HZ_Completeness_Results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This notebook takes results from running Integration_Time_full_spec.py for individual stars (see run_Int_Time_all.py for a loop over all stars. It takes a couple of hours to run the full set. A set has been included in this distribution. The parameters assumed in the set provided are 120 mas slit for spectroscopy and an end-to-end efficiency of 3.5%.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *\n",
    "rcParams['font.size']=14\n",
    "\n",
    "import os\n",
    "import pandas\n",
    "exocat = pandas.read_excel('./ExoCat1.xls', header=0)\n",
    "\n",
    "from Star import Star\n",
    "from Completeness import Completeness_MC_Calculator\n",
    "\n",
    "CMC_calc = Completeness_MC_Calculator(LUT_path = './')\n",
    "\n",
    "def load_tables(fnm):\n",
    "    f = np.load(fnm)  \n",
    "\n",
    "    '''\n",
    "    NOTE, the spectrograph is no longer an IFS, \n",
    "    even though the label on spectroscopy still \n",
    "    carries the IFS acronym. It is a slit prism spectrograph\n",
    "    '''\n",
    "    e2e_IFS_throughput_array   = f['e2e_IFS_throughput_array']\n",
    "    e2e_IMG_throughput_array   = f['e2e_IMG_throughput_array']\n",
    "    contrast_array             = f['contrast_array']\n",
    "    \n",
    "    psf_mas_array          = f['psf_mas_array']\n",
    "    spc_R_array            = f['spc_R_array']\n",
    "    diameter_array         = f['diameter_array']\n",
    "    \n",
    "    IWA_array              = f['IWA_array']\n",
    "    nzodi_array            = f['nzodi_array']\n",
    "    planet_radius_array    = f['planet_radius_array'] \n",
    "    img_SNR_cuts           = f['img_SNR_cuts'] \n",
    "    spc_SNR_cuts           = f['spc_SNR_cuts'] \n",
    "    T_int_array            = f['T_int_array']\n",
    "    Completeness_matrix    = f['Completeness_matrix'] \n",
    "    solar_glint_level      = f['solar_glint_level']\n",
    "    return diameter_array, psf_mas_array, spc_R_array,  e2e_IFS_throughput_array, e2e_IMG_throughput_array, contrast_array, IWA_array, nzodi_array, planet_radius_array, img_SNR_cuts, spc_SNR_cuts, solar_glint_level, T_int_array, Completeness_matrix\n",
    "\n",
    "data_dir = './HZ_Completeness_Runs/'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n        64924, # 61 Virginis \\n        57632, # Denebola \\n        77952, # Beta Trianguli Australis, companion 155 as \\n        102422, # Eta Cephei\\n        7981, # 107 Piscium\\n        114622, # HR 8832, companion at 106.6 as\\n        15457, # Kappa1 Ceti, candidate binary star but unconfirmed\\n        77257, # Lambda Serpentis\\n        105090, # AX Microscopii\\n        70497, # Theta Bootis\\n        105199, # Alpha Cephei\\n        29271, # Alpha Mensae\\n        28103, # Eta Leporis\\n        116771, # Iota Piscium\\n        23693, # Zeta Doradus\\n        49908, # Groombridge 1618\\n        16852, # 10 Tauri\\n        59199, # Alpha Corvi\\n        71908, # Alpha Circini\\n        3765, # HD 4628\\n        56997, # 61 Ursae Majoris\\n        84478, # 36 Ophiuchi C\\n        50954, # HR 4102\\n        102485, # Psi Capricorni\\n        89962, # Eta Serpentis\\n        24813, # Lambda Aurigae\\n        114046, # Lacaille 9352\\n        68933, # Theta Centauri\\n        54035, # Lalande 21185\\n        32362, # Xi Geminorum\\n        39757, # Rho Puppis\\n        54872, # Delta Leonis\\n        112447, # Xi Pegasi\\n        ] \\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "This list assumes the star has been run through Integration_Time_full_spec.py\n",
    "Additional targets, including onces removed from the list due to other reasons, are included in commented text.\n",
    "'''\n",
    "\n",
    "star_HID_list = [\n",
    "        #16537, #epsilon Eridani, dropped due to high zodi\n",
    "        8102,   #tau Ceti\n",
    "        37279,  #Procyon A\n",
    "        108870, #epsilon Indi A\n",
    "        32349,  #Sirius A\n",
    "        19849,  #omicron 2 Eridani\n",
    "        97649,  #Altair\n",
    "        99240,  #delta Pavonis\n",
    "        15510,  #82 Eridani\n",
    "        96100,  #sigma Draconis\n",
    "        2021,   #beta Hyi\n",
    "        61317,  #beta CVn\n",
    "        22449,  #1 Ori\n",
    "        #91262, #Vega, dropped due to high zodi\n",
    "        #86974, #Mu Herculis, dropped to binary companion contamination\n",
    "        113368, #Fomalhaut\n",
    "        17378,  #Delta Eridani\n",
    "        27072,  #Gamma Leporis\n",
    "        1599,   #Zeta Tucanae\n",
    "        ]\n",
    "        #\n",
    "        #64394,  #Beta Comae Berenices\n",
    "        #105858, #Gamma Pavonis\n",
    "        #14632,  #24 Capricorni\n",
    "        #67927,  #Eta Bootis            \n",
    "        #57757,  #Beta Virginis\n",
    "        #78072,  #Gamma Serpentis\n",
    "        #37826,  #Pollux\n",
    "        # Even more extended list\n",
    "'''\n",
    "        64924, # 61 Virginis \n",
    "        57632, # Denebola \n",
    "        77952, # Beta Trianguli Australis, companion 155 as \n",
    "        102422, # Eta Cephei\n",
    "        7981, # 107 Piscium\n",
    "        114622, # HR 8832, companion at 106.6 as\n",
    "        15457, # Kappa1 Ceti, candidate binary star but unconfirmed\n",
    "        77257, # Lambda Serpentis\n",
    "        105090, # AX Microscopii\n",
    "        70497, # Theta Bootis\n",
    "        105199, # Alpha Cephei\n",
    "        29271, # Alpha Mensae\n",
    "        28103, # Eta Leporis\n",
    "        116771, # Iota Piscium\n",
    "        23693, # Zeta Doradus\n",
    "        49908, # Groombridge 1618\n",
    "        16852, # 10 Tauri\n",
    "        59199, # Alpha Corvi\n",
    "        71908, # Alpha Circini\n",
    "        3765, # HD 4628\n",
    "        56997, # 61 Ursae Majoris\n",
    "        84478, # 36 Ophiuchi C\n",
    "        50954, # HR 4102\n",
    "        102485, # Psi Capricorni\n",
    "        89962, # Eta Serpentis\n",
    "        24813, # Lambda Aurigae\n",
    "        114046, # Lacaille 9352\n",
    "        68933, # Theta Centauri\n",
    "        54035, # Lalande 21185\n",
    "        32362, # Xi Geminorum\n",
    "        39757, # Rho Puppis\n",
    "        54872, # Delta Leonis\n",
    "        112447, # Xi Pegasi\n",
    "        ] \n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Parse the star list above into file names.\n",
    "You will have to check the format of your output filenames to make sure they match what the output of your runs.\n",
    "'''\n",
    "fnm_list = []\n",
    "for hip in star_HID_list:\n",
    "    fnm = 'star_%d_imgspc.npz'%hip\n",
    "    fnm_list.append(fnm)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters Simulated\n",
      "\tdiameter\t 2.37\n",
      "\tpsf_mas\t\t 65.0\n",
      "\tspc_R\t\t 50.0 50.0\n",
      "\tifs throughput\t 0.02328 0.03395\n",
      "\timg throughput\t 0.024 0.035\n",
      "\tContrast\t 1e-10 4e-11\n",
      "\tIWA\t\t 103.0\n",
      "\tzodi\t\t 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0\n",
      "\tpl radius\t 6371.0\n",
      "\timg_snr_cut\t 5.0 7.0\n",
      "\tspc_snr_cut\t 20.0 20.0\n",
      "\tsolar_glint_lev\t 0.0\n",
      "\tint Time (days)\t 0.398 0.631 1.0 1.585 2.512 3.981 6.31 10.0 15.849 25.119 39.811 63.096 100.0\n",
      "=============================\n",
      "8102 tau Ceti\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t0.6 - 1.5\n",
      "=============================\n",
      "=============================\n",
      "37279 Procyon A\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t3.3 - 7.7\n",
      "=============================\n",
      "=============================\n",
      "108870 epsilon Indi A\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t0.4 - 0.9\n",
      "=============================\n",
      "=============================\n",
      "32349 Sirius A\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t7.8 - 18.1\n",
      "=============================\n",
      "=============================\n",
      "19849 omicron 2 Eridani\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t0.5 - 1.3\n",
      "=============================\n",
      "=============================\n",
      "97649 Altair\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t4.0 - 9.4\n",
      "=============================\n",
      "=============================\n",
      "99240 delta Pavonis\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t1.2 - 2.7\n",
      "=============================\n",
      "=============================\n",
      "15510 82 Eridani\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t0.8 - 1.8\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.53\n",
      "=============================\n",
      "96100 sigma Draconis\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t0.6 - 1.3\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.48\n",
      "=============================\n",
      "2021 beta Hyi\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t2.3 - 5.4\n",
      "=============================\n",
      "=============================\n",
      "61317 beta CVn\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t1.1 - 2.5\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.35\n",
      "=============================\n",
      "22449 1 Ori\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t1.9 - 4.4\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.50\n",
      "=============================\n",
      "113368 Fomalhaut\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t5.3 - 12.3\n",
      "=============================\n",
      "=============================\n",
      "17378 Delta Eridani\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t2.1 - 4.9\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.45\n",
      "=============================\n",
