plot_Fig8_compl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from astropy import table\n",
    "from astropy.table import Table\n",
    "from astropy.io import ascii\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "from scipy import stats\n",
    "\n",
    "SAGA_DIR = os.environ['SAGA_DIR']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from palettable.colorbrewer.qualitative import Dark2_8\n",
    "if 'plt' in locals() and hasattr(plt, 'rcParams'):\n",
    "    plt.rcParams['lines.linewidth'] = 2.0\n",
    "    plt.rcParams['font.size'] = 15.0\n",
    "    plt.rcParams['font.family'] = 'serif'\n",
    "    plt.rcParams['axes.prop_cycle'] = plt.cycler(color=Dark2_8.mpl_colors)\n",
    "    plt.rcParams['legend.fontsize'] = 'medium'\n",
    "    plt.rcParams['legend.frameon'] = False\n",
    "    plt.rcParams['figure.dpi'] = 100\n",
    "    plt.rcParams['figure.figsize'] = 7, 6\n",
    "    plt.rcParams['xtick.major.size'] = 6\n",
    "    plt.rcParams['xtick.minor.size'] = 4\n",
    "    plt.rcParams['ytick.major.size'] = 6\n",
    "    plt.rcParams['ytick.minor.size'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# NAMED HOSTS\n",
    "complete_hosts =[147100,166313,165536,61945,132339, 149781,33446]#150887]\n",
    "# 150887 has bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n=0\n",
    "for host in complete_hosts:\n",
    "\n",
    "    # READ BASE CATALOG\n",
    "    basefile  = os.path.join(SAGA_DIR, 'base_catalogs', 'base_sql_nsa{0}.fits.gz'.format(host))\n",
    "    base = Table.read(basefile) \n",
    "    \n",
    "    # FIND GOOD OBJECTS IN BASE CATALOG\n",
    "    m_rmv  = base['REMOVE'] == -1\n",
    "    m_rvir = base['RHOST_KPC'] <= 300\n",
    "    m_fib  = base['FIBERMAG_R'] <= 23\n",
    "    m_gal  = base['PHOT_SG'] == 'GALAXY'\n",
    "\n",
    "    base = base[m_rmv & m_rvir & m_fib & m_gal]\n",
    "        \n",
    "    if n == 0:\n",
    "        alldata = base\n",
    "    else:\n",
    "        alldata = table.vstack([alldata,base])\n",
    "    n=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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USpFEEFsbHh5m0zT5gJgZ5om41bGUL5LtptaB94zzE/slmF8iuya3PP5zDqB3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06dGvQG5TDm6MJZ29hEJBm+w5jhvOjRhrPesYGjR49GxKTnMObowlnb2ESk6AmwZsyuQY0cYQgh\n7hJCfCyEqDD4bq4Q4oAQolUIcVAIcUQI4RmOedrFkSNHsGjRItx8883YvHkzrr/+ehw5csTWtfv2\n7cOyZcv6HX/ssceQmJiIyspKfO9738PKlSvx+eefhxzP7/dj3bp1qKysNPz+D3/4A2bPnt3v+Bdf\nfIGbbrpJ/3vjxo14+OGHcf/992P37t22nsXB8GHhwoU4dOiQHrE70vHKK69g0aJF2Lp1q37sqaee\nwrJly3Dw4MGQ1//lL3/BypUrkZmZicrKStx1113YtWuXrXsfPXoUV1xxBc6dO2f4fVVVFdasWdPv\neF1dHRobGwEA7733Hu666y7ce++9ePzxx/Hss8/aureD2EDU6MksKiyaHwAXAngBwI8B9AKoMDjn\nTQDPoE/LvxJAD4AckzGNIuBsYaAFIYxw55130i9/+UsiInr55ZdpxYoVtq9dtmyZ4fEJEybo/y8r\nK6M9e/bYGu/gwYO0devWsO8nIycnh4gCazRr1izT88JZbweRw1BFoxr9nuHSlM+3k3JzK8jn2zko\nmtq0aRNdeumldOjQIf2Y1XutYu/evVSiRav7/X6aMmWK7fncdddd9Pbbbxt+d/bsWbrrrrssr29o\naKCnnnqKiIhefPFFuvPOOw3Pc+gnNjCU0d0YAVHZ4wFsIaJmIcR31S+FEBcDmA3gR9oDgYjahBB/\nBXADgN9HcjI/LivrC6A5eBC7hYhIAM1HH32ESZMmAQAaGxvR3NyMCRMmYMaMGbj77rtBRFi7di0m\nTZqElJQU03FIMnF9/PHHmDx5MgDg3//93/Gb3/wGf/d3f4cvfelLKCsrwwcffIANGzZg4cKF+Nvf\n/oaJEycCALZu3Yr4+Hi8/fbbuOOOO5CdnQ0iwqOPPoqXX34ZqampePjhh/GrX/0KlZWVOHr0KJ56\n6im89dZbqKysRHp6Oj755BNUVlbi61//OpYuXTro9YlV0AgIJGHEajRqWdmP8dhj16G3NxUHD56B\nELvxk59sHNBY48ePR319PW677Ta0tbVhwoQJOk10dnairKwMs2bNwltvvYUHH3wQs2bNMh3rs88+\nwwUXXKDnet9///245JJL8O677+Kee+7BggULcODAARw4cACLFi3CO++8AwD48MMPsXnzZsydOxet\nra14+umnAQDvvPMOysrKcOTIEezevRsZGRlYs2YNUlJSUFJSggMHDkAIgQsuuAAvvfQSjh8/jsrK\nSnz3u9/FhRdeOKD1GGlw6MkGzDj2cH0A+GGsMb+AAAP+kvb3t7Rz7zcZx0g6sQWrbkHh4s4776Tb\nb7+dtm7dSpmZmXrVmpdeekmXwnK18X/1q1/RPffcQ0REb731lqkGm5CQQFu3bqVrrrmGtm3bRkQB\nyW727Nl0/vx5IiLKy8ujc+fO0b333ks/+9nPiIhoz549umaxYMEC+uijj+jjjz+m//zP/yQioqVL\nl9LZs2fJ7/fTtGnT9PvJ85D/n5KSYvrc4ax3rCOWyviFQqxqzLm5FTJJUW5uxYDnwu/wI488Qjff\nfHPQsZ/+9Kf08MMPExFRc3MzFRQU9Lt+7969lJmZSQ8++CClp6fTsWPHiIjo+eefp3Xr1hER0blz\n5yg3N5d6e3tp5syZukZ99dVX09tvv02/+tWvqKSkhHp7e+nIkSNEFNCYr776aiIi+uUvf6lr5bKl\nau/evbRv3z79uJmGPZroR4VDTwHAQmOOKR9zCHwTwFkA7wshXkfArP08gH+J9I0mXHYZzrjdAIAz\nbjcmpqYOarwVK1agoqICL7zwAlatWgUAuOCCC/DAAw9g+/btOHv2LADgjTfe0DVlKyk/MTERFRUV\n2LNnD55++mkQET7++GN89tln2L17N7Zv346pU6fiww8/NB1z+/btuOWWW7B69Wq4XIHXQAiBmTNn\nQgiBhISEkM9FYyQ4ZST5aYUQWLRoUcxpIpddNgFudyCn2u0+g9TUiYMes6ysDJ999hn27t2rHztz\n5oz+nqekpOCMSR53RkYGfvjDH+I73/kOfvOb3+jXvv/++9ixYwcaGhowadIkfPzxx0hKStI16pkz\nZwIAbrjhBlx88cXIysoK8hNznMYll1yCzz77bNDPOBrh0FNoxIop2xIisCq/AXABgOlE9N9CiH9E\ngFl3Rfp+oQpCDBQTJ07E559/jp6eHqxfvx7PPPMMZs+ejeeeew4A8I//+I944YUXAABnz541ZXx8\nfOrUqViyZAmefvppeDweXHLJJdi4cSPcbjdefvllzJ49G5deein+/Oc/IzMzE2+//bY+RmJiIn73\nu9/hiSeewO7du7F79+6g+9n5P29WZ86cQeoghZeBgKJkEmPidDBwPPpoKYTYjTNn9iM1dSIeeeTe\nAY8lv4N79+7FkiVLcM011wAAUlNT8ec//xkA8Kc//Qnz5s2zHOPuu+/GokWLsHHjRqSlpeHdd9/F\npk2bAADPPPMMJk2ahM7OTnR3d+uuHwA4deoU7rvvPlRUVGDZsmU4evQoLr74Ylt0w3C73fD7/fjk\nk0/wxRdfYOrUqQNek0jAoafYwYhgzAgw4G8A+DoR/TcAENEbQohMAE8CWB3Jm8XFxUXEp9zc3IyT\nJ0+iq6sLf/zjH3Hu3DlUVlYiLi4Oq1evxsaNG/G1r30N7733Hvbv34+CggI8++yzuP/++zF58mSc\nO3cOr7zyCq6++mp9zMcffxw9PT3413/9V6xfvx6lpaVYsWIFJk6ciJ/85CcoLS3FtGnT8MUXX2DZ\nsmUoLy9HUVERzpw5gw8//BBvvvkmzp49i8cffxytra34j//4DxQWFuLQoUM4d+4cDhw4gC9/+cv4\n9NNPsX//fowbNw7nzp3Dc889h08++QTnzp1DbW0t7rjjDnzjG9/A/fffj5kzZw4LY45Vf6qD/oiL\nixuwT1nGq6++ildffRX19fX4zne+g0suuQSPP/44amtrAQDr1q1DWVkZtm3bhj//+c/Yvn170PUf\nfPABnnvuObz//vs4fPgwFi9eDK/Xi7vvvhvbt2/Ha6+9hsrKSpw/fx5Lly6Fy+XCjh07cNttt2He\nvHno7u7G3r17kZOTg+9973tITk7GrFmzkJaWhl27duHkyZN44403sH//fpw8eRKvv/466urqcPbs\nWRw/fhzPPfcchBBYsGABUlNT8c477+DBBx/Exo2DX5vBwqGn2EHM5TELIfwAHiKiSulYOYCHEdCW\n35eO/xBAGQJ+5x5lHNqyZYv+99atW8eM6TUWEI08zGhJ+GMJBw8eNEw72rp1K2R64mMOTQ0NhiOP\n2aGnoYEVTdFIKTBiwpi9ABoBZBJRm3S8DsC3iOjLBuOQ/GxOwn504az36IJTYCS6cNZ29GPEFBix\nwL8BeBvAPwsh4gFACDEfwEoADcM5MQcOHDhw4CCSiBnGLIR4WgjxMgJtz+4UQvxeCHEDABDRZwBy\nAJwHcFgI8SqAWgA/BFA6XHN2MHjYqdbk3C927+cg9jDa37nRfj8ghhgzEXmIaBkRuYloNhHlENHz\n0vdniehWIkonoq8R0SIiqiGiiEdlOxgaEBGOHj0aZKIb7UQ2nPczWm8Howdmv+9Yesejfb9o0VTM\nMOahBufnOp/ofDjfUwZHfR47dmwY3oCxh6Feb4emoks/0f59HfRHtNZ8zDBmzgse7GfLli1DVvWs\no6MD48aNQ0dHR1TuN5TPx0VTZIykwgKjAUO93g5NDd3zGdFPtH9fB/0RrTUfKXnMYwKjndCEcAoL\nRBPOeo9umnJ+3+gjWmvuMOYYgkNoDhxEFg5NORiJiLk85khBCDE6H8yBAwcOHIwK0AjPYx4QoulH\niiX/1Ej6DMfz7fT5cNrtBgE47XZjV0lJTD+f3+9HR0cH/H7/sP1Oo4WeIvWbxPLHeb7QH5WmcnMr\nEMjUDXxycyuG/DmsMKoZswMHRih99FH8tqgIW3Jz8duioog1KRkqONG3DhxEFipNDUX3s8HA8TE7\nGHOIVJOSaGE0BzA5cDAcUGkqkt3PIgGHMTtwEONwApgcOIgsVJqKVPezSMExZTtw4MCBAwcxBIcx\nxziWLl063FMYUozV5+vp6cGukhJsWb4cu0pK0NPTY3jeQEHklOM0w1h950YLhuv5oklTozpdarQ+\nm4ORj10lJbjusceQ2tuLM243fltUFFG/99GjRyPa9F4Ipw2hg7GNIaKp2EqXEkLcJYT4WAhRYePc\nNCHEY0KIV4UQB4UQ7UKI7UKICdGYq4ORhaGQbCM95t9efx2pvb0AgNTeXnx65kxExmU4AWMOIoWR\nQE/RQDRpKuqMWQhxoRDiBQCXA7jI5mUPAZgEYBkRLQXwDQArAPx8KOboYGRjKNKLIj3mhMsuwxm3\nGwBwxu3GxNTUiIzL4OAWIQwFcgcObGMk0FM0EE2airopWwgxDUAyETULIfwAHiKiyhDX/BDA00R0\nVDpWDuBhAFOJ6EODaxxT9hgFEeHYsWNYuHBhxIgo0mP29PRg93334dMzZzAxNRX3PvII4uKilyTR\n09ODH5eV4W+vv44Jl12G0kcftby/Y8oeuxgJ9DQSYWXKHlYfs13GbHKtD8BuADOJ6F2D7x3G7MCB\nCcL1cTuM2YGDyCImfcwRwNcB/MGIKTtw4KB/5Hd3dzc6OjrQ0dGBT8+cGVIftwMHox3sJ/f7/RH3\nl4/IAiNCiAwAywFkDvdcHDiIRfT09OCOr34VU0+exFQA+S+/jB989BF2//rXAIDvrliBM263rjFH\n2sftwMFoB/vJn3zySaxZswY33liCjz8eh8sum4BHHy0dlGtqxDFmIcRMAI0Avk1E/2l17kMPPdTv\n2NKlS0d9np8DBz8uK8P3Tp7EfABnAPzW70fiRx9hz549uPTSS7FgwQL8ZMoU7Jd83ABw8OBBHDx4\n0HBMh54cOOjD5ZdfjieffBIejwfPPnsUv/jFavT2puHgwTMQYrdeScyKpkwxnF1CAPgBVIRx/iwA\npwDk2jiXHDiIJXR3d9NOn48qcnNpp89H3d3dgx7T7/dTR0cH+f3+oOMVublEgP65D6BNXi+NGzeO\nOjo6wr6PQ08OxgLM6MkIHR0dOj3l5lbI5Ea5uRUhr9doypB/jRgfsxBiNoB/A1BMRC9px3KFEF8d\n3pk5cGAPPy4rw3WPPYatL72E6x57DLvvu2/QYxqlnRARvpg0Cae1dKyTAD5YsADVdXVObrMDBxYw\nS+Mig7xrOa850t2pYs6ULYRIBNAGoImICrVj/wDgRQCPAvhMCHEFAAHAA+AQgI5hmq4DB7ahFhXZ\nH4GAK6OiB8eOHcP/evZZiFWrkPTxx5iYmop9WjqW0wzDgQNzmBURYYYtV/2SG2FEujvVsDBmIcTT\nACYj0JX6TiHEUgCPENHzCDDcJO3D+CmAZAA/Mhju0NDO1oGDyGDCZZfhzMGDEQ24Muo8NW/ePJTc\neCMSP/oIEwxypMnJIXXgIAg9PT0oK/sxXn/9b1rw1vwgmrGq+kVEOHnyJHbvvi9i9OTUynbgIEow\nKyrCxT4+PXMG5ydPRk1dHeLj422Pe/78eazJyID73XfRm5yM+dnZuPHxx01zlNWav3YYtZPH7GCk\nIRwBtKRkFx577Dr09qbC7T6DoqLf2m4DORB6AqzzmIc1+GsoP3CCVRyMEOz0+ei0200E0DEtSIso\nOBDFLCilu7ubll9yCZ3Sok5OAbRUG4s/m3NygsZpb2+n9vZ2fSw5iMUMDj05GGkweq+Zjnp7e4Po\nySx4y4ju1GO9vb3U2NhIvb29pvc1AkZD8JcDB6MVsu/5cgCJH30EIDgQxSwo5cdlZZj0X/+FNO3v\nNABf7u3FSe3v0243PoiLw+LFi4PG+eMf/6iP4TS8cDAasXDhQrz22muysqa///v37w+iJzV467LL\nJuDo0aO6NizT3dGjR3HVVVfh6NFAhejjx49jzZo12L9/P4goIvQUc8FfDhyECxqBPlN5zrLv+TiA\nzsmTAfRnmIcOHcLll1+Oo0eP6s/6t9dfhwBwGgGmfBrABQAqk5OReumlOD9pEolxe9wAACAASURB\nVPY+8wweeughXH755RBCYM+ePbjrrrvw+717MbWnRzefj5S1czC0GOn0xHMWQkAIEWRmZpq6/PLL\n8U//9E9YuHAhiAh33HE1gP+N118PBG/dfvvVyM7OxmuvvRZEg0SE06dPo6trKm6//V/gdn+OSZPm\n4mtf+w7uvPNOXHrppZEJsDRTpUf6B47pbczArukoliDPubu7m3aVlNDmnBza5PVSV1eXreuIAmbw\nNiHoVoBuBigPoDYhaFdJCRERtbe3U0JCAiUkJFC510sVubm0w+ejNcuX0zHNbnccoBVz51rmVTv0\nNHYw0ulJhp285HBM3h0dHeR2p5AQJwjYScApAohcrlPkcqVQe3t7REzZw85Ah+rjbCRjB+EUBYgV\nDGTORv5hZuo/yMmhlXPnUrbmo2Ym29vbS/X19bQ2L49Oab7nE0JQXmIi7QSoWypAwszcCA49jR2M\nFXri61SakqEy2d7eXkpLu1vzRQf7pdPTS6mhoYG6u7uDfM5mcBizAwcjFPLG0d7eHrRJqJuRUbBY\ne3s7JSYm0hIhSN5FKrRAsV0Andb+rcjNNZ2HQ08ORgvkYC2Z8YYK9PL7/dTY2Ehudwq5XCf7acxZ\nWV4CQF6vl5KSkhyN2fDBnI3EwShAR0cHJSYmUmJiom4msxNNzd+1t7dTU1MTXZucTPcBtFMzXe/S\nGPSNAK0G6AGAthcXm87DoScHowWNjY0EgBobG4MYbygTdEdHByUlJVFtbS15vZvoyiu/SwsW3EpX\nXPFdAuYRsJjmzs0lALRt27aQ2rsVY3aCvxw4iGEsXLgQhw8fBgAsWrQoKBjHKvqTv5s3bx48qal4\n+N13sQDACQBbAezX/g8A8wDkAvgdOXnKDkY/vF6v/q9coEemmZKSXVKxkUCnKKZFIsI999yDJ598\nEl6vF7fc8iA6OupA9CLefPMvmD49AdOmTQMRDTh4zmHMDhzEOIQQhhGyRlW/iPqiUxcuXIgHbrkF\ns998Ewu07xcAmABgHYCpAH4B4A0AvwXw5iuvDPWjOHAwrCAiHD9+HDfffLMpPcnFRg4ePI2PPvo+\nfvazh9Hb24snnvgdfvGLYxg3biluu+2f8eabb+LAgcMgmgLgOgCpeOedE1i9+puIi4vDrbfeOqB5\nOnnMDhzEEIiCi+Wb5S8bXdfR0YGf/exnWLx4MX7+85/j6NGjOPL00/gKoOc1nwAwRfv/DxGQzFMB\nvAfgwrS0/gM7cDCCMRB6ev31v6G3N1Aut7c3Dfv3H8IttzyAadO+jX/5l2vw4YeN+Otfd4HoZmzZ\nsgcB6vkLApQEBMTfZPzDP/zDgOftMGYHDqIA3iD8fn+/LjU9PT3YVVKCLcuX44FbbtGLgQD9zdXy\nRiP//9ixY1i8eDHWrFmDiooKrFmzBgBwlceD/wLwcwBbAPwMwKtxcfg7BPo0AwFm/f8BmHf99dFZ\nDAcOBgkzOlChMmIj94861qRJX8DlOq19exzTp0/EgQOr8eGHCwHd9pQKIAFE03DPPddg/vz30ecc\nOgHgXbzxxhuDe8DR+IETrOIghsCBJY2Njfq/fr+furu76Zb58/XArGMuF23yek0DRzgYbNu2bdTW\n1hYUUcqR2z09PdTY2Eg9PT3U0tJCN40fT3JE9s1amtQuLTr7awDNAKipqcl0/g49OYglyIFaMm2p\ndGM3j1mmqaSkJPJ6N1F6eil5vZto2bIfaKSzk4DT2v9PE/AgATMIAK1atUr7f7Z+rL6+3vIZ4ERl\nO3AwdOju7qadPh9V5ObSTp/PsFCHXLCgsbFRT6fY6fPRCY1hctrS5pwc03v5/X7atm2bTvh1dXW0\nvaiISjMyaId2b1UIyB0/Puget2qR2QTQCe3vo0LQTp/P9L4OPTmIJRilMakpSnZzm2Waqqmpoba2\ntqC8Zq+3nIBjBHRrzDifxo1bRsB0+va3v01xcXGUkJBAxcXFVFlZSQCoqKjIyWM2fLBRsJHEQqJ/\nLMwh1iE3oTjtdlsW6iAKzqPcnJNDsjZbphUIscpR5uvb2tooxe2mE1qO8kmXi3b6fPq5XV1dVFxc\nTHcuW0YruYgIQC0Afe1LX6Iy7e/jNgQCh55GzxxGG2RrkVypS01/CkVT27Zto8TERGpoaKDExERq\na2ujjo4O6uzsJK93E+XkbKb8/HX00EMPkcvlovXr11NlZSXV1tZSfX09JSYmUnV1NSUkJFBra2vI\n39lhzCMUsVAaLxbmMJSwo+2GQkVubhBzlbs5GUFe03KvVy+NeQKglXPnUldXV1AeMhcKkf9mTeHu\ntLSge5dmZOgbVXFxMQEgl8tFqzIzKQugHIAKMjJobWoqdWvm8wqAvgXQ/QUFps/o0NPomcNIQyhh\nxsisLVun5K5PSUlJQfnLMk0xU29ra6OEhASqqanRtXC/3081NTUEgOLi4sjtdtOGDRsIAAEzKS2t\nkPLy1lJ8fDw1NDT0KwZkBIcxj1DEgnQdC3MYSoSr7doZY5PXa0mU8pp2dnZSYX4+laan02y3m1pa\nWoLOYQJvaGigtrY2amhoCBp7h89HJ10uYo25MD+fWltbKSEhgeLj4ykzM5MAUFVVFblcLn1DmSUE\nPaCZtrle9vaiItNndOhp9MxhpCGUMKOata0q5ckxHr29vdTe3t6PpuTzampqqLW1VdeiXS4X3XTT\nTQTMoJSU7xCQppm5iYDjBGSRx1NOzc3NpmU+GTHJmAHcBeBjABU2z/8ygD0A2gA0IxBgOsXifOtf\n24ED6q/tWpWlNANr3aUZGbTT59M1XqNAFLNeyKomLP+/oaGB4uPjqaamRje1sR/s/PnztC4/nwrT\n0uj6+fN1P1lCQgJVV1dTYmIieTweampqIrfbTUIIAkDLli2jgosu6qdtm8GhJwfRhJ1e5HxeKJqS\nzdvMdBMSEvRgr8TERN23zGbturo62rBhA7ndboqPj6eEhASqr6/XBNxZWhMLIqAsqF42UEEu10ly\nu2ePrJKcAC4E8AKAHwPoDYMxvwTg59Lf/y+AdgDC5PzQv76DMY/BasyqZmvV3UYtr9ne3t4v0ISo\nv4bQ1tamB5g0NjbqHaOYWQMgt9tNACgzM5M6Ozt1k1xcXBwBoOvnzaPFWvQ1tH8zgaAOUzuckpwO\nhhmh6Ek+h/9NSEgglyuF0tNLKT9/HZ0/f96ydG1vby/5fD4CQA0NDTqjjo+Pp/b2dr1kJ7uBCgoK\n6PDhw1oQWDYBmVogGGmR2selSO1dBBBNm+ahnp4ey2eNNcY8DUCm9n+/HcaMQMXAXgBp0rHp2vWr\nTK4J8Qo4cNDXnakiN5d2lZSE7WM20niNvldTmuR2jMyYVXNcW1sbtbW1UX19PSUkJFBDQ4Mu0bNW\nvDYvj26fPZtWZWTomwnX6W1ra6OqqiqaKTHgYwClaf92A/Q9BGplPwinVraD4UcoepLP4e+93nJy\nuU5pzPIBmjhxJfl8O3TLFZusWQhmLbmyspKKi3dQTs5m3T9cX19Pra2ttHXrVhJCaGlQoKwsL7nd\npzQGfEyLziYC2jRzdg4BD2hzOEbADGpsbLR81phizEE3t8+YdwH41OD4OwD2mFwT8iVw4GCwCOUz\nlJmsrBmziZoZrhzcxX4vluJl83VrayvV1NRQXFwcLZk+PYjhZgG0ODmZNm/erEeHxsXF0e2zZ5Ns\nb7s22PZGFTbM+A49OYgG7PjgVZrKydksaa+BPGOX6xR5vZt0mqqtrSUhZpEQ2ZSXt44SEhIoL2+t\n1iWKyOU6SULM1IO7srI8BCymadOuoptuuomuuKJEMVnnELCYOGcZAE2efCUBVxGQRpMnr6Ciou12\nepyPaMb8awBvGBxvAvCqyTWWL4ADB5GCupkYRYOyCVvNs+QgE2bI9fX1FB8fT3FxcVRVVUV1dXXU\n2tpK9fX1FBcXp5umMzIyaLEBg23TNOLFAOXMmUMul4s8mZmGGjOhr/XjCSeP2UEMQaap7u5uKi7e\nQWlpd1Nx8Q49V59pyustJ7f7NKn9kXNyNlNjYyO1traSELP0IC23+xTl56/TzNIyCS0mjrJmhs3a\nb0ArZo35FMXHZ2jH+5hzbm4uuVx993G5TlFJyS7TZxwNjPlFACcNjr8CoMPkmoG8DzEPJ6ozPERy\nvVSGa+YPk9vK8Xmq1iwXHJFTO+Lj4yk+Pp6EEOR2u/WKRImJiTpTdrlcup+YGSwXJ+G2jsyEZwpB\nK1asoBkAZWvMegZA5QDdBNAKjYlnArRx1SpTCX+00hORQ1PhIJJrpQZuqb7jQAWuckpOXqX7cV2u\nk1RSsku/tqmpiQoKNlJy8ioaPz6fgBMS8y2kpKQkqq+v78eE09IKNYbK/uFjGvNdTOPH51KfD5kZ\n9lUU8B9XaP/erDPgQCBY4NqkpBv6CQdmsGLMI6W71GcAZhgcTwTwudlFDz30UL9jS5cuxdKlSyM1\nr6iDa78eOnSoX2ehWAJRX5ejgbY+iwQiuV779+/Xu8Xccsst+tivvfYaDh06hMsvvxxHjx6Fx+MB\n0NdeTgiBr371q2hvb8dVV12Fw4cPw+VyITs7G6+++ireeOMN+P1+rF27Fnv37gURYfXq1ejt7cXK\nlSuxefNm7NmzB88//zwOHDgAIQRy5szB+T/9CbcBuPTCC/GPn3yCLQDWArhfm+/lAKYR4de//jUA\n4IaiIjzxxBO4bvJkTPnoI9yJQMXfkwjUyv7GM8+gxOPBVxZwPeBgjEZ6AkYGTY1GeuL67gBw5MgR\nANDHXrhwIW68sQQHDqyG3/80uEa13z8PZ84c0GnK692EAwduB3A5XK7jEOI2pKZmYdo04JVXavHE\nE0/gN785jgCreBBANYDTSE39Mi67LAO/+MU3AUzHxImETz/dB+AKfP75CQCbEWjzchzjx/fi888B\nIA8BiqkAcB7A7wCkgWg+gHwAj+D8+QcRqJW9AMBxxMV9gIMHD+LgwYPhLY4Zx47GB4P3MZ/DGPMx\njxTpPlYKKQx2vYwqbskaM+cWc31qtSygPA5HUNfU1OiackCaD5TXlKsXtbW1UXV1tR5tnTN3Li0G\naNns2UGa8jFNc07TfMcrFI356wkJNF07Jxug69LSaDpApYoZ/Ach/MyjlZ6IRgZNjRZ6ksfgoCxZ\nY5YtS+nppf18x273ad087Pf7afbs2/tpwpze1NDQoJm5T+mabaCU5gwqLCzUzNuLKTmZNWKZJG4l\nYCklJmYRUErAwwSsIOBbulYeMG3v0rTuXdqxbgpEbWdTVpaXOjs7TdcBo8CUnQMnKntEYSRsdnYQ\nakNks3VxcTElJiZSXV0d1dfXU0tLCzU0NOhMvL29neLi4uj6efNoiRC0Ni+Pmpqa9Ihr3ow4lUq+\nZvHEiVSmmanbEajeJe8ictrTKQRqX1cAdCMCkdcPGDDyhdq58jWnLNLFHHoaXowWeiKypin2HfcF\nYPXVqJ4y5RbKzy+kzs5OPT5D9h0LcZTmzl2hBXitpaamJhJiSRDDnTLlFgKy6eKLv0Z9hUHYjK2m\nPWUqx7YTcK3CwL9JwEICmnRT+7hx6QQsJo+nfPQEf2k2h5MAHleOvwigUfr7/wHQCieP2cEQQvUF\nq9WFenp6aNu2bRQXF0fx8fFUXV1NkPzAtbW1VO710r1XXklpAHVIDHKWy0Xx8fHk8/mop6dHzzuu\nr6+nnp4eqq+vp1UZGboGzH7kpUIEacUqo65AX/1r/lv+fjFA35g4kW4F6DsaU77e7bYsSerQk4NI\nwci3LFuLGhoadHrKzy+kOXNup4A/eDoBmTRx4koqKNhIbrebcnJySIhZlJzsISBNL/zhcp0kr3cT\nCTFLCuQ6QX1pTsGFQa644rt08cVf147vokAaVI7ChMsouNIXa8zHCLic3O6lBHyDgI0EdOv+cDNY\nMeZh8TELIZ4GMBkAAbhTCLEUwCNE9DwAASBJ+8j4NoBHhRBtCGjPbwG4TntABw4iDqI+v57sWwMQ\n5Bt74IEHMGPGDBAR3nrrLbjdbjz55JN47733cOL55/Gdp5/G5Qh4nn4HYBEC/t8VV1yByStW4Pvf\n/z78fj+mTp0KIQRWr16NZ599FgcOHMBiBHeAfRLAtURoBFAGgObMwZ/+9Ccc18Y8DqBp4kScmDIF\nW/70J4AInQhU4nkZwPsA/hvAJ59+ih8BSANwGoC3txfTFi9GXNxICTtxMBLBNAUAS5YsMaSnm2++\nmZUr+P1+rFmzBpmZK9HcPAfA7fj001QcOHASQvwSv//97+H1evHss88iOflGvPvufO26eWht/Qtc\nrncwaVIp/s//SQTwEYA/AOgB8AHYF+xynUJ8/Af461//AOAsgGQI8b+0OTBlncD8+R8gLi4BR49+\nCwHKyQVwr/ZkF6K396sApmrHd8Pv34gzZw4MfKFG4weOhD+mEI6pz6rcn1mtXdm/7Pf79Xzi7u5u\nXcLnqGkuZD9u3DgqTU8P0lbvkzRdT1YWtbS0SMXwAx8umwmAZgkRVJ3rVs08zZrvPffcQ9DM09cn\nJVEmQNekptITTzxB2cnJtBig6Qj4oK207HWXXWbZps6hp7GFcE3nZjSlasdsxm5rawuip/b2dj0t\nkN078fHxeoxFQGsOTodKTS2krCwP5eT8gPLy1hEwUyqVyWlOMxST9YMU8Fkf07TdMk3TDtDcxRd/\njdxuNk9n6WbpOXNyqLCwUKJTOaL7AQMzeAW53SM0XWooP85GMrZg5bdSNxmzbjT8nVw2k5kyF7Fn\n/y/7ln0+HyUmJpLb7abKykraunUrbdiwQa86VO7x9HWPEoJumT+fCtPSaAb6ArJy5s4NYsyVlZVU\nXV1NcXFxVFtbSw8XFVHBxRfT1xITqR19fuMc9JXYTAN0P3SbdJw/as5z3oQJQX7ndfn5luvr0NPY\nQjj0xOdzelNGRim53SnU0tLSL4efr+XqW1zNjoiooaFBF2z5/X/qqaeooKCACgo2akz1tMRkg03X\nzIinTSvQTM5XGZijc+mCCwqUY5y/HJyr3Gem7isi0veZrjHkCgoEhcnjlRGQRV7vppHpYx7Kj7OR\njC7YrbBl1NFFbvemRoIaRVqrvV25fGZcXBzt27ePyr1e+kFODt0wf75exo9LZLLWXF1dTUREzc3N\nlOJy0copU2gGQBs2bKDExERau3y53kf5GEDpX/kKuVwucrlcdPjwYSr3eKgwLY3KPR7q7OzUI76z\np08PCvbigiGsDbNWfa3CnNVI7mvT0mhdfj6tmDyZZgC0b98+y/V36Gn0IdzmEAy5bKZMK3IEtMt1\nkny+nYb0xMc4Z58r37W0tJDb7db7GyckJOitS4UQVFBwP33lK98iIJeWL19Lf//3q4IYYlpaIRUX\nF9Py5WskLfkB6iv4cZI8nnKphCfnIDPjVQO7WEPPVjTlxRrD79a075skjfk4uVxXERCow20FhzE7\nGPGw0/qNmZdaZ1eusMX/yhoybzJcl7qtrU1n2Dwel8lMcbmCGFxhfr6+iWzYsIHWr19PAGj9+vXU\n2tqqm7wDgSz5usm7UOmjvFgj/ISEBPJkZQVrs3l5lOJ2020pKZQG0PVC0E70mbTlEps7EejrzNcy\nc545cybN0O6TBtC3Jk2imRLjrqqqckzZYwyhaIoFUo8noAX7fIGqW3JhHVkbzs0NNjWnp5dSQ0MD\ntba2BtWoZmbPle4SEhJ07Zn7II8bN46Ki4upqamJcnJy9He0p6eHGhoaqL6+Xiuh2cd0vd5NlJiY\nSNOmeaR5dBOQpQWHzaCqqipKSkqi/PxCEmIJZWQUSExX1Zh3EPAAXXjht2niRGbGXA/7Ae1zmjhq\nHFitHZtO6enpIevuO4zZQUxgMCkfbE7m1mzquMxEufuSuuEwo+3p6ekXEdrW1qYzTzalyYxcZvAq\nQ733yiv1Sl1AoMvT8uXL9XHi4uIoMTFR72bj8XgIAK1ZvjzI58sMdNWqVf3MzqsuuohOuly0E8Ep\nTrvQv8RmmXKtrDm73W66csqUfqlTPHcrCd+hp9jDYFOozGhKNje7XCl6VLPsM1UbrbS3t5PPt0Mr\njRlglIF0JwTVe5fdQUR9ndNYS66rq6OEhAQqKirSr5Vpi2vAJyQkaP7ngAabn7+ODh8+rNFesF95\n+vRsEkJQUVERdXZ2UnV1NdXW1lJVVZXElGXmfC0JcQUBGZomvFNi2OxD7jbQsK+lvujxxZSXt84x\nZfd7MGcjCYlQpqxI502qEno4pjQ2fXE3JvU72V8lX9vd3U3btm2jlpaWoGYQiYmJ1NLSQjU1NTqB\nVlVVUXV1NbW2tlJ3d/f/z967B8d1Zndi597bD1BjjS3NSHJMicRDZUsA6JqV2AAEyp4RQGCthz2S\nAHTLu9Z4YxGPfjDDsUWOXZYoohvArEjFmyrXVra8HpH9AJR/UqlKbZJKPGslmbWB7ka3OHq4kmyy\niZPa7P6xf+wfOzPE8+SPe39fn/v1vbe7QZCiMviqush+3O8+8J3vnPM75/wOLy0tcbFYdHmSMmZ8\nk4gTIyMcCoXYMAxeXFzkVCqlElamp6cV1L2+vs4TExP84x//mNPpNL/yyit8gohfeuABpTjPnj3L\nyWSyCXYedv6vlz29Eom42jjCG9Z5sG+STcXppfRHifjphx7iM0T8Zjx+VC51m+NuylQn8oTv/WQK\ncLKUp83NTR4auuBSPrHYBS6VSpzP59myeriv71scj7/JkUiENzY2OJG4xL29r7NhnFSxYtT1+9X3\nR6NRpYiheJPJJIdCIfXv1NQUE5EqJ8Q1zM/Pczab5UKhoEoUATn39PwOP/bYsy7Fqyda2or0DA8M\nvMDnzp1jIuKBgQHnu1Hnvt1IgP3+Y6e8SiaWuZPNbqdc6nNXoHfqdbSRtB6tCv07EXo5vH7rFbNq\nh2gAMBkEuFQquTovYU5sJDrXLjzUVCqlPNhSqcTRaNQVv8rlcgpWKxaL6jh4kvAurl+/zt2GwWeI\n+KVf/VX+yU9+oubB5pJMJp2G6qZi9ELsGUKPGszFxUXO5/Ocy+VUz+VYLKYU7Qmys6pvEvG75Iap\n/8HYGL8wOMhjjvJ+jIh/8zd/k18YGODnHaUMuHuMiH/j/vu5n+zEscvOv/1SkZvmEcHIbY67JVOd\nyhO+95KpdDqtvNq1tTUX5Gx7wZ8qj9km9CA2jEYGtGF8zKbZo6oTIFOFQoGXlpY4HA7z4OCLTDTK\njz9uw9KQY2RiW5bFoVCIy+WyQpcymYwK/RQKBbYsi1OpFFerVRd3PHjlLcvi48dHuafnd3h4OM7v\nv/++MpABh5umyePj40xE/MADX3Mp1sHBF50qhxMciTzneM+bmsd8k23iEcSl3Y0sGsrcfo2PX/b9\nmx4p5qPhOTqx7tulBNzb2+OVlZWm33od76fAdbo+UFTC2+3q6lKbieyxCsWKzQoN0C3L4nK5rOBq\nwG/FYlFZ4ktLSyq7Ggo5FouxYRhcrVabmqej9eLExARHo1EFj1mWxZFIRBGNLCwscKFQULFnOQf+\nv7y8rDJS4f2eIeIXBgd5fn6eLcvi2COP8JBQqn9INnuXbGLxXSKXBy3LrC6SHX/+bed3O87nX9fi\n1T+LlJyHOe6WTPnJU6VS4UzmKo+PX+ZM5pqqDtApL/ECvLy8vKwMVnjPe3t7jifazbHYBc5krvHi\n4iITURMV5tDQBd7Z2eFUKqWUKWRAh5ZHRuKKitayLI7H42yaJluWxclkUkHZlUpFzVEqldT/k8kk\nFwoFVSqIl02t2fBWAaXjGFRO3Lhxw1OJNhSs3tjCbuV4330TAQqZ2DC6uavrRUYc+qhc6kgx39Gh\nc9jK7Ep9QIGtrKy4sp53d3ebuinV63XFMY16RhkrhnLG5oFNAzXCMp68trbG0WhUedHyumXiyfLy\nMu/u7roUJxFxsVhUyShQzpZluRQ5mqanUiknCzWhlCw2tWKxyKurq7yxsaFBa+RSzg24zM6I3t3d\n5eXlZe4WrF4fGwafIOKJiQkmai55ek17f5mIX37oIZeCHyXi5x94gN91lDeUuGQFA+R9RMl5d4Zu\nfOq9uvUhZUo/RpenVOpdERP+TCVEAU3a2triiYlzbBhn2DR7uFAoKHmq1Wqu5Eh42FKmIM/p9FVX\nL+N0+qqSB6BNxWLR4Ydv7uwEQxrQciqVUlzygKyBXi0vL/P29rZCpPB9c3zYrWhPn/42W5bFMzMz\nHA6HeWVlRcSm7faOzVB0s7KWvAJExMPDM01tIQcGXtBqqI/KpY4UszNuN77lVyIBWBhWu7Te9WN0\nIo6VlRUmarRAhKUPZQsFubq66jpPV1eXauIA+Hl1dbUp61oaDYgZ7+7uNt0bkkwgoBBswM6VSkXN\nEw6HeXx8nPP5PG9ubnK1WuVwOMz5fJ5DoRBXKhXe3d3lbDbLc3NzyitGRimuXXrMqVSKs9msI9jD\nfOvWLbWBmaapMsG/1dfnUravOz2VDcPgqVhMecx/5ChmSdf5R0T8/GOPuTaShYUFfjMeV8QmUolL\nBf0CEV9KJI5izNrwk5t2YWg/yBnG5+bmpspK9pInZne7UYRFTLObh4YucCJxkbu6upQ82UQcjT91\nLHZBGbRdXV38+OPj3OCW/pgtq7dJnqDooTxLpVJTxv7Ozo7T03iUR0biam2PjIzwrVu3eHl5mSuV\nCm9ubvLExBuuc87MvMnhcFjB2OFwmMvlMpdKJcUFkM1mOZvNqhg49pGZmRkuFou8sbGhkixN0+R8\nPu/A5Q1FG49fVHvJ8vIyV6tVhZRBRoaH45rnK737HzHRMBtGt4o/E51w6qCvsZ0AZjetCIV+S1Po\nz/PExBu8vb3tuzaOFPPPyGgV38Jm4LeZyGQQHSKDJ4vewJhHj1t5XQ8sfcwlCQZyuZwSUnwHjxaW\nrfRa0clJbopQujMzMy4jwMtjRiy3UCio2DCUP5pJYBMIhUK8urqqlCzg7lqtpqx7bA6GYagyDzRn\nj0QiXK1W1XzZbJaTyaRKKNvd3VXXjBh3j2m66pvjw8PKU5eZ3B8T8ZtE6SBaWgAAIABJREFU/HXL\n4t8l4mmyY9DfvP9+l2cOWPwpw+Cac+xNx5MGpH2TbIYxmS2rj59FeWL2lylZZlev13l7e5szmWsK\nQoaB4yVTsnwP+RJIXAySJ8ynZ0onEpfU+raJOH6kvstkrnG9XuednR0HeXF7hLHYhSYUbG9vj5eX\nlxVi5Gc0QO4gTzA0M5mMqyIBcDjRGTbNbrWeYcSura25oG1URxiGoZ4NOOnxzCF/U1NTqoxqa2uL\nJyfPOeVSJ3hqakqFlRCDXlpa4u3tbXWtyAUhIhWH7ut7jmVplGF87JRVSaUNAhKwfv1DtkumLjv/\nvstENzmRuOS7to4U8wHHnchMPqwhLVvd2pUJIrqwyU1AH3p2s9/5/Ajo2z0G362trSmSAUBzUKLS\no5Wdl2AUSI+9EcuyuzzBCMA8UKa1Ws2lZKvVqlK2qVRK/Q6GQS6XU6QhyC6FZ1ssFtk0Tb5+/bry\naFGvKTc6eODpdJpDoZAyNlCLifeGYag4W3x4mEeJXHXGsViMZ/v7Gzsq2QldJwX0jTjzSSIeGhri\nE+Sm4RxwFPJYXx+/9dZbrgSzhYWFwHV+WIr5XpUpL3mSn+vvZT1tJBJxyDWaWxP6yZR+Pvmvnzzh\nuEY7RPs1Nva2mq9QKDDRCe7re50TiUtcLpeV7MPjkzDs5ORsU30/lCQUZalU8qyM2N/fVx5vOBx2\n4tsZlQMilSpKoQBHp9NplaGN70OhkPKULctiwzA4n89zOn2Vh4YucDp9lcvlMq+urnI4HFZyB2N8\nf39fVVj09/czMq6HhqYZJUxEJ/jVV19Vihgxc/my806kAbPD0eivs3d5FJjFvseSZMRW1Pbfxm8c\nKWaPxd3O5tBucsadGK2uUYeEvbwdef0yXqRb71KxwysNIpto9Vy8klyi0agSaPwGsBIUHhRvqVRS\nViw8ab8Yd71eV5y6UPD4vFgssowJ4zpg0cuYdSQSUV2e8DuZxIK5lpaWXFnhUK6maap7AUSP9/Cc\nl5aW2DRNlVkaiURcGwKseni8c3Nz/OSTT3qWQv3IUaiWZfEw2dA0srBHifj3fu/3PEujhoaGVBbr\n/Py82tjK5XLgejwMeWpn7dzJEXSduC6Z49Dqd1jT4XDYU1kyB9ffe83bjkwlEhdZQraJxCUlIwjn\nIHMZaxxK7Pr165zJXOOxsbd5cnKWb9261WSQQDGDpQ5KGFnXurKGnGDtwwFYXl52GdXwoqGUsRct\nLy+7jpNe7shIXBkShvExT06eU/dULBZVQtfm5iaXy2XVY7mRUc3Os/quUJreCVzI1jYMgx98UGZs\ngz3MnZ19/DiOvck2uYgUt989Sv7yvLGAjaTdzeHztO7lNQZlLyNGhbIcPU7kFYuVv8NGhN8Bdi0W\ni3wtk+HL4+N8LZNR2Z3tJKx4lYVAmVWrVeVpQFilR4sELAkly/INffhlmnZ1dSlrHlAcYnU7Oztc\nKpUUjA5KTVje2EyLxaJS+oh3wQOH4q1Wq1wsFtXmJL1iwPCI00GBYxMql8s8NzfnWPfkOiadTrNh\nGC66zU0ifoUapU7TQ0NsmiZPDw01kYYAxpYe8+ijj7rOIz3mdig5WxmJ7Sjbe0GmdGY4eV2S8U1m\nJ3vFYKUcSHINCWGCPcsrPNSJQSyfsQ3FXnR6C4/w+Ph/zPl8XhkJujxJBGp5eVnJjJ9M6WQ8evY2\nYuKYH7HiQqHAoVBIJUDCMMDxUp5yuZyaDzHz3t7XeWLiDV5fX1cedHMy1hleWVlRGeCAo6PRKE9M\nvCHQgB+xDTXjOFmLLBUqFPcJvnLlijKM//zP/1wobnjFO86crzFRv/h+gIme5QaP92dM9OtHyV+e\nN3ZIFv7dGEGKF//6JVtBucrGC1CCfjFDzCkzmvEZ4sjvplL8qWUxE/FnlsWXEglX4hcET0J0fnCg\n/A5lQVLp6gYFYEKwAEGJy/vxgsl1uFtunqDGlHE9KFHElnGMRCMAfVuWpa4XMLZlWUpJh8NhLhaL\nXKlUOJvNqjiaYRg8PT2tEADJDDY5OelSlPh+enqa8/k8n6RmJi9JMhI/fpyJiJ977jnuNk2eHRjg\nFwcH1XmJbFgb3vYz5F1OdZOIXzx1yjNpDoOI2kZI7oURJFNY99JwDJIpJBAF5Wdsb29zInGJY7EL\nbJo9CoGQ9ffSkLV5oXs4FrvAk5PnlPKW+RjymnWZymSuqfpioptsGN3Ka/Uy0MFuF4lEmmqV9ecl\nk81kzBufyaQw3JvsBiX5BfDykifMF49fdGU043nkcjk+e/YNF/Q+MPCCkhHIzNzcnBOXdmd/N3ou\no6uU/f++vjE2DP23ZzibzbrkFMZ3X98Y692jbD5sqdifYaJXmOgtJvou9/Z+47YoOQ37+///DcMw\nWN6bYRh09P7o/dH7o/dH74/e3wvviYiY2SCPYXp9eDSOxtE4GkfjaByNz2fcdcVsGEa/YRh/YRjG\nDw3DqBmGsWwYhtXGcROGYfzPhmH8C+ff/94wjKeCjvnoo4+UlSL//eCDD+jYsWP00Ucfqd/qyMFB\n3jOzOmc7v9/f36cPPviA9vf3Xd9/9NFHruuT7+v1OkUiEQqHw7S5uel5vN/5MA+Okeev1+t03333\nqXNWKhXqC4VobnCQvvvaa7S9vU0fffQR3XfffVSv12l1dZWi0Sitra0RM1O9Xqeuri6KRqPU1dVF\na2tr1NXVRV1dXfTBBx+of/HbaDRK9XrddX0ffPABGYZBH3zwAe3t7VEmkyHDMKjHNOljwyAyDPrU\nsmj27/5dIrKtzkwmQ5VKhUKhEBWLRarX6+rZrq2tUTgcpkgkQsvLy7S5uUmrq6vqt0tLS2QYBi0v\nL9Pe3p665nq9TpubmxSJRNQzDofDZFkWpdNp6urqonA4TOFwmKLRKKXTaSIiGhkZoa6uLvre975H\n2WyWDMOgs2fPUjgcpmQyqf5mmUxGXb9lWZTL5cg0TQqHwxQKhWh6epoMwyDDMKjbMOj13l6KPfKI\n+uykYdAoEZ00DJqenqZCoUDhcJhmZmYoFAopaxy/HzEM+sz+gP7GeT/qvMdrlIiq1arv+pFrSK5v\nfFar1Vzr5zDkSc6/v7/f8vdYW/V6ven3cn3r8hSNRikSidDa2hrVajXP45mZdnd36fz59+js2Xco\nk7lG1WpVyUO9Xqe9vT2XPEJe8Mxw3u9973uUTl+lUOhvnMf/I5qcnKVaraZkqlarueQHcgVZikQi\n9Npr36Xh4e/Qa699l6LRqJIvXK+UJ2amWq2m1kcqlaJoNEqrq6u0ublJyWTSWS8naXBwjiYm3qBw\nOEylUkk9D2amlZUVMgyDbty4QZOTszQwMEem2U3Xr1+nzc1NeuyxBNnLj8kwiAYH5+i+++6j1dVV\nikQiNDk5S+Pjl2lkJK6uIxwOk2F0k2k+S6Ojr1EoFCIiolQqRcvLy+o6LMsiwzAoFAqp41ZXV2l1\ndZWIGuv91KkXiehHZBhEpvkJGUY39ff3k2EY9Oqrr5JpdtNDD71MhnGCDOMkGcYZIhogw6ipYwYH\nX6RoNEqmaZJhDBLR82QYg+ocsVjMOXacDOMSEb1LhvEHZBh/nwyjRkQnKJ/Pt73emwYW9N14EdFX\niejfElHSeX8fEd0kon/U4rg+IrpFRN8Rn/0BEf07IvqKzzHsN+5UTKzTjFO/2LFfyQZippI6r1Xs\nSw7EznTWLK8EM6/3Mg6H0gmZBCI7zeiUmnqJk1f8T9YpIxkrlUo1dXR61jBUI3VyElpkYhaeh0wM\nQ1wZ3MCIjeEYzIEMcZ3eEzF1JLpIUhI7vtiIwXd1danSka2tLRUvRwxwZ2fHFSObn593EmDsfsy4\nr+npaZUwRmRnjc4MDTV1pZJz4fWNb3xDZeI+RjaFJ5LGHiPigfvvb0oYu53uUl8kmdLjqUhGbJWV\nbcd1GyVRqBFGvBYMWl4VD/p7xKTHxt7mROISb29vu2RK5l/osqTHbvVktp2dHZV5PTKS4J/+9Keu\nUsF4PN60BywtLTkZzQ1KS8StpUzhecXjjZ7GiHHb2d89rnhxPH6R19bWuFKpsGF0q2Ms61MeGZlh\ny+pRSV9gwKtUKipzGwmikDs0wkBeR6lU4mq1qhjETp8+7eRX2KVSIyNxlW8xMTHhIjtp1CAzNyeL\njTqy5G4F+eUvN5jAcrmcQws6IOZBffMo53K5wLVKATHmu62Yc45iNsRnrxPRNhH9YsBxrxLRHhE9\nKT7rJ6J9Ivotn2NaS/Ehj043Jz3BSxILeM2FDE8keKDo3msj8bsWmYkdlJ2qH+eVaS2VHjK7/YZM\nKJH/95ofJVDoTiMT0T41Te5xmLJw7p2dHV5dXVUCq9dqywQc1EBalsX5fJ5TqZRKMkM2NBS43vIR\niUMgQJBsZsgCx0YhaUOxuZimyd2Gwd8+fZrjIyMuhi500kFnnUKhwIuLiy6lHAqFmkqg4sePcz6f\nV4p8dnbWlW19+vRpJiJ+cXCQzzgUnzjnCSJ+zulQFYvF2mEpuuvjsGTKb63j7wNFiDXl1fJT77SE\nBgVYX7KJSqt78DI4YJyifKiVMeLXylQaEESb/Nhjz7NhPMum2cMzMzNN/Nl4BgMDs677QycpyIck\nQTGMZ9m9FM/wjRs3eHp6mlOpdx0GshM8PT2tMtz1Y44fn3Ildplmj5L/Y8eOuZLMUP5Vq9UURSk6\nuYFbG8Yt1vfMzAxHo1FeXFzkZDLpsJONsl32dJmbS5y+w41ey2NMdJIjkZe13zzPRKQ4DOye0Hp9\n83eY6ARvbGwE/v3uJcW8SUT/TPusz1Gw3wo47itE9P8Q0btEZJANwV9zlHXM55jAh9LJOKg30Mlx\nukeKzV5uDtKilgsYylluSHrmaavranWcX3YorkHfxHSSBLkRQbCg2JhZGRm7u7vKKoeFn8vl+Fom\nw2+PjfHFuE3/hwxiaWiAsATUm7gHkHygllTWY2J+2f5xenqai8Ui7+zsNBkTu7u7qiYZlIN4wcvP\nZDLKC69Wq6qkaqyvz50JPTjIlmXx7Oys8pbRWQe8w3gZTgcsSShyk4i/9uCDvL6+ru5nxqd0Sm+i\nMTU1pRp44LW0tOS7Pr9o8oTfB3mXUn4kyQY2f2m81mo1tqwe0WnpM1dv4iCF7CVT8hjdmGhHnpib\na6qxL4yPy9Kga0zU6AJFdELR38L7hiGZTl91ZXqDfESSiOC4kZEEN7zrT3lyclbJUzwe50qloljt\n0JBCljNZ1qf8wANTQkle4/7+N3h3d9elkJGtjv9Ljxk0oKg/zuVyXCgU2DAMjsViqnwRe4LbA95h\nopfZ3bCiny1rVPvsayw9Zvt425C2O2Xp9c0/Ur/5InnM/56I/kz7rMtRzLkWx54koh8S0f9LRP+a\niH4adEynG0mQ0LeC09r1Mju9Hgn/MjeTFXgpS5zTy0MIumYYArIvq9fw8py9vAGdVtCvHAMDyjqd\nTnOpVOKuri5Fj4nzgcwA8LXu3eD56GVc0qPBRlQsFhXDEBSypAEFEQI5EK/k/AZyAcGHd93V1cWJ\nRELxcUvCFMuymrzdt557jmu1moLhZmZmeGdnR9V2yt6xhmGoUqwTZJc+gckr9vDDfOPGDQ6FQnxG\nO8eo4w1L0v+vf/3rHIlE2HA8aHjX169f912PB1HMtyMX7XqZnV6LVJJ+DVakbEmFbocnbNrN8+ff\nc5XD+F1vpzLVrjzJc+psYe5Squ/I5cADA3O8ubmpZAZNHYAgTU7OsmE8y5OTs7y1tdUE1UunYXJy\nVkHx+Xxe9RxHyRSQKShHW7ZO8MDALD/zTIJ1mJio3+kIZVN3gmgkFAqpDm9An3DNhmEo5Ons2bMq\nrAR5AtcAFHajLvoaE9XZhp1/n+1Sp8e4uW76Gec6n2eiAX7iiSeYyCbqafzWrm/u6nqVZbepbDYb\nuCbvJcW8S0R/qn1mOIr5TwKO+4+I6P8goj8BDE5EzxNRPOCYwIeij6CC+1aWut+G0cqSDrL8W3md\nXnP5WdZBc0GBe8WB9WOC5pcbm2zTGMQgJo/d3t5WXZRCoZCq55SQueyjDNYlyTCkewD6hqsrVMBz\nUIblcpmLxaLi04aCxUaWTqcVEX40GnV1m0KXLNRronYa8WVAx+DB3iTi8V/8Rb4Qi/FJZ3MJhUKc\ny+U4mUxyOBzmXC7H586dU8pzZnhYtYHUyUPOPPooh8NhfmFw0NNj1jtdnT59uqmW+dzEhO/f6SCK\n2U+m2ln/rdZ6p/MFGbFB6JDXemfmJuPSS7lKmZJypit/v7p/GVduV56YGzXVdmvGAZdnm8lcUwas\nbCJh5zJ0s2Gc4ZGRhIsRDBwAOzs7ns8QRt+pUy+yYTzLExNv8I0bN7hYLKoXEJ1sNuvweY+zab7k\nUoJf+pK7wYbkAMe9b25uuih4oQDBDQDq23g8royO4eE4Hz8eZ8Po5t7ebzjnuOw6N9Flvv/+32Cv\nFpXNtJ02sUgo9DV2s4udcP2uHdIevkcU84E8Zkch/wciiorPTCL6N0T0D3yO4XfeeYcvX77Mc3Nz\nPDc3x5cvX+YPP/zQ8yF5bSLtQmd+Csvrd1AQgF5l20M52t2YoGxglfsR7vt5sF4JYNhQ9GNabT6S\nHjTIc9cJDAAnEjUK+70aZYAcQRKHyKQueK2Ag726YEnKzHA4rJ6bpFfEhlUsFpXCB/wNOA2xaih5\nGfOvVqt87NgxlRAG6sBXX32Vuw2DZwcGePyRR1Tc/CYRx0dGPFvZ6coTitaPbjMUCrm8YPACe730\nOS7EYvzhhx/yO++80/SCPEGmXn311UB5wtr0I7FoNTqRKaxVuTb8YN92FD1kCoQ0Xh66Ho7xgqPl\nupPIjsyl8JInyCW8QylPXl61l5EQjTZ6i5869ZKL+ETuB3hWejIXYGysey95wvWWSqWmxDF4vLo8\nuclE/ogBs9sQ95hcjhyLXXDF3NEz+tixYyr/Yn5+niORCFcqFV5ZWeFyuawYBu3rOsluIpAB5zwx\n8flnbMeVbcXa1fUrDHYwyC1kZnDwBZZQt2H8Kut9maG4Jybe4J2dnUCZ4ntEMXvFmHupdYz5vyGi\n/83j83Ui+kufY1yL1A9WDRLSTqCzII9bnw8xLKmQZLy13U2J2btRQzsec6v79oLgdGUM40K2sJPW\nra74MbCpSW5qsH3BI5Uc03iPJhFy88VxUKBSOcPCx/1Ho1ElwIgfI4YGTxmKV2Z5Y2NFhnQoFFKw\nuLxOQN+lUkkZB6lUSsV3scGZpsmvPPywSyn+Tk+PS2liM/BSwAsLCzwzPOzizR6g5j7PSBibm5tz\nKesvf/nLnkr/UiLhu86kxxyklILWVifyBIXbKqlQGnZe8VYMr/CJ33lhdMEQ3Nra4kTiIo+Nva06\nRwXN5xcmksaoHlvG+pS9kWXnqSBPH3+P5eVll3xAOSEHAsoNlLT4bn9/n8fG3nYpxbGxtxWMHYlE\nOJ1Ou+QJ94R5dK7w3t7XFfIEKs5QKKQlgO0w0TQTvcCTk7M8M/MHLuNgZCSujGHIk2manE6nXTkT\npmnylStXOJfLqUS1Rqa2Dk0D2t9km1ZzjImGeWDgeSfD2tuIJbJDQg89pCeDjWq/a3jcpvnJF4Mr\nm4iydLCs7P/c8bYN7fO/1RW9+E4JSTuQsC5Urbq86EMq5nY2KvxfesxeXqzXsfp59YzJILrBVvO1\nMgqwYcq4rrwnbKaguNze3lZJUPjN5uamUmIrKyueCADiq8iyhnerb77SitevT24iIM3HvHheiLPh\nHLDKJY0i/h7pdJqLxaIrAxf3D6pRy7J4c3PTKVnJON1+bKG141LNnvDHhqE6SaHE6caNGxyLxZp+\nFx8Z4Wq1yu+//z5/jeyezH+PiDfIjjV7bSg67P30V7/Kv/zLv8xEdunUMNmdqv7AiXF7DaIGV3YQ\njOu1Xg4iT5hTX2NB8+vnCVJmfuvfyyiVTSNQJqUr5YPKE34DtElmdnvJEyoQpJEKrxJKTDoH2Jek\n4Qi0SCaSejXGwLXpuS64JyBBRMQjIwkV14bHrPNil0ol5zzwmD9joj/kyclzfOzYMS6Xy6qETCaT\nwZiQCZpE5KpqwAvna/xO76/cKIl64IGXVB/ofD6vGmDcf/9g0/xPPvmkx3wN+Pqpp55y4s9uQwCZ\n+17jXlLMX3Xg5wXn/X1EVCcRXyaiKBF9QkT/VHz2d4hoi4jeFJ+9QXZWtmecGRtJpzEpuSF0kmji\nBWW1SsDyO7fXcfhOcjp73YuXte7lQfhtptK693s+Qd/j/FBUsHQB98r7kNnkzOxqTYnkjkqlomJU\nlUqF6/W6ElIoSUDL0vvWoXJkTMPjBUwNrwLxYB0yh7eOuBYI+gGp4VlXKhWVfQrjIxKJ8Pz8vILc\n3n//fR4fH+e33npLKd1RIn6ur4+JyNXMAoktUKyjRHzRyXY9duwYvzEx4Wr3+B7ZCWEvDA4qzxhz\neXndXgbCp5bF750/77k2icgTRvVac15r+jDkye/8fiPouHbliZmbvMmBgTnWUa6DylOr3/jJE+Bd\n3cPWUbtqtap43GVnqEgkwgsLCxyPv8ljY29zMvk9B6Y9o7pOAYnSw224Vyj6iYkJjkajPDk5y7HY\nBY7HL3I+n1feK+Rpf9+u356ZeZO/9KXfYqIRNoxuzufz6lx4hru7u5zL5XhpaUmVg0WjUc5mszw1\nNcWWZfE777zDU1NTPD8/z++8846SF8MwVEnjU089xe5mE42YsGF0czx+kYeGLrBhDLKdDOZWuMlk\n0tVeEsr5+PE4Dw6+6GEINxT3F6q7FBE9SUQ/IDvDuk5ES0Rkie+7iOhfElFRO+7XiegvieiviOiv\niWiDiBIB53Epunatdd0Cb1epes3RqiuSPn8reKxWazQI1z1Nr+vGMV7tEoM8aSngreJZfvePhBF4\nzF7z6XNJSE42rsCmBA8Wz6BUKvHaWqPJul72IjdkKHOp6JExKolE5DOp1+tKwFOplErIQmIYNpta\nraase1wTEr7wkslXaCyPY2CEoGQKxoNhGC7IDqUi8Xi8Kfv6O0SuTlSIR7/88su+cWovpX15fNzz\nbyoN3XYyjYPWZadDD6G0m2gZ5K12Ik92r+UGMUYicUnBy14eu24oS4RJh7Db9ahhIKKHMep6pYft\nJZuQHeRt4LrtEFqjNzNKqTKZjJqHiJRS9fKY0WISMi5lyi+0JmUK+SQ4BveytbXlZGePsml2c6FQ\nEN2mmhEhGBuQGcuyOJlMujK38duBgReYaNRRqo8FdKMa5dOnT7NlWTw1NcWGYfBXvvIV1znL5bIr\n/vxrv/ZrQjmP8sTEuaPuUk03JjYSWFvtFO0f5giKO3tZ2K3gNni/qJOVnrWXZ6LP1+q9l0Gh34PX\ndesbjR7n89s05bXu7e1xsVhUFrKXApc9m3O5HF+Mx/lZw+CL8bir7hieM863u7vLmUxGJbPA+11a\nWlIGAGojC4WCirshseXKlSuqSxRijzLuhzg/ejcT2TWMssMUFDMyr3O5nEtZ53I5zufzKnMVkDvK\nWBAOwOY0/vjjrhjz3wmHPT3jkydPMhFx7JFHeOb4cVeWdigU4tjDD7vmCfKY5d/t85CnIM8yyGP1\nkyldnuTnCF80yuAu8fj4ZU4kLrlQniCZ0g0K1EXL2Hm78uRncO7v7yumL72zlZQpGR/Gd7bn31gy\njz6aUM8Ba79UKjXJE5AtScyCUBBCT0ChdJmu1WpKoRmGwfl8XskUFLOE1Q3jYzbNboVyoWaZiFRM\neHFxUclTMplU8rawsMCWZfH3v/99p0PUKPf1jXEoFOJyucx9fd9it8jIblTuDGsoWzsxzH7/8MOv\nuH6H8+H9zMxM4Hr+mVXMWOTwGoN6CB/G6MS7DIIEZdzIK2lNT6ySlqm02PXzt7o+HUrX64JlNrRf\nzB4xYiSW+MW2kZmOsijdQtc9EP3cVzOZBhuYZfG1TMbF4iTPiwQ71BibpsnxeJx3dnbU9cgSDBAp\n1Go2oYuEmJeWllzPIhqNqsSxYrHI5XKZZ2ZmVC1nPB5XZVimaXIqleJIJGIrV2eTgWWPjQZQHBK5\nZmZm1N8F81y+fJnjIyMKtn7rrbdcnvGPNM9YbmRExGNjY8owGOvr45cfeoivOgk+XgPyhHUjE+4O\n6gm3Gl5hmk6SGPVjkDWvK2GvWDQgbqmUvM4fJFO6kpbZ2VijMnnRS55gjIB8x+v80qOX8WHIFMh1\n5H3U63VOp6+qZCuUUsl7kbIsz4l5wbqFWHMul1PPC8diva2srCh5QlkklBmqK4AC6IlkdqmTTbAD\nb3h9fZ1nZt5kojMOBN+QG0lrS0SOUpZx4ZPONfe4EINHHhlnolE2jG5+4oknhKd9gt2kIwOsx5lP\nnjypPH/IdhCTHvPPuGLWF/lBCQraGa080k7mwaLWITY5dOu+lbcQdL1emw6ES9JcyrIoJFDJzUl6\n2F7nl9nRmUxGWcrIjAa5iN73GXExKMULsRhL6b0wNKTiXrJGuVaruWBsCYmVSiWXtx4KhXhhYUEx\nesnELcSbJDUgNhrMB68ZmwSYjwDN47pAJajXFkvO67GxMZe3PTk5ydvb27y7u6vm1WE6eMYyxmwY\nBi8uLnI2m1WKub+/n9fX1zkSiSijo1qtBq5JqZjvlky1gyp1Mg8MtIPKVDvnlr/RDV+ZqCkrGJDX\n4CVP8Jj9POx6vZkeMxa7oLKyAWHL0i9ch91/uIcHBmY5kbjE5XLZZaSAMQ8KWBoTMqsboST0WIZT\nAHKQZDLJOzs7rvBOLBZjIjtnA3sC0Cs3n3XDe8VaX1pa4kTCXd5FdFKFgQBnN+RC7708yvPz8zw0\nNMOPPvq7/OCDz7MN65uua4PM65nY4fA3m+abnZ3lSCSiZDiZTAb2N2f+GVbMujXsZ1W3Gu3Gybws\n/Fbn8/OcOyEUCPIWWl2v9La9KEBR8yuhJghsJBJp2qQktabX+TGnjO0hDoZzo1wFUDOytKXC7TFN\nV9ISPGYkm2BTQPwYmxsIPxAvxuYIYwDPQNaBTk9Pczab5Wq1yuu5tA6cAAAgAElEQVTr69xtGHwh\nFuOr6TQXCgWVAYpEERlXjsfjynvG/QDyzufz6vdDQ0PKGIMHTWTHtiHsExMTrs0tmUy6aDVPEPEZ\nsslG5EYmk1dwPrtpQUOpp1KpwHUKefJSHAeVqXZiq36oUicyhfd+nAGHeW6pjPUwEN7L+nv8H2tV\nlyd9L/C6t8nJcy5FNjk566pThjwtLy8rpWqapsvotnmme3hgYI4tq0c1aEH2Npq8oK5a3gvKC6U8\nQfnLOmwZWy6Xyy7KWtQ712o1h9DDhortOmS38WobpG5le/x4nPP5vKskEQpWesyG8TGfOvUSN2dr\nn+ChoSFF80lE/Eu/9EtMRDw8PMPNtdCN948/Pu6SN4muBY2fWcV8kExOrwErNSiz9KDnCLLCb+e6\n2z1WwtdeSSqyTlnCeYCU9c2uVY0rjkcW9N7engsaxffYLMD0hcUOer7r16/zy48/zq/dfz+//Pjj\n/OMf/1htEnrdJrzc5eVlV5wPG41e+oRQAig7IfDhcJjfmJhwGQS9TnkUoDjJEjQ+Pq42I8Br0mDQ\n+asNw1AGyJUrV3h6elpldMs5JW3oK6+8opSyTPDqduayLIt7nBrpsbExnpqa4nw+z5VKpcljD/Ka\npTzd7trEuguKU9/O/HdCpjqVJ3imXsiSjF/v7+/z1tYWT0yc41jsAqfTV7lcLrsSEFvla2xtbamM\n6ETiEm9tbbmUOeQJYSIJMefzeY5EIi4ea9O0E9yANFWr1aawVrVaVTC35DGAwa4nfu3v73OhUGDT\nNLlQKCj4W6IGUOBdXV3qvLqXjdwLNzxtK1bI09jYmCtD2zAMHhqa4QceeIkNo5vn5+ebvOD77ptQ\nil9Hyex5TzK6VTXg7VF+9NEz/P3vf1/B7ER2Zzc826DxM6uY9bjRQeG2djzmg24GQd/dznW3e2wQ\nbMbcHIfWvQBdEetK1i/OphNHSONHlimhpAmW8NLSEtdqNe6xLBdz1uzkZFOpEzxfKEPAZjJe7Wdg\nYEMMhUKcSqWUcm2C0GMxXl1d5Y2NDVecGK/l5WV1/NmzZ5U3nMvl1OaBWPPZs2fVOU3TdJGCkPaS\n12QYBv/G/fe7ruvlhx5yYMGJpmNHRka4XC6rRDOcf3l52XedSHm63bWJdRfkMbea/27LlN9xSLwa\nH7/Mmcw1z85N+lrXZcpdJ/0pm2aPSxFLJatfBzKs/eQJz1jS1G5ubroaTNRqtaa4rmE8q6B/2fQC\nnn4ul3N5uZAr2fHNL0M8nU4rgxXEJfgtzgFkCWtTEuckEgnHqGx41boBa5omh8NhdTyM0MHBQec3\nA2wne11ju4TqpILsIZdSnmFkVyoVzzarqVRKJW8iU/sL08Tibr6cm2bmg0PK7Y7b3WTu1DUdFIrz\nU6zMzYrZK8FNV8byX1j/ss2ebLenx9IwNyxsQGxeClIm1+CcUM6S+xpKFHXROgMbNkPE0aLRqEra\nejMe509Mk5mIPzFNPjc5qZSq3BTm5+c5m81yqVTi7e1tzmQyKjkE9Zmw6P/qr/6KR0ZGXAL+jd5e\n3xKnWCzG6+vrnEqleHFxUXFn67+3LEu14sNm83M/93PKqzBNU0F3ExMT7ZR2tL2W7qQ86Wus07kP\ny2Pe39/XSqk+a6pbhXzIJDL92nWliBixlxGkXwcMY4SP9Npj2bBCeum7u7sqH8Ors9QzzySUwpT1\nxRLNQn9xhJvAJe8lT/W6XbIInvtiscjpdNqVR4KEuwZLWAMaRukg+qBjP4A8pVIppXwXFha4UCi4\n4r3f//73ube3V3i70tt+mkOhb/KDD/4av/322y4kyY7DN0JB6XSa19fXVVc4qZzPnj2rPHTsL0Hj\nZ14xtzMO2zs9aByu3XhxkBfvN0c7XgigLj/orB30wO/8UulDwcsOTbL8C54ArkPCgseOHeOLiQR/\nJjzmS4mEZxxRKlkoQzBwzczMqPuQm5osr8OmgVjc4uIid5smv97Xx70OzB0Oh1VnHcBhaEpB1KAe\nhbcBKBu9nwHVnzp1Sj0Pr9InHXqGEQAIb/TRR3mUbBh7bm6Ow+GwnYQ2PMyjRNzrUQc6NzfHpmmy\nZVmBa/9ekiev936jXXnCZ51kfdfrzYlXOtOTvm69ZCqTucqyTjqTudb2PUhvGkoaytarwkIygyGG\njGTM8+ffU6xbsuWlzk6oG616ToSebS6he8SdEZdGUiZg8WPHjilZk8oVHqzkq5exepwbc+MzwzAU\nhE5E/PDDD3MzTefvO//abR1TqZQqkZybs8lkwuGwuhaURV2+fJl/4Rd+wVMmTdP0LJOV41AUM9lk\nIB+2+/vP+9XJRqILZKfegJ/QdroxQTG2k2EtPcp2s0f9Nh4JMcOC1UlJvKg32yV68LounFNmW2Nu\nCXfLRBrZsWlra4vfO3+e3x4b40uJBG9sbDRtgPp5UYKmc17r5S04l2QEk4rUiysbz2plZcVl6QNK\nQ8mJZBrD3Ldu3VIe88zMDF+/fp2/0duryEI+Ngy+lEjw5uam2hy+/vWvc39/vyoRGR4edm0O6FLl\nRy6iw+hLS0uBSq5TxSyRlE4Rq8OSJ2Z/LgGv+SSi00r+cM06+cj58++5jFcYdmhb6NViEnC4nXjV\n6/K0OoHlJXOeV96ETDjDZyACkhnEOKfMHPfae2RimST0QAmkDqdL+a1UKgo6hoLHM5KVD+AIwLp+\n+eWXFfoFGQQHgeyPXi6XFYqVTCb58uXLKtdicPAFkdHtJhaxrN9S3q80hMfHxz26TDVev/Irv8JE\nJ1SHqoWFhZZJhoelmP9vInqw3d9/3q9OoGw/pee3CbSzSRzEY67XW1Pz3Y7H7Hft2JAkdZ6u+GUS\nmFcGd6tno0PkuE7dUNDhbpR8gCtbT4SRZSFeNZf6eZCgsrS05Cr1kLzYOuwO+lBAbIhV4zkAwkPG\nq0zqAhsSMlYR78ZmJAn6oZyxIZxxPOBzExMqIUjfHGRpSAOqa8zhR8c5NDTkIkBB60q/0SmULddB\nO/LSDppzEI/ZTzEfhsfMbMeYE4lLbBjPqoQpKTtSZvzkCXN7yVSQka1XbvjJkzzf0tKSig8jCVJC\nzjL3AtUL1Wq16dnLuDKUcz6fV4xdExPneGtrq8noxXWAKQxUuxIyl6VY29vbnE6nVfUBSH6QRAZY\nXLLxQSb1EBNeV65cUYQsx46NCljb9phhtKJkUcqan2JurnU+0W4J4m0r5v864LtvtjvP3XrJjeSw\nhb7TOFW71n6n83Z6bCuPWX4uFT+EVYfHvIwOP1ITWRIln4u+GUHQZa9YKDhYyLrylJCxDt9J6BBo\nAJSi7GIF3m3MLRNmkIGq14TKZhzhcFj93zAMHh8f53feeUdtguFwmDOZjKuGGh4HYtiGYSgeYLkB\nLS4uKrgf8TQ8FyKb+Ugmg0HhDg0N8UnD8CUdQd02ykpaJX/J0YlMdeoxtzPutEx1ApUHyY7+DLzk\nSYe39f97za+HnOQz0UNCa2trKsNayhMUq1Se0WhUrddWdLU4F+TUbh2JzO5PXJA4Xru7u8qrhzxJ\nj1k24wBBDxHx1NQUDw0N8U9+8hMlZ5AthKiWl5fV/SCB8sqVKyrcMz8/rz6XtLxf+cqvM9ELfOxY\nTCnkhYUF9awgT5BLIFbIzD5+/BluhsdHOZvNBq6dw1LMrxHRt4ko5PHdX7Y7z916deIx38lxECu8\n3e/l0Deqw4QDvQRe/073tPXNolqtuuqbJVwMeE2fD7Dx0tJSE/R7MZHgt8fG+GIiwevr6662kOgk\nlcvlXFAa4HFZ9gFFC+8AljqgayhNCDOOgReEOC6oACX9JizsmZkZXlxcVKQOkiIRmaxQrFDySDrx\ngp1lvBybbLlc5nQ6zeVy2UWM0t/fzyfJnd09NzfnYknCbzc2NnzXQKce850cd0Om/KDrw5CpIHli\nbvby/WRqeXlZIVDwoGXsF/P7yZOsStArFSTsDCQIyloqdMDd4XDYM7Nb8t4vLy8rjnjTNJWnDOga\nsgV2L8RqZVe2+fl5NgyD5+fnlXLN5XKuFpZIbEMzHChV5FJIWSIi1UQD5yMiJUtI9pJybVkW9/Y+\nx7I22mYNc5OizM7OBq6Dw1LM/ycR/QciukV2u8V/JV4/aXeeu/XqNCZ2p0aQ4LYSasCuOpTkNfRN\nw0tRe1nerbJf5dw7OztN5CE6D7Cf9wAoD/9HXSWIRlZXV12/x0YhNwEkdCRGRjzJRSDc0nuUm4dX\n3A1ZoFCAgJsb7exGlDB6URtKRi/AZsji1En3pScgvSXAidiAsIHA89bhtEKhwIVCQWWqAionIn5x\ncJDPGAaPOR2rsAlJJiSQ8utzBxEi3CvyxHx3ZKodJazLT6fyJMuLZCxectUHecyoYMB7KEjAutK7\nlfKEObEWYWh6cRhAoUqlhHkgU3ifyVxlycY1OPiiWvPwfCXRjeQVkNcImYLRi7ULvnppwELWJbMZ\n5CmdTrtCP/Pz82xZVhNEnUwmlcFw5coVnpmZ4VAopDzzhpGMLlX4t2GE9PT8fe17O0ksaBymYn7H\n43WFiP5Vu/Pcrde9spEEQXqtrHfAv3LRecFk7ZzLa8OScdQgTxufIxa6srLigqF17m6vTVHOC6GG\nlwtrFxsVqDlR17iysqKgr1KpxG+NjbGUitmBAVWigftJp9OqtErf6HTlDAHEZoHNDbWesg+zvlki\nOQUb18zMjKrzLBaLSoFCUGXZl4QbUV4iX1DQr776qspIhdLH+ZLJpILiJWx901HS8/PzykPW5yey\nyRC+0dvLZ4j4TYc/3GvcK/LEfLgyFRSWaXUuL0/WK3nMb15ArkSkEBhpQHohUF5zQhbhLcMbBnQs\nY7ZYb7K2X5YrSjRIypSUP8lqJq91e3ubE4lL3Nv7OoPiEusWsLQk2pGGgITU8TeS1LPJZJIrlYor\nwzoUCqna4UKhoGQT8hQOh10xZklAAphbZpRD+Uums1Ao5BgDJ5wOXOjEJdm/fqSUsWma/PTTT7Np\nmqqZiN84LMX8jwO+e7fdee7W63Y2kiDldzujE9gL0JTsMywFtd1s7KB72dtrdJDRG1no14hNJ5PJ\n8M7OjiuL20+pByl5qcgltCxjp2tra8r6Rfy4Xq/zxUTCrYBOnVLsRDKWhU1FxvSQYAIu7nq9ztvb\n26rZhKy1BJQnjQbJcyw5s2U8WsbnisWisvIBhRPZpR0yEW53d1f9DhuP9GjhFWMTkR4Muve83tvr\nMlieNQwXa5rXq5N+zAcdQcrtdke7MoXzovRHNlsISlILChHp9yLzI7wSsLzmlUpTGpCdPDs/VEpS\n38LDlB6lpMuUVQtAjbDu9bAT7g2/h4GDPalSqSgoGBnTKIWSvdMxL/YSyLxXXFyylkFOkOwJzz+T\nyfCtW7c4k7nGY2NvK5YukJNIfnrZFENS7+J3qJVuGLTNMWTdQ9bf3xXF/EV73c5GgkXtVXpzO6MT\nhd/KUm4nG1uHvLzOIRWM7knqc0mouh1vo9WmKaE7vVUc2s3pxsne3h6Xy2WenZzkZw2DY488woBh\nJVQniRf29/eV4KNfMgQZG7VuCCwvLzd523IOLy5zPCPpgUhKzmq1yjs7O6re2XLoPOv1uqsRwJUr\nV7jbMHi2v5/HHn9c8Rqj0QdegK8jkQivrKy46EI/NgzuNgxeX193QXehUMjVQ7aTfswHHTJm2olx\n2s5oV6ba8Txbecz6vfidAwoqSKbupDxJqFo2h4HBIGuMpTELhj0oI8iKTAyTilk2BUGIADFlXZ4w\nFxIpE4mLPDR0QbV0lEawvufIOLhElsAaJttBjozEVfkaekwD8ga9LqBqIGvlclkZSEAw0um0ug/D\nMHh4eKapdzWuAx61HmNOpVKBa/fQFTPdg8leHtcY+FCChhdMc5DhZ+G2szkdhmeB8+htEOU5dE9S\nwnISPtetY30D0WHsduNt+K3XZuZlVGADKBQKyhtEo3QJI4ItLJfLcbVa5UqlosqUpICDk1fSFcpM\n7Gw2y+FwmMvlsquZh9ygca9eLf1KpZLKHkVHLQi7JIIARFcsFvlqOq3YxW4ScXxkRLV7NE2Tl5aW\nXNAl2LvOnTvHo1/+Mj9PxAPOpgFoXW4iErqTDGN3ymPWn9VhyJTNptagwqxUKh1VXhx0QOGh7t7r\nHDrrli5TkAtZHdCOQe3Fsuc17Lroq6ohRblcVgawblTgforFoorXWpbVpGD39/dVWeKNGzc4Hn+T\nDeNZHhmJ88bGhpJL2axlb29PIVdI8LKrD7pdSs40e1xxa6wTyQiG73d2dlSNPhLgZIKXV8tIJI6i\n0xUgfEDbhUJBGdHyOWSzWa3cyvaIBwdfVJ73wMCAo8ybPer19fXAv9OdUMwfHuQ459h+IvoLIvoh\nEdWIaJmIrDaPnSWi/5GI/jkR/S9E9JdE1O/z28CHEjQOS4jbsdIPc+jWv4Sg/CAyP2tdKtV6vdGG\nMp1Oe9JvSlISCbnrXoPfPeOcuFZAVCiBwG8AzYEAH5mpEnKDdQ0lCMUtk2J2d3c5k8mohBkQjcgO\nVBL6goDieiDIuG+5KSEGLMs5EKPDXJZlKWNgdXVVKddiscgXhoZcXuwZp85Yer0TExPK0ACx/wki\nRUoiyUSGhoaa6AOffPJJJrKztk8Qcfz4cX7v/Pk7EmM+zPWOdWUTe3zGRDYVpt5L+DCGlzcNRSPj\no/rvW8kUFBDqeHUlj3uUWdf4XM/r8LrfTOaaejam+SlPTp5TpXZY67hedLaSnqKMRQP2r9VqAhZv\nxFtN8xM2zR6FEmGd4p5AalIsFlUCmK7EBgZmlccuiUNwXdIoRWOZUCikck+wpkulkotFjegmG0a3\nymOR7VlLpZJCBlCaSGRnaOfzeV9+ALzef/99VWZoz9NcxxxUfsh8D3nMRPRVIvq3RJR03t9HRDeJ\n6B+1cWyWiH5ARF9y3v8cEf0NEb3g83tPAfN6f6fG3TiPFyQm42WSBk8qZl1xBlnr+/t29jGYeqSh\ngdIEPdNYwtR+lIRemaJSGJExit9CEGVilx8agXuAx1ipVFT9JOJcIDrAxoHSCyhZWM26Zy4TY/QY\nObwM/B6QGWA8fCbJ+1EiAki+x7KUgkXtseTrxb+Tk5OujHIvMhH0W5alIHhhwxkfH+dKpRK4TnXF\n/HnL1NjY267NXafCvN35oZS85Em2CPWKG7dClKTCkomdGDZpScK1/nFt8vx+8qR7jURnlJEpKTmR\nuAj+aT00I+8LMHYul+Pjx+NN80MuJDPe5uamWndYp6lUioeH4yxLjRKJSy45ksaLLlOybSmYzojs\nJMhSqcS3bt3iZ55JMNGoaseIRDfMYXebGmoqgcK/+Mw0TcUrgGOfeuopl3HtVtiPMdEwE43xwMAL\nvL6+3o5M3Z5iJqKT4v8tFanPHDlHMRvis9eJaJuIfjHguD4i2iGiJ7XP+4noqz7H+CZfHHacq9UI\nStpoF+71G15xYrkh6Mqv1XnlBqR7A17HIIYEGFb/PijTFEpJ8mHrXLgS1oJFH5SMA5IC1AnDMJAe\nuGQ5w/OBNwvyjlwupzYYkCIgM1smsNTrdVd7vJ2dHQWXIStVJtLAwIH3jXMhblypVBSV5ijZ/Nfw\nepFIg0xRwzB4cXFR0SF60W/KTQYNLSzLciWWTU9Pu2rJvYaUJ7lOPg+Z2t/3bh7Rbrw5aOgKWJYP\n+cWLcU1e8hFkAPtdq1Q2XnkMXqxhUp5Ms8flNY6MxD1Z8aDwpLHtZXABwkaM2rJ6NI+529XqEXKL\nlo1AjyBPNgVtggcGZtmyenljY8OVNKdThe7s7CjEamFhwcW+t7u761K8+L9sdiGTJlOplKubG5Ax\nlFXNz8+7+qTDCNa9ZnANjI+PczQadUod3c0xLKs3UB4OSzHfdlyZiDaJ6J9pn/UR0T4RfSvguD8k\non/d4bmUZfh5WfcYfpuWVKp+gqEPr3vxSkQKOlaH2vSa4U6eDaxWeIZBsL3X5iYzmgGZSdIRPTEl\nl8s1wXvSq4FljqQvbJjwiBFvArcu5u/q6lKZ39gkoCzRcxbUnDp9KYwGQGTI9CwWi8ojx3cSzi4U\nChwOhxUSgbnRajIajbo4f5/r7eVxIn7+wQd52ql5xsaRTCb5/fff5/jwMD9DxMNf+lJTy0iZ9HX9\n+nWOxWL89EMPqd8F9Y8lIpex9nnKFCDdROISj49f5vPn32uiwvRi1PIaXvchlUvQ/chj9fgxZOkg\nhjfWerFYbCsBVJen9fV1npyc5bGxtzmRuKR45KGY8ftyuawM6iAnRsrU3t4eVyoVjscvMtEZnpg4\npwh+4HVLak0YBOBA2NraUiEkPBugB0D1arWaOmexWOREIsFEpCpCYNAiHAQDYHp6Wv0fSBmMnFQq\npUq0bty4wUQ2NSeM7Vwu5ypBBIPe+vq6Qwd60unZfMLlYQNxIiKHJ7uBJDz6aPyueMw/JTepiHz9\nr0T03xHRqy3m+PdE9GfaZ12OYs4FHPcBEZWJ6Ftkx5f/BRH9l0Q0FHCMZ+Zku+N2N5r9/X2uVCp8\nNZNRDFXb29uu7/WYqF+ChtcGIN/riVd+Q7fecZzuuQY9B6/nolvVfs/Mz0CBYgPJiISxJF81Ysay\nP6xO0CBLPvB88T4UCrmSWmBMSApQGAmS6hPZpsVikSORCCeTSZWABUMCGynivbFYjPP5vDImwEIU\nCoVUrE3SZ8KTBSfw4uIil0olLpfLPD097S5rIuJXiHiMiE8KxYuNJy4IWG4S8UkB2RHZ8LY+500i\nvhiP+64dIrptj/gwZMrPy5Qeq1fjhjspT/qxWLNeJY3tylOtVlPhFz8Ocz950isS4GWDwhPXBSOx\nVCq59iNplEiZgjEqaTXB9S7DP6hokEgSZBxrH+WCCFklk0kVX0bOBpK8iOwkq62tLWX4gk4Tx0rP\nXJKQAFECMU+1Wm3q24z2l4CxZXx5eXmZDaObib7LRJedfx9rgrGffvpptmubG3HmePxi4Po5LMX8\nnzmKdZWIlpzXGtnQ9FUi+jMi+nctPN9dIvpT7TPDUcx/EnDc/0BEW0T0XxBRxPnsP3HmG/E55kAb\nQJBwdrKx1Ot17rEs/tRpTfiZlvHqZZnKmKdXPFj3APxgMr978oLfvOBuPc4mY2mtlKseg9Y3THke\nzI+2b/CEvX4vyzFgkWOzkSUc8j5xDAQM1jOymfFCzA5WObwLQGDYNEC5CaWO7Gtci7T0IfTyfIi1\nlctlxY0N71rSBeIlSUH0+PHvO//Wifj0sWM8SsRjfX3813/91zw7MOD67Wx/P+fzeZ6fn1fXdJKI\nnyfia0S84/zuQizmu56J6EBK1U8J6t+1M4Lgcq/v8PcAjaTXbw8iT7h2L2/Yz0DoVJ6kpwqDVT+H\nbtRDpoDMyHv0+j0qFcCCBiQI8Ws9NCYpbHWZQvtGUGHW6416aVkOBUKQbDbrivtCNvT4spQn5LLA\nCAUMjeoJEJIgT0WyjEGeZBc2Gf+GcicifvjhGBvGGSY6ye+//z4/+ODzTPSZI06f8Ze+NM43btzg\nbDYrUKgTTLTJdqeq7zDRwN3Jyiai60T0hMfnT5BDPkJE3US0HjDHQT3m/5aI9oioV/v8fyei/8rn\nGH7nnXeaXh9++GHgwwoSzqCNQR/7+/tN2bWyRlSHeAHpBGV76htFu3BiJ9fN3CihAGWejLPpVjWE\nHPAbrG95bgn9ShgdyhAesJ8Hg/PqJTfSctcFWmZOp1IpFf/FBoJnXSqVuFAo8LupFH+rr497HBYh\nwHCg54SQp1Ip1SlKkorgWWxubvLi4qKqmcS1IH4OTwBzZrNZV+ccGALIPsVmMTM0xB8bBjMRf0zE\n7zlr6hq5M7FfOnWKe0zT5Ql3OxsQYtN69vbfI+LvEPFvnzrFP/jBDzzl5nblyQtW7nRdBilyr5CJ\nTDw8THnq9Nr95InZ3XVNDykBidFj/weVKX1P02VKoktyXhigyJrO5/PKk0eMFwxiMptcyie48lE7\nLHMfZmZm1N9JMpWhfBHyVC6XXQiZHe+2vWfI0MLCAhcKBVW7PzY2xqFQiOfm5vjRR0cZBCDPPfcc\nb2xsKBkGv/yDD36N3fXIJ5loXG7jTDSmGMLAJXD8+Iz2m1EulUrMzPzhhx/6yhQfgmL+MOC7fy7+\n/z8F/M4rxtxLrWPM/8RRzGHt878gok99jmkpMF6jXeFvZ1zLZPgzH49Zjk4s9aDht1l0et3wNPR6\nTcwvoW+9/lg/BzYeeKbYhJAVjbiV17X6KWMoQ1k3DCgdyWO4Lhn/lnzC2ChDoRB3m6ZCNj41TT43\nOelKQsMmMjg46IqNwVsAUxi8Mlw3+suCWhQew0lqNJWQHsGVK1fUxijLOYhsfuvfO3uWpx94gGNE\nXHN2gO9onjS84zdnZjh+/DifNAx++eWXXZ74yw89pH4vFfudqGM+THlqd9xpeWLu7NrL5TIbRjcb\nhh2TlSEtKFm5PhFS8jsH1hLio1hnktXOS6b0MBk+h8EKdi7IKBKwkB2OsIDsOiU9+1KppKgwZTXF\n2tqaCh/B65WlSpgbzgm8XhgkMoFUR9NkGRRRg6IW769fv87hcJgnJt5wPNpryqMdHh7m9fV1FUKy\n5fyMS8F2db3Ip09PqWQ3opvc2/sNNf9bb73FRMSnT081KXQZu/cah6WY/5aIHvL4/GEi+lvx/ocB\nc2TpYFnZM45i7tY+/5SIfuBzTAtxufNjZ2eH3zt/ni+PjwfWiEoBlBb07cCGtzMAW6EESr+uTpLF\n5CYpifLbQQakYsaQ8BgUMMo+AKnpMXsoYZ0GEBuVDv0+61jD0oJH3Ap13PCAp6enlTcqLXl4H7gm\nGCJvxuPK871Jds9lCasBdcA92ZvKhNoIYCSccBTs2MMPu7zfbqcRhyyr0SFyGVvWFfudYP662+Ne\nkye7uUOjHWImc42Zmyk8200Wg0zJMr2gzO8geWJm5WWHQiElT4gZS6UplaFEpnSSIslpAK8b3amA\nVskuU4hD68mOKInSw1kwmGGUILcDYSF4z1DUjc5X17gBSRztvlgAACAASURBVNv81rJz1auvvspD\nQ9PciBN/xogpT07Ocix2gU2zx9Vv3f06waHQ19mu8zaawnr6OCzF/I+J6P8iu2nF685r0fnsT4ko\n7HznqSidOb5KRP+GiBac9/cRUZ1EfJmIokT0CRH9U/GZ5fzun4jPvkl2jPk3fM4VKCztDC9oy09w\n2hHiVr/Z32/Q3IHX9iDztHMf7VyrTF6RtJUYOhOR17xecWy/5B2ZqANoTc8299rMYESk02lX5yts\nzIDQJDGD3FDeTaUU09YnpsmXEgnX3IC0dWMgEokoRTk1NaU8XZm4Fo/HVRxsdXWVX+/razICAItJ\nTm3EuGUJFhHx5cuXmzaEE0T8G/ffzycNg/P5vOsZ7+zsuJJduru7mYg49vDDPErEp7u67jjzl76u\n2pGpw5AnZnZ5YHdKnlrNMT5+2eWFDQ1dYGZukin9WbSC3HXeb/169LIqkKPoyWSAnME3L0lBZGKl\nlGMYmlJpS3mFEkVZk24oA/5GQhqQIkDD2ANxHsgclHmhUFDHQVkvLS2pnAL0Tm4Yt28w0e+7/g5E\nY9zT8zss6TXt1zDbiV7vMdEOf/nLr/DW1pb6+5TLZVe5Ich6iIj/+I//mIns1qqtjMHDUswRIvpP\nyc7O3ndePyWia45S/nmyu019vcU8T5JNFPJDR9kukWD+Ijvm/C+JqKgd9zARFYnoI7LZvz4kom8E\nnMf3gbQ7dChLwk66kPtZo17z+UFs+F5S2rVzXQe5j3aOlxsFNgF5TfrGIqFufWPRzym/29nZ4YuJ\nBD/r8DuDQtDrWXt5AVIoZUJWvd4ghgAhhPTWMX+xWORu0+RRsltKbm9vq40NZRlQurJVXT6fV/XG\nJ4lUiRQ2NTB+wWioVqvcY1kuj/nc5KSKOYN4BCUfeLYoSdM3LiKbhjOZTPL29rbLGCkUCpxKpVRp\nSCgUUokvXh50/PhxvuaUo3gNL3myqR8btJh+x7Zai15/58OQJ2Z28bD7eaK3K0+t5pBMXJb1qWIp\n02VKfxa6PDG3zijXw06SCMXLuPaTJ8R5JWQt0S4gYDLTW147DCLLslwGMUh2iEjxAcCYXVtbc/US\nlyWH2BfBLFYsFjmTySg0DOQnoVBIJZZB7uCtu7OmP2aiP+QG9Gwr57GxMR4cfMH53vaaDeOPeHLy\nnJJpGAde7V1Pnz6t/l+tVgPX0aEoZm4ovGNEdMp5Hev0+Lv1aqWYD2KRB3nM7WwkmE9m8npZ3VKx\n+M3TDuQlrdxOPWb8TnqxuoesW+DSO8XG4kUhqHvj1zIZV4z3Wibje49y45BwNDaRIKIV/V/pwUsu\ncBlvg0UOeA6e7+7urqssCU0jdKsdNclENidvNpvlcxMTfMb5faFQcJVSAcpGWZZeaiLb3mHzgreD\nki+pvGHZI3t0fn6eS6WSguPADEbUnP0rh5c8uRVOe7SY7crUQeTJS0G3I1N+cuJ3zk49/J2dHT5/\n/j1lwCB5EPNgLh121+VJfy7ynPrz8ronL65tPR8CkDIQH6++zlK29Jivfi+Qo0QioTxsWV2Bz7DO\ny+WyIu+AwQq2PxiYy8vLLkIgGBG4XnyHLG0YAJFIhB9/fJxPn/42E40w0Y7wnkeVHNy4cYO//OVJ\nl9csv8e8RHaMPJ/PK3kyTVORkQT1N2c+ZMX8RXm1UsydWMntkMe3A1Prismv61M7irOd6+/UE/A6\nHot9dXVVKQjpIUPxenEIQ7HrG4n0YlZXV/ltrb/yhVisaQOQG5CMf2EzDorPBUGP0muRoQTE2yRc\nKO9ldXW1qYTp9b4+DofDqqMPKDxlhjfmlrXT+XxesX9hk0RiWiQS4fX1ddW6zjRNBWsnk0mlhOHZ\nY35Y/9evX+dQKKQU9NDQEIfDYbWRSBrSVsxf+vCCaNtZb4chT/I3WCvtKN/PW6Zk9zIoCy9DFqEc\nyIn0aGV8F/eEnATEZZm5SdHrexBkMRqNumqaW8mT3/OEfOp10OAfyGQyrnnlngG5QyIlng1kqFKp\nqEqHePwiG8azfOrUi2rNywTQcrmsGL+wrnd3dxVRSS6X4+HhGZY1x4ZxUtVB24Z0c7eo/v5+Xlxc\n5PX19SbjGFz3/f39imL3rnrMX5TXYXjMGF4QUKcjCMo9yPDyPDqJe7V7DiihY8eOqUYPUvABlckW\ncHLDgIeLDQLQMRQCOXAusteRwCS5tfHs0JADkJVMtpJw+87OjmtD8nr2MjlK/m0Rb6tUKq4GIFCu\nSIbb3NzkbsNwxabPTU6q7FFkiyIbXfaXBeMYMlUl/y+8X2xK4PXFJmAYhooZb2xsqKzxQqGgvGLA\nabLumoj4oYceUspZbiw3btxoib4clsfMfDjyxPzFlCkYJTJRSnqZEnqGIQxlKY1drEXMgUYP8rnK\nREm/EA8Mx0KhoDKhcY0yWVInHvGC0WE0SGNjf79BPoR1r6NcQKCADkB28b1sQhGPX2TTtOlGTfMT\nnpw8p5IlJXEQEi9zuZyLb3tmZkbIRKNblGEYPD09rZLS5ufnnX7OdnnVE088oeTF3XGqIUOYd2Bg\ngAuFQksmxiPFfJvDz8LvREhvV6D95pRw2O2UiARdnx/UJ+NOsv5QtruTsDU2FHh8hUKBV1ZW+Nat\nW/ze+fN8IRbjbod0Q2ZO4//YqMLhsIsnGLEtSb+H38mNQ2aVwoPVyfvlPYH9S2aBgvMb1ITXMhm+\nMDTEPQ5RAVrcSTJ/mVUqY2F6PBplJ2jSsbS0pDZbKFPZZWp8fFwloEm2oxs3bvDy8rKy4r1elmW5\nklbatO5dQ0K0oMVsZwR5zPeiTOkhp3bnaYWg6R6pHlO2Y/iZppaWUIAwlGXoRFLWSrpcEPhIhSup\nMBHjhbzCyIaBCEhZKly5rmFwI3FRN4plDBrIk5dMSeQDv0OuRigUamrQYRjPciqVcskTPGxZZzw4\n+CIPDMw6DTQacpDNZjkajbrISEzTVE0qIFe2px5Xv4ORC9ga/Phy7qAwDPORYu54tCvwncBad2IT\nqdfrivkKi7tVfNpvtIrpecHv+j1BoCW5ATO7LPC1tTUX4byMQcPqlUItOXThzZbLZV5dXVWeMTK0\noRDhkSOBTipnQPOI5+qxPgldS8MAVjvgZrmZ4NxIDAOMnUwmXR2kZOII7h/zw2MB5AyID4mAyLzd\n3d11lWtMTEy4krnQNAMN5BcXFxXZApGd1T01NcXhcNiVrd1mPOzA47Bl6k7IE84vZSoo5NRqHp1M\nRI7DkCeEX1ZXV13dybCm9QqGWq3m6oyFLOpKpaLoaLHW4cUiJowQjZfXLPngZYhJ97ZxrI5sQab0\n0kxcBwyDfD7PExPnXKVnp069pNYw+iqjhAqo3ODgiy5YemQk7qrqgEH/wANfY6JRh2TEnjMSiXAq\nleJKpcKVSoWz2azLCD537hwvLCyovu1oBWmaZmBoiPlIMXc8DnNzkDBPqzk73Wz0GC5z+83U9eGX\nPOJnzXsNKVBSGFHygPlA3be9va28acBYukUue0njGiR9poTuvDx76V1gPn2TkfWRMutUvrDxgJ8X\n/NeSKAVeh8yqRjcsNHRH6710Os1dXV0KopNMSCD7R8IMnmG5XOalpSXV/QaQWi6X41wu52ILQwa2\nzB4FvC0/l5taKyi73cxrr3HYMuXFQ32QubyOkTJ1UHnyukZ5Pe3KE4xErAEoS0nbK9m3kLyFhiuQ\nDz0sJZm9dOIdmWyme/a4bkkUJOHnIJnCc5QIGtY+ciRwPI6t1RpEPki6OnXqRTaMM2wYJxWXADxl\nzCMN4IGBWZeXffr0t5UxXSwWOZvNOjHnhvL++Z8/w/39s2wY3YreUza58EOiTNPkWCx2pJh9b+wu\neMztDCzkdiDmg2xe+rW2k83qNZeetawbEu0+E5mcBeEC/y42N0DSgJABmcl4m/7cZCwZnWq2t7dd\nHoH+PPb3912K2I+EQW4usOZBP4hkrFQqpWB46XHLjFAZf4YwowNOpVJRSjEejytPGlDb2JhN87ew\nsKC8b2wCsNCxMSCGhuxWPA9cH34Xj8eVd21ZlkogAznJSbGZeJUAymHPYbdXPMg4bJkK8kbl7w5i\nDMj3B5UnuRa9ZKqT51Gr1RTKJDuQYeOX0DPWLzKeZc6HnzxJNj3pMXvxE+jwuJc81evudqsyXizh\naSRnycoFZGujDAqseVjvIOGxm0s0jMxsNsvx+JtOr+gTPDY2xqZp8sTEBCeT/9DVBnNg4IWmksHm\nHtO/r34PD3p6elpdh5RHvOT16PzsXuNIMXc4DnMTuRMxs6DNwus7vxgxs38zDakkD3JtgMaguKR3\nAGsaJRpetc9+8WyZuFKr1ZrqJ2WyGf7v1d+Z2d2yEuQk0iPAPLImE5mviKdFIhFFZAB4G5A7GlMA\nAkesOBqNqo42xWKR0+m0i48brfhk3SaRXeoE0hHZGhClJBMTE+qcCwsLXCwWVdMAKPevPfBAU89m\nQKBBXqF9DXYm9kHG5yFTd0qe5NxB61SfA+vjoDIFIwzQsYR+ZeKUV9y2XXmq1xtJZ0CiIK+YEzIF\nNj1daYPhC/kWMl6M8ABoM1GrDM8V0HGxWHSFuSSknUqleGdnR2WS273Ju1n2QjaMbtXJamhoiA2j\nmwcGZnlkJO5qf5pMJvnKlSv8+OPfZJuq8xoT1dguk4KSHuWzZ89yuVzmarWqyrh0xSxfqVTqKPnL\n88buApT9eY2gjcRL2CVspt+Xl7fg9bt2PX85n66AJZwNq9qPj9vPEt/b2+NyucwXEwm+EItxj1Oq\nAK9ZQmgyaUySi+C+4GnAepYJZXr9pyxvwvxQiETEiURCJb3hPPgehgG8bEDwMsa9ubmplCcITZAk\ntrCwwMlk0vX8YDjAk5LkIZZlKdgPxkS5XOZMJsNn3G4Bv/TAA644vt+4XY/5XpapTuWJ2V+mvH7v\nN3+nMiUzl2X2Nn6jh36CzqF7wTJTXCZxYq3Bm9WZv7zYAYHkyPi4Xv4IL1qiQ1CkMBR1UiAY0ZBV\nIFO2/Lo5rgcG5rhcLqv54vG4qyJkamqKc7mc09f7oopb2yVUTwsl/ykTDSjjWybDFQoFZRwQ2Wxf\n4+Pjnh3NvMaRYu5wBEHFh2n5tzp30G/0jM527qEdAgW/a8BnnSSX6QIdtGEFwX76+XssS5UpgdhD\nlnTBatcTtPT6ykqlwqlUisvlsmsjCnoOuGZkimazWU6lUi5OYZncgk0OXi6S1vTND0hFMpnkxcVF\nF0nI1NQU91gWf6uvj991vAl4LLL1nd6LNpVKKRakarXKu7u7/PzgIH+XiC8T8XfJZixDY4FWirmT\nzGt9+K3BL6I8yfftkpJ4/aZTmYLis2HbixyLXeBM5qr6m+gQv7yvoNAU1jQ6QCH0BMW/u7urei5j\nbhn/xnOAN1kqlXh7e9uT/lN/FjAcsAaz2ayqlpD12pBficLJ5E+7jApJYTa7GsJqyWSSFxYWXMhR\nMplUyJ6e6R2JvOx4zCAYeUbJE3i5UcaFagrZO3pmZqalPDEfKeam0Ymy9YJ6D2r5t7NJtDt/q9/d\nKQ/Fj2/X65lWq1WV/OR1HfIYHULXPVZQUm5ubvKc1nTijGF4dthBnO1iImGXNFkWVyoVNR+gKMDV\nkh9YEop4lW4hhgY4T4fqcM/YHJAYhnvE+UDVic0HChmK1rIs7jYMReP5iWlyr8P2pYcMdDYkcAuf\nIJv85NzEhKuBxU0ifq6vz1Uq4zfuRXlqda5O5v+85Aneo4zp4nP9mcLLtHmfbY9Oohj6MTKso3us\netIW/g/Zwb8yPp3JZJrgb4ngwHvFOVGSCDQLe4dMaJRIl2Qg03u0S5mSBj+M8WKxyPH4RSY6wyMj\ncd7a2lLGA2RqZmZGebwok7Ih8h5VG010kx977HnVTcr+9wTH43GVd4Jzw9NHBYaML7dTYnekmDVB\n0OGnIKHrZNM5jE3Caw6vzNAgr76dYzq5bjn8yCG8NlyQDbSzSPF3kbAqrkvGeDc3N/mqRuF5yYGR\npWIEn/XFRML129nJySYiAll3DGUNC172ZEb2KBQ0oGpdscusb0DfiKMhiSwSiSjoDiVOUKREpDyP\nSCTS1PjiQiymmtwjuQzPWibZ7e/v85szM6r71CemyS+Fw26jhhplWwdRzJ+nPLU6l98cuods1wtf\n5aGhhgfqdZ26V327147r9+IK9zNg1tbW2DCedXl4fnF/md0svWjUPqPCwCseDRmSmdTSgMPzgIKF\n3IIlEDkQUFaSKlNSakKmobSRi5FOp5VXKkmMwJcQCoU4n8+rJDIcA7QIHAmSXx59pFHWBaPEDvNc\n44GBWac9p8Gm2cMDA3Nsmj08PBznsbG3OZO5yuvr6yrHBLkiQK1wP9lsNrDXAcaRYhZDh3uYDw9O\nO8gm0c5oxZSkW69+8JjX9cmNtR0F2opsxYuLut2NS1rysjwK8DSyQLe3t/lSIsFvj42pdpoSFpfX\neHl83KWIRolUYhWEp1arKcU7Nzen4rJS4ereADZTeOZ4flD6shuVTCCDR1soFHhxcdHFzFUqlXhj\nY4PPnj3L169fV3Gs+PCwgu5vEvHs5KQrtoUm9To70/7+Pn9LU+q/RcSfCY/5pOOdt5OV7bf2Pi95\nOui5dGWoM5idP/9eWzLld32dyJQfjK4b3vjN3t4eJxIXVZZxkMeM47xY9MCG5cW7Le8BBq6XUQJl\nLcl6CoUCp9NXHXjYbgyB2vxisahQLdBWwrDFHoeezlhTkBsYyvCS0fcZBnYqleJ8Pq9q9gFZZ7NZ\nTiaTSokbhsGDgy9wb+/rHI+/qbz3lZUVZUgsLCwo7gAJkVvWp443/SybZrfyuuGxI4FTZ2DzG0eK\nWROEOxXTOqy59Xla1VLqkLBfQonX9aEUIx6Pq/6hQRy7ra5V36ykwpYEItjkvJJS5Cbk9X+vshO/\n2mXpMYPuExuSJEKQig4eL7ypdDrN5XLZpfwkCQMSWHZ3d9WzKxaLqlxqaWmJt7e3XSUi2JTAfQ3P\nQVr9MiHmpGFw/NFH+ZyTeW0Yhuo7a5qmi6hflqOcJFIe88dE/C4Rv0d2jHmY7IQVUCUe1GO+1+VJ\nn0tXhjrn9/j45bZkKsi4RKY/lL+UqaC4tNecukxtb29zInGJx8be5snJc5zP512Qs24MdCJT8h5x\nLVCkUoHXajZZiSydWltba4KFH398XK1jyAdixGDdQ5029gbJIog4Mrxky7I4Ho/z9va2ivFKjxVe\naz6fd7VSHRwcdL4/qcIAhvExT06eUx711NSUqmpAKWRf3+uutWFnbjfoQBHOQlcs8G0fecwdKubD\nGgeBhTuBt+5kHFsOvUYSljAsUUlE4KXs/RQxfiPLp3AeQNW60OtzohwKSSjI4pZMRl7HIzMbPNXw\nri8lEryxseHyLLHBIEYL2K3hnSTUs5HXBiUIT15mdQOSh4DjeChMEIGg3AnXsre310ROcuvWLVc9\nMsqskMCCTG/AiOl02pU4Zpomn3Ag6+cfe4w3oaSdpDmZOXsQxXxY46BhlsOQKS+P+SDnwHlQZ+xF\ncwkkxq+e2Q/V8pIpacThPDr5iH7/UqbgTcvYLghK5PEyVq3vBTC4oWCHhtyJVKdPf9tFCYqKBDgB\nuDYoeRmfxn3JhEt4oysrK+q6lpaWeHFxUXnIUr43NjZc8mR3lpKK9oyL0hSQO+TJME6qeLOdsd0o\nozKMZ9X+gHCXNFJarZcjxXwHhp+gB20A7QidH7R1p8b+fnPyCax71B8jbgvINuj69SEVczgcVvR2\nuD+vUhIdOpSbDuAuyR6kQ9jYXLDZyJgr4rYgZ5Cwow7ZYTNNJBIq2Q3XtrGxwd2myWcMg9+YmODF\nxUVlMKC0Q2Zio7YZmwSUMjbparWqkmvg/cpjkP29sbHB2WxWNahA/1nLshSMh2xsEC9cuXKF34zH\n+dunT/MAEU8fP84X43FFWYgs01ZZ2XdyHESevL73kicdUdHHQTm/vQbWtF/NvEwmbJUt7TcQOwaE\nWygUFLrlJU9yXilTUGzItfCTJ1QZyL0AMoWMbZQdPfroNKOXscyOhhEABSlzGiQsjvJK1P4DnYJB\nLtEpmZOBcqZisajuETzjMkkzkbjoSuyamDjHhULBRcRTLped2mjbcB0ZiXN//ywTDbBhfCQ85lll\nIKF9a6FQUAbzEfPXISvmdoTjsDxmfWO5HW+51ejE8pcGgh/MJSGxVvcsqfhkdqcXbA/IT28kLw0G\nmVGqK9KlpSX1nVTs9XrdlVXpt3lL/ms9WxbjaibDn4rOUt2OkgSxgk5ruLm5qUqXZKwZSSgyo1pS\nmcLzlvWT8JBfGBzk2YEBjjs1zPYmMqKeARRBt2m6ystOkJ0cs7a21pSE4zfuRXny+v5uylM71yd/\nJ40+HR5vNzdDzgWjThqbfoY+1tTGxgYnEhdVMtPGxoZaJ37yJOk3dZkC7eepUy86EPYOG8Yf8cMP\n/zZnMtd4e3tbnT9InuR1wjhA2SNKE2Fs4B6RAIkYNjKiZUgIiY2Axm0vuJuJzvCpUy+6kuCQ0yGR\niImJCeUF26x6NlGJafbwjRs3FBOgaZq8vLzM+Xye0bVqYuKNQCPvnlLMRNRPRH9BRD8kohoRLROR\n1cHxlnPcXovf+T6QVqNdYT6MGFhQ7Ouwh5d34Xc+Ga/yYwxDnFZ6nEHPY29vT0FZgM31IUk/sGFI\nWEhelw5jSyYvbDQ6T/fm5qanoNdqbh5t3BsoAvVykwtDQywxsfjx42ojALwmN0ooZMS/AEkjKxZZ\nrIVCQWXShsNhvnHjxv/X3reHV1Wc67+zw0WoUFspP60eCSk9HhIOl9ZEFFtjKKm1F2mr2acHlVQI\nIElqAgSwLWCCIAQq4umjPXoKmAAt0NZSbY+eaomPiCQhIchFrYoBK/VBqlZRgVy+3x97fyuzJ7Nu\n+5YdmPd59pPstdeaNWvWvPNd5ptvaNqkSXRrRgZdBljBJXkjRljLnvYHAlQwfryVv7ukpIT69+9v\nzV+rSUWuDisAAwYMoIaGBiv5QqIs5rOVT0TdAy+98Emev9X1azn+wU0pkeMjNm3apK2fHLwoRLoU\nzHSQgsH5ERa0XEfZAyUn7ZGnl9iro+ajzs4ui0jFyZy045PcfnweB4jxqgZ52dd5551nCVBeriSv\nmmA+saLMXiredpWv5RgOnovmnASzZ8+mpUuXWuMIPwfv4NWV2KQrHWf//v2poGCeZZEHAgccE/Kk\njGAGMATA2wDuCH8fCKAFwBofZSwG8I9oBbMXDdwrmeOljatupkRq97KVy3Ohuohc1ph5LkptM7ks\nXsfLrm+7+suks9OaZcHJUc9ymXIgnM5tx+453ulJdb3J9eDreJ6dhefGjRupqakreT5Hg/Jg1Nzc\nTMPT0iKCyqaHl2GxcOeBjI/xxhPyxhc8+HAbLl++3Fq/yW1QMH68JYD3CkGTR4ygWzMyKE8RtmXZ\n2RFWl1z3iSNGRNR14ogR1gYi7CaUs6/p4CSY3bxEyeYT31cWIImC2h/5vTvxSecpkr027K1h97JT\n/Z04JSucLBzVYKa8vEVW/e3aTOaH7IaXlYpQ0FdX9HIgkB6R23rjxo0RfVK3OQ2PgfL6fOYBX8ur\nKXhZIgtSjmGRd6LjlKDMNR6feEwpKSmh06dP07Jly2j9+vVWXVmpVgNK+Xn4vo8++qilKPPOVmqy\nEqcUtqkkmJeGBbOQjt0K4AyAizxcPwZAc9jKjkow+52zckIsLjrdPdU5znhCN8fEc0p29/OqMHCn\nlTMD2Z3nZJmxJiy71NRtGHVLPFTtW57jk9c36zR0tf5y/t8zZ85QQUGB5T7mwa+9vZ2WLl1K1cXF\ntCgvj4ry8yPm0Wtra62sYuw6lwcYeUtJdu/xMiueP+fnnhBOLEIArUIospoQytolJwoZHghEDCK8\nnzMnPvjGqFH0vUsuoe8OHkzLw9vU8RwlByRFK5j9xlXYIV58ku/r1L9jhSrIuH/btaVcd6d2kV3E\ndp4lhhOn+B6yG7q4uNqKmk5LO0BXXRXUrk+W24wVZl6frONTQ0MDlZSsookTF1NJySrL88N9q6Mj\ntH/5zTffbMU2sHUrl8/9npdL9e/fn0pKSujUqVMUDAatoDDe3pLjSDh6myO9mU/8/tkY2bRpU4R7\nnN3mbP1y3IZsrMiR5DU1NVYAGseCMNf69u1LkyZNkyzm/VRSssr23aWSYN4D4Anl2BcAdAK4zeXa\nPgAaAVwBYIkXwawjsd85q1jhZWDySlgnuNWbCawmE/AyCKpLLFRNnzVydf9jXXk60qvE0eX6ZZLK\n7l+G3byibC2oOYTl52OLXw2gYS2dkx2wxSAPmvIyMDmBgrxuGQCtX7/eijqV3YZcFpOc92VmjT0o\nWcxzJFW8DaDxAN15xRVUlJ9PL7zwglUPeV9cDmoZJkS3eWYejAoKClz7HAtmv5zqCT7J943WC+Vl\nnJAFiE7hcypXjp/QKYocVNTe3u7LI6HjrDzl09bWRsHgfBLiGrrqqlBEcTAYdJ2X52PMJ3kvddXq\nZze4vDyMk5oAsKZauJ93dnZaXJs0aRplZ5dRQcE8a+0xcxDoWhP9ve99z1peJe8xLfOJBTbPVXNb\ncArNvn37Wvmud+/ebQVBytn/5PlqViiY07zRhpz7PpQPIZ0uvbSA0tIyqKGhwbYvpJJgfh/Aw8qx\n88KCeanLtZUAloX/9ySY4+Eai3Vg8Xq9jrBu56vBJE5b4blZq6r1qWb/US1tblOOjJSXQLl5JOTU\nm1wv1lDVeqoDDQ8MOgGrDhDs1pZdZ7Kmz8uv7rnnnog6cx3YmuUoUXkpR35+vvUc7OYLBAKWEN+4\ncSPNnj07Ygca1fXGebP5HlwW0JXkoF+/fhQcP54mCEFZkpX8ohCUHghYAUByVCu712bPnm0FjBUp\naUyLsrJo1qxZ1rl20wsMFsyxcipV+cTXOAU8ylDnjJ3qoOOUnItablM5ToJd4m5tLlvqOp7acYrd\nveryHifliwVb3759LZcwtxErANy35aQmvCSR+7ycvHwI6QAAIABJREFUMIjHEHn+m1NhcoRzKGgt\nGBE8yS5ytp45yQkLbHlKh9cXy9u5yklFODWunJpUViYmTpxoBYexAJbn25ctW0b19fU0c+ZMkoO/\nzpw5Y9vfUkkwtwP4L+WYCAvm+xyuGxd2YfchH4I5VtdYsuap+D46wWeH5ubI7EVyGXbuMae2kAca\n+S8PIGr0KJcjL4HQaf+6erBwY2KqGr+TO9+rxcblyMuVeJBiZYPdXRxopiZUkQdS+flZi+e5KFkw\n19bWWnPHvHyDBSRHfsprJnkwZaHKu0IBsAZMtsrXr19P3/z3f6cJCG3XuHTp0ogE/yzIOThFDraJ\nSGOalkYF4ehtFtxeNrHw0o+czkllPhFFcorft53F7aUd3Dils7Q5+HH8+ALKyiqi4uJqOnPmjOO9\nZGGp46kTp5zelVdOyQKZ6yFP/chl8TPK7nPmYfc9kSdY3JQz6tXU1FiJe5hncmY+uX1lQa66ttva\n2qypHP5NVuY40Ym8PlndPlZuk5BlfRlxApNAYH+vCf7ybTED6ItQFPYV0rG7vQjmJUuW0JIlS2jx\n4sU0Y8YMWrx4Me3YsSOicbzMj8nBHfGar1Ld13JeWq8Wgc4tHK01otPuvbi8Ozo6um1B5/a8bnNx\n6jvxqlTI1o3sepPnspqaupaZcGpO3q1JVnLkQVm1iuT5YJ5/4jkrHiD4Oy9T4WAW+Ttr7jy/xglR\n7rjjDhJCROyRzEKDXW08V617fvYEcMDa5s2b6cyZM7SqpITKcnKoIjxvDsBa+5yWlkZNTU20Y8cO\nizfyxyufdO9PPZ4IPsn9RFa+vPKJr+9pTnV0dFB+/vSIeWC7wV22Yp34RBRfTvG6ZdVSljdT4a1L\n5WvlMmROsXIbspgPWEItEEi3hC6nuGVLmL1MKp/klJ08RcXpdjn5CN+XlaClS5dq+cR1lpMJ6RQq\nrj8A+sxnvtUt+MuJU5Qiglk3x5zhNMcMIAfAywD+Ev7sAPAGgA7p2AWa62w7pa5z6ywwmUCqheoH\nunuoL1/WKGNx9SUSOpexHGjlZOHq0vq5uQBlDdtLEgnZuuFjchCIPNiwm0ret1muGwt13XaQciSu\nLDB5QOIyZdef2m/ktuMoba6DKrRZe+cBiJdF2cULqIkh2DrhurBVXhBOMsJRpU59ziufnN5PIvkk\n10sWJr2JT0Shd5eVNaPb4K67Ro0b8erFUDnlNAaqnJLjK1R3P08dsWWp8kleT6xurtPU1ET19fXW\n/HcgkE6VlZWWUJYVOt1qEr4PB2YGAsMpJ6eMiourI3aSkgMC2aUux3XolDOVT3LWNE4qwqk8hRhG\nXTt/9Z7lUlWIISpbusaTK1vXKe06qwzdwNPZqd+ezQvsyvM6uNjBTqBHK+jdrpMte6+DKz+PGtTl\nFXwPedckO8GgqzPPo7W1tUXk49UFmsnlMPl1QlU9V45Yld1de/aEktuzi9vJOyBvsSdHpvMcGbvz\n5LSmuoA2tX1Y4+/fv39ETuFJkybR6dOnI3bIcoIXPtn9lgw+ebm3Fy7olAin425w4pTKJyK24i6L\nWGssD+7y87jFjdiBBShb2H4MFzmoiwVbY2Oj9Xw6TnEZPO3Vt29fz3ySYz3k9clLly7tpjBz+8hL\ntwKBA5SWlmEJVt78ghVpOW+9PK0k11/Xh1jJDQQClqJ700030YYNG2j8+AICrqb8/KLekWAEoXXM\nfwcwK/x9YHju+D7pnP4A9gN4xKEcT65sN/ghuNP5bohGOHqJJpUJJp8v19PLM+ruqeuMqjCTtUu7\nAUvWZP0E4ThZzG6beqhtxPNfcqIDO0+FPGcr59x18qhwu/F7YFeyvDb6nnvu0SpNakCbrJ1zW8vL\nroCu7GByUhLe91YuV10yJm8EwHXiPaHV/bVVeOETUXRKaDL45CaA+Fw5OEsXqWw3Z20ngO24yO+I\nvSVyUo/a2loqLuZtKFdFDO5qP/UyFaC2lyp8/PBJbiOORpYFrcoNuZ7q6gdGaFOOUDayYLCC6uvr\nLQtbXmvPwpTvqfO+6TwO2dllETzgDV7kJVLsyuY55dra2gglXh3nuCxOUDJp0iRrSkjeuc4JKSOY\nQ3XBSABPI5T5qxnAPZAyf4XnnF8FUKu5dkzYlX1YcmWvtbmPp04WrfYbLbyW4eW8pqYmy6Kzc9/Z\nkU7VvFX3k25w0WXb0pWng1yG27OrZeksDad76ZKQsHXJz6gqEnL9dF4A1X0tD97sGlfXs9bW1obd\nagFr8JIHRZ03QbagOZBGtpRZKPPAzoE2CEepyoJEDWSSo9DlZP1uebKJvAvmaIVmtIjn/eT3arcU\nyE4gqgJY3j/YSWBzdjq75UlufPLiWYuVT0Rd/V9dnqQLmlTHDB2fZKU+GKyw3L9ACwWD87spvOyK\nbmhosNzG/OyqF0+1mIPB+RHBabLCPHv27IhNceQlmbLSoSpmzHkONuPEQGw9u60/J0oxwZysTyIG\nknggWitBB7lzqxadHO3spuHLAl5XLznAS42i1pVn913e+cap3d3eidvvco5fO4teHdjURCbq86ll\nqpaObuDmtpc3pZDbQLaOZUWKl7DI6Qs5E1RjY2OE1cXucl4KwkFei/LyqKKggGpqaiylTPV8yMI5\nlgQjft5NvBFvPsnWnvwMqmLm5BXo7Oyai928ufuevKq1rOOUrh113hY15ayT1ypaPhF19X85AY56\njcwpOWGInSLDsRlqOs+8vEUR7SR7rdiIkBP1yIqNzuOwe/du6tu3r5V8pLGxkRoaGiKSCcn15i0p\n5fgSXV1UA4h5y3tHe1R2jWDWQXUvJXpQSfQ92NXEwRG6lJS6a5wGBp1W6jYY6s7RWYvxGFDV+sra\nvd091HPcgmHU8+XBlAfr06dP0/T8fLpGCKoIBq1lLjrN364d+Zi8E5FchjqIq2WsKimhg7wsKhCw\n1jqrlh5HktbU1Fhrq50Gkmhd2cno73L5bW1tERmoYtkpSr6HLGi9uH5153Bd1aCpaDmleqESoaTI\nLmInTqnGgGol6xR1FqryHs68I5VsNKhrsNXlZ3J2N7txR42q5ncQyptdTVlZM6i4uJoaGhps21Cd\nm5efmeecly1bRrt377bydDvBCGYH6Nwv8RIWPQHZReZkMcvQDZ5OCovToONH44+nG5OJIWcEc7qH\najGrSSDkdZa6e6oJWAYMGEDTJ02yEoAcSEujVSUlEVa0mhxFp4VzuRz4Ultba5FcHvg4QEzW/ImI\nFk+cSLL5MUFaD82DJS9pkV12ffr0iYtgjsZNGk+47a0cDWTXtJ3FLMOu39m5n508X25eKDfOeQUr\nNPI8r/zMXp9NFqiyZWnHqc7OrnSe2dllFAgMp5qaGqsslddy2RzMpabPVOex2ZplhZc9RY2NjRQM\nVkQs0SourraNHOcpJTnxC+cf4PXQnLGNAzadYASzR8TiXk0VRENWJ9ecbu2lTmh7UQDiDbkeHPHp\nFl3MsFMc5CQQcgCKmihBtlbYRa1m1yrLzo6YR9RlZmNyq5nP2FKWk5mwxd3Y2GgNSHJEaWdnJ1UE\ngxEbVqSHl0JVFxdTWXY23XTllXQZQjtM5X3hC7RkyZKI/XztEA2f7NrZy2/RYuLExbZLjaKFX05x\n31DzR7MHRfVe2HkZkskpWaHheV4nd7wKJ0Wcn0NOm+mUilSNwuY9mlXvk45TKp+4XLZ0WdADodSg\nQlwT0V+ysmZEJByRn4ED0Pg4JxvifZvvuOMOK72nrCzbwQjmOMBJeMV70InngOWlLC+WpZ2r184K\niLXOOgvC6Rw/kaVqOfKzqIkpdOugdW3Qv39/ShciYmOJioKCiGkFnbXAFjNbzbzHLEdYy3PLvHxL\n3q+WNwvg6/v160cVBQVUlJVFFQUFVF9fH5H1qwKRm19cFtbsndqaKHrB7AQ/bm+vnLCzmOPFKT98\nKimp1tZFFR52XoZ4csqNH3l5i7rN88bKKdVlL6fIVPmkXsftw31dTtjjxKmOjg6qqamhgoJQlHdB\nQQVVVlZG5CtgTp133nk0fnzQCjwT4kXLYpY5xffnIDG+Hwv5tLQ0Ki4utpRkL5vCEBnBHBf4dffG\n4sKL1lXmpSy/A5RqOdu5duNp9cgDl25wYsvWLtuYzsJR54PlpU5OWaKYfHLkpq4NmpqaaPfu3TQ/\nGLT2Tn700Ue7uarV51SX0vDAxUE26l7SnGuYrQ95fo0HES6DB62y7GxrxJ0qj74AfXfw4IgsTXZI\nhGB244xOAXTjU1tbG5WWrqaJExdTaelqa445XpxyUtDPnDkTYSHn5f1Ua727KZ6J4JQcvKjjU37+\ndOpKjNF9CkCtkxq4qHrU1L7JkF3TdoJf5SsHKrJS6rRUrLk5FJWdlnbAsv6By6yNZdRAuVOnTlF+\nfhFlZRVRIJBuBUuGvB3VlJ1dRkKkR/CJn1f2ZnHaXF6F4SVxjhHMCYLTwJFIi9mP0Fc7v59ro3GL\nxwMyMWXBqbq8dMu7+HnVIBkW9HLgCA8e6nIVWQiq+YB1EeU663nZsmVWqk+7uVu5zizAZTeeuryM\nBzze5KC4uFjb5+T1ok1NTTQ8ELCs5O8AdDD8/0GAvjN0qLUZQLItZrv20AnqWPtevDilEyZ8bTBY\nEWEhjx79nxHf5aAmdU7XabODeEAXvKgK0vHjg5Sd3bV22olTLOh5ykdeRqRGSvP9OSJaXpFg5xmQ\n31dTU5M1j+sW7dzZ2dltT+SsrCItn4i63Ny8rpkFqux5CQQOUH5+UUTmL071mZaWZrnZ5bq6bQpD\nZARzUpAsoeX3XtEqDGyVygOjk5WaKKj1IIrcTUe1OHnuVV2DKmvg8rIIXUrD5uZma99XLkde5qS2\nqU6AcB3lOSn5mWRLml1+uukCNVEDu/bUjQJUyO7Q+vp6K1r8uqFDaQFAiwG6Swia9rWvecoelWw+\nEaUmp5wsZtUdfN11P42w3uvr6622Drm5u6y6YHB+oh8xoq7y/DVzTE1zKfNMVY5li1nuy6zAqpxi\nQS6v59clR9K1M9eP+aRLQSpzatKkLuuf21bHJ1a4eS0/p//cs2dPt3c5ceJiq05yPgdOJsLjohxo\nZizmJAhmP4NEMgcUt3tHWxfWotWlGHYJQhL5zHbPJLvPZMVB50KTy5LP02VAam9vt+ah5HWO6ppl\npzbmgUJ2n/Fxds2xhi4vuVDrzffmuvI5nJ+XI0TV++7evZsqgkFalJdHK2fPpntnz6YZWVk0DKB0\nIagsO5um5+dbmYo8avdxhR8lsTfwyS0iXF76k5MTadXx2l27+ybjuVT3sByI6DSNo0uuI0+vNDU1\ndUvfqZ7nZDGr9VMFubyGmKOm8/OLKC9vEeXnF9Hp06etcu341NjYaO3K1q9fvwjvhxAv0tCh36GC\nglC0ujzeyHPdvIaaM4e5vTsjmOMAP3NUftzF8UC87q26VmUienUFesns5VT/aCG7n+W1lnbuUdnV\ny0STo1Bli9Npntjvs7E1zoLZaU2zPBiploi8gbycHIUHi+GBgBXw9aIQdJcQRADtDwRofjBITU1N\nVvRpWlqaV+0+rvDKqZ6I+Oc0kdnZZZSWNtx1TSqRfn7bjlOyxawT4nL/i4Yb8XD9y7nM7eoj815N\nXSkv0VPd1n5Siro9G1vjnNjDjlM6PvH9N2/eHJE+N7T/83waOvQHBNxFQFs3z0Z7e3uEsrFnzx4r\nAMxtqRSREcxxgVfBFI/5ML/wc2+n32WS+XV/R+vqdrvO6d6qQqILGFMHFDXXLVvLLDBLSkqovb29\nm1CIpk3snlcn6HXl2bnIBwwYYC2ZYmHMgwGvfVaXbi2W/584kTo6OmjWrFl0GUC3ZmRQdUmJl4T7\ncYVXTsUzOtkr5DSRaWmhpBd28Mupzs7OiKVUcpCaWqZfpcTtumj4pOt/fEwVxPLUEsdacKCXer1O\ncEbznnUZ9HTl2Y2T7B3kDH08NoS2opxg69lg3rExwkGXBQUFrkuliIxgTgr8dKpoOqBXQrnByUqR\ntXu7Du23jjoXma4+dpa2073t3GY6l5g6oKhubN0x+Xp1Ew61XtEsLXFz4Tm1Jwt2jgaVd+PZtGkT\nDU9L67KYAVoYHlkOpKXR6tJSam5upuFpafRi2JI+GD5uh1SeY45FSbK7TjfHaAe/nPLj1XJy6Trx\nxU6Zcbo3CyjV+rXrn/K95GQ73HfVY2p7qOWrgtOvRW33Pu3GNfl35pT82bhxIwUCw6WdvkLbODLX\n29rauqVpVROyOMEI5hSArkP7sSyjucatHom8hqFq3n7Ldvpdl+3L7jqdNSCTyE4wy8+h0+6ZlHKC\nE69tJSsWuvvYDSgcOMMZvdTBbM+ePVRfX0+rSkqoLDub0oWgYQglPFldWmq5WMtycogUS9oOqcYn\nothd3U6c8pNBzC8/UpVPck58L2XL7a/jky46W34Op6RGqhs8Wj6p99IJfFYiOKhS9hLwHtFytDpz\nfdmyZVqFw+u7NYI5wfBCNDs3SjT3iIXYyYZKhHjW3Y6IRO5WjLxsSnZnOQW36TR4vg8nPlBzCdsN\nZrx8xM5Cl+ukDpaskGzcuLGbJd/Q0EDD09KoLCeHqktKrE3g+/TpQ7W1tRH7H6+SEo8cSDGL2Q+n\nYp2H1Q2mduuhexqJ5FO03iuZT/wuWFmyezdy2/OaYV7/3dDQELGUUVUU7DjFSXhURV3lFMd4yNNY\nvAyxsbExYppJp5Bzak5eZmhn9bvBCOYEw8vLcHOz+CFWvKxnr4hFC1fPcbNu/LSHeq7sTvZSZzWB\nhx/lyq4OqgKlzp/LwpbdznaDrDzg8fwXuwDtNgOpVoRtRjjAbNOmTVZwC6dYbGtro1UlJTQjK4tW\n9cAcsxOSyamzmU9ey9Od50cJUOvjVVlqbo5MCKKu99YZJOozMx/U5Y86hUv2jDU2NkYETTJPnYwo\nWYhzrnk1V7fXKS0jmBOMWF1TfgeFZFvMbnV007Tlc9wIG4sVJAdjeEGiXZC6dlGtdQ40sVvPyfdk\n9xmT3k4BUTexKMvJsQY1OcpWraNb/0tFi9kOfjmVqnxy4oAfnnjhp911Tm5zHaLhlLp0zG4+3+6Z\n5WVdzc1dgZ3Lli1zDIDzyindtXI+hLPCYgaQCeDPAJ4D0ARgGYA0l2uyADwUvqYufN1KAIMcrnFt\nmFRAb3BLu1km8k5LsVrCfiwBFdHm9o0FXkis/ibXs6mpyUpw4OT20y1d0bVNxLaPaWnW0iind+cl\nyKa38Iko9Tnl1mfs1ut6LUN3brQrJqJd0uQHkfP5B6yEILr62PFDridzqm/fvo5TU1455XQvtW5+\nloGljGAGMATA2wDuCH8fCKAFwBqX67aFP32kcl4B8EeHayIaYdiwYQTAfHr4M2zYMMfOqnb0VB9c\n1cHTr0vcy+CnzqU73aOtrY1Wl5bSorw8KsrP7xbMIitSdstWdOhNgjnRSMSezwwnN2osSGU+cXvm\n5JRRMDjfdU5ZBz+c0ikqXqcP5KQisiLtNL1kh1QSzEvDgllIx24FcAbARQ7X3QtgnHKsAkAHgKE2\n1+gawaCHob6Hnh4w/MznyefYudVimRu1+80pyM0OXA/dJvK6pS1ubWD404Vo93yOtq/5QU/zyWsd\ndHO3dq75WDjlxN1oXPsyp2SlXF266eUdpJJg3gPgCeXYFwB0ArjNZ1klYcF8qc3vukYw6GGo7yGa\nOXa/0BHFj8tcV0evbrVo56Xd7uVlekH93S4RgxcY/nQhmj2fo3Un+0Uy+EQUf0458USdovLDqXjx\nScdlmet+lkkxUkkwvw/gYeXYeWHBvNRnWVsB7HD4XdcIBj2MnrCYdeT0Ezzjt47RuLUYXufJ7cp1\nup+8lMotCluFE39SwUpLJnQWs5t7285Ki3fbJetdJJNTatv54VQi+eTldyekkmBuB/BfyjERFsz3\n+SgnB8C7AL7ocI6uEZKOV155hcrKymjp0qVUUVFBBQUF1NraGnV5O3bsoOHDh3c7/qc//YnS09Np\n5syZtGjRIrrxxhvpxRdf9FTmhg0bKDc3V/tba2sr5eXl0bPPPtvtt8mTJ9OpU6eIiGj9+vV01113\n0fe//336/e9/T0eOHNGWZ/ceEjmg+NGQ43EPnVvLi0bNVpUXS8LuuFN0aXVJCR0IBIjgvm5ZhRN/\nkmWlpQp0a5zd3Nt27yuRbXe2cEoNLvXKKR2f1Hq6bb/pFq3tNp/thFQSzDFbzACGAXgNwHUu5+ka\nIak4efIkXX311fTxxx9bxx5++GHatWtXTOVed911tsd5HnL79u106623xlwmEVFlZaVWMMuYNGkS\nEYUGrcLCQtvz7d5DNPOoKpI9EMnH1ZSfXubJdPNsdntPq+3iZaBX76kupXLK9KXiXLOY/T5TNO7t\naO7jFfFynSeyfm4Kp9x/o+GUjk+qsHaLCHfik1M9vMBJMPdBcvEagM8rx/j7q24XCyHSATwBYCYR\n7XA7/+677/ZXOwlEhJaWFowdOxZCiKjKeOKJJ3DllVdiwIAB1rGioiLr/5qaGhw8eBBCCIwaNQq3\n3HIL/vGPf+DHP/4xRowYgddffx0rVqzABRdcgAceeAB79+7Fv/3bv+Gf//ynbZ0ZJ06cwNChQwEA\nb7zxBu655x6MHDkSR44cwf33349AIIBp06ZhyJAhGD58uHXd1q1bsXfvXgQCAVx88cUoKSkBEWH7\n9u1Yt24djh07hieffBL79u3D7bffju3bt+P111/Hq6++iqqqKtx8881oaWnBhg0bcPjwYRQWFkbV\ndtGgpaUFEyZMwPPPP49x48a5nu/nHduVzcd37tyJ559/3ipLPofJtnPnTowdO9a2zLFjx2LXrl3a\n+rz88ssYN26cddyuPmPHjrXqQUT45S9/iTFjxgAABo0ciYN1dcjq6MDBtDScGjIEzc3NEeXW1dWh\nrq5O2wY6PuXm5iI3N9dTe/cm+O1LI0cOQl3dIXR0ZCIt7RAyMwd7uo/aV+KFlpYWTJs2DevWrYvo\nc9GU47Ud4skn7sOxcErHp5aWFtx+++345S9/CSLCoUMfoKMjEwDQ0ZGFrVt3YcGCFutech0AYOfO\nnda9uUw+Z8yYMWhubgYAz5xybMxkfQBUIYqo7PB5GQAOArhWOjYRwJdsztdpJ54RDxdTdXU13Xvv\nvdb33/3ud1RcXEy1tbV04sQJGjNmjPXbuHHj6N1336V58+bRr3/9ayIi2rJlC1VUVNDx48cpKyvL\nOlfnyiYiys3NpZkzZ1JhYSEFg0H65JNPiIjopptuop07dxIRUVVVFdXW1tJjjz1GM2fOJCKiw4cP\nWxbzjTfeSPX19URE9MILLxAR0d13303r168nIqJvf/vblvX2wx/+0HJZyxZ3NBZzPDRzp0ANHezm\n/fzUz8991L7k5VrV8vHjGlfv67SUyg1++dPb4bc/