      <td>4.268654</td>\n",
       "      <td>19.286290</td>\n",
       "      <td>0.144029</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9517</th>\n",
       "      <td>0.345865</td>\n",
       "      <td>12.375690</td>\n",
       "      <td>0.440541</td>\n",
       "      <td>0.554423</td>\n",
       "      <td>8.843360</td>\n",
       "      <td>9.458440</td>\n",
       "      <td>8.457669</td>\n",
       "      <td>0.584499</td>\n",
       "      <td>4.968681</td>\n",
       "      <td>0.150288</td>\n",
       "      <td>...</td>\n",
       "      <td>2.182133</td>\n",
       "      <td>16.464721</td>\n",
       "      <td>137.771986</td>\n",
       "      <td>17.023031</td>\n",
       "      <td>10.119150</td>\n",
       "      <td>19.124855</td>\n",
       "      <td>0.123341</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fracDeV_g   deVRad_g  fracDeV_r  fracDeV_i   deVRad_r   deVRad_i  \\\n",
       "166    0.717212   2.940557   0.932047   1.000000   2.679756   2.766071   \n",
       "263    0.318288   7.201929   0.230645   0.428023   7.318970   6.074058   \n",
       "363    0.000000  15.355510   0.000000   0.000000  15.352960  15.351270   \n",
       "867    1.000000   5.803886   1.000000   1.000000   5.920506   5.754169   \n",
       "1011   0.000000  16.349600   0.000000   0.027798  15.349750  15.447710   \n",
       "1241   0.075056  24.907900   0.123616   0.117243  23.819670  23.815450   \n",
       "1262   1.000000   2.699520   1.000000   1.000000   2.901767   2.700522   \n",
       "1838   1.000000   2.664938   1.000000   1.000000   2.533591   2.491115   \n",
       "2271   0.091871  28.664020   0.050821   0.087072  23.826890  16.677420   \n",
       "2815   0.739246   4.405424   0.924393   1.000000   3.426312   3.323353   \n",
       "3556   0.297359   9.890799   0.389718   0.494101   8.415833   7.108675   \n",
       "3843   1.000000   8.958399   1.000000   1.000000   7.417218   6.876963   \n",
       "3849   0.000000  19.113420   0.000000   0.002170  16.001540  15.321790   \n",
       "3913   1.000000   3.866488   1.000000   1.000000   3.610112   3.591066   \n",
       "4425   0.000000   6.663974   0.000000   0.006250   6.373273   6.467491   \n",
       "5424   1.000000   4.244821   1.000000   1.000000   3.184031   2.735376   \n",
       "5545   0.000000  10.684710   0.000000   0.000000   8.411237   6.747405   \n",
       "5680   0.000000  18.508050   0.000000   0.000000  15.376740  15.353930   \n",
       "6229   0.000000  25.131450   0.000000   0.033541  23.454620  22.975660   \n",
       "6415   0.739806   7.107142   0.946085   1.000000   5.717090   5.375099   \n",
       "6470   1.000000   3.844259   1.000000   1.000000   3.868090   4.026062   \n",
       "6913   1.000000   3.911883   1.000000   0.971544   4.380702   4.792568   \n",
       "7137   0.000000  29.674070   0.000000   0.013757  29.671590  29.662300   \n",
       "7304   0.000000  16.556330   0.000000   0.000000  14.955840  15.348480   \n",
       "7431   0.461522   8.836692   0.724658   0.935505   5.898221   4.374843   \n",
       "8227   1.000000   7.856993   1.000000   1.000000   5.913469   5.300271   \n",
       "8260   0.064067   4.025874   0.356862   0.378442   3.687500   3.753201   \n",
       "8293   0.000000   7.942748   0.071109   0.121650   7.941637   8.109632   \n",
       "8340   0.000000  22.895770   0.000000   0.026620  23.813960  24.005180   \n",
       "9105   1.000000   3.087050   1.000000   1.000000   3.042571   2.958752   \n",
       "9438   1.000000   2.075045   1.000000   1.000000   1.559542   1.420120   \n",
       "9517   0.345865  12.375690   0.440541   0.554423   8.843360   9.458440   \n",
       "\n",
       "       deVRad_z  fracDeV_z   expRad_g  fracDeV_u ...   expFluxIvar_g_ext_cor  \\\n",
       "166    2.029882   1.000000   1.638167   0.000000 ...                6.333846   \n",
       "263    7.460191   0.350046   3.949730   1.000000 ...                4.543920   \n",
       "363   15.354580   0.013564   6.268068   0.000000 ...                1.607556   \n",
       "867    4.392871   1.000000   3.555619   1.000000 ...                0.503626   \n",
       "1011  17.452160   0.062294   6.593308   0.000000 ...                1.035562   \n",
       "1241  24.448400   0.197838   9.387571   0.000000 ...                0.317466   \n",
       "1262   2.568494   1.000000   1.589764   1.000000 ...                9.493425   \n",
       "1838   2.013384   1.000000   1.536320   1.000000 ...               13.676961   \n",
       "2271  22.877510   0.165537  10.702100   0.233626 ...                1.881342   \n",
       "2815   2.901511   1.000000   2.483638   0.000000 ...                6.799768   \n",
