{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# COSMOS master catalogue\n", "## Convert COSMOS2016 to help format for comparison and homogeniety\n", "\n", "This catalogue comes from `dmu1_COSMOS2015`. At present we will only cross match the ids into the HELP masterlist. to go into the cross id table. This will allow comparisons for our internal testing as well as allow users of the COSMOS catalogue to get other fluxes and HELP products.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "017bb1e (Mon Jun 18 14:58:59 2018 +0100)\n", "This notebook was executed on: \n", "2018-06-19 17:15:22.503799\n" ] } ], "source": [ "from herschelhelp_internal import git_version\n", "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n", "import datetime\n", "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/seaborn/apionly.py:6: UserWarning: As seaborn no longer sets a default style on import, the seaborn.apionly module is deprecated. It will be removed in a future version.\n", " warnings.warn(msg, UserWarning)\n" ] } ], "source": [ "%matplotlib inline\n", "#%config InlineBackend.figure_format = 'svg'\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rc('figure', figsize=(10, 6))\n", "\n", "from collections import OrderedDict\n", "import os\n", "\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.table import Column, Table\n", "import numpy as np\n", "\n", "from herschelhelp_internal.flagging import gaia_flag_column\n", "from herschelhelp_internal.masterlist import nb_astcor_diag_plot, remove_duplicates\n", "from herschelhelp_internal.utils import astrometric_correction, mag_to_flux, flux_to_mag" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "OUT_DIR = os.environ.get('TMP_DIR', \"./data_tmp\")\n", "try:\n", " os.makedirs(OUT_DIR)\n", "except FileExistsError:\n", " pass\n", "\n", "RA_COL = \"cosmos_ra\"\n", "DEC_COL = \"cosmos_dec\"" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## I - Column selection" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "bands = OrderedDict({\n", " 'ks': 'vista_ks',\n", " 'y': 'vista_y',\n", " 'h': 'vista_h',\n", " 'j': 'vista_j',\n", "\n", " #CFHT Megacam\n", " 'u': 'megacam_u',\n", " #SUBARU Suprime\n", " 'b': 'suprime_b',\n", " 'v': 'suprime_v',\n", " 'ip': 'suprime_ip',\n", " 'r': 'suprime_rc',\n", " 'zp': 'suprime_zp',\n", " 'zpp': 'suprime_zpp',\n", " 'ia484': 'suprime_ia484',\n", " 'ia527': 'suprime_ia527',\n", " 'ia624': 'suprime_ia624',\n", " 'ia679': 'suprime_ia679',\n", " 'ia738': 'suprime_ia738',\n", " 'ia767': 'suprime_ia767',\n", " 'ib427': 'suprime_ib427',\n", " 'ib464': 'suprime_ib464',\n", " 'ib505': 'suprime_ib505',\n", " 'ib574': 'suprime_ib574',\n", " 'ib709': 'suprime_ib709',\n", " 'ib827': 'suprime_ib827',\n", " 'nb711': 'suprime_nb711',\n", " 'nb816': 'suprime_nb816',\n", " #CFHT WIRCAM\n", " 'hw': 'wircam_h',\n", " 'ksw': 'wircam_ks',\n", " #SUBARU HSC\n", " 'yhsc': 'suprime_y',\n", " #Spitzer IRAC\n", " 'splash_1': 'irac_i1', #'irac_i1'???\n", " 'splash_2': 'irac_i2', # #irac_i2\n", " 'splash_3': 'irac_i3', # #irac_i1\n", " 'splash_4': 'irac_i4', # #irac_i4\n", "})" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "imported_columns = OrderedDict({\n", " 'help_id': 'help_id',\n", " 'id': 'cosmos_id',\n", " 'alpha_j2000': 'cosmos_ra',\n", " 'delta_j2000': 'cosmos_dec',\n", " 'class': 'cosmos_stellarity',\n", "})\n", "\n", "for band in list(bands):\n", " if 'splash' not in band:\n", " imported_columns.update({band + '_mag_auto': 'm_cosmos-' + bands[band]})\n", " imported_columns.update({band + '_magerr_auto': 'merr_cosmos-' + bands[band]})\n", " imported_columns.update({band + '_flux_aper2': 'f_ap_cosmos-' + bands[band]})\n", " imported_columns.update({band + '_fluxerr_aper2': 'ferr_ap_cosmos-' + bands[band]})\n", " elif 'splash' in band:\n", " imported_columns.update({band + '_flux': 'f_cosmos-' + bands[band]})\n", " imported_columns.update({band + '_flux_err': 'ferr_cosmos-' + bands[band]})\n", " imported_columns.update({band + '_mag': 'm_cosmos-' + bands[band]})\n", " imported_columns.update({band + '_magerr': 'merr_cosmos-' + bands[band]}) \n", " \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", "catalogue = Table.read(\"../../dmu0/dmu0_COSMOS2015/data/COSMOS2015-HELP_selected_20160613.fits\")[list(imported_columns)]\n", "for column in imported_columns:\n", " catalogue[column].name = imported_columns[column]\n", "\n", "epoch = 2015 #Various epochs\n", "\n", "# Clean table metadata\n", "catalogue.meta = None" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: divide by zero encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:81: RuntimeWarning: invalid value encountered in true_divide\n", " errors = 2.5 / np.log(10) * errors_on_fluxes / fluxes\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/herschelhelp_internal/herschelhelp_internal/utils.py:77: RuntimeWarning: invalid value encountered in log10\n", " magnitudes = 2.5 * (23 - np.log10(fluxes)) - 48.6\n", "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/astropy/table/column.py:965: RuntimeWarning: invalid value encountered in less\n", " return getattr(self.data, op)(other)\n", "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/astropy/table/column.py:965: RuntimeWarning: invalid value encountered in less\n", " return getattr(self.data, op)(other)\n", "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/astropy/table/column.py:965: RuntimeWarning: invalid value encountered in less\n", " return getattr(self.data, op)(other)\n" ] } ], "source": [ "# Adding flux and band-flag columns\n", "for col in catalogue.colnames:\n", " #print(col)\n", " if 'irac' in col:\n", " if col.startswith('f_'):\n", " catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name=\"flag{}\".format(col[1:])))\n", " mask = ( (catalogue[col] < 90.)\n", " | (catalogue[col.replace('f_', 'ferr_')] < 90.)\n", " )\n", " catalogue[col][mask] = np.nan\n", " catalogue[col.replace('f_', 'ferr_')][mask] = np.nan\n", " if col.startswith('m_'):\n", " \n", " mask = ( (catalogue[col] < 90.)\n", " | (catalogue[col.replace('m_', 'merr_')] < 90.)\n", " )\n", " catalogue[col][mask] = np.nan\n", " catalogue[col.replace('m_', 'merr_')][mask] = np.nan\n", " continue\n", " if col.startswith('m_'):\n", " \n", " errcol = \"merr{}\".format(col[1:])\n", " \n", " \n", " mask = (catalogue[col] > 90.) | (catalogue[col] < 0.)