{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Make depth summary figures\n", "\n", "This notebook is to produce some figures for the ELAIS-N1 paper.\n", "\n", "First we will, for every helpix 13 in HELP get the highest K or Ks depth, g depth, and irac 1 depth. We will then plot cumulative area histograms and generate the 25%, 50%, 75% areas to summarise the depths across all HELP.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This notebook was run with herschelhelp_internal version: \n", "1407877 (Mon Feb 4 12:56:29 2019 +0000)\n" ] } ], "source": [ "from herschelhelp_internal import git_version\n", "print(\"This notebook was run with herschelhelp_internal version: \\n{}\".format(git_version()))\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", " return f(*args, **kwds)\n" ] } ], "source": [ "import pyvo as vo\n", "import glob\n", "import time\n", "import numpy as np\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", "\n", "import random\n", "\n", "import herschelhelp as hh\n", "from herschelhelp_internal.utils import flux_to_mag\n", "\n", "from astropy.table import Table, Column, vstack, join, unique\n", "\n", "from pymoc import MOC\n", "\n", "import pandas as pd\n", "\n", "import yaml\n", "\n", "from pcigale.sed import SED\n", "from pcigale.sed_modules import get_module" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "\n", "#Then we establish the VO connection to our database\n", "service = vo.dal.TAPService(\"https://herschel-vos.phys.sussex.ac.uk/__system__/tap/run/tap\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "herschelhelp_python_loc = ('../../../../herschelhelp_python/')\n", "\n", "filter_locs = glob.glob(herschelhelp_python_loc + 'database_builder/filters/*')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "filters = [f.split('/')[-1].split('.')[0] for f in filter_locs]\n", "filters.remove('readme')\n", "\n", "bands = ['u', 'g', 'r', 'i', 'z', 'y',\n", " 'J', 'H', 'K', 'Ks',\n", " 'i1', 'i2', 'i3', 'i4']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['wfi_696nm',\n", " 'suprime_nb816',\n", " 'wfc3_f125w',\n", " 'mmt_g',\n", " 'omegacam_r',\n", " 'decam_z',\n", " 'wfi_416nm',\n", " 'sdss_i',\n", " 'omegacam_g',\n", " 'suprime_ib464',\n", " 'wfi_571nm',\n", " 'mmt_r',\n", " 'wfc3_f105w',\n", " 'spire_350',\n", " 'decam_y',\n", " 'suprime_ib505',\n", " 'cfht12k_b',\n", " '90prime_z',\n", " 'omegacam_u',\n", " 'suprime_ip',\n", " 'isaac_k',\n", " 'galex_nuv',\n", " 'acs_f775w',\n", " 'moircs_k',\n", " 'sdss_z',\n", " 'acs_f606w',\n", " 'cfht12k_r',\n", " 'decam_i',\n", " 'quirc_hk',\n", " 'mmt_u',\n", " 'suprime_zpp',\n", " 'suprime_ia624',\n", " 'megacam_r',\n", " 'bessell_v',\n", " 'wircam_ks',\n", " 'mips_24',\n", " 'omega2000_j',\n", " 'wfi_r',\n", " 'spire_250',\n", " 'suprime_r',\n", " 'gpc1_i',\n", " 'suprime_g',\n", " 'bessell_b',\n", " 'bessell_u',\n", " 'mosaic_r',\n", " 'wfi_753nm',\n", " 'acs_f850lp',\n", " 'ukidss_y',\n", " 'megacam_g',\n", " 'wfc_z',\n", " 'lbc_u',\n", " 'suprime_ia527',\n", " 'wfc_i',\n", " 'wfi_b123',\n", " 'gpc1_y',\n", " 'suprime_b',\n", " 'wfi_b',\n", " 'wfi_u',\n", " 'suprime_ia679',\n", " 'ukidss_j',\n", " 'suprime_ib827',\n", " 'suprime_ia484',\n", " 'ukidss_k',\n", " 'megacam_u',\n", " 'newfirm_j1',\n", " 'suprime_n816',\n", " 'gpc1_z',\n", " 'newfirm_j3',\n", " 'suprime_v',\n", " 'wfi_v',\n", " 'mosaic_b',\n", " 'mosaic_u',\n", " 'ukidss_h',\n", " 'bessell_r',\n", " 'suprime_ib574',\n", " 'newfirm_j2',\n", " 'suprime_zp',\n", " 'wfc3_f140w',\n", " 'suprime_z',\n", " 'irac_i3',\n", " 'irac_i2',\n", " 'bessell_i',\n", " 'megacam_z',\n", " 'vista_z',\n", " 'wfc_g',\n", " 'wfi_856nm',\n", " 'wfc_r',\n", " 'suprime_y',\n", " 'vista_ks',\n", " 'irac_i1',\n", " 'acs_f814w',\n", " 'mosaic_z',\n", " 'megacam_y',\n", " 'suprime_ib427',\n", " 'wfc3_f160w',\n", " 'wfi_646nm',\n", " 'vista_y',\n", " 'suprime_nb711',\n", " 'spire_500',\n", " 'pacs_red',\n", " 'gpc1_g',\n", " 'newfirm_h1',\n", " 'suprime_ia738',\n", " 'vista_j',\n", " 'mosaic_i',\n", " 'irac_i4',\n", " 'wfi_815nm',\n", " 'lbc_y',\n", " 'newfirm_h2',\n", " 'wfi_485nm',\n", " 'megacam_i',\n", " 'moircs_ks',\n", " 'nicmos_f160w',\n", " 'wfi_i',\n", " 'acs_f435w',\n", " 'vista_h',\n", " 'suprime_i',\n", " 'gpc1_r',\n", " 'wfc_u',\n", " 'wfi_461nm',\n", " 'cfht12k_i',\n", " 'wfc3_f098m',\n", " 'newfirm_h',\n", " 'decam_r',\n", " 'omegacam_z',\n", " 'pacs_green',\n", " 'nicmos_f110w',\n", " 'wfi_914nm',\n", " 'sdss_u',\n", " 'suprime_rc',\n", " 'wircam_y',\n", " 'newfirm_k',\n", " 'omega2000_ks',\n", " 'mmt_z',\n", " 'wfi_518nm',\n", " 'decam_g',\n", " 'tifkam_ks',\n", " 'newfirm_j',\n", " 'wircs_j',\n", " 'suprime_ia767',\n", " 'sdss_r',\n", " 'suprime_n921',\n", " 'mmt_i',\n", " 'wfi_604nm',\n", " 'wircs_k',\n", " 'galex_fuv',\n", " '90prime_r',\n", " 'wircam_j',\n", " 'suprime_ib709',\n", " 'wircam_h',\n", " 'suprime_rp',\n", " '90prime_g',\n", " 'hawki_k',\n", " 'omegacam_i',\n", " 'sdss_g']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filters" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Job still running after 0 seconds.\n", "Job still running after 11 seconds.\n" ] } ], "source": [ "irac_i1_query=\"\"\"\n", "SELECT DISTINCT hp_idx_O_10, ferr_ap_irac_i1_mean\n", "FROM depth.main\n", "WHERE ferr_ap_irac_i1_mean IS NOT NULL\n", "\"\"\"\n", "\n", "#Then we execute the query\n", "#resultset = service.run_async(irac_i1_query)\n", "job = service.submit_job(irac_i1_query)\n", "job.run()\n", "job_url = job.url\n", "job_result = vo.dal.tap.AsyncTAPJob(job_url)\n", "start_time = time.time()\n", "while job.phase == 'EXECUTING':\n", " print('Job still running after {} seconds.'.format(round(time.time() - start_time)))\n", " time.sleep(10) #wait ten seconds and try again\n", " \n", "table = job_result.fetch_result() \n", "i1_table = table.table" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table masked=True length=83313\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
hp_idx_o_10ferr_ap_irac_i1_mean
uJy
int64float64
15475061.43366666666667
15475071.52794117647059
15475101.66105263157895
15475111.94072727272727
15475121.62485294117647
15475131.03740540540541
15475141.42491329479769
15475150.732934782608696
15475161.19547872340426
......
11870851206.8402
11870852106.708244
11870853261.2762
11870854265.9595
11870855260.12
11870856303.03732
11870857232.01556
11870858557.28467
11870860679.73
11870864152.82677
" ], "text/plain": [ "\n", "hp_idx_o_10 ferr_ap_irac_i1_mean\n", " uJy \n", " int64 float64 \n", "----------- --------------------\n", " 1547506 1.43366666666667\n", " 1547507 1.52794117647059\n", " 1547510 1.66105263157895\n", " 1547511 1.94072727272727\n", " 1547512 1.62485294117647\n", " 1547513 1.03740540540541\n", " 1547514 1.42491329479769\n", " 1547515 0.732934782608696\n", " 1547516 1.19547872340426\n", " ... ...\n", " 11870851 206.8402\n", " 11870852 106.708244\n", " 11870853 261.2762\n", " 11870854 265.9595\n", " 11870855 260.12\n", " 11870856 303.03732\n", " 11870857 232.01556\n", " 11870858 557.28467\n", " 11870860 679.73\n", " 11870864 152.82677" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i1_table" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1.8694e+04, 3.2534e+04, 3.2980e+03, 1.0970e+03, 1.2085e+04,\n", " 1.4502e+04, 1.0610e+03, 3.7000e+01, 2.0000e+00, 3.0000e+00]),\n", " array([-0.49291727, -0.01875222, 0.45541283, 0.92957788, 1.40374293,\n", " 1.87790798, 2.35207303, 2.82623808, 3.30040313, 3.77456818,\n", " 4.24873323]),\n", " )" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(np.log10(i1_table['ferr_ap_irac_i1_mean']) )" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "273.1409041542108" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "irac_i1_moc = MOC(10, i1_table['hp_idx_o_10'])\n", "irac_i1_moc.area_sq_deg\n", "total_area = irac_i1_moc.area_sq_deg\n", "total_area" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "cells, fluxes = np.histogram(np.log10(i1_table['ferr_ap_irac_i1_mean']*1.e-6), bins=50)\n", "ax.plot(fluxes[1:],\n", " np.cumsum(cells)*total_area/cells.sum() ,\n", " drawstyle='steps')\n", "\n", "ax.set_xlabel('Log$_{10}$ ( Mean IRAC 1 flux error [Jy] )')\n", "ax.set_ylabel('Cumulative area [deg.$^2$]')\n", "ax.set_xlim([-6.5,-3.0])\n", "#y_vals = ax.get_yticks()\n", "#ax.set_yticklabels([n for n in y_vals])\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "column_width_cm = 8.9\n", "width_cm = 1.4 * column_width_cm\n", "hieght_cm = width_cm / 1.618\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.tight_layout()\n", "plt.savefig('./figs/IRAC_i1_cumulative_area_depth.pdf')\n", "plt.savefig('./figs/IRAC_i1_cumulative_area_depth.png')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nThe IRAC i1 band has coverage over 273.1409041542108 square degrees with 3 sigma \\ndepths at the 25th, 50th and 75th percentiles of 2.955037033451454, 5.86515255980631, 176.669196 respectively.'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "i1_p25, i1_p50, i1_p75 = np.nanpercentile(i1_table['ferr_ap_irac_i1_mean'] * 3.,[25, 50, 75])\n", "\"\"\"\n", "The IRAC i1 band has coverage over {} square degrees with 3 sigma \n", "depths at the 25th, 50th and 75th percentiles of {}, {}, {} respectively.\"\"\".format(total_area, i1_p25, i1_p50, i1_p75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In mags as per reviewer request" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "i1_pix_mag_depths, _ = flux_to_mag(i1_table['ferr_ap_irac_i1_mean']*1.e-6*5)\n", "cells, fluxes = np.histogram(i1_pix_mag_depths, bins=1000)\n", "#We want cumulative from faint to bright\n", "cells =np.flip(cells)\n", "fluxes = np.flip(fluxes)\n", "ax.plot(fluxes[1:],\n", " np.cumsum(cells)*total_area/cells.sum() ,\n", " drawstyle='steps')\n", "\n", "ax.set_xlabel('5$\\sigma$ IRAC 1 depth [mag]')\n", "ax.set_ylabel('Cumulative area [deg.$^2$]')\n", "ax.set_xlim([23,16])\n", "#y_vals = ax.get_yticks()\n", "#ax.set_yticklabels([n for n in y_vals])\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "column_width_cm = 8.9\n", "width_cm = 1.4 * column_width_cm\n", "hieght_cm = width_cm / 1.618\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.tight_layout()\n", "plt.savefig('./figs/IRAC_i1_cumulative_area_depth_mag.pdf')\n", "plt.savefig('./figs/IRAC_i1_cumulative_area_depth_mag.png')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nThe IRAC i1 band has coverage over 273.1409041542108 square degrees with 5 sigma \\ndepths at the 25th, 50th and 75th percentiles of 17.727476044402813, 21.424679843133582, 22.16897080604381 respectively.'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mi1_p25, mi1_p50, mi1_p75 = np.nanpercentile(i1_pix_mag_depths ,[25, 50, 75])\n", "\"\"\"\n", "The IRAC i1 band has coverage over {} square degrees with 5 sigma \n", "depths at the 25th, 50th and 75th percentiles of {}, {}, {} respectively.\"\"\".format(total_area, mi1_p25, mi1_p50, mi1_p75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## g bands\n", "\n", "There are multiple g type bands so we the the maximum depth of all of them." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ", ferr_ap_mmt_g_mean, ferr_ap_omegacam_g_mean, ferr_ap_suprime_g_mean, ferr_ap_megacam_g_mean, ferr_ap_wfc_g_mean, ferr_ap_gpc1_g_mean, ferr_ap_decam_g_mean, ferr_ap_90prime_g_mean, ferr_ap_sdss_g_mean\n" ] } ], "source": [ "g_filters = [f for f in filters if f.split('_')[1] == 'g']\n", "spaced_list = ''\n", "for g in g_filters:\n", " spaced_list = spaced_list + ', ferr_ap_' + g + '_mean'\n", "print(spaced_list)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nSELECT DISTINCT hp_idx_O_10, ferr_ap_mmt_g_mean, ferr_ap_omegacam_g_mean, ferr_ap_suprime_g_mean, ferr_ap_megacam_g_mean, ferr_ap_wfc_g_mean, ferr_ap_gpc1_g_mean, ferr_ap_decam_g_mean, ferr_ap_90prime_g_mean, ferr_ap_sdss_g_mean\\nFROM depth.main\\n\\n'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_bands_query=\"\"\"\n", "SELECT DISTINCT hp_idx_O_10{}\n", "FROM depth.main\n", "\n", "\"\"\".format(spaced_list)\n", "g_bands_query" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Job still running after 0 seconds.\n", "Job still running after 11 seconds.\n", "Job still running after 21 seconds.\n", "Job still running after 31 seconds.\n", "Job still running after 42 seconds.\n", "Job still running after 52 seconds.\n", "COMPLETED\n" ] } ], "source": [ "#Then we execute the query\n", "#resultset = service.run_async(irac_i1_query)\n", "job = service.submit_job(g_bands_query)\n", "job.run()\n", "job_url = job.url\n", "job_result = vo.dal.tap.AsyncTAPJob(job_url)\n", "start_time = time.time()\n", "while job.phase == 'EXECUTING':\n", " print('Job still running after {} seconds.'.format(round(time.time() - start_time)))\n", " time.sleep(10) #wait ten seconds and try again\n", " \n", "print(job.phase)\n", "table = job_result.fetch_result() \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ferr_ap_mmt_g_mean',\n", " 'ferr_ap_omegacam_g_mean',\n", " 'ferr_ap_suprime_g_mean',\n", " 'ferr_ap_megacam_g_mean',\n", " 'ferr_ap_wfc_g_mean',\n", " 'ferr_ap_gpc1_g_mean',\n", " 'ferr_ap_decam_g_mean',\n", " 'ferr_ap_90prime_g_mean',\n", " 'ferr_ap_sdss_g_mean']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_depth_cols = [\"ferr_ap_{}_mean\".format(band) for band in g_filters]\n", "g_depth_cols" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "g_table = Table(table.table)\n", "for col in g_depth_cols:\n", " g_table[col].fill_value = np.nan\n", "g_table = g_table.filled()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:1: RuntimeWarning: All-NaN axis encountered\n", " if __name__ == '__main__':\n" ] }, { "data": { "text/plain": [ "array([2.07127300e-07, 2.18198880e-07, 1.93754770e-07, ...,\n", " 6.84728411e-01, 4.62498951e-01, 5.92936370e-01])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.nanmin([g_table[column] for column in g_depth_cols], axis=0)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ferr_ap_mmt_g_mean 0\n", "ferr_ap_omegacam_g_mean 0\n", "ferr_ap_suprime_g_mean 0\n", "ferr_ap_megacam_g_mean 4\n", "ferr_ap_wfc_g_mean 0\n", "ferr_ap_gpc1_g_mean 2\n", "ferr_ap_decam_g_mean 42483\n", "ferr_ap_90prime_g_mean 9564\n", "ferr_ap_sdss_g_mean 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in less\n", " from ipykernel import kernelapp as app\n" ] } ], "source": [ "for col in g_depth_cols:\n", " print(col, np.sum(np.log10(np.array(g_table[col])*1.e-6) < -9))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in less\n", " if __name__ == '__main__':\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in less\n", " from ipykernel import kernelapp as app\n" ] } ], "source": [ "decam_low = (np.log10(np.array(g_table['ferr_ap_decam_g_mean'])*1.e-6) < -9)\n", "prime_low = (np.log10(np.array(g_table['ferr_ap_90prime_g_mean'])*1.e-6) < -9)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AKARI-NEP has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "AKARI-SEP has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "Bootes has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "CDFS-SWIRE has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "COSMOS has 4.993602528615098 sq deg of bad DECam and 4.993602528615098 sq degrees of bad 90Prime\n", "EGS has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "ELAIS-N1 has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "ELAIS-N2 has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "ELAIS-S1 has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "GAMA-09 has 58.83676922758017 sq deg of bad DECam and 58.83676922758017 sq degrees of bad 90Prime\n", "GAMA-12 has 41.50292732171963 sq deg of bad DECam and 41.50292732171963 sq degrees of bad 90Prime\n", "GAMA-15 has 24.055567671853485 sq deg of bad DECam and 24.055567671853485 sq degrees of bad 90Prime\n", "HDF-N has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "Herschel-Stripe-82 has 7.849065563976479 sq deg of bad DECam and 7.849065563976479 sq degrees of bad 90Prime\n", "Lockman-SWIRE has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "HATLAS-NGP has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "SA13 has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "HATLAS-SGP has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "SPIRE-NEP has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "SSDF has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "xFLS has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "XMM-13hr has 0.0 sq deg of bad DECam and 0.0 sq degrees of bad 90Prime\n", "XMM-LSS has 0.04267160683965466 sq deg of bad DECam and 0.04267160683965466 sq degrees of bad 90Prime\n" ] } ], "source": [ "decam_bad_moc = MOC(10, g_table['hp_idx_o_10'][decam_low])\n", "prime_bad_moc = MOC(10, g_table['hp_idx_o_10'][decam_low])\n", "\n", "fields = yaml.load(open('../../../dmu2/meta_main.yml', 'r'))\n", "\n", "\n", "for field in fields['fields']:\n", " field_moc = MOC(filename=field['region'].replace('dmu_products/', '../../../'))\n", " decam_bad_area = decam_bad_moc.intersection(field_moc).area_sq_deg\n", " prime_bad_area = prime_bad_moc.intersection(field_moc).area_sq_deg\n", " print(\"{} has {} sq deg of bad DECam and {} sq degrees of bad 90Prime\".format(field['name'], \n", " decam_bad_area, \n", " prime_bad_area))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "g_table['ferr_ap_decam_g_mean'][decam_low] = g_table['ferr_ap_decam_g_mean'][decam_low] * 1.e6\n", "g_table['ferr_ap_90prime_g_mean'][prime_low] = g_table['ferr_ap_90prime_g_mean'][prime_low] * 1.e6" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:1: RuntimeWarning: All-NaN axis encountered\n", " if __name__ == '__main__':\n" ] } ], "source": [ "g_table.add_column(Column(data=np.nanmin([g_table[column] for column in g_depth_cols], axis=0), name='ferr_g_min'))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=394298\n", "
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hp_idx_o_10ferr_g_min
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......
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" ], "text/plain": [ "\n", "hp_idx_o_10 ferr_g_min \n", " int64 float64 \n", "----------- -----------------\n", " 1048576 0.01926224\n", " 1048577 0.020717567\n", " 1048578 0.021378249\n", " 1048579 0.021092668\n", " 1048580 0.020199573\n", " 1048581 0.022394473\n", " 1048582 0.020951325\n", " 1048583 0.020383537\n", " 1048584 0.022677435\n", " 1048585 0.021453133\n", " ... ...\n", " 12042983 0.470593447975413\n", " 12042985 0.401245270265415\n", " 12043008 1.12891318891637\n", " 12043009 0.508966908339133\n", " 12043010 0.494158403628982\n", " 12043011 0.577247280662618\n", " 12043012 0.445860992796356\n", " 12043016 0.684728410968042\n", " 12043017 0.462498951222175\n", " 12043018 0.592936370182139" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_table['hp_idx_o_10', 'ferr_g_min']" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1292.6302392940547" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_moc = MOC(10, g_table['hp_idx_o_10'])\n", "g_moc.area_sq_deg\n", "total_area_g = g_moc.area_sq_deg\n", "total_area_g" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in greater\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in less\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g_table['hp_idx_o_10', 'ferr_g_min']\n", "\n", "fig, ax = plt.subplots()\n", "\n", "good = np.log10(np.array(g_table['ferr_g_min'])*1.e-6) > -20\n", "good &= np.log10(np.array(g_table['ferr_g_min'])*1.e-6) <20\n", "\n", "cells, fluxes = np.histogram(np.log10(np.array(g_table['ferr_g_min'][good ])*1.e-6), bins=100)\n", "ax.plot(fluxes[1:],\n", " np.cumsum(cells)*1270./cells.sum() ,\n", " drawstyle='steps')\n", "\n", "ax.set_xlabel('Log$_{10}$ ( Mean g flux error [Jy] )')\n", "ax.set_ylabel('Cumulative area [deg.$^2$]')\n", "ax.set_xlim([-8,-4.0])\n", "#y_vals = ax.get_yticks()\n", "#ax.set_yticklabels([n for n in y_vals])\n", "\n", "\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "column_width_cm = 8.9\n", "width_cm = 1.4 * column_width_cm\n", "hieght_cm = width_cm / 1.618\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.tight_layout()\n", "plt.savefig('./figs/g_cumulative_area_depth.pdf')\n", "plt.savefig('./figs/g_cumulative_area_depth.png')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nThe g band has coverage over 1270 square degrees with 3 sigma \\ndepths at the 25th, 50th and 75th percentiles of 0.30178651661213773, 0.367626929893692, 0.8854647236256772 respectively.'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_p25, g_p50, g_p75 = np.nanpercentile(g_table['ferr_g_min'][good ] * 3.,[25, 50, 75])\n", "\"\"\"\n", "The g band has coverage over {} square degrees with 3 sigma \n", "depths at the 25th, 50th and 75th percentiles of {}, {}, {} respectively.\"\"\".format(1270, g_p25, g_p50, g_p75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In mags as per reviewer request" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in greater\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in less\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g_table['hp_idx_o_10', 'ferr_g_min']\n", "\n", "fig, ax = plt.subplots()\n", "\n", "good = np.log10(np.array(g_table['ferr_g_min'])*1.e-6) > -20\n", "good &= np.log10(np.array(g_table['ferr_g_min'])*1.e-6) <20\n", "\n", "g_pix_mag_depths, _ = flux_to_mag(np.array(g_table['ferr_g_min'][good ])*1.e-6*5)\n", "cells, fluxes = np.histogram(g_pix_mag_depths, bins=1000)\n", "cells =np.flip(cells)\n", "fluxes = np.flip(fluxes)\n", "\n", "ax.plot(fluxes[1:],\n", " np.cumsum(cells)*1270./cells.sum() ,\n", " drawstyle='steps')\n", "\n", "ax.set_xlabel('5$\\sigma$ $g$ depth [mag] ')\n", "ax.set_ylabel('Cumulative area [deg.$^2$]')\n", "ax.set_xlim([27,20])\n", "#y_vals = ax.get_yticks()\n", "#ax.set_yticklabels([n for n in y_vals])\n", "\n", "\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "column_width_cm = 8.9\n", "width_cm = 1.4 * column_width_cm\n", "hieght_cm = width_cm / 1.618\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.tight_layout()\n", "plt.savefig('./figs/g_cumulative_area_depth_mag.pdf')\n", "plt.savefig('./figs/g_cumulative_area_depth_mag.png')" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nThe g band has coverage over 1270 square degrees with 3 sigma \\ndepths at the 25th, 50th and 75th percentiles of 23.47744996638206, 24.43185983247774, 24.646128545411877 respectively.'" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mg_p25, mg_p50, mg_p75 = np.nanpercentile(g_pix_mag_depths,[25, 50, 75])\n", "\"\"\"\n", "The g band has coverage over {} square degrees with 3 sigma \n", "depths at the 25th, 50th and 75th percentiles of {}, {}, {} respectively.\"\"\".format(1270, mg_p25, mg_p50, mg_p75)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=9564\n", "
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hp_idx_o_10ferr_ap_mmt_g_meanferr_ap_omegacam_g_meanferr_ap_suprime_g_meanferr_ap_megacam_g_meanferr_ap_wfc_g_meanferr_ap_gpc1_g_meanferr_ap_decam_g_meanferr_ap_90prime_g_meanferr_ap_sdss_g_meanferr_g_min
uJyuJyuJyuJyuJyuJyuJyuJyuJy
int64float64float64float64float64float64float64float64float64float64float64
2652662nannannannannan0.987825059481603nan0.20031881999999998nan0.20031881999999998
2652663nannannannannan42.3153061976883nan0.1769414nan0.1769414
2652667nannannannannan0.777482608504748nan0.200318nan0.200318
2652668nannannannannan1.21649049703478nan0.20031815nan0.20031815
2652669nannannannannan26.3897794278116nan0.18906452nan0.18906452
2652670nannannannannan0.909743596951121nan0.2003182nan0.2003182
2652671nannannannannan0.907613326799461nan0.20031814nan0.20031814
2652983nannannannannan0.759221342327773nan0.19815181nan0.19815181
2652985nannannannannan0.709532414323448nan0.19815195nan0.19815195
.................................
2758725nannannannannan0.962989379875482nan0.16304098nan0.16304098
2758726nannannannannan1.592190840778nan0.16239852nan0.16239852
2758727nannannannannan1.68717381553481nan0.1647915nan0.1647915
2758728nannannannannan1.41524050155837nan0.17226333nan0.17226333
2758729nannannannannan1549.36707196568nan0.16137253nan0.16137253
2758730nannannannannan1.5850016725736nan0.16344784nan0.16344784
2758732nannannannannannannan0.1635113nan0.1635113
2758736nannannannannan0.829615297934712nan0.20959587nan0.20959587
2758737nannannannannan0.892086609466304nan0.22579443999999999nan0.22579443999999999
2758784nannannannannan0.88569399826493nan0.1444143nan0.1444143
" ], "text/plain": [ "\n", "hp_idx_o_10 ferr_ap_mmt_g_mean ... ferr_ap_sdss_g_mean ferr_g_min \n", " uJy ... uJy \n", " int64 float64 ... float64 float64 \n", "----------- ------------------ ... ------------------- -------------------\n", " 2652662 nan ... nan 0.20031881999999998\n", " 2652663 nan ... nan 0.1769414\n", " 2652667 nan ... nan 0.200318\n", " 2652668 nan ... nan 0.20031815\n", " 2652669 nan ... nan 0.18906452\n", " 2652670 nan ... nan 0.2003182\n", " 2652671 nan ... nan 0.20031814\n", " 2652983 nan ... nan 0.19815181\n", " 2652985 nan ... nan 0.19815195\n", " ... ... ... ... ...\n", " 2758725 nan ... nan 0.16304098\n", " 2758726 nan ... nan 0.16239852\n", " 2758727 nan ... nan 0.1647915\n", " 2758728 nan ... nan 0.17226333\n", " 2758729 nan ... nan 0.16137253\n", " 2758730 nan ... nan 0.16344784\n", " 2758732 nan ... nan 0.1635113\n", " 2758736 nan ... nan 0.20959587\n", " 2758737 nan ... nan 0.22579443999999999\n", " 2758784 nan ... nan 0.1444143" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g_table[prime_low]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## K or Ks filters\n", "the same for K" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['isaac_k',\n", " 'moircs_k',\n", " 'wircam_ks',\n", " 'ukidss_k',\n", " 'vista_ks',\n", " 'moircs_ks',\n", " 'newfirm_k',\n", " 'omega2000_ks',\n", " 'tifkam_ks',\n", " 'wircs_k',\n", " 'hawki_k']" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K_filters = [f for f in filters if f.split('_')[1].startswith('k')]\n", "K_filters" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ", ferr_ap_isaac_k_mean, ferr_ap_moircs_k_mean, ferr_ap_wircam_ks_mean, ferr_ap_ukidss_k_mean, ferr_ap_vista_ks_mean, ferr_ap_moircs_ks_mean, ferr_ap_newfirm_k_mean, ferr_ap_omega2000_ks_mean, ferr_ap_tifkam_ks_mean, ferr_ap_wircs_k_mean, ferr_ap_hawki_k_mean\n" ] } ], "source": [ "\n", "spaced_list = ''\n", "for K in K_filters:\n", " spaced_list = spaced_list + ', ferr_ap_' + K + '_mean'\n", "print(spaced_list)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nSELECT DISTINCT hp_idx_O_10, ferr_ap_isaac_k_mean, ferr_ap_moircs_k_mean, ferr_ap_wircam_ks_mean, ferr_ap_ukidss_k_mean, ferr_ap_vista_ks_mean, ferr_ap_moircs_ks_mean, ferr_ap_newfirm_k_mean, ferr_ap_omega2000_ks_mean, ferr_ap_tifkam_ks_mean, ferr_ap_wircs_k_mean, ferr_ap_hawki_k_mean\\nFROM depth.main\\n\\n'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K_bands_query=\"\"\"\n", "SELECT DISTINCT hp_idx_O_10{}\n", "FROM depth.main\n", "\n", "\"\"\".format(spaced_list)\n", "K_bands_query" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Job still running after 0 seconds.\n", "Job still running after 11 seconds.\n", "Job still running after 21 seconds.\n", "Job still running after 31 seconds.\n", "Job still running after 42 seconds.\n", "Job still running after 52 seconds.\n", "COMPLETED\n" ] } ], "source": [ "#Then we execute the query\n", "#resultset = service.run_async(irac_i1_query)\n", "job = service.submit_job(K_bands_query)\n", "job.run()\n", "job_url = job.url\n", "job_result = vo.dal.tap.AsyncTAPJob(job_url)\n", "start_time = time.time()\n", "while job.phase == 'EXECUTING':\n", " print('Job still running after {} seconds.'.format(round(time.time() - start_time)))\n", " time.sleep(10) #wait ten seconds and try again\n", " \n", "print(job.phase)\n", "table = job_result.fetch_result() " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ferr_ap_isaac_k_mean',\n", " 'ferr_ap_moircs_k_mean',\n", " 'ferr_ap_wircam_ks_mean',\n", " 'ferr_ap_ukidss_k_mean',\n", " 'ferr_ap_vista_ks_mean',\n", " 'ferr_ap_moircs_ks_mean',\n", " 'ferr_ap_newfirm_k_mean',\n", " 'ferr_ap_omega2000_ks_mean',\n", " 'ferr_ap_tifkam_ks_mean',\n", " 'ferr_ap_wircs_k_mean',\n", " 'ferr_ap_hawki_k_mean']" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "depth_cols = [\"ferr_ap_{}_mean\".format(band) for band in K_filters]\n", "depth_cols" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "K_table = Table(table.table)\n", "for col in depth_cols:\n", " K_table[col].fill_value = np.nan\n", "K_table = K_table.filled()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "394298" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(K_table)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:1: RuntimeWarning: All-NaN axis encountered\n", " if __name__ == '__main__':\n" ] } ], "source": [ "K_table.add_column(Column(data=np.nanmin([K_table[column] for column in depth_cols], axis=0), name='ferr_K_min'))" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "44649" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(np.isnan(K_table['ferr_K_min'] ))" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1146.2587389655478" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K_moc = MOC(10, K_table[~np.isnan(K_table['ferr_K_min'] )]['hp_idx_o_10'])\n", "K_moc.area_sq_deg\n", "total_area_K = K_moc.area_sq_deg\n", "total_area_K" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: divide by zero encountered in log10\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in greater\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: divide by zero encountered in log10\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in less\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "K_table['hp_idx_o_10', 'ferr_K_min']\n", "\n", "fig, ax = plt.subplots()\n", "\n", "good = np.log10(np.array(K_table['ferr_K_min'])*1.e-6) > -20\n", "good &= np.log10(np.array(K_table['ferr_K_min'])*1.e-6) <20\n", "\n", "cells, fluxes = np.histogram(np.log10(np.array(K_table['ferr_K_min'][good ])*1.e-6), bins=100)\n", "ax.plot(fluxes[1:],\n", " np.cumsum(cells)*total_area_K/cells.sum() ,\n", " drawstyle='steps')\n", "\n", "ax.set_xlabel('Log$_{10}$ ( Mean K or Ks flux error [Jy] )')\n", "ax.set_ylabel('Cumulative area [deg.$^2$]')\n", "ax.set_xlim([-6.5,-4.5])\n", "#y_vals = ax.get_yticks()\n", "#ax.set_yticklabels([n for n in y_vals])\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "column_width_cm = 8.9\n", "width_cm = 1.4 * column_width_cm\n", "hieght_cm = width_cm / 1.618\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "plt.tight_layout()\n", "\n", "plt.savefig('./figs/K_cumulative_area_depth.pdf')\n", "plt.savefig('./figs/K_cumulative_area_depth.png')" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'\\nThe IRAC i1 band has coverage over 1146.2587389655478 square degrees with 3 sigma \\ndepths at the 25th, 50th and 75th percentiles of 8.507107317226373, 15.31927155, 18.434229287923927 respectively.'" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K_p25, K_p50, K_p75 = np.nanpercentile(K_table['ferr_K_min'][good ] * 3.,[25, 50, 75])\n", "\"\"\"\n", "The K band has coverage over {} square degrees with 3 sigma \n", "depths at the 25th, 50th and 75th percentiles of {}, {}, {} respectively.\"\"\".format(total_area_K, K_p25, K_p50, K_p75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And in mags as per reviewer request" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: divide by zero encountered in log10\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in greater\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: divide by zero encountered in log10\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in less\n", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "K_table['hp_idx_o_10', 'ferr_K_min']\n", "\n", "fig, ax = plt.subplots()\n", "\n", "good = np.log10(np.array(K_table['ferr_K_min'])*1.e-6) > -20\n", "good &= np.log10(np.array(K_table['ferr_K_min'])*1.e-6) <20\n", "\n", "#cells, fluxes = np.histogram(np.log10(np.array(K_table['ferr_K_min'][good ])*1.e-6), bins=100)\n", "\n", "K_pix_mag_depths, _ = flux_to_mag(np.array(K_table['ferr_K_min'][good ])*1.e-6*5)\n", "cells, fluxes = np.histogram(K_pix_mag_depths, bins=1000)\n", "cells =np.flip(cells)\n", "fluxes = np.flip(fluxes)\n", "\n", "\n", "\n", "ax.plot(fluxes[1:],\n", " np.cumsum(cells)*total_area_K/cells.sum() ,\n", " drawstyle='steps')\n", "\n", "ax.set_xlabel('5$\\sigma$ $K$ or $Ks$ depth [mag]')\n", "ax.set_ylabel('Cumulative area [deg.$^2$]')\n", "ax.set_xlim([22,19])\n", "#y_vals = ax.get_yticks()\n", "#ax.set_yticklabels([n for n in y_vals])\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "column_width_cm = 8.9\n", "width_cm = 1.4 * column_width_cm\n", "hieght_cm = width_cm / 1.618\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "plt.tight_layout()\n", "\n", "plt.savefig('./figs/K_cumulative_area_depth_mag.pdf')\n", "plt.savefig('./figs/K_cumulative_area_depth_mag.png')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The IRAC i1 band has coverage over 1273 square degrees with 5 sigmadepths at the 25th, 50th and 75th percentiles of 20.181315663428137, 20.38228283956797, 21.020923347289685 respectively.'" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "K_p25, K_p50, K_p75 = np.nanpercentile(K_pix_mag_depths,[25, 50, 75])\n", "(\"The K or Ks band has coverage over {} square degrees with 5 sigma\" +\n", "\"depths at the 25th, 50th and 75th percentiles of {}, {}, {} respectively.\").format(total_area_K, K_p25, K_p50, K_p75)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make a table of bands available on each field" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "bands = ['u', 'g', 'r', 'i', 'z', 'y',\n", " 'J', 'H', 'K', 'Ks',\n", " 'i1', 'i2', 'i3', 'i4']" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "spaced_list = ''\n", "for m, band in enumerate(bands):\n", " specific_bands = [f for f in filters if f.split('_')[1] == band.lower()]\n", " for specific_band in specific_bands:\n", " spaced_list = spaced_list + ', ferr_ap_' + specific_band + '_mean'\n", " \n", "spaced_list = spaced_list.replace(' ferr_ap_lbc_u_mean,', '')\n", "spaced_list = spaced_list.replace(' ferr_ap_lbc_y_mean,', '')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "', ferr_ap_omegacam_u_mean, ferr_ap_mmt_u_mean, ferr_ap_bessell_u_mean, ferr_ap_wfi_u_mean, ferr_ap_megacam_u_mean, ferr_ap_mosaic_u_mean, ferr_ap_wfc_u_mean, ferr_ap_sdss_u_mean, ferr_ap_mmt_g_mean, ferr_ap_omegacam_g_mean, ferr_ap_suprime_g_mean, ferr_ap_megacam_g_mean, ferr_ap_wfc_g_mean, ferr_ap_gpc1_g_mean, ferr_ap_decam_g_mean, ferr_ap_90prime_g_mean, ferr_ap_sdss_g_mean, ferr_ap_omegacam_r_mean, ferr_ap_mmt_r_mean, ferr_ap_cfht12k_r_mean, ferr_ap_megacam_r_mean, ferr_ap_wfi_r_mean, ferr_ap_suprime_r_mean, ferr_ap_mosaic_r_mean, ferr_ap_bessell_r_mean, ferr_ap_wfc_r_mean, ferr_ap_gpc1_r_mean, ferr_ap_decam_r_mean, ferr_ap_sdss_r_mean, ferr_ap_90prime_r_mean, ferr_ap_sdss_i_mean, ferr_ap_decam_i_mean, ferr_ap_gpc1_i_mean, ferr_ap_wfc_i_mean, ferr_ap_bessell_i_mean, ferr_ap_mosaic_i_mean, ferr_ap_megacam_i_mean, ferr_ap_wfi_i_mean, ferr_ap_suprime_i_mean, ferr_ap_cfht12k_i_mean, ferr_ap_mmt_i_mean, ferr_ap_omegacam_i_mean, ferr_ap_decam_z_mean, ferr_ap_90prime_z_mean, ferr_ap_sdss_z_mean, ferr_ap_wfc_z_mean, ferr_ap_gpc1_z_mean, ferr_ap_suprime_z_mean, ferr_ap_megacam_z_mean, ferr_ap_vista_z_mean, ferr_ap_mosaic_z_mean, ferr_ap_omegacam_z_mean, ferr_ap_mmt_z_mean, ferr_ap_decam_y_mean, ferr_ap_ukidss_y_mean, ferr_ap_gpc1_y_mean, ferr_ap_suprime_y_mean, ferr_ap_megacam_y_mean, ferr_ap_vista_y_mean, ferr_ap_wircam_y_mean, ferr_ap_omega2000_j_mean, ferr_ap_ukidss_j_mean, ferr_ap_vista_j_mean, ferr_ap_newfirm_j_mean, ferr_ap_wircs_j_mean, ferr_ap_wircam_j_mean, ferr_ap_ukidss_h_mean, ferr_ap_vista_h_mean, ferr_ap_newfirm_h_mean, ferr_ap_wircam_h_mean, ferr_ap_isaac_k_mean, ferr_ap_moircs_k_mean, ferr_ap_ukidss_k_mean, ferr_ap_newfirm_k_mean, ferr_ap_wircs_k_mean, ferr_ap_hawki_k_mean, ferr_ap_wircam_ks_mean, ferr_ap_vista_ks_mean, ferr_ap_moircs_ks_mean, ferr_ap_omega2000_ks_mean, ferr_ap_tifkam_ks_mean, ferr_ap_irac_i1_mean, ferr_ap_irac_i2_mean, ferr_ap_irac_i3_mean, ferr_ap_irac_i4_mean'" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spaced_list" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1563286732.845642\n", "Job still running after 0 seconds.\n", "Job still running after 51 seconds.\n", "Job still running after 101 seconds.\n", "Job still running after 152 seconds.\n", "Job still running after 202 seconds.\n", "COMPLETED\n", "Fetching result 1563286985.4900851\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: W06: None:2:20168: W06: Invalid UCD 'stat.error;phot.flux;em.IR.I': Unknown word 'em.IR.I' [astropy.io.votable.tree]\n", "WARNING:astropy:W06: None:2:20168: W06: Invalid UCD 'stat.error;phot.flux;em.IR.I': Unknown word 'em.IR.I'\n", "WARNING: W06: None:2:20456: W06: Invalid UCD 'stat.error;phot.flux;em.opt.J': Unknown word 'em.opt.J' [astropy.io.votable.tree]\n", "WARNING:astropy:W06: None:2:20456: W06: Invalid UCD 'stat.error;phot.flux;em.opt.J': Unknown word 'em.opt.J'\n" ] } ], "source": [ "\n", "query = \"\"\"\n", "SELECT DISTINCT hp_idx_O_10{}\n", "FROM depth.main\n", "\"\"\".format(spaced_list)\n", "job = service.submit_job(query)\n", "job.run()\n", "job_url = job.url\n", "job_result = vo.dal.tap.AsyncTAPJob(job_url)\n", "start_time = time.time()\n", "print(time.time())\n", "while job.phase == 'EXECUTING':\n", " print('Job still running after {} seconds.'.format(round(time.time() - start_time)))\n", " time.sleep(50) #wait ten seconds and try again\n", " \n", "print(job.phase)\n", "print('Fetching result', time.time())\n", "table = job_result.fetch_result()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table masked=True length=394298\n", "
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hp_idx_o_10ferr_ap_omegacam_u_meanferr_ap_mmt_u_meanferr_ap_bessell_u_meanferr_ap_wfi_u_meanferr_ap_megacam_u_meanferr_ap_mosaic_u_meanferr_ap_wfc_u_meanferr_ap_sdss_u_meanferr_ap_mmt_g_meanferr_ap_omegacam_g_meanferr_ap_suprime_g_meanferr_ap_megacam_g_meanferr_ap_wfc_g_meanferr_ap_gpc1_g_meanferr_ap_decam_g_meanferr_ap_90prime_g_meanferr_ap_sdss_g_meanferr_ap_omegacam_r_meanferr_ap_mmt_r_meanferr_ap_cfht12k_r_meanferr_ap_megacam_r_meanferr_ap_wfi_r_meanferr_ap_suprime_r_meanferr_ap_mosaic_r_meanferr_ap_bessell_r_meanferr_ap_wfc_r_meanferr_ap_gpc1_r_meanferr_ap_decam_r_meanferr_ap_sdss_r_meanferr_ap_90prime_r_meanferr_ap_sdss_i_meanferr_ap_decam_i_meanferr_ap_gpc1_i_meanferr_ap_wfc_i_meanferr_ap_bessell_i_meanferr_ap_mosaic_i_meanferr_ap_megacam_i_meanferr_ap_wfi_i_meanferr_ap_suprime_i_meanferr_ap_cfht12k_i_meanferr_ap_mmt_i_meanferr_ap_omegacam_i_meanferr_ap_decam_z_meanferr_ap_90prime_z_meanferr_ap_sdss_z_meanferr_ap_wfc_z_meanferr_ap_gpc1_z_meanferr_ap_suprime_z_meanferr_ap_megacam_z_meanferr_ap_vista_z_meanferr_ap_mosaic_z_meanferr_ap_omegacam_z_meanferr_ap_mmt_z_meanferr_ap_decam_y_meanferr_ap_ukidss_y_meanferr_ap_gpc1_y_meanferr_ap_suprime_y_meanferr_ap_megacam_y_meanferr_ap_vista_y_meanferr_ap_wircam_y_meanferr_ap_omega2000_j_meanferr_ap_ukidss_j_meanferr_ap_vista_j_meanferr_ap_newfirm_j_meanferr_ap_wircs_j_meanferr_ap_wircam_j_meanferr_ap_ukidss_h_meanferr_ap_vista_h_meanferr_ap_newfirm_h_meanferr_ap_wircam_h_meanferr_ap_isaac_k_meanferr_ap_moircs_k_meanferr_ap_ukidss_k_meanferr_ap_newfirm_k_meanferr_ap_wircs_k_meanferr_ap_hawki_k_meanferr_ap_wircam_ks_meanferr_ap_vista_ks_meanferr_ap_moircs_ks_meanferr_ap_omega2000_ks_meanferr_ap_tifkam_ks_meanferr_ap_irac_i1_meanferr_ap_irac_i2_meanferr_ap_irac_i3_meanferr_ap_irac_i4_mean
uJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJyuJy
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10485760.3888968----------------0.101949260.01926224----0.914651119968882.071273e-07----0.13300735--------0.030870374------2.426566489712313.1425782e-07--------2.17693319900341----------0.041768204----0.231943522.2510854e-07------146.9257488416710.06151843--0.46263823--------3.296671268.08458139610890.14352015--------4.8629651.92945241928101------4.81787541.7355504155159--------6.3801355--------2.36790292603629--------------
10485770.3287----------------0.0970321740.020717567----1.949018876440962.1819888e-07----0.117325865--------0.03293594------2.403271712280063.3950195e-07--------2.99968585945623----------0.044771805----0.201813282.5253004e-07------5.54786802087020.06634453--0.46295264--------4.163386.020027352056440.1526159--------5.27893731.91976457834244------5.3912741.66691474914551--------6.573922--------2.3642338684627--------------
10485780.3846171----------------0.089479180.021378249----14.21975710784781.9375477e-07----0.11576792--------0.035876982------24.81654425113292.86895e-07--------2.29361599869134----------0.047181558----0.197261212.2915148e-07------387.5245888181730.06980607--0.5684549--------4.156373593.64581868093710.15956911--------6.45714572.23670622490454------5.9206472.36486881526548--------6.919189--------3.06828086159446--------------
10485790.37277982----------------0.106243430.021092668----1.580319762576192.2411628e-07----0.12313704--------0.03373764------1.957406343990243.246678e-07--------2.56385495210278----------0.04277464----0.212814032.2523724e-07------3.834828259274940.063606665--0.6386163--------3.80732635.78493689656930.14877433--------4.9358572.08620972292764------5.1515131.8796404004097--------6.4581065--------2.60780441455352--------------
10485800.2911744----------------0.093643980.020199573----1.476383890712892.1773542e-07----0.11573956--------0.03168788------1.440282604289523.1325948e-07--------1.59535184757387----------0.042326976----0.192295692.3032695e-07------2.487705507793430.06250173--0.62160707--------2.93573955.265480305286730.14537382--------4.0921221.78109755970183------4.37455131.78018177407128--------5.6970916--------2.52248514782299--------------
10485810.33193266----------------0.1093838140.022394473----82.05316576932732.130428e-07----0.12735996--------0.03486983------17.67839109213823.2084301e-07--------21.478622746853----------0.04694038----0.226708812.0998698e-07------116.2183699641380.069550596--0.79693997--------4.0269303191.8318905083770.16066769--------4.4635111.90774633487066------4.5666032.03642669806244--------6.03468--------2.77342678705851--------------
10485820.31823373----------------0.0949250460.020951325----0.9361374869963682.0650837e-07----0.11751947--------0.033160187------3.232261196815833.2076025e-07--------6.7363921433714----------0.04638167----0.196685512.1693563e-07------3.774653560201090.06910887--0.7444448--------3.83193685.792177155908660.16291834--------4.9496671.95878115219948------5.11814741.83907379195804--------6.4513354--------2.62036830054389--------------
10485830.25143299----------------0.091945610.020383537----0.902632847560151.9091698e-07----0.11914382--------0.031862717------1.280343734325423.0067554e-07--------0.914074638367121----------0.04413113----0.199043262.1251756e-07------2.122515736625010.06531107--0.6908586--------2.69367464.723645287475570.1515935--------4.0148271.84046977950681------4.31528661.75781759311413--------5.647315--------2.52217631393604--------------
10485840.36621407----------------0.094525430.022677435----1.395758479687152.0709993e-07----0.11679542--------0.03732341------1.333858640375253.0700738e-07--------1.56957843197039----------0.045192935----0.202252162.3029378e-07------2.637798383751430.06752495--0.72929496--------3.72044665.470301809319240.15900145--------4.8780651.98236000979388------5.06775141.81433707039531--------6.44909--------2.55789082050323--------------
..................................................................................................................................................................................................................................................................
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120430083.43117117596714----------------1.12891318891637--------------1.56644287506227----------------------------------------------1.66926372262749------------------4.31668842759001------------------------------------------------------------------
120430091.81201014966394----------------0.508966908339133--------------0.890091881795282----------------------------------------------1.1312017674427------------------3.09896943585728------------------------------------------------------------------
120430102.49268285441886----------------0.494158403628982--------------0.909613505597342----------------------------------------------1.136793192659------------------3.12670346706475------------------------------------------------------------------
120430112.09373553263053----------------0.577247280662618--------------0.991298854740798----------------------------------------------1.22674660501041------------------3.39733806461695------------------------------------------------------------------
12043012------------------0.445860992796356--------------------------------------------------------------1.02642671074533--------------------------------------------------------------------------------------
120430163.58693278533988----------------0.684728410968042--------------1.19586028947091----------------------------------------------1.38443063072352------------------3.92742906404126------------------------------------------------------------------
12043017------------------0.462498951222175--------------0.85118486016068----------------------------------------------1.08096952471151------------------2.88149622026963------------------------------------------------------------------
120430182.01032084617047----------------0.592936370182139--------------1.04206028405916----------------------------------------------1.31215521774914------------------4.08352324846221------------------------------------------------------------------
" ], "text/plain": [ "\n", "hp_idx_o_10 ferr_ap_omegacam_u_mean ... ferr_ap_irac_i4_mean\n", " uJy ... uJy \n", " int64 float64 ... float64 \n", "----------- ----------------------- ... --------------------\n", " 1048576 0.3888968 ... --\n", " 1048577 0.3287 ... --\n", " 1048578 0.3846171 ... --\n", " 1048579 0.37277982 ... --\n", " 1048580 0.2911744 ... --\n", " 1048581 0.33193266 ... --\n", " 1048582 0.31823373 ... --\n", " 1048583 0.25143299 ... --\n", " 1048584 0.36621407 ... --\n", " ... ... ... ...\n", " 12042983 -- ... --\n", " 12042985 -- ... --\n", " 12043008 3.43117117596714 ... --\n", " 12043009 1.81201014966394 ... --\n", " 12043010 2.49268285441886 ... --\n", " 12043011 2.09373553263053 ... --\n", " 12043012 -- ... --\n", " 12043016 3.58693278533988 ... --\n", " 12043017 -- ... --\n", " 12043018 2.01032084617047 ... --" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "depth_table = table.table\n", "depth_table " ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "cosmos_coverage = Table.read('../../../dmu1/dmu1_ml_COSMOS/data/depths_cosmos_20190402.fits')" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=10\n", "
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94MnAA8BHBvo/3MVeCXy0y2nJlSSN2NBuwpTkTOCnwDeAZ3bNrwRuqKqvdn0uAG5Lcjjwj/TuAX5iVe0Cvp7kz+kVifP3lltVO4c1bklSu6HsaSRZDbwLeOtAaC2wdfZJVX2X3p7Fs7rHI1X1nb7+W7uchXIHv/+GJFNJpmZmZpY+IUnSnIZ1eOpi4JNV9fcD7auAHQNtO4DDF4gtlLuHqtpUVZNVNTkxMbEPw5cktVjy4akk64HfAn51jvAuYPVA22pgJ73DU/PFFsqVJI3BMNY0TgHWAD9IAr09hEOS/ApwE7ButmOSpwOHAt+hVzRWJDm+qv5v12UdMN1tT+8lV5I0BsMoGpuAP+t7/of0isgbgF8A/meSk4H/TW/dY8vsQnaSLcC7kvx7YD3wMuA3u9f5zN5yJUmjt+Q1jap6oKq2zT7oHVZ6qKpmqmoaeD29AvBjeusRb+xLfyPw+C72p8AbuhwaciVJIza0U25nVdVFA88/C3x2nr73Aqfv5bXmzZUkjZ6XEZEkNbNoSJKaWTQkSc0sGpKkZhYNSVIzi4YkqZlFQ5LUzKIhSWpm0ZAkNbNoSJKaWTQkSc2Gfu2pn1drzr/x0e3vX/qSMY5EkvYf9zQkSc0sGpKkZhYNSVKzJReNJIcm+WSSO5LsTPLNJC/qi5+a5PYkDyS5OclxA7lXJLkvybYk5w289ry5kqTRG8aexgrg74HnA08ELgA+n2RNkqOALV3bkcAU8Lm+3IuA44HjgBcAb09yGkBDriRpxJZ89lRV3U/vj/+sv0jyPeDXgCcB01V1DUCSi4CfJDmhqm4HzgJeU1Xbge1JPgGcDdwEvHyBXEnSiA19TSPJk4FnAdPAWmDrbKwrMN8F1iY5Aji6P95tr+22580d9pglSW2GWjSSPBb4DPCpbm9gFbBjoNsO4PAuxkB8NsYCuYPfd0OSqSRTMzMzS5uEJGleQysaSR4DXA08DJzbNe8CVg90XQ3s7GIMxGdjC+Xuoao2VdVkVU1OTEzs8xwkSXs3lKKRJMAngScDZ1TVz7rQNLCur99hwDPorVVsB37YH++2pxfKHcaYJUmLN6w9jY8CzwZ+p6oe7Gu/FjgxyRlJVgLvAG7tW8i+CtiY5IgkJwCvAzY35kqSRmzJZ091n504B9gNbOvtdABwTlV9JskZwIeATwP/CzizL/1CegXnDuBB4LKqugmgqmYWyF22vA6VpIPVME65vQPIXuJfBE6YJ7YbeG33WFSuJGn0vIyIJKmZRUOS1MyiIUlq5k2Y5tG/mC1J6rFoLEOefSVpufLwlCSpmUVDktTMoiFJambRkCQ1cyF8Pxs8C8uFbUkHMovGiHlmlKQDmUVjmfBzIZIOBBaNZc49E0nLiUWjj//bl6S98+wpSVKzZV00khyZ5Nok9ye5I8krxj0mSfp5ttwPT30YeJjevcfXAzcm2VpVB8V9whd7OKylv+sekvanZbunkeQw4AzggqraVVVfB/4c+L3xjkySfn4t5z2NZwGPVNV3+tq2As8f03gOOPPtmbTsjSz2rK3Ffq9hfujRM8z082ocHx5OVe33b7IvkpwMXFNVT+lrex3wyqo6ZaDvBmBD9/SXgb+d52WPAn4y/NGO3MEyD3Auy9XBMpeDZR6w/+dyXFVNLNRpOe9p7AJWD7StBnYOdqyqTcCmhV4wyVRVTQ5neONzsMwDnMtydbDM5WCZByyfuSzbNQ3gO8CKJMf3ta0DDopFcEk6EC3bolFV9wNbgHclOSzJc4GXAVePd2SS9PNr2RaNzhuBxwM/Bv4UeMMST7dd8BDWAeJgmQc4l+XqYJnLwTIPWCZzWbYL4ZKk5We572lIkpYRi4YkqdlBVTRar1WVnsuS3NM9Lk+SUY93bxYxl7cl+XaSnUm+l+Rtox7r3iz2+mFJHpfk9iR3jmqMrRYzlyT/PMlXk+xK8qMkbx7lWBeyiN+vQ5N8rJvDvUluSHLMqMc7nyTnJplKsjvJ5gX6viXJtiQ7klyR5NARDbNJ61ySvDrJLUnuS3Jn9/drZB+fOKiKBnteq+qVwEeTrJ2j3wbgdHqn8J4EvBQ4Z1SDbNQ6lwBnAUcApwHnJjlzZKNcWOs8Zr2N3okPy1HTXJIcBdwEfBx4EvBM4L+PcJwtWn8ubwb+Bb33ydHAT4EPjmqQDe4GLgGu2FunJP8KOB84FVgDPB145/4e3CI1zQV4AvAH9D7s9xx6c/rD/Tu0PlV1UDyAw+i9CZ7V13Y1cOkcfb8BbOh7/u+Avx73HPZlLnPkfgD44LjnsC/zAH4JuA14EXDnuMe/hN+v9wBXj3vMQ5rLR4HL+56/BPjbcc9hjnFeAmzeS/yzwHv6np8KbBv3uPdlLnP0Pw+4YVTjO5j2NOa7VtVc/3ta28UW6jcui5nLo7pDbCezfD4Audh5fBD4Y+DB/T2wfbCYufwGcG+SbyT5cXdI59iRjLLNYubySeC5SY5O8gR6eyVfGMEYh22u9/yTkzxpTOMZpucxwvf8wVQ0VgE7Btp2AIc39N0BrFpG6xqLmUu/i+j9TK/cD2PaF83zSPJvgBVVde0oBrYPFvMz+UXg1fQO7RwLfI/e54yWi8XM5TvAD4C7gPuAZwPv2q+j2z/mes/Dwu+pZS3Ja4BJ4D+O6nseTEWj+VpVc/RdDeyqbl9vGVjMXIDeIhq9tY2XVNXu/Ti2xWiaR3cZ/MuB3x/RuPbFYn4mDwLXVtXfVNVD9I6d/2aSJ+7nMbZazFw+CqyktzZzGL2rNByIexpzvedhL++p5S7J6cClwIuqamQXZTyYisZirlU13cUW6jcui7ruVpLX0i3yVdVyOuuodR7H01uc/FqSbfT+MD21O9NlzQjG2WIxP5Nbgf7/gMxuL5c92cXMZR294+v3dv8Z+SDw691i/4Fkrvf8j6rqnjGNZ0mSnAZ8AvidqvrWSL/5uBd9hryA9Gf0DgMcBjyX3i7o2jn6vZ7egusx9M4ImQZeP+7x7+NcXglsA5497jHv6zzoXW35KX2Pl9M7k+QpwCHjnsM+/ExeCGynd7fJxwL/GfjauMe/j3O5EvhvwBO7ufwxcNe4xz/wu7MS+BN6i/kr6R3mHOx3Wvc++RV6Zxp+iYYTS5bpXF4I3AM8byzjHPc/1JD/0Y8ErgPup3cc9hVd+8n0Dj/N9gu9wyH3do/L6S6pslwei5jL94Cf0dv9nn18bNzjX+w8BnJOYZmdPbXYuQBvoLcOsB24AXjauMe/j79fTwI+Q+806J8CXwd+fdzj7xvfRfT25PofF9FbS9oFHNvX9zzgR/TWZq4EDh33+PdlLsDNwD8MvOe/MKpxeu0pSVKzg2lNQ5K0n1k0JEnNLBqSpGYWDUlSM4uGJKmZRUOS1MyiIUlqZtGQJDWzaEiSmv1/1+lrrg82BaYAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(cosmos_coverage[~np.isnan(cosmos_coverage['ferr_irac_i1_mean'])]['ferr_irac_i1_mean'], bins=100)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "#We use total here because apertur fluxes are absent\n", "cosmos_coverage['ferr_irac_i1_mean'].name = 'ferr_ap_irac_i1_mean'\n", "cosmos_coverage['ferr_irac_i2_mean'].name = 'ferr_ap_irac_i2_mean'\n", "cosmos_coverage['ferr_irac_i3_mean'].name = 'ferr_ap_irac_i3_mean'\n", "cosmos_coverage['ferr_irac_i4_mean'].name = 'ferr_ap_irac_i4_mean'" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "cosmos_coverage['hp_idx_O_10'].name='hp_idx_o_10'" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "99243" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(cosmos_coverage)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1645" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(np.unique(cosmos_coverage['hp_idx_o_10']))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "cosmos_coverage.sort('hp_idx_o_10')\n", "is_duplicate = cosmos_coverage['hp_idx_o_10'][1:] == cosmos_coverage['hp_idx_o_10'][:-1]\n", "#np.append(is_duplicate,False)\n", "cosmos_coverage = cosmos_coverage[~np.append(is_duplicate,False)]" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1645" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(cosmos_coverage)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "shared_cols = set(cosmos_coverage.colnames).intersection(set(depth_table.colnames))" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ferr_ap_decam_g_mean',\n", " 'ferr_ap_decam_r_mean',\n", " 'ferr_ap_decam_z_mean',\n", " 'ferr_ap_gpc1_g_mean',\n", " 'ferr_ap_gpc1_i_mean',\n", " 'ferr_ap_gpc1_r_mean',\n", " 'ferr_ap_gpc1_y_mean',\n", " 'ferr_ap_gpc1_z_mean',\n", " 'ferr_ap_irac_i1_mean',\n", " 'ferr_ap_irac_i2_mean',\n", " 'ferr_ap_irac_i3_mean',\n", " 'ferr_ap_irac_i4_mean',\n", " 'ferr_ap_megacam_g_mean',\n", " 'ferr_ap_megacam_i_mean',\n", " 'ferr_ap_megacam_r_mean',\n", " 'ferr_ap_megacam_u_mean',\n", " 'ferr_ap_megacam_z_mean',\n", " 'ferr_ap_omegacam_g_mean',\n", " 'ferr_ap_omegacam_i_mean',\n", " 'ferr_ap_omegacam_r_mean',\n", " 'ferr_ap_omegacam_u_mean',\n", " 'ferr_ap_suprime_g_mean',\n", " 'ferr_ap_suprime_i_mean',\n", " 'ferr_ap_suprime_r_mean',\n", " 'ferr_ap_suprime_y_mean',\n", " 'ferr_ap_suprime_z_mean',\n", " 'ferr_ap_ukidss_h_mean',\n", " 'ferr_ap_ukidss_j_mean',\n", " 'ferr_ap_ukidss_k_mean',\n", " 'ferr_ap_ukidss_y_mean',\n", " 'ferr_ap_vista_h_mean',\n", " 'ferr_ap_vista_j_mean',\n", " 'ferr_ap_vista_ks_mean',\n", " 'ferr_ap_vista_y_mean',\n", " 'ferr_ap_wircam_h_mean',\n", " 'ferr_ap_wircam_j_mean',\n", " 'ferr_ap_wircam_ks_mean',\n", " 'hp_idx_o_10'}" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shared_cols" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "cosmos_pixels = np.in1d(depth_table['hp_idx_o_10'], np.unique(cosmos_coverage['hp_idx_o_10']))" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1645" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(cosmos_pixels)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "394298" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(depth_table)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "392653" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "depth_table= depth_table[~cosmos_pixels]\n", "len(depth_table)" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "394298" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "depth_table = vstack([depth_table, cosmos_coverage[list(shared_cols)]])\n", "len(depth_table)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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", "/Users/rs548/anaconda/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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['omegacam_u', 'mmt_u', 'bessell_u', 'lbc_u', 'wfi_u', 'megacam_u', 'mosaic_u', 'wfc_u', 'sdss_u']\n", "['omegacam_u', 'mmt_u', 'bessell_u', 'wfi_u', 'megacam_u', 'mosaic_u', 'wfc_u', 'sdss_u']\n", "['mmt_g', 'omegacam_g', 'suprime_g', 'megacam_g', 'wfc_g', 'gpc1_g', 'decam_g', '90prime_g', 'sdss_g']\n", "['mmt_g', 'omegacam_g', 'suprime_g', 'megacam_g', 'wfc_g', 'gpc1_g', 'decam_g', '90prime_g', 'sdss_g']\n", "['omegacam_r', 'mmt_r', 'cfht12k_r', 'megacam_r', 'wfi_r', 'suprime_r', 'mosaic_r', 'bessell_r', 'wfc_r', 'gpc1_r', 'decam_r', 'sdss_r', '90prime_r']\n", "['omegacam_r', 'mmt_r', 'cfht12k_r', 'megacam_r', 'wfi_r', 'suprime_r', 'mosaic_r', 'bessell_r', 'wfc_r', 'gpc1_r', 'decam_r', 'sdss_r', '90prime_r']\n", "['sdss_i', 'decam_i', 'gpc1_i', 'wfc_i', 'bessell_i', 'mosaic_i', 'megacam_i', 'wfi_i', 'suprime_i', 'cfht12k_i', 'mmt_i', 'omegacam_i']\n", "['sdss_i', 'decam_i', 'gpc1_i', 'wfc_i', 'bessell_i', 'mosaic_i', 'megacam_i', 'wfi_i', 'suprime_i', 'cfht12k_i', 'mmt_i', 'omegacam_i']\n", "['decam_z', '90prime_z', 'sdss_z', 'wfc_z', 'gpc1_z', 'suprime_z', 'megacam_z', 'vista_z', 'mosaic_z', 'omegacam_z', 'mmt_z']\n", "['decam_z', '90prime_z', 'sdss_z', 'wfc_z', 'gpc1_z', 'suprime_z', 'megacam_z', 'vista_z', 'mosaic_z', 'omegacam_z', 'mmt_z']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:20: RuntimeWarning: All-NaN axis encountered\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['decam_y', 'ukidss_y', 'gpc1_y', 'suprime_y', 'megacam_y', 'vista_y', 'lbc_y', 'wircam_y']\n", "['decam_y', 'ukidss_y', 'gpc1_y', 'suprime_y', 'megacam_y', 'vista_y', 'wircam_y']\n", "['omega2000_j', 'ukidss_j', 'vista_j', 'newfirm_j', 'wircs_j', 'wircam_j']\n", "['omega2000_j', 'ukidss_j', 'vista_j', 'newfirm_j', 'wircs_j', 'wircam_j']\n", "['ukidss_h', 'vista_h', 'newfirm_h', 'wircam_h']\n", "['ukidss_h', 'vista_h', 'newfirm_h', 'wircam_h']\n", "['isaac_k', 'moircs_k', 'ukidss_k', 'newfirm_k', 'wircs_k', 'hawki_k']\n", "['isaac_k', 'moircs_k', 'ukidss_k', 'newfirm_k', 'wircs_k', 'hawki_k']\n", "['wircam_ks', 'vista_ks', 'moircs_ks', 'omega2000_ks', 'tifkam_ks']\n", "['wircam_ks', 'vista_ks', 'moircs_ks', 'omega2000_ks', 'tifkam_ks']\n", "['irac_i1']\n", "['irac_i1']\n", "['irac_i2']\n", "['irac_i2']\n", "['irac_i3']\n", "['irac_i3']\n", "['irac_i4']\n", "['irac_i4']\n" ] } ], "source": [ "\n", "for col in depth_table.colnames:\n", " if depth_table[col].dtype == 'float64':\n", " depth_table[col].fill_value = np.nan\n", " if col.startswith('ferr'):\n", " depth_table[col][depth_table[col] < 1.e-3] *= 1.e6 \n", " \n", "depth_table = depth_table.filled()\n", "coverage = Table()\n", "coverage.add_column(Column(data=depth_table['hp_idx_o_10'], name='hp_idx_o_10'))\n", "for band in bands:\n", " specific_bands = [f for f in filters if f.split('_')[1] == band.lower()]\n", " print(specific_bands)\n", " for specific_band in specific_bands:\n", " if 'ferr_ap_{}_mean'.format(specific_band) not in depth_table.colnames:\n", " specific_bands.remove(specific_band)\n", " print(specific_bands)\n", " lowest_depths = np.nanmin([depth_table[column] for column in \n", " ['ferr_ap_{}_mean'.format(s) for s in specific_bands]]\n", " , axis=0)\n", " \n", " coverage.add_column(Column(data=lowest_depths, name='ferr_ap_{}_mean_min'.format(band)))" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=394298\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
hp_idx_o_10ferr_ap_u_mean_minferr_ap_g_mean_minferr_ap_r_mean_minferr_ap_i_mean_minferr_ap_z_mean_minferr_ap_y_mean_minferr_ap_J_mean_minferr_ap_H_mean_minferr_ap_K_mean_minferr_ap_Ks_mean_minferr_ap_i1_mean_minferr_ap_i2_mean_minferr_ap_i3_mean_minferr_ap_i4_mean_min
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" ], "text/plain": [ "\n", "hp_idx_o_10 ferr_ap_u_mean_min ... ferr_ap_i3_mean_min ferr_ap_i4_mean_min\n", " int64 float64 ... float64 float64 \n", "----------- ------------------ ... ------------------- -------------------\n", " 1048576 0.3888968 ... nan nan\n", " 1048577 0.3287 ... nan nan\n", " 1048578 0.3846171 ... nan nan\n", " 1048579 0.37277982 ... nan nan\n", " 1048580 0.2911744 ... nan nan\n", " 1048581 0.33193266 ... nan nan\n", " 1048582 0.31823373 ... nan nan\n", " 1048583 0.25143299 ... nan nan\n", " 1048584 0.36621407 ... nan nan\n", " 1048585 0.3486292 ... nan nan\n", " ... ... ... ... ...\n", " 7000392 nan ... nan nan\n", " 7000393 nan ... nan nan\n", " 7000396 nan ... nan nan\n", " 7000400 nan ... nan nan\n", " 7000401 nan ... nan nan\n", " 7000402 nan ... nan nan\n", " 7000403 nan ... nan nan\n", " 7000404 nan ... nan nan\n", " 7000405 nan ... nan nan\n", " 7001088 nan ... nan nan" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coverage" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "#for m, band in enumerate(bands):\n", "# field_coverages.add_column(Column(data=np.full(len(field_coverages), np.nan), name='ferr_ap_{}_mean_median'.format(band)))\n", "\n", "fields_table = Table()\n", "for n, field in enumerate(fields['fields']):\n", " field_moc = MOC(filename=field['region'].replace('dmu_products/', '../../../'))\n", " field_cells= Table()\n", " field_cells.add_column(Column(data=list(field_moc.flattened(order=10)), name='hp_idx_o_10'))\n", " field_cells.add_column(Column(data=np.full(len(field_cells), field['name'] ), name='field'))\n", " fields_table = vstack([fields_table, field_cells])\n", " \n" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [], "source": [ "coverage = join(coverage, fields_table, join_type='left', keys='hp_idx_o_10')" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "coverage.write('./data/coverage_band_overview.fits', overwrite=True)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "coverage = Table.read('./data/coverage_band_overview.fits')" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table masked=True length=449146\n", "
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................................................
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120430083.431171175967141.128913188916371.566442875062271.669263722627494.31668842759001nannannannannannannannannanN/A
120430091.812010149663940.5089669083391330.8900918817952821.13120176744273.09896943585728nannannannannannannannannanN/A
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12043012nan0.445860992796356nan1.02642671074533nannannannannannannannannannanN/A
120430163.586932785339880.6847284109680421.195860289470911.384430630723523.92742906404126nannannannannannannannannanN/A
12043017nan0.4624989512221750.851184860160681.080969524711512.88149622026963nannannannannannannannannanN/A
120430182.010320846170470.5929363701821391.042060284059161.312155217749144.08352324846221nannannannannannannannannanN/A
" ], "text/plain": [ "\n", "hp_idx_o_10 ferr_ap_u_mean_min ... ferr_ap_i4_mean_min field \n", " int64 float64 ... float64 bytes18\n", "----------- ------------------ ... ------------------- -------\n", " 1048576 0.3888968 ... nan GAMA-09\n", " 1048577 0.3287 ... nan GAMA-09\n", " 1048578 0.3846171 ... nan GAMA-09\n", " 1048579 0.37277982 ... nan GAMA-09\n", " 1048580 0.2911744 ... nan GAMA-09\n", " 1048581 0.33193266 ... nan GAMA-09\n", " 1048582 0.31823373 ... nan GAMA-09\n", " 1048583 0.25143299 ... nan GAMA-09\n", " 1048584 0.36621407 ... nan GAMA-09\n", " 1048585 0.3486292 ... nan GAMA-09\n", " ... ... ... ... ...\n", " 12042983 nan ... nan N/A\n", " 12042985 nan ... nan N/A\n", " 12043008 3.43117117596714 ... nan N/A\n", " 12043009 1.81201014966394 ... nan N/A\n", " 12043010 2.49268285441886 ... nan N/A\n", " 12043011 2.09373553263053 ... nan N/A\n", " 12043012 nan ... nan N/A\n", " 12043016 3.58693278533988 ... nan N/A\n", " 12043017 nan ... nan N/A\n", " 12043018 2.01032084617047 ... nan N/A" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coverage" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make field summaries" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0032784908016061202" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "order10_area = MOC(order=10, cells=[1]).area_sq_deg\n", "order10_area" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/numpy/core/fromnumeric.py:688: UserWarning: Warning: 'partition' will ignore the 'mask' of the MaskedColumn.\n", " a.partition(kth, axis=axis, kind=kind, order=order)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Field: AKARI-NEP, i1 band coverage: 7.173337873914191, median depth = 1.6617190000000002, max depth = 9.11136\n", "Field: AKARI-SEP, i1 band coverage: 7.294642033573617, median depth = 1.0387509, max depth = 62.539757\n", "Field: Bootes, i1 band coverage: 10.117422613756487, median depth = 0.7535689270149595, max depth = 23.952327804264\n", "Field: CDFS-SWIRE, i1 band coverage: 8.22245493042815, median depth = 0.5481367645686981, max depth = 7.6217866678337\n", "Field: COSMOS, i1 band coverage: 2.212981291084131, median depth = 0.08034571709348072, max depth = 1.2336000204086304\n", "Field: EGS, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: ELAIS-N1, i1 band coverage: 9.710889754357328, median depth = 0.78441163003663, max depth = 6.62454545454545\n", "Field: ELAIS-N2, i1 band coverage: 4.484975416597172, median depth = 0.8212270942408375, max depth = 5.89333333333333\n", "Field: ELAIS-S1, i1 band coverage: 7.206122781930253, median depth = 0.706632612529864, max depth = 10.5114285714286\n", "Field: GAMA-09, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: GAMA-12, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: GAMA-15, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: HDF-N, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: Herschel-Stripe-82, i1 band coverage: 98.92518144766308, median depth = 1.9088288565758451, max depth = 80.1761464394222\n", "Field: Lockman-SWIRE, i1 band coverage: 11.612414419288879, median depth = 0.6888698618707021, max depth = 4.76666666666667\n", "Field: HATLAS-NGP, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: SA13, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: HATLAS-SGP, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: SPIRE-NEP, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: SSDF, i1 band coverage: 94.2795599817872, median depth = 79.68621, max depth = 17731.0\n", "Field: xFLS, i1 band coverage: 4.091556520404438, median depth = 5.391215873751545, max depth = 1211.70427483696\n", "Field: XMM-13hr, i1 band coverage: 0.0, median depth = nan, max depth = nan\n", "Field: XMM-LSS, i1 band coverage: 10.02234638050991, median depth = 0.669229317030779, max depth = 15.175\n" ] } ], "source": [ "#Make a list of all bands\n", "#this was too slow so tried to run whole query at once\n", "field_coverages = Table()\n", "field_coverages.add_column(Column(data=[field['name'] for field in fields['fields']], name='field' ))\n", "\n", "for m, band in enumerate(bands):\n", " field_coverages.add_column(Column(data=np.full(len(field_coverages), np.nan), \n", " name='{}_band_area'.format(band)))\n", " field_coverages.add_column(Column(data=np.full(len(field_coverages), np.nan), \n", " name='ferr_ap_{}_mean_meadian'.format(band)))\n", "\n", "\n", "for n, field in enumerate(fields['fields']):\n", " in_field = coverage['field'] == field['name']\n", " for m, band in enumerate(bands):\n", " has_band = in_field & ~np.isnan(coverage['ferr_ap_{}_mean_min'.format(band)])\n", " area = np.sum(has_band) * order10_area\n", " \n", " field_coverages['{}_band_area'.format(band)][field_coverages['field'] == field['name']] = area\n", " \n", " if np.sum(has_band) != 0:\n", " depth_median = np.nanmedian(coverage['ferr_ap_{}_mean_min'.format(band)][has_band])\n", " depth_max = np.nanmax(coverage['ferr_ap_{}_mean_min'.format(band)][has_band])\n", " else:\n", " depth_median = np.nan\n", " depth_max = np.nan\n", " \n", " field_coverages['ferr_ap_{}_mean_meadian'.format(band)][field_coverages['field'] == field['name']] = depth_median\n", " \n", " if band == 'i1':\n", " print(\"Field: {}, {} band coverage: {}, median depth = {}, max depth = {}\".format(field['name'],\n", " band,\n", " area,\n", " depth_median,\n", " depth_max))\n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=23\n", "
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fieldu_band_areaferr_ap_u_mean_meadiang_band_areaferr_ap_g_mean_meadianr_band_areaferr_ap_r_mean_meadiani_band_areaferr_ap_i_mean_meadianz_band_areaferr_ap_z_mean_meadiany_band_areaferr_ap_y_mean_meadianJ_band_areaferr_ap_J_mean_meadianH_band_areaferr_ap_H_mean_meadianK_band_areaferr_ap_K_mean_meadianKs_band_areaferr_ap_Ks_mean_meadiani1_band_areaferr_ap_i1_mean_meadiani2_band_areaferr_ap_i2_mean_meadiani3_band_areaferr_ap_i3_mean_meadiani4_band_areaferr_ap_i4_mean_meadian
str18float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
AKARI-NEP0.0nan9.599421067102721.5368271627509259.6026995579043271.610832395729739.6026995579043271.497358855946639.6026995579043273.213156208794219.6026995579043277.155204004839740.0nan0.0nan0.0nan0.0nan7.1733378739141911.66171900000000027.199565800327041.36674930.0nan0.0nan
AKARI-SEP0.0nan9.2519010421324720.1049150959250059.2551795329340770.1256984090109999.2519010421324720.207567666613907529.2486225513308650.3914428092489999.219116134116411.3777517479915259.143710845679473.65498928.576531937001615.85628530.0nan7.8913273594659316.4569577.2946420335736171.03875097.1700593831125850.951545660.0nan0.0nan
Bootes0.0nan11.8714151926157620.1305940611.9042001006318220.098377114945024811.9042001006318220.18848300001729811.897643119028610.19468352043495711.8583012294093374.3415297166517711.8287948121948821.3523338665036059.7141682451589341.538835720505499.6781048463412672.994388418991675.294762644593884512.143566609840110.1174226137564870.753568927014959510.1075871413516691.02598004541859.9830044908906365.412508497441949.9731690184858187.358867777855445
CDFS-SWIRE13.2188749120758782.17129283430091513.4713187037995490.097766642448822413.4713187037995490.11508273055559713.4680402129979410.1124551077331185113.4647617221963360.19043903842073713.4516477589899120.17857073932005510.579689816782952.2673593891991510.1961063929950340.89360693250535650.0nan10.1600429941773670.9476835372500458.222454930428150.54813676456869818.1601636051976330.6159647735745468.0716443535542684.6535157894736858.0224669915301774.809375
COSMOS2.19658883707610060.020699859135001885.3931173686420680.0239928555701159485.3931173686420680.0263500579895864025.3931173686420680.0293231678789122675.3931173686420680.043215650985510945.3865603870388550.173438906594167775.2914841537922783.8473968045278035.3013196261970964.7104326135971975.3373830250147645.8740630801615682.19986732787770660.196193212664675432.2129812910841310.080345717093480722.21953827268734340.09256590098443322.13101902104397833.84571046293058762.1015126038295236.694406648476918
EGS3.8489482010855850.061362351379113953.85878367349040370.03308972534961093.85878367349040370.06444148667547043.85550518268879740.094220335656375553.85550518268879740.1821299863446553.6751881886004615.092251786820183.7932138574582816.2902920.56390041787625270.10585615687138750.87535704402883411.497008382040220.56717890867785880.1322655993106710.0nan0.0nan0.0nan0.0nan
ELAIS-N113.209039439671060.01519586513.9106364712147690.01084685913.9139149620163740.020674653513.8975225080083430.056162783114406513.9139149620163740.04801705150000000513.8352311827778270.16750753656569859.2682934961405030.54818150.0nan9.2846859501485320.68103642000000010.0nan9.7108897543573280.784411630036639.7305606991669651.0321712823600259.6387629567219944.94245833333333459.6518769199284184.86182194616977
ELAIS-N28.2683538016506350.0184354689.5109018154593560.0169387769.5207372878641740.0260510259999999989.5207372878641741.2765486048155559.5207372878641740.0613261189.5207372878641745.13743589233718550.0nan0.0nan0.0nan0.0nan4.4849754165971720.82122709424083754.478418434993961.0535974128073154.4226840913666564.787567567567574.4062916373586264.64675
ELAIS-S10.0nan9.4617244534352630.1233860144933989.4420535086256270.13219586916889659.4584459626336570.2439562063219729.4617244534352630.44263761577839.455167471832051.082285432183999.4322180362208082.578623890876778.2290119120313623.738987103104590.0nan9.2289516065212285.284153900146487.2061227819302530.7066326125298647.3897182668201950.97094507152505056.7110706708877285.277692307692316.7045136892845165.45833333333333
GAMA-0959.062011790934260.2832419663.491252863904120.0936017663.514202299515370.09253827563.504366827110550.24837012563.5010883363089460.5290056563.5338732443251.449528929305463.4683034282928841.9007867455482563.415847575467183.1148492813110459.976710724582366.0982916556.557244818507183.086330014539050.0nan0.0nan0.0nan0.0nan
.......................................................................................
Herschel-Stripe-82115.750396241505680.28281020784016797366.856563718121660.13312250161150452366.94836146056660.1607005552674815366.85000673651840.2505212891630565366.95491844216980.438992619723303368.04009889750151.66655107462496356.07688596244074.01589206055859351.280453919690965.46538630775783233.903926240588656.27123086397038241.218239218971926.512134994521284598.925181447663081.908828856575845199.026814662512861.849502379897950.0nan0.0nan
Lockman-SWIRE14.8056644600532380.01216064299999999922.1199774384364930.01090886222.12325592923810.015816610522.097028002825251.2276592117475722.0511291316027640.04248597121.9757238431658246.234801020608620.4348331664109475.506308058033820.0nan8.619152317422490.62190370.0nan11.6124144192888790.688869861870702111.6124144192888790.94208082122122711.4517683700101785.0186666666666711.4517683700101785.26111111111111
HATLAS-NGP0.0nan179.576055167173621.13681224196556179.589169130380041.0795281666189451179.572776676372031.2273446313397179.61867554759451.0867627179.572776676372033.2590716177.199149336009194.1193457177.25488367963655.139570300000001178.353178098174545.71264840.0nan0.0nan0.0nan0.0nan0.0nan
SA130.0nan0.281950208938126330.190771620.285228699739732470.3101130.0nan0.285228699739732471.03674320.0nan0.33768455256543046.80165240.0nan0.0nan0.0nan0.0nan0.0nan0.0nan0.0nan
HATLAS-SGP0.0nan179.576055167173621.13681224196556179.589169130380041.0795281666189451179.572776676372031.2273446313397179.61867554759451.0867627179.572776676372033.2590716177.199149336009194.1193457177.25488367963655.139570300000001178.353178098174545.71264840.0nan0.0nan0.0nan0.0nan0.0nan
SPIRE-NEP0.0nan0.163924540080306021.550829528225660.163924540080306021.5831337938013250.163924540080306021.634765615651940.163924540080306023.88239396282783030.163924540080306027.089256997006440.0nan0.0nan0.0nan0.0nan0.0nan0.0nan0.0nan0.0nan
SSDF0.0nan112.770248102845710.107691356484737112.780083575250540.13076024198950348112.776805084448920.209138197405894112.776805084448920.367745113347188112.727627722424841.1696262000282052111.675232175109272.695057697.967862133594084.071280.0nan107.491877912259875.46475494.279559981787279.6862192.9714421519463686.925160.0nan0.0nan
xFLS4.724305245114422.43899157.8257575434338090.162669947.8552639606482640.089791157.8421499974418391.454303613332127.8388715066402342.477237551390067.8290360342354156.222903159755045.6160547431512846.08841470.0nan0.0nan0.0nan4.0915565204044385.3912158737515454.0653285939915896.0016732110300553.963695379141799321.01481105297033.963695379141799320.4360184558011
XMM-13hr0.0nan0.84912911761598520.2031490.84912911761598520.202700730.0nan0.84585062681437910.837000320.0nan0.86552157162401586.185498750.0nan0.0nan0.0nan0.0nan0.0nan0.0nan0.0nan
XMM-LSS18.8677145632432220.052211232239810322.4478265185971040.022237319847412222.4511050093987130.0386317723433906522.4511050093987130.04362370374584885522.4478265185971040.090036108488459222.4379910461922880.1806634960360695222.3298008497392862.5914106919215321.0708603819225363.558621031897417.1176035302868870.59675937181954921.9921162971738555.131535547429864510.022346380509910.6692293170307799.8223584416119360.78783616395257859.337141802974235.8489454545454549.3469772753790486.38166666666667
" ], "text/plain": [ "\n", " field u_band_area ... ferr_ap_i4_mean_meadian\n", " str18 float64 ... float64 \n", "------------------ ------------------ ... -----------------------\n", " AKARI-NEP 0.0 ... nan\n", " AKARI-SEP 0.0 ... nan\n", " Bootes 0.0 ... 7.358867777855445\n", " CDFS-SWIRE 13.218874912075878 ... 4.809375\n", " COSMOS 2.1965888370761006 ... 6.694406648476918\n", " EGS 3.848948201085585 ... nan\n", " ELAIS-N1 13.20903943967106 ... 4.86182194616977\n", " ELAIS-N2 8.268353801650635 ... 4.64675\n", " ELAIS-S1 0.0 ... 5.45833333333333\n", " GAMA-09 59.06201179093426 ... nan\n", " ... ... ... ...\n", "Herschel-Stripe-82 115.75039624150568 ... nan\n", " Lockman-SWIRE 14.805664460053238 ... 5.26111111111111\n", " HATLAS-NGP 0.0 ... nan\n", " SA13 0.0 ... nan\n", " HATLAS-SGP 0.0 ... nan\n", " SPIRE-NEP 0.0 ... nan\n", " SSDF 0.0 ... nan\n", " xFLS 4.72430524511442 ... 20.4360184558011\n", " XMM-13hr 0.0 ... nan\n", " XMM-LSS 18.867714563243222 ... 6.38166666666667" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "field_coverages" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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" ] }, { "data": { "text/html": [ "Table length=1\n", "
\n", "\n", "\n", "\n", "
fieldferr_ap_i1_mean_meadian
str18float64
SSDF79.68621
" ], "text/plain": [ "\n", "field ferr_ap_i1_mean_meadian\n", "str18 float64 \n", "----- -----------------------\n", " SSDF 79.68621" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "field_coverages['field', 'ferr_ap_i1_mean_meadian'][field_coverages['ferr_ap_i1_mean_meadian'] > 10]" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "field_coverages.write('./data/field_coverages_band_overview.fits', overwrite=True)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "field_coverages = Table.read('./data/field_coverages_band_overview.fits')" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "table = 'field '\n", "for band in bands:\n", " table += '& \\\\multicolumn{2}{c}{' + band + '}'\n", " \n", "table += ' \\\\\\\\ \\n'\n", "\n", "table += '\\\\cline{1-29} \\n'\n", "\n", "for band in bands:\n", " table += '& a & \\\\sigma '\n", "\n", "table += '\\\\\\\\ \\n'" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "29" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len('AKARI-NEP&&&9.6&1.5&9.6&1.6&9.6&1.5&9.6&3.2&9.6&7.2&&&&&&&&&7.2&1.7&7.2&1.4&&&&\\\\'.split('&'))" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "field & \\multicolumn{2}{c}{u}& \\multicolumn{2}{c}{g}& \\multicolumn{2}{c}{r}& \\multicolumn{2}{c}{i}& \\multicolumn{2}{c}{z}& \\multicolumn{2}{c}{y}& \\multicolumn{2}{c}{J}& \\multicolumn{2}{c}{H}& \\multicolumn{2}{c}{K}& \\multicolumn{2}{c}{Ks}& \\multicolumn{2}{c}{i1}& \\multicolumn{2}{c}{i2}& \\multicolumn{2}{c}{i3}& \\multicolumn{2}{c}{i4} \\\\ \n", "\\cline{1-29} \n", "& a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma \\\\ \n", "\n" ] } ], "source": [ "print(table)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AKARI-NEP&&&9.6&1.5&9.6&1.6&9.6&1.5&9.6&3.2&9.6&7.2&&&&&&&&&7.2&1.7&7.2&1.4&&&&\\\\\n", "AKARI-SEP&&&9.3&0.1&9.3&0.1&9.3&0.2&9.2&0.4&9.2&1.4&9.1&3.7&8.6&5.9&&&7.9&6.5&7.3&1.0&7.2&1.0&&&&\\\\\n", "Bootes&&&11.9&0.1&11.9&0.1&11.9&0.2&11.9&0.2&11.9&4.3&11.8&1.4&9.7&1.5&9.7&3.0&5.3&12.1&10.1&0.8&10.1&1.0&10.0&5.4&10.0&7.4\\\\\n", "CDFS-SWIRE&13.2&2.2&13.5&0.1&13.5&0.1&13.5&0.1&13.5&0.2&13.5&0.2&10.6&2.3&10.2&0.9&&&10.2&0.9&8.2&0.5&8.2&0.6&8.1&4.7&8.0&4.8\\\\\n", "COSMOS&2.2&0.0&5.4&0.0&5.4&0.0&5.4&0.0&5.4&0.0&5.4&0.2&5.3&3.8&5.3&4.7&5.3&5.9&2.2&0.2&2.2&0.1&2.2&0.1&2.1&3.8&2.1&6.7\\\\\n", "EGS&3.8&0.1&3.9&0.0&3.9&0.1&3.9&0.1&3.9&0.2&3.7&5.1&3.8&6.3&0.6&0.1&0.9&1.5&0.6&0.1&&&&&&&&\\\\\n", "ELAIS-N1&13.2&0.0&13.9&0.0&13.9&0.0&13.9&0.1&13.9&0.0&13.8&0.2&9.3&0.5&&&9.3&0.7&&&9.7&0.8&9.7&1.0&9.6&4.9&9.7&4.9\\\\\n", "ELAIS-N2&8.3&0.0&9.5&0.0&9.5&0.0&9.5&1.3&9.5&0.1&9.5&5.1&&&&&&&&&4.5&0.8&4.5&1.1&4.4&4.8&4.4&4.6\\\\\n", "ELAIS-S1&&&9.5&0.1&9.4&0.1&9.5&0.2&9.5&0.4&9.5&1.1&9.4&2.6&8.2&3.7&&&9.2&5.3&7.2&0.7&7.4&1.0&6.7&5.3&6.7&5.5\\\\\n", "GAMA-09&59.1&0.3&63.5&0.1&63.5&0.1&63.5&0.2&63.5&0.5&63.5&1.4&63.5&1.9&63.4&3.1&60.0&6.1&56.6&3.1&&&&&&&&\\\\\n", "GAMA-12&62.1&0.2&64.2&0.1&64.2&0.1&64.2&0.4&64.2&0.5&64.1&1.4&64.1&1.7&64.0&2.7&62.4&6.3&61.8&2.9&&&&&&&&\\\\\n", "GAMA-15&62.0&0.3&63.2&0.1&63.3&0.1&63.2&0.3&63.2&0.6&63.2&1.4&63.0&1.5&63.1&2.5&62.1&6.6&60.6&2.8&&&&&&&&\\\\\n", "HDF-N&0.3&0.0&0.8&1.2&0.8&1.2&0.8&1.5&0.8&2.8&0.8&5.0&&&&&&&&&&&&&&&&\\\\\n", "Herschel-Stripe-82&115.8&0.3&366.9&0.1&366.9&0.2&366.9&0.3&367.0&0.4&368.0&1.7&356.1&4.0&351.3&5.5&233.9&6.3&241.2&6.5&98.9&1.9&99.0&1.8&&&&\\\\\n", "Lockman-SWIRE&14.8&0.0&22.1&0.0&22.1&0.0&22.1&1.2&22.1&0.0&22.0&6.2&20.4&5.5&&&8.6&0.6&&&11.6&0.7&11.6&0.9&11.5&5.0&11.5&5.3\\\\\n", "HATLAS-NGP&&&179.6&1.1&179.6&1.1&179.6&1.2&179.6&1.1&179.6&3.3&177.2&4.1&177.3&5.1&178.4&5.7&&&&&&&&&&\\\\\n", "SA13&&&0.3&0.2&0.3&0.3&&&0.3&1.0&&&0.3&6.8&&&&&&&&&&&&&&\\\\\n", "HATLAS-SGP&&&179.6&1.1&179.6&1.1&179.6&1.2&179.6&1.1&179.6&3.3&177.2&4.1&177.3&5.1&178.4&5.7&&&&&&&&&&\\\\\n", "SPIRE-NEP&&&0.2&1.6&0.2&1.6&0.2&1.6&0.2&3.9&0.2&7.1&&&&&&&&&&&&&&&&\\\\\n", "SSDF&&&112.8&0.1&112.8&0.1&112.8&0.2&112.8&0.4&112.7&1.2&111.7&2.7&98.0&4.1&&&107.5&5.5&94.3&79.7&93.0&86.9&&&&\\\\\n", "xFLS&4.7&2.4&7.8&0.2&7.9&0.1&7.8&1.5&7.8&2.5&7.8&6.2&5.6&6.1&&&&&&&4.1&5.4&4.1&6.0&4.0&21.0&4.0&20.4\\\\\n", "XMM-13hr&&&0.8&0.2&0.8&0.2&&&0.8&0.8&&&0.9&6.2&&&&&&&&&&&&&&\\\\\n", "XMM-LSS&18.9&0.1&22.4&0.0&22.5&0.0&22.5&0.0&22.4&0.1&22.4&0.2&22.3&2.6&21.1&3.6&7.1&0.6&22.0&5.1&10.0&0.7&9.8&0.8&9.3&5.8&9.3&6.4\\\\\n" ] } ], "source": [ "\n", "\n", "for n, field in enumerate(field_coverages):\n", " latex = ''\n", " for element in field:\n", "\n", "\n", " if isinstance(element , float):\n", " if np.isnan(element):\n", " element = ''\n", " elif np.isclose(element, 0.0):\n", " element = ''\n", " else:\n", " element = round(element,1)\n", "\n", "\n", " \n", " latex += str(element) + '&' \n", " print(latex[:-1] + '\\\\\\\\')\n", " table += latex[:-1] + '\\\\\\\\ \\n'\n", "\n", "table += '\\\\hline'" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "field & \\multicolumn{2}{c}{u}& \\multicolumn{2}{c}{g}& \\multicolumn{2}{c}{r}& \\multicolumn{2}{c}{i}& \\multicolumn{2}{c}{z}& \\multicolumn{2}{c}{y}& \\multicolumn{2}{c}{J}& \\multicolumn{2}{c}{H}& \\multicolumn{2}{c}{K}& \\multicolumn{2}{c}{Ks}& \\multicolumn{2}{c}{i1}& \\multicolumn{2}{c}{i2}& \\multicolumn{2}{c}{i3}& \\multicolumn{2}{c}{i4} \\\\ \n", "\\cline{1-29} \n", "& a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma & a & \\sigma \\\\ \n", "AKARI-NEP&&&9.6&1.5&9.6&1.6&9.6&1.5&9.6&3.2&9.6&7.2&&&&&&&&&7.2&1.7&7.2&1.4&&&&\\\\ \n", "AKARI-SEP&&&9.3&0.1&9.3&0.1&9.3&0.2&9.2&0.4&9.2&1.4&9.1&3.7&8.6&5.9&&&7.9&6.5&7.3&1.0&7.2&1.0&&&&\\\\ \n", "Bootes&&&11.9&0.1&11.9&0.1&11.9&0.2&11.9&0.2&11.9&4.3&11.8&1.4&9.7&1.5&9.7&3.0&5.3&12.1&10.1&0.8&10.1&1.0&10.0&5.4&10.0&7.4\\\\ \n", "CDFS-SWIRE&13.2&2.2&13.5&0.1&13.5&0.1&13.5&0.1&13.5&0.2&13.5&0.2&10.6&2.3&10.2&0.9&&&10.2&0.9&8.2&0.5&8.2&0.6&8.1&4.7&8.0&4.8\\\\ \n", "COSMOS&2.2&0.0&5.4&0.0&5.4&0.0&5.4&0.0&5.4&0.0&5.4&0.2&5.3&3.8&5.3&4.7&5.3&5.9&2.2&0.2&2.2&0.1&2.2&0.1&2.1&3.8&2.1&6.7\\\\ \n", "EGS&3.8&0.1&3.9&0.0&3.9&0.1&3.9&0.1&3.9&0.2&3.7&5.1&3.8&6.3&0.6&0.1&0.9&1.5&0.6&0.1&&&&&&&&\\\\ \n", "ELAIS-N1&13.2&0.0&13.9&0.0&13.9&0.0&13.9&0.1&13.9&0.0&13.8&0.2&9.3&0.5&&&9.3&0.7&&&9.7&0.8&9.7&1.0&9.6&4.9&9.7&4.9\\\\ \n", "ELAIS-N2&8.3&0.0&9.5&0.0&9.5&0.0&9.5&1.3&9.5&0.1&9.5&5.1&&&&&&&&&4.5&0.8&4.5&1.1&4.4&4.8&4.4&4.6\\\\ \n", "ELAIS-S1&&&9.5&0.1&9.4&0.1&9.5&0.2&9.5&0.4&9.5&1.1&9.4&2.6&8.2&3.7&&&9.2&5.3&7.2&0.7&7.4&1.0&6.7&5.3&6.7&5.5\\\\ \n", "GAMA-09&59.1&0.3&63.5&0.1&63.5&0.1&63.5&0.2&63.5&0.5&63.5&1.4&63.5&1.9&63.4&3.1&60.0&6.1&56.6&3.1&&&&&&&&\\\\ \n", "GAMA-12&62.1&0.2&64.2&0.1&64.2&0.1&64.2&0.4&64.2&0.5&64.1&1.4&64.1&1.7&64.0&2.7&62.4&6.3&61.8&2.9&&&&&&&&\\\\ \n", "GAMA-15&62.0&0.3&63.2&0.1&63.3&0.1&63.2&0.3&63.2&0.6&63.2&1.4&63.0&1.5&63.1&2.5&62.1&6.6&60.6&2.8&&&&&&&&\\\\ \n", "HDF-N&0.3&0.0&0.8&1.2&0.8&1.2&0.8&1.5&0.8&2.8&0.8&5.0&&&&&&&&&&&&&&&&\\\\ \n", "Herschel-Stripe-82&115.8&0.3&366.9&0.1&366.9&0.2&366.9&0.3&367.0&0.4&368.0&1.7&356.1&4.0&351.3&5.5&233.9&6.3&241.2&6.5&98.9&1.9&99.0&1.8&&&&\\\\ \n", "Lockman-SWIRE&14.8&0.0&22.1&0.0&22.1&0.0&22.1&1.2&22.1&0.0&22.0&6.2&20.4&5.5&&&8.6&0.6&&&11.6&0.7&11.6&0.9&11.5&5.0&11.5&5.3\\\\ \n", "HATLAS-NGP&&&179.6&1.1&179.6&1.1&179.6&1.2&179.6&1.1&179.6&3.3&177.2&4.1&177.3&5.1&178.4&5.7&&&&&&&&&&\\\\ \n", "SA13&&&0.3&0.2&0.3&0.3&&&0.3&1.0&&&0.3&6.8&&&&&&&&&&&&&&\\\\ \n", "HATLAS-SGP&&&179.6&1.1&179.6&1.1&179.6&1.2&179.6&1.1&179.6&3.3&177.2&4.1&177.3&5.1&178.4&5.7&&&&&&&&&&\\\\ \n", "SPIRE-NEP&&&0.2&1.6&0.2&1.6&0.2&1.6&0.2&3.9&0.2&7.1&&&&&&&&&&&&&&&&\\\\ \n", "SSDF&&&112.8&0.1&112.8&0.1&112.8&0.2&112.8&0.4&112.7&1.2&111.7&2.7&98.0&4.1&&&107.5&5.5&94.3&79.7&93.0&86.9&&&&\\\\ \n", "xFLS&4.7&2.4&7.8&0.2&7.9&0.1&7.8&1.5&7.8&2.5&7.8&6.2&5.6&6.1&&&&&&&4.1&5.4&4.1&6.0&4.0&21.0&4.0&20.4\\\\ \n", "XMM-13hr&&&0.8&0.2&0.8&0.2&&&0.8&0.8&&&0.9&6.2&&&&&&&&&&&&&&\\\\ \n", "XMM-LSS&18.9&0.1&22.4&0.0&22.5&0.0&22.5&0.0&22.4&0.1&22.4&0.2&22.3&2.6&21.1&3.6&7.1&0.6&22.0&5.1&10.0&0.7&9.8&0.8&9.3&5.8&9.3&6.4\\\\ \n", "\\hline\n" ] } ], "source": [ "print(table)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "round(np.nan,2)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(np.isnan(field_coverages['ferr_ap_u_mean_meadian']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot general overview of depths" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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/rs548/anaconda/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": [ "def depths_sample(band):\n", " mask = ~np.isnan(coverage['ferr_ap_{}_mean_min'.format(band)])\n", " mask &= coverage['ferr_ap_{}_mean_min'.format(band)] > 0.\n", " mask &= coverage['ferr_ap_{}_mean_min'.format(band)] <1.e3\n", " area = (np.sum(mask)/len(coverage)) * 1270.\n", " return np.log10(np.array(coverage['ferr_ap_{}_mean_min'.format(band)][mask]) *5.e-6 ), area\n", " \n", "\n", "data = [ depths_sample(band)[0] for band in bands ]\n", "areas = [ depths_sample(band)[1] for band in bands ]" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "14" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(data)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "# fake data\n", "fs = 10 # fontsize\n", "#pos = [1, 2, 4, 5, 7, 8]\n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "def set_axis_style(ax, labels):\n", " ax.get_xaxis().set_tick_params(direction='out')\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.set_xticks(np.arange(1, len(labels) + 1))\n", " ax.set_xticklabels(labels)\n", " ax.set_xlim(0.25, len(labels) + 0.75)\n", " ax.set_xlabel('band')\n", "\n", "#ax.violinplot(np.array(coverage['ferr_ap_{}_mean_min'.format('g') ] ) )\n", "#ax.set_title('Custom violinplot 1', fontsize=fs)\n", "\n", "ax.set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", "set_axis_style(ax, ['$' + band + '$' for band in bands])\n", "ax.set_ylim(-7, -3)\n", "\n", "\n", "\n", "parts = ax.violinplot(data, showmeans=False, showmedians=False,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1)\n", "\n", "cax, _ = mpl.colorbar.make_axes(ax)\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('area [square degrees]')\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.savefig('./figs/band_depths_overviews.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.3454938 0.7776233 0.71631326 0.75628717 0.64232469 0.65254824\n", " 0.61656475 0.6314781 1. 0.5538689 0.18692627 0.18278592\n", " 0.07429385 0.06284298]\n" ] } ], "source": [ "widths = [np.max(np.histogram(data[n], bins = 100, density = True)[0])*areas[n] for n in np.arange(len(data)) ]\n", "widths /= np.max(widths)\n", "\n", "print(widths)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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G3IJhZRtQCwRqmBQb94ftUNVjGziuF9g+Y9sw0FfaV16fua/esaHjxKEjIlr7ouNwOBqjszNptH8vIMW8ONSi+ak3Le05VCj4mz/YTMaA/hnb+oHR0r7yenrGvnrHho75X0SL08o/tmC498Ee3GfhsIeUaXEYEyuilW24VdgwtN1i3AkcWV4RkR5gP7y5hIPA5sr9pb/vrHdsyDYDThw6IsNddDzMewcctHQViCDY8Ks0WR0FvCTY8YT5W2GxYMG1wQaFGiJhRSuLSEJEOoE4EBeRThFJAL8ADhORF5b2fwS4vSKg5DLgAhFZJCIHA28ALi3tq3dsqJj/RTjaBAsufFbcCh0ORyXJZNy0CSST5mdY2TDKZIVADQkvICXW0OKDC4BJ4APAK0t/X6Cq24EXAp8EBoGnAOdWHPdRvCCTR4AbgM+q6jUAPo4NFfO/CEebYP7CZ4cNDoejEtMBKQAJCwRqPlcwbQKFfOuKQ4BCSDm8VPVCvJyF1fb9ETi4xr4M8LrSEujYsDH/q2xxnByxCfdpACBuwMDhYYO3ygZhlrBgWLmQy5s2gULevEANi3ISbIc/nDh0OCLF/M3Y4bCJRMK8OEwanvcIdngOc5mcaRNCRPwOETtwcw4dbYUTZg4PGzxmDg8bxKENNtjgtctlzXsvHXawW4hDETlARNIicvkcbUREPiMiA6XlYnEhiY5pOEHgcFRiwy/ChiFdG4JibAgGyWVaWxwWkYaWdsS8L90fXwH+WafN+cDZeHmBFPgD8BDw9XBN2x0wf9GxAVVt05+5w1EdtUAe2lCdxIZ5jzaIQxu8l2FhKAn2bov5X2UdRORcYAj4U52mrwE+p6obVXUT8Dm8AtYORwnzN0KHwyZsGF2PWeA5tMF7aQM2CNQwCTGVTcth9f9aRPqBjwPv9tF8WpFqphewnnne88sFtLdvn1m6sBWx4A7gcDgsxPy1IW5B6bq4BXMObaCVZ2J5eQ7DSYLdipj/Vc7NRcC3VPUxH217mV3AurfavENVvURVj1XVY5ctW9YkUx1zY/4m5IbXS6j598EFhNiBDR+DFcPKFngO4xYk4rahUkyYuDmH/jH2TRCR60VEayx/EZGjgNOAL/g85cwi1f3AmLq7kB13ACtw74PDUYkNcw5jcfM3XxuilW0QZsnOlGkTHJZg7FFFVU+Za7+IvANYCzxacv714tUsPERVn1jlkHKR6n+U1isLWBvDDm1qgw02YMP7YIMNDodH0YKvow2eQyuEmQW5Fm2oVhMWLgl2MGz+JlwCXFGx/h48sfjmGu0vA94lIr/B+x68G/jfMA107G5YcCe0Avc+2ICqGp/jZYPn0PR7ALZ4Ds3bkLBAoIZJuwaXNIK13wRVnQAmyusiMgakS8WoEZETgd+qam+pyTeAfYE7SuvfLG1zWHADcDaUscEGh8PDhoGNmA2eQxtssCCdTrIjadqE8Gjj4JJGsFYczqRU2Lpy/Ua8oebyugLvKy0O27DhLuQo4T4Lh4cNnsNYzPwN24ZhZSsEqgXey7BQaNvgkkbYbcTh7oqbc2gT7n3wsOF9UDB6oTbdvx0ULbg+WSEOLRBmYkFKH7HgswgT5zn0j29xKCIrgGfhBXosxEtMvR74g6puCcc8R+tg/iZkQwoXO7Dgs3BYQcGC30TMAlFkhTi0QLfY8D6EhQtICUbdb4KIPEFEfgrcBbwKSAJbSq+vAu4UkZ+KyCGhWurYzbFBkNhggw2498GG4VQbvHYFC8KVbfBWWSGKbFCHDkcJP57DS4HPAq9Q1czMnSKSAp4PfAs4rqnWOVoI8zchO2ywAfPeIvOfhen+7SBbNF9LN2aBKLJhzqENUdutLlCd59A/dcWhqj6lzv4s8JPS4piBuwWVseGdsMAGC7xFdgyvW/A+GMaGdyBbMC8OnefQEQXl8nmtiIhc5rNpRlXf4KehC0gJGRu0gB04QQJY8mRuwftgGLXi+2ieTCFv2gQ7PIdOHLYFLRyt/FLgUz7avRtw4tBhEzbcjJ0oAm++nflLpNnPQrVoPFjZhnmP6bx5cWiD59AGG6zIbGGDDWGhLT2s/JiqfqxeIxF5md8TOnHoiARVG57ZbBCoNmD+BmBaoCrmh1NtCEixQhyavzBY4TlUC4KDrBCoIdHK0cqqur/Pdgf7Paf5X0SLY4N3wFHGfRY24N2ATHsOzYtDG+7D47msaROsCMSwIZ2ODcLMAhMcTUZE9hWRNUGPM/+LaHHs+LFZYYR57PgwLMC0B9W8OCxqzmj/Hua/j2NZJw7BjkTcVngOi6avDeFSLJXQC7rsTojID0Xk+NLf/wHcCdwlIq8Pcp66w8oi8mef50qr6rOCdO5wOAxg3GumnlA3eM21QRwWLHhYGc3Oyk4WORZoQysEatECYWbBVzI0WjlaeQbPAF5T+vtdwGl4RUuuxEs56As/cw6fBLypThsBvuS303bCDSvbhPmLrx2496EwO2Vr5OQtEAM705OmTbBCmNkQkGLDraLVPYfaHuIwpapZEVkNLFbVv8JUlTvf+BGHf1PV79ZrJCIvD9Jxu9DKT2LBsOGNsMAGK74QpoMQzL8HueIEqmpUmEzmcyzo6DTWP8D2yXGj/QPGo8YB4haIw0LBvDArWjC0HSY2hEVGwG0i8kFgDfBrgJJQHAlykrpzDlX1GX5O5IaUq2PDJGM7sOFH6T4LANSsOFSKxvMM5nWSomGRPGiB1277xLgVUdPGscB7acODY9ECgRoWqu0x5xB4PXA40AVcUNp2HPD9ICcJFJAiIp8XkaOCHNPu2DGsbIMNNmDD+2DeBjXtOdQCGE4lkykMkinsNGrDwOSE0f4BHhzayaMjQ6bNMI4N2rCQNz0X2A4bHPNDVR9U1Zer6mtUdVtp209V9f1BzhM0WjkJ/E5E/i0i7xeRPQMe33ZY8DDocEzHcDCGUjCeSmYiv5WJ/DajNtyxY6vR8nUjmQwPDu1k/bYtxmywBRvmPdrgtcvnWlscqkpDy+6EeLxBRP4kIreXtp0kIi8Jcp5A4lBV3wqsAj4AHAXcLSJ/FJFXi0hvkHO1C04b2oQNn4b5GwCYTV+i5I0noR7LbWIst9GoDY+MDLFpLNA0oKayfvtmAG7dttmYDbZggTa0QpgVLLAhPBobUt4Nh5U/jje0/H/A3qVtG4FQPYeoakFVr1bVlwFPBZYBlwJbROSbpYmPjhJuzqFNuM9CtQiaNmxDHjXsvdw2uY5tk7cYteH27Vu4fbs5Yfb7DQ8A8IeHH3DzDi3ABs9hNmM+xVOYtIPnEHgt8FxVvYJdN70NwL5BThJYHIpIv4i8XkSuA/4M3AycCDwBGAN+G/ScrYwThxah5i++5gVqzrgwU82iBr2Xk/kdjGQfYuvEzcZ+nxuGB7lrYBu/fuheI/3nCgWuLvW9aWyEdVs2GbED7Ej+bAM2RCtnJ80nRQ+Lcvm8NvAcxvG0GOy64fRWbPNF0ICUnwKbgBcAXwdWqer5qvpXVX0ML+HiPkHO2erY8URugw024N4Hb76haXGYQYvmvJePjf0BgIn8FgYy/zZiw1UP3AXA9Y9tYMhA1PKfNz48LVr6F/ffFbkNZVo9fYpfihYEg4wPmw+SCo1S7v1Glt2M3wCfF5EO8OYgAhcBvwpykqCew5uAA1T1TFX9ker0TLKqWgQCJVpsdewQhw4P80/mxgWqTnqLQQo6TlHN3ISKWuC+oSum1u8b+mHkNmybGOObt68DIFso8Pl1f43cht9tuH/ausmhZRvEoQ2XadOeQ1VleMeoURscTeFdeLEhw8ACPI/hGgLOOfSTBBsReV3pz53AGVUiuxQYAG4teRAdJewQhzbYYAPufVCdRA2Lw2JxzFg6ncfHb2A8//jU+saxaxnPbaEnuUdkNnz6phsYy+0avrv87tt46cGHc+jSaJ6ri6pc++hD07btmJxg/bYtHL1iZSQ2VGLDsLIN039MB4NMjE62tueQ9kiCraojwNkishxPFD6mqoFTEvgSh8CrfLTpBw4Wkfep6leCGtKqOHFYxgYbbMB5DgvFUSPisKBZbh+YfmlSCtw+8GWO2+MTkdhw5f138YsHpg/hFlV5x3W/5qfPe3kkFVNu376FHVVyLF776INGxKEN10gbBKrpaOWxwXHGBgNNS9utUNqmfB4isgR4NrBSVS8WkVVATFV9p2jwJQ5V9VSfBh0KXAM4cVjChiET44IEcEO6dqA6ihbNDh3lCluMiMP7hn7AaO7RWdsfHfsd+02cw/LuY0Lt/2+bHuG9N1SP17t/cIDzf38ll53xIjrifp/ZG+Pmx6sP7tz8uJnUPjZE6dogUPNZs8npR3eOMbqzdcVhOZVNqyMiJwM/A9YBJwAXAwcA7wHO8nuewNHKc6GqdxKwREurU7DgomPFhBorhJn5m5BpG7Q4hBbNVsTIFR4nVzG0GwUT+a3cufNbNfffsuMzFEMsK/jPLRt54++vJFes/fnfvPkx3v6nqxnPhRsx+s8akcm3bd9MphC9QLHhAbpYMG9DNm02Unh4xwhD283l3YyCNglI+SLwUlU9Haaewm8GnhzkJHXFoYhc5OdEIvIxAFX9QBADWp3CHDeD9sKC98GGVDaGrzRaHER10KgN2fwmsoVovVS3D3yVwhz5HUeyG3ho5Mqm95stFPjMP/7MS391BaM+RN81D9/PGT/7LrdsDSe1TFG1ZtqabKHA7QaqpeQtiNItWnCdTk+YFYfjwxNMjJqv9x0mbZLncK2q/qn0d/mGk8X/NELAn+fwHSKyj4jsO9cCvC1Ix+1C3oKLjqOMDY+Ahr8PxUEomq0pPJm9g8nsHZH1tzN9F4+M/qZuu38PfINsoXnDavfu3M7ZV17O1267OdCw5SMjQ7z4lz/kf/55Y9PL6909sJ2hTG2R/PcaQ85hYsWwsgXey/SY2eT0E6NpJltcHIaJiKwVkd+IyKCIbBGRL4tIorTvKBG5RUQmSq9HVRwnIvIZERkoLRfL/Oo53iUiz56x7TQg0EXXjzjsAR7wsXQE6bhdyFtw4XOUcZ+FFrejhR3G+i8Uh8nkHyCbf4R8YSD0/lSV9QP/z1fbTHGIewYvnXefA5MTXPi3P/Hcn1/GXQON1W8uqvLlW2/i9J9+h2s23Ne0aNq/bXpk7v2Pz56TGTamU7iAHeJwbGjcaP+FXN54UEyYeEPEoXoOvwpsA1bilRc+GfhPEUkBVwGXA4uA7wJXlbYDnA+cDRwJHAE8F3jjPP6r7wa+LyLfBbpE5Bt4VezeG+QkdcWhqsZUNV56nWvpbui/4QMROUBE0iJy+RxtLhSRnIiMVSyBysWEgR2eQxtsMH/xteN9MHvxLRa2osXGBEszmMjcNvX3eOZfofe3ZeKmQGXy7hu+gsn89ob6Gs9l+dItf+OkKy7h0n//a875hX55aHiQN/3hKs656vvcvHn+Xr1bts491/NfWx+PPDgjnzf/u7QhWnnY8Hy/YlGt8OKGScgVUvYBfqyq6VLqmGuAQ4FT8IZ0v6iqGVX9f4AATy8d9xrgc6q6UVU3AZ/DK4HXEKp6E57IvBP4Nl7pvCer6j+DnKepASkh8hXAz3/sR6raW7E8VP+QcMk1eVhot8WKWb02fBZmbdDiVrS4w1gJveHJP079PZL+0xwt549qkdt3BkucUNAMd+78ZsB+lF/cfycnX/F/fOGWvzKea/57e9u2zbz0V1fw+mt+zuaxxqPN6x2bLRTYmY42152bc+jVNN65xWygGNiR7zFM5hGQslRE1lUs51c5/ZeAc0WkW0RWA89hl0C8Xae/ubeXtlN6XV+xb33FvkCISFxErgcGVPViVf0vVf3vIClsylgvDkXkXGAICPdOEhJ2iEMbfvAW2GBFQIphcVjYAiha2Bp931pkeHLX3L+RiWvQECOEt6dvYygTvHbxhtGrfc89HMlmePu1v+ad1/2mau7AZvOnRx/k9J9dyjUb7mvo+K0T9f9fW8ejHd4s2OA5NHx5GtkxwvAOw5HCpt+ECJjHsPIOVT22YrmkyulvwBN1I8BGvFQyV+LVNR6M20f1AAAgAElEQVSe0XYY6Cv9PXP/MNDbyLxDVS3geTDnre2sFoci0g98HG8M3Q9nichOEblTRN48x3nPLz8BbN/e2BCSX7IWPBVbIcyssMGGz8KcDapFtOANK2ohnGjYuRjPrCNf2DWknS8OMJa5KbT+/AShVKOoWTaO138W/dfWxznjZ9/llw/e3VA/jTKcSfOmP1zFh278Pem8fy9lppBn+0R94bdpLFqRYoXn0LAw2rFpJwOPmw0UKxSKLT2srDQmDP3MORSRGPA74Od4cRpL8eYXfgavfF3/jEP6gbIbf+b+fmBMG3fjfgz4moisKXkSY+UlyEmsFod4xaK/5bMk34+BJwDLgDcAHxGRl1VrqKqXlJ8Ali1b1jxrq5DOm01s6mGDMLPBBvM3IdRcugotbsfLaADFiFPJAGwb+fLsbcNfCWUoK1+c5LGxxgcbNoxcXbfN5XfdysbRmQ6B6PjB3et5ZMT/UOSGoUFfeVfvHww/UKgSGzyHpr1mWzZsY8sGc3OBwSvf18oBKSGzGNgL+HJpXuEA8B3gDLy5f0fM8ASW5wRSej2yYt+RFfsa4ZvAq4GH8C74Obx8h4HmuxgThyJyvYhojeUvpVDv04Av+Dmfqt6lqo+rakFV/4Y3/v+iMP8PfsjknDi0BTU8pOthUBwWHqv4O1pxOJr+C6Pp62ZtH8v8hdH09U3v776hK8gVG09LsyN9G1sn1s3Zpj+CUnf1CFJuz6/oeyBqcWiDt2pemUPmz6b7tzC8Y9RoxPL48AQTI61dW1kbXOqeV3UHXuDHm0UkISIL8QJN1gPX43km3iYiHSLyltJh15ZeLwPeJSKrS2Xu3o0XXdwo+5SWfSuW8rpvjIlDVT1FVaXG8jS8CJ+1wKMisgWv9MsLRcRviKOC+SrbGQuGTOzABoFqwU1IM8a6LuYfqvp32KgW2Tz4yZr7Nw99sqnCPVsY4Z6h7837PHcMfHVOr+Yz1+zP6t6Zo0XRcea+B7G403+SiIdH/CU/f2g42uFNG4aVTbPxfm+6x2P3Rls5qJKRgVHjEdOhEn4qmxcApwPb8dL75YF3qmoWL1XNq/HiJ14HnF3aDvAN4Fd4eQj/Dfy6tK2x/6bqI7WWIOcJt4jn/LgEuKJi/T14YrHqXEIReT7wZ7w3/0l4Sbk/FK6J9ZkMIXIxMDYEYlghDi3w4hoVh/fsMiMfPFCjUbYOf4HJ3L9r7k/n7mXL8GdZubA5xZXuH/4RuSbUjx7I3MGWiZtY2XNc1f0nrF7DdS89jx/fewdfvvXvbBmPpi7ts9buzzuPOYEnLFke6LjNY/7s2xrR/6OMDZ5D016ErQ97c9+3PbKdJzzlACM27Nw6xMjAGPlcnkTSZmkwD0K8DanqbXhOrWr7bgWqFm8vzS18X2mZNyLyPar/TzN4gTJXqur6KvunYe2cQ1WdUNUt5QVv0mZaVbcDiMiJIlJ5FTsXT62P4rlpP6Oq343c8BlMZC0Qh1Zggzi0wAY1N2xTzO2KcC3mH4gknc3QxNVsHfli3XbbRr7C4Pgv5t1fUQs8NHLVvM9Tpl5JvVQ8zisPOYrrX/oGPnLc01naFVq6V07eax9+ec6ruORZ5wQWhgBbxv0J5m0T45HmZ7VhzuH8ClLMn+0bB6a9mmDg8UFUlcGt5ubRhk2blM8bBp6P98yzsfT6PLyh7ScAfxeRV9c7SaDHAxE5FXhYVTeIyErgv0sdfqgk4EJDVS+csX4jXgh4eb1q8IlpJrI5VNXwxcf8xdcOYWbB8JVG65WZ6laVYr4yqjaH5h9EkgeH1udE9t88OvBO3+0f2/leOhL70N1xVP3GNdg68Q8m8s1L0/P4+J9J53fSmVg8Z7vORILXHX4ML3vCEXz/rvV8ff3NTUttc8pe+/D2Y47n6OWr5nWevfoW+Gq3urc/Uk+aFZ5Dw/f/zESm9GpuTnJ5vqH3usSYHWHSBtl6AA4EzlDVv5Y3iMhxwMdV9ZkicjrwRTwnWk2Ceg6/yq6Qz88BSby7frWcPw4gk8/7ihAMF9P9Y4cws2FYuQnDnY2g+fuhOL1sXiH799D6yxW28fD216Pqv16saoYNO84jl2/8OXOy0NyIzyJ50gX/c/C6EknOO+JYbnzZ+Xz4uFNZ1tXTcN9P33tfrjz7lVz6nBfNWxgCvPqwo321e81hRxOPRTeoZIM4NK0OyylkCgbnX2bT3khCLmPBdTIElLbxHD4FuHnGtnXAk0t//w7Ys95Jgl4BVqvqo6Vi0s/Gqwn4ZuD4gOdpG7L5Ahnj6WwsEIc2EGLCZV/dawE1NKxcyP5l9rbM7G3NoKhpHt7+BnKF4JPr84WtbNhxHsXiZEN9L+k8vKHjapGM9bIgFbwKZ1ciyesPP5YbX/YGPviUk+lJJn0fe8yKVVx59iv59ukv5KjlKwP3XYv9Fi7m5L3WztmmK5HgxQcd1rQ+/dDKufX80rPAm47Qu7Dxh4n50tndAUBHd6pOS4fl3AZ8UkQ6AUqvF7GrCss+QN0n3qDicEREVuAVlL5LdWqMzP+Vr83I5POWpLMxjQ03AMPzPzXtLQYoZG6cvS17U9PnHaoqGwc+wES28brJk9n1PLbzPQ3lP+xPriUV8zd86oelnUcQMHfsNDoTSd545JO57qXn8YIDDpmz7fLuHr5w6hn89Hkvb6oorOS9TzqR2Bxesv86+qmB0uM0AxtqK5seb1ywrH/aqwn6l3oFO/qX9NVpuZuigEpjy+7Fa4AT8fTaFryKLSeVtoOXk/E/650k6FXvf/FqHH8fr94xwAnAPTWPaHPS+bwFibBt8BxaMKxs2HNoShyqpqsPIesYxWygWux1GZn8I4MTP5v3eYYmfjmt1J5fRGIcuPCl8+6/zIELmzOVeXl3L58/9Ux+9ryXc+CipbP2n3/Ek7j2JedxzgGHhjo/+bBlK3jd4U+suu/ARUs4/8gnhdZ3LWxIvFwsmr1Grtx3Rek1eKBRs9hj7XK6+7paVxwyr9rKuw2q+rCqHg/shxeYsr+qHq+qG0r716lq3Sz/gcShqn4GLzH1CapaTjOzEXh9IOvbiEw+T9q459CCJ3MrbDA32RsAnfSWiClkbqjZbz59TVP7Gpn8YxPP1ViFkwMXvqwp3sNlnUezousp8z5PJcfssZrLz3wxK7qnYul405FP5kNPPYXeVDTDee889gRW984WAJ866Vmk4vFIbKgkZ/z6SChVeoKwen/PU7xq/z2M2bD3E/Zkr4NXGY/cDpWwsmBbhogswUurc3JpKuAqEak7z7CSQOJQRFKlDt8tIpeJyGXAh2lSfp5WJJ3Lm59zaEMwiA25Fg3mGPT6NyMO83N44Arp36JN+mxUtWoVlEYZnbyhIduSsV4OXHjuvPs/ZPHrQ7lRLu/u5evPej6pWJyT9lzLe590YtP7mIueZIq3HTN9mvgz1+7PsXusjtSOMpm0eXFoemh7z4NW0be4l4XLmjclIih7HbSKPQ+af+CTvYRXW9kmRORk4F7gFXj6DOAA4GtBzhN0WPm7wDvwcgk+OGNxVGEim7Mg16EFwswGG0yLQzKlJTpUMxQytT1wWtxGMXdLU/oqFIfIFTY35VwA+eI28jMirP2ypu/0efXdGV/M8q5j53WOuTh6+SpedNBhfPyE0yKNDC5zzgGHsKrCe/iWo5vrIQ1CJmP6+ghZwzasPmAlqw16DT0b9pjyYLYs7eE5/CLwUlU9nV0pOm5mV7SyL4KmQT8d2EdV/Vd7b3PGs1kLxKEFnkPD4tDzQJkeVs55S4QUMjeCzp0+Jz/5G+Kp+c81i8cWEo8tolD0V6atHjHpIxFb1tCxvck9WdhxEEOZxirBrO45lZiEO8S638LF7Okz92CzScXjnHfEsXz8b9dx/Kq9OTKkABg/ZNKmr4+QMZy+ZY+1y1ixtrHverNYsmoxS1bNnc/TsVuwVlXLHoGytM0SUO8FfWR9FOgIeExbM5HNMZ41LEps8NoZF6gF1HRACtGLw/zkL+u2KaSvbkptYxGhK/mEeZ+nTGfq4HkN667tfU7jx/Y1fqxfjl6+ioQBr2GZlT2e53BllfmHUTI+Ztqjb16g9i/pY/leswOVoqSrt5MlqxYZtSFUwq+tbAt3icizZ2w7Da92s2/qKkkReXrF6mXAVSLyJWBaGQJVvTZIx+3CeCbLuOlhE+OiCAvmPRYwLlA1S5TeS9U0hUz9ABEtbqOY/SfxjqfOu8+u1JGMZf427/MAdKeOmNfx+/SfxR07v0Yh4HSChR0HsaRzfn37oS+iAJRajOe87+JEzuzD6+hw9PNwZzI+ZibFVBkRYc8Dzc73ExEW77HQqA2hs/sNETfCu4GrReTXQJeIfAM4Cy9y2Td+3IzfqrLtUzPWFQieKbYNGMtkGc+YfjI2P2xjXpjlzQtUzUUq1Avp60DHfbXNp69uijhc3Ptito8Gmvdc+1w9L5nX8al4P2v6nlO3PvJMDljw4taO2CwxmPZE2c60WXE2NmpeHNogUJesNj+k29XXZdqEkGn937Wq3iQiR+IFpHwbeAx4sqpuDHKeuuJQVfdpzERHJp8nWygwkjYdJWuB59C0OKRogQ0Foizh58drONU2/Se0/6J5i6LO5AH0dDyZ8cw/5nWe7tTRdKXmThrth4MXvooNI79CfX723YkVrIlgSBkgXzQ33SOdz/PdO28F4ObNG7lt2+bQEm/XY2hw3Hj9+dER8+LQhvyCnT0tPmusPTyHqOom4OL5nCPQBEUReY+q/k+V7e9S1c/Px5BWZLQkCkczpuccmhZFWCBQ85ivrRydDaoF8hn/aWW0+DjF/N3Ek/MXZEt6XzNvcbik99XztgOgL7U3a/vOYMPor3y1P2TR64hLNMO9JsXht++4hY2jI1Prn/j79fzkeedGLtBUlaGd42QzeTo6zRTaKuQLDA+aKWtZSXdftJVpqpEy9BlERouKQxH5Hj7+d6rq+8IadDb0R2psvyDgedqCssdw1Ljn0LQ4BePCzIph5XxkIrmYuw2KA4GOKaSbk8B6YfdZ9HY2nruvp+M4FvW8oCm2ABy8yN/1sCO+iH36n9e0fuuRLZr5Pj46MsRXb7152rZ1Wzbxk3v/Hbkt2UyefL5oNChlYiLLmOE5hwAd3ea9dslU0AQmDkt4gF1pBYeBs4E4XpGSGN58w0BZZnx9EyqCUuIicirTB+73xct76JhBWRSOpE1feMyLw2bX8A1OHuNzLzUTWa7FYv7+SI6phoiw1+KLuXfzaRR9znksE5Mu9lp88bzqGc+kM+4vCjQVW0BMors5mqjK8cjwEC+7+keMVQlC+eCff093Mslz9zs4MnvKomxsdJLFS3vrtA6H8bE0ExaIQxu8dvFE9BVyIqNcW7kFUdWPlf8Wkd8BZ6rqjRXbnsauhNi+8HslLAeldOJNcJyyCdgCvDVIp+3C0GR62qsxrPAc2pBj0LT3chw0muErkeA3Wok17+acSuzJyoUfYNNgoOsRKxa8m47k2qbZAfgeKo16SDXqcr6PDA9x7q9+xObx6s/yBVXe/qdfowpn7R+NQBwa8B4ehnaOs/c+ZvL8jY+mmRjPUsgXjIqjZIcTh2Gzu9VJbpCnAjfN2HYzcFyQk/h6PFfVfUqBKd8v/11a9i0VdK6fTK0NGS6JwmHD4lBtEIfGbbBgzqFORCYOkZ5ojpmDRT3nEHTmyuKeFzXVBoBC0Z+31m+7ZlGIsKTkA4MDcwrDMgVV3n7tr/n5fXdGYtfOHZ49OwfGIumvGkODnkAdHjI77zCRtECYtaZjbRftUSHlVuBTItIFUHr9JHBbkJMEunIHmczogKGJydKrac+h+SGTqJM/z+4/b9wG1XEgF4lYl1jwyhsi/U21IR5bQFfqcN/tO5OHkIgvaaoNAKO5R321m8hviVwgRsE/Nm/khVf9sK4wLFNU5V3X/Zav3Hpz6EPfZVFYFokm2LnDs2HQoEAFiMXNJUSfssFgUvZIUGls2b14LXACMCwiW/HmID4NCKTfAn0TRCQlIh8XkQdEZFxE7heRi0TEfJiVhVQOK5uYXzSFmk/TYF6gZjA/tD02/TVEYskjkPiegY5JdJ3ZdDt6O0/w3bYvQNsgjGQf9tVOKTKaeywUG6r3Fz5XP3gPr7z6Jwxngv/+PvuPG7ngL38MNap625bhaa8mGJwSqGbFYasP6dqAaGPL7oSqPqyqxwP7Ac8D9i+N8D4c5DxBHxO+Bjwdb47hk4C3AScDXw14nrZg57gnynKFgtkSegGDAsLBtPc0a35ouzg6/TVERBIke17vu3284zRiif2abkdXgNQ4nU1Io1ON8fzjobSdL7GQx/Bu2bKJt/zx6nlFRX//rvV8cV1zKt5UY+MjA9NeTWCDQAWQ2G7noXJYjKo+pqo3q6q/oZMZBBWHZwPPVdXfqupdqvrb0razG+m81RmY2DWHZWDcoPfOBnFo2HupmsEb0jWYzmbKcxjNEFqi6yXgc6g42fOGUGzoSKzx3zbpv20Q0nn/wiNI2/kSCzkAZuPYSP1GPnhsNDzRtPGRHaVXc+Lw8cd2eq8bdxqzAaIPiGo7Gp1vuJt5DptFUHG4Beiesa0L2Nwcc1qLneOV4tDgZOeogiDmtMG05zAz/dUExZI4jMBzCF70cbL7FXXbxZJHEEs9JRQbUgEij1MBhGQQMgX/N/10gLbzJR6yGBhpUtnOkWw4v91iUdn4qCcKt24eIpsxEzA2JQ4fMysOHWHT4HzD3W/OYVMIKg6/B1wjIm8QkeeIyPnAb4DLROTp5aX5Zu6eVHoLdxoVh+Oo4TQualqglr2nJu0o9x2hFzXR8x/A3BU/kj1vCs1rkYgtJBGrn2cwLgtIxMJJZZKKL/TdtiO+KBQbqhG2p+iwpSvoTsw/PcqT9gg2d9UvmzfuJD3pBYkVi8rDD24LpZ+5yGbzbN3i5QYuC1VTOM9hBDjPoW+CisM3An3Ah/DmGX4Q6AfehJcL8VvAN5tp4O7MwNguIbJjzKQomTTvuSs2Z4irYSIMBqltw+T01wiIxVeQ6Dqn5n6JryHeeXqoNnQm6+fM60wdFNrNsTe5OpS28yXsYeWjV6zke2e+mL5U45U3PviUk/jPo8PxKj9w75YZ69EPQD328A6KBe/uv+mRAbJZ0yU2HaHSJuJQRJ4gIh8Wka+U1g8WkSOCnCNoKpt9fCz7Bjlnq5LO5RmtGNbZPmZw3l9xwvy8w2Kgyj0h9F8ayo1ovl91zAxtJ3vfSK0EZsme8xEJN0qyM3VQ/TbJJ4TWf29yrwBtw/GSVSMRQdqQY/ZYxQ/PegmLO7sCH3vR057BG496cghWeTxwz3QxaEIcbrh/69TfhUKRxx7eEbkNjghpA3EoIi8GbgBWA68qbe4FPh/kPIGvTiLyTBH5loj8qrR+rBtKns2O8elibIdJcagTRgNCVIugI0bT+WjJY6hFGzyH0XqRY4n9kPg+VffFO8P/6Xannli/TcfRofW/IOUvCjshXfQkVoZmx0zC9hyWOWzpCn76/JdxwCJ/OSS7E0m+ctpZvOrQ8D4TgAfvm+45fOi+rTVahseGB6b3WSkWHY7dlI8Dz1LVNwHlCMz1wJFBThI0z+Fb8dLZ3A+cVNo8CXwiyHnagZnDyOaHlU3O+cvgfUcNJqEuDpZeDU46n5r3GX3EtMRXVdmaQGIrQu+7r/NE6l1q+jpPDq//1BqE+t7R/tR+Ta3pXI9ULLq8dvsuXMyV57yC5+43txd3v4WL+eULXsmZddo1g5lzDB9+cBvFiGsKPjLDhkcein7eoyMiyrWVWz8gZTmeGIRdfs/APtCgV8J3AKep6n8D5cyo9wDhX0l2M2YGoJiKVvZSuKTNzvkrloNBTA6tl7wURZOegeKM1+iIxWfPpZP4HqEPKQMk4ovoTh1Vc39X6nCS8fpBK40SlyTLu+p7L/foDm8ItRpRDCtX0pNM8b/PeC4fOf7Uqn2fse+BXHXOK9nfp4dxPgwPTTCwffoUj8mJLFsfj3b6ySMPbZ9z3dFatEMSbOAWdg0nlzkX+EeQkwS9OvUB5RIC5bcsSUilJ0TkehFJi8hYabl3jrYiIp8RkYHScrEYDP+aWU95JG0ohUrZY6aDZvoH0JIwNShQtbBt2qsZzIlDic+OBJaQooOr0dNRO6ihpyN8UbbfghfWaSHs2x9tutaoxSF4EbGvO/wYPnr8qdO2n7LXPnzltLPoTc0d2d4sHq0hwqKMWJ4Yz8xKfF3LLkeL0AZzDvGKk3xCRG4AekTkd8BFwDuDnCTo1enPwAeqGHJdwPME4S2q2lta5vJQno+XjPtI4AjguXjR1UYYnszMWDcULVwOBDEZEGJDMEjZY2iD59BAIu5C5qZZ24q5O9GIBPtcCa47EmtD7391z8l0xhfX3L+y+3h6ktHNNwRIxc2VS3vFIUfx1FVeoE5fKsWnT3pWpKlUBmrUUo6yvvGmx2anrtny+CC5nItYduy+qOo9wMHAV4ALgO8Ah6vq/UHOE1QcvhU4R0QeBvpKnrwXA+8KeJ4weA3wOVXdqKqbgM/hFaA2wizPoan6ylNz7cx5DrXkOdSIkj/P7j8DRe9GoIXoyqPNpvRzi2Aot5JiYRPF3Loqe7Lk07+PxIZUYu+G9jWLmCTYs/cZNffv3fes0G2YSZRzDmcSE+Hik59NVyLBBcedysrevkj7Hx6qPs1maCi6qSeDVWopq8LwoAVFAxyh0OrDyiISF5EHgYKq/lhVP6uqV6gGz+EWNJXNZryayi8BXo4nyJ6iqlvmPHB+fFpEdojIX0XklDnaHcquSZiU/j60WkMROV9E1onIuu3bwxlGmFlLOVcski0YKN1WFkVFgwlep7yXhoJB8vczNTaQv89g1HR8xms0FCavrr0v/ctIbIhJZ819Io3n4QvCnj2nVt0uxFnVfWIkNlQSNzCsXMne/Qs5YfUazjkgnJrWczG0s7oIHK6xPRQbBqv3Vcs2h8N21KsPW8CrXDcvgkYrH4I3fPsMYDEwqqphTqB6P7AvXr6eS4BfiUitvBS9QOUEkmGgt9q8Q1W9RFWPVdVjly0LZ95VtSGaqFJXTKOwcfqrCaaGdM3M59H8fRUro1A0Ve0xenGoxQlyEz+oub+Q+QvF3N2h2zE0XlugDk/U3tdMlnUdTTI220O2vOsYUvFoPWdg6Howg+NW7W1keLtYrH7bKEQYrVxLBA7uNJjuyhEu7RGt/EXgRyJysojsJyL7lpcgJ/ElDkvBHt8G7sCrjvI84P8D1ovIdxoJ/CgFm2iN5S8Aqnqzqo6qakZVvwv8FTijxinH8Kq1lOkHxtSQmyhZxStgwlOghUe9P/KPRt73Lhs8caiG5vtpbnoc0zSxGCWSKL1GdzPOjlyIFh6eo0WB9NBb0GJ4Q2lFzTI4cWXN/UPjv6IYQQWfmCSqRi2v6H5S6H1XI+zayn44aa9w6lnXo6ureuBLre1hkM/XEKiF6APGbKGlS/g1GoyyGw0rl/gy8Ey8WJD7gQdKSyhzDs8HTgGeqqprVPU4Vd0bOA44kQYCP1T1FFWVGsvTah1GrVIPcCfTkzweWdpmhJlCMCZi3HMYrpN3DoqlCMSCoWCQ/D3T1yPwlFVFS1MNNJp8j/nJX5Gf/FHddpp/gOzIx0OzY2TyTxTmmFJQ0GGGJ64Jrf9KllURh8u6jomk75nYcCNe2dNfv1EIdHXXEIc1todBPF799ldru6MFaANxqKqxGksgr4TfX8GrgLep6j9nGPFPvNyHM3PqzBsRWSgizxaRThFJiMgr8BJv/67GIZcB7xKR1SKyCng3cGmz7fJLYsYFxkTaCgDy5cxDmV0iLWK0sGXaa6R9axrN3jJ9W/bvkdvhdTwx/TVEivlHyQx/0Hf7/OQPyU/+uul2qCrbRr5ct922ka9FMhd0Zffx09ZTsQUs7givdN9cSM3n3OhIxMzY0NNXfQ5qre1hEItX/7/HDM8FdYRHqwekNBO/v4JD8Gr1VeOG0v5mk8SrvLId2IEXKX22qt4LICInikjl5JBvAL/CG/r+N/Dr0jYjLOvtmb7e11OjZXhocQSKm3ZtMDWcmn/Iey1siLxrzfwdmD5kqdl/RB45rZqjXCFGQxaHqhkyQ/8VOHVQZvj9FPMPN9WW0fQNTGZvr9sunbuLkck/NrXvavSn1rKwY1dGrL16TyNWHu6PGAsch8QjrAhTyYqVCwNtD4NUsvrnnkqZiyJ3hEzInkMROVdE7haRcRF5UEROLG1/hojcIyITInKdiKypOKZDRL4tIiMiskVE5pX9peRMe5uI/ExEbhCRP5eXIOfxe2WIq1a/05S2N/0Ko6rbVfVJqtqnqgtV9amq+oeK/Teqam/Fuqrq+1R1cWl5n6n5hgB7LlowfX3hghotQyR3x4z1+jfpZqPFgV0JuIvb0eLw3Ac0u/9MtRSceTR7Y6R2UJlJIHhWgUBkRy6i2MhnraNkBt+MNnH+37aRr4TSdj6s6T191999z4mkT1sxFRSzx6pFNbZHJw5t8F46IiZEcSgizwQ+A/wHXsGQk4CHRGQp8HPgw3iBvOuAyvk+FwIHAGuAU4H3icjpNM4X8Kb6/Rk4BvgZXkm9a4OcxK+oS4rIqSLy9GoLYObR22JWL+yfcz0SZggEza2v0TBE8g/OvR4iqopmqv8eNB3odzJ/Mn+d9ndYzy35ySvJT3yv4eOL+bvIDn+kObYUBhnP3Oy7/UT2FnKF8CPa9+p9OgCd8aUs7Tw89P4cs1myrI9kcraHLkpx2FtDBNba7nDU4WPAx1X1JlUtquqmUs7lFwB3qupP1HvyvhA4UkQOLh33auAiVR1U1buB/2N+OZpfADxHVb8E5EuvZ+MJT9/4FXXbgG/X2e+oYNWC6akx9jQgDmeJwex6VDXSifCaf2DG+n1Iqn6d26aQu2VXTeUZaOZPqE4iMu90UL7QyV/sWik8DLn1MEe94UYo5jcGmmdYi/zkj4h3nESi6/lS9qEAACAASURBVLnzOs9Y5u8Enc09lv4ri3rCLWPXk1zFgtQBLOk8DDE0rNruxGLC0hX9bN64Kzn/goXddEYYrVzTc9jrxGErMs/5g0tFpLKSwCWqesnUub0i9ccCvxSRB4BO4ErgvczIwayq46VE1YeKyFZgFbNzNM/nItjNrjLHkyLSrar3iMjRQU7iSxyq6tqAxrU9HYkEqxb08/iwVx1kzZLqwyhhoaqeAJm2cQgKj0IiuvQVmrtzxvpdkfVdnPhh7Z06ik7+Gul+Ueh2aGE7ZP86fVv6SqTJ4jA/8f2mBbvkxi+ZtzgcT88u2VePscxNoYtDgFU9J7Ck47DQ+5kLc5Ne7GDZigXTxOGyFdE+QHd0VL/9dXQmI7XDESGN5yzcoarHzrF/BV6cxIvwMrjkgKvwytf14sVOVDKMN/TcW7E+c1+j3I1XrOQfeEPYF4rICLBpzqNm4B6bQ+SA5Uum/j6w4u9IKDwyVR1lGrlbZm8LEc3dNud6aP0WB9H0b+dsU5ycQzw2k/TVTNVVLjP5G1SzVZs3gmqW3OSPm3a+Ym49hdy/53WOjuQBgY/pTB44rz790pNYRU9ydSR91cKGgBSTzBSDS1dEOy87mZotDkVcKpuWJrw5h5Ol1/9V1c2qugP4PF5e5pk5mCmtj5b2wewczfOJmHw7UC4Q/i7gicBZeCkJfeN+BSFSFoTJWCxyzyHZarV0QWtsDwMtjs2OkM7fG2rC5am+J38O1BFfufWzPJuh2JL+TZWNQ9DElDqF9B+guKNp5wPIz+V59cHC7jMJNh05xsLu+Xkr/dIRX0hHPLr5bbZi0nm5eEnvtPVFi6PN6FBtzmMylbAi/6QjHMJKZaOqg8BGqv+kpuVgFpEeYD+8eYiDwGaamKNZVf+pqv8q/X2/qp6mqk9R1UBRmE4chsiBy5cCsHbposhLVGmuhgjMRuc51NztzP6tFCB/R7XmzetXleJE/eTPgO92DdtSeHz28H55X7pWys7gFLL/aNq5dp3TfzBJNRLxxfR1nuy7fW/nCSTjy+fVp19S8X5SVUrpOaJjpucuVWOYNyykSo5HJwtbnHBT2XwHeKuILBeRRXg5oK8GfgEcJiIvFJFO4CPA7apars5wGXCBiCwqBam8gXnkaK4VOFwKHvaNE4chsu+yxQDst3Rx9J3X8hAWNqCF5nqYalJjCFmzIQ8t59ZD4SFfTTV9NaqZ8GxJ/36OfX8s5T+cPxKC0BGZ/zn3WPB2/N5yV/S/Y979+SVGgpi4uWUmmem5SySifYBOT87+7WUy+Zp1nx2OOlwE/BO4D2/e363AJ1V1O/BC4JPAIPAU4NyK4z4KPAg8gpc3+rOqOp+SUd+asfwSuAb4ZpCTBHpUE5Fb8RJRPxLkuHZldSm3YdQ5DrU45gWe1CJ/N8RPDN+OGrn2am1vFsX0L+o3KqMjaOY6pHM+aaXmOP1c8x7LQ8sdJ827H5HmT+aX2PzP2d1xNIt7XsbO8R/M2W5Rz4vo7XzyvPvzS0ySLlLZMPHEjCpSVYZ5wyQ9UX3aSSado6u7I1JbHBEQcrUT9Z70/7O0zNz3R+DgWQd5+zLA60pLM+zYp3K9FEl9AQHnMQa9Oh4JfE5ErhWRH4jIy0sdO6qwoLODnlQq+hyHNdK3TBFBGTtVrZlXMUxxqJpFA5aBm5Zmppm2ZG+D3K1ztxn/bnM6i4Uwp7VJ51y58P3EY7Xn98Wkn5ULP9SUvvzivIbmyaRzc66HzeRkdXE4UUM0OlqANqitPBNVLeB5Ld8X5LhGHp37gR/jlal7B/BXETEwbmo/IsKqhX2siloc1hF/Wtgcvg3FLVCskdC4uBkthJMaUzM3eB65gMdocWfzbRn3Ub0xe2NTgmISXWcRSzSxiqX0kOp9a1NOlYgvZo8F7665f48F7yAZX9aUvvwibkaNccZH03Ouh83YyGSg7Y4WoA3FYYlnMitlxtwEvULmgeer6tdV9dOq+mTgeuB/Ap6nbVi1oJ+VCyKe+F5P/NXzLDYBnVm6L+D+hilsbOCgPBS2NtUMzd0HmT/5azt+Sf1GdRDppGPR10B66zf2QceCTxNL7NeUcwEs6X0lHVXS1KQS+7Ck7zVN68cvNgwp25Dn0GCFUcbGMjPWoxWHI8PVRWCt7WHjoqTDJ6xoZZsQkcdE5NGKZQfwE+ADQc4TNDxsE7CIXTl9wCsFE11NtN2MBV0dLOiKOON+sY5XrslCqHof9QRqWN7LQsTHVUcnLvXfOH0Nmt+IJPacV5+xxFo6FlxMZmjWlJdAJLpfSaLr+fM6x0xEEqxaeAEbtr962vZVCy8gJtFVxZiyx1X8NM7I8MSM9WhF2cz+y4zW2O5w7Ca8csb6OHCfqo4EOUnQx+cfAD8VkX0rtkWTtXY3pSeVoicV8c1P6ojRCErGabUE3JWEMIzrdZyv36bqcc0Vh+SDxGxpgx7P2SS6ziTZ23jUbyx1Aqn+DzfFlpn0d51KT8eTpta7U0fR3/XMUPqqR0zMi0NtkfGqRhkaGJ++vnOsRstwmBivnqVgfCzE7AUOR8io6g0zlnVBhSEE9xx+tHTMv0u1AQfxsm9/MmjH7UJfZwfdqYgnv8fqzN+KLw3fhjriL4w5fqWOGzyuyeJQA3pBgrafg1TfO5H4arLDH8Kr4uSPRPfLSfV/HAkxWGNp3xsYz/yz9Pd5xobSbIijMzmkO2WDwb53DkwXg4MD0YrDQqH6taJYNP+5OEKiDT5aEfkePv6nqvrqufYH8hyqal5V3w8sB96Pl0PnZFX9dJDztBPLenuIRX0DrCMOJRZBouF64i8scZhY28BBSYg3uZSajtdvM5/2dUh2v4TOxZeD+KkCIqT6Pkyq/1OhCkOABV3PIhXfi2R8JQu7zwi1r7mIWTCsbMN9ytQsN1Wd5SmcGM+SjjBiuZCvLg4L+SY/KDrsoMH5hrvbnENgCDgbiONVbYkBzy9tf7BimZNGr5DLgH3wri2B3ZXtRH+ngXxZ9SI/YxF4Dut54po9jFtCOp4F8b3nzvM485iu5yPxFU22JOjwdvNvivGOp9K19CrSO1+HFmpcC6SHjoX/S6LzGU3vv2p3Emdhz/NRzYUuROvZYZqiBZ5DU2QyefJVxNn4WJrOzmi+F7U8twXnOWxd2uOjPRA4s7Jcnog8Dfiwqj7b70l8eQ5F5O6Kv08G1gNn4hWV/lfQsiztRCLisnkAxPaYe398VQQ21PFOhlQmTSRBrOcNQY4I2N4nqacFsoHU8c23AS9IpXPJFRBbUnV/x6KvRyYMp/pM7ENHct/6DUNEMC8OC20sDmsloK61PQz6+qvPve5fEP6cbIch2iOVzVOBm2Zsuxk4LshJ/A4rV4ZRfgJ4i6qeoapnAG8qbXNUIRmPPmWGxHohvk/tBskm5sOrZUMd76XUmxc5n767XuDbOyodz0SamLJllw1n+W+cemoInstdxOLL6Vjw2VnbEz3nkWhCdZagpBJ7k0rsHXm/ldiQNaTQxmXaJiaqB31EmYB60eLqaZ8WL2lOOiiHXQhtM6x8K/ApES/ytPT6SSBQ3Vq/yqXy7TkI+GHF+hXUKAvjgKQJzyFA8tDq22OrkVgEOcvreQ7DFIfSQaznPF9tY71vDseI5BMh5m8eo3QGEJINkuh8BonuXfkEY4lDSPUFSpjfNJKJVSTjK430bRO5YvvObas2pAzRzvdbuKSn+vYaotHh2E14LXACMCwiW4Fh4GnAnAEoM/ErDpMi8h8i8jo8oViZmyUBFozRWErcVDRmLXFYa3uzqXfzD1kcSPerIT63R1C6XoIkDw+nf4lB57P8tPTZbv6k+j+ExPcFYnQs/BIiZurHCknEeCoZ80mwswXz4tCUUyRVo45yMhnd92KPVbPLQ4rAsj0irmjliI42GFZW1YdV9XhgP+B5wP6qeryqPhzkPH6vkDfjqc5XAXcBleOSJwP3BunUEQGJw6purikam4wkj5xrb539TehfUsT6L5yjwSJife8N1wY/EdDSj8SiuRmJdBLvPBVJHECsSrWSqBDMl68TY3G6u5jMR1tL2CaSqeoiMJmKzs+wZt9ls6YXrNprMZ2d0Sdld0RAm0Qri8gyEelV1ceAdcAzRORVErAslK/HNFU9ZY7dN+MFpjiqYOx7lTwMT/vPGL4JWZSVkdhCSBwI+ftm70wcjMTCLykY6zgO7Xwemv7l7H1970Nisz0HzTVggY820Xop4onDoDgcaZ+zkBioaXFmun8YzUY3v64WphJxpzqq33pSHdFFsHd2pdhj9SI2bxyc2rZ2vwjSfDnMsZsJvQa5Gi8W5Fa8uYZn4aXDeCLwTr8nmffju6oOq+qO+Z6nVTGV6FZiPZA4aMbWGCSPiM6G5LHVt6eeVHV7GMT6Pji73nDyaKTrhRF07kMcio82TSSWPJxYSEPpuxM21FYeybZvJY6e3k4SidmfwcJF3ZHaMVMMrnHisLVpg2FlvFQ25eCTVwLPAZ4OnBvkJOavkC2O0e9V6ujp64mDvEjmiKglAiVVXTSGYkN8GdL53GnbYt2vjUYczBSl1Yjw8wCQxL7EZj00RIwVKVzMew6H0tHWEraJWExmBX5096To7Ip2SHef/VfMue5oLdphWBkvyXBKRA4HhlX1UbwE2IFuNk4chozJUkySfOL0DTPFYtj91xCBkozOcwggnZX1e5NIVOlb/AzfRjzEKxIPfzi9Lrvn43iz2ZmeNF5Cz2T3i2ZEC5uIEl67//I51x2O3ZDfAj8GvoaXTQa8OJFNQU7ixGHI5E3mMkseNW017CCQmUh8JcT3nL4xvqZuDsSm25E6bsqLJx0nRDLfEYDi9ua0aTYRz3O0ERsCUoYzadKFoJV0Wocly6Z/D5cui+h3WUGlpzDVkWD1nhGk+XKYoz2Glc8Dfo1X3vhTpW1LgQuDnMR0PomWp6gGxWF8T5Au0P+/vTOPl6Ss7v731913X2afYZNNQTYdUFSioigYNdGogURW96CiMW6vGiKIgERMXk2MRsUXRKImbsSFuMcFx6ivvCAgosg27DAzzHbvzF2667x/VPWdvj136e7bVU/N7fOdT32qu5bnOdO3u+pX53nOOcnwVYDhRHU9Bavct+t9hvMNp/pUN+p5Ljb2jbi8XkZY5ZH5D4o2YVbONLWLlP1NuJb4ervnXXHbzfaJcbaNj9NXCldGMCRr9p4+33b13o3UAW8ve++3DCn2oO617zKKJc/KtmjZM4Ve05jZOHBZ3bYfN9vOvJ5DSW/RPAnRJPVIekuznXcC5ZDDyirU5PorQAqVQOa1oW5oOesh5V39xkPq6n7SPEe2kejhBg4yiDKO51Jvtv3tRvirdB7E6baJcbZNjAW2ItznsHqvJXO+z4KurhLLV8YPS2sC9O9ky2KdcyjpogaPe3+jbTYyrLwXcLukT0k6XdKTJR2arE+T9CngD0DbJ2tI+rGkMUkjyTJrPkVJF0iarDl2RFLYAq5AOXSi29Ih8bp4QJCkx6of2u7OLlp6esfVr3qGMynKdzV43J3p2pE7IgjpUc8J2ybG2DbeuRHLeRCHAKsTD+YqF4eLn8U7rPxWSQdJOniuBWjYiTfvWJaZnSvpw8QlWV4LPAFYCmwGbgK+BZxrZpta+R81wJvN7P80eOwXzezMlOxoidBVENR1KDbGLpGYNdNq6AqKoWrqWt06A8p3NH5cz9PTtWUaYefbGRFWn3+zA9k2Ph48nU3IgJSVq4fnfJ+ZHcncx1D9O9mxJ3gBW2QAuJ35L+4ND1U0mgR7I/CPyeI0wWQl8E2wsFe8DlTLVuqDwpp4iLWwT7CSbbvugtlcHSx6FGzz/AcCVr4jB+ERGWIV4mwLnc14pcx44ICUkMPr9WJsVSBxNjAUT7MYHAw93cJxWsPM2j4ktidEK/+9pI2SfibphHmOfbGkRyXdIumNsx0k6WxJ10m6bsOGdKNFJzIsJD8jSpLKqi+cDcUDYhNKB4SzoSpGLKO/R/n2dI5dBBgRltXfIceUo4jJkNkMgIBTolm+cnBa+boVq8MESvX3x7kV+wa8bN6iZ/EOK7edhsWhpMMlXSLp65J+mKwvkXR4iva9GzgY2Jc4+uabkmaLqvgScDiwCvgr4HxJp810oJldZmbHmtmxq1alm1ZlbDJw/dQk+EABxeGUKAw2pAxWuWfaOnUmf9f4seXfBc93lyVmkxiBU7jkYM5jQQUK9cV9Myak57BUKjK8pD95XWBwKIznrm8gHs3o6w80quFkQ6vCsHMuzdNoSBwmIuvnwH7AtcAXgJ8Qi7b/kfTyZjtOgk1slmUdgJn90sy2m9m4mX0W+Bmz1HE2s9+a2QNmVjGz/wH+GTilWbvazejEBFHIG39VFIaMUC2smr4OQdU7V/5DJt1ZuQlxaNsgejA9Y3JHBaxz8/tVGe7pYUl3WEES9NoEDC2Jr0/DS/pRIKHck9R57pml3rOzONAClk6k0V/DJcCfmtnP6ndIegbweeCLzXRsZic0c3z1NBr/WzVzbGqMlytMlCv0dgW68FRFYUBxqMKK+I9RXBHMBktEoWU1hNuM57B6fHGfdGzZjdBVOcqYzzlkuLuHJT1h57mFDpiLPYebpkRiCKq5DUtdnuNw0dOhXsBWaHRYeRVw/Sz7biDOvt1WJC2V9HxJvZJKks4AngV8d5bjXyJpmWKeShyy/fV229UsE+UKE0EnnUd16wAUlk9fZ4xFW6YqkVgGnkOLdkD5tubOmbwpJWvyh1EJPucwD3kOV/UPsKp/YP4DUyR0QMzQcCwKB4fDicNSqZCsXRwudhZrnsPZSPRQobo0c26jB38fuKJ+vl/y/tPJ/nbTBVwMbAA2An8NvNTMfp/0fbykkZrjTyUO5d4OXAVcmgxFB2W8UmZsMuAF2Canr0NQiD2GKgTyHJZr0mOWb8fS/izGvg1MNHfOzq9jmc2DC32182hlgL36B1nZF1Ycjk42+T1tM9UgkIGBcMPrJfccdg4dMOdQ0j6Srpa0CSgDkzVLwzQqDl+TrH8raVTSA4kwu4V46PY1s5/aGma2wcyeYmZDZrbUzI4zs+/X7P+pmQ3WvD/NzFaY2aCZHWZmH223Ta0wMjbByHjIC3DSd0BxKCU3QIW5Edpkbe70ycaTU7fa384vNX9SdD9M7DZrIyXCBmOYVXxYGThwyTJKhbAJI+7a0li6pbSoRgr39oWLFO7qLiVrF4fOouBTxELwRGAEeBLwDeANzTTSaJ7DzcBpkvqBQ4HBpNPbzGxHMx12GlvHxtg2FjDR7ZQoDOg5VPWiG2bepZVvrXv/O9R1aDp9Td4Gkze0du6OL6Ke49tsUR6xsNmXqzYEZp/B8EmXb9u8iRcE7L8qCvv6w9WX7q6Kw1Dzwp3sCP+zz4KnA/ub2agkM7MbJb0W+B/ikd6GaOqx1cx2mNmvzWxdst4hqSjp/CaN7xi2j42zbWfI+qmxhyb1odQ5qYrDMF4SqwsOaSqSuNm+dlzZ+snjP8TKGaXaCU7o2srhU9ms6R+c/6CUuXPLo0H776vmGAzoOezucc9hR9DifMM9cM5hBaZyhW2RtAoYJc4u0zDtuFuXgPe1oZ1Fybad42wN6TnMBclFV9k/mZtFu6evaTaSuNG+xq+FnV9ZQAtlbOu7ggdrdAJ5yCvZ1xXOW1bl0bGdQSOWq57DnoDisKe3a5otziKmA+YcAr9kV8q/7xJnkrkauK6ZRhq6W0u6YqFtdCLlKGLDyCgPb9se2pSwKKDnsPIA9eUkrdL+OYcWbca2nrvwhiavh9FPw2BT00Oaw6KgSZ6kEuGvuOE9h92B5xsC7ChPsrM8SXcxjNesNxFmVQ9iCKp99we0wcmGPdAL2Apnsetm+1bgHcAQ8E/NNNKosDsduByYaQzCffGz8MCWbZSjiPWbtoQ2JTDVG3H2v0yr3Ln7xsp9mI23rc6zmWFb3wfRI+1pb+Sj0PMs1HVEW9qboYeU2m2UEqHFWR6GlYs5EIeRGZWAJfyqwqwqEkPa0BPQBicjQl/6MsDMttS83kmc9aVpGhWHNwPfNbNv1O+Q1Au8p5XOFzvrH43/Rus3d7g4nBrCC3ATmjEy2aC8HtoVlDJ5PYx/pz1tAVDGtn8QLb+qjW3WElYYxZ7D8BHToSmEz9HPqr4BlvaGyzHYn6SwGRgMlwy8f6CHvv5uCjkQ646zUBR7Pc4HTgNWmNkSSX8MHGpmH2u0nUZ/DVfOcewk8P5GO+wk7t4Up4kI6zmMn4rb5SVrjeRGHOCGPNsQcnuHltNwnqc5WyPs47PoQgo7hFcJGqAVE6pcXC2HLFsRtL5zVRz2B8xz2D/Qw8Cg11XuBDokIOUjwFHAGey62N8CvLGZRhpNZfPxOfZVcHE4I7c+FFfleHj7CBtHRlk5GCDPX6FaW7k/+76nCFilpbJhlu3tGQIG0qn8kmo1mbBXu4K6iQJfcCMLHyQWXhrCwUuXBe2/KgqrybBD2RDSc+lkxJ4ZXNIKLwMel6SyiQDM7H5JmUcrOzNgZvz09l3eqXV3rA9jSFUUKtzQ0a6vWfZfN4tmTtUx2/aW2OPEYehh5W4KgT2HY5UtRBa2dFweeMzQkqD9V0XZYEBxVioVWb5yKFj/ToakHK0s6RBJY5I+V7PtdEnrkwIiX5O0vGbfckn/mexbL+n0Bf8f48oX0xx/STqbTc000tTYlaQLZ9k1DtwHfMfMHm6mzcXKbx96hA0ju/KD/+QPd/HStWkFGMzBlDgM6TkMl8qG2USgtVEcagC61sLkjW1qsIC6j2tTWzMQeL6dKAUfqxmvbGa8soW+UtvLwu9R7BdYHA4OJ+IwYG1lgBWrXBwudkQml52PA7+a6lM6krhiyZ8C1wOXAf9KXO63evwEsAY4GvgvSTea2S0LsOHLwGclvS2xYW/iSOX/aKaRZl05hwLvBp4DPC5Zvxs4hng8+05JIRPu54Zr/3D3tPfrbl9POURUYNVjGNJzOCUKAwS2zyYOo/aVDZOEln0Guv+oDa11oaUfRb3PbUNbMxM6UlfqRoSNDB2vbGG80uGBYsDSnrDDqYNDcf9DQ2HtWLYifEJyJwNS9BxKOhXYAvx3zeYzgG+a2bVmNgKcB/y5pCHFdWVPBs4zsxEzW0dc5u6sBf4vzwXuJg4kXgr8AXiAJqf/NSsOC8CpZna8mZ1uZscDfwlUzOw44Bzgg022uSj579/fMe399vFxrlt/XwBLkonWQQNSwngOLdoBNrMItMr9be1LhUG07NPQ87wFNNKPln0a9f5x+wybkdDisIQUVhxWbJxKDuYdhiZ0UEypVKSvvzv4nL/lLg6duVkp6bqa5ezanZKGgQuJcwrWciQwNaRkZncQewoPTZaKmd1Wc/yNyTktIakAPBN4t5kNEnskh8zsbWY20UxbzYrD5xMr21quAV6YvP4c8Ngm21x03Hz/Q/zmgd1H1//9upuyN6Y6tyvoHK/k0csyFiUz5TisUr6j7VUypG609J+h789bOHkpWvZZ1PP0tto0I8Hn2hUJPd3ZrIwF/xzCk4OAaVasGqJYCvt9WLo8QLCgkzkya2kBNprZsTXLZXVNXwRcbmb31m0fBLbWbdtKnJR6rn0tYWYR8HWz+MnXzDZYize6Zn+Rd7B7OPQbku0AK4lr+HU0n/u/v55x+w9uvZ0Htm7L1JY4p1wxrOfQkq+E7Zj7uHZ3W759jp0jbUtaXYtUQsOXQF8T84q1FC3/HOpe23Z7ZiasKBIF4gfccBhR8OF1J2b1XmHnPQL0eyqbxU+rQ8rzSCtJRwMnEaeQqWcEGK7bNgxsn2ffQrhW0oInrTc7zvc64GpJ7wbuB/YjvtNUXSWPJx5T71g2jozyrd/8fsZ9FTP+/Vc38Y6TnpmtUeqhmu8wCFVRaNk+N1j5jnn2346Ka9rer1SA4fMw2wxj357n4L54KLldCbkbIXSOPxXAworDYqGXYtAI/nyQgxLTuRCHoYe1nWxIKSDlBOBA4J5kmsYgUJR0BPAdYOqpX9LBxHO9biOe31OSdIiZ/SE5ZC1xTsKFsB74tqSvA/dSI2/N7PxGG2lKHJrZ9ZIOAY4D9gEeBH5uFt9tzOxa4Npm2lxsfOn/3czkHIEnX77+Zt707OPo7cpm/p2ZJWIgnLfIEnFotiOz3G5mETbxy7mPmfgF9Dwjlf6lIiz5ByzaAhM/n+WoUhx8kpnHsEro4VQReli5pD5KBRcEeUj8lodgEE+C3SGk83W/jOmRwO8kFotvBFYDP5d0PHG08oXA1Wa2HUDS1cCFkl5HHK38EmChc4v6gK8lr/drtZFWFMqBxFHK+xJ7Dx8kjoZxgP+axWtYZcvOMf7nzvU89/EZTc20HcAkRAEjM6ORxJaFessbx3Z8Li5rN9cxo5/Gel+Aulqe/zsnUjcs/Tj26FlQ3v1hUEsuQT3PTqXvubDQnsMc0FUYpKsQXpSEJ/ykw2U5mO/X0+N1lTuBNDyHFns/puZMSRoBxsxsA7BB0huAzwMrgB8Ar645/RzgCuAR4jyEb1xgGhvM7NUzbVeTc3maOljSi4H/BxwGPEo8jHydpD9rpp3Fyt2bNnPHxvnz5/3o93MESrSbasqWNqZuad6GB+N1JZsUmFa+g2j7hxo4skxl6zuwFKNW4yjmy5iKGq/S/wrU99LU+p2bpoLWFiXdxWG6i/XTfZwQLF0eXqR3dQdIs+UsSszsAjM7s+b9F8xsfzMbMLOXmO1Ksmtmj5rZS5N9+5vZF9ptj6QnSPoH4lzUDdPs2M4lwEuSNDZ/a2ZnELtBL2mynUXJDxsUfT+67U6irCb7WOIxDCgOrfJAsm5v+pgZ+7JJKlveCYw1dkL5dqLt/5iqTSqugt7p6T/Vf+YsR2dAcxkN0jCA0MOZA6W9KAau0pIH8hCtPBQ4ATZAd3eABP1O9qRcISUvSFol6W8kXQ/8Gngq8DfNtNGsONwP+GndtnUsYFx7MfHD2+YOgKiycXQHN93/w/Nv4gAAIABJREFUUMrWJCRJoC2k57CSPLBkIA6jkY9B+eamzrEdnyEaX5eSRTHq/8tdb7qPQ6UDU+1vNuKsBqGjdMNfcbsK7jUEKORgWLk/YF3lKsWSew4XPRYPK7ey7AlI6pJ0sqRvEk/5ez3wn8SJuf/CzL7cTHvNisNfs3uSx7cn2zue9ZsaF2B3N3HsgqgKsgyE2Wxk4Tk0K1PZdjE2+vGWzo82n020sz6FZxvpOhaKBwOgvr+c5+A0qWCELZ8Xh8iGveIWQpRydGakty+8OCwUwotkJwMWt+fwYeJSfb8HjjOzI8zsIlqcR9SsODwHeJ2kByT9UtKDwF+xe+7DjmTNcOO5K/cazmaejVWSnJyVezLpb7f+zVL3HFq0jWjz67AdVy6glQmirW+nsv0fsRSSdUuC3qR6Su9JbW+/cSrBaysbFjzHoIvD/NDXH14cFotho+ed9KnWVl6snkPgJuJyeU8DniJp2UIaa+oXYWa3AocTl8z738BfAEck2zuevZc0Lg73XpLRsFY5EYWVEKX7iMvXWTVaeQsWtTcJuJXvorLpFGyiPcPCNvpJoi3nYNUI6zaiwnLQAFLAFCpWJnwqm0r21XJ2wz1FeaHUFX5IN3QZQScjzFpb9gDM7ATiCnXfI06n81AyxDwAzRezn1ccSnpu7QI8izij8sZkfXyyvePZO4eewymPYfQIZjuz6bOWcl01oTZ6MKPxdVQ2nTx3mbwWsPEfUHn05Vi5zYJaS+IlKJPBk2BbHoa2HSAfI2alHMz3kw8rO4sAM1tvZheZ2SHAicSpBiPgRkmNpPCYopGxlcsbsQk4uJmOFyOPXbW8oeMes2wJPaWMhrVq08dUHoHSAdn0mzA1rF3zXl1HtaXtaPMbaDgquVnKvyfa/n6Kyz7dvjYLw/ESELNxSDF1T2NGVLyucU7ILGvCHJQC11V2Ooc9aIh4wZjZOmCdpLcALwNe0cz58yoUMzuoRds6jj896jD+4fs/ZWR87vmfpx77xIwsgmn1jDMuXwfs7ikst8dzGEfdpixy2i2iNBQvIbFxLO3PbR4iG8eCp9NxoPo7Ckse5vv5qHIHsGcFl7QNMxsD/j1ZGib8r3IRMdjTzSnHzO0V6+/q4i+e1B7P2XyYVZgmoGqFYkZYZf2c71unQtq/9LZXEin0Qeh6vjYKUYCHhFoTbDzVxOMNWhG4f2eKHCgzn3PYGShqbelEXBy2mTOfdjSFOS40Lzv6SIZ7MwpIqJ9jGEIcluvEYNvEYRbz5trs3VJ/vATEbAQL4UGuIbIdRAG+i87uWA5E8lzXy6wIb4GTCYs7lU1bybU4lHSqpFsljUq6IylePduxb5P0kKStkq6QFKSS+n5Ll/C8wx834z4BZz3t6OyMsbqI2yDDynWew3qx2DIFKKY740HFNte/VnjPoUUjWIY1rmeiEm2lHG0NaoOTHzwYxMmKRZ7Kpq3kVhxKeh5wKXGR6iHiKOkZw1IlPR94D3F0zoHEwTHvz8TQGTjrqTMLwGcdchAHrlhQ6qHmqE8bk/EN2aLtEG2os+GhtqSJkXoorvga6jt5wW3tTheFofdSWHJpm9vthTDPLLuINkO0JagJ5WgLFReHTkIOHIeO49SRW3FILO4uNLNfmFlkZveb2WxZlF8JXG5mt5jZZuAi4FVZGVrPk/ffl8evWbnb9jNnEY2pYXUiIGNRYDs+P/P2nf/RlvZVGKC45FIKSz4MGmhLmxQPorjiyxQGXtX+eUgqgMKm7bBoAxY9EtSGKBolCjy07ThOh2Es6jyH7SaX4lBSETgWWCXpdkn3SfqYNOuY3JHAjTXvbwTWSFoxS/tnS7pO0nUbNmyY6ZAFIYkznjJdCB6wfCnPeGy2aWSoq6dsGYpDi7YRjV42475o5JOxV7FNFPr+jOKKb0LX2gW1o75TYm9km1Lt7E6B0D85izZilY1BbSgVl1MqNJb2yUmXPMz3c5ys8GHlxsmlOATWEGf0PgU4HjgaOAZ47yzHDwK141TV1zPmDTGzy8zsWDM7dtWqVe2xuI4XPeEwhnt3DSGe/pS12V+I68Thbp7ENLse/T9gs1RDsS1Eo1e0tT+V9qe4/D/QwNktnDxAYclHKC75ICq0yQM5I8VkCYdFG7EotDhcQ1dxTVAbnCrhxaFyYIPTIXhASsMEEYeSfizJZlnWAdUw238xswfNbCPwYeBPZmlyBKjNLlx9HWzmfX93F895/K684C888tDMbbD6knn11UpS63fTvHWObccVWPRoW/uVuigOvYvC8AVNnDRIcdmVFPpe3FZb8opF27DZRHtGdBVXUSqm81DmNId7Dp1OoQNqK7eVIOLQzE4wM82yPDOZN3gfjWv2W4DaMcW1wMNmtqndtjfDkx+zLwD7L1vC6qGMyuUlmBmMfXv6xsnrsNqKKan0O0609V3zp82xUaKt704l312h/0wKwxfNf6CGKS6/CnUf03YbZiZKlnCYjYDtDFqhpKA+CoFT+jgxLg2djqHV+YY+5zB3fAb4a0mrJS0D3gpcM8uxVwGvlXREcux7gSuzMXN2jj0gFodPTtaZMvlrqNR7Cg3G/iu1Ls3GiTafg038pLHjx39EtOXNKQnE0ygMf4BZb39aEgvDrgyr1VBJloBU53oGDAiRupGargPvLFLykGvR6Qzcc9g4eRaHFwG/Am4DbgVuAD4AIGl/SSOS9gcws+8AHwJ+BKxPlveFMLqWg1YsY1l/35QHMUts7Bszb9/5zXT6s3GizW9oWBhOnTf+I6Itb0pJIL6cwvDfs5tA1NJEGGZTqWYKq8RLQMzi6bhZBifVI1wcOvmiQ+//jjMruRWHZjZpZueY2VIz28vM3pLUCMTM7jGzQTO7p+b4D5vZGjMbNrNXW/j6XEjiqH3W8IR9s518bzYJO781887yLVj59jb3N0a0+fXYxE9bO3/8x7HHMRWBeArqP3P6tiUfQF1Htr2v+TFCDiubGVaJo/OtPv9khkjho7bzQB5KtuXBBsfJDA9IaRi/QqfMysF+Vg6mGQE7A6OXgW2edbdt+2A8J7ENmBnR5jdhE+sW1s7ET4i2/HXb7Kql0PuiXW80gHpOaHsfDVpC0Ghl20611nZVJHYqPpSZHzp0SpcTAB9WbhwXhymzcqCfpX0Z1VIGbOJ6bORjcx80cS3suKo9/Y19q+mh5FnbGv8hNv7dtrQ1ja5joBB7b9VzEoEqK8alIAJ6amq9haETYYenQ6/4daTxMLYn4p9DB2BAZK0tHYiLw5Q5eOVyioVsPmaLtmNb3kEjQQ+2/UPY5K0L68/GiLa3t8RctP3Stg8vSwXU+8L4dbIOQ9jhVKs8WPP6oWB25AGzsFHjeaEzb3u749qwQ/Bh5YZxcZgyj101Y5GWtmNm2LbzIZqtwmA9k9iWt2G2c/5DZ+tz9AqIHmj5/Bmp3IuNXtneNgH1nAh0oZ7j29524xQJ+ZOLavJeRvU5MDuODr3i1+HD6wmuDjsCH1ZuHBeHKTPUm9EQ5s4vN5+mpnIntu2iloZUrLKBaPSTTZ/XCNHov2KV9qaoVGEFFIbCDSkDoeccWuX+mtedLQ5dFMXkQRPlYUg3ByY4Tq5wcZgyPaX0xYCNX4ttu6C1k3d+BVoVeXHwePtJo131ArOV5s4IleIlEBZtnfF1Z+LDyjE5UEV5MMHVYWfgSbAbxsVhynQX0xWHNnkztuUtQOsVL2zkI9iOrzZ1joqrUPfTWu5zzra7/wgV2zwcr34oBBaHgWsrF7oOm/F1J+JiICYPH0Me/hZ5sMFJHx9WbhwXhynTnaLn0Mr3YJvPZt5SdY20te292HhzUcfq/dMF95tZu+oFsosan5mwASmFrifUvM6yMkz+sNCVanJClAO3XXgLIOrQiNSOotVglA79arg4TJm0IpWtsgnb/FqI2jU3r4JteQs2eVPDZ6j3j4F2D5N2Je22GRXjJSgiZDXbQunxQDznshhQHLqXJj9U8iCKcvB98O/k4keAzFpaOhEXhymT1vfKtn8QKuvb3OhObMs7Gr5QqrAc9f5JW01Q74tQYUlb2wRycQMKjdRNoetQAApBKsRUCf847gEpMVEOUvrk4afpnsMOIWpx6UBcHKZO+y86Fo3C+Pfa3i4QC87JGxo+vDB0LqhNYk7LKAy/pz1t7UYEObgRhqZQOhwV901HgO9J+HcBCC3RY/LgtcuDDY6TJ1wcpkwq15zx78MC8hPOh+38esPHqriSwlB7BF1h+Nw45UwqhPdWxcnJw851K3QdTqF0eFAb8vC3cM9hfsiDMDP3HHYEPqzcOC4OUyZK4YvVjHhribFvYTbR8OHqOwV1H7egLtX9DNT70gW1MT+hvUWTLCSqvB0USo9P5h6GJHx6CAv+XXCq5GFINxdzL5108YCUpnBxmDLtFocWbYeJn7e1zd072QoTv2z4cEkUhi+mGuzQPL0Uhi9EqdcdDvx1t0loQnSngQor258maI+kQ6/4dYQLj9pFLrx2Heod6ixazHHYod8NF4cp025xqMIQ9L28rW3uRukoaDKHoUoHUhh6Z0vdFYbehUoHtHRu44SNFAYScTgZ1obCEGg4rA05EGYKmG8yT6T/QDY/aYyuNEulEt4GJ308z2HjuDhMmUrU/uErDZ8LpZSSGGsILf1npO7mT+1/JXQ9tblzuv8I9Z/ZdF/NUwCF/rqPJ0s4pKH4ASMglvwLiUI/KDhTRDkQZnmY9+hkgHsOGyb03XLRk8ZTsdSDln4UNND+tpf8PSo9prVzVaC45NLG7dIAhSWXoixEWw48JNgYqZUcbBQNIA2GtSEHKHjOy3xQyMHvIg/CLA82OE6ecHGYMuUUPIcQD+Nq+KL2Ntr/ygUnoFbpMRSG/rahYwtD56HiPgvqr3FKQFdGfc2CjYGF9hwWkmoxoQl7My4ErHHtTKdS8eAgJwMMFLW2dCIuDlOmnOKFT30vQoNvbU9jPSehof/VlqbU93Loesrcx3Q/DfWd3Jb+GqOEFFocjof3HEJcZ7rDUdsr++yZ5GF43b12TmakNKwsqUfS5ZLWS9ou6QZJL6zZf6Kk30naIelHkg6oO/cKSdskPSTp7Sn975vCxWHKpDHnsBYNnoOW/BMLqhs88Ea09GMtzTOc0SaJ4vDfMXsAiCgM/V2mk+ElhfeYRVsg2hrWBkDqC21CcPIgivJAMRfDyqEtILQj28mK9FLZlIB7gWcDS4DzgC9JOlDSSuDqZNty4DrgizXnXgAcAhwAPAd4l6QXtP6fbA/++JwyaQ0r16K+P4HS/tjmN0L0cBNndsdzDPte3H6buo5CfX+O7fzq7vv6/gJ1HdH2Puc3KrAoih5u8u+TEmo15dAiIheiyIJHCxcKOfgc8pDKJvzH4GRAWgmtzWyUWORVuUbSXcCTgRXALWb2ZQBJFwAbJR1mZr8DXgG82sw2A5slfRp4FfCdVIxtEPccpsxEOZuKGOo6Cq34KnQ9sbETCqvR8s+nIgynuhh8++5DmBqIt4cg8HCqVR6GaCMWOp1N4DQuefDayS99ABSDR/BDlMEDtOMAmUUrS1oDHArcAhwJ3LjLBBsF7gCOlLQM2Kd2f/L6yAX8L9tC+CvDIme8nF1FDBVXo+Wfg955BF/pSLTiK6h7bcr2rEH9r5i+rf/VqLgy1X5nNyjwXLvoYcAg2hjUDJ9v51TJQ7Sy4+wBrJR0Xc1y9mwHKp7c/nngs4lncBCon0+0FRhK9lG3v7ovKH6XSJnxjDyHVaReWHIpVr4Lyr+Z4YBhtOxTqLg6E3sKvS+gMvrJmvfPz6TfGQktDiv3J+v7oLh3ODsCp3EJnePQ2YVLw5hCwf0kix5jIRVUN5rZsfMdpDgv278BE8Cbk80jQH3lgWFge7Kv+n6sbl9Q/BeRMll6DqtIJbTkEmbS/hp6d2bCEIDSEVBI+ivslV7y7gZQaHFYXp+s7w5qhksCJ0/kISDFHaiLH2HIWlsaaj+eQHw5sAY42XbNH7oFWFtz3ADwWOJ5iJuBB2v3J69vWfj/eGG4OEyZscnsxSGAug6Dgb+avrH7OOg7JVs7VEA9z45f9zwn7AT8gOLQbAyiB+LXlbuD2eFUcTUA+SiflwMTcvE5OBmQ7pzDTwCHAy82s5012/8TOErSyZJ6gfOBm5IhZ4CrgPdKWibpMOCvgCvb8v9dAC4OU2bnZLjgAw2eA8WDkne9aPjiIBdB9TwnWZ+Qed/TDQmYyqZ8T83r9eHsyA2h3UWh+3emyIEwUw6itp0MSC/P4QHA64GjgYckjSTLGWa2ATgZ+ACwGXgacGrN6e8jDlBZD/wE+AczCxqpDD7nMHV2TAQUh+qB4b/FNp8NA69Gpf3D2FE6ZNo6GCFTuNjIzK+DEDY6VAjz51InIQfakKKLw8XPwuYczt202XrmGI4wsx8AM86pMrNx4DXJkhv8Cp0yIcUhAKUnAKCuJ4SzobBy+joUIcVhbd/B8wyG9poVcpHOxskHefgueECK40wn178ISadKulXSqKQ7JB0/y3GvklSpceWOSDohY3NnJOSwMgCF5aAhKB0YzgYNQmElKoQu2xZSlHXP8joAFjqvnCAH+fVCE1qi54VcDOnmwAQnfdIMSFls5HZYWdLzgEuBlwP/F5gv98fPzeyZqRvWJBPlCpUoohjoyVQSVjoYimGGlKs2qHR4sP53GRLw6z7NcxhWHBpR4HuhCH03zkM6HdcjMXmo0lIs+sNKR9ChQq8VcisOgfcDF5rZL5L394c0plXKURRUHALQ/Yy21U1umdJB8x+zmCmuJnbUR1DcJ7Q1QZEE5jdjJyYP4tDpBFqrdtKp5PIKLakIHAusknS7pPskfUyaszjuMZI2SrpN0nnS7G4iSWdXM51v2LCh7fbXUo4iyoFrh6rr8UH7j41YEtqCoEi9UDo4fp0HL2pwAguC4EPrTpU8zPfzVDYdgJFZ+bzFQPhf5cysAbqAU4DjicPDjwHeO8vx1wJHAauJQ8ZPA/7XbI2b2WVmdqyZHbtq1ap22r0b5UpEOcq2SsruBPYaAirUJ4jvQEpHxOuuI8La4eRiWNmJyYPnMA82OBkQtbh0IEHEoaQfS7JZlnVANYHkv5jZg2a2Efgw8CcztWdmd5rZXWYWmdnNwIXEwjI4hoV/8FBXYAMA5UAc2njQ7tV1eJyIO+D8T8hHdGhoz6F16hU/h+RBmLnnsDPwgJTGCTLn0MxOmO8YSffRekCfEfrukyCUgzxe4T2HFAbnPyZtoo1h+y+shsIqFDxSN3T/4cmHQHYgH9HK4a/RjpMv8nyX+Azw15JWS1oGvBW4ZqYDJb1Q0prk9WHAecDXM7N0DqQc3Ijy4DnMA6HFoXrDVmmZsqMY2oIc4GogL+TBc+h0CD7nsGHyLA4vAn4F3AbcCtxAXH4GSfsnuQyr43MnAjdJGgW+BVwNXJK9ybuTD89hHr7coeddgkWbwhqgvngJTvAvpEM+fpV5IBcBKS5QFz8GRNba0oHkNpWNmU0C5yRL/b57gMGa9+8E3pmddY1TKIhC6GFECy/MQkeHmhlUHgxqA+rJh+cw18+E2SD3nuaG8A/PTmfQuV7AVsitOFwsdBeLdJdC34hyIA5D2xBtwir3hLWBAvnw2uVBHEaEtCP4VA+SBxZXRrkIBsmDDU4GuDhsGBeHKdPf3UUp+LBJDiIzoy2B+38AKg9iZgFvBJV8eHFzIIxCI0I/sPmwchUXZk5muDhsmNCqZdEz2BOynm+VcmgDsMp9gft/AGwUbFtAI8oE96A6QD4EiedaTAj/p8iDCY6TK9xzmDKDPTlII2PhxSGBxSGV+3etC6GqtVTIg1An9BxYwP1mUImMrjz8KQKTB6HudADVgBSnIfzSlDJDvTkQh0yGNiAHnsP7cmBHXi5M4W/G7jXzz6BKwcWhkwkWB0a2snQg7jlMmVwMKwf2HJpVoLIesyhcAuhyEowSPCglD/gzYR6ouBcjJgfa0FPZdAg+57BhXBymzEAehpVDew6jh8B2QPQIFPcKYoJV7o3XZReHoe/G5hdox3GyxoeVm8JdCCkz0J0DcRg6x2Bgr13subw3qA2O48yMzzl0MsMrpDSMi8OU6evKg3M28Je7sj62IpTXzrZR9Z5atCGMDQAUk6XTMYJ/J3OAj2Q6jpNX8qBcFjWlYh70d2DPYXVIN5TXzmqG1UPOv1QX+fjJ5UGY5cGGsBSD5z91nA6jQ72ArZCHO9WipquYB09RaM/hI/E6eiSQAbVzLicC2QBQAvlPLsbdZqVcpBRynE6hc4eIW8HvVCnj4hCINk1fZ800z2HA4Bz3HCa4MAT3HFbxKYdOJhgQdWZamlbIw51qUVPMw5UvpCACLNqYrAOJw2mEFEbdoBykNgosDiVhloPfheMkeFBMh+Cew4ZxcZgy+bjmhBxKZZfHsBJIHBZW73odKJUOEAtDF4cAyGPhHMfJGheHDeNX6E4gsOdw15y/MHao0A+F5fHr4r5BbIg7z4nnMBcZ/3Px1OTkAPfaOU7+cHHYAZgF9hwW1iTr1XMflybF/aavg9AD5CDvZR7qO7s4dHKEC9ROwOIk2K0sHYiLw5TJhRc7ejho9yqsitfFVeFsSDyGKu4TzIbYc5gHcVgJbQAuDh3HyRQDs6ilpRPxOYcpE+VBHZbvwKyMQqVRKSYew6oHMYgNj5m+DoFKScRyWMwmg0sz99Q4jpM5HeoFbAX3HKZMLsRhtAGiR8P1PzWsHNJz+JhkvX8wGyAvqWxCz0F1HMcJgJfPa5g83KkWNaHFoZlBZSNEG3d58DJGxVUYoGJIz+H+QAFCBqRQRLkISAk8B9VxHCdrzDzPYRO45zBlKqG/jDYCjMXew1BUA1FCew4Le6OAc/4kgQaD9T+Fi0PHcRxnDtxzmDKV0HMckrrGVO4LZsKugJSQ0cp7Q8hglCqFodAWYLkISHEcx8mYDh0ibgUXhykT3HNYuQcAK68PF4RQzIHnUF1hI5WnDAkvDh3HcToRC30/3oNwcZgy5dBfxvL6eJ2IxCCoP1kPhLMBwkYqV8nDsLLjOE7H0bnBJa3g4jBlQnsOrZKIw6pI7GBUChmpnFDIgzj0C6TjOB2G4alsmsDFYcpMVgJ7DqON09dByEdOu6Cl86aMCOw9BUT4XIuO4ziZ06EJrVvBo5VTZjLyyf+7xGFgkVgd3u54G/JQpcVxHMfJK+45TJngnsNcCLM82ADkwWOWA89hHqq0OI7jZIkB5sPKDZNbz6GkkbqlIulf5jj+bZIekrRV0hXKRbZhKAcXh3mgGK9Cle+rkgdRFPozAHIhkh3HcbLELB5WbmVpAEnLJf2npFFJ6yWdnvL/KFVyKw7NbLC6AGuAncCXZzpW0vOB9wAnAgcCBwPvz8jUOZmshB5WzsGfeEoQFYOakQ9HeejPgKCJwB3HcUJhkbW0NMjHgQlivXIG8AlJR6b1f0mbHCiHhjgFeAT46Sz7Xwlcbma3mNlm4CLgVRnZNifj5XJYAwrLkvWKgEYU69ahCD2sDfn4yeVBJDuO42RMSp5DSQPAycB5ZjZiZuuAbwBnpfw/So095S7xSuAqs1mTFB0JfL3m/Y3AGkkrzGxT/cGSzgbOTt6OSPp9W63dnZVAyHBh4A8rQSFtWAmFwJ9BHv4OboPb4Dbk0IbQ/XeSDQek3P6MbGfzd39gX1nZ4um9kq6reX+ZmV1W8/5QoGJmt9VsuxF4dov9BSf34lDS/sQf8GvnOGwQ2Frzvvp6CNhNHCZ/1Mvqt6eFpOvM7Nis+sujDaH7dxvcBrfBbchr/25D+pjZC1Jsvl6DkLzfY0tiBRnjkvRjSTbLsq7u8FcA68zsrjmaHAGGa95XX29vp92O4ziO4zh11GsQkvd7rAYJIg7N7AQz0yzLM+sOfwXw2XmavAVYW/N+LfDwTEPKjuM4juM4beQ2oCTpkJpta4m1yR5JHmbHz4qkpwP7MkuUcg1XAa+VdISkZcB7gStTNq8ZMhvCnoPQNoTuH9yGKm5DjNsQ4zaE7x/chj0WMxsFrgYulDQg6RnAS4B/C2tZ62j2GI/wSPoU0G9mZ9Vt3x/4LXCEmd2TbHs78G6gD/gq8AYzG8/YZMdxHMdxOgxJy4ErgOcRxzq8x8y+ENaq1sm1OHQcx3Ecx3GyJdfDyo7jOI7jOE62uDh0HMdxHMdxpnBx6HQMilkv6bGhbXHCIGlZkjLroLrt/yLp0xn2f0DyXpIulHS3pLXznb+n9j2DLcF/i6FtCN1/Xmxw8omLw0WMpIKk8yXdK+kBSS+WNJFEdIe0K0gdPYs5wMzuCNF/KCQNSqpI2rtm21GSHpSUeZJWSasTkbJX1n0DRxPnJLu7bvtRwK8z6n+zma1PSm59BXgu8FQzu3ER9z2N2t+ipDdLuk7SuKQrs7YBuE/S5YlI2i7pBkkvzKr/5DP4XPJ73CbpNkmvS7v/ehuq2yQdImlM0ueysMHJJy4OU0LSByT9U837/SSNSsryM78AOAk4DjgCOJc4/+PmDG1A0uskfS+5AG8G3p5l/3lA0pckjdQsJunNWfRtZiPA74An1Wz+IHCJmYVI0roW2GBmDwXo+2jgtzOU4jwSuCGj/n+dZFxYR1xF4blm9kjIviUdLOkaSRslbZX0/QzsqfIAcDFxpGcISsC9xJW4lgDnAV+SdGCGNvw9cKCZDQN/Blws6ckZ9l/Lx4FfBerbyQkuDtPjaOLailXWAreYNVDFuw1IWgW8DXiNmd1vZluA7wE3Z9F/HU8E/oi4/vUK4KMBbKiK1K+F6NvM/tLMBs1sEDif2EuVZZqDX5GIQ0nPIn5Y+FSG/dfyROCmQH0fA/ymdoOkNcQ1ZbOw6RigC/gF8G9m9hozm8ig3/n6vgr4NrAmWS5I05Da36KZXW1mX2OGUqdZ2GBmo2Z2gZndbWaRmV0D3AWkKs7qPoNbalKvWbKkPtRbf02UdCqwBfjvtPt28o2Lw/Q4muk3m7Vke0M8EbgKptfRAAAGPklEQVTdzG6v2bacMOJwLfCPZvaN5OIbKv/kE5ku2DNH0t8QV/05ycwezbDrKXEIfAg4L0NRUs8TCCcOjwZOSzxkGyVtJPaq/iHxsGbR/5HATWb24Qz6a7TvxwJFoGhmY2b2s5RtCf5bnM2G5GHhUNKvbjGtf0n/KmkH8ffxQeBbKfc/zQZJw8CFwDsy6NfJOS4OUyDx2q1h+sVlLdleDFcSD9dUbSoCLyTMTfmJzF/lJguy/htMIxlGfi2xMMy6tOOvgCdJOpk4Ufy/Z9x/LUE8h5J6gMOBM4iFUnX5BBnMN6zp/2TgcElvTbvPJvo+g7iiwwPJ9I/lKZsU9Lc4mw2SuoDPA581s99l2b+ZnQMMAccTV9vI4iG61oaLgMvN7N4M+nVyjovDdDiS2BMxBiCpBDyHbG+ItwJPl/S45Inwo8TegUw9h4ojI7uIn4ZDE8xbIemNwOuBE81sYwATbgT2Av43ceb+TKY31JM8pBxOmL/DUcTXvO+b2X3VBTiEbOYbHgVUgJ8CLwMukvTcDPqdt28z+6GZnUg83WAt8KqU7cmd5zCZD/5vwASQxXzg3T4DM6uY2TpgP+CNWdkg6Wji+ekfyaBPZw/AxWE6COiXVEouOB8CVpGhODSz/yb2Dt0AXEfsGakOWWTJWuDmUGKkSiJSS8CdAfo+m/hmc5KZbci6f4BkKP9m4G4z+3YIGxIOJf47/DZA38cQB6PUDx8/hWwilY8BfmNmZTO7HngTceDDQfOcl2rfkv48iVAVsedqGSl+HiF/i7PZkPzfLyce8TnZzCaz7H8GSqQ857DOhhOAA4F7JD0EvBM4WdL1adrg5BcXh+nwU2Ih+Dvg+8A9wH1ZRwmb2TlmNmRmhxLPJ/phgHlma8nmxjsfa4nnWoWoF/kh4gv9HTXRymfNd1I7kdQNrAbek2W/M/BE4LZA806Ppi4KM5kCcgDZpbGZ6sfMriIOSvpaklomSN/Ew5g/AbYTz3P7oJn9MEVbpv0Wk4foXpI5j5J6k9GWNKm/HnyC2KP9YjPbmXLf0/pPUjudqjjlVFHS84HTgDT/BtNsAC4jvkZVp1p8Evgv4Pkp2+DkFK+tvEiRdBzxpOZ7iYNTPg/8mZn9IqhhgZB0HrCPmWUxVJM7JH0AONj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake data\n", "fs = 10 # fontsize\n", "#pos = [1, 2, 4, 5, 7, 8]\n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "def set_axis_style(ax, labels):\n", " ax.get_xaxis().set_tick_params(direction='out')\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.set_xticks(np.arange(1, len(labels) + 1))\n", " ax.set_xticklabels(labels)\n", " ax.set_xlim(0.25, len(labels) + 0.75)\n", " ax.set_xlabel('band')\n", "\n", "#ax.violinplot(np.array(coverage['ferr_ap_{}_mean_min'.format('g') ] ) )\n", "#ax.set_title('Custom violinplot 1', fontsize=fs)\n", "\n", "ax.set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", "set_axis_style(ax, ['$' + band + '$' for band in bands])\n", "ax.set_ylim(-7, -3)\n", "\n", "\n", "#areas/np.max(areas)\n", "parts = ax.violinplot(data, widths=widths, showmeans=False, showmedians=False,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1)\n", "\n", "cax, _ = mpl.colorbar.make_axes(ax)\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('Area [square degrees]')\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.savefig('./figs/band_depths_overviews_areaweighted.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews_areaweighted.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake data\n", "fs = 10 # fontsize\n", "#pos = [1, 2, 4, 5, 7, 8]\n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "def set_axis_style(ax, labels):\n", " ax.get_xaxis().set_tick_params(direction='out')\n", " ax.xaxis.set_ticks_position('bottom')\n", " ax.set_xticks(np.arange(1, len(labels) + 1))\n", " ax.set_xticklabels(labels)\n", " ax.set_xlim(0.25, len(labels) + 0.75)\n", " ax.set_xlabel('band')\n", "\n", "#ax.violinplot(np.array(coverage['ferr_ap_{}_mean_min'.format('g') ] ) )\n", "#ax.set_title('Custom violinplot 1', fontsize=fs)\n", "\n", "ax.set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", "set_axis_style(ax, ['$' + band + '$' for band in bands])\n", "ax.set_ylim(-7, -3)\n", "\n", "\n", "\n", "parts = ax.violinplot(data, widths=np.log10(areas)/np.log10(np.max(areas)), showmeans=False, showmedians=False,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1)\n", "\n", "cax, _ = mpl.colorbar.make_axes(ax)\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('area [square degrees]')\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "plt.savefig('./figs/band_depths_overviews_logareaweighted.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews_logareaweighted.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add SED to plot" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [], "source": [ "#s.add_contribution(\"HELP_SED\", orig_spec['WAVE'], orig_spec['LUMIN'])" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [], "source": [ "cigale_filternames = {\n", " 'u': 'u_prime',\n", " 'g': 'MCam_g',\n", " 'r': 'MCam_r',\n", " 'i': 'MCam_i',\n", " 'z': 'MCam_z',\n", " 'y': 'WFCAM_Y',\n", " 'J': 'WFCAM_J',\n", " 'H': 'WFI_H',\n", " 'K': 'WFI_K',\n", " 'Ks': 'Ks_2mass',\n", " 'i1': 'IRAC1',\n", " 'i2': 'IRAC2',\n", " 'i3': 'IRAC3',\n", " 'i4': 'IRAC4'\n", "}" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z = 1:\n", "u band: 2.114516433453811e-14 mJy\n", "g band: 3.0563981403425805e-09 mJy\n", "r band: 0.00023904458353796008 mJy\n", "i band: 0.0009553789491208575 mJy\n", "z band: 0.0017674393225071221 mJy\n", "y band: 0.002210527019337881 mJy\n", "J band: 0.0031530530488951374 mJy\n", "H band: 0.004319244360765199 mJy\n", "K band: 0.006317239316778982 mJy\n", "Ks band: 0.006425892513915043 mJy\n", "i1 band: 0.018534434102073255 mJy\n", "i2 band: 0.022325640838740227 mJy\n", "i3 band: 0.028361672884062546 mJy\n", "i4 band: 0.03943146563662903 mJy\n", "z = 2:\n", "u band: 3.2109771603860785e-14 mJy\n", "g band: 1.472095477474736e-14 mJy\n", "r band: 1.8720567509109372e-15 mJy\n", "i band: 3.854106891800718e-09 mJy\n", "z band: 2.1468245393122928e-05 mJy\n", "y band: 0.00016607597536633694 mJy\n", "J band: 0.0003941947948125535 mJy\n", "H band: 0.0006761987050407453 mJy\n", "K band: 0.0010020216486769902 mJy\n", "Ks band: 0.0010142964936523775 mJy\n", "i1 band: 0.002347266655974899 mJy\n", "i2 band: 0.0043217328844773 mJy\n", "i3 band: 0.0052289219194516685 mJy\n", "i4 band: 0.006923820648659838 mJy\n", "z = 3:\n", "u band: 9.829977022524199e-15 mJy\n", "g band: 1.5021676773415236e-14 mJy\n", "r band: 8.226487430259352e-15 mJy\n", "i band: 1.7866685802931346e-15 mJy\n", "z band: 2.5764041139481603e-16 mJy\n", "y band: 2.2897794934742463e-18 mJy\n", "J band: 3.0398181580537362e-05 mJy\n", "H band: 0.0001731234078584286 mJy\n", "K band: 0.00031012822532282215 mJy\n", "Ks band: 0.00032463657441220823 mJy\n", "i1 band: 0.0006490423409499602 mJy\n", "i2 band: 0.0009478071477916342 mJy\n", "i3 band: 0.0020350690916562104 mJy\n", "i4 band: 0.0026444128595119257 mJy\n", "z = 4:\n", "u band: 8.958612478427203e-17 mJy\n", "g band: 4.745942464644878e-15 mJy\n", "r band: 8.905185022589142e-15 mJy\n", "i band: 5.9291611466690814e-15 mJy\n", "z band: 1.6402212404316308e-15 mJy\n", "y band: 5.927868224683764e-16 mJy\n", "J band: 1.5860746804993732e-18 mJy\n", "H band: 3.5530840494120205e-05 mJy\n", "K band: 0.0001271513501834063 mJy\n", "Ks band: 0.00013800427652824684 mJy\n", "i1 band: 0.00030794458759961604 mJy\n", "i2 band: 0.00041479209264370335 mJy\n", "i3 band: 0.0006499537085758418 mJy\n", "i4 band: 0.0013850168940527713 mJy\n" ] } ], "source": [ "\n", "\n", "\n", "gal1 = './data/HELP_J095946.083p021914.438_best_model.fits'\n", "gal2 = './data/HELP_J100130.443p020929.494_best_model.fits'\n", "gal3 = './data/HELP_J095809.302p013203.775_best_model.fits'\n", "gal4 = './data/HELP_J095822.986p013145.336_best_model.figs'\n", "gal5 = './data/HELP_J003412.527-441056.846_best_model.fits' #z=4\n", "gal6 = './data/HELP_J095738.934+021508.530_best_model.fits' #From hedam - high fluxes\n", "gal7 = './data/HELP_J095818.598+013057.910_best_model.fits'\n", "gal8 = './data/HELP_J095809.488+013225.513_best_model.fits'\n", "gal9 = './data/HELP_J100108.952+022730.528_best_model.fits'\n", "orig_spec = Table.read(gal9)\n", " \n", "s = SED()\n", " # This is wrong because the best SED we get from CIGALE is redshifted (written by Yannick)\n", "s.add_contribution(\"HELP_SED\", orig_spec['wavelength'], orig_spec['L_lambda_total'])\n", " \n", "#z=1\n", "zs = np.arange(1, 5, 1)\n", "\n", "gal_fluxes = np.full([len(zs), len(bands)], np.nan)\n", "for n, z in enumerate(zs):\n", " print('z = {}:'.format(z))\n", " sed = s.copy()\n", " mod = get_module(\"redshifting\", redshift=z)\n", " mod.process(sed)\n", " for m, band in enumerate(cigale_filternames):\n", " flux = sed.compute_fnu(cigale_filternames[band])\n", " print(\"{} band: {} mJy\".format(band,flux))\n", " gal_fluxes[n, m] = flux\n" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "z = 1:\n", "z = 2:\n", "z = 3:\n", "z = 4:\n" ] }, { "data": { "text/plain": [ "(-7, 0)" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for n, z in enumerate(zs):\n", " print('z = {}:'.format(z))\n", " sed = s.copy()\n", " mod = get_module(\"redshifting\", redshift=z)\n", " mod.process(sed)\n", " ax.plot(np.log10(sed.wavelength_grid), np.log10(sed.fnu * 1.e-3), label=str(z))\n", "#ax.plot(np.log10(orig_spec['wavelength']), np.log10(orig_spec['Fnu']))\n", "\n", "plt.legend()\n", "ax.set_ylim(-7, 0)\n", "#ax.set_xlim(0, 14.75)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2.11451643e-14, 3.05639814e-09, 2.39044584e-04, 9.55378949e-04,\n", " 1.76743932e-03, 2.21052702e-03, 3.15305305e-03, 4.31924436e-03,\n", " 6.31723932e-03, 6.42589251e-03, 1.85344341e-02, 2.23256408e-02,\n", " 2.83616729e-02, 3.94314656e-02],\n", " [3.21097716e-14, 1.47209548e-14, 1.87205675e-15, 3.85410689e-09,\n", " 2.14682454e-05, 1.66075975e-04, 3.94194795e-04, 6.76198705e-04,\n", " 1.00202165e-03, 1.01429649e-03, 2.34726666e-03, 4.32173288e-03,\n", " 5.22892192e-03, 6.92382065e-03],\n", " [9.82997702e-15, 1.50216768e-14, 8.22648743e-15, 1.78666858e-15,\n", " 2.57640411e-16, 2.28977949e-18, 3.03981816e-05, 1.73123408e-04,\n", " 3.10128225e-04, 3.24636574e-04, 6.49042341e-04, 9.47807148e-04,\n", " 2.03506909e-03, 2.64441286e-03],\n", " [8.95861248e-17, 4.74594246e-15, 8.90518502e-15, 5.92916115e-15,\n", " 1.64022124e-15, 5.92786822e-16, 1.58607468e-18, 3.55308405e-05,\n", " 1.27151350e-04, 1.38004277e-04, 3.07944588e-04, 4.14792093e-04,\n", " 6.49953709e-04, 1.38501689e-03]])" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gal_fluxes" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [], "source": [ "pos = [3561., #u (SDSS)\n", " 4866., #g (GPC1)\n", " 6215., #r (GPC1)\n", " 7545., #i (GPC1)\n", " 8680., #z (GPC1)\n", " 9633., #y (GPC1)\n", " 12510., #J (UKIRT)\n", " 16377., #H (UKIRT)\n", " 22081., #K (UKIRT)\n", " 21496., #Ks (WIRCam)\n", " 36000., #i1\n", " 45000., #i2 \n", " 56000., #i3\n", " 80000. #i4\n", " ] \n", "fwhms = [\n", " [3048., 4028.], #u (SDSS)\n", " [3943., 5593.], #g (GPC1)\n", " [5386., 7036.], #r (GPC1)\n", " [6778., 8304.], #i (GPC1)\n", " [8028., 9346.], #z (GPC1)\n", " [9100., 10838.], #y (GPC1)\n", " [11690., 13280], #J (UKIRT)\n", " [14920., 17840.], #H (UKIRT)\n", " [20290., 23800.], #K (UKIRT)\n", " [19578., 23431.], #Ks (WIRCam)\n", " [31296, 39614 ], #i1\n", " [39173, 50561], #i2 \n", " [48983, 65089], #i3\n", " [62994, 95876] #i4\n", " ]" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3000.0, 100000)" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "line_styles = [':', '-.', '--', '-']\n", "colours = ['b', 'g', 'r', 'k']\n", "for n, z in enumerate(zs):\n", " sed = s.copy()\n", " mod = get_module(\"redshifting\", redshift=z)\n", " mod.process(sed)\n", " ax.plot(sed.wavelength_grid*10, \n", " np.log10(sed.fnu * 1.e-3),\n", " #c='k',\n", " c= colours[n],\n", " #linestyle = line_styles[n],\n", " )\n", " for m, band in enumerate(cigale_filternames):\n", " if m == 0:\n", " lab = 'z = {}'.format(z)\n", " else:\n", " lab=None\n", " ax.plot([fwhms[m][0], fwhms[m][1]], [np.log10(gal_fluxes[n, m] )-3, \n", " np.log10(gal_fluxes[n, m] )-3], \n", " # c='k',\n", " c= colours[n],\n", " #linestyle = line_styles[n],\n", " label=lab\n", " )\n", "\n", "\n", "plt.legend()\n", "plt.xscale('log')\n", "ax.set_ylim(-7, -3)\n", "ax.set_xlim(3000., 100000)" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake data\n", "fs = 10 # fontsize\n", "# Angstroms \n", " \n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "def set_axis_style(ax, labels, positions=False):\n", " ax.get_xaxis().set_tick_params(direction='out')\n", " ax.xaxis.set_ticks_position('bottom')\n", " if not positions:\n", " ax.set_xticks(np.arange(1, len(labels) + 1))\n", " ax.set_xticklabels(labels)\n", " ax.set_xlim(0.25, len(labels) + 0.75)\n", " else:\n", " ax.set_xticks(positions)\n", " ax.set_xticklabels(labels)\n", " ax.set_xlim(3000., 100000)\n", " plt.xscale('log')\n", " \n", " ax.set_xlabel('band')\n", "\n", "#ax.violinplot(np.array(coverage['ferr_ap_{}_mean_min'.format('g') ] ) )\n", "#ax.set_title('Custom violinplot 1', fontsize=fs)\n", "\n", "ax.set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", "set_axis_style(ax, ['$' + band + '$' for band in bands])\n", "ax.set_ylim(-7, -3)\n", "\n", "\n", "#widths\n", "log_widths = np.ones(len(pos)) * (pos) * .2 * widths\n", "#areas/np.max(areas)\n", "parts = ax.violinplot(data, \n", " #positions=pos, \n", " widths=widths, \n", " showmeans=False, \n", " showmedians=False, #widths=widths,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1.)\n", "\n", "cax, _ = mpl.colorbar.make_axes(ax)\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('Area [square degrees]')\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "\n", "\n", "line_styles = [':', '-.', '--', '-']\n", "for n, z in enumerate(zs):\n", " for m, band in enumerate(cigale_filternames):\n", " if m == 0:\n", " lab = 'z = {}'.format(z)\n", " else:\n", " lab=None\n", " ax.plot([m + 0.5, m + 1.5], [np.log10(gal_fluxes[n, m] *1.e-3 ), \n", " np.log10(gal_fluxes[n, m] *1.e-3 )], \n", " c='k',\n", " linestyle = line_styles[n],\n", " label=lab\n", " )\n", "\n", "ax.legend(loc=2)\n", "\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_seds.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_seds.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake data\n", "fs = 10 # fontsize\n", "# Angstroms \n", "\n", "\n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax2 = ax.twiny()\n", "\n", "#ax2.set_xlim(3000., 100000)\n", "#ax2.set_xticks(pos)\n", "#ax2.set_xticklabels(['$' + band + '$' for band in bands])\n", "#ax2.set_xlabel('band')\n", "#ax2.set_xscale('log')\n", "ax2.set_xlim(np.log10(3000.), np.log10(100000))\n", "ax2.set_xticks(np.log10(pos))\n", "ax2.set_xticklabels(['${}$'.format(band).replace('$Ks$', '').replace('K', 'K/Ks') for band in bands])\n", "ax2.set_xlabel('band')\n", "#ax2.set_xscale('log')\n", "\n", "line_styles = [':', '-.', '--', '-']\n", "colours = ['y', 'b', 'g', 'r', 'k']\n", "for n, z in enumerate([0.25, 1, 2, 3, 4]):\n", " sed = s.copy()\n", " mod = get_module(\"redshifting\", redshift=z)\n", " mod.process(sed)\n", " ax.plot(sed.wavelength_grid*10, \n", " np.log10(sed.fnu * 1.e-3),\n", " #c='k',\n", " c= colours[n],\n", " #linestyle = line_styles[n],\n", " label= 'z = {}'.format(z),\n", " alpha=0.5\n", " )\n", " for m, band in enumerate(cigale_filternames):\n", " continue\n", " if m == 0:\n", " lab = 'z = {}'.format(z)\n", " else:\n", " lab=None\n", " ax.plot([fwhms[m][0], fwhms[m][1]], [np.log10(gal_fluxes[n, m] )-3, \n", " np.log10(gal_fluxes[n, m] )-3], \n", " # c='k',\n", " c= colours[n],\n", " #linestyle = line_styles[n],\n", " label=lab,\n", " alpha=0.7\n", " )\n", "\n", " \n", "\n", "\n", "\n", "\n", "ax.set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", "\n", "#ax.set_xticks(pos)\n", "#ax.set_xticklabels(['${}$'.format(band.replace('Ks', '').replace('K', 'K/Ks')) for band in bands])\n", "ax.set_xlim(3000., 100000)\n", "ax.set_xscale('log') \n", "ax.set_xlabel('Wavelength [nm]')\n", "ax.set_ylim(-7, -3)\n", "ax.legend(loc=2)\n", "\n", "#widths\n", "log_widths = np.ones(len(pos)) * (pos) * .2 * widths\n", "#areas/np.max(areas)\n", "parts = ax.violinplot(data, \n", " positions=pos, \n", " widths=log_widths, \n", " showmeans=False, \n", " showmedians=False, #widths=widths,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(.9)\n", "\n", "cax, _ = mpl.colorbar.make_axes([ax, ax2])\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('Area [square degrees]')\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_seds_wave.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_seds_wave.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake data\n", "fs = 10 # fontsize\n", "# Angstroms \n", "\n", "\n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax2 = ax.twiny()\n", "\n", "#ax2.set_xlim(3000., 100000)\n", "#ax2.set_xticks(pos)\n", "#ax2.set_xticklabels(['$' + band + '$' for band in bands])\n", "#ax2.set_xlabel('band')\n", "#ax2.set_xscale('log')\n", "ax2.set_xlim(np.log10(3000.), np.log10(100000))\n", "ax2.set_xticks(np.log10(pos))\n", "ax2.set_xticklabels(['${}$'.format(band).replace('$Ks$', '').replace('K', 'K/Ks') for band in bands])\n", "ax2.set_xlabel('band')\n", "#ax2.set_xscale('log')\n", "\n", "\n", "line_styles = [':', '-.', '--', '-']\n", "colours = ['y', 'b', 'g', 'r', 'k']\n", "for n, z in enumerate([0.25, 1, 2, 3, 4]):\n", " sed = s.copy()\n", " mod = get_module(\"redshifting\", redshift=z)\n", " mod.process(sed)\n", " ax.plot(sed.wavelength_grid*10, \n", " np.log10(sed.fnu * 1.e-3),\n", " c='k',\n", " #c= colours[n],\n", " #linestyle = line_styles[n],\n", " label= 'z = {}'.format(z),\n", " linewidth=1.0,\n", " alpha=1.\n", " )\n", " for m, band in enumerate(cigale_filternames):\n", " continue\n", " if m == 0:\n", " lab = 'z = {}'.format(z)\n", " else:\n", " lab=None\n", " ax.plot([fwhms[m][0], fwhms[m][1]], [np.log10(gal_fluxes[n, m] )-3, \n", " np.log10(gal_fluxes[n, m] )-3], \n", " # c='k',\n", " c= colours[n],\n", " #linestyle = line_styles[n],\n", " label=lab,\n", " alpha=1.0\n", " )\n", "\n", " \n", "\n", "\n", "\n", "\n", "ax.set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", "\n", "#ax.set_xticks(pos)\n", "#ax.set_xticklabels(['${}$'.format(band.replace('Ks', '').replace('K', 'K/Ks')) for band in bands])\n", "ax.set_xlim(3000., 100000)\n", "ax.set_xscale('log') \n", "ax.set_xlabel('Wavelength [nm]')\n", "ax.set_ylim(-7, -3)\n", "#ax.legend(loc=2)\n", "\n", "\n", "#widths\n", "log_widths = np.ones(len(pos)) * (pos) * .2 * widths\n", "#areas/np.max(areas)\n", "parts = ax.violinplot(data, \n", " positions=pos, \n", " widths=log_widths, \n", " showmeans=False, \n", " showmedians=False, #widths=widths,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1.0)\n", "\n", "cax, _ = mpl.colorbar.make_axes([ax, ax2])\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('Area [square degrees]')\n", "\n", "\n", "\n", "plt.figtext(0.49, 0.83, 'z = 0.25')\n", "plt.figtext(0.71, 0.645, 'z = 1')\n", "plt.figtext(0.71, 0.51, 'z = 2')\n", "plt.figtext(0.71, 0.425, 'z = 3')\n", "plt.figtext(0.71, 0.365, 'z = 4')\n", "\n", "\n", "\n", "\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_black_seds_wave.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_black_seds_wave.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fake data\n", "fs = 10 # fontsize\n", "# Angstroms \n", "\n", "\n", "#data = np.array([np.array(coverage['ferr_ap_{}_mean_min'.format(b)]) for b in bands]).T\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax2 = ax.twiny()\n", "\n", "#ax2.set_xlim(3000., 100000)\n", "#ax2.set_xticks(pos)\n", "#ax2.set_xticklabels(['$' + band + '$' for band in bands])\n", "#ax2.set_xlabel('band')\n", "#ax2.set_xscale('log')\n", "ax2.set_xlim(np.log10(300.), np.log10(10000))\n", "ax2.set_xticks(np.log10(np.array(pos)/10))\n", "ax2.set_xticklabels(['${}$'.format(band).replace('$Ks$', '').replace('K', 'K/Ks') for band in bands])\n", "ax2.set_xlabel('band')\n", "#ax2.set_xscale('log')\n", "\n", "\n", "line_styles = [':', '-.', '--', '-']\n", "colours = ['y', 'b', 'g', 'r', 'k']\n", "for n, z in enumerate([0.25, 1, 2, 3, 4]):\n", " sed = s.copy()\n", " mod = get_module(\"redshifting\", redshift=z)\n", " mod.process(sed)\n", " ax.plot(sed.wavelength_grid, #*10 factor ten turns angstrom to nm fix unit error\n", " #np.log10(sed.fnu * 1.e-3), #This is for flux in Jy\n", " flux_to_mag(sed.fnu * 1.e-3)[0], #This is for flux in mag\n", " c='k',\n", " #c= colours[n],\n", " #linestyle = line_styles[n],\n", " label= 'z = {}'.format(z),\n", " linewidth=1.0,\n", " alpha=1.\n", " )\n", " for m, band in enumerate(cigale_filternames):\n", " continue\n", " if m == 0:\n", " lab = 'z = {}'.format(z)\n", " else:\n", " lab=None\n", " ax.plot([fwhms[m][0], fwhms[m][1]], [np.log10(gal_fluxes[n, m] )-3, \n", " np.log10(gal_fluxes[n, m] )-3], \n", " # c='k',\n", " c= colours[n],\n", " #linestyle = line_styles[n],\n", " label=lab,\n", " alpha=1.0\n", " )\n", "\n", " \n", "\n", "\n", "\n", "\n", "ax.set_ylabel('5$\\sigma$ depth [mag]')\n", "\n", "#ax.set_xticks(pos)\n", "#ax.set_xticklabels(['${}$'.format(band.replace('Ks', '').replace('K', 'K/Ks')) for band in bands])\n", "ax.set_xlim(300., 10000)\n", "ax.set_xscale('log') \n", "ax.set_xlabel('Wavelength [nm]')\n", "ax.set_ylim(27, 16)\n", "#ax.legend(loc=2)\n", "\n", "\n", "#widths\n", "log_widths = np.ones(len(pos)) * (pos) * .2 * widths\n", "#areas/np.max(areas)\n", "parts = ax.violinplot([flux_to_mag(10**d)[0] for d in data], #flux_to_mag assumes Jy\n", " positions=np.array(pos)/10, #fix unit error \n", " widths=log_widths/10, \n", " showmeans=False, \n", " showmedians=False, #widths=widths,\n", " showextrema=False)\n", "\n", "for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1.0)\n", "\n", "cax, _ = mpl.colorbar.make_axes([ax, ax2])\n", "n_ticks = 7\n", "values = np.linspace(0,1200, n_ticks)\n", "ticks = values/np.max(areas)\n", "\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels([int(d) for d in values])\n", "cbar.set_label('Area [deg.$^2$]')\n", "\n", "\n", "\n", "plt.figtext(0.49, 0.81, 'z = 0.25')\n", "plt.figtext(0.70, 0.645, 'z = 1')\n", "plt.figtext(0.70, 0.52, 'z = 2')\n", "plt.figtext(0.70, 0.435, 'z = 3')\n", "plt.figtext(0.70, 0.385, 'z = 4')\n", "\n", "\n", "\n", "\n", "\n", "plt.rc('font', family='serif', serif='Times')\n", "plt.rc('text') #, usetex=True)\n", "plt.rc('xtick', labelsize=12)\n", "plt.rc('ytick', labelsize=12)\n", "plt.rc('axes', labelsize=12)\n", "\n", "#fig.suptitle(\"Violin Plotting Examples\")\n", "#fig.subplots_adjust(hspace=0.4)\n", "#plt.ylim(-10,10)\n", "column_width_cm = 8.9\n", "width_cm = 3.0 * column_width_cm\n", "hieght_cm = width_cm / 1.9\n", "width_inches = width_cm/2.5\n", "hieght_inches = hieght_cm/2.5\n", "fig.set_size_inches(width_inches, hieght_inches)\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_black_seds_wave_mag.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/band_depths_overviews_areaweighted_with_black_seds_wave_mag.png', bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['$u$',\n", " '$g$',\n", " '$r$',\n", " '$i$',\n", " '$z$',\n", " '$y$',\n", " '$J$',\n", " '$H$',\n", " '$K/Ks$',\n", " '',\n", " '$i1$',\n", " '$i2$',\n", " '$i3$',\n", " '$i4$']" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "['${}$'.format(band).replace('$Ks$', '').replace('K', 'K/Ks') for band in bands]" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(204388,)" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[0].shape" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "fig, ax = plt.subplots()\n", "#cmap = mpl.cm.get_cmap('viridis', 256)\n", "#norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.min(areas))\n", "\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "colors[:, 3] = 1\n", "\n", "#cb = mpl.colorbar.ColorbarBase(ax, cmap=cmap, norm=norm)\n", "\n", "#cmap = mpl.colors.ListedColormap('viridis')\n", "#colors = cmap(np.array(areas))\n", "\n", "\n", "im = plt.scatter(np.arange(len(areas)), areas, c=colors)\n", "\n", "\n", "cax, _ = mpl.colorbar.make_axes(ax)\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap)\n", "cbar.set_label('area [square degrees]')\n", "#cax.set_yticklabels(['$-\\pi$', '0', '$\\pi$'])\n", "#cb = mpl.colorbar.ColorbarBase(ax, cmap=cmap, norm=norm)\n", "# Optionally add a colorbar\n", "#cax, _ = mpl.colorbar.make_axes(ax)\n", "#cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap)\n", "\n" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.151918, 0.500685, 0.557587, 1. ],\n", " [0.993248, 0.906157, 0.143936, 1. ],\n", " [0.983868, 0.904867, 0.136897, 1. ],\n", " [0.983868, 0.904867, 0.136897, 1. ],\n", " [0.993248, 0.906157, 0.143936, 1. ],\n", " [0.9553 , 0.901065, 0.118128, 1. ],\n", " [0.814576, 0.883393, 0.110347, 1. ],\n", " [0.657642, 0.860219, 0.203082, 1. ],\n", " [0.120092, 0.600104, 0.54253 , 1. ],\n", " [0.120565, 0.596422, 0.543611, 1. ],\n", " [0.273006, 0.20452 , 0.501721, 1. ],\n", " [0.274128, 0.199721, 0.498911, 1. ],\n", " [0.267004, 0.004874, 0.329415, 1. ],\n", " [0.267004, 0.004874, 0.329415, 1. ]])" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.9990204970624408, 0.0, 0.0, 1.0) (1.0, 1.0, 0.13529325294031186, 1.0) (0.13425359648991364, 0.0, 0.0, 1.0)\n" ] }, { "data": { "text/plain": [ "array([[1. , 0.17402028, 0. , 1. ],\n", " [1. , 1. , 1. , 1. ],\n", " [1. , 1. , 0.98455881, 1. ],\n", " [1. , 1. , 0.98455881, 1. ],\n", " [1. , 1. , 1. , 1. ],\n", " [1. , 1. , 0.93823523, 1. ],\n", " [1. , 1. , 0.72205855, 1. ],\n", " [1. , 1. , 0.49044067, 1. ],\n", " [1. , 0.45196101, 0. , 1. ],\n", " [1. , 0.44166691, 0. , 1. ],\n", " [0.43280407, 0. , 0. , 1. ],\n", " [0.42250923, 0. , 0. , 1. ],\n", " [0.0416 , 0. , 0. , 1. ],\n", " [0.0416 , 0. , 0. , 1. ]])" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cmap = mpl.cm.hot\n", "norm = mpl.colors.Normalize(vmin=np.min(areas), vmax=np.max(areas))\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "colors = scalmap.to_rgba(areas) # The color is the angle\n", "#colors[:, 3] = 0.5\n", "print(scalmap.to_rgba(500),\n", " scalmap.to_rgba(1000),\n", " scalmap.to_rgba(100))\n", "colors" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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/rs548/anaconda/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": [ "(array([7.0000e+00, 1.7000e+01, 1.8600e+02, 3.4000e+02, 5.3000e+01,\n", " 2.6000e+01, 3.5000e+01, 5.0000e+00, 2.8000e+01, 3.2990e+03,\n", " 2.6680e+03, 5.9660e+03, 1.2033e+04, 4.6040e+03, 3.4370e+03,\n", " 1.7157e+04, 2.4320e+03, 1.5290e+03, 4.9600e+02, 6.8100e+02,\n", " 2.0300e+02, 1.0200e+02, 1.6800e+02, 2.9900e+02, 4.3800e+02,\n", " 7.4700e+02, 1.4360e+03, 3.1540e+03, 5.1880e+03, 5.7870e+03,\n", " 4.5870e+03, 3.0960e+03, 2.1500e+03, 1.0010e+03, 3.9300e+02,\n", " 1.2300e+02, 6.0000e+01, 2.6000e+01, 1.3000e+01, 8.0000e+00,\n", " 3.0000e+00, 2.0000e+00, 0.0000e+00, 1.0000e+00, 1.0000e+00,\n", " 0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 2.0000e+00]),\n", " array([-7.47225542, -7.35783564, -7.24341587, -7.1289961 , -7.01457632,\n", " -6.90015655, -6.78573678, -6.67131701, -6.55689723, -6.44247746,\n", " -6.32805769, -6.21363791, -6.09921814, -5.98479837, -5.8703786 ,\n", " -5.75595882, -5.64153905, -5.52711928, -5.4126995 , -5.29827973,\n", " -5.18385996, -5.06944019, -4.95502041, -4.84060064, -4.72618087,\n", " -4.61176109, -4.49734132, -4.38292155, -4.26850177, -4.154082 ,\n", " -4.03966223, -3.92524246, -3.81082268, -3.69640291, -3.58198314,\n", " -3.46756336, -3.35314359, -3.23872382, -3.12430405, -3.00988427,\n", " -2.8954645 , -2.78104473, -2.66662495, -2.55220518, -2.43778541,\n", " -2.32336563, -2.20894586, -2.09452609, -1.98010632, -1.86568654,\n", " -1.75126677]),\n", " )" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask = ~np.isnan(coverage['ferr_ap_{}_mean_min'.format('i1')])\n", "mask |= coverage['ferr_ap_{}_mean_min'.format('i1')] > 0.\n", "mask |= coverage['ferr_ap_{}_mean_min'.format('i1')] <1.e3\n", "plt.hist(np.log10(np.array(coverage['ferr_ap_{}_mean_min'.format('i1')][mask ]) *1.e-6), bins=50)" ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[577.9251290226341,\n", " 1265.6398587541692,\n", " 1259.37392295601,\n", " 1260.7368205438765,\n", " 1263.7397193785541,\n", " 1243.774117992813,\n", " 1178.0213783491338,\n", " 1107.826497397283,\n", " 703.7980523037053,\n", " 699.6075663592685,\n", " 237.4354218895415,\n", " 235.95942076741193,\n", " 56.67335343073299,\n", " 56.5970085451056]" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "areas" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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" ] }, { "data": { "text/plain": [ "17" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(np.log10(coverage['ferr_ap_{}_mean_min'.format('i1')] ) >3)" ] }, { "cell_type": "code", "execution_count": 138, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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" ] }, { "data": { "text/plain": [ "28723" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(coverage['ferr_ap_{}_mean_min'.format('i1')] >10.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## All field comparison" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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/rs548/anaconda/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": [ "def depths_sample_field(band, field):\n", " \n", " mask = ~np.isnan(coverage['ferr_ap_{}_mean_min'.format(band)])\n", " mask &= coverage['ferr_ap_{}_mean_min'.format(band)] > 0.\n", " mask &= coverage['ferr_ap_{}_mean_min'.format(band)] <1.e3\n", " mask &= coverage['field'] == field\n", " area = (np.sum(mask)/len(coverage[coverage['field'] == field])) #* 1270. #Return fractional areas\n", " \n", " pixel_depths = np.log10(np.array(coverage['ferr_ap_{}_mean_min'.format(band)][mask]) *5.e-6 )\n", " if np.sum(mask)== 0:\n", " pixel_depths = np.full(100, -9.)\n", " return pixel_depths, area\n", " \n", "f = 'ELAIS-N1'\n", "data_field = [ depths_sample_field(band, f)[0] for band in bands ]\n", "areas_field = [ depths_sample_field(band, f)[1] for band in bands ]" ] }, { "cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.9446658851113716,\n", " 0.994841735052755,\n", " 0.9950762016412661,\n", " 0.9939038686987104,\n", " 0.9950762016412661,\n", " 0.9894490035169988,\n", " 0.6628370457209848,\n", " 0.0,\n", " 0.6640093786635405,\n", " 0.0,\n", " 0.6944900351699883,\n", " 0.6958968347010551,\n", " 0.6893317702227433,\n", " 0.6902696365767879]" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "areas_field" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/rs548/anaconda/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/rs548/anaconda/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", "/Users/rs548/anaconda/envs/herschelhelp_internal/lib/python3.6/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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85hbBNmfdGEokqVuAwMqgrPczQxkhsmEregSAAyLnYiiZx5n3LwmRdcxnB/74oLlfAUAgfSc031cR+4f1ewRDS/4Fm1ICAHDYBmB46VMYVHxHj847JPdoScm+qNDJfCbZzy0pF+U+Ra4nQb5OVl5yQmu4CqSHBxoIIWDLuya4ypB7eczTqRQwtbxVu1fDrTVhbXPsYB4AKMwyV65k0/tEW97Nd5Rj0oDrcOnI1zC66Azsmzcel458DScNuhX9MveRalsWXV0HXV1naZvJxqqm7wz/78ji+sUx38tS4CjAuH7h+RtzbDmYVDYp6jEyk0U9igwR4czPXsBbVeElAb/atQm/+uQ5NPmDtqu9Xje2tNTD3ylqd5erGVUt5pOdaKS00kdE1UQkiCiLiPI6vF5u3f85EY0iomwiOo6Iqnq9U3pT8GWGjHLY5iQfIzyfnA+j7eZNrieiJsEV9hEQWSdDyb3CvG9JimyEHSt9vU+g+R/QvB+0v2+aCc0XntRUCAUleefhgAEfozD7FBww4FMU5ZwWt+jqQJSluI6k3lixUpGVVfqMI0X1Dr9/+I6doECkE7ySdRKUzEkQSmdX606Hk2qagPvr2jcAAN/VzYOqx47iLcjOirk/eE654BGzketQsjGh/BpMLP8DsmyxP2d30f3LoQV+6LP5SLe5N6HW157E+4eGbyM+KxGhsqEybNvSvUu7fc5pQ6Yh29bu7vTrQb9Gnj26G4JUnawov48QAv+ZMB2/Gnpw2HiaMGBfvDDxfBRmBPuxT0E/zDruAnw29SpUlA5Bnj0DT44/G1+e9n+Yts+hMh/LkJRW+pIS2gvoe83l9AZAN8nI3qbsRVH6yPcV4P2ofYO6EXBFXzoT2WdB2KzIVZUYZG9yimRZG6Z7aL6FUDtHYFILAo0zQQYTngz7QIwoew52m3UO9TJ4NPO0FrFyvaUu1qViIfJHXWlQsn5tfJBtGITj8MheiUzD7RHnNLGjLK57G5udPwAA9vp34aNdT8dsL8NmHjQkGzEcrd5vR2zCgTxH7wTtEAWg+b8D9L3QVXOfxlRkeUO4H3BDoBZbXOFeWbW+WrSo4cEsu7y74NG6l7+w0FGIqQOmAgD6Z/bHieUnxj5A4lkUTekDgFFF5Xj02LPw5LhzIAD85fDJmDXpAlSUDY2QHZFfgnvHTsVv9j8KU4Yc2ONJMz8drUbbY5qvCgCg15k747YtqxgsrxB5QU1/i9zufAKkGoevC3vsXFXJjleiiDmQHFGbfRm1NfVGZ0jfCS2Jajv7JMaLmZWobyNh5dSbADKugKLkXQ2Rc2n4RtsQ2IpnQyi5URo0V65U3R+1Lu265iX4vCZ8/P3Q8Bm+rX3LtN1YyKYQ6lG9XAvQfN8EU3QB0LyfJLQvvcWPTUsMtn0vdWxPrJ8n9D8BGUoGJpdPNvTxCzuPRHuxlL42Th5yIC7avwKXHRg7k8Z+BaWYMuRAibOaw0qfxZC2tVWhMwnm0HYAqokzZtvyr9HyivdDQDeqYxkAuecYt9fqW5Wq2IVDSq5vOucnD478GyBskbnPlMyTYMs+LQE9MkaTepD3xQmC7LKtTHm1OkAzTgcFALaCv0Dk/Cb4RimHreRlCHuktSJ4OgKpxvnrOuLVXfBGsdJ+vOs5Q/+7+XvmwNPFqhYdka3nbBpU18to3vdD/6ue92NIpiaN/no0Buojtle5wn0YyzLLUOwIXzkYmj0UOfacbp87x56DUfmjcGTxkaayVj5jLhpZISU3umSgJedjpc9q1PWtfzdEFSFtR1CR00zCr9uWf43StsRK5aJHXjQAIGJEOqYCAUmrTOr5aaUWimM0Mss+h6PwfkAUQTgOQ2a/t5BZ8lxSpQFSTSo2pCZWjm2JB5e2NTiRjYGSfxtg2xdKwZ0QtsHRBfW6YOCZCS2BerSokfcwIoJTNXad0UiFR+1+7jrZ5d1ETihJd0Pt4M5D2hZo/uUJ609vEM0lo/N2IUTEEuxJ5Sf1+PyHFByCkgxz44hi4WRxWJ6c24tikS80K30WQqQCbVnpAzEuRn9rdTh1c+wkm3prCLqRRS/rVCDKA1Zkny3R29TDJ1FvFQB0q1NWMBEIYYc951xkFD2AzJKXoWQckeguRaD1yaVbK6N3Ja4TdVPQVzgGQjig5F4CkXlCTDnStoC0KtNT7vJsQI0n8pxCCBxUMM7gCGBA1r4oyey+JUTa0iepHPYGqve9iOA/1f1qgnrTOxRnlEIxUEv6ZUb6op/U/yQU2IPBMuWZ5RhXajw2usLwXOuqt0SL3u2MXYmvGsZKn5X4l4aWYsm3MKoY+dscVXXA/210uUDQakgGVkNhGwRR8PfIg3Iugcjs+eBPRpyq3PKNrEWQiY1RpY3OCNsgCMUs2W5i0GT8x/qkNVAWiXx46rr21YsYiIxJpg7mFFgL6PUgLXat0c3O5djoNK6GcHz5DGQq4Ut4AgomD4idBsacnuXpiweqa1bkNs87IJnAwRQhy5aDA/Mjg33GFB4TsS3TlomTB5wMAJg6cKqpH54MbUqkFcj49CUCVvoshDr4W8D/HUiLrJ1I5Ae8n3c45sMImRCB1hB0v3Eousg+E8j9XfuGjPEQ+X/uUp9TiQ0tm6QUP7WLdV2ZKJhFlwPQA2uStiSURzWP3tVJTzF3AAstfTKRy2o1oO0wD3SwmUer6v4lYX+NWN+8BLu8G7DZuRzb3JFVFoozBuCC4bcjszVQRIENZw65AfvkHWZ6/ljI+urJWgStRvMvha4aVZ3wIeB+Pe796U1OLA8vHVrkKMWRxRMMZceXjkeGkoFflvzSknM7FDm/cRkrXrLeV1jpswjSW4LBFSE0wPNmpKD3E4A6PEy9n4MMHKUpsArQWgM91PWgKEssIu9GwD4KEPkQhQ90uaZpqqDqKr6uW4wFtd+YyrKlzxqijblUoUU1z5epkyYZ8JFCyH6eGPk/22WaAaimVYaEiJ0AmXQX9NY8jrrHeKLr09z4eNdTbUfgw52PGy7RD8s9GOcPvw2AwKmDrsKhRdGT6MriUiMn6MmE5q+Muk+PsS9ZibWKMCL3QBxccFTo/ZQB02FXjP3RCx2FOKbfMci0da+6SWdkrYWqbq7QebXETBDMYKXPKjxvRkTZkvuViGTJ5Hqx04EBkPvliObI9d+Y79sQwg6RewWQfS6ErV/X+50C/Ni0Gn9bfQ82u7Zg7tY38dzmF1Hvi76ksaub5XiYdkjfCxgk2O2MkjEWQpEpcxV/nBJKn1f3wi+ZCigpkFlelE0rEiUVSzitlo8eBoHpnv+FFEfd92UwmK1jV4jwwc5H0aK2T4DrfFvxaY1x3tHhuaNxZPHJOLJ4So/61YZHk1siDSRorNhilM1UHCmYistk7E0uD/qll2T0x1FRrHxtjC6If0nRgG4+sfJqgYhqGskAK30WQe7XIjfquwFfeyFl8v8AGCXU9LwWphySWgV4O4Xje/4H0qIoMxnHQmSO70avk59Xt76B+9c+hK3ubQCC0XMLa7/BLav+gvUtkZYor+bDOzveT1rTeipApENv+jN056OgQOxyT0qU9ByJRicdzQHzUkUtgSY0ByQq6CQNEgpdlAoaEWgS+URt5cFVhB5EZRP5oDqf6bBFhep8MkxmZeNnWNMcnpQXAJY3fIh1UUqtHV58omXVXWRze7rV6OlrehNb5gTY21LjdEBxHAFH3u8T0KMeohlnmGhjaM5+GJQ1HL8oOc402X7/rP5W9kwKGWXOowbgUZMv+TsrfRZAgfWAZpyKgDokq6WoZYvqAX/7jY1czyDSydoPilJtQ9j69Vml77SBp+Di4RfiwPz9AQCZSibGlR6Da0ZeiZF5+4bJrmhchbt+vg/rWjbg3jUPYH1L9LQ5jDEU+BHa3gtAvi8B+KHtPR+66z9SQR3JRIvaiKaAufXGp3ulkjgnDbrMkqykj2WUe1YYtsGAbYhce9FO4341IgOB7n4DpG4LvTfy32tje5R9nQM6eoIimQPUGYgdhNKbZBTcBXv2OaH3in00skpmQwjzMnNJh2GO2XAOyD8MB+SZlxvLUuL/+WWUuSa/B43+7lUI6U1SO3FbshAruq3jvhi5+6BuADIntAZ6RAnu8LwLyr8NIo3KjOU78jF5wAmYPOAEfFP3HQ4uGIXijKIwGa/mw/1rH8QGZ3vi13UtG3DXz//EEUVjcMMB18at3msqQ4GfoNVPB9BhCZFaoLfcB6FuhK3wvoT1ravs9FRjl3cbdNJjWgq8mgfebpZuSggmvnUAghGyRObRtP7l5vYtZQBg635xdyKC5nrBYI8KzT0b9oK/AABGFRyLVY2fR0gJKDiwIDJyEwAyFOtyQqq61/Q7I9Kx1fUdDu93oWXn7QpCKMgovB+6tg26uglZJbMglMKE9KXHSLgglGcNRXmW+UqCFVG7bciuEDX4PRhhIrO2aQ+aAl4Mz0uuogjpoz30JjFniR32KUXRxUTrPr0huoM1Ncnd9Psovyg5MkLhA4AsWyYu2+dinDvkLIzIGQYAODD/AFw4bDpmDD+PFT5ZbPtCyb8ZcIxp3yaKILIvgGKwtJSs6KRjUe2HcGstWNYQPXUSADQFGtAUMI9SThpkgi90p+kSL+kuwL80wue4M0IpBET3fTZJ3dAekNa5m975of/3zx+LMUUnR8gcUzoNQ3IOMjw+w2ad0ufVGuEziUL/ufFd7PKsRLUzepqt3kYIGzLyb4Yj9woIiYjppEWiBnxJRhmyLPyNZQhIpuTZ7Yntk0hEeLtqFd6qWmVFtyyFLX1W4DgCQf3ZYJaQ0V5iRWRNBXXIqN6+IxvIOj74v1IGKP2N6/fahiZtTrR44IihXA/NGYyhOYNxxuBfoXLvD6goSb5kwcmOUHIgci+Hkns5dN8CkPdDKPl/g7BwGa23aQ40YO62p7HeGbzZvrHtWbSoTTiu7PQIi59P86Jy77fIdxSgouRYSy0GvQWR29w6Rx6AvABi3CvU9QD8weTLjlExGhPokW0gRg456lRVaMrAK1Hj3YTd3qDFfnjOoZjU/6Kox9ssrDDkVuvgVuuQZYvM0xbQ3fi+9j/4qTFY3/fTHX/Fkf0uwuH9ZsAmuSxsJYrjKECkzjVpiBKjcksr+XY5K6aVyfh9mlwQVI0n9gThuXWLsbqxBqsba/D+1tU4bdghVnTPEtjSZwHC1h/IOsVgjx0ip8NSQObJgOOoCCmRezWEEizFIoQCkXu18Xlyr7GiuymLrMXu0MIUjGZLMpTMSVAK/pFSCl+jvx7/Xncz1rW0Rx3r0PDhrlfw3+qHwmTXNf+Ef637K3b7dmKjcy0eXn8ntro2x7vLXUfG0gcdptU22mrgqiafmdw9Wl0Q9pGI9pgRjgPC3juULJw79DZkKNnIsRVi2tD/ByWGIi5bi1uGGu+P2O35MWK7T3Pizarf4qfG/6Et/6EOFZX1L2Je9e8TUotXCAWKbZ+4n9dSJO4rdsmceVamXGpWzSPam/weLN4T3R92U3MdHvyp3Yp9W+X7aPAlzwodK30WIfJuBNApV1DOBRD2Ee0yQkAU3gugg+Op/WAg94pOx80AMo4O35Z5AkTONCu73GexKmdTupNqvqN59gIcUlgREYmZY8vHoYXtyVu/2P0BHt/4D9R4d4a2Vbk24l/rbseyvYvj1t/uIfGAE1nBVyzaLHAm1RxIbwy6lXQTYSuFkmWcVsWWE+kbV5jRH4cUTsIRxacgx6Q6gmzErRnVzkXY7VmFlXv/C68W/lkdShYG5RwBdDqXAhuG5P4icddIKgZvdMRCZVnVrQsy2+beZirz2ubl+LpmE7Y6jX1dM2y2sFUFh2KLe6m1WCRPT1IcYR8WXh1DKYHIu95Abh+IvP9rewdReDdEp2WKoHJ4PyAK2tsqvLeXes4wfQO74sD0oVfjxgPux/CcYLT3pLLTMPOgx3BkcXt0+y/7TcTx/U9FZoeov1xbHk4ZeBZGFya7W4DEErRSBAgTN5A2pcFsmZCapSqzxMKe/+eIOuHCUQEl63RD+QPyf4kDCswrLFihcG13LcGXu4Ki4YfuAAAgAElEQVTlLJ3qbny0/UZ41PaHuSLsmDTgFkwb/gzKsoLL4ENyKnDuPrPxy7Ire3z+7pPqfsrmippsUIVsTXYZ1resR4M/euDSj3t34rGfFyBAOm5c8hb8BgmYh+YW46ZDjw+9v+uoqch3JI+SzkqfhYjcKwClpPX/30dPWpt7aTBwI3MyRJSkm8I2IKQcirzrIZTkigBimGRlYPYwXL3f7Zg2+Lc4fdBFEc7gefZ8TBvyG/ztkIdwcMEYjC2ZgL+PfgRTB56NTFvy3JyNEK3lx2JiG2SuENn3a/27b2w58skne46CsA+FLe/GDlsyYS+6L6q7RlnmcJRlDuvROWXY6vwWn+74M7QOn6/etx7vb/8DvFr4Ml9Z1oE4Y+ijOLjwDEwd8gCKMhKbnzL1g9PMLdaaZJqoFtWaMpBu1Y01LWvwQ+MPUWVmLnsvlKNvVcNOzN74vaHcJfuPxeCcQhzRbwhOHZpc7kas9FmIUHKA7PODs+ycc6PLiWwga0qwdm4sss8LOrxmn2VxTxmmb+NQMnBsaWQ0aEfyHQU4d+ilOG/YZUmv7IUQMTIAtKFIVOZxjAGQCZhVcxCZwVcPseVeBmEPOrPb8q6GEkPZzLLlwRGH3Gvrmt6HbmBxavJXY5d7ecR2u5KJCQP+lHJuD0mJhG+qKllOc6dnp7mQBB/XfAy/7seHuz6MGtChdbI+RivHZhMKDi4agEOLB1rSNyvh0WsxImsykDnBNIO9KLgDyDwxtoySB6X/fNO6lgzTFYjMi4WnC8UZ/ZChpND1pcT2cwti7gAvlLzgxFNkmJyvNJhRoIcIocCW/0dA5MPW0Q3GgExbfIKHxpffjKG5x4Zts4kMjC39P+yTf1xc+pC26OZ+on6JZVsiwrKGZT3uzibnJnxYE8yPW++vx6vbXjWU+/2o9pJwhY4szNivwlCOiLC5pR6bWhJTwSUWrPRZjf1ACIe5X5AQtj5gomdSk+QrDZQolFS7BcqUQ5OK8AVE5nHmMrbBgG2QVHtmKJnHQck+TW6JOg5k24sxZfA/Mb7/zRCwocAxGGcOex6HlSQm+XJaoZlX5PDo5hGvG5wbsNm1uUfWPpfqwlObngqLAl5QuwCL6yODuk4ZcjCUVn/K8QP2Q0FGpEVaIx33rfocm1rq8M3uLXh+nXEZwUSRYne85EcIe3hyW4ZJNqLVcE5DUm/iZWKZAwBd0rpg5s8HALYBEDZrlqiEsMOW9WtL2gKss1iPKjoDh5VcgJMG3YvizBGWtJmuUMA8GTGRDmg7TOV8msc0Mvedne8AAN7d+a5cBw14a8dbqPdH1gJ+ufpleDpV61nTWAO9NXXP+qY9Ecu7W50NOO/L2Xhh/ZLQtvtWfY7LF76Cep9kTexehpW+3sCimTHD9AbU8kCwIgOTesjkppNV+oSExU3km0cCdwElc6xlbVmpsB9ZchlKMiWUYCYm5HoeZJY3j1xSaYA8mhseLfp96ovdX+Dn5mBd5iV7l2BJ/ZKosrFY2bjScLtLc2Gjc2O7XP0O/N+3r4feb2iuxR8Wv4GWQHvt7nnVq7Byb6RCu2j3Zny5M0a51jjCFTl6g1Sth8j0aUh3glzPAr5PQc02IP/PlllxmHghMU8nH4jUiFRQkXIyy8AapHIDpjg2RcKCysSEtDrA+ymQvRjIHB9dUG8A6Y2mSWda1EY41SbkOyKfp5udm/HattfCts2qmoVhOcMwMLtr97SZB82MmuC5yBEMnNrUXIcLF8xGQA+X+2LXelzx9at47fjLAADXHTIJIwvK8OBP81Hdmsfv4KIBuOnQ4zFhwH5d6ldvwZa+XkCkeuJMps9Brjmg2hMA19PBDd6PQLWToTf/AySZGqEv0jeDWhRI5XGTWGKDthukGZSEZJjOBJYB0EB+4zQmIbTtUesxd6TWtxN7fJFjlIjwQtULUDvdt7y6F7OrZ3elxwCAkowSlGWWGb4crVVBhueV4IoDjkGOLdzSvk9eCa45aGLYtqlDD8YHJ1+F04Yegkv3H4u3T/pt0ih8AFv6GCYtIHVNaz3WjqiAtgWgFkAUJ6RfiSb1fPo0mCdoVqXkKLASIit2BgFS1wDqz13pIBMHiCj5xq5aHfyrVZvIbQi+YkBE2OxcgxxbPsYUHRO2r95fjx0e4wnLupZ18Gk+y6sy2RUF148+HheNHIu7V36Cz3esw8wxJ2P6vkfAZpDCJ9Nmx0NHJ2eqNVb6GCYNUArvAeXPDC7ttvwLIvcyIOssCJtETjcmedBbALPfTG8IvmzlUUWIdMD7EZB/Y1QZAIBvMaBuApGXVzDSGBl3AdJbI2i12JG0pK4DtO0g3RlMHWTAupaVaFYbsLLxW5w2aEZYWqWSjBL8e8y/o7bfm2U4+2Xl4sGxZ2FNYw0OTsIcfDLw8i7D9BGIYqdiEUouRPZZECWvQuT+jhW+FIRUc2dw0qrNrS2BVYBWDQpEb4+0nYC6GoAX8H3dxZ4yfQrymbtC6K3ly0zqOUOtCv6NMkZ10vFRTdBfz6W1YEHt+2H7FaGgJKMk6qu3EUKkrMIHsNLHMH0HbTdIi0w90Blh7/0SV4z1kO4C+SUiFANrgq9YbXneCvtriPfjdvkO/zNpCLmCr1gEfgr+1XaAYtVrbrMERvEpXVT3IXZ4toTef7H7bezxSvifMlKw0scwCcQ0vUFIzjzggDyvAZ7XTOWYFEVd22p5M5PbBFI3Rt1N6nagTdnzzAVptcZy3o/a3/i+APWwBi+Twmjbgq8okLq1w34d8MdISKzXhv/twF7/Hny8a27YNpUCeGP7s13tMRMFVvoYJqHI1ZcEjGs8AgBp9dBb/gW4ngM5HwU5HwO1LbUwfQYKrAYFJIIqyBnTKkMt9yBUlYVcoJZ/Rsqo24FAh/xl5AJ8C7rYY6avQN7PQd7Pogv4vw2X931j3I7uROieZ3CPWlj7IQIGbipbXGuxyckBRVbASh/DJBTZlCHGyiH5l4LqpgCutpkwBZW+2lNAfuOko0xqQupGQN8N0ptjC4qC4MuoDe98wPdF+EbvuyBfp2Vj73uRx3q6X/WASV7MVhFIdwdXENxzovoNk29h+AbfoiiNtSdlNhrHuzzRfVFj7WPk4ehdhkkk5JGrp6rtAYx88URmsOyffzGCqToAwAFkjAEEJ5w1IylTX0RDa/Vz0qoA5bDocrYyCFuZ4S5yPhZ1u8j8Zft7z9uRQr75IL0BQknP9D59FxVAjEovgR/bLcfqOsBxaNhu0nZHWoH1nSDfNxCZ48K3d3Ql0CPzP5479Cp4NeOauyUZ/aP3kZGGlT6GSSR6I6BIRJz5FgL230RsFo7DIEqeB2l7QM4HAWRC5F3Hkbl9DCINFFgb/D/wE4QjhtIHB4we4qQ3AOpPxocElobSslDgx6BiGSkEeD8Bcs7vavcZyyFIJeCWaqoFEDHuQYFl7f/7l0UqfS33wWglglr+AWS8DdGhdCB5O1iLfd+C9L0QHe5/pZkDutx9pmvw8i7DJBKJqgjkXw5yvxBzWU/Y+kPk3w5R8BdW+Pog5H0XoKAPlO5+BUQxfEG17SBDp3sHoisKdoQeB/6l0fsRYx8TT6L7+LZBUmX2APiiB10QaSDPvPb3nv9FLgdHGxPqllCwBhFBb/k34J7TofFG0N7fBINAmLjBSh/DJBDyLwZFVMrosF+tBjXeGHyQN90S9K+JglBywmbVTN+AAj9Bb/57+wZ1LfTm2w19sci/AtB3Ab4vI/yvhJIHZB5vfJKsKRBt7gCxlAVZRYLpZSR8gVXzUmfk+wrUcm/0Unved8Ktvuo6wPdJmIgofgFQOk80HRBFj0LYBgXf+hcBrmcM+rgR1HFsM70OK30MkyBIdwGe9wEj/ykA5HwCVHca0Jbp3vclqO6USKdpps9CeiO0ht8GI3I7bve8AXI/H76NCNTyr+AbvRZwvRjRnsj/c6QPqciDyL+5/b1jTNT+xF5WZuKHhKXP+y5IbzLeR75gwFfDNYC+B1Q/DeQNV+ZId4JaHo48tuXfYRNV4TgAougRdLQii/ybw0r8icyJEAV3dlIOBZA1FaLwHtPPwlgHK30MkwBId4Ia/w/Qa0DN94K8X4bvV7eDnE8D6JQbTa8BtUQvQcR0jaQP4lC3ALpxwm3yV4Zv8LwCBNqL3ZPz8Yh8fcI+AiL/lvBtBX+FsHWoMJAxDrAfGHlCkQvkTO9a/5mEQJ4PANcLoMbrI9xCSN8LqjujNainLX3KHlDjH6A3/qld0PMaoNdENq5VA57/hW0SGWOBrDOCb+wHAjkXRRwmcs6HKP0UyDwBEPkQxbOgFD0MYWM/vnjCSh/DxBnyfw+qP7NDAlMfqPH30JvvDS3JCfsQiNL3Wm+erTVPlUEQ+bdClLyckH4z8UdkHAEl7zpE+OLZR0LJnxl6S1oNqOWBTkf7QE1/iWwzZwbg+EXwTcZEiOzwwvBCCIiC2yPOKfJuCHO6Z5IP0nZBb7gG1HQDgADg/wZUd2qYXx6QAdiMyojZAduQDm1F9zcmg/q6Iv9PADIg8v8MIWyGxwklH6LwAYi830NkHiP3oRhLYaWPYeIMuV4CtM7Oyzrgfg3oYJkR9hFQCv4K0e91IOOXEKXvQ+T+NmqRcqZvouRdB6XwYbQp/yJjEmwlb4SV0yP3HIAM/D0Dyw2DL0T+H1v/Xm94TpHxCyD77PYN9tFATmT0OJNcUOOfAF+nJMp6bdAf2B+MwhVKHpSSWRBFjwGiMChjHw1R+i6U/Bs6NBajAotBvj5hGwDknBOZpqWznJIPZE6S+jyM9aS00ieEyBRCPC+EqBZCtAghfhBCnNq6b4QQgoQQzg6vvya6zwwjih6GyP9/4f4tmSdClL4F4Tg4Ut4xCqLoEVb20hgl+1dQCu4A7PtDKX4q+ODsSKxauwb7RMbY4ETCMTrqYSLvhpD/nyj4M4RI6cdFH8M4kEMUPdi+zNqGMhii8GGIjKPCZbOmtPpyCojiJyHsI8P3Z4yPenaRcazx9sxTzLsOAEqpnBxjOamep88OYBuASQC2ApgK4HUhRMdEQkVEpBodzDCJQAg7kHsZkH0uqOEqiNzfQWRFiapsO4aX1dIekT0Nishoj7LtiG1w9AOj7BPZ58Y+n60MlHUaEPg5qCQyyQP5AJEVsVnYBkAU/Qt6yxDA9SQgiiFK50EohcbtZB4H2EcZ+9VlnQK4RkfmdnRURL9f2YfK9V/kyMkxlpPSUzcichHRHURURUQ6Eb0PYAuAo8yOZZhEI5Q8iMJ7TRU+hgEAIRQo2WcY78s5D4Y5+JTBQOYE4wYNrMoR7WadBpFzThd6ycSDaFG5bYjs04L/ZE6KrvChNb9n1mTjfUKBKHqofQkYAJR+EEX/inHi6OcKb5urBSWKlFb6OiOEKAdwAIDVHTZXCyG2CyFeFEIY2pSFEFcKISqFEJW1tbVGIgwT4tlnn0VFRQUqKirQ0/Ei7MMt6hWTjFg5VmIhHIcA2RdEbi/4a/QHrGJe1kpkHhMM/GDigux4IX1vzHaEfSQgciE6Vc8wJCN6QIWwD4coehhBVcEBUfRYe+49wwNYmUt2hFmx5VRBBLPSfgRgExFdJYTIAzAKwAoA/QA8ASCfiKbEaqeiooIqKytjiTBMiIqKCvB4YWTo7bFCugtUfzqgtSblzToLStE/e+18TO8SbbwQ+aH7l8JmEjCh150Fkf8niMzovnlAMPLbLG2K3vRXQORAKfh/sdtKpVrWfQghxDIiqpCR7ROWPhH0MP4vAD+AawGAiJxEVElEKhHtbt1+shCiIIFdZRiG6RWEkgvRlsZF5EEU3JrYDjG9hA6pihyOMRF1cg1RikxFRNapEFlTzeVY4Ut6Uj2QAyI4yp4HUA5gKkUvStl2lfCoZBimb5J5ImDbB8icwME/fZYMQGSaSonMcTH9+doxzqkXLjIkmJybSXlSXukD8BSAgwCcRB0qTAshfgmgEcAGAMUAHgXwFRHF9oBlGIZJUYQQoMzjITInJrorTC8hhAJhk/AFth9k3UmVwsjyfUxKktLLu0KI4QCuAnA4gJoO+fhmANgXwMcAWgD8hGA9q0hPZ4ZhmD6EcBwEOCx84DNJh2IzD8KBUizZmoTtJ1qqICblSGlLHxFVI/Zy7avx6gvDMExSYBsKIf3AZ/osUUqhdQ8r22ISSUpb+hiGYZhOdKqswKQr1rmvs5Wv78BKHxOT4447DsXFxfD52uswXnrppfjLX9oLua9evRoDBw7Ev//9b9Nj247PyMhAXl4eSkpKMHnyZKxduza0f9asWRg/PnqagTvuuANCCLzxxhuhbaqqQgiBqqqqiHO0vcaMGQMAqKqqghAitH3EiBG47777uv7lMIYk45hpbGzE5ZdfjgEDBiA/Px8HHHAA/vnP9nQmQgjk5uaGjZf7778fQHC8ORwO5OXloaioCMceeywWL17cvS8nDgglfRMUjBgxAtnZ2cjLy0NxcTF+9atfYdu2bT1qs+1+oaqpVtgpua1zr7zyCioqKpCXl4eBAwfi1FNPxddffx263vLz80PX6rXXXotdu3aFjv3qq6+gKErY9Xr66acDML/WO7N9+3acffbZKC0tRWFhIQ499FDMmjULADBlypTQfQAAduzYASGE4baamhp89dVXGDJkSGjfcccdh6ysLOTl5aG0tBTTpk0L+xwd7y1tr6Ii82jqnsBKHxOVqqoqLFq0CEIIvPvuu4YyK1aswPHHH4/bbrsNf/rTn6SPveWWW+B0OrFjxw4MHjwYv/3tb7vUt5KSEtx+++3QNC2qTNs52l4rV64M29/Y2Ain04lXX30Vd955Jz7++OMu9YGJJFnHzA033ACn04k1a9agqakJ7777Lvbbb78wmZUrV4aNl1tuuSW077zzzoPT6URtbS3Gjx+PadOmoa/kOO1rvPfee3A6ndi1axfKy8vxhz/8IdFdShCyj/f4K7MPPvggrr/+esycORO7d+/G1q1b8fvf/x7vvPMOgOD11tLSgr179+Ltt99GTU0NjjrqqDCFadCgQWHX63vvvQdA7lrvyEUXXYShQ4eiuroa9fX1eOmll1BeXg4AmDhxIhYsWBCSXbhwIUaNGhWxbf/998eAAca5Dh9//HE4nU5s3LgRTqcTN910U9j+tntL26uxsbGL32bXYKWPicpLL72Eo48+Gpdeeilmz54dsf/777/HSSedhHvvvRfXXnttl45tIzs7G9OnT8eKFSu61LdTTjkFGRkZmDNnTpeOM+KYY47BIYccgp9++slcmIlJso6ZpUuX4sILL0RxcTEURcGoUaNwzjldLy/mcDhwySWXoKamBvX19V0+nokfWVlZOOecc/Dzzz8DAJqamnDxxRejrKwMw4cPx9133w1d1wEAuq7j7rvvxvDhw9G/f39cfPHFaGoKJnqYODEYCV1UVIS8vLyQlfeFF17AQQcdhOLiYkyZMgXV1dUAggmKb7jhBvTv3x+FhYU47LDDEnRvkVzeJZ+5jIU0NTXh9ttvxxNPPIFp06YhNzcXDocDp59+Oh544IEwWYfDgUMOOQRz585FWVlZxMqAEV291pcuXYpLL70Uubm5sNvtOOKII3DqqacCCP7233zzTWicLFq0CNdffz0qKyvDtrWNkVgUFRXhzDPP7PKzzmpY6WOi8tJLL2HGjBmYMWMGPvnkE+zevTu07/vvv8cpp5yChx56CL/73e+6dGxHXC4XXn31VYwc2TU/JCEE7rrrLvz9739HIBAtNaM5RIRvvvkGq1evxhFHHNHtdpggyTpmjj76aNx222148cUXsWHDhq5/sFZ8Ph9mzZqFIUOGoLTUsKojkyS43W7MnTsXRx99NADgD3/4A5qamrB582YsWLAAL730El588UUAQfeAWbNmYf78+di8eTOcTmdoUrJw4UIA7SsDxxxzDObNm4d7770Xb731FmprazFhwgRccEEwOcSnn36KhQsXYv369WhsbMTcuXPRr1+/uH9+6UTJ1Ny7HenE4sWL4fV6cdZZZ0kfY7PZ8Otf/xqLFi0yle3qtX700UfjmmuuwWuvvYatW7eG7Rs7dix8Pl9olWjhwoWYPHkyRo4cGbZNRumrr6/HW2+91eVnneUQEb86vI466ihiiBYtWkR2u51qa2uJiOjAAw+kBx98kIiILrnkEsrPz6cRI0aE9sse23Z8ZmYmFRYWkhCCRowYQStXrgztf/HFF2ncuHFR+/a3v/2NZsyYQUREY8eOpSeffJICgQABoC1btkSco+118cUXExHRli1bCAAVFhZSUVERjRo1ih555JFufU88XtpJ5jHjdrvpnnvuoSOPPJLsdjvtt99+9OGHH4b2A6D8/Pyw8fLxxx8TUXC8ORwOKiwspLKyMjr++OOpsrKyy98Pj5XeZ/jw4ZSbm0uFhYVks9lo4MCBtGrVKlJVlTIyMmj16tUh2aeffpomTZpEREQnnHACPfHEE6F9a9euJbvdToFAIHS/CAQCof2nnHIK/ec//wm91zSNsrOzqaqqir744gvaf//9afHixaRpWrc/S7zGi+5bEZfztDFnzhwqLy+Pur/j/b0jTz31FI0cOZKIiObPn09CiLDrde7cuURkfq13Zu/evXTrrbfSwQcfTIqi0JgxY+j7778P7Z80aRI9/PDDVF9fT4MHDyYioltvvTW0TQhBVVVVoX61ybQdm52dTQUFBQSAxowZQ9XV1WGfte3e0vY67rjjZL7GMABUkqSOw5Y+xpDZs2fj5JNPDlkzLrzwwrAlt2uuuQa/+MUvMHnyZDQ0NHTpWAC46aab0NjYiKqqKmRnZ2PdunWG/Xj55ZdDDq5tJveO3H333bjnnnvg9Xoj9rWdo+3VuQ91dXVoaGjAmjVrcN1110l8K0wsknnMZGdnY+bMmVi2bBnq6+sxffp0nHvuudi7t71w/fLly8PGy5Qp7WW6p0+fjsbGRuzZswdffvkljjrqqB58U0xvMm/ePDQ2NsLn8+Hxxx/HpEmTsH37dvj9fgwf3p7UePjw4dixYwcAYOfOnRH7VFWNam2urq7GH//4RxQVFaGoqAglJSUgIuzYsQMnnHACrr32WlxzzTUoLy/HlVdeiebm+FrTuoSIb+a2fv36oa6ursuBMTt27EBJSXuVmUGDBoVdr9OnTwcQ+1q/9957Q/eGq6++GgBQXFyM++67D6tXr8bu3btx+OGH48wzzwz57E6cOBELFy7EokWLQsFi48ePD20bOnRo2NjpzKOPPoqmpiasWrUKDQ0N2L59e9j+tntL22v+/Pld+l66Cit9TAQejwevv/46FixYgAEDBmDAgAF46KGHsHLlypBJ22az4eWXX8awYcMwZcqU0E1N5tiODBs2DI888gj++Mc/wuPxROyfMWNGyMH1o48+itjfZmp/8sknLf4WmK6QSmOmoKAAM2fOhMvlwpYtWyz+JphkwWazYdq0abDZbPjuu+/gcDhCfncAsHXrVgwePBhAUIHovM9ut6O8vNxwmXTo0KF45plnwh7WHo8Hxx57LADguuuuw7Jly7B69WqsX78+wlctqbANi+vpjjnmGGRlZWHevHnSx+i6jvfeew8TJkzo0rk6X+szZ84M3RuefvrpCPnS0lLcdNNN2LlzZ2hCOHHiRCxatAgLFy4MnX/cuHH45ptvpJd2AeDQQw/FX/7yF1xzzTUJDQJjpY+JYN68ebDZbPj555+xYsUKrFixAmvWrMGECRPw0ksvheQcDgfeeOMNlJaWYurUqXC5XNLHdmTy5MkYNGgQnn322W7195577gkLoWfiT7KPmbvuugtLly6F3++H1+vFI488gqKiIhx44IGWfH4m+SAivPPOO2hoaMDo0aMxffp03HbbbWhpaUF1dTUefPBB/OY3vwEAXHDBBXjooYewZcsWOJ1OzJw5E+eddx7sdjvKysqgKAo2b94cavvqq6/GP/7xD6xevRpAMDihLYXU0qVLsWTJEgQCAeTm5iIrKws2W/KmTxFKflzPV1hYiDvvvBPXXHMN5s2bB7fbjUAggI8++igsYh4AAoEA1qxZgwsuuAA1NTW48cYbTdvv6rV+66234qeffoKqqmhpacFTTz2FkSNHhvwwjz32WDQ2NmLOnDkhpa+4uBhlZWWYM2eOtNIHAJdccgn27NkTNbNBPGClj4lg9uzZuOyyyzBs2LCQ5WXAgAG49tpr8fLLL4eZ5TMyMvDWW28hKysLp59+OmbNmiV9bEduvvlm3H///RH52WQYN24cxo4dG7H9/vvvD8t/xI73vUeyjxkhBC677DKUlpZi0KBB+Oyzz/DBBx8gLy8vJDNmzJiw8XL99df3/Ith4s7pp5+OvLw8FBQU4LbbbsPs2bNxyCGH4LHHHkNubi723XdfjB8/HhdeeCEuv/xyAMDll1+Oiy66CBMnTsQ+++yDrKwsPPbYYwCAnJwc3HbbbRg3bhyKiorw3Xff4ayzzsKtt96K888/HwUFBRg9enTIqtzc3IwrrrgCxcXFGD58OPr16xeRpiPdufHGG/Hggw/i7rvvRllZGYYOHYrHH38cZ555JgBg7ty5oZx1Z5xxBvr164dly5Zh0KBBpm3LXOsdcbvdOOuss1BUVIR9990X1dXVYUpZTk4OjjrqKPh8PowePTq0fcKECdizZ0+XlL6MjAxcd911uOuuu0Lb2j5rx9eePXuk2+wqIpFmxmSkoqKCKisrE90NJkWoqKgAjxdGBh4rTFfg8cLIIoRYRkQVMrJs6WMYhmEYhkkDWOljGIZhGIZJA1jpYxiGYRiGSQNY6WMYhmEYhkkDWOljGIZhGIZJA1jpYxiGYRiGSQNY6WMYhmEYhkkDWOljGIZhGIZJA1jpYxiGYRiGSQNY6WMYhmEYhkkDWOljGIZhGIZJA1jpYxiGYRiGSQMEESW6D0lFaWkpjRgxItHdYFKEqqoq9NXxQuSBENmJ7kafoS+PFcZ6ej5edMTfrkMAhISYHyDlkjMAACAASURBVBAZvd6bdGHZsmVERFI/tr23O5NqjBgxApWVlYnuBpMiVFRU9Nnx4vOvQmbGYYnuRp+hL48Vxnp6Ol5Id0MoOeZypEKI2KqAjExQzgshsszl1E0Q9v1M5Rg5hBDLZWV5eZdhGIZh0haZ1T7JFUHSJeUCcnKM5bDSxzAMk2DYzYaxHtkxZaHSJy2nSsoxVsNKH8MwDMP0OaycSEj46XXpnDzJSRSs9DEMEwXZGz3Tc/ghyCQKmbFns7AtyC8DM5bDSh/DMFFgpS9+pLbSx8vTqYyVv52sMsdKX6JgpY9hGEME3x4YaVjpSz6sXGqVVdI0STn26UsUfFdnGIZhGKbnkJzSRxy9mzBY6WMYhkk4qW0pE4IfJcmHrH+djJzs78uWvmSHr1SGYaKQ2opIasHfNSOPqnvifEZJ/15iZS7ZYaWPYRhDCLwEwzDJiEreRHchCrJKn2w0MGM1rPQxDGMI+93EE7b0MfLoUha1BCRn5ntG0sNKH8PEIKDK+qj0PYjcie5C2kCs9PUpejuFjS6lXCViTPkScE6mK7DSxzAxECJ9c9Xpekuiu5BGxP8BTZIJcjkHX/JhraVP6oxyYiSr9HGevkTBSh/DxCCdLTCaXpfoLqQR1o0z65W09L0GkhUt7j59kiserPR1GT3OkypW+hgmBrqeng88XXdB1xvifl5/IE2j/5LampbMfUtO9F7+zjTyS0hZ6NMnG5WbtAEmyUu815JY6WOYGGhJ/TDuPTR9LzR9b9zP6/enpw8lSec3s/asTO+g92JtWZ20BPj06XLuANJKH4+9NuL9TbDSxzAxSNdADk2rgabtift5lbT1oZSzpCTGv44f0F1F9KL9RqeAnNIn/bPJRu/KKH2S+QPTdDJtBFv6GCaJCGjpqfSp2laoarW5nPdzkJUJWdNU5yPJ8lVMatCbvsA6qdAstfRZ2FdZpY8nEiFUPb7+jaz0MUwMVC09HY4DgXUIqBtNLUukVoG0GsvOq6Xp953cZan4MdGGrKVVxtLXXautTn7J6F3Za0lmwiEgNyOTDeTgSU4bXjW+1z5fzQwTg7oWV6K7kBB8/pUgciGgro8pp6troavrLDuvxyPjoN73kE+EbaWFhK0tXUU2hZNfN3+Qd9caqFEAmlSUrGxCZRnlUEBKXZC29PV9pU9WqdckgwWtcu1gpY/pFn7n0wi4X090N3qdFdW7Et2FuEMUgM+/DADg9S2OKav5l0D3L7HkvKqmo8WVnsldSdrSlwilL03X3HuAJrFc391UHRr5oEtF70q3KCGjSCm8JBvIkQbuDLITBNlxYFXOWFb6mG6h+5dA93+f6G70Oj9uq0m7YA6v7zsQBS2cHu+XUeW0wE8gbStU70eWzEI9Hn/aWvrkC9XL3Pit9eVK5wTl3ac3Azn8kilbZJd3LXSpkM7Tx+Xa2nD75e55mkW+f6z0MV2GyAvN/x0031emYfypns1/d5Mz7ZZ43Z73Q/97vAujVuZQ3a8AAEjbCs2/qMfnTWelTz5li5XKRGpfm8mMXzdXamyie49flXySy7uSWFndQ7ZflF731Fjs9cgtiTsllUMzWOljuozm/QggN0ivlXjYJ6+VzCzxsjegYs2OPVhetTNOPUo8RAG4PO912OKDy/NBhJyu7YbqfjP0PuB8ssfn9ngD8HjT1AJg6XKXtVamVJ+4xRu/rsKpmi9z+iQUQyM08kHTLfTpk3It0OUizCWUPiIPoDdJnDO1kV22bfTILYnbFWvUtZRW+oQQmUKI54UQ1UKIFiHED0KIUzvsP1EIsVYI4RZCzBdCDE9kf1MBX/OdcNeeCDXGsl7ANTv0v9rhf2OSNyrRzJH6pUXL4fL58Z/536fNEq/H+2VEJQ6X+38RcgHXM+gYqaf7F0Pr4XK/z6/C50/e8dKbEJel6jO0BNxoCbhN5bqt9OkWW/qk7tEa5CbwEgqMVgtKgxKPslOvbY1yCnDAoswGKa30AbAD2AZgEoBCAH8F8LoQYoQQohTAW63bSgBUApibqI6mAkQeqO65IHUjVM+bhjKafyn0wA/t731fQg9siNFo8lpuYk3Evt+4DU9+Fgxi2FBTj3vfmR+nXiUWl3texDav71uo2u7Qe9IbQ0u7HemptU9VtbRRriNJgDUtDZzpE4FL9cKlmSs/uz3dK3Ookd/i6F0JpY8klT6JQA5SNwLqRvO20oQNdfVScrUua5bEU1rpIyIXEd1BRFVEpBPR+wC2ADgKwDQAq4noDQqGFN0BYIwQYlQCu5zUqJ73AHICADTvZyAtcjAGnM902kIIuJ6N2iZ1UBCTjWiWvnW7anHdS++G5eh7Y8mPeOqz7+LVtYRApMLt/cJgjw6P57PQO9XzrmFqBs23AHoH5bCrBFQdqsoWr/ghp/RxIEfXaJa09FW7u1fxRiMvVBmlT7oUnIyvmCqnHOpOUxHyLwL5l4N09usDgBU7zDNEuPx+/LDDGjejlFb6OiOEKAdwAIDVAA4BsLJtHwXDETe1bu983JVCiEohRGVtbW28upt0qK5ZHd75EXC/HLZf13ZB80UqBarnXZCBsz/pLqjNd4PUHRb31Bqilfy6839fwOmNvBE+9fl32LJnL5599llUVFSgoqICfWm8+AM/g8g4aMPbIS2L5v82Sgs6dH/3FeNAQEUg0LesT8k9VljB7ioy/o21vkbU+syX7Gq9jRHbZMaLRn7JlC2S15J0WzKWvpaYvn+kN4I88wB4QZ7X5PqXosja7rc3NaHFG1uJ31y/F2v3WLMk3meUPiGEA8DLAGYT0VoAeQA6X3lNAPI7H0tEzxJRBRFVlJWV9X5nkxDNvxS6ujpsm+qeE1ZiS/N+CuMHhReab0HoHZEXqutF+OumgtSN8Nf/GmrLwyC9e8sZvYXbb7z07A0Yz2h1IvhUDVdeeSUqKytRWVmJvjReVHWL1D7S90aVI11uqcL4HDoCUb77VCURY0XeMscBGl1FJqHyNncdtrnMFXwjnz6Z8aKRD6pUIIfktSRlNfTLueqQM2Zkrt58L9A6sdSdj4G05DQIWIFMHfEGjwdNXh+qGyMnAB2xKzYO5OiIEEIB8F8E7dTXtm52AijoJFoAwNiUkeYY+WiRvhuarz2gQw+siXq8Hvg59H+g/iJozXcB2rbWnXuhOR+Fv+7XhhbBROBXNdS3GC/B3HH2ScjOcERsv/y4Cowa1HeUvM7oMbLpd9wnlPKocrH2meH39z1LH5MayEYom0l5NT8+3LkEC/asQr2vOabsioZNqPFEn0BFQ9P90KUUNUl/apmEyuSWS7OiNwG6sQKje94Ged/q0KYTWuN1XahGkzx0N7F2Z77csBkA8MWGTTHlvGrAsnJtKa/0ieC09nkA5QDOpvYRtBrAmA5yuQD2a93OdIBIher93HCf5v2ow7voywDUYZ+j5HnYix6HknkSAEA4joS98H5k9PsfhBJhaE0Im/fsxeY9xjfcQ4cNwGOXnBE2szrrF4fgxqkT4tW9hGBT+knts2WOiyYFW8bR3T5/OkfvstUtschaR5UYMZnb3XW4ZcVzqPE2wKV58acfnsG65u0Rcj4tgJervsDKxs24f83r2OHu2rJdMDmzjNVNsiSahDJHeiNgslJD5A1a8Qwic0nbA735jsiDAitBrufk+plEqBYkSt5QV48HvvoaAPD898uwZGvkWGnjvdXrsGhzlXR6l1ikvNIH4CkABwE4nShslL8NYLQQ4mwhRBaA2wGsal36TUuizWZJ3QSQ8axU87cHYgjbsKhtK7ah7XJKAWzZU2EvvBcAYM+/GbaccyBsyWEl8wZUPPDhAjz66TdodBnfGI/efxguODY4ZygvzMNtZ54Qzy4mhIyM0dH3Odr32bNOA0Sk8m7LmgJhi644muH1BeD1pd6s3xqs86+Tz6vHimZXiaYcLtzzIy777gGsamx3g6hy7cbVSx/Bm1vbc5k+v+ljnP31nXhuU3AyvbxhIy5a/E/c/MNzqJPwAwTaonfN/fBINgGyHtsiGZSRSLOitSotbSs8Hfvi/zqqcqmHGRZSA5ml1ljWwHd+WoNzX3oV9e7gapMnoOKy1/6H55ZUhsnN37gZF8yZi/8uX4HtTc045bnZePa7paY+gLFIaaWvNe/eVQAOB1AjhHC2vmYQUe3/Z+/Mw6Sorr//vVW9zsJs7IogiwuKorKIYFBxI0YUiaggRn+KJpIY98QkrtEkJlETY5I3LlFxX4OiUeMOCIiIgAiILMO+DbP19F5V5/2jemZ6qeXOdM1098z9PE8/3X3rdt07PdVVp84953sATANwH4A6AGMBXJy72eYBZHxSsfoxJ8doyV5zT5fsnZjZyHwAKwGkSv45djDf7NiLix55Hks3bsd3ew9g2sPPYfF3Ww37zpxwHABg2pij4XO7OnOaOcEl94PbfaThNr+v1ehlUgncRZdl9HEX/ySr8UPhGELhbmr0OSqALIy5tqJxZ7oac0z5YEzuPwZuJre0SWA4pfexGN+rNXfw+/1HY+rB41Hh0W+aJCZhYu9jMf2Q76HKkx6NZAx3IgePMQcAxBFrre3XH1YounQXKZkSXsx9PACP4ceY5yT78fMMHr+wWR+NCA8vWoJgWkx5XNPw2NIvUJeo0PH17j349TvvY/mO1qzdA6EQ/rJgMZ5avqKdM9d17goWItoKi++fiD4AICRamqEwgPLMdgOvTcsmVtLyWvacAOYaqussJSF5xkByDcr8rFQCd9XrkNxD2ztjR/l0/WZc98z8FCmWPQ0BXP3v13H7eafhohOPTel/cGUZDq7sgROHmns4uxol/gtQF78vpU2W+8HnTT0xu0uuQjz0pB7rA0D2ngLZc0xWYzc1RdEUzH75ohDJjThz50uxEFFeSsAQUVZfR7mnGDceMQ2XDDwF96x5FoF4GHeNuAxDS/un9Ovnr8KVQ87GJQNPxW9WP4mrhkzG8LK21QzgXt7lTKoidb/tn07qHoAVWfeJr9FfND8nwVyDIJX9HlrDLUjxarvHQCq9kWue+UREUeB3Z8Z98yAxhrevvAwvrlyN/7fkCxwIhVDs8eDKMcfjR6OOQw+fDwAwol9fLJwzGws2V+MvC5dga10d5ow/EdNGHIXKIn+7517QRp+gjZi41yX3MOh3YZl3j5I7VeHGXXwVYg2/TGubbTpkvhh8AFDk8cAtyylGH6DXwCzxeQ0/c0jPChzS08BQ7qIUF09HXeMfkVwQvaToYrC0OqFMqoTLfyGUkF6RxV3846zHbgyE0RjonkZfPsunOGmo5aPBB+geNx5U0ixr5vbzV+GOo2dhT6Q2w+BLpsjlxa1HTkdff9tXQVSKc3n6SNsLIi3jt5vShzRAs9eJg7rL1uhDLLE0GV8FIgWMpZoXkv88gELQGm/XG9zHQq54DIwZn3vzGdWmhCegx/25Zdlwm8/twuWjj8eUo47EFS+9jofPPwcDKzKvM7Ik4dShg3Fs/37477pvcekJI7Oee0Ev7wpa4YrjIeNsVcb8kL3GLnbZNynlvcs/DUzq3fpZeQhk7xn8E80howcfjFd/NhPDD2qd/4DKMjzzk4twzkhjh3B5kQ/lWdxVFRouuTeK/GcltUgoLZ5p2NddpLczeTBk77isx66tC6K2rrsKtuZvHF6+Gmq5gGcZuJ+/Ekf2sF8daI/Bp88hyhXTB63eNFa7BaoHLCSYWrqp20DqNvPtWhMQ/zrxJgjEVxr2k4ouAfP/EGAlkMv/DiYV246dj/CE+/Ake1QW+fHglMmGBl96v2nHZEgMtwth9HUZeIw+8z4u//TMRlasB+0nNzEPXEWzWt67i/+voC4Kg3pVYO410zFiQF8cXNkDz197MY4Z0Ne0v1uW4ZK718+kpKg19NXn/R5croMM+0nuw8HkQ+Hyne3IuLv2NmDP3gZoHHfRXY9cGH3d67i2gkd/DwAUztJ1Ptk4fs0JVIpzGn219ku8Wi1gUHkpGSINUKoBCx1PxJYgWReQop+ZdpWK54D5fwgmm593uwK8YbpDqviM//YuJ6cjfvVdBCsXfgsWd1Wy7ywwqV9Km8t/gaHEiqtoOvRDxweX//w2zjT3+D1uXHPqGFw2/nhUllgvWXhcxu75rozfdwqkRPJNSdFUy76yZywkz5isx4zHVWzbfgDRmILtO9uuXVbo8BodziJO/82onIkcYaX9WZNOoVEMGkdMH2n7QKpNqTd1j56Za2WhaHsARACqA2kmyYDpVXpMq/YAzDUAkkM3irkirtob/07p6jmN+NV3J5h5dhhjLriKZ6W0uYsvN+wryX0huY+F7B0PJpUY9sl3DqosQ/8K+2y5vuX5oSvYmTAmw+87FQCDP215Px3JfRgk9+FZj/nN+l2IJYSZv/rafBmpy+Jo9i6v512c/pvhFdsNKJzadx2ISgqX0Qd1l328nroDQBzQLGpmq0n7UE3qv8bSsknja0BW3kh3dklfhUBEibdBPqnzEL/6PIc470C5Di5mbaC5/dPRnNsjeU6E5DJPwpA9J2YlxJtrPLIMN4cX75Cq7pPEkYzPcyLcrsMg2+juMal/VlU4mlmwZEPS60zJh65PDi4OnMkL3YE48Xll6mK5ryjE4+kjCusePMX6BqolTk8xlq0CAEqSaiED2RYiApT0ihJxy30y1nHL351BjMPTF4zFM2RZ8gHxq897eO/aOYxDmywpJvdqqbZgt2wruY+AZKLpVgjEVBUxxf6Hy+MN7Ip4PEfD47EPHGbyQWAsuyXwaEzBB5+0lvj7avU27N7LJ1Tb/eA5H/AakIUTi9vRxDQ+o29ryMIj1kloPJ4+pVp/Vqtt+m1O9NtsMeAB49fNUACG1ZrsxJwLmIaIvcrAzoZG7Gzk1ErsRITR12WwN/p4Ls6yd1Li2boCBZMPBpONA/wLAYkxLlX1qlIbmYI8gjiDzHlwuQ6FSz7Uth+TsveEfvDpOjQEWpfNNI0w7+2vLD7RFcmFoSaMvmaa4nzLtluaOORNOhgNCjRYG6mU8LxRhgcuDQtB5ZZ9qUmGrmpk9JqdR7vu8VUTNFbCSKa6rg7VdRzC152MMPq6DM78KyXPaDD5EEiy9ZIdkyrB8qjSRjIqR6p8qc+LYp/9EkNhVeJwbolQlsrgdnGIxjJf1mO9+ubyjLa3/rca4QhHhmKXIf9if/Id3uQLHnaF+YSMG+O5lxTSEkvRmsWSdIsRp2wCmfQjLdhaOi2+wbBP8z5a95vpEWRSibGGn801pJDZWldv20cl4tLz62yE0Zf38B40Dhl9rqEZgsyGsGL9UaC4ZQkeE+HM1H7dL3u3GReH0ceyPO42bNyLzdWZy0BNwSgWdqvYvlxItnS+Jybbcmcdta89kVo0cSRp1MebHBuzvRB0j76V0QelucR8zHyJV/kOLceT8q3xWESgeGuSBsUzb9AAAK60UB9WDMhtqzRSSHyz1yYrGrq2XjaVMzoKYfTlPXwnNucU8z2Q3Sdw9POBMWd0g5xG4vguJMbA85W5u5lGXzKy3IujV3ZGyFdrzAPNV67ZkdW+Cwn+Mmw833f+xvQ5abK6Jee88DFNsY3ri6lxbA/ZX+w7muYwDqtjhpQNhq9TSF7SpQZjeZf48tSau+o2UPybzH7pSX2eMXwyYgXIgVAI3x3g8AyTw0n5DtE1/yuCrGA8Ehx5nH3FYwB73C6upVueDN9Cg3jkHgBIFjWZW/aF7OIIAxZl1wKB3MtjdB5OXh2cvdLwKgjw4KSZ6eTyLoP93N7evRS1sQAW7f/asXHbQ7OxZ/Z/IYoC6s7WBpO4Pkr3ABqIL2vBf3G1Md/pqe+91lJPhYyiahmlPI34es9erNmT+8SfdITRlyP49Xs6/25cShNpNqbz58UTq8dLkcfNVV6Nx2uYP/D+nPmOPcYTr8dpQJrRr0+Z6ba+Ftu6HA4aMPy/TeeObd7zWUhxLk7TyfNBVIubevoUTcWTm9/BP76bBwC4d+0zeH3HgpxpsLUaeyZ/v7otZRs1Z/Jm9EvzpGs7U95S7EtQ9JPM8SPvgOJrU9qY+yhAak7sk4E0I7ArUebzocxnfW5cvHUbXlr9NR5d9gXWciwFdybC6MsZnRur1xb4MjLz0G/dRnr47Qt984q2Fha8xxRPv+xU508cNRiyyRL6+DHGOpGRcNdL8OBf3uUx1Jw1+viW6fh+JwdiuY+JM2JnuAY7QpkadBE1hutWPIxnt74PLfE3xjUFf/9uHm5b/ZijcYVOQWpahrG2x7ijVmf5XovMNxsBWuStzOZmQ899XN4m+TlBWIkjrBjf7NYEg/jlO//Dj156DVFFRV04gmnPvID7PvoUgWjuq7kAwujLGbzxDjmpa2uUiZVOlh6e9uC0140nSYNH1iV/yEElhiyPg6rKEkwcf1hG+9DBvTFyxADDz0Qi+Sd4mj2FfnPBd+xtbHRO8mRbyBkduOrgHnyydxWerf4AipYarqCShoBiLM+R+0xek+88zXgjzSTTlCLW7y0rOGV64Zn35MTz90w/1xV4avlXWLp1O9bvy7xJeOXrb/Dq19+k/Jrjmoa5X36FD76zkc/pJArpitYtyckSAk+CBnV+iRmnDWCe/eXE6G4nvHPlveEg8BhX2WsDXjZ9HCQpde4/unic+Qe6pPc1f+H5nfMcexppWFGXGTfWXh797sMMI62tLK1Zh+tX/AMRLYaV9Ztwy8p/YX+k1UgqdvnwjxNuwCm9R6Z87vyDJuCh4+ZAykGyQvPv1/Q7tzPmWki/6U39W6TiHwFGoT7yIWBFMzLb3cfr+/CMMhmvsNGI8Mjipfj7ks8R1zRc/srr+HJH6pL4T04cgzd/NBOTDx8GQHdUXHTM0Xjvqssx9ejhuZh2BsLoy3tycYGz94ARYjBUYW8nkXh+FqfuzpDpxSKpD+fybixqbkAeOrAnJp7U6u0bemgvnHziMNP+KkcQdeHh5PIuL50rE7P8wGb8b9dqxDmrX1jx/JbP8PHeb3D/2jfbvY/nqj/EbasfR0OSx25l/SZcuezP2BhorTFb6i7C7UddhisHfx8MDL88cgZ+dtgF8Mq5SWaTEqUymel5mvOyLqV569LCephUBbni8VSPH6uAXPE4mJSZ5MWkEkAelCnf0gUIRKOY+cIr+MuiJS1tNcEQZrzwCv65dFlK3+F9euNv5/0A108Yh9+ffQbuO/sMDKrIn3KewujrIjjrdeO4sFDU4g6y7TgZlC1wBs1sWSgZzpimfXusy6rNmDa25fXFF4yx9BzFouIGwRmcO2fwZPjO3bwA9fEQ3tzxZVZjLT+wCY98+y4AYN72L/Cf7V+0az8+2QPJ4FznkdyGYR0zBp6Os/qOxhl9c+vJaq6sxJiJ+kB6eI5ZuI7cN/W91DejC3MfDqn8IejXBAlyxSNgrsHmk/OMAZMKV7/VjKZYzLAKh0qE7fXG57aZxx2Lc4cf0dFTazPC6OsAOt0A00d1cEyO0SikF/V2iIawcwakwBlUdZd9J87lXclG7/CwoX1w+NC+KC3x4ZTx1pJB8bhz5ea6N056F63PP5/XbMTnBzYCAJ7Y+DFCSvuC2v+8dj7mLPs34kklB3+35j+4ZcWzCLcxM3jagJPx0PHXosJd0tI2uvJwPDH2ZgwqzjSAAKDCU2LY3plIifCbZo9fZofUJAomVRl2Y3Ka8WZizEneiWDeU8B8k8E8Yw37tOzTfazl9kKlX2kp/vt/s/DbMyehwq9n7h5/UD+8cunF+N3ZZxh+psLv5yoA0NkIo69DcFA/Kl9jyrQAoDlTTJqIsHaXc2ntWh6WvskneGr0EqmIG5RcyoTPAHO57E81J48bhhNHDYbbbX2i1Lrk8m6hY/4/2RE8gLtWv9Lyfn+0Eb9e+VK74vFckvmx4bbYZsYx5YPxy+GXAAB6ectw74grUOY29lQFlQg+3LvCUX3A9iAzNyTmMb02MLl/2gdMJLhcSTdXrAiQjROnAIB5zwDznmk/Odcg+z4FiluWccnIY/DKzItx8qED8cIl03Fcfx55s/xCGH05wukkCJ7gfP4x7fsRNYLIGaNv0/5aLNu8nWNMvvl3TZkVJ7FfHlXV3VAU82oZrfB912ayLMmMOPIgHHPUwbb9JA4DsnuTixtFY0Morim44cu5qIkGUtoX7V+Pv2/4X5tHuf6I7+PNU27FNcNOR4nLh4sHnoSXT74efzr+UkuD0IrhZXq5sCEl/eGRzZPY5u9cjH3Reny6b2W7xnEKiXkgW4njy/2BJC8gkw8x7ucejpZjxXWE5TWEuQ4Fcx3KMTmeKj6FzaDKCtx75umQC0rZoZXCnHWXIBeGCW8slP2dLGl1IM06TouXD77ZiA/XbbI16nhtOSp4+YsOhuylJuLKd4jHOWrfcngNAb7/3cgRAzDlbPvlIR4DUsCDk6EjxueMtQ07UR3MlLYAgP/u/Ipz/FT6+Mtw1dDTMPekObhp+A9waEnvdu2nmbUNW/Xnxq2W56ClB9amPOcKF/NBZuYao4y5U712Jsu2TCpt7WdXb12qyEj0MO6X++XvzuCgMnM5m3xHnD1zRg4ME+IVRuW4kGt1IK02q+k089aq9dhdH8CXW3da9lM4kz2Eo88G1USsNYlobCWi8VUcO+P7suMx55Ivol1Sp89JA8zpaj8c+zPRaxxY3AtDSvoYbpvU92jO8Y0ZUGwcq9ZWXInECI9ZYkSCSX2OT3nOFbLkg8umWk5ysoVl4kUi05bZZdyyEv1hC0cVH0FOEUZfh5CnVgdXYD64BHdJqwMcMPp21jVg8359Pwu+rbYek9Oai6si0N8So4LpaUSiS6BptYjF19v05DPEG+qNBW7bw6Zv7Y3WQsNcfiOdPE0SI+MkinJPEZ4+6VpcNLBVd7HE5cN9Iy/GrUdN4Ry/Yzm+chhO73M8fn74BZYx1D/oPw4XDjgFY6tyq7fmYn64JJsSkq4hiRcyIA+06Dcs9dkM5tMfdtgYzoLcI4y+DsHJu3EHiXMWjHAmqgAAIABJREFUCufRZ9NqdcMvS1Zt35P02lqtn9fTF4x2wTJdDlZAocjbIDL3vGlaEyLRzwEA4ciHjsxr62bjJb728NnH61BbE7DvWFDk4lTMa/RxeGktMvm9shs3Dz8Xtww/F6UuH/497sc4s98xnGN3Dr8+aiYm9Bph2Ycxhh8Pzb2h6pb8cEnWVZNYs6EnHwRmEf/X4gW08gYCADyW+2lFGH35jjD6ckbnG30U/xqk2cdz8UixkFbviNG3Lilrd51NBi9vgsbuhq5mEABOHS+k1gCxz/WHCbqhpxvOofA7Nnvk86qu+sKZSgyxaBxfLduMZYs44g0LCeaktIPD5xZez78N0weOwy3Dp2Qdg9fdcUlFcNsYfUgkb5gmcbT0GwCwckOx5RS4DD5BISCMvpyRgww7ZSugbrXtRmSfoOGUp297XetYTdEY6kPmBidv7V0n5V+6GhT6NwAVFHzMtE8w/N+W19HYCiiWMYD2XqBoJI7PP9vQhlma8/VXWxEJx7Hss65l9DFwlD4EkJNVBI5YYOI4rwDAyb3zT6y20HBLxXDZ1EdnciILXrbJhpf7Zoo0G8JrKuSpxJigBWH0dQg8B37n/jiINED5FrCN0QJIPWDfR6t1JJEjlFaeKxQz9yrwpsjbLRN3Fp1dm9g2+zm+Bgg+rb+JLQaFXzfYh4pw5JPkFoQjH1nsVLWtxrBl4140NUawe0f2x8uWjbpBX71pb9b7yicYT71rANlk0rZnX0QEcNzcUXwl11J/iVsE+meLWyrm8PT1AcDA7Aw6qScg2Xteeet1C/If8Z/sEHi+1k7+6mOfA9QAitprY5G6w76Pth+kZe9R292QqvW3q95c+4/XiNrbyJul3ME4WLGED/OLPWkBUP3PAbRemKnxHpCyMaVfLP5Nhv5iJLrUZkzrJd7NG3RP4aYN2SdgNGfudrUMXmYhwZEKj0HnYE1dCnBJM2nx9SAuMW9BtrhYkX1MH/MArNzWoGNMBlw2S8CCLoUw+joEey9eZ1baIIqBAn/U30Q/AkU/s+yvqdYnb9ICADWC1D1ZebPW796PTftSvT9vr/rWtH8kzif70bMkX2o/Ounp4/mpmhtCFPgzoKYJYFMI1PDL1D0omcum8bjV0qwCO6Nv53b9f7xre/aePo/XlXjm9YwVBtxGH0/iDHfSD4800wH9YTUcxfR44Vj7auAK2oZbKoKbp76tXAWYlGBLhtktAbcJUS0n3xFGXweQT6XTiDRQw62A0irTQfXXg0yEd4nCGd6fdLSW+J0oSGv/Uuq8FZnSIfNXroNqkqVba1Dw2ojD+/Zs95ychXP5jAuOY4oiIBPpDETeMG6Pr07x0GgGpfU0i8orRCpgkQkMAE2Nuscz0Ji959Pn0409r7drZQnye/p4jD7O79nsWEnuou4BNGsPrRb5AKAmqGGTY0zgKFyJHADAKsCkCvt+HMu7/Ai5rHxHGH1dneDfgch/U9uoAVQ3G6RlLoOSsh2aYr28S0neIM3GQLTCyDi2StbYvL+Wy0jqXZovnj4e6RherwyHcajVmcdfMTM1fRlgZa3dDALEjdpaicMumaO5FjI5URM5cXwwKX9urJxA4jX6DH6zmX04E6x4yihqe0Cq+Y0dqfuhBO7XX8e/hBp8nm9sQbtxS0VwMRudPgBMKuesouGMyDWANniZBblCGH1dBDNjiMJvGn9A2wUYLMdo6haQul334JigxVuXYCm+rm0TTeKSscfCLadKVcwcN9I0YWNbbT32BewlZ9R8Kcmh1fN0cm48ZQNgsDwLAKzkWuPP+C8Ck1tP+j7vSagouzvlUd7jJvMxKQaiaDazbhORcCzx3LUuLgyckhjEkVSh7eXzIPOItav7Aa3GeBx1P+K1l6aEDSiNd0ANZSYICZxDF2fm8PRJZQDjKBcmldn34abzzgWC9iGMvi6DibfF9wPjdqkf4Bmd0azFNwCIWQZla8q6pNfmMXh2HFJVjh8c2yrh4HO78H8njzLtXxcMoy5ov3SVD+LMRGHAwkPS2tHBTMvIu6CIsa4eK5oO+M5JbXQdAdYjNabP7RqAstKrUx7F/slWgwI2Rl8sUYIt5kApti0b9KzdvbvqEWyyFxEvGDizd0nlyFrWagEe2aX4Svs+FALIOKxCabgtxeufGBxKw63QFGd0GQWZyJIPLomnOkYxXy1c5uDKiEP12AUdhzD6ugzGF1RW8jPAe1ZaYylYxb/ADE4ImrI28WzuwdOUTYav28MxA1olBQb1rECpz3yZq8jjQZHH/uLYEM4DY0DZoj/s4JZCsDaYKL4GiLwDhOeZxmuyHvcm6Xb5wMofBuMprWQ1LkVsK7g0BfTtTY3Z/V92bj+AhR/px6eqanj12cVZ7S+fYLyVDNKTcYzQ9nMkXxC08Dz7eTEXTKssmAr2usHEpaXDcDEvZJ5wANaD7/ed5TkgBc25yjuCjkH8MrsKJhdexiSw8j8B8pDWtrI/gLmNRVK1RF1WzaQ+KxHpwd3N73nr+ZqwM0mceU9DwDSJAwD8Hjf8HEbfhj3Gy1GdSuxzkEXVi1Y4f4IWd9CkNYLqb4S+VBwHNdxgWHmFScVgxXP0N0XTwFyD+Ma2gsK6N8iCnVt1A2THNnv9RzOCTRHcc/NLiEVbjd8X/70QSxe239OcV3BU5CAtALIx5gB92ZVUGzklZQ2gfGubtAWpUn8Y4OrxW7CMmq0SXGW/B3NZ1HsVZIXMafQxmTOhjTuJiAMh25P3CKOvq2CxxMaYD6z4cv2NaxiY7wzjXWhBkLoNAKDF15rsLYoUrTcOtX4zmiJR/GdF6zj1oQjeXmUuHh2MxizFmwFgR20DFmzYkvMlXgr/B4h+wlG1hLP8lmL8vRCRbvCp1Ul9N4AabjPej1uvecrcx/KNawNR2NLT9/WKauzeqX8HG9buRPXG9okqP3L/26jelGrIaBrh/t+8hgNdoA4vl2dMq+OLE9X2AzYamlp4fuL5Let9yf3B5P6Gm5hcBXfls2DyoOYWuMp+B7nofPs5CtqNxNyQeMIBeDJ3AThZL5cnZECQW4TRl+dYJVSkdrQxclrK8hxk2kVfqqXEazMPQPrJpv16ab+d/xEONKV6iX7/9qfYXW98Ed/b2IQ9NnV1//rBYkQVFf/6hMfL1jFQdGHCSIuCgnNtetv/BInCoNDLxhsj84HYgsz26LugyAeZ7VrC6LLzBPFCUX2J14CG+iD+fHfrEiIR8IfbX0c41PZg7w3fGHuUQ8EYdlTngWc3azhOxVTPafTVWmbwEkVbqrFQ+BWQheQOkw+2LOXF5F5wVT4OwAO5+ArIRdPt5yfICsYkyDy1cE0z9tNx0AyIfQEykH0S5A/C6Mt3ODIjici+PmZzyTSL5SFKihcibY/hxYAxWS/d0/xe7mM7PyM+37wd81dmeq8awhH8+d1MI4aIsLx6B5ZtMY5pUlQNd7/5Id5ere/z8YXL8fePlrRrbtlCTX9vfROaq4tZZ0PoZSD6seFSHMW+NJ+HwTaKfpJ4tiit1iYUmMUb/u5Xr2LPzlQjZct3e/Hgb00yyi348U1no6SHD/4iT8rj1LNH4JgTBrVj3oUHaQ18CRoWyRcAQJG3dQMSALS9oOj75juT+ugPCyTXYEj+qZBLfmo7N4Ez8Bh9RjHbHQlpdYC6A4h/3anjCtpGQSucMl3R9B8ATgdQCWAjgF8R0TuMsUEAtgBIDm66n4h+29nzzAoKA7BJz6dGew9Ai4fHfHmNUi4omr5flhnP4y6a0ZKdJ7tHWI9rglXsntG2f3y8FFsP1GPu4q8w6cihGN6/VVBU0wjXPvsGFn1XnfKZv3+0FLvrA7j3gjPbNcf2QLGVQHxFUkMACL8MFF/Zvv3FvwM1/QWACqq/Fah6PiU4m3nHg8IvGH6WeSdkNkYX6s/xlSCtyYELgwoj2Zl9exqwcplxIsuC97/BzXeeD6+P30s8evwwvPbRL+07dmUoaKit2ebdpC3pUvhtwGecoc2kUi6tN7lomq4LJ+gUGEcMKDi0/HQcko2KLQVAoNgSMO94Z/YpcJxC9/S5AGwHMBFAGYDbAbycMPiaKSeiksQjbww+7koMPOr66jb9YT0gx1hpS8kmciKe0pvgq3gEvopH4C65xn6/Bpw0dCBuOvtkjB08IOVx5lFDccd5k1r6xRQFt//nffz9I73+azAaw2WPv4x317SWBqsNhbB0s/Hf//7ajZYGptNQOHMZ1nRpFoCVgj2pe0F1swFK3Lcoa0D1N4CS/i/MdybgM4ihKpphfOJtEeTV7L3DXMgwOo307luGE8YNyewO4LTJI9pk8HUPeH6fMfCIeTPmBywEtSlNZokUqzJ74MruZK6htn0ETsJz/eDNynUm/pkiCY9xxMJzLMg5Be3pI6IggLuSmt5ijG0BcAIA83WvDoaIOEqxKeCLh+OIf4qtBClrwXCxeR+5l/4s9TLtwqTS1IYOXh648uRRuNJClw8A7nrjQ8z7KjWpJBSL46aX3kYPnxcnDR2IniXFePCic/CLV95BOKk+b3mRDw/PONdU7NlpiGJA5N3MDeoWUPwbMPdRBp9SYPQz1BM0fqqLaCcT/RDU9AhY6XUtTazsXn1pPp445D0ngZX+xniSriOB2CJdhd9m2Y4L5jGV7rjjTxfh43e/hqa2XqBcbhmTvn9M9uN2OXgu4hK4yvFJvS1/5xkGoZ1HiEd827Jqi8BpNJvShwC4tR+hBU0ztHkhrQlojiFWt4Biq8A8ziSLCZyloI2+dBhjfQAcBiBZb2QrY4wAvA/gFiLqhKhvFbZfLUX4fpQ8MX3xlYCJxEoLUkIPT+5r2oUlJ3lIVVlruDnBoJ7GGWhFHg96JZVbO334UDx55YW48snXEIzG0Ku0GHOvmo6BVZ245BRbZuo9o8gHxkafVm/8P4mvBuKrjMcJPQskG33MA5Q/BKo5G4AbrOyBhL5aJsx3Jii2CPBOcqZGNPPpniUDfD4PJp9/QvZjdAMIKpjdTaCNB68FuT+Y3M98N57RoPCWpPdjzOelHgBpe8BwuM2ghb5oVFgoGk8yFOf/hBoADMhmOkB4HoDWhC4KvySMvjyly/xSGWNuAM8BeJqI1gOoATAawEDonr/SxHajz17NGFvOGFu+f78D4pI2YrV6H87gfp46mmo1oG5PWfbLoPnCbHFXL7mGw1P2EDxlD8Fb9ie++XUwV08cg7vOm5RSrm1Qzwq8cM3FGNYnVYfqmIP74tpTTwQA3HbOKR1m8D366KMYNWoURo0ahZTjxaoknYnkCswqmsj9YeoJNtDXY3JffZm36MKUsmoZeMbp/T0nmfdpA4wVCy+PBabHSjoclVmYVA7GUTKLyX0tb+4k/wVp76eaTyu2QM9GF3QKPMeLqkWh8lxjeGP1eKq82EDhF1MbIm9nn8Am6BC6hNHHGJMAPAM9OOGnAEBETUS0nIgUItqbaD+TscxihET0KBGNIqJRvXpZLIvwwiWrwPmDMPP2JKNsBxAHNIuyX80eKAtjk0nFcBddAHfRBXD5Jpn262ymjz4Gj8ycAokxDO5VieevvhhDexsbNpNHHAaPS8ZpRwzusPlcffXVWL58OZYvX46U48VqmcxkG0X+B1IzM6qZ3Aus9AaDT3jAetxhPIT7aDDX0VZTB+REAozswHEOgLESMFZq37GbYnqspEEcsXr6kryFQc/Zj3lGAfKh+hvXUWDu4cZzUvdBCzwACr0Ain1lP64ga3iOlziFENesBdEBAMRZn1rNrmQexVbpdb9TGsO6nJQg7yh4o4/pa1RPAOgDYBqR6ZHeHDTjwJqWDcRj9HEUTicFFH7VMumDogtaxwtbSGE0l8dRC1PT7OTDBuGMo4bixjMnoLzIfNm5b1kpThh4EDyuHEQu+E4HYBzfxtLr3qI5DuY9IGxSoL7oSsBzcup+Sm8Fc5sZdnHYBmU3a2g5kAUKAC7/D03FewX8mJ+2kpCqUuSSzGBSGZhNlQXm1W/qJN/pxvNRd0KtnQFoewBEodZdaSkP1BmnVYFOTA0gxlPj1qZSDgAQaaAsJVYo8rZxe/i/We1X0DEUvNEH4J8AjgRwLlFrqitjbCxj7HDGmMQYqwLwMIBPiDiErrKlWRPPCqoH2WXmxlfpukdm1Riin4Pqb2p93/Q3UOglswHTnguPsYMHYMyh5kKxzZww0FyAuiNhcl+wkjmZG7yngBl4TinwR4AaQcH/B1J2ZO6PMbDSW1sbpIOAoktNx6f4WpDVEjMANGv9Kcb1edsKk6tM4wcF/NiVsgP0RCtmsWzb2tF+uZ25DtFfGAgvk1YL9cAlqVVeqBFq3RV6jWej/fFIiAgcIazWIKLYX2PsqwEBUHcC8SxLGcaWGrfHv9KT2wR5RUEbfYyxgQCuATASwB7GWFPiMRPAYADvAggAWAM9DfaSbMbjLu3FVRS93rKeKgAg9kXqcxIUXQiquyJNrFUBNd4OCj6euS9Xotau+0j7ueUpQ3tXocRnXyfy4Er7uKcOo/gawHNi63upN1jZHzK6UehFoDkOhgKg+p8YarAx9+EAS2RRe46FHslgQnQhEP3Uen6JcAFRLim/UHlCQgBA5gi456jiQ8rWxHOm1BGFXs3MGtc3QAs+Zj++oEMJxfcgqOyx72gV7tOMshZQqw1rdXOjmC0Px/muhYJOpaBv0YloK6zXFYxVa9uJxJHtSGoNSNlqu9hB6m4wdZdlwDW1XKBXGexPgrnNnpkAwNxHgvW10ePKc3qWFNt3AtC7lK9fR8CYBJT9HrR/MoAIWI87wNLkECj6Oajx7tQPKt+CGm4AqzC4qLJiPSaTmf9dpGxsuVCTust0ybXFU2NaW1mQCxR1JwB7KRvGEdNH1ABYyEaRuhMUflV/HX4eVHQJmNwqdg63eaYucx1mO76gY2mMb0MgbqPLCt2wJ9IsbxQp9gUATZd78n6vnTOyCE0Qnr68o6A9fZ2NxiNwbCW1kaBZz40sYvBI3Q1EP9PfRD8GpcXiMe94sJ7zgOTAfakvWMVTYMU/sp9nAeLi1NurKsltNimTDwL8UwHX4bp4chrU9AAMRZmjn4KiBjWDmwunW2lpJd9tm955I6kcH0cIgqDTiPEusTEO7UytzrRcG2m1UOtmt27XDkCtuzqlXqrknQhmFEbgHgNWPJtvnoIOoy76HeqiHOEZ6k5As4nhjn4MAKDEc7uQerdvmyAnCKOvDRxoso67ISJQ6Em9xFVshUmfKKj+Bj1jKvw8KDg3s49aA6q7Gi26R9QEqr8mI0aDuYaCVT6bENllYJXPgnmdkeJoD88//zxGjRqFkpIS9OvXD5MnT8aiRYsAAGvXrsWUKVNQVlaG0tJSnHrqqVi8eHHK55944gkcccQRKC0tRZ8+fXDOOecgENCzjS+//HIMqCrHm2+mGsrXX389GGN46qmnWtoCB2owc+ZMVFVVobi4GGPGjMFbb6WWnnrjjTcwcuRI9OjRAz179sSkSZNQXV3t2HfBvN8DvCebbLTIdjUSxE6UujNP4EBqHJeFR7DlJOxQ9m4hMGjQIPj9fpSUlLQ8fvpTvU7s7t27MXv2bPTv3x8lJSUYPHgwLr/8cqxf3xpHa3VcOkUouhjEsSxrJoSdgkmFHiINat21mZmWyhpo9T9LaZJK7wTzJUm7uEdCrnhM14Q0gLvCUBsw+7899dRTmDDBoMRgEpdffjlcLhd27Updpr7rrrtw6aWtBm1bzgPV1dVgjOGcc1KTsi699FLcddddAIBYLIYf/vCHGDRoEBhj+OSTT9r8d9tRG/0GMa0RgZj10imp2wB1q/n22KrW5dfIe4a11rnwHGfcLg+wlo9ykEI8VpYuXYozzjgDlZWV6NWrFy688ELs3s2xJJ8lwuhrA/9d/a3pyY2IQIG7gdjnAAhUf52+5JbcR90POnAxkFTgnAL3Qmu4vUVjj0gB1c7K1G+Lfw2qvSJzfOZCa3JG7v6dDz74IK6//nr86le/wt69e7Ft2zZce+21eOONN7Bp0yaMHz8eI0aMwJYtW7Br1y5MnToVZ555JpYsWQIA+PTTT/GrX/0KL7zwAgKBANatW4fp06enjHHYYYfh6aefbnmvKApeeeUVDBnSWu6rtrYWU79/NjweD7755hvU1NTghhtuwIwZM/Dqq/qS1saNG3HZZZfhgQceQENDA7Zs2YJrr70WkpOVO+T+qWLXSbAedxpXTCieYyjgzDzjAciAlbae+2jokQ5u6+W55psCT/eqjTl//nw0NTW1PB555BEcOHAAJ510EkKhEBYuXIhAIIAVK1Zg4sSJeP99/TfKc1xmC5GGaGwVp7fP/hjVootA0UUGG+qA+HLjOcQWp8R1McYglf0ezDMRcA2DXPE4mNT5YRNG/zc7gsEgXnvtNZSVleG55wylWQG0/zywdOlSfPbZZ6bbJ0yYgGeffRZ9+3Ik3bSR+uhmRFT95n9v2DybmogAZUNGyb2UPuFXWt9oNUD0k3bNiXnPMN7g7bya50DhHSt1dXW4+uqrUV1dja1bt6K0tBRXXHGF7ZyzpaBj+jqT99ZswD8+WopQLIbrz5gASWqNlyEiUMMtQCTJC6XtAx2YAVQ+1aKDRYHfAYpB5YzwS4DneH1J0FZQU4Ne7zQxduB+QNunv274BVA5t9Mz6RoaGnDHHXfgySefxAUXtHoHzj33XJx77rmYNWsWxo0bh/vuu69l23XXXYd169bhF7/4BRYsWIAvvvgC48aNw3HH6XeNlZWV+NGPUpepzz33XDz77LOoq6tDRUUF3n33XRxzzDEpXpeHHnoIpSUleOKJJ1p+kJdccgm2bduGm266CdOmTcPKlStx6KGHYtIkPaO2tLQU06ZNc/ZLYV4AxkknzHUIUPm0fnw0y+0UXQ6p9OfG+/KcALiGZZbJS96nVAZyHQ6wItMKGQAA31lA4z1gvu9z/iFdl4ceegg9evTAM88803KslJeXp5x4eY7LbInGVkKjAELRhfB6jDXzWrDxxmjRxUB8BTR1K1jRrJRjhslVYP4ZoPDzGZ9jxVdlGHWMyZArn+D6Gxyp7uIQr732GsrLy3HzzTfjsccewy233GLYr73ngVtvvRW/+c1v8PHHmUuiHo8H119/PQBAlp0/D+8OLWl5vSu0BEPLDOpuA4C6SU8Qi600zPjX5aJSpVYo9CKYiYSPJb4zgOj3MxKIWJGzN0cdQS6PlcmTJ6e8/+lPf4qJEye2469oG8LTZ8O2A/X42bNv4oYX3oaiaXh8wXJc+I/nsWxzkms9+kmqwdcM1YMa72l5y7wTYZh3wsoBt35RYcwDVvUy4Es9IOA7D6zyuRSDjtQDQOiZ1j7xL4CY+R1oR7FkyRJEIhFMnWqs7P/+++/jwgsvzGifPn06PvvsM4RCIYwdOxbvvfce7rzzTnz22WeIRjPLDPl8PkyZMgUvvqhnvc6dOxeXXXZZxljTpk3LuAObPn06tm3bhg0bNuD444/H+vXrccMNN+Djjz9GU5MzmnWpEKzkcZhraKscizwYrPQX5n3lPgBPAL1rGGBT+J5JFWDlDwOe0fb76+J88MEHmDp1quXdOs9xmS2B8Dz9OTSPo7e5zBNpB6A1JI4p7QC0hl9m9JF63J0Rr8eKZ0O2OP4KjaeffhqXXHIJLr74Yqxfvx4rVhiH2rT3PDBnzhxs2LABH3zwgZPT5mJnsLU6yp7Q51BNyrFRVA+dodgSw+2I/BegtIzd2CI9lryNMOaFVP4XSBV/S3kw16Ft3ldnk0/HyoIFC3DUUUb12Z1FGH02zHz0JXy4blNK27rd+3DFv19FdU0ixs59pKloKkvKiGL+8/ULbnL8lTwYrOplsKTSWkwqhVT+V8D/Q72haBak8j9l3onLVUCyx8Y1HHB3fq3TAwcOoGfPnnCZCCLX1NSgX7/MWqD9+vWDpmmoq6vDySefjNdffx0rVqzAOeecg6qqKtx4441Q1dS7x8suuwxz585FQ0MDPv30U5x/fuqdrtVYzdsHDx6MTz75BDt37sT06dPRs2dPXH755c4af6TAMFkjGXeiNqX7aHvvrNzHfkzmSXgYbbr5Tu92umrnn38+ysvLWx6PPfYYampqUpbg3nzzTZSXl6O0tBRnnqkvTfEel+1F05rQGNSX2aLxVQhHrQSQ0SqunYYer/ezhJhyoi36HrSmf6b0Y4xB7nEXpPJ/Qip7AFL5o3lt8Bn936zYtm0bPv74Y8yYMQN9+vTBpEmTUkJCkmnvecDn8+HXv/41fvOb37T772oPEaUW+yOtSYIKhbE7vMywb0tihrYXZFCXnSJvZbQBmm4MFiiFfKysXr0a99xzD/70p44vfyqMPhvOGG7sOTmib28MSOjBMbkvWOWLraWN9Fa9ekLJtSmfY76zwCoeB+AC5EFgVS+kGHwpff0X6c9F5vKCrOzPYOV/Ayt7CKzy6ZzE3VRVVaGmpgaKYrz01LNnT8MA1d27d0OSJFRU6NmpkydPxvz581FbW4s33ngDTz31FB5/PFVzcMKECdi/fz/uvfde/OAHP4Dfn7qUaTVW83YAOPHEE/Hyyy9j//79WLhwIRYsWJCy/Jw9cXu5guaAeh6hZJ4ga3UfoDpQO7oLMm/ePNTX17c8Zs+ejaqqqpRjZcqUKaivr8dDDz2EWKz1f8dzXLaX+qYnoSVl2tYF/mrZn9Sdxu3h54F4pgGgNf0VZJDNLfnOgOQ/D5LvtDbOuHMx+r9Z8cwzz+DII4/EyJEjAQAzZ87E888/j3jcWFbE6jyQnBSwbVtqYszs2bOxd+9ezJ/feaXGdgQXgNLCf7Y3ZS4bklaX4uHTIu+kbqcIYFJdpZDrLBfqsbJx40ZMnjwZf/3rX3HyySbJfw4ijD4bfnzqWMP22RNHQ05aFmKuQ8CqXgAkXR+Nld4GVnyV4WeZZxTgOwOs+MdgzXIcRjRnVhkIqLbsi7l0Q9J/Dlcx9o5g3Lhx8Pl8mDfPeHnq9NMwdxwAAAAgAElEQVRPxyuvvJLR/vLLL2PcuHEoKkqVWJEkCZMmTcJpp52GNWsyKwBceumleOCBBzKWdpvHeu2116BpqSfHl19+GQMGDMBhh2Uuk44ePRoXXHCB4VjtRmtqrXdsQrMOI5T1+onYcn8cRdGV7zITgASmTJo0CfPmzcs4VsywOy7biqYFUdf0/1LagpEPEIkZSz6RFgClZ942b4uYLR8poHYG6Bcic+fOxebNm9G3b1/07dsXN954I2pqavDOO+/Yfjb9PJCcFHDIIYek9HW73bjzzjtx++23d0jmshE7gpkG2a7gopYkwGYo8g4AJen9W6lzVDbDVFsvLfmwK5MPx8rWrVtx+umn4/bbb8esWbOc++MsEEafDdtqjZXyjdqZVAlWPEuvkVmUaZCkIA8AXObq+kRRUPAf+uumv7U/nb4TKCsrwz333IM5c+Zg3rx5CIVCiMfjeOedd3DrrbfizjvvxOLFi/HrX/8atbW1CAQC+Nvf/oa5c+fi/vvvB6Cnw7/44ouoq6sDEWHZsmX49NNPceKJJ2aMd9111+H999/H976XKSZ6ww03oLGxEVdeeSX27NmDSCSCF154Affddx/+9Kc/gTGGRYsW4bHHHsO+fXoCzPr16/Hmm28ajtVutP2gRIKNOc1LrFZC2wBpDYBqUCEhpU+tvrSnbrUv7ycAANx4442oq6vDrFmzsGnTJhARAoEAVq5srVbSluOyrTQGX4BmUCqrLvBPg94AWWmAGpRT49pWoBARIpFIymPJkiXYtGkTli1bhpUrV2LlypVYs2YNZsyYYbhsl+15YNasWYhGo3j33XdT2qPRKCIR/SYuFoshEolkbRhqpGBvODPzOqo1oDaaWqZTS9d/VXcA8aRYNauSf+lxfl2AfD1Wdu7cidNOOw1z5szBj3/8Y2f+WA6E0WdDqc8Lrysz/qmXWdUH+SBdJNmqXBag//AsfmDU9HBSndRvgOCjvFPOCTfeeCMefPBB3HvvvejVqxcGDBiARx55BOeffz6GDRuGRYsWYdWqVRg0aBD69euH1157De+99x7Gj9elQyoqKvDYY49h2LBh6NGjBy699FLccsstmDlzZsZYlZWVmDRpkmHGYFVVFRYtWoRIJILhw4ejqqoKDz74IJ555hlcdJG+XF5eruv9jRgxAiUlJTj77LMxdepU3HrrrRn7ay+kbE7cUZvTIp/iPsFU/wyA7vE10F1L7dNcu1fTRVkFKZx77rkpSzBTp05Fz549sXTpUvh8PkyYMAGlpaUYOXIkAoEA/vlP3fBqy3HZVgLhNwzbg+H3oBkY7hSZD4otNwy2l4qvhFElHrgOA/Pm9xKuFUb/NwBYvHgx/H5/yuOJJ57AeeedhxEjRrR4b/r27Yuf//zneOutt1BbmypInu15QJZl3H333Rn7Pfzww+H3+7Fz506cddZZ8Pv92LrVXC+Ph7roBqgmqwH7I6tbXpO6R6+ukYaWnKlrJfLOUfElXym0Y+Xxxx/H5s2bcffdd6fMu6NhneWaLhRGjRpFy5en3lEt3bQNc559E+GY7hK/ZfL3cMUE44QJCr8OavoXpF7vWY6jHZgB5p0IVnKN8fa6a4Fo0pKNbwqk8j+34S8RdAajRo1C+vECAFrtFUB8JVjv5aZJE0QKaN9osJLrLauoUPgNUMMtYL2XgUnlxn1iX4FqEzGgVfP1mr2CvCL9WNm4c4ipV/aQ3u/D62nN5COlGmrNZABxMP8MyGX3ZHxGDfwRlHZzKFU8C8nroAdb0GkkHy+bG9/C0n2/New3pMd5GNv7VwAALfQCtMbbMztJB8HVW6/LTaSB9o0GyEBg3DdZTyIUFBSMsS+JaBRPX+Hp4+DEIYfggYv0LNmrvjfK1OADAFK2A+puS3V9in8HxFeAwq+ByDi2gpX+KlFpA4B8MFipsX6QIP8giumB0hQE4l+b9mPMlZBZOcJ6fy0eX4uEjxYhaAaY1N0V5BfMyDPXsjF1m9Z4J5rjsCj8ol5NIQ2p+NoULw7znikMvi5CRDUvmxhRWrfptXQN0HaCEiEijEmA91TDbszbDp0+QUEhjD5OTjliMAZUlmHWScdbd4yvABABFPOC9tR4N/RluGog+KRhH+Y6GKznfLCe74BVzdO12gSFQXwNWkromVRAaEVOVFWxoHlpVzEvu8Tk3nrsluswSxFnQf7g8xifSySpAh7X4Jb3Wvg/oBT9TQ1q420ZN5ZMKoFUPCfxToZUerPDMxbkivRkjZRtSdJQpGwy75cUbsL8F2R2YD2A9ogzCwoKUZGjDRzRr5d5LB8SKucx/SJPkY/AEjVTU/vUpkgrUORdsJKrDffHpHLAZDlPkMckxdSRusNIjrsVrca+KLqWyAI2Wo5JxjM+VQNSkNf0KL4YIYPM2h5FF+heYABEcWgBg7AOZQMo/B+woh+mNLOiS4DgY2Cek8CSDEdBYeORe1hsS1JtsDpHJG/zjAMr+wOQbEzK/a2r+Qi6BMLoawM3n22joaOsQ0sqfHy1YRcmVYLcI4G4niHI8lwnS9AOkj1tzPxkTRTTDUSbhI+W/Unm+wIA5p8CwCIhRJBXlPjPgds1FPEkmQwGL8pLWjP5KPa5qVwPReYB6UYf84D5fwjJm5nZLihcengGmW4rcydtszLakrYxxgAjb5+gyyOWd9vAgEo7r1vSBdeiMgIr+73e13U4UGzs5RMUMO7j0ZxJyTxjzPsp1QCU1pg9E1hzzJ9N7B/zjAbzHMs/T0FOYUxGVY/UJdiykkvhdh3U2qAdMP08mWxjnhNbq70IugRV3iMhmfhoevpaV5SYPNB0H0w+xHSboPsgjD4ncR8JMD3l2upiz1xDwHp9lKilKzwzXQ0m9QArvQ0ong14xpl3VKtTn83wjNXrM7tERm5Xo8R/Ltwt9ZLdqCidk7KdeUbD7DTNPMbC8cx9eLcrs9fVcUl+9PIfl9HulorR039My3tmEicKVpFWMUrQXRFGn4Mw5gG8pwFggO9M675ybzCb5TpB4cKKL4VUeov1xbdZp1GzEUR1Hw0UTbfXfhQUHIwxlJfocj0l/u/DJfdN3S73BysykPNh5UlJG2mbrKr8CAqWQ0pOA4Oc8ji4eCLkpExvs+xb5jPWNRV0P0RMn8Mw39kgdSuYkM0Q2CH105/lfpbdGHODiUzMLkuPoovgdg2BxzXMcLtUejPU2DJdpB0AwCCV/QFM7tV5kxTknGFlF2BYmXUcHnMNAlwjACVVKor5pnTgzASFhHAdOAzznQ5W+VKupyEoBDwjAeYHs1oCFnR5JKkExb5TUmP5kmDMC7n8L2iOGWb+SyAJaQ2BCVJacg+kg8Q5RtCC8PR1AGIZTsADYz6Q92xAZHALbGCuQ8GKLgWFX4RUelOupyPIY5hvCqRkDUfXMLG0K2hBGH0CQQ6Ryu/P9RQEBYJUfA3gOwNMKrPvLOi2MKkUrPiyXE9DkKcIo08gEAgKACZXAXJVrqchEAgKGEZEuZ5DXtGzZ08aNGhQrqchKBCqq6shjhcBD+JYEbQFcbwIePnyyy+JiLjiyoSnL41BgwZh+XK7eqkCgc6oUaOyOl6IyLF4GyKlpXyXIP/I9lhxGiePPYHz5NO5RZDfMMZW8PYVGQcCQZdBeO0FAoFAYI4w+gSCLoMw+gQCgUBgjjD6BAKBQCAQCLoBwugTCAoAkXAlEAgEgmwRRp9AIBAIBAJBN0AYfQJBASCy8AQCgUCQLcLoEwi6DGIJWCAQCATmCKNPIBAIBAKBoBsgjD6BQCAQCASCboAw+gQCgUAgEAi6AcLoE3RbhAyKQCAQCLoTwugTdFsKKSNWGKgCQBwHAoEgO4TRJ+i2FNIFtJAMVIFA0LUopHOlwBph9AkEXQZxYu7qCONfIBBkgzD6BIIugzD6BAJBRyDOLV0FYfQJui0atFxPgRsnl1fEUo1AIBB0T4TRJ+i2qAVk/HAt63H/PYXzdxtBVDjGukAgEOQTwugTdFskdLX4qMI25nghqLmeQs4QXlqBQJANwugTdFu0LmckOfv35KtHjbrc/40fZxM5uu/3KBB0V4TRJ+i2KJqS6yk4jvAECQQCgcAMYfQJ2kV9dCPCSo0j+yIixKOLHNlXW4hpcds+UTXWCTNxkq5v9LEutywvaA/iBkcgaDvC6BNkQBS17RNS9iKk7HVmPHUnlDw1+mQmd8JM7OG/wHX9CyGDK9dTyBnC0ElGfBcCfsRvR0cYfYJMyN671RjbjIbYJkeGU2JfQIl94ci+eIlrCmIcy7tRrdA8fU6SnydJQtdblhe0B/ubNkHXR9wQtw1h9AkyoaBtl5rIKhyIrHZkuHjkv1BjX0JTHfIccpwE4pqCONkbDwoVUqao0yc1+/3l4u5ZI3GxF/CtSAicIp9DKnjP0eJmERBGn8AIbb/lZpVi2Btajt3BJVlf9FVlG+KRDwCoiAbnWk/LwWzSqBZDhCNeL6hEHBuz8OD5vjvf6Isoezp9TIE1zoqH8/3O1fg6x8YU5B/cxxSFufZFmjMx6IVOQRt9jDEvY+wJxthWxliAMfYVY2xy0vZJjLH1jLEQY+xjxtjAXM63UKD4Bsvte0JLoFAQYXVf1t6+SOBBNN+BRYP/hqaa/zB5A/h5ZC1CagQhDoOuOriLa8yOh+cE6LABxuEJ5b17Jk6PKU+/QHwj1766K7mJXercMYkI4aaHQVqoU8ftvuRCDopzTNX+JlCNvg8lPI9vf12cgjb6ALgAbAcwEUAZgNsBvMwYG8QY6wng9URbJYDlAF7K1UQLBSIVFFts2WdL41utrwNvt3ssJfYV4uHXkwZvQiTwR9P+TmqU7Y/UoyZaZ9uvOrjbsTE7BydPzhzLqBx32QAAdQdfNxsvMwAomn34gaCzcVLT0fq4I9IQarwL8einCNTNAfEeg4I8gud44Tym1M3Wm2OrEa2/BfHAg1Ai7/LtswtT0EYfEQWJ6C4iqiYijYjeArAFwAkALgDwDRG9QkQRAHcBOJYxdkQOp5z/KOtB8RWmm0PKPuwKLmx5vy3wHuJaU7uGCjfei3QjJRZ6CWr823btry0ElCAaFXsvgUvKj+zdnMTUcMR2cht90Q9tu4SjyxAM2/cLxbfxjSnoRPgu0FxeSK0BZOJlJoojUDcbkeATAIB49H001EyDptVnN6agk+Hx/HPG4GkHLDfHmx4CqB5AHLHGP3CvOnRVCtroS4cx1gfAYQC+AXAUgFXN24goCGBToj39c1czxpYzxpbv32/vaejKUHQBoG4HKVsMt29seCWlDJZCIWxufLPN46jxtVBjywy2aIgGn27z/trKgVgDamMNtv0C8UzD59FHH8WoUaMwatQo5N/x4uAFjisGhnPZ1iY7Oxj+ALsPzMb+htvREHze8sQciG1AxCG5oI4mv48VB71zXKEAANeFnBoBzfi3GWy4DfHIeyltanw1ArVXgUwTfAongD8Xx0tOjGIOhQhwJupQbJVxu1qDaP1NUKMfJbVtQaT2R9AUa+9gV6bLGH2MMTeA5wA8TUTrAZQASD9zNAAoTf8sET1KRKOIaFSvXr06frJ5jBaZrz+H38jYpmpRbGr4T0b7d/Uvt/nEocTNYwHV+Ndt2ldbiahRvL3rM3y0bzlqY42m/WqjDfh0/wp8vG95SvvVV1+N5cuXY/ny5civ48Xhk7eJ4Z86JEeGr9YExJYZGnLR+Hps3XMadh24DKq2H0QR7Ku7GdV7xiIY+cRwfy6pBLJUbD+3PCAXx4qzpdp44cyk5cm8VvcC2j7DTYpJ8oamVIPI2HNvbgzmH93meDH5X6X2sf+/EREQW2C4TY19DiX8aka7FlsIJfyWwSe6B13C6GOMSQCeARAD8NNEcxOAHmldewAIdOLUCgqKLQcUPYmDwq9knCx3hRYhZnAHHlR2Yn/EfEnYCEnqabqNWWzLlu2hvbht9T+wI7wPjfEgbl31N6xvrM7o8+im/2D28t9hf7Qef1z/DO5Y8y8sqfk6z5eKNDhl+JFWC+K5G7ZZWgEAhF8HKABE3sn8uNYAxvwZ7Yz5oWnGBnmxeyDcUon9uAIbnPQK88ZZ8nh4mvSHAZLcx7CdSWVgzGc8NZ5jVNC5mHhyU7H30DLGAKmf4TaX/xz4Kp+D5D6+tb88BN7yh+EpvY53pl2Ogjf6mH6b8gSAPgCmUaul8g2AY5P6FQMYkmgXGKAl4mT0N3tBkdQkjX1hc8Nuv8U2I1zeCWCS8Z2sp+h8w/ZsDa7Palbh2i/vx/pAdUvbzvA+3LjyL3hj56ctbe/v/Rzv7lmCkNqa3ftF7Vq8sfNT1HAsCecOgmNZvspGQPnOvp+6GWRxwSelGhS4X3/d+BuQmnoB9nvH4pA+b2Ng38/g84yF2zUYA3q9hUF9F6K0aIrxkCJw3yEc9PDwymHweHj0joatXv95kN1Hp7RJ8kB4iy4B4M7cCxHiseUZ7YIcY+LJbRfecaabZO8E+Kpeg6voCsjeSfD3ehsu/3nOjV2AFLzRB+CfAI4EcC6lpnH9B8DRjLFpTL8FvAPA6sTSryANUraAoh+ktGnBx1PeWyVsxNqYzMGYD77SmzPaZfcxcPuML/bE6ZkwMw6P6jEEZ/QZC1dSaTUGhgk9j8XYytYLyf8dOgXPjr0HswZ+HxIYji0fhn+Nug1/OPan6OUt55qD1Tw6DCI4FqcVWwVYLMG3DBlfBygWemnMg5a4P1IT7zPxuA7FQT2fQf+qp+HzHm/YpxlV687aiXmKTQZlm2A+wMD7C+hGX3mvd+EtugwA4PacjPLen8JfchX0BZ90NJCJ11DQFhw+l3Fl8/OVW2TyYOvtTIKnx2/gKbvPcFUhGV6NyEKmoItYJnT3roEeULInKTbhGiJ6jjE2DcAjAJ4F8DmAi3My0QJAC81Fxg9bWQ8tugRS4k6qh3uQ6eettpnhKbokka3b7CWU4C/7fdYxJho0yMjMui33lOC6wy7C+QdPxJ1rHkVIieCOo67CUWWZJ40ilw8zBp6FsBrFzIFnwycbGytWdH6sjAaQxuHA4YjDi68AtH0gZQeY62DzjvG1gHww4BlluJnJ/UGe8UBsIeA7C0zKCKltQZJK4OFZts1JzJrACoqvA3xx6KHVVnD4GdS9eqC/e4Rpl+Ky+yC7BsNXPAuMmV/GGJMhSRX2Y3ZjiMixcxXPvkgLAlqt/c5MlutTx4vpK1L+qSZGf2JXzAUmGy8Dp+2Ro09hU9CePiLaSkSMiHxEVJL0eC6x/QMiOoKI/ER0ChFV53jKeQmRCjIJbKVIa0LHIaVnghkYUzLz4uCSU9s8LmMM/rK7W957iqbD5TnGvD/ncpRmk5J/SFFf3Hz4pbhmyFRDgy+Ziw85s10GHz9O1o10cHk3noiCUOyiISIAmXveiLTWpT+HlnQao+sRcKjuc1ckJ3Gn6g5A5dG0tP5tEmmg0FxQ8CnLfoyxhHfPyzFmQV/muh7UxOd9ZRxyWcEngNgiIPxy9vMCoHEIPRc64tcg0OO3yFioOFlqo8R9EAaVfj+jz7Cy6fDK/Mueybg8I+HyngyAwVsyp137SIenXNvwHofi+Ap7ycZil/3dZv7AuzRhf+GFlpBEsbiQE4UBZTPIxDAkioIaftG6/BtbCq3xXlMNNh6+q/sn6qIrsGr/LxFVRFmlvEHd03rMWGJzyQm/pieTxb8ARew1G+0IBZ9DsOkxaKJyRyfBccNBEU59T/ub/Oa48/T48/bS2HgvVGWnI/vKV4TRJwBZudrTto2o+glcSXERXrkCR1b8X1bju/3nQXaPhOwalNV+miEO44cxhnKP+VJj5+Gkp4+zn63RlewxtPgu4+v0LEsDDT6iGKj2UiCSJv0Tmguqu7rN3iiN4li57xf4rv7vAIDG2Dp8tusiNMasSwYKrHAwfkmr5Vuys4CCj4Mab299X38dKPRK+6aj1aO+9mdorL8F8djnOLDvDMSiS7Kan8AhKAjwGOE2On0U/7pFbQKxL0HK1qymFQq+gHD4DdTV3wCtC1f9EUafAEy2kEhJy7D1u3rh8PKZLe+HV1wJj5ydfIbLMwYuk5iwZDTOi1RE7a6B/ryJHNZGH2MywMr0N1KleUe5f+LZIFaGooBSbTL8d7ZzSGfl/l9iVzD1bj6i7sXS3ZcjFN/epn0J2gaXgU4NgInETgom8ikUWwEK/BGpx28c1Hg7SGnbUn48vg41+yYhEn6tpU1Vt6C25kI0BR5u074EzTh9g8Aho6Oae9xI3QOqS5ZdUUD1PwFp9qU104lGF2H/vjNRX38TACAWXYS9e0ajseEPWa1K5CvC6BMA8hDARBuPecZmtA0rvxgy88IrlWNID2N5lbYgyQMguQ617adofD/Aulg3XfYjBVwVMnjKEEkJo6/Z+DNCSZTLU7ZkaDoyqRQomm74MVZ8OUfAfyp9/BPhzbg5kdCn6BR4ZAvDVGCBM95j0oK6FItN3WSiGCiUKZYLAGSg4aijAZH/8UwyaWdxaKrRxV+DpuZbVZSOxMnYTgf3pWwGVA7h9+gCc63Q8HxASzMKlY1A5OM2T6cp8P8Qj69JaSOqRzD4BBQegfoCQxh9AjAmgfmMjTfJPzWjzSuXoV/ReBxcMgmyxBNIbTe+C5JskSGaIM6prL870hU9Pzwn3ThfeSOb6glE1CqeahLrCQAUSUj8UBMQW5qxnbF0bfTmDW1fVj+odApOOfg9HFZxHRhcqPKNwfcOegPH9vodXAVSnSP/4PXe2Bx7iq6CRYpNzezYEiDyNshg2Y55x5t/znOS3QRTcHuOQa++n6O07G7IriEA88FffBkqe76JHuW/bdO+ChsnQ0ec8/RRbHGi1Kd1DW2KrwLixiXWzNQC4DmhzfOprPo3SnvclpLl7fOdg1693oXbPazN+8t3hNHXjbCqfyoVX4YMcVP3cWAeY820Xv7j0dtvrafWFhizXyLmjQPbHeHRgMoXHLrwAvqSKk+9SrvYq9gyfbkOSYadEfGkBI60O2UAQPGVgH9GWttPAP+F9nM0QJa8GFp+Ncb0fQyj+/4LJR5777DAAm5NMhtPX/MxEl0EsgjQp/B8/biKfpKxjXlPASv5GVLOQcwPVvprMM+xGf3tkOVeKC6ZjZ69P0TPXu+irPwP8HjtQ0gEJvCsDgCwO5+Rsh2I6mXTKPSc9a6YB0aC2/j/7J13mFxV+cc/75Sd7btJNgUEEkAQUJoEoiJGQERAioAIhCYoigoiCP6wBRFURAQLIp2EJk0MIL1XkQDSayB00rdPvff9/XFny+zcO/fM7mRbzud55snOuWfOPZs9c+973/O+3xfQzr8GtP8ldIZFp5EqGhqOZfKUe4lGZ9DcfC4TJ11ELL5h2WONBazRtwahyZvQgPI3El0bGaBUHqk7JnCshvh61MfXq9zkSmht9WDq6VuZGUtbOJVM0DDMiiuxZaGa662gAUD6HjT9REDnvrWkPvFcIjEiTadC9Ve9htpDiTT8aMiaYJNqtiVS5vawxQ/TmrTBa0/dlX1yGdoBpW7kvRnh/lm+Un8sMulGoAqkCZl0C1J3uOEc/RGpIhbfeEhjjH/MZJ4qIVysHWfQG8/bfSWae8O/n7qQfc3Xe6zOUk/3048AT7IJ0eg0Jk26gto6/7CU8YI1+tYQ1O2C1N2QCi40Hak7mt40+djGRKp3CuybiDYPWqbFn/ALyvK0mTHnGD+VjjO028jo01xwURrt/BvkBsS3tP0EHVBxRZO3gtNvGz31L9T5wHdMSexU8K9llGAcpF7C6Gv/jWfs9bzvPA/NFXvatft6yHgPD9p1fnCsVmz9/PkEiVXwoXKcYbbrUWkNUBOjL7iPJv8J6fv6tWTR1pOLkiVUHbTtBHAWQdclaPKmguMSnYI0ngYyIKxDmpGmPxhqN/ozXr17/bFG3xqCdp0H2oZ2/g11W337SGwDpMqrvhGpnePbp4eoVBENKKk1uAmGx6J9kHyflBNu1CQqEGc4JtF8QH0YQRmUzkfQdYFP/w/Qrgv7+iVvQdtOKhpTVxzobd8MpHpniG0MVZ8Jn5tlGBmcR6QHTT8EqZsHNHah7XP73qritv8Kbf9ZXx93BbpiP9Q3QSPiJRHZKholMfOWm3rmTPo5hv3856VuJ9r+u+IDuReg+5oBnbsh3ePJy/nqNUrtgUjL7X3XlMTOyOQ7kJo9DOa4ZmONvjUATT/gKZcDuMvQ1h8HuuqlendAkMRXSo4pEqloOSwtUdWhh/ZcO23ZcFmI7tx41FgyuOBqxiymjwDB6cx/CdzySz/S7zRP4Zsl7C4DZ3FRs0gNUn+iJwVjGQYMPTyD3Abr/XjPNWUgmYfRbF4/Lfei/5avdqHtvy1qFokjDT9BGn9W/BnLasI0kWPwRh+Zh0H9nQ1FwspSC7GP972N+8d0SnQaMuFCqPoM0vwXpJS8lKUXa/SNczT3Ltp6IgVf7MxDgQGvEv80RGcg0Uklx3XVQSu4jaoGHqqMmyHrlvYIvtv9Fou6XqE1MzSh2EpQ2XJYFdyCiQRk1ZYSx+4nqSONc5GGX0L/4uXR9ZGJ1yCJHXw/LtXll+mz+DG0LbYChmj0ldRayx+T+KeQxt9CdMBWbdXnkQkBQffVX4Uq/3VkKYdKJompYeJPwFj9rxVFx2r7Pp19EV3xdcg+09fW+QfctlMC4oarkabfl6y/bCnEGn3jHO04qyDmppeuC1A/8cvYdCQ2PXRcV7O4hokVJqjfHIv6KFriAvVe92IufescHM1x4Zu/Z3napCzUOELi3iuMgL+bxLeAhJ9xFkfq+5J6RASpOwSZeIUXVxPfCpl0I1K11SAnbqkspjf7oQnPSs3+/gei6xdIZ0jtfkjLnUj9CV7c1cSriEy8FIl/yn9ciQ452ccCI6LTFxSmU/X5Au9df6TusL6Pp+4tiikGhdQdeVF3n89Hp5nNzQJYo2/8k3sp6ABki0tYiSQChZr742gaZ6iegjyqGaOyPC5uYF3dOz/6J/CsEG0AACAASURBVGe/+vPezN33k29z5is/4bHlQ6/fOWaQ2uLgZj9yLwd6IaXxjKK/vzSchPhcsCW+BVJ/HNJ0NhIZWlUWiylD8LZUmtrDoXrPwjZpRiachwyI9xWJIvXfRRpORKq2HZ75jWPMqqRU0NOHmIXzuP7C+CIxpOmPxR6/2kOQxBd730YajkMmXgPxzXtaoGZ/pOUuZBAafJZirNE33kl8yb9dmoMFLiUg5qsfjqYqaPR1GW3vduY66MgVewS7ch3c/dEC3AFxZhk3zb8/vC7QUBxbGHxVpQGCBJHzqGa8rZMAIV2JtniGXw9Vn0fqjggesGYfJLZu+NwsFaKSBp3hlljA91xEkKYzoeoL+YYaZOIlvg8IvVTvUuYcLYPH1DtncH0UwegalHkqeIj4JoXXlvg2SMNPi/tVbYNMvN4z9prPI9L0GyQ6uaifZXBYo2+cIw0/hNimA1ojSNOZXqksPwykHHJuN9kKFaVWtyN0e3dFejn/a32G+5beU2TE1cUa+OHGpzKt+mMF7TPqNuL4jX9FRNaQZR5p8F6lSN4MmkST1wZ2keod89u8EaTxFyWHE5tlOcwYemVMMJC28KqzLA0eQmJI8zkQmeglYPR6aAL622D7UUjlHiS0+8oieaf+SM1XoXo3IIE0nRUYiycSQRpOQap3rtjcLB5ryN1wzUWkBplwAUT6npSk4eTSgfVGSRWdZEt8uctB3RVoiaDwl9tf4nevnEHSSfJc27P89Y0/0ZYtzARbt3Z9TvzEGWxYvwkAWzXP4riN5jI5MV7iPUxu5FX5lz/qLEU7/+C96b4WzTwdfLbaQyGxA2JQE9kynAyzp8/9qKSYN3h1lqX28EFXWrGsLkzjIg3MAHVDnQGaftRfgmXgrOq+A9W7I7HSpTcDnRKWIWGNvnGAqpZUIZfoNKQpr5FU9QWk7sgSY7lmRp/TRtrxT8EvF9d5Hzfnk1QC3P7hvzn7td+zKttXA/a5tmeZ++LPebe7UBOuKpJgt2nejWfPtQ8iOiYkQky/giYX8CxBkiuqKbT1+/1KsOXQ1uNQ50P/oaq2Q3q27SxjDMM1ZaLpmL4fTRsUsa/eA7FVUkYXptc/k8xXbfNPCOw57KxA2zyZHe38M9q/ROPA08U3Q6q/bDY3S8WxRt94IPcaJP9dsoskdoD4p5GG40qPpSnvFUJ3bgnJXGXKnTnZF3ECEk5ikRjiY/AIEaI+27Y10dqCf0cWk+xI06dxg37OB+CXkQ1oxx+Ki5e7S/OajcXeI5EqqJplODfL8FFBUd7s/0oeVmeFV6El+S80+3zpsWwG5TBj8jc2feg1MANybwRmz6rbia46CtyeijxpdNW30Nw7weMFaO9ZVj/W6BsHaPIaNHltaEaX1OztyXKUHqxk0fQeOjJv05FZXMYsg8mlH0bdleSyA1P1YZepu3LcRj+iOtKXXLJB3YbM3ew01q75WFH/95LeVtS73aW3pIYDt0KJLqZo+kGvSoIfyX/6t2efBCfg4hxduzITs4RiXtfUwOjT8LALdbvQrou9xB7f4yvzN/KlQBZd9Z0+wWVfrJdveDHRSDW9vYf309S9vpUxALT9l8UqEe4Kb80E3ZMiTYZzs1Qaa/SNcTR1hxdDkX0G7Ty3dOd8ibXSA4aX8nLVYUX6RZannitjpv44ubdw8h6obPe/fPts3rQF+67jaYJFJcqxH/8hzVXFdX+7c53cvcQrC3X7hzeQcyunIzgYHB8x0cETEk/jfATJG6H7atTxiY+MbeT/QWkuiPcsPFZCUNVSYUw18wy8N66BMHn3FZ6x3/2PokPqdqArDy28kbvL0ZWHoLk3fIezunrDjMnWfIVCR9RZCqnbILXAv4Rn5r/+H3Te9Kr0+GLFlEcKa/SNYTR5M9p6Ar3B3V3n43acFfyBoJt7waBdoZ6CpcmFZN0OunIfsCrtL/1hSqbfTSeTvAENEA5ekfb0n1x1C+L7emjNrOTPr5/G8vRHACzufp3zF/2WpGNycaw8qi5Z56PKDVgiU9orUH5S/m/Xhrb9pOgJ27dAOYI0nYZE/LfCbdm0YcS4uo1J0H3psTT3Ftp5nvdz5znFIu3Jm/y38rQV7brYcJ6W1YlWSDnBo7SXWbsuBTJe6byu+UXHpf5H+BqOtYcg0SmVmaKlYlijb4yimSfRtpMp8hB0XeT7xQSMJBrQTs94KMHrrdf2+/m68DFLkE3e0ndqdwW59GOFx90sV7x9OXcuucPrg3LWq7/jqVVPFvS77cPr+DBVmNjxRufL3L+0dKzj6iLjvEcmt7hyAwZtwZKP18s80e/kxWX2JP4JpPlc+j9hS/2JSHXpGsuW4cEtVdKsXCQ4nlXVzV838qEH2oW2nVL4kJD4gqf5WETErpdRgGoONyB2d3AE74io2wnJftf47quKQgKkdj+v1F5/c6L2CCIl5Z6GSUDcUoQ1+sYq2ZcJekIrlTkVhrptUGJbclX6VT7ofrj3/dsdt9GV/SCwf+lzdeA6hYaaky2MDXlsxaM8uOyBgrakk+TCN/9OV67POP3KWvsxo7ZwC3PTxq2YPXlkblKdqUfpSD1i0LN0LeG+bv5bKJr5H3RfWnyg63w0+0pBkyRmQ913vTeJnZH6o83ObVntaEAx+kFRals+eW1xQk/mP5Ba0Pfx2AyvxF6kv5cmgTT/qaB6gmVkUHcZ6lZwF6GUAZb8Z+HOj66Cfg/qPUjtvkjjXO9NzTeINBaLLhcyHgTzxybW6Bur1B7o1TMcSHR9pOGEwY/rLgdyaEBc0MurLi/sTo6XVwV4FsOQKmKJnYgldux9RQbowu3Q8gUOXPdgaqN9W5PTa2dw0if+j7pYX9vEqsn8cONT2XXavkyr/hj7rXM4393wJ9TFhl/rSdVheccltHbfQjYXIInSg9tmNmZALJWmbsf/ou2gqTuLWqX+aJBapP5Yo/Nahodc1jRMwiBONcDTp+qiXRf5H+u6sHCI+GbIxKvzJfniyIQLkOpdDedoWZ24uUW4uTcrOKJ/TJ9qFu26rLi960LfxCOpPQjiWyMN/2dwTmv0jRQ2mnKUo24XEimupypSBRMuRNt/4QXwg1fWZsL5SKQ4ySE/Wvj5erYkc29DVaF6ftbt5v3OB4o+807nXWwz+WSkzMoXIgnqJ80r2SciEb409ctsO3EW9yy5i4lVE5k9eUffKhsRibD7Wl9n97VGViR2WcdFpPI38fdW/YL1J5eIg3LeNZO7iEzybZZIc+BfVXwy5ESq0dgmSHyz8HNaho108mYSNfuEx1G6qyA6tXSfoDAOdwU47/kfy71RdK2R2HrQ8BM0+yKS+Fzpc1qGjWzydpzMUyQajg/pmcMsqzrgup26E1yfbWTnLUjfDz7VMqTum773q2Ks0TdSWE/faEeDt1pFYkjjbyCxM0Q3QCZcXMLgA5Mvmmafy/9brMuVyi3D9ckyzLodZNzSZdSGSlO8if3W+To7Ttl5VJdV60o/zUetfck07ck7WdZR/LTcy4Dt7SAktoH/geqv4p/RGYfq3f0/o8lAqQ7L8OO6rWTSj5DNPGrQuTiJaSCBhmOkCSRAKiMyxb/mdmJHJFGiek8ec8kZy1Bwc2+T7b4ON/cS2eStpTsHJMWZotngij2afcb/QHS66eCDmJGlElhP32gn/TjU7ht4WESg/njIvRj+hKXZkskc6nZC/susmYeh7rCC47XxtZg52adAtkRIRK3ukuN28Pby76AD4vQ+XPVr6hIzqa3yqUtqWsouoGapxNZFEztC+p7CA9W7+WbOqdvlCa1mX4KqrczObVmtJDsvBFJ0d5xHvGqH1SZ/IlIFdUehnX8sPlZ3tK+xKJFGtOSDZA85SpUAtAwd1TTJVcfTk4STavsF0fjWRGLFeqUepoa4/0O0xDYL3kUoqufec6De7JSaBmyZtZFg9LpMxjkmnhZNP4J2/BrNPFm6Y2xjiBvcwEPEgjX9AD0xQ5p+tKhwdlSq2LDpa0WvDRr3Dj/3GsCqrn/5yrQoWZa3D1HqosSNVxI+2yzVX/LvnHkEyBqV1tKQWpuWoZNO3k53PtM6m3mErvYzSn+gRGauEXXfhvjMwraqHaD20BLnNNgirKgmpWUgqkpy1Qk42af62twVdK883Eu+GxIBZkDN1/wrZ1TNCt5FMNraxUhA3LJ6sEbfSOEncplH3Tbctrnoqm95kgorD8NtP6PICOtBRAZk2gUNXHprSAu8RRk0E1DdweJLrIRhFjXylpQgIKbPO7HP1m90hm9XzfzH+6G/xEsQw1xRZE0jm3mOjtYf0t8jk+w6n6SPYHIvQzT6RKJI8zkgjV5DZBLSfFZp76LJOZ1KJhZYBpJN3kAuVZw16+ZeI9X+G/8PGets+v/tvbXyp8KQgEgL0nRO8HoxXZ8mAuKW1UKo0SciDxm+7hqOCY8FwsqhAWhuEeojoqqaQpfvBslr6LsZONA9D13+1WDvi8kXfICER9G5B0g5aGboFTfWJJpqv0pTzR5F7YnYRkxt/pH/h0yKnUPpskV+AqhBoqg9Dw4mT9rukvA+lkHTvuoY1EcTs7P1pzi5AB02gyopoeUYo1OR+u97P9cfjwSEDvR9oDH8nKn7UIOa3ZbBESnxUC+Bx4Yuri7RtZGGU/reN/4SibaU+IRhOb6ghCLLasfE07ctcEnI61LAVmfvxSBhIvNYb/xcf0Sqkea/QfVe9H2BElCzLzLhPCTISDBQ9Nf0faizvFSHAQ3W01MOIsJ6LX+msaZvazUR24ANp1wd7AU0LnVWIl4qslbhjTkyJfBGLvF8XGHskyXPppox8wZaBk28ahvf9mhsAyKRCf4fEpO4OQPh25r9vDVTs1d4X5M1mr4bBgirWypHrHo2icZTGWjIxWv2J9Fw3Oo9ec0+3jUmun6oOLdJFR9129DcyNdGX1MxcTM8pqqldTUAETm4AvMZH2g7SMBFG6+WoXZfg5t9jsiEy4qkTqRqK6RqK9T5MdrxW6Th50g0pISapoHgIFp127w0+/hmUPct3z5SvTvk+vTCpOozpc9pKSIiVUxvuYBFS75OOreYDaZcQzxWQpIl6OZeRPD2m0gETXwRUl7dYUplW1bvCh1nIDW7lT5d+iE0dbunvWVZLTQ0n0s0NoPujnPoeVCsqt6dhuY/BpbGM3tODzf6JNKI1nwNMTDowm7kmnkSnHfR5I1I9U4G87MMhkT9kUTjm5Jc+R1UO6humktV3RHBH9Cc4UOCQylTQCSCVn9p6PGkPWRfgVzpXSfL6iPU6FPV4ihx/35fHvp0xgm5N6BqW99DmluMs+o7Xh3LzKO4bScSafqN78VXotOg8QwkYpARpcnShzv/7MUHdl7oeQ19PEHRxp+Hn8cSSkSqWHvCXLrST1IVW7t0Z99yV36kgeAgaanZC80bfVLCeyPRqWh8c6gK1l1TTaOd50LuNTR1P1IdLtlhKR+RCHUNJxCLbUb7qmOorT+GusaTQz7lEL5tZ9IHJLGD4UxL4yWAAemHUXVszebVSCzxWWom/IVcZmFpgw/yIRwmhlqWMFNA4ltXzOjT9J2QeRx1O5CIzeAdbmwiR4VRZ6lvJQQAN/04zop9wFnU1z91C86K/QO3XU0MPtVcySQNzT4P3Vfl37Si7b8LHdMyNGqrtqaxxsDrYZoh64RU9qj6LFAN0gxx/23DHiTxZU++I2hK7adD7jXv57b/Q8PObRkSiZqv0DjhfAODj9C62F6fbrMTxzY06xdGb/ZoCuOygpZBE6ueTVXtAeEd89/hUEIcBt5JN4AB1ZIGgzrLvGIC2g3doRuIltVAWUafiJwW8PqZiBwuIiFS8eMbVQftOBOSN6LZl4uPJ6/2D6DPvdr3tDwYcm963sWgeXVdTEGcYepm1EdaxFI5RISq2LrhHbPPoSFCpaqKpv4dcr44xNaD2PrhlVHiwfF8mn7Aq8/a27AKbftZ6fEsQ6bKp7qBLyW+5319XjMT3y4RglIOEtvY+yG6jtF2sWXoSDRIm68PTRpe5zPPhveJtEAkJMTIAO26uNfI1K5LPd1Qy7BSrqdvY+AnwI7Ax/P//gTYGjgGeFNERqbC/QijzlJ01TchdUteZuUgNHlzQZ9I42k+6vYxIvXHITXBAsyh5+6eh3Zf6ZsN7J24xfME9b5mmXkMLENCQrZMVDNo8nqv3FEpUrdC9zXhHjep9q+qMJDoWsFzSj9Y3Jh5xGr2rXbCw6u9XYS7w/t1XwVh1RqgpFB7WVTvDAgkdqnMeBYDSgt4a+4tSP0b7fxb6X6Zp9H209CwikpS572GSnZhv5N3FsSQW4aHco2+CHCgqu6gqger6g7AAYCjqp8BvgescXuHmn0OXb4r9GigAWg32vZj3Naf9DZJZALRCRchdd/Jt0SJTLjMM/oGWVpMu6+F5PWQfR5tP823T6TxF0Qmzit4SaW2diyBqE/Jut5j6qBtPwFnMdr+SzT7on+/zP/Q9rneg8SqY9AS+o49Zw2lhGq+JL5Y3Fj1+eCscUtFMKnCoV2XQPKmwFAQ1RRu2y8gdTva/gs0eVPIOc1i78JKrEn0YxDf0ksEs4w46ixHV30XyELyH2j31f79Uvejq44C93105cForkRJSEmYrdEwD3N8i35j1kLs46FjWipLuZbGrsDNA9puBXpSAa8E1jxrIrYx1HydIlmN2GaIT+xFpHrP/A8tRBKfHfRpNfOkZxD0kLwG7bpi0ONZKovr+sfKqNuJrvo29GzZaie68hA0VVglQzNPet7jnpCA3EvoykNRZ0XAGQ09fSU0+iQx26vaEPu494pvgTQFiL9ahg3tvs6LgdI2tPW7RVUYNPceunyvflvzWbTtJ7irvh+8A2BMuHSTNJ4KcZ8yg5bVQpABpu5KdNXh4PRJomj7r9Du6wv6uR3noq3f7dvxyb2KrtgbTT8ccEbD5JwQ0WWp+36v2LPUfR+JhGtAWipLuUbfIrxt3P58N98O0AKscfuGItVEGn+KtNzmBdID1H0HmXQT4qPF5abyWy/uEjSzsOi4Mbl3vaD9+La9L3WXDn48S0VxA7bQteN3+XJo/Ru70LYfoY4nyqtuB7rq+8Xb8LlX0faAGLvoVLPKLCFrLtJwEpGW27zXpBu8LHLLiOF2/Alt/zm9cbnZ59AVBxR4ZrT7SnAWF384fTdk/ju0CRjEhUl8s0HvVlgqh7b/DnKvD2z1PL/5v6M6H0HX3yjaFdBOtOMs33GN60GHaDVKtAWZOB9pvgDqvmk2pqWilPst/RbwYxF5V0T+IyLvAScBR+WPfwL4RSUnWAoRSYjIJSLytoh0iMgzIrJb/tgMEVER6ez3Wq1zk9h6eY20KFI7J/iL0k+YUnODL18ktfsSmXRV4avhxEGPZ6ksuaCn3sCn22ifLIKmQANqajr+lTIkNgMJybBTzaJdF9hEnjGCZp6FrvOKDzhvoR2/7X0rDcdD0dZ8BKn/MTKU3YTcu+ZZoJZRTBXkt14lOs1fq1XqkPqAykEGqDpo119Dry0S3xSp3tGGjIwQZRl9qvo0sBFwMHBO/t+N8u2o6kOqelHFZxlMDHgXmA004Rmc14nIjH59mlW1Pv/69eqekNQfAzUHlPaO9A+grlQwtWVU4bjdOI5//J3UHw81+xc2RqYgEy5F8mLNEp2MNJ5B0bZKZDLSFBA2G10foj51ePvTdYEnpNt+mlG5QMsIE9/Mx5gDpBmpndP3Vqrz3pPv5xtqvPVUf/TQzp95pMSWn2W0IY0/9xL1ChprvPrKsfV6myINJyPNf+5LzohugEy6cWianMmbwHkP7bpg8GNYVjuD8cdPBJrxVB8/DhwqIkdWdFaGqGqXqp6qqotV1VXVW4G3gNJCZasRic1A6o8NPK7qoP229nyzJS1jHtdtw3H9PXUiVUSafgM9CT2RtZFJ1yFVWxb2q90faT6vrwxWdH1k4rVI/BP+J41O9V4BaPLfaOdfvDfpe9DOs8v6nSzDj0icyIQLkcZfAfl4zarPIC23IIntB/QVIg0/hMRXkIZfIIlgAW5TNHUbpO6qQFygZTiQSCMy4TLoiRuXRmTiVUj1rsV9q7+CTLgIIh9DJl2PxEIeGEugqmjX37033deViDu2jDTl6vTtgxe/dxpwAXBs/t9DKz+18snrBG4M9E+FfFtE3hORy0TEt1K0iBwtIgtFZOGyZcuGPo9SBak1CW6fkLIVvh17XHjhhcycOZOZM2cStF4c7cDR0jIIkYYTIb410vx7JOpfuUOqd0Iafp733FyIxNYZ1JzV+QhtO4WCOJ6uC9H0o4Maz2KGyVoxQWoPQib8GapmIRMuQ0oY91I7x6ymrgnZ50Bb/eMFLRWnEutFJIY0nQnxzZHmc5D4p4L7Vs1Emn5XgcoYGXDeyf+ctetlFFOup+904JuqujXQlf/3aOCpis+sTEQkDlwFzFPVV4DlwLbAdDzPX0P+eBGqeqGqzlTVmZMnD12AsuQ8I/VehmSeiJU5GHMcffTRLFy4kIULFxK0Xly3C9dAeFSq90CqtivdqWY/qNkfiU0v3S/3enD8lfMRXsWEge2LQ+doGTwma8UUSXwRaTg5XGolvnnJiis9hEmxeCedAEgZNaItQ6FS60UkhtQfa1Zqr2rr0C6hsj2S6NtWjrRACUPTMrKUa/Stp6rXD2ibBxxWofkMCvHSxq7AqwH0AwBV7VTVhaqaU9Ul+fYvi8iI54hH6k8GYl4cRe2BIz0dy2pApNq7EIYRDb+wi0SQmn1K9lHNoN1Xo91X+AopS9VWUP3VwsbYJsWxhZbRTSxga78/xlUxDKp2VO/meRd9anVbRjnxmaYdw7toMjQGWOpPhEgLUv9Ds2ufZUQoN31mqYhMzRtRi0Xks3getRGrsC1eiuwlwFRgdw2uadWzYg1zz1cfEt8Yqd4dSXzBK59lGXfEIhOIRSeFd3TbzQYMKbuk7af21cvtOBNpLJZ1kaYzIbYpqt2AIHWH2IvzGMPEg2eM82FoPVWp/z62nu4YxbiCRo5Qw89dBZE0SLDxL1VbIVNKS7ZYRp5yjb6LgM8DN+Jl796PJx41khHh5wObAl9S7ascLSKzgFbgdWAC8GfgAdUgHYzhRRI7IYkvjPQ0LKuJWHQy8RLlznpQZ4nZU0iJouhux+8heUNfQ/c8NNKE1P+goJ9IHOq/PfJPPZbVjIvRc3jqdqj/XskuEgmu3mIZ3Rhr67ltUCoOHSD3oudBtvesMU+5ki1nquqN+Z/n4yVNbKOqw6bN1x8RmQ58B9gK+KifHt8cYAPgDqADeAFPVv6gkZinHxLfwqqRj2NEoiRCvCiAV2HB7Q7vl1zg//H0w9B1cXF751/QzJPh41rGHyUqrvR2yb1j9RotHpmHQrt4oSPXDMNkLKubIUmoq+o7qvpypSYziPO/raqiqtX9tPjqVfUqVb1GVddX1TpVXUtVD1PV0XOFi6470jOwrGZiUYPgd00Ttn2m3f9AO/+O5hYXH+wn9D3gU+DX3zL+yT4fWgNVO8/x4rQ6/zxMk7IMNyaJOpp9Hu34U8n1oqnbIfM4pO9F0w9UcIaWkaBcyZYmEfmFiPxTRO7q/1pdExyvGLveLeMbqSlZL1czC9H204EUuuo7qDtA8LlmX4j6yLhEN4CaPSs7V8uIYyKoranbIH1/8HHnfUjd5r1J3oSG1Eu1jFXCYzG1/QxwP4SuS/yPa8rr09v/1wSHzVvGAuV6+q4HvgjcB1w74GVZQ5kxYwY1NTXU19f3vn7wgx9w+eWX8/nPf77kZ4844ghisRgffPBBQfupp57KIYcc0vt+wYIFbLXVVjQ2NtLS0sLOO+/M4sWLA8e95JJL2GSTTWhoaGDq1KnssccedHR4unn3338/O+64I01NTcyYMWPQv3clkOg0pJTR1/EHei/ezlswYItFIvXIhMsh0k+3LboOMvHykuOOFP/4xz+YNWsWdXV1TJkyhVmzZvG3v/2twJg59dRTERH++9/CmrGXX345IsIJJ5xQ0P6vf/0LEeGII44oaO/q6qK+vp7ddzeTRTrnnHOYNm0aTU1NHHnkkaTT6d5jjz32GNtttx0NDQ1sscUWPPLIIyVGWp34yO70Q902SN6MdvuqU+U7penLa3N6y3ONNkbrWnnhhRfYddddaWlpKXp4T6fTHHXUUUyfPp2Ghga23nprbr/99jJ/8woRUK6xB3U+hOzT3s899eAHkn0e+tdzd0ZvWb6xuF4AvvjFL1JdXd177/zEJwwy9IdAuUbfZ4CvqOpfVfWS/q/VMTnL2OGWW26hs7Oz9/XXv/419DNdXV3ceOONNDU1cdVVwTepN954g8MOO4yzzz6btrY23nrrLb73ve8Rifgv3wcffJCf/vSnXHPNNXR0dPDyyy9zwAEH9B6vq6vjyCOP5Kyz/IuLDythGXbxzUu/x6v5LBP+DpEpEFnLE3EuVQZwhDj77LP54Q9/yEknncRHH33EkiVL+Pvf/86jjz5KJuMZHqrKFVdcwcSJE5k3b17RGBtuuCHXXnstuVyfLM38+fPZeOONi/recMMNJBIJ7rrrLj78sLQI+p133snvfvc77r33XhYvXsybb77J3LlzAVi5ciV77bUXJ510Eq2trZx88snsueeerFq1quSYq4XA7fw87go8odwPArtIbAOoORiqPg+137RrhfLWSjwe54ADDuCSS4pve7lcjnXXXZcHH3yQtrY2fv3rX3PAAQeUfEBdbaTvK11JJTIFInlVgHiAVl90PQqTguL+OwsjzFhdLz389a9/7b13vvrqq6a/9uBQVeMXcBuwRTmfGWuvbbbZRi3lMX36dL377ruL2i+77DLdfvvtAz83b948XWeddfTcc8/VT37ykwXH5s6dq3PmzFFV1euvv1633HJL4/mcddZZuvfee4f2u/vuu3X69OnG4/ox1PXitP5UXWdF4HHXTamz/BvqfLiROu3nDulcI0lra6vW1tbqDTfcULLfHHDC+QAAIABJREFUgw8+qNXV1XrFFVfoxIkTNZ1O9x7rWU+77rqr3nrrraqqumLFCp06dar++Mc/1sMPP7xgrB133FF/+tOf6tZbb61nnXVWyfMedNBBesopp/S+v+eee3Tq1KmqqnrLLbfoZpttVtB/o4020osvvjj09+7PUNeKm3tfnba5of2cZXuo0/77IZ1rJBnta6WH119/Xb1baGk233zz0N/Fj6GsF9d11Vm2h7rJe0v3S96pzkdblbwGOW1neNefDzdSp/2cQc9pdTHW18vs2bP1oosuMhojCGChGto45Xr6jgAuFZHzROSX/V8VtUQtawTz5s3joIMO4sADD+SVV17h6aef9u336U9/mldeeYUf/ehH3H///XR2ls5OnDVrFnfeeSdz587l0UcfLdimG02oswJSd0D31YF9RBJI01lQvXvJms6jnccff5x0Os3ee+9dst+8efPYc889+cY3vgHArbcWbzsddthhzJ8/H/C2dPbee28SiUK9wXfeeYcHHniAOXPmMGfOnN7+Qbz44otsuWVf7eMtt9ySJUuWsGLFiv4Pvb2oKi+88ELJMSuNdl2Wj8Er7WGU+h8htQcP06wqz2hfK+WwZMkSXnvtNT75yU9WbEwjUrdA7jW089yS3j6p/jJS/4OS4ttSfzxUf82rChQi8TMSjIf1csopp9DS0sL222/PAw88MOTxSlGu0XcGsC6eEPJG/V4fr/C8LGOMffbZh+bm5t7XRRddVLL/O++8w/3338/BBx/M1KlT2XnnnX1d7gAbbLABDzzwAO+//z4HHHAALS0tHHHEEYHG3w477MA///lPnn76afbYYw8mTZrECSecgOOMrqLx2nEGaAfaeQFaYttOYusSaT4Xr/DM2GT58uW0tLQQi/VJg37uc5+jubmZmpoaHnroIbq7u7n++us5+OCDicfj7L///r5r4mtf+xoPPPAAbW1tzJ8/n8MOKy4INH/+fLbYYgs222wzDjroIF588UWeeeaZwPl1dnbS1NTU+77n546ODj73uc/xwQcfcM0115DNZpk3bx6LFi2iu9tAaqeSpO7x9BpD6iVL9c5IiJj3aGa0rxVTstksc+bM4fDDD2eTTTYZ8njloF0Xej/kXoF0iCRL7aElD0uklkjzmUSaflNZcfAKMdbXy5lnnsmbb77J+++/z9FHH82ee+7JokWLBj1eGOXeRQ4EtlLV/VX10H6vES3DZhl5/vWvf9Ha2tr7+va3v12y/xVXXMGmm27KVlttBcCcOXO4+uqryWb9M8M+85nPcN1117Fs2TIefvhhHnroIc44w8sq659A8s47XtHv3XbbjVtuuYWVK1eyYMECLr/8ci6+uFjPbqTQ3FvQGzyd7rtIj1MmTZrE8uXLC+JlHnvsMVpbW5k0aRKu63LTTTcRi8V6g6PnzJnD7bffXlR4vqamhj322IPTTz+d5cuXs/322xedb/78+cyZMweAtddem9mzZ/de5K+66qre9bLbbrsB3hpqb++rjtLzc0NDA5MmTWLBggX88Y9/ZOrUqdxxxx186UtfYp11hjm2Kf4JQCBWHGM0nhjta8UE13U59NBDqaqqMopvrjgFGbals21HoyFXDmN9vcyaNYuGhgYSiQSHH34422+/Pbfddtug/i9MKNfoe5OwFWSxGDB//nzefPNNpk2bxrRp0zjhhBNYvny5Uabbtttuy7777tu7vdY/gWS99dYr6BuJRNh5553Zaaedhn07rjQRCr9+I1bJcFj47Gc/SyKRYMECf5Fp8LZfev6G06ZN4+tf/zrZbJZrrikWhe1J7Dn00GIvxWOPPcbrr7/Ob3/729719cQTT3DNNdeQy+WYM2dO73rpWW+f/OQnefbZZ3vHePbZZ5k6dSqTJnml9GbPns2TTz7JypUrueKKK3j11VfZbrvthvrfUhZS912o+RoSH99G32hfK2GoKkcddRRLlizhxhtvJB4f/lKbvaEgsU0h8aVhP/9wMtbXy0BExEiaabCUW4btCuBmEfkLUJAPrqr3VWxWlnGDqpJKFcpMPPPMMyxatIhnnnmGyZMn97afeOKJzJs3j7322qug/yOPPMLLL7/M3nvvzZQpU3jllVe4+eabOfzww33PuWDBApLJJLvuuivNzc08+eSTPPjgg5x77rmA9xSeyWTIZrO984tEIlRVDd8Tr8SmozUHQvJqkPp8jdPxS3NzM3PnzuV73/seqspXvvIVamtree655+jq6uL999/n3nvv5fbbb2eLLbbo/dy5557LvHnzOO644wrGmz17NnfffTdbb12cdThv3jx22WWXglibZDLJFltswe23386eexbrFx522GEcccQRzJkzh7XWWovTTz+9QKbhmWee4VOf+hTJZJJf/vKXrLPOOuy6664V+J8xR6q2Qqq2GtZzjgSjfa2oKul0ujcrNJVKISK9sV/HHHMML7/8Mvfccw81NTUV+T8pF6n5KmgXVG0zpsNCTBjL66W1tZUnnniC2bNnE4vFuPbaa3nooYd671WrBdOMj7zl+VbA681yxhnNL5u9Wz7Tp0/X6upqraur633ts88+etlllymeIFjB66ijjtJ99923aJwnnnhCq6qqdMWKFQXZu88//7x+9atf1SlTpmhdXZ1Onz5dTz75ZM1kMr7zefDBB3WnnXbSSZMmaX19vW600UZ65pln9h6///77i+Y0e/bsQf3uQ8uw61ZnyQ7qdl0z6DHGGldeeaVuu+22WlNToy0tLbrddtvpBRdcoL/61a/005/+dFH/999/X2OxmD7//PMls8F/9rOf6eGHH67JZFKbm5v15ptvLupzzDHH6H777Rc4t7PPPlunTJmiDQ0NesQRR2gqleo9duCBB2pjY6M2NjbqAQccoEuWLCn7d7fXlvIYrWvlrbfeKrp+9KgALF68WAFNJBIF18Mrr7yy7N/frpfyGIvrZenSpTpz5kytr6/XpqYmnTVrlt51111l/+6Ukb0ruhrdiGORmTNn6sKFC0d6GpYxwsyZMxnKetGuS6H2kDEfV2MJZ6hrxbJmYdeLxRQReUpVZ5r0Hd9+X4tltFO9qzX4LBaLxTIshBp9IvJrk4FE5FdDn47FsoYRmTLSM7BYLBbLGoJJIsfxInIpUFw0rpDjgLlDn5LFsuYgMvyZfRaLxWJZMzEx+uqANwg3+kpXArdYLBaLxWKxjBihRp+q2rg/i8VisVgsljGONegsFovFYrFY1gCs0WexWCwWi8WyBmCNPovFYrFYLJY1AGv0WSwWi8VisawBlFV7VzwV2SOArYD6/sdU9bDKTctisVgsFovFUknKMvqAecCWwC3AkspPx2KxWCwWi8WyOijX6PsKsL6qtq6OyVgsFovFYrFYVg+iquadRZ4Fvqyq49bL19LSojNmzBjiKC7h4ZImfQCUcF1sy0ixePFihrZeTNeBST+7VkYzQ18rhmgGjOo5O0C0QuOZrmOLKcO2XixjnqeeekpNNZVDPX0islO/t/OBBSLyJwZs76rqfWXNcpQyY8YMFi5cOKQx1O1AIg0hfTqRSH3JPgCqKUSqhzQfy+pj5syZQ1ovJmvF6xe+XuxaGd0Mda2Y4ubeJRJbN7SfOiuQ6KTwfrn3kdjHSvdxVyKRicZztIQz5GuL5hAJ38xTdREpbS+Y9CnnnJbKIiJPm/Y1+etc4tP2mwHvFdjA9KQW8P7LTLq51nljwWi92LViATwPngmG1yAxWFRqek6LxTKSmJRhW384JjK+MLmYmm6rm2+/W8YilVwHdq1YoOJGn9GTRM5wLMvwYe8xlmLKCsIQkR8HtJ9QmemMF+yXyGKKXSuWCqOmBlgljQLXcCyLxTKSlBt5+8uA9p8PdSJrHvYiaSkHu14soJox6FVho8/EiLTbu+Mc+3A6XjCKuOyXzBEVkR0p9PdvAHRUemJjm0o+Gdsv2/jG9O9rjT4LoKnwTFrNGg5mFgCqmjLoadfn6MPeOyzFmKbZ9CRzVAOX9mtXvCzeYys5qbGPSdC9vdlbwNzbYhbTZ5plZxmrhK8DNfb0hZtyqi5o0mAsa2CMPkYips+ug9GOkdHXk8whIvNtuTUTKhl0b42+8U2lEznsRXeNx9jTZ0IWs+1imzY+vrFJIeOFcl0C3xaR00TkdRHpyv/7a7HiYAOo5PauNfrGN5U0/u0Fd/xj8jc2ifsDI0NNc5hlA1vvssUyFihXRfFvwCeA44C3genAKcDHgCMrO7UxjNG9twydPsv4xdhOM/X0uRhVWbCMUUxCRxxUFTHR16vQKbGCvGMYe49Zkyj3m7oPsGG/2rsvicgTwBtYo68flfwS2S/k+KaSf1+7vWsBzzPnEH55NzAKJWImzkzcoI/FYhlpyvXJfwTUDmirAT6szHTGC5XcirM38fFNJbO47VoZ/5iuA5MtWZOxohj5Bqynb5xjry3jhXK/qVcAd4jIX4D3gHWB7wPz+9foHS91eAdPpb03lvGL9fRZysAk3EMdKrcOYpiFC1ijb/RhrwWWYsr9pn4n/+9PB7R/N/8CW4fXEJvIYSkHq+toATMPnusZfqG7suFrSiSCisnWrY0jHbvYXYQ1ibKMPluH1xSTrDhrzFnAWOrCrhcLngZf2IrR3oSeMEylXUy2d61ki8UyFrB59iOGvYlbysGuFwugaYNOLkYizkZjGWIfSkYhldYAtfSgxsUVRh9lG30isouIXCoit+Tfz+wfz2cBW4bNYo4V6baUgVF1DDOjz8yANKWSY1mGF3uPWZMoy+gTkWOB84HXgC/km5PA6RWe1xingkaffYIe51TQ6FPDm71lzKLaZdIJs/ViMJbXMbyLazqWZdgYw94oy+qjXE/f8cCXVPV39F1VXsETbLaUhZVssZRDJWv0WsYspp4+oyzfbsOTmozVZjiWxTJ6GcvbtqaUa/Q1AO/mf+7534ljXvdnzaCi3rnxvwjXaCq+Vux6GdcYbcmaefrUrZzRp+4Kw7EsFstIUq7R9xDwfwPajgPur8x0xgtWnNliSqVjO+16Gc+omjxfm+r0pQzParBG3ZWGY1lGHRX1btnrz2inXJ2+Y4FbROTbQIOIvAq0A3tWfGZjGhPJFhO9LRiJAP6K1u20hGAq2VKpCguVxa6V0YhjJO2C5kJH8ra7DNaVs8xkYpYxy+g15ip5DUo5WWpiVRUZa7RSrk7fhyKyLbAtMB1vq/e/qjbboHxGr9Fngr3ZDzeGoryj9OJs18swojnDhwRToWeT7d2lqLqIWBWwscfolGwxv2ZkgXBDzWS8VZmuihl9o/WaF2r0ichpJQ5vDuwuIqjqLys3rbHOWM/eVcI9UCZ9LOFUWrJluC/OpuvArpdKIEa7CFkqF2adw2x7txW0HaS5Que1jE0q+R03u2aodiNSGUPt3e6VrF07IbTfaDTmTDHx9K3b7+dqYD/gSeBtYD1gO+DGyk9tLFPJJ6fKGX3mT+Imc8sCCYNzOojYEk3BVFpAtXJGn9l6MT2fXS/DhZIuI+EjrEsub0SG4C4FZylErNE3LlE1tOeGPz7QcVYQMVh3OXWJh1xbXm3/kFktG4bPbJR68UwItQBU9Zs9L7w/+0Gqur2qHqyqnwcOXO2zHHOYLNbRvGAMYn3cFagaBIJrZwXmM54xvUiO1vUSvlYA1HnPbDi7XkqiJluymjT7bhqRwUR4WXNvoc5bFTqnZbxjJo1iFgKVTN1m1G9FuvS15f3ulVz11qP8Y/HjRuONVcoNwNgN+NeAtgXA7pWZTnmISEJELhGRt0WkQ0SeEZHd+h3fWUReEZFuEblfRKaPxDyHRiW3d023lMMv8m7madzMU+Fj5V4zO6elQlTySdtEn82sEkOu8+LwoTQJzruh/dZoDLxuqt2VM541HVquTZ3l4H6IZp+rzDkto5BK70wZxIkaZKqnM/+jrf0PZHOlHzgWdSzj+VXvBx5/ZOmrzHn0fJalO/j9S//m9Of/RdY1e6ANYrR6Ass1+t4Avj+g7XvAospMp2xieMkks4Em4BfAdSIyQ0RagH/m2yYCC4Frh2dao1WyxdArox2hfZzkzTjJm0P7ual/G51zzaWSsXqVTuQwye4MXytu9iWc5PW4uRBvX+51NPe66eTWUEy2WjvANTH6TOrzdocKQmvmIe/f9EMG57RUAjNP2ehM6vII9+KFXVscZykrVn4HyLBixVG4brBA+LxFj3PFm//B9YmRf6NjCT966iras33r/J/vLuTcV+4MneNYpFyj71vACSLynog8ISLvASfm24cdVe1S1VNVdbGquqp6K/AWsA2wL/Ciql6v3l7HqcCWIrLJ6p+ZyRaM6c3eNMvXAEMFfs0tLnnczb6Bm74HJ3kT6iwNHsf5AO2+Ac08Wc4s1zAM/75GFRYqLM5ssF4093Zon1zn+YBDruuikv3c5D/R5D9NZ7dmYpCVq+4q1G01GMvAgNQuwsq1aTIf0p17Gc2+HD6mZchU1os0AsahgRfPKeG9c91uli0/EMd5B4Bs7mWWrTgM9ZEhuui1h7nx7ad5asU7/PJ/N5NzC79DTyxfhONzfX182RuhcyzFaK3uUZbRp6rPABsBBwF/BA4GNlLVp1fD3MpGRKYCGwMvAp8Enu05pl7RykX59oGfO1pEForIwmXLKqA3ZfTHNt1qreDCKfEk1Hc6BydV+gknl7wG70KRJZe83n8ctxVn1XeBFE7r8aGG5FjiwgsvZObMmcycOZMhrxfjv28lvcempwxfL272edwShp86y3FSdwHgJBegATVa3e7r0e6r0MzjOB1nDm6+o5CKrhXAJCtX3eWouzy8n4nQs9uKuquCx8g8hWae6OveeV74mJZATNfLqPX0mTozDMr25bIv4ATsDqxsPYFsrvABI5P5L61tc3vfO+pyylP/5NyX7+1tu+md//Gtx+bTme2Lec26/g9SOWMt3bFF2aJKqppV1YdV9VpVfUjV5HFx9SMiceAqYJ6qvgLUAwNXVhteKbkCVPVCVZ2pqjMnT55cgdmYLHzTBVXBhed8GNrFTd+Xvzn7bw85mSdxuq7ofZ/rPB93wNO9Zl/EWfENyL2UH3QJzsqDcNMPDn7uo4ijjz6ahQsXsnDhQoa+Xirp8dXKSvwYrBcneWvgNr9qjkzb/9FrqGgb2fa5BTcsVRen4yzc9p/Rc5PSrotwWk8oo0zY6KWya4XQ+DoAnCWo+5HBYOF1fNX5AHX8x1JVnI7fFLal70BNYn0tvpiuFzNPX2UzaStqaIaFegDZzEKymYW+x9LpR33bU/3ar1/8FDe/Vxxn+uSKt/nTy/f1vv/autuwVk1x9u/3Nt45dI5jkXGhpCmersQVeHeXH+SbO4HGAV0bgfAgpCFjshU3/EafZp6klI62aops+5mg7WQ7zy067mZfJ7PymxTEFWkX6ZWH4Dof5MdQnNYfgDMgzNNdhtt6bEmvwZqJ6ZPx8Iozq9sN2edL9sl134DmXiDXdRHqYyBm236Gm763oM1J3kiu4/d950ktQLsuYOD/g6Zuxu366+B/gXGIaiY0cUY1g7pL0JxBQoxBPKaWiLPU1G2Qfbao3en4bfi5LUNitG4dmsXqpSBX+tqSzTxJJnUX3Z1/wh3ggPDkUvx1+Tzfj8fu63yKPdbZvEj3YFbL+hzx8c/2vv/ba/fwYbI4HOLvr93He93jr7zgmDf6xHvkuQSYCuzXz/P4IrBlv351wIb59tWMyY3cNDOogl/uzH/6vG8+ZFv/D80ba07XJTjJwlT4XPdl/vE97kqc7qsB7wk0OuFikLoBnWJEmv+CRMKFL9csTD1zJuulgjF92f+h2eCoDTf7Etm2X+RP20F65dEF24XqLMVJXuf72VzXRb0eq0jN15CabxT1kapZROp/NIRfYByiyfCkitw7gGskn2LyAOam/4tm/ucrAaPJa/w/lP2fje1bzQx/ZqjptcXg4TT7ApoJzvROp+6mbcVhQBYn9xptKw7AyfVl3jrOBzh5J0PR0NkXetdqY7yG32+zH9d/8Tts0jSNaTWNXPjZQ7h0+8P5WD8B5o0ap1ETLTYip9e30BSv9T3PqLW5DRjzRh9wPrApsKdqwRXxJuBTIrKfiFQDvwSey2/9rmZMbuSGu+IG9TGNhnE+guwzaOp23+NO6gGc1IKCtkz7qajb3vs+UjUrcPz+xyS2IdGWO5HaQ4AIktiVaMstRBKzh/ZLjEtMjT6T9aJljBcyUuoOSD9W8PfvPaZKpu3nQJ8h4Hn8Lu3rFGlBohv4jh2Jb4VIn1BzpPF0Is3nQXQDiLR47ydcVvDUbgHVTjQsqSIvkaTOB4Hxk719Q+L+3NxbaO4FIIWburfouDrBEhgacFO2VIZR6+kziBPV9GOQ+Q/qs3vR3fFX2lcegWrfdSeXfZZVy75ENvM/AKLRtamp3sN37LraQ/Bu931s2rQWl3zuMH6/zX5sP+XjRZ/Zf73t+Pusbxa07b3Op/nTzENpiFcX9QdwDf7/TfqMBGUZfXkdvFGjdZefy3eArYCPRKQz/5qjqsvwqoecAawCZjFcQtJGWXGub6ZRcb/KiKxq10WAC93XoW6hy1o1Q7bdp4qeu4xsx9m9b6PVeyHxrYq6RRI7Ek3sUNAm0WlEG08lOulGohPOQ2IbVeT3GHeYhsSaBN2bVk8IG8b5EFILgDTadVnRcSf5DzT7TFF7ruNPvdu8IhHijT/3GV2IN/6ssEWESPWu3lqZeA2R2gMrVlZpXOF2enIspbpkezYyFLeEVx+AEMPM6bq838/F60Di2wR8Mo7Etyh9bsuQMPP0VbqEp0kFF4M43NyroK3gLik65Lr+26neA4+3OyAiTJp4EfV1haIhjQ0nMXHCWb6fb66qZa2apsApbd68Lr/Z6us0xmvY82Nbc8KmpaWHTYxudceB0Ye3XXq2iNwnIleLyMEygjWTVPVtVRVVrVbV+n6vq/LH71HVTVS1RlW/qKqLh2deJnUvDWtamsqslFiEmvw3dF+Zf9OGtv6oYLtGsy+iAaK4TuqO3p9FhKrmc5Bon90vsU9Q1RQcwyPxzY3mv6ZitlYwM/rIYqatV2KtuJ1o63F924hdF6DpBwr69F8ThaRw0vf3votW70is/lj6qolEiTf+nEjV1r6flkgDEls/dP5rKt6NL8zo69s2K7WFppopKbejzge43X2yppp9GjdVmIgVqT8OpHj7S+qORKKVSIizBGHm6aukBqjh9m7IQwkA0clAFCKTig7VNf6UeOKLRe31Tb+mKtFvN0mExob+ksERGhu+V/K0pYw+gK+svSUXzDqSX225X6CHr4f2znBnTDpTmV26SjOY7d1G4DrgeeB44FERmVjRWY11QrZgvD6GXhlDoy/oC6mpu9G2EwuPZx5HVx3T616X+KeIVH3G59MRYnVHFbbEZpBo+TfR6t2J1hxIomUBEp1qOEdLESZrBcw8vsaevoC1ohl01VEDgvNz6Krve1syeWK1h+CV4S5EojOIJnYpaIs3nEDVxHlIdAZVk64jVnekwfwsvmhnyfWi6uD2+9s52RJZtM4SIIM6K3wP5zr+xEB5mFzHHwqMDYmtR6Tx1MIPxrckUn988HktFcHI01dRLVhToy88a1yiMyAyrSDEo/eYxKhv/FVBWzS2MTV1RxT1jUanUZX3NldXf6loW7d47PD/s080rhXaB+DlReG/57JVnSRTo0LcpIByjb4csLeq/l1Vf6uq2wEPAH+o+MzGMgZZcd4F1WTLztAoCPp453n4PvFlHoV8oL5InKqJVxNvOqs3AUNim5BoWUC8/uiij0qkjnjT74k3ne77xbWUgdFaAbOyWhmMY0X9SD8MPtu2kEW7/t77Llq9C4nJd/eL0YwQq/8Ricl3+Hp4ookdSLQsIFr16cHPzQJuZ8k4Pc29VLCe3BKi6Ooszv9bnPChzlLc5E0+47+IZh4uaIvU7ItU57fCpIZo0x9tLOYwYObpM70WmGqAGpwzpBwaAFINJe4bsfjHCxL+4lXbBvatrzss/++h4eetIM++HC458+HSdj5YGq5HONzEyuz/PjAB6J8wcSojV4ZtdOL6Pz0XoOlQ+QUAdduKUs4Devo3J3bwz9iNTIZYX3ESESFWuz9IjGzbXBIT5yHRKYFnk8jA7FzLoDBZKxiuA01h9hwXsFbiW4I0ghYnb0jV5wveR2LrUDXhAtLL9yKa2JF4w3ElzyiRgepJlnJRtw00OOPWST9R2OAux80tIhLbsKhvT+yfZl+CqpmFx9L3EhQm4KTuJJL4QkFbpOHnaPzTEFsfiY2akG+Lcbxw5Yw+zf7P4H4VgZCosIg04eCtdYkEb8smqndApIaE707V6mNlW/gOXGt7N1XxEYt+C6RcT9/VwA0i0j8tb+MKzmfMo26nYQmkbjMvnhtc5mzAgL6tUn881BZmJhFdH5l4BRIp0qkmVrMP8aZflzT4LJVDnaWGlRHCKyyYlMzKd/RtlWgLMnE+RD7WvxXqfgB13y7uLwniDScRq/9B0TFL5VH3I9RZFqi16Wb+U9yWLm4D0Mx/85/5b/GxgC1fAHyOSXQKkbojbHb+qMM0CdB0e9eA3OtoWFyf1PrGgvYn3m8tVQ14yOhPLLoWjQ3HEYnUm82vQsQNjLlIJEIkMvoEUsqd0VzgYeAFEXleRB4CHgOsimoP7lJww8stqbsKTIxDE3c5EPTFFYkSaTwFachnTMY2RSbdgMT85TQAotVfNTynZcgYrBfVDDjh2wm4rWAkfh18kZf4ZkjLjRD1Eiqk6bdEGo4LjIeJJHZChvmCu6aiuUV4cXjFSVeqiutTvcDXqHO7eo1BN/1wUZUPiQc/x0v8E2XO2jJiGCs/mKhIGMpBuSvDdy80FTq32vrvAhCLb07VAGWIgVRX71LyeKVJZ3JGiRxaSd3UClJu7d2cqv4EmAL8BE8UebaqWgn2PJp7E829Gd7R+SC0zJVqGrIvmsVvhBiQUnc4JHZGms8e1OmhAAAgAElEQVTx9fAV9JXR93QyXjFaL86HofIakNdNM9FHC1srkYlI8x+gei+kZt/Sfe1aGTbcvE6Z6xN3qbk38Ktn6vokc7ipW+n1AmkHburuguORxBchurbPDKqI1ny93GlbRgrTJECjbeAcRpqxmgkdT3OvQe7tkiUFo7H1iMY2pSoRbtDFouuGz6tCpDM5Lr3hcf773Ns89cI7Jfs+9cI7oX1GgnJj+nqYDKyPp8VQHAC0BqPZp/OGWqp0NlHuVTQ6FaGEVy23CEiB8zbEZpQ+sbsKQrJopf6HJT18luFF3Q7IvebVKi31NJt7BZy3UbcbiZTYFsm9gUpteEyNuzJ8rcQ3hwE6WJaRw82+irqeQe+mHoCafQYc9y9rpc77qLsSifQJLPSvne29n0e0pu86JFJFrOH/yLUWxmlG676JxNYZyq9hGUbU7TKLBzfyCDoYbQNLHZTw/KvbCanbgBQkb4ba4IeIqsT2xBOfDzzeQ2QY4oVXtnVz6Q2Pcd/jr9Ha4aU0HPvr69loxmT23HFz9v+KJ0PV2Z3mgSdeY8G9z/Pi655T5/nXPmDPnTZnh5kbkqga+SQno8d0EXm538+zgWeBPYDdgadFZKfVM72xharraeKRQVN3BvdzlkLuNUg/UnrAnq2ZEll4kDceXIMsIWvwjSq86ig5NHVrae289KOAC5nHgvuoA5mnIKBAeQElKikUYNfLqMFJ3tz3c/oerzZyPzT3RuBn3X7H3PSjXpZv/89mn+r1IvYQrfkq0j+WKvoxoiHJOpZRhhqED4GhxFjGTCs0th5E/OPBVXNo28m9cera8Vs0+0LwUFWfJj5KdF4T8SgfX28yG82YTCQf6tJQl2CT9aeyzrTm3n6PP/MWV978ZK/BB/Dk8+9w5YInefYVw+vuasbU09f/8e504AeqOh9ARObk2z5X4bmNOTR5I7jeH9bt/BtSvQciPv/FyesBhdzLaPYFJP6pgPG8C72mbkFKPBGRe90zIhPBZdIAW+VgFKGawe2RQXEWe3/jmr2K+7ldkPq393PyeqT6S/4Dph/yLvLaimaeRErIHGjqdm+rP0S3yq6X0YFqllzy+n4NXTipfxGrPbivqVRJtNx7ULUdAE6PSPsAnO4riFQVVtuJNfycbPZwQIk1nIJIzeB/Ccvw44THlgNmslEhNZ97qfoMfvUaVF209VhI9yvnp53oyiNg4mW+Iv6J6t1HjfxPXW2CfXbZkn122ZK3P1jJ+Vc/zGk/3IOqeOH9fZftN2GX7Tfh9cVLOe+qh2huqOGor3+OddcaPTXnTQNy+rshPgH0r7T9D2AT1nDUWYHb8fu+BmcR2nVhcT93JdqvvJF2nOM/XvpByOWfgjL/QQO8faoZtOtCNPmP0ll3llGF23keOH3xHm7Hb7zkngFo18V9F+X0A2jGJ55LFe36W9/7zr8EnlfTT0DqDui+agiztwwnTurfRck+ua75Be/91k7fQc/jo5rxtoZ9cFP3FHmbI/GPk5j6KImpjxGtKV2WyjL6UHdZybi5XkySv7TdSCs0MHwo80ShwddvXO38W3E7jBqDbyDT157ILttvUmTw9WejGVM4bJ9ZfOuA7UeVwQfmRl9cRL4pIkfiGYD9XQAxYPSJ0QwzbvvcIg0tt/MvaO71gjbt+ENhwHXm4aKtYHW70PZCVXJt+6XvF1jbT4X0fV6q/KpvB8o5WEYPmn0J7bqgsNFdjtv+68J+ubeg6+L+LWj7qcU1m5NXF1bRyPwH9RXXfduruEEW7TgNTd0ztF/EMiw4XcXeOc29OkCXLzh4XnsC691lQIARoB3m24GWsYG7wkwH1MAjqO4K1GQsCUgSrNoG/LT0pA6pPSJ83FFGc0O417uxvpqmhtJVQkYCU6PvCeAw4FDgJWCzfsdmA69WeF5jCs2+gqb96pH+P3tnHt5Ulf7x77k3S9OmpS1FKptlUxQVBirgoKKigGwuMIjgAirqoI7LuPzGZdBxGccZdUbHDXQUFEEYcWMEHRcQGReqgoAsspSlbC1tadMlyb33/P5ImyZN7r2n7U2TtO/nefI0OffNOSfpyb3vfc+7+KGF3MVw3wag5t+R7694JEyh456/RqboUHeCe/4RZYgtDc+V7RAKvyfiiub5J6L9n3jth3VpOepeVzyKiIu0sgWofqtBRtkHXhlZZJxXPBrwHQ17byDlR1hfREKjKfujRuACgFr7YcMLg6CxYECZlA19j56UQGJuou2gHQEanwOiwFX9GsxB1MN1pfvMiG7/YcwBlvUq4JrS0Ch1Bct+C8zELSkROVRiHr+qahpUNfGMMEJKH+f8XM75eSGP0L3GbxEI6Gi3NLbmhR/b3vBcx58G2hGgLm0C1yqA6iXR5areCCqHXD0ErewGQNkcIuADP3q5oXMsEX/01wsPOuRzZS/QqORVUCpka5ZXL4yemoFXglcvDpFbCn7sD+EinrnQoiiMROLAdaJyAUDz/xR8zqQcXbn6Y4y5dBMoSykXRvXFIpIXrh4B14wVtUAO0Mi8j5F9HRKqq2sUucuYHVKHR4HUqwDpOLCOi8DsJ5v3mWAU7j+KZZ9sME2l9kthMX4pFPSrbEVanGSLc36Mcy5QLqDtwhyDoPdVstASR1HyaEUek/TrEjIXUB+E798QcN5vjLIZvDaK7wSRMDDHYJ0jNjB7IPRfbK0Yl8MLPcar34xwPwC8QPVScK3UZMZEvOBGaTJ4wzEm65c/Cy2NFojAbXyuskF239rMGRIJi1Zq7q+nHhEL+OAe80obACCQU5a57wBz/xZMzjXvL8GoqvHhwX8sx5adhzD37bW6ckWHy/HK0rX4x4JVKKsQzJfYSlBmVQtgclew1CuiHEiDlPbbhpe6lS4cgDMQlckkN1jGnxD5r5HBOjwWjKhkKaPBst8GEK4gsownIKXf1rwPQrQKUtotdQp8OCz1qobyd7b+waoYEaSMa3ieOhOwRYn+tg8GUqc39N1xafj7AMB2KlinlWE53IjEQrIPBHSyrUmOQSFyehYTO5itb4jcaZBSLw+TkNNmQLL3aelUiVZEE0nYL1LfnZdFuRmMAksxdCFowDzRM5PcgHOMQF+JR9mxahSXBgJathfqb53/699f48hRD3btK8GSj35orekJQUqfRUjuuwEpPIu9lP5/YXczzDURcI6KeC/L+CNYSLJc5poA1uFvaPC/sYNlPgfWqNwMcwwA0q5paHCcBZZqXEGBiD/M1g2S+67wRrkHpPQ7G2SYBJb5V4THTAGQe4G5Q+QkN1j264C9QQGA40ywrFfCkoMz5gBLfwBgDdsvLOMBUvgSHMnWDZIzWpoeCbbUqxpeOX6FaMohs58C1mjnwJZ+N8DqithLOZDddJPYJpHSwn7vURHMv8fkLmByV1M5UyUzOLfEimgVpVtuJl59fDrGjuiPv9x9ia7cvTdciPHnnYprLh2K66ckVjY7UvosgkluSB1Coi/tQ8BcUyPlOjwJ2ELiYFKvBkudEinnGg+W8ce69/xZNz8bS/stIJ8AsNSgPJH4sNSrgPqtXABSxmMRedCY/XSwDo+HNGSCZb0csaXLpAywrFcAuRdg6weW9VLUbV8mdwRLvzvwwjWlzi2BSHTsGX9AY+VfTp0GKaRGLpOywWyRNXPlKBGTTMqEnDYjcDxtlqGLAJHEyN3BbCYlyphbP+I2FFtfMJuANdjILSV02CQu39i1cybuu2k0bLL+Z3DYbbjtmvMwa8pwyFJifdbEmk2SIzlHgDnPA8AgZzwYNfktk1LBMp8FkALY+oOl/yFCJiibOhVwTYqatLehvzSwtBsA1yVgZqXaiISBMQlyxoOB587RkJxnRpdzTQxGvLEOj4X5Z4XJSW6w9NvA0n9vnETXNRWQu4GRdSdpkGw9YasrQB9o6Ah7vfIeKhelZFW0NgCQUycDkCGb1FYmkhdm6wPYehsL2XoAOueUsL7k7oBAjVuuJF6t2VggSeYF7tJcDiG51oaUPothqTPBHL82jEpith6A61Iw9y2mEXMs7TrzQZ3ngzmpEl6yweynA/Z8SGnXGsu5bwvcIKSYFB+3Dwr48hn1xVhg3cmdmjpdIo7Y3DcBUuB/Znf/DixKvVHZ0ah+M3NB0qnMwuSukFIuAJM7Wj5XIvZwTSBgwj4YTDL+/zLmAouWP68xUgdAyjSX8xVQrtgEh5Q+i2GOIWApkX57EXL2AYB9oKmcyN0VkzsCCVKjkGgaUsrYsG3eaDC5E1iKQFYkKTPgJG2G8zzB2RGJAmOuQNk1lg65USBGPZJzKEK3gSXHkAh/vnB5k5sIIqkRTodiULKxATvMqrZyrQLw/xR4EAkLKX0Ww5gNzCHguMlkQMivQbAUDRO4CyMSDuY8W8y/RagkEa2VtoycMgqy8xxdRY6xFEghKaIkk/MQcwywdH5E66FoAtY06XixzkysgQH8MEv8z6teA1ALXvWyaQ47In6Q0hcLRPIPaaWBhyliPgHR/AeJJEAwV1VopQ59aK20ZZjtJEjOsw1lJEdDdYNoQRxhsmb+XkTCogps74I5zGUACFVR1TyBhw68ZhlQX//b+xl45Z8FxyZaG1L6YoL5j41rJYBqntOaLtBtHYG1wmsBZbepHK2Vtg1jMmw6W7v1SPWJv5kLzH6KoSyRvPj9Bkm76+G1gr0JlO7klYFHtEOeeXXVfkIU0erXoZXf01D3mUgYSOmLCQJ3YVpV9PJZRDuD1gphHVKdb69kOwWMGftgEcmJqmk4eNS89iu4vmUuXE5gq5hXA7xKZ5i/I+p5rPY9QCkUmwPRapDSFwt4jYgQhC74RNuG1gphIUzKCCTStfeL91SIGLFhxwF88/Mec0GtXLBHc2sc18rA9Uq6hVT+CcNxDqCTJ5B8/uIHKX2xQBX4QcIPkR8b0cYRWisKhLZgCAIAk3tB0ivhRyQ1O4tKMOfVlXhl+TdY9eMOQ1musx0bKRjdghcm4t8I+DdGPSZl3AeW+RxCXVWY+3awrHlhVYEa9Sg2N8JySOmzGM5rwL1fmguqRYC6P/YTIhIarfYzcLMySOp+QD1A+a8IIZjcFczWLd7TICxm4Sff46pHFuLg0Qp4fQruev4DPDr/E9R6dYwHQoGCADRj33KulYLXLIFW/Ra4Fl2RZCmj6xQ/Gcx9B5h7tomPsYBPIhETSOmzGO79BpqJ0se5D/CvB/d910qzIhIV7l0F7iswFvJ9F3CiVn5unUkRSQ2TO4NJx8V7GoSFvLP6JzyzZDV8Sriy9N6aTXj8zU+jv0ktEutcPaB7iGtlUEtnANpRQDsItexaA8XvPCBlPJB2g8CgZOmLF6T0WQz3rQL8P4JrBjUIfT8GfLl835BvQzuGq0cBZRO4d5WxnPd/gSfer2M/KSLpYVJW0ha0J6IzqG9X5GZH1shNddpx7sDoNXG5VgyuiWzdboveruyBenRy+M2m/0eopb8B11EUWdr1plWmApDqES/om7caZTcA1dhXq97XgteC/PraMepuABxQdxnL1a0XzgUi9giCucFYWrxnQVhIzy4d8e9HZuCWSWchPdUJWWL4zXkD8M5jM3H+4L7R36SWAFqxYb9c2adbQUOreTv6dUzZAV7zbvQO5a6G4xHxh2L6rYal1v01OOnaBwBggXqqwgk0iTaHyFoBAmXalC1g9kGxnxOR/DAnoOtATyQrKU47Zlw0BBcMPhHb9h3ByMEnGspzdT+4WgRmy9MXqn0fUHeB+zeCNSrlKbl+A7V2JaDuDX+PrQ9YysXR+6PrWcJDlj6rsfUCYDOsmcvkToDjTDDXhNabF5F42HoCYIBJpCVzTQBYFuAc3jrzIpIaBglCVRaIpKTbcZnmCp9WCShbwH3f6sv4CsA9LwWel98F3ijFC7P1hNzxHTBHw3mHOc+DnL2UAoWSGFL6LIbJXQCpk6kFjznPB5wjW2lWRCLCmAuQssDMtkTsgwDXxWQVJpoA+Qq3Z3jNYgAqeM0y8Ci5QLn/Z/Cy2QDqMgeou8HLbgRvVGqNSVmQsl4Dc5wN5hwFKXMumBTpW9h0KBNBvCClz2pYFiAJFLR3DAaz9Yj9fIjERmC9MMbAXDrbKQTRCE45QNs1XD0EzfN84IV2CJrnxUbHi8BLrwF4o+TN/h/By66P6I8xCVL6HZDS77Cw1CPdlMQLUvoshsmdxNIlyCfEfjJEwkPrhbAcXg1OZfvaJZxrUMvvDCvBxqteDk8Pph4CuE52CWV79Ha5JyD3snCmZOmLF6T0WY3cEzBynK2HuWI+FSIJoPVCWAzXKgCdXGpE24bXLAP8jfO/qlCPPdiQ3N1+KuA4K+r7WdqN0TtmqYKpWIQnal1fRJMgpc9imNwJzH6qgCR99QTAHIPApA4CkrReCEG0UnDtaLxnQcQBrXp+9APqTnDfGgAAY06wrBcBx5lhIoFKGjpKn9XnH/Wwtf0RwtCVJAY0Dn2PKmOZbwSRzIisFYDWCyEOV4+Aa0fiPQ0iLhj4cvKGY4w5wTJfAGz9Aw2p14C5f2vB+ILnKe4xlyFiAil9sUA6Pt4zIJIFWiuExdTnZyPaH1LazYiqeNkHgjnPDmtiUhpYxpxAton0u016Fg28EJSjijFxI6mVPsaYkzH2KmNsD2OskjH2I2PsorpjeYwxzhjzhDwebJV5SZQNnxCD1gphNZpaCK4UxnsaRByQXBMgpd8T3ij3gpz1MhhzRsgzx0Aw960WpoMSVfqoNnS8SPaKHDYA+wCMALAXwFgASxhjoXtmmZxzJR6TIwiCaE24dgzQjkJTdsZ7KkSckNJmAdwPzfM0IPeEnP0mmNRR/w06QR3hCCpzXAlUhDGDucX6IywnqS19nPMqzvlDnPNCzrnGOV8OYDeAwfGeG0EQRGujKdsAAFz5BZxTLrT2iuSeDdhOhpTxAJhsYlWTcwR6FFX6aoXEGEtq1SOpaVPfPGOsM4ATAWwOad7DGNvPGHuNMRZ1dTPGbmCMFTDGCoqLjQtUE8TcuXORn5+P/Px80HohjGjttcL9W+ueeMDV/TEfj7AWK9eLlHYdmOMcEckWjRMGp1RBiQ5rK3eDjDE7gBUAdnLOb2SMuQH0A7AeQEcAzwNI55yPNuonPz+fFxQUxHy+RNsgPz8ftF4IEYzWSq1PQYqj5d42vmP3Qa1eBABwZL0MOWVUi/sk4kNLzy1c2QNmM0/qzrk3qr9fuIwCxszXJ/f/BGY/XXiOhDUwxr7nnOeLyLYJSx8L2IrfQKCQ4C0AwDn3cM4LOOcK5/xwXfsoxlhGHKdKEAQRBuccS1dvQI2v5aXTuH9b8LlWb/Uj2idGfnwxI9nDBNo+Sf8fYoEEZq8C6AxgLOdc78xZb9KkhGcEQSQEhYdK8eTiL/DNlr1458ufcN/0kRjSr/k1uTXll+BzTsEc7RsBy5zlUCqWhCfplT4ALwI4GcAFnDfUdmGMDQVQDuAXAFkAngWwinO9ooMEQRCtxwP/WoEV321FvYfN3iPluOmZd3BqXi6evfUSZLqbVnqPq0fDfKo0tdDC2RJtFwvtIEykuhART5Ja6WOMnQDgRgBeAIdCqhbciEBF58cBHAegAsB/AVwRh2kSBEFE8JsRA+By2vFJwXZUVnthkyWcc3ovXDSkHzJSU5rcH9cOhL+mBM2EZQj6/oukayHiSlIrfZzzPTC+TVnUWnMhCIJoCgN6d8GA3l3wu8vOxu9f+AD3TR+JvNzsZvfH1dLwBq0MnHMq4dduEf2/iyh0mmBfbSJMoE1D/yGCIIg4ku5y4sU7JrVI4QMQpZ6pBjR4vBBE8+FKs/M+zpgxAw888ECrv1eUm266CY888khMx0gkSOnTIS8vD59++mlY2+uvv46zzgrPXn7uueciKysLXq832HbRRRfB7XbD7XbDbrfD4XAEX990001YtWoVunXrZjj+Qw89BMYYvvvuu7B2n8+H3//+9+jWrRvcbjd69uyJO+64w7CfK6+8MqKdMYYdO3ZEfD7GGJYsWRJsW7hwYXDuLpcLkiQFX7vdbt3vKpTdu3dDkiTMnj074tj777+PgQMHIiMjAzk5ORg5ciQKCwuj9lNYWAjGGMaNGxfWfuWVV+Khhx4Kvq6srMSdd96JvLw8pKWloUePHpg8eXLYd8kYQ1paGtxuN7p27Yo777wTqqrqfgYRaM0ESKQ1AwCvvvoq+vXrh/T0dHTu3Bnjxo1DZWWD71tBQQHGjx+PrKwsZGZm4pRTTsH999+PsrKy4GeUZRlutxsZGRkYOHAgli9frjtec5Cllp+KeVSLTdtIydUczH6PeXl5cLlcSE9PR2ZmJn7961/jpZdegqY1WLVmzJgR9lt0u914++23o45X//tbunRpsE1RFDDGDNdnLBE7J4lY8bwIJMdILL766iv8+te/RocOHZCdnY3hw4dj3bp1AKKfe6Px0ksv4cEHY1OhtbCwEGPHjkVWVhZyc3Nxyy23QFECBcK2b9+Oiy++GJ06dUJ2djZGjx6Nbdu2mfTYckjpawGFhYVYs2YNGGP44IMPgu0rVqyAx+OBx+PB9OnTcc899wRfv/TSS6b9cs7xxhtvIDs7G/Pnzw879uc//xkFBQX47rvvUFlZiS+++AK/+tWvLPk88+fPjxhz+vTpwbmvWLECXbp0Cb72eBpbFqKzYMECZGVlYfHixWGKzo4dO3D11VfjqaeewrFjx7B7927Mnj0bkskF8JtvvsHatWujHvN6vTj//POxceNGLF++HBUVFdiyZQumTp2Kjz76KEx2w4YN8Hg8+Oyzz/DWW29h3rx5Qp+nJdCaad01s3r1atx3331YtGgRKisrsWXLFkyZMiV4/H//+x/OPfdcDB8+HFu3bkV5eTlWrlwJm82GDRs2BOXOPPNMeDwelJeX47rrrsOUKVNQWloabci4wVgUP0DysTLkww8/RGVlJfbs2YP/+7//w1/+8hdcd911YTKhv0WPx4PLL79ct7/s7Gz88Y9/bPENpDWIVtEQmCuvheI3/+22pitBRUUFxo8fj1tvvRWlpaUoKirCnDlz4HSKr/lY/59mz56N4447DgcPHsT69euxevVqvPDCCwCA8vJyTJw4Edu2bcPhw4cxZMgQXHzxxTGdD0BKX4tYsGABhg0bhhkzZkRcaFvCmjVrcODAAfzjH//A4sWL4fM13GGtW7cOl156Kbp06QLGGPLy8nD11Ve3eMw9e/Zg9erVmDt3Lj7++GMcPny4xX3Ws2DBAjz66KOw2+348MMPg+3r169Hz549MXLkSDDGkJ6ejkmTJqFHD+OUFffcc4+uyf+NN97A/v378d577+HUU0+FLMtIS0vD5MmTw6yBofTr1w9nn302Nm3a1OzPKAqtGTGsWjPr1q3DmWeeGVRys7Ozcc011yA9PR1AYC3NnDkTf/jDH9C5c2cAQI8ePfDwww/j3HPPjehPkiRce+21qKmpwa5duyz7vFYgOc+Cs9OqkMdqoYS6BNChQwdMnDgRb7/9NubPn9/sc8GYMWPgcDjw5ptvWjzD5mCu9B04cACTJ09Fp06d0LNnTzz77LPBYw899BAmT56MK6+8Eh2yTsLrry/Ed999h/z8fGRkZKBz58648847g/L1VrfMzEx0794dr7/+evBYWVkZxo0bh/T0dAwdOhQ7dzakE9q6dSsuvPBCZGdn46STTgrbNTBi+/btAIArrrgCsizD5XJh1KhROP3007FlyxbcdNNN+Prrr+F2u5GZmQkgYLn97W9/i7FjxyItLQ1ffPFF2BZy/Y7K448/jpycHOTl5WHhwoXBMb1eL+666y706NEDnTt3xk033YSaGn0Xit27d2PKlClISUlBbm4uxowZg82bAwXDhgwZguuuuw7Z2dmw2+244447sG3bNhw9elTo8zcXUvpawIIFCzB9+nRMnz7d0ove/PnzMWHChOAdZehW0rBhw/D000/jhRdewMaNGy2rr7lgwQLk5+dj0qRJOPnkk8MWektYs2YN9u/fj6lTp2LKlClYsGBB8NigQYOwdetW3HHHHfjiiy+ErUA333wztm/fHnV78NNPP8Xo0aORlpYmPMeff/4Za9asscz6ZQStGXOsXDNDhw7Fxx9/jDlz5mDt2rVhVsOqqip8/fXXmDRpkvDcFEXBK6+8Arfbjb59+zb9w8UQxlIg2U4IeTQ/3197ZciQIejWrRvWrFnTrPczxvDII4/g4Ycfht/f8mTbLcLEgqdpGiZMmIDTT++LoqIifPbZZ/j73/+Ojz/+OCjz/vvvY/LkySgr3YXpV16F2267DbfddhsqKiqwc+fOoNV87969uOiii3DrrbeiuLgY69evx8CBA4P9LFq0CHPmzEFZWRn69OmD+++/H0DgN3jhhRdi2rRpOHLkCBYtWoTZs2cHFSMjTjzxRMiyjGuuuQYrVqwIumMAwMknn4yXXnopzEJfz1tvvYX7778flZWVUbd/Dx06hJKSEhQVFWH+/Pm44YYbgtuu9957L7Zv347169djx44dKCoqwp/+9CfdOd52221YvHgxqqurUVRUhBUrVmDMmDFRZb/88kvk5uaiY8fYJtUmpc+ASy65BJmZmcFHqH/RV199hT179mDKlCkYPHgwevfujbfeeqvFY1ZXV2Pp0qWYNm0a7HY7Jk+eHGYR+sMf/oB7770XCxcuRH5+Prp27WpqMVqyZEnY56i/6wllwYIFmDZtGgBg2rRpllmh5s+fj4suughZWVmYNm0aVqxYgSNHjgAAevXqhVWrVqGoqAhTpkxBTk4OZsyYYXohT0lJwf333x/V2ldSUoLc3Nzg6/Xr1yMzMxMZGRk46aSTwmQHDRqErKwsTJgwAddffz1mzpzZ4s9La6blWLlmzj77bCxbtgw//PADxo0bh44dOwb9N8vKyqBpWth6ueeee5CZmYm0tDQ8+uijwfZvvvkGmZmZyM3NxaJFi/Duu++iQwfKSZboGP0e9ejSpUvY1v3f/va34Ptzcm/7ss8AACAASURBVKKWbw9j4sSJ6NSpE1555ZUWzb3lBHzH9L6DdevWobi4GH+8fyYcDgd69eqFWbNmYfHixcEezjzzTFxyySWQpFS4XJmw2+3YsWMHSkpK4Ha7MWzYMAABP94LLrgAV1xxBex2Ozp27Bim9F122WUYMmQIbDYbpk+fjvXr1wMI3Jzm5eVh5syZsNlsGDRoECZNmoR///vfpp8uIyMDX331FRhjmDVrFjp16oSJEyea3khffPHFGD58OCRJQkpK9NRIjzzyCJxOJ0aMGIFx48ZhyZIl4Jxj3rx5eOaZZ5CdnY309HTcd999Yd9XY0aMGIHNmzcjIyMD3bp1Q35+Pi655JIIuf379+Pmm2/G008/bfq5W0qyK30xdVh57733UF5eHnzU78UDgQvTqFGjgicBqy567777Lmw2G8aOHQsg4B+1YsWKYPFtWZZx8803Y+3atSgvL8f999+Pa6+9Flu2bAlzoL/ooouCfU6ZMiXsc4Te9QDA2rVrsXv3bkydOjX4WTZu3Bj8YTaXmpoaLF26FNOnTwcQOIH06NEjTNEZNmwYlixZguLiYqxZswZffvklHnvsMQAIc57eu3dvWN+zZs3C4cOHw7b+AKBjx444ePBg8PXAgQNRXl6OZcuWhVl5AOCHH35AWVkZdu7ciUcffdTUl1AEWjOJt2YuuugifPjhhygtLcX777+P119/Ha+88gqysrIgSVLYennyySdRXl6OSy+9NOhwXT9meXk5SkpK8M033+CCCy5o0eckWgej36MeRUVFyM5uiKS+6667gu8vKSkBAN3fTT2PPvooHnvsMdTW1lr3YZoKrwag/x3s2bMHBw4cQFans4IK4eOPPx6mNHXv3h0AwKQ0MMbw6quvYvv27ejXrx/OOOOM4I7Cvn370Lt3b92phN5YpaamBm/S9uzZg2+//TZMKV24cCEOHToU0UdosFv9rsLJJ5+M119/Hfv378emTZtw4MAB3H777YZfS/1n0iMrKytsp+iEE07AgQMHUFxcjOrqagwePDg41zFjxgTPs43np2kaRo8ejcsuuwxVVVUoKSlBWVkZ7r333rDxiouLMWrUKMyePRtXXBH7VMLJ7vDhNRexnpqaGixZsgSqqgYXs9frRXl5OTZs2IABAwY0u+/58+fD4/EEfZQ45/D7/Vi0aBF+97vfhcm6XC7cfPPNmDNnDn7++efgtmFzxuSch92ZAQFLTuO2pvDuu++ioqICs2fPxq233gog4Ly6YMGCqD/MM844A5dddlnQn6ax9SY0As5ut2POnDl48MEH0b9//2D7yJEjMWfOHFRVVTVpizfW0JoRw+o1E4okSRg5ciTOP/98bNq0CWlpaRg6dCiWLVuG8847r9lzJtoO69atQ1FRkWnUp9nv5sILL0SfPn2ElMyYoZUZHu7evTt69uyJ7Vs+AbP1jCrTODCjb9++WLRoETRNw7JlyzB58mQcPXoU3bt3j8gaIEL37t0xYsQI/Pe//zWVXbFiheHxfv36YcaMGXj55Zejzr0es2CTsrKysOvH3r17ceqppyInJwculwubN29G165dTedXUlKCffv24ZZbboHT6YTT6cTMmTPxwAMP4MknnwyONWrUKEycODG45R1rkt3SFxfee+89yLKMn3/+GevXr8f69euxZcsWnH322WH+R2bU1taGPer9KpYvXx7sd8OGDbj33nuDFqG///3vWLVqFWpqaqAoCubPn4/Kyspm+6PV1tZiyZIlmDt3bnDM9evX47nnnsPChQvDrB1G+P3+sM9SP7drr702aAFav3491q5di/Xr12Pjxo346quvMG/evODW3datW/HBBx8EtwzMuOqqq+D1erFy5cpg29VXX43jjz8el156KTZt2gRVVVFbW4uCgoKmfzkWQmsmktZYM++//z4WL16MsrJAouLvvvsOq1evDso/+eST+Ne//oUnnngi2Of+/fuxe/fuZn03RHJSUVGB5cuXY+rUqbjyyitx2mmntbjPxx57LHhxjwdcqzQ8PmTIEGRkZOAvf5uPmpoaqKqKTZs2BVOeROPNN99EcXExJEkKunzIsozp06fj008/xZIlS6AoCo4ePSpk9R8/fjy2b9+ON954A36/H36/H+vWrcOWLVtM37t161Y89dRT2L9/P4CAtXHRokXB33bnzp2xf//+sKA2UebMmQOfz4c1a9Zg+fLl+M1vfgNJkjBr1izccccdwXNFUVFRmA9kKDk5OejZsydefPFFKIqC8vJyzJ8/P3iDX1FRgdGjR2P48OF44oknmjzH5kJKXzOYP38+Zs6ciR49eiA3Nzf4uOWWW4QvekVFRXC5XGGP1157DQMHDsSoUaPC+v3d736Hn376CZs2bYLL5cLvf/975ObmIicnB88//zzeeecd9OrVq1mf5b333oPL5cLVV18dNuZ1110HVVXDFCojxo4dG/ZZZs2ahc8++wy33357WL+DBw/GmDFjMH/+fGRmZuKDDz7AaaedBrfbjTFjxuDSSy/FPffcIzSmLMt4+OGHw/xvUlJS8MUXX+CUU07BuHHjgr5869atE44KiwW0ZiJpjTWTlZWFefPmoW/fvsjIyMCVV16Ju+++O2ilOeuss/D555/jyy+/xIknnhjcsjn33HODlkai7TJhwgSkp6eje/fueOyxx3DnnXfitddes6Tv4cOHY8iQIZb01RzMLFqyLOPDDz/Ehg1b0LNnT+Tk5OD666/HsWP65elXrlyJ/v37w+12B4MUUlJS0KNHD3z00Ud46qmnkJ2djYEDB4alPNIjPT0dn3zyCRYvXowuXbogNzcX9957b4Qrjt57v/32WwwdOhRpaWkYNmwYTj31VDz11FMAgPPPPx/9+/cPnvdEyc3NRVZWFrp06YLp06fjpZdeQr9+/QAAf/nLX9CnTx8MGzYMGRkZuOCCCwxz6y1btgwrV65Ep06d0KdPH9hsNjzzzDMAArsa69atw2uvvWboymQ1zKpIvrZCfn4+j7dViEge8vPz425FJJIDWitEU2jpeuH+rWD2fuZyXANjZP8BAilbrrzyyqD1MFlgjH3POc8XkaX/NEEQBEG0NSTR1B9Um7k9QUofQRAEQbQ1pIx4z4BIQEjpIwiCIIg2R7In52h9zj333KTb2m0qpPQRBEEQRJuDLu9EJLQqCIIgCKKdYhblS7QtSOkjCIIgCIJoB5DSRxAEQRAE0Q4gpY8gCIIgCKIdQEofQRAEQRBEO4CUPoIgCIIgiHYAKX0EQRAEQRDtAFL6CIIgCIIg2gGMcx7vOSQUOTk5PC8vr4W9KDDPhq4CkAX64hCrjajBTIfn3AfGHAJ9EaIUFhai5euFaA/QWiGaQqutF3UvIPcwluE1ACSAOY3ltFJAyjYdkvMqMJYmPkfCkO+//55zzoWMeFSnpRF5eXkoKChoUR+qWgJZzjGW0cogS1mmfYkqappWBUky/hH5lb2w20x+3ESTyM/Pb/F6IdoHtFaIptAa64VzBbxsFqTs14zlfD8ALBXM3s9YrnohWOp003EV77ewOYc2aa6EPoyxH0RlaXs3weFQBSU1od4IgiAIIoAf4D4BOQlCO05c5DoEQPi6RlgNKX0xQWDhi26rC/+IzPvjpPQRBEG0C7jnRXCtykRIQcAdyQQmAUzUHUlETETRJGIBKX0xQVRRs7IvK8ckCIIgkhmu7gN4rYmUH+B+gd5sEPNBF4NzE2WUiBmk9MUAay1qYsocF7IIkqWPIAiiXcB9AEwsalyF2DVGgpjSJxJ0CHCtQkiOsB5S+mJCPJQrgTEpUpsgCKKdIGLFE1T6mE1se5eZqxSc+8H5MfO+iJhASl8sEFKuRBUwq+UIgiCINg/3AxDZuhW5djCIWfoEVApeZe5rSMQMUvpigpVKn3VjMiFH3PYD5agkCKLtogoEAnKIK30imMtxzQNwj2B/hNWQ0tcExJWEBN3epX93GIyJnsgIgiCSDQ3mqVFElT7r4LyKAjniCGkBCY54UAhZrQiCIIg6hIwUoje+GsQCPsxlOK8G59WC4xJWQxU54obVKVbisaVMEARBJCSMwVypE5GBeD4/LiLjozx9cYQsfTHBmjsiq+GUy48gCKKdIFhFQ0hGgVBQCK8R7IsqcsQLsvTFBAurYwhW5BBT6MjSRxAE0T6QhFKoCMF9EIne5bxKQIUUVUaJWEBKX7wQLq8mKEeRqARBEEQQGyy7xHMvhFK2aCL590QTPROxgJS+uGF1eTXauk00OOcUIUwQRHxgosqVSGL/Wgh5g2klAuORpS+ekNIXA8S2bkWVNIrejRUa55BIKSMIok0iC1TREC3DVgshRU07ItBX66eJIRqgQI6YIOLTZ7Vljix9BEEQRD0STC/xXK2rv2sCr6nb4jVBKwskXzZEMBKYiAmk9MUNsTsdUeVQSE7kx92OELHxUdUOgiDaLqKpWLxikblaOaCVGXfFa8A1ytMXL0jpixeigRyiippAfiQucqcGgLcT5VAlhY4giDYLh+kOkGjOPO41tfRx7gN4BaCVGstp5eC8wnxMIiaQ0hcLhJSJ1vfp0wTrHbaXfH6KZq7cUiAGQRDJiSJgNPCJbdtyr7lyWB/EYeLXx7VicFXE94+IBaT0xQQrffpEt3fNFRhVKxcbsZ1kS6/0mn9OrUXWwMS0JHLhdEEEQSQt3A/ThMrcC0DkfC/Ql3qw7u8hQzFN2QOuHQHXRBI5E1ZDSl8TsLYOrqgyJ5rEWUTpMza71+PXKsXGJEwg5YogiDghsCULXluXjsVEjCvmLkRqUUBW3W8s5t8EgENVNpuOS1gPKX0xwcLqGAJWmUCwgXl/Sv2dmAlV/n1CcsmOopl/ty2z9JHSRxCh8Hayi5AQcK+5QsdrAGgC/t7mpdO4Uhh4ou7Rl9HKofk3BcS8X5mMScQCUvqaALMyoaSwMiGqQJrLKUoRNK3KVK645n8CYyY3flVFjWIe/FIrIKNPYm7vEkTcEPQrJiyAV5tH3NYrhaaRuRym9XLVnYG/yg5dEX/Nf4L9+Gs+MBmTiAWk9MUAK5Mzi/jqiY7oV/fAr+jfhQFApW8nDlV9LjS3ZKa8thbHas19Smr8AkXG9WgnUdAEIY4z3hNoP/DqwMNQpu4caGoRVM2zOvi3BP6q+3Rz9fmq5gefa8oOKN61xn0SlpPUSh9jzMkYe5UxtocxVskY+5ExdlHI8ZGMsa2MsWrG2BeMsRNaZ2YWKn0C/haBvoz749wHr+8n1PjWGcrtrVwGj383jtb+IDS/REQkt55XUYSseCXVLcknRQlICaIezjlQsyze02g/8Cpzy2r9tq7p9q4Go+sa1ypCtnU5ULeFG4q/9nNoytawNq/nBZNxCatJaqUPgTJy+wCMANABwIMAljDG8hhjOQCW1bVlAygA8HbrTEskelcwObNIQkyopr5/lTXLoXEPKqqW6Mp4fLtRWBH4in4++rc2na/Pq6rwKuafb2epWPBLNMh/iSBC8H0F7vkHOG+B9ZwQh3sCip+hTG34X11Mkjj7f0DYdc8faTTweV6OaFO9a6D6fzYZm7CSpFb6OOdVnPOHOOeFnHONc74cwG4AgwFcBmAz53wpD5jLHgIwgDHWL/YTs7A6hkAOpUAKDv0fpKpV4uixJwAAXv+PURU/lfvww5H/C6ZrKfduwvayl8TmmGCIqNPHamtR4TX/bvccE0tzE3UeZifcuCFqZU5Mn8REnRehD1eLwT3PArwC3PNMIBqUiBmca4GtXZNMDFxU6TOJ3uWNdpC477uw15qyD6rv66jv9VcvNR6bsJSkVvoawxjrDOBEAJsB9Aewof4YD1yBd9a1N37fDYyxAsZYQXFxsW7/4ilbRBQ60QuvSBUNn+7dM+cch0t/ByUkjP5I+X3w1vtf1LGt9Dkc84W3bS+fh9LaH4XmmUiIKAWHPB4c9JinpqnwRp4M586di/z8fOTn58NwvfBETX1jdWJwgZ4sVNSSKWG26Fppq3CugHv+CV4yCvDXnY6rXgEvGQfuXRXXuSUilq0XXgmAm5+Dgj59ZjtKPhjm8/M2Uuh8P4TtdKh+/euI6ku+a0wy02aUPsaYHcBCAPM551sBuAEcayR2DEB64/dyzudyzvM55/mdOnXSHUM8S59AmhVBfy+RKhoar9H1/Sv3zENV7cfhY/NqHDw6C1qdQlmrlGDXsYXResaW0meF5plsrD90EBsOGScRBYCj1TXwNvL9u+GGG1BQUICCggLorRfOfQE/l0RE2MpCFrWWIrJW2jTKbnDPPyO3GdXd4BVPxGdOCYxl60Wru/SZ1MENBnqYbgPX6CqGXDsGKI23aGuBUGXOYGdL9FpIWEObUPoYYxKANxC4FbmlrtkDIKORaAaA2JtfRLZuBS+8mkBCZa55oOnc0R2rejNqu1/ZhZq6u7Mqf6HuD6/St1NonslESXU1lm35GZ/v3oUdpUd15QoOFOGTnTvw92++FsrpFwrXjgVOhq2IsDXNLKKvQbDZc4nsKTGthkRsYfa+YNlvAfZBIa0y4LocLDv6uYmwgPrrhqnSVxn+V7e/Sv2tYt86RNs94L5vgs8lx+m6Xcv2AcZjE5Zii/cEWgoL7PW8CqAzgLG8YZ9zM4BrQuTSAPSua48pXKCsjejdjaqWmMpoWhk0nRJrKY5fwR8lbxJjLjjtJwEAOjhPgUPKgi/KCeK41LOE5pksfFe0H3d//DFKawJ3rVcsXYLHRl6IUX36BGU+2r4dr6//EQUHAhnmXy5Yh/e3bsGkU/rjhsH5SHeap53g2lFwzfx/Zy0cEMglyXkFGHIE+rPuntCv+eGUKV1He4Q5BoF1XAxe+1mgPqt9AJg99q7V7RrtcN1fkxq39dcNsxKdWhnAorsa8ShBGwAA//fBp7KtN2THUKi+byPEHKlTjccmLKUtWPpeBHAygAk8PNT1XQCnMsYmMcZSAPwRwE91W78xRcgPTyTvEQBNQHFQtSNQ1cNRjx2X+ThSHIPC2hgcyM1+CTb5eACATUrFaTkPRLzXKefglOw7TcdPNPSqaCzftg1XLF2CfRUNFrijNTW4afkHeLmgwRFZ0TSojSx7qsahaJqwvUpTD0HT+Z80ByutW9ykNmaIpGVjHvFaV2DdSqsh0XqwlJFgqZeTwtcaBOvgHjCW0wJ+g1wz8R/UjugrkFHSswTaw7d8U7PnIe24VWEP93FrIDtOMx6bsJSkVvrq8u7dCGAggEOMMU/dYzrnvBjAJACPASgDMBRAq9xSiKRZ4WYh8HVoZuZ5AIqyF6oavXSaJKWha85ipDgG17XYcXzHV+F2XRgm18V9Ifp0uDb4msGG/M5PIcUmYhFqHUQVHz2l76wTemDa6QNglxqWPQMw/sSTMOGkhgvRxH798M7UK/D6JZfBZbPh6gEDsea663HvWWcjQ8DKBwCaWghN3S0kK4JY8IJYRDhXtgmOap1ytcOz3bK+1DacSoggrIDX58zTjuomSgYQrJdrpBxyXhOw0KoHohsq6suvRbyxAjzEPYlJmZBtvcIekq2VUucSQZJ6e5dzvgcG+1mc808BtPptpeGPLCjkB+d+MGasRGgCaT8UZQf8yi+6xyXJjeM7voo9h0YgK/1mpLlGRpXrl30rimu/xTHvZpyYdROyU35lOnYykZniwiPnj8SMgb/C9R+8h0qvF3MnXIxBXbpElT8nLw9XDRiIu4efBVlq2v2R6t8Uh/xTYn6HmiJaWzkxo2S9mhd2yR7vaRBE4qLsDH/uiPSb42pJQ/JmPcUNAJRdCNwA+gF1L2DrGX5c0/eLhloMSNmisyZagaS29CUqGjfP7cZRK7QNrOerF4rPvxl+/y+GSU9t8nHo4L4amenX6cowJqNf1q1wSFno3eEaXbl4IR49bSzZOzsb/3fW2bjpjCG6Cl89HVNTm6zwAYDi+x6aslPIUmsdYt8QYw7L+uMiOSkBHKw9KDimOUU1RZb1RRBtEn+IF5OyJbpMqMVf2aGfOzF0+9YfzSXeKNk2JeJONEjpawIidg/OfdAELH2c1wqVWDOL3uXcB79/KwBv3V990lMvgcRchjKdXMPQ1X0RZCnxnO79qti2niag+/TokIkeGR1M5frlNH17W1OKoCk7AHAo3i+b/P5oCG1tCypgoj5VmkCEuU8zX8M/ln2PL458hu2VotvKxnx+5HMc87duZDRBJAtc2QuEGB64/6fogv6NIS+8gM5uEfc1BGRwf0GkgNE1haUZTZWIA6T0WYymeYSS8mpatWnFBs41aEamcwB+/xbUJ830+TcYytrlHqbzYkxCbtr5pnLxQDRtiohy2DU9Hd07mCt9Z5+QJzRm2Pi1Kxue16w0kLQase9Hsp0oJCdiiT7qM94q/ql8PV7e9SI4OP7xyzMtVvwKSgvwfdn3eHPPm/CqIonLCaKd0TiaVif5MQ+Jrg3IfR9VDqGVNBonYQYA6XidiTBAztU5RsQLUvqagMjmGecV0ASS8orIce4B5zWGJYt8IZU1/H4dM34dZv6D9aTauwvJtTZ6ARqNKas1D6TJSEnByTFKluureS/43O/9DNygFJJ4VK6InCa03coksc8tEiVb4t0Hnxb9+/6+rAAv7PwnlLr169Vq8Y9fnsGWiqb7Olb4K/DcL8/h+Z3PQ+UqCsoKcN+m+7Dx2EbzNxNEO6JxCTSoO8MCKgAEAjIaKXkR7wPA/Vsa0r8AgaTayt5wIccggGVFPuyngpnsLBGtDyl9TUAoflIrFUqorGpHTf296v35jPz61PooLQCKskdXLoBsOi8AsCXoD1UV3L7cU978erktRfFvhupf39DAa+CreceCnkU+u3EN5iBSRFGa6L0JRKGX+w6h3BeZAsajeDBv10tBha8er1aL53c82+QI3H/t/hd+KA+3YJT6SvHsL8+i2Nv+ypsRhC5RlLeINv+mhiCOoMy3kTeh3s8j+/J+FvZS6vA4pM7fRj46WnHeI6yGlL4mwATUPk0tgWqyJRuQK4ZqkhtJrcu1pBrkVVNDFEezoA/xmqWJGbUpahTbdrS1kyI34PW8GKVtnkFORuvqOXPuAwyCeRoQi3z1qebBF0d9e1Hi3RvR7pJdupZCm2SHzMRuQOoZ32U88lLzIsa4uMvFyHEkTlohgognXD0UiLBt3N44KbIvyjYtLwOUcL9wHkXpi9ZGJA+k9FmMqh0yVNKCcuqhoFKnh78u7F5R9EuhsdB/oUWF6EVqB8cDvyZmHfr+YHyiOzX1APw1y6O07w3z8wtHON2zuQivBhcusWYyGvfD49NJuhqCnqVPZjLO7RTdN/SC4y6M2m5EH3cfzOk/B7N7z4bb5sZ5nc7DX0//K8Z3Gd+EmxmCaOP4dKpjNGrnvigBGQAQ0s610uiJl30/gGvmqcSIxISUPosJKHOHTX21FHU/FJ2EyvX46nwuvHo/UACS1DH4XA553hKMfAjjycFK8wAZRdOw8bB1lTCagr/2v9CzyOkrfYIKtsB2qHi9X3NFs8r3MyprdRy766jwF+NI7W7s8ETZTgJwRY/pOK9TeE7ICcdfjPFdJgrMMTpnZJ+B2/rehqvzrkaajSIDCSIUrujcqCm/BHYC6tGJ6A2L9PUVIPq5wg+EurAQSQUpfRajqAcA+KAZbN1qWjU0rRiqgQ8e5wpqav8LAKip/VhXibTbGmrG2kKetwQuWNmhtdlVVmYametXVdSq8VFajfIk6m+7iil9ItZXTT0AblZ2qa43M8pqPkd57RrdbWm/Vov39j8BDSqKarbgq+K3ospd3PXSoFuEQ3Jg3PETBOZnTB+3NeucINocUeqs1x0AlECFoEBSZh1XoND3G1XuUWJezZSIEaT0WYyq7AcAKKr+FqNSV57LKPCipuajoOKoqvtQq+NH4XAMjPq8JYjkZ4sHe46VY3+lccSzxBjkOG33OVzjARY9SEK/qLjoVrpAnWblF6iKSLkzY6VP434Ue96FopXjaPXHEceP+Y/gzcJ7UFTTEC2+pvhNfHzwBWiNlES3zY3Lu08DA8NVJ8yATUrqIkAEkdgYuQxph8L/GskA4Aa1w42OEYkNKX1NQBOwkDQEX+hbXOqVPVU7FDVBM+cqjlU+HdZ2rOKvUa19NtsJkKTjAABOxxmm8xNBEwoGaH0OeSpxyGSLt9rvR7U/PvOX5FykZT2LxlHSTvfvYE85N/qbRKNTBLZ3Vf8GqHqJWMM7Mzy6r/wf8KqBm5fCssfhV8OtAp8dmodDtZEWhR/KlmNLRWQy6gs6X4jf9r4FZ3b8tcDcCIJoNka5X+v98Iyi8sN8go3OOYlpGCDMIaXPYlTtSOCvekRfpu6CCvC67eBwqqqXQmlkWvf7N6Cm9j9R+3M68iHLPSDLnZs36UYYblPGEUXTTBM0f7LzF9QoCr7cU9g6k2qEPeUCuDo8HnztcP0Groy7Dd4hur1r/j/RlN3QDIJ+Gjrz6R46VLkQRRUvBF/71APYcmQG1BDH7bM6TUOK5I54b/fUU3FienTFblDWYPN5EQTRQgwi84MR80bR+yHHpCz9rqiebtJCey1NgHNumM2Ecx94Xe4jo1x9qtrg76epRwBbr7DjlZ7no76vovKfSHWNj2h3OAYCzLp/pSpQiUEUTeOQJGu2WxljupGanHO8teknPPLlFwCAW1csx59HjsLYvmLVJ6zEmTYNivcLKP5NcGU+ZiIt6D8pkDNPFK6VgkXJ1efxbcbu0oejtP+E3WWPoE/HJwAAx6X0xPiuv8e/9/0J9VbDdFtHXNbtftgTsHwf0TSq/MUoOPoaahpZeG3MiYHZVyAnpfV/U4QgUjZQ5z4UeSwn/K+RDABm66O/J2Dr26zpEfGHlL4mYLYRp4WY1o1SZ2ghZdq0RtUaVLUYio4zrt+/AZpWBUkKj1q020+21DqnmJSHawqlVdXISbcmytKrKPAqkdsKflXFrA/fw5d7C4NtlT4vblnxISYV9sdfLxxjyfhNwZF6clPYvQAAIABJREFUFSRfgUBGesF6wlqZaWptydYLTKDWJVd2ArYTItqdchd0SBmO8trVYe02qSOyXOHpV/qmD8UFnWfh08Nz4ZIz8JseDyHVZl7Wjkh8ynx7sKtyNXyNaohLsCHXdRopfYmMrSfQuLxa6DEAkLsALLXRVm69TEiQlGMYYB8UvS+LXImI1oeUviZhovaFVYww2LYLCZTgjXwjmOQG4AQQaW1jzB21lJpN7gHNZl0Ber9qXV/fbt+HcYP7tbgfr6Lg+4NF6JSWhpG9eocfU1XsLo9e3WTb0RJwzls9l5tsP0moBrNYMmVAE0iULDvOAGOppnKq9wvIKZE59OxyFk7p/BrKalZj19EH4FUPoEvGdejW4VbYolgGz+h4CbxaNfqmD0XnlN4Rx4nkpFtaPqb2XIj/HvgjDtYE6nm7bbkY2+1JZDkjbxaIxIHZTwev+XfkAblbcEuWMQncfjrg+yby/Y5fNTyXu4B1XByzuRLxgZQ+CwlXyBwGcikhz8PlJOZCunsmKj0vRbwv3X0DWJRtXFnuDFnT9yFsKtWKdcmNC4vNS9KZUe33465PVuCgx4MFG37EyTmdMPmUU4PH3Q4H3rt8Ou785COsDvHlu+Skk/HY+RfGJXkvY6lCCpih43UIqn8rEGVrPxTZPhBMMrb0cc0DteZdaKlTINlPiyqT5RqB/p0X4mDlG8jLus+wv2EdJ8Mm6a91IjmxSy4w1uDyzRjgkCP9OIkEw3GmTvuwsJfMfSvgvyBSLqXpidOJ5IICOZqAWRm2wNZaQCmTJP2tLinEQVaKklC5Q8a9sNtPDWtzOPKRkX67zrgZYX22BM5VHPNal4Np874jpomqjdhSfATjF72BlTt/AQConOOeTz/G7R//B7VKg5Usy+XCvAmXYljX7gCAiSf1w9Ojx8JlFys5FjdMSucBgcAaxb/BVE6SO4PVRXLroVS9CvAq+CufNZRLsfdAl4yZpmOSwtc2WXP4aRyo/jH4utJ/CB/tuyuOMyJEYLYTALlnZLvz3PDXjjPA0q6OfMjHt9JMiXhBSl8TMLMYMcYgy4GLbv3faMhyl+BzW5QfGWMpyMmeF/QHYywdHbNfBmPRFRjGGCSLoqk8/r2o8P1iSV/rduzD2q2FWPr1xmb38fQ3a1EYZev2g21b8emu8EhVmyTh6gGB7YmZA+MbLRrYthfYuhWw0GpqEVSlUGBMDiMXBE3ZDaWuNrDm/RRqXfJvPRx0AWi39M44H46QCG0JMvpljovjjAhRmPsWwHV5yGMq4Dw73tMiEgRS+ixGlrsCAGxyN10ZW50TPWOuYI69SJk8pLtvBABkpN8KW4iiGA2JWbP1UubdAI9/N3yqcRJkM345WILfz/8PNM7xxLursHZrYbP6eWjESORlRloxp5xyKsb2PSmi3WULWFpTbXG28HGfWHCNQRLvejR1PzSRShtaBbim70eoeOYi1FfUzNpHNW3bLz3ShmJy3qvIdvRCitwBE3s8h9OyJsd7WoQAzDUBUodHQh5/EggoI9oLpPRZjE3uAQCQbT10Zey2gNO7zdbL8MKalnoFAIY03WoODTT2DWwuJdVfA9BQUhPp5CvKkv9twBXPvIWyqkCaEb+q4rfz3sXTH0Ym7jWja0YG3rh0MjJTGvwgL+zVG38eOQpSo++Oc44lPwdqTy7eLJKkOHZw7jPMhxeUU4vANf1IbwDQtAoA3qiJvMPlisGNyv81yv3Ihap3EO2VdHsuLjvhZUzrtQSdXf3jPR2CICyAlL4mIOKbZrP1AOCELOlvjclyZzDmhs1mHPFos/WA0zEMstxJYHYtj8nhXMORmq8BAMU1a5vdz1tr1sOrhKci4Rx4f90WlHqMFZxodE3PwFWnN5SYu3f4ORHKcqXXi5s/+hArdgQUmdfW/4AHv/gUPpNavTGDVxum7QmiFpla+2RbDzCpU1gAUNQh1f3Qgom/o43VuPySL1CHkyB0kCUH7JLxuiMIInmg6N0mIBKOIMs9YLN1M90as9nyYJPzTPtLSRkpNjmTIBMRPP498NcFFpTWNt9S9u+7rsJnG3dg6dc/4dtf9qFf1064/NcDMHZQP6Q6m77tqnGOTUcafN9+OnwIvbLCfRhv+/g/WFUYnpR04cYN0DjHY+e3fkQa1yrANYEtcrUIUPcDdv1kp7LtFDhNIncBQFV26UYMa74N4Fpk2he19mPY0qabz5MgCIJIekjpaxIClj65C2QBB3ib3BU2m77fXz0Ou9i2Smh6hebiDbH6eFX9bUIzbLKE0QNPxOiBJ+Ldbzfh4jP6N7sqR4W3Fret/Air9zQodPd8+jFKa2pw7a8agjWuOf1X+Ln4CI5UNaRAOaFDJqacEh4F3Vpo2hFomkBRcvWIaTAHYxKcrknmY/q36ip9fs/TUdsVzwuQUydHzf9IEARBtC1oe9diZLkzZJO0GQAgybmQpVzz/qJUTogVDrkhzYxDyrSkz0uGNF/hAwLWulCFDwjU4H38q9XYX9GQRHpEXk98etW1uLz/aTguLQ03DR6CldOvwYDc+ESgakohNGWPZf1JJoE8gTF3QlOil2DSfOujtnPtILhA4mcieajyN/+GjSCItg1Z+pqAJGBNk6QsofQpspQDSY7M0RdNrrVIt/eGTUqHolUiK2WAJX22NAJ01qAzYJdk/HPdN6jwBiJP+3c6Dg+ecx66ZYTnQnQ7HPjzyFEtGs8qVGUrVEUg36Etr6E8kiHmay9QASS6NdqR9Sx8ZTc3SgYtwZ7xACRbnsD4RLJQ6FmLHu6hSLdTyh2CIMIhpa8JiKgvTEqHJGWYyklSB0gC1jQpSvmrWMGYjNzUEdjvWY7j06Jka48DNknC9YPycXG/k/Hwqs/RL6cTZp8xNCJyN9FQ/ZsCgRVamXHibPtAwCayBW3uWsCQAuhs08rOEXB2fAe+suvA1SKApcKR+SxkYZ9RIlk4UP0DZMmOfh0orx5BEOGQ0tcEzCpyAIEyakwgZx5jbkis9RQ6UY5PuxAHqz5Hp9Th8Z5KGJ1S0/DPsRPiPQ0huOaBpuwCAKi+nyCljNCVZc6zwCTzcm3cJNEz5xyc14AZlHyW7CfBkb0Y3qMXw5H5T8hOnZJNRNKhaF7sqfoffi7/AAeqf0Ch5yscrtmMEzPGINd1GuVcJAgCAPn0xQQhSx9LtTRhZktKnYWS4xqKftm3QLYo7197RFV+AaDVPd9mLGyQxDsUrhnX6FW8nyKQy68civd/unKSrRscHf5CCl8bY9uxFfjy0N9woPoHAIAGFVuP/QerDj2B4lrryioSBJHckNLXBETvlvUiKMNlUkzzrjUNa5Q+m5SKXh2utKSv9ooWkg+Pq2YRvIIWGIPEzKp/B2rL7w++rim/2zCIRKKSTEnJrsr/4cP9D0Y91j/rEkzr9Tby3PX/W4ZhnWZjas83cZzr5NabJEEQCQ0pfTFApDoGY6lgjHbX2yJhNZJ16iUH4V7j4/ViOkqfph5B9dHLwbVQRXMfqo5OBa/LuRg5P0rPkoyU+4pwuHY7FC16pRen7MaFXR5CT/c5OLvzHRiQfbklqZwIgmg70BkhBohY8JhkTa1cIvGQbA2JlmWbftJlAIBI1Q4Amha99q5S+3nU0mtcLYLibXrZOyLxqPQfwReHnsPa4ldQpRzF4j2zsaNyTVSXDonZcE7uXTgl8+I4zJQgiESHlL4YwCCbykgCW8BNw8CDn2hVZNsJkGwnArDB5jzXWFjHGhcK5xr8OgqcI20qnOl3IvynbIMz437YXRNFp0wkKBX+w1iw61r8VP4+NARKCh71FuI/RQ/jq+K5Ud+TIneI2k4QBEFKXyww29KDmN9f07DGp4+wBofrEtic55jnYlT3mfbl934KX81H0LSyqMed6bcjteNSMOk4MLkr0nLehdN9Y3OmTSQYR727oehs7R+s+bmVZ0MQRLJDSl8MYBBR+qwuYq5a3B/REuwpF8CeYp7rkPs3GQZ7KP6f4Sm/B5xXorL0emhaZVQ5m/MMpGQ8iJQOj0B2WJNYm4g/Pd3DML7rw8hx9g62ycyBQdm/wfiuD8dxZgRBJCOk9MUCoUAOc8WwKXBOSl8iIdlOhGw/3VCGax7Auxqo/TDqcW/N+zhWcjG4FqiJrPi+xbHisVD826PKyynnwEaRuW2O3unDcUXeC+jtPgsSZFx+wnM4+7gbkWqzplQiQRDtB1L6YoCYQme10icWBUq0DozJkO36lTY494Efuxvg5eCe58B9P4QdV5Vd8JTdCvCasHZN3Y3K0ut0xsygyNw2isRk9M0YgWznCeiU0tv8DQRBEFEgpS8GMIhY+syDPZoCF4wCJVoPvf8x18rAS2cC3s/qGmrAS2eA164Iykhyd9id50frFSlpV+uMSD/ntkxe2hBccPxd8Z4GQRBJDF0lYoBInj4rv3rOOTReYVl/RGzhx+4H/OsatdaCl98OruwAELAWp2e/CqdrcogMgzvz73C5Z7XaXInEwSmnoXPKifGeBkEQSQwpfTFBJOmydbUwOa+CJpD6g0gMmGNY9ANyHiDlNsgxGWmZf4FUV6rNmToFztRJrTBDgiAIoi1CSl8MELP0WYeqFUNTS1p1TKL5sLSrwTKeQNjNgf0MsI5vRyTtZswJpyug6DlTr2jFWRIEQRBtjaRW+hhjTsbYq4yxPYyxSsbYj4yxi+qO5THGOGPME/KIXrjS8omJWPqsy6unKIVQ1ELL+iNiD0u9DCzz6cAL+2Cw7H+BSdGjMR2ucZCkXNjsg1txhgRBEERbI9mLv9oA7AMwAsBeAGMBLGGMnRYik8k5V1pzUiIVOThUyzZ4/f5N8Pk3WdQb0VqwlDHgztFg6XcYRt3a7KcgxX0dGLPOJYAgCIJofyS10sc5rwLwUEjTcsbYbgCDAXwfg/EEL7wCkblcscytr9a7Bn7/ZnCuWh4VTMQWlnYtmK2XqVxKql7ELkEQBEGIkdTbu41hjHUGcCKAzSHNexhj+xljrzHGcnTedwNjrIAxVlBcHFm8vhkzMZWwKq+eqpbA6/0amlYKr/d/lvRJGDN37lzk5+cjPz8fLV4v9pOFxJiU1rJxiLhg6Voh2jy0XohYwzhvGzVbWSAj8goAOznnNzLG3AD6AVgPoCOA5wGkc85HG/WTn5/PCwoKoh4TtfRpWjUkybi2rqLsg83W3bQvMyoqn8OxiscBAC7XRORkv9ziPglx8vPzobdeRBC3HrduX4T1tHStEO0LWi+EKIyx7znn+SKybcLSxxiTALwBwAfgFgDgnHs45wWcc4VzfriufRRjLCOOUw2iacda3AfnPng8rwVf19R8BEXZ1+J+idaDlDSCIAiitUh6pY8FrpqvAugMYBLn3K8jWm/SbIWrrHnciKoVo6VW1urqd6FqB8PGrfSQpY8gCIIgiEiSXukD8CKAkwFM4LyhUCljbChj7CTGmMQY6wjgWQCrOOfNNrFpgmlWOPeZ96WVgbewikZl1SsRbVXVi6Fpnhb1SyQrbcNVgyAIgogNSa30McZOAHAjgIEADoXk45sOoBeAlQAqAWwC4AXQKtltQ3RPXTT1CFT1SLPH8Pu3wR8lTQvnVaipXdnsfolkhraKCYIgCH2SPWXLHhhf6RZZPJ7QdVXjlaYyirofiroPdnvfZs3F6/3G8FhaaqBm60MPPYQd/8/eeYdJUaQN/FeTZ3c2AksWFCSpYNhTRBA8FdRDRT0QTBhBD9PdqecZEMN53pkxfoZDBcWcUDFgQlRURERRopLD7rJxdid3fX8MzO7s9Ez3srOR+j1PPztd9Xb1O2G73656w9q1zJkzZ4/Oo1AoFAqFon3Qpmf6mhtpcvnMTEm0UHgN4fBa3b7evXuzYMGClMdHtOSzhKn6WhN33nkn++67Lx6Phx49enDmmWcCMHfuXAYNGhQne/zxx+u23XXXXUD8Z/bMM89gtVrxeDxkZ2czZMgQ3nnnndhx69evRwiBx+OJ21566aWmfLsKhaIVsWjRIoYNG0ZOTg75+fkcddRRfPfdd7H+zz77DCEE//3vfxOOnTJlCv3798disfDMM8/E9b344ov079+fnJwcCgoKmDx5MpWVjXPlUURJ9p3Vv+YffPDBsWv+Z599Ro8ePWJjjBo1CpfLhcfjoWPHjpx++uls21brGz9jxgzsdnvcvSE3V79aEsD555+PEIJvv/021rZ27dq4IL2659y9nXzyyTH9LBYLHo+HrKws+vfvz6xZsxLOky6U0dcAQppmSi4cMY6gDQV/Ihj8cY91sViSByFbWkeAckqeffZZZs+ezYIFC/B6vSxZsoRjjz0WgJEjR/Lrr7/G8lSFw2F+/PFHampq4tq+/vprjj76aN3xjzzySLxeL+Xl5fzlL39h4sSJlJeXx8mUl5fj9Xpj226jU9G+6d27N507d6a6ujrW9tRTTzFq1KjY/ldffcWwYcNi+1JK9ttvv4QHD0XbpLKykrFjx3LFFVdQWlrKli1buOWWW3A6ayvjPPvss+Tn5/Pss88mHD9kyBAeffRRDj300IS+o446ii+//JKKigp+++03wuEwN910U5O+n70Bo++s7jX/oosuYsKECZSWluqO9fDDD+P1elm7di1er5drrrkmrv/MM8+MuzfUv3fUJz8/3/A73n3O3du8efNifd26dcPr9VJZWcl//vMfLrnkEn755RczH0uDUUZfAwhEkgUG16Jp1UQMjL5QaBWaLCMQ/KZB53/yySfp27cv+fn5TJr4PNu3R2J9q1aFOGtSKQcdsIOBA2Zz55136pw3xKRJkzjjjDMIBoPMmDGD8ePHc84555CVlcVBBx3E6tWr+fe//01BQQE9e/bkww8/jB0/a9YsBg4cSFZWFvvttx//93+1kcK7n6buvfdeCgoK6Nq1a8qnle+++44xY8bQp08fALp06cKUKVOA6D/Afvvtx8KFCwFYunQpBxxwACNHjoxr0zSNwsLUqYksFgvnnnsu1dXVrFmzxugjVuwlhMNhHnzwwaT97733HieddFJsf+HChRQVFfHbb7/FzQYp2iarV68GYNKkSVitVtxuN6NHj2bw4MEA1NTU8Oqrr/LII4+wZs2ahHx506ZN49hjj8XlciWM3bNnTzp2rK0DYLVaWbtWf1VHYR6j72w3FouFCy+8EJ/Px2+//ZZyzNzcXMaNG8eyZcsapdvkyZNZvnw5n3/+eaPGEUIwbtw48vLylNHXGqgOG0flRiKbCYc3p5Tx+z/dJbuFUMicIfLJJ5/wz3/+k5dffplt27bRu/dgrpgWDRjxejXOmlTGqFFOvvu+G6tXL43Nmu3G5/Mxbtw4nE4nL7/8Mg6HA4B58+Zx7rnnUlZWxiGHHMKYMWPQNI0tW7Ywffp0pk6dGhujoKCAd955h8rKSmbNmsVf//pXli5dGuvfvn07FRUVbNmyhaeffppp06ZRVlam+36GDh3Kc889x913382SJUuIRCJx/UcffXTMwFu4cCEjRoxg+PDhcW1Dhw6NvY9kRCIRZs2ahd1up1evXmY+asVewLXXXss999yT9Am+vtH37LPPcuqpp3LSSSfpzvwo2hb9+vXDarUyefJk5s+fn3Cdeu211/B4PIwfP54xY8bw3HPPNWj8RYsWkZOTQ1ZWFq+99hpXX311OtXfKzH6znYTDod56qmn8Hg87L9/ap/5nTt38vrrr9O3b99G6ZaRkcENN9zAjTfe2KhxNE3jjTfeoLy8nIMOOqhRYyVDGX0NoDRYbSgTCq8hlMRXbzd1o2t9/vmmzv38889z4YUXcuihh+J0Ornrrrv4/vsaNm0K8/GCAAWdLEyZmkl+3ink5vbhiCOOiB1bWVnJCSecQJ8+fZg1axZWa2193hEjRjBmzBhsNhvjx4+nuLiY66+/HrvdzsSJE1m/fn3sxvinP/2JPn36IIRg5MiRjB49mi+++CI2lt1uZ/r06djtdk466SQ8Hg+rVq3SfT/nnHMODz30EB988AEjR46koKAg5p8HxM3qffHFF4wYMYIRI0bEtY0cOTLp57V48WJyc3NxuVxcc801zJkzh4KCgjiZjh07kpubG9t+/fVXU9+Fou1TWFjIqFGjuOeeexL6tm3bxo4dOzjkkEOA2lmfs88+m7PPPpsXX3yRYND4AVDResnOzmbRokUIIbjkkkvo1KkTp5xyCjt27ACiRv6ZZ56J1WrlrLPOYu7cuYRCxis9uxk+fDgVFRVs3ryZa6+9lt69ezfRO9l7MPrOdl/zu3Tpwty5c3njjTfIycnRHevKK68kJyeHjh07UlJSwkMPPRTX//LLL8fdG4455hhD/aZOncrGjRuZP1//nn7llVfGjXnzzTfH+rZu3Upubi4dO3bk1ltvZfbs2fTv39/sR9MglNHXADZX6z9Z1CUY/JFQaGXS2rrhyFYCwVqHz5qat0yde+vWrXEzVR6Phw4dOlJc1IVt2zLo1TsTq6ULWVmXJxy7ePFili9fzvXXX59QAaJz586x1263m44dO8aMQrfbDYDXG837N3/+fIYOHUp+fj65ubm89957lJTUBq106NABm602IDwjIwOv18vGjRvjHFh3c/bZZ7NgwQLKy8t5/PHHmT59Oh988AEQnelbvnw5ZWVlLF68mCOPPJIBAwawbds2ysrKWLRoUVJ/PojOJJaXl1NWVsYpp5wSZ5zupqSkhPLy8tg2cKC5OriK9sFtt93GQw89lFDj9L333uOEE06I/a+8/vrrOJ1ORo8ezdixYwmHw7z77rstobIijQwcOJBnnnmGzZs38/PPP7N161auvvpqNm3axKeffsrZZ58NwKmnnorf79+j77x79+6ccMIJTJw4Md3q75Uk+86g9ppfUlLC4sWLOe6445KOM3PmTCoqKmL3mM2b41fnJkyYEHdv+PTT6OrcnXfeGbuPXXrppXHHOJ1Obr75Zm6++WbdwgszZ86MG/P222+P9XXr1o3y8nJKS0tZtmxZk/5elNHXAL7fud5QJhBYBAQIBPX9fmpq3qRuEt1Q+BdCIf3ZsLp069aNDRs2xParq6vZubOUIYPnceAB97J1y7506/oDDnuio/no0aP55z//ybHHHht7KmoogUCAM844g2uuuYYdO3ZQXl7OSSedZKqqyD777BPnwFofu93O+PHjGTx4MD//HM09uN9++9GtWzeeeOIJ9tlnn5ixeOSRR/LEE0/g9XoZOnSo4bk9Hg+PPvoos2fP5ocffmjgu1a0Zw488EDGjh0bN8MM+ku7EyZMwGaz4XQ6Of3009USbztjwIABnH/++fz888/Mnj0bTdM4+eST6dKlC/vttx9+v7/BS7y7CYfDrFu3Ls0aK+p+Z3vKQQcdxE033cS0adNM3ctuuOGG2H3s8ccfT+i/4IILqKio4I033thjnZoaZfSZJKxFWLDtV36rKk4uE9lKMBSNyPX59Kd4a3yJP4bqmtcT2kKhEH6/P7ZNmDCBWbNmsWzZMgKBADfccANHHHEEvXv3ZuzYsWzfvp0HHniAQCBAVVUV33wTHyRy3XXXcdZZZ3HsscfGzc6ZJRgMEggE6NSpEzabjfnz58cFeTSUZ555hnfffZeqqio0TWP+/PmsWLEibll6xIgR3HfffYwYMSLWNnz4cO677z4KCwtjM5FGdOjQgYsvvpjbbrttj/VVtE9uvfVWnnzySbZs2QJE/+8+//xzjj/+eAA2b97MJ598wpw5c+jSpQtdunTh1VdfTZjlVrQtVq5cyb333hub4dm0aRNz586N+RrfcsstLFu2LLa99tprvPvuu+zcuROIXg/9fj9Syti1WtuV3eH5559n48aNSCnZsGEDN954Y4KPtaLhpPrOGsPkyZMpKiri7bffbrSONpuNGTNm8J///KfRYzUVyugzyesbl1IarObJNQuTytSdxavxvY2U8TV4w+ENulU0fP7EZYOTTjoJt9sd27744gtuv/12zjjjDLp27cq6det48cUXAcjKyuKjjz5i3rx5dOnShf333z82HV2Xm2++mXHjxnHcccclDWVPRlZWFjNnzmTChAnk5eXxwgsvcMoppzRojLpkZ2dz5513ss8++5Cbm8t1113HY489xvDhw2MyI0eOpKioKK5txIgRFBUVpVza1ePqq6/mvffeY/ny5bG23NzcuGXn++67b4/fj6Jt0rdvX84880xmzpwJRH1FBw8eTHZ2NO3R7Nmz6devH6tWrYoZAKtXr6ZHjx7MnZvW3O+KZiQrK4tvvvmGI444gszMTIYOHcqBBx7Iueeey/r165k2bVrMyO/SpQunnHIKffv2jX3no0ePxu1289VXXzFlyhTcbnfM3/iXX35h2LBheDwejjrqKPr378+TTz7Zkm+3XZDsO7v33nsbNa7D4eDKK6+MW2596aWXEvK4FhWZy387adIkunbtmtB++eWXx4132GGHNUrvPUWYmdLcmygsLJT1w/NXlG/lgi9n4YtEnbdvO3gcp+1zSMKx23aMiEu43CH/aTLctctEXu+zlFVcr3verp2/xWbrmY63oGhGCgsLE9I5tBRSygSfTUUivXv35qmnnor5/GzatIn999+foUOHUlhYSJcuXWJ5uwYMGMC0adO44oor4sb473//y8svv9yg7741/VYUrR/1e1GYRQjxvZQydf6yXbTpMmzNwdaaci7/5vmYwQdw249v09mVxbCC2jBvf+DrhAob1dVz4oy+3Uu/egRDy5XRp2gUyuAzx/r16+P2e/bsid/vB2DQoEG8+uqrsb6VK1fqjnHddddx3XXXNZmOCoVC0RSo5V0Dpi97k5JAfPBBWGr8c+nrVIdrI3SraxJLePkDnxOJbI/tRyLbEmRq+7Yn7VMoFE1PMBjkvPPOU1U3FApFu0UZfQYsL9NPtFwarI6lcJEyjN/3gY6URo3v/Tr7qZbSzZV4UygUTYPD4eD66/XdLxQKhaI9oIw+A87vc5Ru+7BOfeiXHc1xFwrq4/MuAAAgAElEQVT9iib1M/sHgl/HXlut3ZOex2pNdPxUKBQKhUKhSBfK6DPgLwOO4aK+w+Pajiroy/1/mBjzoQqHf096fDhcW/vP4Ugszl3b1zKRPAqFQqFQKPYOlNFngqsHHc+UftEUIUcV9OWhwyeRYaut+SpJXpJJytrSPW7Xceh95Hb7YGxqpk/RSFQkvkKhUChSoaJ3TTKt/zHs8FVy9cDjsVviPzartUvS46zWznGvXc4R+AOfx8lkZoxPr7KKvRIVvatQKBSKVKiZPpNYhIU7DjmNji5PQp/DfhBg1T3OYT84bj8z89y4fYGLzIw/p01PhUKhUCgUCj2U0ZcGLJYcnA79vIjRJd26+2OwWApq991jsVhym1Q/hUKhUCgUCmX0pYmMjNMT2qzWfXDUMwaFsOHJPBuLpRMWSycyMyc1l4oKhUKhUCj2YpTRlyYy3KchhDuuLTNjoq6fVU72dXTvupzuXZfjcg5rLhUVCoVCoVDsxSijL01YLFm43afUabGSmTmxxfRRKBQKhUKhqIsy+tJIZsaE2GuXc7hKw6JQKBQKhaLVIFRur3g6duwoe/fu3dJqKGJIiv3rkPXK1Lms2WTbu+yS0NjhX6d7tAULBa4+Tabd+vXrUb8XhRnUb0XREFrX70UCZlJCaZiaS5LVIDIbqVP7wOwnm0ru+++/l1JKU5N4Kk9fPXr37s2SJUtaWg3FLjZWf88bm/6R0J5hzefivi8hhEBKyT2/jk0wDAGy7QVcuv+zTaZfYWGh+r0oTKF+K4qG0Jp+L1IGEcJhLKdVIyzGxpwMLkEkyXjRFpBSpi0vqj8cwmWzG8oFIxEcVv3UcEKIpWbPp5Z3Fa2a373f6LbXREop8q8GokmJM2z6aW8ybXlNpptCoVDsHZhdETQr17YTyaczEb4/HDYlZ03TOZXRp2jVlAT0l20Biuv0eWwddGWStSsUCoXCLOl2A1Omx27CmrnP1mpJz2emPnlFq0aTkaR9UtYu53ps+boyyuhTKBSKWh5++GEKCwtxOp2cf/75Jo8ya/Qlv17HIfSXKfdGpMnPVktT/IUy+hStmnzHPkn78hw9Yq899mQzfWp5V6FQKHbTrVs3brrpJi688EIg6p9miEz0l9bHpNHXxk0PM5+ZaSPNpFhNKGRO0IC2/ckr2j37eobqtrssWXTNODC2n2lN5tOnPwOoUCgUbZUVZdv3+NjTTz+dcePG0aFD9EHZnH+aNGkcBvdYr7aEGTvNtNFn0lUvEDHn+2eEit5VtGp6e47AY+uEN1wc1z4o9wSsovbnmyxgQwVyKBSK9kRE0/jb6X3YWao/q+YL98Htrq0ONWXKFKZMmZKGM5tILiIDJsdq//NNZgMvfCZn8IIRs7OoqVFGn6JVYxFWCjtM5LMdD8XarMLBofnj4+Sy7QW6xydrVygUirZITSRESWmYr97vrtt/1MnuJkj1IjE1vyX9aT5v68SMOWc2wrfU52OfHP2Vqrqky+hr/+a2os0zKGcMTktWbL9/9h8Tlm3zHN10jhTkOro0sXYKhULRfFSFAmhIAjKsu+0mvYUXTBp9mJ3pa/9FIcx+/mZ99QImU7sYoYw+RavHbnExMOf42P7g3JMTZHIcXRD1fs4eWz52i6vJ9VMoFIrmoixQgwRCaLpbi2J6ebdtG31m/PUiJo0+X9ic0Wc2n58RyuhTtAkGZB8LQK6jB53d/RP6rcKWsJSrP/unUCgUTUtTljct8nmRQEBqulsdLXSPD4fD+P1+IpEIkUgEv99P2NCg0JKOF4f0mXwXbdvos5hYujUbyLGurNSUXLoCOZTRp2gTFLj64bBk0CPj4KQyeY6ucfu59fYVCoWiOWjKkvbbairQpMSfZKujhe7xd9xxB263m7vuuos5c+bgdru54447DM4qwcwsolmjL0X+1faCGcMQ4Ift20zJqZk+xV6FEIJ8xz50cPRKKlPfyMu1K6NPoVA0P2YT7u4JG7zlSAR+adHd6mqhx4wZM5BSxm0zZsxIfVKpkVajz3Q+v7aLmejdiKbx5aYN7PTVpJQr9/tYU7ozLXopo0/RZsiyF5CVIho3v06yZoB8Z48kkgqFYm+hKZdaW4L1VTujPn3SorvVkuZADlN5+swafemZtWopwpqxAWzm01+ybStVwSCfrf89pdzPRUX8XFRkUrvUKKNP0WZwW/PISJF3r5Ozd8p9hUKhaA6a0s5c7y1DQ+CXNt2taTCbssVcIIc0XeGjdaKZ+CzMPGzMWvY9AM8t/yGlD+AHv61h4cbf8ZsM+kiFMvoUbQanNROnJTNpfyfXvrHXNuFUgRwKhaJFSFed1PpIKdkYM/rsulstJks9mMLkWHtLRY40RO/ev/hLPvhtLQDLi3Zw/ccf6M4gzl+7mhdX/MROn4/rP/6w0QEdKjmzos3gtHhwWrOS9mfYcnFZs/BHqsh3dkcI9UyjUCian7Cm4cCaUkZKaTqB726K/F6CWgSrFCZm9dJp9Jk1Ys366qVTt+YnrBl/Hv5wGIdV/zfw94/m8/rKX+LaXvl1BVurqph1yunYrVb84RC3LvyUF1f8FJN5a/VKVu4s4YHRJzGgY6c90l3dFRVtBoclA7vFnVJmd/CGCuJQKBQthZmZoD2ZDNxeUwmwa6bPobu1LO3LfzIZZmZyk/0GpJR8u3Wzbt9PxTuoDkVnS7/ftpWX6hh8u1m1s4Snfvi+AdrGo4w+RZvBImxYhT2lTM6uQI9se+fmUEmhUKSZ9hB4ETHh6L8nS8DbfVUASARBadXdakn352hmPLO+em07ejesGesfSvIbEELw2p8n8cfe+8W1H9KlK2+MP4tcV3Ri46ievXjlzxMZXFB7L8t2Opk+4hj+/cfj2VOU0adoM9gtTqwi9ZJGtiP6D5LjUDV3FYq2Sbqd/JvfiPSHjP2u9iStS5HPC+ya6dPsulvdM5jBvJFtvCRrOkDDRJRvazb+zWjmC4eSRvkWZHp4+uTTmD7iGAAuOvgwXv3zJPbLiy8veljX7rw+/iwmDDqQ7lnZzJ90HhccfCj2JMvGZlA+fYo2g9OabShTO9OnjD6Foi0iCSMM/OEaOmJzY6YEVyiiNfjmXeSPGn1SCgIy2apHU6RDsZBWPzyt3FCkof6OzYmZWdpAJEwoEsFmST63dsHBh7JyZzE3Dh+Z9P1aLRZuHD6Sd9asoluW8T3QCGX0KVoFUmqGgRcui8dwnExb9EnJY8s3kFQoFK0RqdWA1ZnGEUPQBEEVqcYys7y7JzNZJfVm+vTZPYuWRmNXCBDGBqowWIkBkDIImnHpsXR+J+lmp6+GThnJM0kAIM0ZhxcdfJjh+8x2uji2d5+GqJgUtbyr2CPSP/VufJG0W1yGMpm2XAAyrLmN1kihUKQXM9cNTXrRtNQVChp0Tq3CUCadxoU/FMYXNM6nFtiDslo7ds/0ASFp091qaYnlURMzl1op0oTR15opqTH+fYalZmrGt29+B1Pn7JCRYUrOCGX0KdoMVmEcmebetQTstuU0tToKdj21KxRpJBzZQTiyI23jaaFVaRvLDMFIhGDE2NF/a3llg8fePdMnmzs5szRZe9fETB+RLRDZZDxUK53lA5KmYqlLdSgUi8RNhdkavWbKuplBGX2KPaT5nyLNzPS5rdlYhR2HCVlF49Ei6SkNpNhbMDYcAqHVBMOr03O28AbCwUVpGcsspd4ayqqNAxW+/V0/bUcqdvv0aVIQ0Oy6W9MQAtn4ahAAMrQMGVpuKGcmQralCJsIWCnz+Sj1mS1LZ0y6jOA2bfQJIZxCiKeFEBuEEFVCiB+EECfW6T9WCLFSCFEjhPhUCNGrJfVtT0TSPMMjTURzGaVrAXBZs2KzfQpFW6E1RyqmF+MlTV9gMTX+xWk5W7DmVUI1bxnOSKfz8y/21lDirTaUC4QbZtRENI2dgeiyokxj9K7p8mqmSqyZWL73vwfaNmRwaUq5dd7Nrfb/YvXOEkOZEl81Jb70uSmkizZt9BENRNkEjARygJuBl4UQvYUQHYHXd7XlA0uAl1pK0baEmX+0oFaZNsNPygi+gPHTuMWEI7FFWMlROfqajXBouSmDXaEADA0HTQbw+hdQ5Xu30Td8LbyeYPWTSG0bQe8jjRqrIZTX+Cir8RvK2SwNr8axOzBAIghLq+7WJEifqTQrRmXYNP+nEPox+tr7YErZXyrXURRofb5/NaEgi7caL09vqKhgY6VxlHJz06aNPilltZRyhpRyvZRSk1K+A/wOHAacDqyQUr4ipfQDM4AhQogBLahyG8H4YusNbcIbavjyhB4+/yd4q+eakDR3kcyyd2ycQgrTBHyvEfS919JqKNoKBg783pp30bRywpHN1AQ+3+PTSOmnpvQykNGZlkDVTMImHizTwe8lZfxeYmysLFq7gS1lxkEmu9laU+sDqElBIGLT3ZoE6Yt9linF6hh9mqaxbdu22j6tEq1yeu1+8Eu0mld0x/FHgny0/Rve2fpFI5RuGn4q3sGPO7anlNGk5IPf1vDe2vS4KaSTNm301UcI0RnoB6wADgB+3N0npawG1u1qr3/cFCHEEiHEkuLi4uZSt02zseojNlZ92OhxIpESyir+hc//MdU176SUFSZ/rhm2po3cfeKJJygsLKSwsJC99fciZQSf92lC/g+orryTUHDPywK1Z9RvJR4ZXpuyv6zqydjr0qon9vg8/orpaOEVdVoi+MquaHIf1EAozOtLf+b9n9dQWq1vJBVVernpzY/49vfNTJ39ZpxvX6rfy9aaWgNRIghoNt2tSZA1Jmf6aigvL+ess87C5XLRt29fAN5++21uvO540LbFiWtVtyPD8bNmmtS4b9UcNtRs4/XNn/DJju/S9jbSwcfr17GtuooVJcl/Sw98+xW/lZfx9ZZNzF1h7L/YnLQbo08IYQeeB56VUq4EPED9x6gKIKv+sVLKJ6SUhVLKwk6d9qyIcfvC2NekxL+MEv+yxp1Fq2BHySRC4VWARnHpNGp8C5LKm3VkdVkSvuK0MmXKFJYsWcKSJUvYG38vkfBGKopHU1N5CwBS20Flyal4y/6GlE2RGLbtsrf/VuoipUQGFibtr/YvxB+KPadT4/8MfzCx9qgRkdAvhGpeTDy/tpNA1Ux93Rp8lkSKKr385fm32FhaQZU/wCXPvcHvxfEzfrfN+4Rj732a177/GYB1xaVM/t8rjHt4NlvLK1P+XrZW1870SUkzG33Vpmb60Mq59NJLycnJYcOGDTgc0YwLQwvzeeX1H3TGrUHzPhTbXV+9lWuXPcCXJbX3lvtXzeHRNS9TEzZeMm9qfikp4rmfo7rN+OJjApH4611VMMDlH7zDzCW1Pqn//Owjbln4samI7uagXRh9IprVdzYQBC7f1ewF6nv0ZwNVzaha20Q2z0fkrX6eYOjnOi1BSiumJ5U3O9PnshokzVQ0Cn/1s0R0oisD/ncIB9PjgK9oh4RXIVNE0pZWPqzT1nBfvEhoRdI+LUmfWf/BZMl2v1y7gZNmPstX6zbG2n7ZWsSpj8zhjaW157xw+GGcN+wQsly1yadH9d+Xa8aMoFtu6gC0Hb7a67KGIKhZdbcmQXrN3Rcim/n444+ZOXMmXbt2jT2od8yroKhE3+iRkd9irxcVL2Nl1fq4fg3JJ0Xfsb566x6rnw6W7djGOW+/gn9XfsXvtm3h4vfejEvLcu/iL3lnbWKKoGd/WsasH1MHrjQXbd7oE9Ff1dNAZ+AMKWNx5SuAIXXkMoE+u9oVKZChlSn7S/w/URFcR4nvRyqD6/fsHFKjuubNhPZw+HcCQZ0nQkCY9OlzWo0rdyj2nMycm8nrvJTM3HuwOYbhcJ9KVv5s8rssx+4c3tLqtUlac06ydCEDH0JkIzKk88AQWkONjs9dle89wpGGLYvbHMMA/Uh/m+tofd1Mjp3MOOyZn8PBPbsmtA/o0pF+nWt9jHvk5XDtmKN558rz6NMpnwcnjuWxc8YxfP/ehucuqmP0IQVhzaq7NfxdmfjtaRXRzYjIenJyPJSUxEe3btqxD107638nFtdJsdfn9D6Jfw68gFx77WrNoOz9ePCQaxmUs5/x+ZuITZXlnP32y5T645e4v9i0nkvffzu2P+WQQoZ13yfh+BP77M/EQQc1uZ5maPNGH/AYMBA4WcaHEb4BHCiEOEMI4QKmA8t3Lf0qkiCln4jO0khd1pRH+zXCrKnQd8Q1IhhaTkTbqdPjpCZpYIBJo8+SnszliuRYrJ1wZUwkp+PLZOU9gsN1DNF/M4UiESkjaDWvAaD5Xk7o9/reT3JkGK+vYb7DFlt3nNnX6rT3w+G5rEFjmWWf/Fyemnw6z144nh552eRmuHhw4lhevvQsDuiemE2gIMvDY+ecyugD9jd9ju1xM30Yz/TJ9C0nSq0UtLLUMpEtoJVw0eQ/csYZZ/Dpp5+iaRpff/01519wJVOnXET9ih3COQqRcX5c2/BOh/BY4Q0clNOXs3udyH+HXEX3jJatpZ7rcjO4oEtCu81i4agetUZet6xsXhg3nvuPOxGXzUaey8Wssafz2AmnkONqHdfHNl17d1fevalAANhe52l5qpTyeSHEGcDDwBzgG2BiiyjahpDBJWiBz5L2/175Lhu9tRfhdRWv0TVjKN0yRzToPE7HwfTosgR/4DNKSv9GRNtOTtYV5GRdjsXSuDx7TrW8q1C0KqR/Pmhboq99ryA9lyMstQFXwdC6pMcGw8n7kuHInEo48CWRWASwE3feY0kfTMzOsxrNnR2+bw+eOPc0NpdXMsJg9q5nfsMCzrZUxwdyGC/lpjHHnVaGFIHUn5N/PgDXXZ6FO2sC06ZNIxQKceGFFzJ16lSuuuoqZM1zaFW3R+VtA7DkPKhbcz3bnsntB12G3dJUyaYbRpbDydxTz+SNVb8wY9EnVAT8HNq5G/85ZjT75ydmizit/yCKa6rp5snmmF77toDGyWnTRp+UcgMp/l+llAsAlaKlAWihn0F60cK/Y7HF/1iLfEtZWvyfuDZJhG923Mqo7o+Q5+zfoHMJIXC7jqGg4zPU+OaTl3O9wRESM5dnh8XdID0UCkXTIaUW56yPrEarfgpr1jWxJpEi8bpIslSbCiEEruzpVBcfC4Aj8xys9uSzambNI03TwJJ6gWzfTvn06pBnVlVT1ISDsWocEA3kCEaMjL40Bg7IKlIl1pbSj6x+FgAR+JCrLn+Pq6++OkFOZE5GBr9EBr7AmvsgwpL8Ab21GHx1Oa3/IPbNzeP6Tz9g7rgJOK3JTajhPXvR3dP6CgW0aaNPkV6k1NBqoksvWs1LWLLjjbAS34+EdcL2g1oFpf4VDTb6duN0DMHpGGIsaPJ53EyNXoVC0TxI/5sQiZ+tkzXPIjPOR1ijsyRO+8Ckxzsdg/bovFb7/ghLJ6RWjNV5ZEpZM8YcQEjTMHN1sTQw8bIRG73xS6sSQUQz0Nd08nwTD9OaF0Ry01h6HwUtWi/5k0WVYLsMS70ldqfTSY8ePdin61/QRCbC1sekfq2Lgzt35erDj0pp8AH0y++IzcRvqrlRRp8ihgx+iYysByBS8wrWrL8iRG2U2aD8C+joHsLykofZGYimUihwFzKkwxXku/bswtw0tM7SPQrF3oaUEs37uE6HD63mmdhsn8d9PEXlNyWICRxkukY1QoFQ/N8kRExG7/qCITIdzf9QudEbX9lBSggZLe82yOgzEkmerkWGVkH1U7H9S/62g607tgIL6NChEzt3Rn23CwoK2L59O4MHD+aFZ6bSv2nTqTYpJ+xn7IvZGg0+aB+BHIo0ICPFhOumS5FlhCsSL8IF7kM5psf/ke3Yj46ugxnV7ZFWZvBBJE2FwRUKhTmS1lGNrIU6KTnijvHX+gbbbT1xO49IkMl0H4fVkrNnOmkVSBk1lrTwxtSyJp8Ti03U1G0K4iJ3ic70hSJW3a0Ws3ntzBh91dFNr8t7L3WXfi+clMMVF+VQuvoItmzZRHl5OVdddRWXXnop5eXl/OEPf+Dyv84xqZsi3aiZPgVSSkLlf0VGNsS1a77XiNgPwpp5Xly7VdgZmDcZpyVP1wm3pQlqLZ/EU6HYm5D+j8A+GCHq3VJS1tqNn4nKyTwLX+CbhLY91qlOtKk0KP8W1iIkS/NSl3UlpQzs0vyRpFWh+M9RSggnWd6NtZquiW3C6NPKQSSZOQx8Gbc786lytvy4LzZbEYTX4Xb341//+hfdunXjxhtv5N5776VHjx4mdVOkm9Z3x1Y0OzL0IzL4lW5f2PuYbnu3jKPpnPGHplRLB3OP4yHN7MVOoVCkhfAK0IuytQ0Aa0/dQ4TzuLj9LPefsIjaHJs2axcyXSP3WCVh6cBuX7XdvoPJCEU0U2Mu27LNWKgJCMv6+gkimv62G6lVkg6kDIFWEvPZS6BewF9mhuC7ZX7ABdZo7sLvv/+ejIxoKi1LK1323FtQn/5ehEy4cEQRtn4IxzCdHhvWzHN0j3FYPVjqP9U3MRrmLsxhtbyrUDQbUgYguAyC3yT0CWHDknVD4kGWDlg8f4lvsmTgcY+J7We5T0GI5H5rRlU0hCULiy3qemJzJC4d16XCb251YOnGlqkKYamXvFtKCEesulsMrRxzGDxMh9cBYZDVSJ1lcpH1D+oGgsy4tgMnTNzCuVe5+ecN/+Kcc85hzJgx3H57NFXLxx9/zJ///GeTuinSjTL69ir0Q/iFJQNHhznY8p4Ea3cALM6R2Dt9gM0zrTkVTIk0mWzUbOUOhUKRBgKfAX6kXz/BssV1PMJ1cnxb9gyEJT9B1uOurc7gyTix0arZXMcjLJ2x2FNnB9hWYVxiTJOSkmoT9WebALsl3viVCCIRi+4WQyshLdQ15nXKLArnCMisNeDPm5DN1x+Np/8Bf6KiooJ+/frx9ddfc955UTehsWPH8uSTT6ZHN0WDUT59exPSDynyYVldx2Kx9SNUPg1b3v9aYWkoc/q4UuR+UigU6UX63oi+CH2PDG9E2BLLUFmyrififx8Igf0PWFz6Bl2mawRgwyIycDsKU59XgtElyuY6GhnZbHgt21ZZRTAcxmFLfku0CEFBVstcW2z1faclaFrq9yS1EqSUJq7jqR+mpf/dOq/fQ2RMSJARnsuRgY8hvBKEhwOOeIwDj0w06hUtjzL69ibCq8DgQipsPbFmnNUKDT6wWcylSujoal0Z0BWK9orUKiCwcPce+N+Besu2AMLaGeEcjgx8isV9RtLxLBYPLscQrJa8lEu70bMZ55ez2g8kYj/A6G1Q7K2m2FtD99zUyXSzXc6U/U1F/euxhPhZPT2kNxrMIQzKUkqZ9GOUwaUQWlbbEPwaGfoVUS+vohBWpPPYqNHn+APz3lnE559/TklJSdwy/HPPPZdaF0WTo5Z39yJkzUvISJGhnLCZrwfZGsm0teEEUApFWyK4lLrpOmTw26Siwj447m8y3I5DcTsPMzy1mTQrQriwmrieFXmrKfJ6U8pU+Pys2FZERDPnW9ykSJCa0N1qZXwmI3iTvx9ZdXfCiWXVPfrC4V8AuO3fHzJ16lQ0TeOVV16hQ4cOfPDBB+Tmqutya0AZfXsJ0j8f/POQFTcgtdR+KW3d6FMoFMYYBUKYG6RehGiqiFHLroL1uyI6k+Gw98dhN67uY1Z/YTVeZpTS2Ih8fNG3lPv8zPluWWrBJsCSMBUnkBH9LYYMYC5Xn/7yrgx8AaHvEzuCXyCD8e3S9wYEPgVg1gub+eCN8dx///04HA7uv/9+5s2bx/r1603oomhqlNHXzpFSQ3ofRpZfBWgQXIgsnYQMb0p6jLC0vnqBivQQjhSzafsoAsHlLa2Koj1g7R2/b+uVXNbSAbAjLFkph7TbumPfFVCWirDJNCtm8u+57TZcdn1vJykl/12wkP8tjho6d320kGe+SZKMuomoH8gRnemz6G61MkHDSiRROf1cinKXEWfUJ4PfIesk8i+v1Diw9/vI6jk4HA5CoRCHH344n3/+ubEuiiZHGX3tHFl+BdI7M74x/Cty56nR8jmKvYqSsr8TCq9ie8l5xsKKdk1a/HbtB4GlduZOuMYkFRXWjmBJnS8PwCJysFqMlwKralIlfm4YVYEA3oB+8uG3lv/K01/XzmxpUvLvDz9n8frkD87pJtNWz59ZAhGhv8UIA2aMPv33LSydkh5Sty+63Ft7nj697KxYFUB67+HAAw/gscceY/bs2eTl5RnromhyVCBHeydJYlQsubCH5Y0UbRebrTcAdpsKdlE0HiEskHEm0vsAWArAeWxyYUsnsCY3JGrHdCCEcdDW6s3FFOR5DOWMCEYifPX7RrJdTg7vlVgp4ojePemem82W8tql64GdO3FAM1bmyHclBmMkSbtaB81kfbmwfnPGRKiZA1pxfLulG9QNxrHEfw63Xd+BnaUaWDrw73/fydlnn43X6+XRRx81oYuiqVEzfe0cS/b1iLz/gXX3Td4OmZcgOryDsHZpUd0UzYuUklBoDQDhyGY0A99OhcIU7vGAFdynI1KkhMKShxDGsz0CC2bSM7362Y+NDqrwhUL89fX32FxeyazFS3lt2YoEma45Wbx4/pl0z4m6vRzQpYA5k8eT1YyRvF0z6rncSBARobvVYkma06a0tJTTTjuNzMxMevcZzgsvvJAgIyx53PrQIJw915DdZ+2ubR2/l/0DYak1tkX29NjkgqZJXE7B0MJ8RO4DHHHEEaxdu5bt27dz+umnN/pzUDQeZfTtBQjncETHt8A2CJH3GJasaxEWgzB+RbsiHNnBjp2T8QU+27W/ma3FpxII/tKyiinaPMLaCeyHIlwnpJYTLjDw5zPLyo1FfLH8dz76brUJaX3DsNznZ8KsF/lo1VoAIlLyz3c+5O5PvkiQLcjycMHQQwH4y4gj8DibN3VLz8xcrHEGXJKl3Tijz0Yyf8Zp06bhcDjYsWMHc557iMsuu4wVKxINXmHtzplnHEHlur5UrutLVfFs+vQ/scc1OQEAACAASURBVJ5MJ0T+c2Drj8XWjdPO346z81MI+0GNf+OKtKOMvr0EIVyI7JsQzqNbWhVFC7C16E/U+D+KawuGfmZr0UmEwy1TWkrRfhDOYdE6u4aCLkMRiUaq0mC+QIhbZ30IwH0vf05xeepUKzJJ2pJ1JTtZVZRYtWL+Cn1DcvfMXrbb+D2kG6fVRs/MOrOkZmb6hFv3866urua1117j9ttvx+PxMHz4CE455RRmz56tf3JbH7D2Alt/cE/SFRHW7lg6zsNS8DlHjxzDN0vNVU9SND/Kp29vwn5wS2ugaCE65v6H4rK/EqnjnyNwkZ97EzZbtxbUTNHakVoFwsj/13FE1L/PEDO3HEkyoy+iadz41HxWb47+jndW1nDVzDd58roJZLr0/QBlknJkh/Xszk1jRnHHB5/F2jp5Mnhi4jhd+XJfNP1JWY2Z3HfpZ0BuAeu9pbUNSezi4uJiCgsLIbIVrJ8wZcqlTJkyJda/evVqrFYr/fr1izYIO0OGDEkaXTtv3jt0fDdM1y45XH7l41x22WUp9ezVqxcnnngip556Kj179owLGLrtttvMvVlFk6GMvnaClBHDDPZCqK97byXDfSzd7G+zZccJaLICsNC54ywyXKNaWjVFK0ZqXgitBOcRqQWTBYwlYCZiOIKU+sEF//fW13y+bF1c26pNxcyY9QF3X3ZygrzUvGjh35Oe6bw/HMKv24t57cfo0ubdp55I304ddGUX/x6N1l28fhNjBjZ/LtMBuQW8v3lldGfXTJ8enTp1YsmSJUSq7sbi+XuCMe71esnJqWvEW8nJyaGqKrH+8IQJE5gyZQoFOT/xzXe/8+dJt5Kbm8ukSfozfgA+n49x46KG8+bNmxv2JhVNjrIC2gtaOVj1L1YKBYDd1pvOHf/Hjp0Xk5d9nTL4FMaEV0U3I6NPmPXVM172kzKA1EkjIqVkzgL9/HifLF3L1pIKunWMn5GMhFYQCSX6qtVl1P778tqPK3DarAzbN7FusJSSRxYu5tM1vwHw4vfLGdi5ExMObV6ftQG58VGywuCjFJZ83dlXj8dDZWXdJNqCyspKsrISv8NBgwYBIIPFDBveh6uuKufVV19NafTNmjUrtWKKFkX59LUDpFYNkd9aWg1FG8DtHEbvbr+Q4zm/pVVRtDBSmvC7Cn6LDH5nLGdqaZekOeHqomleNJk46ySEoFeBfvRvTqaLvKzE4LRw4BMigYUp3+uqHdHl30A4wvrSsri+iKZx+SvzeGjh4lr9pOTmdxdw6/xPDN9LOumfU2v0CTM+fRb9SiT9+vUjHA6zZs2aXS0BfvzxRw44IEWNYosHhAchhKlKKL/++iu33347l19+OQCrVq1i+XKVEL41oIy+9kDoh101MBUKhcIsSfKz1UEGv4HgN8ZDmaj8IKUEaZwmKBzZQTiyQ7fvtotOwOOOj5y1Wi3ceuEY3M7ESNWwbz5SKyUSXJzQB/DJ6nU8+XWtUXvla++yo6o2MKQqEGTJxi26xy5c+3uz1uHtmZkbl6RZRPS3WgF9P8zMzExOP/10pk+fTnV1NV9+8QFvvfUW5557boLsW2+9RVlZGRIX3363gpkzZ3Lqqaem1POVV17h6KOPZsuWLTz33HMAVFVV8be//a3hb1qRdpTR18aR0o/03o+seQYZ2d7S6igUirZCkqjWOMLrQJYjIzsNxqo2ccJA0pJfdQmG1xIMrdXt279HRx644lScjqhnkhBwy+TjGTF4vwRZLVKKFlkPQETnofi5737gspffJhCutZRW7ijm9KefZ11JNGAi1+3izUvOYWTf+GTmpxw0gFcvOgurpfluoUII+uXsSm4tQWj6W0zekpl0rEcffRSfz0dBQQFnnXMVjz32GAcccABffPEFHk9tDr4XX3yRvn37kp3bj8nn/4V//OMfTJ48OaWe06dP56OPPuLxxx/Hao36mQ8ZMoQff/xxz9+8Im0on742jJQ+ZNkUCP0U3S89C/JfUEmXFQqFMVpptDJPSnbdIgyCxIjsAKPrjgxhpiyYL7gUa5KlSYBD9u/OtRNHccdzCzjruEP505GDkp0w5Xl2VHp1JaoDISr9/th+15wsnpg0joc//5qHFi7mXycfz58PPtDwfTQFA3IL+GFndOZRGE0yiuS5WPPz83nzzTcBkN7HEZ6zABgxYgReb+1M59y5c6MyWjmIDFOVUoqKihgyZEhUhV2Ru0KI9JT9UzQaNdPXyknmPyG1KmTpufFLL5HNyJ1/RoY3NJN2CoUiHZjxk0o7YRO1t+37g6UAYWQchtek7geiQRyp/Qg1GcAf+AFfIPWS8rjhB9KnWwcuGZs8wMRi7YBlVyUiq+OwhP5rjx3BExPH0SO3ttrF8f378u7U8zikR2Iao2lHD+XMQw9qMYMP4IC8XYa1NLG8aypSGpIlr47HBhgY/rs47LDDEnL+vfjiixx++OEm9VE0JWqmr9XjB9yJzb6XIKTjGKsVIb0PIHLvb3LNFApF20XWvIpwnZhayDbQTJFXZGgpaCfEledKFEqeimU3wdBqJAEiWoBQeCv2JDkkhRCcM/owsjJSJ0q2uU8hVDMXq2Oobv+ovvty2MXncPbsV/jj/vtx9ahhSccSQvD3Pw5Peb6mZkh+9PMQGEfvSs1rzuyzGufpTPm91mPmzJmMHj2ap59+murqasaMGcPq1av58MMPTY+haDqU0dfaCa8B++DEdtdYCHwNwXolg2wDEBnnN4tqCoWibSKDyyD4BTK0GmHvl1ROWHKRhkvAQPB7CC2FVBV/ZLVhIEdEK497bSe5QTKwV2dDteyu45FaUcrE0VkuJzceP5JDenQ1HC+nBapx1KV/TgEZNnvMpy8l4dXg1Dd247DuayzTAAYMGMDKlSt55513GDt2LD179mTs2LFxvoKKlkMZfa0YKSNI78OQ+38J/hDC2gWR/zQytBxZMQO0YkT2reA8RvlOtCECwZ9x2AeZrGagUBhjJlG7rH5q198nEbl3J5fTKkEnfUqcTOhniPyO9L2dusyjVgxaUcqxbNYC3dd6eNzG/mUW+0Cs9kMM5Y7obTa5dMtitVgYnN+NtZiY6QstQYZHIWyJuQfjsKQ3v+ubb77J2LFjmTBhQlrHVaQHdadpAsz455jy4al+AgKfQc2zSUWEfTAi9wFE9o0I1x+VwdeGkDJISdnV+PzGyx7BkBmfKUV7x9R1Q/pTd0eKIbAguuN/L2rYJUMrhkhx8n6IPpjuHiucPF+oDK8HbSdSS25EOmx9sYgc7NZ9sFk7pjyvxcS1Tgg7Fnt/Q7m2xJD8boY+fVIGkIFFyICJXIKW5AEfe8KMGTMoKCjg4osv5rPPPkvr2IrGo4y+FiP53LyUEbTKu5DeqF+erLoTrerBpBd8YdsHnMc0iZaK9CNlhGrfu2wtOpFQaAVFOy+ltPw2wpFtOrJhKr3/Y0fxn/EbOLcrFIBx+hRtG7XXn1Dq2TfpB5KnWZH+DyFmWISRlbckDz7bnUA+krwsmhBWMlzDyHCNSK5TTNbcA67Fop/Qua3yp30GGRt9vrdBVqLVvIA09Mk0njFtCMuWLWPRokV06dKFiy++mB49evD3v/+d77//Pq3nUewZyuhrEozTEkDyzPSycjrU/C++sfoRZNWdSY8Rwpm0T9G6KC6dQvHOiwmFftnVEqDS+xibtw0lGIrW1oxoZews+webth1MafmNRLQithePY8v2kVRUPdpyyitaGOOEykQM6p3aBoDYZQhZuqT26bLkJC2xJrVKZOWM+MbgN+Cbm0SvqHEpkyRe3o3beThup3Gkp+lFDRNpRtoSB+R1iQZySP0NJFr1rmtE5Dekf16z6zho0CDuuOMO1q5dy6uvvspPP/2kondbCcroaxJMlDdKcuGTWiX4XtE/pma2bk1KRduiY95McrOvoe4TttNxGF07vYHDPgAAIVx4a15G0+KT4obCq/EHvmxOdRWtCYOlWwBCK5DhjUm7hXAgMi+Mvs68OKX/n7B0AksnfVWqnwStJLG96gGkbuLn3VZa6tuOw7Y/zhTBJbuxWc3evtrnbS7pTJ+2EyKbYnJa1X3IlEmxTdyv9oBNmzZx9913c9lll7FkyRIuuOCCJjmPomG0z/+GlkarMJYJfqf/xCuyICOxHA4AmReZSo6paN1YLJnkZv8dd50lrA65/8HpPDS2L3CR5bkYUS/BqtXaA09G8mLninaOlnqWDED654N/fmqhjEkgciFjfGo5kQHJKjv4FyRRoDwazVufXalBhLV7ylNaLfkpkzPXypmt92tidrStIcES0d9kpN5yvbYFWf10irG8yfv2gEcffZThw4czaNAglixZwi233ML27dt56qmn0noexZ6hjL6mIPRrym4pA8iaZ5HVTyT0CSGwZN+MyHsGrH2ijbYBiPznsWRd2wTKKpqbcKSI7cVn4PN/HGvbVnw63uqXY/tCCPJzbqRH1+/xZJ4NWOiQdz89uiwmM2NsC2itaFpMBn8Z1NiWwR8gtARZ81zK2R1hyQb3aQihkwM0bkB/8nJtIlXyh8TZQ2E/AHCArU/KUwphR4jEOrr1MTvTJ83MjrY1Uvr0JfrwadVPIrUkvp4R/drCe8q8efOYOnUq27Zt46WXXmLcuHE4HGqyorWgjL40I8ObU0ZMSa0SWTY1mkOpZnbSAA3hHIbo+AZknI/o8CrC8YemVFvRnMiwzjK9hqZzc7VacsnLuRGX82iyMicapuJQtE00M1URwmuQwW9Tikjfq7sGLIbA5yllhYlUHTJF9K5wjdY/yNIRdCpgCPuBYB9gwqAzTuIMYDVt9KVOOdMW2Z2cOXVFjjrIKmRoiX6fwW+qocyfP59zzz2XjIwMtm3bhqaZqfihaC6U0ZdmZM0z0dQF9afYARleiyw5BYJf1TZWP4Isu1jXB0YIFyLrerWk286w2brRtWAenTvOJT/3Ljrk3UOPLt+Q7dEvZG615O3yAVS0VzQzS5CBjyGw0MCvVyZ5rSMZXm18zsiG6KZHxgW6/n7Cc6XuNUtYuyJsxiXMNOnTfQCqj5mULdEBTbjbtDUkCE3qbskQOgE5MrId6f8grapVVVUxefJkXC4X3bt3x+12M3nyZCoq2uH30AZRRl8akdWzoOa56FNV2flIrTS+v+oe0LYmHhj8Anxv6I6pkva2X9yuUWR7JpOVeTZWa2ofJpczceZE0X6IpHS0jyL970T95VIE8gj3n6MvLJ3AOTL5WDIIgc9T5v2TUoPQT6AVIyOJ1y1h8SCy/hHfaB8M7jOT62cfmLSv9ry+JIEge0Z7nOmD5D59utgPRzgOTWz3vQHhVchYJoHGc8UVV+D1evn555/x+Xz89NNP1NTUcOWVV6btHIo9R1kUaUJ6H0ZW3VXbEF6LLD03LopOZF0Ljvq1He2QcQ64Tm0eRRUKRavDHzGoeiH9EF4bfR36KamccBwKjqGIzAsQIkXJsPA6kBVxUZ6JSr0XjQQFZPVs/fO5TwHbAbX7Wf9InT/PREk3KYNpzlJgIqF1WyOFT5+on9Ta0hmrTi12GdlaW5ml8g6kTE8U7/vvv8/s2bPp168fTqeTfv36MWvWLN5///20jK9oHMroSwMy8BXSO5OEi0t4DbLy5tiusPXBkv8MIucBwAHWfRAd38KSPR2RLEJOoVC0e8qCSZZQYzhj+fISbur1EI4R4DBIbhzZHv2rbdftlqFfkZW31DbUzEL69W/aIuPs6AtbfxO+x8ZLslJGkCZ8HDUz1UkAjIJV2iACsESk7oalC8I5apekHWvuowhrfJ1iqXmRZX+pLbEXWoKs+ldadHO5XBQXx/uBlpSU4HSqXLKtgTZt9AkhnEKIp4UQG4QQVUKIH4QQJ+7q6y2EkEIIb53tZqMx9whHITiP11EwF5F5WWKz+yRE1tWI3EcQtr5NopJCoWg7bKxO7UwvhADnHwGryeo7BsZVzG0k8RYgI1uRZRfUq7mrIcv/jtSrCuM8MvrXcaSxWtI4cb0QFoSJW1M4Yi5AQJhI/9LmMKjIYcn+NxbPNVhyH0Q4hiQeXn41hOst6dbMQVY/12jVLr74Yo4//ngef/xx5s+fz+OPP86YMWO45JJLGj22ovGkirlvC9iATcBIYCNwEvCyEOKgOjK50kwoWCMQwoHIewTpmxd9WtJKwTUWkfVPhFU/sSnu8QhLTlOqpVAo2gCajLCu6nMOyh1HvjN5dQyRMQEpyxDWrinHk6Fl0WtLqgTHu3Pl7cqdF4fv3eg1LIEQ0jcX4TwivtnSFRAIvbF0xjDGil7Kl/oEQ2EyXcZBbu3X6EsRtGHthPBcqn9oaCUEF+r3VT+JyDyvUardeOONdO/eneeff56tW7fSrVs3rrvuOi666KJGjatID216pk9KWS2lnCGlXC+l1KSU7wC/Ay3i9S7cJyPyZoFrLJbc+5IbfKAMPoVCAcDKivn4IxUsK305taD9EITzhJQiUquGwBdJl2JjWHsCjl0GWz3cY8FSoHOQo3Yptw7RYDNbmsudGS/d+oPmnuXbW+1d2L2824BAjrrY9oVkUdTuUxqt21VXXUX//v1ZsGABv/zyCwsWLGDgwIFcffXVjR5b0XjatNFXHyFEZ6AfsKJO8wYhxGYhxCwhRGpnmHToYB+IyDinqU+jUCjaAVWhIr4piVZLWFX5IZuqk+RSg2iORschqQcMfgX4IbgYqdWkGMsJ1gLdoAth7YrIfwEsdWbuhBuR92QKn72IycoXxkmXpQwhTcwI1vjNBXsIE8EjbQ4pERH9zQghnIj85yDm9wcgEJ6/paUAwNy5cyksLIxrO+yww3jhhRcaPbai8bQbo09EM34+DzwrpVwJlAB/AHoRnfnL2tWvd+wUIcQSIcSS+g6oe4SJtASKtssTTzxBYWEhhYWFCQ7LCkVdUv1WIlqQ97dOxxcpB0Ci8eHW26kM6QdXANGyaKmI7M4WENZPD7ULKUOglSc/jW0fRN7/sdsDSGTfjnCm8tkzG1Rh7MwvCUT1M6DapNHXljB9bdm1vLsnRh/sSreT+xgi+w5E1g2IvKeSLgc3FCEEkUj8lGMkElFJmlsJ7cLoE9H1hdlAELgcQErplVIukVKGpZQ7drWPFkJk1z9eSvmElLJQSlnYqVPyJVnz+rS/aDFFLVOmTGHJkiUsWbKEdPxeFO2XVL+VL4oeoti/Kq4toFXywZbpRJIZPXr1upORKro19DNILzK0KqmIsPcH+8EgMsFlVPrPacqgM+Orh9QA41nDnZXJZzLbKg25tjTG6IPozLHImIDIPB/hNIj2bgAjRozg5ptvjhl5mqYxY8YMRoxI3zkUe05bD+RARNcnngY6AyfJ5I+Iu/8bTKZxVygUiqZjXdVnuu3FgTVUBLeS7+wV1y5lCEJLQScaMyYTXle7E/kN7Pvry+3Oz1b9FCL37uRKChfgME4Sb8mJboaYcDoTVszcmjbuKDNxvvaJkNGULa2RBx98kLFjx9K1a1d69erFxo0b6dq1K/PmzWtp1RS0A6MPeAwYCBwn66RxF0IcAZQDa4A8YCbwmZRS1YJRKBQtTr5zX7b5EhMtu615uK06BlT4V2RwCSLzAt3xZPA78L1Vu191DzgOR9QLZJA1z0Pgo+iO/y2k70iE+3R9JbVikOVIGUxdDtKSB8JEwISsNhSximysloQFmQRWbkwsdbnXIEGEW6fR16NHD5YuXcq3337Lpk2b6NmzJ4cffjgWS7tYWGzztOlvQQjRC5gKHAxsr5OP72xgP+B9oAr4GQgAk1pMWYVCoajDmG4z8Njik+ZahYMTut2K26YTfBBYCMGvdf3dZOhXZNlU4lKiRDYgSy+KK2km/QuQlbfHH1txEzJZabfIVkDWJnNOhnDtmhU0QPMaimS6j8HlGGwot27LzpRl5No3EhHRdLfWgMViYejQoYwfP56hQ4cqg68V0aa/CSnlBimlkFK6pJSeOtvzUsq5Usp9pZSZUsquUsrzpJQGVy6FQqFoHjJs+Zza814ybdGkAhZh54Rut9E14yBdeRlYBNILoWWJfd6Hon31Cf8cm/2TMoisuAESql2EkRX/SCjDJSNFtQma6y4b6ypXY2oWL/rsnR78obDpBM3tjkYGcij2Xtq00adQKBTpZMaMGZxzTvOlXMpxdOe4rjcAUNjhPHp5jkguHNpVQSH0a0KXcA5PcpArWjEoRiqjoJ67c+jH2qNCy5MeJWUYwhsh/FuKsZOcoxF0zc/GbjMRGNJOac0zfYrWizL6FK2aRYsWMWzYMHJycsjPz+eoo47iu+++IxgM8ve//50ePXrg8XjYd999+etf/xo7rnfv3rjdbrKyssjNzWXYsGE8/vjjcWkDzj//fBwOBx6PJ7a99P/t3Xt4VNW9//H3NxeSIcRcQAWUitJGBblIqYCFikI9jwYiqMfqQS5a4ciPi5QqKP09Yr20UoX28bTaqu0JWAXxckClhdpzDBVaFQ9WBe3PqlW5WARDTBACSVi/P2aSzCQze3bCJBOYz+t55nn2zPruvdZMvuy92Je1nnwyGV/zuLF//3569+4dMSZXVVUVX/nKV3j66aeZOnUqZsZzzz0Xsd7cuXMxM0pLSwEoLS3FzJg3b15E3OrVqzEzpk6dGrMNq1at4vzzz6dz586MGjUqomzv3r1885vfpGvXruTn5zN8+HA2bYpxabOdnNL5XHp1HsLAgitjxri63UB1aLn5PL3W+d+gc9N7/TKxgv9omOrRrBOWv4TmY+VlY/lLmz+sEf6k8BGPp4Zr3gCqcYdfiR3T0NDc+DH4y6OHb/nXNs2jUaNG8eijj0Yt+/Wvf81ZZ51Fbm4uJ598MsXFxVRVBc+K7tixgyuuuIJu3bqRl5dH//79G9qTKOYcVnsk6kuSY9SoUWRnZ0ccT/7yl78AwWFs3n///WbrxDuOtQV1+qTDqqysZOzYscyePZvy8nJ27tzJokWLyMrK4sc//jGvv/46r732GlVVVbz00kuce27kwLXPP/88VVVVfPzxx9x6660sXry42VRA8+fPZ//+/Q2v73znO+35FY87Xbp04eGHH+amm25qGGds/vz5DBkyhCuvDHZqioqKWLZsWcM6tbW1PPXUU/Tp0ydiW3369OHJJ5+ktrZx+I7ly5dTVOQxvRhQWFjI3LlzufXWW6O27ze/+Q179uxh3759LFiwgHHjxkXU4Vdr1onl7LxLyUzzuCeu5o3G5cNvRA2x3Fshc3Dj+y4zsKwLImOyvoUVPEhjxy8rNOjyec03mNm3cb2Mvs3LQ1z1f4fatRHnvC/fWob3365eR8ijWDZs2MDChQtZsWIFVVVVvPvuu1x11VUN5ZMmTaJXr158/PHHfP755yxfvpyTTz7ZY4ut4FCnrwP6+c9/HnE8GT7cez5qP8exRFOnTzqs9957D4BrrrmG9PR0AoEAF198MQMGDGDz5s1MmDCBnj17Ymb07t2byZOjzxmZl5dHSUkJTz75JMuWLWPr1q3t+TVSzsUXX0xxcTFz5syhrKyMVatW8Ytf/KKhfNy4cWzatIl9+4JDbqxbt44BAwbQvXv3iO10796d/v37s379egDKy8v585//TEmJ91RRY8aM4aqrrqJnz+ZzwWZnZ3PmmWeSlpaGc4709HT27dtHeXnjXLOHDx9m8uTJ5Obm0q9fP15/vXGWjN69e7N48WIGDBhATk5Owjp+BVm9Y5Y5V42rWtr4Qe1WXNhTuvXMDDqFXR7udH7U7VnWBVhu8GyCnXBb87l06+M6DYZOIyH9dAhMiN34w6Ezpe4AHN4SOw6wToM8y8MlO49i2bx5M8OHD284OBcWFjJlyhRyc3MbyqdOnUpOTg4ZGRmce+65XHLJJa2qKzYHR45Ef0mb+eCDDygsLGTLlmCe79q1i27dulFWVtaq7bXkOJYo6vRJh1VUVER6ejpTpkzh97//fcPOHWDYsGEsXbqUBx98kLffftvXU3znnXcep556Ki+//HJbNluAn/70p5SVlXHllVdy//3306NH4xyv2dnZlJSUsHLlSiB41iXWjm7y5MksX74cgJUrV3LZZZeRleVnEGBvAwYMaGjHDTfcwEknNc41+9xzz3H11VdTUVFBSUkJs2bNilh3xYoVrF27loqKCjIyEjPqVae02AO6u4r5UPdR5GeVi3D19/iFq9kSfbmpwDXBQZcDsS8pA6QV/pq0E9djaTmxg2r/0bjcpJ1HqyPm0dChQ1m/fj2LFi1i06ZNHDoUeXZz2LBhzJw5k5UrV/LJJ5/E2MpR0pm+pOjTpw+LFy9m4sSJHDhwgOuuu46pU6c2u43Er9Ycx46WOn3SYZ1wwgls3LgRM2PatGmceOKJlJSUsHv3bm677TYWLFjA448/zpAhQzjllFMiLvXE0rNnz4izOvfffz/5+fnk5+fTrVubT82cMgoKCujXrx8HDhzg8subjwFXfxD+4osv2LBhA+PHj4+6nQkTJlBWVsYXX3zheVBvqbfeeovKykqeeOIJRoyIfAhixIgRXHrppaSnpzNp0iTefPPNiPI5c+bQq1cvAoHEzbwTa2fvat6BQ+uiFBzAfflw5Ee12+Hwq2EhT8esz9JyIP1077H3/AofqsXXrBz+dcQ8GjlyJM8++yxbtmyhuLiYrl27Mm/evIapx5566ilGjhzJXXfdxemnn86gQYPYvHlzq+uLyjmorYv+kjY1bdo0vva1rzF06FA+/fRT7rnnnoayOXPmNBxPBg8e7LGVoNYex46GOn3SoZ199tmUlpayY8cOtm7dyq5du5g7dy7p6enMnDmTTZs2UVFRwQ9+8AOuv/563n23+ZON4Xbu3ElhYWHD+5tvvpmKigoqKirYu3dvW3+dlPHb3/6Wjz76iDFjxrBgwYJm5SNGjGDPnj3cfffdjB07NmYHKhAIUFxczN13393wEEa4G2+8seGm6R/96EctamN2djbXXHMN9957b0THLvzyYOfOnamu/IGBRQAAEChJREFUro64jNurV68W1ePHkVjTjnnNQdv0/rm6HU3eb4+9qnPeD2e0RObA6MsJ0FHz6JJLLuH555+nvLycNWvWUFpa2vDQR0FBAffeey/btm1j9+7dDBo0iPHjxyf2LI5Dnb4kmjZtGlu3bmX27NkRZ4wfeOCBhuNJ/SVgL609jh0NdfrkmHHWWWcxderUZvfkBQIBZs6cSUFBAe+8E+WSV8jmzZvZuXNnszM7klifffYZ3/ve93jkkUf41a9+xapVq/jTn/7ULO7aa69lyZIlcc+6TJ48mSVLljBp0qRmZb/85S8bbppeuHBhq9pbU1PDhx/6GW4kKDjzY2LFmmvXOg2EjH7Ryzo3GVom88zI9xlN3oc78hkc2YPzMVhyPBa4ItjZy/o2ltEn/go+HQt5lJaWxujRo7noooui3ivcrVs3br75Znbt2hVxheGoOQe1tdFf0qb279/P3Llz+e53v8sdd9yRsL+r3+PY0VKnTzqsv/3tbyxZsoQdO4JnMLZv386KFSsYNmwYP/vZzygrK+PgwYPU1taybNkyqqqqoj75VFlZyQsvvMDVV1/NtddeS//+0Qe/lcSYNWsW48eP58ILL6RHjx785Cc/Ydq0ac3ufZozZw4vvvgi3/rWtzy3d8EFF/Diiy8ye/ZsX/XX1dU1nJ07cuQI1dXV1NQEO1WvvPIKGzdu5PDhwxw8eJDFixeze/duhg71GB+vHdQcORizzDpHmUgo/bRmD2pYWiGkNd6bSOZZsSusPyvocTbQLwtcSlrXp0gr+EX84BZIdh5B8Ing6urqhldNTQ1r1qxh5cqV7Nu3D+ccr732Ghs2bGDYsGEALFiwgK1bt1JbW0tVVRUPPfQQX/3qV+natWvLfwQvepAjKW666Sa+/vWv8+ijj1JcXMyNN97oa73Dhw9H5FJdXV2LjmOJcjzMvSvHqdzcXF599VWWLl1KRUUF+fn5jB07lvvuu48VK1bw/e9/n/fffx8zo6ioiGeeeYYzzjijYf1x48aRkZFBWloaffv2Zd68eb7/gUrrrF69mo0bN0b8T/WGG25gxYoV3HnnnRGxhYWFjB49Ou42zcxXXL3HHnuM665rHLMuEAgwZcoUSktLOXToEHPmzOHDDz8kMzOT/v37s3bt2qhP+ran6rrK2IVZFxEc1Djs8mDWhdHPOLoDYcseM2TUz94RbRaPDqAj5BHAjBkzmDFjRsP7iRMnMn36dB544AFmzZrFoUOH6NGjB7fccgsTJ04E4MCBA0yYMIFPP/2UQCDA0KFDm40neNTqz/RJu1qzZg3r1q3j7beDc2YvXbqUQYMG8fjjj8ddt1+/yDP2jzzyCIFAIO5xLNEsdecujG7IkCEufIgGES9DhgxB+SJ+eOXK2/v+i/4FsYdFOVI+BQ7/peG9dX0Gy4w8Y+3q9uL2hJ39y+hLWrfVUbfnat7CfX4l1u13DYM3S8filS95GScyPD96vuztvUX7pBRjZv/rnBsSP1KXd0VEku6Lmp2e5ZYdNs5b+qnNOnzBoCwipjkzj8GeM74WHLIlvXeL2ikdhHNQUxv95UN5eTkTJkwgJyeH0047LWLmEzm+6fKuiEiSfX7oQ5xzsR8SCX9AIv30qCGWlovL/DrUBM/yWNaFMeszC+AC4zHTIeBY5epa/6TuzJkz6dSpE7t37+avf/0rxcXFDBw4sNklSDn+6EyfiEiSVdX8k4N1+2IHhA/HUrcrZph1rp9GMAMCV3jWmXbCoha0UDqUo3h698svv+SZZ57hrrvuokuXLowYMYKSkhIee+yxdmi4JJs6fSIiSXagdh8HamMP/eBq3mt8U/dR7Dlus8YAmdBpCJauwcaPV845jtTURn3F895775Genh4x9/DAgQPZtm1bWzZZOgid2xcRSaIjro5aV83hIx5P21LXZDn6ZWBLy8F1Gox1GpnIJkoHc/6/nMfevR9ELTt48CBDhjTe0z99+nSmT5/e8H7//v3k5eVFrJOXl0dVVVXbNFY6FHX6RESSKM3SMdJI87i/zjLPaRywJaPIc/o0yx4P0R70kOPGunVRpubzqUuXLlRWRg4RVFlZSW5u7tE2S44BurwrIpJkndJyyErzOOhmfxvsBAAs0HwO2nDW+Qoss8gzRlJXUVERtbW1/P3vf2/47M0339RDHClCnT4RkSTLSs8lK71LzHKzQOjBjGwI/Gv7NUyOOzk5OVx++eXcfvvtfPnll2zatIk1a9ZEnZ5Ojj+6vCsikmSZadlkpgU8YyxwGY5azOuMoIgPDz74INdffz0nnXQSXbt25aGHHtKZvhShTp+ISJJ1SutChtdgyoBl9sUy+7ZTi+R4VlhYyOrV0WdrkeObLu+KiCRZfqdesQdmFhFJEHX6RESS7OTss5LdBBFJAeacix+VQsxsD/CxR0g3YG+czfiJSca2UqXO9mz/YGBLO7RDf5Njv06vXEnE9pO5rVSpU/uWjlfnsd7+RNR5mnPuRB/1BEf21sv/C3g9ETHJ2Faq1JmM9h+P36mjbut4qLMjfKeO/Pt01Dq1b+l4dR7r7U90nfFeurwrIiIikgLU6RMRERFJAer0tdzDCYpJxrZSpc5ktL+tt6+/yfFVZ1tv/1j/fTpqndq3dLw6j/X2J7pOT3qQQ0RERCQF6EyfiIiISApQp09EREQkBajTJyIiIpIC1OkTERERSQHq9PlgZl3M7OnQ8klmtswjdqGZvWxm99WvE2fbnn8DM5tvZl09yrubWVno9bmZfTNKzBVmdlNoeaCZ/cxHu/5oZl1ilK03s4LQcp6Z/c4jLs/McsxsrZkVt6S8Sex8MzvFzLaYWbWZZcSIGWpmfw79DX7qsa0LwuL+05pMfBr+u5vZPDPbGKttUbbvK19amiuhdVqdL35yJRTXonzxypVQedx8ae9cCYvzzBc/uRIW1+J80b6lWZn2LR60b2lWrn1LSyVihOfj/QUMBe4KLY8GbokR9w3g4dDyvwN3e2zzNaAUuD1BbRwP/N8YZb2B34SWVwLdfWzvVY+yLWHL5wFLYm0DOAFYD4xpaXmU+GygACgDMmLEdAeyQ8uPA/1jxGWGLf8n8I0YcVnAMmBjIvOlJbmS6HzxypXW5ItXrvjNl2Tkit988ZsrrckXP7nS0nxJZK7Ey5eW5kq8fPGTK8nKFz+50pJ8aWmu+M2XluRKovPFK1daky9eueI3X5KRK37zxW+utDZfor10ps+f/sC2sOWtMeIuo3EsnZqwdSKY2WlAAJjhnLvTq+JY/9NtEjMaGOacuztauXPuI+BUMxsKfOKc+2ec7eUBFTHKcoGDYR8VAe/GiMsEniW40/ljvHIzyzKzZ8xso5n9oUn875xz1c65fR7t/p1z7p/OuerQR7VAXYy4mrCPDgHbm8aEFm8g+A+tJfzki69cCbUlYfkSL1egZfnilSuh8rj5koxcCYvzzBc/uVIfF1psab5o39JYpn1LfNq3NJZr39IK6vT5czrwSWj5MmLvmPOBI6FTvlOAd2LEnQMsd84djFEONPyDbJYETWKGEfzf1W1ecQQTaiHwkzhxAGcDf/MoO6P+VD5wD1F2zKG43gT/p/ayz/JhwDvOuRHAv9QH+vwdImLMbADQzTn3Tqw4Mysxs63AScDnTWPMLBO4wDn3P151R+EnX/zmCiQoX1qQK+A/X7xypb48Xr60a65Ei4uWL35yJTyulfmifUtkmfYt3rRviSzXvqWF1Onz50/AnWb2QyDPORfrD/8YsJxgb7wzsRP2HGCLj3rPIfZBoN5/AYOAl8xsrkfcO8Bbzrm9Puo9m+g7W4C+wGLn3Cjn3CjgjRixfYEfAiPNrMhn+atAjpk9AQwOi/XzOzTEmFkh8HPgu15xzrnnnHPnADuBsVFiJgFPxKk3Gj/54jdX6tuTiHzxmyvgP1+8cgX85Ut750pEnEe++MmV8LjW5Iv2LY20b4lP+5ZG2re0gjp9Pjjn1jvnxjjnFjnnBnuE/m/oj3cH8A/n3KEYcf2At31UHTfJnHM9nHMjQ4nvdVPsV4ElPuqE+Dvm8MsFJzvnymPEvQXcCDxizW94jVZuzrl5wL0E70Wp53vHHNrObwne6xLt0kF9XFbYZ5VEXiaor+9MYIaZrQP6mdnsOG0AfOeL31yBBOVLC3IF/OeLnx1zvHxp71xpiIuTL35yJbzOFueL9i0RtG+JQ/uWCNq3tIY7yhs39Yq40fJ+YAPwInBGAra3HDjxKLfRHXgBmNSCdV6IVS/wPNAjtJwD/I9HXPfQ8veBRfHKgVXAptBrQNPfgeC9F38E9gH/DQyNEnMNsIfgTbZlwPBovynBSyMbQq9HgTSv352jvHm2rXMlWfnilSt+86W9c8VvvvjJlWM1X5KRK/HyxU+uJCNf/OSK33w5FnMlWfnilSt+86W9c8VvvvjJlbbKF829KxHMLAdY64Kny0ViUq5ISyhfxC/lStvR5V1pYGaDgD8Ank9xiShXpCWUL+KXcqVt6UyfiIiISArQmT4RERGRFKBOn4iIiEgKUKdPREREJAWo0yciIiKSAtTpExEREUkB6vSJiIiIpAB1+kRERERSgDp9IiIiIilAnT4RERGRFKBOn4iIiEgKUKdPREREJAWo0yciIiKSAtTpExEREUkB6vSJiIiIpAB1+kRERERSgDp9IiIiIilAnT4RERGRFKBOn4iIiEgKUKdPREREJAWo0yci0g7MbJSZ7Uh2O0QkdanTJyIpzcw+MrODZrbfzPaZ2Voz65XsdomIJJo6fSIiMM451wXoAewG/iPJ7RERSTh1+kREQpxz1cDTQF8AMys2szfMrNLMtpvZHfWxZtbbzJyZTTGzT8xsr5n9IKw8YGalobOH7wDfCK/LzBaY2U4zqzKz/2dmo9vnW4pIqspIdgNERDoKM+sMfAd4JfTRl8BkYBtwDvCimf3VObc6bLURwJlAEfCamT3rnHsXWAT0Cb1ygN+H1XMmMAv4hnNul5n1BtLb8KuJiOhMn4gIsNrMKoBK4NvAfQDOuTLn3NvOuSPOubeAFcAFTdb9oXPuoHPuTeBNYGDo86uAe5xz5c657cADYevUAVlAXzPLdM595Jz7oO2+noiIOn0iIgDjnXP5BDtis4ANZtbdzIaa2UtmtsfMvgBuBLo1WfefYcsHgC6h5Z7A9rCyj+sXnHPvA3OBO4DPzGylmfVM6DcSEWlCnT4RkRDnXJ1z7lmCZ+JGAE8AzwG9nHN5wC8B87m5T4Hwp4C/0qSuJ5xzI4DTAAcsPsrmi4h4UqdPRCTEgi4DCoB3gVyg3DlXbWbnAf/Wgs2tAm4zswIzOxWYHVbPmWZ2kZllAdXAQYIdTRGRNqNOn4gIPG9m+wne03cPMMU5tw34P8CdZlYF3E6wI+fXDwle0v0H8AfgsbCyLOBeYC/By8MnAQuP9kuIiHgx51yy2yAiIiIibUxn+kRERERSgDp9IiIiIilAnT4RERGRFKBOn4iIiEgKUKdPREREJAWo0yciIiKSAtTpExEREUkB6vSJiIiIpID/D7lqfMEmz7CqAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dim = [4,6]\n", "fig, axes = plt.subplots(dim[1], dim[0], sharex=True, sharey=True)\n", "plt.rcParams.update({'font.size': 12})\n", "cmap = mpl.cm.viridis\n", "norm = mpl.colors.Normalize(vmin=0, vmax=1)\n", "scalmap = mpl.cm.ScalarMappable( cmap=cmap, norm=norm)\n", "\n", "for n, f in enumerate(np.unique(coverage['field'])):\n", " \n", " x, y = np.floor_divide(n, dim[0]), np.remainder(n, dim[0])\n", " \n", " \n", " #axes[x,y].scatter( np.arange(20), np.arange(20))\n", " #axes[x,y].legend()\n", " \n", " \n", " #####MAKE FIELD DATA#####\n", " data_field = [ depths_sample_field(band, f)[0] for band in bands ]\n", " areas_field = [ depths_sample_field(band, f)[1] for band in bands ]\n", " data_field_mag = [flux_to_mag(10**d)[0] for d in data_field]\n", " \n", " \n", " colors = scalmap.to_rgba(areas_field) # The color is the angle\n", " colors[:, 3] = 1\n", "\n", " \n", "\n", " def set_axis_style(ax, labels):\n", " axes[x,y].get_xaxis().set_tick_params(direction='out')\n", " axes[x,y].xaxis.set_ticks_position('bottom')\n", " axes[x,y].tick_params(axis='x', labelsize=8)\n", " axes[x,y].set_xticks(np.arange(1, len(labels) + 1))\n", " axes[x,y].set_xticklabels(labels)\n", " axes[x,y].set_xlim(0.25, len(labels) + 0.75)\n", " #axes[x,y].set_xlabel('band')\n", " #axes[x,y].set_ylabel('5$\\sigma$ depth [mag]')\n", " #ax.violinplot(np.array(coverage['ferr_ap_{}_mean_min'.format('g') ] ) )\n", " #ax.set_title('Custom violinplot 1', fontsize=fs)\n", "\n", " #axes[x,y].set_ylabel('log10( 5$\\sigma$ Depths [Jy] )')\n", " set_axis_style(axes[x,y], ['$' + band + '$' for band in bands])\n", " axes[x,y].set_ylim(28, 17)\n", "\n", "\n", "\n", " parts = axes[x,y].violinplot(data_field_mag, widths=0.75, showmeans=False, showmedians=False,\n", " showextrema=False)\n", " \n", " \n", " #This is just to add the field name\n", " axes[x,y].scatter([-99],[-99], \n", " label=f.replace('SGP', 'HATLAS-SGP').replace('NGP', 'HATLAS-NGP'), \n", " c='w', s=0.0001)\n", " axes[x,y].legend(frameon=False, loc=4)\n", "\n", " for n, part in enumerate(parts['bodies']):\n", " part.set_facecolor(colors[n])\n", " part.set_alpha(1)\n", "\n", "#cax, _ = mpl.colorbar.make_axes(axes[dim[1]-1,dim[0]-1], panchor=(-8.8, 10.6)) #5 3 depend on 4 x 6\n", "#This bit moves the color bar\n", "cax = inset_axes(axes[dim[1]-1,dim[0]-1],\n", " width=\"5%\", # width = 5% of parent_bbox width\n", " height=\"85%\", # height : 50%\n", " loc=3,\n", " bbox_to_anchor=(0.05, 0.08, 1, 1),\n", " bbox_transform=axes[dim[1]-1,dim[0]-1].transAxes,\n", " borderpad=0,\n", " )\n", "\n", "ticks = [0, 0.5, 1]\n", "cbar = mpl.colorbar.ColorbarBase(cax, cmap=cmap, ticks = ticks)\n", "cax.set_yticklabels(ticks)\n", "cbar.set_label('coverage')\n", " \n", "axes[dim[1]-1,dim[0]-1].tick_params(axis='x', labelsize=8)\n", "#axes[dim[1]-1,dim[0]-1].set_xlabel('band')\n", " \n", "fig.text(0.5, 0.07, 'Bands', ha='center')\n", "fig.text(0.04, 0.5, '5$\\sigma$ depth [mag]', va='center', rotation='vertical')\n", "\n", "fig.set_size_inches(10, 12)\n", "fig.subplots_adjust(hspace=0, wspace=0)\n", "\n", "plt.rc('axes', labelsize=12)\n", "plt.savefig('./figs/fields_depths_comparison_grid.pdf', bbox_inches='tight')\n", "plt.savefig('./figs/fields_depths_comparison_grid.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summarise the filters" ] }, { "cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['omegacam_u', 'mmt_u', 'bessell_u', 'lbc_u', 'wfi_u', 'megacam_u', 'mosaic_u', 'wfc_u', 'sdss_u']\n", "['mmt_g', 'omegacam_g', 'suprime_g', 'megacam_g', 'wfc_g', 'gpc1_g', 'decam_g', '90prime_g', 'sdss_g']\n", "['omegacam_r', 'mmt_r', 'cfht12k_r', 'megacam_r', 'wfi_r', 'suprime_r', 'mosaic_r', 'bessell_r', 'wfc_r', 'gpc1_r', 'decam_r', 'sdss_r', '90prime_r']\n", "['sdss_i', 'decam_i', 'gpc1_i', 'wfc_i', 'bessell_i', 'mosaic_i', 'megacam_i', 'wfi_i', 'suprime_i', 'cfht12k_i', 'mmt_i', 'omegacam_i']\n", "['decam_z', '90prime_z', 'sdss_z', 'wfc_z', 'gpc1_z', 'suprime_z', 'megacam_z', 'vista_z', 'mosaic_z', 'omegacam_z', 'mmt_z']\n", "['decam_y', 'ukidss_y', 'gpc1_y', 'suprime_y', 'megacam_y', 'vista_y', 'lbc_y', 'wircam_y']\n", "['omega2000_j', 'ukidss_j', 'vista_j', 'newfirm_j', 'wircs_j', 'wircam_j']\n", "['ukidss_h', 'vista_h', 'newfirm_h', 'wircam_h']\n", "['isaac_k', 'moircs_k', 'ukidss_k', 'newfirm_k', 'wircs_k', 'hawki_k']\n", "['wircam_ks', 'vista_ks', 'moircs_ks', 'omega2000_ks', 'tifkam_ks']\n", "['irac_i1']\n", "['irac_i2']\n", "['irac_i3']\n", "['irac_i4']\n" ] } ], "source": [ "for band in bands:\n", " specific_bands = [f for f in filters if f.split('_')[1] == band.lower()]\n", " print(specific_bands)\n", " for specific_band in specific_bands:\n", " if 'ferr_ap_{}_mean'.format(specific_band) not in coverage.colnames:\n", " specific_bands.remove(specific_band)\n", " #print(specific_bands)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [], "source": [ "filter_info = Table.read(herschelhelp_python_loc + 'documentation/filters.csv')" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table masked=True length=80\n", "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
filter_filetelescopecamerafiltersurveysdescriptionwavelength_regionucd
str17str83str83str6int64int64str3str12
90prime_g.xmlThe Bok TelescopeThe 90-inch prime focus camera (90prime)g----optB
90prime_r.xmlThe Bok TelescopeThe 90-inch prime focus camera (90prime)r----optR
90prime_z.xmlThe Bok TelescopeThe 90-inch prime focus camera (90prime)z----optI
bessell_i.xmlStandardised telescope response (telescope uknown)Standard camera response (camera unknown)i----optI
bessell_r.xmlStandardised telescope response (telescope uknown)Standard camera response (camera unknown)r----optR
bessell_u.xmlStandardised telescope response (telescope uknown)Standard camera response (camera unknown)u----optU
cfht12k_i.xmlCanada France Hawaii Telescope (CFHT)The CFH12K prime focus 12K camerai----optI
cfht12k_r.xmlCanada France Hawaii Telescope (CFHT)The CFH12K prime focus 12K camerar----optR
decam_g.xmlBlancoThe Dark Energy Camera (DECam)g----optB
decam_i.xmlBlancoThe Dark Energy Camera (DECam)i----optI
........................
wfc_r.xmlIsaac Newton Telescope (INT)The Wide Field Camera (WFC)r----optR
wfc_u.xmlIsaac Newton Telescope (INT)The Wide Field Camera (WFC)u----optU
wfc_z.xmlIsaac Newton Telescope (INT)The Wide Field Camera (WFC)z----optI
wfi_i.xmlThe Max Planck Gesellschaft (MPG) telescopeThe Wide Field Imager (WFI)i----optI
wfi_u.xmlThe Max Planck Gesellschaft (MPG) telescopeThe Wide Field Imager (WFI)u----optU
wircam_h.xmlCanada France Hawaii Telescope (CFHT)Wide-field InfraRed Camera (WIRCam)H----IRH
wircam_j.xmlCanada France Hawaii Telescope (CFHT)Wide-field InfraRed Camera (WIRCam)J----IRJ
wircam_ks.xmlCanada France Hawaii Telescope (CFHT)Wide-field InfraRed Camera (WIRCam)Ks----IRK
wircs_j.xmlPalomar 200-inch Hale Telescope (P200)Wide Infrared Camera (WIRC)J----IRJ
wircs_k.xmlPalomar 200-inch Hale Telescope (P200)Wide Infrared Camera (WIRC)Ks----IRK
" ], "text/plain": [ "\n", " filter_file telescope ... ucd \n", " str17 str83 ... str12\n", "------------- -------------------------------------------------- ... -----\n", "90prime_g.xml The Bok Telescope ... B\n", "90prime_r.xml The Bok Telescope ... R\n", "90prime_z.xml The Bok Telescope ... I\n", "bessell_i.xml Standardised telescope response (telescope uknown) ... I\n", "bessell_r.xml Standardised telescope response (telescope uknown) ... R\n", "bessell_u.xml Standardised telescope response (telescope uknown) ... U\n", "cfht12k_i.xml Canada France Hawaii Telescope (CFHT) ... I\n", "cfht12k_r.xml Canada France Hawaii Telescope (CFHT) ... R\n", " decam_g.xml Blanco ... B\n", " decam_i.xml Blanco ... I\n", " ... ... ... ...\n", " wfc_r.xml Isaac Newton Telescope (INT) ... R\n", " wfc_u.xml Isaac Newton Telescope (INT) ... U\n", " wfc_z.xml Isaac Newton Telescope (INT) ... I\n", " wfi_i.xml The Max Planck Gesellschaft (MPG) telescope ... I\n", " wfi_u.xml The Max Planck Gesellschaft (MPG) telescope ... U\n", " wircam_h.xml Canada France Hawaii Telescope (CFHT) ... H\n", " wircam_j.xml Canada France Hawaii Telescope (CFHT) ... J\n", "wircam_ks.xml Canada France Hawaii Telescope (CFHT) ... K\n", " wircs_j.xml Palomar 200-inch Hale Telescope (P200) ... J\n", " wircs_k.xml Palomar 200-inch Hale Telescope (P200) ... K" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "has_broad_typical = np.full(len(filter_info), False)\n", "for band in bands:\n", " has_broad_typical |= filter_info['filter'] == band\n", " \n", "filter_info[has_broad_typical]" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "has_optical = np.full(len(filter_info), False)\n", "optical_bands = ['u', 'g', 'r', 'i', 'z', 'y']\n", "has_near_infrared = np.full(len(filter_info), False)\n", "near_infrared_bands = ['J', 'H', 'K', 'Ks']\n", "\n", "for band in bands:\n", " if band in optical_bands:\n", " has_optical |= filter_info['filter'] == band\n", " if band in near_infrared_bands:\n", " has_near_infrared |= filter_info['filter'] == band" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "$grizy$\n" ] } ], "source": [ "def get_bands(camera):\n", " band_list = []\n", " for b in filter_info['filter'][filter_info['camera'] == camera]:\n", " #print(b)\n", " band_list.append( b)\n", " band_list_ordered_string = ''\n", " for band in bands:\n", " if band in band_list:\n", " band_list_ordered_string += band\n", " \n", " return '$' + band_list_ordered_string + '$'\n", "\n", "\n", "\n", "print(get_bands('HyperSuprimeCam'))" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From HyperSuprimeCam on the Subaru telescope we have the optical bands $grizy$. From MegaCam on the Multiple Mirror Telescope (MMT) Observatory we have the optical bands $ugriz$. From MegaPrime/MegaCam on Canada France Hawaii Telescope (CFHT) we have the optical bands $ugrizy$. From Mosaic-3 Wide Field Imager on the Kitt Peak National Observatory we have the optical bands $uriz$. From OmegaCAM on the Very Large Telescope (VLT) Survey Telescope (VST) at Paranal we have the optical bands $ugriz$. From the 90-inch prime focus camera (90prime) on the Bok Telescope we have the optical bands $grz$. From the CFH12K prime focus 12K camera on Canada France Hawaii Telescope (CFHT) we have the optical bands $ri$. From the Dark Energy Camera (DECam) on Blanco we have the optical bands $grizy$. From the Pan-STARRS Gigapixel Camera on the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) we have the optical bands $grizy$. From the Sloan Digital Sky Survey (SDSS) standard camera on the Sloan Digital Sky Survey (SDSS) dedicated telescope at Apache Point Observatory we have the optical bands $ugriz$. From the Wide Field Camera (WFC) on Isaac Newton Telescope (INT) we have the optical bands $ugriz$. From the Wide Field Imager (WFI) on the Max Planck Gesellschaft (MPG) telescope we have the optical bands $ui$. \n" ] } ], "source": [ "\n", "\n", "optical_sentence = ''\n", "for camera in np.unique(filter_info['camera'][has_optical]):\n", " if camera == 'Standard camera response (camera unknown)':\n", " continue\n", " row = filter_info[filter_info['camera'] == camera][0] \n", " optical_sentence += 'From {} on {} we have the optical bands {}. '.format(row['camera'].replace('The', 'the'), \n", " row['telescope'].replace('The', 'the'),\n", " get_bands(camera))\n", " \n", "print(optical_sentence)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From National Optical Astronomy Observatory (NOAO) Extremely Wide-Field Imager (NEWFIRM) on National Optical Astronomy Observatory (NOAO) Gemini Observatory we have the optical bands $JHK$. From the High Acuity Wide field K-band Imager (HAWK-I) on the Very Large Telescope (VLT) at Paranal we have the optical bands $K$. From the Infrared Spectrometer And Array Camera (ISAAC) on the Very Large Telescope (VLT) at Paranal we have the optical bands $Ks$. From the Infrared Wide-Field Camera OMEGA2000 on the 3.5m telescope at Calar Alto we have the optical bands $JKs$. From the Instrument Formerly Known as Mosaic (TIFKAM) on the Michigan Dartmouth MIT (MDM) Observatory we have the optical bands $Ks$. From the Multi Object Infrared Camera and Spectrograph (MOIRCS) on the Subaru telescope we have the optical bands $KKs$. From the VISTA InfraRed CAMera (VIRCAM) on the 4.1m Visible and Infrared Survey Telescope for Astronomy (VISTA) we have the optical bands $JHKs$. From the Wide Field Camera (WFCAM) on the United Kingdom Infrared Telescope (UKIRT) we have the optical bands $JHK$. From Wide Infrared Camera (WIRC) on Palomar 200-inch Hale Telescope (P200) we have the optical bands $JKs$. From Wide-field InfraRed Camera (WIRCam) on Canada France Hawaii Telescope (CFHT) we have the optical bands $JHKs$. \n" ] } ], "source": [ "near_infrared_sentence = ''\n", "for camera in np.unique(filter_info['camera'][has_near_infrared]):\n", " if camera == 'Standard camera response (camera unknown)':\n", " continue\n", " row = filter_info[filter_info['camera'] == camera][0] \n", " near_infrared_sentence += 'From {} on {} we have the optical bands {}. '.format(row['camera'].replace('The', 'the'), \n", " row['telescope'].replace('The', 'the'),\n", " get_bands(camera))\n", " \n", "print(near_infrared_sentence)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Count the number of flagged objects\n", "\n", "For each band lets count the number of objects that have a flag" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "# Import the module\n", "\n", "# Setting up the service object \n", "service = vo.dal.TAPService(\"https://herschel-vos.phys.sussex.ac.uk/__system__/tap/run/tap\")\n", "# Downloading the data\n", "query = \"SELECT COUNT(*) as n, flag_merged FROM herschelhelp.main GROUP BY flag_merged \" \n", " \n", "\n", "\n", "#Then we execute the query\n", "#resultset = service.run_async(irac_i1_query)\n", "job = service.submit_job(query)\n", "job.run()\n", "job_url = job.url\n", "job_result = vo.dal.tap.AsyncTAPJob(job_url)\n", "start_time = time.time()\n", "wait = 10\n", "while job.phase == 'EXECUTING':\n", " print('Job still running after {} seconds.'.format(round(time.time() - start_time)))\n", " time.sleep(wait) #wait ten seconds and try again\n", " wait *= 2\n", " \n", "print(job.phase)\n", "table = job_result.fetch_result() " ] } ], "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": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }