{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook that demonstrates how to crossmatch the masterlist to a radio catalogue using a likelihood ratio method\n", "\n", "#### the likelihood ratio method used for this is done using the method outlined in Fleuren et al 2012\n", "\n", "This code is adapted from code from https://github.com/nudomarinero/mltier1 which was developed for crossmatching LOFAR sources to WISE and PanStarrs which is described in Williams et al 2018. The version used for Williams et al 2018 uses a more sophisticated version that uses the color of the sources as well as their magnitudes. This code uses the magnitude only portion of the code as this is sufficent for our purposes.\n", "\n", "---------------------------\n", "\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import astropy\n", "from matplotlib import pyplot as plt\n", "from astropy.table import Table, hstack\n", "from astropy import units as u\n", "from astropy.coordinates import SkyCoord, search_around_sky\n", "from IPython.display import clear_output\n", "from scipy.optimize import curve_fit\n", "import pickle\n", "import os\n", "from pymoc import MOC\n", "from pymoc.util import catalog\n", "import matplotlib.patches as mpatches\n", "\n", "from Q0_calc import Q0_calc\n", "from likelihood_ratio import likelihood_ratio\n", "from mltier import (get_center, get_n_m, estimate_q_m, Field, SingleMLEstimator, MultiMLEstimator,\n", " parallel_process, get_sigma, get_q_m, get_threshold, q0_min_level, q0_min_numbers)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# read in the radio catalogue\n", "lofar_data = Table.read('../dmu8/data/lockman-lofar-final_HELP.fits')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# read in the masterlist\n", "lockman_data = Table.read('../dmu32/dmu32_Lockman-SWIRE/data/Lockman-SWIRE_20180219.fits')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# read in the moc for the field used\n", "# alternatively a moc could be created that covers a specific subset of masterlist sources (such as IRAC sources).\n", "MOC_loc = '../dmu2/dmu2_field_coverages/Lockman-SWIRE_MOC.fits'\n", "MHz150_MOC = MOC(filename=MOC_loc)\n", "\n", "mask = ~np.isnan(masterlist['m_irac_i1'])\n", "irac_sources = masterlist[mask]\n", "irac_coords = SkyCoord(irac_sources['ra'],irac_sources['dec'])\n", "irac_lockman_moc = catalog.catalog_to_moc(irac_coords,10,11)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "MHz150_MOC = irac_lockman_moc" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.003459196584660735" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "MHz150_MOC.area" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "field area is defined by a MOC\n", "filtering in a MOC\n", "filtering in a MOC\n", "starting to find Q0. This will take a while\n", "finding Q0 with radius = 6.0 arcseconds\n", "finding Q0 with radius = 6.461538461538462 arcseconds\n", "finding Q0 with radius = 6.923076923076923 arcseconds\n", "finding Q0 with radius = 7.384615384615385 arcseconds\n", "finding Q0 with radius = 7.846153846153847 arcseconds\n", "finding Q0 with radius = 8.307692307692308 arcseconds\n", "finding Q0 with radius = 8.76923076923077 arcseconds\n", "finding Q0 with radius = 9.23076923076923 arcseconds\n", "finding Q0 with radius = 9.692307692307693 arcseconds\n", "finding Q0 with radius = 10.153846153846153 arcseconds\n", "finding Q0 with radius = 10.615384615384617 arcseconds\n", "finding Q0 with radius = 11.076923076923077 arcseconds\n", "finding Q0 with radius = 11.538461538461538 arcseconds\n", "finding Q0 with radius = 12.0 arcseconds\n" ] } ], "source": [ "# find the radius used for the crossmatching that gives the optimal value of Q0\n", "Q0_r,search_rad_r = Q0_calc(lofar_data,lockman_data,'RA','DEC','ra','dec','m_irac_i1',min(lofar_data['RA']),max(lofar_data['RA']),min(lofar_data['DEC']),max(lofar_data['DEC']),6,12,14,moc=MHz150_MOC)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = search_rad_r\n", "y = Q0_r\n", "plt.plot(x,y)\n", "plt.xlabel('Radius (arcseconds)')\n", "plt.ylabel('Q0')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#select the maximum value of Q0 and the corresponding radius to use for the liklihood ratio crossmatch \n", "Q0 = max(Q0_r)\n", "search_rad = search_rad_r[Q0_r == Q0][0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "Q0 = 0.6974\n", "search_rad = 6.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### crossmatch\n", "perform the liklihood ratio crossmatching using the optimal Q0 and radius found above the positional errors in the masterlist are hard to compute because the masterlist has been compiled as a combination of several other catalogues. So i have assumed an error of 1 arcsecond. This will create a systematic bias in the liklihood of the crossmatches. However the cutoff threshold to select good matches is dynamic so this shouldn't create a problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "cut down the catalogues using the moc given (or with a rectangular area if no moc is given)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "field area is defined by a MOC\n" ] } ], "source": [ "ra_down, ra_up, dec_down, dec_up = min(lofar_data['RA']),max(lofar_data['RA']),min(lofar_data['DEC']),max(lofar_data['DEC'])\n", "field = Field(ra_down, ra_up, dec_down, dec_up, MHz150_MOC)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "filtering in a MOC\n", "number of radio sources being crossmatched is 2551\n" ] } ], "source": [ "# Filter the radio catalogue with the field\n", "radio = field.filter_catalogue(lofar_data, colnames=('RA','DEC'))\n", "print('number of radio sources being crossmatched is {}'.format(len(radio)))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "filtering in a MOC\n", "number of masterlist sources being crossmatched too is 2964418\n" ] } ], "source": [ "# Filter the masterlist with the field\n", "masterlist = field.filter_catalogue(lockman_data, colnames=('ra','dec'))\n", "print('number of masterlist sources being crossmatched too is {}'.format(len(masterlist)))" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "coords_masterlist = SkyCoord(masterlist['ra'], \n", " masterlist['dec'], \n", " unit=(u.deg, u.deg), \n", " frame='icrs')\n", "\n", "coords_radio = SkyCoord(radio['RA'], \n", " radio['DEC'], \n", " unit=(u.deg, u.deg), \n", " frame='icrs')\n", "\n", "catalogue_i = masterlist\n", "catalogue_i['m_irac_i1'][np.isnan(catalogue_i['m_irac_i1'])] = 0\n" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "image/png": 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XgY9V1T/Octlh0IIaoj6oh+X0TmPxile8YkstSwMtXHHFuFuQJt5IRxpJXkQvMC6uqsta+Yft1BLt++Otvg5Y0Lf6fODRLdTnD6hvoqrOqaqpqpqaO3fuKD+SJGkWo9w9FeA84J6q+nzfolXA9B1Qy4DL++ontbuoDgeeaqevVgNHJ9m7XQA/Gljdlj2d5PD2Wif1bUuSNAajnJ56G/BvgDuSfL/V/gg4A7gkycnAw8AJbdmVwLuAtcAzwAcAqmpDkk8DN7Vxn6qqDW36FOArwO7At9qXJGlMhg6NqvpbBl93ADhqwPgCTt3Mts4Hzh9QXwMcPGyPkqRty3eES5I6MzQkSZ0ZGpKkzgwNSVJnhoYkqTNDQ5LUmaEhSeps5GdPSdL2rutzxx4647jnuZPJ55GGJKkzQ0OS1JmhIUnqzNCQJHXmhXDt8PxwJWnb8UhDktSZoSFJ6szQkCR1ZmhIkjozNCRJnRkakqTODA1JUmeGhiSpM9/cp+2Wb9qTXngeaUiSOjM0JEmdGRqSpM68pqGJ47UKTSo/4c8jDUnSVpj4I40ki4EvAHOAc6vqjDG3pBF4FCFt3yY6NJLMAb4I/DawDrgpyaqqunu8nWkmw0DaOUx0aACHAWur6gGAJCuBJYCh8QIxDKSttyNf+5j00JgHPNI3vw5485h62Wr+gytpNtv634gXIoQmPTQyoFabDEqWA8vb7I+T3Dfk6+0H/GjIdZ9vk9wbTHZ/9ja8Se7P3mbImZ2Gba63V3ZZedJDYx2woG9+PvDozEFVdQ5wzqgvlmRNVU2Nup3nwyT3BpPdn70Nb5L7s7fhjNrbpN9yexOwKMmBSXYFlgKrxtyTJO20JvpIo6o2JvkQsJreLbfnV9VdY25LknZaEx0aAFV1JXDlC/RyI5/ieh5Ncm8w2f3Z2/AmuT97G85IvaVqk+vKkiQNNOnXNCRJE2SnDY0k5yd5PMmdfbXPJbk3ye1JvpFkr0nprW/Zf0hSSfabpN6SfDjJfUnuSvLH4+htc/0leUOSG5J8P8maJIeNqbcFSa5Jck/7PX201fdJclWS+9v3vSeot7HvE5vrrW/52PaJ2XqbhH1ilv+uw+8TVbVTfgHvAA4B7uyrHQ3s0qbPBM6clN5afQG9mwJ+AOw3Kb0BvwX8NbBbm99/wv67fgc4tk2/C7h2TL0dABzSpl8G/B1wEPDHwIpWXzGO/+9m6W3s+8TmemvzY90nZvm9TcQ+MUt/Q+8TO+2RRlVdB2yYUftOVW1sszfQe1/IC25Qb81ZwB8y4A2OL5TN9HYKcEZV/bSNefwFb6zZTH8F7NGm92TAe31eCFX1WFXd0qafBu6h99SDJcAFbdgFwPGT0tsk7BOz/N5gzPvELL1NxD4xS39D7xM7bWh08EHgW+NuYlqS9wB/X1W3jbuXAV4D/EaSG5P8TZI3jbuhGT4GfC7JI8CfAKeNuR+SLATeCNwIvLyqHoPeTg7sP77ONumt39j3if7eJm2fmPF7m7h9YkZ/Q+8ThsYAST4JbAQuHncvAEleAnwS+E/j7mUzdgH2Bg4H/gC4JMmgR8CMyynAx6tqAfBx4LxxNpPkV4GvAx+rqn8cZy8zba63Sdgn+ntrvUzMPjHg9zZR+8SA/obeJwyNGZIsA94NvK/aCb8J8OvAgcBtSR6id4rgliT/bKxdPWcdcFn1fA/4Bb3n20yKZcBlbfq/03t68lgkeRG9nffiqpru6YdJDmjLDwDGcipjM71NxD4xoLeJ2Sc283ubmH1iM/0NvU8YGn3S+8CnTwDvqapnxt3PtKq6o6r2r6qFVbWQ3v+Qh1TVP4y5tWnfBI4ESPIaYFcm60FyjwK/2aaPBO4fRxPtL83zgHuq6vN9i1bR24lp3y+flN4mYZ8Y1Nuk7BOz/DediH1ilv6G3yfGcUV/Er6ArwKPAT+n9z/cycBaeo9i/377+m+T0tuM5Q8xvrunBv3edgX+ArgTuAU4csL+u74duBm4jd753EPH1Nvb6V2AvL3v/7F3AfsCV7cd92pgnwnqbez7xOZ6mzFmLPvELL+3idgnZulv6H3Cd4RLkjrz9JQkqTNDQ5LUmaEhSerM0JAkdWZoSJI6MzQ0sZL8r3H3sC0kOTfJQW36j4ZYf+GgJx4P2culSV7VN//G9oTYY2aMe7Y9AfW2JLckeWurz03y7W3Ri7ZPhoYmVlW9dWYtyZxx9DKKqvq3VXV3m93q0NhWkrwOmFNVD/SVTwT+tn3v95OqekNVvZ7ec4n+C0BVrQceS/K2F6JnTR5DQxMryY/b9yPaZwL8JXBHq30zyc3tMwKW962zuP1lfFuSqwds8/1t3b9K8mCSDyX590lubZ8vsE8b97tJbmrb+Xp7/hdJfr2NuynJp2b0eG37S/7eJBdPP2uo1aeSnAHs3v6Cv3jmEUR6nwvxn9v0oe21rwdO7RszJ73PuLgpvc+4+HetfkCS69q270zyGwN+pe+j793mrb/3Au8Hjk7y4s38p9gDeKJv/pttW9oJGRraXhwGfLKqDmrzH6yqQ4Ep4CNJ9k0yF/gz4HfaX8gnbGZbBwP/um3zs8AzVfVG4HrgpDbmsqp6U9vOPfTeWQ7wBeALVfUmNn2c9BvpPUzvIOBVwC/9NV5VK3juL/gt/aP758BHquotM+onA0+1138T8LtJDmw/z+qqegPwenrv/J3pbfTeBdw//2BV/W/gWnrvFJ42HW73AucCn+5btgYYFEraCRga2l58r6oe7Jv/SJLb6H3GwwJgEb0nil43Pa6qBn0mCcA1VfV0O9XyFPBXrX4HsLBNH5zkfya5g95f1a9r9bfQe8AbwF8O6HFdVf2C3j/aCxlCkj2Bvarqb1rpor7FRwMnJfk+vcc/7EvvZ78J+EA7UvmX1fvshJkOANb3zZ8IrGzTK/nlU1TT4fZaYDFwYd9TWh8Hfm2Yn03bv13G3YDU0f+dnkhyBPBO4C1V9UySa4EXA6Hbh/H8tG/6F33zv+C5feIrwPFVdVuS9wNHbOV2n2XL+9dGfvkPt+nTQ7P9HAE+XFWrN1mQvAM4Drgoyeeq6sIZQ34y/Rrt2tDvAO9J77HnAfZN8rKZgVNV16f3Uapz6QXGi9u2tBPySEPboz2BJ1pgvJbeEQb0Ti/9Zjtdw/T1iSG9jN4F3xfxy+fvb6D3jy3A0iG2+/O2TYAfAvu3U2u70Xv8OFX1JPBUkre3cf2vvxo4ZXobSV6T5KVJXgk8XlV/Ru+ppocMeO17gFe36XcCt1XVguo9KfaV9B6fvcmnBrbf8Rzg/7TSa+g9iE87IY80tD36NvB7SW4H7qP3DzlVtb5dFL8sya/Q+6v4t4d8jf9I7/TPD+idtnpZq38M+Iskvw9cQe/01tY4B7g9yS1V9b4kn2qv8yBwb9+4DwDnJ3mGXlBMO5feaa9b2umi9fT+oT8C+IMkPwd+zHPXZvpd0cb9Nb1TUd+Ysfzr9D6c5yLaNY1WD7Csqp5t87/VtqWdkE+5lbZCu4vqJ1VVSZYCJ1bVknH31UWS3YFrgLf1BcAw27kOWFJVT2xxsHY4HmlIW+dQ4L+2v/KfpPe52duFqvpJktOBecDDw2yj3aH2eQNj5+WRhiSpMy+ES5I6MzQkSZ0ZGpKkzgwNSVJnhoYkqTNDQ5LU2f8DnjaUSWFCJnYAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "y = catalogue_i['m_irac_i1']\n", "bins = np.arange(12,28,0.5)\n", "plt.hist(y,bins=bins)\n", "plt.xlabel('irac magnitudes (AB)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "bin_list_i = np.arange(12., 28., 0.5) # Bins of 0.5\n", "\n", "center_i = get_center(bin_list_i)\n", "# get the mid point of each bin i.e if there is a bin from 1 to 4 then the centre will be at 2.5\n", "\n", "n_m_i = get_n_m(catalogue_i['m_irac_i1'], bin_list_i, field.area)\n", "# get a cumulative distribution of the magnitudes divided by the field area\n", "q_m_i = estimate_q_m(catalogue_i['m_irac_i1'], bin_list_i, n_m_i, coords_radio, coords_masterlist, radius=search_rad)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.965796910010799e-09" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "3923*np.pi*2.93E-7*(5/3600)**2" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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Q04vfYfmGXVzzkaG670TeJ7KEYmYJYDJwPlACTDSzknrVJgHb3H0ocAtwc9i2BJgAjADGAb8L91fnG8CSqGIXkYa5O7+dUc7gnl24UI/2lXqi7KGMBsrdfaW71wBTgPH16owH7guXpwJjLRiQHQ9Mcfdqd18FlIf7w8wGAhcAd0UYu4g0YMbSjSxat4OvnjWEhHonUk+UCWUAsCZjvSIsa7COuyeBSqDHftr+H/A9IL2vNzezq8yszMzKNm3adKCfQURC7s5vnn2TgYd24uJR9Q9lkWgTSkN/vngT6zRYbmYXAhvdfc7+3tzd73D3Uncv7dVLN12JHKwX39zMvIpKvnrWUDokdD2PfFCU/ysqgEEZ6wOBdY3VMbN8oAjYuo+2ZwAXmdlqgiG0j5jZn6MIXkTe4+789tk36VfUkUtOUu9EGhZlQpkNDDOzwWZWQHCSfVq9OtOAy8PlS4EZ7u5h+YTwKrDBwDBglrtf7+4D3b043N8Md/9shJ9BRIBXV26h7K1tfOWsIRTm63kn0rD8qHbs7kkzuwaYDiSAe9x9kZndAJS5+zTgbuB+Mysn6JlMCNsuMrNHgMVAErja3VNRxSoi+/bbZ8vp3a2Q/ywdtP/K0m5Z0CFo20pLS72srCzuMERapbLVW7n09lf50QXH8IUPHRl3ONJCzGyOu5c2p43OrInIPv1mRjk9uhTwmVOOiDsUyXFNSihmdpqZTTaz+Wa2yczeNrMnzexqMyuKOkgRicfcNdt5cfkmvvChI+lUoHMnsm/7TShm9hTwBYJzIeOAfgR3vv8I6Ag8YWYXRRmkiMTjthlvckjnDnzuNPVOZP+aclL+c+6+uV7ZLuD18OdXZqZnf4q0MYvX7eBfSzZy7TnD6VoY2fU70obs939J/WRiZt0z27n71gYSjoi0cne+tJLOBQkuO6047lCklWjynx1m9iXgBmAv793x7oAu+xBpY9Zt38vf5q3jstOKKercIe5wpJVoTj/2O8AI9UZE2r57X16FA1eOKY47FGlFmnPZ8ApgT1SBiEhu2FFVy0Oz1nDBsf0YeGjnuMORVqQ5PZTrgVfMbCZQXVfo7l/PelQiEpuHZr7NruokV31Yo9nSPM1JKH8AZgAL2M/U8SLSOtUk09z78mpOH9KDkQN0i5k0T3MSStLdvx1ZJCISu7/NW8c7O6q46ZJj4w5FWqHmnEN5LnxoVT8zO6zuJ7LIRKRFuTt3vrSSo/p048zheoaQNF9zeiifDl+vzyjTZcMibcSLb25m6Ts7+eUnjyd4ErdI8zQ5obj74CgDEZF43fHiCvp0L+Si4/vHHYq0Uk2Zy2vMfrZ3N7OR2QtJRFrawrWVvFy+hSvOGExBviYhlwPTlB7KJWb2C+CfwBxgE8GkkEOBs4EjgGsji1BEInfnSyvpUpBg4ujD4w5FWrGmzOX1LTM7lOARvZ8E+hJMv7IEuN3dX442RBGJ0trte/n7/PVccXoxRZ00zYocuCadQ3H3bWb2J4I75Ysz2o0FlFBEWrF7/r0KgCvG6DSpHJzmXOX1BLCdYMr6qmjCEZGWVLm3limz3ubjx/VjwCGd4g5HWrnmJJSB7j6uOTs3s3HArUACuMvdb6q3vRD4E3ASsAX4lLuvDrddD0wCUsDX3X26mXUEXgQKw9inuvt/NScmEXnPgzPfZndNii9qmhXJguZczvGKmTX59lkzSwCTgfMJnvA40cxK6lWbBGxz96HALcDNYdsSYAIwguApkb8L91cNfMTdjwdOAMaZ2anN+AwiEgqmWVnFmKE9GdFf06zIwWtOQhkDzDGzZeGz5ReY2fx91B8NlLv7SnevAaYA4+vVGQ/cFy5PBcZacEfVeGCKu1e7+yqgHBjtgV1h/Q7hjyMizfbE3LVs3Fmt3olkTXOGvM5v5r4HAGsy1iuAUxqr4+5JM6sEeoTlr9VrOwDe7fnMIbhsebK7z2xmXCLtXt00K0f37caHh+kJ3pIdzblT/q1m7ruhuRvq9yYaq9NoW3dPASeY2SHA42Y20t0XfuDNza4CrgI4/HBdWy+S6fnlm1i+YRe/0jQrkkVR3hJbAQzKWB8IrGusjpnlA0XA1qa0dfftwPME51g+wN3vcPdSdy/t1UsT3YlkuvPFlfTt3pGPa5oVyaIoE8psYJiZDTazAoKT7NPq1ZkGXB4uXwrMcHcPyyeYWaGZDQaGAbPMrFfYM8HMOgEfBZZG+BlE2pyFayt5ZcUWrjijWNOsSFY15xxKs4TnRK4BphNcNnyPuy8ysxuAMnefBtwN3G9m5QQ9kwlh20Vm9giwGEgCV7t7ysz6AfeF51HygEfc/e9RfQaRtuj2F1bQrTCfiadoKFiyK7KEAuDuTwJP1iv7ScZyFcF0Lg21vRG4sV7ZfGBU9iMVaR/e3rKHJxes54sfPpLuHTXNimSX+rsi7cidL60kPy+PK8/QNCuSfUooIu3Ell3VPFK2hk+MGkCf7h3jDkfaICUUkXbivldWU5NK60ZGiYwSikg7sLs6yX2vvsU5x/RhaO+ucYcjbZQSikg78PDsNVTureVLZw6JOxRpw5RQRNq42lSau/+9itHFh3HSEYfGHY60YUooIm3c3+evY+32vXzpTJ07kWgpoYi0Ye7OH15YyfA+XTn7qN5xhyNtnBKKSBv2/PJNLH1nJ1d9eAh5eZoEUqKlhCLSht3+/Ar6FXXkIk0CKS1ACUWkjXrj7W3MXLWVSWMGaxJIaRH6XybSRv3hhZV075jPhNGaBFJahhKKSBu0ctMupi9+h8tOK6ZrYaRzwIq8SwlFpA2686WVdEjkcfnpxXGHIu2IEopIG7NxZxV/mbOWT540kF7dCuMOR9oRJRSRNubel1eTTKf54od0I6O0LCUUkTZkZ1Utf37tLc4f2Y/inl3iDkfaGSUUkTbkoVlvs7MqqWlWJBZKKCJtRE0ymATy9CE9OG7gIXGHI+2QEopIG/H4GxVs2FGtKeolNpEmFDMbZ2bLzKzczK5rYHuhmT0cbp9pZsUZ264Py5eZ2Xlh2SAze87MlpjZIjP7RpTxi7QWVbUpfvNsOccNLOLDw3rGHY60U5ElFDNLAJOB84ESYKKZldSrNgnY5u5DgVuAm8O2JcAEYAQwDvhduL8kcK27HwOcClzdwD5F2p0HZr7N2u17+f64ozHTJJASjyh7KKOBcndf6e41wBRgfL0644H7wuWpwFgLjobxwBR3r3b3VUA5MNrd17v76wDuvhNYAgyI8DOI5LydVbVMfq6cMUN7csZQ9U4kPlEmlAHAmoz1Cj745f9uHXdPApVAj6a0DYfHRgEzsxizSKtz50ur2Lq7hu+NOyruUKSdizKhNNTv9ibW2WdbM+sK/AX4prvvaPDNza4yszIzK9u0aVMTQxZpXTbvquaul1ZywbH9dGWXxC7KhFIBDMpYHwisa6yOmeUDRcDWfbU1sw4EyeQBd3+ssTd39zvcvdTdS3v16nWQH0UkN902o5zqZJprzx0edygikSaU2cAwMxtsZgUEJ9mn1aszDbg8XL4UmOHuHpZPCK8CGwwMA2aF51fuBpa4+68jjF0k563ZuocHZr7Ff5YO4sheXeMOR4TI5rV296SZXQNMBxLAPe6+yMxuAMrcfRpBcrjfzMoJeiYTwraLzOwRYDHBlV1Xu3vKzMYAnwMWmNnc8K1+4O5PRvU5RHLVr59ZTp4Z3xg7LO5QRIAIEwpA+EX/ZL2yn2QsVwGfbKTtjcCN9cr+TcPnV0TalSXrd/DXuWv50oeH0LeoY9zhiAC6U16kVfrl9GV0K8znK7orXnKIEopIKzN79VaeXbqRL581hKLOHeIOR+RdSigirYi7c/NTS+ndrZArTh8cdzgi76OEItKKzFi6kbK3tvGNjw6jU0Ei7nBE3kcJRaSVSKWdX/xzGYN7duE/Swftv4FIC1NCEWklnpi7lmUbdnLtucPpkNChK7lH/ytFWoHqZIpfPb2ckQO687GR/eIOR6RBSigircCDGdPT5+XpVizJTUooIjluV3WS22aUc/qQHozR9PSSw5RQRHLcHS+sYMvuGj08S3KeEopIDlu8bge/f2EF40/oz/GDND295DYlFJEcVZtK851H51HUqYCffnxE3OGI7Fekk0OKyIGb/Fw5i9fv4A+fO4lDuxTEHY7IfqmHIpKDFq2r5LYZ5Vx8Qn/OG9E37nBEmkQJRSTH1CTTXPvIPA7tUsBPL9JQl7QeGvISyTG3PVfO0nd2cudlpRzSWUNd0nqohyKSQxaurWTyc+X8x6gBnFPSJ+5wRJpFCUUkR1QnU3zn0Xn06FLAf+mqLmmFNOQlkiN++2ww1HX35aV6cJa0SuqhiOSA+RXb+f0LK7jkxIGMPUZDXdI6RZpQzGycmS0zs3Izu66B7YVm9nC4faaZFWdsuz4sX2Zm52WU32NmG81sYZSxi7SUuqGunl0L+MnHS+IOR+SARZZQzCwBTAbOB0qAiWZW/2iZBGxz96HALcDNYdsSYAIwAhgH/C7cH8AfwzKRNuHWf73J8g27uOmS4yjqpKEuab2i7KGMBsrdfaW71wBTgPH16owH7guXpwJjLZj9bjwwxd2r3X0VUB7uD3d/EdgaYdwiLWbumu3c/sIKPnnSQM4+qnfc4YgclCgTygBgTcZ6RVjWYB13TwKVQI8mtt0nM7vKzMrMrGzTpk3NDF0kelW1wVBXn+4d+dGFGuqS1i/KhNLQPNvexDpNabtP7n6Hu5e6e2mvXr2a01SkRfxy+jLKN2qoS9qOKBNKBTAoY30gsK6xOmaWDxQRDGc1pa1Iq3Xvy6u469+r+Oyph3PmcP3BI21DlAllNjDMzAabWQHBSfZp9epMAy4Ply8FZri7h+UTwqvABgPDgFkRxirSYv76xlp+9rfFnFPSR9PSS5sSWUIJz4lcA0wHlgCPuPsiM7vBzC4Kq90N9DCzcuDbwHVh20XAI8Bi4J/A1e6eAjCzh4BXgaPMrMLMJkX1GUSybcbSDVz76DxOPfIwfjtxFPkJ3QombYcFHYK2rbS01MvKyuIOQ9q5Wau28rm7ZzK8Tzce/OIpdOuo8yaSu8xsjruXNqeN/jwSaQGL1+1g0n2zGXBIJ/54xclKJtImKaGIRGz15t1cds8suhbmc/8XTqFH18K4QxKJhBKKSIQ27Kjis3fPJJVOc/+k0Qw4pFPcIYlERglFJCLb99Rw2d2z2La7hj9eMZqhvbvFHZJIpDR9vUgE9tQkufKPs1m1eTf3XnEyxw86JO6QRCKnHopIltUk03z5z68zd812fjPxBM4Y2jPukERahHooIlm0s6qW7z46nxeXb+LmS45l3Mh+cYck0mKUUESy5JXyzXx36nzWV+7lxxeW8KmTD487JJEWpYQicpD21CS5+aml3PfqWxzZswtTv3I6Jx5+aNxhibQ4JRSRgzDnra1c+8g8Vm/ZwxVnFPO9846mU0Fi/w1F2iAlFJEDUJ1Mccszb3LHiyvoV9SJh754KqcN6RF3WCKxUkIRaaaFayv59iNzWb5hFxNHD+KHF5TQtVCHkoiOApEmqk2lmfxcObfNKKdH1wLuveJkPbZXJIMSish+7KpO8o/56/jjK2+xZP0OLj6hPz+7aCRFnTXBo0gmJRSRBqTTzsxVW3l0zhqeWvAOe2tTHNmrC7//zImcf6zuLRFpiBKKSIaKbXv4y5y1TH19DWu27qVrYT4Xj+rPJ0sHMWrQIZhZ3CGK5CwlFGn3qmpTTF/0Do+UreGVFVtwh9OH9ODb5wxn3Ih+ugxYpImUUKRdSaedVVt2s3BtJQvXVrJgbSULKirZXZNi4KGd+MbYYVxy4kAGHdY57lBFWh0lFGmzUmln1eZdLFhbycK1O1iwtpLF63awqzoJQEF+Hsf07cYnThzAx47tx6mDe5CXpyEtkQMVaUIxs3HArUACuMvdb6q3vRD4E3ASsAX4lLuvDrddD0wCUsDX3X16U/Yp7YO7s3V3Desrq9iwo+rd13cqq3gnfK3Ytpe9tSkACvPzKOnfnf84cQAj+xcxckARw/p0pUNCE26LZEtkCcXMEsBk4BygAphtZtPcfXFGtUnANncfamYTgJuBT5lZCTABGAH0B/5lZsPDNvvbp7QSyVSaPbUp9lSn2FOTZHdtr6voAAALGUlEQVR1isq9te/+7Kiqff96+LNldw0bd1RTk0q/b395Br27daRvUUeG9OrKh4b1oqR/d44dUMSQXl3IV/IQiVSUPZTRQLm7rwQwsynAeCDzy3888NNweSpwmwWX0YwHprh7NbDKzMrD/dGEfbYr7o47pN1Jv/v63rKng9eUO+m0k0w7qbofD16TqaBN3bZkKk1tyqlNp0mmwvW0U5tMk0wH25KpNDWpNNW14WsyTU0yTXUyRXUycz1NVW2KvTUpdtck2VuTYk9NsF4/ITQkP88o6tSBok4d6N6pA0WdCyju2YW+RR3p1z1IHn2LOtG3e0d6di1Q0hCJUZQJZQCwJmO9AjilsTrunjSzSqBHWP5avbYDwuX97TNrLvztS+ytCYZMvK7Q3/cSfKG/uwxO8AVft95YnbTXbfew7L167u8lCue9JOFhm3fXM94jTmbBkFJhfoKC/DwK8/PC12C9U4c8enUr5PCCznTukKBzQYJOBfl0LqhbDl67FOQHyaNzmEA6dqBzQUKX6oq0ElEmlIa+Bep//TVWp7Hyhv78bPAr1cyuAq4COPzwA3suxdBeXalN+bvR1AVV9wX33vr7t1nGRsPe3V73vZhnFi6/f9v76wbLeWbkWQPrBK+YkQjL8vKCOsH6++vn5RmJvKBuIu/9P/l5RiIvj0ReUL9DIo8OiTzyE0aHvPA1kUeHhJGfyKNDXvBalzzy80xf+iISaUKpAAZlrA8E1jVSp8LM8oEiYOt+2u5vnwC4+x3AHQClpaUH9Hf8/00YdSDNRETapSgHnGcDw8xssJkVEJxkn1avzjTg8nD5UmCGu3tYPsHMCs1sMDAMmNXEfYqISAwi66GE50SuAaYTXOJ7j7svMrMbgDJ3nwbcDdwfnnTfSpAgCOs9QnCyPQlc7e4pgIb2GdVnEBGRpjPPhbO6ESstLfWysrK4wxARaTXMbI67lzanja6xFBGRrFBCERGRrFBCERGRrFBCERGRrFBCERGRrGgXV3mZ2SbgrSZU7Qlsjjicg5Hr8UHux5jr8YFizIZcjw9yP8aj3L1bcxq0i+ehuHuvptQzs7LmXibXknI9Psj9GHM9PlCM2ZDr8UHux2hmzb7XQkNeIiKSFUooIiKSFUoo73dH3AHsR67HB7kfY67HB4oxG3I9Psj9GJsdX7s4KS8iItFTD0VERLKiXSYUM7vHzDaa2cKMsv81s6VmNt/MHjezQ3Itxoxt3zEzN7OeccSWEUeDMZrZ18xsmZktMrNf5FJ8ZnaCmb1mZnPNrMzMRu9rHxHHN8jMnjOzJeHv6hth+WFm9oyZvRm+HpqDMebM8dJYjBnbYz1e9hVfDh0rjf07N+94CR41275+gA8DJwILM8rOBfLD5ZuBm3MtxrB8EMH0/W8BPXMtRuBs4F9AYbjeO8fiexo4P1z+GPB8jPH1A04Ml7sBy4ES4BfAdWH5dXH+X9xHjDlzvDQWY7ge+/Gyj99hLh0rjcXYrOOlXfZQ3P1FguevZJY97e7JcPU1gqdBxqahGEO3AN+jkUcft6RGYvwKcJO7V4d1NrZ4YKFG4nOge7hcRCNP/GwJ7r7e3V8Pl3cCS4ABwHjgvrDafcDF8UTYeIy5dLzs4/cIOXC87CO+XDpWGouxWcdLu0woTXAl8FTcQdRnZhcBa919Xtyx7MNw4ENmNtPMXjCzk+MOqJ5vAv9rZmuAXwLXxxwPAGZWDIwCZgJ93H09BAc60Du+yN5TL8ZMOXO8ZMaYi8dLvd9hTh4r9WJs1vGihFKPmf2Q4CmRD8QdSyYz6wz8EPhJ3LHsRz5wKHAq8F3gETOzeEN6n68A33L3QcC3CJ4aGisz6wr8Bfimu++IO56GNBZjLh0vmTESxJRTx0sDv8OcO1YaiLFZx4sSSgYzuxy4EPiMh4OGOWQIMBiYZ2arCYYYXjezvrFG9UEVwGMemAWkCeYsyhWXA4+Fy48CsZ2UBzCzDgQH8APuXhfXBjPrF27vB8Q2FBLG0FCMOXW8NBBjTh0vjfwOc+pYaSTGZh0vSighMxsHfB+4yN33xB1Pfe6+wN17u3uxuxcT/Gc80d3fiTm0+v4KfATAzIYDBeTWBHjrgDPD5Y8Ab8YVSPjX6N3AEnf/dcamaQQHMuHrEy0dW53GYsyl46WhGHPpeNnHv3POHCv7iLF5x0tcVxXE+QM8BKwHagn+o00CyoE1wNzw5/Zci7He9tXEf5VXQ7/HAuDPwELgdeAjORbfGGAOMI9gjPikGOMbQ3DSc37G/7uPAT2AZ8OD91ngsByMMWeOl8ZirFcntuNlH7/DXDpWGouxWceL7pQXEZGs0JCXiIhkhRKKiIhkhRKKiIhkhRKKiIhkhRKKiIhkhRKKtFpm9krcMWSDmd1lZiXh8g8OoH1xQ7NS76dNp3C6j0RG2bfMrMrMijLKzjKzynC22flm9i8z6x1uu9DMftbceKXtUkKRVsvdT69flvkF2Vq4+xfcfXG42uyEcoCuJLhLO5VRNhGYDXyiXt2X3P0Edz8u3H51WP4P4KJwWiARJRRpvcxsV/h6VvgshweBBWHZX81sTvhsh6sy2owzs9fNbJ6ZPdvAPj8ftv2bma0ys2vM7Ntm9kb4XIjDwnpfNLPZ4X7+UvelamZDwnqzzeyGejE+b2ZTLXiOyAN18zaF5aVmdhPQKewNPFC/52HBcz1+Gi6fFL73q7z3BY+ZJSx4VsnssEfxpUZ+fZ8h4w58MxsCdAV+RJBYGvp9G8HU5tsAPLiJ7XmC6VdElFCkzRgN/NDdS8L1K939JKAU+LqZ9TCzXsCdwCXufjzwyUb2NRL4dLjPG4E97j4KeBW4LKzzmLufHO5nCcFd+AC3Are6+8l8cKrvUQQTF5YARwJnZG509+uAvWFv4DP7+bz3Al9399PqlU8CKsP3Pxn4opkNzqxgZgXAke6+OqN4IsHMAi8BR9UNa4U+ZGZzgbeBjwL3ZGwrAz60n1ilnVBCkbZilruvylj/upnNI3hWxyBgGMGsri/W1XP3hp43A/Ccu+90901AJfC3sHwBUBwujzSzl8xsAcFf+yPC8tMIJtEDeLCBGCvcPU0wtUUxByA8x3GIu78QFt2fsflc4LIwAcwkmMZlWL1d9AS21yubAEwJY3uM9yfbuiGvQQSJLPPJghuB/gfyOaTtyY87AJEs2V23YGZnEfwlfZq77zGz54GOgNG0By1VZyynM9bTvHfM/BG42N3nmdnngbOaud8U+z/+krz/j76O4eu+PocBX3P36fvY796MfWFmxxEknWfCUbgCYCUwuYG20whmpM2Mae8+3kvaEfVQpC0qAraFyeRogp4JBENWZ9YNAdWdDzlA3YD14ZTfmcNTrwGXhMsTDmC/teE+ATYAvcPhukLCcxXuvh2oNLMxYb3M958OfKVuH2Y23My6ZL6Bu28DEmZWl1QmAj/1cGZed+8PDDCzIxqIbwywImN9OMHkhiJKKNIm/RPIN7P5wM8JvuQJh7CuAh4Lh8MePoj3+DHBkNIzwNKM8m8C3zazWQTP6a5s5n7vAOab2QPuXgvcEL7P3+u9zxXA5PCkfGYP4S5gMcGzPxYCf6DhntDTBMkBgsT3eL3tj/NeQvxQeKHAPOBzwLUZ9c4muNpLRLMNi2RTeLXXXnd3M5sATHT38XHHVZ+ZjQK+7e6fO4h99AEedPex2YtMWjOdQxHJrpOA28JLbLcT3O+Rc9z9jfBS60S9e1Ga43De31uRdk49FBERyQqdQxERkaxQQhERkaxQQhERkaxQQhERkaxQQhERkaxQQhERkaz4/8Gf/SQIRa+gAAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(center_i,n_m_i)\n", "plt.xlabel('irac magnitude (AB)')\n", "plt.ylabel('n(m)')\n", "plt.show()\n", "plt.plot(center_i,q_m_i)\n", "plt.xlabel('irac magnitude (AB)')\n", "plt.ylabel('q(m)')\n", "plt.show()\n", "plt.plot(center_i,q_m_i/n_m_i)\n", "plt.xlabel('irac magnitude (AB)')\n", "plt.ylabel('q(m)/n(m)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### $Q_0$ and likelihood estimators" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Q0 = Q0\n", "likelihood_ratio_i = SingleMLEstimator(Q0, n_m_i, q_m_i, center_i)\n", " \n", "idx_radio, idx_i, d2d, d3d = search_around_sky(\n", " coords_radio, coords_masterlist, search_rad*u.arcsec)\n", " \n", "idx_radio_unique = np.unique(idx_radio)\n", "\n", "radio[\"lr_i\"] = np.nan # Likelihood ratio\n", "radio[\"lr_dist_i\"] = np.nan # Distance to the selected source\n", "radio[\"lr_index_i\"] = np.nan # Index of the source in masterlist\n", " \n", "total_sources = len(idx_radio_unique)\n", "masterlist_aux_index = np.arange(len(masterlist))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Function to calculate the crossmatch with the greatest liklihood ratio\n", "def ml(i):\n", " \n", " idx_0 = idx_i[idx_radio == i]\n", " \n", " d2d_0 = d2d[idx_radio == i]\n", " \n", " i_mag = catalogue_i['m_irac_i1'][idx_0]\n", " print(i_mag)\n", " print(catalogue_i['f_irac_i1'][idx_0])\n", " \n", " \n", " radio_ra = radio[i][\"RA\"]\n", " radio_dec = radio[i][\"DEC\"]\n", " radio_pa = radio[i][\"PA\"]\n", " radio_maj_err = radio[i][\"E_Maj\"]\n", " radio_min_err = radio[i][\"E_Min\"]\n", " c_ra = catalogue_i[\"ra\"][idx_0]\n", " c_dec = catalogue_i[\"dec\"][idx_0]\n", " # Error for masterlist given as the true error is difficult to compute due to the \n", " # composite nature of the masterlist\n", " c_ra_err = 1/3600\n", " c_dec_err = 1/3600\n", " helpids = catalogue_i['help_id'][idx_0]\n", " \n", " sigma = get_sigma(radio_maj_err, radio_min_err, radio_pa, \n", " radio_ra, radio_dec, \n", " c_ra, c_dec, c_ra_err, c_dec_err)\n", " \n", " lr_0 = likelihood_ratio_i(i_mag, d2d_0.arcsec, sigma)\n", " \n", " if len(lr_0) == 0:\n", " result = [np.nan,np.nan,np.nan]\n", " return result,np.nan,np.nan\n", " chosen_index = np.argmax(lr_0)\n", " lr_sum = np.sum(lr_0)\n", " rel = lr_0/(lr_sum + (1-Q0))\n", " result = [masterlist_aux_index[idx_0[chosen_index]], # Index\n", " (d2d_0.arcsec)[chosen_index], # distance\n", " lr_0[chosen_index]] # LR\n", " helpid = catalogue_i\n", "\n", " \n", " return (result,rel[chosen_index],helpid)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "starting the crossmatch\n", " m_irac_i1 \n", "------------------\n", "21.737240063397174\n", " 0.0\n", " 0.0\n", " 21.4799607150159\n", "f_irac_i1\n", "---------\n", " 7.33\n", " nan\n", " nan\n", " 9.29\n", " m_irac_i1 \n", "------------------\n", "19.505371640786045\n", "21.066951358168758\n", " 0.0\n", "18.929160110359867\n", "19.173668020180635\n", "f_irac_i1\n", "---------\n", " 57.26\n", " 13.59\n", " nan\n", " 97.35\n", " 77.72\n", " m_irac_i1 \n", "------------------\n", " 0.0\n", "18.981247063192903\n", " 0.0\n", " 0.0\n", "f_irac_i1\n", "---------\n", " nan\n", " 92.79\n", " nan\n", " nan\n", " m_irac_i1 \n", "------------------\n", " 0.0\n", "21.053453915251275\n", " 0.0\n", "20.469101043689186\n", " 0.0\n", " 0.0\n", " 0.0\n", " 20.43911646465623\n", "f_irac_i1\n", "---------\n", " nan\n", " 13.76\n", " nan\n", " 23.57\n", " nan\n", " nan\n", " nan\n", " 24.23\n", " m_irac_i1 \n", "------------------\n", " 21.27986706721098\n", " 0.0\n", "19.773017400870167\n", " 0.0\n", "f_irac_i1\n", "---------\n", " 11.17\n", " nan\n", " 44.75\n", " nan\n", " m_irac_i1 \n", "------------------\n", " 0.0\n", " 21.05582365690811\n", " 0.0\n", " 0.0\n", "20.579050991403157\n", "f_irac_i1\n", "---------\n", " nan\n", " 13.73\n", " nan\n", " nan\n", " 21.3\n", " m_irac_i1 \n", "------------------\n", " 0.0\n", "21.367906938237063\n", "20.356320766991765\n", " 0.0\n", " 0.0\n", " 0.0\n", " 21.20839889082415\n", "f_irac_i1\n", "---------\n", " nan\n", " 10.3\n", " 26.15\n", " nan\n", " nan\n", " nan\n", " 11.93\n", " m_irac_i1 \n", "------------------\n", " 0.0\n", " 0.0\n", "22.623637474483466\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", "f_irac_i1\n", "---------\n", " nan\n", " nan\n", " 3.24\n", " nan\n", " nan\n", " nan\n", " nan\n", " nan\n", " m_irac_i1 \n", "------------------\n", " 0.0\n", " 0.0\n", " 0.0\n", "17.419863599567698\n", " 0.0\n", " 0.0\n", "f_irac_i1\n", "---------\n", " nan\n", " nan\n", " nan\n", " 390.89\n", " nan\n", " nan\n", "m_irac_i1\n", "---------\n", " 0.0\n", " 0.0\n", "f_irac_i1\n", "---------\n", " nan\n", " nan\n", 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0.0\n", " 0.0\n", "20.884284888968914\n", " 0.0\n", " 21.43083433768431\n", " 0.0\n", "f_irac_i1\n", "---------\n", " nan\n", " nan\n", " 91.09\n", " 33.7\n", " nan\n", " nan\n", " nan\n", " nan\n", " 16.08\n", " nan\n", " 9.72\n", " nan\n", " m_irac_i1 \n", "------------------\n", " 20.78256071402651\n", "22.348498653982183\n", " f_irac_i1 \n", "----------------\n", "17.6593906716877\n", "4.17446225345835\n", " m_irac_i1 \n", "------------------\n", "23.348468756480365\n", "19.961745866641188\n", " 0.0\n", "18.717991115342393\n", " 19.61422658182952\n", " 0.0\n", " 21.60462228647686\n", " 0.0\n", " 0.0\n", " 0.0\n", " f_irac_i1 \n", "----------------\n", "1.66192911958192\n", "37.6098545050722\n", " nan\n", " 118.25065468798\n", "51.7975677666769\n", " nan\n", "8.28230244143867\n", " nan\n", " nan\n", " nan\n", " m_irac_i1 \n", "-----------------\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", " 0.0\n", " 0.0\n", " 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"20.242508469929852\n", " 0.0\n", "f_irac_i1\n", "---------\n", " nan\n", " 30.7\n", " 31.29\n", " 10.02\n", " nan\n", " 29.97\n", " 29.04\n", " nan\n", " m_irac_i1 \n", "------------------\n", "21.201142481492745\n", " 21.42526359070306\n", "f_irac_i1\n", "---------\n", " 12.01\n", " 9.77\n" ] } ], "source": [ "print('starting the crossmatch')\n", "res = []\n", "rel = []\n", "helpid = []\n", "nomatches = 0\n", "for i in range(len(radio)):\n", " result,reliab,helid = ml(i)\n", " if result == [np.nan,np.nan,np.nan]:\n", " nomatches = nomatches + 1\n", " continue\n", " res.append(result)\n", " rel.append(reliab)\n", " helpid.append(helid)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of sources with no possible match is 1\n" ] } ], "source": [ "print('number of sources with no possible match is {}'.format(nomatches))\n", " \n", "(radio[\"lr_index_i\"][idx_radio_unique], \n", "radio[\"lr_dist_i\"][idx_radio_unique], \n", "radio[\"lr_i\"][idx_radio_unique]) = list(map(list, zip(*res)))" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "liklihood ratio threshold using Q0 only is 8.887837817719514e-85\n" ] } ], "source": [ "radio[\"lr_i\"][np.isnan(radio[\"lr_i\"])] = 0\n", " \n", "threshold_i = np.percentile(radio[radio['lr_i'] !