Astroplan-scheduling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: OldEarthOrientationDataWarning: Your version of the IERS Bulletin A is 69.6 days old. For best precision (on the order of arcseconds), you must download an up-to-date IERS Bulletin A table. To do so, run:\n",
      "\n",
      ">>> from astroplan import download_IERS_A\n",
      ">>> download_IERS_A()\n",
      " [astroplan.utils]\n"
     ]
    }
   ],
   "source": [
    "from astroplan import Observer\n",
    "%matplotlib inline\n",
    "apo = Observer.at_site('apo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Couldn't create 'astropy.coordinates.angle_parsetab'. [Errno 13] Permission denied: '/usr/lib/python3/dist-packages/astropy/coordinates/angle_parsetab.py'\n"
     ]
    }
   ],
   "source": [
    "from astroplan import FixedTarget\n",
    "\n",
    "deneb = FixedTarget.from_name('Deneb')\n",
    "m13 = FixedTarget.from_name('M13')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.time import Time\n",
    "\n",
    "noon_before = Time('2016-07-06 19:00')\n",
    "noon_after = Time('2016-07-07 19:00')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroplan.constraints import AtNightConstraint, AirmassConstraint\n",
    "\n",
    "# create the list of constraints that all targets must satisfy\n",
    "global_constraints = [AirmassConstraint(max = 3, boolean_constraint = False), \n",
    "                      AtNightConstraint.twilight_civil()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroplan import ObservingBlock\n",
    "from astroplan.constraints import TimeConstraint\n",
    "from astropy import units as u\n",
    "\n",
    "# Define the read-out time, exposure duration and number of exposures\n",
    "read_out = 20 * u.second\n",
    "deneb_exp = 60*u.second\n",
    "m13_exp = 100*u.second\n",
    "n = 16\n",
    "blocks = []\n",
    "\n",
    "half_night_start = Time('2016-07-07 02:00')\n",
    "half_night_end = Time('2016-07-07 08:00')\n",
    "first_half_night = TimeConstraint(half_night_start, half_night_end)\n",
    "# Create ObservingBlocks for each filter and target with our time\n",
    "# constraint, and durations determined by the exposures needed\n",
    "for priority, bandpass in enumerate(['B', 'G', 'R']):\n",
    "    # We want each filter to have separate priority (so that target\n",
    "    # and reference are both scheduled)\n",
    "    b = ObservingBlock.from_exposures(deneb, priority, deneb_exp, n, read_out,\n",
    "                                        configuration = {'filter': bandpass},\n",
    "                                        constraints = [first_half_night])\n",
    "    blocks.append(b)\n",
    "    b = ObservingBlock.from_exposures(m13, priority, m13_exp, n, read_out,\n",
    "                                        configuration = {'filter': bandpass},\n",
    "                                        constraints = [first_half_night])\n",
    "    blocks.append(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroplan.scheduling import Transitioner\n",
    "\n",
    "# Initialize a transitioner object with the slew rate and/or the\n",
    "# duration of other transitions (e.g. filter changes)\n",
    "slew_rate = .8*u.deg/u.second\n",
    "transitioner = Transitioner(slew_rate,\n",
    "                            {'filter':{('B','G'): 10*u.second,\n",
    "                                       ('G','R'): 10*u.second,\n",
    "                                       'default': 30*u.second}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Schedule containing 6 observing blocks between 2016-07-06 19:00:00.000 and 2016-07-07 19:00:00.000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astroplan.scheduling import SequentialScheduler\n",
    "from astroplan.scheduling import Schedule\n",
    "\n",
    "# Initialize the sequential scheduler with the constraints and transitioner\n",
    "seq_scheduler = SequentialScheduler(constraints = global_constraints,\n",
    "                                    observer = apo,\n",
    "                                    transitioner = transitioner)\n",
    "# Initialize a Schedule object, to contain the new schedule\n",
    "sequential_schedule = Schedule(noon_before, noon_after)\n",
    "\n",
    "# Call the schedule with the observing blocks and schedule to schedule the blocks\n",
    "seq_scheduler(blocks, sequential_schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=11</i>\n",
       "<table id=\"table140635034395648\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>target</th><th>start time (UTC)</th><th>end time (UTC)</th><th>duration (minutes)</th><th>ra</th><th>dec</th><th>configuration</th></tr></thead>\n",
       "<thead><tr><th>str15</th><th>str23</th><th>str23</th><th>float64</th><th>str32</th><th>str32</th><th>object</th></tr></thead>\n",
       "<tr><td>M13</td><td>2016-07-07 02:45:00.000</td><td>2016-07-07 03:17:00.000</td><td>31.999999999999886</td><td>250.423475</td><td>36.46131944</td><td>{&apos;filter&apos;: &apos;B&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 03:17:00.000</td><td>2016-07-07 03:17:30.000</td><td>0.5000000000000782</td><td></td><td></td><td>[&apos;filter:B to R&apos;]</td></tr>\n",
       "<tr><td>M13</td><td>2016-07-07 03:17:30.000</td><td>2016-07-07 03:49:30.000</td><td>31.999999999999886</td><td>250.423475</td><td>36.46131944</td><td>{&apos;filter&apos;: &apos;R&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 03:49:30.000</td><td>2016-07-07 03:50:00.000</td><td>0.5000000000000782</td><td></td><td></td><td>[&apos;filter:R to G&apos;]</td></tr>\n",