\n", " catalogue[col][mask] = np.nan\n", " catalogue[errcol][mask] = np.nan \n", " \n", " flux, error = mag_to_flux(np.array(catalogue[col]), np.array(catalogue[errcol]))\n", " \n", " # Fluxes are added in µJy\n", " catalogue.add_column(Column(flux * 1.e6, name=\"f{}\".format(col[1:])))\n", " catalogue.add_column(Column(error * 1.e6, name=\"f{}\".format(errcol[1:])))\n", " \n", " # Band-flag column\n", " if \"ap\" not in col:\n", " catalogue.add_column(Column(np.zeros(len(catalogue), dtype=bool), name=\"flag{}\".format(col[1:])))\n", " if col.startswith('f_'):\n", " errcol = \"ferr{}\".format(col[1:])\n", " \n", " mask = (np.isclose(catalogue[col] , -99.9) )\n", " catalogue[col][mask] = np.nan\n", " catalogue[errcol][mask] = np.nan \n", " \n", " mag, error = flux_to_mag(np.array(catalogue[col])* 1.e-6, np.array(catalogue[errcol])* 1.e-6)\n", " # Mags added\n", " catalogue.add_column(Column(mag , name=\"m{}\".format(col[1:])))\n", " catalogue.add_column(Column(error , name=\"m{}\".format(errcol[1:])))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "Table masked=True length=10\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "catalogue[:10].show_in_notebook()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## II - Removal of duplicated sources" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We remove duplicated objects from the input catalogues." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "SORT_COLS = []\n", "for f in list(bands):\n", " if 'splash' not in f:\n", " SORT_COLS += ['merr_ap_cosmos-' + bands[f]]\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The initial catalogue had 694478 sources.\n", "The cleaned catalogue has 694478 sources (0 removed).\n", "The cleaned catalogue has 0 sources flagged as having been cleaned\n" ] } ], "source": [ "#SORT_COLS = ['merr_ap_gpc1_r', 'merr_ap_gpc1_g', 'merr_ap_gpc1_i', 'merr_ap_gpc1_z', 'merr_ap_gpc1_y']\n", "FLAG_NAME = 'ps1_flag_cleaned'\n", "\n", "nb_orig_sources = len(catalogue)\n", "\n", "catalogue = remove_duplicates(catalogue, RA_COL, DEC_COL, sort_col=SORT_COLS, flag_name=FLAG_NAME)\n", "\n", "nb_sources = len(catalogue)\n", "\n", "print(\"The initial catalogue had {} sources.\".format(nb_orig_sources))\n", "print(\"The cleaned catalogue has {} sources ({} removed).\".format(nb_sources, nb_orig_sources - nb_sources))\n", "print(\"The cleaned catalogue has {} sources flagged as having been cleaned\".format(np.sum(catalogue[FLAG_NAME])))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## III - Astrometry correction\n", "\n", "We match the astrometry to the Gaia one. We limit the Gaia catalogue to sources with a g band flux between the 30th and the 70th percentile. Some quick tests show that this give the lower dispersion in the results." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "gaia = Table.read(\"../../dmu0/dmu0_GAIA/data/GAIA_COSMOS.fits\")\n", "gaia_coords = SkyCoord(gaia['ra'], gaia['dec'])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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3eXl5WCyWCp9brVaOHTtW4fPzq7J33nmHF198sXgdYmJ4++23adu2bYX5evfujdPpBGDChAk8+OCD1ba7priSCCtq9AkwNzeXadOmkZiYyPz58zl69CjPPvssqqpy//33Vzmfw+Hg008/JTk5mT59+vDLL79UOW1CQgJz584t91nZHUVV/twrCqNe8dll7dUp6bw5djcf78zD7dEIYO7DEqLjhqRIDDrF7zd1w+/rm+dwk57lJMwaXZr8AKzNW9Miviun0nfR8/JrObZrI4qio1Pfy1A97tLp4nr0Y//6b1FVDzqdnvys3/j507c49usGCnLPwrmdQ+vOyQCoFJ+zQlE4aE5Cl+tCPVdex7bvzI7Vywm3xtA+aRDN2nUqt1M5lb6L9j0GlCY/gJadumNp3poT+7eXS4Dtew4ot77RbeKxnT3FT0cLiI8ylW6DYcOG1bitevTowaefflrjdJVVkCXtd3pUnB44nPP7+hYr+aN+/+YRUc25bMpfS/+O63YxYdYY1ix6gWeXp9G8QxdUDQZMmM6ACdO9WmZC38vK/d2p73B2rfmK/Oziw9+V6T78Gjr0HkKYQWFMZwuGSq5YPv9Ky8pUlkyqe8hD2apswoQJDBo0iDNnzrBkyRJuv/12PvroIxITE8vNs2zZMux2Ozt27OCNN97gySef5PHHH6+xbVXFFRU1+gS4bNkyioqKmDdvHmazmSFDhpCfn8+8efOYMWMGZrO50vmsVitpaWkoisLixYurTYBhYWE1npivjEEXmORXVo5DBS2wyQ8gKlSPAgFJfmXlFmloQJi14tWCYdZoCnLOAuCw5aCpHt6+Y3SlyynMOUtEVHP++8qDOB2FDJw4g8iWcRhCQkn77F0K87LLTR8SbgG9sVwy6Hf1NBSdjh2rP2PdJ/OJiI7l4rE30usPk0tjxLSteGgsPDIGR35euc9MEeUrCL3BiNvlJMKoL618IyMjvdoZR0RE0K1btxqnKzkEWhlTuBWnoxC3p3igUKKoMB+DKRR9NfPWVWL/EaxZ9AInD+8jpn2XWs9/fp8o+bsg52yVCTAishnh1miaheu56CIrJkPFSrGmSslqtZKVlVXhc5vNhtVqrXZeRVGIjY0tHYwMGzaMcePG8fbbb/Pcc8+Vm7ZHjx4A9OvXj+joaGbPns306dNp3759tTGqiisqavQJMDU1laFDh5ZLdOPGjeOFF14gLS2Nyy+/vMp5/f2PLl0qcOznJaiSz0ouggmJsKLT6/njI28VP4rmPGHWaHJ+O8bpI/sY/9eX6JA8qPQ7t7OowvSV9R2DKYSBE2cwcOIMck5msPP75fz40atEtepAh+RBhEc1q7SdhblZtIjvWqv1rY26HgItq0N8RzTVQ+6pY0S37lD6efaJI+X+9ql6/j7P39Ylf0dENatynrQv3mfDuUOgT1UxTU2HQBMSEti0aVOFz9PT02s8XH0+g8FA165dycjIqHa67t27A3Ds2LE6JUBRuUafANPT0xk0aFC5z9q0aUNYWBjp6enVJkBvHTx4kIsvvhin00lSUhL3338/AwYMqHlGETD2vGxO7N9B687Fh0FtZ09y+sheul1afNFIXPe+qKpKkT2/wuHFEp5ziU5v/P1cVt6ZE5zYv51m7RIrnacqUa3aMeT6WexY9RlZxw/RIXkQLRN6sPP75TjtBaWHQU+l78J25kTpIVZ/qM8h0NJl9OqNKSyCA2mr6X9N8eX4riIHh7espcfwa3zW1rIObvi+uF3xF9Vp/vRNa0gaOfH35W36gfCo5pijq1nP4dcQ33sIUaF6RnaMqPS+1Zqq7mHDhjF//nw2btxIv379ANixYwcZGRleHbIuq6ioiF9//bXcvZGV2bx5M+DdqRnhvUafAKs74ZyXl1fJHLXTrVs3kpOTSUxMJCsriwULFjB9+nSWLFlCcrL/dlqidkItUXz71hMM/ONfMBhDWL/8HcIs0Vx07oKU6NYd6DliAv+b/xgXj7uJFvHd8LiKyMo8RM7JDC6/9e9Et+6AOaYFPy19nYETZ+B0FJK2/D0iomO9akPKqw8RG9+V2A5dMJhCOLDhe1SPhzZdiw+f97nyenZ+v5wvX7ifi8f9GZfDzs//fpNmcZ3o1H+E37aN2WwmKSmp5gmrERISwoCrbuaXLxYQEmElunUHtv5vKZqmkTx6Uul0e9auYNV7c7j5+U+wNm8NFA8ifju0GwCP20V25mEObFiN0RRGh16XALB++bu4HIW07pyMKSyC43u3sjnlIxL6Dad5+98HH+uXv8uGz9/36irQrMxDfL/gWTr1G87xvVvZlfo1l950X7W3ZZijYzFHx9I8XE+P7pUfAq1Jnz59GDp0KLNnz2b27NnodDqef/55+vbty+DBg0une/jhh9mwYQPffvstAF9//XXpEa2WLVty+vRplixZwunTp0vvAQS49dZbGTx4MImJiej1ejZv3syCBQsYO3asVH8+1ugTIFR9wtkXhzinTp1a7u/hw4czduxY/vWvfzF//vx6L1/4hqVZK/qNn8K6T97EdvYkLeIv4oqZT5TeAgFw2ZT/I6pVe3b98CXrP3sXU1gEMW3i6T5sPAB6o4kxs+awZtGLrJj3COaYFvQbP5XMPVs4eyy9qtClWnVOYv/6VWxZsQRN04hpE8+YWc/QsmPx+bcwazTXPvQ6a5e+zjdv/j90BiMdki/h0hvvrdcVlIHSf/zNuFWVTV8vwpGfS4uO3bjmwVdK72EE0DQVTfWUu+8mc/dmVr37TOnfBzas5sCG1Viat2Lqi58BxQOULSuWsmvNV7idRViateTisTfRb3z535/bWUSYJcqr9g6efBeHt/7EinmPoDea6H/1LSSP+lPNM/rAyy+/zNy5c3n44YdRVZURI0bwyCOPlJtGVdVyt6d07NiRL7/8kmeffZbc3FxatGhBcnIy//nPf+jcuXPpdElJSSxfvpzMzEz0ej3t2rXjgQce4Prrrw/IugUTRWvklwddcskl3HTTTdx9993lPu/Tpw933XUXt912W43LWLx4MU899VSlt0FU5oknnuD777/nhx9+qHY6VdV8eh+cNw5nO/nffhvOAF8F0y7SyNjO5jqNmMt6/PHHa3UlW0aui3v++iCnM9KZ/ET1l69fCFqZDVxzkaXe27m2Tua7+GJXXsD71fk+m3Mncd36VrihvaySG+HH3f986e0ttdU8XM8f61gB1ldTuDfvrVX7/Lr8icmt/br8smJjKx5BLNHoX4ibkJBQ7uZSgBMnTlBYWFjpzai+0tg7qBAXGtXj5uyx9HLn9YTwp0afAIcNG8batWvJz88v/SwlJYXQ0FC/XKjicDhITU0tvQRZCBEYOr2BGfNXVnrLixD+0OjPAV5//fV8+OGHzJo1ixkzZpCRkcG8efOYNm1auVsjRo8eTf/+/ZkzZ07pZ2vWrMFut7N7d/EJ+pUrVwLFx9jbtm2LzWbj9ttv5+qrr6ZDhw5kZ2ezcOFCTp06VeGhtaLhXHn7P3DW/KQvESSssa29ukhGiJo0+gQYGRnJwoULefLJJ7njjjuwWq1MnTqVWbNmlZvO4/GgquVPYDzxxBNkZmaW/n3vvfcCMHfuXCZOnIjJZCImJoY333yTs2fPEhISQu/evVm8eHG9r6oTQgjRuDX6BAiQmJjIokWLqp2mshtXq7uZFYov/Z43b1692iaEEKJpavTnAIUQQgh/kAQohBAiKEkCFEIIEZQkAQohhAhKkgCFEEIEJUmAQgghgpIkQCGEEEGpSdwHKIQQovEK5MOtfUkqQCEamUb9ehYhLiCSAOtBUYpfbRJIlhAdKhDod1XYXR50OiXg6xtmUHA3wCt69ErxNg70ds53qiiKgqoGdjtHGHRoBH59G2o7F7mLt3Og+3NJvIaKK8qTBFgPJa9MCkTn0jSt+CWsYXquT4oiJkxPIF5lpgAGHXRvEYpe+b0t/qZqGi6Pxs7fHAQ4F2DQweD2YdzcK5LYiMBs5xL5TpUl23I4XejG5QlcvzKH6LipVxQtAri+egX6tw1jSu8oWpoNAYtr0EGPFqHoz8UL5O8XGma/ISon5wDrqWQUeX7n9qXzlx0dpuf65Eg2ZtrZdNzutwrJoIOoUD1ju1iIDNWXa48/19flUcl1qKTst5HrCFz5Z9CBNaR4faPDitf3up6RbD5uJy3Tf9v5fDanyic78+jdKpRB7cLR60AXgH5lCdEzqWck207a+TnDjkf1z+FYgw7MJh1ju1hoFl68C/pTDyvbTzpYl1Ho17gR5+I2D/991xfo32/JfzdEXFGeJEAfKDui8+XbnsuO3M5fpk5RGBAXTkK0iZR9NgqcKm4f7jX0CvRvE8bFbcMq7Hz9ub5ulXOJ3RHQc2F6BS5uHUr/uPBy66soCn3bhtMxxkTKXhu2It9u5+psPengcI6zeAASoseg93+/UhSF3q3D6RAVwop9NnKLPD5N/HoFkluWJPby27lX6zA6RJtYsc9Gjt3j8/5cWdyS2OD7/lyyzLIxGkNc8Ts5BOpDvjy0UbYDV9eJm0cYuKlXFEmtQn1yCKm46tMxOSmSfuclg/Od/wOuD7dHI9eh8u+duWwMYPIzKGAN0XFdz0gGtouocn1jwgzc2CuKPm1+PxQcCDkOlaXbc4srfU/9t7O3/arkKEPfNmE+6Vd6pbjq+2OPSIZ0iKiQhEpEheqL+15b38ad2MNabVzwbX8um9RqSkINFVdIBehz5yfB2nbC6kbnVdHrFIZ2iCAxprgadLg16nL6yKBAr5ahDKxklFwVX4xi3R6NbSft/HLMHtDzfQYFerYMZXB779ZXpygMahdBQkwIKXttFLrUOm3n2tKAtEw76dnF1WCEUVenarC2fdJXRxkMOujWPISh8REYvNzO/ePCSTjXn/Odap2qUIMCF8WGcKmXccE3/bkuv/2GihvspAL0k7pUg96OzqvSymLk5t7RXBRrqtXo+fdRciSDaxglV6Uu6+v2qOQXefhsVx7rMgKX/PQKRBgVru1u5dL42q9viwgDN/eOomfLkNILKQLhTKGHxdty2HHKUasLZOpbFZQeZWhZu6MMeqX4Kt6rL7IyPMHsdRIq0SzcwI3JUSS3rF3VXRJ3/EVWRtQhLtStKvNF9dVQcYOVVIB+VJtq0FejN6Ne4fIEC52buVi534bLU301WNvReXVqs74uj8ae007WHi0I6G0OBh10aWZiWLwZYz2OZep1CsPizSTGhLBiv42iOlbdtaVqsPZoIQeynIztYibEoKv2381X/UqvUxgaH0FiM++OMhh00CnaxPAEM6Z6buchHSLo1MzEin352Guoug06SIg2MaJjBKZ6HkOtTVXmy+qroeIGI6kAA6C66shfo7d2kUam9I4iIabyarC+o/PqVLe+qqZR6FL5am8ePxwOXPLTKxBqULiqq5WRnSz1Sn5ltbEWV92dm9Wu6q6vk/luFm3NYe/pokqrQX/1q5qOMugUCNErjOls4Q+dLfVKfuXimo3c3DuKbrEhGCpZpI7iuFd2tnBFZ0u9k19ZDfH7bci4wUQqwACprDry9+gtxKDjys4WDmU7+eZAPm6Phsq50XmMieEd6zc6r05l6+vyaGTZPSzamhOQe9xKGHTQ8VxVEOKHLGXSK4xOtNA1x8nKA/m4PFpADue6VVh9qID9Z51c0bn431Kv83+/quoog0EH7axGRiWaCfXDdjboFEYkmOncLISV+204y8RtazEyOtFMmNE/o5CG+P02ZNxgIRVggJXt0IEavXWMNjGldxTtooyEGs6NzhN9NzqvjqIoOD0adpfKyv02DmY5A5b8TLriquCKRAtXdrb4JfmV1T6qeDu3sQR2XJmR52LR1hwycl0B7VdljzKY9AqjOpm56iKrX5JfWXGRRqb0iaZTjAmTHi7vGMHV3ax+S35l1fcit6YW90InFWADaIhHMIUZdVx9kdXn9xt547cCDyn78nB6AhqWWLOBq7r49nBYTUINOgbGhfPb3sCur9OjkZZpp43FgKmyY4R+UnKUIdD9yqRXuKIB4sLvv99giXshkwpQCCFEUJIEKIQQIihJAhRCCBGUJAEKIYQISpIAhRBCBCVJgEIIIYJSk0iABw4cYOrUqfTq1YuhQ4fy6qtLr2h1AAAgAElEQVSv4vFUf4250+nk2Wef5cYbbyQ5OZmuXbtWOe13333H+PHjSUpKYuzYsaSkpPh6FYQQQjQyjT4B5ubmMm3aNBRFYf78+dx1110sWLCA1157rdr5HA4Hn376KWFhYfTp06fK6TZu3Mg999zDwIEDeeedd7jssst44IEHWLt2ra9XRQghRCPS6G+EX7ZsGUVFRcybNw+z2cyQIUPIz89n3rx5zJgxA7PZXOl8VquVtLQ0FEVh8eLF/PLLL5VO9+abb9KvXz/+8Y9/ADBo0CAOHDjAG2+8wdChQ/22XkIIIRpWo68AU1NTGTp0aLlEN27cOBwOB2lpadXOW9MTE5xOJ+vXr2fMmDHlPh83bhxbt27FZrPVveFCCCEatUafANPT00lISCj3WZs2bQgLCyM9Pb1eyz569Cgul6vC8hMSElBVlUOHDtVr+UIIIRqvRp8A8/LysFgsFT63Wq3k5eXVa9m5ubmlyyorMjKyNLYQQogLU6NPgFD5oUxfPhT2/OXIE9eFEOLC1+gToNVqrfRcXH5+fqWVYW1UVemV/F3f5QshhGi8Gn0CTEhIqHCu78SJExQWFlY4d1db7du3x2g0Vlh+eno6Op2Ojh071mv5QgghGq9GnwCHDRvG2rVryc/PL/0sJSWF0NBQBgwYUK9lm0wmBg4cyMqVK8t9vmLFCnr37i0VoBBCXMAafQK8/vrrMZlMzJo1i3Xr1vHxxx8zb948pk2bVu7WiNGjR/Pwww+Xm3fNmjWsXLmS3bt3A7By5UpWrlxJZmZm6TQzZ84kLS2NZ555hvXr1/Pcc8+xZs0a7rrrrsCsoBBCiAbR6G+Ej4yMZOHChTz55JPccccdWK1Wpk6dyqxZs8pN5/F4UFW13GdPPPFEuWR37733AjB37lwmTpwIQL9+/Xjttdd45ZVXWLp0KXFxcbz44otyE7wQQlzgGn0CBEhMTGTRokXVTrN69WqvPqvMqFGjGDVqVJ3aJmqmVyDOaiQ92xXQuB5V42iui04xpoBe0XumwMWhs3baRIYGNK5H1Tic66RzTIhcwSyEF5pEAryQlNxiUfLfgdpRNWTc1hYDLSIsnMx38a0ucDvmk/kevjmQTyuzgSs6W4gw+feIv6ZpfL07m6dXH8fl0Yg1GxnROYYIk96vcUucKfTw3YECtkUUMaazGXNIYOKW9K1A9iuJKwMcX2j05wAvJGXvLyzpwGUT04Uc16BXaGMx0qtVKJ1iTH6PXcKjwXGbmw+3ZrP/bJHf4mQVurnr80M8vSoTu0vFrWqcsjn595ZTHDhdEJDtDcXreyrfzeJtOez+ze7XuJqmle6My/Yrf6+rxA1M3GAgCTAAynbWsiO3kg7tz87cmOLqdAp6ncLoTmbGdjETog9QFQq4VPjuYD5f7cnD7lJrnKc2vj+Yy/gFe1h/NB+7+/ftqWrgUjV+TM/lm71ncbiqf4WXr5Ss7w+HC/nSD+sL1ferst9L3KYZN1hIAvSzyqqv8/mjM1c2amwscY16hfgoE1P6RNEhyuiz2DVxq3A018WHW3M4nO2s9/JsRR7+9t8jzE45is2pUlWecasamTlFfLzlFEey7PWO6y23Csdy3SzamkN6Vv3XFxp3v2oMcX0Vu6HiBhtJgH5Um0eq+bIqawpx9TqFUIOOMZ0tjO4UgTFAPVHVoMijsWK/jf/tt+H01G2dfzlqY9z7e/j+YB4Od83L8Gjg9Gis3p/N6n1ZON2+r8oqo1Ic938HbKTszaOoHnGbQr9q6Lhl52tqcYORXATjB/V5lmjZH29t52+KcY16hcRmIbSPNLFiv43jNnetl1EXbhUOZjnJyM3mys4W4iK9q0TtLpVnf8gkZU+OV4mvYlyNI9l2MrcUcXmXGNpGhtR6GXXhVuFQjotFW3O4orOZ9pHen4dtiv1K4gbWZ9tP1Gr6icmt/dSS2pEK0Md88SDtuoxim3Jcg04h3KTj6ousXBYfToBODeLRwO7W+GpPHt+n5+NWq1/nbScKGL9gD//dXbfkV8KtgsOt8s2eM6w9mI3b411VVt/NomrgcGv8d6+NVQdtuLyofptyv5K4oiZSAfqILzrw+bwZ1V1IcY16he4tQukYbSJln43fCgJz0Yhbg92nizic42JsFwstzeV/Fk63yms/neDj7VkU1SPxVYirwv4zdo7mOBjVpRktLNVXZb6K7FZh7xknR3KyGdPFQmtLxeq37A7Ul29d8bZfSVz/xhXFpAL0AX8kgxLVnei+EOMadApmk46J3SMZFBdGoG4b9GiQ71T5z6+5/HSkAM+5anDvaTsTFu3jEx8nvxJuVaPAqfL1rtOsP5xTGtffPBoUuDQ+353Hj4cLysX15sKtuqquSpG4gYkrficVYD34MxGUVV0y8mfshoxr1EOf1mEkxJj47Nc8HHW8WKW2PBpsP+XgYFYRZ2wOlm47i9Ot+az6qjKuCrtOFXA428H4HrGEB+jmebcKO085SM928sceViLOXY0UiD4d6H4V7HGlGqxIEmA9BbJTlXTmYIlr0Cs43CquAFVFJdwq7DtTRMquszWeF/R1XJNeh8kQ2AMzbg1M+uKrcoOhXwVjXFE5OQRaDw1xZKGhOnNDxVU1BX0D9FJV0wJ2MU5ZujI7yUDSKwpqgAcaEHz9WZJR4yIJUAghRFCSBCiEECIoVXkO8LnnnqvTAqdOnUrLli3r3CAhhBAiEKpMgO+//36tF6YoCuPGjZMEKIQQotGr9irQpUuXkpyc7NWCPB6P19MKIYQQDa3KBGixWDCZTOj13t2PpCgKFovF6+mFEEKIhlRlAtywYUOtFqTT6Wo9jxBCCNFQ5CpQIYQQQcmrBJiWlsby5csr/e7zzz+Xyk8IIUST41UCfPXVV8nMzKz0u8zMTF577TWfNkoIIYTwN68S4P79+0lKSqr0u6SkJPbt2+fTRgkhhBD+5lUCLCoqqvI7TdOw2+0+a5AQQggRCF4lwA4dOvDjjz9W+t2PP/5I+/btfdooIYQQwt+8SoATJ05k6dKlvPPOO9hsNgBsNhtvv/02S5cuZeLEiX5tpBBCCOFrXr0PcMqUKWzdupUXX3yRl156idDQUBwOB5qmMWbMGG655RZ/t1MIIYTwKa8SoE6n45VXXuHnn38mNTWVnJwcYmJiuPTSSxk0aJC/2yiEEEL4XK3eCH/JJZdwySWX+KstVTpw4ABPPfUUW7duxWKxMGnSJO6+++4aH7tms9mYM2cO3333HaqqMnz4cP7xj38QHR1dOs1DDz1U6T2OKSkpdOrUyefrIoQQonGoVQL86aefSEtLIzs7m5kzZ9K6dWu2b99OXFwcMTExfmlgbm4u06ZNIzExkfnz53P06FGeffZZVFXl/vvvr3be++67j0OHDvH000+j0+l44YUXuOuuu1iyZEm56RISEpg7d265z+Li4ny+LkIIIRoPrxKg3W7nzjvv5Oeff0ZRFAAmT55M69atef/992nVqhUPPfSQXxq4bNkyioqKmDdvHmazmSFDhpCfn8+8efOYMWMGZrO50vm2bNnC2rVrWbx4Mf379wegZcuWTJo0iXXr1jF48ODSacPCwujdu3et23ZuUwg/UgCP2jBx3aoW+MBopb+xwEZtmLhCNCSvrgJ98cUX+fXXX3n99dfZuHEjmvb7jmHIkCGsW7fObw1MTU1l6NCh5RLduHHjcDgcpKWlVTtf8+bNS5MfQHJyMnFxcaSmpvqsfWW3hb9pmlYaLxjiFrnc6D2FHEnfj8vlClhcj9tJmDsP5761qC5HwOLqFbA5PBS5PAGLWeJ0gYfdp4twey78ftVQcT2qhsOtcuCsE1cAt7NH1ShyN8AosgnwKgH+73//495772X06NGEhoaW+65NmzacOHHCL40DSE9PJyEhoULMsLAw0tPTazUfQKdOnSrMd/DgQS6++GJ69uzJDTfcUG1iLUtRFBRFKfeD8peS5ZfEvNDj2oucfJG6mUtufZL3PljMsmVLKSwsRPX4Nzk4HXa2rv6Spyf1Z9ebd3H43Vm4C7JRPP5NwAadQnxMGNf1aUlESK3OTPiEBqw5XMAXe/IodKl+r36DrT+7PBpHc118uDWHlQfy+Xx3HvlOj9+3s8ujkZHrYtHWHL/Gaaq8+qXl5ORUe0GI0+n0WYPOl5eXh8ViqfC51WolLy+vTvMdO3as9O9u3bqRnJxMYmIiWVlZLFiwgOnTp7NkyRKvX/Bb9kfk68NIZX+wwRDX5XaTby/ijmc/YNXGXaWfpx88yGuvvcqEa66hU2JnjEajT+OqbhcOewGLHp/JrnXfln5esD+Nvc9cRbvJ/w9Lt2EoptBqllJ7egX0OoXhidF0iAnz6bLr4rjNzaIt2YzoGEFCTAhG/YXRrxoqrqppuFWN1ekF7D/7+37yZL6bD7fmMKxDBF2a+347F8eF1en55eKK8rxKgK1bt2bXrl2V3vLw66+/+v1JMJV1Sm86a1XzlTV16tRyfw8fPpyxY8fyr3/9i/nz59eqjWVHkr74IXmzrJLvLoS4hQ4nqVv2cPeLi8nJL6zwfZHDwccff8xF3bpxzTUTCDGZUHT1f6OX02HnwKYfWfTkXRTmZVf4XnXkc+SDv2HpMZx2Nz6NISQMTVf/Ks2gU2hjNXFZYjShxsbzImmXCt8cLCD+rJPRiWaMOgW9run2q4aK6/Jo/JbvZuUBG4WuipWeW4XVhwrYd7aIKztbMOl9s51dHpXTBR5W7rdRUElc8Tuv9h5jxozhrbfe4ueffy79TFEU9u7dywcffMD48eP91kCr1Vr69Jmy8vPzK63wys5XWYVos9mwWq1VzhcaGspll13Grl27qpymKiWHVKB+5xbKjka9/SE25bhujwdboYO7X/yQPz/xdqXJr6w9u3fz+uuvcejwoXqdG9Q8HhwF+Xz09Cz+9X83Vpr8yrL9+gN754wnf38amrPu5wZ1Chh1CpcmRPKHi5o1quRX1uGc4kN2R3Nc9TpnVZeBoS/7c6DjqqqGy6OReriAz3bnVZr8yjqW52bR1hzSs+p3brAk7tojhfxnV54kPy94NYy988472bx5M9OnT6dZs2YAzJw5kzNnztC3b1+/PgkmISGhwjm7EydOUFhYWOk5vrLzbdq0qcLn6enpjBo1qsa49RkB1qcarM8ItCnGLXQ42bTnMLc/u4DfsisOdKqcr6CADxctIjk5mXFXjcdoMNSqGnQ67GTs3sKCR2eQd/aU1/N5CrJJf2smURePpe2kR9EbQ9B03icwgw5iI0yM6BJDhKlxJr6yHG6Nr/fZSIwxMbJTBAadgi6A/aquy2iouC6PRrbdQ8p+G7Yi7y88cXo0Vh7Ip2O0kdGdzBhqWXWXxF2x30ZeLeIGO68SYEhICAsXLuSrr74iNTWVM2fOEB0dzbBhw5gwYUKNN6TXx7Bhw3jvvffIz88vvRI0JSWF0NBQBgwYUO188+fPZ+PGjfTr1w+AHTt2kJGRwbBhw6qcz+FwkJqaSo8ePerV7rI/Im8O15YdcdY3+TaFuB6PisPp4u9v/psl3/xS57jbt2/n8OHDTJp0HS1btarx3KCmenAWOfjslUf4+cvFdY6bszmF/IMbiZ/6PKFtL0IxVn9uUKeATlEY2MFKt5YRTe6WgwNZTo7bXFyRaKal2VjjOStf9auS+QPdn+sS163C+mOFbDlR96MDh7KLL1j5QyczbazebWe3CmnHCtlcj7jBStECeR1wHeTm5jJu3Dg6d+7MjBkzyMjI4J///CdTpkwpdyP86NGj6d+/P3PmzCn97NZbb+Xw4cPMnj0bnU7H888/T7NmzUpvhLfZbNx+++1cffXVdOjQgezsbBYuXMiuXbtYunRple9ArK2afpS+PO/Q2OM+/vjjPPjQw+w9coJbnnmXY79Vf9ixNgYMGMio0aMxGPQoSsVq0OWwc/LwXt77+y1knczwWdyYwdfR+uoHiqvBSuIadQqRYQZGdonBGhr4Kzx9rVtsCJfFR6BXQFdJlRJM/RmKqy9bkYeUfTayHb6rvro0MzEiwYyhiu3s9mjYnB7+uy+fbHvNV0fPGtTM69hvrfLvO14nJrf26/LLio2t+lSZV7/G/Px8nE5nuae9fPHFF+zZs4dhw4b59fFokZGRLFy4kCeffJI77rgDq9XK1KlTmTVrVrnpPB4Pqlq+87388svMnTuXhx9+GFVVGTFiBI888kjp9yaTiZiYGN58803Onj1LSEgIvXv3ZvHixT5LflB1deTL0WpTiKuqKi63h2cWfsnbX6zx+aXnaWnr2X9gP5MnT6ZZTDMM56pBTVVxOYv479tz+GHZv3weN2vdJ+TvXUf8LS8REtsBzlWDCqDTQZ84C8ltzE2u6qvK7tNFHMtzMaazmZgwQ2mVEmz9uaT62nzczoZMO76uJPaddZKZl8OVnc3ERugx6nWlcT0qbDlhJy3TToM8r+EC4VUFOGvWLCwWS2l19cEHHzB37lx05865zJs3j8svv9y/Lb1AnL+5A7VTbOi4RS43h0+cYdS1N+JoUb/DyzVRFIVLL72UoZcOA9VD1omjvPPQFH47csCvcVF0tLh8GrF/uB2jKRRLqIFRXWKIDvftLRuNSZ9WoQxqF865fXPQ9GePBoVOlZR9Nk4X+v/BBT1bhDC0QwQKUOg+F7egdnGlAqzIqwpwx44dPPjgg6V/L168mOuuu47HHnuM2bNn895770kC9FLZ0WMgK4KGjLtpz2E+/m49H6T8hC2vgPAW/o2paRqpqansXJ9KrK6AX7760O830BcHVvlt1ftYXFkMmfU83dtEVXro6kKy5aSDvCIPV3S2+OQSfm81ZH/OsrvZecrBjlNFAau+dv5WxNFcFx2ijOz6rYgAPkjmgubVZXNZWVm0aFG818rMzCQjI4ObbroJg8HAtddey4EDfh5ZiyatwF7Ep6s34FEDe3XaqROZrE9ZGpjkV4aWc4LO0YYLPvmVsDlVPEF0HE7VYNdvgUt+JfKKVHackuTnS14lwIiIiNJ78TZs2IDVaqVr164A6PV6ioqK/NdCIYQQwg+8OgTas2dPlixZQvv27VmyZEm5NykcO3aM2NhYvzVQCCHEheWz7f57fnQJb84zelUB3nvvvWzfvp2rrrqKI0eOMHPmzNLvVq1a5fUzM4UQQojGwusKcNWqVRw8eJCOHTuWe5TYpEmTiI+P91f7hBBCCL/w+q5cs9lMr169Knw+cuRInzZICCGECASvDoEuWbKEF154odLvXnrpJZYtW+bTRgkhhBD+5lUCXLp0Ka1atar0u1atWrF06VKfNkoIIYTwN68S4LFjx6p8IW5CQgIZGb57rqIQQggRCF4lQJ1OR25ubqXf5eTk+PzZikIIIYS/eZUAu3fvzvLlyyv97j//+Q/du3f3aaOEEEIIf/PqKtDp06czc+ZMZsyYweTJk2nVqhUnT55k2bJl/PTTT7zxxhv+bqcQQgjhU14lwBEjRvDoo4/ywgsvsHbtWqD4IbTh4eE89thj8iBsIYQQTY7X9wHedNNNXHPNNWzatIns7Gyio6Pp27dv6VvahRBCiKakxgTodDp56qmnuPbaa7n44ou57LLLAtEuIYQQwq9qvAjGZDLx1Vdf4Xa7A9EeIYQQIiC8ugq0Z8+e7Nmzx99tEUIIIQLGqwT40EMP8cEHH7By5UpcLpe/2ySEEEL4nVcXwdx22204HA7uv/9+FEXBarWiKL+/7VpRFNatW+e3RgohhBC+5lUCHDp0aLmEJ+qnoZ6c01BxL4pvxcpX/o+7XviQnw6kBSSm3mDgykl/psMDD7Lk6Vkc/nVTQOIC/JZxkOduGcVN/3idhOQBAYvbUHIdHpbtyGVkJzNtrcaAxW2o/mw2KtyQHMXq9HyO5QXm2ggF6N82jItiQ/j+UAEZuXIkzhcUTZ5jFlCVbe5ADC4aOq6iKNiLnEz8861stcfgcnv8FrN1mzZMnjwZc4QZvcGA02Hnp+UL+HL+U3jc/t1xKIqCoiioqooxJJShE6Zx1Z2PYjSF+DVuY2DQwUXNQ7g0PgKDzr99q2y/Kvvf/lY2lsujse9MEalHCnCr/osZHapnbBczlhA9Rn1x3P1nilhTy7izBjXzetq3Vu2rQ0sbl5I3wsfGWqqcxqtzgKL+NE1D07TSHWTJ/0q+u1Djwu87prAQE327xvPTW/+gW3wbn8fU6XSMHDmSW26ZTqQ1Er2h+ACHKTSMS/84nUeW/UybTv55bF/ZbaqqxXslV5GDn774gDk3DObYvh1+iduYuFXYfbqID7fmcCrfP5VRZf2qofqzUa/QNTaEm3tH0dLs9S3VtdKndSiTkyKJDitOfiVxu8SGMKV3FK38FDdY6B9//PHHvZ344MGDbNq0iV27drFv375y/+vatasfm9m0VTdCPf8zX45iG0Pc85ebmprKNVeN5cY/DEJBYf2ug/hiv9U8NpZbbplOYudEjEYjnBdXpzcQZrEycNwN6PR60rev99kOs2Q9K1uex+3GbsslbcXHqC4XCb0GodNduONODXB6NPacLsLp0WhrNaLzUd+qrl+d/28QqP6sUxRMeh1dmoUQaoBjeW580assITqu7WYlsVkIRn3Vcbs2CyHMoHAsz1Vj3IFx4V7H33TobB1a3bh0a1lc+UVEVH30xatDoAUFBdx5552kpRWfv6msk+3evbtejb0Qld203vwgffXjbcxxH3/8cUrGXPYiJ+mZp5n21DscOnGmTjEVRWHw4CFcNnw4RoOhQuKrjKvIzpnMw7w7eyqnj6XXKe75bfAmmYaEhhPTpj3T5yygZYfO9Y7b2Bl0YDbpGNvFQrPwulcqjbk/l+X2aBS4VP67z8bZwrof4u8eG8Kw+Aj0Cui8OJRcEjdln40z1cSVQ6AVeTUUnTdvHpmZmSxcuBBN03j11Vd59913GT16NO3bt+ff//63b1p8Aalu1FiVsody6lqdNKW4YSEmusW3Zs2bD3Pr1cNqHTMqOpq//OV2hg8fXmnVVxVjSBit4rsy+8M1DJ/8lzrtKOtyKLnIUcjJw/t4buoIvl86v/RQ6YXKrUKOQ+WTnblsPFaIWoe+1ZT6s0GvYA3RcV2PSPq3DaW2vSrcqHBtNwvD4iMw6hWvkl/ZuH/qEcmAtmG1jhvMvEqA33//Pbfffjv9+vUDIC4ujqFDh/Laa6+RlJTExx9/7NdGNiWVnSuojbqeo2uqcXU6HeGhJv7f9AmsePmvtGke5dV8/fr1586Zd9KyZUsMxtpfeajodJhCwxh/x6M88M5Kolu2rfUy6rJz1VQVV5GDlHf+ycszriTrxIX/Mmm3ChuO2/l4Ry65Du8qo6banxVFwaBX6NsmjBuSI4kK9e5wd+dmJm7uHUUbq7H0XF9t4xr1Che3CePG5EiivYwb7LzaSidOnKBjx47o9XqMRiN2u730u/Hjx7Nq1Sq/NRDgwIEDTJ06lV69ejF06FBeffVVPJ6af0g2m42///3v9O/fn759+/LXv/6V7OzsCtN99913jB8/nqSkJMaOHUtKSkqd2