diff --git a/Cluster_Mass_Error_Demonstration/Error Demonstration 10 million years old-0.1Z_solar.ipynb b/Cluster_Mass_Error_Demonstration/Error Demonstration 10 million years old-0.1Z_solar.ipynb new file mode 100755 index 00000000..5450708d --- /dev/null +++ b/Cluster_Mass_Error_Demonstration/Error Demonstration 10 million years old-0.1Z_solar.ipynb @@ -0,0 +1,1789 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## (Terse) Testing and Debugging the BPASS isochrone at 10^7.0 years age. (Nonsolar metallicity)\n", + "\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for 1 billion years age, 0.1 times solar metallicity, AKs=0.0, and distance of 1000 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram (B-V vs M_V), and the current-mass luminosity relationship of the cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**This notebook is meant to demonstrate the errors I have found in the generated cluster mass of in the Binary_Cluster.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:346: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1794: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater\n", + " result = getattr(super(), op)(other)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=2.50 for T= 11956 logg=1.83\n", + "Changing to T= 50000 for T= 51512 logg=4.31\n", + "Changing to logg=5.00 for T= 51512 logg=4.31\n", + "Changing to T= 50000 for T= 58383 logg=4.33\n", + "Changing to logg=5.00 for T= 58383 logg=4.33\n", + "Changing to logg=4.50 for T= 47267 logg=4.32\n", + "Changing to T= 50000 for T= 53429 logg=4.31\n", + "Changing to logg=5.00 for T= 53429 logg=4.31\n", + "Changing to T= 50000 for T= 56650 logg=4.32\n", + "Changing to logg=5.00 for T= 56650 logg=4.32\n", + "Changing to logg=5.00 for T= 49421 logg=4.31\n", + "Changing to T= 50000 for T=181176 logg=5.89\n", + "Changing to logg=5.00 for T=181176 logg=5.89\n", + "Changing to logg=2.50 for T= 15844 logg=1.75\n", + "Changing to logg=2.00 for T= 11558 logg=1.16\n", + "Changing to logg=2.00 for T= 9831 logg=0.95\n", + "Changing to logg=3.50 for T= 27883 logg=2.95\n", + "Changing to T= 50000 for T= 91287 logg=5.21\n", + "Changing to logg=5.00 for T= 91287 logg=5.21\n", + "Changing to T= 50000 for T= 62004 logg=4.40\n", + "Changing to logg=5.00 for T= 62004 logg=4.40\n", + "Changing to 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logg=5.34\n", + "Changing to logg=2.50 for T= 17655 logg=1.94\n", + "Changing to logg=4.00 for T= 37066 logg=3.26\n", + "Changing to T= 50000 for T=117666 logg=5.25\n", + "Changing to logg=5.00 for T=117666 logg=5.25\n", + "Changing to logg=2.50 for T= 14304 logg=1.57\n", + "Changing to logg=3.00 for T= 23460 logg=2.65\n", + "Changing to logg=2.50 for T= 16915 logg=1.83\n", + "Changing to logg=3.00 for T= 24780 logg=2.51\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31288 logg=3.32\n", + "Changing to logg=2.50 for T= 11892 logg=1.82\n", + "Changing to logg=2.50 for T= 15120 logg=1.64\n", + "Changing to logg=2.00 for T= 10823 logg=1.08\n", + "Changing to logg=3.00 for T= 24136 logg=2.46\n", + "Changing to logg=3.50 for T= 28318 logg=2.79\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31390 logg=3.78\n", + "Changing to T= 50000 for T= 50807 logg=4.01\n", + "Changing to logg=5.00 for T= 50807 logg=4.01\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25766 logg=2.81\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 42386 logg=3.68\n", + "Changing to logg=4.50 for T= 42597 logg=3.69\n", + "Changing to logg=3.00 for T= 25037 logg=2.57\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 26892 logg=2.88\n", + "Changing to T= 50000 for T= 91441 logg=5.19\n", + "Changing to logg=5.00 for T= 91441 logg=5.19\n", + "Changing to logg=2.50 for T= 18565 logg=2.03\n", + "Changing to logg=3.50 for T= 29837 logg=3.14\n", + "Changing to logg=4.50 for T= 43228 logg=3.50\n", + "Changing to logg=2.50 for T= 12039 logg=1.25\n", + "Changing to logg=2.50 for T= 16928 logg=1.85\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 32154 logg=3.01\n", + "Changing to logg=3.50 for T= 29569 logg=3.12\n", + "Changing 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+ "Changing to logg=4.50 for T= 41093 logg=3.42\n", + "Changing to logg=4.00 for T= 34521 logg=3.99\n", + "Changing to logg=4.00 for T= 31640 logg=3.88\n", + "Changing to T= 50000 for T= 55978 logg=4.20\n", + "Changing to logg=5.00 for T= 55978 logg=4.20\n", + "Changing to T= 50000 for T= 68751 logg=4.61\n", + "Changing to logg=5.00 for T= 68751 logg=4.61\n", + "Changing to logg=3.50 for T= 28358 logg=2.75\n", + "Changing to logg=4.00 for T= 38923 logg=3.84\n", + "Changing to logg=3.00 for T= 25744 logg=2.62\n", + "Changing to logg=3.50 for T= 26551 logg=2.62\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 14269 logg=1.80\n", + "Changing to logg=2.50 for T= 13108 logg=1.41\n", + "Changing to logg=4.00 for T= 34029 logg=3.99\n", + "Changing to logg=4.00 for T= 35090 logg=3.35\n", + "Changing to logg=2.50 for T= 15507 logg=1.71\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 34623 logg=3.33\n", + "Changing to 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+ "Changing to logg=3.00 for T= 21885 logg=2.60\n", + "Changing to logg=4.00 for T= 31444 logg=3.23\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 11440 logg=1.17\n", + "Changing to logg=1.50 for T= 8792 logg=0.97\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31982 logg=3.74\n", + "Changing to logg=4.00 for T= 31305 logg=2.91\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 15268 logg=1.66\n", + "Changing to logg=2.50 for T= 17775 logg=1.91\n", + "Changing to logg=3.50 for T= 27083 logg=2.67\n", + "Changing to logg=2.00 for T= 11491 logg=1.18\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 33759 logg=3.43\n", + "Changing to logg=2.50 for T= 14006 logg=1.53\n", + "Changing to logg=4.00 for T= 38582 logg=3.95\n", + "Changing to logg=3.00 for T= 23453 logg=2.43\n", + "Changing to logg=2.50 for T= 11953 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+ "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 75074 logg=4.80\n", + "Changing to logg=5.00 for T= 75074 logg=4.80\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 34106 logg=3.57\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 13740 logg=1.50\n", + "Changing to logg=4.50 for T= 39732 logg=4.04\n", + "Changing to logg=4.50 for T= 48595 logg=3.72\n", + "Changing to logg=5.00 for T= 49831 logg=3.98\n", + "Changing to logg=3.50 for T= 27016 logg=2.69\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 22211 logg=2.33\n", + "Changing to logg=2.50 for T= 12986 logg=1.40\n", + "Changing to logg=4.00 for T= 36650 logg=3.99\n", + "Changing to logg=2.50 for T= 18218 logg=1.98\n", + "Changing to logg=3.00 for T= 25570 logg=2.57\n", + "Changing to logg=2.50 for T= 15182 logg=1.65\n", + "Changing to logg=3.50 for T= 26057 logg=2.83\n", + "Changing to T= 50000 for T= 73220 logg=4.77\n", + "Changing to logg=5.00 for T= 73220 logg=4.77\n", + "Changing to logg=2.50 for T= 12959 logg=1.38\n", + "Changing to logg=4.00 for T= 35845 logg=3.39\n", + "Changing to T= 50000 for T= 93530 logg=5.28\n", + "Changing to logg=5.00 for T= 93530 logg=5.28\n", + "Changing to logg=3.50 for T= 29547 logg=3.12\n", + "Changing to T= 50000 for T= 61709 logg=4.38\n", + "Changing to logg=5.00 for T= 61709 logg=4.38\n", + "Changing to logg=3.00 for T= 21602 logg=2.26\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 18634 logg=2.02\n", + "Changing to logg=5.00 for T= 49091 logg=3.72\n", + "Changing to logg=4.00 for T= 38042 logg=3.27\n", + "Changing to logg=4.50 for T= 42502 logg=3.76\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 36247 logg=3.91\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 34751 logg=3.40\n", + "Changing to logg=3.50 for T= 27871 logg=3.02\n", + "Changing to logg=3.50 for T= 26760 logg=3.30\n", + "Changing to logg=4.50 for T= 41371 logg=3.71\n", + "Changing to logg=3.00 for T= 22718 logg=2.37\n", + "Changing to logg=4.50 for T= 42537 logg=3.48\n", + "Changing to T= 50000 for T= 89135 logg=4.69\n", + "Changing to logg=5.00 for T= 89135 logg=4.69\n", + "Changing to logg=3.50 for T= 26913 logg=2.70\n", + "Changing to logg=3.50 for T= 29164 logg=2.80\n", + "Changing to logg=4.00 for T= 36211 logg=3.20\n", + "Changing to T= 50000 for T= 68914 logg=4.62\n", + "Changing to logg=5.00 for T= 68914 logg=4.62\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 18668 logg=2.00\n", + "Changing to T= 50000 for T=127476 logg=5.37\n", + "Changing to logg=5.00 for T=127476 logg=5.37\n", + "Changing to logg=2.00 for T= 9547 logg=0.86\n", + "Changing to logg=2.50 for T= 17342 logg=2.14\n", + "Changing to T= 50000 for T= 51713 logg=4.05\n", + "Changing to logg=5.00 for T= 51713 logg=4.05\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 18110 logg=1.96\n", + "Changing to logg=2.50 for T= 11837 logg=1.20\n", + "Changing to logg=2.00 for T= 10456 logg=0.99\n", + "Changing to logg=3.00 for T= 20335 logg=2.15\n", + "Changing to logg=4.50 for T= 47211 logg=3.94\n", + "Changing to logg=3.00 for T= 22591 logg=2.36\n", + "Changing to logg=4.00 for T= 31584 logg=2.95\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 23583 logg=2.91\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 12020 logg=1.51\n", + "Changing to logg=2.00 for T= 11226 logg=1.14\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=5.00 for T= 49077 logg=4.01\n", + "Changing to T= 50000 for T= 63558 logg=4.45\n", + "Changing to logg=5.00 for T= 63558 logg=4.45\n", + "Changing to logg=3.50 for T= 26826 logg=2.95\n", + "Changing to logg=4.00 for T= 37265 logg=3.81\n", + "Changing to logg=4.00 for T= 37732 logg=3.69\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 30631 logg=2.92\n", + "Changing to T= 50000 for T= 77582 logg=4.87\n", + "Changing to logg=5.00 for T= 77582 logg=4.87\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 11161 logg=1.12\n", + "Changing to T= 50000 for T= 73590 logg=4.78\n", + "Changing to logg=5.00 for T= 73590 logg=4.78\n", + "Changing to logg=3.00 for T= 20480 logg=2.42\n", + "Changing to logg=2.00 for T= 11466 logg=1.18\n", + "Changing to logg=2.50 for T= 12749 logg=1.60\n", + "Changing to logg=4.00 for T= 34580 logg=3.72\n", + "Changing to logg=2.50 for T= 17643 logg=1.89\n", + "Changing to logg=3.00 for T= 21235 logg=2.28\n", + "Changing to logg=3.00 for T= 20714 logg=2.51\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 52159 logg=4.06\n", + "Changing to logg=5.00 for T= 52159 logg=4.06\n", + "Changing to logg=4.00 for T= 37973 logg=3.30\n", + "Changing to logg=3.50 for T= 27450 logg=2.72\n", + "Changing to logg=2.50 for T= 12126 logg=1.26\n", + "Changing to logg=3.00 for T= 21875 logg=2.53\n", + "Changing to logg=4.00 for T= 36722 logg=3.21\n", + "Changing to logg=4.00 for T= 34304 logg=3.84\n", + "Changing to logg=4.00 for T= 38324 logg=3.32\n", + "Changing to logg=2.00 for T= 10767 logg=1.04\n", + "Changing to logg=2.50 for T= 16899 logg=2.09\n", + "Changing to T= 50000 for T=142479 logg=5.64\n", + "Changing to logg=5.00 for T=142479 logg=5.64\n", + "Changing to logg=4.50 for T= 39839 logg=3.95\n", + "Changing to logg=2.00 for T= 9858 logg=0.92\n", + "Changing to logg=2.00 for T= 11192 logg=1.14\n", + "Changing to logg=2.50 for T= 15819 logg=2.21\n", + "Changing to logg=3.00 for T= 21609 logg=2.31\n", + "Changing to logg=2.00 for T= 10828 logg=1.08\n", + "Changing to logg=3.50 for T= 29084 logg=3.47\n", + "Changing to logg=2.50 for T= 15717 logg=1.72\n", + "Changing to logg=2.50 for T= 14900 logg=1.62\n", + "Changing to logg=4.00 for T= 31863 logg=3.96\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 29162 logg=3.07\n", + "Changing to logg=3.50 for T= 28103 logg=2.96\n", + "Changing to T= 50000 for T=147550 logg=5.57\n", + "Changing to logg=5.00 for T=147550 logg=5.57\n", + "Changing to logg=2.00 for T= 10524 logg=1.00\n", + "Changing to logg=4.50 for T= 39184 logg=3.36\n", + "Changing to logg=3.50 for T= 29445 logg=3.49\n", + "Changing to logg=4.00 for T= 32981 logg=3.24\n", + "Changing to logg=2.00 for T= 11423 logg=1.17\n", + "Changing to T= 50000 for T=115931 logg=5.21\n", + "Changing to logg=5.00 for T=115931 logg=5.21\n", + "Changing to logg=3.50 for T= 29023 logg=2.83\n", + "Changing to logg=4.50 for T= 41273 logg=4.12\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25115 logg=2.77\n", + "Changing to logg=2.50 for T= 12202 logg=1.29\n", + "Changing to logg=3.50 for T= 26208 logg=2.84\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31852 logg=2.96\n", + "Changing to logg=4.00 for T= 33907 logg=3.29\n", + "Changing to logg=2.50 for T= 13972 logg=1.49\n", + "Changing to logg=4.50 for T= 40163 logg=3.39\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=1.50 for T= 8974 logg=1.35\n", + "Changing to T= 50000 for T= 60853 logg=4.36\n", + "Changing to logg=5.00 for T= 60853 logg=4.36\n", + "Changing to logg=4.00 for T= 38122 logg=3.28\n", + "Changing to logg=4.00 for T= 37802 logg=3.94\n", + "Changing to logg=3.00 for T= 24518 logg=2.53\n", + "Changing to logg=2.00 for T= 9851 logg=0.92\n", + "Changing to logg=4.00 for T= 34071 logg=3.51\n", + "Changing to logg=4.00 for T= 31093 logg=2.90\n", + "Changing to logg=3.50 for T= 30635 logg=2.90\n", + "Changing to logg=2.00 for T= 11171 logg=1.12\n", + "Changing to logg=4.00 for T= 36400 logg=3.20\n", + "Changing to logg=2.00 for T= 10012 logg=0.94\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 27219 logg=2.68\n", + "Changing to logg=2.00 for T= 9847 logg=0.90\n", + "Changing to logg=2.50 for T= 12773 logg=1.33\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=2.50 for T= 13987 logg=1.53\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 22118 logg=2.62\n", + "Changing to T= 50000 for T= 53696 logg=4.19\n", + "Changing to logg=5.00 for T= 53696 logg=4.19\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 32195 logg=3.67\n", + "Changing to T= 50000 for T=201085 logg=6.01\n", + "Changing to logg=5.00 for T=201085 logg=6.01\n", + "Changing to logg=3.50 for T= 28787 logg=2.99\n", + "Changing to logg=2.50 for T= 16504 logg=1.82\n", + "Changing to logg=3.00 for T= 21150 logg=2.27\n", + "Changing to logg=2.50 for T= 12282 logg=1.89\n", + "Changing to logg=3.50 for T= 27196 logg=3.34\n", + "Changing to logg=2.00 for T= 9486 logg=0.85\n", + "Changing to logg=4.00 for T= 33993 logg=3.30\n", + "Changing to logg=4.50 for T= 43934 logg=3.54\n", + "Changing to logg=2.50 for T= 17793 logg=2.18\n", + "Changing to logg=4.00 for T= 33069 logg=3.97\n", + "Changing to logg=2.00 for T= 10291 logg=0.98\n", + "Changing to logg=2.50 for T= 13946 logg=1.76\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 33050 logg=3.78\n", + "Changing to T= 50000 for T=117685 logg=5.25\n", + "Changing to logg=5.00 for T=117685 logg=5.25\n", + "Changing to logg=2.00 for T= 9953 logg=0.92\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25814 logg=2.61\n", + "Changing to logg=2.50 for T= 17855 logg=2.19\n", + "Changing to logg=3.00 for T= 25112 logg=2.84\n", + "Changing to T= 50000 for T=127803 logg=5.38\n", + "Changing to logg=5.00 for T=127803 logg=5.38\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 73891 logg=4.77\n", + "Changing to logg=5.00 for T= 73891 logg=4.77\n", + "Changing to logg=2.50 for T= 15920 logg=1.66\n", + "Changing to logg=4.00 for T= 36888 logg=3.22\n", + "Changing to logg=2.50 for T= 16490 logg=1.78\n", + "Changing to logg=3.00 for T= 22951 logg=2.61\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-01-08 01:22:41.114369 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 9.100 Msun T = 26537 K m_ubv_U = 6.90\n", + "Primaries\n", + "M = 14.000 Msun T = 30266 K m_ubv_U = 5.28\n", + "M = 3.981 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 19.000 Msun T = 11953 K m_ubv_U = 2.10\n", + "M = 14.000 Msun T = 30298 K m_ubv_U = 5.29\n", + "M = 14.000 Msun T = 30266 K m_ubv_U = 5.28\n", + "M = 3.981 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 10.000 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 3.162 Msun T = nan K m_ubv_U = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_U = 5.62\n", + "M = 12.000 Msun T = 29563 K m_ubv_U = 5.98\n", + "M = 13.000 Msun T = 30037 K m_ubv_U = 5.62\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 15.000 Msun T = 30279 K m_ubv_U = 4.96\n", + "M = 22.000 Msun T = 14210 K m_ubv_U = 1.59\n", + "M = 31.623 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 22.000 Msun T = 6594 K m_ubv_U = 0.95\n", + "M = 12.000 Msun T = 29563 K m_ubv_U = 5.98\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 14.000 Msun T = 30266 K m_ubv_U = 5.28\n", + "M = 10.000 Msun T = 27642 K m_ubv_U = 6.62\n", + "M = 8.500 Msun T = 25805 K m_ubv_U = 7.13\n", + "M = 31.623 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 39.811 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 6.310 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 11.000 Msun T = 28479 K m_ubv_U = 6.21\n", + "M = 3.981 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 19.000 Msun T = 11953 K m_ubv_U = 2.10\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 23.000 Msun T = 23483 K m_ubv_U = 2.34\n", + "M = 16.000 Msun T = 30080 K m_ubv_U = 4.65\n", + "M = 21.000 Msun T = 32719 K m_ubv_U = 3.36\n", + "M = 10.000 Msun T = 27642 K m_ubv_U = 6.62\n", + "M = 8.000 Msun T = 25082 K m_ubv_U = 7.29\n", + "M = 5.012 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 19.953 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 7.943 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 23.000 Msun T = 38042 K m_ubv_U = 3.51\n", + "M = 22.000 Msun T = 22718 K m_ubv_U = 2.43\n", + "M = 10.000 Msun T = 27642 K m_ubv_U = 6.62\n", + "M = 16.000 Msun T = 29888 K m_ubv_U = 4.71\n", + "M = 21.000 Msun T = 21235 K m_ubv_U = 2.46\n", + "M = 6.310 Msun T = 3090 K m_ubv_U = 26.74\n", + "M = 10.000 Msun T = 27642 K m_ubv_U = 6.62\n", + "M = 18.000 Msun T = 29119 K m_ubv_U = 4.09\n", + "M = 2.512 Msun T = nan K m_ubv_U = nan\n", + "M = 3.162 Msun T = nan K m_ubv_U = nan\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 8.000 Msun T = 25087 K m_ubv_U = 7.30\n", + "Secondaries\n", + "M = 4.200 Msun T = 18622 K m_ubv_U = 8.76\n", + "M = 22.000 Msun T = 11274 K m_ubv_U = 1.01\n", + "M = 9.500 Msun T = 28981 K m_ubv_U = 6.59\n", + "M = 2.800 Msun T = 14620 K m_ubv_U = 9.97\n", + "M = 9.800 Msun T = 29450 K m_ubv_U = 6.50\n", + "M = 23.000 Msun T = 31779 K m_ubv_U = 4.34\n", + "M = 19.000 Msun T = 32266 K m_ubv_U = 3.80\n", + "M = 18.000 Msun T = 29113 K m_ubv_U = 3.84\n", + "M = 5.200 Msun T = 21001 K m_ubv_U = 8.17\n", + "M = 10.800 Msun T = 30802 K m_ubv_U = 6.27\n", + "M = 10.400 Msun T = 30291 K m_ubv_U = 6.35\n", + "M = 10.200 Msun T = 30016 K m_ubv_U = 6.40\n", + "M = 7.500 Msun T = 25655 K m_ubv_U = 7.21\n", + "M = 19.800 Msun T = 29572 K m_ubv_U = 3.68\n", + "M = 16.000 Msun T = 30076 K m_ubv_U = 4.41\n", + "M = 11.000 Msun T = nan K m_ubv_U = nan\n", + "M = 1.200 Msun T = 7334 K m_ubv_U = 13.61\n", + "M = 10.200 Msun T = 30016 K m_ubv_U = 6.40\n", + "M = 2.800 Msun T = 14621 K m_ubv_U = 9.97\n", + "M = 1.000 Msun T = 6421 K m_ubv_U = 14.76\n", + "M = 4.250 Msun T = 18735 K m_ubv_U = 8.73\n", + "M = 16.000 Msun T = 30076 K m_ubv_U = 4.41\n", + "M = 20.000 Msun T = 19444 K m_ubv_U = 2.21\n", + "M = 3.500 Msun T = 16547 K m_ubv_U = 9.18\n", + "M = 23.000 Msun T = 15139 K m_ubv_U = 1.17\n", + "M = 13.000 Msun T = 30035 K m_ubv_U = 5.33\n", + "M = 1.100 Msun T = 6851 K m_ubv_U = 14.15\n", + "M = 10.000 Msun T = 27641 K m_ubv_U = 6.22\n", + "M = 15.200 Msun T = 34258 K m_ubv_U = 5.10\n", + "M = 10.000 Msun T = 27641 K m_ubv_U = 6.22\n", + "M = 11.500 Msun T = 31278 K m_ubv_U = 6.08\n", + "M = 9.600 Msun T = 29150 K m_ubv_U = 6.56\n", + "M = 2.100 Msun T = 12092 K m_ubv_U = 10.96\n", + "M = 7.000 Msun T = 24655 K m_ubv_U = 7.40\n", + "M = 1.600 Msun T = 9738 K m_ubv_U = 12.07\n", + "M = 12.000 Msun T = 29563 K m_ubv_U = 5.66\n", + "M = 12.000 Msun T = 29563 K m_ubv_U = 5.66\n", + "M = 11.000 Msun T = 28462 K m_ubv_U = 5.84\n", + "M = 13.800 Msun T = 33633 K m_ubv_U = 5.54\n", + "M = 2.200 Msun T = 12490 K m_ubv_U = 10.79\n", + "M = 8.000 Msun T = 26460 K m_ubv_U = 7.06\n", + "M = 3.200 Msun T = 15747 K m_ubv_U = 9.54\n", + "M = 14.700 Msun T = 33343 K m_ubv_U = 5.19\n", + "M = 12.000 Msun T = 29622 K m_ubv_U = 5.68\n", + "M = 7.000 Msun T = 24655 K m_ubv_U = 7.40\n", + "M = 10.800 Msun T = 30825 K m_ubv_U = 6.24\n", + "M = 8.000 Msun T = 25028 K m_ubv_U = 6.80\n", + "M = 10.000 Msun T = 27641 K m_ubv_U = 6.22\n", + "M = 3.500 Msun T = 16547 K m_ubv_U = 9.18\n", + "M = 3.200 Msun T = 15869 K m_ubv_U = 9.55\n", + "Starting filter: ubv,V Elapsed time: 39.11 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 9.100 Msun T = 26537 K m_ubv_V = 8.07\n", + "Primaries\n", + "M = 14.000 Msun T = 30266 K m_ubv_V = 6.56\n", + "M = 3.981 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 19.000 Msun T = 11953 K m_ubv_V = 2.61\n", + "M = 14.000 Msun T = 30298 K m_ubv_V = 6.57\n", + "M = 14.000 Msun T = 30266 K m_ubv_V = 6.56\n", + "M = 3.981 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 10.000 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 3.162 Msun T = nan K m_ubv_V = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_V = 6.89\n", + "M = 12.000 Msun T = 29563 K m_ubv_V = 7.24\n", + "M = 13.000 Msun T = 30037 K m_ubv_V = 6.89\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 15.000 Msun T = 30279 K m_ubv_V = 6.25\n", + "M = 22.000 Msun T = 14210 K m_ubv_V = 2.31\n", + "M = 31.623 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 22.000 Msun T = 6594 K m_ubv_V = 0.49\n", + "M = 12.000 Msun T = 29563 K m_ubv_V = 7.24\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 14.000 Msun T = 30266 K m_ubv_V = 6.56\n", + "M = 10.000 Msun T = 27642 K m_ubv_V = 7.82\n", + "M = 8.500 Msun T = 25805 K m_ubv_V = 8.28\n", + "M = 31.623 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 39.811 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 6.310 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 11.000 Msun T = 28479 K m_ubv_V = 7.44\n", + "M = 3.981 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 19.000 Msun T = 11953 K m_ubv_V = 2.61\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 23.000 Msun T = 23483 K m_ubv_V = 3.50\n", + "M = 16.000 Msun T = 30080 K m_ubv_V = 5.94\n", + "M = 21.000 Msun T = 32719 K m_ubv_V = 4.70\n", + "M = 10.000 Msun T = 27642 K m_ubv_V = 7.82\n", + "M = 8.000 Msun T = 25082 K m_ubv_V = 8.41\n", + "M = 5.012 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 19.953 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 7.943 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 23.000 Msun T = 38042 K m_ubv_V = 4.89\n", + "M = 22.000 Msun T = 22718 K m_ubv_V = 3.57\n", + "M = 10.000 Msun T = 27642 K m_ubv_V = 7.82\n", + "M = 16.000 Msun T = 29888 K m_ubv_V = 5.99\n", + "M = 21.000 Msun T = 21235 K m_ubv_V = 3.53\n", + "M = 6.310 Msun T = 3090 K m_ubv_V = 24.18\n", + "M = 10.000 Msun T = 27642 K m_ubv_V = 7.82\n", + "M = 18.000 Msun T = 29119 K m_ubv_V = 5.37\n", + "M = 2.512 Msun T = nan K m_ubv_V = nan\n", + "M = 3.162 Msun T = nan K m_ubv_V = nan\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 8.000 Msun T = 25087 K m_ubv_V = 8.42\n", + "Secondaries\n", + "M = 4.200 Msun T = 18622 K m_ubv_V = 9.86\n", + "M = 22.000 Msun T = 11274 K m_ubv_V = 1.70\n", + "M = 9.500 Msun T = 28981 K m_ubv_V = 7.90\n", + "M = 2.800 Msun T = 14620 K m_ubv_V = 10.91\n", + "M = 9.800 Msun T = 29450 K m_ubv_V = 7.81\n", + "M = 23.000 Msun T = 31779 K m_ubv_V = 5.67\n", + "M = 19.000 Msun T = 32266 K m_ubv_V = 5.14\n", + "M = 18.000 Msun T = 29113 K m_ubv_V = 5.15\n", + "M = 5.200 Msun T = 21001 K m_ubv_V = 9.34\n", + "M = 10.800 Msun T = 30802 K m_ubv_V = 7.60\n", + "M = 10.400 Msun T = 30291 K m_ubv_V = 7.67\n", + "M = 10.200 Msun T = 30016 K m_ubv_V = 7.72\n", + "M = 7.500 Msun T = 25655 K m_ubv_V = 8.47\n", + "M = 19.800 Msun T = 29572 K m_ubv_V = 4.99\n", + "M = 16.000 Msun T = 30076 K m_ubv_V = 5.73\n", + "M = 11.000 Msun T = nan K m_ubv_V = nan\n", + "M = 1.200 Msun T = 7334 K m_ubv_V = 13.67\n", + "M = 10.200 Msun T = 30016 K m_ubv_V = 7.72\n", + "M = 2.800 Msun T = 14621 K m_ubv_V = 10.91\n", + "M = 1.000 Msun T = 6421 K m_ubv_V = 14.55\n", + "M = 4.250 Msun T = 18735 K m_ubv_V = 9.84\n", + "M = 16.000 Msun T = 30076 K m_ubv_V = 5.73\n", + "M = 20.000 Msun T = 19444 K m_ubv_V = 3.35\n", + "M = 3.500 Msun T = 16547 K m_ubv_V = 10.21\n", + "M = 23.000 Msun T = 15139 K m_ubv_V = 2.14\n", + "M = 13.000 Msun T = 30035 K m_ubv_V = 6.65\n", + "M = 1.100 Msun T = 6851 K m_ubv_V = 14.08\n", + "M = 10.000 Msun T = 27641 K m_ubv_V = 7.51\n", + "M = 15.200 Msun T = 34258 K m_ubv_V = 6.46\n", + "M = 10.000 Msun T = 27641 K m_ubv_V = 7.51\n", + "M = 11.500 Msun T = 31278 K m_ubv_V = 7.41\n", + "M = 9.600 Msun T = 29150 K m_ubv_V = 7.87\n", + "M = 2.100 Msun T = 12092 K m_ubv_V = 11.73\n", + "M = 7.000 Msun T = 24655 K m_ubv_V = 8.65\n", + "M = 1.600 Msun T = 9738 K m_ubv_V = 12.58\n", + "M = 12.000 Msun T = 29563 K m_ubv_V = 6.97\n", + "M = 12.000 Msun T = 29563 K m_ubv_V = 6.97\n", + "M = 11.000 Msun T = 28462 K m_ubv_V = 7.15\n", + "M = 13.800 Msun T = 33633 K m_ubv_V = 6.89\n", + "M = 2.200 Msun T = 12490 K m_ubv_V = 11.59\n", + "M = 8.000 Msun T = 26460 K m_ubv_V = 8.34\n", + "M = 3.200 Msun T = 15747 K m_ubv_V = 10.54\n", + "M = 14.700 Msun T = 33343 K m_ubv_V = 6.54\n", + "M = 12.000 Msun T = 29622 K m_ubv_V = 7.00\n", + "M = 7.000 Msun T = 24655 K m_ubv_V = 8.65\n", + "M = 10.800 Msun T = 30825 K m_ubv_V = 7.57\n", + "M = 8.000 Msun T = 25028 K m_ubv_V = 8.05\n", + "M = 10.000 Msun T = 27641 K m_ubv_V = 7.51\n", + "M = 3.500 Msun T = 16547 K m_ubv_V = 10.21\n", + "M = 3.200 Msun T = 15869 K m_ubv_V = 10.55\n", + "Starting filter: ubv,B Elapsed time: 74.13 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 9.100 Msun T = 26537 K m_ubv_B = 7.82\n", + "Primaries\n", + "M = 14.000 Msun T = 30266 K m_ubv_B = 6.28\n", + "M = 3.981 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 19.000 Msun T = 11953 K m_ubv_B = 2.51\n", + "M = 14.000 Msun T = 30298 K m_ubv_B = 6.29\n", + "M = 14.000 Msun T = 30266 K m_ubv_B = 6.28\n", + "M = 3.981 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 10.000 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 3.162 Msun T = nan K m_ubv_B = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_B = 6.62\n", + "M = 12.000 Msun T = 29563 K m_ubv_B = 6.97\n", + "M = 13.000 Msun T = 30037 K m_ubv_B = 6.62\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 15.000 Msun T = 30279 K m_ubv_B = 5.97\n", + "M = 22.000 Msun T = 14210 K m_ubv_B = 2.17\n", + "M = 31.623 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 22.000 Msun T = 6594 K m_ubv_B = 0.72\n", + "M = 12.000 Msun T = 29563 K m_ubv_B = 6.97\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 14.000 Msun T = 30266 K m_ubv_B = 6.28\n", + "M = 10.000 Msun T = 27642 K m_ubv_B = 7.56\n", + "M = 8.500 Msun T = 25805 K m_ubv_B = 8.03\n", + "M = 31.623 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 39.811 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 6.310 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 11.000 Msun T = 28479 K m_ubv_B = 7.18\n", + "M = 3.981 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 19.000 Msun T = 11953 K m_ubv_B = 2.51\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 23.000 Msun T = 23483 K m_ubv_B = 3.27\n", + "M = 16.000 Msun T = 30080 K m_ubv_B = 5.67\n", + "M = 21.000 Msun T = 32719 K m_ubv_B = 4.41\n", + "M = 10.000 Msun T = 27642 K m_ubv_B = 7.56\n", + "M = 8.000 Msun T = 25082 K m_ubv_B = 8.17\n", + "M = 5.012 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 19.953 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 7.943 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 23.000 Msun T = 38042 K m_ubv_B = 4.60\n", + "M = 22.000 Msun T = 22718 K m_ubv_B = 3.34\n", + "M = 10.000 Msun T = 27642 K m_ubv_B = 7.56\n", + "M = 16.000 Msun T = 29888 K m_ubv_B = 5.72\n", + "M = 21.000 Msun T = 21235 K m_ubv_B = 3.32\n", + "M = 6.310 Msun T = 3090 K m_ubv_B = 25.89\n", + "M = 10.000 Msun T = 27642 K m_ubv_B = 7.56\n", + "M = 18.000 Msun T = 29119 K m_ubv_B = 5.10\n", + "M = 2.512 Msun T = nan K m_ubv_B = nan\n", + "M = 3.162 Msun T = nan K m_ubv_B = nan\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 8.000 Msun T = 25087 K m_ubv_B = 8.18\n", + "Secondaries\n", + "M = 4.200 Msun T = 18622 K m_ubv_B = 9.73\n", + "M = 22.000 Msun T = 11274 K m_ubv_B = 1.79\n", + "M = 9.500 Msun T = 28981 K m_ubv_B = 7.67\n", + "M = 2.800 Msun T = 14620 K m_ubv_B = 10.87\n", + "M = 9.800 Msun T = 29450 K m_ubv_B = 7.58\n", + "M = 23.000 Msun T = 31779 K m_ubv_B = 5.43\n", + "M = 19.000 Msun T = 32266 K m_ubv_B = 4.89\n", + "M = 18.000 Msun T = 29113 K m_ubv_B = 4.92\n", + "M = 5.200 Msun T = 21001 K m_ubv_B = 9.18\n", + "M = 10.800 Msun T = 30802 K m_ubv_B = 7.36\n", + "M = 10.400 Msun T = 30291 K m_ubv_B = 7.43\n", + "M = 10.200 Msun T = 30016 K m_ubv_B = 7.48\n", + "M = 7.500 Msun T = 25655 K m_ubv_B = 8.26\n", + "M = 19.800 Msun T = 29572 K m_ubv_B = 4.76\n", + "M = 16.000 Msun T = 30076 K m_ubv_B = 5.49\n", + "M = 11.000 Msun T = nan K m_ubv_B = nan\n", + "M = 1.200 Msun T = 7334 K m_ubv_B = 14.08\n", + "M = 10.200 Msun T = 30016 K m_ubv_B = 7.48\n", + "M = 2.800 Msun T = 14621 K m_ubv_B = 10.87\n", + "M = 1.000 Msun T = 6421 K m_ubv_B = 15.10\n", + "M = 4.250 Msun T = 18735 K m_ubv_B = 9.71\n", + "M = 16.000 Msun T = 30076 K m_ubv_B = 5.49\n", + "M = 20.000 Msun T = 19444 K m_ubv_B = 3.21\n", + "M = 3.500 Msun T = 16547 K m_ubv_B = 10.12\n", + "M = 23.000 Msun T = 15139 K m_ubv_B = 2.09\n", + "M = 13.000 Msun T = 30035 K m_ubv_B = 6.41\n", + "M = 1.100 Msun T = 6851 K m_ubv_B = 14.56\n", + "M = 10.000 Msun T = 27641 K m_ubv_B = 7.29\n", + "M = 15.200 Msun T = 34258 K m_ubv_B = 6.20\n", + "M = 10.000 Msun T = 27641 K m_ubv_B = 7.29\n", + "M = 11.500 Msun T = 31278 K m_ubv_B = 7.17\n", + "M = 9.600 Msun T = 29150 K m_ubv_B = 7.63\n", + "M = 2.100 Msun T = 12092 K m_ubv_B = 11.78\n", + "M = 7.000 Msun T = 24655 K m_ubv_B = 8.45\n", + "M = 1.600 Msun T = 9738 K m_ubv_B = 12.76\n", + "M = 12.000 Msun T = 29563 K m_ubv_B = 6.74\n", + "M = 12.000 Msun T = 29563 K m_ubv_B = 6.74\n", + "M = 11.000 Msun T = 28462 K m_ubv_B = 6.92\n", + "M = 13.800 Msun T = 33633 K m_ubv_B = 6.64\n", + "M = 2.200 Msun T = 12490 K m_ubv_B = 11.62\n", + "M = 8.000 Msun T = 26460 K m_ubv_B = 8.12\n", + "M = 3.200 Msun T = 15747 K m_ubv_B = 10.47\n", + "M = 14.700 Msun T = 33343 K m_ubv_B = 6.29\n", + "M = 12.000 Msun T = 29622 K m_ubv_B = 6.76\n", + "M = 7.000 Msun T = 24655 K m_ubv_B = 8.45\n", + "M = 10.800 Msun T = 30825 K m_ubv_B = 7.33\n", + "M = 8.000 Msun T = 25028 K m_ubv_B = 7.85\n", + "M = 10.000 Msun T = 27641 K m_ubv_B = 7.29\n", + "M = 3.500 Msun T = 16547 K m_ubv_B = 10.12\n", + "M = 3.200 Msun T = 15869 K m_ubv_B = 10.48\n", + "Starting filter: ubv,R Elapsed time: 107.14 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 9.100 Msun T = 26537 K m_ubv_R = 8.17\n", + "Primaries\n", + "M = 14.000 Msun T = 30266 K m_ubv_R = 6.67\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 3.981 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 19.000 Msun T = 11953 K m_ubv_R = 2.62\n", + "M = 14.000 Msun T = 30298 K m_ubv_R = 6.68\n", + "M = 14.000 Msun T = 30266 K m_ubv_R = 6.67\n", + "M = 3.981 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 10.000 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 3.162 Msun T = nan K m_ubv_R = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_R = 7.00\n", + "M = 12.000 Msun T = 29563 K m_ubv_R = 7.35\n", + "M = 13.000 Msun T = 30037 K m_ubv_R = 7.00\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 15.000 Msun T = 30279 K m_ubv_R = 6.36\n", + "M = 22.000 Msun T = 14210 K m_ubv_R = 2.34\n", + "M = 31.623 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 22.000 Msun T = 6594 K m_ubv_R = 0.33\n", + "M = 12.000 Msun T = 29563 K m_ubv_R = 7.35\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 14.000 Msun T = 30266 K m_ubv_R = 6.67\n", + "M = 10.000 Msun T = 27642 K m_ubv_R = 7.92\n", + "M = 8.500 Msun T = 25805 K m_ubv_R = 8.37\n", + "M = 31.623 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 39.811 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 6.310 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 11.000 Msun T = 28479 K m_ubv_R = 7.55\n", + "M = 3.981 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 19.000 Msun T = 11953 K m_ubv_R = 2.62\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 23.000 Msun T = 23483 K m_ubv_R = 3.59\n", + "M = 16.000 Msun T = 30080 K m_ubv_R = 6.05\n", + "M = 21.000 Msun T = 32719 K m_ubv_R = 4.81\n", + "M = 10.000 Msun T = 27642 K m_ubv_R = 7.92\n", + "M = 8.000 Msun T = 25082 K m_ubv_R = 8.51\n", + "M = 5.012 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 19.953 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 7.943 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 23.000 Msun T = 38042 K m_ubv_R = 5.01\n", + "M = 22.000 Msun T = 22718 K m_ubv_R = 3.64\n", + "M = 10.000 Msun T = 27642 K m_ubv_R = 7.92\n", + "M = 16.000 Msun T = 29888 K m_ubv_R = 6.10\n", + "M = 21.000 Msun T = 21235 K m_ubv_R = 3.61\n", + "M = 6.310 Msun T = 3090 K m_ubv_R = 23.23\n", + "M = 10.000 Msun T = 27642 K m_ubv_R = 7.92\n", + "M = 18.000 Msun T = 29119 K m_ubv_R = 5.47\n", + "M = 2.512 Msun T = nan K m_ubv_R = nan\n", + "M = 3.162 Msun T = nan K m_ubv_R = nan\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 8.000 Msun T = 25087 K m_ubv_R = 8.51\n", + "Secondaries\n", + "M = 4.200 Msun T = 18622 K m_ubv_R = 9.89\n", + "M = 22.000 Msun T = 11274 K m_ubv_R = 1.63\n", + "M = 9.500 Msun T = 28981 K m_ubv_R = 7.98\n", + "M = 2.800 Msun T = 14620 K m_ubv_R = 10.89\n", + "M = 9.800 Msun T = 29450 K m_ubv_R = 7.89\n", + "M = 23.000 Msun T = 31779 K m_ubv_R = 5.76\n", + "M = 19.000 Msun T = 32266 K m_ubv_R = 5.23\n", + "M = 18.000 Msun T = 29113 K m_ubv_R = 5.22\n", + "M = 5.200 Msun T = 21001 K m_ubv_R = 9.39\n", + "M = 10.800 Msun T = 30802 K m_ubv_R = 7.68\n", + "M = 10.400 Msun T = 30291 K m_ubv_R = 7.75\n", + "M = 10.200 Msun T = 30016 K m_ubv_R = 7.79\n", + "M = 7.500 Msun T = 25655 K m_ubv_R = 8.54\n", + "M = 19.800 Msun T = 29572 K m_ubv_R = 5.07\n", + "M = 16.000 Msun T = 30076 K m_ubv_R = 5.80\n", + "M = 11.000 Msun T = nan K m_ubv_R = nan\n", + "M = 1.200 Msun T = 7334 K m_ubv_R = 13.43\n", + "M = 10.200 Msun T = 30016 K m_ubv_R = 7.79\n", + "M = 2.800 Msun T = 14621 K m_ubv_R = 10.89\n", + "M = 1.000 Msun T = 6421 K m_ubv_R = 14.24\n", + "M = 4.250 Msun T = 18735 K m_ubv_R = 9.86\n", + "M = 16.000 Msun T = 30076 K m_ubv_R = 5.80\n", + "M = 20.000 Msun T = 19444 K m_ubv_R = 3.38\n", + "M = 3.500 Msun T = 16547 K m_ubv_R = 10.22\n", + "M = 23.000 Msun T = 15139 K m_ubv_R = 2.13\n", + "M = 13.000 Msun T = 30035 K m_ubv_R = 6.72\n", + "M = 1.100 Msun T = 6851 K m_ubv_R = 13.81\n", + "M = 10.000 Msun T = 27641 K m_ubv_R = 7.58\n", + "M = 15.200 Msun T = 34258 K m_ubv_R = 6.55\n", + "M = 10.000 Msun T = 27641 K m_ubv_R = 7.58\n", + "M = 11.500 Msun T = 31278 K m_ubv_R = 7.49\n", + "M = 9.600 Msun T = 29150 K m_ubv_R = 7.94\n", + "M = 2.100 Msun T = 12092 K m_ubv_R = 11.67\n", + "M = 7.000 Msun T = 24655 K m_ubv_R = 8.71\n", + "M = 1.600 Msun T = 9738 K m_ubv_R = 12.45\n", + "M = 12.000 Msun T = 29563 K m_ubv_R = 7.05\n", + "M = 12.000 Msun T = 29563 K m_ubv_R = 7.05\n", + "M = 11.000 Msun T = 28462 K m_ubv_R = 7.22\n", + "M = 13.800 Msun T = 33633 K m_ubv_R = 6.98\n", + "M = 2.200 Msun T = 12490 K m_ubv_R = 11.54\n", + "M = 8.000 Msun T = 26460 K m_ubv_R = 8.41\n", + "M = 3.200 Msun T = 15747 K m_ubv_R = 10.53\n", + "M = 14.700 Msun T = 33343 K m_ubv_R = 6.63\n", + "M = 12.000 Msun T = 29622 K m_ubv_R = 7.07\n", + "M = 7.000 Msun T = 24655 K m_ubv_R = 8.71\n", + "M = 10.800 Msun T = 30825 K m_ubv_R = 7.65\n", + "M = 8.000 Msun T = 25028 K m_ubv_R = 8.11\n", + "M = 10.000 Msun T = 27641 K m_ubv_R = 7.58\n", + "M = 3.500 Msun T = 16547 K m_ubv_R = 10.22\n", + "M = 3.200 Msun T = 15869 K m_ubv_R = 10.55\n", + "Starting filter: ubv,I Elapsed time: 140.30 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 9.100 Msun T = 26537 K m_ubv_I = 8.40\n", + "Primaries\n", + "M = 14.000 Msun T = 30266 K m_ubv_I = 6.93\n", + "M = 3.981 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 19.000 Msun T = 11953 K m_ubv_I = 2.67\n", + "M = 14.000 Msun T = 30298 K m_ubv_I = 6.94\n", + "M = 14.000 Msun T = 30266 K m_ubv_I = 6.93\n", + "M = 3.981 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 10.000 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 3.162 Msun T = nan K m_ubv_I = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_I = 7.26\n", + "M = 12.000 Msun T = 29563 K m_ubv_I = 7.60\n", + "M = 13.000 Msun T = 30037 K m_ubv_I = 7.26\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 15.000 Msun T = 30279 K m_ubv_I = 6.61\n", + "M = 22.000 Msun T = 14210 K m_ubv_I = 2.44\n", + "M = 31.623 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 22.000 Msun T = 6594 K m_ubv_I = 0.05\n", + "M = 12.000 Msun T = 29563 K m_ubv_I = 7.60\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 14.000 Msun T = 30266 K m_ubv_I = 6.93\n", + "M = 10.000 Msun T = 27642 K m_ubv_I = 8.17\n", + "M = 8.500 Msun T = 25805 K m_ubv_I = 8.60\n", + "M = 31.623 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 39.811 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 6.310 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 11.000 Msun T = 28479 K m_ubv_I = 7.79\n", + "M = 3.981 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 19.000 Msun T = 11953 K m_ubv_I = 2.67\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 23.000 Msun T = 23483 K m_ubv_I = 3.79\n", + "M = 16.000 Msun T = 30080 K m_ubv_I = 6.30\n", + "M = 21.000 Msun T = 32719 K m_ubv_I = 5.07\n", + "M = 10.000 Msun T = 27642 K m_ubv_I = 8.17\n", + "M = 8.000 Msun T = 25082 K m_ubv_I = 8.73\n", + "M = 5.012 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 19.953 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 7.943 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 23.000 Msun T = 38042 K m_ubv_I = 5.27\n", + "M = 22.000 Msun T = 22718 K m_ubv_I = 3.85\n", + "M = 10.000 Msun T = 27642 K m_ubv_I = 8.17\n", + "M = 16.000 Msun T = 29888 K m_ubv_I = 6.35\n", + "M = 21.000 Msun T = 21235 K m_ubv_I = 3.80\n", + "M = 6.310 Msun T = 3090 K m_ubv_I = 21.84\n", + "M = 10.000 Msun T = 27642 K m_ubv_I = 8.17\n", + "M = 18.000 Msun T = 29119 K m_ubv_I = 5.72\n", + "M = 2.512 Msun T = nan K m_ubv_I = nan\n", + "M = 3.162 Msun T = nan K m_ubv_I = nan\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 8.000 Msun T = 25087 K m_ubv_I = 8.74\n", + "Secondaries\n", + "M = 4.200 Msun T = 18622 K m_ubv_I = 10.02\n", + "M = 22.000 Msun T = 11274 K m_ubv_I = 1.60\n", + "M = 9.500 Msun T = 28981 K m_ubv_I = 8.19\n", + "M = 2.800 Msun T = 14620 K m_ubv_I = 10.96\n", + "M = 9.800 Msun T = 29450 K m_ubv_I = 8.10\n", + "M = 23.000 Msun T = 31779 K m_ubv_I = 5.98\n", + "M = 19.000 Msun T = 32266 K m_ubv_I = 5.45\n", + "M = 18.000 Msun T = 29113 K m_ubv_I = 5.44\n", + "M = 5.200 Msun T = 21001 K m_ubv_I = 9.54\n", + "M = 10.800 Msun T = 30802 K m_ubv_I = 7.90\n", + "M = 10.400 Msun T = 30291 K m_ubv_I = 7.96\n", + "M = 10.200 Msun T = 30016 K m_ubv_I = 8.01\n", + "M = 7.500 Msun T = 25655 K m_ubv_I = 8.73\n", + "M = 19.800 Msun T = 29572 K m_ubv_I = 5.28\n", + "M = 16.000 Msun T = 30076 K m_ubv_I = 6.02\n", + "M = 11.000 Msun T = nan K m_ubv_I = nan\n", + "M = 1.200 Msun T = 7334 K m_ubv_I = 13.16\n", + "M = 10.200 Msun T = 30016 K m_ubv_I = 8.01\n", + "M = 2.800 Msun T = 14621 K m_ubv_I = 10.96\n", + "M = 1.000 Msun T = 6421 K m_ubv_I = 13.86\n", + "M = 4.250 Msun T = 18735 K m_ubv_I = 10.00\n", + "M = 16.000 Msun T = 30076 K m_ubv_I = 6.02\n", + "M = 20.000 Msun T = 19444 K m_ubv_I = 3.52\n", + "M = 3.500 Msun T = 16547 K m_ubv_I = 10.32\n", + "M = 23.000 Msun T = 15139 K m_ubv_I = 2.21\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 13.000 Msun T = 30035 K m_ubv_I = 6.94\n", + "M = 1.100 Msun T = 6851 K m_ubv_I = 13.48\n", + "M = 10.000 Msun T = 27641 K m_ubv_I = 7.79\n", + "M = 15.200 Msun T = 34258 K m_ubv_I = 6.78\n", + "M = 10.000 Msun T = 27641 K m_ubv_I = 7.79\n", + "M = 11.500 Msun T = 31278 K m_ubv_I = 7.71\n", + "M = 9.600 Msun T = 29150 K m_ubv_I = 8.15\n", + "M = 2.100 Msun T = 12092 K m_ubv_I = 11.67\n", + "M = 7.000 Msun T = 24655 K m_ubv_I = 8.90\n", + "M = 1.600 Msun T = 9738 K m_ubv_I = 12.36\n", + "M = 12.000 Msun T = 29563 K m_ubv_I = 7.26\n", + "M = 12.000 Msun T = 29563 K m_ubv_I = 7.26\n", + "M = 11.000 Msun T = 28462 K m_ubv_I = 7.43\n", + "M = 13.800 Msun T = 33633 K m_ubv_I = 7.21\n", + "M = 2.200 Msun T = 12490 K m_ubv_I = 11.55\n", + "M = 8.000 Msun T = 26460 K m_ubv_I = 8.60\n", + "M = 3.200 Msun T = 15747 K m_ubv_I = 10.62\n", + "M = 14.700 Msun T = 33343 K m_ubv_I = 6.85\n", + "M = 12.000 Msun T = 29622 K m_ubv_I = 7.29\n", + "M = 7.000 Msun T = 24655 K m_ubv_I = 8.90\n", + "M = 10.800 Msun T = 30825 K m_ubv_I = 7.87\n", + "M = 8.000 Msun T = 25028 K m_ubv_I = 8.30\n", + "M = 10.000 Msun T = 27641 K m_ubv_I = 7.79\n", + "M = 3.500 Msun T = 16547 K m_ubv_I = 10.32\n", + "M = 3.200 Msun T = 15869 K m_ubv_I = 10.64\n", + " Time taken: 173.30 seconds\n", + "Isochrone generation took 418.823939 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "import time\n", + "t1=time.time()\n", + "bps=evolution.BPASS()\n", + "iso1=synthetic.Isochrone_Binary(7.0, 0.0, 1000,math.log10(1/10), filepath='/g/lu/scratch/ryotainagaki/BPASS_iso_filesTimedIsolated/')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a sanity check, I would like to make sure thatk, for primary stars, we only have (from the isochrone) designated phase 5 stars ( anything that is not a compact remnant), white dwarves (101), and non-white dwarf compact remnants that are NEWSECMODS primaries. Also make sure that our secondary stars are non-compact-remnant, white dwarves, or are merged." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 31270 logg=4.00\n", + "Changing to logg=4.00 for T= 31267 logg=3.99\n", + "Changing to logg=4.00 for T= 31259 logg=3.98\n", + "Changing to logg=4.00 for T= 31245 logg=3.97\n", + "Changing to logg=4.00 for T= 31226 logg=3.96\n", + "Changing to logg=4.00 for T= 31200 logg=3.94\n", + "Changing to logg=4.00 for T= 31169 logg=3.93\n", + "Changing to logg=4.00 for T= 31139 logg=3.92\n", + "Changing to logg=4.00 for T= 31109 logg=3.91\n", + "Changing to logg=4.00 for T= 31072 logg=3.90\n", + "Changing to logg=4.00 for T= 31032 logg=3.89\n", + "Changing to logg=4.00 for T= 31013 logg=3.64\n", + "Changing to logg=4.00 for T= 31102 logg=3.64\n", + "Changing to logg=4.00 for T= 31185 logg=3.64\n", + "Changing to logg=4.00 for T= 31263 logg=3.65\n", + "Changing to logg=4.00 for T= 31337 logg=3.65\n", + "Changing to logg=4.00 for T= 31405 logg=3.65\n", + "Changing to logg=4.00 for T= 31467 logg=3.65\n", + "Changing to logg=4.00 for T= 31520 logg=3.65\n", + "Changing to logg=4.00 for T= 31564 logg=3.66\n", + "Changing to logg=4.00 for T= 31593 logg=3.66\n", + "Changing to logg=4.00 for T= 31604 logg=3.66\n", + "Changing to logg=4.00 for T= 31594 logg=3.66\n", + "Changing to logg=4.00 for T= 31559 logg=3.66\n", + "Changing to logg=4.00 for T= 31501 logg=3.66\n", + "Changing to logg=4.00 for T= 31423 logg=3.65\n", + "Changing to logg=4.00 for T= 31328 logg=3.65\n", + "Changing to logg=4.00 for T= 31225 logg=3.65\n", + "Changing to logg=4.00 for T= 31112 logg=3.64\n", + "Changing to logg=3.50 for T= 29088 logg=3.49\n", + "Changing to logg=3.50 for T= 29029 logg=3.49\n", + "Changing to logg=3.50 for T= 28969 logg=3.49\n", + "Changing to logg=3.50 for T= 28909 logg=3.48\n", + "Changing to logg=3.50 for T= 28848 logg=3.48\n", + "Changing to logg=3.50 for T= 28787 logg=3.47\n", + "Changing to logg=3.50 for T= 28726 logg=3.47\n", + "Changing to logg=3.50 for T= 28664 logg=3.46\n", + "Changing to logg=3.50 for T= 28603 logg=3.46\n", + "Changing to logg=3.50 for T= 28541 logg=3.46\n", + "Changing to logg=3.50 for T= 28479 logg=3.45\n", + "Changing to logg=3.50 for T= 28417 logg=3.45\n", + "Changing to logg=3.50 for T= 28355 logg=3.44\n", + "Changing to logg=3.50 for T= 28293 logg=3.44\n", + "Changing to logg=3.50 for T= 28230 logg=3.44\n", + "Changing to logg=3.50 for T= 28168 logg=3.43\n", + "Changing to logg=3.50 for T= 28106 logg=3.43\n", + "Changing to logg=3.50 for T= 28044 logg=3.42\n", + "Changing to logg=3.50 for T= 27982 logg=3.42\n", + "Changing to logg=3.50 for T= 27920 logg=3.42\n", + "Changing to logg=3.50 for T= 27858 logg=3.41\n", + "Changing to logg=3.50 for T= 27796 logg=3.41\n", + "Changing to logg=3.50 for T= 27734 logg=3.40\n", + "Changing to logg=3.50 for T= 27672 logg=3.40\n", + "Changing to logg=3.50 for T= 27610 logg=3.40\n", + "Changing to logg=3.50 for T= 27548 logg=3.39\n", + "Changing to logg=3.50 for T= 27486 logg=3.39\n", + "Changing to logg=3.50 for T= 27425 logg=3.38\n", + "Changing to logg=3.50 for T= 27363 logg=3.38\n", + "Changing to logg=3.50 for T= 27301 logg=3.37\n", + "Changing to logg=3.50 for T= 27240 logg=3.37\n", + "Changing to logg=3.50 for T= 27179 logg=3.37\n", + "Changing to logg=3.50 for T= 27118 logg=3.36\n", + "Changing to logg=3.50 for T= 27056 logg=3.36\n", + "Changing to logg=3.50 for T= 26995 logg=3.35\n", + "Changing to logg=3.50 for T= 26934 logg=3.35\n", + "Changing to logg=3.50 for T= 26873 logg=3.34\n", + "Changing to logg=3.50 for T= 26812 logg=3.34\n", + "Changing to logg=3.50 for T= 26751 logg=3.34\n", + "Changing to logg=3.50 for T= 26690 logg=3.33\n", + "Changing to logg=3.50 for T= 26629 logg=3.33\n", + "Changing to logg=3.50 for T= 26568 logg=3.32\n", + "Changing to logg=3.50 for T= 26507 logg=3.32\n", + "Changing to logg=3.50 for T= 26447 logg=3.31\n", + "Changing to logg=3.50 for T= 26386 logg=3.31\n", + "Changing to logg=3.50 for T= 26326 logg=3.31\n", + "Changing to logg=3.50 for T= 26265 logg=3.30\n", + "Changing to logg=3.50 for T= 26205 logg=3.30\n", + "Changing to logg=3.50 for T= 26144 logg=3.29\n", + "Changing to logg=3.50 for T= 26084 logg=3.29\n", + "Changing to logg=3.50 for T= 26024 logg=3.28\n", + "Changing to logg=3.00 for T= 22390 logg=3.00\n", + "Changing to logg=3.00 for T= 22334 logg=2.99\n", + "Changing to logg=3.00 for T= 22278 logg=2.99\n", + "Changing to logg=3.00 for T= 22222 logg=2.98\n", + "Changing to logg=3.00 for T= 22167 logg=2.98\n", + "Changing to logg=3.00 for T= 22111 logg=2.97\n", + "Changing to logg=3.00 for T= 22056 logg=2.97\n", + "Changing to logg=3.00 for T= 22001 logg=2.96\n", + "Changing to logg=3.00 for T= 21946 logg=2.96\n", + "Changing to logg=3.00 for T= 21891 logg=2.95\n", + "Changing to logg=3.00 for T= 21836 logg=2.95\n", + "Changing to logg=3.00 for T= 21782 logg=2.94\n", + "Changing to logg=3.00 for T= 21728 logg=2.94\n", + "Changing to logg=3.00 for T= 21673 logg=2.93\n", + "Changing to logg=3.00 for T= 21619 logg=2.93\n", + "Changing to logg=3.00 for T= 21565 logg=2.92\n", + "Changing to logg=3.00 for T= 21512 logg=2.92\n", + "Changing to logg=3.00 for T= 21458 logg=2.91\n", + "Changing to logg=3.00 for T= 21405 logg=2.91\n", + "Changing to logg=3.00 for T= 21351 logg=2.90\n", + "Changing to logg=3.00 for T= 21298 logg=2.90\n", + "Changing to logg=3.00 for T= 21245 logg=2.89\n", + "Changing to logg=3.00 for T= 21192 logg=2.89\n", + "Changing to logg=3.00 for T= 21140 logg=2.88\n", + "Changing to logg=3.00 for T= 21087 logg=2.88\n", + "Changing to logg=3.00 for T= 21035 logg=2.87\n", + "Changing to logg=3.00 for T= 20982 logg=2.87\n", + "Changing to logg=3.00 for T= 20930 logg=2.86\n", + "Changing to logg=3.00 for T= 20878 logg=2.86\n", + "Changing to logg=3.00 for T= 20826 logg=2.85\n", + "Changing to logg=3.00 for T= 20774 logg=2.85\n", + "Changing to logg=3.00 for T= 20723 logg=2.84\n", + "Changing to logg=3.00 for T= 20671 logg=2.84\n", + "Changing to logg=3.00 for T= 20620 logg=2.83\n", + "Changing to logg=3.00 for T= 20569 logg=2.83\n", + "Changing to logg=3.00 for T= 20518 logg=2.82\n", + "Changing to logg=3.00 for T= 20467 logg=2.82\n", + "Changing to logg=3.00 for T= 20416 logg=2.81\n", + "Changing to logg=3.00 for T= 20365 logg=2.81\n", + "Changing to logg=3.00 for T= 20315 logg=2.80\n", + "Changing to logg=3.00 for T= 20282 logg=2.80\n", + "Changing to logg=3.00 for T= 20249 logg=2.79\n", + "Changing to logg=3.00 for T= 20216 logg=2.79\n", + "Changing to logg=3.00 for T= 20183 logg=2.79\n", + "Changing to logg=3.00 for T= 20151 logg=2.78\n", + "Changing to logg=3.00 for T= 20118 logg=2.78\n", + "Changing to logg=3.00 for T= 20085 logg=2.78\n", + "Changing to logg=3.00 for T= 20053 logg=2.77\n", + "Changing to logg=3.00 for T= 20021 logg=2.77\n", + "Changing to logg=3.00 for T= 19988 logg=2.77\n", + "Changing to logg=3.00 for T= 19956 logg=2.76\n", + "Changing to logg=3.00 for T= 19924 logg=2.76\n", + "Changing to logg=3.00 for T= 19892 logg=2.76\n", + "Changing to logg=3.00 for T= 19860 logg=2.75\n", + "Changing to logg=3.00 for T= 19828 logg=2.75\n", + "Changing to logg=3.00 for T= 19796 logg=2.75\n", + "Changing to logg=3.00 for T= 19764 logg=2.74\n", + "Changing to logg=3.00 for T= 19732 logg=2.74\n", + "Changing to logg=3.00 for T= 19701 logg=2.74\n", + "Changing to logg=3.00 for T= 19670 logg=2.74\n", + "Changing to logg=3.00 for T= 19638 logg=2.73\n", + "Changing to logg=3.00 for T= 19607 logg=2.73\n", + "Changing to logg=3.00 for T= 19576 logg=2.73\n", + "Changing to logg=3.00 for T= 19546 logg=2.72\n", + "Changing to logg=3.00 for T= 19515 logg=2.72\n", + "Changing to logg=3.00 for T= 19484 logg=2.72\n", + "Changing to logg=3.00 for T= 19193 logg=2.69\n", + "Changing to logg=2.50 for T= 17579 logg=2.48\n", + "Changing to logg=2.50 for T= 17390 logg=2.46\n", + "Changing to logg=2.50 for T= 17170 logg=2.43\n", + "Changing to logg=2.50 for T= 16944 logg=2.40\n", + "Changing to logg=2.50 for T= 16719 logg=2.38\n", + "Changing to logg=2.50 for T= 16498 logg=2.35\n", + "Changing to logg=2.50 for T= 16279 logg=2.33\n", + "Changing to logg=2.50 for T= 16062 logg=2.30\n", + "Changing to logg=2.50 for T= 15848 logg=2.27\n", + "Changing to logg=2.50 for T= 15637 logg=2.25\n", + "Changing to logg=2.50 for T= 15428 logg=2.22\n", + "Changing to logg=2.50 for T= 15222 logg=2.20\n", + "Changing to logg=2.50 for T= 15018 logg=2.17\n", + "Changing to logg=2.50 for T= 14816 logg=2.15\n", + "Changing to logg=2.50 for T= 14618 logg=2.12\n", + "Changing to logg=2.50 for T= 14421 logg=2.10\n", + "Changing to logg=2.50 for T= 14227 logg=2.07\n", + "Changing to logg=2.50 for T= 14036 logg=2.05\n", + "Changing to logg=2.50 for T= 13847 logg=2.02\n", + "Changing to logg=2.50 for T= 13660 logg=2.00\n", + "Changing to logg=2.50 for T= 13476 logg=1.97\n", + "Changing to logg=2.50 for T= 13294 logg=1.95\n", + "Changing to logg=2.50 for T= 13115 logg=1.92\n", + "Changing to logg=2.50 for T= 12938 logg=1.90\n", + "Changing to logg=2.50 for T= 12764 logg=1.87\n", + "Changing to logg=2.50 for T= 12591 logg=1.85\n", + "Changing to logg=2.50 for T= 12421 logg=1.83\n", + "Changing to logg=2.50 for T= 12253 logg=1.80\n", + "Changing to logg=2.50 for T= 12087 logg=1.78\n", + "Changing to logg=2.50 for T= 11924 logg=1.75\n", + "Changing to logg=2.50 for T= 11762 logg=1.73\n", + "Changing to logg=2.00 for T= 11603 logg=1.70\n", + "Changing to logg=2.00 for T= 11446 logg=1.68\n", + "Changing to logg=2.00 for T= 11291 logg=1.66\n", + "Changing to logg=2.00 for T= 11138 logg=1.63\n", + "Changing to logg=2.00 for T= 10987 logg=1.61\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=2.00 for T= 10837 logg=1.58\n", + "Changing to logg=2.00 for T= 10690 logg=1.56\n", + "Changing to logg=2.00 for T= 10545 logg=1.54\n", + "Changing to logg=2.00 for T= 10402 logg=1.51\n", + "Changing to logg=2.00 for T= 10261 logg=1.49\n", + "Changing to logg=2.00 for T= 10121 logg=1.46\n", + "Changing to logg=2.00 for T= 9984 logg=1.44\n", + "Changing to logg=2.00 for T= 9848 logg=1.42\n", + "Changing to logg=2.00 for T= 9715 logg=1.39\n", + "Changing to logg=2.00 for T= 9583 logg=1.37\n", + "Changing to logg=2.00 for T= 9453 logg=1.34\n", + "Changing to logg=2.00 for T= 9324 logg=1.32\n", + "Changing to logg=2.00 for T= 9198 logg=1.30\n", + "Changing to logg=2.00 for T= 9073 logg=1.27\n", + "Changing to logg=1.50 for T= 8950 logg=1.25\n", + "Changing to logg=1.50 for T= 8828 logg=1.23\n", + "Changing to logg=1.50 for T= 8709 logg=1.20\n", + "Changing to logg=1.50 for T= 8591 logg=1.18\n", + "Changing to logg=1.50 for T= 8474 logg=1.16\n", + "Changing to logg=1.50 for T= 8359 logg=1.13\n", + "Changing to logg=1.00 for T= 7703 logg=0.99\n", + "Changing to logg=1.00 for T= 7599 logg=0.97\n", + "Isochrone generation took 50.693185 s.\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-01-08 01:26:25.118701 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_U = 23.25\n", + "M = 1.571 Msun T = 9909 K m_ubv_U = 12.45\n", + "M = 6.785 Msun T = 23952 K m_ubv_U = 7.89\n", + "M = 18.749 Msun T = 30008 K m_ubv_U = 3.92\n", + "M = 18.788 Msun T = 25605 K m_ubv_U = 3.48\n", + "M = 18.806 Msun T = 20118 K m_ubv_U = 2.98\n", + "M = 20.402 Msun T = 6878 K m_ubv_U = 1.98\n", + "Starting filter: ubv,B Elapsed time: 1.23 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_B = 22.13\n", + "M = 1.571 Msun T = 9909 K m_ubv_B = 12.48\n", + "M = 6.785 Msun T = 23952 K m_ubv_B = 8.74\n", + "M = 18.749 Msun T = 30008 K m_ubv_B = 4.95\n", + "M = 18.788 Msun T = 25605 K m_ubv_B = 4.45\n", + "M = 18.806 Msun T = 20118 K m_ubv_B = 3.79\n", + "M = 20.402 Msun T = 6878 K m_ubv_B = 1.76\n", + "Starting filter: ubv,V Elapsed time: 2.43 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_V = 20.54\n", + "M = 1.571 Msun T = 9909 K m_ubv_V = 12.47\n", + "M = 6.785 Msun T = 23952 K m_ubv_V = 8.97\n", + "M = 18.749 Msun T = 30008 K m_ubv_V = 5.22\n", + "M = 18.788 Msun T = 25605 K m_ubv_V = 4.69\n", + "M = 18.806 Msun T = 20118 K m_ubv_V = 4.00\n", + "M = 20.402 Msun T = 6878 K m_ubv_V = 1.59\n", + "Starting filter: ubv,R Elapsed time: 3.63 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_R = 19.67\n", + "M = 1.571 Msun T = 9909 K m_ubv_R = 12.47\n", + "M = 6.785 Msun T = 23952 K m_ubv_R = 9.06\n", + "M = 18.749 Msun T = 30008 K m_ubv_R = 5.33\n", + "M = 18.788 Msun T = 25605 K m_ubv_R = 4.78\n", + "M = 18.806 Msun T = 20118 K m_ubv_R = 4.07\n", + "M = 20.402 Msun T = 6878 K m_ubv_R = 1.47\n", + "Starting filter: ubv,I Elapsed time: 4.79 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_I = 18.44\n", + "M = 1.571 Msun T = 9909 K m_ubv_I = 12.48\n", + "M = 6.785 Msun T = 23952 K m_ubv_I = 9.27\n", + "M = 18.749 Msun T = 30008 K m_ubv_I = 5.58\n", + "M = 18.788 Msun T = 25605 K m_ubv_I = 5.01\n", + "M = 18.806 Msun T = 20118 K m_ubv_I = 4.25\n", + "M = 20.402 Msun T = 6878 K m_ubv_I = 1.23\n", + " Time taken: 5.96 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(7.0, 0.0, 1000, math.log10(1/10), recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the distribution of logg values. for the stars in the MISTv.1 isochrone." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'spisea.synthetic' has no attribute 'Binary_Cluster'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclus_1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynthetic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBinary_Cluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miso1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKennicutt_1983\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiplicityResolvedDK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mifmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mifmr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIFMR_Spera15\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mclus_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynthetic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResolvedCluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miso2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKennicutt_1983\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiplicityResolvedDK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mifmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mifmr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIFMR_Spera15\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'spisea.synthetic' has no attribute 'Binary_Cluster'" + ] + } + ], + "source": [ + "clus_1=synthetic.Binary_Cluster(iso1, imf.Kennicutt_1983(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2, imf.Kennicutt_1983(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize the isochrone we have created so far with a color magnitude diagram. There we can see the end of main sequence and perhaps the M type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Checking if I am getting the right amout of mass (10000 solar masses)\n", + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparison with the MIST v.1 cluster (10000 solar masses)\n", + "clus_2.star_systems['systemMass'].sum()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cluster_Mass_Error_Demonstration/Error Demonstration 10^7 yr old Kennicutt.ipynb b/Cluster_Mass_Error_Demonstration/Error Demonstration 10^7 yr old Kennicutt.ipynb new file mode 100644 index 00000000..96997d7c --- /dev/null +++ b/Cluster_Mass_Error_Demonstration/Error Demonstration 10^7 yr old Kennicutt.ipynb @@ -0,0 +1,2271 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS cluster at 10^7 years age.\n", + "Here, I try to create a 10000 solar mass BPASS cluster and ResolvedCluster. **This is to demonstrate the issue I have with the mass-matching inside of the Binary_Cluster implementation.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:346: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1794: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater\n", + " result = getattr(super(), op)(other)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.50 for T= 48500 logg=3.98\n", + "Changing to T= 50000 for T=164324 logg=5.78\n", + "Changing to logg=5.00 for T=164324 logg=5.78\n", + "Changing to T= 50000 for T=102353 logg=5.41\n", + "Changing to logg=5.00 for T=102353 logg=5.41\n", + "Changing to T= 50000 for T= 69632 logg=4.38\n", + "Changing to logg=5.00 for T= 69632 logg=4.38\n", + "Changing to T= 50000 for T= 69390 logg=4.71\n", + "Changing to logg=5.00 for T= 69390 logg=4.71\n", + "Changing to T= 50000 for T=102877 logg=5.42\n", + "Changing to logg=5.00 for T=102877 logg=5.42\n", + "Changing to T= 50000 for T= 71453 logg=4.77\n", + "Changing to logg=5.00 for T= 71453 logg=4.77\n", + "Changing to T= 50000 for T=103588 logg=5.41\n", + "Changing to logg=5.00 for T=103588 logg=5.41\n", + "Changing to T= 50000 for T= 82465 logg=5.17\n", + "Changing to logg=5.00 for T= 82465 logg=5.17\n", + "Changing to logg=4.00 for T= 31125 logg=3.66\n", + "Changing to T= 50000 for T= 59860 logg=4.13\n", + "Changing to logg=5.00 for T= 59860 logg=4.13\n", + "Changing to logg=4.00 for T= 35763 logg=3.88\n", + "Changing to T= 50000 for T= 70651 logg=4.75\n", + "Changing to logg=5.00 for T= 70651 logg=4.75\n", + "Changing to T= 50000 for T=102849 logg=5.40\n", + "Changing to logg=5.00 for T=102849 logg=5.40\n", + "Changing to T= 50000 for T= 74116 logg=4.48\n", + "Changing to logg=5.00 for T= 74116 logg=4.48\n", + "Changing to T= 50000 for T= 80581 logg=5.10\n", + "Changing to logg=5.00 for T= 80581 logg=5.10\n", + "Changing to logg=4.00 for T= 35293 logg=3.85\n", + "Changing to T= 50000 for T= 72197 logg=4.43\n", + "Changing to logg=5.00 for T= 72197 logg=4.43\n", + "Changing to logg=3.50 for T= 27580 logg=3.14\n", + "Changing to logg=4.50 for T= 46078 logg=3.89\n", + "Changing to T= 50000 for T=102400 logg=5.42\n", + "Changing to logg=5.00 for T=102400 logg=5.42\n", + "Changing to T= 50000 for T= 69217 logg=4.37\n", + "Changing to logg=5.00 for T= 69217 logg=4.37\n", + "Changing to T= 50000 for T= 72205 logg=4.44\n", + "Changing to logg=5.00 for T= 72205 logg=4.44\n", + "Changing to T= 50000 for T= 52802 logg=3.95\n", + "Changing to logg=5.00 for T= 52802 logg=3.95\n", + "Changing to T= 50000 for T= 69325 logg=4.37\n", + "Changing to logg=5.00 for T= 69325 logg=4.37\n", + "Changing to T= 50000 for T= 68431 logg=4.34\n", + "Changing to logg=5.00 for T= 68431 logg=4.34\n", + "Changing to logg=4.00 for T= 34157 logg=3.71\n", + "Changing to T= 50000 for T= 74287 logg=4.48\n", + "Changing to logg=5.00 for T= 74287 logg=4.48\n", + "Changing to T= 50000 for T= 80053 logg=4.57\n", + "Changing to logg=5.00 for T= 80053 logg=4.57\n", + "Changing to logg=3.00 for T= 21306 logg=2.99\n", + "Changing to T= 50000 for T= 83155 logg=4.66\n", + "Changing to logg=5.00 for T= 83155 logg=4.66\n", + "Changing to T= 50000 for T=162637 logg=5.76\n", + "Changing to logg=5.00 for T=162637 logg=5.76\n", + "Changing to T= 50000 for T= 66874 logg=4.31\n", + "Changing to logg=5.00 for T= 66874 logg=4.31\n", + "Changing to T= 50000 for T= 65318 logg=4.27\n", + "Changing to logg=5.00 for T= 65318 logg=4.27\n", + "Changing to T= 50000 for T= 93573 logg=5.40\n", + "Changing to logg=5.00 for T= 93573 logg=5.40\n", + "Changing to T= 50000 for T=101974 logg=5.40\n", + "Changing to logg=5.00 for T=101974 logg=5.40\n", + "Changing to T= 50000 for T= 59869 logg=4.39\n", + "Changing to logg=5.00 for T= 59869 logg=4.39\n", + "Changing to logg=4.50 for T= 46562 logg=3.91\n", + "Changing to T= 50000 for T=102209 logg=5.40\n", + "Changing to logg=5.00 for T=102209 logg=5.40\n", + "Changing to T= 50000 for T=102582 logg=5.41\n", + "Changing to logg=5.00 for T=102582 logg=5.41\n", + "Changing to T= 50000 for T= 98215 logg=4.84\n", + "Changing to logg=5.00 for T= 98215 logg=4.84\n", + "Changing to T= 50000 for T= 74427 logg=4.49\n", + "Changing to logg=5.00 for T= 74427 logg=4.49\n", + "Changing to T= 50000 for T= 64973 logg=4.27\n", + "Changing to logg=5.00 for T= 64973 logg=4.27\n", + "Changing to T= 50000 for T= 64880 logg=4.26\n", + "Changing to logg=5.00 for T= 64880 logg=4.26\n", + "Changing to logg=5.00 for T= 49825 logg=4.03\n", + "Changing to T= 50000 for T= 88965 logg=4.76\n", + "Changing to logg=5.00 for T= 88965 logg=4.76\n", + "Changing to T= 50000 for T= 79382 logg=5.04\n", + "Changing to logg=5.00 for T= 79382 logg=5.04\n", + "Changing to T= 50000 for T=101728 logg=5.41\n", + "Changing to logg=5.00 for T=101728 logg=5.41\n", + "Changing to T= 50000 for T= 52338 logg=4.13\n", + "Changing to logg=5.00 for T= 52338 logg=4.13\n", + "Changing to T= 50000 for T= 67151 logg=4.32\n", + "Changing to logg=5.00 for T= 67151 logg=4.32\n", + "Changing to T= 50000 for T= 60679 logg=4.16\n", + "Changing to logg=5.00 for T= 60679 logg=4.16\n", + "Changing to T= 50000 for T= 56498 logg=4.28\n", + "Changing to logg=5.00 for T= 56498 logg=4.28\n", + "Changing to T= 50000 for T= 65814 logg=4.28\n", + "Changing to logg=5.00 for T= 65814 logg=4.28\n", + "Changing to T= 50000 for T= 51387 logg=4.09\n", + "Changing to logg=5.00 for T= 51387 logg=4.09\n", + "Changing to T= 50000 for T= 66434 logg=4.30\n", + "Changing to logg=5.00 for T= 66434 logg=4.30\n", + "Changing to T= 50000 for T= 71685 logg=4.42\n", + "Changing to logg=5.00 for T= 71685 logg=4.42\n", + "Changing to T= 50000 for T=103748 logg=5.41\n", + "Changing to logg=5.00 for T=103748 logg=5.41\n", + "Changing to logg=3.50 for T= 30090 logg=2.88\n", + "Changing to T= 50000 for T= 61468 logg=4.45\n", + "Changing to logg=5.00 for T= 61468 logg=4.45\n", + "Changing to T= 50000 for T= 66141 logg=4.29\n", + "Changing to logg=5.00 for T= 66141 logg=4.29\n", + "Changing to T= 50000 for T= 75866 logg=4.52\n", + "Changing to logg=5.00 for T= 75866 logg=4.52\n", + "Changing to T= 50000 for T=102669 logg=5.40\n", + "Changing to logg=5.00 for T=102669 logg=5.40\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T=102676 logg=5.41\n", + "Changing to logg=5.00 for T=102676 logg=5.41\n", + "Changing to T= 50000 for T=174237 logg=5.88\n", + "Changing to logg=5.00 for T=174237 logg=5.88\n", + "Changing to T= 50000 for T= 92342 logg=5.39\n", + "Changing to logg=5.00 for T= 92342 logg=5.39\n", + "Changing to T= 50000 for T= 79287 logg=4.87\n", + "Changing to logg=5.00 for T= 79287 logg=4.87\n", + "Changing to T= 50000 for T= 67916 logg=4.33\n", + "Changing to logg=5.00 for T= 67916 logg=4.33\n", + "Changing to logg=4.50 for T= 42917 logg=3.76\n", + "Changing to T= 50000 for T= 67022 logg=4.33\n", + "Changing to logg=5.00 for T= 67022 logg=4.33\n", + "Changing to T= 50000 for T= 71698 logg=4.79\n", + "Changing to logg=5.00 for T= 71698 logg=4.79\n", + "Changing to T= 50000 for T=103200 logg=5.41\n", + "Changing to logg=5.00 for T=103200 logg=5.41\n", + "Changing to T= 50000 for T= 66924 logg=4.31\n", + "Changing to logg=5.00 for T= 66924 logg=4.31\n", + "Changing to T= 50000 for T=102308 logg=5.41\n", + "Changing to logg=5.00 for T=102308 logg=5.41\n", + "Changing to T= 50000 for T=102056 logg=5.41\n", + "Changing to logg=5.00 for T=102056 logg=5.41\n", + "Changing to T= 50000 for T= 74967 logg=4.50\n", + "Changing to logg=5.00 for T= 74967 logg=4.50\n", + "Changing to logg=4.00 for T= 32053 logg=3.87\n", + "Changing to T= 50000 for T=102638 logg=5.40\n", + "Changing to logg=5.00 for T=102638 logg=5.40\n", + "Changing to T= 50000 for T= 68096 logg=4.34\n", + "Changing to logg=5.00 for T= 68096 logg=4.34\n", + "Changing to T= 50000 for T= 72709 logg=4.44\n", + "Changing to logg=5.00 for T= 72709 logg=4.44\n", + "Changing to T= 50000 for T= 83506 logg=5.33\n", + "Changing to logg=5.00 for T= 83506 logg=5.33\n", + "Changing to T= 50000 for T=101368 logg=5.42\n", + "Changing to logg=5.00 for T=101368 logg=5.42\n", + "Changing to T= 50000 for T= 72790 logg=4.45\n", + "Changing to logg=5.00 for T= 72790 logg=4.45\n", + "Changing to T= 50000 for T= 69328 logg=4.36\n", + "Changing to logg=5.00 for T= 69328 logg=4.36\n", + "Changing to logg=4.50 for T= 42028 logg=3.72\n", + "Changing to T= 50000 for T= 65670 logg=4.28\n", + "Changing to logg=5.00 for T= 65670 logg=4.28\n", + "Changing to T= 50000 for T= 69175 logg=4.37\n", + "Changing to logg=5.00 for T= 69175 logg=4.37\n", + "Changing to T= 50000 for T= 83603 logg=5.19\n", + "Changing to logg=5.00 for T= 83603 logg=5.19\n", + "Changing to T= 50000 for T= 67391 logg=4.32\n", + "Changing to logg=5.00 for T= 67391 logg=4.32\n", + "Changing to T= 50000 for T= 68099 logg=4.34\n", + "Changing to logg=5.00 for T= 68099 logg=4.34\n", + "Changing to T= 50000 for T= 56169 logg=4.27\n", + "Changing to logg=5.00 for T= 56169 logg=4.27\n", + "Changing to T= 50000 for T= 72061 logg=4.43\n", + "Changing to logg=5.00 for T= 72061 logg=4.43\n", + "Changing to T= 50000 for T=170381 logg=5.84\n", + "Changing to logg=5.00 for T=170381 logg=5.84\n", + "Changing to T= 50000 for T= 68290 logg=4.34\n", + "Changing to logg=5.00 for T= 68290 logg=4.34\n", + "Changing to T= 50000 for T=102040 logg=5.39\n", + "Changing to logg=5.00 for T=102040 logg=5.39\n", + "Changing to T= 50000 for T= 69287 logg=4.37\n", + "Changing to logg=5.00 for T= 69287 logg=4.37\n", + "Changing to T= 50000 for T=101167 logg=5.41\n", + "Changing to logg=5.00 for T=101167 logg=5.41\n", + "Changing to T= 50000 for T= 65088 logg=4.27\n", + "Changing to logg=5.00 for T= 65088 logg=4.27\n", + "Changing to T= 50000 for T=103870 logg=5.41\n", + "Changing to logg=5.00 for T=103870 logg=5.41\n", + "Changing to T= 50000 for T= 94748 logg=5.39\n", + "Changing to logg=5.00 for T= 94748 logg=5.39\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 32498 logg=3.50\n", + "Changing to T= 50000 for T=103343 logg=5.41\n", + "Changing to logg=5.00 for T=103343 logg=5.41\n", + "Changing to T= 50000 for T=100716 logg=5.42\n", + "Changing to logg=5.00 for T=100716 logg=5.42\n", + "Changing to T= 50000 for T= 65121 logg=4.26\n", + "Changing to logg=5.00 for T= 65121 logg=4.26\n", + "Changing to T= 50000 for T= 74024 logg=4.48\n", + "Changing to logg=5.00 for T= 74024 logg=4.48\n", + "Changing to T= 50000 for T= 69684 logg=4.38\n", + "Changing to logg=5.00 for T= 69684 logg=4.38\n", + "Changing to T= 50000 for T=101513 logg=5.42\n", + "Changing to logg=5.00 for T=101513 logg=5.42\n", + "Changing to T= 50000 for T= 74302 logg=4.48\n", + "Changing to logg=5.00 for T= 74302 logg=4.48\n", + "Changing to T= 50000 for T= 93233 logg=5.17\n", + "Changing to logg=5.00 for T= 93233 logg=5.17\n", + "Changing to logg=4.50 for T= 41743 logg=3.70\n", + "Changing to T= 50000 for T= 74305 logg=4.48\n", + "Changing to logg=5.00 for T= 74305 logg=4.48\n", + "Changing to T= 50000 for T= 72751 logg=4.45\n", + "Changing to logg=5.00 for T= 72751 logg=4.45\n", + "Changing to T= 50000 for T= 67353 logg=4.32\n", + "Changing to logg=5.00 for T= 67353 logg=4.32\n", + "Changing to T= 50000 for T= 74218 logg=4.48\n", + "Changing to logg=5.00 for T= 74218 logg=4.48\n", + "Changing to T= 50000 for T= 71038 logg=4.43\n", + "Changing to logg=5.00 for T= 71038 logg=4.43\n", + "Changing to T= 50000 for T= 67855 logg=4.34\n", + "Changing to logg=5.00 for T= 67855 logg=4.34\n", + "Changing to T= 50000 for T= 72058 logg=4.80\n", + "Changing to logg=5.00 for T= 72058 logg=4.80\n", + "Changing to T= 50000 for T= 67593 logg=4.33\n", + "Changing to logg=5.00 for T= 67593 logg=4.33\n", + "Changing to T= 50000 for T= 75950 logg=4.93\n", + "Changing to logg=5.00 for T= 75950 logg=4.93\n", + "Changing to T= 50000 for T= 59627 logg=4.39\n", + "Changing to logg=5.00 for T= 59627 logg=4.39\n", + "Changing to logg=4.50 for T= 45606 logg=3.87\n", + "Changing to T= 50000 for T= 65516 logg=4.27\n", + "Changing to logg=5.00 for T= 65516 logg=4.27\n", + "Changing to T= 50000 for T= 61100 logg=4.37\n", + "Changing to logg=5.00 for T= 61100 logg=4.37\n", + "Changing to T= 50000 for T= 74444 logg=4.49\n", + "Changing to logg=5.00 for T= 74444 logg=4.49\n", + "Changing to T= 50000 for T=163298 logg=5.77\n", + "Changing to logg=5.00 for T=163298 logg=5.77\n", + "Changing to T= 50000 for T= 67276 logg=4.32\n", + "Changing to logg=5.00 for T= 67276 logg=4.32\n", + "Changing to T= 50000 for T= 61518 logg=4.45\n", + "Changing to logg=5.00 for T= 61518 logg=4.45\n", + "Changing to T= 50000 for T= 81204 logg=4.62\n", + "Changing to logg=5.00 for T= 81204 logg=4.62\n", + "Changing to T= 50000 for T= 66357 logg=4.29\n", + "Changing to logg=5.00 for T= 66357 logg=4.29\n", + "Changing to T= 50000 for T=102237 logg=5.40\n", + "Changing to logg=5.00 for T=102237 logg=5.40\n", + "Changing to logg=4.50 for T= 42780 logg=3.75\n", + "Changing to T= 50000 for T= 66322 logg=4.29\n", + "Changing to logg=5.00 for T= 66322 logg=4.29\n", + "Changing to T= 50000 for T=102438 logg=5.42\n", + "Changing to logg=5.00 for T=102438 logg=5.42\n", + "Changing to T= 50000 for T= 65749 logg=4.28\n", + "Changing to logg=5.00 for T= 65749 logg=4.28\n", + "Changing to T= 50000 for T= 69107 logg=4.36\n", + "Changing to logg=5.00 for T= 69107 logg=4.36\n", + "Changing to T= 50000 for T= 55505 logg=4.24\n", + "Changing to logg=5.00 for T= 55505 logg=4.24\n", + "Changing to T= 50000 for T= 76591 logg=4.95\n", + "Changing to logg=5.00 for T= 76591 logg=4.95\n", + "Changing to logg=4.50 for T= 43710 logg=3.79\n", + "Changing to T= 50000 for T= 57582 logg=4.32\n", + "Changing to logg=5.00 for T= 57582 logg=4.32\n", + "Changing to T= 50000 for T=101277 logg=5.41\n", + "Changing to logg=5.00 for T=101277 logg=5.41\n", + "Changing to logg=4.00 for T= 31018 logg=3.75\n", + "Changing to T= 50000 for T=171475 logg=5.85\n", + "Changing to logg=5.00 for T=171475 logg=5.85\n", + "Changing to T= 50000 for T= 74381 logg=4.48\n", + "Changing to logg=5.00 for T= 74381 logg=4.48\n", + "Changing to logg=3.50 for T= 27462 logg=2.95\n", + "Changing to T= 50000 for T= 85743 logg=5.01\n", + "Changing to logg=5.00 for T= 85743 logg=5.01\n", + "Changing to T= 50000 for T= 67318 logg=4.32\n", + "Changing to logg=5.00 for T= 67318 logg=4.32\n", + "Changing to T= 50000 for T= 67786 logg=4.33\n", + "Changing to logg=5.00 for T= 67786 logg=4.33\n", + "Changing to T= 50000 for T= 68757 logg=4.35\n", + "Changing to logg=5.00 for T= 68757 logg=4.35\n", + "Changing to T= 50000 for T=168597 logg=5.82\n", + "Changing to logg=5.00 for T=168597 logg=5.82\n", + "Changing to T= 50000 for T= 70824 logg=4.40\n", + "Changing to logg=5.00 for T= 70824 logg=4.40\n", + "Changing to T= 50000 for T= 69530 logg=4.37\n", + "Changing to logg=5.00 for T= 69530 logg=4.37\n", + "Changing to T= 50000 for T= 71035 logg=4.40\n", + "Changing to logg=5.00 for T= 71035 logg=4.40\n", + "Changing to T= 50000 for T= 67265 logg=4.33\n", + "Changing to logg=5.00 for T= 67265 logg=4.33\n", + "Changing to T= 50000 for T= 78201 logg=4.55\n", + "Changing to logg=5.00 for T= 78201 logg=4.55\n", + "Changing to T= 50000 for T= 75421 logg=4.51\n", + "Changing to logg=5.00 for T= 75421 logg=4.51\n", + "Changing to T= 50000 for T= 72729 logg=4.45\n", + "Changing to logg=5.00 for T= 72729 logg=4.45\n", + "Changing to logg=4.00 for T= 31309 logg=3.80\n", + "Changing to T= 50000 for T= 78703 logg=4.56\n", + "Changing to logg=5.00 for T= 78703 logg=4.56\n", + "Changing to logg=4.00 for T= 32897 logg=3.88\n", + "Changing to T= 50000 for T= 67582 logg=4.33\n", + "Changing to logg=5.00 for T= 67582 logg=4.33\n", + "Changing to T= 50000 for T= 75507 logg=4.92\n", + "Changing to logg=5.00 for T= 75507 logg=4.92\n", + "Changing to T= 50000 for T= 77190 logg=4.51\n", + "Changing to logg=5.00 for T= 77190 logg=4.51\n", + "Changing to T= 50000 for T=101506 logg=5.42\n", + "Changing to logg=5.00 for T=101506 logg=5.42\n", + "Changing to T= 50000 for T= 71598 logg=4.42\n", + "Changing to logg=5.00 for T= 71598 logg=4.42\n", + "Changing to T= 50000 for T= 67806 logg=4.33\n", + "Changing to logg=5.00 for T= 67806 logg=4.33\n", + "Changing to logg=3.00 for T= 23512 logg=2.25\n", + "Changing to T= 50000 for T= 68965 logg=4.36\n", + "Changing to logg=5.00 for T= 68965 logg=4.36\n", + "Changing to logg=4.50 for T= 42214 logg=3.72\n", 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67781 logg=4.34\n", + "Changing to logg=5.00 for T= 67781 logg=4.34\n", + "Changing to T= 50000 for T= 67816 logg=4.33\n", + "Changing to logg=5.00 for T= 67816 logg=4.33\n", + "Changing to T= 50000 for T= 66161 logg=4.29\n", + "Changing to logg=5.00 for T= 66161 logg=4.29\n", + "Changing to T= 50000 for T= 65203 logg=4.27\n", + "Changing to logg=5.00 for T= 65203 logg=4.27\n", + "Changing to T= 50000 for T=103060 logg=5.41\n", + "Changing to logg=5.00 for T=103060 logg=5.41\n", + "Changing to logg=5.00 for T= 49828 logg=4.04\n", + "Changing to T= 50000 for T= 67185 logg=4.64\n", + "Changing to logg=5.00 for T= 67185 logg=4.64\n", + "Changing to T= 50000 for T=102183 logg=5.41\n", + "Changing to logg=5.00 for T=102183 logg=5.41\n", + "Changing to T= 50000 for T= 68459 logg=4.35\n", + "Changing to logg=5.00 for T= 68459 logg=4.35\n", + "Changing to T= 50000 for T= 72000 logg=4.43\n", + "Changing to logg=5.00 for T= 72000 logg=4.43\n", + "Changing to T= 50000 for T=103319 logg=5.40\n", + "Changing to logg=5.00 for T=103319 logg=5.40\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.50 for T= 42886 logg=3.75\n", + "Changing to T= 50000 for T= 67473 logg=4.32\n", + "Changing to logg=5.00 for T= 67473 logg=4.32\n", + "Changing to T= 50000 for T=102134 logg=5.40\n", + "Changing to logg=5.00 for T=102134 logg=5.40\n", + "Changing to logg=2.00 for T= 11391 logg=0.99\n", + "Changing to logg=4.00 for T= 31478 logg=3.74\n", + "Changing to T= 50000 for T= 69920 logg=4.38\n", + "Changing to logg=5.00 for T= 69920 logg=4.38\n", + "Changing to T= 50000 for T= 68509 logg=4.34\n", + "Changing to logg=5.00 for T= 68509 logg=4.34\n", + "Changing to T= 50000 for T= 68223 logg=4.34\n", + "Changing to logg=5.00 for T= 68223 logg=4.34\n", + "Changing to T= 50000 for T= 69174 logg=4.37\n", + "Changing to logg=5.00 for T= 69174 logg=4.37\n", + "Changing to T= 50000 for T= 71717 logg=4.43\n", + "Changing to logg=5.00 for T= 71717 logg=4.43\n", + 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50000 for T= 70500 logg=4.40\n", + "Changing to logg=5.00 for T= 70500 logg=4.40\n", + "Changing to T= 50000 for T=102771 logg=5.42\n", + "Changing to logg=5.00 for T=102771 logg=5.42\n", + "Changing to T= 50000 for T=103863 logg=5.41\n", + "Changing to logg=5.00 for T=103863 logg=5.41\n", + "Changing to logg=4.00 for T= 32467 logg=3.86\n", + "Changing to T= 50000 for T=103314 logg=5.40\n", + "Changing to logg=5.00 for T=103314 logg=5.40\n", + "Changing to T= 50000 for T= 65990 logg=4.29\n", + "Changing to logg=5.00 for T= 65990 logg=4.29\n", + "Changing to T= 50000 for T= 67344 logg=4.32\n", + "Changing to logg=5.00 for T= 67344 logg=4.32\n", + "Changing to T= 50000 for T= 72382 logg=4.44\n", + "Changing to logg=5.00 for T= 72382 logg=4.44\n", + "Changing to T= 50000 for T= 90738 logg=5.12\n", + "Changing to logg=5.00 for T= 90738 logg=5.12\n", + "Changing to logg=3.00 for T= 21835 logg=2.72\n", + "Changing to T= 50000 for T=102953 logg=5.41\n", + "Changing to logg=5.00 for T=102953 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logg=5.00 for T= 66604 logg=4.62\n", + "Changing to T= 50000 for T= 73320 logg=4.46\n", + "Changing to logg=5.00 for T= 73320 logg=4.46\n", + "Changing to T= 50000 for T= 72249 logg=4.43\n", + "Changing to logg=5.00 for T= 72249 logg=4.43\n", + "Changing to logg=4.50 for T= 47669 logg=3.95\n", + "Changing to logg=4.00 for T= 34489 logg=3.74\n", + "Changing to logg=4.50 for T= 44957 logg=3.84\n", + "Changing to T= 50000 for T= 75943 logg=4.93\n", + "Changing to logg=5.00 for T= 75943 logg=4.93\n", + "Changing to T= 50000 for T= 62689 logg=4.49\n", + "Changing to logg=5.00 for T= 62689 logg=4.49\n", + "Changing to T= 50000 for T= 71702 logg=4.79\n", + "Changing to logg=5.00 for T= 71702 logg=4.79\n", + "Changing to T= 50000 for T=103240 logg=5.41\n", + "Changing to logg=5.00 for T=103240 logg=5.41\n", + "Changing to logg=4.50 for T= 46309 logg=3.90\n", + "Changing to T= 50000 for T= 70066 logg=4.39\n", + "Changing to logg=5.00 for T= 70066 logg=4.39\n", + "Changing to T= 50000 for T= 79066 logg=4.86\n", + "Changing to logg=5.00 for T= 79066 logg=4.86\n", + "Changing to T= 50000 for T=102440 logg=5.41\n", + "Changing to logg=5.00 for T=102440 logg=5.41\n", + "Changing to T= 50000 for T= 64536 logg=4.26\n", + "Changing to logg=5.00 for T= 64536 logg=4.26\n", + "Changing to T= 50000 for T= 71793 logg=4.42\n", + "Changing to logg=5.00 for T= 71793 logg=4.42\n", + "Changing to T= 50000 for T=102922 logg=5.42\n", + "Changing to logg=5.00 for T=102922 logg=5.42\n", + "Changing to T= 50000 for T= 67724 logg=4.33\n", + "Changing to logg=5.00 for T= 67724 logg=4.33\n", + "Changing to T= 50000 for T=103383 logg=5.41\n", + "Changing to logg=5.00 for T=103383 logg=5.41\n", + "Changing to T= 50000 for T= 73748 logg=4.47\n", + "Changing to logg=5.00 for T= 73748 logg=4.47\n", + "Changing to T= 50000 for T=103674 logg=5.41\n", + "Changing to logg=5.00 for T=103674 logg=5.41\n", + "Changing to T= 50000 for T=103770 logg=5.41\n", + "Changing to logg=5.00 for T=103770 logg=5.41\n", + "Changing to T= 50000 for T= 94724 logg=5.40\n", + "Changing to logg=5.00 for T= 94724 logg=5.40\n", + "Changing to T= 50000 for T= 66161 logg=4.60\n", + "Changing to logg=5.00 for T= 66161 logg=4.60\n", + "Changing to T= 50000 for T= 71973 logg=4.43\n", + "Changing to logg=5.00 for T= 71973 logg=4.43\n", + "Changing to T= 50000 for T= 67596 logg=4.65\n", + "Changing to logg=5.00 for T= 67596 logg=4.65\n", + "Changing to T= 50000 for T=101445 logg=5.42\n", + "Changing to logg=5.00 for T=101445 logg=5.42\n", + "Changing to T= 50000 for T= 85269 logg=4.71\n", + "Changing to logg=5.00 for T= 85269 logg=4.71\n", + "Changing to T= 50000 for T= 56171 logg=4.27\n", + "Changing to logg=5.00 for T= 56171 logg=4.27\n", + "Changing to T= 50000 for T= 64999 logg=4.26\n", + "Changing to logg=5.00 for T= 64999 logg=4.26\n", + "Changing to T= 50000 for T= 65074 logg=4.27\n", + "Changing to logg=5.00 for T= 65074 logg=4.27\n", + "Changing to T= 50000 for T= 68832 logg=4.36\n", + "Changing to logg=5.00 for T= 68832 logg=4.36\n", + "Changing to logg=4.50 for T= 44431 logg=3.82\n", + "Changing to logg=3.50 for T= 28262 logg=3.29\n", + "Changing to T= 50000 for T=101391 logg=5.41\n", + "Changing to logg=5.00 for T=101391 logg=5.41\n", + "Changing to T= 50000 for T= 54789 logg=4.22\n", + "Changing to logg=5.00 for T= 54789 logg=4.22\n", + "Changing to T= 50000 for T=101932 logg=5.39\n", + "Changing to logg=5.00 for T=101932 logg=5.39\n", + "Changing to T= 50000 for T= 74855 logg=4.49\n", + "Changing to logg=5.00 for T= 74855 logg=4.49\n", + "Changing to T= 50000 for T=103024 logg=5.41\n", + "Changing to logg=5.00 for T=103024 logg=5.41\n", + "Changing to T= 50000 for T= 92805 logg=5.39\n", + "Changing to logg=5.00 for T= 92805 logg=5.39\n", + "Changing to T= 50000 for T= 65967 logg=4.28\n", + "Changing to logg=5.00 for T= 65967 logg=4.28\n", + "Changing to T= 50000 for T= 72778 logg=4.45\n", + "Changing to logg=5.00 for T= 72778 logg=4.45\n", + "Changing to T= 50000 for T= 67101 logg=4.31\n", + "Changing to logg=5.00 for T= 67101 logg=4.31\n", + "Changing to T= 50000 for T= 74686 logg=4.49\n", + "Changing to logg=5.00 for T= 74686 logg=4.49\n", + "Changing to T= 50000 for T=172687 logg=5.87\n", + "Changing to logg=5.00 for T=172687 logg=5.87\n", + "Changing to T= 50000 for T= 59073 logg=4.37\n", + "Changing to logg=5.00 for T= 59073 logg=4.37\n", + "Changing to T= 50000 for T=176181 logg=5.90\n", + "Changing to logg=5.00 for T=176181 logg=5.90\n", + "Changing to T= 50000 for T= 72869 logg=4.45\n", + "Changing to logg=5.00 for T= 72869 logg=4.45\n", + "Changing to T= 50000 for T= 72644 logg=4.44\n", + "Changing to logg=5.00 for T= 72644 logg=4.44\n", + "Changing to T= 50000 for T= 82065 logg=4.94\n", + "Changing to logg=5.00 for T= 82065 logg=4.94\n", + "Changing to T= 50000 for T= 65928 logg=4.29\n", + "Changing to logg=5.00 for T= 65928 logg=4.29\n", + "Changing to T= 50000 for T=103295 logg=5.41\n", + "Changing to logg=5.00 for T=103295 logg=5.41\n", + "Changing to logg=4.50 for T= 41746 logg=3.70\n", + "Changing to T= 50000 for T=103359 logg=5.41\n", + "Changing to logg=5.00 for T=103359 logg=5.41\n", + "Changing to T= 50000 for T= 73782 logg=4.47\n", + "Changing to logg=5.00 for T= 73782 logg=4.47\n", + "Changing to T= 50000 for T= 72367 logg=4.44\n", + "Changing to logg=5.00 for T= 72367 logg=4.44\n", + "Changing to T= 50000 for T= 72629 logg=4.45\n", + "Changing to logg=5.00 for T= 72629 logg=4.45\n", + "Changing to T= 50000 for T= 66968 logg=4.31\n", + "Changing to logg=5.00 for T= 66968 logg=4.31\n", + "Changing to T= 50000 for T= 73797 logg=4.47\n", + "Changing to logg=5.00 for T= 73797 logg=4.47\n", + "Changing to T= 50000 for T= 52235 logg=4.13\n", + "Changing to logg=5.00 for T= 52235 logg=4.13\n", + "Changing to T= 50000 for T=101726 logg=5.41\n", + "Changing to logg=5.00 for T=101726 logg=5.41\n", + "Changing to T= 50000 for T= 73925 logg=4.47\n", + "Changing to logg=5.00 for T= 73925 logg=4.47\n", + "Changing to T= 50000 for T= 63838 logg=4.53\n", + "Changing to logg=5.00 for T= 63838 logg=4.53\n", + "Changing to T= 50000 for T= 65131 logg=4.26\n", + "Changing to logg=5.00 for T= 65131 logg=4.26\n", + "Changing to T= 50000 for T= 68547 logg=4.68\n", + "Changing to logg=5.00 for T= 68547 logg=4.68\n", + "Changing to T= 50000 for T=170781 logg=5.85\n", + "Changing to logg=5.00 for T=170781 logg=5.85\n", + "Changing to logg=3.50 for T= 27719 logg=3.22\n", + "Changing to T= 50000 for T= 74005 logg=4.47\n", + "Changing to logg=5.00 for T= 74005 logg=4.47\n", + "Changing to T= 50000 for T= 53145 logg=4.16\n", + "Changing to logg=5.00 for T= 53145 logg=4.16\n", + "Changing to T= 50000 for T=102958 logg=5.41\n", + "Changing to logg=5.00 for T=102958 logg=5.41\n", + "Changing to T= 50000 for T= 65822 logg=4.29\n", + "Changing to logg=5.00 for T= 65822 logg=4.29\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 50000 for T= 70672 logg=4.40\n", + "Changing to logg=5.00 for T= 70672 logg=4.40\n", + "Changing to logg=3.50 for T= 26974 logg=3.45\n", + "Changing to T= 50000 for T= 74307 logg=4.48\n", + "Changing to logg=5.00 for T= 74307 logg=4.48\n", + "Changing to T= 50000 for T= 64892 logg=4.26\n", + "Changing to logg=5.00 for T= 64892 logg=4.26\n", + "Changing to T= 50000 for T=132468 logg=5.53\n", + "Changing to logg=5.00 for T=132468 logg=5.53\n", + "Changing to T= 50000 for T=132764 logg=5.53\n", + "Changing to logg=5.00 for T=132764 logg=5.53\n", + "Changing to T= 50000 for T= 73264 logg=4.46\n", + "Changing to logg=5.00 for T= 73264 logg=4.46\n", + "Changing to T= 50000 for T= 74659 logg=4.49\n", + "Changing to logg=5.00 for T= 74659 logg=4.49\n", + "Changing to T= 50000 for T=101623 logg=5.42\n", + "Changing to logg=5.00 for T=101623 logg=5.42\n", + "Changing to T= 50000 for T=170279 logg=5.84\n", + "Changing to logg=5.00 for T=170279 logg=5.84\n", + "Changing to T= 50000 for T= 74425 logg=4.48\n", + "Changing to logg=5.00 for T= 74425 logg=4.48\n", + "Changing to T= 50000 for T= 72404 logg=4.44\n", + "Changing to logg=5.00 for T= 72404 logg=4.44\n", + "Changing to T= 50000 for T= 69256 logg=4.37\n", + "Changing to logg=5.00 for T= 69256 logg=4.37\n", + "Changing to T= 50000 for T= 66293 logg=4.31\n", + "Changing to logg=5.00 for T= 66293 logg=4.31\n", + "Changing to T= 50000 for T=100772 logg=5.42\n", + "Changing to logg=5.00 for T=100772 logg=5.42\n", + "Changing to T= 50000 for T= 71623 logg=4.42\n", + "Changing to logg=5.00 for T= 71623 logg=4.42\n", + "Changing to T= 50000 for T=101623 logg=5.40\n", + "Changing to logg=5.00 for T=101623 logg=5.40\n", + "Changing to T= 50000 for T= 66816 logg=4.31\n", + "Changing to logg=5.00 for T= 66816 logg=4.31\n", + "Changing to T= 50000 for T=102009 logg=5.40\n", + "Changing to logg=5.00 for T=102009 logg=5.40\n", + "Changing to T= 50000 for T= 68824 logg=4.36\n", + "Changing to logg=5.00 for T= 68824 logg=4.36\n", + "Changing to T= 50000 for T=103302 logg=5.45\n", + "Changing to logg=5.00 for T=103302 logg=5.45\n", + "Changing to logg=5.00 for T= 49043 logg=4.00\n", + "Changing to T= 50000 for T= 69946 logg=4.39\n", + "Changing to logg=5.00 for T= 69946 logg=4.39\n", + "Changing to T= 50000 for T= 84300 logg=4.58\n", + "Changing to logg=5.00 for T= 84300 logg=4.58\n", + "Changing to T= 50000 for T=102134 logg=5.40\n", + "Changing to logg=5.00 for T=102134 logg=5.40\n", + "Changing to T= 50000 for T= 78170 logg=5.01\n", + "Changing to logg=5.00 for T= 78170 logg=5.01\n", + "Changing to logg=4.00 for T= 32382 logg=3.55\n", + "Changing to T= 50000 for T= 68735 logg=4.35\n", + "Changing to logg=5.00 for T= 68735 logg=4.35\n", + "Changing to T= 50000 for T=101414 logg=5.41\n", + "Changing to logg=5.00 for T=101414 logg=5.41\n", + "Changing to T= 50000 for T=102353 logg=5.41\n", + "Changing to logg=5.00 for T=102353 logg=5.41\n", + "Changing to T= 50000 for T= 67324 logg=4.32\n", + "Changing to logg=5.00 for T= 67324 logg=4.32\n", + "Changing to T= 50000 for T= 80553 logg=5.09\n", + "Changing to logg=5.00 for T= 80553 logg=5.09\n", + "Changing to T= 50000 for T= 74186 logg=4.48\n", + "Changing to logg=5.00 for T= 74186 logg=4.48\n", + "Changing to T= 50000 for T= 65654 logg=4.28\n", + "Changing to logg=5.00 for T= 65654 logg=4.28\n", + "Changing to logg=4.50 for T= 47335 logg=3.94\n", + "Changing to T= 50000 for T=101007 logg=5.42\n", + "Changing to logg=5.00 for T=101007 logg=5.42\n", + "Changing to T= 50000 for T=171609 logg=5.85\n", + "Changing to logg=5.00 for T=171609 logg=5.85\n", + "Changing to T= 50000 for T=102249 logg=5.40\n", + "Changing to logg=5.00 for T=102249 logg=5.40\n", + "Changing to T= 50000 for T= 73712 logg=4.47\n", + "Changing to logg=5.00 for T= 73712 logg=4.47\n", + "Changing to T= 50000 for T= 67075 logg=4.31\n", + "Changing to logg=5.00 for T= 67075 logg=4.31\n", + "Changing to T= 50000 for T= 61608 logg=4.45\n", + "Changing to logg=5.00 for T= 61608 logg=4.45\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.70 dist = 100\n", + " Starting at: 2021-01-08 01:19:44.536688 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 8.000 Msun T = 21294 K m_ubv_U = 13.30\n", + "M = 2.550 Msun T = 10591 K m_ubv_U = 17.13\n", + "Primaries\n", + "M = 8.500 Msun T = 21966 K m_ubv_U = 13.10\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 14.000 Msun T = 26300 K m_ubv_U = 11.26\n", + "M = 3.981 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = 6559 K m_ubv_U = 19.99\n", + "M = 3.162 Msun T = nan K m_ubv_U = nan\n", + "M = 15.000 Msun T = 26270 K m_ubv_U = 10.93\n", + "M = 15.000 Msun T = 26270 K m_ubv_U = 10.93\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_U = 21.20\n", + "M = 3.981 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 16.000 Msun T = 24883 K m_ubv_U = 10.48\n", + "M = 16.000 Msun T = 24883 K m_ubv_U = 10.48\n", + "M = 8.000 Msun T = 21305 K m_ubv_U = 13.29\n", + "M = 11.000 Msun T = 24577 K m_ubv_U = 12.20\n", + "M = 14.000 Msun T = 26300 K m_ubv_U = 11.26\n", + "M = 16.000 Msun T = 27959 K m_ubv_U = 10.26\n", + "M = 5.012 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 14.000 Msun T = 26300 K m_ubv_U = 11.26\n", + "M = 13.000 Msun T = 26107 K m_ubv_U = 11.60\n", + "M = 1.200 Msun T = 7594 K m_ubv_U = 19.03\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 19.000 Msun T = 101672 K m_ubv_U = 15.05\n", + "M = 15.000 Msun T = 26270 K m_ubv_U = 10.93\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 20.000 Msun T = 65184 K m_ubv_U = 12.26\n", + "M = 3.162 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 8.500 Msun T = 22028 K m_ubv_U = 13.12\n", + "M = 9.000 Msun T = 22647 K m_ubv_U = 12.93\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_U = 21.20\n", + "M = 14.000 Msun T = 26300 K m_ubv_U = 11.26\n", + "M = 3.162 Msun T = nan K m_ubv_U = nan\n", + "M = 0.900 Msun T = 6908 K m_ubv_U = 19.58\n", + "M = 3.981 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 1.400 Msun T = 6560 K m_ubv_U = 19.99\n", + "M = 13.000 Msun T = 26107 K m_ubv_U = 11.60\n", + "Secondaries\n", + "M = 1.700 Msun T = 7893 K m_ubv_U = 18.67\n", + "M = 11.000 Msun T = 24743 K m_ubv_U = 11.89\n", + "M = 8.400 Msun T = 23278 K m_ubv_U = 12.90\n", + "M = 18.000 Msun T = 3545 K m_ubv_U = 11.11\n", + "M = 1.400 Msun T = 6571 K m_ubv_U = 19.83\n", + "M = 0.700 Msun T = 4039 K m_ubv_U = 24.94\n", + "M = 12.000 Msun T = 25146 K m_ubv_U = 11.47\n", + "M = 4.500 Msun T = 16036 K m_ubv_U = 14.73\n", + "M = 9.000 Msun T = 24171 K m_ubv_U = 12.69\n", + "M = 16.000 Msun T = 25206 K m_ubv_U = 10.23\n", + "M = 0.960 Msun T = 5540 K m_ubv_U = 21.88\n", + "M = 21.000 Msun T = 3288 K m_ubv_U = 10.72\n", + "M = 4.800 Msun T = 16705 K m_ubv_U = 14.53\n", + "M = 6.400 Msun T = 19911 K m_ubv_U = 13.67\n", + "M = 6.400 Msun T = 19914 K m_ubv_U = 13.67\n", + "M = 4.400 Msun T = 15811 K m_ubv_U = 14.80\n", + "M = 12.600 Msun T = 28070 K m_ubv_U = 11.60\n", + "M = 3.200 Msun T = nan K m_ubv_U = nan\n", + "M = 14.000 Msun T = 26173 K m_ubv_U = 10.92\n", + "M = 11.200 Msun T = 26902 K m_ubv_U = 12.01\n", + "M = 9.100 Msun T = 24311 K m_ubv_U = 12.67\n", + "M = 0.480 Msun T = nan K m_ubv_U = nan\n", + "M = 18.000 Msun T = 8770 K m_ubv_U = 8.43\n", + "M = 4.500 Msun T = 15386 K m_ubv_U = 14.68\n", + "M = 1.900 Msun T = 8701 K m_ubv_U = 18.07\n", + "M = 13.500 Msun T = 28501 K m_ubv_U = 11.32\n", + "M = 20.000 Msun T = 62008 K m_ubv_U = 11.55\n", + "M = 16.000 Msun T = 27341 K m_ubv_U = 10.45\n", + "M = 19.000 Msun T = 3554 K m_ubv_U = 11.08\n", + "M = 10.000 Msun T = 17119 K m_ubv_U = 13.88\n", + "M = 3.400 Msun T = 13364 K m_ubv_U = 15.66\n", + "M = 1.800 Msun T = 8304 K m_ubv_U = 18.35\n", + "M = 20.000 Msun T = 70920 K m_ubv_U = 11.90\n", + "M = 0.840 Msun T = 4847 K m_ubv_U = 23.09\n", + "M = 1.400 Msun T = 6689 K m_ubv_U = 19.85\n", + "M = 17.000 Msun T = 24319 K m_ubv_U = 9.90\n", + "M = 0.630 Msun T = nan K m_ubv_U = nan\n", + "M = 13.000 Msun T = 26170 K m_ubv_U = 11.29\n", + "M = 1.120 Msun T = 5956 K m_ubv_U = 21.25\n", + "M = 7.800 Msun T = 22323 K m_ubv_U = 13.11\n", + "Starting filter: ubv,V Elapsed time: 25.27 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 8.000 Msun T = 21294 K m_ubv_V = 13.52\n", + "M = 2.550 Msun T = 10591 K m_ubv_V = 16.48\n", + "Primaries\n", + "M = 8.500 Msun T = 21966 K m_ubv_V = 13.35\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 14.000 Msun T = 26300 K m_ubv_V = 11.67\n", + "M = 3.981 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = 6559 K m_ubv_V = 18.64\n", + "M = 3.162 Msun T = nan K m_ubv_V = nan\n", + "M = 15.000 Msun T = 26270 K m_ubv_V = 11.35\n", + "M = 15.000 Msun T = 26270 K m_ubv_V = 11.35\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 1.200 Msun T = 5987 K m_ubv_V = 19.59\n", + "M = 3.981 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 16.000 Msun T = 24883 K m_ubv_V = 10.87\n", + "M = 16.000 Msun T = 24883 K m_ubv_V = 10.87\n", + "M = 8.000 Msun T = 21305 K m_ubv_V = 13.51\n", + "M = 11.000 Msun T = 24577 K m_ubv_V = 12.55\n", + "M = 14.000 Msun T = 26300 K m_ubv_V = 11.67\n", + "M = 16.000 Msun T = 27959 K m_ubv_V = 10.74\n", + "M = 5.012 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 14.000 Msun T = 26300 K m_ubv_V = 11.67\n", + "M = 13.000 Msun T = 26107 K m_ubv_V = 12.00\n", + "M = 1.200 Msun T = 7594 K m_ubv_V = 17.87\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 19.000 Msun T = 101672 K m_ubv_V = 15.71\n", + "M = 15.000 Msun T = 26270 K m_ubv_V = 11.35\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 20.000 Msun T = 65184 K m_ubv_V = 12.92\n", + "M = 3.162 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 8.500 Msun T = 22028 K m_ubv_V = 13.38\n", + "M = 9.000 Msun T = 22647 K m_ubv_V = 13.21\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_V = 19.59\n", + "M = 14.000 Msun T = 26300 K m_ubv_V = 11.67\n", + "M = 3.162 Msun T = nan K m_ubv_V = nan\n", + "M = 0.900 Msun T = 6908 K m_ubv_V = 18.33\n", + "M = 3.981 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 1.400 Msun T = 6560 K m_ubv_V = 18.64\n", + "M = 13.000 Msun T = 26107 K m_ubv_V = 12.00\n", + "Secondaries\n", + "M = 1.700 Msun T = 7893 K m_ubv_V = 17.93\n", + "M = 11.000 Msun T = 24743 K m_ubv_V = 12.32\n", + "M = 8.400 Msun T = 23278 K m_ubv_V = 13.29\n", + "M = 18.000 Msun T = 3545 K m_ubv_V = 8.23\n", + "M = 1.400 Msun T = 6571 K m_ubv_V = 18.71\n", + "M = 0.700 Msun T = 4039 K m_ubv_V = 22.47\n", + "M = 12.000 Msun T = 25146 K m_ubv_V = 11.91\n", + "M = 4.500 Msun T = 16036 K m_ubv_V = 14.89\n", + "M = 9.000 Msun T = 24171 K m_ubv_V = 13.11\n", + "M = 16.000 Msun T = 25206 K m_ubv_V = 10.67\n", + "M = 0.960 Msun T = 5540 K m_ubv_V = 20.34\n", + "M = 21.000 Msun T = 3288 K m_ubv_V = 7.64\n", + "M = 4.800 Msun T = 16705 K m_ubv_V = 14.72\n", + "M = 6.400 Msun T = 19911 K m_ubv_V = 13.99\n", + "M = 6.400 Msun T = 19914 K m_ubv_V = 13.98\n", + "M = 4.400 Msun T = 15811 K m_ubv_V = 14.95\n", + "M = 12.600 Msun T = 28070 K m_ubv_V = 12.08\n", + "M = 3.200 Msun T = nan K m_ubv_V = nan\n", + "M = 14.000 Msun T = 26173 K m_ubv_V = 11.38\n", + "M = 11.200 Msun T = 26902 K m_ubv_V = 12.48\n", + "M = 9.100 Msun T = 24311 K m_ubv_V = 13.09\n", + "M = 0.480 Msun T = nan K m_ubv_V = nan\n", + "M = 18.000 Msun T = 8770 K m_ubv_V = 7.88\n", + "M = 4.500 Msun T = 15386 K m_ubv_V = 14.80\n", + "M = 1.900 Msun T = 8701 K m_ubv_V = 17.51\n", + "M = 13.500 Msun T = 28501 K m_ubv_V = 11.81\n", + "M = 20.000 Msun T = 62008 K m_ubv_V = 12.23\n", + "M = 16.000 Msun T = 27341 K m_ubv_V = 10.92\n", + "M = 19.000 Msun T = 3554 K m_ubv_V = 8.21\n", + "M = 10.000 Msun T = 17119 K m_ubv_V = 14.09\n", + "M = 3.400 Msun T = 13364 K m_ubv_V = 15.66\n", + "M = 1.800 Msun T = 8304 K m_ubv_V = 17.71\n", + "M = 20.000 Msun T = 70920 K m_ubv_V = 12.60\n", + "M = 0.840 Msun T = 4847 K m_ubv_V = 21.18\n", + "M = 1.400 Msun T = 6689 K m_ubv_V = 18.77\n", + "M = 17.000 Msun T = 24319 K m_ubv_V = 10.32\n", + "M = 0.630 Msun T = nan K m_ubv_V = nan\n", + "M = 13.000 Msun T = 26170 K m_ubv_V = 11.75\n", + "M = 1.120 Msun T = 5956 K m_ubv_V = 19.89\n", + "M = 7.800 Msun T = 22323 K m_ubv_V = 13.49\n", + "Starting filter: ubv,B Elapsed time: 49.95 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 8.000 Msun T = 21294 K m_ubv_B = 14.12\n", + "M = 2.550 Msun T = 10591 K m_ubv_B = 17.27\n", + "Primaries\n", + "M = 8.500 Msun T = 21966 K m_ubv_B = 13.93\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 14.000 Msun T = 26300 K m_ubv_B = 12.22\n", + "M = 3.981 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = 6559 K m_ubv_B = 20.07\n", + "M = 3.162 Msun T = nan K m_ubv_B = nan\n", + "M = 15.000 Msun T = 26270 K m_ubv_B = 11.91\n", + "M = 15.000 Msun T = 26270 K m_ubv_B = 11.91\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_B = 21.22\n", + "M = 3.981 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 16.000 Msun T = 24883 K m_ubv_B = 11.43\n", + "M = 16.000 Msun T = 24883 K m_ubv_B = 11.43\n", + "M = 8.000 Msun T = 21305 K m_ubv_B = 14.11\n", + "M = 11.000 Msun T = 24577 K m_ubv_B = 13.12\n", + "M = 14.000 Msun T = 26300 K m_ubv_B = 12.22\n", + "M = 16.000 Msun T = 27959 K m_ubv_B = 11.27\n", + "M = 5.012 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 14.000 Msun T = 26300 K m_ubv_B = 12.22\n", + "M = 13.000 Msun T = 26107 K m_ubv_B = 12.56\n", + "M = 1.200 Msun T = 7594 K m_ubv_B = 19.04\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 19.000 Msun T = 101672 K m_ubv_B = 16.17\n", + "M = 15.000 Msun T = 26270 K m_ubv_B = 11.91\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 20.000 Msun T = 65184 K m_ubv_B = 13.37\n", + "M = 3.162 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 8.500 Msun T = 22028 K m_ubv_B = 13.96\n", + "M = 9.000 Msun T = 22647 K m_ubv_B = 13.79\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_B = 21.22\n", + "M = 14.000 Msun T = 26300 K m_ubv_B = 12.22\n", + "M = 3.162 Msun T = nan K m_ubv_B = nan\n", + "M = 0.900 Msun T = 6908 K m_ubv_B = 19.66\n", + "M = 3.981 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 1.400 Msun T = 6560 K m_ubv_B = 20.07\n", + "M = 13.000 Msun T = 26107 K m_ubv_B = 12.56\n", + "Secondaries\n", + "M = 1.700 Msun T = 7893 K m_ubv_B = 19.21\n", + "M = 11.000 Msun T = 24743 K m_ubv_B = 12.94\n", + "M = 8.400 Msun T = 23278 K m_ubv_B = 13.93\n", + "M = 18.000 Msun T = 3545 K m_ubv_B = 10.87\n", + "M = 1.400 Msun T = 6571 K m_ubv_B = 20.21\n", + "M = 0.700 Msun T = 4039 K m_ubv_B = 24.81\n", + "M = 12.000 Msun T = 25146 K m_ubv_B = 12.53\n", + "M = 4.500 Msun T = 16036 K m_ubv_B = 15.66\n", + "M = 9.000 Msun T = 24171 K m_ubv_B = 13.74\n", + "M = 16.000 Msun T = 25206 K m_ubv_B = 11.28\n", + "M = 0.960 Msun T = 5540 K m_ubv_B = 22.10\n", + "M = 21.000 Msun T = 3288 K m_ubv_B = 10.46\n", + "M = 4.800 Msun T = 16705 K m_ubv_B = 15.48\n", + "M = 6.400 Msun T = 19911 K m_ubv_B = 14.67\n", + "M = 6.400 Msun T = 19914 K m_ubv_B = 14.67\n", + "M = 4.400 Msun T = 15811 K m_ubv_B = 15.73\n", + "M = 12.600 Msun T = 28070 K m_ubv_B = 12.67\n", + "M = 3.200 Msun T = nan K m_ubv_B = nan\n", + "M = 14.000 Msun T = 26173 K m_ubv_B = 11.98\n", + "M = 11.200 Msun T = 26902 K m_ubv_B = 13.08\n", + "M = 9.100 Msun T = 24311 K m_ubv_B = 13.71\n", + "M = 0.480 Msun T = nan K m_ubv_B = nan\n", + "M = 18.000 Msun T = 8770 K m_ubv_B = 9.05\n", + "M = 4.500 Msun T = 15386 K m_ubv_B = 15.60\n", + "M = 1.900 Msun T = 8701 K m_ubv_B = 18.69\n", + "M = 13.500 Msun T = 28501 K m_ubv_B = 12.40\n", + "M = 20.000 Msun T = 62008 K m_ubv_B = 12.71\n", + "M = 16.000 Msun T = 27341 K m_ubv_B = 11.52\n", + "M = 19.000 Msun T = 3554 K m_ubv_B = 10.84\n", + "M = 10.000 Msun T = 17119 K m_ubv_B = 14.84\n", + "M = 3.400 Msun T = 13364 K m_ubv_B = 16.52\n", + "M = 1.800 Msun T = 8304 K m_ubv_B = 18.94\n", + "M = 20.000 Msun T = 70920 K m_ubv_B = 13.07\n", + "M = 0.840 Msun T = 4847 K m_ubv_B = 23.16\n", + "M = 1.400 Msun T = 6689 K m_ubv_B = 20.25\n", + "M = 17.000 Msun T = 24319 K m_ubv_B = 10.95\n", + "M = 0.630 Msun T = nan K m_ubv_B = nan\n", + "M = 13.000 Msun T = 26170 K m_ubv_B = 12.35\n", + "M = 1.120 Msun T = 5956 K m_ubv_B = 21.54\n", + "M = 7.800 Msun T = 22323 K m_ubv_B = 14.14\n", + "Starting filter: ubv,R Elapsed time: 74.45 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 8.000 Msun T = 21294 K m_ubv_R = 11.01\n", + "M = 2.550 Msun T = 10591 K m_ubv_R = 13.88\n", + "Primaries\n", + "M = 8.500 Msun T = 21966 K m_ubv_R = 10.84\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 14.000 Msun T = 26300 K m_ubv_R = 9.18\n", + "M = 3.981 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = 6559 K m_ubv_R = 15.75\n", + "M = 3.162 Msun T = nan K m_ubv_R = nan\n", + "M = 15.000 Msun T = 26270 K m_ubv_R = 8.86\n", + "M = 15.000 Msun T = 26270 K m_ubv_R = 8.86\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_R = 16.63\n", + "M = 3.981 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 16.000 Msun T = 24883 K m_ubv_R = 8.37\n", + "M = 16.000 Msun T = 24883 K m_ubv_R = 8.37\n", + "M = 8.000 Msun T = 21305 K m_ubv_R = 11.00\n", + "M = 11.000 Msun T = 24577 K m_ubv_R = 10.05\n", + "M = 14.000 Msun T = 26300 K m_ubv_R = 9.18\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 16.000 Msun T = 27959 K m_ubv_R = 8.25\n", + "M = 5.012 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 14.000 Msun T = 26300 K m_ubv_R = 9.18\n", + "M = 13.000 Msun T = 26107 K m_ubv_R = 9.51\n", + "M = 1.200 Msun T = 7594 K m_ubv_R = 15.10\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 19.000 Msun T = 101672 K m_ubv_R = 13.24\n", + "M = 15.000 Msun T = 26270 K m_ubv_R = 8.86\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 20.000 Msun T = 65184 K m_ubv_R = 10.45\n", + "M = 3.162 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 8.500 Msun T = 22028 K m_ubv_R = 10.87\n", + "M = 9.000 Msun T = 22647 K m_ubv_R = 10.70\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_R = 16.63\n", + "M = 14.000 Msun T = 26300 K m_ubv_R = 9.18\n", + "M = 3.162 Msun T = nan K m_ubv_R = nan\n", + "M = 0.900 Msun T = 6908 K m_ubv_R = 15.48\n", + "M = 3.981 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 1.400 Msun T = 6560 K m_ubv_R = 15.75\n", + "M = 13.000 Msun T = 26107 K m_ubv_R = 9.51\n", + "Secondaries\n", + "M = 1.700 Msun T = 7893 K m_ubv_R = 15.08\n", + "M = 11.000 Msun T = 24743 K m_ubv_R = 9.78\n", + "M = 8.400 Msun T = 23278 K m_ubv_R = 10.75\n", + "M = 18.000 Msun T = 3545 K m_ubv_R = 4.71\n", + "M = 1.400 Msun T = 6571 K m_ubv_R = 15.75\n", + "M = 0.700 Msun T = 4039 K m_ubv_R = 19.10\n", + "M = 12.000 Msun T = 25146 K m_ubv_R = 9.37\n", + "M = 4.500 Msun T = 16036 K m_ubv_R = 12.28\n", + "M = 9.000 Msun T = 24171 K m_ubv_R = 10.57\n", + "M = 16.000 Msun T = 25206 K m_ubv_R = 8.13\n", + "M = 0.960 Msun T = 5540 K m_ubv_R = 17.27\n", + "M = 21.000 Msun T = 3288 K m_ubv_R = 4.02\n", + "M = 4.800 Msun T = 16705 K m_ubv_R = 12.12\n", + "M = 6.400 Msun T = 19911 K m_ubv_R = 11.42\n", + "M = 6.400 Msun T = 19914 K m_ubv_R = 11.42\n", + "M = 4.400 Msun T = 15811 K m_ubv_R = 12.34\n", + "M = 12.600 Msun T = 28070 K m_ubv_R = 9.56\n", + "M = 3.200 Msun T = nan K m_ubv_R = nan\n", + "M = 14.000 Msun T = 26173 K m_ubv_R = 8.85\n", + "M = 11.200 Msun T = 26902 K m_ubv_R = 9.95\n", + "M = 9.100 Msun T = 24311 K m_ubv_R = 10.55\n", + "M = 0.480 Msun T = nan K m_ubv_R = nan\n", + "M = 18.000 Msun T = 8770 K m_ubv_R = 5.08\n", + "M = 4.500 Msun T = 15386 K m_ubv_R = 12.19\n", + "M = 1.900 Msun T = 8701 K m_ubv_R = 14.70\n", + "M = 13.500 Msun T = 28501 K m_ubv_R = 9.29\n", + "M = 20.000 Msun T = 62008 K m_ubv_R = 9.76\n", + "M = 16.000 Msun T = 27341 K m_ubv_R = 8.40\n", + "M = 19.000 Msun T = 3554 K m_ubv_R = 4.69\n", + "M = 10.000 Msun T = 17119 K m_ubv_R = 11.50\n", + "M = 3.400 Msun T = 13364 K m_ubv_R = 13.00\n", + "M = 1.800 Msun T = 8304 K m_ubv_R = 14.88\n", + "M = 20.000 Msun T = 70920 K m_ubv_R = 10.14\n", + "M = 0.840 Msun T = 4847 K m_ubv_R = 17.99\n", + "M = 1.400 Msun T = 6689 K m_ubv_R = 15.83\n", + "M = 17.000 Msun T = 24319 K m_ubv_R = 7.79\n", + "M = 0.630 Msun T = nan K m_ubv_R = nan\n", + "M = 13.000 Msun T = 26170 K m_ubv_R = 9.22\n", + "M = 1.120 Msun T = 5956 K m_ubv_R = 16.87\n", + "M = 7.800 Msun T = 22323 K m_ubv_R = 10.94\n", + "Starting filter: ubv,I Elapsed time: 99.96 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 8.000 Msun T = 21294 K m_ubv_I = 7.68\n", + "M = 2.550 Msun T = 10591 K m_ubv_I = 10.38\n", + "Primaries\n", + "M = 8.500 Msun T = 21966 K m_ubv_I = 7.52\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 14.000 Msun T = 26300 K m_ubv_I = 5.89\n", + "M = 3.981 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = 6559 K m_ubv_I = 12.00\n", + "M = 3.162 Msun T = nan K m_ubv_I = nan\n", + "M = 15.000 Msun T = 26270 K m_ubv_I = 5.57\n", + "M = 15.000 Msun T = 26270 K m_ubv_I = 5.57\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_I = 12.81\n", + "M = 3.981 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 16.000 Msun T = 24883 K m_ubv_I = 5.07\n", + "M = 16.000 Msun T = 24883 K m_ubv_I = 5.07\n", + "M = 8.000 Msun T = 21305 K m_ubv_I = 7.68\n", + "M = 11.000 Msun T = 24577 K m_ubv_I = 6.75\n", + "M = 14.000 Msun T = 26300 K m_ubv_I = 5.89\n", + "M = 16.000 Msun T = 27959 K m_ubv_I = 4.97\n", + "M = 5.012 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 14.000 Msun T = 26300 K m_ubv_I = 5.89\n", + "M = 13.000 Msun T = 26107 K m_ubv_I = 6.22\n", + "M = 1.200 Msun T = 7594 K m_ubv_I = 11.45\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 19.000 Msun T = 101672 K m_ubv_I = 10.01\n", + "M = 15.000 Msun T = 26270 K m_ubv_I = 5.57\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 20.000 Msun T = 65184 K m_ubv_I = 7.22\n", + "M = 3.162 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 8.500 Msun T = 22028 K m_ubv_I = 7.55\n", + "M = 9.000 Msun T = 22647 K m_ubv_I = 7.39\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_I = 12.81\n", + "M = 14.000 Msun T = 26300 K m_ubv_I = 5.89\n", + "M = 3.162 Msun T = nan K m_ubv_I = nan\n", + "M = 0.900 Msun T = 6908 K m_ubv_I = 11.77\n", + "M = 3.981 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 1.400 Msun T = 6560 K m_ubv_I = 12.00\n", + "M = 13.000 Msun T = 26107 K m_ubv_I = 6.22\n", + "Secondaries\n", + "M = 1.700 Msun T = 7893 K m_ubv_I = 11.42\n", + "M = 11.000 Msun T = 24743 K m_ubv_I = 6.46\n", + "M = 8.400 Msun T = 23278 K m_ubv_I = 7.42\n", + "M = 18.000 Msun T = 3545 K m_ubv_I = 0.31\n", + "M = 1.400 Msun T = 6571 K m_ubv_I = 11.99\n", + "M = 0.700 Msun T = 4039 K m_ubv_I = 14.87\n", + "M = 12.000 Msun T = 25146 K m_ubv_I = 6.06\n", + "M = 4.500 Msun T = 16036 K m_ubv_I = 8.89\n", + "M = 9.000 Msun T = 24171 K m_ubv_I = 7.25\n", + "M = 16.000 Msun T = 25206 K m_ubv_I = 4.82\n", + "M = 0.960 Msun T = 5540 K m_ubv_I = 13.37\n", + "M = 21.000 Msun T = 3288 K m_ubv_I = -0.49\n", + "M = 4.800 Msun T = 16705 K m_ubv_I = 8.74\n", + "M = 6.400 Msun T = 19911 K m_ubv_I = 8.07\n", + "M = 6.400 Msun T = 19914 K m_ubv_I = 8.07\n", + "M = 4.400 Msun T = 15811 K m_ubv_I = 8.94\n", + "M = 12.600 Msun T = 28070 K m_ubv_I = 6.25\n", + "M = 3.200 Msun T = nan K m_ubv_I = nan\n", + "M = 14.000 Msun T = 26173 K m_ubv_I = 5.53\n", + "M = 11.200 Msun T = 26902 K m_ubv_I = 6.64\n", + "M = 9.100 Msun T = 24311 K m_ubv_I = 7.23\n", + "M = 0.480 Msun T = nan K m_ubv_I = nan\n", + "M = 18.000 Msun T = 8770 K m_ubv_I = 1.48\n", + "M = 4.500 Msun T = 15386 K m_ubv_I = 8.78\n", + "M = 1.900 Msun T = 8701 K m_ubv_I = 11.10\n", + "M = 13.500 Msun T = 28501 K m_ubv_I = 5.99\n", + "M = 20.000 Msun T = 62008 K m_ubv_I = 6.52\n", + "M = 16.000 Msun T = 27341 K m_ubv_I = 5.09\n", + "M = 19.000 Msun T = 3554 K m_ubv_I = 0.30\n", + "M = 10.000 Msun T = 17119 K m_ubv_I = 8.12\n", + "M = 3.400 Msun T = 13364 K m_ubv_I = 9.56\n", + "M = 1.800 Msun T = 8304 K m_ubv_I = 11.26\n", + "M = 20.000 Msun T = 70920 K m_ubv_I = 6.90\n", + "M = 0.840 Msun T = 4847 K m_ubv_I = 13.97\n", + "M = 1.400 Msun T = 6689 K m_ubv_I = 12.07\n", + "M = 17.000 Msun T = 24319 K m_ubv_I = 4.46\n", + "M = 0.630 Msun T = nan K m_ubv_I = nan\n", + "M = 13.000 Msun T = 26170 K m_ubv_I = 5.90\n", + "M = 1.120 Msun T = 5956 K m_ubv_I = 13.03\n", + "M = 7.800 Msun T = 22323 K m_ubv_I = 7.61\n", + " Time taken: 125.14 seconds\n", + "Isochrone generation took 347.334178 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "import time\n", + "import numpy as np\n", + "t1=time.time()\n", + "bps=evolution.BPASS()\n", + "iso1=synthetic.Isochrone_Binary(7.0, 0.7, 100,math.log10(1), mass_sampling=1, filepath='/g/lu/scratch/ryotainagaki/BPASS_iso_filesTimedIsolated/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "\n", + "\n", + "\n", + "\n", + "
massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass L Teff R logg ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.primaries[np.where(iso1.primaries['mass']< 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
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masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.secondaries['mass'] < 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
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masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.singles['mass'] < 0)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a sanity check, we can see that the primaries, secondaries, and single stars have phasees of 5 or of 101. Note that we will have a phase of 110 in order to indicate a mystery compact remnant that BPASS provides for NEWSECMODS (secondary star models with compact primaries). This is since we haven't added neutron stars or black holes yet. Stars with phase of -99 exist, and those are the secondary stars that have already merged." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=3.50 for T= 26068 logg=3.41\n", + "Changing to logg=3.50 for T= 26167 logg=3.41\n", + "Changing to logg=3.50 for T= 26263 logg=3.42\n", + "Changing to logg=3.50 for T= 26355 logg=3.42\n", + "Changing to logg=3.50 for T= 26443 logg=3.42\n", + "Changing to logg=3.50 for T= 26528 logg=3.43\n", + "Changing to logg=3.50 for T= 26609 logg=3.43\n", + "Changing to logg=3.50 for T= 26685 logg=3.43\n", + "Changing to logg=3.50 for T= 26758 logg=3.43\n", + "Changing to logg=3.50 for T= 26826 logg=3.44\n", + "Changing to logg=3.50 for T= 26889 logg=3.44\n", + "Changing to logg=3.50 for T= 26943 logg=3.44\n", + "Changing to logg=3.50 for T= 26985 logg=3.44\n", + "Changing to logg=3.50 for T= 27008 logg=3.45\n", + "Changing to logg=3.50 for T= 27006 logg=3.45\n", + "Changing to logg=3.50 for T= 26955 logg=3.45\n", + "Changing to logg=3.50 for T= 26861 logg=3.44\n", + "Changing to logg=3.50 for T= 26740 logg=3.44\n", + "Changing to logg=3.50 for T= 26600 logg=3.44\n", + "Changing to logg=3.50 for T= 26446 logg=3.43\n", + "Changing to logg=3.50 for T= 26289 logg=3.42\n", + "Changing to logg=3.50 for T= 26128 logg=3.41\n", + "Changing to logg=3.00 for T= 21082 logg=2.99\n", + "Changing to logg=3.00 for T= 20939 logg=2.98\n", + "Changing to logg=3.00 for T= 20797 logg=2.97\n", + "Changing to logg=3.00 for T= 20655 logg=2.96\n", + "Changing to logg=3.00 for T= 20513 logg=2.94\n", + "Changing to logg=3.00 for T= 20372 logg=2.93\n", + "Changing to logg=3.00 for T= 20231 logg=2.92\n", + "Changing to logg=3.00 for T= 20091 logg=2.91\n", + "Changing to logg=3.00 for T= 19951 logg=2.90\n", + "Changing to logg=3.00 for T= 19813 logg=2.88\n", + "Changing to logg=3.00 for T= 19675 logg=2.87\n", + "Changing to logg=3.00 for T= 19538 logg=2.86\n", + "Changing to logg=3.00 for T= 19401 logg=2.85\n", + "Changing to logg=3.00 for T= 19265 logg=2.83\n", + "Changing to logg=3.00 for T= 19131 logg=2.82\n", + "Changing to logg=2.50 for T= 15896 logg=2.49\n", + "Changing to logg=2.50 for T= 15781 logg=2.48\n", + "Changing to logg=2.50 for T= 15667 logg=2.47\n", + "Changing to logg=2.50 for T= 15554 logg=2.46\n", + "Changing to logg=2.50 for T= 15442 logg=2.44\n", + "Changing to logg=2.50 for T= 15330 logg=2.43\n", + "Changing to logg=2.50 for T= 15219 logg=2.42\n", + "Changing to logg=2.50 for T= 15109 logg=2.40\n", + "Changing to logg=2.50 for T= 14999 logg=2.39\n", + "Changing to logg=2.50 for T= 14891 logg=2.38\n", + "Changing to logg=2.50 for T= 14782 logg=2.37\n", + "Changing to logg=2.50 for T= 14675 logg=2.35\n", + "Changing to logg=2.50 for T= 14569 logg=2.34\n", + "Changing to logg=2.50 for T= 14463 logg=2.33\n", + "Changing to logg=2.50 for T= 14358 logg=2.31\n", + "Changing to logg=2.50 for T= 14254 logg=2.30\n", + "Changing to logg=2.50 for T= 14150 logg=2.29\n", + "Changing to logg=2.50 for T= 14048 logg=2.27\n", + "Changing to logg=2.50 for T= 13946 logg=2.26\n", + "Changing to logg=2.50 for T= 13844 logg=2.25\n", + "Changing to logg=2.50 for T= 13744 logg=2.23\n", + "Changing to logg=2.50 for T= 13644 logg=2.22\n", + "Changing to logg=2.50 for T= 13545 logg=2.21\n", + "Changing to logg=2.50 for T= 13447 logg=2.19\n", + "Changing to logg=2.50 for T= 13350 logg=2.18\n", + "Changing to logg=2.50 for T= 13253 logg=2.17\n", + "Changing to logg=2.50 for T= 13157 logg=2.16\n", + "Changing to logg=2.50 for T= 13061 logg=2.14\n", + "Changing to logg=2.50 for T= 12967 logg=2.13\n", + "Changing to logg=2.50 for T= 12873 logg=2.12\n", + "Changing to logg=2.50 for T= 12779 logg=2.10\n", + "Changing to logg=2.50 for T= 12687 logg=2.09\n", + "Changing to logg=2.50 for T= 12595 logg=2.08\n", + "Changing to logg=2.50 for T= 12504 logg=2.06\n", + "Changing to logg=2.50 for T= 12413 logg=2.05\n", + "Changing to logg=2.50 for T= 12323 logg=2.04\n", + "Changing to logg=2.50 for T= 12234 logg=2.02\n", + "Changing to logg=2.50 for T= 12145 logg=2.01\n", + "Changing to logg=2.50 for T= 12058 logg=2.00\n", + "Changing to logg=2.50 for T= 11970 logg=1.98\n", + "Changing to logg=2.50 for T= 11884 logg=1.97\n", + "Changing to logg=2.50 for T= 11798 logg=1.96\n", + "Changing to logg=2.00 for T= 11712 logg=1.94\n", + "Changing to logg=2.00 for T= 11628 logg=1.93\n", + "Changing to logg=2.00 for T= 11543 logg=1.92\n", + "Changing to logg=2.00 for T= 11460 logg=1.90\n", + "Changing to logg=2.00 for T= 11377 logg=1.89\n", + "Changing to logg=2.00 for T= 11295 logg=1.88\n", + "Changing to logg=2.00 for T= 11213 logg=1.86\n", + "Changing to logg=2.00 for T= 11132 logg=1.85\n", + "Changing to logg=2.00 for T= 11052 logg=1.84\n", + "Changing to logg=2.00 for T= 10972 logg=1.82\n", + "Changing to logg=2.00 for T= 10893 logg=1.81\n", + "Changing to logg=2.00 for T= 10814 logg=1.80\n", + "Changing to logg=2.00 for T= 10736 logg=1.78\n", + "Changing to logg=2.00 for T= 10658 logg=1.77\n", + "Changing to logg=2.00 for T= 10582 logg=1.76\n", + "Changing to logg=2.00 for T= 10505 logg=1.74\n", + "Changing to logg=2.00 for T= 10429 logg=1.73\n", + "Changing to logg=2.00 for T= 10354 logg=1.72\n", + "Changing to logg=2.00 for T= 10280 logg=1.71\n", + "Changing to logg=2.00 for T= 10206 logg=1.69\n", + "Changing to logg=2.00 for T= 10132 logg=1.68\n", + "Changing to logg=2.00 for T= 10059 logg=1.67\n", + "Changing to logg=2.00 for T= 9987 logg=1.65\n", + "Changing to logg=2.00 for T= 9915 logg=1.64\n", + "Changing to logg=2.00 for T= 9844 logg=1.63\n", + "Changing to logg=2.00 for T= 9773 logg=1.61\n", + "Changing to logg=2.00 for T= 9703 logg=1.60\n", + "Changing to logg=2.00 for T= 9634 logg=1.59\n", + "Changing to logg=2.00 for T= 9565 logg=1.58\n", + "Changing to logg=2.00 for T= 9496 logg=1.56\n", + "Changing to logg=2.00 for T= 9359 logg=1.54\n", + "Changing to logg=2.00 for T= 9225 logg=1.51\n", + "Changing to logg=2.00 for T= 9093 logg=1.49\n", + "Changing to logg=1.50 for T= 8963 logg=1.46\n", + "Changing to logg=1.50 for T= 8836 logg=1.44\n", + "Changing to logg=1.50 for T= 8711 logg=1.41\n", + "Changing to logg=1.50 for T= 8589 logg=1.39\n", + "Changing to logg=1.50 for T= 8468 logg=1.36\n", + "Changing to logg=1.50 for T= 8350 logg=1.34\n", + "Isochrone generation took 47.311578 s.\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.70 dist = 100\n", + " Starting at: 2021-01-08 01:22:39.336609 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_U = 29.86\n", + "M = 2.350 Msun T = 10414 K m_ubv_U = 17.42\n", + "M = 9.793 Msun T = 24007 K m_ubv_U = 12.72\n", + "M = 17.853 Msun T = 25534 K m_ubv_U = 10.06\n", + "M = 17.889 Msun T = 15781 K m_ubv_U = 9.07\n", + "M = 17.971 Msun T = 6489 K m_ubv_U = 9.12\n", + "Starting filter: ubv,B Elapsed time: 1.18 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_B = 29.64\n", + "M = 2.350 Msun T = 10414 K m_ubv_B = 17.53\n", + "M = 9.793 Msun T = 24007 K m_ubv_B = 13.62\n", + "M = 17.853 Msun T = 25534 K m_ubv_B = 11.04\n", + "M = 17.889 Msun T = 15781 K m_ubv_B = 9.79\n", + "M = 17.971 Msun T = 6489 K m_ubv_B = 8.83\n", + "Starting filter: ubv,V Elapsed time: 2.37 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_V = 27.65\n", + "M = 2.350 Msun T = 10414 K m_ubv_V = 16.73\n", + "M = 9.793 Msun T = 24007 K m_ubv_V = 13.05\n", + "M = 17.853 Msun T = 25534 K m_ubv_V = 10.48\n", + "M = 17.889 Msun T = 15781 K m_ubv_V = 9.12\n", + "M = 17.971 Msun T = 6489 K m_ubv_V = 7.51\n", + "Starting filter: ubv,R Elapsed time: 3.59 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_R = 22.85\n", + "M = 2.350 Msun T = 10414 K m_ubv_R = 14.13\n", + "M = 9.793 Msun T = 24007 K m_ubv_R = 10.55\n", + "M = 17.853 Msun T = 25534 K m_ubv_R = 7.98\n", + "M = 17.889 Msun T = 15781 K m_ubv_R = 6.56\n", + "M = 17.971 Msun T = 6489 K m_ubv_R = 4.66\n", + "Starting filter: ubv,I Elapsed time: 4.79 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_I = 17.49\n", + "M = 2.350 Msun T = 10414 K m_ubv_I = 10.62\n", + "M = 9.793 Msun T = 24007 K m_ubv_I = 7.24\n", + "M = 17.853 Msun T = 25534 K m_ubv_I = 4.69\n", + "M = 17.889 Msun T = 15781 K m_ubv_I = 3.17\n", + "M = 17.971 Msun T = 6489 K m_ubv_I = 0.88\n", + " Time taken: 5.99 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(7.0, 0.7, 100, math.log10(1), mass_sampling =1 , recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater_equal\n", + " result = getattr(super(), op)(other)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1522 star systems had to be deleted\n", + "1706 companions had to be deleted\n", + "Found 659 stars out of mass range\n", + "Found 129 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1, imf.Kennicutt_1983(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2, imf.Kennicutt_1983(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure that I am getting just about enough star mass for my cluster. (It was a bug before I used an adjustment factor.)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2632.0817331932444" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1571 star systems had to be deleted\n", + "1782 companions had to be deleted\n", + "1483 star systems had to be deleted\n", + "1659 companions had to be deleted\n", + "1526 star systems had to be deleted\n", + "1718 companions had to be deleted\n", + "1493 star systems had to be deleted\n", + "1659 companions had to be deleted\n", + "1620 star systems had to be deleted\n", + "1812 companions had to be deleted\n", + "1391 star systems had to be deleted\n", + "1612 companions had to be deleted\n", + "1274 star systems had to be deleted\n", + "1436 companions had to be deleted\n", + "1018 star systems had to be deleted\n", + "1148 companions had to be deleted\n", + "1433 star systems had to be deleted\n", + "1639 companions had to be deleted\n", + "1500 star systems had to be deleted\n", + "1680 companions had to be deleted\n", + "1468 star systems had to be deleted\n", + "1644 companions had to be deleted\n", + "1252 star systems had to be deleted\n", + "1395 companions had to be deleted\n", + "1471 star systems had to be deleted\n", + "1649 companions had to be deleted\n", + "1308 star systems had to be deleted\n", + "1469 companions had to be deleted\n", + "1394 star systems had to be deleted\n", + "1561 companions had to be deleted\n", + "1456 star systems had to be deleted\n", + "1624 companions had to be deleted\n", + "1698 star systems had to be deleted\n", + "1890 companions had to be deleted\n", + "1500 star systems had to be deleted\n", + "1704 companions had to be deleted\n", + "1551 star systems had to be deleted\n", + "1755 companions had to be deleted\n", + "1475 star systems had to be deleted\n", + "1643 companions had to be deleted\n", + "1475 star systems had to be deleted\n", + "1668 companions had to be deleted\n", + "1566 star systems had to be deleted\n", + "1757 companions had to be deleted\n", + "909 star systems had to be deleted\n", + "1014 companions had to be deleted\n", + "1497 star systems had to be deleted\n", + "1697 companions had to be deleted\n", + "1530 star systems had to be deleted\n", + "1730 companions had to be deleted\n", + "1253 star systems had to be deleted\n", + "1425 companions had to be deleted\n", + "1371 star systems had to be deleted\n", + "1567 companions had to be deleted\n", + "1461 star systems had to be deleted\n", + "1654 companions had to be deleted\n", + "1230 star systems had to be deleted\n", + "1381 companions had to be deleted\n", + "1381 star systems had to be deleted\n", + "1563 companions had to be deleted\n", + "1444 star systems had to be deleted\n", + "1618 companions had to be deleted\n", + "1500 star systems had to be deleted\n", + "1688 companions had to be deleted\n", + "1695 star systems had to be deleted\n", + "1879 companions had to be deleted\n", + "1379 star systems had to be deleted\n", + "1556 companions had to be deleted\n", + "1593 star systems had to be deleted\n", + "1806 companions had to be deleted\n", + "1319 star systems had to be deleted\n", + "1505 companions had to be deleted\n", + "1379 star systems had to be deleted\n", + "1564 companions had to be deleted\n", + "1752 star systems had to be deleted\n", + "1937 companions had to be deleted\n", + "1556 star systems had to be deleted\n", + "1735 companions had to be deleted\n", + "1684 star systems had to be deleted\n", + "1910 companions had to be deleted\n", + "1728 star systems had to be deleted\n", + "1918 companions had to be deleted\n", + "1391 star systems had to be deleted\n", + "1550 companions had to be deleted\n", + "1449 star systems had to be deleted\n", + "1656 companions had to be deleted\n", + "1191 star systems had to be deleted\n", + "1341 companions had to be deleted\n", + "1458 star systems had to be deleted\n", + "1625 companions had to be deleted\n", + "1539 star systems had to be deleted\n", + "1720 companions had to be deleted\n", + "1632 star systems had to be deleted\n", + "1834 companions had to be deleted\n", + "1448 star systems had to be deleted\n", + "1616 companions had to be deleted\n", + "1554 star systems had to be deleted\n", + "1758 companions had to be deleted\n", + "1431 star systems had to be deleted\n", + "1588 companions had to be deleted\n", + "1495 star systems had to be deleted\n", + "1667 companions had to be deleted\n", + "1523 star systems had to be deleted\n", + "1706 companions had to be deleted\n", + "1516 star systems had to be deleted\n", + "1722 companions had to be deleted\n", + "1376 star systems had to be deleted\n", + "1590 companions had to be deleted\n", + "1436 star systems had to be deleted\n", + "1626 companions had to be deleted\n", + "1380 star systems had to be deleted\n", + "1572 companions had to be deleted\n", + "1491 star systems had to be deleted\n", + "1673 companions had to be deleted\n", + "1585 star systems had to be deleted\n", + "1780 companions had to be deleted\n", + "1108 star systems had to be deleted\n", + "1250 companions had to be deleted\n", + "1620 star systems had to be deleted\n", + "1799 companions had to be deleted\n", + "1488 star systems had to be deleted\n", + "1679 companions had to be deleted\n", + "1550 star systems had to be deleted\n", + "1734 companions had to be deleted\n", + "1613 star systems had to be deleted\n", + "1807 companions had to be deleted\n", + "1508 star systems had to be deleted\n", + "1689 companions had to be deleted\n", + "1573 star systems had to be deleted\n", + "1752 companions had to be deleted\n", + "1361 star systems had to be deleted\n", + "1534 companions had to be deleted\n" + ] + } + ], + "source": [ + "arr =[]\n", + "for x in range(100):\n", + " clus_1=synthetic.Cluster_w_Binaries(iso1, imf.Kennicutt_1983(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + " arr.append(clus_1.star_systems['systemMass'].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "hist, bins, patch = plt.hist(np.array(arr), bins=30, histtype='step', label='BPASS clusters with x mass')\n", + "plt.title(\"BPASS cluster mass distribution (# of models within mass bin over mass)\")\n", + "print(np.median(np.array(arr)))\n", + "plt.xlabel(\"Initial total mass of the cluster in solar masses\")\n", + "plt.ylabel(\"# of times cluster of that mass was generated\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clus_1.companions['mass']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.primaries['mass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.secondaries['mass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.singles['mass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(clus_1.star_systems['systemMass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(clus_2.star_systems['systemMass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['systemMass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['systemMass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the companions on logarithmic scale of the horizontal axis.\")\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['mass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['mass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the companions on logarithmic scale of the horizontal axis.\")\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.companions['mass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.companions['mass'], bins=30, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the companions on logarithmic scale of the horizontal axis.\")\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparison with the MIST v1 cluster\n", + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.primaries['mass'], bins=30)\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the primaries on logarithmic scale of the horizontal axis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.singles['mass'], bins=30)\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the singles on logarithmic scale of the horizontal axis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.secondaries['mass'], bins=30)\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the secondaries on logarithmic scale of the horizontal axis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.where(iso1.secondaries['mass']>iso1.primaries['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.unique(iso1.primaries[np.where(iso1.secondaries['mass']>iso1.primaries['mass'])]['source'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.companions['log_a'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.companions['log_a'], bins=30, histtype='step', label='MISTv.1')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cluster_Mass_Error_Demonstration/Error Demonstration 10^9 yr old Kennicutt.ipynb b/Cluster_Mass_Error_Demonstration/Error Demonstration 10^9 yr old Kennicutt.ipynb new file mode 100644 index 00000000..7ada8d8d --- /dev/null +++ b/Cluster_Mass_Error_Demonstration/Error Demonstration 10^9 yr old Kennicutt.ipynb @@ -0,0 +1,1266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS cluster at 10^9 years age.\n", + "Here, I try to create a 10000 solar mass BPASS cluster and ResolvedCluster. **This is to demonstrate the issue I have with the mass-matching inside of the Binary_Cluster implementation.