ploKz3hZutH0t0TzSb6XX06pHq7QvssHbC1m\nL/eVy/STkAUp5MoeAuDvAGaFvw8E0AxpfhlAfwD7ATwiHfsigCMAygB8Ofy5AsB/wyaaO1bBHI+O\n/etf/5ruvPPOiGP3338/VVZWUkNDA11//fXW8RtuuIEaGhrohhtuoN27dxNRKFjnm9/8JtXX10ec\ne+2112rvl5uba9X56quvptdff52IiDIzM2nRokW0YsUKuvPOO2ndunW0cuVKWrFihfWsLFhfeeUV\nCgaDNGHCBHrqqaeIKCSYWdDKQrewsDBugjke0LnBnAJRvLj74q0wRFOmnXvP730ZXIa8lMoN55pg\nNghBFWSx8ik0p1tNOTllVFJSHbUCEy9Ohfbdnk9CXEPB4HzX+ji51OXEMD441fOCOVQXjATwNEJZ\nvJoB3AMp8xdCc86vAqiVjj2F0NIo3Schgjke+Oijj+iqq66ijz76yDr2s5/9jCorK+kf//iH1mKu\nqKigX/3qV0QUEuwLFiyg48eP06hRo4go1DHS09O198vNzbWs2W3bttGsWbOIiKigoMAiymuvvUYt\nLS3dLGYO/vrLX/5CRESvvvoqjR49moi8CWY5eGzatGm0Y8cOOnjwYLc6JvI96OakYs3NHe/gHK9z\nf14sCq+DkRygEu0G9UYwn5tQFdxY+RSa0+2yUL2uAbdDPHjuxyugO1/dSMYrUkowJ+uTCoKZKGSB\nlpaWUlVVFc2fP5+mTJliCaxHH32U5syZQ3PnzqWNGzcSEdGJEydo+vTptHz5cioqKqL33nuPiEKW\n9tSpU+nuu++mrKwsevTRRyPu89RTT1FGRgYVFxfTyZMnqaOjg7KysujBBx+k1tZWKioqsizm999/\nnzo6OqiwsJDmzp1LK1asoIyMDKqrq6NFixbRPffcQ+Xl5fTggw/Sm2++SXl5eTR79mx6+eWX6Utf\n+hLNmjWLWlpa6Mtf/jItXbqUnn32WcrIyLC2Qdu0aRNNmTKlm7eAyN97iMWyjLaMWOugQ1tbG1WX\nlFBZTg5VBIOOu1gxvCgEXpWG5uaunMfLly+PJVDFoJcjVk7Fyodog+Ts6qbuahWLgu2HT+yyHzBg\nQKycMoLZoOfB7yGWudnehojtFQMBKsrPd9WuvVjGfi1m3lVH3tzd6wB7LvInURHJ8UC0SmhPcyra\nrGk6cNS1lykZLwqGHz6xxcz7OPuZFmIYwdzVCAY9DH4PXgaIWAbGVBpU1WVK1wjha2CM12Aa7Vw1\n0bnJn54WYk7Q1a03cCqeQXJe3dg6xJtT0dTBCOauRjDoYfixmGNBKg2quh2f/Dx3PLX9aM/X8cds\nDNNzm70kYtrGDanEKRnRPLcbpxLNJyIjmOVGMOhhyO8hkQNJIqKpowUvU1o8cSKtLi2ltra2qIK4\nGNFaS7FAxx/DqcQh2rY9VzjlVG4091D5kwwlxAjmrkYw6GHI7yFVNXBGIusnl+33Pj1hLRnBnFxE\n27bnKqdi4RNR/IPcvMBJMIvQ72cfhBAkP5sQAmfrs/YmyO+BKLbsarFe35Ply2UDSOhz+KmL3f11\n/DGcShyEEOjs7PTdL2Lps4nmUyLvIZfb3t5lPjU4AAATh0lEQVSOW2/9Cd555zxkZg7GmjVl6NMn\neUkuvT5jmD/6E+wkdm//IAUs5l27dtHYsWMpGAzST3/6U7rhhhs858m221jiwQcfpH79+lFlZSUt\nXLiQJk+eTCdPnnQtr6Ojg6ZNm0aVlZXa35999lltRrGPP/6Yvvvd71rf58yZQ/feey/NnTuX7r//\nfk/PIiOe7yFa7dtNG45WW07GvFQiyvTSjrr31hOcqquro7Fjx9Ldd99tHVu3bh3l5ubSjh07XK8/\nduwYTZ48mXJycqiyspIKCwtp1apVnu7d3NxMX/rSl2w3aKmqqqIf/vCH3Y7X1NTQpk2biIjob3/7\nGxUWFtKPfvQjevjhh+n3v/+9tiwASbd+E8Unr+fEeg2fX1JSHbfI72jq4bUdYVzZPTOIEIWScPz2\nt78lotDOUJMnT/Z8rd3GEoMGDbL+Ly8vp3Xr1nkqr66uzlYwO91PRl5eHhGF5k3tEp04IZ7vIVrB\n5kacaAcov9clYuBVy4zXoJkqgpmIaP78+XT55ZfT888/bx1z6tcqNmzYQKXhnbU6Oztp6NChnqOD\n5TS0KlpbW7WCWcamTZus9LZPP/00FRYWas8DkPSVBYnik9dzYr2Gz8/JKSOArE8sa6V19YiXYu8k\nmJO9iUWvQHt7O+4vL8eHL72EQSNHomzNmri4Qt555x0MGTIEALB582bU19dj0KBBuOyyyzBjxgwQ\nkXZjCRWhdxrCiRMn8LnPfQ4A8NRTT+EPf/gDLrroIpx//vkoLy/H22+/jTvuuANjx47Fhx9+iMGD\nQ8n1Kysr0bdvXxw5cgS33XYbJkyYACLCmjVrsGPHDmRmZmLFihV47LHHUFVVhb1792L9+vU4fPgw\nqqqqkJ2djffffx9VVVX46le/itzcXM/tsHfv3ri4sqLdAEBNTO/3d7dyx4wZ4+kZo72PlzrILnI5\nqb/OxZaojRRktLe3o7z8frz00ocYOXJQTO7FT33qU9i4cSOmTJmCPXv2YNCgQRYnTp8+jfLycqSn\np+Pw4cNYuHAh0tPTbcs6efIkBg4caNVl3rx5uPDCC/G3v/0NM2fOxOjRo7Ft2zZs27YN48aNw5tv\nvgkAOH78OBYtWoQRI0agsbERW7duBQC8+eabKC8vxwsvvIC1a9ciJycHt99+O4YPH47S0lJs27YN\nQggMHDgQzzzzDPbt24eqqir86Ec/wgUXXBBRN68uX6L4uIcTxSev5zhd4+UZ+fx1655GU5PzhiJ+\n+qNfTsWFT3YSu7d/EIPFLC9vOehz31oVhYWFdOutt1JlZSVdeeWVltb1zDPPWBrVxPD2e3YbS6hg\nV/b1119Py5cvJ6KQlpaRkWHtjzxp0iQ6evQo3XnnnVaKz3Xr1lmWxejRo+mdd96hEydO0KuvvkpE\nobSara2t1NnZSZdccol1P7ke8v92m2k4AYDjVm5nA5LlgvRrDcdSLx1//HAqnokluA/fd9999B//\n8R8Rx37+859bOeDr6+vp5ptv7nb9hg0b6Morr6SFCxdSdnY2tbS0EBHRE088QdOnTycioqNHj9LE\niROpo6ODhg0bZlnU1157LR05coQee+wxKi0tpY6ODmuzl9bWViuP/W9/+1vLKpc9VRs2bLCy9tXV\n1dla2DJP3MDv1S37VW9FW1sbBYMV4XzWFa7eDS9rpe36Yw9wSiu/ArGJ9bMTH770EjI7OgAAmR0d\n+ODQoZjKmzx5MhYvXownn3wSN910EwBg4MCBWLBgAVauXInW1lYAwF//+lfLUnbS8vv374/Fixdj\n3bp12Lp1K4gIJ06cwMmTJ7F27VqsXLkSF198MY4fP25b5sqVK/GDH/wAU6dORSAQ6gZCCAwbNgxC\nCPTr18/1uYiiC/yRNc0JEyagpaUlqnLiDSLC3r17fT9Xe3s7VpeWYsnXvobVpaUYNWpU3C1hHby0\nH2vvQoiEWOhe8dJLH0rb62Xi0KEPYi6zvLwcJ0+exIYNG6xjhw4dsvr58OHDcciGuzk5Obj33ntx\nyy234A9/+IN17bFjx1BdXY1NmzZhyJAhOHHiBM477zzLmho2bBgA4Fvf+hY++9nPYvz48di+fbtV\nbkZGBgDgwgsvxMmTJ2N6Pq/vit8rgJTiExA9p2SUl9+P3/xmKoiew29+MxVz5qx1PL9Pnz544IG5\nePrpSjzwwNwIS7i9vR2lpauxadM+bX9MFU4ZwazBoJEjcSgtDQBwKC0NgzMz41Lu4MGD8dFHH6G9\nvR2zZs3CrFmzsGDBAlx00UUAgH/913/FG2+8AQBobW217cx8/OKLL8Y111yDrVu3YsiQIbjwwgsx\nd+5cLFiwAIWFhcjIyMDll19ulXnkyBGrjP79++PPf/4zJk+ejLVr10aU6/V/7vB2g58dUkFQ6BCt\nonB/eTlueOghVD7zDG546CE8MHduxH6sscBpYHNrP/VaeUBJNkaOHIS0tFA/8bNfsQ5yW2zYsAEr\nV67Ee++9BwDIzMy0+vvrr7+OUaNGOZYxY8YMbN68GR9//DGysrIwYsQIzJ8/HwsXLkRBQQGGDBmC\n06dPo62tDUAXhw4cOIA5c+Zg9+7d2LVrF/bu3dutbnb/M9LS0tDZ2Yn3338ff//737v97vVd8Xsd\nN25cSvEJiJ5TMkJKXRaA0J7JsSh15eVr8NBD38B7741BaBfhyP6YKpwyc8walK1Zg7VCYMuhQxic\nmYk777svqnLq6+uxf/9+nDlzBq+88gqOHj2Kqqoq9OnTB1OnTsXcuXPxla98BW+99Ra2bNmCm2++\nGdu3b8e8efPwuc99DkePHsWzzz6La6+91irzkUceQXt7O37xi19g1qxZKCsrw+TJkzF48GA88MAD\nKCsrwyWXXIJPPvkE1113HSoqKjB79mwcOnQIx48fx2uvvYbW1lY88sgjaGxsxMsvv4yioiI8//zz\nOHr0KLZt24ZPf/rT+OCDD7BlyxYMGDAAR48exeOPP473338fR48eRU1NDW677TZ8/etfx7x58zBs\n2DBkRqG8JGNu0w+iVRRUD8sWjaJCLnNkdr87bVSvaz+5HD+b3Ccaa9aUQYi1OHRoCzIzB+O+++6M\nqpznnnsOzz33HDZu3IhbbrkFF154IR555BHU1NQAAKZPn47y8nIsX74cb7zxBlauXBlx/dtvv43H\nH38cx44dw65du3D11VcjGAxixowZWLlyJXbu3ImqqiqcOnUKubm5CAQCqK6uxpQpUzBq1Ci0tbVh\nw4YNyMvLw1133YVLL70U6enpyMrKwurVq7F//3789a9/xZYtW7B//3689NJLqK2tRWtrK/bt24fH\nH38cQgiMHj0amZmZePPNN7Fw4ULMnTs35jZONT4B8YmjGDlyEOrqus8ZO3HK7reGhmNhIX85gLX4\nzGdW4JZbxlr9MWU4Zefj7u0fpEhUtkEkzsb34CUmQZ6L0s1j2c1VxbJUI55z+Lr3dja+y1SBadsu\n2M0ZO82t2/EptJTqQLe5ZSckmFNa+WUSjBgkFWfje2hvb8faOXPwgeRhUSM8yUXrln+PJdnKmDFj\nsG/fvrgncDAJRpIL07bu4D5PRLjmmms88am9vR1z5qzFoUMfWJ4bu2jsJHFKW6ARzAZJhXkP8c1+\n5DQ4xRNGMCcXpm29IxF8SobbOqUEsxAiE8BaAOcBGAjgSQCLiajD5bpPA1gDYDSADgCHAdxJRMdt\nzjeCOQVh3kN8sXfvXkyYMAE7d+60AuoSEdxlBHNyYdq2Z8B8clr3Hy84CeakRmULIYYA+AuA3xHR\nVwB8BcA3Aaz2cPnvAAwkoiuI6EoAHwL4X9ETIaYGcUNdXV1PVyGhSPTzcXANR+UaOhgYTkUPOVit\nJ1cxJHu5FIdi/gIAiOhjAD8DUCyEuMjuIiHERAC5AJZKh5cCGAfg+15uzOtzzadnP5dccollCRAR\nfvWrX53VloFuEKE4rO1k9OTgYTiVuA+vl3aCrh8ZTsX23D3JJxnJFszfALCHIltvF0LLtvJdrvuI\niA7yASJ6E8BbAG7wcmNeF9zbPkuWLOmR+3Z2dqK5uRmdnZ1xK7O5uRnvvvuutaaxpaUF69atS6mE\nCMlAqiVWiRaGU4njFCcdcoKuH53LnLr66qvxgx8swNe+tgSlpavR3t7e09WKGskWzCMAHFOOvRX+\n+0WX697WHH/L5TqDKJEI4aGuaRw7dixuv/32lEqIkAyoObWJzl7rxqAL8eaUbo3wucypG28sxbZt\nU/HMM5V46KEbXDOEpTKSLZjPB3BaOcbfP+XzOr7W6TqDKJGIrFyqm0gIgYsvvrjH3UbJBrfDvn37\nzgrL2cAb4s0pndv1XObUG2+0obOTM4TFJ+1rTyHZgvkkgP7KMf7+kc/r+Fqn6wyiRKrMtZzNSLWU\npAaJheFUYpGT83mkpXVPs9kbkdTlUkKIPQDeJqJvSccyALwGoJCIamyuWw1gBhENVo4fBfA0Ed2u\nucb4Bw0MDAwMUhZks1wq2bmy/wRghhARi4wnAGgH8H8u15ULIbIoHAAmhPgXAJeGf+sGuwc2MDAw\nMDBIZSTblf0AAAIwEwCEEAMBlAP4ORG9HT7WXwixXwjxCF9ERH8BsAPAT6SyfgqgCcBvk1R3AwMD\nAwODhCOpgpmITgDIA3CTEOI5ADsRsngrpNMEQlnBzlMu/z6AU0KIPUKIegCDAdxAJqTVwMDAwOAs\nwlmbK7s3QgjxQwCrADxARFXKbyMA3AsgHaGAt/4A1hDR1mTXM1q4PN9FAJYBGImQwtgJYBERPZP0\nivqAEOJ6AEUAPoPQ1NAAAP9DRP+tOfcnCCmYJxHyHM0hoqYkVvecguGT4VNvhdmPOQUghLgAwK8B\nvIxQh9ThSQAvAsghIhJCXAFgtxDiRNjVn7Jwez4hxKcA7A5/JoSf7xsA/iSEuIaIGpNaYX+oBbCM\niO4HACFENoDnhBCDiWgVnySEWAxgCoAriOhDIcQUAM8IIUYT0dEeqflZCsMnw6ceqXk80dNZeMyH\nAOASAFeG/+9EaFMP+ffPho/foRw/DuC+nq5/HJ5vKkIbk4xTjjcDeLyn6+/ybH8EkKY5dkj6fj5C\nWn2xct5rCMVX9PhznE0fwyfDp97+SXbwl4EGRPQWEdU7/P4uQlHrNwshzgcAIcR3AAxB90xqKQe3\n5wPw/8J/1exubyGUIz1lQUTfpO47o30CoJ/0PRchl5zaBi/AY0pZA+8wfDJ86u0wgrn34NsAWgEc\nE0K8hNBuW08AeLAnKxUnvBr+O1w5fhmAgSK0K1mvgBAiAOAqhFxyjBHhv7p0tMOEEGZKKfkwfOoF\nOFf5ZARzL4AIpQr6A4AvAPgXIhoJIBPAswDO9GTd4oQ/AngJwN2SBXMbgMvDv/cmopUDOIFQYBHj\n/PBfu3S0AxNdKYMuGD4ZPqU6jGDuHfg2gK8D+AkR/RMAiOivAK4E8MuerFg8QERnAHwVocHk/4QQ\ndQgNlPchFG35Xs/VzjvCATYzAFwffibGyfBfu3S0Hye6bgYRMHzqBTiX+dSbNKdzGZcjRKjDyvHX\nEcqINo2Ieu8eZ7Dm/e6UjwkhHgTwMhHpNjBJKQghvg6gGsBEIvq78vNr4b+fR6T77fMAjvT2d9cL\nYfiU4jjX+WQs5t4BDv//vHL8UgCnz4aOKISYpDmcC2BTkqviG0KIbyI0iOQT0d/Cx4qEEJ8On1IH\n4BRCFpmMq2CTUtYgoTB8SmEYPhnB3FvwRwBHAPxYCNEXAIQQ/w7gu+gFRPOIzUKIXP4ihJgHoA3A\nmh6rkQcIIW5EyP35YwCfF0J8ObwmdiaATwMAEZ0EsAJAqRBicPi6/0QoCri6Ryp+bsPwKUVh+BSC\nyfyVIhBCbAXwOYTmho4gFDF6HxE9Ef49HcByAF9ESFs8H8BvAKxS5l9SEh6e7xEA1yHkmhIA9iGU\nqSil58OEEKehnxIiABkkJTsQQvwYwM0APgz/Xk5EzUmp6DkGwyfDp94MI5gNDAwMDAxSCMaVbWBg\nYGBgkEIwgtnAwMDAwCCFYASzgYGBgYFBCsEIZgMDAwMDgxSCEcwGBgYGBgYpBCOYDQwMDAwMUghG\nMBsYGBgYGKQQjGA2MDAwMDBIIRjBbGBgYGBgkEIwgtnAwMDAwCCFYASzQdwhhPi2EOIFIUSnEGKq\nEOJX0vfRLtcOFkL8txDigBBid/i6bySr7gYGqQbDp3MPZj9mg7iDiB4XQrwI4A0AUwB8h4hOCSE2\nA+hwufxPAD4BMJaI2sPb1/2vECKfiP6S2JobGKQeDJ/OPZhNLAwSAiHEMIQGkkIiqvF4zdcA/B+A\nSUT0jHR8F4AzRJSbiLoaGKQ6DJ/OLRhXtkGi8aaPc8chtH3bq8rxVwB8OW41MjDovTB8OgdgBLNB\nouHmajMwMPAOw6dzAEYwG6QSmsJ/L1eOXy79ZmBg4A2GT70URjAbJArC7wXhYJTnAMwXQvQDACFE\nPoBsAFXxrZ6BQa+C4dM5BBP8ZRB3CCGuA7AcQA6AfQDqiegOj9eeD6AawFcAfAygE0AlET2ZoOoa\nGKQ0DJ/OPRjBbGBgYGBgkEIwrmwDAwMDA4MUghHMBgYGBgYGKQST+csgaRBCrAEwFqG1lSpaiGhO\nkqtkYNBrYfh09sLMMRsYGBgYGKQQjCvbwMDAwMAghWAEs4GBgYGBQQrBCGYDAwMDA4MUghHMBgYG\nBgYGKQQjmA0MDAwMDFII/x/HmgTkV+3rVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c33f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = alldata\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)#,sharex=True,sharey=True)\n",
    "\n",
    "\n",
    "\n",
    "# WHICH PASS GRI CUTS\n",
    "gmag = base['g'] - base['EXTINCTION_G']\n",
    "rmag = base['r'] - base['EXTINCTION_R']\n",
    "imag = base['i'] - base['EXTINCTION_I']\n",
    "\n",
    "gr = gmag - rmag\n",
    "ri = rmag - imag\n",
    "\n",
    "grerr = np.sqrt(base['g_err']**2 + base['r_err']**2)\n",
    "rierr = np.sqrt(base['r_err']**2 + base['i_err']**2)\n",
    "\n",
    "cgmr = gr - 2.*grerr\n",
    "crmi = ri - 2.*rierr\n",
    "\n",
    "m_r    = base['r'] - base['EXTINCTION_R'] <= 20.75\n",
    "msk1 = cgmr < 0.85 \n",
    "msk2 = crmi < 0.55\n",
    "\n",
    "\n",
    "# DEFINE THE GOOD, BAD AND UGLY\n",
    "gribase = base[msk1 & msk2 & m_r]\n",
    "\n",
    "zgood = gribase['ZQUALITY']  >= 3\n",
    "zbad = (gribase['ZQUALITY']  < 3) & (gribase['ZQUALITY']  != -1)\n",
    "znot = gribase['ZQUALITY']  == -1\n",
    "\n",
    "\n",
    "r = gribase['r'] - gribase['EXTINCTION_R']\n",
    "g = gribase['g'] - gribase['EXTINCTION_G']\n",
    "gr = g - r\n",
    "\n",
    "\n",
    "ax1.plot(r[zgood],gribase['SB_EXP_R'][zgood],'ko',ms=1,label='_nolegend',zorder=1)\n",
    "ax1.plot(r[zbad],gribase['SB_EXP_R'][zbad],'ro',ms=4,label='Bad Redshift',zorder=2)\n",
    "ax2.plot(r[zgood],gribase['SB_EXP_R'][zgood],'ko',ms=1,label='_nolegend_',zorder=1)\n",
    "ax2.plot(r[znot],gribase['SB_EXP_R'][znot],'bo',ms=4,label='No Redshift',zorder=2)\n",
    "ax3.plot(r[zgood],gr[zgood],'ko',ms=1,label='Good Redshift',zorder=2)\n",
    "ax3.plot(r[zbad],gr[zbad],'ro',ms=4,label='Bad Redshift',zorder=2)\n",
    "ax4.plot(r[zgood],gr[zgood],'ko',ms=1,label='_nolegend_',zorder=1)\n",
    "ax4.plot(r[znot],gr[znot],'bo',ms=4,label='No Redshift',zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "mlim = 20.9        \n",
    "ax1.set_xlim(17.1,mlim)\n",
    "ax1.set_ylim(17.5,27)\n",
    "ax2.set_xlim(17.1,mlim)\n",
    "ax2.set_ylim(17.5,27)    \n",
    "xl = [-0.3,1.7]\n",
    "yl=[-0.55,1.1]\n",
    "ax3.set_ylim(0,1.3)\n",
    "ax3.set_xlim(17.1,mlim)\n",
    "ax4.set_ylim(0,1.3)\n",
    "ax4.set_xlim(17.1,mlim)\n",
    "\n",
    "ax1.get_xaxis().set_ticklabels([])\n",
    "ax2.get_xaxis().set_ticklabels([])\n",
    "ax2.get_yaxis().set_ticklabels([])\n",
    "ax4.get_yaxis().set_ticklabels([])\n",
    "ax3.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax4.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "# LINE INDICATING GR CRITERIA\n",
    "tgr=0.85    \n",
    "ax3.axhline(tgr, c='w')\n",
    "ax3.axhline(tgr, c='k', ls=':')\n",
    "ax4.axhline(tgr, c='w')\n",
    "ax4.axhline(tgr, c='k', ls=':')\n",
    "\n",
    "# LEGENDS\n",
    "ax1.legend(loc=3,fontsize=9,frameon=True,numpoints = 1)\n",
    "ax2.legend(loc=3,fontsize=9,frameon=True,numpoints = 1)\n",
    "ax3.legend(loc=3,fontsize=9,frameon=True,numpoints = 1)\n",
    "ax4.legend(loc=3,fontsize=9,frameon=True,numpoints = 1)\n",
    "\n",
    "# X/Y TITLES\n",
    "ax1.set_ylabel('SB_EXP_R')\n",
    "ax3.set_ylabel('g-r')\n",
    "ax3.set_xlabel('r_o')\n",
    "ax4.set_xlabel('r_o')\n",
    "\n",
    "\n",
    "#print nall,nbad,nnot\n",
    "\n",
    "plt.tight_layout(w_pad=0,h_pad=0)\n",
    "plt.savefig('fig_spec_complete1.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 248.115825921 19.8299650421\n",
      "3 248.104747393 19.3651460525\n",
      "4 248.301318154 19.8489304545\n",
      "3 234.325873352 16.7594685034\n",
      "3 233.858425052 16.2141530694\n",
      "3 234.435960893 16.5933516313\n",
      "4 354.341279085 0.114771996855\n",
      "-1 353.73947361 0.389640537593\n",
      "-1 353.995940343 0.422725931919\n",
      "2 353.95316085 0.342462149508\n",
      "1 353.961531446 0.345279405166\n",
      "3 353.94636117 0.380188628021\n",
      "3 353.805870993 0.17915963988\n",
      "2 39.254585762 -1.3337251497\n",
      "1 336.313164965 -3.41816217748\n",
      "1 336.006377514 -3.42790486398\n",
      "1 335.509494633 -3.57712303563\n",
      "1 336.261343959 -3.6494946462\n",
      "-1 336.21215169 -3.53094145243\n",
      "1 335.786072196 -3.5476566353\n",
      "-1 123.290881182 36.2456215891\n",
      "3 123.66458031 36.0510911321\n",
      "-1 123.528276197 36.5829995167\n",
      "4 123.272077307 36.1267582887\n",
      "-1 123.279286477 36.1059066433\n",
      "3 122.776884677 36.3543992399\n",
      "-1 123.445379884 36.3004508503\n",
      "-1 122.715274563 36.3567450954\n",
      "-1 122.78361092 36.4990724732\n",
      "-1 122.69934657 36.2689687378\n"
     ]
    }
   ],
   "source": [
    "m = gr > 1.2\n",
    "for obj in gribase[m]:\n",
    "    print obj['ZQUALITY'],obj['RA'],obj['DEC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:12: RuntimeWarning: invalid value encountered in divide\n",
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:27: RuntimeWarning: divide by zero encountered in divide\n",
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:27: RuntimeWarning: invalid value encountered in divide\n",
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:44: RuntimeWarning: divide by zero encountered in divide\n",
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:44: RuntimeWarning: invalid value encountered in divide\n",
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:58: RuntimeWarning: invalid value encountered in divide\n",
      "/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:1744: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not \"\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "must be matplotlib._image.Image, not None",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-b9d884af62ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fig_spec_complete2.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fig_spec_complete2.pdf'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    686\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# need this if 'transparent=True' to reset colors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1563\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1565\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1567\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2233\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2234\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/backends/backend_pdf.pyc\u001b[0m in \u001b[0;36mprint_pdf\u001b[0;34m(self, filename, **kwargs)\u001b[0m\n\u001b[1;32m   2534\u001b[0m                 \u001b[0mRendererPdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_dpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2535\u001b[0m                 bbox_inches_restore=_bbox_inches_restore)\n\u001b[0;32m-> 2536\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2537\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2538\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0mdsu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mitemgetter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m             \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/marlageha/anaconda/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2300\u001b[0m             im = mimage.from_images(height,\n\u001b[1;32m   2301\u001b[0m                                     \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2302\u001b[0;31m                                     ims)\n\u001b[0m\u001b[1;32m   2303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2304\u001b[0m             \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_grayscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: must be matplotlib._image.Image, not None"
     ]
    },
    {
     "data": {
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BaiLTYpa5F/BD4IGE5Pbhz41ldt8AtDGz+u6+I069IiKSfbI8SLu7l6z6m7hU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\n4AOGDRtG27ZtKyyruLiYRo0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v0/0gRp1RF9hoH6PONRHzNYlRZ8oyeCVtZj2BYwmaPtnd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\nSdrMzCxRTtJmZmaJ+n/1ReOn1PaL1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064ce7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)#,sharex=True,sharey=True)\n",
    "\n",
    "bn=14\n",
    "minN = 9\n",
    "rlim = 16.75\n",
    "lims = (r > rlim) & (gribase['SB_EXP_R'] > 18)& (gribase['SB_EXP_R'] < 26.25)\n",
    "\n",
    "#SB BAD REDSHIFT\n",
    "N, xedge, yedge = np.histogram2d(gribase['SB_EXP_R'][zgood&lims],r[zgood&lims], bins=bn)\n",
    "N2,xx,yy = np.histogram2d(gribase['SB_EXP_R'][zbad],r[zbad],bins=(xedge, yedge))\n",
    "m= (N+N2) < minN\n",
    "frac = 1. - N2/N\n",
    "frac[m]= np.nan\n",
    "frac[0,0] =0\n",
    "ax1.imshow(frac,interpolation='none',extent=[yedge[0], yedge[-1], xedge[-1], xedge[0]],aspect=0.48)\n",
    "ax1.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax1.yaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "ax1.set_ylim(xedge[0], xedge[-1])\n",
    "ax1.get_xaxis().set_ticklabels([])\n",
    "\n",
    "\n",
    "#SB No REDSHIFT\n",
    "\n",
    "N, xedge, yedge = np.histogram2d(gribase['SB_EXP_R'][zgood&lims],r[zgood&lims], bins=bn)\n",
    "N2,xx,yy = np.histogram2d(gribase['SB_EXP_R'][znot],r[znot],bins=(xedge, yedge))\n",
    "m= (N+N2) < minN\n",
    "frac = 1. - N2/N\n",
    "frac[m]= np.nan\n",
    "frac[0,0] =0\n",
    "im = ax4.imshow(frac)   # MAKE LEGEND IMAGE\n",
    "ax2.imshow(frac,interpolation='none',extent=[yedge[0], yedge[-1], xedge[-1], xedge[0]],aspect=0.48)\n",
    "ax2.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax2.yaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "ax2.set_ylim(xedge[0], xedge[-1])\n",
    "ax2.get_xaxis().set_ticklabels([])\n",
    "ax2.get_yaxis().set_ticklabels([])\n",
    "\n",
    "\n",
    "#GR BAD REDSHIFT\n",
    "lims = (r > rlim) & (gr > 0) & (gr < 1.3)\n",
    "N, xedge, yedge = np.histogram2d(gr[zgood&lims],r[zgood&lims], bins=bn)\n",
    "N2,xx,yy = np.histogram2d(gr[zbad],r[zbad],bins=(xedge, yedge))\n",
    "m= (N+N2) < minN\n",
    "frac = 1. - N2/N\n",
    "frac[m]= np.nan\n",
    "frac[0,0] =0\n",
    "ax3.imshow(frac,interpolation='none',extent=[yedge[0], yedge[-1], xedge[-1], xedge[0]],aspect=2.8)\n",
    "ax3.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax3.yaxis.set_major_locator(ticker.MultipleLocator(0.2))\n",
    "ax3.set_ylim(xedge[0], xedge[-1])\n",
    "\n",
    "\n",
    "#GR NO REDSHIFT\n",
    "\n",
    "N, xedge, yedge = np.histogram2d(gr[zgood&lims],r[zgood&lims], bins=bn)\n",
    "N2,xx,yy = np.histogram2d(gr[znot],r[znot],bins=(xedge, yedge))\n",
    "m= (N+N2) < minN\n",
    "frac = 1. - N2/N\n",
    "frac[m]= np.nan\n",
    "frac[0,0] =0\n",
    "ax4.imshow(frac,interpolation='none',extent=[yedge[0], yedge[-1], xedge[-1], xedge[0]],aspect=2.8)\n",
    "ax4.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax4.yaxis.set_major_locator(ticker.MultipleLocator(0.2))\n",
    "ax4.set_ylim(xedge[0], xedge[-1])\n",
    "ax4.get_yaxis().set_ticklabels([])\n",
    "\n",
    "\n",
    "# LINE INDICATING GR CRITERIA\n",
    "tgr=0.85    \n",
    "ax3.axhline(tgr, c='w')\n",
    "ax3.axhline(tgr, c='k', ls=':')\n",
    "ax4.axhline(tgr, c='w')\n",
    "ax4.axhline(tgr, c='k', ls=':')\n",
    "\n",
    "\n",
    "ax1.set_xlim(17.6,20.9)\n",
    "ax2.set_xlim(17.6,20.9)\n",
    "ax3.set_xlim(17.6,20.9)\n",
    "ax4.set_xlim(17.6,20.9)\n",
    "\n",
    "\n",
    "\n",
    "# X/Y TITLES\n",
    "ax1.set_ylabel('$\\mu_{r, \\mathrm{eff}}$ [mag arc$^{-2}$]')\n",
    "ax3.set_ylabel('$(g-r)_o$')\n",
    "#ax1.set_xlabel('r_o')\n",
    "#ax2.set_xlabel('r_o')\n",
    "ax3.set_xlabel('$r_o$')\n",
    "ax4.set_xlabel('$r_o$')\n",
    "\n",
    "ax1.text(17.8,24.75,'Bad Redshift',fontsize=9)\n",
    "ax2.text(17.8,24.75,'No Redshift',fontsize=9)\n",
    "ax3.text(17.8,1.15,'Bad Redshift',fontsize=9)\n",
    "ax4.text(17.8,1.15,'No Redshift',fontsize=9)\n",
    "# COLOR BAR\n",
    "cbar_ax = fig.add_axes([0.88, 0.15, 0.03, 0.78])\n",
    "\n",
    "fig.colorbar(im, cax=cbar_ax).set_label('Fraction Complete')\n",
    "cbar_ax.tick_params(labelsize=9)\n",
    "\n",
    "\n",
    "plt.tight_layout(w_pad=0,h_pad=0)\n",
    "plt.gcf().subplots_adjust(right=0.9)\n",
    "plt.savefig('fig_spec_complete2.png')\n",
    "plt.savefig('fig_spec_complete2.pdf')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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