{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DIA Analysis: Supernovae from the Run 1.2p Test\n",
    "Michael Wood-Vasey\n",
    "Last Verified to Run: 2019-07-06\n",
    "\n",
    "After completing this Notebook, the user will be able to\n",
    "1. Get a list of simulated SN from the Run 1.2p truth catalog\n",
    "2. Select SNe within the test patch in the Run 1.2p DIA Test\n",
    "3. Plot lightcurves from the DIA analysis\n",
    "4. Plot lightcurves from the truth catalog\n",
    "5. Compare the input and recovered lightcurves.\n",
    "\n",
    "See the Truth GCR Variables for a basic overview of the Truth information and how to access it for variables.\n",
    "https://github.com/LSSTDESC/DC2-analysis/blob/master/tutorials/truth_gcr_variables.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inject gcr-catalogs that supports DIA source into path.\n",
    "import os\n",
    "import math\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from astropy.coordinates import SkyCoord\n",
    "import astropy.units as u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Requires issues/337 to fix obshistid query.\n",
    "sys.path.insert(0, '/global/homes/w/wmwv/local/lsst/gcr-catalogs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lsst.afw.display as afwDisplay\n",
    "import lsst.afw.geom as afwGeom\n",
    "from lsst.daf.persistence import Butler\n",
    "from lsst.geom import SpherePoint\n",
    "import lsst.geom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import GCRCatalogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "repo = '/global/cscratch1/sd/rearmstr/new_templates/diffim_template'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "butler = Butler(repo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.5.12/envs/lsst-scipipe-1172c30/lib/python3.7/site-packages/GCR/base.py:373: UserWarning: Native quantity `psFluxMean_{band}` does not exist (required by `magMeanErr_i`, `magMeanErr_z`, `magMeanErr_r`, `magMeanErr_y`, `magMeanErr_u`, `magMeanErr_g`)\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "diaSrc = GCRCatalogs.load_catalog('dc2_dia_source_run1.2p_test')\n",
    "diaObject = GCRCatalogs.load_catalog('dc2_dia_object_run1.2p_test')\n",
    "truth_cat = GCRCatalogs.load_catalog('dc2_truth_run1.2_variable_summary')\n",
    "truth_lc = GCRCatalogs.load_catalog('dc2_truth_run1.2_variable_lightcurve')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(We presently will get a warning from the catalog reader in the initalization above because there is no u-band in the subtractions.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select SNe from the truth catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.5.12/envs/lsst-scipipe-1172c30/lib/python3.7/site-packages/h5py/_hl/dataset.py:313: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  \"Use dataset[()] instead.\", H5pyDeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "columns = ['ra', 'dec', 'redshift', 'uniqueId', 'galaxy_id', 'sn']\n",
    "truth_all_sne = pd.DataFrame(truth_cat.get_quantities(columns, filters=[f'sn == 1']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll get a dataset.value deprecation warning.  Don't worry about this.  The Data Access Team will fix this someday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 76689 simulated SNe.\n"
     ]
    }
   ],
   "source": [
    "print(f'We found {len(truth_all_sne)} simulated SNe.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select SNe from the truth catalog in the DIA test region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tract = 4849\n",
    "patch = (6, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "skymap = butler.get('deepCoadd_skyMap')\n",
    "tract_info = skymap[tract]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = tract_info.getPatchInfo(patch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bar = foo.getOuterSkyPolygon(tract_info.getWcs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tract_box = afwGeom.Box2D(tract_info.getBBox())\n",
    "tract_pos_list = tract_box.getCorners()\n",
    "wcs = tract_info.getWcs()\n",
    "corners = wcs.pixelToSky(tract_pos_list)\n",
    "corners = np.array([[c.getRa().asDegrees(), c.getDec().asDegrees()] for c in corners])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 54.72776196 -29.78294629]\n",
      " [ 52.93572545 -29.78294586]\n",
      " [ 52.949112   -28.22767319]\n",
      " [ 54.71437636 -28.2276736 ]]\n"
     ]
    }
   ],
   "source": [
    "print(corners)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra = corners[:, 0]\n",
    "dec = corners[:, 1]\n",
    "min_ra, max_ra = np.min(ra), np.max(ra)\n",
    "min_dec, max_dec = np.min(dec), np.max(dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_cut = [f'ra > {min_ra}', f'ra < {max_ra}', f'dec > {min_dec}', f'dec < {max_dec}']\n",
    "sn_cut = ['sn == 1']\n",
    "all_cuts = area_cut + sn_cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.5.12/envs/lsst-scipipe-1172c30/lib/python3.7/site-packages/h5py/_hl/dataset.py:313: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  \"Use dataset[()] instead.\", H5pyDeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "columns = ['ra', 'dec', 'redshift', 'uniqueId', 'galaxy_id', 'sn']\n",
    "truth_sne = pd.DataFrame(truth_cat.get_quantities(columns, filters=all_cuts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 8508 simulated SNe.\n"
     ]
    }
   ],
   "source": [
    "print(f'We found {len(truth_sne)} simulated SNe.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "avg_dec = (min_dec + max_dec)/2\n",
    "size = 8\n",
    "dec_size = size\n",
    "ra_size = dec_size * np.cos(np.deg2rad(avg_dec))\n",
    "aspect_ratio = dec_size / ra_size\n",
    "\n",
    "# fig = plt.figure(figsize=(size, size))\n",
    "ax = plt.gca()\n",
    "ax.set_aspect(aspect_ratio)\n",
    "\n",
    "patch_region = Polygon(corners, color='red', fill=False)\n",
    "\n",
    "ax.scatter(truth_sne['ra'], truth_sne['dec'])\n",
    "ax.set_xlabel('RA')\n",
    "ax.set_ylabel('Dec')\n",
    "ax.set_xlim(plt.xlim()[::-1])\n",
    "ax.add_patch(patch_region);\n",
    "ax.set_title(f'tract: {tract}, patch: {patch}');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Lightcurve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search for diaObjects that match input simulated SNe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "sn = truth_sne.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ra           5.300728e+01\n",
      "uniqueId     3.082506e+14\n",
      "galaxy_id    3.010259e+11\n",
      "redshift     2.376888e-01\n",
      "dec         -2.838755e+01\n",
      "sn           1.000000e+00\n",
      "Name: 0, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(sn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oops, we need to fix up the `dtype`s somewhere.  Those shouldn't all be floats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the lightcurve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['obshistid', 'mjd', 'mag', 'filter']\n",
    "\n",
    "sn_lc = pd.DataFrame(truth_lc.get_quantities(columns,\n",
    "                                             native_filters=[f'uniqueId == {sn[\"uniqueId\"]}']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "sn_lc.rename(columns={'filter': 'filter_code'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Translate filter codes to filter names\n",
    "filter_names = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "sn_lc['filter'] = [filter_names[f] for f in sn_lc['filter_code']]\n",
    "sn_lc = sn_lc.sort_values('mjd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lightcurve(df, plot='mag', flux_col_names=None,\n",
    "                    title=None, marker='o', linestyle='none',\n",
    "                    colors=None, label_prefix='',\n",
    "                    **kwargs):\n",
    "    \"\"\"Plot a lightcurve from a DataFrame.\n",
    "    \"\"\"\n",
    "    # At lexigraphical order, if not wavelength order.\n",
    "    # Assume fixed list of filters.\n",
    "    filter_order = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "\n",
    "    if colors is None:\n",
    "        colors = {'u': 'violet', 'g': 'indigo', 'r': 'blue', 'i': 'green', 'z': 'orange', 'y': 'red'}\n",
    "    \n",
    "    if flux_col_names is not None:\n",
    "        flux_col, flux_err_col = flux_col_names\n",
    "    else:\n",
    "        if plot == 'flux':\n",
    "            flux_col = 'psFlux'\n",
    "            flux_err_col = 'psFluxErr'\n",
    "        else:\n",
    "            flux_col = 'mag'\n",
    "            flux_err_col = 'mag_err'\n",
    "        \n",
    "    for filt in filter_order:\n",
    "        this_filter = df.query(f'filter == \"{filt}\"')\n",
    "        if this_filter.empty:\n",
    "            continue\n",
    "        # This if sequence is a little silly.\n",
    "        plot_kwargs = {'linestyle': linestyle, 'marker': marker, 'color': colors[filt],\n",
    "                       'label': f'{label_prefix} {filt}'}\n",
    "        plot_kwargs.update(kwargs)\n",
    "\n",
    "        if flux_err_col in this_filter.columns:\n",
    "            plt.errorbar(this_filter['mjd'], this_filter[flux_col], this_filter[flux_err_col],\n",
    "                         **plot_kwargs)\n",
    "                        \n",
    "        else:\n",
    "            if marker is None:\n",
    "                plt.plot(this_filter['mjd'], this_filter[flux_col], **plot_kwargs)\n",
    "\n",
    "            else:\n",
    "                plot_kwargs.pop('linestyle')\n",
    "                plt.scatter(this_filter['mjd'], this_filter[flux_col], **plot_kwargs)\n",
    "\n",
    "\n",
    "\n",
    "    plt.xlabel('MJD')\n",
    "\n",
    "    if plot == 'flux':\n",
    "        plt.ylabel('psFlux [nJy]')\n",
    "    else:\n",
    "        # Ensure that y-axis decreases as one goes up\n",
    "        # Because plot_lightcurve could be called several times on the same axis,\n",
    "        # simply inverting is not correct.  We have to reverse a sorted list.\n",
    "        plt.ylim(sorted(plt.ylim(), reverse=True))\n",
    "        plt.ylabel('mag [AB]')\n",
    "\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plot_lightcurve(sn_lc, plot='mag', linestyle='-', marker=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match to DIAObject Catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53.00727812651823 -28.387552632959427\n"
     ]
    }
   ],
   "source": [
    "ra, dec = sn['ra'], sn['dec']\n",
    "print(ra, dec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index: 22 is 0.028836 arcsec away\n"
     ]
    }
   ],
   "source": [
    "# Match on RA, Dec\n",
    "sn_position = SkyCoord(sn['ra'], sn['dec'], unit='deg')\n",
    "\n",
    "diaObject_cat = diaObject.get_quantities(['ra', 'dec', 'diaObjectId'])\n",
    "diaObject_positions = SkyCoord(diaObject_cat['ra'], diaObject_cat['dec'], unit='deg')\n",
    "\n",
    "idx, sep2d, _ = sn_position.match_to_catalog_sky(diaObject_positions)\n",
    "print(f'Index: {idx} is {sep2d.to(u.arcsec)[0]:0.6f} away')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "diaObjectId = diaObject_cat['diaObjectId'][idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How did we do with the lightcurve?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mjd</th>\n",
       "      <th>mag</th>\n",
       "      <th>obshistid</th>\n",
       "      <th>filter_code</th>\n",
       "      <th>filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60554.288025</td>\n",
       "      <td>26.432639</td>\n",
       "      <td>657401</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60555.258471</td>\n",
       "      <td>25.581090</td>\n",
       "      <td>658296</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60556.250832</td>\n",
       "      <td>25.019064</td>\n",
       "      <td>659233</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60557.246086</td>\n",
       "      <td>24.602021</td>\n",
       "      <td>660201</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60558.249432</td>\n",
       "      <td>24.266763</td>\n",
       "      <td>660882</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60559.245016</td>\n",
       "      <td>23.986753</td>\n",
       "      <td>661527</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60567.219180</td>\n",
       "      <td>21.397669</td>\n",
       "      <td>664557</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60567.228879</td>\n",
       "      <td>21.461969</td>\n",
       "      <td>664577</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60567.234411</td>\n",
       "      <td>21.431454</td>\n",
       "      <td>664587</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60567.244111</td>\n",
       "      <td>21.719140</td>\n",
       "      <td>664607</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60567.256310</td>\n",
       "      <td>21.978863</td>\n",
       "      <td>664633</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60578.271042</td>\n",
       "      <td>21.354127</td>\n",
       "      <td>671659</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60578.280324</td>\n",
       "      <td>21.694316</td>\n",
       "      <td>671678</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60578.285857</td>\n",