       "<tr><td>M13</td><td>2016-07-07 03:50:00.000</td><td>2016-07-07 04:22:00.000</td><td>31.999999999999886</td><td>250.423475</td><td>36.46131944</td><td>{&apos;filter&apos;: &apos;G&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 04:22:00.000</td><td>2016-07-07 04:23:26.384</td><td>1.4397302563017433</td><td></td><td></td><td>[&apos;filter:G to B&apos;]</td></tr>\n",
       "<tr><td>Deneb</td><td>2016-07-07 04:23:26.384</td><td>2016-07-07 04:44:46.384</td><td>21.333333333333364</td><td>310.35797975</td><td>45.28033881</td><td>{&apos;filter&apos;: &apos;B&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 04:44:46.384</td><td>2016-07-07 04:45:16.384</td><td>0.5000000000000782</td><td></td><td></td><td>[&apos;filter:B to R&apos;]</td></tr>\n",
       "<tr><td>Deneb</td><td>2016-07-07 04:45:16.384</td><td>2016-07-07 05:06:36.384</td><td>21.333333333333364</td><td>310.35797975</td><td>45.28033881</td><td>{&apos;filter&apos;: &apos;R&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 05:06:36.384</td><td>2016-07-07 05:07:06.384</td><td>0.5000000000000782</td><td></td><td></td><td>[&apos;filter:R to G&apos;]</td></tr>\n",
       "<tr><td>Deneb</td><td>2016-07-07 05:07:06.384</td><td>2016-07-07 05:28:26.384</td><td>21.333333333333364</td><td>310.35797975</td><td>45.28033881</td><td>{&apos;filter&apos;: &apos;G&apos;}</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=11>\n",
       "     target         start time (UTC)    ...     dec       configuration  \n",
       "     str15               str23          ...    str32          object     \n",
       "--------------- ----------------------- ... ----------- -----------------\n",
       "            M13 2016-07-07 02:45:00.000 ... 36.46131944   {'filter': 'B'}\n",
       "TransitionBlock 2016-07-07 03:17:00.000 ...             ['filter:B to R']\n",
       "            M13 2016-07-07 03:17:30.000 ... 36.46131944   {'filter': 'R'}\n",
       "TransitionBlock 2016-07-07 03:49:30.000 ...             ['filter:R to G']\n",
       "            M13 2016-07-07 03:50:00.000 ... 36.46131944   {'filter': 'G'}\n",
       "TransitionBlock 2016-07-07 04:22:00.000 ...             ['filter:G to B']\n",
       "          Deneb 2016-07-07 04:23:26.384 ... 45.28033881   {'filter': 'B'}\n",
       "TransitionBlock 2016-07-07 04:44:46.384 ...             ['filter:B to R']\n",
       "          Deneb 2016-07-07 04:45:16.384 ... 45.28033881   {'filter': 'R'}\n",
       "TransitionBlock 2016-07-07 05:06:36.384 ...             ['filter:R to G']\n",
       "          Deneb 2016-07-07 05:07:06.384 ... 45.28033881   {'filter': 'G'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequential_schedule.to_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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aZhd9LG6j2J64TmbHAwuB53Cbjh6oGAM/aQuv9Yb8NPc7duVy+Kaq+TJLaEoArsGNQN6Fm4bXB9c1r9JjLhEJPyqURKTF7T6KdGdneESjSFHrO9xakwtw644+BKYARzfDYx+e6EYo7+7iNiu+ZKlrN6+1S5EvHbgbVzBdAvwe6IsrnEQEjDFccskl2z+vq6sjJyeHs88+G4Bly5Zx3HHHkZiYyAMPPLD9ftXV1QwePJh+/frRp08f7rrrrhbP3lJUKIlIi9pSB9ev3DGK9FofOFejSFGpDrcnTm/gA9yUu7m4DUWbkzFwdms3YtkvDX77Ldy5xnVUlMh3GPAXYBquY95ZwMWAtmKTaJeamsrixYupqnJD7VOnTqVDhw7bb8/OzubRRx/l5ptv3uXrEhMT+fDDD1m4cCELFizg3Xff5bPPPmvR7C1FhZKItJjFFe6M/twyuK2TG0U6TKNIUWkebv3IjcBJuPUkNxPcjUPbxMNjPeCq9vDeJrh0KazQXKyocTKwANda/l/AUbj9l1QvSzQ766yzmDJlCgCvvPIKY8aM2X5b27ZtOeaYY4iP33XzQmMMaWlpANTW1lJbW4uJ0LOd2sxaRILOWphcBH8sgJx4+POR0DvVdyrxoQy4E3gUaAu8ilts31IvsbEGrjzcrVv61Wr4r2Xwi04wqrVGNaNBIq61/Gjg57j9l14EngH6e8wl0e2Pa9009OZ0ZArc1IQFnqNHj2bixImcffbZLFq0iHHjxjF9+vT9fl19fT0DBw5k5cqVXH311QwZMqQZUocejSiJSFBV1cOv18B9a2FwOrx8lIqkaPURblH9w8B4XHeyi2m5Imlng9Lhb713TMX79Rqo0NBC1DgCeA/4G25frmOA/0WjSxJ98vLyWLNmDa+88gpnnXVWk78uNjaWBQsWUFBQwJw5c1i8eHEQU/qjESURCZo11XDLKvim2k13GtfOdSOT6FKLG0W6D+iB2yD0eK+JnNaBqXh/WQ+TvoclFXBfN+jR2EZNElEMMAY4Aze6dAeueHoJ6Owxl0Sfpoz8BNO5557LzTffzLRp0yguLj6gr83KymL48OG8++679O3bN0gJ/dGIkogExdRNcNlS2FwHj/dw051UJEWflcBQXMexK4D5hEaRtM22qXhP9YTKBhi3HKZv8Z1KWlIr3MjSS7g1TP1wG9eKRItx48Zx1113cfTRTes1WlRUxJYt7omyqqqKqVOn0qtXr2BG9EYjSiLSrKyFp7+HP6+HvFT4XTc1bIhGFrf242rcvjaTce2/Q9XAwLTQG1bCTavg5o5wcVvfqaSlGFwL8eMDl2OAt3HNHjI85hJpCbm5uVx33XV7XL9+/XoGDRpEaWkpMTExPPzwwyxZsoTCwkIuv/xy6uvraWho4OKLL97eUjzSqFASkWZTZ916j38Xu8Xxt3WCeI1bR50twP/gzsqfjDtT73lmSZO0iYdJPeGX38Af1roNka/PdaNOEh26AZ/g1itNBGYALxNao6AizaW8vHyP64YNG8awYcMAaNeuHQUFBXvcJy8vjy+++CLY8UKC3sKISLOoqIcJK12R9N+Hwx2dVSRFo5lAPvAP3JvNDwiPImmb5Fh4oDuMbgt/2wC3robqBt+ppCXFAXcB03EjoyfhWorr10Ak+uhtjIgcso21MH45fF4Kv+4M/6+9Wi1HG4tr+X0yEIsrmH4Z+DjcxBo39e7mjvDxFve7vbHWdyppaccDC3GtxO8EzgNKvSYSkZamQklEDsmaahi3DL7dCg8eAaPa+E4kLW0r8P+A64EfAV/gNpMNd6PbutGl1dXws2Wwqsp3ImlpGbipo48BU4DjcA1KRCQ6qFASkYO2sByuWOamJk3qCUMzfSeSlvYDcCrwZ1x75TeIrMXvJ2fBn3pCTQNcsRzmlvlOJC3NANcA7+N+348JfCwikS9ohZIx5jljzAZjzF53oDLGjDXGLDLGfGmMmWWM6bfTbWsC1y8wxswNVkYROXgfbYaffw2ZcfBcL20iG42+wL1pnI9r3PAbIvPs21Gp8MJR0DYerl8Bn2n+VVQ6Bfgct+buTOCPuCmnIhK5gvma9jxuH7fGfAOcbK09Gvf6Omm324dba/OttYOClE9EDtLbxXDLarcx53O9IDfRdyJpaf/A7Y8ErjPYTzxmaQntEmDSkdApCW5cCZ+W+E4kPnQFZuHWK90MXAZoRqZI5ApaoWSt/QTYtI/bZ1lrNwc+/QzIDVYWEWk+726Cu9fAoHR4uidkaZOBqNIA/Bq4GOiPO8M+wGuilpMV5zam7ZLk9lqapWIpKqUBr+Hah7+M64q3ZwNlkdBWXFxMfn4++fn5tGvXjg4dOmz/vKamptm/39q1a/nJT9wptfnz5/Puu+9uv+2NN97g/vvvb/bv2RxC5S3OFcA7O31ugfeNMRZ4xlq7+2jTdsaY8cB4gE6dOgU1pEi0+89muOsbyE+Dh46ApEicZyWNqgLG4tYhjQOeBKJtMDErDp7sCVd/7Yql+7vDCVqbF3VicCcM8nAb1A7BrVvq4zOUyAFo3bo1CxYsAODuu+8mLS2Nm2++eZf7WGux1hITc+gv9h07duTVV18FXKG0ePFizjjDTTw777zzDvnxg8X72xxjzHBcoXTrTlefYK0dgJsGfLUx5qTGvt5aO8laO8haOygnJyfIaUWi17Qt8KvVcHQaPKwiKeqU4p6Q3wQeAp4l+oqkbbYVS92T4RerYIZGlqLWKFwr/AbcyNJsv3FEDtnKlSvp3bs3Y8eOpU+fPhQWFjJ+/HgGDRpEnz59mDhx4vb75ubmcvfdd9O/f3/y8vL4+uuvAfjwww/p168f+fn5DBgwgIqKClauXEl+fj5VVVVMnDiRv/71r+Tn5zN58mSeffZZJkyYAMA333zD8OHDycvL4/TTT9++4e0ll1zC9ddfz/HHH0+3bt144403AFi3bh0nnHAC+fn59O3bl1mzZjXrv4fXESVjTB7u9fZMa23xtuuttesClxuMMW8Ag3GbZYuIB9O3wG2rXcOGR46AlHDcHEcOWhGuSFoI/BUY4zdOSMiMgyd7wNUr4OZV8IducFKW71TiQx6uWDod1wHyjcDHIk01YcKE7aM7zSU/P5+HH374oL522bJlvPjiiwwa5NoE/P73vyc7O5u6ujqGDx/OhRdeSO/evQE47LDD+OKLL3j00Ud58MEHefrpp7n//vuZNGkSQ4YMoby8nKSkpO2PnZyczJ133snixYu353v22We33/7zn/+cK6+8krFjxzJp0iQmTJjA5MmTAdiwYQMzZ87kyy+/5OKLL+a8887j5Zdf5pxzzuHWW2+lvr6eqqrmXTXo7ZywMaYT8DpwqbX2652uTzXGpG/7GBgB7LVznogE38wS17ihZzI81gNSVSRFlQLcmfKvcKNJKpJ2yAgUSz2T3d/Ix1t8JxJfuuGamnTD7SU22W8ckUPSvXv37UUSwCuvvMKAAQMYMGAAS5cuZcmSJdtvO//88wEYOHAga9asAWDo0KFcf/31PPbYY5SWlhIb2/Q3DrNnz2b06NEAXHbZZUyfPn37bT/+8Y8xxpCXl8e6desAOOaYY3j22We55557WLx4MWlpaQf9c+9N0EaUjDGvAMOANsaYAuAuIB7AWvs0bqPr1sCTxhiAukCHu8OANwLXxQF/s9a+u8c3EJGg+6zUTS3qngyP94A0FUlRZQXuzPgm4D1cwSS7So+DJ3rANSvg1tUaWYpmhwMfA2fjmp08g9uIWWR/DnbkJ1hSU3fs97FixQoeeeQR5syZQ1ZWFpdccgnV1dXbb09MdJOwY2NjqaurA+COO+7g3HPPZcqUKRx77LF88MEHBN7XH5Jt3wvc+imAU045hWnTpjFlyhQuu+wybrnlFsaOHXvI32uboBVK1tp9nni01l4JXLmX61cD/fb8ChFpSZ+Xwk0rXYevx3u4s+cSPYqBE4F6YBrR09nuYKTHwRM93b5it6+GZ46EvtpXLCq1wjV1uBDXZWoTuy7AFgk3paWlpKenk5GRQWFhIe+99972JgyNWbVqFXl5eeTl5TF79myWL19Or169tt+enp5OWdned+8+9thjee211xgzZgwvv/wyJ52071N03377Lbm5uYwfP57Kykq++OKLZi2UtBxbRPawpAJuWAUdEt3ZcrUAjy7rgf/DTQGYjoqkpkiLdU1O2sTDDSuhYKvvROJLKvAWbprqbcAtaGNaCV8DBgygd+/e9OrVi8suu4yhQ4fu92seeOAB+vbtS15eHmlpaYwYMWKX20855RQWLlxI//79t68/2uaJJ55g0qRJ5OXl8eqrr/LQQw/t83t98MEH9OvXj/79+/P6669z7bXXHvgPuQ96+yMiu1hf497oZQc6e7WK951IWtJa3BnxNNyai85+44SV7Hh4pAdcsQyuW+E2Y9ZJhuiUgNtjqRVwP1ACPA0c+uQjkeZ39913b//4iCOO2KWxhDGGl156aa9ft60jHbiRoP/85z8APPXUU3vcd+fHzcnJYe7cuXt9zK5du/LRRx/tcf3LL7+8y+fl5eUAjBs3jnHjxu31sZqDRpREZLuKelckVTe4fZLaqEiKKutwRVIWcA4qkg5GlyT44xHuhMONgb8liU4xwOO4UaVJwI1oZEkk3KhQEhEA6iz8cjWsroL7ursGDhI9fsA1bMjAde1K8RsnrOWnwcSu8GWF26C5Qe+Oo5YB7gWuAx7GdbUSkfChQklEAHhoLcwshVs6wbEZvtNISyoG3sEVRz8CkvZ9d2mC01rB9bnwwRZ4pGD/95fIZXCbNI8DfgP8wW8cETkAmj0tIvx9A7xaBGPbwgU5vtNIS9oCvI1r3KCRpOY1ti0UboW/boDDE2F0W9+JxJcY3PS7ClwXvDTg514TSSiw1jZL22xp3LY24gdLhZJIlJtRAg+uhZMy4bpc32mkJZUDU3DrJn4EpPuNE3GMgRs7wg+18Me10C4BhmmPpagVC7wEVAJX44qly7wmEp+SkpIoLi6mdevWKpaCxFpLcXExSUkHP09ChZJIFFtR6dYl9UiG33aFWD1XR41KXJFUi9sgU+/fgyPWuL+tq76GX2mPpagXD7yG+5v7Ga6V+AVeE4kvubm5FBQUUFRU5DtKREtKSiI39+DPAqtQEolSG2thwkq3/8tDR0BKrO9E0lK24qbbVQBnAW38xol4STHwUHe4fBncugr+2lttw6NZEvAmMBK319KbuL9DiS7x8fF07drVdwzZDzVzEIlCWxtc6+LSenjwCGib4DuRtJRaXOOGLcAIoJ3fOFGjVTz8vhtsqlMnPHHT7qYAfXEjSh/7jSMijVChJBKFHimAJZXwm67QS6v3o4cxTAWKgFMBLUlrWb1T4YZc113yhfW+04hvWbh9y7oA5wErvKYRkb1RoSQSZT7aDK8VwU/bamF51DnuOAqAEwFN+PDjohwY0Qqe+h7mlvlOI761Af6NezN2Dm6kV0RChwolkSjy/VaY+C30ToFrO/hOIy1pCcDRR3M00MtzlmhmDPyqM3RMdM0dNtb6TiS+dQdeB1YDFwN1fuOIyE5UKIlEiToLvwqsjbi3G8Trrz9qfA/MBPjuO4Z4ziKQGgv3dYfyelcs1Wu9UtQ7CXgKmArc4DmLiOygt0oiUeKpdfBlBdzRGXITfaeRllKKe/OVCfDBB3rSDxFHJMNtnWBeOTzzve80EgquAG4CHgee9JxFRBy9ZopEgVkl8MIPcH4bOD3bdxppKTXAe4GPRwLU1PgLI3s4pw2c2xqeWw8zS3ynkVBwH27z5+uA/3jOIiIqlEQiXlEN3LXGncG+saPvNNJSGoAPcYvDTyMwoiQh55ZObsPnO7+B9apjo14s8DfgKOAiYLnfOCJRT4WSSASrt/DrNVDVAL/r6ja+lOgwB/gOGAqob0foSopx+yvVWbh9NdQ2+E4kvmUA/wfE4zrhbfIbRySq6W2TSAR7rtC1IL61I3RN9p1GWsrXwCKgd+CQ0NY5CX7dxa0hfLbQdxoJBV1wnfC+xY0sqTmiiB8qlEQi1Lwy+FMhnJkNZ7f2nUZayg/AJ0B74HjPWaTpTmsFZ2XD8+theaXvNBIKTgAm4abQ3uo5i0i0UqEkEoHK692ah9xE11nLGN+ytAUxAAAgAElEQVSJpCVU4xaAp+HWJekJPrzc1BGy4uCeNW4qnsjlwDXAQ8AUz1lEopFeR0Ui0OProKgWJnZ1e7ZI5LO4kaQq4FQgyW8cOQiZce7ExtdVbmRJBOB+oB/wX8A6v1FEoo4KJZEIs6AcJhfB6LbQN9V3GmkpS4E1wGAgx28UOQTDW8HprdxapZVVvtNIKEgC/g5UApcA9X7jiEQVFUoiEWRrA/z2W2ifAP/T3ncaaSmbgE+BjsDRnrPIobulI6THwsQ1moInTi/gCWAacK/fKCJRRYWSSAT5y3pYUw23d4JkTbmLCnXAB0ACcDKg5Wjhr1W8K5aWVMLLP/hOI6HicmAscDcw3W8UkaihQkkkQqysgr8Uus5Zx2l30ajxKbAZGA6keM4izee0VjA8CyZ9705+iBjgKaAr8FO0v5JIS1ChJBIB6q2bcpceBzd29J1GWkzXriwF8oBc31mkWRkDt3aC5Bg3Ba9eU/AESMetV/oBGIdr4iIiwaNCSSQC/KMIFlfAzYH2whL5ygFOPpkc4BjPWSQ42sS7luGLKuDvG3ynkVAxCLgPeAu3bklEgkeFkkiYK9wKT6yD4zNgZCvfaaQlNOA2ocQYTgG0HC1ynZkNJ2bCk+tgrabgScAE4EfATcACz1lEIpkKJZEwZi387jv38e3aWDZqzAfWA0yfjpajRTZj3N92fAz85lto0Fwrwa1X+gvQGvgJgRFmEWl2KpREwth7m2FWKVzdAQ5P9J1GWkIh8AXQA2DlSr9hpEW0TYAbcmF+Ofy72HcaCRU5wF+BFcDtnrOIRCoVSiJhaksdPLDWbSp7kXYYjQp1wMdAGjDUcxZpWee0dn/rT6yDSu04KgHDgWtwa5Vmec4iEomCVigZY54zxmwwxixu5PZhxpgSY8yCwHHnTredYYxZboxZaYy5LVgZRcLZwwVQVgd3dIZYTbmLCvOBUuBE3L5JEj1iDNyUC8V18MJ632kklPwvruvllcBWz1lEIk0wR5SeB87Yz32mW2vzA8dEAGNMLO7kyJlAb2CMMaZ3EHOKhJ2lFW4KziWHwRHJvtNISygGFgI9USvwaHV0mmvY8vIPromLCLiW4c8AS4Hfec4iEmmCVihZaz/h4PZDGwystNauttbW4LYMGNWs4UTCmLVuNCkrDn52uO800hIagE+AROBYz1nEr2sDVfJj6/zmkNByJm4T2nuBrzxnEYkkvtcoHWeMWWiMeccY0ydwXQdg7U73KQhct1fGmPHGmLnGmLlFRUXBzCoSEmaUwLxyGH84pKkvdFRYAhQBxwNJnrOIX+0S3Ejy+5thoVqdyU4eBjJwU/C0jE2kefgslOYDna21/YDHgDcP5kGstZOstYOstYNycrSiXSJbnYVH1kGnRDhfv+5RoRyYA3QEunvOIqHh8nZuM9oH16pduOyQgyuWPgOe9JxFJFJ4K5SstaXW2vLAx28D8caYNsA63HuCbXID14lEvbc2wppquC4X4tTAIeJZYEbg4xNwe6eIpMTCNR3gq0p472AmuEvEGguMxLUL/85zFpFI4K1QMsa0M8Ztj2mMGRzIUgx8DvQwxnQ1xiQAo4F/+copEioq6mHS95CfBidrl9GosBr3ZmcQbsG2yDZnZcNRKW6tUpXmWUmAwTV2ALgKd7JFRA5eMNuDvwJ8ChxpjCkwxlxhjLnKGHNV4C4XAouNMQuBR4HR1qnDbQvwHq6Jy2vWWq1NlKj30g+uNfCEXDAaWoh41bh9UXKAvp6zSOiJMXBjLmyodc8NItt0xrUMfwd4xXMWkXAXF6wHttaO2c/tjwOPN3Lb28DbwcglEo421MBL6+H0Vm7TSYl8s3HF0ln477ojoal/OpyaBS/+AKPawGHaXEsCrsEVSdcDI4A2fuOIhC29/oqEgae/d12Mrm60/6NEku+B5UAe0NpzFglt1+VCvYUntJJXdhILPAuUADd4ziISzlQoiYS4lVXwf8VwcQ7kJvpOI8FWh9szKQMY6DmLhL4OiTD2MHh7E3xV4TuNhJK+uKYOLwMfeM4iEq5UKImEuEcL3H5JV2hz2aiwECgFTiSIc6MlovxXO8iOgz+udRtSi2xzO9AFuBHtrSRyMFQoiYSwz0phVqkrkjL1rjnypaSwEOjGPnbZFtlNWixc1R4WVcDMUt9pJJQkAfcBi4AXPGcRCUcqlERCVL2FRwqgfYKbdidRYNAgGoDBvnNI2Dm3DRyeAM8WalRJdnURcBzwK9wG1iLSdCqURELU28Wwoso1cEjQX2rk69sXjjySPrj1SSIHIs64KXiLK2B2me80EkoM8CCwHrjfcxaRcKO3XyIhqM66M8NHpbiW4BIF7r8famro7zuHhK1zWsNh8fDnQt9JJNQcC/wEVygVeM4iEk5UKImEoKmbYF2NW5sUo81lI96KJOCMM2D+fJJ8h5GwlRADl7aDL8phnkaVZDe/BxqAO3wHEQkjKpREQkyDhefXQ7ckOCnTdxoJtgbg3VbAqlXw1Ve+40iY+3EbaB2nUSXZUxfcBrQvAPP9RhEJGyqURELMjBJYVQ2Xt9NoUjSYnwY/JAC33QYNDb7jSJhLCowqzSmDRVq5L7v5JdAGuAlQzw+R/VOhJBJCrIW/rHed7kZm+04jwbbVwAeZ0LEamDzZdxyJEBe0gaw4t85RZGeZwD3ANOBffqOIhAUVSiIhZF45fFkBlx7mulhJZJuZAWVxcOZm30kkkiTHwti2bg+2JRW+00ioGQ/0An4B1HjOIhLqVCiJhJDn10N2HJzTxncSCbbSWJieAX0roJPerUgzu6gtZMRqrZLsKQ54AFgBPO05i0ioU6EkEiKWVsBnpfDTw9w6A4lsH2RCg4HTt/hOIpEoLRZGt4WPS2BFpe80EmrOAk7FTcPTgLZI4/R2TCRE/GW9e3NzYY7vJBJs6+NdE4chZdC6zncaiVSj20JqDPx5ve8kEmoM8EdckfRbz1lEQpkKJZEQsKYaPtoCF+W4Ykki23utIKkBhpX4TiKRLCMOLm4LH2yGb6p8p5FQ0w/4GfAYsMZvFJGQpUJJJAS8sB4SDIxp6zuJBNvKJFiR7IqkFHUDlyAbG5jK+5xGlWQv7sGNLv3edxCREKVCScSz9TXwdrHbKDI73ncaCbZpmZBR56bdiQRbVpybzvveJviu2ncaCTW5uFGlvwAFnrOIhCIVSiKevfyDu7y0nd8cEnxrEmFNEpxY6jpPibSEsYdBvHHrIEV2dxvQAPzBdxCREKRCScSjzbXwRhGc2RraJfhOI8H2cSak1sPAct9JJJq0iYfzctzIdZFa0ctuugCXAn8CVEuL7EqFkohHf98ANRYuP8x3Egm2ggS3NmloKSRY32kk2oxuC/XAGxt9J5FQ9Evc5rN/9B1EJMSoUBLxpLweXiuCYVnQNdl3Ggm2jzMhqR4Ga22SeJCbCMdnwOsboU6FuuzmCGAM8BSgWlpkBxVKIp68UQRl9fAzrU2KeOvjYWkKHFcGSXqTKp5clAMba2GaNjmWvfglUAk85DuISAhRoSTiQYOFyUUwIA16p/pOI8H2SSYkNLhCScSX4zOhfQL8Y4PvJBKKegMX4PZV2uw5i0ioUKEk4sGcMlhXAxfk+E4iwbYxDr5Mce3AtW+S+BRr4PwcmFcOq7QBrezFHUAZ8KjvICIhQoWSiAf/LHL7mwzP8p1Egu2TTIi1romDiG8/buM2t55c5DuJhKJ+wLnAI4CeskRUKIm0uKIa+GQLnNMaEvQXGNE2x8KCVBhUDmkaTZIQkBUHp7eCKcWuoYzI7n6Nm3r3pO8gIiFAb9NEWthbxa5N7/ltfCeRYJuRAQY4QadmJYRc1BYqG+CdYt9JJBQNAs7AtQqv8JxFxDcVSiItqN66bneD06Fjku80EkxlsTAvHfLLIUtn7iWE9EmBo1LgH0Vg1YVR9uIOXJvwZ3wHEfFMhZJIC/q0FH6odQuqJbLNSHcjhydpNElCjDGuVfjqatfYQWR3Q4HhwP2A+n5INFOhJNKC/lkErePcJrMSuSpi4PN0yKuA1nW+04jsaUQ2ZMSqqYM07tfAeuA530FEPFKhJNJC1tfAzBI4tw3EGd9pJJg+TYeaGDhZo0kSopJi3HPRR5tdgxmR3Q3DjSz9Hqj1G0XEm6AVSsaY54wxG4wxixu5/RfGmAWBY7Expt4Ykx24bY0x5svAbXODlVGkJb21ESxwnpo4RLRqA59lQO8KaKt3FxLCLsyBBuD1jb6TSCgywG1AAfCG5ywivgRzROl5XOOUvbLW3m+tzbfW5gO3Ax9bazftdJfhgdsHBTGjSIuos/DmRjguA9on+k4jwTQ/Dao1miRhIDfRPSe9sdE9R4ns7kygG/C47yAingStULLWfgJs2u8dnTHAK8HKIuLbjBIoUhOHiGeBOenQcSt00HQmCQMX5cDGWpi2xXcSCUWxwM+B6cBCz1lEfPC+RskYk4IbefrnTldb4H1jzDxjzPj9fP14Y8xcY8zcoiKtSpXQ9HoRtI2HEzJ9J5FgWpUEG+NhcJnvJCJNc3wmtE+A1zb4TiKh6mdAMvCE7yAiHngvlIBzgJm7Tbs7wVo7ADfqe7Ux5qTGvthaO8laO8haOygnR6frJfR8v9W1BR+lJg4Rb3Y6pNRDX+3SKGEi1sAFOTC/HFaqD7TsRTYwFngZ2Ow5i0hLC4VCaTS7Tbuz1q4LXG7ArSEc7CGXSLN4Y6NbFDtKTRwi2pZYWJYMg8oh3ncYkQMwqg0kGLd9gcjeXI3bT+kvvoOItDCvhZIxJhM4GXhrp+tSjTHp2z4GRgB77ZwnEurqLPxrIwzNhHYJvtNIMH2e7i6P0bQ7CTNZcXBaK3i7GKobfKeRUJSPaxX+JK5Toki0CGZ78FeAT4EjjTEFxpgrjDFXGWOu2ulu5wHvW2t3nqhyGDDDGLMQmANMsda+G6ycIsE0bQsU17mpLRK5bGwMc9PgyCpoVe87jciB+1FrqGhwjWdE9uYaYBXwnu8gIi0oLlgPbK0d04T7PI9rI77zdauBfsFJJdKyXi9yI0nHZfhOIsFU2bcbFbEwRKNJEqYGpUPrOHhvkxtdEtnd+UA7XKvwMz1nEWkpobBGSSQira2GOWXw4zZuwbRErrIhfWhdC92rfScROTixBkZkuxGlsjrfaSQUJQD/DbwDrPScRaSlqFASCZJ/Fbs9KEa19p1EgqkwHmo6t2NwmZ5QJbydkQ21Fj7UnkrSiPG417WnfAcRaSF6XRcJAmth6mY3nSVHTRwi2mfpYGpqGaCW4BLmeqdAx0R4t6lbxUvUaQ9cADwH6ClPooEKJZEgWFoJBVvh9GzfSSSYqmJgUSqkLFxJslpBSZgzxo0qzS2DohrfaSRUXQNsAf7mO4hIC1ChJBIEUze76QnDs3wnkWCanwq1MZA++yvfUUSaxchssMD72llUGjEU13HrcdzvikgkU6Ek0sy2Tbs7LhMyg9ZXUnxrAOakQ6dqSCgs9h1HpFl0SYKjUlz3O5G9MbhRpUXADM9ZRIJNhZJIM/uyAtbXwOlqsRvRViVBcbxagkvkGZkNSyrhW3VxlEb8FMjCjSqJRDIVSiLN7P3NkGDgZE27i2iz0yG1HvpU+k4i0rxGtHKjBmrqII1JAa4AXgfWec4iEkwqlESaUb2F/2yG4zMhLdZ3GgmWzbGwPBkGlQdx124RT9omuI6d721yU4lF9uZ/gHpgku8gIkGkQkmkGS0oh4217oysRK7P093lMZp2JxFqZDZ8t9V18BTZm+7AmbhCSXsUS6RSoSTSjN7fBEkxcGKm7yQSLHXA3DToVQVZ9b7TiATHKVkQbzT9TvbtCmA98B/fQUSCRIWSSDOpC+xof2ImJGvaXcT6OhkqYzWaJJEtIw6GZro1l/WafieN+BHQCnjJdxCRIFGhJNJM5pbB5jp1u4t0C1IhrR66qyOYRLgzst1U4nk6KSCNSAQuBt4A9GsikUiFkkgzmboJUmNcIweJTFUxsDwF8irchsIikeyETPecpul3si+XAlW4Ykkk0qhQEmkGtQ1u2t1JWW6NkkSmxSlQb6Bfhe8kIsGXFAPDW7nntq0NvtNIqDoe6Iqm30lk0ls6kWYwuwzK6tXtLtItTIU2tdC+xncSkZZxRjaU18PMEt9JJFQZ4BLgA7SnkkQeFUoizeD9TZAeC8dm+E4iwbIlFtYkudEk4zuMSAsZlA6t4zT9TvbtUsACr/gOItLM9lsoGWOGGmNSAx9fYox50BjTOfjRRMLD1gb4eAsMz4J4nXqIWAtT3aWm3Uk0iTNwejbMKHEjSyJ70wMYgqbfSeRpytu6p4BKY0w/4CZgFfBiUFOJhJFZJVDRACOyfSeRYLG4QqlTNWRrZ0WJMmdkQ42Fjzb7TiKh7FJgUeAQiRRNKZTqrLUWGAU8bq19AkgPbiyR8PH+ZsiKc1NUJDKtj4cNCZCv0SSJQn1SIDdR0+9k334CxKFRJYksTSmUyowxt+PW6k0xxsQA8cGNJRIequpheonbxT5OC1ci1oJUiLHQt9J3EpGWZ4ybWjyvHCo0/U4a0QY4E/gboF8TiRRNKZR+AmwFrrDWrgdygfuDmkokTEwvgWpNu4toDcCiVOhZBSlqkSxR6sRMqLMwu9R3EglllwLfAx/5DiLSTJo0ogQ8Yq2dbozpCeSjxiYiAEzd7DpC9U/znUSC5ZskKIvTtDuJbnlprrPndLUJl304B8hA0+8kcjSlUPoESDTGdADex50weD6YoUTCwba9RU5rBbGadhexFqRCYgMcWeU7iYg/cQaOy3DPeQ3WdxoJVUnARcA/AZ1bkkjQlELJWGsrgfOBJ621FwF9gxtLJPTNKHGdoE7XtLuIVWNgSQr0qYR4vTmUKHdiJmyqg6Vaqyf7cCmuSHrLdxCRZtCkQskYcxwwFphyAF8nEtFmlrhud3mpvpNIsCxPhq0x2jtJBOC4TPfiP0PT72QfTgQ6oel3EhmaUvBcD9wOvGGt/coY0w2t05Mo12Dh01I3FSVG0+4i1sJUyKiDrtW+k4j4lxUHR6eqUJJ9i8G1SX4fWO85i8ih2m+hZK39xFp7rrX2vsDnq6211wU/mkjoWlIJW+rg+AzfSSRYKmLg62TIq9AQusg2J2a5qXdFNb6TSCi7BNcxVJ2/JNzt9/XfGJNjjLnfGPO2MebDbUdLhBMJVbNKwOCmokhkWpwCDUbd7kR2dkLgOW+m2oTLPhwFDARe9h1E5BA15UTpX4FlQFfgHmAN8HkQM4mEvFml0DvFTUWRyLQgFQ6rgXa1vpOIhI7uSdAuQdPvZP8uBeYDS3wHETkETSmUWltr/wzUWms/ttaOA04Jci6RkLWlDr6qgOM1mhSxiuNgbZKaOIjszhg3qjS7FLZqA2bZhzFALGrqIOGtKYXStvOphcaYHxlj+gP7bYhsjOlojPnIGLPEGPOVMeb6vdzHGGMeNcasNMYsMsYM2Om2y40xKwLH5U3+iUSC7LNSsMBQFUoRa1Ggk2GeCiWRPZyQCVUNML/MdxIJZW2BEcDfca+ZIuGoKYXSb40xmcBNwM3As8ANTfi6OuAma21v4FjgamNM793ucybQI3CMB54CMMZkA3cBQ4DBwF3GmFZN+J4iQTerBDJj4agU30kkGCyu213Xasiq951GJPQMSodEo+l3sn/n49ZrfOk5h8jBakrXu39ba0ustYuttcOttQOttf9qwtcVWmvnBz4uA5YCHXa72yjgRet8BmQZYw4HRgJTrbWbrLWbganAGQf4s4k0u+1twTMhVm3BI9KGeNgYD0drNElkr5JiYHCGK5SshgpkH87BNT5603cQkYO036XoxpiuwLVAl53vb609t6nfxBjTBegPzN7tpg7A2p0+Lwhc19j1Il4tq4TNagse0ZYmu8teVX5ziISyEzJhegmsqYauyb7TSKg6DDel6C3gTs9ZRA5GU3p2vQn8Gfg/XFv8A2KMSQP+CUyw1jZ7Q1FjzHjctD06derU3A8vsotZpYG24CqUItayFOiwFTI07U6kUdvWaE4vUaEk+zYKuA139ruj5ywiB6opa5SqrbWPWms/CnS9+9ha+3FTHtwYE48rkv5qrX19L3dZx65/N7mB6xq7fg/W2knW2kHW2kE5OTlNiSVy0GaVuLVJreJ9J5FgKIuBgkSNJonsT7sE6JmsdUqyf6MCl/tdsyESgppSKD1ijLnLGHOcMWbAtmN/X2SMMbiRqKXW2gcbudu/gMsC3e+OBUqstYXAe8AIY0yrQBOHEYHrRLwpqYPFFep2F8mWBxp0HFXpN4dIODghExaWQ2md7yQSynoBPXHT70TCTVOm3h2N2zfsFHZMvbPsfy+loYGv+9IYsyBw3S+BTgDW2qeBt4GzgJVAJfCzwG2bjDG/YcfGthOttZua8gOJBMtnpe4PQOuTItfSZMiqg8O0yazIfp2QCc+tdw1uRu530xCJZqOAh4ESQOcaJZw0pVC6COhmra05kAe21s7ALefY130scHUjtz0HPHcg31MkmLa1Be+d6juJBEONgVVJcEz5fp64RASAPqmQFeem36lQkn0ZBdwPvAOM9pxF5EA0ZerdYiAr2EFEQtm2tuDHZqgteKRalQR1MVqfJNJUscaNsM8qgXq1CZd9OBa3Aa2m30m4acqIUhawzBjzObB125UH0h5cJNwtr4RNdXC85gxErKUpkNQAXap9JxEJHydmwtub4MsKyE/znUZCVSxuT6V/ADVAgt84Ik3WlELprqCnEAlxMwON7dUWPDI1AMuToUeVe0EXkaY5NsP9zcwsUaEk+zYK1+FrGq5Dl0g42GehZIyJBe621g5voTwiIenTEuidAtlqCx6RChKhIlbd7kQOVHqcK5Cml8DV2hZe9uE0IAU3/U6FkoSLfa5RstbWAw3GGE04kqhVWuemlWjaXeRamgwx1o0oiciBOTELVlbB+gNq+STRJhlXIP0L1zpZJBw0pZlDOa7F95+NMY9uO4IdTCRUzA60Bde0u8i1LNmtTUrWq7fIATshcBJJm8/K/owCCoD5voOINFFT1ii9HjhEotKsUsiIhb5qCx6RNsZBUQIM1k5tIgelcyJ0SHDrlC7M8Z1GQtnZuDP0bwEDPWcRaYr9FkrW2hdaIohIKGqwrvXtELUFj1jLk91lL61PEjkoxsAxGfDBZtcmXM+V0pg2wFBcoTTRcxaRpmh06p0x5rXA5ZfGmEW7HQtbLqKIP19XQXEdDNX6pIi1NAXa1UCret9JRMJX/zQoq4dVWucn+zEKWAR84zuISBPsa43S9YHLs3Ht788BzgWuBtYFOZdISJgVmHOv9UmRqT45kW8TNZokcqgGprvL+eV+c0joGxW4/JfXFCJN02ihZK0tDFx+C2QD1+Da308E3m6JcCK+zSqFXinQWm3BI1L1kZ2wBnrpLLjIIWmXAO0TYF6Z7yQS6o4AegNv+g4i0gSNrlEyxvQExgSOjcCrgNGeShItyurgy3K4vJ3vJBIslb06k14H7dXWWOSQ9U9zm3Nb69YtiTRmFPAHYBPuTLxIqNrX1LtlwCnA2dbaE6y1jwGaxS9RY165+4XXtLvIVAdU9+zIkVVN2ydBRPZtQDpsqYNvqn0nkVD3Y9zr6xTfQUT2Y1/vD84HCoGPjDF/MsacCugckUSNBeWQYKCP2oJHpNVJYBMTOErT7kSaxYA0d6l1SrI/g4DDcd3vRELZvtYovWmtHQ30Aj4CJgBtjTFPGWNGtFRAEV8WlLsiKUHDDRFpWQqYmlq6qVASaRa5iZATD/O1Tkn2IwbXHexdQAOQEsr2+xbQWlthrf2btfYcIBf4Arg16MlEPKpugKUVkJ/mO4kEgwWWJUPSigLUp0OkeRjj1il9Ue7WKYnsyyigAvjQdxCRfTigc+XW2s3W2knW2lODFUgkFCyucPOn+6lQikiFCVAaB8lL1/iOIhJRBqRDUS0UbPWdRELdKUAamn4noU2TikT2YkG5W5DXT+uTItLSZDAWkpd/5zuKSEQZqHVK0kSJwGnA+76DiOyDCiWRvVhQDkckQ3qjDfQlnC1Lho5bIbZSs+NFmlOXJGgVp3VK0jSnAmuA1Z5ziDRGhZLIbuosLCrX+qRIVREDhYnQU00cRJrdtnVKGlGSpti2juMDrylEGqdCSWQ3K6ugskHrkyLVN0nusrsGk0SCYkA6FNZAodYpyX70wrUJV6EkoUqFkshuFgTOhGpEKTKtToLEBmhf4zuJSGTSfkrSVAbX1OFDoMFzFpG9UaEkspsF5XB4ArRL8J1EgmF1EnSphljfQUQiVPdkSI91bcJF9udUoAj4yncQkb1QoSSyE2tdoaTRpMhUEgsb46Gbpt2JBE2scc+hauggTfH/27vzMDmrMu/j31+6093pJCxChACBBEgggGELGMddFATX12UGVERfvRh3XMZ9XEZHR51xV0QUcRcdEGVc0HlFcUGWgNkghCUkZAPCTpZOp7vv949zCmp6OklXUtVP1VO/z3Xlqu6qp6rv5KSrnvs597mP1ylZM3OiZFZlTT/cu9Xrk8pqeV6f5ETJrLGOnwx3bknvp2bbcyBwKE6UrDk5UTKr4vVJ5ba8ByYMwj4+eTNrqGMr65Q8q2SjcBJwJTBQdCBmwzhRMquycAPs1gEH9xQdidVbkBKlg/v8xmfWaIf1Qu84N3Sw0TkJeAS4ruhAzIbx+YJZlb9tSGV341R0JFZv93fCQ50uuzMbC51K76WeUbLReEa+dfmdNRsnSmbZA1thRZ/XJ5WV1yeZja3jJsHyPnjQ9VS2A1OAo3GiZM3HiZJZtnBjuvX6pHJa3gOTB2Bvn7SZjYljJ6fbv3lWyUbhJOAqYHPRgZhVcaJkli3cAF2CI3qLjsTqrXp9kqsqzcbGkb3QLa9TstE5CegH/lJ0IGZVnCiZZQs2wBETocu/FaVzz3jY2OGyO7OxNH4czPE6JRulpwGduPzOmkvDTgklTZP0e0k3Se/LsXAAACAASURBVLpR0jkjHPNKSYskLZZ0laSjqx5bke9fIGl+o+I0A+gbgps2uuyurLw+yawYx06CWzbDIy55tR2YBDwRJ0rWXBp57XwAeFdEHAHMA94s6Yhhx9wBPD0ingB8HDh/2OPPjIhjImJuA+M0Y8lGGMSJUlkt74E9t8Keg0VHYtZejpucSl8XuPzORuFZwPXAg0UHYpY1LFGKiHURcUP++hFgKbD/sGOuiogH8rdXAwc0Kh6z7VmwIa1dmTOx6Eis3oaAO7o9m2RWhKMmwnivU7JROon0nv2HguMwqxiT1RiSpgPHAtds57DXAb+u+j6A30q6XtLZjYvOLDVyOGQC7NZZdCRWb+u6oM/rk8wK0TMOjpzodUo2OvOACcAVRQdiljU8UZI0CbgEeHtEPLyNY55JSpTeW3X3UyLiOOBUUtne07bx3LMlzZc0f/369XWO3trBYMCiDS67KyuvTzIr1rGT4OZNsMmlr7YD3cBT8Tolax4NTZQkjSclST+IiJ9u45g5wDeBF0XEfZX7I2JNvr0HuBQ4caTnR8T5ETE3IuZOmTKl3n8FawO3boaNQ06Uymp5Dzy+HyYPFR2JWXs6fnJaA7poY9GRWCs4CbgJWFd0IGY0tuudgAuApRHxuW0ccyDwU+DMiLil6v6JkiZXvgZOBpY0KlZrb5VFxk6UymcAWOn1SWaFmjMROnD5nY3OSfnW5XfWDBq5IuPJwJnAYkkL8n0fAA4EiIjzgA8DewHnpryKgdzhbh/g0nxfJ/DDiLi8gbFaG1u4AfbtSn+sXNZ0Q/84J0pmRertSGtAb9pUdCTWCo4B9iCV372y4FjMGpYoRcSfSY3EtnfM64HXj3D/cuDo//0Ms/qKSDNKx08uOhJrhNt7QAHTtxQdiVl7O7wXrnwwvedqu2cG1u46gGeSEqVgByeSZg02Jl3vzJrV2n5Yv9Vld2W1vAem9kOv1yeZFWp2Lzw0CHf1Fx2JtYKTgDuB24sOxNqeEyVra3/z+qTS6hes8voks6YwO+9Rt9TldzYKXqdkzcKJkrW1hRtgcgcc3FN0JFZvd3bDoJwomTWDQyekkionSjYahwH74TbhVjwnStbWFmyAoyfBOBdBl87yHhgXcJDXJ5kVrmccHDwh7adktiMizSpdAbhy2orkRMna1oMDcEefy+7KankPHLAFuqPoSMwMUkOHpZtSQwezHTkJuBdYXHQg1tacKFnbqlzZPLK32Dis/voEa7pcdmfWTGb3pgtUd28tOhJrBc/Kty6/syI5UbK2VUmUZjlRKp0VPRBen2TWVGbn99qlG4uNw1rDNOAQ4I9FB2JtzYmSta1lm2BqF+zeyG2XrRDLe6BzCKZ5fZJZ05jZ64YOVpsnAdeQ9lMyK4ITJWtbyzbBYZ5NKqUV3XDgFhhfdCBm9ig3dLBazQPuIu2pZFYEJ0rWljYNwqotcNiEoiOxeusX3NUF07yxpVnTcUMHq8W8fHtNoVFYO3OiZG3pls1pKt8zSuWztguG5LI7s2Y0uxcecEMHG6U5QA9wddGBWNtyomRtaVku/XCiVD6ru9PtAU6UzJpOpaGDy+9sNMYDx+NEyYrjRMna0i2bYI9OeLwXsZTOqi7YcytM8i6FZk3n0YYO7nxnozQPuAHwtS8rghMla0vLNqf1SVLRkVi9re6GA7w+yawp9YyDGRPc+c5Gbx4pSVpYdCDWlpwoWdsZCLh9s8vuyuiRDnio0+uTzJrZ7N5UeueGDjYalYYOLr+zIjhRsrazfDNsDSdKZbSqK916fZJZ8zq8F+4fgHvc0MFG4QBgf9z5zorhRMnajhs5lNfqbugImOrSO7OmVWno4PI7G615eEbJiuFEydrOss2pTn5ad9GRWL2t6oZ9+73RrFkzm9WbTj7c+c5Gax6wHLin6ECs7ThRsrazbBPMmgAdbuRQKkPAmi6vTzJrdj3jYEaPO9/Z6HnjWSuKEyVrK0ORWoO77K587hkP/ePc8c6sFcye6IYONnrHAZ24/M7GnhMlaytrtsDGISdKZeSNZs1ax+G9cN8ArHdDBxuFXmAOTpRs7DlRsraybHO6PWxCsXFY/a3qggmDsNdA0ZGY2Y64oYPVah5wLTBYdCDWVpwoWVtZtintCn+IE6XSqWw066VnZs3vMDd0sBrNAzYAS4sOxNqKEyVrK8s2wcEToMv/80tli9IaJZfdmbWGnnEwvcczSjZ63njWiuDTRWsry9zIoZTWdEHIHe/MWsnsXne+s9E7FHgcTpRsbDlRsrZx79a0eNjrk8pnVaWRgzvembWM2RNzQwf/3tooCG88a2PPiZK1jWW5xGOWZ5RKZ3U37LUVeoeKjsTMRutwN3SwGs0DbgIeKjoQaxtOlKxtVBIll96VSwCru7w+yazVHDYhnYQ4UbLRmkd6z7+u6ECsbThRsraxbBPs3wWTOoqOxOrpoQ54pBOmuXzHrKVM6HBDB6vNiaQSPJff2VhxomRtY9lmzyaVkTeaNWtdh7uhg9Vgd+Bw4JqiA7G24UTJ2sKGQVi9xYlSGa3qgs6AfT2jZNZyZve6oYPVptLQIYoOxNqCEyVrC7d4fVJpre6GqVugs+hAzKxmsyemW5ff2WjNA+4FlhcdiLWFhiVKkqZJ+r2kmyTdKOmcEY55hqSHJC3Ifz5c9dhzJS2TdJuk9zUqTmsPlUYOhztRKpVBYG2X1yeZtapZE9KaEydKNlreeNbGUiMvwg4A74qIGyRNBq6X9N8RcdOw4/4UEc+vvkNSB/BV4DnAauA6SZeN8FyzUVm2GfbqhL3HFx2J1dPd42HrOK9PMmtVvbmhw81OlGyUjgQmkhKlVxYci5Vfw2aUImJdRNyQv34EWArsP8qnnwjcFhHLI6IfuAh4UWMitXawbJPL7spotTeaNWt5h/d6RslGr4N0kugZJRsLY7JGSdJ04FhGblTyJEkLJf1a0pH5vv2BVVXHrGYbSZaksyXNlzR//fr1dYzayqJ/CJa7410preqGiYOw50DRkZjZzprdC/duTX/MRmMesADYXHQgVnoNT5QkTQIuAd4eEQ8Pe/gG4KCIOBr4MvCzWl8/Is6PiLkRMXfKlCm7HrCVzu2b01qWWROKjsTqbXV3KrtT0YGY2U6bnS9iuU24jdY80vqOvxUdiJVeQxMlSeNJSdIPIuKnwx+PiIcjYkP++lfAeEl7A2uAaVWHHpDvM6vZsnzJyTNK5bJZsH68y+7MWl3lvXmZpwdslJ6Yb11+Z43WyK53Ai4AlkbE57ZxzL75OCSdmOO5D7gOmClphqQu4HTgskbFauW2bBNMHAcHdBcdidXTmjye09zIwayl9XbAvl2woq/oSKxV7ANMx4mSNV4ju949GTgTWCxpQb7vA8CBABFxHvAy4I2SBkilpqdHRAADkt4C/Ia0bu9bEXFjA2O1Elu2CWb2wjjXZ5XKqm5QuOOdWRnM6IE7PKNkNZgH/KXoIKz0GpYoRcSf2cHSgYj4CvCVbTz2K+BXDQjN2shgwK2b4YV7FR2J1dvqLth7K/R4e3azlje9B/62AYbCF7VsdOaRWiKvYfQtlc1qNSZd78yKsmoLbB7y+qSyCdKMktcnmZXDjB7oG4K7/Ttto1TZeHakdspm9eJEyUptWd6bw4lSudwBbOrw+iSzspjek27v8DolG6VjgPGkRe1mjeJEyUpt2SboFBzcU3QkVk+VK4hen2RWDjOcKFmNuoHZwKKiA7FSc6JkpbZsExzSA+P9P71UrgHGD8E+3qDSrBT2HA+7d7jzndVmDk6UrLF8+milFZH25XDZXfncAOzbn1pimlk5zJjgRMlqMwdYDdxfdCBWWk6UrLTuH4AHB2DmhKIjsXoKYDGwr2eTzEplRo9L76w2c/Lt4kKjsDJzomSltTJ/4B7k9Umlshp4kDSjZGblcVBPurj14EDRkVirqCRKLr+zRnGiZKXlRKmcKlcOvT7JrFwebejgjWdtlPYF9saJkjWOEyUrrZVboEuwb1fRkVg9VT4Q9/GMklmpuPOd1Uq4oYM1lhMlK62VfTCtGzq8y3upLAamAROi6EjMrJ727YKecU6UrDZzgCXAYNGBWCk5UbLSWtnnsrsyWsxjdelmVh7jBAd1P1Y2bTYaRwObgOVFB2Kl5ETJSmnrEKzZ4kSpbPqBpcATig7EzBpixgTPKFlt3NDBGsmJkpXSmv40De9EqVyWAQN4RsmsrKb3wLp+2Ow6KhulI0gns06UrBGcKFkpVTYtnO5EqVQqH4SeUTIrp8p79sotxcZhraMHOAxYWHQgVkpOlKyUHm0N3l1sHFZfi4HxpA9FMysftwi3neHOd9YoTpSslFb2weM6YXJn0ZFYPS0CZpOSJTMrnwO7oYPHqgLMRmMOcAfwcNGBWOk4UbJScse7cnLHO7NyGz8O9u92QwerTeVzYUmhUVgZOVGyUlrpjnel8wCwGq9PMiu76T2eUbLauPOdNYoTJSudhwbgwQGvTyqbxfnWiZJZuc3ogTu3wIA3lbZRmgbsjhMlqz8nSlY6jzZy8IxSqVQSJZfemZXbjAkpSVrtznc2SsINHawxnChZ6ThRKqdFwJ7AfkUHYmYNVWkR7vI7q0UlUfJEpNWTEyUrnZVbUtek/V16VyqVRg4qOhAzaygnSrYz5gCPACuLDsRKxYmSlc7KPjigGzp9Rl0aQ6REyeuTzMpvUgc8frz3UrLaHJ1vXX5n9eREyUrHrcHLZyWwAa9PMmsX03vcItxqcySp4mBh0YFYqThRslIZDFjl1uClU7lC6Bkls/YwvSdd9AovOLFRmgQcgmeUrL6cKFmprOuHreFEqWwqHe+OKjQKMxsrM3pg4xDcs7XoSKyVuPOd1ZsTJSuVyuLf6U6USmURcDDpiqGZld+MCenWDR2sFnOAW4FNRQdipeFEyUrl0dbg7nhXKpWOd2bWHioXu7xOyWoxh9Qe/MaiA7HScKJkpbKyD3brgD06i47E6mUzcAten2TWTvbqTN3vPKNktahcUHP5ndWLEyUrlUrHO7k1eGksJbUHd6Jk1j6ktE7JLcKtFjOAiThRsvpxomSlstId70qn0sjBpXdm7WV6j2eUrDbjSBfVnChZvThRstLYMAj3bvX6pLJZBPQAhxYdiJmNqRk9cN8APDxQdCTWSiqd79xZ3uqhYYmSpGmSfi/pJkk3SjpnhGPeLWlB/rNE0qCkx+XHVkhanB+b36g4rTzurDRy8IxSqSwmbSTYUXQgZjamZuT3cs8qWS3mAPcDa4sOxEqhkTNKA8C7IuIIYB7wZklHVB8QEf8eEcdExDHA+4ErI+L+qkOemR+f28A4rSRWOlEqpUV4fZJZO6q0CHfnO6tFpUx7YaFRWFk0LFGKiHURcUP++hHSmuz9t/OUM4AfNSoeK787t6T/0NNcelca9wB34/VJZu1oahd0yTNKVht3vrN6GpM1SpKmA8cC12zj8V7gucAlVXcH8FtJ10s6ezuvfbak+ZLmr1+/vn5BW8tZ0Zc/WL3yrjQqjRw8o2TWfjoEB/Z4RslqsztwEE6UrD4afkopaRIpAXp7RDy8jcNeAPxlWNndUyLiOOBUUtne00Z6YkScHxFzI2LulClT6hq7tZZKa3ArD3e8M2tvbhFuO6PS0MFsVzU0UZI0npQk/SAifrqdQ09nWNldRKzJt/cAlwInNipOa31DkUrvnCiVyyLg8fmPmbWf6T2wth+2DBUdibWSOcDNwJaiA7GW18iudwIuAJZGxOe2c9zuwNOBn1fdN1HS5MrXwMnAkkbFaq3vnq3QN+REqWwW49kks3Y2oyfV4d/p8jurwRxgkLQ43mxXdDbwtZ8MnAkslrQg3/cB4ECAiDgv3/d/gN9GxMaq5+4DXJpyLTqBH0bE5Q2M1Vrcox3v3MihNAZJV0feWHQgZlaYSovwO/pgZm+xsVjrqG7ocEyRgVjLa1iiFBF/BjSK474NfHvYfcuBoxsSmJWSW4OXz+1AH55RMmtn03rSiYQbOlgtDiVtVO51Srar3B/MSmFlH/SOgynji47E6sUd78ysZxzs1+UW4VabTtJG5U6UbFc5UbJSWLkltZHVDucwrVUsIr1BHbGjA82s1GZMcKJktXPnO6sHJ0pWCiv7vD6pbBYDM4EJRQdiZoWa0ZPe4wej6EislcwhbVh+d9GBWEtzomQtr28I7ur3+qSyWYTL7swstQjvD1jXX3Qk1kqqGzqY7SwnStbyVvWl9rFOlMpjA7AcN3Iws5QogTeetdoclW9vLDQKa3VOlKzlrcw7yjlRKo8bScmvZ5TM7MBcVr3GM0pWgynA7sCtRQdiLc2JkrW8Fd5DqXQqHe88o2Rme3Sm7nfrthQdibUSkda5OlGyXeFEyVreyj7YZzxM6Cg6EquXJcBEYHrBcZhZ8aTUInytZ5SsRrNwomS7xomStbyVfS67K5tbSFcC/QZlZgD7drmZg9VuJrCStHm52c7weYi1tAgnSmV0G+kDzswMYL9ul95Z7WaR1rsuLzoQa1lOlKyl3TcAG4ecKJXJAHAHTpTM7DFTu+ChQdgwWHQk1koqnyO3FBqFtTInStbSVrqRQ+msICVLTpTMrGK/rnR7l8vvrAaVzxGvU7Kd5UTJWtqjiZJnlEqj8oF2aKFRmFkzmZovhq11+Z3VYA9Sm3AnSraznChZS1vZB91KC32tHCofaJ5RMrOKyoySO99ZrWbi0jvbeU6UrKWt3ALTemCcio7E6uVWYDLw+KIDMbOmsWdnuijmhg5WK7cIt13hRMla2so+r08qm1tJVwCd+5pZhZQ633lGyWo1E1gLbCg6EGtJTpSsZQ1G2lfjACdKpeLW4GY2kqldbuZgtat8ntxWaBTWqpwoWcu6bysMxGOLfK31bSV1vXOiZGbDTe1yMwer3ax86/I72xlOlKxlVa4supFDedwBDOKOd2b2v03tTnspbfReSlaDyueJGzrYznCiZC3LiVL5uOOdmW1LpfPdOpffWQ0mAvvjGSXbOU6UrGWtc6JUOk6UzGxb9stl1u58Z7Vyi3DbWU6UrGXd1Q+TO2BSR9GRWL3cCuwO7F10IGbWdKZ6LyXbSTPxjJLtHCdK1rLu6vdsUtlUOt65NbiZDfe4yl5KTpSsRrOAe4EHig7EWo4TJWtZdztRKp3KHkpmZsNJqaGDO99ZrSqfK55Vslo5UbKWtc6JUqn0Aytxxzsz27b9ujyjZLVzi3DbWU6UrCVtGIRHBp0olclyYAjPKJnZtnkvJdsZB5NOeN3QwWrlRMla0t3ueFc67nhnZjtS2Utpk/dSshp0AwfiGSWrnRMla0mV0oupTpRKw4mSme3IVO+lZDtpFk6UrHZOlKwlebPZ8rkN2BPYq+hAzKxpedNZ21mVvZSi6ECspThRspZ0dz90AHuNLzoSqxd3vDOzHZmaN531OiWr1SzgYWB90YFYS3GiZC2psodShzfcKY1bccc7M9u+vbyXku2kyoU4N3SwWjQsUZLUI+laSQsl3SjpX0Y4plvSjyXdJukaSdOrHnt/vn+ZpFMaFae1pnX9sI/L7kqjD7gTzyiZ2fZJ6SKZZ5SsVt5LyXZGI2eUtgDPioijgWOA50qaN+yY1wEPRMShwOeBTwNIOgI4HTgSeC5wrqSOBsZqLeYu76FUKstJdeNOlMxsR/br9oyS1W460IkTJatNwxKlSDbkb8fnP8PX0L0I+E7++mLgJEnK918UEVsi4g7SOu8TGxWrtZaBgPX97nhXJu54Z2ajNbUL1jpRshp1kvZTcumd1UIRjev/kWeBrictPfhqRLx32ONLgOdGxOr8/e3AE4GPAldHxPfz/RcAv46Ii7f38yZPnhzHH3983f8e1lz6AxZvgIN6YG83cyiFVaRZpSeTPsxGY2Xfrv/cdevWMnXqfk3xOiuuuRJmzmTqfqN/nXVr1zb0+Fk7PsRszN3VD2u2wLGTYJzXqVoNFpPKneYWHYgV7sorr7w+Inb4X6GhidKjP0TaA7gUeGtELKm6f5cTJUlnA2fnbw8DljXwr9IK9gbuLTqINucxaA4eh+bgcSiex6A5eByag8eheM0wBgdFxJQdHTTai7e7JCIelPR70nqjJVUPrQGmAasldQK7A/dV3V9xQL5vpNc+Hzi/EXG3IknzR5MhW+N4DJqDx6E5eByK5zFoDh6H5uBxKF4rjUEju95NyTNJSJoAPAe4edhhlwFn5a9fBlwRaYrrMuD03BVvBmnpwrWNitXMzMzMzKxaI2eUpgLfyeuUxgE/iYhfSPoYMD8iLgMuAL4n6TbgflKnOyLiRkk/AW4CBoA3R8RgA2M1MzMzMzN7VMMSpYhYBBw7wv0frvq6D3j5Np7/CeATjYqvxFyGWDyPQXPwODQHj0PxPAbNwePQHDwOxWuZMRiTZg5mZmZmZmatpJEbzpqZmZmZmbUkJ0pmZmZmZlY3kkqxy5kTJbMaSZpX6ehoxfE4NAdJp0nap+r7Unw4thKPQXPwv3tz8Dg0hyjJ2h4nSi1G0iskzcjdBP2GMIYkPUPSWuCfgYskHV90TO3I49AcJP2dpJXAW4CvS/oHKM+HYyvwGDQX/7s3B49DsST9X0mXSnqfpGk7fkZzc6LUIiQdL2kZ8CrgX4H3gd8QxoqkccCzgHMi4vnA74EzJZ2aH3fCOgY8Dk3lWOCTEXEa8APg2ZLOgkfHyRrPY9AEJL1a0h8k/aukJxUdT7vyOBRP0rOAtwFfAQ4B3ifpyfmxlvx89htp65gO/Ch/IP4HcJSk94A/EBtF0m6S9gKIiCHgBOCA/PC3gVuB50ma7IS1cTwOzUHS7pIOqXq/+Ttgv/z15cCvgJdLelweJ6szj0HzkXQC8E7go8CDwDslnZYf82fzGPE4FGdYAvQk4LsR8TvgX4AbgTdD617Y93+eJiVpD0knSBqf7zoe2C1/vRD4AvBqSfv4A7H+JJ1DOgE/V9IX893fAI6VNDEi7gauBbYATy8ozNLzODQHSW8mjcPnga/nu78InCxpt4h4BLgauAX4+2KiLDePQfOolL5nM4HLIuIPwLnAJcCH4dELO9YgHofiSfow8JFKVQewFDgdICJWA78GBiSdmY9vuVklJ0pNSNLZwDLSlZFvStqXtDnXGZL2jIihiLgG+C3w1uIiLSdJM4BTgaOAfwSOlvQq4F7gLuAV+dBlwGRA+Xkt9wbQzDwOzUHS3qRyx2OAFwM9kt4PbAb+CLwjH/oQcA/+XKk7j0HzyCeGX5ZUSUbXkEriiYhNwI+BtZLelY/3+1EDeByKJalT0meAJ5Iu4HxW0suBvwK3SHpNPnQ98DvgUEmdrTir5DfTJiOphzR1+dSIeB5wJ2nR+hDwHeDT+bjxpP+QHZK6Cwq3rB4G9gB6I+JB4HOkEpdJpNmLkyUdGREPA33ANGjdaeUm5nFoDvcDs4G985XZc4HHAScCPwGeL+nEfHLSDexeWKTl5TFoApI+SHoPuhx4s6R/Av4MLJT07qpDvwYcI2mC34/qz+PQFDqBJwNviIgfAB8D5uX7fkhaOzw5IjbkYydFxEArJqxOlJpMRPSREqVKq9fvkTLys0hrk06TdFJEbAWm5OdsKSLWsqmqY54EXAUcARARlwEbgcOAK4BFpJm+C4FTgGvGPtry8jgUp7qUpWocJgAXA08BiIi/AsuBGcBtwHeBT0r6MWmWz+NQJx6D5iGpE3ga8J78XvQhYF/gtcBHgDdK2j2fkPeT1sn0teKJYTPzOBRP0rh8rnoD8Px896XAOlIFyFLgJtLn81RSAjUArXkh04lSk5A0rupD8VvAiwAi4hbSh95UIID3AGdL+gWps4g/EHeSpJdKOqZyclipY46IlaQSluMkHZQP/zlwZkTcHxEfJ5W6XAOcEBHXFxB+aUj6e0knSpoMHoeiSHonqZTlYPgf47CRtO5lhqRj8+FXAU8FBiPiy8AbgF8CcyPiijEPviQkvVjSUSO8J3kMxtDwk2pJHRExACwBzsh3X0VaE/ZEUtnXpcCFSt3WzgB2i2zsIi83j0MxJO1bOT/N5XNDOWFdAhwo6aB8wf6vpHPVLcA/kd6zLgS6SEtJWpITpYJImlX9fV53VFlw+GdgD0nPzt/fQrpisndE/BB4E+k/3wkR8bOxirkMlBwk6TrSv+MHgI8qNc/olPQhSUcCPyN1VjsJICL+DKyvjFtEXB0R5+WyL9sJkp4u6Y/A60lrkD4gqVfSJEkf8Dg0Xv596JV0Hmk92DdJ5b5I6pH0UUnPIK2D2QS8FCAiFgJbgcPz97dFxHdzQwGrkaRZkuYDZ5Pfl5Q3U5b0cY/BmOuqfJGvng/mby8H9sslvwPAYtLFnJnAe4FfkE4Qx5HG0XaB0rYokyrfexzGlqSTJP0J+Cqp3JdcPnc8qRT4CmA88Nz82F9IWxYcFxFbIuJDwEsi4qyI2FzIX6IOnCiNsTyDsQL4hdJi9erHPinpZaQs/Qbg9Tl7Xw70APsDRMR9EXGJPxBrU7WQcD/g2og4iTRtvxvw0fyG+6OIuDGfhPwSOEXSeZJ+TprGv7Oo+MtCUoekCcCrgS9FxMmkDkWTSYvTBfzY49BYksbn34dO4PHAaRFxA+m9plIG/I2I+ENE3Ekq/Zol6YeS/os0w317QeGXQr4qC6mk7ieRtn/4NHAQ6YQP4Oseg7Eh6WRJvybNrJ4J6SKmpHmSnk6auVgOVB5bRjo5PzgiBiLiW8ArI+LsPAtoO6HqBP31pP/jlftP9DiMjXwx8pOkzppvJM0cPSvPLE0HeiLiVuA64OmSXiOpl9RM5q7K6+R1ky2tc8eHWD3kKeNBUv3mp0gL3l4k6dyI6M+HfSbSonUk/ZD04XmRpIlAL6m7l9Uon4x8CujKNfxH8dhi59uBz5IS1xMj4tqcUA1ExC/yzNM/AEsj4osj/gAblapx6CEt9nxT1fq6lwEHktrg3xYRt3scGqNqHMbnxPNeUm15h6RPAkdIuh64/BjHxgAADHJJREFUJCIW5oRqa0QslvRa4DRgSkScW9hfosUNe0/6Dukq7ARIJaeS+oCXSfpxRCyofH54DOovl9l1kPbg+XvSOpfdSQ0yHo6In5Mu4myNiEck/ZJUhfBOUpn8eNJsBvDoBQarUR6HcaTqgg8B74iIi4Ydthsw4HFojEp5Xa5uOoZ0QfliSbuR1gffAoyLiEuqnvafpM+PdwHvBn4aEfPHNvLGkss2GyvXmn+clJT+Erg5Iu6WNI+Urb8zIhZs47njSY0dDouIb4xVzGWS33y/SnqD/Q3phPwvwPuAJ0fE0nzcO4CjI+I1+funAmsjwldr62DYOPwaeA2prO580mZ0TyBdJZwNdEbE2/LzPA51NGwcLifV8P+etM3At0knKj8gNQR4cUTMyc87hTQOiwsIu1SGjcFvgZeQuji+g3SSOInU0Wsj0B0Rb8nPOxlY5zGon+oTQ0lnAPMj4tZc7vUR0onif47wvGOAc0gXdn6eS4xsJw0bh7OAo4FP53Ol00hrXx7JVR/Vz/M41Em+APMJ4MKI+KCkw0kJ6I3AycBKciVHRLyq6nkTI2JjLhXeWsYZPM8oNVCeIv4i6Zf8euAzpIz77oi4WtICUgvFFZWZpPy8F5NOSq4l1aX/ceyjL43JpCsjp+SrUA+QTsZvJl21ekVOZucDMyXtDkwEDiVN7Vt9DB+He4EXAM+PiC9UDsq1zx+UdCCpxM7jUF/Dx+FBUlfBtcDrSOUqNwMflvQCSU8jXVjYk7RXhu264WNQaf19Fank7tmkHe0fT5ppIlcVPI7U4c7qoOrE8Nuktao/A7bkGdQNkvYnJa3Dnzc5z/L9I9DRymsvmsEI4/Ar4GBSx7RDSLMYZ5Hef/656nkehzrJFwZeRCr7PUvSdyPiZkkvIl3UvCEivqa0fc0qSU+KiL9Kegtpa45vVp/Dlo3XKDXWEPDZiHhjRHyTdMX81KrHP0e6EnIkQD5Jh1QG4PVHdRBpkf8K0i87wJXA3aSTv+dKelUuiewl7dfzUESsjYgLI2JNETGX0Qjj8BdScvocpQ2VK6YAD0bEnRFxl8ehvkYYhz+S6sn/RFobdgiApP1IJyg35pKvi/JaSdtF23hPugdYDfwwIp6XL5LtR76YGREbPQb1M+zE8LmSDo2IzZGaKm2V1EUqEb5u2PPeQpoBJyL6fXK+a0YYh1kRsZ70+bACOCMiXgz8K/ACSUfl570Jj0PdRNrr6G25rP23PNah7j5Ss5gl+bg+0ka+e+bHL8rntqXmGaXGuh64tmp90tU8doWwMyJWS/om8N489bweeO2w+k/bdZeS3oSnRsQ6SctIXQQ/AbxU0gtJCet/QCqNCdekNsLwcVgEzAGm5itVryCVgn0ZPA4NNNLvw36kDQOfIOl7pHG5JCLuKzLQEhs+BktIs0z7Sxogbf3wKlLLb6uzPGP0toi4U2mfl38BXll1SGWj6yV5ZulJEXEx6cq5177UyTbG4Qzg/wFXx2PdTG8G/sZjF/cvdHJUX5GaxQB8AbhM0qkR8WtJtwHn50qnl5LWzn82P+feYqIdW55RaqCI2BSpRWKlpeUpwKr8WKXW9khSa8WFEfHaAsJsB38mLVh/DUBEXAO8GPgdcDrwfeAZEfG1/LhPzhtj+DjcAJyYHzuDVJd+WkScnx/3ODTGSL8PLyStz3gHqUX4qRHxscIiLL/hY3A9cALpM3ke6UT9WRHx30UFWHbDTgxn5jVgFQcDu0s6h9RqujLr7c3d62zYOBwi6ZRIzQSq17q8h7RNxOr8HCdJDRIRdwEXkMogiYh/I62v/zBpLfELIuKO4iIce55RGgN5DUwA+5Dqb5E0m9TlaAMwKyJWFBZgyeUrtj8HPpWvjlxHqqvtjtR17bJCA2wT2xiHraQduz9TdUHBGmgb47CZ1DWKiLiyyPjawfZ+FyLil6QTExsDEXGXpAuAD5LKjiAlq3OBhcALI6JygdMXbxqkahw+APwmIgYlPY+UJK0BzoqI+wsNsg0o7Rv2dUnPkXQuae+2nwCL2zVBdde7MZC7HHWRrtReSlo0vQ54d0Q8UGRs7UTSqcDLSR2lvhIRXyk4pLbkcWgOHofieQyKl08MhyRdTFqvdz9p5uLmiHAjpTEybBzWkS4iLwBuzdUHNkaU9kO6nNTo52MR8aWCQyqUE6UxktuBX5X/XBgRFxQcUlvKLdcjhrUZtbHlcWgOHofieQyKN+zE8OPhvdoK4RP05iDpn0ilju+Nx/Y6bFtOlMaIpANIu0h/zv/xzMzMmoNPDJuDx6E5VGb3io6jWThRMjMzs7blE8Pm4HGwZuREyczMzMzMbBi3BzczMzMzMxvGiZKZmZmZmdkwTpTMzMzMzMyGcaJkZmZmZmY2jBMlM7MWJmkvSQvyn7skran6/qoG/cwfSVok6R2NeP2qn/MDScskLZH0rbznEEq+JOm2HMdxVc+5XNKDkn4x7LUk6ROSbpG0VNLbtvEzZ0i6Jr/2jyV15fs/X/XveoukB7fx/O78vNvy60yvemyOpL9KulHSYkk9Izx/RX5sbtX3e1c9/gxJv5D02qp4+vNzFkj6VD7uVEnzJd0k6W+SPpvvf4ekOyV5c1szsx3oLDoAMzPbeRFxH3AMgKSPAhsi4j8a9fMk7QucEBGHjvBYZ503Tv0B8Kr89Q+B1wNfA04FZuY/T8z3PTEf9+9AL/CPw17rNcA04PCIGJL0+G38zE8Dn4+IiySdB7wO+FpEPJoUSnorcOw2nv864IGIOFTS6fn1/kFSJ/B94MyIWChpL2DrNl7jmRFx7zYeAyAiLgQuzPGsqH6OpKOArwDPi4ibJXUAZ+fnfV7SA8Dc7b2+mZl5RsnMrLQkbci3z5B0paSfS1ou6VOSXinp2jwTcUg+boqkSyRdl/88eYSX/S2wf569eKqkP0j6gqT5wDmSpku6Is/0/E7Sgfm1vy3pa5KuzjE8I88SLZX07ZHij4hfRQZcS9qMEuBFwHfzQ1cDe0iamp/zO+CREV7ujcDHKvu0RMQ9I/x7CXgWcHG+6zvAi0d4rTOAH40Uc47tO/nri4GT8uueDCyKiIX5598XEYPbeI1d9R7gExFxc/5ZgxHxtQb9LDOz0nKiZGbWHo4G3gDMBs4EZkXEicA3gbfmY75Imk05AXhpfmy4FwK3R8QxEfGnfF9XRMyNiM8CXwa+ExFzSDNCX6p67p7Ak4B3AJcBnweOBJ4g6ZhtBZ5L7s4ELs937Q+sqjpkdb5vew4hzezMl/RrSTNHOGYv4MGqWbH/9bqSDgJmAFds4+c8Glt+nYfy684CQtJvJN0g6T07iHdXHAVc38DXNzNrCy69MzNrD9dFxDoASbeTZoYAFgPPzF8/GzgiTYAAsJukSRGxYQev/eOqr58EvCR//T3gM1WP/VdEhKTFwN0RsTjHcyMwHViwjdc/F/hjVWK2M7qBvoiYK+klwLeAp+7E65wOXLwTs0GdwFOAE4BNwO8kXZ9nwLZnpF3hvVO8mdkY8IySmVl72FL19VDV90M8dtFsHDAvzxYdExH7jyJJAthYYwzVP394DP+DpI8AU4B3Vt29hrTeqOKAfN/2rAZ+mr++FJiTX/83uYzwm8B9pDK+Siwjve7pVJXd5QYRCyRVkrxHY8uvs3t+3dWkZO/eiNgE/Ao4jh27jzQTV/E4YLvrl4AbgeNH8dpmZrYdTpTMzKzitzxWhsf2yuG24ypSMgHwSmCnZ4EkvR44BTijsrYouwx4tZJ5wEOV2bLt+BmPzZw9HbgFICJOyUnh6/NaqN8DL8vHnQX8vCqew0lJy18r90XEByuJZVVsZ+WvXwZckV/3N6QSw96cQD0duGkU/wx/IJUdkpsyvCrHuD3/DnxA0qz8vHGS3jCKn2VmZlWcKJmZWcXbgLm5EcNNpDVNtXor8FpJi0gn+OfsQjznAfsAf82zNh/O9/8KWA7cBnwDeFPlCZL+BPwnqYnCakmn5Ic+Bbw0l/39G6mD3kjeC7xT0m2ktUUXVD12OnBRTny25QJgr/z8dwLvA4iIB4DPAdeRSgxviIhfjuLf4OPAoZIWAn/Lf+fvb+8JEbEIeDvwI0lLgSXAwaP4WWZmVkXbf783MzOzsZJbfc/dUXvwXfwZr8k/4y2N+hlmZmXgGSUzM7PmsZ7U6KEh+xwpbRL8fuDhRry+mVmZeEbJzMzMzMxsGM8omZmZmZmZDeNEyczMzMzMbBgnSmZmZmZmZsM4UTIzMzMzMxvGiZKZmZmZmdkw/x8OjG//WctE1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astroplan.plots import plot_schedule_airmass\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot the schedule with the airmass of the targets\n",
    "plt.figure(figsize = (14,6))\n",
    "plot_schedule_airmass(sequential_schedule)\n",
    "plt.legend(loc = \"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Schedule containing 6 observing blocks between 2016-07-06 19:00:00.000 and 2016-07-07 19:00:00.000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astroplan.scheduling import PriorityScheduler\n",
    "\n",
    "# Initialize the priority scheduler with the constraints and transitioner\n",
    "prior_scheduler = PriorityScheduler(constraints = global_constraints,\n",
    "                                    observer = apo,\n",
    "                                    transitioner = transitioner)\n",
    "# Initialize a Schedule object, to contain the new schedule\n",
    "priority_schedule = Schedule(noon_before, noon_after)\n",
    "\n",
    "# Call the schedule with the observing blocks and schedule to schedule the blocks\n",
    "prior_scheduler(blocks, priority_schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=11</i>\n",
       "<table id=\"table140635034310472\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>target</th><th>start time (UTC)</th><th>end time (UTC)</th><th>duration (minutes)</th><th>ra</th><th>dec</th><th>configuration</th></tr></thead>\n",
       "<thead><tr><th>str15</th><th>str23</th><th>str23</th><th>float64</th><th>str32</th><th>str32</th><th>object</th></tr></thead>\n",
       "<tr><td>M13</td><td>2016-07-07 03:54:20.020</td><td>2016-07-07 04:26:20.020</td><td>31.999999999999886</td><td>250.423475</td><td>36.46131944</td><td>{&apos;filter&apos;: &apos;R&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 04:26:20.020</td><td>2016-07-07 04:27:00.020</td><td>0.6666666666665577</td><td></td><td></td><td>[&apos;filter:R to B&apos;]</td></tr>\n",
       "<tr><td>M13</td><td>2016-07-07 04:27:20.021</td><td>2016-07-07 04:59:20.021</td><td>31.999999999999886</td><td>250.423475</td><td>36.46131944</td><td>{&apos;filter&apos;: &apos;B&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 04:59:20.021</td><td>2016-07-07 04:59:40.021</td><td>0.33333333333327886</td><td></td><td></td><td>[&apos;filter:B to G&apos;]</td></tr>\n",
       "<tr><td>M13</td><td>2016-07-07 04:59:40.021</td><td>2016-07-07 05:31:40.021</td><td>31.999999999999886</td><td>250.423475</td><td>36.46131944</td><td>{&apos;filter&apos;: &apos;G&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 05:31:40.021</td><td>2016-07-07 05:33:00.021</td><td>1.3333333333334352</td><td></td><td></td><td>[&apos;filter:G to R&apos;]</td></tr>\n",
       "<tr><td>Deneb</td><td>2016-07-07 06:54:00.026</td><td>2016-07-07 07:15:20.026</td><td>21.333333333333364</td><td>310.35797975</td><td>45.28033881</td><td>{&apos;filter&apos;: &apos;R&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 07:15:20.026</td><td>2016-07-07 07:16:00.026</td><td>0.6666666666665577</td><td></td><td></td><td>[&apos;filter:R to G&apos;]</td></tr>\n",
       "<tr><td>Deneb</td><td>2016-07-07 07:16:20.027</td><td>2016-07-07 07:37:40.027</td><td>21.333333333333364</td><td>310.35797975</td><td>45.28033881</td><td>{&apos;filter&apos;: &apos;G&apos;}</td></tr>\n",
       "<tr><td>TransitionBlock</td><td>2016-07-07 07:37:40.027</td><td>2016-07-07 07:38:20.027</td><td>0.6666666666665577</td><td></td><td></td><td>[&apos;filter:G to B&apos;]</td></tr>\n",
       "<tr><td>Deneb</td><td>2016-07-07 07:38:40.028</td><td>2016-07-07 08:00:00.028</td><td>21.333333333333364</td><td>310.35797975</td><td>45.28033881</td><td>{&apos;filter&apos;: &apos;B&apos;}</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=11>\n",
       "     target         start time (UTC)    ...     dec       configuration  \n",
       "     str15               str23          ...    str32          object     \n",
       "--------------- ----------------------- ... ----------- -----------------\n",
       "            M13 2016-07-07 03:54:20.020 ... 36.46131944   {'filter': 'R'}\n",
       "TransitionBlock 2016-07-07 04:26:20.020 ...             ['filter:R to B']\n",
       "            M13 2016-07-07 04:27:20.021 ... 36.46131944   {'filter': 'B'}\n",
       "TransitionBlock 2016-07-07 04:59:20.021 ...             ['filter:B to G']\n",
       "            M13 2016-07-07 04:59:40.021 ... 36.46131944   {'filter': 'G'}\n",
       "TransitionBlock 2016-07-07 05:31:40.021 ...             ['filter:G to R']\n",
       "          Deneb 2016-07-07 06:54:00.026 ... 45.28033881   {'filter': 'R'}\n",
       "TransitionBlock 2016-07-07 07:15:20.026 ...             ['filter:R to G']\n",
       "          Deneb 2016-07-07 07:16:20.027 ... 45.28033881   {'filter': 'G'}\n",
       "TransitionBlock 2016-07-07 07:37:40.027 ...             ['filter:G to B']\n",
       "          Deneb 2016-07-07 07:38:40.028 ... 45.28033881   {'filter': 'B'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "priority_schedule.to_table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astroplan.plots import plot_schedule_airmass\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot the schedule with the airmass of the targets\n",
    "plt.figure(figsize = (14,6))\n",
    "plot_schedule_airmass(priority_schedule)\n",
    "plt.legend(loc = \"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}