lmXUWNVajOKvRDihoea6NOlPeveeZTrRlZ964DFYmH69On84Yo/YDSZUOp5Ps0YGkb7br15eOk6Bl11Q7XT6nQ6n11wUWQvJGPvNubcOIR1X3x4wb9U2q3C2UIPS7bnsO2kvdr1vRD6s1GvIzpMz/VJUfRqGVrldKEGhau6WhiZYMak19X7fKlRrxAVpmdyUhR9WlUdVxTzau8RGRlJQUEBAC1atODgwYOl3xUUFOBwOPzTOiA3N5dp06ahKArz58/nrrvuYsGCBbz22ms1znvfffexfv16nn76af75z3+yc+dO7rrrrnLTbNy4kXvuuYeBAwfyzjvvcNlll/HAAw+UvvbJG/UdNValplHshRbXoNdjDgvhhVnX8+mcu2keWf5NIz2Tkrj77lm0jWuH0WjyWVxFpyckLII//fVZZr2+HEt0bJXT+jJRqR4PTkchn73yCG/MupbcMyd9tuzGSKM4Ea47Wsh/fs0j31lxENsQ/dlfcXXnqrJL2oczqYcVs6n87jY+ysjNvaNoH1m3qq+muAPbhXNdTyuWEKkGq+LVVaDr168nPDycPn36cOTIEZYtW0arVq04dOgQL774Ip07d2bChAl+aeAHH3zAhg0b+OSTT0hMTCQpKQm9Xs/bb7/NzTffjMlU+Y5wy5YtvPTSS7z55psMGzaMTp060atXL1599VX69u1Lu3btAHj00Udp1aoVL7/8cumh3W3btvHTTz/xpz/9qcb2+XLUWJXzn7rTlOP+8MMPDB8+vNppjAY9cbHRTLvqUvYfO0XmWRuTJ1/PgIEDMJlC/La+eoOR6JZtGTrxFn47epBTh/eVu2LTX1Wax+0i5/QJ1i5fSPO28bROuMgvcRoLVYMCp8rOUw4ijArNy1wgE6j+XPJ3IH5Hep1CuFFHUstQ8l0quUUqoxIi6B8XToih/lVftXENOnq2DKXQpZIQ4/2gMViuAvVqaHDTTTcRElK8kJkzZ2KxWHjggQe455578Hg8PPLIIz5obuVSU1MZOnRoufcOjhs3DofDUXpValXzNW/enP79+5d+lpycTFxcHKmpqUDxq57Wr1/PmDFjys07btw4tm7dis1mq7ZtgbwBt+xl3sEQ12DQY40I44X7pvC3//srHRM6+rTqq4pObyA0wsLVdz6GMSQMCMwTRzxuF0WF+Xw1/ymK7AV+j9fQNMClwi/H7LjUwPdnCOzvV6crrsqGx0dwW9/o0tsbAhV3WHyE32M1RV5dm3zppZeW/ndsbCxff/01u3fvRlEUEhMTq6zCfCE9PZ1BgwaV+6xNmzaEhYWRnp5e5dvo09PTSUhIqPB5p06dSE8vvvT96NGjuFyuCtMlJCSgqiqHDh0iOTm5yrZpmneXKftSSTIKxI+2McQt0vQYDQZcAb5gMvfMSQwGI64ie80T+5C1eQt8cmNkExFm1CtR+YQAACAASURBVIEWmCRUVkP1Z6O+4eKKiup0c45Op6NHjx6+bkul8vLysFgq3sdhtVrJy8ur03zHjh0Dis8vlnxWVmRkZOkyqvP555+zffu26lfADxriB+SruD/88AO1ePYCuQ4P+88W4QlwTsg6kYHL6b9z21XJPpXJNx+8jD4A1W5jEGHU8WtsCPoADyShaf+O6qI2v7tgUWUCPHDgAO3bt69VdVeXebxRWWfxphNVNV9N03l7aGTChAlMnHhttdP4Q1P+4Za9Ed4bGbkuUvblUcn1En51YMs6dqSm4HY5Axo3umVb/jD1PkLCq75590ISG6FnYjcrJkPgL9Royr8j4RtV9rrx48fX6iW4Ho+H8ePHs3//fp80rITVaq30XFx+fn6lFV7Z+Sqr4Gw2W2nFV1WlV/J3dcsXQgjRtFVZAWqaxt69e3G7vXuoraqqfrlYICEhofScXYkTJ05QWFhY6Tm+svNt2lTxFTjp6emMGjUKgPbt22M0GklPT2fAgAHlptHpdHTs2NFHayGEEKKxqfYc4GOPPeb1gvxV1g8bNoz33nuP/Pz80itBU1JSCA0NLZe0Kptv/vz5bNy4sfQJNjt27CAjI4Nhw4ofu2UymRg4cCArV67k+uuvL513xYoV9O7dWypAIYS4gFWZABcsWFCnBfq6arr++uv58MMPmTVrFjNmzCAjI4N58+Yxbdq0crdGjB49mv79+zNnzhwA+vTpw9ChQ5k9ezazZ89Gp9Px/PPP07dvXwYPHlw638yZM5kyZQrPPPMMo0aNYs2aNaxZs4Z3333Xp+shhBCicakyAV5yySWBbEeVIiMjWbhwIU8++SR33HEHVquVqVOnMmvWrHLTeTyeCg8Xfvnll5k7dy4PP/wwqqoyYsSICvcs9uvXj9dee41XXnmFpUuXEhcXx4svvsjQoUP9vm5CCCEaTpN4m2JiYiKLFi2qdprVq1dX+MxqtTJ37lzmzp1b7byjRo0qPS8ohBAiOMhD4oQQQgSlJlEBCiGECG4lL7j1JakAhRBCBCVJgEIIIYKSJEAhhBBBqd4J8KuvvuKrr77yRVuEEEKIgKn3RTB/+9vfUBSF8ePH+6I9QgghREDUOwE+9dRTvmiHEEIIEVBVJsDjx48TGxuL0WisdgGTJk3yeaOEEEIIf6vyHODIkSPZvXs3AFOmTOHgwYMBa5QQQgjhb1UmQKPRiMvlAiAtLY2CgoKANUqIYOaHt4oJISpR5SHQdu3asWjRIrKzswHYuHEjp0+frnJBI0eO9H3rGjlFCfzbnUveuRgscU06cHkCH9cS2xqX241Op0dVA/c6+tyzp1F0ejRVRdEF7i4lvQLqucQbyPxb5FbRKUrQ9OeGjCtvoa+oygR4xx138Pe//51vvvkGRVF47rnnqlyIoiilh0uDiXLuhxuIzlX2ZcPBEFdVNYo8Gu9t+I2UPTmM6hpDVJgRvc7PP2JNw61qHHRF0+mv/yZj0YMU/ZaOp8ju17CKooCip8jSlvlvvsnkydfTokULDDWcg/cFvQ4Gtg2jczMTK/fnc9buwa3WPF+94yrQPTaUkjx/IffnxhBXVFRlAhw/fjxDhgzh0KFD3HTTTTz22GMkJiYGsm1NQtnOXPK3r1W27As5rsPl4WiOkwe+PsLRHCcAy7efJrmNmYvjLBh0SnH57WMeVSXP4eHbvVnkOtyYmsWRcO9izqYu4uSK+eB2+mWHojMY0YVZMCdfid4cTV5eHu+8+w6DBg7i8pEjMRj0KIrvq0GDAuYQHeO6WIgJL94VTOoZybaTdn7OsONR/VMNGnRgNukY28VCs/Dfd0EXan9uTHFFeVUmwA0bNtC9e3f69u3Ltddey6WXXkq7du0C2bYmo6SD+XpUd/6o8UKPq2oaTrfGO2m/8d6G06WH5KB4R7zteD5Hsx2M7hqDOcTgu2pQ03BrsC3TxpZj+eV2+opOR/Ph0zB3G0bGwr/iyj6Bx+m7alDRGQiN70Nox77lD3lqGr/8//buPLypMm38+PckadN9o6ylULoChbIXUGQVCooKSMVBkUW2ERkdHRRwQR1fgRl5HVBZxeEnOgoWxGGADo76guDCyKIgUGAqpSwFBLrvyfn90WlsuqZpkqbN/bkuLpqTc879PCdPcp/nbM+333D27BkmTXqQwMBAm/YGtQr0bOtBfHsvs+2oKAo923rRMUDPnjM5ZBXZtjeoVSCutQcDQr2qfH72alfl66wYw5Xjil/VuFtZ8cpPuQjGMhUbdENVbMB1NWJniNvQ2EWlRi5mFvPQh+fYcMg8+VV0q6CUj3+4xvEruZQajTS0j2IwquQUGfj0+DWOVEp+FXm0DifyDx8TPGQKipu+QTEBtDo3tF5++PWfgGdEvxrP9924cYM1a9dw4MCBsovSGridtQr4umuYGOvPwA7eNe5EBHpqeTDOnz7tPNHZoPOpVcp6fffH+nN7x5rjgm3bVcXk4sj23BTiilp6gHq9nqKiIgAuXbpEcXGxwwrVlFVORvVthHX1vpw5rrV7sYUlRj489gtvfn2V0poyn1lZ4d8Xsvn5RgEjY4LwctOisaI3WGowcupqHocuZNeYcCtStDpajZmHb/fhXNj0FIbcmxiKC+sdV9Hq0LePxSNqAIpGW+f8qtHIvn3/x+nTp5j04G/w9fVBp6t/b1Cnga4t9dze0bvsMHIdNIpCfHsvwgPd2X0mh7xiI6VW/D7rNNAlWM+gMMvigm3alTXfBVeL6+pqTIDh4eGsXLnSNFL67t27OXbsWLXzKorClClT7FPCJsqaE922aMBNKW5RqZHMglJ+vzONE1frf1jxl7wSth69yoAwf6JbeqHTWtZVMRpVCkuNfJZyg2u5JfWO69m+C1ELP+Xarr9w4+skjCVFFi2n0bmB1h2fHgm4BdZ/bLOrV6/y9ltvcuedd9Knb786H1JRTquAXqcwOsqXEL/6J85gbx0P9Qjgmwv5HL9WaPEhUa0C7lqFMdHWxQXrzpXZuj27QlxXpag19LkPHjzIU089RVZWlulDqXElLnoVqKUsaaD2aMTOGPell17ipZdeorDEyM5TN/nzvisUWtOtqKSNrzt3Rgeh12lq7Q2WGlT+80seB89nY7Ck21eHvNQjpL+3AGNBDoZaEqGi1eHRLhrP6EEoVvTeKmsfGsoDD0zCy8sTrbbmJxrqNBAZ5M6QTj64axv+GWfklLD7TA6FpSqGWjafTgMRge4MDbdNXHDO9twc4wKs+/yMTddnC9YOiNuypW+N79WYAKFs4167do0hQ4bw1ltv0aVLlxpXFBISYlXhXEVNDdXee2/OFveFF5fw+4UvsGBXGv++aNvzym4ahUHh/oQFeVbpDZZdYGPk8zO3uJxtWY/NUoaifK5+soxbR/+JscT8kKhGp0NVdPjEjcI92LYXkbm5uTHmrjF06xZXpTeoUcq2R0KUDx0D3G0at8Sg8lVaLim/FFfpDZbHHRXpQ1igbeOC87Xn5hpXEmAFb731FomJibRu3dqqAohfVWy4jjx04QxxC0uMPPr7RaR2mUJesf1uNGsfoGd4VBDu2rILAUoNRi7cKmR/aiYltXVbGign5WvSNy+EkgIMJcVl5/padcKzyxA0NrhwpiadwsOZOHEiHnoPNFotOg108HdjRIQPHra4gqUG6VklJJ/NocRQ1hvUaSDUz407I+0bF5yjPTfnuJIAhd001jH7xoqbU1SKwQjP/zOdj9f8idajf2v3mHqdwpCIQFr76tn3n1tcuFX/i1WsYcjP5uKHz5Nz5lt8Yofj3jrcIXH1ej2TJz1AZFQkI8J9iGxhv4RbUVGpkS9/ziMts4Th4d5EOSguWH/hlsStm6skwBpPHixatIjHHnuM0NBQFi1aVGsARVF47bXXrCqcK6rrnGpzi/vT1QKe2plGrh17fZUVlarsTbmJgmMf7aX18qPNhMV4HE3GUOK4K6eLior48dBXvDiuB75ejktCep2G0VG+Nr+PzRLl7VniCmvVmAC/++47pk6davq7NvKBCCGEaGpqTIBffPFFtX8LIYQQzYHjHjcvhBBCOJFaR4Svj3bt2jW4MEIIIYSj1JgAhw8fXq9ze/a8EX7r1q288847XLlyhaioKBYsWMDAgQPrXO7w4cMsW7aMlJQUgoODmTZtGo888ojZPDExMVWW69GjB1u3brVZ+YUQQjifGhPga6+9ZkqAJSUlrFmzBr1ez913303Lli25du0au3btoqSkhN/+1n6Xte/atYslS5bw+OOP06dPH7Zv386cOXNISkoiOjq6xuXS0tKYOXMmQ4cO5amnnuL48eMsW7YMT09PEhMTzeadMWMGCQkJptfe3t52q48QQgjnUGMCnDBhgunvP//5z4SHh7NhwwY0FZ5a//jjjzNr1izS0tLsVsBVq1Yxbtw45s2bB0B8fDynTp1i/fr1vP766zUut3HjRlq1asWf//xndDodAwcO5PLly7z11ltMnDjRrHcbEhJCz5497VYHIYQQzseii2D+/ve/8/DDD5slPwCNRsNDDz3Ep59+apfCpaenc/78ecaMGWMWMyEhga+++qrWZffv38/IkSPR6X7N8XfffTcZGRmcOeN8N3kKIYRwLIsSYFZWlmlopMoKCwvJzs62aaHKpaamAmUjU1QUERFBZmYmN2/erHa5/Px8rly5Uu1yFddb7s0336Rr167079+fRYsWkZmZaasqCCGEcFI1P0a+gq5du7JmzRoGDBhAQECAafqtW7dYs2ZNrQ/JboisrCwA/Pz8zKb7+/ub3g8KCqqyXE5OTrXLlb+umLDHjx/PsGHDCAoK4sSJE6xevZqUlBQ+/vhjtNq6x2oTQgjRNFmUAJ999lmmT5/O8OHDuf322wkODuaXX37h4MGDqKrKX//6V4sD5uTkcO3atTrnK++tge2fhF5xuWXLlpn+7tevH+Hh4cyePZsvv/zSNBaiEEKI5seiBNirVy8+/vhj3n77bf7973+TmZlJQEAAQ4cO5be//S1RUVEWB0xOTub555+vc76UlBRTTy87Oxtf318faFreg6vcwytXPm95T7DychXXVdngwYPx8vLip59+kgQohBDNmEUJECAqKoq//OUvDQ6YmJhY5TaEmpSfw0tNTTUbbzA1NZWAgIBqD38CeHl50bZt2yrn+mo6p1hRee9Qnm8qhBDNm1M/Ci00NJSwsDCSk5NN04xGI8nJydxxxx21Ljt48GA+++wzDAaDadru3btp27ZtrfcP7t+/n/z8fGJjYxteASGEEE7L4h5gY5k/fz4LFiwgJCSE3r17s2PHDtLS0lixYoVpnkOHDjFt2jQ2bdpEfHw8AI8++ig7d+7kmWeeITExkePHj7NlyxZeeuklU+9uy5YtnDhxgoEDBxIYGMjJkydZs2YNcXFxDB06tDGqK4QQwkGcPgGOHTuW/Px8NmzYwOrVq4mKimLdunVmvThVVTEYDGZj3XXs2JENGzawbNkyZs2aRcuWLXn22WfNDr926NCBTz75hL1795Kbm0twcDDjxo3jiSeekCtAhRCimXP6BAjwwAMP8MADD9T4fv/+/UlJSakyvW/fviQlJdW43MCBAy16pqgQQgjrWTuau7059TlAIYQQwl4kAQq7KypVaeOrb5TYat2z2Jy33o3ICvexOkqQnzducuheCItZlABXrlzJc889V+17zz33HG+++aZNC9WcqapqOldZ8Zxlc4yrqio/XS3g5C8lDI4MZGzXYHRax99e4siIEcGePDy0G/fffz/Tp8/Ap5Z7Tm1p/JA+bFg0Hb27zqHtCmiU9ixxhS1YlAB3795d42gJvXv3Zvfu3TYtVHNV8Qk25VeiOqIxN0bc/GIjO05lsz8tn1IjaDUKbfzciW3jTacgD7vGVnBs0gPw0GkY06UFg8MDcNNqcHNzIzQ0lMcfn0+3bt3sFjfQ15sPX5nLyt8/hI+nh9nna+/PuDxG5XYlcZtHXFdg0UUwGRkZdOjQodr3QkNDuXLlik0L1dxUbKgVb7CvnIzscfN9deu2d9xzN4r4V2ouBgMYK0xXFAWtRmFoZCBRWUXs+88tikrt8yVWa/hbg3mZbKFDoAfDIgNx0yrm21mjQa/Xc+994+jRowfbP/mEgvx8m8UdGR/Lmmem4u2hx03366HPyj+SjdGuJG7TjusqLOoBenh4cP369Wrfy8jIwM3NzaaFak6q631VZo9eWXV7jfaOW1hqZFdKNp/9J5cSQ82JRqfV0CHQg0m92tA+wDbnBivW0FH7xW5ahTujAxkRFYi7TlPjdnZzcyM8IoLf/e6JWh/CYCkfLw/WL5zOu889SoCPl1nyq6i5tKumFNdWsRsrrquxKAH27NmTTZs2mT1VBcBgMPDee+/JYLI1qE8Pq7yh26IxN0bctMxi3juayfnMEkot6GIpioJep2FkdBDDIgNw0zRsL9bSpc16pA2I185Pz4O9WtMxyBOdtu6vkUajxcPDg4mJDzAxMRF3vXWJf1BcFN+/u4Sxt/fAU+9e5/xNvV01tbgVl2tqcV2RRYdA586dy5QpU7j33nuZOHEirVu3JiMjg23btpGWlsbmzZvtXc4mpSGHFit+eeu7fGPELTao7Ps5l3M3iy1KfJXptBrCW3gR4u/Bv87cJCOn2PIy82tvz9aHNWui1SjcHuZHRLCXRYmvMjc3N7p07kJYWCeSkj7m/M8/W7Sch7sb/zP3fh4YEW9R4qusqbUrieu4uK7M4tEg3n77bV555RWWL19umh4aGsrq1avp1auX3QrY1NjivJo15+gaI+6l7BKSz+ZQVKpiaMDOp0aj4OmuZUyXFpy5ns+357MsWp+iQEN2eisuqlD3odNWPm7cGdMCT50GTQN6rBqtFm9vbyZPfojjP/7AnuRkSktKapy/V3RH/t8LMwny98HD3frTDU2lXUlcx8V1dRY/CWbIkCF8/vnn/Pzzz9y6dYugoCDCwsLsWLSmxR6NzpK9usaIW2pUOZiWx8nrRVb1+mqi02ro3MqbDoEefJZyk1/yqiYFs16fDY/41JZMNQrEd/CjSxtvdBrb3Trr5uZGjx49iYyKZuuWj7h06ZL5+zoti6eOZea9Q6zq9dWkPu2qfH6J27ziijL1fhRap06d6NSpkz3K0mTZc4+r8vmMijEaI+613FJ2n8khv8TYoF5fTTQaBR+9lntigzlxJZfvL+Y0qJdnqZqSaZCXjlExLfBy16Jt4HnK6mh1Ovz8/Jg6bTrf//sQ//r8c4wGA13C2vH/XphFu+AAPPS2v8istt6CvduVxG3cuOJXFifAs2fPmg2Iu3XrVmJjY1m1ahX9+vVzyWdqOqpx1Xa1lz1jV4xrMBo5dLGAo1cK7ZL4KkVGp1Xo3s6HjkEe/OOnXyj87+0SjjjNX75Fe4X40CPE978379v3M3Zzc6NffH9iOnfBPz+dOfcMQu/uhsYBbcvR7UriNk5cSYJVWXQ85/jx4yQmJnL06FFuu+02jMZfj3sVFRXx4Ycf2q2Azs6RjapirNouj7ZH3Ms5pRzLcETy+5VWo6GoxEiJI4NSlmRb+rj9N/lpsHfyK6fT6ejcviXzxg/FU+9u9+RXrjHblcR1TFxJftWzKAGuWLGCbt26sXfvXpYuXWq2F9OtWzdOnDhhtwI6s8a46rixGrKqNs6DYw0o6Oxw6LEuGkVplOeIajSKS7UriSsak0W/aT/88APTp09Hr9dX+QCDg4O5ceOGXQonhBBC2ItFCVBRlBoHiM3MzMTDw77PdhRCCCFszaIEGBsby65du6p977PPPiMuLs6mhRJCCCHszaKrQGfOnMncuXMxGAzcc889KIrCiRMn+PTTT9m1axcbN260dzmFEEIIm7IoAQ4ZMoQ//vGP/OlPf2LPnj2oqsqSJUvw8fHhj3/8IwMGDLB3OYUQQgibsvg+wIkTJ3L33Xdz9OhRbty4QWBgIL1798bLy8ue5RNCCCHsol5PgvH09OS2226zV1mEEEIIh6kzARYXF7N7926+//5705iArVq1Ij4+ntGjR8tYgEIIIZqkWhPgqVOneOyxx8jIyKjyCJ+kpCT+8pe/sGbNGpsM8imEEEI4Uo0JMDc3l7lz51JQUMAzzzzDsGHDaNeuHaqqcvnyZb744gvWrVvH3Llz2blzJ97e3o4stxBCCNEgNd4HmJSURFZWFh988AHTp08nLCwMd3d39Ho9nTp14tFHH+X999/n1q1bbNu2zZFlFkIIIRqsxgS4f/9+xo8fT1RUVI0LR0dHc99997Fv3z67FE4IIYSwlxoT4NmzZ+nfv3+dKxgwYABnzpyxaaGEEEIIe6sxAWZnZ9OyZcs6V9CyZUuysrJsWqjKtm7dyqhRo+jevTsTJkzgm2++qXOZ48ePs3DhQhISEujcuTMLFy6sdr7i4mKWLVvGwIED6dmzJ7Nnz+bixYu2roIQQggnU2MCLCoqsugWB51OR0lJiU0LVdGuXbtYsmQJ9913Hxs2bCAyMpI5c+bU2es8cuQIhw8fpnv37gQHB9c436uvvsonn3zCs88+y8qVK7l16xYzZsygqKjI1lURQgjhRGq9DSItLQ13d/daV3D+/HlblqeKVatWMW7cOObNmwdAfHw8p06dYv369bz++us1LjdlyhSmTp0KwIQJE6qdJyMjg6SkJF577TXGjRsHQOfOnRkxYgR///vfSUxMtHFthBBCOItaE+AzzzxT5wpUVbXbII/p6emcP3+e5557zjRNo9GQkJDA5s2ba11Wo6l7oIsDBw4AMHLkSNO01q1b07t3b/bv3y8JUAghmrEaE+DSpUsdWY5qpaamAhAeHm42PSIigszMTG7evElQUFCD1t+mTZsq9zBGRERw6NAhq9crhBDC+dWYAMePH+/IclSr/OIaPz8/s+n+/v6m9xuSALOzs/H19a0y3c/Pz6ILe+zU8XVKiqJgbIy4gFGtczabU2mcz1cFNK7UsIRoRPV6GLYt5OTkcO3atTrni4iIMP1d+RBr+WPZbHHotaZ1WLpuex4CrqzUqFJUauR6noEQPzfctI6Jq6oqbX20RAa5c+5mMaUOyoRaBcKC9AyP9GNfajaFpY7JhDoFcopKKSlV0dV+CtymtApkFxooLDXirdE4rF1VfMyhI9uzxHVsXEfFa0ocngCTk5N5/vnn65wvJSXF1NOr3FPLzs4GqvYM68vPz8+0roqys7MtWnd5g3JEAysxqJz5pYj9aXmUGqFjgBujIn1w0yhoNfaLW143nVbDyEhfojOL+efZXEqMql17ZjoNRAS5M7STD+7aFnydlsOzuy+QX2ygxI4JWKdRCAvy4PbwANy1dZ9Htl1c6BTozrBO3uh1ZXEd0a6qiyFxm3dc8SuHJ8DExESLLy4pP/eXmppKSEiIaXpqaioBAQENOvxZvv6MjAzy8/PNxjVMTU2tct6xNoqioKqqXfbqDEaVYoPKP8/mkp796+0maZklvHc0k5ERPrT3t31vsKYvTscAdx7pFcDn/8nlQlaJzXuDWqUsCY2K9CEs8Nfu120dfdk1vTMvfZbOgfM5Nu8NahXQahSGRQXRIdDDpuuujQbQacvq2ynQvLtpz3ZV2w+jxLV93OpiN1Zc8SvH7eJaITQ0lLCwMJKTk03TjEYjycnJ3HHHHQ1e/6BBgwD47LPPTNOuXr3K4cOHGTx4cL3WVbE3WHnkDGuVGFRSbxXz3rFMs+RXrsig8o8zOfzrP7kUG4wYbRS3rr1GD52Gu2P8GBnhg5vGdo1Ip4EO/m480ivALPmV8/PQ8r/3hPHa6A74uGvQ2SiwTqPQPsCDSb1aOzT56TQQ6q/jkZ4BVZJfOUVRzH4obcGSXoHEtW3c2k61NEZcUcbhPcD6mj9/PgsWLCAkJITevXuzY8cO0tLSWLFihWmeQ4cOMW3aNDZt2kR8fDwAN2/eNF3JmZ2dzaVLl0yJdPTo0QC0adOGiRMn8tprr6GqKkFBQbz11lu0a9eOe++9t95lrZwErW18RqNKiVHlX//JJfVW3Q8ZOHezmMs5JSRE+tDax/reYH0Pl0S20NPW142953LIyC21ujeoAbQaGBHhQ1QLfZ3z3xnlT68QbxbvucDRy3lW9wY1CmgVhUHhAUS29Kp7ARtRKEt+Qzt5ExOst2h726K3YE2vQOI277iuzukT4NixY8nPz2fDhg2sXr2aqKgo1q1bZzYGoaqqGAwGswZw9uxZnnjiCdPr9PR0U0JMSUkxTX/++efx9PRk2bJlFBYW0q9fP1asWIFeX/cPcU0q79HVpzGWGFQu55Sw91xuvX7Y80tUPjmVQ5eWeoaEeaNVQFOPc4PWnivwdtcwrosfJ68Vsj8tH4Ox7EpGS+k00MZHx6hIX7zdLe/StfDSsXZCJ3aevMlrX16hqNSIoR6BdRqFVj5uDIsKwstdW48SN4xOAy29dYyO8sWnHvWFhrWrhpwLasi5bonr/HFtYUJc20aJ21CKaqt+t6iWpQ3TaFQpVeHL1FzO3ChuUExfvYYxUT4Eeerq7A3acq8xp8jAnjM53Cgw1Ngb3P3Ocu6a+SwKZb2+wR296NrKo0GxM3KKWbDrAmeuF1BQx06DRim7zWBgmB8xrbwd9oNRXt/bO3jRvXXD6guWtytb9wokbvOOW27d5/Ub4MCZE2DLllVvdSvn9D3Aps6SvfYSQ9mtDcnncskrbvhVJTlFRraeyKZnGw8GhHqh1VR/b5mt9xp99VoSu/nzQ0YB36QX1JgEdRoI8tQyJtoXP33De19tfN15b1IEfzv2CysPZFBsqP4KVTeNQoCnjhHRQfh6OK7p6zQQ6KllTJQv/h626W1a0q7s0SuQuM07rquRBOgANZ0bNKoqBiMcSMvnxDXbP3z7WEYhaZnF3BXti69ea+oN2vNcgaIo9GzrRccAPXvO5JBVZN4b1CgwoL0nPdt62vwH46FeLbmjkx9P7UzjQmaR6RCyQtnh4D6hvnRrns23lQAAH8FJREFU6+PQHwytBuJDPOndzrb1hZrblb3PBUnc5h3XlTj1VaDNTcUGXGpUuZFv4G8/Ztol+ZW7VWjkbz9mceRyAaUG8z1Ke355Aj21PBjnT5+2Hug0ZbcZBHlq6dbKg17tvOwWu0OAni0PRTGzX0v0OgWdBgI8dUyIa0n3dr4O+8HQKhDgoeHBbv70CbFffaH6K5AdcQWgxG3ecV2B9AAdTFEULmWXkPJLESevFdXrghFrqcChSwXklxi5I8wLnQUPCrcFjaIQH+pNpyA913JL6dJKz9lP7B9bq1GYPaANXVp7s/X4LSJbejn08WLBXlq6ttLTrZWHXR9SUFHFH0lH/jBK3OYdt7mTBNgIig0qZ39xTPKrKLvIiNGIw/v9Lb11BHtpHf7F7RTkQbc23tjgtGq9uGkVOgfrHZb8hBDWkUOgQgghXJIkQCGEEC5JEqAQQgiXJAlQCCGES5IEKIQQwiVJAhRCCOGSJAEKIYRwSZIAhRBCuCRJgEIIIVySJEAhhBAuSRKgEEIIlyQJUAghhEuSBCiEEMIlSQIUQgjhkiQBCiGEcEmSAIUQQrgkSYBCCCFckiRAIYQQLkkSYCNo6aVlYjd/WnppHRZTp4FurfS4aRWHxSynqqrDY5YaVY5fLaDY6Ni4WqVsO7u7yHaWuM0/bnOma+wCuBpVVfF21+ANTIz158iVAv59qQCjHdt2ax8dd0X54KHTmMoAoCj2/5GuGEtVVYd8ia/mlrLnTA75JY7NfsFeWu6O9sXLXWOqLzTOdpa4ElfUTRKgg1TXaHVa6N3Wk8ggd3afyeVWocGmMTUKDAz1JK61J7pKPZLyZGSvL1HFRFceo/x/e8U1GFW+S8/nh4xCSh24s6wA8SGe9G7niVZjXt/G2s4SV+KKukkCdIDa9th0WoVATy2Tuvtz6GI+R64U2iRmCy8td0X54uOuqZL8ystSsUdmyy9SbeusmARtGfdmfim7zuSQW2R0aPIL9NByV7QPfnqt021nV4tb1zwSV1TWJBLg1q1beeedd7hy5QpRUVEsWLCAgQMH1rrM8ePH+eCDDzh69ChpaWmMGzeOZcuWVZkvJiamyrQePXqwdevWBpe7ur236iiKgpsW4tt7EdVCz56zOWQXWXf4TgH6tPOgb4gXOk3dccvLaYu9yfrU11ZxjarKkctlh5FLHXy+r1dbD/q3d43t3BTilr8vcYWlnD4B7tq1iyVLlvD444/Tp08ftm/fzpw5c0hKSiI6OrrG5Y4cOcLhw4fp0aMHeXl5tcaYMWMGCQkJptfe3t4NLrc1e2ZuWoVgLy2T4wL4Ki2Pn64V1Sumv4eGu6J88ffQ1OtiF1vstVuzbEPjZhUa2HMmh1uFBocmP1992XYO9NS6xHaWuM0/rqty+gS4atUqxo0bx7x58wCIj4/n1KlTrF+/ntdff73G5aZMmcLUqVMBmDBhQq0xQkJC6Nmzp03KW5+9t+poNAoa4I6O3sS00PPPcznkldR9TC+utZ7bOnij1YDGirjW7rU3tL7WxFVVleNXCzl4IR+DERx5bVzXlnoGh3mjVco+q/pqSttZ4jb/uK7OqW+DSE9P5/z584wZM8Y0TaPRkJCQwFdffVXrshqN46tWcQ+soQ3QTavQ1k/Hwz0DiGrhXuN8Pu4aEmP9uK2DN25axarkV1HlL1JtbFlfS+PmFhvY9lM2By/kU+rA5OflpjC+iy+Dw/67na1IfhU5+3aWuM0/rnDyBJiamgpAeHi42fSIiAgyMzO5efOmTeK8+eabdO3alf79+7No0SIyMzPrtby9Dj9oFAV3rYYR4T7cE+OLh8583Z2D9TzcI4BW3jqb3t9X8YtY3ZfIXvWtLa6qqpy+Vsj7xzK5mlvq0EOeUS3cmdIzgHZ+bg7dzhWnO2o7S1zXiCvKOPUh0KysLAD8/PzMpvv7+5veDwoKalCM8ePHM2zYMIKCgjhx4gSrV68mJSWFjz/+GK227hvVHXHc3U2rEOrvxpSeAXx2LpereaUkRPrQxse2P8iVVXeC3RH1rRy3oMTIZ+dyuJTj2MTnoVO4M8KH9jZOfJU5w3au+FriNt+4wpzDE2BOTg7Xrl2rc76IiAjT35U/OFt+oBWvDO3Xrx/h4eHMnj2bL7/8kjvvvLPWZR3ZsLQaBa1GYXSUL4pS1nVv6GE4S1S3J+mI+pbHyC4s5aPj2ZQYVBx5kae3m4bJPfxx++92t7fG3s6O/qGUuI6PK6pyeAJMTk7m+eefr3O+lJQUU08vOzsbX19f03vZ2dlA1Z6hLQwePBgvLy9++uknCxKgY5JQRW5apVFOcpfvTTo6blaRiopjkx+UXVGrAYckv4oaaztL3OYfV1Tl8ASYmJhIYmKiRfOWn/tLTU0lJCTEND01NZWAgIAGH/6sTnlDkQYjZJ9ZiObNqc8BhoaGEhYWRnJyMnfccQcARqPR7LWt7d+/n/z8fGJjY+2yfiGEaOomxLVt7CLYhFMnQID58+ezYMECQkJC6N27Nzt27CAtLY0VK1aY5jl06BDTpk1j06ZNxMfHA3Dz5k0OHToElB0yvXTpEsnJyQCMHj0agC1btnDixAkGDhxIYGAgJ0+eZM2aNcTFxTF06FDHVlQIIYRDOX0CHDt2LPn5+WzYsIHVq1cTFRXFunXrzJ4Co6oqBoPB7ETv2bNneeKJJ0yv09PTTQkxJSUFgA4dOvDJJ5+wd+9ecnNzCQ4OZty4cTzxxBMWXQEqhBCi6XL6BAjwwAMP8MADD9T4fv/+/U1JrbZplQ0cOLDOZ4oKIYRonpz6RnghhBDCXiQBCiGEcEmSAIUQQrgkSYBCCCFckiRAIYQQLkkSoBBCCJckCVAIIYRLkgQohBDCJUkCFEII4ZIkAQohhHBJkgCFEEK4JEmAQgghXJIkQCGEEC5JEqAQQgiXJAmwARQFszEIHaE8nqPjlhiMqCoYHRzXx13BqILi0KhQUGJEq1Fc5vOVuK4RV5iTBNgAilL2s+yIxqWqqimOI+MaVZUSg8qBtHze/yGTm/kGSgyOq2+Ah5bJcQG08NKic1Br1WmgW2s9WuXXsthbY32+Etd14oqqmsSAuM5MUZRqG7ctVbduR8QtMahkFhrYfSaH7CIjAB8dz6J3Ow/iQ7zQahxTX38PLZO6+3PkcgGHLhVgMNo8JFCW+Pz0Wu6K9iXQU2tWnub4+Upc14srzEkCtIGKe3SqqtqswVXcc6tunfaMazDCvy/mc+RKIRX3H1Xg8OVCzt8q4a5oX3zcNei09q+vRlHoG+JFp0B3dp/JIbfISKkNd2y1CvRu60G/9l5oKsVujp+vK8WtGFviiorkEKgN2fLQRsUGXFcjtmXcsl6fkS0nsjhcKflVdKPAwAc/ZnIso4BSQ8MPs1ha3xZeOibHBdCjjQe2yLs6Bfz0Gh7o5k//UO8qya+iyj9YDWHt5ytx6x+3PLlIXFGZ9ABtrHIyqm8jrGsv2Z5xS43wQ0YB310swGjB99CowjfpBaTeLGFMtA+eOgWdtv77VPUts1ajcFtHbyJb6Nl9Jof8EiPWnJbUKdCttQe3dfBCq7Esti322q35jCSuxLVHXFcnPUA7saZXVp+9ZFvGLTUYyS02sv1kFt+kW5b8KrqaV8rmY5mcul5crwtkGrq32spHx8M9A4htpa/XBTJaBbzdFMZ39eOOMG+Lk19F1n6+Dd07d+W4lsaWuMJS0gO0o/r0ymy591afuKUGlZPXiziQlm9VL6qcQYX/O5/H2ZtFjI7yRa9Vak0stqqvTqMwpJMPkS307DmbQ3GpWms9dBqIbuHO4DAf3Bp4DLUpfL7NLW5dvSOJK+pDeoAOUNvesz333mqLW2pUyS828unpbPadb1jyq+hSdinvHcvk3I2ianuD9qpviJ8bj/QMJLKFe7W9Qa0CHjqFsTF+jIjwbXDyq8gZP1+JK3FF3aQH6CDV7T07Yu+turglBpX/3Czi/37Oo8QOtxSUGFT2/iePsBvFjIz0wU2jmN1Ubq/6umsVRkX6cv5WMXvP5VJqLOsN6jTQKdCdYZ280dvpZkJn+nwlrsQVlpEeoINVbNCO3HtTFIUig0pBiZE9Z3P47D/2SX4Vnc8sYfOxTK7klDi0vmGB7jzSK4COAW7otQoJkb5lh2UdcCd9Y36+rhq34muJK+pDeoCNoOJenCNdzS0l+WwOxQbHxSwsVfn3pQJaeetw1znuS+uh03B3jJ/VV9Q1RGN9vq4at7E+X1eJ25xJD1AIIYRLahIJcOvWrYwaNYru3bszYcIEvvnmmzqX+eijj5g+fTq33XYbffr04cEHH+TAgQNV5lNVlbVr1zJkyBDi4uJ46KGHOHXqlD2qIYQQwok4fQLctWsXS5Ys4b777mPDhg1ERkYyZ84czpw5U+tya9eupX379rzyyiusWrWKjh07MnPmTD7//HOz+davX8/q1auZNWsWa9euxcvLi2nTpnH9+nV7VksIIUQjc/pzgKtWrWLcuHHMmzcPgPj4eE6dOsX69et5/fXXa1xu+/btBAUFmV7ffvvtpKWlsWnTJkaMGAFAUVER69evZ/bs2Tz88MMA9OzZk+HDh/P+++/z+9//3o41E0II0ZicugeYnp7O+fPnGTNmjGmaRqMhISGBr776qtZlKya/cl26dOHmzZum10eOHCE3N9ds/V5eXgwbNqzO9QshhGjanDoBpqamAhAeHm42PSIigszMTLNkZoljx44RERFhtn6tVktYWFiV9ZfHFkII0Tw5dQLMysoCwM/Pz2y6v7+/2fuWSEpK4uTJk0yePNk0LTs7Gy8vL7Rardm8/v7+FBQUUFxcbG3RhRBCODmHnwPMycnh2rVrdc5XsadW+b6X+t4MeuLECV599VUeeeQRBgwYYPZedeuQm02FEKL5c3gCTE5O5vnnn69zvpSUFFNPLzs7G19fX9N72dnZQNWeYXXS09OZM2cOAwYMYOHChWbv+fn5kZeXh8FgMOsFZmdn4+npiZubm0V1EkII0fQ4PAEmJiaSmJho0bzl5/5SU1MJCQkxTU9NTSUgIKDaC10qunHjBo8++ijt2rXjjTfeqHKoMzw8HIPBQFpamtl5xtTU1CrnHW2tMXqXHQPcmdOvhcPjVvTSSy85NF5j9eIlrsRtynHnjIh2SJzG5tTnAENDQwkLCyM5Odk0zWg0kpyczB133FHrsnl5ecyaNQuAdevW4enpWWWe3r174+PjY7b+goICvvzyyzrXL4QQomnTvuToXfJ6CgwMZNWqVWg0GgwGA2+//Tbff/89y5cvp0WLst7MoUOHGDlyJP369TP1FOfOncuPP/7I4sWLAcjIyDD9a9OmDQA6XVkHeO3atabDoUuXLiUjI4Ply5fj5eXVCDUWQgjhCE5/I/zYsWPJz89nw4YNrF69mqioKNatW0d09K9ddFVVMRgMZg/kPXjwIAB/+MMfqqwzJSXF9Pfs2bMxGo2sW7eOzMxMunXrxl//+leCg4PtWCshhBCNTVEb4zHuQgghRCNz6nOAQgghhL1IAhRCCOGSJAE2ImuGeTp+/DgLFy4kISGBzp07V7m3sbGcO3eOqVOn0qNHDwYNGsTKlSsxGOoeeTcnJ4dFixbRr18/+vTpw9NPP82tW7ccUOL6s6aOxcXFLF++nMmTJxMXF0dMTIyDSms9a+r5448/smjRIkaOHEmPHj1ISEjgrbfeoqioyEGlrh9r6nj27FkeffRRBg0aRLdu3Rg6dCjPPfecRQ/2EM7J6S+Caa7Kh3l6/PHH6dOnD9u3b2fOnDkkJSWZXeBT2ZEjRzh8+DA9evQgLy/PgSWuWVZWFtOmTSMyMpLVq1dz4cIFli9fjtForHNEjSeffJKff/6ZV199FY1Gw+uvv868efP429/+5qDSW8baOhYWFpKUlERcXBy9evXi22+/dWCp68/aeu7Zs4cLFy4wa9YsOnbsSEpKCitXriQlJYU333zTgTWom7V1zMnJoX379owbN45WrVpx8eJF3n77bX766SeSkpJMV5WLJkQVjWLUqFHqwoULTa8NBoM6duxY9emnn651OYPBYPp7/Pjx6rPPPmu3Mlpq7dq1at++fdWcnBzTtPXr16txcXFm0yo7cuSIGh0drR46dMg07YcfflCjo6PVgwcP2rXM9WVtHVVVVY1Go6qqqrp582Y1OjraruVsKGvreePGjSrTPvroIzU6Olq9ePGiXcpqrYZ8lpUdOHBAjY6OVk+cOGHrYgoHkEOgjaAhwzxpNM73ke3fv59Bgwbh4+Njmnb33XdTWFjIoUOHal0uODiYfv36mabFxcXRvn179u/fb9cy15e1dYSm9UxZa+tZ0/BjUPZEJmfSkM+ysoCAAABKSkpsWkbhGM73a+oCbD3MU2Or7tFx7dq1w9PTs9ZhpWp65JwzDkdlbR2bGlvW8+jRo2g0Grs/VrC+GlpHo9FIcXExqamprFixgu7duxMXF2ev4go7koPWjcCSYZ7qes6pM6n8sPJyfn5+pgeX13e5ixcv2rSMDWVtHZsaW9Xz+vXrrF27lvvuu8+sp+UMGlrHWbNmceDAAQBiY2PZsGGDUx6ZEXWTBGgjjTHMkzOpaVipuupS23BUzsbaOjY1Da1ncXExTz75JF5eXixatMjWxbOJhtTxhRdeICsri/Pnz7NmzRpmzZrFhx9+iF6vt0dRhR1JArQRRw/z5Ez8/PzIycmpMj03N7faPe2Ky1V3uDcnJ8fptoG1dWxqGlpPVVV59tlnOXfuHH/7299Mbd2ZNLSOYWFhAPTo0YO+ffsyYsQIdu7cycSJE21dVGFnkgBtxJHDPDmb8PDwKudOrly5Qn5+fq3nf8LDwzl8+HCV6ampqdx55502L2dDWFvHpqah9Xzttdf4/PPPeffdd82OdjgTW36WISEh+Pv7k56ebssiCgeRA9eNoCHDPDmjwYMHc+DAAXJzc03Tdu/ejYeHB/Hx8bUud/36db7//nvTtOPHj5Oens7gwYPtWub6sraOTU1D6rlu3Tref/99/vznP9O3b197F9VqtvwsU1NTyczMpH379rYupnAApx8Oqbmydpinmzdvsm/fPs6dO8eXX36Jqqp4eXlx7tw5IiMjG6UuUVFRbNmyhe+++45WrVrx9ddf87//+79MnTqVIUOGmOYbOXIkp0+fZsSIEQC0bduWY8eOkZSURNu2bfn555956aWXiIiI4Mknn2yUutTE2joC7Nu3j9OnT3P48GFOnjxJVFQU586dw9PT0+kO9Vpbz507d/Lyyy8zfvx4+vfvbzb8mLu7e7XjcTYWa+u4fPlyvv32W/Ly8rh+/Tr79u3j5ZdfJjAwkBdffBE3N7fGqpKwkhwCbSTWDvN09uxZnnjiCdPr9PR0071LFYd5ciR/f382bdrEK6+8wty5c/Hz82Pq1KnMnz/fbD6DwYDRaDSb9sYbb7B06VIWL16M0Whk2LBhPPfcc44svkUaUseXX36ZS5cumV6Xf35Lly5lwoQJ9i98PVhbz/Lhx7Zv38727dvN5nW2elpbx27durF582a2bt1KUVERbdu2ZdSoUcyePVvGDm2iZDgkIYQQLknOAQohhHBJkgCFEEK4JEmAQgghXJIkQCGEEC5JEqAQQgiXJAlQCCGES5IEKIQQwiVJAhROZfv27cTExJj+de3alcGDB7No0aJaR9soLCykb9++xMTE8N133zW4HG+++SYxMTFm02JiYnjzzTfNpu3cuZO77rqL7t27ExMTYxrGadOmTYwYMYLY2Ngq66nLxx9/bLYN8vLyGlYZJ5ednW1W3/fff7+xiyRchDwJRjil5cuXExYWRkFBAV9//TUbN27k2LFj/P3vf6/2kVP//Oc/TU/437ZtG/3797d5mbZs2UKbNm1Mr2/cuMGiRYsYNmwYL7/8Mm5ubrRq1YqTJ0+ydOlSfvOb3zB27Fh0Ouu+ZmvWrCEoKMipHiNmD97e3mzZsoWMjAyzpxwJYW+SAIVTiomJoUuXLgAMHDiQGzdusG3bNr7//nsGDhxYZf5t27bh7e1NbGwse/fu5cUXX7T5QKw9e/Y0e33+/HlKSkq455576Nevn2n6uXPngLIRQmJjY62O17VrV7OE21DFxcW4u7vbbH22otVq6dmzJ2lpaY1dFOFi5BCoaBLKE0l14wdevHiRQ4cOMXr0aCZPnkxBQQG7du2yeN1ffPEF9957L926dWP48OGsX7++2kF5Kx4CXbhwIZMnTwZg/vz5xMTEMGXKFKZMmcKCBQsAmDBhAjExMSxcuLDe9a3Jzp07mTZtGrfffjs9evRg7NixrF27luLiYrP5fvOb3zBhwgS++uorJkyYQPfu3XnnnXdM73/yySckJibSs2dPevfuzf3338+ePXtM7x88eJCHHnqI+Ph44uLiGD58OE8//bRZjMzMTF599VWGDh1q2narVq2itLTUbL7CwkJWrlxJQkIC3bp1Y8CAAcyYMYPTp0/bbLsIYQ3pAYomofxh0uWDkVa0bds2VFVlwoQJxMXFERAQwLZt25g0aVKd6z1w4ADz5s2jT58+vPHGG5SWlrJhw4ZqE21Fjz32GN27d+eVV17hD3/4A/369TP1OP/xj3+wZs0a02FcW47vmJaWxogRI5g+fTp6vZ7Tp0+zdu1a0tLSWLp0qdm8ly9fZsmSJfz2t78lNDTUNNjrn/70JzZu3MjYsWOZPXs2Hh4enDp1ynT+Mi0tjTlz5pCQkMDs2bPR6/VcuXKFAwcOmNadm5vLb37zG3Jzc5k7dy7h4eEcO3aM1atXc+XKFVNZSkpKmD59OidOnGDGjBn07duXoqIiDh8+zNWrV+ncubPNto0Q9SUJUDglg8FAaWkphYWFfPvtt3z00UeMHTu2yiFFo9HIjh07CAsLM41Bd88997B582aLhohauXIlrVq14t133zUdHhw0aJDZcEbV6dChg2ndnTp1Mjs82qFDB8D8MK6tPP7446a/VVWlb9+++Pr68sILL7B48WKzEc1v3brF2rVrzcp2/vx5/vrXvzJp0iReeeUV0/SK41CeOHGCkpIS/vjHP5qNcjB+/HjT35s2beLChQvs2LGDqKgooOxQtV6vZ/ny5cycOZOIiAh27NjBkSNHWLFiBWPHjjUt72wDHgvXJIdAhVO6//77iY2NpU+fPsybN48uXbqwbNmyKvN9/fXXXL582ezH+f777wfKeoa1yc/P5/jx4yQkJJidG/P19WXYsGE2qoltnT9/nmeeeYYhQ4YQGxtLbGwsixcvxmAwVDmH1rJlyyrnLQ8ePIjRaOTBBx+sMUbXrl1xc3Nj/vz57Nmzp9qrb/fv309sbCydOnWitLTU9K98IOPyQY6/+uorfH19zZKfEM5CeoDCKb3++uuEhYWRm5vLJ598wqeffsprr73GkiVLzObbtm0biqIwfPhwsrOzAQgJCSEqKopPP/2Up556qsaBSrOzs1FVleDg4CrvtWzZ0vaVaqCcnBwmT56Mr68vv/vd7+jYsSN6vZ6jR4/yP//zPxQWFprN36pVqyrruHXrFkCtF9d06tSJjRs38s4777Bo0SIKCgqIjo5m1qxZ3HvvvQD88ssvXLp0qcaLfMrj3Lp1i9atW1tVXyHsTRKgcEqRkZFmV4Hm5OTw4YcfMn78eOLi4gDIysriX//6F6qqcs8991S7nn379tV4uM3Pzw9FUfjll1+qvHf9+nUb1cR2vv76a27cuMFbb71F7969TdN/+ukni9cRGBgIQEZGRq3nJvv370///v0xGAwcP36cDRs2sGDBAtq0aUN8fDyBgYEEBATw8ssvV7t8edILCgri1KlTFpdPCEeSQ6CiSVi8eDE6nY6VK1eapu3cuZPi4mKefvpp3nvvPbN/GzduxN3dnaSkpBrX6eXlRVxcHHv37jW7ijI3N5cvv/zSrvWxhqIoAGY9WlVVa61jZYMGDUKj0fDRRx9ZNH/5LQrPPvssACkpKQAMHjyYtLQ0WrVqRffu3av8K+993nHHHeTk5PCPf/zD4jIK4SjSAxRNQmhoKJMmTeL999/n6NGj9OrVi23bthEUFMS0adOqvb9t1KhRJCcnc/369RoPaT7xxBPMnDmTGTNmMG3aNEpLS1m/fj1eXl5kZWXZtA5vvPEGa9eu5YMPPjBdsFMfffr0wcfHhxdffJHHH38cVVX58MMPTYd+LdGxY0dmzJjBO++8Q15eHmPGjMHT05OUlBSMRiMzZ87kgw8+4Pvvv2fIkCG0bduWvLw8PvjgA9zd3U0PGHj00UfZu3cvkydPZtq0aURGRlJcXMylS5fYt28fL7/8Mm3atOG+++5j27ZtLFq0iLNnz9KvXz+Ki4s5fPgw8fHxDBkypN7bQQhbkR6gaDIee+wxvL29WbVqFadPn+bkyZOMHz++xpu7J02aRGlpKTt27Khxnbfffjtvv/022dnZPPnkkyxfvpzRo0ebLqRxJi1atGDt2rVotVqeeuopXnrpJaKjo1m0aFG91rNgwQJeffVVUlNTeeqpp5g/fz67d++mffv2AHTu3JmioiL+8pe/MHPmTBYvXgzAxo0biY6OBsDHx4ctW7aQkJDA5s2bmTVrFgsWLCApKYnOnTvj5+cHlPVW3333XaZPn86uXbuYO3cuixcv5vTp0za9yV8IayhqdXf8CiEazccff8zzzz/PF198QevWra1+lFpTUlpayoULFxgzZgwvvPACDz/8cGMXSbiA5v/NEqKJGj58OABHjhzB29u7kUtjP9nZ2WaPkhPCUaQHKISTuXnzpunJN1D2GDiNpvmerTAYDJw8edL0OiQkxKZPzxGiJpIAhRBCuKTmu1sphBBC1EISoBBCCJckCVAIIYRLkgQohBDCJUkCFEII4ZL+PwbDyK9vGcS5AAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n", " gaia_coords.ra, gaia_coords.dec)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RA correction: -0.07898549040419311 arcsec\n", "Dec correction: -0.010563906203397977 arcsec\n" ] } ], "source": [ "delta_ra, delta_dec = astrometric_correction(\n", " SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]),\n", " gaia_coords\n", ")\n", "\n", "print(\"RA correction: {}\".format(delta_ra))\n", "print(\"Dec correction: {}\".format(delta_dec))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "catalogue[RA_COL] += delta_ra.to(u.deg)\n", "catalogue[DEC_COL] += delta_dec.to(u.deg)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_astcor_diag_plot(catalogue[RA_COL], catalogue[DEC_COL], \n", " gaia_coords.ra, gaia_coords.dec)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## IV - Flagging Gaia objects" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "catalogue.add_column(\n", " gaia_flag_column(SkyCoord(catalogue[RA_COL], catalogue[DEC_COL]), epoch, gaia)\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "46 sources flagged.\n" ] } ], "source": [ "GAIA_FLAG_NAME = \"cosmos_flag_gaia\"\n", "\n", "catalogue['flag_gaia'].name = GAIA_FLAG_NAME\n", "print(\"{} sources flagged.\".format(np.sum(catalogue[GAIA_FLAG_NAME] > 0)))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## V - Flagging objects near bright stars" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# VI - Saving to disk" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "catalogue.write(\"{}/COSMOS2015_HELP.fits\".format(OUT_DIR), overwrite=True)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python (herschelhelp_internal)", "language": "python", "name": "helpint" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }