{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yoachim/gitRepos/sims_featureScheduler/python/lsst/sims/featureScheduler/utils.py:29: UserWarning: Could not import ts.scheduler. This is required to load the FieldsDatabase. In this case\n",
      "it will fallback to loading fields from the local \"fieldID.lis\" file.\n",
      "  it will fallback to loading fields from the local \"fieldID.lis\" file.''')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import lsst.sims.featureScheduler as fs\n",
    "from lsst.sims.speedObservatory import Speed_observatory\n",
    "import matplotlib.pylab as plt\n",
    "import healpy as hp\n",
    "import time\n",
    "import matplotlib.pylab as plt\n",
    "from drive_cadence import Cadence_enhance_basis_function\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: ErfaWarning: ERFA function \"d2dtf\" yielded 1 of \"dubious year (Note 5)\" [astropy._erfa.core]\n",
      "/Users/yoachim/gitRepos/sims_seeingModel/python/lsst/sims/seeingModel/seeingModel.py:133: RuntimeWarning: invalid value encountered in power\n",
      "  airmass_correction = np.power(airmass, 0.6)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "progress = 97.5%Skipped 0 observations\n",
      "Completed 1000 observations\n"
     ]
    }
   ],
   "source": [
    "\n",
    "survey_length = 1.2 #365.25*10  # days\n",
    "nside = fs.set_default_nside(nside=32)\n",
    "years = np.round(survey_length/365.25)\n",
    "t0 = time.time()\n",
    "\n",
    "target_map = fs.standard_goals(nside=nside)\n",
    "norm_factor = fs.calc_norm_factor(target_map)\n",
    "\n",
    "# Set up a map of where to drive the cadence\n",
    "cadence_area = target_map['r'] * 0\n",
    "cadence_area[np.where(target_map['r'] == 1)] = 1.\n",
    "# hp.mollview(cadence_area, title='Where to drive cadence')\n",
    "\n",
    "# set up a cloud map\n",
    "cloud_map = target_map['r']*0 + 0.7\n",
    "\n",
    "\n",
    "# Set up observations to be taken in blocks\n",
    "surveys = []\n",
    "filter1s = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "filter2s = [None, 'r', 'i', 'g', None, None]\n",
    "pair_surveys = []\n",
    "for filtername, filtername2 in zip(filter1s, filter2s):\n",
    "    bfs = []\n",
    "    bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Target_map_basis_function(filtername=filtername,\n",
    "                                            target_map=target_map[filtername],\n",
    "                                            out_of_bounds_val=hp.UNSEEN, nside=nside,\n",
    "                                            norm_factor=norm_factor))\n",
    "    if filtername2 is not None:\n",
    "        bfs.append(fs.Target_map_basis_function(filtername=filtername2,\n",
    "                                                target_map=target_map[filtername2],\n",
    "                                                out_of_bounds_val=hp.UNSEEN, nside=nside,\n",
    "                                                norm_factor=norm_factor))\n",
    "    bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Strict_filter_basis_function(filtername=filtername))\n",
    "    bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))\n",
    "    bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., zenith_pad=20.,\n",
    "                                                   nside=nside))\n",
    "    bfs.append(fs.Quadrant_basis_function(quadrants=['N', 'E', 'S'], azWidth=90.))\n",
    "    bfs.append(fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))\n",
    "    bfs.append(fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))\n",
    "    weights = np.array([0., 0.3, 0.3, 3., 1., 0., 0., 0., 0., 0.])\n",
    "    if filtername2 is None:\n",
    "        # Need to scale weights up so filter balancing still works properly.\n",
    "        weights = np.array([0., 0.6, 3., 1., 0., 0., 0., 0., 0.])\n",
    "\n",
    "    if filtername2 is None:\n",
    "        survey_name = 'blob, %s' % filtername\n",
    "    else:\n",
    "        survey_name = 'blob, %s%s' % (filtername, filtername2)\n",
    "    surveys.append(fs.Blob_survey(bfs, weights, filtername=filtername,\n",
    "                                  filter2=filtername2,\n",
    "                                  survey_note=survey_name, az_range=180.,\n",
    "                                  search_radius=90, ignore_obs='DD'))\n",
    "    pair_surveys.append(surveys[-1])\n",
    "\n",
    "\n",
    "#filters = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "filters = ['y']\n",
    "greedy_surveys = []\n",
    "for filtername in filters:\n",
    "    bfs = []\n",
    "    bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Target_map_basis_function(filtername=filtername,\n",
    "                                            target_map=target_map[filtername],\n",
    "                                            out_of_bounds_val=hp.UNSEEN, nside=nside,\n",
    "                                            norm_factor=norm_factor))\n",
    "\n",
    "    bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., nside=nside))\n",
    "    bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))\n",
    "    bfs.append(fs.Strict_filter_basis_function(filtername=filtername))\n",
    "    bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=0., max_alt=76.))\n",
    "    bfs.append(fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))\n",
    "    bfs.append(fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))\n",
    "    weights = np.array([3.0, 0.3, 1., 3., 3., 0, 0., 0.])\n",
    "    # Might want to try ignoring DD observations here, so the DD area gets covered normally--DONE\n",
    "    surveys.append(fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername,\n",
    "                                           dither=True, nside=nside, ignore_obs='DD'))\n",
    "    greedy_surveys.append(surveys[-1])\n",
    "\n",
    "\n",
    "# Set up the DD surveys\n",
    "dd_surveys = fs.generate_dd_surveys()\n",
    "surveys.extend(dd_surveys)\n",
    "\n",
    "survey_list_o_lists = [dd_surveys, pair_surveys, greedy_surveys]\n",
    "\n",
    "scheduler = fs.Core_scheduler(survey_list_o_lists, nside=nside)\n",
    "n_visit_limit = None\n",
    "observatory = Speed_observatory(nside=nside, quickTest=True)\n",
    "observatory, scheduler, observations = fs.sim_runner(observatory, scheduler,survey_length=survey_length,\n",
    "                                                     filename='tight_mask_simple_%iyrs.db' % years,\n",
    "                                                     delete_past=True, n_visit_limit=n_visit_limit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ca81780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hp.mollview(pair_surveys[3].calc_reward_function())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAFzCAYAAACjPchOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuQLNtV3vntfFV2VVf3OfehBwpgwODhNYYxEcY8bBzYQMDMYAfGhsEWWAIU/MEY8xgDHocVmhhsxkZmYIgwIRmBEEaP0UCAeRiBQRfp+jKO4RnMwATmbUlIuvfq3tunq/K954/Mnbn32rse3af7nH58vxsdtzMrsyorK/vkV2utby2ltQYhhBBCCCHRwz4AQgghhBByNaAwJIQQQgghACgMCSGEEELIAIUhIYQQQggBQGFICCGEEEIGKAwJIYQQQggACkNCyA1FKfX3lFLvtpa1UupjLvD5/45S6h1bHn+nUuqrL+r1rOe9p5T66It+XkIIASgMCSFXEKXUHyqlKqXUY2L9rw0C7794OEc2obX+N1rrz3sIr3uotf79B/26hJDbAYUhIeSq8gcA/nuzoJT6rwDMH97hEELIzYfCkBByVXkTgK+wlr8SwA/ZGyiljpVSP6SU+qBS6o+UUv9YKbX13zWl1EcppZ4z2ymlXq+U+oD1+JuUUv/Aev7vV0q9Tyn1HqXU/6KUiofHZKr6c5VSv6OUel4p9b0AlHjdVyqlflsp9SGl1M8qpT5yw/H9jFLq68S631BKffHw+5gSV0rNlFLfqZT6Y6XU+5VS36eUOhgee0Ip9TeH3z9z2O+/GZb/qlLq17edJ0LI7YTCkBByVfllAEdKqY8fxNiXAfhhsc3/DuAYwEcD+Gz0QvIV255Ua/0HAF4A8F8Pq/4ygHtKqY8flj8bwBPD7z8IoAHwMcP2nwfAqxscUt4/CuAfA3gMwO8B+Ezr8b8O4B8B+GIAjwN4F4A3bzjEN8ONlH4CgI8E8FOBbb8DwJ8F8CnDMb4MwD8ZHnsCwF+x3tPvD+9VvkdCCBmhMCSEXGVM1PBzAfw2gPeYByyx+G1a6xOt9R8CeC2Al+/xvE8A+Gyl1EuG5bcPyx8F4AjAbyilXgzgCwH8A631qdb6AwC+a3hNyRcC+H+01m/XWtcA/jcAf2o9/rUA/pnW+re11g2AfwrgUzZEDX9MPPZ3APyo1rq0N1JKKQCvAvANWutntdYnw/Oa43sCvQAEekH4z6xlCkNCSJDkYR8AIYRs4U0AfgnAR0GkkdFH5lIAf2St+yP0UbNdPAHgiwD85+H534leUBYA3qW17gZhlgJ4X6/BAPRfpv8k8HwfZq/XWmullL3dRwL4bqXUa611ajhW+/ihtT5RSv0UeoH3v6KPHn5N4DUfR19z+SvW8SkA8fD7UwD+7CBwP2V4v68Zopt/YXjfhBDiQGFICLmyaK3/SCn1B+gjcl8lHn4aQI1edP2/w7qPgBVV3MITAP4FemH4BIB3A/g+9MLQRNL+BEAJ4LEhyreN9wH4cLMwRPM+3Hr8TwB8u9b63+xxbECfTn61UuqXAOQAfjGwzdMA1gA+UWvtvWet9Uop9SsAvh7Ab2mtK6XUfwDwjQB+T2v99J7HQgi5RTCVTAi56nwVgM/RWp/aK7XWLYC3Afh2pdRyiPB9I/w6RA+t9e+iF1V/F8ATWusXALwfwN/EIAy11u8D8A4Ar1VKHSmlIqXUn1FKfXbgKX8KwCcqpb5YKZUA+PsAXmI9/n0Avk0p9YnAaGr5W1sO8afRC97/GcBbtdZd4D10AF4P4LuUUi8anvdlSqnPtzZ7AsDXYRK77xTLhBDiQGFICLnSaK1/T2v9f294+H8AcIreWPFuAD8C4A17PvUTAJ7RWv+JtawA/Kq1zVcAyNBHJD+EvhbxpYFjfBrA30JvBnkGwMcCeNJ6/MfQp4XfopR6AcBvAfiCTQc21BP+KIC/NrynTXwLgP8E4JeH5/15AP+leI9LTGljuUwIIQ5Ka/2wj4EQQgghhFwBGDEkhBBCCCEAKAwJIYQQQsgAhSEhhBBCCAFAYUgIIYQQQgYoDAkhhBBCCIDr3eCadmpCCCGEEB+1e5Mw11kYEkJuCa95zWse9iFce1796lc/7EMghFwDrnMfw2t74ISQHgq+qwcFJCE3gnNHDCkMCSGXBoXfzYPCkZBrAYUhIeTyodAjZ4VCkpCHAoUhIeTioAAklw0FIyGXCoUhIeR8UASSqwLFIiEXBoUhIcSFgo/cVCggCdkJhSEhtx0KQXJboVAkxIPCkJDbBoUgIWEoFAmhMCTkxkHhR8jlQOFIbgEUhoRcdygECXk4UCiSGwiFISHXBQpAQq4HFIzkGkNhSMhVhUKQkJsBhSK5RlAYEnKVoBgk5GZDkUiuOBSGhDwMKAAJITYUjOSKQGFIyIOCYpAQsg8UieQhQmFIyGVCMUgIuR8oEskDhsKQkIuCIpAQ8iCgWCSXCIUhIfcDxSAh5GFCkUguGApDQs4DBSEh5CpBgUguCApDQnZBEUgIuY5QLJJzQGFISAiKQULITYIikewJhSEhAIUgIeR2QaFINkBhSG43FISEkNsMBSIRUBiS2wfFICGE+FAkElAYktsAhSAhhJwdCsVbCYUhublQEBJCyP1DgXiroDAkNw8KQkIIuXgoEG8FFIbk+kMhSAghDx4KxRsJhSG5vlAQEkLIw4cC8UZBYUiuDxSChBBy9aFQvNacWxhGF3kUhOyCopAQQq4H/Pf6dsKIIbl0+I8LIYRcfxhBvFYwlUyuFhSDhBByc6FIvPJQGJKrAQUhIYTcHigQryysMSQPH4pCQgi5XfDf/ZsHI4bk3PAfBEIIIRJGEa8EjBiSBwtFISGEkBC8P1xvGDEkZ4J/8IQQQvaF0cOHBs0n5PKgGCSEEHK/UCQ+UCgMycVDQUgIIeSioUB8IFAYkouBYpAQQsiDgiLx0qD5hNw/FIWEEEIeJLzvXD0YMST8wySEEPLQYfTwQmEqmZwdCkJCCCFXDQrEC4GpZHI2KAoJIYRcRXh/ergwYnjL4B8cIYSQ6wKjh+eGqWSyGYpBQggh1x2KxDPBVDIJQ1FICCHkJsD72YOBwvAGwz8iQgghNwne1y4fppJvGPyjIYQQcltgenkjTCUTikJCCCG3C973Lh5GDG8A/MMghBBy22H00IGu5NsIBSEhhBDiQoEIgKnk2wdFISGEEOLD++P9wYjhNYMXPCGEELIftzh6yIjhbYCikBBCCNkf3jfPDoXhNYEXNyGEEHJ2eP88G0wlX3F4QRNCCCEXwy1KLdOVfNOgICSEEEIuh1sgEFljSAghhBBC7g9GDK8QjBISQgghD5YbGj1kxPC6Q1FICCGEPHh4/3VhxPAhwwuSEEIIuRrcoOghI4bXEYpCQggh5OrA+zKF4UODFx8hhBBy9bjt92emkh8wt/2CI4QQQq4L1zi1zFTydYCikBBCCLk+3Mb7NoUhIYQQQggBwFTypXMbv20QQgghN5FrlFpmKpkQQgghhNwfFIaXCKOFhBBCyM3hNtzXmUq+BG7DhUMIIYTcZq54WpmpZHJ7+NZXvXHr49/0NW94QEdCCCGE3CySh30ANwlGCi+Wf/iqHxh//+evewUAVxTav3/H677SE4Rf99WvG3//3n/9qss6TEIIIbcQc8+/4pHDM8OI4QVBUXi5fOur3rg1UsgoISGEkIfBTbv/s8bwArhpF8Vl89Vf9X3419//tRsftyOFhmjHd5hat+PvHTrUunMetyOGX/7K78OPvGHz6xNCCCFn5YpFDs9dY8hU8n1CUbg/X/1V3+cs//2vef34+/e8/ms8QbhLDHboti7X4rvDl7/SfX0A+O/+3vcDAP7tD37V1tcihBBCtvGa17zmqonDc8GI4X1AUbg/UhTOo9hZzpX7HeWsotCOGALASiyfdO7l8iNv+NpRFAIUhoQQQi6GKyIO6Up+0FAUbuaLX/F6Z/kVQhTmyr3sUrgicRdSFEpq3SHd8jdR6Qhf8orXbXz8z7/87fjzL3/7mY6JEEIIAa6/PmDE8Bxc9w/9ovjsr/zh8fcn3vh3HUH4oz/wNY4gjOEKwkgpRxDGyhVyoYihFIStde2G6gpPreVy2LTSkbUuRqGnSOXPvfErHUH4q2/6EnzYl/38uPzet/w175gIIYSQEA85cnjuiCGF4RmhKOyxReGRKpEqV5QdR1MqN1PKi+DNROp4lzAMRQltYVjqxnvcFoa1BgrtPufz3Wz8vUKMky53Hv/T+o6zTGFICCHkLDxEcchU8oPgNorCj/7yn3GWP+Mr3ozP+Io3b9x+rlrMVbvxcQBIVeQIvX2ihbtIrWhkDe0ZT2xqRKgRYR7Vm7fRCR5N7o3LhU7PfEyEEEJuN9dRNzBiuCfX8cO9H2xB+Ps/8gWeGExViyNVjsvHQmSlqo8UjstQjnjrt3FrC+/XcFLrzhGEp8JwUuheENo8J6KEz7aHzvJ76rvu42/97K3HSAghhEgeQuSQEcPL5LaJQruuDoBnxFhGhSMK55GfxrUlX0gUnjUquI/hZFuUEABi5T5ei9RyrbebYMqOUUNCCCFn5zrpCEYMd3CdPsz7xRaEeVTjTnw6Lasay6gYlzN0jiBMh48jt76jeO5jFTmC8DyGE2CqLTSP2aaTGnp8vNBm3fQ6hY4dQXiqM08QfrA5Gn9/Zogg2qLwPaVbe3jnJz7BO0ZCCCFE8gAjh4wYXga3RRQ+8qVPeFFCWxQCcEQhAEQi+pYrVxSGuIgooW04qbXvRLYfz5UrCvt9zhYlXEQlI4WEEEIuhOugKygMbxnrv/GbWP+N39y6zZ145SxLUZgLc0muNOwKw1QppHZ9oYq8VPJFG04AoBVBZGktqXXkHbtNodOdJpNCiEQpGn/lcz64dX9CCCHkKsNUcoDroOjPihSDL5t9yFnOVe0IwseTF7zHpai6I2oLU8ARhHnAXHJWQRjqXWivq3XnCcKVuKZPOvc4nrPa1AC+IDxpDzyB+N5qMqE8Uy88Qfjin/xYRxR+6i88vvE9EUIIIZecVmYqmWzmmS/6HWf5buqmie/EKy9KKImF+Fqo7aJwH84qCkN4UUItl3cfh3xvUhSeCiF5EPttbv7wC/5w/H3dMPVMCCHkekJhKLhp0cJdojDEMlo7ywtViWVfFErsNG+EaG9jyTZaT/R1iK0vRfuIQr/G0G20fdrNMI9KZ3kX69Y/A+/+y8/t3I8QQsjt5arqDaaSLa7qh3QepCCUqeMPS33h8ng8pY8fjX0BeSdyBeI8ECCcR27aVs5Bvt8JJ4A/5aQQBpRV4Mo4Eanf57oDZ/kD7dJ9zi7FibXN+yrXiQwA7yuOneX/fOpv81m/5K8jhBBCDJeUUmYq+X65zqLwqb/yrLP8x1/4B87y3cRNEx/HbkRwGa29KGEhImm5alFscfBKwwkAJ5oH+KIQ2B01lKLQqzmE9l43FS9TiSihfG8ydSwNJgBwLM7huk1xJ92efi/bZOvjhBBCyFXTH4wY4up9KPsgxeCnv/MRRxAuktIThPO4dEThSxI3apirGgsrjZqrxjOcLCN3WdYWpiryBKGccCKJEAVNJjb+1BPXdCINJ7V2BWGp46AgtEXhC13uicL3N25U8E9Ld/k9hRsR/FAxdwThX33SnaRCCCGEhLjgyCEjhreZR2ar3VHCZOWIQtmnEABi5Yovacrw29TsjhLu04bmfkXhRSFF4UrUF67a3fWGB4lrTPmpTys2bEkIIYRcPW69MLxu0cJf+KwT/MJnnWzd5pHMFX0yDSpZRKUTKQSAhXIFzqb+f7Zku9+xdwY75bwp1XxW04lNqF/haTfDwqqh3CQKj5JJ6K27zH3eHW7kN31cjDd93PboKSGEkNvJVdEjtzqVfFU+hH2QYvBli+e9bV524KaGX5r5BpMPS10TyqPxPXc5ks2ta7HsR6dncvTdDsPJvtR6EqMtNDot28psN51Iwwngm07MyDubDzauEeX9tZs+fn915Cy/dyXSzafu/gDw7PMLZ/nlv7O50TYhhJDbywWllJlKvsn89F9c79xGRgkfSbb3KgxHCV3X8Uy1nnGjEuJMXkAylXweWq29dLK3jfhe0AKYiZeeiShnhchpRRNCRhK9yKGIEq7bFHdnIm0/c9PHZZ1gMZ9et6lj/MCfcZ+HEEIIuQrcWmF4XaKFRhQWTW9ouDtb4e5shZWVttxHFO4iJAolUnhtEoW7aga3EdrWF4G7n88TtJDO5D6dbMRxKL1sROEyLoblXswtE79u0IhD2dy6rOlMJoQQsj8PW5/cylTywz7p+yCjhHfzNXJr4sbdbI1F4ka/XjZzU8fH8Qq5SAXLUXfLqECGSQTOoxqpEHbLyF3OlHJkVsiJLOsLd6WTQ4KwhitOO60dUVhrDSlfaw0UliisEXltdp4V6eNKx07PwqJL8Xw7d7Z5f+2mj9dtipMmd9a999RNKZdtgufX0zbPP+8+p64ivPJPaE4hhBDic58pZaaSbxI/9qm1E2m6m4dTyafNlOa8m66cNOdxIEq4jIqtyyFy5YtCm5Dh5KyiMEQHd6qJqS/0eiNav0vTST0ch22cKXTqpJOrLb0Zx31EraKcdHLaZDhtpnNftonXw7BqYhwsptfVFf/0CCGEXD1u3d3pqkcLf+xT3QifFIV3szXuZmJdKhtYT8tFl2IZFaMILEw6VIjCeVRjPkQXa0TIVTeKQiO4Msct7IrCTendfUThrvY0Ic4zE1mmiqUoLLp0FIHmHE7L/Tm3ReEyKRxBeDwLC/iqmV7nYFE6ovANH56HdiGEEHLLeVh65VYJw6ssCt/6ydoRhccHBfLEHf2Wx+7yI9kpZtG07jheI5UNqWMZJVwjFQnYSEQFZWuaVLmiMIJ74cRQiKHOFSncJgqN2LTdyC20IwpD3t5Y6TFaCPTzkW1RWOsYqZz3rOSYvRQzKw1f6sRLy5edaJjdppiJz2yWust1LSKUTYQ3vNRNMRNCCCHAw9Ett0YYXlVR+NZP1njrJ7vi6PjAFXN3sjXuiCih16tQjrmLi6AotMlVjXxHv0IzXs64kbddMOcxneyanQwA0bCNEYrpuOxTD6lm8z7qod5QmmsAOMYTAFgO51BGFm1MdNaIwrvZZmOPMQwtD/rXMaIwPfSPhRBCCAnxoPXLrRGGV5E3fbx7+mdpExSFzjZxs1MUyqhhqpqgKLSJoTeKwm1cRHuaENucyB3C0cjaaXodjaIQ6Ocjx+hQW+lj6UQuutSLHOZRjdIapbdqMydSuG5T5HGNwkoxz+JmFIVA70zOEqsnYx0jmrVAMx3fDzzq9jkkhBBCHgbspfGQkKJwKQThceYbQ6RIPE7EctBw4m4jexcCwEKmVYUok4YTwDedhDiPE1lGHWVT6xCe6UTvPjYZFZQGEwAo5Yi8dnfvwbJx/6T2aVejqssR14QQQshZuRURw6uWRt4mCgshLFaDucEWhaftzBGFsuny+LyWKJRCyETObFFY68gRhZsMHdtMJ0bYndeJbIihHFE49Uh0sY9RCloTAS2siF+uaudchIQy4IrC43jtiEIzFs82otzJVo4olE2uDa1VYxgtakcUMmpICCEkxIPUMTdeGF4lUfiDH5M6onBxUCGNpxTj8azA8azAzDKZ3J2tnOU7yQp3EulCdk0ny3jtpETNlJPYMpnkqkFsCalUdUitx1Plp5LTwWRiiMVyv1987hF429inqXUtjkX2LwSAWgvDSJc55wHwU/FSeJdd4ph+gEnAG9ZNiiwWqflU9GWs3OOLyghvPPTH6RFCCCEPSs/ceGF4VfjBj+mjSyZitDjYbUCQo9akIARCppPd4/Nyz5G7uz1MukctYap29wTchjSemMikLQoj9EYYY4Yx4lWKQhvzfo0oDBlRDIXuBd6hmHYi0/YAcCft1xlRuBzS/3L6CTC1rMnn/WsbUagXvoWG4pAQQsjDgjWGl4wRhDZSFIbSjlIUrtsUd6xPa9VmeGn2vLONFIWFTvFofM9ZJ0VhoWNHGNZQmCsRQROiq9PaqzG8X1G4icuMFLr77K4fPE7W+EDlijYZKVxmhSMMl3mJZ+657WhkpFAvWsTPbnZCE0IIIQ+KGz0S72Gnkd/wEX3z4ijro0ImWgRgdKku877GbZ72j90ZmiQfDOPvjtNJ7M2jfhs7ejWPKhzHk0vZ9Nqza+dyVTtRslS1jgPZCMNcuZE523RixGGk3FSyHeU7Twp5nz6Gte6Gx3pMtNCuLzTi0HUix54L2f4/AJx2M2ebF7oDp77w+bYflWebU15ocqytmsMPVcM2Q83hC2X/udv1oqdFv31d9evq1fB8tXX+TvrjMO0SX16egBBCCLHZc1QeR+JdJd7wEfkoCoE+QmSLQqAXhEYUAsCqzkZRCPhj1wyhlKZEGiq2pU4NudrtRI529Bzcp2+hZFMfw9apf4wc00no2ADfjVwHIoebTDjb8EfiuVHCu9naaVdzNCs8E9Eir0ZRCADp3G0XZLB7aL9pxpQyIYSQB8uNFYYPO1pos0kE2NwRo++O07UTLQTgGR6O45XXd08aKULIbfbZZx/2aVYt2WccnokY3i9ywkuIzKu/DLXRdjkNGE8ksm1NU8dQqWjNM7uY90kIIeTmctn65kamkh+WKHRGm6WdJwjTrMEin6J3edI4gnCeDKliSxAexDXuCtNJHtVOz8J5VGIRuVHBx5MXnOUMrVNfmKou2NDaljSxUl59YaQUUrhRtlAK2RaFm2Yf7xKFhRYuXkxpZKBPJcv6wlBj60oc76qbOXOSV93MiSSeDOlj24180uZeFPeF5sARhS/UuScKn77ntqBZrzM0VssavUq8PobJvQhxOa37crifJSGEELIjpcxU8sPmBx45dJZlNAiAIwoBeHN1E9V5UUIpCgG/kbUUhSGk6SQK6Gp5MYQujrOKwk3sEym8CEJRwkqkj+ci9b6M116LmlBqf59IoSROxPHIZQBd5p6bt1RHO5+XEEIIuQjoSr5PpCAEADXvRVhTx0jSFgeDC7msE8zS/rHj3HUiH6X98mkzwyIpp1YoXTaaTpZjC5UZ5lE5Lhc6Ra5qt6F1lyKPplnI1SDWMnSYDZHCSkfIVIdZ4HtFas0njqFGF3KHbhR++6aOI0Se8IuVcsSh2bdDZ81EjjzjSaYUKq1H40kKjRoqOO3EvGfT1NrUCtpNrjdNQDHi+/l2jqJLx6bWLzQ51t3UdPy50XiS4HhoQv58dTA2u17mJU6KWX88Ip2sq/749LyFWsWIAhNQjOZ/S3WEL8sYOSSEEHK53LiI4YNMI3tRwnkzikLDQaBfoS0K1006ikLDaTNzllddNopAg1yWAgfw5yEDGEXhtOxtMorCadm9TDp0Z64n3CeKCITb02ybdgL4E09S1Y2i0CANJHICyjwqg2PxQuu8bVoxBk8YT5Z56YjC2UE9ikKDnrufS73ssEcgmBBCyC3lsvTOjROGD4o3Hi69CI+82c8OXGG2zEtvGsZxtrlx8rhfHB6v5mwjZiLnUe2JRVlTmKvOE1n7tJzZV+Tt2k++lowq7ms6CdUZSqRTu0bsrCu61EsprzpfoCeRe0z2VJpNrAPGk0R8gbDb1gBAVCnUS/fDefsLTCkTQgi5XG6U+eRBRAvlVIr2rh+Vy49dgZGljdOaBgBesnDTgnncOIJwkZSeIHxp+pz3WtJkkqt67GVouBO5z5OiQy7G38kWMDPli6uLqC8M1RK2Wnv9C6UoLALX6UpvN55UiLx2NKfdDLX1Pk67mRcVfLpxBdhJm3s1hx8o/VYy71+7614oc0cUroqZYzwBgOZ593nj08j5wpGeKCSB7wVfcsS0MiGEkI0mFJpPHgQ/nO3uKxfLSBCAJN7d8kS2otmnTYrXr1DMQ95ErM7Rb3CPa+y8kcTzICOdrQ7XGUrk+dmnVY/8LEJGlNC6VHzuSbL7M5WZ6y4wkCUuru2XOUIIIVccCsMzElt1X+1h606uCIjCuYgUHs0KHIkReMdpOFVsR7KO47WT2jSiUKY7bRaq8ppby3SyoXKMIG4a14hCO6p3np6FIaQ72TaeGDq4EU0pCg12/aSsMQTCdZiA6+oudDbOSQY21xgurTCeFIXrJt3oUM6tmtO2jpyaVDWUIjQL9w3apmSKQkIIIZfJjUklX1Ya+c3dEdpc3Kgf8cVVfCzmHy/9NjMvPnTnFh9lBRaW0ryTrsZReOM+qZ8ylOljuQwAd2TNoWqdmci50p50ypTy1uViBnJoJvI+onBTCtmmhntey0CN4Wkn29z07mqbk8B0E1sU1jrBqRDUz7S+u/zpWsxF7jK80OTOOplSXjUZTqrpuYsmwb3Cfa3TF9znAAA854YGZ8/65/TgA6IxeQF80UdwbB4hhNx2AulkppIvk7iYzm+z0IjK6bQZJ3JnRQ4PDipUwpl6JCOHWThKaEeflnHh1LYt4zWW8dpvseKZTGqxHI4Sbhszl6oIqYo8l7AUcxeVPu7Qeelq2Vy70hqpsvfxqcXxbBp5F0rD25Rd6tR4yhpDwB+Nt2oCeV8Ah+Kzz4QpSVex50qWUcPYfQqYQ/uJP+bYPEIIIRcHheEW3ty5JgR5sw5ht6cx4tCIwm0NkO+kboRxLyfyIBSBSRwaUVhov0WlqcHbZy6yJBr+AyZxeBZRuG1bGU3stEY3vIYRh9WWyHY2REKNKDRCOCQKa+u8LKKy7284nDsjDsstLWpMP0MjCpdJuXFbe17yYV6irmPUg/nEiENd+cdopp6Y682Iwur43F8ACSGEkL1gg+sAby2OvKL/Tp6p2Bcq0lxwmJfIrekmx0OUME+miFEStTgU4SBpdpDzkEPbLFTlbLcYBGIqzBZSFEpZso/JZJ+WNhdFHTCHxGpzneG4DTRqsdw/30SouXXsNeLucNK6qd9UtbAT9Yk4x1nUfzaF9eclZyUDQJRotFYFgjbTcsrpU+ky7YzHa/P+d9YaEkIIuQxuRMTwIusL31r0UULjR6gPNepDUdc291OzJgJkIkImfVg0viAohsihGX93r51q0I5EbxI5/g4AlkP7mWJQr8ZgUm+NErbIVesIqt2tm33jyXlF4baRd2MkchBv0fAaIVFonsVOKcuLBhb8AAAgAElEQVQaw+k9+4J6iqi6UcJtjazt6K1nNAm4kQ1H2RCBHEThfBiJ2NQJmoBQlJiuQ6afoUwnA6wxJIQQcrE66EYIw4vCiEJDK2bW6lRDp6LGLtFezZhXUxa1Y7TQIGciJ1HniMJUtV5UMEI3ikKDbLcSQ4/RQkOoxtCWM61I207PdTGRwX2MJ6GJJ+6x+NFNwBWFKbrg+ZCCWUYKZZQwUh0i1Tl1halqHRF4lKyRRu55zaLGm3oiI4VxLCKS86afl1xbdayHLUQrSq+NTZtTFBJCCLl4KAwtvjSf3L0yShgiXrgRqXleYZ5XjhgwUSO7vvAoWzsiQ046CWGmcpxaYkUaJkL1hTPVuuIpoPXiDRE6W6zdj8kktK8deZTGk24Pp7yJfGZWGrfzJqD0UtI245i50uPylkjhoajztIX7OtRgEMAyc8cdHs6mXLG5Lma51bLGTMs5mERmVEZo5tM5sM1PNj/2ARpPCCGEXCzXXhhedJuaL81fcEShSSm3hyLSY4nCfdKChiNrBN66TR1RaCJU0ngiR7UBrijc1J/P6esn063D/4PTS6x1LbQj7M5jPNmG0xsRyhGFJoLZOuvC9YW2KNzkwrbPk3RujynlwLm0xeNRUjii0My5lvOS7S8Ctji0aSv/HNqO92auHVFYDx117PpCikNCCCHAxemhay8ML5I3t0d4c+s7kTuRPkbiLs/yykkRHs4qpFGH1Jqru8wKJCL1eCQcrcu4QCxSmJHSKLQlRIZtbKTICZGJfYxsaQNCzEamk89TY7itvtDGa42Dvtdi6ghVYKbsbZQ3yaXWkZdSjsT7r3SMyDquQqeIVecI7lWXefvJtjXrNvVSynHU4TCzhHuTILGuj7qOEcUtUlGCIA1NchZ3VAFtBlRH0/pkDfzbP6I4JIQQcjFQGA7YglC2CwEANazTQ8qvHZyjdlpwE3Z60fS6M6LwtNk8ucRLZdoTOUR7GhtjuiiHVKoUhSFSkcqNoYITT8Zt9hR7IVqtvcijNJ4E97N+n6leFI7Hv+U9VkN1oom8Vht6G9rYAnAel866ZWB48TIdtrF6GR5mpWM+Wmy7VobooTE2GVG4raQhsSoQKA4JIYRcBNdaGF7WtBMACGVn9YEbGWpLV2CUdeKlDZeBRtZSxJw2My99LEWhHTU0bBOF4zEJEVRr340cihRKOnSXFi2UaelIqUD0cD/secm5akZRaJBp+fnQy9BGmlEAP1K4TArPkbypwbXz3MJ8kh7Uoyg0SNd7fagRCU2Z7C5LJYQQcsu4CF10rYXhRfC21RHetjpyWoE0Q5QmHqI23bxDN++cG3h6UCM9qFFXbkTIjhAdzQoUbYqiNe1pCm8u8lGyxlGyxsqaonEcr1F26dho+Sha4yhaO7Vuc1Wi0vEY/dpkPAmZT2w5mQ4izI7YSWFmmlu3+uxmlG3Gk876b4xODs2t7RR2rbXjSq61Ebja2iYa3s8kvCpEyFU9nZsuRdGlnhnFFovmHBv3t/kcZMuadZt685KPsmKcaGPmJS8yaw5zoBa1rWNE8wbRMDNZ1RFUHaFd9O8jqvofO3IYF0B5rFAODa+TQiMpNH7m//PH+hFCCCFn4dYLQ5u4nEShoZuL6FEV+bVhcNOERZPgaCYifoH+d0eBsM9x7K47EjOPiy7FXIXdyOM2OnGMJwbbkVzDrd0DNtX4BQwSZ7xsdrmSNxFDoRYOZZkETqGdKKGhEq8p3ce5qp1I4TwqvW1ka6DQNJpQ5FBOuFlklSMK87z/vNrafTeqdo/ZiENDfaghD6EU01AoDgkhhNwPt1oY/p/PukaT+hCISvdGq4QBQM39pskyPbic+S7iw1QYTdLCS0/KvoUhQqlPmS4NNXaWH3TIx+zPKt5di7cP+/QxDBHqbSjPUIfNLuRthNrWyDRzyKEs08xyXjIALDPxPE2ChSgxiAOTcyTyWoxLhVqUEsal/zw/9+vznc9NCCGEhLiVI/FsQRgXfbPgWgRaGitSqCoFnelRFDZmusnRFM0r6gR52oyi0ESNHj+4N25z2mZYxBWWVjp51WV4afa8swwAjyV98+LTrjenLKLSEy651QW5QowjK5JoaguPrW1a3Y+TM3LHRONyNcnGTmtESnmiUPYdBPaLHEpRGEpHd+icVHanp9S2iWoWtnt6GIlnP3OuWpxYEb8MHSpEk0knstLJgVF4hnlU4tl2uhhMrecHqiNrXYl77cybl/z+YlJty6zESTVzzSezCs/dm0RbljeoigSdmZc81LCqdTyKwnauEa+UMxavXgL509P5KO70jx2+bxLIP/frc3zup/hTcwghhJBt3LqI4Y+/13dvyh7HXRKI5iSuwJkF0slpvDtyJVvWHO/pIpBtU/ZhploUIs26zwd+URNP9uG87uZYHKLs07h5v/O7qW32ie5m4rMu6wQHInIYBSKHWlx/G9pU7uQXn9zseCeEEEJC3Bph+OPvXY6iMLEaBMvUXDMW/StElYKet9CDS1QP5pPZOBe5jwYt8xJLMRt5mZZYpiWKJvVmI5uaNCMKV12GVZfhMC7GHxPVWsZrLIe6Q9mixjZT5KpGhWisrdtWY9iOy8ozn6RqiuLZyBYz5zWfxEo55hOJaXA9tcrpfzKlkKlpnf1+jCg0ZhtgqjE056bW8TgJRTazHqOJOkOhM8yjCvPBBrxqZ1i1Mxxa7qSy6z9jUyNqzChHaYGjtEDZJONoPDkv2cZEnpODBslBA9RR/4OpvjAaRuWNhqiy/6mXCvVyMJ+s+5/iTozizvCcp/15oDgkhBByFm6NMJR0mSsK43IShduIROQwifx9lqlfYyhnI+8TcQrVuO3TzDoSDZ8LHXmj8KTxpN9vd6TwoiaeSOTUEwDnjCX6xpPg64nIoWySDWAUh4bDuBxFoUE2tw6RimvkYFaNonAbsrF6aHqfEYc2RhQSQgghZ+VWCMOf/P2l4+Y0kyPsFjV9rzjh0hVCMQ4YTwBgXW13pR6mZbCRtd2i5jD2zShL4UheRCVqUfu3iMoxEgaEjRi56rwxclJ0pUqmnMNO4vtpbG0IGU9aaE+YyiOoxH6hecmAfw5q0cvRCG45a1oul0KJlTrBYeKLfptVk2JhfTEw14Ld37JqEsxyV+C3wpEsTU8GWQsbi8NJCo3i0XPmngkhhNx6brz55Cd/fwoLxgWwfpF0egLFo/ZsZIX6riUshht2fDzd2Os6xtGhK9rWVYrHF6fTcsh80szw0vx5Zz/ZyNqIwxcn/XbGGPFoMj1PjRh3ItdYUOsYSyvCVQ+yamk5lGsNLCLbRNIzs0RhCx10I5/HfLLLjWz2ry2vcaQUat1Z2/QUzn6ANONmqsOp1cOxN6Nk1nIvxJ7rXMdurd0/gUVU4tlmUl+mRc3T1rrDpMQHq+m6MvOS/3Q9rTPiUM5MfvZ0ev1ZXmN1Mn1hUAfDZ/X8tE8z7xCVEZKVOzM5f2Y65mpoWTN/vzXi79EUX/Bx0zVDCCGE7MOtiBgaqiPl9YGrD+E4PgEgKkX05qCdnKPo+9BVja+pZaRQTj05Sguctm7kcNX6kUTZP28RVShENFGmmUMtakLRQxl1kxdAjIuJCoYIRQrt5tYAvObW/TZyH3ip8UpHSMWWMl0vW9QA4dS8dH/LSO6qzbAQobp7gaknsndl0SSYC/NJmrufm65i6IPd579euMtx4Z7bdNXh53+VbWsIIYScjRsvDP/bj+7bvpj08S7a+WCwGMShOthcr2WLw02j8IrWF5BGHBpDgy0OQ6JQMk05MWaUXly44+D6466decLDcY8mEgz77RYim0TdJra1qdlnf9t8AmA0nuzCiMNiSB8bcRiq1zRiW/YztDGiUNYbAhjFoRGFdq9KKQpD1EOdoRGHuvIjteY6bObu5BNgEodGFMpm1wAoDgkhhJyJGy8MAUAG5eKiL+S367V0MonCaUMRXYs6RMJIkMSdIwrXTepFCtOo9UwK0iCyameOKFx153OT1jpCHGgMLSNsuz748zalvh+k+WQX0pVsKPT5GnPLfoaSCBoRZMTVXT5MS0cUHmYl4qhz+hnGUYdYXEdKXGv6oPPb1iT+5BNpSGlnCu3M/bDf+W46kwkhhOzHjReGP/6evu7LnhBh3MiycN82n3SiRc027Jv+YVZiZaUVQw5lM2PXTlEu48IRgyFhIgnNR940BcQ2n2RKObJPGk+A/cbYbasxDLWp2YVtPhkbXg/LMgUOhM0nNSLnHNRilrRNLqKAdsNwKcxlJBGYpp4cJW6k8DBgPrFnJo+vN+tfzziUowMrrVyHz5c9sjEWT5n4E/tGKA4JIYTsw40Xhjah8WEA0Cxc84kRhTaJ5SKthpSfXS9WNAkOrVFoq0DNGTCJQmddvDlSeGrSnZaICdXKFToRgqj/aFMrolVrNyUbSuSGxtAB+wm7XWwSlqEZze522LpsqOGm0qUbWc5HNtjr8qgWAt0XdIA/Cu8oEZHCwBcCwO9nKNvWOOIQQLvoP1NbBDaH2lkOtawp7g79DFdsXUMIIWR/brQw/Kn/5Pb2qJcKwkeALtWw7/3tvIMSUUIVd2hr13wSRR0K6ya/yCrHfHKUFV6Pw0S1Y3NrQ6paJxp1GBdOmtnUGNrmk4WqfDetqp0aw3SIoskaQzvylkJ5Ro9NU08uIo0sn8Okq3cdgxSwte7H+0ls80mtI8+QU+sECyVmFqvOaVMjZyYXOvGmzkRKYyHa1qzbdByNB/SRQ/n5x1HnmU9k25quip0ShqiMoBPt1BhGpXLMJ3EJdJl7zpJ1hyZXKB6ZrpN3vSPGu95xMfOvCSGE3ExurDA0ojBZ9zdUO6piUsj1oYhUOfOR+1MjIzgh7DThuklxZNUYSiForzuOrVnLXeq0ril0OOJoY8Thwooe1htGw9k1hpXWSIUTWLJPjeH9mk+8faxt9jGfTGlmSxCjEyYcY8zxTUBy3bYaw7msOwBGcWh/xksROVxm7kQc5/WbXqQZcWg735GHz21kz0wWzuRtBqt4NV3HFIeEEEI2cSOF4c/+lnvHlOVh9QLoEiAuLHGUaL+psDAExLFGLNcJY8FS1BgCQKI6JGp7K5VlXDgNlRdR6U3jiKE9Y4lcTgOzgGMViLptSBkb9qkxBHwB2A3/nYd9Jq+4r9UjG1zLcxA6Z/46ETnUqRcpjFXnTUyR26zb1GmCvW76yOGh9eUhZD6JIvF5VJH319klbtkDEDCfZP2PTXPgCsG/9HlMLxNCCAlz44ShEYVmHvI45aTYLxVqxKEe2tR0gRYihoWoMbQx4nAZmJRxJMwndo2hnLaxrSZOmk9sQWSiZrklLmX0zYjDfQTZthrDfYRgaP9t5pV9zCchTJ3hmErfZj6xZk4b5PQTU2MoPxdgihSGIoe7JqQAQJ4NM7er/vNLcj863R1Ms7sNRhyaOkPjrg9d4yZiXj7a101SFBJCCNnGjRKGMlKYiBtlXOhAY2Dl1G8Bkyg0dFWMzLpp13XiiELDMhONkUXkcN1moygc95H9RxAWh1KwhMwnklBaWaZkZeSwhT7XPOR9IoWbxKFtNomU2mk+SQMR0Ex1jvkE8EfhhcwnIbex3EYaUJZx4ZUIyJpDwBeHh1nlmU+MKDQkedNHC3cgHclyVF55HI2i0EBRSAghZBc3Shh+/ied4vM/qR9LVx5HKI8jdx7yUnk31N7habep6foxeGYU3kGD+KAZmxEDwMGsv8HbN/mjrETZJCiHyOFRWuAodQXEUbLGuk2ddasuc9rWzKMS86gcBcsiKrGISkeszKMSFWJUsGckN16D696ZO73XFH2tn6n3S6GQQnm1fZfVwzBUo7hp8olZV2uNWmtnSF+tQ3OU+8knxoBS6wipap0G18HJJ1HttKkxItwI8bJLUXapIw5XbYbDuBwblK/bDOs2wyKxtmky74tB2STBmcmmxrBtIrRNBGXN5Fa1gk60U/8aVUC90KhN5LDsf+w62qTQKO9EKO9MZ+qd75rhne9i2xpCCCGbuVHCcBPypmnEodsTTvWi0KYWEag6nFY+EpFCMzvXsG5THCXubGVAtqnJPMODjGYVOvW2qRA77ttQlLDWvSi0SaXzdw9jyf2Iw137xkKgAr0odLdxCZlP+v3cZVnPKSfHGORyKK28kgV8gkVSOYLQiMVSTMmRIxWlO9kWhzaye04tag7rpfIi5YDbtobikBBCyCZupDCUkx9CdKnr8GzmnW8+CRDHwugQ+QJq1QTq0USfHGliCCGFCRBOfUrkNI5UAbLCbpf55EHTQnu1juk5eyeGDDiSCNtNJEBAmHfpGCWc9tt9HuVnHZqZHCRx95OTUAB/zjcAVKKvYZvffw9KQgght4MbJwx/+neHNjWFRlJoVEuFaqkQlYDRWXadYSRurEYcqryFylt0VTwaUPIhqmOiPSYtuG7SacLF0Nh41aRYNemYUgYmcWhqz0wK+TAucBgXY7rTriksunQUKHM1rQP6yFeualSIUCHCTLWYDdExGUUDJnEYW+aTehBkRpRNpg9/csn9NLkO7R+qZZTHkSqFVCm0AEzMy7Teka7kGlHQfFLreDxXAFANtYfmfI7LVhp/POcitQ9YM66Hz2+RlFgk5VgmcJiWY4NrMyt7nvpC0IjDuo5R1zHSWYN0ZuYm9+/D1Lsa80k712jnGnGpRlFYL/qfuNCjAcWIQxM9LId+hsmqRrKq8e6fjfDun71xf/6EEELukxt1Z3jHby52bhOaNNd5OUo/MiNnJMtUIACvBUkI2fR4JTtub0BGuPah0pE3I/mqcR6jSwjZsmb/1z9va53dkcJEzMeep5XnXg9dM5GcmxyIFO5Dm8nIoX+un3q779YmhBBye7kxwtCIQtuJWQ31ViZq0iz6H1OsD2B0JEeleyp0FY1RGzMOzx6FN5/1N3lzozfj8Exz4+OswHFWOGYTMw5PTjqxDSi22cTUFJq6wlBN3NS6pnUiZpnqeqeunmYKm71D5hNT3zc1lvYbVN+vAWWT+cR5XXEctvnE6HfzfuxPLGQ+ATAaUOxzlW2oOQSmySf2OS90inlcjk2up0jvEHHsElRd4kSHTaTQHo1XtgkWWTU2RDfmpXxWI5/VaJsY7dD0OjbO+KHO1TWfKDQL7bStiau+vtDU0prruzwaruFVv3/5yAzlI32NYXzSb0RxSAghxHBjhKFNstZenVWoNE9GYqIyghYTJxJhCqgCfQ1l1KfYMu1k3EZMOgH6Jsu7KHTquGgBODOSAXhtW0KERN5lGlDOs19oPF/IgBJKm+8iG8SiIVe1V1MojT6AXy8oaw6BSRSO26QlynZ3pNB7LdE2qZ13Tj9DwG94DfiRQiMOnec+cY+b4pAQQghwQ4ThL/xfB85yeRQh0B7Qc3RKunkHJZzIJoJjyPPamZFssOckH6YlTkWrkmWgf2GoaXLIiWyziEqvP18hllN0W2sMgb7mTxpQQo2uL0ocnqc+sYX2DCiyE18N5U2IAXwDimxTUw11h4aQ4JZjCUOfV9klWAhxuEyFu7xNsUj9ZujSgJLN3Ndv5czuKvJ6bkqhGCqV6FvXuNdI9bhbdvHpX7Lb1EQIIeTmc+2FoRGFybobfwxx0f/Y0cOoQjANZ7eqMeIwGlqGmBRfbkUPizpBUSfenGQ7dXjaZFgmxSgKZQ9DYOqTt4ysOckb0pm2S1kaKow4TC0hV+nIqzOs4Yq0kPlENpgONa++CHEYWf+11hQW2eQ6ZD7pndZWC6JBHNrR0lR1o/kEsMw62u7/6Da9NuLQiMJ5VGEeVY4oXMbFIOynLwiLuHTLBtISy9Sdm7zYYECpm3icm5zNamSzehKFeQvk7Ti7G+jLH6JKjaLQmE9sUWhMV3brmvJOjGTVjq1rqscXqB5fUBQSQggZudbC8NWvfrW3rjx2IyN9Xzd3GxllCRIo+K9FpFBOP1lmpZdGXgf63smUsXEk28jIYah1jYwcAn4a2ZsjjECt3x4i76JMIiG8ectDk2ubzYMJXWxh3Gnl9HgE4DQFB4AasXduQxFIOf1k1WWYi27pC9k9PYA0H5V1Mo7GMzSyX2YVQedurFRGDoOvVbglFUmhUT7iXlfJKVPIhBByUwjporNyrYUhAHzOp63xOZ/WN482ojDU4FfizJ6da6eHYWhOcja0ETHi0IhC6TINYYvDsdVJO7Wq2cS2tLKJFG4Th2Mblz2Ce3L6iU1IFN5P6xrndTeYT0LI6SepFVVs9ebjMeLQiEKZMg5hi8VybF3Tf+YhJ7n5jG1xaFrXjM/Tbr5WjDg0ojA1Ixi3jMezxWFc9sYqw7a/ASkOn/zpK25dJ4QQ8sC49sLQIJtaJ4WGff9Oit6AYt88o0rBygZCVcprct1VMSLRvkZGfYomcdYVbYpEdUisaN26zbwWJ3KKRqFTREo7pocY2hEpMrII9OIw3tGwWopD25ls2CdyeJn4UcLJmTytgxjzp733nqLz1skG1nlUO0aSRVQihkZtzZzu17n7ycjhPK68zzVRnVMycJiWzrVwmFWIlfbmJnvN03M32qnzFoi196XGLnFsFr7RqloqtKJvUZvHTrTwyZ9WeOr/2KPpNiGEkBvNtReGdtg0Kaz6uqNwFCRkQLFvtN1B50RpkuHmLNN79k39MNv/hmpHmw7jEqt2Gk9mRIc0PRSBCJUbPQyPT7MNKLKfod3kWhKqMfS2uSARaUcj5fQTu8n1tM5/L4CbMu8CbmbANaB4Bp7hHEuTCuC6kwudOFHecaZ1Igwo1nIRmIQDAPPMfy17NF5Tx1CWM9nUvnYHVh1t5TqTzfVtp5A3BaXLR/Npv9OAa4UQQsi14SLSyMANEIYA8HO/Ph9/t8UhAMSlRlxqVMtpnbl5yjot+4a7LYVnkBEfMwHlMHENKIArHFZd5rQ5scXhJkLCRUYPx959woAizSeS2jJ+bCIkDs9DKAVtm092HYdBTj8BAvWUw3JlXea5qh1RGBKCBj+Vb30Z2KC2bEPKMikdUWjmJq/raZ0Rh3U7HdMsr50vIupAerF77JLGUNsa2bKpXLo9DYFeHNqikFFDQgi53dwIYWg7kYG+j5t9366WCrLdXJdoJ3qoEw1Vi/F4IrUXx52X7ouVdm70gN/HLhKpRQAotUgjQjvO11jpoAkilEqWSANKqMbQjvhF6KNztb4Y8beNTZHGUM9C5/HASL1YudHDdkPrmkyI2m1isD+WDjE6J4Ufqc5JI5ddilT0j0yi1oscHqbS2CIm6LQxkrjDgRU9rOsYmeifKc1QOtZoDsQXG1HC2JdPWOaTUqPNlNO6Jlk3qEXrml9+w3MghBByffjFp/LdG+3JjRCG7/x33+KJQ8BPoclpJyGMOFQHQ6uaOkIrehuGxuEZcbgUky/Mss1xMphlBnG4tA5U9soLCcFp2olprdI/j+zdB7gGlN6wMXGWdPCDcCVvmn5ikykzmWVCppanOcmBXo5W6xrAOpdbmlzan4GsMVxuMQ+ZLwNybvJhVo6TcrZhxKGu3bnJNkYcmpnfzRA8t5349XJ3BNaIQ3WyAkBxSAgh14VffCrHO//dt1zY890IYWhoZxHamWhQLaZAxCUQWzWFURWYRZt048143E5ECpO4Q+JFD+UElMSJFK7bPspkm05i1XkuV2l4iFXnPbc0VxQ6cYRQjQix0miFuLpuzUk2TT+Rcvk8F7KMHOZR5Z9n1fntbKTZZRi7Nx6L6jyzi4wcAkAau0IvUtozsoTmJsvrVc76bubwkGWqba7Q5sJoNd9vbjchhJCHzy/9+wS/9O93d0Y5KzdKGG5CujaBSRy2ZlZyoLehFIe17C8XYGVNPDFiwIjDI9lQ0d6vk4aTPVLGOhldtMaAYsShHJMHTJE1KQ73SSBfVpNrwI9Gyobb/TabacdZ0EOT63FOsv/ObAFX6wQLVWGhqnEZ2N7ORrau2fY52Wnlok1wmJbjNWFa1ywCxqVZZgxP/TZx7s5NDmFHwaMaqJYY62rjsn9M1hwCQHm3P/54cCi3L74DAPiLr7yz8bUIIYTcXG6MMCzvTKo5KTWSUjvOZNnnDXAjh8Dmxtd225C6jp1RZlUbo2pjJzW4ajIvQiRrDGWrGoPjgO3ktJNkFDM20pUsU6gyamiwRZqUUKEJKCEuUhzK15PLkXi9kCRLvWheWPbWosZTLueROxEl1GBcppVDc5MBv+ZU9jNcZJVnZGpkKxvR4NpM6vFG4glNa5uu+mXlG7REk2sjDgkhhFw9ZKQwXoU7k5wXpR9y77r7wDvwv/TX/wWag14UlUN0xKTL6sNpOxOcs52cXdr/3prReOlw47VuyEna/54PM21naf9hmKjPLO6Xj7IpMpjH/bZHQ/ToYLCS2iLCRKhsp6tJcxpBYrYx6+00qPndRMNMtNCIotgZLzf8fzodo1CLxm2iYb/d0b3QmDvDPk5mI/TMtiExakwxndindbYZ/j/2PoyGbabjM2MDbVeyEYRGBJr/m7F5q653jNuRQbnORHuN+L9nucwryxFyUg/7Ddud1v1+pkn6qpy+LJgReVXZb9uZOsOiX2/324xXwwjH4ZJIVv1jtk7NTrSzbvbCdPbioT539kx//RmX8qf/7d1ueUIIIQ8eIwyruxne9eP/Y2iTc08uuDERQ4M0ocSFRiymQGzyGbTWvORNaTsjCoG+XY2dCgxNtgiNyJNj8jalIxciehhqVbPNYVvryIsebpqCEjnbdOcShbu2D+E5jQOvm6rIkZibXteenRyKFIbS64A/UcaepTwPRAoB9zMLRQ43Rw/d2cn25BwTiTai0NBZ16LKw+/BjhSGzFXSmV8e9a8RW38vXuuat7G3ISGEXFWqu5dTF37jhCEAbMjSOsjWHnIZACAK/70ZtgFC4nBTOtNGmhWkCSKE38dw9wSUB8153My7WtcAvjhMlZ9GjgLn4jznJ/Kmn+xOK/fHJAxL0e7PNJGGlMA+Uhw6X2gG9B71yG22+7OhO5kQQmovaj0AACAASURBVK4ebb5bj5yXG5VKBvp0cnk83fCamXLadbQz37XZzdyecDrrAHHzjdLOmUqRJv3jdr1hnjRYpNPyPKmcCRgHQ1rZNiXMowpzEV2aR5UTLcxV7Rki7kQrZzlXtVNraESJLU5iaKe1i5GVtsiKMKWSp+38C3BbCjnEprSyXTPomVuGj1iO6iu03M6NhNZQ3uzkGm701LT4kalkO1pY6DSYQrbrEU/a3OtJ+Ux16CyftplTY3pvSCnbXyLuVRkqK1K4HtLK9peRpkwcQ5RamxSynVZWXu3s7FlnEdmJdmYpZ8/3102ymq759Ol7UPfW4zLNKIQQ8vD5xSenEp/2IN6URgaYSp6wRSHgt+kIZfhMDziDCkw96aRDuXFNKAAcUQi4DmWgr0GTkUHpRg6t26epdWgKioxYhVrX7JPKvQhCwlEaV7xU9Z7HImWrjBwCviEnV40j8GQK2ayzmUel104mjXYX/S5ikWpOSy+yLMcqHswqL0ItXfL6oHNEIbBfpFC6k6vjxBGFABxRCDBySAghD5t3/bwwJa7DpUX3y40Thv/xh77JW2fadRhk1k/WZOm89UbiJTP/A5BO0rJNvBv+aSCvbUePlnHhNbWWaclc1V4d4i4RA4QbPLdaLvsiSkboZCQvVOO3j9FknxrFkICUI/JSsSw/mQ6bx+MZah0756wSy4ZtrWsMcjyenH6yblNHHBZNOo7HM5SiaXrVJE6EGgCi3Beh7YF/3uX1XLsBTO/vIS47lI+411f7omNnWZ+c4Knv/hPvtQghhFw+UhQCwDvf8a2X8lo3ThgCQFK4qTJgmplcDy1roqr/aaw+huZnpIqAKhpFYV0lqKsEeVYjH0aYlXUSFIjA1MfwtM1w2maOYPBG5In+eKfdDKfdzDWcdH1qc67656l07AiaQifjjz39o9YRUugxkjb1/cOwrD2BKKePmD6G2wwnoV6Hkmj4zxAadWe2Cc1PNuvScXl4L2I8HmBNfcHU1zBVnTcrWTq8c9W3qpmmotQodIZC9yJ/FtWYRbU3O9l8PkAvDhdJ6XzOMnJoxKERhYusxiJzxaARh20zpI3zpheItep/0IvD9qBzrt1mrsdrHOjFofkbAIDySKE8UohLy3wyiMN41e/UvugY7YuOoU9Oxm0oDgkh5MHxrp9PPFFY3UlQ3dkjPXRObqQwNMipJwAg7s1e37fg8zS7R+LJGb1JwDwi3cjehBMxLxnYPhJvPJ5ADaCkhhRfPvtcDLv6Fl7m6DxJSKTGe2Sf5Yxjef5qxF7fwn36GG4bj2eQ10XIZBRHcvpJh3QmooW5u5+Sk3oq5dXS1oeBBtdH7ntvDy6voJkQQsj+PPkT/v32Mk0nhhtnPjF8xt/+TpRH082ynfk3xjaTvQynxsEjS1c5xolrQsmSBnMR5Xns4NRZzpPaMaEAwOPZibM8jytHaMyi2hMjuaodR+zY09BStxlar+H1XNTBpcKEEsNPz0oDSgx17lSwTSiiGExnW9u10F56u4V2nqnS2nvm007UhcJv33Oq/dZBtSUUT7uZ1/z6udZVXKsucwT9SZujFDb3k9pdd2/saTitW9WZ074GAF44dQej12WCTtQeqhN3n2TlRr6TlV9bmz/jns/ZC63T6il7Zo1oJb5Fve8D+PSv/3AQQgi5PKQg7OYpSqs1TZsr/Ie3ffOup6H5RCJPWih6KINx3szkAPtEDyVFs9s8IiehlF3q1RVK40oomii32Ydd/Qgvm12vf1GGmPO0qtlnn30ih8vUXXeYlkgiN3IZ79HORsW7j2evVkwC+ffRHgSu2Ze+aPcTEUIIuVBklHAPUXhf3FhhCOw2nYRQotWHdIKms8aZmXyQ1SibxDEPFG3iRIIO09Ifiddljvv4MC5F0+QNDZL3cChX4mOVrVtCSEkiI3T9NrvrB3chawz3ea4W2jGgbBrVJ59VGlAAv8l15llXfKQpRS6XXeqIw1WbeWJx3abe7OQQdsP0uolxYDVUb4Z2NrFtQqlVb5Ya2DTWsRbjIINzk49FSv1RdyfpVCaEEHK51I+4WSPZeeUyuLGpZMNfePlrAQDrx0Sk7667XXXsi4juyE3BZoe+sjxauJGgR+duGvmx3F0GgMdm95zlF6Un3jaPJPd2rpO9DO/E7jIALD1R47/PRSTSyNieVu7XuSLiPLWF+6SV64Bwq7WMnLr7lIEroxAu7hPREuiFzv3j6187RiG2k2nk58UyADwtbMDPCFX2TClUGoBnCnfdc6sDb5sTkVZuT9wvCfELvtjMnhM9DT/kbYL5B91zPH+f+6Uk+6OnvX3Y15AQQi6Pd77bHUl6+pL+3/v/+Ca/88oGmEo+KzIg5zmSAW8sXtvEaMW4skosy7SxjB4CviNZRg8BPzIoI4iLqHRq4UL75KodHbkGKcWkkzfC5ojctE3ku5j3aFcTep5d7JNGzkJTUMRusl1NrhqnFjOP/Cbiklr7phT5ucj0f9kl3ni8ZSr2aVOvB6bskSmvs66OvAkonWhdE9VuDW1cA82O1jVJob0xS17rmnv+lx1CCCEXQ6g1zYPkVgjDfk7s1KrDuDVD2VojDseb7IaZycA0N9nctE0aMFRTaMShSSdKcRhCCr3QTOVd4jCElHDVBUWNzysON7WvMc9ni8NOa8RQVhudHikObUxjb7uFzyaMODTnNR9SwrX2nWBmnRGH5vMJzUk266qh4M+IQzlLGwCqdnjeQRya62s284XrLnEYQopD26S1CSkOn/qe9+zchxBCyNkwojAu+n/by7sJyrsJklAq7JK48alkAPjUr/qXaK2obH2oIDVWdUe4XucddCp6IQqHcjarx9F4QH8jn4lZt48duOnfZVois1zCR0mBmXANL+MCMyt6NY8q5MqNID0q0sq5qp02LH0/PvdYUnRO3V2q/G8GecCdbAuzUJTvrOPxQoREZa3FTGDhTpbOZKBPRdfOst+qZyXcGBUiR1DXOvYE9knrpnYrHY8j8gzPCsVV6gT3mmmbsku9hucfLMQ+bYJVPb12UScoKjHVpo7RlNN70HUEVbjCNXlBOMgrhcSqNIhKIL0nooWlxux5a4Ri2SF7xq0rjH7/vc5yd3KCz/yfPg6EEELOj9+rMEVzMP07Xi4j/Mr3f+NZnpKp5G2ETqadNWwW2lluh5Y1yho3pvLWGYuXBaI3AFC20w36MCudiJCJElVCmNhtTKSj1ZgYCqutyiKqnNq30MSOfp/pWFIhoUyq1V6bKeUsh2oLJRchCkO0WnsGFMCfghKJfdzn6P9vj8iTowKlUcdgn9NaJ06auRKRQqCP1Nozr+X8ZIPd5HrdpmMTdGBqjD5PRV2o1Q6prsM9rGwDiqoVWmv2t5ydPD6X1b5Jfhs1ja+rRydBHK0q4CWPjcvdiV8bSwgh5Gw8+RMa0cq6x9zxM0lnFIX3xa0QhkDfx01m+GRJWVT765SYRRuamVyL+i9bHAJ9ulCmDLeJw345DTS7lj33Mkcg1jr2Up7SdFHpCJXeXnfoReGgd9YdXhb7uJjlo7uT6aGaw8A4PGnc2aMb+lxcZIdJ2F3ubJPu3iYXvTIT2fA6gC0OAXgNrwG/t+dejjdLHALAk9/+O7v3IYQQ4vDkT2ivZ6EUheUyQrl8sFLt1ghDQ5cqRyB2qd/iwzOhxNppWxNFHRoRuZE96OKow1rUGsq6QilOYtV5JhQpDmOlvTSnbMAsJ6oA8EwotdB5MdyIWwwVbFnjHsvl9T+Uz+2ks5UKRg7tJt0pXANKCo0Y2kmvp6pzlnNVB6fROCPzhEllHpWIlHba08Sqc5YPkxKJdX0s4gqJ6pym54nqnPnJsdLeNJ047pBbzdVVrJ2+hjrR0Il26gx1DDSWQOySQJ/DzG1f0+R+T0PZ11AdiUJFQgghe/PUW9zsYP1I7vQrLI/jYP/lB8GtEYYyDOtFDwPiUK6TPQ2lOAzNTQ6JQ1sgrtvMG5UXEoe7RuVJcRiKHobEoRSI+8xM3sT9pJG37RsyoQCb2uj4M5T75/AxphQjDs352pSaB6Y0shGHMoprM5YBWPOTAT86bGOLQwDe7GQAjjgEgCjf3YuxkdFDv2OOIw7L43hnX8P4ZS8FANYYEkLInjz1liIoCm3kv70PMo0M3CJhCPQnN66sqFjppo6jSkGWhulYA1Y6WdeRM/2kqWNEkXYmoMRKOw2v103qzciNoD2BaLuOV13mTTGR0axCp86s3Von3qSOkDiUkShJSBzaqeRu+E9yHleyTcihbNaP6yxHMuCbY8x+tktZtq/JVYtInCd53vrooXaWI/H+ZGp5HlXeHGb5OguRWl4mpXdthMRhGotrQUxAUWJqj4ke2nTCTNVl/Y+NNwFlJsoOFjN0i8lQE7/spXjqe11DCiGEkN1089QRheWdxBGF1VI9cFEI3DJhCAC/9q9ccQiEag0D4VuxTo7GC1GKcXn7jMaTLWkKoVSLwKi8bdHDXDWo9TQj2ETHWiGm6uEH6IVVDY1aGD5scRjqZXhRnKe/4T79Dr3ehlLoWb0NzTk00cMqEE2sdeL0NQy1tAHckXlllzji0Hw5sOsMizbB3OprWLXh581EnaFsXQNMRiqD3ddwE9VQz5IU/bayr6GN6WlIcUgIIfvTPCY6UtwRruSlwq/9qwcvCoFbKAwN+4hDTyDuEIdVk3izk0Pi0BaIoX6GRZfi0HInS3FoWAhXrBQtEtm/T4rDELWIeEkTSqjR9f1GDgFfeG4Si52oi7TJxn6IE6mCY76R4jDErpF4i6h0PovDuHA+P6AXh3YKWUYODXYz9HlaOaJwPqSVa6tcIZs10NZ1GBKHAGB3RJIufGNACbRfHPGaXj9+7G1DcUgIIdtpHjvcSxQ+TG6lMDQqPK40rO4hvStZTL1TdQRl1xbGGp1VWxjFHZomQmPdnKs2dm7oidpuRFm3KZLIvaHLusJCJ05EykQO7ehhpeMxsgX06VEvlWxFD4Fh0skedYbSiHIx4m/7xW83u+639x3Km4wo25blDGWZRpaGE8AXg7u2KbvUSyvPo8qJHkaqcwRiErU4TEsnemjPTu636XAgpqJ4DuXYTyu3+XaHcpcqx6HcZsppfB2XHdr59I9XtKqgFwfAi632NfdO8eR3/C4IIYS4bHIg26KwWkajKHxY0ULglgpDwD3pRhya9h5RFRaINt2GfnI2MgW4y6VcdkmwbQ0ALKM+AmVMJU60UKSWK08M7j7WXeIQwN7icJ9UsBGFu8QhcL5eifuIw0x16CwDimwIvimNHIrGms9AikXZlzLEpuhh3U0TdRZZ5YzFM+LQRK2NODQGKZ1vMQkNpiojDrdFCuVUlOoRf6Y00ItCA8UhIYRMhAShaUuTFEPPWKslzcMUhcAtFoaA28MtrvzHzyoOZeQQCIvDtUglL1NXPGwSh9vYRxzaAlFGDoGwOEx31PI9iMhheB8/cijT3HLUXwTfhS3ZJA63Lfvzkd3lZVw4n+k8qrwvBVIc7tPbUEYOJTrvHGd9m2vPaS8jh/Wh8uYnm2bXBk8cvtjtawhQHBJCCBAWhRJbFG77ov6guBUj8bbx577+u0ZnprlJ2u7NZhj80GX9unHCxLBNnE9pvCRpMcum5VnaOFMsZnH/2GK46efDshGGB/G0rZmta5yvJgVppm3YUzcy1TrLuXJ77WVD8xZ3ZF479lG0J4MYg0Y8Lg/1ZybCZ0XvxnWDSIuVOlO0MESH3VFJE800j4VG5fWPT1R6Gp9ni0NTZ2lEsvm/qeushjNhi796XJc5Yvt0GJFnr1t1s3Hfe8NcRlskmnWm4flJ7S6f1lNtn6lXXQ0j8kw9azWMx7NrXrsiARrrs1pFTrP2ZD18doO2TIeAXzTU3mYn9uSU/vfZ8/31E6+H6+mZfie1mr7YtO97Pz7zWz8WhBBym3nqbf09ucv7f8ON+7jNrTF3gwPZdIOolgq/+d3fcFGHwJF45+U3v/sbvMhgyHjiNb2ulWdGkciehmUrjChiWfY4BPxI4KqbeTN6JdKlXGF7KrmG8uYJSwvDpsknzti6C/iSIUXjRtOJJfvO6lCW7Wv6dZ0TQTXu5HF5ENq1dS5zceHY6f0Qh3E5Cn57nT0FZykihYvBnWybmOZZ7ZicpDs5hHQnA25EvBZ9DU2di23SKo/jURQCfl9DQgi57Tz1tnIUhYZQn0LZq/CCReF9ceuFoSGq9mhbk2wXPs0e4/HKNnHG40lxCPi1h7IXnqTSsScGvdQy3FSyHJW3D54jGdqL6O1KLYeihf/0dV/hLH/H675S7ONepvtEJn2xGDoWF39MXlgcbn1d2a9S7U61L0QdwyZxuI1I9DmUBhRJc6C91jVSHLaz3V847Z6GABDd8d3KhBByW+nmotRICsJD9dBdyJJbn0o2/Lmv/y5nGkSXAs1cODmPrFFjSf+73R4kSjtkVio5TVrMrTqwWdxva/eoeyy/N/5uUsl2VOkgrh0nqxEndjuUXFWOaMlV7Y1xy2CPgnONFptSyvblK1PK/Trb3Tylk+U6m32Eoc23vuqNjlD8h6/6AScyGRKitXbX1dDOPjXcesoayokWmt8r6/gLnQphnQ7bTsL+udYt2Cu61BHsq3aG0tr+XjNz6hFPhwk4dmT5mcJVa6d15tStrsrMqWuth7SyU/9aumnkeBU57WuSU+V8KUrvuW797KQ/n6avIQBkz9WIi+lJ4g8+DwDQ6+m6/PSvfREIIeQ2YaKFzSPTv92rl7hfoNuZQmV3gZhdSrSQqeT7RX4ou4JDqomgGmlGESPnAtHD+ZboTyiVHEoty2igjGTtih7uamED+BG2WuutKdpQ/8J9jCnbRCHgRw//+ete4SzvU9MoDTSy9DcV3zFk5BDwz1mu6qA72dlGTkaRaWRhOFnElVduIJHRw7kwoKSzxjNFKdl/c0fTa9uUBfSF0bYoBOCIQqDva2iLQkIIuY00jywcURiiEv/GXpUUsoERQ8EnfbNtRrEiUynGXnA6m9brWR95U+kQVRnqvZIhomg7R7OkxaHVly6NWhxl0800G8I4R4NgmJnooFkeUpt2C5RZVCNX/XPaQsTUu6VDpNA8ZiKHdpo0VZ0jhlJopwZvkxFFziqW4+z2iRjuEobb+KaveYOzbJtRgKm9jp0Cr7V26ieNEcW8e5NiN6YUsyyjhbYoNELcOMFNDWg1bGNmX5vooFm2BftpM3NMKafNsE1rTCiDcWVYXtX9vnYda1klaK0vI00xPDaYUFTRfx6mRCIeDSiWKWXlOvT76OF0/mbPd8M+/f9nz/bXmVr315f64LMApokoAPAZ3/xRIISQm8yTPzncd3Krt/DgQG7yyHEeD8kh1IcKv/WdlyYKGTG8KH7rO30zCuBGEFWloIQZRYtoYah1jcT0qTNUgTY1dlSpDESonCbYgWhiLWJ/u4wo/T7nM6I42+zxheN+RCEAvPb1r3SWQy1sJKlYJ00ouWqdiTCyfU0I2cIGmEQh4I7ECy0DfruaRVI59aeLtHSiibbbfRNJ3jjOZMCtmzV9O7cho4fAJAoBoHxkNopCANCPP7LzOQkh5CZhRKHB7lMIuO1oDJcsCu8LCsMN9E2uN99UAewUh5KqiXfOT5bi0OuRpxNvRJ5Macp9pCFCisNQKlkim0zvIw4fBvuIQ0m8c4ayKw7lpJT+dad95lE5RnGndZXYfvf529XLMI7EBBdpQIFbAxtqei2/a+xqmVke7/FPxtJNo7z7Nb+1ex9CCLmGhEShjTSb1Icq+IX7KsFU8hY+7p981/h7l2m0luPc9DrsDqybbdIhsm7EcdJiZrUSyZIGi8zqa5g0WCSWsSTpH7Ndqo/NJnNK2HjSOD0MU9Vg4ZhVKieiZXoZ2usyiFSy6jwjSuaYSsz6wXAi+hmO71+F1/fr1H1HDIHN6WRgc3/DWhhRxt+H1fYs5RquaO5HE7pp5EqkmQtnlKGbRgb6VHIhlt008gylFU2+N6SRbTf7h4qDaf+yf42mtcYylqnT11AXsdvXcB05X3Ti1fBZDSdE9jUEgEyaUp5vkBTT9Z48s4YqpmtRv+8DAIDudEorf9arPwmEEHKdeert052jyxNUdydx0M36f3dtQdhmrhj8je95IJFCppIvg2Q1/R6KHnrtbETquBXmk0pEC3dFDyWhVPHu6GEmHo89I4VERg/ltBAZd2qht5pAwsaU+9f1UhRKQv0N6y1fhEL9DSVeC5udk1EqbxKK/AxlNDE0BaVoN18b81nlmVBacS3akUMAaA/cz6MVDvx64YpCwC+Yro7da6159MBZVi99kSMKCSHkumOLQgCOKATCPQrdOfSXd2wXBYXhFmT+XzX9zzZ0I2sNhThsY6fdiOxjWDSpEz0KOZVXrWt991zIO5YlkdJOXR3giz85Xk4SEn+7+Eev+qEzbS957etfubXOsIX2UsmyxnDXH0AMvbPOUIrBhVdT6Ao9OT+51rHf+Dpxn0OmlW0TEwBnjjLQu5O3oeoIjagxtCf+AH59oXTqx1WH8u72ayt52YdtfZwQQq4Lv/yme85y/ch8w5Y91aHyvlBf1bpCG6aS9+ATvq1PKcvehjpxW3/oRDuRGZV0zlSKJGmxEJGdO/l6/N2MyLNFwCOZG3GRfQ3ncen2ORTj8ADgTuw+h+xzuBRCRoqhCG4kLVPKEVSpirzoXIToTD0N7zetHIoe1sI202m91aHcwY+OFqIpuIy2vtDlYvt0a2/DVTdznuOkzZ3le+3MqTO912RetPBePXOMS8+u3EhdUaXOF5K6TNz61/L/b+/dw6XLq/rO72/v2lV16twv762bpsEAGnTEBDW0I0Meic8wzjMSc5loHp5RdIYYk2BEkYhGEiZqAMWJjyaOMxANTwA1XogOiQRFYLqbKKiDgEg3dDfd/fZ7Pbc6ddm1L7/5Y+/f3mut366qc97rOe+7PjzNW7/f3rVrV52qXd/6rt9aK2B9v1sjA5k/094j9SoPrCcKe1f5a9vZSRDEZBnFlT7MqH4fp0+f1zCyoignEioI7UKHCcKsE2CyVl+zszYXg3nbVM0DPvMTt1QUaij5ZuL+mPLLUbqHRmSANrmHsk0eZZy1PAcxFskoh3EP54WKpYMoQ8fSPZTJGbTvcHH/ozmFTdwI91AmyMzjqO7hYTKUZaKPRLqHkTjmPOcQANrBHAezzd+oQUucU0eE9ed09DnMQumkx9+n2dYyG4fr63OPoSiKctyY5xJSUQj4S25ukyi8LtQxPAIv/KGfRkreE3lkWckPG1lY8qVroryqawgAYfkF7b64O1GKBVJ2pCMcw26YsvVmnbLOIRUPnSCtQpOu7uEiS0bJxLg4hnMMC/eQ1zQE6szctsmZW+jEk0xGmZWIctiaho5rdQ9/8DX/FkBdKmdaIgp1Dd0evth19zPl2E9CAXiG9ziPqvJAbm0nFekD4ha6Woczk1CyDitR0087mGQ0SaVMbCnXqg4mZTeWcqlCHBfjrExKyUayrmGxH/3BE4742tnWEDO7onT6GYKY1DncKd5rQVkAO7i8CzusXfEHXnsvFEVRTgJOFKbkh27eabHM46xjEJNyNHnEf0zn0W0TheoY3grkH9crXyPGsnyNTAiQ7qHsetHkHkoHkY7jPPISHfwuJ+Ix5riHEzHO4a8/pGSwhyplM00UAtfmHjpRCBRCdJZ7GMIghJn5PCLDndN5fZQBXjOy21QMk9ALYs89lCwK93C5odYhG7eTShQCQKfDncNwgdc1tN0MtjvbfUxnL6FBvCy6+6x3KlEIAPmpNbb94Z95evYBFUVRjhGpiH545WhEjUIZYTlJTqFDheER+dw/nS0OgwP+RZllvjikX940EQUoxCFdU3Y1XmwofD27buG8sS8OZWbz7KxkAKJfMVhZmGIuZ/seNStZisN5YnFea7wQxuvUQlvltY2Z2yqvI8K+gScWRUayEIfRnMwlGVZuoifEoAxd90QYWdY2lNnJknl1DLPO/B+hk42Fmdsfesujc4+hKIpyO3noN1JfFK7zpVvDrenZx8DJSDRpQkPJ14ircZis+JmdrLZhaBEskHByyBNSojBjpUbaYcb64XbDBMskGaUdpN46tM2oTi5xiSfUjVoLh952nnxCEmBK8UIdskUWai7/JY/fla3xjGHJKPMSUWa5h0ARWqbCUI4BLjSr1njWnwOa10SOyZx7ZTJLt9fnHJcu7AQ0tMzXdnpt83Je73CYd5hbO8w6XhKKrG1I15cO07bnKO/S2oZlWJkmoYwHdZ0EW4aRab/vYGSqdbItUdewmKvH7X7x4oTkV0N7L0MY169je3sEE5MDXLgCO6rfa1/3hudBURTlOPHQb5CIRzdiYjBvB4hXaGs7v1j1ZLX497Nvvu2i8JpDySoMr4Np4jBdEc7SEndxFnrc9Vntjdh4KZogCutjnOryxa8AsEHEIBWG1fZWfR8qDJvmqDCs5+pzXGxwunq0l7IxzH0D/D7KkeG/rI4qDuchHcjEioxk4VjK4tfjRrFIb/uOZF+E4fsiQ7mfc+dsP+Pbt7MlNt4TcdurCe8gciXm+2/HfH8qDAFgfygypgeigNaBbw+G/fp50szkeo7sO7FoH/DXfeESf693ntlnY/vYk2ys4lBRlOMCFYUAMD7Hr7mjLRFt2xBLr46PKAR0jeHtwf3xZX1DmZ2cJwFyst5wNORf0JO0xYpfHyR8e1MtQ0psW419lKvj25C5VZJEdPPw7x94aw3p139mLRIhzOQ6w8P0Tp5FaGa/VaWwlGsMG5NfDF1D6PeH5iV6crQNdRUDL0tZhpJ7hju7soyQ10NZZiTLNYYRr38oaxsuitqGMju5JWobeusLxfs2XeR/s6AhEk4z8EJREDuYZEg2F+VdKvLxWNvlKYpy23n4PSM8/B5ukFBROFlteQX9qVOYLhb/AcdGFF4X6hjeAL7sR3+a1TdMV7j7ZFsW6JC6gFGOqE3rG+ZYMRJ7PQAAIABJREFUIF/i7VaKJRJOjsKMiYCFMGFt8zpBgiWavWxSJjK6JmGh5bbJvDFvm5d6mcq0f7DMVI7AhVgEw0RXCDM3S3meY0iFYTanPE4Of02jDCtL4UrDy7K+IQCQxFtMbIBEnD+tcTi2ETIhpIe2U4nzcR5VrfKq7XmbhZJ3hFsoQ8tXY7Fd1DbcaaxtSFr9CffQiJZ5UZ//PVoDw4RhZ4e/fkW7PBJW3k0RTOpXMbo6gBnX79n8mYvIx7XQ1RqHiqLcaqgYtN3Ir09IxGAW+WsI43WSfdw6dqJQHcPbyWff/H3sS1NmJzc5iJRUZCvL1nmJSFCR7qHMRJbuocw8lu6hHEv3sClTmSakiPKOSKb0KK7GDckoRyE0wUwHMUBzeZx6++y3vXQPJe2GWoUheY7SOSyOWb9BpHNYbJ+dFDJvezucvb3Vmr3dylqHgnxOG6dUJKXI2l75Eg9rB+s8W1lRFOVWIh1Crz7hDIdQjtOePW6i8LpQYXiD+NyPvK66HY4MTGK4QGzoo0x7KacieznOWqx8jZe9LErXyMxjWbpmbNvMpWoSi1QgemVu4Ltk7PlYOzNcPK9lXuHy3TwTWIaWXckaOqa0jWG1GmUP5QjcRZV0A78DDWUxiFl9SUkvnF3uZjmaXepGZidLwobsZBpazrr+34IugWwqek3LNsg+ywCQba3MPCdFUZSbzcd+aR8f+yW+9jndImHjtdbMotXJUpFwQr9y6ff/nYCGkm8CL/gXb0fewszi161FUVaklWGB1J1bXeDryTphikUiBjY7PKGkE/Bi2BstnpCyLsayTd5awI+3EvDH7wmRE6FecxcZeKVeaKayzFIu7nO0ZJR5awxleFmKzHnlc2goOQMvdi2LXw9yud6SF78eiDAxLXwNALsZDwMPRJu8otB1/YruJPyXrGybJ8PKg6RdFb0GgP2x6I4zjpCTHxrZAb8Ihn3Rd7nPny9NQAH8sPKCaJe3cLH+ZR5e2QfGXNS+5DVbUBRFuZlIMZjcU3djytuhJwZHG/V1sKloddqzx10Qaij5uBGkPIQsw8vpIEKe1XPUPQSAvVEXMfnylsWv/XDy7NqGO+kiExsyXLybCxsdMrzsi6F6mx9Oplm+skcxcP3JKPOYtWYxNMYTpmx7g3tI6YpQcoScJaL49Qz5WDqFbZH1LZNSllqzk1Lk9ki0zJPuYas1u7Yh7f/dRDI9nwQAEK/OcJYbXMOHf/b87AMqiqJcB1QUZlsrTBQCfOlLvBIyUQjIsDFYTsGdiDqGN5EX/Iu3IyU5ANky+QIOi9MPumXrsNCi0yUJIGHGsko7YYaeqG/Ya9XbF8LEa59HBUY3SNi4EySVQHHr36hgWSaOYbtMxXDr3FyNQyeGQlgvGQWow7dNyShA7RIeJhllnmNIOYp7KMvaFHM0+7hso1d+ThL49Q1zcq5jG7KOKfPqG0q3UI4Psm41PkgL548K/EHWrn40uO102cHBpIO0dDhlbcNJ2TIvp91QBvW5hcPiflVtw5Fh2ffRgGcqR32LICXt8fZyBEn9Wna2Y5hJfYfg0g7spH5PPvAP74GiKMqN5mO/tM9+lBYOYZuMDeKV4rroPJbJMgkfk9+zeXTsnUKHOobHEfnmYSG6zBT/lVD3ECgSTvqjesF+LNYYjrMIw7QWCIdxEIckg0C6h143FLJ9gpA5iLJvcAbjJaNQj6qplA11EG+0eyhF5Cz3MDLhTPfQq88otkv3cF4JGzmW7uGsdYdLrdhzB2l2utwGAEvteq7XTljB63Zn9jrErJcz1ztd4H8n6Rwmy/y1ks5hvMFD2lQUAuocKopyc6CicHx6gYlCAJUoBApBOE0UAnfeesImVBjeZERUz89QJkkpo0Hby1Cm7IwXWEmScRZhQsZSHO6kPe5WzWmVR9e+JQj9BBRvzMVhNkPfxTb32uZR5rXMm1eiRjI7azmYnbVspBiU6yMhxvzc2pBicbYAkz2TqbMbmcwLLS82CMDqsYPUq2/IzkW2zJPZyl1R5qcnx7PFITt24mf2Mc7wtYV2PDuhRlEU5Sg89BspHvx/6vFkg1dHiFdDVrR6IpLqZK94Wdv1TkVDybeQ57317chDsJZ5tMZhUK79ikgh4uVe/WXZCjMskSLGbi2ZEwLtIGOiYSFM2Hq0yGRYDsdsTEVJN0gqEeP6/LqxCyO3SYW/ZSZgctZbOBQJKbI7ymHa5k1z+o4SVgYOF1qmrmVCnqMTs9ThHNN9LZCQ85Ru6gTBzFDyPumWMszLUDDZvpMuku1tJuYHVWi5VY7b7IfCQdJha1P3iAM9LsPKtJd3Mm7BuvE4YD9iXFjZ/dAJh7yuYTQAAmIbRwcWoWuft1cul6Dt864OYZL6dbZPPF3d/rofeC4URVGuFdrBxLZblSDMWwbxKk0qKa5xThAWSSX1cWyrEIOP/uCJdAk1lHxSCDKgdVC/7PTLN08D5iACQH9Yh9/SLMTuqF60mOQhcxAneciyVUdZhMuTOg1/ngNIRcc4b2NMQs+J9R1EfqwAQ7JduocyXCzdQ98xnO4QZjY/koM4L7Q8zz2UDiLlRrqHvSD23EO+3XcO6ZKBxZZIWpnjHEr3MOoSpdfgHFL3e55zSBdrT1ZbTBQCwGRT/BQnPPSTj03dpiiKMo2HfiNlojA+u8RcQioKgUIQTkRiiSNZtXeNQyhRYXgLob86WMbyoMXWG8YD2TIvxISsDYtFAewJcYUK12h6BvM4j5gA3Mv4F7QnFmW9Q7LWsJ+3vWxldizwtYayH/G8TOUbWdfwSOsOESLCdBHsZylbdA1JuvDWGTb0kqu2JUwsJrbF1hlKMUgdX4CHleM8RE+Iw2WxxpCS5gE6M9YZWiEO6RpDkxovzNJU29AhS0HQpBQAMOdOs/GDP/65qcdSFEWRPPwr/IdwQn58xquhJwrH6/V3QrpU9zkGeMj4hLqF14WGkm8Tz3vr21kRYduyrMBwq1eLibCVoUNa6LVbGRYikrHcSplb1A75eCOqaxYuhElDtjIPNy+KcZfsL7OVpejpkfhiBItQZCtHrGg0aZMnMpWL7fUHuUnIHTWk7GgKLcs6h5QEGXM4aTKNrHE4tgaZJSLfy1JuTQ0lN2Us0yQh2Tavn3WZY7g9qS+EQ/EDoT/psA46w0lUZSsDQBxHLKycHdSPa0RYuchOJuOhyE4+sCx7eWGHtFPcdWHlei66OgAmxfvZPnMJdlK/t//bN74AiqIoTVAxaKOQicE8CnjYuMWTSiZrJFoXcjFoW3eEINRQ8klDvulkUko6JD0a0xBDElKeiJqHsxxEANgmcb5RFuEqGcsElHnh5j4RMhOE2Lc805TdV7wvvdZ5oqi0dBApt9I9lF1SjuoeUo7qHlJkhvI895DtO8M5BHz38FqdQ8BfoD3POZRuYbZUv4fUOVQU5TDMcggBHjYerwdTRSHgJ5XcAaLwulBheBt57LXfz8ZmxAVIGk8XJINJhMGkFnX9hAu0QcrD0QdZZ+p4mHeqxAegcKooY9Eo1w83t8g2nnwxtkIcNqw1nJWtzPa9qeLw8JnKMku5O6e38izWQt5xZjkYTdnTJ7MB1lv1/Tfa/FhrYkyxAJa75Ne2NVhYqMVk0OMCNp9R9NpkfI2hyXjpGtoqDwAmq7LoD7nveILwzKmp2xVFUR76NX59Gt9b15QZnmljeGZ6c/fxpiirtc6/Vx55490tCgENJR8bnvszPwUbEit7IauKYANAq5MhCOsv56WeWGvWThCFtTu12a3Dx4utCdok1rcUxuiIMe3O0QviKgsZ4OKlG0x8Z8uQ0LRJWQKGzFbueskaPDu5Y1piuy+OqcN3reFkBw0rU+EpM5UBP1tZ1mek2cqybR4ADImA7uddBOR12hVrPfv5Ahtvp0tiXCuxHWnZgYeVdyc95ESg744X2Jnvj3gJh9GoDZuTMAsJKwfDADS/pjUIQE3RaAA+7lsWZpbt8tp7CYK43qF1fru6nV28rKFkRVEqqCCMz9bXwCCznhgcr5N1/BkXhEHGBaHJ7khBqKHkk85R3cNRzD8E1D2UDNI2+kn95T/LPQTA3EOAJ6CM87bX65fv22KOoUxOGQs57zmIXg9jvzPJzSIQ5XNmFb4OjEFnhihdDGZnTa8JZ1A6h5KN1sHUbdQ1bGIl4j8i1rqzXclZYWXpHKaLYsw1plf0WpL2ZrQmVOdQURQAD70v91xCihSFoy3xvaMu4ZFQx/CY8pyf+8naQVwoxJEpx67OYVg6iAudwrGLyvFie8L65a60C2HQKq2e5WjsOYZu7NatUcewF9SO4mHrHLq1dJHJuYNY/tlcmRcnC1xSSghTJaXQ0K5b99dU3/BGuoaA7xwW+zTXOUyY48hdQ4CH0jNrMLQthOT4co2mcw7D8jWjInyWawjUzqFzB3eThXJcvD67kwW2fS/usvHBuIO8dArHo/pC69xDO2zB6faqXV6ZTd8amHJcnw9tmRf1i+cclmVr2vvFgdx6w/ZOEdZ27mFwdR92UAveB157LxRFubt46H3lBSct/h3fW18DTWYxPFOYFu7rzglCU9ZLG2+U16XyMJM1W40ffcMdLwbVMbyjEe5hEotyNJ57yMf7E27jUPcQ4I5hP+uinwnbhzC2UfFfue5wVjkbwF9zKBNSpB+YwVZiKy//R2mqb3jUrihHgfZzlkkpAM+sDjB7vWFoLHozkk/q/Zqfj3QNN1oDNpbO4VrkO4M0pLza4U7iEllz2C3XG9KQsiHrDmVHlHRxds0v6RxOVgKWhDJZ5wI53+R9qB7+maehKMrdQyUKS6goBFCJQofnEm6Ia87aHe0l3VDUMTzm3P/zb6tumyUuKro9nnm60psdMtzo1MKhE6ZYbvH9N0lZm6KMTX38TpB4WbJrYb1/hIytUwSAFUPr8fFzj2BZcWgpqCITsLWHMqzbEgL0VriGjrHlzyUR9x3OcA0BYCjqTErXkGZ+N4Xt5zmHV0W16e0JcR1jvh4xt6ZyDgFgfyjWGw5Ef+MDfu6tPv87RPt8HU8kIuDd7fq1ChKL9n790yCMc0Q79XvWfPECP9eDga45VJS7gAd/21auHwCMz/DrFm1jBwDxOr/+x6tsiGSlPtbnX3/HO4WOa3YMVRieEO7/+bcxf9f0UgRlaLldrgkLSXLKYmeCVrnObaksVdImySlLrRidsAwflwJxWkKKE4idcuwEYlS6X9XYhZHL/ZrCyuxf8idcDOj6PpT7lW4dzNSwcvHvzQ8tz1r76MrsUIHoQsp5Nb52cQj4AvFaxCFLQJkssLETh1mZNDMYt5GXt+NR+cucuId2GMKU43Dgwjf147UGpgrvOHHIWuL1LR/vZwjj8n1RikMzIj98Lm8jPyh+iKg4VJQ7kwd/uwz1Zhaje+prmBOJw9PFtcjkljmE7loz3nD7A5P1nNzf3E2C0KGh5DudJ7779WxsSZ3DSRxhEk9PPjmYdHAwmV5vsJ920ZdZA4Rh3saQlKwZ5B1W0kaOZXcVWs4G8MPLQFEo2pEDTIZlDQWoM7a/7KBytJZ5ksN0SaEOZgieWQ3UDmhQ/tcT9Q2lg0rd1SaoO9uEDCt729uzt692xpUolHQW/GQUQ0Ritui/1jSsnCx5m1lNMUmyvoBkfWHqdq1tqCh3Fg/+tq1EYRPD01ElCpsYb9SiEOCiELirXMIbggrDE8QTr+HiMBsKO31cf3AGcRsDsfZwe1Tb8Qdpx6t9eIX0VT7IOt5awx3hSs3KXgb4ekOZrQzw9YaJ5eKwmOMFsGetN7yRNQ6BQhxSgShD19d0zCOcI+0w08SsLGUAWI/mZCq3/eOvduu5nshMluLQLvDVoU3ikDLjd0dx/4XZlyKzxN97Kg4V5c5ACsLRuUXmFo42+ffceMswt3Cyzo8nReEXXscrfijz0VDyCeX+X3gbq3MY9lIEpERKp5tUoWSgCC0DqGoduk4Yrr7hcsTHKyK83JStvBSO0S7DxK6tHq1x2DUJW3coW+g1ZSzTdYdtY9gvF7nuMEDAEkJuRcZyKtJlZDmdsTfmb9OYDDOY6w4pA81h5Zy8DjtJr8pMBooah3T7bryAjGzfG3dhSZj5YFRnKwNAMopgSW9vMwpZr+/WwIjsZMNa5BVhZDrOq2xloGibF8SkPeTVA5hR7ahmFy5pOFlRTjgPvr+8ZuTFNXZ0rri2mdyFjYtro7uWjLfItT0XgjCXoWMVhNBQ8t3HE695PfsybnIPB6SNnnQQ+5MO+iS83E+4g7ifdrFPbJ6mbOUDMpbdU4DDZSyzsXgfNzmIVHg1OYhSuN1opHMok2K63pg/pw4ZhrBYnpGwA/jO4byQchO5eJ1lh5RMbKfOIQAmCgEgkqHljG+X2ckyEVuGkSeyM8oafy+nm1z4hmd52zxFUU4OD77f1KIQpUN4rv7BOzzdqkQhUAhCKgonq7NdQhWF1486hncA97/jrcUNV+eQfHEHYY4FEhZshTmWOrX4aAcZlklYsR1kWCYFkTtBWrmHQOEY0h69Mlu5a5LKPQSAtsn4uME1ZP8K17A4xmznUIoz6Rze7GSUrEo0KeYSm7O+z66It5OsccM7t0/WZAbGYrd0CsPymLs5z8qbl4xyRYyvkmUCOQzrjJJbg52Yj/fHtejPcoPBqBb9NjdIhkT05wZmWP8N3PLJcFius0wN5BLIICmcQzru9GtRH8QW7d06+SQcJQiu7tfnMBzigX94DxRFOTl89AMhgrS+dg7vXWA/HIenQ9ZZabRVJ7EBwHjDIiA/RONSELr7PPa9KggJmpWslAKRhJejhYS10QOA5V4t0pY6MdoBd9hoKz0nEGm2Mi1pAxQh5g5xvTZDvvatF8Roi2LZbeLqzWuh50RimzhvsuNIURSbCBP4ySM3s5xNMc6ZQJQlbIr6jLMF4kAk6ezm3UoYFuPrF4e5uFZsi3Z5V8f8mPvjLjLiGB4MuGucDCOWrWyGIWuBFw4NgpSGmSHCyMWL4OY6/QyBeGHauxOEo2KH4Oo+7JA7nioQFeX48tEP0B+NxfVseG+dXDba4D/sTV4IQsdklV8PgsxUgtDtr4KwEQ0lK8AT3/WDbJyMIkxGM7KV4w5LSAGAfVL8up90vWLYsgyKDC9fzUQR0txvt8da7NkW+iTjObGBl5SSCAEVN4guih9ivvG/IeRaRipGAwRez2fqcIbAoVJZ1uYkoMiwskxI2RLjtYaElNWGItiUxfZk5vaoJ5JSOuJv0+OvvchfwmTZIF0gDsCy/8pM1ur3R765AmxtsO0P/+z5meeoKMrtgYpCoBCEVBQOT/Ht4w3DRKEkWc+ZKARUFN4M1DG8g7n/F98CADCla9heSFitQ5d52iL1DVfacZWg4nrsunqHQFHzcFq9w2Jc3McloSwGfHvVUk8kqbiklm7577yklGK+DFUag4hILVnn0OHE3PW4h/OcQ5mMEoti2GOvGLb/GNQ5pKLZMc85BAr3MCufr8smp2sJt5NFZMQp3Jnwdno7cY+Vr9kfFwLfzY3Klnmu1mG1xpWEecJBULXMA8qkFPJyVPUNy7n2vuVJKHvFa+nqGwJAe3tU1ze8sg1AHUNFOU589IPkR3FuMTpXmwdVYkkpCE0GjE6RiEN5+Yw3LJtLnBgsryeP/yMVg4dAHUPF54nveAMbT0YRsqz+kw/jCENR/3CfJKTsJ13mIALw6h3SdnrFmG8f5B1R0zDyklK4gxhibEOvjR51DSfW8rqH1rL+xbLOocTVObwZrfTkekdJV4jSXsNHd5Gop+XAd+zWAtH6riEhJSPXhPU5NQ4BYL09ZCFlyUqXJ8UsLEwqUQgUWfFhb3a7P5mUkiyBhZ0nKyIpZdV/LVnR660Nzz1UFOX2wUQhwEQhUAhC6hJSUQgUgnCqKCxRUXjzUcfwLuA57/oJNjahrXrhAkXHFFfOBgCiIMdKhyakpFgVde/aIimlE6ReUgrNqO02tdQTAoc6hwC8jN2useyXTNuYyjWszt0ErL/xNNfQcS3u4VFdwwQZcvI5S+AL17HlRb3lekPpHErXEPCdw8vpMhtfSfj48sSvPH01Lo7hROI2WXOYW4O9UZeNR0P+wyDr1yLfuYWuMwoARHsiBJ/ytnnhxLKklHBSdvfZLRRkOE7RusqFrhmM8JLvEmmKiqLcMj7ye1HlBgLA8FwHsuX78FTgJZbQcbxu2ThZz1jEAQAe/54fuJGnfaejySfK4XjOu34CJuQv3eJSLeCqeoekBuJKZ1zVNwSA1TYfr4jwMsCTUpxApCHlxSCuWuoBwKKZsPBycT/ulkUmR5d0EAnAk1KcSHSt9GSdQzd3M2odzhOHACpX04nEhNwnsxZjcogc88UhcLiwMhWIUhwCvkC8GvP2edvjRc9N3BmSxeOlOKR6OR9E7KJehJX547Z3hag/qIUgUCSmsPFuinBcv2ecQDSDep2kCkRFuTV85Pd45MfkFsNzHTIuxCAd0/WDTgTG6/VnPF3lFwmTGRWD144KQ+VoPPfdP17dpsLQsbbA57YW/E4bp7p87lRbJD9EfTbumoS5ik0h0M1AZD03hFKXSSZ1BHjir2dm1xps6mJyveIQaHAKG5xDCU2kGeT+W7p/A8ShdA4BLhCbnMPLY5HhPOJjKgwBIMuCuqdyid3n59ra56+xFIYA0N3mr8HCVW47LFzg78twmCC4ssfmVBwqys1FisLRGf5ZH23619Pxhlwq4l/vqDB84u+93tuuHAldY6gcjcf+7hur26NhxwsJHoh2ek0ZynsJFwf73vrDLltzOLYRy2LezRYxFkJnYPnYrTnkcyRrGfDCsonl5WISm7F9UmRlLULq2F3/esMAhrmRkQmZaI3m5CIvBrM/xyEs1oLZPZWBhmzlcHb7PFmCSBbETm2ItS7PXl5Z8H9MeD2VF7mLnIq2ecmSRbJEHGCxRDFIgHhFFL9e94VxvrXqzSmKcuP58Ifb+PCHxTX7Hv7dMTjNr3OjLT/TmK4jBIB0PUW6Xl8AVBTeXtQxVPDcd/84goC/nMtLXAgsdSbotPg3N615CBSlTzoixNwV6wS3WtxFPCXGTS7iWsDPRa49BIBlw89fJnl45WPE+kMZXi72ubbfTVRkNnViGYtMZVl+B/Ddw75tsV7Lu6IMUDHHncOrslVe5juDlyYrbHx54ruLF8d87qoocbRzwMfxKIIlSU4YtCBfhtYgEBnKxhOG3av8Nejs5zxDeWeCcMjfC1/3Sv2tqyg3EikETWaZGAwy64nBWJj2JvfFYLYiPvC5UUF4Y9FQsnL9/IX3/lh1OwhzLC5wd6oV5qxdWhRkWBFJKac7XOh1ghSrrREZJ16bt66XqOKvN5TisGsyVtKmayxkxUYpDmliSj0nu6bc+OSUaf2VeXcUXn1xbC0y8Q4fCudUisOmhJR54jC3Aa4k9VyGANsTHoqWwjDNA+zFtfObZCEORvxc8txgMqy/UGzKu6OY1KA1IH2uk3J9KPldEEzgJaJ09urXMhyXZZiu1u8NM4zxwLdxJ1tRlGvj9z/aYUklg3NtL6lEho3HW2DriScbOSuCDwDZesJKWz3xv/EavMoNQUPJyvXz+W/9YTYejDqsFRoA7I15uHh/wse7yQJ2RYh5L+Xjft5FP+96cxS/pE0LY7Hezu+1XPznyGAPUfw69+ZuNNP6K/O2foVkdc+oawxCA4QzPtoyrCyzvAFgUxS5pmFlGTKu9mnX6iy1ATY7ZJw332eJ/IiQvZVt2fnE9rhAluVrmpB9leOGEjaTTf7+evg9s4t2K4oym9//aAe//1ERIj7HncPh6QDD074opEw2RMH79aQQhQQVhccPdQyVqTz/V/93b25VhJijMMNqh/daXmnzfc4IF7EXTqpC2A4ZUi4SVerjRMgayt3wscxaBoCeaUhwuEXJKfN6LAN+gspYjIcNn89+Pts5LOZmh5WvpDyEDBS1D2lSSm4NrojElIsj7h7m1rDuObKvMgDE++L8MoNgwJ9De1eUFUp4GZsgsejsi7/tRX9JQefJXTZW91BRDocnBO+JvEoCoy3+OY03wepsTTZy7z7Zql/f9InvfIM3p9xwNJSs3DyoQHSdU5xD5LqkOHFIey87gbgQFl/gtO6hFIfdcsEZFYNN4hBAJRDdfbrkSiTFYVTN05Z0flkboLljyvV2S5knDpvK2lyvOAxhcTX3y9ZIcQjUAtEVxHbCkJanoeIwswZXSMZykzAEwMRhngVIBsQBLkNIVBwGqUHrgIaWfWEIgInDsOyp3HE1DkfFv60rxR1VFCrKfD70YPFZpSFiKQqdM1iVmNkkByjnnDvo7pdtlD/caMhYBeGtRIWhcmv4sl//597c+hIPYZ5a4AkkZxb2vfustMbohbwUjezte6rl309m2G42JKvI5BSZmALMT04BfCdxXleTacwriA0AseXnLMUh4AtEKQ6LOT9rV4pEKRAvpn5Wr0xKuRj7SSmXRg01EQeil/YBF2fJvn9+rT3+2rd3/OtZZ8d/zXqX+evae4a70C/9Rv81VBSlFoOO4VlRkzCDFyYGgIlIKpmsiWvbqu/iP/Ht/+Qaz1K5TlQYKrcWKhDDMPfKl0RBjrVO7fa1y37L6+1aRHaCFOsRGRu/ewoArIjEEykOu0GCRUM6t5Q/a5mTCIsu+ZiEot9ycT8/OaUpe/lGuIdN4jCxGXMTD+McArVADMpj7onQ8jz3MEfg1TqUwtA5iK7mYVo+5nZcu4VJHmKXdkYp1yMOSCmkPA+QHQgRnhm0DmhiSvFv1CcOYgpENBElLrKUKa1xjvYO6egzKG5/3V+/NkGvKHcav/ex4vNpSGbb4J4271pymhejH28V2wLi/MWbflcSuzGBpe6gCsLbjQpD5dbjxKELLwN1fTvaOWWtM6qEIVCLQ1raxgnEThVSHpf71L9AnUB0JXB6JmZjAJVAlOIwIm8XJxBD0TUlMlQA3vzw8qxuKU4gHiWsHJDjzROeCtPsAAAfoElEQVSHUhg6mgQiDStLYQjU4jAp55w4pL2UnTikc9lBi4WZnDicJgyLueI5hmR5aWc/R2vMy9gAtTAEVBwqdzdOEAK1KBzcU7j3ThRSh9AJQycKgUIYxpsZ2adMKtuoP2dOGKooPBaoMFRuP1/+vjex8eYiDzFHYYb1Tj3n1iPSLNhOkGK9JXooBwlbj+jWGNJklK5J0POSU0RZnAaRGBneVg/wO6fI1npOJN6I0jaNrfPEXFzWPaQZ1mNrvVzqgcgYPoxzCACXRSKKFIcXYh5qzhDgkihhk8PgsuiOsj3gCTBpHniF1PPUwA5qB9GkBq0DmYhi2HpDoCxlQ9cbTiy6u/x1C0c52lfr95KKQ+Vu4Xf/oFjCwUvNRH7/4tO8X7ETgqzcTIM7CBEyfvxVP3T9J63caFQYKscDKg5bpWu46lzEsL7aOIEoxSFzEUuBSB1BJxClOKR1D3sNc04USnEYkY9Oe0p4Gbi5DuJRxCFQC8Rx+dl113UpDAFfHAK+QJTCsJhzSSj1MS+Vxa+z8rk7cZiT18uJw4ycy17pILpSN04c5ml9PycOTUodxPK1dzUOS3FIuyQ6cUh7KjuBGI5cncPifaTCULkbkKJwcK5eP+hE4PC037NYisJJgzsYlO5gToSiisJjiwpD5Xjx5e97UyUMHasLY08c0ixmoBCIHdECY7019MShLF2zGMReUeymVnBdk3lrDyPx8VlsEHjzOqcA17f+cF6fZYCLQ6AQiGPy+c1x48WhrHX4zMRPVLkwFo7jaMkThk31Dwf7vHalHbRmCkOgEIeyfXZ73zJhCBTi0AlDoBCHKgyVOxknCB0mt54opILQzdFwMVAIQykKgw3+ocszo4Lw+KPCUDm+vOi3/ml1u0XWI64tFGsGO+X6wzVS/9CVuFkr1x7SDObVsoQNXX+4FpbuYikOZZgZoE5iRrYd3kGU7iGAmSHmo4rEpuQUt9aQ9nqWAnHQ0FKvfwMF4sWkFoNZKRQvlWVt3FrDK6RbihOBV0ZLbAzU7iEAZGXbvPGgWOtk07IkBi1jU4rCWiTW5xWR0pdOGDoHsTUuS9ns1K9Vq2yf9/X//c0taK4ot4Lf/XgpBMu38+AcWZJRzo2oEHSlZpwQJB+DZCNjc9E6LVhff36/8G1vvO7zVm4ZKgyVk8GL38+7q0hxCBQC0QnDai4aeuVtTke8nI0Uh0Czk9gLYiYOAWDZcLHlBGJ7RoIK4K8/BPwC2UcRiPPa6TmBeC3isEkYAocXhxkNKwth6Lgg1h5eGS15buHeqFuJQsd40K6EoSPc9UsItXdES8M+PLewvW8rYehYfNrvhqICUTmJVIIQaBSFADDeEpogJ4KQzFWCkByLikIAePTv/Mj1nK5y+1BhqJwcpDgEgDNLfW/unKh/2AlSbLZ5eFiKQwA429rz5jZliRuToCfqHUbI0RWrsxcD/7Mlk1OApp7LwTUnp8wThwAwtily8dk9jDgMjcV2xkO4TUkpTeLwvChilsN4iSlSGALAhYF/rDQPsD/k7uHkQNQ4TA3CA9EhZcd/DYOEJ6IAQO+SKGUzytHZ5l94KgyVkwQThCWDM/6PJykKx1vWSzpJ1vxrSrQae3MqCk80KgyVk8uL3//DaLfEusLuCG2x1nCzM2TrD51I7Ai3b7U19NYgymLZrqwNT1opbkckxtI1ubcGsW0MS1ABDl8DUTJNLEpxCEwXiA4nFMfivhkKgRiSQt9SHDqaROKFZI2NLyarLOEEqDOX6fyl8ZLnKF4ZLXoO4v7QdxAnB22ArDd0AtGw9YYGQtsX6w3F91tnL0drxF+TznaswlA51nzwj3hW/+AM/yyZHBie4Z9Dt2Sbrhs0OZBsirZ0mUFng7voeRaoELyzUGGonHwe+ABvlxQFOTa6fmeTexo6qdzT4T1yu0GCjZbvEtIWewCwGfjHXw38X87LgS8iZJJKkzhs6pbSJBABXyReizh0DHN/v771H/cw7qEUhgDwTMNcZgNcFIWxzw+5o5jaAFeG/mPs9Hve3GSXh75NGiDsi1B+yusdAsDCZf/SsHjBf41e/jV+eFlRbjdSEB7cE/o9i08b7xtwsu6/79N1/33f2xh6P8Q+97d+9NpOVjnOqDBU7jwe+MAbWKFsANjoDtAO+VVyqz3Aglh/KDOZAWCjdeCtN1wOR97coplUfZgdHZN5YebIAF2xvjCcUij7KAKRHc8Ex0YgAoVIzIT4vZSssPWHAHBxslIVvHZcHi8hJfs5gZiQL6mDMrwsv7iyfgQj1iBGDWHlqG+YYyiTUQCgU5azaQ3r1+ZlLxOpzopyC/gv/x8XgSYrhKCcG4lsYlgg3srFfgbplrDQM4PeBq8Lm2WBCsG7AxWGyp3Lf/fB13tzZxe5a7gQJtgS6w87QYqNVoPjGO14c019mTeDoTe3KtclNojDyAQIGz6THRN5c4cRh9OQ4jCxGSuC7bhWcQj4AjGzAS6kDQ7ixJ97arzu7zfy1xs+0+frEtMsZG30ACBPAuBA9HNNDFoH/vPoXmlYF3qpwfE9z8WgikPlViEFIeCHigFgvOm/l8enc89BzLcmsOLHVG/Vd8T/7Fv+2dFOVDnJqDBU7nyoQHT1EDdJqNllMlOB6NYkUoFIO6RUmcxE8C2T3sxV+RviKkr30K1BpALRuYYhK5btWtc1ryu8FpHY5By6GohUJMbWL3tzLeLQOYPzxKFba3h+TObKx7tIElSSrCx3M6y/KNNyjvVYTsrXjIhDut6QCsQwNryUTVyWrtmrn3tVzma7/ruqMFRuJh/4ZPE5MuQzeHCO9AhvKDHDehafzr25fKt4z1JRuLxRXOuo666C8K5EhaFy9/HyD73Om+uEKU51uXO4ECbYiLhz2Asm3hrEXhBjJeRt9Hom9hJZuiZFz/hh26Z1iMuB7wJ0jZ9J2CQWDysUc9iq3qGjqUD2sGEOAHYbcjCaBOKlzM84BnyRmFvjrUFM8hAXY+4WxnnLa6M3yUOvlR4A9Pf8jEyz0/bnEoP2vliQnwDtPX65CGOgK554a5DhG17C//6Kcq38zqcWvWzgg3tC1qYOKISg3G98KoPJxed/K656ETvWNw6qH1eUT33zm6/5vJU7BhWGyt0NFYmuJiIViM5NpAKxV7bQoALRuYlUIPZMU2/mtNyWkrni6k6zmA/rIgLXJw4Bvyg2cHiBSDVSWB7ncuaLsSZxSIVhTlxIJw7pWkMqDuO8EMhUHE7Kfak4TInzMTooXERa8zDok8K+pYtIxaEzg6k4dOsQqThsDYrXRcWhcj38zqdKZ5B8pty6QSoKnTPIehWfyso58rnfKt6sVBSubxTXLCoKVQwqAhWGigIUApEWywYKgSgLZjuB2CP91ZxA7AmHcCUcV+LQURTO5o/TM+mhElSaBGIxH163c+jP5UcSh2HDMY4iEHMRnn4mWfOSUJw4dMIQqMXhhOzrxGEq1k6NDjpeMeyg32KhZaAQh7KcDQAsXPKfY3c3r4ShQwWiclicGHSYvCGJJLdeEknVp/gUaUHnROEWv+bYzFSC0JFkoQpCZRoqDBWliW/6yGur210iDk+XbuICEYabTiyStFbab9mtQ1wjSSl0baKrjdjkIgKz1yICN3Y9YpNIjK2vksZEIFJJ289nC8SgfF4X0rocTV6e6wXaQo+c/9Pxuj83qt1Gl7F8ceivQQSAnUHx+Glaz1VFsRPyOvZpS73iX+ogsqzl0kVsjern292u/34qDhXJf/5MLQLdx5uKQPrbcHSmeF8Z4vbFp8kPkPJmuEVb0BX7ntqoF8rSzP0//h9/7NpPXrmbUGGoKPP4po+8lolDoBCIVBwChUDsySrJKESiLIGzFvhlcRbNpHENYlOx7K7x2+wV84dbh1jM+5//JmEIzBeHjr0GYQgU4jAQruiFdLUShZSnEz8r+el43St3AwBPDDe8uaf6foLLzmCBCUPHZMdfExlt+/sBwMLlhqzlC/5Cy+52qsJQAcDFoKPZFazFYDWXGS4GHRkXhEAhCqkgBApRqGJQuQZUGCrKtfA3Hvz7AIB2UF+4z3Xrlnouq/l0uy5nQ+se0vWJbg3icjBu3He5vN0mwqozZz1iRPou057MRwk5N4nEhDmF9fkcViRendJ3+XzaIAaJQMxJHcOnSIs91w3laZLFPCFu4aXSRaRzewe1g5kRsZj1i8xllrVMCmOHsVuHWJ9jyOoc1re/6QU8dKfcPVAxONURPOscwXqOikDqFEZbRbWDnLh/5zbra417b//BK37iOs9cUQCoMFSU6+NbH35N4/xzele9uWd3/DkAuK/tz58K/R7QALDZ0F1ltaEvMwD0GuofynZ7jmsVh5Rhg6s4zUE8ikB8crLZuO/jY3/+6fEaE4GOJhdx72CBCUNHvu1nLbf6QSUMKU3rDgHgW043//2UO5//sOvX3ASA8Wn/vTLZbP4stdd9x/nMWvN76v/9xrce4ewUZS4qDBXlRkFFoktaOdPhBbB7ZaeVrRa/yPeCGJsNrfgA7iQCwGIZbu6KarWdGQkr1EEEahfxMCJxWni5SSDGZRcVWTCbCkT3iJcPKQ4zG+C8CC8ntng+T8dr3vxTQ18ETvIWLg14iZukFIX9A54kkycB0Och+WBSvCbRvkheKVcTULcQAFoj4H+6X8Xh3cKv9gsx6LWgc86gWHEwOZUCoqxM71SxVlkueXjWZtG2k66bVTGo3ERUGCrKzeTVf/Dqxvnn9q54c89u+3MAcG9Dx5VFM8Fy4BdW7pqMhZnr+eY1iRFCFmp2TFuX2ESOnBXAdsQNrfZ284bih5guEp9MfFfwicmWN5fB4Mmxv94wzlt4ukEoJlmISwdLjfPDfX/dYXDVd18BoHPFf51aY6Cz478eKhRPPr8yWPFEHlCsD2yaTzb9zwAA9Db97khnV5vfHx/6hp860jkqynWiwlBRbhVUJNIyOGc79XohVwZnS7TaWyzn10JecNtlNFORSJ3EeWsRgaOtR5wGXW9IRWKTOARujEAEapGYkWsZFYiutI0Uh859oeKQOjJUHNrJlMzlCXk9SfZyixi8VCCqMDyZ/MqgDg03Zg6Lt3Jyuvxsi6LSy6eLiADtLHL/Rv2jL85ql1rFoHIbUWGoKLeDV//Bq70aiUAhEnsNTuBWa78Sh5S1cFCJQ8qp0O93CjSvR4xM4IWagenrEWchO6kAhVC8We4hUIjDrOFa9uR4g9U8dDzebz7O03ur3txwv8uEoSPsh0wYOqJ9w4Sho7NjVRieEKgQpIxP+V8dJidCkJKZSgiy6SxgYtARZy0Vg8pxQYWhohwnvucTrwIALIRc7J1rF67ikmi9t1nWS5Tt91zNxJ5XEqcWaE1uogw3O8Eow83zBGOTQASAsRCIeXkdoSIxJA91IeuU+/HHezp1tQ39a9gTky0kQhA+QVzElBTDfmpQO4nUsblyUGSWTia+YE72OzBJQwi5XH9Ik1TatRmMkNQ8/JYzKhJvN788Jk4gWRs4Osvfuy5DODsrCkeXxdI3TnN33znPz9vgS0PGWbEc4T+/7F9dx1kryk1HhaGiHFe+/0/+58b5pTDGufauN78YxJVQpNzT2vPmgCLk3DMN3TymrEcstvkOHDBdKB5WIDr28uYszfOp30UFAL6Y+usKAeDR8dnG+c8PTzXOj7II5we+Y3h+u9k9ytIQdq+h53Js0N5vfi0WLvivtQrEW8d7J1PWB57NvaQRR37Kd+PPnN7DpCGb/Us3LmOcNX8+fvPr//WRzlVRbiMqDBXlpECF4hIppE1FInUOqUik7frWWL1E/o1IhWKTizgtuxk43JrEaf2YaRbzNHEIHE4gUnfxC/Fp8nj1uX9xxAXlKKuTS6hAHKf1F/32ft2HmZa5oQLRULdQZjATw8mtPVRheHN576TMFmY1BKUjWN+2xBWkPYbPnCZ1A8nf/ks3Lle3qShUIaicYFQYKspJ501/+kpvznVguSfiziIVjrRt30rZ07kX+E6eE4vTSuEARyuH00RTZxXHds7PiR7xqSlC8fGEZy67rimPjs/wxyUh58eHfO2h+6I/f8CdxDip77Pfrx8/J2HnYLe53E17j7+G39rmYUigLn3yt5f9bUrNe7LmUDAAjO4pJkzqf8eFZ4v1tzQJ5L7TNAmEv5e/YuMCAGCQ+g7xex/4hSOetaIce1QYKsqdxpv+9JWNrfmAQijK9YhALRKdQKT0gnRqyBnw1yUC11cGp0kkUnEojzBNHAKFQGxqpecEYmNyynCzMSToBCIVho79/gIThtW5lgKxKVGlvWcqYejEIEWFIYcKQYcThE4Ism2pqUQgxQlCKgYdcRZWQlAySNsqBJW7ARWGinK38ZbPvAKAXzgbAM5Ge+U2/oW6WSWzTHcUgcMX2Ab8hBaHE4+zXEQA2M2b1ylSoSiTUx5P6jWG45zXJvz8uAg7N4nFxwaFmzjO+H2e2a/FStO6s+FeeS4is7m1W+y7cKk+v3idX5peld49YeZ3mwbRV/55R/fwvzNtF9c569cDzMqkkOee5h2FXHLRV2487d1nlBVu4Du+5hcPf9KKcmeiwlBR7nb+9Wf/auP8cjjG2Zaf5AIUonKtoXwOUAhFKRDrbc2FooHpQhGY3oYPmC4QAeCJtOfNOQfxkbg5QeUzw3sa5+NSSH7hYErZnG2/nR8AJJNCkCR7fhme9mVfTAJAslasg3v1lUHj9pPMuzrLCBscVMf4dPPfc/HsgIV/Kc8/fRlx2pz48eKNL+Igay6B9LN/+d1zzlZR7jpUGCqKwqFCcVmUx6FCsclxpGJxmpPYNQ0hVxLuvdkCUYaWqUCkCSoA8MioXpMY576opSJxlNbbL+2TwtkTX7A4kUiFoRODlJMuDN/VWWZjKgjH55rr/zkWz9bPnQrC55++DAkVhS/e+CLbRkWhCkFFmYsKQ0VRro13PfKS6rYMPZ8VJXJo/UQAWA1qgSfdRSocm9YkUuHY1IqPZj47sdhUNOfxlLfEk7USPxufq26PLReFjw5rwTjI/KSEx/t11vO4wcm6uls8dnql7rBi1n0H9jseqcXTv91c9La/+uqtFY6/uOq3EQzH9d8jOdfgIpMw+vo5vm5yQl6bF57ma/tk2P4l64+xMRV8//JFvzbjrBVFOQIqDBVFuXH85udfNHXbSjDGqXC6kDkTNtc8BAqxeNQuLJQreXPY2/GFpLleIQBMbIjPxPdO3R7nET47aA5Lj7IIT+w3h5gB4NLV6Y8LAGurA+w81nz/YHOCb//c9HWY77yvi+98sqENi9v+7C6+84vTtzeJwOqxz46Rxs1hcAA4e3a3MUnHIYs/U166/ij6md+v2vEjX/HbU7cpinLdqDBUFOXm4wTjSkP4GQATjMuBHy7uHSEUPY+jiMSJ9R+LisSm8DKASiiOsubtTixSYbi22iyanTAMNpvPm4rDd97nCyoqDt/57IbtRBxSMRic9f9WVAyePeuvP6Vi8EWnz3vbacmXl64/6m0HUIlCFYCKcltQYagoyvHj9x77UgDAWtDc89mxUfab7k1Zl+hE5GGE4zzB6PjsZB1JQ5Y15VOj+wAAw9wPMwPAnx8UoehHtut6i/ev+uVTAOCTTz4LAHBms7mDjeP8Uxs4dW72PpcvFCV3WgtFiP1Lzkx37vYnRaj2xVtPNW53IfSXrf35zMfsZ0Vm9vf+xQ/O3E9RlGOBCkNFUU4ef/TEsxvnV0Vv6OXAv8ZJx3GaaJwmFDNxBfmMKKbtGOQ8E/bTo2d5+/zpXnMG9JctX2TjD198XuN+L9rkrtzHL93XuN96t1lgv2SLr9u7OvHDx39t7dNsvJv5md4A8J0veLBxXlGUE4UKQ0VR7lw+/+S5ufusBcHU3tCSy9n0zGfKH85Yk0j5k8H9h9oPAHYbSu80cap9uPqHL1/+NLaz6esIHX/jL/zxoY6nKModgQpDRVEURVEUBcB1CMPp6WbHn2t+0oqiKIqiKIrPtdeNUBRFURRFUe4oVBgqiqIoiqIoAFQYKoqiKIqiKCUqDBVFURRFURQAKgwVRVEURVGUEhWGiqIoiqIoCgAVhoqiKIqiKEqJCkNFURRFURQFgApDRVEURVEUpUSFoaIoiqIoigJAhaGiKIqiKIpSosJQURRFURRFAaDCUFEURVEURSlRYagoiqIoiqIAUGGoKIqiKIqilKgwVBRFURRFUQCoMFQURVEURVFKVBgqiqIoiqIoAIDW7T4BRbmdbJlzdoK4GBgD4zaY6v9qDBk3bhdzRmycOZ533AI77X5Nj+fd1z90831RPVc7a5+m4x/hMY66f3WfI+xP5+2U+UM/9lEfd+o2O/e1bJ633nzjXao/vfeM2Vu4fDeJ+/DHMuIB3DENu48l28m/xrI5dn/ynGZt949d72+845Nt9Dwbjin3m3X8xm3eeTfsZwByRRH7NG2rZz7xyfh3rLWvgKLcBlQYKnc1E8T4K8E3wgQGMEH1LwJTfZOYIChuV/8aGLcPUMzR7dWYbifHbNhujSn8e7Ldum8yMs/2A4qxKY8RuGPV22FQ3cftS7cXtwEEprrdtN3NVfsD5eP52yoBZeT9yfnSx27Yb+p21Nsb73fIY896LP95AzB2+v3pdsjtluzjtllvu3G3Qfd128i/5Xa3vzG2fGvUYtMYW74l6u2BE1FizhiLALZ6u1Rz5N9iu622s/9Q32bbq/m83kbmwmqfentIxsX28jby6tihyRGg/Nfk1X3o/UNyP7c/gPJ28djFPnl1bPd4obt/+Rgh3Lnn1bg6Niw792JcngeA0ABh+UcpxgYBDEKU/xq3LSjHxS0ACM89sgVFuU1oKFlRFEVRFEUBoMJQURRFURRFKVFhqCiKoiiKogBQYagoiqIoiqKUqDBUFEVRFEVRAKgwVBRFURRFUUpUGCqKoiiKoigAVBgqiqIoiqIoJSoMFUVRFEVRFAAqDBVFURRFUZQSY63fT1NR7haMMZ8CML7d56EoikLoWmu/4nafhHJ3or2SlbudsbX2q2/3SSiKojiMMR+/3eeg3L1oKFlRFEVRFEUBoMJQURRFURRFKVFhqNzt/MLtPgFFURSBXpeU24YmnyiKoiiKoigA1DFUFEVRFEVRSlQYKoqiKIqiKABUGConHGPMlxljHjbGxMaYH5ix33ONMf/VGPOoMeaXjTHtWfc3xtxnjPmQMeYzxphPG2O+l2z7Z8aYp40xf1L+900391kqinIcMMa8whjz5+V15J80bDfGmJ8pt3/SGPOXybZ3GmMulbVT6X2+yhjzsfJa8nFjzNeW888xxozIdebnyX3+Tnn8Txtj3kLmX1desz5pjPldY8z9ZNuzjTEfMMb8WbnPc27sq6PcKagwVE462wBeC+An5+z3FgA/ba19HoAdAN815/4pgO+31r4QwEsA/ANjzAvJ9p+21n5V+d/7r/dJKIpyvDHGhAB+DsD/AOCFAL5NXBNQbnt++d9rAPwbsu0XAbyi4dBvBfDPrbVfBeBHy7Hj8+Q6893leWwCeBuAl1trvxzAWWPMy8v9/xjAV1trvxLAfxDH+ncA3mat/YsAvhbApaM8f+XuQYWhcqKx1l6y1v4hgGTaPsYYA+AbUFwoAeCXAPz1Wfe31j5jrf2j8nYfwJ8BuPfGPwNFUU4IXwvgUWvtF6y1EwDvBfBKsc8rAfw7W/AxAGvGmHMAYK39CIofohILYKW8vQrg/Jzz+BIAj1hrL5fjDwL4m+VjfMhaOyznPwbgWQBQCtiWtfa/lPsdkP0UhaHCULkb2ASwa61Ny/FTOILIK0MufwnAfyXT/6gM17zTGLN+o05UUZRjy70AniTjpuvIYfaR/GMAbzPGPIkicvFDZNtzyzDyh40xLy3nHgXwpWWouYXiR+59Dcf9LgD/qbz9AgC7xphfN8b8sTHmbaUDqigeKgwVZQbGmCUAvwbgH1tr98vpf4PiV/tXAXgGwE/dptNTFOXk8/cBfJ+19j4A3wfgHeX8MwCeXYaYXwfg3caYFWvtTnmfXwbwUQCPA8joAY0xrwLw1ShCzkDR/valAH4AwNeguH59x817SspJRoWhcuIwxvwDsiD7nkPc5SqKkI7rDf4sAE8f4nEiFKLw31trf93NW2svWmsza20O4P9CEWJSFOXO5mlwZ67pOnKYfSTfDsBdX34V5fXEWhtba6+Wtz8B4PMonD9Ya3/LWvtXrLUPAPhzAJ9zBzPG/DUAPwzgm621cTn9FIA/KcPgKYDfBFAlxigKRYWhcuKw1v4cWZA9bz0ObFHF/UMA/lY59e0A3jfrPuW6xHcA+DNr7dvFtnNk+C0AWJahoih3JH8I4PllhYM2gG8F8B/FPv8RwP9SZie/BMCetfaZOcc9D+Bl5e1vAPAIABhjTrlwrzHmS1AktHyhHJ8u/10H8D0A/u9y/JcA/J8oRCFNLvlDFD+OT5HH+cxRnrxy96CdT5QTjTHmLICPo1i8nQM4APBCa+2+Meb9AP5Xa+358sL6XgAbKDL3XmWtjafdH8BXogjT/Gk5DwBvtNa+3xjzLhRhZIsijPP3DnHxVxTlhFOWpvo/AIQA3mmt/TFjzHcDgLX258sflD+LIvt4CODV1tqPl/d9D4C/CmALwEUAb7LWvsMY8/UA/hWKcO8YwPdYaz9hjPmbAN6MIjEuL/f/LXKsF5Wn9WZr7XvL+Q8C+G9QhKEB4IvW2m8ut30jimUvBsAnALymTKJRFIYKQ0VRFEVRFAWAhpIVRVEURVGUEhWGiqIoiqIoCgAVhoqiKIqiKEqJCkNFURRFURQFgApDRVEURVEUpUSFoaIoiqIoigJAhaGiKIqiKIpS8v8DHQj14mjtd9kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1164fd828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110618be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12883f860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128953780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129b479b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b316710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a12dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b7c6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for bf in pair_surveys[2].basis_functions:\n",
    "    val = bf()\n",
    "    if np.size(val) > 1:\n",
    "        hp.mollview(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pair_surveys[2]._check_feasability()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['u', 'g', 'r', 'i', 'z', 'y']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_surveys[2].extra_features['mounted_filters'].feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "available_time = pair_surveys[2].extra_features['night_boundaries'].feature['next_twilight_start'] - pair_surveys[2].extra_features['mjd'].feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False], dtype=bool)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "available_time < pair_surveys[2].time_needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_surveys[2].extra_features['sun_moon_alt'].feature['sunAlt'] > pair_surveys[2].sun_alt_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-19.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.degrees(pair_surveys[2].sun_alt_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-35.392903988585012"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.degrees(pair_surveys[2].extra_features['sun_moon_alt'].feature['sunAlt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59581.33866564679"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_surveys[2].extra_features['night_boundaries'].feature['next_twilight_start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ ( 2.12009262, -0.20318495,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.06920649, -0.22716039,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.25487115, -0.23109301,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.34533292, -0.22988971,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.2062453, -0.25771876,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.24974627, -0.28503938,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.34117812, -0.28456796,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.29360616, -0.31233577,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.24522244, -0.33907202,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.20069057, -0.31109727,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.15047093, -0.33575971,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.06117196, -0.27814298,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.01702544, -0.24910309,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.96371174, -0.26885325,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.90951513, -0.28651606,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.85466792, -0.30236124,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.8103857, -0.26875239,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.86511857, -0.25422204,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.91945064, -0.23798175,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.97314674, -0.21965745,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.82129098, -0.22094145,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.77806118, -0.18652461,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.87556103, -0.20623682,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.83206151, -0.17331304,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.99225123, -0.12085362,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.00163157, -0.07076039,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.9596216, -0.04347653,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.01053453, -0.01968194,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.0592371,  0.00727517,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.06534598,  0.06166721,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.05180582, -0.04575837,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.0435111, -0.09758623,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.03477636, -0.14855199,  0.,  30., 'r',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, a', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.12009262, -0.20318495,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.06920649, -0.22716039,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.25487115, -0.23109301,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.34533292, -0.22988971,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.2062453, -0.25771876,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.24974627, -0.28503938,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.34117812, -0.28456796,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.29360616, -0.31233577,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.24522244, -0.33907202,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.20069057, -0.31109727,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.15047093, -0.33575971,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.06117196, -0.27814298,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.01702544, -0.24910309,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.96371174, -0.26885325,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.90951513, -0.28651606,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.85466792, -0.30236124,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.8103857, -0.26875239,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.86511857, -0.25422204,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.91945064, -0.23798175,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.97314674, -0.21965745,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.82129098, -0.22094145,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.77806118, -0.18652461,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.87556103, -0.20623682,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.83206151, -0.17331304,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.99225123, -0.12085362,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.00163157, -0.07076039,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 1.9596216, -0.04347653,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.01053453, -0.01968194,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.0592371,  0.00727517,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.06534598,  0.06166721,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.05180582, -0.04575837,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.0435111, -0.09758623,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')]),\n",
       " array([ ( 2.03477636, -0.14855199,  0.,  30., 'i',  0., 2,  0.,  0.,  0.,  0., 0,  0.,  0.,  0.,  0.,  0.,  0.,  0., 'blob, ri, b', -1, 1, 5)],\n",
       "       dtype=[('RA', '<f8'), ('dec', '<f8'), ('mjd', '<f8'), ('exptime', '<f8'), ('filter', '<U1'), ('rotSkyPos', '<f8'), ('nexp', '<i8'), ('airmass', '<f8'), ('FWHMeff', '<f8'), ('FWHM_geometric', '<f8'), ('skybrightness', '<f8'), ('night', '<i8'), ('slewtime', '<f8'), ('fivesigmadepth', '<f8'), ('alt', '<f8'), ('az', '<f8'), ('clouds', '<f8'), ('moonAlt', '<f8'), ('sunAlt', '<f8'), ('note', '<U40'), ('field_id', '<i8'), ('survey_id', '<i8'), ('block_id', '<i8')])]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_surveys[2]()"
   ]
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}