       "3556   6.747465   0.561581   4.200860   0.374594 ...                4.018406   \n",
       "3843   5.021695   1.000000   7.365738   1.000000 ...                0.323311   \n",
       "3849  13.280440   0.017233   8.138582   0.000000 ...                1.378119   \n",
       "3913   3.465287   1.000000   2.506204   0.825140 ...                0.948294   \n",
       "4425   6.890406   0.001572   2.738735   0.000000 ...                5.862085   \n",
       "5424   2.024805   1.000000   1.858546   0.896192 ...               10.468866   \n",
       "5545   6.038580   0.089139   3.388911   0.000000 ...                4.146163   \n",
       "5680  16.040930   0.000000   6.736097   0.000000 ...                2.430776   \n",
       "6229  24.344390   0.087027  10.556900   0.000000 ...                0.479044   \n",
       "6415   4.932603   1.000000   3.775403   0.086426 ...                1.809905   \n",
       "6470   3.188996   1.000000   1.895784   0.000000 ...               10.398773   \n",
       "6913   4.859884   0.814038   2.902982   1.000000 ...                0.255989   \n",
       "7137  29.664730   0.066381  11.216200   0.000000 ...                0.479685   \n",
       "7304  16.120200   0.000000   6.512290   0.000000 ...                3.303372   \n",
       "7431   4.005365   0.890636   4.077615   0.626466 ...                6.007095   \n",
       "8227   4.315418   1.000000   3.959027   1.000000 ...                2.304686   \n",
       "8260   4.094726   0.569483   2.348300   0.000000 ...                2.335356   \n",
       "8293   7.700018   0.128764   3.610440   0.000000 ...                2.485943   \n",
       "8340  23.815390   0.022968   8.214314   0.000000 ...                0.651777   \n",
       "9105   2.164743   1.000000   1.771134   1.000000 ...               11.146166   \n",
       "9438   1.503109   1.000000   1.286409   1.000000 ...               12.931437   \n",
       "9517   8.457669   0.584499   4.968681   0.150288 ...                2.182133   \n",
       "\n",
       "      deVMag_g_ext_cor  deVFlux_u_ext_cor  expMag_g_ext_cor  petroRad_g  \\\n",
       "166          17.586729          11.116547         17.971199    5.166017   \n",
       "263          17.336736          40.356270         17.675196    6.734111   \n",
       "363          16.434399         113.037695         16.859049    9.325549   \n",
       "867          15.088230         152.685630         15.557800   16.871460   \n",
       "1011         15.912493         180.938609         16.354383    9.674854   \n",
       "1241         15.018878         384.049449         15.541808   17.127680   \n",
       "1262         18.243580           7.374698         18.578990    5.566939   \n",
       "1838         18.623287          22.691908         18.992777    5.260222   \n",
       "2271         16.828328          49.865805         17.397598   15.538140   \n",
       "2815         17.807893          30.693164         18.190853    5.749047   \n",
       "3556         17.416280          28.898746         17.911541    8.347596   \n",
       "3843         14.804058         345.559621         15.141938   15.892600   \n",
       "3849         17.012063          37.097363         17.373323    9.946127   \n",
       "3913         15.568998         124.598971         15.885928    5.890203   \n",
       "4425         17.282339          29.209637         17.738539    4.971918   \n",
       "5424         18.052987           1.900080         18.651707   14.138590   \n",
       "5545         17.614087          41.523225         18.338087    5.823877   \n",
       "5680         16.974855          82.974073         17.499105    8.966158   \n",
       "6229         15.547171         122.811229         15.868681   14.536830   \n",
       "6415         16.316914          85.246305         16.747544    8.420620   \n",
       "6470         17.841480           2.891788         18.318500    7.851704   \n",
       "6913         14.592833         626.918215         14.966333    7.883327   \n",
       "7137         15.626874         176.617171         16.109924   15.275850   \n",
       "7304         17.277314          48.021836         17.699154    8.538476   \n",
       "7431         18.260596          11.297445         18.687086    6.693216   \n",
       "8227         16.475748          81.481517         16.919258    9.245286   \n",
       "8260         16.540108         113.097889         16.803058    4.857699   \n",
       "8293         16.693414          64.789528         17.099524    6.101304   \n",
       "8340         15.563293         228.696281         16.093653   12.176750   \n",
       "9105         18.595325           9.174294         19.001715    8.277800   \n",
       "9438         18.403246          10.958748         18.721816    4.268654   \n",
       "9517         16.464721         137.771986         17.023031   10.119150   \n",
       "\n",
       "      fiber2Mag_r_ext_cor         z  actual  pred  cor  \n",
       "166             18.725587  0.102298       3     3    1  \n",
       "263             19.426438  0.102254       3     1    0  \n",
       "363             19.698901  0.057433       3     3    1  \n",
       "867             16.896179  0.029821       3     3    1  \n",
       "1011            19.166269  0.047730       3     3    1  \n",
       "1241            19.281560  0.024614       3     3    1  \n",
       "1262            19.068358  0.178933       3     3    1  \n",
       "1838            19.184969  0.239132       3     3    1  \n",
       "2271            20.155508  0.035076       3     3    1  \n",
       "2815            19.231804  0.118640       3     3    1  \n",
       "3556            19.625023  0.103373       3     3    1  \n",
       "3843            16.935662  0.027521       3     3    1  \n",
       "3849            19.530310  0.052914       3     3    1  \n",
       "3913            17.032371  0.047818       3     3    1  \n",
       "4425            19.691489  0.088047       3     0    0  \n",
       "5424            19.112847  0.102771       3     3    1  \n",
       "5545            20.253351  0.204254       3     3    1  \n",
       "5680            20.038627  0.063472       3     3    1  \n",
       "6229            18.805255  0.033542       3     3    1  \n",
       "6415            18.331618  0.073760       3     3    1  \n",
       "6470            18.925867  0.177715       3     1    0  \n",
       "6913            16.490880  0.029443       3     3    1  \n",
       "7137            19.597371  0.017060       3     3    1  \n",
       "7304            20.075040  0.072409       3     3    1  \n",
       "7431            19.829158  0.155356       3     0    0  \n",
       "8227            18.174258  0.066802       3     2    0  \n",
       "8260            18.612504  0.074825       3     3    1  \n",
       "8293            19.265089  0.075858       3     0    0  \n",
       "8340            19.495780  0.034164       3     2    0  \n",
       "9105            19.285158  0.219191       3     3    1  \n",
       "9438            19.286290  0.144029       3     3    1  \n",
       "9517            19.124855  0.123341       3     3    1  \n",
       "\n",
       "[32 rows x 53 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unfortunately, overall accuracy is down, though we are able to pick up many of the merger cases\n",
    "# This may or may not be a worthwhile tradeoff, depending on what our overall goal is\n",
    "print len(res_df)\n",
    "print res_df.actual.value_counts()\n",
    "res_df[res_df['actual']==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rudygilmore/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:11: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
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    "# We can compare the error matrix here to the unsampled set, definitely improvement in the \n",
    "# last two classes\n",
    "# Classes:\n",
    "# 0 - Ellipticals\n",
    "# 1 - Spirals (face-on)\n",
    "# 2 - Spirals (edge-on)\n",
    "# 3 - Mergers\n",
    "\n",
    "imax = int(max(res_df.actual))\n",
    "rep = np.zeros((imax+1,imax+1),dtype=np.int32)\n",
    "for i in range(len(res_df)):\n",
    "    rep[res_df.actual[i]][res_df.pred[i]]+=1\n",
    "pd.DataFrame(rep) \n",
    "\n",
    "# rows are actual class, columns are predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The resampled model is less accurate overall, \n",
    "# but it does correctly classify most of the merger cases in the test set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Regression with a PCA transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "[ 0.41075225  0.25792565  0.10767951  0.05235836  0.03059819]\n",
      "(65787, 5)\n",
      "3219.0 3027.0 9466\n",
      "Overall correct fraction =  0.9466\n"
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       "          0         1         2         3         4  actual  pred  cor\n",
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   "source": [
    "# To avoid having to select features by hand, and avoid problems with collinearity and \n",
    "# overfitting, it makes sense to run the feature set through a PCA transform, and just \n",
    "# model based on a few variables.\n",
    "\n",
    "# Let's see if this improves performance over our previous method of selecting variables \n",
    "# based on correlation\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=5)\n",
    "\n",
    "\n",
    "#rescale and center input params\n",
    "mod_df_rescale = mod_df.copy()\n",
    "for col in input_set:\n",
    "    s = np.std(mod_df[col])\n",
    "    m = np.mean(mod_df[col])\n",
    "    mod_df_rescale[col] = mod_df[col].apply(lambda x:(x-m)/s)\n",
    "    \n",
    "# run PCA on a subset of our data - doing the full set gets expensive\n",
    "pca.fit(mod_df_rescale[input_set][0:10000])\n",
    "print(pca.explained_variance_ratio_) \n",
    "pca_df = pca.transform(mod_df_rescale[input_set]) #apply transform to full set\n",
    "print pca_df.shape\n",
    "\n",
    "\n",
    "# and rerun our logistic regression model, using the new PCA components as the training set\n",
    "\n",
    "test_size = 10000\n",
    "\n",
    "\n",
    "#print mod_df[ycols+['gal_class']]\n",
    "lm = LogisticRegression(multi_class='multinomial',solver='lbfgs')\n",
    "X = np.array(pca_df[test_size:])\n",
    "y = np.array(mod_df['gal_class'])[test_size:]\n",
    "X_pred = np.array(pca_df[:test_size])\n",
    "y_pred = np.array(mod_df['gal_class'])[:test_size]\n",
    "\n",
    "\n",
    "lm.fit(X,y)\n",
    "res_df = pd.DataFrame(pca_df[0:test_size-1])\n",
    "\n",
    "res_df['actual'] = pd.Series(y_pred)\n",
    "res_df['pred'] = pd.Series(lm.predict(pca_df[0:test_size]))\n",
    "res_df['cor'] = res_df.apply(lambda row:int(row['actual']==row['pred']),axis=1)\n",
    "print sum(res_df['actual']),sum(res_df['pred']),sum(res_df['cor'])\n",
    "print 'Overall correct fraction = ',sum(res_df['cor'])/float(test_size)\n",
    "res_df.head()\n",
    "\n",
    "# I've printed out the explained variance ratios below.  The rapid falloff in the \n",
    "# numbers means that the first few variables contain most of the model's explanatory power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
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       "        -0.09155798,  0.1604988 , -0.04837489,  0.25589351,  0.11217436]])"
      ]
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     "execution_count": 67,
     "metadata": {},
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    "pca.components_[:2]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9999\n",
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     "name": "stderr",
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      "/Users/rudygilmore/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:16: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
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   "source": [
    "# Let's look at the usual confusion matrix\n",
    "\n",
    "print len(res_df)\n",
    "print res_df.actual.value_counts()\n",
    "\n",
    "# 0 - Ellipticals\n",
    "# 1 - Spirals (face-on)\n",
    "# 2 - Spirals (edge-on)\n",
    "# 3 - Mergers\n",
    "\n",
    "imax = int(max(res_df.actual))\n",
    "rep = np.zeros((imax+1,imax+1),dtype=np.int32)\n",
    "for i in range(len(res_df)):\n",
    "    rep[res_df.actual[i]][res_df.pred[i]]+=1\n",
    "pd.DataFrame(rep) \n",
    "\n",
    "# rows are actual class, columns are predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Performance here is impressive compared to previous cases, with a much smaller features subset.\n",
    "# ~95% accuracy with first 5 PCA features\n",
    "\n",
    "# Unfortunately, we are back to misclassifying our merger cases, so the next step would be to try  \n",
    "# to boost these cases in the training set with resampling, if recovering those is a priority."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
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   "outputs": [
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mn0EpV5pJeW6fO992d77uhKSFmsRxsu6a/OtxC4TnGSRCfVZz2qK8BRQKbm+1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\ncTcia7dRXpBlCUo2a0VE7Xb3+VKUYDkWXy3vq8Fyc5DWdjh5ect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ZuyvJ3zeGbcClVFT8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DvwbxRnjZa5H9Az4sfo+ls+xuXBsg1o8HceMI9x46qAfbS2fjgl/x0DSMQ5ISHhMKMCEaU4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\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FziIiIiIieVDgLCIiIiKSBwXOIiIiIiJ5UOAsIiIiIpIHBc4iIiIiInlQ4CwiIiIikgcF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0b0d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# positions of galaxy targets on the sky (not essential to this project, but interesting)\n",
    "plt.scatter(phot_df.ra,phot_df.dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}