SMWLV-metrics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.13.0.sims-61-g3ee711b3+fffdf3dce1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import lsst.sims.maf\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import lsst.sims.maf.db as db\n",
    "import lsst.sims.maf.metrics as metrics\n",
    "import lsst.sims.maf.slicers as slicers\n",
    "import lsst.sims.maf.metricBundles as metricBundles\n",
    "\n",
    "lsst.sims.maf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Metrics are computed against OpSim simulations, the outputs of which are stored in SQLite databases.  The latest simulation outputs are avaialable at http://astro-lsst-01.astro.washington.edu:8082.  They're described in https://community.lsst.org/t/january-2020-update-fbs-1-4-runs/4006.  In order to run metrics against a simulation, you need to have downloaded the associate .db file.  In this notebook, we use the simulation database available at http://astro-lsst-01.astro.washington.edu:8082/fbs_db/footprints/footprint_add_mag_cloudsv1.4_10yrs.db."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "pp = sys.executable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gatspy in /home/jail/dlusers/knutago/.local/lib/python3.7/site-packages (0.3)\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \n",
    "/data0/lsst_stack/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/bin/python3 -m pip install --user gatspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gatspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gatspy in /home/jail/dlusers/knutago/.local/lib/python3.7/site-packages (0.3)\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$pp\"\n",
    "$1 -m pip install --user gatspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version\n",
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      "backports.weakref             1.0.post1\n",
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      "Flask                         1.1.2\n",
      "future                        0.18.2\n",
      "gatspy                        0.3\n",
      "h5py                          2.10.0\n",
      "healpy                        1.14.0\n",
      "humanize                      2.5.0\n",
      "idna                          2.8\n",
      "imagecodecs-lite              2019.12.3\n",
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      "itsdangerous                  1.1.0\n",
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      "pip                           20.2\n",
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      "python-dateutil               2.8.1\n",
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      "pytz                          2020.1\n",
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      "sqlparse                      0.3.1\n",
      "sshpubkeys                    3.1.0\n",
      "statsmodels                   0.11.1\n",
      "tables                        3.6.1\n",
      "tabulate                      0.8.7\n",
      "termcolor                     1.1.0\n",
      "terminaltables                3.1.0\n",
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      "tifffile                      2020.6.3\n",
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      "traitlets                     5.0.5\n",
      "TreeCorr                      3.3.11\n",
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      "xmltodict                     0.12.0\n",
      "zipp                          3.1.0\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'gatspy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-80fe8ceda18a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgatspy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gatspy'"
     ]
    }
   ],
   "source": [
    "import gatspy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "add_fbs_proposals.py\n",
      "footprint_add_mag_cloudsv1.5_10yrs.db\n",
      "footprint_big_sky_dustv1.5_10yrs.db\n",
      "footprint_big_sky_nouiyv1.5_10yrs.db\n",
      "footprint_big_skyv1.5_10yrs.db\n",
      "footprint_big_wfdv1.5_10yrs.db\n",
      "footprint_bluer_footprintv1.5_10yrs.db\n",
      "footprint_gp_smoothv1.5_10yrs.db\n",
      "footprint_newAv1.5_10yrs.db\n",
      "footprint_newBv1.5_10yrs.db\n",
      "footprint_no_gp_northv1.5_10yrs.db\n",
      "footprint_standard_goalsv1.5_10yrs.db\n",
      "footprint_stuck_rollingv1.5_10yrs.db\n",
      "index.html\n",
      "index.html?C=D;O=A\n",
      "index.html?C=D;O=D\n",
      "index.html?C=M;O=A\n",
      "index.html?C=M;O=D\n",
      "index.html?C=N;O=A\n",
      "index.html?C=N;O=D\n",
      "index.html?C=S;O=A\n",
      "index.html?C=S;O=D\n",
      "__pycache__\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls /sims_maf/fbs_1.5/footprints\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbdir = '/sims_maf/'\n",
    "opsdb = db.OpsimDatabase(dbdir+'fbs_1.5/footprints/footprint_add_mag_cloudsv1.5_10yrs.db')\n",
    "outDir = 'output_directory' #results from the metrics will be stored here\n",
    "resultsDb = db.ResultsDb(outDir=outDir)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What information is in the OpSim output?  You can get the names of the columns in the database by running the `columnNames` method on `opsdb`.  The database populates these columns with information about every visit in the simulated cadence.  In order to access this information, we will need to issue a query against the database, as described below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Proposal': ['proposalId', 'proposalName', 'proposalType'],\n",
       " 'SummaryAllProps': ['observationId',\n",
       "  'fieldRA',\n",
       "  'fieldDec',\n",
       "  'observationStartMJD',\n",
       "  'flush_by_mjd',\n",
       "  'visitExposureTime',\n",
       "  'filter',\n",
       "  'rotSkyPos',\n",
       "  'numExposures',\n",
       "  'airmass',\n",
       "  'seeingFwhm500',\n",
       "  'seeingFwhmEff',\n",
       "  'seeingFwhmGeom',\n",
       "  'skyBrightness',\n",
       "  'night',\n",
       "  'slewTime',\n",
       "  'visitTime',\n",
       "  'slewDistance',\n",
       "  'fiveSigmaDepth',\n",
       "  'altitude',\n",
       "  'azimuth',\n",
       "  'paraAngle',\n",
       "  'cloud',\n",
       "  'moonAlt',\n",
       "  'sunAlt',\n",
       "  'note',\n",
       "  'fieldId',\n",
       "  'proposalId',\n",
       "  'block_id',\n",
       "  'observationStartLST',\n",
       "  'rotTelPos',\n",
       "  'moonAz',\n",
       "  'sunAz',\n",
       "  'sunRA',\n",
       "  'sunDec',\n",
       "  'moonRA',\n",
       "  'moonDec',\n",
       "  'moonDistance',\n",
       "  'solarElong',\n",
       "  'moonPhase'],\n",
       " 'info': ['index', 'Parameter', 'Value']}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opsdb.columnNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As described in the sims_maf_contrib Introduction notebook, to run a metric against OpSim output, you need to:\n",
    "* specify what you want to measure (referred to as the `metric`), e.g. 5-sigma depth\n",
    "* specify how you want to project the output (referred to as a `slicer`), e.g. a map of the metric on the sky, or a one-dimensional vector ordered by time of observation, or a single number over the whole survey\n",
    "* specify the subset of the OpSim output that you want to consider (referred to as a `constraint`), e.g. only output for visits in r-band.\n",
    "\n",
    "Once these are defined, you collect them into a `MetricBundle`, assemble a dictionary (`bundledict`) describing all of yuor `MetricBundles`, associate your `bundledict` with the input simulation database and output results database through a `MetricBundleGroup`, and use the `MetricBundleGroup` to run your metric.  Following the Introduction notebook, here's an example that returns the lowest airmass at which r-band observations were made at each point in the sky, at roughly 1-degree resolution, using the footprint_add_mag_cloudsv1.4_10yrs simulation output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healpix slicer using NSIDE=64, approximate resolution 54.967783 arcminutes\n"
     ]
    }
   ],
   "source": [
    "# metric = the \"maximum\" of the \"airmass\" for each group of visits in the slicer\n",
    "metric1 = metrics.MinMetric('airmass')\n",
    "\n",
    "# slicer = a grouping or subdivision of visits for the simulated survey\n",
    "# based on their position on the sky (using a Healpix grid)\n",
    "slicer1 = slicers.HealpixSlicer(nside=64)\n",
    "\n",
    "# sqlconstraint = the sql query (or 'select') that selects all visits in r band\n",
    "sqlconstraint = 'filter = \"r\"'\n",
    "\n",
    "# MetricBundle = combination of the metric, slicer, and sqlconstraint\n",
    "minairmassSky = metricBundles.MetricBundle(metric1, slicer1, sqlconstraint)\n",
    "\n",
    "# Our bundleDict will contain just this one metric\n",
    "bundleDict = {'minairmassSky': minairmassSky}\n",
    "\n",
    "# Group our bundleDict with the input simulation (opsdb) and location for string results (outDir, resultsDb)\n",
    "group = metricBundles.MetricBundleGroup(\n",
    "    bundleDict, opsdb, outDir=outDir, resultsDb=resultsDb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, run the metric against the simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Querying database SummaryAllProps with constraint filter = \"r\" for columns ['airmass', 'fieldDec', 'fieldRA']\n",
      "Found 492149 visits\n",
      "Running:  ['minairmassSky']\n",
      "Completed metric generation.\n",
      "Running reduce methods.\n",
      "Running summary statistics.\n",
      "Completed.\n"
     ]
    }
   ],
   "source": [
    "group.runAll()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAF can plot the map of the minimum airmass, the histogram, and the power spectrum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting figures with \"filter = \"r\"\" constraint now.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data0/lsst_stack/conda/miniconda3-py37_4.8.2/envs/lsst-scipipe-cb4e2dc/lib/python3.7/site-packages/healpy/pixelfunc.py:1583: UserWarning: monopole: 1.23128  dipole: lon: 81.2218, lat: 79.439, amp: 0.24138\n",
      "  mono, lon, lat, amp\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting complete.\n"
     ]
    },
    {
     "data": {
      "image/png": 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BHMDhcOBwOOjRowerVq3ikUceISYmhrq6upAldjU+n4+9e/cya9YsSktLkVIyYMCA67bF7/ej1Wo5evQoRqORu+6666bt93g8/PnPf+a5557DYDDc9PyOKtcgJ0+exGKxMGvWrJue6/f7eeedd3j00UeJiYm54Tyooijs27ePadOmUV5ejtvtZsiQIdc81+l0UlFRweDBg9m2bRs5OTmMGjWKiooKsrOzr+inYOBYamoq77zzDgsWLMDtdnPhwgWmTp2Kx+NBr9cjpaSoqAifz0dZWRlnz54lNjaWxsZGjEYjeXl51NfXM3XqVN544w3VylUBVbmq3MmYzWZ+9KMfUV1dTVVVFdXV1ej1erKyssjMzAz9S05OvsJ6slgsoUChtWvXMn36dDIyMrh06RLDhg27piwpJZ999hnjxo2jqKiIhoYGpk6delML8fLlyzgcDvLz8zl+/DijR4++qTUkpWT16tU8+OCDoejem2G324mNjQ1Z3R2hs8pVURSAUJDTjSzo9mzfvp0+ffowfPjwm55bVVWF1+ulT58+FBQUMGPGjOveL7/fj9/vR0rJxo0beeqpp6isrESj0ZCTk3PNa1pbW6mvr2fAgAFs2rSJESNGMGDAAM6ePcuoUaOuOLe5uZm6ujqsVisFBQWkpaVRXV2NEIL8/PzQkqZ169bd1EugcsehKleVOwOPx8P3vvc9ysvLqayspKqqCo/HQ3Z2duhfr169SEhIuOI6RVHwer0YjUY2bNjAwoULsVgslJeXM3HixJCFeD2KiopwOByMHj2aPXv2MHHiREwm0w3rKqVk3759TJ48maamJlwuF/369btpG91uNydPnmTixIk4HI5QAFBH2LRpE2PHjiU3N7fD13RWuQZ59913mTx5Mn369OnQ+W63G41GQ1NTE01NTQwdOvSm1/h8Pr744gvGjh1LUVERMTExoeCsG1FUVIRGo6F///5s2bKFBQsWXFc5SymRUuJwODhy5Aj33Xcfn332GXFxcSHlebXSdLvdlJSU4HA42LNnD3q9ntbWVqSUjBkzBoPBQG5uLmvXru3wy4fKbYmqXFVuTywWCy+99BLl5eWUl5dTXV1NWloaffr0IScnJxTgcq0BzO/3c/HiRfLy8jh69CgOh4NZs2ZRXl5+RYTutQgOuGvXruWJJ56gtbUVr9cbioS9ES0tLZjNZvLy8jhx4gRDhw694fxke4Lu6UOHDjF58uROZTwKBgIFA6Y6SleVa/CF5GYvJldTXV2NxWJh2LBh+Hy+DrexpKQkFDlcWFjI6NGjb9pOv9/P+fPnyc/P5/PPA3vDjxs3LjTfez2ampoQQpCQkMDLL7/M97//fVpbW/H5fGRkZFzzGqvVypkzZxBCcPjwYdxuNyaTCZfLxfTp08nOzub111/vVMS2yjceVbmq3B60tLTw4osvcvnyZS5fvkxzczO9evWiT58+IYV6M9fb2bNnMRqN9O/fn82bN/PQQw+h1WqvmIe7FkElcfLkSZqampg9ezbV1dVkZmbe9FoIBPHEx8fjcDi4cOFCaKlKRzGbzezcuZOnn366U9cBlJeXc+jQIZ588slOX9tV5QrQ0NDAli1beOaZZzptodXU1LB7926WLVvWqetaW1s5efIkM2bMoKqqivT09A7NLdtsNnw+H/Hx8bz++uusWLECrVZ70xeDYMDUhQsXsNlsjB07loKCAiZOnHhDr4KUErPZzK5du0hOTubcuXN4PB4GDhyI2+1m8uTJrFu3rkPPlso3FlW5qnwzcTgcvPDCCyFlWl9fT3Z2Nrm5ueTm5tKrV6+bBsEoioLH42HDhg08/fTTVFVVhRIcdASfz4dGo+EPf/gDzz33HEIItFpthwZsCLgIjUYjBQUFDBgwoEOu3/bY7Xbq6+vp168fTqfzpu7mayGlDM2BdpbuKFcI1L8zruv2OJ1ODAYDRUVFDBkypNMKeuvWrUyYMIHU1NRQhqeO0NraSmJiIvv27UOv1zN58uQOW9FSSk6dOsWoUaMoLi6mqqqKe++9F6/Xe9N5dIvFQlVVFdu3b0ev12O320OJQNLT01m9erXqRr69UJWryjcDKSUvvfQSFy9e5OLFi1RWVpKZmUm/fv3Izc0lJyfnpgOUoig0NDTQs2dPtm/fTlZWFnfddRdms5mMjIwODU7BgfTLL7+krKyMBx54oEPLVq7G4/Hw+uuv88ILL3RYGV9NVVUVJSUlTJ06tUvX7927lwEDBtC7d+8uXd9d5Qrw8ccfM2bMmNB63M5gs9nYt28f8+bN67IVd+LECZqamrjvvvs6dV1wLl5KyRtvvMFLL72ElLLDrmq3243dbic1NZU33niDRYsWER8fj9vt7lA0cVlZGWfOnMHv93P69GkMBgN5eXm43W4efvhhfv3rX3eqPSpRR1WuKrcOq9XK8uXLQwoVYODAgQwcOJDc3NwOKzSz2YzJZEKj0bB582aWLVt2RcagjhCca3vzzTdZtGgRiYmJnbJ4gpSWllJUVMScOXM6ZLFci5KSEqqqqrqsVINUVFSQlpbWJasVwqNcy8rKyMzM7NZ8otPpZMeOHTz44IOd3l0nmD3K7XazdetWHn/88U4r6qAHYvv27WRnZzNq1Kibzs22x+v1otPpKC0t5dy5cyxYsIDS0lKysrI6dF/8fn8o2cWpU6dCSj47O5uZM2fy+uuvq1btNw9VuapEl3/8x3/kwoULnD9/nrKyMnJychg0aBADBw6kR48eHR4kXC4XRUVFjBw5ksOHD5ORkXHDdaE3oqKigkOHDvHEE0+EBtLOUl5eTnx8PCaTCavV2iVLDb5OjGCxWMjOzu5SGVJKCgsLGTp0aLe2eguHcgWor6/H7XZfd/nLzVAUhZKSEgYOHHhFYojOllFdXU1OTg7nz5+nX79+nfZG+P1+FEWhpaWFrVu38swzz3S6HkG2bNnC9OnTkVJSV1fH4MGDO3yt1+tl165dWK1WampqQhmlsrKyGDRoEL/5zW+6XC+VsKEqV5XI84Mf/IDz589z/vx56urqGDhwIEOGDGHQoEGdHuDOnz9PYmIiaWlp7N27lzlz5nT5rX3Hjh0MGjSI3NxcXC5Xl+YHg9bpiRMnSE1N7dRSl6u5ePEihYWFPPzww10uAwIu6d27d3P//fd3K0l9uJRrcXExTqeTkSNHdquc1tZWNmzYwHPPPddlN7GUkp07dzJlyhSMRiM6na7Tz4+UEpvNRkJCAu+//z6TJk3qULT4taipqaGqqopx48Zx/PhxhgwZ8jfLxW6E3+/n3LlzNDc3c+jQIbxeL8OHD8fr9bJgwQJV0d46VOWqEn6klJw8eZIf/ehHnD9/HqfTSV5eHkOGDCE3N7fT1pTL5eKzzz5j2rRpIeXa1cHM6XTyySefsGDBAiwWSyi5fleQUrJq1Sq+853vkJKS0qUyguU4nU6MRiNOp/Nv0iF2hvbrdrtLuJRrkK7MXV9NMODI4XB02d0dZOfOnfTs2bND2a2uR2NjI0lJSVRWVlJXV8eECRO6XNbhw4cZM2YMzc3NNDQ0MGLEiE6X0dLSQmFhIXv27EGj0ZCUlER6ejr79+8nLS2ty3VT6TRdUq5qfLjKNSkqKmLGjBmkp6dz3333oSgKDz30ED/+8Y9ZuHAhgwYN6pQiO3/+PJcuXcJgMKDT6VAUhSFDhnRJsVZWVnLx4kViYmJC7uPU1NQuKVa3283BgwcBePrpp7ulWCFgvWzevBmtVtstxRosa8OGDd0qIxLU1tayfv36bpeTmJhIXV0dGzdupLsv+ffeey8jRozAbDZTWFjYpTLS0tLQ6XQkJyeTlZWFlJIjR47g8Xg6XdbkyZOJiYlBo9GEguD27t2L3W7vcBlJSUncc889/OxnP+NHP/oRWVlZNDQ0kJ2djV6v54EHHuhUeSrRRbVcVUJUVVXx7LPP8uWXX2K1WsnPz2fEiBFkZ2d3yV3r8/k4cOAA06dPp7KyEp1O12UrFQLBTunp6VRXV2O326+bk7ajeDyeUE7fiRMndntv0KamJlJTUzuVNOFmhKuscFuu4W4jBJb7dDeXr9lsprm5maFDh+LxeLoc3Q2BaYLDhw8zderUkFXb1fKklHz++eeMGTOGiooKPB5Pp+Zm22Oz2di6dSt+v5+SkhLi4+NZsGABb731lpqaMTKolqtK57FYLKxatYrc3FwGDhxIfX099913Hz/+8Y+ZN28eOTk5XUocUFxcHNptxufz0adPny4r1uDge+TIERoaGujdu3e3FWtNTQ3vvvsuWq2We+65p9uK1W6388EHH+D3+8OidEpLSzl27FjYFFi48Xg8bN26tdsWJwT2lC0pKQl5ELpDZmYmQ4cOpba2lnfeeadbZen1eqZPn45Go+H06dOUlZWFttLrLEIIxo8fH9quL+i9OXToUOj57ijx8fE8+eSTLFmyhGeffZb8/HwOHDiAyWRi1KhRbN26NbQ3sMqtQ7Vcv4VIKdm/fz8/+MEPuHDhAgMGDGDEiBGddvVezenTpxk2bBi1tbVYrdYO5ZW9GVarlbVr1/LCCy+EJctNc3MzjY2NoQw64ZjPbGxsJDU1FSBsyygsFgtWq7XL61qvJtyWq5SSc+fOMXTo0LBlHwoGFxkMhrD0i9vtRqfTcebMGUaNGhWWel64cIFz587xyCOPdLssj8fDkSNHmDZtGmVlZcTHx3d484ZrcfHiRcxmM/v370dRFKZPn86vfvUrxo8f3+26fsvp0o/6m/larBIRzGYzS5cu5eTJk2i1WsaMGcPcuXO7nHkHAgNEXV0dOTk5NDc343A4urxMoz0lJSU0NjYyfvx4nn322bAN4E6nM7RpeTgGcCkl27dvZ8GCBSEF212sVisajSZsijUSCCEYNmwYNTU13XL1X13msWPHyMzM7NCuOjfDaDTicDhobGwMQ+0CDB48mL59++L1etmxYwfz58/v8gupwWBg+vTpQOBlSghBcnIy1dXVHd4ooT3BdeWTJk2irKyML774ggkTJoQi+tetW0diYmKX6qrSeVTL9Q7H5/Oxc+dOfvKTn1BaWsrQoUMZM2ZMl9y97XE6nQghsFqtHD16lIULF4alvjU1NSQnJ+NyubDb7WFR1ABffvklFoul28kc2mO1WjEajdfd/LyrnD17lsbGRqZNmxa2MsNtuUJgTnLdunU89dRTYdtoPJjQITh/HS48Hg/r16/niSeeCMtLlc/no7i4mKFDh1JRUUFGRka35neD1NfXh35PFouFpKSkbj1bdrudCxcusHPnTvx+PxMnTuTHP/5xWCzvbxHqUhyVr2lqauLxxx/n+PHjxMfHM3bsWPLz87s9sAQT32/fvp3c3NywuH7h68TpBQUFDBo0qEtv7tfC4/GEEgb4/f6wvrnv27cvdG+/6URCuUYKRVFYs2YNjz76aFg3K6+pqSErKyu0BChcbNu2jXHjxpGenh7WBP1r165l7ty5pKWldXoXpGthNps5evQoX3zxBQMGDCA/P5/33nsvLC8FdzhqQJMKFBYWMm7cODIyMqisrGTmzJmh/Sf37NnDkSNHAPjtb3+L1WqlpKSENWvWAIFMMydOnADgl7/8JR6Ph/Pnz4eWXWzcuJH//u//Dq1VHTp0KKdPn2bz5s0ArFu3jqKiIlwuF7/61a8AOH78OB999BEAq1evprS0lJaWFn73u98BcOjQIXbu3MmqVat47bXXGDZsGCaTiVdffRWAPXv2sHfvXgBefvllGhsbqaqq4s033wQC+W5v1KY1a9awdetW4uLieOWVV/6mTZs2beLLL79EURR+/vOfA3SoTR988AHTpk3j9OnT12zTrl27AHj99depqamhvr6+Q206ffo0q1ev7lY/XatNxcXFNDU1daufrtWmV155hTVr1lBQUNCtfmrfpl/96lcsXryYmpoa1q5d261+at8mt9tNU1MTv//977Hb7d3qp/ZtWrBgAWvXruXTTz9l+/bt3eqn9m0SQmCxWDhy5Ai/+MUvutVPr776KpmZmaHlPfn5+WzZsoXExETmz59PcXExKuFFtVzvAPx+P0uWLOHYsWPU19czbtw4xo4dG1pnKaVEo9GgKApCCIQQoc8dOX758mVKS0uZMWMGLpcLk8kUuia4J2r769sKTvgdAAAgAElEQVSXeaPjbrebCxcuMHLkSKxWKwkJCR2u082ONzQ0oChKKA3j9erX2TprNBrq6urYtm0b3/3ud6/IS9vR62/UJqfTicvlIjU1tdNtvpHMiooKduzYwYoVK7rU5hsdr6mpISMjA61W2+06tz9eXV3Nnj17WLJkSVjrrCgKWq2Wr776KhTEF446+3y+0L+KigqGDh3a6X66Vp0VRcHtdgOBHYIee+wxtFptl+5J+zp7vV4qKiooKCigqqqKSZMmMWrUKP74xz/efND5dqG6hb9tuFwuHnvsMQ4fPozJZGLixIkMGzYsbMs3zp49G0pIbrPZrruBdFfw+XyhdYSzZs0Ke7Lys2fP4vf7u52m72q6unl4R7Bardjt9g5vo9cZIukW9vl8VFZWdnobvo7Q1c3hb4bP52P79u3MmTMn7GtD6+rqKC0tZcKECWFdD+z3+6msrKRv376cOXOGvn37hs1tXldXxxdffMHhw4fp1asX8+bN409/+lNYyr4DUN3C3xasVitz5swhPT2d4uJiHnnkEZYvX87IkSPD8kMORld6vV68Xi9xcXFhVazFxcVs3boVk8nEvffeG9ZB8+LFi3z55Zfk5+eHXbECrF+/nurq6rArVghY20VFRWEvN9L4fD6OHTuGoihhL1ur1bJ169awuy11Ol1oZ56dO3eGLMNw0LNnTyZMmIDVauVPf/pT2O6LVqulb9++QCCRhEajwW6343Q6u112z549mTNnDj/4wQ8YOXIka9euxWg0hrwzKp1HtVxvIxoaGli0aBGff/45/fv3Z8qUKWG1coJbev3lL39h2bJlYYmqbE9NTQ2KopCZmYnX641INpm6ujrcbnfElrE4HA5MJtNtty3Y7RTQdDWRvOeKonDmzBlGjhwZ1mCkIMG8ycE14OGKqg7yxRdfYLFYmDFjRqe2zrsZDoeDo0ePUlhYiMViYeHChWzevDki9+g2QLVc71TMZjOTJk2id+/e2Gw2nnvuOR577LGwKtby8nLef/999Ho9zz//fFgVa/AFrqWlhdbWVrRabdgVa0lJCQUFBfTs2TMiirWsrIxt27YRGxsbkUG+qqqKLVu2hL3caNHY2Mh7770XkbJjY2NpaWlh/fr1YbeONRoNo0ePxuFwsGHDhi5lX7oRsbGx+Hw+ampqQvOh4WT06NFMnz6d5uZm1qxZE7byY2NjmTVrFi+++CKjR4/ms88+Iz4+nrvuuivs9+hORbVcv8E0NTXx0EMPceLECUaOHMk999wT9kXgJSUlGAwGsrKysNlsYV36EGT37t1kZ2czbNiwsJcNXwdHtbS0hNV93R6v14vFYiE9PT1i5Tc3N3d579ibEWnL1efz0djYGLH7L6XEbDaTlZUVsfIrKiro06dPWC3Aq2W88847zJ07t1uZmK5XtsViISUlhePHjzN8+HBMJlNYy9+yZQvl5eVAwI189uzZ286D00VUy/VOwWq1MmvWLHJycnA6nbzwwgvMmzcvrIrVbrcjpcTr9eLz+dBqtWFXrI2Njfh8PsaPH8+gQYPCWnYQs9nM+vXriYmJidjAfvz4cVpaWiKmWCGg/MI94EYTnU5HXFwcdXV1ESlfCEFWVhafffZZaElRuMvv06dP6HmKBEKI0LrVhoaGsFqAQghSUlJQFAW73Y5Wq8Vut4fN0hdC8NBDD/HSSy8xfPhwLl68SO/evZkzZ05E5trvBFTl+g3C5XJx//33h7aWev7551m4cGFErMlNmzZRV1dHXl5eRKI8AQ4ePEh1dTXJyclhn2uCQPRkRkYG8+fPD3vZ7TEYDBHdbcTlcnH06NHb3gqorq6O+HrJuLi4iN6njIwM5s2bh5QyIu7P4NKw4G8j3Gg0GmbMmIHBYGDv3r189dVXYS1fCMHMmTP553/+ZyZOnMgnn3xC7969efbZZ8Mq505AdQt/A5BSsmHDBl544QUyMzOZNWtWRKwwj8fD7t27mTt3LkKIiES8QmD3mvz8/CvWrYYbl8vF6tWref755yOiuCEQ7FJaWkr//v0jUn40uZ0Dmq7G5XJRX18f0dzLp0+fpra2ljlz5kSk/KDr+dSpU2RnZ0dkOiBoUZaUlGC1Wru1kfz18Pl8nDlzhm3bthEfH8/JkyfviN/LVaiJ+29Hjh8/zqJFi/D5fDz66KOhUPtwEpyPSU5Opk+fPhFTrMEBIxjZGSnF6vP5iImJYenSpRFTrBBwz585c4bc3NyIWksffPABM2fOJDk5OWIyosXRo0dJSkoKW1rMa9HY2MiFCxciqlzz8/MZPHhwaMok3P0fLE+v16PX6yMyzxuM7E1JScFoNKIoCi0tLaSkpIRNhk6nY8yYMfTv359jx44xbNgwjEYjFRUV3/pNAlS38C2iurqa0aNHM2vWLO666y6WL18eEcUKUFtby44dOxBCMGLEiIhZrBs3bqS2tpbRo0eTkJAQERkA77zzDmazOZSBKhJ4vV4SEhJ4+OGHI+6uHTNmTETbEk0GDBgQts0Wrkd2djazZ8/G4/FETIZOp8NkMrF9+3YuXLgQMTnDhw8nJSWFjz/+OGIu9bS0NHr37k1dXR0ff/xxRGQkJyczd+5cnnzySUwmE9nZ2YwcOTKs64dvN1S3cJRxu93813/9F7/4xS8YO3YsU6dODft60iDl5eWYzWYmTJgQsQhI+HotX0NDA6mpqRFbCxdM3eZ0OomNjY2IjCCfffYZNpuNWbNmRVROS0sLBoMhrJGd1yKabuG6ujrS09Mj+lLi9Xp5/fXXWbFiRcR+PxB4tmNiYpBSRuylFAJbzsXFxYW8MpG6d1JKPB4Pn3zyCffff3/E2lRWVsaaNWtIS0tj48aNzJgxIyJyooQaLfxNZ//+/eTk5PD222+zfPlyZs+eHZGBwefz4Xa7SUxMDM3lRHKge//996murqZHjx4RXWR+5MgRjhw5EnHFCjB+/HimTJkScTlnz54Ne9DJrWbHjh1YrdaIytDr9axcuTKiihUC6z0VRWHVqlU4HI6IyQkG/UXSggVCU0J9+/ZFq9VGrE19+/blX//1X5k2bRqzZs2iV69eoX2Uvy2olmsUaGpqYvbs2Vy8eJH58+dHdD4KAkrcYDAwceLEiMoxm82kpaUhhAhb/tTrEcwe5ff7Ixq5C1BQUEB+fn5EcvzeKu6kgKb27Nmzh7y8vLBt2H49bDYb8fHxEfUAAaE53traWtLS0iIaU+D3+3nzzTdZunRpRKclamtrOXbsGOfOnSMnJ4eioqLbLTJetVy/aUgpWb9+PX379kWn0/F3f/d3EVWsRUVFNDY2cs8993D33XdHTE6QkydPYjabI65Ym5ubefvtt9HpdBFXrBCYN4xGcFFDQwP79u2LuJxo09zczKeffhoVWQMGDIjIUrWriY+Pp6ysjA8++CCicnQ6HUIIPv/884itGQ6i1WpZvnw58fHxnD59OmJWbEZGBg8++CCTJk2ipqaGwYMHh7bFu5NRo4UjRFVVFTNmzKC1tZXHH388opGNwbfp4HZlkZwbAigtLSUpKSni60uDJCcns3Dhwqi87V68eJF+/fpF/IUBAutns7OzIy4n2sTGxkbN6u/Tpw+NjY04HI6IJvkAyMnJiYoiB3jggQeAwLTBoEGDIrahuU6nC60mUBQlopb59OnTmTJlClu2bGHu3LnMnTuX7du337H5iu/MVt1iNmzYQF5eHjk5OaxcuTKiihUCezyWlpYyatSoqAzWDQ0NtLS0RFwOwIcffkhtbS2pqakRl+X3+zl9+nTUcqcaDAYGDhwYFVnRxGg0kpeXF9Fo3vZUVVVhNpsjLker1ZKcnMzu3bu5fPlyxOVJKamursblckVUjhCC6dOnEx8fz1//+lcqKysjJkur1fLII4/w4osvUlhYSExMDEePHo2YvFuJOucaRpqbm5k2bRo1NTU8+uijEVd0VquVuLg4mpubSU5OjrjFWlZWhsfjiVgqw2tRXV1Nz549o2JJRmKP1hvx6quvsnTp0qhYQ9Gec921axcpKSmMHz8+KvIgev1XW1tLcnJyxIOpgvj9fg4fPszEiRMjOgcLgTXEycnJOBwO4uLiImpVer1e1q5dS0NDA3fffTe7d++O6u+vE6hzrreSZcuW0a9fP2JjY1m5cmVULMjt27dTVVVFWlpaVB5KjUYTNRdOQ0NDaOPmaChWq9XKG2+8EdU8qd/73vfu2IX2s2fPjqpiLSoq4sMPP4yKrIyMDDweD3v27InaXqeRSGRxLYJjye7duyktLY2oLL1ez7PPPsvSpUvZu3cvubm5/PSnP42ozGiiWq7dxOv1MmPGDAoLC3nooYcYMGBAxGVeuHCBvn37YjAYoqLsmpqa+Oqrr7jnnnsiLitIa2srNTU15OXlRU2m0+mM+HrTIGVlZUgpI5bX+WqibbkqisLhw4eZPHlyVJ5Rr9eLlDJic5PXkldYWMjo0aOjFvna2trKqVOnmDZtWsRlKoqCRqPh0qVLZGRkRDzJicvlYt++fXz++eeMHz+eQ4cORVReJ1Et12hTVVXFwIEDqaurY+XKlVFRrBDYdNxqtUbNijQYDFFNzXfu3Dn0en3UFKvdbufQoUNRU6wQcPXdyftiajQa/H4/Xq83KvL0ej12u53jx49HTd5dd93F5cuXo7Z+02g0kpSUFBVlHhxbgmNNpImJiWHu3LkMGzaMU6dOMXPmzKjN2UcKVbl2kWXLljFkyBAGDhzI4sWLiYuLi7jMgwcPUltby4wZMyIeGQmBbFIff/wxRqOR/Pz8iMsLUl1dHbVBGQKKLhr9157+/ftH7WXsVjF9+vSozUsCUfPktKepqQmbzRYVWUajkdGjR1NWVsaJEyeiInPKlClkZWWxf//+iC8NAnjkkUdYuXIlR44cIS0t7bZ2E6vKtZMoisLMmTP561//yqJFi5g2bVrUftA9e/aMag5anU4XtTlPCNzb5uZmZs+eHbW5SL/fj06nY/To0VGRBwEX2Kuvvhq1+bpbxblz59i+fXvU5MXFxTFmzJioKTuAcePGkZ2dHZE9Zq9HQkICaWlpUZMHRMU1HCQ5OZkf/vCH5OXl8dprr922qRNV5doJbDYbw4YNo6SkhBUrVpCbmxsVuTt37qS4uJjBgwdHzcI6evQozc3NjBw5MmpzSpFMLH49ampqIp4Y4GqMRiPLli273bLUdJr+/ftHPDfz1dTX17Np06aoyqyrq2Pnzp1Rk5eamkq/fv04evQoDQ0NUZGZl5dHbGws7733HrW1tRGXZzKZWLhwIbm5uRw8eJBFixZFXGa4UQOaOkh5eTkTJkygV69eLFiwICrWXDBy1WKxEB8fH7VgDYAvv/yS3NzcqL2tBgMoIp1e7kayo0VtbS1GozGq89i3Kv1hSUkJvXv3jvgSkvZEuz8hsCZVShlVuefOnSM7OztqiS0g8CLRo0ePiG9k0J7a2lpWr16NyWSioaHhViSdUAOaIsXRo0cZPnw4o0aN4sEHH4yam/Tw4cMcO3aM1NTUqClWs9nMuXPnGDFiRFRd0B9//DGFhYVRVayNjY18+umnUf+xlpWVRSXpwTeBwsLCiCa8vxYWi4WCgoKoygT4y1/+EjVLEmDYsGGYTCYOHjwYtSVkPXv2RErJqlWrsNvtUZGZkZHBk08+iRCC4cOHRzTJRThRLdeb8O677/Lcc8/x0EMPRS16VVGUUECPECKqFmtNTQ0WiyXimwtcTXDfx2gGwNjtdmpqau7ILElXc6cm7r8WHo+HS5cuseI/4hjuSuOP70VnKsVqtUZ0H+Nr4ff7OXLkCBMnTozaSz98vZGBy+WKSr5vCCx/evPNN2lqaqKiooKsrKyoyEW1XMPPyy+/zMqVK1m2bFlU11ueP3+eXbt2YTQao6ZYpZQUFhbSs2fPbinW/O+WXvH3yKv+Hvd0GQCDnr0ckrtr1y6G/D+Xr1CswePXKrP9sa6iKAputzvqitXpdLJ58+aoyryVlJeXc+DAgajKNBgM/J+fZWCutLI3vp45Syq5/6kq8p65zMNPRm6+MCEhga+++iqim6tfjVarZcqUKVgsFqqqqqImNz4+HrvdzltvvYXP54uKTL1ezwsvvMDdd9/N0KFDWb9+fVTkdhXVcr1WwVLy85//nFdeeYVly5ZFdW7M6XQSExODz+eL6jyV1+tl586dzJkzp8sKPf+7pZTrteT4/OjbeueyXkum30eCX8MlvUARgky/D6sQ9PRDqluwZnsxYx8dQJIAnYQGrYbUNjeXQRFU6jT08it4hMQuBF4hSFUUit/KJfe5y1z+c+cDyxoaGtixYwdLly7tUlu7isfjoaKiIurLcG6V5dra2kprays5OTlRlVteXk6JZR8PvTydXn4FvQS3kDRoNQxzCw6s7RMRuVVVVWi12qhvV1hUVITT6WTUqFFRlev1etHpdLhcrqiuE1+3bh2XLl3i1KlTjBw5MtLiumS5qsr1KhRF4Yc//CHvvfceS5Ysieq8o8ViYePGjTz//PNRnQdsbm7GaDT+zSbkPZ8vo+5PfQHQrKhCeTMbw/JKXEKD5s3A/pnKimpipEKq4sOu0WIVWnL9blqFhnqNHkUI+vjc2IWGZo2OJOknTvFTqTOS7HZTf6oO4905ZCpe3AgatXo0UpKm+FDgir+NUqFaayBdCbjMmzUBN5jyZja+lTXo3sgK1SlOKjhXBQZ014paYt7MiMatvCkulwu9Xh/1HKq30i3sdDoxGo1Re6Z9K2uIVfz0VLxYhRaP0NAidGQoHjRIarUGZjq8EVOwHo+HhoaGiO8xey2qq6vJysqKauxCSUkJJ06c4Dvf+U7UZEJgPv+jjz5i5cqV/P73v4+kKFW5dhefz8czzzzD/v37Wbx4cdTmEgAcDgexsbH4fL6ozp0AfPbZZ2i1Wvq/kYtL6hglm/lKE1hnmiXt+ISgVsaRjY0qAi8baSIQpFIv49AgSRMOGmXsFceaMaFIQZawUyPj0AhJkgjs8NEiY5Cltez63WnmvjyHBNxohKRZCbz9pgsHtTIOg/CTRNs1xKBFIVZ4aZExKFKQIexYMOJFS5Jw0SIDfZYj7fiBWhGLBkkSbloxkPhmD7xeL1u3buXhhx+OejDTnj17MJlMEd/I/mpupXJ97bXXWLJkScTWLl/98uRbWUOzYsJ8tBzbWTOznhtGKQkYhJ8E4Q49I4OVVhrbXh7DSU1NDadOnYraloxBFEVh/fr1PPjgg1Gf+/X5fCiKghAiqh63rVu3cvr0abZt28acOXMiJUZVrt3B7/fz3e9+l8OHD/PEE09ENYhIURRWrVrFE088EZWw+kvLXQxYFUPlchu2Vh9DNiTTvKKJZmnCLzWkahy0yBj8UkMPjR2f1NBKDIm4sGJEIkgWTiwyoDyThROA1jblZ8SHGx0eqSOlTdE60aNBYhJe7NJArHTjQ4NdBOSka2w0tSnnRFwoCOwY8EktScJJq4zBKHwkCydetAHljCBd2GiVMZjaFC4Qql+TEotEkKGxUqfEIxEIJP3+qOH4oHP0um8Q/VbFcmm5iwxhJf7NyGe9ulXcyQFNrhW1mGUCmcJKg4zDg5YE3NRUunDV2xkwJjX0PGuEDD27Akn/VZFzZd6KJUFBuV6vN6rBgQAFBQUkJSUxbty4qMotLy9n7dq1PPvss7z++uuREKEq166iKAorVqygoKCAxYsXR1Wxer1etFotiqJE3GI9+2zgfyEkiRo79a1GPl34Jg98vAy3ITCvLJDEae3Y/YEIy1itA4c/oKSCSKkhXmcNnROjceJSAoOVRJCotdLqTyBG40Qn/Nj9cShSixAKydpWLP5EGrccpeqLZkb+73nEaJw4/AGLWCP8mDQOPIoRP1piNE78UotXBvokVdtCkz8JgSRW68Duj7viO4kI1V+LH73Gg0sxoUgtBo0bvfDQUOomrncSiTo7Lb4khFCI1way+kRysIXA8qoJEyZE3TtxK5VrVVUVbreb/v37R6R814paLvt7INvFZ3oVAwm6VpqaDeDzkJoONn88RuHGLY3EaQPLSCKlYJ1OJ6tXr2blypVRnwI4efIkDQ0NkbTkronP50Or1eL1eqM6hkJgh7DTp0+ze/dupk6dGu7iu6Rco/sL/wYipeT73/8+u3btYsmSJVF/KA4cOEBCQkLYtuf64plA/V2KiRiNk9GrPRx9OokEbQtW/9dWcSvJ6Ew+Jn/0Y9wGH3Z/Ihr8SAR2fwKxWht2f8CNZ/cnhAYuDYFk8y6PCSEUtMG/2xRYrNZGjScbDX48SgxGjRO/1OGRBmKEk2pPYB4q5v5Z9JnkwO5PRJFanEocUgridS00enuiEX4UqcXhj0eiQaAQq7VR7c1EkdpQvQSSKv/XSy1MWjs2XwIOJT5QVz8oaNHgx6Bx0+RN5ej3/8CUVd/BnBaoi0b4idfaMLuz6U8TXzxjwC+1CBF4f9MJLyPf6n6Sfb/fj8Ph+KbuWRkxfD5fRHNFX/b3AMDqSyJB14LVl4Re46HW04vyt7ZgyEzD/52JmLR2nEosAkmdJzA/rxVeMleUEPtmz7DWyWQysWzZslvS16NGjbol2b90Oh2KovDWW2+xePHiqG6nOH/+fPLy8pgxYwY//elP+Y//+I+oyb4e33rLdfLkyZSVlbFs2bKozrFCwGIOLv7uriVTs7yVEld/pKJtK/vreQ+tzoVW48XrbVNCQqFm8xE8jTb6LZ8NgGxTWFK2KVGN92trVWpAKKHPBr0Ntyex7XzRdr4/dK1W675CPoAQ/lC9mo+eAyB10hA0msCg6/cbQ5+l1IbOl1KLlBqE5utwf6l8fa9C37erY/vjwXYYdA48vsDA+nWdFPx+Y6gcrdaDzxuLVusOtUVKLakx1QDkv3WtO397cCe7hUuXO6hy9UGn8eDxfR2UpxH+0HMopQaNxku8oRmrJ/WK58WotzHhL+HPR+z3+zl06BD33HNP1JWsx+Nh3bp1PPXUU1E3GNxuN0aj8Za4xbdt20ZhYSFLly7ljTfeCFex6jrXzjJv3jwuXLjAo48+SkxMDGazGb/fj8vlCmVaaW5uDmUiqampQVEUnE4njY2NQGBXDIfDgZSS6urAIOxwOELbUDU2NuJyuVAUhZqaGiCQvODy5cusWbOG5uZm/H4/Pp8vlLXHarXS0tICBNKNeTwevF5vKKdncHnDvif6sm2MYOf9Q/iqdjDNZ+14PQlYyzw4zB6kFFi/qsTj0GNv1GO92ITfb8BWaif+rhGk3TeZptNNuJ3xOBuh5aIdrzcO62UrjjqB1x1H86l6fL4Y7LUaWi+58XrjaPhK4mqWeFwmmk41oyh67LV+bOU2fD4TlvN2XBaBx27AcrYORdHTUqbHXuXA5zNhr7Dhdelx2/Q0FVrxuJNwVDuxVfrwehJoPmvBbdPjatFh+aoVn8+EtcyD0+zG60nAUliH16XB3eKntciKzxOPrdSOs1YGjn9Zi9dtxN3spaXYgcueTuMFcNbB5dVHqHz/NF5PAvZaDbbSVvx+A63Fdmx1JnxuA02nGwL1NEtsZQ7qrAMpKzRS8EBP9jyazbHJlZ3qJwikcDt58iQFBQXU19cD0NLSEkoyH+lnL1im3W7HYrEAgeVIbre7S89esE1erxePx3PDNtlsNl555RWklBH5PaX/XuKsasLtScR2sQFPiw+vQ0dzYRM+XwwXfr2FxmNlKIqemrMe7PVJeJ0KrV/V4vPE03TZ2Ok2daSfLBYLer2eurq6Lo0R3eknl8vFtGnTMBgMYW1TR/rJaDRy9uxZ3n333bC2qSPP3vTp05k/fz6rVq3iqaee4lbyrVWuf/3rXzl06BD5+fmhh2PXrl24XC4aGxs5cuQIAGfOnKG8vBwI+PX9fj9mszm0b+TJkyeprq5GSsnWrVuBgJVw8uRJIBCJG3wQgknpS0tLqaio4LHHHuPw4cOhH9cnn3wCwKVLlzh3LmDd7d+/n5aWFj4eN5C9e/fyyaPD+avRzoexLjzOJC7910d4651YL3ope2MbPms6tdtP0njwAh5HGsW//gB3s8Ba3ELZn/bgdaRQ9G/raTxwCV16P4p/vg6vQ0troZnqdwpQfAaqNxyg5Uwlfp+RC//2F3zueFo+q6HmvYP4rT2o/J8CWgvr8Frh8m/ewueOp/nIJcybT6H4jZT/eSe2c27cFh+XfrMZjysRy5HTmLedwVWj0Lj/K/Tp2ThLNZS+8gE+dzx1O87TsKcQnyeW0t+/h9OsYC+1U/7mVvweE3XbTlC/vwS/LZWS32zA3ajBWmSh/K1d+Nzx1HzwGU3HS1D8xkCbbAZazjRQtTaQwKD6vQNYTldi7N2Xsj9tx+c10XyyguqNR/C546l8ey+tZ2txW/QU/3IDXlciTUdLqPnwM3xeE2WrPqH1Kw+OOi1Hf/45u+dPuGY/2Ww29u7dCwQ2tS8uLgbg008/JScnh/79+4eSKpw9e5aSkpKoPHtHjx4NPXtnzpwB4NChQx169m7UJofDgcViuWGbWlpaQus+I/F7+jS3muoPTuHzmSj70ydYL9pwNSlc/M/N+NzxKB5B05Ei3I5ULr1cgNtcj73ESMnL20LP3odxzk61qaP9NGnSJE6dOkVVVVWnx4ju9pPFYuHMmTNs3749rG3qSD+VlZUxZsyYsLepI8/eiBEjiImJYefOnSFlfSv4VrqFjx8/zowZM3jqqafIzs4OZ506xIcffsj48eOvuw5u95wp3Lfr4Nd/z7sbPCYwOEFI8BlA2zaHpfGDKx70gfSBeExgCrzd4UwMXOM1gtERcIO54qnZupH0+2eii00ERQu6tk2JXfEQYwt85zUGygy6gz2mwN/BsgDcsYHzhQxca3CCFOCOC3zWegNleUyBa6Sg9OW3MeVmkDH/gUDdtd5217a96/kMoHeB1heQEWybKx6M9sB13pjAOYo28Fnn+bqsYL3byUXR4nfbEVoNGn3M1+2DwGeTNXCtqTVQXjRh0kcAACAASURBVPB+6zzg1wW+M9oDdQwea7tP9xXs7VC/19bWkpycHPUoTrj1buHGxkZiYmIitqvT/idz8PkCgUmKX4/0mAL97kwEox1fqxVdYkKg74LPePCZ1vjbPrtC5d2341jY6vbnP/+Zhx9+OOrbxEFAISUnJ5ORcWvWee/YsYOhQ4fSr1+/qMqVUrJx40aKi4ux2Wzd/c2p0cL/l703j7LrKO9Ff7WHM3SrNc+TJUvWYMmWbYwdCRnb2AacQDCDmWITuCTh5mZYJCG5L3ncRRLy1oVLEnIJ3PdwMCZOTG4INr4GAwbjAU+yZEvWaNnW1GrNarV6POfsoareH7WrdtXetc853RraMlSvs7pO7fq+7/cNVbVr2Pu0k7q7u7F69Wq8613vwooVK842prbS8ePHMW3aNOs+66Pr35l+IRzcj8RAQX3R0TsUpN4pyt0IpNEBXhKDBAmSR1GqI6I+kNImg/HIKz3ovFQc5iC1CeAdwwAnII0OgBPwihggSVgGLzcAwoS8ckOUlQLAjQWtw4BKDWgk+1yVGhCXQMKS4AMAYRnwxeAteXISw6l3CdmNDjixC9YhBmzSqCoMoL4aQElYBneZ0D8sCdl+KAZ2QGAOKgKzF4oBPtGZhGVwwnHiiQcxvG0flvz+Z0Q9ea0UpHyYCxL5wr4OTW1abohBHxD8CQOCKuCHuOVnT+CnN96EWx4XL4v/6TvW4ZZHns359qGHHsK11147Lh3deA+uP/vZz7B48eJzdmIYAB67/RIAyeAadIgBNOgEHQmx5WO/gTd95z4QJ+lkvVBcZ67wfeJr3jmobijP1gA7Hr/0pKcoijA4ODgug/uJEycwZcqU8/rsq0xhGOKf/umfMH/+fLz44otn4oNfDq6tknwcYNWqVXjLW95yLjA1TYODg3jppZfw1re+1Sj/6S3XYeq8F3B6z/VwYheEpb6MOwJ4NdEh0HIENxBBSksxCBUzPdZRAwkqIIyAMAe0ow5vuAPc4aCVEG6jBOZRxL2D2P2Fz+LSL38Rfm0iuMvAfHEgyA180FIMEA4n8kCog7gawEtouctUHYkhropB3auXEVcDEObADT1V34k8cIeBOxxu4OPIow+gPGcmpl6+Dsyj8BpikGQeBXc4CHXEAEo4SOyKQdePBR+XgVZCeCMVJVvQc9BSBDf0wTxxCMqJXRDqgJYjlY+rAbx6GZwxsCpN61RCgBO4oSfs2ygBhCOuhPDqZdByBBCelmv+kHUAIJ5QA4k9kNgFmyj2lby+abhx0/fPchSNLY334Ho+02O3XwJamwTUJ8KtVUUbGCoBPkCrDXgjYlYbT6jBG+4A82Owcij8Rx2wiojrWx577Kxh+uEPf4jrr7/+vP0es55eeeUV9PT04Oabbz7vsgGxf7tz506sX7/+vMsOggD/8A//gGnTpqGnp2esbMY0uLp/+Zd/OVaB453+crQE69atAyEEt95667jcScoN+F2/+ls4OPNSvLZ0EQ5NvQzeSCcafYvg1ysgzIETe3CoC8JJMmD5cGIxy3VDHw51wVwGr1GCG3kgsQ+/UYYTu+Aegz/cIeipAzcowY18MIeizCdh7o2/DsJ8g6dfq8Kh4g7ea5ThRj64y0Q5c8D8GKXhDriRBy8QszfucIAAJSkLgNcoCWx+jNJIVQ2Ofq0Ch7qgJEBHZQ7K1elwQx+EEyEXgN8ow409MJehNFJVGLygJDB4FP5IBQABCIdfrwg7xQKTG/oAAdzIB2GOqNMoiwHb4XAHXHTf/01MuuwqQUtdoWO9LLAkdgYI4HD4I0J3Worh1StwIw/co/BqQi73aDKwCjxeoiMcDieootQ/GQ510T1nBbpnr8SMfZvx5JNPntOZW7M0ODiIPXv24E1vetO4yJc/ZXg+tmEW7+rDvoXLABA4kY/SUAeGel7F0OatmDRzlbhRDEvwGuUk78OrV0SM1SpwIh9uUEL3rJVYdGz3WcHEGMOUKVPO+/PNADB9+nRcfPHF4zaDJoRgaGjovL9vGRBPYXR0dGDbtm3o6enBu9/97rGw+asxyR4L0YWYvv3tb2PPnj341Kc+NS4B9pCzCB0rL0J57XS4DSG/NFyFG6bH5N3IBxi0GZcP1JPlUABOsqdEuKPoOBEzRMIdMEfM3Lx6GXCAuBzAC8qgfoTBp15A78YnseI//zd4sQuHuQATM1adP6EO4AgsnDBwIgYaN/LB3BjgBF5QRujVUOmfCE5YMvDZsZUHxMshgt4TmDLlMjgdFThR+lgCLYUojXSk+aEOEE5UR2fwZLAewZN6l0Y6RJ0kcZeBliL4tQrioWF41U74DcHTYS7csAROmFoBkHYgPBVS7RN1pH044WJQHqmCcAdRtYHSSAeYQ0FdiT+ll9g2rLoN/dEMPLX6Vly347vFgfIGTZVKBVOmTDl/8mbvRrQ3vZEoBR0ICIEblkBicWNFYhclrf05kaf+82Sb4OlVt2P9zv84YzzLly9Hb28vyuXyuPQ/g4OD+M53voNPfvKT511+pVLBmjVr8Nprr+Giiy46748GXXXVVfB9H3fffTd+93d/F1dcccV5kfsLsSz88ssv4+qrr8add955Pn8DEM8u/xDcSHS0e354F6aueDMmX3IFHErEzA8AYWkeEFuNgLGVCMIBL3QQl0Qnz1xx0aEkRydTuoUoMozGCPt7UZk2Gw4lqtyNHIMfczlKNRdRlYpl5oyVZV2ZqCe++0HKhzBi8OQOx/6H74ZXnYCFN3xY4XNiB1GFGnoAMOxhS5wAbkysekt6vZxwwG04oBWm8Ml6UraND/WZ8p/8LnXSeejL+DY9CCPGrGFoXh8A4OanHmmq59lMr4dl4fGYOW1Y+uFUPgG8uoO4Km+iRDlzeS4G9URLHG/Z9e9nhINzjn/+53/G+973vvP6cgU9nT59+rze4GTTo48+ijVr1mDGjPF5zejXvvY1dHV1Yc+ePaONw18+52pLURThzjvvxI033njeBtanLns/nr/4I+o7PT2Ii9/925h8yRUo1dJZm193VeftBS68IFkiDRx13W+k9b1QlJfqrspn6SStvAYAIzt2oH/3i6hMm41S3YVfF+XlYQ+elNVwjYFE8ndDB0S+KCLpgHSZ5ZqL8ki6AOLGjpYniueyt/0OFr/lowl9Wkfq5zCiBkw3dlL9Qseqq94ZSjoAxs2AHziK/wtf/S8YeOkFUT9y4GgDohObzcAL0ute4CgdSjUPfpD6Q7eXrqvE42u23v/Du/Had8Qvd0zunnZeB9bXQ+Kc4wtf+MI5fVOTLRFGVMxs+59/hJM7xSl8L3JUOQDlJyCNYT2Wn151Zr/4QgjBxz/+8XEbWAHxe7PPPfecenHN+U4333wzpk2bhnq9Pi7yP/WpT2FwcBDvete7zou8N/yy8Be/+EX09/efU4M+9Ruzcd19x/DSrDsxNLOGrhPJYaKYAIMBnv7CR7D+c99DGenbY9xYzPQ4OMojHhxKQL00Lxu+mEl6cGMi7ry1DiEuMfiBg6gs/ruRk8yEgVLNVbS1egxCI3SeLoG5HMzh6Ogviad6fIaOAR/U42AuR2XYA2EEsUvFgBsTIOkPqcdRih01gLnJni1hBJHLUR4Rh7HiEoPfEINlVGbof/4Z1E8fxYIbP4TKsJfMPB2lQ3nEA+FAVKFqoI7LFJUhX9jRT+okM2k/cIyZJnc43MhcBZCzybjEUBny8aZPfhlwCcqDPpjLEbsclSHfwBB0xiglnSwDR3XQV/pUhj3EPgN3oTBST/AgPJ3Re6G45gUpPocRrHjr74DRCK52I/LSrDtBOLDmxL+MJewuqEQIwWc+85nzempUv8H16y5Wf/yv0YEpIIMEUZXCCx1jxaU66CNOVifcxI9+3c2t1ow1hWGIhx9+GO95z3vG5YX+ruuqFzOMx+NgALBlyxacPn16XA5XeZ6Ha6+9Fj/+8Y/R3d2Niy46+7+IpKc39LLw1q1bsW7dOnzqU586Z782s236x+CqN/BpMyiWwqM8huP6Rhl3iPpOk/42LjGUa476Lh7zTMv05Ss5wBFO1P6QnjgRnXpUH4Jb6QBJGrOchXLCVT6LV+H2TDl5GSYP6jFjyVbiHTnZg3p8GjNmrwEnHNTj8CKzc5HyuWPqo5avY0eVO9S0taRnljfMORSoDZ3A8In9mL7sWoWXOdxYepczVeoxcVNSd8AdgthncCiBGwONTopSQxw0i8pU1dFxFdnw5GsbMHHWJShNmpbYM12KdmNgVf+9AIDNc+/AVUf+Nc/oDNPrYVm4u7sbU6ZMOa+ztxcW/IZ6VJucrqF37ybMWf028d0S/3rMy8RcrrY/ztQ3L7/8MpYtWzau75duNBrn/VWvMlFK4TjOuD6a9NRTT2H79u3o6elpd//3l8vCeoqiCB//+Mdxyy23nLWB9dXqnQCAVzo/hj3lO7Fr0sfgicchk/chcLgRhxdyuDHQv/clbL3/8yhRT5V5UfIJuaLzIqDUADoGHbgxUK6LDwCUaw68SDzrXq4RlOsi74XyvQlc5fWPH4hre564B3sfuweEiwHCoQKj4MHVR1wTHUupIbCVGkC5xpPn7LmiLdWZoinXmNK7cwCojBD4AVAZ4SjXCFAPUEIJs6ddDi/kKDWA6jAxZOvyvVDI8wMhvzJCUK4RITtO9IplPa5kC1pBI+m9UNSNTh3HwL6tSn+HcnXdD5D4UOpGUBl2lD/LNaJunjoGXSGjzlAdEr6qjDAlr1wXGPVPx5CQ1ffyBvDeXuW/6jBHuS7wepGIqZe7PobqkIOXuz6GnZM/dlZi9vWUenp61Ovrzle6uuc+AECp4YIziuM7Hlf+IoyrDyDiT29bMn78gKBcc87KTc/y5cvPuw30xDnH3XffjaGhoXGR77ou+vv78d3vfhfjNbFbu3Ytent78ad/+qfnVM4bdub65S9/GV/72tdwxx13nJW7pFerd8KPgOTJEXgxEIpHH+FHQOSbh5C8GGiwGgZ7D2DWjEuNumEJcBjgUsGPk5QHJ0C5wRFUCMoNjrBMFD/qAswlKAUcyYqs4ifzkS9mdaoO4+CMwnE9uBQIy8IW5Ya4zpyER5nAjTm8ONVF6ijrS0xBhcCLhQ56XYnJpWm+v3sbdjx1F9bf8dWcnXQ7SsyRL/j6oZAj7RR7coaZ0mYn0/LwFwBQT+gDmLNEiQ8QusvEMreZpRAIKoKHH3I0OhwQxlEKU79Ln0ie0pcuFfwcJv7LsuRpJ/ghR1QiuXJpC4eJfFAhWD5yb5OobD+9Hmau45m2T0tXmLzQ3nU4LI0T5spzBvoqivDHq9U7saw+tqX8EydO4NFHH8VHP/rRMdGfjUQpHdeZM2MMhw8fxoIFC8YNw8mTJ3HPPfdgx44dWLx4cavqv5y5ynTixAl89rOfxTvf+c4zGlgPuGKmut+7E9VacnfLgFKQnJCNgEo97cAB0SlW6hynjuxE/Vg35k5ZCUAMTuWGqFQKofIOA6ojKT8pR/L1YqBjOM13DiZ7QtTEIen8KK1z8MWHcGrX0yhxT9UtNzg6hpnip+TVGCoaj1IgZpSlUNRxExyEC/x+mMwAI8FTDkKVWlpeqXPMmX4ZbvzQV1GpizqlQNRxaUonMbkxV7Zx48S+Na4G/Y5hk7Y6IsrLjfR/x7CQ3TnEUKmL8uf+7Y8RH+5RvvHD1BaStmNYyK7UUz91DjKV7xhmyg/VGjfsKeuUG1z5TV4vBRz+SITH//VTKNeYQSf9rsus1rjygywDgEP0juJAvUDSa6+9huefP3uvFRxNuuyUuEl57WffwLEtj6hYkX7vGBY+danwRykQMVmpp2WVGsMB906UwrHjmDlz5rgOrADgOA5+9KMfodFotK58juTPmzcPW7duHbfDVTNmzEBHRwdWr159zmS8IQfXz372s7j88svP6Mj3seA3UG5w7PfuRKkhOmQ/aXBy4PEDsRzpxqKjLAWizIuB4ORhNI4fVINQKUyXM2WH7kUc1RqHF3NV7ofJABaI/9UREXx+yFEdYcmb2jhK+mCd8PMDngwoHF7EMb06D50dM+EHYsDxohSfHNSkXqUQBg59EJCDkRfnyyo1ll6PgORRUVRqDCTmePbHfwVvsK7qSDnVEZYpg+It89URpviVGukeqej8mMqrsjDlIXlXRxhWv+lOVCfMQHWEKd4dw2neDzhclg7qepnEZ9O3OsLUPqscyCVv+V/YkWP15R9BKUrxyX3AjmGNR42letUYKjWRlwPrsWB8f+XjTNOMGTPO+SGSZunSgXsxd+XbMGf2lcYAKduRH2l+TuLKD0VbA5C0YZE/k5udvXv3Yvv27WOmP9NECMHChQvHbVlWYjh69Oi4DfAA8JGPfASEEPVjA2c7veGWhbds2YLrrrsOv//7v49qtToqht3kDnSMxKCuC5dSUNcFdQlKYQxOCJjjwGEMhHMEZQ+lkIIlB4Uc7Q6MRQFcT75IQX9MhKsyMka7S35FPGR5ozEA1y0BHRNQCmPEvgcvikEYEJU9uHHy+6qaTlJvACqv6y3/Szq9rhdRcAeIfQ9+EIM7QN1lOLD1IaxcfVuOnxdRxL4LwjkcxhD7AhPhPIfVi8SoFZY8+BGFQzmo5xj6Szo3pspGDmMIwhHEUR2dndNb2lTnJRPVls+kvlJ/m/31ZUT5LglKQzBG4ftVpX/WzkV5pQvliH1RxhwHsyrfLtTHll4Py8KMMYRhOG6HaQBgL/kI2NEeTJy80GizgIhdwrgq54QYsUQ9F14UgzkOIl+cmp/njX4f9vjx4wjDcFyXRQHxYonxfDQIAOI4Hpe3Vsn0+OOP45lnnkGj0Wi2VP7LZWEA+Iu/+Atcf/31oxpYu8kd6CZ3YMJgBDdmKAURvIiiFESo1kIQBrgxgx/GcGMGL6LoHA5UnVIg6OTn2Se/hFe2fQ9eRA0aeV3Pq4NCSbkXURAm3h0uy+VHL5M8JH/58cMYXkSxf9eP8NKzX0elLnSp1AJBS0VeypK6Sr0ln1IQgTAYdfTrutxSEMFhIl+uh3CY0MsfruHSFb8OAIpW2oRwbvJuRHComNGV66GwDUv4JTaSujjMtKG0Y7keKv4SX2/PFrzw1D8a9XQ6m290n0heWR9LGodyZUuHio5Z3qxIPscOb8FPH/pjxVPnpcv2wzg9cJWJG8K5uu5FlmPJF0A6deoU7r337OwhjzXNHLwLj33/z5S/dJ+W6yHKjVC1Iel3GaMyFr2IolqL4MUMxxujX+KdNWsWZs+eDUrHz4+UUtx7770IgqB15XOU5OEq+Rux45HWr1+PUqmEhx9++KzzfkPNXDdu3IhbbrkFf/iHf1h4NzR46v2YOO1+HMQdmHyqjonT7sdQ7/sQln3VaXFC1KzDoVwdbpDfFQAiH4sxDz1QGoFEETw/HeAJ52qW60UU1JMPqjNQzwEnBA5juVmUmNW5quNm6pEaMXuz4ZF0+htxmPZcnY5XnwFn87YkZ/RFecK50ulHj/xfWLbkZlx8yS2qTuynd4dSJ+q64I6wC3Mcq111+2XtDaS/jFeko+KhHVTJ2j47o5a0+kyGuQR+QBGVXXXzoc/AmeMY8aLwyMdBMtiL/Jf1Qxa7nncYQ9f0B3Iy9fR6mLm+HpIcDOVNCyBsW9SWZdJXEGQd5hIMTxTPiy4go5vB3nvvvbjxxhvHdfY63r/WA4gX64/XM7cy7dixA4888ghOnz5dNHv95cz1b/7mb7B+/XpjYB05/l6VHz7xPgDAUO/7MP3YsMoDYmYlZ1RixpTOkkqNWM1O/DBWsxM/jOFSqvIOY9i/84c4ffRllElZlalZHhX0ANQMBhANWuZ1XrJMypbXZV7OdnQeXkTRe3wXtm7+VzhIHz2RM0UvouksiDFDJ3mnLmV7EYVLkxlr8l/ikXm9XNfPYQzvvuHzWLr4BqOOH1CU6xH8QLMFpajUQkWnz6b1maosl3jdOPWNxO1FFOVGqGyyY/N9GD7drTD4QerHrB8kXdbeshMWs5u8DWTsABAz3URHyaev+yWc6NmsbCxvQMr1yIglfTXBWCGg5gqIvmpQNIutHbvNWj7eadOmTeN2iAUAZlW+jT3bHkT/4Z3GCojDRPuU8Sb9KP0kVn9C5WdAtK0Jg4HaJx9NuuOOO8Z9WXhkZET9UPl4pXK5jKeffhq1Wm3cMKxYsQIjIyO4++67zyrfN8zgumXLFjzxxBO48sorVVn96HvgMIaR4+9FcPjd8GOt86LmsmgppKqsHCSDZrIEJJc5S2HS4dJ0MNQ751IQwXVLKBEffqSVMXkggsKLRefqR5FaPiwHMVwmBrxSSOEyIcsYVKiZd2OGciAauj6gOIyhCxVMm7xIdM5RBD+KUB0RBwf8KB0ISiGFn+y9loIIXhynmJObhlIQqcHAZclSaUxV3mYHhzGMnNyHV/b+BB6FOQgl8nT7qOuhNgjHNK2T+EaW6zi8ODYw6PqUGyFKfhVl5infEZ760Y9SPNKWfpzapFIPVHm5ESo8fhQZNxS6f6X+yiYhRb3Rj0atzxi8S0GY01/6KotDDf5BrAbTchArfqUgQv3oewCIQVUOrK/HAbavrw9xHLeueA7TtImb4bi+sWog/Qyk8e9HkdpK0dtXpS6WUiv1AB3DYzuQ02g08MILL5yBFmeeKpUKpk+fPq4HmwCgs7NzXJfIPc/De9/7Xnz+858/q7Z4wywLf/SjH8WpU6ewbt06VRYfvDWtTBwQzlReP5wjU3q4h4G64nADAUfseWrgYa4Hh4p87CWHcCCWLMPGIMp+Bwix37NIvg5j4CDqLU0E3OAr8+LgSyzwuh4IpaquG4eq3KGx0o0RF/VgENXyRBDNRBxEfc/qQyiFw6nSWeYlTuq58KMQ1CvlMBalk6f34Wjvy1i9XPzEk66nG4egrgeXxqBu8ksk2kEp/W1NbiJDYlO2tJxn03WUvCTvrH3dOAQjLrjrKlsqH3klgDOBzyupugDgcKrqSpkSj5RhK8v6Xtcje1BM6e+6AE9jVNqCEVfpqZbKE/tGvrix4yCIfQ8dsx8E8Mtl4WwaPPX+5CY5PTZsiyl9O8VhDLHnqdUG6TcOgtKCH4xKfhAE2LBhA66//vqxK3EWEuccjUZj1Ic/z3aq1+vjioExhs9//vP43Oc+h8997nPZy7+4y8InT57EAw88gCuvvBK0+50AANr9ThDGQJj8JZNYfXdoDC8K4dDY+HhRCDcKQRgTeRrBoTFKQUPUYRReFMBhckbRgMMpCGfw4ggbtn4LW3b9R46v/Ei+Do3h0gheFAp+NIYXNlIcSd5LsACAFwVwWYIhbCTL1xRuHKa6cY5g5CT++aHfhEMjOIyqj8tSHKWgIcoTWS6LDZ0BwIsjhbMUBsmycqD46XnJS9rIYRSzJl2EKy5+R6JjCIendIRzeHGk9q68KEz5xhFcFsNlsaoj68ty6ZfsR9pHDmYDfftw74O/qexHuLhxkhhcJvSXMZH6KFC2l9ddKuRLuyuZNEptlthEx+iFDfxsw9+j5+hmw/fy49AYfqD5PvGBSyN4YQNeHIlPgkfaTK/vRaEaGPwoAuEMDqcoheN3WKUoPfzwwzh8+PC4Yti/fz9+9OgH1cAqfeswqmKEcKbiUT9B7MVxmo+S7QdwhD3vUp92UrlcHveBFQB27tw57kvDnHN861vfQn9//7hhcBwHa9euxf3333/WeL4hZq5f+tKXcN/XH8cNv/63mHzqtKrgxjFizweBdtjEceHFkTg4AwKHp4eI9BlFUTkBNw5A6IeMAIhHLhgH9Twwx4UfBog9H46aWTptzboUDo2/wxiokxwgYhTUce28OLPOnnU9XGY+RiTzbhyDel6ufLTpnh//Ht537Z9h0pSL1ACZlaHLapWyvpF2lL7QeUm/g1M06v3oqKY/syUehRE2JIyBKxuky8a5g2HJIxnSHhykqf1lkrx6B7rRWZmCajl97MGNYzDXVYfIpEyJ3eHMsFmWL7P4l4Abs2aZap1d6N/7DB7e/FX81q/+E5yLf1qI+VynEydOYOLEieP6OE4UReIdtyc/AJdG6gZWT45aaaBG32FLLFl9Ge0M9rHHHsPkyZNx1VVXjU2Rs5AYY+PyIwKvRxy1Wg1/97d/j8ce/xne+ta36pd+MWeujDHcddddePPSX8e0YyfgJnuZbrJ35TKq8mIPsa7y8s7TTe5GXUbhR4EaCCUvNXuII7hxnKN3GMOxYzuwc/+j8ClTZX7QSDEkszCJh3AONxL83DiGHwUqr8qlDno+wQiIDlTM+pjSeetrP8Bgf4+SIa97UShka/S6nbL20HWTeCQvZeMEr8zrWD609i8wqXOWuK7ZTOmRkSvp5D60bhs3SmzPqDFbyPKS+ko7ne7bjyAaUYN76lMxI/GjIPWrkhelOkmMVJObzKKlDIlZ6iAx6LxKTglV+OqwnIohSg0fSxn6rF7H4sbyMFQEhzM4nKl4dbjgq/LaT7tN7D+VYE9i85Xrsd+7s6hJndM0YcKEcd/j830fhw4dQqD9brCMAyCdkfpBQ/lKxqCKFxrDDwOUgjpKQR1+2EC1Ngjn1RvgvHpDWziuueYaXHbZZWdXuVEmx3Gwbds2nDx5clxxRFGEJ554YtzkO69cjwk9t6KrOh3/9m//dnZ4nhUu45h+/vOfY6iX4aLJS9WelBuHcJOlQZVnLFlCFUt6bpwMHlQeUkrLxLKgGIC8MIDDJN8IDhODhRcGcCgFoWJptgSCjuQX/Ahn8OSSHOcq74UBSCLPiwKF12HJnqnqOKmQwwV/KV/qIHVMB8MwlUcp/KTP8IOGskFWb0KpopE66Dpm+coyLxS4JS6HxQZGh8aoDR1FLRiEn6GTvNw4Ujx1feRSp7wpkfbI2tEPGig16sqOkpe6mYkjuHGIQ727cPT4NrixWFJV16Mo9XUYpLEg7az5z41DQ4dUhoyf2Nh7ltflQEY4w4NPfx5D9V61/Csxyjp+0DBiTCY9blJ7GgZmTQAAIABJREFUpUvQMu/GkbKDHzQUhlKjnt4k8eQgVEOcylz8yhGcGvoQznd6/vnn8corr5x3udm0detWhFH+hKq0j25b3dcySXvLculPScf23dISQ7lcHvclcgDjPmMExA1PqVQal5Pkzitied6lMT7wK/8V373v4bNyAzj+Vj3D9J3vfAeXL7xRPa8lOxEg7eAAqNkagHTmwJOZKovVIKvTuFpjMjo9Le/HITjnmN61AMvnXAuXxundLo3TAVQ7DadjlJ1qlq/OX9Lonb9NR3CGNy25FZM7Zlp5pTcSNJ19FWA0bBenMyob1qysEwPdePXIc+q7bkdZV/IEUn/oeR1XNq/0Sexo4w8Ab154E9YsFD8vps945ZJpkU9suPW6aoaZ3GDJ617BHudv3fh3mFGeqr7rsajjlTZ34yh/I6LHjIZFt53OV7dvyh+KlxwcyjvXpnzbnHGdSbrxxhtxxRVXnHM5rdL73/9+TFv0uPWa9Lu+tK5iLQ7TG9SCduha2p4tMcawYcOG0QE/B2n16tWYPr34DWbnIzmOg3Xr1o3rY1oAcNH01egd6sE3v/nNM+Z1QQ+ulFLcf//9WDNnLVwqnt90KU0eI0mW5ShNl3kphUNDlXejTAdGGdwoEP+12ZiczYnlSv2OVfAaGT6Or/7kd5K38qTLhw4N1TKdmIky+FEj5U/D5D9TfMWSXmCWJXlVN5aPEol64hNix/6f4uGNX05nkVynS/XWrzs0VHoRzpPlxSi1o0an66VwqcElOQgUR1g2bRXetvxDIEzwlfbX5WZ9IOmVjMRe6n+CVc/rPxeW1culFD/Zfg9ODx7MzJTjZHk/UvuaSt84XYq2xYmcqQNQesllYh2XWOJNlvmjEE/s/Few5LoX1lXs6LNul9Lk4EyslrGN2En8LOldSlVeLGun8aPik6dbGg5lAJKYTPw2t/uAqPfK9SjtXKfy5zIdOnQIe/bsOacy2kkbN25Ed3c33EWPqH13fUtIrPSImCJM9CvSjoTxxB/U2CrST7OXGzV0k+bvHy6Xy/jwhz98jjRsP9VqNXzlK18Z9+X6V199FQ880PxlKGcr+bvegvL2N6O841qAEPhx0g8yjmsuvhXd3d1nLOOCHlyfe+45zJkzB9MmzAGhUTqwxQGIPKBCIxAaASwGiZPOm0UqLx91cKM6CIvUdVWu82LJ6VUWiXLOARZjcnky/uDmr8GJxXKcEzdUnsSBkK/lCafiP6MAZ2ldFqm6ShdNL7A4rZvQE3A4sTg9vGbeOrxjpXi5O+E01TcOFE+VjxtmnlFlF8KosFeCRdqOxEE6sGo6OnHDwPuznf+K4/174UobsxhuVDcw6HYRHX5o5ZXDwhmglqBDuHEgfCFtTNNZw+KpK9DhdSq+Wd9nbZvaQi4JpnwNGSzFYvhD6qPZBXEDlIn9Yt0fMqZkXXCm7GXYQPOdGvCjusKrsOh1NXvJ2AfStw85cUOVgcXwQ7EUWqqf+98Z5ZyP++wEAObMmaPeq+sueiSd9SexKj+A8L0ec+IGKL3xJCyCF9TUdenH5Vt3t8TxzDPPjPvScEdHB377t3973N/WtGTJEnzgAx8453KcV65XPiKMotQQLxSSbWz5nGvxj//4j2cu54w5jGN65JFHMKW+VA0MAODEdRAuZ1MBCJedENXKQxAwOCxSgwQBU8Z1JC+N3qFBWs4i0Ukz0Tnv6Pk5gnqvanBKDotAwDRe2oDP40RmoOgcJsodNcjTVD6LUvlxIy1P9CUswt5jW+CCC6w0UHSKXs04gxRXglHag4AJbIlsnU7SGLbU5Ds0AGERFk9bgQl+V47e8Icun6UDgcGLx4KH6sRoOnBpg43uI8JjgSmqYemMS9HpV4wB0MCiyXWkTBkXNEzlM9NfqT3lYFg3/KT8CgaXAO9Y8UGNPrVlqovmLz1O4iQ24vRFBemNHtXioa4N1nUTV6KLKOMpPv1GjkVwYzE4lBsDxlLx2U4LFizAsmXLzhn/dtP8+fPR1dWlvgernktvQmScgxk2cmIx2Io4Ev2BE9WUTb1oRPFTN5Zt4JgwYcJZ0elM0sDAwLi+4xcQP6S+d+/e84KDaP2p7DNk+dIpyzA4MIienp4zknHBD67Lpl+mDUbJbI7FqsMhjKqGIA4cNEDknT3n6pk26O+xpRHccFjQJw1JzkxUXtv/7Bs5ChYHgq96Ri5KZIdJR05VuaNwUlVOODPKZIerZMryRA9ZR6ffefhpxFJvjc5hkdJPDF5SbpoH52k5NTGamCOFQXbQ0hbgHIwzLJq8FBP9jgJ6qtlGs4vmo5wtkoFM1I/haoONE9cTDJHyEaERGo3T+O8//i3lYyeDG2oWV1d2BueaHdIYUXGR4HHVTJ+avtFiTn5OjxzH3U9/TtkYgCnfiIXUnrq+aWwnNx+cGvFltTENctjBkeLgHIRndElwOVEN5Z1rUX1pDaovrVHPjp+NdODAAXz/+98/a/zGmrZt24Yf/CB9bKa8cy3geGlfog2w8mYFQHqzpbVdaU9xAK+hfCc77GZp4cKF4/4CBwA4cuTIuA+uADA0NHRWf4ZOnt6uvrRGlZXr/ap9p/1H0pY5Q9UtY9Wca/HYY4+dkewL9jnXoaEhPnv2bHz2xm+gRADuJM9LcgaevMGGsNgoB3GszwESFoM5PgDR2ck84VTx0vmqJPe3iKNk2WRKGarclrS6+lt5ePJmIMJpel1e03QhTLzJSWJhjq9w6/hs+ufwabZyWGTFzUHUDIsTV9mqv3YS//Tc5/GnN30l74MCnRUvTf+s3tlnjbM8dB+qtxdxwEHKq5neOVtm40nTVeeVjZ2srjEN0V/vw/QJswttkI0raY9cvMlrRWWcgUsbsMiIl+7Te/B/tt+DP7j+Cyq+JB8OAhAHDg1APdHROzQQvJK6YccUxCuetuswihQEARqNBiZNmnTGvM4k2V5aX9l6lRpU05tR+Z2l9iIuCItSW8vYyPpNi+PaVTutOHbv3o3t27fj9ttvP6v6/TKJ5Lx6A6pDx8QX4rQ1PhBO8fXn/h+coofQ19cH/KI957pp0yZcccUVKCVqq5kGYCx5ATD2n9QdfjJDSGeg6R6KwyK4tKHK9b1BRUfFLGh/7y7c/9LXTTnJzMOW1+nBmWi0Gl06U0mXsM09yDi962Lpcuah/n348cv/22oDQ3+5/JzhlZNPNRvIevI615Y35UwwsdXU8iT82Q1/q2xl8JW6ajpDo9fLjX3YZKlUztiyddV/7frQyAm8emwTAMClDUWv7J71l5r5xapM5tW+rLSr5MW0JXsdqzaDpiwGaGj6mrPCeDXODnCa1pV2lxgzttL3g/U9YSWXUwAsnf0mnYvCH8tHjOpwk0dU9JlyeaQXZyMRQhCG+ZPM5zvV63Xs3m3uiTbWmD+a7SarGvqWEGDuqRtbPdBXJuR2kNgm6nxxhRXH8uXLXxcDa39//+tiReH06dO47777zhq/jsEjxkofYPpHHyfSdgLcePGvYtq0aWck+4IdXDds2AD/6ETRkSSHjMT+nLaXpu2TyH0zAMmSmLbcxlPDyz0/SWPuhaUDsczP75qPG5e8K5WZobeVS3pjH1fDrQdBdi9Pp9c70inlSVg96wqlk1p2VAN7ZOisyvS8Za84V0ff+9VpkuXRE8NHsP/UTtO+Fr3kd92+RXvdkMvG2l5ilpfuQyeuox4N4+hQj8lfz2t7LKqD5OnBNZkK6XW7ahhUo032a08NduPxvd/Px5IlRuTerYwNw3+ZGC3a77XZMrVRGhOqXcSZg3xIlz4lT5nvfHEFOraIlx7I/5VtV6Nr42J0bVyslo+7Ni3BhBfEvmrXxsXo2rRE5eOnu/Dkk0+ia+NiTHhhWVpv05K03qYl6Nq4WNHIgcmos3ExqluvQHXrFaqufLY0K1PPV7aKtyH5my8SB4n23mTI8oIBAGJgzflXP7Smx6p2Y6T2xPWbzqTMpp+7/Wq8/L2jwqabV6nyytarDFzyAwATNl1SqF+WRuablXW+uAIdHR24xrslLU8w2mgAYMILy3LlHVsuU/nSznVWel2H8vY35/jOe+VyvP3tb7fqlJVp5LX6uq2zN5vWNhjX4cYNtaRPOMMlU5dj/74D2LVrF8aaLthl4Q996EPc3z0Jb5prOXyhLy86rghufclVX17VlnYNGs5zeRv/EyPH0FWaiGppQl6mXj8rx4KFE2LQcfmaviL5Gk0tGkGpNAEe59bl5SKd1W+4FuGzydT0445r3P3tO/0qTtV68eb563NL3TlbFvlBl6ulnC+zdBbMud+r1f3ShK4Qc1EqsJuit1xrltqSm5HJXV/4whLr3adfw/de/jY+vfa/5bYXFAtpn3b9QhyE1eko1Qtmtdm21kqWLs+in1EtY5+cnVvIpF7F2LuX1wGY8aL/vqvjq8N+Ep96RSrxtJtvyytJCUm2L6ghh9IQ33v52/jA6o8btg4rU1Fq9FlxFMaErd9rNxEHtbiGklOC53hpu27ig6zMuDwJXjhkr18QB0YbS9KJocOYWJmMilewFy3bdeLLs2IfSx/9Xx/5HfyPv/siPv3pT/9iLQvv2rULcybMVS8FUB+uLdslL14Q5TRdDtOuSxq5JKAO+STX5LF7ueRj0kd47uDjODJ0EIQFJo2BiaYvL9CW6QqxJh/xjJ02G8piZ5GieeS1B7Ht8LNGHZkMOhYYuHT9cvx5lJZL/ViglrrU6WgN85Ipl+CaeWst+kXNZelydF9m7VFEl8G8/dgLeKb7p6lMi31tdFneOcwFflX2ydQ9PtCNjT1PWq9l9cvpmqkPwIzFjEzlC93/KtbkjNqMuaZtRfcFM0+/ynrl2vFCfrZPLRjAz/Y8ZPel5gehX7KqoMd/gX2cZOldX8pWy+p6u5ZxHw7h6xu/JE4IJ3RGe5OH6nT70EDhVr5Qr6msQT6frWNWPlCHz5KX1jCx8uQ5Lj6w6mPGni44Q7l+0vSL1CvTfq2xbO2Donz9DO23XvyfODl8OG3Xeqwkcg3/Z2T6Qb+aKeb6D0ssqKVzrY0SFuHJ7p/g5MjRpu1Ojx/DPlm5zeyj95Esj/VNc689ox9yvyBnroQQr1KpRH+1/ksoOZ59VsoZQDxAHlCAA/C4eAarrssN7hhwSgALzTLJU9FrfGWZko1imc3o9XueHH0Gi44Z2Tu27KzAgsWGo8hWNv3AUjk8xqP7f4I5XfOwavqqNvRjpp9s9rHiQwtbOThdP4WQBpjVOTPDP+uXLL6MTXP5oviyl50cPoajI8dw+awr7PJz+ltiLcvfGoco0E+Udw8cxAMv/xv+aO2fW2ya9WUzX2QOp+VwOmZ5xn71OMSWY5uwbv5b7LNWVabFiIGpWayzjE7M7ifiASzEq32vYenU5XCIJot4mYHRZh+k/YPcasrNU1hBmY5XpG9v/ybevuTXML1jhqZHkV6t2mxG76JYyaWC9t80vvRYYJY4scVEC9y2ftjmc1v/oMeKYbsi/TyzLMP/rs3/C9HkOl577bUxzVzb+0mS11+aPWXKFJQczWkAAAdgEUCIyPMwLUeclDEYTjRSQsM5QFyANbR6LMeTswjf2/0feO+KD4Eo/rGFRsNXlGeRkMlZilXSF+nEAkEDho2HnsKqmVeh00/utIin1dX1izX9LDKbYrXoJ+UkPC+fsRplv5r3C9ftr9s9o1MRDZdYNfxW/wmaqldObaH71JCl6c0ik2eBz02fJfYnOtZEFgAQghmdMzGjc7p53WpTpvHM+jqrH0zd5Z4ScTP0ui0jjS4TBxKz41viVutYpS1zmJIyGQuqYwxzOlU9D+vmr83roOsnOz09vgybk2SAjARmKUvRabJ1zPJ6otfcCXM0HRNZtJ5gzeiXHUiA5MZbiyNSgtlBZ+mg6ScxlXDdwuvRpdpMBiscIUPGmhEzsOSzeltiTotPZUdC8OqpV9HpVzFv4kWaf2z8s7IyNx22mLBhtfovxL7T+9Df6MNVc65OeenxqQ+8Ok9bn6fbUacBM9uSEf8C3+LJi3G0a+zPuhbdxrze0/z58+eLHKfiAyQdCEvKovSa6lgirW763GH6nJq8zkyeBk1azliEWZ0zxcCqZPKMTF1OlJdl8MzQKZ1gp0cy6LAA/UE/OGukdCzQ6mpydCw5mVEB1iZ2ZDoNRdXvQKdXLbB/RlfdPsommsym9qcWfCnNz7t/ig2HnmlBEyGnl5IZFeDLy4JcMtXtl8TQlqPP43u7/8NihwJZgIaPZz4F9Hp8KHpLXXCtLONn5UtNR00Pwxc5OyZ5ZYc4r1NC34hq+O9P/7XFjhpmlo0vvW6Ch2l1VV7Kp8jHPdWui2tf2/T3GA4GTFnqeiYOWQSzM6ZpHtqNhxy8dXw5nFqcswBTy5PEkqsuX48ppvsyGxu2mLC01Wz7z8nhqEdDCGiQxmRhP9pOm9FsbvW1btco9SnnqLgeJpYmZGIXpp9byZLXWDN8UjZP+Wr4lk1dDqq9x3u06UJdFn7/bbfd9t3rh2/IXMjcZbfFzEKjz0ReLyl3oMSy7DQW/dtKtiUue/rWtntww8IbsGjyRW3TtJ1aHa6Qdc7U/2cDR5JCGiJiETr9ztHxLeLf7ABQE37dAwfwwO7v4o+u+ePW+EehX1O5uTbFwDjDUDiESeUmz7mOxifN6rY6xGjUzSwd6/V0WuKaA1T2lYEsys9WxYUC+Qz/e9d3sGbmGqycvjKP32qDTHscTcwX1S20Y/ttP8f/TPujtuKwCb4mcZirZ+G/s3cXvvHSXeCc/0IdaJo0ZcoUqNmbmq3G5l2lvJb9yDtgsPSO1CgL8vT69UTuwcGD+F8vfrU9OVmsRTT69SxO43qKj3OKv3/+78AZzeti42XFV5BXnUJ7NB+//DexaNLCFvaTmFrYJEvDY9EIdFtkP8m1V07txtGhQ23gjk2bFdaL8jQ5n2Tqcop6PIKRcKg9++l81fKl2RGLu/MW8nOxHUO9Szgrxxp/zfhnUyYeeZS3qeSZ1OmtncjEQYEdcjItduYMdv/BvJbViVNsOvI8hsPB1K6AJluLe/lcJGcAbWj2DaBmpEyuYOgxFWqfIpsDH155ezKwtmrzlvbYrF5RTBTYfPPRTXjswE9z+Fr2Ezk/Z9vWKPCB4eDAAfy/m7/WZpwX4MvFIfL4WtjvkskXo1KpYKzpgh1c5Uu3zSXEzHKdsdwll2EiO52qR9ujYRHmd87AJy7/GNRyi05jXZrILFno/PVlLB1Tjp5b6T+w4v0gRMOq65LVr4lOef3T5RqrHhk5Gw5vwHA4bOLTl4Nsy2UFOln1l52gYeu8n0bCgWSJK2tzi/11XeR/m82tMdHEvwAO9u/Hrt4dmXpN/GSzhb70ZdUjkzfKNP2sy8LUlJWlKZIlwLeJKV2C5ozhkX2PNG97OXzJ8p3VzpY4lzQGvmx7ZxgMBkGZtkeulhXtWw3psnWgYYkUP5WUfGTk2327+9RuHB48YOJr2uYzGJvavM04ZxEumbwEV85a0x7/bDvOxatFZqt+LMnP65yJT1z28TbivKAd2tqWvk3WZt/nOx6CIAAhpANjSBfqgabOzs5OpAcJtAMQMqmDNoB9ox8wToupzX+9kVgOJaklBIZaNIy+xmksnLQYucMDuY18/a7TMWXmMIV5nbI4SSqLcY6qmxxm0Q/IGEsd2Q19i0xAo8nii1vTEA/1aAScBwA6rFgNvtlDMvqBFF2mXkfliw78iGtXJS/UMGYgxuETTRZx8vrJfA6TxWdWLCJdNmMV0jtmmx0kRkfDwTT7yvtfhtzhDKWTfqBDOwRj2DUCwPM66Vsg8nBL1iZZWboOOcwF+JITpC6h+C9X/naidzqbTe1Q5JvMIR3bNonUWZ745Vn7mDF900VvtVxPMOT0S5JcINRpwEQ/Q9y0gwaELUlycKco3oiDIK6j6nomX6lr7nBX3FSn1H66nSyHw4w4F7x8B2CUWOxcEJN6mzEOaWXzep9q08mki1mMw0OHsWTKIs3uHsz4kH7S8Fn7luxhJs03WZ30PpqHIHBACAHnfDqAgxhlulD3XP9l0aJFd5QGxvfeoBE3MByNYHr1zF6TdaaJMorDw0ewcOKCccXxekq99VOoeBVMaGev8xymWlRHzGJMLHe1rnwOUz1u4GTt5LjHCOccJ+q9mNUxY1xxAMDRkeOYVpmCkltqn4gQINtnyn1Xzs38BZaGwxHU4hpmjrNvIhbhVP00ZnfOHFccALBv6ADiOL6Yc75/tLQX6sz16Nq1azFpl74ezqHdVrbIy8AnmbJW9TI0hCR3O0lZdjNf3bGNln8mSTnESRqtjT7LN8uniUyFVeOjZLWiN2X/aP+jeMu8azCxNLEN/Xh7djLsbNMpj28gGELJ9VFVb3nJ4Lf5SreD0YlaaAp9aqa+xmk04gbmTphdIF/TL8dfk52VmY29Qnwif3zkJJ45sgFvW7jewifr56xN9JiDWbdIDyBvUwCMU+zuew2XTlsBtBOvtsGsqI6tneTiGGoA7Kv1oas0Ab6cNeYOi3HTzoYcWxxlytUyPDdpZVmCb/OxlzCx1IWlUy5OMefsl/VJpr2Otu8y8Ba1jUwMFvYTRXGo+VTprdfV47iAzqafLc6tfWMLGltfm7HFa9v2AsCY7owv1MG1b8GCBZhzbDLU8mezB9KbnQ6zPijd4kRZkj8+chLbel/GLRe91Y6yaLBtdSpUf/g7t2SFHD7KKH7W8wzeLnEYdfWlpFHaoR2dZN2kfHLpNkytToXv+qZurTAV4WtG3wTfcDgCz/VRkbOSZrLakdOu/Qz9LDibxYQtDmU+S589SdkCX8Wt4MXjW7B86lK7H6z6t/CPbjsb5qxNkra6curSYj0Nm2svP1DyitpEQZwTB+nzs2b+dHU6ukod8IhlmbmVHu0cVyEEYpnYyZRx6FtYM6tT4RMXE0qdxe2wyP45O7Qb53nfHxw8jMFoBKunZX5vt52YLcIGtOczTde+Rj+eO7oZv7b4bXk5uk2a4WsndnV8xp55Suf7PsIwPFJMXJzaiJDXZRocGhqCOoUnG6D6yDL9xKX2yZ5oy9Y19oJYpgyqXodfxoKuOVo9mHQ2OUqWhjWLkYfmfyuvLD5aUDfMl7VrB0OvIj1io9xzHHB18CB/vRBTEb6cT2DaJUcj/j9x6Bm8fGp3a18U6NHU/i39m/rv+aMv4IHXHm4vJmxYVKdg6peehLTtr9swy0Mc7cY+ivO6nByOJrbkMSIa4L8988U0/oFMmw1zNM3boZ7PXFd4tL1tLfa+s/sB9NV77f5taZOs7lqSJ4SNpw5kNki/J3SEU7gOgeFHnrWfjg/I26TIPkU6aW2Q63WoSZPlUxQ/Re0xR2Mpz8R52fWwSO9X1d6uTVYBvlZ9i4FPO82t1Y3jBuI4BoA+jCFdqDPXwYGBAXA+HSS5m+E8AiEuYF0OAThnqm57iRrfBL25vNRV6sTyKYs1OTYaJ5Mn1ro2Ojt9Hp/rOLjlovUKR7u6inrCZu3Kal4PeHjfo7jpovWYN2EOdF9wTnN0rXDmZbXSjyRx4OBdF9+cDvJKPz0+snRUw1RcryimivJvmnU5rpyZLi/n67lJnmZ0krKyMRVZ6gFpp2uP/zSfLns152XGtMCatUc28Uyc67JEued4+Itr/wDtxJRunzRJOumz7DKorJNdJmUJz9TOn1rzGxnb6DZA5hpL9YALc16in6LObHvwWMOQyDfYx3jy0AYsnbwIq6YvR3ttTfePtIPejkfbD4g4WDhxbkbnRDfNljZMzfsEktEp0urq8ZfW6fAqWDV9mcJh63vT8ixezc5NY8q+1aDHb1+jD4wxcN5s2lucLtSZ68mTJ08CEMaQunMeaXmqrvHkzofzOJNPbSbrpfTNaTiP0Vc/jb95/iuqPP2vY2IGjcBFM3VjA4PM22SaeJnC9w+bvykegVHXi/UzMdFC/UwZre3IeYw7L30f5nbOVL4QH2qpa5ej428lK69fpK7tPvUKDgwessaEKd+ky9fT8UU5rMX4RL3BYAAnaicgOqbYuJbKstknMurm4yhW9QQ9z+DT6aVducHbrl8an0Wysh/BN6tfZJVFeYz9A90WnbL5WKOX/Kl23Z7Xy9J2pmNN4+Xnh55HLaplYoIbetljhyK96Yhy9OkHxv/UJjoOivcsfTsunbbU6rNsrOVl6jeQrduxzdYyPXbwaTx75AVLXTMmTFs3b5tmP2zzr9m/cM6wvXcX7tnx75nYLeoTs7Gst6dsO7W1rXyfqNe5+OKLMdZ0oQ6uh3p6eqDuRhEjvXNnYOqYuX7DoRsuVnRp55Gnl/85WCJDXJf0k8oT8OfX/K6GIZWjY+KI1fUs1vSOWl7PLlskMpHv7FL+DHesfDcqXkVcz2DVy3QsOr0NKzLlpp5mmbTVi8e34ejI0Rx93i5Z3qkdUrunNrHh1/2X1YlxBqoanilL92+WzrCDZmeb/3Rb2PTjiHGi1ovX+veZMZezJbPaMo1LZsEJA58Z89IGqX4ssYUZ2/k4tMWkjtXqv1xM5jteWRaxBjYeeylTN2vfjExus0leV0Nmk/Yn60Q0XYI2OuGkvg2/wYunvxRkG6zkd3OQSDt4ltA/e2QTTtX7muqX9wkzdM22GcN+mfYl+eo0jIe4ZvbluHz6srxNM31WVhd7P8sK209xXyj+XzZ9Oe5ceRt0m5tt1mx/jIcWrPY2I8oTn1jan457e+/LCALz16BGky7UZeFDPT09YDMpCNEHCIAk9wscMQgcYTQZ8JYlEg6W1NPoOYN84RUHU/QcZsNxiIftva9i6aT5qPhV7Xpm0JL0Os9EFs8GJmdmmYbZxGrippwhoDVUvUoea0a+tE9OZqEsEzfnsaqbve46RCwVSfqkrs4rh404Fv/l61qxZMqknBVTF6XXLf43O6EUKyGeMajrqRATZ4Iu0VW/vmzKQiybuigtU76QHXRot6VmYx1zNlaLfGHQZWeuNj9YUqFUVCaHAAAgAElEQVT/rZ1pa3oAKLslfOzS2zI3JpK/ZYA19MvfzOh5o83ncCJTznDjwmvVd5K8zD2vr11XljwHKcrMuFIHho3u1cnhk/XFgSpu+lwfpLX2k/N/Jo6zuFX8E6cwpmQaCocwsTQhjRfiFMan3q/BWK7WYFt0NbDq/U7CixAP3YOHUPUqmNkxvdD/Nh/n2g/MPs/si7UbJ2t8AQPh0C/eG5o45wMdHR0YDAZhHmRK7jq0vL4Ek61bSAOufW+2Nxpj/8BBBMlvSRo0uowC+brcQlkZHlb9OMPTh1/AsZETdh2b6N/MPll8qjxrH+3amunLMas6JaVP+OpYc9jakGm1axNfvnh8J37a/QyyS3BN6TL2bVcmkM4Ms9d6ho7i8YPPFfuiyC+ajZvFqqJvpp+42LQN2HRVHXMznK1smikbCobw4J6ftGfXrFxbu2gzZrNYGYvxPzbdBZ78fquafY/WPsL74DwUA5GGWZbp12w2vXrWKkyVz0Hr/tfiVmLL+b8drDKuW/j/2SNbcGwk2W6T8ae1X6tvdMxZ+7Swp9EuVT7G0ZETON3obwtzodyiONFlNolrcIay4+OOO+7AWNOFOnPFpZdeimN9vZhYnoDs3aNIaZkMVAICDv1ovKyTp9fr2WlE/l0X3wBxx8thk2mjaY5VyhJlAjMHmg3yoHjv0pubYLXJz8sstk8ed5F9NhzdguGwjpsvWmelz+rXXI48PGLqmseap18+dTGoWjZrz5ftxEIzrNxSPqHUifldM7V4aEdvm1xb0mh4BNO+5nWxtN5aF+N6wjOXz8hvFusErtE2PMfF0skLEkyjsQkD4VC8Ur42uXYsZtsk+M1LbwNItn8w+woZf7LckMlpYq80EXnASB6sAVd8CbRnOiE7eOALm+7CZ67+ODzHfJmFbrfR2iqlaa8PABhuW3pTIpeilV+LeGX7wfbpU7nXzF6FZrFsiyu73NHYRyQ9Bn5++AXMO5B5LGkU6YKcuQLAqlWrsPI/rUV+f4KpsuxeUNoQWIYOBo2NPlsu7lYZXjqxExuObjX4NKMpwlqEqRgzM2TtH+jB5uM7Cnnl5ef1ay4r1a1of43zGJdPX451cy+30uf3nez7S3o54xE4RCemY2XGfmqKTeYdIn6RppUvm9vKjslmk6I6k0pVLJm0oGlc5e1ZFJ/2GM3XNXXJLQs3tXuxrHZtZdgE1Cgvux4unbpkTPrpvJhlP93U3d4HSDrKQzCuD1gmf9kWZOyZemVl5turiFtzQNb/6/nfufx2eI5n2C0bV+3EXzbf2q8pb8oZHtzzKBiLCngVy2qFuZhG85G0G4tw944HENEAYv+6dVzZ2lORTWztTvcp035kYNa82fjEJz6BsaYLdnB985vfjI0bN6LVaUPxPTbqZE/aZmnyJ8fsMgBgQddsLJk03yizybTLt52iYxaZJkYbXdUro6vU0QYv28m4Ihvm6XQaWzkBx+nGUNu8xpK34c6WHRk+jqcOvdiUppld2rFfM77yc2DwCL760rdzPEeHq/WJz6J4SQXYdWnH1vZ2Y6dpZa+DQ0fwlZfuS67bY7kdPjq9rne2TOel0w2GI/jh/idhdrzZwSjvAxOXOeDZ6WPjw3hkyAxZgHrcaKpXHkv77bWVX2WiLMbszmnJu3TzWIp52ePR9KHdflb9AFw39yp4jmvwHU1/kLVXuzR6/sjQCZw8eRLr16/HWNMF+W5hANi9eze/9dZb8XsLbjvHkuSShz1xztEfDGFKZeKY6ItpkNC1T884h9P6YcQxpPYxHBvpxdNHXsIHLrl5TPRnKv/1lmT7ImPyi/2gyFjoDw4exYP7nsAfXvGRMcpshqUdnGmd9uL0XPvctmzZrA4rKM9eb3e+IuofHzmFnx/ZgtsveXsTHDZcZ9M2Dhino+w7zk2brkUNOMRBxWv1vudzGx8/PvAsNvTtxPDwMJB/+LmtdMHOXJctW4bBwUH0B0OFyy7Zv2bXbPXEf1pYBwAop/jGju8hYtrzVsafnb45vsxSlCaviCZiMf56w12gjBr18nlaaIti+9Bcvoh+Vuc0vP+SmwzMUofR/qU6t29D+RfSCD868Ewbti7WpcjmzW1l+qpBA7zct78tOrvdaY5n+1j0Jdk8vV1vBru9aUF5q2tmnf5gCPsGDsGWTFzRKOxU7AebXwCgZ+gItpzYZeibry+vZc876OUmfdFysX3JX7SX2y+5WZW3Gxet0ujsxPDc0a14eP9TOdsV8xp9mzR5USvvnX178cShFwrpU1ytbVVsi/awf/rTn25p52bpgh1cHcfBTTfdhCX/+S3QByAz8MxATw1uC/p8vVa8ODhch+AzV98hXvunBY2NpnmeQR9QpXwdSx5/itFzXPz5Nb8Jx5EHAZhxPat/M15ZXPnUnP4H+57CSDSiBbJtLzcrJ5/XG3+e3o5LJodwTPA7MuUpva5r1u7ZAcaGMbuXmMcteEYsxqv93RZZdlzFsXEmWDiyj4kVYbENyM1wNdclTz8U1dAzdMzS0RX5o5msIoxSl+wNHlUf3/FR9SoFcpGj1+PYpDHrpPnsmQD7DfvmEy/j1dMHR9F36HiyA0XzmDXzZqyvnXM5bl745pwupg1b82rmr2xs2fS9etYKvGPRWtjpeQZXMx31m57R9MMiPde7A3PnzsWZpAt2cAWAd7zjHXjkkUcA5Pc/mjlc7gOYSet4Wxx+Sfe1RPn+gcPY3rsns39TvImfymdauY4DmWvNdZF1euv96ii9zjd/8KC1jnk602bNDugs7JoJRz5XZ2A098aKZRblTV7StrYDDa7j4i1zV+fsldXLdigjTc1wZfXK8+I8RlepivcseeuoeeVjg2UwNtfL6DwlHW9+oKn1YGmr23oA1HHP65yKGxZcXahj84Hd3kbztinGJffVZnVOxvKpC6x803aejwlxsM5svwY9qBpY9cG86IZ9amUSukpVq142P+Z9l8rX9zdb8UrLRNrVt9eqV1HMZW1j7qk280u+XOYpi/Cj/c8iZqFlr9YuPxsHZh+fj610X7W47xmJ6mCM4cMf/jDOJF3Qg+s73/lO/OQnP0FE7a+Ks2+EF2/At3swI3vddzyUk1+BKT5EYZfVrLzZgQ5zkBF1jo30oq8xUKjDaHRshbsZr8umL4GvHUjIXi+yTzt5G62NP+cM/9+2B3BspLctXjYbjAaLjRcAMEZxz47vg3E+Kl6t6o7WRjzX6bSjY/51ju1+Unqzve3o3Yfnj24rlNssDu06tsJoP5Dz0N6fY/OJV6w+M8viQjwi2XHZ+JoHwRgAB/O7ZmF259TCtpTVPVvWqr0W8cqW7Tq1H1yLUVscFPmk3fht5VPGGSb4Vbjae4CL9G1mo3auN8P07OGXcPPNN2Pq1Kk4k3RBD67z5s3DypUrsaf/YEENsxHarqezyaKZS/M85zHmTZiBJZPmg1oaXqu7+tYYTbzN6K+ceQlWyjcTFehrL2sPo32Gnef7xKHN+NnBTS3qtvZNK7pWuO9Y+Q7MqE5OZIzGJ81wZeU2w8NACMGvzFkF/R2orWMhy9esl12BsCV7POu88j5tJ9+Ml12+iWHOhGlY2DUrU97cntmbSBvfdn0qy965aC0unXqRlW9z25m4igbWLF+bDTnn+Jvnv4FGHBa+YN9O16odZmO8GQZR9oFLbkTZK3rtQbO4tNXL5lvxSvPr560ZA680b66yjabfSvNPH3kJjUajQGb76YIeXAHg9ttvB79mJoqcZe9Mi4KkHXp7/r7dP9YG+Wadbrv5djpwM884x13bH0zemZqt24y+XYztXV8/9zLcsvCaMfIqwtWMzs6rEQfoawyMUt9WOrbqxPO8Fk+ac4Z8xx6XaWrnwM1o+Y6eV5ffgRnViW3wOhOftLbHwcEj2tbFmfHSk30ZVk9Mux7h/77mP6HsOpabhjPXsd1433hsJzYeK3pGvpl/bG127G3nX17+UXLYrRV9c33MNDpc9bgOv7OC7373uxZeo0sX/OD6wQ9+EA8++CAaNLRs6iOX1x2QrdPuX5YvAHx42U1YPmXhWeMv6PQTdflTwNnvhADvXHSN8dNgRQcfbLZplm/vT+4tcWw+ubvA/mOxjXm6r4hvtmz/4FEcGj6BbEzoKW97ViDLbrO8nfN/X9/+f9DbGGipQzO+7dkqf9AmW94qZTHm5ZoHkEzceaxZHo90b8ALJ15pQ5ci25gymuE2k+5XimePbgc1TvjTJrSC3mYn2wBTfIaCGbKO1/pwaOiIum4eRMy3leLYHEsbTuN1+ZQFWDJ5nvV6FkNrvsWyWvG6c+XbsXDirBaymp9Mt/uofdvcu+vHmDNnDiZPnowzTRf84Dp37lxcd9112N67Ny3kxQFqC87RJg5uyODgcB0XTx3eipjnB/ex8DfoC/Sx6TerYypOJ48nNZNto20mbzTJIQ4ODBxFzLTlvILAzr0zto1U5Nds2dWzVmDNjKWGjGaNMcuniK9VnyZ6/MEV71fL06Pma8PVRFZR7LXbIRbxKpLVTl7//q6L1+Ga2SsLeY5WRjPchbpxhjtWvgNlr2S1czt/o8Ftu+EGgIFwBH2NwUJ9ivLt2iaPI58Y5xiJG5ha8Jy+NZ44a8sGbfFK8vsHjuLwcG+63zoKHWwyRmUbrT31h8P44Ac/2La8ZumCH1wB4JOf/CQOTWvA+rLvZp9W9Zpdz1wjYAhZhIBmXuDeLpZmdUfB7+DgMTx3ZEf7MsdilxYfQgjet/Q6uIS05tuq7Ayw9NUH8KP9z7Vn19HaJ0vbRI/dpw6gr97fHp+x+qclL7Gi0NS+2byeimQ1k2ups7N3HwYa+R/cMOo2s/lobGbThzMcr53Go92b7Lzb8btWV/12qN5pa2Vc3mzrvyeb8Fo2eQGunLmsePBot500w95Cp6FgCD8+sGF08diunUbBI6SBeF3paGWNJQ4Krm85/grKUzrxuc99zu6PUaY3xOD6a7/2azh+/DgODB5Fq58qM5KtweiX9YMUFh7ZsrfNvxJlx7V2QK1+Oi0rrxhUMWYOhqWT5+HdF6+DeFF7c/3OVrL9BNuLJ17FM0e2ZyrmbWHlp9uhQN+WPMDQ4ZWxeOIco8yIjxa2LMxbOltrfCR69AdDqMdBnhePW8ZgK2xpYXN9snWzP4FnzRcNLJlOqTBupb81PfsagwhZk0M6RTq0GuiayM9i6PDKWDRxtigHay07yy+DQR9M9V9HsvKVNmEUX9/2IBpxI69bkc3bxYO8vKI0qTwBn7j01kSP2IyNJqlpf9gMj4ZL/7nMSybPw9LJ86z12klN+/uiGM+kRw5uwtSpU+G6bmGd0aQ3xODqeR7+5E/+BEcvNn93MNeZWsr0T/b6aHgAQBCH+LvN/w7K8jS2fLM6rWQ1o3vh+G7s7utpS59WGIrwNLMbB8OSSXOwetqilvKaXW/HhkW8AKDseVg6eR4ot9vtTPC0wqnLunbOSsydMO2M+Z4NbO3Yspk+Wd1G67vr5l2OGdXJTf1ok5PlW1S/HT3LbglLJs01y4tOXWf5NRk01GGmNmzIAfzqol9Rj/C1kt0qFprJtflGfh7a9ywGgqGmdK3afLOyduJjy8lX8YP9G1r6uV3ZrfI2u/Q1BuB0lvDMM88U+mO06Q0xuALAJz7xCTz33HM4UesvrNNuwyy63qq85Hn4gzXvheOYdUfLt92OsIjf7M6pmF4191CadUztBGEz7LY0qdyJ4agB1mS2Ohq7jAabnv/GzodxdPiU+hH2VjJGk4p0ytp6y4k9eO7orrZ5jWXgaY+H/dpofZ7VbzR47tr+ffWqUBuf9jvF4oGsFYYH9z2Fbb17WupVNHhZMbZYBcny6g+GMaXS1dK+7cZ6O36y1Vk8cRY6/PwPgo+2LbSbbHyvmnEJbln4ptz1VnYYrbxm+a9v/wGWLFmCSZMmtc27VXrDDK4dHR34vd/7PfRf0YXi4+jyO7Pks4k1+dh4///svXeYHcWVN/yr7psmaUajNKNRQhEFUAAhYTAmmIwXbPw6gO1dG/DuvstrjP14/e6++7GPd9fL4sD37prPCzYmBxsjoklCAgllCUmjNNJImtHknO7cfG931/dHd9+urq7u23ckbCzmzHOf6a4+deqcqlN1Kp7Sv0mEYnNHvTFV5EVflJ5/HvTGxVQ8+3GCuvJqhOUgcloW/uQrFkTxnDyub/sQsWzCU47CsnqlXZjGXcuuR13FRKYyeekDT4PHKwRiOnMrp+HciTMKyFUobS/d80OnUJru8d0aOX/pWuGUqrh69gX5TSvieLx+u+mAXSbqSoOPA/yPBZ/BsslzXaXw6jx4xfGLQ6HhyOApHBtutaVHHTwXC37qtIUTzcSweNLs/A00TjwR3UI/N354HP25M96Ho0MthqP+YtoE0bOfNpTVFR0SuSRoRMZ//Md/COKPHc4a4woAd999N15//XVj9OrVCAOFC9EL3GkHpAAoaN6hhDt9L4XxwwPgdbPH683b0Z0Y8khfJI8/5fQXD/irJVejMlxWQAY/jYGfZzGNwfQojg93wj2f3WiIbkQpVHZiXsqCEV8Gxf27KFxEAwXwKZwy+0lP/2/vxPnl0wpXqYaJ4TLjhqBi9HzsOsjnjapp2N1zTHADTCH998LxQ8cOl9Ytw6qpCzzS8aLtpYduuE66G9v34+RIhwC3GHm9ePTCscDSB56Gn/h+5BZ9t54fO/IOrr/+elx55ZUeaRYPZ5Vxra6uxg9/+EM0zTR7f/wuvGIVxS9YE24SIbh8xnKk844cTo+eM4wFDXA5w3jbuVdgVsXUMaRdqJL7z8OB1CjWt+4tIm33M4piHBG+PSyj5DCaTYwxTT5//fDnhK7EIN5o2eWJc2bAb9mocNMbf3TNfGGNdmEYTI/i5aZtEJdnsUc7xnZMLKtlMJyJcaF+DLZXgw5j2cEbx4ShVBzrWz/0hWuBWV6F9N8NnGl8Yf6lWDRxhg96Y8vrQpBWsphWWoXFeR/PZzIdf/k0mBpFXyaKu+666wymrcNZZVwBffR68OBBnIr2GCHU4we4N9BjrewUiqbi14feRNrYIeqF656uF19uMljhlFK82rQdyRzPQ2GDdKagPBjBzIopPujzFUGEr0KcN94dkRkVk3HhtIWeOIV1wK1cCvGtv8+qmIqvLrpckIYoTS+e3HD9hI8ljUI0/NYTHWda6UT81ZJrHOHF0fPCKVxGJYEwrp29Cpax4o2OyIiJwG58rVtw3PAs3FCA5HcrizsarKFmO0Jnouz0uHt7T+D4cIctzPrP/0TXmXq1P/yzmO7+/pPY1HHQR/qF9EIU140f+/urzdvxz//8z7jqqqs86I8NzjrjGolE8OMf/xj7S/qhFbwIvlDl9jKConAdApKEe1beYqwjFNMQeOEVAjseIQQLJ9ZBIuY3N7nc+NAEOKL03PmLBEJYUFVnHEMphO+Wx27g38A+fuQdjORHK16G0k9FFvHhxQuFqqlGI+K3HNwaGi/+RWmL+BPx7/XuF9x0xOKpIzaQ38VeHE9u4V55Is6z99r340B/sxHOdtj4uDwNN35EswAS3C/z1gAKLJw43fgu0jkz3G12wcvI+mtXppZWYmKk3BeuN3jXPa88vLh2Ma6cuVz4bex8+Met72vCiZEu3H333WeIBzucdcYVAL7yla8gEAig+utriohVrBH0VmKJSHjp5DYMp+PwbhCBwpV4bLC4ehZGs0lQWqgCuIW5GR/+uzvvW7sO48PeEwL8sRiyYkGnf+M5F6E8WPIR0Pb3TSIEOU1lOnuFyqFYPkT5WEzeioyKV1qF6IiAQIPp2acQX+ZIqRCuaETlDRdOW4h5lbVMiEnDqyn0U1cMKqQEEgm7UtIoxcOH3kBacVs2KlYf2Dwo1FbpzyOZOKaWVmFKSaWP9NxGracHH3QcwskR3b9z8eC38+MOqqahsWwUjz/++BlxdSgCQguO7j624Mn4kSNHcPnll+POOVf62FRTLBAuebYxsKA52oMZ5ZMRkt1umygmLXEaXkApxWMN63Hr/EtQFS4vHMGWZnFpOSshzfNAHJtHTo9msZBSMohn05hS6rXN3k1mtqz5cv84g12etlg/Xm/eib9b/rkx0LDo+I8nwvdDz6scJBS3VmwHjWrojA8aSxU87bEYcSuORKzjLLpXJvddv/Y6IZLTSd8fX4XbJADY3HEQZcESXDhtgeNb8emMDboTQygNhJm2uVCbM1ZdFMMv6l9FJkLQ39/vp30aUwN2Vo5cAWDp0qX4u7/7OzRMT+PMdyDceoYEbDnMrazBQCpq7F4mDE6xQDye3ekRQnDH0msLGFYvbyRjNYpWXEIIdvc0MjuXiwEzX81ptrHx0RkfxN6+Ex44MpOGqJyISzhccJyw7sRWNEe7ffDLlgdPr5jyOJ0REP/up8Hjw5zfXjyxhdkL4ZceYJUP+27ii8pJXF8SuTTeaz/gm18LvPSPwF5mkhFq71ATEgIhIWzrOoJBw5+wRSGYfyoMIhmBYkbXn5lxvg/DWkw6xUHDYCsmRSagMt8umR0nEQ/s/zOT/kAqinQY+M1vfnMaHf/CcNYaVwD4h3/4B5w4cQLzv3uDEeLWGJg/GfaK6wV8peK/6f97ksMYSscc4WJ8vyBqQNyN5O+Ob8ZAapSLI3PPsiCc5c3N6BSGCaFSBCV+9O7VYXCj7ywbq2FiZbC/z6+qw3VzLuRomHHNd42LK0rfjUeRQbTjfHbWSswsF42YRMDL4YXrFtdssAjz7AVsx0KGvREbizs4vhGUcc3sC1BXPokLZ+ueH+DlY2mI6JpxZFSESvGXS66FOE8lFx7YY0O80TQ6kJAYF4gqYEx1mgaVkBCIgVseLEGJXMp8Dxv4bAdPxI+oHEUdDv6ZjS9jQ9t+42jaWMGtE0lcvtt50ShwfKSLOarIykQh1l/An6kS8WVPP6uqeOT4etx11124+eabfdAcO5y108Im7Ny5E7fccgu+OfMzTE9J1HBoEJ9t9Oq1s9NU3o1fIpdGmc0Tir8pHP+NKsu7feqsMz6AaaUTEcgbuEKKWuhoADtSLwQ6blrJIixHQAhbqfzQEZWJRdc0rvZD/Cp4o7mxfT/OnzQPU0qt9RUCCWLnEpIgjAeW98L4sWwSWTWHSSUT4N4T9zIwbseRCsXV47XFBvB683aPaWGZSYPfjOM2HcvqLW+owMWR0Trag5kVUwRnTEVpFmvc3fLe0p+tnYcwZ0INZlRMEeA7ebZ0S2HCAo5360Vcr0zDmsqlEJaD1miJwXf3Ky7SS9F3M/9EeQ+YeTiQGkFEDqA85LUHQdTh5EHUfrHgbI8opUgqWa4dFOGbaYvCillOMOWw8mJzxyHUZzrQ3NyMkhLf+zDGp4VFsHbtWnznO9/B9tKevCs+9ykPdsu82YuUOXw2niagIzl+HbFBvHB8M4fjNoVljrgK0xXzrjE09F9d+TS0xQYRy6YEcorALQ2Wz0IVisUFfnNkvTEdZvLF5q0XuPFJXAwrIJqmm1k+FZFAANYIxM1os+9e+U0MHgK+8FtG+9Aw1A77CEUyZDDz0+851WJGe/zOVV6P2RE/30lg5XCrA+CezcZNs+GqGsXbrR9CQgBiXeZ37rLpaPByX+nk1aQQYOJpqC2rRkWoUMNu1Rs3j0k63YDNsBIiuLDD+Jk34mxo34u9fceNCJIN100fiS2/vIA3Rnw7oqFppAslgaDAsPLl6sd5hFsbCoYP8yy0jteXiuK5Y+958C+iwYexOsv+RO21KYf+/l5bPbYMHMWOHTuKMaxjhrN+5AoAqqri2muvBY4P4rOzVnFf3UYdXj0zt2327t9VTYMsSR44eu9f7xl7OXHw4lmEo8OmjnosqJqBuvJqiKbs3KFYxxti3lSqQSYB4bfCtLx4cWt02MZGj5dWssbxKEmAw89A8Dhinu3l5cWr26hGEjTgprHxHo2K+RRDW6zH2ND0Fy4YbjIW0nXRZjtng8saImd+qTZMN3DPaxPs38w0KRTkjJt4nMsTgLfhskZLBEFQxtBT5GAtS5iJSnDzM0wphYZcfocsISHYLwJg9UBi0nGT2UvHnHHWt+7FiinzMNW2sW9Mg7ICwM90WGk420E+HuDUb69ZBn7mUZxXaSWLn+79Pa6+8Tq88sorhQTgYXzk6gayLOPpp5/GkVwPmqNdLlh8VrANG7/l27shE1InBI83rM9fjiw+R2pWYqtiicHskRXiwTqDd/mMFZheNgk51ezJ8XjsfzadYlVErNwSCN48tQtZ1c2xxpkCca87mkngvw/+wQW/kBcm9wbd3hFy7xQlcmmsb93jiWMB6+CgkI9q73QLp1UI3OLyeut9vKgz3o+d3UcEdLx2/zrpFB7Bmnh2I9o00o0XT2zxiOGWx5R5yjmeKRS7UXQxrIcGmnEy2uk4emL5G7Z3sAgZywkDdvbKDjlVwTWzL+AM60cFzjJ9p/VDHB5o8TCsgJh3fiqYwnm22Fv/VVXFw4f+gK/f8VdjMaxjhk+EcQWA2tpaPPnkk/jD4AEMp2OwT1+x02BgwnkjxBtYN+CnuABCCG6ccxEmhis4XHsa7p5eROA1suXXIFVs6z6CDzoPQlzsXga2WHDGIYSgtmySD8cegM6fiee38XUbPerhleEy3LPylgL4LF2/Rz74BkFMNyRJmBSZYMOxr/m68SFKrxgYS/kVKiO/k0ZS/n8kEMTECK/7XvTFZzadzvhFDas14jPz99zqmfjywssdKRESgnv5iXXAXkcp/DSjFaFSlAbso1zrijresIZcjbSYT29QNBX/Vf+KwGPcRzFqFR8t+lTtEsy1nS8WxeE7a4U6bW5LKfY473XUY1hJnrFL0P3CJ8a4AsA111yDH/zgB3g714iMyhsx97U98bMfsCvD1NIqtMX6saP7qEcaeuWlUJj1Fv9peMGaaefiipkrhWnaYexnCb1g5dT5gnxngV9b4g2++Z2tdKIerBjaY/1oHG5HcV3xAhYAACAASURBVIaTh7EtowTlAFZNne/oXPgbibFxiqmyxRhW0bKI+fOz5seCc3NTVbgCiybO4joTxRl+ywgV7tDYrhSjFJs6DkClznKnlHfm4Lbkwu9GZ8NdwBilxnIJzCifgunMbnF+ZO3Gk/eSgz9dDEgS/teKvzCWRCwO/IGbYwo/QJBRcniteQdKA2GUBsMCOiLD6qTjL8zpVOKN5t04kuvBiRMnUFvrZtw/GvhEGVcA+O53v4vVq1ejviZubHAqZrrXWXjiMLfdoEBluBRTS3iPIGLF9X/9lGja2glBmWAgNYK3W/b4oDnWqUR21MlRpBRPHd3AbKxyS9PN+JnfRRvRCkGxTgK8Gp+xGdgnj76LU9FusOUlLmPRsRI/wBqGsZafl2xuZet2DMOCZ49tyF+x5s2f23nNse8NUakGSikCxO+uY1bP2I1jHnyzO3+ZqWIKDfv6TmBH92GDYkCwduzciFWYN7cpeTsMpWP4Q/MuhByXsvvJ07EYVnajE4UkAXMqphrTwaKRqR8ohlfrvTc5hJ39jfjWt76FWbNm+UzrzMEnzrgSQvDLX/4SQ0NDWN+6FwOpKEYycQBAy2gPFE1FMpdFV1x3etCXHMFoJgkAaI52Q6Ma4rlU3ilCT2IYsWwKlGpoGtHPj8WyCfQmhwHonkgSuRRUqqE52o2qcDkmRSrwXns9cqqCzvgAUkoGOU1By2gvAGA4HcdAKgoAaI/1IqMoyKo5tMX6AABD6dH82dnW0T5kVQVpJYt247slE0HLaC8UTcvLVBUuQ3WkzJCJMDKlOZl0t4lNI12GTElGpkEkcmmoFHnHCNFMIn/Vny5TGjktZ5NpKB3F3ctvwUgmjoyS85Cpn5Gp35BpyJBJY8opg674IACKvuQwRjMJj3IaQXWkAgsqp+PkcCcAysk0ZMikoTnaA4AyMulrhrpMZjlRo5xGjXLq95CJonW0F/9jwWWoK5+cl6k/NYCRTBIANWRSGJmIoXspRiYF8VxCoHtmOWmIZeOCctIMmuBkctM9Xqa0IZOGoXSM0z3VKKcBTvfY+pTGFTOWY35lHfqSQ4L6xOteykX3qE0mp+7xMvUA0BDNJHDe5LkghKA91mfIlEV7bJArJ8lF94YxkhnlZDLLSUV/KobRTAIUCpqi7Xnd64rrDjMWVs3A+ZPnApTg5EgnKDRO94bzMp2K9uVl6k0OuZSTf90rDYQwMVIhkElUTpm8nuj1yU+7x5ZT2qZ7hwdb0J+KojxUehq6x8pk6h4p0O4B27sa8Ojhd/D444/j3//93/GngE+ccQWAUCiEdevWobMkhfWt+/IKt7XrCLJqFiOZGPb1nwQAHBtuR5ehUJs7D0HVNAykojg00AyA4shQK/qSw6AA3jecs/ckh9Ew2AYAONDfjMH0KBRNxZZOvffaGR9E62gv0moW+/pOYiSTQEZVsL2rAQDQHuszFJZiT28jYrkUkkoGO7uPAdDdKuoNB7Cz5xhSShaj2aThhYjixEgnJ5OCkUwc+/pPIiQHkFKy2NZ1BKqmcTKdAgBOJt2jjV2mU4ZMOZtM5i0b+/qaGJmOAKB5mQiheOH4ZvQkh1xk0rCz56ghU8qQieDESBcnk2rI1MSUk25oLZlGGZna0JccxW+Pb8abxsjdu5wIIxPlZOLLCUY5JT3K6SgGUlEcG27Le4vSZerlyinGyNRhyAQXmcxyokY5SehJjnDlFIWiKag3aNrL6aTe0VFzRjnxMh03ZMpiZ3cDI1MvAGLoXobRPTC6R7G16xByqoqhdAwb2vdDlthyAjZ3HhToXhtXn9hyIozuqS66x8qkf3+u8T3s6j5q1KfjiOUSSCoZ7DA2WNl1rwEpJY3RbMI4NuNVn/Q8PTp0Cl2JARAScpTTtq7D2Nx5EP2pKDRoeL9jvyHTIBoGWwBoONB/EoPpOBSNGnsi9M7c8eF2Q6YTGMmMFiinhEP39vYdRyyXQke8X1BOXfkOEd9GAJQrp0NQNcrpXgv6kkOG7h00dG/QVp96jE6DvZw6DZn4+kTQHutH00g3AMLoHl+fevP1SZcp4ZApo+TwZsseTJlRg6997Wv4U8En4iiOG7S0tODTn/407r//fnQ8/B4+iqlAC5y0M2oOHbEBzKsy1wLc0mAP+HtBoYPdBhaleKtlDy6tW4YJoVKXeLxjADfwc5SJBX20rPtcFk1V8XwUAr9rNDqfujOLIAgx1xJZvk2Z+SMlbmnweSQ6CmDFPRXtQU9yGBfXLhZKYoGfQ/xO+hZfzm+Wb2G3ozgiWjw9P8AfbQLSioo3W3biC/MvLZCe+3KK/XvhI1qss4ecpuiH3Gw7VQudz2SBrX8int3qgISexBDCMr+Zy/0IjfvxG34Jq7ATln19J1AeLMHCiXXC7+7rlsXi2PVV0VQ0R7s90vWCsTrU0fHj2RReGNmLK664Ag8//PCZcm84fhSnWJgzZw7Wr1+PH/zgB1j8g5txums7xa6VRTMJHBtuY0JEziMA0e5j9/Td173yWITghnMuQokcQTzHr396V1gnngkiJwsiOhRzK2vRkxxGRsnlwwrT98ODF+gVPyQHsbv3mLGxiG/A3NYOvTa1+d1BDpxTWYOLa5cIvojy2s8xKNFRFdYLV7FtwploiPijTfrVg07D6gXFNKyiPLfODrfH+tGTGEZA8ute0W29X6TfRBBurat2xAZQFalEdcTf8ZfC51oLOfCwYCQTx6qp8/8IhhXgy3wkE0fjcPsYfLq7lXHhNg0gUDUNG+UW9Pb24pe//OVH6jfYD3yijSsALF68GK+//jruuusuXPQv5hTCWHvwZoPGKoN7vKmlVbjxnDUYSI0aRo5tUP0q9thhd+9R7OttgnPH45na6MB6YrLHPzzQgoF0tIg0AX/q6lVOGiRCMJyOIaO6Xfnlh66XgXWLR0ApxRMN66FoXq4E2eMFXketRHyYDbOUfxubX+DCuusvPgDI2N93Eh/2Hh9jfPu7fnSGBd4xi2QLT+TSXAeS3aDkpONwCmHzRR0AQBgckUcoazPSkaFW9CeHwTqeEI1a/Z1rFwE7yrN+iqbi6aMbjWvtRMZYpL/8qLiY9seawWmL9aE6Uo7PzV3rYty8DCj/7seo6pBVs3hqcAeqqqrQ398PWR6L3p9Z+ERPC7OwefNmfPGLX8Rzzz2HvT96HmOfIvY7hWvBpo4DmFpShSWTZhsh7PSin97k2ECjFBIhUCkgk2I6FG7TQoC31xjTo4qFo1HNOFjvPo1cGNjRdqHpbGN0QKkxNTxWGAu/FM3RbsyqmGqMpMyRTzH6ImqkTZndGmgVbbFBw7cwPy1cbPqFeGF5AgAZw+kYFKoY94eycflOBuG+ueWlvzEBpaqggXdOwTriGZ6X+BEkgQTWAxPN7951GkcN0PWaO6/q5k7R+u519IaXg58q1us0AQEFOB/ObvWimPaFXzZh41BQSvF84yZcP2c1cxG7G92xLAGJQaUq9tfG8c477+DEiROoqak5LXoCGJ8WPh34zGc+g5dffhm33347lv7w87AKX5RFbj0ydtThP2svn7EcSybNRmd8CJSyNFi6rP9XvrdeLOhxJEKQUXP4Rf1LyKpe02FuU74m6F6EiBCXlcXuw3Zj237s7DnmQtOt1+rGj9/RloZ4NoX/rH/JyOvC4HUmsViYUT4ZWZV3hFAsuE1nu0Ghm3XOdDNgb3wlwjrQMIE/VqV3Dqy89p6BQB7LbeSn4fBgC15p2s5yksfzGik6R6/mB/txG9YZCAuUUjxUvw5RY+cqiO4sRGw4rW/eDkV4cBpWgGBv30msb9sruBzBq/Nlfi889SoGiqF0DCkli9vOvcIwrIXq5OnOjuiQVtL4r5NvYWBgAL29vR+FYR0zjBtXBi699FK88cYbuPPOOzHvnuuZLxKcjbrXNW2Ae29edE5WH8G9174fo9k43IvF6ZDe/iziEwI8y2CE5SDuXHo9QnIIzgpGubgsbedUr+74wmsUaoLe27+4dgnW1JzLhPOdCBH/fg7Qe6t1eSiMO5Zeb4xqClcB+1Qryyv73x/s6D6K/f0nfcRz46vQVLEbmI2xvzPRPHifvXQHSlU8duQtZhpetKnIXzlYYOmB11nwpZPm4LOzVjJxzNT8pcUa7bw7Qip25s96bSKE4FtLbtBv4SISc+NN8R0psRtE987lqqnz8enp59k403/ika4O/H25gLjum89OQ3x4sAXHRzoE37z0/HQMq4ScpuHDyVHIsoxnn30WpaWlp0HvzMO4ceVg9erVWL9+Pb7zne+g9q7LYC3W270EEYeRERk1tw1SbLg+pSIRgq8vvhITQuVoHRVdKK1xPzbcTJ8NE/Giwpxy09eB9IpbHirBvr5GbOtqcOFX5X6iqURipGI2JHwD7uS7NBhGX3IEO20eqzR4Vzrq8sxCoUZMz5uGwdYCeCw9EU03A+terT4z43xcMn2pzzSLBT6fzfIq5Gmq8OYpsRErxCMBITLuXfVFlARKYG+g3dLJwemRiwfW97IZz85LWsni8MAplAftt5+4jQ4tH7+aPkIl/o0xa1jTShZvtexCaVAGpRnuKjk7HYmEPekSsNPKbOeWn9nSd+g+3rAeWTXHeELiy9jNsLrtcDeNMqtDdtyMkkN3YgiX1Z2HFVPmC+IXs/RCuZ8bT0Ail8G/7/8tMpkMWltbUVc3lp3JHy2MG1cBLF++HO+99x7uu+8+qNfPZXa9WUaWQmTsTMMjUgx/O2LjuTi2dh1irsdj8Vg6vIHlPb64TR1aLgXZhmZe5XScN3mOgEfRdLFo97Jok5MIx17JSwNhVIREPU6vtSfe0b5bhXTD0aBSBd2JQZd0igG3tSO+zPT3rKpgk3F+05tXnnYh4OUodBmBKL0zBdbopTPehyODLT7j8Tx7dQooE+I0/IlcGkOZGBNi1VUe32ZYAeSvi2PoU5rNuyW0RvFc3hkGeVppJbPWq3KjT91ASiQMCgpCAq4bmixPT6Jmml3/pAhIMj47cyVKAiGIy9OrI8qOank8L1ehBF2JQdT3N8O+uYpPT1wX7N8L6aj5n2AwOYo3lSNYtWoVXnvtNQSDLtP4f2IYN64usHjxYuzYsQMvvfQSTp4rQdVEo0UIwkxl9Ntg2fEqQqW4/dwrkVVzeQcEzk0EY20MRWulOlSGy1AWjODVph1IORx8+wXR8Q9vI1wZLsOS6lloHe0bY5o8+MubqnA5rpq1guvEmFBsteDTdJ+6DkgSNKqBOtJ1y6c/Fnw0/qQBfX+tDmOd0i6+mZoYKcflM5Z74vhxL+rfBSkwmk0goWaxcuo8H3T9LJ0UpgIA9f3NaBhsw8yKyT7iuKXlxY9zJkmlGo4Pd+CcylpcP2e1jR8vXv0Db6x16I4P4uf167B06VJs3br1j3Iv61hh3Lh6wPTp07F582b09vZic3mXsbUdcO9Vn84agj3uYHo0781EDKLeHj96LQ5kImFuZa3LnZd+Njzwm1TMZyoIRz48qyl4v+NA/s7N04dCHRE97FS0B8+6Xt58Oum6NyQSkXDlzBUuxxSKGXGMFdzW1E8H3GcO6sonY8mkmRAvZbi989/8rLNbMJCK4uGDb3jisL5/C+HYw8yRr33GgiCIjlg/DvY32vAJgtzUsHgdtfCFBqKZLB2mlVah2rE71+LA+91Jz44rrvPxbAqHB1t83nLlBaL41oichfWte/F813Y88MADeOqpp/7k51gLwfhRHB+gKAruuecefPDBB7ghfC4m5nc+mlMzIvDLHj8qtR8n6UkMIZ5LYX5VnQdN0ZpfoZ6vm8EDBlOjODbcjkumL2FwWFlF/IppuafLgrVuRAiBTAp1DIrJW3d8RdOgahrCgdOZVuJpi44q2GFTx0HIRMan65Z58uekWyxflvx2D01+9chvOu40/u/+l3HH0utQESo0wvCmU/i7XWcSuTTKgvyZWBGYdcW5i1h8iT1gH+npfORUIChT6GuYqvElaNBhzrkS8yiYsfnPtuHJBK8RvjXDEM+msLv3OK6YsRx2O+PWgSqmrRLjqlTD7p5GrKlZ5Lib9vTqpXd57+5uxKstO/Hggw/innvu8ZnOGYPxozgfFQQCATz00EO488478WTHFmZEWWgnHD+CE71T5tm5QSat5pDMT9MSLq4b+JlScqcRDgRRGggzeICTd3+0xOmKaACvNu3AsaH2IumInv301gkCkox4LpX3lzo2cDtm4F7+a2rO9eEC8XSAn54vlB9n6sC9My/uWHodyoMRjzjmJh2WB9Hu4UJHO/QRbk7NYlvXYUZ//eyiZv+LeOPpOHfMPnH0bXQnRhgc1vmEYVChr+XajKljeUBcd+27tfW0JSKjKlxmjOC88qbY+umOazrwV4XLKXxcGW51QNzuwfEtreRw/57f4SDpRUNDw5/CsI4Zxo2rTyCE4J577sELL7yAt4cP4v32emi+zkn6MbLuMGfCNJw/eS4O9DcbhqeQITl9KA+WYOXU+WgYbEdnfMAFi+0c+AVT3fhOgt6IfW7uGizNO9Jwi8vH5/kxgTcY4jynoKexxsynLSoLZwMTkoM4MdJpC3PSEn3zA36rNIE9j/jjGMU0DSLZJQykRpHIpV2m70Q7gc3jbW7HsMy03CGtKlCpxh2z8iuLtbGJnaZ1HkNyGpa/XHw1assmGW8y3FwZ2u6ZFZ5ttfPqvKJO9/S1resIKChWTV1gw3ZCIf3xMnIWaFTDm6d2I6squHrWKpelIxPMcmSXifx0eO3fBlNR/ObI25gwtRqvvPIKFi1a5C3KxwzGjWuRcNlll2HPnj2I1YWxe8oA0orbbj6/wDdoYlqTIhNQFS4v0l+nF1+FeSYEPhwtmN953kXv3umG5DC64oPGwX8ez+24iFtjzPJg/pyVfHJJJS6qOZfxc8zz6wbFnsu0gIBgX18TFI2fFnPr9YumQIvVOTb/2bh8J6RQOfK03Aynfl2jeV2YOH4hYMvOjRcLKCUISQFcVne+By13cN6zyn937uodSiXxu8YPHMaGN5zFOYgwwUtWCll4BtYrvigPvOqvlZZEJEwvn4Sw47INnvaZaQsbBlvxs33r8LW770RbWxuWLVvmEe/jCePGdQxQV1eHTZs2YcaMGXhuaAe64gMQTyH5aaS0/Dfi2ohImFExGTVlk7CxfT/29zULuPIyZoUMnbgSLq6eg7ryamzpPIxETuQEgKfnxodXJbd/m1I6EaunnSvAc6ZlH1H4qdhi3tpifXi28T3PtAq/FzIE1rtECG4/9yrDBWKhcvJqtLziioA1hH6qfqE0eRw7D0smzcFFNYtcv58+2Gn1JofxxNF3fMUpyusWcRpVc0RZFS7HJdOXQfJ1ETscdMRh9vaAMlPXhwZa0J0YwqV15yMSCEGsc+ZPg7te8fjgwvS4GiV47Mg7GMnEsWLKAgSkoAs+D6Lz9yKw85BVFTzZ8C7eHTmCX/3qV7j//vs/9huX3GB8Q9NpwvPPP4977rkHF5adg0unnydwO+YfCt+KoWE0m0bI6CWHZMnYVMBX/EJ07LjUtu7krAy7uo9i2eRzUGZbOyvseMA7LW/4oOMglk+Zh8pwmSstoLCMftKklEKjFLIk2fD95M1Y4fDgKcSzKVxcu2yMoxoLvGXU+W6L9eH15h34u+U3n7F0vNLVKMUzxzbgtkVX5Ud1pysnQz1vHM1NR+aoU9FU4Q04fLnycU1w+BMmAcG6qA57eo9hZvlU1JRVC+Pzu4IJCDSag0Ss0R+lpkMXS5/tm6rsaR8ZbMHkkkpMK50o5OlMQkrJoCQQRn9yBJNLKos0cnx9KfSu3xL27LEN6M1GcfDgwY/TNPD4hqY/BXz1q1/F7t27MVoXxHslLYhmEgC8e6ii/yy+ze0a7CPaCaFSlAQi2NC+Dwf6Txk9aMmRhoiOW5qWf1a+MdDD19QuRkCS8W7rXn1Hr4HrjC9+F9Es9F4VLjcmSsUdBx7fK/1C7xLRNza9bVyi7pU3IrlE+Szil8WbXjYZ86tm2HBEeVFsvjnlE8sugkI0RXnnFhcguKR2mW261C1vCpWVMy8CtmeCAHqTw3i/vR5BKViw3Pm4VoCkO3Vgfuw3HiJyyBg96pA33nxc6IYVkOyGFTSfpmseGbTaYn3Y33cSSyfNERpWv3XQr57qNzi9g4FUFFNLq20jczc95fXOjsuXqf37m6d24af7X8Df/MN3kUwmP06GdcwwblzPAMyZMwebNm3CZZddhsfaNmLR928A4Gz42V4p/y4CUUNnht0wey1WTFmIjng/BtOjBRtNzzRNd2/EaajMeEESRGW4zL7OI5wuK9ToisP59+VT5iMoh9A43OZpdNxk9fvdfC8LRlBbPtlzTduLX/a7m0Fi8SZFKhGUAvr1c8Q9Lwrlp5/8t54B3q2fW3kXk9+iNBPZFGZXTCsYR5gO8ZePLJTIIcxg0hP75GWZENQBn7g5VcHhwVM4b/JcVIXLjfRkSCToka6dhsOJhIgfJqw0WIryUJnNcBNIeQ9PtqhF6CX/n0DC4cFmUBB8e9nnMKVkoo2Oq546OhNOXL79I5AwkIpiU0UbOiMJ3HffffjHf/zHj8V1cWcCxo3rGYJAIID77rsPr7zyCr7//e/j6PwMkrm0w3B5vQOwv3MVjH2X5QAkQtCTGEJvcti7sWDiERJwpsfH43kBIEsS1tQuw3A2gdebt4OC2OO7yWC8O0YBPuIllSxazQ0xJg2eb7f849MqgB+Uw1g+eT46EgNF0cnLxX13C2efX27agiHm5hRbWsz/gnriI//BHtdw0T1C5OLzlXs25d7e04CGoVbvfOH/+5FP8B7NJEAJwaKJs+wyFODVkd8cEOPPfDbjxnIpdMQH8nWAEPfLJghkEO7CDaF3JqqBENnBf19yGG+e2oGpJROxcOJMi65ZF6jmK58c+mg82zo0RIIGgqZoN5JKFgE56K88/OS3QOeaol14tPkd7N+/H/X19bjvvvuE5fDnCmKtGocxw8UXX4wDBw6goqICv25/F/O/+1nrY6FK7VLJhQ2b8f/CaYuwZNI5+LD3GOoHmoyKJNsaA6/0LNd0gjT59KmGylAZFlXPtq+/sHS8ZPLiRxBvckklrpl9EXpTI/bjMl755JYWw6cbrwpVsb51D3Ksq0sjP13TYd4ddAvkxTeXXI+p7BQf3yjBarht5VQoj81HUwf4+AI8m4xu8vA8uqVNJFw3Zw2WT13gyG+WJ9d84qFAfrfG+lDff1IcT8CfyVPemMGZP+w7YXakt8f6UBkux3Vz1oAQM98kWNOcsu3HA4XKfCc2XnSwy1oVrsDCibPc3SWyeeFVpgxu/pkxzJqm4YXG95BSMrh53qft/r5ddErIC1vHODyzXUorGTzU8gf8tnUznnrqKfT09KCiokJM888Yxjc0fYSwbds23HHHHVi6dCnO7ShDZchyUUZBXQ2b2zc+nH0fTEWhgaIqXA5V02xrQV7gxYcXNEe7cHDgJG6Zd1lRNPzKxsLbLbswv6ouv0Y5FiiGR/MS+dOh4YePkUwc+/uP44oZqwrieoX74ast1ovXmrbi7hW3FozjTV9iwlVBuIaBVBQNQ6dwWd2KongsBBYNiweNqkVtIhTz4X+M8cLxjfjMjJWYVlpdGNkXOF2WUqiIZlJYd/J9fHPJDae1SdIJki1NCopELo3yYAmOD7djflVd3vOS3zIrRpd+f/w9dAfjOP/88/HAAw/gvPPOE8b7mMH4hqaPG1xyySWor6/Hueeei1+fehtTvrXaWNOTmN6w88dPIznDrV6y+W1SyURMKanGkcFWrG/bA6toWdr8uxsfzrT536yKWlxSuxyUEn3d0APXnh4RpOXNx3Vz1mB+1Sz0J6O+0+HpeOU3/3uy4S10xvs9ysV/umL5dQjLYUwIlnvklZhnezgEeSpK336e0T2/nWXEp2eCOFxCQJJRHZ7gS5aC+kICjJzOM80vnfwAx4bafNHR1ydFZcjIRKT8j4WUkkEsm8CXFl4lNKzWJigrLiH2DVLidAI2GQFApQSVoTLcMOdTzEYiLx3z+u6tk2lFwa8PvY6cpmHhxNmQ8rz413fC6Zao3o2kE9g/YxgHhptx77334s033/xzMaxjBqkwyjicDkQiEfz4xz/G+vXr8ctf/hKv4xCu+cWdwgpsr5hMo2LD5f/bYcWUBbhxzqcQzcSxt/e4Lb7bZgueD1vlcJkCCkgyppROxIGBJrx5aqcPmixtCOQB1yjZK2pGyeLFk+8jq/LOHmCLw6ZbSG43Pr8w/3JML5vqoMPz6wWidHmZSgJhrJq6CDm18IUFbvkparjZ9M0G3/mNNwISLOMQsOHwPzaOCMoCpVg2eb4tTETDS1axkXLGv2HOxZhbWWfLC1F8O/1QHp9PU6T7hEhoHG7Hju4jLnlibjCSQRAEQRASCdumfgGNwxeXSUrJ4hf7X4AGMB6fnPnF6qPYqHuXUyKXxq6eIygJhHHPyi8jJLvNdPE6IqLt1l5I0ADELp+I+z98CtOmTcPIyAi+//3vu6R1dkERJ6nH4XRgxYoV2LVrFx577DFcd911+MIXvoBp+xWUBkthP3CtCRoFNlyCm/9R81tAlpDLJqCCghAJiqYhILFG2xnfi7b9mx1n+ZQFWFw9B2k1h4yqoIo5l8pXNJ62yKjblnKZ75FgKf72/FuhgSCaTaAqXOGaD3y+uaUF5ugES6sqMgFHh1ogE5nbROLVYRD7prXksZetCZs79kKhGj47azUfmaNtzxv3dFy+QwIIcdEpUZqAt0yi+BI0quCne5/F9y+4XbA0YdEV65tBEzKo7cJ05ONZYQQb2nbhkukrEA7YfRez8cUdHMMQg4CQoIEr0g9AIjKG0sNYOXVhftZJ5CDBPtskqEPMKJCXjYCAQkVvYgTTyqrxt8tvNZw18DLb618x9YfVcUXTIEuyvuESBHKBtsGevlc69nbq3dYd2Bs/iZXhlXjppZfw+c9/3oX2U6T3tAAAIABJREFU2QnjI9c/IkiShDvvvBMNDQ2QJAm/bH4VE25fDN0LnmjKVDQ1A+6bGG9KSTXW1JyHaCaJh+pfgEaJDYefpvP6Zp/iseNIREZJQF+v2d1z2IUv+xSRXxz+m0QCaB7pwLutu4S4fuiJpqzYZ/N/iRxBRA670PWiJTG0SEGcT9etwlUzL3JNx6ThNVXLfnPmH78m6owjpuemJ05++DL64eq/QiQQgbWpR8QLS4svB3ZjEPtn3yRUFixB2DDg1q5eNr77bUf2DUtBLh3rL5qJ47eN70KjxJgyhY0P23N+ZMd2Kuy6kjf6tlEtkFFUvNz0HnKqgtJAqU1mUf3zq++sLhLIaBxuxwvHN6AkUIIrZ62BaEOWWJfE9UWkG0cHW/FBZRMOpFvwqU99Cu++++4nzrAC4xua/qRw4MAB3H333Ugmk/jZz36Gvf/8mgCL7bGym0j4HjTfm7Z6oCkljYgcxu7eI1g5ZRFCctgT35k2/yzGoZSgN9mPkUwM51af44OWF313+TSqIplLISgHEPacznKTsZBcOr6i5dCZ6MPsirox5XshOcznD3sbsKBqDirDpR54fmVg8aznttFuvNK8Cd9Z8VUBb3bahATyN7ewz07azvQPDZzEtNJqTC2tLoJnlh8xPstHTlXRmxwwzrWK5PUGQsxr3lh8jZMVGEpHUR2pNDa4sY7o7ek4lww0UBevTnraltcvRdOwq+cgLq5dDv3cqtmseZVpMTqgv7fHuqBRipkVtciqGUQCJZ747nWd5cPOUzKXgnTjDPzoRz/C2rVr8fbbb6OsTOxh7c8Mxjc0/bnB8uXL8cEHH+AHP/gB7rzzTmwsO4au+CCHZVdk+6YOyeXZ/l4SiEClGgZTIyBENtYt3fD5hkIUBiEOIRQZVUHGtoboVSG9ZNBcv0lExrauAzg80OTCd+E8ET/b8aOZBLZ1HgCldAz5zoeLeNAhp2nGRfEiWhCE+eWDj0Pg5MmOZ02jMmuJjjVCN30A0mrGuOzBi2c+/eLKqS81jN29DS48+QVvXdYowe+Or8dwOi7YVGSBcO+AyxV+dsNqxUnkMtAoNQyrnzL1rwM5VQWlFPFcGkklA4lILoZVRIsPF8dRNRVbO+vxzzsfwc6dO3Hs2DFs2bLlbDGsY4bxkevHBLLZLH71q1/h3/7t33D11VdjZmMYk0q8/YdSaq15sc8WmBXC3hN94sir+HTdSsyttI62uNHR3wN5OpQqtkbCbR2yYbAJzdF23HjOZZ54Jm98T99704sESlUAFP2pYUwuqRYfnRHmCRjeA77kyao5hOSgLb59dGfPr8L822UBNMfoqBAdNr+c65f28gaA1tFuvNK0Efes/JqDjil/4TV9b9CnTu0jL7cy8AZWb20pIKvmEJQCMM9wmuXnTceNpljOwdQIJoTLEWDOwXrnCWtY7XpImcvNWfxELo5nj76BO5Z9ATLjC1lc7nyZiMvIqRPAkw2v4tLpqzCvaqaNtr1eFwYRbUoJ3ji1CbuHjmDt2rX49re/jS9/+cu+6P2ZwfjI9c8ZQqEQ7r77bpw4cQLz5s3DI00vo2OVgkQuCbeRELtTjxDROqZohCjhq+dej3mVc3BsuA1Hh1oFoxWWboCJb24Ekbldj2ya+vOCqnOwpnYFNEgYzaYs+sxaVV4OyHAeZXCOAG1xiB7n7Zbt6E0OOWQEWD+u3LoU60LOkIFNl19P+vWhdboXLC5P7K7oZIYOm2c87/z6NoGqUfzHnkdtMwrOXcDOsne625PyNO1lonPA47F6Yx+divPBfYSp/7Z2foh3W3faeLCOvzhpEhJ0hnF5YwcJrzdvxoGBE0w+WPT5tdA8DkvT5jiCd/igy7alcz+aRjpsuF754XAEQaT8jxg6aO4eBiQMp0dRFizHTXOvQECO2GTPr9t6rFXbZ3XsdVXv9El489QW5DQFX110E+ZVzWS+i+o1XzbO0Te/03ndiXfxaN9r6JuQwM0334wNGzacrYZ1zDA+cv2YQm9vL/71X/8Vzz//PO644w6UbUmhIlQG07MKBdUrM/cMACD6yM70kkKpKsRti3WDUg0zKmoxmBrG1LIpNhpC2iYY39n0ADj4aB5pw/au/fjaEuM2FtMrjIge+x2w+ObCWfqapkCSAjg+1Iz5VbOQ95ojWvNiZTIaLC/5zDzMqFmE5GA+P93ymc0vP7yzz4lsAmWhMne5PdIsFA6qoXW0C680bcA9K78h1BcH7xwd23qkGc6VmUY1ZJUMIsESezxoQnzXvBGBkaaiKSBEhkyIqxxC/lheABBIDnkGUsMgRMakyETAHIGztL14N2kyI0H9UgC7TIOpEfy28XX87fLbQAhx1QfRMyEBaDRnLyMGNAADyUFMLZ2EXd0HsHLqEgTlgKNMTf1y010hHwA0quHdtu3InhPE3r178cUvfhGPPvooJOmsH6ONj1zPJpg2bRoeeugh1NfXI5VK4T8bn0X7qgyimRhgjFrMyp0foTGjJnNkByD/nZCA7Xn2hJmYXTkTfclhvN26zahIMtiRGYufp88eZ2DSYHHN8HlVc3D74luQyCXxTssWmJdzs/TMa70I06MmsN8gwjsr192sSZClEDSNYnfvISTVrDUq4Uan9mdZ+Mzylc83IiEsh3BypAOvNG10xXHSM0dHUp7X/DPj59V0vE6IhP19R41n2Y7PxMt/Nx22C8J52qwLR5NHPh/ZcFMmwoyGbGWGgEOmaCaOo4OnEAmWOOOJ8sBsehzHRcznkPWdSIhlE3jk0O8gSyF9GtVV7614+ZuMSCAfLpFQPm02nJAQTo12oWW0U++gwZnvJp6NLky5WGcX5m071sxRX3IYWzr3oLpkAv52+TeYuhIS1ifRM4WWL8f8rEVedhm9iX682bIFhASwZvpKhAMl+XTYWQ42jHVBaasLDE+qqmJvbwPW5bZgQ/sO3HLLLRgaGsJjjz32STCsY4bxkeufCXR3d+OnP/0pnnjiCdx2222o/lDGxEilz9hum4nsz6pG8Yv6J3HXeV9GWbBMgCOi5w8SuRQaBhuxumYFt4bpj7fCz4CqqdjTexAX1aww1v7EeMWDhoyqIJ5NYFJJZRF8+eMb0Hd0b2zbhpvmXuWLn0J0KRRY16lpaIv14KUTb+G7q+4QxnHuoOU20pGA6w5YAOhO9KFppBWX1onO67qnZ617G/ezCtIgRIKm5dCTHEJt2RT4KU/dUIibCCtdPa3+1BCGUsNYVD0P/G5e0XQ8v9PYvmYfBKWsoxMJOS2HjJpF49BJXDDN3SuReO2YgNIcCGGPFVEbD9u79iCt5HDlrE9hLPXGDXKqisahk3hreAsGBwfxxBNP4Etf+tJZc2tNETCmkeu4cf0zg97eXvz85z/Hb37zG3z+85/HpIMh1DLehE4XBlJDmFxSjX29hzCtdArqKmrhzGoiCPMHaSWD/7vvUXzvgm8bBtZOa2wbYCzaG9u24ro5l0OW5NOixYPeeBK83vwurpj5KVQwfqKdYNbF4vPIza+xfxCXTetoJ1468RbuveBOF9yxlynA8y2ixYeJZGS/WxuJdnXvQ3WkCgsmnmOUaTGNO2VoAchvMCL56dHW0Q70JPqxpvYCViIPeiIZRHqmYXf3fnQn+nDz/GsNHHaDExu/UN6whlGH99u2YXXNClBQBKUAIoFwAbpuYMdXNRX1/Q343cnXMXXqVDzwwAO49dZbEYlEXCmc5TA+LfxJgGnTpuEnP/kJGhsbMWfOHDzV/jLeiexC41Az9H4S4X6y/p8Eref8f+dvcskkADICUgBBOYiUkkNXfIDDg0FD5mgav3xa5rMVNxIoxb2r/hohOYJtnR8ilo3beNIbTnf+LFm4ZxJEJBDBjXM/i5SSwwuNb+jOOUS4hX5sXhlxiDGtNnvCLISkUIF4JrDyu8jF8NQc7cLjh19w4ZXPXz6+GS4JyoYvN/OdxZXs5eaWnoOXMPqSw/jF/seZ+JIgDji5eedwEkDCDG0rfErpFFSFJzL6IRnxzSlN5wYyiyZ3bVpefgmto93Y2LYNsyvnWIaVCGja6LP5x3834hMJhwaOoTPej1U1K3HjPNOwMvnPjkRJyAqzjVANPsxpWiJBpQQto90ACaAsVA6VqqgIVyMSiDB8sXkg1md7+eiQzKXw4vE38fdb/x2dNcP42c9+hpaWFtx+++2fZMM6Zhgfuf6ZQzqdxvPPP48HH3wQAPC9730Pg0+2ISCZjY8xBUQVo4LKAGXdvTFTmFQDpKD+nWoACeDUSDMODTTiL+Zfi6yqICSzDQ1zzIEqTCPDH/9xPlNKsKltMy6uWwtAQ1gOG5spDD55muYz+91xPEFPW9Uojg41YtmkhTontiMHkoAG/99DPqpgODOKkyOtWF2znMN1mxbmaBp5y+e9AgJFzRgNpRFulheRofeeCuetSMbWaKs+cr3wb3RUky7LiyiMxXXwYEE8G0N5yLw2jOebdfcnosnRNsLSShp7eg7g0rq1IOw0PyEuNIMAzXE6zn0zZNO0LBJKGv3JAcytmsukyzYr4ry0lXN+45kKkCDSuRgigQgO9R9DVaQSMyvqOHqALe9ssxSSwb/BK6S8rJRS5CiQzsWx7sQb+MulX8p7jNL5MOOYdArUR0afB9Ij+P3QH9DW1oZbb70VCxcuPOvuVj1NGB+5fhIhEongm9/8Jg4ePIgHH3wQL7zwAn7e+DAOndOMnmQ/otkEdnfvBeQSNI60om20E5DC+KBzNzJKBkPpUeztrQeIjIahJnTGugAi4/2OnVA0BWXhCZg9YSZymoYf7/w52mJdoJCwoXUzQGR0JwZxeLARkMKo7zuE/tQwchqwqX0rQGR0xHtxdOg4QGTs7TmI4UwUaTWHbV27cMWcq9Cb6MczDetwoL8Bu7r3Iaakkcilsb1rDyAF0TRyCs3RVkAKYlvXbiRVBdFMHLt79gFERuNQM9piXQCADzp2IqPkEM0mkFFzGMkk8MDuX6A91qnL1L4dClXRlx7Bgb4GgBAcGmhET6IXFAFDpiC64326TERGfd8R9KcGkaOGTFIYPYkBtIy2MzKNIq2q2NqxAyAyWqOdODHcDEDCru69iOUYmYiMpmi7LhMkXSYli2g2iX29BxDLpbC+ZZMhk4QP2rcjoyoYSg1jb+8BABIaBhv1W3tIEO+3b4WiaehLDeFA/xEA+qipJzUKSkLY0LYFgISBdAwpJaXL1N+A/uQAcpqGTR27rHIaPAGQIPb2HrZk6tRvWGod7cSJoSaAyHo55eJI5JLY2rkLWzt3oic5YMgEbOv6EMlcytI9SGgcPoW2WAdAZHzQsRsZJafrXs8BXfcGjqMz3q2XU5suU1diEB2xbhBJwqGB40Y5ARtaPwAgGbp3AoCE+v6jhkzApratAAmiI96Do4MndZl69mM4E0dao1h3/DU83/g6hlJRqJRYMmUTSOSS2N71IQCgKdqhy0QC2Na1F0klg2g2ZegeQeNwi16fIOkyqSr+u/4JbGzdhvOmLEEsm0Rnoh+Qgni/fRtTTg0AkfVySvSCUmLIBF2mgQaABFHffyQv0+OHf4uNrZswqmSwtvZCSHIJ9vbWW+Vk6GbraCdODDcBUhC7uj9ELJdEQklje9duQ/c60DzSAkpC+H3ja/inPQ/gkZNPYdmyZXjxxRfxzDPPjBvWMwTjxvUsAUIIrr76arz11lvYsGEDent78Ujj49g1oR7VV9VAM44x5DS9x59WUqBSACpkZDUKkAByVINCAZCAfjm5FIRGNWSoimCgBDfMux6yFMZrTW/rDSUAFRRZVe/ZZzRV7xsTCSk1C0CCQql++TgJIK0p0CCBEhkpJQ0QCTmqYe30i3D+tBXojHdjd3c9NCIjo+YMnigUqvOXUTLQIBnf9Z63AoqcRgEpqHsHkgIGTzlUlU7GZ2Z9Ghok9Cb6dJlIABqVkNH0YxhZTYUKCZQQpNWsPvVGJEOmgCGTPvWXMr6XBEuxqHoBTo60YjAzCg0ElBCktJwuEyhyxixARlOgUohlkoLIqDmbTDlKEcul9HIiMtJKBpTIUEGQ1VRACiJHAcXYKa7LRCyZpCCymgKVqqCgSCtpvRzzh0QkZFSFkSkDELOcFKOcckY5kfz3HDVkMspZpTrP8VwKSSULhRJGJp2+ZqQFKQyFQi8nEtDLiRBDJsXIE4XRvTQODjRClmTMmzgXIAFdJkigJIi0kgGksFHOWUCOGGkSppwCur4b5ZDWFGQ1DYPpEZSHJ+Bz82/Qy0lTGJnMcsrq+awpjExZaFTX77zuURU5jSKl5LCv9wBUaPjKkq+iNFQOSGFdJk0fTaby5aDrFIiErJqFSmVd94z6oOeJoTtKBr899hL6U0OYVj4N1869xpKJakirKjQig0oBpAwPXzlKkDPriykTjPoCCbFcCrtDB/Av+/8DTeopXHDBBTh8+DDWrVuHG2+88Y/YYp39MD4tfBZDLBbDc889h4cffhijo6P49re/jeybGVSEKqBPOUnc2ULNmnqllJvSQ/49no1DIhKSuSR2dO3C5+YblZIQI54xiyKiYdLJT7mpefyB5CCimVHMrZyN9ngnZk0wb6Sx47nS5fg0IZ6N48nDT+NvVnzb5g3HzosJ/PQZz7sl4/aO7agpq8HcifP1OKK6JJSfkYPHI/qaWU7NIJjfUe3BEyHcd9jzx0irNdqKlxpfxr0XfZdj0GtKm6EnBQEtp9MjAf0ZQE5TEJTDNt4dm4FMfHYjEjGmJgVAKcEbJ1/HpTMvRVWkyi436zHJ1FUzDfOZ59eIf2TgME4ON+PmBTc5+XPslObyx1ZWug5kNRWDqUHUlE/Hvp69WDH1PEa/jLVYmgVISP9fME90PvqTvdjZuRufW3Aj+pODmFQyCRIoIIXtdNzkz79r0JdgKJqGm/FM0zMA9Bu61q5di/vvvx/kjF7EftbCmDJp3Lh+AoBSij179uCRRx7BSy+9hGuuuQZ//dd/jfr7D1nrNlSxGiTzuistZ1Va/oA+gISSQke0DYsmL8Xm1o1YMW0VKiMTjQYlAOswOkfDBP4wvrEeNpIexLpjv8c3l38boLqTCAeeGZ93GpBfL4RtXYlC1g/BN7+JK+dchVAg4kGHXdsFF2asRRphVMvh0EADlk05DxI4PF5m06AQgTz5tBQ0DDRib8+H+Pp5f4l8g883oraG1Vk2fFhrtAUvNb6Eey/6nv49b3zYzTjECGN5clvHBaLpEfz3vofww4v/DwhUS4a84ml2+rpSGf95o67L2J3ogywFMLWkWjcmWsYwKiqXrwGjA5HjaPFrxxKah08ink3g/JpVoJrGrN+K1yCFZcjoE6X6FXGnRlpwqK8etyz6otXpcoDAULNrw0A+Xn3vXlSHJ6JmwkwcH2jAsmmruLVXo+PKriezNGzfgEQ2igNT6/HOO++gpqYGdXV1eOihh7BkyRIBn+PgAePGdRwKw8jICJ555hk8+uijGBoawu23346vf/3rWH/vB9zowwRuFGIbPVhhW9q34KLpazGYGkQil8CCiQsAvlfMjmpttJgRA0OfUopf7HkQX13yNUwpn+bNh4MvdqSjy6BoOezs2IZPzbwMmpZDQPa6GcSLpiWDqlGsO/o8blp4i3E3rwsUkJUFVVNBqYJA/so7ni8qGHGz6QQdcrRG2/DSsd/j3rX/20Nml1UiFz4BIKukEAoyDtrZ0VO+vFkDmtONJd9pMHjY37MPlGpYVXuRmAeRN6d8Z8UY1RmOITQtC6pl0ZPoRTwbw6LJS+wy5/MRXJhIRy14rfFl1FbMwOrpa9xpEUnvqMglDHlu9Ao9//b27MPaGZfi2MARTAhPQF2F6fOb75wynTJH2eu6qVAF8/7nLHzjG98AAKxduxbLli3Dgw8+OD5KHTuMG9dxKA4OHTqEp59+Gs8++yxqamrw9a9/HbFXFVSEJ4BtENVAKWQlbh8RAlYDJ4Xy4U3DJzCaHsHK2tU40HcISycvNnYuA44GVQpBlSOQlSTDldXQUymIeKof5cEy1PfuQ0COYNm0VSA0B2p6uLEd2GfSMEeRbING9Kk6Tcvi/915P+5Y+T9RFZlo4dl2FfPA7PRlaJmyZNUsjvQfxMqaCyE0iAJ6lMgG/xa+JkcgKUn0JYdwfLABl866zEU+iKdW+elBA1qjbXjp6HO49+J/ystAOWNAzFErByYe0TJ5HhLZBLa0vYfrFtzsHO0BduNn0jFnRKgGYkxtm7Tj2RjaRpqxZKq+A5uAQpNDIGoO1JhqldQsNDkESXUaKQBQg2WQlDS0QASSksbbJ17GhHAlLpn5GeesiZl/NgbF09QgAShSAC8fegJ/sfAW5DQFJaFKfUaFak4ddIzWndAf70JKUzGjvBZ/OL4O18//CwTlICgJCnTaYI8EQahq6I01U6AqGbSNtuDDwFbs2rULl156KWpra/GjH/0ICxcuLMjLOBSE8d3C41AcnHfeefjJT36CtrY2PPDAA9i/fz8erP8x1kuvYNpfliGj5kClIGQ1DU2OQDV+IAFogVJQuQRULoFmGE9NjmBe9WKsqLsUGUrQ0H8QVC5BR7wHPbkUqBTUf0Y8CgJJzUANlOZ/VApDkyM6HiEoK50KBEoxpWIWqkunIKfmsL+3Xm9wib7Bxf6Tocmh/DNIAJBCoHKJPqVHZEhSCH+9+u9RVTIFh/sPI5oeAZX0A/iUBA08/cyo+d/2TS6BaRzNsLSqoD3aCs0YNemymrLo/zU5YuOTygzPAf07AGiBUoTC5SgJT7DFp1IYVApCC5j5o8tHpXCeZ2ps6tHMcjL+s2CWg3l2EkSCZpYNkfMGz/pPjHhhiwc5iEmlU/SyJwFQEFv+AzDKM2ilZ9IiEjQ5pJeTJINKMkZzcXQnuvU8kYN5GpRZIzfDtEDE0plgWf5nfjvcswdRNYMrzrkWF55zvSFfIJ+mJbPOt/nfks/6jagqjvYfgUwpFkxaDMgRlEUmgci6+1ECauW/8V8NlFi8mnISGRoFDvbVQwVBX3oYPbF2IBDB5879CgKG7ut5Y+lJ/tnQz3y+EBmtI80YWdOGv3/vbqzrfBaXXHIJnnvuOWzatAnPP//8uGH9E8P4yHUcbJBIJPDKK6/g6aefxo4dO/DZz34Wt956K049lUIkWAqi5UAFPXNi9PqpFHTg7On4ACWBUiyevgbHevZicc2F+SkqOZfUG1iPfp7ZwBJNxVCyH7va38f1534Z/bEuTC6dBGKMnAk0EE21NcjsaMXxDcDGpj/g/JoLUV0yBRKR7FNnIu9OAkfuLN3BZD9aR05i1fSLxXS4+PlRIbtJiWq6QaIU/YkuTCudKuSHQoKk5aBJQRBulKxJljMCScuhdfgkXm54Bvdc8i9OmTggVAGhVKfLrusakM4mkNYUVJZU6/SN0SI/Gs2vQRICSc1CCZblcSRVHwWn1Bw+bP8Al8y5xvDpq+Vl8wJqbPYimjXaVDUVkhzEluY3sWDyeajJb4iz0tPkcF4WolojRDMfTRhNjyAgB5HIxrCvc5s+QufyIc8Le/42n4e6nJoUxGh6BNFYB2ZPWoR1hx7H1QtuRkVkso5n6KxpNFkgxqYxTQ5DUjOgUhDbWzdg2edn4JFHHkF/fz/uvvtuXHDBBbj99ts/iW4J/1gwPnIdh9OHsrIy3H777Xj77bfR3NyMm266Cc899xx+suN72ITnsLtzO5K5hI7Mrj0G9PVGSiRjpGKMeuQgLpx1BZZOX4NEZgRHevcCAE4NHsNQog9qsBSaHAaBZsWRZJ02IaCSDGIcH6KSjInlNbh+0ZdAiYQ/HHtePweoZqESYoy+LEOtGaMz09CbIz6dTz3syvm3YEpZDd5t+gN2tL1npGPiWLTy/00fuMbI1aRrftM0VT8qwdEB9EZSC5RYvBn5xeJQIufjxbOjePHgY1DksI0HM69hyAxCGP743dDmqDSQj5P/AcJGXQuU6HEMA5bn2eCjdbQVG0++yuBb3nv49E36rGG1voVBKUUGFBQUqrFmnZdFkqHkHVMY9Jl7da009Rt5/mvr/4PR9Ag+PfeGvGE1DZMmhx2GlaWlSfpoOWMY4c3Nb+DUcCMmT5iJ6xbcrOcBkfJ5QCHlf2x+6nzreX2w7wAyShpDyV6cHD4OCglfOO8OVEQm61Pe0GwjfZYXouWgyWGoUgh72zfj13sexKMn/wWvHHkChw8fxjPPPINkMon//M//xDe+8Y1xw/oxhPGR6zj4gmg0ijfeeAMvvvgiNm7ciLVr16KsfyGW1K5BpbmRx8OxO+GKa1vLBtRMmI2ZE2Zhf+dWrJl9le07NTqLbLx8mNlIGw3ljpYNGEr24oYlX4NGNch+Nm5wvObULFSqIpWN48TAIVw068rCNFhy3Ai0se8AhpL9uPica4RyAVbD7xjxGWDKTikFIcRpOJn0+HVUNo8oCNqGT+DVQ7/Bdz79Y0dapjEgpvN5wREnk2+TlzxvvDE1OgbsSJodIZpyZ5QU3jz0GD53/rcQlOzGhaUp4gXQR5oAEM+Mor5rBy6ZewPimSjKw5UGDWvcYI5u3XgGAE1ToGo5/Pz97+N7l/8c4YCz02HjEcSafWBmVoaTfegaOYWltavxzrHf4sKZl2NSWY09smgmg/mmUQ0dQ8fw1IcPYuK0CUgkEqiqqsIzzzyD1atXj99E88eHMY1cC6+8j8M4AKisrMRtt92G2267DYlEAm+99RbWrVuH/+/t/4X58+fjpptuwsDmSkyvmgcJsG+8gN4TMjeaaIEIPnXOtdDkMGKJXsRyCWhSEAc6d6CmvBZTqhdCVtJQAxHISjo/AiWavgYsKSmowVJIahZUCuKic65DDkBKy+GhTd/Hdy9/ELIxTUmJnJ/ykzTd/R2Vg/mRC9FUaFIQASmIAIBYNgE5WAZKZIykh1FZOgVEUxxx8iNqpnGkchBQ9f+Tq85BJFypO2OQZL12EmJNIVNqGwlJmrVxh6j6qEXSclABNes2AAAgAElEQVRJAL/e+k/4yqq7UVk61UbDnE4kmgpq/jdoU2Lw+f+3d+dhUlTn4se/b1Vvsw/MDMO+byKyiYmICxpwQQ1xjdcsXmJMYmJiEs3mvUlu4pLNJL9cE01i4jUxmxolMREXAgKCCMI4gOwMMMAwMzAzzDBrL1Xn90dV9/QsoInNxOX9PE8/3X3qnDrnVC9vn1PVVWKljao6ZxXSp13FOLjBbOxYC04o17sPZnuvVfIAIv+vIM9v/T1FBSOZPvx877VJjgytniPKJCecj5Voxw1kIYkornEJ2RHGDZhKwAql1u0GIj0CsRPMwY630v3I86gdpi3WTHYgi5h3MTZyIv0w9BLwgyHsRIf3HrTDqfdR8r58/1IOHt3N5VNu5Itz7yMkNq6/fTu3p516PwH+qDOAmAQGi7UVf+eUIWeRcBM0RI/i2mHmTV7ovS9S/xv3XjsnEOk8KEssxLjUth+ltmEb5R3PsWPHDsaOHcvI8cO48847ufzyy/VI37chHbmqNyUej7Nq1Sqefvpp/v73v9PY2Mj8+fOJbR3I2JKpZHW7ekxqKq6XfZcbDyxnUMEYskN5LNv+R94//TOQtk/NCyLBzmniLlNx3nrbWg+TGynkmS2/YXj/CUxK3/fZjZzgvd8Rb+UXK7/KZ87/EbYEXvfLLflF3d0r+56nsb2eCydek/oy9vZlJkdR/nqTo0c/uKT3ra65iqJ870Lw6dujtz4YkR77DwH2N2znb+W/4DMX/Ngv2O2o4xNJ1mkFaIsdwzjeaTGTbe7ephSr52/3eEcTv1j1X9w05weEA1k9Rm7JHzC9jS7BO/5aRCjft4TapkoumfzREza9x/ZJmwr+7apvMn/yQgrC+ZhAhFAvU+TpfRRjUgfSAVQ37GR7zTrOn3gdG/YtYXTxafTPKe0xdZ2acXE7fzgYY9iwbwmVDdvIHRlj+fLlnHfeeZx11lmcccYZXHHFFSfsl+pT+lcc9e9XUVHB008/zeLFi1m9ejWnnnoq8+bN4/CqQoYMmIz4++eSU5CW42AswXYcXLGwjMsxcTh0qIwxw2azZMNPGTvwDMaXTifmOoREcAJBbMchEQgSSMSJB0LYTtcv+Ob2BqxAgHjHMZ7a+HNumH2nX69DIhjGTiR6TFW7YmG5CVwrgOUmSIhFwLg8uu5ezhxzKSOKTkktc60AlnFxbBvbcXoEieTyqBMnHm9GQtm4sXZysoq61JG8Tw9w8UCIYCJGPBjGTnhfyFtqXiHbCjFy4DRsx8GxbSzH7dEHjOttn0S8Sxurajfzt42/4Oa592GstB8lfr2BRLzXqfh4MEwgHiMRDLF682+ZPu5yCuzs1PYCcOwAwUQMJ22/nxGLQCKeeo0c26a+uYqivCE0ttZSkDuoc3nc28+ZCIa7vTdcolnZhNvbcAIBLMfhiVd+yNSx8xk+YAqCEHDiuGJj+TMk4vb8WnBtO7XOqsOvsXbvM1w98zZq2g4xIKsUEwx3aWsiEMROJHACgdS95TgknDhBK8Cjr/yAc8ddRV5WEbWNFYweeDqWcbtsKwDLTWA5Lq5tIa6hqaOBbftXsHr/n4hEIhw7dozhw4fzne98h1mzZlFSUtKj7eotQYOremvp6OjgpZdeYsmSJSxZsoRdu3Zx7rnnMnfuXHY8l0Np4RgIdP2VbyW6BsmjHXWEAlnEYy38bvlXueXSh2loOUQkUkBWKA9xuh0kE/BGGpaf7roO9c0H6V80hr+t+T4zRsxjWPGpSHK/1Rt4/ze1VpMb6U/10d3UN1cxbdQ8r+g/8Zl7bf8LVDfsZt60m3oGRF/39aX3paJmA1mhXAb3n5AKFMetv5fTKx6o28risp/yyYse6L2Iv87OwODvf/XvHYF1O57kjNGXEkgf4aUOjPL/i9rt9TN+enu8jd8tu52F5/+QYCCcqs/p9vonA3ZyG1muS0esldVb/8AFUz9OfVstBTmlhJxuP2YCve/hMsabAP/b2h8ybcT7GNRvDMdizfTPG5yaHUiObpN9TStMbeMeivNHsKd2Axsqnua6c77N4aZ99M8d3HU7pEn4fWo8doD1O//CrEuGcP/99+M4DpdccgnV1dU8/PDDTJw4Uad73x40uKq3trq6OpYtW8bSpUtZsWIFhw8f5uyzz+bcc89l57ICSkpPJeS4xEJZBOMdOIFgly/reKKDYCDCyi2/pySrhPGj51G+/Ulmjns/bjCM5Xqj0mCsg3goQjDWkRoNO4EgRxsryc4qoqFxLy++9juum3M3CQwh1+D4+ztt18G1LO+vKGJhuw6OZWMZl5qmfbS1HmH40DPZsuvvTB7xPiQYSeV5vXtxHY40H6Syej2nT7yqy7pT7bQDPb/kfY6b4EDVOkYOOwvbdUgEQwTiMVzLwnLd1H2yzuTzRCBIdc1Gnln/Ez4+/0FcO4CdiKdGuE4giOUkMHgHKrmWje0kcOwAgUScBjdKS+M+Bg+Y3LVBfmCwnAROIEggHktt93goQiAepSXeStm2x5k97Ubvh5BtI66Lsawu924ggJVIYKzOv7UcObqXjo5GhpVMZsPOvzJ97GUELZtoJJtwRxvRrFzC7S3EQlmEYu2dgTqRwLWEo3U7eW7jg3x0znc51F5Dcbg/JqsQ2596t51Earsng7qxbeoa99HUUsOYoWfy+NIvM+/0WyiMFBGPZBN2HFzLTo1wARw7iO3EaWw7Qk39dtbt/hnBYJCqqiqMMdxxxx1MmDCB+fPnk5vbdTeJelvQA5rUW1txcTHXXnst1157LQA1NTWsXLmSFStW8NL+FRxYfYBZs2Yxa9Ysdqzpx5CSyYT8oz/joSCWm00UmHX6TbgCHa1HaYg1kojksHHnUwTsMKeOuYg2C4JANJKN5brE7QCWkyC7dALBeJwBkSlcmHc7iWCYR5+7ldlTFzKwZDKxRDtZ4Xws4+AEQtiJGPFANnYihguUFI2DonFEEx0cbNzNpHGXUtdUSU52MdmhAozxAnTcH0XF7YgX3BAs4+BKGCuaRVbuAIxt0+K0Ew7kefsRjcHBu0KKsazUdK2kTTe3d7Ty6v5lDB0xGzcQ9reLd58cvzv+uhK2d1BOUiIYxoiVmnpNRLzAkAwQqRkEv97kTxrHDtJQs5P9tWUMHDStS3uSYhHvBA7JNsdDEYwxtLpRgsEswln9MAKxnFx/eYCcllZa8vPIaWnFWBbxYAiCIUKxKAcPb6Zk4Gk0xRpp7jjCoEg2U6d+yLsijV9nNCsXcRzacvIIxuMcy8/HPVpDTlZ//vLSN5k85hJGDTmT+Wf/N9FwNkXh0d70sOMQC0ewEw6JUARJxEk4UaxQFk8uuZ0PnPN1OuKtNMabcC2bBZf8BDvhEAt427IDvLJWgMamfTz70veZe9k0/vq3v3LkyBHmzZvHmDFj+NCHPsSHP/xhQqETH3Ws3rl05KreMurq6li1ahVr1qxhzZo1lJWVMWLEiFTAXb2kH0X9RiP+l7jluiSC3pdeU8N+wJBXOJQHf3MRn7jmSRpNC+ZYPYXDphJpj6b2CaYfLBSLt2HZIY7U72T1up9x1WX3U3tkGwX5Q4iE8739qWksA674X7DBAOs2/Iqi4vGMGnYW9U2VFPcfSzAWJxHs/XdrIJ7ACdg0N1Xx5NIvs/Dy3+CE0s5eBDgBv39Oz7e4saCt/Sg4LjnZRanpW9txiAeDhKKxHnUbEQ7VbmLZqu9x/bV/6FJXcko0WVfAcUnYnft/6xoqKCzteqafQLxzmyTrd7v9PWT//jVs3LGI98/9HvFQgGAsQTzU+zaxEy4JE6ex6QBF/Ufz7NJvMGvqQgr7jyKYcHAswXYNjr+vOFmXawuHj2yjf9ZAjjVX88KaH/HBy39BNNqM5BYiIoSi3ii1I9sLctmtMTpIcHD/GkaOOo9Xyv6PhNPB7NM/yb7qDQwunYIdjKS2afI909h+mLJXH+HKq8fxwAMPcOTIEUpLSzl27Bg333wzF154ITNnziQvL69799Tbn04Lq3eWeDzOpk2bWLNmDS+//DJr1qzhyJEjTJs2jdNPP50ZM2aw5PkC+vUbhWUlT9QgWO3tEIpwYO+LNBw7yLQp17F0+V2cPv0GsgoH4zgxsq0snICV+tIPxhIYY4hlhVmz7HuMmXARRblD2bbneWZMuqZLwPOOyHUxIt6IM2DRdHg3q9b9jAUX3Uv90b3k5w9BIlkE4k7nQU/gHYjk/yiwWtsw4QirVn6fGe+9ibxQYZd6XFuwHJP6ARFujxPNCrJx7a8JhfOYMulKnIBFIO4QC3sBOhSNkwjaXeqNhYPUV25g2YtecHUCFnbC7RL0QtF4lwOSEkEbu6ODJ/76SS696PtE8oq7tCV9uyWDZjCWYOveJQTsMCPHnI90dECWtw1ikQB2IrmfNXnCCyFWXUEkUkh9035e3fIYl1zw7dTypOR2TmqrP8D+w+VMPOVyVq34AePHXkTJ4NMAeu7DdF06OhrJyupH+Ybf0q9oNMMHv4dn/nEHl869h3jAIuRIat8wQGNjJRUVL/CBKwZy7733YoyhqamJeDzO5z73OUpLS5k5cyYXXPDP/RdavW1pcFXvfPX19bz66quUlZWxYcMGysrKqK6uZurUqcyYMYPp06czefJkHnw0hFOQR+6xOK15Qeq3r2bA4KlUH9tN+fL7uPDDv6a6bDF5A8fRv2hs6uQI6SPVluYadlcsY9q061n78gMMHjSNYSNm4Xa0EwhGiEWCBKMJEkE7FWQCcYdlz32dCdOvYUD+KBobKykZODkV0NI5AQs7FmfrrmeYMOESjjbsISermKysfqkAGY0ECMW8SdqOrADhDoeEbRFwXBob9lHQb4R/ggkhEHdoyw2S3eKN1pKBEKBufxkrl93DtR96lPbszoOIgnF/5GlbqSNb7YRDc1MVWTnF2HaIRChAMO4QD9pE2r11xyJBQh1xYpEgluNSc7CMAYOnUFe7DTcni4H5Y0kErFTbk9qzAwSOtXKkfhelg6ey8vn/Yeykyxg0/AzAmxFIikZsslviJOIdmJxcli/+L6a990aCdoi9e1Ywdep1QNq5j0VSr+PmrU8wetjZNMebWPvsnSy4/hHqarcRyepHTsEg7281xlBVuZb3ndlANBrlzjvvpKioiJqaGizL4sYbb6SlpYWLL76YK6+8kmDw+P/lVe9oGlzVu1NTUxPl5eVs2LCB8vJytmzZwvbt2yktLWXy5Mmp22OrCigoHoUJhwkmoGLTU/QvnUh23gD++ssPcM2tL9BQs5WE5VJa6o+E/HdZU+1uwpFC2nOE5392NdfcuowjtVuw7CD9B05MTatK2ruyrvo1dpUv4oz3f4PqzUspGTiZrHzvPMGuH/csP5bEw8LWZQ/Qr2Q8Q8eeRyLWhpWfTyDmrbAjWwhFO+swxrDksZs544Lb6FcyDieQ/C9lZwMsP5a7Fhw5WM7ap+9kwY1PAOAk/75pC4G4SeVLWvfs3ZSOmcWoMV1HZ4lgt5M5tDUSzi7kpSe+wpTZnyQ0YkxqWW5TcqTqPT+w50UKSsYChrIlP+Tca36Enej5MY4FoXL9Isac9n4qtz/P/p3LeM/199KyexMFxaMIhnP99QrGddn16hNMOvUq9m17lsq9K5hz6XfZXvYnBo6bTW7hUL+dDWxa9UsuP7eEvXv3smjRIiKRCPF4nPz8fK6//nqqqqr49Kc/zdlnn62nE1TpNLgqleQ4DhUVFbz22mtdbnv37mXYsGGMHz+ecePGpe5//HScgtJxVO5bgd0aY/jki3jm51fx3o/9DBGhZecmhp06j2gYjJvAsoMc2rAYyw5ROvV9vPzLT3LWdT8kIS5OIkpW/oAu+0y3LLufEVMuIxGxOVT2DKfMuSm1zHT76NbtK2Pb0geYc8MvcRJR3JwIwWjX4N2eK9hRb5RWs+NFBow7KzU13pv6ynJeXfQtLv7Mk0DPIAmQCEHz4T2I2BRlDUNEaM8VIq3+CS/8fZ7RkIvVkaDtaBVr//glLvjCn3usK9neY82HOFD+NOMu/CQVLz5C/xHTGFCadtRxPEFLfSX5JaPZu/EpErF2Rp59HRv//G0mz/0sgaw8xLJJBCHW1sjhHasZMfUytiz5KeGcQsaf+WE2/PXbnHrZbUTrammtr+SW+fl84xvfYObMmaxcuZKDBw+mjtKdMWMGCxYsYOfOnXzrW9+iuLj4uNtMKZ8eLaxUkm3bjB8/nvHjx3PllVem0qPRKBUVFezatYudO3fy6quv8uijj7Jr1y6OHj3KmDFjGDduHIPr9/LFj1/N5MlHMMbw9Vf2UJIjvPzI5xk88xIGTrmAurptnHLZF2gy9Qw572qiJTkc2r6Uo1vXMfmqO9i69KcMmzKf7OKhlFx0FXaklFhjDfbwwcQisOEPX2Xoey6neNyZtNdVkVM8HID8iTM4Y9z9tEQMy+++ljM+8RMihQMJOSHEsolmO4TbbJyw0B7poGLTIrImnEKgXyF2PLnP1vs+aO0XI7c+TCLkjUzbczvTc44mL4tmUvdH9q4nEIpgnzeYcGsAMLQWGgIxCyfWgWSF2fTneygYMoEBFy3gzK/+jo6AgxNycWJRYs0NZBUNovwXX2Lkef9B7uCxyMD+GDGMnP1BWuMNtBa47H3hEQrGTCGveDQvP3g7Z/33Y0ROPY1oUx1tBQkGnbWAg1Vr6TfnAsru+hgTL7uV7LxB1FS8zL2fmME9S7czYfgEdi7/LAfLXqBt3z+oqqoiPz+f3zXMobm5mdNOO40PfOADFBcXc8455+h/SlWf0uCq3lXC4TCTJk1i0qRJPZa1tLSwe/dudu7cSUVFBZWVlaxatYr9+/dTWVnJgRW/YNiwYZTs+xuDnXJc6yDXFa1n48aNPLJrL7kzTqdq2xJGLLiBpmAdHfEmYkMjdBzYztY/3MPpdz1M7asvEsrvT9PAdorPvojwqHHUOZVs/dMdnPm1R6ja9DzhYB5Fk2Zh4u1Mv+shyMrh4NJnqd+xjok3/w9tNZXkFg7DDVmAMPmW75KwXTb/+uuMvuJTZPX3ThQfjNoYG5oHRGk7GsMJGJqLo6n+thV6aQBVi/9IaMggihdcTqgtQCw7QSw7gdN4jISdQCI26750DWfdt5ihCz+FFY7gtsUgL8S2R3/AkA/9Jx1btlPzynOMu/UORsz7MIHcfjiDIzQ8WUbx9PNoOLqFQ089yoxP/wR3aB51R18ja8okis6+gIMV/yArMpCyX36WRX94mHt+fS/ZoRDZDf+gcc869v/5S+zfvx/btrnttgp2795Nfn4+s2fPZtSoUdx2220MGzZMp3PVW4YGV6V8ubm5TJs2jWnTpvVYZoyhoaEhFWj3799PTk4Ojz/+ONXV1YRra1n7xfOJxWLY29fSv39/Iu3tjC8WduzYwZxJQ/mgs5nvrPsjX/zylxkx6Chzf/8/zLj/9xxetpTgsEG0FrdwtGoj2aeOJNS/gZevu5D3/u5pGmu205HbxsjP3UpzcyVbf/5VJn3lHtyWOC1VOymZ6e0X7XfObGRYNvWV5VjhCNlDhuNEvBPER/u1YGyHWH5rlz7Vr1lB0XvPITxjDOHiUuI57bSbZmrXL2HIeVeyb9FDBAv7MeCi+Uy971dULL6foQs/yoFfPUiiuZmxN99Ga6KG1iN7CE0ZzNEnNhJNNHKocgXN21/jld8/xPVtexmz6SFmDRjAD7e+yOCX7qV88WKi0Sj28sfYvWULgUCAcDiM5Tj8+Mc/prq6mpkzZzJ37lxKSkq4/fbbGTJkCFlZWX3zZlDqTdJ9rkplUFtbG7W1tce97dy5k3g8Tl1dHY2NjYgIwWCQgoIChg8fztq1a5k3bx4dHR1s2LCBW265hccff5xAIMCtt97K1772NRYuXEhhYSH33XcfN9xwA/X19axevZrPf/7zlJeXU1FRwfnnn08wGKSmpoYFCxawZcsWHnjgAe6++25CoRBPPfUUAwcOZP369bS1tTFnzhxWr17N5s2bufvuu7n33ntpaGjggQceYOHChYwfP56LL76Yu+66i5tuuokXXniBffv2cc011/DYY49RUFBAQUEBe/bsIT8/n2PHjmFZFsOHD+fAgQNMnz6dwsJC9u3bx6c+9Sm2bdvGxIkTufDCC6mtrWXOnDl6NK56q3rXHdCk1NueiESAfv6tEOgPZAEDgCIgBkwB4ng/KM8FtgCDgOHARuC9QDtQ7edtAGygAO+kQsmrmSe/JFqAHP/5HmCUv/7tfvnNft0lwGLgfUAVsB64EPgtMATIBR4FLgCeAw75ZZYDTcYc52KsSr0LaHBVSimlMkwvaa+UUkplmAZXpZRSKsM0uCqllFIZpsFVKaWUyjANrkoppVSGaXBVSimlMkyDq1JKKZVhGlyVUkqpDNPgqpRSSmWYBlellFIqwzS4KqWUUhmmwVUppZTKMA2uSimlVIZpcFVKKaUyTIOrUkoplWEaXJVSSqkM0+CqlFJKZZgGV6WUUirDNLgqpZRSGabBVSmllMowDa5KKaVUhmlwVUoppTJMg6tSSimVYRpclVJKqQzT4KqUUkplmAZXpZRSKsM0uCqllFIZFvh3N+DdaHy/oaYtEfWfSbel0i2l5/LjPpY3ki/tubxe3m7l5PjLjJyonV3LmvRlvbTBpJ52X2dv9fj5JK1sL00wPfqclrfbOk23fJ1196zWdM/UI62XtknXHL2tt2tbetTSsx7pUkOX9Zj09NS6TY+6Unl79L17edO1aG/t67Ke7su712G6v/rHLSu9ttt0fzemZeks31sd3fN0L9fzHd/b+tLa1b1pJ1gmx007Tt3m9cufsI7ON+AJ6u7+OO37qMuml9fPn5a+cUvtc8aYi3kX0eD6b9AWj/LZ6VfgvRGTkwcWIlbXNLH8x+Iv8/P1kgYW+OWTZV83Lb38cdK8YOalGbFAxLtPpck/nc8k6xDxn3tpycDZNa+k1pl63ktaKp/FcdPcE+QzfpM60+iWBq7VGQiTy7uWpZd84udLS7OMX977tjR+cPLymdR9ehrJ/Km6/XUkA5tl/HwmrWzac8v1y/ppaflT6d3SENd7nLzvlibWG8uXTBP/seAiyWV+uuCvM628YPwyyXxeWcRPxwtcVjIvnfks0soDlv9Y8Jalry99Hcm8FsbP66eJSaVbqeDXMy2Zr0tZjP8JNdjJlystzTKk8tq9pKWX7ZoGlhFsUm8BP12wUvWAbcTPm0zrfG776+hcp2AZwfK/M5KPk+nJ8rbpmXa8sgClp/6gmHcZnRZWSimlMkyDq1JKKZVhGlyVUkqpDNPgqpRSSmWYBlellFIqwzS4KqWUUhmmwVUppZTKMA2uSimlVIZpcFVKKaUyTIOrUkoplWEaXJVSSqkME2N6O+m2OplE5Fng9c61WQzU9UFztH6tX+vX+k92/XXvthP3a3B9ixKR9caYmVq/1q/1a/3vtvrfCXRaWCmllMowDa5KKaVUhmlwfev6pdav9Wv9Wv+7tP63Pd3nqpRSSmWYjlyVUkqpDNPg2sdE5CEROSwirx1nuYjI/4rIbhHZJCIz0pbtE5HNIlIuIutPUv0f8uvdJCIvicjUtGUXi8gOv21fPUn1L/DrLheR9SJydtqyvuj/HBFp8usoF5FvpC076f1Pa0O5iGwRkRVp6X3R/wIR+ZuIbPTrX5i2rC9e/34issh/D6wTkclpy95U/0VkmIi8ICLb/L7d2kueiSKyRkSiInJ7t2Vvqv9vsP4Tff5Oev/T8p4hIo6IXJ2W9qZf/3cVY4ze+vAGnAvMAF47zvL5wDOAAGcCa9OW7QOKT3L9ZwH9/MeXJOsHbKACGA2EgI3ApJNQfy6duyumANv7uP9zgL/3kt5X/S8EtgLD/ecD+rj/dwDf8x+XAA1+f/uq/z8Avuk/nggszVT/gUHADP9xHrCzex+AAcAZwN3A7Zl8/d9g/b1+/vqq/2l9XQYsBq7O5Pv/3XTTkWsfM8asxPvCOp4FwG+N52WgUEQG9VX9xpiXjDFH/acvA0P9x+8Bdhtj9hhjYsCf/LZmuv4W43+agRwgowcFvIHtfzx90n/geuBJY8x+P//hf6Gtb6Z+A+SJiOD90GkAEvRd/ycBS/2824GRIlL6z9ZznLqrjTFl/uNmYBswpFuew8aYV4B4t+Jvuv9vsP7jff7etDdSv++zwBNA+nsvI6//u4kG17eeIcCBtOcH6fwAGOB5EdkgIp/og7bciDeKfr12ZZSIXCEi24GngY+lLeqr/s/yp0WfEZFT/bS+6v94oJ+ILPf7+dG0ZX3R/58CpwCHgM3ArcYYl77r/0bgSgAReQ8wgs4Ak7H+i8hIYDqw9g0WyWj/32D96Z8/6IP+i8gQ4Arg592K9Nnn/50i8O9ugOpBeklLjt5mG2MOicgAYImIbPdHAplvhMj5eB/u5D7PE7Uro4wxi4BFInIucCcw11/UF/0vA0YYY1pEZD7wF2Acfdf/AHA68D4gC1gjIi8bY3bSN/2/CCgHLgDG+PW8SN/1/7vAT0SkHC+4v4o3coYM9V9EcvFGZp83xhx7o8V6SfuX+v9G6u/l8wd90///B3zFGON4kxedxXpZlf7V5AR05PrWcxAYlvZ8KN4oAmNM8v4wsAhvqibjRGQK8CtggTGm/vXadbL4XxxjRKTYf37S+2+MOWaMafEfLwaCfv191f+DwLPGmFZjTB2wEpjqt6cvXv+FeNPSxhizG9iLt++zT/rvb/+FxphpwEfx9vvu9Ze96f6LSBAvsPzeGPPkP1E0I/1/I/Uf5/PXV/2fCfxJRPYBVwP3i8gH+Dd8/t/uNLi+9TwFfFQ8ZwJNxphqEckRkTwAEckBLgSOe8Tpv0pEhgNPAh/xR0tJrwDjRGSUiISA6/y2Zrr+sf7+PsQ7UjoE1Pdh/wem1f8evM9IPX3Uf+CvwDkiEhCRbOC9wLa+6j+wH2/UjL+vcwKwh757/Qv99QN8HFhpjDmWif77r5IeoOMAAATsSURBVOuvgW3GmB/9k0170/1/I/Uf7/PXV/03xowyxow0xowE/gx82hjzF/ru/f+OodPCfUxE/oh3RGqxiBwEvgkEAYwxP8c7Qm8+sBtowxtJAJTiTZWC97r9wRjz7Emo/xtAEd4vVoCEMWamMSYhIrcAz+EdOfiQMWbLSaj/KrwfF3GgHfigMcb4X/R90f+rgZtFJOHXf51/gFWf9N8Ys028qyZtAlzgV8aY10RkdB/1/07gYRHZjDcV+BV/BE0fvf6nAL8VEQfvqOkb/aKZeP1nAx8BNvvTzuAdHT08Wb+IDATWA/mAKyKfxzsq9lgG+v+69XOcz19f9f94BTP1+X830TM0KaWUUhmm08JKKaVUhmlwVUoppTJMg6tSSimVYRpclVJKqQzT4KqUUkplmAZXpTJMRIyIPJL2PCAiR0Tk7/7z90uGrioiIotFpDAT68oUEfm8+KdtFJGnROQjacseFJEv+Y//JCLj/l3tVOpk0r/iKJVhItIC7ALOMsa0i8glwHeAg8aYy/qgfsH7bLsnu65e6g7gnUJyhv/fyJHAC3jnsZ2Ed87a040xcRE5D/iwMeamvm6nUiebjlyVOjmeAS71H/8H8MfkAhH5TxH5qf/4YfGu3/uSiOyRtOtnphORv4h3wvYtknbSdvGu8VksIiPFu07n/XjBbZiItIjI9/xy/xCR94h3QYA9IvJ+v/xIEXlRRMr821l++iARWSnetUNfE5FzRMT22/uaeNcV/UIvTb0AKDPGJACMMfuAXwLfB+4HbjHGJK848yIw1w/ISr2jaHBV6uT4E3CdiETwrkt7oqufDMI7QftleCeu783HjDGn45379XMiUtRLngl4lyucboypxLtk33K/XDNwFzAP76on3/bLHAbmGWNmAB8E/tdPvx54zj/H71S8k/lPA4YYYyYbY04D/q+XNswGNnRLuxe4GNiSfqJ5f2S921+/Uu8o+otRqZPAGLPJnxL9D7xTWp7IX/xAs1WOf+3Sz4nIFf7jYXhX6qnvlqfSeNcATooByVPkbQai/nTsZmCknx4Efioi0wAH75J34J1L9iHxTvT+F2NMuYjsAUaLyH14lwN8vpd2DsK7Tmi6KXinUpwoIla36erDwGB6BmSl3tZ05KrUyfMU3qjtj6+TL5r2uMelvURkDt5l92YZY6biXYYt0st6Wrs9j5vOgyrcZD1+cEv+sP4CUIs3epyJd6GE5BWJzgWqgEdE5KPGu4j3VGA58Bm8K7d0157eNhGx8KaDP4K3H/rmbvkjfhml3lF05KrUyfMQ3lWNNvsB8l9VABw1xrSJyETgzIy0rnPdB40xrojcgHdSdkRkBFBljHnQvwrLDBFZDMSMMU+ISAXwcC/r2waMTXv+SWCXMWa5iOzEuz7tY8aYI/7y8YCeAF6942hwVeokMcYcBH6SgVU9C3xKRDYBO4CXXyf/P+N+4AkRuQbvqN7k6HcO8CX/6kQteNdWHQL8nz8aBfhaL+t7BngEQLyLen8F/8eAf6Hvn+Ad3LTQnwJvN8ZUZ7A/Sr0l6F9xlFIZJSKLgC8bY3a9Tr4vAMeMMb/um5Yp1Xd0n6tSKtO+indg0+tpBH5zktui1L+FjlyVUkqpDNORq1JKKZVhGlyVUkqpDNPgqpRSSmWYBlellFIqwzS4KqWUUhmmwVUppZTKsP8PVmAUodp6do0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group.plotAll(closefigs=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MAF has lots of metric provided in its BaseMetric class.  You can get the list of names of them, as shown in the Writing a New Metric notebook, but they won't be very useful unless you can also see what they do.  Fortunately, the name of the module is also stored in the BaseMetric registry:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassMetric <class 'lsst.sims.maf.metrics.simpleMetrics.PassMetric'>\n",
      "Coaddm5Metric <class 'lsst.sims.maf.metrics.simpleMetrics.Coaddm5Metric'>\n",
      "MaxMetric <class 'lsst.sims.maf.metrics.simpleMetrics.MaxMetric'>\n",
      "AbsMaxMetric <class 'lsst.sims.maf.metrics.simpleMetrics.AbsMaxMetric'>\n",
      "MeanMetric <class 'lsst.sims.maf.metrics.simpleMetrics.MeanMetric'>\n",
      "AbsMeanMetric <class 'lsst.sims.maf.metrics.simpleMetrics.AbsMeanMetric'>\n",
      "MedianMetric <class 'lsst.sims.maf.metrics.simpleMetrics.MedianMetric'>\n",
      "AbsMedianMetric <class 'lsst.sims.maf.metrics.simpleMetrics.AbsMedianMetric'>\n",
      "MinMetric <class 'lsst.sims.maf.metrics.simpleMetrics.MinMetric'>\n",
      "FullRangeMetric <class 'lsst.sims.maf.metrics.simpleMetrics.FullRangeMetric'>\n",
      "RmsMetric <class 'lsst.sims.maf.metrics.simpleMetrics.RmsMetric'>\n",
      "SumMetric <class 'lsst.sims.maf.metrics.simpleMetrics.SumMetric'>\n",
      "CountUniqueMetric <class 'lsst.sims.maf.metrics.simpleMetrics.CountUniqueMetric'>\n",
      "UniqueRatioMetric <class 'lsst.sims.maf.metrics.simpleMetrics.UniqueRatioMetric'>\n",
      "CountMetric <class 'lsst.sims.maf.metrics.simpleMetrics.CountMetric'>\n",
      "CountExplimMetric <class 'lsst.sims.maf.metrics.simpleMetrics.CountExplimMetric'>\n",
      "CountRatioMetric <class 'lsst.sims.maf.metrics.simpleMetrics.CountRatioMetric'>\n",
      "CountSubsetMetric <class 'lsst.sims.maf.metrics.simpleMetrics.CountSubsetMetric'>\n",
      "RobustRmsMetric <class 'lsst.sims.maf.metrics.simpleMetrics.RobustRmsMetric'>\n",
      "MaxPercentMetric <class 'lsst.sims.maf.metrics.simpleMetrics.MaxPercentMetric'>\n",
      "AbsMaxPercentMetric <class 'lsst.sims.maf.metrics.simpleMetrics.AbsMaxPercentMetric'>\n",
      "BinaryMetric <class 'lsst.sims.maf.metrics.simpleMetrics.BinaryMetric'>\n",
      "FracAboveMetric <class 'lsst.sims.maf.metrics.simpleMetrics.FracAboveMetric'>\n",
      "FracBelowMetric <class 'lsst.sims.maf.metrics.simpleMetrics.FracBelowMetric'>\n",
      "PercentileMetric <class 'lsst.sims.maf.metrics.simpleMetrics.PercentileMetric'>\n",
      "NoutliersNsigmaMetric <class 'lsst.sims.maf.metrics.simpleMetrics.NoutliersNsigmaMetric'>\n",
      "MeanAngleMetric <class 'lsst.sims.maf.metrics.simpleMetrics.MeanAngleMetric'>\n",
      "RmsAngleMetric <class 'lsst.sims.maf.metrics.simpleMetrics.RmsAngleMetric'>\n",
      "FullRangeAngleMetric <class 'lsst.sims.maf.metrics.simpleMetrics.FullRangeAngleMetric'>\n",
      "fONv <class 'lsst.sims.maf.metrics.summaryMetrics.fONv'>\n",
      "fOArea <class 'lsst.sims.maf.metrics.summaryMetrics.fOArea'>\n",
      "TableFractionMetric <class 'lsst.sims.maf.metrics.summaryMetrics.TableFractionMetric'>\n",
      "IdentityMetric <class 'lsst.sims.maf.metrics.summaryMetrics.IdentityMetric'>\n",
      "NormalizeMetric <class 'lsst.sims.maf.metrics.summaryMetrics.NormalizeMetric'>\n",
      "ZeropointMetric <class 'lsst.sims.maf.metrics.summaryMetrics.ZeropointMetric'>\n",
      "TotalPowerMetric <class 'lsst.sims.maf.metrics.summaryMetrics.TotalPowerMetric'>\n",
      "StaticProbesFoMEmulatorMetricSimple <class 'lsst.sims.maf.metrics.summaryMetrics.StaticProbesFoMEmulatorMetricSimple'>\n",
      "NChangesMetric <class 'lsst.sims.maf.metrics.technicalMetrics.NChangesMetric'>\n",
      "MinTimeBetweenStatesMetric <class 'lsst.sims.maf.metrics.technicalMetrics.MinTimeBetweenStatesMetric'>\n",
      "NStateChangesFasterThanMetric <class 'lsst.sims.maf.metrics.technicalMetrics.NStateChangesFasterThanMetric'>\n",
      "MaxStateChangesWithinMetric <class 'lsst.sims.maf.metrics.technicalMetrics.MaxStateChangesWithinMetric'>\n",
      "TeffMetric <class 'lsst.sims.maf.metrics.technicalMetrics.TeffMetric'>\n",
      "OpenShutterFractionMetric <class 'lsst.sims.maf.metrics.technicalMetrics.OpenShutterFractionMetric'>\n",
      "CompletenessMetric <class 'lsst.sims.maf.metrics.technicalMetrics.CompletenessMetric'>\n",
      "FilterColorsMetric <class 'lsst.sims.maf.metrics.technicalMetrics.FilterColorsMetric'>\n",
      "BruteOSFMetric <class 'lsst.sims.maf.metrics.technicalMetrics.BruteOSFMetric'>\n",
      "ParallaxMetric <class 'lsst.sims.maf.metrics.calibrationMetrics.ParallaxMetric'>\n",
      "ProperMotionMetric <class 'lsst.sims.maf.metrics.calibrationMetrics.ProperMotionMetric'>\n",
      "ParallaxCoverageMetric <class 'lsst.sims.maf.metrics.calibrationMetrics.ParallaxCoverageMetric'>\n",
      "ParallaxDcrDegenMetric <class 'lsst.sims.maf.metrics.calibrationMetrics.ParallaxDcrDegenMetric'>\n",
      "RadiusObsMetric <class 'lsst.sims.maf.metrics.calibrationMetrics.RadiusObsMetric'>\n",
      "PairFractionMetric <class 'lsst.sims.maf.metrics.visitGroupsMetric.PairFractionMetric'>\n",
      "VisitGroupsMetric <class 'lsst.sims.maf.metrics.visitGroupsMetric.VisitGroupsMetric'>\n",
      "fSMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.fSMetric'>\n",
      "TemplateExistsMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.TemplateExistsMetric'>\n",
      "UniformityMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.UniformityMetric'>\n",
      "RapidRevisitUniformityMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.RapidRevisitUniformityMetric'>\n",
      "RapidRevisitMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.RapidRevisitMetric'>\n",
      "NRevisitsMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.NRevisitsMetric'>\n",
      "IntraNightGapsMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.IntraNightGapsMetric'>\n",
      "InterNightGapsMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.InterNightGapsMetric'>\n",
      "VisitGapMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.VisitGapMetric'>\n",
      "SeasonLengthMetric <class 'lsst.sims.maf.metrics.cadenceMetrics.SeasonLengthMetric'>\n",
      "HourglassMetric <class 'lsst.sims.maf.metrics.hourglassMetric.HourglassMetric'>\n",
      "FftMetric <class 'lsst.sims.maf.metrics.fftMetric.FftMetric'>\n",
      "TgapsMetric <class 'lsst.sims.maf.metrics.tgaps.TgapsMetric'>\n",
      "NightgapsMetric <class 'lsst.sims.maf.metrics.tgaps.NightgapsMetric'>\n",
      "NVisitsPerNightMetric <class 'lsst.sims.maf.metrics.tgaps.NVisitsPerNightMetric'>\n",
      "MaxGapMetric <class 'lsst.sims.maf.metrics.tgaps.MaxGapMetric'>\n",
      "LongGapAGNMetric <class 'lsst.sims.maf.metrics.longGapAGNMetric.LongGapAGNMetric'>\n",
      "ExgalM5 <class 'lsst.sims.maf.metrics.exgalM5.ExgalM5'>\n",
      "SlewContributionMetric <class 'lsst.sims.maf.metrics.slewMetrics.SlewContributionMetric'>\n",
      "AveSlewFracMetric <class 'lsst.sims.maf.metrics.slewMetrics.AveSlewFracMetric'>\n",
      "TransientMetric <class 'lsst.sims.maf.metrics.transientMetrics.TransientMetric'>\n",
      "ChipVendorMetric <class 'lsst.sims.maf.metrics.chipVendorMetric.ChipVendorMetric'>\n",
      "VectorMetric <class 'lsst.sims.maf.metrics.vectorMetrics.VectorMetric'>\n",
      "HistogramMetric <class 'lsst.sims.maf.metrics.vectorMetrics.HistogramMetric'>\n",
      "AccumulateMetric <class 'lsst.sims.maf.metrics.vectorMetrics.AccumulateMetric'>\n",
      "AccumulateCountMetric <class 'lsst.sims.maf.metrics.vectorMetrics.AccumulateCountMetric'>\n",
      "HistogramM5Metric <class 'lsst.sims.maf.metrics.vectorMetrics.HistogramM5Metric'>\n",
      "AccumulateM5Metric <class 'lsst.sims.maf.metrics.vectorMetrics.AccumulateM5Metric'>\n",
      "AccumulateUniformityMetric <class 'lsst.sims.maf.metrics.vectorMetrics.AccumulateUniformityMetric'>\n",
      "PhaseGapMetric <class 'lsst.sims.maf.metrics.phaseGapMetric.PhaseGapMetric'>\n",
      "PeriodicQualityMetric <class 'lsst.sims.maf.metrics.phaseGapMetric.PeriodicQualityMetric'>\n",
      "StarDensityMetric <class 'lsst.sims.maf.metrics.starDensity.StarDensityMetric'>\n",
      "CrowdingM5Metric <class 'lsst.sims.maf.metrics.crowdingMetric.CrowdingM5Metric'>\n",
      "NstarsMetric <class 'lsst.sims.maf.metrics.crowdingMetric.NstarsMetric'>\n",
      "CrowdingMagUncertMetric <class 'lsst.sims.maf.metrics.crowdingMetric.CrowdingMagUncertMetric'>\n",
      "OptimalM5Metric <class 'lsst.sims.maf.metrics.optimalM5Metric.OptimalM5Metric'>\n",
      "BaseMoMetric <class 'lsst.sims.maf.metrics.moMetrics.BaseMoMetric'>\n",
      "BaseChildMetric <class 'lsst.sims.maf.metrics.moMetrics.BaseChildMetric'>\n",
      "NObsMetric <class 'lsst.sims.maf.metrics.moMetrics.NObsMetric'>\n",
      "NObsNoSinglesMetric <class 'lsst.sims.maf.metrics.moMetrics.NObsNoSinglesMetric'>\n",
      "NNightsMetric <class 'lsst.sims.maf.metrics.moMetrics.NNightsMetric'>\n",
      "ObsArcMetric <class 'lsst.sims.maf.metrics.moMetrics.ObsArcMetric'>\n",
      "DiscoveryMetric <class 'lsst.sims.maf.metrics.moMetrics.DiscoveryMetric'>\n",
      "Discovery_N_ChancesMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_N_ChancesMetric'>\n",
      "Discovery_N_ObsMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_N_ObsMetric'>\n",
      "Discovery_TimeMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_TimeMetric'>\n",
      "Discovery_DistanceMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_DistanceMetric'>\n",
      "Discovery_RADecMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_RADecMetric'>\n",
      "Discovery_EcLonLatMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_EcLonLatMetric'>\n",
      "Discovery_VelocityMetric <class 'lsst.sims.maf.metrics.moMetrics.Discovery_VelocityMetric'>\n",
      "ActivityOverTimeMetric <class 'lsst.sims.maf.metrics.moMetrics.ActivityOverTimeMetric'>\n",
      "ActivityOverPeriodMetric <class 'lsst.sims.maf.metrics.moMetrics.ActivityOverPeriodMetric'>\n",
      "MagicDiscoveryMetric <class 'lsst.sims.maf.metrics.moMetrics.MagicDiscoveryMetric'>\n",
      "HighVelocityMetric <class 'lsst.sims.maf.metrics.moMetrics.HighVelocityMetric'>\n",
      "HighVelocityNightsMetric <class 'lsst.sims.maf.metrics.moMetrics.HighVelocityNightsMetric'>\n",
      "LightcurveInversion_AsteroidMetric <class 'lsst.sims.maf.metrics.moMetrics.LightcurveInversion_AsteroidMetric'>\n",
      "Color_AsteroidMetric <class 'lsst.sims.maf.metrics.moMetrics.Color_AsteroidMetric'>\n",
      "LightcurveColor_OuterMetric <class 'lsst.sims.maf.metrics.moMetrics.LightcurveColor_OuterMetric'>\n",
      "InstantaneousColorMetric <class 'lsst.sims.maf.metrics.moMetrics.InstantaneousColorMetric'>\n",
      "PeakVMagMetric <class 'lsst.sims.maf.metrics.moMetrics.PeakVMagMetric'>\n",
      "KnownObjectsMetric <class 'lsst.sims.maf.metrics.moMetrics.KnownObjectsMetric'>\n",
      "ValueAtHMetric <class 'lsst.sims.maf.metrics.moSummaryMetrics.ValueAtHMetric'>\n",
      "MeanValueAtHMetric <class 'lsst.sims.maf.metrics.moSummaryMetrics.MeanValueAtHMetric'>\n",
      "MoCompletenessMetric <class 'lsst.sims.maf.metrics.moSummaryMetrics.MoCompletenessMetric'>\n",
      "MoCompletenessAtTimeMetric <class 'lsst.sims.maf.metrics.moSummaryMetrics.MoCompletenessAtTimeMetric'>\n",
      "PairMetric <class 'lsst.sims.maf.metrics.pairMetric.PairMetric'>\n",
      "NightPointingMetric <class 'lsst.sims.maf.metrics.nightPointingMetric.NightPointingMetric'>\n",
      "StringCountMetric <class 'lsst.sims.maf.metrics.stringCountMetric.StringCountMetric'>\n",
      "AreaSummaryMetric <class 'lsst.sims.maf.metrics.areaSummaryMetrics.AreaSummaryMetric'>\n",
      "ExgalM5_with_cuts <class 'lsst.sims.maf.metrics.weakLensingSystematicsMetric.ExgalM5_with_cuts'>\n",
      "WeakLensingNvisits <class 'lsst.sims.maf.metrics.weakLensingSystematicsMetric.WeakLensingNvisits'>\n",
      "PeriodicDetectMetric <class 'lsst.sims.maf.metrics.periodicDetectMetric.PeriodicDetectMetric'>\n",
      "YearCoverageMetric <class 'lsst.sims.maf.metrics.coverageMetric.YearCoverageMetric'>\n",
      "DcrPrecisionMetric <class 'lsst.sims.maf.metrics.dcrMetric.DcrPrecisionMetric'>\n",
      "KuiperMetric <class 'lsst.sims.maf.metrics.kuiperMetrics.KuiperMetric'>\n"
     ]
    }
   ],
   "source": [
    "# List of provided metrics\n",
    "\n",
    "for metric, module in zip(metrics.BaseMetric.registry.keys(), metrics.BaseMetric.registry.values()):\n",
    "    print(metric, module)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to see the documentation and code for a particular metric, try e.g.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mlsst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msims\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrowdingMetric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCrowdingM5Metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcrowding_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfiltername\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mseeingCol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'seeingFwhmGeom'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmetricName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmaps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'StellarDensityMap'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m        \n",
       "\u001b[0;32mclass\u001b[0m \u001b[0mCrowdingM5Metric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseMetric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Return the magnitude at which the photometric error exceeds crowding_error threshold.\u001b[0m\n",
       "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrowding_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltername\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseeingCol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'seeingFwhmGeom'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                 \u001b[0mmetricName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'StellarDensityMap'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"\u001b[0m\n",
       "\u001b[0;34m        Parameters\u001b[0m\n",
       "\u001b[0;34m        ----------\u001b[0m\n",
       "\u001b[0;34m        crowding_error : float, opt\u001b[0m\n",
       "\u001b[0;34m            The magnitude uncertainty from crowding in magnitudes. Default 0.1 mags.\u001b[0m\n",
       "\u001b[0;34m        filtername: str, opt\u001b[0m\n",
       "\u001b[0;34m            The bandpass in which to calculate the crowding limit. Default r.\u001b[0m\n",
       "\u001b[0;34m        seeingCol : str, opt\u001b[0m\n",
       "\u001b[0;34m            The name of the seeing column.\u001b[0m\n",
       "\u001b[0;34m        m5Col : str, opt\u001b[0m\n",
       "\u001b[0;34m            The name of the m5 depth column.\u001b[0m\n",
       "\u001b[0;34m        maps : list of str, opt\u001b[0m\n",
       "\u001b[0;34m            Names of maps required for the metric.\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m        Returns\u001b[0m\n",
       "\u001b[0;34m        -------\u001b[0m\n",
       "\u001b[0;34m        float\u001b[0m\n",
       "\u001b[0;34m        The magnitude of a star which has a photometric error of `crowding_error`\u001b[0m\n",
       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mseeingCol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0munits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mag'\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrowding_error\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrowding_error\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfiltername\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltername\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseeingCol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mseeingCol\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mmetricName\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mmetricName\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Crowding to Precision %.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcrowding_error\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetricName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetricName\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataSlice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslicePoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# Set magVector to the same length as starLumFunc (lower edge of mag bins)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmagVector\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslicePoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'starMapBins_{self.filtername}'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# Pull up density of stars at this point in the sky\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mlumFunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslicePoint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'starLumFunc_{self.filtername}'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# Calculate the crowding error using the best seeing value (in any filter?)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mcrowdError\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_compCrowdError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagVector\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlumFunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                                     \u001b[0mseeing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSlice\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseeingCol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;31m# Locate at which point crowding error is greater than user-defined limit\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0maboveCrowd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrowdError\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcrowding_error\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maboveCrowd\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mcrowdMag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagVector\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maboveCrowd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcrowdMag\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m           /data0/lsst_stack/stack/miniconda3-py37_4.8.2-cb4e2dc/Linux64/sims_maf/2.13.0.sims-61-g3ee711b3+fffdf3dce1/python/lsst/sims/maf/metrics/crowdingMetric.py\n",
       "\u001b[0;31mType:\u001b[0m           MetricRegistry\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "??lsst.sims.maf.metrics.crowdingMetric.CrowdingM5Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Science Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magellanic Clouds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A 3-D Map of the Magellanic System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will map structure and detect satellites using MSTO and RR Lyrae as tracers.\n",
    "\n",
    "We desire:\n",
    "- Footprint: WFD + SCP, avoiding most crowded parts of Galactic Plane\n",
    "- Filters: ugriz\n",
    "- N visits per field: 40 per filter in SCP, + WFD\n",
    "- Depth: u: 25.6 g: 26.8 r: 26.4 i 25.8 z: 24.9 in SCP, otherwise WFD depth\n",
    "- Cadence: not strongly constrained, but want spacing for proper motions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From previous notes:\n",
    "\n",
    "1.  The total number of visits per filter per field in the WFD + SCP area with Dec < -60, avoiding the Galactic Plane; could be aggregated into total or average number of visits of all fields over all filters\n",
    "\t\n",
    "Existing metrics in MAF: Nvisits\n",
    "\n",
    "2.  The average total depth per filter in the fields above\n",
    "\n",
    "Existing metrics in MAF: \n",
    "- CoaddM5 - Coadded depth per healpix, with benchmark value (depending on filter) subtracted.\n",
    "- fiveSigmaDepth - is this the single-visit depth? (it's ~2 mags shallower than coaddM5)\n",
    "\n",
    "3.  For a candidate stellar population parametrized by age, metallicity, distance, surface density, and spatial profile, the signal to noise of the population, evaluated as:\n",
    "\n",
    "    T/sqrt(T^2 + B^2), \n",
    "\n",
    "where T is the number of target stars and B the number of foreground contaminant stars plus the number of background unresolved galaxies\n",
    "\n",
    "Adriano Pieres has a metric similar to this (T/sqrt(B)).\n",
    "\n",
    "Need to include: contamination filtering (e.g. S/G separation) and proper motion selection\n",
    "\n",
    "Existing metrics in MAF: \n",
    "\n",
    "not much! but possibly relevant to create it, there are:\n",
    "- skyBrightness\n",
    "- CoaddM5 / fiveSigmaDepth\n",
    "\n",
    "4.  Given an input distribution of dwarf satellites, the number that would be detected above a set S/N threshold, evaluated through @3.\n",
    "\n",
    "5.  Given an input distribution of low surface brightness streams, the number that would be detected above a set S/N threshold, evaluated through @3.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A Detailed census of variables and transients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will target periodic and irregular variables, SN light echoes, giant planet transits, microlensing events, and interstellar scintillation.\n",
    "\n",
    "We desire:\n",
    "\n",
    "- Footprint: 9 fields on LMC main body, 3 SMC main body fields\n",
    "- Filters: ugriz\n",
    "- N visits per field: u: 50 g: 1300 r: 300 i: 300 z: 30 y: 20\n",
    "- Depth: will be confusion-limited\n",
    "- Cadence: Two campaigns of 500 15-second consecutive exposure sequences in g for interstellar scintillation; 300 30-second visits in gri spaced roughly logarithmically over 10 years; uzy visits such that reach confusion depth under excellent image quality conditions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Primary Science: LSST will enable the identification and locations of variable populations in the Clouds using high time and areal coverage, with 2000 visits in each of 12 fields during the 10 years.\n",
    "\n",
    "Baisc metric: \n",
    "- N_visits/300 per main body field in gri\n",
    "\n",
    "\n",
    "I. Obtain light Curves and periods of all variable objects to Mv=6.5 to map populations\n",
    "\n",
    "    A. Find all eclipsing binaries (periods of hrs-yrs), Mv=0-6.5\n",
    "\n",
    "    Existing metrics in MAF: \n",
    "    - periodicDetectMetric (for P=0.5-2 days, amp=0.05-1 mag, 21 and 24th mag stars) https://github.com/rhiannonlynne/notebooks/blob/master/periodicity%20check.ipynb\n",
    "\n",
    "    Metrics needed\n",
    "\n",
    "    B.  Find all outbursting cataclysmic variables\n",
    "        B1. New novae and Recurrent novae (outbursts 7 mag in 1-5 yrs), Mv =-7 to -9\n",
    "        B2. Dwarf novae (outbursts 2-8 mag in weeks to years), Mv = 4.5-5.5\n",
    "\n",
    "    Existing metrics in MAF: ObservationTriplets, PeriodogramPurity, FieldstarCount ( https://github.com/LSST-nonproject/sims_maf_contrib., Lund et al.2016, PASP, 128,025002)\n",
    "\n",
    "    Metrics needed\n",
    "\n",
    "     C.  Find all periodic pulsating variables\n",
    "          C1. Delta Scuti (periods 0.5-7 hrs), Mv=0.7, amp 0.1-1 mag\n",
    "          C2. RR Lyrae (periods 0.2-1 day), Mv=0.6, amp 0.2-1.2 mag\n",
    "          C3. Long P variables (periods 60-400 days), Mv=-4, 0.3-5 mag\n",
    "\n",
    "    Existing metrics in MAF: \n",
    "    - periodicDetectMatric (for Delta Scute, RR Lyr)  https://github.com/rhiannonlynne/notebooks/blob/master/periodicity%20check.ipynb\n",
    "    \n",
    "    Metrics needed:\n",
    "    - coverage for LPV\n",
    "\n",
    "  II.  Explore light echoes for past SN to probe the physics of explosions (weeks timescale)\n",
    "\n",
    "    Existing metrics\n",
    "\n",
    "    Metrics needed\n",
    "\n",
    "  III.  Use interstellar scintillation to probe invisible baryonic matter (min timescale), need two sets of 500 continuous 15s g filter visits separated by a few months) \n",
    "\n",
    "    Existing metrics\n",
    "\n",
    "    Metrics needed\n",
    "    - The number of 500-visit campaigns divided by two.\n",
    "\n",
    "The challenge in this experiment is to detect small stochastic flux fluctuations at the minute scale. The better the photometric resolution, the better the sensitivity to turbulent structures. The turbulence is characterized by the parameter Rdiff, the average transverse separation corresponding to a given column density fluctuation. Rdiff increases when the turbulence decreases.\n",
    "-> We can distinguish two effects when dividing the number of visits:\n",
    "- Basically, the precision of the measured modulation index from every scintillation signal scales with the square root of the number of visits.\n",
    "- The ultimate sensitivity is also downgraded. Considering a Kolmogorov-Smirnov test to distinguish between a stable light-curve from a scintillating one with an error risk of 10% (a reasonable cut), we can connect the number of measurements with the maximum turbulent parameter Rdiff. Assuming the best 0.5% LSST photometric precision, decreasing the number of measurements from 500 to 250 would degrade the ultimate sensitivity from Rdiff max = 13000km down to 10000km.\n",
    "\n",
    "IV. Find giant planet transits in LMC to probe planet formation under low metallicity (hrs)\n",
    "\n",
    "    Existing metrics\n",
    "\n",
    "    Metrics needed\n",
    "\n",
    "\n",
    "V. Use microlensing events in the Clouds and Galactic halo to probe compact objects (timescales weeks-months)\n",
    "\n",
    "    Existing metrics\n",
    "\n",
    "    Metrics needed\n",
    "    - Largest gap between visits for microlensing\n",
    "    - The probability that the visit distribution was drawn from a logarithmic distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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