      "27072 Gamma Leporis\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t1.6 - 3.8\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.42\n",
      "=============================\n",
      "1599 Zeta Tucanae\n",
      "=============================\n",
      "\tdiameter [m]\t2.37\n",
      "\tpsf [mas]\t65.00\n",
      "\tspc_R\t\t50.00\n",
      "\tthroughput\t3.40e-02 3.50e-02\n",
      "\tContrast\t4.00e-11\n",
      "\tIWA [mas]\t103\n",
      "\tzodi\t\t4.5\n",
      "\timg_snr_cut\t5\n",
      "\tspc_snr_cut\t20\n",
      "\tsolar_glint_lev\t0.0\n",
      "\tPeriod (yr): \t1.1 - 2.7\n",
      "=============================\n",
      "\t*Imaging integration time limit imposed:\n",
      " \t\tt_int index 7\n",
      "\t\ttime limit 6.31 days\n",
      "\t\torbit completeness 0.41\n",
      "i, T_int_array[i]/(60.**2 * 24.)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Loop over the list of files to extract completeness values. \n",
    "'''\n",
    "\n",
    "'''\n",
    "A completeness target is set to determine the integration time needed for imaging.\n",
    "There is another limit on integration time (6.3 days) set even if the completeness \n",
    "target is not met.\n",
    "''' \n",
    "Completeness_target = 0.5\n",
    "\n",
    "'''\n",
    "Initialize lists that will collect infromation in the loop below. \n",
    "'''\n",
    "HIP_list       = -1*np.ones(len(fnm_list))\n",
    "orb_T_int_list = -1*np.ones(len(fnm_list))\n",
    "svs_C_list     = -1*np.ones(len(fnm_list))\n",
    "orb_C_list     = -1*np.ones(len(fnm_list))\n",
    "spc_C_list     = -1*np.ones(len(fnm_list))\n",
    "bth_C_list     = -1*np.ones(len(fnm_list))\n",
    "exozod_vals = []\n",
    "\n",
    "\n",
    "'''\n",
    "Loop through targets.\n",
    "'''\n",
    "count = 0\n",
    "for fnm in fnm_list:\n",
    "    diameter_array, psf_mas_array, spc_R_array, e2e_IFS_throughput_array, e2e_IMG_throughput_array, contrast_array, IWA_array, nzodi_array, planet_radius_array, img_SNR_cuts, spc_SNR_cuts, solar_glint_level, T_int_array, Completeness_matrix = load_tables('%s%s'%(data_dir,fnm))\n",
    "    if(count==0):\n",
    "        '''\n",
    "        Print all parameters simulated in the npz files produced by Integration_Time_full_spec.py\n",
    "        '''\n",
    "        print('Parameters Simulated')\n",
    "        print('\\tdiameter\\t',*diameter_array)\n",
    "        print('\\tpsf_mas\\t\\t',*psf_mas_array)\n",
    "        print('\\tspc_R\\t\\t',*spc_R_array)\n",
    "        print('\\tifs throughput\\t',*e2e_IFS_throughput_array)\n",
    "        print('\\timg throughput\\t',*e2e_IMG_throughput_array)\n",
    "        print('\\tContrast\\t',*contrast_array)\n",
    "        print('\\tIWA\\t\\t',*IWA_array)\n",
    "        print('\\tzodi\\t\\t',*nzodi_array)      \n",
    "        print('\\tpl radius\\t',*planet_radius_array)   \n",
    "        print('\\timg_snr_cut\\t',*img_SNR_cuts)\n",
    "        print('\\tspc_snr_cut\\t',*spc_SNR_cuts)  \n",
    "        print('\\tsolar_glint_lev\\t',solar_glint_level)  \n",
    "        print('\\tint Time (days)\\t',*np.around((T_int_array/(60.**2*24.)),3)) \n",
    "\n",
    "    ''' \n",
    "    Get target infromation\n",
    "    '''\n",
    "    hip = int(fnm.split('_')[1])\n",
    "    HIP_list[count] = hip\n",
    "    i_entry = np.where(exocat['HIP'] == hip)[0][0]\n",
    "    exocat_star =  exocat.iloc[i_entry]\n",
    "    star_model = Star(L_bol = exocat_star['Lbol'], d_pc = exocat_star['d(pc)'], Temperature=exocat_star['Teff'], Mass = exocat_star['M*(Msun)'], MV = exocat_star['Mv'])\n",
    "\n",
    "    # get HZ period for eta_Erth a la HabEx\n",
    "    Period_min_yr = CMC_calc.get_orbital_period(0.95 * np.sqrt(exocat_star['Lbol']), star_model.Mass)/CMC_calc.year_in_seconds # Using Kepler's law\n",
    "    Period_max_yr = CMC_calc.get_orbital_period(1.67* np.sqrt(exocat_star['Lbol']), star_model.Mass)/CMC_calc.year_in_seconds # Using Kepler's law\n",
    "    #print Period_min_yr, Period_max_yr\n",
    "    #scale_val = (0.95 * np.sqrt(exocat_star['Lbol']))**0.5/(Period_min_yr)**(1./3.)\n",
    "    #print 'uxuxu', scale_val\n",
    "    #GM_sun_AU3_yr2 = 39.4876393\n",
    "    #GM_star = GM_sun_AU3_yr2*star_model.Mass\n",
    "    #scale_val = (GM_star/(2.*pi)**2)**(1./6.)\n",
    "\n",
    "    '''\n",
    "    Print out target parameters\n",
    "    '''\n",
    "    print('=============================')\n",
    "    print(hip, exocat_star['COMMON'])\n",
    "    #thr_val, con_val = 0, 0 #Required (e2e eff 2.4%, contrast 1e-10)\n",
    "    thr_val, con_val = 1, 1 #CBE (e2e eff 3.5%, contrast 4e-11)\n",
    "    for i_thr in [thr_val]: \n",
    "        for i_con in [con_val]: \n",
    "            for i_iwa in [0]: # 0 is 103 mas\n",
    "                exozod = 4.5 # can set this value. It will round to the neares 0.5 from 0 to 10. \n",
    "                i_exozod = int(round(exozod * 2))\n",
    "                exozod_vals.append(exozod)\n",
    "                for i_exz in [np.min([i_exozod,20])]: # This makes sure that i_exz does not exceed its range.\n",
    "                    for i_rpl in [0]: # 1 is 6371.\n",
    "                        for i_snr in [0]: # 0 is snr 5 and 1 is snr 7 for imaging, it is 20 for spectral with R=50 in both cases\n",
    "                            '''\n",
    "                            Print the perfomance parameters used for the estimates as well as target parameters. \n",
    "                            '''\n",
    "                            print('=============================')\n",
    "                            print('\\tdiameter [m]\\t%1.2f'%(diameter_array[0]))\n",
    "                            print('\\tpsf [mas]\\t%1.2f'%(psf_mas_array[0]))\n",
    "                            print('\\tspc_R\\t\\t%1.2f'%(spc_R_array[0]))\n",
    "                            print('\\tthroughput\\t%1.2e %1.2e'%(e2e_IFS_throughput_array[i_thr], e2e_IMG_throughput_array[i_thr]))\n",
    "                            print('\\tContrast\\t%1.2e'%contrast_array[i_con])\n",
    "                            print('\\tIWA [mas]\\t%1.0f'%IWA_array[i_iwa])\n",
    "                            print('\\tzodi\\t\\t%1.1f'%nzodi_array[i_exz])      \n",
    "                            #print('\\tpl radius\\t%1.0f'%planet_radius_array[i_rpl])   \n",
    "                            print('\\timg_snr_cut\\t%1.0f'%img_SNR_cuts[i_snr])\n",
    "                            print('\\tspc_snr_cut\\t%1.0f'%spc_SNR_cuts[i_snr])\n",
    "                            print('\\tsolar_glint_lev\\t%1.1f'%solar_glint_level)\n",
    "                            print('\\tPeriod (yr): \\t%1.1f - %1.1f'%(Period_min_yr, Period_max_yr))\n",
    "                            print('=============================')\n",
    "                            \n",
    "                            '''\n",
    "                            Loop through integration times to find the values that meet the completeness target\n",
    "                            limited by a maximum values of 6.3 days. \n",
    "                            '''\n",
    "                            for i_tint in range(0, len(T_int_array)): \n",
    "                                #print Completeness_matrix\n",
    "                                # i_comp: \n",
    "                                # 0 is single visit obs completelenss, \n",
    "                                # 1-4 are at least n of 4 obs for n in 1-4 \n",
    "                                # 5 is at least 1 of 4 spectral measurements with SNR>20\n",
    "                                # 6 is at least 3 of 4 obs with SNR>5 and 1 of 4 spec with SNR>20\n",
    "                                #i_comp  = 3 # 3 of 4 imaging detections \n",
    "                                i_comp_svs  = 0\n",
    "                                i_comp_orb  = 3\n",
    "                                i_comp_spc  = 5\n",
    "                                i_comp_bth  = 6\n",
    "                                '''\n",
    "                                The code below limits the integration time for orbit follow-up observations to a maximum of 6.3 days.\n",
    "                                This is even if a single visit completeness of >= 50% is not achieved.\n",
    "                                '''\n",
    "                                if Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_orb]>Completeness_target and T_int_array[i_tint]/(60.**2 * 24.)<30. and i_tint>=2 and i_tint<=6:\n",
    "                                    #print('--', '%1.1f'%(T_int_array[i_tint]/(60.**2 * 24.)),'%1.2f'%(Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_orb]))\n",
    "                                    if(orb_T_int_list[count]==-1):\n",
    "                                       orb_T_int_list[count] = T_int_array[i_tint]/(60.**2 * 24.)\n",
    "                                       svs_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_svs]\n",
    "                                       orb_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_orb]\n",
    "                                       spc_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_spc]\n",
    "                                       bth_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_bth]\n",
    "\n",
    "                                if i_tint>6 and orb_T_int_list[count]==-1:\n",
    "                                       print('\\t*Imaging integration time limit imposed:\\n \\t\\tt_int index %d\\n\\t\\ttime limit %1.2f days\\n\\t\\torbit completeness %1.2f'%(i_tint, (T_int_array[6]/(60.**2 * 24.)),Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, i_tint, i_comp_orb]))\n",
    "                                       orb_T_int_list[count] = T_int_array[6]/(60.**2 * 24.)\n",
    "                                       # orb_C_list[count]     =  Completeness_matrix[i_thr, i_con, i_iwa, i_exz, i_rpl, i_snr, 13, i_comp]\n",
    "                                       svs_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, 6, i_comp_svs]\n",
    "                                       orb_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, 6, i_comp_orb]\n",
    "                                       spc_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, 6, i_comp_spc]\n",
    "                                       bth_C_list[count]     =  Completeness_matrix[i_snr, 0, i_thr, i_con, i_iwa, i_exz, i_rpl, 6, i_comp_bth]\n",
    "                                if i_exozod > 20:\n",
    "                                       svs_C_list[count]     =  0.\n",
    "                                       orb_C_list[count]     =  0.\n",
    "                                       spc_C_list[count]     =  0.\n",
    "                                       bth_C_list[count]     =  0.\n",
    "\n",
    "    count+=1\n",
    "    #print 'count', count, 'len(spc_C_list_cut)', len(spc_C_list_cut)\n",
    "print('i, T_int_array[i]/(60.**2 * 24.)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orb_T_int_list [1.         1.         1.58489319 1.         3.98107171 1.58489319\n",
      " 3.98107171 6.30957344 6.30957344 3.98107171 6.30957344 6.30957344\n",
      " 6.30957344 6.30957344 6.30957344 6.30957344]\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Cut out targets that require too much time to achieve sensitivity.\n",
    "For the nominal set of 16, this won't cut any out.\n",
    "However, for extended lists, it will as some become too faint to meet the goals.\n",
    "This has already be cut in the loop above (max time 6.3 days), but is included here\n",
    "in case additional cuts are desired.\n",
    "'''\n",
    "cut = np.logical_and(orb_T_int_list>0., orb_T_int_list<30.)\n",
    "print('orb_T_int_list', orb_T_int_list)\n",
    "HIP_list_cut       = HIP_list[cut]\n",
    "orb_T_int_list_cut = orb_T_int_list[cut]\n",
    "svs_C_list_cut     = svs_C_list[cut]\n",
    "orb_C_list_cut     = orb_C_list[cut]\n",
    "spc_C_list_cut     = spc_C_list[cut]\n",
    "bth_C_list_cut     = bth_C_list[cut]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(0, 16)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "Sort targets and save their common names in an array\n",
    "'''\n",
    "\n",
    "'''\n",
    "There are several ways to sort targets. \n",
    "In this case, we choose to keep them as listed at the beginning \n",
    "as target ranking has already been through many iterations.\n",
    "'''\n",
    "#idx = argsort(orb_T_int_list_cut)\n",
    "#idx = argsort(orb_C_list_cut)[::-1]\n",
    "#idx = argsort(spc_C_list_cut)[::-1]\n",
    "#idx  = argsort(bth_C_list_cut)[::-1]\n",
    "idx = range(len(bth_C_list_cut))\n",
    "print(idx)\n",
    "#idx = [ 2,  5, 14,  9,  7, 15,  6,  1, 11, 12,  8, 13, 10,  4,  3,  0]\n",
    "#idx = [ 2,  5,  9, 14,  7, 15, 13, 12, 11, 10,  8,  6,  4,  3,  1,  0]\n",
    "\n",
    "'''\n",
    "We can include additional sortings depending on the plots needed.\n",
    "'''\n",
    "idx2 = argsort(spc_C_list_cut)[::-1]\n",
    "#idx = idx2.copy()\n",
    "idx_o  = argsort(orb_C_list_cut)[::-1]\n",
    "idx_s  = argsort(spc_C_list_cut)[::-1]\n",
    "idx_b  = argsort(bth_C_list_cut)[::-1]\n",
    "\n",
    "'''\n",
    "Save the common names sorted as desired. \n",
    "This is useful for having sorted values plotted by target name. \n",
    "'''\n",
    "name_array = []\n",
    "count=1\n",
    "for i in idx:\n",
    "    i_entry = np.where(exocat['HIP'] == HIP_list_cut[i])[0][0]\n",
    "    exocat_star =  exocat.iloc[i_entry]\n",
    "    #print 'exocat_star[\\'COMMON\\']: ', exocat_star['COMMON']\n",
    "    name_array.append(exocat_star['COMMON']+' (%d)'%count)\n",
    "    #print 'count', count\n",
    "    count+=1\n",
    "\n",
    "name_array2 = []\n",
    "count=1\n",
    "for i in idx2:\n",
    "    i_entry = np.where(exocat['HIP'] == HIP_list_cut[i])[0][0]\n",
    "    exocat_star =  exocat.iloc[i_entry]\n",
    "    #print 'exocat_star[\\'COMMON\\']: ', exocat_star['COMMON']\n",
    "    name_array2.append(exocat_star['COMMON']+' (%d)'%count)\n",
    "    #print 'count', count\n",
    "    count+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HIP\tName\t\tMax Imaging T_int (days)\n",
      "-----------------------------------------------\n",
      "8102\ttau Ceti          \t1.00\n",
      "37279\tProcyon A         \t1.00\n",
      "108870\tepsilon Indi A    \t1.58\n",
      "32349\tSirius A          \t1.00\n",
      "19849\tomicron 2 Eridani \t3.98\n",
      "97649\tAltair            \t1.58\n",
      "99240\tdelta Pavonis     \t3.98\n",
      "15510\t82 Eridani        \t6.31\n",
      "96100\tsigma Draconis    \t6.31\n",
      "2021\tbeta Hyi          \t3.98\n",
      "61317\tbeta CVn          \t6.31\n",
      "22449\t1 Ori             \t6.31\n",
      "113368\tFomalhaut         \t6.31\n",
      "17378\tDelta Eridani     \t6.31\n",
      "27072\tGamma Leporis     \t6.31\n",
      "1599\tZeta Tucanae      \t6.31\n",
      "-----------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('HIP\\tName\\t\\tMax Imaging T_int (days)')\n",
    "print('-----------------------------------------------')\n",
    "for k in range(len(orb_T_int_list)):\n",
    "    print('%d\\t%s\\t%1.2f'%(HIP_list[k], name_array[k].split('(')[0].ljust(18)[0:18], orb_T_int_list[k]))\n",
    "print('-----------------------------------------------\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaAAAAGgCAYAAADsNrNZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOydeXhUVfK/32KJBBAUlbiggIMCko1VYUCCLI6K3wERBVlER0cFxxUY3FFQdAZF/YGOoyM7oqCogNuwhMU1yrAvgoIIKiiCkgUCpH5/3O6mk3SSm+R2kgv1Ps950n3vuZ9TCaEr55w6VaKqGIZhGEZZU6m8DTAMwzCOT8wBGYZhGOWCOSDDMAyjXDAHZBiGYZQL5oAMwzCMcsEckGEYhlEumAMyjDJERAaJiIpIg/K2xTDKG3NAhu8Qkboi8qSIrBORDBHJFJHVgWtnlLd90UJE4kVkpDkv41ihSnkbYBjFQURaAe8BtYDXgPFADpAI3AxcBZxfbgZGl3jgESAV2FaulhiGB5gDMnyDiJwEvA0o0FJV1+W5fz8wojxsMwyj+NgSnOEnbgHOAu7N63wAVPU3Vb0v/JqI/J+IfB5YptsrInNEpEmePiMD+zKNRWRSoN8eEXlKRCoFlvzeEJF9IvKziDyS5/kGgedHiMjfRGSriGSJyGcicqGbb0xEWonIvMAYWSLyhYh0D7s/CGfGB7A4MJ4GrrvSCOoEnusgIk+IyI+Bvh+JSMPi2hXoU0VEHhSRrwN9fhGRT0Tk6rA+cSLyioh8LyIHReSHgG6Cm5+PcWxiDsjwE/8HHADecNNZRPrizJhigAeB54GOwCeRPmyBmUAscD+wDBgeaB8B+4H7gFXASBHpEeH56wL9/w2MBs4BFohIoUuCItIRWA7EBZ4bDmQD74rIVYFuS4EJgddPAAMCbWkxNMIZB3QAxgD/BNoD00tgFzjLgo8BS4A7An03A+HOdzZwNTAZGIzzb1EJaFzYz8Y4xlFVa9Z80YBfgZUu+1YFfgQ2AjXCrjcHjgDTwq6NxFnW+0/YNQG+wdlfeizserWAHXPDrjUIPH8QaBB2/XzgEDAj7NqgQN8GYeNsBBYBlcL6VQI+Bb4Ju9Yn8GxKnu+1OBrB8Zfk6XtX4HqzEmj+D5hXyL9F7YD20PL+HbJWsZrNgAw/UQtnJuKGlsDpwAuqmhG8qKr/AxYAl4uI5HnmlbB+CnyB80H8atj1AzizoD9EGPNdVd0W1vdr4EPgskLsTMKZBUwD6ojIqSJyKlAHeB84V0TqF/G9lkTjJVXNCXu/JPD13BJo/g40K2SmdwDHEaeISJ0ivhfjOMKCEAw/8Ttwosu+DQJfN0a4tx7ohuPQfgu7vj1Pv+C97yNcb0J+Nke49jVwhYjUVtXfItwPfmj/J8K9IHWB7wq5XxKNvHp7A1+DDqI4mo8Ac4BNIrIex+m+pqppAKp6UERGAP8AdonI5ziRjNNUNe/P3DiOMAdk+IkNQAsRiVHV7FLo5J35BDkS6aKqRroeSSNSca2CxgoSXIUYAXxVQJ9NUdCI+L1y1F7XmqqaKiLnAlfiOPaBwF0i8oCqjgn0eUZE5gB/BroCDwH3i8ifVXVhgd+ZcUxjDsjwE+8C7YDe5Nkwj8C2wNcmOEEE4TTB+Yv/dy+NI/L5o/OAfQXMfsDZZwLYr6oLitAvqHpkcTTcUixNVd0LTAGmiEgszjLdIyLyj6ADV9WtwLPAsyJyNs7e0QjAHNBxiu0BGX7iJWAn8LSINM17U0RqicgTgbdfAT8BtwU+EIN9knD+An8vsM/jJVeGZykI7IlcCnxQyDNf4SzdDRWR2nlvishpYW+De1knl0LDLa41ReSU8HuqmoUzWz0BqC4i1cP/DQJ9vgd2R/hejOMImwEZvkFV9wXCn98DVojIDCANJ1ItAegL7AHuV9VDInIPzkzpYxGZihON9TecPZyHomDi18AyEZmA88fd7TiRcY8W8j3liMiNOPsm60XkVZx9lTOAtkB9oFmg+wqc7/U+cQ7lZgGfq+rWYmi4oph2bRCRpTj/Fr/gBDDcBMxX1f0ikgwsEpFZwLrAz+RyoCkwrDh2GccW5oAMX6GqX4pIPHAvzp5DX5x9i804M6Tnw/q+JiKZwAM4Z2cO4qSxGRFYDvKaGTizlHtwPqhXAXeraqRAiBCqulxE2gAP4xy2rY0zO1iJs8Ef7PeDiPwVZ9nqZaAycAOw1a1GcSiG5rM4Z7QuwTlH9T3wJPBU4P73OH8IdMY5K6U4zvovqvoqxnGLeL8KYRjHF4Flt63Afar6ZPlaYxj+wfaADMMwjHKhTB2QiFwsIu+KyM68eawC9yeF5bgKts/K0kbDMAyjbCjrGVBNYC1wJ84GaiQW4KyfB9vlZWOaYRiGUZaUaRCCqr6HE8GEiEwqoNtBVf2pzIwyjFISSL9T1IFTwzDyUBH3gNqLyO5AaveXRaRueRtkGIZheE9FC8P+AHgLJ6KoAU5a90Ui0lJVD+btHAhJ/StAbGxsy7PPPrvEA+fk5FCpkvf+2HT9ZavfdP1kq990/WRrRdb9+uuvf1HViIehyy0MW0TSgdtVdVIhfc7EOfx2raq+VZheq1at9MsvvyyxPampqaSkpJT4edMtW03TjZ5mNHQPHTrEjh072LdvH9WqVfNMN8iBAwc8142G5vGkW61aNerVq0dMTMxXqtoqUp+KNgPKReDg3Q6cfFqGYfiUHTt2cOKJJ3LKKadQq1atgjsePgzjxzuvb78dqrj7iNq/fz8nnpg/UfqXX37JuHHjmD69qNSBBWtu27aNVq1a8csvvxRbI5wXXniBF198EXAccvfu3Rk7diwAIsKf/vQn3n///VB/EWH//v3UrFmTlJQUtm/fTq1atcjKymLAgAE8+OCDEe0tCBEhISEhNJuZOnUqCQlFF6QtSjcSqsqePXvYsWNHof0qtAMK1B85C6ewmGEYPuXAgQM0aNCA9PT0wjvOmAF33+28rlMHBg4s1bitWrUqkfPxmrS0NMaNG0daWhqVK1emevXqrFuXu6r8xo0bWbJkCR07doyo8fzzz9O9e3d+/PFHmjZtSteuXbnwwgvJzs4mO9tdcvhPPvmEmjVrlvr7KQoR4ZRTTuHnn38utF9ZnwOqKSLJgdxQlYBzAu/PCdwbKyJtRaSBiKQAc3FSf8wpSzsNw/Ce/PX/8nD4MIwadfT9qFHONRdkZmbSu3dvLrjgApKSkrjmmmsAZymxVStn9Wfbtm2ceuqpPPDAAzRv3pzGjRuzfPnykMb48eM577zzaN26NY888ggNGjSIONbnn39Op06daNmyJS1btmT+/PlF2rdjxw5q164d+vCvXLkyiYmJufqMHDmS++67r0itM844g8aNG7N9u1NKae/evTRr1oxBgwYxd+5cDh06VKRGWVDkvzdlHwXXCicF+/9wckY9Gnj9GE59kgTgHZw8UZNx6o20VVW3VTANw/ArM2bAli3QqJHTtmxxrrlg4cKF7N27l/Xr17Nq1SpeeumliP327NlD27Zt+d///sfDDz/M3//+dwBWr17NmDFj+OSTT0hLS+O33yJXz9i3bx+33norM2bM4KuvvmLevHnccsst7Nu3r1D7unXrRpUqVahfvz433ngj//73v8nMzMzVp1evXhw+fJh33nmnUK2vv/6aPXv2hPbo4uLi+Oabb7juuuuYNWsW559/PrfddhvLli0j7x5/SkoKycnJ3HfffRw8mC+uq8wpUwekqqmqKhHaIFXNUtVLVbWuqsaoav3A9bzVKA3DONYIn/089JDTwPUsKD4+no0bNzJkyBBmzZrFCSecELFfzZo16d69OwAXXXQR33zjlD1KTU3l8ssv57TTnGCtG264IeLzn3zyCVu3buWyyy4jOTmZyy67DBFhy5YthdpXo0YNPv30U95++21atGjBK6+8Qtu2bfMtnY0ZM4YHHniAnJycfBp33HEHzZo1o2nTptx9990hWwGqVKlCt27dmDJlChs2bKBly5Z0796dHj16hPps376dL7/8kqVLl7J+/XpGhc82y4mKeA7IMIzjjfDZz3XXOa0Ys6CGDRuyYcMGunbtyoIFC0hKSuLAgQP5+oU7psqVK3M44NxU1dWSkaqSmJjIypUrQ+37778PLfMVhojQunVrbr/9dpYvX853333H2rVrc/Xp3Lkzp59+OtOmTcv3/PPPP8+6detYunQpw4YNY82aNbnuZ2Rk8Nprr3HttdcyduxYhgwZwlNPPRW6HzymUqtWLW666SY+/vjjIm2ONuaADMMoX8JnP1u2QNWqTgvOKlzMgnbu3EnlypXp0aMH48aN4+eff+bXX391bUJKSgrvvfdeKNJt8uTJEfu1a9eOzZs3s3jx4tC1tLS00FJX586d+eKLL/I9t3HjxlzOZtOmTWRnZ1OvXr18fZ988klGjhxZoK1//OMfGTJkCA8//DAAv//+O3379qVNmzYsW7aM4cOHs2HDBp544gmaNGkCOPtEWVlO9rPDhw8ze/ZskpOTC/uRlAkVOgrOMIzjgODspyCCs6BCIuLWrVvHtddeC8CRI0e47777OPPMM/n6669dmZCUlMTw4cNp27Ytp59+Ol26dIkYLn7yySfz7rvvMmzYMO666