=0][\"lr_i\"], 100*(1 - Q0))\n", "print('liklihood ratio threshold using Q0 only is {}'.format(threshold_i))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (15,6)\n", "plt.subplot(1,2,1)\n", "plt.hist(radio[radio[\"lr_i\"] != 0][\"lr_i\"], bins=200)\n", "plt.vlines([threshold_i], 0, 1000)\n", "plt.xlabel('liklihood ratio')\n", "plt.ylim([0,200])\n", "plt.subplot(1,2,2)\n", "plt.hist(np.log10(radio[radio[\"lr_i\"] != 0][\"lr_i\"]+1), bins=200)\n", "plt.vlines(np.log10(threshold_i+1), 0, 1000)\n", "plt.ylim([0,200]);\n", "plt.xlabel('log(liklihood ratio)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "radio[\"lrt\"] = radio[\"lr_i\"]\n", "radio[\"lrt\"][np.isnan(radio[\"lr_i\"])] = 0" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "updating threshold of liklihood ratio that can be trusted\n" ] } ], "source": [ "print('updating threshold of liklihood ratio that can be trusted') \n", "def completeness(lr, threshold, q0):\n", " n = len(lr)\n", " lrt = lr[lr < threshold]\n", " return 1. - np.sum((q0 * lrt)/(q0 * lrt + (1 - q0)))/(float(n)*q0)\n", " \n", "def reliability(lr, threshold, q0):\n", " n = len(lr)\n", " lrt = lr[lr > threshold]\n", " return 1. - np.sum((1. - q0)/(q0 * lrt + (1 - q0)))/(float(n)*q0)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "liklihood ratio updated threshold considering completeness and reliability is 0.1\n" ] } ], "source": [ "completeness_v = np.vectorize(completeness, excluded=[0])\n", "reliability_v = np.vectorize(reliability, excluded=[0])\n", " \n", "n_test = 100\n", "threshold_mean = np.percentile(radio[\"lrt\"], 100*(1 - Q0))\n", " \n", "thresholds = np.arange(0., 10., 0.01)\n", "thresholds_fine = np.arange(0.1, 1., 0.001)\n", " \n", "completeness_t = completeness_v(radio[\"lrt\"], thresholds, Q0)\n", "reliability_t = reliability_v(radio[\"lrt\"], thresholds, Q0)\n", "average_t = (completeness_t + reliability_t)/2\n", " \n", "completeness_t_fine = completeness_v(radio[\"lrt\"], thresholds_fine, Q0)\n", "reliability_t_fine = reliability_v(radio[\"lrt\"], thresholds_fine, Q0)\n", "average_t_fine = (completeness_t_fine + reliability_t_fine)/2\n", " \n", "threshold_sel = thresholds_fine[np.argmax(average_t_fine)]\n", "print('liklihood ratio updated threshold considering completeness and reliability is {}'.format(threshold_sel))" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/im281/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/astropy/table/column.py:965: RuntimeWarning: invalid value encountered in greater\n", " return getattr(self.data, op)(other)\n", "/Users/im281/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" ] }, { "data": { "image/png": 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7nT9/PscddxxHHHEE3/jGN/gqONj9/vvvZ/jw4YwePZqLLrqoTf+7SvOG9xjOsQOP5a8L/0qtq412OCLiIRojIOI9+12Bc859QPh9bbPDHAu95w7gjnbEJQe55cuX87e//Y2HHnqI6urqZq/79a9/zTvvvEOvXr3Yvn07Xbp04dZbb2Xp0qX83//9HwC/+MUvmDx5Mk888QTbtm1jwoQJnHLKKfzpT38iIyODJUuW8Mknn5Cf33Tw/Zo1a8jOzqZr165hP7+srIyxY8fym9/8hoqKCnJycnjnnXcYMmQI3/ve93j44Yc577zzmD17NsuWLcPM6pKwxrGHLFu2jEWLFrFr1y6GDh3KfffdxyeffMJPf/pTnn76aaZOncp5553HlVdeCcBNN93EE088wY9//GNOP/10vvOd79SVSB5xxBFMmzaNs846i8rKSmpra1m7di2LFi1i+fLl9OzZk4kTJ/Lxxx8zZswYrrnmGmbNmkVWVhbTp0/n//2//8fDDz/M3XffzcaNG0lMTGwQq0TOj474ERe9eBFvr3+bkwefHO1wRMQjVEIp4j2t6kIp0lGGDBnCuHHj9nvd0UcfzZQpU3j00UeprQ2/cvHGG29wxx13kJeXxwknnEBlZSVffPEF7733Xt1q0pgxYxgxYkSr40xMTORb3/oWACtWrGDo0KEMGTIEgClTpvDee+/RvXt34uLiuPzyy3nxxRdJSUnZZ+wnnngiKSkp9OrVi65du3LWWWcBkJubW7eCtmTJEo499lhyc3OZMWMGy5YtaxLbtm3bKCkpqXt/UlISycnJAEycOJE+ffrg8/nqVutWrFjBsmXLOPnkk8nLy+POO+9k06ZAQ9kRI0Zw0UUXMX36dBISElr9z0n279vDv01ml0w1MxGRiFITExHv2e8KnEg0hJIcgLi4uAYzsuqXOj7yyCPMnTuXl19+mdGjR7NkyZIm93LOMXPmzLrEqr7AFs/mDR06lPXr11NeXt4gppAuXbrU3aO5OV4JCQksWLCAN998kxkzZvDggw/yxhtvNBu73+9v8L2HXsfFxdWtRk6ZMoVXX32VkSNH8uijj/Lxxx+H/ezmvr/6n+Hz+aiursY5x6hRo3j//febXP/666/z7rvv8tJLL/Gb3/yGpUuXRn2/n9ckxSdxyehLeGDeA3y560t6d+0d7ZBExAM0RkDEe7QCJ51eXFwcGRkZrFmzhtraWl588cW6c+vWrWPixIncfvvtZGRksHnzZlJTU9m5c2fdNaeddhoPPPBA3etPPvkEgEmTJjF9+nQAPv3007CrWKmpqUyZMoWf/exnVFVVAbBly5a699U3fPhw1qxZw7p16wB4+umnOe6449i5cyc7duzgzDPP5P7776/7/HCxt1R5eTm9e/emqqqKZ555pkG8oe89IyODrKysuj1/lZWVVFRUNHvP4cOHs3nzZubNC/Qc2rt3L8uWLaOmpoaCggJOPPFE7rnnHoqLi/d5H2m7K464guraav72yd+iHYqIeESohFIrcCLeoQROYsJdd93F5MmTOemkk+jfv3/d8WuvvZbc3Fxyc3M5+eSTGTlyJCeeeCKffvopY8aM4fnnn+dXv/oVFRUV5ObmMmLECKZNmwbA1KlTKS0tZdSoUdx///1h98AB3HnnnaSnp3P44YeTm5vLueeeS8+ePZtcl5yczGOPPca5555Lbm4ufr+fyy+/nLKyMs444wxGjx7NiSeeyH333dds7C112223MX78eE455RSGDx9ed/yCCy7gt7/9bV1Z5PTp07n33nsZNWoUxxxzDMXFzbep9/v9PP/88/z85z9n9OjRjBkzhrlz51JdXc2FF17IqFGjGDt2LDfeeCOpqaktjlVaLicrhxMGncDDix5WMxMRiYi6JibaAyfiGdZc2VdHys/PdwsWLIh2GJETrmytE/xzbmzFihUcfvjhda87eoyAeEfjf5cAzGyhcy58VhxDOvrn0zOfPcP3Xvge//3BfzlywJEd9rkiBxMv/Hxq6c+m0opSsu7J4g+T/8DVE67ugMhEpK1a+rNJe+CkjpItkeg7ftDxAMzbPE8JnIi0m5qYiHiPSihFRDqRvql96Zval/lb5kc7FBHxAI0REPEeJXAiIp3MuL7jlMCJSEQkxAVGv2gFTsQ7lMAd5DrDHkiJbfp3KPLG9R3H6tLVbK/U0HQRaR8zI9GXqDECIh6iBO4glpSURGlpqX4BlzZzzlFaWkpSUlK0Q/GU/L6B/cuLChdFORIR8QK/z68VOBEPUROTg1j//v0pKCjYZ2t5kf1JSkpqMNpB2i+UwM3fPJ8Ts0+McjQiEusSfYnaAyfiIUrgDmIJCQlkZ2dHOwwRaSQzOZPBGYO1D04kRpnZ48CZQJFzbmTw2DTgciD0V9NbnHOzg+duBn4I1ABXO+dej2Q8/ni/SihFPEQllCIinZAamYjEtCeAyWGO3++cyws+QsnbcOB8YETwPX8xM18kg0n0JaqEUsRDlMCJiHRC4/qO44uyLygqL4p2KCLSSs6594CtLbz8HGCGc26Pc249sBYYH8l4/D6twIl4iRI4EZFOqP4+OBHxjKlmtsTMHjezjOCxfsCmetcUBI9FjFbgRLxFCZyISCc0ts9YDGPBlgXRDkVEIuNBYAiQBxQC9waPW5hrw7aHNrMrzGyBmS1oTQMyf7xfTUxEPEQJnIhIJ5TqT+XwHodrH5yIRzjnvnLO1TjnaoFH+LpMsgAYUO/S/sCWZu7xsHMu3zmX36NHjxZ/tkooRbxFCZyISCcVamSiWY0isc/M+tR7+S1gafD5LOB8M/ObWTYwFJgXyc9WCaWItyiBExHppMb1HUdReRGbdmza/8Ui0mmY2bPAR0COmRWY2Q+Bu83sMzNbApwAXAvgnFsG/ANYDrwGXOWcq4lkPCqhFPEWzYETEU8ws8nAHwAf8Khz7s5G5+8n8EsTQDLQ0znXrWOjbJ1x/cYBgUYmA9MHRjkaEWkp59wFYQ4/to/r7wDuOFDxaAVOxFu0AiciMS84M+nPwDeA4cAFwdlKdZxz14bmLwF/BF7o+EhbZ1SvUcTHxauRiYi0i/bAiXiLEjgR8YLxwFrn3Drn3F5gBoHZSs25AHi2QyJrh6T4JEb1GqVGJiLSLlqBE/EWJXAi4gUtnqNkZocA2cDbHRBXu43rO44FWxZQ62qjHYqIxCi/T3vgRLxECZyIeEGL5ygB5wPP76tJQFtnLR0I4/qOo2xPGWu3ro1qHCISu/zxKqEU8RIlcCLiBS2eo0Qggdtn+WRbZy0dCPUbmYiItIVKKEW8RQmciHjBfGComWWbWSKBJG1W44vMLAfIINDeOyYM7zGcLvFd1MhERNpMJZQi3qIETkRinnOuGpgKvA6sAP7hnFtmZreZ2dn1Lr0AmOFiaDJ2fFw8Y/qMYd6WiM71FZGDSGgFLoZ+9InIPmgOnIh4gnNuNjC70bFbG72e1pExRcopg0/htndvY922dQzOGBztcEQkxvjj/Tgc1bXVJPgSoh2OiLSTVuBERDq5y8deTpzF8eD8B6MdiojEIL/PD6BGJiIeoQRORKST65fWj3MPP5fHPnmMiqqKaIcjIjEm0ZcIoEYmIh6hBE5EJAZMHT+VbZXbeOazZ6IdiojEGH98cAVOjUxEPEEJnIhIDDh24LGM6jWKP837U4sbETww9wGueuWqAxyZiHR2WoET8RYlcCIiMcDMmDpuKp9+9Skfbvpwv9c757j/4/t54tMnqHW1HRChiHRW2gMn4i1K4EREYsSFuRfSLakbf5z3x/1eu7x4ORu2b6CiqoLNOzZ3QHQi0llpBU7EW5TAiYjEiJTEFH6Q9wNeWPHCfpOyl1e/XPd8VemqAx2aiHRi2gMn4i1K4EREYshPxv2EmtoaHl748D6ve3nNy/RL7QfAqhIlcCIHM5VQiniLEjgRkRgypPsQTh96On9d+Ndmy6G27t7Kfzf9l0vzLqVrYletwIkc5FRCKeItSuBERGLM1PFT+ar8K/657J9hz7+29jVqXS1n5ZxFTmaOEjiRg5xKKEW8RQmciEiMOXXIqRyWdRh3fXhX2A6TL69+mZ4pPcnvm09OVo5KKEUOclqBE/EWJXAiIjEmzuK45Zhb+KzoM2atmtXgXHVtNa+ufZXTh55OnMWRk5nDxrKNVFRVRClaEYk27YET8RYlcCIiMeiC3AsYkjGE29+7vcFg7/9u+i/bK7dz5tAzAcjJzAFgTemaqMQpItEXWoFTCaWINyiBExGJQfFx8dx8zM0sKlzEq2tfrTv+yupXSIhL4JQhpwCQkxVI4LQPTuTgFdoDpxJKEW9QAiciEqMuHn0xA9MHNliFe3nNyxw36DjS/GkADMscBmiUgMjBTCWUIt6iBK6jmDV9iIi0Q6IvkZuPuZmPCz7mP+v/w7pt61hevLyufBIgOSGZgekDtQInchBTExMRb1ECJyISwy7Nu5R+qf24/b3beWX1KwCcMeyMBtfkZOawsmRlNMITkU5AYwREvEUJnIhIDPPH+/nF0b/gvY3vcc9/7yEnM4dDux/a4JrQLLj6zU5E5OChFTgRb1ECJyIS4y4fezm9Unqxaccmzhx2ZpPzOVk57Nq7i8JdhVGITkSiLSEuAdAeOBGvUAInIhLjuiR04fqjrgfgrGFnNTkfGiWgRiYiByczw+/zq4RSxCOUwImIeMDPJv6Mt6e8zaRDJjU5d1jWYYBGCYgczBJ9iSqhFPGI+GgHICIi7RcfF88J2SeEPdcvrR/JCclagRM5iPnj/SqhFPEIrcCJiHhcnMUxLHMYK0vViVLkYKUVOBHv2G8CZ2YDzGyOma0ws2Vmdk3weHcze9PM1gS/ZgSPm5k9YGZrzWyJmY090N+EiIjsW05mjlbgRA5ifp9W4ES8oiUrcNXAdc65w4GJwFVmNhy4CfiPc24o8J/ga4BvAEODjyuAByMetYiItEpOZg4btm+gsroy2qGISBRoBU7EO/abwDnnCp1zi4LPdwIrgH7AOcCTwcueBL4ZfH4O8JQL+BjoZmZ9Ih55Z2LW8CEi0skclnUYDsfarWujHYqIRIE/Xl0oRbyiVXvgzGwQMAaYC/RyzhVCIMkDegYv6wdsqve2guAxERGJkpwsjRIQOZiphFLEO1qcwJlZV+BfwM+cczv2dWmYYy7M/a4wswVmtqC4uLilYYiISBsMyxwGdM5RAqUVpRTu1JBxkQNJJZQi3tGiBM7MEggkb9Odcy8ED38VKo0Mfi0KHi8ABtR7e39gS+N7Ouceds7lO+fye/To0db4RUQAMLPJZrYq2EDppmau+a6ZLQ82ZHqmo2OMpq6JXemX2o+VJZ2vE+WVr1zJ2TPOjnYYIhFjZo+bWZGZLa137B4zWxls8PaimXULHh9kZrvNbHHw8dCBiEkllCLe0ZIulAY8Bqxwzt1X79Qs4JLg80uAl+odnxLsRjkRKAuVWoqIHAhm5gP+TKCJ0nDggmCzpfrXDAVuBo52zo0AftbhgUZZTlZOp1yBW7hlIUuLllLraqMdikikPAFMbnTsTWCkc24UsJrAz6OQz51zecHHlQciIK3AiXhHS1bgjgYuBk6s99eh04E7gVPMbA1wSvA1wGxgHbAWeAT4SeTDFhFpYDyw1jm3zjm3F5hBoKFSfZcDf3bObQNwzhVxkAmNEnCuSVV7q5VWlHLXB3dRXVvdrvvsrtpd1x2zYEdBu+MS6Qycc+8BWxsde8M5F/o/zMcEKpQ6jPbAiXhH/P4ucM59QPh9bQAnhbneAVe1My4RkdYI1zxpQqNrhgGY2YeAD5jmnHutY8LrHA7LOoyyPWUUlRfRq2uvdt3rdx/8jns/upcJ/Sdw/KDj23yfVaWrcMFt0mtK1zAwfWC74hKJET8Anqv3OtvMPgF2AL90zr0f6Q9M9CWqhFLEI1rVhVJEpJNqSfOkeALzKY8HLgAeDe1BaXIzjzZZyskMdqJsZxnlrr27eHTRowDM3zy/XfdaXry87vnq0tXtupdILDCz/yUwY3d68FAhMNA5Nwb4OfCMmaU18942/2zyx/tVQiniEUrgRMQLWtI8qQB4yTlX5ZxbD6wikNA14dUmS6FRAu1tZPLUp09RtqeM5IRk5m2Z1657rShegc98dIlNzgRkAAAgAElEQVTvwpqta9p1L5HOzswuAc4EvhesWMI5t8c5Vxp8vhD4nGDFQGPt+dmkEkoR71ACJyJeMB8YambZZpYInE+goVJ9M4ETAMwsi8AvSOs6NMooG5g+kIykDF5e/fI+r5u3eR4zV84Me67W1fLA3AcY13ccZw47k3mb25fALS9ZzpDuQxiWOUwrcOJpZjYZuBE42zlXUe94j2AjJsxsMIE/LEX8Z5OamIh4hxI4EYl5wcYAU4HXgRXAP5xzy8zsNjML9ad/HSg1s+XAHOCG0F+9DxZxFse1E6/l36v/zcItC8Nes2vvLs6ZcQ7f/se3+fCLD5ucf+PzN1hVuoprJlzDhH4T+KLsC77c9WXYeznnuPODO1lWtKzZmFYUr2B4j+EMzRyqFTjxDDN7FvgIyDGzAjP7IfAnIBV4s9G4gEnAEjP7FHgeuNI5tzXsjdvB79MYARGvUAInIp7gnJvtnBvmnBvinLsjeOxW59ys4HPnnPu5c264cy7XOTcjuhFHxzUTryEjKYNfvfOrsOfv/vBuvtz1JT2SezBl5hR27tnZ4Pwf5v6BPl37cN6I8xjfbzzQ/D64NVvXcPN/buZP8/4U9nxVTRVrtq7h8KzDGdZ9GOu2rWt3V0uRzsA5d4Fzro9zLsE5198595hz7lDn3IDG4wKcc/9yzo1wzo12zo11zv37QMSkFTgR71ACJyJyEEnzp3HDUTfwyppXmFswt8G5gh0F/P6/v+d/RvwP/zzvn6zftp7r3riu7vzKkpW8tvY1fpz/YxJ9iYzpPYY4i2P+lvAJ3JufvwnARwUfhT2/dutaqmur61bgqmur2bB9Q2S+URFpwB8f2AMXiTEiIhJdSuBERA4yU8dPJSs5i2nvTmtw/Jdv/5IaV8PvTvodxx5yLDccdQOPLHqkbs/cH+f+kURfIj/K/xEAKYkpjOw5stl9cG+uCyRwnxV9xq69u5qcX1GyAiCwApcZ6NmgfXAiB4bf5wegqrYqypGISHspgRMROcik+lO54agbeG3ta3y0KbA69knhJzz16VNcM+EasjOyAbjthNvI7ZnLZbMuY+3WtTz56ZNcmHshPVN61t1rfN/xzNs8r8lf9atqqnh7/dsMzhhMratlwZYFTeIIjRA4LOswhnYPNARdU6p9cCIHQqIvEUBllCIeoAROROQgdNW4q+iR3INfvfMrnHNc98Z1dO/SnVuOvaXuGn+8n79/6+9s3b2Vox47ivKqcq6ZcE2D+4zvN55tldv4fNvnDY7P2zyPnXt3ctPRNwHUJYr1rShZwSHph5CSmEJWchbdkrppBU7kAPHHB1bg1MhEJPYpgRMROQilJKZw0zE38ea6N7nprZuYs2EO046fRrekhrPNR/ceze0n3E5xRTGTDplEXu+8BudDjUwal1G+ue5N4iyObw//NjmZOXy8+eMmMSwvXs7hPQ4HwMwY2l2dKEUOFK3AiXiHEjgRkYPUlflX0iulF3f/925yMnP40RE/Cnvd9Uddz62TbuWByQ80OTei5wi6xHcJm8Dl982ne5fuTOw/kY82fdSgzLKmtoaVJSsZnjW87phmwYkcOKE9cBrmLRL7lMCJiBykkhOS+d9j/xeAe065hwRfQtjrfHE+fn3Crxnde3STc/Fx8YztM7ZBJ8qyyjLmFszllMGnAHBk/yMprihm/fb1dddsLNtIZXVl3QocwNDuQ/mi7Asqqysj8v2JyNdCK3AqoRSJfUrgREQOYlPHT+Xzqz/nrJyz2nyP8f3Gs6hwEVU1ge52czbMocbV1CVwE/tPBBrug1tRHOhAObxHwxU4h+PzrQ3304lI+4X2wKmEUiT2KYGLJrOGDxGRDmZmDM4Y3K57jO83nsrqSpYWLQUC899SElI4csCRQKDMMiUhhY8Lvt4HF+pAeXhWvRW4zGAnSu2DE4k4lVCKeIcSOBERaZfGjUzeXPcmxw86vq5kKz4unvH9xjdoZLKiZAW9u/Ymo0tG3bHQKIGW7oP776b/1g0LF5F9UxMTEe9QAiciIu2S3S2bzC6ZzNs8jw3bN7Bm65q68smQif0nsvjLxeyu2g0EErj6q28A6Unp9Ezp2eJZcFNnT+Xq166OzDch4nEaIyDiHUrgRESkXcyMcf3GMX/L/LoVsVOGNEzgjux/JNW11SwsXIhzLjBCoFECB8FOlFv3vwK3bfc2Fn+5mE1lm5oMEReRprQCJ+IdSuBERKTdxvcdz7LiZcxcNZO+qX2bJGcT+k8AAo1MCncVsmPPjgYNTEKGdh/aohW49za+h8NRXlVO2Z6yyHwTIh6mPXAi3qEETkRE2m18v/HUulpmr5nNKYNPwRo1ZuqZ0pPBGYP5ePPHXzcw6RF+Ba5wVyE79+zc5+fN2TCn7vmmsk0R+A5EvE1jBES8QwmciIi027h+4+qenzrk1LDXHNn/SD7a9FFdAtfcChzA2q1r9/l5czbMISMp0AClYEdBm2IWOZhojICIdyiBExGRduuZ0pNB3QYBcPLgk8NeM7H/RAp3FfLG52/QLakbvVJ6NblmWOYwYN+dKEsqSljy1RL+Z8T/ALBph1bgRPZHJZQi3hEf7QC8yKY1PebCHBMR8ZLThpzGypKV9EzpGfZ8aKD3q2tfZWL/iU3KLAGGdB8C7HsW3Lsb3gXgwtwLeXjRwyqhFGkBNTER8Q4lcCIiEhF/OeMv++wIObrXaJLik6isrgzbgRIgOSGZAWkD9rkCN2fDHFISUpjYfyJ9uvahYKdKKEX2R2MERLxDJZQiIhIRcRaHL87X7PkEXwL5ffOB8PvfQoZmDt3nCtycDXM4ZuAxJPgS6J/WXytwIi1Q18REJZQiMU8JnIiIdJiJ/QJllM2twAEM6z6s2RW4ovIilhcv54RBJwAwIH2AmpiItEBoD5xKKEVinxI4ERHpMGfnnE3vrr05ou8RzV4zNHMoW3dvpbSitMm5dza8A8AJ2YEErn9qfzbt0DBvkf2Jj4vHMJVQiniAEjgREekwxx5yLIXXFTbb6AS+7kQZroxyzvo5pCamMrbPWCCwAldRVcG2ym0HJmARjzAzEn2JWoET8QAlcCIi0qmEZsGFK6Ocs2EOkw6ZRHxcoAfXgLQBgGbBibSEP96vPXAiHqAETkREOpXBGYPJ7JLJnR/cybbdX6+sbdm5hVWlq+r2vwH0T+sPEPFGJuV7y3lp5UsRvadItGkFTsQblMCJiEinkuBL4F/f/Refb/ucbz73TSqrK4Gv978dP+j4umsHpB+YFbjpn03nm899Uyt74il+n1974EQ8QAmciIh0OscNOo4nv/kk7218j0tmXkKtq2XO+jl0S+pGXu+8uuv6dO2Dz3xs2hHZFbjCnYUAbN29NaL3FYmmRF+iSihFPECDvEVEpFM6f+T5bCrbxC/e+gUD0wbyzsZ3mHTIpAaz5nxxPvqk9ol4AldUXgRAWWVZRO/bGs45zCxqny/e44/3q4RSxAOUwImISKd1/VHXs7FsI7//6PcAXDXuqibXDEiL/Cy44opiAHbs2RHR+7bGxS9eTJzF8dS3nopaDOItfp+amIh4gUooRcQTzGyyma0ys7VmdlOY8983s2IzWxx8XBaNOKV1zIw/TP4D5+ScA8BJ2Sc1uaZ/Wv+INzGpW4HbE70VuCVfLWFFyYqofb54j5qYiHiDVuBEJOaZmQ/4M3AKUADMN7NZzrnljS59zjk3tcMDlHbxxfmY8Z0ZzN88n9xeuU3OD0gbwMurX45oyWFnKKEsqSghzZ8Wtc8X7/HHq4mJiBdoBU5EvGA8sNY5t845txeYAZwT5ZgkgpLikzj2kGPDnhuQPoDd1bsj2nAk2iWUzjlKKkrYuXdnVD5fvElNTES8QQmciHhBP6B+DV1B8Fhj3zazJWb2vJkN6JjQ5ECrmwUXoUYmNbU1lFaUAtErody5dydVtVXs3KMETiLH71MTExEvUAInIl4Qrm7ONXr9b2CQc24U8BbwZLM3M7vCzBaY2YLi4uIIhikHwoC0yM6CK91digv+6xOtFbiSihIAdu3dhXON/1UWaZtEX6JKKEU8QAmciHhBAVB/Ra0/sKX+Bc65Uudc6DeXR4AjmruZc+5h51y+cy6/R48eEQ9WIis0zDtSjUxC+98geitwoQTO4SivKo9KDOI9GiMg4g1K4FrLrOlDRKJtPjDUzLLNLBE4H5hV/wIz61Pv5dmA2vt5RK+UXsTHxUdsBa5BAhelJiahBA5QGWUUmVmCmV0dLLt+3sx+amYJ0Y6rrTRGQMQblMB1JkoORdrEOVcNTAVeJ5CY/cM5t8zMbjOzs4OXXW1my8zsU+Bq4PvRiVYizRfno29q34jtgSsuD5TNZiVnRb2EElAjk+h6kMBq/V+Cj7HBY/tkZo+bWZGZLa13rLuZvWlma4JfM4LHzcweCI5AWWJmYw/Q96IxAiIeoTECIuIJzrnZwOxGx26t9/xm4OaOjks6xoC0ARFL4EIrcId2PzRqJZShJiqgFbgoG+ecG13v9dvBPwLtzxPAn4D6U9hvAv7jnLszOKvyJuBG4BvA0OBjAoEEcUIEYm/C79MYAREv0Arc/mhFTESk0+uf1j9iJZTFFcUYxuCMwZ2jhFIrcNFUY2ZDQi/MbDBQs783OefeAxrPtTiHr5snPQl8s97xp1zAx0C3RiXfEaMxAiLeoARORERi3oC0ARTsKIhIx8ai8iKykrPISMroHCWUWoGLphuAOWb2jpm9C7wNXNfGe/VyzhUCBL/2DB5v6RiUdlMTExFvUAllBNi0aEcgInJw65/Wn8rqSkp3l5KVnNWuexWVF9EjpQdp/jTK9pThnMM6uAKjZHcJCXEJgVlwWoGLGufcf8xsKJBDYFzJynrdbCOlJWNQAheaXQFcATBw4MBWf1BoDlw0/p0WkcjRCpyIiMS8SI4SKK4opmdKT9L96VTXVlNZXdnue7ZWSUUJA9MDv6BrBa7jmdmJwa/nAmcAhwJDgDOCx9riq1BpZPBrqN3pfseghLR3xEmiLxGAqtqqVr9XRDoPJXAiIhLzQsO8I9HIpKi8iJ4pPUnzpwHRmQVXUlFCdkY2oD1wUXJc8OtZYR5ntvGes4BLgs8vAV6qd3xKsBvlRKAsVGoZaf54P4AamYjEOJVQiohIzOuf1h8gIo1MisuL6ZHcg/SkdCAwC653197tvm9rlFSUcFT/owDYtXdXh362gHPuV8Gntznn1tc/Z2bZ+3u/mT0LHA9kmVkB8CvgTuAfZvZD4AvgvODls4HTgbVABXBpJL6HcEIrcNoHJxLblMCJiEjM69W1FwlxCe0uodxbs5dtldvqSiiBDm9k4pyjtKKUnik9SU5IVglldP2LwOy3+p4nMBuuWc65C5o5dVKYax1wVZuiayW/L7gCp06UIjFNCZyIiMS8OIujX1q/dpdQhro/RrOEsmxPGTWuhqzkLFITU1VCGQVmdhgwAkhvtOctDUiKTlTtF1qBUwmlSGxTAiciIp4QiVlwxeXFAE1KKDtSKInMSs4i1a8ELkpyCOx160Zg31vITuDyqEQUAaE9cCqhFIltSuBERMQTBqQNYO7mue26R1F5oDFgNEsoGyRwiakqoYwC59xLwEtmdqRz7qNoxxMpKqEU8Yb9dqE0s8fNrMjMltY7Ns3MNpvZ4uDj9HrnbjaztWa2ysxOO1CBi4h3mVn3aMcgsSe0AteeYd6hBC40Bw46voRSK3CdyidmdpWZ/SX4+9DjZvZ4tINqKzUxEfGGlowReAKYHOb4/c65vOBjNoCZDQfOJ1A3Phn4i5n5IhWsiBw05prZP83sdNO0WWmhAWkD2Fuzl+KK4jbfI/Te+nvgtAJ3UPs70Bs4DXiXwIy2mP0fRGMERLxhvwmcc+49YGsL73cOMMM5tyfYdnctML4d8YnIwWkY8DBwMbDWzH5rZsOiHJN0cpEY5l1UXkR8XDzdkrrhi/ORkpCiPXAHt0Odc/8PKHfOPUlgqHdulGNqs7omJiqhFIlp7RnkPdXMlgTLCTKCx/oB9f/LWRA8JiLSYi7gzWAr7ssIDL2dZ2bvmtmRUQ5POqlIzIIrLi8mKzmLOAv85zE9KT0qJZQJcQl0TeyqFbjoqwp+3W5mI4F0YFD0wmmf0B44lVCKxLa2JnAPAkOAPKAQuDd4PFypU9jNCGZ2hZktMLMFxcVtL3cREe8xs0wzu8bMFgDXAz8FsoDrgGeiGpx0WgPSgitw7RglUFRRRM+UnnWv0/3pUSmhzErOwsw0RiD6Hg7+kfqXwCxgOXBXdENqO40REPGGNnWhdM59FXpuZo8ALwdfFgAD6l3aH9jSzD0eJlAiRX5+ftt3nIuIF31EYO/JN51z9ZdTFpjZQ1GKSTq5nik9SYpPYsP2DW2+R1F5ET2Se9S9TvOndfgKXOnuUrKSswBI9adSUVVBTW0NvjhtKe9IZhYH7HDObQPeAwZHOaR20xgBEW9o0wqcmfWp9/JbQKhD5SzgfDPzm1k2MBSY174QReQg9Evn3O31kzczOw/AORezf/2WA8vMGNRtEOu3r2/zPYrLixuuwCWlR2UPXF0Cl5gKwK69uzo0BgHnXC0wNdpxRJLGCIh4Q0vGCDxL4K/hOWZWYGY/BO42s8/MbAlwAnAtgHNuGfAPAiUGrwFXOedqDlj0IuJVN4U5dnOHRyExJ7tbNuu3tT2BKyrvPCWUEFiBA1RGGT1vmtn1ZjbAzLqHHtEOqq00RkDEG/ZbQhlsItDYY/u4/g7gjvYEJSIHJzP7BnA60M/MHqh3Kg2ojk5UEksGZwzmo4K2zV2urK5k596dUS+hDLcCp0YmUfOD4Ner6h1zxGg5pcYIiHhDe7pQiohE2hZgAVAJLKz3mEVgDpPIPmV3y2Z75Xa27d7W6vcWl389Ay5kXytwzjlu+c8trC5d3bZgw6iprWHr7q1agesknHPZYR4xmbyBxgiIeEWbmpiIiBwIzrlPgU/NbLpzTitu0mrZGdkArN++nowuGfu5uqGi8iKgYQKX5k9j195dYZuIbN65md998Dt85uP2E29vZ+QB2yu3U+tqtQdODgiNERDxBiVwHcSmNT3mwhwTOZiZ2T+cc98FPjGzJt1pnXOjohCWxJDBGYHFkfXb1jO2z9hWvbe4IrAC1yPl6xLK9KR0AHbs2dEkISzcWQjAqtJVbY63sdAQ78wumQB0TewKqIRSIkNjBES8QQmciHQm1wS/nhnVKCRmZXf7egWutcKtwKX7m0/gvtz1JXBgEjiVUMqBEB8Xj2FagROJcUrgRKTTcM4VBr9ujHYsEpvSk9LJSMpg3bZ1rX5vaA9c4yYmQNhGJoW7Aitwa0rXUOtqibP2bysv3V0KoCYmUWZm+1y+dc4t6qhYIsnM8Mf7tQdOJMYpgRORTsPMdhLo8NbkFOCcc2kdHJLEoOyM7DavwCX6EuuSNmhYQtlYaAVud/VuCnYUMDB9YBsj/ppW4DqNe4Nfk4B84FMCP4dGAXOBY6IUV7sl+hJVQikS49SFUkQ6DedcqnMuLcwjVcmbtNTgjMFtmgVXVBGYAWdmdcdCJZThhnmHEjiAVSWRKaNsnMClJKRgmFbgOphz7gTn3AnARmCscy7fOXcEMAZYG93o2sfv86uEUiTGKYETkU7DzNKCX7uHe0Q7PokN2d0CK3C1rrZV7ysuL25QPgn7L6EMNRuJ1CiBkooSkuKTSE5IBgIlb10Tu2oFLnoOc859FnrhnFsK5EUxnnZL9CWqhFIkxqmEUkQ6k2cINDBZSKCU0uqdi9nhudKxsrtls7dmL4U7C+mX1q/F7ysqL2rQwAT2X0I5ps8YPi74OGKNTEJDvOuvAqb6U7UCFz0rzOxR4GkCP4MuAlZEN6T28cdrBU4k1mkFTkQ6DefcmcGv2c65wa0Znmtmk81slZmtNbOb9nHdd8zMmVl+pOOXzqFulEAr98EVVxQ3GCEA9VbgwpRQFu4spE/XPgzLHBbxBK6+1MRUrcBFz6XAMgIdcn8GLA8ei1l+n5qYiMQ6rcCJSKdkZucSaBTggPedczP3ca0P+DNwClAAzDezWc655Y2uSwWuJtCEQDyqbpj3tvUcM7DlvSaKyovomdxwBa5LfBfi4+KblFA65/hy15f07tqb6tpqPir4qP2BE0jgQmWZIal+JXDR4pyrNLM/A28R+Fm0yjlXFeWw2iXRl6gVOJEYpxU4Eel0zOwvwJXAZ8BS4MrgL1HNGQ+sdc6tc87tBWYA54S57nbgbqAywiFLJ3JI+iEY1qpRAuV7y6moqmhSQmlmpPvTm5RQbq/czp6aPfTp2oeczBw2bt/I7qrd7Y692RU4lVBGhZkdD6wB/gT8BVhtZpOiGlQ7+eP96kIpEuO0AicindFxwEjnnAMwsycJJHPN6Qdsqve6AJhQ/wIzGwMMcM69bGbXRzhe6UT88X76pvZtVQllcUVwBlyjEkoIlFE2XoELdaDs3bU3fVL74HCs3bqW3F657Yg8MAeuSQLnT2XD9g3tuq+02b3Aqc65VQBmNgx4FjgiqlG1g5qYiMQ+rcCJSGe0Cqg/VGsAsGQf11uYY3Xz5MwsDrgfuK4lH25mV5jZAjNbUFxc3JK3SCczOGNwqxK4ovIigCYrcBBoZNJ4BS40xLt3194MyxwG0O59cNW11WzbvU0rcJ1LQih5A3DOrQYSohhPu2mMgEjsUwInIp2Gmf3bzGYBmQS6v71jZnMIdH1rujTytQICSV5If2BLvdepwEjgHTPbAEwEZjXXyMQ593Bw7lN+jx77+ljprLIzsls1C26fCZw/vUkTk9AKXJ/UPnUJXHtHCWzbvQ2HUxOTzmWBmT1mZscHH48Q6JIbszTIWyT2qYQyimxaw9duWrirRA4qv2/j++YDQ80sG9gMnA9cGDrpnCsD6n4rNrN3gOudcwvaHqp0Ztndsvn7jr+zp3oP/nj/fq8vLg+WUCaHL6HctGNTg2P1Syi7JnalX2q/dq/ANR7iHaIxAlH1Y+AqAs2PDHiPwF64mKUxAiKxTwmciHQazrl32/i+ajObCrwO+IDHnXPLzOw2YIFzblYk45TOb3DGYByOL8q+YGjm0P1ev78SymXFyxocK9xZSFJ8Eun+wJy4YZnDWFVygBK4xFT21OyhqqaKBN/+q/c+/fJTfHE+RvYc2a54BJxze8zsT8CbeKQLpcYIiMQ+lVCKSKdjZhPNbL6Z7TKzvWZWY2ZNJynX45yb7Zwb5pwb4py7I3js1nDJm3PueK2+eVt2t+AogRbugyuuKKZLfBdSElOanEtLTGtaQlkeGCEQGridk5nDqtJVBPvutMm+VuAAdu3d1aL7XDX7Kn7++s/bHId8zYtdKFVCKRL7lMCJSGf0J+ACAr84dQEuCx4TaZH6s+Baoqi8KOzqGwRW4Mr2lDVIzkJDvENysnLYXrm9Lglri9B7m8yBSwwkcC3dB1dcUaw9c5ET6kJ5nHNuEnAagYZIMUtNTERinxI4EemUnHNrAZ9zrsY59zfg+CiHJDGkb2pfEn2JLZ4Ft88Ezp9OdW01ldVfjw8MDfEOiUQnyroELrlhAtc1sStAi/fBbdu9LSIz6QTwYBdKjREQiX1K4ESkM6ows0RgsZndbWbXAk1r20SaEWdxDOo2qFUllOFmwEGgiQnQYBZc4a5GK3CZOUD7OlGW7i4lOSGZ5ITkBsdDJZQtWVVzzrGtcluDZFPaxXNdKNXERCT2qYnJfqhTpEhUXEygGclU4FoCIwK+HdWIJOZkd8tucQJXVF7EqF6jwp5LTwo0KtmxZwe9u/ZmT/Uetu7e2mAFblC3QST6EtvVyKSkoqTJ/jeoV0LZghW48qryJquF0i6e60KpPXAisU8JnIh0Os65jcGnu4FfRzMWiV3Z3bJZsGX/vWqccxSXF9MzufkSSqCukclX5V8BNEjgfHE+Du1+aLtLKMMmcK1Ygdu2exsAu6tVQhkJzrk9wH3Bhyf4fX6qaqtwztU14RGR2KIETkQ6DTP7h3Puu2b2GYGW3Q0458IvkYiEMThjMKW7S9mxZ0ddGWQ4hbsK2VOzhz6pfcKeb1xCWX+Id33tHSUQiRW4bZWBBE4rcJFhZkcD04BDqPc7k3NucLRiaq/QXMS9NXtbNCNRRDofJXAi0plcE/x6ZlSjEE+o34lydO/RzV738uqXATgp+6Sw5+uXUELDId715WTm8MrqV6iurSY+rvX/eS2pKGFI9yFNjrdpBU5NTCLlMQJl3AuBmvbezMxygOfqHRoM3Ap0Ay4HioPHb3HOzW7v54WT6EsEYE9NwyH3W3dvpXuX7gfiI0UkwtTEREQ6DedcYfDrxnCPaMcnsaWls+BmrpzJ4IzBzQ6+rluBC5ZQFu4sBGjQxAQCCVxVbRUbtm9oU7wlFSVkdYnMClxVbRU1te3ONwTKnHOvOueKnHOloUdbb+acW+Wcy3PO5QFHABXAi8HT94fOHajkDQIllECDRiafffUZPe7pwdyCuQfqY0UkgrQC10qNm5qISOSY2U6+Lp0Mbc5wwefOOdd8HZxII6EVuH2NEti5Zyf/Wf8frhp3VbP7ger2wNUroTSsydiB0CiB1aWrObT7oa2KtaqmirI9ZU1GCAAkxSfhM1+rVuAgsMKSHJe8j6ulOWY2Nvh0jpndA7wA1HX+cM4tisDHnAR87pzb2JF70epW4Oo1Mnl7/dvUulrWbF3DhP4TOiwWEWkbJXAi0mk451KjHYN4R0ZSBun+9H0O837989fZW7OXc3LOafaa0ApcqISycFchWclZJPgajgPLyQqMElhVsorTh57eqlhLdwcWdcLtgTMzUv2prVqBg0AZZeORBNJi9zZ6nV/vuQNOjMBnnA88W+/1VDObAiwArnPObQv/tvapvwcu5KOCjwDaNYheRDqOEjgR6ZTM7BhgqHPub2aWBaQ651rWE16EQOKTnbHvUQIzV84ks0smRw88utlrfHE+UhJS6kooGw/xDslKzqJ7l4X9PCgAACAASURBVO5t6kRZWtF8AgeBMsrWrsCpkUnbOedOOJD3D865PBu4OXjoQeB2Asnh7QQSyB+Eed8VwBUAAwcObNNnh0oo6w/z/rjgY0AJnEisUAInIp2Omf2KwF+8c4C/AYnA00Dzv2WLhJHdLbvZhKqqpopX1rzCOTnn7LfpSHpSeoMVuHAJHATKKNsyzDv0i3OzCZy/hQlc/RU4jRJoMzO7yDn3tJn9PNx551x7xwp8A1jknPsqeL+v6n32I8DLzXzuw8DDAPn5+U069bZE4xLKwp2FbCwLbDFWAicSG9TEREQ6o28R+Ot0OYBzbgug8kpptcEZg1m/bT3ONf1d9/0v3md75fZ9lk+GpPvTG+yBa27kQE5mTptW4PabwCW2voRSK3DtkhL8mtrMo70uoF75pJnV/xfqW8DSCHxGWI1LKOduDjQuibM4JXAiMUIrcCLSGe11zjkzcwBmlrK/N4iEMzhjMLurd7OiZAXDewxvcG7mypkkxSdx6pBT93ufNH8aZXvKcM4FSihTwq/A5WTm8OSnT7Jzz8669v8tEfrFObPL/2/v3uPjrMv8/7+uzCST46Rpk55ToKVAORZoESygaFEQpborAquIoLKrsvLddX+r+3WXZfXhPvyurqu7i7siorig6AIKKiKCKMpBWqCllDMF2tLSpm2aNIdJMpnP74+5Z5gkM8lMMpN77sn7+XjMI5l77pn7M0n7aa+5rs/1GdvEBArIwPWPXAMnk+Oc+5b39Z+K/dpmVg+cDfx5xuF/MbOVJEsoXxn1WFFlbiMAyfLJ6qpqTlxwogI4kYBQAFfuRnemyvIpskgF+rGZfQuYZWYfJ7kW5HqfxyQB9N6j3svnf/N5Pnbnx/j9Zb8nVBUCwDnHHc/dwdlLz6ahZuLPB1IllJ2xTgaHB3Nm4BY2LQSgo6+joAAuld2bVTsr6+NNNU3p7QvG0xnrpMqqSLiEMnBTYGb/Pt7jzrlPT/a1nXN9wJxRxy6Z7OsVavQ2Ao/seIQTF5zIoqZFkyr/FZHppxJKESk7zrmvArcCt5FcB3e1c27c/1CJZLOwaSHffNc3eXjHw3zloa+kj2/avYltXdvyKp8ELwMX68q5iXfmefBGx8p8dcW6CFkoZ9fIpkgTPYM9E75OZ39nemwK4KbksQlugZW5Bi6eiLN+53pOXXQqrfWt6W6oIlLelIETkbLknPs18GsAMwuZ2Qedczf7PCwJoIuOvYifPvdTrr7/as45/BxWzl/JT5/9KYbxniPfk9drpNbA5drEO2WyAVz3QDfRSDTnXnR5d6GMddIebWfnwZ1qYjIFzrkbM++bWYNzrtev8RRT5hq4p/Y8Rd9QH6cuPpXNezazt28vzrmcfw5FpDwoAxc0ZmNvIhXCzKJm9ndm9p9m9g5LuhLYCnzA7/FJMJkZ33zXN5lTP4dLfnIJA/EB7njuDt7c/uYxm3Hn0hxJllCWLAM30EVzbXPOx/NpYuKco7O/M13GqQzc1JnZaWb2NPCMd/8EM/umz8OaksxtBB7entz/7dTFyQxcPBEv+M+uiEw/BXAiUk7+h2TJ5GbgY8A9wAXAOudcfrVuIlnMqZ/Dd87/Dk/teYrL77ycja9vzLt8EpKBWc9gDzu6dwDkXAOXCsImFcBFcgdwjTWNDCWG0q3fs+kb6mMoMZTODqqJSVF8HXgnsA/AObcJONPXEU1RZgnlI689wryGeRw669B0B1Q1MhEpfyqhFJFystQ5dxyAmV0P7AWWOOcmrh0TmcC7lr+LK066gusevw6AdUflH8ClArMX9r9AXbiOpprsDUpSGbjUpt/5SpVQ5pJqiHJw8GC6BG601BYCqeBSGbjicM5tH1VSOOzXWIohs4TykR2PcOriUzGzEQHcstnL/ByiiExAGTgRKSdDqW+cc8PAywrepJj+9Z3/ytKWpRw791iOmHNE3s9LZcee2/cc8xvn51wjNJUmJhOVUALjllGmthBIlVBqDVxRbDezNwPOzGrM7G/wyimDKpWB29Wzi+f3Pc+pi08FUAZOJECUgRORcnKCmaX+52tAnXffAOecy52iEMlDY00jD17+IPFEvKDnpQKzZ/c+O27gVxeuI2ShSTUxWRFZkfPxzAxcLukMXKMycEX0F8A3gEXADpJl3Z/ydURTlFoD9/ttvwdQACcSQArgyohdM/aYy3JMpFI550J+j0EqX64GJONJZcf29u3lzENyL4EyM6KRaNHXwBWSgfO7hLJ3sJczv3cm33zXN3nT4jf5MoYiSjjnPph5wMwOw1sTF0SpDNyD2x6kyqpYtXAVoABOJEhUQplJHR5FRCSLzPVpubYQyDy3e3By2wjkUkgGbnbdbCKhiG9NTLZ1bePxXY/z02d/6sv1i+xnZpb+xZjZCuBnPo5nylJr4HqHejlu7nE01jQCyQ8JqquqFcCJBIACOBERkQlkZscmyuAVmoGLxWMMDg8WLQPXUttCbbjWvwzcUHK7tMd2BXq/65R/JhnENZrZycCtwId8HtOUhKvCVFnyv3+p8klIZo/n1M9RACcSACqhFBERmUBmg5GJMnDNtc0FdaFMBXvFyMAZRnNtM3XVdb41MekdTAZwG3ZuCPym0M65X5hZNcm1b03Ae51zL/g8rCmrCdUQi8c4bfFpI4631reyt18BnEi5UwAnIiIygczgKp8M3O6e3Xm/dirYK0YXyubaZqqsqiwycJ2xTl4+8DJLW5b6Mo6pMLP/AFzGoSiwFfhLM8M592l/RlYckVCEWDw2IgMHyQBuX19gl/eJzBgK4ERERCZQF64jXBUmnojnFcC9sC//JE0xM3AttS0A/gZwXgYO4LGdjwUygAM2jLpfEfWgKTWhGlpqW1g+Z/mI4631rWzZs8WnUYlIvhTAiYiITMDMaI40s69/X7rLYy7RmsLWwHUNeBm4cdbA1YRqqAnVjJ+Bi3XSUpcM4OrCPpZQDr0RwG3YuYELjrnAl3FMhXPuRr/HUEp11XWsaF2RXguX0lrXqjVwIgGgAE5EKoKZnUNyv6YQcL1z7sujHv8Lkvs3DQM9wBXOuaenfaASWNFIlP39+5nbMHfC8woJ4PLJwEGyjHLcDFx/eWXg2qPtgW1kYmY/ds59wMw2M7KUEgDn3PE+DKtovv7Or7OkecmY4631rezr30fCJcYEdyJSPhTAiUjgmVkIuBY4m+Rmu+vN7M5RAdoPnHP/7Z1/PvA14JxpH6wEVnNtM20NbYSrxv+nMxqJ0h/vZ2h4iOpQ9YSvm88aOEiWUfYM9uR8vDPWyaLoIiCZYfFrG4FUBu7MQ87k58//PKiNTK7yvr7b11GUyPtWvC/r8db6VhIuwYHYAWbXzZ7mUYlIvmb2xyva802kUpwCvOic2+qcGwRuAdZlnuCcy0yJNJDlU3WR8UQj0bw2AU8FYvlm4So1A3fmIWfSNdDFS50v+TKOqXDO7fK+vprt5vf4SkWbeYsEgzJwIlIJFgHbM+7vAN40+iQz+xTw10AN8LbpGZpUis+c9hkG4gMTnpcKxLoHuplTP2fC81Nr4CYM4CJNOdfAOedGNDHxew1cXbiO1QtXA8lGJofPPtyXsUyWmR0k+4c8Bjjn3Pi/rIDKDOCOmHOEz6MRkVxmdgZORCpFthR6tnUr1zrnlgGfBf4+54uZXWFmG8xsQ0dHRxGHKUF2/pHn59WQIzOAy0f3QDe14VpqQjXjnjdeBq4/3s/g8GC6iYnfGbiGmgaOmXsMkVCEDTtHN3Qsf865JudcNMutqVKDNyD9gYMycCLlbcIAzsxuMLM9ZvZUxrHZZvZrM3vB+9riHTcz+3cze9HMnjSzk0o5eBERzw6gPeP+YmDnOOffArw314POueucc6ucc6va2tqKNESZKQoN4LpiXeN2oEwZLwPX2d8JUB4llEO9NFQ3UBOq4fh5xwe2kclMpBJKkWDIJwP3PcYu9P8ccJ9zbjlwn3cf4FxguXe7Aviv4gxTRGRc64HlZnaYmdUAFwF3Zp5gZpkbHp0H5L9Rl0gBCg7gBromLJ8EaKxpzJmB64wlA7hZtbMAr4TSxyYmDTUNAKxauIrHdj1GwiV8GYsURgGcSDBMGMA55x4A9o86vA5I7ZFyI298kr0O+L5LegSYZWbjb5gjIjJFzrk4cCXwK+AZ4MfOuS1m9gWv4yTAlWa2xcw2klwHd6lPw5UKN5kSyok6UIJXQjlRBq5cSiir3wjguge6eWl/8BqZzEQN1Q1EQhH29e3zeygiMo7JNjGZl9GhaZeZpTbFydZIYBGwa/QLmNkVJLN0LFkydi8SEZFCOOfuAu4adezqjO+vGvMkkRJIlUOmmpNMpGsgzxJKbw1ctrb8qQxcuolJdbKJiR8t/DMzcCcvOBlIbui9fM7y8Z4mZcDMaK3XZt4i5a7YTUzyaiQAWmMiIiKVaTIZuHxKKJsiTSRcImt3yWwZOIDB4cG8xlBMmRm4o9uOpjZcG8hGJjNVa30re/sVwImUs8kGcLtTpZHe1z3e8UIbCYiIiFSU+up6qqyqsCYmeZZQAlnLKEdn4FIBnB9llJkZuOpQNSfMO0GNTAJEGTiR8jfZAO5O3lg/cilwR8bxD3vdKE8FulKlliIiIjOBmRGNRAvLwNXkl4EDsjYySWXgMpuYAL7sBZeZgYPkOrjHdz2uRiYBoQBOpPzls43AD4GHgSPNbIeZfRT4MnC2mb0AnO3dh+T6k63Ai8C3gU+WZNQiIiJlLN8ALuESBTUxgewZuAOxA0QjUUJVIaAMMnCjAriDgwd5YZ8avwaBAjiR8jdhExPn3MU5Hnp7lnMd8KmpDkpERCTI8g3gegd7cbi818BBjgxcrDNdPgnJJiaAL1sJpDbyTslsZHJk65HTPh4pTGt9K539ncQTccJVk+11JyKlVOwmJiIiIjNeNBLNqwtl6px8u1BC7jVwqQYm4F8Gbmh4iKHE0IgM3Iq2FdSF69TIJCDm1M3B4dJluSJSfmb0Ryt2jd8jEBGRStQcaaajr2PC81JZuiln4PpHZeB8WgPXO9QLMCIDF64Ks3L+SjUyCYjMzbzbGtQlXKQcKQNX5uyakTcRESl/+ZZQdsW8DFwRulAWkoHb07uHm5+8ecJrFqp30AvgMjJwoEYmQZIZwIlIeVIAJyIiUmT5BnCFZODmNc6jJlTDC/vHNgMZnYGbKIC76cmb+NBPPsTWzq0TXrcQ2TJwAEtbltI71MuB2IGiXk+KLxXA7evf5/NIRCQXBXAiIiJFlncGroA1cDWhGo6bexyP73p8zGOFNjFJZf7Wv7Z+wusWIlcGLvX+8t1aQfyjDJxI+VMAJyIiUmTRSJS+oT7iifi45xWSgQM4acFJPL7rcZJNn5Ni8RixeKygEsrUOrpiNxbJlYFLvb9U4Cjla079HEABnEg5UwAnIiJSZKmAZaKMUyFr4CDZkr8z1skrB15JH0t1CyykiUlqHd36ndOUgatVBi4o6qvrqa+uVwAnUsYUwImIiBRZviWD3QPdGEZjTWNer3vSgpMARpRRdsa8AK6ADFzPUA8Aj+16rKiNRSbMwOWxtYL4T5t5i5Q3BXAiIiJFlncGbqCLpkgTVZbfP8fHzTuOcFV4REv+rBm4CdbApTJwPYM9PLf3ubyunY+J1sCphBLM7BUz22xmG81sg3dstpn92sxe8L62TPQ6paQATqS8KYATEREpskICuHzXv0Eys3ZM2zFTz8AN9tBWn9zjq5hllLkycCqhHOMs59xK59wq7/7ngPucc8uB+7z7vlEAJ1LeFMCJiIgUWb4BXPdAd14dKDOdvODkEY1MsmXgwlVhQhYat4nJyQtPpqG6oaiNTHJl4FRCOaF1wI3e9zcC7/VxLMypm6MATqSMKYATEREpskKamOTbwCTlpAUn0dHXwY7uHUD2DBwkyyjHa2LSHGnmpAUnTUsGri5cR7gqrBLKJAfcY2aPmdkV3rF5zrldAN7Xub6NDmXgRMqdAjgREZEiy7dtfvdAd0EllDC2kUkqAzerdtaI82rDteOWUDbVNLF64Wo2vr6RoeGhgsaQS+9gLzWhGsJV4RHHzYzmSLNKKJPWOOdOAs4FPmVmZ+b7RDO7wsw2mNmGjo6Okg2wtb6VroGuov25EJHiUgAnIiJSZPmu+eoa6Cq4hPKE+SdQZVVvBHCxTppqmsYETXXhcTJwgwdpijSxauEqYvEYWzq2FDSGXHqHeseUT6ZEI1GVUALOuZ3e1z3AT4BTgN1mtgDA+7onx3Ovc86tcs6tamtrK9kYU5t57+vfV7JriMjkKYATEREpsobqBgzLaw1coRm4+up6VrSuSHei7Ix1jimfhNwZuIRL0DvYS2NNI6sXrQaKt6F371DvmPLJlOZaZeDMrMHMmlLfA+8AngLuBC71TrsUuMOfESalA7g+BXAi5UgBXCUwG3kTERFfmRnRSDS/NXAFZuAATl548ogSyswGJil11XVZA7i+oT4cjqaaJpa1LGNW7SzWv1acdXC9g8rATWAe8Acz2wQ8CvzCOXc38GXgbDN7ATjbu++bVACndXAi5Sk88SlSTuyascdclmMiIuKvaCRK92DuAG5oeIj+eH/BGTiAk+afxPc3fZ9dB3eNm4HLtg9cag+4xppGzIxVC1cVrZHJuBm4SDPburYV5TpB5ZzbCpyQ5fg+4O3TP6LsFMCJlDdl4EREREpgogxc6rFCu1BCMgMHyUYmuTJwuUooewZ7AGiKNAGweuFqNu/ZnLPhSSHGy8CphDI4FMCJlDcFcCIiIiUQjUTH7UKZCmYmk4E7Yd4JGJYM4GI5SihzNDE5OJjMwDXVvBHAxRNxNr2+qeBxjDZeBi5aoxLKoJhTNwdQACdSrhTAiYiIlMBEGadUMDOZNXBNkSaOmHMEj+16LJmBK6CJSSoD11jTCMCqhauA4jQyyScDl9qAXMpXJByhsaZRAZxImVIAJyIiUgL5llBOJgMHyTLKR3Y8Qn+8P2cTk/HWwKVKKBdHFzOvYV5R1sGNm4GLRIkn4jm3NpDy0lrfyt5+BXAi5UgBnIiISAlEa8YP4FLllZNZAwfJRia7e3cDYzfxhtwZuFQJZSoDl2pkUvIMnJdpnGhzcykPrfWtysCJlCkFcCIiIiUwHRm4lGwllHXh7NsIpJuYeGvgILkO7pm9z6Qfm6zxNvLOd3NzKQ8K4ETKlwI4EakIZnaOmT1nZi+a2eeyPP7XZva0mT1pZveZ2SF+jFNmjmgkSu9QL8OJ4ayPT2UNHMCJ809Mf5+rC2XWJiajSigBVi9aTcIleGLXE5MaC8BwYphYPDZuCSWgRiYBoQBOpHzNnABu9GbX2vBapGKYWQi4FjgXOBq42MyOHnXaE8Aq59zxwK3Av0zvKGWmSQUsuTJOU83ANdc2c/jsw4HsGbh8m5jAG41MprIOrm+oD2DCEkpl4IJhbv1cdvfszvkBhIj4Z+YEcCJSyU4BXnTObXXODQK3AOsyT3DO3e+c6/PuPgIsnuYxygwzUQDXFeuiuqqa2nDtpK9x0oKTgOwZuLpwHfFEnHgiPuL4wcGD1IZrCVeF08fmNsxlSfOSKQVwvUO9ABNn4LQGLhBOXHAi/fF+Nu/Z7PdQRGQUBXAiUgkWAdsz7u/wjuXyUeCXJR2RzHgTrfnqGugiGoliU6gIedOiN1FlVbQ1tI15LBUYjs7CHRw4OCL7lrKidQUv7n9x0mPpHfQCuAnWwKmEMhjWtK8B4MFtD/o8EhEZTQGciFSCbP8DzrrZlJl9CFgFfCXni5ldYWYbzGxDR0dHkYYoM00+JZST7UCZ8snVn+TByx9kdt3sMY/VVdcBjNlKoGeoZ0QDk5QlzUvY3rV9zPF8TZSBUwllsBw661AWNC7goR0P+T0UERmlcgM4rXcTmUl2AO0Z9xcDO0efZGZrgc8D5zvnBnK9mHPuOufcKufcqra2sZkNkXxMWELpZeCmojZcy6mLT835GGTPwGU2MElpj7azu3c3A/GcfzXGNVEGLnVNlVAGg5mxZskaZeBEylDlBnAiMpOsB5ab2WFmVgNcBNyZeYKZnQh8i2TwtseHMcoMk1cGbpIdKPNRF05m4EYHcD2DPVlLKNubk5+B7OjeManrTZSBC1eFaahuKHoJ5XBimIMDB3Eua9JdpmBN+xpe7XqV17pf83soIpKhYgM4u2bkbUZRx02ZYZxzceBK4FfAM8CPnXNbzOwLZna+d9pXgEbgf81so5ndmePlRIpiorb5XbGuKZdQjieVgRu9lcDBwYNZSyjbo8kAbnv35MooJ8rAwcR7403G1s6tRL8c5aYnbyrq60rGOrjtysKJlJPwxKeIiJQ/59xdwF2jjl2d8f3aaR+UzGj5ZOCmWkI5nvFKKFPBWqZUBm6y6+DS2wjkyMBBspFJsTNwnbFOIPtWCjI1K+evpL66nge3PcgHjvmA38MREc+MCeBmXBZORER81VjTiGHjroEraQllriYmgz1Z18AtjiZ31ph0Bm5o4gxcc6S56Bm4zn4vgMuylYJMTXWomlMWnaIMnEiZqdgSShERET9VWRVNkaasAYtzzr8MXI4SyvrqeubUzZl0Bi5dQjlOBi4aiRa9iYkycKW1pn0NG1/fmN4AXkT8pwBORESkRHKt+eqP9xNPxKe9iYlzLmcTE0huJbCte9ukrpdXBq4UJZTKwJXUmvY1DLthHn3tUb+HIiIeBXAiIiIlkiuASx2bjgxcZhOTgeEB4ol41gwcJNfBTSUDF7IQNaGanOdEa4rfxEQZuNI6rf00DNN2AiJlRAGciIhIiUQj0awZp1QZ4XR0oczMwB0cOAiQMwPXHm2f0hq4hpoGbJzOx821zcUvoezvpDZcm36/UlyzamdxzNxjtA5OpIwogBMRESkRPzNw2ZqYpNYxZWtiAskA7kDswKTWO/UO9o5bPgnJJia9Q70MJ4YLfv1cOmOdKp8ssTXta3h4x8NF/b2JyOQpgBMRESmRXF0XU1m5Uq6By5qBG0xm4MYroYTJbSWQysCNZ6KtFSajM9ap8skSW9O+hu6BbrZ0bPF7KCKCAriKMKM3LRcRKWO+ZuC8JiaZa+DyKaGEyW0l0DuURwbOKxktZiOTzn5l4EptzRJvQ2+tgxMpCwrgRERESiRXADcda+BqQjUYNiIDN2EJpZeB29ZVeCfK3kFl4CrVYbMOY37jfB7a8ZDfQxERFMCJiIiUTDQSpWewZ8zaoenIwJkZteHagkooFzUtwrDJl1DmsQYOKGojE2XgSs/MWNO+Rhk4kTKhAE5ERKREUgFaKnBKSZUQljKAg+Q6uGxNTHKVUFaHqlnQtGByJZR5ZOBSGceiZ+AUwJXcmvY1vHzgZXYd3OX3UERmPAVwIiIiJZKrZLAr1kV9dT3hqnBJrz8mA+etgctVQgmT30ognwxc6udRrDVww4lhuge6VUI5DdLr4LSdgIjvFMCJiIiUSK4Arnugu6QdKFPqqutGNjEZHL+JCUx+M+98txGA7CWUCZfgaw9/jc7+zryveSB2AEAZuGlw4vwTqQvX8Ydtf/B7KCIzngI4ERGREkkFLGMycANdJS+fhLEZuJ7BHsJVYSKhSM7npDJwzrmCrpXPNgLjlVBu3r2Zz9zzGW575ra8r9kZSwZ7ysCVXnWomjVL1nD/K/f7PRSRGU8BnIiISImMm4ErYQfKlLpw3ZgSyqaaJsws53Pao+30DfWxv39/3tdxzuWVgasL1xGyUNYSyle7XgVgT++evK+bytYpAzc91h62lid3P8nunt1+D0VkRlMAN1OYjb2JiEhJ5VwDN9A1LSWUteHaESWUPUM945ZPQsZm3gWsg4vFYzjchBk4M6O5tjlrCWVq64KO3o68r6sM3PRau3QtAL95+Tc+j0RkZlMAJyIiUiLpph2jApbuge5pKaGsq86SgRungQnAkuYlAAWtg+sd6gWYMAMH3t54g2NLKNMBXF8BAZwycNNq5fyVzK6bzb1b7/V7KCIzmgI4ERGREhmvC+W0ZeCGRjYxmTADFy08A9c76AVwE2TgILkucLwMXEEllMrATatQVYizDj2LX2/9dcFrJEWkeBTAiYiIlEgqWMq2Bs6vJia5NvFOmdc4j+qq6pJl4Jprm7M2MVEGLhjWLl3L9u7tvLj/Rb+HIjJjTSmAM7NXzGyzmW00sw3esdlm9msze8H7qllVRERmpFBViKaaphEBy3BimIODB6eticmIbQTyKKGssioWRReVLAMXjUSL18Qk1kkkFKGuui7v58jUpNbBqYxSxD/FyMCd5Zxb6Zxb5d3/HHCfc245cJ93X0REZEaKRqLpAG5oeIjrH78+fbzUsmXgJiqhhGQZZSojlo+CMnBZSigHhwfZdXAXVVZFR29H3uV5nf2dgSqfNLN2M7vfzJ4xsy1mdpV3/Boze837QHyjmb3L77HmsqxlGYc0H8K9LyuAE/FLuASvuQ54q/f9jcBvgc+W4DqSg10z9pjLckxEREovGomyr38f1z9+PV/6/Zd45cArnLzgZC485sKSX3vMNgKDBycsoYRkJ8qHtj805nhHbwf3v3I/HzjmAyOOF5qBG11C+Vr3azgcK1pX8HTH03lvs9AZ6wxa+WQc+Ixz7nEzawIeM7Nfe4/9m3Puqz6OLS9mxtqla7ntmdsYTgwTqgr5PSSRGWeqGTgH3GNmj5nZFd6xec65XQDe17lTvIaIiEhgRSNR7njuDj7+s4/TWt/Kzy/+Oes/vp5F0UUlv/aYJiYDEzcxgWQG7rXu10i4xIjj/3D/P3DhrReOKXMsOAM30DUiy5bK9q1amCzmybeMsjMWrAycc26Xc+5x7/uDwDNA6f8gFNnapWs5EDvA47se93soIjPSVAO4Nc65k4BzgU+Z2Zn5PtHMrjCzDWa2oaMj/wXLIiIiQXL20rM5Y8kZ/OLPfsGjH3uU8444b9yNtIuprrqOgeEBnHMMDQ8xMDyQXwYu2s5QYmjEhs2xeIwfbfkRAsbaSAAAIABJREFUwJgGFgV1oaxtJp6Ij8gMpgO4BckALt9GJgdiB4KWgUszs0OBE4E/eoeuNLMnzeyGcu8f8LbD3gZoHZyIX6YUwDnndnpf9wA/AU4BdpvZAgDva9aP0Zxz1znnVjnnVrW1tU1lGCIiImXri2/7Ig9c9gDvWv6uaQvcUmrDtUAy+OoZ7AGYsIkJZOwFl9HI5GfP/YwDsQMAvLT/pRHnF7oPHDCikUmqgclJC04CCsjABWwNXIqZNQK3Af/HOdcN/BewDFgJ7AL+NcfzyuLD77kNczlh3glaByfik0kHcGbW4NVvY2YNwDuAp4A7gUu90y4F7pjqIEVERKRw2QK4vEoom7294DK2Erhx040saFyAYbzUOSqAK3AfOBi5ufm2rm3MbZibDhw7evMLTgK4Bg4zqyYZvN3snLsdwDm32zk37JxLAN8m+YH4GOX04ffapWv5w7Y/0DfU5+s4RGaiqWTg5gF/MLNNwKPAL5xzdwNfBs42sxeAs737Uo7MRt5ERKSi1IWT7fX74/0cHDwIkHcJJbxR2ri7Zzd3v3g3Hz7hwyyOLmZr59YR5/cO9WJY+nrjyba5+baubSxpXkJbQzIoyaeEMuESdMW6AhXAWTIF+x3gGefc1zKOL8g47X0kPxAva2uXrmVweJAHtz3o91BEZpxJd6F0zm0FTshyfB/w9qkMSkRERKYuMwN3cCAZwOWTgZtdN5u6cF26hPIHm3/AsBvm0hMu5Y+v/TFrBq6+uj6vEtFUd8nMEsptXdtY0baC2nAtTTVNeZVQdsW6cLiglVCuAS4BNpvZRu/Y/wUuNrOVJJvDvQL8uT/Dy98ZS86guqqae7fey9nLzvZ7OCIzSjH2gRMR8Z2ZnWNmz5nZi2Y2Zv9JMzvTzB43s7iZvd+PMYpMt9QG14WugTMz2pvb0wHcjZtuZPXC1axoW8GylmVZ18DlUz4Jb5RQpjJwzjle7XqVQ5oPAaCtoS2vDFxnrBMgUBk459wfnHPmnDve20N3pXPuLufcJc6547zj56e6eZezhpoG3tz+Zq2DE/GBAjgRCTwzCwHXkuyIezTJT7OPHnXaNuAjwA+md3Qi/kll4PqHCiuhhGQZ5fau7Wx6fRObdm/iwyd8GIClLUvZ3bs7HRCCF8Dl0cAEMpqYeGvg9vfvp2+oL73+bW7D3LzWwHX2ewFcsDJwFWXt0rU8seuJvNcsikhxKICbIeyasTeRCnIK8KJzbqtzbhC4BViXeYJz7hXn3JNAItsLiFSi1Jq0QpuYAOkM3Pc3fZ/qqmouPvZiAJa1LAMYsQ6ud7CADNyoEsrUOrtUANdW35ZXCWUQM3CV5t1HvBuAM757RtaN30WkNBTAiUglWARsz7i/gwBujitSbOkMXLw/vQYunxJKgCXRJew6uIubNt/Eu494N3Pq5wCwbHYygMssoywkA5fKAKZKKLMFcHmVUCoD57uV81dyzyX3EIvHOP2G0/nrX/21ulKKTAMFcCJSCbJ1TnCTfrEy2WtJZKpGNDEZzL+JCSQzcA7Hnt49XHrCpenjqQxcZiOTQjJw1aFq6qvr0yWUqQAutQYuVULp3Ph/hZWBKw9rl65l8yc284lVn+DfHvk3TvjvE9SZUqTEFMCJSCXYAbRn3F8M7Jzsi5XTXksiUzG6iYlheWfKUlsJzKmbw7nLz00fb6lroaW2ZdIZOEg2MkmVUL7a9Sq14Vpa61uBZBOTocTQiC6V2SgDVz6aIk1ce961/ObDv2E4Mcw5N5+TzviKSPEpgBORSrAeWG5mh5lZDXARcKfPYxLx3YgmJgMHaaxpzKvVP7yxmfefHfdn1IRqRjy2bPaySWfgINnIJLOEcknzkvS42uq9veAmaIzRGeukJlST195zMj3OOuwsfvCnP6BnsIcfb/mx38MRqVgK4EQk8JxzceBK4FfAM8CPnXNbzOwLZnY+gJmtNrMdwAXAt8xsi38jFpkemU1MDg4ezLt8EuCo1qP44llf5HOnj9mVg2Uty0Y2MSk0A1fbPKKJSWr9GyRLKIEJG5l09nfSUtuSd0Aq0+NNi97EUa1H8d2N3/V7KCIVa9IbeZcT+ydN3iIznXPuLuCuUceuzvh+PcnSSpEZI7OJSc9gT94NTACqrIq/P/Pvsz62rGUZtz1zG/FEnHBVOJmBKyCAG52BO/fwN0o02xq8DNwEjUw6Y50qnyxDZsZlKy/js/d+luf2PseRrUf6PSSRiqMMnIiISIXKXANXaAZuPMtmLyOeiKcbkBSykTd4a+BiXQzEB9jVs4tDZh2SfizvDFysUw1MytQlx19CyEJ8b+P3/B6KSEVSACciIlKhIqEIkFwD1zPYk/cm3hNJd6Lc/xKDw4PEE3Hqq+vzfn6qicmO7h0AI0oo814D168MXLla0LSAc5efy42bbiSeiPs9HJGKowBORESkQoWqQlRXVSczcAMHCyqhHE96L7jOl+gd7AWYVAnl6D3gACLhCE01TfmVUCoDV7YuX3k5u3p2cc9L9/g9FJGKowBORESkgtVV1xW9hHJh00IioQgv7X+J3iEvgCukhLK2mZ7BHl4+8DIwMoCDZBllvk1MpDydd8R5tNa3csMTN/g9FJGKowBORESkgtWGa99oYlKkEsoqq2Jpy9JJZ+CaI80AbNmTbAa7ODqyv1BbQ9u4GbiES3AgdkAllGWsJlTDJcdfwp3P3cnevr1+D0ekoiiAExERqWB14bp0CWWxMnDwxl5wk8nARSNRADbv2cz8xvnpbpkpE2Xguge6cThl4MrcZSsvYygxxM1P3uz3UEQqigI4ERGRClYbrqVvqI/eod6iZeAAls5amiyhnEwGrjaZgXty95Njyich2chkvCYmnf2dAMrAlbnj5h3HqoWruGHjDTjn/B6OSMVQACciIlLBasO16RK2YjUxgWQGrneoN72ObTIZuN29u3MHcH0dOf/T3xnzAjhl4MreZSsv48ndT/LE60/4PRSRiqEAbgaza0beRESk8tRV16XXkxW1hNLbSuDJ3U8Ck1sDB7AkOjaAm9swl3gizoHYgazPVwYuOC4+9mIioQh/9au/Yn//fr+HI1IRFMDJ+MxG3kREJFBqw7XpcsRillCmthLYvGczUHgXypTMTbxT2hq8veByNDJJZeBm1c7K+5rij5a6Fr79nm/zyI5HWP3t1Ty15ym/hyQSeArgREREKlhduC5dQlnMDNxhsw7DsEll4FIllDB2CwFIZuCAnI1M0hk4lVAGwiUnXMJvL/0t/UP9nHr9qdz+zO1+D0kk0BTAiYiIVLDacC3Dbhgo7hq4SDjC4uhiXu95HSgwA5dZQpljDRyQs5FJeg2cSigD47T209hwxQaOnXssf/rjP+Xq+68m4RJ+D0skkBTAiYiIVLC66rr098UsoYQ3yiihsAxcfXU9IQsBOQK4iUoo+zsJV4ULuqb4b2HTQn73kd9x+crL+eIDX+Rjd36M4cSw38MSCRwFcCIiIhWsNvTGHmvFLKGENxqZREIRQlWhvJ9nZkQjUerCdcypmzPm8VQGLmcJZayTltoWTGuzAycSjnD9+ddzzVuu4bsbv8uHfvIhhoaH/B6WSKCE/R6AlBH9QygiUnEyN8kuZgklvBHAFVI+mdJc28y88LysQVgkHCEaiY5bQqnyyeAyM/7xrf9IXXUdn733swzEB/jhn/6QSDji99BEAkEZOBERkQqWWUJZ9AycV0I5mVLG1vpWlrYszfn43Ia545ZQqoFJ8P3tmr/lP879D37y7E9434/eR/9Qv99DEgkEZeBEREQqWGYGrlQllJPJwH1v3fdGBJejtdW3jVtC2VrfWvA1pfxcecqV1IXr+PjPPs4Z3z2DT67+JH+y4k+0RYTIOJSBExERqWB14br013BVcT+3nUoG7pi5x4ybgWtraFMGbob46Ekf5Ufv/xGdsU4+eudHmffVeay7ZR23PHWLsnIiWSiAExERqWCpDFyxs2+Q3Eh7dt3sSWXgJjK3fu6ETUykclxwzAW8+Jcv8ujHHuVTqz/Fhp0buPi2iznmm8dw94t3+z08kbKiAE7GZdeMvGU/yUbeRESkbKTKFIvdwCTl5AUnZ90KYKraGtrY27cX59yI4wmX4EDsgJqYVCAzY/Wi1XztnV9j+19t55cf/CWRcIRzbz6XC2+9kF0Hd/k9RJGyoABOCjM6WFPAJiJS1kqZgQO4/cLbue7d1xX9ddvq24gn4hyIHRhx/ODAQRIuoQxchauyKs45/Bw2/vlGvvDWL3DHs3dw1LVHce2j1xKLx/wenoivFMCJiIhUsFQAV+xNvFMaaxrHbUYyWXMb5gJj94LrjHUCKAM3Q0TCEf7hLf/A5k9s5pRFp3DlL69kwb8u4BM//wSP7HgknaF1zvHKgVe46cmbuOqXV/Hz53/u88hFSkddKEVERCpYqolJqUooS6WtIbmZd0dfB0dyZPp4Z78XwCkDN6Msn7Ocez50D/e9fB/f2/g9btx0I//92H9zxJwjOH7e8Ty8/WFeO/gaACEL8e+P/jufP+PzfOGsL1BlyldIZVEAJ2k517iJiEhglbqEslSUgZPRzIy1S9eydulauge6ufXpW7lx042sf209py85nTOWnMHpS05n+ZzlfPqXn+ZLv/8SG1/fyE1/cpO2JZCKogBORESkgqWbmJSohLJU2uq9DFzvyK0ElIETgGgkyuUnXs7lJ16e9fFvv+fbnLzgZD5996c55duncMdFd7CibcU0j1KkNBTASfFla2wyqouYiIhMj6Bm4FIbdY/eC04ZOMmHmfGJ1Z/g2LnH8v7/fT8nfutEFkUXUV9dn77Nqp3FkugSDpl1CEual7CkeQnzGuYxu242jTWNmBq1SZlSACflo1iBnwJIEZG09Bq4gGXgIuEIzZHmsSWUysBJAc445Aw2fHwDX33oq+yP7advqI++oT56B3t5ft/z3Lv1XnoGe8Y8r7qqmtl1s5nXOI/TFp/GWYeexVmHnZUu7U2JJ+Ls7tlNW0MbNaGa6XpbMsMpgBMREalg6S6UAWtiAslGJtkycCELBS6jKP5pb27nG+d+I+tjzjm6BrrY1rWNbV3b2NO7h319+9jfv599/fvY3r2dH2z+Ad967FsAHNN2DMtmL2PnwZ281v0au3t3k3AJmmqaeMeyd3De8vN41/J3Ma9xHs45dnTv4OmOp3m642niiTintZ/GqoWr0n8vRSZDAZyIVAQzOwf4BhACrnfOfXnU4xHg+8DJwD7gQufcK9M9TpHpFtQSSkg2MsmWgWupa6m48raJ5jApDTNjVu0sZtXO4vh5x2c9J56I8/iux/nNy7/h/lfu5+XOl1nYtJDj5x7P4uhi5jXOY+PrG/nFC7/gtmduA+CIOUew8+DOrNm9mlANqxeuZk37GpbPWc7chrnp2/zG+dRX15f0PUvwKYCTgmTrVOmyHBOZTmYWAq4FzgZ2AOvN7E7n3NMZp30U6HTOHW5mFwH/D7hw+kcrMr3am9v5u9P/jvOPPN/voRSsrb6NrZ1bRxzrjHVWXPlknnOY+CRcFeaURadwyqJT+Nzpn8t5nnOOTbs38fPnf876net557J3cnTb0RzddjQrWpMNVB7a/hB/2PYHHtz+IP/2yL8xlBga8RpVVsUZS87gT1b8Ce876n20N7cDkHAJtuzZwm9f+S0Pbn+Qppomjpl7DMfOPZZj5x7LvIZ5APQM9nAgdoCugS5qQjUsbVlKuEr/3a80+o3K9NC6NCmtU4AXnXNbAczsFmAdkPmfn3XANd73twL/aWbmnP4gSmWrsir++e3/7PcwJqWtvo0/vvbHEcc6Y52V2MAknzlMypyZsXL+SlbOX5nznHVHrWPdUesAGIgPsLt3N3t696RvL+x7gTueu4Or7r6Kq+6+itULV7M4upgHXn2Aff37AFgcXUz/UD/XP3F9+nXrq+uJxWMkXGLE9WpCNRw550iOmXsMR7cenS6lds7hcFRZFc2RZlrqWmipbaGlroXacC2Dw4Pp20B8gI6+DnYd3MWunuTtQOwAs2pn0VrXSmt9K3Pq59AcaaY6VE1NqCZ9a440px9X2WjxKICTKRudlcs7I5dP+cvoc/R/bcluEbA94/4O4E25znHOxc2sC5gD7J2WEYpIweY2zGV3z25O+85p6WNP7XmKNe1rfBxVSeQzh0mFiYQj6e6Xmb709i/x/L7n+ckzP+H2Z29n0+5NvOfI9/CWQ97CWw99K4fOOhTnHHt697ClYwtb9mzh5QMv01DdwKzaWTTXNjOrdha9g71s6djC0x1P88iOR7jlqVumPOaQhZjfOJ+Wuha6Yl109HUQi8fyem59dT0ttckAMTPQq7Iq4ok48UScoeEh4ok4ZkbIQoSqQumvRvL/hKny6eHEMAPDA+kgc3B4kHBVmEg4QiQUSV9naHiIweFBhhLJr6kgN/P1wlVhqquqqQ5VU11VTagqlB7PUCI5Judc+vHqUDXhqvCYTeJTgXHCJXAu+fXE+SfynXXfmfLPPpMCOAkWZfIku2yfBoz+g5HPOckTza4ArgBYsmRJtlNEZBq8b8X7eOL1Jxh2w+ljb25/M5etvMzHUZVEXvOT5qaZ44g5R/DZ0z/LZ0//bNbHzYx5jfOY1ziPtx32trxes2+oj4H4QDoAMoyES3AgdoDOWCed/Z10xjoZiA8QCUdGZNLm1M1hQdMCWutbxwQtfUN97O3bS/dAdzpYGhweZGB4gK5YF3v79rKvfx/7+vbRGesckd0bSgwxnBhOB0Spm3OOYTfMcGI4/RXAZfy1qLIqIqHkOCPhCDVVNcQTcQaGB5I3L6irCdWkA8ZUcJYqvnE4nHPJYC0xlA7YMseUCtiAEQHd0PDQiPFkjsuw5Fcz2hra8vr9FCJwAZz9U2UtWhaRotgBtGfcXwzszHHODjMLA83A/mwv5py7DrgOYNWqVfqEQMQnqxau4q4P3uX3MKZDPnOY5iaZktT+d6O11LVwGIdN6XVHZxGltKomPkVEpOytB5ab2WFmVgNcBNw56pw7gUu9798P/Ebr30SkTOQzh4mIAAHMwEnlmPTauVJReWZgeWvargR+RbIF9w3OuS1m9gVgg3PuTuA7wP+Y2YskM28X+TdiEZE35JrDfB6WiJQpBXBSdNpqQPzgnLsLuGvUsaszvo8BF0z3uERE8pFtDhMRyUYBnJS1omXpJptdUxdMERERESkjCuCkbGTL3FU0BYciIiIiUiA1MREREREREQkIZeBkWsy47Fq5U8MWERERkUBSACeBkrVBSrZgJK8XC+Ceggq8RERERGY0BXBScfLN9hWtM2ax1rL5HZz5fX0RERERmVDJAjgzOwf4Bsn9TK53zn25VNcSmYx8OlyOOSefrF0xA6FiZQmLlaVUQCciIiLiq5IEcGYWAq4FzgZ2AOvN7E7n3NOluJ5IMZR0nV4pyzWDWAoqIiIiIpNSqi6UpwAvOue2OucGgVuAdSW6loiIiIiIyIxQqhLKRcD2jPs7gDeV6Foyw01nh8usTVSuKfycYr52Ptcq2oboIiIiIuIrcyVY02JmFwDvdM59zLt/CXCKc+4vM865ArjCu3sk8FyeL98K7C3icKdbkMevsfsjqGM/xDnX5vcgpsrMOoBXxzklqL+fTEF/Dxq/v4I4/sDPT3nMTRDM300mjd9fGv/0y2tuKlUGbgfQnnF/MbAz8wTn3HXAdYW+sJltcM6tmtrw/BPk8Wvs/gjy2CvBRBNpJfx+gv4eNH5/BX38QZXPf/KC/rvR+P2l8ZevUq2BWw8sN7PDzKwGuAi4s0TXEhERERERmRFKkoFzzsXN7ErgVyS3EbjBObelFNcSERERERGZKUq2D5xz7i7grhK8dMFll2UmyOPX2P0R5LHPBJXw+wn6e9D4/RX08VeyoP9uNH5/afxlqiRNTERERERERKT4SrUGTkRERERERIosUAGcmZ1jZs+Z2Ytm9jm/x5MvM2s3s/vN7Bkz22JmV/k9pkKZWcjMnjCzn/s9lkKY2Swzu9XMnvV+/qf5PaZ8mdlfeX9enjKzH5pZrd9jkpGCOidBZcxLENy5CYI9P4HmqHIW5LkJKmN+CvLcBJqfyl1gAjgzCwHXAucCRwMXm9nR/o4qb3HgM865FcCpwKcCNPaUq4Bn/B7EJHwDuNs5dxRwAgF5D2a2CPg0sMo5dyzJZkAX+TsqyRTwOQkqY16C4M5NEND5CTRHlbMKmJugMuanIM9NoPmprAUmgANOAV50zm11zg0CtwDrfB5TXpxzu5xzj3vfHyT5l2CRv6PKn5ktBs4Drvd7LIUwsyhwJvAdAOfcoHPugL+jKkgYqDOzMFDPqL0UxXeBnZMg+PMSBHdugoqYn0BzVLkK9NwEwZ+fgjw3geanIAhSALcI2J5xfwcB+sucYmaHAicCf/R3JAX5OvC3QMLvgRRoKdABfNcrY7jezBr8HlQ+nHOvAV8FtgG7gC7n3D3+jkpGqYg5CQI7L0Fw5yYI8PwEmqPKXMXMTRDY+SnIcxNofip7QQrgLMuxQLXQNLNG4Dbg/zjnuv0eTz7M7N3AHufcY36PZRLCwEnAfznnTgR6gUCsBTCzFpKfmB4GLAQazOxD/o5KRgn8nATBnJcg8HMTBHh+As1RZa4i5iYI5vxUAXMTaH4qe0EK4HYA7Rn3FxOgdKiZVZOchG52zt3u93gKsAY438xeIVmG8TYzu8nfIeVtB7DDOZf61O5WkhNSEKwFXnbOdTjnhoDbgTf7PCYZKdBzEgR6XoJgz00Q7PkJNEeVs8DPTRDo+SnocxNofip7QQrg1gPLzewwM6shuRjxTp/HlBczM5J1xM84577m93gK4Zz7O+fcYufcoSR/5r9xzgXiUwzn3OvAdjM70jv0duBpH4dUiG3AqWZW7/35eTsBWkA8QwR2ToJgz0sQ7LkJAj8/geaochbouQmCPT8FfW4CzU9BEPZ7APlyzsXN7ErgVyS7ydzgnNvi87DytQa4BNhsZhu9Y//XOXeXj2OaKf4SuNn7R2wrcJnP48mLc+6PZnYr8DjJblxPANf5OyrJFPA5CTQvlYNAzk+gOaqcVcDcBJqfyoHmpzJmzgWyLFpERERERGTGCVIJpYiIiIiIyIymAE5ERERERCQgFMCJiIiIiIgEhAI4ERERERGRgFAAJyIiIiIiEhAK4HxmZp83sy1m9qSZbTSzN/k0jllm9smM+wu9FqzFev1bzWxpsV7PT2b2ETP7z3EeP87MvjeNQxIpCc1PwaP5SWYCzU3Bo7mpuBTA+cjMTgPeDZzknDue5M7x20t4vfH2/ZsFpCch59xO59z7i3TdY4CQc25rAc8JFePafnDObQYWm9kSv8ciMlman8Z9juYnEZ9obhr3OZqbZggFcP5aAOx1zg0AOOf2Oud2ApjZyWb2OzN7zMx+ZWYLvOO/NbOvm9lDZvaUmZ3iHW8wsxvMbL2ZPWFm67zjHzGz/zWznwH3mFmjmd1nZo+b2ebUecCXgWXeJ1lfMbNDzewp7zVqzey73vlPmNlZGa99u5ndbWYvmNm/5HifHwTuSN0xs/8ysw3ep2f/lHH8FTO72sz+AFxgZoeb2b1mtskb7zIzW2BmD3jjfMrMzvCe+w4ze9g773/NrNE7vtr7WW0ys0fNrGky78fMLjOz583sdyQ3GE0dv8AbxyYzeyDjPf8MuKiQPwwiZUbzk+YnkXKkuUlzkzjndPPpBjQCG4HngW8Cb/GOVwMPAW3e/QuBG7zvfwt82/v+TOAp7/t/Bj7kfT/Le80G4CPADmC291gYiHrftwIvAgYcmnot77FDM177M8B3ve+PArYBtd5rbwWavfuvAu1Z3ufvgOMy7qfGEvLez/He/VeAv80474/A+7zva4F6byyfz3h+k/c+HgAavOOfBa4GarzxrfaOR733X9D7IfmPxTagzXvNB4H/9J6/GViU+rlnjH0N8DO//4zppttkb5qfND/ppls53jQ3aW7SzTFeWlhKzDnXY2YnA2cAZwE/MrPPARuAY4Ffmxkk/7LtynjqD73nP2BmUTObBbwDON/M/sY7pxZIpaF/7Zzb731vwD+b2ZlAAlgEzJtgqKcD/+Fd81kzexU4wnvsPudcF4CZPQ0cwthShgVAR8b9D5jZFSQnhAXA0cCT3mM/8l6rieRf7p941415x9cDN5hZNfBT59xGM3uL9xoPej+vGuBh4Ehgl3Nuvfca3d5rFPp+WoHfOuc6vOM/yjj/QeB7ZvZj4PaM97gHWDjBz1WkbGl+0vwkUo40N2luEhTA+c05N0zyk5Tfmtlm4FLgMWCLc+60XE/Lct+AP3XOPZf5gCUX9vZmHPogyU9DTnbODZnZKyQnrPHYOI8NZHw/TPY/U/2pa5jZYcDfkPxkp9OSC1Yzr58aa9ZrehPvmcB5wP+Y2VeATpIT7cUjBm12PGN/VpN9P9leB+fcX3g/4/OAjWa20jm3z3tP/eNcR6TsaX7S/CRSjjQ3aW6a6bQGzkdmdqSZLc84tJJk6vk5oM2SC3Uxs2pLLmZNudA7fjrQ5X3q8SvgL837GMXMTsxx2WZgjzcBnUXyUxKAgyRT6tk8QHLywsyOIPnp1HM5zs3mGeBw7/soyYmmy8zmAedme4L3ic8OM3uvd92ImdWb2SHe+L8NfAc4CXgEWGNmh3vn1nvjfBZYaGarveNNllyMXOj7+SPwVjOb4316dUHqATNb5pz7o3PuamAvybIBSH7K9FT+PyKR8qL5SfOTSDnS3KS5SZSB81sj8B9eGj9Osqb6CufcoJm9H/h3M2sm+Xv6OrDFe16nmT1E8i/05d6xL3rnPOlNRK+Q7NI02s3Az8xsA8ka8mcBnHP7zOxBSy6+/SVwbcZzvgn8t/cpVxz4iHNuwJvv8vEL4K3Avc65TWb2hPdetpJMo+dyCfAtM/sCMETyL/8ZwP9nZkNAD/Bh51yHmX0E+KGZRbzn/r1z7nld0OR2AAAA4ElEQVQzu5Dkz7iO5Kc6awt9P865XWZ2DcnSgl3A4yRLMwC+4v1DYsB9wCbv+Fne+xYJKs1Pmp9EypHmJs1NM545lzW7KWXKzH4L/I1zboPfY8mXNwHcD6zxyh4qmjcR/g443TkX93s8ItNF81P50/wkM5HmpvKnuakwKqGUknPO9QP/SHLR70ywBPicJiCR8qf5SUTKkeYmGY8ycCIiIiIiIgGhDJyIiIiIiEhAKIATEREREREJCAVwIiIiIiIiAaEATkREREREJCAUwImIiIiIiASEAjgREREREZGA+P8BPzSw2gXHVgAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (15,6)\n", "bins = np.arange(0,search_rad,search_rad/50)\n", "plt.subplot(1,3,1)\n", "mask = ~np.isnan(radio['lr_dist_i'])\n", "plt.hist(radio['lr_dist_i'][mask],bins=bins,color='red')\n", "mask = (radio['lrt'] > threshold_sel) & (~np.isnan(radio['lr_dist_i']))\n", "plt.hist(radio['lr_dist_i'][mask],bins=bins,color='green')\n", "plt.xlabel('Seperation (arcseconds)')\n", "green_patch = mpatches.Patch(color='green',label='Trusted Crossmatches')\n", "red_patch = mpatches.Patch(color='red',label='All Crossmatches')\n", "plt.legend(handles=[red_patch,green_patch])\n", "\n", "plt.subplot(1,3,2)\n", "reliability_r = []\n", "mask1 = (~np.isnan(radio['lr_dist_i']))\n", "for n in range(len(bins)-1):\n", " mask = (radio['lr_dist_i'] > bins[n]) & (radio['lr_dist_i'] < bins[n+1])\n", " mask = mask[mask1]\n", " rel_temp = np.array(rel)[mask]\n", " reliability_r.append(np.mean(rel_temp))\n", "rad_cent = get_center(bins)\n", "plt.plot(rad_cent,reliability_r,color='green')\n", "plt.xlabel('Seperation (arcseconds)')\n", "plt.ylabel('Reliability')\n", "\n", "plt.subplot(1,3,3)\n", "lrt_r = []\n", "mask1 = (~np.isnan(radio['lr_dist_i']))\n", "for n in range(len(bins)-1):\n", " mask = (radio['lr_dist_i'] > bins[n]) & (radio['lr_dist_i'] < bins[n+1])\n", " #mask = mask[mask1]\n", " rel_temp = radio['lrt'][mask]\n", " lrt_r.append(np.mean(rel_temp))\n", "rad_cent = get_center(bins)\n", "plt.plot(rad_cent,lrt_r,color='green')\n", "plt.xlabel('Seperation (arcseconds)')\n", "plt.ylabel('likelihood ratio')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams[\"figure.figsize\"] = (15,6)\n", "plt.subplot(1,2,1)\n", "plt.plot(thresholds, completeness_t, \"r-\")\n", "plt.plot(thresholds, reliability_t, \"g-\")\n", "plt.plot(thresholds, average_t, \"k-\")\n", "plt.vlines(threshold_sel, 0.9, 1., \"k\", linestyles=\"dashed\")\n", "plt.vlines(threshold_mean, 0.9, 1., \"y\", linestyles=\"dashed\")\n", "plt.ylim([0.9, 1.])\n", "plt.xlabel(\"Threshold\")\n", "plt.ylabel(\"Completeness/Reliability\")\n", "black_patch = mpatches.Patch(color='black',label='Average Reliability and Completeness')\n", "green_patch = mpatches.Patch(color='green',label='Reliability')\n", "red_patch = mpatches.Patch(color='red',label='Completeness')\n", "plt.legend(handles=[black_patch,red_patch,green_patch])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating masterlist table of cross matched sources\n", "number of crossmatched sources is 1867\n" ] } ], "source": [ "print('creating masterlist table of cross matched sources')\n", "radio_match = radio[~np.isnan(radio['lr_dist_i'])]\n", "radio_match = radio_match[radio_match['lr_i']>threshold_sel]\n", "\n", "lockman_match = masterlist[radio_match['lr_index_i'].quantity.value.astype(int)]\n", " \n", "new_table = astropy.table.hstack([radio_match,lockman_match],join_type='outer')\n", "print('number of crossmatched sources is {}'.format(len(new_table)))" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/im281/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/astropy/table/column.py:965: RuntimeWarning: invalid value encountered in greater\n", " return getattr(self.data, op)(other)\n", "/Users/im281/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" ] }, { "data": { "text/plain": [ "Text(0,0.5,'likelihood ratio')" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mag_r = []\n", "mask1 = (~np.isnan(radio['lr_dist_i']))\n", "for n in range(len(bins)-1):\n", " mask = (radio['lr_dist_i'] > bins[n]) & (radio['lr_dist_i'] < bins[n+1])\n", " id_temp = radio['lr_index_i'][mask].quantity.value.astype(int)\n", " mag_temp = catalogue_i[id_temp]['m_irac_i1']\n", " mag_r.append(np.mean(mag_temp))\n", "\n", "rad_cent = get_center(bins)\n", "plt.plot(rad_cent,mag_r,color='green')\n", "plt.xlabel('Seperation (arcseconds)')\n", "plt.ylabel('likelihood ratio')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "new_table[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "catalogue_i[4365644]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Demonstarting that the likelihood_ratio function has the same output as this notebook\n", "cross_match_table = likelihood_ratio(lofar_data,lockman_data,'RA','DEC','ra','dec','m_irac_i1',Q0,search_rad,min(lofar_data['RA']),max(lofar_data['RA']),min(lofar_data['DEC']),max(lofar_data['DEC']),moc=MHz150_MOC)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask = ~np.isnan(cross_match_table['lrt'])\n", "table_temp = cross_match_table[mask]\n", "bins = np.arange(12,26,0.5)\n", "y = []\n", "for n in range(len(bins)-1):\n", " mask = (table_temp['m_irac_i1'] > bins[n]) & (table_temp['m_irac_i1'] < bins[n+1])\n", " lrt_temp = np.mean(table_temp['lrt'][mask])\n", " y.append(lrt_temp)\n", "x = get_center(bins)\n", "\n", "plt.plot(x,y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mask = ~np.isnan(cross_match_table['lrt'])\n", "len(cross_match_table[mask])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(rel)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "radio" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:herschelhelp_internal]", "language": "python", "name": "conda-env-herschelhelp_internal-py" }, "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": 2 }