       "      <td>21.346081</td>\n",
       "      <td>671688</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60578.295556</td>\n",
       "      <td>21.825918</td>\n",
       "      <td>671708</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60578.307755</td>\n",
       "      <td>22.263662</td>\n",
       "      <td>671734</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60581.174302</td>\n",
       "      <td>21.450943</td>\n",
       "      <td>674105</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60581.189152</td>\n",
       "      <td>21.472849</td>\n",
       "      <td>674134</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60581.198851</td>\n",
       "      <td>21.983958</td>\n",
       "      <td>674154</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>60581.211050</td>\n",
       "      <td>22.407560</td>\n",
       "      <td>674180</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60582.204745</td>\n",
       "      <td>24.257999</td>\n",
       "      <td>675069</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>60583.180252</td>\n",
       "      <td>24.374555</td>\n",
       "      <td>675910</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>60584.176755</td>\n",
       "      <td>24.494031</td>\n",
       "      <td>676739</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>60585.171706</td>\n",
       "      <td>24.608092</td>\n",
       "      <td>677595</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60586.172558</td>\n",
       "      <td>24.727928</td>\n",
       "      <td>678467</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>60587.166237</td>\n",
       "      <td>24.855251</td>\n",
       "      <td>679317</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>60588.163054</td>\n",
       "      <td>24.992185</td>\n",
       "      <td>680167</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>60593.164657</td>\n",
       "      <td>22.093300</td>\n",
       "      <td>684469</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>60593.173940</td>\n",
       "      <td>23.101931</td>\n",
       "      <td>684488</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60593.179472</td>\n",
       "      <td>22.013917</td>\n",
       "      <td>684498</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>60608.151733</td>\n",
       "      <td>22.571821</td>\n",
       "      <td>696296</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>60608.163933</td>\n",
       "      <td>22.559708</td>\n",
       "      <td>696322</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60611.119189</td>\n",
       "      <td>23.094521</td>\n",
       "      <td>698622</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>60611.128471</td>\n",
       "      <td>24.440580</td>\n",
       "      <td>698641</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>60611.134004</td>\n",
       "      <td>22.555700</td>\n",
       "      <td>698651</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>60611.143703</td>\n",
       "      <td>22.708548</td>\n",
       "      <td>698671</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>60611.155902</td>\n",
       "      <td>22.649910</td>\n",
       "      <td>698697</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>60613.097971</td>\n",
       "      <td>26.392147</td>\n",
       "      <td>700187</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>60614.092032</td>\n",
       "      <td>26.435882</td>\n",
       "      <td>700968</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>60615.087792</td>\n",
       "      <td>26.482756</td>\n",
       "      <td>701754</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>60616.088773</td>\n",
       "      <td>26.532553</td>\n",
       "      <td>702553</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>60617.085683</td>\n",
       "      <td>26.583687</td>\n",
       "      <td>703320</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>60620.131032</td>\n",
       "      <td>26.722439</td>\n",
       "      <td>704240</td>\n",
       "      <td>0</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>60621.122783</td>\n",
       "      <td>23.508854</td>\n",
       "      <td>704746</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>60621.132065</td>\n",
       "      <td>24.736442</td>\n",
       "      <td>704765</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>60621.137598</td>\n",
       "      <td>22.996806</td>\n",
       "      <td>704775</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60621.147297</td>\n",
       "      <td>23.206187</td>\n",
       "      <td>704795</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60621.159496</td>\n",
       "      <td>23.115017</td>\n",
       "      <td>704821</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>60624.078591</td>\n",
       "      <td>23.592920</td>\n",
       "      <td>706923</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>60624.093405</td>\n",
       "      <td>23.095192</td>\n",
       "      <td>706952</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>60624.103104</td>\n",
       "      <td>23.344373</td>\n",
       "      <td>706972</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>60624.115303</td>\n",
       "      <td>23.258472</td>\n",
       "      <td>706998</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>60627.276161</td>\n",
       "      <td>23.667327</td>\n",
       "      <td>709647</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>60627.291393</td>\n",
       "      <td>23.191451</td>\n",
       "      <td>709677</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>60627.301092</td>\n",
       "      <td>23.484316</td>\n",
       "      <td>709697</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>60627.313291</td>\n",
       "      <td>23.379125</td>\n",
       "      <td>709723</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>60632.036052</td>\n",
       "      <td>23.747787</td>\n",
       "      <td>712881</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>60632.051295</td>\n",
       "      <td>23.312274</td>\n",
       "      <td>712911</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>60632.060994</td>\n",
       "      <td>23.667778</td>\n",
       "      <td>712931</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>60632.073193</td>\n",
       "      <td>23.491572</td>\n",
       "      <td>712957</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             mjd        mag  obshistid  filter_code filter\n",
       "0   60554.288025  26.432639     657401            0      u\n",
       "1   60555.258471  25.581090     658296            0      u\n",
       "2   60556.250832  25.019064     659233            0      u\n",
       "3   60557.246086  24.602021     660201            0      u\n",
       "4   60558.249432  24.266763     660882            0      u\n",
       "5   60559.245016  23.986753     661527            0      u\n",
       "6   60567.219180  21.397669     664557            2      r\n",
       "7   60567.228879  21.461969     664577            1      g\n",
       "8   60567.234411  21.431454     664587            3      i\n",
       "9   60567.244111  21.719140     664607            4      z\n",
       "10  60567.256310  21.978863     664633            5      y\n",
       "11  60578.271042  21.354127     671659            2      r\n",
       "12  60578.280324  21.694316     671678            1      g\n",
       "13  60578.285857  21.346081     671688            3      i\n",
       "14  60578.295556  21.825918     671708            4      z\n",
       "15  60578.307755  22.263662     671734            5      y\n",
       "16  60581.174302  21.450943     674105            2      r\n",
       "17  60581.189152  21.472849     674134            3      i\n",
       "18  60581.198851  21.983958     674154            4      z\n",
       "19  60581.211050  22.407560     674180            5      y\n",
       "20  60582.204745  24.257999     675069            0      u\n",
       "21  60583.180252  24.374555     675910            0      u\n",
       "22  60584.176755  24.494031     676739            0      u\n",
       "23  60585.171706  24.608092     677595            0      u\n",
       "24  60586.172558  24.727928     678467            0      u\n",
       "25  60587.166237  24.855251     679317            0      u\n",
       "26  60588.163054  24.992185     680167            0      u\n",
       "27  60593.164657  22.093300     684469            2      r\n",
       "28  60593.173940  23.101931     684488            1      g\n",
       "29  60593.179472  22.013917     684498            3      i\n",
       "..           ...        ...        ...          ...    ...\n",
       "43  60608.151733  22.571821     696296            4      z\n",
       "44  60608.163933  22.559708     696322            5      y\n",
       "45  60611.119189  23.094521     698622            2      r\n",
       "46  60611.128471  24.440580     698641            1      g\n",
       "47  60611.134004  22.555700     698651            3      i\n",
       "48  60611.143703  22.708548     698671            4      z\n",
       "49  60611.155902  22.649910     698697            5      y\n",
       "50  60613.097971  26.392147     700187            0      u\n",
       "51  60614.092032  26.435882     700968            0      u\n",
       "52  60615.087792  26.482756     701754            0      u\n",
       "53  60616.088773  26.532553     702553            0      u\n",
       "54  60617.085683  26.583687     703320            0      u\n",
       "55  60620.131032  26.722439     704240            0      u\n",
       "56  60621.122783  23.508854     704746            2      r\n",
       "57  60621.132065  24.736442     704765            1      g\n",
       "58  60621.137598  22.996806     704775            3      i\n",
       "59  60621.147297  23.206187     704795            4      z\n",
       "60  60621.159496  23.115017     704821            5      y\n",
       "61  60624.078591  23.592920     706923            2      r\n",
       "62  60624.093405  23.095192     706952            3      i\n",
       "63  60624.103104  23.344373     706972            4      z\n",
       "64  60624.115303  23.258472     706998            5      y\n",
       "65  60627.276161  23.667327     709647            2      r\n",
       "66  60627.291393  23.191451     709677            3      i\n",
       "67  60627.301092  23.484316     709697            4      z\n",
       "68  60627.313291  23.379125     709723            5      y\n",
       "69  60632.036052  23.747787     712881            2      r\n",
       "70  60632.051295  23.312274     712911            3      i\n",
       "71  60632.060994  23.667778     712931            4      z\n",
       "72  60632.073193  23.491572     712957            5      y\n",
       "\n",
       "[73 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sn_lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can't use a direct filters = match in the GCR wrapper for the diaSrc table.\n",
    "# So we have to use a lambda function here to match the ID\n",
    "dia_lc = pd.DataFrame(diaSrc.get_quantities(['visit', 'mjd', 'psFlux', 'psFluxErr', 'mag', 'mag_err', 'filter'],\n",
    "                                            filters=[(lambda x: x == diaObjectId, 'diaObjectId')]))\n",
    "dia_lc = dia_lc.sort_values('mjd')                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5hIaGcvLkSaqrqwFYvXq1n6sT8b/09KZzogHCw+12ERGRrs5nI9HGmGPGmJz6r08D+4DLgRnA2vpua4HbfFWDLx07dozIyEhCQ0MBiIyMZMiQIQC4XC4aNoeJiIhgyZIljBs3jpSUFHbs2IHL5SI+Pp4NGzY0u+5Pf/pTkpKSSEpK4vLLL+fuu+/uuDcl4kWpqZCRATExYFn2c0aGbioUEZHuoUN2LLQsKxZ4FxgF5BtjLjvn3CljTLMpHZZlpQFpANHR0ePy8vKanG+yY1/2g3Aq17tF902CcU+0erqsrIwJEyZQXl5OSkoKs2bNYuLEiYAdon/5y1+SnJyMZVls2rSJW265hZkzZ3LmzBlee+019u7dy9y5c8nNbbnu0tJSbrrpJp5//nnGjRvX7Lx2LBQRERHxvk6zY6FlWRHAeuBBY8wXF/t9xpgMY0yyMSZ5wIABvivwEkVERJCdnU1GRgYDBgxg1qxZrFmzplm/kJAQpkyZAkBCQgITJ04kODiYhIQE3G53i9c2xpCamsq//Mu/tBigRURERMS/fLpOtGVZwdgBOtMY83J98wnLsgYbY45ZljUYKGj3C51nxNiXnE4nLpcLl8tFQkICa9euZd68eU36BAcHY1kWAA6Ho3H6h8PhoLa2tsXrPvLIIwwdOlRTOUREREQ6KZ+FaMtOjs8C+4wxK845tQGYCzxW//yKr2rwpU8//RSHw8FVV10FQG5uLjExMe2+7saNG9m8eTNbt25t97VERERExDd8ORL9VeBOYJdlWQ0Tf3+CHZ5fsixrPpAPfNeHNfhMWVkZDzzwACUlJQQFBTFs2DAyMjLafd1f/epXHD16lOuuuw6A6dOn8+ijj7b7uiIiIiLiPR1yY2F7JScnm4bVLhoE+o11gf7+RURERHyh09xYKCIiIiLS3ShEi4iIiIi0kUK0iIiIiEgbKUSLiIiIiLSRQrSIiIiISBspRIuIiIiItJFCdDukp6czcuRIEhMTSUpKYvv27QAsWLCAvXv3+rk6EREREfEVn2773Z1t27aNjRs3kpOTQ2hoKCdPnqS6uhqA1atX+7k6EREfOZQJHy+F8nwIj4bR6RCX6u+qREQ6nEaiL9GxY8eIjIwkNDQUgMjISIYMGQKAy+WiYXOYiIgIlixZwrhx40hJSWHHjh24XC7i4+PZsGFDs+veeeedvPLK2Z3QU1NTW+wnItLhDmXCjjQozwOM/bwjzW4XEQkw3WMk+sEHITf3wv3aIikJnnii1dOTJ0/m0UcfZfjw4aSkpDBr1iwmTpzYrN+ZM2dwuVw8/vjjzJw5k4cffpjNmzezd+9e5s6dy/Tp05v0X7BgAb/+9a+ZMWMGpaWlvP/++6xdu9a7701E5FJ8vBTqypu21ZXb7RqNFpEAo5HoSxQREUF2djYZGRkMGDCAWbNmsWbNmmb9QkJCmDJlCgAJCQlMnDiR4OBgEhIScLvdzfpPnDiRAwcOUFBQQFZWFrfffjtBQd3jbx0R6eLK89vWLiLSjXWPdHaeEWNfcjqduFwuXC4XCQkJrF27lnnz5jXpExwcjGVZADgcjsbpHw6Hg9ra2have+edd5KZmcmLL77Ic88959P3ICLdUGYmLF0K+fkQHQ3p6ZDqhZHi8Oj6qRwttIuIBBiNRF+iTz/9lP379zce5+bmEhMT45Vrz5s3jyfq/zAYOXKkV64pIgEiMxPS0iAvD4yxn9PS7Pb2Gp0OzvCmbc5wu11EJMB0j5FoPygrK+OBBx6gpKSEoKAghg0bRkZGhleuPXDgQEaMGMFtt93mleuJSABZuhTKvzRvubzcbm/vaHTDvGetziEigmWM8XcNF5ScnGwaVrtosG/fPkaMGOGninyrvLychIQEcnJy6NOnT4t9uvP7F5F2cDjsEegvsyzweDq+HhGRLsayrGxjTPKF+mk6Ryfz17/+lWuuuYYHHnig1QAtItKq6FbmJ7fWLiIil0QhupNJSUkhPz+fBx980N+liEhXlJ4O4V+atxwebrd7S2Ym9Ohhj27HxnpnvrWISBejEC0i0p2kpkJGBsTE2CE3JsY+9sbqHHD2xsWqKvvYmzcuioh0IQrRIiLdTWoquN32HGi323sBGs5/46KISABRiBYRkYuX38rGKq21i4h0UwEVoh9yvcBDrhf8XYaISNelGxdFRIAAC9He5nQ6SUpKYuTIkYwePZoVK1bgqV9CauvWrUydOrVJ/xkzZnDjjTf6o1QREe/oiBsXRUS6gIAJ0Vsyd/HpB4fZ/U4ed8f+hi2Zu9p9zbCwMHJzc9mzZw+bN29m06ZNLF++vMW+JSUl5OTkUFJSwqFDh9r92iIifuHrGxdFRLqIgAjRWzJ3sTLtNWqq6gAozCtlZdprXgnSDaKiosjIyGDlypW0tIHN+vXrmTZtGrNnz+bFF19s8RqFhYV84xvfYOzYsdx3333ExMRw8uRJr9UoIuIVvrxxUUSkiwiIEP3C0i1Uldc0aasqr+GFpVu8+jrx8fF4PB4KCgqancvKymLOnDnMmTOHrKysFr9/+fLlfP3rXycnJ4eZM2eSrxt1RERERDqlgAjRJ/NL29TeHi2NQp84cYIDBw4wYcIEhg8fTlBQELt3727W77333mP27NkATJkyhb59+3q9PhERERFpv4AI0ZHRLW+f3Vr7pTp48CBOp5OoqKgm7evWrePUqVPExcURGxuL2+1ucUpHSwFcRES6iMxMewdHh0M7OYoEgIAI0XelTyI0PLhJW2h4MHelT/LaaxQWFrJw4UIWL16MZVlNzmVlZfHGG2/gdrtxu91kZ2e3GKInTJjASy+9BMCbb77JqVOnvFafiIj4UMNOjnl5YIx2chQJAAERoielJrA441sEhzoBGBDTh8UZ32JSakK7rltRUdG4xF1KSgqTJ09m2bJlTfq43W7y8/O54YYbGtvi4uLo3bs327dvb9J32bJlvPnmm4wdO5bXX3+dwYMH06tXr3bVKCIiHUA7OYoEnCB/F9BRJqUm8JfffQTAY1vv8so16+rqWj3ncrlwuVwAHDlypNn5nJycZm19+vThL3/5C0FBQWzbto0tW7YQGhrqlVpFRMSHtJOjSMAJmBAN3gvPvpKfn88dd9yBx+MhJCSE3/3ud/4uSURELkZ0tD2Fo6V2EemWAipEd3ZXXXUVH330kb/LEBGRtkpPt+dAnzulQzs5inRrATEnWkRExKe0k6NIwFGIFhGRi5a5K5PYJ2JxLHcQ+0Qsmbu0+kQjX+7keCgT/hwLf3DYz4f0cxfxN03nEBGRi5K5K5O0V9Mor7GnLOSV5pH2ahoAqQkacfWZQ5mwIw3q6qeKlOfZxwBx+rmL+EtAjUS7XPZDRETabulbSxsDdIPymnKWvqVl3Hzq46VnA3SDunK7XUT8JqBCtLc5nc7GdaJHjx7NihUr8Hg8AGzdupWpU6c26T9jxgxuvPFGf5QqItJu+aUtL9fWWrt4SXkrP9/W2kWkQwRMiM7MhA8+gHfe8d5urGFhYeTm5rJnzx42b97Mpk2bWL58eYt9S0pKyMnJoaSkhEOHDl3w2rW1te0vUETEi6L7tLxcW2vt4iXhrfx8W2sXkQ4RECG6YTfWqir72Be7sUZFRZGRkcHKlSsxxjQ7v379eqZNm8bs2bNb3PIb4JFHHiEtLY3Jkydz112de01rEQk86TenEx4c3qQtPDic9Ju1jJtPjU4HZ9OfO85wu11E/CYgQnRH7cYaHx+Px+OhoKCg2bmsrCzmzJnDnDlzyMrKavUa2dnZvPLKK/zhD3/wbnEiIu2UmpBKxrQMYvrEYGER0yeGjGkZuqnQ1+JS4dhceNAJqdjPx+bqpkIRPwuI1Tk6cjfWlkahT5w4wYEDB5gwYQKWZREUFMTu3bsZNWpUs77Tp08nLCzM+4WJiHhBakKqQnNHy8yEpWuhvM4+Lqyzjwd8VetQi/hRQIxEt7brqrd3Yz148CBOp5OoqKgm7evWrePUqVPExcURGxuL2+1udUpHz549vVuUiIh0bR31caqItElAhOj0dHv31XN5ezfWwsJCFi5cyOLFi7Esq8m5rKws3njjDdxuN263m+zs7FZDtIiISBMd+XGqiFy0gJjO0fBp1/z59s2FMTF2gG7vp2AVFRUkJSVRU1NDUFAQd955Jz/60Y+a9HG73eTn53PDDTc0tsXFxdG7d2+2b9/O9ddf374iRESke4uOtu+Ib6ldRPzGamkOb2eTnJxsdu7c2aRt3759jBgxok3XadhoZetW79TlT5fy/kVEpAtqWGLq3Ckd4eGQkaE50SI+YFlWtjEm+UL9AmIkukF3CM8iIhJgGoLy0qX2FI7oaO98nCoi7RJQIVpERKRLSk1VaBbpZALixkIREREREW9SiBYRERERaSOFaBERERGRNgqoEO1a48K1xuXvMkRERESkiwuoEO1tTqeTpKQkRo4cyejRo1mxYgUejweArVu3MnXq1Cb9Z8yYwY033tjq9TZs2MBjjz3m05pFREREpP0CJkRn7srkg8Mf8E7eO8Q+EUvmrsx2XzMsLIzc3Fz27NnD5s2b2bRpE8uXL2+xb0lJCTk5OZSUlHDo0KEW+0yfPp2HHnqo3XWJiIhctMxM6NEDLAtiY+1jEbmggAjRmbsySXs1jaq6KgDySvNIezXNK0G6QVRUFBkZGaxcuZKWNrBZv34906ZNY/bs2a1u+b1mzRoWL17stZpERETOq2Ejlyr79yN5efaxgrTIBQVEiF761lLKa8qbtJXXlLP0raVefZ34+Hg8Hg8FBQXNzmVlZTFnzhzmzJlDVlaWV19XRETkkixd2nQnRLCPl3r396NIdxQQITq/NL9N7e3R0ij0iRMnOHDgABMmTGD48OEEBQWxe/dur7+2iIhIm+S38nuwtXYRaRQQITq6T3Sb2i/VwYMHcTqdREVFNWlft24dp06dIi4ujtjYWNxud6tTOkRERDpMdCu/B1trF5FGARGi029OJzw4vElbeHA46Tene+01CgsLWbhwIYsXL8ayrCbnsrKyeOONN3C73bjdbrKzsxWiRUTE/9LTIbzp70fCw+12ETmvIH8X0BFSE1IBmP/KfKrqqojpE0P6zemN7ZeqoqKCpKQkampqCAoK4s477+RHP/pRkz5ut5v8/HxuuOGGxra4uDh69+7N9u3buf7669tVg4iIyCVLrf89uHSpPYUjOtoO0Knt+/0oEgislubwdjbJyclm586dTdr27dvHiBEj2nSdho1Wts7b6qXK/OdS3r+IiIiInJ9lWdnGmOQL9QuIkegG3SE8i4iIiIj/BcScaBERERERb1KIFhERERFpI5+FaMuyrrAsa4tlWfssy9pjWdYP69u/W3/ssSzrgvNNREREREQ6G1/Oia4F/tUYk2NZVi8g27KszcBu4NvAMz58bRERERERn/FZiDbGHAOO1X992rKsfcDlxpjNQLO1lDvEX132c8rWjn9tEREREek2OmROtGVZscAYYHtHvF5HcTqdJCUlMXLkSEaPHs2KFSvweDwAbN26lalTpzbpP2PGDG688UZ/lCoiIiIiXuTzEG1ZVgSwHnjQGPNFG74vzbKsnZZl7SwsLGx/IYcy4eQHUPAO/DnWPm6nsLAwcnNz2bNnD5s3b2bTpk0sX768xb4lJSXk5ORQUlLCoUOH2v3aIiIiIuI/Pg3RlmUFYwfoTGPMy235XmNMhjEm2RiTPGDAgPYVcigTdqSBp8o+Ls+zj70QpBtERUWRkZHBypUraWkDm/Xr1zNt2jRmz57d6pbft956K0lJSSQlJdGnTx/Wrl3rtfpERERExHt8uTqHBTwL7DPGrPDV61yUj5dCXXnTtrpyu92L4uPj8Xg8FBQUNDuXlZXFnDlzmDNnDllZWS1+/6ZNm8jNzeXZZ58lJiaG2267zav1iYiIiIh3+HJ1jq8CdwK7LMvKrW/7CRAK/BYYALxmWVauMeabPqwDyvPb1t4OLY1CnzhxggMHDjBhwgQsyyIoKIjdu3czatSoZn1PnjzJnXfeyUsvvUSfPn28Xp+IiIiItJ/PRqKNMe8ZYyxjTKIxJqn+sckY8ydjzFBjTKgxZqDPAzRAeHTb2i/RwYMHcTqdREVFNWlft24dp06dIi4ujtjYWNyZYOTzAAAgAElEQVRud4tTOurq6pg9ezY//elPWwzYIiIiItI5BMaOhaPTwRnetM0Zbrd7SWFhIQsXLmTx4sXNlu/LysrijTfewO1243a7yc7ObjFEP/TQQyQmJjJ79myv1SUiIiIi3hcYITouFa7LAEeofRweYx/HpbbrshUVFY1L3KWkpDB58mSWLVvWpI/b7SY/P58bbrjhbDlxcfTu3Zvt25uu+PfLX/6SN998s/Hmwg0bNrSrPhERERHxDV/Oie5c4lLh89/ZX3tps5W6urpWz7lcLlwuFwBHjhxpdj4nJ6dZW0vzqUVERESk8wmcEA3aqVBEREREvCIwpnOIiIiIiHiRQrSIiIiISBspRIuIiIh0Nocy4cUe8AcL/hzr1V2WxTsUokVEREQ6k0OZsCMNPFX2cXmefawg3akEVoh2ueyHiIiISGf18VKoK2/aVldut0unEVgh2sucTmfjOtGjR49mxYoVeDweALZu3crUqVOb9J8xYwY33nijP0oVERGRrqI8v23t4heBE6IzM+GDD+CddyA21j5up7CwMHJzc9mzZw+bN29m06ZNLF++vMW+JSUl5OTkUFJSwqFDh9r92iIiItIJ+OJT7vDotrWLXwRGiM7MhLQ0qKqfW5SXZx97IUg3iIqKIiMjg5UrV7a4acr69euZNm0as2fPbnHLb4/Hw1VXXUVhYWHj8bBhwzh58qTXahQREZEuYHQ6OMObtjnD7XbpNAIjRC9dCuVfmltUXm63e1F8fDwej4eCgoJm57KyspgzZw5z5swhKyur2XmHw8H3v/99MuuD/V//+ldGjx5NZGSkV2sUERERL/HBp9yAvcvydRkQHgNY9vN1GXa7dBqBEaLzW5lD1Fp7O7Q0Cn3ixAkOHDjAhAkTGD58OEFBQezevbtZv3vuuYcXXngBgOeee467777b6/WJfNmWzF3cHfsbpjn+i7tjf8OWzF3+LklEpPPz9afccalwmxu+57GfFaA7ncAI0dGtzCFqrf0SHTx4EKfTSVRUVJP2devWcerUKeLi4oiNjcXtdrc4peOKK65g4MCBvP3222zfvp1bbrnFq/WJfNmWzF2sTHuNwrxSjIHCvFJWpr2mIC0iciEd9Cm3dF6BEaLT0yH8S3OLwsPtdi8pLCxk4cKFLF68GMuympzLysrijTfewO1243a7yc7ObjFEAyxYsIDvf//73HHHHTidTq/VJ9KSF5Zuoaq8pklbVXkNLyzd4qeKRES6iA78lFs6p8AI0ampkJEBoaH2cUyMfZzavo9GKioqGpe4S0lJYfLkySxbtqxJH7fbTX5+PjfccENjW1xcHL1792b79u3Nrjl9+nTKyso0lUM6xMn80ja1i2RmQo8eYFnenQIq0uV00Kfc0nkF+buADpOaCr/7nf311q1euWRdXV2r51wuF676JW+OHDnS7HxOTk6L3/fxxx8zevRorrnmGq/UKHI+kdF9KMxrHpgjo/v4oRrp7FqbAgrtHpMQ6XrS0+3/AM6d0uHlT7kbl87zUm4R7wqMkegGW7d26n+Ijz32GLfffjs///nP/V2KBIi70icRGh7cpC00PJi70if5qSLpzDQFVOQcPvqUW7oOq6XVJDqb5ORks3PnziZt+/btY8SIEX6qyP8C/f3LxamqraK4opiiiiKKK4oprijmZHkxJ74oouB0MSfPFFP8Vh2sj8JxqgcRg0NI+59buPn7o/1dunRCDge09CvDsqB+s1YR8ZbMTJg/3/7oJybGHuFWQO8QlmVlG2OSL9QvcKZziHRiNTVQUWE/ysubPldUQGlZFYVniikqtx+nKosorS6mtKaY07VFnPEUU+4ppsIqotIqptpZTE1QEZ6g8tZftC4YKvpBSH+4rR/0PQS9j/D6579lzovz+cmUBcRcprl9clZ0tD2Fo6V2EfEizZ3qErp0iDbGNFsJIxB0hU8PujpjoLKyebBt79dlFdWc8RRzxhRRborrA28RntBiCCuGsKL652IIP+frkDMtFxoMOIJwVPXHWdeP4Jr+hNTFcJkZQw/Tj3D6E+HsR4SzH72D+3NZSD8u69GPfj36c1l4T8LDLcLCICwM/vFZLS9se51PI54mw/w3GfvSiau9lXvHLORH06cQGqLVYgJdR0wBFRHOP3dKIbrT6LLTOQ4dOkSvXr3o379/QAVpYwxFRUWcPn2auLg4f5fToTwe+/9DvBVoWxrxbfi6srLlj60bOavPBtwvhd+g3kU4I4px9LSDsOlRjCe0mLqQIuqcrYRhwEEQPa1+9ArqT6+gfvQO7kffHv3p26Mf/cP7MaBnfwZE9GNg734M7tOfwZf1Y0BEPyJCIrz638CJE/D8n9w8+9FqDvR+FiKO4/gimoSae1l043xmTx1Mr15eeznpYjIz7d/j+fn2CLQ+YRbxAc2d8quLnc7RZUN0TU0Nhw8fprKy0k9V+U+PHj0YOnQowcHBF+7cjXzyCYy+hKm6Tqc9WhYWdva54evQ8GqcEadwRhRhhRfbgbdHEXUhxdQGFVMdVESVwx4xPmPsaRNldcVU1JW1+npBjiD6hfWjX1g/+of1b/z6y8f9w/s3afd2GPaGki9q+NnLG/j9P57mWNhfwePE+mwG47iPeyamcNsMB4MH+7tKEZFuJja25blTMTHgdnd0NQGn24doCTxFRfDcc82DcFgYBIVWUxt8iipHMRXY84LPeIo5XVdEaZV9Q11xZTFF5WdvsCuqKKKsuvUw7LScLYbdCwXiXiG9Ol0Y9oZ/FBxg+cYMXsl7ngrHSSiOh+w0xlh3851bopgxA6691h4oERGRdmiYE/3luVNa/aNDKERLt1NWXca/v/nvFFfWh+BzAvHp6tOtfl9DGP5y2O3X4zwBObx/tw3D7VVVW8X6fS+z4t2nyT75LpYnGLP327BzIfHOidw2w+K22+ArX7E/BRCR9svclcn8V+ZTVVdFTJ8Y0m9OJzVBYapbC+TVOf7qsp9Ttvrl5RWipdupqq1i6K+HNh8B7tHy9IiGr3uH9lYY9pF9hft4JvsZ1ny0ltLqEnpWXE3le/dRlzOXyJ79mDoVZsyAKVPsXe5EpO0yd2WS9moa5TVnRyXDg8PJmJahIN3dBepmKwrR3qMQLWDfVLlx1U7iRg8kPmkg4b1C/V2S1KuoqeClPS/xdPbTfHD4A4KtUIaW3kHh6/dRtu8rDBlisWQJ3HuvPf1GRC5e7BOx5JU2nx8b0ycG94Puji9IOk4ghuhDmbB9PniqIDwGRqdDXMf+sagQLd3O8UOnWBC/ErDn3Q65qh9Xjh1M/JhBDBs7iPgxg+jdP9zPVcrHxz/mmexn+P0nv+d09Wliw0YRlLuQA+vvYlC/XixZAvfdpzAtcrEcyx0Ymv+utrDwLNNKDdKNHMqEHWlQd85ccGc4XJfRoUFaIVq6peJjp/k85ziff3Scz3OO8XnOcQryShvPR8X04cqxg7hy7GCuHDOIK8cOot9grcfmD2XVZWTtyuLp7KfJOZZDmDOCvvl3cfTPixjouJYf/xgWLrTvlRGR1mkkWgLGn2OhvIVVScJj4DZ3h5WhEC0B44uicg5+dJwDOXawPvjRcY58Vtx4vu+giPpgbYfrYWMHMSC6j+ZJdxBjDDuO7GDVh6tYt2cd1XXVXFbiouTNRQwonsGP/zWYH/wAevb0d6UinZPmREvA+IMDWvjUBSz4Xsd96qIQLQGt/IsqDn58onG0+vOcY/xz30k8dfa/94i+PezR6vpwPWzsYAYP64fDoWDtS4VnCnn2o2d5audT5JfmE1o1hKr376Of+16W3D+Y+++HiAh/VynS+WTuymTpW0vJL80nuk+0VueQ7kkj0d6nEC3eUFVRg3tXQWOo/jznOO5dBdRW1wEQFhFC/JhBjXOsrxw7iCtGDMAZ5PBz5d1PnaeO1/a/xqoPV/Hm529ieYIwe2+nz6eLWDJnAosXW9oVUUSkI3Smmxc1J9r7FKLFV2qq6/jnvsL6YF0/HST3BFXlNQCE9AgiNjHq7Kj1mEHEjIoipEeQnyvvPj4r+oynPnyK1dnPU1ZbCscTCd97P/82OZV/fSCC3r39XaGISDfWmUI0aHUOb1OIlo5UV+fh6GdF9XOsj3Ow/ibGM6VVADiDHESPHNBkjnXc6IH06Bni58q7tjPVZ/jDrj/wi3dWsf/0x1DZm9B/zOO+sffzXz+8WmFaRMQXOluIBq0T7U0K0eJvxhhOHCrhQOMcaztYlxbaHzlZFlx+df/GEeth9UvvRVymHUbayhjD+/98n0ffWMXmI3/EOGoIykvhOzGLWPn/TaV/X30KIOJN2g0xwHXGEO1nCtEiPmaMoejo6SZzrD/POc7Jw1809hkU35f4MQMZ1ngT42Aui9IyFBfreNlxlm9czZpdz1AZchjriyuY1GshT6ct4KohUf4uT6TL08ofohDdnEK0iJ+UFp6pX8f6bLg+9vmpxvP9L+/VbC3ryKHamvx8aj21PPH6Bh7fsoqTvd6G2hASnN/lF3csYvKIG/SzE7lEWoNaFKKbU4gW6UTKSio5mNswv9oO14f/UYTHY//31zsyvMlye/FjBjEovq+W3GvBy3/bx7+/9CQHe62F0NMMMmP4j5sXseCGOYQHa+cWkbbQbogBLjMT5s+HqiqIiYH0dEjVJxAK0SKdXOWZag59UtC4QcznOcfJ211AbY39iyu8dyjxYxqCtT1yffnV/XE6teQewLbs0yzK+D0fBa2CqD30MH25e8zd/OimHzCs3zB/lyfSJWgkOoBlZkJaGpSfs5xceDhkZAR8kFaIFumCaqpqydtT2GQqyKGPT1BdWQtAaFgQsaPPnWM9iOiRUQSHOP1cuf988onhhyveZWv5KrjmT+Cs5evRU/iXry7ilmG34HQE7s9G5EI0JzqAxcZCXgsbm8TEgNvd0dV0KgrRIt1EXa2Hw5+ebLIqyOcfHafidDUAQcEOYhKi6udX2+E6NnEgPcKD/Vx5x9qzB37y86NsOJKBlZyBiThGdO84Fl23kPlj5tM/vL+/SxTplLQbYoByOKClDGhZ4AnsqTwK0SLdmMdjOPZ58TlzrO1w/UVRBQAOh8XQEZGNG8RcOXYw8UkD6dmn+y+5t3cvPPrfNaz75E84bliF54p3CXWGMnvUbBaNX8T4y8f7u0QREf/TSHSrFKJFAowxhsJ/fnF2ub36gF189HRjn8HD+jVuaX5l/Q2MfSK75814//gH/Pd/wx/e2oXj+idxJP0fNdYZxg8Zz6Lxi5g1ahY9grr/HxUiIi3SnOhWKUSLCACnjpfVB+qz4frEoZLG8wOu6M2VYwcxfupwJs9P6nbLxX32mR2mf//HUpxjX6DX15/klPMf9A/rz/wx8/nB+B8Qe1msv8sUEel4Wp2jRe0O0ZZl9buI1/EYY0ou3K19FKJFvOt0cQUHc89OA/nsw2McO1DM5AVjuP/JWwgK7n434+3fb/9++L/fG5zD3ib69lUcCn0FYwzfGv4tFo1fxOQrJ+OwtPqJiAQQrRPdjDdCdCVwFDjfsJTTGBN9aSVePIVoafT2N8AZDj0Gnn2EDWx6HNzHvjFCLpoxht//51bWpb/HuClXsuSl2wnvFervsnzi88/tMP3CC+Ds+08S73kGd7/fcbKygGH9hvGD5B9wd9Ld9A3r6+9SRUR8TyG6GW+E6I+MMWMu8CIX7OMNCtECgPHAlilQeRwqT0DVSbvtyxwh0COqabBu7RHaDzTy2OiN3+Xw5A82EZsQxbLX5tB/SC9/l+QzBw/Cz34Ga9eCFVzFxIXrOXXVKrIL3icsKIzvJXyPReMXMWawz/8vTkTEfxSim/FGiO5hjKm8wItcsI83KERLizx1dpCuPHHOo+BLx+e0m9rm17CcduAOjWp9ZLsxcEeCI6jj32cH2/n6AR777h/p1T+cRzbNJmZklL9L8im32w7Tzz9vr/g0PS2XoK+s4pWDmVTUVnDj0BtZNH4R37n2O4QGdc/ReREJYArRzXj9xkLLssKBa4E8Y0xhO+trE4VoaTfjgepTZ0N1xQmoKmh6fG7o9lS1cBHLDtI9BrY+0t0QwEOjwBnS4W/TWw7kHGP5t16kuqKGpX+6g8RJsf4uyefy8uDnP4fnnrOPv3fPKa6YvoYXP3+SA8UHiOoZxYIxC7gv+T6i+/h8FpuISMdQiG7GGyPR04HfAMXAw8Aq4AQQCywxxqz1WrUXoBAtHcoYqPniIke4T0DtmZavE9L3/IH73EdQWMe+x4tQkFfCI7dmcXR/MT98fjqTUhP8XVKHyM+Hxx6DZ5+19xuYO8/DTXM3s/6fq9j42UYsy2L61dNZNH4RN8fd3O1WMxERCXTeCNEfA98F+gBbgERjzEHLsqKAt4wxHfYbVSFaOrXaM01Hs6sKmo9sNzxqSlu+RlCvsyPZF5paEhTRYTdOlpVUkj7zJXZtzeOu9El89z++GjCh8Z//hMcfh9/9zg7Td90Fc3/oZlPB06zOWU1RRRFX97+a+8ffz9zRc+nTo4+/SxYRES/w6o2FlmXtOjc0d9QNhQ0UoqXbqKv80qj2eUa4q4pavoYz7EvB+jxTS4Iva3fgrqmq5X/nv8rWzN18894x3P/krTiDAudmzCNH7DCdkQG1tXDnnfBvD1WSXfkSqz5cxY4jO+gZ3JPvJ36fReMXkTAwMEbsRUS6K2+NRLsAB/B2/dcNv423GGNGe6XSi6AQLQHJUwOVhRcZuAsvcqWS80wtCe3f6kolTZbAu2UYS9Z9u9sugdeao0fhf/4HnnkGamrs/QgefhhKe+5k1YereHH3i1TWVnJT9E0sGr+ImSNmEtKF58WLiAQqb4RoN+ChlXWijTFx7SmwLRSiRS7AUwfVRa0H7nOnl1QV2AH9y0L7wzU/guEPQHDLS9u9kZHDk/dvIi5xIMtem02/wd13CbzWHDsGv/gFPP20vcnX975nh+nIK4p4Pvd5ntr5FAdPHWRQxCDSxqaRNi6Ny3tf7u+yRUTkImnbbxFpmTHnrFRyTtg+9hc4+lp9mP43GL4YgiOafXugLYHXmuPH4Ze/hCeftMP07Nnwn/8Jw6/28MaBN1i5YyVvHHgDh+Vg5oiZLBq/iIkxEwNmTrmISFflkxBtWdaVwGxgjjFmVDvqaxOFaJEOcnIH7HoEjr1uL+c34scw/H4I6tmkWyAugdeaggI7TK9aBRUVMGuWHaavvRY+L/6cp3c+zbMfPcupylOMHDCS+8ffz52Jd9IrNPBG8UVEuoKLDdEXvDvIsqzBlmU9aFnWDmAPEATM8UKNItLZRF4HkzbB5G3Qbxzk/hg2xMO+X0FteWO3YWMH86sP7qb/5b346Tcz2ZK5y49F+1dUlD1X2u2GH/8YXn0VRo2yw3TF0Sv5xeRfcORHR3hu+nOEBoWyaNMiLl9xOYs3LWZv4V5/ly8iIpfofHOi78UOy0OBl+ofr3TkXOgGGokW8ZPCbbBrGRzfbN98eO0SGLawcV3rslMVpH/7/wXkEnitOXkSVqyA3/4WysrgO9+Bn/4UEhLsGzR3HNnBqg9XsW7POqrrqpkUO4lF4xcx45oZBAXAjpgiIp2dN24srAa2Af9qjNlZ33bQGBPv1UovgkK0iJ8VvGdP8zjxFvQYBNc+BMPSICiMmqpanrjnVd75Q2AugdeaoiL49a/hN7+B06fh29+2w/To+nWNCs8U8uxHz/LUzqfIL83n8l6Xc9+4+7h33L0Mihjk3+JFRAKYN0J0JPZmK3OAgdgj0fOMMVd4s9CLoRAt0kkUvFsfprdA2GC49j9g2L0YRyj/9/AWXvrZ3xl3yzAeeul2wiK0vBtAcTE88QT87//CF1/AbbfZYXpM/Ur7dZ46Xtv/Gqs+XMWbn79JsCOY26+9nUXjF/HVKzSyLyLS0bx6Y6FlWUOpv6EQCAf+ZIz5SburvEgK0SKdzImt9jSPgnch7HIY+R9w5QLeeHaPvQTe6IEs2xiYS+C15tSps2G6tBRmzLDD9NixZ/t8VvQZT334FM/nPk9pVSmJAxNZNH4RqQmp9Azp2frFRUTEa3y2xJ1lWVcDs40xyy+1uLZSiBbphIyxR6R3LYPC9yB8KIxcys7PbuKxWa/Sq384y1+fQ/S1A/xdaadSUmIH6SeesL+eNg2WLYNx4872OVN9hj/s+gOrPlzFxyc+pk9oH+YlzeP+8fczvP9w/xUvIhIAvDGdY6wxJucCL3LBPt6gEC3SiRljz5X+ZBmcfB/CoynovZgf3wUVZYalf76DRFesv6vsdEpL7fnSv/61PUr9rW/ZYXr8+LN9jDG8/8/3WfXhKv6494/UeGr4Rvw3WDR+EVOHT8XpcPrvDYiIdFPe3Pb7fBPy3jLGjGnl+68AXgAGYe98mGGM+V/Lsn4BTAOqgc+Bu40xJecrUiFaGtXWQpBWMOiUjLFX8fhkGRR9QF3oFWSt/xp/+tNwHnh2Jq7vJfi7wk7piy/slTxWrLDnT99yix2mr7++ab/jZcdZnbOap3c+zZHTR4juE83CcQtZMHYBA3pqtF9ExFt8vu13vUJjzHWtfP9gYLAxJseyrF5ANnAb9pJ5bxtjai3LehzAGLPkfEUqREujMWPA6YSvfQ1uugkmTIABChCdijH27oe7lkHRDoq+GMD6V8Zx+dfv5tZ/+65ulGvF6dOwciX86lf2yh7f/KYdpm+8sWm/Wk8tGz7dwKoPV/H2obcJcYZwx8g7WDR+Eddffr1+viIi7dTptv22LOsVYKUxZvM5bTOB7xhjUs/3vQrRAtjh7JFH4J13YPt2qKy026+5xg7UDY+YGFCQ8D9j4OjreD55BMepDwEoOhNN33FzcFwxHfpfD5qO0Mzp0/ZW4r/8pb3m9De+YYfpr361ed99hft48sMnWfvxWk5Xn2bs4LEsGr+I2aNmEx4c3vHFi4hXuda4ANg6b6tf6wg0nSpEW5YVC7wLjDLGfHFO+6vAOmPM71v4njQgDSA6OnpcXl6ez+uULqSqCrKz4W9/sx9//7t9lxbA0KFnA/XXvgYjRoBD6xb7k6f0ADtX/5rQotcZdW0+TkcdhA6AIbfC0OkwaDIER/i7zE6lrAyeegp+8QsoLISbb7bD9E03Ne97uuo0v//k96z6cBV7CvfQt0df7hlzD7dedSuJAxOJDI/s+DcgIu2mEO0fnSZEW5YVAbwDpBtjXj6nfSmQDHzbXKAIjUTLBXk8sHu3Hajffdd+PnbMPtevnz3toyFYjx0LwcH+rTdAvf5MNi/828t8c1ohc+aXEnpqM1SfAkcIDJwEl0+zHz2j/V1qp3HmDDz9tL21eEEBTJpkh+mJE5v3Ncbwbt67rPpwFS/ve5k6UwfA4IjBjB40msSoRBIH2o+rI68mxKm1vL0lMxPmz7f/vo+JgfR0SD3vZ6wi55e5K5P5r8ynqq6KmD4xpN+cTmqC/lF1hE4Roi3LCgY2An8xxqw4p30usBC42RhTfqHrKERLmxkDBw+eHan+299g/377XHg43HDD2VB9ww3QU2vwdpQPN+3n8TvW0zsynEde+y7RkZ/BkVftx+nP7E6XJZ4N1P3Hg6VPEsrL4Zln7DB9/LgdopctA5er5dlLReVFfHT8Iz458UnjY0/hHqrrqgEIdgRz7YBrG0N14sBERg8czcCIgR37xrqBzExIS7P/N2oQHg4ZGQrScmkyd2WS9moa5TVn/1GFB4eTMS1DQboDeC1EW5Y1toXmUiDPGFN7nu+zgLVAsTHmwXPapwArgInGmMILFQgK0eIlx4/De++dHa3++GM7bAcF2Yv0NoTqCRPs0WvxmQPZx1g+9UWqK2t5+M93kDAxxj7xxTmBuvA9MHXQYyBcPtUO1INSICiw/+CpqLDD2eOP2x+23HSTHaa//vUL3wpQU1fDZ0WfNYbqj098zCcnPuHI6SONfaJ6RtmhOirRHr0emMiIyBGEBoX6+J11XbGx0NKMw5gYcLs7uhrpDmKfiCWvtPk/qpg+MbgfdHd8QQHGmyH6A2As8An2Sh2j6r/uDyw0xrzZyvdNAP4G7MJe5QPgJ8BvgFCgqL7tA2PMwvPVoBAtPlFaCu+/f3akescOqLZH6Rg5sunNild0+G733d4JdwmP3JrFsc9P8eDz05ovgVdVDEdftwP1sTegphScPWDg1+tHqafaG7wEqIoKWL0aHnsMjh61bzxctgxSUtp+X21ReRG7Cnbx8XE7VH9S8Am7C3ZTWWvfvOu0nFwTeU3jaHXiwESSBiUxuNdgH7yzrsfhsP8e/zLLsmeaibSVY7kDQ/N/VBYWnmX6R+Vr3gzRLwL/ZYzZU398LfDvwH8BLxtjkrxQ73kpREuHqKyEDz9serPi6dP2uZiYs8vq3XQTXH21VgDxgrJTFfz3zP/H7nfymPvzr/OdJV9peYk2Tw0U/O3sKHXZ53Z737F2oB46zf46AP83qayEZ5+Fn/8cjhyxl8RbtgwmT27fj6POU8f+4v1NpoN8cuKTJqNjV/W7ikmxk5gUN4lJsZMCdiqIRqLF2zQS7V/eDNG5Xw7KDW0tnfMFhWjxi7o6e8rHufOqCwrscwMGNL1ZMSlJm8BcopqqWp64ewPvZO1hyn1j+cHKW3AGnWcOtDHwxb6zgfrkNjAeCBtydtrHwJshKKzj3kQnUFUFzz0HP/sZHD5sT/VPT7eneXhTSWUJu07s4sOjH7LFvYV3897liyp70aVrB1xrh+rYSUyMnRgwq4JoTrR4m+ZE+5c3Q/Q6oBh4sb5pFhAJ3Am8Z4wZ39r3eotCtHQKxtg3J54bqg8etM9FRNhDgA3L6l13HYQFVohrD4/H8MLSt/njY++TfOswlqy7nbCIi1w5ovIkHN1UP+3jL1B7GpxhMOgb9dM+vgVhgTPtoKoK1qyxA/Q//2nvgPj445Dgow0jaz21fHTsI7a4t7DFvYW/5f2NMzVnAEgcmNgYqr8W8zX6hvX1TRGdgFbnEG/T6hz+484AbcgAACAASURBVM0QHQbcD0zAnhP9HvAkUAmEG2PK2l/u+SlES6d15EjTmxV377bDdnAwjB9/dqT6q1+Fyy7zd7Wd3uvPZPPU/a8TlzSQR16bQ99BbVw7uq4KCt45O0p9pv7j0H7jz077uGx0QEz7qKy0d0BMT7en/8+dC48+6vvp/TV1New8urMxVP89/+9U1FZgYTFm8JjGUH1TzE30Du3t22JEujitE+0fnWKJO29RiJYu49Qpey51w0j1zp1QU2OHtoSEpjcrDhni72o7pQ9f289jd6ynz4Bwlr8+hytGXOK27sZA6W47TB9+FYq2AwbCrzi7fN5Al32zYjdWXGxP8fjtb+0b4H74Q3jooY77m66qtoodR3bw9qG32eLewrbD26iuq8ZpORk3ZBxfj/06k+Im8dUrvkrPkMBeeUVEOgdvjkRfBfwcuBZo/G1jjIlvb5EXSyFauqzycnvVj4ZQ/f779u4ZAFde2TRUDxsWECOkF2P/zqMsn/oiNVV1TZfA+//Zu++4qsv3j+OvDxtUUHGjgCKO3IIjU3Pkytl0laWWoyxtfvu2s+zXHtY3zRxloWnLVY7cIxduUxwoIm4Q2eOMz++PW5YMUQ6cwfV8PM5DPPMGLd/nPtd9XSWRdgnO/3m97GMNmFJVu7xavXPKPjxqlPx1bFRUFLzxhio7qFIFXnsNnn4a3Mu4c12aIY3tMdvZcFrtVO88txOj2Yirkyvt/dpnH1S8s+6deLpKSZQQouxZMkRvBd4CPgcGAqOvP+4tSyy0OCREC4dhNMK+fTmheutWiI1Vt9WqlfewYsuW4Oxs3fVa0aWoa7zVbwEXT13jue8Hcffw5pZ7clM6XNqQU/aRGgNo4NtBjSH3Gwg+zRzyTc2+ffCf/8Dff6uuEtOmwbBhapfaGlIyU9h2dlt2qA4/H45JN+Hu7E7Huh2zQ3UHvw7Sq1oIUSYsGaL36LoeomnaIV3XW1y/bouu610stNabkhAtHJauQ0RE3sOKWb2yvL2hU6ec1nrt2pX9tqGVJcen8d6QxRzeHM3jH/TggZcLaYFXEroO1w5AzDIVqK9e/39NhcCcso8ad4ODjcheswZeflk1oGnbVk1C7NnT2quCxIxEtkZvzS7/2HdhHzo6ni6edKrXiR71e9A9sDuhdUJxdXa19nKFEA7IkiF6G9AF+BVYD5wDPtB1vbElFlocEqJFuRIdnTdUHzmirnd3V10/snaqO3VSQdvBGTKMfP74Mjb//C/9JoQw4au+RbfAK6nU8zllHxfXgikNXCpB7T5ql7rOveDuW3qvX4bMZliwQJV2REdDnz6qk0erVtZeWY74tHg2n9mcfVDx4KWDAFRwrUCXgC7ZBxXb1G6Di5O0mRRClJwlQ3Q74ChQGTVgxQf4SNf1HZZYaHFIiBblWmxs3sOKe/aoHtZOTirt5K6rrumYwy5yt8Br1z+Yl3++v/gt8ErCmAqX1qtd6vMrIO0CaE5QrVPOLrV3E7sv+0hPh//9T5V2XLsGjz4K774L/v7WXll+samxbIralB2qj1xRbzK93b3pGtA1O1S3qtUKJ81KNSpCCLsm3TmEcFQpKbBjh2qpt2WL+jotTd3WqFHeUF2/vt0HvNz+mrmHmU+vpEGbWry1Ytitt8ArCd0MV/fm1FHH71PXVwy63j5vEFTvDE72W2IQH68mH06frn7/7LPw3/+qg4i26lLyJTZGbWRD1AbWn17PiasnAKjiUYW7A++me2B3etTvQbPqzSxfCiSEcEiW3IkOBV4DAoDsz8p0XW9Z0kUWl4RoIYqQmQl79+Y9rBgfr26rUydvqG7e3HonyCxk14rjfDj095K3wCup1Bg4t+J62cc6MGeAqw/U6adCdZ1+4GbD6bMIZ86oTh4//aRa4WV18vCwg26A5xLPqV3q6wcVT187DUB1r+p0C+yWfVCxsW9jCdVCiAJZMkQfA14CDgHmrOt1Xc8/1L2USIgW4haYzaqOOitUb96shsKA2lK8666cUB0SAm72d2Audwu8N5Y+TPOuFmiBVxLGFLjwtwrU51dA+mXQnNXOdHbZRyPrrvE2HDigOnmsXq2m8L33HowYYV/vw85cO5Nd+rHh9AbOJp4FoHbF2nlCdVCVIAnVolySgS75WbTFna7rnS22stsgIVqIEtB11SQ492HFY8fUbZ6e0KGDCtQ9eqhOIHaSkPK0wPthEHcPs2ALvJLQzRC3O6fs45o6CId345xAXa0T2NEhuLVrVSePffugTRt1+LBXL2uv6tbpus6p+FPZoXr96fVcTL4IQF3vutn11D3q9yCgspXfmAlRRiRE52fJEN0TGA6sAzKyrtd1/feSLrK4JEQLYWGXL+eMK9+yRaUjsxmaNYMXX1TbjXawQ10mLfBKKuWMmph4bjlc3gBmA7hVzSn7qN0X3HysvcqbMpth4UJV2nHmjArR778PoTf9Z8Z26brOsbhj2aUfG6M2ciX1CgD1K9fP3qXuHtgdP28/K69WiNIhITo/S4bon4AmwL/klHPouq6PKfEqi0lCtBClLDERli6Fjz+GQ4fAzw+mTIFx42y+jV7uFnj3Tgxh/PRSboFXEoYkNS3x3HLVRi8jFjQX1YfabyDUHQgVy2wY7G1JT4dvvlEBOi4OHnhAdfJo2tTaKys5Xdf598q/eUJ1fLo6XxBcNTg7VHcL7EatirWsvFohLENCdH6WDNHZQ1asRUK0EGVE19UUjo8+gvXrVYCeMAEmT1aHFG2U2awz/9X1/PrhP7QbEMx/fr4fjwo2vpNuNkHcjpyyj4Tr/cB97sgp+/DtCE62ObUyMRE+/RQ++0xNtx81Ct5+W9VOOwqzbubgpYPZoXrTmU0kZiQC0LRa0zyhuppXNSuvVojbIyE6P0uG6O+Az3VdP2Kpxd0qCdFCWMGePWpn+pdf1PjxkSNVqUezZtZeWaH+mhHOzEmrrNMCr6SST+Uq+9gEuhHcq6nhLn6DoHZvcK1k7VXmc+WKaov3zTeq5GPCBFXy4Ygty41mI/su7Muuqd5yZgsphhQAWtRokR2q7w64myqe9tmZRZQ/EqLzs2SIPgoEAadRNdEaqpxDWtwJUR6cOgWffw5z5qh+1AMGwEsvqcOItlZ/TE4LvMo1KvD2X8Os1wKvJDIT4MJqOLcMzv8FmfHg5AY1uuWUfVSwrS3fs2dVWcfcuWq45pQp6q9J5crWXlnpMZgMhJ8Pzw7V26K3kWZMQ0OjTe022QcVuwR0wdvdtsuiRPklITo/S4boAv9PLS3uhChnYmPVduNXX6mvO3RQKWnIELVTbUNOhJ/nnf4/YzSYeH2JDbTAKwmzEWL/ySn7SLzeWaVyi+tlH4PAt52apGgDjh+Ht96Cn39WAfo//4FnnoEKFay9stKXYcxg17ld2aF6+9ntZJgycNacCakTkh2qO/t3poJbOfiBCJsXdiiMsUvHkmHKIMAngGk9pzGyxUhrL8vqZGKhEKJ0pKbCDz/AJ5+oXeqGDeGFF+Cxx1TLPBtx8XQ8b9+70PZa4JVU4onrgXoZXNkKugk8akKd/te7ffQCF+sHtP37VVnHX39BrVrw+uvw5JN20fTFYtIMaWyP2Z5dU73z3E6MZiMuTi6092ufHao71euEp+vN/9vp1k39unGj5ddams8tbFPYoTDGLR9HqiE1+zovVy9mDZxV7oO0hGghROkymeCPP9QhxN27oXp1teX41FPg62vt1QGQdFW1wPt3SzSPf9iTB1660/Za4JVExlW4sOp6t4+VYEgAJ3eo2UOVfPgNBK+6Vl3i1q3w6quqk2JgILzzjiqvt7EPL8pESmYK285uyw7V4efDMekm3JzduLPundk11R38OuDu4p7v8RKihSUFfhHImYT8RQUBPgFETYkq+wXZEAnRQoiyoetqKuJHH6ltRy8vGDsWnn9epSYry0w38sVoO2mBVxJmg9qZjlmmQnVypLq+Spucbh9V21ql7EPX1dTDV19VLcnvuENNPxwyxCbL6stMYkYiW6O3ZofqvRf2oqPj6eJJp3qdskN1uzrtWPyzK2PHQkaG6oAybZp6M2IJYWGU2nML2+X0jhM6+TOghob5LXMBjyg/JEQLIcre4cOqzGPBAtWq4aGHVN1027ZWXZZdtsArCV2HxIicOurYf9QkRc/aUGeACtS1eoKLV5kuy2yG336DN95QQzPbtVP9pu+5p0yXYbPi0+LZfGZzdk31wUtq2qXbv49jXDoDc6ZH9n29vGDWrJKH3bAw1Q4+NecTfYs9t7BtshNdOAnRQgjriYmBL7+Eb7+FpCRo3Vq1xmvSRF0aN4bgYPDwuPlzWVBWC7ygtrV4c7mdtcArifRYuLBS7VJfWA3GJHD2hFr3XN+lHqACdhkxGmH+fNVX+uxZNXF+2jTo2LHMlmAXYlNj2RS1ice7dSP5Sv4SqYAAiIoq2WsEBqoJlKXx3MK2SU104SRECyGsLyFBbWmtXQsRERAdnXObpkH9+jmhOnfArlGj1D7nz9MCb+Vw6jUpZ0MyTJmqD3XWLnVKlLq+amhO2UeV1mVSZ5GRATNnqgB95QoMGqTKPFpYdbyX7XFyUh8u5KOZCY/ZR0idEIs/t6apTw6EY5PuHAWTEC2EsD0pKXDihArUERHqM/2sX9PScu5XuXLeUJ31dVAQuLqWeBl5WuAtHUrzLv4lfk67pOuQ8G+uso8dgA5e9dTutN9AqNkdnEv3E4PkZPXBxUcfqQ8uRoxQBxCDgkr1Ze1GYbvFWuVo9CkB9G3Yl1c7v0qXgC4We27ZiS4/pE90fhKihRD2w2xWn+tnhercIfv8+Zz7ubhAgwY5oTp3yK5a9ZZe8uLpeN7ut5CLp6/x/PzBdB1qu5MYy0z6ZTj3pwrUF9eAMUW1y6vVSwXqOv3Bs/RGEV69qoL09OlgMMATT6j6aRueOF8mCqtb/vKbVGKDpvPZ9s+4knqFLv5deK3La/QO6l3sLjRSEy0kROcnIVoI4RgSE/PuWGcF7BMnIDMz537Vq+fdtc4K2IGBKnwXIOlqGu8OXsSRrWcZ/VFP7n/RwVrglYQpHS5tzNmlTj0LaODb/vrBxF7g5gNOrqC5gpPL9V9zXTSX2yoLOX9elXV89536o3vmGTW0xUY6J1pFUR00Ug2pzN47m4//+ZiYxBhCaofwWpfXGNxkME7F6MYi3TnKNwnR+UmIFkI4NpNJfd584851RIQqsM3i5qYOMRZUe+3tTWa6kc8fX8qWRUfo/1Qo477s45gt8EpC1+HaAYi5Hqiv7i7+YzXnXEH7hoCd/XXBt6WkuXI0wpXIKHWfJk1daNrcFTf3W3i+3Nfnua2g0H+z21xu+41BWcg0ZTL/wHw+2PoBkfGRNKvejP92/i9Dmw/FxangN5JZpE90+SUhOj8J0UKI8isuTgXqG8tDIiNV+M5SuzY0aYLeuDG7jmv8uT4Z357tGP/Hk3hUKtvOIXYl7YKqnzalq/7UukH9mnXRjbm+vo3bbrg+I93A1TgjmekG3F0NeFcy4OluQMt937JUYDC/HrydvaBaB6jZTdWTW2HYjdFsZPG/i3l/y/v8e+VfGlRpwCt3vcKoVqMKHOIiyjcJ0flJiBZCiBtlZqpR5TeWhkREwLVrOXdzcsOpaWNcmt+Rd+e6USOoYP2R2uXVzp1qYMv69VCvHrz1lpo27+ICmE0FhHIDmAsO5je9zWSAlGS4mgDxiTmXa8kQnwzXUuBaKiSkwrU0SEiHxAxIMUANDeqZoR7QqCaEdIU2A6F2D/DyK7Ofl1k3s/zYcqZtmcbu87vxq+THi51e5Mm2T1LBTf4eC0VCdH4SooUQorh0XZWARERwatEm/p21mgDXeO6olopLzJm8PcD8/Quuva5Tx2Y/5nc069apML1rl3pf8+678OCDql1bgYxGdWoxLi7vpaDrcl9y19zfqFIlVaTt66sOtWZ9XbEiRJ6E/eEQGZ3zd8cdqAsEeUOL5hDaAzoPg7qlf6BV13XWnlrLtC3T2HRmE9W8qvFcx+d4ut3T+Hj4lPrrC2FvJEQLIcRtOr77PFMHqBZ4byweTLNamQXXXicn5zyoYsWC2/I1bFjmQ2Uclq6rn3lcHHpsHP+siGPJnDjSYuJoXiuOvu3jCKgYh3ZjOE5IKPw5XVxyAvCNl9zh+Mbr3Yox8TI1FY4cgf37IXw97N8NR89AYq7yE18XaFQbWraGDn0gtKv6+1Oc578N26K3MW3LNFaeXImPuw+T2k9iSscpVPMqZ/3ShSiChGghhCiBm7bA03XVRqKg0pCzZ3Pu5+SkOoQUFLCrVy+/u9cGQ/F3hHPfz1B4/XMC3qR4+FIp0JdKAcUMxpUqle2fga7D+XPwzx+wazUc2A/HL0CMGbLK9Z2dILgutG6nLi1bqosFP+3Ye2Ev7295n9+P/o6nqyfjQ8bzwp0v4OddduUmQtgqCdFCCFFCt90CLyUFjh8veKhMenrO/apUKbg0xEJDZcqErqs2hIWF3sICcVJS4c/p5lb0TnABl8wKVZgz35WpU+HiRejXT7Vqa9Om7H4Ut81shEu7YPevsGs9HD4CZwxwFojLdb8qlaFDR+jbF/r0UX9XShiqj145ygfbPiDsYBiapnFfk/uYGDqRboHdpN2jKLckRAshhAVkphv5/LGlbFl8vQXe9D44O99mCzyzWY0+L2iozIULOfdzcVFBuqCAfYtDZW6brkNsLMTEqMu5c3l/PX9e3X71qqo5LkzlysUrkch9qVDhtsNhaip89RV8+CHEx8PQoTB1qqqdthtmA1zdC5c2QOTfsPcfiEqHaOCEJ8Rcn+4ZEKDCdN++0KMH+Nx+ffPp+NN8vetr5u2fR3x6PI19GzMhdAKPtXqMKp5VLPN9CWEnJEQLIYSFmM0637+yjt8/3k77gcG8vPB+PCpYuGY1IaHwoTK5SxiqVy+4NCQwEJydi/daRqMK7VmBOHc4zvr63Ln8B+ucnFRbwLp1VWlB9epFh+MqVQoddFParl2DTz+Fzz9Xm/+jR8Obb6quHnbHbIC4cLi0Dk7Ng9On4Kg3nKgDu2MgKVn92d95Z84uddu2RZy0LFyaIY1fjvzCjPAZ7IjZgYeLB8OaD2Ni6ETa1Wknu9OiXJAQLYQQFrbif7uZ9exqgkJq8+byoVSpWbH0X9RozDtUJnfAjo3NuV/uoTJZAdvJqeCQfOmS2hXPzd1dhWM/P/Vr7q+zfq1Z02qh+HZdugT/938wY4b6/VNPqc4e1atbd123zWyCC6vg+NfqV7MLJHZTgXrzYdi7V92venXo1UuF6t691Z/dLdp/cT8zw2fy08GfSDGk0KZWGyaGTmR4i+FUdCuDv/tCWImEaCGEKAU7lx3jo2G/U7lWRd5ZOZy6ja3Y1SBrqMyNpSE3DpXx8ckfiG/8umpVhz7keOaMKuv4/nvw8oLnnoMXXihRBYT1JR6HE9+o3WlDIlRpC76PwdGKsHYDrF6dM72zTRu1Q92nD3TqdEvdPxIzEgk7GMaM8BkcunwIb3dvHm35KBNCJ9C8RvNS+uaEsB4J0UIIUUqyWuCZjGZeX/owzTr7W3tJeWVmqiCtaSooV6pk7RXZjIgIVdbxyy/qfcMrr8CkSeDpae2VlYAhGaJ+VLvTCUfA3ReCnoCg8XAiHlatUoH6n3/UJxsVK6oa6qzSjwYNivUyuq6zPWY7M8Jn8Mu/v5BhyqCzf2cmhEzgwTseLJfTEGVcumOSEC2EEKXo4ql43uq3gMtnEnh+/mC6PFz6QzOE5ezZA6+/rvJlnTrwxhswdqz9NEUpkK7D5Y1w7Cs4t1Rd5zcIGj2jRpAnJalxj1mhOipK3Sc4WIXpe+9VpR/FqK2PTY3l+/3fMzN8JpHxkVTzqsbo1qMZHzKeoKpBpfYt2hoJ0Y5JQrQQQpSyxLhU3huymCNbzzLm43u474WOcvDKzmzerGqkt21TG7JTp8Lw4bd1Js+2pETDiZkQ+R1kxIJ3U2g0CeqPAteKKnAfP67C9KpVKgWmpamOH08/DU88oQ6G3oRZN7Pu1Dpm7pnJ0oilmHQTfYL6MCF0AgMaDcDFyb5q6G9FWJh645WRoX5s06bByJHWXpWwBAnRQghRBvK0wHs6lHFflqAFnrAKXYeVK1WYPnAAWrSA996DgQMdoEzclA5nFsHxr+DqHnD1hvqPQ6OnwTtX37/0dPjrL9UfcONGVTj+6KPwzDPQrHifspxLPMecfXOYtWcW55LO4VfJjyfbPskTbZ9wuCEuYWEwbpxqqZjFywtmzZIg7QgkRAshRBkxm3W+/886fv9kOx0GNeKlBfdZvgWeKHVmMyxerEo7Tp6Ejh3h/fehe3drr8wCdB3idqq66ejFqm1e7T5qd7p2P3DKVcJx4IAK02FhKlz37AnPPgv9+xer1MNoNvLn8T+ZET6D1ZGrcdacGdR4EBNDJ9KzQU+cNPt/kxkYqA6r3iggIKdKRtgvCdFCCFHGrNICT1icwQA//ADvvKM6AvbqpT6qb9fO2iuzkLSLcPI7ODkT0s5DxQYQ/BQEjQG3XCUcsbEwezb873/qB9GggTqFOXq0GqJTDJFXI5m1ZxZz988lNjWWhlUbMj5kPKNbj8bXy7eUvsHS5+Sk3pfcSNPyd48U9kdCtBBCWIFNtcATJZKervpLv/++ypP33w/vvgt33GHtlVmI2QBn/1C701e2gLMXBI2FJs9DxcCc+xkMsGQJTJ8OW7eqiZKPPaZKPZo0KdZLZRgz+O3ob8wIn8HW6K24O7vzULOHmBg6kTvr3ml3ZwlkJ9qxSYgWQggrObbrHFMH/IzZpNtmCzxxSxIT4Ysv4JNPICVFlQq//bYKUg4j/gBEfA5nFoBuBv+HoelLULVN3vvt3avC9MKFqpVinz6q1KNv32Kfxjx8+TAzw2cy/8B8kjKTaFGjBRNDJzKy5Ui83b1L4ZuzPKmJdmwSooUQwopyt8B74cchdH7IUbYvy6/YWPjwQ/j6azXLZvx4eO01qFXL2iuzoNQYOPYlnPgWjElQqxfc8TLU7Jn3lOXlyyoxfvONGiEfHKx2ph97DLyLF4STM5NZeGghM8JnsO/iPiq6VWRki5FMCJ1A61qtS+kbtBzpzuG4JEQLIYSVJcal8t7gxRzZJi3wHMm5c6qsY/ZsNS198mR46aVidYSzH5nX4OS3EPEFpF+EKm2g6cvg/yDkbluXmQm//aZ2p3fsUIN9Ro9WtdPBwcV6KV3X2X1+NzPCZ/Dz4Z9JN6bTsW5HJoRM4OFmD+PparuTcKRPtGOSEC2EEDYgM93IZ6OWsPWXowyY1I4nv+gtLfAcxMmT8NZbqrLBxwdefllVNlSoYO2VWZApA6J+gqOfQGIEVAiEJi9A0GhwueEb3bVLdfVYtEhNRrz3XvUD6dWr2L0C49Pi+eHAD8wMn8mxuGNU8ajC460fZ0LoBBr5Nrr5EwhhARKihRDCRuRrgbfwfjy87Hk0nsjt4EE1/XD5cqhRA158ESZOVNO1HYZuhnMr4MiHEPuPGi0ePEn1m/aonve+Fy7At9/CzJlw6ZI6fPjMMzBqVLF/KLquszFqIzPCZ/BHxB8YzUZ61O/BxNCJDG48GFdn+e/HkVl7h19CtBBC2JjlX+/mu8mraRhamzeXD6NyDUfashT//KMOHP79N/j6wnPPqaoGHx9rr8zCrmyDox9DzFJw9oQGY6Dp86pVXm4ZGfDLL/DllxAern4QY8eqiYgNGhT83AW4mHyROXvnMGvvLKIToqlVsRZPtHmCJ0OexN9HDu06IgnRFiQhWgjhKHYsPcbHw3+nSu1KvP3XMGmB54B27FATD//8U7VTfvZZVTddtaq1V2ZhCUch4lM4PR90E9R7UB1CrBqS9366rn4o06fDr7+qU5kDB6ofTI8exS71MJlNrDy5kpnhM/nrxF9omkb/4P5MDJ1I76DeODvdfBCMsA8Soi1IQrQQwpEc23mOqQNVC7w3lg3ljrvqWXtJohTs3avC9B9/qCqGSZPU7nSNGtZemYWlnofj0+HEDDAkQs0e6hBi7d75A/K5c6r59rffqnYnzZqpMP3II6pHXDFFXYviuz3fMXvfbC6nXCawciDjQ8Yzps0YalRwtB9w+SMh2oIkRAshHM2FyKu81W8hV6KlBZ6jO3RItT9bvBg8PGDCBNXNo3Zta6/MwgyJcHKW6jeddh4qt1K9pgMeBqcbapjT0+Hnn1Wpx/79qrXJE0+oUo+AgGK/ZKYpkyURS5gRPoONURtxdXLlgTseYGLoRLr4d5FuOHZKQrQFSYgWQjiixLhU3h20iKP/xDDmk3u473lpgefIjh1T0w/DwsDFRWXG//wH6jnaBxGmTDW05ejHkHAEvPzVFMSgseB6w8FCXYdt21SY/uMP9fshQ9TudNeuxS71ADh65Sjf7vmW7/d/T0JGAndUv4MJIRN4tNWjVPYo3phyYRskRFuQhGghhKPKSDPw2ailbPtVWuCVF5GR8MEH8MMP6vePPw6vvHJLZ+3sg26G83/BkY/UWHG3KhD8NDR+BjwKKLmIjlalHrNmwdWr0LKlCtMjRoBn8XtFpxpSWXR4ETPCZ7D7/G68XL0Y3nw4E0InEFrnprlIWJktDLGREC2EEHbCbNaZ9/Ja/vh0Bx0HN+LFBdICrzyIjlYTEGfPVmftHnkEXn0VGjliO+Qr26939FgCTm7QYDQ0fQEqNcx/37Q0laSmT1e1ML6+asb2xIm3vG2/5/weZobPZMHhBaQaUgmtE8qEkAkMaz6MCm7SHcfW2Mo4dQnRQghhZ5Z/tYtZk1cT3K6OtMArR86fh48/VmftMjJg6FA1TrxZM2uvrBQkHoOjn8LpH8BsgHoPqLrpau3z31fXYdMmFaaXLlWlHfffr3an77rrlko9EtIT+PHgj8wIn8GRK0fwcfdhVKtRTAidwB3V5TyCrQgMhDNn8l8fEABRUWW3DgnRQghhh3K3wHtn5XD8NkBd3AAAIABJREFUGvlae0mijFy+DJ99Bv/7HyQnq7z4+uvQpo21V1YK0i7CsayOHtegxt2qo0edfgWH46go9YOZPRuuXYO2bVWYHjpUndYsJl3X2Rq9lRnhM/j1yK8YzAa6BnRlYuhE7m96P27Obpb7HsUtc3JS751upGlgNpfdOiRECyGEnZIWeOVbXJw6Zzd9OiQkwIAB8MYb0L6AzVq7Z0iCyNkQ8RmkxoBP8+sdPYZBQYE2JQV++kn9cI4cgerVYfx4VepRp84tvfTllMvM2zePb/d8y+lrp6lRoQZjWo9hXMg46lepb6FvUNwK2YkuBRKihRDlTZ4WeD8NofOD8pFzeXPtGnz9NXz+uTpn17u3CtOdO1t7ZaXAbIAzP6tDiAmHwasuNH4OGj4JrpXy31/XYd06FaZXrABnZ3joIbU73aHDLZV6mHUzayLXMCN8BiuOr0DXdfo27MvE0IncG3yvDHEpQ1ITXQokRAshyqOE2FTeG7yIiO0xjPmkF0Oe6yAt8MqhpCTVtOLTT1XJR/fuKkx363ZLWdE+6DpcWKXC9OWN4OoDwU9B42fBs1bBj4mMVKUec+ZAYiK0a6fC9MMPg9utlWecTTjLd3u/Y/be2VxIvkA973qMCxnH2DZjqV3J0Rp72ybpzqEWUA+YD9QCzMAsXde/1DTtXWDw9esuA4/run6+qOeSEC2EKK9yt8Ab+Ew7nvhcWuCVV6mpakfuo4/gwgW1I/3GG9CrlwOGaYDYXaqjx9nf1LCW+o+pjh7ejQu+f1ISzJ8PX32lmnLXqqUm24wfr76+BQaTgWXHljFzz0zWnlqLi5MLQ5oMYWLoRLoHdpc3s6Ws3PeJ1jStNlBb1/W9mqZVAvYAQ4AYXdcTr9/nWeAOXdcnFPVcEqKFEOVZnhZ4QxrzYth90gKvHEtPV5uuH3wAMTGqeuGNN+Deex00TCeeUDXTp+aBORPqDoE7XoZqHQu+v9kMf/+tCstXrgRXV3UAcfJkCL31PtHH444za88s5u2fx9W0qzTybcSEkAk81voxqnpWLeE3JwpiLyG61LYzdF2/oOv63utfJwFHAb+sAH1dBcD260mEEMKKnJw0xn7Si/HT+7Bz6TFe6/Ej1y6nWHtZwko8PNR07JMnVVu8S5fU4cPQUDX0ryy7GJQJ72BoPwMGn4Fmr6kyjzV3wt9dIWa5GuqSm5MT9OkDf/2ldqQnTIAlS1SZR6dOaty4wVDsl2/k24hPen9CzHMxzB8yH19PX55f8zx+n/nx+JLH2X52O/ZQGissr0xqojVNCwQ2A811XU/UNG0aMApIALrrun6lqMfLTrQQQijbl0Tw8fA/qFqn6BZ4r3SbD8AHG0eV5fKEFRgMqo502jQVrFu0UK3xHnhAnbdzOIZkiJxzvaNHNPjccb2jx4iCO3qAqpX+/ntV6nHypOrk8dRT6hRb9eq3vIQDFw8wM3wmPx36ieTMZPx9/BnUaBCDmwzm7oC7cXWWT4pKwl52oks9RGuaVhHYBEzTdf33G277L+Ch6/pbBTxuHDAOwN/fP+RMQT1PhBCiHIrYEcPUgYtAVy3wmnbK3wJPQnT5YzTCokXw3nsQEQFNm6qhLUOHgouLtVdXCswGOLMYjn4E1w6CZx1oPAUajgM3n0IeY1YlHtOnw5o14O4Ow4erg4i30ZA7KSOJX4/8ypJjS/g78m/SjGn4uPtwb/C9DG48mH7B/fB29y7hN1r+SIhWi3AFVgCrdV3/rIDbA4A/dV1vXtTzyE60EELklbsF3oth93HXA03z3C4huvwymeC33+Ddd+HwYQgOVuPER45U5cEOR9fh4t+qo8eldeDqDQ0nQOPJ4FVE7+ijR9XO9A8/qFObnTuruukhQ27rXUeqIZW/I/9m6bGlrDi+giupV3B1cqV7/e4MbjyYgY0GUs9Her4XR7kP0Zo6uvoDcFXX9Sm5rg/Wdf3E9a+fAe7Wdf3Bop5LQrQQQuSXuwXe2E97MXiKaoG3IewQ08cux5BhonqAD6Omdaf7yBbWXq4oY2azmpb97ruwbx/Urw///S889tgtd36zH1f3wJGP4ewvoDlD4KPQ9EXwaVr4Y65dg7lzVVPu06ehXj1V6vHkk+B7exNDTWYT22O2s+zYMpYeW8rxuOMAtK3dlsGNBzO48WBa1mwpXT5slC2E6M7AFuAQqp0dwKvAWKDx9evOABN0XT9X1HNJiBZCiIJlpBn49NEl/PNbBAOfbU9waG3+N+EvMlJzDk65e7kyaVZ/CdLllK7Dn3+qML1rl8qIr7wCY8bc0sRs+5J8Co5+BqfmgikN/Aapjh7V7yr8MSaTGtwyfTqsX69+OI88As88Ay1blmg5EbERLI1YytJjS9kRswMdncDKgdl11F38u0gdtQ2xeoi2JAnRQghROLNZZ+5La1ny2Q7cPF3ITDPmu0/1AB/mRT1rhdUJW6HrqvPb1KmwbZs6W/fyy2rD1cvL2qsrJelX4Pj/4MTXkBEH1TqpMO03ELQiGpQdPqxKPX78EdLSVH3Bs8/CoEElPq15MfkiK46vYOmxpfwd+TcZpgwqe1Smf3B/BjceTN+GfankXsCURlFmJEQLIUQ5s2z6LmZNXl3gbZoGy81vWOR1pN7avum6qjWdOlX9WqMGvPSS6gRXsaK1V1dKjCkQOQ8iPoWUKDWwpcmLUP9RcHYv/HFXr8Ls2WoiYnS0GqE3aZIaqVelSomXlZKZwprINdl11HFpcbg5u9E9UNVRD2o8CD9vvxK/jrg1EqKFEKIcGlHjUxKvpOa73pI70RKiHcfWrarMY80a8PFRjSrGjFE9px2yXNdshOhfVUeP+H3gUUsdQAyeAG6VC3+c0QjLlqlSj02b1Nb9o4+qUo9mzSyyNKPZyD9n/8ku+4iMjwSgda3W9AnqQ5+gPtzlfxduhbXxExYjIVoIIcqhDWGHmP7EcgzppuzrLF0TLSHa8ezcqTZbf/1VVS+0aKHC9MiRt9VG2fbpuurkceQj1dnDpZJqjddkCnjVLfqx+/erUo+wMMjIgHvugSlToF8/NejFIsvTOXLlCMuOLWNV5Cr+OfsPRrORim4V6R7Ynb4N+9InqA9BVYMs8noiLwnRQghRTm0IO8SXY5ZjzDShafDAy514/IOeFnt+CdGOKyFBDfSbO1cdQnR1VWXAY8aoIYAOObzl6j44+jFELwY0CBypOnpULrL7LsTGwnffqXcf585Bkybw/PNqh9rCJzYTMxLZcHoDq06uYnXkak5fOw1AUJWg7EDdvX53Kro5aj1O2ZIQLYQQ5dgr3eZjyDCSkWog5mgsz80fzN3DbhIKbuG5QUK0ozt8GObNg/nzVV7081Pt8UaPhoYNrb26UpB8GiI+V9MQTalQp//1jh5diq5tycyExYvh00/VLnX16mou+1NPlco2vq7rnLx6ktWRq1l1chUbojaQakjF1cmVzv6dVelHwz60qtlKWujdJgnRQgghSL6WznuDF3F4czTjvuzDoGfbl/g5JUSXL5mZqvPb3Llq2J/ZDF27qrN1DzwAFSpYe4UWlhEHx7+B49MhIxZ8O1zv6DEYnIrYitd12LBBhem//lK70aNGqd3pxo1Lb7nGDLad3Za9S33w0kEAalWsRe+g3vQN6kuvoF5U86pWamtwNBKihRBCAJCZbuTjEb+z/Y9jPPTfuxg1rXuJdqgkRJdf58+rnem5c+HECahUCYYNU+UeHTo42GFEYyqc/gGOfqL6TlcKVmUe9UeB803KNY4ehc8+Uy3yMjJg4EB44QX17qOUf0jnk86zJnINqyNXsyZyDVfTrqKhEVInhL5BfenTsA8d63bExckRZ8FbhoRoIYQQ2UwmMzOeWsmqWXvpNaY1k77tj7PL7R2CkhAtdF119pg7V1UypKbCHXeoMP3II1CzprVXaEFmE8T8Dkc+VBMRPWpAo2cheCK4Vy36sZcuwTffqEtsLISEqDD94INlMoPdZDax58IeVp9czarIVeyI2YFZN+Pt7k3P+j2z66kDKgeU+lrsiYRoIYQQeei6zoK3N7Fw6hbaDQjmP4sewMPr1v8hlxAtcktKUkF6zhzYvh1cXGDAABWo+/VTv3cIug6XN6qOHhdWgUsFCHoSmjwHFfyLfmxamtrC/+wzOH5cjY2cPBmeeEL1Fiwj19Kvse7Uuux66rOJZwGoX7k+nf0709m/M138u9CkWpNyXU8tIVoIIUSB/poRzoynV9Lkzrq8uXwYlap6FvuxG8IOMX3scgwZJqoH+DBqWncZJy6yHT2acxjx0iWoVSvnMGIplgWXvfgDqszjzEJAA/8HVb/pah2LfpzZrGawf/qp6jddqZIaGTl5MvjfJIhbmK7rRMRGsCZyDVuit7AleguXUy4D4Ovpy13+d9G5ngrWIXVCylV/agnRQgghCrX11yN8MnIJdRpWYerqkVSr633Tx2wIO8TX4/4kI9WQfZ2le1ALx2AwqEOIc+aozGgywV13qd3phx92oMmIKWcg4ks4NQcMieDbXpV6+D8ENwud4eFqZ3rxYvX7hx5SpR6hN81upSKr68fW6K1sjd7KlugtnLh6AgAPFw86+HXI3q2+s+6d+HiU3Q56WZMQLYQQokgHN0Tx3pDFVPBxZ+rqEdRrWnQ7rtGB07lyJiHf9Zachigcz8WL6nzdnDlw7Jjq5jF0qArUnTo5yGFEQ7I6hHhsOiQdB8/a0HAiBI9XNdRFiY5WkxBnzVK1MV27wosvQv/+FhvecrsuJV9i29lt2cF674W9mHQTGhota7bMLv/o7N/ZocaTS4gWQghxU6f2X+TNvgswGcy8uWIYTe8sfFrbQKd3KeifDE2D5eY3SnGVwhHoOuzYocL0okWQnAyNGqkwPWoU1K5t7RVagG6GC6vh2JfqVyd3CByuSj2qtC76sYmJMHs2fPmlCtaNG8Nzz6mTmjbSRzA5M5mdMTtVqD67le1nt5NiSAEgsHKg2qmu15kuAaqu2kmz7puA2yUhWgghRLFcPBXPG73DuHo+iVd+eZB2/YMLvJ/sRAtLSU5WI8bnzoUtW9QkxHvvVYG6f/8yaVxR+hKOwvGv4NQPanhLja4qTPsNgqLayxkM8Ntvqm46PFwF6MGDYcQI6N3bpn44RrORAxcPsCV6S/Zu9aWUSwBU9azKXfXuyi4BCakdgruLu5VXXDwSooUQQhRb/KVk3r53IacPXGLynIH0fKxVvvtITbQoDcePq8OIP/wAFy5AjRpqcvaYMaptnt3LjFdTEI9/rWqoKwRA8NPQ8Alwq1L443Qdtm1TtTC//gpXr0LVqqp2evhw6NLF6uUeN9J1ncj4SFVTfWYLW89u5XjccUDVVbf3a599WPHOendS2aOylVdcMAnRQgghbklqUgbT7vuFA+tO8/iHPXngpTvztbmS7hyitBiNsHq12p1etkz9vmNHFaaHDgXvm599tW1mE5xbpko9Lm8CZy81uKXxs+DTtOjHZmbCmjWwYAEsXaoac9etq34wI0ZAmzY2W1x+OeUy26K3ZR9WLKiuOutS17vwcrKyJCFaCCHELTNkGPnssaVsWXSEIc93ZMzH9+DklPcfZ+kTLUrb5cvw00+qfvrIEfD0VBuwY8eqDVgbzYvFF79fHUKMWgDmDKjVW5V61OkLN6sjTklR7zIWLoRVq1T5R6NGKkwPH66+tmEpmSnsPLczu/xje8x2kjOTAQjwCcg+rHh34N00qdbEKmuUEC2EEOK2mM06301ZzfKvdtNtZHMmzx2Eq5tz9u0SokVZ0XXYvVvtTi9cqM7eNWyo+k4/9hj42XtDiPQrcHIWnPgG0s5DpUbQ6Blo8Bi4Vrr5469eVfXTCxfCxo3qBxYSosL00KFqt9rGZdVVZx1W3HJmC5dSLtE/uD8rRqywypokRAshhLhtuq7zywfbmP/qBtr2CeK/vz6IZ0XV91ZCtLCG1FSVF+fOVXnRyQn69FHlHoMGgZs9zwIxZcLZ31SpR9xOcPWGBmOg8TNQsUHxnuPcOdVzesECdSBR01S7vBEj4IEHwNe3dL8HC9F1nVPxp0g1pNKipnVKxSRECyGEKLE1c/bx9bg/CQqpzdt/DsOnegUJ0cLqIiPh++/VJSYGqlVTneDGjIEW9l6iH7tTlXpELwbdBHXuheAJULsfODnf/PEAJ06o3ekFC1RzbhcX6NtX7VAPGuRA025Kh4RoIYQQFrFz2TE+HPo71f29mbp6JJ8/vgyQEC2sz2SCv/9Wu9NLlqjy4NBQVTs9bBhUts3mD8WTeh5OzoTI2ZB2AbzqQdCTEDQWvOoU7zl0HfbvV4F64UL1jsPLSwXp4cNVsLbrLfzSISFaCCGExfy7NZqpAxfh7ulC1TqV8KjoJiFa2JTYWLXxOmcOHDwIHh6qimHMGOjWzea6wRWf2QDnlsOJmXDxb9Ccoe5gaDgeat1z84OI2c9jVi3zFiyAX36BuDioUkX9kPr3V61QatUq3e/FTkiIFkIIYVFRhy/zVt8FxF9MJqB5Db7aP87aSxIiH12HvXvV7nRYGCQkQP36OYcR/f2tvcISSDqpDiKemgcZsapeuuF4aDAaPKoX/3kMBrWFv3Ah/PGH6vgBEBiownTWpXVrcLePASmWJCFaCCGExV2OTuDN3mFcPH2NMR/fw8Bn2uXrJS2ErUhLU2Uec+bAunXqrF2vXmp3evBgtVttl0wZcPZ3Ve5xeTM4uUG9B1SgrtH11noApqfDvn1qJnvWJTpa3ebmBm3b5g3W/v4O0GOwaBKihRBClIqkq2l8/vhSdi0/QfuBwUyZNwhvXy9rL0uIIkVFqYOI8+apjFilSs5hxNatrb26Ekg4cn13+gcwXAPvJtBwAjQYVfRExKKcPw87d+aE6t271TsSUCUfuUN1aKgaTe5AJEQLIYQoNbqus/yr3cx9aS0+1b14acF9NO8aYO1lCXFTZjOsX692p//4AzIy1MC/MWNUN7iqVa29wttkTFUdPU7MVG3ynD3Af6jq7OHboWS7xwYDHD6cd7f6uBrnjbOzaomSO1gHB9txEbqEaCGEEGUgct8FPhz6Oxcj4xn2ZheGvt4FZ2f7/cdTlC9Xr6qy4LlzVR21mxvcd5/q7tGzpx3nwPj9cOJbiPoJjMlQuZUK04EjizfEpTji4vLuVu/cqabhgNrm79AhJ1S3b6+usxMSooUQQpSJ1KQMZk5axfr5B2ne1Z8Xw+6jWl1vay9LiFuyf78K0z/9BPHxqvT38cfVpX59a6/uNhmS1GjxkzNVsHapoIJ0wwlQtY1lX8tshoiIvLvVhw+rk54ATZrk3a1u1kz1r7ZBEqKFEEKUqfU/HuSbiX/h6u7ClO8H0WFgI2svSYhblp4Oy5apQL1mjcqAPXuqco/77gNPT2uv8DboOsTtVmH6zM9gSoOq7dTudMBQFa5LQ1KSqqfOHayvXFG3VagA7drlDdY1a5bOOm6RhGghhBBl7tzxOD4a9juR+y4yaHJ7Rn/YE1d329xtEuJmoqPhhx/UYcTTp8HHR9VNjxkDISF22qQiMx5O/wgnv1WHEl19IGCY2qGuflfx+07fDl1XP8jcoXrfPjAa1e03tthr394qP2QJ0UIIIazCkGHk+1fWs/SLnQS1qcXLP9+PXyNfay9LiNtmNsOmTWp3+tdf1W51y5YqTI8cqcaO2x1dhytbVZg++weYUqFCAASMUIG6crOyWUdaWv4We2fPQkCAaqliBRKihRBCWNXO5cf5cvQyMtONPDXjXno82tLaSxKixK5dg59/VoF6927VnKJDB1Xycc89agPV7iZpG5IhZglEhampiLoJqrRWYTpgOHj5le16zp1TI8o7dCjb171OQrQQQgiri41J5JORf3B4czTdH23BxP/1w6tS+ZuAJhzToUOwaBGsXasCtdkMXl7QpYsK1D17QqtWdtblI+0SRC9SgTpuF6BBze4qUNd7ANx8rL3CUichWgghhE0wmcwsem8LP0/dQq2gKrz88/00bFvb2ssSwqKuXVMlH+vWqVB99Ki63tcXevTICdUNGthRLXXiCRWmo8Ig+SQ4uYPfQBWo6/QDZ8d8QywhWgghhE05vPkMH4/4g4QrqYz+qCeDnm0vI8OFwzp/Xg11WbtWBeuYGHV9YGBO6UePHlCjhlWXWTy6rnalo8JUd4+MK2oaov9DEPhI6R9ILGMSooUQQticxLhUvhi9LHtk+OS5g/CpJiPDhWPTdTXgL2uXesMGtXMNathf1i51165QyUKzUEqN2QAX16pAnXUg0csfAkeoQF1WBxJLkYRoIYQQNunGkeEvht1Hi7tlZLgoP0wm1ZAia5d661bV8cPFJe8hxQ4dbPyQoiEZYpaqyYhZBxIrt4L6j1jnQKKFSIgWQghh0yL3XeCjYb9z4WQ8Q9/owrA3ZGS4KJ/S0+Gff3JCdXh4ziHFdu1UmM66+NlqLk27BNGLVaDOPpDYLdeBxMrWXmGxSYgWQghh89KSM5k5aSXrfpCR4UJkuXYNNm5UZR9Z80gMBnVb3bp5Q3VIiBr+Z1MST8CZBXD6p1wHEgeocg87OJAoIVoIIYTdyDMyfN5AOgxqbO0lCWEz0tNh/37YuTPncuqUus3ZGZo3V4G6Y0f1a5MmNtJWL2vceFQYnFmoDiS6VlYHEus/AtU72+SBRAnRQggh7ErukeEDn23PmI9kZLgQhblyBXbtUjvVO3eqrxMS1G3e3vnLQGrWtO56MRuvH0j8qYADiSOhcnMrLzCHhGghhBB2R0aGC3F7zGbVASRrp3rHDjh4UB1iBNVaL3eobtsWPDystFhjijqQePonuLjm+oHElqrcI3A4eNW10sIUCdFCCCHsVu6R4RO/6UfPUa2svSQh7E5qKuzdmzdYnz2rbnNxUdMUO3RQ5SBNmqhLrVplPAwm/TKcyZqQuBPQoMbd0OAxaPB4GS4kh4RoIYQQdk1GhgtheRcu5K2tDg+HpKSc2318cgJ17ktQELi6lvLisg4kRoVBhfrQY3Upv2DBJEQLIYSweyaTmcXTtrLwnc0yMlyIUqDrcO4cRESoy9GjOV+fP59zPxcXFaQLCtiVLd29TtfBkGC1tngSooUQQjiMw5vP8MnIJVy7lMzoj++RkeFClIHERDh2LCdUZ11OnMhpuQeqBKSgcF2vno10CblFEqKFEEI4lMS4VL4cs5ydy47TbkAwU+bJyHAhrMFohNOn8+5aZ+1iZ40zB/D0hMaN84frRo3UbbZKQrQQQgiHo+s6K77ezZwX1+JdzYuXFsjIcCFsha6r1ns37lxHREBUlLod1MHFwMCCd6+rVy/jg40FkBAthBDCYeUbGf56F5xd7PBzYyHKidRUVQZyY7g+dgzS0nLuV6WKCtOPPgoTJ1pnrcUN0dLFXgghhN0JalObL/Y8ycxJK1n4zmYObYiSkeFC2DAvL9VSr9UN3SrNZtV278ZwnZpqnXXeCtmJFkIIYddkZLgQwpKKuxMtn30JIYSwaz0ebcmXe5+kRoAP7w5ezLfPriIz3WjtZQkhHJyEaCGEEHbPr5Evn2wfzeApHVj+1W5evHMuMcdirb0sIYQDkxAthBDCIbi6u/Dk5715c/lQYs8mMiVkNuvmH7D2soQQDkpCtBBCCIfSfkAjph8YR8PQOnz+2DI+fXQJqUkZ1l6WEMLBSIgWQgjhcKr5eTNt3SOMfOduNi04zOS2szm594K1lyWEcCASooUQQjgkZ2cnhr/Zlfc3PIoh3ciLHeey9Iud2ENXKiGE7ZMQLYQQwqE17xrA9P1PEtKvId89t4apgxaREGsHTWiFEDZNQrQQQgiH5+3rxetLHmb89D7sW3OKZ1rN4uDGKGsvSwhhxyRECyGEKBc0TWPgM+35dMdoPCu68lqPHwl7ayMmo9naSxNC2CEJ0UIIIcqVrJHhPUa1ZOHULbza40eunE2w9rKEEHZGQrQQQohyx7OiG899P5gXfhzMqX0Xebb1d+xcdszayxJC2BEJ0UIIIcqt7o+05Iu9T1AjUEaGCyFujYRoIYQQ5ZpfsC+f/CMjw4UQt6bUQrSmafU0TdugadpRTdP+1TRt8g23v6hpmq5pWrXSWoMQQghRHAWODP/hgPSUFkIUqjR3oo3AC7quNwU6Ak9rmnYHqIAN9AKiS/H1HZY5yUzmoUwMUQZMcSZ0g/xPXgghLCHPyPDHl/HZqKUyMlwIUSCX0npiXdcvABeuf52kadpRwA84AnwOvAwsLa3Xd2SpK1MxHDPkuU7z1HCq5ITmreHk7YRTJSf1a66vNXfNSisWQgj7kTUyfPG0rSx8ZzMRO87xn5/vp2FIbWsvTQhhQ0otROemaVog0AbYqWnaIOCcrusHNK3wUKdp2jhgHIC/v38ZrNI+mJPNGE4YcGvjhlszN8yJZsxJZvREPftrw3kDemoBu9PuFBius77WfDQ0d42i/lyEEKI8yBoZ3qJbAJ+MXMKLd85l9Ef3MGhye/l/pBACAK206700TasIbAKmAauADUBvXdcTNE2LAkJ1XS/yBEdoaKgeHh5equu0F+nb00lbm4b3BG+cqzsXej/dqGNOMmNOvB6wr3+dFbTNiWb05AL+7F0pMGRrlbScrz0laAshyo/EuFSmj13OjqXHaTcgmCnzBuFTzcvayxJClBJN0/bouh560/uVZojWNM0VWAGs1nX9M03TWgDrgNTrd6kLnAfa67p+sbDnkRCt6LpO4sxENA8N79HeJX8+k46erOcL13mCdpION/4Vcc4btAsqIdEqSNAWQjgOXddZ8b9w5rzwN97VvHgxbAgtuwVae1lCiFJg9RCtqQT1A3BV1/UphdwnCtmJLjZjjJGkeUl4DfDCvY17mbymbtbRUwoO2npSzvXcODXXiZyAXUgJiVZRQ3OSoC2EsB+n9l/kw6G/cf7EVYa+3oXhb3bF2aVsusW+0m0+AB9sHFUmrydEeVXcEF2aNdF3AY8ChzRN23/9uld1Xf8xXELkAAAb8ElEQVSrFF/ToWXszwBXcLvDrcxeU3PSVClHJSd1LLQAuq6jp+YN2rlrtE0XTRiOG1S/ljxPDlrFIg5CXg/gmrMEbSGEbWjQuhZf7HmSmZNW8vO7Wzi4IYqXFtxH9Xo+1l6aEKKMlWZ3jq1AkelH1/XA0np9R6Nn6mT+m4lbUzeb67KhaRpaBQ2nCk5QyOF1XdfR0/VCa7RNsSYMpwyQWcDzVyigXCRXjbZTJSc0V9v6mQghHFfWyPDW99Tnm4kreabVLKbMG0THwY2tvTQhRBkqk+4couQyj2ZCJri1KbtdaEvSNE214fN0gpqF30/PKKJG+5oZY7QRPT1/CZLmqRUasLOvd5OgLYSwnO6PtKRRBz8+GvY77w1ZzIBJ7Rjz8T24ecg/rUKUB/Jfup3I3J+JU1UnXOo59h+Z5q7hXN256M4jmTfsZN9YQnLOXGCLP81dK7qPtre0+BNC3JqskeHfv7KepV/s5PDmMzw9416adqpn7aUJIUqZYycyB2GKM2GMNuLR3UMCHqC5aTj7OuPse5MWf7m7jNxQQmK4ZJAWf0IIi8gaGd6qZyBfj/+Ll+76nrtHNGf0hz2pVrfknZSEELZJQrQdyDyQCRq4tyqbjhyOQHPRcK7qjHPVIoK2SQXr3F1G8gTtKIO0+BNCFFv7AY349lggv364jT8+2cGOPyJ48JW7uO/FO/HwcrX28oQQFiYh2sbpZp2Mgxm4NnRVHTKExWjOGs6VnaFy4ffRzdd7aRfUQztRxxhjlBZ/QohsnhXdePTd7vQe24Z5L68l7K1NrJm9j9Ef30OXh++QN9dCOBAJ0TbOGGlET9Jx62OfBwrtneaUU0ddZIu/lLxBO3f5SLFa/BVWQiIt/oSwSzUDK/PK4gc5vPkMsyav5qNhv7Pi692M+6IPDUMKaWMkhLArpT722xLK87CV5F+SMUYb8ZniI2HKjum6jp6m5xtSc+MBSQz5H6tVzL+bfWMJieYifzeEsFUmk5m18w4w/9X1JMamcs/o1oya1p0qtSoW+zk2hB1i+tjlGDJMVA/wYdS07nQf2cJia5RBLkLksIVhK6KEzClmDMcNuLd3lwBt5zRNQ/PScPJygloF30fXdcig0BHspqsmjGcKafHnpeUbUpOv+4i0+BPCKpydnejzRBs6P9SURe9tZdmXO9n6yxGGvt6FwZPb4+pe9D/FG8IO8fW4PzFkmAC4ciaBr8f9CWDRIC2EuDUSom1Y5qFMMMuBwvJC0zTwAGcPZ5xr3KTFXwFB+6Yt/jy0QntoZ32NO1KzKUQpqeDjwZiP76HPuDbMeeFvvv/POlbP2svYT3vRYVCjQv/bm//aBjJS835MlZFqYP5rGyREC2FFEqJtlK7rZOzPwLlO0YFKlD+am4ZzNWecqxURtA3Xa7JvtcWfG4UfhLwewKXFnxAl4xfsy5vLhrF3TSSzn1vDe0MW06pnfZ78ojeBzWvku39sdEKBz1PY9UKIsiEh2kaZLpgwXzHj1d/L2ksRdkhztUCLv9OFtPhzIW/Azl2jnfV7afEnxE217R3E9P3jWDlzD2FvbeLZVrPoNyGEkVPvxts35//91fx9uHImf2Cu5u9TlssVQtxAQrSNytifAS7gdod05RCl45Za/BUyit0YbcScVEiLvyL6aDtVkhZ/QgC4uDoz8Jn23D2iOQve3sxfM8LZtPAwI96+m3snhuDi6syoad35etyfeUo63L1cGTWtuxVXLoSQEG2DdINO5uFM3Jq6oXlIyBDWk6fFXyGyW/wV0EfbnGTGdN6EIcIAphufnOw2fh7tPHBrIW8YRfnl7evFhK/60m9CW2Y/9zezJq9m5cw9PPF57+y659LsziGEuHUSom2Q4ZgBMsCttYQKYfs0TVNt+Co6QZ2C75O7xd+Nbf6MF4ykLE0BV3BrIn/nRfkW0KwGU1ePYNfy48x+YS1v9V1AuwHBPPFpLxp3rAtIGzohbIWEaBuUeTQTrZKGS4D88QjHUFSLPz1TJ+mnJFJ+T0F7RMPVX8Yji/JN0zQ6DGpM2z5BLJu+i5/f3cLTzWfiU7MCNaQOWgibIXOkbYyeqWM4acCtsZsczBLlguamUXFYRZwqO5GyKAXTpRvrPoQon1zdXXjgpU7MOvE0PUa1JC4miWM7zvHd82u4bMHOHBvCDnFsRwyHN51hdOB0NoQdsthzC+HIJETbGEOkGg/t2lR240T54eTlRKWRlcAVkhYmYbomQVqILFVqVuTZ2QNpGFKbStW8WD59F08Gfc2no5Zw+uClEj13YYNcJEgLcXMSom2MIcKA5qnh4i+lHKJ8cfJxotLwSpAJyQuSMafe2PJDiPLNo6Ib9ZpWY3bkJAZMasf23yN4ptUs3uq3gIMbotTU01tU1CAXIUTRJETbEN2ok3kiE9fGrtL6S5RLzjWdqTCsAuZrZpJ/TkbPvPVQIISjqxFQmSc/7833Zyczalp3Ivde5NUeP/JcuzlsWfwvJmPx34DKIBchbp+EaBtiPG1UXTmaSocCUX65+rtS4f4KmM6bSP4tGd0kQVqIglSs4snDr3Zm7plnmfRtf1ITM/hw6O+Mb/wNf34TTvoNO8wFKWxgiwxyEeLmJETbkMyjmeAOLoFSyiHKN7cmbnjd64XxpJHUFam39TG1EOWFm4cLfce1ZcbRibz6+0P41PBixtMrGRMwnQXvbCIhNrXQx46a1h13r7xncGSQixDFIyHaRuhmHcNxA27BbmguUsohhHtbdzzu9iDzYCbp69OtvRwhbJ6zsxOd7mvCJ/+M5sMtj9HkTj8WvL2ZMf5fMmPSSi6eis/3mO4jWzBpVn9c3Z0BqB7gw6RZ/WWQixDF8P/t3XmUXGWZx/HvU0t3dVV1ZyEJSeiEpBOSGJYsREwAMxlDwuYaZVFhBMajZ0YZ8KhzUI+OznLOOG7HM6POOCAqKqIZQERiOmziFjDpLHRIIHQSQjaTEOmlqpda3vnj3jRN6CzdqepbVf37nFOnb92+9ebph/fST9/73ve1crjCs2DBArdu3bqgwyiqzM4MHT/qIHFNQgtOiPicc6RXpelZ30PN5TXELooFHZJIWdn93CEe+NpanrhnM/mc45Jr3sR7P30x0y+c8Lrj7ljyQ0ALuYgAmNl659yCkx2nK9ElIrM1A1GITtPUdiJHmRnxK+JEZ0bpXN1Jz5aeoEMSKSuTZ4/ltrvewZ07b+U9n1rI+lUt3L7gTj679B7Wr27RUCmR06AiugQ45+h5vofotCgW1VAOkb4sZCRWJIhMjpD6RYrMzpM/LCUirzfmrDpu/vJl3L37H7jlK5exZ9sr/NMVP+HWud/liR9txuVVTIsMlIroEpDbk8N1OM3KIXIcFjES1yYIjQ7R8bMOsgeyQYckUpYSI2Ks+NQi7tp5K7ff/U7y2Txfu/EXvPDMXg7tbmVX80FdnRY5RRoTXQLSjWm613Uz8pMjsWpdiRY5nnxbnra72yAPtTfVEh4VDjokkbKWzzvWr3qRr3zwAdKt3QCMnpBk3vIG77WsgRFjEwFHKTK0TnVMtOZSC5hzjsy2DNGGqApokZMI1YWo/UAt7d9vp+MnHdTeVEsooRtqIoMVChlvvvocGuaOp6cryxUfmc+Gxhae+eV2HvvBZgCmzR/PvOUNzF8+jTddXE+0WqWDCKiIDlzuQI58a57YYs06IHIqwmPDJK9P0v6jdjp+2kHtjbVYlf4AFTldVbEIy2+Zy/Jb5pLL5WlpOsCGxhY2NO7gga+uZeW//4HqeJTzl5ztF9UN1M8ag5nOPxmeVEQHLLM1AwbRGZqVQ+RURSZFSKxIkPp5io6VHSSvS2Jh/SIXKZRwOMSMN09kxpsnct3n3kq6vZtnn3yJDY072NDYwrpHXgRg7KS63qEfc5ZOpe6MeMCRiwwdjYkOkHOOtm+3ERoRovaG2qDDESk73Ru6ST+cJjo7SuI9CSykQlpkKBzY+Rc2rtlJU2MLmx7bRerVLsxg+oKJzPeL6pkL64lW6bkFKT+nOiZaRXSAcgdztP1PG/Er41QvqA46HJGy1PXHLjof7aRqXhXxq+O6tSwyxHLZPNvX7fOvUu9g29o95HOOmmQVF7xtSu+V6onTR+v8lLKgBwvLQM82b+GI6EwN5RAZrNiiGK7L0fW7LqzaqLmsRr+oRYZQOBJi1sJ6Zi2s5/1fWEyqtYvNT+yiafUOmla38PRDLwBw5pSRrw39eNsUkqNqAo5c5PSoiA5QZmuG8KQwoVrNLiByOmJLYrhuR/fabq+QXqxfziJBSYyIsejds1j07lkA7G85wobGHTQ17uA39zbz6+82EQoZ51w0kfmXT2P+8gZmXHQW4Yh+F0p50XCOgOSO5Gj7Vhs1y2qILdTMHCKnyzlH+pdpejb1ULO8hthbdF6JlJpsJscLz+yjabU368f2P+0jn3fE66qZs3QK85Z7RfX4hlFBhyrDmIZzlLjMNm/p4ugsDeUQKQQzI/72OK7b0dnYiVUb1XP1rIFIKYlEw8y+ZBKzL5nEDf+8hPYjnWx6fKd3pXr1Dv74wPMATJg2qs/Qj6nE63QuS+nRleiAtN3VBg7qPlwXdCgiFcVlnbc0+I4siRUJqmZXBR2SiJwC5xz7th+hyZ9Gb/Pju+hKZQiFjVmL6nsXfJm+YALhsIZ+SPFodo4Slm/L0/rNVmJ/HaPmUo3dFCk0l3F0/LiD7N4syeuSRKfrjo9Iucn05Nj2xz29C768uH4/zkFyVIw5S6f2Lvgy7uyRQYcqFUZFdAnr3thN+pdp6j5aR3ic5tAUKQbX5Wi/p53c4Ry1N9YSqdfoNZFy1no4zabHjg79aOGVve0A1M88o3fox/lLplCT1N0nOT0qoktY6sEUmR0ZRnxihKbiEimifCpP+/fbcZ2O2g/VEh6rP1pFKoFzjj3bDnvT6DXuoPnJXXR3ZolEQ8y6eFLvgi/T5k8gpEWYZIBURJco5xyt32wlMjlCckUy6HBEKl7uLzna727HIkbtTbWE6jSWUqTSZLqzPPf7l3sXfGnZcACAujNqmHPZVOZfPo15yxoYU6/nkOTkVESXqNzhHG3faSN+dZzq+XraWGQoZPdnaf9hO6ERIa+QjqmQFqlkrx5MsfFRb8aPDY07+MuBDgAmzx7DvOXTmLe8gfMWTyaW0NAPeSMV0SWqe1036VVp6j5WR3i0bi2LDJXMjgwd93YQqY+Q/GASi+gWr8hw4JzjpeaD/qwfO9jy1G56urJEqsLMvnRS74IvUy44U0M/BFARXbI6VnaQ25ej7tY6jYcWGWI9zT2kHkgRnRUl8d4Epl+YIsNOd2eG5373cu8DiruePQjAyHEJ5i7zZv2Yt6yB0RNqA45UgqIiugQ552j9WivRGVES70wEHY7IsNT1dBedjZ1UXVhF/Mq4/pgVGeaO7G9nwxrvKvXGNTt59WAKgCnnj+ud9ePct06mukZTZQ4XKqJLUPZAlvb/bSf+rjjVF2g8tEhQ0o+l6f5DN7G/ilGzWHO1i4gnn3fs2vzn14Z+/HY32Z4cVbEI5y6e3Ds39dnnjdMf4BVMRXQJ6lrbReeaTkbcNkIzBIgEyDlH+qE0PZt79JCviBxXV6qH5qd2+7N+tLD7ucMAjJ6QZO6yht6hHyPH6e5yJTnVIlqrDwyh7K4sodEhFdAiATMz4m+Pk0/lST+SJjQqRHSqbtWKyOvFElUsuHI6C66cDsDhPW29Qz/W/Wo7j/9wMwD/vPoDzF8+LchQJQAqooeIyzsyL2WoOl/T6YiUAgsbyRVJ2r7fRmplitqbawmP0Yw5InJ8Y+rrWHbzXJbdPJd83rFjwwGaGluYcdFZQYcmAdAl0SGS25+DHohO0dUukVJhMSN5XRJC0HFfB/l0PuiQRKRMhELG9AsncO1nLiU5MhZ0OBIAFdFDJLMzA0DkbF38Fykl4VFhktcmybfmSa1M4XKl/5yIiIgET0X0EMnuyhIeFyaUUMpFSk1kUoT4O+JkX8qSfiRNOTxwLSIiwVJFNwRc1pF9OUtkqq5Ci5Sq6vOriV0ao2djD91ru4MOR0RESpyquiGQ3ZuFLESmKN0ipSy2JEbulRydj3YSGh2iaqYeBBYRkf7pSvQQyO7MgkF0sh4qFCllZkbiXQnCE8OkHkiRPZANOiQRESlRKqKHQHZXlvCEMBbT6kYipc6iRvLaJFZj3owd7ZqxQ0RE3khFdJG5Hkd2b1ZT24mUkVBtiOR1SVyno2Nlh2bsEBGRN1ARXWTZl7OQRw8VipSZyPgIiXckyO3J0bmmM+hwRESkxKiyK7LMzgyEvCm0RKS8VJ1bRXZflu613YQnhqm+oDrokEREpEToSnSRZXdlidRHsKjGQ4uUo5qlNUTOjpD+VVoPGoqISC8V0UWU78yTO5DT1HYiZcxCRmJFAqsxUitT5Dv1oKGIiKiILqrs7iw4jYcWKXehZIjk+/ylwR9MaUVDERFREV1M2V1ZiEDkLBXRIuUuUh8hfkWc7ItZup7qCjocEREJWNGKaDObZGZPmNlWM9tiZrf5+79oZnvNbKP/uqpYMQQtszNDZHIEC2s8tEglqJpfRdWcKrqe6qLnhZ6gwxERkQAV80p0Fvikc+5NwELgY2Y22//eN5xzc/3XI0WMITD5jjz5Q3nNDy1SQcyM+JVxwuPDpB9MkzuSCzokEREJSNGKaOfcfudck7/dDmwFzirWv1dqsi95T/FrPLRIZbGokbgmASFI/TxFvlUPGoqIDEdDMibazKYA84Cn/V0fN7PNZvY9Mxs1FDEMtcyuDFRDeHw46FBEpMDCI8MkViTIvZKj9VutpFenyXeomBYRGU6s2E+Zm1kS+A3wb865+83sTOAw4IB/ASY4527p53MfAT7iv50JPF/UQCvHGLz8ysAob4On3A2O8jY4ytvgKG+Dp9wNTjnn7Wzn3NiTHVTUItrMosDDwGrn3Nf7+f4U4GHn3HlFC2KYMbN1zrkFQcdRbpS3wVPuBkd5GxzlbXCUt8FT7gZnOOStmLNzGHAXsLVvAW1mE/oc9h6guVgxiIiIiIgUQzGfersEuBF41sw2+vs+C7zfzObiDefYBXy0iDGIiIiIiBRc0Ypo59zvgP4mSK7IKe1KyHeDDqBMKW+Dp9wNjvI2OMrb4Chvg6fcDU7F563oDxaKiIiIiFQaLfstIiIiIjJAKqJLlJmNNLOVZrbNXzp9kZmNNrM1Zrbd/zrKP3aJmbX2WUr9Cydqx9/fb1vlrhB5M7OZffZtNLM2M7vd/15F5g0K2uc+YWZbzKzZzO41s5i/f6qZPe23dZ+ZVQX1sxZSAfN2m5+zLUf7m7+/IvvcQPLmH7/Ez9kWM/tNn/1XmNnzZvaimd3RZ/+w72/+8cfL2/fM7KCZNR/TfkX2NyhM7sxskpk94X9+i5nd1uf4isxdgfIWM7NnzGyTv/9LfY4v33PVOadXCb6AHwAf9rergJHAfwB3+PvuAL7sby/BmyrwlNrxt/ttq9xfhcpbn/bCwAG8OSMrNm+Fyh3eqqQ7gRr//c+Am/psX+9v/zfwd0H/zCWUt/PwZiqK4z2r8ihwTiX3uQHmbSTwHDDZfz/O/xoGWoAGv41NwGz1txPnzd9eDMwHmo9pvyL7WwH73ARgvr9dC7zQp89VZO4KlDcDkv52FG/xvYX++7I9VwMPQK9+/qNAHV4hYsfsfx5vcZqjJ/Lz/vYS+v/F3G87J2qrnF+Fytsxn10O/L6S81bgPncW8DIwGq8YfNjPoeFNuh/xj1uEN3984D97ieTtGuDOPu8/D/xjpfa5QeTt74F/7aed1/Uj4DP+S/3tBHnr87kpvLGIrrj+Vozc9fn8L4BllZq7YuQN72JBE/CWcj9XNZyjNDUAh4C7zWyDmd1pZgngTOfcfgD/67g+n1nk3yZZZWbnnqQdTtJWuSpU3vq6Hri3z/tKzBsUKHfOub3AV4HdwH6g1TnXCJwBvOqcy/qf3YNXcJe7QvW5ZmCxmZ1hZnHgKmCS/71K7HMDzdsMYJSZPWlm683sb/z9R/9oO+pov1J/8xwvbydSif0NipA78xaMm4d3VZUTtFXOCpY3MwubN+XxQWCNc+5pyvxcVRFdmiJ4t9m+45ybB6TwbpccTxPecIM5wH8CDw6ynXJXqLwB4I/Leifw8+KEW1IKkjt/XNy7gKnARCBhZjfQ/3SXrnDhB6YgeXPObQW+DKwBfo03LCF7vEYqwEDzFgEuBK4GLgc+b2YzOH6/Un977fj+8jYcFTR3ZpYE/g+43TnXVrSog1ewvDnncs65uUA9cJGZnUeZn6sqokvTHmCP/1cawEq8Tvxn81d89L8eBHDOtTnnOvztR4ComY05QTscr60yV6i8HXUl0OSc+3OffZWYNyhc7i4DdjrnDjnnMsD9wMV4t+tGmtnRuenrgX1D86MVVcH6nHPuLufcfOfcYuAIsN1vsxL73IDy5h//a+dcyjl3GHgKmOPvn9Sn3aP9Sv3tteP7y9uJVGJ/gwLmzsyieAX0j51z9/f5NyoxdwXvc865V4EngSso83NVRXQJcs4dAF42s5n+rqV4A/UfAj7k7/sQ3lgszGy8mZm/fRHef9dXTtAOx2urnBUqb32afD+vH8rB8doqdwXM3W5goZnF/e8vBbY65xzwBPC+Y9sqZ4Xsc2Y2zv86GVjBa32v4vrcQPPmf32rmUX84S5vAbYCfwLOMe/p/iq84VcPqb+dNG8nUnH9DQqXO//8vQvv/2tfP+afqbjcFTBvY81sJICZ1eBdcNlW9udqMQZa63X6L2AusA7YjHfLdxTe2KHH8K5QPQaM9o/9OLAF7xbwWuDiE7Xj7++3rXJ/FTBvcbziZsQx7Vdk3gqcuy8B2/DG+d4DVPv7G4BngBfxhshUB/0zl1jefov3y2kTsLTS+9xA8uYf/2k/P814t9CP7r8Kb4aEFuBzffYP+/52krzdi/fcQgbv6uHfVnJ/K1TugEvxhhtsBjb6r6sqOXcFytsFwAa/jWbgC32OL9tzVSsWioiIiIgMkIZziIiIiIgMkIpoEREREZEBUhEtIiIiIjJAKqJFRERERAZIRbSIiIiIyACpiBYRKUNm5szsnj7vI2Z2yMwe9t/fZGb/5W9/0cz2mtlGM9tuZveb2eygYhcRqQQqokVEylMKOM9fuABgGbD3BMd/wzk31zl3DnAf8LiZjS12kCIilUpFtIhI+VoFXO1v97fCZr+cc/cBjcAHihSXiEjFUxEtIlK+fgpcb2YxvBXBnh7AZ5uAWUWJSkRkGFARLSJSppxzm4EpeFehHxngx63gAYmIDCMqokVEyttDwFc5xaEcfcwDthY+HBGR4SESdAAiInJavge0OueeNbMlp/IBM3svsBz4ZDEDExGpZCqiRUTKmHNuD/DNfr4VAbr7vP+Emd0AJIBm4G3OuUNDEKKISEUy51zQMYiISIGZ2TeA7c65bwcdi4hIJVIRLSJSYcxsFVAFrHDOtQYdj4hIJVIRLSIiIiIyQJqdQ0RERERkgFREi4iIiIgMkIpoEREREZEBUhEtIiIiIjJAKqJFRERERAZIRbSIiIiIyAD9Py+a/LEGhAOdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plot_lightcurve(sn_lc, plot='mag', linestyle='-', marker=None, label_prefix='Sim')\n",
    "plot_lightcurve(dia_lc, plot='mag', label_prefix='DIA')\n",
    "\n",
    "sim_date_range = [min(sn_lc['mjd']), max(sn_lc['mjd'])]\n",
    "sim_date_delta = sim_date_range[1] - sim_date_range[0]\n",
    "buffer_fraction = 0.05\n",
    "plot_date_range = sim_date_range\n",
    "plot_date_range[0] -= sim_date_delta * buffer_fraction\n",
    "plot_date_range[1] += sim_date_delta * buffer_fraction\n",
    "\n",
    "plt.xlim(plot_date_range)\n",
    "plt.ylim(25, 19);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The shape seems good.  But the calibration is magnitudes off.  It does look like it's a constant magnitude offset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mjd</th>\n",
       "      <th>mag</th>\n",
       "      <th>obshistid</th>\n",
       "      <th>filter_code</th>\n",
       "      <th>filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60567.219180</td>\n",
       "      <td>21.397669</td>\n",
       "      <td>664557</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60567.228879</td>\n",
       "      <td>21.461969</td>\n",
       "      <td>664577</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60567.234411</td>\n",
       "      <td>21.431454</td>\n",
       "      <td>664587</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60567.244111</td>\n",
       "      <td>21.719140</td>\n",
       "      <td>664607</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60567.256310</td>\n",
       "      <td>21.978863</td>\n",
       "      <td>664633</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mjd        mag  obshistid  filter_code filter\n",
       "6   60567.219180  21.397669     664557            2      r\n",
       "7   60567.228879  21.461969     664577            1      g\n",
       "8   60567.234411  21.431454     664587            3      i\n",
       "9   60567.244111  21.719140     664607            4      z\n",
       "10  60567.256310  21.978863     664633            5      y"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sn_lc.query('(60567 < mjd) & (mjd < 60568)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mjd</th>\n",
       "      <th>psFlux</th>\n",
       "      <th>mag_err</th>\n",
       "      <th>psFluxErr</th>\n",
       "      <th>visit</th>\n",
       "      <th>mag</th>\n",
       "      <th>filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60567.219782</td>\n",
       "      <td>51954.298318</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>430.379216</td>\n",
       "      <td>664557</td>\n",
       "      <td>19.610946</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60567.235013</td>\n",
       "      <td>47920.015598</td>\n",
       "      <td>0.010489</td>\n",
       "      <td>461.672183</td>\n",
       "      <td>664587</td>\n",
       "      <td>19.698708</td>\n",
       "      <td>i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60567.244713</td>\n",
       "      <td>49375.749296</td>\n",
       "      <td>0.012521</td>\n",
       "      <td>568.633628</td>\n",
       "      <td>664607</td>\n",
       "      <td>19.666216</td>\n",
       "      <td>z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mjd        psFlux   mag_err   psFluxErr   visit        mag filter\n",
       "5  60567.219782  51954.298318  0.009033  430.379216  664557  19.610946      r\n",
       "6  60567.235013  47920.015598  0.010489  461.672183  664587  19.698708      i\n",
       "7  60567.244713  49375.749296  0.012521  568.633628  664607  19.666216      z"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia_lc.query('(60567 < mjd) & (mjd < 60568)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MJD for the same visits (recall `visit` == `obshistid`) are slightly different between the truth catalog and the DIA lightcurve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-52.0128000061959"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(60567.219180 - 60567.219782) * 24 * 3600"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why the 52 second difference?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's match on `obshistid == visit` to get a matched set of dataframes that we can compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "joint_lc = pd.merge(sn_lc, dia_lc, left_on='obshistid', right_on='visit', suffixes=('_sim', '_dia'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "joint_lc['mjd'] = joint_lc['mjd_dia']\n",
    "joint_lc['filter'] = joint_lc['filter_sim']\n",
    "joint_lc['delta_mag'] = joint_lc['mag_dia'] - joint_lc['mag_sim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mjd_sim</th>\n",
       "      <th>mag_sim</th>\n",
       "      <th>obshistid</th>\n",
       "      <th>filter_code</th>\n",
       "      <th>filter_sim</th>\n",
       "      <th>mjd_dia</th>\n",
       "      <th>psFlux</th>\n",
       "      <th>mag_err</th>\n",
       "      <th>psFluxErr</th>\n",
       "      <th>visit</th>\n",
       "      <th>mag_dia</th>\n",
       "      <th>filter_dia</th>\n",
       "      <th>mjd</th>\n",
       "      <th>filter</th>\n",
       "      <th>delta_mag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60567.219180</td>\n",
       "      <td>21.397669</td>\n",
       "      <td>664557</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60567.219782</td>\n",
       "      <td>51954.298318</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>430.379216</td>\n",
       "      <td>664557</td>\n",
       "      <td>19.610946</td>\n",
       "      <td>r</td>\n",
       "      <td>60567.219782</td>\n",
       "      <td>r</td>\n",
       "      <td>-1.786722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60567.234411</td>\n",
       "      <td>21.431454</td>\n",
       "      <td>664587</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60567.235013</td>\n",
       "      <td>47920.015598</td>\n",
       "      <td>0.010489</td>\n",
       "      <td>461.672183</td>\n",
       "      <td>664587</td>\n",
       "      <td>19.698708</td>\n",
       "      <td>i</td>\n",
       "      <td>60567.235013</td>\n",
       "      <td>i</td>\n",
       "      <td>-1.732747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60567.244111</td>\n",
       "      <td>21.719140</td>\n",
       "      <td>664607</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "      <td>60567.244713</td>\n",
       "      <td>49375.749296</td>\n",
       "      <td>0.012521</td>\n",
       "      <td>568.633628</td>\n",
       "      <td>664607</td>\n",
       "      <td>19.666216</td>\n",
       "      <td>z</td>\n",
       "      <td>60567.244713</td>\n",
       "      <td>z</td>\n",
       "      <td>-2.052925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60578.271042</td>\n",
       "      <td>21.354127</td>\n",
       "      <td>671659</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60578.271644</td>\n",
       "      <td>47085.850480</td>\n",
       "      <td>0.008895</td>\n",
       "      <td>385.539974</td>\n",
       "      <td>671659</td>\n",
       "      <td>19.717774</td>\n",
       "      <td>r</td>\n",
       "      <td>60578.271644</td>\n",
       "      <td>r</td>\n",
       "      <td>-1.636353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60578.280324</td>\n",
       "      <td>21.694316</td>\n",
       "      <td>671678</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "      <td>60578.280926</td>\n",
       "      <td>10983.583655</td>\n",
       "      <td>0.020358</td>\n",
       "      <td>205.931007</td>\n",
       "      <td>671678</td>\n",
       "      <td>21.298140</td>\n",
       "      <td>g</td>\n",
       "      <td>60578.280926</td>\n",
       "      <td>g</td>\n",
       "      <td>-0.396176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60578.285857</td>\n",
       "      <td>21.346081</td>\n",
       "      <td>671688</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60578.286459</td>\n",
       "      <td>53817.660622</td>\n",
       "      <td>0.010054</td>\n",
       "      <td>498.132297</td>\n",
       "      <td>671688</td>\n",
       "      <td>19.572688</td>\n",
       "      <td>i</td>\n",
       "      <td>60578.286459</td>\n",
       "      <td>i</td>\n",
       "      <td>-1.773393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60578.295556</td>\n",
       "      <td>21.825918</td>\n",
       "      <td>671708</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "      <td>60578.296158</td>\n",
       "      <td>57613.595367</td>\n",
       "      <td>0.012146</td>\n",
       "      <td>644.364773</td>\n",
       "      <td>671708</td>\n",
       "      <td>19.498688</td>\n",
       "      <td>z</td>\n",
       "      <td>60578.296158</td>\n",
       "      <td>z</td>\n",
       "      <td>-2.327230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60578.307755</td>\n",
       "      <td>22.263662</td>\n",
       "      <td>671734</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60578.308357</td>\n",
       "      <td>47248.056561</td>\n",
       "      <td>0.020698</td>\n",
       "      <td>900.609948</td>\n",
       "      <td>671734</td>\n",
       "      <td>19.714040</td>\n",
       "      <td>y</td>\n",
       "      <td>60578.308357</td>\n",
       "      <td>y</td>\n",
       "      <td>-2.549622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60581.174302</td>\n",
       "      <td>21.450943</td>\n",
       "      <td>674105</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60581.174904</td>\n",
       "      <td>38596.129793</td>\n",
       "      <td>0.011141</td>\n",
       "      <td>395.892130</td>\n",
       "      <td>674105</td>\n",
       "      <td>19.933641</td>\n",
       "      <td>r</td>\n",
       "      <td>60581.174904</td>\n",
       "      <td>r</td>\n",
       "      <td>-1.517302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60593.173940</td>\n",
       "      <td>23.101931</td>\n",
       "      <td>684488</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "      <td>60593.174542</td>\n",
       "      <td>2152.571205</td>\n",
       "      <td>0.074385</td>\n",
       "      <td>147.471570</td>\n",
       "      <td>684488</td>\n",
       "      <td>23.067606</td>\n",
       "      <td>g</td>\n",
       "      <td>60593.174542</td>\n",
       "      <td>g</td>\n",
       "      <td>-0.034324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>60593.201370</td>\n",
       "      <td>22.608499</td>\n",
       "      <td>684544</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60593.201972</td>\n",
       "      <td>28137.340049</td>\n",
       "      <td>0.026534</td>\n",
       "      <td>687.511028</td>\n",
       "      <td>684544</td>\n",
       "      <td>20.276792</td>\n",
       "      <td>y</td>\n",
       "      <td>60593.201972</td>\n",
       "      <td>y</td>\n",
       "      <td>-2.331706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>60596.178013</td>\n",
       "      <td>22.270623</td>\n",
       "      <td>686469</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60596.178615</td>\n",
       "      <td>10590.229808</td>\n",
       "      <td>0.041427</td>\n",
       "      <td>404.050880</td>\n",
       "      <td>686469</td>\n",
       "      <td>21.337737</td>\n",
       "      <td>r</td>\n",
       "      <td>60596.178615</td>\n",
       "      <td>r</td>\n",
       "      <td>-0.932887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60596.187712</td>\n",
       "      <td>23.396907</td>\n",
       "      <td>686489</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "      <td>60596.188314</td>\n",
       "      <td>1853.087931</td>\n",
       "      <td>0.159190</td>\n",
       "      <td>271.697872</td>\n",
       "      <td>686489</td>\n",
       "      <td>23.230260</td>\n",
       "      <td>g</td>\n",
       "      <td>60596.188314</td>\n",
       "      <td>g</td>\n",
       "      <td>-0.166647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>60596.193244</td>\n",
       "      <td>22.072659</td>\n",
       "      <td>686499</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60596.193846</td>\n",
       "      <td>21364.683865</td>\n",
       "      <td>0.025884</td>\n",
       "      <td>509.285207</td>\n",
       "      <td>686499</td>\n",
       "      <td>20.575759</td>\n",
       "      <td>i</td>\n",
       "      <td>60596.193846</td>\n",
       "      <td>i</td>\n",
       "      <td>-1.496901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60596.202943</td>\n",
       "      <td>22.472107</td>\n",
       "      <td>686519</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "      <td>60596.203545</td>\n",
       "      <td>29860.670592</td>\n",
       "      <td>0.021702</td>\n",
       "      <td>596.795208</td>\n",
       "      <td>686519</td>\n",
       "      <td>20.212251</td>\n",
       "      <td>z</td>\n",
       "      <td>60596.203545</td>\n",
       "      <td>z</td>\n",
       "      <td>-2.259856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>60596.215142</td>\n",
       "      <td>22.573407</td>\n",
       "      <td>686545</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60596.215744</td>\n",
       "      <td>26717.792021</td>\n",
       "      <td>0.033466</td>\n",
       "      <td>823.473242</td>\n",
       "      <td>686545</td>\n",
       "      <td>20.332999</td>\n",
       "      <td>y</td>\n",
       "      <td>60596.215744</td>\n",
       "      <td>y</td>\n",
       "      <td>-2.240409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>60605.134462</td>\n",
       "      <td>22.778590</td>\n",
       "      <td>693871</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60605.135064</td>\n",
       "      <td>5489.243765</td>\n",
       "      <td>0.048667</td>\n",
       "      <td>246.036487</td>\n",
       "      <td>693871</td>\n",
       "      <td>22.051219</td>\n",
       "      <td>r</td>\n",
       "      <td>60605.135064</td>\n",
       "      <td>r</td>\n",
       "      <td>-0.727371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60605.171175</td>\n",
       "      <td>22.502005</td>\n",
       "      <td>693946</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60605.171777</td>\n",
       "      <td>23313.259952</td>\n",
       "      <td>0.033979</td>\n",
       "      <td>729.486718</td>\n",
       "      <td>693946</td>\n",
       "      <td>20.480992</td>\n",
       "      <td>y</td>\n",
       "      <td>60605.171777</td>\n",
       "      <td>y</td>\n",
       "      <td>-2.021012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>60608.127220</td>\n",
       "      <td>22.936279</td>\n",
       "      <td>696247</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60608.127822</td>\n",
       "      <td>3967.161602</td>\n",
       "      <td>0.059354</td>\n",
       "      <td>216.864375</td>\n",
       "      <td>696247</td>\n",
       "      <td>22.403800</td>\n",
       "      <td>r</td>\n",
       "      <td>60608.127822</td>\n",
       "      <td>r</td>\n",
       "      <td>-0.532479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>60608.136502</td>\n",
       "      <td>24.292038</td>\n",
       "      <td>696266</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "      <td>60608.137104</td>\n",
       "      <td>755.784122</td>\n",
       "      <td>0.173746</td>\n",
       "      <td>120.944561</td>\n",
       "      <td>696266</td>\n",
       "      <td>24.204006</td>\n",
       "      <td>g</td>\n",
       "      <td>60608.137104</td>\n",
       "      <td>g</td>\n",
       "      <td>-0.088033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60608.142034</td>\n",
       "      <td>22.400639</td>\n",
       "      <td>696276</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60608.142636</td>\n",
       "      <td>9545.790759</td>\n",
       "      <td>0.035801</td>\n",
       "      <td>314.726180</td>\n",
       "      <td>696276</td>\n",
       "      <td>21.450470</td>\n",
       "      <td>i</td>\n",
       "      <td>60608.142636</td>\n",
       "      <td>i</td>\n",
       "      <td>-0.950169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>60608.151733</td>\n",
       "      <td>22.571821</td>\n",
       "      <td>696296</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "      <td>60608.152335</td>\n",
       "      <td>21319.168260</td>\n",
       "      <td>0.022675</td>\n",
       "      <td>445.118346</td>\n",
       "      <td>696296</td>\n",
       "      <td>20.578074</td>\n",
       "      <td>z</td>\n",
       "      <td>60608.152335</td>\n",
       "      <td>z</td>\n",
       "      <td>-1.993746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>60608.163933</td>\n",
       "      <td>22.559708</td>\n",
       "      <td>696322</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60608.164535</td>\n",
       "      <td>19782.167896</td>\n",
       "      <td>0.034725</td>\n",
       "      <td>632.615017</td>\n",
       "      <td>696322</td>\n",
       "      <td>20.659315</td>\n",
       "      <td>y</td>\n",
       "      <td>60608.164535</td>\n",
       "      <td>y</td>\n",
       "      <td>-1.900393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>60611.119189</td>\n",
       "      <td>23.094521</td>\n",
       "      <td>698622</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60611.119791</td>\n",
       "      <td>3932.489517</td>\n",
       "      <td>0.071238</td>\n",
       "      <td>258.012421</td>\n",
       "      <td>698622</td>\n",
       "      <td>22.413331</td>\n",
       "      <td>r</td>\n",
       "      <td>60611.119791</td>\n",
       "      <td>r</td>\n",
       "      <td>-0.681190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>60611.128471</td>\n",
       "      <td>24.440580</td>\n",
       "      <td>698641</td>\n",
       "      <td>1</td>\n",
       "      <td>g</td>\n",
       "      <td>60611.129073</td>\n",
       "      <td>674.906577</td>\n",
       "      <td>0.168484</td>\n",
       "      <td>104.731225</td>\n",
       "      <td>698641</td>\n",
       "      <td>24.326891</td>\n",
       "      <td>g</td>\n",
       "      <td>60611.129073</td>\n",
       "      <td>g</td>\n",
       "      <td>-0.113689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>60611.134004</td>\n",
       "      <td>22.555700</td>\n",
       "      <td>698651</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60611.134606</td>\n",
       "      <td>8362.159167</td>\n",
       "      <td>0.044743</td>\n",
       "      <td>344.553900</td>\n",
       "      <td>698651</td>\n",
       "      <td>21.594204</td>\n",
       "      <td>i</td>\n",
       "      <td>60611.134606</td>\n",
       "      <td>i</td>\n",
       "      <td>-0.961496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>60611.155902</td>\n",
       "      <td>22.649910</td>\n",
       "      <td>698697</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60611.156504</td>\n",
       "      <td>16851.248510</td>\n",
       "      <td>0.038859</td>\n",
       "      <td>603.064822</td>\n",
       "      <td>698697</td>\n",
       "      <td>20.833420</td>\n",
       "      <td>y</td>\n",
       "      <td>60611.156504</td>\n",
       "      <td>y</td>\n",
       "      <td>-1.816491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>60621.122783</td>\n",
       "      <td>23.508854</td>\n",
       "      <td>704746</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60621.123385</td>\n",
       "      <td>3054.910661</td>\n",
       "      <td>0.072668</td>\n",
       "      <td>204.460426</td>\n",
       "      <td>704746</td>\n",
       "      <td>22.687504</td>\n",
       "      <td>r</td>\n",
       "      <td>60621.123385</td>\n",
       "      <td>r</td>\n",
       "      <td>-0.821351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>60621.159496</td>\n",
       "      <td>23.115017</td>\n",
       "      <td>704821</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60621.160098</td>\n",
       "      <td>11238.585132</td>\n",
       "      <td>0.060528</td>\n",
       "      <td>626.510025</td>\n",
       "      <td>704821</td>\n",
       "      <td>21.273221</td>\n",
       "      <td>y</td>\n",
       "      <td>60621.160098</td>\n",
       "      <td>y</td>\n",
       "      <td>-1.841796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60624.093405</td>\n",
       "      <td>23.095192</td>\n",
       "      <td>706952</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60624.094007</td>\n",
       "      <td>5518.862250</td>\n",
       "      <td>0.068156</td>\n",
       "      <td>346.433388</td>\n",
       "      <td>706952</td>\n",
       "      <td>22.045376</td>\n",
       "      <td>i</td>\n",
       "      <td>60624.094007</td>\n",
       "      <td>i</td>\n",
       "      <td>-1.049815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>60624.103104</td>\n",
       "      <td>23.344373</td>\n",
       "      <td>706972</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "      <td>60624.103706</td>\n",
       "      <td>12068.193213</td>\n",
       "      <td>0.042003</td>\n",
       "      <td>466.842536</td>\n",
       "      <td>706972</td>\n",
       "      <td>21.195894</td>\n",
       "      <td>z</td>\n",
       "      <td>60624.103706</td>\n",
       "      <td>z</td>\n",
       "      <td>-2.148479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>60624.115303</td>\n",
       "      <td>23.258472</td>\n",
       "      <td>706998</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60624.115905</td>\n",
       "      <td>10180.921902</td>\n",
       "      <td>0.071504</td>\n",
       "      <td>670.470176</td>\n",
       "      <td>706998</td>\n",
       "      <td>21.380532</td>\n",
       "      <td>y</td>\n",
       "      <td>60624.115905</td>\n",
       "      <td>y</td>\n",
       "      <td>-1.877940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>60627.291393</td>\n",
       "      <td>23.191451</td>\n",
       "      <td>709677</td>\n",
       "      <td>3</td>\n",
       "      <td>i</td>\n",
       "      <td>60627.291995</td>\n",
       "      <td>4111.582997</td>\n",
       "      <td>0.140924</td>\n",
       "      <td>533.663023</td>\n",
       "      <td>709677</td>\n",
       "      <td>22.364977</td>\n",
       "      <td>i</td>\n",
       "      <td>60627.291995</td>\n",
       "      <td>i</td>\n",
       "      <td>-0.826474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>60627.313291</td>\n",
       "      <td>23.379125</td>\n",
       "      <td>709723</td>\n",
       "      <td>5</td>\n",
       "      <td>y</td>\n",
       "      <td>60627.313893</td>\n",
       "      <td>8028.566688</td>\n",
       "      <td>0.120211</td>\n",
       "      <td>888.906650</td>\n",
       "      <td>709723</td>\n",
       "      <td>21.638405</td>\n",
       "      <td>y</td>\n",
       "      <td>60627.313893</td>\n",
       "      <td>y</td>\n",
       "      <td>-1.740720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>60632.036052</td>\n",
       "      <td>23.747787</td>\n",
       "      <td>712881</td>\n",
       "      <td>2</td>\n",
       "      <td>r</td>\n",
       "      <td>60632.036654</td>\n",
       "      <td>2517.807350</td>\n",
       "      <td>0.136071</td>\n",
       "      <td>315.546726</td>\n",
       "      <td>712881</td>\n",
       "      <td>22.897444</td>\n",
       "      <td>r</td>\n",
       "      <td>60632.036654</td>\n",
       "      <td>r</td>\n",
       "      <td>-0.850344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>60632.060994</td>\n",
       "      <td>23.667778</td>\n",
       "      <td>712931</td>\n",
       "      <td>4</td>\n",
       "      <td>z</td>\n",
       "      <td>60632.061596</td>\n",
       "      <td>9233.395339</td>\n",
       "      <td>0.071836</td>\n",
       "      <td>610.905922</td>\n",
       "      <td>712931</td>\n",
       "      <td>21.486596</td>\n",
       "      <td>z</td>\n",
       "      <td>60632.061596</td>\n",
       "      <td>z</td>\n",
       "      <td>-2.181181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mjd_sim    mag_sim  obshistid  filter_code filter_sim       mjd_dia  \\\n",
       "0   60567.219180  21.397669     664557            2          r  60567.219782   \n",
       "1   60567.234411  21.431454     664587            3          i  60567.235013   \n",
       "2   60567.244111  21.719140     664607            4          z  60567.244713   \n",
       "3   60578.271042  21.354127     671659            2          r  60578.271644   \n",
       "4   60578.280324  21.694316     671678            1          g  60578.280926   \n",
       "5   60578.285857  21.346081     671688            3          i  60578.286459   \n",
       "6   60578.295556  21.825918     671708            4          z  60578.296158   \n",
       "7   60578.307755  22.263662     671734            5          y  60578.308357   \n",
       "8   60581.174302  21.450943     674105            2          r  60581.174904   \n",
       "9   60593.173940  23.101931     684488            1          g  60593.174542   \n",
       "10  60593.201370  22.608499     684544            5          y  60593.201972   \n",
       "11  60596.178013  22.270623     686469            2          r  60596.178615   \n",
       "12  60596.187712  23.396907     686489            1          g  60596.188314   \n",
       "13  60596.193244  22.072659     686499            3          i  60596.193846   \n",
       "14  60596.202943  22.472107     686519            4          z  60596.203545   \n",
       "15  60596.215142  22.573407     686545            5          y  60596.215744   \n",
       "16  60605.134462  22.778590     693871            2          r  60605.135064   \n",
       "17  60605.171175  22.502005     693946            5          y  60605.171777   \n",
       "18  60608.127220  22.936279     696247            2          r  60608.127822   \n",
       "19  60608.136502  24.292038     696266            1          g  60608.137104   \n",
       "20  60608.142034  22.400639     696276            3          i  60608.142636   \n",
       "21  60608.151733  22.571821     696296            4          z  60608.152335   \n",
       "22  60608.163933  22.559708     696322            5          y  60608.164535   \n",
       "23  60611.119189  23.094521     698622            2          r  60611.119791   \n",
       "24  60611.128471  24.440580     698641            1          g  60611.129073   \n",
       "25  60611.134004  22.555700     698651            3          i  60611.134606   \n",
       "26  60611.155902  22.649910     698697            5          y  60611.156504   \n",
       "27  60621.122783  23.508854     704746            2          r  60621.123385   \n",
       "28  60621.159496  23.115017     704821            5          y  60621.160098   \n",
       "29  60624.093405  23.095192     706952            3          i  60624.094007   \n",
       "30  60624.103104  23.344373     706972            4          z  60624.103706   \n",
       "31  60624.115303  23.258472     706998            5          y  60624.115905   \n",
       "32  60627.291393  23.191451     709677            3          i  60627.291995   \n",
       "33  60627.313291  23.379125     709723            5          y  60627.313893   \n",
       "34  60632.036052  23.747787     712881            2          r  60632.036654   \n",
       "35  60632.060994  23.667778     712931            4          z  60632.061596   \n",
       "\n",
       "          psFlux   mag_err   psFluxErr   visit    mag_dia filter_dia  \\\n",
       "0   51954.298318  0.009033  430.379216  664557  19.610946          r   \n",
       "1   47920.015598  0.010489  461.672183  664587  19.698708          i   \n",
       "2   49375.749296  0.012521  568.633628  664607  19.666216          z   \n",
       "3   47085.850480  0.008895  385.539974  671659  19.717774          r   \n",
       "4   10983.583655  0.020358  205.931007  671678  21.298140          g   \n",
       "5   53817.660622  0.010054  498.132297  671688  19.572688          i   \n",
       "6   57613.595367  0.012146  644.364773  671708  19.498688          z   \n",
       "7   47248.056561  0.020698  900.609948  671734  19.714040          y   \n",
       "8   38596.129793  0.011141  395.892130  674105  19.933641          r   \n",
       "9    2152.571205  0.074385  147.471570  684488  23.067606          g   \n",
       "10  28137.340049  0.026534  687.511028  684544  20.276792          y   \n",
       "11  10590.229808  0.041427  404.050880  686469  21.337737          r   \n",
       "12   1853.087931  0.159190  271.697872  686489  23.230260          g   \n",
       "13  21364.683865  0.025884  509.285207  686499  20.575759          i   \n",
       "14  29860.670592  0.021702  596.795208  686519  20.212251          z   \n",
       "15  26717.792021  0.033466  823.473242  686545  20.332999          y   \n",
       "16   5489.243765  0.048667  246.036487  693871  22.051219          r   \n",
       "17  23313.259952  0.033979  729.486718  693946  20.480992          y   \n",
       "18   3967.161602  0.059354  216.864375  696247  22.403800          r   \n",
       "19    755.784122  0.173746  120.944561  696266  24.204006          g   \n",
       "20   9545.790759  0.035801  314.726180  696276  21.450470          i   \n",
       "21  21319.168260  0.022675  445.118346  696296  20.578074          z   \n",
       "22  19782.167896  0.034725  632.615017  696322  20.659315          y   \n",
       "23   3932.489517  0.071238  258.012421  698622  22.413331          r   \n",
       "24    674.906577  0.168484  104.731225  698641  24.326891          g   \n",
       "25   8362.159167  0.044743  344.553900  698651  21.594204          i   \n",
       "26  16851.248510  0.038859  603.064822  698697  20.833420          y   \n",
       "27   3054.910661  0.072668  204.460426  704746  22.687504          r   \n",
       "28  11238.585132  0.060528  626.510025  704821  21.273221          y   \n",
       "29   5518.862250  0.068156  346.433388  706952  22.045376          i   \n",
       "30  12068.193213  0.042003  466.842536  706972  21.195894          z   \n",
       "31  10180.921902  0.071504  670.470176  706998  21.380532          y   \n",
       "32   4111.582997  0.140924  533.663023  709677  22.364977          i   \n",
       "33   8028.566688  0.120211  888.906650  709723  21.638405          y   \n",
       "34   2517.807350  0.136071  315.546726  712881  22.897444          r   \n",
       "35   9233.395339  0.071836  610.905922  712931  21.486596          z   \n",
       "\n",
       "             mjd filter  delta_mag  \n",
       "0   60567.219782      r  -1.786722  \n",
       "1   60567.235013      i  -1.732747  \n",
       "2   60567.244713      z  -2.052925  \n",
       "3   60578.271644      r  -1.636353  \n",
       "4   60578.280926      g  -0.396176  \n",
       "5   60578.286459      i  -1.773393  \n",
       "6   60578.296158      z  -2.327230  \n",
       "7   60578.308357      y  -2.549622  \n",
       "8   60581.174904      r  -1.517302  \n",
       "9   60593.174542      g  -0.034324  \n",
       "10  60593.201972      y  -2.331706  \n",
       "11  60596.178615      r  -0.932887  \n",
       "12  60596.188314      g  -0.166647  \n",
       "13  60596.193846      i  -1.496901  \n",
       "14  60596.203545      z  -2.259856  \n",
       "15  60596.215744      y  -2.240409  \n",
       "16  60605.135064      r  -0.727371  \n",
       "17  60605.171777      y  -2.021012  \n",
       "18  60608.127822      r  -0.532479  \n",
       "19  60608.137104      g  -0.088033  \n",
       "20  60608.142636      i  -0.950169  \n",
       "21  60608.152335      z  -1.993746  \n",
       "22  60608.164535      y  -1.900393  \n",
       "23  60611.119791      r  -0.681190  \n",
       "24  60611.129073      g  -0.113689  \n",
       "25  60611.134606      i  -0.961496  \n",
       "26  60611.156504      y  -1.816491  \n",
       "27  60621.123385      r  -0.821351  \n",
       "28  60621.160098      y  -1.841796  \n",
       "29  60624.094007      i  -1.049815  \n",
       "30  60624.103706      z  -2.148479  \n",
       "31  60624.115905      y  -1.877940  \n",
       "32  60627.291995      i  -0.826474  \n",
       "33  60627.313893      y  -1.740720  \n",
       "34  60632.036654      r  -0.850344  \n",
       "35  60632.061596      z  -2.181181  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint_lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_lightcurve(joint_lc, flux_col_names=('delta_mag', 'mag_err'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm.... so not really a constant offset in magnitude.\n",
    "Nevertheless, I can only suspect there's some calibration or flux interpretation error somewhere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If one wants to look at the postage stamps of this SN, get started with the examples in\n",
    "[dia_source_object_stamp.ipynb](dia_source_object_stamp.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "desc-stack",
   "language": "python",
   "name": "desc-stack"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}