y6ys7M599xzmTt3Ljk5OaxatSqiU8nMzOSuu+5i9+7dxMTEULVqVaZNm0bduvkTvbRq1YoWLVqwdWvBJaseeOABGjVqxMqVK2nYsCH9+/fnhRde4OSTIxeY3bhxI7fccgsiwqFDh2jXrl2FWIIzB2QYRvkycGCpw627detGr1698l1PSUkheEC9QYMGuc7y5H1/ww03cMcddwBORFqbNm0i9mvdujWpqan5xlqxYgU9evTgzDPPzHevRYsWLF26FIh8riZvsMDs2bNzvc873kknnZTLpiuuuIL9+wuO1Wrbti2rV68u8H55UaHKMeTp++9An6FlaKJhGMcpI0aMIDk5mQsuuIAvv/yy2DOEYHCB4Z6yngEFyzFMCbSIiMjVQGvghzKyyzCM45wJEybkel/YjMLwhrIOw35PVe9X1dlA/jhDQETqA8/h1I6vGCeqDMMwDM+pUFFwIlIFeA0YraobytsewzAMI3pUqGzYIvI4kKiqVwbebwPGq+rYAjRC5Rji4uJazpw5s8T2pKenRyVHkun6y1a/6frF1tq1a9OoUSOOHDlC5cqVPdMNEg1dP9laUXW3bNlCixYtCsyGjaqWSwPSgUFh7zsCO4HTwq5tA4a60WvZsqWWhsWLF5fqedMtW03TjZ5mNHTXr1+vqqq///67p7pBoqHrJ1srqu769esV+FIL+NyuSEtwnYAzgB9F5LCIHAbqA08FSjIYhmEYxxAVyQG9ACQCyWHtB2Ac0Lkc7TIMI8qIFN7c0KNHD5KSkmjevDkdOnRg5cqVxbZj8+bNNG/enObNm/P6669H7KOqdO7cmVNPPbXY+tu2bSswy3ZJyM7OZvDgwTRr1oy2bdsSHx/PjEDqotTUVEQkVzqe8Ozg4KQHSkxMJCkpicTERN59991ijZ+amkr16tVJTk4mOTmZCy+8sFjPl2kYtojUBBoF3obKMQC/qup2nNIL4f0PAT+p6qaytNMwDP8xefJkateuDcA777zDjTfeyIoVK4ql8dZbb9GuXTsmTJhQYBj2+PHjadCgAatWrSq1zaXlueeeY8+ePaxevZrMzExEhB9/PFo+7YwzzmDcuHHccsstnHTSSRE1gjWC3n//fa655hr27t1LlSpV+O2331ztAwbPTZWEilSOwTCM4xzV3K04BJ0PwG+//Raq/JmX9PR0brjhBuLj44mPjw/NEKZPn864ceOYNWsWycnJfPvtt/me3bx5MzNnzmTEiBGF2pKWlkbbtm1JTEykbdu2pKWl5bo/dOhQ2rRpQ0JCAsuWLQNg9+7ddOnShYSEBBISErg7WJivEHbs2MHpp58eChKoWbMm5513tID0mWeeSe/evXPNggoiJSWF9PR09u7dC8Dy5cv5wx/+wPDhwyPmt/OCMp0BqWoq4HJCDaraIGrGGIZxzHHTTTfx0Ucfoap88MEHEfuMGjWKnJwc1qxZw/79+0OOol+/fmzevJn09HTGjh2bbwaUk5PDzTffzIQJE6hatWqBNmRnZ9OrVy9effVVunTpwsKFC+nVq1eoZMOvv/5KYmIiY8eOZcmSJfTt25dvvvmG6dOnU79+fRYsWAAQcgRFfb+XXnopixYtok2bNlx55ZW5SjAAPPTQQyQkJITSDBXEnDlzuOSSS0JlHq644go6dOjAjBkzePjhh/nuu++45ppr6NevH+eff37oua+//poWLVpQtWpVBg8ezPXXX1+k3UEq0h6QYRhGqXjllVfYvn07TzzxBMOGDYvYZ8GCBdx8882ICLVq1aJv376hD/3CGDt2LBdffHGRWaQ3bdpETEwMXbp0AZwM2TExMWza5OwkxMTE0L9/fwA6duxIbGwsmzZt4qKLLuKjjz5i2LBhzJs3z9XyV0JCAt9++y1PP/00p556Kn/729+49dZbc/WpW7cuN998c4Gphdq1a0fDhg256aab8s2UatWqRb9+/fjggw9YunQpGRkZNG3alHHjxgFO+qHvv/+eFStWMHPmTB577DFXP8sgrhyQiFwjIt3C3j8sIjtE5EMROcP1aIZhGGXAgAEDWLx4MXv27Ml3TyPU/nFTC2jp0qVMmjSJBg0a0L59e/bu3UuDBg34/fffi9QvbIxg/7Zt27Jy5UpatmzJ1KlT6dSpU5E2AVSrVo1u3brx4IMPMnv27FAQQjjDhg1jzpw5oQJ84QSL7D322GP06tUrXx2ln376ieeee44rr7ySZcuW8cwzz4QcaK1atUJLnw0bNqRHjx7FqjPkdgY0MvhCRFoA9wPPA1WBp12PZhiGUQgliX4DZ1/n+++PFk+eO3cuderUoU6dOvn6du3alVdeeQVVZf/+/cycOTM0WymMefPmsX37drZt28by5cs5+eST2bZtW76yDU2aNOHgwYOhmkGLFy/m0KFDoWWr7OzskJNYtmwZBw4coHHjxmzdupVatWrRp08fnnnmGb766itycnLYuXNnqK5PXpYtW8auXbtC71esWEHDhg3z9atduzb33nsvo0ePLvD7u/fee4mLiwuVM1+5ciWdO3eme/fu7Nu3j2nTpvH5559z5513hpbpfvzxx1Am719//ZWPPvqoWHWG3O4B1QeCkWg9gbdV9R8i8hHwoevRDMMwokBmZib9+vUjIyODypUrU6dOHebOnRtx1vHQQw9x++23k5CQADizpT/96U+e2RITE8Obb77JHXfcQUZGBjVq1GD27NnExMQAUKdOHTZv3syFF15IZmYmr732GjExMaSmpvL0009TpUoVcnJy+Ne//kWlSpX44YcfqFIl8kf1tm3buOOOO8jOzkZECqymCnD77bfz3HPPFWi3iDB27Fj69OnDLbfcQtWqVXnyySdp0qRJvvIRQd58801efPFFqlatyuHDhxk4cCB//vOf3f+wCjqhGt6APUB84PUnwE2B1w2BTDcagf4XA+/iZDxQwjIhBO6PAjYCGcBeYCHQzo22ZUKouLp+stVvun6x1TIhlFz36aef1qlTp3qu65ZoZkJwOwNaBjwtIstxQqmvDlw/H/i+wKfyU1Q5hk3AEGArTpj23cAHInKequ6K0N8wDOOY5p577ilvE6KG2z2g24FsHMdzq6oG6/RcRjGW4LSIcgyqOk1VF6rqt6q6DrgHOBEnK4JhGIZxDOFqBqSqO4ArI1y/y3OLAohIDE6m69+B4ufUMAzDMCo0rsoxiMhpAKr6c+B9AnAtsE5VXyvRwBHKMQSudwdmAtWBH4GeqhrxGK6VY/CHrp9s9ZuuX2y1cgzHp64n5RiAxcCNgden4gQIrAN+A+51oxFBM1c5hrDrNXDyxV0E/AenJMMZRelZEELF1fWTrX7T9YutxQlCWLNrja7dtbZY+sdyEIKfdb0qx5AIfBZ4fTWwRVWbAQOBW4rjEYtCVTNUdYuqfqaqf8Epy32Tl2MYhlExSc9Op/es3vSe1ZuM7IzyNseIMm4dUCzOjAWgC04oNcAK4GyvjcpDJeCEKI9hGEY5o6oMnj+Yjb9sZMMvGxjy3pBiPT906FAaNmyIiLB27dooWeme7777ju7du5OYmEhCQgKtWrUK2TVy5EhEhM8//zzUf+TIkQwdOhTIXeYgPj6ejh07snHjxmKNP2rUKJo1a0ZSUhItW7bkww+PxotlZmZy7bXX0qhRI5o0acK8efM8+I6Lj1sHtBm4SkTOBroBHwWuxwH73A4mIjVFJDlQgiFUjkFEzhGRWiIyWkQuDLxvKSKvAvWAN9x/S4Zh+JFJKycxdfVUYqvEElsllsmrJjNp5STXz/fo0YOlS5dSv3796BlZDAYPHsyll17K6tWrWbNmDe+88w5169YN3a9fv36hWbUvuOACVq5cydq1a7noootyZceOlGIoL23atCEtLY1Vq1bx6quvcu2115KVlQU4ee1OPPFEtmzZwty5c7nppptIT08vQtF73DqgR4GncPZjPlPVoNu+FKecglsKK8dwGGgGzMFxeHOBU4CLVXV1McYwDMNnrN29NjTjeeGKF5hw+QQABs8fzLrd61xptG/fnrPPLnpB5p133iEhISE0u0hNTQWccgR33XUXKSkpNGrUiEcffTT0zM6dO+nVqxeJiYkkJiYyZsyYIsfZsWMH9erVC70/66yzcjmgXr16sXfv3lwzk4JISUlh+/btoffjxo0jPj6eJ554gm3btkV85tJLL6V69eoAJCYmoqohx/X666+Hkpaed955tGrVivfff79IO7zGbRj2WyJyDnAmEF6FaQHwptvBtOhyDD3dahmGcWyQkZ3BNbOuIetwFtcnXc+g5EGoKqnfpTJl1RR6z+pN2s1p1Iip4cl4Dz/8MC+88AIdOnTgyJEjZGQc3Wtav349CxYs4MCBA1x44YWkpKTQvXt3+vfvz+WXX86bbzofd7/88kuR4wwfPpyBAwfSokULLrroIq6++mpat24dui8iPP7449x///1069atQJ2cnBzeeecd+vTpE7o2evRobrnlFmbOnEmvXr2IjY3lqquuYuDAgRErtU6ZMoU//OEPIYe4ffv2XDPFc845J1cuvbLCdTkGVd2lqv8DThORSoFrn6tq8RYmDcMwypFLLrmEe++9l3/+859s2LAhVzLR66+/nipVqlCzZk169erFokWLSE9P55NPPsm1BOamHHe/fv3Ytm0bQ4YMISMjg06dOvHaa7lPrVxxxRVUr16dWbNm5Xt+/fr1JCcnExcXR2pqKkOG5N4TO/vssxk2bBhfffUVr7zyCu+//z5nnHFGvuJ3S5Ys4aGHHso3dkXAbTmGqiLyDxHZj5PHrUHg+lMiMjiK9hmGcYxTI6YGb/R+I9e+z6SVk5iyagqxVWKZ1XuWZ7MfcJav/vOf/xATE0Pv3r15+eWXI/bTAsoqFIdTTjmFa665hvHjxxfoBMaMGcNDDz3E4cOHc10P7gHt3LmTCy64gNtuuy3fs2vXruWBBx6gR48eVKlShalTp5KYmBi6/+mnn9K/f3/efvttGjduHLp+zjnn8N1334Xeb9++3dXypde4nQE9gpMJoT9wMOz6F8Agj20yDOM4I75ufK59n/D9oGZ1m3k61qZNm0hISODOO++kf//+uWYMU6dO5fDhw2RkZPD222/TqVMnatasSbt27UJF2ODoEtycOXMYOHBgxHHmz58fqq1z5MgRVq9eHbFUQvv27TnvvPOYPn16RJ2YmBhefPFF3n//fVaudJLCvP766yQmJnLbbbdx9tln8/HHH/PGG2/Qp08fTjjBCRpOS0vj2muvZfbs2bRo0SKXZu/evUNlFzZv3kxaWpqnGcHd4tYB9cXJAfcOuXO4rcVJSGoYhlEqBiUPYmDSQLIOZ+XaD3LLHXfcQb169dixYwddunShWbPIjmvEiBHEx8eTnJzMf//7X/7+97+H7rVo0YIuXbqQnJzMpZdeSvfu3QGYNm0aH3/8MfHx8SQlJfGf//wHcE76560HFCQ1NZXmzZuTkJBAQkIClStX5rHHHovY94knnsgVZJCXuLg4hg4dGgqMqFu3LnPnzmXZsmXceuutnHLKKfmeGTx4MFlZWdxyyy0kJyeTnJzMmjVrAKdA3b59+2jUqBHdu3fn3//+d4ElF6JKQSdUNXd2giygQeD1fuDcwOtmQLobDS2iHANOcbungNU45Rh+BGYA57jRtkwIFVfXT7b6TdcvtrrNhJB+MF2bjm+qTcc31fSD6a71vcgC0LFjR507d26xNK+66irdunVrscapiBkLoqXrVTmGdQHnsS3P9WuAr4rh7worx1AdaAE8jpN8tDZOtdUPRCRRVQ9jGMYxTXA/SBBP932iRTAqzigZbh3Qo8C0wEHUykBvEWkCXAdc4XYwVX0PeA9ARCblufcb0DX8mojcguP8mgJr3I5jGIZ/ia8bXy7jBs8DGWWHqz0gVZ2LM9vphrMH9AhwHnClqi6InnkEF1f3RnEMwzAMoxxwVY4hKgMXUI4h7H4MThbuPar6fwX0sXIMPtD1k61+0/WLrVaO4fjU9aQcg+YOJDgJqBPeiquhhZRjCNyrgpP/bR1wihs9C0KouLp+stVvun6xtTjlGEqClWOomLqeBCGISH3gX0AnnGi10C2caDbP3K6IVAFeAxKAFFUtOuueYRiG4TvcBiFMxJn53Aj8gON0PEdEquJUQ43HcT4/RWMcwzAMo/xxexC1DTBQVWeoaqqqLglvbgcrohxDFWAWTiXUvoCKyOmBFlvcb8wwDP+RmZnJ9OnTGT16NDNmzAiVD3BDjx49SEpKonnz5nTo0CGUNaA4bN68mebNm9O8eXNef/31iH1Ulc6dO7vKB5eXbdu20aBBg2I/l5fHHnuM888/n/j4eIYPH15qvfLC7QxoK94UhWuFE1gQ5NFAmwyMBP4cuJ73bNENwCQPxjcMo4KSlpbGlVdeya5du0LX4uLimDt3bq4s0gUxefJkateuDTglF2688UZWrFhRLBveeust2rVrx4QJE9i/f3/EPuPHj6dBgwasWoimzSwAACAASURBVLUq4v1os2nTJl566SW2bNlCbGwsW7ZsKRc7vMDtDOhOYIyINCrNYIHZk0Rog1R1WwH3RAuIlDMM49ggKysr5HySk5MZPnw4SUlJ7Nq1iyuvvNLVTCjofAB+++03KlWK/PGWnp7ODTfcQHx8PPHx8Tz11FMATJ8+nXHjxjFr1iySk5P59ttv8z27efNmZs6cWWghOXCcadu2bUlMTKRt27b5MlQPHTqUNm3akJCQwLJlywDYvXs3Xbp0CaXuCc++HU5MTAz79+/n119/BaBRo8I/lh999FGaNGlCcnIyzZs3Z98+p4aoiDBy5EjatWtH48aNcx2q/fTTT2nfvj1JSUm0a9eOjz76qCD5UuF2BvQOzgxok4gcxCkeF0JVIydDMgzDcMGcOXNCzueLL76gatWqjB49mtatW7Nq1SrmzJnDddddV6TOTTfdxEcffYSq8sEHH0TsM2rUKHJyclizZg379+8POYp+/fqxefNm0tPTGTt2bL4ZUE5ODjfffDMTJkygatWqEbUBsrOz6dWrF6+++ipdunRh4cKF9OrVKzRT+fXXX0lMTGTs2LEsWbKEvn378s033zB9+nTq16/PggXO0cq9eyMff6xevTrnnHMOf/rTn1i0aBGnnXZagbbs3buXsWPHsnv3bmJjY9m/fz+xsUd3NCpVqsQnn3zCpk2baNeuHR06dKBKlSr07NkzNBvct29fMDrZc9zOgG4HbsYJQrgN+FueZhiGUWKCs41u3bqFPtyrVq0aKtQWaTYSiVdeeYXt27fzxBNPMGzYsIh9FixYwM0334yIUKtWLfr27Rv60C+MsWPHcvHFF5OcnFxov02bNhETE0OXLl0A6Ny5MzExMWzatAlwZjD9+/cHoGPHjsTGxrJp0yYuuugiPvroI4YNG8a8efMKPIfVo0cPnn/+ee688066du3Knj172LVrF/Hx+TNI1KpVi8aNG9O/f39efvll0tPTqVLl6LzjL3/5CwCNGzemRYsWfPbZZ3z66adccMEFtGvXDoDKlStz8sknF/nzKQluK6JOjsrohmEYwLnnngvAhx9+yOjRo6latSqHDh0KLf0E77tlwIAB/PWvf2XPnj35MkVrhDo/bur+LF26lNWrVzNlyhQOHz7M3r17adCgAatXr86VETuSfmFjBPu3bduWlStX8t///pepU6fy5JNPsnz58lx9f/75Z1asWEGnTp245JJLyMnJoWvXrlxxxRX069cvn3blypX57LPP+Pjjj1m0aBEtW7bkgw8+yFUzKK8d0ZrtRMJ1RVQRiRORoSLyooicGrj2RxHJX+DCMAyjGPTs2ZO4uDhWrVpF69atGT58eGj5LS4ujp49exb6fHp6eq6S0nPnzqVOnTrUqVMnX9+uXbvyyiuvoKrs37+fmTNnhmYrhTFv3jy2b9/Otm3bWL58OSeffDLbtm3LV46hSZMmHDx4kMWLnXirxYsXc+jQIc4/36lck52dzYwZMwBYtmwZBw4coHHjxmzdupVatWrRp08fnnnmGb766itycnJyaZ966qmceeaZTJ7szAn++te/0rhxY8aMGcOAAQPy2bx//35+/vlnOnbsyKOPPkp8fDxr164N3Z84cSLg7G2tXLmSCy+8kHbt2rF+/Xo+/fRTwMmEUNByYGlxexC1JbAQJxquGfBP4Bec5KHn4yQldaNzMTAUaAmcCdwQHmAgIlcBt+BkxT4V6KSqqe6+FcMw/EpsbCxz587lyiuvZNWqVaEIs2AUXPi+RSQyMzPp168fGRkZVK5cmTp16jB37tyIs46HHnqI22+/nYSEBMCZLXlZjC0mJoY333yTO+64g4yMDGrUqMHs2bOJiYkBoE6dOmzevJkLL7yQzMxMXnvtNWJiYkhNTeXpp5+mSpUq5OTk8K9//StfIIWIMG/ePO68807Gjh1LbGwsHTp04B//+Ae9e/dmyZIluWZ8v/32G7169SIrK4ucnBxatGjBVVddFbp/wgkn8Mc//pFffvmFl156ibp16wJONOA999xDRkYGAM8884wrJ11sCkqREN5wQqcf1fz1gNoC37nRCPS/HHgCuBrIJE8qHmAATqLTATiHXVPcalsqnoqr6ydb/abrF1vdpuLJzMzU6dOn66hRo3T69OmamZnpSt9S8RRfF9D9+/d7rhuOV/WAWgJ/iXD9RyCuGM6uwHIMgftTA/eKf8LLMIwKjbrYW4iNjXUV7WZUfNz8e7t1QFlApDCIJsDuYthkGMZxSLVq1dizZ09oGcoof9w4iNJo79mzh2rVqhXarzjngB4Rkd5BfRFpgFNC20oCGoZRKPXq1WPHjh3s27evyA+lknDgwAHPdaOheTzpVqtWjXr16hXax1U9IBGphbN0lgjUAH7CWXr7GLhcVTOKa1xh9YACS3A/U0QQgtUD8oeun2z1m66fbPWbrp9srci6nTp18qYeEHAJThTbcKBLcZ6NoFVYPaBTsSCEY0bXT7b6TddPtvpN10+2VmRdPKgHNBB4XVUXAYvCrscAfVR1Sgmdo2EYhnGc4vYg6kSgdoTrJwbuuaKwcgyB+3UC94I5JRoF7p/udgzDMAzDH7h1QMHKp3k5B/itGOO1Av4XaLE4pRj+BzwWuP9/gffBkg0vB97fWowxDMMwDB9Q6BKciKzBcTwKLBGR8CzYlYH6BM71uEGdgIICky6pE5Awya2eYRiG4V+K2gOaHfgaD8zHCRwIkg1sw8KwDcMwjBJQqANS1UcBRGQbThDCgbIwyjAMwzj2cbUHpIFyDCJytYj8XUROCrz/g4jkTzdrGIZhGEXgNgy7EbAAqAmcBMwC9uEUpzsJuClaBhqGYRjHJm6j4J4FPsLJfhBenP1doJPbwUTkYhF5V0R2ioiKyKA890VERorIDyKSJSKpItLMrb5hGIbhH9w6oHbAWFU9kuf6dpy6Pm6pCawF7iS3IwsyHLgXp8x3a5xEp/8VkROLMYZhGIbhA1xXRAWqRrhWrHNAqvqeqt6vqrOBXKX+xKkcdRfwpKq+qaprgetxDrtafnbDMIyy5PBhePZZ6s2e7byOAm4d0EfAPWHvNZCg9FGc8GwvaAicHhjLGUQ1C1iKMwMzDMMwyooZM+Duu2k0YYLzOgq4dUD3AO1FZBNQDXgd5wzQ6cAIj2wJptvZlef6rrB7hmEYRrQ5fBhGjTr6ftSoqMyCXJVjABCRWKAv0ALHca0ApgdmKcUfOE85BhFph1Pe4RxV/T6s30TgDFXNV7TdyjH4Q9dPtvpN10+2+k3XT7Z6rRv34Yc0ffJJMs86C83JocaPP7JhxAh2XXppsbU8K8fgZSNPOQbgXJyUP63z9JsPTC5Kz8oxVFxdP9nqN10/2eo3XT/Z6qnuoUOqjRqpgurkybp+xAjndaNGzr1iQknKMYjIVW49nKq+5bZvIWzFKXTXFUgL2FAN6AAM80DfMAzDKIoZM2DLFmjUCK67jt1LltB09mzn2owZMHCgZ0MVdhB1diH3wlGcxKRFIiI1gUaBt6FyDMCvqrpdRJ4FHhCRjcDXwIM4M6Xo7IAZhmEYRwnf+9myBapWpWP4/VGj4LrroIqrHAZFUmAQgqpWctlcOZ8ARZVj+AfwDDAB+BI4A+imqvtL8L0ZhmEYxSE4+ymI4CzII4pzDqjUqGqqqkqENihwX1V1pKqeoarVVLWjOueBDMMwjEh4eV5n4EBwdnxCLXXx4tzXymgJLhci0gLnoOgFgUsbgHGqusIzawzDMIziETyvA9CmjacOItq4mgGJSD+cwIAzcArQvYeTF+4LEekfPfMMwzCMAimj8zrRwu0M6HHgIVV9IvyiiNwHjAameW2YYRiGUQRhEWuZWVlUj0KkWjRxuwd0GvBGhOuzgLremWMYhmG4Inz289BDfDdggPPaR7Mgtw5oMZAS4XoKsMQrYwBE5EQReVZEvguUZPhERFp7OYZhGIbvyXtep0sX57XHkWrRxO0S3PvAGBFpBXwWuHYRcBUwMvzQqgeHUl8BEnEyYe8A+gMLROQCVd1ZSm3DMAz/U8bndaKFW+v+X+BrKPdaGOPDXrs+lBqJQL65XkAvVU0NXB4pIlfiVF99sKTahmEYxwxuz+tU8L0gV0twUTqUGokqOA7sQJ7rWUD7UmobhmGUHz4+rxMtKtT8TFX3i8inwIMishYnN1xfoC1QiLs3DMOo4Pj4vE60KE45huZAJ5yot1wzJ1Ud7plBIn8AXgUuBo7glH34Gmihqhfk6WvlGHyg6ydb/abrJ1v9puulphw5Quvrr6f6TmcbO/Oss0ibPBmtXNpFo6NU1J9tqcsxAMNxSmhvxQlC+DSsfeJGo7gNqIFTBwicAnjzC+tv5Rgqrq6fbPWbrp9s9Zuup5qTJ4dKGmScdVao1IGXVNSfLYWUY3Abhn03cJuqNlTVi1S1bViLSrlsVc1Q1R9F5GTgUuCdaIxjGIYRVY6B8zrRwq0DqgQsjKYhQUTkUhG5TEQaikhXnDNIm4CJZTG+YRiGpxwD53WihVsH9CJwQzQNCaM2Tmj3RmAKsBynJMOhMhrfMAzDGyKd1+nS5WgI9XE+C3IbBfco8J6IrATWALmcgare6JVBqvoGkdP+GIZh+Itj5LxOtHA7A3oc6IbjeE7GyQ0X3gzDMIy8HCPndaKF2xnQYOA6VX09msYYhmGUO4cPw/jx1NuyBdq3r/DpbPyM259sFk7pbMMwjGMbOzBaZrhdghsH3CUiEk1jDMMwyhWfF3jzG25nQB1wMhNcISLryR+E8H9eG2YYhlHm+LzAm99wOwP6BXgLWISTn21PnuYJIlJZREaJyFYRORD4OlpEbBHWMIzoYgdGyxxXH+yqWlZngP4ODMGpBbQGpy7QZOAgMKqQ5wzDMEpH3gOjS5bQdPbs4z5UOpoUa2YhIucCF+DU/dmgqt96bE87YK6qzg283yYi7wIXejyOYRjGUY6RAm9+w9USnIjUEpFZOCUR3sbJy7ZZRN4QkRM9tGc50ElEmgTGvQC4BHjPwzEMwzBy4/bA6HGASO7WqVNKrveejqUuyjGIyESc2clfgU8Cl/8I/Av4WFX/4okxTpTdaOA+nFIMVYDHVTViJVQrx+APXT/Z6jddP9kaDV05coSz5szh4MGD/NKnz3FR3iDaup06pRR6f/Hi1GLqlb4cwx6gQ4TrFwN73Gi4HKcP8H3gawIwAPgV+EtRz1o5hoqr6ydb/abrJ1ujohssc3AclTeItm7wx5lXN+9193qlL8cQS+Rot1+Bam49oQv+CYxV1ZmqukZVpwLP4MyIDMMwjmJndnyPWwf0MTBKRKoHL4hIDZwkpZ8U+FTxqY6z9BbOEdzbaRjG8UL4mZ2zzjqu9mmOFYpTkO5CYKeILBGRVGBH4NpdHtozFxghIleISAMR6QncA8zxcAzDMPyOndkp02CBaOHKAanqWuA8nNLcXwIrgGHAeaq6zkN7/gbMBl4ANgBPAy8DD3g4hmEYfseKvEWdvI4tGrgOalfVLBxnEDVUdT/OjMrLWZVhGMcSdmYnF8FA5tTUVFJSoucsokGhMyARaSsiaSJSK8K92iLyhYi0iZ55hmEYebAzO1ElT/kiFi9OzfXeS4pagrsHeFNVf89vpP6Gs1w21FuTDMMwCsGKvB0zFDVHbYkT6VYQ7+EUqzMMwyiY47jIW/4lsZRc77yeVfiJon4LzgQyCrmfCZzunTmGYRyTWJG3qHHUwaWUoxUlo6gluN1A00LuNw308QQR2SYiGqHN92oMwzDKGDswCuTfUzGKdkD/BR6MVAlVRCrhhEf/10N7WgNnhLUWOJm33/BwDMMwyhI7MBoVyjJYIFoU5YAexym/kCYi14pIUqD1AdIC957wyhhV/VlVfwo24HLgd2CWV2MYhlGG2IFRoxAKdUDq1PvpgpPv7TWcA6grgBnACUBXVf0mGoYFZl1/AaapamY0xjAMI8r46MDosZBZwG+4KscAICLJONkQBPhaVVdG1TCRbsCHQPOCxrJyDP7Q9ZOtftOtyLbKkSO0vv56qu/cGfF+5llnkTZ5siclFLyw1+syBNHWzUtF/V0odTmG8mg4y25fuO1v5Rgqrq6fbPWbboW2NbxUQkHNoxIKXtjrdRmCvLoFNa+oqL8LeFCOoUwRkbrAn4ly6h/DMPJw+DA8+yz1Zs8u/R6NHRgFjo1ggWhRUU+DDQIOAiVfUzMMo/jYeR2jDKlwM6BA8MFNwEx1kpMahlEW2Hkdo4ypcA4I5zjvedjym2GULXZeByibMgSGg2sHJCJxIjJURF4UkVMD1/4oIg29NEhVF6uqqOoXXuoahlEIdl7HKAdcOSARaQlsAvrhnM0JlmfoinNY1TAMP+Oj8zrRwoIFyh63QQhjgedU9RERCd+X+RC4wXuzDMMoM3xY4M0yTB8buF2CawlMjnD9RyDOO3MMwyhzrMCbUU64dUBZwMkRrjfBw2zYhmGUAz4+r2MZpv2NWwf0DvCIiJwQeK8i0gB4CnjTS4NE5AwRmSwiP4vIARFZLyIdi37SMI4jvDwwahjlhNtF3aE41U9/BqoDy3GW3j4GHvTKGBE5KaC5HLgiMN652CzLMHJjB0aNYwBXDkhVfwfai8glODV6KgErVHWBx/YMB35U1fD/TVs9HsMw/E2kA6MVLEjAMNzg6jdWRJJUdZWqLgIWRdGeHsAHIvI60An4AXgFmBBIamcYRviB0awsqgeDBCrgLCja0Wp+LkdtuCzHICI5wDpgKjBDVXdExRiRA4GX43CqoCYD/w8YoarjI/S3cgw+0PWTrRVdN7zEwYYRIzhw8CDNx43ztLSBV7aCv0scVOTfAz/plrocA3A+8CjwNXAYWAzcCNRy87zbBmQDn+S59gSwoahnrRxDxdX1k60VXjdY4qBRI9VDhzR1wQLntYelDVS9+xlEq8RBXuz3tuLqUtpyDKr6tao+oqrnA38E1gQcw08i8kaJXWN+fgTW57m2ATjHwzEMw59EOjDapcvRMzyWNsfwGcVORqqqn6vqHTj1ejYBvTy052OgcZ5r5wPfeTiGYfiTKB4YtXLURnlQLAckIueKyIMisgEnVHovTukErxgHXCQiD4hIIxHpDdwBTPBwDMPwJz4+MGoYkXCbjHSIiHwCbAZ6AxOBBqp6iapO9MoYVU3DiYS7BliLk+j0IeAFr8YwjDLHR4dGo5VZwEocGJFwe3BgBPAacIuqromiPajqfGB+NMcwjDLFDo0aRkTcLsGdo6rDo+18DOOY4zivMmolDozCKHAGJCItgJWqmgM0l0Lmzaq6Igq2GYb/8dGhUcMoawpbgvsSOB0nD9uXgAKRvJAC3px+M4xjibxVRjdsoOmTT1bo1DmWWcAoSwpbgmuIkww0+PrcwNe87dxoGmgYvsWqjBpGoRTogFT1u8ApVnBmOdsD13K1wD1PEJGRIqJ52k9e6RtGmRGlQ6PROq9jezVGeeA2CGErcFreiyJyCt5nq94EnBHWEjzWN4zoY1VGDaNI3DogIfJMpyZwIML10nBYVX8Kaz8X/YhhVDCifGjUKoEaxwKF7oKKyPOBlwqMEZHMsNuVgTbASo9tOldEduIkJv0cuF9Vv/V4DMMwDKOcKbQcg4gsDrzsCHyK4xSCZAPbgLGqutkTY0QuA04ENgJ1caqtNgGaqeqeCP2tHIMPdP1iqxw5wllz5nDw4EF+6dPHs9IGQbywN1iGIFhuIKiZ93pp8cu/WTR1/WRrRdb1ohzDRDwuveBy3Jo4YeD3FNXXyjFUXF3f2BosdeBxaYMgXtjr5/IGftP1k60VWRcPyjHcoE5Z7jJFVdNxCuGdV9ZjG8cZPstYYLnVjGMB1yfhRKQT0BenNk9M+D1VvcRju4JjVsNZgltcVF/DKBVRyFgQ7XLUhuF33GbDHgS8j7M/k4JzQPVkoAX5C8iVGBEZKyIdRaShiFwIzAZqAJO9GsMw8pE3Y8GAAc7rCjgLsvM6xrGE2zDsocDtqtoXOATcp6rNgWlAuof21MPJur0JeAs4CFykzoFXw4gOUc5YYCHThhEZt0tw5wILAq8P4gQHAIwHUnHKNZQaVe3jhY5huCZSxoLw+xU4b5th+B23M6A9OMtvADuB+MDrU4BYr40yjDLDMhYYRrnh1gEtA7oFXr8BPC8iE3GWy/4bDcMMo0ywMteGUW64XVe4HagWeD0GOAz8EccZjY6CXYZxzGAlDgwjMq4ckKr+GvY6B3gqahYZRlEcPgzjx1NvyxZo377U+zMWLm0Y5UOBS3AiUsdti5ZxInJ/oCTD+GiNYfiQGTPg7rtpNGFChd6fsZBpwyicwvaAfsE571NYC/bxHBG5CLgZWB0NfcOnRDFjgYVLG0bZUtjaRacysyIPIlIbmA78BXi4vOwwKiBRyFhgGEb5UKADUtUlZWlIHv4NzFbVRSJiDshwyJuxYMMGmj75pJ3VMQyfUmg5hlCnIvZ5woMUSm2QyM3ArUBbVc0WkVRgrareHqGvlWPwga5XmnEffkjTJ58k86yzSJs8mfT0dDoNGUL1nTvZMGIEuy69tES6fi5x4KffA7/p+snWiqzrRTmGHOBIQc2NhstxGuPsKTUJu5YKjC/qWSvHUHF1PdE8dEi1UaO8+/pHW6NGTp8SUJCkH0oc+On3wG+6frK1IutSSDkGt2sWefeDqgLNgdtwisZ5RVvgVGCtHI2NrQxcLCK3AjVU9aCH4xl+wW3GAtsLMgzf4PYcUKT9oAUi8i1wE+BVLOzbwJd5rk0ENgNPkLsiq3E8MXBgPueSmppKSkpKqaXzrkJ7pWsYRuGUdtd2JXCxF4YAqOo+YF/4NRHJAH5V1bVejWP4EzswahjHFm5zweVDRGoCdwHfe2eOcUxx+DA8+yz1Zs+ucHV1DMMof1zNgERkPxD+96UA1YEMoF8U7AqhqinR1DeiSDBjAUCbNp7tzwRnOsGlMitJbRj+pDjJSMPJwYlW+1xV93prknFMECljgZ3VMQwjDLdBCFYS2ygelrHAMIwiKNYeUCD5aBMRuSC8Rcs4w6fkzVgwYIDz2sO8bYZh+B9XDkhEmovISpxlt3XAWmBN2FfDOErY7IfrrmN3ly7Oa4+qi4o4rVMn2/8xDD/jdkH+VZxS3HcCu8gdkOAZIjIEuAVoELi0DhitqvOjMZ4RBcJnP1u2QNWqdAy/b3tBhmEEcLsEdx5wp6ouUdWNqropvHlozw7g70ALoBWwCHhbRBI9HMOIJjNmIFs2I2jktmVziWdBVl/HMI4t3Dqg5UDTaBoCoKrvqOr7qrpFVb9W1QeA/Tgpegw/4CbIwAIRDMPAvQP6CzBYRO4Ukc4icnF4i4ZhIlJZRPoANYFPojGGEV38WOBt7e61bM3Y6gtdP9nqN10/2epH3SBuF+LPA5KBSPnuFSdhqCeISALwKVANSAd6qqoFOkSbw4dh/HjqbdkC7dtX+D2a4H+MlDzpeEpDenY6vWf1Jiszi2u6XkONmBoVVtdPtvpN10+2+lE3HLefMi8BC4ExRDEIIcAmHGd3EtALmCwiKZFyweWpB0RqamqJB01PTy/V837XDdbaaQRsOPHEEtfWcUgBnEwFWzO2kpmZ6RTVCLteGrKOZHHrilvRHOX0aqcTWzm2VHrglCUZs2kMG3/ZCECvV3sxosmICqnrJ1v9pusnW/2oG3GgohpOyp0/uOnrdQMWAP8pqp/VAyoFeWvtlKK2jupRmf0H92uT8U20/j/qa/rBdE/q6+Tk5OiAtwYoI1FGotfPub50ggFeXfGqMhKNHR2rJzx2gjISnfi/iRVS10+2+k3XT7b6RZdC6gG53QP6L9DSe/fnikrACeU09vFBeNaCs87y6LyOcmKfwWz8ZSPfZX5HzWuHeGLqpJWTmLp6KrFVYjmh0glMXjWZSSsnlUpz7e61DHnPse+FK17gzvPuBGDw/MGs272uQun6yVa/6frJVj/qRsKtA/oAeFpERovItSJyVXjzyhgReVJEOohIAxFJEJExOOs2070aw8hDtLIWJE+CpKlwKNZpyZOda6UgGv8xMrIzuGbWNWQdzuL6pOsZlDyIP8X9iYFJA8k6nEXvWb3JyM6oELp+stVvun6y1Y+6BeF2D+iFwNf7I9zzMgjhdGBa4OtvwGrgMlX90CN9Iw9StQpOvT9g2FrQlsAY2AJULdn5mjW71tLm5SFkHYaJV7/Aho0b+MemfxB79WDSPmwNNCu2ZqT/GIv3Luanaj8xZdUUes/qTdrNaVHZKDUMIzq4mgGpaqVCmmcRcKo6SFXrq+oJqlpXVbuY84ki4TOcmHTo3Ruu6Q1VMyL3cUFZ/wVVWmrE1OCN3m8QWyU2tJz3wa4PmLJqCrFVYpnVe1aJnFo0dP1kq990/WSrH3ULosQF6YxjgNA+jzLgihPhtI1w2gauv6JmhD7lSzT/Y8TXjWfC5RMAZznvuc3PAc4yX7O6xZ+tRVPXT7b6TddPtvpRNyIFRSdo7ki0ewprbjSi3Y6nKLg1u9boq/Ne9UQLVEnOH/FC8sQSR6yt2bVGY0fHhiJnhr82PKS/dtfaUtkbraifnJwcHThnoOfRddHQ9ZOtftP1k61+0aWQKDhRF4v8IpL3KGxV4AwgC9itqud65hFLSKtWrfTLL78s8fPB6ppe47VuenY6rV9uTVZmFuvuXFfq6bDErYWb20DVLCb+eWJov4ZDsfDvNHR3yf7imfi/idz47o3EVoklJyeHgzkHmfjniQxKHlQqe1WVQe8MYsqqKQBcn3Q9k3pMKpVmkIzsDFq/3JrMzExPfrbR1PWTrX7T9ZOtftAVka9UtVWke24L0jWMIBoHTAReLpFVRrFRVQbPuzV0OGzI/NuY1HNKifUysjOg9zVQNfLGPtf0JiO7ZBv7g5IHkfpdai5HUVrnAyAivHD5C6TtTCMzMzO0VOAFwWW+tDRvgxmioesnW/2m6ydb/agbTonzrajqLhF5AHgDmOOF+37PjwAAIABJREFUMSJyH3AV0Bg4CHwG3KcRsiAcj0xaOYmpa6YTe8h5P3n1VFIaXuLJB/vkyTC5J0AK9Jjk5KIoBX50FOCsf/9S4xdPNaOl6ydb/abrJ1v9qBuktAm/KgFxXhgSIAUn5DsNEOAxYIGIXKCqv3o4ju/IdQZmvhP7fmMPZ5Ow9ZmtS7Q5WCOmBsx6w1mCS54M21IAheQpzhLcG7OoMb7kH/B+cxSZmZnMmTOHRYsW8cMPP9CzZ09iY0uf5icaun6y1W+6frLVj7q5KGhzKLzhzErCWy/gdmADMN+NRkkaTibsI8CVRfU9loMQ0g+ma9PxTZ3NwB5Oqpz0s87UgT2cDcKm45tq+sH0EutHa2M/iNc/24yMDJ02bZreeOONOn36dM3MzCy15hdffKFxcXGK49sV0Li4OP3iiy8qnK6fbPWbrp9s9YsuhQQhuHUEOXnaEeAnYAZwhhuNkjScQAcF2hfV97hyQJMn67oRf/fMAUUrksYvjiIzMzOkmZycrH369NGkpKSQdkntjoaun2z1m66fbPWTbqkdUHk1nP2l/wGVi+p7LDsgVVXqrlEeiD0aIh0IneaBWOW00oU2qx51csHEoaXFT45i+vTpIc3s7GxdvHixZmdnh7SnT59eYXT9ZKvfdP1kq590C3NAFbboi4g8A7THmf0cKaDPcVGOQY4cgd2dYf4E6HEjXDH46M35L8DPzViycCFaueRJKQ4cOED7re35ccePjHlsDB06dOCEE0qWA/bgwYP07duXvXv30qhRI5KSkli5ciXffPMNl156Ka+99lqJtBcsWMCuXbto1KgR//znPzlw4ADVqlXj1ltv5ZtvvuHxxx+nS5cuxdZduHAhAE2aNOHjjz8mPT2djz/+mCZNmrBq1SoWLlzImWeeWSF0/WSr33T9ZKsfdSNSkGdyHBeXAduA2hHu1Q7c61aYRkkaMA74EWji9pljegY0eXKglEFOaNktuBznXHf6lBSvZyvR+sts1KhRCujw4cNV9ejPdtiwYQroqFGjSmVvUlJSVP6S9FLXT7b6TddPtvpJl5IuwQHzgSGF3L8Nj4MQgOdwit41Lc5zx7QDUg05mrxLZSEHVEKisazlN0cR/jNISkrSa6+91vO1dK90/WSr33T9ZKufdEvjgL4HGhdyvzGwozCN4jRgAvA7cAlORuxgq1nUs8eLA1LNnYqntA4ommvpfnEUqv6IJvKjrX7T9ZOtftEtjQM6AJxXyP3zgazCNIrTwr/ZPG1kUc9WOAd06JCuGTtc37ujT6mqiwYJOpq8kWWQWSoHFI3Zih8dRdDu6dOnexq1Fy1dP9nqN10/2eoH3dI4oC1Ar0LuXw1sKUyjrFpFc0D7J76kTYagTYeg6RP/XWo9xwF9oRCXxznHKZT8wzdasxU/OoogFX02HG1N042e5vGoW5gDKioKbj4wSkTeU9Ws8BsiUh0nU8H8IjSOO/TQIQYvHsrGQIrWIYvuZVL/G6BKaYIOs4ArcbbHkoFuwIfAKuBKsrK2luiUcs+ePYmLi2PVqlW0bt2aJk2acNddd7Fq1Sri4uLo2bNniaxt3bo1W7duZc6cOSxcuJDOnTt7dpI6NjaW6667jjPPPDMqCWQNwygbiqoH9DhOtNtmEfm7iPw50EYAXwfuPRFtI/2ECFRqM42p5+4PlaOe/If9SOtpiJRcd/r0OcAukpOTyc7+gsWLLyM7O42kpCRgF3PmlCwdX2xsLHPnzg05oddffz3kfObOnVsqhxF0FAMGDOC6667zPo2HYRi+ptA/yVV1t4i0A17EcTTBj1DF+fN7sKruiq6JPqPuWvj/7Z13uBvF1YffnwslppleQu+mmNA7AUILCSQQegkQEhIIoXcChITw0UNIgNCb6b33Tui9V4MhFBswxQWM7fP9cUa+umtJVxrtXuly530ePdbdXZ0dr1YzO2fO+Z2NXbONW04HrCN3539x5agB3nnnHQDWX399+vfvD0D//v1Zf/31ef755yftj6HI2UoikUhUo8uKqGb2npn9FJgZWAlYGZjZzH5qZu8W3L4eRafyBm9Piz25PfedMDc7vj0t9B8byhvElaNeYAH3591xxx18953LYX/33XfceeednfbHkmYriUSiu6m7JLeZjTSzJ83sCTMbWVSDJK0p6UZJ/5NkknYq6ly5M358x/uvv4b+/VlrvfX8faVjGiC7VnPmmWeywgorNL1Wk0gkEq2i7gGoG5kGeAnYC1957zEMuPoGL2/w3dRcOAh2mwc2nx0u6gOMmcrLG1x9Q5TtItdqEolEohW0nRacmd0K3Aog6YLuOOdLw19i6Oih/JgfN2doxx3h18BV+8P//spZJW/btUC/KWD8GD8mkrRWk0gkvk+03QDU3YwaN4otrtqCsWPGsuV6W+ZQOG0svHGWv50dWBB4cSB8NZJmwqVLpBDkRCLxfaEdXXDdhpmx+y2789qnr/HemPcmVRxtDg+XhqXhF4vBMovDmHeA5sKlE4lE4vuGPFG1PZE0CvijmV1QZX95OYblLr/88obs3/bxbRz/+vFMZf3AjG/6TOCgRQ9iw9k3jG7zxRdfzHnnncfWW2/N+tuvz5gxY1hiliU488wzueKKK9hll13YYYcdou2XGDVqFNNMM03TdrrDbk9qa0+z25Pa2tPs9qS2trPdtdde+2kzW77izmoSCe3wAkYBO9VzbKNSPEUVeCtK2iZLu8pudJfNZLc4m8lucTZ7o116YkG6IinP1+G5X8NzOwEG890Py1wU8nWejFoPKkraJpFIJL5vtN0AJGkaYKHwZx9gHknLAJ+b2bC8z7fd+AvZaLbbeX3MGO57/2sejhMqmEQpXPrnP/85zz//PM8//zxACpdOJBKJDG03AAHLA/eV/f2X8LoQ2CmPEwyYYoDn62y8PEPu+5Yho4Oa0NPAa0D/Ixjwr/houBQunUgkEl3TdgOQmd1Ph+ZccQxfEC6eEsZ/i+YQfRbsw4Q3J3gAG3szduymKVw6kUgkCqR3hmGPHw9cB+O/YuD0YLsaE34ygR1Wgknh0ldf3do2JhKJxPec3jkAXXop4OrRI7/eBz5fHEYszsW3jcLr7MA711/fuvYlEolEL6DtXHDdwo47wq8v9fcT74UrhwBTwHdTAEFdevPNW9a8RCKR6A30zgEIGDPml8w//2x88snzDJ5zZxZbbDFee+21FC6dSCQS3USvHYBSuHQikUi0lrZcA5K0u6Shkr6R9LSkNYo4TylcesiQIeyyyy4MGTKEoUOHssIKKxRxukQikUiU0XYzIElbAacCuwMPh39vkzSoiETUFC6dSCQSraEdZ0D7AheY2dlm9qqZ7Ql8BPyhxe1KJBKJRI601QAkaQpgOUqhaB3cCaza/S1KJBKJRFG0mwtuZqAvQY+gjE+An2QPLi/HAIyS9HqT5/60ic8nu91rM9ktzmayW5zN3mh33mo72m0AKpEtUqQK2zCzs4Cz8jihpKesWs2KZLftbCa7xdlMdouzmex2pq1ccPgoOwEvZl3OrEw+K0okEolED6atBiAzG4drUq+X2bUe8N/ub1EikUgkiqIdXXAnAxdLegJ4BPg9MCdwZsHnzcWVl+x2m81ktzibyW5xNpPdMuQVU9sLSbsDBwJzAC8B+5jZg61tVSKRSCTypC0HoEQikUh8/2mrNaBEIpFI9B7acQ2oRyNpSnzNampghJmNaHGTatLT2tuTSNe2WCT1AaYyszGtbksijl7rgpO0ALAWMB+hgwCeAR4xs28atDUtsD2wDbAi0J+O3KUPgduBs8zsyYh2zgBsVqWtt5lZw9GBBbe3D/DjKu2908zeb9Rmme3cvrMym1MBq2RtmtnbkfYKubZFXdciv69gf/6M3Rdjv6tgbyP82q4FzIVf229Ce28HzjezDyPsFnV9Fy9rb9bubcA1ZvZthN0ifgvTAJtUsXu7mT0dY7fmOXvbACRpO2AvYHk8t+hDYCwwI7AgfjMPAY4zs/fqsLcPcDheYvVG4ImMzSWBNYBfAo8Be5rZm3XYnRM4Gtgu2KtkdzngPeAvZnZFnf//oto7Na7jt3uw81wFu3PiskpHm9lj9bQ32M71Ows2Vws2f44PEl+W2ZwSvz5nAWea2dd12sz92hZ1XQv+vubDtRu3oWOQKDEOeAi/tteY2cQ6bf4COB6YFriV6td2FeAC4M/1zDgLvL7LhvaujkfzVmvvdOG4f9QzEBX0W5gFOBL4NfBVlbYOBl4BjjKzG+qxWxdm1mtewLPAk8AewNwV9k+JPwWdiY/8W9Rh8ypgyTqOmxK/yXets63DgROAJWocMzX+I38c2L9Ou0W194Nge2Ogf5Vj5gUOwQfN37bwO7sB+F+4vmsAU2f2L4D/GG/HhXDXa9W1LfC6FmX3VHwwvwrYAVgUHzT64Qnl6+Cd3Wt4hOsKddp9HPgZ0KeL4+bCO/T9Wnwd3gv37MAujlsFuBI4tBW/hfC54cBpwHI1jpkW2Dm0YZ967Nbz6lUzIEkbm9ktdR47MzC/Rbih8kDSLNbAmkGjx+eNpCXN7KU6j50CmNfqm1nl/p1J2gM42zzxuSubSwBzmtld9bQhbwq8rkXZPQF/+u5SO0zST4EfmNnV9bSjCAq8DlPUc381cnxR/ZekOa0Bt6WkOczso3qPr2mrNw1AidpIWhgYambjW92W7xthjQGr0+WUSPQG0gCUQVI//Im3oeJ3khYENgRGAjea2aiyfdPhPt5dItrTF3cJvWtm34VF883x6fbNZja8UZs1zjUOGGxmr+ZocyCwEPCRmX2Qk82p8LWbeXBXx83WxMJ2nkgaAPwDWAm4xcwOkfQX4AB8LeQm3I3zZYTtBfA1hTlwzcShwF1m9lUT7RWuNL8qrsFo+NrCI8A91mYdRAjKWQ3/nT1a3r5w7fczs6MjbXeahYR1wimBhxuZzdRxnmHAuvXMpBqwOQvwhZl9F/n5aYAJZjY2/L0oXmmg9Bs728yaqTZQmbx8ed+XF77YNqHBz6wGjMLXC0YCwyjzpwKzNWozfG4x4H1gIr6wvQAekTIKGA18BiwSYffGKq8JwH2lvyPs/h13qYAv7J8VbE4M/16Lh802avcC4Jfh/fzAu8AY4K1wHd7B3Q2N2PwaOAdYKef751+hPUeF7+oC/Ae8Hb5e9yZwWoM2B+DrFBPLruVHwPjw/9gjsq1z4T79Cfh6zD3AveH9BFyXca48r0847+LAOxGfW6Ls/z0ReAp3iTX7O5sTDwqZgA+8M+IRaqXr/Rb+UNqo3X2rvMbj61T7Avs2aPN3wJThvYBD8T5nQvgtnEwX62RV7D4IbBberwF8G77/S/CghLHA6rnfC3kb7Okv4gage4Ezwvsp8cXtL4BVwrbYH8b1+IL5UsApeBTK9XjnPiU+UFwcYXcicD9wfuY1Abiu9HeE3QnArOH9oYSFUPwpalN8wffPEXZHEIIx8EHsJmC68Pc0eOd8Q8Q1eCv8+wLwR2CGHO6f94Efh/fzBPublu1fD3ivQZv/wcvTL4nPJq8CjgN+AOwSOp5tI9p6A/7A8cMK+34Y7uvrm70mFWw3/BsLn7sxfP8D8FngleGeWjjsj/2dXYwPPD8HLg/vH8QH6HnCtf9XhN2J4X4YmnlNDO0eSoMDceY3thv+MHoAsCYeHfcF8MeItn5Zdh3vB07I7D8K+G/u90LeBtv9hT+d1np90OhNjD+BLJzZtlf4Uldr4ocxHFgmvB8QbtzVy/av2mhnFj63Tfhh7JDZ/h0wqIlrO7Hsx/EssEtm/5bAKxF2xwILhPcfAMtn9g8CPmvQ5gQ8Kms1fJYyCp9VXQSs0cQ1GAPMU/b3OMoiGfH8itEN2hxB5xn1wHBNSrPNPYBnI9o6qnR/Vdn/I2BUhN3zunhd38TvYYnMtpPxiMZFmvidfQisHN7PGO7jdcv2rwO8HWH3LHwWvGhme/TvLPMbe4JMRBqwK/B85L2wWHj/Me6KL9+/IPB1TJtrvXqjFM8cwF3Av6u8roywORF/Gp2EmZ0KHIFP5deIbOs0wOfB3mj8Sbc8+uR9/EfXEGZ2GZ5stoekS8MaVV6UfPJz4z+Qcp6gRnXEGryGr6mAD+oDM/tnoELBwi4QgJk9YmY74W6YA/DZ5gOSXpO0X0Rb38PXaZC0UmjXimX7V8IH0Uboh+dnlBgVtg0If9+Ju2sbpZTnUY0ZwzGNsiM+IMxS5ZX9/uplSjLfs5nti/9mH8BdezEMxAcxzOxz/CGiPIfmLfz+aAgz+x3ulr47VG/Oi9I1mB93m5ZzL+6qb5TH8VB08AfxJTP7lyb0RbmS94jW7i9CHH2N/TEuuAeB3avs2xf3p8Y8mb0FrFb29x+Aacv+Xhb4uIlr0Q/3RQ/Fp/DjaH4GdET4P38IrFXh2n4eYXd7vINYB19LeQVYF+8U1sZdaGc1aHOSK6PCvhXx9aGvItq6F54MeB8+M94df2g4KVzrL2jQDYkPMKeX/X0Q8GHZ3z/CpX4abetp+GC4NTBT2faZwrb3gVMj7L4K7Fhj/zKRv4cnqtmlI/8oxu57wIplf/8fMGPZ30vHXN+yz8+Lu/WuD9e22RnQzrg6yvvAqpn9SwBfRthdNdybh+KuvY+Bw8J5DsGLhXaZq9TwefM22O4vPEKp6o8Kn2re16DNXamxFoM/WQ+NaOtZ1Eh8w0tW3JrDNVk/dPATYn8Ywc67dPZ1753ZvzceuRRj+090uMm+DW0tva4DBjRob5Iro8Yx00S2ddvQuW8V/l4Lf0h5Ck/CbGiRGH/Q+Ax3Qf0v/P+3Ltu/B3BhRDunAM4ou57jwmtC2HY6MEWE3SF41Ge1/YOBiRF2D8Hlp6rt/3ek3RuAP9XYvztwb8y9UGajL3AMPuCPb3IAKn8dltm/Ky4lFWN7DTzwIHuO4cBBzfz/q71SGHYPJoR+f2s5hDdLmgmfdj9hIRQzbyStjLf32cjPT48PlgvgSu4f4dpXDYezSjof73TqktlpNZLmwJUApsQ7w1dytD0dLus0e9j0MfC0RYZ3S5odj9SqSwqm1YRQdKxKZxju27Fm9nwO51oLVyv4t9WRrBth/2fAd2Z2RxM25qbzb+wNKyh/LQ1AiUQikWgJvSoIISjz1nuswpNAV8fdLWn1Oo6bQdJhkvas8/zbl57M6jh2Pkl1BToU2N4ubZYdO42kpeo8tojvrK7r2ujxRVzboq5rnfZmk3REXvaKRtKCku4twO5ASTsWYHduSeflbHOApDXztBnsziLpwLzt9qoBCHhU0rmSVql2QLjZ/oAvdG9ah81LgMskvSHpRElbS1pL0kqSNpS0r6Rr8UX5JfD1inrYBXhd0qGSlsx2gpJmlLSJpCvxxdnp67RbVHvPlXSPpG2qRdVJWlrS8XhwxeA67Rbxnb0WBvipah0kaXFJZwMH19nWIq5tUde1HmbH16xyRdJgSRPytotHja5VgN158Ny4vJkRF73Nk4XwAJi8mRM4Nm+jvcoFJ5fxOAz4DR2Z3h/hUUsD8XySxfEO/eh6/aiS+uMJl9vgIbilwcDwTvEOIqQsJG2ML77/JLRxeFlbZwl/nw+cYo0Jl+beXrmE0W54QufCeGdYfm0XBabCEwmPqXcNo4jvLPjhjw9tuhsPDsjaXB0PJf4ncGy9a0V5X9uirmuw3dWT8sJ4dGHfem3Wed7BeN5SQw/AdczGZgd2a7S9kubp4pAlcLmnRu12NWuaBy+lktv1Ddf2mYi2rtjFIYvggS753gu9aQAqIa8BsjHeQcyLlzX4FE+evMPqVMetYX/6YPMzi9RmytibuUpbn81jcbCA9i5P5fbeZ55nEWMz9+9M0qr4QLFGJZvAJWb2RUx7y86R27XN+7pKmogPjLVcjBbRmXXlBpsGT6xt1G5JkqpakMxUeMJyjN1aHaGIuw4T8ajNarZLFV3rtlvvzLGJa5DrvdDleXvjAJRIJEDScGAfvO5RJZbCBUkb7czGATcTkjsrMDOwZYTdt/Gw48ur7F8Gj95r1O5I3NX4SJVDFsXTLBq1+wEeaXltXu2VNApPJakWSTo/Xg6j0baOAPbHPQKVWAIPgc91AOqXp7FEItGjeAafMXxWaWfomBsK2Ai8iuennVPF7jK4LFOjPIPnRFUcgOj6Cb4az+JFCSuWnJY0PtLu03h7Kw5AxLX3OXw2fU2lncEFd1yDNsGv7dxmVvGhIXhhYq5BTdIAlEj0Xv5Dh5xPJYbhWfeNUhooqvFtsN0oR5KRvMrwCj4DaJTLurD7MfCXCLsn4u7GaryFK3k0wq3UDjj6HNczbJRzqN3W93ElllxJLrhEIpErkqYE+prZmFa3JdHepAEokUgkEi2ht+UBdTuSppa0k6SHC7A9QNKuOdssrL3NIKm/pOMlxahpJzJIOlxeQbSeY1eT9PM6j522wXbUdbx6XsL3OfXcq3K2k7RDHccWley9f1c5cWXHLidpo3rb0RW9ag1I0rLAc2Y2Mbyvipk90+S5VsCFAbfCFxtvbMZexvYqeF7MVvjCYMXF3gZtNtVeSZsBN5mXDd+s1rHVooK6+Mx3knbHBTJzQ17KmFIeVfjRbgW8bF62ohFbL+AK4CMlvUiN0F4zWzqirVviZZfvDH8fgVfIfBnYycw+qvX5DAsBwyRdg3/XT5nZx8HuVHTkQm2PKzjXmzD5pqR/ARdU0yiU1AfYAFdNvwdXn+6KUpLv2FJ7qZy7tSGuOl1v1v4uwBGSLgh2Xy7XhJM0Ix3X4cfh+Hr4AHhB0uNdtHcrXMR3tzpsnhsi687By71PptUnaenQ1h3xqLYX67C7HPCepCvouBe+CPaE59mVrsF8wXY+FKFw2q4vOhdzKpU2ziq/TiRC0j3YHIgnjj6P1+6ZgHfq/XNo+0x4yOzLwe6NeCTRD5qwmVt7K1zbaq+oaxvsXkOmyF0O1/W+kk08PHhkuMZfAvs1aOtIOorEHVnrFdnWV4D1w/tl8c7sQLwGzKUR9pbCAxFKJZ3H4zk2pd/FU/gAV7ciNp68eiWuqv0UcGb4Px+EDzQ34gnU7+Gdbt3K4Hgl4G3xirgjy+8pvKM9kUzxtzrtboznfZXKWg/FI/k+pqP8+d+BWRq0Owte3uBFOqu3T8BLH1xd+j7rtNcPVz5/NXxXr4X79za8pPjI8P0NoUG1bXwQuhBXnJ+A5y+NDOcplWz/Ex4tmNvvr1etAYUp8TAzs66mx9aAkq+kdYHfApvghZ0uwjvLz/DKgtHKxZI2wAeFn+M32SW4hH603SLbWyRhBnQEHob7NN5ZTMIiZlaSPsMroL4i6ffAb8xsBUmb4mWJF8mh6bkgaTTesbwn6a94Fd6tQ1jzHWbWcHHCYLcPXvOmPMH1OWtCrVmuybcl1ZN8b7Umk6h7UsK3pIG48kHJ7tvWROdbRLJ3sNsfH4w62bUq4dnN0qsGoHKC/Mb7lW4CSfOYWd1hoiFP4GTgNDN7v2z7dzQ3ULyLP+VeDAwxs3dzsltIe4smZGtXwywiSU7SGLwU8TBJV+PljP8aOtA3zGzq2PbmTRgs1zKzlyT9FzjPzM6RC7a+bGa1QokTibajV60BZRiKl+ceXr5RXhdnKF5Aql5uxYtWzS/pYtw/m4fY4ux4sazn8Dj8vCiqvZMIHfgawKxkgl3M7OQYm9agdlidvAlsFtZC1gdOCNtnw90kUYS1g2Pwyq2VrkFMGfSHgJNCgMjywK/C9kXI9/5IJLqF3jwAicqLxNPgs466MbNN5EW4dsL90OfKVaqpco56mbvM5nmSLsddcE1NWwtsLwCStgPOw/3HIzI2DZ99tQt/wRMRT8JlZx4P2zegutxJPZyLl8o+C1e/zsPV8Efc/for4Pdm9mHYvhHu1kokehS9zgUn6Z/h7R64knR5slxfYEVgnJmt1sQ51sajZTbHZ1hXA1eVdW4xNtfAI99+hWdtn4YrFb8ca7Oo9so1u64A/tzszErSvsDpZvZNeF+V2JmVpNlwufnnS75+SSsBX5rZa5E2vwLWa+Y7TyS+7/TGAahUK2Mt4FE8WqfEODwk8kSLKPNc4VzT46GLuwDLxKxRVLA5LbBdsLk88LqZLd6s3WA7l/bKBROXNrN3cmjTUGB5M/ssvK+GmdkCzZ4vLyS9BWyaxwNCIvF9pdcNQCUknQ/sZZF17yPO9yMza8alU8nm0njU1l552g22o9sb3HnXWYN5NN1FmAUfYmajy2bEFTGzP0WeYys8CuzXZjYqxkaw8xUuGPqppK+pnVsUs66UCMjr91xhZt9mtk8BbG1mMRprPZbwQPpVM9F6XZ6jtw5AJULo5YJ42Om3XR2f6BpJvwX+jId3vwh0CpGNCZfOkzAL/qWZfVE2I66Emdk6ked4EU/a64vnvGSvQV2JqJJ+DVxuZt+G97Uae2ED7St8EC4715xUDsSoO9m76CTfcI4JwBxmVikwaXgjHgFJNwLbm9lX4X1VzGyTiLbmmZRcbrcfnuqwBy56uoiZvSPpGOBdMzs7xm41em0QgqRp8IXyX+E388LAO5LOBD42s6Na2Lyezn/Cv4dW2Gc0FmHYiRBdtiGeUzFFJ8NmR9djw8zWrvQ+Z67Ow0j5oNLIAFMHS+GJnaX3VZsQewJJP8KDZhZjcin/Ru+Da3AVbcjp2lagWmDSPHhiciN8VmarYrmLJjkK2BsmKbwcig8cG+IBNdtG2j0ML9K4O94/lngO2A/IdQDqtTMgSacDg/GR/mHCmoWkn+GljQe3tIGJyZC0MnAL3hHNghc8myP8/W7sk29PRF6qPDujiE5ALAJJT+Kd79FUiARsJNm7zGYffEAb1oxrM2OzNKNaAngdj94s0RdPyrzVzGJqGBVCgUnJbwG/M7N7g8t3cOgXFwMeNbOB+f0vevEMCFcB+KWZPSep/IfxKtA2i9mJTpyAy4zsBXwFrIOrIVyGhz1HEdZrquXrNOweKYqg3nEmXkOmf/kumphZSvoFruNatZnXAAAgAElEQVSXay4Yrnf2IzN7I0ebhj+ND8Lr6eRBaUa1JP6AUz6wlQKTKhaAayHfACUh13XpmK18WbY9hrmAtyts70PG45AHvXkAGkjlqfG0uPZRr0PS1MBqwJsxT6cZW027yipQCrqw4K+fMjydHQRcig9OjbbzBNyVcR/55euUFq5L7ox56DxgEBlheD4wAx6lmFtb8ev2taQLgfNjQ88r8CKeTJ3bABS++9fxGXAuA5CZ/QUmKY9cYWYN5QF2hVzcdS+qP+TEzNyLSkp+FZf4yf7+N6e5vLiK9OYB6El8FvSP8Hfpx7wb8N8Yg3neaCpYXTqc4wLgCTM7PXSYT+BuiHGSfmlmt0Xarekqw10yMZSHzH+Cu0ZexZ9Y54y0uSOwjZnlva7wV1zp+FjgFOAAPChhazxAI4YVgZXN7KU8GljG7Piawc7A/pIexWeUV5rZ6JqfrM2hwPGSDqdyMEqsy/BA4ARJf8Rzt3IZiHNeYyvndOCXwFV439LOScl/A84Jiep9gE0kLYo/9GzahN3KWI7Kpj3pBawKfI0vqo3FEzvvxTuzZSNtnocryJ6FLxJGKyDTPerSH5X+r/iN/C4+cB4MPN6E3YeAf+Kuoa9xl+Zs4fpu14TdO0qfxwMdnsLLBNyF+6djbI4AFirg/hoKbBjefw0sGN7/Abg60uaLwHJ5tzVzjkH4IvbHuJvzbHzQi7HV6T4tezV7336ND2YT8Iear8pfTdr9qtqrCbufAz8p8nvL+R4oiRSPw9fDngJ+XsS5em0QAkyq/bI/rv7aB69lf5yZ1VNDo5K9z4Etzezu/FpZHJK+wTvfDySdg2f+7ydpPuBFM4vyJUv6EljBzN6Q9AWwipm9Kq85dKmZLRxpd3lgWjO7T17H5yLcZfgGXlLhhQibxwDfWc5Rj+oscvoR8DMzezoIhz5vETk7ktbBHw52N7O81j8qneeHeEjvgXgnNDX+2/htI9dY0lq19pvZA5Htyy0cPWN3JzrPTvrjckqb44FJp0Xa/QBY18xej/l8K5EkK3CQ6M0uOMJAU2+hrXoYQ88ShfwYWDJ0kBvgnQ64Hl4z8vZFuMows6fK3o/A3Q3NMgOwraT1gBeY3E0UmwMzDP+/DsPXKjbAS0isgs+4Y7gBmBJ4XdK3dI7WImZQKyGX4f8l7mpZF38C/j0uqTQQOC6871J1Q9JCZvZW7ADTFbEDTB12L6i0XdIz+DWJGoCA44F9Jf3Bmijr0Iqk5CIHH+iFA1CIJDoM2N8yKggh8/d44G9WVqagAXK50UJb6q46aPEZ2ufhncqHuDvjnrB9JbzYVSzPACvgM5P7gb8FvbXt8U4+Ckn3AptZqNZYtn064HqLSxodhEdVgYf3ltPMj+86vNN6DDgVr+b5WzzK6IRaH6zBH5toT1UknYYHSxhe+mNf61ySY6ykw3AXbT28IelB3H13jeW/qD8nPitZFG/zG+E8H9b8YDz30bFWHMN6uDL8hpJeYfKHnHojLffE3YRQ3L3QD1+vLAXPZAOIci350etccCHrW2ZWsWZ82G8WIW8j6Sb8RvsSr14Ze6MRnnDKmQJ3CZQGtj7B/rdNPvlujt9oV1kooRxcHF+Y2Q2RNmu5ynZuwsU5EZjdJs9UnxX4n5n1r/zJ1hMCM1bFawzd3Or2lCPpHnywuNbMxlU5ph+wWj2zGkk/xQMaNsG9AkOAc8zsuZofrK+tu+GDwZT42gzAdPha0N5m9p9qn23inIfi7sf5Iz9/fq39ZrZzg/b64aVDHjezXJNcJf0NV8k/Aa9gezQwP7AZcJSZ/SvX8/XCAegVPJT30Sr7V8ZDURsW+Mz7RiuzuzEdmc8ldeWV8LIGf223Di1vQqY3+GLo+viibom+uHtrVzObr4lzTAUshD9Rv533U3teSJoSF6MdhLf1ZeAya0MZKbnM1Y74YDQIL/1+Nr4O2KiyAJI2BG7GXWEnlGY8YUZ0EJ69v7EFeZoI+1mJH+HBMzMCf7CcZWiaIazfLmahSGWOdt8B9jSzW8JD8DJm9rakPwGrmtnWuZ6vFw5AkxaHq+yfB3gt76lmM0h6FV9kfzSzfRXgAjNbNNJuIeHdeRNmPqUbNSvpAr6msqeZnVdhX1e2+wN/x10aUwT73+Kd3GHWQKnnot2mkgYBt+NP/KVZ5FL4jHtDM3u1QXtr1nOcmT3YiN0q51oJLyeyFe76v8bM6r5ewcY9wJNmdnCV/f+HB7+sG9nGIzObJuJRkvdbfrlRuSDpcfz+zDXgqULwzMZm9oykBfDS3NPner5eOACNAH5VzZUQIneuNrNZmjjHAnQ8ob5qTZYlkDQWWCkbgSRpMPCYRZaNVvUS1waNJUvKSyXUdTNZg2UTwrqdgHfwXJgRZbvH4UKRUcnDkk7G/d0H45JM4G7UY/Ey6Ps3YKtQt6mku3CX1g6l9cuw/nUJnpS7QYP2SgN7aVAvf1/CGrkPujifcIXw04EZGrUboitXrjbQSloc/z3k2knmgaSdqb6u0rDyiqSNcBfZkXhwS6d8LYvMsZL0Bi6i+oSkR/C11RMkbQH828xmjbFbFWuDuPPufAE3AefV2H8BcHOk7enwZLOJeITSeHxx/0p8TSS2zfeH11xl2+bC82ruy/Ha9MODB+7H/f2NfHa/steR+FP5XbgP+ejw/kvgiFbfA5l2fwz8tML2jYGPmrC7MZ7svFq4rv3C+8fxkOwYm2OAJSpsXwoYHWFvprLXzPgC9/KZ7TPlcI0XwmeZH+AD8C24DFajdkYD89XYP1/MdeiGe+wA3G18LC6hczJwI17y/fBIm0XlWJ2EF5IEHzC/wyNYv8Hdnrlem14XBYdf4LvD09RxZvYxQMj8PRiP1Fov0vapuFzM2nSoKayG63f9A3dBxPAb4HrgXUn/C9vmwoUTfxFpczLMbDzwZFh0PQMXa633syeV3ssVFo4zs7+XHyPpEFxpoW66chNm2hDjMpyeytpXb+Mh2rGcyORu00ck7U14yImw+U2VNk1Pg2XkASyzgO0TFD7Pbo9BLuu0JR7WvToeQXcmvr76vxofrcXr+BrgWVX2b0ATsj8qRj4J4Le4wOfVcvWGf5lLSP0ZT1GIoRAVdzPbr+z9ZZI+JAQQWf5qIb1vBhRG9t3wH+wEXLng8/D+G3yxMdbuZ8AaFbavCXzWZJuF//j+hMv9rEdwoRZwfQYBo5r4/FdUUBfAn4QbyiintgpE04oQeJj0vytsP4NIdYXw+bG4wnp2+2BgbKTNC/HoytXw4Iu+eOf+Mt6xN/u9f43nmTRr5yx8tjsWF4pdN6f7cs/wW50sKx+PuPscXwuMtX8cPlDuhs8298UVPYYDuzVhdwwwT3g/HF/YL/0ePs/j2vTUV2+cAWFm/5F0M/6EthDeub+Br/180ITpqakscPo5MFUTdjG/Y+8Mr1woiy6btAnXbDuI5oQHRwM/ZnKxyB/jP8a6MbM+XR/VFAcCt4ZE1EfxdZBV8CTSZhJdHwf+KWk7C0/8kubCdeEei7S5Fz4IPUSHYG4f3J2zdxNtzZuVcL27i81sZI52/4UPvjeEtYpX8e9rEF7P65pwTCxb4rpqt0s6EbjBPALsVfyBLzbE+2PcxTkMF/lcBc89K0VdRhFy6/agc0TkGWb2SazNMrurUVnP8vRmbE92rjASJ3IgLBJ/hS8SjwnbBuB5MNOZWaxrrxRFVE3kNLZsdHYRusRjeL5OlHSIpANxMc7z6ehsV8ZVJ44ys+Ni7HZxzp9YZERQGBh2p6Nw2ivA6dZEYqOkBXG36WK4GCuUuU2tCSkdSQvhigQCXmnGVsbu1/isbWiTdgTMjRd2HJfZNyUwm1WJQq3T/pa4eOoiYdMbeGj3lbE2g93c5ZOC3XOAD8zsKEm/p+MhZFlc8PW3ETZXwyMiP8EfnMAHtlmBDaxKmkkddrfA+6u++INz+QBhZhatZFLxfGkAyg9JS+I3xQA8499wl8to/KZ4OdLu/rjKwltMLsNvFl82Out/ngiMsBxyYEInsRcd0i2vAqc220lkzjEXnmPyG9zF0bCPXlJfy78OTsm28Cfn8oHtbov80YU1ij7Z7yfkME3MdvZ12MvmvZRq7HSyYw2WC5C0NXA4LnRbaQB6GviLmV3ViN2ikfQaXs76MUkPAbeZ2d8lbQucYvFF3vrg39v48PdWdCRm/8caCPUvs/koHor/ewuqK+E8ZwJLmtmqkW19C5d8OqTR+ynqfGkAypew+Lo9nTudIWYWq/+FpPfxRf1cs5B7IpL64v7+XfE1sRdwOaGrYp7cQ1j+ZcAlZvZEnm3NG0k3AA+Y2cmZ7XsDPzazhgJSKuS9VMRCvZwG7N4HXGjVtdV2wGfYUQ9ORSHpWHzt8xhJv8Lviw8I8klmdlhLG1hGSM1YJuulkFcufdbiUzNGA0tZk6kjdZ8vDUD5IWkWc5HMvO1+iVeWbPqmUDfUGSoCeU2SXfHM+tF4AbqD8JLBr9T6bBd2f4e7c9bAI98uwR8YKkXGdWVrX9x19014X5XsIFKn/U/xgealzPYl8HD8fHM0Ignuq9WrXcPgnnzEzGbv3pY1hpqQT6o3yRfiEn0lfYzP1m7PbN8ITzOZo1Gb4fPX4soa3TI7TQNQjkgah+cZnQPcHutqqWD3TOCFPBYAVaanViMRFRpMQFSBSr3BHbIkXjr5EgtJxJK+o8kBqOwcP8QlbrbF82oexxfR677mIRl3eTP7LLyvhllc8uEY3K31Wmb74sAzsU+9eRPauZxVTxgdBDxlbaQ2kjdVknyp8DeRruN/AFvgQTSlIner48mpV5pZzQegGnZ3wmW/zqFyEcFbY+xWo1dGwRXIxviaxDXA53JtuAtinqYzvA/8JSw8VioZUPfTdHlUWc4RZkUq9a4C/Bs4O/v0nxch+vE44LgQHXguLsdT9wBkZWKVFilc2QUv4DkqWdfZtkAh1yWSoXhCczVpoBWpX1m7W5E0Nz4brhTs08istVxJZSU8L+wYOgcMHIoPIDEciA9m5+H9uPC1uzPwfMZYSnJWlaoWGx6ckBu9dgZUYQG2E40uvGZsz4A/Te+MF7R6AO/QoqTpC3qa7o+7mw7NYYAst5u7Uq+kZfBkvm3xjusi3D//PjnNgMJ5Vse/ty1wuZTrzKzhelHh2j4M7BgbSVjF7sZ4ZN2VuAoGeGTkFriyQFuI0kr6K66ovKKZfZTZNyc+u7zAzGJLkxeCpO3wDng8LveUDfZp+HcW7D4NHGxmd2W2rwccb2Y/imwykn4ALIgPQG+Vom+bsDdlrf2Ws+htbx6Ask+R/YFl8OiUf5vZ4TmdZw9cfWEKXHrjLLze0Kg87DeDpJG4qyTXBUcVp9Q7Fd7Z/gb/nvrgT3vnWGS+SVg/2Q6fWcwF3I0PzNc1GTgyHF8Hic7Mr2J3QzzCrNRpPYtX67wtz/M0g6Rp8DDjefFrWXIZLo5f62G4pltWO6/Z886NR9ftEvn5t/GAlj/nGRkZAgaWzbokgyvy6WZcpyHoacHw59vN3LOtoNcOQNWQdAAwr5lFu5EkzYHnvOyMd2pX4zOgOYFDgE/N7Cc5NLcpJJ2Li6WemLPdQpR6M+dYiI6ghJmAe82s4cTR4Kt/Eq9Zc1leQSSSTgAwswPysNfTkBd3PBZXvx4YNo/EO/hDLVNUMKdzDsbXwqLcRJJG4XlQeT+QPYWHt+9cGiDCwHE+rhiyfITNKXGX8W50VnE/CzgoxtNSZntaPH2gknDq8bF2K54rDUCdCRE6T5nZwC4Pnvyzm+HaV+vjPvlz8EXzr8qOGQQ8Z2ZTVLbSyV5dC4kxEVXB/pHAPriL8CkmV9SNtVuIUm+Vc/UFfobrrm0a8fmFzezNvNpTZvd0/Gl/KJWvQWyp70IIrtMVqdzpxFbcLeVCzYx3kCOaCcxR1+Uu5sFnQLED0JX4zPeymM/XsLsCrv3Xn46KwEvhahYbm9mTETbPw/uZg+i8rnQsnmsWOwtcHrgVX+uZHndFzoormHxkZovU+Hjj50sDUGfksul/M7O5Ij77Jb42cbaZPV3lmKmBA62O3Iou1n5KNOObzn1tKdgtj67LFvhqKLqupxJyYaphFpEDo4LEMkPuyE3A/Ph3NAFf2G664m6ehPtqDNXXbvsAUzUYvVmeijATLiF0EZUjwKLTEsJaTTY/8FIzG13zg9XtfY2Xp6+0rnRN7Hcm6YHQtj1wPb/BeHDDpcBpeYdn99oBSNKN2U24DtqP8KeoSlEgXdn8QbOLgN8H5DWVqmJ1lHXuLorq1ItA0nG4S+tYXM7lcLwEwdb4ukWUVpmk2/H1yd/gumXL4E+/Z+DlAu6q8fFuQ9IHwJ+qDQQhWOXpBgegWqkI5bTVg1PIA1onG4ATPCz3Wbxqwxd47bHXw/tVzOxVuRTYRRZZ/LIavTkMOxuhNREX8zvUIkv6AhtLGmdmN5RvlLQp0N+KkDNvQ9ppgKmDv9K5Uz+Ask69UWOSprAaEibBJTV/5DpDUWKZKwBrmdno0CH3M6+CeSAeih4dEZozT+P6adVmIpV0DWuScyrCJFR8wvdpwJGSdsqsK/057ItlPB0FFIfjD2Wv4g8oczdhtyK9dgAys50LMHsULuGeZTReD6jlA5Ckf+I6T6PD+6o0uk4h6e+4+7IkxPpT/Gms9AOZDq+F0lAp5oLJu1MfK2kOMxsOIOlhYCvrqIEzK/AmcfkUs+HuEYBRdNQGuh1fkI5FdKiUj6BDNPUDXLG5XTgRmKbG/rcoqE5OBFcDs+OdeK3ffWxuzcrAWsD/JJWvK/UDBpR7eMxskwbsPgssh9+jDwJHhbSSHSkg16zXDkAFsQD+w83yVtjXDixFh5tpqRrHxfhmD8IH2lJndjnuzik97U+NL8y30wCUd6eefQIfDGRzKxp6Si9jGB5JOQy/pzbAZwWr4LV3YnkJb+c7wBPAQZIm4LlXuSht54GZPdTF/tF4QE3d1BvoE+y3Q8J3iU/xhPdymlIxDxxBxyB/OL6mfRE+IO2Qg/1O9OoBSDnXacfDTBdm8izvRehQCWgpZrZ2pfc5ke1YYzva7qSoTr0WsQuv1+GJp4/h1Xcvk/RbglhmE+05BldwB+90bgbuwzu5rZqwW1h0XY7sWedxhpfSboiiEr4L8uBgZWUczKtFFzqj7LUDUMj3OQR3sayJS64sFN7H5sXcAJwiabNSAqJcRPNkPIO9rQmdxVTtkCTbjRTVqeeOmR1S9v5quUp6qVxytAqCmd1R9v4dYJCkGYGRTYZN14yuw5+sW4oVI5lUbv87SevjfU3uhLDpBYGbg1t9AB65OD7S3iL4GmCl4Ibvck9ZsDYoy9qKF16L41fh/aRSxPgi3tmRNqcFHsEX8t4Pr/G4WOB0ObV7BmDG8leEjXWBLTPbDsZLko/H3U8zRNidCMxa9nenEs+4uyuqdHY33hcr4et4P4v8/ARglrK/v8KDDpq6Brjb9ApgwQL+z+cB01bYPgBXVo61ezvuhh0Q7oUF8SCCx4H1Wv1dd+M9dS6wf842ZwvXcWK450r913/wuluxdh/CC2pmt2+LlwLJ9dr05jDs8uqHw4H1zey5kGH/hJnN2ITt9fC1DwHPAPdYExdaXjjuTHw6XB4mHJVXI6/cepsFn7akFfEZwLl4xMsBeAJtQ1n8IYLqKHwtBdy1czIdEYfTAkc02t4iCRnl/SwyH6OCvYl40Enp+54m87eAH8RcgwKlkyYAkwInyrbPjFc1jfKUSPoMj657KeTIrWge3rsWnlPSLtF1kwgzvw2p7DJsODUj2Mw94VvSpfjAvhPuPh5sZu9I+gl+bRev9fkadr/CZYPeymxfCE/Qn6HyJ+PotS44CqrTDmCeN5Fn7sT5+MxnFyaviBrDUnRWzN0C+K+F0sDBtfM3fCBqhGG4/FCJj/Enp+wxLSd0rhfi2eR9gnzQ9jl07oX45gPXApsR7yLuROhsFV4DJZW7bfri6u6fNHMKekZ0HQDy+j+34O7BWfBS6nOEv9+lskJ0PeyErw8vzeQh7VFrS7gXY10zG+mR/ZN4Gx88YzH8QTHLdOSshA29ewC6F6+s+Qz+5H+KvIz0srjacBRyxeKD8PLGhkdYHWfN1dFYERdvzCsMcgY8PLTEarj8Rokn8c6iIcxsvuaa1a0ci4ebHom7Hn+Puy/Wa8aomV3YfNOqMgw4XNIa5PMk/Sl+j5bu0yzG5KUfGqFHRNeVcQKuCbgX7jpdB7/Gl+F9RBRWzDrT1GRKpwdmwe/nWB4CDpa0jXUu9X0wrvCeK73ZBVdEnfZd8WCGIXR8WWvgkXZ/MLPzqn22C7sv4tUPK8r7RNgbigsj3h9cUF/gax73hP1LAfeb2Ux5nK8dkTQMz/+5Nfy9GN5hTh3z3XcHeUsnBVeY8IexzYFyjb5xwHtm9mHDDe2wvwEwwMyulbQAHl23GCG6zsxqyRV1O8FNuIKZvZFRAVgBl81ZuIBz9il19A1+7ma8SOWhQZZnafwB5Up8jXHLyPYsief/fEpHSPuaeP7ammb2YozdquS9qNRTXvg0VRW2C5gn0uabwB8rbN8Tj1SKbes6wJ24cm4e//fT8SfSdfC8nU+BKcr2b4fX82n591Tg9z8eX/co3zYGV0Jvefu6+VrMW+m3UNC5Zuyuc0W0bQSwSHj/OrBheL84MDrC3ofATGV/D6FzkE50UA7uYRmBu/rH4e7Z13G3d1OBKqFvPAm4B384ObGo30VvngFVW3idCRhucYvE3wJLWOUFvJfNrGaxpxp2v8aTGfvi/uhOIZbWeInrmfEbdnU8YODXZnZd2f57gEctp5pI7Uj4/me3svILYQF2sJnlkdDXowiz3t3wSLVdzOwjSb/AZ0HPRto8D9jLMnV/QqjwaRap2FwUku7A9c6GSPoP7qI9DRcRncbMVmnQ3kT8HiupYnxNCBYIf8+GK0xHJapKmh34Q2hnH3w54d+WKQLYzvTmNSBReTF/GuJ9qMPwNYSsf3t9PNAhllxLXJvZp8Ca8poto2zy4ltb0BHJ9n1FwAOZhfcfALdJmuRbtzaJ1Ao6XwfirrIF8Hv3HeAq4CRrrnje+sCNwG34rLhUIG1BfAH9F5Gmf42vHWSTsKfG1TDaagDCRWlLC/CH43lKp+Fu+aKCS6JnAOaJop3W6CTNJGl3M6u7lHzm84O6OGcu1YdL9LoBqEz/zIBjQzh2ib74gv9zkeZPBE6TtCye+2P4LGMH6s+4ngwraGHbzL6ssj23ej1tTKVyGFlpk7YgJAjfiwfI3I5Hagl3wxwBbCRpLYtMPsQFWfc1s9PDU3qJ+4H9ItpbdHRdIZjZU2XvRwANFzhsA36ID5pRAxC+DpoVdS0fJHONhOt1AxAd+mfCfbvlkSTj8GlsVJirmf0n5BTth4fLgufVbGkZhexGCcEC29ERXfcyXsEz1xrtvQWrox5TLCHAZXFcSeDDzL6p8PuhERWA3+Fhy8ua2csZe0visjm/I77TWYLOUZAlPsfXbBql6Oi6nkLpGmS3tTPZ/KH+eImagyhAzaE3rwGdj/unv+ry4Prs9cNdbY+bWbbUQ7O2B+FPvtPhhbLAB9Iv8YXSV6t9truRtDXuJhqJRxM+XbZvZjzJt12EWXNH0ty4K6v0oHAjvqYyMuyfDfiwkTVGSfcCN5rZP6rsLyk3NFzkLnz+fWBrM3ukfJ1C0uZ4CkFDOTtFR9cVhaSBeCL12njUV6e1GTObtUF7E4HX6FizHYS750sPvf2ARWPWm2ucs6my5DXsboSr6K+Zq93eOgAVgaRvcHWFd3O2exceobVDacCUlza4BJjSzDbI83yxyMVdz8IDHKbD1xP2NLOzwv6GO9+ehqQheN2U3fCibicRroWZjYgcgD7BpWteqLJ/abwMc0MdZNnnj8PTBbbEZyzL4wmYFwDnW7wCwLzAMOshnYykm/DZ4IW4i7BTu63Bgn9BAaFL8pyNFzgALQQ8b2YDujy4Ebs95N7oEYRs+sPM7O6c7Y7B8xOy7pelgMfyvilikfQ8cIaZnRn+/hkeenq4mZ3WSwagD4FNSusJcjXkIcCSwI/xmUGjA9A4PAy2YnSTpDnwWcUUlfbXYb8/PthsHdo3Mfx7KZ5/lg1SacR27tF1RRFmf2uZ2TOtbks11HX5iDmBfWJ/Y/LS4Z024Q8jR+MRvoNj7FajN64BFclRwEnhyedpJs9Uj13c/4aOOjXlTE9zWc95sxDuKgTAzG6WF6W7VVJfPKP8+850uGsUmKSGvA0uyvkAnpTcKH3JhN5nmEgTi8PmibfbSToC9/f3AZ61JpWPC4yuK4q3ybjd2pB6gpmakbsaReV1qk9osjRHJdIAlC+3hH+vpfOXWAr5ju0kbgLOlpcJeCxsWwWXjrmx6qe6ny/xp6V3SxvCusLG+CL3HC1qV3fyNi4/M6nzNrMJYW3sSvy7bBQBl4Q8s0pE5ZdlMa8E+0l4n0cYfq7Rdd3AXnhk7P7AS83M/IrCCi4fweSRfxPxhNdXrEap+VjSAJQv61BMlMteuF/6IVx6HfxJ7UZg7wLOF8sT+A38aPlGM3s4uOOa0cMrDOVbNO12XOusUxnmMAhtFbbP2aDNesLwm6qtI2lvvAzFXOHvD3GRzH80sYaTd3Rd0byFz9KeAVBnkU++567jI4ATraw2VLecN60B9RwkLYxraQl/ImkrQccQ/bSqmR1bZf+PcdWFIhWjG0JdFE2LUJnoh5dbqBhdGVyRPzSzZhKTc0XS8XgY9wl0PDysAuyP18Y6MNJurtF1RSPpQWAgXvqkUhBCW+aJ5UE1ZZjCz5sGoOYJC3cn4D7t/sDdwJ+C4kCijZF0Oy7G+htcR2sZfG3tDDx4Is+yGm2JpM+B35nZ1Zntv8JD6aNEaYuKriuKEOyzouWnOt9jyMoGdRfJBZcPf8EXVYfgQQHb4B3YFrEGyxQbusTM/hR7ngQr4JFPo8OPsJ+ZPSPpQDyjvCEpnjDDORivrIJ/gh4AABK2SURBVHpLSE7eGU/i64OvDx5ehD+9SSqFeL9Ac4vyh+ODzXuEWTsd0XXHNGG3KF7Bg0hyJ2c3b1F0+2wkDUD5sBnwGzO7HEDSJcAjkvo2sZC5VNeHAO2fWd3u5F007ShgD3x97jBJc4W/T8YXdPfBBWX/3FSr8+UivI17Zbb/Abg41mhR0XUFcjhwsqTD8YTvTmU5YqNYu3Lz0uT6XY48FVxxVck7iTwNQPkwNx4gAICZPRH0r+YE3o8xaGZr59S2RG3yLpq2HbBjCEEfhHdkO5rZEABJr+Hu2pYOQJkZdj9ge3n9nlKU5Ur4/Tuk2XMVEF1XFKWAiTvJN4r1H3haxjJUcPNG2iyC85lcOLZQ0gCUD32ZvDrheNL17QkcA5QSeQ/Hi6bdRyiaFmFvDoI7y8xeCYNZubjtM7RHOHp2hl2STJo3/PtxeC3WzEkKiq4riqIe+nJ18wJImgJX794Gd+v1L98fGbF3eloD6plUytOYCs/dmaS2bWab1G3Qn1APCTdtzfWgtAYUT3nYqXmdlkFBzXlkZAf5Ea56MEzSovjDySBcPBY8NLmpH7mkOam8lvBgvTa6Y4ZdI7ruCHwQjoquKwoze6Dro6LI280LnmO1FV5a/hTgAGA+XM0iZnbdkoeBNADlQ6U8jUuatLkUHU81tdaD2u0pEqBU4nxdKos61j0QF40qFE0zs88lDZAUUzRtCHBR0BVbG+8gTgwyRBPxAIWra3y+VlvnxBfw16RDMr8wqfwc2BXYNRNdd6+k1/Ek6rYagGCSXuEedFadP8PMmikfkbebFzyy8PdmdrukE4EbgqvzVbwmWUO6dXQuv9BtpDDsRO5IOgFPkL0PL0uczadopzygapVxZwY+NrOGHtLkpRgOxp/0Hzaz44IKwvF4wbub8LLto2uYqWb7SmAmvIN8EtgQL+t8NK7/1VYh4yG8e2UzeyOzfRFcNX5ga1pWGUmr4YnEn9B5xjYrsIGZPVrts13Y3QAYYGbXSloAd/MuRnDzmtl9ETbH4MLHwyR9hKuhPy1pflw0tNH8tZYIx6YBqIcS1Gk/MLN20oIDJqk375HNK2kn1FE0bQReA2VE2e5S0bRjzGyuFjSvIuG6bmxmT8nLhy9vZm8EqaM/m9nKLW5iJyT9A+9j9spsPwXo226uY0mP4kEjvzeziWFbHzwxdUkzWzXHczXj5i0Fs+xkZo9Jegi4zcz+Lmlb4BQzmy2vthZJcsH1ACT9HXjdzC6U64Pcibu3vpS0kZk9VttCt9OH+Kqy3UVPLJo2Nd5ucDmbWfFy0a8QsZBdBN0ZXVcAy+Cd+sTSBjObKOlkIFq5uwA3L8B1eB/wGHAqcFnQipwLX3PrEaQZUA9A0nv4VP2xoC59If6Evh2wdLuFbEs6BvjOzI5qdVuqoR5YNE3SE8ARwe9/Pa5cfBiukLypmS3c0gYCkup1J5lFFtArCkkf4wPQ7ZntGwHnmVlU9GLebt4q51gZWBV4w8xubtZed5FmQD2D2fCIGYCfAleGXKPPgaeqf6xlzABsK2k9PCQ5m9DXctdLKeIp+Mx7StG0U4HZw/uj8fWKbfBkxl+3qlHltNvDUINcDpwbwqP/i8+CVwf+j4hSImVuXgEDQ25giZKbt5nghkkEL0i7eUK6JA1APYPP8PyMD/Cy36Xa7P1oUfRKFwyiwwWXzSNpeUcvadnMppmyysclrI2Kk5WSWcP7ZyTNh1/fYUl3MBcOxH9P59HRN36HJ4weHGGvUDevpNnxWU+lSNPTY+1m7GdD/ZupNTT5OXrGg1/vJvjVN8X9/T/Cq2OODtFVB5jZci1tYA8jJAOWwphrYZEJfYkeTBAXXhC/P94yszFdfKSancLcvJK2B84J9kfS+cHOzKzRkh8lu9MD/8TDvCersJv37yHNgHoG++KCjvMAB5aF8M6BP521JZKmwhPtDHi7jSL2ii7qVRiSfonnF1V66t2yJY3q4UhaGi9ANxEgDDgvNmu3YDfvMXho/9FmVqtabqOciOcs/QIXzt0FD2zYiwKKCKYZUCJ3JPUH/g78EX+KEr5OcRpwWBCpTDSIpJPwgINHqFyvJqbcd68nGyQg6RY8gfajJmxm3bxViXHzShoJLBfUO3JD0gfANmb2UAj1X9bM3pKXld/FzNbL83xpBtRDkDQHrk48KGx6Fdduiv6RFMhx+OL474GHw7Y1cFWAPnihs7ZB0lLAbrjbZRcz+0jSL3AXSXT4bQH8GtjCzG5odUO+Z2RdsWviIe/N8BR1unmJU7AYggcxnBbx2VrMgHtbAL7EE5/fwhNzz8n5XGkA6gmEaLIbcGXtx8PmLYD9JP3CzO5sWeMqsy3ekZeXY35b0gj8Jm6bAUjS+njphNvwkuqljmdBvMbTL1rTsoqMAV5rdSMSdVG0m3df4HpJ61K5dERssb+3gQWAYfhD7tYh/H8zOq9h5UJywfUAgr7TXXgym5VtPxVY38wWb1njKiBpLLCMmb2e2b4YXg+m2afL3JD0OHChmZ2uzmWjlwNuil3MLQJJuwPLAbvl7Pfv1QQX3OxmNiL8/TWeXze0tS2rjqQ98bD8T3Fx22wQQlRisqR9gAlm9k9J6+CyQf1xz8VeZvav5lqeOV8agNqf0KEPrqKp9ZyZ/aA1LauMpMeAp81sj8z2M/CBaZXWtGxyJI3CZVbezQxA8wOvmtlULW7iJMLa2g34IPQGkz/1tlViZ08hREXeha9TAmwEPECHgjXQnIhu3m5eScOBY83slNg21XmeefBS6m+aWdOBGVmSC65n8BSuiP1GZvtSNCERUiAHArcG1+Gj+NPZKrgEy0atbFgFRuJRPu9mti9LR/Jvu3AmvpZWEsxMT4/5kFWzb1bJvhMFuXn7Bpu5ImlH4Aoz+xYm5f0MkzSFpB0t5/LhaQbUAwgRKMcBp9OR7bwyHpRwMF5bBGifxEl56YA98ERJ4Yl4p7ehvM1xeKe+Jd7G5fHw9guA85vwpedOmKFtZm2mep2oTRFuXnkJhq/yvj9ryAbNBAzPOw8oDUA9gOAiqIeWJ04GN9ExwL/N7L2ujm81ob0X4IW8hNfsEV53Zyczm9C61nVG0lBcDbtSVn2iTSnCzSvpdDzY52VylLsKfc1spfWwsu0/Au4xsxlj7FYjueB6Bj0mcdLMvguL5U1LgXQHISdpO0lH4CoTffBAiTdb27KKHAkcLWknMxvV6sYk6qYIN+/idLjfm5a7kvQiHbJBD1TQrZsXuLXSZ5shDUA9gJ4wk8hwB+7rPq/VDakXM3sbD0FtZ0pllz+RNIzJn3rboiRDYjIuBU6QtCXewfcLMj0nAufHGCxA9LVUu2tJ4BZcab3EOHzwvCbnc6YBqKcQJOH3wGP0NzCz9yXtCgw1s3ta27rJuAf4e5A4eRroVP3TzK5tSasCoT5LXVhcrZaiaNsCf4maHI67ed+jYz205OY9pnXN6sDM/gIg6V08CKFbZLPSGlAPQNJ2eATUObi6wBLBh7wbvii9QUsbmKGLNat2WKe6KbNpTXztpxRmuiTuinuwmdDbRKIcSQuSk5tX0pTA7lTXBVyxCdtTAT/Do/T+Y2ZfhLaPNLNck1HTDKhncCDwWzO7PMx6SjyG14VpK8ysT9dHtQ4z+3npvaRDgLHAziWRV0kDgHPJQZAykSiRs5v3bHyQuAGfUeUyk5C0EHA3MA0uy3MV8AUecTsDsGv1T0ecL82A2h9JY4DFzey9TBTNgriKb9soC/Q0JH0ErJuNLJO0BB71M3vlT3Y/ZWUkKvENrtl1rpn9s8oxiW5G0tT4A+TmuPvcgHfwjv0kMxsbafcLvAruA3m1Ndi9GfgQH3C+oKOvWRNPS1gwz/OlGVDP4ENgETpEAkusSRsunIc1lpfN7KTM9n2BQWaW61NUk0yDJ8hmQ5vnANpKYQJXFz8KuI4OTcCV8ETG44C5gf+TZGaWt0hlokEk9cNrAS2LJw/fgq/9DAKOADaStFakrNJwXIYnb1YFVjazCZkijcPw30mupAGoZ3AW8M8y99vcktbA64Ec1bJWVeenVFbpvZc2EiINXAOcL+kAOif5HofXQ2knNgAOMbNzy7adF8QiNzGzTSW9jpdsSANQ6/kdXg9rWTN7uXyHpCWB+8IxMSkLh+KBPjuZ2cimW9qZ/hW2zYOrY+dKcsH1ECQdA+wDlJLWvgVONLM/t65VlZH0DbBUdpFV0sLAi22mrzY1cBJeeKv0wxuPrwHtb5HVMIsgJDQuY2ZvZbYvBDxvZgOCW/bFdtMH7I1Iuhe40cz+UWX/vsDPYjT8JE2HR0WuDXzM5CH5CzTeYpB0OTDazH5TEmUFPsPXmt4xs9/E2K16vjQA9RzkpYIH4REvr7RrMqKkF/C1iFMz2/fGC30t2ZqWVScEHpSXYR7dxUe6HUnvAaeZ2YmZ7fsDe5rZvJIGA7eb2RwtaWRiEpI+AdYzsxeq7F8auNvMZo2wfT2wAh7KXak44UmVPleH3TnxmRn4mtWz+CzuE2DNrEJCsyQXXA8iPI0/1ep21MFJwJmSZsXdbgDrAnvjuUxtRxhwKnYUbcRfgLODTP4TeKezIrA+8NtwzHq4knOi9QwEanXYI/DIshjWA9Yxs8e7PLIBzOxDScvgBSWXxR92zwKGxAZM1CLNgBKFEHKUDsclSAD+BxxjZme2rlU9H0mr4Gs8JZHXV4F/mtljNT+Y6HaydYYq7J8N+DAmL05eI2wbM3uuyWa2lDQAJQpF0iz4fTa8y4MTie8RFeoMZZkS+EnkALQhXhV19+yaYAyS7sEFhCsG3jQzWNY8bxqAEon2RdKMpexzSTWViPPOUk80h6S6dN7MbOcI21/jA1hffIDrFMptZtM1aG8irgZyjJkdWWH/bMBHeSeZpzWgRC6EwIO1zGxkmbJuRZJoZkOMkFSqz/Ipla+rwvaWShwlOhMzsDTAHwuw+QdcNHVpYIcKQU65z1bSAJTIi2vocDUk0cz8WAcozWzyVkBO9FDMLFvFNQ9uAB4GrgcelbSpmb1TwHkmkVxwiUQi0YORNDswRfk281LajdiYiAdMDJc0PXAZrrKxlZndXdQaUFuLRiYSiQ4kDZK0aNnf60m6RNIhkpL7rRchaXpJF0oai0eYDs28ojGzL4GNccHTWyXt02x7q5EGoETuSBoo6VRJL0j6WNLw8ler29eDOReX80fSD3GXyYx4btXfWtiuRPdzIjAY1wH8Bi/PfQBeYXWrCHvZRFYzs4OBHYC/4qVgcietASWK4CJgCeBCKmRpJ6JZHHgmvN8CeNzMfippbbyy5iEta1miu9kIzwN6KOQbPW1mVwR1991ofB1WlTYGm6/j60K5kwagRBH8GI+Ie6arAxMN0RcvjwyuLHFreP82MFtLWpRoFTPQoY7/JTATXo7jUeJmK2vTEezSCTN7TtJyuFsuV5ILLlEEb5PurSJ4CfhDUEJfF5f4B1ebKEKaP9G+vI1rtYGrYWwtr5+wGVUGklqY2QO1ykKY2WdmdlFUS2uQOolEEewFHCtpcFocz5WDcM23+4HLzKxUsXUTXBsu0Xu4AFeqBvg/3O02DjgBLyXSI0hh2InckTQXcAWwSqX9eYdy9ibCgD5deQ0YSfMBY5LcUe9F0jzA8sCbZQ8mbU8agBK5I+lBXAn4TCpLxV/TinYlEon2Ig1AidyRNAZY0cxeanVbEonvE6Fu1c/N7PLw9+l0FKkEmADs3Y71rCqR1oASRfAK0JAYYiKRqIudgS3L/t4BmBeYJbw2oE1rblUizYASuROk4o/C6wG9yOTlgpNqcyIRgaSHgRPN7Prw99fA4JJmm6Rt8BnQSi1sZt2kPKBEEZTyU+6k8/pPUm1OJJpjYeCNsr+/wN1uJZ7CE5Z7BGkAShRBUm1OJIphOrxuDwBmNndmfz+gf7e2qAnSAJTIHTN7oNVtSCS+p7wPLAW8VmX/4HBMjyCtASUKIci37wEMwt1uLwNnmNknLW1YItGDkXQKsD6wnJl9k9k3AHfB3W5mhSlY50kagBK5I2k1XCbmE1ybCjwpdVZgAzN7tNpnE4lEdSTNCjyLl+D+Fx3rQYvhVVL7AD/qKUnJaQBK5I6kR/Hot9+b2cSwrQ+emLqkma3ayvYlEj0ZSfPiv6X16VCxNjzoZ3cza6oeUHeSBqBE7oQiWcuY2euZ7YsBz5rZ1K1pWSLx/UHSQDwqDuCtnpjekIIQEkXwJTA/8Hpm+/x42GgikWiSoAfYo0Vo0wCUKILLgXMlHQj8F3cPrI6r9l7WyoYlEon2IQ1AiSI4EPdNn0fHPfYdcAZwcKsalUgk2ou0BpQoDEk/ABbEB6O3zGxMi5uUSCTaiDQAJRKJRKIlJDXsRCKRSLSENAAlEolEoiWkASiRSCQSLSENQIlEIpFoCWkASiQSiURL+H8CMz06BSO7+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(6,6))\n",
    "plot(cumsum(svs_C_list_cut[idx]), 'r^', mfc='none', mew=2, label='single, SNR>5')\n",
    "plot(cumsum(orb_C_list_cut[idx]), 'bs', mfc='none', mew=2, label='3 of 4 obs, SNR>5')\n",
    "plot(cumsum(spc_C_list_cut[idx]), 'gd', mfc='none', mew=2, label='1 spc, SNR>20')\n",
    "plot(cumsum(bth_C_list_cut[idx]), 'ko', mfc='none', mew=2, label='3 of 4 obs & spc')\n",
    "ylim(0.,15.)\n",
    "xticks(range(0, len(orb_C_list_cut)), name_array, rotation=90)\n",
    "yticks(range(0,16))\n",
    "#plot(5.*np.ones(len(orb_C_list_cut)), 'r--')\n",
    "ylabel('Cumulative Completeness')\n",
    "title('Completeness')\n",
    "grid('True')\n",
    "legend(loc=0, borderaxespad=0.1, borderpad=0.1, handletextpad=0.1, fontsize=11)\n",
    "tight_layout()\n",
    "savefig('cumulative_completeness.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "star_8102_imgspc.npz 0.72438 tau Ceti (1)\n",
      "star_37279_imgspc.npz 0.72285 Procyon A (2)\n",
      "star_108870_imgspc.npz 0.68672 epsilon Indi A (3)\n",
      "star_32349_imgspc.npz 0.66143 Sirius A (4)\n",
      "star_19849_imgspc.npz 0.66782 omicron 2 Eridani (5)\n",
      "star_97649_imgspc.npz 0.60342 Altair (6)\n",
      "star_99240_imgspc.npz 0.66444 delta Pavonis (7)\n",
      "star_15510_imgspc.npz 0.66114 82 Eridani (8)\n",
      "star_96100_imgspc.npz 0.59421 sigma Draconis (9)\n",
      "star_2021_imgspc.npz 0.60309 beta Hyi (10)\n",
      "star_61317_imgspc.npz 0.52562 beta CVn (11)\n",
      "star_22449_imgspc.npz 0.59506 1 Ori (12)\n",
      "star_113368_imgspc.npz 0.56831 Fomalhaut (13)\n",
      "star_17378_imgspc.npz 0.55696 Delta Eridani (14)\n",
      "star_27072_imgspc.npz 0.54158 Gamma Leporis (15)\n",
      "star_1599_imgspc.npz 0.51325 Zeta Tucanae (16)\n",
      "<a list of 11 Text xticklabel objects>\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create plot\n",
    "rcParams['font.size']=16\n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
    "\n",
    "n_groups = len(svs_C_list_cut[idx])\n",
    "\n",
    "\n",
    "figure(figsize=(8.5,8))\n",
    "ax = subplot(111)\n",
    "index = np.arange(n_groups)[::-1]\n",
    "bar_width = 0.2\n",
    "opacity = 0.8\n",
    "\n",
    "cc=-1\n",
    "for ii in idx:\n",
    "    cc+=1\n",
    "    print(fnm_list[ii], svs_C_list_cut[ii], name_array[cc])\n",
    "rects1 = plt.barh(index + 2.5*bar_width, svs_C_list_cut[idx], bar_width,\n",
    "                 alpha=1.0,\n",
    "                 ls =':',\n",
    "                 color='salmon',\n",
    "                 edgecolor='k',\n",
    "                 lw=1.5,\n",
    "                 label='Single-Visit')\n",
    "\n",
    "rects2 = plt.barh(index + 1.5*bar_width, orb_C_list_cut[idx], bar_width,\n",
    "                 alpha=1.0,\n",
    "                 lw=1.5,\n",
    "                 ls = '--',\n",
    "                 edgecolor='k',\n",
    "                 color='dodgerblue',\n",
    "                 #hatch = '///', \n",
    "                 label='Orbit Det.',)\n",
    "\n",
    "rects3 = plt.barh(index + 0.5*bar_width, spc_C_list_cut[idx], bar_width,\n",
    "                 alpha=1.0,\n",
    "                 lw=1.5,\n",
    "                 ls = '-.',\n",
    "                 edgecolor='k',\n",
    "                 color='lightgreen',\n",
    "                 #hatch = '\\\\', \n",
    "                 label='Spectral Char.')\n",
    "\n",
    "rects4 = plt.barh(index - 0.5*bar_width, bth_C_list_cut[idx], bar_width,\n",
    "                 alpha=1.0,\n",
    "                 color='k',\n",
    "                 edgecolor='k',\n",
    "                 lw=1.5,\n",
    "                 label='Target HZ')\n",
    "\n",
    "yticks(index+bar_width, name_array, rotation=0)\n",
    "xlabel('Completeness')\n",
    "#title('Completeness')\n",
    "ylim(-0.5, 16.)\n",
    "xticks(np.arange(0.,1.05, 0.1))\n",
    "plt.legend(loc=4, fontsize=14, borderaxespad=0.0, handlelength=0.8)\n",
    "grid(True, which='both', alpha=0.5)\n",
    "\n",
    "print(ax.get_xticklabels())\n",
    "for label in ax.get_xticklabels()[1::2]:\n",
    "    label.set_visible(False)\n",
    "\n",
    "xlim(0., 1.)\n",
    "subplots_adjust(left=0.33)\n",
    "savefig('Completeness_by_target.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}