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:346: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1794: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater\n", + " result = getattr(super(), op)(other)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 23286 logg=5.78\n", + "Changing to logg=5.00 for T= 17733 logg=5.24\n", + "Changing to logg=5.00 for T= 24377 logg=5.87\n", + "Changing to logg=5.00 for T= 24403 logg=5.91\n", + "Changing to logg=5.00 for T= 26316 logg=5.57\n", + "Changing to logg=5.00 for T= 26930 logg=5.65\n", + "Changing to logg=5.00 for T= 24518 logg=6.01\n", + "Changing to logg=5.00 for T= 25618 logg=5.53\n", + "Changing to logg=5.00 for T= 26119 logg=5.75\n", + "Changing to logg=5.00 for T= 24305 logg=6.01\n", + "Changing to logg=5.00 for T= 26134 logg=5.73\n", + "Changing to logg=5.00 for T= 16799 logg=5.12\n", + "Changing to logg=5.00 for T= 20198 logg=5.51\n", + "Changing to logg=5.00 for T= 27134 logg=5.64\n", + "Changing to logg=5.00 for T= 26114 logg=5.72\n", + "Changing to logg=5.00 for T= 24452 logg=5.99\n", + "Changing to logg=5.00 for T= 22084 logg=5.78\n", + "Changing to logg=5.00 for T= 22961 logg=5.92\n", + "Changing to logg=5.00 for T= 21333 logg=5.59\n", + "Changing to logg=5.00 for T= 23774 logg=6.99\n", + "Changing to logg=5.00 for T= 23264 logg=5.77\n", + "Changing to logg=5.00 for T= 23096 logg=5.56\n", + "Changing to logg=5.00 for T= 27240 logg=5.65\n", + "Changing to logg=5.00 for T= 22114 logg=5.26\n", + "Changing to logg=5.00 for T= 26757 logg=5.79\n", + "Changing to logg=5.00 for T= 20983 logg=5.17\n", + "Changing to logg=5.00 for T= 26427 logg=5.72\n", + "Changing to logg=5.00 for T= 22135 logg=6.94\n", + "Changing to logg=5.00 for T= 24306 logg=5.43\n", + "Changing to logg=5.00 for T= 24820 logg=6.17\n", + "Changing to logg=5.00 for T= 24008 logg=6.91\n", + "Changing to logg=5.00 for T= 27188 logg=5.67\n", + "Changing to logg=5.00 for T= 21570 logg=5.64\n", + "Changing to logg=5.00 for T= 25553 logg=5.87\n", + "Changing to logg=5.00 for T= 18719 logg=5.30\n", + "Changing to logg=5.00 for T= 25509 logg=5.94\n", + "Changing to logg=5.00 for T= 25707 logg=5.88\n", + "Changing to logg=5.00 for T= 22982 logg=5.76\n", + "Changing to logg=5.00 for T= 24495 logg=5.62\n", + "Changing to logg=5.00 for T= 23626 logg=5.99\n", + "Changing to logg=5.00 for T= 17042 logg=5.14\n", + "Changing to logg=5.00 for T= 24739 logg=6.19\n", + "Changing to logg=5.00 for T= 25963 logg=5.80\n", + "Changing to logg=5.00 for T= 23984 logg=6.88\n", + "Changing to logg=5.00 for T= 19593 logg=6.97\n", + "Changing to logg=5.00 for T= 24124 logg=5.48\n", + "Changing to logg=5.00 for T= 25973 logg=5.55\n", + "Changing to logg=5.00 for T= 21304 logg=5.48\n", + "Changing to logg=5.00 for T= 26020 logg=5.81\n", + "Changing to logg=5.00 for T= 25999 logg=5.76\n", + "Changing to logg=5.00 for T= 22646 logg=5.73\n", + "Changing to logg=5.00 for T= 25848 logg=5.79\n", + "Changing to logg=5.00 for T= 26959 logg=5.63\n", + "Changing to logg=5.00 for T= 27359 logg=5.67\n", + "Changing to logg=5.00 for T= 26907 logg=5.63\n", + "Changing to logg=5.00 for T= 21104 logg=5.60\n", + "Changing to logg=5.00 for T= 26100 logg=5.77\n", + "Changing to logg=5.00 for T= 22179 logg=5.92\n", + "Changing to logg=5.00 for T= 22891 logg=5.32\n", + "Changing to logg=5.00 for T= 21139 logg=5.58\n", + "Changing to logg=5.00 for T= 24444 logg=5.91\n", + "Changing to logg=5.00 for T= 20689 logg=5.14\n", + "Changing to logg=5.00 for T= 26317 logg=5.76\n", + "Changing to logg=5.00 for T= 26494 logg=5.69\n", + "Changing to logg=5.00 for T= 17875 logg=5.24\n", + "Changing to logg=5.00 for T= 18636 logg=5.32\n", + "Changing to logg=5.00 for T= 25863 logg=5.74\n", + "Changing to logg=5.00 for T= 24009 logg=5.86\n", + "Changing to logg=5.00 for T= 21888 logg=5.45\n", + "Changing to logg=5.00 for T= 23501 logg=5.37\n", + "Changing to logg=5.00 for T= 26095 logg=5.78\n", + "Changing to logg=5.00 for T= 27193 logg=5.64\n", + "Changing to logg=5.00 for T= 26146 logg=5.79\n", + "Changing to logg=5.00 for T= 22008 logg=7.09\n", + "Changing to logg=5.00 for T= 24403 logg=5.88\n", + "Changing to logg=5.00 for T= 19749 logg=5.03\n", + "Changing to logg=5.00 for T= 26204 logg=5.71\n", + "Changing to logg=5.00 for T= 26196 logg=5.70\n", + "Changing to logg=5.00 for T= 22245 logg=6.65\n", + "Changing to logg=5.00 for T= 25914 logg=5.89\n", + "Changing to logg=5.00 for T= 21142 logg=5.20\n", + "Changing to logg=5.00 for T= 24186 logg=5.42\n", + "Making photometry for isochrone: log(t) = 9.00 AKs = 0.70 dist = 100\n", + " Starting at: 2021-01-08 01:19:55.007527 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_U = 19.68\n", + "M = 3.200 Msun T = 10963 K m_ubv_U = 27.99\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_U = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_U = 28.18\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_U = 17.80\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_U = 28.39\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_U = 27.99\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_U = 28.17\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 2.100 Msun T = 6638 K m_ubv_U = 17.64\n", + "M = 3.500 Msun T = 12654 K m_ubv_U = 28.16\n", + "M = 1.700 Msun T = 7133 K m_ubv_U = 17.86\n", + "M = 1.800 Msun T = 7355 K m_ubv_U = 18.42\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_U = 28.17\n", + "M = 4.500 Msun T = 10856 K m_ubv_U = 28.39\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_U = 19.08\n", + "M = 1.700 Msun T = 7242 K m_ubv_U = 18.75\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_U = 28.26\n", + "M = 1.200 Msun T = 6151 K m_ubv_U = 20.85\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 1.900 Msun T = 7312 K m_ubv_U = 18.07\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_U = 17.80\n", + "M = 1.700 Msun T = 7246 K m_ubv_U = 18.74\n", + "M = 1.800 Msun T = 7355 K m_ubv_U = 18.42\n", + "M = 4.000 Msun T = 11144 K m_ubv_U = 28.23\n", + "M = 3.000 Msun T = 11468 K m_ubv_U = 27.77\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 3.700 Msun T = 12543 K m_ubv_U = 28.16\n", + "M = 3.200 Msun T = 10973 K m_ubv_U = 27.99\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 1.800 Msun T = 7355 K m_ubv_U = 18.42\n", + "M = 5.000 Msun T = 10786 K m_ubv_U = 28.41\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_U = 17.88\n", + "M = 0.320 Msun T = nan K m_ubv_U = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_U = 17.30\n", + "M = 0.200 Msun T = 3299 K m_ubv_U = 29.80\n", + "M = 3.500 Msun T = 11388 K m_ubv_U = 27.96\n", + "M = 3.000 Msun T = 12026 K m_ubv_U = 15.93\n", + "M = 1.900 Msun T = 7266 K m_ubv_U = 17.88\n", + "M = 4.500 Msun T = 11882 K m_ubv_U = 28.16\n", + "M = 2.500 Msun T = 17317 K m_ubv_U = 26.55\n", + "M = 2.880 Msun T = 8175 K m_ubv_U = 15.62\n", + "M = 3.700 Msun T = 11133 K m_ubv_U = 28.18\n", + "M = 3.600 Msun T = 9273 K m_ubv_U = 14.60\n", + "M = 1.000 Msun T = 5608 K m_ubv_U = 21.96\n", + "M = 0.420 Msun T = 2424 K m_ubv_U = 30.76\n", + "M = 1.750 Msun T = nan K m_ubv_U = nan\n", + "M = 0.170 Msun T = nan K m_ubv_U = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_U = 18.71\n", + "M = 1.200 Msun T = 6207 K m_ubv_U = 20.65\n", + "M = 7.000 Msun T = 17765 K m_ubv_U = 28.78\n", + "M = 1.350 Msun T = nan K m_ubv_U = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_U = 13.74\n", + "M = 2.500 Msun T = 15710 K m_ubv_U = 18.08\n", + "M = 0.960 Msun T = 5540 K m_ubv_U = 21.88\n", + "M = 0.680 Msun T = 3924 K m_ubv_U = 25.25\n", + "M = 2.000 Msun T = 7152 K m_ubv_U = 17.60\n", + "M = 40.000 Msun T = 10250 K m_ubv_U = 26.90\n", + "M = 3.500 Msun T = 11388 K m_ubv_U = 27.96\n", + "M = 1.200 Msun T = 5961 K m_ubv_U = 20.86\n", + "M = 0.600 Msun T = 3462 K m_ubv_U = 26.66\n", + "M = 0.600 Msun T = 3950 K m_ubv_U = 26.04\n", + "M = 0.570 Msun T = 3878 K m_ubv_U = 26.34\n", + "M = 3.000 Msun T = 11498 K m_ubv_U = 27.77\n", + "M = 1.400 Msun T = 6747 K m_ubv_U = 19.64\n", + "M = 0.340 Msun T = 1962 K m_ubv_U = 33.22\n", + "M = 0.720 Msun T = 4155 K m_ubv_U = 24.64\n", + "M = 2.000 Msun T = 7108 K m_ubv_U = 17.60\n", + "M = 1.500 Msun T = 7084 K m_ubv_U = 19.30\n", + "M = 1.200 Msun T = 6155 K m_ubv_U = 20.66\n", + "M = 7.000 Msun T = 17707 K m_ubv_U = 28.78\n", + "M = 0.740 Msun T = nan K m_ubv_U = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_U = 17.86\n", + "M = 1.800 Msun T = 7808 K m_ubv_U = 18.08\n", + "M = 0.900 Msun T = 5193 K m_ubv_U = 22.45\n", + "M = 4.000 Msun T = 11312 K m_ubv_U = 13.81\n", + "Starting filter: ubv,V Elapsed time: 26.91 seconds\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_V = 18.36\n", + "M = 3.200 Msun T = 10963 K m_ubv_V = 27.51\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_V = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_V = 27.73\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_V = 16.54\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_V = 27.98\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_V = 27.51\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_V = 27.74\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 2.100 Msun T = 6638 K m_ubv_V = 16.26\n", + "M = 3.500 Msun T = 12654 K m_ubv_V = 27.82\n", + "M = 1.700 Msun T = 7133 K m_ubv_V = 16.59\n", + "M = 1.800 Msun T = 7355 K m_ubv_V = 17.20\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_V = 27.84\n", + "M = 4.500 Msun T = 10856 K m_ubv_V = 27.93\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_V = 17.83\n", + "M = 1.700 Msun T = 7242 K m_ubv_V = 17.52\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_V = 27.75\n", + "M = 1.200 Msun T = 6151 K m_ubv_V = 19.34\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 1.900 Msun T = 7312 K m_ubv_V = 16.84\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_V = 16.54\n", + "M = 1.700 Msun T = 7246 K m_ubv_V = 17.52\n", + "M = 1.800 Msun T = 7355 K m_ubv_V = 17.20\n", + "M = 4.000 Msun T = 11144 K m_ubv_V = 27.78\n", + "M = 3.000 Msun T = 11468 K m_ubv_V = 27.31\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 3.700 Msun T = 12543 K m_ubv_V = 27.81\n", + "M = 3.200 Msun T = 10973 K m_ubv_V = 27.51\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 1.800 Msun T = 7355 K m_ubv_V = 17.20\n", + "M = 5.000 Msun T = 10786 K m_ubv_V = 27.95\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_V = 16.98\n", + "M = 0.320 Msun T = nan K m_ubv_V = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_V = 15.43\n", + "M = 0.200 Msun T = 3299 K m_ubv_V = 26.72\n", + "M = 3.500 Msun T = 11388 K m_ubv_V = 27.51\n", + "M = 3.000 Msun T = 12026 K m_ubv_V = 15.82\n", + "M = 1.900 Msun T = 7266 K m_ubv_V = 16.98\n", + "M = 4.500 Msun T = 11882 K m_ubv_V = 27.76\n", + "M = 2.500 Msun T = 17317 K m_ubv_V = 26.44\n", + "M = 2.880 Msun T = 8175 K m_ubv_V = 14.95\n", + "M = 3.700 Msun T = 11133 K m_ubv_V = 27.73\n", + "M = 3.600 Msun T = 9273 K m_ubv_V = 14.14\n", + "M = 1.000 Msun T = 5608 K m_ubv_V = 20.45\n", + "M = 0.420 Msun T = 2424 K m_ubv_V = 27.31\n", + "M = 1.750 Msun T = nan K m_ubv_V = nan\n", + "M = 0.170 Msun T = nan K m_ubv_V = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_V = 17.86\n", + "M = 1.200 Msun T = 6207 K m_ubv_V = 19.40\n", + "M = 7.000 Msun T = 17765 K m_ubv_V = 28.75\n", + "M = 1.350 Msun T = nan K m_ubv_V = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_V = 13.51\n", + "M = 2.500 Msun T = 15710 K m_ubv_V = 18.23\n", + "M = 0.960 Msun T = 5540 K m_ubv_V = 20.34\n", + "M = 0.680 Msun T = 3924 K m_ubv_V = 22.69\n", + "M = 2.000 Msun T = 7152 K m_ubv_V = 16.67\n", + "M = 40.000 Msun T = 10250 K m_ubv_V = 26.24\n", + "M = 3.500 Msun T = 11388 K m_ubv_V = 27.51\n", + "M = 1.200 Msun T = 5961 K m_ubv_V = 19.51\n", + "M = 0.600 Msun T = 3462 K m_ubv_V = 23.72\n", + "M = 0.600 Msun T = 3950 K m_ubv_V = 23.49\n", + "M = 0.570 Msun T = 3878 K m_ubv_V = 23.74\n", + "M = 3.000 Msun T = 11498 K m_ubv_V = 27.31\n", + "M = 1.400 Msun T = 6747 K m_ubv_V = 18.58\n", + "M = 0.340 Msun T = 1962 K m_ubv_V = 30.01\n", + "M = 0.720 Msun T = 4155 K m_ubv_V = 22.26\n", + "M = 2.000 Msun T = 7108 K m_ubv_V = 16.66\n", + "M = 1.500 Msun T = 7084 K m_ubv_V = 18.34\n", + "M = 1.200 Msun T = 6155 K m_ubv_V = 19.39\n", + "M = 7.000 Msun T = 17707 K m_ubv_V = 28.74\n", + "M = 0.740 Msun T = nan K m_ubv_V = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_V = 17.30\n", + "M = 1.800 Msun T = 7808 K m_ubv_V = 17.32\n", + "M = 0.900 Msun T = 5193 K m_ubv_V = 20.74\n", + "M = 4.000 Msun T = 11312 K m_ubv_V = 13.62\n", + "Starting filter: ubv,B Elapsed time: 53.08 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_B = 19.76\n", + "M = 3.200 Msun T = 10963 K m_ubv_B = 28.56\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_B = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_B = 28.78\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_B = 17.78\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_B = 29.02\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_B = 28.56\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_B = 28.78\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 2.100 Msun T = 6638 K m_ubv_B = 17.63\n", + "M = 3.500 Msun T = 12654 K m_ubv_B = 28.82\n", + "M = 1.700 Msun T = 7133 K m_ubv_B = 17.84\n", + "M = 1.800 Msun T = 7355 K m_ubv_B = 18.42\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_B = 28.84\n", + "M = 4.500 Msun T = 10856 K m_ubv_B = 28.99\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_B = 19.12\n", + "M = 1.700 Msun T = 7242 K m_ubv_B = 18.77\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_B = 28.83\n", + "M = 1.200 Msun T = 6151 K m_ubv_B = 20.90\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 1.900 Msun T = 7312 K m_ubv_B = 18.06\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_B = 17.78\n", + "M = 1.700 Msun T = 7246 K m_ubv_B = 18.76\n", + "M = 1.800 Msun T = 7355 K m_ubv_B = 18.42\n", + "M = 4.000 Msun T = 11144 K m_ubv_B = 28.83\n", + "M = 3.000 Msun T = 11468 K m_ubv_B = 28.34\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 3.700 Msun T = 12543 K m_ubv_B = 28.81\n", + "M = 3.200 Msun T = 10973 K m_ubv_B = 28.56\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 1.800 Msun T = 7355 K m_ubv_B = 18.42\n", + "M = 5.000 Msun T = 10786 K m_ubv_B = 29.02\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_B = 18.36\n", + "M = 0.320 Msun T = nan K m_ubv_B = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_B = 17.38\n", + "M = 0.200 Msun T = 3299 K m_ubv_B = 29.53\n", + "M = 3.500 Msun T = 11388 K m_ubv_B = 28.55\n", + "M = 3.000 Msun T = 12026 K m_ubv_B = 16.74\n", + "M = 1.900 Msun T = 7266 K m_ubv_B = 18.36\n", + "M = 4.500 Msun T = 11882 K m_ubv_B = 28.78\n", + "M = 2.500 Msun T = 17317 K m_ubv_B = 27.25\n", + "M = 2.880 Msun T = 8175 K m_ubv_B = 16.20\n", + "M = 3.700 Msun T = 11133 K m_ubv_B = 28.78\n", + "M = 3.600 Msun T = 9273 K m_ubv_B = 15.26\n", + "M = 1.000 Msun T = 5608 K m_ubv_B = 22.18\n", + "M = 0.420 Msun T = 2424 K m_ubv_B = 30.95\n", + "M = 1.750 Msun T = nan K m_ubv_B = nan\n", + "M = 0.170 Msun T = nan K m_ubv_B = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_B = 19.21\n", + "M = 1.200 Msun T = 6207 K m_ubv_B = 20.98\n", + "M = 7.000 Msun T = 17765 K m_ubv_B = 29.63\n", + "M = 1.350 Msun T = nan K m_ubv_B = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_B = 14.51\n", + "M = 2.500 Msun T = 15710 K m_ubv_B = 19.01\n", + "M = 0.960 Msun T = 5540 K m_ubv_B = 22.10\n", + "M = 0.680 Msun T = 3924 K m_ubv_B = 25.09\n", + "M = 2.000 Msun T = 7152 K m_ubv_B = 18.07\n", + "M = 40.000 Msun T = 10250 K m_ubv_B = 27.29\n", + "M = 3.500 Msun T = 11388 K m_ubv_B = 28.55\n", + "M = 1.200 Msun T = 5961 K m_ubv_B = 21.15\n", + "M = 0.600 Msun T = 3462 K m_ubv_B = 26.41\n", + "M = 0.600 Msun T = 3950 K m_ubv_B = 25.88\n", + "M = 0.570 Msun T = 3878 K m_ubv_B = 26.17\n", + "M = 3.000 Msun T = 11498 K m_ubv_B = 28.34\n", + "M = 1.400 Msun T = 6747 K m_ubv_B = 20.05\n", + "M = 0.340 Msun T = 1962 K m_ubv_B = 34.21\n", + "M = 0.720 Msun T = 4155 K m_ubv_B = 24.54\n", + "M = 2.000 Msun T = 7108 K m_ubv_B = 18.06\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 1.500 Msun T = 7084 K m_ubv_B = 19.75\n", + "M = 1.200 Msun T = 6155 K m_ubv_B = 20.99\n", + "M = 7.000 Msun T = 17707 K m_ubv_B = 29.62\n", + "M = 0.740 Msun T = nan K m_ubv_B = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_B = 18.48\n", + "M = 1.800 Msun T = 7808 K m_ubv_B = 18.62\n", + "M = 0.900 Msun T = 5193 K m_ubv_B = 22.60\n", + "M = 4.000 Msun T = 11312 K m_ubv_B = 14.59\n", + "Starting filter: ubv,R Elapsed time: 80.18 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_R = 15.49\n", + "M = 3.200 Msun T = 10963 K m_ubv_R = 24.87\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_R = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_R = 25.09\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_R = 13.74\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_R = 25.35\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_R = 24.87\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_R = 25.11\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 2.100 Msun T = 6638 K m_ubv_R = 13.39\n", + "M = 3.500 Msun T = 12654 K m_ubv_R = 25.23\n", + "M = 1.700 Msun T = 7133 K m_ubv_R = 13.78\n", + "M = 1.800 Msun T = 7355 K m_ubv_R = 14.41\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_R = 25.26\n", + "M = 4.500 Msun T = 10856 K m_ubv_R = 25.28\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_R = 15.00\n", + "M = 1.700 Msun T = 7242 K m_ubv_R = 14.72\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_R = 25.06\n", + "M = 1.200 Msun T = 6151 K m_ubv_R = 16.40\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 1.900 Msun T = 7312 K m_ubv_R = 14.05\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_R = 13.74\n", + "M = 1.700 Msun T = 7246 K m_ubv_R = 14.71\n", + "M = 1.800 Msun T = 7355 K m_ubv_R = 14.41\n", + "M = 4.000 Msun T = 11144 K m_ubv_R = 25.14\n", + "M = 3.000 Msun T = 11468 K m_ubv_R = 24.69\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 3.700 Msun T = 12543 K m_ubv_R = 25.21\n", + "M = 3.200 Msun T = 10973 K m_ubv_R = 24.87\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 1.800 Msun T = 7355 K m_ubv_R = 14.41\n", + "M = 5.000 Msun T = 10786 K m_ubv_R = 25.30\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_R = 14.09\n", + "M = 0.320 Msun T = nan K m_ubv_R = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_R = 12.26\n", + "M = 0.200 Msun T = 3299 K m_ubv_R = 23.10\n", + "M = 3.500 Msun T = 11388 K m_ubv_R = 24.89\n", + "M = 3.000 Msun T = 12026 K m_ubv_R = 13.13\n", + "M = 1.900 Msun T = 7266 K m_ubv_R = 14.09\n", + "M = 4.500 Msun T = 11882 K m_ubv_R = 25.15\n", + "M = 2.500 Msun T = 17317 K m_ubv_R = 23.91\n", + "M = 2.880 Msun T = 8175 K m_ubv_R = 12.12\n", + "M = 3.700 Msun T = 11133 K m_ubv_R = 25.09\n", + "M = 3.600 Msun T = 9273 K m_ubv_R = 11.36\n", + "M = 1.000 Msun T = 5608 K m_ubv_R = 17.38\n", + "M = 0.420 Msun T = 2424 K m_ubv_R = 23.19\n", + "M = 1.750 Msun T = nan K m_ubv_R = nan\n", + "M = 0.170 Msun T = nan K m_ubv_R = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_R = 14.98\n", + "M = 1.200 Msun T = 6207 K m_ubv_R = 16.41\n", + "M = 7.000 Msun T = 17765 K m_ubv_R = 26.22\n", + "M = 1.350 Msun T = nan K m_ubv_R = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_R = 10.79\n", + "M = 2.500 Msun T = 15710 K m_ubv_R = 15.61\n", + "M = 0.960 Msun T = 5540 K m_ubv_R = 17.27\n", + "M = 0.680 Msun T = 3924 K m_ubv_R = 19.29\n", + "M = 2.000 Msun T = 7152 K m_ubv_R = 13.76\n", + "M = 40.000 Msun T = 10250 K m_ubv_R = 23.59\n", + "M = 3.500 Msun T = 11388 K m_ubv_R = 24.89\n", + "M = 1.200 Msun T = 5961 K m_ubv_R = 16.49\n", + "M = 0.600 Msun T = 3462 K m_ubv_R = 20.16\n", + "M = 0.600 Msun T = 3950 K m_ubv_R = 20.10\n", + "M = 0.570 Msun T = 3878 K m_ubv_R = 20.32\n", + "M = 3.000 Msun T = 11498 K m_ubv_R = 24.69\n", + "M = 1.400 Msun T = 6747 K m_ubv_R = 15.64\n", + "M = 0.340 Msun T = 1962 K m_ubv_R = 25.45\n", + "M = 0.720 Msun T = 4155 K m_ubv_R = 18.91\n", + "M = 2.000 Msun T = 7108 K m_ubv_R = 13.75\n", + "M = 1.500 Msun T = 7084 K m_ubv_R = 15.43\n", + "M = 1.200 Msun T = 6155 K m_ubv_R = 16.39\n", + "M = 7.000 Msun T = 17707 K m_ubv_R = 26.22\n", + "M = 0.740 Msun T = nan K m_ubv_R = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_R = 14.51\n", + "M = 1.800 Msun T = 7808 K m_ubv_R = 14.47\n", + "M = 0.900 Msun T = 5193 K m_ubv_R = 17.61\n", + "M = 4.000 Msun T = 11312 K m_ubv_R = 10.92\n", + "Starting filter: ubv,I Elapsed time: 107.47 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_I = 11.75\n", + "M = 3.200 Msun T = 10963 K m_ubv_I = 21.39\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_I = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_I = 21.62\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_I = 10.05\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_I = 21.89\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_I = 21.39\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_I = 21.65\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 2.100 Msun T = 6638 K m_ubv_I = 9.65\n", + "M = 3.500 Msun T = 12654 K m_ubv_I = 21.80\n", + "M = 1.700 Msun T = 7133 K m_ubv_I = 10.09\n", + "M = 1.800 Msun T = 7355 K m_ubv_I = 10.74\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_I = 21.84\n", + "M = 4.500 Msun T = 10856 K m_ubv_I = 21.80\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_I = 11.30\n", + "M = 1.700 Msun T = 7242 K m_ubv_I = 11.04\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_I = 21.54\n", + "M = 1.200 Msun T = 6151 K m_ubv_I = 12.60\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 1.900 Msun T = 7312 K m_ubv_I = 10.38\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_I = 10.05\n", + "M = 1.700 Msun T = 7246 K m_ubv_I = 11.03\n", + "M = 1.800 Msun T = 7355 K m_ubv_I = 10.74\n", + "M = 4.000 Msun T = 11144 K m_ubv_I = 21.67\n", + "M = 3.000 Msun T = 11468 K m_ubv_I = 21.23\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 3.700 Msun T = 12543 K m_ubv_I = 21.79\n", + "M = 3.200 Msun T = 10973 K m_ubv_I = 21.39\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 1.800 Msun T = 7355 K m_ubv_I = 10.74\n", + "M = 5.000 Msun T = 10786 K m_ubv_I = 21.81\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_I = 10.39\n", + "M = 0.320 Msun T = nan K m_ubv_I = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_I = 8.25\n", + "M = 0.200 Msun T = 3299 K m_ubv_I = 18.60\n", + "M = 3.500 Msun T = 11388 K m_ubv_I = 21.43\n", + "M = 3.000 Msun T = 12026 K m_ubv_I = 9.66\n", + "M = 1.900 Msun T = 7266 K m_ubv_I = 10.39\n", + "M = 4.500 Msun T = 11882 K m_ubv_I = 21.70\n", + "M = 2.500 Msun T = 17317 K m_ubv_I = 20.56\n", + "M = 2.880 Msun T = 8175 K m_ubv_I = 8.48\n", + "M = 3.700 Msun T = 11133 K m_ubv_I = 21.62\n", + "M = 3.600 Msun T = 9273 K m_ubv_I = 7.79\n", + "M = 1.000 Msun T = 5608 K m_ubv_I = 13.49\n", + "M = 0.420 Msun T = 2424 K m_ubv_I = 18.15\n", + "M = 1.750 Msun T = nan K m_ubv_I = nan\n", + "M = 0.170 Msun T = nan K m_ubv_I = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_I = 11.29\n", + "M = 1.200 Msun T = 6207 K m_ubv_I = 12.60\n", + "M = 7.000 Msun T = 17765 K m_ubv_I = 22.87\n", + "M = 1.350 Msun T = nan K m_ubv_I = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_I = 7.28\n", + "M = 2.500 Msun T = 15710 K m_ubv_I = 12.21\n", + "M = 0.960 Msun T = 5540 K m_ubv_I = 13.37\n", + "M = 0.680 Msun T = 3924 K m_ubv_I = 15.03\n", + "M = 2.000 Msun T = 7152 K m_ubv_I = 10.05\n", + "M = 40.000 Msun T = 10250 K m_ubv_I = 20.09\n", + "M = 3.500 Msun T = 11388 K m_ubv_I = 21.43\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 1.200 Msun T = 5961 K m_ubv_I = 12.65\n", + "M = 0.600 Msun T = 3462 K m_ubv_I = 15.73\n", + "M = 0.600 Msun T = 3950 K m_ubv_I = 15.85\n", + "M = 0.570 Msun T = 3878 K m_ubv_I = 16.05\n", + "M = 3.000 Msun T = 11498 K m_ubv_I = 21.24\n", + "M = 1.400 Msun T = 6747 K m_ubv_I = 11.89\n", + "M = 0.340 Msun T = 1962 K m_ubv_I = 19.95\n", + "M = 0.720 Msun T = 4155 K m_ubv_I = 14.72\n", + "M = 2.000 Msun T = 7108 K m_ubv_I = 10.03\n", + "M = 1.500 Msun T = 7084 K m_ubv_I = 11.71\n", + "M = 1.200 Msun T = 6155 K m_ubv_I = 12.58\n", + "M = 7.000 Msun T = 17707 K m_ubv_I = 22.87\n", + "M = 0.740 Msun T = nan K m_ubv_I = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_I = 10.90\n", + "M = 1.800 Msun T = 7808 K m_ubv_I = 10.80\n", + "M = 0.900 Msun T = 5193 K m_ubv_I = 13.65\n", + "M = 4.000 Msun T = 11312 K m_ubv_I = 7.43\n", + " Time taken: 134.25 seconds\n", + "Isochrone generation took 370.792576 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "import time\n", + "import numpy as np\n", + "t1=time.time()\n", + "bps=evolution.BPASS()\n", + "iso1=synthetic.Isochrone_Binary(9.0, 0.7, 100,math.log10(1), mass_sampling=1, filepath='/g/lu/scratch/ryotainagaki/BPASS_iso_filesTimedIsolated/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
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massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
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masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
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" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.secondaries['mass'] < 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
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masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.singles['mass'] < 0)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a sanity check, we can see that the primaries, secondaries, and single stars have phasees of 5 or of 101. Note that we will have a phase of 110 in order to indicate a mystery compact remnant that BPASS provides for NEWSECMODS (secondary star models with compact primaries). This is since we haven't added neutron stars or black holes yet. Stars with phase of -99 exist, and those are the secondary stars that have already merged." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 82.923034 s.\n", + "Making photometry for isochrone: log(t) = 9.00 AKs = 0.70 dist = 100\n", + " Starting at: 2021-01-08 01:23:32.274266 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_U = 33.40\n", + "M = 1.215 Msun T = 6415 K m_ubv_U = 20.55\n", + "M = 2.038 Msun T = 7816 K m_ubv_U = 17.29\n", + "M = 2.055 Msun T = 4955 K m_ubv_U = 19.05\n", + "M = 2.071 Msun T = 4749 K m_ubv_U = 18.64\n", + "M = 2.275 Msun T = 4576 K m_ubv_U = 18.06\n", + "M = 2.287 Msun T = 3677 K m_ubv_U = 17.25\n", + "M = 2.287 Msun T = 3493 K m_ubv_U = 17.19\n", + "M = 2.287 Msun T = 3478 K m_ubv_U = 17.19\n", + "M = 2.288 Msun T = 3244 K m_ubv_U = 17.39\n", + "M = 2.288 Msun T = 3188 K m_ubv_U = 17.69\n", + "M = 2.288 Msun T = 29326 K m_ubv_U = 12.80\n", + "M = 2.288 Msun T = 134813 K m_ubv_U = 17.53\n", + "Starting filter: ubv,B Elapsed time: 3.20 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_B = 33.52\n", + "M = 1.215 Msun T = 6415 K m_ubv_B = 20.63\n", + "M = 2.038 Msun T = 7816 K m_ubv_B = 17.21\n", + "M = 2.055 Msun T = 4955 K m_ubv_B = 18.57\n", + "M = 2.071 Msun T = 4749 K m_ubv_B = 17.98\n", + "M = 2.275 Msun T = 4576 K m_ubv_B = 17.26\n", + "M = 2.287 Msun T = 3677 K m_ubv_B = 16.33\n", + "M = 2.287 Msun T = 3493 K m_ubv_B = 16.54\n", + "M = 2.287 Msun T = 3478 K m_ubv_B = 16.56\n", + "M = 2.288 Msun T = 3244 K m_ubv_B = 17.33\n", + "M = 2.288 Msun T = 3188 K m_ubv_B = 17.85\n", + "M = 2.288 Msun T = 29326 K m_ubv_B = 13.88\n", + "M = 2.288 Msun T = 134813 K m_ubv_B = 18.72\n", + "Starting filter: ubv,V Elapsed time: 6.33 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_V = 30.94\n", + "M = 1.215 Msun T = 6415 K m_ubv_V = 19.16\n", + "M = 2.038 Msun T = 7816 K m_ubv_V = 16.12\n", + "M = 2.055 Msun T = 4955 K m_ubv_V = 16.52\n", + "M = 2.071 Msun T = 4749 K m_ubv_V = 15.80\n", + "M = 2.275 Msun T = 4576 K m_ubv_V = 14.98\n", + "M = 2.287 Msun T = 3677 K m_ubv_V = 13.46\n", + "M = 2.287 Msun T = 3493 K m_ubv_V = 13.68\n", + "M = 2.287 Msun T = 3478 K m_ubv_V = 13.71\n", + "M = 2.288 Msun T = 3244 K m_ubv_V = 14.82\n", + "M = 2.288 Msun T = 3188 K m_ubv_V = 15.44\n", + "M = 2.288 Msun T = 29326 K m_ubv_V = 13.30\n", + "M = 2.288 Msun T = 134813 K m_ubv_V = 18.29\n", + "Starting filter: ubv,R Elapsed time: 9.49 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_R = 25.82\n", + "M = 1.215 Msun T = 6415 K m_ubv_R = 16.25\n", + "M = 2.038 Msun T = 7816 K m_ubv_R = 13.39\n", + "M = 2.055 Msun T = 4955 K m_ubv_R = 13.40\n", + "M = 2.071 Msun T = 4749 K m_ubv_R = 12.64\n", + "M = 2.275 Msun T = 4576 K m_ubv_R = 11.77\n", + "M = 2.287 Msun T = 3677 K m_ubv_R = 9.85\n", + "M = 2.287 Msun T = 3493 K m_ubv_R = 9.72\n", + "M = 2.287 Msun T = 3478 K m_ubv_R = 9.72\n", + "M = 2.288 Msun T = 3244 K m_ubv_R = 9.98\n", + "M = 2.288 Msun T = 3188 K m_ubv_R = 10.31\n", + "M = 2.288 Msun T = 29326 K m_ubv_R = 10.78\n", + "M = 2.288 Msun T = 134813 K m_ubv_R = 15.84\n", + "Starting filter: ubv,I Elapsed time: 12.72 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_I = 20.31\n", + "M = 1.215 Msun T = 6415 K m_ubv_I = 12.48\n", + "M = 2.038 Msun T = 7816 K m_ubv_I = 9.75\n", + "M = 2.055 Msun T = 4955 K m_ubv_I = 9.42\n", + "M = 2.071 Msun T = 4749 K m_ubv_I = 8.61\n", + "M = 2.275 Msun T = 4576 K m_ubv_I = 7.70\n", + "M = 2.287 Msun T = 3677 K m_ubv_I = 5.31\n", + "M = 2.287 Msun T = 3493 K m_ubv_I = 4.87\n", + "M = 2.287 Msun T = 3478 K m_ubv_I = 4.84\n", + "M = 2.288 Msun T = 3244 K m_ubv_I = 4.46\n", + "M = 2.288 Msun T = 3188 K m_ubv_I = 4.56\n", + "M = 2.288 Msun T = 29326 K m_ubv_I = 7.48\n", + "M = 2.288 Msun T = 134813 K m_ubv_I = 12.62\n", + " Time taken: 15.99 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(9.0, 0.7, 100, math.log10(1), mass_sampling =1 , recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/ryotainagaki/Desktop/PyPopStar/spisea/synthetic.py:2977: RuntimeWarning: overflow encountered in power\n", + " 10 ** iso.secondaries['log_a']])))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater_equal\n", + " result = getattr(super(), op)(other)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "736 star systems had to be deleted\n", + "762 companions had to be deleted\n", + "Found 167 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1, imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2, imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityUnresolved()), 10000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure that I am getting just about enough star mass for my cluster. (It was a bug before I used an adjustment factor.)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6743.40950221685" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "690 star systems had to be deleted\n", + "722 companions had to be deleted\n", + "738 star systems had to be deleted\n", + "772 companions had to be deleted\n", + "736 star systems had to be deleted\n", + "775 companions had to be deleted\n", + "768 star systems had to be deleted\n", + "802 companions had to be deleted\n", + "704 star systems had to be deleted\n", + "736 companions had to be deleted\n", + "712 star systems had to be deleted\n", + "750 companions had to be deleted\n", + "746 star systems had to be deleted\n", + "781 companions had to be deleted\n", + "729 star systems had to be deleted\n", + "759 companions had to be deleted\n", + "699 star systems had to be deleted\n", + "729 companions had to be deleted\n", + "764 star systems had to be deleted\n", + "811 companions had to be deleted\n", + "728 star systems had to be deleted\n", + "755 companions had to be deleted\n", + "715 star systems had to be deleted\n", + "749 companions had to be deleted\n", + "758 star systems had to be deleted\n", + "794 companions had to be deleted\n", + "715 star systems had to be deleted\n", + "755 companions had to be deleted\n", + "711 star systems had to be deleted\n", + "738 companions had to be deleted\n", + "730 star systems had to be deleted\n", + "752 companions had to be deleted\n", + "743 star systems had to be deleted\n", + "771 companions had to be deleted\n", + "722 star systems had to be deleted\n", + "752 companions had to be deleted\n", + "697 star systems had to be deleted\n", + "736 companions had to be deleted\n", + "758 star systems had to be deleted\n", + "799 companions had to be deleted\n", + "697 star systems had to be deleted\n", + "735 companions had to be deleted\n", + "712 star systems had to be deleted\n", + "740 companions had to be deleted\n", + "708 star systems had to be deleted\n", + "735 companions had to be deleted\n", + "730 star systems had to be deleted\n", + "767 companions had to be deleted\n", + "723 star systems had to be deleted\n", + "752 companions had to be deleted\n", + "726 star systems had to be deleted\n", + "765 companions had to be deleted\n", + "746 star systems had to be deleted\n", + "773 companions had to be deleted\n", + "700 star systems had to be deleted\n", + "743 companions had to be deleted\n", + "724 star systems had to be deleted\n", + "752 companions had to be deleted\n", + "778 star systems had to be deleted\n", + "808 companions had to be deleted\n", + "708 star systems had to be deleted\n", + "742 companions had to be deleted\n", + "699 star systems had to be deleted\n", + "726 companions had to be deleted\n", + "703 star systems had to be deleted\n", + "747 companions had to be deleted\n", + "749 star systems had to be deleted\n", + "778 companions had to be deleted\n", + "685 star systems had to be deleted\n", + "728 companions had to be deleted\n", + "727 star systems had to be deleted\n", + "772 companions had to be deleted\n", + "728 star systems had to be deleted\n", + "768 companions had to be deleted\n", + "721 star systems had to be deleted\n", + "758 companions had to be deleted\n", + "718 star systems had to be deleted\n", + "760 companions had to be deleted\n", + "738 star systems had to be deleted\n", + "774 companions had to be deleted\n", + "751 star systems had to be deleted\n", + "776 companions had to be deleted\n", + "739 star systems had to be deleted\n", + "781 companions had to be deleted\n", + "695 star systems had to be deleted\n", + "731 companions had to be deleted\n", + "756 star systems had to be deleted\n", + "794 companions had to be deleted\n", + "740 star systems had to be deleted\n", + "776 companions had to be deleted\n", + "705 star systems had to be deleted\n", + "735 companions had to be deleted\n", + "744 star systems had to be deleted\n", + "785 companions had to be deleted\n", + "706 star systems had to be deleted\n", + "742 companions had to be deleted\n", + "700 star systems had to be deleted\n", + "734 companions had to be deleted\n", + "768 star systems had to be deleted\n", + "802 companions had to be deleted\n", + "699 star systems had to be deleted\n", + "735 companions had to be deleted\n", + "693 star systems had to be deleted\n", + "713 companions had to be deleted\n", + "701 star systems had to be deleted\n", + "721 companions had to be deleted\n", + "740 star systems had to be deleted\n", + "771 companions had to be deleted\n", + "719 star systems had to be deleted\n", + "757 companions had to be deleted\n", + "704 star systems had to be deleted\n", + "745 companions had to be deleted\n", + "734 star systems had to be deleted\n", + "766 companions had to be deleted\n", + "707 star systems had to be deleted\n", + "731 companions had to be deleted\n", + "752 star systems had to be deleted\n", + "784 companions had to be deleted\n", + "724 star systems had to be deleted\n", + "744 companions had to be deleted\n", + "729 star systems had to be deleted\n", + "761 companions had to be deleted\n", + "711 star systems had to be deleted\n", + "749 companions had to be deleted\n", + "773 star systems had to be deleted\n", + "799 companions had to be deleted\n", + "755 star systems had to be deleted\n", + "783 companions had to be deleted\n", + "711 star systems had to be deleted\n", + "742 companions had to be deleted\n", + "724 star systems had to be deleted\n", + "755 companions had to be deleted\n", + "687 star systems had to be deleted\n", + "711 companions had to be deleted\n", + "745 star systems had to be deleted\n", + "781 companions had to be deleted\n", + "752 star systems had to be deleted\n", + "787 companions had to be deleted\n", + "764 star systems had to be deleted\n", + "798 companions had to be deleted\n", + "702 star systems had to be deleted\n", + "739 companions had to be deleted\n", + "761 star systems had to be deleted\n", + "793 companions had to be deleted\n", + "757 star systems had to be deleted\n", + "800 companions had to be deleted\n", + "742 star systems had to be deleted\n", + "769 companions had to be deleted\n", + "741 star systems had to be deleted\n", + "769 companions had to be deleted\n", + "754 star systems had to be deleted\n", + "787 companions had to be deleted\n", + "738 star systems had to be deleted\n", + "778 companions had to be deleted\n", + "697 star systems had to be deleted\n", + "731 companions had to be deleted\n", + "718 star systems had to be deleted\n", + "754 companions had to be deleted\n", + "720 star systems had to be deleted\n", + "746 companions had to be deleted\n", + "711 star systems had to be deleted\n", + "735 companions had to be deleted\n", + "708 star systems had to be deleted\n", + "746 companions had to be deleted\n" + ] + } + ], + "source": [ + "arr =[]\n", + "for x in range(100):\n", + " clus_1=synthetic.Cluster_w_Binaries(iso1, imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + " arr.append(clus_1.star_systems['systemMass'].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "hist, bins, patch = plt.hist(np.array(arr), bins=30, histtype='step', label='BPASS clusters with x mass')\n", + "plt.title(\"BPASS cluster mass distribution (# of models within mass bin over mass)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['systemMass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['systemMass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.yscale('log')\n", + "plt.title(\"mass distribution plots of the cluster system masses on logarithmic scale of the horizontal axis.\")\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['mass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['mass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.title(\"mass distribution plots of the cluster single stars on logarithmic scale of the horizontal axis.\")\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(clus_1.star_systems['systemMass'], bins=30)\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.companions['mass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.companions['mass'], bins=30, histtype='step', label='MISTv.1')\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the cluster companion stars on logarithmic scale of the horizontal axis.\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Please compare me with the MIST v1 cluster mass\n", + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.primaries['mass'], bins=30)\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the isochrone_binary's primaries on logarithmic scale of the horizontal axis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.singles['mass'], bins=30)\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the isochrone_binary's singles on logarithmic scale of the horizontal axis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.secondaries['mass'], bins=30)\n", + "plt.yscale('log')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.title(\"mass distribution plots of the isochrone_binary's secondaries on logarithmic scale of the horizontal axis.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.where(iso1.secondaries['mass']>iso1.primaries['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.unique(iso1.primaries[np.where(iso1.secondaries['mass']>iso1.primaries['mass'])]['source'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.companions['log_a'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.companions['log_a'], bins=30, histtype='step', label='MISTv.1')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cluster_Mass_Error_Demonstration/Error Demonstration 10^9 yr old Salpeter.ipynb b/Cluster_Mass_Error_Demonstration/Error Demonstration 10^9 yr old Salpeter.ipynb new file mode 100644 index 00000000..ac1b3e2d --- /dev/null +++ b/Cluster_Mass_Error_Demonstration/Error Demonstration 10^9 yr old Salpeter.ipynb @@ -0,0 +1,1154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS cluster at 10^9 years age.\n", + "Here, I try to create a 10000 solar mass BPASS cluster and ResolvedCluster. **This is to demonstrate the issue I have with the mass-matching inside of the Binary_Cluster implementation.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:346: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1794: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/column.py:1020: RuntimeWarning: invalid value encountered in greater\n", + " result = getattr(super(), op)(other)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:477: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 23286 logg=5.78\n", + "Changing to logg=5.00 for T= 17733 logg=5.24\n", + "Changing to logg=5.00 for T= 24377 logg=5.87\n", + "Changing to logg=5.00 for T= 24403 logg=5.91\n", + "Changing to logg=5.00 for T= 26316 logg=5.57\n", + "Changing to logg=5.00 for T= 26930 logg=5.65\n", + "Changing to logg=5.00 for T= 24518 logg=6.01\n", + "Changing to logg=5.00 for T= 25618 logg=5.53\n", + "Changing to logg=5.00 for T= 26119 logg=5.75\n", + "Changing to logg=5.00 for T= 24305 logg=6.01\n", + "Changing to logg=5.00 for T= 26134 logg=5.73\n", + "Changing to logg=5.00 for T= 16799 logg=5.12\n", + "Changing to logg=5.00 for T= 20198 logg=5.51\n", + "Changing to logg=5.00 for T= 27134 logg=5.64\n", + "Changing to logg=5.00 for T= 26114 logg=5.72\n", + "Changing to logg=5.00 for T= 24452 logg=5.99\n", + "Changing to logg=5.00 for T= 22084 logg=5.78\n", + "Changing to logg=5.00 for T= 22961 logg=5.92\n", + "Changing to logg=5.00 for T= 21333 logg=5.59\n", + "Changing to logg=5.00 for T= 23774 logg=6.99\n", + "Changing to logg=5.00 for T= 23264 logg=5.77\n", + "Changing to logg=5.00 for T= 23096 logg=5.56\n", + "Changing to logg=5.00 for T= 27240 logg=5.65\n", + "Changing to logg=5.00 for T= 22114 logg=5.26\n", + "Changing to logg=5.00 for T= 26757 logg=5.79\n", + "Changing to logg=5.00 for T= 20983 logg=5.17\n", + "Changing to logg=5.00 for T= 26427 logg=5.72\n", + "Changing to logg=5.00 for T= 22135 logg=6.94\n", + "Changing to logg=5.00 for T= 24306 logg=5.43\n", + "Changing to logg=5.00 for T= 24820 logg=6.17\n", + "Changing to logg=5.00 for T= 24008 logg=6.91\n", + "Changing to logg=5.00 for T= 27188 logg=5.67\n", + "Changing to logg=5.00 for T= 21570 logg=5.64\n", + "Changing to logg=5.00 for T= 25553 logg=5.87\n", + "Changing to logg=5.00 for T= 18719 logg=5.30\n", + "Changing to logg=5.00 for T= 25509 logg=5.94\n", + "Changing to logg=5.00 for T= 25707 logg=5.88\n", + "Changing to logg=5.00 for T= 22982 logg=5.76\n", + "Changing to logg=5.00 for T= 24495 logg=5.62\n", + "Changing to logg=5.00 for T= 23626 logg=5.99\n", + "Changing to logg=5.00 for T= 17042 logg=5.14\n", + "Changing to logg=5.00 for T= 24739 logg=6.19\n", + "Changing to logg=5.00 for T= 25963 logg=5.80\n", + "Changing to logg=5.00 for T= 23984 logg=6.88\n", + "Changing to logg=5.00 for T= 19593 logg=6.97\n", + "Changing to logg=5.00 for T= 24124 logg=5.48\n", + "Changing to logg=5.00 for T= 25973 logg=5.55\n", + "Changing to logg=5.00 for T= 21304 logg=5.48\n", + "Changing to logg=5.00 for T= 26020 logg=5.81\n", + "Changing to logg=5.00 for T= 25999 logg=5.76\n", + "Changing to logg=5.00 for T= 22646 logg=5.73\n", + "Changing to logg=5.00 for T= 25848 logg=5.79\n", + "Changing to logg=5.00 for T= 26959 logg=5.63\n", + "Changing to logg=5.00 for T= 27359 logg=5.67\n", + "Changing to logg=5.00 for T= 26907 logg=5.63\n", + "Changing to logg=5.00 for T= 21104 logg=5.60\n", + "Changing to logg=5.00 for T= 26100 logg=5.77\n", + "Changing to logg=5.00 for T= 22179 logg=5.92\n", + "Changing to logg=5.00 for T= 22891 logg=5.32\n", + "Changing to logg=5.00 for T= 21139 logg=5.58\n", + "Changing to logg=5.00 for T= 24444 logg=5.91\n", + "Changing to logg=5.00 for T= 20689 logg=5.14\n", + "Changing to logg=5.00 for T= 26317 logg=5.76\n", + "Changing to logg=5.00 for T= 26494 logg=5.69\n", + "Changing to logg=5.00 for T= 17875 logg=5.24\n", + "Changing to logg=5.00 for T= 18636 logg=5.32\n", + "Changing to logg=5.00 for T= 25863 logg=5.74\n", + "Changing to logg=5.00 for T= 24009 logg=5.86\n", + "Changing to logg=5.00 for T= 21888 logg=5.45\n", + "Changing to logg=5.00 for T= 23501 logg=5.37\n", + "Changing to logg=5.00 for T= 26095 logg=5.78\n", + "Changing to logg=5.00 for T= 27193 logg=5.64\n", + "Changing to logg=5.00 for T= 26146 logg=5.79\n", + "Changing to logg=5.00 for T= 22008 logg=7.09\n", + "Changing to logg=5.00 for T= 24403 logg=5.88\n", + "Changing to logg=5.00 for T= 19749 logg=5.03\n", + "Changing to logg=5.00 for T= 26204 logg=5.71\n", + "Changing to logg=5.00 for T= 26196 logg=5.70\n", + "Changing to logg=5.00 for T= 22245 logg=6.65\n", + "Changing to logg=5.00 for T= 25914 logg=5.89\n", + "Changing to logg=5.00 for T= 21142 logg=5.20\n", + "Changing to logg=5.00 for T= 24186 logg=5.42\n", + "Making photometry for isochrone: log(t) = 9.00 AKs = 0.70 dist = 100\n", + " Starting at: 2021-01-08 01:19:55.017408 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_U = 19.68\n", + "M = 3.200 Msun T = 10963 K m_ubv_U = 27.99\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_U = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_U = 28.18\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_U = 17.80\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_U = 28.39\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_U = 27.99\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_U = 28.17\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 2.100 Msun T = 6638 K m_ubv_U = 17.64\n", + "M = 3.500 Msun T = 12654 K m_ubv_U = 28.16\n", + "M = 1.700 Msun T = 7133 K m_ubv_U = 17.86\n", + "M = 1.800 Msun T = 7355 K m_ubv_U = 18.42\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_U = 28.17\n", + "M = 4.500 Msun T = 10856 K m_ubv_U = 28.39\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_U = 19.08\n", + "M = 1.700 Msun T = 7242 K m_ubv_U = 18.75\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_U = 28.26\n", + "M = 1.200 Msun T = 6151 K m_ubv_U = 20.85\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 1.900 Msun T = 7312 K m_ubv_U = 18.07\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_U = 17.80\n", + "M = 1.700 Msun T = 7246 K m_ubv_U = 18.74\n", + "M = 1.800 Msun T = 7355 K m_ubv_U = 18.42\n", + "M = 4.000 Msun T = 11144 K m_ubv_U = 28.23\n", + "M = 3.000 Msun T = 11468 K m_ubv_U = 27.77\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_U = 33.22\n", + "M = 3.700 Msun T = 12543 K m_ubv_U = 28.16\n", + "M = 3.200 Msun T = 10973 K m_ubv_U = 27.99\n", + "M = 2.000 Msun T = 7250 K m_ubv_U = 17.80\n", + "M = 1.800 Msun T = 7355 K m_ubv_U = 18.42\n", + "M = 5.000 Msun T = 10786 K m_ubv_U = 28.41\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_U = 17.88\n", + "M = 0.320 Msun T = nan K m_ubv_U = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_U = 17.30\n", + "M = 0.200 Msun T = 3299 K m_ubv_U = 29.80\n", + "M = 3.500 Msun T = 11388 K m_ubv_U = 27.96\n", + "M = 3.000 Msun T = 12026 K m_ubv_U = 15.93\n", + "M = 1.900 Msun T = 7266 K m_ubv_U = 17.88\n", + "M = 4.500 Msun T = 11882 K m_ubv_U = 28.16\n", + "M = 2.500 Msun T = 17317 K m_ubv_U = 26.55\n", + "M = 2.880 Msun T = 8175 K m_ubv_U = 15.62\n", + "M = 3.700 Msun T = 11133 K m_ubv_U = 28.18\n", + "M = 3.600 Msun T = 9273 K m_ubv_U = 14.60\n", + "M = 1.000 Msun T = 5608 K m_ubv_U = 21.96\n", + "M = 0.420 Msun T = 2424 K m_ubv_U = 30.76\n", + "M = 1.750 Msun T = nan K m_ubv_U = nan\n", + "M = 0.170 Msun T = nan K m_ubv_U = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_U = 18.71\n", + "M = 1.200 Msun T = 6207 K m_ubv_U = 20.65\n", + "M = 7.000 Msun T = 17765 K m_ubv_U = 28.78\n", + "M = 1.350 Msun T = nan K m_ubv_U = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_U = 13.74\n", + "M = 2.500 Msun T = 15710 K m_ubv_U = 18.08\n", + "M = 0.960 Msun T = 5540 K m_ubv_U = 21.88\n", + "M = 0.680 Msun T = 3924 K m_ubv_U = 25.25\n", + "M = 2.000 Msun T = 7152 K m_ubv_U = 17.60\n", + "M = 40.000 Msun T = 10250 K m_ubv_U = 26.90\n", + "M = 3.500 Msun T = 11388 K m_ubv_U = 27.96\n", + "M = 1.200 Msun T = 5961 K m_ubv_U = 20.86\n", + "M = 0.600 Msun T = 3462 K m_ubv_U = 26.66\n", + "M = 0.600 Msun T = 3950 K m_ubv_U = 26.04\n", + "M = 0.570 Msun T = 3878 K m_ubv_U = 26.34\n", + "M = 3.000 Msun T = 11498 K m_ubv_U = 27.77\n", + "M = 1.400 Msun T = 6747 K m_ubv_U = 19.64\n", + "M = 0.340 Msun T = 1962 K m_ubv_U = 33.22\n", + "M = 0.720 Msun T = 4155 K m_ubv_U = 24.64\n", + "M = 2.000 Msun T = 7108 K m_ubv_U = 17.60\n", + "M = 1.500 Msun T = 7084 K m_ubv_U = 19.30\n", + "M = 1.200 Msun T = 6155 K m_ubv_U = 20.66\n", + "M = 7.000 Msun T = 17707 K m_ubv_U = 28.78\n", + "M = 0.740 Msun T = nan K m_ubv_U = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_U = 17.86\n", + "M = 1.800 Msun T = 7808 K m_ubv_U = 18.08\n", + "M = 0.900 Msun T = 5193 K m_ubv_U = 22.45\n", + "M = 4.000 Msun T = 11312 K m_ubv_U = 13.81\n", + "Starting filter: ubv,V Elapsed time: 26.81 seconds\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_V = 18.36\n", + "M = 3.200 Msun T = 10963 K m_ubv_V = 27.51\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_V = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_V = 27.73\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_V = 16.54\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_V = 27.98\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_V = 27.51\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_V = 27.74\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 2.100 Msun T = 6638 K m_ubv_V = 16.26\n", + "M = 3.500 Msun T = 12654 K m_ubv_V = 27.82\n", + "M = 1.700 Msun T = 7133 K m_ubv_V = 16.59\n", + "M = 1.800 Msun T = 7355 K m_ubv_V = 17.20\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_V = 27.84\n", + "M = 4.500 Msun T = 10856 K m_ubv_V = 27.93\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_V = 17.83\n", + "M = 1.700 Msun T = 7242 K m_ubv_V = 17.52\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_V = 27.75\n", + "M = 1.200 Msun T = 6151 K m_ubv_V = 19.34\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 1.900 Msun T = 7312 K m_ubv_V = 16.84\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_V = 16.54\n", + "M = 1.700 Msun T = 7246 K m_ubv_V = 17.52\n", + "M = 1.800 Msun T = 7355 K m_ubv_V = 17.20\n", + "M = 4.000 Msun T = 11144 K m_ubv_V = 27.78\n", + "M = 3.000 Msun T = 11468 K m_ubv_V = 27.31\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_V = 29.80\n", + "M = 3.700 Msun T = 12543 K m_ubv_V = 27.81\n", + "M = 3.200 Msun T = 10973 K m_ubv_V = 27.51\n", + "M = 2.000 Msun T = 7250 K m_ubv_V = 16.55\n", + "M = 1.800 Msun T = 7355 K m_ubv_V = 17.20\n", + "M = 5.000 Msun T = 10786 K m_ubv_V = 27.95\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_V = 16.98\n", + "M = 0.320 Msun T = nan K m_ubv_V = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_V = 15.43\n", + "M = 0.200 Msun T = 3299 K m_ubv_V = 26.72\n", + "M = 3.500 Msun T = 11388 K m_ubv_V = 27.51\n", + "M = 3.000 Msun T = 12026 K m_ubv_V = 15.82\n", + "M = 1.900 Msun T = 7266 K m_ubv_V = 16.98\n", + "M = 4.500 Msun T = 11882 K m_ubv_V = 27.76\n", + "M = 2.500 Msun T = 17317 K m_ubv_V = 26.44\n", + "M = 2.880 Msun T = 8175 K m_ubv_V = 14.95\n", + "M = 3.700 Msun T = 11133 K m_ubv_V = 27.73\n", + "M = 3.600 Msun T = 9273 K m_ubv_V = 14.14\n", + "M = 1.000 Msun T = 5608 K m_ubv_V = 20.45\n", + "M = 0.420 Msun T = 2424 K m_ubv_V = 27.31\n", + "M = 1.750 Msun T = nan K m_ubv_V = nan\n", + "M = 0.170 Msun T = nan K m_ubv_V = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_V = 17.86\n", + "M = 1.200 Msun T = 6207 K m_ubv_V = 19.40\n", + "M = 7.000 Msun T = 17765 K m_ubv_V = 28.75\n", + "M = 1.350 Msun T = nan K m_ubv_V = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_V = 13.51\n", + "M = 2.500 Msun T = 15710 K m_ubv_V = 18.23\n", + "M = 0.960 Msun T = 5540 K m_ubv_V = 20.34\n", + "M = 0.680 Msun T = 3924 K m_ubv_V = 22.69\n", + "M = 2.000 Msun T = 7152 K m_ubv_V = 16.67\n", + "M = 40.000 Msun T = 10250 K m_ubv_V = 26.24\n", + "M = 3.500 Msun T = 11388 K m_ubv_V = 27.51\n", + "M = 1.200 Msun T = 5961 K m_ubv_V = 19.51\n", + "M = 0.600 Msun T = 3462 K m_ubv_V = 23.72\n", + "M = 0.600 Msun T = 3950 K m_ubv_V = 23.49\n", + "M = 0.570 Msun T = 3878 K m_ubv_V = 23.74\n", + "M = 3.000 Msun T = 11498 K m_ubv_V = 27.31\n", + "M = 1.400 Msun T = 6747 K m_ubv_V = 18.58\n", + "M = 0.340 Msun T = 1962 K m_ubv_V = 30.01\n", + "M = 0.720 Msun T = 4155 K m_ubv_V = 22.26\n", + "M = 2.000 Msun T = 7108 K m_ubv_V = 16.66\n", + "M = 1.500 Msun T = 7084 K m_ubv_V = 18.34\n", + "M = 1.200 Msun T = 6155 K m_ubv_V = 19.39\n", + "M = 7.000 Msun T = 17707 K m_ubv_V = 28.74\n", + "M = 0.740 Msun T = nan K m_ubv_V = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_V = 17.30\n", + "M = 1.800 Msun T = 7808 K m_ubv_V = 17.32\n", + "M = 0.900 Msun T = 5193 K m_ubv_V = 20.74\n", + "M = 4.000 Msun T = 11312 K m_ubv_V = 13.62\n", + "Starting filter: ubv,B Elapsed time: 52.82 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_B = 19.76\n", + "M = 3.200 Msun T = 10963 K m_ubv_B = 28.56\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_B = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_B = 28.78\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_B = 17.78\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_B = 29.02\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_B = 28.56\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_B = 28.78\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 2.100 Msun T = 6638 K m_ubv_B = 17.63\n", + "M = 3.500 Msun T = 12654 K m_ubv_B = 28.82\n", + "M = 1.700 Msun T = 7133 K m_ubv_B = 17.84\n", + "M = 1.800 Msun T = 7355 K m_ubv_B = 18.42\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_B = 28.84\n", + "M = 4.500 Msun T = 10856 K m_ubv_B = 28.99\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_B = 19.12\n", + "M = 1.700 Msun T = 7242 K m_ubv_B = 18.77\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_B = 28.83\n", + "M = 1.200 Msun T = 6151 K m_ubv_B = 20.90\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 1.900 Msun T = 7312 K m_ubv_B = 18.06\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_B = 17.78\n", + "M = 1.700 Msun T = 7246 K m_ubv_B = 18.76\n", + "M = 1.800 Msun T = 7355 K m_ubv_B = 18.42\n", + "M = 4.000 Msun T = 11144 K m_ubv_B = 28.83\n", + "M = 3.000 Msun T = 11468 K m_ubv_B = 28.34\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_B = 33.10\n", + "M = 3.700 Msun T = 12543 K m_ubv_B = 28.81\n", + "M = 3.200 Msun T = 10973 K m_ubv_B = 28.56\n", + "M = 2.000 Msun T = 7250 K m_ubv_B = 17.78\n", + "M = 1.800 Msun T = 7355 K m_ubv_B = 18.42\n", + "M = 5.000 Msun T = 10786 K m_ubv_B = 29.02\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_B = 18.36\n", + "M = 0.320 Msun T = nan K m_ubv_B = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_B = 17.38\n", + "M = 0.200 Msun T = 3299 K m_ubv_B = 29.53\n", + "M = 3.500 Msun T = 11388 K m_ubv_B = 28.55\n", + "M = 3.000 Msun T = 12026 K m_ubv_B = 16.74\n", + "M = 1.900 Msun T = 7266 K m_ubv_B = 18.36\n", + "M = 4.500 Msun T = 11882 K m_ubv_B = 28.78\n", + "M = 2.500 Msun T = 17317 K m_ubv_B = 27.25\n", + "M = 2.880 Msun T = 8175 K m_ubv_B = 16.20\n", + "M = 3.700 Msun T = 11133 K m_ubv_B = 28.78\n", + "M = 3.600 Msun T = 9273 K m_ubv_B = 15.26\n", + "M = 1.000 Msun T = 5608 K m_ubv_B = 22.18\n", + "M = 0.420 Msun T = 2424 K m_ubv_B = 30.95\n", + "M = 1.750 Msun T = nan K m_ubv_B = nan\n", + "M = 0.170 Msun T = nan K m_ubv_B = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_B = 19.21\n", + "M = 1.200 Msun T = 6207 K m_ubv_B = 20.98\n", + "M = 7.000 Msun T = 17765 K m_ubv_B = 29.63\n", + "M = 1.350 Msun T = nan K m_ubv_B = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_B = 14.51\n", + "M = 2.500 Msun T = 15710 K m_ubv_B = 19.01\n", + "M = 0.960 Msun T = 5540 K m_ubv_B = 22.10\n", + "M = 0.680 Msun T = 3924 K m_ubv_B = 25.09\n", + "M = 2.000 Msun T = 7152 K m_ubv_B = 18.07\n", + "M = 40.000 Msun T = 10250 K m_ubv_B = 27.29\n", + "M = 3.500 Msun T = 11388 K m_ubv_B = 28.55\n", + "M = 1.200 Msun T = 5961 K m_ubv_B = 21.15\n", + "M = 0.600 Msun T = 3462 K m_ubv_B = 26.41\n", + "M = 0.600 Msun T = 3950 K m_ubv_B = 25.88\n", + "M = 0.570 Msun T = 3878 K m_ubv_B = 26.17\n", + "M = 3.000 Msun T = 11498 K m_ubv_B = 28.34\n", + "M = 1.400 Msun T = 6747 K m_ubv_B = 20.05\n", + "M = 0.340 Msun T = 1962 K m_ubv_B = 34.21\n", + "M = 0.720 Msun T = 4155 K m_ubv_B = 24.54\n", + "M = 2.000 Msun T = 7108 K m_ubv_B = 18.06\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 1.500 Msun T = 7084 K m_ubv_B = 19.75\n", + "M = 1.200 Msun T = 6155 K m_ubv_B = 20.99\n", + "M = 7.000 Msun T = 17707 K m_ubv_B = 29.62\n", + "M = 0.740 Msun T = nan K m_ubv_B = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_B = 18.48\n", + "M = 1.800 Msun T = 7808 K m_ubv_B = 18.62\n", + "M = 0.900 Msun T = 5193 K m_ubv_B = 22.60\n", + "M = 4.000 Msun T = 11312 K m_ubv_B = 14.59\n", + "Starting filter: ubv,R Elapsed time: 79.69 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_R = 15.49\n", + "M = 3.200 Msun T = 10963 K m_ubv_R = 24.87\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_R = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_R = 25.09\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_R = 13.74\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_R = 25.35\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_R = 24.87\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_R = 25.11\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 2.100 Msun T = 6638 K m_ubv_R = 13.39\n", + "M = 3.500 Msun T = 12654 K m_ubv_R = 25.23\n", + "M = 1.700 Msun T = 7133 K m_ubv_R = 13.78\n", + "M = 1.800 Msun T = 7355 K m_ubv_R = 14.41\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_R = 25.26\n", + "M = 4.500 Msun T = 10856 K m_ubv_R = 25.28\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_R = 15.00\n", + "M = 1.700 Msun T = 7242 K m_ubv_R = 14.72\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_R = 25.06\n", + "M = 1.200 Msun T = 6151 K m_ubv_R = 16.40\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 1.900 Msun T = 7312 K m_ubv_R = 14.05\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_R = 13.74\n", + "M = 1.700 Msun T = 7246 K m_ubv_R = 14.71\n", + "M = 1.800 Msun T = 7355 K m_ubv_R = 14.41\n", + "M = 4.000 Msun T = 11144 K m_ubv_R = 25.14\n", + "M = 3.000 Msun T = 11468 K m_ubv_R = 24.69\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_R = 25.90\n", + "M = 3.700 Msun T = 12543 K m_ubv_R = 25.21\n", + "M = 3.200 Msun T = 10973 K m_ubv_R = 24.87\n", + "M = 2.000 Msun T = 7250 K m_ubv_R = 13.75\n", + "M = 1.800 Msun T = 7355 K m_ubv_R = 14.41\n", + "M = 5.000 Msun T = 10786 K m_ubv_R = 25.30\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_R = 14.09\n", + "M = 0.320 Msun T = nan K m_ubv_R = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_R = 12.26\n", + "M = 0.200 Msun T = 3299 K m_ubv_R = 23.10\n", + "M = 3.500 Msun T = 11388 K m_ubv_R = 24.89\n", + "M = 3.000 Msun T = 12026 K m_ubv_R = 13.13\n", + "M = 1.900 Msun T = 7266 K m_ubv_R = 14.09\n", + "M = 4.500 Msun T = 11882 K m_ubv_R = 25.15\n", + "M = 2.500 Msun T = 17317 K m_ubv_R = 23.91\n", + "M = 2.880 Msun T = 8175 K m_ubv_R = 12.12\n", + "M = 3.700 Msun T = 11133 K m_ubv_R = 25.09\n", + "M = 3.600 Msun T = 9273 K m_ubv_R = 11.36\n", + "M = 1.000 Msun T = 5608 K m_ubv_R = 17.38\n", + "M = 0.420 Msun T = 2424 K m_ubv_R = 23.19\n", + "M = 1.750 Msun T = nan K m_ubv_R = nan\n", + "M = 0.170 Msun T = nan K m_ubv_R = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_R = 14.98\n", + "M = 1.200 Msun T = 6207 K m_ubv_R = 16.41\n", + "M = 7.000 Msun T = 17765 K m_ubv_R = 26.22\n", + "M = 1.350 Msun T = nan K m_ubv_R = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_R = 10.79\n", + "M = 2.500 Msun T = 15710 K m_ubv_R = 15.61\n", + "M = 0.960 Msun T = 5540 K m_ubv_R = 17.27\n", + "M = 0.680 Msun T = 3924 K m_ubv_R = 19.29\n", + "M = 2.000 Msun T = 7152 K m_ubv_R = 13.76\n", + "M = 40.000 Msun T = 10250 K m_ubv_R = 23.59\n", + "M = 3.500 Msun T = 11388 K m_ubv_R = 24.89\n", + "M = 1.200 Msun T = 5961 K m_ubv_R = 16.49\n", + "M = 0.600 Msun T = 3462 K m_ubv_R = 20.16\n", + "M = 0.600 Msun T = 3950 K m_ubv_R = 20.10\n", + "M = 0.570 Msun T = 3878 K m_ubv_R = 20.32\n", + "M = 3.000 Msun T = 11498 K m_ubv_R = 24.69\n", + "M = 1.400 Msun T = 6747 K m_ubv_R = 15.64\n", + "M = 0.340 Msun T = 1962 K m_ubv_R = 25.45\n", + "M = 0.720 Msun T = 4155 K m_ubv_R = 18.91\n", + "M = 2.000 Msun T = 7108 K m_ubv_R = 13.75\n", + "M = 1.500 Msun T = 7084 K m_ubv_R = 15.43\n", + "M = 1.200 Msun T = 6155 K m_ubv_R = 16.39\n", + "M = 7.000 Msun T = 17707 K m_ubv_R = 26.22\n", + "M = 0.740 Msun T = nan K m_ubv_R = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_R = 14.51\n", + "M = 1.800 Msun T = 7808 K m_ubv_R = 14.47\n", + "M = 0.900 Msun T = 5193 K m_ubv_R = 17.61\n", + "M = 4.000 Msun T = 11312 K m_ubv_R = 10.92\n", + "Starting filter: ubv,I Elapsed time: 106.77 seconds\n", + "Starting synthetic photometry\n", + "Singles\n", + "M = 1.440 Msun T = 6673 K m_ubv_I = 11.75\n", + "M = 3.200 Msun T = 10963 K m_ubv_I = 21.39\n", + "Primaries\n", + "M = 0.398 Msun T = nan K m_ubv_I = nan\n", + "M = 3.200 Msun T = 11170 K m_ubv_I = 21.62\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_I = 10.05\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 5.000 Msun T = 11503 K m_ubv_I = 21.89\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 3.200 Msun T = 10978 K m_ubv_I = 21.39\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 4.000 Msun T = 11435 K m_ubv_I = 21.65\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 2.100 Msun T = 6638 K m_ubv_I = 9.65\n", + "M = 3.500 Msun T = 12654 K m_ubv_I = 21.80\n", + "M = 1.700 Msun T = 7133 K m_ubv_I = 10.09\n", + "M = 1.800 Msun T = 7355 K m_ubv_I = 10.74\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 12885 K m_ubv_I = 21.84\n", + "M = 4.500 Msun T = 10856 K m_ubv_I = 21.80\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.600 Msun T = 7037 K m_ubv_I = 11.30\n", + "M = 1.700 Msun T = 7242 K m_ubv_I = 11.04\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 7.943 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 4.000 Msun T = 10127 K m_ubv_I = 21.54\n", + "M = 1.200 Msun T = 6151 K m_ubv_I = 12.60\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 1.900 Msun T = 7312 K m_ubv_I = 10.38\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 2.000 Msun T = 7188 K m_ubv_I = 10.05\n", + "M = 1.700 Msun T = 7246 K m_ubv_I = 11.03\n", + "M = 1.800 Msun T = 7355 K m_ubv_I = 10.74\n", + "M = 4.000 Msun T = 11144 K m_ubv_I = 21.67\n", + "M = 3.000 Msun T = 11468 K m_ubv_I = 21.23\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 5.012 Msun T = 2739 K m_ubv_I = 21.09\n", + "M = 3.700 Msun T = 12543 K m_ubv_I = 21.79\n", + "M = 3.200 Msun T = 10973 K m_ubv_I = 21.39\n", + "M = 2.000 Msun T = 7250 K m_ubv_I = 10.07\n", + "M = 1.800 Msun T = 7355 K m_ubv_I = 10.74\n", + "M = 5.000 Msun T = 10786 K m_ubv_I = 21.81\n", + "Secondaries\n", + "M = 1.900 Msun T = 7266 K m_ubv_I = 10.39\n", + "M = 0.320 Msun T = nan K m_ubv_I = nan\n", + "M = 2.300 Msun T = 4923 K m_ubv_I = 8.25\n", + "M = 0.200 Msun T = 3299 K m_ubv_I = 18.60\n", + "M = 3.500 Msun T = 11388 K m_ubv_I = 21.43\n", + "M = 3.000 Msun T = 12026 K m_ubv_I = 9.66\n", + "M = 1.900 Msun T = 7266 K m_ubv_I = 10.39\n", + "M = 4.500 Msun T = 11882 K m_ubv_I = 21.70\n", + "M = 2.500 Msun T = 17317 K m_ubv_I = 20.56\n", + "M = 2.880 Msun T = 8175 K m_ubv_I = 8.48\n", + "M = 3.700 Msun T = 11133 K m_ubv_I = 21.62\n", + "M = 3.600 Msun T = 9273 K m_ubv_I = 7.79\n", + "M = 1.000 Msun T = 5608 K m_ubv_I = 13.49\n", + "M = 0.420 Msun T = 2424 K m_ubv_I = 18.15\n", + "M = 1.750 Msun T = nan K m_ubv_I = nan\n", + "M = 0.170 Msun T = nan K m_ubv_I = nan\n", + "M = 1.620 Msun T = 7460 K m_ubv_I = 11.29\n", + "M = 1.200 Msun T = 6207 K m_ubv_I = 12.60\n", + "M = 7.000 Msun T = 17765 K m_ubv_I = 22.87\n", + "M = 1.350 Msun T = nan K m_ubv_I = nan\n", + "M = 4.050 Msun T = 10873 K m_ubv_I = 7.28\n", + "M = 2.500 Msun T = 15710 K m_ubv_I = 12.21\n", + "M = 0.960 Msun T = 5540 K m_ubv_I = 13.37\n", + "M = 0.680 Msun T = 3924 K m_ubv_I = 15.03\n", + "M = 2.000 Msun T = 7152 K m_ubv_I = 10.05\n", + "M = 40.000 Msun T = 10250 K m_ubv_I = 20.09\n", + "M = 3.500 Msun T = 11388 K m_ubv_I = 21.43\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M = 1.200 Msun T = 5961 K m_ubv_I = 12.65\n", + "M = 0.600 Msun T = 3462 K m_ubv_I = 15.73\n", + "M = 0.600 Msun T = 3950 K m_ubv_I = 15.85\n", + "M = 0.570 Msun T = 3878 K m_ubv_I = 16.05\n", + "M = 3.000 Msun T = 11498 K m_ubv_I = 21.24\n", + "M = 1.400 Msun T = 6747 K m_ubv_I = 11.89\n", + "M = 0.340 Msun T = 1962 K m_ubv_I = 19.95\n", + "M = 0.720 Msun T = 4155 K m_ubv_I = 14.72\n", + "M = 2.000 Msun T = 7108 K m_ubv_I = 10.03\n", + "M = 1.500 Msun T = 7084 K m_ubv_I = 11.71\n", + "M = 1.200 Msun T = 6155 K m_ubv_I = 12.58\n", + "M = 7.000 Msun T = 17707 K m_ubv_I = 22.87\n", + "M = 0.740 Msun T = nan K m_ubv_I = nan\n", + "M = 1.920 Msun T = 8764 K m_ubv_I = 10.90\n", + "M = 1.800 Msun T = 7808 K m_ubv_I = 10.80\n", + "M = 0.900 Msun T = 5193 K m_ubv_I = 13.65\n", + "M = 4.000 Msun T = 11312 K m_ubv_I = 7.43\n", + " Time taken: 133.39 seconds\n", + "Isochrone generation took 373.063306 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "import time\n", + "import numpy as np\n", + "t1=time.time()\n", + "bps=evolution.BPASS()\n", + "iso1=synthetic.Isochrone_Binary(9.0, 0.7, 100,math.log10(1), mass_sampling=1, filepath='/g/lu/scratch/ryotainagaki/BPASS_iso_filesTimedIsolated/')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
\n", + "\n", + "\n", + "\n", + "
massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass L Teff R logg ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.primaries[np.where(iso1.primaries['mass']< 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
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masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.secondaries['mass'] < 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
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masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.singles['mass'] < 0)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a sanity check, we can see that the primaries, secondaries, and single stars have phasees of 5 or of 101. Note that we will have a phase of 110 in order to indicate a mystery compact remnant that BPASS provides for NEWSECMODS (secondary star models with compact primaries). This is since we haven't added neutron stars or black holes yet. Stars with phase of -99 exist, and those are the secondary stars that have already merged." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 83.553300 s.\n", + "Making photometry for isochrone: log(t) = 9.00 AKs = 0.70 dist = 100\n", + " Starting at: 2021-01-08 01:23:32.100342 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_U = 33.40\n", + "M = 1.215 Msun T = 6415 K m_ubv_U = 20.55\n", + "M = 2.038 Msun T = 7816 K m_ubv_U = 17.29\n", + "M = 2.055 Msun T = 4955 K m_ubv_U = 19.05\n", + "M = 2.071 Msun T = 4749 K m_ubv_U = 18.64\n", + "M = 2.275 Msun T = 4576 K m_ubv_U = 18.06\n", + "M = 2.287 Msun T = 3677 K m_ubv_U = 17.25\n", + "M = 2.287 Msun T = 3493 K m_ubv_U = 17.19\n", + "M = 2.287 Msun T = 3478 K m_ubv_U = 17.19\n", + "M = 2.288 Msun T = 3244 K m_ubv_U = 17.39\n", + "M = 2.288 Msun T = 3188 K m_ubv_U = 17.69\n", + "M = 2.288 Msun T = 29326 K m_ubv_U = 12.80\n", + "M = 2.288 Msun T = 134813 K m_ubv_U = 17.53\n", + "Starting filter: ubv,B Elapsed time: 3.16 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_B = 33.52\n", + "M = 1.215 Msun T = 6415 K m_ubv_B = 20.63\n", + "M = 2.038 Msun T = 7816 K m_ubv_B = 17.21\n", + "M = 2.055 Msun T = 4955 K m_ubv_B = 18.57\n", + "M = 2.071 Msun T = 4749 K m_ubv_B = 17.98\n", + "M = 2.275 Msun T = 4576 K m_ubv_B = 17.26\n", + "M = 2.287 Msun T = 3677 K m_ubv_B = 16.33\n", + "M = 2.287 Msun T = 3493 K m_ubv_B = 16.54\n", + "M = 2.287 Msun T = 3478 K m_ubv_B = 16.56\n", + "M = 2.288 Msun T = 3244 K m_ubv_B = 17.33\n", + "M = 2.288 Msun T = 3188 K m_ubv_B = 17.85\n", + "M = 2.288 Msun T = 29326 K m_ubv_B = 13.88\n", + "M = 2.288 Msun T = 134813 K m_ubv_B = 18.72\n", + "Starting filter: ubv,V Elapsed time: 6.27 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_V = 30.94\n", + "M = 1.215 Msun T = 6415 K m_ubv_V = 19.16\n", + "M = 2.038 Msun T = 7816 K m_ubv_V = 16.12\n", + "M = 2.055 Msun T = 4955 K m_ubv_V = 16.52\n", + "M = 2.071 Msun T = 4749 K m_ubv_V = 15.80\n", + "M = 2.275 Msun T = 4576 K m_ubv_V = 14.98\n", + "M = 2.287 Msun T = 3677 K m_ubv_V = 13.46\n", + "M = 2.287 Msun T = 3493 K m_ubv_V = 13.68\n", + "M = 2.287 Msun T = 3478 K m_ubv_V = 13.71\n", + "M = 2.288 Msun T = 3244 K m_ubv_V = 14.82\n", + "M = 2.288 Msun T = 3188 K m_ubv_V = 15.44\n", + "M = 2.288 Msun T = 29326 K m_ubv_V = 13.30\n", + "M = 2.288 Msun T = 134813 K m_ubv_V = 18.29\n", + "Starting filter: ubv,R Elapsed time: 9.40 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_R = 25.82\n", + "M = 1.215 Msun T = 6415 K m_ubv_R = 16.25\n", + "M = 2.038 Msun T = 7816 K m_ubv_R = 13.39\n", + "M = 2.055 Msun T = 4955 K m_ubv_R = 13.40\n", + "M = 2.071 Msun T = 4749 K m_ubv_R = 12.64\n", + "M = 2.275 Msun T = 4576 K m_ubv_R = 11.77\n", + "M = 2.287 Msun T = 3677 K m_ubv_R = 9.85\n", + "M = 2.287 Msun T = 3493 K m_ubv_R = 9.72\n", + "M = 2.287 Msun T = 3478 K m_ubv_R = 9.72\n", + "M = 2.288 Msun T = 3244 K m_ubv_R = 9.98\n", + "M = 2.288 Msun T = 3188 K m_ubv_R = 10.31\n", + "M = 2.288 Msun T = 29326 K m_ubv_R = 10.78\n", + "M = 2.288 Msun T = 134813 K m_ubv_R = 15.84\n", + "Starting filter: ubv,I Elapsed time: 12.57 seconds\n", + "Starting synthetic photometry\n", + "M = 0.106 Msun T = 2909 K m_ubv_I = 20.31\n", + "M = 1.215 Msun T = 6415 K m_ubv_I = 12.48\n", + "M = 2.038 Msun T = 7816 K m_ubv_I = 9.75\n", + "M = 2.055 Msun T = 4955 K m_ubv_I = 9.42\n", + "M = 2.071 Msun T = 4749 K m_ubv_I = 8.61\n", + "M = 2.275 Msun T = 4576 K m_ubv_I = 7.70\n", + "M = 2.287 Msun T = 3677 K m_ubv_I = 5.31\n", + "M = 2.287 Msun T = 3493 K m_ubv_I = 4.87\n", + "M = 2.287 Msun T = 3478 K m_ubv_I = 4.84\n", + "M = 2.288 Msun T = 3244 K m_ubv_I = 4.46\n", + "M = 2.288 Msun T = 3188 K m_ubv_I = 4.56\n", + "M = 2.288 Msun T = 29326 K m_ubv_I = 7.48\n", + "M = 2.288 Msun T = 134813 K m_ubv_I = 12.62\n", + " Time taken: 15.82 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(9.0, 0.7, 100, math.log10(1), mass_sampling =1 , recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'spisea.synthetic' has no attribute 'Binary_Cluster'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mclus_1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynthetic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBinary_Cluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miso1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIMFSalpeter1955\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiplicityResolvedDK\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mifmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mifmr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIFMR_Spera15\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mclus_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msynthetic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mResolvedCluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miso2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIMFSalpeter1955\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultiplicity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiplicityUnresolved\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mifmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mifmr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIFMR_Spera15\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'spisea.synthetic' has no attribute 'Binary_Cluster'" + ] + } + ], + "source": [ + "clus_1=synthetic.Binary_Cluster(iso1, imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2, imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityUnresolved()), 10000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure that I am getting just about enough star mass for my cluster. (It was a bug before I used an adjustment factor.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "arr =[]\n", + "for x in range(100):\n", + " clus_1=synthetic.Binary_Cluster(iso1, imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()), 10000, ifmr=ifmr.IFMR_Spera15())\n", + " arr.append(clus_1.star_systems['systemMass'].sum())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "hist, bins, patch = plt.hist(np.array(arr), bins=30, histtype='step', label='BPASS clusters with x mass')\n", + "plt.title(\"BPASS cluster mass distribution (# of models within mass bin over mass)\")\n", + "print(np.median(np.array(arr)))\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clus_1.companions['mass']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iso1.secondaries[3870]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.primaries['mass'], bins=30)\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.secondaries['mass'], bins=30)\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.singles['mass'], bins=30)\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(clus_1.star_systems['systemMass'], bins=30)\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(clus_2.star_systems['systemMass'], bins=30)\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['systemMass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['systemMass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['systemMass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['systemMass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.star_systems['mass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.star_systems['mass'], bins=bins, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.companions['mass'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.companions['mass'], bins=30, histtype='step', label='MISTv.1')\n", + "plt.xlabel(\"initial mass in solar masses\")\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Comparison with the MIST v1 cluster\n", + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.primaries['mass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.singles['mass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(iso1.secondaries['mass'], bins=30)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.where(iso1.secondaries['mass']>iso1.primaries['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.unique(iso1.primaries[np.where(iso1.secondaries['mass']>iso1.primaries['mass'])]['source'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "hist, bins, patch = plt.hist(clus_1.companions['log_a'], bins=30, histtype='step', label='BPASS')\n", + "plt.hist(clus_2.companions['log_a'], bins=30, histtype='step', label='MISTv.1')\n", + "plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Cluster_Mass_Error_Demonstration/iso_7.00_0.00_01000_m10.fits b/Cluster_Mass_Error_Demonstration/iso_7.00_0.00_01000_m10.fits new file mode 100644 index 00000000..846fbba7 --- /dev/null +++ b/Cluster_Mass_Error_Demonstration/iso_7.00_0.00_01000_m10.fits @@ -0,0 +1,273 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 97 / length of dimension 1 NAXIS2 = 662 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 13 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_ubv_U ' TFORM9 = 'D ' TTYPE10 = 'm_ubv_B ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_R ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_I ' TFORM13 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' LOGAGE = 7.0 AKS = 0.0 DISTANCE= 1000 METAL_IN= -1.0 HIERARCH METAL_ACT = -0.9761970853273753 WAVEMIN = 3000 WAVEMAX = 52000 END 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TODO: replace "/g/lu/scratch/ryotainagaki/BPASS_tester_newReformatTest/" + # with absolute path to your directory of + # reformatted fitsmodels + # that were created by the reformatter function + # TODO: replace "/g/lu/scratch/ryotainagaki/BPASS_iso_filesTimedIsolated/" with + # absolute path to your intended directory + # of isochrone files + extractor(round(6.0 + 0.1 * x,1), met,"/g/lu/scratch/ryotainagaki/" + + "BPASS_tester_newReformatTest/", + "/g/lu/scratch/ryotainagaki/BPASS_iso_filesTimedIsolated/",0.05) + t2 = time.time() + print(t2 - t1) \ No newline at end of file diff --git a/GenerateIsoFiles.py b/GenerateIsoFiles.py new file mode 100644 index 00000000..f0794295 --- /dev/null +++ b/GenerateIsoFiles.py @@ -0,0 +1,6 @@ +from spisea import evolution + +bpass = evolution.BPASS() +# Temporary comment out: +# bpass.run_formatter() +bpass.run_extractor() \ No newline at end of file diff --git a/README.md b/README.md index cbb42b51..cb81a531 100755 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# SPISEA: Stellar Population Interface for Stellar Evolution and Atmospheres +# Ryota Inagaki's fork of SPISEA SPISEA is an python package that generates single-age, single-metallicity populations (i.e. star clusters). It gives the user control over many parameters: diff --git a/Test_Plots/TestPlot_7.0_MIST_0.1.ipynb b/Test_Plots/TestPlot_7.0_MIST_0.1.ipynb new file mode 100755 index 00000000..54fb0b3c --- /dev/null +++ b/Test_Plots/TestPlot_7.0_MIST_0.1.ipynb @@ -0,0 +1,4166 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS isochrone at $10^{7.0}$ years age and Comparing with MIST Model ($0.1Z_{\\odot}$ metallicity)\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for 10^7.0 years age, a tenth of solar metallicity, AKs=0.0, and distance of 1000 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram (B-V vs M_V), and the current-mass luminosity relationship of the cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 50000 for T= 58383 logg=4.33\n", + "Changing to logg=5.00 for T= 58383 logg=4.33\n", + "Changing to logg=4.50 for T= 47267 logg=4.32\n", + "Changing to logg=2.50 for T= 11956 logg=1.83\n", + "Changing to T= 50000 for T= 51512 logg=4.31\n", + "Changing to logg=5.00 for T= 51512 logg=4.31\n", + "Changing to T= 50000 for T= 53429 logg=4.31\n", + "Changing to logg=5.00 for T= 53429 logg=4.31\n", + "Changing to logg=5.00 for T= 49421 logg=4.31\n", + "Changing to T= 50000 for T= 56650 logg=4.32\n", + "Changing to logg=5.00 for T= 56650 logg=4.32\n", + "Changing to T= 50000 for T=181176 logg=5.89\n", + "Changing to logg=5.00 for T=181176 logg=5.89\n", + "Changing to logg=4.00 for T= 38042 logg=3.27\n", + "Changing to logg=2.50 for T= 14006 logg=1.53\n", + "Changing to logg=2.00 for T= 11179 logg=1.12\n", + "Changing to logg=3.50 for T= 29023 logg=2.83\n", + "Changing to logg=4.00 for T= 31860 logg=2.94\n", + "Changing to logg=3.00 for T= 24369 logg=2.72\n", + "Changing to logg=2.50 for T= 18993 logg=2.03\n", + "Changing to logg=2.00 for T= 11466 logg=1.18\n", + "Changing to logg=2.50 for T= 18565 logg=2.03\n", + "Changing to logg=3.50 for T= 29402 logg=2.84\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 20480 logg=2.42\n", + "Changing to logg=2.50 for T= 18634 logg=2.02\n", + "Changing to logg=3.50 for T= 28755 logg=3.07\n", + "Changing to logg=4.50 for T= 39184 logg=3.36\n", + "Changing to logg=2.50 for T= 16578 logg=1.80\n", + "Changing to logg=4.50 for T= 44236 logg=3.48\n", + "Changing to logg=2.50 for T= 14174 logg=1.52\n", + "Changing to logg=2.00 for T= 9831 logg=0.95\n", + "Changing to logg=3.00 for T= 20335 logg=2.15\n", + "Changing to logg=2.50 for T= 12635 logg=1.33\n", + "Changing to logg=4.50 for T= 40163 logg=3.39\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 29097 logg=3.02\n", + "Changing to logg=2.50 for T= 12008 logg=1.84\n", + "Changing to logg=3.00 for T= 19573 logg=2.34\n", + "Changing to logg=2.50 for T= 17181 logg=1.89\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 71367 logg=4.69\n", + "Changing to logg=5.00 for T= 71367 logg=4.69\n", + "Changing to T= 50000 for T=111828 logg=5.43\n", + "Changing to logg=5.00 for T=111828 logg=5.43\n", + "Changing to logg=2.50 for T= 16590 logg=1.83\n", + "Changing to logg=3.50 for T= 26782 logg=3.32\n", + "Changing to logg=4.00 for T= 33050 logg=3.78\n", + "Changing to logg=4.00 for T= 33304 logg=3.86\n", + "Changing to logg=3.00 for T= 21103 logg=2.55\n", + "Changing to logg=2.00 for T= 9248 logg=0.79\n", + "Changing to logg=3.00 for T= 24136 logg=2.46\n", + "Changing to logg=3.50 for T= 27432 logg=2.93\n", + "Changing to logg=4.00 for T= 37265 logg=3.81\n", + "Changing to logg=2.50 for T= 18038 logg=2.20\n", + "Changing to logg=3.50 for T= 30994 logg=2.93\n", + "Changing to logg=4.00 for T= 32981 logg=3.24\n", + "Changing to T= 50000 for T=105619 logg=5.06\n", + "Changing to logg=5.00 for T=105619 logg=5.06\n", + "Changing to logg=2.50 for T= 13637 logg=1.45\n", + "Changing to logg=2.50 for T= 17655 logg=1.94\n", + "Changing to logg=2.50 for T= 14357 logg=2.20\n", + "Changing to T= 50000 for T=142479 logg=5.64\n", + "Changing to logg=5.00 for T=142479 logg=5.64\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 66555 logg=4.55\n", + "Changing to logg=5.00 for T= 66555 logg=4.55\n", + "Changing to T= 50000 for T=124474 logg=5.34\n", + "Changing to logg=5.00 for T=124474 logg=5.34\n", + "Changing to logg=3.50 for T= 29162 logg=3.07\n", + "Changing to logg=3.00 for T= 20547 logg=2.50\n", + "Changing to logg=2.50 for T= 13282 logg=1.40\n", + "Changing to logg=2.50 for T= 18668 logg=2.00\n", + "Changing to logg=2.50 for T= 17697 logg=1.94\n", + "Changing to logg=3.50 for T= 26101 logg=2.62\n", + "Changing to logg=4.00 for T= 38158 logg=3.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 36247 logg=3.91\n", + "Changing to T= 50000 for T= 89824 logg=5.14\n", + "Changing to logg=5.00 for T= 89824 logg=5.14\n", + "Changing to logg=4.00 for T= 37562 logg=3.98\n", + "Changing to logg=2.50 for T= 12773 logg=1.33\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T=127803 logg=5.38\n", + "Changing to logg=5.00 for T=127803 logg=5.38\n", + "Changing to logg=3.50 for T= 29084 logg=3.47\n", + "Changing to logg=3.00 for T= 19914 logg=2.45\n", + "Changing to logg=3.00 for T= 24633 logg=2.81\n", + "Changing to logg=2.50 for T= 18378 logg=2.23\n", + "Changing to logg=4.00 for T= 32815 logg=3.05\n", + "Changing to logg=3.50 for T= 27706 logg=2.94\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 37726 logg=3.29\n", + "Changing to logg=4.00 for T= 31093 logg=2.90\n", + "Changing to logg=3.00 for T= 20819 logg=2.85\n", + "Changing to logg=4.50 for T= 44410 logg=3.77\n", + "Changing to logg=4.00 for T= 31852 logg=2.96\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 16448 logg=1.80\n", + "Changing to T= 50000 for T= 62004 logg=4.40\n", + "Changing to logg=5.00 for T= 62004 logg=4.40\n", + "Changing to T= 50000 for T= 52353 logg=4.07\n", + "Changing to logg=5.00 for T= 52353 logg=4.07\n", + "Changing to T= 50000 for T= 84122 logg=4.59\n", + "Changing to logg=5.00 for T= 84122 logg=4.59\n", + "Changing to T= 50000 for T=103498 logg=5.02\n", + "Changing to logg=5.00 for T=103498 logg=5.02\n", + "Changing to T= 50000 for T= 61709 logg=4.38\n", + "Changing to logg=5.00 for T= 61709 logg=4.38\n", + "Changing to logg=3.50 for T= 29547 logg=3.12\n", + "Changing to logg=0.50 for T= 6805 logg=0.34\n", + "Changing to logg=2.50 for T= 12942 logg=1.36\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 34029 logg=3.99\n", + "Changing to logg=2.50 for T= 15951 logg=1.99\n", + "Changing to logg=2.50 for T= 15171 logg=1.65\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31753 logg=3.87\n", + "Changing to T= 50000 for T= 53696 logg=4.19\n", + "Changing to logg=5.00 for T= 53696 logg=4.19\n", + "Changing to logg=2.50 for T= 15485 logg=1.67\n", + "Changing to logg=3.00 for T= 19341 logg=2.10\n", + "Changing to logg=3.50 for T= 29877 logg=2.87\n", + "Changing to logg=2.00 for T= 9152 logg=1.27\n", + "Changing to logg=2.50 for T= 17527 logg=1.90\n", + "Changing to logg=2.50 for T= 15819 logg=2.21\n", + "Changing to logg=2.50 for T= 16928 logg=1.85\n", + "Changing to T= 50000 for T= 69716 logg=4.65\n", + "Changing to logg=5.00 for T= 69716 logg=4.65\n", + "Changing to logg=2.50 for T= 16899 logg=2.09\n", + "Changing to logg=4.00 for T= 34106 logg=3.57\n", + "Changing to logg=2.50 for T= 12020 logg=1.51\n", + "Changing to logg=1.00 for T= 8146 logg=0.84\n", + "Changing to logg=5.00 for T= 49077 logg=4.01\n", + "Changing to logg=2.50 for T= 13843 logg=1.48\n", + "Changing to logg=2.50 for T= 11892 logg=1.82\n", + "Changing to logg=3.00 for T= 20680 logg=2.21\n", + "Changing to logg=4.00 for T= 31579 logg=2.96\n", + "Changing to logg=4.50 for T= 47203 logg=3.65\n", + "Changing to logg=2.00 for T= 9029 logg=0.76\n", + "Changing to logg=4.00 for T= 32394 logg=3.96\n", + "Changing to logg=3.50 for T= 30989 logg=2.93\n", + "Changing to logg=2.50 for T= 13598 logg=1.71\n", + "Changing to logg=4.50 for T= 40325 logg=3.59\n", + "Changing to logg=4.50 for T= 41093 logg=3.42\n", + "Changing to logg=2.50 for T= 14210 logg=1.53\n", + "Changing to logg=3.50 for T= 30512 logg=3.17\n", + "Changing to logg=2.00 for T= 9256 logg=0.81\n", + "Changing to logg=3.50 for T= 29837 logg=3.14\n", + "Changing to logg=2.50 for T= 11837 logg=1.20\n", + "Changing to logg=4.00 for T= 35285 logg=3.43\n", + "Changing to logg=3.50 for T= 26486 logg=2.86\n", + "Changing to logg=3.50 for T= 30731 logg=3.37\n", + "Changing to logg=2.50 for T= 16346 logg=1.77\n", + "Changing to logg=3.00 for T= 24191 logg=2.51\n", + "Changing to logg=3.50 for T= 27796 logg=3.16\n", + "Changing to logg=3.00 for T= 20516 logg=2.16\n", + "Changing to logg=2.00 for T= 9630 logg=0.86\n", + "Changing to T= 50000 for T= 70362 logg=4.66\n", + "Changing to logg=5.00 for T= 70362 logg=4.66\n", + "Changing to logg=3.50 for T= 27010 logg=2.67\n", + "Changing to logg=4.00 for T= 31982 logg=3.74\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 20509 logg=2.63\n", + "Changing to logg=4.00 for T= 37692 logg=3.28\n", + "Changing to T= 50000 for T= 56351 logg=4.20\n", + "Changing to logg=5.00 for T= 56351 logg=4.20\n", + "Changing to T= 50000 for T=147550 logg=5.57\n", + "Changing to logg=5.00 for T=147550 logg=5.57\n", + "Changing to logg=4.50 for T= 42386 logg=3.68\n", + "Changing to logg=4.00 for T= 37066 logg=3.26\n", + "Changing to logg=2.00 for T= 10524 logg=1.00\n", + "Changing to logg=3.50 for T= 29669 logg=3.06\n", + "Changing to T= 50000 for T= 73590 logg=4.78\n", + "Changing to logg=5.00 for T= 73590 logg=4.78\n", + "Changing to logg=3.00 for T= 21918 logg=2.32\n", + "Changing to logg=5.00 for T= 49091 logg=3.72\n", + "Changing to logg=4.00 for T= 36115 logg=3.99\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 29992 logg=2.87\n", + "Changing to logg=2.50 for T= 12959 logg=1.38\n", + "Changing to T= 50000 for T= 74437 logg=4.79\n", + "Changing to logg=5.00 for T= 74437 logg=4.79\n", + "Changing to logg=4.00 for T= 38923 logg=3.84\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 21150 logg=2.27\n", + "Changing to logg=4.00 for T= 38582 logg=3.95\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31946 logg=2.97\n", + "Changing to logg=2.00 for T= 9851 logg=0.92\n", + "Changing to 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T= 26158 logg=2.63\n", + "Changing to logg=3.50 for T= 29863 logg=2.84\n", + "Changing to logg=2.00 for T= 9547 logg=0.86\n", + "Changing to logg=3.00 for T= 24650 logg=2.50\n", + "Changing to logg=4.50 for T= 39406 logg=4.07\n", + "Changing to T= 50000 for T=124934 logg=5.35\n", + "Changing to logg=5.00 for T=124934 logg=5.35\n", + "Changing to logg=2.50 for T= 15268 logg=1.66\n", + "Changing to logg=4.00 for T= 34751 logg=3.40\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T=117666 logg=5.25\n", + "Changing to logg=5.00 for T=117666 logg=5.25\n", + "Changing to logg=4.50 for T= 46698 logg=3.63\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 12150 logg=1.25\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 20333 logg=2.17\n", + "Changing to logg=4.00 for T= 35749 logg=3.85\n", + "Changing to logg=2.50 for T= 13946 logg=1.76\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 11558 logg=1.16\n", + "Changing to logg=4.50 for T= 40713 logg=3.41\n", + "Changing to logg=4.00 for T= 34521 logg=3.99\n", + "Changing to logg=4.00 for T= 34642 logg=3.39\n", + "Changing to logg=2.00 for T= 11423 logg=1.17\n", + "Changing to logg=4.00 for T= 37221 logg=3.23\n", + "Changing to logg=3.00 for T= 24047 logg=2.47\n", + "Changing to logg=3.00 for T= 22118 logg=2.62\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 23026 logg=2.42\n", + "Changing to T= 50000 for T= 52159 logg=4.06\n", + "Changing to logg=5.00 for T= 52159 logg=4.06\n", + "Changing to logg=3.00 for T= 21691 logg=2.52\n", + "Changing to logg=3.00 for T= 23453 logg=2.43\n", + "Changing to logg=2.00 for T= 10291 logg=0.98\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 19398 logg=2.11\n", + "Changing to logg=3.00 for T= 24174 logg=2.77\n", + "Changing to logg=3.00 for T= 23460 logg=2.65\n", + "Changing to logg=2.50 for T= 14721 logg=1.54\n", + "Changing to logg=3.50 for T= 28086 logg=2.77\n", + "Changing to logg=3.50 for T= 29934 logg=3.46\n", + "Changing to T= 50000 for T= 89135 logg=4.69\n", + "Changing to logg=5.00 for T= 89135 logg=4.69\n", + "Changing to logg=2.00 for T= 11226 logg=1.14\n", + "Changing to T= 50000 for T=116915 logg=5.23\n", + "Changing to logg=5.00 for T=116915 logg=5.23\n", + "Changing to logg=2.50 for T= 13814 logg=1.51\n", + "Changing to T= 50000 for T=125974 logg=5.36\n", + "Changing to logg=5.00 for T=125974 logg=5.36\n", + "Changing to logg=3.50 for T= 27028 logg=2.70\n", + "Changing to logg=2.00 for T= 10846 logg=1.08\n", + "Changing to logg=4.50 for T= 41371 logg=3.71\n", + "Changing to logg=4.50 for T= 44609 logg=3.55\n", + "Changing to logg=2.50 for T= 15447 logg=1.67\n", + "Changing to logg=4.00 for T= 31100 logg=3.62\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 33624 logg=3.94\n", + "Changing to logg=4.00 for T= 36722 logg=3.21\n", + "Changing to logg=2.50 for T= 18221 logg=2.30\n", + "Changing to logg=2.00 for T= 11367 logg=1.16\n", + "Changing to logg=4.00 for T= 33652 logg=3.06\n", + "Changing to logg=3.50 for T= 28103 logg=2.96\n", + "Changing to logg=3.50 for T= 28358 logg=2.75\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 28668 logg=2.79\n", + "Changing to logg=4.00 for T= 33805 logg=3.10\n", + "Changing to logg=3.50 for T= 29227 logg=3.14\n", + "Changing to T= 50000 for T= 91287 logg=5.21\n", + "Changing to logg=5.00 for T= 91287 logg=5.21\n", + "Changing to logg=2.00 for T= 9384 logg=0.82\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25766 logg=2.81\n", + "Changing to logg=2.50 for T= 13972 logg=1.49\n", + "Changing to logg=4.00 for T= 38122 logg=3.28\n", + "Changing to T= 50000 for T=117744 logg=5.25\n", + "Changing to logg=5.00 for T=117744 logg=5.25\n", + "Changing to logg=4.00 for T= 34972 logg=3.13\n", + "Changing to T= 50000 for T= 59465 logg=4.31\n", + "Changing to logg=5.00 for T= 59465 logg=4.31\n", + "Changing to logg=4.00 for T= 35401 logg=3.16\n", + "Changing to logg=2.50 for T= 12514 logg=1.30\n", + "Changing to logg=4.50 for T= 40654 logg=3.39\n", + "Changing to logg=2.00 for T= 9486 logg=0.85\n", + "Changing to logg=2.50 for T= 16452 logg=1.78\n", + "Changing to logg=2.50 for T= 17855 logg=2.19\n", + "Changing to logg=3.00 for T= 25861 logg=2.89\n", + "Changing to logg=4.00 for T= 36650 logg=3.99\n", + "Changing to logg=4.50 for T= 41273 logg=4.12\n", + "Changing to logg=1.50 for T= 8425 logg=1.22\n", + "Changing to logg=3.00 for T= 25112 logg=2.84\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 22445 logg=2.57\n", + "Changing to logg=2.50 for T= 14360 logg=1.56\n", + "Changing to logg=2.00 for T= 11741 logg=1.19\n", + "Changing to logg=3.00 for T= 25570 logg=2.57\n", + "Changing to logg=2.50 for T= 18912 logg=2.28\n", + "Changing to logg=2.50 for T= 17793 logg=2.18\n", + "Changing to logg=3.50 for T= 27083 logg=2.67\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 22193 logg=2.31\n", + "Changing to logg=4.00 for T= 35348 logg=3.72\n", + "Changing to T= 50000 for T=115931 logg=5.21\n", + "Changing to logg=5.00 for T=115931 logg=5.21\n", + "Changing to logg=2.00 for T= 11591 logg=1.18\n", + "Changing to logg=2.50 for T= 12749 logg=1.60\n", + "Changing to logg=4.50 for T= 39152 logg=3.32\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 40326 logg=4.02\n", + "Changing to logg=4.00 for T= 37802 logg=3.94\n", + "Changing to logg=2.00 for T= 10012 logg=0.94\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 12147 logg=1.26\n", + "Changing to T= 50000 for T= 63192 logg=4.44\n", + "Changing to logg=5.00 for T= 63192 logg=4.44\n", + "Changing to logg=4.00 for T= 31444 logg=3.23\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 13987 logg=1.53\n", + "Changing to logg=3.50 for T= 28685 logg=2.77\n", + "Changing to logg=4.00 for T= 34304 logg=3.84\n", + "Changing to logg=4.00 for T= 38324 logg=3.32\n", + "Changing to logg=3.50 for T= 26208 logg=2.84\n", + "Changing to logg=3.50 for T= 26826 logg=2.95\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 20714 logg=2.51\n", + "Changing to logg=2.00 for T= 9977 logg=0.94\n", + "Changing to logg=2.00 for T= 9988 logg=0.92\n", + "Changing to logg=3.00 for T= 22718 logg=2.37\n", + "Changing to logg=2.00 for T= 11171 logg=1.12\n", + "Changing to logg=4.00 for T= 35845 logg=3.39\n", + "Changing to logg=3.50 for T= 27580 logg=2.74\n", + "Changing to logg=2.50 for T= 12971 logg=1.39\n", + "Changing to logg=3.50 for T= 30706 logg=2.90\n", + "Changing to logg=3.00 for T= 21235 logg=2.28\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 35090 logg=3.35\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 42502 logg=3.76\n", + "Changing to logg=0.50 for T= 6490 logg=0.44\n", + "Changing to logg=3.00 for T= 19495 logg=2.07\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31778 logg=3.86\n", + "Changing to logg=3.50 for T= 28012 logg=2.73\n", + "Changing to logg=3.00 for T= 23583 logg=2.91\n", + "Changing to logg=4.00 for T= 31262 logg=3.34\n", + "Changing to logg=2.50 for T= 15753 logg=1.97\n", + "Changing to logg=4.00 for T= 36735 logg=3.43\n", + "Changing to logg=4.00 for T= 37540 logg=3.54\n", + "Changing to logg=3.50 for T= 26511 logg=2.65\n", + "Changing to logg=4.00 for T= 33912 logg=3.36\n", + "Changing to T= 50000 for T= 57711 logg=4.25\n", + "Changing to logg=5.00 for T= 57711 logg=4.25\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 28527 logg=3.46\n", + "Changing to logg=4.00 for T= 34071 logg=3.51\n", + "Changing to logg=3.00 for T= 20501 logg=2.17\n", + "Changing to logg=2.00 for T= 11639 logg=1.18\n", + "Changing to logg=2.50 for T= 13649 logg=1.64\n", + "Changing to logg=4.00 for T= 33759 logg=3.43\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 15717 logg=1.72\n", + "Changing to T= 50000 for T= 75074 logg=4.80\n", + "Changing to logg=5.00 for T= 75074 logg=4.80\n", + "Changing to logg=2.00 for T= 11159 logg=1.11\n", + "Changing to logg=2.50 for T= 12126 logg=1.26\n", + "Changing to logg=2.50 for T= 16251 logg=2.02\n", + "Changing to logg=3.00 for T= 22951 logg=2.61\n", + "Changing to logg=3.00 for T= 25037 logg=2.57\n", + "Changing to logg=2.50 for T= 12585 logg=1.31\n", + "Changing to logg=2.50 for T= 13489 logg=1.46\n", + "Changing to logg=2.00 for T= 10999 logg=1.08\n", + "Changing to logg=2.50 for T= 16536 logg=1.78\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 17713 logg=1.93\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 12202 logg=1.29\n", + "Changing to logg=4.50 for T= 41570 logg=3.99\n", + "Changing to logg=2.50 for T= 13740 logg=1.50\n", + "Changing to logg=3.50 for T= 26046 logg=2.60\n", + "Changing to logg=2.50 for T= 12251 logg=1.30\n", + "Changing to logg=2.00 for T= 11491 logg=1.18\n", + "Changing to logg=2.50 for T= 13162 logg=1.40\n", + "Changing to logg=2.50 for T= 11787 logg=1.21\n", + "Changing to logg=3.00 for T= 25063 logg=2.53\n", + "Changing to logg=2.00 for T= 9079 logg=0.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 43228 logg=3.50\n", + "Changing to T= 50000 for T= 50807 logg=4.01\n", + "Changing to logg=5.00 for T= 50807 logg=4.01\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25286 logg=2.56\n", + "Changing to logg=2.00 for T= 11440 logg=1.17\n", + "Changing to logg=3.00 for T= 24173 logg=2.47\n", + "Changing to logg=4.00 for T= 32195 logg=3.67\n", + "Changing to logg=3.50 for T= 27883 logg=2.95\n", + "Changing to logg=2.50 for T= 13405 logg=1.44\n", + "Changing to logg=3.00 for T= 23627 logg=2.47\n", + "Changing to logg=4.00 for T= 37110 logg=3.51\n", + "Changing to logg=2.50 for T= 15182 logg=1.65\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 21885 logg=2.60\n", + "Changing to logg=2.50 for T= 15824 logg=1.74\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 17615 logg=1.90\n", + "Changing to logg=2.50 for T= 13454 logg=1.43\n", + "Changing to logg=2.50 for T= 15920 logg=1.66\n", + "Changing to logg=2.50 for T= 17238 logg=1.87\n", + "Changing to logg=3.00 for T= 21609 logg=2.31\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 33993 logg=3.30\n", + "Changing to logg=2.00 for T= 10562 logg=1.00\n", + "Changing to T= 50000 for T=127476 logg=5.37\n", + "Changing to logg=5.00 for T=127476 logg=5.37\n", + "Changing to logg=4.50 for T= 43153 logg=3.49\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 53132 logg=3.86\n", + "Changing to logg=5.00 for T= 53132 logg=3.86\n", + "Changing to logg=2.50 for T= 14880 logg=1.60\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31111 logg=3.98\n", + "Changing to logg=3.50 for T= 29585 logg=2.86\n", + "Changing to logg=3.00 for T= 22331 logg=2.57\n", + "Changing to logg=2.00 for T= 10456 logg=0.99\n", + "Changing to logg=3.50 for T= 26057 logg=2.83\n", + "Changing to T= 50000 for T=108863 logg=5.37\n", + "Changing to logg=5.00 for T=108863 logg=5.37\n", + "Changing to logg=2.50 for T= 16790 logg=2.08\n", + "Changing to logg=3.50 for T= 29681 logg=3.44\n", + "Changing to logg=3.00 for T= 21579 logg=2.87\n", + "Changing to logg=3.50 for T= 28787 logg=2.99\n", + "Changing to logg=2.50 for T= 16915 logg=1.83\n", + "Changing to logg=2.50 for T= 14052 logg=1.52\n", + "Changing to logg=3.50 for T= 28695 logg=2.81\n", + "Changing to T= 50000 for T= 89759 logg=5.15\n", + "Changing to logg=5.00 for T= 89759 logg=5.15\n", + "Changing to logg=2.50 for T= 14531 logg=1.58\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 19170 logg=2.05\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 17643 logg=1.89\n", + "Changing to logg=4.00 for T= 31305 logg=2.91\n", + "Changing to logg=2.50 for T= 12568 logg=1.34\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 60853 logg=4.36\n", + "Changing to logg=5.00 for T= 60853 logg=4.36\n", + "Changing to logg=3.50 for T= 27648 logg=2.71\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 27016 logg=2.69\n", + "Changing to logg=2.00 for T= 10035 logg=0.92\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 18738 logg=2.02\n", + "Changing to logg=2.00 for T= 10529 logg=1.02\n", + "Changing to logg=2.50 for T= 12986 logg=1.40\n", + "Changing to T= 50000 for T= 69306 logg=4.64\n", + "Changing to logg=5.00 for T= 69306 logg=4.64\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 28929 logg=2.81\n", + "Changing to logg=4.00 for T= 34555 logg=3.12\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 31430 logg=2.97\n", + "Changing to logg=2.50 for T= 12282 logg=1.89\n", + "Changing to logg=3.00 for T= 21301 logg=2.28\n", + "Changing to logg=2.00 for T= 10844 logg=1.08\n", + "Changing to logg=4.00 for T= 36400 logg=3.20\n", + "Changing to logg=2.50 for T= 18110 logg=1.96\n", + "Changing to logg=3.00 for T= 24518 logg=2.53\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 20172 logg=2.15\n", + "Changing to logg=4.50 for T= 39576 logg=3.97\n", + "Changing to T= 50000 for T= 69499 logg=4.63\n", + "Changing to logg=5.00 for T= 69499 logg=4.63\n", + "Changing to logg=4.00 for T= 33813 logg=3.59\n", + "Changing to logg=2.50 for T= 15543 logg=1.94\n", + "Changing to logg=4.00 for T= 34365 logg=3.11\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 12246 logg=1.30\n", + "Changing to logg=1.50 for T= 8974 logg=1.35\n", + "Changing to logg=3.00 for T= 22211 logg=2.33\n", + "Changing to logg=2.50 for T= 17342 logg=2.14\n", + "Changing to T= 50000 for T= 73891 logg=4.77\n", + "Changing to logg=5.00 for T= 73891 logg=4.77\n", + "Changing to logg=4.50 for T= 45850 logg=3.82\n", + "Changing to logg=4.50 for T= 47211 logg=3.94\n", + "Changing to logg=2.00 for T= 10044 logg=0.93\n", + "Changing to logg=3.00 for T= 21113 logg=2.20\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25919 logg=2.63\n", + "Changing to logg=3.00 for T= 24761 logg=2.55\n", + "Changing to T= 50000 for T= 77582 logg=4.87\n", + "Changing to logg=5.00 for T= 77582 logg=4.87\n", + "Changing to logg=4.00 for T= 31640 logg=3.88\n", + "Changing to T= 50000 for T= 51713 logg=4.05\n", + "Changing to logg=5.00 for T= 51713 logg=4.05\n", + "Changing to logg=4.50 for T= 39663 logg=3.36\n", + "Changing to logg=3.50 for T= 28880 logg=3.47\n", + "Changing to logg=2.50 for T= 12952 logg=1.38\n", + "Changing to logg=2.50 for T= 14983 logg=1.88\n", + "Changing to logg=4.00 for T= 33366 logg=3.77\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 12157 logg=1.28\n", + "Changing to T= 50000 for T=118899 logg=5.27\n", + "Changing to logg=5.00 for T=118899 logg=5.27\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25665 logg=2.88\n", + "Changing to logg=4.00 for T= 34623 logg=3.33\n", + "Changing to logg=4.00 for T= 32138 logg=3.62\n", + "Changing to logg=3.00 for T= 21608 logg=2.26\n", + "Changing to logg=2.00 for T= 9772 logg=0.87\n", + "Changing to logg=2.50 for T= 17007 logg=1.84\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 34770 logg=3.86\n", + "Changing to logg=3.50 for T= 27749 logg=2.73\n", + "Changing to logg=5.00 for T= 49831 logg=3.98\n", + "Changing to logg=3.50 for T= 28205 logg=2.96\n", + "Changing to logg=2.00 for T= 9953 logg=0.92\n", + "Changing to logg=3.00 for T= 24540 logg=2.73\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 33069 logg=3.97\n", + "Changing to logg=2.50 for T= 14949 logg=1.88\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 14001 logg=1.50\n", + "Changing to T= 50000 for T= 68751 logg=4.61\n", + "Changing to logg=5.00 for T= 68751 logg=4.61\n", + "Changing to logg=2.00 for T= 10082 logg=0.94\n", + "Changing to logg=2.50 for T= 18199 logg=1.95\n", + "Changing to logg=2.50 for T= 13299 logg=1.44\n", + "Changing to logg=0.50 for T= 6594 logg=0.24\n", + "Changing to logg=3.00 for T= 23610 logg=2.66\n", + "Changing to logg=2.00 for T= 11278 logg=1.40\n", + "Changing to logg=2.50 for T= 16490 logg=1.78\n", + "Changing to logg=4.00 for T= 31390 logg=3.78\n", + "Changing to logg=3.00 for T= 19250 logg=2.07\n", + "Changing to logg=2.50 for T= 13396 logg=1.45\n", + "Changing to logg=3.00 for T= 21618 logg=2.26\n", + "Changing to logg=3.00 for T= 24419 logg=2.72\n", + "Changing to logg=2.00 for T= 11130 logg=1.10\n", + "Changing to logg=3.50 for T= 30157 logg=3.42\n", + "Changing to logg=4.00 for T= 32021 logg=3.01\n", + "Changing to logg=2.50 for T= 15549 logg=1.69\n", + "Changing to logg=3.00 for T= 21686 logg=2.30\n", + "Changing to logg=2.50 for T= 14971 logg=1.65\n", + "Changing to T= 50000 for T= 68914 logg=4.62\n", + "Changing to logg=5.00 for T= 68914 logg=4.62\n", + "Changing to logg=3.00 for T= 25115 logg=2.77\n", + "Changing to logg=3.00 for T= 25744 logg=2.62\n", + "Changing to logg=3.00 for T= 25879 logg=2.59\n", + "Changing to logg=2.50 for T= 16504 logg=1.82\n", + "Changing to logg=4.50 for T= 41172 logg=3.92\n", + "Changing to logg=3.00 for T= 23641 logg=2.44\n", + "Changing to logg=4.00 for T= 31316 logg=3.34\n", + "Changing to logg=4.00 for T= 36115 logg=3.40\n", + "Changing to logg=3.00 for T= 21155 logg=2.70\n", + "Changing to logg=4.00 for T= 33735 logg=3.08\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 29164 logg=2.80\n", + "Changing to logg=4.50 for T= 43934 logg=3.54\n", + "Changing to logg=2.50 for T= 12586 logg=1.34\n", + "Changing to logg=2.00 for T= 10556 logg=1.00\n", + "Changing to logg=4.50 for T= 40168 logg=3.97\n", + "Changing to logg=4.00 for T= 33087 logg=3.25\n", + "Changing to logg=4.00 for T= 37430 logg=3.75\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 44039 logg=3.75\n", + "Changing to logg=4.50 for T= 41157 logg=3.63\n", + "Changing to T= 50000 for T= 75264 logg=4.84\n", + "Changing to logg=5.00 for T= 75264 logg=4.84\n", + "Changing to logg=4.50 for T= 44376 logg=3.83\n", + "Changing to logg=4.00 for T= 32605 logg=3.00\n", + "Changing to logg=4.00 for T= 34831 logg=3.59\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25413 logg=2.86\n", + "Changing to logg=3.50 for T= 26892 logg=2.88\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 42597 logg=3.69\n", + "Changing to logg=0.50 for T= 7388 logg=0.40\n", + "Changing to logg=2.00 for T= 10415 logg=1.01\n", + "Changing to T= 50000 for T= 62596 logg=4.42\n", + "Changing to logg=5.00 for T= 62596 logg=4.42\n", + "Changing to logg=4.00 for T= 36888 logg=3.22\n", + "Changing to logg=4.00 for T= 33346 logg=3.07\n", + "Changing to logg=4.50 for T= 39839 logg=3.95\n", + "Changing to logg=2.50 for T= 11892 logg=1.82\n", + "Changing to logg=2.00 for T= 10049 logg=0.92\n", + "Changing to logg=2.50 for T= 15120 logg=1.64\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 24868 logg=2.76\n", + "Changing to T= 50000 for T= 72991 logg=4.76\n", + "Changing to logg=5.00 for T= 72991 logg=4.76\n", + "Changing to logg=4.00 for T= 34383 logg=3.53\n", + "Changing to logg=4.00 for T= 33612 logg=3.78\n", + "Changing to logg=4.50 for T= 40154 logg=4.02\n", + "Changing to T= 50000 for T=117685 logg=5.25\n", + "Changing to logg=5.00 for T=117685 logg=5.25\n", + "Changing to logg=3.50 for T= 28503 logg=3.21\n", + "Changing to logg=4.00 for T= 34051 logg=3.89\n", + "Changing to T= 50000 for T= 92255 logg=5.23\n", + "Changing to logg=5.00 for T= 92255 logg=5.23\n", + "Changing to logg=4.50 for T= 48116 logg=3.91\n", + "Changing to logg=3.50 for T= 28030 logg=2.73\n", + "Changing to logg=4.00 for T= 31018 logg=3.55\n", + "Changing to T= 50000 for T= 93530 logg=5.28\n", + "Changing to logg=5.00 for T= 93530 logg=5.28\n", + "Changing to logg=3.00 for T= 22591 logg=2.36\n", + "Changing to logg=2.00 for T= 11161 logg=1.12\n", + "Changing to logg=3.00 for T= 23483 logg=2.40\n", + "Changing to logg=3.00 for T= 19791 logg=2.10\n", + "Changing to logg=2.50 for T= 14680 logg=1.77\n", + "Changing to logg=3.50 for T= 26760 logg=3.30\n", + "Changing to logg=2.50 for T= 18483 logg=1.98\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 15844 logg=1.75\n", + "Changing to logg=3.50 for T= 26090 logg=2.91\n", + "Changing to logg=2.50 for T= 14900 logg=1.62\n", + "Changing to logg=4.00 for T= 37732 logg=3.69\n", + "Changing to logg=2.00 for T= 9562 logg=0.86\n", + "Changing to logg=2.50 for T= 17581 logg=1.89\n", + "Changing to logg=2.50 for T= 13108 logg=1.41\n", + "Changing to T= 50000 for T= 91441 logg=5.19\n", + "Changing to logg=5.00 for T= 91441 logg=5.19\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 18747 logg=2.35\n", + "Changing to logg=3.00 for T= 21875 logg=2.53\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 10828 logg=1.08\n", + "Changing to logg=4.00 for T= 35200 logg=3.17\n", + "Changing to logg=4.00 for T= 32154 logg=3.01\n", + "Changing to logg=3.50 for T= 26228 logg=3.44\n", + "Changing to logg=4.50 for T= 42029 logg=3.73\n", + "Changing to logg=4.00 for T= 32770 logg=3.30\n", + "Changing to logg=2.50 for T= 14269 logg=1.80\n", + "Changing to logg=3.50 for T= 27450 logg=2.72\n", + "Changing to logg=4.00 for T= 32910 logg=3.21\n", + "Changing to T= 50000 for T= 64838 logg=4.49\n", + "Changing to logg=5.00 for T= 64838 logg=4.49\n", + "Changing to logg=4.50 for T= 39732 logg=4.04\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 21639 logg=2.59\n", + "Changing to logg=4.00 for T= 34611 logg=3.12\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11982 logg=1.23\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 36065 logg=3.46\n", + "Changing to logg=3.50 for T= 29569 logg=3.12\n", + "Changing to logg=0.50 for T= 7215 logg=0.35\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 25242 logg=2.78\n", + "Changing to logg=2.00 for T= 9858 logg=0.92\n", + "Changing to logg=3.50 for T= 29295 logg=3.10\n", + "Changing to logg=3.00 for T= 21295 logg=2.25\n", + "Changing to logg=3.00 for T= 23329 logg=2.40\n", + "Changing to logg=2.50 for T= 18218 logg=1.98\n", + "Changing to logg=2.00 for T= 11350 logg=1.15\n", + "Changing to logg=3.50 for T= 27871 logg=3.02\n", + "Changing to logg=2.50 for T= 15351 logg=1.66\n", + "Changing to logg=4.00 for T= 36211 logg=3.20\n", + "Changing to logg=3.50 for T= 28199 logg=2.97\n", + "Changing to logg=2.50 for T= 13964 logg=1.51\n", + "Changing to logg=3.00 for T= 23724 logg=2.47\n", + "Changing to logg=3.50 for T= 28915 logg=3.01\n", + "Changing to logg=3.50 for T= 30156 logg=2.88\n", + "Changing to logg=3.50 for T= 30635 logg=2.90\n", + "Changing to T= 50000 for T= 52552 logg=4.08\n", + "Changing to logg=5.00 for T= 52552 logg=4.08\n", + "Changing to logg=2.50 for T= 15105 logg=1.62\n", + "Changing to logg=2.50 for T= 17775 logg=1.91\n", + "Changing to logg=3.00 for T= 23971 logg=2.45\n", + "Changing to logg=2.50 for T= 14304 logg=1.57\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 33907 logg=3.29\n", + "Changing to T= 50000 for T=126981 logg=5.37\n", + "Changing to logg=5.00 for T=126981 logg=5.37\n", + "Changing to logg=4.00 for T= 31584 logg=2.95\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=1.50 for T= 8792 logg=0.97\n", + "Changing to logg=4.50 for T= 45586 logg=3.62\n", + "Changing to logg=2.50 for T= 12039 logg=1.25\n", + "Changing to logg=4.00 for T= 34580 logg=3.72\n", + "Changing to logg=4.00 for T= 31496 logg=2.94\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 10767 logg=1.04\n", + "Changing to logg=4.00 for T= 34488 logg=3.79\n", + "Changing to T= 50000 for T= 62553 logg=4.41\n", + "Changing to logg=5.00 for T= 62553 logg=4.41\n", + "Changing to logg=3.50 for T= 26146 logg=3.43\n", + "Changing to logg=4.00 for T= 31288 logg=3.32\n", + "Changing to logg=3.00 for T= 20055 logg=2.17\n", + "Changing to logg=3.00 for T= 24780 logg=2.51\n", + "Changing to logg=2.50 for T= 15507 logg=1.71\n", + "Changing to logg=2.00 for T= 10049 logg=0.95\n", + "Changing to logg=3.00 for T= 21692 logg=2.52\n", + "Changing to logg=3.50 for T= 28318 logg=2.79\n", + "Changing to logg=3.00 for T= 19889 logg=2.13\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.00 for T= 32764 logg=3.03\n", + "Changing to logg=4.50 for T= 41138 logg=3.44\n", + "Changing to logg=4.00 for T= 33778 logg=3.90\n", + "Changing to logg=3.00 for T= 25568 logg=2.87\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.00 for T= 21602 logg=2.26\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to T= 50000 for T= 63558 logg=4.45\n", + "Changing to logg=5.00 for T= 63558 logg=4.45\n", + "Changing to logg=2.50 for T= 16585 logg=1.81\n", + "Changing to logg=3.50 for T= 28474 logg=3.41\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 45926 logg=3.89\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 10030 logg=0.91\n", + "Changing to logg=3.50 for T= 30631 logg=2.92\n", + "Changing to logg=4.00 for T= 35244 logg=3.15\n", + "Changing to logg=3.00 for T= 22562 logg=2.36\n", + "Changing to logg=2.50 for T= 11886 logg=1.24\n", + "Changing to logg=3.50 for T= 27196 logg=3.34\n", + "Changing to logg=3.50 for T= 27742 logg=2.91\n", + "Changing to logg=4.50 for T= 48595 logg=3.72\n", + "Changing to logg=2.00 for T= 9454 logg=0.84\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 11587 logg=1.20\n", + "Changing to logg=4.00 for T= 32681 logg=3.01\n", + "Changing to logg=3.50 for T= 26913 logg=2.70\n", + "Changing to logg=4.50 for T= 43817 logg=3.81\n", + "Changing to logg=3.00 for T= 25219 logg=2.58\n", + "Changing to T= 50000 for T=201085 logg=6.01\n", + "Changing to logg=5.00 for T=201085 logg=6.01\n", + "Changing to T= 50000 for T= 70870 logg=4.69\n", + "Changing to logg=5.00 for T= 70870 logg=4.69\n", + "Changing to logg=2.50 for T= 15185 logg=1.68\n", + "Changing to logg=3.00 for T= 20326 logg=2.48\n", + "Changing to logg=0.50 for T= 6551 logg=0.46\n", + "Changing to logg=2.50 for T= 11921 logg=1.23\n", + "Changing to logg=2.50 for T= 17089 logg=1.84\n", + "Changing to logg=3.00 for T= 25814 logg=2.61\n", + "Changing to logg=3.50 for T= 26551 logg=2.62\n", + "Changing to logg=2.50 for T= 13054 logg=1.39\n", + "Changing to T= 50000 for T= 73220 logg=4.77\n", + "Changing to logg=5.00 for T= 73220 logg=4.77\n", + "Changing to logg=4.50 for T= 42111 logg=3.67\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=4.50 for T= 42537 logg=3.48\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=2.00 for T= 11192 logg=1.14\n", + "Changing to logg=2.50 for T= 11953 logg=1.83\n", + "Changing to logg=3.50 for T= 30042 logg=3.15\n", + "Changing to T= 50000 for T=110685 logg=5.39\n", + "Changing to logg=5.00 for T=110685 logg=5.39\n", + "Changing to logg=3.50 for T= 27219 logg=2.68\n", + "Changing to logg=3.00 for T= 19799 logg=2.35\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.50 for T= 44007 logg=3.82\n", + "Changing to logg=4.00 for T= 32887 logg=3.99\n", + "Changing to logg=4.00 for T= 32733 logg=3.96\n", + "Changing to logg=4.00 for T= 35818 logg=3.45\n", + "Changing to logg=3.00 for T= 20368 logg=2.17\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.00 for T= 10637 logg=1.30\n", + "Changing to logg=4.00 for T= 32582 logg=3.96\n", + "Changing to logg=4.00 for T= 32635 logg=3.97\n", + "Changing to logg=4.00 for T= 33254 logg=3.97\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to T= 50000 for T= 77730 logg=4.53\n", + "Changing to logg=5.00 for T= 77730 logg=4.53\n", + "Changing to logg=3.00 for T= 20512 logg=2.50\n", + "Changing to T= 50000 for T= 55859 logg=4.25\n", + "Changing to logg=5.00 for T= 55859 logg=4.25\n", + "Changing to logg=0.00 for T= 4176 logg=-0.07\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.00 for T= 9574 logg=0.87\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=3.00 for T= 25689 logg=2.88\n", + "Changing to logg=3.00 for T= 24268 logg=2.78\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=2.50 for T= 18086 logg=2.21\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 32819 logg=3.30\n", + "Changing to logg=2.50 for T= 14090 logg=1.54\n", + "Changing to logg=4.50 for T= 40683 logg=4.38\n", + "Changing to logg=3.00 for T= 24079 logg=2.70\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=3.50 for T= 28005 logg=2.77\n", + "Changing to T= 50000 for T= 84772 logg=5.05\n", + "Changing to logg=5.00 for T= 84772 logg=5.05\n", + "Changing to logg=4.00 for T= 32743 logg=3.97\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=4.00 for T= 32315 logg=3.96\n", + "Changing to logg=4.00 for T= 32593 logg=3.96\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 35768 logg=3.18\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to T= 50000 for T= 55898 logg=4.26\n", + "Changing to logg=5.00 for T= 55898 logg=4.26\n", + "Changing to logg=3.00 for T= 21667 logg=2.31\n", + "Changing to logg=4.00 for T= 38001 logg=3.56\n", + "Changing to logg=2.50 for T= 12522 logg=1.33\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.00 for T= 19181 logg=2.38\n", + "Changing to T= 50000 for T= 89826 logg=5.13\n", + "Changing to logg=5.00 for T= 89826 logg=5.13\n", + "Changing to logg=3.50 for T= 29491 logg=3.44\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to T= 50000 for T= 85247 logg=4.71\n", + "Changing to logg=5.00 for T= 85247 logg=4.71\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.50 for T= 30990 logg=3.20\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=3.50 for T= 29564 logg=3.48\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=2.50 for T= 12652 logg=1.35\n", + "Changing to T= 50000 for T= 67809 logg=4.30\n", + "Changing to logg=5.00 for T= 67809 logg=4.30\n", + "Changing to logg=3.50 for T= 27564 logg=3.36\n", + "Changing to logg=2.00 for T= 11203 logg=1.14\n", + "Changing to logg=4.00 for T= 31591 logg=3.62\n", + "Changing to logg=3.50 for T= 27537 logg=3.00\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to logg=3.50 for T= 30252 logg=2.90\n", + "Changing to logg=2.00 for T= 11540 logg=1.17\n", + "Changing to logg=3.50 for T= 30959 logg=2.93\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.00 for T= 11564 logg=1.20\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to T= 50000 for T= 50325 logg=4.06\n", + "Changing to logg=5.00 for T= 50325 logg=4.06\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=3.50 for T= 26216 logg=2.84\n", + "Changing to T= 50000 for T= 86127 logg=5.03\n", + "Changing to logg=5.00 for T= 86127 logg=5.03\n", + "Changing to T= 50000 for T= 67144 logg=4.28\n", + "Changing to logg=5.00 for T= 67144 logg=4.28\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to T= 50000 for T= 74561 logg=4.76\n", + "Changing to logg=5.00 for T= 74561 logg=4.76\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=2.50 for T= 15570 logg=1.72\n", + "Changing to logg=4.50 for T= 47637 logg=3.88\n", + "Changing to logg=4.00 for T= 32582 logg=3.99\n", + "Changing to logg=2.50 for T= 15655 logg=1.70\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to logg=4.00 for T= 32558 logg=3.87\n", + "Changing to T= 50000 for T=105862 logg=5.32\n", + "Changing to logg=5.00 for T=105862 logg=5.32\n", + "Changing to T= 50000 for T=104205 logg=5.02\n", + "Changing to logg=5.00 for T=104205 logg=5.02\n", + "Changing to logg=2.00 for T= 9246 logg=1.21\n", + "Changing to logg=2.00 for T= 10719 logg=1.04\n", + "Changing to logg=4.50 for T= 42350 logg=3.75\n", + "Changing to T= 50000 for T= 86435 logg=5.42\n", + "Changing to logg=5.00 for T= 86435 logg=5.42\n", + "Changing to T= 50000 for T= 95984 logg=4.90\n", + "Changing to logg=5.00 for T= 95984 logg=4.90\n", + "Changing to T= 50000 for T= 52139 logg=4.06\n", + "Changing to logg=5.00 for T= 52139 logg=4.06\n", + "Changing to T= 50000 for T= 81943 logg=4.62\n", + "Changing to logg=5.00 for T= 81943 logg=4.62\n", + "Changing to logg=0.00 for T= 3997 logg=-0.36\n", + "Changing to logg=2.50 for T= 16091 logg=2.00\n", + "Changing to T= 50000 for T= 75436 logg=4.78\n", + "Changing to logg=5.00 for T= 75436 logg=4.78\n", + "Changing to logg=2.00 for T= 11272 logg=1.15\n", + "Changing to logg=4.00 for T= 34929 logg=3.41\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.00 for T= 31060 logg=2.93\n", + "Changing to logg=4.00 for T= 34077 logg=3.97\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to T= 50000 for T= 70473 logg=4.68\n", + "Changing to logg=5.00 for T= 70473 logg=4.68\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.00 for T= 10241 logg=0.97\n", + "Changing to T= 50000 for T= 69383 logg=4.64\n", + "Changing to logg=5.00 for T= 69383 logg=4.64\n", + "Changing to logg=0.00 for T= 4182 logg=-0.07\n", + "Changing to logg=3.50 for T= 29392 logg=3.04\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=3.50 for T= 30687 logg=3.18\n", + "Changing to T= 50000 for T= 77543 logg=4.93\n", + "Changing to logg=5.00 for T= 77543 logg=4.93\n", + "Changing to logg=2.50 for T= 18787 logg=2.03\n", + "Changing to logg=2.50 for T= 16928 logg=1.86\n", + "Changing to T= 50000 for T= 89092 logg=5.11\n", + "Changing to logg=5.00 for T= 89092 logg=5.11\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 33820 logg=3.98\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to T= 50000 for T= 73544 logg=4.78\n", + "Changing to logg=5.00 for T= 73544 logg=4.78\n", + "Changing to logg=4.00 for T= 38291 logg=3.57\n", + "Changing to T= 50000 for T= 91251 logg=5.20\n", + "Changing to logg=5.00 for T= 91251 logg=5.20\n", + "Changing to logg=3.50 for T= 28428 logg=2.78\n", + "Changing to T= 50000 for T= 99993 logg=5.35\n", + "Changing to logg=5.00 for T= 99993 logg=5.35\n", + "Changing to logg=4.00 for T= 36456 logg=3.24\n", + "Changing to logg=4.00 for T= 32744 logg=3.97\n", + "Changing to logg=2.50 for T= 14200 logg=1.54\n", + "Changing to logg=2.50 for T= 12393 logg=1.31\n", + "Changing to T= 50000 for T= 78213 logg=4.55\n", + "Changing to logg=5.00 for T= 78213 logg=4.55\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=4.00 for T= 31270 logg=3.83\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=4.00 for T= 36091 logg=3.47\n", + "Changing to logg=0.50 for T= 6228 logg=0.10\n", + "Changing to logg=2.50 for T= 12228 logg=1.26\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to T= 50000 for T= 82899 logg=4.67\n", + "Changing to logg=5.00 for T= 82899 logg=4.67\n", + "Changing to logg=0.00 for T= 4314 logg=-0.49\n", + "Changing to T= 50000 for T= 77053 logg=4.91\n", + "Changing to logg=5.00 for T= 77053 logg=4.91\n", + "Changing to logg=4.00 for T= 31617 logg=3.17\n", + "Changing to logg=3.50 for T= 28096 logg=2.95\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=0.00 for T= 4603 logg=-0.14\n", + "Changing to logg=3.50 for T= 27307 logg=2.70\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=2.00 for T= 9812 logg=0.89\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=4.00 for T= 37119 logg=3.26\n", + "Changing to logg=0.00 for T= 4271 logg=-0.49\n", + "Changing to logg=4.00 for T= 31955 logg=3.00\n", + "Changing to T= 50000 for T= 70906 logg=4.67\n", + "Changing to logg=5.00 for T= 70906 logg=4.67\n", + "Changing to logg=4.00 for T= 31408 logg=3.83\n", + "Changing to logg=3.00 for T= 24408 logg=2.52\n", + "Changing to T= 50000 for T= 88908 logg=4.75\n", + "Changing to logg=5.00 for T= 88908 logg=4.75\n", + "Changing to logg=2.50 for T= 15640 logg=1.95\n", + "Changing to logg=4.50 for T= 46620 logg=3.92\n", + "Changing to logg=4.00 for T= 36554 logg=3.49\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to logg=2.50 for T= 18198 logg=1.97\n", + "Changing to logg=0.00 for T= 4474 logg=-0.18\n", + "Changing to logg=3.50 for T= 27038 logg=2.70\n", + "Changing to logg=0.00 for T= 4166 logg=-0.09\n", + "Changing to logg=4.00 for T= 33881 logg=3.98\n", + "Changing to logg=3.00 for T= 24494 logg=2.80\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=2.50 for T= 16350 logg=1.78\n", + "Changing to logg=3.00 for T= 25993 logg=2.90\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=3.00 for T= 21688 logg=2.29\n", + "Changing to logg=3.50 for T= 28586 logg=3.06\n", + "Changing to logg=4.00 for T= 32623 logg=3.99\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to T= 50000 for T= 68102 logg=4.33\n", + "Changing to logg=5.00 for T= 68102 logg=4.33\n", + "Changing to logg=3.50 for T= 26457 logg=3.40\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=4.50 for T= 39842 logg=3.56\n", + "Changing to logg=3.50 for T= 28746 logg=3.07\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=3.00 for T= 22329 logg=2.64\n", + "Changing to logg=4.50 for T= 44438 logg=3.57\n", + "Changing to logg=4.50 for T= 40054 logg=3.39\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 33634 logg=3.86\n", + "Changing to logg=2.50 for T= 18594 logg=2.33\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=0.00 for T= 4164 logg=-0.21\n", + "Changing to logg=0.00 for T= 4524 logg=-0.42\n", + "Changing to logg=3.00 for T= 20136 logg=2.18\n", + "Changing to logg=4.00 for T= 36215 logg=3.22\n", + "Changing to logg=4.00 for T= 37002 logg=3.43\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=2.00 for T= 11274 logg=1.13\n", + "Changing to logg=4.00 for T= 37696 logg=3.27\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.00 for T= 32438 logg=3.28\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.00 for T= 23358 logg=2.45\n", + "Changing to logg=2.00 for T= 11672 logg=1.20\n", + "Changing to logg=4.00 for T= 37458 logg=3.46\n", + "Changing to logg=4.50 for T= 43216 logg=4.36\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=3.00 for T= 21217 logg=2.24\n", + "Changing to logg=2.50 for T= 13908 logg=1.50\n", + "Changing to logg=4.00 for T= 32737 logg=3.96\n", + "Changing to logg=4.50 for T= 44142 logg=3.55\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to logg=2.00 for T= 9740 logg=0.90\n", + "Changing to T= 50000 for T= 50389 logg=3.78\n", + "Changing to logg=5.00 for T= 50389 logg=3.78\n", + "Changing to logg=4.00 for T= 33048 logg=3.05\n", + "Changing to logg=4.00 for T= 32137 logg=3.85\n", + "Changing to logg=3.00 for T= 24401 logg=2.71\n", + "Changing to logg=4.00 for T= 32475 logg=3.99\n", + "Changing to T= 50000 for T= 52009 logg=4.12\n", + "Changing to logg=5.00 for T= 52009 logg=4.12\n", + "Changing to logg=3.00 for T= 21447 logg=2.57\n", + "Changing to logg=3.50 for T= 28081 logg=2.76\n", + "Changing to logg=3.00 for T= 22637 logg=2.59\n", + "Changing to T= 50000 for T= 74922 logg=4.82\n", + "Changing to logg=5.00 for T= 74922 logg=4.82\n", + "Changing to T= 50000 for T= 95163 logg=4.88\n", + "Changing to logg=5.00 for T= 95163 logg=4.88\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 37545 logg=3.28\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to T= 50000 for T= 55325 logg=3.93\n", + "Changing to logg=5.00 for T= 55325 logg=3.93\n", + "Changing to logg=0.00 for T= 3997 logg=-0.36\n", + "Changing to logg=4.00 for T= 38104 logg=3.28\n", + "Changing to logg=4.50 for T= 46762 logg=3.65\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.00 for T= 37113 logg=3.44\n", + "Changing to T= 50000 for T=115030 logg=5.23\n", + "Changing to logg=5.00 for T=115030 logg=5.23\n", + "Changing to logg=4.50 for T= 45080 logg=3.86\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=2.00 for T= 10127 logg=0.96\n", + "Changing to logg=3.50 for T= 27107 logg=2.90\n", + "Changing to logg=0.00 for T= 4531 logg=-0.42\n", + "Changing to logg=4.00 for T= 32215 logg=2.98\n", + "Changing to logg=4.00 for T= 31546 logg=3.83\n", + "Changing to logg=3.00 for T= 22713 logg=2.35\n", + "Changing to logg=3.00 for T= 21118 logg=2.55\n", + "Changing to logg=4.50 for T= 44511 logg=4.36\n", + "Changing to logg=2.50 for T= 18524 logg=2.25\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=4.00 for T= 32451 logg=3.99\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.00 for T= 9804 logg=0.91\n", + "Changing to logg=2.50 for T= 12837 logg=1.34\n", + "Changing to logg=3.50 for T= 27778 logg=2.74\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=5.00 for T= 49246 logg=3.95\n", + "Changing to logg=2.50 for T= 14134 logg=1.78\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=4.00 for T= 33622 logg=3.34\n", + "Changing to logg=3.50 for T= 26928 logg=3.43\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=2.50 for T= 15341 logg=1.93\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.50 for T= 42384 logg=3.50\n", + "Changing to logg=4.00 for T= 31297 logg=2.96\n", + "Changing to logg=3.50 for T= 28557 logg=2.99\n", + "Changing to logg=4.00 for T= 35427 logg=3.17\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=4.00 for T= 31663 logg=3.95\n", + "Changing to logg=3.00 for T= 19049 logg=2.09\n", + "Changing to logg=3.00 for T= 25145 logg=2.84\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to T= 50000 for T= 83620 logg=5.48\n", + "Changing to logg=5.00 for T= 83620 logg=5.48\n", + "Changing to logg=4.50 for T= 41588 logg=4.33\n", + "Changing to logg=4.00 for T= 31468 logg=3.73\n", + "Changing to logg=2.00 for T= 10174 logg=0.96\n", + "Changing to logg=3.00 for T= 22050 logg=2.34\n", + "Changing to logg=3.00 for T= 24227 logg=2.49\n", + "Changing to logg=4.00 for T= 33080 logg=3.97\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=3.00 for T= 21010 logg=2.46\n", + "Changing to logg=4.00 for T= 31109 logg=3.90\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.00 for T= 22900 logg=2.40\n", + "Changing to logg=4.00 for T= 32492 logg=3.66\n", + "Changing to logg=2.50 for T= 15788 logg=2.06\n", + "Changing to logg=3.50 for T= 26275 logg=3.40\n", + "Changing to T= 50000 for T= 76883 logg=4.88\n", + "Changing to logg=5.00 for T= 76883 logg=4.88\n", + "Changing to T= 50000 for T= 56136 logg=4.20\n", + "Changing to logg=5.00 for T= 56136 logg=4.20\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=4.00 for T= 31573 logg=3.83\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to T= 50000 for T= 78708 logg=4.53\n", + "Changing to logg=5.00 for T= 78708 logg=4.53\n", + "Changing to logg=4.00 for T= 34782 logg=3.15\n", + "Changing to logg=3.50 for T= 29068 logg=3.02\n", + "Changing to logg=0.00 for T= 4278 logg=-0.49\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 33558 logg=3.97\n", + "Changing to T= 50000 for T= 90402 logg=5.15\n", + "Changing to logg=5.00 for T= 90402 logg=5.15\n", + "Changing to T= 50000 for T= 67144 logg=4.28\n", + "Changing to logg=5.00 for T= 67144 logg=4.28\n", + "Changing to logg=4.50 for T= 46385 logg=4.32\n", + "Changing to logg=3.00 for T= 21097 logg=2.54\n", + "Changing to logg=4.00 for T= 34005 logg=3.99\n", + "Changing to T= 50000 for T= 62584 logg=4.42\n", + "Changing to logg=5.00 for T= 62584 logg=4.42\n", + "Changing to logg=0.00 for T= 4124 logg=-0.51\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to T= 50000 for T= 59039 logg=4.30\n", + "Changing to logg=5.00 for T= 59039 logg=4.30\n", + "Changing to T= 50000 for T= 89646 logg=5.09\n", + "Changing to logg=5.00 for T= 89646 logg=5.09\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to T= 50000 for T=103889 logg=5.04\n", + "Changing to logg=5.00 for T=103889 logg=5.04\n", + "Changing to logg=3.50 for T= 30588 logg=2.92\n", + "Changing to logg=4.00 for T= 32165 logg=3.96\n", + "Changing to logg=4.00 for T= 34871 logg=3.41\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.50 for T= 39602 logg=3.63\n", + "Changing to logg=4.00 for T= 31483 logg=3.84\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=1.00 for T= 8242 logg=0.85\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to T= 50000 for T= 50163 logg=3.77\n", + "Changing to logg=5.00 for T= 50163 logg=3.77\n", + "Changing to T= 50000 for T= 62435 logg=4.41\n", + "Changing to logg=5.00 for T= 62435 logg=4.41\n", + "Changing to logg=3.00 for T= 23364 logg=2.65\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=4.00 for T= 36338 logg=3.89\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=4.00 for T= 32266 logg=3.27\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to logg=3.50 for T= 27521 logg=2.73\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 33918 logg=3.29\n", + "Changing to logg=4.00 for T= 32840 logg=3.97\n", + "Changing to logg=3.50 for T= 28511 logg=2.80\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.00 for T= 20687 logg=2.51\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=3.00 for T= 23454 logg=2.72\n", + "Changing to T= 50000 for T= 86127 logg=5.03\n", + "Changing to logg=5.00 for T= 86127 logg=5.03\n", + "Changing to logg=4.50 for T= 39074 logg=3.34\n", + "Changing to logg=3.50 for T= 28153 logg=2.96\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to T= 50000 for T= 57768 logg=4.32\n", + "Changing to logg=5.00 for T= 57768 logg=4.32\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to T= 50000 for T= 79788 logg=4.90\n", + "Changing to logg=5.00 for T= 79788 logg=4.90\n", + "Changing to logg=2.50 for T= 17227 logg=1.89\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=4.00 for T= 31233 logg=3.83\n", + "Changing to logg=1.50 for T= 8768 logg=0.96\n", + "Changing to logg=4.50 for T= 40319 logg=3.41\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=3.50 for T= 26352 logg=2.85\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=3.00 for T= 23116 logg=2.70\n", + "Changing to T= 50000 for T= 87084 logg=5.05\n", + "Changing to logg=5.00 for T= 87084 logg=5.05\n", + "Changing to logg=3.00 for T= 22731 logg=2.38\n", + "Changing to logg=3.50 for T= 27796 logg=2.95\n", + "Changing to logg=3.50 for T= 28279 logg=2.75\n", + "Changing to logg=3.00 for T= 20719 logg=2.43\n", + "Changing to T= 50000 for T= 57412 logg=4.22\n", + "Changing to logg=5.00 for T= 57412 logg=4.22\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.00 for T= 9201 logg=1.18\n", + "Changing to logg=4.00 for T= 31259 logg=3.83\n", + "Changing to logg=3.00 for T= 19292 logg=2.32\n", + "Changing to logg=3.00 for T= 19444 logg=2.32\n", + "Changing to logg=3.00 for T= 19187 logg=2.09\n", + "Changing to logg=4.00 for T= 32089 logg=3.93\n", + "Changing to logg=2.50 for T= 16344 logg=1.78\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.00 for T= 31779 logg=3.65\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=4.00 for T= 32705 logg=3.97\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.00 for T= 32458 logg=3.01\n", + "Changing to logg=4.50 for T= 44994 logg=3.60\n", + "Changing to T= 50000 for T= 78139 logg=4.89\n", + "Changing to logg=5.00 for T= 78139 logg=4.89\n", + "Changing to logg=3.00 for T= 25653 logg=2.81\n", + "Changing to logg=4.00 for T= 32018 logg=3.61\n", + "Changing to logg=2.50 for T= 14308 logg=1.55\n", + "Changing to logg=2.50 for T= 12187 logg=1.27\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=4.00 for T= 33752 logg=3.28\n", + "Changing to logg=2.50 for T= 18431 logg=1.99\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=2.50 for T= 18387 logg=2.31\n", + "Changing to logg=2.50 for T= 13082 logg=1.41\n", + "Changing to logg=3.00 for T= 21938 logg=2.28\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=0.00 for T= 4575 logg=-0.42\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to T= 50000 for T= 54349 logg=4.14\n", + "Changing to logg=5.00 for T= 54349 logg=4.14\n", + "Changing to logg=3.00 for T= 21843 logg=2.53\n", + "Changing to logg=0.00 for T= 3926 logg=-0.48\n", + "Changing to logg=2.50 for T= 12933 logg=1.37\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to T= 50000 for T= 69440 logg=4.64\n", + "Changing to logg=5.00 for T= 69440 logg=4.64\n", + "Changing to logg=0.50 for T= 6491 logg=0.44\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=3.50 for T= 29607 logg=3.12\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=3.00 for T= 19167 logg=2.38\n", + "Changing to logg=3.00 for T= 20743 logg=2.18\n", + "Changing to logg=0.00 for T= 3975 logg=-0.43\n", + "Changing to T= 50000 for T=105475 logg=5.05\n", + "Changing to logg=5.00 for T=105475 logg=5.05\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to T= 50000 for T= 95589 logg=4.89\n", + "Changing to logg=5.00 for T= 95589 logg=4.89\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=4.00 for T= 37311 logg=3.53\n", + "Changing to logg=1.00 for T= 8218 logg=0.85\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=3.00 for T= 25777 logg=2.88\n", + "Changing to T= 50000 for T= 61234 logg=4.33\n", + "Changing to logg=5.00 for T= 61234 logg=4.33\n", + "Changing to logg=3.50 for T= 30541 logg=2.92\n", + "Changing to logg=2.50 for T= 11931 logg=1.21\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.00 for T= 25287 logg=2.85\n", + "Changing to logg=3.00 for T= 20590 logg=2.50\n", + "Changing to logg=2.50 for T= 15139 logg=1.63\n", + "Changing to logg=4.00 for T= 32573 logg=3.02\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=2.50 for T= 12786 logg=1.37\n", + "Changing to T= 50000 for T= 63320 logg=4.18\n", + "Changing to logg=5.00 for T= 63320 logg=4.18\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=4.50 for T= 41303 logg=3.44\n", + "Changing to logg=3.00 for T= 23542 logg=2.37\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to T= 50000 for T= 68011 logg=4.56\n", + "Changing to logg=5.00 for T= 68011 logg=4.56\n", + "Changing to logg=2.50 for T= 14588 logg=1.60\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=3.00 for T= 24826 logg=2.82\n", + "Changing to logg=4.00 for T= 33999 logg=3.98\n", + "Changing to logg=4.50 for T= 44674 logg=3.56\n", + "Changing to logg=3.50 for T= 29870 logg=3.14\n", + "Changing to logg=4.00 for T= 32551 logg=3.99\n", + "Changing to logg=2.50 for T= 14303 logg=1.57\n", + "Changing to logg=4.00 for T= 31957 logg=3.74\n", + "Changing to logg=2.50 for T= 12718 logg=1.95\n", + "Changing to logg=3.50 for T= 29334 logg=3.03\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to T= 50000 for T= 78053 logg=4.93\n", + "Changing to logg=5.00 for T= 78053 logg=4.93\n", + "Changing to logg=3.00 for T= 22428 logg=2.34\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=0.00 for T= 4144 logg=-0.11\n", + "Changing to logg=4.00 for T= 32485 logg=3.03\n", + "Changing to logg=4.00 for T= 31841 logg=3.85\n", + "Changing to logg=0.00 for T= 3997 logg=-0.36\n", + "Changing to logg=0.00 for T= 3997 logg=-0.36\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=4.00 for T= 33294 logg=3.84\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=2.00 for T= 11041 logg=1.08\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=4.50 for T= 42436 logg=4.35\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.50 for T= 17397 logg=1.89\n", + "Changing to logg=0.00 for T= 3967 logg=-0.42\n", + "Changing to T= 50000 for T= 50863 logg=3.97\n", + "Changing to logg=5.00 for T= 50863 logg=3.97\n", + "Changing to logg=2.00 for T= 11070 logg=1.12\n", + "Changing to logg=0.00 for T= 4183 logg=-0.51\n", + "Changing to logg=0.00 for T= 3997 logg=-0.39\n", + "Changing to logg=3.00 for T= 21072 logg=2.26\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=0.50 for T= 6965 logg=0.31\n", + "Changing to logg=0.00 for T= 3998 logg=-0.36\n", + "Changing to logg=4.50 for T= 41118 logg=3.43\n", + "Changing to logg=4.00 for T= 31312 logg=3.99\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to logg=2.00 for T= 9689 logg=0.87\n", + "Changing to logg=4.00 for T= 33313 logg=4.00\n", + "Changing to logg=3.50 for T= 26284 logg=3.42\n", + "Changing to logg=0.00 for T= 3950 logg=-0.38\n", + "Changing to T= 50000 for T= 53343 logg=4.17\n", + "Changing to logg=5.00 for T= 53343 logg=4.17\n", + "Changing to T= 50000 for T= 65145 logg=4.22\n", + "Changing to logg=5.00 for T= 65145 logg=4.22\n", + "Changing to logg=3.00 for T= 23136 logg=2.40\n", + "Changing to logg=3.00 for T= 21648 logg=2.29\n", + "Changing to T= 50000 for T= 74748 logg=4.79\n", + "Changing to logg=5.00 for T= 74748 logg=4.79\n", + "Changing to T= 50000 for T=122481 logg=5.32\n", + "Changing to logg=5.00 for T=122481 logg=5.32\n", + "Changing to T= 50000 for T= 65983 logg=4.52\n", + "Changing to logg=5.00 for T= 65983 logg=4.52\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=4.00 for T= 33539 logg=3.97\n", + "Changing to logg=2.50 for T= 12561 logg=1.93\n", + "Changing to logg=3.50 for T= 30993 logg=3.13\n", + "Changing to T= 50000 for T= 64292 logg=4.47\n", + "Changing to logg=5.00 for T= 64292 logg=4.47\n", + "Changing to logg=1.50 for T= 8984 logg=0.76\n", + "Changing to logg=0.00 for T= 4183 logg=-0.07\n", + "Changing to logg=0.00 for T= 3998 logg=-0.33\n", + "Changing to logg=2.50 for T= 11942 logg=1.83\n", + "Changing to logg=3.50 for T= 26289 logg=3.42\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-08-13 11:06:20.867185 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 9.500 Msun T = 26977 K m_ubv_U = 6.74\n", + "M = 8.500 Msun T = 25805 K m_ubv_U = 7.13\n", + "M = 15.849 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 6.310 Msun T = nan K m_ubv_U = nan\n", + "M = 23.000 Msun T = 127803 K m_ubv_U = 8.44\n", + "M = 19.000 Msun T = 11953 K m_ubv_U = 2.10\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 19.953 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 20.000 Msun T = 26486 K m_ubv_U = 3.23\n", + "M = 18.000 Msun T = 29119 K m_ubv_U = 4.09\n", + "M = 19.000 Msun T = 11953 K m_ubv_U = 2.10\n", + "M = 7.943 Msun T = nan K m_ubv_U = nan\n", + "M = 6.310 Msun T = nan K m_ubv_U = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_U = 5.62\n", + "M = 1.995 Msun T = 3090 K m_ubv_U = 28.16\n", + "M = 3.162 Msun T = 3090 K m_ubv_U = 28.32\n", + "M = 15.000 Msun T = 30279 K m_ubv_U = 4.96\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 25.119 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 10.000 Msun T = 27642 K m_ubv_U = 6.62\n", + "M = 16.000 Msun T = 30080 K m_ubv_U = 4.65\n", + "M = 13.000 Msun T = 30037 K m_ubv_U = 5.62\n", + "M = 21.000 Msun T = 11491 K m_ubv_U = 1.43\n", + "M = 8.500 Msun T = 25805 K m_ubv_U = 7.13\n", + "M = 11.000 Msun T = 28461 K m_ubv_U = 6.21\n", + "M = 18.000 Msun T = 29119 K m_ubv_U = 4.09\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 19.953 Msun T = nan K m_ubv_U = nan\n", + "M = 1.995 Msun T = 3090 K m_ubv_U = 28.16\n", + "M = 3.162 Msun T = 3090 K m_ubv_U = 28.32\n", + "M = 19.000 Msun T = 11953 K m_ubv_U = 2.10\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 1.995 Msun T = 3090 K m_ubv_U = 28.16\n", + "M = 18.000 Msun T = 29119 K m_ubv_U = 4.09\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 19.000 Msun T = 11953 K m_ubv_U = 2.10\n", + "M = 15.000 Msun T = 30279 K m_ubv_U = 4.96\n", + "M = 18.000 Msun T = 29119 K m_ubv_U = 4.09\n", + "M = 1.400 Msun T = 3090 K m_ubv_U = 28.08\n", + "M = 10.000 Msun T = nan K m_ubv_U = nan\n", + "M = 23.000 Msun T = 23329 K m_ubv_U = 2.36\n", + "M = 17.000 Msun T = 29685 K m_ubv_U = 4.36\n", + "M = 15.849 Msun T = nan K m_ubv_U = nan\n", + "M = 12.589 Msun T = nan K m_ubv_U = nan\n", + "M = 23.000 Msun T = 10030 K m_ubv_U = 1.13\n", + "M = 22.000 Msun T = 201085 K m_ubv_U = 9.22\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 6.800 Msun T = 24284 K m_ubv_U = 7.98\n", + "M = 17.000 Msun T = 29679 K m_ubv_U = 4.36\n", + "M = 12.000 Msun T = 29622 K m_ubv_U = 6.00\n", + "M = 9.000 Msun T = 26339 K m_ubv_U = 6.89\n", + "M = 16.100 Msun T = 29012 K m_ubv_U = 4.88\n", + "M = 1.900 Msun T = 11240 K m_ubv_U = 11.83\n", + "M = 10.200 Msun T = 30016 K m_ubv_U = 6.73\n", + "M = 35.000 Msun T = 67809 K m_ubv_U = 5.93\n", + "M = 19.000 Msun T = 11942 K m_ubv_U = 2.10\n", + "M = 10.000 Msun T = 29174 K m_ubv_U = 6.77\n", + "M = 16.200 Msun T = 34251 K m_ubv_U = 4.99\n", + "M = 5.700 Msun T = 22094 K m_ubv_U = 8.47\n", + "M = 22.000 Msun T = 69383 K m_ubv_U = 6.98\n", + "M = 16.000 Msun T = 30076 K m_ubv_U = 4.65\n", + "M = 7.800 Msun T = 26175 K m_ubv_U = 7.57\n", + "M = 20.000 Msun T = 4144 K m_ubv_U = 6.01\n", + "M = 10.000 Msun T = 27641 K m_ubv_U = 6.62\n", + "M = 12.000 Msun T = 32222 K m_ubv_U = 6.14\n", + "M = 17.000 Msun T = 29314 K m_ubv_U = 4.30\n", + "M = 50.000 Msun T = 26457 K m_ubv_U = 4.35\n", + "M = 11.000 Msun T = 28462 K m_ubv_U = 6.20\n", + "M = 2.000 Msun T = 11677 K m_ubv_U = 11.64\n", + "M = 6.400 Msun T = 23549 K m_ubv_U = 8.14\n", + "M = 6.500 Msun T = 23738 K m_ubv_U = 8.10\n", + "M = 14.700 Msun T = 32704 K m_ubv_U = 5.34\n", + "M = 0.850 Msun T = 5742 K m_ubv_U = 15.89\n", + "M = 4.400 Msun T = 19127 K m_ubv_U = 9.20\n", + "M = 10.800 Msun T = 30825 K m_ubv_U = 6.54\n", + "M = 6.800 Msun T = 24328 K m_ubv_U = 7.97\n", + "M = 24.000 Msun T = 76883 K m_ubv_U = 7.43\n", + "M = 16.000 Msun T = 30076 K m_ubv_U = 4.65\n", + "M = 10.000 Msun T = 27641 K m_ubv_U = 6.62\n", + "M = 7.600 Msun T = 25815 K m_ubv_U = 7.65\n", + "M = 15.300 Msun T = 34143 K m_ubv_U = 5.21\n", + "M = 10.000 Msun T = 27641 K m_ubv_U = 6.62\n", + "M = 3.600 Msun T = 17023 K m_ubv_U = 9.78\n", + "M = 15.300 Msun T = 34143 K m_ubv_U = 5.21\n", + "M = 10.200 Msun T = 30016 K m_ubv_U = 6.73\n", + "M = 7.600 Msun T = 25815 K m_ubv_U = 7.65\n", + "M = 13.500 Msun T = 33441 K m_ubv_U = 5.70\n", + "M = 7.200 Msun T = 25077 K m_ubv_U = 7.81\n", + "M = 12.000 Msun T = 29563 K m_ubv_U = 5.98\n", + "M = 11.000 Msun T = 28462 K m_ubv_U = 6.20\n", + "M = 20.700 Msun T = 30303 K m_ubv_U = 3.82\n", + "M = 3.400 Msun T = 16457 K m_ubv_U = 9.95\n", + "M = 30.000 Msun T = 78053 K m_ubv_U = 7.63\n", + "M = 20.000 Msun T = 4183 K m_ubv_U = 5.93\n", + "M = 13.800 Msun T = 32235 K m_ubv_U = 5.66\n", + "M = 15.400 Msun T = nan K m_ubv_U = nan\n", + "M = 15.000 Msun T = 31233 K m_ubv_U = 5.12\n", + "Starting filter: ubv,V Elapsed time: 16.77 seconds\n", + "Starting synthetic photometry\n", + "M = 9.500 Msun T = 26977 K m_ubv_V = 7.92\n", + "M = 8.500 Msun T = 25805 K m_ubv_V = 8.28\n", + "M = 15.849 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 6.310 Msun T = nan K m_ubv_V = nan\n", + "M = 23.000 Msun T = 127803 K m_ubv_V = 9.88\n", + "M = 19.000 Msun T = 11953 K m_ubv_V = 2.61\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 19.953 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 20.000 Msun T = 26486 K m_ubv_V = 4.45\n", + "M = 18.000 Msun T = 29119 K m_ubv_V = 5.37\n", + "M = 19.000 Msun T = 11953 K m_ubv_V = 2.61\n", + "M = 7.943 Msun T = nan K m_ubv_V = nan\n", + "M = 6.310 Msun T = nan K m_ubv_V = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_V = 6.89\n", + "M = 1.995 Msun T = 3090 K m_ubv_V = 24.52\n", + "M = 3.162 Msun T = 3090 K m_ubv_V = 24.52\n", + "M = 15.000 Msun T = 30279 K m_ubv_V = 6.25\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 25.119 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 10.000 Msun T = 27642 K m_ubv_V = 7.82\n", + "M = 16.000 Msun T = 30080 K m_ubv_V = 5.94\n", + "M = 13.000 Msun T = 30037 K m_ubv_V = 6.89\n", + "M = 21.000 Msun T = 11491 K m_ubv_V = 1.93\n", + "M = 8.500 Msun T = 25805 K m_ubv_V = 8.28\n", + "M = 11.000 Msun T = 28461 K m_ubv_V = 7.44\n", + "M = 18.000 Msun T = 29119 K m_ubv_V = 5.37\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 19.953 Msun T = nan K m_ubv_V = nan\n", + "M = 1.995 Msun T = 3090 K m_ubv_V = 24.52\n", + "M = 3.162 Msun T = 3090 K m_ubv_V = 24.52\n", + "M = 19.000 Msun T = 11953 K m_ubv_V = 2.61\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 1.995 Msun T = 3090 K m_ubv_V = 24.52\n", + "M = 18.000 Msun T = 29119 K m_ubv_V = 5.37\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 19.000 Msun T = 11953 K m_ubv_V = 2.61\n", + "M = 15.000 Msun T = 30279 K m_ubv_V = 6.25\n", + "M = 18.000 Msun T = 29119 K m_ubv_V = 5.37\n", + "M = 1.400 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 10.000 Msun T = nan K m_ubv_V = nan\n", + "M = 23.000 Msun T = 23329 K m_ubv_V = 3.52\n", + "M = 17.000 Msun T = 29685 K m_ubv_V = 5.65\n", + "M = 15.849 Msun T = nan K m_ubv_V = nan\n", + "M = 12.589 Msun T = nan K m_ubv_V = nan\n", + "M = 23.000 Msun T = 10030 K m_ubv_V = 1.41\n", + "M = 22.000 Msun T = 201085 K m_ubv_V = 10.65\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 6.800 Msun T = 24284 K m_ubv_V = 9.06\n", + "M = 17.000 Msun T = 29679 K m_ubv_V = 5.65\n", + "M = 12.000 Msun T = 29622 K m_ubv_V = 7.26\n", + "M = 9.000 Msun T = 26339 K m_ubv_V = 8.05\n", + "M = 16.100 Msun T = 29012 K m_ubv_V = 6.12\n", + "M = 1.900 Msun T = 11240 K m_ubv_V = 12.06\n", + "M = 10.200 Msun T = 30016 K m_ubv_V = 7.98\n", + "M = 35.000 Msun T = 67809 K m_ubv_V = 7.37\n", + "M = 19.000 Msun T = 11942 K m_ubv_V = 2.60\n", + "M = 10.000 Msun T = 29174 K m_ubv_V = 8.01\n", + "M = 16.200 Msun T = 34251 K m_ubv_V = 6.35\n", + "M = 5.700 Msun T = 22094 K m_ubv_V = 9.48\n", + "M = 22.000 Msun T = 69383 K m_ubv_V = 8.41\n", + "M = 16.000 Msun T = 30076 K m_ubv_V = 5.94\n", + "M = 7.800 Msun T = 26175 K m_ubv_V = 8.72\n", + "M = 20.000 Msun T = 4144 K m_ubv_V = 2.88\n", + "M = 10.000 Msun T = 27641 K m_ubv_V = 7.82\n", + "M = 12.000 Msun T = 32222 K m_ubv_V = 7.45\n", + "M = 17.000 Msun T = 29314 K m_ubv_V = 5.58\n", + "M = 50.000 Msun T = 26457 K m_ubv_V = 5.56\n", + "M = 11.000 Msun T = 28462 K m_ubv_V = 7.43\n", + "M = 2.000 Msun T = 11677 K m_ubv_V = 11.93\n", + "M = 6.400 Msun T = 23549 K m_ubv_V = 9.20\n", + "M = 6.500 Msun T = 23738 K m_ubv_V = 9.17\n", + "M = 14.700 Msun T = 32704 K m_ubv_V = 6.67\n", + "M = 0.850 Msun T = 5742 K m_ubv_V = 15.41\n", + "M = 4.400 Msun T = 19127 K m_ubv_V = 10.08\n", + "M = 10.800 Msun T = 30825 K m_ubv_V = 7.82\n", + "M = 6.800 Msun T = 24328 K m_ubv_V = 9.06\n", + "M = 24.000 Msun T = 76883 K m_ubv_V = 8.86\n", + "M = 16.000 Msun T = 30076 K m_ubv_V = 5.94\n", + "M = 10.000 Msun T = 27641 K m_ubv_V = 7.82\n", + "M = 7.600 Msun T = 25815 K m_ubv_V = 8.78\n", + "M = 15.300 Msun T = 34143 K m_ubv_V = 6.57\n", + "M = 10.000 Msun T = 27641 K m_ubv_V = 7.82\n", + "M = 3.600 Msun T = 17023 K m_ubv_V = 10.54\n", + "M = 15.300 Msun T = 34143 K m_ubv_V = 6.57\n", + "M = 10.200 Msun T = 30016 K m_ubv_V = 7.98\n", + "M = 7.600 Msun T = 25815 K m_ubv_V = 8.78\n", + "M = 13.500 Msun T = 33441 K m_ubv_V = 7.03\n", + "M = 7.200 Msun T = 25077 K m_ubv_V = 8.92\n", + "M = 12.000 Msun T = 29563 K m_ubv_V = 7.23\n", + "M = 11.000 Msun T = 28462 K m_ubv_V = 7.43\n", + "M = 20.700 Msun T = 30303 K m_ubv_V = 5.12\n", + "M = 3.400 Msun T = 16457 K m_ubv_V = 10.67\n", + "M = 30.000 Msun T = 78053 K m_ubv_V = 9.06\n", + "M = 20.000 Msun T = 4183 K m_ubv_V = 2.89\n", + "M = 13.800 Msun T = 32235 K m_ubv_V = 6.98\n", + "M = 15.400 Msun T = nan K m_ubv_V = nan\n", + "M = 15.000 Msun T = 31233 K m_ubv_V = 6.43\n", + "Starting filter: ubv,B Elapsed time: 32.95 seconds\n", + "Starting synthetic photometry\n", + "M = 9.500 Msun T = 26977 K m_ubv_B = 7.67\n", + "M = 8.500 Msun T = 25805 K m_ubv_B = 8.03\n", + "M = 15.849 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 6.310 Msun T = nan K m_ubv_B = nan\n", + "M = 23.000 Msun T = 127803 K m_ubv_B = 9.55\n", + "M = 19.000 Msun T = 11953 K m_ubv_B = 2.51\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 19.953 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 20.000 Msun T = 26486 K m_ubv_B = 4.19\n", + "M = 18.000 Msun T = 29119 K m_ubv_B = 5.10\n", + "M = 19.000 Msun T = 11953 K m_ubv_B = 2.51\n", + "M = 7.943 Msun T = nan K m_ubv_B = nan\n", + "M = 6.310 Msun T = nan K m_ubv_B = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_B = 6.62\n", + "M = 1.995 Msun T = 3090 K m_ubv_B = 26.41\n", + "M = 3.162 Msun T = 3090 K m_ubv_B = 26.45\n", + "M = 15.000 Msun T = 30279 K m_ubv_B = 5.97\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 25.119 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 10.000 Msun T = 27642 K m_ubv_B = 7.56\n", + "M = 16.000 Msun T = 30080 K m_ubv_B = 5.67\n", + "M = 13.000 Msun T = 30037 K m_ubv_B = 6.62\n", + "M = 21.000 Msun T = 11491 K m_ubv_B = 1.86\n", + "M = 8.500 Msun T = 25805 K m_ubv_B = 8.03\n", + "M = 11.000 Msun T = 28461 K m_ubv_B = 7.17\n", + "M = 18.000 Msun T = 29119 K m_ubv_B = 5.10\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 19.953 Msun T = nan K m_ubv_B = nan\n", + "M = 1.995 Msun T = 3090 K m_ubv_B = 26.41\n", + "M = 3.162 Msun T = 3090 K m_ubv_B = 26.45\n", + "M = 19.000 Msun T = 11953 K m_ubv_B = 2.51\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 1.995 Msun T = 3090 K m_ubv_B = 26.41\n", + "M = 18.000 Msun T = 29119 K m_ubv_B = 5.10\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 19.000 Msun T = 11953 K m_ubv_B = 2.51\n", + "M = 15.000 Msun T = 30279 K m_ubv_B = 5.97\n", + "M = 18.000 Msun T = 29119 K m_ubv_B = 5.10\n", + "M = 1.400 Msun T = 3090 K m_ubv_B = 26.40\n", + "M = 10.000 Msun T = nan K m_ubv_B = nan\n", + "M = 23.000 Msun T = 23329 K m_ubv_B = 3.29\n", + "M = 17.000 Msun T = 29685 K m_ubv_B = 5.38\n", + "M = 15.849 Msun T = nan K m_ubv_B = nan\n", + "M = 12.589 Msun T = nan K m_ubv_B = nan\n", + "M = 23.000 Msun T = 10030 K m_ubv_B = 1.36\n", + "M = 22.000 Msun T = 201085 K m_ubv_B = 10.33\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 6.800 Msun T = 24284 K m_ubv_B = 8.84\n", + "M = 17.000 Msun T = 29679 K m_ubv_B = 5.37\n", + "M = 12.000 Msun T = 29622 K m_ubv_B = 6.98\n", + "M = 9.000 Msun T = 26339 K m_ubv_B = 7.80\n", + "M = 16.100 Msun T = 29012 K m_ubv_B = 5.85\n", + "M = 1.900 Msun T = 11240 K m_ubv_B = 12.01\n", + "M = 10.200 Msun T = 30016 K m_ubv_B = 7.71\n", + "M = 35.000 Msun T = 67809 K m_ubv_B = 7.04\n", + "M = 19.000 Msun T = 11942 K m_ubv_B = 2.50\n", + "M = 10.000 Msun T = 29174 K m_ubv_B = 7.74\n", + "M = 16.200 Msun T = 34251 K m_ubv_B = 6.05\n", + "M = 5.700 Msun T = 22094 K m_ubv_B = 9.27\n", + "M = 22.000 Msun T = 69383 K m_ubv_B = 8.09\n", + "M = 16.000 Msun T = 30076 K m_ubv_B = 5.66\n", + "M = 7.800 Msun T = 26175 K m_ubv_B = 8.47\n", + "M = 20.000 Msun T = 4144 K m_ubv_B = 4.41\n", + "M = 10.000 Msun T = 27641 K m_ubv_B = 7.56\n", + "M = 12.000 Msun T = 32222 K m_ubv_B = 7.16\n", + "M = 17.000 Msun T = 29314 K m_ubv_B = 5.31\n", + "M = 50.000 Msun T = 26457 K m_ubv_B = 5.30\n", + "M = 11.000 Msun T = 28462 K m_ubv_B = 7.17\n", + "M = 2.000 Msun T = 11677 K m_ubv_B = 11.88\n", + "M = 6.400 Msun T = 23549 K m_ubv_B = 8.98\n", + "M = 6.500 Msun T = 23738 K m_ubv_B = 8.94\n", + "M = 14.700 Msun T = 32704 K m_ubv_B = 6.38\n", + "M = 0.850 Msun T = 5742 K m_ubv_B = 16.03\n", + "M = 4.400 Msun T = 19127 K m_ubv_B = 9.90\n", + "M = 10.800 Msun T = 30825 K m_ubv_B = 7.54\n", + "M = 6.800 Msun T = 24328 K m_ubv_B = 8.83\n", + "M = 24.000 Msun T = 76883 K m_ubv_B = 8.54\n", + "M = 16.000 Msun T = 30076 K m_ubv_B = 5.66\n", + "M = 10.000 Msun T = 27641 K m_ubv_B = 7.56\n", + "M = 7.600 Msun T = 25815 K m_ubv_B = 8.54\n", + "M = 15.300 Msun T = 34143 K m_ubv_B = 6.27\n", + "M = 10.000 Msun T = 27641 K m_ubv_B = 7.56\n", + "M = 3.600 Msun T = 17023 K m_ubv_B = 10.39\n", + "M = 15.300 Msun T = 34143 K m_ubv_B = 6.27\n", + "M = 10.200 Msun T = 30016 K m_ubv_B = 7.71\n", + "M = 7.600 Msun T = 25815 K m_ubv_B = 8.54\n", + "M = 13.500 Msun T = 33441 K m_ubv_B = 6.74\n", + "M = 7.200 Msun T = 25077 K m_ubv_B = 8.68\n", + "M = 12.000 Msun T = 29563 K m_ubv_B = 6.96\n", + "M = 11.000 Msun T = 28462 K m_ubv_B = 7.17\n", + "M = 20.700 Msun T = 30303 K m_ubv_B = 4.85\n", + "M = 3.400 Msun T = 16457 K m_ubv_B = 10.53\n", + "M = 30.000 Msun T = 78053 K m_ubv_B = 8.74\n", + "M = 20.000 Msun T = 4183 K m_ubv_B = 4.39\n", + "M = 13.800 Msun T = 32235 K m_ubv_B = 6.69\n", + "M = 15.400 Msun T = nan K m_ubv_B = nan\n", + "M = 15.000 Msun T = 31233 K m_ubv_B = 6.14\n", + "Starting filter: ubv,R Elapsed time: 48.68 seconds\n", + "Starting synthetic photometry\n", + "M = 9.500 Msun T = 26977 K m_ubv_R = 8.02\n", + "M = 8.500 Msun T = 25805 K m_ubv_R = 8.37\n", + "M = 15.849 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 6.310 Msun T = nan K m_ubv_R = nan\n", + "M = 23.000 Msun T = 127803 K m_ubv_R = 10.01\n", + "M = 19.000 Msun T = 11953 K m_ubv_R = 2.62\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 19.953 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 20.000 Msun T = 26486 K m_ubv_R = 4.54\n", + "M = 18.000 Msun T = 29119 K m_ubv_R = 5.47\n", + "M = 19.000 Msun T = 11953 K m_ubv_R = 2.62\n", + "M = 7.943 Msun T = nan K m_ubv_R = nan\n", + "M = 6.310 Msun T = nan K m_ubv_R = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_R = 7.00\n", + "M = 1.995 Msun T = 3090 K m_ubv_R = 23.50\n", + "M = 3.162 Msun T = 3090 K m_ubv_R = 23.49\n", + "M = 15.000 Msun T = 30279 K m_ubv_R = 6.36\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 25.119 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 10.000 Msun T = 27642 K m_ubv_R = 7.92\n", + "M = 16.000 Msun T = 30080 K m_ubv_R = 6.05\n", + "M = 13.000 Msun T = 30037 K m_ubv_R = 7.00\n", + "M = 21.000 Msun T = 11491 K m_ubv_R = 1.93\n", + "M = 8.500 Msun T = 25805 K m_ubv_R = 8.37\n", + "M = 11.000 Msun T = 28461 K m_ubv_R = 7.54\n", + "M = 18.000 Msun T = 29119 K m_ubv_R = 5.47\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 19.953 Msun T = nan K m_ubv_R = nan\n", + "M = 1.995 Msun T = 3090 K m_ubv_R = 23.50\n", + "M = 3.162 Msun T = 3090 K m_ubv_R = 23.49\n", + "M = 19.000 Msun T = 11953 K m_ubv_R = 2.62\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 1.995 Msun T = 3090 K m_ubv_R = 23.50\n", + "M = 18.000 Msun T = 29119 K m_ubv_R = 5.47\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 19.000 Msun T = 11953 K m_ubv_R = 2.62\n", + "M = 15.000 Msun T = 30279 K m_ubv_R = 6.36\n", + "M = 18.000 Msun T = 29119 K m_ubv_R = 5.47\n", + "M = 1.400 Msun T = 3090 K m_ubv_R = 23.50\n", + "M = 10.000 Msun T = nan K m_ubv_R = nan\n", + "M = 23.000 Msun T = 23329 K m_ubv_R = 3.60\n", + "M = 17.000 Msun T = 29685 K m_ubv_R = 5.76\n", + "M = 15.849 Msun T = nan K m_ubv_R = nan\n", + "M = 12.589 Msun T = nan K m_ubv_R = nan\n", + "M = 23.000 Msun T = 10030 K m_ubv_R = 1.40\n", + "M = 22.000 Msun T = 201085 K m_ubv_R = 10.78\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 6.800 Msun T = 24284 K m_ubv_R = 9.16\n", + "M = 17.000 Msun T = 29679 K m_ubv_R = 5.75\n", + "M = 12.000 Msun T = 29622 K m_ubv_R = 7.37\n", + "M = 9.000 Msun T = 26339 K m_ubv_R = 8.15\n", + "M = 16.100 Msun T = 29012 K m_ubv_R = 6.23\n", + "M = 1.900 Msun T = 11240 K m_ubv_R = 12.08\n", + "M = 10.200 Msun T = 30016 K m_ubv_R = 8.09\n", + "M = 35.000 Msun T = 67809 K m_ubv_R = 7.50\n", + "M = 19.000 Msun T = 11942 K m_ubv_R = 2.61\n", + "M = 10.000 Msun T = 29174 K m_ubv_R = 8.12\n", + "M = 16.200 Msun T = 34251 K m_ubv_R = 6.46\n", + "M = 5.700 Msun T = 22094 K m_ubv_R = 9.56\n", + "M = 22.000 Msun T = 69383 K m_ubv_R = 8.54\n", + "M = 16.000 Msun T = 30076 K m_ubv_R = 6.05\n", + "M = 7.800 Msun T = 26175 K m_ubv_R = 8.82\n", + "M = 20.000 Msun T = 4144 K m_ubv_R = 2.20\n", + "M = 10.000 Msun T = 27641 K m_ubv_R = 7.92\n", + "M = 12.000 Msun T = 32222 K m_ubv_R = 7.57\n", + "M = 17.000 Msun T = 29314 K m_ubv_R = 5.68\n", + "M = 50.000 Msun T = 26457 K m_ubv_R = 5.66\n", + "M = 11.000 Msun T = 28462 K m_ubv_R = 7.54\n", + "M = 2.000 Msun T = 11677 K m_ubv_R = 11.96\n", + "M = 6.400 Msun T = 23549 K m_ubv_R = 9.29\n", + "M = 6.500 Msun T = 23738 K m_ubv_R = 9.26\n", + "M = 14.700 Msun T = 32704 K m_ubv_R = 6.79\n", + "M = 0.850 Msun T = 5742 K m_ubv_R = 15.08\n", + "M = 4.400 Msun T = 19127 K m_ubv_R = 10.15\n", + "M = 10.800 Msun T = 30825 K m_ubv_R = 7.93\n", + "M = 6.800 Msun T = 24328 K m_ubv_R = 9.15\n", + "M = 24.000 Msun T = 76883 K m_ubv_R = 8.99\n", + "M = 16.000 Msun T = 30076 K m_ubv_R = 6.05\n", + "M = 10.000 Msun T = 27641 K m_ubv_R = 7.92\n", + "M = 7.600 Msun T = 25815 K m_ubv_R = 8.88\n", + "M = 15.300 Msun T = 34143 K m_ubv_R = 6.68\n", + "M = 10.000 Msun T = 27641 K m_ubv_R = 7.92\n", + "M = 3.600 Msun T = 17023 K m_ubv_R = 10.60\n", + "M = 15.300 Msun T = 34143 K m_ubv_R = 6.68\n", + "M = 10.200 Msun T = 30016 K m_ubv_R = 8.09\n", + "M = 7.600 Msun T = 25815 K m_ubv_R = 8.88\n", + "M = 13.500 Msun T = 33441 K m_ubv_R = 7.15\n", + "M = 7.200 Msun T = 25077 K m_ubv_R = 9.01\n", + "M = 12.000 Msun T = 29563 K m_ubv_R = 7.34\n", + "M = 11.000 Msun T = 28462 K m_ubv_R = 7.54\n", + "M = 20.700 Msun T = 30303 K m_ubv_R = 5.23\n", + "M = 3.400 Msun T = 16457 K m_ubv_R = 10.73\n", + "M = 30.000 Msun T = 78053 K m_ubv_R = 9.19\n", + "M = 20.000 Msun T = 4183 K m_ubv_R = 2.22\n", + "M = 13.800 Msun T = 32235 K m_ubv_R = 7.09\n", + "M = 15.400 Msun T = nan K m_ubv_R = nan\n", + "M = 15.000 Msun T = 31233 K m_ubv_R = 6.54\n", + "Starting filter: ubv,I Elapsed time: 63.89 seconds\n", + "Starting synthetic photometry\n", + "M = 9.500 Msun T = 26977 K m_ubv_I = 8.26\n", + "M = 8.500 Msun T = 25805 K m_ubv_I = 8.60\n", + "M = 15.849 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 6.310 Msun T = nan K m_ubv_I = nan\n", + "M = 23.000 Msun T = 127803 K m_ubv_I = 10.28\n", + "M = 19.000 Msun T = 11953 K m_ubv_I = 2.67\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 19.953 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 20.000 Msun T = 26486 K m_ubv_I = 4.78\n", + "M = 18.000 Msun T = 29119 K m_ubv_I = 5.72\n", + "M = 19.000 Msun T = 11953 K m_ubv_I = 2.67\n", + "M = 7.943 Msun T = nan K m_ubv_I = nan\n", + "M = 6.310 Msun T = nan K m_ubv_I = nan\n", + "M = 13.000 Msun T = 30037 K m_ubv_I = 7.26\n", + "M = 1.995 Msun T = 3090 K m_ubv_I = 21.80\n", + "M = 3.162 Msun T = 3090 K m_ubv_I = 21.79\n", + "M = 15.000 Msun T = 30279 K m_ubv_I = 6.61\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 25.119 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 10.000 Msun T = 27642 K m_ubv_I = 8.17\n", + "M = 16.000 Msun T = 30080 K m_ubv_I = 6.30\n", + "M = 13.000 Msun T = 30037 K m_ubv_I = 7.26\n", + "M = 21.000 Msun T = 11491 K m_ubv_I = 1.97\n", + "M = 8.500 Msun T = 25805 K m_ubv_I = 8.60\n", + "M = 11.000 Msun T = 28461 K m_ubv_I = 7.79\n", + "M = 18.000 Msun T = 29119 K m_ubv_I = 5.72\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 19.953 Msun T = nan K m_ubv_I = nan\n", + "M = 1.995 Msun T = 3090 K m_ubv_I = 21.80\n", + "M = 3.162 Msun T = 3090 K m_ubv_I = 21.79\n", + "M = 19.000 Msun T = 11953 K m_ubv_I = 2.67\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 1.995 Msun T = 3090 K m_ubv_I = 21.80\n", + "M = 18.000 Msun T = 29119 K m_ubv_I = 5.72\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 19.000 Msun T = 11953 K m_ubv_I = 2.67\n", + "M = 15.000 Msun T = 30279 K m_ubv_I = 6.61\n", + "M = 18.000 Msun T = 29119 K m_ubv_I = 5.72\n", + "M = 1.400 Msun T = 3090 K m_ubv_I = 21.80\n", + "M = 10.000 Msun T = nan K m_ubv_I = nan\n", + "M = 23.000 Msun T = 23329 K m_ubv_I = 3.81\n", + "M = 17.000 Msun T = 29685 K m_ubv_I = 6.01\n", + "M = 15.849 Msun T = nan K m_ubv_I = nan\n", + "M = 12.589 Msun T = nan K m_ubv_I = nan\n", + "M = 23.000 Msun T = 10030 K m_ubv_I = 1.39\n", + "M = 22.000 Msun T = 201085 K m_ubv_I = 11.06\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 6.800 Msun T = 24284 K m_ubv_I = 9.38\n", + "M = 17.000 Msun T = 29679 K m_ubv_I = 6.00\n", + "M = 12.000 Msun T = 29622 K m_ubv_I = 7.62\n", + "M = 9.000 Msun T = 26339 K m_ubv_I = 8.38\n", + "M = 16.100 Msun T = 29012 K m_ubv_I = 6.48\n", + "M = 1.900 Msun T = 11240 K m_ubv_I = 12.14\n", + "M = 10.200 Msun T = 30016 K m_ubv_I = 8.35\n", + "M = 35.000 Msun T = 67809 K m_ubv_I = 7.77\n", + "M = 19.000 Msun T = 11942 K m_ubv_I = 2.67\n", + "M = 10.000 Msun T = 29174 K m_ubv_I = 8.37\n", + "M = 16.200 Msun T = 34251 K m_ubv_I = 6.73\n", + "M = 5.700 Msun T = 22094 K m_ubv_I = 9.76\n", + "M = 22.000 Msun T = 69383 K m_ubv_I = 8.82\n", + "M = 16.000 Msun T = 30076 K m_ubv_I = 6.30\n", + "M = 7.800 Msun T = 26175 K m_ubv_I = 9.05\n", + "M = 20.000 Msun T = 4144 K m_ubv_I = 1.31\n", + "M = 10.000 Msun T = 27641 K m_ubv_I = 8.16\n", + "M = 12.000 Msun T = 32222 K m_ubv_I = 7.83\n", + "M = 17.000 Msun T = 29314 K m_ubv_I = 5.93\n", + "M = 50.000 Msun T = 26457 K m_ubv_I = 5.89\n", + "M = 11.000 Msun T = 28462 K m_ubv_I = 7.78\n", + "M = 2.000 Msun T = 11677 K m_ubv_I = 12.03\n", + "M = 6.400 Msun T = 23549 K m_ubv_I = 9.50\n", + "M = 6.500 Msun T = 23738 K m_ubv_I = 9.47\n", + "M = 14.700 Msun T = 32704 K m_ubv_I = 7.05\n", + "M = 0.850 Msun T = 5742 K m_ubv_I = 14.64\n", + "M = 4.400 Msun T = 19127 K m_ubv_I = 10.32\n", + "M = 10.800 Msun T = 30825 K m_ubv_I = 8.19\n", + "M = 6.800 Msun T = 24328 K m_ubv_I = 9.37\n", + "M = 24.000 Msun T = 76883 K m_ubv_I = 9.27\n", + "M = 16.000 Msun T = 30076 K m_ubv_I = 6.30\n", + "M = 10.000 Msun T = 27641 K m_ubv_I = 8.16\n", + "M = 7.600 Msun T = 25815 K m_ubv_I = 9.11\n", + "M = 15.300 Msun T = 34143 K m_ubv_I = 6.95\n", + "M = 10.000 Msun T = 27641 K m_ubv_I = 8.16\n", + "M = 3.600 Msun T = 17023 K m_ubv_I = 10.75\n", + "M = 15.300 Msun T = 34143 K m_ubv_I = 6.95\n", + "M = 10.200 Msun T = 30016 K m_ubv_I = 8.35\n", + "M = 7.600 Msun T = 25815 K m_ubv_I = 9.11\n", + "M = 13.500 Msun T = 33441 K m_ubv_I = 7.41\n", + "M = 7.200 Msun T = 25077 K m_ubv_I = 9.24\n", + "M = 12.000 Msun T = 29563 K m_ubv_I = 7.60\n", + "M = 11.000 Msun T = 28462 K m_ubv_I = 7.78\n", + "M = 20.700 Msun T = 30303 K m_ubv_I = 5.48\n", + "M = 3.400 Msun T = 16457 K m_ubv_I = 10.88\n", + "M = 30.000 Msun T = 78053 K m_ubv_I = 9.47\n", + "M = 20.000 Msun T = 4183 K m_ubv_I = 1.35\n", + "M = 13.800 Msun T = 32235 K m_ubv_I = 7.36\n", + "M = 15.400 Msun T = nan K m_ubv_I = nan\n", + "M = 15.000 Msun T = 31233 K m_ubv_I = 6.80\n", + " Time taken: 79.07 seconds\n", + "Isochrone generation took 754.068313 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "import time\n", + "import numpy as np\n", + "iso1=synthetic.Isochrone_Binary(7.0, 0.0, 1000,math.log10(0.1), mass_sampling=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking if all phases represented in the isochrone are valid phases." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x==102 or x==103) for x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x==102 or x==103) for x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == -99 or x==102 or x==103) for x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries, single stars, and secondaries. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.893166216900485" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.014527259205064" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.477660824834683" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg']))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.346046095913214" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.8669286871048065" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.061781883661361" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.369644990441944" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.087974438060869" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.310810637133002" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median([x for x in iso1.secondaries['logg'] if np.isfinite(x)])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (secondaries) of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]), np.arange(-10, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values (primaries) of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]), np.arange(-10, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1dAX8E9Vln+PbMv85yS2S3JY6kPUHpZTRwcOvo95Ns2cp5VGllJeUUt7Q6v1bk1jnje29axsWdKQN6utdpZRM8HroYIZSyt9KKe8ppexE3S/7tW3dB/hKhu5m6jON5+y4VudcHTbp/TWRUso5pZS9qN9cH0Tt0rg9dWBo751Fw5LcjtrFcz5w11LKP5dSXlVKOaQdP9dNVIQ1WLbZvmYMDM6fu6TnbsgxDO5se25GHlJJ7a6Dep7/3aoWVEr5aynl/dSxklBvQFjVPMuoY3cuot69d/dJb8GKZY1Tr1OpuyuAzcbZB6up65idiWNt2s79Wf+tt1LKL0opH6E2pV5NDTwGBneK9UWq36Nuw0M6pj2kzXfeSH6od1zdTOtS2Hr8kt/kTu392I5pu3akzZSfUh/0ec/U53+MGhwcXc8TmQ4zUTeDdax0a2rXclalfbP6LPUujIdRv5Wuz0hrUnMnamvWGR3TJlPvy9t71/Yt6Uj7NjW4evAk1nGTUsofSinHlVKeSG0RuyOTvIiv4pydLhMdD5tQW+3GsVr7q08p5bpSytmtG+bFLXnca9d21OP25FLKn4YnpD4aYLs1XLY+g32+2+iEJHeidvlfOE3dbhMZvl5N+jMqyRJql82lwEd6Xj+ktmo8cRKLHtRV7xCUYaWUG6njhsaeZxXLm6hep3K9/2Yr1yNXt2xTcNP53fNFdzo+n6bt3J/xQCnJtm1sx6jNqF0Vw82Ay6nR6KKexX20vR+a5JZD67gl9c4YqCfFwDHUJsIXJdl6KH+oo+b7ArKJLG3vuw0npj5o7N9HM8+U1sx7NLVZ8d+Gp6U+Uv/F1O7BT6yhIsxE3Qz6rt/SmscH82wKvH6K5T6qvT+jvW6g7sdRS6mtWfcYTkzy/6i3no5r0H9/wPAFo+2DN4xmLqX8oZVnSepzjVa6yCS5Y7v1dvCcqT3afhzOswF17BWs4sn5kzxnp8sJ1G+ET0uy08i01zFml/Fk99dEkjy4p4Vj0Lo1vB8Ht5p3XbuWtvddktx0XLcA8ENMYUjEJMvWZ3DOvq61pg6WfQvq3V3rcfNzdk15WXv/YSll+YQ5uw2exP2eUsqzu15D67jpJ01Sn/m2c9cCk9yNets61MdhDNLfmJ5neSV5PHXs0HJq6+GkjVuvU7zeD1rW/jPJSuMpu9KmSynlEmr36mJWPJJmsN77U8eELWdofNkU1jFt5/6Mj1GiDlw8Psl3qQfPpdSmyX2pY29uCi5KHbT5LeDBSY6m3t3wN+DEUsoPSynHJNmX+q3ggtQfYyzUZ5hsC3y2lHL00PJ+meQN1GeC/CDJZ1jxrJ6F1Gez3OyDbwwnAb8AXpZkR2qkvIh6q+kX6Q/yZsKrqdH0C5Pcl/oMi8FzNW5FvcvowjWx4hmqm48DT6Z+Izo/yYnUY2g/6oC/u7Kia2vccp+V5BfUi+IGwEnthBv1bmpA9I0kg2cTLaG2gHyOOp5gnPV9K8nXqa1s305yGvUiuDd17EpXS9MLqd1f/wY8Pck3qGN2tqAOTLwv9fEMF1K7B08FlrZz6dfUQecPb3lPLKX8eBXFHPucnS6llKuSPB/4JHB228eD5yjtRB2Qvivj1e9k9tdE/hXYM/Uhpr+itqbtQO2eXc7Nfxrj9Fa2Q1N/5mJ52663lFJ+l+TT1GP3+0lOprZuPBz4C3Xg/D3H2K6plq1TKeXsJIdRn5Nzfurg4WvaMu5OHVj/H5Ms10QW5+aDbBdS6/c+1OB7pfFDq9KCzadQv+B0tQQPnEbdTw9MfZbOBdTryOGpNyadRR0gvBH12HkE9Vh/bylleHDyS4FDknyPes25jFqX96Y+buEG4KBSB31PxWTqdVLX+1LKyUneTP1S+eN2jb6Yev3ZhdritP8Uyz2Og6j7+T+S7Endf4PnKN0IHDDa4joF03Pul/FuWyyM3JrdkWcpI7frDs17xtD/W1E/DM+iDvK6jvqMly8Dj+pY7p2owcgf284r3Pz2yfWoD946lxpd/5l6x9gL6Hl2CHVA3PeoF6XLqBfjLagfAleM5N2trfOQCbZ9a2rkOhiQeQH1YrP+6PaXm982uVvHsvYf3cbJlKVjngXUD7LB80muoEbynbebMk2PB5iJumn5N6aeBBe27VtKfcDilm1ffX4y29KWOXhUQQH2myDfXtSLyZ/afj2ZGvB01mHfvm119CHgD20bzqd+011Mx632bZ4NqReBs1nxjJSLqAPLXwLcpuXboB2LX27TB/v1m9QL1YZj7I+xz9m+MrPi8QCLO5bfe1xTPxDObsfOcmpL092AL7R5Fqxq3ZPZX6vYD3tSBwL/qC3jGmqXx3uBbTry/zM16LmWkWso9Q6lt1K/ZP2F+gH1fuoA1DMYOZ8m2kdTKdsqtvPJ1KDoT61sF1AHE2/ckfcQpu/xANdRg4EPU8du9a2rcx+0PM9peY4boxyvaXnf0/6/CzUw+XKrl2uGjpPjGHqO2tAydmn1+I2W77qhff8hYMdJ7Jf9WfnzbbLH3AImcb1v8zyaeufbsjbPxdSWnN0ncfwtZRKPPhnKsyV1jOOvqXcwXk598OV9x9k/I9NX+qydrnM/bUHrvNTbSX8PfL+U8oDZLo9WmErdJHk4NXB5eynl4DVZPs2s1hX0K+pzofoGg0rStJj1wdwzLclts/Lv46xPfbDXxqxGn6hWz1TqJskWHWm3YcU4KOtzLZVkwfD4tpYWaovfIuq3fElao9a5FqUkB1G7ak6lNjEupHaX3IXaTP7AMrnnSmiaTKVu2liPnajNqpdRu4ke1eY9opRy0EyVX9MrySOpzyU7mdq0vwn1IXn3pB4fS0r3+DFJmjazMZh7tn2L2p/8EFY8kOpCaj/zvxskzaqp1M1xrBj8vIAVYyo+Sh3roLXXT6ljkR5EHUexPnVs1HupT2E2SJK0xq1zLUqSJEnjWufGKEmSJI1rXex6U4/NN9+8LF68eLaLIUmz7rvf/e7lpZTbrjqn5jsDJd1k8eLFnHtu1w8zS9K6JcmvZ7sMmhvsepMkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnq4ZO5tU5Y/Oovjp136dsfswZLIklam9iiJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkpzVJIHJTk5yR+SXJXkvCTPGsmzWZIPJ7k8yTVJTk2y42yVWZKk+cZAaQ5Kcg/gVGAD4DnAfsB3gI8keV7LE+BE4JHAi1qeDYDTk2w1G+WWJGm+WX+2C6BOTwZuAexdSrm6pZ2SZCfgGcDhwD7ALsDupZTTAZKcA1wIvBJ48YyXWpKkecYWpblpQ+B64NqR9CtYUWf7AJcOgiSAUsqVwEnAvjNQRkmS5j0DpbnpqPb+3iRbJFmQ5DnAHsC72rQdgPM75r0AWJRkkzVfTEmS5je73uagUsr5SXYDjgee35KvBw4qpXy6/b8QWNox+7L2vhlwdcd0SZI0JgOlOSjJnYFjqa1DB1G74PYFPpjkL6WUo4EApWv2CZZ7IHBg3/RFixatTrElSZp3DJTmprdRW5D2KqVc39K+luQ2wHuSfIracrSwY97N2vvy0QmllCOBI/tWumTJkq7AS5KkdZZjlOamHYEfDAVJA98GbgPcjtratEPHvNsDFw3dLSdJkqbIQGlu+h1wzyQbjqTfH/gLtTXpRGDLJLsOJia5NbB3myZJklaTgdLc9D5gW+CkJPsm2TPJ+4CnAIeXUv5KDYbOAT6Z5MlJHtHSAhw2WwWXJGk+MVCag0opnwMeDWwEfJg6sHsX4AXAK1qeG4G9gFOAD1DvkPsb8NBSysWzUGxJkuYdB3PPUaWULwNfXkWeZcCz2kuSJE0zW5QkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgNIcleXSSrye5OslVSc5NsvvQ9M2SfDjJ5UmuSXJqkh1ns8ySJM0nBkpzVJLnAicA3wUeBzwB+B/glm16gBOBRwIvAvYDNgBOT7LVbJRZkqT5Zv3ZLoBWlmQx8G7gFaWUdw9N+urQ3/sAuwC7l1JOb/OdA1wIvBJ48UyUVZKk+cwWpbnpWcCNwAcnyLMPcOkgSAIopVwJnATsu2aLJ0nSusFAaW7aBfgJ8OQkv0xyQ5JfJHnBUJ4dgPM75r0AWJRkk5koqCRJ85mB0ty0BXBn4D+AtwN7AqcA70vyLy3PQmB5x7zL2vtma7qQkiTNd45RmpvWA24F7F9KOa6lndbGLh2c5L1AgNIxb/oWmuRA4MC+6YsWLZpygSVJmo8MlOamP1JblE4ZST+ZepfbHagtRws75h20JK3U2lRKORI4sm+lS5Ys6Qq8JElaZ9n1Njdd0JM+aC26seXZoSPP9sBFpZSr10TBJElalxgozU3Ht/dHjKQ/AriklPI76jOUtkyy62BiklsDe7dpkiRpNdn1Njd9CTgdOCLJ5sCvgMdTB3Uf0PKcCJwDfDLJK6hdbQdTW50Om/ESS5I0DxkozUGllJLkscChwJuo445+AjytlHJMy3Njkr2AdwAfADamBk4PLaVcPCsFlyRpnjFQmqNKKVcBL2ivvjzLqA+nfNZMlUuSpHWJY5QkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNAaS2Q5CtJSpK3jKRvluTDSS5Pck2SU5PsOFvllCRpvjFQmuOSPAXYqSM9wInAI4EXAfsBGwCnJ9lqRgspSdI8ZaA0hyVZALwLeFnH5H2AXYCnl1I+VUr5SktbD3jljBVSkqR5zEBpbjsMuKCU8qmOafsAl5ZSTh8klFKuBE4C9p2h8kmSNK8ZKM1RSXYBngE8vyfLDsD5HekXAIuSbLKmyiZJ0rrCQGkOSrIBcATwjlLKT3uyLQSWd6Qva++brYmySZK0Lll/tgugTq8C/g546wR5ApSe9O4ZkgOBA/umL1q0aNzySZK0TjBQmmOSLAJeCzwb2CjJRkOTN2oDvP9EbTla2LGIQUvSSq1NpZQjgSP71r1kyZKuwEuSpHWWXW9zz3bAxsAnqcHO4AXw8vb3jtSxSDt0zL89cFEp5eo1X1RJkuY3W5Tmnu8DD+1IP50aPH0E+AX1GUoHJNm1lHImQJJbA3sDx8xMUSVJmt8MlOaYUsoVwBmj6fX5kvy6lHJG+/9E4Bzgk0leQW1pOpg6RumwmSmtJEnzm11va6lSyo3AXsApwAeA44G/AQ8tpVw8m2WTJGm+sEVpLVFKWelutlLKMuBZ7SVJkqaZLUqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQmoOSPD7JsUl+neTaJD9NcmiSW43k2yzJh5NcnuSaJKcm2XG2yi1J0nxjoDQ3vRz4G/Aa4JHA4cDzgFOSrAeQJMCJbfqLgP2ADYDTk2w1G4WWJGm+WX+2C6BOe5dSLhv6/8wky4CPAbsBpwH7ALsAu5dSTgdIcg5wIfBK4MUzWmJJkuYhW5TmoJEgaeA77X3L9r4PcOkgSGrzXQmcBOy7ZksoSdK6wUBp7bFre/9xe98BOL8j3wXAoiSbzEipJEmaxwyU1gJJtgT+DTi1lHJuS14ILO/Ivqy9bzYTZZMkaT5zjNIc11qGTgBuAA4YngSUrlkmWNaBwIF90xctWjTFUkqSND8ZKM1hSTam3tm2HbBrKeWSocnLqK1KowYtSSu1NpVSjgSO7FvfkiVLugIvSZLWWXa9zVFJNgCOBe4HPLqU8n8jWS6gjlMatT1wUSnl6jVcREmS5j0DpTmoPSvpaGAPYN9Syjc7sp0IbJlk16H5bg3s3aZJkqTVZNfb3PR+4AnAW4Frkuw8NO2S1gV3InAO8Mkkr6B2tR1MHaN02AyXV5KkeckWpbnpUe39tdRgaPj1bIBSyo3AXsApwAeA46lP835oKeXimS6wJEnzkS1Kc1ApZfGY+ZYBz2ovSZI0zWxRkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPVYf7YLIK2Oxa/+4mwXQZI0j9miJEmS1MNASZIkqYeB0louydZJPpfkyiRXJTkuyaLZLpckSfOBgdJaLMktgdOAuwHPBJ4O3Bk4Pcnfz2bZJEmaDxzMvXZ7DrAdcNdSyi8AkvwQ+DnwXOCds1g2SZLWerYord32Ab45CJIASikXAmcB+85aqSRJmidsUVq77QCc0JF+AfCEGS7LWMa9nX/p2x+zhkvSb20ooyRpZtiitHZbCCzvSF8GbDbDZZEkad6xRWntVzrS0pUxyYHAgRMs6+okP51iOTYHLp/ivCvJv0/XktactaCM01onmjbWy9w0Wi/bzFZBNLcYKK3dllNblUZtRkdLUynlSODINVGQJOeWUpasiWVraqyTucl6mZusF/Wx623tdgF1nNKo7YEfzXBZJEmadwyU1m4nAjsn2W6QkGQx8KA2TZIkrQYDpbXbh4ClwAlJ9k2yD/UuuIuBI2azYJIkzQcGSmuxUso1wO7Az4BPAEcDFwK7l1Kuns2ySZI0HziYey1XSrkI2G+2yyFJ0nxki5IkSVIPAyVNlzXy2AGtFutkbrJe5ibrRZ1SStfzCiVJkmSLkiRJUg8DJUmSpB4GSpqyJFsn+VySK5NcleS4JItmu1zriiRbJfmvJOck+XOS0h44OppvsyQfTnJ5kmuSnJpkx1ko8ryX5PFJjk3y6yTXJvlpkkOT3Gokn3Uyg5I8IslpSX6X5LoklyT5bJLtR/JZL1qJgZKmJMktgdOAuwHPBJ4O3Bk4Pcnfz2bZ1iF3Ap5I/V2//+3KkCTUp7Q/EngR9VESG1DraasZKue65OXA34DXUPf54cDzgFOSrAfWySxZCHwXeCGwJ3Aw9eefvplkG7Be1M/B3JqSJP8CvBO4aynlFy1tW+DnwCtLKe+czfKtC5KsV0q5sf39bOqT2rctpSwdyrMv8HnqQ0hPb2mbUh9M+slSyotnutzzWZLbllIuG0l7BvAxYI9SymnWydyQ5K7AT4CXl1L+03pRH1uUNFX7AN8cBEkApZQLgbOAfWetVOuQQZC0CvsAlw4u/G2+K4GTsJ6m3WiQ1HynvW/Z3q2TueGP7f369m69qJOBkqZqB+D8jvQLgO070jU7JqqnRUk2meHyrIt2be8/bu/WySxJcoskGya5M/X3MH8HfLpNtl7UyUBJU7WQOjZm1DJgsxkui/pNVE9gXa1RSbYE/g04tZRybku2TmbPt4DrqL+PeQ9qN9sf2jTrRZ0MlLQ6uga4ZcZLoYkE62lWtBaIE4AbgAOGJ2GdzJanAzsDTwWuog6yX9ymWS/qZKCkqVpO/QY2ajO6v5Vpdiyjv57AulojkmxMvYNqO+ARpZRLhiZbJ7OklPLjUsq3SimfAvYANgFe3SZbL+pkoKSpuoDapz9qe+BHM1wW9Zuoni4qpVw9w+WZ95JsABwL3A94dCnl/0ayWCdzQCnlCuAX1MdsgPWiHgZKmqoTgZ2TbDdIaE3YD2rTNDecCGyZZDCgmCS3BvbGepp27VlJR1NbK/YtpXyzI5t1MgckuT31OXC/bEnWizr5HCVNSXuo5A+Aa4HXUfv23wzcCriH375mRpLHtz/3AA4Cng9cBlxWSjmzfXB/A9gaeAW1++Bg6kDWnUopF898qeevJIdT6+GtwBdGJl9SSrnEOpl5SY4HzgN+SB2bdBfgpcA/APcrpfzMelEfAyVNWfu5kncBD6cOePwa8JLhBx5qzUrSdwKfWUrZreVZCLwDeCywMXAO8LJSyg9moozrkiRLgW16Jr+plHJIy2edzKAkr6I+xf6OwIbAxcAZwKEjD2i1XrQSAyVJkqQejlGSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUpLVYksVJSpKjZrssqyPJIW07dpvtskjSMAMlSZKkHgZKkuaC9wH/CHx7tgsiScPWn+0CSFIp5XLg8tkuhySNskVJmqeS3CHJ+5MsTfLXJJclOS7JfXryb5rk3UkuSfKXJD9J8rIk2/WNg0pylyTHJlme5JokZyd5TJL92zz7j1nWzjFKLe2MJJsnOTLJb5Ncl+SCJAdMYZ9sleS9SX7etnFZkm8neX1H3kckOatt17Ikn09ytyRHtXItHsm/T5KvDZXx0iRnJnn+ZMspae6wRUmah5JsS/0l9C2A04BPUX8V/QnAY5LsV0r5wlD+jVu+ewPfA44GNgVeCzy4Zx13A84CFgJfpP4y+3bA8cCXpnFzFrT1/BX4HPXHSh8PfDTJjaWUj42zkCRLgK+28n4dOA64JbA9cAjw5qG8TwKOAa4DPgv8Fngg9UdSV/qB1CQHAkcAvwNOoraO3Y76y/MHAB+Y1BZLmjMMlKT56YPUIOl1pZS3DhKTfIAaJHwsyTallKvbpFdQg6RPA08t7deyk7wVOK9nHe+nBh3PL6UcPrSORzG9gdJOwEeA55ZS/tbW8S5qYPYqYJWBUpINgf9p5X1aKeWYkelbD/19K+r+uwF4wPAvxyd5e1vnqOdSA7mdSil/GFn25mNso6Q5yq43aZ5JshWwJ3ARcNjwtFLK2dTWpYXAPw1NeiZwI3DwIEhq+S8G3t2xjq2B3YFfUFtShtfxZeDUadiUgT8DLxsESW0dP6K2Mv1jC2xWZW9gMXDiaJDUlnfx0L/7Uluxjh4Okpq3AFf0rOMG4PqOZTv2SlqLGShJ88+92vv/llJW+uCmdrHdlC/JrYE7Ar8ppSztyP+NjrR7tvdzSik3jjnPVP28lHJVR/oguFkwxjJ2bu9fHiPvYP+ttA2tBe77HfMcTe3GuyDJu5I8Nsltx1iXpDnOQEmafzZt77/tmT5IX9Deb93ef9+Tvyt90wmmTZQ+FVf0pN/Q3m8xxjIWtPffjJF30ttWSnkntVXuIuDF1HFav09yehsbJWktZaAkzT9Xtvd/6Jl+h5F8g9aa2/fk70qfyjyz6Yr2vuUYeae0baWUj5dSdgZuAzyGOq7qIcBXk9xu/KJKmksMlKT553vtfZckXTdsPLS9nwfQurV+BWw5esv7YDkTrOMBSbquI13zzKZvtvdHjZH3pv03OiHJJqzoduxUSrmilPKlUspzgKOo48E67xyUNPcZKEnzTCnlEuAU6uDllwxPS3J/4KnAcmr30MDHqdeDQ5NkKP/Wo8to67gYOAO4E/WOr+F1PBJ42OpuxzQ7CVgK7JPkKaMTkwy3NJ1AbW17WpKdRrK+jo4xUUke2ROUDlqS/jyFMkuaA3w8gDQ/HUS9K+w/kuwJnMuK5yjdCBxQSvnTUP7DgMcCTwbumuRk6lidJ1IfJ/DYNt+wF7R1fCDJo1nxHKX9qMHGvh3zzIpSyl+TPAE4GTgmyXOprUwbU386ZQ/a9bCUclV7SOQngbOTDD9HaSfgTGBXbr5tnwb+kuQb1IAs1Fak+wLfZXrvApQ0g2xRkuahUsqvgCXU5wHdFXg5tdvpK8CDSiknjOS/ltol91/UsU0vbf+/DTi0ZbtqZJ4fAQ+gtkw9mNrytBh4HCvuGOu6W21WlFLOpXabHQ5sA7wMeDq1heiNI3mPoY4z+gHwJOB51FamBwCDZ08Nb9urqQ+jvDfwfOpDJjegPnPpoT13H0paC2TokSmStJIkzwGOBA4qpRyxqvxtnqOpXXx3K6X8dE2WbyYluQV1PNdGpZS+wfKS5hFblCQBkGSLjrStgddTb8X/wsi09ZKsFCwk2YPaCvOjtTVISrIgyS1H0kIdo7SI+vMnktYBjlGSNHBskg2oY2quoHaj7UV9kOLBpZTRZxBtCFyc5HTgJ9Rgagfg4dSf83jBzBR7jdgZ+Ewbq7UU2KSl3ZP6oMtDZqtgkmaWXW+SAGgDmJ8O3Jk6kPtq6q3y7yulrNSC0rqh3k39KZOtqAHV5dTB328vpXxvdJ61RftR4bcADwJuS/1SeQm1Ve1tpZTpfKCmpDnMQEmSJKmHY5QkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSj/8PQVBgPDDboGsAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values single stars of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What exactly could be causing the vertical line in the top left corner of the plot? First, I want to zoom in...\n", + "Plotting the CMD for the Color magnitude diagram for high surface gravity stars (surface gravity greater than 5 cgs). Remember to account for distance modulus for dist = 1000 pc" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4qgO3davGXYV8V/zJlBs51frjIqS5UnBdtYdpe0Nl/99SOyClNC8i/khe32pjiu6oBq+s+4+iuwn58XYrRNXfPebxiHg/+UnKGPKd64E1o6xLLijCwnzZZd+llJ6IiCfJd67ruaubNAwp0vAJ8jYZSuf1WLfedLQoXzewMbmNyZ9SSjPqDL8J+Bb1j9VWuIL8+PYXEbEz+dHxn4AHqwvei3hO2bro1p6cu3NvgxsIT7Iwn9eqt88XZ7t22d8ppbkR8Sydzwn/QS5cPgJ8K7/IqIvZ5OOu4lxywfOBiLiQfJz/KaX0fL2JGyl5PPWWRb3GlV9QDtjOJxfkzk8pfb8Fsz2X/CTugYi4iLwvbk8p1Rbiursmd0TEreTz0bvpYZsXgcY3yAWZ9chVS6o1Ogc1PK91s6zVWdgIeAPyk8VmltXsNbG7a9UrEXEvsF2z6e0FtfnzNvIT0ScBKsdrsb8PjYifpoVvZ9q86u/KeG8A34uIM1h4jq9nYtHtrqC8KMoebyeTq5tNqqQpIrYgn5d/X1NeKl2GKN7cti/5ycYoct5YvmqaRi/9aJSXLySf0y6LiP8jV9f8U72goDslj7HuyhrzI6LypqxqLSlvNRNEPEW+kJV9t28lGn26wfCnyRtmGLk+2qLOA+pHtM/U6desehF0RxPDVqjpfyG5oPdPcj3tZ8hVAAC+TK5OUrFK0a191Sg99K94uZt0LV9nWG+r7LtG6a63f1Yvugf1MO+VFylF9a1TdOeRH+01FBGfIN8hfYNc1ecx8l2e+eRHnNvReZ/2tA2epXEQUTf/FoHpTeQ7afeT89jzLLwjfGRNGqq1Kl/XszjH6mIrgrL3ku8c7kKuAwzwZEQcl1L6+WKks/J3dxfcWi836N9B4+/z1Nvni7Ndu0tD9TmhctxtSNVbXOpYcNyllH4aES+QG2L/N/l8liLiFuBrde7odrEIx9OiquTtYQ2Gr1IzXm/6MbkwfBcLn9Yvrv8hb7vPkp9wHg50RMTV5DYHjxbjteQYLd5sdxe5sPUHcpuKV8jn0JHkJ+qN9lup63JEDAf+TK7+dRf5qf8Mch4eTi6sNVrWy3X61bsmLsq1qpVK5c/iTnbDV62mqle6pgavdy2GNZxHRKwGfJJ88+DsRuM10NLjLaV0c0RMBfaJiK+k/Aa1ScXg2gBnUcoQPyWfv54m33yazsKnCONp/GatuvkipXRX5G9QHUEOfvYHiIiHge+mlM6vN121RTjGmilr1GpJeauZIOKP5Le27EiuG9usSgZ5C/WrrqxdM15P86inu3n0+l2l7kTEaHIAcQOwa0ppbtWw5YCv10wys+iu1WCWjfovKZVHcPXyzPA6/Sr7pFG66+3TyjSjUkp/az5pi2X7ontPddWWBo4m35UYXV0tCCAifkXXu1XV+7RLNRi636eN8u84cgBxZkppfE0a1qb7QmBvWpxjtSWKfbJX5I86jSI38DsE+FlEzEop/XoR0/ly0V2XHj6Et5jq7fMlsV0r016aUtq92zGrpJTOAs4qCnrbkM93nwWujYhNiuph3Sl7PC2qR8kX3w0iYkCd43zDolvvkX/LRMRnyW/amw58PFW9UnpxFE+8fkbO52sC25Kffn8K2CwiNiueyrcqLx1GLoB8pra6XETsQy7gNEpr2evyBHIA8d1U86ruiBhDDiIW16Jcq1rp4aJbe6e4otv8WXsdaKR2+/WgUkg9s1FNkW4s1vo0cAo5j+8bEWeSnxxMJ788olqpMkRxvPw3+YbcNqnmFc9Ffm6kYV5OKd0OfDQiBpHfhrYL+Vp0XkQ8n1K6oYeklT3GFqX82JLyVjNtIs4g3+X8ZE91o4oNVvHXoju2znjvJD/ZeLy7DFrs0MeAdSNiwzqjVAqAf+kuXW3yzqJ7RXUAUXgvNY+mUkozyU8s1o2IkXXm17DuYotUql40empRuUtf74uV9T4wWNknXQoCEbE89dfnjqK7RL4kHRErsfC97LXtXOp5J7l6TG2BZznqr0/lGOgyLCLWp/62bCYNkBvG1WrnI/eHyW952DIi6lWfW2LHakqpI6V0T0rpWPKbdyC/DWpRzymVfPmRXkhuT5bEdn2IHChtXTzpKiWl9HJK6eqU0kHkRq2r0dwxXPZ4gnwzo9ST1aIAfRu5Wli9dFX2a5dqPq1S3Jn8JXlfjivqerdcSum5lNIlKaU9yevzDnK9eej+mjyAhdu8p7y0JM9BS2JZ3V2rhlHT1rEX3EG+8/3+orpl9fKXIzfwhfzGtyWlcne6zLchKnpjfc4kP6WcRP5A5HDg13WqjJYtQ2xALgdfVyeAeGsxfJGllOaklG5LKX2HHKxAvhHYk7L5vruyxnLkmzy1WlLe6jGISAs/QjIQuKq4w95FROxC5zrDvym634qIEVXjLU9+HdZyNPdk4zfkOnQ/LqatzGcN8tuMqpfVl0wrumOrexaR7y8aTHMWebscE1UVkyN/WfLLLU9hZy8W3UaN6ir1/zo9+irqJta7G3QbuQD0wYioPWi+RNf2EJAD1peBI4uqKZ1ExHIRMbZB+kopGp1dRa6q91eaq/c5DdgwIipVoCr1KY8kt3mpdR758fkhxT6snuYYFq2a2bSiO7a6Z/H489hFmF9LFA2+ziU/+vxe9bCIeAf5BDqX8o/GmxIR742IendbKv1er+pX9pzyS/J+/Ha9GynFxaZXLIntWtyZP5F8J/rnEVFb95aIWLt63SNil6LgWWvNovt6nWG1plHueIJ8nlqU4PuXRfd/I39sq7K8rciFkuepuWBHxHoRsXFxs2GRFeeaS8jVAsenlOq1P1rUeQ+KiB2rrxdF/xVY+KKIyr64jFwVaJ+I2JrOvkwuMN1Q3Ri5gWlFd2zNMnemuZellNFoWe8mv2WtFS4n3yT7dJ3yzVE0rpbTEiml18jH7xC6fvDtS+TqK9emJfSF5yLg3YT8EpluG1QXx8fG1f16Y32K9h7nkwO6/yXf9Dy9zqhlyxDTiu62NdeClckNi5v6IHPNMj5QBJ+16l2LGqmka2zNvBsdY38i3xzbPiJqb3ZNpP5ToZaUt5raQCmlHxQXjCOBP0dupHE3+Y0ha5Hfd70hVQ2ZUkq3RcSPyNV27o/cwGQW+a7P5uRqUj9uYvHHFdOMA+6LXM9zJfKj2jWBH6WUemp03A5/Ju/Y3Yvt9UfytvoIuXD9VJ1pfkS+Y7o3sFFEXEc+ge1JfjXdx1lYrajVbidn7i9Hrg9ZqUN3YnEAX05ueLlPUWi6kxxwjCuG7Vk9s5RSiojPkes6XxwRlQ/jVKqZXEPNFytTSi9GxB7k9zXfERE3kqsBzS+WNYb8iG8wzRseCxsNDSDXMRxVzGu5Ih0HNtkI/3jyY9W/RsTF5MLb+8kFniuBj9Wsz2MR8R1yg9/7IjdAfYX8IcPVyG/IeBflXEnejocVAdxfydvmo+SgqBVvs1lUh5PvanypKJzdTH4DzZ7kxt9fSik93kvL/jRwcOQ6+Y+SCwXvIO+TOXR+u1mpc0pK6cGI+CIL933lWFid/BTuVRY+EegNS2K7Hk0+Lj4PfCwibiJXF1iTfG5/P7mOb6WB3QXAG5FfkDCNHJR9gPyxo3vI1Th7Uup4KtwI7B0RVxbL6SC/Ae7WHpZ1AbmdzB7F8q4k77+9yMH8QcXT4Gpnke/6bU9+RSmwINg8rmq8NYruryOiUsXhh1UN9ytvYnqEonpRN+l8OaV0Qg/rUm1F8raeFhF3kt8KNph8jtmE/CR8KuTCXeQqVRcBt0RuhP0vcnWLnch1vCd1XUQXJwOfAS4q9tt08jV9F/K3GfYqkf6enEVuVH1CRGxP3oYbks93l7RiWcV2mUhuX/aH4jz9NPmu7ubka+8Hm53fIuQPgG+SC4yHRcSW5Jt2m5DPUc+RX5u6pFQaVDfzFKLyFLH2bQy9sT4nkwvQ6wJX1mvTUbYMkVJ6JiIuIJe57q0qc32Y3FbrXso/ifoKsFNETCHXLnmN/CKFj5CvS81s11LHWNF4egK5PHNFMc1j5PLFh8k3+D9CVfmxZeWtVO59x5uQ71jdT66D9Sb5YPs9+dsH9d61uze5AP0qeac8QL4Y1fuy7jTqf7F6MDlT3k9+TPZqMc996ow7Erp+T6HJ9RtfTDu+zrCxxbCjml0muaB4crFebxQ79QfkAkujdR1Ofm3jU+TCz0PkTPneYhkn1Iw/mQbvsG6U5m6WvQs5mHiNOu/GJt8BvJB8N2s2OVDavYdt8x4WfrH6VZr7guJI8mtgHym228xiO5xNrkvczL6s7JPq3xss/ADYicC23UzfaBuNJ59YZpHfr3wpsEUP67M/ubD/BvmO5znkBt33kwsNTeWzmv1wLgsbgD1ADtYHFNNOqRm/u7SNZxHyfDdpG05+IvIIOf++TA4kdyqznXtYxuTaNAPvI99tvq8qfz5KvtuyeZ15NH1OqZpmDAu/9Psm+Ri9Btijp3NB1fApFFXDm9kHi7Jde9pvjbY5uSCwP7mgPqNYx+nFdvkmnb918nly3v8n+ebDDHIe/zo1H9HsYV+Op8TxRA5qziPf5JhXJn+Sj4//IX9kcTb5gn41jb/gPaVBGir7uLvf2Krxexq3+lf2WFih2Oa/JwcElXPMHcU+Glhnmq2K7fx8sY//RT521unmWBtZ038bcnWpl1h47Hy8Ud6jTr4vsY6bkt++9lyRT+4hFyYr+2FyM2nu6dggF7b+WOTnl8g3xzbubn4N0lsqf1RNtxq53v8TLCxb/YY6X03urR/5JlvlQ2nDmxg/NdqvvbE+5HNMAnZrYh80VYYgl8e+T75evEFutP4LcgG6S77tLg8Vw3ciX3ceJN80nEW+cfxzYP0S61rqGCumeR/5ulBb3qp82XvLxdlW9X5RzER9XEQcRI5gP5+6fzexlgKRX+n7LPlVoI1e+SlJ0jIvcvuKp8g3LN6emvzegiAi/kQOMIallGa1ct7NNKzWElRdP7iq39vIdbU76Po2AvVhETEiahqrFlUDf0K+G35pWxImSdLS4wvktmEnG0B0FRErRX5TXm3/8eSnGte1OoAAfBLR10TE7eRH1PeQqyyMJNf/XIn8xdofti1xKi0iPk9uEHsD+THpauT6tf9BrsaxTer8NWVJkpZ5RQPlL5DbQRxEfgqxUap5k5JyI3dyda/ryVWzBpA/QrctuSy5Tap5E15LlmsQ0bcUDTj3JzceG0Zun/BX4KSU0iXtTJvKK94i8m1ym5bKx10eJzcKPNaToaRqRUPUjzczbir37n9pqRL5dfePk9uB3QMcklLqi6/0b7vIrwD/MfllEG8hf+fjGfINzO+nkl/Mbnq5BhGSJPUNRfWDM5oZN6VU+1YcSVpiDCIkSZIklVL6QxqSlow11lgjjRw5st3JkKS2u+eee15IKY3oeUxJS4pBhNRHjRw5krvvvrvnESWpn4uIJ9qdBkmd+YpXSZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJKMYiQJEmSVIpBhCRJkqRSfMWr1N/cfjtMmQJjx8KYMe1OjVpk5syZPPfcc8ydO7fdSZFaZoUVVmDNNddklVVWaXdSJJVkECH1J7ffDjvuCG++CQMHwo03Gkj0AzNnzuTZZ59l3XXXZcUVVyQi2p0kabGllJg9ezbTp08HMJCQljJWZ5L6kylTYM4cmDcvd6dMaXeK1ALPPfcc6667LiuttJIBhPqNiGCllVZi3XXX5bnnnmt3ciSVZBAh9Serrw7z5+e/58/P/2upN3fuXFZcccV2J2OxTJ48mYhY8Ft++eVZd9112XPPPXn44Ye7HXfo0KGMGjWKk046iY6Ojk7jzp49m2HDhhER3HfffXWXPXPmTI488kg23XRThgwZwqqrrsoWW2zBpEmTOhVe58yZw/HHH8+oUaMYOnQoq6yyChtvvDEHHnggjzzySLfrd9RRR7UtwJsyZQoRwQ033NCW5S+uFVdc0Wp60lLI6kxSf3LuuV3/nzixPWlRS/WXJxAXXXQRb33rW5k3bx6PPfYYRx99NDvuuCMPPPAAw4YNqzvuzJkzueiiizjkkEN47rnn+N73vrdgnEsuuYSZM2cCcNZZZ/GTn/yk0zzmzZvHhz70IaZNm8Y3vvENttxyS2bNmsX999/P+eefz1NPPcWaa64JwD777MN1113H17/+dbbeemvmzZvH1KlTueiii3jwwQfZcMMNG67XhAkT2GWXXVq1mZYp/SVvS8uclJI/f/764O8973lPKm348JRg4W/48PLzUJ/z4IMPtjsJi+2MM85IQHrkkUc69b/++usTkK6++uoexx07dmwaOnRop34f/vCH02qrrZbe9773pbXWWit1dHR0Gn7jjTcmIF122WV10zVv3ryUUkqPPfZYAtIJJ5zQ7Xh90c0335yAdP3115eetqOjI82dO7cXUlVOT3kcuDv1gfOyP3/+Fv6sziT1J5WqTI3+l/qYSmPaZqqzbLXVVrz66qsLqiBNnz6dG2+8kb333psJEybw7LPPcu2113aaZsaMGQC85S1vqTvP5ZZbrtR4jdSrzvSzn/2MTTbZhBVXXJFVV12V0aNHc+mlly4YnlLi+OOPZ6ONNmLgwIGsvfbafOlLX1rwZKWio6ODY489lk033ZTBgwczYsQIdtllFx566KFO473++ut86UtfYo011mDEiBHst99+vPzyy53GiQiOOOIIfvjDH/L2t7+dgQMH8ve//x2Ac845h1GjRjF48GDWWGMN9t9/f55++ulO048cOZL99tuPCy64gE022YQhQ4YwevRo/vjHP3bZJrfccgs77rgjQ4cOZciQIey8887cf//93W5HSUsPgwipPxk0qPv/pTabN28eHR0dzJkzh6lTp/LNb36TNddck7Fjx/Y47eOPP87yyy/PyiuvDMDZZ5/N/PnzOeCAA9hzzz0ZPHgwZ555Zqdp/vM//5MBAwYwadIkLr30Ul566aW68954441ZZZVVOPzwwznnnHN49tlnF2s9zz33XL7yla+wzz77cPXVV3Puueeyxx57LAhWAI444ggOO+wwPvzhD3PllVfy9a9/ncmTJ7Pbbrsxv+oGwN57780RRxzBrrvuymWXXcZpp53Gpptu2qWAf+ihhxIRnHfeeXznO9/h4osv5tBDD+2StsmTJ3PVVVdx3HHHcdVVV7HOOutw6qmnsv/++7PJJptwySWX8MMf/pBrr72W7bbbjtdee63T9H/4wx/4yU9+wtFHH82FF17IvHnz+OhHP9opYLnqqqvYcccdWXnllTnnnHM477zzePXVV/nABz7Ak08+uVjbVlIf0e5HIf78+av/W6TqTPvumzpVZ9p33/LzUJ/TsupMt92W0g9+kLtLWKWKUu1vnXXWSXfddVfdcR966KE0d+7cNGPGjHTKKaek5ZZbLo0bN27BeJtssknaaKONFvy/9957p0GDBqWXXnqp0/xOO+20NGTIkASkiEibbrpp+upXv5qmT5/eabwrrrgirbHGGgvStsEGG6SDDz44TZ06tcf1O/LII1O+pGYHH3xweve7391w/BdffDENGjQoHXjggZ36n3322QlIl19+eUppYXWsn/3sZw3nVanOdMABB3Tqf/DBB6dBgwal+fPnL+gHpLXXXju9/vrrC/p1dHSkNddcM40dO7bT9H/4wx+6LHv99ddPw4cPTzNmzFjQ789//nMC0rnnnrug3zve8Y60ww47dJrfK6+8klZfffV06KGHdlkHqzP587f0/XwSIfUnQ4d2/7+WXZVviHz727l7++1tScall17Kn//8Z+666y4uu+wyNt10U3bddVemTp3aZdyNN96YFVZYgdVWW40vfvGL7LvvvvzmN78B4K677mLq1Knsv//+C8Y/8MADmTNnDr/97W87zWfChAk8+eSTnHPOOUycOJH58+dz3HHHsdlmm/HAAw8sGO9jH/sY06ZN45JLLuGQQw5h+PDhnHzyybz73e8u/eajrbbainvvvZdDDjmEG264gddff73T8DvuuIM5c+aw3377deq/9957M2DAAG655RYArrvuOiKCgw46qMdl7rbbbp3+32KLLZgzZ06Xpyq77LJLp7d9Pfzwwzz33HPsu+++ncbbdtttWX/99RekpWLMmDGsuuqqnZYD8K9//QuARx55hMcee4x9992Xjo6OBb+VVlqJMWPGcOutt/a4LpL6PoMIqT958MHu/9eya8qU/BHCefNyt03fENl8880ZPXo0W221FePGjeOKK64gpcRRRx3VZdxKwPHQQw8xa9YszjrrLFZbbTWABdWWPvaxj/Hyyy/z8ssvs9VWWzFixIguVZoAVl11Vfbdd19OOeUUpk6dymWXXbbg1a/VhgwZwic+8Ql+/vOfc88993Dbbbex/PLLc/jhh5dazwMOOIBf/vKX3Hnnney8886sttpq7L777kybNg1Y2AZj7bXX7jTdgAEDWH311RcMf/HFF1lttdWaesVvZdtUDCqqM77xxhud+tcus1FaILcRqa6C1cxyKm1WPve5z7HCCit0+v3ud7/jxRdf7HFdJPV9vuJV6k9eeKH7/7XsGjs2f8W88jXzJtogLAkrrrgiG2ywAX/729+6DNt888155zvf2aX/m2++yQUXXADAqFGjugx//vnnefTRR+tOWzFu3DhGjRrFgz0E2ltvvTU77bQT11xzTU+r0klEMGnSJCZNmsRLL73Eddddx1e+8hX22msv7rzzzgUF8WeeeYbNNttswXQdHR28+OKLrF5842WNNdZgxowZzJ49u2XfCqltAF6dllrPPPMMo0ePLjX/StqPOeYYPvShD3UZPnDgwFLzk9Q3+SRC6k/WWKP7/7XsGjMGbrwRjj46d8eMaXeKgPxGoccee4wRI0Y0Pc2VV17JjBkzOPLII7n55ps7/SrBxVlnnQXACy+80OVOPMCsWbN48sknF9x9f/XVV7u8FQlyQ/BHHnmk7l36Zq266qrstdde7LnnngveTrT11lszaNCgBemtuPDCC+no6GC77bYDYKeddiKlxOmnn77Iy+/JRhttxFprrdUlLbfddhtPPPHEgrSUmd/IkSN54IEHGD16dJffu971rlYmX1Kb+CRC6k9qC0F1CkVaho0Z0/bg4d577+WFF14gpcTTTz/NSSedxIwZMzjkkEOanseZZ57JyiuvzFe/+tUFb2qqdvzxx3PWWWfx3e9+lylTpvCFL3yB8ePH84EPfIDhw4fzxBNPcOKJJzJjxgwOO+wwILcL2H777dl///3ZcccdWXPNNXn66ac5/fTTuf/++zn55JNLrefEiRMZOnQoY8aMYc011+Qf//gHZ599NjvttBOQ7/4fdthhHHPMMQwZMmRBu5BvfetbbLvttgvaN2y//fZ88pOf5LDDDuPJJ59khx12YO7cudx6663stttuTb3VqifLL7883/ve95g0aRL77bcf++23H9OnT+eII45gww035DOf+Uyp+UUEv/jFLxg3bhxvvvkme+65J2ussQbPPvsst912G+utt96C7S5p6WUQIfUnjz3W/f9Sm33qU59a8PeIESPYfPPNueaaa9h5552bmv7555/n97//PQcccEDdAAJyXfyJEydyyy23sPXWWzNhwgRuuukmzjzzTF566SWGDx/OVlttxfXXX88OO+wAwDvf+U6+9rWvccMNN3DppZfywgsvsPLKK7Plllty0UUXsccee5Raz/e///2cccYZnH322bzyyiuss8467Lfffnz3u99dMM73v/99RowYwSmnnMLJJ5/M6quvzgEHHMAxxxzT6bsUF1xwAcceeyxnnnkmJ5xwAsOGDWOrrbZiwoQJpdLUnYkTJ7LSSivx4x//mHHjxrHyyiuz66678qMf/ajhdu7Orrvuyq233sr3v/99JkyYwOzZs3nLW97C1ltvzV577dWydEtqn0gptTsNkuoYPXp0uvvuu8tNNGhQrvNeMXAgzJnT2oRpiZs6dSqbbLJJu5Mh9Zqe8nhE3JNSKtc4Q1Kvsk2E1CIR8baI+L+IeCUiZkbEJRGx3hJNRG2DRRswSpKkXmAQIbVARKwE3ARsDBwI7A9sCNwcEUOWWEJq30bTzdtpJEmSFpVtIqTWOAjYANgopfQoQET8DXgEmAT8dImkorZ6otUV+42UUpdXc0r9gdWqpaWTTyKk1vgv4I5KAAGQUnoc+BMwbomlovarv3W+Aqylz4ABA+jo6Gh3MqRe0dHRwYAB3tOUljYGEVJrbAbcX6f/A8CmSywV8+d3/7+WSoMHD+a1115rdzKkXvHqq68yePDgdidDUkkGEVJrrAa8VKf/DGDVJZaK4nWVDf/XUmnEiBE8//zzvP7661b9UL+RUuL111/nhRdeKPWxQUl9g88PpdapV7prWIk9IiYCExsNX2+9RXix0yc/Cddd1/l/LfUGDx7MWmutxTPPPMMcX9mrfmTQoEGstdZaPomQlkIGEVJrvER+GlFrVeo/oSCldCpwaqMZjh49uvwt54sv7vr/xIZxipYiw4YNY9iwYe1OhiRJgNWZpFZ5gNwuotamwINLLBVbbtn9/5IkSS1gECG1xhXA1hGxQaVHRIwE3l8MWzJmzuz+f0mSpBYwiJBa4zRgGnB5RIyLiP8CLgeeBH61xFLxzDPd/y9JktQCBhFSC6SUZgE7AP8AzgbOBR4HdkgpLbl3c77lLd3/L0mS1AIGEVKLpJT+lVL6ZEpplZTS0JTSx1NK05ZoIg44AAYNgojcPeCAJbp4SZK0bPDtTFJ/MmYM3HwzTJkCY8fm/yVJklrMIELqb8aMMXiQJEm9yupMkiRJkkoxiJD6m9tvh2OOyV1JkqReYHUmqT+5/Xb44AehowMGDIBbb7VqkyRJajmfREj9yeGH5wACcvfww9ubHkmS1C8ZREj9yWOPdf+/JElSCxhESP3Jvvt2/78kSVIL2CZC6k+OPTZ3L7kEdt994f+SJEktFCmldqdBUh2jR49Od999d7uTIUltFxH3pJRGtzsdkhayOpPU3/iKV0mS1MusziT1J7ffDjvuCG++CQMHwo03+opXSZLUcj6JkPqTKVNyADFvXu5OmdLuFEmSpH7IIELqT8aOzU8gll8+d8eObXeKJElSP2R1Jqk/GTMmV2GaMiUHEFZlkiRJvcAgQupvxowxeJAkSb3K6kySJEmSSjGIkCRJklSKQYQkSZKkUgwiJEmSJJViECFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJKMYiQJEmSVIpBhCRJkqRSDCKkFoiIPSLi4oh4IiJmR8TDEXFMRAxtd9okSZJazSBCao2vAvOAbwK7AL8EvgBcHxEeZ5IkqV8Z0O4ESP3Ex1JKz1f9f0tEzADOBMYCN7UlVZIkSb3AO6RSC9QEEBV/LrrrLsm0SJIk9TaDCKn3bFd0p7Y1FZIkSS1mECH1gohYF/gecENK6e52p0eSJKmVbBMhtVhErAxcDnQAn+lmvInAxEbD11tvvdYnTpIkqQUMIqQWiojBwBXABsB2KaV/Nxo3pXQqcGqj4aNHj06tT6EkSdLiM4iQWiQiVgAuBt4LfCil9Pc2J0mSJKlXGERILVB8C+JcYEdgt5TSHW1OkiRJUq8xiJBa4xfAp4DvA7MiYuuqYf/urlqTJEnS0sa3M0mt8ZGiewRwe81vQrsSJUmS1Bt8EiG1QEppZLvTIEmStKT4JEKSJElSKQYRkiRJkkoxiJAkSZJUikGEJEmSpFIMIiRJkiSVYhAhSZIkqRSDCEmSJEmlGERIkiRJKsUgQpIkSVIpBhGSJEmSSjGIkCRJklSKQYQkSZKkUgwiJEmSJJViECFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEVpmRMRfI+LzETG03WmRJElamhlEaFkyCvgF8FREnB4RW7c7QZIkSUsjgwgtS7YFzir+/izwp4i4LyIOjohhbUyXJEnSUsUgQsuMlNJtKaXPAOsAXwT+CmwB/Jz8dGJyRLy/nWmUJElaGhhEaJmTUno1pXRKSmk08G7gFOBN4ADg1oi4PyIOjYhV25pQSZKkPsogQsu0lNJ9KaWDgbWB8cBtwKbAT4HpEXF2G5MnSZLUJxlESEBK6Y2U0lkppQ8AmwB/BAYDn25vyiRJkvqeAe1OgNRXRMRq5CpNE8iBBMAr7UuRJElS32QQoWVeROwAHAR8HBgIBHAHcCpwYftSJkmS1DdZnUnLpIh4S0T8v4h4FLge2At4HTgJ2CKltE1KaXJKafZiLOOaiEgR8b8tSrYkSVKf4JMILTMiIoDdyNWVdgWWJz91+CNwGvDblNKcFi1rH/LH7SRJkvodgwgtS/5F/kZEAC8CZwOnppQeauVCImI4cDzwP8B5rZy3JElSX2B1Ji1L1gVuBfYF1k0pHdbqAKLwI+CBlNL5vTBvSZKktvNJhJYlG6WUHlmcGUTEOGBcSumzDYZvS37Dk1WZJElSv+WTCC0zFjeAKGwJHFhvQESsAPwKOC6l9HALliVJktQn+SRCap1vACsC329m5IiYCExsNHy99dZrUbIkSZJayyBCaoGIWA84gvzmp0ERMahq8KCisfWrKaV5lZ4ppVPJ36Koa/To0amXkitJkrRYrM4ktcYGwGDgHOClqh/AV4u/t2hP0iRJklrLJxFSa9wLbF+n/83kwOLXwKNLMkGSJEm9xSBCaoGU0svAlNr++ft2PJFS6jJMkiRpaWV1JkmSJEml+CRC6kUppWh3GiRJklrNJxGSJEmSSjGIkMqZBtza7kRIkiS1k0GEVEJK6cyUUr23MEmSJC0zbBOhZUZEHLAo06WUzmp1WiRJkpZmBhFalkwGynwFOorxDSIkSZKqGERoWdMB/A54sN0JkSRJWloZRGhZcgvwQeDjwJrAacBvU0pvtDNRkiRJSxsbVmuZUTSI3gg4DngncAbwdEScGBHvamviJEmSliIGEVqmpJQeTSl9A3gbsCdwJ/AF4K8RcVdEfC4ihrQ1kZIkSX2cQYSWSSmljpTSxSmlXYB3AD8A1gZOBZ6KiDFtTaAkSVIfZhChZV5K6YmU0reBicB0YGVgRHtTJUmS1HfZsFrLtIhYB/hs8VsfeAM4B/hLO9MlSZLUlxlEaJkTEcsBHwUmALuQj4O/A4cCZ6eUXmlj8iRJkvo8gwgtMyLi7cDngM+Q2z/MAs4ETksp3dXOtEmSJC1NDCK0LHm06N4NHAmcn1Ka1cb0SJIkLZUMIrQsCWAu+SnEd4DvRERP06SU0vq9nTBJkqSliUGEljUrAG9tdyIkSZKWZgYRWmaklHylsSRJUgtYqJIkSZJUikGEJEmSpFIMIiRJkiSVYhAhSZIkqRSDCEmSJEmlGERIkiRJKsUgQpIkSVIpBhGSJEmSSjGIkFooInaNiFsj4rWImBkRd0fEDu1OlyRJUisZREgtEhGTgMuBe4BPAJ8CLgJWame6JEmSWm1AuxMg9QcRMRI4AfhaSumEqkHXtiM9kiRJvcknEVJrfBaYD5zS7oRIkiT1NoMIqTW2BR4C9o6IxyKiIyIejYiD250wSZKkVrM6k9Qa6xS/HwPfBB4jt4k4KSIGpJR+1s7ESZIktZJBhNQaywFDgfEppUuKfjcVbSX+X0T8PKWUqieIiInAxEYzXG+99XorrZIkSYvFIEJqjReBDYHra/pfB+wCrA08VT0gpXQqcGqjGY4ePTo1GiZJktROtomQWuOBBv2j6M5fUgmRJEnqbQYRUmtcWnR3rum/M/DvlNIzSzg9kiRJvcbqTFJrXA3cDPwqItYA/gnsAewEfKadCZMkSWo1gwipBVJKKSI+DhwDfBdYlfzK131TSue1M22SJEmtZhAhtUhKaSZwcPGTJEnqt2wTIUmSJKkUgwhJkiRJpRhESJIkSSrFIEKSJElSKQYRkiRJkkoxiJAkSZJUikGEJEmSpFIMIiRJkiSVYhAhSZIkqRSDCEmSJEmlGERIkiRJKsUgQpIkSVIpBhGSJEmSSjGIkCRJklSKQYQkSZKkUgwiJEmSJJViECFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJKMYiQJEmSVIpBhCRJkqRSDCIkSZIklWIQIUmSJKkUgwhJkiRJpRhESJIkSSrFIEJqkYh4f0RcFxHPRcTMiPhLRHy23emSJElqNYMIqQUi4l3ADcAKwEHAJ4E/A7+OiC+0M22SJEmtNqDdCZD6ib2B5YGPpZReK/pdHxGjgAOAX7YtZZIkSS3mkwipNQYCc4HZNf1fxuNMkiT1MxZupNaYXHR/HhHrRMTwiDgI2BE4vn3JkiRJaj2rM0ktkFK6PyLGApcCXyx6zwU+n1K6oN40ETERmNhonuutt16LUylJktQaBhFSC0TEhsDFwAPA58nVmsYBp0TEGymlc2unSSmdCpzaaJ6jR49OvZRcSZKkxWIQIbXGD8hPHj6aUppb9LsxIlYHfhYR56eU5rcveZIkSa1jmwipNbYA7qsKICruAlYH1lzySZIkSeodBhFSazwDbBkRA2v6vw94A5ix5JMkSZLUO6zOJLXGScBFwJURcTK5TcR/AfsAx6eU3mxn4iRJklrJJxFSC6SU/g/YFRgEnE5uZL0tcDDwtTYmTZIkqeV8EiG1SErp98Dv250OSZKk3uaTCEmSJEmlGERIkiRJKsUgQpIkSVIpBhGSJEmSSjGIkCRJklSKQYQkSZKkUgwiJEmSJJViECFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJKMYiQJEmSVIpBhCRJkqRSDCIkSZIklTKg3QmQJEn90O23w1ln5b8POADGjGlveiS1lEGEJElqrdtvh+23hzlz8v+nngrrrAOf/jQce2x70yapJazOJEmSWmvKFHjzzYX/z58P//43/OhH8I1vtC1ZklrHIEKSJLXW2LEwcGD9YZdcskSTIql3GERIkqTWGjMGbr4ZPv952HLLzsN2370tSZLUWraJkCRJrTdmzMLG1N/4Rn4CsfvutomQ+olIKbU7DZLqGD16dLr77rvbnQxJaruIuCelNLrd6ZC0kNWZJEmSJJViECFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJK8RWvUh8VEc8DT7Q7HX3cGsAL7U7EMsDtvGS4nRtbP6U0ot2JkLSQQYSkpVZE3O2743uf23nJcDtLWppYnUmSJElSKQYRkiRJkkoxiJAkSZJUikGEJEmSpFIMIiRJkiSVYhAhSZIkqRSDCElLs1PbnYBlhNt5yXA7S1pq+J0ISZIkSaX4JEKSJElSKQYRkiRJkkoxiJDUp0TE2yLi/yLilYiYGRGXRMR6TU47OCJ+HBFPR8TsiLg9Ij7Y22leGi3mdk4Nflv2crKXOhHx1og4sciLrxfbaWST05qfJfVZBhGS+oyIWAm4CdgYOBDYH9gQuDkihjQxi18DBwHfAT4KPA1ca+G2sxZsZ4DJwJia3z9antil3zuBPYGXgD+UnNb8LKnPGtDuBEhSlYOADYCNUkqPAkTE34BHgEnATxtNGBGjgE8Dn00pnVH0uwV4APge8F+9m/SlyiJv5yrTU0p39F4S+41bU0prAUTEBGCnZiYyP0vq63wSIakv+S/gjkrBFiCl9DjwJ2BcE9POBS6smrYDuADYOSIGtT65S63F2c4qIaU0fxEnNT9L6tMMIiT1JZsB99fp/wCwaRPTPp5Ser3OtAPJ1UqULc52rvhCRMwp6vnfFBEfaF3yhPlZUh9nECGpL1mNXHe81gxg1cWYtjJc2eJsZ4BzgC8CHwImAqsDN0XE2BalT+ZnSX2cbSIk9TX1voAZTUwXizHtsmiRt1VKaf+qf/8QEZeTn2z8L7BtC9Im87OkPs4nEZL6kpeof4d1Verfla02o5tpK8OVLc527iKl9CpwFbDVYqZLC5mfJfVpBhGS+pIHyHXBa20KPNjEtG8vXl9aO+2bwKNdJ1lmLc52bqTRnXMtGvOzpD7NIEJSX3IFsHVEbFDpUXyY6/3FsJ6mXQH4VNW0A4C9gOtSSnNantql1+Js5y4iYhVgN+DOViVQ5mdJfVuk5I0jSX1D8aGz+4DZwLfId7aPBoYC70opvVaMtz7wGPC9lNL3qqa/ANgZ+BrwOPAF8ke6tkkp/WUJrkqftjjbOSK+CmwE3Aw8BawPVPrtmFIq+0G1fi8i9ij+3BH4PLlR+vPA8ymlW8zPkpZGNqyW1GeklGZFxA7A8cDZ5CoyNwJfrhRsCwEsT9enqZ8Bvk9u4DucXFDexQJXZ4u5nR8GPlH8hgEzyd+X+FxK6a4lkPyl0UU1/59cdG8BxmJ+lrQU8kmEJEmSpFJsEyFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJKMYiQJEmSVIpBhCRJkqRSDCIkqZ+LiFTnNycipkXEmRGxSZPzua2Y9iNNjPtwMe6Wi70CkqQ+x4/NSVI/FxGVE/13q3oPA94LbAPMArZNKd3bw3zGA2cAl6aUdu9mvO2AKcDdKaWtFjnhkqQ+yyBCkvq5ShCRUoo6w04EvgScmVIa38N8VgSeBoYAb00pPdtgvLOB/YCJKaXTFi/1kqS+yOpMkrRsu67ojuhpxJTSbOAcYAAwvt44ETEc2AN4DTi/JSmUJPU5BhGStGz7UNG9u8nxTy26n2swfD9gMHB+Sum1xUmYJKnvsjqTJPVzDdpErAJsBbwfuAr4dErp1Sbndye5PcX2KaUpNcPuBUYBW6WUmg1MJElLmQHtToAkaYk5sk6/B8lPDZoKIAqnkoOICeQG1ABExFbkAOKvBhCS1L/5JEKS+rl6DasjYgiwGfBDYHvgBymlI4phI6nT5iGldFTVtE8DKwDrpJReKvqfChwEfCGldEqvrZAkqe0MIiSpn+vh7UzDgX8Dg4ANUkpPRsRY4ObacWuCkFOAScB/p5ROrAosliMHFjNbvyaSpL7ChtWStAxLKb0MPEyu3vqfRb8pKaWo/dVMWmlgfVDR3RsYClxoACFJ/Z9BhCRp1aLb9DUhpfQX4B5gi4iotI+AhcGFJKkfM4iQpGVYRHwceDswF7it5OSVD8kdB2wN/C2ldGfrUidJ6qtsEyFJ/VyDV7wOATYFPgIE8LWU0nEl5zsUeApYueh1SErppMVMriRpKWAQIUn9XFUQUW0e8DxwF3BSSun6RZz3aeSqTLPJDapfXtR0SpKWHgYRkiRJkkqxTYQkSZKkUgwiJEmSJJViECFJkiSpFIMISZIkSaUYREiSJEkqxSBCkiRJUikGEZIkSZJKMYiQJEmSVIpBhCRJkqRSDCIkSZIklfL/AW5E9183o2EOAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "indices1= np.where(iso1.singles['logg'] > 5)[0]\n", + "indices2= np.where(iso1.primaries['logg'] > 5)[0]\n", + "indices3= np.where(iso1.secondaries['logg'] > 5)[0]\n", + "plt.plot(iso1.primaries['m_ubv_B'][indices2] - iso1.primaries[\"m_ubv_V\"][indices2],\n", + " iso1.primaries[\"m_ubv_V\"][indices2] - 5 * np.log10(1000 / 10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'][indices3] - iso1.secondaries[\"m_ubv_V\"][indices3],\n", + " iso1.secondaries[\"m_ubv_V\"][indices3] - 5 * np.log10(1000 / 10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'][indices1] - iso1.singles[\"m_ubv_V\"][indices1],\n", + " iso1.singles[\"m_ubv_V\"][indices1] - 5 * np.log10(1000 / 10), \"r.\",\n", + " label=\"BPASS isochrone\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "indices2= np.where((iso1.primaries['logg'] > 5) & (iso1.primaries['m_ubv_B'] -\n", + " iso1.primaries['m_ubv_V'] > -0.4) &\n", + " (iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] < -0.2))[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=1>\n", + "
\n", + "\n", + "
5.0
" + ], + "text/plain": [ + "\n", + "5.0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(iso1.primaries[indices2]['phase'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "indices2= np.where((iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] > 1.2) &\n", + " (iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] < 1.5))[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 332, 1134, 3546, 4014, 4260])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "indices2" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=1>\n", + "\n", + "\n", + "
5.0
" + ], + "text/plain": [ + "\n", + "5.0" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(iso1.primaries['phase'][indices2])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
0.41.2869803887833635e+254038.778168243262260540453.18446245.1424924959836815False0.71255.0121.3974131985619519.27536083748358620.57747683714516318.54567585397026317.62335684775594
0.41.2620655992139642e+254022.4439387259845260105876.51229885.119227217859879False0.67315.0121.450908091348819.30960074183060420.62150300546367318.5750642296182517.64547085076375
0.41.2620655992139642e+254022.4439387259845260105876.51229885.119227217859879False0.67315.0121.450908091348819.30960074183060420.62150300546367318.5750642296182517.64547085076375
0.41.2620655992139642e+254022.4439387259845260105876.51229885.119227217859879False0.67315.0121.450908091348819.30960074183060420.62150300546367318.5750642296182517.64547085076375
0.41.3187483495325182e+254054.711884519506261667728.40945665.162280956389513False0.75225.0121.34117801755890319.2368799339786720.5299535511115218.5117160061947317.596290777432472
" + ], + "text/plain": [ + "\n", + " mass L ... m_ubv_R m_ubv_I \n", + "solMass W ... \n", + "float64 float64 ... float64 float64 \n", + "------- ---------------------- ... ------------------ ------------------\n", + " 0.4 1.2869803887833635e+25 ... 18.545675853970263 17.62335684775594\n", + " 0.4 1.2620655992139642e+25 ... 18.57506422961825 17.64547085076375\n", + " 0.4 1.2620655992139642e+25 ... 18.57506422961825 17.64547085076375\n", + " 0.4 1.2620655992139642e+25 ... 18.57506422961825 17.64547085076375\n", + " 0.4 1.3187483495325182e+25 ... 18.51171600619473 17.596290777432472" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.primaries[indices2]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(100), \"r.\",\n", + " label=\"Secondary stars BPASS isochrone\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1 times solar metallicity\\n\" +\n", + " \"and 10**7.0 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmm, I think those are stars that are going to be white dwarves very soon or may unintentionally be brown dwarves. At least according to HOKI's criterion for what is a white dwarf or not. At the very least, at least the stars we just saw were low mass stars so they should be **LOW** on the CMD" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 31270 logg=4.00\n", + "Changing to logg=4.00 for T= 31267 logg=3.99\n", + "Changing to logg=4.00 for T= 31259 logg=3.98\n", + "Changing to logg=4.00 for T= 31245 logg=3.97\n", + "Changing to logg=4.00 for T= 31226 logg=3.96\n", + "Changing to logg=4.00 for T= 31200 logg=3.94\n", + "Changing to logg=4.00 for T= 31169 logg=3.93\n", + "Changing to logg=4.00 for T= 31139 logg=3.92\n", + "Changing to logg=4.00 for T= 31109 logg=3.91\n", + "Changing to logg=4.00 for T= 31072 logg=3.90\n", + "Changing to logg=4.00 for T= 31032 logg=3.89\n", + "Changing to logg=4.00 for T= 31013 logg=3.64\n", + "Changing to logg=4.00 for T= 31102 logg=3.64\n", + "Changing to logg=4.00 for T= 31185 logg=3.64\n", + "Changing to logg=4.00 for T= 31263 logg=3.65\n", + "Changing to logg=4.00 for T= 31337 logg=3.65\n", + "Changing to logg=4.00 for T= 31405 logg=3.65\n", + "Changing to logg=4.00 for T= 31467 logg=3.65\n", + "Changing to logg=4.00 for T= 31520 logg=3.65\n", + "Changing to logg=4.00 for T= 31564 logg=3.66\n", + "Changing to logg=4.00 for T= 31593 logg=3.66\n", + "Changing to logg=4.00 for T= 31604 logg=3.66\n", + "Changing to logg=4.00 for T= 31594 logg=3.66\n", + "Changing to logg=4.00 for T= 31559 logg=3.66\n", + "Changing to logg=4.00 for T= 31501 logg=3.66\n", + "Changing to logg=4.00 for T= 31423 logg=3.65\n", + "Changing to logg=4.00 for T= 31328 logg=3.65\n", + "Changing to logg=4.00 for T= 31225 logg=3.65\n", + "Changing to logg=4.00 for T= 31112 logg=3.64\n", + "Changing to logg=3.50 for T= 29088 logg=3.49\n", + "Changing to logg=3.50 for T= 29029 logg=3.49\n", + "Changing to logg=3.50 for T= 28969 logg=3.49\n", + "Changing to logg=3.50 for T= 28909 logg=3.48\n", + "Changing to logg=3.50 for T= 28848 logg=3.48\n", + "Changing to logg=3.50 for T= 28787 logg=3.47\n", + "Changing to logg=3.50 for T= 28726 logg=3.47\n", + "Changing to logg=3.50 for T= 28664 logg=3.46\n", + "Changing to logg=3.50 for T= 28603 logg=3.46\n", + "Changing to logg=3.50 for T= 28541 logg=3.46\n", + "Changing to logg=3.50 for T= 28479 logg=3.45\n", + "Changing to logg=3.50 for T= 28417 logg=3.45\n", + "Changing to logg=3.50 for T= 28355 logg=3.44\n", + "Changing to logg=3.50 for T= 28293 logg=3.44\n", + "Changing to logg=3.50 for T= 28230 logg=3.44\n", + "Changing to logg=3.50 for T= 28168 logg=3.43\n", + "Changing to logg=3.50 for T= 28106 logg=3.43\n", + "Changing to logg=3.50 for T= 28044 logg=3.42\n", + "Changing to logg=3.50 for T= 27982 logg=3.42\n", + "Changing to logg=3.50 for T= 27920 logg=3.42\n", + "Changing to logg=3.50 for T= 27858 logg=3.41\n", + "Changing to logg=3.50 for T= 27796 logg=3.41\n", + "Changing to logg=3.50 for T= 27734 logg=3.40\n", + "Changing to logg=3.50 for T= 27672 logg=3.40\n", + "Changing to logg=3.50 for T= 27610 logg=3.40\n", + "Changing to logg=3.50 for T= 27548 logg=3.39\n", + "Changing to logg=3.50 for T= 27486 logg=3.39\n", + "Changing to logg=3.50 for T= 27425 logg=3.38\n", + "Changing to logg=3.50 for T= 27363 logg=3.38\n", + "Changing to logg=3.50 for T= 27301 logg=3.37\n", + "Changing to logg=3.50 for T= 27240 logg=3.37\n", + "Changing to logg=3.50 for T= 27179 logg=3.37\n", + "Changing to logg=3.50 for T= 27118 logg=3.36\n", + "Changing to logg=3.50 for T= 27056 logg=3.36\n", + "Changing to logg=3.50 for T= 26995 logg=3.35\n", + "Changing to logg=3.50 for T= 26934 logg=3.35\n", + "Changing to logg=3.50 for T= 26873 logg=3.34\n", + "Changing to logg=3.50 for T= 26812 logg=3.34\n", + "Changing to logg=3.50 for T= 26751 logg=3.34\n", + "Changing to logg=3.50 for T= 26690 logg=3.33\n", + "Changing to logg=3.50 for T= 26629 logg=3.33\n", + "Changing to logg=3.50 for T= 26568 logg=3.32\n", + "Changing to logg=3.50 for T= 26507 logg=3.32\n", + "Changing to logg=3.50 for T= 26447 logg=3.31\n", + "Changing to logg=3.50 for T= 26386 logg=3.31\n", + "Changing to logg=3.50 for T= 26326 logg=3.31\n", + "Changing to logg=3.50 for T= 26265 logg=3.30\n", + "Changing to logg=3.50 for T= 26205 logg=3.30\n", + "Changing to logg=3.50 for T= 26144 logg=3.29\n", + "Changing to logg=3.50 for T= 26084 logg=3.29\n", + "Changing to logg=3.50 for T= 26024 logg=3.28\n", + "Changing to logg=3.00 for T= 22390 logg=3.00\n", + "Changing to logg=3.00 for T= 22334 logg=2.99\n", + "Changing to logg=3.00 for T= 22278 logg=2.99\n", + "Changing to logg=3.00 for T= 22222 logg=2.98\n", + "Changing to logg=3.00 for T= 22167 logg=2.98\n", + "Changing to logg=3.00 for T= 22111 logg=2.97\n", + "Changing to logg=3.00 for T= 22056 logg=2.97\n", + "Changing to logg=3.00 for T= 22001 logg=2.96\n", + "Changing to logg=3.00 for T= 21946 logg=2.96\n", + "Changing to logg=3.00 for T= 21891 logg=2.95\n", + "Changing to logg=3.00 for T= 21836 logg=2.95\n", + "Changing to logg=3.00 for T= 21782 logg=2.94\n", + "Changing to logg=3.00 for T= 21728 logg=2.94\n", + "Changing to logg=3.00 for T= 21673 logg=2.93\n", + "Changing to logg=3.00 for T= 21619 logg=2.93\n", + "Changing to logg=3.00 for T= 21565 logg=2.92\n", + "Changing to logg=3.00 for T= 21512 logg=2.92\n", + "Changing to logg=3.00 for T= 21458 logg=2.91\n", + "Changing to logg=3.00 for T= 21405 logg=2.91\n", + "Changing to logg=3.00 for T= 21351 logg=2.90\n", + "Changing to logg=3.00 for T= 21298 logg=2.90\n", + "Changing to logg=3.00 for T= 21245 logg=2.89\n", + "Changing to logg=3.00 for T= 21192 logg=2.89\n", + "Changing to logg=3.00 for T= 21140 logg=2.88\n", + "Changing to logg=3.00 for T= 21087 logg=2.88\n", + "Changing to logg=3.00 for T= 21035 logg=2.87\n", + "Changing to logg=3.00 for T= 20982 logg=2.87\n", + "Changing to logg=3.00 for T= 20930 logg=2.86\n", + "Changing to logg=3.00 for T= 20878 logg=2.86\n", + "Changing to logg=3.00 for T= 20826 logg=2.85\n", + "Changing to logg=3.00 for T= 20774 logg=2.85\n", + "Changing to logg=3.00 for T= 20723 logg=2.84\n", + "Changing to logg=3.00 for T= 20671 logg=2.84\n", + "Changing to logg=3.00 for T= 20620 logg=2.83\n", + "Changing to logg=3.00 for T= 20569 logg=2.83\n", + "Changing to logg=3.00 for T= 20518 logg=2.82\n", + "Changing to logg=3.00 for T= 20467 logg=2.82\n", + "Changing to logg=3.00 for T= 20416 logg=2.81\n", + "Changing to logg=3.00 for T= 20365 logg=2.81\n", + "Changing to logg=3.00 for T= 20315 logg=2.80\n", + "Changing to logg=3.00 for T= 20282 logg=2.80\n", + "Changing to logg=3.00 for T= 20249 logg=2.79\n", + "Changing to logg=3.00 for T= 20216 logg=2.79\n", + "Changing to logg=3.00 for T= 20183 logg=2.79\n", + "Changing to logg=3.00 for T= 20151 logg=2.78\n", + "Changing to logg=3.00 for T= 20118 logg=2.78\n", + "Changing to logg=3.00 for T= 20085 logg=2.78\n", + "Changing to logg=3.00 for T= 20053 logg=2.77\n", + "Changing to logg=3.00 for T= 20021 logg=2.77\n", + "Changing to logg=3.00 for T= 19988 logg=2.77\n", + "Changing to logg=3.00 for T= 19956 logg=2.76\n", + "Changing to logg=3.00 for T= 19924 logg=2.76\n", + "Changing to logg=3.00 for T= 19892 logg=2.76\n", + "Changing to logg=3.00 for T= 19860 logg=2.75\n", + "Changing to logg=3.00 for T= 19828 logg=2.75\n", + "Changing to logg=3.00 for T= 19796 logg=2.75\n", + "Changing to logg=3.00 for T= 19764 logg=2.74\n", + "Changing to logg=3.00 for T= 19732 logg=2.74\n", + "Changing to logg=3.00 for T= 19701 logg=2.74\n", + "Changing to logg=3.00 for T= 19670 logg=2.74\n", + "Changing to logg=3.00 for T= 19638 logg=2.73\n", + "Changing to logg=3.00 for T= 19607 logg=2.73\n", + "Changing to logg=3.00 for T= 19576 logg=2.73\n", + "Changing to logg=3.00 for T= 19546 logg=2.72\n", + "Changing to logg=3.00 for T= 19515 logg=2.72\n", + "Changing to logg=3.00 for T= 19484 logg=2.72\n", + "Changing to logg=3.00 for T= 19193 logg=2.69\n", + "Changing to logg=2.50 for T= 17579 logg=2.48\n", + "Changing to logg=2.50 for T= 17390 logg=2.46\n", + "Changing to logg=2.50 for T= 17170 logg=2.43\n", + "Changing to logg=2.50 for T= 16944 logg=2.40\n", + "Changing to logg=2.50 for T= 16719 logg=2.38\n", + "Changing to logg=2.50 for T= 16498 logg=2.35\n", + "Changing to logg=2.50 for T= 16279 logg=2.33\n", + "Changing to logg=2.50 for T= 16062 logg=2.30\n", + "Changing to logg=2.50 for T= 15848 logg=2.27\n", + "Changing to logg=2.50 for T= 15637 logg=2.25\n", + "Changing to logg=2.50 for T= 15428 logg=2.22\n", + "Changing to logg=2.50 for T= 15222 logg=2.20\n", + "Changing to logg=2.50 for T= 15018 logg=2.17\n", + "Changing to logg=2.50 for T= 14816 logg=2.15\n", + "Changing to logg=2.50 for T= 14618 logg=2.12\n", + "Changing to logg=2.50 for T= 14421 logg=2.10\n", + "Changing to logg=2.50 for T= 14227 logg=2.07\n", + "Changing to logg=2.50 for T= 14036 logg=2.05\n", + "Changing to logg=2.50 for T= 13847 logg=2.02\n", + "Changing to logg=2.50 for T= 13660 logg=2.00\n", + "Changing to logg=2.50 for T= 13476 logg=1.97\n", + "Changing to logg=2.50 for T= 13294 logg=1.95\n", + "Changing to logg=2.50 for T= 13115 logg=1.92\n", + "Changing to logg=2.50 for T= 12938 logg=1.90\n", + "Changing to logg=2.50 for T= 12764 logg=1.87\n", + "Changing to logg=2.50 for T= 12591 logg=1.85\n", + "Changing to logg=2.50 for T= 12421 logg=1.83\n", + "Changing to logg=2.50 for T= 12253 logg=1.80\n", + "Changing to logg=2.50 for T= 12087 logg=1.78\n", + "Changing to logg=2.50 for T= 11924 logg=1.75\n", + "Changing to logg=2.50 for T= 11762 logg=1.73\n", + "Changing to logg=2.00 for T= 11603 logg=1.70\n", + "Changing to logg=2.00 for T= 11446 logg=1.68\n", + "Changing to logg=2.00 for T= 11291 logg=1.66\n", + "Changing to logg=2.00 for T= 11138 logg=1.63\n", + "Changing to logg=2.00 for T= 10987 logg=1.61\n", + "Changing to logg=2.00 for T= 10837 logg=1.58\n", + "Changing to logg=2.00 for T= 10690 logg=1.56\n", + "Changing to logg=2.00 for T= 10545 logg=1.54\n", + "Changing to logg=2.00 for T= 10402 logg=1.51\n", + "Changing to logg=2.00 for T= 10261 logg=1.49\n", + "Changing to logg=2.00 for T= 10121 logg=1.46\n", + "Changing to logg=2.00 for T= 9984 logg=1.44\n", + "Changing to logg=2.00 for T= 9848 logg=1.42\n", + "Changing to logg=2.00 for T= 9715 logg=1.39\n", + "Changing to logg=2.00 for T= 9583 logg=1.37\n", + "Changing to logg=2.00 for T= 9453 logg=1.34\n", + "Changing to logg=2.00 for T= 9324 logg=1.32\n", + "Changing to logg=2.00 for T= 9198 logg=1.30\n", + "Changing to logg=2.00 for T= 9073 logg=1.27\n", + "Changing to logg=1.50 for T= 8950 logg=1.25\n", + "Changing to logg=1.50 for T= 8828 logg=1.23\n", + "Changing to logg=1.50 for T= 8709 logg=1.20\n", + "Changing to logg=1.50 for T= 8591 logg=1.18\n", + "Changing to logg=1.50 for T= 8474 logg=1.16\n", + "Changing to logg=1.50 for T= 8359 logg=1.13\n", + "Changing to logg=1.00 for T= 7703 logg=0.99\n", + "Changing to logg=1.00 for T= 7599 logg=0.97\n", + "Isochrone generation took 53.533239 s.\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-08-13 11:08:34.637766 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_U = 23.25\n", + "M = 1.571 Msun T = 9909 K m_ubv_U = 12.45\n", + "M = 6.785 Msun T = 23952 K m_ubv_U = 7.89\n", + "M = 18.749 Msun T = 30008 K m_ubv_U = 3.92\n", + "M = 18.788 Msun T = 25605 K m_ubv_U = 3.48\n", + "M = 18.806 Msun T = 20118 K m_ubv_U = 2.98\n", + "M = 20.402 Msun T = 6878 K m_ubv_U = 1.98\n", + "Starting filter: ubv,B Elapsed time: 1.42 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_B = 22.13\n", + "M = 1.571 Msun T = 9909 K m_ubv_B = 12.48\n", + "M = 6.785 Msun T = 23952 K m_ubv_B = 8.74\n", + "M = 18.749 Msun T = 30008 K m_ubv_B = 4.95\n", + "M = 18.788 Msun T = 25605 K m_ubv_B = 4.45\n", + "M = 18.806 Msun T = 20118 K m_ubv_B = 3.79\n", + "M = 20.402 Msun T = 6878 K m_ubv_B = 1.76\n", + "Starting filter: ubv,V Elapsed time: 2.79 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_V = 20.54\n", + "M = 1.571 Msun T = 9909 K m_ubv_V = 12.47\n", + "M = 6.785 Msun T = 23952 K m_ubv_V = 8.97\n", + "M = 18.749 Msun T = 30008 K m_ubv_V = 5.22\n", + "M = 18.788 Msun T = 25605 K m_ubv_V = 4.69\n", + "M = 18.806 Msun T = 20118 K m_ubv_V = 4.00\n", + "M = 20.402 Msun T = 6878 K m_ubv_V = 1.59\n", + "Starting filter: ubv,R Elapsed time: 4.08 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_R = 19.67\n", + "M = 1.571 Msun T = 9909 K m_ubv_R = 12.47\n", + "M = 6.785 Msun T = 23952 K m_ubv_R = 9.06\n", + "M = 18.749 Msun T = 30008 K m_ubv_R = 5.33\n", + "M = 18.788 Msun T = 25605 K m_ubv_R = 4.78\n", + "M = 18.806 Msun T = 20118 K m_ubv_R = 4.07\n", + "M = 20.402 Msun T = 6878 K m_ubv_R = 1.47\n", + "Starting filter: ubv,I Elapsed time: 5.42 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3516 K m_ubv_I = 18.44\n", + "M = 1.571 Msun T = 9909 K m_ubv_I = 12.48\n", + "M = 6.785 Msun T = 23952 K m_ubv_I = 9.27\n", + "M = 18.749 Msun T = 30008 K m_ubv_I = 5.58\n", + "M = 18.788 Msun T = 25605 K m_ubv_I = 5.01\n", + "M = 18.806 Msun T = 20118 K m_ubv_I = 4.25\n", + "M = 20.402 Msun T = 6878 K m_ubv_I = 1.23\n", + " Time taken: 6.72 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(7.0, 0.0, 1000, math.log10(1 / 10), recomp=True) # New MIST isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the distribution of logg values. for the stars in the MIST isochrone." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.583447298562488" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 72.,\n", + " 42., 93., 208., 247., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0.]),\n", + " array([-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2,\n", + " 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n", + " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n", + " 29]),\n", + " )" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.xlabel(\"logg in cgs\")\n", + "plt.title(\"Histogram of logg values of stars in the MIST isochrone\")\n", + "plt.hist(np.array([x for x in iso2.points['logg'] if np.isfinite(x)]), np.arange(-10, 30, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.556386609995295" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.2602644297347076" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the Parsec isochrone. Here I use the MultiplicityResolvedDK to test the ability of my code to match primary-secondary pairs from initial masses and separation." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/ryotainagaki/Desktop/PyPopStar/spisea/synthetic.py:801: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " compMass = np.array([compMass[x] for x in indices])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Interpolator for: Teff\n", + "Creating Interpolator for: L\n", + "Creating Interpolator for: logg\n", + "Creating Interpolator for: isWR\n", + "Creating Interpolator for: mass_current\n", + "Creating Interpolator for: phase\n", + "Creating Interpolator for: m_ubv_U\n", + "Creating Interpolator for: m_ubv_V\n", + "Creating Interpolator for: m_ubv_B\n", + "Creating Interpolator for: m_ubv_R\n", + "Creating Interpolator for: m_ubv_I\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n", + " result = self.as_array() == other\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1,\n", + " imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()),\n", + " 20000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2,\n", + " imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()),\n", + " 20000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize the isochrone we have created so far with a color magnitude diagram. There we can see the end of main sequence and perhaps the M type" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B']-iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"],\n", + " iso1.singles[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(iso2.points['m_ubv_B'] - iso2.points[\"m_ubv_V\"],\n", + " iso2.points[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"b+\",\n", + " label=\"MIST isochrone\", alpha = 0.2)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do have several outliers but otherwise the pattern does not seem too terribly off from the MIST isochrone's shape. Now I look at the primary stars and see if there are any problems caused." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r+\", \n", + " label=\"BPASS isochrone \\n (primary stars)\", alpha=0.7)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar\\n\" +\n", + " \" and 10**7.0 years age (Primary stars only)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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YW6wlWBErWHuARjHT7hx8x7exIMXw0Jok6aqfYt7KEP52idId9fvsEnxfkGLZzcqUomi7Bd/bsSqJhETkdKwqahNWhTcXu0vbgZVo9aTkb5pqHywlcTAWmX+DAP91rHH/Z1geW45Vh4K1A2kUNS8Vl6+jlOdYLTe19lbzgBtEZF+spOcS7GGCeHsmuOGJNTz4Hhe3ns9E5APgsCDYK3VhD5Z9f/BBRHbHqs76YzUGB0fMM5uYIEFEumPVIN2w3/SyFOlNZBSWT3+DPQR1FbBNRP4L/E5Vvwumy/T3C78XlpoyuTURw5Kdp5ZpUNwQp8z5LcHNf1QaKux8JCJHYsdtWFL0HFZ6twPLD6eS+LjNRHgct0gwvkXcdJVGRC4Bfotd/36lKdqHp0NVbxWRFVhg93/YcaEiMh17+CY8hirkfBS0g30f62pmFtb4fxWWX3KxG7tEv1tGMUg6wdhbWGR7AtY2Kl3hj90OOxnE2zVuulTLiJJsGVUV+UcK7nRPx9o7/CI2I4pIPazBaKzw4YJSnUKmGF5VwgcTovJMbsSw8DdJlO6o3zScp1tQYlAVjgu+P1DVVH053Yg1UC5Q1S9jR4jI/VgwFiv2N/08YnnJftNE+fdULBB7WFWHxaVhV6qwUW6c8hyrFe09LBjrXpaZReSgmHnfSdLn6nCiS1lKUNUfRORMLNjvJiK7qOrKFPPMEpGLsfNHqgdwki1nO9aQ+A4RaYOVBpyJNd4/QEQOCGoQMv391gTflV2ak+g4iE1v1LFVEfmtIs9H12EPkR2nqtNiR4jI1dhxXW6qul5EFgLtEzxI0in4/qYi1peIiJyIVeOuA05R1RUVtWxVfQR4JAiUjsaus78BXhKR/VV1GRV3PjofC8SuV9UxsSNE5CiKS9kjk5pi2SWk0wP/WOyu+1ci0iXZhCISGyF+GHz3iphuH6yk7ftEVZQAak8azsUyVqeIScILaYU8XVfBwh6Kn4u4I+iOHZhFVHUtdgfRXqJfGdKjwlNYUvikR6JStLDUaI+IcVEd/Ya/SXyAElYpRW3Pu8F3lfSMLyI7YR0jghV/p7IP8EVEIFaP6O0Jj4FS40SkI9H7Mp00gLVNildqX1ehr7G2TgcHT9nFq8pjNVx/WV/3FpaKTcNuQKM+G4FBIpJuKe1mip+GTVf4pHXCaDATqrpMVZ9W1TOwUpq9gQOD0cnO1zkU5+Hw95uF3aAdG1SbV7Vk6c3FSps2YY24y6oiz0f7AKviA7FAouN2O2Wr1Qir5vpGjDspbpoKFzw5+QSWbwepalSwXG6qukZV/6uqF2Al2HkU/1YJ80fc8FTnoyo736Y8WQVF7mOwtjHPByU+pQSPAL8QM+jfwfd1ItI6Zrr62GtP6pFeSdu/sR/1H8G84XJaYU8fxq6rOikMvnvFDgzuTu9JMM8j2H65WWJux0VkD8peTZGu8E69Q4LxYf13iSJ7EelK9N3BTOwCfayIxN/1XUzp9mJggf8aYHRQTVOCiNQTkV4J0pcREdkTe+S4M3bg3p98DsB+004iElZtEvxOo7E2gfEmYsXZlwS/Yew8N1O2E21h8N0rdqCI7IU1XM8Ktd7zJ2BVNjfEjhORvbEqha1Y32WVJggEw+4JppVh/iZYqdp27Cms86M+2Mm5GdZvGyLSVET+KBGvOwpcFkz/RVgqJiLdxV5D1iR+4qA6+g/Bv0nbtCTZlkYickLsuSRm2XnBvxuC78lY9cugoEotPu17Aa+q6nyAoI3OY1gJwy3BDUnsOpqJSKKqsorwKJafLpHSr+a5EWv28Wg52g1DxZ6PCrEuZA6KW8Z5WPvCKCuB1lH5I4WwLeO1sTdGwU3+SOzGYGxcOnYVe91huX6zYH1TsNqSP6jq8+VZXsTy+0r0a8vaBN9hfn4bu/70EJEBccsYgLXb+4bUbbELg+9eccs4BOvpocKk9TokVb0p2AGjgfdFZCZWPB++DulYrPhzdsw8M0Xk71h13Gci8hTWvuYk7G7sLeAfaaz+lmCeU4GPg7YOO2HF7G2Av6tqqh2aDe9jGeKXwf56C9tXJ2GZZFHEPH/HOpg7E9hP7FU9LSjuOPc0iqsLK9o7WEa+TETyKG7ndFfQ2PdZ7MnWQUEbmPewwO3UYNwZsQtTVQ1ONK8A/xGRsJ+xbkBvrA+cvnHzrAwOlGeAd0XkNawKYkewrqOwdhyZvAM1N6bxbQ5WatItWFa9IB1D0zxp34ad6D4Ukf9gF4NjsEBsCtYmKHZ75oq9RuwmLO8+TnE/Y3lYw+ISJ+c0hH0dXR4Ewh9i+6YfFlwmCqarwlXYnenFYn38vEFxP2PNsaflvq/A9Z0WU4ocNuDvj+WR94lrYJ+mgdiFZIqqRh2joYewftiGY+3AGmBB6GgRmYX1B7Ua+52PwfojXI+1oQnthl0U7xZ7oOULrDRnV+zYaIf91iWC2ww0wao5C0XkPaxNXWMs/+2Pldp/CaCqP4nIb7D+uqaLNfafjz1Q0Adr/zIibvkXY+fy32L9272Elf7tiQUYp1CGgDgdqlooIpdhN7ZzROQJrO1kT+zY/oriYLas66jI89Ht2D55K0jrj1iNQg+sHeqAiHlewx6UeFHsAYrwDRBTUqR7pojcij1o8klw7W2I5e084JKINpM3Yw+/nEtcO0mx/vVCnYPvv4lIWHL7UMw1eDwWCyzH+twbkyKtScdHeAzYFBwvhVhBzc+w/fQBlt/D689Q7PrzuFg/il8B+2HX0XXYa/BSXU8fwRrv3y4ix2HXwE7Y+fZpbJ9WjEwevcQO4LuwhsNrsQNvMVYidh7RveqfiQUi67ATzefYY8lRPYUXEt0Df2Osx+TPsOqBdcEyB0VMm09EFxBpbt8wEnTvQPJe5iPXiWX8e4Pt2oRVud6EBZOJtjUX6yZhEXbwfYVVpXUP1nF73PTjyKDLiRT7uS8WlP1E8WP2+THj98AajK8Kfof3sc4Pk+2b2B7415FeD/z52Pv/vg3229pgP4wHTkvztwx/k9jPJqxjyHexfNwjyfyJ9tEw7EK7Hut/6BnsQptse87GgqZN2EnqUexC/BlxPbYn25dxv8MErPH0RuyYuhILNpW4np5TpG0YZcjzSdKWi5XQhb2br8FOiH0y2c8p1jEu4rfVIJ/Mwk6eic4vkcdKzDRvB9OckkY6vg6mPQQL7PtiPcW/hx2/W7E8/wl2Mc6Pm785VrI2NphmBVaSugorWb4KaJ7BfplGTNcWWIB4JXZ+nh+T/97FAqhSPc5jF7Vngum2BPP9C9gtwTqbYufzT7CbuXVYUHk7JbuKCX+zcp+n4qbpg3UGuzrIb99hN7W5qfZPBsdBPuU8HwXL6Rfs+3XYcfEyVpARue5g3/4L6yduGxle17Dg6n2Ku5yZTkzXHQmOqajtjzrWYj/DYqYtTGP6ok8mx32w/N8G+fN/QX5bhZ1bryTiWMGCr/FYnLI1+H6UiLcnkLifsS7YAxfLgn35AdaWLD/qNyFJXk/2kWBmV82JyAVYz+i/1eJezF0NJdaVyVLgI1U9KtX0zjnnaq+yNnB1lSS2PVLMsD2w9nHbsFc0uRpCRFoHbXRih+VgfeU0xu7ynHPO1WFptRlzVeo/wcX7A6woOx8r3t4JuFpVM+3Xx2XXr7C+r17FOpbMw6om9sWqO+/KXtKcc85VB15NWc2IyEVYG6NOWOP9n7A68btV9elsps1lLnjq5o9Ym7+wE8nvscaff9OYF8U751xdEjyAMyzNyW/XJF1h1XQejDnnnHOuygVdg7yR5uTpvDmjxvJgzDnnnHMui7wBv3POOedcFnkw5pxzzjmXRR6M1XDB61RURIZlOy3OVXcicoOIbIp9PZUrTUSmiYi3YalDRGRMcC3pVY5lHBYs47yKS1nd4MFYHSMiuSLyexGZICJfiMi24ODpnWK++iJymYh8IiIbRWSViPxXRI5OMd8wEZmWRrp6iUhhkvGaxufsVOsp7/a4misIwK4AHlDVBXHj6ovIBSIyPcgLW0VkWZA/HhKRU7KTalfRRKQw2bnGlZ2qfoC95/TPItIsy8mpUbyfsbonH3tdCNhrNlZg78xMSEQEeyfYAOwVMHdj/WUNBGaIyK9U9dmY6Tuq6rwEy2oFbFDVDSmma4q90iZ8gfn1CZLXDHtd1DbstTspZbo9rtb4I9CIuHfiikh9rDPlvljffs9jx0Ye9kL7wdg7+Z6rwrQ6V1PdjL0S7P+w1/+5NHgwVvfMw17U/aGqrgpeAjs0xTxnYoHLTOAEVd0EICL3Ye8IfVBEXlfVdSLSHHuZ/EvYOwIJpq2PvWj4RuxF0o8DLwR3qJfGrkxEBmI91N8N/BUSv1BWRMKXF09R1SVp7YEMtifN5blqTkRaAGcBr8WXimHvh+yLvbi9p6r+GDfvTsARVZJQ52o4VZ0lIl8BI0Tkb6q6Pdtpqgm8mrKcgjryO0Tk46B6Y5OIfCsi/xSRlhHTF7XxEpHjgrYZ60RkrYg8LyL7J1jPPiLypIisFpH1IjJTRE7ONL2qulpVX1PVVRnMdmHwfV0YuATLeh97cXhrLLghCGA6Y+9d/Bi70O0OzAZ+DZykqpNUdQdQgL2Y+S3gcqC5iLwB/AE4T1X/mkbahgffmbyvM+3tSUZEHgt+y2MTjB8QjL8rbnieiNwsIl8GVaQ/ishrItInYhktgmrl10XkBxHZIiLLReQ5ETkywXo1yFftgiq2hSKyXYJ2hSLSVkRuEZGvg7y0Jvh7nIjslWq7g2UcJyIPiFV1rw224zMRGS0ijRPMs6uIjA2q/zaKyEciMlSsilpFZEzEPGnvqxQGYW+xeDxiXFg1PS4+EANQ1Q2qGtkXkogMEpE3guNyU5DO60SkUYLpO4vIv8WqyjYH++JNEbkwYtoTROTFmPPKNyLy1yCwjJ92WrAPc0TkmuActFlEFojI30SkYYL0nCkiHwT7dpmIjJeIV7IF0zYUkYvFqvPnBctfJSKvishJCeYpDD47i8itwd9bxdon/TVI8zkJ5g3bH02JGh83rQR5aWZwfGwKtv0lsZu7sCmEAh2BjlKyicO4mGWdJiKPBvt7vYj8FOyj/xORUtfM4LhREdlLRC6R4qYP09JNWzrEzgU3ix2rm4I895JENDGJPaZE5GCxa8saEdkgVhWfsjmGiLQMpp8rIpJgmqnBeg6LG/UY0AG78XfpyPSt6f4p9ab3+7DA4wmsNOd2YAb21vYviHuTPNbbsAJPYW+Rfw6rNnk+GL4MaBU3TyesOlGB/2JFv0/EzK/AsDKmf1wwf+8E4xthVYDrgZyI8YOC+cdHjDs6mE+B25KkYd9gH4b7pX6aaT80mOd7oF6a85R5eyKmPTaYdkKC8S8H47vGDOsYpFeDfHIb9gL4RcAO4IK4ZRwJbAFexQLOvwITsTczbAX6RqxXgU+AQuAz7JVLdwAnYQHJd8E0LwO3BPn2KWA10C/N/fhisPyJQf69C5gTLPeN+N8QaBOz3dOxqoyxwe/wTDB8TNw8Ge2rFOl9KljOfhHjbgzG3ZvhsfP/gvkWBH//E7u5CPdBTtz0JwMbgO3Y8X4z8C+shPb7uGlHBNu4Dvh38Lu/Gyz7cyA3bvppwbgngMXBPLcD3wTDx0akf1QwbnWQt/6GvaKrELuR0rjp2wVpfxN4KEj/OGBlsJzzI9ZRGPxes4H/Bb/fLVhpfH6wvLcT7N8HguWmzJPYOVGDddwT/D8Wy/9PBdPkA2Owqug1wd/h57SYZX2FnbvHB/v9X1hzhkTnuXHBuCnBcicE8/0l3bSlsX25we+uwKxg+Q8Ba4N8MiJu+l7BtFOxPPdasN+fCPb5RuKOhWA/KNArZti/g2EnRqRpd+xcOjtiXO9gvlsyOabq8ifrCajpH+yCUSp4AM4LMuMf4oYPC4Zvw6rIYsfdHIy7Mm54eFG/NG74qcHwygzGDgjGf5pgfEEw/r2YYXnYBXpZkPbvsFc6vQ50j5luJ+AaLBCbgp3U38Au6n3SSPv9wbqvyWB7M96eFMv7DNhE6QB6z+Ak+Xbc8GnB8DPjhudiF8KNQNuY4S3ilx0M3x27yH0ZMS7ME49QOiDoT4LgGGhI3M1Dku3ei6DT6LjhYWAzMG54GLj8LW54N2Az0cFYRvsqRXqXYBeuqDQfggW8O7AL8C+BjimWNyxI89NAk7hxY4g7XoFWwI/BenpG/Z4xf3cM9slaoHPcdPcGy34gYl8p9k7bvJjhTbHjbzvQLmZ4frCOVUB+zPB6wH/CPBS3jkax6YzLo58Fy4rfF4XBsl4FmkbMO5W4G5ZgeDMsEJ1PGjdn2LnjB2CniHHxx2YhUJhkWXtHDKsHPByk9Yi4ceOC4QuxXuLLnLYkaQrPdffH5mHsRv3H4LeM/R17keDagAX6StzNB9HBWHg+LBU0xkxf6qYoyBMKzEpn+/zjwVjl7ViQ4CB5PW74sCCTPhoxz57xGR+76IZ3VVFB37SoAy6DdIYnkkTB2NHB+LcSjO8UjP86+L85FoQ9CuwabO80oD4wMjgxDQpObl9gJX2dgpNHYbCMM4OT11VJ0t0Mu1htJeYik8b2ZrQ9aSxvZDD97+KGh4H1OTHDugXDnkywrDC4vijNdd8ZTN8hbrgGJ+c2EfOEwdhNlZTvdwmW/++YYQ2xu/M1RAR7wIPEBWMVua+C9SvwTZJpzsBKlDTmsxIrtesfMf2HQd7LjRhXHyvJnhUz7HfBMu9II73XJvqNgJZBvt8INIoZPo0ExzH28IsSU8IUs47rI6bfCwveNIPf/fJgecfGDS8MhndLMN/Jwfi74oaHAcOf0lz/SqwUtVEa0xaSJBhLMt+hUWmi+Bx6aXnTlmD+BlgJ8jpiAu2Y8TfGp4viYKzUeS5Y3lbiSrSICMaC4e8Td54N8viCIC82S5DujcCSsmxzXfx4A/5yEpEG2InjTKALdkcQ266gfYJZZ0cMCxsWx7Y1OyT4fkujG0JOA3qmm95KELYlsCjAGvEfrsFTkmFTgyDt94g13N+gqjtE5KSY6Yr2k6o+JiJTsTvxRAZhgd/Tmn7D/Yy3Jw2PYFUGw7FqqjBPDMOqf56Imfao4LuFRLSPwtqqAZRoNygix2APORyFVffFt/9pj5UgxCpU1WUR65iO3cFfJSKHYsHw28BHCfJXJLGnXS8FTseqmZtTvO/CNIX2A5pgJ/+ohyLeAs6PG1amfZVA+IL21YkmUNUnROQZ4DigB3bc9QBOA04TkUewGx4Va9DfDQu4LkvQnGZzXNrC9n0vpJHeQ4Pv1yPSuVpEPsSqyDtj1Ymx0j2vhOuYHrGO/4nIAqyErgQROQB7MOdY7GYrvn1g1PluE1ZtHuUFLFA5W0T+oKobguHDsYDwoQTzxZsAXAJ8LiJPYtv1jka0AUxFRHbBtvEXWGDaNG6SROf0WZWUts5YLcLbGt3W93XgOoqvFbFK5QdV3SoiSymZH5K5F6uu/A3FT0f+Aiso+Jeq/pRgvlWkeFLfFfNgrPwexy5I/wOexapDNgfjLiNxQLEmfoCqbgtO7PVjBrcIvpcmWE5FBiJRwhNGiwTjd46bDk3QXUUwbkWa0/2EtYtKJGy4/0CSaaJkvD3JBMHno8BvReQ4tYbep2Lta27XmAcEKA4KTgw+iRT1zyMip2PtnTZhXXfMxe6Sd2B3vz2JzmOR+UJV14o1/L8eOAX4eTBqhYjcC/xZVbcmSVsYbL4OdMeqpx4HlmN3zwCj49KUKg9HDc94XyWxMfiOfLAgFGz3y8EnfAL4V9iF6ByslGwydhETLCAcncb6wapWwQLhVML9tTjB+HB4bvwIVV0TMf224DvT80qJYCzIN69j143XsPaqYZulg7F8H5UXl2lQVBKR3h0iEraFHAiMDRqDHwpMVtVFCdIXbxR2bPwGuCr4bBOR/2Kl1t+lsxARycVKgvbEgqtHsKBiG7a/L02wjZD4XFzetJU5PxBxnQlso2R+SOYx7EbzAhH5q9rDV+FT7MkenGpC8bHnUvBgrBxEpAALxF4FfhF7EQueurmyAlYTBgWJ7jDaVcA6kgnbm+wlIjmqui1ufKfg+5uomVV1HFaMn5SqTsPasaQkIgdjbRm+J7hwZqBc25PAv4DfYieoN0gcKIa/5aWqemeay74Ra2dUoKpfxo4ILmKJSkUTluyp6g/AecETUl2A47Hq1j9hpbp/TJGmU7FA7GFVHRaXpl0pHaCsDb4T5eGo4WXZV5FUdY2IbKE4wEt3vu3AEyLSFSt5OB4LxsK0faiqhyaYPd6a4Ls98GmKacPlt8MabcfbNW66sog9r0StI+q8ch12gT0uOF6LiMjVWL6IkqqU+d/YzcEIrGF7Ohf6kiuw3+oO4A4RaYOVap6JPcF9gIgcoKqbky0jcD4WiF2vcd3piMhRxHXDE5+MSkpbbH6IUhH5ISFV3Rg8bToK6CMin2FdwbynqvEls0DR9S8XO0e7NHjXFuWzT/D9XERpQnfsxFVeHwbfPYI79Xi9KmAdCQUniZlYMfnPIiYJH2kvVaVSicKT9UOJ7rgTqYztUdVPsKq+00XkCOxJohnxwRP2NBwJ1pvIPsAXEYFYPeykXmZqPlfVuygufTotzTSBNfSOFxUcfoXdIR8k1g9dvKjtKMu+SuZTYFcR2TnllKWFVasCRaW2n2MX0rw0lxFuT2QXEHHCY75X/Iig5OZgrKQ0Pn9lYk7wXer3EuveJOp1UfsAq+IDsUTLSZeqLsdKf48IquQHYe26Mr3RCpe3TFWfVtUzsON4b+DAmEm2k7hUKNO8XdFpi/I11ubyYInoLgmrWofi37Qy/AsLNkdgAWt9kgfL+2HHy0eVmKZaxYOx8ikMvnvFDgzufu6piBUEpRivYHdrF8et51Sqpr3Yv4LvP0tMH1IicjhWtbCc6JNXhQvaKg3Gitn/nWLaDmL9Ou0UN6oytudfWFuu/2AnofviJ1DV2Vi3AL8Ukd8kSHPXIP+ECoFOEtP3U1CiNRor1cqIiBwoIvkRo8LSqQ0R4+IVBt+94pa9F9Y9QgmqugWrymyBla7EztMNqwKMn6cs+yqZadj5rnvEcgaJyIkS3YdUO+CC4N8ZMaNuxX7vfwcBUvx8LYM2eaGHsRLCCyWiXzoR2T3m30exKt9LRGSfuElvxKrSH02zpCeRCTHryI9JRz3sSeioa0MhkCciB8Wl/TyKq7vLKjwmH8eqnh8IqsNSEpFGYn2ySdzwBtiT3VAyX68EWotI1M1yYfDdK25ZhwBXp5OecqatlOD4mYDtlxvilrM31tP9VuxJ4Eqhqt9iVdP9sFqANUT32RcK20i+UVlpqnWy/QRBTf5gdwdvYXcMM7HXDD2MPU34JtY+pDBunmEkefoxGDctblhsP2PPU85+xrD+ZsYFn7DPqZdihp0WN70ATwbTfRls5//D2nRtA06twn0edhnynzSmnUb000EVvj3YhXlZsMzlJHhyCmv0Gvb99BHF/TtNwEpvFDgyZvrwqbKlWEPaO7BGuRtifvv47SuVh2LGXYa18XkLaxx9E9Yu5kesxGBAGtvaFPg2WM/LQfonYSVIjyXIw22xtz9o8LuEfS3F9jMW/5RaRvsqRZqPCqb/R8S424Nxi4N88ffg82SwnxWrnpS4+e6h+KnLiVi7pwewm6fNwH1x05+MlRBux7p0uAl7y8QMSvczdlGw7LUU9+k1k+I8mxc3/TQSPP1IgnMOxU9ArsZuHlL1M9Y3Lk3/xBqjb6f4eIpfRyFpPrkYrFuxavm0uiwJ5ssN5vs+yH9/w46TL4Lhz8ZNHz7pPB0Lbq8jeGIW2C34Pbdj3Zb8LfjeQnHeHhe3vHHB8Pzypi3JNuYFv7tipaw3Y08h/4gdzxfFTd+LuCeUU/0uJHiaMmb86RQ/aXxnivROws6le6T7O9b1T9YTUNM/wUFyb5C5N2ENNW/CqsGiMnzkiTFmfOSFFCs+fwq7I1kPvIOd3JMuL8E6CmMOqqjPmIh5crA2A59iF5TV2JN4R1fx/n4vSOPP05h2WqKTS2VsD9YpaeQFP2665lj/ah9gAeDG4GT9PNberGnc9MOwC9V6LCh/Buia6OSZKA8F4/bHSnVmY0Hj5iA/PJXJtmPVWBOwG46NWLXdlcF+TZSH22M3K8uDeT7COv8cEMxzWXn3VYo0z8H6ZovvkHYPrM3cM1iV0Frs4rs4yBNDSNCpMFZSMBULxLdgjbhnAX8mro+wYPoDsOB3YTD9UiwoGB4xbR8s2F0d/E7fYUFibqK8niCNw0hwjsCqBOdg567lWKncbomWF2zvu1jgvSZI37GJ1kFmwdilwTIiuzNJMl+DIO+9gD1VHG7Lu1gpTsO46ZtiJXE/YAFDiQALK3F+LvhN1wd573ysTWumwVhGaUuxnblYMPdtkB/WYIF/qT4ZqZxgrH6QdgUOSJLOFthxOjmT37GufyTYec65chJ7/cmxWM/W32Y5OTWGiPwFC7j6qupLlbieQVgJ1i9V9ZnKWo8rGyl+T25vVX0ty8lxcYJmCN9hXWwkbMspIpdgfSAeq6pvVlX6ajoPxpyrACLSHSu1e1FV02mkXeeIyG4a11VB8KTiTKyUqL2W7AqkotcvWIlyE+Bg9ZNftSEie2AlPv/DSl38t6lmgq5vLsTeiBHZXixohzcXmKmqKd/v64p51xbOlYPYC57bA+dibTdGZzdF1dpsEfkO65tsPdYW8mSssfhvKzMQA6tzE5Hh2OuOdiO9Pr9cJRKRwVinwWdi/Xf90QOx6kNEOmAPTHXCznEfY+0DE8nH2k2Oq+y01TZeMuZcOYhIIdbQ/H9Y+4yJ2U1R9SUio7GuM/KxtmBrsLYzt2h0dwmuloup2l+AvS/19qwmyJUgIr2wJyI3YA/9XKiq/8tmmmorD8acc84557LI+xlzzjnnnMsibzPmyqVVq1aan5+f7WQ451zWffDBBytUtXXqKZ0ryYMxVy75+fnMnj0728lwzrmsE5F52U6Dq5m8mtI555xzLos8GHPOOeecyyIPxpxzzjnnssiDMeecc865LPJgzDnnnHMuizwYc86VNtFfJOCcc1XFgzHnqpPqEgRNmpTtFDjnXJ3hwZhz1YkHQc45V+d4p6/OOTNxYslgsH9/+x40CAYPzk6anHOuDvAXhbtyKSgo0FrdA39YbRgVjEycWDFBSnwQFMpmENS/P0yZkp11O1dDicgHqlqQ7XS4msdLxpxLZOJEuOYayMuLDoomTaqYYGnw4OLleBDknHN1jgdjzsULS8OuuQYWL4aNG7ObnrIob6ndoEEVlxbnnHNJeTDm6raooOWaa2D1ali7FkRgzRpo2xbat4ejjoL584unTdWuauRIuOee9NNTUUFQeUvtvI2Yc85VGQ/GXN12yy0lA48TT4R58ywIA1CFLVtg1Spo0gSOOaY4uDr0UJgzJ/nyn3oqs2DMgyDnnKtzvGsLVzeFVZFz5xb/f+ih8OqrpacVgeHDobCwZLC0cGGlJzMjEydaSV1YWhf+XV36LnPOORfJS8Zc3TNxIowaZVV5mzZZwBIGViJWGta6NSxfDo0bQ+fO0aVb7dtHL3/kSCsRC7Vta98DBmRWSpYpfxDAVYWKeorYOVfEgzFXN23aBO++a39/+qm1A4vt5mXZMvveZRe44ori4bHdULRvH91m7J57ioOutm1h6dLK2w7nqlpFPUXsnCviwZirO8JAauFC+Okn2LGjuG1Y69YWXH3+ubUR69gROnWCV14pnjcseaoJpU/+NKSrKsn64ivr8jzYc3WMtxlzdcfbb1uQMmeOlYI1bAjNm1tbsKVLixvj5+RA167FgRhYQ/+yGDCg3MkuE7+YuYqUrD3ipEkV+xovfyWYq4M8GHO1X3jn/uCDMGwY1KtnwdjWrdZ9RU6OPUUJsOuu0KtX6WVENdZPp/SpMtuIOVdVBg+2UuCwJDj8u7KD/okTEz+AUhEPpjRrVv5lOFcBvJrS1W6xbby2brXSsNatrauKbdtg551hyBALmiZOtN72Gze26Q891IKwsKF+fPswL31yddXEiVZaHHuTEvbFd8UVmR8bid6LGh5/mb4BI92qzvXrM0unc5XES8Zc7XbNNfDiixZwhdassUCsfn0rGQtLrwYPtqrK8O7/iiuge/fET006V1fElkKFNyNz5lj1fvfu9gmr+styk5Ko5K2sx14mVZ3htoXtR53LAi8Zc7VT+F7J8KnI0I4dVvq1erWVlCUTW/rVtm31bazvXGWLLYVKJ9gqTyP8hQuLS6VDZX0DRpRmzUqWiJ11ln0qIu3OlZEHY672Cas8fvjBuqZYvtyqJpcts0DstttKnnyjxLcH89Ix56JFtZ0sT/cXsdWcYbAVdSMU/zRzoqrO+EAtVdWkd93hskA0tm8l5zJUUFCgs2fPznYySjvxRPjkE6s+mTXLvl96CcaNSx6IJToe/G7Z1TXxwU0onVKoiur2JVkwJgITJthT0vfcAw0aFJd2J1t/utWRZbg2isgHqlqQ8YyuzvNgzJVLtQvGTjzRgq+1a62x/tat9vRkw4Y2ftOm6JNsvXqwfXvVptW5miKd4Ko8wVuyZUL0/CLQr58d70uXFr89I1V6Y/sb/PBDOzc0bmznjDZtrBQ8LAnPMO0ejLmy8mpKV7usXAnHHgsvvwy5udbP1zvvWNVHshIxD8ScK5/K6BC5rEFcsm5nYtMpAn362N9Tp1pQV507c3a1lj9N6Wq22Ke8TjzR7nSff97+X7YM7r0XPvrI7oT79Sv+xPLSYeeSqy5vdBAp/oAFUMuWFf8fjhs6NP1lVpdtc3Wal4y5mu2WW6zNyPPPw8aNVuWQm2sn6Fat7NVGe+9td7qxd7wiXjXpXLoyLaGqrAAn9sYpWTVluiZMKN628NzgwZnLAi8ZczVLWBIW9sw9d65VQ27caI30c3OtXRhYILZpk7UNadsWZswoWZLmgZhzlaOmPOwS1V1HTUm7q1W8ZMzVLJMmWUnYU0/Z/2GwBXZHO2sWtGxpJWRHHmkBGFig1qFD8YnWqyadq/kGDbLjGuy1Zs7VUJ57Xc3z/PPWd5iIdeK6Zo0Nv/BCKyFr0sTGAzRqZA36vUGuc7VLeEMV3mCl6sTZuWrMqyld9TdxorX3OvRQK+maN89OxDt22PitW+3R9H/9Cw48ELp2LZ63cWNvA+Kcc65a82DMVX+DB1tAFfb906SJfTdsaH/Xr2+N9MO+g8I2Ym3bWpXlpEkWzMW2F3POOeeqCa+mdDXHtGnw00/F/2/ZUvz3qlUWrMX2uD9okAViXkXpnHOuGvNgzNUMYbuQ88+3dmEhESsZ27jRgrANG6BXr+IALKpHcOecc64a8WpKV33FdmORnw/nnlvcbUWocWNrO9a9u302bSrZRszbiznnnKvmvGTMVV+33GLfYanYpEnwwgvF/YPVr28vB9640dqJXXGFNfD3PoOcc87VIB6Muerrq6/gmmvs77Bxfvg4+847WxAWVlkuXAijRllJWdu2NmzAALjnnqpPt3POOZcBD8Zc9TJxYnE7r40bYf58+79DBwu4duyw1xht3Vrcr9DOOxfPv26dtRtzzjnnaggPxlz18vbbFnTNnVs87LXXoE0bWL3aGuw3a2bDN22y/xs3tnfTQXGpmHPOOVdDeDDmqpfnn4dFi4pLvVSthGzePCsRy8mBtWuLp1e1l4LXrw/HH29Vk84551wN4sGYq16WLLHSrngdO1rP+s8/b//vvLOVjG3dalWYN93kjfWdc87VSN61hcu+8HVH+fmweXPp8WFj/alTrSQsJ8feNxmWnnkg5pxzrgbzYKwOEZFCEdGIz2lZTVj4uqMffrD/41/4u25d8SuQAPLy4OWXrQStfXsPxJxzztVoXk1Z97wEjIkb9nUW0lHsxBOtf7Cw/7Cw+4qQanGg1qYNLF9u/Ysdfzy88krVptU555yrYB6M1T0rVPXdbCeiyMSJ8O23sG1b8unCQG3ZMvtu1swDMeecc7WCV1O67Ova1Z6UDMX2G9akCRxyCPTrZ/83bGiN+e+6q2rT6JxzzlUSD8bqnv4iskFENovIu1lvL/b229aPWGzJWPj+SRE44QSYM6f4HZPnnw+Fhd5OzDnnXK3h1ZR1yxTgfeB7oC1wMfCMiJytqo9WeWpGjoRHHy398u+dd4YVK6xdGMChh1pHsDk5/noj55xztY4HYzWUiPQG0mk0NV1VewGo6iVxy3gGeBe4GYgMxkRkODA80cI7dOiQZoojHHMMPPJI6Qb7K1bY95Yt1p0FWNXkww+XfV3OOedcNSUafyF0NYKI7ASkEwltUNX5SZZzJfA3YDdVXZxpOgoKCnT27NmZzmYN92+5BT75pLhxfjJhmzGwKkuvpnTOVTMi8oGqFmQ7Ha7m8ZKxGkpVNwBfVcCiwu7usxOVpxOIAcyaBd27eyDmnHOu1vFgrA4TkRzg18B8VV1SZSueOBGuuQYWLKiyVTrnnHPVlQdjdYSIDAJOBf4LLMAa8I8EDgMGVXmCuna1nvQ//DC96du0gSlTKjdNzjnnXBZ4MFZ3fA+0Af4B5AEbsCcr+6rqS1Wakrffth73161Lf565c61EzasonXPO1TIejNURQa/7x2c7HYA9RTl/vrUDC3vUT2XrVmvwDx6QOeecq1U8GHNVK3yK8quvoHnz9OapV8+qNBcu9IDMOedcrePBmKtagwfbp21b2LEjvXl27LDP8uWVmzbnnHMuC/x1SC579tgD6tdPPk1OcL+wZYuVqjnnnHO1jAdjruqMHGklYm3bwurV8NFHqfsZC99ZqQqTJnlA5pxzrtbxHvhduZS5B/569eDkk4tfd5RM8+awdm3m63DOuSrkPfC7svKSMVe1Jk60F3+r2tOUIZHE86xbZ1WazjnnXC3kDfhd1Rk5Ep56yv4WgVWr7O+cnOLqyCiHHAJXXFH56XPOOeeywEvGXNW55x5YuhRuu81KxuoF2S9ZIAbWS/8f/lD56XPOOeeywEvGXNWZONEa4S9caP+n27VFw4bQuXPlpcs555zLIi8Zc1Vr4cLiYCwvL7156teHlSv9SUrnnHO1kgdjruoMHgxz5lhVpQh0757efBs32rsp3367ctPnnHPOZYEHYy47wi5Vdt899bT16sGQIdbmzDnnnKtlPBhzVSe201eA116DH35IPd+OHXDvvZCf71WVzjnnah0PxlzVOeYYq5oMqydPOCH9ecOXhTvnnHO1jD9N6arO2LEwY0bx/y+/nP68ItC+vT2NCdb+zDnnnKsFPBhzVWfffeGTT+zvZcsgN9e+UxGxz6BBHoQ555yrdbya0lWd+GrK7t2hX7/U86lax7BDhkD//t5uzDnnXK3iJWOu6tx8M3z+efH/zz+f/rz169sn7KPMOeecqyU8GHNV5+qri9t8TZ0KBx9s7cA2bYI33oDt2xPPu307NG1qwZi3G3POOVeLeDDmqk58ydiHH8JHHxX3OZaOTZsqPFnOOedcNnmbMVd1rr4aTj7ZPgBt2kCHDunPv3atfYelY952zDnnXC3gwZirWgsXwqxZxf/PmweHHJJ6vjZtoGNHaNzYqjb9yUrnnHO1hAdjrmq1b1/6nZRXXGHtwZJZtgzmz4fly62vslGjrEd/55xzrobzYMxVnbFjraPX//7X/g/7GBs2DNavTz2/qvU3duyx9v9TT3lVpXPOuRrPgzFXdc49F/r0gT32sP9zgudHmjSxICuVJk3se9YsK13btMnbjjnnnKvxPBhzVa9r15LfLVtC69ap59u40V4avmxZyVcpeUDmnHOuBvOuLVz2rV5tDfP79bP+x1LJy4Ojj7a2Y1OmVH76nHPOuUrkwZirOm+/XfJJyoULreqxZUsr9ZoyJb3qylWr4NNPrZqyf38b1qED3HNP5aTbOeecq0ReTemqTvy7KQGaN7eSrjCwuuii1Ms55BAoLLTStClT7DN/fnFVpVdZOuecq0FEM+n93Lk4BQUFOnv27PQm7tq1uAf+8MlIsG4t7r+/uN+wdErHABo2hAMOsO4yFi607ylTLKjz6kvnXBUTkQ9UtSDb6XA1j5eMuarTrh00aGAfKP67cePiQGziRJgwIfWyGja0T/v29n/79taGrH9/q8J0zjnnaghvM+aqzrnnWuAF1lC/Tx/7O/aVSJMmWanW4MHJS8jCeQcNsnlmzLCqzvA7P99K4rynfuecc9WcB2Ou6owda8FSKOyeIuzENRNTp1rbsUmTLOCC4v7HZs2yQMyrKp1zztUA3mbMlUtGbcZiiVi7MbCqyUmTSk8zaBCcdVbiZajaK5Hmz7eqycWLYft2qF/fSuD23tteteQlY865KuBtxlxZecmYqzonnliyZKxRI/s+9lh45RX7O77xfbLqShHrmywsGQtfr7RjhwVjc+cWB3kekDnnnKumPBirRCLyIXA/MEFV12U7PVm3777wySf297JlkJtbPLyspk4tbmPWv39xVSXYE5ZeVemcc66a82CscnUD7gH+ISKPAw+p6rtZTlP2zJgBy5cX/x/+HVtaFpZyxYrtBiNKgwbWV1mTJiUb8TduXNwprDfkd845V0151xaVqwfwSPD3b4C3ReRjERkpIi2ymK7sSNS1Rbt2xdOUJWDatg0GDLCOYPfeG3780R4WiO0U1gMx55xz1ZQHY5VIVWeq6rnAbsBFwIdAV+BOYJGIjBORY7KZxhpD1Uq/ouTlWe/+of79i9uK9e9vH++V3znnXDXlT1NWMRHpBgwHBgMtAAW+BB4EHlHV1VlMXsYyepoy9qnJqVOt8T1kVoWYrLpS1dYRLuvQQ2HOnPSW65xz5eRPU7qy8pKxKqaqH6vqSGBXYBgwE+gC3AosFJHxlbl+EdlDRJ4SkR9FZK2IPC0iHVLPmWVhyVayYKxZs5JBXdg7v3POOVeNeTCWJaq6SVUfUdWfAfsDbwGNsRKzSiEiOwGvA52BocDZQCfgDRFpWlnrrRCTJllAdvLJiadZv75klWTUwwDOOedcNePVlFkkInnAOcD5WEAmwBpVTdA4qtzruxQrgdtPVb8Lhu0JfAtcqaq3ZrrMCun0NR2x/Y8lKx3Ly4OVKzNPj3POlZNXU7qy8pKxLBCR40VkErAQ+CdWTfke9sRlZdatnQK8GwZiAKr6PfA2cGolrtdMnFjcoF4kdeP62Omh+O9kLxJftco6l3XOOedqCO9nrIqISDvgXOA8YE+sFGw18ADwgKp+XgXJOAB4NmL458Cvq2D9xZKVboUGDy5uAxbfMz8kflXSq69mXvLmnHPOZYmXjFUiMf1EZDIwH/gzsBdWEjUU2E1VL62iQAwgDwsA460CWlZRGkx5A6XBg1MvY489yrcO55xzrgp4yVjlmo/1MSbASmA8Vgr2VRbTFBXBJCymEpHhWFcckTp0yOBBzNiSrgYNMntVUaLG+Hl5VjUZ5YcfrMoyfO+lc845Vw15MFa52gPTsarI/6jqliynZzVWOhavJdElZqjqA1j6IxUUFKRfxBXbz9hOO6X/qqLYvsPirVyZvMrz1Vdh5Ei45560k+mcc85VJX+ashKJSCdV/bacyzgVOFVVf1MB6XkdaKiqPeKGT8PyQs9Ml1nmpykz6ZA1qr1YvFRt0DyfO+cqmT9N6crK24xVovIGYoGDsfZlFeE54EgR2SscICL5wDHBuKpT0R2ypgq2/HVIzjnnqikPxuqWB4FC4FkROVVETsGerlwA3F+lKUnV1ixRtxZlDaoSPXnpnHPOZZm3GatDVHW9iBwP3IY9TCDAa8BlqvpTlSZm/vzk4+O7tUjn/ZVh6ViiKstwuFdZOuecq0a8ZKyOUdX5qvorVd1ZVZur6mmqWpjtdCW1cGFxw//y8kDMOedcNePBmKs6sVWPCxeWr+ox2TzJAq50Opt1zjnnqpBXU7qqE1v12LZt6qcpw64wFi60Bv+xXWFMmhRdbRl2g6GavLpywoTU1Z7OOedcFfCSMVc9xfZJ1r07zJplf6dqOxZbnRlfQtamTcWm0TnnnKsA3s9YNScio4E/qWr9bKclSkb9jMUGWGFpF6QOsOJLxOLFzh/VJ1myqknP/865CuL9jLmy8mpKV3VSvfg7mTDgipo/bIsWiu/ZP6rKsmFD2LLFX5fknHMu6zwYq/4KgRnZTkRWpSo5K0uQtyV4M9Wrr5Y/fc4551w5eJuxak5VH1bV47KdjgqX6MXfUaICsUzmBysdi62SDP/u189KzUaOzGx5zjnnXAXxNmOVSETOKct8qvpIRaelspT53ZSVJdlLxcHbjznnKo23GXNl5dWUlWsckMkVXoLpa0wwVu2k00u/iJWITZ1a/A023AMy55xzVcyDscq3DZgKfJHthLhAbIP+MBALiUDTpvBT1b4dyjnnXN3lwVjlmg4cC5wGtMFe1P2Eqm7KZqIcyTuFXb/eS8mcc85VGW/AX4mChvf7AbcA+wBjgcUicpeIHJTVxDkPtpxzzlULHoxVMlX9TlX/AOwBnAG8B1wIfCgis0TkPBFpmtVE1nRlebdlKNV7LP1dls455yqZB2NVRFW3qep/VLUvsDdwE7Ar8ACwSESOymoCa7KoXvkzkazKEorHeWDmnHOuEngwlgWqOk9V/wgMBxYCzYDW2U1VHbdjR+lhYamZV2c655yrRN6Av4qJyG7Ab4JPR2AT8CgwJ5vpqnFi33MJpV+BVBaq0KABbNtm/ycrEfMG/s455yqIB2NVQETqAf2A84G+2H7/FLgUGK+qP2YxeTVTed5zmczWrfYdBlvxgViq6kwP0JxzzmXIg7FKJCJ7AucB52Ltw9YDDwMPquqsbKbNpRD16qTYYMvbjznnnKsgHoxVru+C79nAaGCSqq7PYnpqp0zfU1kesUFY7N+pSsS81Mw551wC/m7KSiQiO4CtwNIMZlNV7VhJSapw1e7dlJUpvmQs1VOYsceWB2PO1Xr+bkpXVl4yVvkaALtnOxGuAkQFUx5wOeecKyfv2qISqWq9snyynW5XQcJOY2OfykzWkaxI+Tqwdc45VyP5hd+5RJIFRumUgKkWf6L+j1LeDmydc87VOB6MOZdIpoFRqgCtvKVeJ55Yvvmdc85VSx6MOVdVooK7+KrLqVOL/48P3mbMKD2vc865Gs+DMediTZxonciGPfqHf5e3VCtRqVl81WW/fsX/p/smga5dy5c255xzWeVPUzoXq6J79i/va5tOPLFkiVijRvZ97LHFwz7/vHxpdM45l1UejDlXmTIN7uI7sH3lleK/GzWCLVvs71dfte/YJzW9Ww3nnKuRPBhzLpGq7Nkf0n9CE6xq8rPPSrYbq1cPDjgAPv205DweqDnnXLXmbcacSyTdNluxkrUtK29wF1s1GQZcJ59s3zt22Cc+EHPOOVfteTDmXEVK1h1GWYK7WLFVls4552oND8acq2nCJz779bP/mzYt/cRnpr3/O+ecyxpvM+ZceZX3iclMxT8UsG5d6Wn8nZnOOVdjeDDmXHlVdHcY2eSBm3POVTmvpnSuJkv0UEBlvXDcX2TunHMVzoMx5ypSVXeHkagaNLbatCJLuiZN8oDMOecqmAdjzlWkymgjVtnSaewfG4Bl+gJ155xzSXkw5lxtUdb3asa/HzP+/4kTYdSo4uXOmpV6uV565pxzafNgzLnaZMqU4gcIwr/LW1o3eDC0b29/z5oF3bvb35MmJe4qw0vPnHMubf40pXO1Qdi9RkVWk8Z22REGYwALF8KcOfa391vmnHPl5iVjdYiI9BIRjfisyXbaXDnFl0Sl+yBBfHVibGP/wYNLl7TddlvJwCx+WWWpJnXOuTrOS8bqpv8D3o/5f1u2EuLKKSy9CqsPYzucTUempWmDB8NZZ5UsEYv9OwzmKqO/tYkTa+YDEs45l4KXjNVNX6rquzGf2dlOkCuD2GrE7t0tIIPK6fk/NrhL1eC/sng7NOdcLeUlY87VVPE9/3fvnl5pVFle35RpcFfV/a0551wN5sFY3TRBRFoBa4CXgKtUdX52k+TKLd0AqCpe31RRJXNV/d5P55zLAg/G6pYfgX8C04G1wCHANcA7InKIqi7LZuJcOaQTnFRGm6vKrpqsTe/9dM65BDwYq0NU9UPgw5hB00VkBjALa9R/Xfw8IjIcGJ5omR06dKjoZLqySCfIimqsX5uqE72Bv3OuhvJgrI5T1Tki8g1weILxDwAPJJq/oKCgkotGXKWqScHLoEH25Gai0riK7mfNOeeqiD9N6QAE8KCqNqpNfX95oOWcq6W8ZKyOE5ECYF/giWynxVWC2t7myhv4O+dqAQ/G6hARmQB8D8zBnqQ8BLgaWAjclb2UOZdE/CuX4juZrc3BpnOuTvBqyrrlM+AUYCzWpcVlwNPAEaq6IovpclWhOjXWz6SatDydzPq7M51zNYAHY3WIqt6sqgepagtVbaCqe6jqcFVdnO20uSpQnmq7im5jVhm96VenYNM55zLgwZhzLrWa8CoibyPmnKuhvM2Yc65qVERj+7JUTUa9yNw556oRUT85uXIoKCjQ2bP9PeO1UnzwFIoPnsrS2WpVNbZP1i+ZcxVMRD5Q1YJsp8PVPF4y5pyLlm63GN7ZqnPOlYu3GXPOVT1vbO+cc0W8ZMw5l1p88FTe9l9VVZLmVZTOuRrA24y5cvE2Y847W3XOeJsxV1ZeTemcc+moie/zdM7VCB6MOefKp660/6oJfa0552okD8acc+XjT1I651y5eAN+55xLpCI6qnXOuRQ8GHPOuUTS7WvNOefKwaspnXPOOeeyyIMx55xLR115UME5V+U8GHPOuXR4GzHnXCXxYMw555xzLos8GHPOuUx456/OuQrmwZhzzmWisjp/9SDPuTrLgzHnnKsOvId/5+os72fMOedS8c5fnXOVSFQ122lwNVhBQYHOnj0728lwrupUZOev8UFeyIO8GklEPlDVgmynw9U8XjLmnHPZ4j38O+fwNmPOOZcZ7/zVOVfBPBhzzrlMVFb1oQd5ztVZHow551x14G3EnKuzPBhzzjnnnMsiD8acc84557LIgzHnnHPOuSzyYMw555xzLou8nzFX6dauXcuyZcvYunVrtpPinHOV5pVXXun68ccfF6Y5+Q4RWbJt27brDz300JcqM12u+vNgzFWqtWvXsnTpUtq3b0+TJk0QkWwnyTnnKsX27du3HXjggSvSmXbHjh2ycePGFoWFhXfPmTPnYg/I6javpnSVatmyZbRv356ddtrJAzHnnAvUq1dPmzZtujE/P39LTk7O6Gynx2WXB2OuUm3dupUmTZpkOxnlMm7cOESk6FO/fn3at2/PGWecwddff5102ubNm9OtWzfuvvtutm3bVmLajRs30qJFC0SEjz/+OHLda9euZfTo0XTp0oWmTZvSsmVLunbtyogRI1i2bFnRdJs3b+a2226jW7duNG/enJ133pnOnTszdOhQvv3226TbN2bMmKwFytOmTUNEePXVV7Oy/srSv39/Lrnkkmwno1L16tWLXr16ZTsZabv99tt5+umns7Luc889d49evXrtEzWuSZMmm1S1XVWnyVUvXk3pKl1tKRF78skn2X333dm+fTtz587lxhtv5IQTTuDzzz+nRYsWkdOuXbuWJ598kksuuYRly5Zxww03FE3z9NNPs3btWgAeeeQR/vnPf5ZYxvbt2+nduzeFhYX84Q9/4OCDD2b9+vV89tlnTJo0iUWLFtGmTRsABg0axMsvv8yVV17JkUceyfbt2/nyyy958skn+eKLL+jUqVPC7Tr//PPp27dvRe2mOm/GjBm88sorzJ07N9tJcTFuv/12evTowS9/+csqX/eYMWOW7Lfffl2fe+655qeccsq62HH16tVTvGDEqap//FPmz2GHHabJfPHFF0nH1wRjx45VQL/99tsSw1955RUF9L///W/KaXv16qXNmzcvMezEE0/UvLw8PeKII7Rt27a6bdu2EuNfe+01BXTy5MmR6dq+fbuqqs6dO1cBvf3225NOVx298cYbCugrr7yS8bzbtm3TrVu3VkKqyqdfv346YMCAbCej0vXs2VN79uxZIcvatGlThSwnmY4dO+pZZ51VocuMT/enn366XlVnR31OOumkVccee+yaqHEfffRRoVaD87l/svfxaNy5Mtp5550B0npK9PDDD2fdunVFVYsLFy7ktdde48wzz+T8889n6dKlvPRSyfa7q1atAqBdu+gajHr16mU0XSJR1ZR33HEH+++/P02aNKFly5YUFBTwzDPPFI1XVW677Tb2228/GjZsyK677srFF19cVNIX2rZtG3/729/o0qULjRs3pnXr1vTt25evvvqqxHQbNmzg4osvplWrVrRu3ZohQ4awZs2aEtOICNdeey1//etf2XPPPWnYsCGffvopAI8++ijdunWjcePGtGrVirPPPpvFixeXmD8/P58hQ4bw2GOPsf/++9O0aVMKCgp46623Su2T6dOnc8IJJ9C8eXOaNm3Kz3/+cz777LOk+xFg0aJFvPDCCwyOe7XRkiVLGDp0KLvtthuNGjVi1113pV+/fiWqmjds2MAf/vCHom3bc889+ctf/sKOHTtKLGv58uVcdNFF7LHHHjRq1Ig99tiDs88+m82bNxdN8+KLL3LUUUfRpEkTWrRowWmnnVaqSr1Xr1706NGDV199lUMPPZSddtqJAw88kMmTJ5farscee4zOnTvTqFEjDjjggBJ5IbRp0yZGjRrFgQceSLNmzWjXrh39+/cv9VuHVfkzZszg17/+Nbm5uRxxxBHccsstNGrUiOXLl5eYXlXZa6+9GJTi3Z3J8mx+fj7z5s1jwoQJRU0Ihg0bBsB3333H2WefzZ577kmTJk3Ya6+9uPDCC1m9enWJ5Q8bNozdd9+dd955h6OPPpomTZpw5ZVXAjBx4kQOOeQQunfvvlPz5s0P3nfffbv84x//aBU7/8CBA1e99dZbLb777rsGSTfE1U3Zjgb9U7M/VVoyNmFCxS0rA2Fp11dffaVbt27VTZs26RdffKEnnHCCtmnTRn/88cdS08aXjA0YMEDr16+v69evV1XVm2++WQF999139ccff9TGjRvrGWecUWKeuXPnak5Ojnbr1k2ffvppXbVqVWT61q1bpzvvvLPm5+fr+PHjdcmSJRlt3+jRo9VOBebRRx/V+vXr6/XXX6+vv/66Pv/883rzzTfrQw89VDTN1VdfrYCOHDlSX3zxRb311lu1adOm2qNHjxIlcb/61a+0fv36+rvf/U5feOEFfeaZZ3TUqFH6+uuvq2pxyVh+fr5efPHF+tJLL+mdd96pjRs31nPOOadEOgHdbbfdtEePHvrUU0/pCy+8oEuWLNH7779fAR04cKA+//zz+uCDD2rr1q21U6dOum7duqL5O3bsqB06dNCCggJ98skndcqUKXrwwQdrixYtdPXq1UXTTZ06VevXr6+nnHKKTp48WSdPnqxHHXWU5ubm6vz585Puy3Hjximgy5YtKzG8d+/e2qlTJ3300Ud1+vTp+sQTT+iIESP0+++/V1XVrVu3ao8ePTQvL09vu+02ffXVV/XPf/6zNmrUSC+//PKi5axatUr32WcfzcvL01tvvVVfffVVnThxog4cOFDXrl2rqqovvPCC1qtXT3v37q3PPvusTpgwQffee29t1aqV/vDDD0XL6tmzp7Zr1067dOmi48eP1xdeeEF79+6t9evXL5F/X3nlFRUR7devn06dOlXHjh2re+yxh7Zr165EydiaNWv0vPPO00mTJum0adP06aef1t69e2uLFi108eLFRdOFx8juu++uv//97/WVV17RF154QVeuXKmNGzfWv/3tbyX23YsvvqiAvvHGGwn3e6o8O2fOHG3Xrp3+/Oc/13feeUffeecd/e6771RVdfr06XrVVVfp5MmTdfr06Tp27Fjt1KmTHnnkkSXWMXToUG3WrJl26NBB77zzTn3jjTf03Xff1TfffFNFRC+99FK9//77N/7nP//55sYbb5x/zTXX/KAxpV+LFi36CNDbbrvte/WSMf/EfbKeAP/U7E+VBmP9+lXcsjIQXjziP7vttpvOmjUrctowcFu1apXed999Wq9ePT311FOLptt///11v/32K/r/zDPP1EaNGpUIClRVH3zwQW3atKkCKiLapUsXveKKK3ThwoUlpnvuuee0VatWRWnba6+9dOTIkfrll1+m3L74YGzkyJF6yCGHJJx+5cqV2qhRIx06dGiJ4ePHj1dAn332WVUtrma94447Ei4rDMbiA6+RI0dqo0aNdMeOHUXDAN111111w4YNRcO2bdumbdq00V69epWY/8033yy17o4dO2pubm6JoPb9999XQCfEBPp77723Hn/88SWW9+OPP+ouu+yil156acJtUVX97W9/q7vttlup4U2bNk26Hx555BEFdPr06SWG//nPf9YGDRro0qVLVVX1j3/8o9arV0/nzJmTcFmHHXaY7rPPPiWqcP/3v/9pTk6Ojho1qmhYz549NScnR7/55puiYUuXLtV69erpX/7yl6JhRx99tO6///4lgux3331XgaTVlNu2bdP169drs2bN9NZbby0aHh4jl112Wal5hg4dqnvvvXeJ3/30008vcaxESZVnVdOvpty6dWtR/ondz0OHDo1sNvCPf/xDW7ZsqarJqylVdXbbtm23nHnmmcvjh3sw5h+vpnQuTc888wzvv/8+s2bNYvLkyXTp0oVf/OIXfPnll6Wm7dy5Mw0aNCAvL4+LLrqIs846i3//+98AzJo1iy+//JKzzz67aPqhQ4eyefNmnnjiiRLLOf/881mwYAGPPvoow4cPZ8eOHdxyyy0ccMABfP7550XT9e/fn8LCQp5++mkuueQScnNzuffeeznkkEMyflLx8MMP56OPPuKSSy7h1VdfZcOGDSXGv/vuu2zevJkhQ4aUGH7mmWeSk5PD9OnTAXj55ZcRES644IKU6zz55JNL/N+1a1c2b97M0qVLSwzv27dviadzv/76a5YtW8ZZZ51VYroePXrQsWPHorSEjjrqKFq2bFliPQDz588H4Ntvv2Xu3LmcddZZbNu2reiz0047cdRRRzFjxoyk27Fo0SJat25davjhhx/OP/7xD+644w4+/fRTVLXE+BdffJGOHTty9NFHl1hvnz592Lp1K++++y5g+/Twww/nkEMOiVz/+vXrmTNnDgMHDiQnp/j5rD333JNjjjmm1P7o1KlTiYc72rRpQ5s2bYr2x/bt23n//fcZMGBAieruI444gvz8/FLrf+KJJzjiiCPIzc0lJyeHpk2b8tNPP5WqIgU4/fTTSw276KKLmDt3Lq+99hoAixcvZsqUKYwYMSJye0Op8mwyW7Zs4aabbqJz5840adKEBg0a8LOf/QygVLpzcnLo169fqXWvXr2aIUOG8MYbb9RfsWJF/UTrysvL27pkyRKvpnSleDDmqreJE6F/f/tA8d8TJ1Z5Ug488EAKCgo4/PDDOfXUU3nuuedQVcaMGVNq2jBw++qrr1i/fj2PPPIIeXl5ADz88MOABVBr1qxhzZo1HH744bRu3bpoXKyWLVty1llncd999/Hll18yefLkoi4vYjVt2pTTTz+dO++8kw8++ICZM2dSv359rrrqqoy285xzzuFf//oX7733Hj//+c/Jy8vjl7/8JYWFhUBxG7Vdd921xHw5OTnssssuReNXrlxJXl5eWl2bhPsm1KhRI8DaIcWKX2eitIC1oQvHp7uesA3XeeedR4MGDUp8pk6dysqVK5Nux6ZNm4qWGevxxx/nlFNO4e9//zsHHXQQ7du354YbbihqD7Zs2TLmzZtXap3du3cHKFrvypUr2X333ROuf/Xq1ahqmfdHuE/C/bFixQq2bt1K27ZtS00XP2zKlCkMHDiQ/fffn4kTJ/Lee+/x/vvv07p161K/I0T/Zt27d6egoID77rsPgIceeoicnByGDh2acJshdZ5N5uqrr2bMmDEMGTKE559/nlmzZhV1gRGf7jZt2lC/fslYq2fPnjz55JMsWLCAUaNGNWrXrl23o48+et/33nuvVMZv3Lixbtq0ya+7rhTv2qKWEJHdgT8ABUA3oAmwp6oWxk3XEvgHcFowzTvAKFX9tCrTm7bBg+0DFoRNmZLd9MQIG/t+8sknpcYdeOCB7LNP6W6FtmzZwmOPPQZAt27dSo1fvnw53333XeS8oVNPPZVu3brxxRdfJE3fkUceSZ8+fXjxxRdTbUoJIsKIESMYMWIEq1ev5uWXX+Z3v/sdAwcO5L333iu6gC9ZsoQDDjigaL5t27axcuVKdtllFwBatWrFqlWr2LhxY4X1NRf/oEFsWuItWbKEgoKCjJYfpv3mm2+md+/epcY3bNgw5fzff/99qeFt2rThnnvu4Z577uHrr7/m4YcfZvTo0bRu3ZoLL7yQXXbZhT333LNUyWgoLIVq1aoVCxcuTLj+li1bIiIJ90e4felq1aoVDRo0KFVCCbB06VI6duxY9P9jjz3GPvvsw7hx44qGbd26tVQAGErU5c2FF17IiBEjWLhwIQ899BC//vWvI4PG+GUly7PJPPbYY5xzzjlcd911RcN++umnjNI8YMAABgwYwHvvvbfhf//736I//vGPu/fv37/T4sWLP4kN3tasWVN///3335I0Qa5O8gi99tgHOANYDbwZNYHYmeQ5oC9wCfAroAHwRhDMuQxs2LCBuXPnRlZLJTJlyhRWrVrF6NGjeeONN0p8wiDtkUceAaxUIqpEYf369SxYsKCoZGHdunWlnmIEq2L69ttvI0sg0tWyZUsGDhzIGWecUfQ04ZFHHkmjRo2K0ht6/PHH2bZtGz179gSgT58+qCoPPfRQmdefyn777Ufbtm1LpWXmzJnMmzevKC2ZLC8/P5/PP/+cgoKCUp+DDjoo6fydO3dmwYIFpTr4jV/HTTfdRMuWLYv2ad++fVmwYAHNmjWLXG+rVvZgXp8+fZg1a1bCToKbNm3KYYcdxpNPPsn27duLhs+bN4+ZM2dmvD/q16/P4YcfzlNPPVXiqc733nuvVKnThg0bSlSNAowfP75EOtIxaNAgmjdvzuDBg5k/fz6//e1vM5o/Ks+Clfht3Lix1PQbNmygQYOSNYdjx47NaJ2hpk2bMmjQoB+HDRu2fPny5Q2WLl1atEO2bdvGkiVLGnbq1Kn0Qe3qPC8Zqz1mqGpbABE5H+gTMc0pQA/geFV9I5j2HeB74Erg/6oorWWT4tH2yvbRRx+xYsUKVJXFixdz9913s2rVqox6Wn/44Ydp1qwZV1xxBc2aNSs1/rbbbuORRx7h+uuvZ9q0aVx44YUMGzaMn/3sZ+Tm5jJv3jzuuusuVq1axeWXXw5Yu5bjjjuOs88+mxNOOIE2bdqwePFiHnroIT777DPuvffejLZz+PDhNG/enKOOOoo2bdrwzTffMH78ePr0sSyVl5fH5Zdfzs0330zTpk2L2s1dd9119OjRo6j913HHHcevfvUrLr/8chYsWMDxxx/P1q1bmTFjBieffHKF9N5ev359brjhBkaMGMGQIUMYMmQICxcu5Nprr6VTp06ce+65GS1PRLjnnns49dRT2bJlC2eccQatWrVi6dKlzJw5kw4dOhTt9yjHHnsso0eP5pNPPuHQQw8F4Mcff6R3796cddZZRW0Jn332WVavXl20T8866yzGjh3LCSecwO9+9zu6devGli1bmDt3Ls899xyTJ09mp512YtSoUUycOJHevXtz3XXX0bVrV1asWMGzzz7LfffdR/Pmzbnxxhs5+eST6devHxdddBE//fQTo0ePpkWLFvzud7/LeB9ff/319OnTh9NOO40RI0awfPlyRo8eXaorlb59+zJ58mRGjRpFv379+OCDD7jzzjvJzc3NaH1NmjRh2LBh3HbbbXTt2pWjjz465Typ8ixAly5dePPNN5k6dSrt2rWjVatW5Ofn07dvXx5++GG6du3KPvvsw9NPP83MmTPTTu+f/vQnli5dynHHHcfGjRvrvfXWW3kPPPBAm86dO2/cbbfdiqLy999/v8nGjRvr9erVa12y5bk6KttPEPin4j/A+dhTdflxw/8fsDBi+oeBeWVZV13q9DX207p1az3uuOP0xRdfjJw2vmsLVdVly5ZpTk6O/uY3v0m4rgceeKDoMf4FCxboVVddpd27d9fWrVtrTk6OtmrVSk866SR97bXXiuZZvXq1Xn/99fqzn/1M27Vrpzk5OZqbm6u9evXSJ598MuX2xT9NOW7cOO3Zs6e2bt1aGzZsqPn5+XrZZZeV6MJjx44deuutt+q+++6rDRo00Hbt2ulFF11UYhpVezLtz3/+s3bq1EkbNGhQlP6vvvpKVRN3+hrux7DrB1VVQK+99trIbRg/frwedNBB2rBhQ83Ly9MhQ4bookWLSkyT6Gk6QEePHl1i2MyZM/Xkk0/W3NxcbdSokXbs2FEHDhyoM2fOTLwj1Z4g3G233XTMmDFFwzZt2qTDhw/XLl26aNOmTbV58+ZaUFBQ4glOVdWNGzfq6NGjdb/99tOGDRtqy5YttaCgQEePHl3iycilS5fqBRdcoO3atdMGDRro7rvvruecc06JDkhfeOEFPfLII7Vx48a688476ymnnFK0z0M9e/bUY445ptQ2dOzYsdSTshMnTtR9991XGzZsqF26dNGnn366VKev27dv12uvvVZ33XVXbdKkiR577LE6Z86cUstLdoyoquqKFTpz5kwF9O677060q0tIJ89++eWX2qNHD23SpIkCRWlavny5Dhw4UHNzczU3N1cHDx6ss2bNUkDHjh1bNP/QoUO1ffv2pdY9depU7dOnT9Hv0bZt2y2//vWvl3///fcfa8wTk1deeeUPrVq12rJlyxbv9NU/pT6iWvKpHlfzBSVjDxLXZkxE3gV+VNWfx01/JfA3oLmqRjeWSKCgoEBnz56dcPyXX37J/vvvn8kinavRxowZw4QJE/jmm29qzavAqtS333LtuHHccccdLFq0qKhz5Zrgs88+23DggQeWfrwa2HvvvQ/o16/f6jvuuGNR/LiPP/64Vbdu3fIrPYGu2vI2Y3VLHtamLF7YwrZlxDjnXAZGjRrFmjVr+M9//pPtpNQ4H374IY9Nncodd9zB8OHD2fmbb7KdpArx6KOP5q5cubLBH//4x9JPQjiHtxmrawSrZosaHj2DyHBgeKLxHTp0qIBkOVd7tGjRgvHjxyd8itBFWLkSVq3i9P79WbpiBT/v0YPrzz4bMmz8X11t2LBBHnjggf+1atWqdmyQq3AejNUtq7DSsXhhiVipUjNVfQB4INECCwoKUtZzq6pX17g6pW/fvtlOQs2yyy6wyy4U/vADfPsthB3RJmkCUZMMHz48qkYCgB07dgiwI9F4Vzd4MFa3fE70U5ZdgPmZthdLR4MGDdi4cSM77bRTRS/aOVcb/fhjySAs9u8M+42rCTZu3NhYREp3DOfqFA/G6pbngHNFpKeqTgcQkZ2B/kCldGnfpk0bFi5cSPv27WnSpImXkDnnkttzTyspAwvEamEABlYitnHjxsaFhYUNt23bdn220+Oyy4OxWkREBgR/HhZ8nyQiy4HlQfD1HNbj/qMi8nusWvJqrM3Y3ysjTeGTUIsWLWLr1q2VsQrnXG0TvJaKFSsg4t2v1dWSJUtytm/f3irNyXeIyJJt27Zdf+ihh75UqQlz1Z53bVGLiEiiH3O6qvYKpskDbsFeh9QYC84uV9XoLr1TSNW1hXPOlZkI1KBrlIh8oKq1syjPVSovGatFVDVlHaCqrgJ+E3ycc676qkGBmHPl4f2MOeecc85lkQdjzjnnnHNZ5MGYc84551wWeTDmnHPOOZdFHow555xzzmWRd23hyiXox2xettORJa2AFdlORA3h+yo9vp/SVx33VUdVbZ3tRLiax4Mx58pIRGZ7n0Lp8X2VHt9P6fN95WoTr6Z0zjnnnMsiD8acc84557LIgzHnnHPOuSzyYMw555xzLos8GHPOOeecyyIPxpxzzjnnssiDMefK7oFsJ6AG8X2VHt9P6fN95WoN72fMOeeccy6LvGTMOeeccy6LPBhzzjnnnMsiD8aciyEie4jIUyLyo4isFZGnRaRDmvM2FpF/iMhiEdkoIu+IyLGVneZsKee+0gSfgys52VVORHYXkbuC/LAh2M78NOeta3mqPPuqzuQpV/t4MOZcQER2Al4HOgNDgbOBTsAbItI0jUX8P+AC4E9AP2Ax8FJtvBhUwL4CGAccFff5psITm337AGcAq4E3M5y3zuSpQHn2FdSdPOVqmZxsJ8C5auQCYC9gP1X9DkBEPgG+BUYAtyaaUUS6AYOB36jq2GDYdOBz4AbglMpNepUr876KsVBV3628JFYbM1S1LYCInA/0SWemOpinoIz7KkZdyVOulvGSMeeKnQK8GwYXAKr6PfA2cGoa824FHo+ZdxvwGPBzEWlU8cnNqvLsqzpFVXeUcda6lqfKs6+cq9E8GHOu2AHAZxHDPwe6pDHv96q6IWLehlj1S21Snn0VulBENgdtg14XkZ9VXPJqhbqWpyqC5ylXI3kw5lyxPKytSrxVQMtyzBuOr03Ks68AHgUuAnoDw4FdgNdFpFcFpa82qGt5qrw8T7kay9uMOVdSVC/IksZ8Uo55a6oyb6+qnh3z75si8ixW0vZnoEcFpK02qIt5qsw8T7mazEvGnCu2mujShpZEl1DEWpVk3nB8bVKefVWKqq4DngcOL2e6apO6lqcqlOcpV5N4MOZcsc+xdjrxugBfpDHvnkGXD/HzbgG+Kz1LjVaefZVIopKguqqu5anK4HnK1QgejDlX7DngSBHZKxwQdDh5TDAu1bwNgF/HzJsDDAReVtXNFZ7a7CrPvipFRHYGTgbeq6gE1gJ1LU9VKM9TribxNmPOFXsQuBh4VkSuw+6obwQWAPeHE4lIR2AucIOq3gCgqh+JyOPA7SLSAPgeuBDYEzirSreiapR5X4nIFcB+wBvAIqAjcAXQjtq5rxCRAcGfhwXfJ4nIcmC5qk73PFWsLPuqLuYpV7t4MOZcQFXXi8jxwG3AeKyK4zXgMlX9KWZSAepTumT5XOAvWIPhXOBjoK+qzqnkpFe5cu6rr4HTg08LYC3WP9l5qjqrCpKfDU/G/X9v8D0d6IXnqVhl2Vd1MU+5WkRUvTrdOeeccy5bvM2Yc84551wWeTDmnHPOOZdFHow555xzzmWRB2POOeecc1nkwZhzzjnnXBZ5MOacc845l0UejDnnnHPOZZEHY865akFENOKzWUQKReRhEdk/zeXMDOY9KY1pvw6mPbjcG+Ccc2Xknb4656oFEQlPRtfHDG4BdAeOBtYDPVT1oxTLGQaMBZ5R1V8mma4nMA2YraqHlznhzjlXTh6MOeeqhTAYU1WJGHcX9i7Mh1V1WIrlNAEWA02B3VV1aYLpxgNDgOGq+mD5Uu+cc2Xn1ZTOuZrg5eC7daoJVXUj8Cj27t1hUdOISC4wAPgJmFQhKXTOuTLyYMw5VxP0Dr5npzn9A8H3eQnGDwEaA5PiXmzunHNVzqspnXPVQoI2YzsDhwPHAM8Dg1V1XZrLew9rb3acqk6LG/cR0A04XFXTDfCcc65S5GQ7Ac45F2d0xLAvsFKstAKxwANYMHY+1lAfABE5HAvEPvRAzDlXHXjJmHOuWohqwC8iTYEDgL8CxwE3qeq1wbh8ItqEqeqYmHkXAw2A3VR1dTD8AeAC4EJVva/SNsg559LkwZhzrlpI8TRlLvAD0AjYS1UXiEgv4I34aeOCufuAEcD/qepdMQFaPSxAW1vxW+Kcc5nxBvzOuWpPVdcAX2NNKw4Nhk1TVYn/xM0aNuS/IPg+E2gOPO6BmHOuuvBgzDlXU7QMvtM+b6nqHOADoKuIhO3HoDhIc865rPNgzDlX7YnIacCewFZgZoazhx263gIcCXyiqu9VXOqcc658vM2Yc65aSNC1RVOgC3ASIMDvVfWWDJfbHFgENAsGXaKqd5czuc45V2E8GHPOVQsxwVis7cByYBZwt6q+UsZlP4hVUW7EGu6vKWs6nXOuonkw5pxzzjmXRd5mzDnnnHMuizwYc84555zLIg/GnHPOOeeyyIMx55xzzrks8mDMOeeccy6LPBhzzjnnnMsiD8acc84557LIgzHnnHPOuSzyYMw555xzLos8GHPOOeecy6L/D79lxvVxvvQtAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r+\", \n", + " label=\"BPASS isochrone (secondary stars)\", alpha =0.7)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of BPASS isochrone at 0.1Z_solar\\n\" +\n", + " \" and 10**7.0 years age (Secondary stars only)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare this to a BPASS Cluster(Salpeter IMF, 10^7.0 yr old, 1/10th of solar metallicity)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Where are some of the goofy outliers coming from?" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
\n", + "\n", + "\n", + "\n", + "
masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass log_a L Teff R ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(iso1.secondaries[\"m_ubv_V\"] -\n", + " 5 * np.log10(1000/10) > 20.0)[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "issue = iso1.secondaries[np.where(iso1.secondaries[\"m_ubv_V\"] -\n", + " 5 * np.log10(1000/10) > 20.0)[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='mass' dtype='float64' unit='solMass' length=0>\n", + "
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(issue['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=0>\n", + "\n", + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.log10(iso1.primaries['Teff']), np.log10(iso1.primaries[\"L\"]), \"r.\", alpha =0.3)\n", + "plt.plot(np.log10(iso1.secondaries['Teff']), np.log10(iso1.secondaries[\"L\"]), \"r.\", alpha =0.3)\n", + "plt.plot(np.log10(iso1.singles['Teff']), np.log10(iso1.singles[\"L\"]), \"r.\",\n", + " label=\"BPASS isochrone\", alpha =0.3)\n", + "plt.plot(np.log10(iso2.points['Teff']), np.log10(iso2.points[\"L\"]), \"b+\",\n", + " label=\"Paresc\", alpha =0.1)\n", + "plt.xlabel(\"log(T in Kelvin)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"HR Diagram of Isochrones at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A good sign here is that there are plenty of compact remnant (that means noticable number of stars have gone through their main-sequence and post-main sequence). I also notice stars turning to the red giant branch. This is going to be really important when we decide to use the clusters as reference for finding ages of actual star clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "from astropy import constants as cs\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']), np.log10(iso1.primaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['mass_current']), np.log10(iso1.secondaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['mass_current']), np.log10(iso1.singles[\"L\"] / cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['mass_current']), np.log10(iso2.points[\"L\"] / cs.L_sun),\n", + " \"b+\", label=\"MIST\", alpha =0.2)\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which table is causing that line segment at around log10(L/L_sun)=-2.5?\n", + "Let's find out.\n", + "First I take a look at the shape of the primary stars' plot for log current mass log L." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone (primaries)\")\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which type of secondary star is it that is causing this strange pattern?" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "bad_line = iso1.primaries[np.where((np.log10(iso1.primaries[\"L\"] / cs.L_sun) > -3) &\n", + " (np.log10(iso1.primaries[\"L\"] / cs.L_sun) < -2))[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "\n", + "\n", + "\n", + "\n", + "
massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass L Teff R logg ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "solMass W K m ... \n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bad_line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sorts of characteristics (logg, Teff, L) can be causing it? Let's find out" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='L' dtype='float64' unit='W' length=0>\n", + "
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(bad_line['L'])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='Teff' dtype='float64' unit='K' length=0>\n", + "\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(bad_line['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='logg' dtype='float64' unit='' length=0>\n", + "\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(bad_line['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='mass' dtype='float64' unit='solMass' length=0>\n", + "\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(bad_line['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=0>\n", + "\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(bad_line['phase'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**It turns out that the line was caused by some compact remnants.**" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log10\n", + " import sys\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:9: RuntimeWarning: divide by zero encountered in log10\n", + " if __name__ == '__main__':\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot((clus_2.star_systems['mass_current']), np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b.\", alpha =0.1)\n", + "plt.plot((clus_2.companions['mass_current']), np.log10(clus_2.companions[\"L\"]),\n", + " \"b.\", label=\"Cluster made from MIST\", alpha = 0.1)\n", + "plt.plot((clus_1.star_systems['mass_current']), np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"Cluster_w_Binaries made from BPASS\", alpha =0.3)\n", + "plt.plot((clus_1.companions['mass_current']), np.log10(clus_1.companions[\"L\"]),\n", + " \"r.\", alpha = 0.3)\n", + "plt.xlabel(\"Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"Mass-logL of Cluster at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK. It makes sense that our compact remnants are causing this. (Remember, there really is no significant fusion in the insides of neutron stars or white dwarves." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in log10\n", + " \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.plot(clus_1.star_systems['mass_current'], np.log10(clus_1.star_systems[\"L\"]), \"r.\",\n", + " label=\"Cluster_w_Binaries made from BPASS (Star Systems)\", alpha =1)\n", + "plt.xlabel(\"log Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log-Mass-logL of Cluster at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in log10\n", + " \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.plot(clus_1.companions['mass_current'], np.log10(clus_1.companions[\"L\"]), \"r.\",\n", + " label=\"Cluster_w_Binaries made from BPASS (Companions)\", alpha =1)\n", + "plt.xlabel(\"log Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log-Mass-logL of Cluster at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now go back to the shape of the clusters' Color magnitude diagrams." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='log_a' dtype='float64' length=2683>\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", + "\n", + "\n", + "
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"cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Remember to use a distance modulus!\n", + "plt.plot(clus_1.star_systems['m_ubv_B'] - clus_1.star_systems[\"m_ubv_V\"],\n", + " clus_1.star_systems[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\",\n", + " label=\"BPASS\", alpha =0.3)\n", + "plt.plot(clus_2.star_systems['m_ubv_B'] - clus_2.star_systems[\"m_ubv_V\"],\n", + " clus_2.star_systems[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"b+\",\n", + " label=\"MIST\", alpha=0.1)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of clusters at 0.1Z_solar \\n\" +\n", + " \" and 10**7.0 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: divide by zero encountered in log10\n", + " \n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log10\n", + " \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(clus_2.star_systems['Teff']), np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b+\", label=\"MIST\", alpha=0.15)\n", + "plt.plot(np.log10(clus_2.companions['Teff']), np.log10(clus_2.companions[\"L\"]),\n", + " \"b+\", alpha=0.15)\n", + "plt.plot(np.log10(clus_1.star_systems['Teff']), np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", alpha=0.5)\n", + "plt.plot(np.log10(clus_1.companions['Teff']), np.log10(clus_1.companions[\"L\"]),\n", + " \"r.\",label=\"BPASS Cluster\", alpha=0.5)\n", + "plt.xlabel(\"log($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log(L in watts)\")\n", + "plt.title(\"HR Diagram of clusters at 0.1Z_solar and 10**7.0 years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at which star system's primary/single stars are the white dwarves?" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 3, 4, ..., 2677, 2678, 2680])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.where(clus_1.star_systems['phase'] == 101.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Black Holes?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=int64)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 103.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Neutron Stars?" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=int64)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 102.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Total mass of the cluster?" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19800.974080058662" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What Phases representedin each cluster? Sanity check for mergers not being included in the BPASS" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=3>\n", + "\n", + "\n", + "\n", + "\n", + "
-99.0
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masssystemMassTeffLloggisWRmass_currentphasemetallicityisMultiplemergedm_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
float64float64float64float64float64float64float64float64float64boolfloat64float64float64float64float64float64
1.06817965351566671.6350846101200718nannannannannan-99.0-1.0True0.0nannannannannan
0.81333406896257271.244420375099297nannannannannan-99.0-1.0True0.0nannannannannan
8.44514973051459426.5972273590802nannannannannan-99.0-1.0True0.06.6361488422267657.6361488422267677.6361488422267678.2474250108400478.247425010840047
0.50887446451892520.9225955681633743nannannannannan-99.0-1.0True0.0nannannannannan
0.467459444761455960.8722183637630846nannannannannan-99.0-1.0True0.0nannannannannan
3.9319986294257894.662599889788373nannannannannan-99.0-1.0True0.0nannannannannan
1.81467496973968964.947363728710449nannannannannan-99.0-1.0True0.015.014.015.014.014.0
1.13214581857035721.62849939542187nannannannannan-99.0-1.0True0.0nannannannannan
1.12721139081857482.189725290254545nannannannannan-99.0-1.0True0.0nannannannannan
0.53464882847602660.9445725381933454nannannannannan-99.0-1.0True0.0nannannannannan
................................................
0.41120494770845730.4112049477084573nannannannannan-99.0-1.0False0.0nannannannannan
0.74950713211416140.7495071321141614nannannannannan-99.0-1.0False0.0nannannannannan
0.70256167763273560.7025616776327356nannannannannan-99.0-1.0False0.0nannannannannan
0.50967591146882350.5096759114688235nannannannannan-99.0-1.0False0.0nannannannannan
0.52928915835219110.5292891583521911nannannannannan-99.0-1.0False0.0nannannannannan
0.49126644076313020.4912664407631302nannannannannan-99.0-1.0False0.0nannannannannan
0.52776405269196610.5277640526919661nannannannannan-99.0-1.0False0.0nannannannannan
0.74306184169760570.7430618416976057nannannannannan-99.0-1.0False0.0nannannannannan
0.47819096418248440.4781909641824844nannannannannan-99.0-1.0False0.0nannannannannan
0.43255528581240850.4325552858124085nannannannannan-99.0-1.0False0.0nannannannannan
" + ], + "text/plain": [ + "\n", + " mass systemMass ... m_ubv_R m_ubv_I \n", + " float64 float64 ... float64 float64 \n", + "------------------- ------------------ ... ----------------- -----------------\n", + " 1.0681796535156667 1.6350846101200718 ... nan nan\n", + " 0.8133340689625727 1.244420375099297 ... nan nan\n", + " 8.445149730514594 26.5972273590802 ... 8.247425010840047 8.247425010840047\n", + " 0.5088744645189252 0.9225955681633743 ... nan nan\n", + "0.46745944476145596 0.8722183637630846 ... nan nan\n", + " 3.931998629425789 4.662599889788373 ... nan nan\n", + " 1.8146749697396896 4.947363728710449 ... 14.0 14.0\n", + " 1.1321458185703572 1.62849939542187 ... nan nan\n", + " 1.1272113908185748 2.189725290254545 ... nan nan\n", + " 0.5346488284760266 0.9445725381933454 ... nan nan\n", + " ... ... ... ... ...\n", + " 0.4112049477084573 0.4112049477084573 ... nan nan\n", + " 0.7495071321141614 0.7495071321141614 ... nan nan\n", + " 0.7025616776327356 0.7025616776327356 ... nan nan\n", + " 0.5096759114688235 0.5096759114688235 ... nan nan\n", + " 0.5292891583521911 0.5292891583521911 ... nan nan\n", + " 0.4912664407631302 0.4912664407631302 ... nan nan\n", + " 0.5277640526919661 0.5277640526919661 ... nan nan\n", + " 0.7430618416976057 0.7430618416976057 ... nan nan\n", + " 0.4781909641824844 0.4781909641824844 ... nan nan\n", + " 0.4325552858124085 0.4325552858124085 ... nan nan" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems[np.where(clus_1.star_systems['phase']==-99)[0]]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/TestPlot_7.0_MIST_1.0.ipynb b/Test_Plots/TestPlot_7.0_MIST_1.0.ipynb new file mode 100644 index 00000000..95e926ca --- /dev/null +++ b/Test_Plots/TestPlot_7.0_MIST_1.0.ipynb @@ -0,0 +1,4170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS Isochrone at $10^{7.0}$ Years Age and Comparing with MIST Model (Solar Metallicity)\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for 10^7 years age, solar metallicity, AKs=0.0, and distance of 1000 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram ($B-V$ vs $M_V$), and the mass luminosity relationship of the cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This is for example as well as debugging purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We found files from which we can create our isochrone\n", + "Evolution Models of potential saved isochrone and desired isochrone\n", + "BPASS\n", + "BPASS\n", + "Atmosphereic Models of potential saved isochrone and desired isochrone\n", + "get_merged_atmosphere\n", + "get_merged_atmosphere\n", + "Reddening Laws of potential saved isochrone and desired isochrone\n", + "N09\n", + "N09\n", + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 35763 logg=3.88\n", + "Changing to T= 50000 for T=102134 logg=5.40\n", + "Changing to logg=5.00 for T=102134 logg=5.40\n", + "Changing to T= 50000 for T= 83086 logg=4.67\n", + "Changing to logg=5.00 for T= 83086 logg=4.67\n", + "Changing to T= 50000 for T= 73318 logg=4.84\n", + "Changing to logg=5.00 for T= 73318 logg=4.84\n", + "Changing to T= 50000 for T=101667 logg=5.41\n", + "Changing to logg=5.00 for T=101667 logg=5.41\n", + "Changing to logg=4.00 for T= 31761 logg=3.51\n", + "Changing to logg=4.00 for T= 31615 logg=3.92\n", + "Changing to T= 50000 for T= 63838 logg=4.53\n", + "Changing to logg=5.00 for T= 63838 logg=4.53\n", + "Changing to T= 50000 for T= 67724 logg=4.33\n", + "Changing to logg=5.00 for T= 67724 logg=4.33\n", + "Changing to T= 50000 for T= 72205 logg=4.44\n", + "Changing to logg=5.00 for T= 72205 logg=4.44\n", + "Changing to T= 50000 for T= 90738 logg=5.12\n", + "Changing to logg=5.00 for T= 90738 logg=5.12\n", + "Changing to T= 50000 for T= 71698 logg=4.79\n", + "Changing to logg=5.00 for T= 71698 logg=4.79\n", + "Changing to T= 50000 for T=101368 logg=5.42\n", + "Changing to logg=5.00 for T=101368 logg=5.42\n", + "Changing to T= 50000 for T=102045 logg=5.39\n", + "Changing to logg=5.00 for T=102045 logg=5.39\n", + "Changing to T= 50000 for T=168597 logg=5.82\n", + "Changing to logg=5.00 for T=168597 logg=5.82\n", + "Changing to T= 50000 for T=165067 logg=5.79\n", + "Changing to logg=5.00 for T=165067 logg=5.79\n", + "Changing to T= 50000 for T= 72058 logg=4.80\n", + "Changing to logg=5.00 for T= 72058 logg=4.80\n", + "Changing to T= 50000 for T= 73797 logg=4.47\n", + "Changing to logg=5.00 for T= 73797 logg=4.47\n", + "Changing to T= 50000 for T= 65131 logg=4.26\n", + "Changing to logg=5.00 for T= 65131 logg=4.26\n", + "Changing to T= 50000 for T= 65318 logg=4.27\n", + "Changing to logg=5.00 for T= 65318 logg=4.27\n", + "Changing to T= 50000 for T= 73264 logg=4.46\n", + "Changing to logg=5.00 for T= 73264 logg=4.46\n", + "Changing to T= 50000 for T=103240 logg=5.41\n", + "Changing to logg=5.00 for T=103240 logg=5.41\n", + "Changing to logg=3.00 for T= 21835 logg=2.72\n", + "Changing to T= 50000 for T= 65996 logg=4.30\n", + "Changing to logg=5.00 for T= 65996 logg=4.30\n", + "Changing to logg=4.00 for T= 32897 logg=3.88\n", + "Changing to T= 50000 for T= 72367 logg=4.44\n", + "Changing to logg=5.00 for T= 72367 logg=4.44\n", + "Changing to T= 50000 for T= 69470 logg=4.37\n", + "Changing to logg=5.00 for T= 69470 logg=4.37\n", + "Changing to T= 50000 for T= 71035 logg=4.40\n", + "Changing to logg=5.00 for T= 71035 logg=4.40\n", + "Changing to T= 50000 for T=101167 logg=5.41\n", + "Changing to logg=5.00 for T=101167 logg=5.41\n", + "Changing to T= 50000 for T=100716 logg=5.42\n", + "Changing to logg=5.00 for T=100716 logg=5.42\n", + "Changing to T= 50000 for T= 66764 logg=4.30\n", + "Changing to logg=5.00 for T= 66764 logg=4.30\n", + "Changing to T= 50000 for T= 64973 logg=4.27\n", + "Changing to logg=5.00 for T= 64973 logg=4.27\n", + "Changing to T= 50000 for T= 78703 logg=4.56\n", + "Changing to logg=5.00 for T= 78703 logg=4.56\n", + "Changing to T= 50000 for T= 85269 logg=4.71\n", + "Changing to logg=5.00 for T= 85269 logg=4.71\n", + "Changing to T= 50000 for T= 59869 logg=4.39\n", + "Changing to logg=5.00 for T= 59869 logg=4.39\n", + "Changing to logg=4.50 for T= 42214 logg=3.72\n", + "Changing to T= 50000 for T= 72144 logg=4.80\n", + "Changing to logg=5.00 for T= 72144 logg=4.80\n", + "Changing to T= 50000 for T=101414 logg=5.41\n", + "Changing to logg=5.00 for T=101414 logg=5.41\n", + "Changing to logg=4.50 for T= 47507 logg=3.94\n", + "Changing to T= 50000 for T=168846 logg=5.83\n", + "Changing to logg=5.00 for T=168846 logg=5.83\n", + "Changing to T= 50000 for T= 83778 logg=4.69\n", + "Changing to logg=5.00 for T= 83778 logg=4.69\n", + "Changing to T= 50000 for T=102223 logg=5.38\n", + "Changing to logg=5.00 for T=102223 logg=5.38\n", + "Changing to T= 50000 for T= 67185 logg=4.64\n", + "Changing to logg=5.00 for T= 67185 logg=4.64\n", + "Changing to logg=4.00 for T= 32746 logg=3.73\n", + "Changing to T= 50000 for T= 67151 logg=4.31\n", + "Changing to logg=5.00 for T= 67151 logg=4.31\n", + "Changing to T= 50000 for T= 70500 logg=4.40\n", + "Changing to logg=5.00 for T= 70500 logg=4.40\n", + "Changing to T= 50000 for T=169805 logg=5.84\n", + "Changing to logg=5.00 for T=169805 logg=5.84\n", + "Changing to T= 50000 for T=102527 logg=5.39\n", + "Changing to logg=5.00 for T=102527 logg=5.39\n", + "Changing to T= 50000 for T=102958 logg=5.41\n", + "Changing to logg=5.00 for T=102958 logg=5.41\n", + "Changing to logg=4.50 for T= 46562 logg=3.91\n", + "Changing to T= 50000 for T= 73782 logg=4.47\n", + "Changing to logg=5.00 for T= 73782 logg=4.47\n", + "Changing to T= 50000 for T= 65074 logg=4.27\n", + "Changing to logg=5.00 for T= 65074 logg=4.27\n", + "Changing to logg=3.50 for T= 26285 logg=3.28\n", + "Changing to logg=4.00 for T= 31478 logg=3.74\n", + "Changing to T= 50000 for T= 60679 logg=4.16\n", + "Changing to logg=5.00 for T= 60679 logg=4.16\n", + "Changing to T= 50000 for T=100772 logg=5.42\n", + "Changing to logg=5.00 for T=100772 logg=5.42\n", + "Changing to logg=4.00 for T= 31125 logg=3.66\n", + "Changing to T= 50000 for T= 67630 logg=4.33\n", + "Changing to logg=5.00 for T= 67630 logg=4.33\n", + "Changing to T= 50000 for T=102249 logg=5.40\n", + "Changing to logg=5.00 for T=102249 logg=5.40\n", + "Changing to T= 50000 for T= 74781 logg=4.49\n", + "Changing to logg=5.00 for T= 74781 logg=4.49\n", + "Changing to T= 50000 for T= 56171 logg=4.27\n", + "Changing to logg=5.00 for T= 56171 logg=4.27\n", + "Changing to T= 50000 for T= 73020 logg=4.45\n", + "Changing to logg=5.00 for T= 73020 logg=4.45\n", + "Changing to T= 50000 for T= 69669 logg=4.38\n", + "Changing to logg=5.00 for T= 69669 logg=4.38\n", + "Changing to logg=3.50 for T= 28262 logg=3.29\n", + "Changing to T= 50000 for T= 58381 logg=4.34\n", + "Changing to logg=5.00 for T= 58381 logg=4.34\n", + "Changing to T= 50000 for T= 65755 logg=4.28\n", + "Changing to logg=5.00 for T= 65755 logg=4.28\n", + "Changing to T= 50000 for T= 93573 logg=5.40\n", + "Changing to logg=5.00 for T= 93573 logg=5.40\n", + "Changing to T= 50000 for T=103619 logg=5.41\n", + "Changing to logg=5.00 for T=103619 logg=5.41\n", + "Changing to T= 50000 for T=173680 logg=5.88\n", + "Changing to logg=5.00 for T=173680 logg=5.88\n", + "Changing to T= 50000 for T= 56498 logg=4.28\n", + "Changing to logg=5.00 for T= 56498 logg=4.28\n", + "Changing to T= 50000 for T= 67248 logg=4.32\n", + "Changing to logg=5.00 for T= 67248 logg=4.32\n", + "Changing to T= 50000 for T=103193 logg=5.40\n", + "Changing to logg=5.00 for T=103193 logg=5.40\n", + "Changing to T= 50000 for T= 65928 logg=4.29\n", + "Changing to logg=5.00 for T= 65928 logg=4.29\n", + "Changing to logg=4.00 for T= 31018 logg=3.75\n", + "Changing to T= 50000 for T=174237 logg=5.88\n", + "Changing to logg=5.00 for T=174237 logg=5.88\n", + "Changing to logg=3.50 for T= 26831 logg=3.32\n", + "Changing to T= 50000 for T=102953 logg=5.41\n", + "Changing to logg=5.00 for T=102953 logg=5.41\n", + "Changing to T= 50000 for T=102669 logg=5.40\n", + "Changing to logg=5.00 for T=102669 logg=5.40\n", + "Changing to logg=4.50 for T= 41746 logg=3.70\n", + "Changing to T= 50000 for T= 74427 logg=4.49\n", + "Changing to logg=5.00 for T= 74427 logg=4.49\n", + "Changing to logg=4.00 for T= 32382 logg=3.55\n", + "Changing to T= 50000 for T= 67149 logg=4.32\n", + "Changing to logg=5.00 for T= 67149 logg=4.32\n", + "Changing to T= 50000 for T=101932 logg=5.39\n", + "Changing to logg=5.00 for T=101932 logg=5.39\n", + "Changing to T= 50000 for T= 67855 logg=4.34\n", + "Changing to logg=5.00 for T= 67855 logg=4.34\n", + "Changing to T= 50000 for T= 72947 logg=4.83\n", + "Changing to logg=5.00 for T= 72947 logg=4.83\n", + "Changing to T= 50000 for T=101726 logg=5.41\n", + "Changing to logg=5.00 for T=101726 logg=5.41\n", + "Changing to T= 50000 for T=102282 logg=5.39\n", + "Changing to logg=5.00 for T=102282 logg=5.39\n", + "Changing to T= 50000 for T= 71623 logg=4.42\n", + "Changing to logg=5.00 for T= 71623 logg=4.42\n", + "Changing to logg=3.00 for T= 20951 logg=2.96\n", + "Changing to T= 50000 for T= 67101 logg=4.31\n", + "Changing to logg=5.00 for T= 67101 logg=4.31\n", + "Changing to T= 50000 for T= 67624 logg=4.65\n", + "Changing to logg=5.00 for T= 67624 logg=4.65\n", + "Changing to T= 50000 for T= 72000 logg=4.43\n", + "Changing to logg=5.00 for T= 72000 logg=4.43\n", + "Changing to T= 50000 for T= 73308 logg=4.46\n", + "Changing to logg=5.00 for T= 73308 logg=4.46\n", + "Changing to T= 50000 for T= 52802 logg=3.95\n", + "Changing to logg=5.00 for T= 52802 logg=3.95\n", + "Changing to T= 50000 for T= 71269 logg=4.77\n", + "Changing to logg=5.00 for T= 71269 logg=4.77\n", + "Changing to logg=3.50 for T= 27462 logg=2.95\n", + "Changing to T= 50000 for T=101277 logg=5.41\n", + "Changing to logg=5.00 for T=101277 logg=5.41\n", + "Changing to T= 50000 for T=101445 logg=5.42\n", + "Changing to logg=5.00 for T=101445 logg=5.42\n", + "Changing to logg=4.50 for T= 44222 logg=3.81\n", + "Changing to T= 50000 for T= 74381 logg=4.48\n", + "Changing to logg=5.00 for T= 74381 logg=4.48\n", + "Changing to logg=5.00 for T= 49067 logg=4.01\n", + "Changing to logg=3.50 for T= 30090 logg=2.88\n", + "Changing to T= 50000 for T= 71302 logg=4.41\n", + "Changing to logg=5.00 for T= 71302 logg=4.41\n", + "Changing to T= 50000 for T=102113 logg=5.40\n", + "Changing to logg=5.00 for T=102113 logg=5.40\n", + "Changing to T= 50000 for T= 69859 logg=4.38\n", + "Changing to logg=5.00 for T= 69859 logg=4.38\n", + "Changing to T= 50000 for T= 88965 logg=4.76\n", + "Changing to logg=5.00 for T= 88965 logg=4.76\n", + "Changing to T= 50000 for T= 68314 logg=4.34\n", + "Changing to logg=5.00 for T= 68314 logg=4.34\n", + "Changing to T= 50000 for T= 83044 logg=5.15\n", + "Changing to logg=5.00 for T= 83044 logg=5.15\n", + "Changing to T= 50000 for T= 68290 logg=4.34\n", + "Changing to logg=5.00 for T= 68290 logg=4.34\n", + "Changing to T= 50000 for T= 71598 logg=4.42\n", + "Changing to logg=5.00 for T= 71598 logg=4.42\n", + "Changing to T= 50000 for T= 66293 logg=4.31\n", + "Changing to logg=5.00 for T= 66293 logg=4.31\n", + "Changing to T= 50000 for T= 65569 logg=4.28\n", + "Changing to logg=5.00 for T= 65569 logg=4.28\n", + "Changing to T= 50000 for T= 52338 logg=4.13\n", + "Changing to logg=5.00 for T= 52338 logg=4.13\n", + "Changing to T= 50000 for T=102353 logg=5.41\n", + "Changing to logg=5.00 for T=102353 logg=5.41\n", + "Changing to T= 50000 for T=102527 logg=5.40\n", + "Changing to logg=5.00 for T=102527 logg=5.40\n", + "Changing to T= 50000 for T= 75943 logg=4.93\n", + "Changing to logg=5.00 for T= 75943 logg=4.93\n", + "Changing to T= 50000 for T= 67022 logg=4.33\n", + "Changing to logg=5.00 for T= 67022 logg=4.33\n", + "Changing to T= 50000 for T=103200 logg=5.41\n", + "Changing to logg=5.00 for T=103200 logg=5.41\n", + "Changing to T= 50000 for T= 66816 logg=4.31\n", + "Changing to logg=5.00 for T= 66816 logg=4.31\n", + "Changing to logg=4.50 for T= 42930 logg=3.76\n", + "Changing to logg=3.50 for T= 28029 logg=3.30\n", + "Changing to logg=4.50 for T= 45454 logg=3.86\n", + "Changing to T= 50000 for T= 69799 logg=4.38\n", + "Changing to logg=5.00 for T= 69799 logg=4.38\n", + "Changing to T= 50000 for T= 78201 logg=4.55\n", + "Changing to logg=5.00 for T= 78201 logg=4.55\n", + "Changing to T= 50000 for T=102040 logg=5.39\n", + "Changing to logg=5.00 for T=102040 logg=5.39\n", + "Changing to T= 50000 for T= 68468 logg=4.34\n", + "Changing to logg=5.00 for T= 68468 logg=4.34\n", + "Changing to T= 50000 for T= 61423 logg=4.19\n", + "Changing to logg=5.00 for T= 61423 logg=4.19\n", + "Changing to T= 50000 for T= 91289 logg=5.38\n", + "Changing to logg=5.00 for T= 91289 logg=5.38\n", + "Changing to T= 50000 for T= 69920 logg=4.38\n", + "Changing to logg=5.00 for T= 69920 logg=4.38\n", + "Changing to logg=3.00 for T= 19883 logg=2.48\n", + "Changing to T= 50000 for T=103383 logg=5.41\n", + "Changing to logg=5.00 for T=103383 logg=5.41\n", + "Changing to T= 50000 for T= 66702 logg=4.30\n", + "Changing to logg=5.00 for T= 66702 logg=4.30\n", + "Changing to T= 50000 for T= 73925 logg=4.47\n", + "Changing to logg=5.00 for T= 73925 logg=4.47\n", + "Changing to T= 50000 for T= 72249 logg=4.43\n", + "Changing to logg=5.00 for T= 72249 logg=4.43\n", + "Changing to T= 50000 for T= 72622 logg=4.45\n", + "Changing to logg=5.00 for T= 72622 logg=4.45\n", + "Changing to T= 50000 for T=102056 logg=5.41\n", + "Changing to logg=5.00 for T=102056 logg=5.41\n", + "Changing to T= 50000 for T= 75866 logg=4.52\n", + "Changing to logg=5.00 for T= 75866 logg=4.52\n", + "Changing to T= 50000 for T= 65814 logg=4.28\n", + "Changing to logg=5.00 for T= 65814 logg=4.28\n", + "Changing to T= 50000 for T= 76554 logg=4.95\n", + "Changing to logg=5.00 for T= 76554 logg=4.95\n", + "Changing to logg=4.50 for T= 41743 logg=3.70\n", + "Changing to logg=4.50 for T= 42917 logg=3.76\n", + "Changing to T= 50000 for T= 68712 logg=4.36\n", + "Changing to logg=5.00 for T= 68712 logg=4.36\n", + "Changing to T= 50000 for T= 67059 logg=4.33\n", + "Changing to logg=5.00 for T= 67059 logg=4.33\n", + "Changing to T= 50000 for T= 68757 logg=4.35\n", + "Changing to logg=5.00 for T= 68757 logg=4.35\n", + "Changing to T= 50000 for T=103674 logg=5.41\n", + "Changing to logg=5.00 for T=103674 logg=5.41\n", + "Changing to T= 50000 for T= 73889 logg=4.47\n", + "Changing to logg=5.00 for T= 73889 logg=4.47\n", + "Changing to T= 50000 for T= 67473 logg=4.32\n", + "Changing to logg=5.00 for T= 67473 logg=4.32\n", + "Changing to T= 50000 for T=102237 logg=5.40\n", + "Changing to logg=5.00 for T=102237 logg=5.40\n", + "Changing to T= 50000 for T=102991 logg=5.41\n", + "Changing to logg=5.00 for T=102991 logg=5.41\n", + "Changing to T= 50000 for T=101728 logg=5.41\n", + "Changing to logg=5.00 for T=101728 logg=5.41\n", + "Changing to logg=4.50 for T= 45086 logg=3.85\n", + "Changing to T= 50000 for T= 66364 logg=4.29\n", + "Changing to logg=5.00 for T= 66364 logg=4.29\n", + "Changing to T= 50000 for T=102308 logg=5.41\n", + "Changing to logg=5.00 for T=102308 logg=5.41\n", + "Changing to T= 50000 for T= 94748 logg=5.39\n", + "Changing to logg=5.00 for T= 94748 logg=5.39\n", + "Changing to T= 50000 for T=102400 logg=5.42\n", + "Changing to logg=5.00 for T=102400 logg=5.42\n", + "Changing to logg=4.00 for T= 32467 logg=3.86\n", + "Changing to T= 50000 for T= 73320 logg=4.46\n", + "Changing to logg=5.00 for T= 73320 logg=4.46\n", + "Changing to T= 50000 for T= 74218 logg=4.48\n", + "Changing to logg=5.00 for T= 74218 logg=4.48\n", + "Changing to T= 50000 for T= 69844 logg=4.38\n", + "Changing to logg=5.00 for T= 69844 logg=4.38\n", + "Changing to T= 50000 for T= 64999 logg=4.26\n", + "Changing to logg=5.00 for T= 64999 logg=4.26\n", + "Changing to T= 50000 for T=101623 logg=5.40\n", + "Changing to logg=5.00 for T=101623 logg=5.40\n", + "Changing to T= 50000 for T=162637 logg=5.76\n", + "Changing to logg=5.00 for T=162637 logg=5.76\n", + "Changing to T= 50000 for T= 68091 logg=4.34\n", + "Changing to logg=5.00 for T= 68091 logg=4.34\n", + "Changing to T= 50000 for T= 69256 logg=4.37\n", + "Changing to logg=5.00 for T= 69256 logg=4.37\n", + "Changing to T= 50000 for T= 67185 logg=4.32\n", + "Changing to logg=5.00 for T= 67185 logg=4.32\n", + "Changing to T= 50000 for T=103279 logg=5.41\n", + "Changing to logg=5.00 for T=103279 logg=5.41\n", + "Changing to T= 50000 for T= 69328 logg=4.36\n", + "Changing to logg=5.00 for T= 69328 logg=4.36\n", + "Changing to T= 50000 for T= 68509 logg=4.34\n", + "Changing to logg=5.00 for T= 68509 logg=4.34\n", + "Changing to T= 50000 for T=102134 logg=5.40\n", + "Changing to logg=5.00 for T=102134 logg=5.40\n", + "Changing to T= 50000 for T= 60392 logg=4.41\n", + "Changing to logg=5.00 for T= 60392 logg=4.41\n", + "Changing to T= 50000 for T=103343 logg=5.41\n", + "Changing to logg=5.00 for T=103343 logg=5.41\n", + "Changing to T= 50000 for T= 72629 logg=4.45\n", + "Changing to logg=5.00 for T= 72629 logg=4.45\n", + "Changing to T= 50000 for T=102866 logg=5.41\n", + "Changing to logg=5.00 for T=102866 logg=5.41\n", + "Changing to T= 50000 for T=102433 logg=5.39\n", + "Changing to logg=5.00 for T=102433 logg=5.39\n", + "Changing to T= 50000 for T= 82949 logg=5.17\n", + "Changing to logg=5.00 for T= 82949 logg=5.17\n", + "Changing to logg=3.50 for T= 26486 logg=3.38\n", + "Changing to T= 50000 for T=103319 logg=5.40\n", + "Changing to logg=5.00 for T=103319 logg=5.40\n", + "Changing to T= 50000 for T= 57106 logg=4.30\n", + "Changing to logg=5.00 for T= 57106 logg=4.30\n", + "Changing to T= 50000 for T= 66876 logg=4.31\n", + "Changing to logg=5.00 for T= 66876 logg=4.31\n", + "Changing to T= 50000 for T=103409 logg=5.41\n", + "Changing to logg=5.00 for T=103409 logg=5.41\n", + "Changing to T= 50000 for T= 72729 logg=4.45\n", + "Changing to logg=5.00 for T= 72729 logg=4.45\n", + "Changing to logg=3.00 for T= 23512 logg=2.25\n", + "Changing to T= 50000 for T= 74855 logg=4.49\n", + "Changing to logg=5.00 for T= 74855 logg=4.49\n", + "Changing to T= 50000 for T= 72709 logg=4.44\n", + "Changing to logg=5.00 for T= 72709 logg=4.44\n", + "Changing to T= 50000 for T= 50619 logg=4.06\n", + "Changing to logg=5.00 for T= 50619 logg=4.06\n", + "Changing to T= 50000 for T= 72056 logg=4.43\n", + "Changing to logg=5.00 for T= 72056 logg=4.43\n", + "Changing to logg=4.50 for T= 43710 logg=3.79\n", + "Changing to T= 50000 for T=101391 logg=5.41\n", + "Changing to logg=5.00 for T=101391 logg=5.41\n", + "Changing to T= 50000 for T= 66924 logg=4.31\n", + "Changing to logg=5.00 for T= 66924 logg=4.31\n", + "Changing to T= 50000 for T= 65516 logg=4.27\n", + "Changing to logg=5.00 for T= 65516 logg=4.27\n", + "Changing to logg=4.50 for T= 42028 logg=3.72\n", + "Changing to T= 50000 for T= 82065 logg=4.94\n", + "Changing to logg=5.00 for T= 82065 logg=4.94\n", + "Changing to T= 50000 for T=170279 logg=5.84\n", + "Changing to logg=5.00 for T=170279 logg=5.84\n", + "Changing to T= 50000 for T=101747 logg=5.38\n", + "Changing to logg=5.00 for T=101747 logg=5.38\n", + "Changing to T= 50000 for T= 68431 logg=4.34\n", + "Changing to logg=5.00 for T= 68431 logg=4.34\n", + "Changing to T= 50000 for T= 72644 logg=4.44\n", + "Changing to logg=5.00 for T= 72644 logg=4.44\n", + "Changing to T= 50000 for T= 61468 logg=4.45\n", + "Changing to logg=5.00 for T= 61468 logg=4.45\n", + "Changing to T= 50000 for T= 67391 logg=4.32\n", + "Changing to logg=5.00 for T= 67391 logg=4.32\n", + "Changing to T= 50000 for T= 67360 logg=4.33\n", + "Changing to logg=5.00 for T= 67360 logg=4.33\n", + "Changing to T= 50000 for T= 66968 logg=4.31\n", + "Changing to logg=5.00 for T= 66968 logg=4.31\n", + "Changing to T= 50000 for T= 64880 logg=4.26\n", + "Changing to logg=5.00 for T= 64880 logg=4.26\n", + "Changing to T= 50000 for T= 67191 logg=4.32\n", + "Changing to logg=5.00 for T= 67191 logg=4.32\n", + "Changing to T= 50000 for T=101962 logg=5.39\n", + "Changing to logg=5.00 for T=101962 logg=5.39\n", + "Changing to T= 50000 for T=103464 logg=5.41\n", + "Changing to logg=5.00 for T=103464 logg=5.41\n", + "Changing to T= 50000 for T= 67276 logg=4.32\n", + "Changing to logg=5.00 for T= 67276 logg=4.32\n", + "Changing to T= 50000 for T= 93233 logg=5.17\n", + "Changing to logg=5.00 for T= 93233 logg=5.17\n", + "Changing to T= 50000 for T= 66595 logg=4.30\n", + "Changing to logg=5.00 for T= 66595 logg=4.30\n", + "Changing to T= 50000 for T= 74182 logg=4.48\n", + "Changing to logg=5.00 for T= 74182 logg=4.48\n", + "Changing to T= 50000 for T= 71973 logg=4.43\n", + "Changing to logg=5.00 for T= 71973 logg=4.43\n", + "Changing to T= 50000 for T=102353 logg=5.41\n", + "Changing to logg=5.00 for T=102353 logg=5.41\n", + "Changing to T= 50000 for T= 52235 logg=4.13\n", + "Changing to logg=5.00 for T= 52235 logg=4.13\n", + "Changing to T= 50000 for T= 65822 logg=4.29\n", + "Changing to logg=5.00 for T= 65822 logg=4.29\n", + "Changing to T= 50000 for T= 65203 logg=4.27\n", + "Changing to logg=5.00 for T= 65203 logg=4.27\n", + "Changing to T= 50000 for T= 68099 logg=4.34\n", + "Changing to logg=5.00 for T= 68099 logg=4.34\n", + "Changing to T= 50000 for T= 61325 logg=4.18\n", + "Changing to logg=5.00 for T= 61325 logg=4.18\n", + "Changing to T= 50000 for T=171609 logg=5.85\n", + "Changing to logg=5.00 for T=171609 logg=5.85\n", + "Changing to T= 50000 for T= 72778 logg=4.45\n", + "Changing to logg=5.00 for T= 72778 logg=4.45\n", + "Changing to T= 50000 for T= 67324 logg=4.32\n", + "Changing to logg=5.00 for T= 67324 logg=4.32\n", + "Changing to T= 50000 for T= 69021 logg=4.36\n", + "Changing to logg=5.00 for T= 69021 logg=4.36\n", + "Changing to T= 50000 for T=101974 logg=5.40\n", + "Changing to logg=5.00 for T=101974 logg=5.40\n", + "Changing to T= 50000 for T= 64536 logg=4.26\n", + "Changing to logg=5.00 for T= 64536 logg=4.26\n", + "Changing to T= 50000 for T=176181 logg=5.90\n", + "Changing to logg=5.00 for T=176181 logg=5.90\n", + "Changing to T= 50000 for T= 74444 logg=4.49\n", + "Changing to logg=5.00 for T= 74444 logg=4.49\n", + "Changing to T= 50000 for T=164324 logg=5.78\n", + "Changing to logg=5.00 for T=164324 logg=5.78\n", + "Changing to T= 50000 for T=103719 logg=5.41\n", + "Changing to logg=5.00 for T=103719 logg=5.41\n", + "Changing to T= 50000 for T= 69390 logg=4.71\n", + "Changing to logg=5.00 for T= 69390 logg=4.71\n", + "Changing to T= 50000 for T= 71038 logg=4.43\n", + "Changing to logg=5.00 for T= 71038 logg=4.43\n", + "Changing to T= 50000 for T= 66434 logg=4.30\n", + "Changing to logg=5.00 for T= 66434 logg=4.30\n", + "Changing to T= 50000 for T= 75950 logg=4.93\n", + "Changing to logg=5.00 for T= 75950 logg=4.93\n", + "Changing to T= 50000 for T=101887 logg=5.41\n", + "Changing to logg=5.00 for T=101887 logg=5.41\n", + "Changing to logg=4.50 for T= 43082 logg=3.76\n", + "Changing to T= 50000 for T= 65498 logg=4.28\n", + "Changing to logg=5.00 for T= 65498 logg=4.28\n", + "Changing to T= 50000 for T=102419 logg=5.40\n", + "Changing to logg=5.00 for T=102419 logg=5.40\n", + "Changing to T= 50000 for T= 80053 logg=4.57\n", + "Changing to logg=5.00 for T= 80053 logg=4.57\n", + "Changing to T= 50000 for T=169602 logg=5.84\n", + "Changing to logg=5.00 for T=169602 logg=5.84\n", + "Changing to logg=4.00 for T= 32053 logg=3.87\n", + "Changing to T= 50000 for T=103359 logg=5.41\n", + "Changing to logg=5.00 for T=103359 logg=5.41\n", + "Changing to T= 50000 for T= 59860 logg=4.13\n", + "Changing to logg=5.00 for T= 59860 logg=4.13\n", + "Changing to T= 50000 for T= 66357 logg=4.29\n", + "Changing to logg=5.00 for T= 66357 logg=4.29\n", + "Changing to logg=4.00 for T= 34489 logg=3.74\n", + "Changing to T= 50000 for T=170381 logg=5.84\n", + "Changing to logg=5.00 for T=170381 logg=5.84\n", + "Changing to logg=4.00 for T= 32498 logg=3.50\n", + "Changing to T= 50000 for T= 66487 logg=4.30\n", + "Changing to logg=5.00 for T= 66487 logg=4.30\n", + "Changing to T= 50000 for T= 59709 logg=4.13\n", + "Changing to logg=5.00 for T= 59709 logg=4.13\n", + "Changing to T= 50000 for T= 62689 logg=4.49\n", + "Changing to logg=5.00 for T= 62689 logg=4.49\n", + "Changing to T= 50000 for T= 68000 logg=4.33\n", + "Changing to logg=5.00 for T= 68000 logg=4.33\n", + "Changing to logg=4.50 for T= 48500 logg=3.98\n", + "Changing to T= 50000 for T= 71723 logg=4.42\n", + "Changing to logg=5.00 for T= 71723 logg=4.42\n", + "Changing to T= 50000 for T= 73712 logg=4.47\n", + "Changing to logg=5.00 for T= 73712 logg=4.47\n", + "Changing to T= 50000 for T= 65990 logg=4.29\n", + "Changing to logg=5.00 for T= 65990 logg=4.29\n", + "Changing to T= 50000 for T= 74967 logg=4.50\n", + "Changing to logg=5.00 for T= 74967 logg=4.50\n", + "Changing to T= 50000 for T= 92342 logg=5.39\n", + "Changing to logg=5.00 for T= 92342 logg=5.39\n", + "Changing to T= 50000 for T=101007 logg=5.42\n", + "Changing to logg=5.00 for T=101007 logg=5.42\n", + "Changing to T= 50000 for T=103750 logg=5.41\n", + "Changing to logg=5.00 for T=103750 logg=5.41\n", + "Changing to T= 50000 for T= 68223 logg=4.34\n", + "Changing to logg=5.00 for T= 68223 logg=4.34\n", + "Changing to T= 50000 for T= 59073 logg=4.37\n", + "Changing to logg=5.00 for T= 59073 logg=4.37\n", + "Changing to T= 50000 for T=102806 logg=5.41\n", + "Changing to logg=5.00 for T=102806 logg=5.41\n", + "Changing to T= 50000 for T=132468 logg=5.53\n", + "Changing to logg=5.00 for T=132468 logg=5.53\n", + "Changing to logg=4.50 for T= 42780 logg=3.75\n", + "Changing to T= 50000 for T= 61518 logg=4.45\n", + "Changing to logg=5.00 for T= 61518 logg=4.45\n", + "Changing to T= 50000 for T=102197 logg=5.40\n", + "Changing to logg=5.00 for T=102197 logg=5.40\n", + "Changing to T= 50000 for T= 74305 logg=4.48\n", + "Changing to logg=5.00 for T= 74305 logg=4.48\n", + "Changing to T= 50000 for T= 65130 logg=4.27\n", + "Changing to logg=5.00 for T= 65130 logg=4.27\n", + "Changing to T= 50000 for T= 68970 logg=4.36\n", + "Changing to logg=5.00 for T= 68970 logg=4.36\n", + "Changing to T= 50000 for T= 69746 logg=4.38\n", + "Changing to logg=5.00 for T= 69746 logg=4.38\n", + "Changing to T= 50000 for T= 64874 logg=4.27\n", + "Changing to logg=5.00 for T= 64874 logg=4.27\n", + "Changing to T= 50000 for T= 54789 logg=4.22\n", + "Changing to logg=5.00 for T= 54789 logg=4.22\n", + "Changing to T= 50000 for T= 80822 logg=5.09\n", + "Changing to logg=5.00 for T= 80822 logg=5.09\n", + "Changing to logg=4.00 for T= 32240 logg=3.61\n", + "Changing to T= 50000 for T= 70824 logg=4.40\n", + "Changing to logg=5.00 for T= 70824 logg=4.40\n", + "Changing to T= 50000 for T= 74024 logg=4.48\n", + "Changing to logg=5.00 for T= 74024 logg=4.48\n", + "Changing to logg=4.50 for T= 46746 logg=3.91\n", + "Changing to T= 50000 for T= 92805 logg=5.39\n", + "Changing to logg=5.00 for T= 92805 logg=5.39\n", + "Changing to logg=4.50 for T= 46309 logg=3.90\n", + "Changing to T= 50000 for T= 78712 logg=5.02\n", + "Changing to logg=5.00 for T= 78712 logg=5.02\n", + "Changing to logg=4.00 for T= 31309 logg=3.80\n", + "Changing to T= 50000 for T= 56169 logg=4.27\n", + "Changing to logg=5.00 for T= 56169 logg=4.27\n", + "Changing to T= 50000 for T= 67593 logg=4.33\n", + "Changing to logg=5.00 for T= 67593 logg=4.33\n", + "Changing to T= 50000 for T= 74602 logg=4.49\n", + "Changing to logg=5.00 for T= 74602 logg=4.49\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 55242 logg=3.98\n", + "Changing to logg=5.00 for T= 55242 logg=3.98\n", + "Changing to logg=4.00 for T= 33232 logg=3.72\n", + "Changing to T= 50000 for T= 76516 logg=4.96\n", + "Changing to logg=5.00 for T= 76516 logg=4.96\n", + "Changing to T= 50000 for T= 65967 logg=4.28\n", + "Changing to logg=5.00 for T= 65967 logg=4.28\n", + "Changing to logg=4.00 for T= 34156 logg=3.75\n", + "Changing to T= 50000 for T= 72751 logg=4.45\n", + "Changing to logg=5.00 for T= 72751 logg=4.45\n", + "Changing to T= 50000 for T= 72197 logg=4.43\n", + "Changing to logg=5.00 for T= 72197 logg=4.43\n", + "Changing to logg=4.00 for T= 36458 logg=3.95\n", + "Changing to T= 50000 for T= 67178 logg=4.31\n", + "Changing to logg=5.00 for T= 67178 logg=4.31\n", + "Changing to T= 50000 for T=103863 logg=5.41\n", + "Changing to logg=5.00 for T=103863 logg=5.41\n", + "Changing to T= 50000 for T=103060 logg=5.41\n", + "Changing to logg=5.00 for T=103060 logg=5.41\n", + "Changing to logg=4.50 for T= 48012 logg=3.96\n", + "Changing to T= 50000 for T= 51387 logg=4.09\n", + "Changing to logg=5.00 for T= 51387 logg=4.09\n", + "Changing to T= 50000 for T=103870 logg=5.41\n", + "Changing to logg=5.00 for T=103870 logg=5.41\n", + "Changing to T= 50000 for T= 65184 logg=4.27\n", + "Changing to logg=5.00 for T= 65184 logg=4.27\n", + "Changing to T= 50000 for T= 94724 logg=5.40\n", + "Changing to logg=5.00 for T= 94724 logg=5.40\n", + "Changing to T= 50000 for T= 66243 logg=4.29\n", + "Changing to logg=5.00 for T= 66243 logg=4.29\n", + "Changing to T= 50000 for T=102009 logg=5.40\n", + "Changing to logg=5.00 for T=102009 logg=5.40\n", + "Changing to T= 50000 for T= 72942 logg=4.45\n", + "Changing to logg=5.00 for T= 72942 logg=4.45\n", + "Changing to T= 50000 for T= 67151 logg=4.32\n", + "Changing to logg=5.00 for T= 67151 logg=4.32\n", + "Changing to T= 50000 for T= 67344 logg=4.32\n", + "Changing to logg=5.00 for T= 67344 logg=4.32\n", + "Changing to T= 50000 for T= 69946 logg=4.39\n", + "Changing to logg=5.00 for T= 69946 logg=4.39\n", + "Changing to logg=4.50 for T= 45774 logg=3.87\n", + "Changing to T= 50000 for T= 67186 logg=4.31\n", + "Changing to logg=5.00 for T= 67186 logg=4.31\n", + "Changing to T= 50000 for T= 68127 logg=4.34\n", + "Changing to logg=5.00 for T= 68127 logg=4.34\n", + "Changing to logg=3.50 for T= 27299 logg=3.50\n", + "Changing to T= 50000 for T= 65605 logg=4.58\n", + "Changing to logg=5.00 for T= 65605 logg=4.58\n", + "Changing to T= 50000 for T= 68824 logg=4.36\n", + "Changing to logg=5.00 for T= 68824 logg=4.36\n", + "Changing to T= 50000 for T= 69684 logg=4.38\n", + "Changing to logg=5.00 for T= 69684 logg=4.38\n", + "Changing to T= 50000 for T=172675 logg=5.87\n", + "Changing to logg=5.00 for T=172675 logg=5.87\n", + "Changing to logg=5.00 for T= 49828 logg=4.04\n", + "Changing to T= 50000 for T= 67582 logg=4.33\n", + "Changing to logg=5.00 for T= 67582 logg=4.33\n", + "Changing to T= 50000 for T= 83603 logg=5.19\n", + "Changing to logg=5.00 for T= 83603 logg=5.19\n", + "Changing to T= 50000 for T=102922 logg=5.42\n", + "Changing to logg=5.00 for T=102922 logg=5.42\n", + "Changing to T= 50000 for T= 67806 logg=4.33\n", + "Changing to logg=5.00 for T= 67806 logg=4.33\n", + "Changing to T= 50000 for T= 67816 logg=4.33\n", + "Changing to logg=5.00 for T= 67816 logg=4.33\n", + "Changing to T= 50000 for T= 76206 logg=4.80\n", + "Changing to logg=5.00 for T= 76206 logg=4.80\n", + "Changing to T= 50000 for T=102440 logg=5.41\n", + "Changing to logg=5.00 for T=102440 logg=5.41\n", + "Changing to T= 50000 for T= 79382 logg=5.04\n", + "Changing to logg=5.00 for T= 79382 logg=5.04\n", + "Changing to T= 50000 for T= 95984 logg=4.82\n", + "Changing to logg=5.00 for T= 95984 logg=4.82\n", + "Changing to T= 50000 for T=109252 logg=5.51\n", + "Changing to logg=5.00 for T=109252 logg=5.51\n", + "Changing to T= 50000 for T= 69247 logg=4.37\n", + "Changing to logg=5.00 for T= 69247 logg=4.37\n", + "Changing to T= 50000 for T= 74302 logg=4.48\n", + "Changing to logg=5.00 for T= 74302 logg=4.48\n", + "Changing to T= 50000 for T= 69725 logg=4.38\n", + "Changing to logg=5.00 for T= 69725 logg=4.38\n", + "Changing to T= 50000 for T= 74425 logg=4.48\n", + "Changing to logg=5.00 for T= 74425 logg=4.48\n", + "Changing to T= 50000 for T= 65100 logg=4.26\n", + "Changing to logg=5.00 for T= 65100 logg=4.26\n", + "Changing to logg=4.50 for T= 42886 logg=3.75\n", + "Changing to logg=5.00 for T= 49043 logg=4.00\n", + "Changing to T= 50000 for T= 72790 logg=4.45\n", + "Changing to logg=5.00 for T= 72790 logg=4.45\n", + "Changing to T= 50000 for T=103548 logg=5.41\n", + "Changing to logg=5.00 for T=103548 logg=5.41\n", + "Changing to logg=4.00 for T= 34157 logg=3.71\n", + "Changing to logg=3.00 for T= 22218 logg=2.77\n", + "Changing to T= 50000 for T= 69174 logg=4.37\n", + "Changing to logg=5.00 for T= 69174 logg=4.37\n", + "Changing to T= 50000 for T= 77190 logg=4.51\n", + "Changing to logg=5.00 for T= 77190 logg=4.51\n", + "Changing to T= 50000 for T=102438 logg=5.42\n", + "Changing to logg=5.00 for T=102438 logg=5.42\n", + "Changing to T= 50000 for T= 65088 logg=4.27\n", + "Changing to logg=5.00 for T= 65088 logg=4.27\n", + "Changing to T= 50000 for T= 70940 logg=4.41\n", + "Changing to logg=5.00 for T= 70940 logg=4.41\n", + "Changing to T= 50000 for T=123259 logg=5.43\n", + "Changing to logg=5.00 for T=123259 logg=5.43\n", + "Changing to T= 50000 for T=101672 logg=5.40\n", + "Changing to logg=5.00 for T=101672 logg=5.40\n", + "Changing to logg=4.50 for T= 44957 logg=3.84\n", + "Changing to T= 50000 for T= 69287 logg=4.37\n", + "Changing to logg=5.00 for T= 69287 logg=4.37\n", + "Changing to logg=5.00 for T= 49567 logg=4.02\n", + "Changing to T= 50000 for T= 52571 logg=4.14\n", + "Changing to logg=5.00 for T= 52571 logg=4.14\n", + "Changing to T= 50000 for T=103024 logg=5.41\n", + "Changing to logg=5.00 for T=103024 logg=5.41\n", + "Changing to T= 50000 for T= 80581 logg=5.10\n", + "Changing to logg=5.00 for T= 80581 logg=5.10\n", + "Changing to T= 50000 for T=103153 logg=5.41\n", + "Changing to logg=5.00 for T=103153 logg=5.41\n", + "Changing to T= 50000 for T= 67123 logg=4.31\n", + "Changing to logg=5.00 for T= 67123 logg=4.31\n", + "Changing to T= 50000 for T=163298 logg=5.77\n", + "Changing to logg=5.00 for T=163298 logg=5.77\n", + "Changing to T= 50000 for T= 64892 logg=4.26\n", + "Changing to logg=5.00 for T= 64892 logg=4.26\n", + "Changing to T= 50000 for T=102638 logg=5.40\n", + "Changing to logg=5.00 for T=102638 logg=5.40\n", + "Changing to logg=4.50 for T= 40176 logg=3.63\n", + "Changing to T= 50000 for T=170781 logg=5.85\n", + "Changing to logg=5.00 for T=170781 logg=5.85\n", + "Changing to T= 50000 for T= 85743 logg=5.01\n", + "Changing to logg=5.00 for T= 85743 logg=5.01\n", + "Changing to T= 50000 for T=101838 logg=5.38\n", + "Changing to logg=5.00 for T=101838 logg=5.38\n", + "Changing to T= 50000 for T= 77477 logg=4.99\n", + "Changing to logg=5.00 for T= 77477 logg=4.99\n", + "Changing to T= 50000 for T= 71210 logg=4.41\n", + "Changing to logg=5.00 for T= 71210 logg=4.41\n", + "Changing to T= 50000 for T= 67205 logg=4.32\n", + "Changing to logg=5.00 for T= 67205 logg=4.32\n", + "Changing to T= 50000 for T= 66141 logg=4.29\n", + "Changing to logg=5.00 for T= 66141 logg=4.29\n", + "Changing to T= 50000 for T= 75507 logg=4.92\n", + "Changing to logg=5.00 for T= 75507 logg=4.92\n", + "Changing to T= 50000 for T= 74399 logg=4.48\n", + "Changing to logg=5.00 for T= 74399 logg=4.48\n", + "Changing to T= 50000 for T= 78170 logg=5.01\n", + "Changing to logg=5.00 for T= 78170 logg=5.01\n", + "Changing to T= 50000 for T= 74116 logg=4.48\n", + "Changing to logg=5.00 for T= 74116 logg=4.48\n", + "Changing to T= 50000 for T= 72404 logg=4.44\n", + "Changing to logg=5.00 for T= 72404 logg=4.44\n", + "Changing to T= 50000 for T=103588 logg=5.41\n", + "Changing to logg=5.00 for T=103588 logg=5.41\n", + "Changing to T= 50000 for T=103555 logg=5.41\n", + "Changing to logg=5.00 for T=103555 logg=5.41\n", + "Changing to T= 50000 for T= 70066 logg=4.39\n", + "Changing to logg=5.00 for T= 70066 logg=4.39\n", + "Changing to T= 50000 for T= 74521 logg=4.49\n", + "Changing to logg=5.00 for T= 74521 logg=4.49\n", + "Changing to T= 50000 for T=102537 logg=5.40\n", + "Changing to logg=5.00 for T=102537 logg=5.40\n", + "Changing to T= 50000 for T=100788 logg=5.42\n", + "Changing to logg=5.00 for T=100788 logg=5.42\n", + "Changing to T= 50000 for T=102054 logg=5.41\n", + "Changing to logg=5.00 for T=102054 logg=5.41\n", + "Changing to T= 50000 for T= 66874 logg=4.31\n", + "Changing to logg=5.00 for T= 66874 logg=4.31\n", + "Changing to T= 50000 for T= 84848 logg=4.70\n", + "Changing to logg=5.00 for T= 84848 logg=4.70\n", + "Changing to T= 50000 for T=102530 logg=5.42\n", + "Changing to logg=5.00 for T=102530 logg=5.42\n", + "Changing to T= 50000 for T= 66324 logg=4.31\n", + "Changing to logg=5.00 for T= 66324 logg=4.31\n", + "Changing to T= 50000 for T=102676 logg=5.41\n", + "Changing to logg=5.00 for T=102676 logg=5.41\n", + "Changing to logg=3.50 for T= 26974 logg=3.45\n", + "Changing to T= 50000 for T= 65151 logg=4.27\n", + "Changing to logg=5.00 for T= 65151 logg=4.27\n", + "Changing to T= 50000 for T= 71659 logg=4.42\n", + "Changing to logg=5.00 for T= 71659 logg=4.42\n", + "Changing to T= 50000 for T=101871 logg=5.40\n", + "Changing to logg=5.00 for T=101871 logg=5.40\n", + "Changing to T= 50000 for T= 67619 logg=4.34\n", + "Changing to logg=5.00 for T= 67619 logg=4.34\n", + "Changing to T= 50000 for T= 67596 logg=4.65\n", + "Changing to logg=5.00 for T= 67596 logg=4.65\n", + "Changing to T= 50000 for T= 67916 logg=4.33\n", + "Changing to logg=5.00 for T= 67916 logg=4.33\n", + "Changing to T= 50000 for T=103548 logg=5.46\n", + "Changing to logg=5.00 for T=103548 logg=5.46\n", + "Changing to T= 50000 for T= 74598 logg=4.49\n", + "Changing to logg=5.00 for T= 74598 logg=4.49\n", + "Changing to T= 50000 for T= 72382 logg=4.44\n", + "Changing to logg=5.00 for T= 72382 logg=4.44\n", + "Changing to T= 50000 for T= 68735 logg=4.35\n", + "Changing to logg=5.00 for T= 68735 logg=4.35\n", + "Changing to T= 50000 for T= 69107 logg=4.36\n", + "Changing to logg=5.00 for T= 69107 logg=4.36\n", + "Changing to T= 50000 for T= 65670 logg=4.28\n", + "Changing to logg=5.00 for T= 65670 logg=4.28\n", + "Changing to T= 50000 for T= 80553 logg=5.09\n", + "Changing to logg=5.00 for T= 80553 logg=5.09\n", + "Changing to T= 50000 for T=171475 logg=5.85\n", + "Changing to logg=5.00 for T=171475 logg=5.85\n", + "Changing to T= 50000 for T= 50852 logg=4.07\n", + "Changing to logg=5.00 for T= 50852 logg=4.07\n", + "Changing to T= 50000 for T= 71059 logg=4.41\n", + "Changing to logg=5.00 for T= 71059 logg=4.41\n", + "Changing to T= 50000 for T= 68997 logg=4.70\n", + "Changing to logg=5.00 for T= 68997 logg=4.70\n", + "Changing to logg=4.50 for T= 44431 logg=3.82\n", + "Changing to T= 50000 for T= 55505 logg=4.24\n", + "Changing to logg=5.00 for T= 55505 logg=4.24\n", + "Changing to T= 50000 for T=103295 logg=5.41\n", + "Changing to logg=5.00 for T=103295 logg=5.41\n", + "Changing to T= 50000 for T=102877 logg=5.42\n", + "Changing to logg=5.00 for T=102877 logg=5.42\n", + "Changing to T= 50000 for T= 69175 logg=4.37\n", + "Changing to logg=5.00 for T= 69175 logg=4.37\n", + "Changing to T= 50000 for T=102582 logg=5.41\n", + "Changing to logg=5.00 for T=102582 logg=5.41\n", + "Changing to T= 50000 for T=101384 logg=5.42\n", + "Changing to logg=5.00 for T=101384 logg=5.42\n", + "Changing to T= 50000 for T= 74005 logg=4.47\n", + "Changing to logg=5.00 for T= 74005 logg=4.47\n", + "Changing to T= 50000 for T= 67059 logg=4.31\n", + "Changing to logg=5.00 for T= 67059 logg=4.31\n", + "Changing to T= 50000 for T= 69217 logg=4.37\n", + "Changing to logg=5.00 for T= 69217 logg=4.37\n", + "Changing to T= 50000 for T= 79066 logg=4.86\n", + "Changing to logg=5.00 for T= 79066 logg=4.86\n", + "Changing to T= 50000 for T= 71685 logg=4.42\n", + "Changing to logg=5.00 for T= 71685 logg=4.42\n", + "Changing to logg=3.50 for T= 27049 logg=3.39\n", + "Changing to T= 50000 for T= 69250 logg=4.36\n", + "Changing to logg=5.00 for T= 69250 logg=4.36\n", + "Changing to T= 50000 for T=103183 logg=5.45\n", + "Changing to logg=5.00 for T=103183 logg=5.45\n", + "Changing to T= 50000 for T= 67126 logg=4.31\n", + "Changing to logg=5.00 for T= 67126 logg=4.31\n", + "Changing to T= 50000 for T= 68547 logg=4.68\n", + "Changing to logg=5.00 for T= 68547 logg=4.68\n", + "Changing to T= 50000 for T= 70317 logg=4.39\n", + "Changing to logg=5.00 for T= 70317 logg=4.39\n", + "Changing to T= 50000 for T=102702 logg=5.40\n", + "Changing to logg=5.00 for T=102702 logg=5.40\n", + "Changing to T= 50000 for T= 54474 logg=4.21\n", + "Changing to logg=5.00 for T= 54474 logg=4.21\n", + "Changing to T= 50000 for T= 96381 logg=5.41\n", + "Changing to logg=5.00 for T= 96381 logg=5.41\n", + "Changing to T= 50000 for T= 61844 logg=4.19\n", + "Changing to logg=5.00 for T= 61844 logg=4.19\n", + "Changing to T= 50000 for T= 68368 logg=4.68\n", + "Changing to logg=5.00 for T= 68368 logg=4.68\n", + "Changing to logg=4.50 for T= 46078 logg=3.89\n", + "Changing to T= 50000 for T=102160 logg=5.40\n", + "Changing to logg=5.00 for T=102160 logg=5.40\n", + "Changing to T= 50000 for T= 67265 logg=4.33\n", + "Changing to logg=5.00 for T= 67265 logg=4.33\n", + "Changing to T= 50000 for T= 69440 logg=4.37\n", + "Changing to logg=5.00 for T= 69440 logg=4.37\n", + "Changing to T= 50000 for T= 74307 logg=4.48\n", + "Changing to logg=5.00 for T= 74307 logg=4.48\n", + "Changing to T= 50000 for T=172330 logg=5.86\n", + "Changing to logg=5.00 for T=172330 logg=5.86\n", + "Changing to T= 50000 for T= 57361 logg=4.31\n", + "Changing to logg=5.00 for T= 57361 logg=4.31\n", + "Changing to T= 50000 for T= 98215 logg=4.84\n", + "Changing to logg=5.00 for T= 98215 logg=4.84\n", + "Changing to T= 50000 for T= 61608 logg=4.45\n", + "Changing to logg=5.00 for T= 61608 logg=4.45\n", + "Changing to T= 50000 for T= 84300 logg=4.58\n", + "Changing to logg=5.00 for T= 84300 logg=4.58\n", + "Changing to T= 50000 for T= 80369 logg=4.61\n", + "Changing to logg=5.00 for T= 80369 logg=4.61\n", + "Changing to logg=4.50 for T= 44419 logg=3.82\n", + "Changing to T= 50000 for T= 53145 logg=4.16\n", + "Changing to logg=5.00 for T= 53145 logg=4.16\n", + "Changing to T= 50000 for T= 74186 logg=4.48\n", + "Changing to logg=5.00 for T= 74186 logg=4.48\n", + "Changing to T= 50000 for T= 71793 logg=4.42\n", + "Changing to logg=5.00 for T= 71793 logg=4.42\n", + "Changing to T= 50000 for T= 70672 logg=4.40\n", + "Changing to logg=5.00 for T= 70672 logg=4.40\n", + "Changing to logg=3.50 for T= 26443 logg=3.24\n", + "Changing to logg=4.50 for T= 47335 logg=3.94\n", + "Changing to T= 50000 for T= 82465 logg=5.17\n", + "Changing to logg=5.00 for T= 82465 logg=5.17\n", + "Changing to T= 50000 for T= 68965 logg=4.36\n", + "Changing to logg=5.00 for T= 68965 logg=4.36\n", + "Changing to T= 50000 for T= 72869 logg=4.45\n", + "Changing to logg=5.00 for T= 72869 logg=4.45\n", + "Changing to logg=4.00 for T= 32875 logg=3.84\n", + "Changing to T= 50000 for T= 63810 logg=4.24\n", + "Changing to logg=5.00 for T= 63810 logg=4.24\n", + "Changing to T= 50000 for T= 83155 logg=4.66\n", + "Changing to logg=5.00 for T= 83155 logg=4.66\n", + "Changing to T= 50000 for T= 67786 logg=4.33\n", + "Changing to logg=5.00 for T= 67786 logg=4.33\n", + "Changing to T= 50000 for T= 87573 logg=4.75\n", + "Changing to logg=5.00 for T= 87573 logg=4.75\n", + "Changing to logg=3.50 for T= 27580 logg=3.14\n", + "Changing to T= 50000 for T=102783 logg=5.40\n", + "Changing to logg=5.00 for T=102783 logg=5.40\n", + "Changing to T= 50000 for T= 68979 logg=4.36\n", + "Changing to logg=5.00 for T= 68979 logg=4.36\n", + "Changing to logg=4.50 for T= 47669 logg=3.95\n", + "Changing to T= 50000 for T=103748 logg=5.41\n", + "Changing to logg=5.00 for T=103748 logg=5.41\n", + "Changing to logg=4.50 for T= 45540 logg=3.87\n", + "Changing to T= 50000 for T= 69632 logg=4.38\n", + "Changing to logg=5.00 for T= 69632 logg=4.38\n", + "Changing to T= 50000 for T=102771 logg=5.42\n", + "Changing to logg=5.00 for T=102771 logg=5.42\n", + "Changing to logg=4.00 for T= 35832 logg=3.76\n", + "Changing to T= 50000 for T= 65433 logg=4.28\n", + "Changing to logg=5.00 for T= 65433 logg=4.28\n", + "Changing to T= 50000 for T= 73748 logg=4.47\n", + "Changing to logg=5.00 for T= 73748 logg=4.47\n", + "Changing to T= 50000 for T= 70651 logg=4.75\n", + "Changing to logg=5.00 for T= 70651 logg=4.75\n", + "Changing to T= 50000 for T= 69859 logg=4.40\n", + "Changing to logg=5.00 for T= 69859 logg=4.40\n", + "Changing to T= 50000 for T=103443 logg=5.40\n", + "Changing to logg=5.00 for T=103443 logg=5.40\n", + "Changing to T= 50000 for T=103633 logg=5.41\n", + "Changing to logg=5.00 for T=103633 logg=5.41\n", + "Changing to T= 50000 for T= 54369 logg=4.20\n", + "Changing to logg=5.00 for T= 54369 logg=4.20\n", + "Changing to T= 50000 for T= 74466 logg=4.49\n", + "Changing to logg=5.00 for T= 74466 logg=4.49\n", + "Changing to T= 50000 for T=101506 logg=5.42\n", + "Changing to logg=5.00 for T=101506 logg=5.42\n", + "Changing to T= 50000 for T=132764 logg=5.53\n", + "Changing to logg=5.00 for T=132764 logg=5.53\n", + "Changing to T= 50000 for T= 71717 logg=4.43\n", + "Changing to logg=5.00 for T= 71717 logg=4.43\n", + "Changing to T= 50000 for T= 65890 logg=4.28\n", + "Changing to logg=5.00 for T= 65890 logg=4.28\n", + "Changing to T= 50000 for T= 72778 logg=4.45\n", + "Changing to logg=5.00 for T= 72778 logg=4.45\n", + "Changing to T= 50000 for T= 72500 logg=4.44\n", + "Changing to logg=5.00 for T= 72500 logg=4.44\n", + "Changing to T= 50000 for T=102183 logg=5.41\n", + "Changing to logg=5.00 for T=102183 logg=5.41\n", + "Changing to T= 50000 for T= 69325 logg=4.37\n", + "Changing to logg=5.00 for T= 69325 logg=4.37\n", + "Changing to T= 50000 for T= 81204 logg=4.62\n", + "Changing to logg=5.00 for T= 81204 logg=4.62\n", + "Changing to T= 50000 for T= 73497 logg=4.46\n", + "Changing to logg=5.00 for T= 73497 logg=4.46\n", + "Changing to logg=4.50 for T= 45606 logg=3.87\n", + "Changing to T= 50000 for T= 74686 logg=4.49\n", + "Changing to logg=5.00 for T= 74686 logg=4.49\n", + "Changing to T= 50000 for T= 79299 logg=5.03\n", + "Changing to logg=5.00 for T= 79299 logg=5.03\n", + "Changing to T= 50000 for T=102716 logg=5.41\n", + "Changing to logg=5.00 for T=102716 logg=5.41\n", + "Changing to T= 50000 for T= 66604 logg=4.62\n", + "Changing to logg=5.00 for T= 66604 logg=4.62\n", + "Changing to logg=4.00 for T= 35293 logg=3.85\n", + "Changing to T= 50000 for T= 69530 logg=4.37\n", + "Changing to logg=5.00 for T= 69530 logg=4.37\n", + "Changing to T= 50000 for T= 65121 logg=4.26\n", + "Changing to logg=5.00 for T= 65121 logg=4.26\n", + "Changing to T= 50000 for T= 85889 logg=5.01\n", + "Changing to logg=5.00 for T= 85889 logg=5.01\n", + "Changing to T= 50000 for T= 57582 logg=4.32\n", + "Changing to logg=5.00 for T= 57582 logg=4.32\n", + "Changing to T= 50000 for T= 79287 logg=4.87\n", + "Changing to logg=5.00 for T= 79287 logg=4.87\n", + "Changing to T= 50000 for T=172687 logg=5.87\n", + "Changing to logg=5.00 for T=172687 logg=5.87\n", + "Changing to T= 50000 for T= 72337 logg=4.44\n", + "Changing to logg=5.00 for T= 72337 logg=4.44\n", + "Changing to T= 50000 for T=103770 logg=5.41\n", + "Changing to logg=5.00 for T=103770 logg=5.41\n", + "Changing to T= 50000 for T= 68832 logg=4.36\n", + "Changing to logg=5.00 for T= 68832 logg=4.36\n", + "Changing to logg=3.50 for T= 27719 logg=3.22\n", + "Changing to T= 50000 for T= 61100 logg=4.37\n", + "Changing to logg=5.00 for T= 61100 logg=4.37\n", + "Changing to logg=5.00 for T= 49825 logg=4.03\n", + "Changing to logg=3.00 for T= 25019 logg=2.99\n", + "Changing to T= 50000 for T= 69868 logg=4.38\n", + "Changing to logg=5.00 for T= 69868 logg=4.38\n", + "Changing to T= 50000 for T= 67318 logg=4.32\n", + "Changing to logg=5.00 for T= 67318 logg=4.32\n", + "Changing to T= 50000 for T= 83506 logg=5.33\n", + "Changing to logg=5.00 for T= 83506 logg=5.33\n", + "Changing to T= 50000 for T= 67845 logg=4.33\n", + "Changing to logg=5.00 for T= 67845 logg=4.33\n", + "Changing to T= 50000 for T= 67217 logg=4.32\n", + "Changing to logg=5.00 for T= 67217 logg=4.32\n", + "Changing to T= 50000 for T= 69239 logg=4.36\n", + "Changing to logg=5.00 for T= 69239 logg=4.36\n", + "Changing to T= 50000 for T= 65169 logg=4.27\n", + "Changing to logg=5.00 for T= 65169 logg=4.27\n", + "Changing to T= 50000 for T= 70627 logg=4.40\n", + "Changing to logg=5.00 for T= 70627 logg=4.40\n", + "Changing to T= 50000 for T= 67075 logg=4.31\n", + "Changing to logg=5.00 for T= 67075 logg=4.31\n", + "Changing to T= 50000 for T=101719 logg=5.39\n", + "Changing to logg=5.00 for T=101719 logg=5.39\n", + "Changing to T= 50000 for T= 71038 logg=4.43\n", + "Changing to logg=5.00 for T= 71038 logg=4.43\n", + "Changing to T= 50000 for T=102948 logg=5.41\n", + "Changing to logg=5.00 for T=102948 logg=5.41\n", + "Changing to T= 50000 for T= 61191 logg=4.44\n", + "Changing to logg=5.00 for T= 61191 logg=4.44\n", + "Changing to T= 50000 for T=175890 logg=5.90\n", + "Changing to logg=5.00 for T=175890 logg=5.90\n", + "Changing to T= 50000 for T= 72061 logg=4.43\n", + "Changing to logg=5.00 for T= 72061 logg=4.43\n", + "Changing to T= 50000 for T= 66322 logg=4.29\n", + "Changing to logg=5.00 for T= 66322 logg=4.29\n", + "Changing to T= 50000 for T=101695 logg=5.38\n", + "Changing to logg=5.00 for T=101695 logg=5.38\n", + "Changing to T= 50000 for T= 67526 logg=4.32\n", + "Changing to logg=5.00 for T= 67526 logg=4.32\n", + "Changing to T= 50000 for T= 66161 logg=4.60\n", + "Changing to logg=5.00 for T= 66161 logg=4.60\n", + "Changing to T= 50000 for T= 67781 logg=4.34\n", + "Changing to logg=5.00 for T= 67781 logg=4.34\n", + "Changing to T= 50000 for T=101623 logg=5.42\n", + "Changing to logg=5.00 for T=101623 logg=5.42\n", + "Changing to logg=4.50 for T= 41893 logg=3.71\n", + "Changing to T= 50000 for T= 68096 logg=4.34\n", + "Changing to logg=5.00 for T= 68096 logg=4.34\n", + "Changing to T= 50000 for T= 75421 logg=4.51\n", + "Changing to logg=5.00 for T= 75421 logg=4.51\n", + "Changing to logg=4.50 for T= 44663 logg=3.83\n", + "Changing to T= 50000 for T= 74287 logg=4.48\n", + "Changing to logg=5.00 for T= 74287 logg=4.48\n", + "Changing to T= 50000 for T=102193 logg=5.41\n", + "Changing to logg=5.00 for T=102193 logg=5.41\n", + "Changing to T= 50000 for T= 76591 logg=4.95\n", + "Changing to logg=5.00 for T= 76591 logg=4.95\n", + "Changing to T= 50000 for T= 65654 logg=4.28\n", + "Changing to logg=5.00 for T= 65654 logg=4.28\n", + "Changing to T= 50000 for T=101513 logg=5.42\n", + "Changing to logg=5.00 for T=101513 logg=5.42\n", + "Changing to logg=3.00 for T= 21306 logg=2.99\n", + "Changing to T= 50000 for T=173760 logg=5.88\n", + "Changing to logg=5.00 for T=173760 logg=5.88\n", + "Changing to T= 50000 for T= 59627 logg=4.39\n", + "Changing to logg=5.00 for T= 59627 logg=4.39\n", + "Changing to T= 50000 for T= 66240 logg=4.30\n", + "Changing to logg=5.00 for T= 66240 logg=4.30\n", + "Changing to T= 50000 for T= 74659 logg=4.49\n", + "Changing to logg=5.00 for T= 74659 logg=4.49\n", + "Changing to T= 50000 for T= 68459 logg=4.35\n", + "Changing to logg=5.00 for T= 68459 logg=4.35\n", + "Changing to T= 50000 for T=102209 logg=5.40\n", + "Changing to logg=5.00 for T=102209 logg=5.40\n", + "Changing to T= 50000 for T= 70529 logg=4.34\n", + "Changing to logg=5.00 for T= 70529 logg=4.34\n", + "Changing to T= 50000 for T= 71453 logg=4.77\n", + "Changing to logg=5.00 for T= 71453 logg=4.77\n", + "Changing to T= 50000 for T= 67352 logg=4.32\n", + "Changing to logg=5.00 for T= 67352 logg=4.32\n", + "Changing to T= 50000 for T= 71702 logg=4.79\n", + "Changing to logg=5.00 for T= 71702 logg=4.79\n", + "Changing to T= 50000 for T= 70233 logg=4.39\n", + "Changing to logg=5.00 for T= 70233 logg=4.39\n", + "Changing to T= 50000 for T= 65749 logg=4.28\n", + "Changing to logg=5.00 for T= 65749 logg=4.28\n", + "Changing to logg=3.50 for T= 26165 logg=3.21\n", + "Changing to T= 50000 for T=103314 logg=5.40\n", + "Changing to logg=5.00 for T=103314 logg=5.40\n", + "Changing to T= 50000 for T= 66161 logg=4.29\n", + "Changing to logg=5.00 for T= 66161 logg=4.29\n", + "Changing to logg=2.00 for T= 11391 logg=0.99\n", + "Changing to logg=4.00 for T= 31789 logg=3.54\n", + "Changing to T= 50000 for T=102849 logg=5.40\n", + "Changing to logg=5.00 for T=102849 logg=5.40\n", + "Changing to T= 50000 for T=110499 logg=5.22\n", + "Changing to logg=5.00 for T=110499 logg=5.22\n", + "Changing to T= 50000 for T= 67353 logg=4.32\n", + "Changing to logg=5.00 for T= 67353 logg=4.32\n", + "Changing to T= 50000 for T=103302 logg=5.45\n", + "Changing to logg=5.00 for T=103302 logg=5.45\n", + "Changing to T= 50000 for T=102840 logg=5.41\n", + "Changing to logg=5.00 for T=102840 logg=5.41\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 72167 logg=4.44\n", + "Changing to logg=5.00 for T= 72167 logg=4.44\n", + "Changing to T= 50000 for T= 72257 logg=4.44\n", + "Changing to logg=5.00 for T= 72257 logg=4.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T= 70999 logg=4.40\n", + "Changing to logg=5.00 for T= 70999 logg=4.40\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 72382 logg=4.45\n", + "Changing to logg=5.00 for T= 72382 logg=4.45\n", + "Changing to T= 50000 for T=102464 logg=5.41\n", + "Changing to logg=5.00 for T=102464 logg=5.41\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 70881 logg=4.41\n", + "Changing to logg=5.00 for T= 70881 logg=4.41\n", + "Changing to T= 50000 for T= 69402 logg=4.37\n", + "Changing to logg=5.00 for T= 69402 logg=4.37\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 63378 logg=4.21\n", + "Changing to logg=5.00 for T= 63378 logg=4.21\n", + "Changing to logg=0.00 for T= 3545 logg=-0.23\n", + "Changing to T= 50000 for T=102099 logg=5.41\n", + "Changing to logg=5.00 for T=102099 logg=5.41\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T= 68910 logg=4.35\n", + "Changing to logg=5.00 for T= 68910 logg=4.35\n", + "Changing to T= 50000 for T= 72362 logg=4.44\n", + "Changing to logg=5.00 for T= 72362 logg=4.44\n", + "Changing to T= 50000 for T= 67408 logg=4.31\n", + "Changing to logg=5.00 for T= 67408 logg=4.31\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 71836 logg=4.43\n", + "Changing to logg=5.00 for T= 71836 logg=4.43\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T= 66501 logg=4.29\n", + "Changing to logg=5.00 for T= 66501 logg=4.29\n", + "Changing to T= 50000 for T= 66483 logg=4.28\n", + "Changing to logg=5.00 for T= 66483 logg=4.28\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 68919 logg=4.35\n", + "Changing to logg=5.00 for T= 68919 logg=4.35\n", + "Changing to T= 50000 for T= 66374 logg=4.28\n", + "Changing to logg=5.00 for T= 66374 logg=4.28\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 77947 logg=5.46\n", + "Changing to logg=5.00 for T= 77947 logg=5.46\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 50000 for T= 66185 logg=4.27\n", + "Changing to logg=5.00 for T= 66185 logg=4.27\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=104042 logg=5.41\n", + "Changing to logg=5.00 for T=104042 logg=5.41\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3285 logg=-0.77\n", + "Changing to T= 50000 for T=101067 logg=5.42\n", + "Changing to logg=5.00 for T=101067 logg=5.42\n", + "Changing to T= 50000 for T= 72280 logg=4.44\n", + "Changing to logg=5.00 for T= 72280 logg=4.44\n", + "Changing to logg=3.50 for T= 26699 logg=2.90\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=100228 logg=5.42\n", + "Changing to logg=5.00 for T=100228 logg=5.42\n", + "Changing to T= 50000 for T=100823 logg=5.41\n", + "Changing to logg=5.00 for T=100823 logg=5.41\n", + "Changing to T= 50000 for T= 84680 logg=5.43\n", + "Changing to logg=5.00 for T= 84680 logg=5.43\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 72399 logg=4.45\n", + "Changing to logg=5.00 for T= 72399 logg=4.45\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to logg=2.00 for T= 11252 logg=1.72\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 69785 logg=4.37\n", + "Changing to logg=5.00 for T= 69785 logg=4.37\n", + "Changing to T= 50000 for T= 66757 logg=4.29\n", + "Changing to logg=5.00 for T= 66757 logg=4.29\n", + "Changing to T= 50000 for T=102994 logg=5.41\n", + "Changing to logg=5.00 for T=102994 logg=5.41\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 87809 logg=5.45\n", + "Changing to logg=5.00 for T= 87809 logg=5.45\n", + "Changing to T= 50000 for T=100376 logg=5.42\n", + "Changing to logg=5.00 for T=100376 logg=5.42\n", + "Changing to logg=2.50 for T= 17098 logg=2.30\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 71110 logg=4.41\n", + "Changing to logg=5.00 for T= 71110 logg=4.41\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 72419 logg=4.45\n", + "Changing to logg=5.00 for T= 72419 logg=4.45\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 72509 logg=4.45\n", + "Changing to logg=5.00 for T= 72509 logg=4.45\n", + "Changing to T= 50000 for T=102558 logg=5.41\n", + "Changing to logg=5.00 for T=102558 logg=5.41\n", + "Changing to T= 50000 for T= 72552 logg=4.46\n", + "Changing to logg=5.00 for T= 72552 logg=4.46\n", + "Changing to logg=4.50 for T= 45560 logg=3.71\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 67791 logg=4.33\n", + "Changing to logg=5.00 for T= 67791 logg=4.33\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=101906 logg=5.42\n", + "Changing to logg=5.00 for T=101906 logg=5.42\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 67759 logg=4.33\n", + "Changing to logg=5.00 for T= 67759 logg=4.33\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3289 logg=-0.83\n", + "Changing to T= 50000 for T= 72205 logg=4.44\n", + "Changing to logg=5.00 for T= 72205 logg=4.44\n", + "Changing to T= 50000 for T= 67588 logg=4.31\n", + "Changing to logg=5.00 for T= 67588 logg=4.31\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 70651 logg=4.40\n", + "Changing to logg=5.00 for T= 70651 logg=4.40\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3337 logg=-0.66\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 72414 logg=4.45\n", + "Changing to logg=5.00 for T= 72414 logg=4.45\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T=101046 logg=5.41\n", + "Changing to logg=5.00 for T=101046 logg=5.41\n", + "Changing to T= 50000 for T= 73646 logg=5.52\n", + "Changing to logg=5.00 for T= 73646 logg=5.52\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=102908 logg=5.41\n", + "Changing to logg=5.00 for T=102908 logg=5.41\n", + "Changing to logg=2.00 for T= 9391 logg=1.55\n", + "Changing to T= 50000 for T=101233 logg=5.40\n", + "Changing to logg=5.00 for T=101233 logg=5.40\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 72171 logg=4.44\n", + "Changing to logg=5.00 for T= 72171 logg=4.44\n", + "Changing to T= 50000 for T= 67906 logg=4.33\n", + "Changing to logg=5.00 for T= 67906 logg=4.33\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T= 70351 logg=4.40\n", + "Changing to logg=5.00 for T= 70351 logg=4.40\n", + "Changing to T= 50000 for T= 72349 logg=4.44\n", + "Changing to logg=5.00 for T= 72349 logg=4.44\n", + "Changing to T= 50000 for T= 72304 logg=4.45\n", + "Changing to logg=5.00 for T= 72304 logg=4.45\n", + "Changing to T= 50000 for T= 67222 logg=4.30\n", + "Changing to logg=5.00 for T= 67222 logg=4.30\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 65076 logg=4.24\n", + "Changing to logg=5.00 for T= 65076 logg=4.24\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 61732 logg=4.16\n", + "Changing to logg=5.00 for T= 61732 logg=4.16\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 72302 logg=4.45\n", + "Changing to logg=5.00 for T= 72302 logg=4.45\n", + "Changing to T= 50000 for T=104937 logg=5.55\n", + "Changing to logg=5.00 for T=104937 logg=5.55\n", + "Changing to T= 50000 for T= 65399 logg=4.25\n", + "Changing to logg=5.00 for T= 65399 logg=4.25\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3299 logg=-0.75\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 70066 logg=4.37\n", + "Changing to logg=5.00 for T= 70066 logg=4.37\n", + "Changing to logg=0.00 for T= 3291 logg=-0.79\n", + "Changing to T= 50000 for T= 67298 logg=4.32\n", + "Changing to logg=5.00 for T= 67298 logg=4.32\n", + "Changing to T= 50000 for T= 72379 logg=4.45\n", + "Changing to logg=5.00 for T= 72379 logg=4.45\n", + "Changing to T= 50000 for T=101681 logg=5.41\n", + "Changing to logg=5.00 for T=101681 logg=5.41\n", + "Changing to T= 50000 for T= 76273 logg=5.44\n", + "Changing to logg=5.00 for T= 76273 logg=5.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T= 71010 logg=4.41\n", + "Changing to logg=5.00 for T= 71010 logg=4.41\n", + "Changing to T= 50000 for T= 70963 logg=4.40\n", + "Changing to logg=5.00 for T= 70963 logg=4.40\n", + "Changing to T= 50000 for T= 68456 logg=4.34\n", + "Changing to logg=5.00 for T= 68456 logg=4.34\n", + "Changing to T= 50000 for T= 72300 logg=4.43\n", + "Changing to logg=5.00 for T= 72300 logg=4.43\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T= 71000 logg=4.40\n", + "Changing to logg=5.00 for T= 71000 logg=4.40\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T=102712 logg=5.41\n", + "Changing to logg=5.00 for T=102712 logg=5.41\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to logg=2.00 for T= 9845 logg=1.65\n", + "Changing to T= 50000 for T= 70307 logg=4.39\n", + "Changing to logg=5.00 for T= 70307 logg=4.39\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 50000 for T= 81506 logg=5.45\n", + "Changing to logg=5.00 for T= 81506 logg=5.45\n", + "Changing to T= 50000 for T= 72029 logg=4.43\n", + "Changing to logg=5.00 for T= 72029 logg=4.43\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 72557 logg=4.46\n", + "Changing to logg=5.00 for T= 72557 logg=4.46\n", + "Changing to T= 50000 for T= 70265 logg=4.39\n", + "Changing to logg=5.00 for T= 70265 logg=4.39\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T= 70487 logg=4.40\n", + "Changing to logg=5.00 for T= 70487 logg=4.40\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=102341 logg=5.42\n", + "Changing to logg=5.00 for T=102341 logg=5.42\n", + "Changing to T= 50000 for T= 65896 logg=4.27\n", + "Changing to logg=5.00 for T= 65896 logg=4.27\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=3.00 for T= 20116 logg=2.54\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8927 logg=1.48\n", + "Changing to T= 50000 for T= 81495 logg=5.50\n", + "Changing to logg=5.00 for T= 81495 logg=5.50\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 50000 for T=100510 logg=5.42\n", + "Changing to logg=5.00 for T=100510 logg=5.42\n", + "Changing to logg=4.00 for T= 31386 logg=3.65\n", + "Changing to T= 50000 for T= 70306 logg=4.38\n", + "Changing to logg=5.00 for T= 70306 logg=4.38\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T= 87789 logg=5.30\n", + "Changing to logg=5.00 for T= 87789 logg=5.30\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T=100422 logg=5.42\n", + "Changing to logg=5.00 for T=100422 logg=5.42\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 68979 logg=4.35\n", + "Changing to logg=5.00 for T= 68979 logg=4.35\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 83774 logg=5.42\n", + "Changing to logg=5.00 for T= 83774 logg=5.42\n", + "Changing to T= 50000 for T= 68243 logg=4.33\n", + "Changing to logg=5.00 for T= 68243 logg=4.33\n", + "Changing to logg=0.00 for T= 3337 logg=-0.66\n", + "Changing to T= 50000 for T= 56673 logg=4.23\n", + "Changing to logg=5.00 for T= 56673 logg=4.23\n", + "Changing to T= 50000 for T= 63749 logg=4.21\n", + "Changing to logg=5.00 for T= 63749 logg=4.21\n", + "Changing to T= 50000 for T=102681 logg=5.41\n", + "Changing to logg=5.00 for T=102681 logg=5.41\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 69700 logg=4.38\n", + "Changing to logg=5.00 for T= 69700 logg=4.38\n", + "Changing to T= 50000 for T= 96627 logg=5.44\n", + "Changing to logg=5.00 for T= 96627 logg=5.44\n", + "Changing to T= 50000 for T= 93401 logg=5.43\n", + "Changing to logg=5.00 for T= 93401 logg=5.43\n", + "Changing to T= 50000 for T= 70002 logg=4.38\n", + "Changing to logg=5.00 for T= 70002 logg=4.38\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 61686 logg=4.15\n", + "Changing to logg=5.00 for T= 61686 logg=4.15\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T=101375 logg=5.40\n", + "Changing to logg=5.00 for T=101375 logg=5.40\n", + "Changing to T= 50000 for T= 69929 logg=4.39\n", + "Changing to logg=5.00 for T= 69929 logg=4.39\n", + "Changing to T= 50000 for T= 68126 logg=4.67\n", + "Changing to logg=5.00 for T= 68126 logg=4.67\n", + "Changing to T= 50000 for T= 99591 logg=5.45\n", + "Changing to logg=5.00 for T= 99591 logg=5.45\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 72354 logg=4.44\n", + "Changing to logg=5.00 for T= 72354 logg=4.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 54200 logg=3.98\n", + "Changing to logg=5.00 for T= 54200 logg=3.98\n", + "Changing to T= 50000 for T= 62987 logg=4.19\n", + "Changing to logg=5.00 for T= 62987 logg=4.19\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T=102887 logg=5.41\n", + "Changing to logg=5.00 for T=102887 logg=5.41\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=101904 logg=5.42\n", + "Changing to logg=5.00 for T=101904 logg=5.42\n", + "Changing to logg=4.00 for T= 35481 logg=3.37\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=2.00 for T= 9849 logg=1.64\n", + "Changing to logg=1.50 for T= 8783 logg=1.48\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 67894 logg=4.31\n", + "Changing to logg=5.00 for T= 67894 logg=4.31\n", + "Changing to T= 50000 for T= 68524 logg=4.35\n", + "Changing to logg=5.00 for T= 68524 logg=4.35\n", + "Changing to T= 50000 for T= 65306 logg=4.25\n", + "Changing to logg=5.00 for T= 65306 logg=4.25\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T= 95993 logg=5.47\n", + "Changing to logg=5.00 for T= 95993 logg=5.47\n", + "Changing to T= 50000 for T= 72205 logg=4.44\n", + "Changing to logg=5.00 for T= 72205 logg=4.44\n", + "Changing to T= 50000 for T= 66362 logg=4.28\n", + "Changing to logg=5.00 for T= 66362 logg=4.28\n", + "Changing to T= 50000 for T= 72364 logg=4.45\n", + "Changing to logg=5.00 for T= 72364 logg=4.45\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 72177 logg=4.44\n", + "Changing to logg=5.00 for T= 72177 logg=4.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=2.00 for T= 9842 logg=1.63\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T=101295 logg=5.42\n", + "Changing to logg=5.00 for T=101295 logg=5.42\n", + "Changing to T= 50000 for T= 69454 logg=4.37\n", + "Changing to logg=5.00 for T= 69454 logg=4.37\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 66211 logg=5.49\n", + "Changing to logg=5.00 for T= 66211 logg=5.49\n", + "Changing to T= 50000 for T= 69387 logg=4.36\n", + "Changing to logg=5.00 for T= 69387 logg=4.36\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T=101042 logg=5.42\n", + "Changing to logg=5.00 for T=101042 logg=5.42\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=101335 logg=5.42\n", + "Changing to logg=5.00 for T=101335 logg=5.42\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 70791 logg=4.41\n", + "Changing to logg=5.00 for T= 70791 logg=4.41\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 72300 logg=4.45\n", + "Changing to logg=5.00 for T= 72300 logg=4.45\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=3.00 for T= 19590 logg=2.49\n", + "Changing to logg=2.00 for T= 9832 logg=1.63\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 72322 logg=4.44\n", + "Changing to logg=5.00 for T= 72322 logg=4.44\n", + "Changing to T= 50000 for T= 68251 logg=4.33\n", + "Changing to logg=5.00 for T= 68251 logg=4.33\n", + "Changing to T= 50000 for T= 77780 logg=5.39\n", + "Changing to logg=5.00 for T= 77780 logg=5.39\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 50000 for T=101824 logg=5.41\n", + "Changing to logg=5.00 for T=101824 logg=5.41\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T= 71017 logg=4.41\n", + "Changing to logg=5.00 for T= 71017 logg=4.41\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 70317 logg=4.40\n", + "Changing to logg=5.00 for T= 70317 logg=4.40\n", + "Changing to T= 50000 for T= 72419 logg=4.45\n", + "Changing to logg=5.00 for T= 72419 logg=4.45\n", + "Changing to T= 50000 for T= 63596 logg=4.20\n", + "Changing to logg=5.00 for T= 63596 logg=4.20\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=2.00 for T= 9034 logg=1.64\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T=101478 logg=5.42\n", + "Changing to logg=5.00 for T=101478 logg=5.42\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 61937 logg=4.17\n", + "Changing to logg=5.00 for T= 61937 logg=4.17\n", + "Changing to T= 50000 for T=102393 logg=5.41\n", + "Changing to logg=5.00 for T=102393 logg=5.41\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 67861 logg=4.32\n", + "Changing to logg=5.00 for T= 67861 logg=4.32\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 66627 logg=4.29\n", + "Changing to logg=5.00 for T= 66627 logg=4.29\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 50000 for T= 63602 logg=4.20\n", + "Changing to logg=5.00 for T= 63602 logg=4.20\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T= 72134 logg=4.44\n", + "Changing to logg=5.00 for T= 72134 logg=4.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T=101226 logg=5.40\n", + "Changing to logg=5.00 for T=101226 logg=5.40\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T= 69480 logg=4.37\n", + "Changing to logg=5.00 for T= 69480 logg=4.37\n", + "Changing to T= 50000 for T= 64500 logg=4.23\n", + "Changing to logg=5.00 for T= 64500 logg=4.23\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T= 72096 logg=4.44\n", + "Changing to logg=5.00 for T= 72096 logg=4.44\n", + "Changing to T= 50000 for T= 99582 logg=5.43\n", + "Changing to logg=5.00 for T= 99582 logg=5.43\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T= 53224 logg=3.92\n", + "Changing to logg=5.00 for T= 53224 logg=3.92\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 64353 logg=4.22\n", + "Changing to logg=5.00 for T= 64353 logg=4.22\n", + "Changing to logg=0.00 for T= 3317 logg=-0.68\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to T= 50000 for T=102948 logg=5.41\n", + "Changing to logg=5.00 for T=102948 logg=5.41\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 86137 logg=5.29\n", + "Changing to logg=5.00 for T= 86137 logg=5.29\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 1200 for T= 808 logg=4.44\n", + "Changing to T= 50000 for T= 67602 logg=4.32\n", + "Changing to logg=5.00 for T= 67602 logg=4.32\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=0.00 for T= 3294 logg=-0.74\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to T= 50000 for T= 68173 logg=4.34\n", + "Changing to logg=5.00 for T= 68173 logg=4.34\n", + "Changing to T= 50000 for T=101167 logg=5.42\n", + "Changing to logg=5.00 for T=101167 logg=5.42\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 70474 logg=4.40\n", + "Changing to logg=5.00 for T= 70474 logg=4.40\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T= 70920 logg=4.41\n", + "Changing to logg=5.00 for T= 70920 logg=4.41\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T=132114 logg=5.77\n", + "Changing to logg=5.00 for T=132114 logg=5.77\n", + "Changing to T= 50000 for T= 72540 logg=4.46\n", + "Changing to logg=5.00 for T= 72540 logg=4.46\n", + "Changing to logg=2.00 for T= 9671 logg=1.71\n", + "Changing to logg=2.00 for T= 9787 logg=1.68\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=4.50 for T= 43064 logg=3.76\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=2.00 for T= 10354 logg=1.62\n", + "Changing to T= 50000 for T=100244 logg=5.42\n", + "Changing to logg=5.00 for T=100244 logg=5.42\n", + "Changing to T= 50000 for T= 82393 logg=5.42\n", + "Changing to logg=5.00 for T= 82393 logg=5.42\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 73592 logg=4.46\n", + "Changing to logg=5.00 for T= 73592 logg=4.46\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T= 72259 logg=4.44\n", + "Changing to logg=5.00 for T= 72259 logg=4.44\n", + "Changing to T= 50000 for T= 68454 logg=4.35\n", + "Changing to logg=5.00 for T= 68454 logg=4.35\n", + "Changing to T= 50000 for T= 66393 logg=4.29\n", + "Changing to logg=5.00 for T= 66393 logg=4.29\n", + "Changing to T= 50000 for T= 72375 logg=4.45\n", + "Changing to logg=5.00 for T= 72375 logg=4.45\n", + "Changing to T= 50000 for T= 72410 logg=4.44\n", + "Changing to logg=5.00 for T= 72410 logg=4.44\n", + "Changing to T= 50000 for T=100316 logg=5.42\n", + "Changing to logg=5.00 for T=100316 logg=5.42\n", + "Changing to T= 50000 for T= 57932 logg=4.06\n", + "Changing to logg=5.00 for T= 57932 logg=4.06\n", + "Changing to T= 50000 for T= 72417 logg=4.45\n", + "Changing to logg=5.00 for T= 72417 logg=4.45\n", + "Changing to T= 50000 for T= 67825 logg=4.33\n", + "Changing to logg=5.00 for T= 67825 logg=4.33\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to T= 50000 for T=101037 logg=5.40\n", + "Changing to logg=5.00 for T=101037 logg=5.40\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3330 logg=-0.71\n", + "Changing to T= 50000 for T= 62008 logg=4.16\n", + "Changing to logg=5.00 for T= 62008 logg=4.16\n", + "Changing to T= 50000 for T= 62552 logg=4.18\n", + "Changing to logg=5.00 for T= 62552 logg=4.18\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 63004 logg=4.19\n", + "Changing to logg=5.00 for T= 63004 logg=4.19\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 71333 logg=4.40\n", + "Changing to logg=5.00 for T= 71333 logg=4.40\n", + "Changing to T= 50000 for T= 71130 logg=4.41\n", + "Changing to logg=5.00 for T= 71130 logg=4.41\n", + "Changing to logg=2.00 for T= 9833 logg=1.68\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T=102747 logg=5.41\n", + "Changing to logg=5.00 for T=102747 logg=5.41\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 69322 logg=4.37\n", + "Changing to logg=5.00 for T= 69322 logg=4.37\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 72437 logg=4.45\n", + "Changing to logg=5.00 for T= 72437 logg=4.45\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=101606 logg=5.41\n", + "Changing to logg=5.00 for T=101606 logg=5.41\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T=101265 logg=5.40\n", + "Changing to logg=5.00 for T=101265 logg=5.40\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 63756 logg=4.21\n", + "Changing to logg=5.00 for T= 63756 logg=4.21\n", + "Changing to T= 50000 for T=101237 logg=5.41\n", + "Changing to logg=5.00 for T=101237 logg=5.41\n", + "Changing to T= 1200 for T= 692 logg=4.44\n", + "Changing to logg=2.00 for T= 9772 logg=1.67\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=2.00 for T= 9759 logg=1.72\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=0.00 for T= 3288 logg=-0.82\n", + "Changing to logg=5.00 for T= 1616 logg=4.44\n", + "Changing to T= 50000 for T= 62509 logg=4.18\n", + "Changing to logg=5.00 for T= 62509 logg=4.18\n", + "Changing to T= 50000 for T= 72345 logg=4.44\n", + "Changing to logg=5.00 for T= 72345 logg=4.44\n", + "Changing to T= 50000 for T= 67934 logg=4.33\n", + "Changing to logg=5.00 for T= 67934 logg=4.33\n", + "Changing to T= 50000 for T= 69696 logg=4.37\n", + "Changing to logg=5.00 for T= 69696 logg=4.37\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 73315 logg=4.45\n", + "Changing to logg=5.00 for T= 73315 logg=4.45\n", + "Changing to T= 50000 for T= 65349 logg=4.25\n", + "Changing to logg=5.00 for T= 65349 logg=4.25\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to logg=0.00 for T= 3554 logg=-0.25\n", + "Changing to logg=0.00 for T= 3317 logg=-0.70\n", + "Changing to logg=1.50 for T= 8770 logg=1.44\n", + "Changing to T= 50000 for T= 68454 logg=4.35\n", + "Changing to logg=5.00 for T= 68454 logg=4.35\n", + "Changing to T= 50000 for T= 95192 logg=5.08\n", + "Changing to logg=5.00 for T= 95192 logg=5.08\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 65770 logg=4.26\n", + "Changing to logg=5.00 for T= 65770 logg=4.26\n", + "Changing to T= 50000 for T= 72282 logg=4.44\n", + "Changing to logg=5.00 for T= 72282 logg=4.44\n", + "Changing to logg=0.00 for T= 3337 logg=-0.66\n", + "Changing to logg=0.00 for T= 3325 logg=-0.66\n", + "Changing to T= 50000 for T= 73018 logg=5.46\n", + "Changing to logg=5.00 for T= 73018 logg=5.46\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-08-13 12:08:41.330391 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6110 K m_ubv_U = 14.69\n", + "M = 5.300 Msun T = 16984 K m_ubv_U = 8.36\n", + "M = 17.000 Msun T = 24307 K m_ubv_U = 3.94\n", + "M = 18.000 Msun T = 71698 K m_ubv_U = 7.27\n", + "M = 1.400 Msun T = 6559 K m_ubv_U = 13.78\n", + "M = 6.310 Msun T = nan K m_ubv_U = nan\n", + "M = 19.000 Msun T = 28262 K m_ubv_U = 2.75\n", + "M = 12.000 Msun T = 25081 K m_ubv_U = 5.56\n", + "M = 0.251 Msun T = nan K m_ubv_U = nan\n", + "M = 19.000 Msun T = 102113 K m_ubv_U = 8.77\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 8.000 Msun T = 21305 K m_ubv_U = 7.05\n", + "M = 11.000 Msun T = 24577 K m_ubv_U = 5.95\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 13.000 Msun T = 26107 K m_ubv_U = 5.36\n", + "M = 15.000 Msun T = 26270 K m_ubv_U = 4.69\n", + "M = 17.000 Msun T = 24307 K m_ubv_U = 3.94\n", + "M = 14.000 Msun T = 26300 K m_ubv_U = 5.01\n", + "M = 8.500 Msun T = 22028 K m_ubv_U = 6.88\n", + "M = 3.162 Msun T = 2739 K m_ubv_U = 29.73\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_U = 15.00\n", + "M = 15.000 Msun T = 26270 K m_ubv_U = 4.69\n", + "M = 14.000 Msun T = 26300 K m_ubv_U = 5.01\n", + "M = 3.162 Msun T = 2739 K m_ubv_U = 29.73\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 20.000 Msun T = 65151 K m_ubv_U = 6.00\n", + "M = 12.000 Msun T = 25081 K m_ubv_U = 5.56\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 12.000 Msun T = 25081 K m_ubv_U = 5.56\n", + "M = 1.400 Msun T = 6559 K m_ubv_U = 13.78\n", + "M = 21.000 Msun T = 69632 K m_ubv_U = 6.23\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 3.162 Msun T = 2739 K m_ubv_U = 29.73\n", + "M = 0.600 Msun T = 5463 K m_ubv_U = 16.50\n", + "M = 5.012 Msun T = nan K m_ubv_U = nan\n", + "M = 17.000 Msun T = 24307 K m_ubv_U = 3.94\n", + "M = 5.012 Msun T = nan K m_ubv_U = nan\n", + "M = 13.600 Msun T = 28461 K m_ubv_U = 5.27\n", + "M = 16.200 Msun T = 26347 K m_ubv_U = 4.27\n", + "M = 0.280 Msun T = 1616 K m_ubv_U = 33.62\n", + "M = 15.000 Msun T = 25833 K m_ubv_U = 4.59\n", + "M = 11.400 Msun T = nan K m_ubv_U = nan\n", + "M = 2.400 Msun T = 10468 K m_ubv_U = 11.13\n", + "M = 1.400 Msun T = 6571 K m_ubv_U = 13.77\n", + "M = 17.100 Msun T = 24815 K m_ubv_U = 3.89\n", + "M = 12.000 Msun T = 25146 K m_ubv_U = 5.54\n", + "M = 2.400 Msun T = 10468 K m_ubv_U = 11.13\n", + "M = 1.100 Msun T = 5905 K m_ubv_U = 15.39\n", + "M = 16.000 Msun T = 25206 K m_ubv_U = 4.25\n", + "M = 5.200 Msun T = 17553 K m_ubv_U = 8.50\n", + "M = 3.000 Msun T = 12276 K m_ubv_U = 10.27\n", + "M = 3.400 Msun T = 13365 K m_ubv_U = 9.82\n", + "M = 8.400 Msun T = 23278 K m_ubv_U = 7.04\n", + "M = 5.100 Msun T = 17340 K m_ubv_U = 8.56\n", + "M = 11.000 Msun T = 24743 K m_ubv_U = 5.98\n", + "M = 18.000 Msun T = 8770 K m_ubv_U = 2.31\n", + "M = 19.000 Msun T = 102681 K m_ubv_U = 8.82\n", + "M = 11.000 Msun T = 24743 K m_ubv_U = 5.98\n", + "M = 1.400 Msun T = 6561 K m_ubv_U = 13.77\n", + "M = 1.080 Msun T = 5851 K m_ubv_U = 15.53\n", + "M = 13.500 Msun T = 28501 K m_ubv_U = 5.31\n", + "M = 11.200 Msun T = 26902 K m_ubv_U = 6.07\n", + "M = 15.000 Msun T = 25833 K m_ubv_U = 4.59\n", + "M = 1.500 Msun T = 6905 K m_ubv_U = 13.36\n", + "M = 12.000 Msun T = 27449 K m_ubv_U = 5.79\n", + "M = 10.800 Msun T = 26377 K m_ubv_U = 6.13\n", + "M = 1.500 Msun T = 6905 K m_ubv_U = 13.36\n", + "M = 19.000 Msun T = 101226 K m_ubv_U = 8.78\n", + "M = 3.600 Msun T = 13885 K m_ubv_U = 9.63\n", + "M = 0.840 Msun T = 4847 K m_ubv_U = 17.71\n", + "M = 6.300 Msun T = 19695 K m_ubv_U = 7.93\n", + "M = 13.000 Msun T = 26170 K m_ubv_U = 5.33\n", + "M = 18.000 Msun T = 8770 K m_ubv_U = 2.31\n", + "M = 0.360 Msun T = nan K m_ubv_U = nan\n", + "M = 21.000 Msun T = 3317 K m_ubv_U = 8.13\n", + "M = 15.300 Msun T = 28171 K m_ubv_U = 4.68\n", + "M = 16.000 Msun T = 25206 K m_ubv_U = 4.25\n", + "Starting filter: ubv,V Elapsed time: 12.40 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6110 K m_ubv_V = 14.10\n", + "M = 5.300 Msun T = 16984 K m_ubv_V = 9.18\n", + "M = 17.000 Msun T = 24307 K m_ubv_V = 5.13\n", + "M = 18.000 Msun T = 71698 K m_ubv_V = 8.72\n", + "M = 1.400 Msun T = 6559 K m_ubv_V = 13.37\n", + "M = 6.310 Msun T = nan K m_ubv_V = nan\n", + "M = 19.000 Msun T = 28262 K m_ubv_V = 4.03\n", + "M = 12.000 Msun T = 25081 K m_ubv_V = 6.74\n", + "M = 0.251 Msun T = nan K m_ubv_V = nan\n", + "M = 19.000 Msun T = 102113 K m_ubv_V = 10.21\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 8.000 Msun T = 21305 K m_ubv_V = 8.08\n", + "M = 11.000 Msun T = 24577 K m_ubv_V = 7.11\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 13.000 Msun T = 26107 K m_ubv_V = 6.56\n", + "M = 15.000 Msun T = 26270 K m_ubv_V = 5.91\n", + "M = 17.000 Msun T = 24307 K m_ubv_V = 5.13\n", + "M = 14.000 Msun T = 26300 K m_ubv_V = 6.23\n", + "M = 8.500 Msun T = 22028 K m_ubv_V = 7.94\n", + "M = 3.162 Msun T = 2739 K m_ubv_V = 26.84\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_V = 14.35\n", + "M = 15.000 Msun T = 26270 K m_ubv_V = 5.91\n", + "M = 14.000 Msun T = 26300 K m_ubv_V = 6.23\n", + "M = 3.162 Msun T = 2739 K m_ubv_V = 26.84\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 20.000 Msun T = 65151 K m_ubv_V = 7.44\n", + "M = 12.000 Msun T = 25081 K m_ubv_V = 6.74\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 12.000 Msun T = 25081 K m_ubv_V = 6.74\n", + "M = 1.400 Msun T = 6559 K m_ubv_V = 13.37\n", + "M = 21.000 Msun T = 69632 K m_ubv_V = 7.67\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 3.162 Msun T = 2739 K m_ubv_V = 26.84\n", + "M = 0.600 Msun T = 5463 K m_ubv_V = 15.45\n", + "M = 5.012 Msun T = nan K m_ubv_V = nan\n", + "M = 17.000 Msun T = 24307 K m_ubv_V = 5.13\n", + "M = 5.012 Msun T = nan K m_ubv_V = nan\n", + "M = 13.600 Msun T = 28461 K m_ubv_V = 6.53\n", + "M = 16.200 Msun T = 26347 K m_ubv_V = 5.50\n", + "M = 0.280 Msun T = 1616 K m_ubv_V = 29.23\n", + "M = 15.000 Msun T = 25833 K m_ubv_V = 5.80\n", + "M = 11.400 Msun T = nan K m_ubv_V = nan\n", + "M = 2.400 Msun T = 10468 K m_ubv_V = 11.28\n", + "M = 1.400 Msun T = 6571 K m_ubv_V = 13.36\n", + "M = 17.100 Msun T = 24815 K m_ubv_V = 5.09\n", + "M = 12.000 Msun T = 25146 K m_ubv_V = 6.72\n", + "M = 2.400 Msun T = 10468 K m_ubv_V = 11.28\n", + "M = 1.100 Msun T = 5905 K m_ubv_V = 14.69\n", + "M = 16.000 Msun T = 25206 K m_ubv_V = 5.45\n", + "M = 5.200 Msun T = 17553 K m_ubv_V = 9.35\n", + "M = 3.000 Msun T = 12276 K m_ubv_V = 10.69\n", + "M = 3.400 Msun T = 13365 K m_ubv_V = 10.37\n", + "M = 8.400 Msun T = 23278 K m_ubv_V = 8.14\n", + "M = 5.100 Msun T = 17340 K m_ubv_V = 9.39\n", + "M = 11.000 Msun T = 24743 K m_ubv_V = 7.14\n", + "M = 18.000 Msun T = 8770 K m_ubv_V = 2.39\n", + "M = 19.000 Msun T = 102681 K m_ubv_V = 10.26\n", + "M = 11.000 Msun T = 24743 K m_ubv_V = 7.14\n", + "M = 1.400 Msun T = 6561 K m_ubv_V = 13.36\n", + "M = 1.080 Msun T = 5851 K m_ubv_V = 14.79\n", + "M = 13.500 Msun T = 28501 K m_ubv_V = 6.58\n", + "M = 11.200 Msun T = 26902 K m_ubv_V = 7.28\n", + "M = 15.000 Msun T = 25833 K m_ubv_V = 5.80\n", + "M = 1.500 Msun T = 6905 K m_ubv_V = 13.02\n", + "M = 12.000 Msun T = 27449 K m_ubv_V = 7.02\n", + "M = 10.800 Msun T = 26377 K m_ubv_V = 7.33\n", + "M = 1.500 Msun T = 6905 K m_ubv_V = 13.02\n", + "M = 19.000 Msun T = 101226 K m_ubv_V = 10.23\n", + "M = 3.600 Msun T = 13885 K m_ubv_V = 10.23\n", + "M = 0.840 Msun T = 4847 K m_ubv_V = 16.05\n", + "M = 6.300 Msun T = 19695 K m_ubv_V = 8.88\n", + "M = 13.000 Msun T = 26170 K m_ubv_V = 6.54\n", + "M = 18.000 Msun T = 8770 K m_ubv_V = 2.39\n", + "M = 0.360 Msun T = nan K m_ubv_V = nan\n", + "M = 21.000 Msun T = 3317 K m_ubv_V = 5.03\n", + "M = 15.300 Msun T = 28171 K m_ubv_V = 5.95\n", + "M = 16.000 Msun T = 25206 K m_ubv_V = 5.45\n", + "Starting filter: ubv,B Elapsed time: 24.65 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6110 K m_ubv_B = 14.69\n", + "M = 5.300 Msun T = 16984 K m_ubv_B = 9.01\n", + "M = 17.000 Msun T = 24307 K m_ubv_B = 4.89\n", + "M = 18.000 Msun T = 71698 K m_ubv_B = 8.39\n", + "M = 1.400 Msun T = 6559 K m_ubv_B = 13.83\n", + "M = 6.310 Msun T = nan K m_ubv_B = nan\n", + "M = 19.000 Msun T = 28262 K m_ubv_B = 3.77\n", + "M = 12.000 Msun T = 25081 K m_ubv_B = 6.49\n", + "M = 0.251 Msun T = nan K m_ubv_B = nan\n", + "M = 19.000 Msun T = 102113 K m_ubv_B = 9.88\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 8.000 Msun T = 21305 K m_ubv_B = 7.86\n", + "M = 11.000 Msun T = 24577 K m_ubv_B = 6.87\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 13.000 Msun T = 26107 K m_ubv_B = 6.31\n", + "M = 15.000 Msun T = 26270 K m_ubv_B = 5.66\n", + "M = 17.000 Msun T = 24307 K m_ubv_B = 4.89\n", + "M = 14.000 Msun T = 26300 K m_ubv_B = 5.98\n", + "M = 8.500 Msun T = 22028 K m_ubv_B = 7.72\n", + "M = 3.162 Msun T = 2739 K m_ubv_B = 28.28\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_B = 14.98\n", + "M = 15.000 Msun T = 26270 K m_ubv_B = 5.66\n", + "M = 14.000 Msun T = 26300 K m_ubv_B = 5.98\n", + "M = 3.162 Msun T = 2739 K m_ubv_B = 28.28\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 20.000 Msun T = 65151 K m_ubv_B = 7.11\n", + "M = 12.000 Msun T = 25081 K m_ubv_B = 6.49\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 12.000 Msun T = 25081 K m_ubv_B = 6.49\n", + "M = 1.400 Msun T = 6559 K m_ubv_B = 13.83\n", + "M = 21.000 Msun T = 69632 K m_ubv_B = 7.34\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 3.162 Msun T = 2739 K m_ubv_B = 28.28\n", + "M = 0.600 Msun T = 5463 K m_ubv_B = 16.26\n", + "M = 5.012 Msun T = nan K m_ubv_B = nan\n", + "M = 17.000 Msun T = 24307 K m_ubv_B = 4.89\n", + "M = 5.012 Msun T = nan K m_ubv_B = nan\n", + "M = 13.600 Msun T = 28461 K m_ubv_B = 6.26\n", + "M = 16.200 Msun T = 26347 K m_ubv_B = 5.24\n", + "M = 0.280 Msun T = 1616 K m_ubv_B = 31.39\n", + "M = 15.000 Msun T = 25833 K m_ubv_B = 5.55\n", + "M = 11.400 Msun T = nan K m_ubv_B = nan\n", + "M = 2.400 Msun T = 10468 K m_ubv_B = 11.25\n", + "M = 1.400 Msun T = 6571 K m_ubv_B = 13.82\n", + "M = 17.100 Msun T = 24815 K m_ubv_B = 4.85\n", + "M = 12.000 Msun T = 25146 K m_ubv_B = 6.48\n", + "M = 2.400 Msun T = 10468 K m_ubv_B = 11.25\n", + "M = 1.100 Msun T = 5905 K m_ubv_B = 15.35\n", + "M = 16.000 Msun T = 25206 K m_ubv_B = 5.21\n", + "M = 5.200 Msun T = 17553 K m_ubv_B = 9.17\n", + "M = 3.000 Msun T = 12276 K m_ubv_B = 10.59\n", + "M = 3.400 Msun T = 13365 K m_ubv_B = 10.25\n", + "M = 8.400 Msun T = 23278 K m_ubv_B = 7.91\n", + "M = 5.100 Msun T = 17340 K m_ubv_B = 9.22\n", + "M = 11.000 Msun T = 24743 K m_ubv_B = 6.90\n", + "M = 18.000 Msun T = 8770 K m_ubv_B = 2.39\n", + "M = 19.000 Msun T = 102681 K m_ubv_B = 9.93\n", + "M = 11.000 Msun T = 24743 K m_ubv_B = 6.90\n", + "M = 1.400 Msun T = 6561 K m_ubv_B = 13.82\n", + "M = 1.080 Msun T = 5851 K m_ubv_B = 15.47\n", + "M = 13.500 Msun T = 28501 K m_ubv_B = 6.31\n", + "M = 11.200 Msun T = 26902 K m_ubv_B = 7.03\n", + "M = 15.000 Msun T = 25833 K m_ubv_B = 5.55\n", + "M = 1.500 Msun T = 6905 K m_ubv_B = 13.40\n", + "M = 12.000 Msun T = 27449 K m_ubv_B = 6.77\n", + "M = 10.800 Msun T = 26377 K m_ubv_B = 7.08\n", + "M = 1.500 Msun T = 6905 K m_ubv_B = 13.40\n", + "M = 19.000 Msun T = 101226 K m_ubv_B = 9.90\n", + "M = 3.600 Msun T = 13885 K m_ubv_B = 10.10\n", + "M = 0.840 Msun T = 4847 K m_ubv_B = 17.03\n", + "M = 6.300 Msun T = 19695 K m_ubv_B = 8.68\n", + "M = 13.000 Msun T = 26170 K m_ubv_B = 6.29\n", + "M = 18.000 Msun T = 8770 K m_ubv_B = 2.39\n", + "M = 0.360 Msun T = nan K m_ubv_B = nan\n", + "M = 21.000 Msun T = 3317 K m_ubv_B = 6.53\n", + "M = 15.300 Msun T = 28171 K m_ubv_B = 5.68\n", + "M = 16.000 Msun T = 25206 K m_ubv_B = 5.21\n", + "Starting filter: ubv,R Elapsed time: 36.83 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6110 K m_ubv_R = 13.80\n", + "M = 5.300 Msun T = 16984 K m_ubv_R = 9.24\n", + "M = 17.000 Msun T = 24307 K m_ubv_R = 5.22\n", + "M = 18.000 Msun T = 71698 K m_ubv_R = 8.85\n", + "M = 1.400 Msun T = 6559 K m_ubv_R = 13.12\n", + "M = 6.310 Msun T = nan K m_ubv_R = nan\n", + "M = 19.000 Msun T = 28262 K m_ubv_R = 4.14\n", + "M = 12.000 Msun T = 25081 K m_ubv_R = 6.83\n", + "M = 0.251 Msun T = nan K m_ubv_R = nan\n", + "M = 19.000 Msun T = 102113 K m_ubv_R = 10.35\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 8.000 Msun T = 21305 K m_ubv_R = 8.16\n", + "M = 11.000 Msun T = 24577 K m_ubv_R = 7.21\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 13.000 Msun T = 26107 K m_ubv_R = 6.66\n", + "M = 15.000 Msun T = 26270 K m_ubv_R = 6.01\n", + "M = 17.000 Msun T = 24307 K m_ubv_R = 5.22\n", + "M = 14.000 Msun T = 26300 K m_ubv_R = 6.33\n", + "M = 8.500 Msun T = 22028 K m_ubv_R = 8.03\n", + "M = 3.162 Msun T = 2739 K m_ubv_R = 25.46\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_R = 14.03\n", + "M = 15.000 Msun T = 26270 K m_ubv_R = 6.01\n", + "M = 14.000 Msun T = 26300 K m_ubv_R = 6.33\n", + "M = 3.162 Msun T = 2739 K m_ubv_R = 25.46\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 20.000 Msun T = 65151 K m_ubv_R = 7.58\n", + "M = 12.000 Msun T = 25081 K m_ubv_R = 6.83\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 12.000 Msun T = 25081 K m_ubv_R = 6.83\n", + "M = 1.400 Msun T = 6559 K m_ubv_R = 13.12\n", + "M = 21.000 Msun T = 69632 K m_ubv_R = 7.81\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 3.162 Msun T = 2739 K m_ubv_R = 25.46\n", + "M = 0.600 Msun T = 5463 K m_ubv_R = 15.06\n", + "M = 5.012 Msun T = nan K m_ubv_R = nan\n", + "M = 17.000 Msun T = 24307 K m_ubv_R = 5.22\n", + "M = 5.012 Msun T = nan K m_ubv_R = nan\n", + "M = 13.600 Msun T = 28461 K m_ubv_R = 6.64\n", + "M = 16.200 Msun T = 26347 K m_ubv_R = 5.60\n", + "M = 0.280 Msun T = 1616 K m_ubv_R = 26.30\n", + "M = 15.000 Msun T = 25833 K m_ubv_R = 5.90\n", + "M = 11.400 Msun T = nan K m_ubv_R = nan\n", + "M = 2.400 Msun T = 10468 K m_ubv_R = 11.30\n", + "M = 1.400 Msun T = 6571 K m_ubv_R = 13.12\n", + "M = 17.100 Msun T = 24815 K m_ubv_R = 5.19\n", + "M = 12.000 Msun T = 25146 K m_ubv_R = 6.82\n", + "M = 2.400 Msun T = 10468 K m_ubv_R = 11.30\n", + "M = 1.100 Msun T = 5905 K m_ubv_R = 14.37\n", + "M = 16.000 Msun T = 25206 K m_ubv_R = 5.55\n", + "M = 5.200 Msun T = 17553 K m_ubv_R = 9.41\n", + "M = 3.000 Msun T = 12276 K m_ubv_R = 10.72\n", + "M = 3.400 Msun T = 13365 K m_ubv_R = 10.41\n", + "M = 8.400 Msun T = 23278 K m_ubv_R = 8.23\n", + "M = 5.100 Msun T = 17340 K m_ubv_R = 9.46\n", + "M = 11.000 Msun T = 24743 K m_ubv_R = 7.23\n", + "M = 18.000 Msun T = 8770 K m_ubv_R = 2.35\n", + "M = 19.000 Msun T = 102681 K m_ubv_R = 10.40\n", + "M = 11.000 Msun T = 24743 K m_ubv_R = 7.23\n", + "M = 1.400 Msun T = 6561 K m_ubv_R = 13.11\n", + "M = 1.080 Msun T = 5851 K m_ubv_R = 14.46\n", + "M = 13.500 Msun T = 28501 K m_ubv_R = 6.69\n", + "M = 11.200 Msun T = 26902 K m_ubv_R = 7.38\n", + "M = 15.000 Msun T = 25833 K m_ubv_R = 5.90\n", + "M = 1.500 Msun T = 6905 K m_ubv_R = 12.81\n", + "M = 12.000 Msun T = 27449 K m_ubv_R = 7.13\n", + "M = 10.800 Msun T = 26377 K m_ubv_R = 7.43\n", + "M = 1.500 Msun T = 6905 K m_ubv_R = 12.81\n", + "M = 19.000 Msun T = 101226 K m_ubv_R = 10.36\n", + "M = 3.600 Msun T = 13885 K m_ubv_R = 10.27\n", + "M = 0.840 Msun T = 4847 K m_ubv_R = 15.53\n", + "M = 6.300 Msun T = 19695 K m_ubv_R = 8.96\n", + "M = 13.000 Msun T = 26170 K m_ubv_R = 6.64\n", + "M = 18.000 Msun T = 8770 K m_ubv_R = 2.35\n", + "M = 0.360 Msun T = nan K m_ubv_R = nan\n", + "M = 21.000 Msun T = 3317 K m_ubv_R = 3.89\n", + "M = 15.300 Msun T = 28171 K m_ubv_R = 6.05\n", + "M = 16.000 Msun T = 25206 K m_ubv_R = 5.55\n", + "Starting filter: ubv,I Elapsed time: 48.81 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6110 K m_ubv_I = 13.44\n", + "M = 5.300 Msun T = 16984 K m_ubv_I = 9.40\n", + "M = 17.000 Msun T = 24307 K m_ubv_I = 5.44\n", + "M = 18.000 Msun T = 71698 K m_ubv_I = 9.13\n", + "M = 1.400 Msun T = 6559 K m_ubv_I = 12.82\n", + "M = 6.310 Msun T = nan K m_ubv_I = nan\n", + "M = 19.000 Msun T = 28262 K m_ubv_I = 4.38\n", + "M = 12.000 Msun T = 25081 K m_ubv_I = 7.06\n", + "M = 0.251 Msun T = nan K m_ubv_I = nan\n", + "M = 19.000 Msun T = 102113 K m_ubv_I = 10.63\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 8.000 Msun T = 21305 K m_ubv_I = 8.37\n", + "M = 11.000 Msun T = 24577 K m_ubv_I = 7.43\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 13.000 Msun T = 26107 K m_ubv_I = 6.90\n", + "M = 15.000 Msun T = 26270 K m_ubv_I = 6.25\n", + "M = 17.000 Msun T = 24307 K m_ubv_I = 5.44\n", + "M = 14.000 Msun T = 26300 K m_ubv_I = 6.57\n", + "M = 8.500 Msun T = 22028 K m_ubv_I = 8.24\n", + "M = 3.162 Msun T = 2739 K m_ubv_I = 22.12\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 1.200 Msun T = 5987 K m_ubv_I = 13.65\n", + "M = 15.000 Msun T = 26270 K m_ubv_I = 6.25\n", + "M = 14.000 Msun T = 26300 K m_ubv_I = 6.57\n", + "M = 3.162 Msun T = 2739 K m_ubv_I = 22.12\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 20.000 Msun T = 65151 K m_ubv_I = 7.86\n", + "M = 12.000 Msun T = 25081 K m_ubv_I = 7.06\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 12.000 Msun T = 25081 K m_ubv_I = 7.06\n", + "M = 1.400 Msun T = 6559 K m_ubv_I = 12.82\n", + "M = 21.000 Msun T = 69632 K m_ubv_I = 8.09\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 3.162 Msun T = 2739 K m_ubv_I = 22.12\n", + "M = 0.600 Msun T = 5463 K m_ubv_I = 14.59\n", + "M = 5.012 Msun T = nan K m_ubv_I = nan\n", + "M = 17.000 Msun T = 24307 K m_ubv_I = 5.44\n", + "M = 5.012 Msun T = nan K m_ubv_I = nan\n", + "M = 13.600 Msun T = 28461 K m_ubv_I = 6.89\n", + "M = 16.200 Msun T = 26347 K m_ubv_I = 5.84\n", + "M = 0.280 Msun T = 1616 K m_ubv_I = 22.86\n", + "M = 15.000 Msun T = 25833 K m_ubv_I = 6.14\n", + "M = 11.400 Msun T = nan K m_ubv_I = nan\n", + "M = 2.400 Msun T = 10468 K m_ubv_I = 11.34\n", + "M = 1.400 Msun T = 6571 K m_ubv_I = 12.82\n", + "M = 17.100 Msun T = 24815 K m_ubv_I = 5.41\n", + "M = 12.000 Msun T = 25146 K m_ubv_I = 7.05\n", + "M = 2.400 Msun T = 10468 K m_ubv_I = 11.34\n", + "M = 1.100 Msun T = 5905 K m_ubv_I = 13.97\n", + "M = 16.000 Msun T = 25206 K m_ubv_I = 5.78\n", + "M = 5.200 Msun T = 17553 K m_ubv_I = 9.58\n", + "M = 3.000 Msun T = 12276 K m_ubv_I = 10.81\n", + "M = 3.400 Msun T = 13365 K m_ubv_I = 10.52\n", + "M = 8.400 Msun T = 23278 K m_ubv_I = 8.45\n", + "M = 5.100 Msun T = 17340 K m_ubv_I = 9.62\n", + "M = 11.000 Msun T = 24743 K m_ubv_I = 7.46\n", + "M = 18.000 Msun T = 8770 K m_ubv_I = 2.30\n", + "M = 19.000 Msun T = 102681 K m_ubv_I = 10.68\n", + "M = 11.000 Msun T = 24743 K m_ubv_I = 7.46\n", + "M = 1.400 Msun T = 6561 K m_ubv_I = 12.82\n", + "M = 1.080 Msun T = 5851 K m_ubv_I = 14.06\n", + "M = 13.500 Msun T = 28501 K m_ubv_I = 6.94\n", + "M = 11.200 Msun T = 26902 K m_ubv_I = 7.63\n", + "M = 15.000 Msun T = 25833 K m_ubv_I = 6.14\n", + "M = 1.500 Msun T = 6905 K m_ubv_I = 12.56\n", + "M = 12.000 Msun T = 27449 K m_ubv_I = 7.38\n", + "M = 10.800 Msun T = 26377 K m_ubv_I = 7.67\n", + "M = 1.500 Msun T = 6905 K m_ubv_I = 12.56\n", + "M = 19.000 Msun T = 101226 K m_ubv_I = 10.64\n", + "M = 3.600 Msun T = 13885 K m_ubv_I = 10.39\n", + "M = 0.840 Msun T = 4847 K m_ubv_I = 14.92\n", + "M = 6.300 Msun T = 19695 K m_ubv_I = 9.15\n", + "M = 13.000 Msun T = 26170 K m_ubv_I = 6.88\n", + "M = 18.000 Msun T = 8770 K m_ubv_I = 2.30\n", + "M = 0.360 Msun T = nan K m_ubv_I = nan\n", + "M = 21.000 Msun T = 3317 K m_ubv_I = 1.35\n", + "M = 15.300 Msun T = 28171 K m_ubv_I = 6.30\n", + "M = 16.000 Msun T = 25206 K m_ubv_I = 5.78\n", + " Time taken: 60.57 seconds\n", + "Isochrone generation took 568.174965 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "iso1=synthetic.Isochrone_Binary(7.0, 0.0, 1000,\n", + " math.log10(1), mass_sampling=1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Will check if all of the indices in the BPASS isochrone are valid, i.e. are 5, 101, 102, 103, or -99 (-99 is for merged))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x==102 or x==103) for x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x==102 or x==103) for x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == -99 or x==102 or x==103) for x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries and secondaries accounting for NaNs in the columns. (For max we make NaNs the same as - infinity and for median and mean, we do not include them)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.591806913365015" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.898517399939001" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.7676806790772" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.178243141326868" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.108995846829808" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.96292885544605" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.194452095013302" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.075886239648769" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/plain": [ + "4.239978886358815" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.secondaries['logg'][[np.where(~np.isnan(iso1.secondaries['logg']))]])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Distribution of logg values (secondaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Distribution of logg values (primaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Distribution of logg values single stars of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, for comparison, I will create a MISTv.1 isochrone of the same metallicity, age, distance from earth, and AKs." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=3.50 for T= 26068 logg=3.41\n", + "Changing to logg=3.50 for T= 26167 logg=3.41\n", + "Changing to logg=3.50 for T= 26263 logg=3.42\n", + "Changing to logg=3.50 for T= 26355 logg=3.42\n", + "Changing to logg=3.50 for T= 26443 logg=3.42\n", + "Changing to logg=3.50 for T= 26528 logg=3.43\n", + "Changing to logg=3.50 for T= 26609 logg=3.43\n", + "Changing to logg=3.50 for T= 26685 logg=3.43\n", + "Changing to logg=3.50 for T= 26758 logg=3.43\n", + "Changing to logg=3.50 for T= 26826 logg=3.44\n", + "Changing to logg=3.50 for T= 26889 logg=3.44\n", + "Changing to logg=3.50 for T= 26943 logg=3.44\n", + "Changing to logg=3.50 for T= 26985 logg=3.44\n", + "Changing to logg=3.50 for T= 27008 logg=3.45\n", + "Changing to logg=3.50 for T= 27006 logg=3.45\n", + "Changing to logg=3.50 for T= 26955 logg=3.45\n", + "Changing to logg=3.50 for T= 26861 logg=3.44\n", + "Changing to logg=3.50 for T= 26740 logg=3.44\n", + "Changing to logg=3.50 for T= 26600 logg=3.44\n", + "Changing to logg=3.50 for T= 26446 logg=3.43\n", + "Changing to logg=3.50 for T= 26289 logg=3.42\n", + "Changing to logg=3.50 for T= 26128 logg=3.41\n", + "Changing to logg=3.00 for T= 21082 logg=2.99\n", + "Changing to logg=3.00 for T= 20939 logg=2.98\n", + "Changing to logg=3.00 for T= 20797 logg=2.97\n", + "Changing to logg=3.00 for T= 20655 logg=2.96\n", + "Changing to logg=3.00 for T= 20513 logg=2.94\n", + "Changing to logg=3.00 for T= 20372 logg=2.93\n", + "Changing to logg=3.00 for T= 20231 logg=2.92\n", + "Changing to logg=3.00 for T= 20091 logg=2.91\n", + "Changing to logg=3.00 for T= 19951 logg=2.90\n", + "Changing to logg=3.00 for T= 19813 logg=2.88\n", + "Changing to logg=3.00 for T= 19675 logg=2.87\n", + "Changing to logg=3.00 for T= 19538 logg=2.86\n", + "Changing to logg=3.00 for T= 19401 logg=2.85\n", + "Changing to logg=3.00 for T= 19265 logg=2.83\n", + "Changing to logg=3.00 for T= 19131 logg=2.82\n", + "Changing to logg=2.50 for T= 15896 logg=2.49\n", + "Changing to logg=2.50 for T= 15781 logg=2.48\n", + "Changing to logg=2.50 for T= 15667 logg=2.47\n", + "Changing to logg=2.50 for T= 15554 logg=2.46\n", + "Changing to logg=2.50 for T= 15442 logg=2.44\n", + "Changing to logg=2.50 for T= 15330 logg=2.43\n", + "Changing to logg=2.50 for T= 15219 logg=2.42\n", + "Changing to logg=2.50 for T= 15109 logg=2.40\n", + "Changing to logg=2.50 for T= 14999 logg=2.39\n", + "Changing to logg=2.50 for T= 14891 logg=2.38\n", + "Changing to logg=2.50 for T= 14782 logg=2.37\n", + "Changing to logg=2.50 for T= 14675 logg=2.35\n", + "Changing to logg=2.50 for T= 14569 logg=2.34\n", + "Changing to logg=2.50 for T= 14463 logg=2.33\n", + "Changing to logg=2.50 for T= 14358 logg=2.31\n", + "Changing to logg=2.50 for T= 14254 logg=2.30\n", + "Changing to logg=2.50 for T= 14150 logg=2.29\n", + "Changing to logg=2.50 for T= 14048 logg=2.27\n", + "Changing to logg=2.50 for T= 13946 logg=2.26\n", + "Changing to logg=2.50 for T= 13844 logg=2.25\n", + "Changing to logg=2.50 for T= 13744 logg=2.23\n", + "Changing to logg=2.50 for T= 13644 logg=2.22\n", + "Changing to logg=2.50 for T= 13545 logg=2.21\n", + "Changing to logg=2.50 for T= 13447 logg=2.19\n", + "Changing to logg=2.50 for T= 13350 logg=2.18\n", + "Changing to logg=2.50 for T= 13253 logg=2.17\n", + "Changing to logg=2.50 for T= 13157 logg=2.16\n", + "Changing to logg=2.50 for T= 13061 logg=2.14\n", + "Changing to logg=2.50 for T= 12967 logg=2.13\n", + "Changing to logg=2.50 for T= 12873 logg=2.12\n", + "Changing to logg=2.50 for T= 12779 logg=2.10\n", + "Changing to logg=2.50 for T= 12687 logg=2.09\n", + "Changing to logg=2.50 for T= 12595 logg=2.08\n", + "Changing to logg=2.50 for T= 12504 logg=2.06\n", + "Changing to logg=2.50 for T= 12413 logg=2.05\n", + "Changing to logg=2.50 for T= 12323 logg=2.04\n", + "Changing to logg=2.50 for T= 12234 logg=2.02\n", + "Changing to logg=2.50 for T= 12145 logg=2.01\n", + "Changing to logg=2.50 for T= 12058 logg=2.00\n", + "Changing to logg=2.50 for T= 11970 logg=1.98\n", + "Changing to logg=2.50 for T= 11884 logg=1.97\n", + "Changing to logg=2.50 for T= 11798 logg=1.96\n", + "Changing to logg=2.00 for T= 11712 logg=1.94\n", + "Changing to logg=2.00 for T= 11628 logg=1.93\n", + "Changing to logg=2.00 for T= 11543 logg=1.92\n", + "Changing to logg=2.00 for T= 11460 logg=1.90\n", + "Changing to logg=2.00 for T= 11377 logg=1.89\n", + "Changing to logg=2.00 for T= 11295 logg=1.88\n", + "Changing to logg=2.00 for T= 11213 logg=1.86\n", + "Changing to logg=2.00 for T= 11132 logg=1.85\n", + "Changing to logg=2.00 for T= 11052 logg=1.84\n", + "Changing to logg=2.00 for T= 10972 logg=1.82\n", + "Changing to logg=2.00 for T= 10893 logg=1.81\n", + "Changing to logg=2.00 for T= 10814 logg=1.80\n", + "Changing to logg=2.00 for T= 10736 logg=1.78\n", + "Changing to logg=2.00 for T= 10658 logg=1.77\n", + "Changing to logg=2.00 for T= 10582 logg=1.76\n", + "Changing to logg=2.00 for T= 10505 logg=1.74\n", + "Changing to logg=2.00 for T= 10429 logg=1.73\n", + "Changing to logg=2.00 for T= 10354 logg=1.72\n", + "Changing to logg=2.00 for T= 10280 logg=1.71\n", + "Changing to logg=2.00 for T= 10206 logg=1.69\n", + "Changing to logg=2.00 for T= 10132 logg=1.68\n", + "Changing to logg=2.00 for T= 10059 logg=1.67\n", + "Changing to logg=2.00 for T= 9987 logg=1.65\n", + "Changing to logg=2.00 for T= 9915 logg=1.64\n", + "Changing to logg=2.00 for T= 9844 logg=1.63\n", + "Changing to logg=2.00 for T= 9773 logg=1.61\n", + "Changing to logg=2.00 for T= 9703 logg=1.60\n", + "Changing to logg=2.00 for T= 9634 logg=1.59\n", + "Changing to logg=2.00 for T= 9565 logg=1.58\n", + "Changing to logg=2.00 for T= 9496 logg=1.56\n", + "Changing to logg=2.00 for T= 9359 logg=1.54\n", + "Changing to logg=2.00 for T= 9225 logg=1.51\n", + "Changing to logg=2.00 for T= 9093 logg=1.49\n", + "Changing to logg=1.50 for T= 8963 logg=1.46\n", + "Changing to logg=1.50 for T= 8836 logg=1.44\n", + "Changing to logg=1.50 for T= 8711 logg=1.41\n", + "Changing to logg=1.50 for T= 8589 logg=1.39\n", + "Changing to logg=1.50 for T= 8468 logg=1.36\n", + "Changing to logg=1.50 for T= 8350 logg=1.34\n", + "Isochrone generation took 44.786056 s.\n", + "Making photometry for isochrone: log(t) = 7.00 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-08-13 12:10:29.305912 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_U = 24.31\n", + "M = 2.350 Msun T = 10414 K m_ubv_U = 11.18\n", + "M = 9.793 Msun T = 24007 K m_ubv_U = 6.48\n", + "M = 17.853 Msun T = 25534 K m_ubv_U = 3.82\n", + "M = 17.889 Msun T = 15781 K m_ubv_U = 2.83\n", + "M = 17.971 Msun T = 6489 K m_ubv_U = 2.91\n", + "Starting filter: ubv,B Elapsed time: 1.03 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_B = 23.44\n", + "M = 2.350 Msun T = 10414 K m_ubv_B = 11.29\n", + "M = 9.793 Msun T = 24007 K m_ubv_B = 7.37\n", + "M = 17.853 Msun T = 25534 K m_ubv_B = 4.80\n", + "M = 17.889 Msun T = 15781 K m_ubv_B = 3.55\n", + "M = 17.971 Msun T = 6489 K m_ubv_B = 2.59\n", + "Starting filter: ubv,V Elapsed time: 2.03 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_V = 22.45\n", + "M = 2.350 Msun T = 10414 K m_ubv_V = 11.32\n", + "M = 9.793 Msun T = 24007 K m_ubv_V = 7.61\n", + "M = 17.853 Msun T = 25534 K m_ubv_V = 5.04\n", + "M = 17.889 Msun T = 15781 K m_ubv_V = 3.70\n", + "M = 17.971 Msun T = 6489 K m_ubv_V = 2.21\n", + "Starting filter: ubv,R Elapsed time: 3.03 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_R = 21.45\n", + "M = 2.350 Msun T = 10414 K m_ubv_R = 11.34\n", + "M = 9.793 Msun T = 24007 K m_ubv_R = 7.70\n", + "M = 17.853 Msun T = 25534 K m_ubv_R = 5.13\n", + "M = 17.889 Msun T = 15781 K m_ubv_R = 3.75\n", + "M = 17.971 Msun T = 6489 K m_ubv_R = 2.01\n", + "Starting filter: ubv,I Elapsed time: 4.01 seconds\n", + "Starting synthetic photometry\n", + "M = 0.112 Msun T = 3025 K m_ubv_I = 18.66\n", + "M = 2.350 Msun T = 10414 K m_ubv_I = 11.38\n", + "M = 9.793 Msun T = 24007 K m_ubv_I = 7.93\n", + "M = 17.853 Msun T = 25534 K m_ubv_I = 5.36\n", + "M = 17.889 Msun T = 15781 K m_ubv_I = 3.88\n", + "M = 17.971 Msun T = 6489 K m_ubv_I = 1.72\n", + " Time taken: 4.98 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(7.0, 0.0, 1000,\n", + " math.log(1), recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding the max, median, and mean of the MISTv.1 isochrone's logg values" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.3301956912058674" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 60., 57., 78., 149., 210., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0.]),\n", + " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.xlabel(\"logg in cgs\")\n", + "plt.title(\"Histogram of logg values of stars in the MIST v1 isochrone\")\n", + "plt.hist(np.array([x for x in iso2.points['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.44315451781853" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.1148461826295915" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1, imf.IMFSalpeter1955(multiplicity=None),\n", + " 2000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2, imf.IMFSalpeter1955(multiplicity=None),\n", + " 2000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize the isochrone we have created so far with a color magnitude diagram. There we can see the end of main sequence and perhaps the beginnings of the subgiant branch. That turnoff will turn out to be quite important when using the clusters to find age of real stellar populations.\\n But first, let's look at the isochrones and plot the observers' and theorist's HR diagrams and juxtapose the evolution models.\n", + "\n", + "Now we try Taking a look at the BPASS Cluster vs MIST cluster observer's HR Diagram.\n", + "\n", + "Remember to account for distance modulus! (dist to cluster = 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(1000 / 10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(1000 / 10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"],\n", + " iso1.singles[\"m_ubv_V\"] - 5 * np.log10(1000 / 10),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(iso2.points['m_ubv_B'] - iso2.points[\"m_ubv_V\"],\n", + " iso2.points[\"m_ubv_V\"] - 5 * np.log10(1000 / 10), \"b.\",\n", + " label=\"MISTv1\", alpha=0.2)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at \\n\" +\n", + " \"solar metallicity and 10 million years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then compare with a Hoki cluster (Salpeter-slope IMF from 0.1 to 0.5 M⊙ and a maximum mass of 300 solar masses). First I overaly the cluster with the cluster, and, in the next cell, put the Hoki cluster by itself. This is to show that the photometry values of the stars in the BPASS isochrone are about appropriate for the age and metallicity." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/lu/scratch/ryotainagaki/hoki/hoki/cmd.py:196: RuntimeWarning: divide by zero encountered in log10\n", + " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n", + "/g/lu/scratch/ryotainagaki/hoki/hoki/cmd.py:212: RuntimeWarning: divide by zero encountered in log10\n", + " self._log_ages = np.concatenate((np.array([0]), np.log10(self._my_data[1,1:])))\n", + "/g/lu/scratch/ryotainagaki/hoki/hoki/cmd.py:348: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"Greys\").copy()\n", + " colMap.set_under(color='white')\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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aMXJeht+G4oMrLkiU4XJP7WJIW+gzmxwwb5KfNy2Oa/RKP+9PUT6HItwXtFXY9NB1WO3mhPvFrsDJdf0e1OO7ernfzlFRjn//gHUe8PMGh02b5adNClh+DHEEaFygVeCjKPPv8/MnBsz7QdC+qLxBzgtYJw33Q+hrICNgfif/vXoibFoGLge2JeJz7uS/Q58Q/OOih0/Hm3F8RtHuN0Gv3ARdp2m49i6Ku6+0DthGLvEF6NC9LqZ7bcD1c13ANRp4TVFzgD4whn3OjjyfYfOG+Hlzo6w73s+/OJbrry6v+hZxd/Hv22pY5jtgRcD0kf59iAQ/93agfx8IrAYO8/+/rsHFtAtxLZKjCY2V27WGZYJMUt9ITFzDhr1x9RbX4Ypkh6rqtwHrXebfFZcLWoEL1PeELXM20BG4V6s2NLsfd0OdErE8qroN+Im4xwJOAIbiipIP8a+LRGScho3Rq6orgMPEjek7FlekPAI4yr+m+GLUmj7HmjwMXAqsEjde8iJgqapGtvr8nn+vVqenqqUishh3MzgMlwsehruBLFbVnXGkZ3nAtFC9fE7AvGjXaE3p3SYi7+HO3wDcD6S6pCHe70GNxDVG/I1PV09czjZc74DVClX1kxjTGzoni6juNWJ7EiCktvtHxXc+YN6btexrWcC0A/0+PwaukuAnC3fjzjVQcV3eB1yDa3UcavR1Hu6Ji5nq78wR6nq/QVWjPvLo60f3iZhcn+v0SVwx713Ar1S1PN701iDovDSGh3HVQ2+JayC8AHhDVb+Mczuh72Z2lO9mN/8+MGBe0PUXt/oG6FDDsNZhf0faFOUCDn05L6xlH+39e7Z/j6xvDImsA4rUxr9HS2et/A+D9cANInIgLkd4Ke5Z60j7alhdahRT/PvsiP2sFJF3gMP9D4BqN3u/7Xv9CxHZG1ecdQoul3NowDrLCQsc4p4NfwD3S/Fa4Fe1pDea/4cr1voZ8Hv/KhWRF4Bfh938Q59htHqp0PROEe8b40zP9oBpoZt50HOX0a7ReNNbQYPr+4LSEO/3ICoRGYG7SWcAr+Dq34pwucJDcb/4WwWsGpTWaOmN+j1U1TIR2Ro5vQbh948g9dlX0P0gdK4PwF3v0USe65nAH3F1q6EAPQVXH35/lG3U+34Thzpfp7jAXQo8l8Dg/DXucdS9cdUTjUpVn/JtXX6NuydNBfD31D+o6ssxbip0vRznX9EEfTdri0cxqW9HJZv8e5calon2KyqUuxqiqlLD64GI5QNb7QJ7RZkeEkrjphqXil2oAVdcHaCEiHscKrTuUonoFAWXy4XKIF4j/+vwbNxNY4iI1PSZhNZZBkzz/wY1noqJuhaZd6jqENzn8xPcY3en4hpShIJC6DOM9ln1jFhuu38PyvUlUm3XaKzprYt4vwc1uQoXGI5X1R+q6q9U9RpVvY7qDQ7rm96gznDSqfleUIX/EVNcwzpF9dhX0GcaSvu8Ws71vhHp3IhrWHaUiAwMaxw2T6M3Ek30/aYm9blOx+JKMJ6T2p+GiVWoxCNRvamVEz0z2Slooqo+r6pH40p/jsFVFR0E/EdEBsW439D5uqyW62VSwLoJKT2ob4AOFQvW5VGCN/37kTEu/55/HyXBvc+MqWX9/lQ2ZkqEULFfXc9hKPAuxDVOCXrtBs4RkVpzT94e4mtBC7DDvyekJzFV3aSqT6nqmbjc3H64mxlUfoZjItcT18PaKP9vqMXyMtyX8ygRaZeI9MWppvR2wuVKvwPW1GMf8X4ParI/UKCqCwPm1VT9E4/QZxO0vSOJv1TuQ6CniHQMmFfxnQ+YN6IO+1qL+9E3wrfmjscM/z6Fyu/uvTUsH7onvh/nfuqiztepr/4ajSuSf0pETktAemb69ykS5THIsPQFlehE2gb0iPKZDa1pRVXdqaqvqurluEa+WUD4I4eh6tKgmJLI72ad1DdAL/TvI+qw7v24L8u1EtANp4ikhT/b5nOIL+N6v5oWsex4argBici+uF/hC6MUZcbFP/oT+tW0sA7rt8EVj5fhemCbHPTC1Q+1xz0rioi0E5Gra7jof+WXX62qW/06R/hn8tpELuwv+N/5fxfHexx+G63EPX8pEdMzcY82gGs0Aq5FfAHuR0fkNfMrXLHYfPWdofjcyWO4HMDfJKLrRBFpL3V79jZWc3CP8VwqIvtHzLsR135gjtbvUZG4vge1yAM6+9KZ8G38HNdeIRFm+/crRST0+SIirQmu6qnNQtx9KKgk6sGwfVV8zuKeJf9TvDtS1VJcXWtP4M4o34meUXJYrwAf4dqfnIlr2Laght2Fru+alkmUel2nqroGV9SdD8yVgL4L4qGqb+Cq2brgStAOiFzGX9fnALGMk7AM92Ms8vHEC3CNBSO3fUzQZ0tlScyusGmhapK+kQv7KsHXgB+LSGAfGiJysLj+KRpEfeugX8XdXE7AFa/FTFW3isjpuKLQN0XkFWAVLsfUF1dB34Wq9VOX4B7luF3cQ+If4HINP8IVQZ0SZXehB8qfjCeN3mlS2U9rqJHYKT5tbxPRiCtGZ+GKZp5T1a9qWO4+3IP8U3AXfCauV7BrRWQZ7tf5Nlwg/AFwMO45yF+EbaMXLghMF5HXcQ2NvsPdpMbhisU+8dutiza4R5TyROQtXB19a1ydzUBcJzZrAFT1W3+hzwUW+QZlX+CK84/H1dtMjdj+NFwO/BfAGBH5H66UYF/cdXcqdfiRFAtVzRORX+GexX1XRJ7APVs8Gnd9rqXyB05d91GX70E0t+POyes+rYW4HMYo3ON59e5SUVXfEJG7cG0vVorIv6l8Dnob0etBo3kSV1d4Au46Ct/XIhGZibv+V4nIk35fp+CO7SvceYrHjbg2F7/APR/8Kq6NQ3dc3fQPcK2iqzTIU1UVkXuAv/tJNeWewV3P2wlouJVoibhOVfUTETkSl96HRaSVqj5Y0zq1uASXAfkFsEZEFuLu13twVVZH4+6lsYzfcBcuON8tbhCSDbjP8Pu4jp1Ojlj+ViDX7zMPd7843O9zPe5Hf8gruEaVs/y1/C2wXVVDPTn+FHdO/ikiv8RVFW33aT8Ed28aSUNVZWg9m4FT+RjIwIB5edTyyAWu1e50XMvK73D1Tmtxv6xOC1h+f9yHuh0XjJYCJ1HZvP2CgHWW+BOYFcdxzSb48YYi3C+63xD8SEIeNTxX55d5wy9zagzpWOeXPQyX0xiHu0m8hbtBleCKqVfgbtC5Eet3wOXA7/fLbME1Cinw5+X3xPhsuN/eQsIes8L9aPgt7jG2L/xnuBlXPPSLoHOOa509zy9X7Ne7m+iP67XD3TRX4H797sDdQG8Hugd8ZtXOPXV4hCNsmeNxz+Bvw91gPsF1htKptvMTMa+mazSXOL4HNaT1ZH/ud+C+Iy/hckeB+67p+Al4hMVPF9wPpzX+fHyFCw7ZsZzPgP2867cR9PhkGq4R4tqAfe0A3o/1HEek/zzczbnAX4MbcXWnfwT6RFkvBxd0vgO61LD9A30abo/zPGi0ayfi84p2jdf7OsUFz7X+OC8MuzaVGB+zitjecFx13Ue44LcHF2Dn4Uoi0sKWHUPAd9TPG4Ur5dvlvxvP4wJktWvUb/dR3HfpW7/8SlyHSt0Ctn152LWskdcv7h76R1zfBt/iqh4/92mYQtgjoLFcf/G8xG+0znzuci3uUaHL6rWxBuCL+z4ArlbV/0t2eowxVfmizkeAH6vqvBjXOQB3039MVc9pyPSF7XMMrsh6jqqeV8Nyt+J+wAxU1c8aI22meap3gAYQkb/girz2V9fiMWWIyNO44o0DNaC/cGNMcvn2C0tx1SWHathNSVwf15s07BEgEWmLqyY5EThLVZ9opHS+gGtgNEJVA1vFi+tP/lNghqpe0RjpMs1XIkazAteD1U5cUUjKBGj/RX4PV9RkwdmYFKSqKiJTcJ1L9KLqPeRXuEaFC3H123vhHpvZG1etMrch0yYiB+OqDQ7HBef/RAvOXi6ul8E7GjJdpmVISA7aGGMagm8UdAXuUaHOuPYTH+GKxG9XNxRiQ+7/Alz7jSLgf7huHbc05D6NCbEAbYwxxqSg+j4HbYwxxpgGYAHaGGOMSUEWoE0FERnj+wK/LmL6Qt8/eJ2WNS1TqlwL0a5VU3e+d0L1dfTh0/PEjbhV67Jx7q/adluCFhWgE3GhBGxzoIhcLyLPiMgXUjngRY0t5EWks4jc7i+8PSLylYj8S9yoVMYYkzAiMtvfl3KTnZZEaYj7eapJ1GNWLdkJuLFiy6jsBarGbhnFjTS1BNfj0Ku4rucG4LqzO0lERiapg4NluO4569NKdSLQNjHJMcY0A/NwvdvF2w1suESNjNWkWICuvxdxnSysUNXdEjygeqQ/4YLzbepGWQHA9/V6B27knHENk9zoVHUXrle4+mzjiwQlxxjTDKhqIfUbkhVV/TRByWlSmnQRt4gcLiJ3iMgHIlIgIt+JyMcicqu4EafCl11I5eDq90vV8Zdz65oGVV2nqm/F2hGKuGETz8N17HJtxOzpuL52TxCRfjFuL8+/2ovIbSKyQUR2i8j74oeOE5EMEfmjPzfficinIjItYFv1rquLVu8obvSaX4jI2yLyrYjs9H9fJBGjVPnl1W+rq4jMFJGvfVXAKhEJGn81KC3p/nwUSZQhO0Vkut/XTyKmD/DFghv8fvNF5BER6R+wjQNF5BYRWS4im/3y6326q1VZhJ9ncaONPe+v34prUUQOEZFHw6pANovIu75aJKahEn0R4JMi8pm/JopE5A0RmRBl+YWh6pmw62WPPwd/FjeKVNB6Z4vIO34fm0TkIRHpFUsaI7YT8zGLSLaI3Cwi6/w1vU1E/icix8axv5jvH375iiJVERnnz1dh0PUesG6oiHlfEZkmIqv9/vL8uRa/3Bkissx/Pzb56zOwRC7Wa9Sn73z/7+dSed/Lq+u5iIfUUBQtInuLyJ1SeW8q8Md/dcRyeRHpXUgN93Nx30cVkYlR0nS4n/9cfY6toTX1HPSFuJGsFuFGwkkHvofr/PyHIjJcVUPjHc/GDR4wHniGquO0bm+U1DojcV0avhSWNgBUtVxEXsJ1wD4WiLWYOxM3FGdn3LFl4QbIeFLcqF8X4zqtfxHXIfwZwF0isllVH6//IcXkIdzIMBtwo3Qp7rObgesI/9yAdTrhBhYpxg2Q0ho3ItO/RKRcVR+oaYeqWiYis4DrcedjVvh8qRz28xvg2bDp44CncOf1OdygA3vjero6SUTGquq7YZv6MW5gkAW4qoti3ODwk3EjJg2N0gXuSOAPuEEa/gV0BYrF9R//lj9Hz+I65u+IGyjmYtzIcbF00HE3blCRxbjixS647jEfEpH+qnp1lPUewY2B+yKug44TcQOidKf6kH//Dzd4y3bc8JDbcdU+S4gj1xTPMYsb4/gNYBBuRLnbcefuTOAlEblIVWsbbQriu3+EOx1XwvUibjS73FiPE/gbblCI53ADW5yKG8QhS0QKgFtww7K+hhsR7hKfrovCNxLnNXo9cBpuBKg7qLzfba/cYp3PRZ2JyFBc5y+dcdfoU7jqsUG4QTBurGH12dR8P78HN6DRVCqHLQ0XGjUvluskeRIx4kayXrii5KARcH6O+6L/LmL6BSRwpJEoacrz+8iIMv8SP/+uKPOv8PP/HOf+ngNahU0/0k8vwN3EOoXN64cLIu9FbGsMwSM+LSRi5Js4lz3HL/su0D5sejtguZ/304h1QqOH3Rf+GeO+vKW4Ma9jOT89cTf25QHzQtfDTWHTcnCjAW0BBkUsfxBuNJt3I6b3Dj/3YdOPx7VNuDvKuVNgasB6t/p54wPm5RA2AlAtx75fwLQs3ChOJUDvoM8ON2pP54jP6RN/LHuFTc/F/eArIGx0JVzJ3JOhY4wxrTEfM+6mqv5dwqYfgPtRsCciPdGu1breP8qBcbEcV9i6s/26eeHnHfcjdAuuRG0zYaMCAq1wP7D2UHXUtrpco6H950ZJX0LupQSMZha0rL8OPyfgu+/n96nLdiPm/8fPPzhienvcSGhfBB1zKr2adBG3qq5X1bKAWf/C/fJP1CD1iZTt36PlLkLTO8W53V9p2IDsqvoa7guQg/tybQ+b9xkuB3KwiKTHuZ+6CA12/ntV/TYsHTupHKd2csB6u4DLwz9jVV2NS/tAEelQ245V9WtcjuRwETk8YvZU3M02PGc9EXfur/X7Ct/WKr/sYSIyKGz6xvBzHzb9JdzYztGuw/e15pxetWoTVd2mYQNH1EQD6u1UtRg3XGMG0Rve/E5VC8LW2Qk8jAu8Q8OWOxd3o71LVfPCli/H5V7iHasZajlmX9Q9AReE/qD+juuX+xi406cpsGgzYrt1vX88o6r/rW37UdyoYaUp/nv5LC7neLf6sdP9vD3A47jjGRi2jbiv0dok4V56Cu4H3rOq+khAejYkYB93+/cpEdPPxQXp+6Icc8po0kXc/ss6FTgbl7PKpmq9eu9kpKuexL/H8/zo9qCbMW7s3H1xOaJIG3HFWHvR8AOcfA93s14YMG8RLmd2WMC8j1W1KGB66MvbCfdLuDYzcMWSU/FfVnGDIIwAXgwPLrhiZ4AhElwXf6B/H4jL3YRGYzoX94t+CO5HUfgPn+Io6VoWZfrjwGXA0+IGkZ8PvBHlM45KRPrifgAdA/TFVa2Ei/b9WB4wLXTOw+sjv+ffF0UurKqficgGam8wGRLrMQ/ABbM3wn9EhHkVVxwedD1VUY/7R7TPLRZB5/Yr/x7tewqu+Dok7mu0Nkm4l47w7y8meLvhXsRlUs4Tkd+pawQL7h5QhiudS2lNOkDjvtQ/wtXVPoOrSwzlZH6FKyJKNaEccnaU+R0jlotnm5FKoaIVZeA8XB1WQ8sGCnzurQpVLRWRLbj6zUjbo2wvlPaYcv+qukBE1uBGRfq1urq0aHVQXfz7hbVsNrzR2d9x19vXuDq1jVTmBC8gepD6Jkp6l4nIkcCVuB8W5wGIyDrgelV9tJa0Ia6R4TJcQH0NV99ZiLsx5eIaDQV+P8JLW8IEnfPQNZwfJRnfEGOAjuOYQ/uM9shOaHqnGHZb1/tH4OcWo5q+i7F+T+tyjdamse+lnfx7g2UO1LXpuRdXr38WrjHZ4bgflk+r6lc1biAFNNkA7RsY/Aj3S/tEDRvVRlyr4N8mK221WOffD4wy/wD//lEjpKWxFAKdRSRTI0YfEtehS1dcMVpDugfXQOZcEXkAl+PdiKunikwrwBBVXVHbRkWkO/BLYCXwfY1oSCMi59SwetRSElVdCpwsIq1wQx2Ow425/ohv3De/lqRdjruRT1LV2QFpOj9opTiFzlUPXFF+pL3i2ViMxxzaZ7Rt94xIW6B63j/iKd1qCHFdo7VJ0r10u39v6FLOf+EayU3FtfpuGo3DvKZcB72/f3828qYPHEH14jxwuQeIMefVQN7E5a5+EFmH6r8Mx/t/FzR2whrQe7hr7aiAeUfhPo93A+Yl0gO4hjhTcb+mOwH/DKiDetO/Hxnjdvvhjq1aq3xxj1jF9LhcNKq6R1WXqOo1uB8C4Fqu1ib0/XgyYN7o+qQpTOgzq7Y9n4PvU5eN1nLM63BtEw6N8vjP2Ii0RVOX+0eqiPcahZrvfck4F6Fj+GE9tlHr/VxVN+OeABkuIj/ANVjNw5UopbymHKDz/PuY8Ik+R/OPKOts9e99GyZJtfONpB7CtYy9LmL2NFzx4/80OT2JNZR/+febRaSilzH/9y3+3382ZAJ8Mf+juHGF/4/odVD3437dXysiR0TOFPc895iwSXn+fVR4gztxz13Pog6lVCJypIgEVYH08O+7AuZFCqVrTMS2TyC4QV5dPIxrDX6phPUl4H9o/pU47i+xHrOvJnkYV3x7Q8Q29sMF9BLcd6wmef59TMQ2arp/pIp4r1Go+d6X59+rrNPA5+I5v99Tg0qZRCSWnHWs9/NQY7HHcdfNzFgbWiZbky3ixj069AbwYxFZgnuWtAfuF9k6KhtehFuK+6L/SkQ6U1l3dleUetpaiUhX3LONIV39+z+lsgODW1Q1vIeuP+K+DJeLyKFUdrE5HtiEexSr2VDVR0RkPO451VUi8jSumPA0XCO2J1T14UZIygxccOoNPBfUUlRVt4rI6fjuCUXkFVzxbTnuRjASV3Tc2i//jYg8hmtc875/jj0b9wzrd7jnMw+NM52/Bo73nTF8hmuxfBDu2t4GzIzxWCcBc0XkSVxx/mBcsfETuFKEelHVPBH5Pe4RqfdE5HFc8esJuBKKFcAhMW4unmP+PS73OE1EhuFKm0LPQXcApqnq57Xsry73j5QQ7zXqvYJrWT/LN8L7Fte4dDpJOBeqWiwiZ+Byso+IyFRcrro17l54DLXHp5ju56r6hoh8gGvAWUJlhiH11ecZrWS/cA+4z8D9EvsO+BTXjWZbAp6b8+uMw32w31L5LGpuPdKQG7adaK8xUdJ+B7Ae18r3a9yFs3ec+w88Tj9vIVGeQyXguUga6DloPz0N1+HEctyXaheu1eolBDzX67e9MNa0x3G+3vPrnhTD5zqdyv7Vi3DdoD4EnBaxbFtcZxOf+GU34HIeXeI5d2Hzj8flklbjAt5O3I3yTmCfOI71+7hWzdtwrd1fx/0oiuuz8/MuIMozp7hiw3f9sW8G5gC9atpefY8Z9wPgz/7z2YPLUb4MHB+wbLTjjev+UdM5iOH4ol6zuJK0aPeJms57zNeoX/5yYI0/Xxp+fIk6F/GeN9wPihm41tbFuFzxW8CVtW3XT4/pfo57QkCBufF+dsl8iU+8Mc2er/P/Ctexxr7aRIq5jDH1IyKzcQ0jj1XVV5KcnJg15TpoY+J1Ea4OaoYFZ2NaBhHpg6uCWoMrUWoymnIdtDG18g2PLsLVO1+Iq0qYkdREGWManIj8FPc469m457iv1iZWZGxF3KZZ862LP8fVu70DXKpVB7owxjRDvsHhUbg2Ibep6u1JTVAdWIA2xhhjUpDVQRtjjDEpyOqgE6Rr166am5ub7GQYY0zSvfPOO1tUtVuy09HUWYBOkNzcXJYvDxqoxhhjWhYRWZ/sNDQHVsRtjDHGpCAL0MYYY0wKsgBtjDHGpCAL0MYYY0wKsgBtjDHGpCAL0MYYY0wKssesjGkgRUVFbNq0iZKSkmQnxZiEyMzMpHv37nTs2DHZSWkRLEAb0wCKiorIz8+nd+/etGnTBhFJdpKMqRdVZffu3WzcuBHAgnQjsCJuYxrApk2b6N27N23btrXgbJoFEaFt27b07t2bTZs2JTs5LYIFaGMaQElJCW3atEl2Mupl9uzZiEjFKz09nd69e3PmmWeybt26Gpft0KEDQ4YMYfr06ZSWllZZdvfu3WRnZyMifPDBB4H7Lioq4tprr2XQoEG0a9eOnJwcDj74YKZOnVolOOzZs4fbbruNIUOG0KFDBzp27MiAAQM4//zz+fjjj2s8vuuuuy5pP54WLlyIiDB//vyk7L8+2rRpY9U2jcSKuI1pIM0l5zx37lz23ntvysrK+PTTT7nxxhs55phjWLVqFdnZ2YHLFhUVMXfuXC699FI2bdrEDTfcULHMU089RVFREQAPPvggt956a5VtlJWVceyxx5KXl8fvfvc7Dj30UHbu3MnKlSt59NFH+eqrr+jevTsA55xzDi+99BK//e1vGTFiBGVlZaxZs4a5c+eyevVqDjjggKjHNXnyZMaNG5eo09RiNJfruklQVXsl4HX44YerMSGrV69OdhLq7f7771dAP/744yrTX375ZQX0hRdeqHXZMWPGaIcOHapMO+6447Rz5846fPhw7dGjh5aWllaZ/8orryigTz/9dGC6ysrKVFX1008/VUBvv/32GpdLRQsWLFBAX3755bjXLS0t1ZKSkgZIVexqu76B5ZoC9+Wm/rIibmNMXEKNg2Ip5hw2bBg7duyoKJbeuHEjr7zyCmeffTaTJ08mPz+f//3vf1XWKSgoAGCvvfYK3GZaWlpcy0UTVMR9xx13MHDgQNq0aUNOTg5Dhw5l3rx5FfNVldtuu43+/fuTlZVFz549mTZtWkWJQEhpaSl//vOfGTRoEK1bt6Zbt26MGzeOtWvXVllu165dTJs2ja5du9KtWzcmTJjA9u3bqywjIlx55ZXccsst7LvvvmRlZfHhhx8CMGfOHIYMGULr1q3p2rUr5513Hl9//XWV9XNzc5kwYQKPPfYYAwcOpF27dgwdOpTXX3+92jlZtGgRxxxzDB06dKBdu3accMIJrFy5ssbzaBqOBWhjTI3KysooLS1lz549rFmzhj/+8Y90796dMWPG1Lru559/Tnp6Ou3btwfgoYceory8nIkTJ3LmmWfSunVrHnjggSrrfO973yMjI4OpU6cyb948tm3bFrjtAQMG0LFjR37/+98zZ84c8vPz63WcDz/8ML/+9a8555xzeOGFF3j44Yc5/fTTK34IAFx55ZVcfvnlHHfccTz33HP89re/Zfbs2Zx00kmUl5dXLHf22Wdz5ZVXcuKJJ/L0008za9YsBg0aVC14XnbZZYgIjzzyCNdccw1PPvkkl112WbW0zZ49m+eff56//e1vPP/88/Tq1YuZM2dy3nnnMXDgQJ566iluueUW/ve//zF69Gi+/fbbKuu/9tpr3Hrrrdx44408/vjjlJWVcfLJJ1f5MfD8889zzDHH0L59e+bMmcMjjzzCjh07OPLII9mwYUO9zq2po2Rn4RP9Ai4HngO+BhS4roZlLwTWAnuAdcAv6rpfK+I24RJWxL1kieqf/uTeG1mo2Dry1atXL122bFngsmvXrtWSkhItKCjQe+65R9PS0nT8+PEVyw0cOFD79+9f8f/ZZ5+trVq10m3btlXZ3qxZs7Rdu3YKqIjooEGD9IorrtCNGzdWWe7ZZ5/Vrl27VqStX79+eskll+iaNWtqPb5rr71W3S3QueSSS/Swww6LuvzWrVu1VatWev7551eZ/tBDDymgzzzzjKpWFtHfcccdUbcVKuKeOHFilemXXHKJtmrVSsvLyyumAdqzZ0/dtWtXxbTS0lLt3r27jhkzpsr6r732WrV977PPPtqpUyctKCiomPb2228roA8//HDFtP3220+PPvroKtsrLCzULl266GWXXVZluhVxN86rOeagLwS6A0/XtJCIXAjcCzwJjAPmAjNE5KKGTqAxMVm6FI45Bq6+2r0vXZqUZMybN4+3336bZcuW8fTTTzNo0CBOPPFE1qxZU23ZAQMGkJmZSefOnbn44os599xz+de//gXAsmXLWLNmDeedd17F8ueffz579uzhiSeeqLKdyZMns2HDBubMmcOUKVMoLy/nb3/7GwcddBCrVq2qWO6UU04hLy+Pp556iksvvZROnToxY8YMDjvssLhbSA8bNoz333+fSy+9lPnz57Nr164q899880327NnDhAkTqkw/++yzycjIYNGiRQC89NJLiAgXXnhhrfs86aSTqvx/8MEHs2fPnmqlAePGjavyVMC6devYtGkT5557bpXlRo0axT777FORlpCRI0eSk5NTZT8AX3zxBQAff/wxn376Keeeey6lpaUVr7Zt2zJy5EgWL15c67GYxGuOAfogVR0OXBptARHJAG4CHlLVK1V1gapeBcwGbhSRzMZJqjE1WLgQiouhrMy9L1yYlGQMHjyYoUOHMmzYMMaPH8+zzz6LqnLddddVWzYUzNeuXcvOnTt58MEH6dy5M0BFUfYpp5zC9u3b2b59O8OGDaNbt27VirkBcnJyOPfcc7nnnntYs2YNTz/9dMXjV+HatWvHj370I+68807eeecdlixZQnp6Or///e/jOs6JEydy991389Zbb3HCCSfQuXNnfvzjH5OXlwdU1nn37NmzynoZGRl06dKlYv7WrVvp3LlzTI/Zhc5NSKtWrQD47rvvqkyP3Ge0tICrkw8vlo9lP6E2Aj//+c/JzMys8vrPf/7D1q1baz0Wk3jN7jErVS2vfSlGAt2AORHTHwImAaOABQlOmjHxGTMGsrJccM7Kcv+ngDZt2tCvXz9WrFhRbd7gwYPZf//9q00vLi7mscceA2DIkCHV5m/evJlPPvkkcN2Q8ePHM2TIEFavXl1j+kaMGMHxxx/Pf//739oOpQoRYerUqUydOpVt27bx0ksv8etf/5qzzjqLt956qyLIffPNNxx00EEV65WWlrJ161a6dOkCQNeuXSkoKGD37t0JexY+sjFbeFoiffPNNwwdOjSu7YfSfvPNN3PsscdWm5+VlRXX9kxiNMccdCxC367I5omhsrNBjZgWY4KNHAmvvAI33ujeR45MdooA1/L4008/pVu3bjGv89xzz1FQUMC1117LggULqrxCgfvBBx8EYMuWLdVykAA7d+5kw4YNFbnGHTt2VGs9Da5R28cffxyYu4xVTk4OZ511FmeeeWZFK+YRI0bQqlWrivSGPP7445SWljJ69GgAjj/+eFSV++67r877r03//v3p0aNHtbQsWbKE9evXV6Qlnu3l5uayatUqhg4dWu11yCGHJDL5JkbNLgcdo1B5T2Tz0IKI+SbFFRcXI2++iSxejB51FDpiRFzrp3zOYOTIpAfm999/ny1btqCqfP3110yfPp2CggIuvTRqLVI1DzzwAO3bt+eKK66oaNEd7rbbbuPBBx/k+uuvZ+HChVx00UVccMEFHHnkkXTq1In169dz1113UVBQwOWXXw64etixY8dy3nnnccwxx9C9e3e+/vpr7rvvPlauXMmMGTPiOs4pU6bQoUMHRo4cSffu3fnoo4946KGHOP744wGXa7388su5+eabadeuXUU9/FVXXcWoUaMq6pPHjh3LT37yEy6//HI2bNjA0UcfTUlJCYsXL+akk06KqfV7bdLT07nhhhuYOnUqEyZMYMKECWzcuJErr7ySAw44gEmTJsW1PRHhH//4B+PHj6e4uJgzzzyTrl27kp+fz5IlS+jbt2/FeTeNJ6UDtIgcC7wcw6KLVHVMPJv27xpHWqYAU6LN79u3bxy7N/VRXFzM7t27KSoqonjRIva98EKkuJjyzEw+vO02dgweXG2d8AYyscjOzqZjx460adMm9YN4AzvjjDMq/u7WrRuDBw/mv//9LyeccEJM62/evJkXX3yRiRMnBgZncHWfU6ZMYdGiRYwYMYLJkyfz6quv8sADD7Bt2zY6derEsGHDePnllzn66KMB2H///fnNb37D/PnzmTdvHlu2bKF9+/YceuihzJ07l9NPPz2u4/zBD37A/fffz0MPPURhYSG9evViwoQJXH/99RXL3HTTTXTr1o177rmHGTNm0KVLFyZOnMjNN99c5bnrxx57jD//+c888MAD3H777WRnZzNs2DAmT54cV5pqMmXKFNq2bctf//pXxo8fT/v27TnxxBP5y1/+EvU81+TEE09k8eLF3HTTTUyePJndu3ez1157MWLECM4666yEpdvETlyL+NQkIm2BWCLfLlX9ImLdDKAEuF5Vr4uYdxEwA+ilql+HTe8O5APTVPUf8aR16NChunz58nhWMXEID8qFhYUV07vedx897roLKS+nPC2N9ZMn86VvZRveErZHjx6B2w0P3NGet40M7n369Kk1cK9Zs4aBAwfWfmDGNEG1Xd8i8o6qxlcRbqpJ6Ry0qu7CPaecaKG65oNwz0uHhOqea26FYlLGN/370y0zEykpQTMz+bxvXwoCOqzIz88PDNLRgnLkMjk5ORXLBq0TGcTLysrYvXt3xf/p6elkZGTU2ruVMcaEpHSAbkBLgS3AuUD4w5ITcPXQbyQjUaZm4S1iCwsLXaAcPJgPb7uN9NdfZ/OgQRT07x91/Vhy1NHUFsgj57dv377KKE6hnrgiZWRkkJmZSVpamgVvY0wVzS5Ai8hQIJfKFuqDRCRUGfWCqu5S1RIRuRrXMclGXJA+GvgZcKmqFjd2uk3twou4QwExPz+f/G7d4Ec/imkb8QbmIKHccm1BW1VrHfkn1CFESEZGBunp6RUB24K2STWpXC3a3DS7AA1MA84P+/8M/wLYF8gDUNV7RESBXwO/Ab7A1T3H1/TTNLrw4BwpMgDH0jgstL3a6qNDxdyxFIuDK+bOyIjvKxYesFu1akVaWlrc2zCmIZWWlto12Uia3VlW1QuAC2Jc9l5cd5+miQg1EIt1YITwYBotWAdNj3XZaME6VAfdoUOHqGmrbTSokpISMjNdp3ZWFG5SxY4dO2jdunWyk9EiNLsAbZq/uo5aVFvON95HscLXyc7OBip/QJSXl1NQUEDr1q3JyMgILOoOBd+QoIAdmmY5FpNsqsru3bvZsmWLPVbaSOxbb1JW5KNVNQXYRNQt1/aYVXhReGSxeCgwh56fLioqYtu2beTl5ZGenl5le6FhCcvLywNzw+HDFkYKFXlbTtokQ6tWrejRo4floBuJBWjTpJQsXkz/1aurtdiuS6461qAeGbjD/w8K1KFgnZaWVvGsdHgOOz09nW3btlV59CuU/h49etR6LD169CA3N5cePXq0+E5UjGnOLECbJqPDypUMvuEG0kpKKMvI4JlLL63SDWZkYKstAEcLhPHkxkPbCHrOurbHuoKCcU3BOXxeaH85OTlkZ2dbsDamGbIAbVJaUVEReXl55Ofn0//110krLSVNFSkpof+yZSzu1w+AHp99xvc+/phhzz1HGlAO3Dt9epVtxRp461rHHWtwjSct0bYVCu7hPwwsSBvTvFiANk1Cfn4+7LUXA9PSEF9HO3DpUtYdcQQAp/3974TX9KYDU6dN497p0xNSP51o8f4IiCX45+XlkZubS8eOHSuK1I0xTZcFaJPyKoqR+/VjzfDhHPTGG6QBaar0/vhj9nvnHdJxI6Bo2HtTaUIVXv8cND2edYwxzYcFaJPSQg2uNm3aRPfu3Vk3fDgDli2DsjLK0tPZeMABDHvhhSrrhPo5Koe4xrGta9F2TdsKpbs28QTnyOVCxd0WrI1pXixAmyZj06ZN9AC+GDiQtoWFrBk5knxfBx2pHFgZ53jANQW4eIJ35LKxBOn65IjDg3ROTg4dO3aMexvGmNRjAdo0GXtv2MBpDz5IelkZAN2+/JKCXr0obtWKNrt2AVWLtw+57DJW3HFH4PjQ8QoPnLE8BhVaJpbcc7j65OItB21M82IB2jQZh65YQVpZGaE+udLKy+n/1lu08sEZKuugASgtJfv99+sVoOsaMGN5njlemzZtAtwPlSErViDA+tGjGTBpEn369LGGYcY0MxagTZOw94YNHPbuuxU5ZHDF2IggIohqxfSK94wMyo88MubRp6B6QI43yNaWi61rXXN4cJ4UVopw2HvvsXL//Sk6+WTatGljj1kZ04xYgDZNwr7r17vnn/3/Cmzp25d1RxzBwGXLoKQE0tMpv+wyMnbuBEAmTmS/sI5MwhUXF5Ofn19rF6Lxqi3ghjceg+Ai8NC8SFu2bOF7q1eTHlaKkF5eTvrDD/NWt24VHZdYbtqY5sECtGkSsr77rkruGSCttJTOnTuz+Jpr2PeLL2j7wx+Sc+KJEEcuMtTlJgR3JhLZFWe8wgNxUOCNFoxDtmzZUuXvb/xQlOFF+V26dGH48OH06dOnTmk0xqQmC9Am5YTnbteuXQtLlzJqyRKAKjnobhs3Mvr66/nwjjvYOX48GXHkGrOysujTpw99+vShsLCwSo9lkYICd6wBO95GYrXpWFxMOa4jFgXKRdg0bhz7WsttY5odC9AmpUQGy/z8fIYtWoQQ1vgr7O+0khIGfPMNaQceWOf61+zsbLKzsyuCNcCGDRuqDWgRLt6cdXhuPNb1Nm3aRNeuXQGXe+7atSsbDziAsjVroLTUlSioJqSVujEm9ViANikrPz+fTZs2kb1xY+B89a+1e+1Fxkcf1XnQiGo5di8yMAflnCOnRQvCkYE+lgAflPve0bUraU89VdFLWjow8qij2Ll9e9TtGGOaJgvQJuUUFhZWCVydfaOvICXt27Nj8GBy/P+7d+8GiDtId+zYsSL3HG7AgAH06dOnooV0aIzqUA4bogfb8OAdlAuvbQSsaELBual1aWqMiY8FaJOyQg2oWhcXR13my379qo3qFBqCMdZBI7KyssjKyiI7O5vBtRQXhy9bk8LCQjZs2FBjsXb4KFSh+VOnTasYjaukXTvyBg7k1QsuqJKbDtVBh/9vjGl+LECblLZlyxZalZRUmx5qzb1+0CCgapCLzJkmY4SnUL12eI47FKBDufIq6Vm6FM44o+LfNCBj504GLl9Ov379+PjqqyvqxBf/3/8x+qqrKurht86bR06bNo12bMaYxmEB2qQ8iTJNgW4bNlBAzR2MBBUlh3LZDT2Gcky586VLYdSoqNvInD+fPjNnkp2dTW5uLgwfzu4jjqD1W2+Rccwx9IjyrLcxpmmzAG1S3nfp6bQvDy7IbbtjR0wjOUUuE945SUMH6VpdfDFEOT6AtB/+kOzsbNr4XHJWVhb06QPHHddYKTTGJIEFaJPSunbtSpuAIu6QXTU8/9tkBo/47LOa58+ZA8Tf8M0Y07RZgDYpq6L3LRFQrTIv9N+6I46oMr1Hjx6uGDhMY9c/x+2UU+Dhh4PnRRy3MablsABtUt6uzEw6FBdXHakKH6RHjqQHruFVKAh37NixanFwqvM5ZF58EX74w8r/jTEtmgVok9JOmzePDv4xq6DGYuByzdu2batSrxz+qBWQ+iM9WVA2xkSwPg5Mytq0aROHfPghED04Q/TOPULdhhpjTFNkOWiT0moKzFC9IViTyzkbY0wUFqBNkxFZBw0wfPhwoInUNRtjTBysiNs0aaG+t40xprmxAG1SUiyDRgjQ9oMPLPdsjGmWLECbJik0PnTmo48mOynGGNMgLECblBTUC1hk/bMCxYsXU1zDaFfGGNNUWYA2TVrahg3JToIxxjQIC9CmSanW8WUNg0wYY0xTZgHapJzQuMdbtmypMj3oMav0nTuRN99stLQZY0xjsQBtmpTwHHQoWMvPfpaMpBhjTIOyAG2avPRPP4WlS5OdDGOMSSgL0KZJqyjyfvDBZCbDGGMSzgK0aR4sB22MaWYsQJvmYf36ZKfAGGMSqkUHaBHpIyL/FpFCESkSkadEpG+y02Wcrl27Vn+sioBHrQA6dWrYxBhjTCNrsQFaRNoCrwIDgPOB84ADgAUi0i6ZaWvpcnJyKv4O6j0s8P8zz2zAFBljTONrycNNXgj0A/qr6icAIrIC+BiYCvw9iWlr8Xr06MGmTZtqXU7wQbqoqKGTZIwxjarF5qCBU4E3Q8EZQFU/B94AxictVaZC9+7do+aYI6fvefBBNmzYYP1yG2OajZYcoA8CVgZMXwUMauS0mCgii7ghuA46c9cu8vLybHxoY0yz0ZIDdGdgW8D0AiAnYLpJgqAcdHnAPAFKFi9ulDQZY0xjaMkBGoIzY0GZNkRkiogsj/bavHlzAye1ZYr8MNKAsaNGBXb5eeisWY2TKGOMaQQtuZHYNlwuOlIOATlrVZ0JzIy2saFDhwY+/WMaT6f169mR7EQYY0yCtOQc9CpcPXSkQcDqRk6LiUOvXr3Y4/+O/FVUZK25jTHNREsO0M8CI0SkX2iCiOQCP/DzTIo644wz+Nf06dWCswA9+/blkksu4d///jcbNmxIRvKMMSYhWnKAngXkAc+IyHgRORV4BtgA3JvMhLV02dnZ9OjRgx49ekRdpsdnn1V55CpUD50G1caRNsaYpqjFBmhV3QkcDXwEPAQ8DHwOHK2q3yYzbS1ZmzZtANebWLQA3XndOsbfdRdI9fZ8Ajz8xBMNmURjjGkULbmRGKr6BfCTZKfDRBeeOw7ptno16aWlpKlWa82tuF+dc+fOZcGCBQwaNIjRo0fTp08f2rRpQ1ZWVmMl3Rhj6qVFB2iT+srbtiVt167K/9u3Z/OgQZSlp6NlZaBKGtUD+aNPPMEe4ALfR/fo0aM58MADGzPpxhhTLxagTUrJysqiT58+9OnTJzQBwgI0ImQedRSr7ryT7Pffp/DQQxl88cWkU9miOxSoW+OKuy/r2hWAbdu2kZOTY7lpY0yTIKr2+G4iDB06VJcvX57sZDQ/o0dDeA9hRx0FixZRWFhIUVERhYWFrF27ltPOOIM0XHAOBejQlV0G/HOffXgvO5sep53GscceyyGHHEJ2dnajHooxLYWIvKOqQ5OdjqbOctAmtd1yCxx5JJSVQXq6+z/Ajddcw9U33FAtJ61AOjBl/XoAZmZmMn3tWgYMGMCxxx5Lbm5uZW7dGGNSiAVok9pGjoTXXoOFC2HMGPd/hB49ejBgwACWHnssI486qiInDZVBOvQ+5Z132L59O//56iu2bNnC2LFjASxIG2NSTot9zMo0ISNHwh/+EBicQ7Ztc72zPj13Lv+cNAlwATmoz+7ffvopC15/vWHSaowxCWIB2jQrPXr0YMCkSTx5+eWUU3386FCQTgfunDGDzuvWNXoajTEmFhagTbPSYeVK9p4zh/33358lixdz9/TplPl5kUE6DRh71VV8duihXHXMMfzjH/+w7kGNMSnD6qBNk9SmTZtqA2N0WLmSg//f/yOtpATNymLXs8+SM3o0906fztRp0wIfxVLgqIICjnr1Vd5JS4NTT23EozDGmOgsB22apN27dwOVdc8A2e+/T1pJCVJejpSUkPH66/Tp04fRo0ezdPFiyqha5B1qPBYK1ofPn0+vfv1gwoTGOxBjjInCArRpkrKzs+nTpw9HHnkkw4cPJzc3l/Ijj6Q8M5PytDQ0M5PSUaOqrHPv9OlcevHFVQJ1SEWxd2kp+vDDrNtvP1auXElxcXFjHI4xxlRTryJuEXkPN/LTw6q6IzFJMqZuvtpnH9ZffTX7fvEFhYceSkbPnrBhQ0Uu+5BDDuGQQw7h3kGDqhV5Q9Vi7wM/+4zP3n8frHtQY0yS1KsnMREJNZTdBTwO3KeqbyYobU2K9SSWPMXFxeTn55OXl0d+fj6d161jn0WLAFg/ejRARdDeMXgw+fn55OfnM3XatIoipPCibnAXdTlQtmePdQlqTJysJ7HEqG8jsVHAhcDpwM+ASSKyEpgJzFHVwnpu35ha7d69m8LCQvLz82HpUo66/XbSy8sByH31VVSEtPJyNDOTD2+7jfxu3QC47OKLWbFiBQtef70iNx0epNMAWrWCX/wCJk6s8TlsY4xJtHrVQavqElWdBPQCLgbeAw4G7gS+EpHZIvKD+ifTmNj0f+st0svLK3LEaWVl7uUbjmW//z7gnpceO3Ysd/30p25ADi/oUSzuuQdGjYKZMxvtOIwxJuGDZYjIEGAK8FMgG3fPWwPMAh5U1W01rN5kWRF3aiibMoW0WbMquvZUETQjoyIH/fmsWezYsYP011+nVa9edF20iJzly5GAsaWrSU933Y5aTtqYGlkRd2Ik/DloVf0AuEREfg2ciSsC/wHwd+BmEXlSVc9L9H6NAUifNAkefBD27EHS05EZM/h2333RBx9kx44dlH3wAQffdRdSXIyE/TiN/JkamZMG3IAdCxdagDbGNIpGGW5SRPrj6qWPBFRV0xt8p43MctCpobi4mG0vvMCuF1/knfbtye/Xjx6ffcb4u+4ivbS0oj46chAN/N+lVDYYCx9wowobotWYGlkOOjEatCcxEekMTAQmAwP9ZGs4ZhpU8eGH82WXLuSvWAFA/2++Ib2sjDRVQj9II/voDv2dDlzZtSuPd+zIus8+q9J4rKIRmYh7+YZoxhjTEBqkoxIROVpEHgU2ArcCg4C3cC29ezfEPo2JpmzUKMjKolxcXrjK41Rh00LTf37YYUyYMIGxo0ZFr5dWdUHaGGMaSMJy0CKyFzAJ+DmwL+5+tg1XtD1TVVclal/GRJOVlUWfPn0qehmrcOihlN5/P+kPPEB5SQmi6nLFEY3DAPZZsIC9994bcM9CB/XhXSEtzXLSxpgGUd+exAQ4CVeEfSLuXibA67hW20+o6p76JtKYehs5kvLDD+e7M85gz333kTNvHlpa6hqKiYAP1GlAuiojPvqI8i+/5OxevXjsq69Io3p9NWD10caYBlPfnsQ24J6BFmAr8BAut7w2MclrOqyRWOorLCykqKiIvLw8ShYvZtDcuXRfsYI0VcpF0LQ0RJXytDQXkMvKIDOTBddcw70rVpCzdi3/WLGiIlhXOPdcmDMnOQdlTAqyRmKJkYiuPhfhirGfVNUWO7KABeimo7i4mN27d9P2gw/IHDcOLS6GrCw2/uY3bPvkE3I+/pjeb79dkWMu2H9/buvbl0fz8rg1M5Px69YBEQ3HOneGrVuTdkzGpBIL0IlR3zro/qr6cX02ICLjgfGq+rN6psWYmGRlZbn+tY86Cl55hbJXXuG74cPZ3rMn69au5bCIHsM6f/IJN3z5JUdfdRUF/fuz8cIL6b19e9WW4AUFSMeOsCNszBgr/jbG1EO9AnR9g7N3KHA+roW3MY1r5EgyRo6kPTAYGDx4MPTuDYsWuZw1PodcUsL3iop4d9063unfnw1r1zK8sLCi9bdC1eAMFXXbxhhTFw36HLQxTVHhoEF89/jjZDz6KDlPPw1lZWhmJpvKyznyuutIKy2lWIRv09LoUF5ebZANY4xJBAvQxgQoPvxwPurShQ5jxrDXunW0P+kk+r7+Ohm+F7Is4KVhwzh6+XLal5UF9kpmjDH1YQHamAjZ2dlkZ2fTo0cPGD68cjzo9u3Rv/4VLS5GMjPpcMop/P7ww7lrxowq3YJaobYxJhEsQBsTRVbYMJTgctXFzzxD6fz5bD34YNYUFsJXX1XrzAQiugW1emhjTB1YgDYmRllZWWQddxwcdxxti4uZsHs3RUVFrL3oIgYcfHBFv7nhDcdCnZ8YY0y87N5hTB1kZWVVFIP36dOHtR9+yN3TpxPq9DOUZy4Hyo47DsIf3UpPdznr9GY3qJsxJoEsB21MPYSeqc7OznaPaF1yCaUipFHZjzfz56Pz57OtoIDOV15Z2Xd3ebkL0mVlyTsAY0zKshy0MQlWvmcPqz/8kLx+/YDKFt0dXn65+sAa5eVuwA1jjImQCneGPGBxshNhTKLk5+ezdu1aXsnJASqLu3cfeGDwCqoWpI0x1SS9iFtVHwAeSHY6jEm0V/fbj/Xr13NWejpbx46lW1ERB6WlkRY0PKW19DbGRKjvcJMT67Keqj5Yn/0ak8pC41GffvrpFBYWsmLFCp587DHyn36aOUCmSMV41BUdmoh1bWKMqaq+OejZxNcvQ+jpEwvQpsV5r3VrfjV4ML/cs4eBfkSsCkG5amNMi5aIIu5S4D/A6gRsy5hm7eVvv+UK/3eVPPPMmTBlShJSZIxJVfUN0IuAo4DTgO7ALOAJVf2unts1ptlaNXAg+332WdVBNu64wwK0MaaKejUdVdWxQH/gb8D+wP3A1yJyl4gckoD0GdPk5efnA/DNN98A8NI++7Bj//2rLrR2LSxd2thJM8aksHo/26Gqn6jq74A+wJnAW8BFwHsiskxEfi4i7eq7H2OaojZt2jBgwAC6du3KySefzNChQxk0aBCbr722+qNVCxcmJY3GmNSUsIcvVbVUVZ9U1XHAfsCfgJ7ATOArERmZqH3VRET29jn4pSKyS0RURHIDlssRkftEZIuI7BSR+SJycGOk0bQcu3fvZtu2bWzZsoXly5ezfPlypk6bxr7nnUdZeTlkZrpA3aoVjBmT7OQaY1JIg/SOoKrrVfVqYAqwEWgPdGuIfQXYH5eT3wa8FrSAiAjwLDAOuBT4CZAJLBCRvRspnaYFWvPZZ6Tj6p7TAUpLXd3z+ecnN2HGmJQjmuAOEkSkF/Az/9oH+A74N3Clqn6Z0J0F7z9NVcv935NxDdf2VdW8sGXGA08DR6vqAj8tG/gcmKOqv4x3v0OHDtXly5fX/wBMs1VcXExGq1bVRrsqz8oivawMycqCV16BkY1S2GRMgxGRd1R1aLLT0dQlJActImkicqqIPIvruvMGYAdwGdBLVc9vjOAMEArOtTgV+CoUnP16hcBzwPiGSpsx5b7eOXy0q7SSEqSsDPbssXpoY0yF+vYkti/wc2ASrr55J67bzlmquqz+yWswBwErA6avAiaKSHtV/baR02SasOLiYt566y3mz5/P2rVroy94+unMeeIJ0nEdCMwdPZqfLlrkHrkqL4cuXRopxcaYVFff56A/8e/LgWuBR1V1Zz232Rg643L6kQr8ew5gAdrE7KOPPmL+/Pm8+uqrvPvuu1Xmbd+1i3SgDOjUti2d2ratmHdfQQEqQlpowIytWxs34caYlFXfAC1ACS73fA1wjdTep7Cq6j713G99haoAg6YHryAyBdfoLVDfvn0TkCzTVA0ePJjBgwdz/fXXV50R9ihVGrBz9+6q3XouXQrHHAPFxZCVZS25jTEVEtHVZybQ1Fo+F+By0ZFy/Pu2yBmqOhP3yFigoUOH2nBEprrIRpiR/48c6RqGLVzogrM1EDPGePUK0KraVAexXQUcHzB9EPCF1T+bhBGpGpSDSphGjrTAbIyppqkG2Pp6FugtIqNDE0SkI3CKn2dMYpSXVwZlkeqjVi1dCjffbN18GmOqSUQRd8oRkdP9n4f79x+KyGZgs6ouwgXhpcAcEfkNrkj7D7g66L80dnpNMxdtKMnI+md7BtoYE6ZZBmhgbsT/M/z7ImCMqpaLyMm4QT5mAK1xAXusqm5ovGSaFm3hQhecy8rc+8KFFqCNMRWaZYBW1ViakhdQ2eOZMY1vzBiXc7YW3MaYAM0yQBvTJFgLbmNMDSxAG5NM1oLbGBNFS23FbYwxxqQ0C9DGGGNMCrIAbYwxxqQgC9DGGGNMCrIAbUxjsB7DjDFxslbcxjQ06zHMGFMHloM2pqEF9RhmjDG1sABtTEML9RiWnm49hhljYmZF3MY0NOsxzBhTBxagjWkM1mOYMSZOVsRtjDHGpCAL0MYYY0wKsgBtjDHGpCAL0MYYY0wKsgBtjDHGpCAL0MYYY0wKsgBtjDHGpCAL0MYYY0wKsgBtTCpLSwMR926MaVHsW29MqkpLA1X3t6oFaWNaGPvGG5OqQsE52v/GmGbNArQxqUqk5v+NMc2aBWhjUlV5eWVQFnH/G2NaDBvNyphUZkHZmBbLctDGGGNMCrIAbYwxxqQgC9DGJFJubuWzy6FXVlbl/EGDqs6L9jLGtHhWB21MouTmwvr11aeXlLggvf/+sGZNbNsSsceqjGnhLAdtTKJ88UX0eSUlsG5d46XFGNPkWYA2JlH69o0+LzMT+vdvvLQYY5o8C9DGJEpeHuyzT/U65MxMKC6G1ath4MDYtmXF28a0eFYHbUwi5eXVPH/16kZJhjGm6bMctDHGGJOCLEAbY4wxKcgCtDHGGJOCLEAbY4wxKcgCtDHGGJOCLEAbY4wxKcgCtDHGGJOCml2AFpHTReRJEVkvIrtFZJ2I3CwiHSKWyxGR+0Rki4jsFJH5InJwstJtTIMKDeCR1uy+8sY0W83x23oFUAb8ERgH3A1cBLwsImkAIiLAs37+pcBPgExggYjsnYxEG9Ng0tIqeyZTtSBtTBPRHHsSO0VVN4f9v0hECoAHgDHAq8CpwCjgaFVdACAiS4HPgd8Cv2zUFBvTkCK7DbVuRI1pEprdT+mI4Bzytn/v7d9PBb4KBWe/XiHwHDC+YVNoTCOL7Bvcxps2pklodgE6itH+PTQY70HAyoDlVgF9RaR9o6TKmMZQXl4ZlEXc/8aYlNfsA7SI9AZuAOar6nI/uTOwLWDxAv+e0xhpM6bRlJe7om0LzsY0Gc2xDrqCzwk/A5QCk8JnAUEVcVHL/kRkCjAl2vy+NY0FbIwxxsSp2QZoEWmNa6ndDxitql+GzS7A5aIjhXLO1XLXqjoTmBltf0OHDrWWN8YYYxKmWRZxi0gm8CRwBHCiqn4YscgqXD10pEHAF6r6bQMn0RhjjKlRswvQ/lnnh4FjgPGq+mbAYs8CvUVkdNh6HYFT/DxjjDEmqZpjEfc/gDOAm4CdIjIibN6Xvqj7WWApMEdEfoMr0v4Drg76L42cXmOMMaaaZpeDBn7o36/EBeHw12QAVS0HTgZeBmYA83C9j41V1Q2NnWBjjDEmUrPLQatqbozLFQA/8y9jjDEmpTTHHLQxxhjT5FmANi1PerrrUSs9PdkpMcaYqCxAm5YlPb2yN63ycgvSxpiUZQHatCyRXV1a15fGmBRlAdq0LJFjIdvYyMaYFGV3J9OylJVVBuW0NPe/McakoGb3mJUxtbKgbIxpAiwHbYwxxqQgC9DGGGNMCrIAbYwxxqQgC9DGGGNMCrIAbYwxxqQgC9DGGGNMCrIAbYwxxqQgC9DGGGNMCrIAbYwxxqQgC9DGGGNMCrIAbYwxxqQgC9AtUVoaiNhITsYYk8LsDt3SpKWBqvtb1YK0McakKLs7tzSh4Bztf2OMMSnBArQxxhiTgixAtzSRRdpWxG2MMSnJ7s4tTVlZZVBOS3P/G2OMSTkZyU6ASQILysYYk/IsB22MMcakIAvQxhhjTAqyAG2MMcakIAvQxhhjTAqyAG2MMcakIAvQxhhjTAoSta4eE0JENgPrk5yMrsCWJKchFdh5sHMQYuchOedgH1Xt1sj7bHYsQDcjIrJcVYcmOx3JZufBzkGInQc7B02ZFXEbY4wxKcgCtDHGGJOCLEAbY4wxKcgCtDHGGJOCLEAbY4wxKcgCtDHGGJOCLEA3LzOTnYAUYefBzkGInQc7B02WPQdtjDHGpCDLQRtjjDEpyAK0McYYk4IsQDdDInKgiNwhIitE5FsR+VpEnhWRIclOW0MRkT4i8m8RKRSRIhF5SkT6JjtdjUVETheRJ0VkvYjsFpF1InKziHRIdtqSSUT+KyIqIv+X7LQ0NhE5UUQW+3tAkYgsF5Gjk50uEzsL0M3T8cBY4AHgFOBioBvwlogcnsyENQQRaQu8CgwAzgfOAw4AFohIu2SmrRFdAZQBfwTGAXcDFwEvi0iL/J6LyDlAs/1RWhMRmQo8A7wD/Ag4A5gLtE1mukx8rJFYMyQiXYGtGvbhikg2kAc8p6oTk5W2hiAilwF/B/qr6id+2r7Ax8BvVfXvyUxfYxCRbqq6OWLaRNyPtGNU9dXkpCw5RKQTsBb4f8AjwE2qelVSE9VIRCQXWAP8QVVvT25qTH20yF/WzZ2qbtGIX16qWgh8BPROTqoa1KnAm6HgDKCqnwNvAOOTlqpGFBmcvbf9e3P8zGvzF2CVqj6a7IQkwc+AcuCeZCfE1I8F6BZCRDoDg3G/rJubg4CVAdNXAYMaOS2pZLR/b46feVQiMgqYiKvaaYlG4UoPzhaRT0WkVEQ+EZFLkp0wE5+MZCfANJq7AAFuT3I6GkJnYFvA9AIgp5HTkhJEpDdwAzBfVZcnOz2NRUQygXuBv6nqumSnJ0l6+ddfcW0SPsXVQU8XkQxVvSOZiTOxsxx0EyAix/qWqLW9FkZZ/w/AT4Fp4cXAzUxQYwpp9FSkABFpj2sgVApMSnJyGtvvgDbATclOSBKlAR2Aqao6S1VfVdWLgP8CfxCRFvm9aIosB900LAEGxrDcrsgJIvIL4E/AVar6r0QnLEVsw+WiI+UQnLNutkSkNfAs0A8YrapfJjlJjcY/VnclMBloJSKtwma38g3HdqhqWTLS14i24p5ieDli+ku4Fv49ga8aO1EmfhagmwBV3YWrU4qLiJwHzABuVdXmnKNYhauHjjQIWN3IaUkaX7z7JHAEcKyqfpjkJDW2fkBrYE7AvCv86zDg/UZMUzKsAkYETA/lnMsbMS2mHqyIu5kSkR8B9wP3qeoVyU5PA3sWGCEi/UIT/KMmP/Dzmj3/rPPDwDHAeFV9M8lJSob3cc//R77ABe2xQHOt4gk3z7+fEDH9BOBLVf2mkdNj6sieg26GROQoXHHWamAaVX8x71HV95KSsAbiOyP5ANgNXIWrj74RVw93iKp+m8TkNQoRuRv4Ba7u9T8Rs79sSUXdkUREaVnPQQvwCq6TliuBz4DTgQuBSao6O3mpM/GwAN0Mich1wLVRZq9X1dzGS03j8PWPtwHH4YryXgF+pap5yUxXYxGRPGCfKLOvV9XrGi81qaWlBWgAEekI3IwLzDm4KrJbVPWRpCbMxMUCtDHGGJOCrA7aGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI1JQVFGK9sjInki8oCIxDJ4CiKyxK/7wxiWXeeXPbTeB2CMqTfrqMSYFOR7vwK4PmxyNm4gjO8DO4FRqvp+Ldu5ANcn+zxV/XENy40GFgLLVXVYnRNujEkYC9DGpKBQgFbVamP3ishduD7WH1DVC2rZThvga6AdsLeq5kdZ7iFgAjBFVWfVL/XGmESwIm5jmp6X/Hu32hZU1d24kZwygAuClvHjJJ8OfAs8mpAUGmPqzQK0MU3Psf59eYzLz/TvP48yfwJuHOVHW8LIX8Y0FVbEbUwKilIH3REYhhvn+nngp6q6I8btvYWrvx6rqgsj5r2PG5pwmKrGGvSNMQ0sI9kJMMbUKGjY0NW43G5MwdmbiQvQk3GNwQAQkWG44PyeBWdjUovloI1JQUGNxESkHXAQcAswFviTql7p5+USUMccGgfar/s1kAn0UtVtfvpM4ELgIlW9p8EOyBgTNwvQxqSgWlpxdwK+BFoB/VR1g4iMARZELhsR4O8BpgK/VNW7woJ2Gi5oFyX+SIwxdWWNxIxpYlR1O7AOV0X1PT9toapK5Cti1VBjsQv9+9lAB+BxC87GpB4L0MY0TTn+PebvsKq+C7wDHCwiofpoqAzcxpgUYgHamCZGRE4D9gVKgCVxrh7qhORvwAhghaq+lbjUGWMSxeqgjUlBUR6zagcMAn4ICPAbVf1bnNvtAHwFtPeTLlXV6fVMrjGmAViANiYFhQXocGXAZmAZMF1VX67jtmfhird34xqHba9rOo0xDccCtDHGGJOCrA7aGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI0xxpgUZAHaGGOMSUEWoI0xxpgU9P8BJvsNMtKpic4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import hoki\n", + "from hoki.cmd import CMD\n", + "input_file = ('/u/ryotainagaki/Desktop/ryotainagaki/BPASS/' +\n", + " 'bpass_v2.2.1_imf135_300/input_bpass_z020_bin_imf135_300')\n", + "mycmd = CMD(input_file)\n", + "mycmd.make(mag_filter='V', col_filters=['B', 'V'])\n", + "mycmd.plot(log_age=7.0)\n", + "# actually makes and fills the grids - this is the time and memory consuming step\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B']-iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"],\n", + " iso1.singles[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\",\n", + " label=\"BPASS isochrone\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"CMDs of \\n (red) BPASS isochrone and (grey)\" +\n", + " \" Hoki Cluster \\n at 10 million years and solar metallicity\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/lu/scratch/ryotainagaki/hoki/hoki/cmd.py:348: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"Greys\").copy()\n", + " colMap.set_under(color='white')\n", + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mycmd.plot(log_age=7.0)\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"CMD of 10 million years (grey) Hoki Cluster\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, I try to find the source of the vertical \"line segment\" in the top left corner of the BPASS isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r.\",\n", + " label=\"BPASS isochrone (primaries)\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at \\n\" +\n", + " \" solar metallicity and 10 million years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "line = iso1.primaries[np.where(((iso1.primaries['m_ubv_B'] -\n", + " iso1.primaries[\"m_ubv_V\"]) < -0.3) &\n", + " ((iso1.primaries['m_ubv_B'] -\n", + " iso1.primaries[\"m_ubv_V\"]) > -0.5))[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=495\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", + "
masslog_aLTeffRloggisWRmass_currentphasemergedsourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64float64boolfloat64float64boolint64float64float64float64float64float64
17.1-0.27550040868802932.581023323661638e+3124765.09509725319807809082.936673.372089168724686False17.073465.0False13.88671864007710085.0897523352224594.8495303563610055.1816550045212965.406994997685946
8.41.0445565913119711.408581334391018e+3020178.552077850593451294372.8966464.175145535759932False13.433775.0False16.8178360737518217.7984069515971317.592681992282047.87809637170143258.069519102150933
7.2-0.90931140868802928.085221628723427e+2921282.8605769755162350401217.22028354.241459310736706False7.2582465.0False17.5123024078119298.5359095734064258.3218299723209388.6206297782036328.82283290836493
3.8000000000000003-0.89865640868802917.630656943082565e+2814384.341992261561580782077.5187444.3050579558158235False3.8009345.0False19.45722767682877410.095721013438039.96413439832891410.14352717599941510.265777753515541
10.799999999999999-0.53566540868802933.775304007818359e+3026326.3105495036583319418448.45476964.12056449854774False10.959155.0False16.1718433757535517.37074547570594257.1210103340226427.4720738429405067.710658324370102
14.0-0.44548040868802931.0622626927995523e+3128381.80422481014790733399.1608663.915472420865279True14.235245.0False15.13757007511377056.4004497734948176.1350954456985426.5081153862941226.759560223676305
2.10.119009591311970647.469506836750609e+279446.47979502621146758593.6467414.326191058155279False2.100025.0False111.69905503986772811.66681694102370211.69757840839805711.6617947008961311.663256553314188
13.2999999999999991.06048759131197078.795730802512516e+3023739.4143100643126231061006.9524353.8766183531871814False22.020685.0False15.0608499448162256.1949958966557135.9601259890110146.28725714156266156.508380718313637
16.2-0.69168140868802922.0458412411787787e+3126346.9289331760777714930238.6063153.5683758598609887False16.600775.0False14.2655176781487295.4977001220177925.2446580679526525.5972592941182275.835135243549378
................................................
5.3999999999999995-0.85904140868802912.8108128422388745e+2917956.4301306329871946892640.1588024.277728391093879False5.4137825.0False18.3839045810927389.2530651115940269.0728181822664929.3214252277246469.489233387737537
14.7-0.82843640868802911.2453227703237567e+3128622.6605440391875100138891.5779543.889571214383584True15.19935.0False14.9726949103218296.243075480513015.9762359268043846.3512618409359966.6038560319886
12.0-0.48268040868802925.652908151601763e+3027481.472551625633727451517.5891894.065648618632092False12.177585.0False15.7956232963564547.0274818252181086.7691036562474627.13272897656095057.378887605408695
13.2999999999999990.326131591311970678.797756327563536e+3028068.551428945954457609326.0246053.955457154331543False13.512935.0False15.3336546075480516.5859276155580896.3226927588101216.6928452560717926.942650036328916
8.0-0.18848540868802921.1880423689890658e+3022630.8048691269362519776395.9625684.224570103821256False8.0238445.0False17.19247563150543558.2686775847051668.0442701747718338.3585301375662598.572358764590357
11.40.15133459131197084.679418946083288e+3027007.227325827373511575443.6770394.090742698097661False11.450785.0False15.9773591762329197.1954554116836936.9408379433940397.2990219845602577.5419132514056475
16.0-0.9224334086880293nannannannanFalsenan-99.0True1nannannannannan
14.00.216265591311970871.0613336382203087e+3128531.8544578184234738293991.0924623.91860208113607True14.026025.0False15.1465373562646056.412881415409396.1465599074661186.5210258222045816.773334036765385
10.0-0.89489740868802932.7895999661934836e+3021247.6057786320674380382178.3289334.092717608522885False17.899055.0False16.1538333264082567.183159937392656.9669286744569997.267480996249317.469177235061234
5.7-0.55279840868802913.427929875825102e+2918563.0784474002322011787027.38104034.272278914179298False5.7086225.0False18.2215722203859079.122575082607318.935169827536949.1942692551740559.368920660265113
" + ], + "text/plain": [ + "\n", + " mass log_a ... m_ubv_R m_ubv_I \n", + " solMass ... \n", + " float64 float64 ... float64 float64 \n", + "------------------ ------------------- ... ------------------ ------------------\n", + " 17.1 -0.2755004086880293 ... 5.181655004521296 5.406994997685946\n", + " 8.4 1.044556591311971 ... 7.8780963717014325 8.069519102150933\n", + " 7.2 -0.9093114086880292 ... 8.620629778203632 8.82283290836493\n", + "3.8000000000000003 -0.8986564086880291 ... 10.143527175999415 10.265777753515541\n", + "10.799999999999999 -0.5356654086880293 ... 7.472073842940506 7.710658324370102\n", + " 14.0 -0.4454804086880293 ... 6.508115386294122 6.759560223676305\n", + " 2.1 0.11900959131197064 ... 11.66179470089613 11.663256553314188\n", + "13.299999999999999 1.0604875913119707 ... 6.2872571415626615 6.508380718313637\n", + " 16.2 -0.6916814086880292 ... 5.597259294118227 5.835135243549378\n", + " ... ... ... ... ...\n", + "5.3999999999999995 -0.8590414086880291 ... 9.321425227724646 9.489233387737537\n", + " 14.7 -0.8284364086880291 ... 6.351261840935996 6.6038560319886\n", + " 12.0 -0.4826804086880292 ... 7.1327289765609505 7.378887605408695\n", + "13.299999999999999 0.32613159131197067 ... 6.692845256071792 6.942650036328916\n", + " 8.0 -0.1884854086880292 ... 8.358530137566259 8.572358764590357\n", + " 11.4 0.1513345913119708 ... 7.299021984560257 7.5419132514056475\n", + " 16.0 -0.9224334086880293 ... nan nan\n", + " 14.0 0.21626559131197087 ... 6.521025822204581 6.773334036765385\n", + " 10.0 -0.8948974086880293 ... 7.26748099624931 7.469177235061234\n", + " 5.7 -0.5527984086880291 ... 9.194269255174055 9.368920660265113" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.secondaries[np.where(((iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"]) < -0.3) &\n", + " ((iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"]) > -0.5))[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST Settings cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(line['m_ubv_B'] - line[\"m_ubv_V\"],\n", + " line[\"m_ubv_V\"] - 5 * np.log10(1000/10),\n", + " \"r.\", label=\"BPASS isochrone (vertical line feature)\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrone Vertical Line\\n\" +\n", + " \" at solar metallicity and 10 million years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What exactly is causing this line. We need to look specifically at the properties of the stars there. Specifically current mass, source code of the star, logg, initial mass, Teff." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=495\n", + "
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massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
19.02.351537609741078e+31102133.91979406652550712791.74303515.399585032488795True5.75415.018.78183931936255210.2244582770649569.89445311969597310.35906752427788310.640208585673976
21.06.360070490769697e+3183086.41107791131368548409.36537554.666033340205703True6.563055.016.8051643193625518.2477832770649547.9177781196959738.3823925242778838.663533585673976
18.02.19979357423166e+3173317.897090254021033618239.09201444.839341892580129True5.579235.017.4146643193625548.8572832770649578.5272781196959758.9918925242778859.273033585673979
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Notice the alignment of features." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hP2MtwlsgyvDWi6MS6hGTqidyyS62Vfd/H3u+qTqrxNZ3q+bxauon+Ln4NvDz6K3EzP4HXBW8c2tt6hlbJstjrEpRknUl0WVWkm2rQ/RrboJan9eQPK86WR1inXW+k+IxYjpFj7vBvAPsjXju/gnR7QVmdhfwmxBClf0davF6qouGujaSMu+U+R7+ATgDzxdcT7Q1BA9gUj23pK/7aqiP13cE/0LTk5rls6ZUx3NRW92AwhDC1no+7k4hhFfNbB4eBFwV/TJTKT+1Gv4I/Bi/tl7E32di9Z5A6sanohTrS6j42t7V51Btr7fqil17XVNs75pQrqbHjVdSjW01ms+iluryPlKUZF1VnxuVhBDKzOzv+K/v5+Kd/g/GA/3JIYT4UbVi7/0jqXrQh50dcKPv/dPw945X8L4CG4imO+D9aJK9ppN+xkW/+I/Ff2E4G7g1ummjmT0A/CKEsKnKJ+0exxtnvsCD8pV4AwD4ayy+Tl2iy5rEZzU+V6k0eqfEEMI24B3zXqOfAXeb2SsJF0OdmFkr/BsWwLu7KH4G/uH/QAhhQsJx+lD5A2hDdJl0BIgq1gPR3xqTuxlvtcgLIcxLqMffifZOT4PYm9hueC5boj4J5epVtGX0TuDOaGfQI/BvquOA/aKdBbYn1DOZxHoWRZf13YqWKNX/uzHOa2zfYSGEZC0IlYQQlgEXR1vY9sU7zHwff1NsheenVaWmr6daa8BrI5Ur8Tffb4XKQ3aeTxUtglW87htD7HnNCSEcVGXJ6qv1uaiDIqCHmbVvyKAab+y5E7gg+sF/AR4QT6nOztFr8Yf4F8rDEn9hqMmoAVWI/U9Tfd6kutbrRQhhs5l9BfQzsz6h8ugge0aX8xuyHo2svt5H6uJ+vMHjMuBfpP6yF6vDn0IIV1bz2L/Cf2U6OoQwPX6Dmf0Cf29PJuV7W/DRX34C/MTMhuBxzGXAFfgXj4uqqpCZ5eHB9FTg5PgGnWic97OEXWoTn9XmXCWVzmHzVgD/D+/cUN9pHxPwnyBX4L2sqzIkunwyybZkQexneEvDgWbWOcn2I6pZx2T1+DRJMN2qDsesD3OiyzGJG6I9Y4fjrerzErfXtxDC6hDCUyGEc/Bv0oOB/atRz2zKz+H70eV7+DfvI6MpCo2tMc5rbJSHpD2bqxLcJyGEvwDHRVd/vRq71vT1BN6qCdVsqUmmnq+NVGrz3NIu2gr0Cf4lo3sNdi0j9f8kHefiHbyl/cQ6HKMUwMyqutYewH8dvAxvCewG/LMGaU974J+tLyUJpvtHt9fVZ3hfnuGWfOi1MfXwGLsSS31I9v84KaFMc1Bf7yO1FkJYg/+S/bVoiuj5eB5z4qgusc+3mrz3DwHWJQbTUXV+TYcQFoYQ/hk91iZSB+iJdQJ4Jsmvo4eQkGYWQthAtNOyJczaHZUslqrNuUoqbQF11F/wJvgJZrbnrgrvivn4vN/Bx8AMwE+iLeJVWRxdjkk41h6U/0SxUzT353H8559fJewzDJ/hqzYWA3uaWSxVJZaHdz3eUpguk/Cf638Q/YYZ72b8J5ZJ0ZbAemVmbc3HabaE9a3xIXrAP1TAh1lah/9Umzhu94/xD7GpITphRPSN6TG8VeG26BeX+MfolOKDqr40xnn9F96qd72ZHZK40cxamdmYuPv7p3gTin2r35JkW6LF0eWY+JWpXk9Ra6PLAdU4fux4DXZtVGFxdDkm4TFPoOp+E03BH/E+GfdHv7BVYGaRaN58vLWkni1vcXQ5JuE4DXku/hJd3m5mlX5ZSrYuiV1ea9Gc5EfxL7W/oXJ+6q4sji6PiA/czce3vo96+GU4Glw8jPdPuCF+W7RV74K6PkY13BNdXmtx4wFH30O+j/8s/69GqEdjmUz9vI/U1d3R5eN4GsK9IYSy+AIhhNX49ZFnPg50pWvOzAabWW7cqsX48IQHJpS7mPL0v2ozs9xoTnaiCJ6mUZ1fmRZHl2MSjt0Lj/OSeRCPbX8b//lgZrvj/6cKanmukkrrONQhhC1m9jt8tIGb8G9b1fX1uA//jvgb5Gg8QFoPXBZCeDzFvvFiY/Nead75ZE70WKfi410me+P9Of5T+M/M7Gv4uI19gHPwjlxfx7/x1MSf8DeoOWb2JB5sHY4H089Su+nZ6yya9/5j/OJ938z+jefEHoV3evsM75jUENrjP/UsNrN38TE/2+Etpvvg31rnReu5ycy+jffSf828k9qX+Bihx+N5V5clHP8KvBXzcnx85BfxtJtc/A3kdCqOu1tvGuO8hhDWmtnZRKfRNbNX8JbKMvy6PhT/2T7W8eRYfDKkt6KPvxrv7HNGdJ8/VONha/N6ejV6/N+a2f74UH+EEH5TxeM09LWRzF14z/wnoq/Rr/Dr50R8HOxKk8k0FSGE+83zLb8HLIpe61/iXz5y8RS5f1E+WQd4HuV55pNFzMZzPl8PIbxOGs5FCOEl8xl2fw3MM7PJeMeu3njL0zv4r5NVeQVPCXrKPNd+K7AkST+eu/AvBv2AZ0MIS6mmEMJKM3sMTz+aa2Yv4Q0wx+G/Os3Fg/W6+iXeIfTH0SA6Ng71ufjn0Ok1OZiZ/Rzv7Ehc/b5lZrFWvTdC3DjEIYS3zOyPePrPh2b2H/xL27n4dfWD6nbizAT1+D5S13q8aWYf4J1di/E0kGSuwFNvbsI7Mr6BN2D2xd8jR+IxV360/B34594b0c+j9fgQjEfgreJn17Cqw4CnzWw2nv60HO/HcQae856qgSXeTLxj/JnRz6U38Nf7SfjcEclShX+Px2DnAUPjXn/n4EMVfp3K8VlNz1VyoXrDkwSqGGooVBw+ZVB11sdtb4e/GZcBB1ajLhNj9YneSvG8mUX4N8griBujN2HfCSQfNmd3/BtKrOPIJ3huTjYpho7C32gfwIOgrfib5Dfxiy4AP04oP70a53BC9Dib8R75T+NDz9xAkmHFUtUt7rwvTrFtl3VJss/x+M9KhXjLw0L8wu1W3fO8i+MPImHYPPxF9zO8d/OX+IfRGvyD83KgTZLjjIyetzV4cPwl/o2+b4rH7YgPpfMh3qK5Ee+dfAfQK8l1V+k6JvUQXSn/B7U8ryn/b1Wd8+i5/SuwIHoON+AB80PA1+PK7YO3ZM6Knr/t0efwHzwXtLr/y9q8ni7Er/2tVO/9pkGvjVT/b3ySgmnR/9dG/A3+61VcAyn/Z9U4j/X++sa/2EzBvyztwIOA9/CW2MTx0HvhY7Wvwt9nKzy/+jgXtTlv+OQxL+CthdvxoPppYGw1jpuFpxp+QfnQg6nO8Zzo9lNq8b/rgE88tDB6bS7Fvzz3qMl52NX/G8/nvZ+Kn0MTdnW8qq6bKm4TU+z3TTzw2Ry9Dl4jbvjOaj72DST5jItum0Dq97ZU/+dK5yvVcWpSNrqtzu8j1fmf7+J8/Si67xO7KNcGj4newgPk7dH6voK31vZIKH8q/h66Ef918yX8y3a1z13ctv74a+1NyjsSLsPfs0+qwXPtjn/BXYy/lhZFj9sh1ePjaVp/xgPu7fjn3VV4mkgA7qjruUp2s+iBpJ6Y2S14y8GJIYQX010fERGpmWj/mOV40J4bEn5SF0kn86mzv4nPSfBKmquTMaIpwfcCl4cQajJqT7WkO4c6Y8XnOsetOwDv3b0O/5YuIiKZ57t4fupdCqalKYnmAp+Hd1hvTp0+602K+Gx3PF2shGqO2FNTac2hznCzzGwhnhu0Gc+/OQX/knJ52HVnSBERaSKinZC/i6fzfQcfJequtFZKJMrM/g+fKOs8vFPfr4NSDFJ5MtpBfTaeujIIT2fpgI9/XdP5J6pFKR+1ZGbX4/mCg/Ce1kV47tFtIfmwMyIi0kRFO7nn47mTs/FOdQ02BJpITZjZdDyfeSk+ZvIdaa1QE2Zm38PHuN4T75C4Ce8T8dcQwlMN9rgKqEVEREREak851CIiIiIidaAcakmrnJycMGjQoHRXQ0REmoHZs2cXhBB6prse0vIooJa0GjRoELNmzUp3NUREpBkwsyXproO0TEr5EBERERGpAwXUIiIiIiJ1oIBaRERERKQOFFCLiIiIiNSBAmoRERERkTpQQC0iIiIiUgcKqEVERERE6kABtYiIiIhIHSigFhERERGpAwXUIiIiIiJ1oIBaRERERKQOFFCLiIiIiNSBAmoRERERkTpQQC0iIiIiUgcKqEVERERE6kABtYhIU1RYCAsW+FJERJq07HRXQESkQRQWQkEB5ORAJJLu2tRMYSE88QSUlkJWFowbl3nPQUSkBVFALSLNT6YHpAUFXvf+/WHZMr+fSfUXEWlhlPIhIs1PfEBaWur3M0lOjn8RWLbMlzk56a6RiIhUQS3UItL8ZHpAGol4q3qmpqyIiLQwCqhFpPlpDgFpJJKZ9RYRaYEUUItI86SAVEREGolyqEVERERE6kABtYiIiIhIHSigFhERERGpAwXUIiIiIiJ1oIBaRERERKQOFFCLiIiIiNSBAmoRkcJCWLDAlyIiIjWkcahFpGUrLIQnnvApyrOyfEIYjV8tIiI1oBZqEWnZCgo8mO7f35cFBemukYiIZBgF1CLSsuXkeMv0smW+zMlJd41ERCTDKOVDpCEVFnqLZ05O1WkE1S3X0jXEeYpEPM1D519ERGpJAbVgZicA1wD7AhFgDfAWcEMI4dNomWOAbwGHAn2B5cBLwPUhhNXpqHeTF8vN3bgRtmyBCy+E3NzU5ZTDW7X6OE+pAvJIROdcRERqTSkfAtAdmA1cARwP/ALYD3jHzAZGy1wO9AB+A5wI/BY4PVqmU6PXOFFhITz8MFx3Hbz+erpr4woKPJj+4gv45BOYNCn5KBLK4a2eqs5TYSHMmuW3VCN1xALyqVN9qRE9RESknqiFWgghPAo8Gr/OzN4DPgPOBm4HvhdCWBNX5DUzmw+8BpwD3N9I1a1s7lz44Q/hnXf8/p//DBdf7K3BRxwBw4c33GNXlYKQk+Mt02vXQo8e0KGDl01WriXn8FY3jSPVeSoshIkTPZgOAUaOhAkTKh9r0SJYsQL22guKipL/L0RERGpBAbWksja6LAZICKZjZkaX/RqlRsnk58P3vufBdAi+bv16uOMO6N0b+vSBf/6zYYLqXaUgRCKe5jFpkgfTnTsnD5Zbcg5vTdI4IhE47jhYuBCGDCkvV1Dg//MuXfz+hg2Vg+XCQpgxA+bPh88/96C7pX1xERGRBqOUD9nJzLLMrI2Z7Qn8HVgJPFbFLkdFl/MavHKpLFzowVSisjJo1w62b/cW7IZQnVSN3Fy44go4/fRdB4t77tmygmmoWbpLYSG8/LL/z19+uTxlIycHunb1QDoWWCcGywUFfj2ceqq3UI8e3fLOtYiINBi1UEu8d4GDo38vBMam6nBoZp2BO/BgenJjVC6pIUOgb18PsnbsKF+flQWbNnnQ1FApH9VN1VCHt9Ri53D+fE+NWbo0dSv9okWwfDkMHVoxZSMS8RSP0aO93ODBqdNqior8ehk8uIGfmDQZ3br5F6127eDttxs2BUxEWiwLsZ/JpcUzs32ALsAewE+B3sARIYTFCeWy8SD6KODwEMKHVRzzUuDSVNsHDBhw8JIlS+pW8fx8eOopz6Fdtgx69fIPz2HD4Pjja/YBmp9fnlKQbESORBrurvpSnav8fLjvPp/6u3Xr5DnQsTzpmTPBDPLykudJ1+bxpfmKBdPx5sxRUN2MmdnsEEJeuushLY9aqGWnEEIsdeNdM3seWAz8HB/hAwAzawU8ABwLnFJVMB095r3Avam25+Xl1f0bXW4uXHWV/12XoCk/H379aygpgexsuPlmP3ZVx2yI1ufmGPhVlStdUgJt2niO+datsGpVeepH7DwUFHgaT14erF5du5QN/VLQ8iRLB3vpJQXUIlLvFFBLUiGEIjNbCAxJ2HQPcC5wdgjhlcav2S7UJWhauNCDuz339NbShQu9hashxohOFTRXt5NepgXd8bnSy5ZV7DSYk+Mt0x9/7EFzaSls3gyvvlp+Hg45xFsWY192evRI7/ORzNW5c7prICLNkAJqScrMegN7Aw/HrbsduAT4Zghhcpqq1nCGDPFgbcECXw4ZUnUgWFtVBc2Jj7dokXe4iw+cM3EimPh8823bPFd6/fryfOdDD4UlS2DAAGjfHtasqXge1qyBESOgUyfPjS8pSfczkkzQrp1fb/FOPDE9dRGRZk0BtWBmTwPvAx8CG4C9gJ8AJfgY1JjZNcCV+HjTC8xsVNwh1oQQFjVqpRtCbq6necTnUBcW1v8Y0fFB8/z5nvudF035W7/eA4BY4DljhgcF8YFzQwT5DS02NOCiRfDii/CPf5TnQn/96/DBBx4oz5vn64YM8RSc2HmP3S8trTj8YFUt/ZnUgi8NY+tW/4K2bZtfRwsWVK9vhIhIDSmgFoB38MlZrgLaAEuB6cBv4zoknhRdfjt6i/cAMKGhK9kocnMrfuA2xBjR8SNbzJnjk788/7x/6Hfp4q3jBx3kwcC778KBB0JxcXngnKkTwUQi3tpeUlLecrhqlX+BadcOTjvNz8no0Z5qc9BBvl+sFTvx/5CfX3GM79gXjkxswZeGs3VrumsgIi2AAmohhHArcOsuyoxpnNo0QfXdmS0WHM6a5cH0kiU+XXpRUfl4zAsXemutmU9ac8YZ5YFzpk4EU1joqR6rVvnY4FlZnit96KEeXBcV+UQ8PXpUDIhjQ9zF/x8KCz2Y/uQTL7/HHuVfOFL9ApAp50lERDKOJnYRSYdIpDzNY+lSb0UrLfVxlletgq++8gBz//2hY0cfVq57dw+wzfzvsWPhrrvKJzhpymLD3v3jH/DFF/78TzjBR/eITbwzcqR/USgp2fVkL4sWeYpI69aweLGPYR37wpH4C8CHH3qAngnnSUREMpICapF0iU1NnpvrKQ5du3q+Z9eusNtuHjgvXgxvvumt2ImWLYNf/apioG0GBx8Mf/hDw80QWRuLFnmKRrt2PrFKhw6eL966tc9c2K6dP+/qpLTEphFftMhbqEtK/EtHTKwF/8ADfRKYTp1g48aqZ2GUzFNY6DnR+qIkIk2AUj5E0ik3F665xlugt2zxgLhjRw80hw71gPKtt2p2zPff91u3bvDd78J3vpPejlixAHjFivKRPEaN8lSPDz7wVvktWzx3HHad0hKbRvyww7xl/5hj/ItIfOfMSMQ7Mj7wgAfd2dlw5pmN+7yl4cR+8Vi1Clq1Sv81LiItngJqkXTLzYWrry4PIKH876Ii+PvfPXCoqaIiuO02D8j/9S8PsNORdx0LgM8801vNv/Y1OPpor0NubnnHwpdfhuOO8/QNSD6FOHj9t23zCV46dIAQkrdkl5T4l5IQ/IuKhtprPhYt8l9u1qzxPHzw15Dy5EUkTRRQizQFiR0f41ta334bzjrL84FrqrjYO+X99rcwcKCnh2RleeBaUtI4wXUshaOoyIPkWDAN5XWIdSC87z5Pc6nO9OLt2vmMd0cdlTz4zs6Gzz8vnwwmW293zcr27T4RUJs2/qVt0SK/Zg49FGbP9tSnt99Ody1FpIXQJ4xIU5eb6ykc4AFxTXNGN2+GZ56Bfv08BWT9em8VjgW6DR1cV5XCEZ8vvWWLB0hdu/q29euTT2wTa/HebTcPmGOPkaikRJPBNFeDB3va0Ouv+/Wwbp2nFX33u/4FEnx0nEMPVVAtIo1CAbVIJlm3zjv3zZjh+chz53pKx6pVntoQ06ULbNjg+aXg6R47dviIFz17eqpEt26ew/yXv3j5rl2rbhGui1gLfKwjWSxAjg+2s7PhkUd8CvJ27bzVOtnENtnZ3pr96ac+/nTXrslbqHNyfPvGjRVztJuxwkL/DrJhQ9XlNm707ytduyafiXvjRh9gZcSIiqnJhYX+Q8nnn/spjdehQ/llB/53bOTD2Lq+fX25fr2vLy2t+P0p9neyx64gEoHvfQ/22Qfeew+GDfNfQBI74s6eXfWJEBGpJ83/E0YkE+Xnl8/YmJj7nDj5DHik87//eYA8ZIhHJHfe6QH4xo0ebA8e7LMS5ubC5Mnw7LOer1xQ4AHJ9u0+qUpsOL/6Vp0JVzp08KECW7Xy1sXPP684IyR43T/80IcbHD7cW7WTzRb51FPw73/7cx8zxnO0q5rkJTa7YnZ2g7XYxwLSOXM8BbxTJ3/K1bVlize2V9hv2VJY/hVbuvdn3poc1q0uZnVRO0poDR/FpQkdMALw019U5KvMPIjNyiovVlrqAXC3btC7N3zrWz48+IoV8PjjXvf16z2bqLS0/Dht2pQPNBMTP/O3GbRt648VQvkkhrHyZr6+VSs/7b17wznnlA8AA/64/ft7sA0RCgYcQ86SDUSKivzAw4eXt1CDp32IiDQCBdQi6ZYYPM+ZA/fc45FFWZkHu7Hc51QBYSQCF1zgN/DIbd06b8UuLfU849gMhLEAev16Dx4nTy6PehLFmjw//xxmzoSVK8vTLYqLfVv37rD33v4TfJ8+qQPRZFOmQ3mQXVDgUeKwYd4CDeXjScdamAsK/DkVFnrQ++mnft4SOyT+859w+eXkl/RnBoexJr8bW/YcyqZZpXTavTwY3RmgZm+iw2fzYMdWWPYVW3oNZBMb6JQ3lA7dOyU9NUmD2yps2eKncO5cD6ZLSsr7U1ZHCBUD2KwsIJRASXegO2C0yS6ja8dA9w7rCSuWAXHR7UdzYb/h7Njhx4p9bygurhgE79jhy82b/ceC66/3QHj1ar9kysp8e1ZW+X6tWpXXr3VrX1daWr4uK8vLFBeXP+fEvqKx4LtVK3/sDz/074dt2pT/+JKV5ZfbwQd7+S5dutK78zgmjF5JZHB3uPRS5VCLSFoooBZpLMlaQIuK4Ne/9vux4HnVKg+yjzrKZ0tctcrHVI4FodVpNY39JB7fsh3fQrxtmzf7lZV5Csj69d5cOGcOvPaaj57QqpVHVIsXe+Cc+Bt/vPbtPdIZPx4GDUoe+CcbX7qgoDxdZds2b7Z8+21vLc/OhlNO8Zb02CgghxwCn33G9Qv+j8e2nE6/juu4oWc2R8Y9Vn4+vPCnrcwtuZ03OIqV9GZzfid25LcBy9oZjFYIUEN7ssgDyghAKa0Aw15qRVbryk83aXBbDbEgsqysPBiNtcpWR3y5EKBVKMODZiOLUrIpZVtZW7ZuL6EDIckBPEDdssXr36qVB8Dxx23Txn/UKC72v9u3939NLAgvLvbLNb41uqyscr/PWMAdOzdlZRVbqGMt0rF9Skv977Iyr1/8/rHve23b+vfEqVO9DoMGQd++HRl9wmDyYpeAgmgRSQMF1CKNIRbMbtzoQeuIEZ68mpPjkcGee3rHw1Wr4IADvEPVvHk+JnXv3qknOalK/Mgh+fnw3HPecmfmjzN0qD92rCV7yxZ44QVfF2s+jY+iqlJW5kHw5s3lrc2JAXWyzolLlsCUKeUjcZx3ngdEffv6+NH77FM+CsiyZbBmDddvvZqbNo0BYP7Gwbx6ZRkD/7SNnF7taNcOVi3fwar8i9hMe8qALMoImIedoRRaQQgeWHsgWQplgRACraJBqMeXASx1wFspuN1FUBwLQGMTQcbux1p3dyWE8tbhnfuVAfjKMqBdVjF79d7AZQe+S5+n/lr5IHdMB3adQ71ihQetK1Z4y3RZmQferVv7JbnbbnD66f4dKqa+cqg3bfLH/vJL/3EiFmiDf9+KTRS6dau3pqf6cUVEpDEpoBZpDLF0h06dPKLq1Mnvd+vm0cKCBeXBc0kJnHGGB5MjRtR8/OhYmsb773sqSZs2MG2aR0bLlpUHyy+84FFPcbEfNyvLI5RY82nst/n4fIBUWrXy5sOOHasO/BOHB1yzxkcfiUR8SvIZMzyiWrYMevUqf+7LlnnklJXFlC/2TnxwlixtxZKlpUBW9NYBosGxNyQHiAXVpdCqdSxA9bwEo4xWlBIwAmWUkeXlA7Sy+IC3FIIfrazMm15rEhS3aePB6NChniXTo0cdc6hnzoXnpuzc3vPM0Yz+5hBycw+GE/4PLrusfOfqVDDOySdX7OC4caPPwdOxo2cPNeQ8KrHH/uqrip0Vly+Hjz7yy2HZMj9/hx7q3QNERNJJAbVIfcrP9xborCwPFGOjT8TSHTZu9AB60yZvGhwxAm6+OXUHxJiqAum5cz0dIivLH+/jjz2AfucdD9qLiz3hNPbbfqtW5XkHsSnAN2+umFgbS2xt1cqD7p49fdm6tR/vwAO9/jXJoU5myBCv9wcfeDDfrp0fa906b5Xu0qV8spcZM+Dzzzl1aBveX9Y7xQGjeRjEmosDPVnNCD7i4M6fU7rXPnQanUeHNiXQqSNbCraw6aNFdCouosOij6B7Dyhaz5bcfdjUoVfFHOotm2DuhxBKwbLYMvRANpV2qlHHwg4dPJgeMaKe+jtO2wSfTvb/SVkZnHkwjI1GuuPG+ZjftexcGYk0XP/U6j52ssePH8mkS5fU8/+IiDQmBdQi9SU/Hw4/3H8nj7nySvjVr/zvgw7y5ZlnVhxFIjZyR0x1W6HnzPH84kcf9YC4rMx/U999dw+SoTxAju/9FevsGEsEHjjQUy1ycjxCWbnSW447dvQIsGdPD6Dfe6/qETpqIzfXxw6ePNkjo3ff9S8iu+/ujz9zpj/eQQftHErvxrM+YnHHfXjwmW67OHggm218m4lc0/WfRPbZDX7+c1j1ZPnzOO0QuO1v3vS7ZYk3vfbsCSf0hMF7QCSuQ+KCFZDzQXn6ybE9PVUnnXJzvT4LF3qy88cfw5FHll9XzTDSTGegLyKSigJqkXixjoO1adW78MKKwTTAH//owWAst7guwWhhoU9kERseb8UKvxUXe5Dcrp0HyTt2eG4BeBDdqpUHiX36+GOXlXlLeSzd5IgjfLixXRk4sGGmLh8xwgPCjRu9l9mxx3pgP3NmefAKFTo0PjCxFRe/tYm779zOp4uy2bF6Pas2taGQnnjKB4DRjmL2ZR6RjV/Cujbeqr5woedMbNzoXxxik7+sXu2t7Hl5qac8T+xU2dB2dT2WlPiXnW7d/P9fUlL9jqsiIlJvFFCLxFRnnOSqfPxx8vW33ebDecUPF1eT48bSSF55Bf7zH98/lufcpk15i3NxsecxjxkDY8d64LhwoQfSAwfuYqaMamioFs9IxNM6Jk3ypNiFC/1+fPDao4d/MfnXv+DNN+Hllznyq684csUKGDyYuaOOZ8Ijx0QD6phSBrCU0bzp52fhQk+FWby4vBPkMcd4kF1a6vnrqYLpWD1TzfjYEFJdj/FBdk6O13vpUs8x79KlcQJ9ERGpQAG1SEyycZKrm35RUODpAs89V3n7hx/WvmXz8cfhN7/xoLmoyIMms/JUjtJST8vo1AnOPx9OPbU8QXfs2Oo/TrrFUmBi537t2vIUmR49PEf8ued8CnXwFvqo/MWBb/I7PuQAyluny4hQxO/5Gbks8fSRHTt8v0MOKR+3rWPHmgXJjZlGsatxu2NB9oQJfu1BeUJxXX5pERGRGlNALRJTm5/041sRTz89eUBdVuYtrjWZfS8/H+67z6cF37rVA+iOHX1bbLSG7t19KrncXDj++OqlbTRV8ed+27aKU44fdJCf3wULku76B37MRxxIeUfEUrqygbv5HqcMXQzzzYf0a93ac9zfeqt80OTDD/fHTncudDKpxu1ODLL33LNiUnFdf2kREZEaU0AtElObn/TjA5z58z2ojrWixpsxA047repjxloV338fbr3VA8hNm8pTOtq0gR//2MuEAGef7R3QmoPYuV+0yAcgzs+vnD/drl2l3Z7jBO7nW4SdLdOBTmzi71zOuSduhuw9/YtMhw7+xSM2iUxskpoZMzwVpCkGnamux1196Uu8JmfNqjqVRURE6kwBtUi8mv6kH9+KuGWL57N27+7DvsV06eL5z0uWeMfFZHnMhYVw110+8cqrr3p6wo4d5bOFZGX54LxXXNE8A6PYWGgzZviXhzlzfH3nzp7GMHgwvPRS+XqgkG5cy81sp3zMutZs5w5+wrk8CYfd5P+TNWu8dXrjxvLh+YqK/Nz27du0O/IlXo/V+dIXuybnzy8/X/n5lb80KC1ERKTeKKAWqYv4ACc7Gx55xDsA7tjhqRqdO3vQtnWrDwm3dStcfXXlPNc5c+DJJ71z2caN3rkwluPbpw9cfrnnyjbHwKewECZO9KBv+XI46yxff+CB3rJaVOStyB99VGG3JxjHh+xHfKrH2TzBxTzo52vrVu/A+NFH/r/p1s0ni+nUCfbYw1v/Fy3yvzOpI9+uvvTFrslZs/z+XntV7hOgtBARkXqlgFqkruIDnO99z6dui+UCFxXBZ595x8Q2bXyylcce89k9PvigPE+4XTsfpaNzZ5+xonVrb+m+9NLUrdrNxaJFPkReu3ae7vHBBx7kxoLpX//aR+eIG5KwkG78me8RaLNzXWfW8zP+6AH5iBH+ZeTRRz2YLi31oQuzsvwGfn5btfL89uYWTMYGa87PT54eUtsOuCIikpQCapH6EmtxHjHCb0884QFyt26e9rF5s+dF/+UvPnFJ584e/C1f7sHk7rt7C3VuLuy7r+dLN5cc6V0x81b5gQN9FI6jj/YAb9YsT8mIT6EBnud4PmMIEJsWvZSzeJrhfARjLvUW11df9XO8fbu3+nfs6COhrFjh/4vDDvOAPTbhTXNTVXpIOsbUFhFpxhRQi9SHxJ/QYyNT9O/vAU3v3t76Gol4q2ibNh7ozZ0LX33lAfjhh3tQ3b9/Pc5NnQEGD/bW1PXrPT0hFkyDT02en18p6H2J4yilvJNiZzbyI/7qdw480APxnByfxnzFCg+ojzjCz3vv6LTlRUXNP5hMlR7S2GNqi4g0cwqoRepD4k/oUN4x7PPPvdV5zRoP4tav96D65JM98M7PL89zPfTQpjmEW0OKRDw/PFlwN22ap4PEhgqM2kBnylunIY+Z3jo9ZQrce68H4GVlPuvi229D164+W+TIkR7Ag4LJZjo1uYhIOiigFqkPiT+hx0amiO8YtmWL5+3utZfnS3/9616moEA/vacK7p5/Pmnx9mytcL8PK+HOO70FOjbe92uv+Xn+6itPJfngA9h///IxmxVMiohIPVFALVIfUv2EnpfnU5K/+aZPeZ2dDV98Ud5Sqp/eq3bSSfD005VaqLfRrvL9H33LO3yWlfkwhevXe3rHpk3lrdx33+0pIc25k6eIiDS6VrsuIiLVEol4ukayoHjLFg+mTzjBW6hHjy4vF9sPvNNiYWHj1bmpu/hiH+UkQYhL96hw/5NP4IwzfCKXnBxvnc7Kgvbt/bx36OBD8ImIiNQjBdQiDamgwEfwOPRQH8Vi+XIflzqWxxsT69Q4daovFVSXe/HFSqsSUz523o9EfHKY2JCFZWUwbBgMGuQjfnTo4LnU+uIiIiL1SCkfIg0plltdVOTpH6NHl6d6xE/s0pzHBa7rjHyrVlVaVbpzqvEyoJXfj0R8NsXY48WmHB8wwNNqCgt9fOv33tOEJiIiUq8UUIs0pFQ50onD7B13XPMcF7g+ZuTLzq40bF5v1gCltALKKKU3q/yxXnwRevTwyXGys/2LzJtv+qyJ/fv7qCulpTB8uG9rTl9cREQkbRRQizS0ZCNYJLZIl5Q0z86J9dHyftZZ8PjjFVaNtRk8Es5lB+1owzbGMt03hOCBewieYtO6tQfXM2d6Z9Dt233dsmX+i0Fz+eIiIiJppYBaJB2SzVTXHMcFro8Z+R57DJ55xluZow4McxjOR6yiN71ZxYF87BvMfIzvtm3LA+vY2N9ffOGzVl56qd+P7xgqIiJSBwqoRdKhpQyXl+x51ianemvFTohr6UE3NtCFDWyiE/kMIpclcMABPm37p5/6DIk7dniQ3a2bdw7t0cOD6T59KncMFRERqSUF1CLp0hxbpJOJf571kVMdFYAV9GEjXXiWUxnBB0Q+/BD2288n0Fm3zgtu2wYrV3oQ3b27t0y3pKndRUSkwWnYPJGmID8fXn7Zl81ZfE51aanfr47WrSvcHcwX7G5fsZHOdGYDyxjAIvbwjY8+Ch07+ggfbdtCr14+VOHhh8M++0CXLgqmRUSkXimgFkm3/Hz49a/hn/+En/0Mpk1rvmMkx3Kq58/3YDq7mj+SXX55hbsRijgt/Je+XbbQlxWExPLPPedBe9++/ngA777rk7rMmNF8z6+IiKSFAmqRdFu40Ef56N/f/5440W/NMeiLRHyIwC1bvAX55Zer9zz//OdKq3JZTJeSdWygM5voQDbFFQt87Wtw7rlw8smeWtKnDxx2mOdSV7dlXEREpBoUUIuk25Ah3lL74YceXJaWwqxZsGhRumvWMEpKvKV6r71qlvaReBhac+CWt+navpQysvk351BIt/IC69Z5ML3HHj71eMeOsHq1r1+/vnl+YRERkbRQQC2Sbrm5cPPNcMYZcOCBnuMbKiUxNB+1HUrvG9+oeBgK2Ep7FoY9KGyVw8fsV55H3alTeTrJQQd5J8Rhw/z+3Lnw2mua4l1EROqNRvkQaQpyc+H//s+HeduwwVtvm+uwbrUdMvCpp3yUjmgQHKGIr/EOU7adSnbPHBZv7spXW/qQB7Bpkx9/0qTyqd07dPC/Fy/2gDvWOq4OiiIiUkdqoRZpKiIRmDDBA+sJE3zdggXNsxU1EoE996x5MBsbCi+qHysYRD4lbdtTsmU70xlTnvbx+OMeRPfv78stWzzQLi31jqDbtmmmRBERqRdqoRZpSmJjNtfjeM3N2WC+YDCLyF8xlE5s5FP2ZQ7DGMtrXmDbNk8t6dwZjjnGZ0ssKiof+UNERKQeqIVapCkqKICNG30a7Y0bNSpFvCOO2PlnhCJOYwp9SxcDsJx+PMup5a3Uu+8Oxx4LhxwCzz4L773nQfXQoRrtQ0RE6o0CapGmpLDQ0zw2b4Y5c+DVV31Z3fGaW4IZMyrcHcEH7MunbKU97dmys5Ua8HSPnBwPpufM8ZFTNm6EDz6o2TjYIiIiVVBALdJUxNI8pk71AHDoUDj6aF8uXNg8c6lr6847d/65s5Wa5UBcK/WQkd6JMdYKvW6dt05v3gzbt9dsHGwREZEqKKAWaSrip+Xu0MHXbdoEn3/uY1RrmLdyP/xhhbvlrdSdaG/b+bTDIcxZnwv33+8BNHhwvffecMAB0KaNj/ShdBoREakH+r1TpKmIH5+5c2c480xvmQYfRm/ZMg3zFq9VKygrA8pbqT9lXwqtJ8u3Rnh2yyGMuP8OIh984ON8g39RKSyE2bM9taZDBz/PIiIidaCAWqSpSDY+c7duPsRbTSdBaQlKS8Fs591YK/XUsmNpb9s8l3rrUMbOehtmzoSTTvIxvqdO9ZkT27f3dJqSkjQ+CRERaQ6U8iHSlCSOzxwLso89VkPnJSoshD/+cefdCrnUoZTl9OXZshMp3NIGbrjBg+o33/RAunt3Hz5v61Z9SRERkTpTQC3S1MWCbIBZs/ymXGpvyd+wocKqWCv1JjqRTTGLGOzTkW/aBLfe6ikeW7fC+vW+XLEClixJ0xMQEZHmQgG1SCYoLISJE71F9k9/8r9belCdkwNdulRotY9QxBimk00JEPicoXzOEN+4ZInnph97LPTr5yN9fPghXHedp9WIiIjUkgJqkUwQa43t2tWDyPXrNTpFbKr2//ynwup+rKAvy1hLT9bThX/xbfIZCK1beyA9YoQX3LYNdtvNx6KOdf4UERGpBQXUIpkg1hq7fn15YK3cXw+qx46Ff/xj56rBfEEfVlJCFp3ZyHL6MYPDobgYzjnHc6cnTPDg2sw7JfbsmbanICIimU+jfIhkglhr7OjRfn/wYHVQjHfxxT6u9HnnEaGIrzOZd/kam+nAZtrwNqM4jf8R2VIExxzjE7zcdJOnzvTo4TNSlpTovIqISK2ohVokU0QikJfnNwV9lZ177s4/j+RNjmI6bdnObqzgCwaXT0een++3jh192LwhQ3wEkClTNHmOiIjUigJqEWk+evcGvHPiOJ4kl8VkU1o+HTndvNwee3judFYWzJ/vqR9Dh/rY1i09N11ERGpMAbWINB8rV+78s3w68va0ZjuzyON1Di8vu/fePrb36NEwcKB3TCwo8EBbRESkBhRQi0jzEgJQPtFLT1ZTRDcWM4jbuJK5HODlduzwDooLF/pkLzNn+vqXX1bah4iI1IgCasHMTjCzaWa20sy2m9kyM/u3me2bUC5iZv8wswIz22xmU83sgHTVWySlU04BvJV6LxbQlh10YAsF9OYeLitP/Zg0ydM8evXySV969VLah4iI1JgCagHoDswGrgCOB34B7Ae8Y2YDAczMgGeAE4EfAGcBrYFXzax/OiotktKUKZCdTYQiLufv9GQNW+jIdtownz3LOyhed523UG/aBGVlMG8eLF2qtA8REakRBdRCCOHREMLVIYT/hBBeCyE8BJwJdAbOjhY7HTgCuCha/oXoulbAz9JScSlXWAgLFihVIV5xMQDD+YiruJ2B5NONQtbQu2IHxYICOOoo2GsvWLTIOylOnqxzKSIi1aaAWlJZG10WR5enA8tDCK/GCoQQ1gPPAmc0ct0kXmGhD/c2dWrLHfZtF18ojuRNRjKLYtrQni18yr4Vh9EDH4e6c2fNRCkiIjWmgFp2MrMsM2tjZnsCfwdWAo9FN+8HfJxkt0+AAWbWqZGqKYkKCjzvt3//lpn/W9UXiv33B8o7KPZlOVmUsJpe/Jtx5GfvCevWwZw55a3Tc+fCmjWe+tESv5yIiEiNKaCWeO8C24H5wIHA2BDC6ui27kCy6GJddJl0phEzu9TMZqW6rVmzpr6fQ8uTk+PjKS9b5suWNiV5VV8oPvoI2rUDvIPiwcymM5toSzHzGcqtdjX5K9vDv/4Fe+4JX/+6T5qzaJFPZz5xooJqERHZJfW8kXgXAV2APYCfAi+b2REhhMWAASHJPlbVAUMI9wL3ptqel5eX7JhSE5GIj6dcUODBdEubRXFXXyi2bgUzIhTxHf7JOrrzJoezmhxWlPSj/Q64bulfiOTn+9jUHTpAz57Qti1s2ODntaWdUxERqRG1UMtOIYR5IYR3QwiPAscAnYCfRzevw1upE8UiDTXjpVMk4i2sLTHwi32hOPZYXyY7B9ddB0AuSziHJ+hKEaW0YktoywebB7OoMNoqPWQIjBoF27d72sf27T7iR3yOtjqAiohIArVQS1IhhCIzWwgMia76BB9SL9G+wJchhE2NVjmRRJFI1V8mbrzRlzfdxAg+4EA+4gv2oDXFrCrrzVetc8nLfw4efxzuvNM7Kk6dCj16+Igf4Kkj27aV/52VlTqAFxGRFkUt1JKUmfUG9gYWRVc9A/Qzs6PiynQBTotuE2naokF1hCLO5kn25jO6sJ4SjMlFo8kv6AzPPw+PPAK77+63vfbytI/16z1He/16v9+tG6xY4a3aIiLS4imgFszsaTP7tZmdYWZHm9llwGtACXB7tNgzwNvAJDM7z8xOiK4z4PdpqbhITUVbk0fwAcP4kFLasI22vMPX+DNXUFjSGe6/HzZvLs/L7tIFunb1v1u39unKn3zSRwSZMUOpHyIiopQPAeAd4BzgKqANsBSYDvw22iGREEKZmZ0K3AbcBbTDA+yjQwhL01BnkZpbt25nB8XTmMJM8thKP7bTlpkcxByGMXbLZ/D223DeeeUdPcFbo2fM8M6K27bBCSf42NXqtCgi0uKphVoIIdwaQjg4hNAthNAhhDA0hHBZLJiOK7cuhPDtEEL3aLljQggfpKnaIrUTAhx9NCP4gJHMoj3bKaUV68jhWU6ncFNrePBBWLKkvKNnJOKt1GVl0KuX51AvX+6B9fr1aqUWEWnhFFCLSMszbRqRLmX8kL9yCO/QkS20YwuzGMHrbY/1APmeeyoGytnZPgHMG294SsiQaH/dmTNb7gyVIiICKKAWkZZq/XpyWcJ4JrEbKyiiG4sZxG1rL2Ru4UD49FMPoGNKSmDoUM+tLivz9I+yMu+suHEjzJqloFpEpIVSQC0iLdoIPmAvFtCWHbRlO1/RjztWn0vhonXw0EM+hB54LvXWrX5/2zZP+Vi71jsnzpkDH36olmoRkRZKAbWItFxHH02EIi7n70QopIAerKc7rzGa57cf7RO43HefB8mRiE8e06cPDB4MnTv7/QMPhBEjfIi9xKnPRUSkRVBALSIt17RpcPTRDOcjLuJBIhSRxQ4K6c4f1/4fcwsHwOzZ5akfI0bA2LEwYADsu68PqTdkiAfX8+d7MJ2twZNERFoaBdQi0rJNmwYhcGLbN9idpbQCWrODNeRwx6djKfxyIzz7bHkr9YQJcOqp0KGDd0h8+WU45BDYssXXvfyy0j5ERFoYBdQiIkAu+fyGX9GH5eygDZvoxmsc5akf8R0UY0PotWvnHRJLS2HNGs+xVtqHiEiLpIBaRARg5EiO5C0u5V66U0gWO1hHd/7fkvN5fVFf+Pe/Ye5cz6vOzvaOiW++CUuXQs+ePvqH0j5ERFokBdQiIuDD4PXowYm8zO4sBVpRRjary7rzqyWXkP/BevjFL+CZZ2DyZB/h45NPPIh+9VWlfYiItGAKqEVEYgoKyB1/FL/hV/RmFa0oIWB8WdKLFxbt6WkfX3zhsyOuX+8pHz17woYNSvsQEWnBFFCLiMR74AGO5C1+zm/pwTpKaEUh3bl3zWm8vmooPPAALFsGu+3mgfT69Z7ikZXlaSDLlmlKchGRFkYBtYhIos6dOZmXOIrX6MpG2lDMCvryK24kv2wgLFoEp5wCP/kJXHKJp3l8/rnvO3SoLzUluYhIi6GAWkQk0YYNRCjiR/yZnhRQTDaltOJL+vFC8Vifavzf//YJXnbfvXzEj3btPN0jfgQQpX6IiDR7CqhFRJIJgeHtF/Jj/kR3CimhFeuJ8GDJOOYuy4E33vCc6pwcT/dYtsyXQ4ZoxA8RkRZGAbWISCpbtuxM/ejOenpQwFpyuHXVheTPLIDrr4clS2DcOJ+GfNw4yM2F447TiB8iIi2IAmoRkSpEjh7Bj/gz/fiKLXRkHT2YzUH8edu3KXxrHnz/+1BUBHvu6ZO+AJSUaMQPEZEWRAG1iEhVpk1jOB9xFbfTh+V0YBM7aMcMjuT1cASsWlU+i2JMYhpITk566i4iIo1CAbWIyK6EwJGdP+JIZtCeYjbSgRXsxm3hh8zdvrfnU+fnl5ePRDztY8gQX8ZarkVEpFlSQC0iUg2RDUv44Rc/5SBm0YN1dGITBfTmnmUnUfjxV3DffeW50oWFnju9cKFyqEVEWgAF1BnIzDqb2YlmdqmZ/dLMfmJmF5jZfumum0hzlpsL1/B7dmMVG+nMJjryKfswZ1lPeP/98tSPggLPndbQeSIiLYLGc8oQZtYeOB+4GDiE8i9DFl2GaLkC4Eng7hDCR41dT5HmbjgfcRn3cD03sZ02zGcv7lt5CrnbbiP3pptg61Y47DDlUIuItCAKqJs4M8sGfghcC0SAbcA7wExgJbAOaA/0APYGRgGXA5eZ2VTgqhDCx2moukjzFAJD7WAGsIQF7MUO2vBu4WD+vP40rlt9O5GLL/ZJX8aN85bpnBzlUIuINHMKqJu+z4Bc4AXgAeC/IYTtVe1gZnsBE4DxwBwzuziE8GBDV1SkpRi8bjbDBv2DJRsGsoX2lJLFzLLhzGEEY7dMhylT4MgjFUiLiLQQyqFu+j4FDgohnBJC+PeugmmAEML8EMIvgT2Aq/EWbBGpJ5EI/HDDbxjNDHJYC8AaevLQ5tPJ397XR/yIH/VDRESaNQXUTVwI4fQQwge13HdHCOGOEMLf67teIi1d7uBsruc3HMpbtGM722jHO4zizyWXU7i1HUyapNE9RERaCAXUIiK1sXAhudlfMZ5J9GQ1rSijmNZ8WrYXi95aCZs3w6JF6a6liIg0AgXUzYSZ7R0dPu8yM+ua7vqItAjFxYwY1Z6RzKIjm+nFajqzCQrX+RjUM2aolVpEpAVQp8QMY2bXAd8F9gshrIuuOxZ4FmgTLfYzMzskhLA2TdUUaTEibz/PD20Q7dlGGa3ozWoG8wU8+zEceqiP9KHOiSIizZqFENJdB6kBM3sb2BZCODpu3UxgP+D/AbsB3wN+E0K4Lj21rL68vLwwa9asdFdDpG52243CVdspIIccCohQ5Os7dPCxqBVQizQKM5sdQshLdz2k5VELdeYZBDwdu2Nm/YCDgT+GEH4TXbc38HWgyQfUIs3CypVEdtuNyKqFFddv2VKeRz14sAJrEZFmSjnUmSeCT+YSczg+S+KUuHWzgQGNWSmRFm/lSujdu8KqQrqxYOT5FH7j2/CHP2goPRGRZkot1JlnDdAv7v7RQDHwbty6NujLkkjjW7kSzAAPpifyTdbTha7LNjDhwWeJtG8PV1yhlmoRkWZGAXXmmQucbmb749OQnwu8EULYGldmELCi8asmIhx9NLz6KovYg1nkAYEV9GGPNV9xRocO6qQoItIMqRUz8/we6Ap8AHwe/fv22EYzaweMAdTTTyQdpk3zoBrYSEfeYyQLGMJtO77L3D9Ng5ycNFdQRETqmwLqDBNCmAGcCkzGOyeeHUJ4Pq7IYcBi4jouikgjmzaNwetm03fvCNmU0oEtrGI3bv3qfPJHj9fY1CIizYwC6gwUQnghhHBWCOHsEMLTCdumhRBGhBD+k676iYhndVz+6Bh6spqNdGYdPZjNQfz5k6MonPhfBdUiIs2IAuoMY2b3m9npuyhzqpnd31h1EpHkhg+Hq7idPiynA5vYQTtmcASvf97Tc6lFRKRZUECdeSYAw3dRZhjwzQaviYjs0pHDt3AkM2jPdjbSiWX05y9/b03+e2vUSi0i0kwooG6e2gKl6a6ESLNTWAgLFtQoEI7MmcYP93+d/fmY1uygMxtZTl9mXPg3mDhRQbWISDOggDozpZwv3szaAkcCKxuvOiItQGEhPPEETJ3qyxoEwrkfPcN4HqQ7hZSQzWY6MIcRFN74Z5g1S0G1iEiGU0CdAczsi9gtuuon8evibkuAQmA08Gz6aizSDBUUQGkp9O/vyxrmQB/Z+SNO4AUG8iWjmUF/llOwPhuOP14t1SIiGU4Tu2SGVpS3SgfAordExcBHwCvAbxqnaiItRE4OZGXBsmW+rOF40pENS/hhu32YtH09HdhCZzaRQzQov+oqGDjQx6/WpC8iIhlHAXUGCCEMiv1tZmXAn0IIN6WvRiItUCQC48Z5y3ROTq0C39xt87hi6CgK5q8lhwIiFPmGEOC99/zY48YpqBYRyTAKqDPP0UB+uish0iJFInUOdiOfv0PEEn5gMoNhw6CoSFOTi4hkIOVQZ57r8anFUzKzC81sWuNUR0RqLATo3BmAwnZ9mNX3NGZddCeFt9+vqclFRDKQWqgzzxhg+i7KDASOavCaiEjtbdhAYSFMHPlXZi3qxnbasNeiz/nOyd8j9+1H0107ERGpAQXUzVN7oCTdlRCRqhUUwPrF62hLO/IZSAE5MLMNVxcq60NEJJMo5SMzJR2H2txA4GRgaeNWSURqKicHug7qznL6spx+bKATMzqewOuvp7tmIiJSEwqoM4CZlZlZqZnFZj+8IXY//oa3Sn+BT03+WLrqKyLVE4nAhJlX8H97z2F3llHUtg+Luwzntttg7tx0105ERKpLKR+Z4XXKW6WPBL4EFicpVwqsxceh/kej1ExE6iQSgdPeupa3fgEFr0HrxUtYtWwrtx6/lP/37nHk5qa7hiIisisKqDNACGFM7O/oONT/0jjUIs1HJAKXXw6fPjqHhdty2EGE2WvK+PPhj3LdJ+crn1pEpIlTykfmyQXuTHclRKR+DR8OV225hT4spwOb2EE7ZqzI5fUfPZHuqomIyC4ooM4wIYQlIYT16a6HiNS/I0ft4Ehm0J7tbKQDK+jLbQ/1ZO7PHk531UREpApK+chQZtYHOAboB7RNUiSEEG5u3FqJSF1EZjzDD0eOY+Ws3ZjNQRitKKAn9/zhE3474yQibz+f7iqKiEgSCqgzkJndCPyciv8/o7zjYuxvBdQiGSZ35hNc88N/8sO/rCafXLIo4QOG8fo7z3NGx46weXO6qygiIgmU8pFhzOwC4NfADOBsPHh+APg/4D6gDB8yb2y66igidTP8zxdz1bfX04+lBALL6M9tXMncLYM92bqwMN1VFBGROAqoM893gWXAiSGEp6PrFocQHgshXA6cCpwDdElXBUWk7o687QyG915DB7bRmU2spRc3ch1zPwjwk5+g2V9ERJoOBdSZ5wDgfyGE+KnFs2J/hBBeBF4Erm7siolI/YlE4PIXvkEfWw1AAT34jKFcyR+Y+3Q+/OAHmv1FRKSJUECdeVrjk7fEbAW6JpT5GBhW3QOa2dlm9qSZLTGzrWb2uZn91sw6J5Tbz8yeMrPlZrbZzD4xs6vMTLn4Ig1g+HD44/tjGdptNT1YSzZlrKYXf9rwTfI/XK+AWkSkiVBAnXlWAH3i7n8JHJhQph8+DXl1/RSfZfGXwInA3Xhqyctm1grAzPoC04E9gB8DpwGTgT8At9TsKYhIdQ0fDte/OpY+e3QgENhGR75kAPdxCYWX/yLd1RMRERRQZ6I5eNpHzDRgtJldZGYdzewU4Kxoueo6LYRwTgjh4RDCayGEO4AfAl8DxkTLnArkAOeEEP4dQpgWQrgW+Dcwvm5PSUSqMnw4/PGX6ziAj2nPJjbTkRkcwevbD0531UREBAXUmWgKsJ+Z5Ubv/w5YD0wENgDP4CN//Kq6BwwhrEmyemZ02S+6bBNdbkgoV4SuI5EGN/ziPL6T9SA9WUMBPVjMIG7jGmV9iIg0AQqEMkwIYWIIoUMIIT96fykwEk/TeAm4FxgZQninjg91VHQ5L7p8AigA/mpmuWbWxcy+AVwE3F7HxxKRahix5iX2arWEtuygA9so2Hs099yjUfRERNLNQgi7LiVpZWbXAq8A74UQyhrh8frhKSMfhBCOi1s/BPgvsG90VQBuCCHcVNvHysvLC7NmzapLdUValLlz4Yc/hPx8aN0a+q+ZxSWb/8ppBy8nMvOldFdPJK3MbHYIIS/d9ZCWRwF1BjCzMjx43QS8hgfXr4QQPm6Ax+qEdz7sCxwSQlgWXd8z+rjbgFvxkUbG4jM2/jqEcGuK410KXJrq8QYMGHDwkiVL6vMpiDR7//0v/OEPsOX9T1mztQMDWcxJvMj38mYrqJYWTQG1pIuGO8sMvwGOBg7BOweeAmBmq/FOibEAu06RqZm1w3Ow9wCOigXTUT8DBgEDQwixH5inm1kWcLOZ/TOEUJB4zBDCvXgaSlJ5eXn6RidSQ0ceCe++C6+/WUBrdmM7bXmBE9h99gpOK/QxrEVEpPEohzoDhBCuCyGMBiLASXjO8hygJ3A+PuX4F2a20Mz+bmbnmFlOTR7DzFoDT+JB+8khhI8SihwALIwLpmPew8fGHlLT5yUitROJwHe+A/v3XENHtrCWHL5gD+5t9R3uGvQ7Cr/543RXUUSkRVFAnUFCCFtCCC+GEH4W/UkrBzgT75D4Od6yfAnwKD5edbVEx5p+GDgGOCNFh8aVwBAzS2z7+lp0+VWNnoyI1EluLlzz7lkc3mshvVhDW4rZXtqKqRvymPPgXLg0ZaaViIjUMwXUGSyEUBRCmBxCuALYD/gGPkuiUbP/7d+AccBtwGYzGxV36x8tcw/QHngp2gJ+jJndjE8K83R0tBERaUS5uXDNO2cz/LLD6Ji1jSIiFNCDfzOO/CmfpLt6IiIthgLqDGZm+5vZD83sv8A64Ck8NWM58EgNDnVSdHkt8HbC7RKAaKv1aGANcCeea30mcBNwQZ2fjIjUSm4uXHMNHL7navqzlC5sYhn9uLP7DeTnp7t2IiItg0b5yCBmNhgfWeMYfAbDnnhr9Cp8ZI5XgVdDCAvSVMUa07B5IvUjPx/uPO1lFi4soyhEsLJS+vcr45rJhzN8eLprJ9I4NMqHpItG+cgAZnY/HkT3xwPoNfjwebEA+rM0Vk9EmoDcXPjRs8dx6wlTyV/QjjX0ZNGSEpYc+znX/HMoRx6p0T9ERBqKUj4ywwR8CvAngINDCL1DCOeEEO5WMC0iMbm5cM6au+nERtqxlTKy+GJtZ+64AyZO1IyKIiINRQF1ZliI/6/OAV43s/+Z2dVmlmdmlua6iUgTMuKEXhzHVNqzDSijA1vpO+MRNtw1iUWLYMECBdYiIvVNOdQZwsz64mkfx+CTvOyOz564AXgdT/+YFkL4MG2VrAXlUIvUv8JTLuT1acVMLjmFdSWd6chm9uMTOuw1gHbnn0lW106Mm9BRKSDS7CiHWtJFAXWGMrMheHA9lvIOigEf7WM6Hlzfna76VZcCapEGUFgITzxB4Q+uY9GOfjtXz8w6lP7jDmP+5n4cOOEg8o7urKBamhUF1JIuCqibCTM7AA+uv40PnVcWQmjynU4VUIs0kMJCOPNMmD7d79KNJ1qdx8ayDsxp/TVGfHs4nQ/ai3Hj1FlRmg8F1JIuTT7gkqqZWW88kI7dBsU2patOItIERCLw6qtw6qkwYwaR0mLGbX6MWRwMxYG9nnyBZXv+hUWLOtK1K+TkKLAWEaktBdQZxsy64SkesQB6n9im6PIz4JXoTURauilTfNm+PRE2k8ds8tmDZWvbs+2rtcyY0ZF27SArC7VWi4jUkgLqDGBmx1OeLz0cH/EjFkB/iQfP04BXQggr01FHEWniDj0UXn2VCEWM4wkKuu3F+q0vM3PVsfQf1oP5yzsxaxbk5SmoFhGpKQXUmeEFvMNhbFKXV4kG0SGERemsmIhkiGnTYOxYePNNIrt1IbJiDoX3/JT3W53H/MOOZ06XI4Ee5OfDccdBSYnSQEREqkudEjOAmT0LTMUD6I/SXZ/6pE6JIo2ssBB694biYr9LN2ZxMB8ynL2Gd2DumTexaBGEAN27ww9/6BPGiGQCdUqUdFELdQYIIZyW7jqISDMRiewMpgEiFO3Mq54/t4zXls1lXpvh7NgB7dp5meuuU0u1iEhVNFOiiEhL06lThbuxvOoD+ZB+RZ+Qne2dFM1g82YoKEhTPUVEMoQCahGRTFZYWPP5xDduTBpU5zGbvUo+pmtXaNUKunWDgQM9l1pERFJTyoeISKaKzohIaWnNx73buNH3nzgRrrwS8KD6e9zDoZ/NYdmd/6H/0E6MGKF0DxGRXVELtYhIpioo8GC6f39f1jQ3IxKBCRMqrqKIscUvMn7bfYztMosINWj5FhFpodRCLSKSqXJyvGV62TJf1iY3IxKBjh09WTqmTRuYNQtWrfJtF16ooT5ERKqgYfMkrTRsnkgdFRZ6y3RdB43u1MmD6jZt4Be/gI8/hqefhrIyT6guKKh0/Pp6aJH6omHzJF3UQi0ikskikfqJZjdt8mVhISxaBDfeWL6trMzHrt6xY+equqRvi4g0NwqoM5SZ9QLygAiQlaxMCOHBRq2UiGS+SAR+9rPK64uLIT9/Z+pHfPr2/Plo2nIRadEUUGcYM2sN3AOMJ3WnUsOnKldALSI19847ydffdx9cfTVEIjvTt+fPhzlzfHN+vlqqRaRlUkCdeW4GvgUsAh4GlgIlaa2RiDQvo0bBq69WXHfooTB1KqxZAxddROTIIxk3zlumAfbay/tGJkm1FhFp9hRQZ57/A+YDI0IIW9NdGRFphqZNg7FjvaV6r708f/qrr+CTT2DmTPjHP+Af/yBy8cXk5XnL9Pz5sGULZOtTRURaII1DnXl6Af9TMC0iDWraNI+QX30V5s3zYDreJZfA3LlEInDccV60Qwd4+eWaTdooItIcKKDOPF8CXdJdCRFpIc46C5YuTb5t7lwASkp86Ly+fWH5ch8kRESkJVFAnXkmAieZWdd0V0REWoBUHRQBevYEPJjetg2mTPHUjxkz1EotIi2LAurM8zvgDWCqmR1tZmqtFhFXWOi9BGfNqr+IdtSo5OuPOALefx8KC4lEYPRoT7c+7TRo167ms6CLiGQydR/JPMXRpQFTAcwsWbkQQtD/V6SlKCyEiRO906CZDwo9YUL5kBu1ndYw1kHxrbege3c48EBPAenWzbdHh/UYPBj69PGUD3VOFJGWRm95mWcGPsa0iEi5RYt8uI127aBtW1i/vnwMu7pOazhtWvkxFi+G6dPh0089x+OWW2Dbtp2dEydNKu+cqDGpRaSlUECdYUIIY9JdBxFpYgoLPXF5xQpYsgQGDPD8i5wc3x4/rWFtB4uORDxCHjUKtm8vX799O7RvD1u37uycqNkTRaSlUUAtIpLpCgq8ZfrMM33kja99DY4+ujySjU1ruGyZL2OBdk1FIvDFF5XXb9sGhYXk5EQ0e6KItEgKqEVEMl0sYC4qgsGDKwbTUN66XJsc6kR5eclH/pg1i0heHuPGRTR7ooi0OAqomzgzuw7Pmf5bCGFd9H51hBDCzQ1YNRFpKqoTMEcitY9q4zs0vv22d3pM9Oqr8M47RC68kLy8XM2eKCItioWg/m1NmZmV4QH1PiGE+dH71RFCCFkNWLV6kZeXF2bFmrNEpOlJ1qHxvPPgpZfKy7Rr5z0SO3SA/faDK64gvyiys4Ni585K+5DGYWazQwh56a6HtDxqN2j6jo4uv0y4LyLS8JJ1aHzxRe+c+O67Xqa0FBYuhEMP9Qi6oIASIuTk+Oh6n3/ug5DkKcwRkWZKAXUTF0J4rar7IiINKpafnZi/EZ/2UVwM8+b57e9/h5wcciifPTEEH4Rk8GC1UotI86SZEkVEJLXYANNbtpQPMF1YWD6MR6LLLoNIpMLsiUcf7cNiL1rUuFUXEWksCqhFRKRcYSEsWFBx6vLYANN77eXpHQUFMHLkLg81eDB06eLzwMyf763U9TUjuohIU6KUDxGRli42ikd2trdAJ86omGwc6xkzvAn6jTdSHjbWSr1hg8fiy5drshcRaZ4UUIuItGTxo3gUFHhaR+IA0qmG5ZsxwyPk+NbqX/zCjxktM3gw9OnjwbQmexGR5kopHyIiLUliSkf8KB4dOniudLIZFSMR2HPPylHwz35W8f5tt/kx43YbNw4OPBBGjIC+fT24Vj61iDQnaqEWEWkpEseUPu447y24bZsH0Z07+/TlsZzpSKTipC7JmpTfe6/i/eJi+L//g5kzd66KRDzN4+OPfdSP7dthxw7o0QNycxv4OYuINAK1UDdDZnasmf0w3fUQkSYmvjV640aYNKk88B050puSc3PLW6JjAfjUqb5M1qPw8MMrr5s9u9KqWD717rtD167wxRf+8OqkKCLNgQLq5ukC4E/proSINDHxnQtjw+D17+8zHXbtWrkFOhaAd+sGK1Ykz9N48cXK60Lw4T1OPbXC6sGDoWNH2LwZOnWCTZuU+iEizYMCahGRliKW0HzssXDhhZ7ikZgvHZ9jnZPj6SDPPlv1uHfXXVd53caN8NxzFYLqSMQfdo89PNNk6VINpScizYNyqEVEWpLYqB1QeeSOxBzrceM8T2P9ehg6FIqKykf+iHfjjb78z3/gs8+grKx823PPVSiam+tp2s895x0Uv/pK05KLSOZTC7WISEuVOHJHfI51bBi9wYM98i0qqjzyR7wbb/Qxqffdt/K2JKkfsQlfPv4YnnrKh9ITEclUCqhFRMQlm8AlPk3kuOM8yE6VoxGJwOuvV14/Y0alYuqgKCLNiVI+RETEpZrAJbZMTAdJNoxeJAKnnFIx1WP06ErFYh0UFy+u2EFRqR8ikokUUGcAMxtfw12GNEhFRKT5i8+xjhefDhI/i2Iyv/kNvPaaR8mdOvn9JA9z4YVw333eB7KoyFM/NDa1iGQiBdSZYSIQalDealheRKSixAldkqWDpDJ3rk9f3rWrd1J89FEYPrxSsVgHxf/8B9as8aJ33gk/+pGCahHJLAqoM8ODKEAWkcaSbLSPVOkgyQwf7oNNf/SR33/wQU8DOfLISkVjqR/z58O6db5u0iS44oqqH0JEpClRQJ0BQggT0l0HEWlBUqV3xKeDVDUl+fDhcPzxHhkPHAhr18JbbyUNqGOpH2vX+v3u3ZVPLSKZR6N8iIhIRbtK74i1YD/zDPz1r8nHvDv7bB8bb+1aaNUKDjss5cPl5nqax957+5DXixZpKD0RySwWgjIJJH3y8vLCrFmz0l0NEUlUVQv0ggUeTH/xhQfM++2XPEfj9de9Zfqww+CAA3aZLjJrVnk+dWEhDBigfGqpGTObHULQbxvS6NRC3cSZ2VVm1q4O+x9kZifVZ51EpAVInPQlXk4ObNniwXSPHtChgwfLiY48En7+cw+mv/1tOPFEX8YGnO7VC8x8SXk+dWGh51N/+aXGpxaRzKCAuun7f8AiM7vGzPpVZwdzJ5jZ08BMYFiD1lBEWpZY4vN++8Fuu3lwnV1Fl5xbboHJk71Fe/Jkv9+rlzdFgy979dp52AEDPJc6Pp9aRKQpU0Dd9B0AzAF+Cyw2s9fM7BYz+4aZjTKzoWY2zMzGmtn3zOxBYBnwP+Bw4Arg9+mrvog0S7m5Hv2Ct1C//HLqpuQ33vBl69a+nDq1PJiOid5XPrWIZCIF1E1cCGF+COFU4Ajg38DXgF8A/wHeBD4F3gdeBv4KXAhsAH4CDA4h3B1CKEtH3UWkmSsp8fSPvfbyUUGSpX0AfP3rviwu9mXfvh6Ex+vZc+efsfGpBw8uH8r6zjsVVItI06Vh8zJECOEt4C0zuxw4Eg+wBwA9gK3AauBDYHoI4ZOaHNvMzgbOB/KAXsCXwFPA/wshbEwoOwq4ARgFtAa+AG4JITxW6ycnIpmpupO9/PznvnziCejdG95/39NEYnr2hNWrK+ySOD71jh2a9EVEmi6N8iGY2Tt4EP1fPF1kBB40fwYcFmvhNrNTgKeBR/DW8h3AvsCGEMLE2jy2RvkQyXBVjQaSrOyBB3oAHrPffvDxx0mL5+d7EL1woU9N3qGDj0199dWa9EWS0ygfki4KqAUz6xlCWJOwbjzwAHBMCGGamXUGFgGPhBB+XF+PrYBapBmqKsjOzvb0kJhWrconjkkiPx9uvRXefRfat4fOneGaa2Ds2Aasv2QsBdSSLsqhFhKD6aiZ0WVsZJFxQE/g9kaplIhkptikL1On+jKxo+LIkRXvDxiQOvcaT+845xwfFCQEzwz597+VTy0iTYtyqDOMmd1fjWJleMfEecCUEMKKWjzUUdHlvOjyCGAdcICZ/Q/YB1gB/AP4TQihtPIhRKTFSTVteczbb3tQ/f77Hkz/4hepc6+jRoyAgw/2iV9C8NH37rtPqR8i0nQooM48E4BYno4l2R4S1heb2a9CCH+o7gNEx7u+CZgaQojlY/QFOuD50zcDs4FjgV8D3fBRRZId61Lg0lSPNWDAgOpWS0QyQXU6Ks6cWTEt5Kyz4J13YNQomDatUvFIBL7zHe+c+O67Pjb17NkwZ45SP0SkaVAOdYYxs1w87eII4A7gDWAV0BsYDfwImAH8DhiOB7z9gDNDCP+txvE7AdPxAPqQEMKy6PqXgOOAq0IIf4wrfzdwMdAzhLC+ps9HOdQizVBNOiqOHQuvvlp+/+ijkwbV4KtvvdUPv3UrHHssXHedWqmlnHKoJV2UQ515vo4HziNCCP8vhPB6COHz6PIW4GA8XePwEMJ9+OQuW/EJXqoUneL8GWAP4IRYMB21Nrp8OWG3l/Dh8/arw3MSkeakqmnLE73zTtX344wYAfvu68F0+/bw6afeSi0ikm4KqDPPpcATIYSvkm0MISwFnoiWi92fAhxU1UHNrDXwJHAIcHII4aOEIrGxrRN/0oill2jyGBGpuVGjqr4PPstL9+5ELj6T007zeWFKSrxjYrJ+jyIijU0BdeYZBOwqtaIIiJ/6YDHQKVVhM2sFPAwcA5wRQkjWRDQ5ujwxYf0JwDYg+UCyIiJVmTbN0zzat0+e7nHmmfD00x41P/00I/54EYMHw8qV3lL98svw/PPpqbqISIw6JWaeAjyX+RdVlDme8hQN8E6DVQXhf8OHxbsF2BydDTFmWQhhWQjhYzObCNwUDcDfxzslXgLcHELYVNMnIiICVAyi8/N9JpchQ3zMvOnTKxSNPDeJUQcO45XOP6WwEDZsgL/8BQ49VDMoikj6qIU68zwJHGRmk8yswhAZZjbAzB7GOyP+J27TwcCCKo55UnR5LfB2wu2SuHKXAX8EfgD8DzgTuDKEcENtn4yIyE75+fDrX8M//+nL/HwYM6ZSsdEf/pXuqz5m+3Zo29ZH/5gxo/GrKyISo4A681wHzAH+D1hkZovN7F0zW4zPZHg+8EG0HGbWBygGHkp1wBDCoBCCpbjdEFduRwjhVyGE3UMIbUIIe4UQ7mywZyoiLcvChZ4cveeevly4EJ56Cr7xjQrFclnCFdtup2dPD6g3bvThrZVLLSLpooA6w4QQNgCHAdcDS4ABwMjocglwA3BYtBwhhBUhhMNCCPekp8YiItU0ZIhPTb5ggS+HDPH1Tz0Fp5xSoejJR23huOM8oO7RA776ChYtSkOdRURQDnVGCiFsxydXudnMOgNdgA0hhI3prZmISB3k5sLNN1fMoY6ZMgVOPdVzO0aPJjLlcU78L7z5pgfVixd7UJ2nEYhFJA0UUGe4aBCtQFpEmofc3PJAOnGCmClTKhTt1w8GDYIVK3xK8uefhwMPVOdEEWl8CqgzlJl1wDsFjqB8FI/3gadDCJvTWDURkborLPRBpktLfQrzceMqTRQzeDDsvz9s2wabN8Pq1TBpElxxhWZPFJHGpYA6A5nZycADQHfKJ1YBn3TlT2b2rRDClKQ7i4hkgoICD6b794dly/x+QpQcicB3vgNbtniWSPv25bsqoBaRxqROiRnGzA4CnsJbpR8Gvo0Pe/ft6P1uwH/M7OA0VVFEpO5ycrxletkyX+bkJC2WmwsTJkCrVrB+Pcyc6a3VIiKNSS3UmedavCV6dJIZDSea2d+A6cAvgbMauW4iIvUjEvE0j4ICj5BnzarcUTGqY0cYORI++8xnT3z2WRg4UK3UItJ4FFBnntHAEymmByeE8K6Z/QefElxEJHNFIlBUBLfd5uNSZ2f7KCAJQXWs8XrrVqV9iEh6KOUj83QFlu6izJf4UHoiIpkt2WQvCSIROO0036y0DxFJBwXUmWc5cMguyuQBKxqhLiIiDSvVZC8JYmkfXbtC2XvvMvGQu8g/4bJGrqyItFQKqDPP/4CxZvZzM8uK32BmrczsKuDYaDkRkcwWm+zl4ovh/PPhtddg7txKxXamfbz6FkWrdvBp8R7c99LuFJ56UePWV0RaJOVQZ56bga8DtwCXmdkMvDV6N+AIYBCwEvhNmuonIlK/cnM9l+Oyy8pzqf/+dxg+fGeRSAQuvBC+unke+QyjDJjNQcyZ/i5j01ZxEWkp1EKdYUIIK4HDganAQOBC4GrgIiA3uv6IEIJSPkSk+Zg714PpIUN8maSVOjcXzjlkMb1YzTbakk8uT/S8nMLCRq+tiLQwCqgzUAhhcQjhBGB34HQ8mD4d2D2EcEIIIT+tFRQRqW/Dh3vL9MKFPs94JEKySHnEszczeBCsoD+b2+Qwrc0pvP56o9dWRFoYCyGkuw7SguXl5YVZs2aluxoikgnmzoU33oCVK6Ffv5RTkt99N9x4ozdkb98OJ5wA992nYfRaAjObHULIS3c9pOVRC7WIiGSG4cM9Ou7Xz6ckLy31AacTDB0KnTr5lOSlpfDBBzDnpJ/7ECCnntr49RaRZk+dEps4M7u/lruGEMLF9VoZEZF0q8aU5CNGeOy9ciW0awcblxTw+cK1jGUDPPecB9VTpjR+3UWk2VJA3fRNqOV+AVBALSLNS/yU5Dk5SfM4IhG46CL46CPYtAlKi7fzIQdSSDciFMGMGY1fbxFp1hRQN325uy4iItKCRCK7TIg+8kgYOxZeeQW6ddlAwYbeLGIP8ngfRo9upIqKSEuhgLqJCyEsSXcdREQyTawhe9ky6HnEPmx7YSWs7whHn6J0DxGpdwqoRUQkcxUWpkz/GDHCW6k3bIAuVx/N4AlHg0b6EJEGoIBaREQyU2EhPPGED+WRZAi9SAQmTPB4Ozu7fEAQDZ8nIvVNAbWIiGSmggIPpvv399yOgoJK0XLs7sSJPnt5164eZCuoFpH6pIBaREQyUzWG0ANYtAjefBNat4biYu+TmKepP0SkHimgFhGRzFSNIfTAc6i/+AJatYKyMr8vIlKfFFCLiEjmigXR1UiQLinxFuqNGxuhXiLSomjqcRERyVyxjolTp/qysLBSkS5dPM3aDEKA6dOTFhMRqTUF1CIikrniOyaWlpa3VMcZPNhvJSXQrZunXC9alOJ4hYWwYIEibhGpEaV8iIhI5qpGx8RIBE47DRYu9Nbq9etT5FHvYhg+EZFU1EItIiKZK9Yx8dhjqwyAR4yAUaO8Abu4GN5+O0kjdDVau0VEklFALSIimS0SgT33rLI1ORKBQw+FNm28Efudd2DOnIRC1RyGT0QkkVI+RESkRejSBdq2haVLYcsW78c4YkRcHF7NYfhERBIpoBYRkRZh8GDYay/Pn+7dG9q3TzK5YiSiQFpEakwpHyIi0iJEInDOOZ7NsXYtvPUWbN6c7lqJSHOggFpERFqMkhKfgrywEBYvhgce0Ah5IlJ3CqhFRKRF2b4d1q3z4fNmzapiTGoRkWpSQC0iIi3G4ME+Kl5RkQfWixfDV1+lu1YikukUUIuISPOxi5kOIxE46ijo29c7KPbo4UNO18ipp0LXrr4UEUGjfIiISHNRzZkOR4+GZ5/1ofM6dPCh86rt1FPhuef87+ee8/tTptRP/UUkYymgFhGR5iF+psNly5KMiedyc+H3v/epyIcM8fvVNmNG1fdFpEVSyoeIiDQPNZjpMDcXjjuuhsE0ePN2VfdFpEVSC7WIiDQPjTHT4ZQpnuYxY4YH00r3EBEUUIuISHPSGDMdKogWkQRK+RARERERqQMF1CIiIiIidaCAWkRERESkDhRQi4iIiIjUgQJqEREREZE6UEAtIiIiIlIHCqhFREREROpAAbWIiIiISB0ooBYRERERqQMF1CIiIiIidaCAWkRERESkDhRQi4iIiIjUgQJqEREREZE6UEAtIiIiIlIHCqhFREREROpAAbVgZmeb2ZNmtsTMtprZ52b2WzPrXMU+fzezYGaTGrOuIiIiIk2NAmoB+ClQCvwSOBG4G/gu8LKZVbpGzOww4AJgQ2NWUkRERKQpyk53BaRJOC2EsCbu/mtmtg54ABgDTIttMLPWwL3ALcBljVlJERERkaZILdRCQjAdMzO67Jew/mogC7i9QSslIiIikiHUQi2pHBVdzoutMLPBwK+AU0IIO8wsLRUTERERaUrUQi2VmFk/4CZgaghhVtyme4CnQgivpqdmIiIiIk2PWqilAjPrBPwXKAG+Fbf+QmAksHcNj3cpcGmq7QMGDKhdRUVERESaCAXUspOZtQOeAfYAjgohLIuu7wT8EbgV2GZm3aK7tAJaR+9vDiEUJx4zhHAv3okxqby8vFCfz0FERESksSnlQ4Cdo3c8CRwCnBxC+Chucw7QE/h/QGHcbXfgnOjfpzRqhUVERESaCLVQC9Gxph8GjsE7HL6TUGQlcHSSXR8DPsKH0Pu4QSspIiIi0kQpoBaAvwHj8MB4s5mNitu2LJr6MT1xJzPbBqwKIVTaJiIi9eDUU2HGDBg9GqZMSXdtRCQFpXwIwEnR5bXA2wm3S9JVKRGRFu3UU+G552DDBl+eemq6ayQiKaiFWgghDGrM/UREpBpmzKj6vog0GWqhFhERaYpGj676vog0GQqoRUREmqIpU+CUU6BLF18qh1qkyVLKh4iISFOlIFokI6iFWkRERESkDhRQi4iIiIjUgQJqEREREZE6UEAtIiIiIlIHCqhFREREROpAAbWIiIiISB0ooBYREcl0Z54J3bv7UkQanQJqERGRTHbmmfD001BY6EsF1SKNTgG1iIhIJps+ver7ItLgFFCLiIjEMyu/ZYIxY6q+LyINTgG1iIhITGIQnQlB9VNPwTe+AZGIL596Kt01EmlxstNdAREREakjBdEiaaUWahERERGROlBALSIiEhNC1fdFRJJQyoeIiEg8BdEiUkNqoRYRERERqQMF1CIiIiIidaCAWkRERESkDhRQi4iIiIjUgQJqEREREZE6UEAtIiIiIlIHCqhFREREROpAAbWIiIiISB0ooBYRERERqQMF1CIiIiIidaCAWkRERESkDhRQi4iIiIjUgQJqEREREZE6UEAtIiIiIlIH2emugMiubNiwgdWrV1NcXJzuqojUi+zsbNq1a0fPnj1p165duqsjIiJ1pIBamrQNGzawatUq+vXrR/v27TGzdFdJpE5CCJSUlLBp0ya+/PJLevfuTdeuXdNdLRERqQMF1NKkrV69mn79+tGhQ4d0V0WkXpgZrVu3JhKJ0LZtW1auXKmAWkQkwymHWpq04uJi2rdvn+5qiDSI9u3bs3379nRXQ0RE6kgBtTR5mZ7mMXHiRMxs5y0rK4t+/fpxzjnn8Pnnn1dZtnPnzgwbNoy//vWvlJSUVCi7detWunbtipnxwQcfJH3sDRs2cP3117PvvvvSsWNHIpEIBxxwAJdddhmrV6/eWW779u386U9/YtiwYXTu3JkuXbqw9957881vfpMFCxZU+fxuuOGGtP2Ppk+fjpkxderUtDx+XWX6tS0iIk4pHyKN5IknnqB///6UlpayaNEibr75Zo455hg++eSTSj/5x8pu2LCBJ554gh/84AesXr2am266aWeZp556ig0bNgDw4IMPcvvtt1c4RmlpKcceeyyLFy/mmmuuYfjw4WzevJmPP/6YRx99lOXLl9OrVy8Azj//fF566SV+9rOfMWrUKEpLS5k3bx5PPPEEn376KXvuuWfK53XJJZdw4okn1tdpEhERyTwhBN10S9vt4IMPDlX59NNPq9yeCf71r38FICxYsKDC+pdffjkA4X//+98uy44ZMyZ07ty5wrrjjjsudO/ePXzta18LvXv3DiUlJRW2v/LKKwEIkydPTlqv0tLSEEIIixYtCkC44447qizXFL366qsBCC+//HKN9y0pKQnFxcUNUKuaaQ7XuEhTAcwKTeCzTbeWd1PKh0iadOnSBaBawwGOHDmSjRs37kzT+Oqrr3jllVc477zzuOSSS1i1ahUvvvhihX3WrVsHwG677Zb0mK1atapRuVSSpXzceeed7LPPPrRv355IJEJeXh5PP/30zu0hBP70pz8xdOhQ2rRpQ58+fbjiiit2trjHlJSUcOutt7LvvvvuHGbuxBNP5LPPPqtQbsuWLVxxxRXk5OTQs2dPLrzwQoqKiiqUMTOuvfZafve735Gbm0ubNm346KOPAJg0aRLDhg2jXbt25OTkcNFFF7FixYoK+w8aNIgLL7yQxx57jH322YeOHTuSl5fHG2+8UemcvPbaaxxzzDF07tyZjh07csIJJ/Dxxx9XeR5FRCRzKaCWlqGwEBYs8GWalJaWUlJSwvbt25k3bx6//OUv6dWrF2PGjNnlvvn5+WRlZdGpUycAHnroIcrKyhg/fjznnHMO7dq144EHHqiwz0EHHUR2djaXXXYZTz/9NIUpnvvee+9Nly5d+PnPf86kSZNYtWpVnZ7nww8/zFVXXcX555/P//73Px5++GHOPvvsnYE7wLXXXsuVV17Jcccdx7PPPsvPfvYzJk6cyCmnnEJZWdnOcueddx7XXnstJ598MpMnT+a+++5j3333rRTs/uhHP8LMeOSRR7juuut48skn+dGPflSpbhMnTuS5557jtttu47nnnqNv377ce++9XHTRReyzzz489dRT/O53v+PFF1/kqKOOYtOmTRX2nzFjBrfffjs333wzjz/+OKWlpZx66qkVgvfnnnuOY445hk6dOjFp0iQeeeQRNm7cyOjRo1m6dGmdzq2IiDRR6W4i161l3xol5WPduhD+/vcQ7rrLl+vW1f2YNRBL40i89e3bN7z33ntJy3722WehuLg4rFu3Ltxzzz2hVatW4YwzzthZbp999glDhw7def+8884Lbdu2DYWFhRWOd99994WOHTsGIJhZ2HfffcNPf/rT8NVXX1Uo98wzz4ScnJydddtjjz3C97///TBv3rxdPr/rr78++FuJ+/73vx9GjBiRsvzatWtD27Ztwze/+c0K6x966KEAhP/+978hhPKUlTvvvDPlsWIpH+PHj6+w/vvf/35o27ZtKCsr27kOCH369AlbtmzZua6kpCT06tUrjBkzpsL+M2bMqPTYAwcODN26dQvr4q6fmTNnBiA8/PDDO9cNHjw4jB07tsLx1q9fH3r06BF+9KMfVXoOSvkQqT8o5UO3NN3UQi3NX0EBlJZC//6+LChISzWefvppZs6cyXvvvcfkyZPZd999Ofnkk5k3b16lsnvvvTetW7eme/fufO973+OCCy7g/vvvB+C9995j3rx5XHTRRTvLf/Ob32T79u38+9//rnCcSy65hKVLlzJp0iQuvfRSysrKuO2229hvv/345JNPdpY77bTTWLx4MU899RQ/+MEP6NatG3fddRcjRoyo8QgaI0eOZO7cufzgBz9g6tSpbNmypcL2d955h+3bt3PhhRdWWH/eeeeRnZ3Na6+9BsBLL72EmfGd73xnl495yimnVLh/wAEHsH379kqt7SeeeGKFYRg///xzVq9ezQUXXFCh3BFHHMHAgQN31iXm0EMPJRKJVHgcgC+//BKABQsWsGjRIi644AJKSkp23jp06MChhx7K66+/vsvnIiIimUcBtTR/OTmQlQXLlvkyJyct1dh///3Jy8tj5MiRnHHGGTzzzDOEELjhhhsqlY0F35999hmbN2/mwQcfpHv37gA7UztOO+00ioqKKCoqYuTIkfTs2bNS2gdAJBLhggsu4J577mHevHlMnjx553B68Tp27Mg3vvEN/vznPzN79mzeeustsrKy+PnPf16j5zl+/Hjuvvtu3n33XU444QS6d+/OmWeeyeLFi4HynO0+ffpU2C87O5sePXrs3L527Vq6d+9erXHIY+cmpm3btgBs27atwvrEx0xVF/Cc8vg0leo8TizH/eKLL6Z169YVblOmTGHt2rW7fC4iIpJ5NGyeNH+RCIwb5y3TOTl+vwlo3749e+yxBx9++GGlbfvvvz9DhgyptH7Hjh089thjAAwbNqzS9jVr1rBw4cKk+8acccYZDBs2jE8//bTK+o0aNYrjjz+eF154YVdPpQIz47LLLuOyyy6jsLCQl156iauuuopzzz2Xd999d2dQunLlSvbbb7+d+5WUlLB27Vp69OgBQE5ODuvWrWPr1q31NrlPYufJ+LokWrlyJXl5eTU6fqzuv/3tbzn22GMrbW/Tpk2NjiciIplBLdTSMkQisOeeTSaYBh+ZYtGiRfTs2bPa+zz77LOsW7eO66+/nldffbXCLRZoP/jggwAUFBRUaqEF2Lx5M0uXLt3ZKrtx48ZKo2uAd6JcsGBB0tbb6opEIpx77rmcc845O0e5GDVqFG3btt1Z35jHH3+ckpISjjrqKACOP/54Qgj84x//qPXj78rQoUPp3bt3pbq89dZbLFmyZGddanK8QYMG8cknn5CXl1fpduCBB9Zn9UVEpIlQC7VII5k7dy4FBQWEEFixYgV//etfWbduHT/4wQ+qfYwHHniATp068dOf/nTniB/x/vSnP/Hggw9y4403Mn36dL773e8yYcIERo8eTbdu3ViyZAl/+ctfWLduHVdeeSXgecRHH300F110Eccccwy9evVixYoV/OMf/+Djjz/mrrvuqtHzvPTSS+ncuTOHHnoovXr1Yv78+Tz00EMcf/zxgLcKX3nllfz2t7+lY8eOO/PIf/WrX3HEEUfszIc++uijOeuss7jyyitZunQpY8eOpbi4mNdff51TTjmlWqOj7EpWVhY33XQTl112GRdeeCEXXnghX331Fddeey177rkn3/rWt2p0PDPjb3/7G2eccQY7duzgnHPOIScnh1WrVvHWW28xYMCAneddRESaDwXUIo1k3LhxO//u2bMn+++/Py+88AInnHBCtfZfs2YNzz//POPHj08aTIPn7l566aW89tprjBo1iksuuYRp06bxwAMPUFhYSLdu3Rg5ciQvv/wyY8eOBWDIkCFcffXVTJ06laeffpqCggI6derE8OHDeeKJJzj77LNr9DwPP/xw/vWvf/HQQw+xfv16+vbty4UXXsiNN964s8wtt9xCz549ueeee7jrrrvo0aMH48eP57e//W2Fca8fe+wxbr31Vh544AHuuOMOunbtysiRI7nkkktqVKeqXHrppXTo0IE//OEPnHHGGXTq1ImTTz6Z3//+9ynPc1VOPvlkXn/9dW655RYuueQStm7dym677caoUaM499xz663eIiLSdFgIId11kBYsLy8vzJo1K+X2efPmsc8++zRijUQal65xkfpjZrNDCDXr/CBSD5RDLSIiIiJSBwqoRURERETqQAG1iIiI1K9TT4WuXX0p0gIooBYREZH6c+qp8NxzsGGDLxVUSwuggFpERETqz4wZVd8XaYYUUIuIiEj9GT266vsizZACahEREak/U6bAKadAly6+nDIl3TUSaXCa2EVERETql4JoaWHUQi0iIiIiUgcKqEUa2MSJEzEzzIz58+dX2j59+vSd26dOnQrADTfcgJlRUlKys9yGDRu4/vrr2XfffenYsSORSIQDDjiAyy67jNWrV1c4TlW3CRMmVLvuGzdu5Kc//SljxoyhS5cumBnTp0+v6ykRERFpVpTyIZjZ2cD5QB7QC/gSeAr4fyGEjdEyxwDfAg4F+gLLgZeA60MIq9NR70zTuXNnHnroIW6++eYK6x988EE6d+7Mxo0bU+5bWlrKsccey+LFi7nmmmsYPnw4mzdv5uOPP+bRRx9l+fLlHHTQQbz99ts791mxYgVnnnkmv/jFLzj99NN3ru/Zs2e167x27Vruv/9+DjroII477jieeuqpGjxjERGRlkEBtQD8FA+ifwksA0YANwBHm9lhIYQy4HKgE/Ab4AtgT+BG4AQzOzCEsCkdFc8kZ555JpMmTeKmm27CzADYunUrTz75JGeddRYTJ05Mue9rr73GzJkzmTx5MmecccbO9aeffjq//OUvKSsro1WrVowaNWrntsWLFwOwxx57VFhfEwMHDmTdunUATJ06VQG1iIhIEkr5EIDTQgjnhBAeDiG8FkK4A/gh8DVgTLTM90IIJ4UQ/hUt8w+8VTsXOCcttc4wF110EUuWLOGNN97Yue7pp5+mtLSUs846q8p9Y0HtbrvtlnR7q1Y1eymffPLJHHzwwZXWr1ixguzsbO644w6AnYG/iIiIpKaAWgghrEmyemZ02a+6ZaRqAwcO5Mgjj+Shhx7aue7BBx/kG9/4Bp06dapy34MOOojs7Gwuu+wynn76aQoLC+tUl/Hjx/P+++/z6aefVlj/yCOPAHD++efX6fgiIiItiQJqSeWo6HJeHcs0CYWFsGCBL9Np/PjxPPHEE2zbto0VK1YwdepUxo8fv8v99thjD+6++24WLlzImWeeSY8ePdhvv/24+uqrWb58eY3rccYZZ9ClS5cKwT3AQw89xPHHH0/v3r1rfEwRkRo780zo3t2XIhlMAbVUYmb9gJuAqSGEWSnKdAbuwIPpyY1WuVooLIQnnoCpU32ZzqB63LhxbN++nWeffZaHH36Y3XbbjWOOOaZa+15yySUsXbqUSZMmcemll1JWVsZtt93GfvvtxyeffFKjerRv356zzjqLhx9+mBACAB999BEffPBBtQJ8EZE6O/NMePppf1N++mkF1ZLRFFBLBWbWCfgvUIKP6pGsTDbwKJ7qcV4IoSRZuWjZS81sVqrbmjXJMknqV0EBlJZC//6+LCho8IdMqXPnznz961/noYce4sEHH+SCCy6oUf5zJBLhggsu4J577mHevHlMnjx553B6NTV+/HiWLl26cxi8hx56iM6dO1fo9Cgi0mASh+DUkJySwTTKh+xkZu2AZ4A9gKNCCMuSlGkFPAAcC5wSQviwqmOGEO4F7k21PS8vL9Sp0tWQkwNZWbBsmS9zchr6Eas2fvx4TjnlFMrKynj00UfrdKwzzjiDYcOGVcqFro6jjjqKAQMGMGnSJI466igeffRRzj77bNq3b1+nOomIVMuYMd4yHX9fJEMpoBYAzKw18CRwCHBsCOGjFEXvAc4Fzg4hvNJY9auLSATGjfOW6Zwcv59Oxx13HOeccw7dunVjv/32q9Y+BQUFdOrUiXbt2lVYv3nzZpYuXcqBBx5Y43qYGRdccAF/+9vf+MY3vsGyZcuU7iEijeeppzzNY/p0D6Y1LKdkMAXUEmt1fhg4Bm91fidFuduBS4BvhhAmN14N6y4SSX8gHZOVlVXjlunp06fz3e9+lwkTJjB69Gi6devGkiVL+Mtf/sK6deu48sora1WX8ePH89vf/pbLL7+c3XffnaOOOqpSmeeff57Nmzfz0Uf+Heu1116joKCAjh07ctJJJ9XqcUVEAAXR0mwooBaAvwHjgFuAzWYWPwvIshDCMjO7BrgSuB9YkFBmTQhhUeNVt+UZNWoUl1xyCdOmTeOBBx6gsLCQbt26MXLkSF5++WXGjh1bq+Puvffe5OXlMWvWLH7xi18kHXf6u9/9LkuWLNl5/4YbbgB8GMDY5DEiIiItmcV6+EvLZWaLgYEpNt8YQrjBzKZTPkxeogdCCBNq89h5eXlh1qykA4kAMG/ePPbZZ5/aHFokI+gaF6k/ZjY7hJCX7npIy6MWaiGEMKgaZcY0fE1EREREMo+GzRMRERERqQMF1NLkKS1Jmitd2yIizYMCamnSWrduzdatW9NdDZEGsXXrVtq2bZvuaoiISB0poJYmrVevXnz11Vds2bJFrXnSLIQQKC4uZt26dSxbtowePXqku0oiIlJH6pQoTVqXLl0AWL58OcXFxWmujUj9yM7Opl27dgwYMKDSZD0iIpJ5FFBLk9elS5edgbWIiIhIU6OUDxERERGROlBALSIiIiJSBwqoRURERETqQAG1iIiIiEgdKKAWEREREakDBdQiIiIiInVgmixD0snM1gBL0l2PBpQDFKS7EhlE56vmdM5qRuer5jLpnA0MIfRMdyWk5VFALdKAzGxWCCEv3fXIFDpfNadzVjM6XzWncyaya0r5EBERERGpAwXUIiIiIiJ1oIBaRERERKQOFFCLiIiIiNSBAmoRERERkTpQQC0iIiIiUgcKqEUa1r3prkCG0fmqOZ2zmtH5qjmdM5Fd0DjUIiIiIiJ1oBZqEREREZE6UEAtIiIiIlIHCqhFasDMxphZSHIrqsa+7czsD2a2wsy2mtnbZnZkI1S7STGzF6Ln7DfVKJvsXAczG94IVW0yanjOWtx1ZmYnmNk0M1tpZtvNbJmZ/dvM9q3Gvi3uGqvj+Wpx15dIdWSnuwIiGeqHwMy4+yXV2OefwCnA1cAXwPeBF83s0BDC3HqvYRNkZucDw2q420Tg7wnr5tdLhTJALc5ZS7zOugOzgbuANcAA4OfAO2Z2QAhhyS72n0jLusbqcr5a4vUlskvqlChSA2Y2BngVOC6EMLUG+w0D5gLfDiH8K7ouG/gE+DyEcHq9V7aJMbNuwGfAT4BHgFtCCL/axT6hOuWaq5qeM11n5cxsKH7ufhpCuL2Kci36GoupzvnS9SWSmlI+RBrH6UAx8HhsRQihBHgMOMHM2qarYo3o98AnIYRH012RDFLTc6brrNza6LI4rbXIHNU5X7q+RFJQQC1SOw+bWamZrTWzR8xswC7K7wfkhxC2JKz/BGgDDGmQWjYRZnYEMB74Xi12/240z3NLNO9zdD1Xr0mq5Tlr6ddZlpm1MbM98RSOlXiwtyst9Rqr6flq0deXSFWUQy1SM+uB24HXgA3ACOCXwNtmNiKEsDrFft2BwiTr18Vtb5bMrDX+YX1bCOHzGu4+CZgCLAcG4nmb08zsuBDC9HqtaBNSh3PWYq+zqHeBg6N/LwTGVvGajGmR11hUTc9XS7++RFJSQC1SAyGEOcCcuFWvmdnrwHt4R8VUeZgGJOuwYPVbwybpGqA9cEtNdwwhXBR3d4aZ/Rf4GPgNcET9VK9Jqu05a8nXGcBFQBdgD+CnwMtmdkQIYXGqHVrwNQY1P18t/foSSUkpHyJ1FEJ4Hx8RYGQVxdaRvPUmEre92YmmwlwL/Bpoa2bdoh3tiLufVd3jhRA2As9R9bnOaHU8Zy3yOosJIcwLIbwbzTk/BuiEj15Rk2M0+2ssphbnq0VfXyJVUUAtUj9StdzEfALkmlmHhPX7Ajvwn1uboz2AdvjP6oVxN/AWsULggBoec1fnOtPV5Zy11OuskhBCEf58a5PX29yvsUqqeb50fYmkoIBapI7MLA/YC89HTOUZ/n97dx4kR1mHcfz7mENAZVOIgpCCgBii4QiHCBbhEBLAAoGyQJBI5JAilgSQAKagyiqMBI8qUJMSKLECCIIQIoIKHpxGIwZYNBBOA4YIcoYIBsLCzz/eHtPO9sz2zOzOLNnnUzXVm7ff6fftdzq7v37nfd+GEcARufcNBz4H/CYi3hjQSnZON7BvwQtSwLgvDfwRlrQhaQ3cem39TtdN8202VK+zXiRtAowDnmjwfUPhGuulZHv5+jKrwWOozRog6SpgGXAfsJI0KXEmsAL4QZZnS9IfpfMi4jyAiOiWdC1wUTbhbBkwDdgKOKbNp9E2Wa/XHdXpkgCeqkz6KmozSTOAbUnrflcmjM0ANsVt5ussR9IC0v/Jv5ImC48lrd3dQ5pE7Gssp9n2GqrXl1kZDqjNGrMEOBo4BdiAtMzUDcDXI+KFLI+AYfT+Bug40iSzWcAo4AHgwGwM9lBX1GaPAIdnry7SH/6FwAkRcU/bazj4+DpbaxFwJHAGafm25aSbktm5CXa+xtZqtr1gaF5fZn3ykxLNzMzMzFrgMdRmZmZmZi1wQG1mZmZm1gIH1GZmZmZmLXBAbWZmZmbWAgfUZmZmZmYtcEBtZmZmZtYCB9RmZmZmZi1wQG1mZmZm1gIH1GY2ICSNkRSS5nWwDldIek7SezpVhzIGQ1v1h/46j348zi7ZcU5o5ThmZn1xQG1m6yRJuwJTgAsi4rVc+vNZkFX2dXLnzqJ/Vc6pxr5tJD2R5Tm/3XUbCBFxL/BzYJak93a4Oma2Dhve6QqYmQ2Q84FVwA8rCVlP9dyqfMOBc4A1wOyC49wyUBXMWQF8FHilDWX1ImkX4FfAxsApETGnE/XI6c/2mA38GZhOuibMzPqdIgo7K8zMWiJpDLAMuDwivtjmsscCDwM/ioiT+si7I9AN3BsRu7aheh1T6Z2OCOXS9gcWACOBKRFxXQvHH0OHPvN6JC0FNgC2joi3Ol0fM1v3eMiHmbWdpCMl3SXpFUmrJf1N0kxJ7y7IK0mnSnpI0uuSVkiaI6lL0pOSniwo4nhAwLUlqlMJou9t/oxaU2vMcD49+/kaSS9k7bBY0sEtlnsU8EvgbeDAWsG0pE9Iul7Ss5LWSFou6RJJm5UoY4/sHG6ok2eppDckbZT9u1d7tNgW1wBbAPv3VV8zs2Y4oDaztsrG515L+kr/amAOKfg9H7hV0oiqt8wFLgK6gEuBnwKTgd8C1Xkr9gfeAhaVqNIu2XZx6ZNovy2Be4AxwJWk9tsOuFHSvs0cUNKppPZ/Cdg7Im6vke84YCFwEHA76bNYDJwILJa0Rb1yIuJPwCPAwZLeX3D83YBxwE0R8VKJqjfTFguz7aQSxzcza5gDajNrG0l7ADOB5cD2ETEtIs4EJgA3A3sDZ+byTwSmAY8C4yNiekTMIAVQrwO9ekizcdITgKX5yYh1VALqjvVQl7APMDcido+I0yNiKnAo6Xf4mXXfWUDSbFJg/DjwyYjorpFvLHAJ8CQwNiKOjoizIuJwUnC6CfC9EkVeTrr5Obpg39RcnjL2ofG2+Eu23atkGWZmDXFAbWbtdHy2nRURz1YSI6IHOIM09ODEXP5KsPXNiFiZy7+GFJgX2RwYBjzTV2UkDQd2IE1IXFLuFOoe70uS/i6pR9IltdKa8BQwK58QEbcC/wB2a+J4XwPeJA3zWFYn3zRSIHxqRKyoKv824BfAIZLe10d5V5I+26n5REkjgaOA54Bfl6x7w20REa+QbsDq9qabmTXLq3yYWTvtnG1vq94REY9KehrYStKoLIDeKdv9h4JjLQJ6CtIrwwpeLlGf8cB6pAmJa0rkr0nSONKKIkdkdft3UVqTh++uMZluObBHE8e7FTgAuFrSgfmblSqVY+8t6eMF+z9IunkZS50e/oh4WtLvgUmSPhYRD2W7DgE2Ai7MbqrKaLYtXiL1qJuZ9TsH1GbWTl3Ztlbv8TOkXsQuYGUu/7+qM0bEW5JeLDjG6my7Xon69OeExM8ASyJiQSVBUq+0Jq2skd5Dc980Hgr8jFTn2yRNjogXCvJVbk76GlZSZo3neaRhIlOBs7O0Rod7QPNtsT5rrw0zs37lIR9m1k6VdYU3rbH/Q1X5VmXbXj2LkoaxNuDLey7bFu2rVmpCoqTNlZ66+KKklZLmS9okt/9R4FvAjtlKFAuK0krUpy0i4g3gs6SgeifgDklFn0nlc+iKCNV53Vmi2AWkz3OKpGGSPkCa6PhARDzQD6dVk6R3AaNYe22YmfUrB9Rm1k73Z9t9qndI2gYYDSzLDUGo5N+z4Fi7U/wt2zPA88C2JerT54RESVsB95EeNrInqe4bAxfnsu1Jmjh5LummYGqNtEEjG2LxeVLv8HjgTkmjq7JVVkmZ2A/lrSYF8JuRVmE5hvT5NdI73axtSSvJdLehLDMbghxQm1k7/Tjbnpv1UAL/623+Lul30mW5/Fdk23MkdeXyj6TGU+8iPa3qLmDjLEgv1MCExIuByyJiZkQszVbE+AawXy7PKmBrYGFEPBsRq2qkDSrZWOTjSCt5jAXuyh7OUjGHNHnxwmzFj/8jaWS2EktZ87LtsdmrB7iq8Zo3bPdsW7g0oJlZqzyG2szaJiL+KOnbwFnAEknXA6+RvvrfjjT58Du5/HdKuhQ4CXhQ0nxSgHcIaTjCP0mrR1SbTxrScABpabgifU5IzNZYngxMlDQ9t2sY8J/cv7cj/T7t7iNt0MluQE6WtBo4Dbhb0qci4rGIeFjS8aQboQcl3ULqdR9BGus+kfRtwLiSZS2U9DhpkuYI0trT7RiGMZm0LvmNbSjLzIYg91CbWVtFxNmk9YgfI/VSTif9LjoXmFQQ3E4Dvgq8CpxMGqbwO9IEtw1ZO846bz5pIuOxdapSZv3pCdnxd8h+rry2Z+0KJJV8T1WtllGUNmhFxOmkXv/RpJ7q8Vn6T0htdRWpHb4CTAG2Aa4HvtxgUZU1qSs/D6jsm43DgJsjYvlAl2dmQ5NS54SZ2TuLpI+QekuviYheDwyRNJMUIO4cEfdX7y9ZxkHATcCoiHi1Tr45wOiIOKxemrWfpFOA7wN7RcTdna6Pma2b3ENtZoOapE2zVRryaRuQnvQHafWIIheSHvZxXgvFLyKtZ32lpJ0kfVjSJElzq+o0gd5DO4rSrI0krU96ANB8B9NmNpAcUJvZYHcasEzS5ZIukDQPeAT4NOnpetcVvSkiXge+ACzOHkfesIh4mTS+u4s0oa2bNHny6Yh4G0CSSEMhuivvK0qzjhgDXArM6HA9zGwd5yEfZjaoSdqPFBBNID1Vr4c01ONq4KKIeLNztTMzM3NAbWZmZmbWEg/5MDMzMzNrgQNqMzMzM7MWOKA2MzMzM2uBA2ozMzMzsxY4oDYzMzMza4EDajMzMzOzFjigNjMzMzNrgQNqMzMzM7MW/Bcdo1Wi87KQrwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['Teff']), np.log10(iso1.primaries[\"L\"]),\n", + " \"r.\", alpha = 0.3)\n", + "plt.plot(np.log10(iso1.secondaries['Teff']),\n", + " np.log10(iso1.secondaries[\"L\"]), \"r.\", alpha = 0.3)\n", + "plt.plot(np.log10(iso1.singles['Teff']),\n", + " np.log10(iso1.singles[\"L\"]), \"r.\", alpha = 0.3,\n", + " label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['Teff']), np.log10(iso2.points[\"L\"]),\n", + " \"b.\", alpha = 0.3, label=\"MISTv1\")\n", + "plt.xlabel(\"log($T_{eff}$ in Kelvin)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"HR Diagram of Isochrones at solar metallicity and 10 million years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()\n", + "# Rough pattern seems to fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the mass-luminosity relationship of the ISOCHRONE" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from astropy import constants as cs\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun), \"r.\", alpha = 0.3)\n", + "plt.plot(np.log10(iso1.secondaries['mass_current']),\n", + " np.log10(iso1.secondaries[\"L\"] / cs.L_sun), \"r.\", alpha = 0.3)\n", + "plt.plot(np.log10(iso1.singles['mass_current']),\n", + " np.log10(iso1.singles[\"L\"] / cs.L_sun),\n", + " \"r.\", alpha = 0.3,label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['mass_current']),\n", + " np.log10(iso2.points[\"L\"] / cs.L_sun), \"b.\", \n", + " alpha = 0.3, label=\"MISTv1\")\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"Mass-logL of Isochrones at solar metallicity and 10 million years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting what masses are represented by the cluster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the BPASS Cluster vs MIST Settings cluster Observer's HR Diagram\n", + "\n", + "**Remember to use a distance modulus!** (dist to cluster = 1000 pc)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['m_ubv_B'] - clus_1.star_systems[\"m_ubv_V\"],\n", + " clus_1.star_systems[\"m_ubv_V\"] - 5 * np.log10(1000 / 10), \"r.\",label=\"BPASS\")\n", + "plt.plot(clus_2.star_systems['m_ubv_B'] - clus_2.star_systems[\"m_ubv_V\"],\n", + " clus_2.star_systems[\"m_ubv_V\"] - 5 * np.log10(1000 / 10), \"b+\",\n", + " label=\"MISTv1\", alpha=0.1)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of clusters at solar metallicity\\n\" +\n", + " \" and 10 million years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the theorist HR Diagram of the clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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N9CiQKDrRQzrarNAL/d2uQ/8B+X2Odqz6r4i8gw5R2e6ce9rbfwnavvuS1wY4F63+awscA3RH79dmiieSvIXzBBqEv/Z+bzvQGoST0P4N54d4zedoh59pIvIVOoxukXPug0Kuczo64c8c9POwGX3fg9G/g4f9BzrnXheRwejfylLv9+xvG+0AvBWi13M+zrlcEXkS/TtcLCLvo7UJp6Jju2d6P8erSD8r36IB+CYRaUZev5annHM7YnQvrvfyeQ06F8R09DPaAf3MDSJ0pz8AnHO7RWQu0FtEXkPHnecAU51zPwYc97vo6J4bvaQXopJ7V8IxWPH2wBsfWMQx6UQwzjlgfx20Y0EucEwx8jLOnx/vkYO2Ua1GOwlcDzQL89phhB7nnID2jt2AVlkvRb+p1SDEGEvvNW3QyU62eK9ZiHZWON97zU1Bx88qxj0c5p1nD7AVnQv8aEKM0Q34vRTIW8B9Tw+zr8i8hHhNf3SClSz0H/bPwENAk+Le52Je50y0x/Y27zrrvftwWsAxfQgxztnbdxI6kcvv3ufiI/QfXoF7iAaIScAqNNjuRDsFjgVahDj3zWj1vX8se3rQ/obAnWjP3d3e52Ktl4fhaO/5Yt2jSPNWyP08C/1iswsNAJ+i1cchr49+0X0OyEA7NIUdixrwmi7AY2hv5i3e/UlHvwCcEOL4auhEKmne7+l37579naAxs+E+5+jf5s3AMu8+b0KHNbWnBONqi/F3GfJ3VcRnMew1I/mseMefgQbp3eT970uM5r2I9L55n5V/o3M3/O59xpahXwpbFnZeL70TWvDIRP//h7vHPbx9vwI1Iv39hXqId2JThYjIWPSP7gzn3PRY58cYYyoyr1nqFbTX/11FHF68c1pwrrxE5HAX1MPcqxafg1bvtHFFt3cbY4wJw+uN/j1aO9PBRWkyImtzrtzSRORntKpxDzqX7Z/QKrtrLDAbY0zJiMhJaL+WPmjT3tPRCsxgwbmyewHtxHIx2n60HZiOTkc6K2a5MsaYiu90dKTKNnTo1K3RPLlVaxtjjDFxxsY5G2OMMXHGgrMxxhgTZyw4mwpPRIaJiPOGM1Q53nufFZQ2ykvvU9JjjTGxY8HZVDki0kRERojIayKyTESyveB0ehGvqy4iN4nIjyKyV0S2icjHInJCeeXdGFM1WG9tUxUlojOHgc4ytZUiVury5hB/A51d7SfgaXTqwT8DX4nIEOfc+2WV4SJ0oXjzWIfzNPregtfhNsbEiJWcTVW0Dh0G4XPOJaBTcRblIjQwzwF6OudGOOeuROcEzkHnsw61AliZc86tcM6VOLA657Z65yhNgDfGRJEFZ1NqItJLRP5PRBZ5Vb37RGSViDwqIk1DHH+ojVhEThWRWSKyS0R2ishHItIlzHU6icjbIpIlIntEZI6I/CnS/DrnspxznzvntkXwsmu97X8CJ29xzs1HVwBrQehFGgoQkXHe++8gItd7Vev7RCRdRO70SumIyAUiMs97r5tF5GkRqRPifAXakSNRWJuz6MpT0wJ+rytF5IFQK/p4v0cnuvLand5nYL+IrBeRB0Uk3DrWwed5wztPqCUbEZHzvf1PBaU3E5H7RWS51+ywQ0Q+F5H+Ic7R2Gva+EJEMkTkgIhsEZGpInJ8mOs67z22FpH/icgGEcnx93UQkVYi8oiI/OT9zrZ7P48TXUGuOO/9VBF50ftM7PTexxIRGRnqd++95jARecX7jOwVkYUicrmI9PHyPCrEa4p9r0xsWLW2iYa/AecCXwKfoeufHotOdD9QRFKdc7tCvO4sdFWgT9DVrrqii0ocJyJdnXNb/QeKLtf5LbqQ+Sfo4hud0IVEPimTd5V37droqlG/o0vpBfsEuAxdjemVCE79CDq70AfoYg+D0EUjaonINnR5wineNfuhiy5UJ++LQpkSkavRBSb2AG+jKxD1QVf4OltETnTObQ/x0tfR1dc+QRfDOBOdoKElQUtlhvEs2lxwNbpASLDh3vbFgLy2RxdLSUTv1zR00YOz0NWrrnbOBa6x2wW911+hCzlkoau7DUI/s2c750LVqDRDF+nYDbyLLobwm+hykt8ARwAz0N+poAs7DEYX2FhTjPd+G3AUWkPzEbrozonooih9ROR055x/nWFEl22c473vr7yfW6P38NNQFyjBvTKxEI3VM+xRtR/oP6DqIdKvRFdquS0ofZiXng30Ddp3v7fv1qD0T730fwSlDyZvBZxhJcz/OO/1p4fZ383bvzjM/mRv/9wIr5eOzm/uT2+Ctn/vQVdO6hKwrza6ms5+AlbT8faFWhFpFMVcJSzUsd7vdD8aXI8KOv5Z7/gXg9JneekLCFh1Df3H/zNa/d+6mPdoCbAPaB6U3gENiN+EuHYucFFQehP0i9xeoFVAeuPgc3vpbdGVhZaH2Of/nL1K0MpD6HKpDng8xOtqAQ2L+b474k0OFZQ+xjv/n4PSX/LSHwxK70HeymSjSnOv7BGbh1Vrm1Jzzq1zAd/mA7yM/nMfEOalbzjnPg9K85eGUvwJItIWLTmuRTsvBV77fbTEXpb8Vbg7wuz3pzeJ8LxjnHMb/E+clkKnAvWA55xzywP27Uerz2uhpb6y9hfvWk8751YE7fs3uvTeZV6tQrDbXECTgXNuD7rcaTX0i0xxPId+Ibk8KH04WiI9tGauiPRA5zie7JzLt4a0d09HoiXQIQHpO1xAzUxAegZayj1KREKtk34AuMU5lx0m33tDnPOAC11zVIBzbo3zImWQJ7ztob8lr5ngYvTzd2/QeRahXyLyKcm9MrFh1dqm1ESkJloFeRFaNd2Y/P0Z2oR5aVqItPXeNrCt+g/e9uswXwJmof9wYkW8baRz4YZ6//5VxBaE2OcP5G0jvE5JHOttvwje4ZzLEpEf0DWXjwIWBR1S3N9rYV5Fq/WHA4/Coc/ZMLQK+q2AY//obRuHal9F+wNA0JcaETkR+If3+pbol5FAbSjYgz3dObc5xDW+RH8/t4vIscDHaDX3wjCf2ZBEpL6Xp3OBzuic+BJwSODf0pFAXSAtTPD/GrgqKK1E98qUPwvOJhreRP+ZrAHeRxdS3+/tuwktAYWyPTjBOZft9YeqHpDsL7n+FuY8myLKbeT8JeMCnaA8jYKOi/S8gbKLsa9mhNcpCf973Rhmvz+9SfAOF7od2p/36iH2FeCc2yUiE4FrRORU59xMtAmjNfCEy7+ims/b9vMe4TTw/yAi56Il5H1oG/FqtDkhF21XP4XQn9uQnzXn3E6vI9lotN3aX8LdKiLPouv8Hiwkb/4vH1+gtUZL0L+rLYD/dSOD8lTU30Wo9IjvlYkNC86mVEQkGQ3MnwFnBv4DEpFqRGelFn+gCjcWuXUUrlEYf3tpRxGpEaJKM8nbrizjfJQn/z1vDSwNsf+woOPKwnPANWitzExCdAQLysM/nHNPFvPcY9Aq6uTA5gMAEXmB8DUxYWtHvCrxK0W/XXZFOwj+HbgbrUm6q4g8DUYD83jn3LCgPB2GBudAO71tuL+LUOkluVcmBqzN2ZRWJ287NUTJIAWtdiutH7ztSSISquTVJwrXCMtr752DtgX3DnHIQG9boAq4AvPf8z7BO0SkCdATLXUuD94fLc65H9Gq4XNFJBUdm/5VcDBFe09D6N9NOJ2AZSECczXgpBJmGQCnljrnniKvdHpOMfMEMDnEvlBfFlagbdzHSOgx9qHeR0nulYkBC86mtNK9bZ/ARG+IxzPRuIBXIpmB9tS9Pug6gymf9ubnvO29geNNReQ4dNjPFkL/U62oJqLVqTeISKegfWPQqvyJ3heXsvQc2hY8GW17fT74AOdcGjok6DwRuSLUSUTkaO8z6ZcOJInI4QHHCFo67RppJkWku4gkhtjlL70WZ4KXdG/bJ+jcHYEHgw92zh1Aq74bA/8Jek0PYGiI15TkXpkYsGptU1rz0dLNeSIyB+2E0gotTf5EXgen0vo7Os75CW+ihEVoSeNcdEzp2ZGcTEQeAZp7T/0ljBEi8hfv5ynOuSkBL3kDOA+daOQHEfkAbb/7M9qO+jfn3E4qCedcuojchH7B+l5E3kK/gJyCdipagY7JLWtvA4+jHaG2omOLQ7kErbl4SURuBOaifRraAscA3dF8+ztzPY4G+h9EZDL6ReRENDBH/HlCS/WPeX8DK7zrtEWrqnOBh4txjg/QJpSbReRotPaiHTr++CPv52C3o9Xnt3q1C3PQJocL0U5p53jXDxTpvTIxYMHZlIpzLkdEBqFDOc4EbkR7rf7PS1sWpeus8jrcPID+I+wD/Ij+82lB5P9Mz0fH8gYKnB0pHZ0AxH99JyIXo//8rgBuQKt1v0I7+8yJ8Ppxzzn3rIj8DNyCDq2ph/a6fhi4L0zHr2jn4YCIvIZ2LBwXrqTunMsQkV7o72UIcCn6pWkT+hl8ClgccPwLIrLfO+/laPXwbHSSlCFE/nmajg53OhkNyI3QTnMzgMeK8/lwzu0RkdPQz3gftOp5DVpT8Rj6RTD4Nb+JLrxyH/r3l4p+Kb4O7eB2Dnlt0/7XRHSvTGxI6CF1xhgTH0SnJj0ZONI5tyrG2akwRGQscCdwhnNueqzzYyJjwdkYE7dEJAWtdp3mnBtY1PFVkYgc7pz7NSjtaLSW5wA6C92+kC82ccuqtY0xcUdErkXbmf+KtpkGDyMyedK85oclaFV2EvAntMPvNRaYKyYrORtj4o6IpKMdlNagc0O/HtscxS8RGYm2LSeiM4ptR4dMPeKcmxWrfJnSseBsjDHGxBkb52yMMcbEmSrb5ty8eXOXmJgY62wYY4ypBBYsWLDVOdei6COLp8oG58TERNLSQi2eY4wxxkRGRNZF83xWrW2MMcbEGQvOxhhjTJyx4GyMMcbEGQvOxhhjTJyx4GyMMcbEGQvOxhhjTJypskOpjDHxb+fOnWzevJmDBw/GOiumiqpZsyYtW7akUaNG5XpdC87GmLi0c+dOfvvtN9q0aUPdunURkVhnyVQxzjn27t3Lhg0bAMo1QFu1tjEmLm3evJk2bdpQr149C8wmJkSEevXq0aZNGzZv3lyu17bgbIyJSwcPHqRu3bqxzkapjBs3DhE59KhevTpt2rThwgsv5Keffir02IYNG9KjRw+efvppsrOz8x27d+9eGjdujIiwaNGikNfeuXMnI0eOpGvXrtSvX5+mTZty9NFHc/XVV+cLNPv37+fxxx+nR48eNGzYkEaNGnHUUUdx+eWXs2rVqmK9z2+//ZYLL7yQww8/nFq1auHz+ejXrx/jx48nJycn3/tLT0+P4A4Wz6xZsxg1ahS5ublRPzdA3bp1y71pxaq1jTFxq7KUmN9++23atm1LTk4Oq1evZsyYMfTt25elS5fSuHHjkMfu3LmTt99+mxtuuIHNmzdzzz33HDrm3XffZefOnQC8+uqrPProo/nOkZOTw+mnn056ejq33XYbPXv2ZM+ePSxZsoRJkybx66+/0rJlSwAuvvhiPv30U2699VaOP/54cnJyWL58OW+//TbLli0jKSmp0Pf2xBNPcPPNN3Paaafx4IMP0r59e7Kysvj000+59tpradKkCYMHD47GbQxr1qxZjB49mv/85z9Uqxb9MmdMPofOuSr56NWrlzPGxK9ly5bFOgul9sorrzjArVq1Kl/6jBkzHOA+/vjjIo/t06ePa9iwYb60fv36uWbNmrnU1FTXqlUrl52dnW//559/7gA3ZcqUkPnKyclxzjm3evVqB7gnnnii0OPC+fLLL52IuBtuuCHk/p9//tktWrQo3/tbu3ZtoecsiZEjRzrAHTx4MGrn3LdvX77nRX0egTQXxRhl1drGGFPO/B2LilNVetxxx7Fr165DVdEbNmzg888/56KLLuKqq67it99+Y/r06fles23bNgBat24d8pz+0mVxjwvngQceoFmzZjz00EMh9x9xxBEcc8wxYV8vIowaNSpfWnp6OiLCuHHjDqXNnz+ffv364fP5qFevHh07duS6664DYNSoUYwePRrQntX+ZgG/33//ndtuu40OHTpQq1YtOnTowNixY/NVgc+aNQsR4d133+Vvf/sbLVq0oFWrVoW+97Jm1drGmMpt/XpIT4fEREhIiEkWcnJyyM7OJicnhzVr1nDnnXfSsmVL+vTpU+Rr165dS/Xq1WnQoAEAEyZMIDc3l6FDh9KlSxduuOEGxo8fz5lnnnnoNcceeyw1atTg6quvZuTIkfTp04emTZsWOPdRRx1Fo0aNuP322zl48CD9+vUrdlDKyclh1qxZnHPOOdSpU6d4N6IEdu/ezYABA0hJSWHcuHE0bNiQ9PR05syZA8BVV11FRkYGL730El9//TXVq1c/9Nrs7GwGDBjAsmXLuOuuuzj66KP57rvvGDNmDNu2bSvQHHDDDTcwcOBAJkyYwL59+8rsPRWHBedSyswEny/WuTDGhLR+PYweDdnZUKMGjBwZkwB91FFH5Xt++OGH8+GHH4YcmuMP5Lt27eKtt97i3Xff5eyzz6ZevXqAtjEfeeSRpKamAnDOOefw3nvvsX37dpo0aQJAx44dee6557jppps477zzEBG6dOnCmWeeyT//+U8OP/xwABo0aMDEiRO54ooruOyyyw69duDAgVx//fUF8h1o69at7N27l/bt25f6/hRmxYoVZGVl8dBDD+UrhQ8bNgyAtm3b0rZtWwBSU1OpUSMvrE2aNImvv/6aL7/8kpNPPhmAvn37AjB69Ghuu+22Q23vACkpKfzvf/8r0/dTXFatXUqZmbHOgTEmrPR0DcyJibotg57CxfHee+8xf/585s2bx5QpU+jatStnnnkmy5cvL3DsUUcdRc2aNWnWrBnXXXcdl156KS+//DIA8+bNY/ny5YcCKcDll1/O/v37eeutt/Kd56qrrmL9+vVMnDiR4cOHk5ubyyOPPEK3bt1YunTpoePOPvts0tPTeffdd7nhhhto0qQJzz77LH/4wx/47LPPyuiOFF9SUhJNmjTh6quvZuLEiaxfv77Yr502bRrt27fnhBNOIDs7+9Cjf//+HDx4kO+++y7f8eeee260s19iFpyjpECQnjsXnn5at8aY2EhM1BJzerpuExNjko3u3buTnJzMcccdx+DBg5k6dSrOuQLtrZAXyFesWMGePXt49dVXadasGQDjx48HNKBu376d7du3c9xxx9GiRYtD+wI1bdqUSy+9lOeff57ly5czZcqUQ0OsAtWvX59zzz2XJ598kgULFjBnzhyqV6/O7bffHvY9+Xw+6taty7p160pxZ4rWuHFjZs6cyeGHH851111Hu3bt6N69O5MnTy7ytZs3b2bdunXUrFkz3yMlJQWAzKB/3IcddliZvIeSsGrtEsjMzB+MV66EtWshOdmr4p47Fy65BPbtgzp14PXXwauCMsaUo4QErcqOcZtzsLp169KxY0d+/PHHAvu6d+9Op06dCqQfOHCAN954A4AePXoU2L9lyxZ+/vnnkK/1Gzx4MD169GDZsmWF5u/444+nf//+TJs2LewxNWrUoE+fPsyYMYP9+/dTu3btQs8ZSu3atTlw4EC+tOCACdCzZ08mT55MdnY2aWlp3H///Vx44YUsWrSI7t27hz2/z+ejQ4cOBWoV/BKDvqzF09A9KzmXgM8HnTvrA3TboQN4HR/h009hyxbIyYFNm+DFF7XtyxhT/hISoHfvuAnMoD2IV69eTYsWLYr9mg8++IBt27YxcuRIZs6cme/hD9qvvvoqoO3BoTo07dmzh/Xr1x8qIe7atevQeOlAOTk5rFq1qsiS5O23305mZiYjRowIuX/t2rUhv4D4tW/fniVLluRL++ijj8IeX6NGDY4//njGjBlDbm7uoWYB/xeDvXv35jv+jDPOYP369TRo0IDk5OQCj+bNmxf6/mLJSs6lkJmpJWa/r74C58BXty0+0JLz/v3w7bcwYgQ8/HBc/YMwxpSPhQsXsnXrVpxzbNy4kaeffppt27Zxww03FPsc48ePp0GDBtxyyy2Hem4Hevzxx3n11VcZPXo0s2bN4tprr2XYsGH07t2bJk2asG7dOp566im2bdvGzTffDMBPP/3EqaeeymWXXUbfvn1p2bIlGzdu5H//+x9Llizh2WefLTRPJ598Mo899hg333wzy5cvZ9iwYbRr146srCw+//xz/ve///H666+HHU510UUXce+99zJ27FiOP/54Zs+ezaRJk/Id8+GHH/Liiy9yzjnn0KFDB/bs2cOTTz5Jw4YN+eMf/whA165dAXj00UcZOHAg1atXJzk5mUsvvZRXXnmFvn378q9//YsePXpw4MABVq9ezdSpU5kyZcqhjnZxJ5qDpivSI1qTkGzdqtuffnJu2jTv+S+/OHf22c4dcYRztWs71769Pt5+OyrXNKYqqEyTkAQ+WrRo4U499VQ3bdq0kMcGT0LinHObN292NWrUcFdccUXYa7344osOcDNnznTr1693t99+u0tJSXEtWrRwNWrUcM2bN3cDBw50n3/++aHXZGVludGjR7vevXu71q1buxo1argmTZq4Pn36uLcj+H/1zTffuPPPP//QOZo2ber69evnJkyYcGgik1CTkOzdu9fdeOONrnXr1q5BgwbuwgsvdHPnznWAe+WVV5xzzq1YscJdeOGFLjEx0dWuXfvQ+/juu+8OnSc7O9tdd911rkWLFk5EnIa2vGuMHDnSHXnkka5WrVquadOmLjk52Y0cOfLQpCUzZ850gJsxY0bY91jek5CInrPqSU5OdmlpaaU+z6pVsGYNZGRA27Z52451N5I09VF44QWoWVN7ij70EFxzTRRyb0zlt3z5crp06RLrbBgDFP15FJEFzrnkaF3PqrVLKSlJH5mZkJamTVvaFn0YbD4eJk+G6tW1/TmO2zeMMcbEDwvOUeDvvZ2RoR3DVq6ErCzo1PmP+I4/Xp/UqqUHr19v7c7GGGMKZb21o8Dfe9ubgIbOnaFpU9hWt41WZV91FdSvD2++Cddea2OfjTHGFMqCcxQFr6y2Zg1aSm7VStucV6+GJUvgn/+0AG2MMSYsC85RlJmpNdgrV+rzjAz9ObNxR9i7VwdC79oFv/xiAdoYY0xYFpyjyOeDZs107PPs2dpre+1a+OKnNqwadi8cfjjUrauB+tdfYcwYm5zEGGNMARacoywpCQYM0F7boD/36AHNTu0Jjz8O7dpBw4YaxevWjdlE/MYYY+KXBecyEryMZGYmOr/2449D9+5wxBHQuHHMJuI3xhgTv2woVRkSyWt/9k/z6euUiu+55+JuIn5jjDHxw4JzGfH5oFevvPm3O3TQ9MxMwJeAr7cXlNevt0BtjDEmH6vWLkP+8c/+wNy5s6YdqvJevx5Gj4aXXtKFMd55xzqIGVOJjBs3DhFBRFjpr0YLMGvWrEP7P/vsMwBGjRqFiJCdnX3oOP8azF27dqV+/fo0bdqUo48+mquvvprNmzfnO09hj2HDhhU777t27eKWW26hT58+NGrUCBFh1qxZpb0lppis5FwOfL689Z8zMwOCc3q6jn9u2hQ+/BC2b4dp03T9WStFG1NpNGzYkAkTJjBmzJh86a+++ioNGzZk165dYV+bk5PD6aefTnp6Orfddhs9e/Zkz549LFmyhEmTJvHrr79y7LHH8u233x56zcaNGznvvPO44447GDRo0KH0SJaozMzM5OWXX+bYY4+lX79+vPvuuxG8Y1NaFpzLQXDnsEMSE6FGDVixQp936aIDpdPTLTgbU4mcd955TJw4kXvuuQcRAXTt4cmTJzNkyBDGjRsX9rVffvkl8+fPZ8qUKQwePPhQ+qBBg7jzzjvJzc2lWrVqHH/88Yf2pXujQDp27JgvPRLt27dnm7dI/WeffWbBuZxZtXY58M+97a/VWrlSH6v2JWgp+corITlZA3ONGtaD25go89dcxcpll13GunXr+Prrrw+lvffee+Tk5DBkyJBCX+sPkK1btw65v1q1yP6Nn3nmmfTq1atA+saNG6lRowZPPPEEwKEvESY2LDiXA3/bs65Wlfezc5BZLwHOP1/n4L7ySqvSNqYMxDo4t2/fnpNPPpkJEyYcSnv11Vc599xzadCgQaGvPfbYY6lRowZXX3017733HllZWaXKy9ChQ/n+++9ZtmxZvvTXX38dgIsvvrhU5zfRYcE5xjIzdU1oEhJ05hILzMZUSkOHDuXtt99m3759bNy4kc8++4yhQ4cW+bqOHTvy3HPP8fPPP3Peeefh8/no1q0bI0aM4Ndff404H4MHD6ZRo0b5vigATJgwgf79+9OqVauIz2miz4JzOfOPfQ4c//zxx0Hf7Nev1/k/ree2MSXmb0oKbk6KVSn6ggsuYP/+/XzwwQe89tprtG7dmr59+xbrtVdddRXr169n4sSJDB8+nNzcXB555BG6devG0qVLI8pH3bp1GTJkCK+99hrOOQAWL17MokWLivVlwZQPC87lLCkpr4OYf/zz7t157dL5hleNHm0B2pgSCtecFLaDZhlr2LAh55xzDhMmTODVV1/l0ksvjai9uGnTplx66aU8//zzLF++nClTphwaYhWpoUOHsn79+kNDoyZMmEDDhg3zdTgzsWXBOQZ8Pi1BZ2RoAbl1a2+BjC9g1exNOrwqMVG3Nve2MZXG0KFD+eijj1i8eHGpS6mDBw+mR48eBdqOi+OUU06hXbt2TJw4kdzcXCZNmsT5559P3bp1S5UnEz02lCpGmjXTKba3b9cg3aGDN8Vn3bbaYzs93XpuGxMlsSotB+vXrx8XXnghTZo0oVu3bsV6zdatW2nQoAF16tTJl75nzx7Wr1/PMcccE3E+RIRLL72UZ555hnPPPZeMjAyr0o4zFpxjxD9TWGamBmd/1ZvjMO2xbVN6GhM18RKcq1evzqRJkyJ6zaxZs7j22msZNmwYvXv3pkmTJqxbt46nnnqKbdu2cfPNN5coL0OHDuX+++/nmmuuISEhgVNOOaXAMZ988gl79uxh8eLFgI653rp1K/Xr12fgwIEluq4pHgvOcSB4gYysJgl06pqQ/x+KzcFtTJV0/PHHc9VVV/HFF18wfvx4srKyaNKkCccddxwzZszgtNNOK9F5jzrqKJKTk0lLS+OOO+4IOa752muvZd26dYeejxo1CtChYenW5FamxN9bLyYXFxkA3AZ0BZoCW4A5wCjn3LKA45oCDwPnAHWBb4F/OucWl/TaycnJLi0treSZj6JVq3TMs7+DmH97aB7u9et17u0dO3SZyYcftgBtKr3ly5fTpUuXWGfDGKDoz6OILHDOJUfrerEuOTcDFgDPooG5HXA78J2IHO2cWyf6dW4q0AG4AcgC7gBmikhP51xGbLIePUlJ+Z936KDV3KtWecF57lxYsADq14eff9bnFpyNMabSimlvbefcJOfcCOfcO865L51zE4DzgIbA+d5hg4CTgMu846d5adWAW2OS8TKQmamzd/rXfV65MsT4Z2OMMVVCPA6l8oejg952EPCrc26m/wDn3A7gA6DSDMrz+SA1VafYBi05H5rVz7+jdWvdpqbGLJ/GlKdYNrsZ4xeLz2Gsq7UBEJHqQHWgPfAAsAl4w9vdDVgS4mVLgaEi0sA5t7tcMloOtm3Tsc/+EvSUKdCoUQLtL3mclCarrEOYqTJq1qzJ3r17qVevXqyzYqq4vXv3UrNmzXK9ZlwEZ2Au4F8m5WfgNOfcZu95MyA9xGu2edumQKUJzv7xz02b6hCrtm013TVpDamhV6UxpjJq2bIlGzZsoE2bNtStW9dWSTLlzjnH3r172bBhQ7nPOR4vwfkyoBHQEbgFmCEiJznn0gEBQtUpFPqXKiLDgeHh9rdr167EmS1L/uFTmZmwaZOuhbF2rU5WsmpVwc5jxlRWjRo1AuDXX3/l4MGDRRxtTNmoWbMmrVq1OvR5LC9xEZydc8u9H+eKyCdoSfl24Bq0hNwsxMuaetuQ66c5514EXgx3zeTk5LhtzPIH6IYNtYrbX3r+6iut9u7UKX4mVTCmLDVq1Kjc/ykaEw/iIjgHcs5tF5GfgU5e0lKgf4hDuwK/VKb25kA+H1x6af7e2v4hVsYYYyq3uOutLSKtgKOA1V7SVKCNiJwScEwj4GxvX6XlXyBj7dr8Q6xiueydMcaYshfTkrOIvAd8D/wI7AQ6A/8EsoFHvcOmojOCTRSREeRNQiLAQ+Wd5/KWlKSdxEADsr/kfGiCErCpPY0xppKJdbX2d8CFwL+AWsB6YBZwv9cZDOdcroicBTyCziRWBw3WpzrnqsRix4GdxPzWrPE6h/nXf87O1lWsRo60AG2MMRVcTIOzc+5B4MFiHLcNuMJ7VFmhOoFlLsrA51//OT1dHxacjTGmQot1ydlEYNs2CFyrY/p0yFjemZP3HEZS4PrPVs1tjDEVmgXnCiQpKW+c8/TpMGAArOzgo9mA62HHGg3GYNXcxhhTwcVdb21TtMxMnT3MvwZ02sY2rGzVm8x6CVpi9gfmxYt19QxjjDEVigXnCsjng5NPzuu57R//7POhpeesLHjrLVi+HB58UJeYNMYYU2FYcK6gmjXTknPg+Oe5c9HSc3KyLmnVtasOlJ4/P7aZNcYYExELzhWUz6el5cAlJps29UrP/fvr3J9btmj1drt2Og/o+iox8swYYyo86xBWwfl8IWYLS02FV1/VEnO7djB1qnUQM8aYCsRKzhVcZqY2Mfs7hx2a3rNTKlx/vRans7Pz1qC09mdjjIl7VnKu4Hy+vMlJVq4MsTBGYiLs3w/vvafbxx/XknNqanln1RhjTDFZybmyS0iAU07RwLx3rw6vuvNOa382xpg4ZsG5Egk5vWcm0Lw5VK+uPbdr1oScHB0PbYwxJi5ZcK5Ewgbn1FTo1Qvq14dGjfTx229WejbGmDhlbc5VQUICmWOexbfyW9i6Fb78Ej78EKZNs97bxhgTh6zkXAllZub12gbdpm1sQ+ap50O3blC7tnYUy8626m1jjIlDVnKuhML14D40vWeNGhqUa9SAWrV0ghJbwcoYY+KGBedKLDMzb3pP8JekE/DdOBrfjjUamP/7X9ixQ3ty33WXDbEyxpg4YNXalZjPp9N7+kvOnTt7P7dpA717w4EDGphXr9ZFMsaMsU5ixhgTByw4V3LhenDPm4dWZe/dqwG6cWNwDt5/3wK0McbEmAXnKiI4SC9e7K1gdddd0KULHHYYLFkC//ufTvtpAdoYY2LGgnMVEtiDe9MmSEvz5uB+7jkN0JmZsG4dzJwJH38c28waY0wVZh3CqgifT5uWFy/WwNy6tQbnjAw4+ugEUho3hmrVdJjV77/D5s2xzrIxxlRZFpyrkJQUOOIIDcppaXDBBXn7VnU5m6Tmr8C+fTrdZ//+scuoMcZUcRacqxh/D+6MDH3u78m9kp4waZKuAX3ccTqkau7c/M+NMcaUCwvOVZDPB0cfXTA9s1MqPn8QnjsXhg7VWcRq1IBXX7UAbYwx5cQ6hFVRKSm6SFVgJ7G0NP05MxMtMWdnQ4sWsGsXfPppTPNrjDFViZWcq7CkpLyfV66EDh3yqrk57jgd97xsmT5PS9PhVTbFpzHGlDkrOVdx/kUy/NN8+kvSq5qlwm236RCrCy+Epk1tkQxjjCknVnKu4vyLZPh8GqgPdRBbCZx5Zl71tn+RjHfe0QNSU60UbYwxZcSCswHygnM+CQm63nN6ugbmxx/XjmIHD0KvXvD00xagjTGmDFi1tjkkuIPYypWwcm8Cq1p7i2Rs2gQ7d+pc3F9+CRMnxjbDxhhTSVlwNockJQWsXEXez87BjF86Q/XqulDG77/D7t3w1FNakjbGGBNVFpxNscxa1gruu0+rsWvWhGbNtKp7/vxYZ80YYyodC84mpOAq7g0bYC6pZN71BLRsCfXq6Tzc7drB7Nm2ipUxxkSROOdinYeYSE5OdmlpabHORlybMUPXfd66NS+tXj3o03oZ/eQLDcxTp+b15h450jqIGWOqJBFZ4JxLjtb5rLe2CevYY6F9ex0DPW0anHGGpjdu2pXMI7riWzZbA3NiIixdCu+/D4MHW4A2xphSsmptE5Z//HOTJpCVpTOIgS47OXMmGpRr1NDA/OOP8MkncO211knMGGNKyYKzKZTPB506wZFHagnaH6B/+gky63njoE85RQ/auBGWL4cxY6wN2hhjSsGCsymSzwd9+8KKFfD225q2dStMmQKfLk8g8+RztQfZjh1Qt67uHDfOArQxxpSQBWdTLCkp8Mc/QuvWOhdJ9+7Qtq2Ogd5Wtw3cdZdWc+/YoYtlvPwy3HqrBWhjjCkB6xBmii0lBY44QheoysiAAQMC96Zqe/PDD0PDhjoGOitLp/60DmLGGBMRKzmbiPh80LEjNGqUPz0zE10Mo0MHnXt7zx4dB/3bb1Z6NsaYCFnJ2UQscB1ov8xM8HVO0JLz3LmaMHMmfPihjsOyMdDGGFNsVnI2JRIqQAOs2pcA558PXbtCnTq6DnRGhg2vMsaYCFjJ2ZRYZmb+ZSZXrtSZPJs1Axp3xLd/v5acQdeBtjWgjTGmWKzkbErM5yu4ilXbtpq+YFMbMvtfrOOfzzoLcnJ0BjFrfzbGmCJZcDaltmqVlpinT9fn06frZGEz9/8R2rTRau0ff9Q1oEePtgBtjDFFsOBsSi0pCc45B5KTNQ536KDjoHfUbMHKy8aQmTwAjjkGunXTcdBWgjbGmEJZm7OJCp9PS9Br1ujzTZt0O5vDSGgziF6LF+Pzz8ENsHCh9eA2xpgwLDibqElK0kWpmjTRaT6Tk/1zcbeEY26Fr97TA7t108Uyxo2DLl2so5gxxgSx4GyiKiVFt4GdxFQbMjkX38KFGpi//x7mz4eaNaFXLx0fbQHaGGMAa3M2ZaRnTy1BB/p5bxsybxytq1i1basH1K+v7dDp6eWfSWOMiVMWnE2ZSErSUVSBtm+HzDptdBWrww/XKT737NFhVrNm2UQlxhjjEedcrPMQE8nJyS4tLS3W2aj0AicqWbtWtzt3wmlHbsC38lvtRfbf/2qAzs2FZ56BQYNil2FjjCkBEVngnEuO1vms5GzKXFaWBuaMDH2+cqU3Scmp5+sKVjk5cOCAFq0feMCGWRljqjwLzqZM+XzaGTs54PtkcrKuA52ZCatan6Ql5t27oUYN/dmqt40xVZwFZ1NugpeZBFjbqKdWZXfuDA0aaMR+5x0rPRtjqjQLzqZc+Hx5Pbj9s4iBxuB5rQfBHXfotGI2D7cxxlhwNuUnKSlvvhF/57C2bWHxYphb6yQyfZ3z5uH+5BO49lqr4jbGVEkWnE2585eaA22v3Zptw2/XMdCdOsHGjbB8OYwZYyVoY0yVY8HZlLukJGjaVEvPaWlaes7IgK9WHca89ueDiE5M0rgx/P47PPqolaCNMVWKBWcTEykp2mu7s1eT3bu3BumsWq2ZN+R+nXO7SRNYsABeew3OOEOn+DTGmCrAgrOJGX8nMdBStL+6e3Hu0cy7+n+6zGStWrBtm5ak774bpk6NWX6NMaa8WHA2MZWUBEcfrT/nC9CbW7PqpGGQna2DoqtX1+ruL76IWV6NMaa8WHA2MZeSom3QGRkwe7ZWb2/aBB9v6Mmqvz0EtWvr6lU1a8Jpp8U6u8YYU+ZsyUgTF/xLTS5erJ3EkpN1u6bv38hq0ZmUDe9pYG7VCp5+Go47TsdlGWNMJRTTkrOInC8ik0VknYjsFZGfROR+EWkYdFxPEZkmIrtFZKeITBWRTuHOayqmlBQ4+WRo3VpL0cnJWs29LvEUVv39CQ3MQ4fC44/r1npwG2MqqVhXa98C5AB3AmcAzwHXAjNEpBqAiCQBs4HGwKXAX4FE4CsRaRmDPJsy1KyZTlISaOdO7RPG/PnaBp2QAPv3w2OP6ThoC9LGmEompktGikgL59yWoLShwHigr3PuCxH5H3A+kOic2+4d0xb4GXjSOXdrSa5tS0bGt3nztIq7d++82cQ67FyE747h+LJ/gy0BH5vGjeH5522pSWNMzFSqJSODA7Nnvrdt422PB771B2bvdRnAEuDcMs2giZkjjtDO2YFLTa5t1IOf73yJzAGXQGKidhDLzdVi9Z13WgnaGFNpxLpaO5RTvO1yb5sDHAhx3H7gCBGpUy65MuXK54PBg2HAAO293aGD/px6RXd8/7mWVa1OgoMH9ZGbq4tG21SfxphKIq6Cs4i0Ae4BPnPO+eucfwJ6iUjNgOMaAt0AAZqWe0ZNufD5dNukSdCOhAQWXnAvmf+8V4vYLVpoZ7G6dSE9vZxzaYwx0Rc3Q6lEpAHwPpCNdvry+z/gAuB5EbkbzfOjQANvf26Y8w0Hhoe7Xrt27aKQa1MeOoXol7+zVgsWnPxPOvQ8laRx/9HAXL06/Pablp6De5UZY0wFEtMOYYcyoVXTHwM9gVOcc4uD9l8H3A808pI+B9YBfwEaOOcORnpN6xBW8axaBWvW6M8ZGbBkCTRvDhf23kjS5m9g8mSdsKRGDRg50gK0MabcVKoOYQBedfVkIAU4MzgwAzjnngVaAt2Bds6504HDgbklCcymYmrWLK+Ke9MmDcwAizYfRmadNhqYExN1Hu7337f2Z2NMhRXrSUiqAa8BfYHBzrnvwh3rnNvvnFvqnFsvIkcDp6Pjok0V4fNpgN6wQWcP27pV0z/8EN5f3plVew6DpUvhxx/hyy9h9GgL0MaYCinWbc7PoO3JY4E9InJ8wL4M51yGN6b5WmAO2kO7FzppybvOuUnlnWETW0lJ+mjbVuNu27Y6k9iAAT4yf7wevnpPD+zWTTuHpadb9bYxpsKJdbX2QG/7b+DboMdV3r6DQCo6MclHwOVoj+5LyzWnJq40aaLtz/5uA9Onw5T5bVh1zBCdlCQ9Xcc/z5pl45+NMRVOXHQIiwXrEFbxrVql2zVrdAz0ypXQuTNapP7oI3j4Ydi1C/buhRtvhLFjY5pfY0zlVek6hBlTUklJ2gadkaGBGXS7cm8CmTtqaGDesgV274YHHoCXXoptho0xppgsOJsKzefTlaw6d9bnnTvrw9fnaC0xg84DCjBtWmwyaYwxEbLgbCq8pKQQiampcOONZEpzDc7Vq8MZZ2iV9+zZ1ovbGBPXIu6tLSJHAe2A5sBeYDOw2Dm3M8p5MyYi/uk+Dxk7lgW/n0j/jFc0MPfvr8OrduzQUvVdd2kQN8aYOFOs4CwipwFXomOLm4c4JFdEfgDeAV52zm2NXhaNKZ4CwRlY3/1MePxMfTJ7tgbm1at1O2YMPPecDbUyxsSdQoOziJyHjkHujC4ysQGd/3oTsA2oC/iAo9CpN5OB0SLyKnC3c+63Msu5MWFkZurDz99ZzNe4I769ezUwN26ct1CGBWdjTJwJG5xF5CvgJHTpxjuAN5xzvxRyfC3gVHQc8l+Ai0TkMufc1Ohm2ZjCbdum60D7+X/e3rQNvrvu0hJz3boaoBMTY5JHY4wpTGEl54bAOcUNrs65A8B0YLqItERn8Tqy9Fk0JjL+WcRAR08NGKA/r1wJmb5UfM89pyXmxEQrNRtj4lLY4Oyc+0NJT+qc2wzcVNLXGxMtjRrlf56ZCb7OCfmD8vr1ebOIpaZawDbGxFys59Y2pky1b5/X5gwBVdzbISUFDcy33gpffw379kHPnvDyyxagjTExVepxziLSXETOFZEBIlI9GpkyJlpSUvJ6ca9dCx066M+LF3vTf6an647Nm7Wj2OzZMGFCrLJrjDFABMFZRK4Vkbki0iwgrRfaYewd4GNgjojUj342jSk5n09nDfMH5s6ddTUr52De9iQ4eBBycvRx8CCMH2+LZRhjYiqSkvOfAeec2xaQ9jDQFHgFDc7HAddEL3vGRM/27Vownj5dn8+eDZO+aM2qqx7UZa5q1tSZxLZtgzvvtFnEjDExE0lwTgJ+9D8RkebAKcBLzrmrnHNnA/OBS6KbRWOiIyVF5+Hu0EEXy+jdG5o3B9f3dDKfmKAN1DVqaOl56VL4+ONYZ9kYU0VFEpx96FSdfid62/cC0mYD7UubKWPKSlKSlqCXLIG334bWrbUEPWX/QGYMeFjHPoOuZPXii1a9bYyJiUh6a28j/9SdpwC5wJyANAfUiUK+jCkzKSnQtKm2Oc+eDVdeqelzp6aQufB4fD/N0cUyMjNtik9jTExEUnJeDpwtIj4RaYK2Qc8PWvAiEZ3a05i4lpRUcC7u7bVb8/OwMWS2+4OWoJs10wj+/vvW/myMKVeRBOf/Aw4DMoD1QGvgWf9ObxjVScCiaGbQmLLi80G7djoO2j8WeonrTtpfHmdVYj847DD4+Wf48ktdzcoCtDGmnBQ7OHvTeF4DLAV+Am5xzk0MOOR0tEp7elRzaEwZOvZYyMrSoc4ZGZqW0aALC/98H/O6DCXzyBOgWzfIztYx0cYYUw4imiHMOfci8GKYfdPRYVXGVBj+qu2ff4a0NO0g1rYtZGS04NsG/XCswpf+s84e9ttv2kHswAGbl9sYU6YimYTkbhE5uYhjThKRu0ufLWPKj8+nU2ofd1xeWu/e0CChGdsvHM5k39+0g9ibb8LQofDAA3DttdaT2xhTZiJpcx4F9CnimFOAkSXNjDGx1KuX9gNbsUKHWQFk7G/JxPlJvLLuNDJrtIL9+2HZMi1mX3edBWhjTJmI9sIXNdDhVcZUOD4fnHqq/pyZmTcX96afanL4j7tgzx6t0t6+Xaf63L4dLrsMHnkEBg2KZdaNMZVMqRe+CNIL2BrlcxpTbnw+fSxeDJMm6RBnGjdlUsO/cW+de5h8yv9B/fo61eeBA/DLL3D55TB8uJWijTFRU2jJWUS+CEoaJiJ9QhxaHUhAZwebFJWcGRNDQ4boWtDbt8NHH8HFVzUkI6Mh27fVY17GZaQsfhmqVYPcXF3Natw4ePddePDBvFlNjDGmhIqq1u4T8LNDJxlJDHFcLpAJvAn8Mwr5Mibm+vXT6u05c/Lm4l671kfWYbeTuSIB3/NjYac3B092ti6Yccst0KKFVXMbY0ql0Gpt51w1/wMQYFRgWsCjhnOulXPuEufclvLJujFlz+eDk07Snw+tB92iJZlD/6mLZRx5pC6W4Zz26N67VwP088/bpCXGmBKLpM35r8CUMsqHMXFryBA4+mj9+VCABjJTBpL52jS46SatA69ZU6u5N2zQGcWGDbN2aGNMiUQSnE8hdJX2ISJyloi8XKocGROHUlIgOTkvMHfurA/fMW3goYdgwgTo1EnHYtWsqSXoH3/U9uepU2ObeWNMhRNJcB4G9CzimB7A5SXNjDHxzN+TO6RBg+Cll+CYY6BOHV0TescOWLMG/vY3C9DGmIhEeyhVbSAnyuc0Jm4UGqBTU7XX9ujROgeovy165064806r4jbGFFukwdmF2yEitYGTsSUjTSUXLjhnZqLzbV99NTz8MDRsqDtyc2HzZp2w5L77rKOYMaZIRY1zXhOU9E8R+WuIQ6sDLdCS8/NRypsxFUpmZkDg9g+luvNODczbt+sBo0fDzJnw8su2cIYxJqyiSs7+IVSClpolzOMgsBh4EBhRVpk1pkLxt0O3bKkTloCWoufPh1GjrJrbGBOWOBe2pjr/gSK56Djne8o2S+UjOTnZpaWlxTobpoLLzPSqs4OIQLNmXkl66lS44gotPYtogK5bV6cB/e9/bcISYyoBEVngnEuO1vkiaXM+FRgfrQsbUxn4fHnDqiDvZ+d0jWhAg+/LL0OPHtC8ue78/XfYsgX+8Q8rQRtjCih2cHbOfemcW1eWmTGmMtm+XberVqEBesoUONlbEl1Eg/TGjXDOOdqBzBhjPBEvGen1yj4OaIN2ACvAOfdqKfNlTIUjooVgf1DOyIDp02HBAu3A7UtIgJtvhq+/1lJzbq6uD71pE9x+u75ohHXZMMZEGJxF5ArgIaBpuEPQjmMWnE2Vk5SU1wa9dq0Ode7QAdLSdE0Mnw8dC/3uu/DWWzB+vB7sb4d+9lm46CLrxW2MKX61toicAfwP2Ajcggbi94F/AzO8528DV0Q/m8ZUDP42aBENymvXQuvW8NVXWopetQoN0I8+Crfdpr24ndNtrVpasrbZxIyp8iLpEPYvdFnIE5xzj3tpC51zDzjnzgD+BpwHrI5yHo2pcDp00CDtn4vbX4rOygo4aMQIeOABSEzUebnXrvUWj75Yh2AZY6qsSILzscAHzrldoV7vnHsJ+AYtSRtTpSUlQc+e+vOmTXlBevFimDcv4MARI7RYfcwxOt0naDv0Qw/ZTGLGVGGRBOf6aJW23z6gUdAxaUBqaTNlTGWQlKSl5+OO0+edO2sJOisraGx0QoJO7ekc7NuXNx+3DbEypsqKJDhvQqfo9NsIHBl0TGN0Kk9jjKdJE5g9W9ucQduip0zx2p/9Bg2CG2/UdaHbtYPateHTT2HMGAvSxlRBkfTWXkr+YDwbuEhEejvnZotId+BC7zhjjCclRbdZWRqYk5N1mNWaNbrkc0KCd8z118Mvv8Cvv+oMJq+9pqXo//4X3n5bO5IZY6qESErOnwAnisjh3vOH0OUhZ4nIFmAR0BC4N7pZNKbiS0nRoNy6tQbm3r21HXrFCvjss4AVrR56CC64QIvb2dn62LjROogZU8VEEpxfQCce2QrgnFsG9EWD9lbgU2Cgc+7jaGfSmMrA59MJwtq21eedO2uwdi6gDTohAQYPhqZNNTAfPKjb99+36m1jqpBiV2s75w4CvwWlfQecFe1MGVNZJSVpVfbjj8PevZq2Y4dWd3frBn/4AwwZkqDjoC++WIvZ9evrgZ9+alXbxlQREU/faYwpnSFDdOTUtm3wxhtaSD7jDN23YoV2FEtKTYUnnoBrrtHoffCglp5btIA//clmETOmkit2cBaRdOAzYCbwhXNuY+GvMMaEk5Sk2yVL8sZB+1e2OrSK66BB2jns+ed1aNWyZToH9+TJusqVBWhjKq1I2pzroFNzvgpkiMgyEXlKRM4VkSZlkjtjKrmjj9bRU4FDrfw/r1qFlpLbtoVdu3Rykj179IAJE2Kab2NM2RJ36Gt6MQ4W6YZ2Ajsd6I2Oa3ZALtpb+3O0VD09+lmNruTkZJeWlhbrbBhDZqZWcTuncbd377x9IpC0bS4MHZq3QLSIrg09ZYqVno2JEyKywDmXHK3zRVJyxjm31Dn3pHNuEOADjgf+A8wCuqILYnwUrcwZUxX4fHmziQX25O7cWQP2PEnV9Z6bNoWaNbWDWM2a1nvbmEosouAcyDmXC+wG9gB7gYPoylQlPqcxVV3HjgXTFi+GzBMHaTtzt25aD75una5gdcklFqSNqYQiCqQi0k5E/ioiE0XkV2Ax8ARwDDAZuAwdC22MKYGkJK21XrlSH6Adxr74wgvQd9yhPba3btWFMSZNgn79bJlJYyqZSHprrwL83+u3or22/W3MtkykMVGSlKQrVy1erIG5dWuYM0c7bLer05t+Bw5ATk7eC3btghtu0EHS1gZtTKUQScn5CG87A7gcuMI5918LzMZEX0oKnHOOTvm5aZOOg+7dG3Kbt2LVJXdDtaA/3Q0bYOLEmOTVGBN9kQTne4E5wKlop69tIvKliNwtIieKiE1oYkyUNWlSMG1b3wvJvGGU1n/75ebqAhlnnw3Dh1s7tDEVXERDqQBEpD5wCnCa9+jh7dqDrlT1uXPusWhmsizYUCpTUcyYoYtV+YdYrV2r246f/5ekZ27S2cNq1tS1oHNzdWejRvDYY3DllTHJszFVTbSHUkUcnAucQKQZ2hHsDqAl4Jxzcb+mswVnU5F8+ikkJmpg7tAhL0A3/fFLUt67Q2cSW79ex145p6XqFi20o5jNx21MmYvpOOeATDQWkcEi8iTwNfAYGpgBMsO/0hhTEr166XhoyAvQAIubncKqBybD1VdD7dp5c3/WqKEBetIkDdrGmAql2MFZRPqKyH0iMhftrf0ucD3QFl028hbgD865loWcxhhTAj6fPvzjoAMD9Jq9hzG58x26ikbfvlql3ayZTvU5dSqccIJOYmKMqTAi6cQ1w9vuR9uWv0CHUs1zzuWEfZUxJmqSkiArS4dZZWTojGIZGTrtZ8J1gzjizUH4fp4LL74I776bV/99xx0asK0N2pgKIZJq7QeAfkBT59xpzrl7nXPfWmA2pnylpMDJJ+cF5t69dWbPrCydfntVs1QYOFA7ivnl5sLrr8cu08aYiBS75Oycu7MsM2KMKb6kJPjxRy0xL1kCzZvDtGm6r0EDGDM8Fbp2hfnzNdE5WLpUh1hZBzFj4l5M58EWkfNFZLKIrBORvSLyk4jcLyINg47rJiLvisivIrJHRJaKyL9sbLWpyoYMgeuu0yblVat0opIzzoDdu2HurwnMu2sqnH461K2rq2jUr6/dvmfPtk5ixsS5WAe3W4BfgDuBDOAPwCjgVBE5wTmXKyKHo6tebQBuQjuj9QUeRnuI31buuTYmTqSkwBFH6PSeGRma1r07bN8OX33XmvV9n2bIL4MgO1t3fvUVfPSRVnmPHAmDBsUs78aY8GIdnM92zm0JeP6liGwDxgN90E5nZwHNgROdc95SAHwhIkcAQ7HgbKo4nw9OOkmrs0GDdIcOkJYGGXWPhFdf1ertnTvhuedg82adm/uKK3SlKwvQxsSdmFZrBwVmP6+R7NDqVrW87c6g47Zjy1MaA2gVd3KydgxbskQ7abduDenpMH17KvNSrteq7X378iYq2bkT/vEPW9HKmDgUj8HtFG+73Nu+jVZlPy0iHUSkkYici85K9mgsMmhMPPL5tIq7QQMtNc+erR3F0tLg/fdhxv7eunJVdW8Cv+xsXVXj+uttLm5j4kypp++MJhFpA/wALHLO9QtI7wS8D3T1khwwyjl3TyHnGg4MD7e/Xbt2vdatWxeVfBsTTzIzNVC/9JI+v/LKvLWhZdNGms14E9+4R3VN6IYNdYWrv/8d7rordpk2poKL9vSdsW5zPkREGqABOBv4a0B6C3Q2sj3A+ej0oKcB/xGR/c65B0Odzzn3IvBiuOslJyfHz7cSY6LIP81n48bw0095gXntWsjIOIxqHW9i0P1H4rv1StixQ3e+9Rb06GHtz8bEiYiDs4i0BJKBpkDIBS6cc69GeM46wFSgI3CKcy4jYPetQCLQ3jmX5aXNEpHqwBgReck5tzWyd2FM5XfqqRqgIW+6z4wMHR/d9aKB+O6+G556Ctatg9Wr4ZproFUrGwdtTBwodnAWkZrA82gP6XBt1YJWORc7OHvnnQykAKc75xYHHXI08HNAYPabB9QEOqFt0saYAD4f9OunY6BXrNC259atdSaxzz6DHYnn0avGW/j2r9SFMnbs0Gk/Dz8cEhJinX1jqrRISs5j0Orm1cBrwHq0CrrERKSad66+wJ+cc9+FOGwTcIKINA0K0P6v9xtKkwdjKrukJJ0sbNYs7STWtKmmz09vyU8Jt3LGyi0k1VynPbnnzoU//xluv92quI2JoUiC8yXASnTlqb1Ruv4zwAXAWGCPiBwfsC/Dq95+HrgU+FREHkbbnPugE5i855yzqY6MKUK/fnDssVp6TkuDCy7QkVXTJx7Ltp0DyNz0Db4da7SKe/9+uPxyGD/eArQxMRLJUKqWwMdRDMwAA73tv4Fvgx5XAXil6d7AFuD/0Lbp84B70KBtjCkG/5KT/slKAGjRku3DbyXz+pHQpIkuM3nwoE4xdsMNNs2nMTESScn5F6BRNC/unEss5nHfAWdG89rGVEVJSbqa1ddfaxU3wKZNLUnjDI474mp+XbmQE5lDEqvhl190qcmJE2ObaWOqoEhKzuOAgSLSuIzyYowpB0OGwODBOgf3pk3aSSw5GdyAgUxjIJ/QP+/g11+3GcSMiYFI13P+GvhMRE4VkaiWoo0x5cfn0xFTyclaku7QAQb8owuHdW/OLq+CbB69dJrP666LcW6NqXoiqdb2r9wuwGcAIhLqOOeci5vJTYwx4XXsCJMnwwsvaBymzR+Zu6QOafRiN/UYy0hSNiyAYcNg3LgY59aYqiOSIDobHcNsjKkkkpLgb3+D007T8c/PP9+AP6VuI3nuf5nEn9lOE+bRi5Tx46FbNxgxItZZNqZKiKu5tctTcnKyS0tLi3U2jIkrF1+sw53PWv4Qm37aBsAqkkhmAdfXHQczZ9oMYsaEEO25teNxVSpjTIwMGKAF5Lb/dytb2yWTzPc0JYsldGXy3gHwf/8X6ywaUyVYcDbGHDJsmM4/0rQppHc5g7S6venOUn6jFRO5hBkf7CPzR5uUz5iyFrbNWUTuRtuYn3HObfOeF4dzzo2JSu6MMeUuKUm33Y9rwNacwaR9tpJk0pjFKXy8+wQW/HkGQ6YOO3ScMSb6wrY5i0guGpy7OOdWes+LwznnQq5WFU+szdmYwmVm6nzcE6+Ywe6d2fThS7bSnHQS6X5+d47401GcfXbeEpXGVGXluZ7zqd72l6DnxpgqwOfTCUsafXOAyY+vZRVJXMybpHEsW9Oq8fbvR9GlS8HXGGNKL2xwds59WdhzY0zV0O+xP7HhzTuZ+mtPpvlnD8vO5tdf4fPPdaXJxERN3rZNq8UzMy1QG1MaNlmIMaZIw547nobnTmBObjKf0o8aWfVonriHmTPrM326zjR24YW6XoY/OPtZkDYmchacjTFFGzSIIWOW0GfcFI5vmMuvO+ry6W9n0P/k/TTYuwXqdGDJkiNYs0YXtwrkD85Wmjam+Cw4G2OK57LL8P34Iz1nT6dxVmN+yG3FwEkPkV7nSNYfaMXu7ql0PC2VJUuOAEAE2rTRgOzzWXA2JhIWnI0xxZOQAOefT9Lah2lWfz+//jYH377fYL+QuOcHWPA5i1afQrsHrmWJ606bNvqytLSCpWmwYG1MYSw4G2OKLzUVOnbEl5bGsEZT4EAW7M8FyYF6jWiUk0Xq77No2r87oPN1N22a9/KVK3XrL0n7fzbG5GfB2RhTfAkJ8NBDMHeuPt+xA98TT8CaNZC7n44NN8Fxx2nwXbKRphs20Ll3K0hIYOVK6Nw571SZmVZ6NiacqAZnETkd6OqcezKa5zXGxJGEBH349e8PH30EW7aQ1L8/pKbiW78e/vsIZGfDF/th5EjYWIPMlelkNkyEww4DYO1aPUVggLZgbUz0S86XAkMBC87GVBUJCXDNNfnT0tPx1dgBnRIhPR3mzsU3ZTa+tWmw/ncyL/o7a/teRYcOenhmZv4qcAvQpqqzam1jTPQlJkKNGhqYa+i/Gd/aNPjuO3y5ufgeGQ47d8LNNx+q6va3R2dm6mQmzZpZkDZVlwVnY0z0JSRoVXZ6et70Yb/+CrneFP3O4Xv5YX72HcHcQYPZvj3vpRkZsHs3HH+8BWdTddmSkcaYspGQAL1757VRX3edDn72+HI20+nF20hlLskBywX07g0NGuhsY4EzjRlTlVjJ2RhTPkaM0Ij7iNdRrHZtfLV3w1tv4eswH8nty3LXhbQ0aN1aS9AZGRrXe/XSU1hJ2lQVFpyNMeVn7Fjo2BHuvBNq1oRq1eCNN2DvXnodfIRe//oXmZfcwNq10KFD/qFX/jZpsCBtKr9Cg7OIDI3wfJ1KkRdjTFVw5ZXQvTvMnw8//ggTJsC+ffjIgvtvgU6dWNtiYMiX2sQlpqooquQ8DnARnE8iPN4YUxWlpurjhRdg3Li8dOfwTXqajmdAs8OOITOzTb525+Bx0RakTWVVVHB+FQu2xpiycuaZ8NJLsGCBPs/NhTlzSJo9W3t5//e/0CmVn3+GJUugbVs9LC1NR2L17GlDrkzlJM5VzdibnJzs0tLSYp0NY8z69TBxInz9NSxfrsOv/P+XWrbUAD1oUL42586dC7ZBW4A2sSQiC5xzyUUfWTw2lMoYE1sJCXDHHfD889pJLLDAsHkznHce3HADPp/OIrZ2bV5gXrtWHz//DKtWxSb7xpQFC87GmPiQkAC33npoRrFDcnLg6afxPfZvOnXSzt5ZWTB7dt4hS5bAxx/DjBnlm2VjykrY4Cwi/xKROiU9sYgcKyKhu1waY0woV14J990HtWsX3Pd//4fvm6kkJWlfsu7d83b5Jy6ZNy/0xCXz5sHkyWWXbWOirbCS833AahG5TUTaFOdkogaIyHvAfKBHNDJpjKlCRoyAt96CVq3yp//+O1x2GQwfDnPn0qmTTjiWkaGl6LZtYetW7Sw2b17+ly5eDB98oFXfNuuYqQjCdggTkc7AY8CZQA4wB/gaSAM2AllAHcAHHAUcD/QFWgOZwEjgBedcbtm+hZKxDmHGxLn16+Gqq+Dzz7Ud2v+oVUt7fz3/PJknDuL77zUYb92aV5retAmOOgqOOUZ7cw8ZoiteXXONTm4iAklJtp60iZ5odwgrsre2iJwA/B0YAtQi9NAq/4S5PwHPAa8453ZFK5NlwYKzMRXA+vVwxRVaND54UINzzZq67/DD4f/+DwYNIjNTS8wZGVozvnIlTJsG06dr+/TChXDkkXkvO/ZYGDNGj7Oe3iYaoh2ci5y+0zk3B5gjItcAJwMnAe3QEvNeYDPwIzDLObc0WhkzxhgSEuDll3Wo1fjxOswqO1vHQ2/YAJdeCnffjW/ECJo21QDt78ldvz6ceKLWhi9apGl9+uh2/Xr49FNo3Fif+3xWijbxxcY5G2MqBv946Gefhd9+05I0QPXqcNttMHYskyfDjh1azT1njr4E9NADB6BhQ+jSBf7617zTisBJJ2kJe+dO6NdP26aTksr/LZqKq9xLzsYYExf846G7ddMSsz845+TAAw9AVhZD7rgDEhI46SQ491zd3aED9O+vfclOOkmfL1oEPXpwaIEN0AWzvvpKg/PChTbzmIktG+dsjKlYBg2Cu+/WErNfbi68+KJOWDJ3boGgWrcuDB2qY6R9PmjUSGcZa9Ikb77uDh20CnzlSvj117xe3Ta5iYmFYpecReTlYhyWC+wElgMfOuc2ljRjxhgTln9t6Acf1JIz6PbHH+G66/A9+ywdO6YCWgI+7bT81dQdO+q2UyeYOlXbqv1B+L77tGp83ToYMECbtpOSrKrblK9itzmLSC55PbUlxCEuKP0g8B/n3MOlymEZsTZnYyqBhx+GUaNg3z7tyV2jBtSrp+Om/vMf7bpdBH9HsLlz4ZVXtDo8LQ2SvdbDFSvgL3/RtI4dLUCb0GI5t/YRwPvoGOb/AH2ALt72Li/9PSAVuBr4DXhARAZHK7PGGJPPiBEwaRJ07ap11dWqaa+uX36B666DSy7RqFsIfxV4p07arD1gQN7kJqDjpdPS9OGfMtQmMzFlLZLgfA7QG/iDc+4+59xXzrmfvO1YoBdwCnCic+6/wInoUKvro51pY4w5ZNAgnVj7wQehRQuNrKDds998U/dPnVrkaXw+SEnRn5OTdUrQjh01KPvTNm7UsdOzZ+cFZwvSpixEEpyHA2875zaE2umcWw+87R3nf/4hcGxpM2mMMYVKSICrr9Yq7lq18la2ys2FLVt0QQ3/uKpC9Oun2169NFgnJcFhh2laYFX3xo065Bo0OGdmWscxE12RBOdEYEcRx2wHOgQ8TwcaRJQjY4wpqSuvhKef1nm5/SVo53RNyT//uVglaMg/a1jv3lrVfdhhOtQqIwNat9ZY/9JLugz1zz/r8CtjoiWS4LwV6FfEMf3Rtme/JhQd0I0xJnquvBKmTNGVMPwBOicHvvsOLrpII2oE/B3AeveGk0/W0zdqpKfv3VvHTm/frkOwrARtoiWS4DwZOFZEJopIu8AdItJORF4DegLvBOzqBdhH1RhTvlJTtQTdtKl2EgMN1Pv3a9X3Cy8Uq5o7UFJS3hCsRo10jHR6Orz9dl7nsSlTYNw4DdD+hzElEckMYXejHcIuAf4sIhvQHtmtgDZAdWChdxwichg6nGpCFPNrjDHFM2iQjo0aMULrnXNzNUBv2QJ33aWdyJ5+Wturi8lfiu7YUcdPQ17zNmhJGnSfvyOZDb0yJVHskrNzbidwAroU5Dp08YvjvO06YBRwgncczrmNzrkTnHPPRzvTxhhTLIMGwWefwS23aD10o0YaTX//HWbO1KWpIixBgwZcf7t0crLG/CVLdLaxtm3zhl5t2GC9uU3JlHjhCxFpCDQCdsb78pCh2CQkxlQxc+fq2Odly7R62zmt8k5K0hWvUlNLfOrMTFiwQKcAfe65/HNyr1+vw7AHDrRSdGUWy0lI8nHO7XLObaiIgdkYUwWlpuqKVu3a5XUUy82Fn36C4cPhnXdKVIoGDca9emnwHTAA/v1vLVEnJ+uMYw0bWmA2kYk4OItIPRH5i4g8KiIvichj3vP6ZZFBY4yJmtRUnfKzTp386atWadX3mWcWe7hVMH9puWNHLUlnZOR1FPvySy24WxW3Ka6IgrOInIm2L48H/gn8FbjJe54uImdFO4PGGBNVgwZpj+2aNbVau1o1nZv7l19g6VK4/PISB2jIKyE3bpx/GtAlS7RfmgVoUxyRLHxxLDAH7ZU9CfgC2AgcBpwGXAzkoNN3LiiT3EaRtTkbU8VNnQrvv6/LT331lY6F9vfoTkrSjmQR9OQONmMGzJunhfKmTTVt3z4tvA8bFp23YOJHtNucIwnOk4EzgVOdc9+F2J8KzAI+ds4NiVYGy4oFZ2MMoPXN552nc3IG/j885xx4771SnTozE774Aj76CO68U8dEN2igq1wFrzltKrZYdgjrjc6tXSAwAzjn5qITkPSORsaMMaZcpKbCu+9ql+pA77+v7dOl4PPpWtJt2ujz1q01OG/bVqrTmiogkuDcGCiqK+Mv6PAqY4ypOFJT4b77oHr1vDTn4LHHilxysiirV2uh/L77tN159mxdh+PJJ20GMRNeJMH5VyCliGOS0XZoY4ypWAYNgmuvzRtmBToe+p//LFWATknRAvi110Lz5jqL2Fln6fcB/yxjxgSLJDh/DJwmIreLSPXAHSJSTUT+BZzuHWeMMRXPU0/putCtW2svLud0RYvrritVgPb5NBBv3aql57ZtdfvFF1Z6NqFF0iGsNbAAaI1WX89GS8mtgZPQJSU3AcnOubgvPVuHMGNMWP7ZxJYvhwMHNEg3baqB+8orS3zaefN0BSvndDaxrKxSTUxm4kjMOoQ55zYBJwKfAe2BvwAjgMvQNZw/A06qCIHZGGMKlZoKf/sb1KiRN8QqMxOuvjriJScDHXGEjn8OHPvsX2rSmEAlmltbRNoAf0A7ie0AfnDObYhy3sqUlZyNMYVavx5OOUVXswjk88EPP5R4DLR/Hm7/v14RDdidOtnwqoosLubW9ubU/tA595q3rVCB2RhjipSQoJNkVwv6N5mZqV2vSyExMe/n9etL3SHcVEIlXvjCGGMqvSuvhAceKBig//tfDdwl4PNB5866KAZo57BdtnyQCVIj3A4RebmE53TOuZL3mDDGmHgyYoT24gosLefk6PMmTXR/hFatgoUL9dGxoz5//nk47jhd3cqqt03YNmcRyS3hOZ1zrnrRh8WWtTkbYyJyzjk6a1igbt20V1cJzJsH334L6ek6/hm0/fn003VstKlYyrPNuUMJHx2Le3EROV9EJovIOhHZKyI/icj9ItIw4JhxIuLCPFZE+H6NMaZk7rgD6tbNn7ZuXYl7b6ek6Bzb3bvr+OcLLoBWrbQwbr23TdhqbefcunK4/i3omOk7gQy0B/go4FQROcE5lwuMAZ4Pel0iujJWydd1M8aYSKSm6iQl11+vy0sB7NkDf/87tGihM4xFyOeDk0+G3bv1edu2Gu9FrGq7qgsbnMvJ2c65LQHPvxSRbej60H2AL5xzq4HVgS8SkX7ej+PLJZfGGAPaQaxFC/jrX3X1Cud0is8nnyxRcPYT0Tm327aF117T2cSuvTZvbWhT9cS0t3ZQYPab723bFPLSocAC59zS6OfKGGMKMWgQDBiQP23OHF0fugSSkuDGG7VJ2//cP92nqbricSjVKd52eaidInIi0AkrNRtjYuUf/9C1H/327tWlptYXtXBfaDNmwL33wiOPaM/tWbPg7LPh/vtt7u2qKq6Cszfz2D3AZ865cF2phwIH0Tbnws41XETSwj22bAlVaDfGmGJITdV658AlJletKvHkJP366VTeF1+sz/v00c5h9euXPqumYirR9J1lQUQaALOAw4EU51xGiGNqo4trzHTOnVea69lQKmNMqYSa3rNaNXjvvRK3P2dmagnar3lz7cXt81kHsXgXF9N3RpuI1EF7XncEBoQKzJ7BQBOsStsYE2sJCbo4RuD6z7m5cPfdJT7l99/rfCfp6fo8LQ3Gj9d0U7XEPDiLSE1gMpACnOmcW1zI4ZcDW7E1o40x8eAvfylY97x4cYk7h/Xrp7Xlxx2nc5ucdRbUqwfLlumkJabqiOlQKhGpBrwG9AX+5Jz7rpBjWwH9gWedcwfLKYvGGBNeQoJ2tQ5sa87NheHD4Q9/KNHKVSkp+tizR59fcIFuO3eOQn5NhRHrkvMzwAXAI8AeETk+4NE26NhL0S8TVqVtjIkfY8fqTCKBfvsNbrqpxKfMzNQlJDdt0udr1+rKVTZzWNUR6+A80Nv+G/g26HFV0LGXA0ucc9b6YoyJLw89BDWCKiKnTCnxWpA+n/YpO/JIDcwdOmhb9NSpFqCrilhPQpLonJMwj1FBx/Zwzh0do6waY0x4qalwwgn503JzdaByCfl8cP75GphBt4sL65FjKpVYl5yNMaZyeOih/OOeAb78ssQTk4CWkrOy8kZrrV0LL75opeeqwIKzMcZEQ2oq3HZb/rTt23W4VQn5fLBzJ3z1FTz3HDRuDB98oM3cM2aULrsmvllwNsaYaBk7Fk48MX/a9Onw8MMlPmViIvTsqROS7NihM4dt3Kg/Wwm68rLgbIwx0XTrrQXTnnuuxKdLStI1nn//XScnSUzUZSW/+Ubn4DaVkwVnY4yJpkGDoH37/Gm//Vbintugk5MMG6Yl6PR0rUHv3h0yMmxhjMrKgrMxxkTbXXfln9YzJwfuvLNUncOysqBNG11GOitLx0D/8ANMmGDV25WRBWdjjIm2K6+Eq6+GOnWgdm0N1OvXl6r0nJKii2LceKM+T07WVaw2b9bgbAG6crHgbIwxZeHOOzWCHjwI+/bB6tXwf/9XqtIz6HKSTZvmPd+3T7fbtpXqtCbOWHA2xpiykJAA/ftDw4Z5s4ctWAATJ5bqtD4fHHGEDq+aNEkD9dtvw7PP2vCqysSCszHGlJX+/bVaOztbZwzbu1frpktRvQ1anX355fCnP+nzCy6AM86IQn5N3LDgbIwxZSU1VRfACJw57PfftT26FHw+XaWqZ0/tHAY6vee8efDpp9b+XBlYcDbGmLL0l79AzZr50xYtKvGaz4GSkuCUU/KWk2zdWicnsfbnis+CszHGlKWEBDjuuILpw4dH5fRt28JLL2m7M8BHH8Hdd2sp2lRcFpyNMaashZq+87ffNKqWUr9+upx0586wZAn07q09uMeNswlKKjILzsYYU9ZSU+HMMwumRyE4AzRrpotiZGVpgG7cGL79Ft55x0rQFZUFZ2OMKQ8ffaSrVwRatqzU455BO4g1aaJV3FlZeeOgf/pJV7EyFY8FZ2OMKS99++Z/vmOHTlYSBSkp2iFs5Urta5aYqP3OXn8dbrklKpcw5ciCszHGlJdLLsk/rArgjTei0nMb4PrrYfRoOOooWLECzj8fOnaE5cu1ituGWFUcFpyNMaa8DBoEt92WPy07WycmiZJ+/WDgQP05LU1XrzpwwKq4KxoLzsYYU57GjoXLLsufNn++rgkZJddfr7OHNW6sHcT69NEq7+nTYfLkqF3GlCELzsYYU97GjoVGjfKnjR8fteptgDvugLPO0nm4QYdY1a6tbdIm/llwNsaY8paQAIMHF0z/17+idgmfT9ucjz9exzuvXau9uOfNg5dftjHQ8c6CszHGxMLYsQU7h61eXepFMYJ17Qr798O0aZCeDrt3a9vzyJEWoOOZBWdjjImFhISCQ6ucgxEjonqZlBS44grtGLZ7t24TEzVQ//hjVC9losiCszHGxMr//qezhwSaPRv+/e+oXsY/xWfbthqUzzgD6tfX4Va2BnR8suBsjDGxkpCgHcGqBf0rfvLJqMwcFigpSS+Xng6PPAJt2sCsWfDQQ/D001G9lIkCC87GGBNLgwbBiSfmT9u9GyZOjPql7rkH/vMfHVq1YYPOHNanD+TkWAk63lhwNsaYWHv44dAzh5WBIUO0HRp0kpLkZP0u8PHHNgY6nlhwNsaYWEtNhS5d8qf9+KPOJFIG+vXTkVytW0NGhgZogO+/tx7c8cKCszHGxIObbiqY9vHH0KtXmVzu4ou1qXvJEi1Bgy6S9fbb1gYdDyw4G2NMPLjySvjLXwqmf/99VKf29PP54KSToEEDDc7p6Trd5/z5usz0uHFRv6SJgAVnY4yJF/fdBy1aFEwfP14jZpQlJWmB/eyzNTA3bapTftatq3OhzJsX9UuaYrLgbIwx8SIhQafvqlmz4L6//71MArTPp/Nu79+vQ6s+/FDHQM+apfOhWCex2LDgbIwx8SQ1FZ57rmD6/v0wfHiZlaCvuAL+8Ad9vnWrrgl98CDMmWMl6Fiw4GyMMfHmyivhzjtBJH96bq4ujhHlCUpAe3Bfcw106wb79mla586QmamFeWuDLl8WnI0xJh6NHQsPPlhw9rAdO0L37I6ClBSdE6VHD32elKSP3bujupqlKQYLzsYYE69GjIDbby+Y/u67cMEFZXLJlBQtuLdurWOe/WOg167VIVY2Drp8WHA2xph4NnYsXH55wfR33oEbbiiTS/brB6efrlN8jh6tw6z27YPHH4f+/a2KuzxYcDbGmHg3bhwcc0zB9JdeKpP2Z9BpPi+9VNudQVe0atUKtm/XZadN2bLgbIwxFcGYMQU7iO3dW6bTeQ0bptXav/0GP/ygwTknR4db/etfVsVdliw4G2NMRTBoENxxR8H0Rx4pk+FVftdfDx06wIED8PnnUK+eDrV66y0dem0BumyIcy7WeYiJ5ORkl+afUNYYYyqKk06Cb77Jn1arFnz1lY6RLiOTJ+uQqs8/h1GjtJo7I0MX0yqD2UUrHBFZ4JxLjtb5rORsjDEVya23FqzePnAArr66TC87ZIiu/VyrFmzapKVp0JnExo2ziUqizYKzMcZUJIMGwdChBdMXLYJ//7tMLz1smAZoEZg9W0vPO3bA++/rOOjMzDK9fJVi1drGGFMR9eypATlQvXqwYoXO0V2Gxo3TEvOOHZCYqD/Xras/v/56mV46blm1tjHGGHjhBW3wDfT772U2e1ggfwl62zYdbt28uaZ/+qlWf9s46NKz4GyMMRVRaircf3/B9HffLdPe237DhsHDD8OJJ+qa0KmpunBGu3ZWxR0NFpyNMaaiGjECLrusYPrdd5fL5VNSdMnJFSu0artBA10Hes6ccstCpWXB2RhjKrKxY6F9+/xpv/5abuObnnlGL9WxI6Sl6YxiTZvCF1/AX/9q60GXlAVnY4ypyBIS4M03C7Y/v/deuWXhttvg7LO1U9iGDdqLu04dHQf9+ec2UUlJWHA2xpiKLjVVG38D7dwJf/pTuWVh2DBdMAO0mrtPH/154cLQE5uZwllwNsaYymDiRB1KFejjj+HII8stC888o4tlNG2qbdDdu+t83D/8AGedZSXoSFhwNsaYyiAhQefVDLZyJZx2WrllY9gwGD5ch1elp0Pjxhqgly/XErQF6OKx4GyMMZXFiBHahTrYzJllPntYoOuvhx49YPdu/W7QqpXOMDp3LjzxRLllo0Kz4GyMMZXJO+9oz6xg991XrgH6kUe053ZWlk71edRRGqwnT4YBA8otGxWWBWdjjKlMEhK0pBwuQD/8cLll5ZlntE9ajRo6zCrZm9xy3jzt3W3Cs+BsjDGVTWoqvPEGVAvxL/7WW3UKr3LyyCM6pWft2rB4MZxxhv48b552HjOhWXA2xpjKaNAguP320PtCrWpVhp55Rtug9++HadPg6KNhzx745BO48spyzUqFYcHZGGMqq7Fj4fLLC6bv2AGHH16uWZk+HU46CWrW1KFVSUngnLZHWxV3QRacjTGmMhs3Ds48s2D6xo1ahC1Hd90Fhx0G+/bB6tXQrJmubPXFF3D++eWalbhnwdkYYyq7jz4KvcbzkiXlNgc36Civ117TycyqV9fhVb17g4hOWmJt0HksOBtjTFXwzTc64XWw8ePLdYhVUhK8/jq0aKHDrNLSdEaxHTt0FNjf/15uWYlrFpyNMaYqSEjQGUE6dCi477774IILyi0rPh/ceKMG5d27de3nunW1JD15si03CRacjTGm6khIgDVrdDxTsHfeKfdZxK6/XqcD37tXHzVraqB+7DG45ZZyy0pcsuBsjDFVzYsvwhFHFEx//fVyzcZtt+l8282a6fNataB1a/35zTdhxoxyzU5ciWlwFpHzRWSyiKwTkb0i8pOI3C8iDUMce7yITBOR7SKyR0QWi8hFsci3McZUaAkJ2jMreBax5s3LPSvXX6/zojRrptXaCQla3Z2ZCVdcUXUXyoh1yfkWIAe4EzgDeA64FpghIofyJiJ/Ar4CNgGXAIOB/wIhejcYY4wpUmoqPPWUdpsGrVO+666YZOW22+D007XkvHKlfkfIzYUNG7QGPjMzJtmKKXHOxe7iIi2cc1uC0oYC44G+zrkvvFL0auB159xN0bp2cnKyS0tLi9bpjDGmYpo6VQcan3aazioWQyedBAsX6kxiPh/89pumt2oFS5dqWrwSkQXOueRona9GtE5UEsGB2TPf27bxthcALYBHyyVTxhhTlQwaFPOg7Pf113DssbBokQbmhg1h1y79+Y9/1FJ1VRHrau1QTvG2y73tScA24GivnTlbRNaLyEgRqR6bLBpjjCkL338PRx6pP+/apStagbY9p6bGLl/lLa6Cs4i0Ae4BPnPO+eucDwfqAa8D44DT0Wrvu4BHCjnXcBFJC/fYsiVUod0YY0ysXXedtj8DZGfnpc+bV3Wm+Yxpm3MgEWkAzEKDcYpzLsNL/xToB/zLOfdYwPHPAVcCLZxzOyK9nrU5G2NM/Bo3Tnty79mTP93ng1GjdF88iXabc1yUnEWkDjAV6AgM8Admj7+fXvCIt0+BmkC3ss+hMcaY8jRsGAwenPfcvzR1ZqZOaFbZx0DHPDiLSE1gMpACnOmcWxx0yFJvG1zEF2+bW4bZM8YYEyOvvQZ9+mhgzs2F+vU1ffNmmDIlljkre7GehKQa8BrQFxjsnPsuxGFTvG3wfHMDgH3AkjLLoDHGmJi6805ITNSf9+zR4dg5OTrJWWWe4jPWJedn0KFSjwB7vFnA/I+2AM65JWhHsHtE5FYROV1EHgCuAh50zu2OVeaNMcaUrX794J//1GFVoCVo0I5ikyZp23RlFOvgPNDb/hv4NuhxVcBxVwOPATcAHwPnATc750aVW06NMcbExPXXQ//+Wr2dk6OlZ4Bff9XpwCvjDGJx01u7vFlvbWOMqViOOQYWB/dKQtulZ84s9+zkUyl7axtjjDFFOeccaNSoYPrixfDgg+WenTJlwdkYY0yFcM892gbtr9b2zx6WmQnz54d/XUVkwdkYY0yF8c47eb23A2cPmzwZOneOSZbKhAVnY4wxFUq/fnljngOJaJCuDCw4G2OMqVCeeQZOPTVvKWr/duXKyjO0yoKzMcaYCueuu7T3NujwKr9p0yrH4hgWnI0xxlQ4KSmwb1/B9OxsmDtXl5isyCw4G2OMqZCWLYMWLQqmZ2TAiSeWf36iyYKzMcaYCuuCC6BevYLpBw7A3/9e/vmJFgvOxhhjKqxnnoH27Qum79gR+1nDSsOCszHGmArtzDNDzxy2ejVcemn55ycaLDgbY4yp0B55JHTp+cCB0HNxVwQWnI0xxlR4kyfrAhjBli+vmKVnC87GGGMqvKQkWLq0YHp2tgbuijZzmAVnY4wxlcLmzdCxY8H0/fvh1lvLPz+lYcHZGGNMpdG8eej0NWvg7LPLNy+lYcHZGGNMpTF3bvh9H35YfvkoLQvOxhhjKpWnngo9MQlA167lm5eSsuBsjDGmUrn+emjdOvS+9esrRucwC87GGGMqndWrQ6/5vHs3XH55+ecnUhacjTHGVEqHHx46fc8eGDCgfPMSKQvOxhhjKqWVK+Hoo0Pv27ixfPMSKQvOxhhjKq2RI6Fu3YLpixeHL1nHAwvOxhhjKq0hQ8L33N64Ee6+u3zzU1wWnI0xxlRqW7eG7hwG8Nhj5ZuX4rLgbIwxptIrrHPYMceUb16Kw4KzMcaYSm/lyvD74nFZSQvOxhhjqoSVK6FWrdD7wlV7x4oFZ2OMMVVCUhLUrBl63++/w7hx5ZqdQtWIdQaMMcaY8rJ7t25FdHv55fEVlP2s5GyMMabKKawNOh5YcDbGGFPlJCVpO3OfPrHOSWhWrW2MMaZK8ldxxyMrORtjjDFxxoKzMcYYE2csOBtjjDFxxoKzMcYYE2csOBtjjDFxxoKzMcYYE2csOBtjjDFxxoKzMcYYE2csOBtjjDFxxoKzMcYYE2csOBtjjDFxxoKzMcYYE2csOBtjjDFxxoKzMcYYE2fEORfrPMSEiGwB1hXz8ObA1jLMTlVn97fs2T0uW3Z/y1ZFuL/tnXMtonWyKhucIyEiac655Fjno7Ky+1v27B6XLbu/Zasq3l+r1jbGGGPijAVnY4wxJs5YcDbGGGPijAVnY4wxJs5YcDbGGGPijAVnY4wxJs5YcC6eF2OdgUrO7m/Zs3tctuz+lq0qd39tnLMxxhgTZ6zkbIwxxsQZC87GGGNMnLHg7BGRaSLiROTeCF93h/e6r8sqb5VBce+viCSLyIsiskJEfheRX0TkNRHpUF55ragi+QyLSB0ReVhENorIXhH5VkROLo98VgQiMkBEvhCRTSKyX0QyROQtEelajNe2E5Hx3mf3dxFZKSL3ikj98sh7RVGae+y9vouIvC0iW73P8E8i8o+yznd5qRHrDMQDEbkY6FGC13UE/g1sjnqmKpEI7+9FQDfgSWAp0Aa4C0gTkZ7OufVlk8uKrQSf4ZeAPwEjgDXA34HpIvJH59zC6OewwmkGLACeBbYA7YDbge9E5GjnXMhFc7wA/BlQE/3c/gIcB4wGkoA/l33WK4wS3WPQL/HAF8As4CpgB3p/G5RxnsuPc65KP4AmwCbgYsAB90bw2unAC+gH5OtYv5d4fER6f4EWIdLaA7nAPbF+P/H4KME97uEd99eAtBrAT8DUWL+feH0AR3r37V+FHNPfO6Z/UPoDQDZQL9bvI54fxbzH1dAv7u/FOr9l+bBqbXgIWOqcmxTJi0TkEuBY4I4yyVXlEdH9dc5tCZG2Dv1m3SbKeassIv0MDwIOAm/6E5xz2cAbwAARqR39LFYKmd72YCHH1PK2O4PSt6NBRaKcp8qmOPe4D9AVeKzMcxNDVTo4i8hJwFDgughf1xR4HLjVObetLPJWGZT0/oY4TxegJbA8GvmqTEp4j7sBa51zvwelL0WDS6coZa/CE5HqIlJLRJLQWrJN6JeYcD4DVgEPikhXEWkgIqcB/wCed87tKftcVywluMcneds6IvKdiBwUkc0i8qSI1C3zDJeTKhucRaQm+kF4xDn3U4QvfxhYCYyLdr4qi1Le38Dz1ACeR0vOL0Upe5VCKe5xMyArRPq2gP1GzQX2o3/vxwCnOefC9jFxzu1Dg4e/6nUX8DnwIXB9mee2YoroHgOHe9s3gU+Bfmjt0VXA62WYz3JVlTuE3QbUBcZG8iIR6Y2WVI51XgOICalE9zeEp4ETgD8550IFlKqspPdY0Ha9UOkmv8uARkBH4BZghoic5JxLD3WwiNRBg0ZL77W/ACnA3Wib87XlkOeKJqJ7TF6hcqJz7m7v51kiUh14QES6OueWlWmOy0GVDM4i0g7tZX0VUDuoja22iDQBdjnnckK8/AW0BJfhHQd6H6t7z/c65/aXVd4rglLe38Dz3A8MBy53zn1aVvmtiEp5j7ehPWODNQ3YbwDnnL8pZa6IfAKkoz2KrwnzkivRNtFOzrnVXtpXIrIDeFFEnnfOLSrDLFc4JbjH/nbpGUHpn6Id73oCFT44V9Vq7Y5AHWAiWr3nf4B+c8sCjg7z2i7ohybwdScCx3s/2zfj0t1fAETk3+gf6D+ccxPKLqsVVmnu8VKgg4jUC0rvChwAfo56bisB59x29N4U1iZ/NJAVEJj95nnbLmWQtUqjmPd4qf/woHR/zU9ulLMVE1Wy5AwsBE4NkT4T/Wf3EuH/QYV63RNAdeCGQl5XlSyk5PcXEbkRuBf4t3PuqbLIYCWwkJLf46nouNsLgPFwqG3/z8CnVb3mJxwRaQUcBbxWyGGbgKYi0sk5F3j/U73thrLKX2VQzHv8CdpGfQbalu83wNumlU3uylmsx3LF04OgMaLo+Nps4O4iXjcLG+cclfuLTkKSi/4BHh/06Brr9xDvj+J+htHesFlotXhf4B1gH9qXIubvI9YP4D10EpHB6Jegq4EV6JCozuHuLZCIDqNaCVzuvXaEl5YGVIv1e4uXR0nvsZc+0ku/DzgdrWXbC4yL9fuK1qOqlpyLS9AScVWt/i9roe7vGV76Gd4j0Jdoe54pvnCf4b+iHcnuRScxWQSc4Zz7vlxzF7++Ay4E/oUOL1uPfgm/3+V1VCpwb51z6SJyPDAKvbfNvde+CIx1zlWKKtcoKdE99tyD9oS/Dm3G2YiOohlT1pkuL7ZkpDHGGBNnrERojDHGxBkLzsYYY0ycseBsjDHGxBkLzsYYY0ycseBsjDHGxBkLzsYYY0ycseBsjDHGxBkLzsYYY0ycseBsKiURSRQRJyLjYpiHV71F4OvHKg/FUR73qqyvEY3zRyuPItLLO8+VpTmPqdosOBtTBkQkGfgL8IBzbk9A+hbvH3dxH+GWzTNxyjm3AJgC3CsiDWKcHVNB2dzaxpSN+9DFDp7zJ3gl6GeCjquBrst8ALg/xHmmlVUGA2xAlzLcUQ7XimfRvA/3A3OBG9HPgjERseBsTJSJSGd0pZz/Oef2+tO9EvSooGN7oMF5sXMu377y4pw7iK4GVKVF8z445+aJyArgahF50DmXE43zmqrDqrVNlSMiF4rIVyKyQ0T2ishiEblDRGqHOFZE5B8iskxE9onIBhF5WkQai0i6iKSHuMQV6Go6bxYjO8nedkHJ31HphGtrDUz3fn5DRLZ69yFNRM6KwrWriciT3nXeFZE6QftTReQdEdkkIgdEZL2IvCAihxdx3j/6z1nIMctFZL+INAt+v0HHlfQ+vAG0Q7+oGRMRC86mShGR+9Cg2QV4HXgaDaT3AdNFpGbQS54BngAao8v+TQL6AzOA4GP9Tgdy0CXxitLL28bzAvHtgXnoWsUT0PvXHXhfRE4t6Um9QPwWcAN6n893zu0L2P9X4BtgIDAT/T2koWtQp4lIu3Dnds59C/wEnCUivhDXTgGOAj5wzm0rZpYjvQ/feNt+xTy/MXlivaC0PexRFg/0H6gjYPF14I9e2i9A64D0GsAH3r47A9J7e2k/AU0C0msBX3n70oOuWx9dBH5xMfM51zvPsfF0r4LSHTAyaN8AL/3jklwDaAbMBnKB20Ic3xlth/8ZaBO07zT0y897Rfy+7/DSrg9x/me8fWeX1X1Av9A5YF6s/x7sUfEeVnI2VckV3vZe59wmf6JzLhtd8D0XLZX5Xe5txzrntgccfwD9xx9KG3Rx+I1FZUZEagDHoEFoSfHeQqHn+5uIrBGRbBF5IVxaCawD7g1McM5NR7/kpJQgn+3RUmUqcJlz7sEQh12L1kz8wzm3IejaXwBTgbNFpGEhl5qA/k4vD0wUkVrARcBm4JMIsh7RfXDO7QD2oVXbxkTEOoSZquRYb/tF8A7n3EoRyQA6iEgTLxj/wdv9dYhzfYeWkIP5q1CzipGfbkAdYIEX8EtMRI5Ce4Zf4OVtV6i0Ep5+oQvdoWk9WhsRiSOBb9EahoHOuc/DHOc/7ykiclyI/S3RL0GdCdNe75zLEJHPgX4i0tU5t8zbdTZacn/c+2JWXCW5D9uAVhFcwxjAgrOpWhp723Cl2o1oKacxsD3g+N+CD3TO5YhIZohz+Htn1wmxL1g0O4MNApY4597zJ4hIgbQS2h4mPZvI+610RgPjQuD7Qo7zf8kZUcT5ihpHPA5t870cuM1L85ekxxfx2mDbw6QXdh/qkveZMKbYrFrbVCX+8autw+w/LOi4nd62QMlHRKqTF0ACbfa2ofYFK1ZnMBFpIzrbWKaIbBeRySLSKmD/SuBBoIfXq/i9UGnFyE95+AC4E+gJfC4izcMc5/8dNHbOSSGPL4u43nvo7/EvIlJdRFqgHcwWOecWReH9hCUi1YAm5H0mjCk2C86mKvnB2/YJ3iEinYC2wNqA9mX/8SeFONfxhK552ghsQatvi+IPzmFLziLSAS1hbvDy0QdoDjwfcNhJwErgP+gXjMvDpMUF59z9wD/RZoOZgV80Avh7uvcu5bX2oj3CD0d70V+K/t4iLTWXxJHoSICF5XAtU8lYcDZVycve9j9eCQo4VAp+BP17eCng+Fe97b9FpHHA8bUIM+uTc86hPbmbewE/pAg6gz0PvOScu8M5t9w5txAYA/QNOGYn0BH4xjm3yTm3M0xa3HDOPYF2+uoGfBli3PLTwEHgcW9Sl3xEpJaIFDdwj/O2Q71HNvBaCbIdqeO97cxyuJapZKzN2VQZzrk5IvIQcCuwRETeAfag1Zzd0Y5fDwcc/6WIvAgMB5aKyGQ0YJyNVrv+ivYGDjYZGIIOs/k5THaK7AzmjePtD/QWkRsDdlUHfg943h39W15YRFpccc49LyL70C9EX4nIac65X7x9K0TkCvQL1VIRmYbWBNRE+wX0RmsojirGdb4RkZ/RjnE10bHN5VHV3B8d8vV+OVzLVDIWnE2V4py7TUR+AK5HS1E1gdVo9e+jIQLlteiUjlcD1wCZaDvmnUCG99pgk9FOZEMpOJe2X5FV2mi77M6AYwMdCDpuXeBwrzBpccc5N05E9qO1FP4AvcbbN1FEFqHD3E5Fg90e9EvROxRvBja/8WiNg//nMuXVtJwDfOicW1/W1zOVj2gtnDEmEiKShJbk3nDOXRxi/x1o1fexzrkfgvcX8xoD0Q5UTZxzuws57mmgrXPunMLSTPkRkRuAJ4GTnXOzY50fU/FYm7MxhRCR1l6v28C0euhUkqCl6FAeRyenuKcUl/8OHS89QUT+ICJHiEg/EXkmKE89KVh9HSrNlAMRqYtOUjPZArMpKQvOxhTuJmCtiIwXkQe8RRF+As5EZ5d6O9SLnM4RfRk6B3T9klzYOZeFtoc3RjsVLUQ7rmU453JBF+ZAO5Yt9L8uVJopV4noPOy3xDgfpgKzam1jCiEifdF/sj3RyTOy0ers14EnnC4zaIwxUWXB2RhjjIkzVq1tjDHGxBkLzsYYY0ycseBsjDHGxBkLzsYYY0ycseBsjDHGxBkLzsYYY0ycseBsjDHGxBkLzsYYY0yc+X93owqBrrZ1FQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "\n", + "plt.plot(np.log10(clus_1.star_systems['Teff']),\n", + " np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"BPASS Cluster\", alpha=0.5)\n", + "plt.plot(np.log10(clus_2.star_systems['Teff']),\n", + " np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b+\", label=\"MISTv1\", alpha=0.15)\n", + "plt.xlabel(\"log($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log(L in watts)\")\n", + "plt.title(\"HR Diagram of clusters at solar metallicity \\n\" +\n", + " \" and 10 million years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at which stars are the white dwarves?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=int64)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.where(clus_1.star_systems['phase'] == 101.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Black Holes?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=int64)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 103.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do we have any neutron stars?" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([], dtype=int64)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 102.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure that I am getting just about enough star mass for my cluster. (It was a bug before I used an adjustment factor.)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1997.7272166461355" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1999.431851591159" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_2.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What systems do all of these companions correspond to?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Showing the current-mass and current-luminosity relationship of the clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['mass_current'], np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"Cluster_w_Binaries made from BPASS\", alpha =0.2)\n", + "plt.plot(clus_2.star_systems['mass_current'],\n", + " np.log10(clus_2.star_systems[\"L\"]), \"r.\", alpha=0.2)\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"log-Mass-logL of BPASS Cluster at Z_solar and\\n\" +\n", + " \" 10**7.0 years age (Using mass current)\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What phases exist in the cluster?" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=2>\n", + "\n", + "\n", + "\n", + "
-99.0
5.0
" + ], + "text/plain": [ + "\n", + "-99.0\n", + " 5.0" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(clus_1.star_systems['phase'])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=1147\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", + "
masssystemMassTeffLloggisWRmass_currentphasemetallicityisMultiplemergedm_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
float64float64float64float64float64float64float64float64float64boolfloat64float64float64float64float64float64
0.474292597781683640.47429259778168364nannannannannan-99.00.0False0.0nannannannannan
0.403323796832225630.40332379683222563nannannannannan-99.00.0False0.0nannannannannan
0.65373710849192430.6537371084919243nannannannannan-99.00.0False0.0nannannannannan
0.71309182684031670.7130918268403167nannannannannan-99.00.0False0.0nannannannannan
0.66264605997298790.6626460599729879nannannannannan-99.00.0False0.0nannannannannan
0.402831180283462150.40283118028346215nannannannannan-99.00.0False0.0nannannannannan
0.54997479649529920.5499747964952992nannannannannan-99.00.0False0.0nannannannannan
0.55786246033308980.5578624603330898nannannannannan-99.00.0False0.0nannannannannan
0.66549541230086480.6654954123008648nannannannannan-99.00.0False0.0nannannannannan
0.47069169463416420.4706916946341642nannannannannan-99.00.0False0.0nannannannannan
................................................
0.496129402058592850.49612940205859285nannannannannan-99.00.0False0.0nannannannannan
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0.465918276376730060.46591827637673006nannannannannan-99.00.0False0.0nannannannannan
0.52640476787734390.5264047678773439nannannannannan-99.00.0False0.0nannannannannan
0.56914203794066250.5691420379406625nannannannannan-99.00.0False0.0nannannannannan
0.724190601556780.72419060155678nannannannannan-99.00.0False0.0nannannannannan
0.47161445397055480.4716144539705548nannannannannan-99.00.0False0.0nannannannannan
0.54756879092729720.5475687909272972nannannannannan-99.00.0False0.0nannannannannan
0.7021444705280950.702144470528095nannannannannan-99.00.0False0.0nannannannannan
" + ], + "text/plain": [ + "\n", + " mass systemMass Teff ... m_ubv_B m_ubv_R m_ubv_I\n", + " float64 float64 float64 ... float64 float64 float64\n", + "------------------- ------------------- ------- ... ------- ------- -------\n", + "0.47429259778168364 0.47429259778168364 nan ... nan nan nan\n", + "0.40332379683222563 0.40332379683222563 nan ... nan nan nan\n", + " 0.6537371084919243 0.6537371084919243 nan ... nan nan nan\n", + " 0.7130918268403167 0.7130918268403167 nan ... nan nan nan\n", + " 0.6626460599729879 0.6626460599729879 nan ... nan nan nan\n", + "0.40283118028346215 0.40283118028346215 nan ... nan nan nan\n", + " 0.5499747964952992 0.5499747964952992 nan ... nan nan nan\n", + " 0.5578624603330898 0.5578624603330898 nan ... nan nan nan\n", + " 0.6654954123008648 0.6654954123008648 nan ... nan nan nan\n", + " 0.4706916946341642 0.4706916946341642 nan ... nan nan nan\n", + " ... ... ... ... ... ... ...\n", + "0.49612940205859285 0.49612940205859285 nan ... nan nan nan\n", + " 0.7697468495792291 0.7697468495792291 nan ... nan nan nan\n", + " 0.6044977506782676 0.6044977506782676 nan ... nan nan nan\n", + "0.46591827637673006 0.46591827637673006 nan ... nan nan nan\n", + " 0.5264047678773439 0.5264047678773439 nan ... nan nan nan\n", + " 0.5691420379406625 0.5691420379406625 nan ... nan nan nan\n", + " 0.72419060155678 0.72419060155678 nan ... nan nan nan\n", + " 0.4716144539705548 0.4716144539705548 nan ... nan nan nan\n", + " 0.5475687909272972 0.5475687909272972 nan ... nan nan nan\n", + " 0.702144470528095 0.702144470528095 nan ... nan nan nan" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems[np.where(clus_1.star_systems['phase']==-99)[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "
\n", + "\n", + "\n", + "
massisMultiplesystemMassTeffLloggisWRmass_currentphasemetallicitym_ubv_Um_ubv_Bm_ubv_Vm_ubv_Rm_ubv_I
float64boolfloat64float64float64float64float64float64float64float64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass isMultiple systemMass Teff ... m_ubv_B m_ubv_V m_ubv_R m_ubv_I\n", + "float64 bool float64 float64 ... float64 float64 float64 float64\n", + "------- ---------- ---------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_2.star_systems[np.where(clus_2.star_systems['phase']==-99)[0]]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/TestPlot_8.2_MIST_1.0.ipynb b/Test_Plots/TestPlot_8.2_MIST_1.0.ipynb new file mode 100755 index 00000000..3520aa85 --- /dev/null +++ b/Test_Plots/TestPlot_8.2_MIST_1.0.ipynb @@ -0,0 +1,2276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS Isochrone at $10^{8.2}$ Years Age and Compared with MIST Model. (Solar Metallicity)\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for 1 billion years age, solar metallicity, AKs=0.0, and distance of 2000 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram ($B-V$ vs $M_V$), and the mass luminosity relationship of the cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We found files from which we can create our isochrone\n", + "Evolution Models of potential saved isochrone and desired isochrone\n", + "BPASS\n", + "BPASS\n", + "Atmosphereic Models of potential saved isochrone and desired isochrone\n", + "get_merged_atmosphere\n", + "get_merged_atmosphere\n", + "Reddening Laws of potential saved isochrone and desired isochrone\n", + "N09\n", + "N09\n", + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 50000 for T= 52330 logg=8.47\n", + "Changing to logg=5.00 for T= 52330 logg=8.47\n", + "Changing to logg=5.00 for T= 44758 logg=8.49\n", + "Changing to logg=5.00 for T= 36146 logg=8.54\n", + "Changing to logg=5.00 for T= 38774 logg=8.52\n", + "Changing to logg=5.00 for T= 38361 logg=8.50\n", + "Changing to T= 50000 for T= 73161 logg=8.45\n", + "Changing to logg=5.00 for T= 73161 logg=8.45\n", + "Changing to logg=5.00 for T= 37966 logg=5.70\n", + "Changing to logg=5.00 for T= 39842 logg=5.51\n", + "Changing to logg=5.00 for T= 32888 logg=5.68\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 35701 logg=8.52\n", + "Changing to logg=5.00 for T= 37547 logg=5.70\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38974 logg=5.38\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35701 logg=8.52\n", + "Changing to logg=5.00 for T= 39110 logg=5.45\n", + "Changing to logg=5.00 for T= 38030 logg=5.34\n", + "Changing to logg=5.00 for T= 35710 logg=8.52\n", + "Changing to logg=5.00 for T= 39081 logg=5.47\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38681 logg=5.49\n", + "Changing to logg=5.00 for T= 39333 logg=5.52\n", + "Changing to logg=5.00 for T= 37796 logg=5.52\n", + "Changing to logg=5.00 for T= 39001 logg=5.46\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 39098 logg=5.41\n", + "Changing to logg=5.00 for T= 39018 logg=5.53\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 34722 logg=5.55\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 37968 logg=5.42\n", + "Changing to logg=5.00 for T= 39042 logg=5.41\n", + "Changing to logg=5.00 for T= 39090 logg=5.47\n", + "Changing to logg=5.00 for T= 47516 logg=9.02\n", + "Changing to logg=5.00 for T= 38778 logg=5.38\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 38810 logg=5.36\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 32931 logg=5.73\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38008 logg=5.41\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 38543 logg=5.67\n", + "Changing to logg=5.00 for T= 35665 logg=8.52\n", + "Changing to logg=5.00 for T= 38216 logg=5.28\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38912 logg=5.40\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35759 logg=5.48\n", + "Changing to logg=5.00 for T= 35776 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 37645 logg=8.47\n", + "Changing to logg=5.00 for T= 37474 logg=5.67\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38938 logg=5.41\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38864 logg=5.34\n", + "Changing to logg=5.00 for T= 38164 logg=5.31\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 39126 logg=5.42\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45739 logg=8.21\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 35706 logg=8.52\n", + "Changing to logg=5.00 for T= 38268 logg=5.57\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 35714 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35715 logg=8.52\n", + "Changing to logg=4.50 for T= 39634 logg=4.15\n", + "Changing to logg=5.00 for T= 40536 logg=5.52\n", + "Changing to logg=5.00 for T= 34113 logg=5.11\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 40751 logg=8.18\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 38868 logg=5.45\n", + "Changing to logg=5.00 for T= 35519 logg=8.51\n", + "Changing to logg=5.00 for T= 39136 logg=5.47\n", + "Changing to logg=5.00 for T= 39036 logg=5.38\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 33207 logg=5.01\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38863 logg=5.61\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45954 logg=8.68\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38886 logg=5.50\n", + "Changing to logg=5.00 for T= 39074 logg=5.38\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 38499 logg=5.32\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 39105 logg=5.40\n", + "Changing to logg=5.00 for T= 38901 logg=5.40\n", + "Changing to logg=5.00 for T= 38260 logg=5.41\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38820 logg=5.38\n", + "Changing to logg=5.00 for T= 39086 logg=5.37\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 37638 logg=5.66\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 35706 logg=5.17\n", + "Changing to logg=5.00 for T= 41390 logg=5.50\n", + "Changing to logg=5.00 for T= 39027 logg=5.57\n", + "Changing to logg=5.00 for T= 40930 logg=5.50\n", + "Changing to logg=5.00 for T= 31378 logg=8.25\n", + "Changing to logg=5.00 for T= 35709 logg=8.52\n", + "Changing to logg=5.00 for T= 39059 logg=5.38\n", + "Changing to T= 50000 for T= 52469 logg=8.18\n", + "Changing to logg=5.00 for T= 52469 logg=8.18\n", + "Changing to logg=5.00 for T= 39097 logg=5.40\n", + "Changing to logg=5.00 for T= 40327 logg=8.45\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 38866 logg=5.46\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 33157 logg=5.77\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 37690 logg=5.71\n", + "Changing to logg=5.00 for T= 38205 logg=5.40\n", + "Changing to logg=5.00 for T= 39156 logg=5.49\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 35714 logg=8.52\n", + "Changing to logg=5.00 for T= 35722 logg=8.52\n", + "Changing to logg=5.00 for T= 37950 logg=5.39\n", + "Changing to logg=5.00 for T= 37640 logg=8.47\n", + "Changing to logg=5.00 for T= 35929 logg=8.18\n", + "Changing to logg=5.00 for T= 39474 logg=5.51\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 38945 logg=5.49\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 34757 logg=5.54\n", + "Changing to logg=5.00 for T= 35710 logg=8.52\n", + "Changing to logg=5.00 for T= 33652 logg=5.61\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35715 logg=8.52\n", + "Changing to logg=5.00 for T= 47227 logg=8.14\n", + "Changing to logg=5.00 for T= 38030 logg=5.41\n", + "Changing to logg=5.00 for T= 38796 logg=5.38\n", + "Changing to logg=5.00 for T= 38530 logg=5.55\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38942 logg=5.43\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 34986 logg=5.58\n", + "Changing to logg=5.00 for T= 33657 logg=5.67\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 39010 logg=5.57\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38062 logg=5.44\n", + "Changing to logg=5.00 for T= 35708 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38751 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38924 logg=5.38\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to T= 50000 for T= 55186 logg=8.12\n", + "Changing to logg=5.00 for T= 55186 logg=8.12\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 41051 logg=8.91\n", + "Changing to logg=5.00 for T= 38968 logg=5.47\n", + "Changing to logg=5.00 for T= 38751 logg=8.52\n", + "Changing to logg=5.00 for T= 35722 logg=8.52\n", + "Changing to logg=5.00 for T= 34428 logg=5.62\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 37422 logg=5.67\n", + "Changing to logg=5.00 for T= 34980 logg=5.56\n", + "Changing to logg=5.00 for T= 34191 logg=8.16\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 37820 logg=5.74\n", + "Changing to logg=5.00 for T= 38913 logg=5.59\n", + "Changing to logg=5.00 for T= 38797 logg=5.34\n", + "Changing to logg=5.00 for T= 35649 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35392 logg=8.22\n", + "Changing to logg=5.00 for T= 35709 logg=8.52\n", + "Changing to logg=5.00 for T= 37225 logg=5.46\n", + "Changing to logg=5.00 for T= 38747 logg=5.38\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 39121 logg=5.40\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 37138 logg=5.69\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to logg=5.00 for T= 35098 logg=8.49\n", + "Changing to logg=5.00 for T= 38377 logg=5.39\n", + "Changing to logg=5.00 for T= 39010 logg=5.64\n", + "Changing to logg=5.00 for T= 39290 logg=5.53\n", + "Changing to logg=5.00 for T= 37889 logg=5.54\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 34340 logg=5.63\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39098 logg=5.57\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 44961 logg=8.20\n", + "Changing to logg=5.00 for T= 37700 logg=5.76\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to T= 50000 for T= 80369 logg=8.32\n", + "Changing to logg=5.00 for T= 80369 logg=8.32\n", + "Changing to T= 50000 for T=196662 logg=7.28\n", + "Changing to logg=5.00 for T=196662 logg=7.28\n", + "Changing to logg=5.00 for T= 39296 logg=5.53\n", + "Changing to logg=5.00 for T= 33848 logg=5.68\n", + "Changing to logg=5.00 for T= 42994 logg=5.57\n", + "Changing to logg=5.00 for T= 37933 logg=5.39\n", + "Changing to logg=5.00 for T= 34437 logg=5.46\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 42289 logg=5.55\n", + "Changing to logg=5.00 for T= 37723 logg=5.77\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 31631 logg=5.78\n", + "Changing to logg=5.00 for T= 35046 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39033 logg=5.45\n", + "Changing to logg=5.00 for T= 35123 logg=8.49\n", + "Changing to logg=5.00 for T= 35233 logg=8.21\n", + "Changing to logg=5.00 for T= 39162 logg=5.48\n", + "Changing to T= 50000 for T= 91531 logg=8.30\n", + "Changing to logg=5.00 for T= 91531 logg=8.30\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 44686 logg=8.19\n", + "Changing to T= 50000 for T=172024 logg=7.24\n", + "Changing to logg=5.00 for T=172024 logg=7.24\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38002 logg=5.45\n", + "Changing to logg=5.00 for T= 38718 logg=5.32\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 37054 logg=5.15\n", + "Changing to logg=5.00 for T= 34271 logg=5.61\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 31499 logg=8.21\n", + "Changing to logg=5.00 for T= 39297 logg=5.50\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39015 logg=5.63\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to logg=5.00 for T= 39282 logg=5.39\n", + "Changing to T= 50000 for T=122659 logg=7.98\n", + "Changing to logg=5.00 for T=122659 logg=7.98\n", + "Changing to logg=5.00 for T= 39152 logg=5.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38506 logg=5.66\n", + "Changing to logg=5.00 for T= 39300 logg=5.53\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to T= 50000 for T= 53154 logg=8.20\n", + "Changing to logg=5.00 for T= 53154 logg=8.20\n", + "Changing to T= 50000 for T= 90298 logg=8.31\n", + "Changing to logg=5.00 for T= 90298 logg=8.31\n", + "Changing to logg=5.00 for T= 35044 logg=8.49\n", + "Changing to logg=5.00 for T= 34450 logg=5.60\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35047 logg=8.49\n", + "Changing to logg=5.00 for T= 36175 logg=5.49\n", + "Changing to logg=5.00 for T= 39339 logg=5.50\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35065 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 37969 logg=5.40\n", + "Changing to logg=5.00 for T= 38323 logg=5.43\n", + "Changing to logg=5.00 for T= 38497 logg=5.67\n", + "Changing to logg=5.00 for T= 35161 logg=8.49\n", + "Changing to logg=5.00 for T= 37720 logg=5.76\n", + "Changing to logg=5.00 for T= 35042 logg=8.49\n", + "Changing to logg=5.00 for T= 39061 logg=5.47\n", + "Changing to logg=5.00 for T= 39115 logg=5.45\n", + "Changing to logg=5.00 for T= 27868 logg=6.18\n", + "Changing to T= 50000 for T=197624 logg=7.27\n", + "Changing to logg=5.00 for T=197624 logg=7.27\n", + "Changing to logg=5.00 for T= 39260 logg=5.51\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to T= 50000 for T=170279 logg=7.23\n", + "Changing to logg=5.00 for T=170279 logg=7.23\n", + "Changing to logg=5.00 for T= 39090 logg=5.45\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=0.00 for T= 3294 logg=-0.23\n", + "Changing to logg=5.00 for T= 38348 logg=5.37\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 37267 logg=5.48\n", + "Changing to T= 50000 for T= 81089 logg=8.14\n", + "Changing to logg=5.00 for T= 81089 logg=8.14\n", + "Changing to logg=5.00 for T= 36208 logg=8.26\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39215 logg=5.43\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38042 logg=5.48\n", + "Changing to logg=5.00 for T= 39003 logg=5.46\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39019 logg=5.46\n", + "Changing to T= 50000 for T= 85704 logg=8.31\n", + "Changing to logg=5.00 for T= 85704 logg=8.31\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38521 logg=5.66\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 27144 logg=6.25\n", + "Changing to T= 50000 for T= 52576 logg=8.18\n", + "Changing to logg=5.00 for T= 52576 logg=8.18\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39184 logg=5.47\n", + "Changing to logg=0.00 for T= 3295 logg=-0.23\n", + "Making photometry for isochrone: log(t) = 8.20 AKs = 0.00 dist = 2000\n", + " Starting at: 2021-08-13 12:02:10.878206 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_U = 16.04\n", + "M = 5.500 Msun T = 27287 K m_ubv_U = 21.28\n", + "M = 4.500 Msun T = 4524 K m_ubv_U = 11.80\n", + "M = 2.700 Msun T = 10526 K m_ubv_U = 11.99\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_U = 21.48\n", + "M = 2.500 Msun T = 10121 K m_ubv_U = 12.37\n", + "M = 5.000 Msun T = 38750 K m_ubv_U = 20.43\n", + "M = 3.200 Msun T = 11657 K m_ubv_U = 11.23\n", + "M = 5.012 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_U = 9.67\n", + "M = 3.000 Msun T = 11265 K m_ubv_U = 11.54\n", + "M = 4.000 Msun T = 11805 K m_ubv_U = 9.86\n", + "M = 6.000 Msun T = 30830 K m_ubv_U = 21.36\n", + "M = 6.000 Msun T = 27033 K m_ubv_U = 21.18\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_U = 20.43\n", + "M = 3.000 Msun T = 11287 K m_ubv_U = 11.51\n", + "M = 3.200 Msun T = 45669 K m_ubv_U = 20.18\n", + "M = 5.500 Msun T = 27836 K m_ubv_U = 21.18\n", + "M = 4.500 Msun T = 4524 K m_ubv_U = 11.80\n", + "M = 2.500 Msun T = 10121 K m_ubv_U = 12.37\n", + "M = 6.000 Msun T = 33082 K m_ubv_U = 21.42\n", + "M = 2.300 Msun T = 10194 K m_ubv_U = 12.31\n", + "M = 3.700 Msun T = 11328 K m_ubv_U = 9.19\n", + "M = 3.200 Msun T = 41469 K m_ubv_U = 21.85\n", + "M = 1.650 Msun T = 7676 K m_ubv_U = 14.33\n", + "M = 3.600 Msun T = 13127 K m_ubv_U = 10.62\n", + "M = 1.350 Msun T = 6542 K m_ubv_U = 15.49\n", + "M = 4.500 Msun T = 4534 K m_ubv_U = 11.76\n", + "M = 0.600 Msun T = 3945 K m_ubv_U = 22.59\n", + "M = 2.000 Msun T = 9101 K m_ubv_U = 13.37\n", + "M = 4.000 Msun T = 14082 K m_ubv_U = 10.26\n", + "M = 2.240 Msun T = 9965 K m_ubv_U = 12.85\n", + "M = 7.000 Msun T = 39833 K m_ubv_U = 22.06\n", + "M = 0.810 Msun T = nan K m_ubv_U = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_U = 14.85\n", + "M = 0.400 Msun T = 3660 K m_ubv_U = 24.09\n", + "M = 5.400 Msun T = 12339 K m_ubv_U = 8.58\n", + "M = 3.600 Msun T = 10929 K m_ubv_U = 10.51\n", + "M = 4.500 Msun T = 38506 K m_ubv_U = 13.62\n", + "M = 5.500 Msun T = 26754 K m_ubv_U = 21.29\n", + "M = 3.200 Msun T = 11594 K m_ubv_U = 11.20\n", + "M = 0.500 Msun T = 3758 K m_ubv_U = 23.42\n", + "M = 2.700 Msun T = 11431 K m_ubv_U = 12.02\n", + "M = 0.960 Msun T = nan K m_ubv_U = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_U = 12.92\n", + "M = 2.700 Msun T = 11422 K m_ubv_U = 12.01\n", + "M = 0.500 Msun T = 3758 K m_ubv_U = 23.42\n", + "M = 3.600 Msun T = 13801 K m_ubv_U = 10.96\n", + "M = 0.230 Msun T = nan K m_ubv_U = nan\n", + "M = 0.370 Msun T = nan K m_ubv_U = nan\n", + "M = 1.920 Msun T = nan K m_ubv_U = nan\n", + "Starting filter: ubv,V Elapsed time: 9.52 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_V = 15.52\n", + "M = 5.500 Msun T = 27287 K m_ubv_V = 22.41\n", + "M = 4.500 Msun T = 4524 K m_ubv_V = 9.30\n", + "M = 2.700 Msun T = 10526 K m_ubv_V = 12.17\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_V = 22.50\n", + "M = 2.500 Msun T = 10121 K m_ubv_V = 12.47\n", + "M = 5.000 Msun T = 38750 K m_ubv_V = 21.81\n", + "M = 3.200 Msun T = 11657 K m_ubv_V = 11.59\n", + "M = 5.012 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_V = 10.17\n", + "M = 3.000 Msun T = 11265 K m_ubv_V = 11.84\n", + "M = 4.000 Msun T = 11805 K m_ubv_V = 10.28\n", + "M = 6.000 Msun T = 30830 K m_ubv_V = 22.61\n", + "M = 6.000 Msun T = 27033 K m_ubv_V = 22.30\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_V = 21.81\n", + "M = 3.000 Msun T = 11287 K m_ubv_V = 11.82\n", + "M = 3.200 Msun T = 45669 K m_ubv_V = 21.61\n", + "M = 5.500 Msun T = 27836 K m_ubv_V = 22.34\n", + "M = 4.500 Msun T = 4524 K m_ubv_V = 9.30\n", + "M = 2.500 Msun T = 10121 K m_ubv_V = 12.47\n", + "M = 6.000 Msun T = 33082 K m_ubv_V = 22.71\n", + "M = 2.300 Msun T = 10194 K m_ubv_V = 12.43\n", + "M = 3.700 Msun T = 11328 K m_ubv_V = 9.57\n", + "M = 3.200 Msun T = 41469 K m_ubv_V = 23.23\n", + "M = 1.650 Msun T = 7676 K m_ubv_V = 14.06\n", + "M = 3.600 Msun T = 13127 K m_ubv_V = 11.16\n", + "M = 1.350 Msun T = 6542 K m_ubv_V = 15.08\n", + "M = 4.500 Msun T = 4534 K m_ubv_V = 9.28\n", + "M = 0.600 Msun T = 3945 K m_ubv_V = 20.21\n", + "M = 2.000 Msun T = 9101 K m_ubv_V = 13.27\n", + "M = 4.000 Msun T = 14082 K m_ubv_V = 10.89\n", + "M = 2.240 Msun T = 9965 K m_ubv_V = 12.91\n", + "M = 7.000 Msun T = 39833 K m_ubv_V = 23.43\n", + "M = 0.810 Msun T = nan K m_ubv_V = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_V = 14.54\n", + "M = 0.400 Msun T = 3660 K m_ubv_V = 21.70\n", + "M = 5.400 Msun T = 12339 K m_ubv_V = 9.07\n", + "M = 3.600 Msun T = 10929 K m_ubv_V = 10.76\n", + "M = 4.500 Msun T = 38506 K m_ubv_V = 15.00\n", + "M = 5.500 Msun T = 26754 K m_ubv_V = 22.40\n", + "M = 3.200 Msun T = 11594 K m_ubv_V = 11.55\n", + "M = 0.500 Msun T = 3758 K m_ubv_V = 21.04\n", + "M = 2.700 Msun T = 11431 K m_ubv_V = 12.33\n", + "M = 0.960 Msun T = nan K m_ubv_V = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_V = 12.94\n", + "M = 2.700 Msun T = 11422 K m_ubv_V = 12.31\n", + "M = 0.500 Msun T = 3758 K m_ubv_V = 21.04\n", + "M = 3.600 Msun T = 13801 K m_ubv_V = 11.55\n", + "M = 0.230 Msun T = nan K m_ubv_V = nan\n", + "M = 0.370 Msun T = nan K m_ubv_V = nan\n", + "M = 1.920 Msun T = nan K m_ubv_V = nan\n", + "Starting filter: ubv,B Elapsed time: 19.08 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_B = 16.07\n", + "M = 5.500 Msun T = 27287 K m_ubv_B = 22.27\n", + "M = 4.500 Msun T = 4524 K m_ubv_B = 10.56\n", + "M = 2.700 Msun T = 10526 K m_ubv_B = 12.12\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_B = 22.40\n", + "M = 2.500 Msun T = 10121 K m_ubv_B = 12.45\n", + "M = 5.000 Msun T = 38750 K m_ubv_B = 21.51\n", + "M = 3.200 Msun T = 11657 K m_ubv_B = 11.50\n", + "M = 5.012 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_B = 10.05\n", + "M = 3.000 Msun T = 11265 K m_ubv_B = 11.76\n", + "M = 4.000 Msun T = 11805 K m_ubv_B = 10.18\n", + "M = 6.000 Msun T = 30830 K m_ubv_B = 22.42\n", + "M = 6.000 Msun T = 27033 K m_ubv_B = 22.16\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_B = 21.51\n", + "M = 3.000 Msun T = 11287 K m_ubv_B = 11.74\n", + "M = 3.200 Msun T = 45669 K m_ubv_B = 21.29\n", + "M = 5.500 Msun T = 27836 K m_ubv_B = 22.18\n", + "M = 4.500 Msun T = 4524 K m_ubv_B = 10.56\n", + "M = 2.500 Msun T = 10121 K m_ubv_B = 12.45\n", + "M = 6.000 Msun T = 33082 K m_ubv_B = 22.50\n", + "M = 2.300 Msun T = 10194 K m_ubv_B = 12.40\n", + "M = 3.700 Msun T = 11328 K m_ubv_B = 9.47\n", + "M = 3.200 Msun T = 41469 K m_ubv_B = 22.97\n", + "M = 1.650 Msun T = 7676 K m_ubv_B = 14.32\n", + "M = 3.600 Msun T = 13127 K m_ubv_B = 11.04\n", + "M = 1.350 Msun T = 6542 K m_ubv_B = 15.55\n", + "M = 4.500 Msun T = 4534 K m_ubv_B = 10.53\n", + "M = 0.600 Msun T = 3945 K m_ubv_B = 21.56\n", + "M = 2.000 Msun T = 9101 K m_ubv_B = 13.33\n", + "M = 4.000 Msun T = 14082 K m_ubv_B = 10.75\n", + "M = 2.240 Msun T = 9965 K m_ubv_B = 12.90\n", + "M = 7.000 Msun T = 39833 K m_ubv_B = 23.18\n", + "M = 0.810 Msun T = nan K m_ubv_B = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_B = 14.89\n", + "M = 0.400 Msun T = 3660 K m_ubv_B = 23.08\n", + "M = 5.400 Msun T = 12339 K m_ubv_B = 8.96\n", + "M = 3.600 Msun T = 10929 K m_ubv_B = 10.69\n", + "M = 4.500 Msun T = 38506 K m_ubv_B = 14.70\n", + "M = 5.500 Msun T = 26754 K m_ubv_B = 22.26\n", + "M = 3.200 Msun T = 11594 K m_ubv_B = 11.47\n", + "M = 0.500 Msun T = 3758 K m_ubv_B = 22.42\n", + "M = 2.700 Msun T = 11431 K m_ubv_B = 12.25\n", + "M = 0.960 Msun T = nan K m_ubv_B = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_B = 12.94\n", + "M = 2.700 Msun T = 11422 K m_ubv_B = 12.24\n", + "M = 0.500 Msun T = 3758 K m_ubv_B = 22.42\n", + "M = 3.600 Msun T = 13801 K m_ubv_B = 11.43\n", + "M = 0.230 Msun T = nan K m_ubv_B = nan\n", + "M = 0.370 Msun T = nan K m_ubv_B = nan\n", + "M = 1.920 Msun T = nan K m_ubv_B = nan\n", + "Starting filter: ubv,R Elapsed time: 28.50 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_R = 15.23\n", + "M = 5.500 Msun T = 27287 K m_ubv_R = 22.53\n", + "M = 4.500 Msun T = 4524 K m_ubv_R = 8.73\n", + "M = 2.700 Msun T = 10526 K m_ubv_R = 12.19\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_R = 22.61\n", + "M = 2.500 Msun T = 10121 K m_ubv_R = 12.48\n", + "M = 5.000 Msun T = 38750 K m_ubv_R = 21.94\n", + "M = 3.200 Msun T = 11657 K m_ubv_R = 11.62\n", + "M = 5.012 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_R = 10.20\n", + "M = 3.000 Msun T = 11265 K m_ubv_R = 11.86\n", + "M = 4.000 Msun T = 11805 K m_ubv_R = 10.31\n", + "M = 6.000 Msun T = 30830 K m_ubv_R = 22.73\n", + "M = 6.000 Msun T = 27033 K m_ubv_R = 22.42\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_R = 21.94\n", + "M = 3.000 Msun T = 11287 K m_ubv_R = 11.84\n", + "M = 3.200 Msun T = 45669 K m_ubv_R = 21.74\n", + "M = 5.500 Msun T = 27836 K m_ubv_R = 22.45\n", + "M = 4.500 Msun T = 4524 K m_ubv_R = 8.73\n", + "M = 2.500 Msun T = 10121 K m_ubv_R = 12.48\n", + "M = 6.000 Msun T = 33082 K m_ubv_R = 22.83\n", + "M = 2.300 Msun T = 10194 K m_ubv_R = 12.44\n", + "M = 3.700 Msun T = 11328 K m_ubv_R = 9.59\n", + "M = 3.200 Msun T = 41469 K m_ubv_R = 23.36\n", + "M = 1.650 Msun T = 7676 K m_ubv_R = 13.93\n", + "M = 3.600 Msun T = 13127 K m_ubv_R = 11.20\n", + "M = 1.350 Msun T = 6542 K m_ubv_R = 14.83\n", + "M = 4.500 Msun T = 4534 K m_ubv_R = 8.71\n", + "M = 0.600 Msun T = 3945 K m_ubv_R = 19.44\n", + "M = 2.000 Msun T = 9101 K m_ubv_R = 13.26\n", + "M = 4.000 Msun T = 14082 K m_ubv_R = 10.93\n", + "M = 2.240 Msun T = 9965 K m_ubv_R = 12.92\n", + "M = 7.000 Msun T = 39833 K m_ubv_R = 23.56\n", + "M = 0.810 Msun T = nan K m_ubv_R = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_R = 14.35\n", + "M = 0.400 Msun T = 3660 K m_ubv_R = 20.90\n", + "M = 5.400 Msun T = 12339 K m_ubv_R = 9.10\n", + "M = 3.600 Msun T = 10929 K m_ubv_R = 10.78\n", + "M = 4.500 Msun T = 38506 K m_ubv_R = 15.13\n", + "M = 5.500 Msun T = 26754 K m_ubv_R = 22.52\n", + "M = 3.200 Msun T = 11594 K m_ubv_R = 11.58\n", + "M = 0.500 Msun T = 3758 K m_ubv_R = 20.24\n", + "M = 2.700 Msun T = 11431 K m_ubv_R = 12.35\n", + "M = 0.960 Msun T = nan K m_ubv_R = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_R = 12.94\n", + "M = 2.700 Msun T = 11422 K m_ubv_R = 12.34\n", + "M = 0.500 Msun T = 3758 K m_ubv_R = 20.24\n", + "M = 3.600 Msun T = 13801 K m_ubv_R = 11.59\n", + "M = 0.230 Msun T = nan K m_ubv_R = nan\n", + "M = 0.370 Msun T = nan K m_ubv_R = nan\n", + "M = 1.920 Msun T = nan K m_ubv_R = nan\n", + "Starting filter: ubv,I Elapsed time: 37.62 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_I = 14.88\n", + "M = 5.500 Msun T = 27287 K m_ubv_I = 22.77\n", + "M = 4.500 Msun T = 4524 K m_ubv_I = 8.02\n", + "M = 2.700 Msun T = 10526 K m_ubv_I = 12.23\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_I = 22.83\n", + "M = 2.500 Msun T = 10121 K m_ubv_I = 12.51\n", + "M = 5.000 Msun T = 38750 K m_ubv_I = 22.22\n", + "M = 3.200 Msun T = 11657 K m_ubv_I = 11.69\n", + "M = 5.012 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_I = 10.28\n", + "M = 3.000 Msun T = 11265 K m_ubv_I = 11.92\n", + "M = 4.000 Msun T = 11805 K m_ubv_I = 10.38\n", + "M = 6.000 Msun T = 30830 K m_ubv_I = 22.99\n", + "M = 6.000 Msun T = 27033 K m_ubv_I = 22.66\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_I = 22.22\n", + "M = 3.000 Msun T = 11287 K m_ubv_I = 11.90\n", + "M = 3.200 Msun T = 45669 K m_ubv_I = 22.02\n", + "M = 5.500 Msun T = 27836 K m_ubv_I = 22.70\n", + "M = 4.500 Msun T = 4524 K m_ubv_I = 8.02\n", + "M = 2.500 Msun T = 10121 K m_ubv_I = 12.51\n", + "M = 6.000 Msun T = 33082 K m_ubv_I = 23.10\n", + "M = 2.300 Msun T = 10194 K m_ubv_I = 12.47\n", + "M = 3.700 Msun T = 11328 K m_ubv_I = 9.65\n", + "M = 3.200 Msun T = 41469 K m_ubv_I = 23.63\n", + "M = 1.650 Msun T = 7676 K m_ubv_I = 13.79\n", + "M = 3.600 Msun T = 13127 K m_ubv_I = 11.30\n", + "M = 1.350 Msun T = 6542 K m_ubv_I = 14.53\n", + "M = 4.500 Msun T = 4534 K m_ubv_I = 8.00\n", + "M = 0.600 Msun T = 3945 K m_ubv_I = 18.38\n", + "M = 2.000 Msun T = 9101 K m_ubv_I = 13.24\n", + "M = 4.000 Msun T = 14082 K m_ubv_I = 11.05\n", + "M = 2.240 Msun T = 9965 K m_ubv_I = 12.94\n", + "M = 7.000 Msun T = 39833 K m_ubv_I = 23.83\n", + "M = 0.810 Msun T = nan K m_ubv_I = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_I = 14.12\n", + "M = 0.400 Msun T = 3660 K m_ubv_I = 19.55\n", + "M = 5.400 Msun T = 12339 K m_ubv_I = 9.18\n", + "M = 3.600 Msun T = 10929 K m_ubv_I = 10.84\n", + "M = 4.500 Msun T = 38506 K m_ubv_I = 15.41\n", + "M = 5.500 Msun T = 26754 K m_ubv_I = 22.76\n", + "M = 3.200 Msun T = 11594 K m_ubv_I = 11.65\n", + "M = 0.500 Msun T = 3758 K m_ubv_I = 19.00\n", + "M = 2.700 Msun T = 11431 K m_ubv_I = 12.42\n", + "M = 0.960 Msun T = nan K m_ubv_I = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_I = 12.95\n", + "M = 2.700 Msun T = 11422 K m_ubv_I = 12.41\n", + "M = 0.500 Msun T = 3758 K m_ubv_I = 19.00\n", + "M = 3.600 Msun T = 13801 K m_ubv_I = 11.71\n", + "M = 0.230 Msun T = nan K m_ubv_I = nan\n", + "M = 0.370 Msun T = nan K m_ubv_I = nan\n", + "M = 1.920 Msun T = nan K m_ubv_I = nan\n", + " Time taken: 46.47 seconds\n", + "Isochrone generation took 354.168146 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "iso1 = synthetic.Isochrone_Binary(8.2, 0.0, 2000, math.log10(1), mass_sampling=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check if all phases represented in the BPASS isochroneare valid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x == 102 or x == 103) for x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == 102 or x == 103) for x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == -99 or\n", + " x == 102 or x == 103) for x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries and secondaries accounting for NaNs in the columns. (For max we make NaNs the same as - infinity and for median and mean, we do not include them)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294869501162154" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.288600394549206" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.283702439639246" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's find the mean and median logg values for the single stars, secondary stars, and the primary stars" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.354531279865074" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.109131776599373" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.762344379541872" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.2498758947769355" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.133548174454043" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.30006013240644" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median([x for x in iso1.secondaries['logg'] if np.isfinite(x)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make a histogram of log_g values for each table of the isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (secondaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]),\n", + " np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values (primaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]),\n", + " np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values single stars of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]),\n", + " np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, let's create a MIST v.1. isochrone phot using otherwise same parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 23.235669 s.\n", + "Making photometry for isochrone: log(t) = 8.20 AKs = 0.00 dist = 2000\n", + " Starting at: 2021-08-13 12:03:21.425874 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.111 Msun T = 2982 K m_ubv_U = 28.94\n", + "M = 1.468 Msun T = 7177 K m_ubv_U = 14.83\n", + "M = 4.392 Msun T = 4603 K m_ubv_U = 11.93\n", + "M = 4.423 Msun T = 3455 K m_ubv_U = 12.78\n", + "M = 4.425 Msun T = 23358 K m_ubv_U = 6.34\n", + "M = 4.425 Msun T = 170211 K m_ubv_U = 11.96\n", + "M = 4.426 Msun T = 157705 K m_ubv_U = 17.19\n", + "Starting filter: ubv,B Elapsed time: 1.80 seconds\n", + "Starting synthetic photometry\n", + "M = 0.111 Msun T = 2982 K m_ubv_B = 27.70\n", + "M = 1.468 Msun T = 7177 K m_ubv_B = 14.87\n", + "M = 4.392 Msun T = 4603 K m_ubv_B = 10.80\n", + "M = 4.423 Msun T = 3455 K m_ubv_B = 10.83\n", + "M = 4.425 Msun T = 23358 K m_ubv_B = 7.37\n", + "M = 4.425 Msun T = 170211 K m_ubv_B = 13.15\n", + "M = 4.426 Msun T = 157705 K m_ubv_B = 18.38\n", + "Starting filter: ubv,V Elapsed time: 3.55 seconds\n", + "Starting synthetic photometry\n", + "M = 0.111 Msun T = 2982 K m_ubv_V = 26.40\n", + "M = 1.468 Msun T = 7177 K m_ubv_V = 14.53\n", + "M = 4.392 Msun T = 4603 K m_ubv_V = 9.59\n", + "M = 4.423 Msun T = 3455 K m_ubv_V = 9.17\n", + "M = 4.425 Msun T = 23358 K m_ubv_V = 7.56\n", + "M = 4.425 Msun T = 170211 K m_ubv_V = 13.52\n", + "M = 4.426 Msun T = 157705 K m_ubv_V = 18.74\n", + "Starting filter: ubv,R Elapsed time: 5.43 seconds\n", + "Starting synthetic photometry\n", + "M = 0.111 Msun T = 2982 K m_ubv_R = 25.14\n", + "M = 1.468 Msun T = 7177 K m_ubv_R = 14.35\n", + "M = 4.392 Msun T = 4603 K m_ubv_R = 9.04\n", + "M = 4.423 Msun T = 3455 K m_ubv_R = 8.20\n", + "M = 4.425 Msun T = 23358 K m_ubv_R = 7.62\n", + "M = 4.425 Msun T = 170211 K m_ubv_R = 13.66\n", + "M = 4.426 Msun T = 157705 K m_ubv_R = 18.88\n", + "Starting filter: ubv,I Elapsed time: 7.17 seconds\n", + "Starting synthetic photometry\n", + "M = 0.111 Msun T = 2982 K m_ubv_I = 22.28\n", + "M = 1.468 Msun T = 7177 K m_ubv_I = 14.14\n", + "M = 4.392 Msun T = 4603 K m_ubv_I = 8.36\n", + "M = 4.423 Msun T = 3455 K m_ubv_I = 6.26\n", + "M = 4.425 Msun T = 23358 K m_ubv_I = 7.79\n", + "M = 4.425 Msun T = 170211 K m_ubv_I = 13.97\n", + "M = 4.426 Msun T = 157705 K m_ubv_I = 19.19\n", + " Time taken: 8.91 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(8.2, 0.0, 2000,\n", + " math.log(1), recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: dist/10 pc = 200 for both of our isochrones and clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum logg of the isochrone and the distribution of logg values" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.038804565028379" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 96., 103., 29., 55., 179., 39., 26., 71., 5., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0.]),\n", + " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlabel(\"logg in cgs\")\n", + "plt.title(\"Histogram of logg values of stars in the MIST v1 isochrone\")\n", + "plt.hist(np.array([x for x in iso2.points['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding the log_g median and mean for the new isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.190403645585174" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.7100077975325854" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso2.points['logg'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we want to make clusters for both of these models and plot CMD, HR Diagram, and Mass Luminosity relationship for the isochrones. Then, we want to find the Mass luminosity relationship and CMD for the cluster." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we compare the BPASS and MIST cluster of the same age. Here, we test the functionality of the BPASS cluster, specifically in its ability to match IMF generated stars with their counterparts in the isochrone and to apply the IFMR." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/ryotainagaki/Desktop/PyPopStar/spisea/synthetic.py:801: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " compMass = np.array([compMass[x] for x in indices])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Interpolator for: Teff\n", + "Creating Interpolator for: L\n", + "Creating Interpolator for: logg\n", + "Creating Interpolator for: isWR\n", + "Creating Interpolator for: mass_current\n", + "Creating Interpolator for: phase\n", + "Creating Interpolator for: m_ubv_U\n", + "Creating Interpolator for: m_ubv_V\n", + "Creating Interpolator for: m_ubv_B\n", + "Creating Interpolator for: m_ubv_R\n", + "Creating Interpolator for: m_ubv_I\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n", + " result = self.as_array() == other\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 6 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1,\n", + " imf.IMFSalpeter1955(multiplicity=\n", + " multiplicity.MultiplicityUnresolved()),\n", + " 2000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2,\n", + " imf.IMFSalpeter1955(multiplicity=\n", + " multiplicity.MultiplicityUnresolved()),\n", + " 2000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the BPASS Cluster vs MIST Settings cluster Observer's HR Diagram\n", + "Remember to use a distance modulus!" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Oxe1xNcUfAXsmvdZE4s1Y/dsrwNdz8Hn90tveY1mu9y/cb3IwsK3ANQvbCGwXmO6fA30pxbbm4s4rBmTwuqnOQ88hxf+RN98Pkj7T1X2WYvvf8bZ/RGDamam+V7ia3fdpHyTlZF9lkrjBePc2g2WDRnv3C5NnWGv/66WM3NMYM8ha+0HoCxszAJd9ZJ21Nqxzpr/tA0Pm/dta25RlmX3LQ6a95d0/FzJvnXc/LDjRGNMbd+X2NNzV6O1JTLu+W2DZgbhajTettbGQ11jSiTL7WV/SptjsJv7nvzh5hrW2xRizBPd+g/w0nAekSOX5Ge9+P1zwngsm8LjDY9wYcyjuRKEGd3W5T9Iiu+Fq9SB+XLb77KzLzvMm7iQrzLNpytDfK8NJuH0ygMT3sVvYeuTouE5hX1wfr39aaxtD5i/EBY1h39VceARX7f97Y8wxuCYc/wRWWu8XETr9mzLOu388i/LUW2tbQqa/Sfw4Txb2mXdlv7b7vK21W40xb5P4m/AZXID9CvAjY0zyauCaBO0XeH4vrunGS8aYP+O+5/+01r4btnIqWX6fOqs7j41Qxu3EM3AnHAfg9ndFYJFUiUpSfu8zkIvvt39sDk/xG7y3d78frratQ13YF53lf1/n20DH+lyy1r5kjHkKl8lsdxvPsDbFu78lxartGGP2AK7AZVTdA+iXtEg2v+dh//np/otf8/6Hhmda3m6QfI75MPAP62W383+PrLXrgTO9rHUfGWOewdXsv5m03IfAt40xv7aJ2e0ux/0O/A5Xe9WA+32dDtxrjKm21l7eqTdgzLdwicv+A5yV5eo345rdneeVD+B43PfyD9bajwPL+t/P8caYsSHb2gn33foM3nc+m/PQJKl+i+7FBfDLvN/+Rbjf/g7TwAcZY/bHNU88AhcAJw8hFCxXunOpj40x9bgLG0FZ76swmQRJb+EOpExOlIK29+7Xp5i/HveDsD2uvXJntwHuSmeyhvTFS+vDkGnNGczrnTT9z7gT2ddwX/wGXHUhuKrfvoFlB3r3yamo6WC674M05aoImdfd/M8uVbnDPp8h3v2FHWx7u06VKNyu3r3fxjYlY8xJuA6An+KaqryKu5reivuCjifxM+1oH7xN6iAp9Pj1fvAW4pojvIg7xt7FXYkCl7yhb9i65O64DtOV72qXeUHnQbgricfifsQB3jTG/NJa+7sulNN/vI7MfZBiejOpx6cL+8y7sl/TlSH4m+B/7/bGHT+ptH3vrLW/Nsa8h0v08C3c75k1xiwGvm+tDTuBS9CJ71OndPOxkcqvcftkPS4oW4cLNMEFC6lOSvP9v+UfC1/t4LWy+Q3u7L7orEHefTbf1864GXeCdwFwtZdB7Su4CyQZBbteltVncUHN07hmXx/itSjB1faFfgdSXFwO+8/P5L+4O4Mk/9jbPsX8gcHlrMtYuCnVxvzAxwuAQ9N/B5cDN+4Vrl/NXGvtdwKLrfB+h/4LfNcYc4u19rV0byaZMeYSXM3VSly/qrCLWenMxl1wutAY83PvfU315t2atKz//fx+B9sMfj+zOQ8NCv0tstY+FMhmfZ5fVmPMc7jawH90UDaMMeNw5zGVuGaljxDvY1uNq9HO9lwqWWf2VTuZBElLcO33jsK1Tc+U/8Wowv35JdslabmOthEm3TayrfnKKWPMGNyB+QSuacLWwLxeuHz9QRu9+1RjDnQ4FkE386/IhR0zg0Km+Z9JqnKHfab+OgdYa/8v86J1yQTv/jlrbXPaJV3Tpi3AGGvtquAMY8ytuJO6oOBn+lLI9tJ9pqmO3xNxAdJd1tpzksqwC+lPcrtTV76rOeF9Jl8zxlTirlh/AdfU6rfGmE+stX/qZDk/8O53o4vphjsQ9pn3xH71151rrT057ZIB1tpZwCxjzCBckpaTcH+a840x+9lAmt0Usv0+dVo3HhvtGGN2wgWOL+L6L32UNP/0dEVN+0a6n//eTrTWPtLVjXVxX3TWB959qivkufIQ7uTsfGPMNbhjv5L2J7bpfAd3MneutfbO4Axv35ydg3IG/4vD/odSHe+58rJ3/5kU8/3ayf+GzbTW1mbyIsn/h0m+7N0vCllvkzHmWdzv14G4YCIjxpjLcM26XsQFSB395rVjrd1sjLkTl1zjaGPMi7iLOcustf9OWrwt4LTWbqQDnTgPTShamjI/BjzmtWo5GLd/Lwb+aow50FrbUUufH+FqTCdYa6NJZb4Sd54T1Jnz46z2VSqprmgG3YG7Sj3JGDMy3YLGmGDk97x3Xxuy3AhczdTrqZraAXg/qK8Cuxlj9g5ZxD/BXZGuXHkywrt/JHhgeg4iqUrd+xBfw73XSMj2Dst5CRP5TYNS1Tr5tSy7h8wLG0DY/0zanegYYyoIfz9LvfvDU5Qhp7xB6/yxve7NYJURuCY6ySd0vQh/P/53oN08Y8xwwvdlJmWA8DEYcnZS2Qkv467+VZvwEdR77LtqrW221j5nrb0e174dXFv0zv6m+Mflcd1Q3I70xH79D+7EcpxXU5kVa+0H1tq/WWsvxPULGExm3+Fsv0/gLtZ0uma8G46NMHvh/lsXhAQFw7z5haozv8Hp/jvysS/893CMdyx1Rkf/h3j/63/EBWMn4GqUPiaz/xJfT/yep/sv3ovO/Q9lww9Mjk7+PLwmrofiahaXJq+YQ/656dAU8/3pGTf9NMZcgQuQ6nEn+1kHSAF/wAUlU3HHUQXhwXa238+szkOzZa39xFq70Kuduw7XXDqT/8kRQGNygOQJO+7TnUtth6t9SpaT88kOf0C8/jHTcG/+MS8ybccYcyyJbfZv9+5/ZIwZGliuAtfBrReZ1Uzdjmuz+gtvXX87O+KywQVfq5DEvPva4ETvytrvU6wzC7dfpptAxwBjzO64atHutMG73yPFfL/5QEJTOGPMKFyfgmTP4E7wjjDGJF8V+Cbt+yOBC8g/wDVdOCh5pjGml1dt3mXGmD1x6WD3xX0BM7n6FwP2Nsb4TfT89vZX49r6JrsP1/zhUu8zDK4znc6d7MW8+9rgRO/P7vpObC8nrBsM9l5c1fU1wXnGDfz3LdzFlru74/WNMQcZY8KuJvnTgs03sv1N+QPuc/xx2IUi72SvW/TEfvVqUG/E1ZT8zhjT7o/TGLNL8L0bY471amWS7eTdp2wuExAju+8TuN+prE7quvnYCBPz7g9L2sZ2uIRGGQ3inicP4wLFS4wxx4ctYIyp8S4w+dL9d8S8+x7bF9ba53D/P9W4vj4JjDFDjDHJ/R+SdfR/6JuJC6huwqUdvy85GOxAzLuvTSrjMbiT5Vy4F/cbcWnwAqwXsPyCzC6Wd5p1A7MvwDUfvCRp9k9xWflmWWs/6cZiPO3dTzHGJNQwGmOOwwVqn+KOG396b2PMvt7vLEnr/BiXVe05XA3Se10pnLX2FVyzsy/jxuH6ANdMLtlNuM/yN8aYdjVzxpg+xphgUBDz7muTlkt3HpqWMeaosP8Iwn9PU4kBg40xn0va9vm45CfJHsbVDJ1hjDkgad6PCG/NlO2+CpXRD5S19jrvD/Fq4F/GdZhbjrtqsjOuXe7eBDoSWmufMcbcgKvOe9EY8yCuvflxwGdxzfh+kcHL/9Jb50Tg38aYv+E6Mn8V94d8g7W2o6QG+fAvXOfgk739tQS3r47DBQ9vhaxzA+6q5mnAPsaYBbi2mKfi0nlPJN7sLdfqcAf3ZcaYwcTbeN7odYR8GNex+3TvpHAZ7g/kRG/eqcGNWWutd8D/A5hjjPEHfPObuvwdV6UcXGeDMeYUXNaRpcaYJ3HNA1q916rBNU3o6A8uaJCJd0CuxLX9PsDbVi+vHGdnmOTjN7gOuc8bY+bgvoCH4k7oHsVdTQy+n1eNMT/BXWH5t3GdHD/EDVQ8GDc+RcKPRAYexe3H73gB6vO4ffNlXNDX0Z96d/oB7qrNN43rKLkIl273VFxyiW9aa1/vptf+Ou7EbjFu/7yPC8RPwLW/nhFYNqvfFGvtSmPMN4h/9v53YQiuFvUj4rUM3aEn9uu1uO/FRcAJxpiFuD4dO+F+2w/FZcnym1HMBj41LgFLDBdYHA6MxZ04PJHBa2b1ffI8CZxmjHnUe51mXMbRp9K8TrcdG2GstQ3GmNm43/H6wO/4F3EnYvWEX/nMOy+xx8m4vkOPef9d9bj/ht1xn+9euIDaPxlahPuNnm6M+SxeqwNr7f/mcV+cicuKdp0xZpL32OCO5aNxF8diadZ/GXf8n2aM2YJLHmJxmW/b+rpYa98wxjyG64sE2TW1A9ev6VzgAe87sA53fnQsLj3117LcXjvW2pgx5ge4fi/PB/6HjsGdXP4fWfwPGWP2xf0mBe1gXJMx3/eSAodv4AKQ3xljjsKNQXYw7nfzv7jflu70IO436QvAKmPMXFyfm/1w/50G+IG1dkNgnd28cq4h0HfYGHM27oKVP87Pt0z7ZDex5OaTGbjZK9/OuPOudsGGtfY/xpjzcBdqXjLG/B23/3rj/vsPx/VR3tdbpTPnoR35FRAxxkRx36EtuIySR+L21ewMtjEDd/wtMcb4Y3yOwdUUPQicElzYWrvR+w++B3jGW2c9rpn3AbikJOMJnB93Yl+Fs9ml5tsPd8XxRVwbwS1eQR/HpUcNGwvhNNwH8xHuR/El3Bdim5BlY4SkA8adFF/lve5mb1tLgNNDlo2QZUrWwLrnkJRaODCvlhQpQVO9Ju5E+GbvfX2Ku0J3He5PN9V7HYTLvvIW7g/8P7gmYQd5rzEjafk7SUp92FGZ07z2sbhg6WPi6TEjgfm7465uNHqfw79wnaDT7ZvP4wKRj7zbE7gAZRop0sZ6+/Mm3Inop96x9h/c1fKJGX6W/mcSvH1KfIDPG4HD0qyfah+dg/tj/wSXNnkuMKqD93MWLpj5FPelvAeXMOJF4INMj7Okz+Fe4h2gX8JdjKgkJEVyB2U7h04c82nKNghXo+WPAv8BLlA+Opv93MFr3JlcZtwf7h+Ijyq+GXdCfAeBwUIDy2f8mxJYpwbXLOYd3G/fW7hj+5SOfgsC86O0Tzec8jPozH7t6HNLtc9xJwpn4QKRRu89rvP2y1UkjvV1Ee7Yfw13styIO8YvJ4P0s539PuEClftwF3FaMjk+u/PYSPV5437jf0Z8zKE3cVduh6Q4BtJ+Zh28v9B91ZXvt7eff+7tg024/4RXcCcwZ5KUKt6bVu/tLxt8f7naF9nuN2/71+NOBj/FfWfqvbJsm2673vSxuO/Ch7iTr1T7+ERvXkaDKIesfwiuE/v7gWNtYjb7IcPP+3Rc07vk/6GU20vxGn650t0iIevtjvvOrcf9tqzBJTxoN7RBd9xwJ8eX4f7/N+IusLyDGzQ17Hc04r2XWIrvW7pbtBPlqyA+Hma78fiSlh2F+x9cQ3wMrBdxQfqRSctmdR6a7hjy5p+KG+fxFdzvwkbvtX8GDM3i/X7Z+yw+wn03F+AqXNIdw8fhgu1NuO/Lw7gg56/eOoO6sq/CbsbbiBQ4Y8yFuKr9i6y12V6tkgJjXMr3t3GZkFKlhBYRkQLmtVS4GjcOUDbJrUTaeE3mV+PSafdIv+xS4DXjfQ1XSZPzJCTd2hZVshdsnx+YtjuuPXwzLmKWImGMGWqSOsN7TVd/hbtiPTcvBRMRkS4xLvHARbir0/fnuThS3L6Hq82/Kd8FKUTGmEFJfSH9Pqw/wjWfe6g7XreQO5CWqzneSfVzuCrICK5acltcDvruHvtBcmsScI0x5glcM5PBuCrlz+CaftyYv6KJiEi2jDFfwg3SegKuj8f3bEgfEpF0jBtI+Ou4vnLn4poEP5DXQhWuccCfvX6NMVwyo3G4Po1v4ppB5pya2xUYr3PaWbgvzfa4Np/PAzdZa7slUpbuY4w5EFcLeBDxwc1ex131uN5mlwlJRETyzEtScDauyfTtwI+sGwRUJGPGZetdhOtjswS42GY5mG25MC4j8f/ikvsMxVXyrMW1rrrOWptqoNmuva6CJBERERERkTj1SRIREREREQlQnySRHrDjjjvaSCSS72KIiEgJeO65596z1g7NdzlESpmCJJEeEIlEWL58eccLioiIdMAYs6bjpUSkK9TcTkREREREJEBBkoiIiIiISICCJBERERERkQAFSSIiIiIiIgEKkkRERERERAIUJImIiIiIiAQoBbiIiBSdTz/9lHfffZdPP/2U5ubmfBdHJCd69+7NTjvtxMCBA/NdFJGypyBJRESKyocffsjbb7/N0KFDqaqqorKyEmNMvosl0iXWWjZv3sy6desAFCiJ5Jma24mISFF57733GDZsGDvssAO9e/dWgCQlwRjDtttuy2677cY777yT7+KIlD0FSSIiUlS2bNlCv3798l2MLrnzzjsxxrTdKioq2G233Tj11FN5+eWX0y47YMAADjjgAG666aZ2TQ03b97M9ttvjzGGf//736GvvXHjRq6++mpGjhxJ//792WGHHRg1ahRTp05NODlvamriN7/5DQcccAADBgxg4MCB7Lvvvpx99tm88sorad/ftGnT8ha8RqNRjDE88cQTeXn9rurXrx9bt27NdzFEyp6a24mISNEpldqjBx54gGHDhtHS0sKrr77Ktddey1FHHcVLL73E9ttvH7rsxo0beeCBB7j00kt55513uOaaa9qWeeihh9i4cSMAs2bN4le/+lXCNlpaWvjCF75ALBbjiiuuoLq6mk8++YQXX3yR+++/n7feeouddtoJgNNPP50FCxZw+eWXM27cOFpaWli1ahUPPPAAK1euZO+99075vi644AKOPfbYXO2mslIqx7ZI0bPW6qabbt18+/znP29FJDdWrlyZ7yJ02R133GEB+8orryRM/8c//mEB+7e//a3DZWtra+2AAQMSpn3xi1+0gwcPtgcffLDdeeedbXNzc8L8J5980gJ23rx5oeVqaWmx1lr76quvWsDOmDEj7XKFaNGiRRaw//jHP7Jet7m52W7durUbSpWdjo5xYLktgP823XQr5Zua24mIiBQIv7N+Js2txo4dy0cffdTWRG7dunU8+eSTnHbaaVxwwQW8/fbbzJ8/P2GdxsZGAKqqqkK32atXr6yWSyWsud1vf/tb9ttvP/r168cOO+zAmDFjmDt3btt8ay2/+c1v2GeffejTpw+77LIL3/zmN9tqxnzNzc1cf/31jBw5km222YahQ4dy7LHH8p///CdhuU2bNvHNb36THXfckaFDh3LmmWfywQcfJCxjjOGHP/whP//5z9lzzz3p06cPL7zwAgD33HMPBxxwANtssw077rgjZ511FuvXr09YPxKJcOaZZzJ79mz2228/+vfvz5gxY1iyZEm7fbJ48WKOOuooBgwYQP/+/TnmmGN48cUX0+5HEckfBUkiIlK+6upg+nR3nwctLS00NzfT1NTEqlWruOqqq9hpp52ora3tcN3XX3+diooKtttuOwDuvvtuWltbmTx5MqeeeirbbLMNd911V8I6o0ePprKykqlTpzJ37lzef//90G3vu+++DBw4kB/84Afcc889vP322116n/feey/f/e53Of300/nb3/7GvffeyymnnNIWjAH88Ic/5Dvf+Q5f/OIXefTRR7n88su58847+dKXvkRra2vbcqeddho//OEPOf7445k3bx633XYbI0eObBfAfPvb38YYw3333cdPfvIT5syZw7e//e12Zbvzzjt57LHH+OUvf8ljjz3GrrvuysyZMznrrLPYb7/9eOihh/j5z3/O/PnzGT9+PB9//HHC+k8//TS/+tWvuPbaa/nzn/9MS0sLX/7ylxMCsscee4yjjjqK7bbbjnvuuYf77ruPjz76iMMPP5w333yzS/tWRLpJvquydNOtHG5qbifd5fXXO36cq3m5XjfTZZLlrLndM89Y26+ftRUV7v6ZZ3Kz3Qz4TeiSb7vuuqt99tlnQ5f9z3/+Y7du3WobGxvtLbfcYnv16mVPPPHEtuX2228/u88++7Q9P+2002zfvn3t+++/n7C92267zfbv398C1hhjR44cab/3ve/ZdevWJSz3yCOP2B133LGtbHvttZe95JJL7KpVqzp8f1dffbV1pxjOJZdcYg888MCUy2/YsMH27dvXnn322QnT7777bgvYhx9+2Fobby7429/+NuW2/OZ2kydPTph+ySWX2L59+9rW1ta2aYDdZZdd7KZNm9qmNTc325122snW1tYmrP/000+3e+3hw4fbQYMG2cbGxrZp//rXvyxg77333rZp//M//2OPPPLIhO19+OGHdsiQIfbb3/52u/eg5na66Zb/m2qSREQKSCyW+8e5mpfrdTu7Ts7Gjo1GYcsWaGlx99Fojjacublz5/Kvf/2LZ599lnnz5jFy5EiOP/54Vq1a1W7Zfffdl969ezN48GC+8Y1vcMYZZ3D77bcD8Oyzz7Jq1SrOOuustuXPPvtsmpqa+Mtf/pKwnQsuuIA333yTe+65hylTptDa2sovf/lL9t9/f1566aW25U444QRisRgPPfQQl156KYMGDeLmm2/mwAMPzDpz3NixY6mvr+fSSy/liSeeYNOmTQnzly5dSlNTE2eeeWbC9NNOO43KykoWL14MwIIFCzDGcOGFF3b4ml/60pcSno8aNYqmpqZ2tWLHHntsQrbEl19+mXfeeYczzjgjYbnDDjuM4cOHt5XFV1NTww477JDwOgBvvPEGAK+88gqvvvoqZ5xxBs3NzW23bbfdlpqaGp566qkO34uI9DwFSSIi3ag7gp6wwKFUdWuQVFsLffpARYW7z6CJW6599rOfZcyYMYwdO5YTTzyRRx55BGst06ZNa7esH1D95z//4ZNPPmHWrFkMHjwYoK1Z3QknnMAHH3zABx98wNixYxk6dGi7JncAO+ywA2eccQa33HILq1atYt68eW2pwYP69+/PSSedxO9+9zuee+45nnnmGSoqKvjBD36Q1fucPHkyf/jDH1i2bBnHHHMMgwcP5uSTTybmfcB+s7tddtklYb3KykqGDBnSNn/Dhg0MHjw4oxTw/r7x9e3bF4BPP/00YXrya6YqC7g+WsEmgpm8jt9n7Pzzz6d3794Jt7/+9a9s2LChw/ciIj1PKcBFRHIkFoNIJPFx2LR0jzPR0BCv9GhogBkz3OOKCqivd4+rqmD2bDff73vvL5fNvI6eZ7tuNOqe+8tD+DKQ3T7plJoaePJJ94K1te55nvXr14+99tqL//u//2s377Of/SwjRoxoN33Lli3Mnj0bgAMOOKDd/HfffZfVq1eHrus78cQTOeCAA1i5cmXa8o0bN46jjz6av//97x29lQTGGKZOncrUqVN5//33WbBgAd/97nf52te+xrJly9oCjYaGBvbff/+29Zqbm9mwYQNDhgwBYMcdd6SxsZHNmzfnbKys5AQTwbIka2hoYMyYMVlt3y/79OnT+cIXvtBufp8+fbLanoj0DAVJIiJZSBf8dDbwySToSQ6Agi67zN375/rJj/3np53WtXkdPc9k3eTKmrBl/H3p1yIlB1bbbw8ffeSe9+kD3oX7zqmpKYjgyLdp0yZeffXVhEChI48++iiNjY1cffXV7RI+vP3225x22mnMmjWLa665hvfee4/tttuObbbZJmG5Tz75hDfffJPPfe5zAHz00UdYa9uy7flaWlp45ZVXQmtZMrXDDju0BUe33nor4IKvvn37Mnv2bI466qi2Zf/85z/T3NzM+PHjATj66KP5+c9/zh//+EcuvfTSTpchnX322Yedd96Z2bNnc/7557dNf+aZZ1izZg3f/e53s95eJBLhpZdeyroGTkTyR0GSiEgK6QKhdAFRLBYPaLoj6El+XGoikfj+DAusnn8eBgzo2TJ1l/r6et577z2staxfv56bbrqJxsbGrAKAu+66i+22247vfe97bZnugn7zm98wa9YsfvrTnxKNRrn44os555xzOPzwwxk0aBBr1qzhxhtvpLGxke985zuA65czYcIEzjrrLI466ih22mkn1q9fzx//+EdefPFFbr755qze55QpUxgwYAA1NTXstNNO/Pe//+Xuu+/m6KOPBlztzXe+8x2mT59O//792/pl/ehHP+Kwww5r6180YcIEJk2axHe+8x3efPNNjjzySLZu3cpTTz3Fl770pYyyAnakoqKCa665hqlTp3LmmWdy5plnsm7dOn74wx+y9957c+6552a1PWMMv//97znxxBPZsmULp556KjvuuCNvv/02zzzzDHvssUfbfheRwqEgSUTEk00gFFwnLCDyA5+GBpg40a3bHUFPsEypHudqXq7XzXSZUvbVr3617fHQoUP57Gc/y9///neOOeaYjNZ/9913efzxx5k8eXJogASuL8yUKVNYvHgx48aN44ILLmDhwoXcddddvP/++wwaNIixY8fyj3/8gyOPPBKAESNG8P3vf58nnniCuXPnttVAVVdX88ADD3DKKadk9T4PPfRQ7rjjDu6++24+/PBDdt11V84880x++tOfti3zs5/9jKFDh3LLLbdw8803M2TIECZPnsz06dMTxmWaPXs2119/PXfddRczZsxg++23Z+zYsVxwwQVZlSmdKVOmsO222/KLX/yCE088ke22247jjz+eG264IeV+Tuf444/nqaee4mc/+xkXXHABmzdvpqqqinHjxvG1r30tZ+UWkdwx1tp8l0Gk5I0ZM8YuX74838WQJMnBkB+sJN/HYjBvngt8/GZfft/twYPjfWnq69sHRJkGRv7j7ujDVMzC3usLL6xi1Kj98lEckR6xatUq9tsv9TFujHnOWptd5ygRyYpqkkSkbKSqKUoOQFLVDPlB0sSJbrmwICfTmpZcPS51Ye+1Uv9cIiLSzfRXIyIlK5ugqKHB3YIBUqqmcv42koUFMgp6REREio+CJBEpGV0JinzBWiI/01pQuuBHgY+IiEhpUJAkIiXBTxmdi6DIryVKFwgpIBIRESldCpJEpCiF1Rb507saFCkQEhERKW8KkkRCGGNiwPCQWSdZa+f1bGkkWbDWaOlS93jhQpdpLhdBkQIiERGR8tar40VEytZ8oCbptjivJSpTwVqi5Hs/Jbc3vAvV1S4L3WmnuceZNJlTUCQiIiJBqkkSSe09a+3SfBei3GVSa+Q3rauqitcYgYIiERER6RzVJIlIwfEDI/+xf99RrdG4cW66giIRERHpCtUkiaR2gjFmE1ABPA/8XP2Rup8fIDU0ZF9rpKBIREREckE1SSLhHgUuBY4BzgA+BeYaY87Ma6lKVHLNkR8IQfa1RiLF4s4778QYgzGG//73v+3mR6PRtvlPPPEEANOmTcMYQ3Nzc9tyGzdu5Oqrr2bkyJH079+fHXbYgVGjRjF16lTeeeedhO2ku51zzjkZl/2jjz7ie9/7HrW1tQwcOBBjDFH/ioWISAlQTZKUPGPMF4B/ZLDoYmttLYC19tKkbcwFlgLTgXtCXmMKMCXVhvfYY48sSlxegjVHS5fCkiUwYoSbV18PjY0wcqRqjaR0DRgwgLvvvptrr702YfqsWbMYMGAAH330Ucp1W1pa+MIXvkAsFuOKK66gurqaTz75hBdffJH777+ft956i9GjR1NXV9e2zvr16zn55JO58sor+cpXvtI2fejQoRmXecOGDdx+++2MHj2aL37xizz00ENZvGMRkcKnIEnKwTPAfhkstynVDGttizHmAeB6Y8wu1tr1SfNnAjNTrT9mzBibaWHLQTAFd7DmqLoaTjkl3rTOT9+tPkZSyk4++WTuuecerrnmGowxAGzevJk5c+YwadIk7rzzzpTrLl68mH/961/MmzePE088sW36V77yFa666ipaW1vp1asX4/yqVyDmfQH32muvhOnZGD58OI2NjQA88cQTCpJEpOSouZ2UPGvtJmvtfzK4vdHBpoy/ye4ucynza46WLoXZs+HBB+NN6+rrXR+kqqp4+m7VGkl384P2fDnrrLNYs2YNS5YsaZs2d+5cWlpamDRpUtp1/UClqqoqdH6vXtn9zR9//PF8/vOfbzd9/fr1VFZWMmPGDIC2YE5EpFQpSBLJgDGmEvgq8Ia1tiHf5SlWwaZ1fvKFU06Jz5840fVBGjdOgZH0nHwHScOHD+eII47g7rvvbps2a9YsTjrpJLbbbru0644ePZrKykqmTp3K3Llzef/997tUlsmTJ7NixQpWrlyZMP2+++4D4PTTT+/S9kVEioWCJJEkxpjTjTGzjTGTjTETjDGnAYuAzwNX5Ll4RWvJErj99sxrjhQcSTmZPHkyDzzwAJ9++inr16/niSeeYPLkyR2ut9dee/GHP/yB1atXc/LJJzNkyBD2339/vv/97/PWW29lXY4TTzyRgQMHJgRsAHfffTdHH300O++8c9bbFBEpRgqSRNp7HdgJ+AWwALgVaAKOtdbOzmfBio1fcxSLwfLlLpW3ao6kEMRiLhGInwzEf5yvWqWvfvWrNDU18eijj3LvvfdSVVXFUUcdldG6F1xwAW+++Sb33HMPU6ZMobW1lV/+8pfsv//+vPTSS1mVo1+/fkyaNIl7770Xa13L4hdeeIF///vfGQVtIiKlQokbRJJYa5cCR+a7HMUu2LTO7y5RXR1PylBVpcBI8idYWxmNuuyJ+TRgwAAmTpzI3XffTSwW44wzzsiqP9EOO+zAGWecwRlnnAHAww8/zMknn8zVV1/Ngw8+mFVZJk+ezB133EE0GmXChAncfffdDBgwICExhIhIqVNNkojkXHLfI0gMkMaMaT/OkUi5mzx5Mo899hgvvPBCl2ttTjzxRA444IB2fYsyMX78ePbYYw/uueceWltbuf/++znllFPo169fl8okIlJMVJMkIjkTi8HatbBggRvfKGy8o2DTOpFCUCjH4xe/+EVOPfVUBg0axP7775/ROu+99x7bbbcd22yzTcL0Tz75hDfffJPPfe5zWZfDGMMZZ5zB73//e0466STWrl2rpnYiUnYUJIlITvhpvRsaXN+jI48MH++oUE5IRXyFckxWVFRw//33Z7VONBrl4osv5pxzzuHwww9n0KBBrFmzhhtvvJHGxka+853vdKoskydPZvr06Vx00UXsvvvujB8/vt0yjz/+OJ988gkvvPAC4MZseu+99+jfvz/HHXdcp15XRKRQKEgSkS4LBkigvkciPWXcuHFccMEFLFy4kLvuuov333+fQYMGMXbsWP7xj39w5JGd61657777MmbMGJYvX86VV14ZOi7SxRdfzJo1a9qeT5s2DXApzWP5zqsuItJFxs9eIyLdZ8yYMXb58uX5Lka3WLKkffO61atdbdLRR8OwYQqOJLdWrVrFfvvtl+9iiHSbjo5xY8xz1toxPVgkkbKjmiQR6bRYzAVIyc3rDjtMfY9ERESkeClIEpFOWbLEJWlobEwMkKqqFCCJiIhIcVOQJCJZW7LEDQ4LroldMLW3mtdJT7DWhvaTESl26gYhUhg0TpKIZMVP852sqkoBkvSMPn36sHnz5nwXQ6RbbN68md69e+e7GCJlT0GSiGRsyRK4/XZ371u8GCoq1MROes6OO+7I2rVraWxsZOvWrbryLiXBWsumTZtYt24dO+20U76LI1L21NxORDKSKknDkUfCpZfms2RSbrbffnv69u3Lu+++y4YNG2hubs53kURyonfv3uy8884MHDgw30URKXsKkkSkQ/44SKmSNIj0tG222Ybdd98938UQEZESpSBJRNKKxeDRR6GlRUkaREREpDyoT5KIpLV2Lbz8cuI0JWkQERGRUqYgSURSCqb6rq6G1as1DpKIiIiUPjW3E5FQDzzgMteBa2b34IOwcaNqkERERKT0KUgSkXZiMXjhBRccVVe7AGnECNcP6bDD8l06ERERke6l5nYikiCYya66OjFRgwIkERERKQcKkkSkTSzmbitXJmayGz9eAZKIiIiUDwVJIpKgocHVIvmqqmDs2PyVR0Qka3V1MH26uxcR6QT1SRIRIN7MrqHB1SKtXu1qj5TJTkSKSl0dHHUUbNkCffrAk09CTU2+SyUiRUY1SSICuPGQVq6MPx882NUiiYgUlWjUBUgtLe4+Gs13iUSkCClIEhFiMViwIJ6sYfVqGDnS1SCpFklEikptratBqqhw97W1+S6RiBQhNbcTKXPBbHZ+sobBg9XMTkSKVE2Na2IXjboASU3tRKQTFCSJlDE/QPrb32DgQFeDBLDPPjl6gbo6naiISM+rqdFvjoh0iYIkEWHgwG5I1qDO0yKSii6giEiBU5AkUqaSx0RavTrHzezCOk/rZEhEdAFFRIqAEjeIlLHgmEiDB7tkDTkT1nlaY5eIiLLPiUgRUE2SSBkKq0XK+ZhIwc7TQ4bArFlw223uxKiyEp56SlePRcqRfwHFr0kqoexzsVj88dq1ifMOO6xHiyIiXaQgSaRM+bVIgwd345hIfhB01FGweXN8enMz3HADzJ3bDS8qIgWtyLPP+YFQJJIYFIFLhOP/ltbXJ84bNgz+9S/46le7t3wikhsKkkTKTKpapJyMiRTWGdtvWpNs+XK3fJGdIIlIDhRB9rlUwVC6IKmhIfUFp6VL4S9/UZAkUiwUJImUoW6pRUrVGdtvWvPpp2BtfPl169zy6rQtInkWFvikC4aCGhrcRadYDF5/Hf70J3j33XjleZ8+sN12sHWrq0RfskRN70SKgYIkkTLSrbVIqbLZBZvWvPQSPP64i9CshaYmZb0TkR4Xi2UXEPmBkH9xKRaD666LB0LWwrbbQr9+sOeeib+ny5a55bfbzj3/1a9g5kw44QTVKokUMgVJImUoFuuGWqQ33nAJGcDdP/ssXHwxTJ7spr3xBjzwQGLTu9ZW+OCDHBRARHLqmGPg6afh8MNh/vx8l6ZLUtUShQVEsRgsXNg+GAL3WzlwYHzda65pv359PVRXxx/X17tAqk8f2H57+PBD2G03t+1ddsnhmxSRnFOQJFIm/Fqkhgb3R5+zWqRgM7uKCnd59LHHYN48N/+PfwRjXFuTMPfeCxs3umBKNUoi+XfMMbBggXu8YIF7XkSBUqa1RGEB0UcfwYABbtnGxtTBUNj2wAVSlZXw73+7YGjJEthxRxc4tbbC4sVwyik5qr0XkW6lIEmkzKxc6e5zUot08MEuAUNra3zapk2u4b0v+DjMunVwyy1wxx2waJECJZF8e/rp9M8LSHJA5D9OVUtUXx8fGy44RpwfEJ13Xvv1krcVDG6Cj9euddeJmpth1So38kFLiwuWTj4Zbr/dPVaAJFIcFCSJlJHg4LFdHjj24INdk7qgPn1g0iR3ubSpyU3r1SsxiDroINh773jfJF+wH1NYljwR6RmHHx6vSfKf55Ef9PiPIR64hAVJ/nId1RL5yROCF4vCtpXucfJ669a539nFixNTgP/oR9C/P3zlKwqQRIqFgiSRMpCcsGHFCndi0KUrmitWJD43BmbMgClTYNQoN3gswIEHwre+Fc96d/75cNllieMmgQumamvbN9877zw1xRPpSfPnp++TZEzi86FD4dxz4frrc1aEBx6I99nxB2UNqzHyNTTEb36CBUhfS1Rb667F+Nvz+Y/TBUZhtVV33ul+vgYMcM8//BDef99dLzruODj6aGW1EykmCpJEyoifsCEnVzJHj06sSbLWBUOjRrUfA2XUKHc2cvfdLplDsGbJt88+bpk33ohnyWtpgVtvhbvuUqpwkQ4sWeIGLM3J9ztVH6TkAAlcvusbbnCPOxkoxWJuoFWAsWNdTcyIEYnz/ffV0OB+KhYudM8ffNDdDx4cX/5734PYhT9zqeUOPhh++MPQWqJMAqKwsvrlaWiAZ55xP2nbbQdf/KKrTXrzTTj7bHj5Zfc+Jk5UDZJIsVGQJFLicpKwIaz527JlboMffRRfrqnJ1SCFNZm77DLXUD+VV16BH//Y9XquqHBnHda6W7ApnoiEWrAAjjwyddO00O/7mWe6pq/HHQf33NO1Ajz0UMdBUl0dsbnPw7hxRE4enZBcYfFi93joUJfLZfVq93zwYNef58EHE1vv+kHRKafEB3H1a4ciPzqT2BOuCiryxG3Q/12onZE2OAoT1pxv6VJXW+U3p9trL3fv116dcoor66hR7vmBBypAEilGCpJEykSnEzakGiQWwpMyrFjhBgG57LL4OjNmuAQPQb16ucu99fVugJFHH3U1RwAXXuju77jDvUafPu7sR0RSCnbxg9TJDNqCqImXEXn4XvfkXncf+9972tbJ2sknh05ua5K2YgV8/VxiW3eDyoeJRKcRa4pf+HjlFXd77DGXDLOpyf0kVFTAvvu6YGPQINef8rTT4k3lgs3m2sr++ONEcO3eIqyBp+8GZmT0voL7Kvi4oSF+f+SRbtn6evd76penvt4tP358/EKUAiSR4qQgSaQMvPBCFxI2pBokFtp38AYXDD3/vFu+tdWd6Xzzm+2b2O2zj2uDMmiQSwM1f348qPL7IE2erAQOImksWeK+go2NrpnXgw+62z77uBqZ5AsiCUHSgv8SAWIMByDyyCPELnDzHnsMLrkkJLiylpiJuMDD3+bg0fDVrxK5/gcps80B8Pgq2LobtLZAM8TmPs+d79RQVweffBK/5tK7t/vZ+PznXZ6XwYNdCu3aWoh+91H41VOwcW/4zJTUzeWOO46IF/iBe55OcnKIVH2eqqpcWfzxkKqq4vs4EnEV4QqMREqDgiSREuZfCf3Xv1wXok4lbKitdYGLH8AEa3T8Dt6LF7vUTR98EA+GevVy/ReMCa9xWrUqsYZqxgzYsCExIEru2yQiCYYNi9dqPPhgvAnau++625Il8en+iX7b93/XXeFViBEBILLXIMAt8/DDKYIkILYoRqQ2MC/qpkdInW2uoQFWNh9BIxsZzHvE7J589Jf9GbDvO3ztazvR2Aj//a/7fTrvPFdmv09SW6A3cyaRX3sjuy5fQ+Rn2xGp/XrC67Txmw6maUoYFhj5ZU3V58lPIT5ypPupSk4NruBIpHQoSBIpcQ0N8W5DnfoDr6lxTexS1ej4HbyTm+Vdeqk7oxg6FO67L3zbfoa7LVtcgHTllZ0ooEj5Cp6YP/igC5j86xjBzG3z5sWDDX+c56rJlxP96essbB3PBwyifvwJUO/6Ar3yCsyenf61U/ZzIp5lbuHCePpt2J0BX5zExtdeJfLqQs5782pi60fACb+GI/dvC0bANVfzs9sNG+ZNnD4noQYrsvguuOrrqQvYicAomCXPz4wX2ufJW09BkUjpUpAkUqKWLHEt31ascAMYLl7sOhL7zUGykkmNTjCYuvXWeLarjlRUqM9RV2hMKfHss0/qMXzamqtFXf+ZWAxuv/0zNHzzjwxevZrBI0awZN2evPB31x9ou+3gl790zd4eeAD2398FPH6fxtmz4W9/i9e2NDa65Aoff+zuBw50y/o1WyNH+kHaTnDvP+DVhURaXyPWHIH6eiJf2p/x4+PlDf05mDQpsXnvpElp90eqQWGTAyOIB5D+fUd9nhQciZQ+BUkiJWrYMNfKzR/OaNQod5W57apsd/Cz2K1Z0+GibS68UOMgdVa6pBpSdk44IfMBUCMRv9ZpT6LRPWlogFOIBwmLF7vEd42N8fGF/OZl8+a5oOu889yy9fWJyRSCQUVCgPHSSy4g2tNA73XQXEGkch0ct19mgceUKe5+zhwXIPnPQ8RiLgsdJPYZCguMwE0/7TT3uF0SCNLvSxEpTQqSREqQ3xfJt25dJ9J+dyRYgwHxx8mDzHbkjjtckCTZS5dUQ8pOWCCU/DjVyb7flGzcOBdc1Ne76x1+07JgH6NgrVQmFcCRCO534btfh61bifRdD7+bARs2EKmthZrRHW/EN2VKyuAoOU13Q0N8nv8428BIzepEypeCJJES5vdH2n33LNN+dyRYg1FREU/O0KcP7L134nhIffu6NjupNDW5pnlVVfGG/6pZyky6pBoiITIJnGIxGDMm/TLZbpP750PzapfZbkuFy4C5xx5Zlz8dPz330qWuyR/Apk2uW+SAAfEMn+ed58oUFhileg8iUn4UJImUMH9spEMOyfGGgzUYLS0uSPIHfT3rLNceZ8UK6NfPnaV0xO9J7rvtNrj55rTNaYSOk2qIZCAs6LnsssR56YKIjAKMYEBfUZE4BprfTLSL/ev8ZBHgkm369wMGuPL4zQbVhE5EMqEgSaSExWLuBCGntUjgTmIqKuKDv1rrUn77tRlXXulSgyePoWQM/OxnbpnaWnfCFKalxY2tNGqUTvw7ojTpkmOZBgxZBRbBgP6NN9yFkGAzUehU/zq/71FDg6sp8muLBrhxZBk40DU19oc+6HT5RaTsKEgSKTF+f6SGBneCsHGjm57T/kiXXeZSYCWbMSN+YvP00+3nV1a6WqNrr3UdG/77Xze2UpiWFvWxESklfkBfVwd33ZXYTDRYO93UBNOmueQMyWOnJQn+rgWTRcyYEb84NG5c4vIiIplQkCRSovxmJzk/KTj4YHj22fB5GzbEHx9+ePuapBEj4uum2ga4Gqe+fdXHRqQUpWom2qePC5BaW91vx4IF7regosKNhn3++Rk3wa2qigdHCoxEpDN65bsAItI9/ExPI0fmeMNh2euMcbVEwaBm/nw4+mgX7PiCCR1S6dMHpk5VOmuRYldXB9Onu/tkNTWuWa7/HfcDpy98wf2e+Kx1fZeefdb9LsycmfLlgrVK48ZpPCMR6RrVJImUkOTU3xs3xtvi5+xkYfTo9rVA1rpmMi+8kBjYzJ+feMKTztFHu/sOxj8RkSLQmTG8ampcM7uFC11gFGbOnJS/D0rEICK5pCBJpAT5qb9HjeqGjS9bFt7krqUFLrnEPfb7ESRnrUvljDPgL39x/ZyeeMJNU6CUWhezgBXd60rx6ewYXjU18Pvfu8QtfmKY1tb4/EmTuqO0IiLtKEgSKUF+f6ScN7XzLVsGvXu3v9rrZ6VrbY2PndSRSMQ14fMTQbS2wsUXK7NdKp25Ql/MryvFqStjeE2Z4r7/fkD+wguuBkm1zCLSgxQkiZSgbkv9HRTW7M4YFygFr/x2ZN269pnyWluV2S6Vzl6hL9bXleLU1TG8gqnta2oUHIlIj1OQJFIikvsj+bqt87Lf7G758nhQ1NrqEjgYE1qTFGM4EdYkbicsoOrdW5ntUunKFfpifF0pXhrDS0SKmLLbiZSgjz7qoY7Ly5a5bFRBo0e7cZCeegouv9yl/b78cnjmGe7k7MRljz7anXBXVLjAaORImDgRFi/WyVUq/hX6a6/t2SZv+XpdERGRPFBNkkiJaWhw993WHylZdXXieEjBGobrr3c3gJNO4m/8gGlcE58/bZq7D2uSE0wSkGqZcpWvK/SqGRARkTKhIEmkxKxc2QP9kXwjR8bHPurVCwYNghtucM/79YMnnyS2S41rBrhiIDGGE2U8ABFiRG64AebOjZ94+4HRkCFw2WWuaVdlZTzFuBIGpKbMcyIiIjmjIEmkRPh9jxYudPfd3mXk4IMTB4dtbYXGxvjzzZshGuVfI2pYvBh4+3A+YHse5BQAxhMl8tab8eVnznQpxFtbXcDV2upuLS2uf5O1xZEwIB/BijLPiYiI5JSCJJESEEzaEIu5ICka7eYR51es6HiZ2lpY+DIs/wiaPqU5+Sfn/PPd/cyZLu23n8TBWhco+fznlZXwxhsuKCjEICBfwYoyz4mIiOSUEjeIlJiGhh7qjzR6dPr5w4fDrFmu39GyZQDY5J+cUaNccPSNbyRmuauogBNOcDVI4AKkMWNcsHTbbS4QqauLL19XB9OnJ07Lh7BgpSf4mecqKpR5TkREJAdUkyRSQhoaYNOmHuqP5KcATx4rCVyAtGYN3HIL73AR69i1bdY6dgPgHYbCEUe4gMLa+LrGwO9/7wKo+fOhqckFSbvuCs891762pJCamuUrTXZXx6QRERGRBAqSRErIypWw7bY9+ILLlrnU3UnjIbWl2AN24l12Y533rJUBbGQgH7ET77ZfD+IBU00NzJgB3/ymC4wef9zVlEBiAFJITc38Ms+ZA5Mm9Ww5lHlOREQkZ9TcTqSExGI9VIsUFNbsrqkpdNFetNLAzh1vc84cd79hQzyBw5YtcPzx7cfpKaSmZnV1Livfk0+6+3w3/xMREZFOUZAkUuSCSRuCujVpA8T7Ac2YAQcdlJhoIWAXGhix+xZGsJpt2QTACFazC15tU3W1S8gQNGmSu6+tjc+zFv72t/bNyQppkNN89UnqikLpzyUiIlJA1NxOpIQ0NMC4cT3wQmH9gKJR+OEP2/UvOsz+k2FvriVGhEF8SBVvU009zVQSYziRcePg5pvd+Eovvwz77OP6I4ELeM49F2691W1361ZXQzNjRvtAKdfBUWdSeeerT1JnFVJ/LhERkQKimiSREuEnbWjLbLdiRffVEITVmNTWJgZI0O75SFa2b253xx3u/vLLXZXYo48mZq+bPNmdwPvbe/ZZmDCh+2o+6upcxr3aWvjxj9tn0kvHr9W68EI4++zuKV8uFWPNl4iISA9QkCSSgjFmd2PMg8aYD40xG40xDxlj9sh3uVLxkzZUVQEvvQRf/3r2J/mZCusHdNllHa72ZR5nE/0TJzY3u5Pz4Al7U5NLHe6Ph3TuuYnrdNcJvV+zcuutXQse7rorPFV58mt1JYjNRTO5QurPJSIiUkDU3E4khDFmW2Ah0AScDVjgf4FFxpjPWWs/yWf5wiQkbaivd03TWrsp41tYyukMBpcdxQs8ypcTJ1ZUxE/O+/RxAVJrKzzxBDz9tHudyZPh9tvde/GX644Tej9Q82vAjMn+tTLJttfVZm65aian1OEiIiKhVJMkEu5CYC9gorV2nrX2YeArwHBgal5LFpAyacNx+xHpu757awhqauDKK+Mn1h0NLuupoiFxwnnnxfsUPfkkfOELbnprK3z6aTzIiEbhoovcbdGi7jmhD9as9O0LU6dmH4BkUjvT1WZuuWwml/w5ioiIiGqSRFL4CrDUWrvan2Ctfd0Y80/gRODXeStZCglJG0aP7vkagjSDy0b225bIgM3w7LMs5EhqWRyfOXBg/HFNjct2t2CBe24tfPBBfF5YjcysWe7x5Mldf5/Z1qyEJXfIZBtdTfDgr9/U5Gq7hgzJbn0RERFJy9jkjtYigjGmAXjYWjs1afrNwFettUOz2d6YMWPs8uXLc1lEIF6T1NAAN93kxl2tquqB9N/pVFa6Go6gigpXsHXrmM3XOI0/J84P/g4dc0w8SAI4+miYP7/969TVuQQO/phMffr07ECy2TR5CwumOpM9L2jmzPhAu337KjOdSBkxxjxnrR2T73KIlDI1txMJNxh4P2R6I7BDD5elQwlJG/Lt859vP62lBdatA2AcS9vPNyaegMAfIylo773hiisSp/lNznxbt/ZsdrZMm7z5wVRyEo2uNnNLHmhXmelERERyRs3tRFILq2Y1YQsaY6YAU1JtaI89ujcpXkLShnzzm90991z7GiUgwprw9SZMcH2Npni7cc4cd+/XKt1wgwu07rnHPQ82OQPo3dtN62oNTaYybTKXSSIHyL7cxTYmk4iISBFRkCQS7n1cbVKyHQipYbLWzgRmptrYmDFjct6uNWXShkgem9r5li1z6amvuirtYjGGx4OmYAAxZYq77b134gr33QeXXBLvn7RoUWKfJIg3gauocEkhutJXKV3gkmn/pUyCmc5kqyuUzHQ9FZSKiIj0IAVJIuFeAvYPmT4SWNnDZelQQtKGQlFb62p3tm5NuchSxsWDpF692gcQJ5/sapB81sYDKf/kPBgETZ8er7VpaXHjHd11V+f662QSuIQlk0iWSTCTXNs0a1ZmgUcmr9+dcpWKXEREpMCoT5JIuEeAccaYvfwJxpgIcKg3r6A0NsLIkfkuRZKaGthmm7SLrGQkMYa7Jy0t8MILiQtcfz2ccYbrswTQr58LHGbOhCOOgB/9KLGfj19r4y9vrWuO15n+Oj2ZZjuYNryy0o0J1V0DAafSmcFpc7mPRERECoiCJJFwtwEx4GFjzInGmK8ADwNvArfms2BhmpoKqE9S0KeftpsUYzhRxhNlPI0M5nbOI8p4FyxNDRmC6p574J//hOuuczUV4LK6NTe7pAXBIMivtTnxxPj6ra0ujXi2AUAm4x3lil/ua6+Fc8+N14R1lBAi2/fkrzNzZuK6qZJLdKQn95GIiEgPUnM7kRDW2k+MMUcCvwHuxiVseBK4zFr7cV4Ll6ShwWWALkgTJiSm8w7RyGBeYBQRYm5CXV1ijUtyn5fp0xMTQhiTeHJeUwMHHQSPPOICpF694Ne/drVK2TQJ6+k+P37Tubo610Qw132Y/HWamuL7xU8dnmlyibAyF0K/KBERkRxTkCSSgrX2DSAkH3VhWbkSBoelmCgE8+e7gU4bGxMmN1BFA1WsYzd2Yx2P8iWG8i4AkUMOiY+bFBYM+H2d/Kx2YWO91da6AGDLFhcMtLQkpsoO9mlKd3Kfjz4/nenDlElQ46/T2uqeB/dHVzLl5btflIiISDdQkCRS5Aoq/XeYDRvifYSIpwCPEWEFBzKC1axjN94lMD7vzJkuu11YMHDllS7F+FNPuWVbW11yh7lz4+sHA40hQ+CyyxIDgGDwlSoLXj6ztnUUeHQmqAmmTPdrkvx1VSMkIiKSQEGSSBEq6PTfYQ46CJ59tu1phDWsZRgAixnPbqxjIUfyLkM5mgUcNnWqC4IGDHBBDCQGA8l9nd56y90nBzb+yf6oUe2b7KXLgpeuOVshpLzuTFCTHDhu2JC4rmqERERE2ihIEilyBZn+O5k/wGwgUBrGWo7ncVYykkYGsxvrEte5915337cvXHhhYk3P+ecnbIvzz08f2CQHAH6tyqefuuZ61iY2W0vVnK2QUl53JqhRICQiIpIRZbcTKXIFmf47zLJlrkbJE2ENw1gLQD0HMILVvMAoZnIhSzg0vl5TE+yxR+LJ/ZQprvbn6KPdfXLTvKYm18Tu4ovDM7X5tSpTp7ogLDk7W6qsbUp5LSIiUhZUkyRS5Ao2/XeYZctcRLdqFQCH8U+GsZYP2J7VjGA8i1nNCJ7nQIaxNj7Q7AcftN/WlCnu5kvuc/Pss+52xx2waFHqgWAnT27fbC1Vc7auJDgQERGRoqEgSaTIFWz671RWrnRJAwJZ6fZmNQs5su25//gEHnWB0q9/DRMnpm8q5gc206Ylph3vKPtbqiZowel1dTBrlns8Y0b7/jwiIiJSUhQkiRSxhoYCTv+dTmtrW8a7CGs4gUcBeJl9GMFqAN5lKGsZ5oKk5mY49VT4xjc6Ttk9bVq8WRwk1vh0JulCXZ0b78lPOd6nT+bjCImIiEhRUp8kkSK2cmURNbVL1iv+8xNhDQfyPBsZwGLGu/5J2x/ObE7j91zkFlq7Fq66yiVOCOtn5KupgRtvdP2fJk6MN7Xzky78+McdbyMoGHABbN2qvkgiIiIlTkGSSBGKROKVI/7j2toCTf+dyj77JDw9jH8yhdsYxQuuf9LurzGC1axi/8REDps3x5u+hamrc0kbnnvODWbr62zSBb8fkq93b/VFEhERKXEKkkSKTCzmzu+j0fhYScHHRWPlSthvv4RJw1jLUN5lHbux+sXNAKxjNxZwNA8wKb7gbbe5AWfDpAqGUmWs60hNjauNuugid1NTOxERkZKnPkkiRayhAY4/Pt+l6IKVK919oH/SpdxEL5p5gqMB2jLe3ccZAHyVOS4AuuQSN0hscsCSKgNdqox1mfRTymR8oY62UwiD0OZLOb93EREpSgqSRIpYY2MR90kKOuighMFhL+EWWqmkhQpWM4JTeJAHOYV1DGMJh7r04C1vuCQN06YlnninCob8eZDY1C4Xg8OmGmTWDw6GDHFNAAthENqeVkgD8IqIiGRIQZJIEfMTrhW9Zcvg4IMTAqUTeJR/MYbFjOdBTmEEq1nd97OsbtqbffgPJ9hHiTzxBCxeDOeeCwceCM8/76rXqqrc+EfJJ+PJJ+xnn92+aV5wnUxrQFI18fNfq1cvN6+1teOU5KUmbN+Uy3sXEZGipSBJRArDsmXuPtD0zh9Mdh3DXI3SSS3U73Q8LX/vxdL/vgutQBNEbrml/fZuv739CXnyCTukHhw2mxqQsCZ+wdey1gVKxpTfILQagFdERIqQgiQRKSzWtgVK4PogLeFQVmx7GA/ObmAEvwVgCYexkpGMxPVr8gOqNmG1Fskn7JMnu1tYbVE2NSCpmvgFX6tcB6FN1/xRRESkQClIEilSDQ3Qt2++S9FNkgKlw/gnbIK1DKMB1wnrFB6knmoaqGIp49qWTQiWnn3W1Qj5J+apTtjDTtzDakDSNb9LTu6QLlHE9OnlFTBkkvhCRESkgBhrbb7LIFLyxowZY5cvX56TbcVi7rZwISxd6sZXBTdGUlGNk5SJmTNhzhyYNAmmTiXGcJYyjpWMpJHBjGA1AKsZwWAaGclKxrEU8IIlY2CbbbqWkMEPcqDrCQiUxEBEcsAY85y1dky+yyFSyjROkkiRisVKJLNdOlOmuAFhp0wBXOAzjqWcx+0cxpK2xU7hQQbTSMNOB7C016HEiBBjuKuR2rLFDT47fboLUrJRUwNXXunuOzsYbVAutiEiIiLdTs3tRIqIX4uUrNO1SMGaGi8QKVheE7xgczq/VsnPfsc7b7OEmrZaJUwvIpVvEfvTk0RaX+ta7U0uEhAoiYGIiEhRUJAkUqQaGmDcuI6XC1VXBzfcAPPmuecLFrj7YgiUKiqgtbUtWBrH0tAmeEs4jJWRL3NezSpis5dC6xYiTW+Gj62UTrDJXTYJCMICUCUxEBERKQoKkkSKVGMjjBzZiRX9fjGbNydOnzOn8IMkcE3VAEaOJLJqVdvkKhpooCoxscMJdxD73R+9aS1EWtcQW/AykX8cCjvvDJ/5jNuJYWMqQXgfoiuv7LiMM2fC1KnucXIAqiQGIiIiBU9BkkiRamrqZJ8kv19MskmTulqknrVyZdvDSNvYSjGWMo4GqljIUQz+3W+pp9otjosoG6gCC5GGNcQa+hJ56im47Tb47ndh0KDEGp7ODoQ6Z077550NQDMd0FZERERyRkGSSJHqdPpvv19MU5N7vu++8O1vF0ctUirWtgVKvti+x9Hwn/epooF6qjmShQBtKcMjrCFGxN23DCNyww0uG15FBfz+925/dLYPUXV1vAbJf94ZyoYnIiKSF8puJ1Kk+vXrxEp+rcSll7pgAOD112HUqFwWLT+shYMOIlK5jshBOxN5/A9U0UAti9sCJYBq6mmgiijj25rmxYi4e7sHNDfDN78ZH1/pySfh2muzC1AGDYqP82SMe94ZmWTD88ddyjZzn4iIiKSkmiSRUucHRkOGwGWXuZPtXr3ciXdra3bNyArdsmVtDyMAxIB4cgeAhRzJYBrbgqbZfI0GqogQawuWIlvXEJv7PJFg/yE/QMlkP9XWuvGZuprFrqOaLNU0iYiIdAsFSSJFJDnVd4fn3sGT6GBgZK17bkxJp6KO2FhC2vBIxVrw8j5EiDGPiV6qhwZiRKin2guY1hD7xQNEbvgGsYdWEDnzKNc8sVeveFO8dHKVxa6j7XS2z5SIiIikpSBJpIgkj5PkV26kHCcpeBJtrZvWq5fr0DRjBmzYUPoJAbz3HQE4+GAiz8ba+iJVU98WLFVTTzX11FPtNcXbGSoqiJ03i0hTE7HW3Ym0roFvfAOefx4OPDD9/stVFrt029G4SyIiIt1CQZJIKQsmaWhtddMqK12AVMyJGjpr2TIiBx8Mz7kapQgxIqyhigYgpCle61dp2PYQIq27JyZ5uOWWxO3usgscfDBcfnnnAyO/WeQHH0B9fWYD/KaraQpmxQNlyBMREcmCgiSRItTQEK8YSqumxgVEF18cn9ba6mpAytWyZV7PI692qaKCca1LifR6E2wvsK3xpnjmHap+d25bUzy/35LLjDe8rRkf69e7gXkfeQT+8IfsA1C/WeSnn8Y/2EwH+A2raQo2s6ysdNtsaVG/JRERkQwpu51IEVq5ErbdNsOFkwOiXr3ULCuopcX1XWppIfLaQjC9XFM8839EXluYkBXPD5ZiDG9L8gAQY7i7b93dBaQzZ2ZXBr9ZZHLkmzzeUrbb8/sqbd2aPkOeiIiIJFCQJFKEYrEMB5Ktq4M33oDevV1w1Lu3SzygmoRQkQguULKWyGsLiURo67eUKliCQApxItDaSuwbN2SXkttvFpk01lOnB/j1t1dR4e57944/VoAsIiLSITW3EykSwaQNAwfCxo3uccqkDXV1cMQRbtyfigrXbGvyZAVIHfD3Zdv961Eie5rEJA+7vUf1OhcsxYi0jbfki7XuAXOfd6FTJv2Cgn2LsumTlEpyX6V0ry0iIiLtKEgSKUIbN6YIjIJuuMEFSOCaWjU06AS5EyIRXM3SQyuIvDyf6oFfofaS/ZltTqOKBi+BeBUz+DaAe16xOw2NnyFy1FHEmnYh0vunHfcLylU2vFTby2bbwaQPOmZERKQMKUgSKUINDXD88R0sFBhYNfS5ZCVy8mhgNJGYe161aDa1E1zzuCjjiXz/VObFqqna/nWqqqup/9tbRJtqaGgdSmTL624la12/oFmzCjcI0QC1IiIiCpJEikWwWd2PfwynndbBCh99lP65dEpCczw/0UIUIrVQNRuoOoT6eqg+HniikoatuxLrvTeRra8AxFOI+32EujpeVa5rfVINUKvaJRERKSMKkkSKRNYDyX78cfrn0iXBfe4/HjfOPW5ogPqG/an+9a+prq8ntmQzsf/skphCvGUYkc1rYOpUFzBVVMB552XXb6w7an3CBqhV7ZKIiJQZZbcTEemi5GQP48ZBdTXUXrI/nHEGtWvuopbF8XGVCGbEGx6vtbn1VpgwwaUR97Pj1dXB9Onh2fLCan26yk/6cO218WCoO15HRESkgKkmSaQIbd6c7xJIOsk1e7ExpxB7+o2251HGtxucFnDN95qaXLB0112uKd5ll6WuwQmr9cmF5KQPmbyOmuOJiEgJUZAkItINgrVLkadmETnmGGILXnYpxKlPGG/JJRKP1zK1JXiYMye8f5AvOdV3dwUnHb2OmuOJiEiJUZAkUoT69ct3CSRTbbVK8+cTAaqjEGlcwbxJsxKCJVejFCPSZ308VfikSfD00+lrcHKdOjyVdK+TKtmDiIhIkVKQJCLSgyIRiNSOpnrRaGprIWpqqWWxm2ltu2ZrsR3HEHl5fvsanEJq3tZdzf5ERETyREGSSJFIzmKX9jx05szuLYx0WnKSBxZFoTawQFKNTWzwaCJXjib20Aoi06fHP/hg87auphHvqp5q9iciItJDFCSJFImsUoDPmNEDJZKuaBcspVNXR+z0aURannSpw4P9lzZvdmnEAfr2hUWLuhakZFpDlbxcTzX7ExER6QEKkkSKUNo+STNnwqpV4fPq6grvRLaQmo3lQViQ1C4gvv016rfuT8S+HB9nieGJwRK4zHizZnV+P2aagKE7EjWU+XEgIiKFReMkiZSaOXNSz5s1q+fKkQn/ZPvHP3b3YWMBlaFIxMUKkQjU1wPV1VT3fqktG16M4YnjLAXu26QbXymVTMdDyvW4SToORESkwChIEikyDQ0dLDBpUup5hXbyqUFK04pE3KC0kS/tT/1FtwAkpA5PDJYiUFEBkyd3PujwEzBUVKRPwJDpcpnScSAiIgVGQZJIkfBrFwCqqtxjv7YhwZQpbjBSY9pv5N//hiuu6M5iZifXJ9slyO9zVn3SnkRej1JPNdA+WALg6aeJ7VKTOujoqHbJT8Bw7bXpm9BlulymdByIiEiBMdbafJdBpOSNGTPGLl++vEvb8Pup3H67O2k+8kg3PTRxA8COO7qMZ8l69YIlSwqn34f6omQkFnOfczTq7ufNg6r/dzoN7AwY+M1vqKpyNY0T91gBX/86kebV8T5DEO9HVFEB553nap0KZZ/rOBDJmDHmOWvtmHyXQ6SUKXGDSBEIduQfODA+PWWABLD77uFBkrWFNdinsqJlJJgNz2+GV2vvb4srgveR2tFEfzWHyMZHiO1zDJGa0Yk1iy0trrbxT3+CAw+E8893NZD5pONAREQKiIIkkSKzcSOMGJHBgn36pJ6u5kxFKyx1eCzmJXjwRKO4bHin7+9qoIxpnw3PWti6FZ591t0g/4FSLqhGSkREckB9kkSKTEOD65PUoUGDwqc3N+eyOJInyTVLEycGsuHhapr84CksG1476bIiFgtlyRMRkRxRkCRSBPykDbW1buzQ005LkbQhaOXK8OktLXDDDbkuouRJWDO8dsFSWDa8AaMSN7RggWuS5yd3uOIKOPjg+Ab33BNOOqmwAw9lyRMRkRxRczuRIhDsk9SvX/zcL22fpL32grVrw+e9/HIuiycFJDlYmjcPqhdFqZ5Q2xYsRQ4YRGzGPCL/nUls6nWJzfAOOcQFS2FJfWIxeOwxWLw4903ZkpvJdabZnJ8lzx/kVs1KRUSkkxQkiRSZ7bbLcMEzzoCnngqft88+OSuPFJbQmqXGFcyrHEt16wqqK1cRu/DX1NdDZOIUYtxHhDWJfZbSZT3dujX3iT/8ZnJ+cDNjBlx2Wfx5pmnG/dTk6pMkIiJdpOZ2IkXm448zXDAss51v06aclEUKV0Kw9PJ8qltXEGl9lfqt+0N9fbzPUlIzPMCliU+loiL3NTTJzeTmzOl8s7maGrjySgVIIiLSJQqSRIpAsE8SpBlINqi2NnxAWXD9Twq5b4nkTCQC1NYS6b2OSMVaqnu/ROS4/eJ9lhbNSByU9plniH33Rrj8cth22/YbbGlxTfJyKXkw2UmTEp8PGZJ+EFwREZEc02CyIj2gq4PJBvsk/e538K1vucdp+yQBjBwJq1aFz7vuOnfFXcqD18cnts8xRE4enTAobXV14qINDS45CL17E2veLbHPks8YaG3Nefna9UkaMqRzTe9ESpgGkxXpfqpJEillu+8ePr2yUp3ay43XDC1y8mggaVDaWreIX0PZlmJ+9GhiRMLThlub29qd5GZy/vMNG/KTsc7P8qfaKxGRsqTEDSJFIFhj9NvfZhHfvPZa+PRUzfCkbCQPStvQkBh/RKPA9ctoOP+HEKsg0upqkxISPFx1VXyF667rnmQJ+chYl5xIQrVXIiJlRzVJIkUgFnMnrdGoi2/8x34TvJROPjl8+tatGitJgHiQNG5cvCapoSE+v+pPP6P+V08SXWS9WqVI+Iauuqp7BnD1M9Zde23PBSsab0lEpOypJkmklF1/vbt/6CGXO9zvrQ/w6KPuhFZXyFPrzFg9RSrYt62qyj2Pxdwh4/dZii163QVQw+vgkEMSa5UgHlDkel/V1PTs/td4SyIiZU+JG0R6QFcTNwSddBLMnduJFevq4LDD4p3te/WC//1fJW9IpoQBxGLxoCkadTVLbf2UfBNqaaCK0/hzPFjq1w82b44v068fHH44zJ/fI+XOqTIKkKX4KHGDSPdTTZJIEQhmt/Ob20EG2e2SVVTEgyQlb2gv2BfFz97W2tp9NSQFKnhM+Y/9Q8WPG7BRosZNjBEhct1UYlfdmliztHmzSzd/zDHFFyj1dO2ViIgUFAVJIkVmu+06uWI0Gg+QjIHzztNJYLJgX5RevVxQaUxZN7lKDsITEjwsihLFNcmLTITYVfOJsKZ9M7ynn+6BknaSaoxERCSEgiSRIhCsMbr99k6er9fWxmuSeveGyZNzVr6SkdwXZcYMl4K6zE+gg4HSuHHx/kr+GEvV1V7/JaqJEHM1S8Fg6fDD81DqDCiLnYiIpKAgSaQIBJvbffxxF5rb+am/lQI8nJ9JTTULKQVTh1dXJw5IW71oBrEJ9fFgqc8+cPARRObPyldx0wvLYpfpZ64aKBGRkqYgSaQI5KQmKRqF5mY3CGhzc1n1sclKvvuiFNHJd3BA2rZgaVGUaiDGDOrroaEKIiQmg2iT7/fa2Sx2mdRA5fu9iYhIlyhIkrwzxjwP3Arca639KN/lKUTBmiToZE1S8ISwogLeeEMpwAtNkTX/CtYqhdYsVbv+Sn6GPH/5WAwi6wvgvXa25rCjGqgi+xxFRKQ9DSYrheAA4PfAW8aYPxpjxuW7QCXJPyG88ELX3O6227pn8E/pvCIdxDQ5WPLThQfHWGpoiAf6sRgQjRJr2sW9182b4ZBDXPDe02pqXBr8bIIY/4JDRUV4DVRXPse6Opg+Xd9LEZE8U5AkheAwwO+0cB7wT2PMv40xlxhjts9juQpGJOLOw/xzMf9xVv2RwJ0I7rGHa25XZCfiZaGjk+8C5x+P48a5oldVxccv9pM7+LVK1NYSqxwBQIzhbqHWVhfA77RTYQcJ/gWHa68NryXq7Ofo10D9+Me6gCEikmcKkiTvrLXPWGvPBXYFvgE8D4wCfoerXbrTGHNoPsuYb/7JZTQaT9wQjSY2wctYkZ+Il7SOTr6LpJYhGCxVV7tDrKEhPr+qCqJNNdRfdAsxhhMj0hYoxRgO777rapYK+X2mq4Hq6HNMpUhrEkVESpGx1ua7DCLtGGMOAKYAXwe2ByywCrgNmGWtfb+bX78WWBQy60Nr7aBstzdmzBi7fPnyLpbKmTwZZnU1WZg6lRefIu3n4ids8A+3YOpwAI48knr7Oapo4DT+TJTx1LI4nj68nP6jivQzlp5njHnOWjsm3+UQKWWqSZKCZK39t7X2EmAX4BzgGWAk8GtgnTHm7h4qyreAmsDtCz30ugmCNUnQxZok6Fw/DMmNztYGFWktQ7C/kn/vJ3iorwcWLqSaehqoIsp4GnAdmmK4FWK99urJ4uZXZ2ugREQk55TdTgqatfZTXH+lWcaYfYCZwOG4GqazeqAIq6y1S3vgdTL28cf5LoF0WrY1BcEav86mqy4Qwf5zyQkeFv44yuA7f039m+75bL5GA1VurCW7R2IK8UKtBc1VufKdgl5ERAAFSVIEjDGDgcnABcB+3uQP81einhdM9X3ddUV3fiy+bAYvDQuoSmSg22Cfpbbg6cjvuPThe/4/qmigigZiRKjnQCIxL0ja08Sb4fXrlz7I7MlgSs3kRERKjprbScEyxhxpjLkfWAf8CtfcbhkuA95uPVSMe40xLcaYDcaY+4wxe/TQ6yYINrfbvDkHze0kP7JJmpEqoCqhZpKhTfEW/YaIeYN6qsH0onrRb4jFoH7CZW1JHgBim3dK3eSwp7PEFWlTSBERSU01SVJQjDFVwLnA+cCegAHexzWzm2mtfamHivIhLjBbDGwEDgSuAuqMMQdaa9/poXJIKclm8NIib16XjXZN8VpfozqaNDAt9V7NUrVrhkeESG1tvBleUDY1drnQU59VoTY1FBEpQQqSJO+MMQb4Eq453fFABS44WoLLZvcXa21TT5bJWvs8LhW5b7Ex5ingWVwyhx8FlzfGTMFl4wu1xx65q4DavDlnm5J8yLTPSTYBla+IT6LDapWCSR7mUU019YnB0i414UFSTweYnfmssqUmfSIiPUpBkhSCN3BjJBlgA3A3rtboP3ktVRJr7QpjzH+BsSHzZuJqu0KNGTMmZ3mMm3o0XJS8yqYTf4mdRLcLlhbNIDIhwjwmumBp0QzXDK8+vkxbwHTIIYkb6on90N0JF7pSO1bEwbOISL4oSJJCsBuuWdtMYI61dkuey5OOwY3ZJFJYerqJWQ9JCJZsrH0zvGoXHMVibsDayJ6mbd0Yw4msWgUHHwzLlvVswXOts7VjJRY8i4j0FCVukEKwj7V2grX2/s4GSMaYE40xt+e6YEmvMQb4DC55hEhhySYpRBEKa4bnv0U/Q7qfUtznJ3lgxYruL2B36+wYSkoqISLSKapJkryz1r6Sg81UA2fjMt91mTHmXuB1YAXwAS5xw5W4THs35uI1OmtLIdezSf70RL+YApDcd6mhIfG8P8p4N59YfOLo0QDh/ZeKSWea9JVRAhARkVxSkCQS7kXgdOBSYFugAXgIuNpa+14+C7Z1az5fXQpSsM/JlVfmuzQ9ImyspWgUIq9Hie1Z21aLFN33Irj+D/GxliIlECxlo0yCZxGRXFOQJBLCWjsdmJ7vcoRRkCQJyrzPSXKw4/ouRYF4XODzxxXLa7CUjyQK3Z1UQkSkBClIEikCa9e6LF4An34af1xZWUZXxCVciSZs6Iyw74Kf1MEXjcYz4vV4sFTmAa2ISDFRkCRSBIYNg+Zm97ilxXVa96dLmVOfkzahtUqReCCUnBEvGCwBRNZ3cy2PAloRkaKhIEmkCARrklpaVJMkAepzklLwu5EwMO289sFSw1P/JfLzo4g17UKkb5YZ5DKlgFZEpGgoBbhIkWltzXcJpODU1LiEDQqQ0gqmD49E4hcbqquhYdkaok01LG0d232psjubxltERHqcapJEikCwuV1rq5rbSTfJJqlAPhIQdFHyWEv+uEr19VB9/K7wRCUNW3clVjmCSHfV8iiJgohIUVCQJFIEgs3tWlvV3E66QTZJBYo8AUFy+vCGBqhv2J/qX/+a6vp6YtXnE2van0isDFOGi4gIoCBJSkcMeCrfhRApWtkkFSiRBATBYCkWg9ra/YlG92/XVSgWA/aMEGGNm2Btj5WxoBRh7aGISGcpSJKSYK29C7gr3+XoKa+9Bnvtle9SSF7l+oQ1m6QCJZaAoMNaoglucNoIa4gxnIgx5Rcopao9VOAkIiVKQZLknTFmcmfWs9bOynVZikFlJTz/vIKkstYdzd2yyZJXghn1gv2VoP34SvVUA9BAVbxGqZyE1R5CUTe7FBFJR0GSFII7gWwuyxpv+bIMkvr3h3Xr8l0Kyavuau6WTVKBEk1AEBYs1VNNNfWAC5JiDIdYN/VTKtSambDawxJpdikiEkZBkhSKZuCvwMp8F6QQDRvmao9eftk937QJVq+Gxkb3/LDDuvgChXpiJuHy2dytjI4VPwtew/0zqD/9Mqqpp5p6Yoti1M+DiRNznNihkBNipKo9LKFmlyIiQQqSpBAsBo4AJgI7AbcBf7HWfprPQhWSSAQOPNBdsB061NUkjRiRo40X8omZhMtXc7cyPVbGjYPYohltlSd+LBCsbcpJkFToNTPJtYcl2OxSRMSnIEnyzlo7wRgzArgQmAzcAfzWGHMPcJu19v/yWsACs/ferhYpZwr9xEzC5aO5W5keKwlN7+rj0/1uOQ0N8fldCpaKMSFGiTa7FBHple8CiABYa1dba68AdgdOBZYBFwPPG2OeNcacb4zpn9dCFojPftado+aMf2JWUVE8J2aSH2V8rPhN7yZOdG/bD4zADUobjcK8eYnJHrLm18xce23Z1NKJiBQq1SRJQbHWNgNzgDnGmOHABcA5wEzg18aYY621dXksYt4NHpzjDarJjGRKx0pbTVFVVTxGDGuC58u6dkk1MyIiBUFBkhQsa+0a4MfGmGeAW4HdgKH5LVVhMCbHG/RPyvz2QzpJk1R0Eg903ATPr3nKWX8lERHpUQqSpCAZY3YFzvNuw4FPgXuAFfksV6HoleuGsmXaIV+ks4Kpwv0sd8EapTAKmEREioeCJCkYxphewJdxTeyOxR2fLwDfBu621n6Yx+KVtjLtkC+SC2FN7IJ9k4IJHhQkiYgUBwVJknfGmD2B84FzgV2AT4C7cJntns1n2QrJsGHQ3Bx/Xl0dn95lxZhVS6TABGuX/MfB2iU/WPKpZklEpHApSJJC4Ce0Xg5cDdxvrf0kj+UpSGvXxvs+9OoVf1xZmYMTLXXIF+mysO9hQ0NicBTssxQMkko+YCqjQYhFpDQoSJJCYICtuFqknwA/MR1nJrDW2uHdXbBC1bcvvPYa7LVXDjeqDvkiOTduXHitEiQ2yWsLkoK/fdddVxpBhfo8ikgRUpAkhaI3kIuGY2Whf394/vkcB0kiknPZ9Fdql7byqqugX7/iDyrU51FEipCCJMk7a60GNc5AsE/SoEGwebPrl5STPkki0u38vkp+4DR7thtvCbwBaRnvliNGhDXEGE5ky9riDyrU51FEipCCJJEiEYm4fknLl7vnTU2uX1J9PYwZA4cdlsfCiUiHkmuV2g1Iy+KE+TEiRPq807ZQ0fZb6qjPo/oriUgBUpAkUoT23hueVd4/kaLWLuCxNrHJ3QUXwnnT2wKHog2SIHWfR/VXEpECpSBJpIj4Te6qqtyF13ZpwHVFVqRoBAOetix3r9vEPktNQDS8b1PRBkxB6q8kIgVKQZJIEQmmAd+6NSkN+Po6OOIIF0VVVsJTT+lkQ6RIpBpjyU8V7gdOJTcwrforiUiBUpAkUkTSDih79g/iM5ub4Qc/gMWLkzchIkWkaAam7WwttsZoE5ECpSBJpIgEa5IgqSbp1VcTF05+LiJFJTnoyXRgWoCYiRBhjXtibXcV0elqvyKN0SYiBUipl0WKVO/eSRPOOCP9cxEpKslB0rhxrrLFr03yH7erQTKGGJGE590qrF+RiEiRU02SSJHq3z9pwvXXu/uHHoKTT44/F5GSkOnAtBGGJy7H8GDIlHvqVyQiJUhBkkgRSR5Qtl12u+uvV3AkUgaSB6ZNSPLghUT+4LQNVHVvkKR+RSJSghQkiRSR4EnR3nvrgq1IuQpL0tD2+2CjRE1t2+C00UXxPkndluBB/YpEpMQoSBIpUhMm5LsEIlIo2iV5uD9KtCr+PFXq8LxnxRMRKVBK3CBSpE44Id8lEJFCkWmSh6qqxOWCfZqCj0VEyp1qkkSKlK7+ikgqwd+HdKnDg1SrJCISp5okERGREpZcq+QHQn7NUTTqbg0N8XViMVzqcP9WV9czhRURKRAKkkREREpYcu1QJBIPmoKBUVVVPGBauudpbdNjDIdDDlGgJCJlRUGSiIhImUgOmKqqUvRdIh49tQ1Mq0FiRaSMqE+SiIhImQirVfK167sUGGcJILbPMd073pKISAFRTZKIiEiZCgZJwb5LDfdH26ZX0UD0pheZ98botn5MyoQnIqVONUkiIiKSEDBVVUGtjQKudqm2FogmJn1QJjwRKWUKkkRERCRBMBiqr49PDw5K61PAJCKlSEGSiIiIJPCDnkgEJk5097Nnxwej9TPhQTxgUqAkIqVEfZJEREQkJT/4SZkJryqxj1Js6Fg3ttIuu/RkMUVEcko1SSIiItKhdJnw/CZ5ka+OJfZef5cFr6HBBUrr1/dUEUVEckY1SSIiItKh5Ex4/vP6eqiudo9j7/WPpwxneGLnJRGRIqKaJBEREclKsM8SuFioqgrYYTBV7zcQZTz1VDNxx39qbCURKUqqSRIREZFOi0QC/ZUaH4IdBlPLYqp3eIPIu//SmEoiUpRUkyQiIiKdFmyGF4tB/U8egmr3PBp1zfH8DHkiIsVCNUkiIiLSJckpw4MZ8KqrE8ddAqCuDqZPd/ciIgVINUkiIiKSM5FI+kFoI+vr4KijYMsW6NMHnnwSamp6vqAiImmoJklERERyKqxGyR9TiWgUNm+GlhZ3H8wlLiJSIIy1Nt9lECl5Y8aMscuXL893MUREetzs2V5w5JtQSwNVjGMpEda4aToXyYox5jlr7Zh8l0OklKkmSURERLrNuHHxmiSAWhZTRUM8QBIRKUAKkkRERKTbKKudiBQjBUkipUQZo0SkQMViLnFDdJFrWhdlPFHGs+RpNbUTkcKj7HYipaJOGaNEpHBFIvFapeiiaFvzu6iZAETdk2ee0e+WiBQE1SSJlIpo1AVILS3uXhmjRKTQ9eoFBGqSDjlENeEiUhAUJImUitpaV4NUUeHu/cu0IiIFprLSXceJ2iMA1/RuNl8jxnBd4BGRgqAU4CI9oMdSgNfVuROM2lo1WRGRwterF1F7BLUsJsp4alnsapdaWvJdsoKmFOAi3U99kkRKSU2NgiMRKR6trWAmtJ9WWQnNzfkpk4gICpJEREQkT2IxaLh/EdHTawHX7A4g0hIjkrdSiYgoSBIpLWpuJyJFpC3j3ZlLiLYc5prbgetbKSKSRwqSREpFXZ0LjrZuhd69XbCkQElEikFzM1QcBa0Q67UXkeZX810iESlzym4nUipmzXKpv61197Nm5btEIiIZi7z6JFhL7EkFSCKSfwqSRErFE0+kfy4ikm91dTB9euhYSJE9DRgDE2p7vFgiIsnU3E6kVKxfn/g8FstLMUREQtXVwVFHuZruPn3gySfbmgTHTISYl7QBIGpqYVE03mdJRKSHqSZJpFRsu23i8+ZmuOKK/JRFRCRZNOoCpJYWdx8YNDbCGmpZTIQYALUsprZWAZKI5I+CJJFS0bdv+2l//GNosxYRkR5XW+tqkCoq3H1tbbtFlPhbRAqFgiQpK8aYYcaYG40xdcaYTcYYa4yJhCy3gzHmj8aY94wxnxhjnjDGjMpDkTP39a+3n9bY6Jq3KFASkXyrqXFN7K69NqGpHeASzngixBKei4jkg/okSbkZAZwKPAc8DRydvIAxxgCPAHsClwLvA1cCi4wx1dbatT1X3Cxcf727nzkTNm+Gpib33G/WonTgIpJvNTXtfotiMa8L5SIXGMWAWBT1RxKRvFKQJOXmKWvtzgDGmAsICZKArwCHAUdaaxd5y9YBrwOXA9/qobJm73/+Bw46CKqr4cYb4x2khwxxGaU0yKyIFBg/GIqZPYkxnFrzFLS25rtYIlLmFCRJWbHWZvLP+xXgLT9A8tb70BjzKHAihRokzZwJU6e6xwsWwOWXw6BBLkC65BKXyKFXL/jDH2DKlLwWVUQkQa9exDjCPbbW/VYpUBKRPFKfJJH29gdeDJn+ErCHMWa7Hi5PZubMSXxeXw9XXgmPP+4CJHAnHRdfrD5KIlJYvD5IfnY79UkSkXxTkCTS3mBcP6Rkjd79Dj1YlsxNmpT4vLra3b/8cuL01taE1LsiIvkSi7mfoyi17jkRooxXljsRyTs1txNpzwBhlzFNyhWMmQKkbMO2xx575KBYHZgyBV59FW64wT2/4QbYuBGGDoVVq+LL9eoVmnpXRKSntSVnsIuImgnUEgVj1NRORPJOQZJIe4242qRkfg1Su1oma+1MYGaqDY4ZM6Zn2o74AZLvlltc4obKysQ+SaBEDiLS8+rqXNVR8m+PMcB491gBkogUAAVJIu29RHjWu5HAG9baj3u4PF2zdSvsuiuMHeuSOQBMmBDPfLdokQIlEel+dXVu3Db/t8cfK8m4Svq2/kjGqE+SiOSd+iSJtPcIsJsxZrw/wRgzEDjBm1dcrIV162DePHjhBZg1y42hZK27nzUr3yUUkXIQjboAqaUlPn5bQIQ1eSmWiEgYBUlSdowxpxhjTgE+7006zpvmB0WPAHXAPcaY04wxx3jTDHBD+y0WkI6uviZnwMvGzJlw8MFw0knKjici2autdTVIFRXuXn0jRaSAqbmdlKMHkp7f7N0vBmqtta3GmC8Dv/TmbYMLmiZYa9/suWJ2QkfBy4IF7r5PH9cMr3dvmDy54+1ecUVif6e//hWeekrN9EQkczU1roldcp+kZ56BQw6JL/fMM/konYhIAgVJUnastSmz1AWWaQTO827FIxp1V2lbWtIvt2ULXHddZokb6urgF79InNbc7IKmuXO7UloRKTc1Ne1/c2pqXGAUltBBRCRPFCSJlBK/OYvfMXrz5tTLXnllZtucNSu8Gd+8eTBwIOy+O3z72y4FearMVSIiIWIxiOzVy/3GKPW3iBQQBUkipSSsOYvpsOIsvZUrU8/76CM3f+pUl1r8hRfcyU7fvvHMVSIiKcT2nEDEH5bOWjdMgQIlESkACpJESk1yc5bk9v6QeXrdujpYsiSzZevr4483b3aBmoIkEUkr6bdIqb9FpEAoSBIpdZ1p719X55rZLV7c+au6fnpfNb0TkYBYzN0cQ9QbRDZCjIh5I0+lEhFJZKyu2oh0uzFjxtjly5fnuxiZOfhgePbZ3G2vb18NWCsioaJRqD1SfZKyZYx5zlo7Jt/lECllGidJROKGDMltgARuwNof/CC32xSRotdWm9Ta6oIkBUgiUkAUJImIc8wx0NjYPdt+6ik48EANQisiQLzJXSSS54KIiKSgIEmkVNXVwfTpmQcmTz/dveWpr4dDD4WZM7v3dUSk4Pm1SAqSRKRQKXGDSCnxxykaMgQuuyw+XlIm6bgPPxwWLOje8lnr0oXPmQPz57efr3GWREqaX4NUXw/V/6+WqDc98npUAZOIFBQFSSKloq4OjjrKBUZ+B+jWVvc8k3Tc8+e7JndPP+36EXXUP6C6GsaNg8mT4fzzYdWqzMu6YAGMHJk4BlOw/JkGdiJSNPwAqaHBBUj1VFNNvctqt6dR+m8RKShqbidSKqJRF2C0tLgAp6LC3fr0cTUzmZg/HzZtgi98oeNl6+tdP6OaGhfsWAv9+mVe3lWr4Mwzw8vvB3YiUjIiEfdT9O677nk19dSymAhr8lksEZFQCpJESkVtrQuIKipc2u2bboJrr+1cjcz8+XD00W576cyZk/j8ySehVxY/K/feG+8zFSx/NoGdiBSVl1929xFieS2HiEg6am4nUipqalyQkqs+PfPnQ2UHPxGTJrUvw5IlLuX3U09l9jrnnw9/+lPuyy8iBSPY1A6g4f4onF4L4GqS1NRORAqMBpMV6QFFNZgsuL5JfvO3VM44A+65J/X8K66AX/wis5Offv3UB0mkhC1ZAsuXw4oVMHAgjBjhpo8ZA4cdlt+yFSMNJivS/VSTJCKJjjkmdZY7Y2DsWFf7M2VK+u1cfz1MnAizZsHSpbB6NXz8cfiyTU2ZJZcQkaJ02GHuNmOG+ym47LJ8l0hEJD0FSSLlLjntdrrxkqZOddnsMg1mamoSlz3zTLjvvva1S716qQ+SSInym9r5Nm50PzmRiMZJEpHCpcQNIuXMT7v94x+7+7o6N15SKrfcAhMmZD5AbbJ77oF//hMuusglZ+jVC3r3ht//XrVIImVi7Nh8l0BEpGOqSRIpZ2Fpt4PjJW3e3H6dTMddSsWvXZo8WUkaRMpAsMaofsJlXMpv3RP1iRaRAqaaJJFy5qfd7tXL3YYMcdOnTXOBUphcpeeuqYErr1SAJFIujKGKhoTnIiKFSjVJIuWspsb1pL7kEleb5Pem/uY3YevW8HUWLVJgIyKdMo6l+S6CiEhGFCSJlLtf/hKam93jLVvcmEWpAiQ1jxGRLoiwJt9FEBHJiJrbiZSjujqYPh0OPhheeSU+vaXFDWYSZAzceqsCJBHpmuTfEP2miEgBU02SSLnxM9r5CRuStbbGH1dUwM03dzwmkohIJhQYiUiRUJAkUm6CGe3SqayEp55S/yMREREpO2puJ1Ju/Ix2FRXQt2/q5Y48UgGSiIiIlCXVJImUm5oaePLJ+BhFhx4a3gRm/vyeLpmIiIhIQVCQJFKO/AFdAQYMgI0b81seERERkQKi5nYi5e4Xv2g/TZ2rRUREpIypJkmk3PmZ6+bMgUmTlMlOREREyp6CJBFxgZGCIxERERFAze1EREREREQSKEgSERGRTonF8l0CEZHuoSBJREREOkVBkoiUKgVJIiIikhUFRyJS6pS4QURERDIWi8G8eVBd7Z5Ho+4+EnE3EZFSoCBJREREMhaJuACpttYFSLW1gDHxBTTOmoiUAAVJIiIi0qFYLLGZXTQK9fUQmRAhElzQGAVKIlL01CdJRERE4urqYPp0d5+kttarOfIeT5wIEdb0YOFERHqGapJERETEqauDo46CLVugTx948kmoqQFcLVJynyP1QRKRUqWaJBEREXGiURcgtbS4ez8rQ5KE4Ci5aZ2a2olICVBNkoiIiDi1ta4GyatJiu1zDEtnQ1WVmx3MZJdAgZGIlBgFSSIiIuLU1Lgmdl7aukjNaGLRpEx2IiJlQEGSiIiIxNXUtPVDEhEpVwqSREREJEFYuu+GhvDkDSIipUhBkoiIiCSIRNzND5bUzE5Eyo2y24mIiEioYG2SiEg5UZAkIiIiKal5nYiUIzW3ExERkTZ+E7uGBpf623/uN8ETESkHCpJERESkjR8M+WMiqT+SiJQjNbcTEREREREJUE2SiIiIAO1Tfzc0uBolNbUTkXKjIElERESA9qm/Tzstv+UREckXNbcTERGRBEr9LSLlTkGSiIiItKPmdSJSztTcTkRERNr1R1LqbxEpZwqSREREJCEYUrIGESl3am4nIiIi7ahfkoiUMwVJIiIikkA1SCJS7tTcTkRERID2/ZKiUXevpnciUm4UJImIiAjQvl9Sba17rKZ3IlJu1NxORERE0lKQJCLlRkGSiIiItKPmdSJSztTcTkREREL5fZKCj9U/SUTKgYIkERERaSdV/yQRkXKg5nYiIiKSFfVREpFSpyBJRERE0kpuXqcgSURKnYIkERERSUt9kESk3KhPkoiIiHRIA82KSDlRkCQiIiIdUiIHESknam4nIiIiOaG+SiJSKhQkiYiISFZSNa9LbpInIlKsFCRJWTHGDDPG3GiMqTPGbDLGWGNMJGmZiDc97DYoPyUXESkc6fogKVASkVKgPklSbkYApwLPAU8DR6dZdjrwSNK0j7qpXCIiRSk5KKqvh4UL4bzzlNBBRIqXapKk3Dxlrd3ZWns88EAHy75mrV2adGvpiUKKiBSLYEKH+nqorobGRhc4LVmSt2KJiHSJgiQpK9ba1nyXQUSk1CQHSiNGuMfLl6vpnYgUJwVJIqlNN8Y0G2M+NMY8YowZle8CiYgUsqqqfJdARCQ31CdJpL0m4FZgAfAusC9wFfCMMeYga+2q5BWMMVOAKak2uMcee3RTUUVECoNfm7RkCcye7ZrdVVfH+yxp0FkRKSYKkkSSWGvXAxcFJj1tjPk78BLwQ+DMkHVmAjNTbXPMmDE21+UUESlEhx0Ga9e6wWY16KyIFCs1txPJgLX2TWAJMDbfZRERKXTjxuW7BCIiXaMgSSRzBlCNkIhIB/xmdWpeJyLFSkGSSAaMMXsAhwLL8l0WEZFioSBJRIqV+iRJ2THGnOI9/Lx3f5wx5l3gXWvtYmPMr3AXEOpwiRv2Aa4EWoHrerq8IiIiItKzFCRJOUoeRPZm734xUItL0HAxcA4wAHgPWAj81Fr7cs8UUURERETyRUGSlB1rrelg/u3A7T1UHBEREREpMOqTJCIiIiIiEqAgSUREREREJEBBkoiIiIiISICCJBERERERkQAFSSIiIiIiIgEKkkRERERERAIUJImIiIiIiAQoSBIREREREQlQkCQiIiIiIhKgIElERERERCRAQZKIiIiIiEiAgiQREREREZEABUkiIiIiIiIBCpJEREREREQCFCSJiIiIiIgEKEgSEREREREJUJAkIiIiIiISoCBJREREREQkQEGSiIiIiIhIgIIkERERERGRAAVJIiIi+dCnDxjjbp/5DNTVxefV1UFlZXz+sGFw8MEwc2Z8/vTpsM028WV22gmuuMLddtoJdtkFBgxw8/r0gf33d/NOOsk9Hj8eLr44/jqVlW6bwXL4gmW5+GK3TF2d28bgwdC/P/TrBwceGL6+iEiRMdbafJdBpOSNGTPGLl++PN/FEJGO1NVBNAq1tVBTE77MzJkwZw5MmgRTpqTe1hVXwH33wV57wc9/nri9Pn1g69bE5Y2Bf/7TPT7kkNTbvfxyuPFG2Lw5k3eUvX794Mkn4+WtrISWlsRlevd201pbw7dx0UUweXJ8G8ccA08/DaNGwcSJ4ft3yBBobHRB14YNqctnTPzxM8+k/pxKmDHmOWvtmHyXQ6SUVea7ACIiIgWhrg6OOgq2bHFBTDBQ8M2cCVOnuscLFrj7sEDpiivghhvc47Vr4fDDXZDgby85QAKw1gVoHXnoIVfG7rJliyuHX9bkAAnCyx90yy1w111uH06bFt9Xzz7rbsmBmB8ggbsfMiQ8UAoGSOCCyTINlESke6m5nYiICLjAYMsWFxT4gUKyOXPSP/c99FDi85aWxO317t1+HWNcDUttbfpynnyyC+K6S58+iWWoqGi/TO/e0KuDUwh/Hz79dOp5Pj9ASvU8nUwCSxGRLClIEhERARcY9OnjgoLkQME3aVL6576TT058XlGRuL0tWxIDpb33dk3tamrc7ZlnEoOT3XaDgw6CW2+F6693tTDXXQd9+8aXGTrUNcW7/HL3uKoKttvOzevdG0aOdPMmTnSPjzjCNYvzX6eiwm0zuQatuTmxLBddBIsXw5Ilbhs77BAeSPn78PDDU8/zDR6cOD/5eTodBZUiIp2gPkkiPUB9kkSKRE/1SSpFZ54Jjz8OY8bEa8TUJ6lbqE+SSPdTkCTSAxQkiYhIrihIEul+am4nIiIiIiISoCBJREREREQkQEGSiIiIiIhIgIIkERERERGRAAVJIiIiIiIiAQqSREREREREAhQkiYiIiIiIBChIEhERERERCVCQJCIiIiIiEqAgSUREREREJEBBkoiIiIiISICCJBERERERkQAFSSIiIiIiIgEKkkRERERERAIUJImIiIiIiAQYa22+yyBS8owx7wJr8l2OErMj8F6+C1HitI97hvZz9yu1fTzcWjs034UQKWUKkkSkKBljlltrx+S7HKVM+7hnaD93P+1jEcmWmtuJiIiIiIgEKEgSEREREREJUJAkIiIiIiISoCBJREREREQkQEGSiIiIiIhIgIIkERERERGRAAVJIlKsZua7AGVA+7hnaD93P+1jEcmKxkkSEREREREJUE2SiIiIiIhIgIIkERERERGRAAVJIlJQjDG7G2MeNMZ8aIzZaIx5yBizR4brbmOM+YUxZr0xZrMxps4Yc0R3l7nYdHEf2xS36m4udlExxgwzxtzoHYObvH0UyXBdHccZ6OI+1nEsImkpSBKRgmGM2RZYCOwLnA2cBewNLDLG9M9gE38CLgR+AnwZWA/M14lPXA72McCdQE3S7b85L2xxGwGcCrwPPJ3lujqOM9OVfQw6jkUkjcp8F0BEJOBCYC9gH2vtagBjzP8BrwBTgV+nWtEYcwDwdeA8a+0d3rTFwEvANcBXurfoRaPT+zhgnbV2afcVsSQ8Za3dGcAYcwFwdCYr6TjOSqf2cYCOYxFJSTVJIlJIvgIs9U/eAay1rwP/BE7MYN2twJ8D6zYDs4FjjDF9c1/cotSVfSwZsta2dnJVHccZ6sI+FhHpkIIkESkk+wMvhkx/CRiZwbqvW2s3hazbB9c0R7q2j30XG2OavH4gC40xh+eueGVPx3HP0XEsIikpSBKRQjIY178gWSOwQxfW9edL1/YxwD3AN4AvAFOAIcBCY0xtjspX7nQc9wwdxyKSlvokiUihCRvh2mSwnunCuuWm0/vJWntW4OnTxpiHcTVT/wscloOylTsdxz1Ax7GIdEQ1SSJSSN4n/Er5DoRfXQ9qTLOuP1+6to/bsdZ+BDwGjO1iucTRcZwHOo5FJJmCJBEpJC/h+mQkGwmszGDdPb0U18nrbgFWt1+lLHVlH6eSqvZDsqfjOH90HItIGwVJIlJIHgHGGWP28id4g0Me6s3raN3ewFcD61YCXwMWWGubcl7a4tSVfdyOMWYg8CVgWa4KWOZ0HOeBjmMRSaY+SSJSSG4Dvgk8bIz5Ee6q7rXAm8Ct/kLGmOHAq8A11tprAKy19caYPwMzjDG9gdeBi4E9gTN69F0Utk7vY2PM94B9gEXAW8Bw4HtAFdrH7RhjTvEeft67P84Y8y7wrrV2sY7jruvMPtZxLCKZUJAkIgXDWvuJMeZI4DfA3bjmL08Cl1lrPw4saoAK2teGnwv8DNf5ehDwb+BYa+2Kbi560ejiPn4ZOMm7bQ9sxI2vdL619tkeKH6xeSDp+c3e/WKgFh3HudCZfazjWEQ6ZKxV81sRERERERGf+iSJiIiIiIgEKEgSEREREREJUJAkIiIiIiISoCBJREREREQkQEGSiIiIiIhIgIIkERERERGRAAVJIiIiIiIiAQqSREQkp4wxNuTWZIyJGWPuMsbsl+F2nvHWPS6DZV/2lq3u8hsQEZGyp8FkRUQkp4wx/h/LTwOTtwcOAg4BPgEOs9bWd7Cdc4A7gLnW2pPTLDceiALLrbVjO11wERERj4IkERHJKT9IstaakHk3At8E7rLWntPBdvoB64H+wDBr7dsplrsbOBOYYq29rWulFxERUXM7ERHpWQu8+6EdLWit3QzcA1QC54QtY4wZBJwCfAzcn5MSiohI2VOQJCIiPekL3v3yDJef6d2fn2L+mcA2wP3W2o+7UjARERGfmtuJiEhOpeiTNBAYCxwKPAZ83Vr7UYbbW4brzzTBWhtNmlcPHACMtdZmGniJiIikVZnvAoiISMm6OmTaSlytT0YBkmcmLki6AJegAQBjzFhcgPS8AiQREckl1SSJiEhOhSVuMMb0B/YHfg5MAK6z1v7QmxchpM+RtXZaYN31QG9gV2vt+970mcCFwMXW2lu67Q2JiEjZUZAkIiI51UF2u0HAWqAvsJe19k1jTC2wKHnZpCDrFmAq8C1r7Y2BwKkXLnDamPt3IiIi5UqJG0REpMdYaz8AXsY19x7tTYtaa03yLWlVP4HDhd79acAA4M8KkEREJNcUJImISE/bwbvP+D/IWrsCeA4YZYzx+ydBPHgSERHJGQVJIiLSY4wxE4E9ga3AM1mu7g8U+0tgHPB/1tpluSudiIiIoz5JIiKSUylSgPcHRgLHAQb4vrX2l1ludwDwFrCdN+lSa+1NXSyuiIhIOwqSREQkpwJBUlAL8C7wLHCTtfYfndz2bbimdptxCRs+6Gw5RUREUlGQJCIiIiIiEqA+SSIiIiIiIgEKkkRERERERAIUJImIiIiIiAQoSBIREREREQlQkCQiIiIiIhKgIElERERERCRAQZKIiIiIiEiAgiQREREREZEABUkiIiIiIiIBCpJEREREREQC/j8jsy9xZDZGlQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(200), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(200), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"],\n", + " iso1.singles[\"m_ubv_V\"] - 5 * np.log10(200), \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(iso2.points['m_ubv_B'] - iso2.points[\"m_ubv_V\"],\n", + " iso2.points[\"m_ubv_V\"] - 5 * np.log10(200), \"b+\", label=\"MISTv1\", alpha=0.25)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(200), \"r.\", label = \"Primary Stars from BPASS isochrone\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Primary Stars from BPASS Isochrone\\n at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Finding stars with the very low M_V magnitudes.** This includes looking at the insides of a primary star table" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=2>\n", + "
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5.0
101.0
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massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
1.42.6668705105418817e+232738.734924624995581562431.489620524.648933553569084False1.6250475.0330.73672471688670528.3040530254177629.590381196274226.8904196375954623.585313231822646
0.3981072.6668705105418817e+232738.734924624995581562431.489620524.041574446625068False0.40133485.0430.0425824979413928.298569798494329.29882075936120226.9792126533972923.631401519338752
0.3981072.6668705105418817e+232738.734924624995581562431.489620524.038067441246554False0.3981075.0330.03950025188098428.2987015763748429.29739394293731526.9802280599826223.631908766863493
3.162282.6668705105418817e+232738.734924624995581562431.489620524.938067948646069False3.162285.0431.23949410007064728.34926167509225429.78165948630291726.96471929990474323.621658438647714
0.3981072.6668705105418817e+232738.734924624995581562431.489620524.038067441246554False0.3981075.0430.03950025188098428.2987015763748429.29739394293731526.9802280599826223.631908766863493
0.5011872.6668705105418817e+232738.734924624995581562431.489620524.138067424857716False0.5011875.0430.13101451806891628.29495027553055329.3388327910729326.95164053744141723.617537092071686
0.5011872.6668705105418817e+232738.734924624995581562431.489620524.138067424857716False0.5011875.0430.13101451806891628.29495027553055329.3388327910729326.95164053744141723.617537092071686
3.162282.6668705105418817e+232738.734924624995581562431.489620524.938067948646069False3.162285.0431.23949410007064728.34926167509225429.78165948630291726.96471929990474323.621658438647714
1.02.6668705105418817e+232738.734924624995581562431.489620524.438067627303133False1.05.0430.46356325167995728.28377350849845229.47357815170020226.8701344602487723.57552936322022
..........................................
3.162282.6668705105418817e+232738.734924624995581562431.489620524.938067948646069False3.162285.0431.23949410007064728.34926167509225429.78165948630291726.96471929990474323.621658438647714
0.6309572.6668705105418817e+232738.734924624995581562431.489620524.266478022815469False0.67361295.0330.26111580900598628.29015213670909629.39447490763566526.91600156219367823.599357000580635
0.5011872.6668705105418817e+232738.734924624995581562431.489620524.138067424857716False0.5011875.0430.13101451806891628.29495027553055329.3388327910729326.95164053744141723.617537092071686
2.511892.6668705105418817e+232738.734924624995581562431.489620524.8398356118570485False2.5221335.0431.04145911026798328.33369014362339729.71282625262321626.93890189913049223.609173345996595
0.7943282.6668705105418817e+232738.734924624995581562431.489620524.338067498968938False0.7943285.0430.34104353586467428.2874863304672929.42678014478545726.89662909332884423.589352142607936
1.02.6668705105418817e+232738.734924624995581562431.489620524.438067627303133False1.05.0430.46356325167995728.28377350849845229.47357815170020226.8701344602487723.57552936322022
2.511892.6668705105418817e+232738.734924624995581562431.489620524.838068244279513False2.511895.0431.03820704977337628.33341202022594329.71162692792946626.9384429760622923.608950027401207
3.162282.6668705105418817e+232738.734924624995581562431.489620524.938067948646069False3.162285.0431.23949410007064728.34926167509225429.78165948630291726.96471929990474323.621658438647714
1.258932.6668705105418817e+232738.734924624995581562431.489620524.538069210104485False1.258935.0430.59244508906621728.287205756826929.52513589493962326.86322684141771723.571693650767493
3.162282.6668705105418817e+232738.734924624995581562431.489620524.938082643335787False3.1623875.0431.2395265985636428.34926402124222729.78167011660531626.964723208250323.62166031708847
" + ], + "text/plain": [ + "\n", + " mass L ... m_ubv_R m_ubv_I \n", + "solMass W ... \n", + "float64 float64 ... float64 float64 \n", + "-------- ---------------------- ... ------------------ ------------------\n", + " 1.4 2.6668705105418817e+23 ... 26.89041963759546 23.585313231822646\n", + "0.398107 2.6668705105418817e+23 ... 26.97921265339729 23.631401519338752\n", + "0.398107 2.6668705105418817e+23 ... 26.98022805998262 23.631908766863493\n", + " 3.16228 2.6668705105418817e+23 ... 26.964719299904743 23.621658438647714\n", + "0.398107 2.6668705105418817e+23 ... 26.98022805998262 23.631908766863493\n", + "0.501187 2.6668705105418817e+23 ... 26.951640537441417 23.617537092071686\n", + "0.501187 2.6668705105418817e+23 ... 26.951640537441417 23.617537092071686\n", + " 3.16228 2.6668705105418817e+23 ... 26.964719299904743 23.621658438647714\n", + " 1.0 2.6668705105418817e+23 ... 26.87013446024877 23.57552936322022\n", + " ... ... ... ... ...\n", + " 3.16228 2.6668705105418817e+23 ... 26.964719299904743 23.621658438647714\n", + "0.630957 2.6668705105418817e+23 ... 26.916001562193678 23.599357000580635\n", + "0.501187 2.6668705105418817e+23 ... 26.951640537441417 23.617537092071686\n", + " 2.51189 2.6668705105418817e+23 ... 26.938901899130492 23.609173345996595\n", + "0.794328 2.6668705105418817e+23 ... 26.896629093328844 23.589352142607936\n", + " 1.0 2.6668705105418817e+23 ... 26.87013446024877 23.57552936322022\n", + " 2.51189 2.6668705105418817e+23 ... 26.93844297606229 23.608950027401207\n", + " 3.16228 2.6668705105418817e+23 ... 26.964719299904743 23.621658438647714\n", + " 1.25893 2.6668705105418817e+23 ... 26.863226841417717 23.571693650767493\n", + " 3.16228 2.6668705105418817e+23 ... 26.9647232082503 23.62166031708847" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iso1.primaries[np.where((iso1.primaries['m_ubv_V'] - 10 > 15.0) &\n", + " (iso1.primaries['phase'] == 5))]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-1.4943756354369313" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(([x - 10 for x in iso1.secondaries[\"m_ubv_V\"] if np.isfinite(x)]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the HR Diagram of the Isochrones" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['Teff']), np.log10(iso1.primaries[\"L\"]), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['Teff']), np.log10(iso1.secondaries[\"L\"]), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['Teff']), np.log10(iso1.singles[\"L\"]),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['Teff']), np.log10(iso2.points[\"L\"]),\n", + " \"b+\", label=\"MISTv1\", alpha=0.25)\n", + "plt.xlabel(\"log10($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log10(L in watts)\")\n", + "plt.title(\"HR Diagram of Isochrones at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I wonder if the bloch of stars in the BPASS isochrone in the middle of the plot has blue stragglers? I check initial masses of these stars and see if they are the type of star that would normally be \"dead\" by this age." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.5" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(iso1.primaries['mass']\n", + " [np.where((np.abs(np.log10(iso1.primaries['Teff']) - 4.5) < 0.15) &\n", + " (np.abs(np.log10(iso1.primaries['L']) - 28.5) < 0.6))])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.primaries['mass']\n", + " [np.where((np.abs(np.log10(iso1.primaries['Teff']) - 4.5) < 0.15) &\n", + " (np.abs(np.log10(iso1.primaries['L']) - 28.5) < 0.6))])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.5" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(iso1.secondaries['mass']\n", + " [np.where((np.abs(np.log10(iso1.secondaries['Teff']) - 4.5) < 0.15) &\n", + " (np.abs(np.log10(iso1.secondaries['L']) - 28.5) < 0.6))])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12.0" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.secondaries['mass']\n", + " [np.where((np.abs(np.log10(iso1.secondaries['Teff']) - 4.5) < 0.15) &\n", + " (np.abs(np.log10(iso1.secondaries['L']) - 28.5) < 0.6))])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It turns out that medium-high mass stars are in that island." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare this to the HR diagram of the Hoki cluster (Salpeter IMF, log-age of 8.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "from hoki import load\n", + "import hoki\n", + "to_dat_file_pt1 = (\"/u/ryotainagaki/Desktop/\" +\n", + " \"ryotainagaki/BPASS/bpass_v2.2.1_imf135_300/\" +\n", + " \"hrs-bin-imf135_300.z020.dat\")\n", + "\n", + "bin_hr_diagram = load.model_output(to_dat_file_pt1,\n", + " hr_type = 'TL')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/g/lu/scratch/ryotainagaki/hoki/hoki/hrdiagrams.py:435: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"Greys\").copy()\n", + " colMap.set_under(color='white')\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Xc25LwqIpvzziHI3/QfCUa9x9xG34ZOs8EpKtII4afHXO0xC7o/Gr+A/umfhfoo8lrLMBfz49EKzTHfg5PmG43swed861uE2Jc26ZmR2ALwk4JogboMLMrnbO/TV43Zr/XfTvZN1rpLIhxfRaUt+Ek+xz0JZzrakYCuNeR794RuI/26mk26/OA/gfaR/h2yiuwpeSgu/iI9lnFpLE65yrDZK/+Hibu94kvZ5kQFv+N7vhqxDnAmndGJKG6H5acp62h1PwbVX/D98uC2CrmT0M/LgFn+foefiTZpZLdh629H8evVYmvdkrDRuSTIt2k1OYZF6ifPrOvB/4M/AtM/u98+0Fzw/mJWsLGtWV+uPYKllvIO+c2wq8amZfxDey/LuZTXfOtaabgKSCEoxDg5evNbP4ZHyi1ejLOmiInHhB3hg8p8pym8x+40seElyJL2Ivd87NT4jjH7Tu12t7iCZsg4D3kszfOWG5jHDOfWhm6/AX7d1oXSlatGH80Za6k95jzKzMNdPpX1DK8KCZ7YVPno4gIdlKss5nwK+CkoMJ+JswknU30azgHDnFfHcn++DbhV0EXGdmnznn/kXD/10yyf53G4LnUjJ7O3iy49+aeFsquu5U59xXmlyyGWZWjk+0ngWODRLy6LwCfIP9torGm+q6kupYtbe2/G+ewDdg/x3+hoWjXNwNAK20IXhOVXKYjmjD/FTfg71IeD/Bj7zLgcvNdxJ6KL5q9nR8ddwhae47ut1ezrmNTS6ZoInvkFTLbzCz7dQneNmWN9+Zzrktwd2MP8TfFDUP/4P1Nedc0u+U4LPcG98MpNVy2fXDSvyHrzv1vxDay9n4uxJWklAcnsSuwfMjSeYlS3AW4DPcvYOi9UQT0owxWRzvJzlpCtqwzfbwVvA8MXFGcOfYvvhfFvMT57enIKmIHu8Wn7dmNgh/B9hGfGPNZI+X8b/UvtmCTUeLxVNWsbfTOkk5393Jm865P+DvkgJfvUlQZL8YKA3uaEsU7XssvrQheofqF9saWyssxLft2NeSdJtA8nhbagH+i/oga+HoFUlErx2PxydagQPwv4bbKvr5mxCUpiaa2A77aFYrz6X49afgv+D2A563ZrplSMPr+GTp0KDEuDWi1WrJRqbYlWZK651zFc65e/Al5ovw/6P4hKaO1CU/0c9ZuslZW70L7Gx+dI9sy7fvzL/jf+ydj6++LKTpUq1R+Gv13FbGCeR+bMTr8UWIZ6f4ALeI+b5zvoW/pTjayHdrM6stDZ4nJmxrF+APiQs757bjqw564Us04tfZB1+l1BpLgZEW179K0B/IZfi2ILlyN75K9aLgAhTvSnyj0budc9sardm+LsQ3eF6Lr+5tqW/if8He45w7N9mD+sbD5wQfWMzsNDP7giXpeylI4KL9c82Mm/4TM9sjWRBmNgH/xVSLb8fSYub7wEr2ZRWdFt9+5Fb8heJP8V/WZtYf36N0dJmovwex/cqSDHUU3CKdEcFn6x581ckVCfv9HL4X5xp8W7zW7qMWf93ZGfirxfXDF7evnZO99ySWBs8TE9YfiL8GtZlzbjm+imQE/jMQv5/JZLfEu6XnUgPOuWvxHQjvAbxgSfqSSpdzbjW+Smhn4OrEz6eZFVvz/UAtwP/4mhz8z6LrdsU3sG7AzAaYWbK2rt3xPwRr8SUtUWtJksgFbsCfy9eYWaNuZcz3LdWeidgM/Pd9Yp+DGZdv35nOuUXAdPyP72/jf3w16n8sTrQda3MFN03KaT9bzrnNZvZ7/J0ZV1D/yzwdX7b6cRi740uyDsF/+D4FznfONXUAo57A3357cVAl9FawrePwfXcMTbLOz/HVRj8NPnyzgv2ejL8t+svUF1Gn6xp8O7a3goaXNfhqpjFBjK0Z0qjNgnZ2P8B/ecwxswfxbdoOwzdKXIBvw9BeelvDzu164huXH4Y/pt9NUorQpOADGB1M9J+plguqKl/Af3l+Ef//PxDfAHOVmb1EfVHyCHxj+q74tjoPx23q68Afzfc8HB0eqjv+S+YI/BfWj9pQdf5/wAVBrB/if6F/Dn+ObKPhcEdXB+9lMvC2mT2Jb4T+NXyD0j8652J97zjn3jez71J/Lk7D/2rvh791ehP1pRiZ8HP85/jCoOHw89T3s9UDf/dTm4rz8T8S9sFfaI83s+fwbX8G4ttyfR5/F+77zWznDXxp6FfMbBa+oW8J/ngvpHUjKCRzAT4xv9bMjsJXoe+Kr8LM5rWhRedSMs65m8xsK74keab5obRaOzzPhcCe+P/jRDN7Cp/sjMCXNp1AXL9WSWKpMbPr8IniW+ZvginC96e0gsb/v1J8E5j5+BK8Cvz16Th89epfExqATwdONd+o/k18MjbTOTfTObfAzL6JT07fM7P/AR/gf1BGv8tW428aaQ+PAD/CH5dn22mbLZFv35k34ptelOA7n27qBoej8He+TmthfA2lc8siwa3AzSyzlKa7fhieYr2d8Be6OmDvNGK5PRpP8Ijgf50sxreZuZAkt40H655Nktuk8b8+7gni2IJvm/RT/Acv6e27+A/eHfgPxBZ8EeNZ+A7+HPCDhOVnpHEMzw628xn+9vSpwF6kuCU6VWxxx31pinnNxpJknaPwjcPX47/QP8T3IdU73ePczPaHJ/xfo4/t+IvafcC4JvZ1exPb/kKwzJw04vi/YNlpcefGBcH/YmFwrm3HJ1BP4ttqJPbxsx/+F9xz+ORsC76qdXFwnk1owXGZSOOuHw7El0C9jb9LZkvw/7gN2DPFZ+xSfIngFnzC9BJwWhP7HY+/QH8SvN8VwP+Ak5L8z5Ie+2TnWTrnBr765g/4JG8b/pfnM8BR6RyfdD4H+IT3DPwX4rrgPVYGx+VSoCzN/09f/IV7adz/+Hf4JKTRvpt7/6S+3uyKT+g34K8Pr+CT/WaPZys+LxObiCPtc6mp8wP/w7omOEa7BNMuJ82uH+K20x2fGL+DL9HdhE+SrwUGxi13O8m/nwyfCCwOzoGP8de1Rv+/4Lz8Nf5zXRmcmyvx5/lpJHQHgU9A78XX3kSSnaf46/vt+G5StgXn4jx8tdYRzX2eWvLAJ4grSOhCJNX/KdUxS+dzl2L/Of/OjFuvMIjDkaQ/wbjlegWxPtba4x59WLBBaSdmdhX+YnSMc671vc2KiIi0EzM7DZ/8fcU5NzXX8UTl4jszaCb0Ib6bmZTVtWZ2Eb5K+VDn3Itt2qeSrdYxs8EuoRooqIachf+FVOqaby8mIiKScUFzilfwTR/2dVn+8s+n70wzuxHffvBUl6K5UdB2bzEwyzl3Ulv3meuxEcNstpl9iC/y/Qzf1uNL+EaI31aiJSIi+cI558zsPHy/fIPJfh9lOf3ONLOh+GYiI/E94L+NH+s3leHAzTQc27L1+1fJVuuY2WX4Rn3D8Q13N+AbQ1/tnJuRq7hERETyTa6/M61+ZITN+HaG33HJR1jJzP6VbImIiIhkTq772RIRERHp0JRsiYiIiGSQki1pMzO73MxcUCcu7cDMbg+O6fC4acODabc3t2wL95V0uzuKHf39i0jmKdkKGTM7O/hiODvXsUg9M1tqZktzHUd7amsS15GZ2Tlm9g8ze83MNgfH6bdprHecmc0ws0/NrDpY/6xm1hluqQdOT1y2yf+XmfU0s0vNbK6ZrQ/ieNfMrjSzAensI9jOvsGPrJfNbKWZbTezSjO7z8zGprsdkR2Fki2R8LsEGE3rb+WuDNa/pN0i6vj+DJyHv408rWF5zOxC/DAie+LHHL0Ffwv+7WZ2dcKyZUG/SMm208nMdg7+3tlSDKhtXlnc6174IYauwvfefjt+uJjt+BEP5lj6A0TfhB+DrgvwKH7olHnAqcBrZnZimtsR2SEo2RIJOefcSufcAtfCMSPj1q8J1l/Z3rF1YKfihzHpC6RTojUcP7bgOqDcOXeBc+6HwN74jhN/ZGbj41b5JzDLzPZP2M6R+KFpLgsmXQ68E0yPX25/fAeWt8RNPg/YDbjNOTfOOffD4LE/fhiVIcD56bx5/LBTI51z0ffyM+fc0fjhq4qAW8ysc5rbEunwlGzlmJntb2bXmdnbZrbOzLaa2SIz+7OZ9UlYdgZ+/DuA24Iqg+hjeBr7OsHMpgfF/tvMbIWZvWB+4OHEZUea2Z1B1cD2YNk7zWxkC97bl83sbjP7wMw+C6pN3jSz75lZo3MvrtpqFzO7yMzeMbMtwftubl9Lg0exmV1jZhXBunPN7MvBMkVBFcqi4DgvDkobUm3zaDN70szWBMdrsZn9ycx6xy0zMajiGQYMS/if3N7aY9ES1kR1n5kdYGYPBP/HbcH//mkzOzlumUZtloL3FK3eWhL3npYG8181s0iq887Mfhws/6M04h9sZr82XyW1Ku58u9fMRidZPhZv8Pf9wf9oq5nNNrPjUuynh5n9xcyWB8suMLOLacV10Dn3P+fcshas8k18KdANzrmlcdtZjx9LEfyAylFfAu4CHgf+EsT/MH5MzN/ge7+OrvMb4CYzi3bQ+JdgvTvxgyRH7RI8P5EkvseD57SqEp1z1zvnPkwy/R7qBy7fq7ntmNm3g//lr1PMH2RmNWb2bsL0IjP7bnAebjRflfuWmV2Y4tpytpk9YmYfBdeFjcH5dnqK/c4I4uocnJsLg8/P7cH8zsFnd4756tjNwfVnmiUkvk28993M7PfBObs62P4yM7vZzIakWKeL+erbj4Lll5jZb4PpzpJcK1t6rCQz1IN87n0LOBF4AT8aeyEwFrgY+KKZHejqR5K/Hd8R3GT8CORz47azoamdmO85+B/AKvzFdg1+oNS98b3p3hi37Lgglh74i/D7+NHnvw5MNrNJzrnZaby33+MHGH8NX1XVCz/y+3XAOPxAwMlchx/1/j/4wZ4jaewLoBN+wOK++OPTGT9A7CNmdhTwXfwgzv/FD/r6NeB6M1udOGRDcPH/Db4k4t/4AZn3Bn4MHGtm451zG/GD1f4G+EGw6rVxm5kb93drj0Wrmdm38F/OEfz/cRH+f16OPxYPNrH6b/AdEO4TxLghmB59vhFfGvIt/EDAic7FH+M70gj1UPxgwM/jB7+uxlfPnQScYGafd869nWS9YcDrwEf4xKQvcAowzcyOdM49H13QzLrgB50eh+85+h78wMK/Ag5LI8a2OiJ4/l+Sef9NWAbnXC1wY5BgRcdkGwTs45zbHLecA+43s8fxA8UD7AHs55z7JGE/7wXPX8IP2BsvmpQ9m97baVK0hLU2jWXvxg86fq6ZXeWcS/ysfxP/PfWP6ATz1aZPAEfjB4e/Fz8I+OHA9fjPeOLn6e/469hM/ODR/YBjgbvMbJRz7lcp4nsEf878F3gMfx0Afy0+DV91eid+sOLBwATgGNI7jl/BJ8vPUz9kzR74z87xZlbunIs1DTAzC+L5Ev6zfAP+mnd2sF4jrTxWkgltHclaj7Y98F8YhUmmn4MfkfxnCdPPDqaf3cL9vIn/8huYZF7/uL8NmB/s4+sJy50STF8AFMRNv5wkI6wDn0uyrwL8F7ADDkyYd3swvRIY0cL3tzRY9wmgS9z0Q4Lp6/DtVXrHzdsFf4F7K2FbhwfrzIpfPuH4X5Nk/0ubiK+1x2J43LThwbTb01h2DP5Lbx1JRrUHhrRmu3HzuuAT9pVAp4R5E4P17knzfzcQ6JFk+j74xOu/CdOj8TrgsoR5RwfTn0yYfmkw/ZGEc3dEcIwavf8WnHvRc+K3TSyzOlimX4r51cH8bsHrIvwX8XJ8mygHPIz/kj2F+g6pLXi9KHhvLlh+ebB+Udw+dgJeDpZ5Dd/u7C/4z8VnwKWtef8J7+PAYPvLSXJdS7HODcE6xyVMN3wi/RnQK2765cHy18fvA/9D9V/BvMlpfP464xPwGvy4fPHzZgTbeYe462Mwrxf+h9PsZO8x1f84yXKlxF2r4qYfhf+B9PeE6WcEMc0EOsdN742/JjtgRsI6LT5WemTmkfMA9Ejxj/EXmk+B5xKmRy/sZ7dwe28GF60+zSz3+WD7s1LMfzGYf2jctOgHemKasYwNlv91wvTbg+nfb8XxWhqsm+yi+lEw74gk857H/wKPvxBNDZZvlKQE898CPkmy/6WtiLu5YzE8btpw0k+2rg+m/TCNGNLebsL8PwXzv5ow/b7Ec6QNn4PH8b/EOyWJdynJv+yWAWsSpi3Cf4ElOz+i5+/trYwx+plsKtnaHixTlGJ+ZTB/5+D1U/ihTPaPvt9g+hfwP4ZuCl7/I3h9ZPDaBcuXB+v/L2E/XfCN213C4yFgTBv/V32AD4LtndyC9fYI1nkiYXo0cb41bloB9Ul+o2OJTzzqgAfT3PdXgn2cmTB9BikSEaBnMO9lgqS3vR/4JO+jhGnPpvpc4WsdGiRb7X2s9GjbQ9WIORYU856Pb3A7Bv+rKb4evbSddnUP/pfse2b2AL7a8mXn3OqE5aK3bT+XYjvP4YvK98P/wkrJzPoBP8EX1+8CdE9YJNV7e72p7TZhg3NucZLpK/AlGG8mmVeJ/5U3iPq7+cbjf+1+zcy+lmSdzsAAM+vnnFubTmBtOBatdVDw/N8ml2qbvwM/wp+/jwCYWX98tfh851yT50c8M/sSviSmHOhP4yYO/fFfGvHmusbVTgAV+P9hdNs9gF2BihTnxwzqG5znSvTOQxc8nwssd841aIvnnHvGzPbGHw/wieIal3BzhHNutvkG97G2P8E5+Ai+ScCp+Cp3A47EVxW/FjQRaPHnz8y64xPjkcAfnXNNVVE34Jx7z8xm4ptNlDnnKoJZ5wXPN8Utvhu+CnAR8EtLfsPmFvzdtfHxDQV+BkwChgJdE9ZJ+1rknNtoZk8AxwNzzewR/I/Q11xcFW9zgmrBr+OT9X3wyWph3CLbE1bZD58czUqyuZeSTGvVsZLMULKVew/gv5w+wrczWoWv7gPfDqhLe+zEOfcXM1uDb6vzvWDbzsxeAH7i6ttg9QqeU92ZFp3eu6n9mW9E/gY+yXkd365hHb4UqTfwfVK/t1VNvpnUPk0xvRbAOZdsfrRdSfzt8/3wn43mvoCLgWaTrTYei9bqHTy3tjuIZjnnPjKzp4CjzexzQSJzNv69/KPJleOY2ffwX/br8QnAx/jBYh317caSHZ8NKTZZS8MfLNFzuirF8q0931riU3yC1Ivk50zP4HkjQFzC0UiQWK0M/k55B6nzxRfx2/kzvn3aZOfc43HTHzCzrfg2SX/EVwOnLUi0/oP/EfYX59zPWrJ+4EZ8271zgcvMbBBwAj6hjk94+gXPI2n681kcF98u+M9dH3xS9DT+/xHBlwKeRcuvRafgk7f/w7dvBNgatLH7sXMu1bkW7y/46/BKfElmJT75Af85GpawfC9gnfPt+RIl21+Lj5VkjpKtHDKzcnyi9SxwbPyv0+AukZ+25/6cc3cCdwZf/gcH+/4m8JSZjXa+QW00IRmUYjM7B8+pEpuoc/HJxW+cc5fHzwh+cX+/qVCb2XamfYpv19O3nbbXlmPRWhuC51J8e45M+Tu+QfC38I3cz8VX+92ZzspmVoT/sloFjE1MHqxhdwitFT1XU/Uhlepcb08L8cnWbvguGWLM95nVHV+S1ahkxPm7F5MWSyRZtqnloo3gn08yLzpt/yTzUgpKDf+Dbxv5x1YmWuDbmVUB55jZFSRpGB+I/i+nOue+kua2L8YnHt9wzt0eP8PMTqP+rttGgoQ12fQt+FLFy833ZXYoPkE6HZ/AHdJUQGY2EP+jdx5wsKu/CSo+rkQbgb5mVpQk4Up2brfmWEmG6LbP3No1eH48sRoAOIDGRd1Qf2deYZJ5aXHObXDOPemc+xa+XU5f6i8ObwXPE1OsHp0+p5ndRN/bI0nmHZZWoLnzKtDHzJLe4ZNChNT/k1wci1eD5y+2YRvpnGv/xpdEfSO443MUvg3I+jT30R9fCjcrSaJVTH21dqsFX2QfAqVm9rkki0xs6z7SEK2WPybJvC8mLJMp0dKbZN07RKclVl2lZL6T1Kfx146r2pBoRUvr/on/cXA8Pmmvxjd/iLcA/0PiIEvRmWsSGf38OecqnO/y4mh8ld2EoMq2Kbvgv3+fTpJoDaG+m454bwXrHJxk3oQk01pzrCRDlGzl1tLgeWL8xOBXz99SrBOtghjakh2Z2TFBKUKigcFz9Bf1y/hf4RPM7KSEbZyE/wX3AcnbCMRbGjxPTNjGfuR/T+XXBM+3mNngxJlm1t3MDkqYvBbfjitZgrw0eJ6YsJ1MHou/46vTfmVmYxJnpurHJ0Gz55pzrg64GX8e3RpMvinV8kl8gj/39g+Sq2h8nfBVi/1TrdhCt+Gvd3+I71vIzEbgSxgy7TZ884ALreF4l33wd0pCy45ba0S7kLgs4RgUUl8VNj1+BTPrb2a7B23x4qf3wZfIH4S/I/SX7RDfzfgE/wZ8SfC9iYlIUKJzPb6E/a/JPm/me9WPP+eXBs8TE5Y7Gp/UtYiZDTCzA5PM6o7vLqeW5pPWaEwTguMf3XYxviPaZNfqaGnxby2uw9gg6W3UdUUrj5VkiKoRc+sNfHLzFTObhU9gSvC/dBeSfBiQV/BfTj8ws77U19Vfn6JNUtT9+DYFL+E/6Ib/RToO33D8WfDF5ubHansG35ZjGv4X0ih8+5lN+Dt36pp5b3fiG4Rfa2aH43/xjcRXZTyKb/OQl5xz083s58AUYJGZPQkswbdtGIb/NfwSDUspon04/S9o7LsNeNs59wQ5OBbOuffNd1Z7E/BW8H+MdjZZjv8/Ht7MZqYHcd8StEWpxt+EcEPCcv8Efo0vlXjXOfcKaXLO1ZnZX/FVkO8GcXYOYuuLr95qLs50/Bl//n4VPyzNU/g2MKfgb/Q4oSUbM7NzqS9NiJacHB+XxC5wzv0+urxzbomZ/QT4KzA7uEllO74vsSHAn1ty3FrpZ/hSkTPxyW20JG0S/uacNdQnflEX4tv7/AZfbRb1KP48WgwUmNnlNPaYc25uusE55z42s/9Q/79I1e7vSnw7vm/jj/lz+PZOA/Gfq8/j+357P1j+Rnxfgg8Fjdkr8UMmHYPva66ln79S4FUzm48v4a/At7k7Dl8l/dfEJDHJe11lZvfjb1SYa2ZP48/HL+Cr4ecC+yasdmew/DHAPPN9q3XCn9Oz8dfoxOtyS4+VZEqub4fc0R/4L5Qb8QnQVvzF63dAN1J0J4D/sL1Cfd88KW/Pj1vn2/guDT7CJ2vr8MXSPyV5H0ej8B1FrsTfmbcS3wHhqCTLXk7yfrbG4O9Q+gTf7cSb+F+Sw2lFVwPNvL+kxyqYN4Og+UWSeSn3if8yfRCf9G7H95U0F9+wtTxh2e740qTl+F+2Dd5fexyL1hw3/F15jwT73R68l/8BJzW33WDexfiuBbYFy6Q6xlOD+Re04n9XFOznfXwD4VXBuTesJcehuf83/gvxL/gvm634HxE/wlfZpNxeM+dNqseMFOsdj78TeFNwHrwBnNXSY9baB77E6Cb8dWZbcBwW4UtASpMsf3nwfi5P8nlr6v07Wtg9TbDdycG6bzSznOH7nZqOv5ZtD/6vL+ETxrKE5Q/GV9OuD479S/jke2KK95f0HArm9cb/uIgmLtvw18cZ+I5O0+oOAn+Nvwpfxb0Vn7T9Df+DKNU5vBNwBf7H37bg/3AVPgF0+AS3TcdKj8w8oh3jiYi0SlAl9SG+VHZn53vWF2mxoITsMuBc59y/chxOaJjZF/Dt537vnMv3Zho7JLXZEpG2OglfYnKnEi1preDOxm/jS1/uy3E4eSlFG9J++OHAoPEwTJIn1GZLRFolaNfWF9/55GfUX/BF0ma+Q9ux+CrWEnw/VWl3DrqD+YuZ7YPv2HQ1vr3fF/Gfw3+4VnRIK9mhakQRaRUzc/j2fO/jO8Z9JschSQiZ2e34vq6q8He0/tI1fwPODsnMTga+gx/iqDe+rdd7+OP2T6cv9LylZEtEREQkg9RmS0RERCSD1GYrj/Tv398NHz4812GIiEgH8Oabb65xziUbMUCyTMlWHhk+fDizZ89ufkEREZFmmNmyXMcgnqoRRURERDJIyZaIiIhIBinZEhEREckgJVsiIiIiGaRkS0RERCSDlGyJiIiIZJC6fhCRDmvr1q2sXr2arVu3Ultbm+twRNpFp06dGDhwID179sx1KJImJVsi0iF9+umnVFVVMWDAAAYNGkRRURFmluuwRNrEOceWLVuorKwEUMIVEqpGFJEOac2aNQwZMoQ+ffrQqVMnJVrSIZgZ3bp1o7S0lE8++STX4UialGyJSIe0fft2unbtmusw2uT222/HzGKPwsJCSktLOfnkk1m4cGGTy/bo0YN99tmHG264oVEV6pYtW+jVqxdmxttvv5103xs3buSyyy5jzJgxdO/enT59+rDXXntx/vnnN/iS37ZtG9dccw377LMPPXr0oGfPnuy+++6cddZZLFq0qMn3d/nll+csCZ4xYwZmxrPPPpuT/bdV165dqampyXUYkiZVI4pIh9VRSrMeeughhgwZQiQSYfHixVx55ZVMmjSJ9957j169eiVdduPGjTz00ENcdNFFfPLJJ1xxxRWxZR599FE2btwIwJ133smf//znBtuIRCIceeSRLF26lJ/97Gfsu+++fPbZZ8ybN4/77ruPFStWMHDgQABOO+00nn76aX76059y0EEHEYlEmD9/Pg899BDvv/8+I0eOTPm+zj33XI455pj2Okw7lI5ybu8wnHN65Mlj//33dyLSPt5///1ch9Bmt912mwPcokWLGkx/5plnHOCefPLJZpedOHGi69GjR4NpX/jCF1zfvn3dgQce6EpKSlxtbW2D+dOnT3eAe+yxx5LGFYlEnHPOLV682AHu2muvbXK5fPT88887wD3zzDMtXre2ttbV1NRkIKqWae4cB2a7PPhu08OpGlFEJGyijaLTqUYaN24cmzZtilX9VVZWMn36dE499VTOPfdcqqqqeOqppxqss27dOgAGDRqUdJsFBQUtWi6VZNWI1113HaNHj6Zr16706dOH8vJypk6dGpvvnOOaa65h1KhRdO7cmZ133pkLL7wwVlIXVVtbyx/+8AfGjBnDTjvtxIABAzjmmGNYsGBBg+U2b97MhRdeSP/+/RkwYACnn346GzZsaLCMmfGLX/yC3//+94wYMYLOnTvz7rvvAnD33Xezzz77sNNOO9G/f3/OOOMMVq5c2WD94cOHc/rpp3P//fczevRounfvTnl5OS+99FKjY/LCCy8wadIkevToQffu3Tn66KOZN29ek8dR8p+SLRGR5rzyCkyZ4p9zIBKJUFtby7Zt25g/fz6XXnopAwcOZOLEic2uu2TJEgoLCykuLgbgrrvuoq6ujjPPPJOTTz6ZnXbaiTvuuKPBOmPHjqWoqIjzzz+fqVOnsn79+qTb3n333enZsyc///nPufvuu6mqqmrT+7znnnv40Y9+xGmnncaTTz7JPffcw0knnRRL6gB+8YtfcPHFF/OFL3yBJ554gp/+9KfcfvvtfOlLX6Kuri623KmnnsovfvELjj32WB577DFuueUWxowZ0ygR+v73v4+Zce+99/LrX/+aRx55hO9///uNYrv99tv5z3/+w9VXX81//vMfBg8ezM0338wZZ5zB6NGjefTRR/n973/PU089xWGHHUZ1dXWD9V988UX+/Oc/c+WVV/LAAw8QiUQ47rjjGiR2//nPf5g0aRLFxcXcfffd3HvvvWzatIlDDjmEioqKNh1bybFcF63pUf9QNaJI+2m3asRZs5zr2tW5wkL/PGtW+2w3DdGqwcTH4MGD3euvv5502QULFriamhq3bt06d9NNN7mCggI3efLk2HKjR492o0aNir0+9dRTXZcuXdz69esbbO+WW25x3bt3d4AzMzdmzBj34x//2FVWVjZY7vHHH3f9+/ePxbbLLru4Cy64wM2fP7/Z93fZZZc5/zXkXXDBBW6//fZLufzatWtdly5d3FlnndVg+l133eUAN23aNOdcfTXoddddl3Jb0WrEM888s8H0Cy64wHXp0sXV1dXFpgFu5513dps3b45Nq62tdQMHDnQTJ05ssP6LL77YaN/Dhg1zvXv3duvWrYtNe+ONNxzg7rnnnti0z33uc+6II45osL1PP/3U9evXz33/+99v9B5UjRieh0q2RESaMmMGbN8OkYh/njEj6yFMnTqVN954g9dff53HHnuMMWPGcOyxxzJ//vxGy+6+++506tSJvn378t3vfpevf/3r3HrrrQC8/vrrzJ8/nzPOOCO2/FlnncW2bdt48MEHG2zn3HPPpaKigrvvvpvzzjuPuro6rr76avbYYw/ee++92HLHH388S5cu5dFHH+Wiiy6id+/e3Hjjjey3334tvtNv3LhxzJ07l4suuohnn32WzZs3N5j/6quvsm3bNk4//fQG00899VSKiop44YUXAHj66acxM771rW81u88vfelLDV7vtddebNu2rVEp3THHHNPg7taFCxfyySef8PWvf73BchMmTGDYsGGxWKLGjx9Pnz59GuwH4OOPPwZg0aJFLF68mK9//evU1tbGHt26dWP8+PHMnDmz2fci+UvJlohIUyZOhM6dobDQP6dRddfe9txzT8rLyxk3bhyTJ0/m8ccfxznH5Zdf3mjZaGK2YMECPvvsM+6880769u0LEKsuPP7449mwYQMbNmxg3LhxDBgwoFFVIkCfPn34+te/zk033cT8+fN57LHHYl1CxOvevTsnnngif/3rX3nzzTeZNWsWhYWF/PznP2/R+zzzzDP5+9//zmuvvcbRRx9N3759+cpXvsLSpUuB+jZiO++8c4P1ioqK6NevX2z+2rVr6du3b1pdf0SPTVSXLl0AP/pAvMR9pooFfBu2+KrPdPYTbVN3zjnn0KlTpwaPf//736xdu7bZ9yL5S10/iIg0Zfx4mD7dl2hNnOhf51jXrl3ZZZddeOeddxrN23PPPdl1110bTd++fTv3338/APvss0+j+atXr+bDDz9Mum7U5MmT2WeffXj//febjO+ggw7iqKOO4n//+19zb6UBM+P888/n/PPPZ/369Tz99NP86Ec/4pRTTuG1116LJSyrVq1ijz32iK1XW1vL2rVr6devHwD9+/dn3bp1bNmypd36WktsyB8fS6JVq1ZRXl7eou1HY58yZQpHHnlko/mdO3du0fYkv6hkS0SkOePHwyWX5EWiBf4OusWLFzNgwIC013niiSdYt24dl112Gc8//3yDRzQJu/POOwHf+35iyQ7AZ599RkVFRaw0Z9OmTY3uAgTfoH/RokVJS33S1adPH0455RROPvnk2N14Bx10EF26dInFG/XAAw9QW1vLYYcdBsBRRx2Fc45//vOfrd5/c0aNGkVJSUmjWGbNmsWyZctisbRke8OHD+e9996jvLy80WPvvfduz/Aly1SyJSKS5+bOncuaNWtwzrFy5UpuuOEG1q1bx0UXXZT2Nu644w6Ki4v58Y9/HLszMd4111zDnXfeyW9+8xtmzJjBd77zHc4++2wOOeQQevfuzbJly7j++utZt24dF198MeDbLR1++OGcccYZTJo0iYEDB7Jy5Ur++c9/Mm/ePG688cYWvc/zzjuPHj16MH78eAYOHMgHH3zAXXfdxVFHHQX40qSLL76YKVOm0L1791i7tV/+8pdMmDAh1v7q8MMP56tf/SoXX3wxFRUVHHHEEdTU1DBz5ky+9KUvpXUXZ3MKCwu54oorOP/88zn99NM5/fTTqays5Be/+AUjR47kG9/4Rou2Z2b87W9/Y/LkyWzfvp2TTz6Z/v37U1VVxaxZsxg6dGjsuEv4KNkSEclzX/va12J/DxgwgD333JP//e9/HH300Wmtv3r1av773/9y5plnJk20wLcVOu+883jhhRc46KCDOPfcc3nuuee44447WL9+Pb1792bcuHE888wzHHHEEQDsuuuu/OQnP+HZZ59l6tSprFmzhuLiYvbdd18eeughTjrppBa9z89//vPcdttt3HXXXXz66acMHjyY008/nd/85jexZa666ioGDBjATTfdxI033ki/fv0488wzmTJlSoN+ve6//37+8Ic/cMcdd3DttdfSq1cvxo0bx7nnntuimJpy3nnn0a1bN/70pz8xefJkiouLOfbYY/njH/+Y8jg35dhjj2XmzJlcddVVnHvuuWzZsoVBgwZx0EEHccopp7Rb3JJ95u8OlXxQXl7uZs+eneswRDqE+fPnM3r06FyHIZIxzZ3jZvamc65ljcckI9RmS0RERCSDlGyJiIiIZJCSLRHpsNRMQjoqndvhomRLRDqkzp07s2XLllyHIZIRW7ZsoVOnTrkOQ9KkZEtEOqT+/fuzfPly1q1bR01NjUoCpENwzrF582YqKysZOHBgrsORNKnrBxHpkHr16kWXLl1YvXo1a9eupba2NtchibSLTp06UVJSQs+ePXMdiqRJyZaIdFg77bQTZWVluQ5DRHZwqkYUERERySAlWyIiIiIZpGSrHZjZsWY208yqzWyjmc02syNyHZeIiIjknpKtNjKz84FpwJvAicDXgIeAbrmMS0RERPKDGsi3gZkNB64FfuKcuzZu1lO5iEdERETyj0q22uabQB1wU64DERERkfykZKttJgALgFPNbLGZ1ZrZh2Z2Qa4DExERkfygasS2GRw8/gRcCizGt9m6wcyKnHPX5TI4ERERyT0lW21TAPQAznbOPRpMey5oy3WJmf3VxY0RYmbnAeel2tjQoUMzGauIiIjkgJKttlkLjASeSZj+NHAMsDOwIjrROXczcHOqjZWXl2vwNhERkQ5Gbbba5r0U0y14rstWICIiIpKflGy1zdTg+eiE6UcDy51zq7Icj4iIiOQZVSO2zZPA88A/zKw/8BFwEnAU8I1cBiYiIiL5QclWGzjnnJl9GZgC/Abog+8K4uvOuXtzGZuIiIjkByVbbeSc2whcEDxEREREGlCbLREREZEMUrIlIiIikkFKtkREREQySMmWiIiISAYp2RIRERHJICVbIiIiIhmkZEtEREQkg5RsiYiIiGSQki0RERGRDFKyJSIiIpJBSrZEREREMkjJloiIiEgGKdkSERERySAlWyIiIiIZpGRLREREJIOUbImIiIhkkJItERERkQxSsiUiIiKSQUq2RERERDJIyZaIiIhIBinZEhEREcmgolwH0BpmNgY4FBgK9Ae2AJ8Ac4GZzrlNuYtOREREpF5oki0zGwKcB3wT2Dk6OWExB0TM7Fng78C/nXMue1GKiIiINJT3yZaZ9QUuB84HOgFLgXuBN4BVwDqgK9AP2B0YD0wEjgYWmtmPnHP/zXbcIiIiIhCCZAv4EOgC/BO4wzn3enMrmFlP4FR8Sdi/zeyHzrm/ZjZMERERkcbCkGzdDVzlnKtKdwXn3EbgZuBmM/sysFOGYhMRERFpUt4nW86577Vx/cfaKRQRERGRFlPXDyIiIiIZlPclW4nMrBDo4pzbnDD9CGAysBm42Tm3JBfxiYiIiMQLY8nW1cA6M+sVnWBmpwLPABcBPwNeN7OyHMUnIiIiEhPGZOtQ4Hnn3Kdx0y4DNgBnAj8FegMXZz0yERERkQRhTLbK8N1BAGBmuwCjgOudc3c7564G/gsck6P4REQyprq6OtchiEgLhTHZ6glsjHv9eXzP8f+Lm/YeMCSbQYmIZJoSLZFwCmOytRIYEff6SPzYiG/GTSsGarMZlIiIiEgyobsbEXgVOMHMjgO2AicB051zNXHL7AJU5iI4ERERkXhhLNn6HT7uacBTQGfgqujMYKieicBruQhOREREJF7oSracc++a2YHAWcGkB5xzb8QtsjfwNHBf1oMTEcmg4uJitdsSCaHQJVvgEy7gxynmvQS8lN2IRERERJILYzViA2bWRx2YioiISL4KZbJlZsVm9mczWwWsAZbEzTvQzJ40s7G5i1BERETEC12yFQzT8wrwQ2AFMB+wuEXeBQ4BTst+dCIimaP2WiLhFLpkC/gFsAdwtnNuLPBQ/MxggOoXgEk5iE1ERESkgTAmW18BnnLO3dnEMsuA0izFIyKScdXV1VRWVlJZWakSLpGQCWOyNQR4p5llqoFeWYhFRCRrqqqqqKqqynUYItJCYUy2NgEDm1lmBL7hvIhI6KkkSyTcwphsvQEcZ2Y9ks00s52BY1FfWyLSAcQnWiUlJTmMRERaK4ydml4H/Bd40szOi59hZqOBW4CdgL/mIDYRkVZLpwRLCZdI+IQu2XLOPWVmlwOXA/OAGgAzWwP0wXcD8TPn3KxcxSgi0lLRBvCplJaWUlqq+35Ewih0yRaAc+4KM3sR+B5wENAPcMCTwDXOuedyGZ+ISHuLT8SUdImESyiTLQDn3PPA87mOQ0SkPRQXF1NaWtpk6Rb4OxKVbImESxgbyIuIdEjRhCuZqqoqZs+eTUVFRZajEpG2Cm3JlohIW7SlO4Xi4uJ2jKTxtqMJV7SUq6qqKpZkVVRUUFlZSWlpaUbjEJH2k/clW2ZWZ2a1ZrZb3OtIGo/aXMcuIvmprf1WZaPfq/hEK0qlWiLhFIaSrZn4xu+bE16LiLRIeyZJ1dXVGSlZau6uRFC7LZGwyftkyzk3sanXIrJjamvi1FxCk0p8kpOJEq7WxiUi+Svvky0R2TG1NJHJVpKSuJ90Spiaii3VWIfxnZeq+lAk3EKXbJnZR8C1zrmUPcSb2QXAj5xzu2QvMhFpLy1JtHJdEtRc8tXa+BKTsGjCtXz5cmbN8n02jx07Vo3kRUIgdMkWMBzo3cwyvYFhmQ5ERNpXuklWrhOsprQktlSlWvHiS7WWL1/eaLoSLpH8F8ZkKx3FwPZcByEi6UuWaGU7qWou+Ymv2osum42xChOrEeOTrpKSEnUDIZLnQpFsmdnQhEm9k0wDKASGAicBH2U8MBFpF4mJViaSrHRKkVqzjdYmXc3Fk5hgnXzNNQxdvZqPBwzgL1/7Wmz67NmzARg1alSL9i8i2ROKZAtYSsPuHr4fPFIx4OJMBiQi7SM+0crn6sHmZLKk6/S//Y3STz4BYMQnn3DxQw/x4A9/2O77EZHMCEuydSc+2TLgTOAdYG6S5SLAWmC6c+7prEUnIq0STbSykWSVlJS0S+lWc9or6SorK4uVbu28ciXgL4AOGLpmTYPl1OeWSH4LRbLlnDs7+reZnQlMdc5dkbuIRKQtsplk5Up8YtfWxGvtsGEM+OijWPH+qsGDKSsro6ysrE3bFZHsyPvhehI55wqUaImEX0dOtBIllqilW8IWTaaeueIKVu+yC5GCAiqHDOGF3/++3WMUkcwJRcmWiEiibFQJtqe2xvvMFVfEqhXLqE/EsnE3pIi0Td4nW2Z2K76ZwqXOuargdTqcc+6cDIYmIlkQtqSqPTXXc7y6fRAJh7xPtoCz8cnWH4Cq4HU6HJDVZMvM/gccDVzlnPtlNvct0pHsyAlWVDpD9CjREgmHMCRbI4LnyoTXecXMTgP2yXUcImGmJCs9ZWVl6jleJETyPtlyzi1r6nU+MLPewDXAD4F7cxuNSDgp0aqXrFQrfpraaYmES+juRsxTfwTec87dl+tARMJIiVa9dKoPdbxEwiXvS7YSmdl+wHjgHufcp8G07sCNwGRgM/AH59x1WYpnAr6jVVUhirRCR04ckiVO6htLZMcTumQL+BlwiHPuxrhpU4AzgGqgH/AXM5uf6V7kzawT8A/gaufcwkzuS6SjqK6u3iH62EpVQpVOyZWIdCxhTLbKgRnRF0HCcxbwOjAR6Au8BXwPyPSQPT8DugJXpbOwmZ0HnJdq/tChycbWFuk4EgecltZRmy2RcAljsjUQiP9pWA70AP7hnNsKrDCzacAxmQzCzIYCvwDOBbqYWZe42V2CRvObnHOR6ETn3M3Azam2WV5e7lLNEwm7HSnRaq/Sq1gnphqaRyTUwthA3tEwSZwQTHshbtpqYECG49gF2Am4G1gf9wD4cfD3XhmOQSR04qsQO2J7rfZOtNpzmyKSG2Es2foYOCju9WRguXPuo7hpg6lPfDJlLnB4kunP4xOwfwEfZjgGkVBobalWsuqyfEnQ4hOgXJQ6qY8tkfAIY7L1IPAbM3sY2Iq/M/HahGX2BBZnMgjn3Abi2o5FmRnAMudco3kiUq+5pCk+0crHBCtxensmXCrJEulYwphsXYNvj/WV4PVc4IroTDMbA+wP/C7rkYlIu8v3RCvd+SKy4wpdsuWcqwY+b2Z7BpPed87VxS2yGTgRmJ314ADnnOVivyIdUVgSLRGRpoQu2Ypyzs1LMX0psDSrwYhIWnaE/rXaSomdSMcT2mQLYr237wf0Bj4F5jjnXsppUCLSSHFxMdXV1ZSWluYs4UqVxLSlrZXaaolIOkKZbJnZWPwdf6Oik/DdP2BmC4EznXM5qUYUkeSiCVe+ie/Lqqn5qaa1d8IlIh1P6JItM9sVeA7oCbwU/L0S2BnfFcMhwDNmdoBzblHOAhWRpNIp3Uqnh/ToMum060qnxCjdpClV8pVuwhW/nPrSEtkxhLFT018BxcApzrlDnXOXO+f+ETwfBpyM71H+lzmNUkQaSbdvqKqqqrQbxzeXmLUkicn2HYcqERPZMYSuZAs4EnjMOfdQspnOuYeD4XqOzG5YIpItiQlWSUlJu925mKsBpMvKylS6JdJBhbFkqz+woJllFgTLiUgHUlJSkrIkK5ODM+ciCRq8bBkHzZjB4GXLsr5vEWlfYSzZWg2MaWaZ3YE1WYhFRLKkNclUvpUUpVttOHjZMk77178orK0lUlTEfeecQ0Xc+qWlpRmMUkTaWxhLtp4DTjCzU5PNNLOv4sdLfDarUYlImzSVTGWy1Ko5qRK25cuXs3z58nbdVzSZGrpkCYW1tRQ4R0FtLUOXLGnX/YhIdoWxZOsKfDJ1j5ldgB/4eSUwCJgITAA2Ab/NVYAi0nJVVVVJ7zDMZaKVSmuSrFSlWsnaan08YgSRoiJcbS11RUV8PGJEq+IUkfwQumTLOfehmR0J3Al8Png4fF9bAAuBs9Ttg0h4JUuwoglYrpKvwcuWMXTJEt4sLoZBg1q0brrVh9Gka8WwYdx3zjkMXbKEj0eMYMWwYS2OV0TyR+iSLQDn3BvAaDM7GBgL9ML3IP+Wc+7lnAYnIq0WX7qVOL25ZZJp6g6//osWUTJ/PlWjR7Nm5MgmtxNtQ1VQW8vBhYVcP3kyS4KEa/ny5QwZMiTl/ltrxbBhSrJEOohQJltRzrlZwKxcxyEi+aepxvH9Fy1i0pQpFATVdNMvuaTJhCu+DRWRCCMrK2PJVvz+4pOr9uhDK1pdmZjMVVZWMmrUqGSriEgeCnWyJSI7hvh+tOJLtZLdlZdOf1sl8+dTUFtLQV0d1NZSMn9+k8lWfBuqSGEhi5Lsd/CyZZTOmMG2gw5izciRzQ4D1Ba6G1EkXEKZbJlZJ3wj+QOAPkBhksWcc+6crAYmIhmTWHVYWlraqEf60tLStJKtqtGjqSsqgqBkq2r06JTLlpWVUQGxNlRvFhezZNAgLn7oIYauXs1nnTuz6nOf43MLF1JQV0fd8883KCnT2IkiErpky8wGA8/g+9KyJhZ1gJItkQ4oWaIFfjigsWPHMmfOnCbXXzNyJNMvuSTtNlvQsA3Vxddcw4hPPgGg57Zt9Hz/fcBfkFxNTaOSMiVcIju20CVbwJ+B0cB9wC1ABVCb04hEJCui1WdNjbFYXFxMSUlJsx2arhk5Mq0kK5mhq1cDDX/tOaAOqCsq4p2+fZMWtycTjTM+3vg7HxPbholI+IQx2ToKmOmc+3quAxGR9pfqbsNUpVnJlJaWMnv27CaXaW2bquXLl/PxgAGM+OQTXNz0uoIC3h43jnn77UfhhAmx6S3dfrI7H2uGDIltp7y8PO3jICL5IYw9yO8EvJbrIEQkc5K1u2rPBKO1w/hE7w78y9e+xpKBA3H4Eq0tRUW8PW4cq3bemaFLltB/ke/mrzVVh9E7HwudozC48zGqvLxcdyGKhFAYS7bmAep8RqSDiy/has+771qaaEWXT+w1vktNTawasWttLfu95n8DOog1kqcVyVayOx/LysooKyvTXYgiIRXGZOtPwJ1mNsY5936ugxGRzGpJ9WFT2ntQ6kHr1wNBo/i45wKA7dvZ5/77+eCII1q83Wjv8b3nzmVRaSlLBg1iXFkZY8eOVfWhSEiFMdn6BHgCmGVm1wFvAhuSLeicm5nFuESkHZWUlLRblVl7J1oAq/r0YfD69bF2W/HPBpQsXMiaP/2JdT/5SZPbifZyH9/b/Yphw3i9MN0m9iKS78KYbM2g/nr2K2jQRjWRrlYiIdTWRKsyrp1Tpkz5v//jknvvZdD69Wzp1IluNTUU0LCUa+gbb7AujW0lS7hEpOMIY7J1BU0nWCISQj3mzaP33LkUHHEE/caObbftZiJ5GbFqFSMrK7n/iCNYMmgQI1at4qJp0/yQPtRfoD4eNy6jPcmLSDiELtlyzl2e6xhEpH31mDePfX7wA6y2FnfHHWx98kmYNCnXYSUVS6wiESKFhTw8YQLFW7fy1i67cMAHH8SWqwOqBw6MvVbHpiI7rtAlWyLS8ZQ89RQWvbtv+3a23XIL3bKQbLUm+RlZWem7ZgCstpZTZsxo0Llp9O8C4IDbbuPTsrJmh+5J7Ng08c5HEQk3JVsikpeqq6vz8u67/RcubNA2K/pIbChvAM4x+t//5sUf/jC2fqpqzej042+8kV1XrODDwYP5+wknZOItiEiW5X2yZWbP4a9fZznnlgev0+Gcc/lZDyEiDVQdfTQl//2vr0YsKmLjl7/M1srKduv2IZnWVumVbNgA1JdgRTs2NSAC1JlR5OqblXYNuohoSnyiNSb4e0xFBd95/HElXCIdQN4nW8BE/LWsW9zrdKgRvUhIbNpzT9659lp6z53Lhn33pdt+++U6pJjoHYJDhgxJPVRP8BwpKmLGXnvxhbfeis1fPHFiWvtZvnw5u65YAdSXlEVfi0i45f1wPc65AudcoXPug7jX6TzU7YNIiGzac08qTj+dTXvu2eZtZbKn9ehQPXXAtsJC3h4xgo3dumHAZ50703X7dhbtvDMb+vRhzcCBrFu3joqKigaPVOb17QvU/1IsjEQy9j5EJHvyPtkSEcm1aJXjkCFDAJ9wrevRg86RCHstWULvzZsxoPfmzUx47z1GrlxJ7/Xr6f/JJ3zxscfY+7WGw7nGJ1zxf181YQLx6VUhcO3f/paptyUiWaJkS0TyTrKBqHMtPuG65N576b9pE0Z9z8kW92wJ03Z/771G24tPsuLvPoxelOPvalRHpyLhFoY2W0mZWX9gNDAE6JRsGefcnVkNSkTarOTxxxkwcybbjjsOLr44p7Ek9ugefT0orpF8quF64uet2nnnpNuP3/aKJtpnqX8ukXALXbJlZl2AvwDfBDqnWgx/nVOyJRIiJY8/zm5//rN/8cYbbOvTBy66KCexRBOc6HN8YrS2f38GrF4dS6bq8CVQ1nATsQvRmu3b095vHb60LH7bIhJuYaxGvBr4DvAR8BvgPHziFf/4RvAsIiEyYKYfOz6atNQ+8ADV1dUt3k5bx0ZMVpIUP+3Wiy9mY5cusVIsAGcW6waiwcOM6p12StlRafz0X7z0UoNqyTrgyYMOatN7EZHcC13JFnAy8A4wzjlXk+tgRKRlmuo3a/Whh9LnjTdipTqbjjqKHtkJK6a5KrtoVeI/jzuO70+dCnV1uIICPuvUiR7btjVa3pzj5JkzWdmvHzVBA/tU9li71q9DfXXkotJSxrXyvYhIfghjstUdeEaJlkg4NVVSVRV04Dlg5kxWH3oonHJKVpOtdBOtk269leGLFsVKoQrr6mKJVnyiRNz8AxYs4OXy8ia3/16/foyNq54E2DlIwEQkvMJYjfgekLy1qYiEXtUJJzDv6qtjiVc+qaio4LA//IHPxSVaycZFjO9dvjlD4kq7rpowgcpu3WLbMOCUF16gx7x5bYpbRHIrjMnW1cCJZrZbrgMRkR1PycKFQOM7DqG+nRY0bOBeW1jI67vv3uR2Bw8eDMAN5eVEgvZfBhQ4R8lTT7VX+CKSA6GrRnTOPWRmOwMvmtmNwBzg0xTLzsxqcCLS4VWNGkXpu+82SLIMqKX+jsTovAhw/5gxVI0eTU0zVYhRH/Trx6quXSndvDk2rThJP10iEh5hLNkC6INvu/Vr4DHg+RQPEZG0pdN56As/+xmLR46k1ozNnToRKSigFsCMOhqWdBUCGzt3ZsmgQbFp6fSZ1ae2tsHrrnnYyauIpC90JVtmdglwGbAWeABYgf9RKSIhUFxcTGlpac57ia+oqGh1Z6EPf/ObnHzNNQwPBqR2QKFr2EIrWsI1fsUK3sd38RBtnxW/3/hBrqNViZu7dKFbXN9ctU3cwSki+S90yRa+X62PgP2dc0mrD0Uk/5WUlDRIuFqS/MTf0dhUVxKtkU4cp//tb5R+8gmQvIF8fNutrml2aBpNuADe2nVXvvDWW7FtfPz1r5O5obVFJNPCWI04CHhciZbIjm3OnDlUVla2qtPTqFTVhs1VJw4KhtaxuAfUJ1nxCdioDRs445lnUm4rPrEbMmQIB0QiTHz3XeqAOjOe2W+/vLwzU0TSF8Zk6yOgd66DEJG2KykpadP6qaoiS0vry4GaK6WqqKho8UDPq4Lqvvie4qOSDUi97+LFDbp4SBQf49AlSyiKRGJdSwwcOZKSkpJ2L8ETkewJY7L1d+B4MxvU7JIikpei7bbySUsSrueOO44I9YnW1sJCqgt9epSYfAF0jkQYvGxZk4lfdN7HI0YQKSry3T906sRu553HqFGjWvReRCS/hDHZegJ4AZhlZmeb2V5mNjTZI9eBikhq0YSrNaVblZWVLS6NSkf8Npva/tAlSzCz2AX07QEDOOuEE2JJVrJ2XEOXLGmwjf6LFrHH44/Tf9GiBtNXDBvGc5deyrtf+xrbn3ySbpMmte7NiEjeCGMD+SXUN4v4VxPLOcL5/kR2GPlyZ2K6osP1fDxihB942jkMOHDVKh6aOrXBsvGlW5HCQj4eMSJWNdh/0SImTZlCQW0tdUVFTL/kEtaMHBkr3epaVsauP/4x3VR1KNIhhDEZuZP0RsEQkZBoSelWfIP4iooKxo4d266xpHM34ophw6jo35/hn3wS6+IhVTWBA54+/nhWDBtGGT5h6/n44xTU1lJQVwe1tZTMn8+akSNj66iNlkjHErpkyzl3dq5jEJHcqqqqajYpipZCZUq/oIf3ZFWHidP2eOst3jnwwNj8qtGjqSsqgqBkq2r06IzFKSK5F7pkS0QkqqKigsrKyqw3IC8rK6P7Z58BDYfnibZvSBwzsd+aNQ3WXzNyJNMvuYSS+fOpGj26QakWkHc3D4hI2yjZEpEd0vLlyxmxahUjKyvZsO++rBg2rEXrr+3fnwGrVzcaeDrVsonWjBzZKMkSkY4pdMmWmd2a5qLOOXdORoMRkZxqqhox2g4sWVViNNG6aNo0CiMRIrNnc/+559J/1Sp2f+89PjnkEBYfcUST+7714os5449/ZOf162PT4js3jT5HzHjhi1+MxZrJqk0RyU+hS7aAs5uZHy3Jd4CSLZEOKL5BfbIqt+Li4ljVYrRtV2KSM7KyksJIxI9pWFvLkQ88EEucRixaxM5bttDv5puZM2dOk7FYiukO+PdBB7GotJSaoHG8iOyYwtjP1ogUj/3w4yYuxw9QvUuuAhSR9tFUlxBlZWXN3jVYWlpKeXl5g2nR8QcXlZZSV1BAHf5CGE20Yv1iPfAAxe++m/Rux2jilrhOoqFVVQwYMKDJGEWk4wtdsuWcW5bi8bZz7p/ABOAY4MgchyoiGdCSLhGi/XilSsrM1be0iq8CjA21M2MGxcXFrepewoB9lizh6zffzOBlyxrN779oEeNuvZVxt97aqGPTysrKFu9PRPJXGKsRm+ScqzCzJ4Dv03SnpyISUtGqw9LS0maTr+Li4qT9eB2wYAGFdXWxNgfxDdwdYJ06wcSJsWklJSVUVVU1aHu1sk8fBq9f36irh/iSroK6Og6cOZOpw4bF1u2/aBGTfvtbCiMRAHaZOZPpv/iFGsyLdFChK9lKUxWgq5ZIiDXVkDzaJqstHX/usXRp7O/4QaMNwAy+8Q0YPz7putEqzLt++lNW9OnT6I7ExORt1/nzG5RudXn1VQojkdh+CyIRSubPb/V7EZH81uGSLTMrBI4APs11LCKSn0asWkWfoJ8saJgc1ZnBTjvBmWc2uY34hGtlXMIFDfvaMnx15Z5vvUVFRUVsuJ9IYWFsv3UFBerYVKQDC101opkdmmJWEVAGfAPYF/hntmISkXD52owZQONe3++fOJHxI0dScsopdEtRqhWvrKyML/zwhwyI6/4hUTTh2nv2bObttx8rhg1jxbBh3Putb7HnnDlgxrz99qNQVYgiHVboki1gBk33H2jATOAnWYlGREIhvvuHwevWNZpfB3x89NEMOfhgBrSgQXz/oHf4xDsSE0u6CiIRhi5Z0qDz1I19+vgBqidMSHt/IhI+YUy2riB5slUHrAded869nt2QRKS1WtvuKjogdUvXPyASocA1voSk6r6hORt23pk+K1Y0Sq7i/46Wbm3u2hWAwcuWcdq//kVhbS2RoiKeKylR43iRDix0yZZz7vJcxyAimdVcL+vV1dWx7hHSuSOxtLSU2bNnA3DgzJkNGsRHtTbZak400aoDBq1cyZply5gwfTqFtbUUOIerraVk/nwlWyIdWOiSLRERqO/wtKWDNhdv3JhyXllZWdJuIpKJdgXRe+VKIHmylth3195vvMHes2dTENyJWGdGXVGRGseLdHAd7m5EEQmf0tLSBklOcz3Dt1RxcTGTJ0+mrKyMt4Me5RMrEptqCBrtHLWkpCT2iNqw886x9ZNtM74ProK6OgojEQrwJV1Ld92V+845h7d22qm1b01EQiDvky0z+6iVj8VZiO0kM3vEzJaZ2RYzW2hmU8ysR6b3LdKRxCcz6WhND+vFxcWUl5ez/qSTqEsyP1JU1GDZVDEm+u8f/0iN1ZdrJSZY0Tl1QKSw0D/MqOvUiZcmTYo1mNcA1SIdVxiqEQto+kdnKplqghHvx8DHwKX4MRn3Ay4HDjezg51zya7pIpJEfDLT1JiIUdEBplsiOjh1MjXdu1NWVtZktWQ0xvhkr6Kiwg9mTeOuJFzctGUDB/LoIYcwOhJh9/feY8EeewBw0IwZfDxiBCuGDWvVexKR/Jf3yZZzbniuY2jC8c651XGvXzCzdcAdwETguZxEJRJS8QlXtKSnsrKyQSP46urqWDJWUVHR4nELS0tL2VpaSreE0rGtvXq1KMb4hCs6mHV8cgX1dyI64NFDDgHgyH//m6JIhKEffQT4qsVIURH3nXNOg24hRKTjyPtqxHyWkGhFvRE8t6zVrogkVVVVRWVlJdXV1VRXVzNnzpxYT+yttfzUU4GGReY7mTF27Ni0upKITwrLysooDKbHN4iPf352v/1YMmgQByxYQFFwF2JBJOLbbzlHQW0tQ5csiZVqtbTRv4jkNyVb7e+w4FkDnYm0UXxCVVlZmbStVmvab3UO7kiML4XqtG1bq/v8WtvDN9NMrDqMAMuKixlQWckJs2Yxfv78WGlXXUFBffutoiI+HjECaP+bA0Qk95RstSMzK8V3uvqsc252ruMR6Sii1YZVVVUNSrVaW7q1Yd99gYYlW0VJepVP178uvZS1nTvHXke3WwgMq65m308+4QtvvUVhXV0sEVtbXMw75eW8PW4c951zDoUTJijREumg8r7NVliYWTEwDajFj8+YbJnzgPNSbWPo0KGZCU6kA0in0Xy6Nu25J7VmFMX1JO8ikTZts8/27UDyYXuipVnxBm7cyIDXXiPSqRPz4tqdpXtHpoiEh0q22oGZ7QQ8DuwCHO2cW55sOefczc658lSPAQMGZDVukTAqKyuLlQC1tiSopKSETxOqDAsiEbZef33a2yguLmbs2LGxGBLvREz2HP07umwBxNpriUjHpWSrjcysE/AIcABwrHPu3RyHJNKhpKoqbGtj8g2DBgENk5/Nd90VG3MxHcXFxZSUlFBWVpa0767o9h3w+m678fpuu8Wmgb+L0RUUxNpriUjHpGSrDcysALgHmARMds69muOQRHYIbS3ZAhgQVEvGV/Et6NqVOXPmtCjhKi0tpaysjOu++lWgcXVhQbCP8kWL2P/DDxvMiyZ5qjoU6dhCl2yZ2XNmdmWu4wj8DfgacDXwmZkdFPcYkuPYRDqMZKVbbW1Mvrlbt0bTxr3yCj3mzWvRdqKlWzXl5Q16jo+vQjTAnIs1kLe45ayuji6v6neaSEcWumQLOAhi3drk2heD518AryQ8zs1VUCIdUXsPZ7O9S5cGrw3ffqr33Lkt3la0dCtxe9FHNOGqo+FwPhEAMzZ37drifYpIeIQx2VoE5MX90c654c45S/G4PNfxiXQ07Zlw9fz00wavHYBZrFuIlioPBrhO3GZ8m7CN3bqxZuDAWPuuAnzJ1hf+8x/6L1rUqv2KSP4LY7L1T+BLZqZ+EkQ6sFTVhG1NuKIdlxbW1samxe4OrKujWzCMTku3mayhfnyVIUDvzZvp/8knsXZc4C/ChbW1lMxXP8giHVUY+9l6AvgC8LKZ/QE/PM4qkgxW7Zz7OMuxiUg7KisrS5pcRae1pd1WpKj+8hffF9aAmTNbvc1U4ref2OeWw7fn2trK3utFJP+FMdn6iPpr1nVNLOcI5/sT2aG1ZMic1gxEHRUpqC/Yj0+AVh96aKsGNi0uLqYW36A0WUP5OurbbVnCMnXATtXVbG/FfkUk/4UxGbmTJKVYIrLjiC/ZqqysZNSoUS3eRqe4asQGA1KvWNHquIqco9Ys6R08hm8QXzVkCDsvXx5rwxFtK1Y1ejRqJi/SMYUu2XLOnZ3rGEQkexKrEqN/L1/uB2pI1jA9He/tuy/jZ85sVApV8uyz1DaxXnOiSVRi6RX4Uq/O27ZRZ4bFDRX06iGHsGbkyPy480dE2l3oki0R6fhKS0sbjYXYf9EiSubPJ9K3LyuGDWPIkNZ3ZVdaWsrs8/wwpeNefpmiuHERt/fs2aY7hwpoXI0Yf0di/9WriRQUsGjQIDpFIrwyZgwfH320Ei2RDkzJlojknei4g3PmzAF8ojVpyhQKamvZs6iIe7/5TVYMG9bqBvLFxcVMnjyZacBO27ax32uvxeZV77EHPdvhPcQnWNHXBNMK6+qo6tuXByZOBEA9IIt0bKFNtsxsHHA0UAp0SbKIc86dk92oRKS9xCdcu61YQUFtLQV1dVBby97r1lE4YQLQ+rERownXy4sWEXnjDQrq6qgrKKDq6KPblmwVFEDQUzzUJ1yJDU27BzcCtKWETkTCIXTJlpkZcDtwOg3vpo6Kv7tayZZIiEUTrkXz5lFWVAS1tdQVFVE1ejTQ9iF7iouLOeSQQzCzBheRNhk1CubPb3Bhih+6xwF1hYXM22+/WKJVVlZGWVmZxkgU6aDC2KnphcAZwF1AOf56di1wMHApsAm4H9glR/GJSDvrNmkS0y+5hHdOOonpl1zCmpEj22/bDz9MQSQSq94reeqptm3w/fdT3i4d7eT09c9/nhXDhiVdprUldSKSv0JXsgWcBSyM3pXoC7rY4Jx7FXjVzJ4CXgWeAW7LVZAi0r7WjBzZKMlqSz9bqXRat65dtweNi94HrVzZ5PIt6WtMRPJfGEu2RgHPJUyLJY3OubeAfwPfzWZQIpJd7TZO4pln4oqKYqVRfV97jYK4BvNt5RIeAJ8ccki7bV9E8l8Yky0D4keQ/Qzom7DMImD3rEUkIlkVn2hVVla2bWPjx/PpV78aa2NltbUUvvhiqzdXXV3Noh/9CGjYoHRD795sGj2aD370I7afdVasvdl+W7eyx+OP02PevDa9DRHJX2FMtiqhwWgaHwH7JywzEp+EiUgHk6yD07baOmZM/d02zuH69Wv1tiorK5m9337MCBq/R0uzumzcyLwBA9hSUcHS++6joqKCwcuWMWnKFPZ5+GH2+dGPKAs6ahWRjiWMbbZep2Fy9V/gJ2b2K+BRYCIwGV+VKCLSrJ2CRu0FgCsowNaubdV2qqurmT17NhUVFZwaJE7RJK5rXR3jg0Gu9y4o4H8nnEC3LVsoqKnxvcnX1FD44ot0mzSpXd6TiOSPMJZsPQIUmtmI4PUfgWXAb4B3gOuBDcDPcxKdiGTF8vYqBXrlFXpNnRpLiuoKC4m0ok1VdXV1rBPW5cuXN2gUH70LMfZ3XR1HT5vG5q5diRQVUVdQAJ070+Xoo9v2XkQkL4WuZMs59xjwWNzrdWa2H/At4HPAUuBO51zTt/uISOi0W6P4eDNmYEHXD3VA1Re/SM8DD2zxZiorK6moqEgaY+IYiYavrqxdtYr7zjmHL3btSvcvfYlu48e38k2ISD4LXbKVjHPuU+DqXMchIpkVPyh1u/W8PnEidO5M3bZtRAoLW92DfPx4jomlbvFjJEZFCgtZVFrKoAkTWFdezoBRo1r5BkQk34WxGlFEdmBt7TW+kfHj2fLEE3w4cSLv7rdfqzcT7e0eUieCGwsKmN+lC4t69+ahQw6hprwcUEemIh1d3pdsmdmhrV3XOTezPWMRkfzSnonXLi+9REFNDe7tt9k2ahS0oqF6cXEx5UECVQcU0rA0q2ddHT23baNu2zaGv/QS9+++O2UHH9wu8YtI/sr7ZAuYQeMxXNNV2I5xiEgORHtTj69CjP+7PUqFCl980Q907Rx1wV2BrUm2ovFUVVXxgwsu4K9/+1uDxvHgL2aFAJEIe69bR1eNhyjS4YUh2bqC1idbItIBxLeHSlRZWcmoNrZ3ihxyCIVFRbiaGujUqVV3I8bHE00Ek5VuRafXFRXhDjus1fsRkfDI+2TLOXd5rmMQkdyKbw+V2KlptNquLeoOPJDpl1xCl1dfpdcJJzCyFXcjQsN+thJFG8lH/35j/Hhq99yTbq2OWkTCQg3kRSQUoglXtJ1Wu/WzFVgzciSvTpzIpj33bNX61dXVTJs2rcG06AW2QZcPwd+7v/eehugR2UGEOtkyswlmdpGZ/crMvmdmE3Idk4hkTnwJVz6prq5Oa4zG+O4f+qxdy94XX8xOb72V0dhEJPfyvhoxGTMbC9wNRBtqxErozWwhcKZzbnaOwhORDCsrK+OVV15p122Wl5dTVlbG2LFjY43yW6OsrCzWgH/FtGmULl/eYEDq2BiMQEFNDd1efx2OO67tb0BE8lboki0z2xV4DugJvBT8vRLYGTgcOAR4xswOcM4tylmgIpJR7dapKb7ErLS0lNLS0jYlWvHKysr46J57GHzYYY0SLaLPdXXU9u7dLvsTkfwVumQL+BVQDJzinHsoYd7lZnYScD/wS+CsbAcnItnTnv1stVeSFVVSUtKoW4poohW9S7HOjKING9p1vyKSf8KYbB0JPJYk0QLAOfewmU0LlhORDqyiooKysrJ2LZFqD4lVkdG2WtGSrQIgYobr1InNBxxA19yEKSJZEsYG8v2BBc0ssyBYTkQ6uIqKCubMmUN1dXWuQ0nOuUb9bDlgfd++rB8yhM4ffJCLqEQki8JYsrUaGNPMMrsDa7IQi4jkWLRPq2i1XT6VcKViQL+1a2HtWrjsMrb16QMXXZTrsEQkQ8JYsvUccIKZnZpsppl9FZgMPJvVqEQkq5YvX96gr63Zs2dTWVmZvyVcgfi+tqLPRY89lptgRCQrwliydQU+mbrHzC4AnsffjTgImAhMADYBv81VgCKSG1VVVe0yVmJ7ix+2p9EdiUDtl78cyouxiKQndJ9v59yHZnYkcCfw+eAR343NQuAsdfsgsuOINpTPV4k9yQNsLyhg24ABrD/iCPp94xu5CEtEsiSM1Yg4595wzo3Gl2J9D/h18HyIc260c+71nAYoIlmR2NdWsjEJs63HvHl0uvpqiOt0tS54jm8oX1RXR/EnnzDk0UcpeO21rMYoItkVupKteM65WcCsXMchItk3ZMgQBi9bxtAlS/h4xAjIg5Kt/osWsffvf09BbS388Y8wfTqMH8/iBQvYdffdG/y6NaDAOepqaih88UWYNClXYYtIhoU62YpnZv3xvcdvBp51zkVyHJKIZNDgZcs47V//orCmBldQwBvbt7P4iCNyGlPJ/PkU1NRgdXWwfTvMmEH1Xnsxe/ZsasvKGFNRESvdirbdcs6xrqCAnXMYt4hkVuiqEc3sO2b2mpn1jZu2PzAfeBh4EphlZt1zFaOIZF7vuXMprKmhACioq+OAO+6g/6LcNNWMDvfT58QToUsXKCyEzp1h4kTmzJlDRUUFuwdVnEbDOxILnKNkyhQ2T5+ek9hFJPNCl2wBpwDOObcubtqfgD7Abfhkaxzw7RzEJiJZsqi0lLqCgvq7Y+rqKJk/P2fxFBcXM+zUU7Hp0+HKK2H6dBb27UtFRQXLly9v0DgeaFDCxbZtvipRRDqkMCZbI4F3oi+C6sPDgH855851zh0PvAH8X47iE5EsWDJoEA8eeih1BQXUmREpKqJq9OhchwXjx8Mll7Cwb19mz54da7S/pcBfbpP1Jm/O4fr1y26cIpI1YUy2+gGfxL3+fPA8NW7ai8CwrEUkIjkxa489uOe885h7wAG8u99+AFRWVuY4KpJ2rHrG5MmNEi0I2m6ZYWvXZjwuEcmNMDaQX0fDcQ8Pw99ZHX9XogN2ymZQIpI7e82ZQ2FtLW72bD4pKIBf/zqn8aQaMii+c9OoWoBOnYgcckjmAxORnAhjydZ84Hgz62dmvfFtuN5wzm2MW2Y4sCoHsYlIlg1dsoTC2loKnKOgro6SK6/M28bm+++9NxGCuxCBf/fqxd8GDWL6JZdQd+CBOY5ORDIljMnWdcDOwHKgAj9Mz43RmWZWiO/s9O2cRCciWTFkyBCGDBnCm8XFOLP6hvKRSF43Nr+jf//Y3YjHffopJ6xdy4gXX1THpiIdWOiSLefc4/g7Dd/DD83zY+fc3XGLHImvQnwqB+GJSBYtX76cJYMG8cChhxIpKPClRp07502VXLIhhL6xZg1Q3/XD8JoaRj73HF2/9KUGvc6LSMcRxjZbOOduBm5OMe8pfDcQItLBRYfr+XjIEB4EDqqspOvpp9Mjj6rkDj744Njfs2bNgndiN1M36A7CBZ2gMn589oITkawIXcmWiEiiwcuW8bWXX2b4hx9SMmUKxe++2+ZtVldXJ72rsC1OPPHEBq9d3CPaCaqIdDxKtkQkdEpKShq8jm8kbzU1voSoDaqrq6msrGTOnDksXLiwTduKGjt2LKNGjaIurr+taMnWiiFD2PKf/6hUS6SDUrIlIqFUVlYWaxP18YgRRIqKiJgRKSxk2YgRbdp2wWuv0fcf/6DHvHntESpjx46NdQdRFIkQHbg12gXEsl128Y361WZLpEMKZZstEZF4K4YN475zzmHokiV8PGIEhZEIWxcuZNSoUS3f2Cuv0PX44+m6bRt9O3Vi+e23U11amrLvrNawuGcHjJ85E15+Gf74R5g+XSVcIh2MSrZEpEN4vbCQh3fdlRXDhlFRUcHs2bNb1+ZqxgzYvh2rq8Nqauj2+uutiqe0tBRoXOUJNBonEcAiEYg2kheRDkXJloiESrISpuXLlyf9e9q0aS1PuCZOhM6d/ZiLRUVsPuCA1oaaUrJhe1xBgRrJi3RQqkYUkQ6poqIiaT9XzRo/ni1PPEHVAw/wweDBDA/GXGwv1dXVsbHEGiRddXVw0UWqQhTpgJRsiUiHEu17K6qysrLFbbfqDjyQ7UOG0LWqqtVxFBcXM3bs2Njf8fGMDP5u0M8WwNy5rd6fiOSvUCZbZjYS+D5wAL4D08Ikiznn3OeyGpiI5MSQIUMaVB+2VcFrr9H3P/9h8667QklJqxvHJ65XXV1NVVUVn6PxgNQAdOvm70hU6ZZIhxK6NltmNh6YC3wX2Bc/NI8leYTuvYlI+6qoqGj5SsHdiP2vu469L76YsnZK4qJ9d1VUVPDkQQcBSRrKP/44TJqkLiBEOpgwJiRTgC748RG7OefKnHMjkj1yHKeIZEj0Tr9EI1atYsy0aQxetqz1G58xAwvuRiyorW3XQa2rgmrJRUH80Y5NY0lXXZ3uSBTpgMKYbI0DHnbO3eycq811MCKSfcXFxUyePBmoH+x5xKpVXDRtGl967TVO+9e/Wp9wBXcjUlgInTvT5eij2ylqr6KigiWDBjWoQoz9rTsSRTqkMCZb24GPcx2EiORWcXEx5eXlsdcjKyspjEQodI6C2lqGLlnSug2PH+87Fr3ySqydOxiNr9ZMbK9VB/Db36pTU5EOKIwN5GcB7XsvtoiEUvxdhotKS4kUFkIkQl1RER+3Zcie8ePbPeGprKxs8DqxvZYBXHJJu+5TRPJDGJOtS4FZZnaGc+6uXAcjIrlTXV3N7NmzAVgyaBDXT57MyMpKNuy7LyuGDWuwXHsOt9NS1dXV7PTWW+zx+OOsikRYUVfXYCBqCEq6TjwRPvgAdtsNfvpTlXCJdBBhTLYmA88Bt5vZucCbwIYkyznn3JXZDExEsqe6upo5c+Y0mLZk0CBqgqrFaFcQVVVVKRvUZ0vRbbcx9OKLKYtEGFNYyOqDD45VI0afCwAee8y/eP99+Pe/YeZMJVwiHUAYk63L4/4+JHgk4wAlWyIdUDTRim8DFd+ZaXv2udVmr7xCl4svhtpaCoDC2lr2XLMm1mA2Ohh1I7W1/q5EJVsioRfGZOvwXAcgIrkV7a8qmcREq6KiItaTe07MmAF1dfVJVUEB8/r3p44UHZtGFRXprkSRDiJ0yZZz7oVcxyAi+aesrIyKiopGvclXVFQwbdo0Jk+enJt2WxMnQpcu1G3dijPjgUMP5YM+fdh/77158513KMDfibj92WfpdsMNarMl0gGFLtkSEUkm2t9WshKviooK5syZw9ixY7OfcMUNbP1iYSGvFxbCihUA7L/33uz92Wf8aP/92Q1g6tTsxiYiWRHGfrbyipmVmdnDZvapmW00s0fNbGiu4xLZkZSVlTF27FgmT55MWVlZo8GoAWbNmsWcOXOorq7Oenx1Bx5Ixemns2LYMEasWsVXFi5k788+Y8CHH3LzRx+x90MP0fX44zVMj0gHlfclW2ZWhy9lH+Oc+yB4nbKZQxznnMvo+zOzbvg7I7cBZwVx/RZ43sz2ds59lsn9iwiUl5dTWloaK7EqT7gbsdPs2exfXc3HI0ZQUVZGSUlJg/65sqWkpITBy5Zx6rRpFNbW8lUz7i0qYifnfHuuLVvUIF6kg8r7ZAuYiU9iNie8zgffAnYBRjnnPgQws3eARcD5wF9yGJtIh1VaWkpVVRUlJSUNEq34ea+88kpsCJ+iSIRIURHPlZRAXK/z2VRVVcXQJUt8L/dAJ+c4q6YGiLugXXopfPwxrFoFgwbBmWcq+RLpAPI+2XLOTWzqdY6dALwaTbQAnHNLzOxlfH9gSrZEMqC4uDh2h2FTbbCiQ/gUOIerraXLq6/CaaflrJPTj0eMIFJYiKutxeHvRoSE7h9uuql+hVtvVWmXSAegNlttswcwL8n094AxWY5FZIdSXFzcbMIUHcInYtb2IXzawYphw3h4wgQwo4DGQ/Y0UlPjky0RCbW8L9nKc32B9UmmrwP6JE40s/OA81JtbOhQtasXaav4PriiQ/gcsGAB3bt3z3FkMHjZMvZdvBhzruk+tqI6dVJfWyIdgJKttkt2vUz6g9U5dzNwc6oNlZeX50tbNJFQio6VmNj9w/j58ymoq2Pv2bN5d6+9fFuoLOsxbx6n/etfFNbUYPi7fqIXivihe+zQQ6FvX7XZEulAlGy1zXp86VaiPiQv8RKRDKmurmbatGmNEq1Jc+ZQGPTgbnV1DLnvvpwkW73nzqUwGLLHUZ9gGQ2TrnVbttD5rrtyOnC2iLQvtdlqm/fw7bYSjQHez3IsIjusxEGp43uQ7/VZwx5Yuqxdm7W44m3Yd1+cmS+9widWdcG8+OTr/pqanPUHJiKZoWSrbR4HDjKzXaITzGw48PlgnohkQbSdVrLe418Z4+9ViSYzK489NouR1es2aRJPnXACkYICIkCksJBvfO5zRKhPtiLAox98QN3vfkenX/8ajj4abk7Z8kBEQkLViG1zC3AhMM3Mfom/Xl4JVAD/yGVgIjuy+PERZ+3hC5/3XbyYuZ/7HENOOIHSHMX1zoEHsmbQIEa8+CKfffYZpaWlsHhxrBqxEHhm82bcU09hTz3lJz79tH8+L+W9NSKS5zpkyZaZ/c3MMl5XEPQQfwTwAXAXcA+wBDjCOac6AJEsi46PmGjWHntw4wknxBKvXDvogw84atkyrpg1K3YRjr+rptGF+ZFHshOYiGRERy3Z6gb0zsaOnHMfA1/Nxr5EJH3xpVtRI1atouzuuymoqYFJk3IS19AlS3xDeecojESSLhN/pyIAX9UlRiTMOmqyJSISS7hGrFrFAQsWcNCCBRTW1cFdd8Fzz2W1W4Wqqiog6EW+qAgiEVxREWt79WLA6tWxNmUfdu5M5eGHM37MGLq8955PtFSFKBJqSrZEJPSi4yFCw6rEWbNmcUAkwqnTplFUWxvrZqFu+3ZWP/QQA3LQh9WKYcN484wzGPrGG3w8bhwzRo7k5GuuYegnn7C6qIgrysrYY+JEyi+8kC7q/kGkQ1CyJSKhFz9WYqK6GTP8+IjUd7fggMWffkrXHIyROHjZMsrvvpuC2loGLlzIB9/8JlMPOYQLH3uMAbW13PzRRzy0bFlWYxKRzOqQDeRFZMcTHSsx/jF27Fi2HXQQdUVFRMyoNaPODKurY/+77uLlq6/Oen9WQ5csoaCmhoK6Ogpqaxm6ZAkjKyspikQoAoqcY+iSJVmNSUQySyVbItJhFRcXM/y007ivqoqCmTPpv3kzX1i61Jdy1dRQMn8+lZWVlJaWZq2EK77NVnRw7NWrV1NbWEhdJEJNsEx5VqIRkWwIRbJlZh+1cJX+GQlEREJl4cKFTJ06leWFhawYNYqvz5sXq04sADavWkXXLMTRoJrz4IOZt9de9J47lw377suKjRtZXlgYq+o0gtKv116Dbt3gzjv9ehonUSS0QpFsAcNbsY4GdRbZgUUHpY63y6efAvXD5QxauZKaLMUTTbjmzJnDpj33ZNOee9Jj3jyOeest9nzwQQqD5QqBw556CmbMgLo6qAkivPVWP00Jl0johCXZGpHrAEQkPOIHpY7va+uVwYPZ95NPYr/EFuyxB8VVVb4n9yyIJlyVlZVwyy2MvPZaCAbJhvok0AC3fTu4uN+MNTVKtkRCKhTJlnNOt+aISNqiYyUmenbECHr37h0buufjAw/k4CzHVlxczKh163DXXQeRSCzBIvG5c+eGJVudOsHEiVmOVkTaQyiSLRGR9vJwnz7MOuEE/yKhh/msmTEDq6sDgu4oCgpwdXUU4LumeOnoozngJz+hm9psiXQIeZ9smVlX59yWXG9DRDqWEatWMbKykh5jxkCKPrraW7SbieJ+/aCgAJzDFRTw1PHH808zpk+fDsAlEydSfuCBUFysBEukA8j7ZAtYYmZTgJucc9tasqKZ7QNcAcwGrsxEcCISLg9OnRq7IxEz3Jw5bB81KuNjJS5cuJDZs2fTf9EijvrjH33JVlER2//8Z9b37cvgV17J6P5FJHfC0Knp08BfgJVm9nczO9zMUt6tbWa7mNl3zOwVYA6wD/B8lmIVkTz24NSpFOIboBcAhc5RUFtL4YsvZnS/8R2nlsyfD9u3+/ZYdXXY2rUNhhgSkY4n70u2nHNnmtlfgd8B5wWPiJnNB1YC64GdgH7AKHwfWwZUAb8ArmlpiZiIhFtpaSknnngiU6dOjQ1GPXjw4Nivy2ijdAe+d/lDDsloPMXFxbE7HncqLMSeeMInXIWFbF6wALVxEOnY8j7ZAnDOzQaOMrORwDnAJGBfYK+ERVcDjwKPAI8457LVhY6I5JHi4mJGjRrVKOGqw/djFb3rLwK8e801jDzwwKzFxKhRMGwY3Hkn7rbb6P3QQxxRWMjzapsl0mGFoRoxxjm3yDn3c+fcOKAXviTrYGA/oNQ5V+Kc+5pz7n4lWiISTbgAhgwZwg8uuIAIPtmKAD+44AI27bln9gMbP55tJSVQU4MFYyTuuWYNAGs3buSnl1zCTj16wJQpoLZcIqEXqmQrnnNuc5B8veqce9s5tzLXMYlIfoq2iTogEsF16oThS7iu/dvfkvbHlQ2RQw7Bde5MXUEBdUVFzOvfn7UbN8balBUCXHqpb7ivhEsk1EJRjSgi0hrROwCjhi5ZQlFNTazNViFwyumns3XTpqzH1m3SJDb/+99UPfAALxYWUl1YSMHMmQANOjpl+3b1HC8ScqFLtszsMOAIYDegN/6a9CnwAfCcc+6F3EUnIvkiMdEC+HiEH/krNiQOPuHq/P3vw7nnZj2h6TZpEtuHDKFw9mx45ZVGbcoA35O8eo4XCbXQVCOa2Tgzexd4DvgVcApwNHBM8PevgOfM7B0zK89dpCKSawsXLmTq1KkNqgjLyspYMWwYkeB1NOEC6HTbbTmrristLY1Vc+6/994N2pTxu9/B9Okq1RIJuVAkW2a2O76vrD2Al4BLga8BR+ETrq8F014G9sQnXbvnJloRyQdlZWVJ+6+KNpKH+hIkcw63dauvrsuy6ODUQ4YMAXzCVYe/ONdeeqkSLZEOICzViL8BOgOTnXNPNLHc781sMvAQcDlwahZiE5E8M2rUKIBG1YhRP7jgAorffZfT581jj3XrfNLlHNvffpvO2Qszpri4mAsvvJCysjJOOf103zgeX6VYa0aRc02tLiJ5LhQlW8BE4MFmEi0AnHPTgIeBwzMdlIjkr1GjRlFeXt+iIPGuww/69WPMunVAfXVi0YMPsjkYnzDbiouLmTx5coOOV8FfpBcuXJiTmESkfYQl2eoJtOT+7GXBOiKyA0tMuAYvW8ZRb77JiFWreHDq1FhCA0Fy4xxVDzzQYHidbCouLqYu+DtallUHTJ06VQmXSIiFpRpxGf4OxGaZmeF7mP84oxGJSChEqxQjL73Eaf/6FwW1tUQKCxsN3QMQKSxkw777MiAXgQaKnKPWjAJ8orX/3ntzWg7jEZG2C0vJ1gPAODO738yGpVoomHc/sD9wb7aCE5H8N3TJEgprayl0jsJIpNF8B/z1y1/OTY/yCbZu2sQfp0xh/733ZunSpbkOR0TaKCzJ1hRgFnAy8JGZzTezaWZ2Z/CYFgxM/RH+zsRXgN/nMF4RyTMfjxhBpKiIiBmRwkKmjhwJ1Jdq3T9xIksGDcpZj/KJfnzJJbz1zjus3bgx16GISBuFohrRObfVzI4AfgR8Gz8m4qgki34M3AT8xTm3PYshikieWzFsGPedcw69585lUWkpL9fVUdW9O5OWLWP9Tjuxsl+/XIcYs1OPHg3uSPzxJZewOBjjUUTCJxTJFkCQPE0BppjZ5/DJVq9g9qfAQufc4lzFJyL5raysjArg9cIgjVmxgo979mS39esBOPCRR3hpjz3Y3L9/7oIMJLYnC0sVhIgkF5pkK16QVCmxEpE2uSphLMIJ771H3cKFbDvkEN+jfI4kDttT18SyIpL/9INJRHYo0Z7aP19Q0KDrB/BJV0FtLUX35vb+mq2bNjUYtufqKVMoLS3NaUwi0nodMtkys4PN7MxcxyEi+SExURmxahUXTZsWS7YS+2cveOONrMTVlAfuvpvVQRcV373qqlyHIyJt0CGTLeBbwG25DkJE8tP+1dUURiKNSraiSVfhu+/Cz36W7bAaOP7b32ZgEGOP6mo6jxiR03hEpPU6arIlIhJTWVkJ+B7kD5oxg81duxIpLKwfiDruOZaA3XxzdoNMUBz0Yh8bSmjNmtwFIyJtEooG8ma2SwtX6ZGRQEQktKI9yBfW1lJXUMC8oUPZe8mSBomWIy7Z+vTTnMQZVV1cTI/q6lhCWNu/f04GyRaRtgtFsgV8SONmFSIiaYv2IF/gHBaJsPeSJbHkKvrcoFrR5faS89jpp3P6TTfFYlp5/fWkHD5DRPJaWJItB2wA3klz+d2BgRmLRkRCpbS0lKUHHUTkueegpsbfdQhEzFjfrRt9PvusYakWvruFXLazGBqUukWTwW6vvw6nnprDiESktczl+NdbOsxsEYBzbmSay98GnOmcK2x24TxSXl7uZs+eneswRDqk6upqXr76agb897/s/eabWF0dkcJCrp88mW8/9hjd4hrMR6+Km/fai+7vpPsbr31jje9FHnwXEEUhuF5L/jCzN51z5bmOQ8JTsvUW8BUzK3bOVec6GBEJn+LiYj7/4x8zbeRI5r30EkOXLOHN4mJOfPFFugUDU8dXJzqg67vvsnDhQkaNSjY6WGapF3mRjiMsydbbwEnAPsDLaSzfqPmFiEhxcTGTJ09m869/Tb8lS9i7Wzd6bt4MpL5gzJ49m9LSUoqLi7MXKMl7kVfCJRJOYfns3g6cCCxJZ2Hn3NnOubC8NxHJouJJkxjw0UcUOEefzz6jIKiaS1ZBZ8Dnnnsuq/FFXT1lCpG414UApt+QImEUioTEOVfpnJvmnFuR61hEJOTmzAHqi7+j1XTQMOGKpjX73Xdf1kJLFF+VKCLhFYpkS0SkvdTuuy/gE6vETk2hcQlXpy1bMh9UCtEBqBvE9MorOYhERNpCyZaI7FC2Pv88n44eTcSsQbLVVOlRtAf6bIsv2YrFN2NGTmIRkdYLSwP5GDO7NY3F6oCNwHzg3865lZmNSkTC5J2bbuLgww6LvU5WwkXctH5XXw233JKN0BqoATqTEN/EiVmPQ0TaJnTJFnA2TV8bE/smrDGzXzrn/pTpwEQkHA4+7DCSdcKXrEd5B/R+8MGcJFsDe/bkk40b6YT/BVk0axaMH5/1OESkbcJYjfg5YBqwFvglMBEYHTz/Kpg+FTgQOB+oAn5vZpNzEKuI5KGk1XOBpHclbtyY2YCa0CApVKIlEkphLNn6MnAIsK9zLr4hxUJgppndie8E9UXn3LVm9j98deKF+CRNRHZw8X1YxSdcqfpnz9XdgGs3bowlW7GuH9SLvEjohLFk6zzgoYREK8Y5VwE8FCwXff1vYGzWIhSRvDbrhRdifViluisxH6jrB5GOIYzJ1nDg02aW2QCMiHu9FMhu988iktd+cMEFzBkwoEEfW/FlRo363Lr55myFFpO06wcRCZ0wJltrgC80s8xR+LZbUb1pPkETkR3IihUruGrCBE468UQi1Cdbf99336Sdm3L55VmOEPr17BmLLQKqQhQJqTC22XoE+J6Z3Q1c6pz7ODrDzIYCU4B9gevj1tkfWJTNIEUkPE4+8cQGr789d26jZepWrszJr9P49Grz9Ol0mzQpB1GISFuEsWTr1/gG8P8HLDazpWb2mpktBRYDp+EHrv41gJntjO+u5q7chCsi+aS4uJixY8fy1a9+tcXrVldXZyCi1NZu3EgRvnStEOh65JFsnj49qzGISNuFLtlyzm0EDgYuA5YBQ4FxwfMy4HLg4GA5nHMrnXMHO+duyk3EIpJviouLmTx5csqEa0W3bkmnZ7Mn+bKysqRdVLz+pz9lPekTkbYJXbIF4Jzb5py70jm3K9ALKAN6Oed2dc5d4ZzbmuMQRSTPNZVw3VBeDjRuMD979mwWLlyYnQBp2EA+Gsu/PvyQG264QQmXSIiEMtmK55zb5JyrdM5tynUsIhIuqRKuD/r1i3UNEeWAiooKqqqqspLolJWVcXJC4/0I8Pjq1Sxfvjxn4zWKSMuFsYE8AGbWDfgKsB/1dxvOAaY65z7LYWgiEiLFxcWUl5dTUVHB8uXLY9NrOnWisKamfsGCAgYvWwYHH5yVmGLtyqZOjVUhFuLbcf0g4xGISHsKZcmWmR2Lb591B/BD4BvAD4LXS83suNxFJyIdwTsjfFd90eq7wro6/u+WW+gxb15W9h8tdUvs2DSUF22RHVzoPrdmNhZ4FF+adQ/wTeCLwfM9wfSHzWz/HIUoIiFTWlpKWVkZQ4YMiU2r6tsXqG+cbkBBJIK98ELW4iouLm7UsWldqoVFJG+FsRrxF/jrziHOuVcT5t1uZn8DZgCXAi2/t1tEdmjRhGvRqlXUFhZSFKlvvVVXWEjV6NHsmsV4ipyj1owCfKLVr2dPzsji/kWk7cKYbB2CHxsxMdECwDn3mpk9DByd3bBEJKyiVXaVlZVUVVX5iQcfzH0DBrDnnDl0r67ms+Ji5o0dy/AjjshucAUFDQajXrNxIz/MbgQi0kZhTLZ6ARXNLPMx0DMLsYhIB1FcXExpaSmlpaWxaSUlJcyePZuKivpLzvBsBxYM0WP4Iv3CJhcWkXwUxmRrBXBAM8uUAyuzEIuIdCDFxQ3Hqx81ahSlpaVMmzatQcKVVWbgXKzNVmKXFCKS/0LXQB54EjjCzH5uZg1+5JlZgZn9CDgyWE5EpE2iVYxlZWWUlZVlP4C6OiJmsX62+vdUob1I2ISxZOtK4MvAVcD5ZvYivhRrEDABX8q/CvhtjuITkQ4mvk1XaWlpoxKwjHOu+WVEJG+FrmTLObcK+DzwLDAMOB34CXAGMCKYPsE5l9FqRDPbzcyuM7N3zKzazFaa2eNmtk8m9ysiuVFcXMyoUaOyn2iZUUj9YNRrN27M7v5FpM3CWLKFc24pcLSZleJ7kO+F70H+LedctsawOAo4HN+R6hx8/14/BV4zs887597MUhwisgOINpAP3S9kEQlnshUVJFa5GiDsfuBvztWX75vZc8BS4PvAmTmKS0Q6IHVqKhJe+pHUSs65NfGJVjDtU+ADoDT5WiIiLeRcbDDqCL5TUxEJl7wv2TKzW1u5qnPOndOuwTTDzPoCewK3ZXO/ItKxbd20iRtuuIH77rsPli7NdTgi0kJ5n2wBZ7dyPQdkNdkCrsc3rbg2y/sVkR3AUiVaIqEUhmRrRDZ2YmZHAs+ksegLzrmJSda/BPg/4Bzn3Icp9nEecF6qDQ8dOjS9YEVkh/PjSy7hp/g2Wz/IcSwi0jLm1H8LAGbWDUgn29nsnPs4Yd1vA38Hfumcu6q1MZSXl7vZs2e3dnUR6aBqg+4foiLA4gULGDVqVK5CkhAwszedc+W5jkPCUbKVFc65zcCClq5nZmcANwJ/bkuiJSKSSvROJnX/IBJO+sy2gZmdiG8M/0/n3I9zHY+IdEzR7h7U/YNIOCnZaiUzOxS4D3gHuN3MDop77Jfj8ESkA9m6aVOD7h9+cMEFOY5IRFpCyVbrHQF0wfdg/zLwStxjag7jEpEO6OopU+jTs6f62RIJISVbreScu9w5Zykew3Mdn4h0PMOHD891CCLSCmogLyISEnt/9hlf2baN4lWrch2KiLSAki0RkRAYvGwZ3//oIzo5R920aaw86SRQ1w8ioaBqRBGREBi6ZAmdnKMIKIxE6Pb667kOSUTSpGRLRCQEPh4xgkKCfracY/MBB+Q6JBFJk5ItEZEQ+L+bbmrwuvS003IUiYi0lJItEZEQiO9FPv61iOQ/fV5FREJAvciLhJeSLRGRELh6ypQGvcgvXtDioVxFJEeUbImIhES/nj3p07OnhusRCRklWyIiIiIZpGRLREREJIOUbImIiIhkkJItERERkQxSsiUiIiKSQUq2RERERDJIyZaIiIhIBinZEhEREcmgolwHICIiTSsuLubggw9m0qRJAIwfP57S0tIcRyUi6VKyJSISAoceemjs77Fjx1JcXJzDaESkJZRsiYiExKGHHkp1dbUSLZGQUZstEZEQUaIlEj5KtkREQqS6ujrXIYhICynZEhEJgerqambOnMm0adOUcImEjNpsiYjkuerqaqZNm8YjjzzC4sWLqaio4MILL1SVokhIqGRLRCQEKioqmD59OkuXLmX58uVUVlbmOiQRSZNKtkRE8lxxcTEnnngiy5cvZ8WKFepnSyRklGyJiITAqFGjuOiii6iqqlI/WyIho2RLRCQkRo0axahRo3Idhoi0kNpsiYiIiGSQki0RERGRDFKyJSIiIpJBSrZEREREMkjJloiIiEgGKdkSERERySAlWyIiIiIZpGRLREREJIPUqamISBiZ1f/tXO7iEJFmqWRLRCRs4hOtZK9FJK8o2RIRERHJICVbIiIiIhmkZEtEJGwS22ipzZZIXlMDeRGRMFKCJRIaKtkSERERySAlWyIiIiIZpGRLREREJIOUbImIiIhkkJItERERkQxSsiUiIiKSQUq2RERERDJIyZaIiIhIBinZEhEREckgJVsiIiIiGaRkS0RERCSDlGyJiIiIZJCSLREREZEMUrIlIiIikkFKtkREREQySMmWiIiISAaZcy7XMUjAzDYBC3MdRwfQH1iT6yA6AB3H9qHj2D50HFtumHNuQK6DECjKdQDSwELnXHmugwg7M5ut49h2Oo7tQ8exfeg4SpipGlFEREQkg5RsiYiIiGSQki0RERGRDFKyJSIiIpJBSrZEREREMkjJloiIiEgGKdnKLzfnOoAOQsexfeg4tg8dx/ah4yihpU5NRURERDJIJVsiIiIiGaRkS0RERCSDlGxlkZkNMbPrzewVM9tsZs7MhidZro+Z/dPM1pjZZ2b2rJntlYOQQ8PMPm9mT5vZJ2a20czmmNk3cx1XGJnZsWY208yqg2M528yOyHVcYWVm/ws+67/NdSxhYmYnmdkjZrbMzLaY2UIzm2JmPXIdm0hLKdnKrl2Bk4H1wIvJFjAzAx4HjgEuAr4KdAKeN7MhWYozVMxsb+BZ/HH6Fv6YvQH8y8y+k8vYwsbMzgemAW8CJwJfAx4CuuUyrrAys9OAfXIdR0j9GIgAl+Kvh38HvgM8Y2b67pJQUQP5LDKzAudcXfD3ucAtwAjn3NK4ZSYDjwFHOOeeD6b1ApYAdzvnvpftuPOdmf0Of2Hu65yrjpv+KuCcc+NzFlyIBKWs84FLnHPX5jaa8DOz3sAC4IfAvcBVzrlf5jSoEDGzAc651QnTzgTuACY5557LTWQiLadfB1kUTbSacQKwIppoBet9CjwBTM5UbCHXGagBtiRM34DO8Zb4JlAH3JTrQDqIPwLvOefuy3UgYZSYaAXeCJ5LsxmLSFvpiyj/7AHMSzL9PWComRVnOZ4wuD14/quZDTaz3mb2LWAScE3uwgqdCfiSmFPNbLGZ1ZrZh2Z2Qa4DCxszmwCcCXw317F0MIcFz/NzGoVICxXlOgBppC+wNMn0dcFzH6A6yfwdlnNunplNBKZS/+VWA3zbOXd/ruIKocHB40/4djKL8W22bjCzIufcdbkMLizMrBPwD+Bq59zCXMfTUZhZKXAF8Kxzbnau4xFpCSVb+ceAZA3pLNuBhIWZjQQewZf+fRtfnTgZuMnMtjrn7sllfCFSAPQAznbOPRpMey5oy3WJmf3VqZFnOn4GdAWuynUgHUVQoj8NqAW+keNwRFpMyVb+WYcv3UrUJ3hen8VYwuJ3+JKs45xzNcG06WbWD7jOzO5Ls73cjm4tMBJ4JmH60/i7wXYGVmQ7qDAxs6HAL4BzgS5m1iVudpeg0fwm51wkF/GFkZnthL9DexfgMOfc8hyHJNJiarOVf97Dt9tKNAb4OP5uO4nZC3g7LtGKeh3oBwzMfkih9F6K6dFSVSWszdsF2Am4G//DKPoAf8fsevz5KmkIqmQfAQ4AjnXOvZvjkERaRclW/nkcKDWzaENQzKwncHwwTxpbBexrZp0Tph8IbKW+vZs0bWrwfHTC9KOB5c65VVmOJ4zmAocneYBPwP6/vbsPtrqo4zj+/oiYSoYSqanjY6KGJYopjKLgA0njAxU1OalM9qCUYpZoqDOp+ZwFKZg46eBjVpKZTj6NEipCpgnjA4qiKKAGPiBKIqLf/tg9+vPcc+4993LOuVz9vGbu7GV3z/72/O6F82V3f7tDgGc6pWddTN5L6zrSgy6HRcTMTu6SWYd5GrHJJI3I3/bP6TBJS4AlETGNFFDNAK6VNIb0P+GxpNGFC5vd3y5iAmnjzVskXUpas3UocDgwLiJWdmbnupB/AFOBSZJ6A88CI4CheJ1MTSJiKfDP8vy0VzHPR0SLMqtqIukBjXOA5ZIGFMoWejrRuhJvatpkkqrd8GkRMTjX6QVcBAwnTUnMAH4WEbOb0ceuSNIw0sLkvqR7Ng+4HJjk9TG1y6Oo55GCrI1IW0GcHxHXd2rHurj8996bmraDpPnAVlWKz4yIM5rXG7PV42DLzMzMrIG8ZsvMzMysgRxsmZmZmTWQgy0zMzOzBnKwZWZmZtZADrbMzMzMGsjBlpmZmVkDOdgyMzMzayAHW2ZmZmYN5GDLrIMkbS0pJE3u7L40kqSrJS2W1KOz+9KWRv9Mukr7dWynf27n+6vTjtknnYMtsy5I0ghJl0i6T9Ky/IF4bQ2v20LSlZJelPSOpPmSxkvaqEr93YEjSEf2LC/kL8nXrPXr2Pq9e2uWiHgY+BtwtqRPd3J3zLosH0Rt1jWdDuwCvAUsBHZs6wWStgMeADYGbiade7gHcAJwkKS9IuLVspedCywDfl9opwfpkOCitYHTgJWksxXL3d72W6qLRcBOwBtNut6aqp734TzgX8Bo0u+DmbWTgy2zrulEUpD1DLAvMLWG11xKCrRGR8QlpUxJv83tnQMcW8jvAxwA/CEi3i7l5xGuM4oNS9qFFGw92pkHBEfEu6Qg8hOtnvchIh6U9CRwjKQLfLC7Wft5GtGsASR9W9K9kt6Q9LakRyWNlfSpCnUl6QRJT0haIWmRpAmSeuZpvvnlr4mIqRHxdNR4krykbYGhwHxajkr9ElgOHFm2LutoQMCfarjE7jl9uJb+NEqltUrFvPz9DZJeyff6IUkH1+G6a0m6OF/nr5LWLSvfU9KNkl6WtFLSAkmTJG1WQ9sDS+22UmdOnhbuVf6eC3VW5z7cAGxJCr7NrJ0cbJnVmaRzSQHKTsD1wARS0HIucIek7mUvmQiMB3oClwN/JAVGdwHldTtqv5zeGRHvFwsi4k1gOrA+MKBQdADwHjCzhvb75/Sh1exnI20FPAhsDVxD+hntDNwsaUhHG82B1Z+B40k/yxERsaJQ/j3S/R1GGoEcT7pPPwAekrRla+1HxAzgKeBgSZ+tcP09SNPIt0TEazV0uSP3YXpOD6yhfTMr42DLrI4kDQTGAguAL0XEqIgYA/QDbiVN+Y0p1B8EjALmAn0jYnREnET68FsBtDnyUaMdcjq3SvnTOe2T+9Uj93lOcWF8K0rBVqeObLVhMDAxIgZExIkRMRI4jPTv4JhWX1lFHkm6C/gG8IuIOK4YzOap2EmkEcU+EXF4RJwcEV8nBS6bAL+r4VJXkQLvwyuUjSzUqcVg2n8f/p3TfWq8hpkVONgyq6+jc3p2RLxcyoyIVcDPgfdJIxolpQ/KcyJiaaH+SlLQVi89c1ptwXQpf8Ocbg50A15qq2FJawNfJi2Of6zjXfygvR9KelbSKkmTquV1wPPA2cWMiLgDeIH0oEB7+7kVacRnT+DIiLigQrVRpCDphIhYVHbte4C/A4dI2qCNy11D+t0ZWcyUtA7wHWAxcFuNXW/3fYiIN0jBf6ujcGZWmRfIm9XXbjm9p7wgIuZKWghsI2nDHFztmovvr9DWTGBVQ3rZknJaWgNWmq56vYbX9gXWBR7OQWLHOyHtSHry8Vuk9/9mpbwONj+ryuLuBcDAdra1AzAD6AEMi4i7q9QrtbuvpK9UKN+YFNT2oZVRwYhYKOlu4EBJX4yIJ3LRIUAvYFwO6GvR0fvwGmkkzszaycGWWX2VRpCqjQi9RBod6AksLdT/b3nFiHhPUvlWDB1VGrnqWaX8M2X1Sk8frluhbrl6Lo4/FHgsIm4qZUhqkddBS6vkr6L9o/x9SEHOLOA/rdQrBa1tTVPWsofVZNLU40jglJzX3ilE6Ph9WI8Pfy/MrB08jWhWX6VgZdMq5Z8vq7cspy1GDCR148MP69X1VE77VCnfPqelNV2Lc1rL9WtaHC9pc6Xd6F+VtFTSFEmbFMrnAhcAu+Sn5m6qlFdDf5rhFuBU0rq2uyX1rlLvgyA3ItTK17QarnkT6fflCEndJH2OtOh+dkTMXs330ypJa5GmmBe3UdXMKnCwZVZfj+R0cHmBpC8AWwDPFdZnlervXaGtAdRv9Lm0D9fQ/MFZ7NcGwF6kUYvSk4cvAUv4cGF9a9pcHC9pG9II0CLSex0M9AYuK1TbmxTsnU4KSkdWyVsjRMR5pP3JdgWmFgPHgtL9HFSH671NeupxM9KTot8l/X60Z1Sro3YgTTXPasK1zD52HGyZ1deVOT09jzwAH4xSXUT6O3dFof7VOT1NUs9C/XWo427dETEPuJP0uP9PyorPJK09urr05GHev+teoHcOEitqx+L4y4ArImJsRMyJiFnAr4D9C3WWAdsC0yPi5YhYViVvjRER40mL4PsC0yrsmzUBeBcYl59M/AhJ6+QnUms1OadH5a9VwHXt7HZHlLYEqWXzXDMr4zVbZnUUEQ9IuhA4GXhM0o2kDUOHkbZzuB/4daH+NEmXAz8CHpc0hfThfAhpCupF0lNoHyFpODA8/7E0ZTmwsInlK3kLiaIfk47ruVjS/sAc0pN0Q0ijR6eV1Z8CfBP4Kmmn+kraXByf95EaCgySNLpQ1A34X+HPO5P+TZrVRt4aJSIuk7SCFETfK2m/iHghlz0p6WhSEP64pNtJ97o7ae3eINIIYpvHLeX2pkt6hvSwQHfS3lrNmNobStpz7eYmXMvsY8fBllmdRcQpkh4BjiONPnQH5pGmwn5TISgZRTpa5RjScTmvktbnnEo6kmdehcv0o+WU2rb5C9Lj/R8JtiJintLB0mcBBwFfI00XXgycWWFDzCmkhftH0XLX+ZJa9tfqRxqh6l+hbGVZveeLW2BUyVvjRMRkSe+QRipLAdezuexaSbNJW38MIQUuy0mB9I3UtkN/0VWkUcHS9w2VR1yHA7dGxIJGX8/s40g1nvZhZk0maXvSKMgNEVFpM8tm9GEsaTpzt4h4pK36VdoYRlpQvmFEvNVKvQnAFhExvLU8ay5Jx5MC8n0i4r7O7o9ZV+Q1W2adTNKmFRatr0861gXSKFdnGUfa7PKs1WhjJmm/rmsk7SppO0kHSppY9r770XK6sFKeNYmk9Uib605xoGXWcQ62zDrfT4HnJF0l6fy87uop0jTfbcBfOqtj+Yy/I0ln+PVoq36VNl4nrVnrSVpgPYv0sMDC0tE2kkRaaD+r9LpKedZ0W5PO6yxf/2dm7eBpRLNOlhern0QaxelFesJsLukQ6/ER8W7n9c7MzFaXgy0zMzOzBvI0opmZmVkDOdgyMzMzayAHW2ZmZmYN5GDLzMzMrIEcbJmZmZk1kIMtMzMzswZysGVmZmbWQA62zMzMzBro/68O6CHrqgm1AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import astropy.constants as cs\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['Teff']), np.log10(iso1.primaries[\"L\"]/cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['Teff']), np.log10(iso1.secondaries[\"L\"]/cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['Teff']), np.log10(iso1.singles[\"L\"]/cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "bin_hr_diagram.plot(log_age=8.2)\n", + "plt.xlabel(\"log10($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log10(L in solar luminosities)\")\n", + "plt.title(\"HR Diagram of BPASS isochrone and Hoki cluster (in gray) \\n\" +\n", + " \" at solar metallicity and 10**8.2 years age\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the sake of comparison, we will create a plot of just the Hoki cluster's HR diagram." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5C/B1YEq957lG/9PSPrky13Mt4jexS3GH/8fT7/g5yvz2qp0f/CZ1V9r/v1LZbAq4OpT0dQD+H30w9Xdbus5m1HOu8AAHC9O+/8IV3Jbkuzq8N10vt+L3hPvwB7lPku8K89XUNnOJuaiszab4Peb61N8I7gR+BvA/wFolbXOvzwLn6ei0/3b1/E61rq9q/4sK7SfhD9+3pWt7CR5woi/19Zuy9jW/L/6gcgH+f3k4XZf71nMtpd/RgA/UkHsBvta3Tr3ftdMvJcFXQ9J9uHXPybgBRk3TYXkE/bfgI8Ud8Km+Y6rvFbQKSV8BPgu8ysyaZUEYBOOWNINzE3CimeW5RHUESa/AH/iOMrO2RFBJa/y34NOZm5vZQxXavQB3Lfq8mX25HbI1g2rK7xjgK2ZWy8S20v774k+5pzcuXlAPkqZamYVqmjZdgFtt9lnt9cYgCIA0bf1B4Llm1lY3kQr/5Y1xxTcDeEk9A5EmybI/blPwfTN7X5V2vwF2BLa1nJioY5WKyi/oHiQtw5/QrsenKbfB4wOugUdvOLWD4gVBV5FmsD4KnGdml7X52KfjoQkXsDIG6z74MtSJZvbeNsjw6XS8g/Hllueb2e0V2q4HfAK42MwuarVszSSU3zhA0mH4HP40PCLMA7iZ+De77YIMgolMGm29Dw9kPYWV68U/An5gbbhhp8hFT+K2G5+wxo2txjR1K79kqr2ueT6x0vI9cOfsR/Ggwrc1XcogCIIgaCJFlN/R+BNJr3kiViS9BbcYy1wf/oVbjw3l9xIEQRAEnaeIk/ss4MJM8SUOw6fYDsTNmKfg4XSCIAiCYMxSRPkN4EYVwIqoI4PAsWb2MzP7Jp7S4lXNFbFzqCRLcqdlGS8oJ/O3UlZvpUzy1doWPFZuvxOFif79g6AaRZTfBqwMpAwe8cOA0ozeN+DWSYWRNDf9Uec2sn/QGiQtkaeIGjeMVqmOZyS9S9KJkv4i6dF0nmr6bkl6jaSLJD0oaSTtXylocrbPNOWnhcprW/X3krSBpM9Kuk7S/UmOv0v6UmmYuTqO86L00HuZpDslPSFpWNJp8qzywTihiPK7Ew9nlLEXHk3k6pKyHjxFTRA0g8/gudoa9bUaTvu3xSl4nPAt3MR9G6Cu7CaSDsHD3v0HHn/1//DwaSdL+mZZ2wFJubk4Ja2dxRqVtLk8xVdeO0kaKPk8GbgKj4TyJB6Y40e4j+uheCi8WlkRMr7PytRhv8YjvlyPB+/4izzdVzAOKKL8rgBel57w9sJDZ11gZk+WtNmaNucOC8YvZnanmd1Udo0V2f/JtH+l3HrB6rwFD1W2EVDPiG8aHvvzPjwF1gfM7KPAC/Awfx/TqtkpfgAskLRjWT974VkVsrRdhwN/S+Wl7XYELscVbMbBeOLnH5vZi83so+m1Ix72rx9PzFoPpwLbmFn2XT5lZnuTwqgB/6eSoNNBF1NvHDQ8rmMWu/Np/AnrJSX1G+AO1j9Mn3cEvouHvbkP91e5GX+y3LCs74tYNUZm6WtaHbK9Dg+QeyceD28ZHsfw/Tltt8EDyA7jT4bL0udtctoeTn4cy33xJ9x/pu88go+APwSskdPPyamfrfHIEX/DR801Y/6R4gnio+qj8ezQWfzOfVObtfAwZjen87wYOKRKn3vjQa/vTedrMR5seUpJm9lVfpOTm3AuppWUTSvvt1Lbkrqd8OgTw+k73IlHwdi/Wr9VvtOSVH8FKdFnhXP38dT+Y3X8dlPxwMCX4TE6s+vt5+THjlwhb3p/evqNHsfjar6mwnGeDXwbTyXzOB6e63/T9bbaeS3wn5+b9v9ylTZfTG2OyKl7Z6r7SUnZWniS22F8ZGV4jM6bccWbWaArfb4F+FVq9+u03/tZNZ7nCal+vxwZXp/qjm3kHJT19c/U1451tH1vavuFCvWb4ffQv5eVZ+fnCnyZ6VE8Luoh5P+f5uLBpG/F7wsPpevtfyoc96Ik1zrp2lyE/39OTvXr4P/da/BYoI/i9595wF51nqdt8VisC3FH/X8Dt+NZIvor7LMufr+9NbW/DX/4WpfKMY0LnavyV93JbM3s7/KsAdk8/i/M7KqSJi/Abz6npc/vxhNYXoxHcV8TD8/zv8A+kl5iZg+ntifjVqNz0km+rqTfB6rJJelgPLvwXfjUy714INwX4NHMjy9p++Iky7PxpKH/wHN3HQDMkbSn1ZdA9Cg8+PVf8D/jZDxC/nfxSPNvq7Dfd/Ho9r/Hlc/TdRwLPLL7uXjUhXn4BfpW4Ex5mpn3Ay/BDY7+jUfJP1bSPVaWTULSF/CchffhObvuxs/Vx4FXS9rZPIbfktTuI2nX75R0c13J+0bPRcPIU0WdgJ+/3+I3zk2Bmfi5+GWV3Y/AFfYLk4wPpPJsezw+Wng3Hoi6nIPwc/yTOkSdhWfduBC/QY3gD19vxGdRXmZmf83Zb0s8jVAWTH4jPPr+PEl7mdmFWUNJ6+IPfi/GHzRPxa2uP097kg7vkbZ/yqn7Y1kbzJO0Hi/PdnFJKt4MeKGV+BCb391Ol2c2+HMqfj6wg62eCDdLbPxfeCDmUl6TtucxerIZiHqWdn6GpyE7SNJXzKz8v/5O/Oa9IjN6muY9G384XYQ/JD0O7I6nXHsJq/+fTsDvY/PxB8CNgVcDP5U0aGaVUiGdiV8zf8SD8Gfn9GT83nI9Pih4DH+I2xU3ZqznPL4eV/4XsjLE4vPx/85rJc20krBxaRr8TPz3uxk4Dr/nzU37rUaD52pVRvs0VEX7bwmsmVP+LlyTfyrnCcYoEH0/7Xc1fjPaNKduk5L3wpOcGnBAWbs3p/KbKHlioPLIb3rOsdbAb4hGyYg41Z2cyoeBrQp+vyVp37PxIANZ+W6p/D58vWNKSd3W+AV3bVlfu6d9FlCWAaHk/B+dc/wlVeRr9FxMKymbRp0jPzyt0pPpez8/59j9jfRbUrcu/gB1J7B2Wd3stN+pdf52m1KWxSCVvxBXhH8sK8/kNeCwsrq9U/kfyso/m8rPLLt2t0rnqNUjv3tSm40r1I+k+vXS57XwG+NSVh/5vZlVR35vTuVnsnLktzTtXzrym4SPdgx/CPsWPhK+Cp+N+Gwj37/se7wk9b+UnPtahX2OS/u8pqxc+IPNI8DkkvLDU/tjS4+BDxx+mOrm1PH/Wwd/IHoSj+tbWndR6udvlNwfU91k/EF2Yd53rPQb57Tro+ReVVL+SvyB9YSy8rclmeZTkhUCf4i7ifxMH4XP1WryjOJi2BAYaGA/4WlVLqjwR5tbsL+r00W0YY12mXXqggr1l6T6WTkneHadsswgZ6qDlTfbDzdwvpakffMu8ltT3R45dRfiT6ilF8ZZqf1qSiPVXwvcnXP8JQ3IXetcTCspm0b9yu/YVPbROmSou9+y+m+k+jeUlZ9Wfo00+sJHrI9TomBL5F1C/s3nduDesrKb8RtK3vWRXb8nNyhj9p+spvyeSG3WqlA/nOo3T5/Pwaepdsy+byp/Bf5w+v30+cT0ea/02VL7mWn/P5UdZ13cWMXKXr8Cth/lb7UhK6c89y+w3/PTPmeXlWcPMj8qKVuDlQ9dq51LXBE8g2dTr+fY2XTvgWXlF1FBMeBLV4Y/SGi013gFuf4G3FpWdl6l/xU+K7eK8mvWuap72hNAUg8+bXQA8Jwk1Fqp7iX4YvWhZnZNGpa+B5+33x5/qig1sOkrcuwqnIo/6d0gT7R5MXCZmd1T1i4zU76gQj8X4EP7HfAnkIrIo6x/Ap9e2BpPYFtKpe/WaDT2B8xscU75MvwJ/+qcumH8KWgzVhoh7Yw/Db5J0pty9lkHeI6kjc3sXzn1qzGKc9EoWZbtP1ZtNTpOAD6GX79nAkjaBJ/Gv9HMql4fpUj6L3ykMhPYhNUTSG+C/4lLuc5WnyYDX+9dYTwi6dnAc4GhCtfHRaw0IOkUmWWnpe1BwFIzW8V1wczOlafG2SQVHY4r+lWMncxsYTKgWeFSla7BM/EljLfgSwTCLdK/i1tp7mkNZEOQtD7+oLIN8HUzqzalvgpmdoOk+fgyz4CtjHx1cNqWZkbfFp+yvBk4tIJB7GO49XKpfFvgCZz3xJNdP6tsn7rvRWb2kKQsWfV18uS3lwB/sbKwltVI05gH4A9PL8QfHtYsafJE2S474MpqQU53l+aUNXSuyqlb+SVz4kvxp5nrcM1b2vnf8am4t+KLpb/Abxa34utUd+HTk+DrSOvWe+xqmNm3Jd2Lr/V8KPVtki7Gg7Jma3iT07aS5V9WPqXa8SRNwadTtsIvoFPw6aWn0r4fpvJ3u6vql6nMgxXKnwKwVaPurFKHz51nbIz/5rVuiD14qLqqjPJcNMqUtG2ZVbGZ3SrpHGBvSdOTYpmLf5cTq+5cgqQP4Tff+/Eb8h34oryxct0x7/w8UKHLp1j1ATK7piulHWv0eivCg7jCmkz+NbNB2j4EYFVCHyZFd2d6X9FCNw0VS/v5Fr6+OcfMfltS/gtJj+NrWl9n9YzzVUmK7/f4Q/G3zexTRfZPHI+v/R4EHCZpM9xA77oyZbxx2m5D9f9nT4l8W+P/uw1xJfVn/Pd4Gh8lv53i96I348r0v/GBDsDjaY3241Zfirtv4/fhO/GRfpaYG/x/tGVZ+8nAfebrweXkHa/wucqjyMjvc7jim2tmp8gzCXwhqzSzR5PC2VPSTFzxnQe8uvTpTZ4g8ZMFjlsTMzsFOCXdjHdJx34ncI6k7cwXyDMFsVmFbjZP20qKJuMg/GZ/hJkdXlqRnkg/XE3UGn23mgfxdaGNmtTfaM5FozyQtn34ekCrOAFf4H83brRyED5NeUo9O0taC7953IXHu72zrH7n3B2LkV2rlXzYKl3rzWQRrvy2xV0QVpB89tbHR3qrjRzMbAkrR4ZVMbNq7TKjlgtz6rKyHXPqKpJG1b/HH+i/3qDiA1+nXA68S9IXyTF0SWS/5Vlm9vo6+/5fXBG8w8xOLq2Q9FZWGieuRnqAyCt/DB91H558KWfhCut/cIW6WzWBJG2KD0KuB3axlUaNpXKV8xCwkaS1chRg3rXdyLlajSJ+fq8HzkmKphK34zel56bPvy2ftsBN1MuH5rDS8nHNnLq6MLMHzOwPZvZufF1nI1b+WNem7ewKu2fl19Q4TPbdzsype3ldgnaOK4ANJeVaUFXgaSr/Jp04F1ek7T6j6KOea+13+EjtHcmidhBfQ7i/zmNsgo9SF+Qovh5WTsM3TLqx3AL0SZqe02T2aI9RB9kyQl5Yw33K2rSKbHSTF8klKyufaqtImuX6M37v+MooFF82mv0Bfl98Lf4QNYIv15RyE/5g99JKzv05tPT/Z2ZD5rlA98anGHdNU8zV2BrXK3/OUXz9qb6ca9M+u+TU7ZpT1si5Wo0iyq8fX6ysxgg+hF2SPs8urUxPBd+rsG82ZbJFAZmQ9Kr0lF3OpmmbPXFehj+l7irpjWV9vBF/wvkn+XPMpSxJ29llfezA2I8kcnTa/p+kqeWVktaX9NKy4n/h64B5DyxL0nZ2WT+tPBcn4NN/n5e0fXll+oPVoua1ZmbP4H5Jm+LRQmDVNZpa3I1fezsmZZfJtzY+FbpJpR0L8mP8f/y1NKuSHWcr/Am81fwYX844RKvGa90Qt0SFYuetETKXicPKzsGarJy6O790B0mbSHpeWsstLd8Qn7F6KW5xe2gT5DsJf+A6Dp8p+Xm5YkgjnmPxGahj8v5v8qg3pdf8krSdXdZub1zJFkLSc5LtRjnr4+5hT1H7ISKTadd0/rO+e/DABHn36mxA9eXSAALpIWQ1V40Gz9VqFJn2fJiVCqUSW+FrgVfhyub1khbgCqUXfxJcRH7YpMvxm8VHJG3EyrneYyusaWWcjs9JX4qfeOFPbC/GDUHOAx/my2MNnouvBczDnyAG8fWXh3HLqGdqfMdTcAOP70jaHX8i2gafevk1Pmc+JjGz8+VZmo8Ebpb0B9yZtAefh385/luVPsVnPmR/Sov3/wb+amZn04FzYWb/kPR+/IZ6bfodb8anf2biv+PuNbo5P8n9f2ktYwQ3KjqurN0P8Kn9PtwZ+XLqxMyekXQMPmX69yTnOkm2jfDpuFpy1sO38Ov3DXgYr3PwB9A344ZbryvSmaSDWPm0nY0sXlvyUHGTmR2VtTez2yR9AjgGWJiMzp7AfRn7gW8VOW8N8il81HAg/rCRjTT3xI3t7mWlIs44BF8vOgKf5sv4NX4dLQbWkHQ4q/MbM7uuXuHM7A5Jv2flb1Fp3fhL+Drwe/FzfgG+XrYp/r96Gb789I/U/njcl/lXyThlGA8x9yrc17Xo/68PuELSjfgM2BC+ZvsafAr9mHKlnfNd75Jno38LbjTzZ/x6fAW+bHAd8KKy3U5J7V8FXC/37Vwbv6YX4vfo8vty0XOVK2y9Jqp/wH16np0+HwY8XVK/OX4TOT193gj/cZawMurIV4H1qGA+n7785az0Dapojl6yz3txE/5bceV5Hz6M/iT5PlaDuOPwnbjl4524Q+pgTtvDyffz2x63ALsbd7O4Gn/SmkYDpvU1vl/uubISs+UKdRWPid/cfok/hDyRftfr8IXqmWVt18dHW0vxJ79Vvl8zzkUj5w23ejwzHTeLnPIn4I21+k11/4ub0v87tal0js9K9R9o4LdbKx3nH/iC/13p2tuyyHmo9XvjN6hv43/+LMLLx2ggwkuJXJVeF1XY77W4pfXD6Tq4Cnh70XPW6At/8P4+fp/5NysjSh1Lma+brfrfPjzn/1bt+xsF3bFSv3PSvlfVaCfc7+18/F72RPpdL8UV+EBZ+13waeX707m/FH8Yml3h++VeQ6luCv6wlymSLHLSRbghY13uD/g9/iv4lPzjuBL9Hv6AWukanoRHC7otHXdJ6qMvfY/fjPZclb+KJLPdGzcvvww31d0f9+FaU9J2+JD2pbivRp7JahB0FWkK7RZ81mJz88g3QVCYNII8DDjIzH7YYXG6BkmvwNdfjzKzpi6l1K38kiBfYOUT05P40PR+3NRWeNSWbzRTwCDoFJL2x112vm9m7+u0PEF3kixHb8bvlwNWwGduoiBpqpktKyvbGFd8M/BIUY36Secfs4jySwLtji+kvxQfxj6IW+AdbWattuoKgpaT1kU3wmc41sIj4tzeWamCbkMe4GAGPiX8YtxP7ludlWpsktYJX4g7ut+Drxfvg/8PTzSz9zb9mEWVXxCMd+QJVp/E1+o+YWbndlikoAuRdDLua7cctxg+1Gob1E1I0izL+3Bf8in4WuEN+Hn7gbVAUYXyC4IgCCYcRfz8giAIgmBcUNHPT9IzuG/F9mb2z/S5nmGimVmhgNmdZpNNNrFp06Z1WowgCIKOc/XVV99rZnnRcsYV1ZTUfFzZPVr2edwxbdo0Fi6sJ4dtEATB+EbShDDuqqj8zGx2tc9BEARB0K3Eml8QBEEw4ahb+Um6NeUnq9bmA5JuHb1YQRAEQdA6ioz8plEj0Wuq37JBWYIgCIKgLTR72rOHAnmzgiAIgqATVHVJkFSe72xKThl4UtAt8DQmMe0ZBEEQjGlq+eMtYVX3hg+nVyWEp3AJgiAIOsxDDz3E3XffzVNPPcVaa63FpptuygYbbNBpscYEtZTfKbjyE54o8m943rdynsazY59vZn9upoBBEARBcR566CGWL19OX18fkyZN4vHHH2d4eBggFCA1lJ+Zzc3eSzoQOMvMvthqoYIgCILRcffdd9PX18d6660HwHrrrUdfXx/Lli0L5Uftkd8KzCx8AoMgCLqEp556ikmTJq1SNmnSJJ566qkOSTS2CIUWBEEwDllzzTV5+umnVyl7+umnWXPNNTsk0diiWmDrH+HrfZ81s+Xpcz2Ymb2rKdIFQRAEDTFp0iRGRkbYcMMNV5Q9/PDDq40GJyrVpj3n4srva3gyxrl19mlAKL8gCIIO8pznPIc77riDddddd4XBy7333ssWW+R5q008qim/rdJ2uOxzEARBMMaZNGkSvb293HXXXTzxxBOss8469Pb2xsgvUS2rw+3VPgdBEARjm8mTJzN58uROizEm6WqDF0mvljRf0oikhyQtlLRHp+UKgiAIxjZFsjrsIOn9kiaXlK0v6SeSHpC0TFK16C9NRdJ7gHnA1cB+wJuAXwHrtUuGIAiCoDup288P+BSwm5kdX1J2JPA2YATYGPi2pBtbHeVF0jTgO8AnzOw7JVXntPK4QRAEwfigyLTnTOCi7IOktYG3A1cCm+IGMfcCVXP+NYl3As8A32/DsYIgCIJxRhHltykwVPJ5JvBs4EQze9zMluHTkC9oonyV2BW4CXiLpMWSnpJ0i6QPtOHYQRAEQZdTZNrTytrvmsouLim7B3hOE+SqxdT0+gbwWWAxvuZ3nKS1zOy7bZAhCIIg6FKKKL87gJeWfJ4DLDWz0vx9U4H7myFYDdbAR51zzezXqeyCtBb4GUnHmNmKVEySDgYOrtRZOH0GQRBMLIoov18CR0g6A3gc2Bk3OinlP/BRWKv5F7ANcG5Z+Z+BVwGbA8uyQjM7CTipUmczZ860SnVBEATB+KPImt/RwOXA64H/Bv4KrEhvJGl7YEdWnQZtFTdUKFfaPtMGGYIgCIIupW7lZ2YjZvYy3KDlBcBMM3uwpMmjuL/dCc0VMZez0nbvsvK98anYu9ogQxAEQdClFJn2BMDMrq9QvgRYMkp56uUPwIXAiZI2AW4F3gi8EnhHm2QIgiAIupTCyg9A0q7ADsAU4EHgGjO7tIlyVcXMTNK+uJP9EcCGuOvDAWb283bJEQRBEHQnhZSfpBnAz4DBrAh3d0DSIuBAM1vYVAkrYGYPAR9IryAIgiCom7qVn6TnAhcAGwCXpvd34paVuwO7AedK2snMbm6BrEEQBEHQFIqM/D4P9ABvNrNfldUdLumNwOnAoXjYsyAIgiAYkxRxddgL+E2O4gPAzM7Aw5vt1QzBgiAIgqBVFFF+m+BGJdW4KbULgiAIgjFLEeV3D7B9jTbPwzM7BEEQBMGYpYjyuwB4naS35FVKegMe7/O8ZggWBEEQBK2iiMHLF3HldmpKHXQhbu25GTAbz/LwMPDlJssYBEEQBE2lbuVnZrdI2gs4BXhZehkr42kuAt4ebg5BEATBWKeQk7uZXQVsJ2kXYAYwGY/wcq2ZXdYC+YIgCIKg6TQU3szMFgALmixLEARBELSFIgYvQRAEQTAuKBrbc23c6GUnPJj0mjnNzMze1QTZgiAIgqAlFIntORXPnP48Vhq55GFAKL8gCIJgzFJk5PctYDvgNOD/gCHgqVYIFQRBEAStpIjyeyUw38wOaJUwQRB0PyMjIwD09PR0WJIgqEwRg5dJwF9aJUgQBEEQtIsiyu96YMtWCRIEQRAE7aKI8vsGHtuzVnDrIAiCIBjTFFnzuxs4G1gg6bvA1cADeQ3NbP7oRQuCIAiC1lBE+V3Eylien0/vK5Hn/xcEQRAEY4KiWR2qKbwgCAKGh4cBGBwc7LAkQVCZIlkdDm+hHEEQjANGRkZYvnw5AH19feHuEIxZIrZnEARNIfPvC4JuoKGsDkEQTGyqKbre3l4gnNyDsU1F5SfpAnyN7+1mtjR9rgczsz2bIl0QBGOWbG0vCLqRaiO/2bjyW6/kcz2EUUwQjHN6enro6+tbTQEuX76coaEhBgYGYs0vGNNUVH5mtka1z0EQdJ5G19maoZTKFWCm+IaGhgCYMWPGqI8RBK0i1vyCoAvJU3q1piH7+vpW2b+IAqykZCsdM1OAQTBWCeUXBG2m3tFas9fUKvVXqhRrtc3cGMrJjFxKueaaa5g1a1YBCYOgfRRWfpI2wfP69QNr57Uxs1NGKVcQjDtqKb1WGJAsX748VzE1ctxKii+ry6Y8ly5dCsCCBQsAn/6Mtb9grFEkk/u6wLeBdwLrVGqGG7yE8guCEkoVXzOUXDVFVLRtLeVYi2yKs1TxZSxYsIChoSHmzJkTCjAYUxQZ+X0TeB9wI/ALYJjI5B4EVWm20msFmXIcrRIMgm6iiPLbH/gb8GIze7JF8jSEpD8BewNfMbNDOy1PEIArvrGq8PIoHyGWKsNqo8eBgYEV7g3AKqO/gYGBFeVBMJYoovzWB84dg4rvrcALOy1HEJTSqOIrMp3ZahqRpVTRZYovRpTBWKSI8rsB2LxVgjSCpCnA0cBHgZ93VpogqI+xpOCaQSW3hkzxhbN7MBYp4rj+TWA/Sdu2SpgG+Dpwg5md1mlBgqAWy5cvn1CKDyKzQzB2KZLS6FeSNgcukXQ8cA3wYIW2Lc/kLmlX4EBiyjPoArpJ6ZWu3zWyb7YNC89gLFPUz29DfO3vCzXatTSTu6S1gROBb5rZolYeKwhGS7cpvtJtOZlxSxB0O0X8/D4DHAb8C3d1WEbnXB0+BTwL+Eo9jSUdDBxcqX6LLbZoklhB0H1Wnhn1KLVKbcrLw8IzGOsUGfkdDNwK7GhmudOd7UDSFsDngIOAdZPzfca6yQjmYTN7Ois0s5OAkyr1OXPmzMhEEYyavAgu3TTqa5RQfEE3UkT5bQac0EnFl9gamAT8LKfu4+m1A3BdG2UKghWUZjmoRGb+307l2IrpypgCDbqVIsrvVmBKi+QownXA7jnlF+IK8YfALe0UKAgaodsVXzVi9BeMdYoovxOAwyRtZmZ3tUqgWpjZA8BF5eWSAG43s9XqgmA8Uu90Y57iy8pGo6Ri1Bd0M0X8/M4GLgYWSJor6T8lbZH3apGsQTCmycz681IElTOaUV9pwtjy8nr3L7pPo8cKgrFKkZHfbXjGBuFTi5Wwgv02BTNTu48ZBOWM1q+t1lpgLaVTbUTXqMIq7SuUXjBeKKKkTsEVWxAENejr6ys8uiuNgdnb2zvq0WG1z0Ew0SkS4WVuC+UIgglLpcDP5QqwmVOUWeaF/v7+hvoMZ/eg22n79GQQBE6nsh2UJ5ytRlhtBuOVUH5B0EbKFV7pyK6WMhztaKsZSi9GfMF4oUh4sx/V2dTM7F0NyhMEQQVapXQaCWRdLsvSpUsbnkINgk5QZOQ3t0Z9ZglqQCi/IKiD8tFeeQqgvr4+rrnmmqYcq7+/f7XRX6a0yhXgaDI7BEE3UMTPb6sKrx3wuJ9L8YDXWzdZxiAYN1Sz4BwcHFzNVaKnp4cZM2ZU7bPWiLBeJdZoYOsi06lBMFYoYu15e4Wq24G/SjoH+BtwHtX9AINgQrN8+fJVRnyjSfhamoKoVoSXdhi6ZPvNmDEjcvkFY5qmGbyY2ZCks4EPE8ovCOqiUcXXSqOT0Ux3DgwMMGvWrCZKEwStoci0Zz0sB7Zpcp9BMO7o6+vLneash1ZbW1brP1OMlRRkrSnaIBgrNE35SVoT2APodMqjIBjTjGZKsBWKr3Q6tHQatVo29yDodoq4OlSay1gLGADeAbwI+MHoxQqC8Ulvby/Dw8MMDg42td/RKqRKrgp5sUJLFWMYuwTdSpE1v4uoHttTwHzgE6MRKAjGM5mxy6JFi0Zl6NJuyjNBhNILup0iyu+L5Cu/Z4D7gSvN7MqmSBUEQd00axqy1OevFf0HwViiiKvD4S2UIwgmDOWuDq0mC0mW5+ReTt70Z+nUZzbqW7Zs2Sptpk6d2lyhg6DFNNvaMwiCMUg2eqsnBFklBVnN2KZcGQbBWCeUXxCMUwYGBla8ss9QvwIssq4XI7+g26g47Snp1gb7NDOb3uC+QTAhWL58OX19fS3rv3ydrlo29iJTmGHoEowXqo381sAtOIu+YjQZBB1ktAYqRacwY9QXdCMVR35mNq2NcgRB0GLy1uyKKrp6jGaCoBuIUVoQdDnlSq0ZUWBKlWLeSDIb7cWoL+hWQvkFQYsodWeoFi4sY2RkhEWLFrFo0aJRH3s0CnDx4sWrlZUazmQGM+WKL/wBg24ilF8QtJCi/nzLly9n+fLljIyMFNovT7m2Ig5oqQLMXkHQjYTyC4IW0NPT01D8ztEmpi0SkLp0arN0tDd9+nSmTp1Kf39/7vHKy3beeWf2339/DjjggK4J1xYETcvnFwTB6gwODtLX11doFDY0NNTW1EDl05z1rOOVKsA5c+aE0gu6jhj5BUEbKF0zq8Tw8HCukuzp6Sk8fVqPsq2k5IpYgM6cOTMUX9CVhPILgjFG0bW6LBpLngtCpb5K1+qmT4+YFMHEo27lJ+kCSV9qpTBBEOSv6/X19eWWlyu88s+NWGCG+0IwESgy8nspsGarBAmCiU6m4PIUVk9PD7NmzVqlvpKz+dKlSxkYGGDmzJnMmjUrd/2wlqN6JWOXIBgvFFF+N+MZ24MgaBEDAwP09vZWXEfLFGA9/WTWpj09PaspwEouCpmVJ6yewLYeX8Ug6BaKKL8fAP8laYtWCRMEE4VKhi2zZs0q7CKxbNmyuo1UBgYG2GWXXeoe1bXLfzAI2k0RV4ezgVcAl0n6GnAVcBc52d3N7I7miBcE449mKY/RxNnMlN/ll1++iuLM3ofzejDeKaL8bsUVnYDvVmlnBfstjKQ3Am8FZgKbAncAvwa+amYPt/LYQdBKssgu1dwHark99Pf3jzpTfF5G94xYCwzGA0WU1CnkjPI6xMdxhfdZYCmwA3A4sLukXczsmQ7KFgR1MTw8vMoU58jICNdccw0AM2bMqKoABwYGckeQ9Y7Yent7GRoaor+/v+KUaTaqLO9zaGgoFGDQ9dSt/MxsbgvlKMprzeyeks8XS7oP+AkwG7igI1IFQQ6ZwUmm2EZLqcLMm/YcGBhYLVFuJsPw8DDgMUQr7V8PoQCDbqcrndzLFF/GVWnbuvTYQdAgmTFLb2/vCqVRGsA6G/XVa1E5ODjInDlz6O/vZ+rUqavE4qyklHp6eurKHl8+Eoz8fcF4pCuVXwVenrY3dlSKIKhCufIZHh4urPgyenp62GWXXVYrr+YqEQowCJzChimSXgzsjY+w1s1pYmb2rtEKVlCmPuCLwHlmtrCdxw6C0ZClMCpVfI1ag2aO7fW4SuSt+eXl8SuN9lJqBJONMOtRpEEwFqlb+UkScDLwP7jFZ2b5mWEl5W1TfpJ6gHnAU8A7KrQ5GDi4Uh9bbBGui0H7KV03y7ajcYPI9u3r66tqLJON/jKlWy+lhi+ZM34QdCtFpj0PAd4G/BR3MRDwHWAX3OryYeB0YOvmilgZSZOA36Zj7m1muXMzZnaSmc2s9HrOc57TLpGDoCr1ZH+oxNKlSxkaGuK4446rmQ0+yxSRHas8uHXp5/KktaVrlkHQrRRRfm8HFpnZXDPLzNYeMLMrzOwoYHfgDcAezRYyD0lrA2cCOwGvNrO/t+O4QdBMSqc7y0eBrWZwcJCZM2eumNqsN7tDRHgJxgNFlN8gq7sQrJg2NbNrgd8B72+CXFWRtAZwKrAnMMfMrmj1MYOglZQrwHopn3osapjS19fHzjvvvJoCLF//y0ubFNOeQTdTRPkJeLDk8yPARmVtbgaeN1qh6uB7wJuAbwKPSHppySviMgVjnjwl18zA0fVOSfb09DBnzpxVFGA5YekZjEeKKL9hVvWhuxXYsazNNrhSbDX7pO3ngMvLXge14fhB0DDVRkyjVYCNxOTs6elh5syZ7LzzznXvE1OfQbdTRPldyarK7o/ATpI+L+n5kj4AzAFaPgVpZtPMTBVeh7f6+EHQKKVWmKWjv0zpZUYrjTCaYNSDg4MMDAzkjv4iyHUwHimi/M4E1pS0Vfr8deB24Ajgb8CxwAPAp5spYBCMN/J84zJFWFTRZGt2zVBQeUlvM8r7j9BmQbdTt/Izs9+Y2XZmdlv6fB8eUPqTwEnAZ4D/NLObWiJpEIwTslBnsKoSydwciiiWnp4eDjjgAPbbb79VyltpjBKKLxgPjCq8mZk9aGbfNLP3mdnXzOzOZgkWBOOdLNZnMxgcHOSQQw5pSl+VKPf3C4JuZjzF9gyCrqaZ1p6jZfr06asEy84jQpsF3UzF8GaSZjXaqZnNb3TfIJjodEIBjoyMMG/evFXifC5evJjddtttNeVXGtqsWhi1IBjLVIvteRGNJ69ds8H9gmBC0iyFVynJbTUyxZe33yWXXMKyZctWjAJLCcUXdDPVlN8XGTuZ24NgQlCaOaFdZAluKzmzl0Z76e/vp7e3N6Y8g66novILf7kgaB/ZiK0TBiWlGR7K8/iVk6UxilFf0O2EwUsQjBHaHdg6I3O92GWXXSqGOKtl/BIE3UbhZLYAknbFffym4PE+rzGzS5soVxBMCPLy6nXC4GX+/Pk1jxs5/ILxRKGRn6QZkv4BXIzn8jsCOBq4WNI/JM1svohBMH7JRl3ZiKrdQaRHRkZWKL5M+dWb2igIupkimdyfi6c02gC4NL2/E9gcz+W3G3CupJ3M7OYWyBoE45ZZs2Yxf/58Lr/88lH1kynRRgxSyqc0y9MaBcF4osi05+eBHuDNZvarsrrDJb0Rz+R+KJ74NgiCAvT29o7K4KU0bFojZKHVent7OfbYY4FQgMH4pci0517Ab3IUHwBmdgYwL7ULgmAUzJs3j5GRkbYes7e3l1mzZjE4OMhRRx3FTjvt1NbjB0E7KaL8NgFqBa2+KbULgqAgy5cvX5EtfWhoiHnz5rFo0aJOixUE45Iiyu8eYPsabZ4H3Nu4OEEQlLJ8+fK2jwArMTQ0VHeG+CAY6xRRfhcAr5P0lrxKSW/Ak9me1wzBgiAYOxnTM+f3sSJPEIyWIgYvX8SV26kpa/uFuLXnZsBsYFfgYeDLTZYxCCYE5QYvQ0NDHXMqHx4eXs3tIvs8PDzM4OBgJ8QKgqZRt/Izs1sk7QWcArwsvQxQarIIeHu4OQRBY2RrflA8o3uj9PT0MGPGjFXidS5atIizzjprlVBnpVafy5cvD+UXdD2FIryY2VXAdpJ2AWYAk/EIL9ea2WUtkC8IghbT09OzQpktWrSIhQsX5jrbZ2mOdt55Z0ZGRiK+Z9DVNBTezMwWAAuaLEsQBGUMDQ3FNGMQtIBRB7aWtImk/STtLSny+AVBE8hcHpYuXdoRC8tOZJcIgnZSt/KT9D5Jf5G0UUnZjsCNwBnAH4AFktZvvphBMP4ZbYSXZpOX4WHJkiXtFyQIWkCRac83A2Zm95WUfQPYEPgx0Av8F/Be4FtNkzAIJhjlCrDRTAql/oFF1ueyMGcA+++/Px/84AdXqQ93h2A8UGTacxvgb9kHSZsALwd+aGYHmdlrgauA/26uiEEwsVm4cGFDju7Dw8Ncc801KzK1F2XGjBnMmjWLyy67jN12262hPoJgrFJE+W0M3F3y+WVpe1ZJ2SXAlqMVKggmKqWjrmaEOmt0lNbb27titNjT08P+++8PwLRp0yKhbTAuKKL87mPVuJ0vB55hVatPAyY1Qa4gmHBUS0M0NDTEwoULWx7rM0+GkZGRmOoMxh1FlN+NwGslbSxpCr4GeJWZPVTSZhpwV/PEC4IgoxEF1MgobcaMGSuU4MjICNdcc82o8wwGwVijiPL7Lp64dikwhIc1Oz6rTG4OuwJ/baaAQRCspBG3hyKJbXt6ela8RkZGGB4eXkXphrVnMF6oW/mZ2W9xS84b8FBmHzezn5U02Quf8jynqRIGwQQnL9pKPfT19TVsKZqxfPlyhoaGVgl1FgTjgaLhzU4CTqpQdw7u9hAEQQP09PRwwAEHMH/+/KassZWGLWuUWOsLxiujjvASBEFz6e3trbpW1678ftmUZ6MjzyAYy3St8pM0IOkMSQ9KekjSryVt0Wm5gmA0zJ8/n4ULFwIrnd37+/tXcXvoJNOmTevo8YOgWXSl8pO0Hp5c93nA24G34U74F0Z4taBbyXMpyBRfxtDQENdcc03bZIpRXzBe6UrlB7wb2BrY18x+Y2bzgNfhDvbv6ahkQdAksqnP8nBnCxYs4NRTT23L9Gf5scPaMxgvdKvyex1whZndkhWY2W3AZXi2+SDoagYGBpgzZw6HHHLIKut/2UgsGwG2a/0PViq+Tk+9BkEzaCif3xjg+cC8nPIbgDe1WZYgaBozZ84EWMVKc86cOWPC8GTatGkx8gvGDd068tsIuD+n/D7C3SLoUjLXhFruCaWjv3YTBi/BeKFbR37gcUTLUV5DSQcDB1fqaIstwkg0GJssWrSIs846a7VR39KlSzsWYPq0007jyiuv5CMf+QizZs3qiAxBMFoKKT9J2wAfBnbCR1h5mdvNzKY3QbZq3I+P/srZkJwRYTXnfICZM2fmKdIg6BhZTM1qo7tOrr0tXryYD37wg7z1rW/lkEMOKZQvMAjGAnUrP0k7A+cBzwKeApan7WpNmyNaVW7A1/3K2R74RxuOHwQtZ2hoaMwal2Rrf51ehwyCRimy5ncksC4e33M9Mxsws63yXq0RdRV+C7xU0tZZgaRpeI7B37bh+EHQFiq5O7ST/v5+dtppp1US2k6bNi3W/4KupojyezFwhpmdZGZ5I7528n/AEmCepDmSXodbfw4BJ3ZSsCBoFeUKcOnSpcybN69t7g79/f3stttuTJ/e6lWNIGg9RZTfE8AdrRKkCGb2CLAH8E/gp8CpwG3AHmbWPsenIGgRPT09zJkzh5kzZ7Lffvux3377MTAwsJoCzLK8t1oBZhnm+/v7mTp1KhAO70F3U0T5LQB2aJUgRTGzO8zsDWa2gZk928z2NbMlnZYrCJpFqevD4OAgc+bMYZdddgFYkWIoi/fZagU4Vtceg6BRilh7fhZYIOltZvbTVgkUBEE+w8PDLFiwoG3H6+vrW7HmmG1DCQbjhSLKbw4eTPpkSQcBVwMP5LQzM/tSE2QLgoCVbg8LFixYxbpy2bJlK6YgW6GUsvyCixYtWpFBfmBgYIUMYfASdDNFlN/hJe93S688DAjlFwRtYunSpfT39zM8PDzq5LV5DA4O0tfXtyK/X8aSJUtWsQANgm6iiPLbvWVSBEEwKlrhbzcyMsK8efMYGBhgxowZDA4Osnz58pj6DMYFdSs/M7u4lYIEQTC2GB4eBnxKdcaMGQDMmDGDoaEhpk+fHtaeQVfTzbE9g2BC0NPTs0L5ZOt+2VpfK8mmOzMZSkOuLV68uOXHD4JWEsovCLqAnp4eZs2aRW9vL2eddRbQntBieTE7I6RZMB6o6Ocn6RlJT0natuTz03W8Oh39JQjGHSMjI8yfP5+FCxeuFvIs8/lrNZki7O/vZ/r06WHtGXQ11UZ+83HLzUfLPgdB0EZKXR3ysrqDK8CFCxfS19fX0gwL2ZrflVde2bJjBEE7qKj8zGx2tc9BELSHnp4eent7a+bvu/zyyxkaGmppiqEs7NrQ0BBTp05l5513BjzvYKsVbxA0E5nFYG7mzJm2cOHCTosRBFXJXA/ALTCzkV/ptOfUqVPp7+9nv/32a4nPX7k8AJ/+9Ke55JJLmD59OkceeWTLjxu0FklXm9nMTsvRaorE9gyCoINkEVdqjQCXLl26IiJLq+UJgm4llF8QBKMmfP6CbiOUXxAEQTDhCOUXBF1Gb28vsHpy23LH93YluYUIch10H6H8gqALKV33mzp16grFt2zZMpYtW9b2+Jsx7Rl0G6H8gqALyZRb6eivXc7u1WiHoU0QNIOmKj9J35P0r2b2GQTBqtSjYDqReSHifQbdRLNHfusBU5rcZxAEJWRBrqtx5ZVX8ulPf5pFixa1QaIg6D5i2jMIuoyenh4OOeQQdtlll5ptjz32WE499dS2GL9Mnz695ccIgmYRyi8IupAsy8N+++1Xsc1YWAMMgrFKKL8gGMd0wvIzCLqBUH5B0MVkxi8xyguCYoTyC4IuZ6wovrD2DLqJqpncJd1asL9NRiFLEARBELSFqsoPmNZAn5EjKQiCIBjT1FJ+W7VFiiAIWsrw8HDL8+xNnz6doaEhRkZGIt1RMOapuuZnZrc38mqX8EEw0ent7V0toHU5S5cu5ayzzmL+/Pktl+fMM8/kwAMPDOf6YMwTBi9B0MUMDg5y1FFH8YY3vKFm2wULFjB//vymO7z39/czbdo0Fi9ezOLFizn//PPZaaedOPXUU5t6nCBoJhWVn6RnjbbzZvQRBEF1sgzvRx555GqjwPLPrfD5GxgYiOguQddRbeR3m6QPS1q3aKeSXihpHvDxxkULgqAIfX199Pf3r0hxVGs6tBlkivcjH/lIrgJsZ07BIChCNeX3Z+DbwJ2STpC0e7WRnKStJb1P0uXANcALgQubK24QBNUYGBigv79/tUS3rWbWrFkceeSR7Lbbbm09bhA0SkVrTzM7UNIxwFeBg9PraUk3AncC9wOTgI2BQdzHT8By4HPA0Wb272YLLGlb4APA7sDWwMPAVcDnzeyvzT5eEHQLPT09zJkzZ8Xn4eFhli9fzoIFC9py/GzkGQTdQFVXBzNbCLxS0jbAu4A9gRcB/1nW9B7g18CZwJlm9mTzRV3BK3HF9xN8hDkF+CTwF0kvM7OrW3jsIBjTlLoYDA4OMjg4yIwZM5g3b15HYnyGy0MwVqnl5weAmd0MfBpA0npAHz7iewy428zubJmEq3M68D0zW+FML+kCYAnwYeDANsoSBGOebEQ4PDxMX19fKKQgoE7lV4qZPQrcnF5tx8zuzSl7UNI/caUcBEEZPT09LXdyD4JuYlz4+UnaCPgP4MZOyxIEQRCMfeoe+Ul6ObAHsC2+zmbAg8A/gQvM7OJWCFgnx+LGNt/poAxBEARBl1BT+Ul6MfAjYHtcweRxqKQbgHcmI5m6kbQXcG4dTS82s9k5+38G+G/gXWZ2S4VjZNaquWyxxRb1CRsEQRCMC2qlNHoe7qu3HnAJ8Ed8re9BXBFuAGwDvBrYFbhA0k5mdlMBGRYA29XR7tEc+d6Lu2IcamY/qrSjmZ0EnFSpfubMmZGJIgiCYAJRa+R3BLAOMMfMzq7S7ihJc4BfAYcDb6lXgGRAU0RZAiDpbcDxwLfM7CtF9w+CIAgmLrUMXmYDv6yh+AAws3nAGbgPXkuRtB/wY+AHZhYh1IIgCIJC1Br5bQAU8Yy9Pe3TMiTNAk4D/gacLOmlJdX/NrNrW3n8IAiCoPuppfxuxy08ayJJeASYO0YrVA32ANYFdgAuK6u7ncayzwdBEAQTiFrTnr8AXizpdElbVmqU6k4HdgR+3kT5VsPMDjczVXhNa+WxgyAIgvFBrZHfkfhobn/gTSmKyj9xa0+Aybjf37a49ecC4KjWiBoEQRAEzaFWYOvHJe0BfAx4L569IS9G0h3A94Fvm9kTTZcyCIIgCJpITSf3pMyOBI6UNB1XfpNT9YPAIjNb3DoRgyAIgqC5FApsnZRcKLogCIKgqxkXga2DIAiCoAhNVX6SdpEU+fSCIABgZGSk0yIEQS7NHvm9G4+8EgRBEARjlsLJbIMgCPLo6elhv/32Y+nSpQDMmTMnssYHY5ZaWR22Ltjfs0chSxAEXc7g4CDHHXdcp8UIgprUGvndgietDYIgqItFixYBrgiDYKxSS/kZ8AAeRLoengdsOhqBgiDoTkZGRjjuuOM48sgjATj++OM54IADOixVEORTS/ndCmBmdaUpkvRjIKw9gyAAXCHGul8wFqml/K4FXi+px8zCZjkIgor09PTw6U9/ml122YXe3t6Y9gzGNLWU31+BNwIvZPX0QXkovbqKq6++ekTSok7LMQbYBLi300J0mDgHTpyHiXsOKmbwGU/IrLI9i6Q+YCZwlZkta5tUbUbSQjOb2Wk5Ok2chzgHGXEe4hyMd2pldRgGhtskSxAEQRC0hYjtGQRBEEw4QvkFQRAEE466w5tJ+lEdzZ4BHgJuBH5nZnc2KlgQBEEQtIoisT3nsjLaS55Fp5WVPynpUDP7RoOyBUEQBEFLKDLtOR2YB/wLOBSYDWyXtp9P5WcBLwHeAywHjpI0p3niBkEQBMHoKTLy2xfYDXhRsgLNWATMl3QK7hR/iZl9R9Kf8OnPQ3ClOZY5qdMCjBHiPMQ5yIjzEOdgXFPVz2+VhtKNwIVm9v4qbU4AXm5m26fPpwOvMLONmyFsEARBEDSDItOe04AHa7R5ANiq5PMSIAL7BUEQBGOKIsrvXuAVNdq8El/7y5hCbYUZBEEQBG2liPI7E5gh6WeStiitkLSFpFOBFwFnlFTtCNw8ailHiaR+ScdKulzSo5JM0rScdhtK+oGkeyU9Iuk8Sf/ZAZHbhqSXSfqzpLslPSTpGknv7LRc7UbSqyXNlzSSzsNCSXt0Wq5OIelP6X/y5U7L0i4kvVHSmZJul/SYpEWSjpQUSbrHIUWU3xdwg5b/BhZLWiLpL5KWAIuBt+KBsL8AIGlz4Engp02VuDGeC+wP3A9cktdAkoDfAq8CPgi8AVgbuFBSf5vkbCuSXgCch3/Pd+Pf+Srgh5Le10nZ2omk9+BGWVcD+wFvAn4FrNdJuTqFpLfiwewnGh8HngY+i98HTgDeB5wrKQKCjDfMrO4XsC7u1nAL7tCevW7Bld6kIv216wWsUfL+INwncVpZmzmpfPeSssnAfcAxnf4OLTovXwWeAHrKyq8ALu+0fG06B9OAx4CPdFqWsfDClyruwh9mDfhyp2Vq43d/Tk7Zgek87NFp+eLV3Fehpxkz+7eZfcnMnpsUwwAw2cyea2ZfNLPHi/TXLszsmTqavQ5YZmYXluz3IHA2rhjHI+vgo/PHysofYOKEvnsn/gD3/U4LMkb4OnCDmZ3WaUHajZndk1N8Vdr2tVOWoPU0fIMzs4fNbNjMHm6mQB3k+cD1OeU3AFtIGo9Wqyen7TGSpkqaIundwJ7A0Z0Tq63sCtwEvEXSYklPSbpF0gc6LVi7kbQrPtKp6M40AXl52t7YUSmCplPEyR0ASesBrwd2YKU15zXAWWb2SFOlay8b4a4Z5dyXthsC4yqbvZldL2k2Hpknu+E9CbzXzE7vlFxtZmp6fQNf61mMr/kdJ2ktM/tuJ4VrF5LWBk4EvmlmkdiZFflMvwicZ2YLOy1P0FwKKT9JrwZ+giuK0jieBhwt6R1m9rsmytdOxMrYpeXl4xJJ2+BWvDcA78WnP+cA35f0uJmd2kn52sQawLOBuWb261R2QbIG/oykY8ysvkgQ3c2ngGcBX+m0IGOBNNMzD3gKeEeHxQlaQJGsDjOAXwNrAqcCFwB3ApsDe+AL5GdIepmZXd0CWVvNfbhSL2fDtL2/jbK0i6/iI73XmNmTqex8SRsD35V0Wp3rpd3Mv4BtgHPLyv+MW/xtDixrt1DtJLkufQ43BltX0rol1etKmgI8bGZPd0K+diNpEm75vTUesWpph0UKWkCRNb/P4SOj3czsQDM72czOSdsD8bUTw6eOupEb8HW/crYH7jCzcTXlmfhP4K8lii/jSmBjYNP2i9R2bqhQno34x7vyB7/JTwJ+hj/kZS9w8//78Wtl3JOmf88EdgJebWZ/77BIQYsoovx2A35lZlfkVZrZX3AH992aIVgH+C3QJylb4EbSBsBrU9145C7gRZLWKSt/CfA4K9c7xzNnpe3eZeV7A0vN7K42y9MJrgN2z3mBK8TdcXemcU3y5TsVN/iaU+leF4wPiqz5TQaGarS5A9igcXFah6Q3prc7pu0+ku4B7jGzi3EFdznwM0mfwJ92P4OPAL7ebnnbxHG4M/fZko7H1/xeh09hH21mT3RSuDbxB+BC4ERJmwC3Am/EQ/VNiLUeM3sAuKi83OM+cLuZrVY3Tvkebuz0FeARSS8tqVsa05/jiyJZHW4DbjGzivE9JZ0DbGtmW1Vq0ykkVfqiF5vZ7NRmI+CbePqmSbgy/F8z+2s7ZOwEkvbBjR2ej3/nxXgqlxMn0BrPBsCRuNLbEHd9OMrMft5RwTpM+s98xcwO7bQs7SBFq9qyQvURZnZ4+6QJWk0R5fc93CLwc8A3Sm+Mabrgo/gI6ftmNuF8pIIgCILuoYjy2wyPfbgZPr15CW7tuRlu7DINX0OaaWZ3tkLYIAiCIGgGdSs/gOT7dCL5qY3OxZ2jb2uOaEEQBEHQGgopvxU7eeSDHXAjmAeBa81suMmyBUEQBEFLaEj5BUEQBEE3M1Ei9wdBEATBCir6+Un6UYN9mpm9q8F9gyAIgqDlVJz2lNRoWCczszUbFykIgiAIWku1CC9jzlE9CIIgCJpBxTU/M7u90Vc7v0DQXCRNk2SSTu60LK1E0imS7pa0fqdlqUWrf5Nu6b+J/eyY+onlmQlMGLwEYwZJb5R0rKRLJD2UblA/q2O/fkk/krRM0r8lLZH0HUkbVmg/E/gfPITZIyXl96Rj1vt6b/O+fdAuUsq13wBfTnn7gglI4UzuQdBCDgVeCIwAS4Hn1dpB0nRgAZ5+aR4el3Mn4MPAq1J+yX+V7fZV4CHghJJ+1scDG5eyFh7O7wk89mc5f6r9lZrCMLAd7lM7kWnmeTgS+AvwIfx6CCYYofyCscRHcaV3C/ByPNtCLY7HFd+HzOzYrFDSt1N/X8Fj0mbl2wJ7AT8ws8ey8jQCPLy0Y0kvxJXf3zsZ1DjlW7ypU8cfKzTzPJjZlZJuAt4j6WsTJYh7sJKY9gzqRtL+kuZLelDSY5L+LukzZZm/s7aS9GFJ/5D0uKRhScdJmpymJZeU72NmF5rZzVZn5AVJW+Oph5aw+qjtMOAR4G1l63rvxNNU/aKOQ8xM26vrkadV5K11lZal96dLujed64WSXtOE464h6Zh0nF+nDOel9S+RdIakuyQ9IWlI0omSptbR985Zv1Xa3JimsTcq/84lbUZzHk4HtsAfhoIJRii/oC4kfRVXGNsBP8dzAQqfMjpHngG7lO8B38FD4J0EnIYrqnOB8raNskfa/tnMVnHNMbOHgcuA9YDSvGx7AU8D9SQqzXI/LhylnK1kS+BKPLD8T/Hf6D+AeZJ2r7JfVZKi+yXwQfy3fKOZPV5S/w78/O6Dj9C/g5+ng4CFkrao1r+ZXQ4sAl4jaeOc4++ET3ufbWb1JFVu5DxclrYV07QF4xgzi1e8Vrzwm4cBJ5eU7ZzK7gA2KylfCzg71X22pHy3VLYImFJSvg4wP9UtqSHH7NTuZ1XafCO1+ViF+uNS/fvS5/WBp/BpzHrOxV/S/jPG4G+SlRlwWFn7vVP5HxrpH9gIz9ryDPCpnPbb4uugtwB9ZXV74A8XZ1WTP5V/JpUfknOM76W617bqPOAPZgZc2cnfN16decXIL6iHd6btl83srqzQzJ4CPobfJA8qaf/2tP2KeZbwrP0T+A2vWUxO20oGEFn5lLTtA9bEU3FVRdJawAvwm/z1jYu4or93S7pV0lOSTqxU1gC3A18uLTCzc/AHlZ0akHNLfET0EuBtZva1nGbvw0fvH7aygPZmdgHwW+C1kp5d43A/xa+dt5cWSloHeAtwN/DHOkUvfB7M7EHgcXzqM5hghMFLUA8z0vaC8goz+6ekpcBWkqYkZbdDqr40p68r8NFXO1DaZmuI2fTa/XXsm2W2vzop7caFkJ6HW5a+Cf/+D+eVNdj9dZZvrDGEj9iLMAhcjo+Q9zGz8yu0y/p9uaQX59Rvij9kbEuV9VIzWyrpfOAVkrY3s3+kqtfio8+j0wNWPTR6Hu4Deus8RjCOCOUX1EM2wqo0YroTf3qeDDxQ0n55eUMze1pSuetBo2Qju8kV6jcoa5dZd07KaVtOM41dXgdcb2ZnZQWSVitrkAcqlD9F8TX9bXGlcx1wTZV22UPEJ2r0V48P3cn4mtvbgU+lsmwk+JM69s94oEJ5rfPwLFZeF8EEIqY9g3rIlMdmFeo3L2v3UNqu9kQtaU1W3jxHy6K03bZC/TZp+8+0vTtt6zl+XcYukvrk0WL+JekBSWdK6i2p/yfwNeCFySrxrLyyOuRpB2cDnwVeBJwvaZMK7VY8dJiZqrwuruOYZ+HXy/9IWlPSc3Ajmr+a2V9H+X2qImkNfEr87hpNg3FIKL+gHq5N29nlFZKeC/QDt5Ws72Xtd83p66U0b8Yh8wN8ZbqRlcr1bOBl+FN9Ztl5J3APPr1Xi0z5VRz5SdoKHyEN4991NrAJ8P2SZrviyvdQ/CHh7RXKxgRmdiTuH7kDcGGpIi8hO5+7NeF4j+FWpVNxS9wD8OujyKivUQbxqfHr2nCsYIwRyi+ohyy91aHpyRxYMYr7Jn4d/bCk/Slp+zlJk0var0MTo2mY2WLgz7jF3wfKqo/A165OsRTCzMwMtzbdJCntXAoYu3wf+KGZfcbMbjSz64AvAXuWtHkI2Bq4zMzuMrOHKpSNGczsO7hRy/OBi3P89o4DngSOlgcNWAVJ60gqohhPTtsD0+sp4NSCYjdC5gJTTzCFYJwRa35BTcxsgaSvA58Erpd0Bu5Avg/uS3Up7naQtb9Y0knAwcANks7Eb5avxafMluFWfqsgaV9g3/Qxm2LducSp+V4z+3jZbu/Hw5sdI2lP4EbcUnF3fHT1ubL2ZwJvwM3gb6nwlWsauyQ/tlcCu0n6UEnVmsCjJZ//A/+fXVejbExhZt+X9Dj+UDNf0h5mdkequ0nSO/GHohsk/Qk/12vja7+74SPsmuHpUn+XSboFN/5ZG/fta8dU5Ctxt4x5bThWMMYI5RfUhZl9StK1wCH40/nawGJ86u5bOUrifXgoqvfg4cX+ha/vfBYPYbY45zAvYvUpwK3TC9ycfRXlZ2aL5YGqvwi8Cng1Pr15DHCEre4gfSZuiHMgq0eFyag55ZlkfaikbSlPlLW7vdTlo0LZmMPMTpb0b3wknynAW1PdzyT9FXd12R1XJI/gDzZnUF8EnVJ+go+as/ctJc1I7Av8zsyGWn28YOxRMZltELQCSdvgo4TTzeytHZLhM/j06wwzu7ZW+wp97IMbiEwxs5Eq7Y4D+s1s32plQXuR9EH8AWmWmV3SaXmC9hNrfkFLkLRZjhHKengYLPBRYKc4Gnd+/uIo+rgC9xf8qaQdJE2X9ApJ3yv73i9i9enNvLKgTUh6Fh5s4cxQfBOXUH5Bq/gIcJukn0g6Kq3bLcKnJf8I/KpTgpnHqHwbHoOyoWS2ZnY/vuY5GTeYuA43/llqKc6oJOGGM9dl++WVBW1nGh5vtnz9OJhAxLRn0BKS8cnH8VHORrgF3z/xoNjfMU9PEwRB0BFC+QVBEAQTjpj2DIIgCCYcofyCIAiCCUcovyAIgmDCEcovCIIgmHCE8guCIAgmHKH8giAIgglHKL8gCIJgwhHKLwiCIJhw/D+uXgNjhRxFvgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bin_hr_diagram.plot(log_age=8.2)\n", + "plt.xlabel(\"log10($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log10(L in solar luminosities)\")\n", + "plt.title(\"HR Diagram of Hoki cluster (in gray) \\n at solar metallicity and 10**8.2 years age\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the mass-luminosity relationship of the clusters (primary stars)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log10\n", + " after removing the cwd from sys.path.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from astropy import units as u\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(clus_1.star_systems['mass_current']),\n", + " np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"Cluster_w_Binaries made from BPASS \", alpha =1)\n", + "plt.plot(np.log10(clus_2.star_systems['mass_current']),\n", + " np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b+\", label=\"Cluster made from MISTv.1\", alpha=0.1)\n", + "plt.xlabel(\"log10(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log10(L/L_solar)\")\n", + "plt.title(\"log-Mass-logL of Cluster at solar metallicity and\" +\n", + " \" 10**8.2 years age (primaries and singles)\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, I plot the mass-luminosity relationship of the isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['mass_current']),\n", + " np.log10(iso1.secondaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['mass_current']),\n", + " np.log10(iso1.singles[\"L\"] / cs.L_sun), \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['mass_current']),\n", + " np.log10(iso2.points[\"L\"] / cs.L_sun), \"b+\", label=\"MISTv1\", alpha=0.25)\n", + "plt.xlabel(\"log10(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log10(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at solar metallicity and 10**8.2 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining the phases of primary stars that have luminosities of less than L_sun * 1/sqrt(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log10\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=1>\n", + "
\n", + "\n", + "
101.0
" + ], + "text/plain": [ + "\n", + "101.0" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(clus_1.star_systems[np.where(np.log10(clus_1.star_systems['L']) < -0.5)]\n", + " ['phase'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sanity check: make sure there are no phase 0 stars." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=0\n", + "\n", + "\n", + "\n", + "
masssystemMassTeffLloggisWRmass_currentphasemetallicityisMultiplemergedm_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
float64float64float64float64float64float64float64float64float64boolfloat64float64float64float64float64float64
" + ], + "text/plain": [ + "\n", + " mass systemMass Teff L logg ... m_ubv_V m_ubv_B m_ubv_R m_ubv_I\n", + "float64 float64 float64 float64 float64 ... float64 float64 float64 float64\n", + "------- ---------- ------- ------- ------- ... ------- ------- ------- -------" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems[np.where(clus_1.star_systems['phase']==0)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the BPASS Cluster vs MIST cluster Observer's HR Diagram\n", + "Remember to use a distance modulus!" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['m_ubv_B'] - clus_1.star_systems[\"m_ubv_V\"],\n", + " clus_1.star_systems[\"m_ubv_V\"] - 5 * np.log10(200), \"r.\",label=\"BPASS\")\n", + "plt.plot(clus_2.star_systems['m_ubv_B'] - clus_2.star_systems[\"m_ubv_V\"],\n", + " clus_2.star_systems[\"m_ubv_V\"] - 5 * np.log10(200), \"b+\",\n", + " label=\"MISTv1\", alpha=0.2)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of clusters at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to note about the Cluster_w_Binaries is that it does include the units as information for columns.\n", + "The usual ResolvedCluster does not. Thus the latter has a disadvantage when we do comparisons with solar luminosity.\n", + "\n", + "In any case let's compare the actual masses of the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1990.3916096207872" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(clus_1.star_systems['systemMass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000.2279106848912" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(clus_2.star_systems['systemMass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=295\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", + "
system_idxmassTeffLloggisWRmass_currentphasemetallicityisMultiplemergedm_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_Ithe_secondary_star?log_aeiOmegaomega
int64float64float64float64float64float64float64float64float64boolfloat64float64float64float64float64float64boolfloat64float64float64float64float64
02.49025300361061410782.941649782421.6902127228083314e+284.2835825900191060.02.4902530036106145.00.0False0.012.37432206101816612.57891138832707512.52862152799554212.60051525942661112.6492681685551True-0.3013376382188222nannannannan
11.2648459784579147nannannan0.0nan-99.00.0False1.0nannannannannanTrue5.667496591311971nannannannan
20.49664628391809434nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
30.8745223539341569nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
43.59015997755711813888.0627975161448.210189933828224e+284.2289758407247410.03.44034976492091945.00.0False0.011.08722405567845811.6501249989164911.52715703463908911.6920291701076711.797874912471825True0.6429516321972302nannannannan
50.7104067893639824nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
60.733878544867282nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
70.4876441105227166nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
83.757796246869899512763.0283948934231.2390821263202032e+293.90640700612144440.03.79305982739713345.00.0False0.010.32653632574606710.83475272355212110.72056065660764510.87048416231103310.962179626737656True0.1969835263684048nannannannan
90.6063213846731395nannannannannan-99.00.0False0.0nannannannannanFalsenannannannannan
..................................................................
2730.5762894104911251nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
2741.1041582494333408nannannannannan-99.00.0False1.0nannannannannanTrue5.667496591311971nannannannan
2750.9456174007324465nannannannannan-99.00.0False1.0nannannannannanTrue2.4836839476213326nannannannan
2760.5214536891533125nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
2770.8449975828983857nannannan0.0nan-99.00.0False0.0nannannannannanTrue0.32135379223645705nannannannan
2780.7329050880299302nannannan0.0nan-99.00.0FalsenannannannannannanTruenannannannannan
2793.0675614651869nannannannannan-99.00.0False0.0nannannannannanTrue0.5357302709926124nannannannan
2800.4984383212079005nannannan0.0nan-99.00.0FalsenannannannannannanTruenannannannannan
2810.5470243125816465nannannannannan-99.00.0FalsenannannannannannanTruenannannannannan
2820.563518212023225612280.5529094268533.425034086206858e+284.2968918365716550.02.99068631798252765.00.0False0.011.73581400539629612.15084722501196512.05845485407767212.1852754431490112.269653529882708True-0.44270221045539415nannannannan
" + ], + "text/plain": [ + "\n", + "system_idx mass Teff ... i Omega omega \n", + " int64 float64 float64 ... float64 float64 float64\n", + "---------- ------------------- ------------------ ... ------- ------- -------\n", + " 0 2.490253003610614 10782.94164978242 ... nan nan nan\n", + " 1 1.2648459784579147 nan ... nan nan nan\n", + " 2 0.49664628391809434 nan ... nan nan nan\n", + " 3 0.8745223539341569 nan ... nan nan nan\n", + " 4 3.590159977557118 13888.062797516144 ... nan nan nan\n", + " 5 0.7104067893639824 nan ... nan nan nan\n", + " 6 0.733878544867282 nan ... nan nan nan\n", + " 7 0.4876441105227166 nan ... nan nan nan\n", + " 8 3.7577962468698995 12763.028394893423 ... nan nan nan\n", + " 9 0.6063213846731395 nan ... nan nan nan\n", + " ... ... ... ... ... ... ...\n", + " 273 0.5762894104911251 nan ... nan nan nan\n", + " 274 1.1041582494333408 nan ... nan nan nan\n", + " 275 0.9456174007324465 nan ... nan nan nan\n", + " 276 0.5214536891533125 nan ... nan nan nan\n", + " 277 0.8449975828983857 nan ... nan nan nan\n", + " 278 0.7329050880299302 nan ... nan nan nan\n", + " 279 3.0675614651869 nan ... nan nan nan\n", + " 280 0.4984383212079005 nan ... nan nan nan\n", + " 281 0.5470243125816465 nan ... nan nan nan\n", + " 282 0.5635182120232256 12280.552909426853 ... nan nan nan" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.companions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/TestPlot_8.2_Parsec_1.0.ipynb b/Test_Plots/TestPlot_8.2_Parsec_1.0.ipynb new file mode 100644 index 00000000..6dd70d6b --- /dev/null +++ b/Test_Plots/TestPlot_8.2_Parsec_1.0.ipynb @@ -0,0 +1,1974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS isochrone at $10^{8.2}$ years age and Comparing With Parsec (Solar Metallicity)\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for $10^{8.2}$ years age, solar metallicity, AKs=0.0, and distance of 100 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram ($B-V$ vs $M_V$), and the mass luminosity relationship of the cluster. The BPASS isochrone is compared to a Parsec isochrone in this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Note: This notebook can also be used for demonstration of how the new Isochrone_Binary and Binary_Cluster can be used" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to T= 50000 for T= 52330 logg=8.47\n", + "Changing to logg=5.00 for T= 52330 logg=8.47\n", + "Changing to logg=5.00 for T= 44758 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+ "Changing to logg=5.00 for T= 35701 logg=8.52\n", + "Changing to logg=5.00 for T= 39110 logg=5.45\n", + "Changing to logg=5.00 for T= 38030 logg=5.34\n", + "Changing to logg=5.00 for T= 35710 logg=8.52\n", + "Changing to logg=5.00 for T= 39081 logg=5.47\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38681 logg=5.49\n", + "Changing to logg=5.00 for T= 39333 logg=5.52\n", + "Changing to logg=5.00 for T= 37796 logg=5.52\n", + "Changing to logg=5.00 for T= 39001 logg=5.46\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 39098 logg=5.41\n", + "Changing to logg=5.00 for T= 39018 logg=5.53\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 34722 logg=5.55\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 37968 logg=5.42\n", + "Changing to logg=5.00 for T= 39042 logg=5.41\n", + "Changing to logg=5.00 for T= 39090 logg=5.47\n", + "Changing to logg=5.00 for T= 47516 logg=9.02\n", + "Changing to logg=5.00 for T= 38778 logg=5.38\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 38810 logg=5.36\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 32931 logg=5.73\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38008 logg=5.41\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 38543 logg=5.67\n", + "Changing to logg=5.00 for T= 35665 logg=8.52\n", + "Changing to logg=5.00 for T= 38216 logg=5.28\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38912 logg=5.40\n", + "Changing to logg=5.00 for T= 42300 logg=8.53\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35759 logg=5.48\n", + "Changing to logg=5.00 for T= 35776 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 43455 logg=8.58\n", + "Changing to logg=5.00 for T= 37645 logg=8.47\n", + "Changing to logg=5.00 for T= 37474 logg=5.67\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38938 logg=5.41\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 38864 logg=5.34\n", + "Changing to logg=5.00 for T= 38164 logg=5.31\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 39126 logg=5.42\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45739 logg=8.21\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 35706 logg=8.52\n", + "Changing to logg=5.00 for T= 38268 logg=5.57\n", + "Changing to logg=5.00 for T= 38750 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+ "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 33157 logg=5.77\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 37690 logg=5.71\n", + "Changing to logg=5.00 for T= 38205 logg=5.40\n", + "Changing to logg=5.00 for T= 39156 logg=5.49\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 35714 logg=8.52\n", + "Changing to logg=5.00 for T= 35722 logg=8.52\n", + "Changing to logg=5.00 for T= 37950 logg=5.39\n", + "Changing to logg=5.00 for T= 37640 logg=8.47\n", + "Changing to logg=5.00 for T= 35929 logg=8.18\n", + "Changing to logg=5.00 for T= 39474 logg=5.51\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45669 logg=8.52\n", + "Changing to logg=5.00 for T= 38945 logg=5.49\n", + "Changing 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logg=8.52\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to T= 50000 for T= 55186 logg=8.12\n", + "Changing to logg=5.00 for T= 55186 logg=8.12\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 41051 logg=8.91\n", + "Changing to logg=5.00 for T= 38968 logg=5.47\n", + "Changing to logg=5.00 for T= 38751 logg=8.52\n", + "Changing to logg=5.00 for T= 35722 logg=8.52\n", + "Changing to logg=5.00 for T= 34428 logg=5.62\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 37422 logg=5.67\n", + "Changing to logg=5.00 for T= 34980 logg=5.56\n", + "Changing to logg=5.00 for T= 34191 logg=8.16\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 37820 logg=5.74\n", + "Changing to logg=5.00 for T= 38913 logg=5.59\n", + "Changing to logg=5.00 for T= 38797 logg=5.34\n", + "Changing to logg=5.00 for T= 35649 logg=8.52\n", + "Changing to logg=5.00 for T= 38750 logg=8.52\n", + "Changing to logg=5.00 for T= 35392 logg=8.22\n", + "Changing to logg=5.00 for T= 35709 logg=8.52\n", + "Changing to logg=5.00 for T= 37225 logg=5.46\n", + "Changing to logg=5.00 for T= 38747 logg=5.38\n", + "Changing to logg=5.00 for T= 42302 logg=8.53\n", + "Changing to logg=5.00 for T= 39121 logg=5.40\n", + "Changing to logg=5.00 for T= 45668 logg=8.52\n", + "Changing to logg=5.00 for T= 37138 logg=5.69\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to logg=5.00 for T= 35098 logg=8.49\n", + "Changing to logg=5.00 for T= 38377 logg=5.39\n", + "Changing to logg=5.00 for T= 39010 logg=5.64\n", + "Changing to logg=5.00 for T= 39290 logg=5.53\n", + "Changing to logg=5.00 for T= 37889 logg=5.54\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 34340 logg=5.63\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39098 logg=5.57\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 44961 logg=8.20\n", + "Changing to logg=5.00 for T= 37700 logg=5.76\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to T= 50000 for T= 80369 logg=8.32\n", + "Changing to logg=5.00 for T= 80369 logg=8.32\n", + "Changing to T= 50000 for T=196662 logg=7.28\n", + "Changing to logg=5.00 for T=196662 logg=7.28\n", + "Changing to logg=5.00 for T= 39296 logg=5.53\n", + "Changing to logg=5.00 for T= 33848 logg=5.68\n", + "Changing to logg=5.00 for T= 42994 logg=5.57\n", + "Changing to logg=5.00 for T= 37933 logg=5.39\n", + "Changing to logg=5.00 for T= 34437 logg=5.46\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 42289 logg=5.55\n", + "Changing to logg=5.00 for T= 37723 logg=5.77\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 31631 logg=5.78\n", + "Changing to logg=5.00 for T= 35046 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39033 logg=5.45\n", + "Changing to logg=5.00 for T= 35123 logg=8.49\n", + "Changing to logg=5.00 for T= 35233 logg=8.21\n", + "Changing to logg=5.00 for T= 39162 logg=5.48\n", + "Changing to T= 50000 for T= 91531 logg=8.30\n", + "Changing to logg=5.00 for T= 91531 logg=8.30\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 44686 logg=8.19\n", + "Changing to T= 50000 for T=172024 logg=7.24\n", + "Changing to logg=5.00 for T=172024 logg=7.24\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38002 logg=5.45\n", + "Changing to logg=5.00 for T= 38718 logg=5.32\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 37054 logg=5.15\n", + "Changing to logg=5.00 for T= 34271 logg=5.61\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 31499 logg=8.21\n", + "Changing to logg=5.00 for T= 39297 logg=5.50\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39015 logg=5.63\n", + "Changing to logg=5.00 for T= 35041 logg=8.49\n", + "Changing to logg=5.00 for T= 39282 logg=5.39\n", + "Changing to T= 50000 for T=122659 logg=7.98\n", + "Changing to logg=5.00 for T=122659 logg=7.98\n", + "Changing to logg=5.00 for T= 39152 logg=5.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38506 logg=5.66\n", + "Changing to logg=5.00 for T= 39300 logg=5.53\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to T= 50000 for T= 53154 logg=8.20\n", + "Changing to logg=5.00 for T= 53154 logg=8.20\n", + "Changing to T= 50000 for T= 90298 logg=8.31\n", + "Changing to logg=5.00 for T= 90298 logg=8.31\n", + "Changing to logg=5.00 for T= 35044 logg=8.49\n", + "Changing to logg=5.00 for T= 34450 logg=5.60\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35047 logg=8.49\n", + "Changing to logg=5.00 for T= 36175 logg=5.49\n", + "Changing to logg=5.00 for T= 39339 logg=5.50\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35065 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 37969 logg=5.40\n", + "Changing to logg=5.00 for T= 38323 logg=5.43\n", + "Changing to logg=5.00 for T= 38497 logg=5.67\n", + "Changing to logg=5.00 for T= 35161 logg=8.49\n", + "Changing to logg=5.00 for T= 37720 logg=5.76\n", + "Changing to logg=5.00 for T= 35042 logg=8.49\n", + "Changing to logg=5.00 for T= 39061 logg=5.47\n", + "Changing to logg=5.00 for T= 39115 logg=5.45\n", + "Changing to logg=5.00 for T= 27868 logg=6.18\n", + "Changing to T= 50000 for T=197624 logg=7.27\n", + "Changing to logg=5.00 for T=197624 logg=7.27\n", + "Changing to logg=5.00 for T= 39260 logg=5.51\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to T= 50000 for T=170279 logg=7.23\n", + "Changing to logg=5.00 for T=170279 logg=7.23\n", + "Changing to logg=5.00 for T= 39090 logg=5.45\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=0.00 for T= 3294 logg=-0.23\n", + "Changing to logg=5.00 for T= 38348 logg=5.37\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 37267 logg=5.48\n", + "Changing to T= 50000 for T= 81089 logg=8.14\n", + "Changing to logg=5.00 for T= 81089 logg=8.14\n", + "Changing to logg=5.00 for T= 36208 logg=8.26\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39215 logg=5.43\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38042 logg=5.48\n", + "Changing to logg=5.00 for T= 39003 logg=5.46\n", + "Changing to logg=0.00 for T= 3317 logg=-0.19\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39019 logg=5.46\n", + "Changing to T= 50000 for T= 85704 logg=8.31\n", + "Changing to logg=5.00 for T= 85704 logg=8.31\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 38521 logg=5.66\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 27144 logg=6.25\n", + "Changing to T= 50000 for T= 52576 logg=8.18\n", + "Changing to logg=5.00 for T= 52576 logg=8.18\n", + "Changing to logg=5.00 for T= 35040 logg=8.49\n", + "Changing to logg=5.00 for T= 39184 logg=5.47\n", + "Changing to logg=0.00 for T= 3295 logg=-0.23\n", + "Making photometry for isochrone: log(t) = 8.20 AKs = 0.00 dist = 100\n", + " Starting at: 2021-08-13 13:57:24.378799 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_U = 9.54\n", + "M = 5.500 Msun T = 27287 K m_ubv_U = 14.77\n", + "M = 4.500 Msun T = 4524 K m_ubv_U = 5.30\n", + "M = 2.700 Msun T = 10526 K m_ubv_U = 5.49\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_U = 14.97\n", + "M = 2.500 Msun T = 10121 K m_ubv_U = 5.86\n", + "M = 5.000 Msun T = 38750 K m_ubv_U = 13.92\n", + "M = 3.200 Msun T = 11657 K m_ubv_U = 4.73\n", + "M = 5.012 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_U = 3.17\n", + "M = 3.000 Msun T = 11265 K m_ubv_U = 5.03\n", + "M = 4.000 Msun T = 11805 K m_ubv_U = 3.36\n", + "M = 6.000 Msun T = 30830 K m_ubv_U = 14.86\n", + "M = 6.000 Msun T = 27033 K m_ubv_U = 14.67\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_U = 13.92\n", + "M = 3.000 Msun T = 11287 K m_ubv_U = 5.01\n", + "M = 3.200 Msun T = 45669 K m_ubv_U = 13.68\n", + "M = 5.500 Msun T = 27836 K m_ubv_U = 14.68\n", + "M = 4.500 Msun T = 4524 K m_ubv_U = 5.30\n", + "M = 2.500 Msun T = 10121 K m_ubv_U = 5.86\n", + "M = 6.000 Msun T = 33082 K m_ubv_U = 14.91\n", + "M = 2.300 Msun T = 10194 K m_ubv_U = 5.81\n", + "M = 3.700 Msun T = 11328 K m_ubv_U = 2.69\n", + "M = 3.200 Msun T = 41469 K m_ubv_U = 15.34\n", + "M = 1.650 Msun T = 7676 K m_ubv_U = 7.82\n", + "M = 3.600 Msun T = 13127 K m_ubv_U = 4.12\n", + "M = 1.350 Msun T = 6542 K m_ubv_U = 8.99\n", + "M = 4.500 Msun T = 4534 K m_ubv_U = 5.26\n", + "M = 0.600 Msun T = 3945 K m_ubv_U = 16.08\n", + "M = 2.000 Msun T = 9101 K m_ubv_U = 6.86\n", + "M = 4.000 Msun T = 14082 K m_ubv_U = 3.75\n", + "M = 2.240 Msun T = 9965 K m_ubv_U = 6.34\n", + "M = 7.000 Msun T = 39833 K m_ubv_U = 15.56\n", + "M = 0.810 Msun T = nan K m_ubv_U = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_U = 8.34\n", + "M = 0.400 Msun T = 3660 K m_ubv_U = 17.59\n", + "M = 5.400 Msun T = 12339 K m_ubv_U = 2.08\n", + "M = 3.600 Msun T = 10929 K m_ubv_U = 4.00\n", + "M = 4.500 Msun T = 38506 K m_ubv_U = 7.11\n", + "M = 5.500 Msun T = 26754 K m_ubv_U = 14.78\n", + "M = 3.200 Msun T = 11594 K m_ubv_U = 4.70\n", + "M = 0.500 Msun T = 3758 K m_ubv_U = 16.92\n", + "M = 2.700 Msun T = 11431 K m_ubv_U = 5.51\n", + "M = 0.960 Msun T = nan K m_ubv_U = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_U = 6.41\n", + "M = 2.700 Msun T = 11422 K m_ubv_U = 5.50\n", + "M = 0.500 Msun T = 3758 K m_ubv_U = 16.92\n", + "M = 3.600 Msun T = 13801 K m_ubv_U = 4.45\n", + "M = 0.230 Msun T = nan K m_ubv_U = nan\n", + "M = 0.370 Msun T = nan K m_ubv_U = nan\n", + "M = 1.920 Msun T = nan K m_ubv_U = nan\n", + "Starting filter: ubv,V Elapsed time: 9.55 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_V = 9.01\n", + "M = 5.500 Msun T = 27287 K m_ubv_V = 15.91\n", + "M = 4.500 Msun T = 4524 K m_ubv_V = 2.80\n", + "M = 2.700 Msun T = 10526 K m_ubv_V = 5.66\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_V = 15.99\n", + "M = 2.500 Msun T = 10121 K m_ubv_V = 5.97\n", + "M = 5.000 Msun T = 38750 K m_ubv_V = 15.31\n", + "M = 3.200 Msun T = 11657 K m_ubv_V = 5.08\n", + "M = 5.012 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_V = 3.66\n", + "M = 3.000 Msun T = 11265 K m_ubv_V = 5.33\n", + "M = 4.000 Msun T = 11805 K m_ubv_V = 3.77\n", + "M = 6.000 Msun T = 30830 K m_ubv_V = 16.10\n", + "M = 6.000 Msun T = 27033 K m_ubv_V = 15.80\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_V = 15.31\n", + "M = 3.000 Msun T = 11287 K m_ubv_V = 5.31\n", + "M = 3.200 Msun T = 45669 K m_ubv_V = 15.10\n", + "M = 5.500 Msun T = 27836 K m_ubv_V = 15.83\n", + "M = 4.500 Msun T = 4524 K m_ubv_V = 2.80\n", + "M = 2.500 Msun T = 10121 K m_ubv_V = 5.97\n", + "M = 6.000 Msun T = 33082 K m_ubv_V = 16.20\n", + "M = 2.300 Msun T = 10194 K m_ubv_V = 5.92\n", + "M = 3.700 Msun T = 11328 K m_ubv_V = 3.06\n", + "M = 3.200 Msun T = 41469 K m_ubv_V = 16.72\n", + "M = 1.650 Msun T = 7676 K m_ubv_V = 7.56\n", + "M = 3.600 Msun T = 13127 K m_ubv_V = 4.66\n", + "M = 1.350 Msun T = 6542 K m_ubv_V = 8.57\n", + "M = 4.500 Msun T = 4534 K m_ubv_V = 2.77\n", + "M = 0.600 Msun T = 3945 K m_ubv_V = 13.70\n", + "M = 2.000 Msun T = 9101 K m_ubv_V = 6.77\n", + "M = 4.000 Msun T = 14082 K m_ubv_V = 4.38\n", + "M = 2.240 Msun T = 9965 K m_ubv_V = 6.40\n", + "M = 7.000 Msun T = 39833 K m_ubv_V = 16.93\n", + "M = 0.810 Msun T = nan K m_ubv_V = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_V = 8.03\n", + "M = 0.400 Msun T = 3660 K m_ubv_V = 15.20\n", + "M = 5.400 Msun T = 12339 K m_ubv_V = 2.56\n", + "M = 3.600 Msun T = 10929 K m_ubv_V = 4.26\n", + "M = 4.500 Msun T = 38506 K m_ubv_V = 8.50\n", + "M = 5.500 Msun T = 26754 K m_ubv_V = 15.90\n", + "M = 3.200 Msun T = 11594 K m_ubv_V = 5.05\n", + "M = 0.500 Msun T = 3758 K m_ubv_V = 14.53\n", + "M = 2.700 Msun T = 11431 K m_ubv_V = 5.82\n", + "M = 0.960 Msun T = nan K m_ubv_V = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_V = 6.43\n", + "M = 2.700 Msun T = 11422 K m_ubv_V = 5.81\n", + "M = 0.500 Msun T = 3758 K m_ubv_V = 14.53\n", + "M = 3.600 Msun T = 13801 K m_ubv_V = 5.04\n", + "M = 0.230 Msun T = nan K m_ubv_V = nan\n", + "M = 0.370 Msun T = nan K m_ubv_V = nan\n", + "M = 1.920 Msun T = nan K m_ubv_V = nan\n", + "Starting filter: ubv,B Elapsed time: 18.96 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_B = 9.57\n", + "M = 5.500 Msun T = 27287 K m_ubv_B = 15.76\n", + "M = 4.500 Msun T = 4524 K m_ubv_B = 4.06\n", + "M = 2.700 Msun T = 10526 K m_ubv_B = 5.61\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_B = 15.89\n", + "M = 2.500 Msun T = 10121 K m_ubv_B = 5.94\n", + "M = 5.000 Msun T = 38750 K m_ubv_B = 15.00\n", + "M = 3.200 Msun T = 11657 K m_ubv_B = 5.00\n", + "M = 5.012 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_B = 3.55\n", + "M = 3.000 Msun T = 11265 K m_ubv_B = 5.25\n", + "M = 4.000 Msun T = 11805 K m_ubv_B = 3.67\n", + "M = 6.000 Msun T = 30830 K m_ubv_B = 15.91\n", + "M = 6.000 Msun T = 27033 K m_ubv_B = 15.65\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_B = 15.00\n", + "M = 3.000 Msun T = 11287 K m_ubv_B = 5.23\n", + "M = 3.200 Msun T = 45669 K m_ubv_B = 14.78\n", + "M = 5.500 Msun T = 27836 K m_ubv_B = 15.67\n", + "M = 4.500 Msun T = 4524 K m_ubv_B = 4.06\n", + "M = 2.500 Msun T = 10121 K m_ubv_B = 5.94\n", + "M = 6.000 Msun T = 33082 K m_ubv_B = 15.99\n", + "M = 2.300 Msun T = 10194 K m_ubv_B = 5.89\n", + "M = 3.700 Msun T = 11328 K m_ubv_B = 2.97\n", + "M = 3.200 Msun T = 41469 K m_ubv_B = 16.47\n", + "M = 1.650 Msun T = 7676 K m_ubv_B = 7.81\n", + "M = 3.600 Msun T = 13127 K m_ubv_B = 4.54\n", + "M = 1.350 Msun T = 6542 K m_ubv_B = 9.04\n", + "M = 4.500 Msun T = 4534 K m_ubv_B = 4.03\n", + "M = 0.600 Msun T = 3945 K m_ubv_B = 15.05\n", + "M = 2.000 Msun T = 9101 K m_ubv_B = 6.83\n", + "M = 4.000 Msun T = 14082 K m_ubv_B = 4.25\n", + "M = 2.240 Msun T = 9965 K m_ubv_B = 6.39\n", + "M = 7.000 Msun T = 39833 K m_ubv_B = 16.68\n", + "M = 0.810 Msun T = nan K m_ubv_B = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_B = 8.39\n", + "M = 0.400 Msun T = 3660 K m_ubv_B = 16.57\n", + "M = 5.400 Msun T = 12339 K m_ubv_B = 2.45\n", + "M = 3.600 Msun T = 10929 K m_ubv_B = 4.19\n", + "M = 4.500 Msun T = 38506 K m_ubv_B = 8.19\n", + "M = 5.500 Msun T = 26754 K m_ubv_B = 15.76\n", + "M = 3.200 Msun T = 11594 K m_ubv_B = 4.96\n", + "M = 0.500 Msun T = 3758 K m_ubv_B = 15.91\n", + "M = 2.700 Msun T = 11431 K m_ubv_B = 5.75\n", + "M = 0.960 Msun T = nan K m_ubv_B = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_B = 6.44\n", + "M = 2.700 Msun T = 11422 K m_ubv_B = 5.73\n", + "M = 0.500 Msun T = 3758 K m_ubv_B = 15.91\n", + "M = 3.600 Msun T = 13801 K m_ubv_B = 4.92\n", + "M = 0.230 Msun T = nan K m_ubv_B = nan\n", + "M = 0.370 Msun T = nan K m_ubv_B = nan\n", + "M = 1.920 Msun T = nan K m_ubv_B = nan\n", + "Starting filter: ubv,R Elapsed time: 28.29 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_R = 8.73\n", + "M = 5.500 Msun T = 27287 K m_ubv_R = 16.03\n", + "M = 4.500 Msun T = 4524 K m_ubv_R = 2.23\n", + "M = 2.700 Msun T = 10526 K m_ubv_R = 5.68\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_R = 16.10\n", + "M = 2.500 Msun T = 10121 K m_ubv_R = 5.98\n", + "M = 5.000 Msun T = 38750 K m_ubv_R = 15.44\n", + "M = 3.200 Msun T = 11657 K m_ubv_R = 5.11\n", + "M = 5.012 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_R = 3.69\n", + "M = 3.000 Msun T = 11265 K m_ubv_R = 5.36\n", + "M = 4.000 Msun T = 11805 K m_ubv_R = 3.80\n", + "M = 6.000 Msun T = 30830 K m_ubv_R = 16.22\n", + "M = 6.000 Msun T = 27033 K m_ubv_R = 15.91\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_R = 15.44\n", + "M = 3.000 Msun T = 11287 K m_ubv_R = 5.34\n", + "M = 3.200 Msun T = 45669 K m_ubv_R = 15.24\n", + "M = 5.500 Msun T = 27836 K m_ubv_R = 15.95\n", + "M = 4.500 Msun T = 4524 K m_ubv_R = 2.23\n", + "M = 2.500 Msun T = 10121 K m_ubv_R = 5.98\n", + "M = 6.000 Msun T = 33082 K m_ubv_R = 16.33\n", + "M = 2.300 Msun T = 10194 K m_ubv_R = 5.94\n", + "M = 3.700 Msun T = 11328 K m_ubv_R = 3.09\n", + "M = 3.200 Msun T = 41469 K m_ubv_R = 16.85\n", + "M = 1.650 Msun T = 7676 K m_ubv_R = 7.43\n", + "M = 3.600 Msun T = 13127 K m_ubv_R = 4.70\n", + "M = 1.350 Msun T = 6542 K m_ubv_R = 8.32\n", + "M = 4.500 Msun T = 4534 K m_ubv_R = 2.21\n", + "M = 0.600 Msun T = 3945 K m_ubv_R = 12.93\n", + "M = 2.000 Msun T = 9101 K m_ubv_R = 6.75\n", + "M = 4.000 Msun T = 14082 K m_ubv_R = 4.43\n", + "M = 2.240 Msun T = 9965 K m_ubv_R = 6.41\n", + "M = 7.000 Msun T = 39833 K m_ubv_R = 17.05\n", + "M = 0.810 Msun T = nan K m_ubv_R = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_R = 7.84\n", + "M = 0.400 Msun T = 3660 K m_ubv_R = 14.40\n", + "M = 5.400 Msun T = 12339 K m_ubv_R = 2.59\n", + "M = 3.600 Msun T = 10929 K m_ubv_R = 4.28\n", + "M = 4.500 Msun T = 38506 K m_ubv_R = 8.62\n", + "M = 5.500 Msun T = 26754 K m_ubv_R = 16.01\n", + "M = 3.200 Msun T = 11594 K m_ubv_R = 5.08\n", + "M = 0.500 Msun T = 3758 K m_ubv_R = 13.73\n", + "M = 2.700 Msun T = 11431 K m_ubv_R = 5.85\n", + "M = 0.960 Msun T = nan K m_ubv_R = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_R = 6.43\n", + "M = 2.700 Msun T = 11422 K m_ubv_R = 5.84\n", + "M = 0.500 Msun T = 3758 K m_ubv_R = 13.73\n", + "M = 3.600 Msun T = 13801 K m_ubv_R = 5.09\n", + "M = 0.230 Msun T = nan K m_ubv_R = nan\n", + "M = 0.370 Msun T = nan K m_ubv_R = nan\n", + "M = 1.920 Msun T = nan K m_ubv_R = nan\n", + "Starting filter: ubv,I Elapsed time: 37.53 seconds\n", + "Starting synthetic photometry\n", + "M = 1.240 Msun T = 6225 K m_ubv_I = 8.38\n", + "M = 5.500 Msun T = 27287 K m_ubv_I = 16.27\n", + "M = 4.500 Msun T = 4524 K m_ubv_I = 1.52\n", + "M = 2.700 Msun T = 10526 K m_ubv_I = 5.72\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 6.000 Msun T = 24066 K m_ubv_I = 16.32\n", + "M = 2.500 Msun T = 10121 K m_ubv_I = 6.01\n", + "M = 5.000 Msun T = 38750 K m_ubv_I = 15.71\n", + "M = 3.200 Msun T = 11657 K m_ubv_I = 5.18\n", + "M = 5.012 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 12431 K m_ubv_I = 3.78\n", + "M = 3.000 Msun T = 11265 K m_ubv_I = 5.42\n", + "M = 4.000 Msun T = 11805 K m_ubv_I = 3.87\n", + "M = 6.000 Msun T = 30830 K m_ubv_I = 16.48\n", + "M = 6.000 Msun T = 27033 K m_ubv_I = 16.15\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 5.000 Msun T = 38750 K m_ubv_I = 15.71\n", + "M = 3.000 Msun T = 11287 K m_ubv_I = 5.40\n", + "M = 3.200 Msun T = 45669 K m_ubv_I = 15.51\n", + "M = 5.500 Msun T = 27836 K m_ubv_I = 16.19\n", + "M = 4.500 Msun T = 4524 K m_ubv_I = 1.52\n", + "M = 2.500 Msun T = 10121 K m_ubv_I = 6.01\n", + "M = 6.000 Msun T = 33082 K m_ubv_I = 16.59\n", + "M = 2.300 Msun T = 10194 K m_ubv_I = 5.97\n", + "M = 3.700 Msun T = 11328 K m_ubv_I = 3.14\n", + "M = 3.200 Msun T = 41469 K m_ubv_I = 17.13\n", + "M = 1.650 Msun T = 7676 K m_ubv_I = 7.28\n", + "M = 3.600 Msun T = 13127 K m_ubv_I = 4.80\n", + "M = 1.350 Msun T = 6542 K m_ubv_I = 8.02\n", + "M = 4.500 Msun T = 4534 K m_ubv_I = 1.50\n", + "M = 0.600 Msun T = 3945 K m_ubv_I = 11.87\n", + "M = 2.000 Msun T = 9101 K m_ubv_I = 6.73\n", + "M = 4.000 Msun T = 14082 K m_ubv_I = 4.54\n", + "M = 2.240 Msun T = 9965 K m_ubv_I = 6.44\n", + "M = 7.000 Msun T = 39833 K m_ubv_I = 17.33\n", + "M = 0.810 Msun T = nan K m_ubv_I = nan\n", + "M = 1.500 Msun T = 7078 K m_ubv_I = 7.62\n", + "M = 0.400 Msun T = 3660 K m_ubv_I = 13.05\n", + "M = 5.400 Msun T = 12339 K m_ubv_I = 2.68\n", + "M = 3.600 Msun T = 10929 K m_ubv_I = 4.33\n", + "M = 4.500 Msun T = 38506 K m_ubv_I = 8.90\n", + "M = 5.500 Msun T = 26754 K m_ubv_I = 16.25\n", + "M = 3.200 Msun T = 11594 K m_ubv_I = 5.15\n", + "M = 0.500 Msun T = 3758 K m_ubv_I = 12.49\n", + "M = 2.700 Msun T = 11431 K m_ubv_I = 5.92\n", + "M = 0.960 Msun T = nan K m_ubv_I = nan\n", + "M = 2.200 Msun T = 9709 K m_ubv_I = 6.45\n", + "M = 2.700 Msun T = 11422 K m_ubv_I = 5.90\n", + "M = 0.500 Msun T = 3758 K m_ubv_I = 12.49\n", + "M = 3.600 Msun T = 13801 K m_ubv_I = 5.20\n", + "M = 0.230 Msun T = nan K m_ubv_I = nan\n", + "M = 0.370 Msun T = nan K m_ubv_I = nan\n", + "M = 1.920 Msun T = nan K m_ubv_I = nan\n", + " Time taken: 46.77 seconds\n", + "Isochrone generation took 365.240186 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "iso1 = synthetic.Isochrone_Binary(8.2, 0.0, 100, math.log10(1), mass_sampling=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check if all of the phases represented in the isochrone are valid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x == 102 or x ==103) for\n", + " x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == 102 or x == 103) for\n", + " x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == -99 or x == 102 or x == 103) for\n", + " x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries and secondaries accounting for NaNs in the columns. (For max we make NaNs the same as - infinity and for median and mean, we do not include them)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294869501162154" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.288600394549206" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.283702439639246" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.2498758947769355" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.133548174454043" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/plain": [ + "4.30006013240644" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.secondaries['logg'][[np.where(~np.isnan(iso1.secondaries['logg']))]])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.354531279865074" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.109131776599373" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.762344379541872" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make a histogram of log_g values for each table of the isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (secondaries) of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values (primaries) of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5JN262P66Lptr2za6Av6JyrLP59o6/yHJbZLckdqR9fullNHOw6+jPk2zTynlsaWUl5ZS3tDK/ZuT2ObN7bNrHxZ0TBuU15GllEzw9/DBAqWUP5dS/rOUshv1uBzY9nV/4CsZepqpzzRes+Nam2t12KSP10RKKeeUUval/nJ9MLVJ407UjqG9TxYNS/JX1CaeC4B7lFL+oZTy6lLKYe38uXGiLKzDvM32PWNgcP3cPT1PQ45h8GTb8zIySCW1uQ7qdf4Xa1pRKeWPpZT3UftKQn0AYU3LrKD23bmE+vTePSe9B6vWNU65TqXsrga2GucYrKWuc3YmzrVpu/Zn/V1vpZSfl1I+TK1KvY4aeAwMnhTri1S/S92Hh3XMe1hb7vyR9FCfuLqV1qSw/fg5v8Vd2+fxHfP27Jg2Uy6kDvR5n9TxP0YNTo6u8USmw0yUzWAbqz2a2rWeNWm/rD5DfQrjkdRfpRszUpvU3JVam7W0Y95kyn1l++zavyUd075FDa4eOolt3KKU8ptSygmllKdQa8TuwiRv4mu4ZqfLROfDFtRau3Gs1fHqU0q5sZRydmuGeUmbPO69ayfqeXtKKeV3wzNShwbYaR3nrc/gmO81OiPJXalN/hdNU7PbRIbvV5P+jkqyhNpkcznw4Z6/H1BrNZ4yiVUPyqq3C8qwUsrN1H5DYy+zhvVNVK5Tud+f2/L1mLXN2xTccn33/NCdju+nabv2ZzxQSrJj69sxaitqU8VwNeBKajS6qGd1H2mfhye57dA2bkt9MgbqRTFwHLWK8MVJth9KH2qv+b6AbCLL2+dewxNTBxp7x2jimdKqeY+lViv+2/C81CH1X0JtHvz4OsrCTJTNoO36La16fLDMlsDrp5jvY9rns9rfTdTjOGo5tTbr3sMTk/wf6qOn4xq03x88fMNox+ANo4lLKb9p+VmSOq7RajeZJHdpj94Oxpnaux3H4TSbUPtewRpGzp/kNTtdTqT+InxGkt1G5r2OMZuMJ3u8JpLkoT01HIPareHjOHjUvOvetbx9PiTJLed1CwA/yBS6REwyb30G1+zrWm3qYN23oT7dtRG3vmbXlZe3zx+UUlZOmLLbYCTu/yylPKfrb2gbt7zSJHXMt927VphkZ+pj61CHwxhMf2N6xvJK8iRq36GV1NrDSRu3XKd4vx/UrP1HktX6U3ZNmy6llMuozauLWTUkzWC7f0vtE7aSof5lU9jGtF37M95Hidpx8XNJvkM9eS6nVk0eQO17c0twUWqnzW8CD01yLPXphj8DJ5VSflBKOS7JAdRfBctSX8ZYqGOY7Ah8ppRy7ND6/jfJG6hjgnw/yadZNVbPQurYLLf64hvDycDPgZcnuRc1Ul5EfdT0i/QHeTPhNdRo+kVJHkAdw2IwrsbtqE8ZXbQuNjxDZfMx4O+pv4guSHIS9Rw6kNrh7x6satoaN99nJfk59aa4CXByu+BGvZsaEJ2ZZDA20RJqDchnqf0JxtneN5N8g1rL9q0kp1FvgvtR+6501TS9iNr89W/AM5OcSe2zsw21Y+IDqMMzXERtHjwVWN6upYupnc4f1dKeVEr58RqyOfY1O11KKdcmeSHwCeDsdowH4yjtRu2Qvifjle9kjtdE/gXYJ3UQ019Qa9N2pTbPruTWr8Y4veXt8NTXXKxs+/WWUsqvk3yKeu5+L8kp1NqNRwF/oHacv88Y+zXVvHUqpZyd5AjqODkXpHYevr6t457UjvX/Psl8TWRxbt3JdiG1fO9PDb5X6z+0Ji3YfBr1B05XTfDAadTjtEfqWDrLqPeRD6Q+mHQWtYPwZtRz59HUc/09pZThzskvAw5L8l3qPedKalnejzrcwk3A80vt9D0VkynXSd3vSymnJHkz9Uflj9s9+lLq/ech1Bqng6aY73E8n3qc/z3JPtTjNxhH6Wbg4NEa1ymYnmu/jPfYYmHk0eyONMsZeVx3aNmlQ//fjvpleBa1k9eN1DFevgw8tmO9d6UGI79tB69w68cnN6IOvHUeNbr+PfWJsX+iZ+wQaoe471JvSldSb8bbUL8Erh5Ju1fb5mET7Pv21Mh10CFzGfVms/Ho/pdbPza5V8e6Dhrdx8nkpWOZBdQvssH4JFdTI/nOx02ZpuEBZqJsWvrNqRfBRW3/llMHWNy2HavPT2Zf2joHQxUU4MAJ0u1LvZn8rh3XU6gBT2cZ9h3bVkYfBH7T9uEC6i/dxXQ8at+W2ZR6EzibVWOkXELtWP5S4A4t3SbtXPxymz84rudSb1SbjnE8xr5m+/LMquEBFnesv/e8pn4hnN3OnZXUmqadgS+0ZRasaduTOV5rOA77UDsC/6it43pqk8d7gB060v8DNei5gZF7KPUJpbdSf2T9gfoF9T5qB9SljFxPEx2jqeRtDfv599Sg6Hctb8uonYk370h7GNM3PMCN1GDgQ9S+W33b6jwGLc1zW5oTxsjHv7a0/9n+f3dqYPLlVi7XD50nJzA0jtrQOh7SyvHMlu7GoWP/QeBekzguB7H699tkz7kFTOJ+35Z5HPXJtxVtmUupNTmPmMT5t5xJDH0ylGZbah/Hi6lPMF5FHfjyAeMcn5H5q33XTte1n7aiDV7q46RXAN8rpTxotvOjVaZSNkkeRQ1c3l5KOXRd5k8zqzUF/YI6LlRfZ1BJmhaz3pl7piW5Y1Z/P87G1IG9Nmct2kS1dqZSNkm26Zh2B1b1g7I811NJFgz3b2vTQq3xW0T9lS9J69QGV6OU5PnUpppTqVWMC6nNJXenVpPvUSY3roSmyVTKpvX12I1arXoltZnosW3Zo0opz5+p/Gt6JXkMdVyyU6hV+1tQB8m7D/X8WFK6+49J0rSZjc7cs+2b1Pbkh7FqQKqLqO3M75hLQVKSx1E76A1Guf4p9Z01p7X5W1E7Vz6B2mn3HOBlpZQfzkqG195UyuYEVnV+XsCqPhUfofZ10PrrQmpfpAdT+1FsTO0b9R7qKMwGSZLWuQ2uRml9keR51JdEvpc6Ou1G1F/Sy0opX2hNEN+gPkH2SmpH10OpT0Tcp9THLyVJ0lowUJqD2rgcP6a+GuLdPWkOoD4d8IhSyult2pbUGphPlFJe0rWcJEka34bY9LY++EdqU9t/T5Bmf+DyQZAEUEq5JsnJ1PFtJh0obb311mXx4sWTXUyS5p3vfOc7V5VS7rjmlJrvDJTmpodQ36P196lvpd6B2pn1yFLfOQS1ia1rtNdlwLOSbFHGf1EpAIsXL+a887pezCxJG5YkF892HjQ3bHDDA6wntqGOJvrv1Mfc96EOGvbeJP/c0ixk1XvChq1on13v+5EkSZNgjdLctBF1yPmDSimDsWJOa32XDk3yHurLDLs6mPW+fDHJIQy922jUokWz+bYVSZLmHgOluem31Bqlr41MP4X6PqI7U2uOFrK6QU3SarVNpZSjmeCdT0uWLLFnvyRJQ2x6m5uW9Uwf1Bbd3NJ0vdF9F+CSyfZPkiRJqzNQmpsGr9149Mj0RwOXlVJ+DZwEbJtkz8HM9k60/do8SZK0lmx6m5u+BJwOHJVka+oLQJ9E7dR9cEtzEnUk7k8kGR5wMsARM55jSZLmIQOlOaiUUpI8ATgceBO139FPgGeUUo5raW5Osi/wTuD91JfGngM8vJRy6axkXJKkecaRuXWLJUuWFMdRkiRI8p1SypLZzodmn32UJEmSetj0pg3C4td8cey0y9/++HWYE0nS+sQaJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GCitB5J8JUlJ8paR6Vsl+VCSq5Jcn+TUJPearXxKkjTfGCjNcUmeBuzWMT3AScBjgBcDBwKbAKcn2W5GMylJ0jxloDSHJVkAHAm8vGP2/sBDgGeWUj5ZSvlKm7YR8KoZy6QkSfOYgdLcdgSwrJTyyY55+wOXl1JOH0wopVwDnAwcMEP5kyRpXjNQmqOSPAR4FvDCniS7Ahd0TF8GLEqyxbrKmyRJGwoDpTkoySbAUcA7SykX9iRbCKzsmL6ifW61LvImSdKGZOPZzoA6vRr4C+CtE6QJUHqmdy+QHAIc0jd/0aJF4+ZPkqQNgoHSHJNkEfBa4DnAZkk2G5q9Wevg/TtqzdHCjlUMapJWq20qpRwNHN237SVLlnQFXpIkbbBsept7dgI2Bz5BDXYGfwCvaP++F7Uv0q4dy+8CXFJKuW7dZ1WSpPnNGqW553vAwzumn04Nnj4M/Jw6htLBSfYspZwBkOT2wH7AcTOTVUmS5jcDpTmmlHI1sHR0eh1fkotLKUvb/08CzgE+keSV1JqmQ6l9lI6YmdxKkjS/2fS2niql3AzsC3wNeD/wOeDPwMNLKZfOZt4kSZovrFFaT5RSVnuarZSyAvjH9idJkqaZNUqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgNAcleVKS45NcnOSGJBcmOTzJ7UbSbZXkQ0muSnJ9klOT3Gu28i1J0nxjoDQ3vQL4M/CvwGOADwAvAL6WZCOAJAFOavNfDBwIbAKcnmS72ci0JEnzzcaznQF12q+UcuXQ/89IsgL4KLAXcBqwP/AQ4BGllNMBkpwDXAS8CnjJjOZYa23xa744Vrrlb3/8Os6JJGnAGqU5aCRIGvh2+9y2fe4PXD4Iktpy1wAnAwes2xxKkrRhMFBaf+zZPn/cPncFLuhItwxYlGSLGcmVJEnzmIHSeiDJtsC/AaeWUs5rkxcCKzuSr2ifW81E3iRJms/sozTHtZqhE4GbgIOHZwGla5EJ1nUIcEjf/EWLFk0xl5IkzU8GSnNYks2pT7btBOxZSrlsaPYKaq3SqEFN0mq1TaWUo4Gj+7a3ZMmSrsBLkqQNlk1vc1SSTYDjgQcCjyul/HAkyTJqP6VRuwCXlFKuW8dZlCRp3jNQmoPaWEnHAnsDB5RSzu1IdhKwbZI9h5a7PbBfmydJktaSTW9z0/uAJwNvBa5PsvvQvMtaE9xJwDnAJ5K8ktrUdii1j9IRM5xfSZLmJWuU5qbHts/XUoOh4b/nAJRSbgb2Bb4GvB/4HHU074eXUi6d6QxLkjQfWaM0B5VSFo+ZbgXwj+1PkiRNM2uUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GSJElSDwMlSZKkHgZKkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknpsPNsZkNbG4td8cbazIEmax6xRkiRJ6mGgJEmS1MNASZIkqYeBkiRJUg8DJUmSpB4GSpIkST0MlCRJknoYKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9Np7tDEianMWv+eJY6Za//fHrOCeSNP9ZoyRJktTDQEmSJKmHgZIkSVIPAyVJkqQeBkqSJEk9DJQkSZJ6ODyAZpSPtkuS1ifWKEmSJPUwUJIkSephoCRJktTDQEmSJKmHgdJ6Lsn2ST6b5Jok1yY5Icmi2c6XJEnzgYHSeizJbYHTgJ2BZwPPBO4GnJ7kL2czb5IkzQcOD7B+ey6wE3CPUsrPAZL8APgZ8DzgXbOYN0mS1nvWKK3f9gfOHQRJAKWUi4CzgANmLVeSJM0TBkrrt12BCzqmLwN2meG8SJI07xgord8WAis7pq8AtprhvEiSNO/YR2n9VzqmpSthkkOAQyZY13VJLpxiPrYGrprisqvJO6ZrTevXtqdT3jG9ZaJpY7nMTaPlssNsZURzi4HS+m0ltVZp1FZ01DSVUo4Gjl4XGUlyXillybpYt6bGMpmbLJe5yXJRH5ve1m/LqP2URu0C/GiG8yJJ0rxjoLR+OwnYPclOgwlJFgMPbvMkSdJaMFBav30QWA6cmOSAJPsDJwKXAkfNZsYkSZoPDJTWY6WU64FHAD8FPg4cC1wEPKKUct1s5k2SpPnAztzruVLKJcCBs50PSZLmI2uUJEmSehgoabqsk2EHtFYsk7nJcpmbLBd1Sild4xVKkiTJGiVJkqQeBkqSJEk9DJQ0ZUm2T/LZJNckuTbJCUkWzXa+NhRJtkvyX0nOSfL7JKUNODqabqskH0pyVZLrk5ya5F6zkOV5L8mTkhyf5OIkNyS5MMnhSW43ks4ymUFJHp3ktCS/TnJjksuSfCbJLiPpLBetxkBJU5LktsBpwM7As4FnAncDTk/yl7OZtw3IXYGnUN/r9z9dCZKEOkr7Y4AXU4eS2IRaTtvNUD43JK8A/gz8K/WYfwB4AfC1JBuBZTJLFgLfAV4E7AMcSn3907lJdgDLRf3szK0pSfLPwLuAe5RSft6m7Qj8DHhVKeVds5m/DUGSjUopN7d/P4c6UvuOpZTlQ2kOAD5PHYT09DZtS+rApJ8opbxkpvM9nyW5YynlypFpzwI+CuxdSjnNMpkbktwD+AnwilLKf1gu6mONkqZqf+DcQZAEUEq5CDgLOGDWcrUBGQRJa7A/cPngxt+WuwY4Gctp2o0GSc232+e27dMymRt+2z7/1D4tF3UyUNJU7Qpc0DF9GbBLx3TNjonKaVGSLWY4PxuiPdvnj9unZTJLktwmyaZJ7kZ9H+avgU+12ZaLOhkoaaoWUvvGjFoBbDXDeVG/icoJLKt1Ksm2wL8Bp5ZSzmuTLZPZ803gRur7Me9NbWb7TZtnuaiTgZLWRlcHt8x4LjSRYDnNilYDcSJwE3Dw8Cwsk9nyTGB34OnAtdRO9ovbPMtFnQyUNFUrqb/ARm1F968yzY4V9JcTWFbrRJLNqU9Q7QQ8upRy2dBsy2SWlFJ+XEr5Zinlk8DewBbAa9psy0WdDJQ0VcuobfqjdgF+NMN5Ub+JyumSUsp1M5yfeS/JJsDxwAOBx5VSfjiSxDKZA0opVwM/pw6zAZaLehgoaapOAnZPstNgQqvCfnCbp7nhJGDbJIMOxSS5PbAfltO0a2MlHUutrTiglHJuRzLLZA5IcifqOHD/2yZZLurkOEqakjao5PeBG4DXUdv23wzcDri3v75mRpIntX/uDTwfeCFwJXBlKeWM9sV9JrA98Epq88Gh1I6su5VSLp35XM9fST5ALYe3Al8YmX1ZKeUyy2TmJfkccD7wA2rfpLsDLwP+GnhgKeWnlov6GChpytrrSo4EHkXt8Ph14KXDAx5q3UrSdwGfUUrZq6VZCLwTeAKwOXAO8PJSyvdnIo8bkiTLgR16Zr+plHJYS2eZzKAkr6aOYn8XYFPgUmApcPjIAK2Wi1ZjoCRJktTDPkqSJEk9DJQkSZJ6GChJkiT1MFCSJEnqYaAkSZLUw0BJkiSph4GStB5LsjhJSXLMbOdlbSQ5rO3HXrOdF0kaZqAkSZLUw0BJ0lzwXuBvgG/NdkYkadjGs50BSSqlXAVcNdv5kKRR1ihJ81SSOyd5X5LlSf6Y5MokJyS5f0/6LZO8O8llSf6Q5CdJXp5kp75+UEnunuT4JCuTXJ/k7CSPT3JQW+agMfPa2UepTVuaZOskRyf5VZIbkyxLcvAUjsl2Sd6T5GdtH1ck+VaS13ekfXSSs9p+rUjy+SQ7Jzmm5WvxSPr9k3x9KI+XJzkjyQsnm09Jc4c1StI8lGRH6pvQtwFOAz5JfSv6k4HHJzmwlPKFofSbt3T3A74LHAtsCbwWeGjPNnYGzgIWAl+kvpl9J+BzwJemcXcWtO38Efgs9WWlTwI+kuTmUspHx1lJkiXAV1t+vwGcANwW2AU4DHjzUNqnAscBNwKfAX4F7EF9SepqL0hNcghwFPBr4GRq7dhfUd88fzDw/kntsaQ5w0BJmp/+mxokva6U8tbBxCTvpwYJH02yQynlujbrldQg6VPA00t7W3aStwLn92zjfdSg44WllA8MbeOxTG+gtBvwYeB5pZQ/t20cSQ3MXg2sMVBKsinw/1p+n1FKOW5k/vZD/74d9fjdBDxo+M3xSd7etjnqedRAbrdSym9G1r31GPsoaY6y6U2aZ5JsB+wDXAIcMTyvlHI2tXZpIfB3Q7OeDdwMHDoIklr6S4F3d2xje+ARwM+pNSnD2/gycOo07MrA74GXD4Kkto0fUWuZ/qYFNmuyH7AYOGk0SGrru3TovwdQa7GOHQ6SmrcAV/ds4ybgTx3rtu+VtB4zUJLmn/u2z/8ppaz2xU1tYrslXZLbA3cBfllKWd6R/syOafdpn+eUUm4ec5mp+lkp5dqO6YPgZsEY69i9fX55jLSD47faPrQauO91LHMstRlvWZIjkzwhyR3H2JakOc5ASZp/tmyfv+qZP5i+oH3evn1e0ZO+a/qWE8ybaPpUXN0z/ab2eZsx1rGgff5yjLST3rdSyruotXKXAC+h9tO6IsnprW+UpPWUgZI0/1zTPv+6Z/6dR9INamvu1JO+a/pUlplNV7fPbcdIO6V9K6V8rJSyO3AH4PHUflUPA76a5K/Gz6qkucRASZp/vts+H5Kk64GNh7fP8wFas9YvgG1HH3kfrGeCbTwoSdd9pGuZ2XRu+3zsGGlvOX6jM5Jswapmx06llKtLKV8qpTwXOIbaH6zzyUFJc5+BkjTPlFIuA75G7bz80uF5Sf4WeDqwkto8NPAx6v3g8CQZSr/96DraNi4FlgJ3pT7xNbyNxwCPXNv9mGYnA8uB/ZM8bXRmkuGaphOptW3PSLLbSNLX0dEnKsljeoLSQU3S76eQZ0lzgMMDSPPT86lPhf17kn2A81g1jtLNwMGllN8NpT8CeALw98A9kpxC7avzFOpwAk9oyw37p7aN9yd5HKvGUTqQGmwc0LHMrCil/DHJk4FTgOOSPI9ay7Q59dUpe9Puh6WUa9sgkZ8Azk4yPI7SbsAZwJ7cet8+BfwhyZnUgCzUWqQHAN9hep8ClDSDrFGS5qFSyi+AJdTxgO4BvILa7PQV4MGllBNH0t9AbZL7L2rfppe1/78NOLwlu3ZkmR8BD6LWTD2UWvO0GHgiq54Y63pabVaUUs6jNpt9ANgBeDnwTGoN0RtH0h5H7Wf0feCpwAuotUwPAgZjTw3v22uog1HeD3ghdZDJTahjLj285+lDSeuBDA2ZIkmrSfJc4Gjg+aWUo9aUvi1zLLWJb+dSyoXrMn8zKcltqP25Niul9HWWlzSPWKMkCYAk23RM2x54PfVR/C+MzNsoyWrBQpK9qbUwP1pfg6QkC5LcdmRaqH2UFlFffyJpA2AfJUkDxyfZhNqn5mpqM9q+1IEUDy2ljI5BtClwaZLTgZ9Qg6ldgUdRX+fxTzOT7XVid+DTra/WcmCLNu0+1IEuD5utjEmaWTa9SQKgdWB+JnA3akfu66iPyr+3lLJaDUprhno39VUm21EDqquonb/fXkr57ugy64v2UuG3AA8G7kj9UXkZtVbtbaWU6RxQU9IcZqAkSZLUwz5KkiRJPQyUJEmSehgoSZIk9TBQkiRJ6mGgJEmS1MNASZIkqcf/B0nJ83io1sWUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values single stars of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, let's create a Parsec isochrone phot using otherwise same parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 14.694427 s.\n", + "Making photometry for isochrone: log(t) = 8.20 AKs = 0.00 dist = 100\n", + " Starting at: 2021-08-13 13:58:26.544627 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2491 K m_ubv_U = 26.30\n", + "M = 4.155 Msun T = 11703 K m_ubv_U = 2.87\n", + "M = 4.371 Msun T = 4036 K m_ubv_U = 5.84\n", + "Starting filter: ubv,B Elapsed time: 0.43 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2491 K m_ubv_B = 24.47\n", + "M = 4.155 Msun T = 11703 K m_ubv_B = 3.18\n", + "M = 4.371 Msun T = 4036 K m_ubv_B = 3.89\n", + "Starting filter: ubv,V Elapsed time: 0.86 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2491 K m_ubv_V = 22.76\n", + "M = 4.155 Msun T = 11703 K m_ubv_V = 3.28\n", + "M = 4.371 Msun T = 4036 K m_ubv_V = 2.35\n", + "Starting filter: ubv,R Elapsed time: 1.28 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2491 K m_ubv_R = 20.76\n", + "M = 4.155 Msun T = 11703 K m_ubv_R = 3.31\n", + "M = 4.371 Msun T = 4036 K m_ubv_R = 1.62\n", + "Starting filter: ubv,I Elapsed time: 1.69 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2491 K m_ubv_I = 16.78\n", + "M = 4.155 Msun T = 11703 K m_ubv_I = 3.38\n", + "M = 4.371 Msun T = 4036 K m_ubv_I = 0.70\n", + " Time taken: 2.10 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(8.2, 0.0, 100, math.log10(1),\n", + " evo_model=evolution.Parsec(), recomp=True) # New Parsec isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum logg of the isochrone and the distribution of logg values of the new isochrone." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.8974" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([14., 56., 26., 36., 84., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0.]),\n", + " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the BPASS Cluster vs Parsec cluster CMD\n", + "Remember to subtract from V a distance modulus! 5$log_{10}$(distance to cluster/(10 pc))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"],\n", + " iso1.singles[\"m_ubv_V\"] - 5 * np.log10(100 / 10),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(iso2.points['m_ubv_B']-iso2.points[\"m_ubv_V\"],\n", + " iso2.points[\"m_ubv_V\"] - 5 * np.log10(100 / 10),\n", + " \"b+\", label=\"Parsec Isochrone\", alpha=0.25)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the BPASS Cluster vs Parsec Settings cluster CMD\n", + "Remember to subtract from V a distance modulus! 5$log_{10}$(distance to cluster/(10 pc))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['Teff']), np.log10(iso1.primaries[\"L\"]), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['Teff']), np.log10(iso1.secondaries[\"L\"]), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['Teff']),\n", + " np.log10(iso1.singles[\"L\"]), \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['Teff']),\n", + " np.log10(iso2.points[\"L\"]), \"b+\", label=\"Parsec Isochrone\", alpha=0.25)\n", + "plt.xlabel(\"log10($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log10(L in watts)\")\n", + "plt.title(\"HR Diagram of Isochrones at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the mass-luminosity relationship" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from astropy import constants as cs\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['mass_current']),\n", + " np.log10(iso1.secondaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['mass_current']), np.log10(iso1.singles[\"L\"] /\n", + " cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['mass_current']), np.log10(iso2.points[\"L\"] /\n", + " cs.L_sun),\n", + " \"b+\", label=\"Parsec Isochrone\", alpha=0.25)\n", + "plt.xlabel(\"log10(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log10(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at solar metallicity and 10**8.2 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log10\n", + " \"\"\"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "from astropy import units as u\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(clus_1.star_systems['mass_current']),\n", + " np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"Binary_Cluster made from BPASS\", alpha =1)\n", + "plt.plot(np.log10(clus_2.star_systems['mass_current']),\n", + " np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b+\", label=\"Cluster made from Parsec\", alpha=0.1)\n", + "plt.xlabel(\"log10(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log10(L/L_solar)\")\n", + "plt.title(\"log-Mass-logL of Cluster at solar metallicity and\" +\n", + " \" 10**8.2 years age (for primaries and single stars)\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Taking a look at the BPASS Cluster vs \"Parsec Settings cluster\" Observer's HR Diagram\n", + "Remember to use a distance modulus!" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['m_ubv_B'] - clus_1.star_systems[\"m_ubv_V\"],\n", + " clus_1.star_systems[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"r.\",\n", + " label=\"BPASS Cluster\")\n", + "plt.plot(clus_2.star_systems['m_ubv_B'] - clus_2.star_systems[\"m_ubv_V\"],\n", + " clus_2.star_systems[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"b+\",\n", + " label=\"Parsec Cluster\", alpha=0.25)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of clusters at solar metallicity and 10**8.2 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to note about the Binary_Cluster is that it does include the units as information for columns.\n", + "The usual ResolvedCluster does not. Thus the latter has a disadvantage when we do comparisons with solar luminosity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the total mass of the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1966.64870269735" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=4>\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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" + ], + "text/plain": [ + "\n", + "-99.0\n", + " 5.0\n", + "101.0\n", + "102.0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(clus_1.star_systems['phase'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/TestPlot_9.0_MIST_0.1.ipynb b/Test_Plots/TestPlot_9.0_MIST_0.1.ipynb new file mode 100755 index 00000000..6cf69f16 --- /dev/null +++ b/Test_Plots/TestPlot_9.0_MIST_0.1.ipynb @@ -0,0 +1,2997 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS isochrone at $10^{9.0}$ years age and Comparing with the MIST model. ($0.1Z_{\\odot}$)\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for 1.0 billion years age, 0.1 times solar metallicity, AKs=0.0, and distance of 100 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram (B-V vs M_V), and the current-mass luminosity relationship of the cluster. The control group cluster and isochrone are the MIST model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This is for tutorial as well as debugging and demonstration purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 31808 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 27695 logg=5.91\n", + "Changing to logg=5.00 for T= 30805 logg=6.87\n", + "Changing to logg=5.00 for T= 29247 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25245 logg=5.78\n", + "Changing to logg=5.00 for T= 29247 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 24127 logg=5.39\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=0.50 for T= 6052 logg=0.32\n", + "Changing to logg=5.00 for T= 29242 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 21758 logg=7.13\n", + "Changing to logg=5.00 for T= 27945 logg=5.91\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 26542 logg=5.98\n", + "Changing to logg=5.00 for T= 20293 logg=5.24\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25820 logg=5.63\n", + "Changing to logg=5.00 for T= 25777 logg=5.93\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 25237 logg=5.78\n", + "Changing to logg=5.00 for T= 26557 logg=5.76\n", + "Changing to logg=5.00 for T= 25621 logg=5.91\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25758 logg=5.90\n", + "Changing to logg=5.00 for T= 21277 logg=5.21\n", + "Changing to logg=5.00 for T= 23626 logg=5.84\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=3.00 for T= 24984 logg=2.88\n", + "Changing to logg=5.00 for T= 29246 logg=8.00\n", + "Changing to logg=5.00 for T= 21458 logg=5.08\n", + "Changing to logg=5.00 for T= 29780 logg=5.90\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31785 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25710 logg=5.86\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25181 logg=6.03\n", + "Changing to logg=5.00 for T= 25146 logg=5.76\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25818 logg=6.01\n", + "Changing to logg=5.00 for T= 29233 logg=8.00\n", + "Changing to logg=5.00 for T= 29641 logg=5.84\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 22736 logg=5.74\n", + "Changing to logg=5.00 for T= 18944 logg=5.21\n", + "Changing to logg=5.00 for T= 25787 logg=5.93\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 26654 logg=5.72\n", + "Changing to logg=5.00 for T= 20295 logg=5.48\n", + "Changing to logg=5.00 for T= 25745 logg=5.86\n", + "Changing to logg=5.00 for T= 25833 logg=6.03\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 26555 logg=5.78\n", + "Changing to logg=5.00 for T= 25729 logg=5.88\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 24680 logg=5.95\n", + "Changing to logg=5.00 for T= 25709 logg=5.87\n", + "Changing to logg=5.00 for T= 25905 logg=6.07\n", + "Changing to logg=5.00 for T= 19571 logg=5.43\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 19582 logg=5.14\n", + "Changing to logg=5.00 for T= 25907 logg=6.07\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 24997 logg=5.96\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25762 logg=5.97\n", + "Changing to logg=5.00 for T= 25858 logg=6.05\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 26555 logg=5.68\n", + "Changing to logg=5.00 for T= 25854 logg=6.03\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=1.00 for T= 7909 logg=0.93\n", + "Changing to logg=5.00 for T= 25765 logg=5.92\n", + "Changing to logg=5.00 for T= 25829 logg=6.01\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29292 logg=8.01\n", + "Changing to logg=5.00 for T= 23510 logg=5.82\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 25355 logg=5.78\n", + "Changing to logg=5.00 for T= 21643 logg=5.09\n", + "Changing to logg=5.00 for T= 26611 logg=5.73\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 41397 logg=6.10\n", + "Changing to logg=5.00 for T= 31828 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 28111 logg=5.91\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25764 logg=5.90\n", + "Changing to logg=5.00 for T= 19775 logg=5.37\n", + "Changing to logg=5.00 for T= 31786 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=3.00 for T= 20042 logg=2.76\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25645 logg=5.82\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25785 logg=6.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29247 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 20462 logg=5.35\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 26386 logg=5.66\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 26494 logg=5.67\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25801 logg=6.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 24970 logg=5.77\n", + "Changing to logg=5.00 for T= 24127 logg=5.71\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 43334 logg=6.20\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25857 logg=6.04\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25801 logg=6.01\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 30678 logg=5.89\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 29252 logg=8.00\n", + "Changing to logg=5.00 for T= 38896 logg=6.02\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 25707 logg=5.87\n", + "Changing to logg=5.00 for T= 27689 logg=5.89\n", + "Changing to logg=5.00 for T= 29247 logg=8.00\n", + "Changing to logg=5.00 for T= 29367 logg=5.86\n", + "Changing to logg=5.00 for T= 25785 logg=5.94\n", + "Changing to logg=0.50 for T= 6322 logg=0.41\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=2.50 for T= 12550 logg=1.68\n", + "Changing to logg=5.00 for T= 26671 logg=5.76\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25689 logg=5.86\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 31822 logg=8.00\n", + "Changing to logg=5.00 for T= 25758 logg=5.91\n", + "Changing to logg=5.00 for T= 24130 logg=5.67\n", + "Changing to logg=5.00 for T= 25760 logg=6.22\n", + "Changing to logg=5.00 for T= 29214 logg=8.00\n", + "Changing to logg=5.00 for T= 21696 logg=5.45\n", + "Changing to logg=4.00 for T= 33537 logg=3.83\n", + "Changing to logg=0.50 for T= 6013 logg=0.28\n", + "Changing to logg=5.00 for T= 22770 logg=5.66\n", + "Changing to logg=5.00 for T= 26947 logg=5.77\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to logg=5.00 for T= 25274 logg=6.09\n", + "Changing to logg=5.00 for T= 26207 logg=6.22\n", + "Changing to logg=5.00 for T= 40090 logg=6.12\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 30164 logg=5.87\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 25138 logg=5.98\n", + "Changing to logg=5.00 for T= 25111 logg=5.99\n", + "Changing to logg=5.00 for T= 24937 logg=6.13\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 27088 logg=5.79\n", + "Changing to logg=5.00 for T= 28191 logg=6.01\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 25344 logg=6.12\n", + "Changing to logg=5.00 for T= 26789 logg=5.84\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 28409 logg=6.41\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 28694 logg=6.11\n", + "Changing to logg=5.00 for T= 27485 logg=6.04\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 25092 logg=6.01\n", + "Changing to logg=5.00 for T= 25489 logg=6.13\n", + "Changing to logg=5.00 for T= 26779 logg=5.84\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 29293 logg=8.01\n", + "Changing to logg=5.00 for T= 26673 logg=5.98\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 26797 logg=5.90\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 25251 logg=6.10\n", + "Changing to logg=5.00 for T= 25954 logg=5.83\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to logg=5.00 for T= 26972 logg=5.82\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 25409 logg=6.17\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to logg=5.00 for T= 26884 logg=5.86\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 24975 logg=5.92\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to logg=5.00 for T= 24619 logg=6.09\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 26871 logg=5.88\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 25372 logg=6.18\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 27404 logg=6.06\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 21587 logg=5.69\n", + "Changing to logg=5.00 for T= 26775 logg=5.83\n", + "Changing to logg=5.00 for T= 25196 logg=6.07\n", + "Changing to logg=5.00 for T= 26881 logg=5.88\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 24726 logg=5.82\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 26441 logg=6.20\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 25774 logg=5.93\n", + "Changing to logg=5.00 for T= 37599 logg=6.16\n", + "Changing to logg=5.00 for T= 26745 logg=5.84\n", + "Changing to logg=5.00 for T= 29293 logg=8.01\n", + "Changing to logg=5.00 for T= 26685 logg=5.95\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 23624 logg=5.81\n", + "Changing to logg=5.00 for T= 25823 logg=5.80\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 24353 logg=5.55\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to logg=5.00 for T= 23774 logg=5.96\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to T= 2300 for T= 1731 logg=4.44\n", + "Changing to logg=2.00 for T= 10370 logg=1.99\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to logg=5.00 for T= 25498 logg=6.07\n", + "Changing to logg=5.00 for T= 31205 logg=6.22\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 2077 logg=4.44\n", + "Changing to T= 2300 for T= 1558 logg=4.44\n", + "Changing to logg=5.00 for T= 30086 logg=5.84\n", + "Changing to logg=5.00 for T= 25023 logg=5.94\n", + "Changing to T= 2300 for T= 2000 logg=5.39\n", + "Changing to logg=5.00 for T= 26753 logg=5.85\n", + "Making photometry for isochrone: log(t) = 9.00 AKs = 0.00 dist = 100\n", + " Starting at: 2021-08-13 13:58:03.000974 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 5232 K m_ubv_U = 6.23\n", + "M = 4.500 Msun T = 11258 K m_ubv_U = 17.38\n", + "M = 2.500 Msun T = 9748 K m_ubv_U = 17.07\n", + "M = 2.100 Msun T = 9820 K m_ubv_U = 17.28\n", + "M = 1.100 Msun T = 7045 K m_ubv_U = 8.94\n", + "M = 0.251 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = 3090 K m_ubv_U = 23.30\n", + "M = 0.900 Msun T = 6172 K m_ubv_U = 10.16\n", + "M = 2.700 Msun T = 10637 K m_ubv_U = 17.42\n", + "M = 2.100 Msun T = 26557 K m_ubv_U = 9.08\n", + "M = 1.900 Msun T = 5224 K m_ubv_U = 5.97\n", + "M = 1.700 Msun T = 9348 K m_ubv_U = 6.42\n", + "M = 0.501 Msun T = 3090 K m_ubv_U = 23.19\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 2.500 Msun T = 9747 K m_ubv_U = 17.07\n", + "M = 1.800 Msun T = 8946 K m_ubv_U = 6.02\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 2.100 Msun T = 26654 K m_ubv_U = 8.97\n", + "M = 2.000 Msun T = 31822 K m_ubv_U = 13.65\n", + "M = 1.700 Msun T = 9348 K m_ubv_U = 6.42\n", + "M = 3.500 Msun T = 7901 K m_ubv_U = 17.98\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 2.100 Msun T = 9820 K m_ubv_U = 17.28\n", + "M = 3.500 Msun T = 7821 K m_ubv_U = 18.01\n", + "M = 1.900 Msun T = 5223 K m_ubv_U = 5.97\n", + "M = 3.200 Msun T = 9358 K m_ubv_U = 17.59\n", + "M = 3.000 Msun T = 9943 K m_ubv_U = 17.26\n", + "M = 3.000 Msun T = 9944 K m_ubv_U = 17.26\n", + "M = 2.000 Msun T = 31822 K m_ubv_U = 13.65\n", + "M = 3.162 Msun T = nan K m_ubv_U = nan\n", + "M = 0.398 Msun T = nan K m_ubv_U = nan\n", + "M = 1.100 Msun T = 7042 K m_ubv_U = 8.94\n", + "M = 1.300 Msun T = 8321 K m_ubv_U = 7.98\n", + "M = 3.200 Msun T = 9357 K m_ubv_U = 17.59\n", + "M = 1.300 Msun T = 8321 K m_ubv_U = 7.97\n", + "M = 0.398 Msun T = nan K m_ubv_U = nan\n", + "M = 2.300 Msun T = 10874 K m_ubv_U = 16.46\n", + "M = 0.900 Msun T = 6172 K m_ubv_U = 10.16\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 3.000 Msun T = 12382 K m_ubv_U = 16.51\n", + "M = 3.200 Msun T = 9943 K m_ubv_U = 17.50\n", + "M = 1.800 Msun T = 9701 K m_ubv_U = 17.53\n", + "M = 2.700 Msun T = 9885 K m_ubv_U = 17.27\n", + "M = 0.900 Msun T = 6174 K m_ubv_U = 10.16\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 9885 K m_ubv_U = 17.27\n", + "M = 3.200 Msun T = 9357 K m_ubv_U = 17.59\n", + "M = 1.600 Msun T = 9941 K m_ubv_U = 17.26\n", + "M = 1.250 Msun T = 7745 K m_ubv_U = 8.47\n", + "M = 0.420 Msun T = nan K m_ubv_U = nan\n", + "M = 0.880 Msun T = 5078 K m_ubv_U = 11.67\n", + "M = 1.300 Msun T = 8310 K m_ubv_U = 8.03\n", + "M = 2.000 Msun T = 4717 K m_ubv_U = 3.95\n", + "M = 0.720 Msun T = 4155 K m_ubv_U = 14.28\n", + "M = 0.810 Msun T = nan K m_ubv_U = nan\n", + "M = 1.470 Msun T = 8608 K m_ubv_U = 7.46\n", + "M = 0.380 Msun T = 3862 K m_ubv_U = 17.08\n", + "M = 0.170 Msun T = 3596 K m_ubv_U = 19.41\n", + "M = 2.000 Msun T = 25344 K m_ubv_U = 10.28\n", + "M = 1.500 Msun T = 9188 K m_ubv_U = 7.19\n", + "M = 1.500 Msun T = 9188 K m_ubv_U = 7.19\n", + "M = 2.250 Msun T = 10601 K m_ubv_U = 5.33\n", + "M = 1.440 Msun T = 9220 K m_ubv_U = 7.59\n", + "M = 1.100 Msun T = 7051 K m_ubv_U = 8.92\n", + "M = 7.000 Msun T = 11382 K m_ubv_U = 16.85\n", + "M = 1.050 Msun T = 6395 K m_ubv_U = 9.39\n", + "M = 0.600 Msun T = 4605 K m_ubv_U = 14.04\n", + "M = 0.170 Msun T = 3596 K m_ubv_U = 19.41\n", + "M = 3.150 Msun T = 11242 K m_ubv_U = 3.83\n", + "M = 1.900 Msun T = 5230 K m_ubv_U = 6.09\n", + "M = 0.420 Msun T = nan K m_ubv_U = nan\n", + "M = 0.350 Msun T = 3818 K m_ubv_U = 17.37\n", + "M = 1.140 Msun T = 7217 K m_ubv_U = 8.86\n", + "M = 2.880 Msun T = 13396 K m_ubv_U = 4.54\n", + "M = 1.800 Msun T = 11016 K m_ubv_U = 6.86\n", + "M = 1.500 Msun T = 9315 K m_ubv_U = 7.66\n", + "M = 1.600 Msun T = 10052 K m_ubv_U = 7.00\n", + "M = 5.000 Msun T = 7711 K m_ubv_U = 18.05\n", + "M = 1.100 Msun T = 7051 K m_ubv_U = 8.92\n", + "M = 0.220 Msun T = 3700 K m_ubv_U = 18.55\n", + "M = 0.390 Msun T = 3880 K m_ubv_U = 16.98\n", + "M = 2.880 Msun T = 13407 K m_ubv_U = 4.54\n", + "M = 1.170 Msun T = 7419 K m_ubv_U = 8.69\n", + "M = 1.500 Msun T = 9148 K m_ubv_U = 7.19\n", + "M = 1.380 Msun T = 8679 K m_ubv_U = 7.93\n", + "M = 0.270 Msun T = 1558 K m_ubv_U = 24.92\n", + "M = 1.900 Msun T = 5230 K m_ubv_U = 6.09\n", + "M = 1.800 Msun T = 9333 K m_ubv_U = 6.17\n", + "M = 0.320 Msun T = nan K m_ubv_U = nan\n", + "M = 0.900 Msun T = nan K m_ubv_U = nan\n", + "M = 0.270 Msun T = nan K m_ubv_U = nan\n", + "M = 0.180 Msun T = 3624 K m_ubv_U = 19.18\n", + "M = 3.500 Msun T = 9499 K m_ubv_U = 17.56\n", + "M = 0.270 Msun T = nan K m_ubv_U = nan\n", + "M = 2.560 Msun T = 13752 K m_ubv_U = 5.32\n", + "M = 1.440 Msun T = nan K m_ubv_U = nan\n", + "Starting filter: ubv,V Elapsed time: 21.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 5232 K m_ubv_V = 5.41\n", + "M = 4.500 Msun T = 11258 K m_ubv_V = 18.07\n", + "M = 2.500 Msun T = 9748 K m_ubv_V = 17.58\n", + "M = 2.100 Msun T = 9820 K m_ubv_V = 17.80\n", + "M = 1.100 Msun T = 7045 K m_ubv_V = 8.77\n", + "M = 0.251 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = 3090 K m_ubv_V = 19.52\n", + "M = 0.900 Msun T = 6172 K m_ubv_V = 9.89\n", + "M = 2.700 Msun T = 10637 K m_ubv_V = 18.04\n", + "M = 2.100 Msun T = 26557 K m_ubv_V = 10.22\n", + "M = 1.900 Msun T = 5224 K m_ubv_V = 5.14\n", + "M = 1.700 Msun T = 9348 K m_ubv_V = 6.37\n", + "M = 0.501 Msun T = 3090 K m_ubv_V = 19.52\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 2.500 Msun T = 9747 K m_ubv_V = 17.58\n", + "M = 1.800 Msun T = 8946 K m_ubv_V = 5.91\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 2.100 Msun T = 26654 K m_ubv_V = 10.11\n", + "M = 2.000 Msun T = 31822 K m_ubv_V = 14.93\n", + "M = 1.700 Msun T = 9348 K m_ubv_V = 6.37\n", + "M = 3.500 Msun T = 7901 K m_ubv_V = 18.18\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 2.100 Msun T = 9820 K m_ubv_V = 17.80\n", + "M = 3.500 Msun T = 7821 K m_ubv_V = 18.19\n", + "M = 1.900 Msun T = 5223 K m_ubv_V = 5.14\n", + "M = 3.200 Msun T = 9358 K m_ubv_V = 18.05\n", + "M = 3.000 Msun T = 9943 K m_ubv_V = 17.80\n", + "M = 3.000 Msun T = 9944 K m_ubv_V = 17.80\n", + "M = 2.000 Msun T = 31822 K m_ubv_V = 14.93\n", + "M = 3.162 Msun T = nan K m_ubv_V = nan\n", + "M = 0.398 Msun T = nan K m_ubv_V = nan\n", + "M = 1.100 Msun T = 7042 K m_ubv_V = 8.77\n", + "M = 1.300 Msun T = 8321 K m_ubv_V = 7.81\n", + "M = 3.200 Msun T = 9357 K m_ubv_V = 18.05\n", + "M = 1.300 Msun T = 8321 K m_ubv_V = 7.80\n", + "M = 0.398 Msun T = nan K m_ubv_V = nan\n", + "M = 2.300 Msun T = 10874 K m_ubv_V = 17.11\n", + "M = 0.900 Msun T = 6172 K m_ubv_V = 9.89\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 3.000 Msun T = 12382 K m_ubv_V = 17.30\n", + "M = 3.200 Msun T = 9943 K m_ubv_V = 18.04\n", + "M = 1.800 Msun T = 9701 K m_ubv_V = 18.04\n", + "M = 2.700 Msun T = 9885 K m_ubv_V = 17.80\n", + "M = 0.900 Msun T = 6174 K m_ubv_V = 9.88\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 9885 K m_ubv_V = 17.80\n", + "M = 3.200 Msun T = 9357 K m_ubv_V = 18.05\n", + "M = 1.600 Msun T = 9941 K m_ubv_V = 17.80\n", + "M = 1.250 Msun T = 7745 K m_ubv_V = 8.30\n", + "M = 0.420 Msun T = nan K m_ubv_V = nan\n", + "M = 0.880 Msun T = 5078 K m_ubv_V = 10.71\n", + "M = 1.300 Msun T = 8310 K m_ubv_V = 7.86\n", + "M = 2.000 Msun T = 4717 K m_ubv_V = 2.20\n", + "M = 0.720 Msun T = 4155 K m_ubv_V = 12.30\n", + "M = 0.810 Msun T = nan K m_ubv_V = nan\n", + "M = 1.470 Msun T = 8608 K m_ubv_V = 7.31\n", + "M = 0.380 Msun T = 3862 K m_ubv_V = 14.75\n", + "M = 0.170 Msun T = 3596 K m_ubv_V = 16.64\n", + "M = 2.000 Msun T = 25344 K m_ubv_V = 11.38\n", + "M = 1.500 Msun T = 9188 K m_ubv_V = 7.10\n", + "M = 1.500 Msun T = 9188 K m_ubv_V = 7.10\n", + "M = 2.250 Msun T = 10601 K m_ubv_V = 5.51\n", + "M = 1.440 Msun T = 9220 K m_ubv_V = 7.50\n", + "M = 1.100 Msun T = 7051 K m_ubv_V = 8.76\n", + "M = 7.000 Msun T = 11382 K m_ubv_V = 17.55\n", + "M = 1.050 Msun T = 6395 K m_ubv_V = 9.17\n", + "M = 0.600 Msun T = 4605 K m_ubv_V = 12.53\n", + "M = 0.170 Msun T = 3596 K m_ubv_V = 16.64\n", + "M = 3.150 Msun T = 11242 K m_ubv_V = 4.13\n", + "M = 1.900 Msun T = 5230 K m_ubv_V = 5.26\n", + "M = 0.420 Msun T = nan K m_ubv_V = nan\n", + "M = 0.350 Msun T = 3818 K m_ubv_V = 14.98\n", + "M = 1.140 Msun T = 7217 K m_ubv_V = 8.69\n", + "M = 2.880 Msun T = 13396 K m_ubv_V = 5.06\n", + "M = 1.800 Msun T = 11016 K m_ubv_V = 7.06\n", + "M = 1.500 Msun T = 9315 K m_ubv_V = 7.59\n", + "M = 1.600 Msun T = 10052 K m_ubv_V = 7.05\n", + "M = 5.000 Msun T = 7711 K m_ubv_V = 18.21\n", + "M = 1.100 Msun T = 7051 K m_ubv_V = 8.76\n", + "M = 0.220 Msun T = 3700 K m_ubv_V = 15.96\n", + "M = 0.390 Msun T = 3880 K m_ubv_V = 14.67\n", + "M = 2.880 Msun T = 13407 K m_ubv_V = 5.06\n", + "M = 1.170 Msun T = 7419 K m_ubv_V = 8.52\n", + "M = 1.500 Msun T = 9148 K m_ubv_V = 7.10\n", + "M = 1.380 Msun T = 8679 K m_ubv_V = 7.79\n", + "M = 0.270 Msun T = 1558 K m_ubv_V = 19.92\n", + "M = 1.900 Msun T = 5230 K m_ubv_V = 5.26\n", + "M = 1.800 Msun T = 9333 K m_ubv_V = 6.12\n", + "M = 0.320 Msun T = nan K m_ubv_V = nan\n", + "M = 0.900 Msun T = nan K m_ubv_V = nan\n", + "M = 0.270 Msun T = nan K m_ubv_V = nan\n", + "M = 0.180 Msun T = 3624 K m_ubv_V = 16.46\n", + "M = 3.500 Msun T = 9499 K m_ubv_V = 18.04\n", + "M = 0.270 Msun T = nan K m_ubv_V = nan\n", + "M = 2.560 Msun T = 13752 K m_ubv_V = 5.86\n", + "M = 1.440 Msun T = nan K m_ubv_V = nan\n", + "Starting filter: ubv,B Elapsed time: 41.44 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 5232 K m_ubv_B = 6.17\n", + "M = 4.500 Msun T = 11258 K m_ubv_B = 18.15\n", + "M = 2.500 Msun T = 9748 K m_ubv_B = 17.76\n", + "M = 2.100 Msun T = 9820 K m_ubv_B = 17.97\n", + "M = 1.100 Msun T = 7045 K m_ubv_B = 9.09\n", + "M = 0.251 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = 3090 K m_ubv_B = 21.44\n", + "M = 0.900 Msun T = 6172 K m_ubv_B = 10.38\n", + "M = 2.700 Msun T = 10637 K m_ubv_B = 18.17\n", + "M = 2.100 Msun T = 26557 K m_ubv_B = 9.99\n", + "M = 1.900 Msun T = 5224 K m_ubv_B = 5.90\n", + "M = 1.700 Msun T = 9348 K m_ubv_B = 6.39\n", + "M = 0.501 Msun T = 3090 K m_ubv_B = 21.42\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 2.500 Msun T = 9747 K m_ubv_B = 17.76\n", + "M = 1.800 Msun T = 8946 K m_ubv_B = 5.95\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 2.100 Msun T = 26654 K m_ubv_B = 9.87\n", + "M = 2.000 Msun T = 31822 K m_ubv_B = 14.66\n", + "M = 1.700 Msun T = 9348 K m_ubv_B = 6.39\n", + "M = 3.500 Msun T = 7901 K m_ubv_B = 18.52\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 2.100 Msun T = 9820 K m_ubv_B = 17.97\n", + "M = 3.500 Msun T = 7821 K m_ubv_B = 18.54\n", + "M = 1.900 Msun T = 5223 K m_ubv_B = 5.90\n", + "M = 3.200 Msun T = 9358 K m_ubv_B = 18.25\n", + "M = 3.000 Msun T = 9943 K m_ubv_B = 17.96\n", + "M = 3.000 Msun T = 9944 K m_ubv_B = 17.96\n", + "M = 2.000 Msun T = 31822 K m_ubv_B = 14.66\n", + "M = 3.162 Msun T = nan K m_ubv_B = nan\n", + "M = 0.398 Msun T = nan K m_ubv_B = nan\n", + "M = 1.100 Msun T = 7042 K m_ubv_B = 9.10\n", + "M = 1.300 Msun T = 8321 K m_ubv_B = 7.96\n", + "M = 3.200 Msun T = 9357 K m_ubv_B = 18.25\n", + "M = 1.300 Msun T = 8321 K m_ubv_B = 7.96\n", + "M = 0.398 Msun T = nan K m_ubv_B = nan\n", + "M = 2.300 Msun T = 10874 K m_ubv_B = 17.22\n", + "M = 0.900 Msun T = 6172 K m_ubv_B = 10.38\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 3.000 Msun T = 12382 K m_ubv_B = 17.34\n", + "M = 3.200 Msun T = 9943 K m_ubv_B = 18.20\n", + "M = 1.800 Msun T = 9701 K m_ubv_B = 18.22\n", + "M = 2.700 Msun T = 9885 K m_ubv_B = 17.97\n", + "M = 0.900 Msun T = 6174 K m_ubv_B = 10.37\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 9885 K m_ubv_B = 17.97\n", + "M = 3.200 Msun T = 9357 K m_ubv_B = 18.25\n", + "M = 1.600 Msun T = 9941 K m_ubv_B = 17.96\n", + "M = 1.250 Msun T = 7745 K m_ubv_B = 8.54\n", + "M = 0.420 Msun T = nan K m_ubv_B = nan\n", + "M = 0.880 Msun T = 5078 K m_ubv_B = 11.53\n", + "M = 1.300 Msun T = 8310 K m_ubv_B = 8.02\n", + "M = 2.000 Msun T = 4717 K m_ubv_B = 3.29\n", + "M = 0.720 Msun T = 4155 K m_ubv_B = 13.52\n", + "M = 0.810 Msun T = nan K m_ubv_B = nan\n", + "M = 1.470 Msun T = 8608 K m_ubv_B = 7.44\n", + "M = 0.380 Msun T = 3862 K m_ubv_B = 16.16\n", + "M = 0.170 Msun T = 3596 K m_ubv_B = 18.23\n", + "M = 2.000 Msun T = 25344 K m_ubv_B = 11.15\n", + "M = 1.500 Msun T = 9188 K m_ubv_B = 7.15\n", + "M = 1.500 Msun T = 9188 K m_ubv_B = 7.15\n", + "M = 2.250 Msun T = 10601 K m_ubv_B = 5.46\n", + "M = 1.440 Msun T = 9220 K m_ubv_B = 7.56\n", + "M = 1.100 Msun T = 7051 K m_ubv_B = 9.08\n", + "M = 7.000 Msun T = 11382 K m_ubv_B = 17.63\n", + "M = 1.050 Msun T = 6395 K m_ubv_B = 9.61\n", + "M = 0.600 Msun T = 4605 K m_ubv_B = 13.52\n", + "M = 0.170 Msun T = 3596 K m_ubv_B = 18.23\n", + "M = 3.150 Msun T = 11242 K m_ubv_B = 4.05\n", + "M = 1.900 Msun T = 5230 K m_ubv_B = 6.03\n", + "M = 0.420 Msun T = nan K m_ubv_B = nan\n", + "M = 0.350 Msun T = 3818 K m_ubv_B = 16.41\n", + "M = 1.140 Msun T = 7217 K m_ubv_B = 8.99\n", + "M = 2.880 Msun T = 13396 K m_ubv_B = 4.95\n", + "M = 1.800 Msun T = 11016 K m_ubv_B = 7.02\n", + "M = 1.500 Msun T = 9315 K m_ubv_B = 7.65\n", + "M = 1.600 Msun T = 10052 K m_ubv_B = 7.05\n", + "M = 5.000 Msun T = 7711 K m_ubv_B = 18.57\n", + "M = 1.100 Msun T = 7051 K m_ubv_B = 9.08\n", + "M = 0.220 Msun T = 3700 K m_ubv_B = 17.48\n", + "M = 0.390 Msun T = 3880 K m_ubv_B = 16.07\n", + "M = 2.880 Msun T = 13407 K m_ubv_B = 4.95\n", + "M = 1.170 Msun T = 7419 K m_ubv_B = 8.79\n", + "M = 1.500 Msun T = 9148 K m_ubv_B = 7.16\n", + "M = 1.380 Msun T = 8679 K m_ubv_B = 7.90\n", + "M = 0.270 Msun T = 1558 K m_ubv_B = 22.19\n", + "M = 1.900 Msun T = 5230 K m_ubv_B = 6.03\n", + "M = 1.800 Msun T = 9333 K m_ubv_B = 6.15\n", + "M = 0.320 Msun T = nan K m_ubv_B = nan\n", + "M = 0.900 Msun T = nan K m_ubv_B = nan\n", + "M = 0.270 Msun T = nan K m_ubv_B = nan\n", + "M = 0.180 Msun T = 3624 K m_ubv_B = 18.03\n", + "M = 3.500 Msun T = 9499 K m_ubv_B = 18.24\n", + "M = 0.270 Msun T = nan K m_ubv_B = nan\n", + "M = 2.560 Msun T = 13752 K m_ubv_B = 5.75\n", + "M = 1.440 Msun T = nan K m_ubv_B = nan\n", + "Starting filter: ubv,R Elapsed time: 61.33 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 5232 K m_ubv_R = 5.02\n", + "M = 4.500 Msun T = 11258 K m_ubv_R = 17.99\n", + "M = 2.500 Msun T = 9748 K m_ubv_R = 17.46\n", + "M = 2.100 Msun T = 9820 K m_ubv_R = 17.68\n", + "M = 1.100 Msun T = 7045 K m_ubv_R = 8.58\n", + "M = 0.251 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = 3090 K m_ubv_R = 18.49\n", + "M = 0.900 Msun T = 6172 K m_ubv_R = 9.61\n", + "M = 2.700 Msun T = 10637 K m_ubv_R = 17.95\n", + "M = 2.100 Msun T = 26557 K m_ubv_R = 10.32\n", + "M = 1.900 Msun T = 5224 K m_ubv_R = 4.75\n", + "M = 1.700 Msun T = 9348 K m_ubv_R = 6.36\n", + "M = 0.501 Msun T = 3090 K m_ubv_R = 18.50\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 2.500 Msun T = 9747 K m_ubv_R = 17.46\n", + "M = 1.800 Msun T = 8946 K m_ubv_R = 5.89\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 2.100 Msun T = 26654 K m_ubv_R = 10.21\n", + "M = 2.000 Msun T = 31822 K m_ubv_R = 15.05\n", + "M = 1.700 Msun T = 9348 K m_ubv_R = 6.36\n", + "M = 3.500 Msun T = 7901 K m_ubv_R = 17.97\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 2.100 Msun T = 9820 K m_ubv_R = 17.68\n", + "M = 3.500 Msun T = 7821 K m_ubv_R = 17.98\n", + "M = 1.900 Msun T = 5223 K m_ubv_R = 4.75\n", + "M = 3.200 Msun T = 9358 K m_ubv_R = 17.91\n", + "M = 3.000 Msun T = 9943 K m_ubv_R = 17.68\n", + "M = 3.000 Msun T = 9944 K m_ubv_R = 17.68\n", + "M = 2.000 Msun T = 31822 K m_ubv_R = 15.05\n", + "M = 3.162 Msun T = nan K m_ubv_R = nan\n", + "M = 0.398 Msun T = nan K m_ubv_R = nan\n", + "M = 1.100 Msun T = 7042 K m_ubv_R = 8.58\n", + "M = 1.300 Msun T = 8321 K m_ubv_R = 7.74\n", + "M = 3.200 Msun T = 9357 K m_ubv_R = 17.91\n", + "M = 1.300 Msun T = 8321 K m_ubv_R = 7.73\n", + "M = 0.398 Msun T = nan K m_ubv_R = nan\n", + "M = 2.300 Msun T = 10874 K m_ubv_R = 17.02\n", + "M = 0.900 Msun T = 6172 K m_ubv_R = 9.61\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 3.000 Msun T = 12382 K m_ubv_R = 17.25\n", + "M = 3.200 Msun T = 9943 K m_ubv_R = 17.92\n", + "M = 1.800 Msun T = 9701 K m_ubv_R = 17.91\n", + "M = 2.700 Msun T = 9885 K m_ubv_R = 17.68\n", + "M = 0.900 Msun T = 6174 K m_ubv_R = 9.60\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 9885 K m_ubv_R = 17.68\n", + "M = 3.200 Msun T = 9357 K m_ubv_R = 17.91\n", + "M = 1.600 Msun T = 9941 K m_ubv_R = 17.68\n", + "M = 1.250 Msun T = 7745 K m_ubv_R = 8.18\n", + "M = 0.420 Msun T = nan K m_ubv_R = nan\n", + "M = 0.880 Msun T = 5078 K m_ubv_R = 10.28\n", + "M = 1.300 Msun T = 8310 K m_ubv_R = 7.79\n", + "M = 2.000 Msun T = 4717 K m_ubv_R = 1.69\n", + "M = 0.720 Msun T = 4155 K m_ubv_R = 11.62\n", + "M = 0.810 Msun T = nan K m_ubv_R = nan\n", + "M = 1.470 Msun T = 8608 K m_ubv_R = 7.26\n", + "M = 0.380 Msun T = 3862 K m_ubv_R = 13.97\n", + "M = 0.170 Msun T = 3596 K m_ubv_R = 15.78\n", + "M = 2.000 Msun T = 25344 K m_ubv_R = 11.48\n", + "M = 1.500 Msun T = 9188 K m_ubv_R = 7.08\n", + "M = 1.500 Msun T = 9188 K m_ubv_R = 7.08\n", + "M = 2.250 Msun T = 10601 K m_ubv_R = 5.52\n", + "M = 1.440 Msun T = 9220 K m_ubv_R = 7.48\n", + "M = 1.100 Msun T = 7051 K m_ubv_R = 8.56\n", + "M = 7.000 Msun T = 11382 K m_ubv_R = 17.47\n", + "M = 1.050 Msun T = 6395 K m_ubv_R = 8.91\n", + "M = 0.600 Msun T = 4605 K m_ubv_R = 11.98\n", + "M = 0.170 Msun T = 3596 K m_ubv_R = 15.78\n", + "M = 3.150 Msun T = 11242 K m_ubv_R = 4.15\n", + "M = 1.900 Msun T = 5230 K m_ubv_R = 4.87\n", + "M = 0.420 Msun T = nan K m_ubv_R = nan\n", + "M = 0.350 Msun T = 3818 K m_ubv_R = 14.19\n", + "M = 1.140 Msun T = 7217 K m_ubv_R = 8.52\n", + "M = 2.880 Msun T = 13396 K m_ubv_R = 5.10\n", + "M = 1.800 Msun T = 11016 K m_ubv_R = 7.08\n", + "M = 1.500 Msun T = 9315 K m_ubv_R = 7.57\n", + "M = 1.600 Msun T = 10052 K m_ubv_R = 7.06\n", + "M = 5.000 Msun T = 7711 K m_ubv_R = 17.99\n", + "M = 1.100 Msun T = 7051 K m_ubv_R = 8.56\n", + "M = 0.220 Msun T = 3700 K m_ubv_R = 15.13\n", + "M = 0.390 Msun T = 3880 K m_ubv_R = 13.90\n", + "M = 2.880 Msun T = 13407 K m_ubv_R = 5.10\n", + "M = 1.170 Msun T = 7419 K m_ubv_R = 8.37\n", + "M = 1.500 Msun T = 9148 K m_ubv_R = 7.09\n", + "M = 1.380 Msun T = 8679 K m_ubv_R = 7.74\n", + "M = 0.270 Msun T = 1558 K m_ubv_R = 18.03\n", + "M = 1.900 Msun T = 5230 K m_ubv_R = 4.87\n", + "M = 1.800 Msun T = 9333 K m_ubv_R = 6.12\n", + "M = 0.320 Msun T = nan K m_ubv_R = nan\n", + "M = 0.900 Msun T = nan K m_ubv_R = nan\n", + "M = 0.270 Msun T = nan K m_ubv_R = nan\n", + "M = 0.180 Msun T = 3624 K m_ubv_R = 15.62\n", + "M = 3.500 Msun T = 9499 K m_ubv_R = 17.91\n", + "M = 0.270 Msun T = nan K m_ubv_R = nan\n", + "M = 2.560 Msun T = 13752 K m_ubv_R = 5.90\n", + "M = 1.440 Msun T = nan K m_ubv_R = nan\n", + "Starting filter: ubv,I Elapsed time: 81.37 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 5232 K m_ubv_I = 4.47\n", + "M = 4.500 Msun T = 11258 K m_ubv_I = 17.96\n", + "M = 2.500 Msun T = 9748 K m_ubv_I = 17.36\n", + "M = 2.100 Msun T = 9820 K m_ubv_I = 17.59\n", + "M = 1.100 Msun T = 7045 K m_ubv_I = 8.32\n", + "M = 0.251 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = 3090 K m_ubv_I = 16.79\n", + "M = 0.900 Msun T = 6172 K m_ubv_I = 9.23\n", + "M = 2.700 Msun T = 10637 K m_ubv_I = 17.90\n", + "M = 2.100 Msun T = 26557 K m_ubv_I = 10.56\n", + "M = 1.900 Msun T = 5224 K m_ubv_I = 4.21\n", + "M = 1.700 Msun T = 9348 K m_ubv_I = 6.35\n", + "M = 0.501 Msun T = 3090 K m_ubv_I = 16.80\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 2.500 Msun T = 9747 K m_ubv_I = 17.36\n", + "M = 1.800 Msun T = 8946 K m_ubv_I = 5.85\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 2.100 Msun T = 26654 K m_ubv_I = 10.45\n", + "M = 2.000 Msun T = 31822 K m_ubv_I = 15.32\n", + "M = 1.700 Msun T = 9348 K m_ubv_I = 6.35\n", + "M = 3.500 Msun T = 7901 K m_ubv_I = 17.75\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 2.100 Msun T = 9820 K m_ubv_I = 17.59\n", + "M = 3.500 Msun T = 7821 K m_ubv_I = 17.75\n", + "M = 1.900 Msun T = 5223 K m_ubv_I = 4.20\n", + "M = 3.200 Msun T = 9358 K m_ubv_I = 17.79\n", + "M = 3.000 Msun T = 9943 K m_ubv_I = 17.59\n", + "M = 3.000 Msun T = 9944 K m_ubv_I = 17.59\n", + "M = 2.000 Msun T = 31822 K m_ubv_I = 15.32\n", + "M = 3.162 Msun T = nan K m_ubv_I = nan\n", + "M = 0.398 Msun T = nan K m_ubv_I = nan\n", + "M = 1.100 Msun T = 7042 K m_ubv_I = 8.33\n", + "M = 1.300 Msun T = 8321 K m_ubv_I = 7.64\n", + "M = 3.200 Msun T = 9357 K m_ubv_I = 17.79\n", + "M = 1.300 Msun T = 8321 K m_ubv_I = 7.64\n", + "M = 0.398 Msun T = nan K m_ubv_I = nan\n", + "M = 2.300 Msun T = 10874 K m_ubv_I = 16.98\n", + "M = 0.900 Msun T = 6172 K m_ubv_I = 9.23\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 3.000 Msun T = 12382 K m_ubv_I = 17.26\n", + "M = 3.200 Msun T = 9943 K m_ubv_I = 17.83\n", + "M = 1.800 Msun T = 9701 K m_ubv_I = 17.81\n", + "M = 2.700 Msun T = 9885 K m_ubv_I = 17.59\n", + "M = 0.900 Msun T = 6174 K m_ubv_I = 9.22\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 9885 K m_ubv_I = 17.59\n", + "M = 3.200 Msun T = 9357 K m_ubv_I = 17.79\n", + "M = 1.600 Msun T = 9941 K m_ubv_I = 17.59\n", + "M = 1.250 Msun T = 7745 K m_ubv_I = 8.02\n", + "M = 0.420 Msun T = nan K m_ubv_I = nan\n", + "M = 0.880 Msun T = 5078 K m_ubv_I = 9.71\n", + "M = 1.300 Msun T = 8310 K m_ubv_I = 7.70\n", + "M = 2.000 Msun T = 4717 K m_ubv_I = 1.01\n", + "M = 0.720 Msun T = 4155 K m_ubv_I = 10.76\n", + "M = 0.810 Msun T = nan K m_ubv_I = nan\n", + "M = 1.470 Msun T = 8608 K m_ubv_I = 7.20\n", + "M = 0.380 Msun T = 3862 K m_ubv_I = 12.97\n", + "M = 0.170 Msun T = 3596 K m_ubv_I = 14.59\n", + "M = 2.000 Msun T = 25344 K m_ubv_I = 11.70\n", + "M = 1.500 Msun T = 9188 K m_ubv_I = 7.06\n", + "M = 1.500 Msun T = 9188 K m_ubv_I = 7.06\n", + "M = 2.250 Msun T = 10601 K m_ubv_I = 5.55\n", + "M = 1.440 Msun T = 9220 K m_ubv_I = 7.46\n", + "M = 1.100 Msun T = 7051 K m_ubv_I = 8.31\n", + "M = 7.000 Msun T = 11382 K m_ubv_I = 17.45\n", + "M = 1.050 Msun T = 6395 K m_ubv_I = 8.57\n", + "M = 0.600 Msun T = 4605 K m_ubv_I = 11.29\n", + "M = 0.170 Msun T = 3596 K m_ubv_I = 14.59\n", + "M = 3.150 Msun T = 11242 K m_ubv_I = 4.20\n", + "M = 1.900 Msun T = 5230 K m_ubv_I = 4.33\n", + "M = 0.420 Msun T = nan K m_ubv_I = nan\n", + "M = 0.350 Msun T = 3818 K m_ubv_I = 13.15\n", + "M = 1.140 Msun T = 7217 K m_ubv_I = 8.29\n", + "M = 2.880 Msun T = 13396 K m_ubv_I = 5.19\n", + "M = 1.800 Msun T = 11016 K m_ubv_I = 7.13\n", + "M = 1.500 Msun T = 9315 K m_ubv_I = 7.56\n", + "M = 1.600 Msun T = 10052 K m_ubv_I = 7.08\n", + "M = 5.000 Msun T = 7711 K m_ubv_I = 17.75\n", + "M = 1.100 Msun T = 7051 K m_ubv_I = 8.31\n", + "M = 0.220 Msun T = 3700 K m_ubv_I = 14.02\n", + "M = 0.390 Msun T = 3880 K m_ubv_I = 12.90\n", + "M = 2.880 Msun T = 13407 K m_ubv_I = 5.20\n", + "M = 1.170 Msun T = 7419 K m_ubv_I = 8.16\n", + "M = 1.500 Msun T = 9148 K m_ubv_I = 7.06\n", + "M = 1.380 Msun T = 8679 K m_ubv_I = 7.68\n", + "M = 0.270 Msun T = 1558 K m_ubv_I = 14.72\n", + "M = 1.900 Msun T = 5230 K m_ubv_I = 4.33\n", + "M = 1.800 Msun T = 9333 K m_ubv_I = 6.10\n", + "M = 0.320 Msun T = nan K m_ubv_I = nan\n", + "M = 0.900 Msun T = nan K m_ubv_I = nan\n", + "M = 0.270 Msun T = nan K m_ubv_I = nan\n", + "M = 0.180 Msun T = 3624 K m_ubv_I = 14.45\n", + "M = 3.500 Msun T = 9499 K m_ubv_I = 17.80\n", + "M = 0.270 Msun T = nan K m_ubv_I = nan\n", + "M = 2.560 Msun T = 13752 K m_ubv_I = 6.00\n", + "M = 1.440 Msun T = nan K m_ubv_I = nan\n", + " Time taken: 101.39 seconds\n", + "Isochrone generation took 452.867625 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "iso1=synthetic.Isochrone_Binary(9.0, 0.0, 100,math.log10(1/10), mass_sampling=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking if all phases represented are valid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x == -99 or x==102 or x==103) for\n", + " x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == -99 or x==102 or x==103) for\n", + " x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == -99 or x==102 or x==103) for\n", + " x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries, single stars, and secondaries by looking at the singles, primaries, and secondaries attributes (here tables) of the isochorne." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294221355125417" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294221355125417" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294186013434684" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.953600902940848" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.685477526206641" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.292267279955646" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.551726078546701" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.122828084107825" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.472385193454187" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median([x for x in iso1.secondaries['logg'] if np.isfinite(x)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This may be due to compact remnant primaries from NEWSECMODS type models. 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Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (secondaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]),\n", + " np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (primaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]),\n", + " np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values single stars of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]),\n", + " np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also examine what the CMD of the isochrone is for stars with > 5 cgs surface gravity." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "indices1= np.where(iso1.singles['logg'] > 5)[0]\n", + "indices2= np.where(iso1.primaries['logg'] > 5)[0]\n", + "indices3= np.where(iso1.secondaries['logg'] > 5)[0]\n", + "plt.plot(iso1.primaries['m_ubv_B'][indices2] - iso1.primaries[\"m_ubv_V\"][indices2],\n", + " iso1.primaries[\"m_ubv_V\"][indices2] - 5 * np.log10(100/10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'][indices3] - iso1.secondaries[\"m_ubv_V\"][indices3],\n", + " iso1.secondaries[\"m_ubv_V\"][indices3] - 5 * np.log10(100/10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'][indices1] - iso1.singles[\"m_ubv_V\"][indices1],\n", + " iso1.singles[\"m_ubv_V\"][indices1] - 5 * np.log10(100/10),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at solar metallicity\\n\" +\n", + " \" and 1 billion years age (Examining upper lefthand corner)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining the potential location of caluse of a vertical line in the upper left hand corner of the plot." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "indices2= np.where((iso1.primaries['logg'] > 5) &\n", + " (iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] > -0.4) &\n", + " (iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] < -0.2))[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=2>\n", + "\n", + "\n", + "\n", + "
5.0
101.0
" + ], + "text/plain": [ + "\n", + " 5.0\n", + "101.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(iso1.primaries[indices2]['phase'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previous kind of shows that there may need to be more work done on identifying when white dwarves are. There should be tons of them in the lower lefthand corner of the CMD. That is an issue I may need to fix.\n", + "The following is another look at the CMD of the isochrone, but of only the secondary stars." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"r.\",\n", + " label=\"Secondary stars BPASS isochrone\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at solar metallicity\\n \" +\n", + " \"and 1 billion years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, let's create a MIST v.1. isochrone phot using otherwise same parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 78.293325 s.\n", + "Making photometry for isochrone: log(t) = 9.00 AKs = 0.00 dist = 100\n", + " Starting at: 2021-08-13 14:01:03.925762 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3384 K m_ubv_U = 21.55\n", + "M = 0.781 Msun T = 5867 K m_ubv_U = 11.01\n", + "M = 1.713 Msun T = 9508 K m_ubv_U = 6.29\n", + "M = 1.744 Msun T = 6165 K m_ubv_U = 6.22\n", + "M = 1.790 Msun T = 4388 K m_ubv_U = 5.07\n", + "M = 1.903 Msun T = 5179 K m_ubv_U = 5.56\n", + "M = 1.911 Msun T = 4438 K m_ubv_U = 4.91\n", + "M = 1.912 Msun T = 3995 K m_ubv_U = 5.21\n", + "M = 1.912 Msun T = 3811 K m_ubv_U = 5.52\n", + "M = 1.913 Msun T = 3716 K m_ubv_U = 5.79\n", + "M = 1.913 Msun T = 3729 K m_ubv_U = 5.92\n", + "M = 1.913 Msun T = 4901 K m_ubv_U = 1.46\n", + "M = 1.913 Msun T = 53895 K m_ubv_U = 3.26\n", + "M = 1.913 Msun T = 90166 K m_ubv_U = 10.50\n", + "Starting filter: ubv,B Elapsed time: 3.19 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3384 K m_ubv_B = 20.02\n", + "M = 0.781 Msun T = 5867 K m_ubv_B = 11.18\n", + "M = 1.713 Msun T = 9508 K m_ubv_B = 6.28\n", + "M = 1.744 Msun T = 6165 K m_ubv_B = 6.30\n", + "M = 1.790 Msun T = 4388 K m_ubv_B = 4.29\n", + "M = 1.903 Msun T = 5179 K m_ubv_B = 5.46\n", + "M = 1.911 Msun T = 4438 K m_ubv_B = 4.19\n", + "M = 1.912 Msun T = 3995 K m_ubv_B = 3.79\n", + "M = 1.912 Msun T = 3811 K m_ubv_B = 3.76\n", + "M = 1.913 Msun T = 3716 K m_ubv_B = 3.89\n", + "M = 1.913 Msun T = 3729 K m_ubv_B = 4.09\n", + "M = 1.913 Msun T = 4901 K m_ubv_B = 1.47\n", + "M = 1.913 Msun T = 53895 K m_ubv_B = 4.40\n", + "M = 1.913 Msun T = 90166 K m_ubv_B = 11.68\n", + "Starting filter: ubv,V Elapsed time: 6.32 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3384 K m_ubv_V = 18.26\n", + "M = 0.781 Msun T = 5867 K m_ubv_V = 10.60\n", + "M = 1.713 Msun T = 9508 K m_ubv_V = 6.27\n", + "M = 1.744 Msun T = 6165 K m_ubv_V = 5.85\n", + "M = 1.790 Msun T = 4388 K m_ubv_V = 3.11\n", + "M = 1.903 Msun T = 5179 K m_ubv_V = 4.68\n", + "M = 1.911 Msun T = 4438 K m_ubv_V = 3.03\n", + "M = 1.912 Msun T = 3995 K m_ubv_V = 2.32\n", + "M = 1.912 Msun T = 3811 K m_ubv_V = 2.13\n", + "M = 1.913 Msun T = 3716 K m_ubv_V = 2.18\n", + "M = 1.913 Msun T = 3729 K m_ubv_V = 2.41\n", + "M = 1.913 Msun T = 4901 K m_ubv_V = 0.58\n", + "M = 1.913 Msun T = 53895 K m_ubv_V = 4.71\n", + "M = 1.913 Msun T = 90166 K m_ubv_V = 12.02\n", + "Starting filter: ubv,R Elapsed time: 9.46 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3384 K m_ubv_R = 17.33\n", + "M = 0.781 Msun T = 5867 K m_ubv_R = 10.29\n", + "M = 1.713 Msun T = 9508 K m_ubv_R = 6.26\n", + "M = 1.744 Msun T = 6165 K m_ubv_R = 5.58\n", + "M = 1.790 Msun T = 4388 K m_ubv_R = 2.55\n", + "M = 1.903 Msun T = 5179 K m_ubv_R = 4.28\n", + "M = 1.911 Msun T = 4438 K m_ubv_R = 2.48\n", + "M = 1.912 Msun T = 3995 K m_ubv_R = 1.62\n", + "M = 1.912 Msun T = 3811 K m_ubv_R = 1.34\n", + "M = 1.913 Msun T = 3716 K m_ubv_R = 1.34\n", + "M = 1.913 Msun T = 3729 K m_ubv_R = 1.59\n", + "M = 1.913 Msun T = 4901 K m_ubv_R = 0.09\n", + "M = 1.913 Msun T = 53895 K m_ubv_R = 4.83\n", + "M = 1.913 Msun T = 90166 K m_ubv_R = 12.15\n", + "Starting filter: ubv,I Elapsed time: 12.52 seconds\n", + "Starting synthetic photometry\n", + "M = 0.104 Msun T = 3384 K m_ubv_I = 15.95\n", + "M = 0.781 Msun T = 5867 K m_ubv_I = 9.86\n", + "M = 1.713 Msun T = 9508 K m_ubv_I = 6.26\n", + "M = 1.744 Msun T = 6165 K m_ubv_I = 5.21\n", + "M = 1.790 Msun T = 4388 K m_ubv_I = 1.78\n", + "M = 1.903 Msun T = 5179 K m_ubv_I = 3.73\n", + "M = 1.911 Msun T = 4438 K m_ubv_I = 1.73\n", + "M = 1.912 Msun T = 3995 K m_ubv_I = 0.68\n", + "M = 1.912 Msun T = 3811 K m_ubv_I = 0.30\n", + "M = 1.913 Msun T = 3716 K m_ubv_I = 0.23\n", + "M = 1.913 Msun T = 3729 K m_ubv_I = 0.49\n", + "M = 1.913 Msun T = 4901 K m_ubv_I = -0.57\n", + "M = 1.913 Msun T = 53895 K m_ubv_I = 5.10\n", + "M = 1.913 Msun T = 90166 K m_ubv_I = 12.45\n", + " Time taken: 15.58 seconds\n" + ] + } + ], + "source": [ + "iso2 = synthetic.IsochronePhot(9.0, 0.0, 100,\n", + " math.log10(1 / 10), recomp=True)\n", + "# New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the distribution of logg values. for the stars in the MISTv.1 isochrone." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.941465338001905" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([505., 146., 198., 93., 229., 66., 24., 123., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0.]),\n", + " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),\n", + " )" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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f0gfMS+TblVs2X/IXbZOnV5ak7T6A9VXlt6Rv7DtKKru9UBtF+e6StCoMRt3UtvHWkrTV1tOfiFhOespuc9KH5xGkwKfs2+c2pG99s0rSWqn3RXlatn+TSubdQQrK/qGFbbwoIp6KiKsi4hBSC9xrgL/rZ7FBP5ciYmVE3BURXyS1bkBqearp69qwCSk4vbUkuNqAdEuknWXrpP6uma26LU8HdL71Jwf/F5GCqwDaFaSuoPep77Zo8foxaCKZGxFfo7cV8l2FLK1eq2vnRNPD0VQlIv5Cao3fQtJrS7K87GtRvoM2l/Tlc0x/+fsy6AGWpAm570q90aQm++WFeYtIb7pxDVZ3UZ6eke+b1raxHnBm/rf4hr2M1Bz4EUlbFvILOIOBXZR68nSP4kylcUa+WJ95sOTm3ktJTfefK6ZJeg3wUdJtzO+0qQiDUTffztPT8qO7tWU2pvfbWaum5+m/5NdK0nGs10P61vfG4kxJ/0pvc3wzav0Mjs7fFmvr2ZL0TfMlIuKpXJ5JSmPJrdbyJek1kibkv9eVtLfqOhLkW9u1i0efI4sP1rmkNOZNWctjbV6xnLUWiLJrw1M579/ngKq2/uGklrxN2ly2TurruLQsIu4kDc3wbknHlOWRtENuGR2o/yb1X903Iv4wkBVIWj+/Hxq1XH+cdP4+0KbWq5rpedrf9aOtJP2dyn+2rux8bfVafTGpRes4SasFZfmWbDtdRLqF9yVJL34uSNqE3uv+RWULtuDLpAcULlIaouYlJI3Ofdv61IlbhBOBqyXdReqz9DjpXudUUt+iF4OSiFgm6XbgHyRdShqb5gVgRqRB4y6TNJU0Fs9cST+kt4/FBOB7EXFpYX1/kHQKqbPqfZKuoHespTGkp01e8oHZhGuBh4DjJe1A+jYwjjRGzHVUdKEboBNJ3zw/LOnNpDGBamMXbUh6pHheOzY8SHXzbdKtrsnA/ZJmkM6hA0mdeF9Pa2PpEBG3SHqIdPtsOHBtDmrqnUMKpH4hqTa21CRSy9kPSP0Lm9ne7ZJ+RvqmeIekm0kXwQNIHanLWrY+TBoe4XPAkZJ+Qepztjmp4+WbSS0r80i3MW8CevJ76WFSZ/x9ct4Z+dZXfwbjXDoc+JCk2aT31CJSC9sBpBaIcwp555A+JD6ev2XW+tx9LSKWSPrvXOZfK40pN4L07XZMLvuetKaVsnVSn8dlgOs8nNTaeaFS37bbSbeJxpLek39H6iRc9j7pV0Qs4KXjHA7EcNL74TOS7iA9SLWIVN9vIz29/gzpoYe2aeH6gaT30dvSXrsTckAhQPlNRJy5+pJNeTvwZUm3koaseYpUX1NJ18QvFcrc6rV6gaTDSde5mZJ+BPyKNEbcG0nXrAkDLHczzia1nk0lfVZcT3oI52BSX7GzIqKlh5zqRcRFSmOyfRD4g6QbSP04x5D2bTfSoL59n0/Rz2OG0fuYZfSTp4fmhmkYS/oQvYXUR2IFaYyeHwHvLFnvNqQg5s/0Pn57VCF9nXwQ7iRdWJ4lPYH3IWCdBmU9khQI/ZXUIf0S0ofT/cDiurx7UPJIcF2eLUnfUh4jtcDNJQ22Nqx+/3P+U2n8aP5R9fvYSllKlhlFClp/n4/1YlKn39UeyS3UY0+z68/LzCo7P9pdNzn/SNKFdV7evx7SwJpb5GP1w1b2Ja+zNmREAAf2kW9/UnP5X/JxvZH0xiutw0bHNtfRN0kXwRV5X6fRxzAlpIDhw6QnKGvjUD1C6nD/ceCVOd/wfC7+KKfXjuttpIvDiBaOS1vPJdK4Rd8gBdMLSe+lh0gXsr8ryT+ZFFAsK9TX+Jw2jDRcxgN5PU+SWti2ovdx/PGFdTU81gMpWx/72Or1oM9yNdhGX8dlQNsiBdGfJr1/l+X9n0f6EjkNWL/JsvXkbfQ75AktDNNAutZMJrU83E764v486b35K1IAPL6f7dXOi6Oa2Zc+1tPs9WN6IV/Za1aT21ut3khfnr5MuvY+Te+18QcUxoiqO36tXqu3J33JfYx0+/VPpKcOp9Xla7gvlL8X9yir97rlRubz8f58Lv6F9OT4Ya2+h2jw+ZXT9if9rNNTeR+fJN11OI2SYSLqX8orGfKUfmD0T6RHOfvqBGuDbCB1I2kfUsBzZkSc1M7ymZmZ1et4J/fBJulVuS9Gcd4w0i+bjyQ9CWIdMJC6kbR5ybxX0tt3wPVpZmaDbsi1YEn6AOmW0k2kAcbGkG7rvI503/6tkZ4GsUE2kLqRdDmpX9+tpKbwsaT782OA8yKirX0uzMzMynRkHKwOu510r3Y3egfRm0fqt/NFB1cdNZC6uYreTuGjSH2M5pKeImnLjy2bmZn1Z8i1YJmZmZm125Drg2VmZmbWbkPxFqENwCabbBLjx4/vdDHMzDrurrvuWhARr+o/pw1lDrCsKePHj+fOO+/sdDHMzDpO0sOdLoN1P98iNDMzM6uYAywzMzOzijnAMjMzM6uYAywzMzOzijnAMjMzM6uYA6wuIWkPSVHyWlyXb7SkCyQtkPSMpJsk7VCyvpGSviTpCUnLJc2RtNug7ZCZmdkQ5mEaus9HgV8W/l9Z+0OSgBnABOAjwCLgJGCmpB0j4tHCchcC+wEnAH8EPgTcIGmXiLi3rXtgZmY2xDnA6j4PRsRtDdKmALsCe0XETABJc0i/1/cpUnCGpInA4cAxEfGtPG826Tf6PpfXY2ZmZm3iW4RrlinA47XgCiAilgDXAlPr8j0PXFHItxK4HNhX0rqDU1wzM7OhyS1Y3edSSZsAi4EbgBMj4pGctj1wf8kyc4F/kbRBRCzL+eZFxLMl+UYA2+S/B8X4E69rKl/Pmfu1uSRmZmaDwwFW91gC/BcwG1gK7AR8GpgjaaeIeAoYA/SULLswT0cDy3K+RX3kG1Ndsc3MzKyeA6wuERH3APcUZs2W9DPgDlLfqpMBAVGyuEr+byZfb4I0DZjWKH3cuHGNkszMzKyOA6wuFhF3S/od8OY8ayHlrU+j83RRIV9ZRDS6kF6/rfOB8xuVZdKkSWUBm5mZmZVwJ/fuV2yNmkvqX1VvO+CR3P+qlm+CpPVK8j0HPNSOgpqZmVniAKuLSZoEvA64Pc+aAWwhafdCno2AA3IahXzDgYML+YYBhwI3RsSKNhfdzMxsSPMtwi4h6VLSeFZ3k54g3Ik0iOhjwNdythnAHOASSSfQO9CogLNq64qIeyVdAZwjaXhe73GkAUqPGIz9MTMzG8ocYHWP+4HDSCO0rwc8CVwFfCYiFgBExCpJ+wNnA+cCI0kB154RMb9ufUcDpwOnAaOA+4DJEXF3+3fFzMxsaHOA1SUi4gzgjCbyLQSOya++8i0Hjs8vMzMzG0Tug2VmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYXUrSjyWFpNPq5o+WdIGkBZKekXSTpB1Klh8p6UuSnpC0XNIcSbsN3h6YmZkNXQ6wupCkw4CJJfMFzAAmAx8BDgSGAzMlja3LfiFwLHAKsD/wBHCDpB3bV3IzMzMDB1hdR9Io4CvA8SXJU4BdgSMj4rsR8eM8bx3gU4V1TAQOBz4REd+MiJ8ChwCPAJ9r7x6YmZmZA6zucxYwNyK+W5I2BXg8ImbWZkTEEuBaYGpdvueBKwr5VgKXA/tKWrcdBTczM7PEAVYXkbQr8C/ABxtk2R64v2T+XGCcpA0K+eZFxLMl+UYA21RQXDMzM2vAAVaXkDQcOA84OyJ+2yDbGGBRyfyFeTq6yXxjBlpOMzMz69+wThfAXvTvwN8Ap/eRR0A0mD+QfL0J0jRgWqP0cePG9VEsMzMzK3KA1QUkjQP+A3gfsG5dH6l1c8f3v5BaoMpan2otV7VWq4VAWUQ0upD+EhFxPnB+ozJOmjSpLGAzMzOzEr5F2B22BkYCl5CCpNoL4JP57x1Ifai2L1l+O+CRiFiW/58LTJC0Xkm+54CHKi29mZmZvYQDrO5wL7BnyQtS0LUnKSiaAWwhaffagpI2Ag7IaTUzSONjHVzINww4FLgxIla0a0fMzMzMtwi7QkQsBmbVz0/jivJwRMzK/88A5gCXSDqB1LJ1Eqlv1VmF9d0r6QrgnNx5fh5wHDABOKKNu2JmZma4BWuNEhGrSKOy/wQ4F7gaeAHYMyLm12U/GvgWcBpwHbAlMDki7h68EpuZmQ1NbsHqYhGx2lN/EbEQOCa/+lp2OWk0+LIR4c3MzKyN3IJlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWF1C0r6Sbpb0pKQVkh6V9D1J29XlGy3pAkkLJD0j6SZJO5Ssb6SkL0l6QtJySXMk7TZ4e2RmZjZ0OcDqHmOAu4APA+8ATgK2B26TtBWAJAEzgMnAR4ADgeHATElj69Z3IXAscAqwP/AEcIOkHdu+J2ZmZkPcsE4XwJKI+C7w3eI8SXcAvwEOAv4LmALsCuwVETNznjnAPOBTwEfzvInA4cAxEfGtPG82MBf4XF6PmZmZtYlbsLrbn/P0+TydAjxeC64AImIJcC0wtbDclLzMFYV8K4HLgX0lrdvOQpuZmQ11DrC6jKRXSBoh6bXAecCTpMAI0i3D+0sWmwuMk7RBId+8iHi2JN8IYJvqS25mZmY1DrC6z+3ACuB3wBtJtwOfymljgEUlyyzM09FN5htTTVHNzMysjPtgdZ8jgY2ArYFPAj+RtGtE9AAComQZlfzfTL7eBGkaMK1R+rhx4/outZmZmb3IAVaXiYgH85+3S/oR0AOcCHyA1AJV1vpUa7mqtVotBMoiotGF9Prtng+c36hckyZNKgvYzMzMrIRvEXaxiFgMPERvn6m5pP5V9bYDHomIZYV8EyStV5LvubxOMzMzaxMHWF1M0qbAtsAf8qwZwBaSdi/k2Qg4IKdRyDccOLiQbxhwKHBjRKxoc9HNzMyGNN8i7BKSrgbuBn4FLAVeB3wCWEkaAwtS4DQHuETSCaRbgieR+ladVVtXRNwr6QrgHEnDSeNkHQdMAI4YlB0yMzMbwhxgdY/bgEOAfyMNpTAfmAWckTu4ExGrJO0PnA2cC4wkBVx7RsT8uvUdDZwOnAaMAu4DJkfE3e3eETMzs6HOAVaXiIgvAl9sIt9C4Jj86ivfcuD4/DIzM7NB5D5YZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhVzgGVmZmZWMQdYZmZmZhUb1ukCmHWD8Sde11S+njP3a3NJzMxsbeAWLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcDqEpIOknSlpIclLZf0W0lnSNqwLt9oSRdIWiDpGUk3SdqhZH0jJX1J0hN5fXMk7TZ4e2RmZjZ0OcDqHp8EXgA+DUwGvgEcB/xE0joAkgTMyOkfAQ4EhgMzJY2tW9+FwLHAKcD+wBPADZJ2bPuemJmZDXEeB6t7HBARTxf+ny1pIXAxsAdwMzAF2BXYKyJmAkiaA8wDPgV8NM+bCBwOHBMR38rzZgNzgc/l9ZiZmVmbuAWrS9QFVzW/zNMt8nQK8HgtuMrLLQGuBaYWlpsCPA9cUci3Ergc2FfSuhUW3czMzOo4wOpuu+fpg3m6PXB/Sb65wDhJGxTyzYuIZ0vyjQC2qbqgZmZm1ssBVpeStAXpdt5NEXFnnj0GWFSSfWGejm4y35iqymlmZmarcx+sLpRboq4BVgJHF5OAKFuk5P9m8hW3OQ2Y1ih93LhxjZLMzMysjgOsLiNpJOlJwa2B3SPi0ULyQspbn2otV4sK+coiotGF9JeIiPOB8xuVa9KkSWUBW0f4h5nNzKzb+RZhF5E0HLgSeAvwjxHx67osc0n9q+ptBzwSEcsK+SZIWq8k33PAQ9WV2szMzOo5wOoSeayrS4G9gakRcVtJthnAFpJ2Lyy3EXBATivmGw4cXMg3DDgUuDEiVlS/B2ZmZlbjW4Td4+ukgOh04BlJOxfSHs23CmcAc4BLJJ1AuiV4Eqlv1Vm1zBFxr6QrgHNyq9g80qClE4AjBmNnzMzMhjK3YHWPd+bpf5CCqOLrfQARsYo0KvtPgHOBq0mjv+8ZEfPr1nc08C3gNOA6YEtgckTc3d7dMDMzM7dgdYmIGN9kvoXAMfnVV77lwPH5ZWZmZoPILVhmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlaxYZ0ugFnN+BOv63QRzMzMKuEWLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcAyMzMzq5gDLDMzM7OKeSR3sw5qdvT6njP3a3NJzMysSm7BMjMzM6uYAywzMzOzijnA6hKSxkr6mqQ5kp6VFJLGl+QbLekCSQskPSPpJkk7lOQbKelLkp6QtDyvd7dB2RkzM7MhzgFW99gGOARYBPy8LIMkATOAycBHgAOB4cBMSWPrsl8IHAucAuwPPAHcIGnHdhTezMzMermTe/f4WURsCiDpfcA7SvJMAXYF9oqImTnvHGAe8Cngo3neROBw4JiI+FaeNxuYC3wur8fMzMzaxC1YXSIiVjWRbQrweC24ysstAa4Fptblex64opBvJXA5sK+kdSsptJmZmZVygLVm2R64v2T+XGCcpA0K+eZFxLMl+UaQbkeamZlZmzjAWrOMIfXRqrcwT0c3mW9MxeUyMzOzAvfBWrMIiAbzB5KvN0GaBkxrlD5u3LhmymdmZmY4wFrTLKS89anWcrWokK8sIhpdSH+JiDgfOL/RhidNmlQWsJmZmVkJ3yJcs8wl9a+qtx3wSEQsK+SbIGm9knzPAQ+1r4hmZmbmFqw1ywzgaEm7R8RsAEkbAQcAl9Xl+yxwMHBxzjcMOBS4MSJWDGqpO6TZ3/kzMzOrmgOsLiLpoPzn3+fpOyU9DTydA6oZwBzgEkknkG4JnkTqW3VWbT0Rca+kK4BzJA0njZN1HDABOGJQdsbMzGwIc4DVXb5f9/+5eTob2CMiVknaHzg7p40kBVx7RsT8umWPBk4HTgNGAfcBkyPi7jaV3czMzDIHWF0kIho+5VfIsxA4Jr/6yrccOD6/zMzMbBC5k7uZmZlZxRxgmZmZmVXMAZaZmZlZxRxgmZmZmVXMAZaZmZlZxRxgmZmZmVXMAZaZmZlZxRxgmZmZmVXMA42ataDZ3zfsOXO/NpfEzMy6mVuwzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYg6wzMzMzCrmAMvMzMysYsM6XQCztdH4E6/rdBHMzKyD3IJlZmZmVjEHWGZmZmYVc4BlZmZmVjEHWGsxSVtK+oGkJZKWSrpK0rhOl8vMzGxt5wBrLSVpPeBmYFvgvcCRwGuBmZLW72TZzMzM1nZ+inDtdSywNfD6iHgIQNKvgN8D7we+3MGymZmZrdXcgrX2mgLcVguuACJiHnALMLVjpTIzMxsCHGCtvbYH7i+ZPxfYbpDLYmZmNqQ4wFp7jQEWlcxfCIwe5LKYmZkNKe6DtXaLknkqyyhpGjCtj3Utk/TbAZZjE2DBAJc1QF9sy2pdL93HddKd6utlq04VxNYcDrDWXotIrVj1RlPSshUR5wPnt6Mgku6MiEntWLcNnOul+7hOupPrxQbCtwjXXnNJ/bDqbQc8MMhlMTMzG1IcYK29ZgA7S9q6NkPSeOBtOc3MzMzaxAHW2uubQA9wjaSpkqYA1wDzgfM6WTAzM7O1nQOstVREPAPsBfwO+A5wKTAP2CsilnWybGZmZms7d3Jfi0XEI8CBnS6HmZnZUOMWLDMzM7OKOcCywdCW4R/sZXO9dB/XSXdyvVjLFFE2FqWZmZmZDZRbsMzMzMwq5gDLzMzMrGIOsKwtJG0p6QeSlkhaKukqSeM6Xa6hQtJYSV+TNEfSs5IiDzRbn2+0pAskLZD0jKSbJO3QgSKv9SQdJOlKSQ9LWi7pt5LOkLRhXT7XySCStK+kmyU9KWmFpEclfU/SdnX5XC/WEgdYVjlJ6wE3A9sC7wWOBF4LzJS0fifLNoRsAxxC+t3Jn5dlkCTSqP6TgY+QhvQYTqqnsYNUzqHkk8ALwKdJx/wbwHHATyStA66TDhkD3AV8GHgHcBLpZ8Zuk7QVuF5sYNzJ3Son6WPAl4HXR8RDed4E4PfApyLiy50s31AgaZ2IWJX/fh9pZP8JEdFTyDMV+CFp8NmZed7GpAFpL4mIjw52uddmkl4VEU/XzfsX4GJg74i42XXSHSS9HvgN8MmI+C/Xiw2EW7CsHaYAt9WCK4CImAfcAkztWKmGkFpw1Y8pwOO1D4y83BLgWlxPlasPrrJf5ukWeeo66Q5/ztPn89T1Yi1zgGXtsD1wf8n8ucB2JfOtM/qqp3GSNhjk8gxFu+fpg3nqOukQSa+QNELSa0m/1/okcHlOdr1YyxxgWTuMIfX9qbcQGD3IZbHG+qoncF21laQtgM8BN0XEnXm266RzbgdWkH6/9Y2k24FP5TTXi7XMAZa1S1nnPg16KawvwvXUEbnF4xpgJXB0MQnXSaccCewMHA4sJT18MD6nuV6sZQ6wrB0Wkb7x1RtN+bdA64yFNK4ncF21haSRpCfStgb2jYhHC8mukw6JiAcj4vaI+C6wN7ABcGJOdr1YyxxgWTvMJfVZqLcd8MAgl8Ua66ueHomIZYNcnrWepOHAlcBbgH+MiF/XZXGddIGIWAw8RBruBFwvNgAOsKwdZgA7S9q6NiM3tb8tp1l3mAFsIanW0RpJGwEH4HqqXB7r6lJS68jUiLitJJvrpAtI2pQ0jt8f8izXi7XM42BZ5fJgovcBy4GTSX0XPg9sCLzR3/YGh6SD8p97Ax8APgg8DTwdEbPzB/4vgC2BE0i3OU4idfCdGBHzB7/Uay9J3yDVw+nA/9UlPxoRj7pOBp+kq4G7gV+R+l69DvgE8LfAWyLid64XGwgHWNYW+WdxvgLsQ+oI+lPg48WBLq29JDV6c8+OiD1ynjHA2cC7gJHAHOD4iLhvMMo4lEjqAbZqkPzZiDg153OdDCJJ/0761YPXACOA+cAs4Iy6gXldL9YSB1hmZmZmFXMfLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcAyMzMzq5gDLDMzM7OKOcAyMzMzq5gDLLO1lKTxkkLS9E6X5eWQdGrejz06XRYzs2Y5wDIzMzOrmAMsM+t2/wO8Abij0wUxM2vWsE4XwMysLxGxAFjQ6XKYmbXCLVhmQ5CkzSR9XVKPpOckPS3pKkl/3yD/xpLOkfSopL9K+o2k4yVt3aifl6TXSbpS0iJJz0i6VdJ+ko7KyxzVZFlL+2DlebMkbSLpfElPSFohaa6kowdwTMZK+m9Jv8/7uFDSHZL+syTvvpJuyfu1UNIPJW0raXou1/i6/FMk/bRQxsclzZb0wVbLaWZrBrdgmQ0xkiYAvwA2B24GvgtsCRwM7CfpwIj4v0L+kTnfm4B7gEuBjYH/AP6hwTa2BW4BxgDXAb8CtgauBq6vcHdG5e08B/yA9CO8BwEXSVoVERc3sxJJk4Abcnl/BlwFrAdsB5wKfL6Q91DgMmAF8D3gCeCtpB//Xe2HfyVNA84DngSuJbXGvRp4I3A0cG5Le2xmawQHWGZDz/+SgquTI+L02kxJ55KCi4slbRURy3LSCaTg6nLg8Mi/EC/pdODuBtv4OilY+WBEfKOwjXdSbYA1EbgQeH9EvJC38RVSQPfvQL8BlqQRwPdzeY+IiMvq0rcs/L0h6fitBHaJiPsKaWfmbdZ7PykAnBgRT9Wte5Mm9tHM1kC+RWg2hEgaC7wDeAQ4q5gWEbeSWrPGAO8uJL0XWAWcVAuucv75wDkl29gS2At4iNRyU9zGj4CbKtiVmmeB42vBVd7GA6RWrTfkgKg/BwDjgRn1wVVe3/zCv1NJrWaXFoOr7DRgcYNtrASeL1m3+5aZraUcYJkNLTvl6c8jYrUPfNKtwBfzSdoIeA3wWET0lOT/Rcm8HfN0TkSsanKZgfp9RCwtmV8LikY1sY6d8/RHTeStHb/V9iG3+N1bssylpNuNcyV9RdK7JL2qiW2Z2RrMAZbZ0LJxnj7RIL02f1SebpSnf2qQv2z+xn2k9TV/IBY3mL8yT1/RxDpG5eljTeRted8i4sukVsBHgI+S+qH9SdLM3PfLzNZCDrDMhpYlefq3DdI3q8tXax3atEH+svkDWaaTFufpFk3kHdC+RcS3I2Jn4JXAfqR+Y7sBN0h6dfNFNbM1hQMss6HlnjzdVVLZQy575undAPn22x+BLeqHHqitp49t7CKp7BpTtkwn3Zan72wi74vHrz5B0gb03h4tFRGLI+L6iDgWmE7q71b6JKaZrdkcYJkNIRHxKPATUqfujxfTJP0/4HBgEek2Vs23SdeKMySpkH/L+nXkbcwHZgHbkJ6gK25jMvD2l7sfFbsW6AGmSDqsPlFSsWXrGlLr3hGSJtZlPZmSPl+SJjcIZmstV88OoMxm1uU8TIPZ0PMB0lN2X5L0DuBOesfBWgUcHRF/KeQ/C3gX8B7g9ZJuJPVFOoQ0rMO78nJFH8rbOFfSP9I7DtaBpCBlaskyHRERz0k6GLgRuEzS+0mtWiNJP9GzN/laGRFL8+CglwC3SiqOgzURmA3szkv37XLgr5J+QQrkRGq1ejNwF9U+VWlmXcItWGZDTET8EZhEGs/p9cAnSbfHfgy8LSKuqcu/nHTr8GukvlufyP9/ATgjZ1tat8wDwC6klrB/ILV0jQf+id4n8Mqe/uuIiLiTdHvvG8BWwPHAkaQWqc/U5b2M1I/qPuBQ4DhSq9YuQG3ssOK+nUgahPRNwAdJg4sOJ42ZtWeDpznNbA2nwrA2ZmYtkXQscD7wgYg4r7/8eZlLSbcit42I37azfINJ0itI/dXWjYhGDxGY2RDhFiwz65ekzUvmbQn8J2lIhP+rS1tH0mpBhqS9Sa0+D6ypwZWkUZLWq5snUh+scaSf2TGzIc59sMysGVdKGk7qM7SYdLtvf9IAmidFRP0YUiOA+ZJmAr8hBWHbA/uQfjbmQ4NT7LbYGbgi90XrATbI83YkDXB6aqcKZmbdw7cIzaxfuWP3kcBrSR3cl5GGLPifiFitxSbfLjuH9JM5Y0mB2AJSp/gzI+Ke+mXWFPnHsk8D3ga8ivRF9VFSK94XIqLKgVTNbA3lAMvMzMysYu6DZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFXOAZWZmZlYxB1hmZmZmFfv/6T3Zq2Po+ogAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.xlabel(\"logg in cgs\")\n", + "plt.title(\"Histogram of logg values of stars in the MIST v1 isochrone\")\n", + "plt.hist(np.array([x for x in iso2.points['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.1703298385098044" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.6426943829275906" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone. Here I test the ability of BPASS to use initial mass and separation to match primary secondary pairs.\n", + "\n", + "Note that I use the Spera 15 IFMR." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/ryotainagaki/Desktop/PyPopStar/spisea/synthetic.py:801: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " compMass = np.array([compMass[x] for x in indices])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Interpolator for: Teff\n", + "Creating Interpolator for: L\n", + "Creating Interpolator for: logg\n", + "Creating Interpolator for: isWR\n", + "Creating Interpolator for: mass_current\n", + "Creating Interpolator for: phase\n", + "Creating Interpolator for: m_ubv_U\n", + "Creating Interpolator for: m_ubv_V\n", + "Creating Interpolator for: m_ubv_B\n", + "Creating Interpolator for: m_ubv_R\n", + "Creating Interpolator for: m_ubv_I\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n", + " result = self.as_array() == other\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 51 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1,\n", + " imf.IMFSalpeter1955(multiplicity=\n", + " multiplicity.MultiplicityResolvedDK()),\n", + " 2000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2,\n", + " imf.IMFSalpeter1955(multiplicity=\n", + " multiplicity.MultiplicityResolvedDK()),\n", + " 2000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize the isochrone we have created so far with a color magnitude diagram. There we can see the end of main sequence.\n", + "\n", + "**Remember to apply the distance modulus. (Dist to cluster=100 pc)**" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(100 / 10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"], iso1.singles[\"m_ubv_V\"] -\n", + " 5 * np.log10(100 / 10),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(iso2.points['m_ubv_B']-iso2.points[\"m_ubv_V\"], iso2.points[\"m_ubv_V\"] -\n", + " 5 * np.log10(100 / 10), \"b+\", label=\"MISTv1\", alpha = 0.2)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at solar metallicity\\n\" +\n", + " \" and 1 billion years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do have several outliers but otherwise the pattern does not seem too terribly off from the MISTv.1 isochrone's shape. Now I look at the primary stars and see if there are any problems caused." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(iso1.primaries['m_ubv_B']-iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"]-5*np.log10(100/10), \"r+\",\n", + " label=\"BPASS isochrone\", alpha =0.7)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrone at solar metallicity\\n\" +\n", + " \" and 1 billion years age (Primary stars only)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Color magnitude Diagram of secondary stars in the BPASS isochorne\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(100/10), \"r+\", \n", + " label=\"BPASS isochrone secondaries\", alpha =0.7)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at solar metallicity\\n\" +\n", + " \" and 1 billion years age (Secondary stars only)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['Teff']),\n", + " np.log10(iso1.primaries[\"L\"]), \"r.\", alpha =0.3)\n", + "plt.plot(np.log10(iso1.secondaries['Teff']),\n", + " np.log10(iso1.secondaries[\"L\"]), \"r.\", alpha =0.3)\n", + "plt.plot(np.log10(iso1.singles['Teff']),\n", + " np.log10(iso1.singles[\"L\"]), \"r.\",\n", + " label=\"BPASS isochrone\", alpha =0.3)\n", + "plt.plot(np.log10(iso2.points['Teff']),\n", + " np.log10(iso2.points[\"L\"]), \"b+\",\n", + " label=\"MISTv1\", alpha =0.1)\n", + "plt.xlabel(\"log($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"HR Diagram of Isochrones at solar metallicity and 1 billion years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A good sign here is that there are plenty of compact remnant (that means good number of stars have gone through their main-sequence and post-main sequence). I also notice stars turning to the red giant branch. This is going to be really important when we decide to use the clusters as reference for finding ages of actual star clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SrJ9seUq895udczOAK5xz5Vg+4iNJ3mSg5UB8Butxex3rkVlFtOfsGuyXX/AcTc65o7AaxN8g2oPxmXPuHqwX8PPIuv/nnFuN5b5+H8vj9c65OcCPvffBf65TaP3L8h6iOXaJpLvvg/W7ZN+3Id12ZpT3/j3n3IHYKbnjiZ4Fed85d6P3/qZOtDO4nk5e5rokyxtJPnB3ZYJlndmvbbUhHLwEg8ZGEB38lcjWwZU0jvmknHOnYr0em7AUgrex093N2GnzIwj9n+yoLj42kvk/bJ/UYcH6h0R7ab5L8mAl0TGQqkQ9nY0p3FcUWhYcC99s57m2buf+sI7ui47qH7nMaB51ArdiAdUF2MDCIVgO6QLvfUo/gpwNrHoZC7Kex/I2P8VO+Q/Hvi8S/h/wifNvg/c0/P+7ve+ElWQ/eG7Vtsh38Bos0OtqHf6/n8b7kJT3/jPn3H3A95xzR3obE3MydpZnatyZkuD/6LGRv2Ra/o924rM24eeR9369s4GGv8CO+aCHeLVz7lbgV0k6WlukG5PRsdgm7X2VTEeD5+CDd0iS+4fGrbc+cpms/mMm6kLeAVyB/SLrR+K6z2EnY2/SPd7774bviAwoahU0RE4j/Q/wP865PbADbBJ2irs/oQFU3vsZWK97f+yU4anY6aYnnHOl3vuPI88b89wpCO/7VikDZGffJ5LuMZJxkVPW33bO9cF6uI7B8l3/6Jz7wnv/5w62c13kcifamcClkxKdvuqO/Ro89iHv/WltrhmSyjHfziauw3odx4bTDQCcc9Ow/28Z0YXHRiuRQS3fx74QDonvJXHOndFWU9t8IV0veG0ne+8f6ezGOrkvOmpd5DJZj22mPIh9YZ/vnPsl6Q8UBPsOGwicG59iFdk38Z0tHRG8pzuS+Dsk2fHenXYkrtfTOVeI7Zv1CR+RWel+z3aF27BxSZOwwa6tBgrGteEHoR/+7enoZ21babAfYMe+w8YFHQVcgnU4FmDj3tqSbkzWkdimI/sqoY6Wqns1cvmVyJdPvCMjl/PBfpVggyZ2conLLH2lg+1oETmt+jx2ynE5NoiwLXtELmcmuK/dL2nv/VuRL9gjsPzTk5Ost857/6j3/kIsjWR7OlddJNj3FfF3RIKW/bBfkzVx67fax5Hc1oRlyjLgDaxw/X7OuUSnqmKOka7kvW/03ld776/HcqTAev2JfHm/jR2bIxI8PFE7X4xcnkD36479uhQLOA6O9AakpRPH/B5Y6kT8h3kByT8jmkmxNydJWzN9bCSyG/ZZOztBsLhz5P5cFRzr6XxmBadpE70v2dgXwWsY5xKUWExRW68JsBQorIrNTtgAqguw74b703ieTn0vpSg4XlttL9Lz/aUMPU9nJHqth2E/Rl5NcF+mpfs9m3He+4VY6sOpzrmDsB/4z8V/PtKx/6Md+axNiTeLvfc3E+3dPSXFNkHqx/5S7IzVPi7xLNOJXkdH9lVCHfogifzCeBI7jXR5+L7Im/wdLC/nodBdMyLPN8WFkiidc1+K30YnTMR6u05LIbdteeSyIrww8uHRalCJc25X59zeCbYzADuVsDG07vFJflQEp5s6U4z9Puw0xmWR3u+w67AUmft8dEDOC1gAcKRzLj7Ym0jX5DsHA1zux05//DJ8n3Nud6z3qQEr25ZxzmrjJvrlGSwLvwd3YXlVv4v0bgTbGET01/JdofVvw07D/dw5NyrBc3eqVm1bumO/RgaD3Iz1rtzknIvPuQxqa44K3c7EMb8cGOFCtWYjnxXXYD0ZiawhzS/7Lj42ElkeufxK3Da2xs6Y5fLspw9jnx+XOOe+mmgFZ/XiwzPyrYlcJhpwtDxy2W37wntfjZWw2g/LJY7hnBvoktSQDWnrNYVNxwLtW7CBgn9NdbBUxPLIZUVcG8eRwsDUFN1P9DtkeOg5CoDfkRuTp/083DkQeX+mRG7e3Q3Pn+73bFe5DctFnol9DrXKnY+kxD0PnOacSziY0jk3OnLWJ7Cc9D9rk3LOfTlJx2iiz9RklkcuK+K2nTAmi3wX/h1L3/hZ3GP2JcGg+Q7uq4Q680H1PSww+52zwtLziNZ5bsZOO4U/NG7Afn2cDuzlnJuNvehvYfX5TqGTo3wjvc+pnkb/F1Y+5grn3Gjsl+YwrNbff2j9Ibkv8JBzrho75ViLJZefjOUIht/cB4BNzrn/YgeEw37pHIAN1GmvVzwp7/1y59zl2Mjr+c65f2B5QUdgyfBLCX1BRAYFXoDVMnzEORfUed4H+1X4GNaDmmzf3+ic+zzJff/r257Z6SfY677U2WQKzxKtR7wNcKn3/t32X3WHfAf7wp+Dvc+fYINWT8JK2EwNrXsjtg9OBl5zzj2KDcr7Jhb83eC9bxlw6b1f4mzij9uBV53VzF2GnVIci43EPpKu0x379TrsmP8eVtP0GSxndAcsF/pQbNT5ksj6mTjm/0B0n87EvrwOxT7M/4W9d/GeBk53zv0r8jyNWO9MovqogS47NhLx3q90zj2AffYtCH32HYv1Xi2g9cDhnBAZUHoalpv8H2cDjBdgX4Zfwt7f3bAfWsEX5LPY58kU59yXsf2L9/5XWdwXZ2Elqn7jnBsfue6wY/k4bODX8jYe/wZ2/J/unNuMpRR44F7v/XvBSt77Fc65/2B5n5BeygZY3vS5wD8j/wc+BL6M5eb/A6v/2ymR75CfYAPRXnU2wPdTLE+1P7AQ+35IiXNuJPaZFDbAxU569iOfXqWpGmzwcbjO8+7Yd3OXdLiEpfs924X+iX0u7oQVRXgwyXrfwfKF/+yc+z7wEnb2cGfsvfwy1u4gda4jn7VtOQb4v8jnw9LI8+yMvW/N2I+y9qQbk4Edd0cBV0Y6budin0XfwgYtnkLr2CbdfZWYb7/u3nBIXIMZe0Nvw8pwbcbe3FnAAUm21R8rpVWLfUktxcpeHRh5jqnttSeuTf9Ncf1kdZ6/hP0KDwasLMaKmfchrh5kZMf+BvvBsDLS/g+IBJ9x2/0e1uv+DvaFshY7EK4kxXqHke1cm6jdkfuOwwaTfBJpy1vYD5T+SbZ1EHa24LPI31ORAySoT7pf3PrLidZ1TPa3XwqvoT/2w2JZpJ3rIu04Lsn6y8lMneeDIsfma5H9vzGyj+4GvpxgG/2Aq7EfRhsj++i/wBltPG851iPwceT4r8V+pHwjlf8/Plpz0sct+2786+nMfqX9GrUJ9zkWXJyNBahrI6/xw8h+uRr4Uhcc89/FAqgvsM+Th7Aa69eS+P/wDtikBB9hPX5JX2d3HBvJ3m8s4P410ZrJ72NfzAOTHANtvmed+NxIemy19ZyR/fzbyD7YgKUjLMMGHZ1F67qvZxGtmerDry9T+yLd/RbZ/vVYILwJ+z+zINKWLdvabmT5Adj/hU+xL+Rk+/jkyH2vpPveRR5/CPbl/knoWDslnf2Q4vt9BpbCsQkLDO/Dancn3V6S5wja1dbf8BS3VRlZvy9Wfuxd7PPtHSIDxhI8Jua7uq3/A+msG7kv5e/Zjr4PKeyTP0Qe+7t21tsG+5yqxv5/bozsv/9gZ5m3StCmBaT+Wdtq34XuK8UGAs+LHEv12PfK/yNUBz+F15pyTBZ6zE5YoYVVkccswMYGfCPymMs7u68S/bnIhrLK2Wxt04Hv+Y7XNJUOcM69gAUT23mrRykiIh3krATWNcAF3sbFiHSYs6nHDwf28lbLX1LgnPs1FiAf721OhcxuvzuDZ+dcibfC+uFlX8J6c4div067upxQ3onkIxb7uBI6zuakvxt4zHufMJ9RRERSExm4tAxL5fuS974z41skzzkrqfkS8Lj3PhsD1HNekrhyNJbCsRmbkbOzkyC10t0DVWY6G71fjZ0yG47ls2yJ1UlW4Nw1hmG5TU9ip536YIXmv4K9Dz/MXtNERHo259yJ2MQaJ2GDpH6kwFk6yjl3EZaOcC6WInRNdluU0+Y5597CUsq+wMYxnIgNfP1eVwTO0P09zxdjOZQjsEEin2N5kbd475MlwksnRUYt/w4b7DAEyydbieU9/9onmA5WRERSExkcdw6We38X8DPfRbMZSu/nnFuOjbN6B8tx/2t2W5S7nHPXYGMChmO5zOuwknQ3eu8ru+x5cyHnWURERESkJ8iFeo4iIiIiIj1CLhfnl15q0KBBfvjw4dluhoiI9ALV1dWrvfeDs90OyR8KnqXbDR8+nHnz5mW7GSIi0gs4595rfy2RzFHahoiIiIhIihQ8i4iIiIikSMGziIiIiEiKFDyLiIiIiKRIwbOIiIiISIoUPIuIiIiIpEjBs4iIiIhIihQ8i4iIiIikSMGziIiIiEiKFDyLiIiIiKRIwbOIiIiISIoUPEvanHOPO+e8c+5X2W6LiIiISHdS8Cxpcc6dAeyb7XaIiIiIZIOCZ0mZc64/8Afgiiw3RURERCQrFDxLOm4AFnvv/5bthoiIiIhkg4JnSYlz7ivABODibLdFJKOuugpGjLBLERGRdvTJdgMk9znnioBpwI3e+zey3R6RjLnqKrjhBrseXF5/ffbaIyIiOU/Bs6TiKmAL4NeprOycmwhMTHb/sGHDMtQskU76619b31bwLCIibVDwLG1yzg0DfgpcAPR1zvUN3d03MojwM+99U7DQez8dmJ5sm2PHjvVd1FyR9AwaBB98EHtbRESkDcp5lvbsBvQD7gM+Cf0B/ChyfXR2mibSSQcf3PZtERGROAqepT0LgCMT/IEF1EcCb2WlZSKdNWECFBba9cJCuy0ZVVub7RaIiGSW0jakTd77dUBl/HLnHMB73vtW94n0GIsWQVMk46ipyW6Xl2e3Tb3I/Pnw8cewciWMGZPt1oiIZIZ6nkUkf/3xj23flg554gm7fOEFWLYMHnggu+0REckk9TxLh3jvXbbbINJp3rd9W1JWWwslJXb9jjts7OUrr8COO0JlJVRXw9Ch0XVERHoq9TyLSH6oqoIpU+wycPnlLVcdG7h4sLpIO6K2FhYutOvz58PcuXDnnfDaazBrFixfDuefDzNn2v0iIj2Zep5FpPerqoKjj4bNm6G4GJ5+2nKbJ0bKkc+cCbP7ULlqn+y2s4dauBD+9jfraV6/HjZutPTxd9+13d3UBG+8AUuWwJ57Zru1IiKdo55nEen9KistcG5qssvKyuh9EydGknSLstS4niVcPaO21tIxHn4YPvzQbq9cCV98AX0jFeELC6G+3v6WLYPBg7PTbhGRTFHwLCK9X0WFdYEWFtplRQUAzoFzDTjXADRQUxMsg4svzmaDc1NtLTz7rF2fP9+u19TAihXwzjvw3nuwdi00NsJnn8GmTbBqlfVEe2/rn3WWzYouItJTKXgWkd6vvNxSNa67LpqyAYwYvIo+NNKHxsiKDfTpAyNGwK23Zq+5uSbobV64MFpJ4+OPLaf5tdcsPeOTTyxQfvddC5Q//xwKCmLHYDY32+Vnn6n+s4j0XMp5FpH8UF7eqoZzeWE1DewOwHKG04dmdt5ZpZ7jLVxofw8/bD3N1dUwbx68/bb10m/cCBs2WLrGmjX2mI0boyW0wz7+2ErYPfQQXHJJ974OEZFMUPAsInlrzz2a+WDlcsCC5x22+oLdduubV4Pa5s9vewKT2loLlBcssLSMt96yoiXew0cfWbrG2rUWKG/cCJ9+ao/bsMHSN+LV19ugwnXruuLViIh0PQXPIpK3di3ZzJ4sBzzPUMHX934X9tyeXXfNdsu6z6OPJg6ea2uhrs5SMdavt57njRstj3nDBhsg2NBgy5yz4DnIdwZL40iksBC23Za82sci0rso51lE8tbAg0ZQUvARJdQBULLPYEpKYODALDesGy1YkHh5SYlNarJmjfUu94l0tTQ0WLBcXGy3m5uhXz+77lw0x7mwMPF2m5st+H733Yy9BBGRbqWeZxERfGjQYO83f77VXAZLw7jvPrs+alRsL/TKldabvHGj3f7iC7v0HnbYwVI03n8fttjClm+/vQ0GBCtVt2lT4ufv3189zyLSc6nnWUTy1uC5s9i1+S12ZTn92Miu7zzNrrvmVy3ijz9Oft+QIVZ5ZO+9YautrEfeOauiEeQ2b7lldGDgpk3RihqbNyfeZkGB3ZcsrUNEJNep51lE8taQpXPwWHmIftRTuuwRKD2XIUOy3LBuFPQqJ1JSYpdvvWUBb//+lqIxYID1Mm+9taV0DBtmvdKDB9sgQucsqE607eZm2GYb2GOPLnk5IiJdTsGziOSvtWtbrm7D5/D+ChsZN7R3T9Md1GMGS8EIrifqcS8pgUMOgcWLrXbzypUwblx0/YUL4YQTbKKUQw6xQLuhIXGZOrCAe7vtMv+aRES6i4JnEclbK489i5oZrwAwkDXUMBIeW47bd5+WXtfeaPDgaM7xlltGrydLVxkyBE4+2abXXroUSktt+dChFjiPHWspHSUl1qvc1BQdYBhv3bq2U0VERHKdgmcRyT9VVVBZyZDxx+KrX4XFr7MNn1LKUthieV6lbfTv3/46JSX2t88+8MorUFYWve/cc+2+iy+GSy+FBx6AI46w2tArV7beVkGBDSYUEempFDyLSH6pqoKjj7Yk3qI7YfevxN7/5hvZaVc3GjMmWlXjb3+Ds85K7XElJdGUjfAygNNOs8uyMpvafP58OOyw2EGEYD3SzuXXoEwR6V1UbUNE8ktlpQXOTU2UNLxH2Yj1lDGf7fmUMuZTdu6+vTplI166U5EfeWTi5cE+O/NMu1y1ylI44us9NzRY7eilS9N7XhGRXKGeZxHJLxUVNsPH5s3UFu1C3aizYd1uDF7sqL5wJpSdxtBa8iaA/upX01u/vf0SHkwYVOFoaIjeX6AuGxHp4RQ8i0j+Oeccu9z1UPjf31tPtN8d6ECycyR/moqK9Ltxc0Ciqbkztd3LL4ff/Q4WLYpW3+jXz0rYvfYafOc7XfPcIiJdScGziOSPIN+5vh4KCij52kpKGl4E38ydfJey350OX5sDJeXR9dsKjMP508XF8PTTPTKA7iqffGKVNcI5z599Zrv/nXey165eq6oKZsywkZpDhsD++1uOTC7+sOvhPzolvyl4FpH8UVlpkVtzMzQ3U/vIPOooA5oAT3XjvnD/UobuUk7JeykExqH8aTZvttsKBFoMGGCVNYqLbbeDpW0odaMLVFVZQnqwowNBeZNc+mGnH53Sw+kjTETyR0VFbOTmPRx+GPTpw9ZssC/yoA5bosA40faKi21UXHGx3ZYYu+1mf4Hhw20Xn3pq1prUOwXHa7zm5uTHb7ak8n9LJIep51lE8kNwmviKK+APf7BqG33XUvKbbwDf4IGroOz6w6A8MrtgaGBh0sC4vNx6zXT6OaGKCstt9t4mWGlstIlV+vWzlA7JoOB4TdTznGs/7FL5vyWSwxQ8i0jvF3+a+JZbYM0aavc+lrqlxVBdDTsfS3XxzlBtAV5JqoFxebmC5iRKSuCoo2xK7poaqKuLxkl77JHVpvU+5eXw7LM9I+dZPzqlh1PwLCK9X/xp4jVrYPJkSqqqKPnWV2DzZh5zP6PsshNiv8gVGGfMyJG226UL9aTjtSe1VSSOgmcR6f0SnSauqqL24l9RV783AN43Uf37Sphcbj3PeVLnuauNHg2DBsEBB8A3vxmtKz10aHbb1WMlqqjx6qt234QJdpmJHt1MVMNQRQ3ppRQ8i0jvF3+aGODooynZuJEgRn6McZQNXgFlWWpjLzd4sBV9kE5IVlEj8Oc/W45zY2PnqlhkohqGKmpIL6ZqGyKSH8rLYfJku4ykcdQylGrGUM0YPH2o3v5Yqquhtjbbje2dttkm2y3o4ZJV1Ag0NGSmikUmqmGooob0Yup5FpH8E0njKNn8MSUFq6G5mceajqfsD2fB156OTpIinbZoEbz8sl3fYgt49FG7fuCBSo1Jy/TpcO+9ba9TUGBlE5ubO1fFIhPVMMLbKCy0g+Ciiyy1RD3Q0sMpeBaR/HTOOdR+sR11G7aDhx7CA9X1X45OkqLALiOCnGeAv/1NOc8dMn06TJqU+L4+feCQQ+CFF6wmYEEBnH9+54LUTFTDCLYxY4alk8yaZcvvukuTCUmPp+BZRPJLKBezpLiYkqlT4d8Leaz+WMqKFsKZI0GBs+SSmTOT3+e9Fc4G63FubIRhwzofnGaiGkaQItXYGF3W0KDgWXo85TyLSH4J5WLW1g+kevYaqhv3tZ7nxn2pXrqVcp67yFZbZbsFPdT48YmXBxOgjB+fuzNdBukbgaKi3GqfSAeo51lE8ksoF7OkeA0lTS9D08s8xtGUNb0ML98G596W7Vb2ShowmIagzNvAgVaK7vDDYdUqK1syalTrCVBGj06cZpGsXFx3lZELJm+54QYbiVtRER08qN5n6aEUPItIfgnlc9bufSx1M54EVuBxVDMGVg1jaK0Gs3WFLbbIdgt6gHHjYlMdmpuj9xUVWf5wqkFnsnJx2Sgj98QTVmLv5Zetx7xvX5Wvkx5LaRsikn8iZetKvj6Wsh9WUNZ3MQ4o67uYsh9WKHDuIkFqriQwfTpstx3Mnm1BbXNzbOAMli88Y0brxwbB8M9/bpdVVbZ8xgzYtKl1ubiuKCNXVWXVNC66KPr8geD5gtfT3KzyddKjKXiWdjnnxjnnnnHOrXTO1TvnPnDO/cM5NyrbbRNJaPp068GbPr3N1Wprobq4nOrbXsYfcxzVt71MdXG5cp4z4E9/ar1s06bub0ePEFTTWL++Y49PFAxXVcHdd9uAQrB86CDXOEhdylSOdDB5y+23219kBs8WwfMVREKOIFdbuc/SQyltQ1KxPVAN3AqsAoYBPwFedM6N9t6/l83GicQIl/WaPdsuJ05MuGpJSSQ9o2wfHvsQys7tnibmg7/8BS65JLSPgU8/hTLN4NhaW9U0woqLo1NwhyWqyxxO/XAOzjsvmiKRiVJ0YfGTt8RX1Ag/38CBsbnaIj2Qgmdpl/f+b8Dfwsuccy8DS4FvAL/PRrtEEooPRGbOTBo819ZCXZ1d//RTqK6260OHKue5s1autMvwPv7sM+3jFlVV8JOfwDvvtL0jnLOA+Nxzk9duThYMhwPq+KA7E6XoAkHwHkwbXlAAK1bYawwH0AqWpZdQ8CwdtSZy2ZDVVojEGz8+2uMc3E4i3Cv6wAPqFe2sP/3JCiuAxWzf+AZ88gnsvDMceyxs2AA1NXa/c3kcPFdVwWGHWZoFwAcfRO8rKIAzzrDKGvvtB/37p9ZLGx+cZrp3ub3nvukmuPhie01NTXYG6J57NChQeiUFz5Iy51whUAjsAvwWWAk8kNVGicQLeplnzrTAOUmvM8T2ioJ6RTtrwACrpAbWCRlcD+o7f/FFdtqVcyoro4FzPO9h771h8uTOP0939vauWRN7OzwoUMGz9DIKniUdLwFB39xbwFHe+4/jV3LOTQSSRizDhg3rmtaJBCZObDNolq5RUQF77WXX77kHLrgAli6FtWttmQYMRtx7b/L74gfShes9P/aY/eI7/3w7vsP3xecRx9dxTreuc7rrh1M3mps1KFB6NQXPko6zgW2B3YAfAU86577ivV8eXsl7Px1IWuZg7NixvisbKZKqlrSNqioeeAnKTkO9ZBniXOtlLZXXli2DF5+H+tL82d9BMHrvvdHclbDCQjjpJLjyytgA+OijowFp4OWX4e23YepUG5znfWztZIit4zx1Klx+eep1nRPVgYa2g2kNCpQ8ouBZUua9Dz7xX3LOPQYsx6pufC9rjRLphNpaqHtiIUz8H2g8lerDH4Lp0xk6bh+lbXTAypXRuHDrre368uV2e8CAyErLlsGUKdC4EO5akh85seFgNFm6BsCBB8bui/j6yGH33x9b4SK+dnK4dN3Mma1L2bW1z+NL382YYacS2gu+NShQ8oTqPEuHeO/XYakbe2S5KSKmqsqCsvgJGtpQUgJlL99GWeNLbMenlDW+RNnLtylwzoAtt0xyR02NlVDzGZygI9eFg9FEktVbjq+PHLb77rG3g6ocFRWt6ziPH59eXef4x0O0/fX1cO21af0/E+lt1PMsHeKc2xEYCdyf7baIdHS64dpaqFs1DBij6bkzLCgx3EppKfTpA40ZmqAjl1VVwQ03wIsvRnuPCwtjg+hp05KnOIRTIdatg3//21I0Lr8cRo+2iUk2b7b9ef75saXs4ittjB6deg5zfKUOgLvuis56+NRT8Pzz+XHWQCQBBc/SLufcQ8B8YCGwHtgT+B+gEdV4llyQaIa1FL7US0qg5IcV8O9f8Fj98ZT1XQw/vAUUOHfIkCHRCe2amixOdi46YBCAESOsksSa5+HMXpzzXFUFRxxhOclhTU32o+G885LXbQ4L7g//OBw92pY/+2zygDhR6bp09nV4/aoqeyODN1eVNCTPKXiWVLwIfAv4IVAMvA9UAlPiBwuKZEWiGdZSUFsLdcXlcNvLfHr3F1Sf+20o3kc9zxlQWNjGnSNGwMkjYEy3Naf7VVa2DpwDmzfDsGGJq2AkqnKR7Mdhd+UYh2crhNgUEZE8pOBZ2uW9vx64PtvtEEmqgxNChKfnfmCJpufurPCAwT59kgwYzBcVFVBUlDyAvvdeWyeVqhgd/HGYMeHn79On7dkORfKAgmcR6R060AunSVKky5SXw5w5lvP86qvw3nux9y9b1rpHOVlVjO6cLTDZa8nm84vkGAXPIiKSEeGc58bGJDnPvdFVV8Ff/wqDBsHBB0d7ZcvL4aGHbJ2DDrL6zIEBA+x2YWG0TvPgwbbDCgps+YoVlsYRTtEIqsq0FcQmSv1ob9KTqiorSbdypb2R8T3LyX6cpjuZikgvoOBZRPJWS9oG8MDvP6Rs9gwLAkoUBHREOG2jqSlP0jauusp6lwE++AAWLIC777bBfOFg8vzzY4PnVatg1ixLg3DOfm3cf380ePYe7rjD6isH6RupVJVJNsFJW4+rqrLjPlw3+q672h8Q2MEqNyI9nYJnEclbLWkbCxfCP56i+u8PQdG/GfqPP1Ly9bHZbl6PE+55LizMg57n6dMtTzleokoUM2cm3kZjY2wlC++jJeG8j91WKlVlEq0TtCn+cUGv8YoVrXOzGxrSn0xF1TckTyh4FpG81dLzPPs/PNb0BWW8YvWHFz8JCp7TllcDBqdPh0mTEt/nvU1RHTZ+PMye3XrdoiK7DAevQW90UNYuGCCYysDBZOvELwv3GhcW2nOG21BUlPpkKtkayCiSJQqeRSRvtfQ8DzmRTwufpNofAH2KGLr3sSr13AUSTZTXYyXrSQ789Kexk59MnBh93H77wfr1dnvCBMs1njbNgm7nohOexOcSpzJwL9k68cumTImd9fDCC+0yWc5zvKDXeurU5JO8iPRSCp5FJG/FlKp7cnvKRm+OBAHqde6I9gYM9qrgOb4nuU+f2FrIq1fD1VfDFltEc4EnTowG0fHuust6fgsKYP/9Ww/QCw/Mmzy57bYlGtwXvyy+1zid0nPKdZY815s+ykRE0lJba+XpqquBnXam+rjJVBeXU1vbhU8aVEuoqurCJ5GMC2YM3GEHuxw92nqLDzwQTjkFnnsu8cww4bzjtt577+2vqQkuuyx2nenT4fDD4Wc/s6D3oovs/nSOpfh1gx7q665LP/hNllctkifU8ywikmnJynf18h67RDnPn3wC228PI0fa8tJSuxwyJDtt7JCqKgteg57lVavgK1+B//7X0haC4LGsLLaiBkRLzk2fnngCFGg9g194sF5VFVx6afT+zZstaL/rrmiVjvaOpaoqOPLI6HMHlUA6OkOhcp0lzyl4FpG8FVOq7gGLfTqtrQA5D6sTfPqp/QXpHEFw7VwPmogmPrgFq4Zxww3wxBOx7/Xll8P8+TYF+RFHWNm6O+6wdIymJntc/HtfURE7YC884LCyMpqXHPA+dt32jqUZM6C+3q7X19vtzhx3mjRF8pyCZxHJW+EZBj//PEMzDLYVIPfyHrtwzjP0olJ1yd6n2trW7/VLL0XvnzLFgtyg7Fw46fvll2MnQDn//OigwYICG4QXPHffvhb0BjWgm5utRzvc8xxU0OiugLajvdYivYCCZxHJW13S89xWgNzLe+zCaRuNja1L1TU3Z61pnVNebrnN8SkZmzdHX1RTkwW/L79sQXVFBbz5ZvT+5mY45hh7/5uabIKURx+NzRcuKorWfX78cUv32H9/OOccu3/CBFi0yCp2jB9vedfBsbRoEVx8sT1PODUjeFwwILGoyG6LSIcpeBaRvBXueYYM9Ty3FyD34h679nqee2zwDNajPG6cva+DB8NHH9lsgmHvvWd/0DrQBnjyydgd1NBgKRT33BOd3S+YJOW55+wPrLe5b18LpIO86eeft+Ns8mTrcb744mh6R3xqRjDBSi/90SbS3RQ8i4hkWi8OkPPaE0/Y5ZQpVoYuXd5bukUQ5AYTpITrLScS5EnPnJk4JShRXnQ8HZMiGaPgWUTyVpekbeSxRYtiO1wffdR697fZJsEMg6+9Bk88mjs9oePGWarDttvC1lvDBx9YV/o//xltX5BTPHCgBb7xU1q3p7gYbr4ZXn3VbgfpE/fcA5s2xfZKhxUU2GPHj7ce5yAlaOBAC+TXrWsdlKebmtGd+dIiPZyCZxHJW10yYDCPjR4NgwbZ9bvugq9+FZYuTTBgcNkyuP570PhKbpTsGzcuOuHJmjXRwXoffgiHHgovvGC3w9NZn3iivbBVq+wgCpcUAcuR7t8/djbB/fe3bQeXEE3zmTED7rzTcp4LCqwU3qhR0XWDoDbIcx440FI46uutZ9o5a9dJJ8GVV6a3P3t5CUWRTFPwLCJ5Sz3PmRUeMFhf33rAYIuaGtjcAD5HSvY9/3zy+7yPDuoLUiaamuDhh6Ffv2igGQ7AAebNs4A2yE0GC1CDHmbnYh8PVtauqcl6jn/72+TTb4en1w4SyYPA/cAD09+XeVhCUaQzNMOgiOStmBkGiV7v0hkGe7EhQ2yQYDARSmkp7LorbLdd3IqlpVBcZD2luVCy77DDkt/nnLUvqKLinC0P11cGS6kIC2YLDNaprLRfFEGQ673dDh4/Y4atG9RwvvbatmcODNoTlL8LUjs6si+DbeXK+yGS49TzLCIiGde3bxt3jhgBt90OK3Mo59k5C1y33da6yT/4wKZGHD/eAtkXX4SttoKNG6OPKSiwcnJVVTBxoi2bOdNSNW6+uXW5wsLC2JIjhYXR+sx33x0NrJub4amnor3WyXqgg6ouAwfGpnakq5eXUBTJNAXPIpK3lLaRWWkNGNx3Xzh3325tX0Lx6Raff241lsHSLG6/Pfljm5tt9sB77rHgc+LEaBB9yimtg9FbboFLLrEe6cJCux2kYIRnMHQu8UyE8TpbQSN+kKCCZpGUKHgWkbylAYOZFR4wOH166wGDyYpJZFV8vnNzczSVIpjSOpGgp7q52XqjL78cpk61+4KANKjBPGVKtHf4iiusPvT48dFAO0ibCGYRDNoR7pnOdK/w9Olw6aUWyPftq0GCImlQ8CwieUs9z10nUdnhtmLRbnHVVdZT7L0Frtdfb/nO4Z7ngoLojH1tzepSUBDtIQbrcv/KV6LLioosdSO+IkYw/fbzz9uvjaDHd+pUC2YbG6O/Mpyzdnz/+9EUkPDMgR1VVWU94EFvd5B7nWi7KmEn0oqCZxHJW10yw2AeC1fbKCxsXW0jmEQvK666Cm64IXo7uD5+vOUzB+Xk+vWDn/wEVq9uf5sjRkRfMMQG25s3w3XXRQNniM1pDqdkVFVZrnQ4cA628cc/Rn91xM8c2BFVVZbDHf51E/RwJ1pXJexEWlHwLCIiGdcn8u2yfj189pnFhN7Du+/a8sGDu7lBDz7Yetndd1ud5rANG6LTYicT1FR+66221/vgA7ssKGjd8xwMJAwC1ESTpHhveS+ZEjxXUPUjeB1B7nU8lbATSUjBs4jkLaVtZNaQIdH4r7HRKtLNnQsLF8Kbb9ryIJ24oMDG6nWb006L7XkGq54RHzynYvfd4ZhjLAUkEATG8QoKbN3x4y3nOb4yxpQpsSXsEgmqdHRk5sCwIBhubo6269prkwfEQS52fNUQkTyn4FlE8pYGDGZWOG2jqcmur1plsVe/ftltG5WV0ZrI225rOc+77w6TJrX9uERB8Y9/bPnK99wTDSynTrVpt4NZAgNFRW0HqAMHxqZ7OGePcc62E2y7I6Xo4vOV44PhttoFKmEnkoSCZxHJW+p5zqxwz3NhofU8P/mkxWpBSnH8Zbc46KDYGnp77mmDBauqLL8kCHaD9IqwcOA8fLhV0Jg40R57zjn2i2HIEAumg+oZ06ZF0yLOPbftoPPVV2NvH3BA66odHQlak+UrpxsMq4SdSCsKnkUkb2nAYGaFe57Brm/YYLHbttvaspbLT96FKQ90T4/m/PmJb1dWxla2aM+ee0YD5yB3OEiBuOeeaNBbVGRd78XF6adZjBkT3R+d2S8zZkTzqMP5ygqGRTpNwbOIiGRcUMxh333tb/hweOYZOPeZMwEY9cwSYIEFnv/9b2YCuqoqy2t+800LdK+80rY7Zkxsz3NTk91/6qmx0223V4g6mII7nDsMdllfb6XmmputN/vCCy1wTva6gpSK/fe3OstBD3FncprD277rrujr6dMneb6yStGJpE3Bs4jkLaVtZFY4bQMsbcO56CQpkKBWXXMzXHxx6/SFdFVVweGHR1MwliyBf//bKme89FJs6ob3sGxZ6wGE8UpLYa+97BTF+ecnntQk6HkuKLCgPAiohw1rHYwGgerAgVb/OQiYb7qpc9Nrx6usjP56aSt1RKXoRDpEwbOI5C0NGMysRGkbCxdaqbqSEiigiXcZDsBgQlUuFizo/JNXVsYO1AO7PWOG3Td1Khx5ZOoztRQWwtlnW44z2Ix848ZFe59Hj7YXdcIJFviuWwd/+IPdl6gyRThQDSZSCeo9r1kTfZ5MiB8YmKw3W6XoRDpEwbOI5C31PHedwsLWy5pJsLAzzjoLHnvMAthLLokd/Be4/XYLVsOzAYYlKzHnHKxYYUHvokXRqhzh2QjByt1ts41V2Whqsh7oqVOjk58EKRHhQLWgwHaQc5kpARefepHqwECVohPpEAXPIpK3NGAws4K0jVWrYIstLOth3TpYvNh+nEAKg/JSddZZcP/9dj24fO45OOSQ1usmy2cePNj+iottRsEPP4xWyfDe6jjfc4/1MicTPHegqclSUOJTIqZOjQ1UO1p+Ll6y1ItUBgaqFJ1Ihyh4FhGRjAjSNl54wYLnmhrrvF2xAt5+O7KS6wM+rne4vYF6iTz2WOvb990HO+1kQXAqVq1qPUlK0Csc5C9v3tyxX1LxKRFr1nRNoNrZ1AtV3xBJm4JnaZdz7hvAGcBYYAdgBfAg8Bvv/WfZbJtIZyhtI7OCnucnnoBBg6zn+cYbLYjeHBkreIO/HID9WMC4ub/oeOB2wgmxvb4nnGCX//wnHHpoxwJyiM4GGAzoKyy0F3bllZabvd9+8OKL8PzzsWXugh7rcI5xfEpEOoFqqlUwlHoh0u0UPEsqfoQFzFcDHwD7A9cCRzrnDvHeJ0gkFMl9GjCYOfEpMJs2wUVnrmPFij5sWVxEY2NfmpthSzZwOHP4Z+EEqLyu48HzfffZZZDzHNwuL7eu78pKuPfe2BGM7QlmA1y0yFI1Nm+269OnWzm5qVNjq2Sce66Vmks07TZ0vKc5nSoYSr0Q6XYKniUVJ3nvw+c25zjn1gL3ABXAM1lplUgnqec5c1auhLlzLcf5nXfArV/HNm/MY3e2poG+LHJfBuC4gmc5ofnRzPSSBgFzvKCHd9as1vcFKRmBffe1ADnoOZ41K3EJu/p6mDkzmiIBVo4uKF/XVjvSlW4qRqLnUf1mkS6j4FnaFRc4B16JXO7UnW0RySQNGMycIUOiY/UefxxGb/EWO/MsS9mDasbiPOywQxFbH3I0FG8Nl1+Wfm/sjBl2PUiLqKy0aH3BAku1GD06Wkd5zZroGxrW3BxNsygshB13hNdft+VNTfDXvyZ+/sJCe47nn0+cIpFKsNrZVIxUH6/6zSJdSsGzdNQRkcs0zomKSG+2apUVrfj4Y+DLO+HnF3Fm8z/ZumAzK4r24KNZ89LvDb3qKkubWLcuuuz221uvN3u2BbjeRycuSVaCrqAgGkTvt180IO7TJ/bXVKCwEL71Let5vuwy6N8/+hqCoP7uu61MXrJgtbOpGOk8PlnPtXqjRTJCwbOkzTm3E/BL4Cnv/bxst0eko5S2kVlB5bctt4RVhUN5cdcr2G5kFaP2GMGwhxq574g7GNW4kDH9rkutN/Sqq9qfBTAsnI6RLIBubo7WWG5uhptvjpaNW7HCytOFOWcDEIPBibNnw7RpsQHtpk3R50mWZtHZVIx0Hp+o51q90SIZU5DtBkjP4pzbGngYaATOTbLOROfcvGR/q+JLQ4lkSW2tndmvro4OGKyutuWSvlWrbHxeQ4MF0B+s25YRl4yD3XbjZwc9zVnNMxjjq6PBX3sefLBzDUo0KQpYD3Eww1+Qy1xRYekgxcUWdINd9utnwXHYzJl2GQS04aobyXK5g4C2sLBj+d7pPD7oub4u9CMlUfAtIh2inmdJmXOuH/AIsBtwhPf+g0Tree+nA9OTbWfs2LEdrCElklnqec6clSstZWP5cti4ER56yJbPm2dlly/8xy+ASM9wqsHjaae17nneemv7pdNRQYDrfTSIfuopmDPHqmcEvdDh6hmLFsHLL0e3EUzRHe7hLSyE886zADxRj25nq2Kk+/j4nmuVtBPJGAXPkhLnXBEwEzgQOMZ7vyjLTRLpNA0YzJwhQyzX+cknLYj+/HOLcx97DJhbSRUHM4YFtvLGjakFj9dfb5fTp1ugO2YM/Pa3cP758MYb8KUvWU/qBwl/x7dWWAgXXhgdcHj55fDKK9Ee6GnTrFzdeefFBqjB5cyZliO9Zo2lQXQ2oE2krbzkzkxoopJ2Ihmj4Fna5ZwrAO4HjgZO9N6/mOUmiUiO2mor69wNbLcdDORdBvBJxzZ4yinwxz9acPvcc3DEEdEneO+9aIpFKm69NVparqoKXnstNifae+uZnTbNpuUO5wVPnGjVPJJNhZ0JXZ2XrNkERTJCwbOk4k/AN4FfA1845w4O3fdBsvQNkVyX82kbPaw6wq9/bVXfvvjCbn/+uU3Gdwj9+Q5/T3+DBx1kPcPhALehIXadZHnNQUWNwLRpFgAH+3TFCuvNhtgKHN7b36ZNVkUjvP/DecPB/em+L229p52daltEuoWCZ0lFZN5bfhr5C/sFNtugSI+T02kbPbg6QrjnucMOOig2z7g9228Pa9fGLisqipaPGz3a9umRR1ovdtBI52y9m2+2ihrPPWfLvYc777TLYP9XVEQnWfHeytMly3FOpL33tDN5yT3sh5ZIT6bgWdrlvR+e7TaIdIVwz/Njj+VYz3MP64VcuRImTbLrf/yjDRS86CK44AIYU3Za+xuID/4STXDSlm23jQ2ew73IjY3R3ub6ers/Pl1j9GirrBEW9EwH+3/yZMuHnjYtdruJ3pdEwWx772lH85J78A8tkZ5IwbOI5K1wz/Onn7bT89zdPXs9rDrCkCGx8WhBgY3rG/LBPNhiCxskGIivvRwf/E2dmjwdI5nly2Nv9+ljvcRBz3NFRXSGwnhBEDx+vNVxDisoiN3/EyZYPnRb70uyYDaV97Qjeck97IeWSE+n4FlE8lbKOc/Z6NnLteoIaf54CKbqpqrK9htYMHvdda1Xjg/+gjrK8QoL7ZdNW9U1nIMDDrAAPNh2uM133hntUQ6EA9nSUitWDRY4H3MMXHttbOWN9t6XZMFsV72nPeyHlkhPp+BZRPJWyjnP2erZy5XqCB348RDErgwYEB2Q194EIsH2x4+3ustBigVEH7/NNm23Nei5BqsRXVtrU3sHvc5/+hO8+iosWWKzuuy1F1x5pdVyvvTSaGBdUAB9+8YGzoH23pe2gtmueE9z7YeWSC+n4FlEpD3t9exVVUWDs3QGkGVLe73I8fd39MfDwoVwzTXRKbGnTk19ApHRo6P7dP/9rbbyvfdGe4WT8T4aCAeVOcIDD/v2hZtuiqZeLF8OJ5wAl1wSGzjH9zinIxvBbK780BLJAwqeRSRvpZy20VYwVFVly4LUhLvvhmefzd1Apr1e5ET3dzQtoDoyFXdzs/U+r1mTeL1Ro2DpUhg0KLrtRMHgz3/e/nM2NVnaR3xJu0CQFhKfJhLOsS4s7HjgHOiOYFYVNkSyQsGziPQ+KQYV4bSNzz9vZ8BgsmCosjI2UMulAVvTp1tgOH58dHKQ9nqRE90/eXK7PakrV8Z2CteMPRMAxyBKCuuSB92jRkUfuGqVJUsXFsKee8LZZ0efb/p0a1NbgtSO/fZrPfAvEKSFPP98bJrI889bmkhBAdxyS+dmAuwOqrAhkjUKnkWkd0kjqMjIJCkVFVYnOOh5zpUBW9OnR2vHBYHkxInt9yInu7+dntSYahvzqihlqS3nIwt6k70Pb7zRellTkwXUV18d3fjHH7f9esOpFomqahQUwNe/bvnNQVpIfJpIqsFwdwauyYJ0VdgQyRoFzyLSu6QRVNQ+Mo+62YsiUfM+HZskJcgJzrWc5/iKFTNnWvDcXj5uJ/N1v/lN2Ibi1nck285ee7Wfx7xyZdv3Oxc7uC9R8Ow9HHhgbNWM+BrLqb7W7gpc2wrSVWFDJGsUPItI7zJwoPUyBjPDJQsqqqrg22fB5oHWc3za34GdO/acuThYK75m8fjx0evttbcTr+fdd2EffPsrBpYsieY8FxS0n5oRVlBg9ZzPO88mSbn2WnudEya0LklXVNS5ADPcA9xdgWtbQboqbIhkjYJnEenZwkENwOWXW7BRUJC8ugNAZSUlDe9R0vwONBbywAfvU1bWweA5FwU5zvE5z11oqWVqUDx2LMwL3bHFFvY+JXsvliyJXj/oIJg/v3Ut5rA+feCKK6B/f3vfFy2KTVGZNs2m2Z4xw3qthwxp/4xAcBwNHGgDG4OANKikcvfd0QlXnn66ewLX9oL0XPzRJpIHFDyLSM8Vf1r7nHNSq+4AUFFBbdEu1PmB0KeIz7ffJXHaRrYHhnXGxIndEjTvvbelLgedxvPmwUG8wI7U8S9Oo2Tzx6mnNrz0kgXQ4fJyYO9nnz42bWF8IHzttbHrXncdfO1rqafQBMdRfb0dO0GN56lT7cfYpk3RhO7wIMrumChn6tToD6CedvyJ9FIF2W6AiEiHxZ/WBguiCwvbP51eXk7JM/dR9qtTKXv2RrYeUUJZmaU/xwTORx9tJdKOPtpuSytr1sRnWzTQRCGb6BcdMHjTTRaUDh9uq4waZe/T8OFw0UU2mUpw/yuvtH4S7207w4bZ7SlTou9HOCUFbAbC22+HI49M/p5VVUW3ERxHQbm65ubYknZB4OycHVcDB8Y+f1epqrLg/emn7VLHn0hOUM+ziPRc8ae1J0ywvxR7imt3KafuuOg6rXqek+Wc9uTe6AybPx/GjYMFC2xOFIC+1DOa1zmKZ6MrBoP+3nvPepCDaPu99yzQDbz3XvIna2622QKPPho2brRg9jvfsQlOTjnFurzDU3fX19ssg0OG2O2gJzr+jMXUqXYZ7nmOL2lXWGi51fvvb4Fsd1TaUEUNkZyk4FlEeq5kg6biA4wUgt3PP0+wMFHOqerrxpgyxeaEiZa6bqCeIjbQj31YlPhB6QwKDCsosCh940a77T3cf7/VGQQLyvv0ic2XnjUrev2uu+w4iA9K16yJHkdBzvO6ddbzfNll0dzq8nJ7wd0V0KqihkhOUvAsIj1be4OmghkAGxqs4kIo2AnXeb7zzgR1nhMF590ZPPUAu+0GixdbrLl+vS0rooliGniXXSmlhhLqOv9EQTm68ePhySdDRaWJDcYvvNC6w195JXYdiL5fFRXWkxxMGx68t0Gv9A03RIPu2bOjtaGhewNaVdQQyUkKnkWkd7vhhmg+9ObNVjkhEoSEZxiESNrGwoUMfXMOJV8fGw2owkGLegNj3HUXrF4d3GqI/FvEG4xgV961nOdMOOCAaPWUmTNjy/AVFETzkYPUnfCU6WEDB9qlc7GXED2rEPRsB373O/tlEKR9dGdAq4oaIjlHwbOI9F5VVfCvf6W06tZbY0m7F10EDSvgj2sSp2SoNzDGeefZLl6zJjoJYDGbKePVzD7RmDGxvb/h4HniRBtIGH4/zjsvNpc6sGaNvXeNjdYz3dgYPXsQpHPE897K391zT/SYyPP3XSSfKXgWkd6rsjL21H1hofUeRrSannvlf6DxJWhugs2FyVMyFDy1smn9RqAPDk8BbdRoTsS51ikWhx8Oc+daSkZRUcz7RkWF1Y4ODxSNfz8mTIA//zmcjB07UUqiswfhswpgaR1Bu7xXmo6IAAqeRaQnSLW6Rfx6FRWWJ1tfb4HzLbfEPD6ctvH551A96kTo82+G+hWUFK/J+5SMVGy7LWyseYONfAmAQpqAAj5kZxYymiOpjH3AmWfCX/8aDUqLi21AXkmJbWzTJqvl/Pbb8M47MGgQHHxw6yc+55zoBCiLFrWeHr28HObMsbSdN96wacDDucvJBpqGl0PsBCmFhbBiRdsTvqRDVVtEeiTn43/ti3SxsWPH+nnz5rW/oghYgHHkkdFewmefjQYa8bMLJqqCkWKA8uMfW2qrAprUzZ8Pj5ZdzeOMo56+zGNf+vM5h/Ai1w25jTErH2v9oKKi2N7gsIICuO02C5xvuCH2vr597b2H2AlN4nuti4sz3zucbJbBzjyHqrZkjHOu2ns/NtvtkPyhnmcRyW0zZligBHZ5+eU2cAxig+pzz01cBaONFIuEAwaLyxl6Tnl0ohRp03q2xQO7sILF7MVQ6jjh9P6M+ccTiR+QLHAGC4YvvRR23LH1ffX1diwMGxY7oUl8B1BDQ8eD52Q/nIJ86MbGzFVZUQ1nkR5LwbOI9Cwvv2w9duPGxQbVK1eqCkY3qq2FmoffpJoy+rGZTxjA3izhAKo5ZOb9liZRU5P+hpuaLFUjPNlJYOVKS8uIn9AkCKTBeraDGQDjg+D44DiVMxeBTFVZCZ5z4EAdryI9lIJnEcltEyZYPbRwFYTNmy16CxsyJO0qGK0GDMbXeZakFi2Ch2Z8Sh1DGEUN7zGMsczjZP7NmIaXYYm3KbjfeMNSK1KZGMU5C36XLEl8/5AhsXnJwYQmAwfCq5HqHslmAEw0q2B4vXPOabsnOJ0qK8l6sBO1Yc0apQiJ9DAKnkUkN4UDkMrK1jmn559vs80Fk5+EB4qlqNWAwfjpuSWhJ56AO6Z8zHvLmxnKSpZQylBq2ZsaRvO65SdDbBC81VawYUPiDZ5yCpxwggWSK1bAHXe0Xqdv32jFjbbe50ST2CxaZAntmzZFq2bMnBm7HrTfE5zK8dVWLnOimQ0nT257eyKScxQ8i0juSRSA3HabBU/hHsebb+5Uz516ntNXWwvLlsF7iz9jF97jEwZQxGaOZA6HMJeSotWwKXSW4KCD7FfJ9tvbe/npp5aW8atfRd87iP5QWrTIUjG8t4A5vnc2PtUifDxUVLROr1i3Dq6+OtqeggJbPn48PP98bLm74PjqTE9wW7nMmmBHpFdQ8CwiuaeyMprTWl8fO/gPMlalIOGAQdTz3JaVK2HxZX8CytiX13iEk9iXhXyVxxjDAptk0Dkr6xZO1Vi1Knr9k0+i1xctiqZP9Oljj2lqsiD3sstaB87Be9+nT3SSkyD3uW9fOx7C6RXXXhv7AkpK4Gtfg9Gjk5er64y2AmRNsCPSKyh47gGccyOBYcAgYCPwMbDIe78+qw2T/NaVJd0GDowOAmtujk6pDF1WpeDzzzu9iV7viSfgqeN/y0LK2YUVVFJBXzZxPE9Y4BzWVo5zYyNcfLFdLyiwdZubYx/T1AS//71dD34khd/7+Gobzc3R42Hy5OgxMX587GyEH31kaSHBbIGZTptoL0DWBDsiPZ6C5xzlnDsKOB84Bgua4zU7514F/h9wl/d+dXe2T/JcuAewsNCmQk40y1tHvfpq8tsZPPUdTtu4806lbbQlSNdYyY4t1TX6sZEy5jGSN9LfYDB7n/cWQDtny8MBdLBOEBSH3/tEPc+JjoeJE+1y5kzYckubS7yry8MpQBbp1RQ85xjn3GnAr4E9AQd8CDwMrATWAlsAA4GRwH7AWOAXzrkZwP967z/KQrMl38yYER181dQE06ZFe/K6OmjI4KlvpW2kbtEiWLwYtmYDe/Im8yjjSJ6Npmuko7AwmtYRrjoxcCB8//vR4Dio0hEExYlmAIzPeY7Pi160yALn8eMtVeOJJzKXc5zo7Ism2RHp9RQ85xDn3HPAV4AaYDLwgPd+RRvrFwNHAucAZwGnO+fO9t4/0h3tlTxVVWVVL8KTU4R7BzsbMFRV2WWQ/xpU0gjrgp69rbfO6OZ6ldpaePhhWPXi2+xLLa+xLzuwkrFUM4R2fq8XFlqP79Zbw4gRNnBw7VqrquE97LILPPaYlaGrqLBZBIOptrfd1iqqjB9vt4PazZMnp1YOzjnrmQZL3TjllPbLw6UzFXx87j1o1kCRPKDgObdsA5ySavDrvd8MPAE84ZzbAbga2KsL2ycSnWktEJxyz1RPXnhA2AUXZDYdJI6qbbSvthYefBDeePkT9nztSQA20o8TeJx9WEQJdW1voKkJPvsMvvjCguZglr7Ae+9Fr999N9x0k53FCE+CMmdO9CxHohrNycrBxZs1y3qekwW16UyZnSj3HjRroEgeUPCcQ7z3+3fisR8Dl2euNSJJVFTEVlIoLLSay5kIcuMDn2HDujT4UJ3ntgWB86OPArUrwTfzGvsykDUcQlX7gXNYMKAvfjrtsHD95fCA0aAOc7IazcnKwYV7nsPPkSyoTWcwarLce5WiE+n1FDznMOfc4cB67/2CbLdFpEV5uQ0QnDbNgpnm5swFud1cB1c9z21btAheegnq336fPesqeZMR7MQHiatrtKegwFJw4nuew8L1l8M9z0VFsT3P8TWa2yoHt2gR/PnPNui0ubnt4yqd4y9Z7r1K0Yn0es631QsgWeWcawKmee8vznZbMmns2LF+3rx52W6GdEY6p7dT2VY42OjGAVfhnucHHoDTT7fr6nmG+fPhhRdgyff/RAm1VFLBTnzAmfyNcTzZ+gEFBXYZ9BgXF0crYYRTcABuuMFymd97L1pt4+tfhyuvjD0G1q2L5jyPHt254ySdXGYFvz2Kc67aez822+2Q/KGe59y2GqvrLJJbMlXxoqrKHh9MsR0/GUo3Up3nqNpaqKmBdd//KSVALSX0YyMH8opNv51IEDSDBcPDhsE779ht72PPTjz0kA3++/nPrTfZOTjwwNigeOBAuO46+4H2/PN2vAVTtUP0OKmqig4kzMRxozJzItIOBc+5rRI4JNuNcM7tDFyFlcXbFyuXt6v3fnk22yVZlokgY8aMaD7r5s12uxsDF9V5TmzlSot3d2U5YMHzCTzOacxKLc+5uRnefjvaq5woBSI+RWLgQLjoIrjrrugMg8FkKMGxcc89Hatu0ZVnSkQk7yh4zm0/A15yzl0H/NJ735ClduwBfAuoBp4HjstSO6S3CAKQlStjl8ff7mKq89xabS0sWQI88wylLOVRjmcnPkw9cA4EKYEDB8KvfpU41SI4ezFwoFXPCGqHB48PV3KBjle3CA8ErK+3Kbuvvbb94DdRSpFK0YnkPQXPuW0y8DpWgu5859xr2GQp8Ynq3nt/fhe24znv/Y4AzrkLUPAsYR3JPQ2XowtX7vjXv2D69OiscN1IdZ6jPyY+qX6HQ2ZMpoaR9GcdhzI3vcA5bNUquOQS64n+wx/sve7bNxp4lpdb2kW4Eodztk64JjPE9jynU90i6OUOBiE+9VQ0FSTZMZsoUO6iqeFFpGdR8Jzbvhu6PiTyl4jHpvLuEt775vbXkrzUkZ64G26AjZFU/uZmKC21BNugmsJFF9l93RBAq9pGVG0tLFwIg+sWMuCpR6lpGgF4DmVu+pU14jU22vseqK+3NIzgR1f8tNvnnmsTpAQzA7ZVySKV3Pugl/vaay1wDlJB2gp+EwXK3VwNRkRyk4Ln3LZrthsg0qZ0e+KmT7eJKgLeW46Ac9Flzc1w8cVWXaEbq22ozrMFzmXn7UsN36GUpQylruM9zm1xLprbHPzoii8vN2mSrTt7tl1OnJg4zz7V3Pvycguek5W4i5coUM7g1PAi0nMpeM5h3vv32l8r9zjnJgJJuw2HDRvWja2RLpVOT1xVFfzud4nviy+Z2dTULYMH1fMctfCgC1n9wQZgDACrGNRyX7sBtHOwxx6wbFn7T1RQACedZCk64R9dkydH3+9rr419zMyZmTkTkU7wm2xdVeMQyXsKniXjvPfTgenJ7h87dqyKi/d04TznVIKR6dPh0kutJF0O0YDByD449Bss+2BHDmE+AJ/Qn6N4NvVe5+JiOPVU+Pvf4dNPYf16+0HUpw+ceCI89pi994WFcMstdlbhiSeS/+gaPz7a4xzczpR0gl8FyiKSgILnHsI5VwgMAvomut97v6J7WyR5K1Ge8+TJba9/ySWtp0luS1FRdEKNbpKPdZ6DHw+rln/GAPpSw0hKWcoI3kovXaOhITan2bno7JMHHmiTn8yYYfcF6Tht/egKepmDnOeJE1UiTkRyhoLnHOecGw38FjiSJIEzNmBQ76V0j3TznCsrYyfQiLfFFhZYNzS0nmmui+V7nefg9VezmtUMopSllDGfWoamvpEgUI5f5py9nwMH2rJ77rGBgnfcYWkbV15pP7rCk5xANEAOAug//xnuvx9efDE2RzpTswqG1wk/vwJ0EUlCAVcOc86NBOZGbj4JnAS8BnyEJSYOAp4F1Oss3SfdigMVFVZ2LFzDN2zq1NZTL3eTfE/bqK2Fhb99lNWMBCzPuZoxDE2l13m//WDxYvvhU1gYe2bhjDMshaOpyeo3n3NOtEwc2KDRRx+Fm2+2+zdvtm04Z9spLobLLovtzQ6kWiIulUow4XXin181nEUkCQXPue3nQBFwgPd+kXOuGXjIe/9L59xWwE3AV4ktadclnHPfiFwN+uZOcM6tAlZ57+d09fNLDkm34kCw/uWXw8svx9635ZbRHkYFKt1q/nzwry1k9Z/+TilLmcvB6VXYWLjQLr23v1NOgQ0bLM1izZpo2kYwg2RBQewZiIYGS8sIzmIE93lvyx58sPVzBhOmpFIiLpUzJOF14p9fNZxFJAkFz7mtAvi3935RaJkD8N5/4ZybBCwErqPrA+h/xt2+NXI5B2un5JOODKR69dXWyzZsgKuuguuvz0y70pTPaRsffwzHv3wbNL8OwCG8SFlkwGBKggGBQUAbTrWpqoo9OzFhAuy/v5UgDCbEKSqyQDsoHRff83vaabE9z4WFcOGFtq1Ujr1UzpCE14l/ftVwFpEkFDzntkFAuPZTI7BlcMN73+icexY4tasb4r137a8lEqeqygaKPf548kobDz6YteA5X9M25s+HV259hcH/sjMBNYxkEKupZSglxWvg4IPtvYt/z84809IxmpstFeeyy2DBgtiJTCDx2YnyckvPCQYOBkFwOGUHYh+z++6W81xSkn4efCpnSOLXiX9+EZEEnE+Ugyg5wTlXB/w/7/1lkdu1wBzv/Rmhdf4ATPTeb5WlZqZt7Nixft68edluhnS1qio44oj2y9NdeWVOBM8PPACnn27Xe2vwHMwiuPqEMwEoZSk1jGQUS9qfRdA5+PGPrQwd2AyAwXTbRUU2K2CqvcIiGeScq/bej812OyR/qOc5t70NDA/drgaOdc7t4L3/OJL3fDLwbjYaJ9KmGTPaDpyLiy0POkuBM+TnJClB4Bw2hI/af6D3lkZRUGDBcmNjNAWjvh6mTbNZA887r3UQHV/1It0qGArIRSSHKHjObbOBK51zW3nvvwBuB04EXnXOzcUG7+0C/DCLbRTpmM2bbZBZFuXT9Ny1tbDwgSWA9Tg/yvGUspRRLEmvpnMwCDD+rGUw0G7aNCtLF1SriK96MXVqtMJGKlUwVPlCRHJMQbYbIG26Azgf2ALAe/8f4PLI7fHADsD1WNUNkdwyYYKd6m9LZWW3NCWZkhLrbS4rg623jl7vrYHz6p/c2JKq0Z91DKWu/XSNeAUFFtAWF9v1wkLriQ7e6yCInjHD6jfPmBFb9SJcYSOoahEvUaUMEZEcoZ7nHOa9rwP+HrfsJufcn7DBhB97Ja1LLhs0CFatSnxfUVHWKxrk04DBwXMfZnDDawCpl6QL0jMADjvMBgyuWdN6cB1YkHz33bZ+nz6WwtHUZMF1n8hXTXFxbIWNVKpgqPKFiOQYBc89kPe+CVJJUhTJkqoqC3iCGr/xRoywU/s5dCq+t07PPX8+LHl4Gcx8nVKilTXanQjFOTj/fBg2rO1qFeHrEyZYQL1ihc0kGOREX3hh7HbamxQn3VriIiLdSMGziGRecKo+mQEDciIg6u11nmtr4YUXYMB//9uSrjGI1ezDovZ7nYP6zBCbNpEooA0P7gum3L7nntg6z/GBdiqT6+TAMSIiEk/Bcw5xzj3TwYd67/3RGW2MSGesXNn2/TmSE9Gb0zaC17buyVc45NlbqYlMwZ00cB41CvbcE9autanUzz/flrc3fXV4cF+fPtGSdV3Vc6wqHCKSZQqec0tFBx+nvGfJHVVV8J//JL+/sNBqO0uXCeo5D65byK6P3kKN3xMgcWWNggILeg8/PLZ286JFcM457U9fHR7c19QUW21j8uTMvjBV4RCRHKDgOYd471X9RHqWYAZBiJ6av+GG5PWdBwywwDpTAU8neyF7a9rGypVWz3kwSyHS4zyI1YnrOe+1F7zzDkyfHg2QwWo3Q/vTVweD+zZtssA6PrjOpERVOBQ8i0g3U/AsIh0TPyjw7rvhppvgX/9K/pgjjshs4NzJXsjemrbhy8ooBcqYTw0jOYpnk+c4f/aZ/dgJB85gwfKECdFBgMmmrw4G982YEa2w0VUVMlSFQ0RygIJnEemYysrYHuaghm98EBZwLrPpGuqFbKVl+m1GUspSqhnDJ/Rv+0EffGCXzsVOfHLkkdGc5hUrLDieMCFxKkb8ft9//+ggw0yfZZg6NVouL8/fbxHJDgXPPYBz7nTgAmB/YDtgPTZV95+99w9ks22SxyoqrA5w0PNcXAz77QezZyde/+STMxvsZKAXsjelbQS96MueeIdDIpU1SlnKocxNbQbBnXaKBtJg7+NVV1mwGj678OyzrafYhthBg+Ee6EwEu4lmKcx0cC4ikiIFzznMOeeAGcB3AAc0AauwCVKOBo5yzn3de/+d7LVSeoxMVykIBosFOc/772/TLieS6V7n4Pk7WdGhzbSN93pWVYfgR8CAhXMAmwiljPmpb+Cjj1r3Pj/4YOuzC0HQGg5mkw0srK+HSy+1ZZ0Z4Bc+y1BfD5dcYtvXoEERyQIFz7ltEnAm1st8FTDHe9/knCsEjgB+C3zbOfec9/72LLZTcl1XVSkI1+KdMgU2bky83mGHdU2A01W1gOfNg9N7TlWH2lqoe2IhNfdVwzNPtdRzrmVoar3OYAHuYYfBc89Fl512Gvzf/0VnGQx6+ONTZoL7gp5n76OPaWzs/CDC8FmGgoJokK50HRHJAgXPue08YDlwuPe+JSqJzDD4jHPuCOB14HxAwbMk1x35wcnSJpyD3/42s8+VIUnTNmY/2aPyqVc+8RpcdDHUDwesssZgVqe+gYICC05/+1srUTdzpk2jPXo0/PGPth8KCmxAaLAfwikz8QMLFy2yHucgcA6239EBfuGzDAMH2hkODRoUkSxR8JzbRgHTwoFzmPd+o3NuFtZDLZJcd1QpKC+HLbZo3ft8++05G3gmTdvY+1hKiq/rEQHa/Pmw5L75lNbXU8pSHuV4SlnKUOpS73WeODFaarC83G6DnU0IAmCw3GVInjITXFZWWs9wEDgfcwxce23njoPwWYb2pvcWEelCCp5zm8dyndvS3v0iGckPTsnAgbGDznbeORqI9SRjx3bP/uqk2lrwry2k9LlplFFNNWM4gHnp5To7B8OG2fWLLrLL/fe3QHngQEvDaG62y/CPiHCgHL4NrX+sdTZwjqepu0UkixQ857Ya4DTn3E8T9T4757YATgGWdHfDpAfq6oBj+vTYwBlyvlhym9U2SnI7QKuthWeeAc67nlIaqGYMNYxkVLofB8XFFiSHa3ZDdObBoNfZx01k2lYefXf9WBMRyQLNaJfb7gKGAc855452zvUBcM4VOueOBJ4FdomsJ5I9V10FkxJkD51/fve3JQ21tZaqEaRrBNdra7PbrvYE9Zw5+0xKI2XpwKbfHsOC5A/ceWfYZRfrbQa7PPdc62WOnxWyudmWBWkbTU3RXmZInEcfVl5uNaEVOItIL6Oe59w2DTgMOAOYDTQ759YC22M/fBzwD1XakKyaPt2m5I53+OE9M2Ujx7VMhFK1LCZwbjfHuagIvva1aEnB8GC/4P5EPc+JpuQGzfYnInlLwXMO89574Ezn3L+xyhv7Y4Hzp8CrwF3e+79lsYki8Oc/t16WwxU2wnrqJCmD6xYy+HrLT044OHDrrW2QXthjj8EddySfuOTmm+Hii6P5zeefHw2sE6VfKDVDRPKUguceIBIgK0iW3FNVZTWR42V6NsEu0uYkKTmYrl1ba1XkBsyqprR+U0s956HhwHnatNaB8ZQp8K9/RVMs1qxpPc12UEnDewughw1rXUUjngbuiUgeUvAsIh0XlCQL64rZBKUl0F+3cAWHPHNLS+C8D4tie52///1omkUwiC+VFItcScPI9EyYIiIZpuA5hznnhmO1nud477+ILOsD/ByrsvEF8Dvv/UPZaqPkuYEDWy/rqtkEu0BPSdsIAudVc5aw64uPtOQ5twqcwYLf+Bn92kuxCALWROkc3amrZsIUEckgBc+57Rrg68COoWU/w4LnwD+cc4d571/s1paJgAVazkXLmBUU9Ihc50DCtI2FCxn65hxKvj42pwK3VXOWsPonN0JDPWCzCCZUXGxVMpyDdessZSMIhsvLLUCdMsXuW7AA9tvP8p1zIWDtjpkwRUQ6ScFzbisHnvbeNwI45wqAi4GlwHHAEOAp4H+Ab2erkZLHKiqsSkNDgwXOt97as4OdhQttopCGFfDHNTnV8zl47sMMbniNGkZyFM8mrqwxeDD86lfRqbFvuMHel7597bWA9exu2hT9wTN7dvQHULYD1lxJHRERaYOC59y2I/Be6PZ+wCDgF977D4APnHMPY+XsRLIjqBncp49Nm9yDtErbWPkfaHwJmptgc2FO9HzOnw9LHl4GM1+nFPiE/tQxFKB1AH3ooVb9pKkpGhw3N8fWYQ7SOsKcsyA72wGrKniISA+g4Dm3FWFTdAcOjdx+JrTsA4h8k4qkKlODsioro5NoNDbmRLCZjlZpG0NOhD7/ZqhfQUnxmqz3fM6fD0u+eQ28805LTecRvJW8pvOsWdHrQW9yfFBcXBzb8wzwox9B//65EbCqgoeI5DgFz7ntA2Cf0O2vAqu99zWhZTsA67u1VdKzZXJQVm87zb7PPnDbbfDmHMhyznNtLbxw6u8YsOKtmMlQEg4SjOccHHssjB/fegBg0LMb5DyPH6/JbERE0qDgObf9G/gf59yNwCbgWODuuHVGEpvaIdJauKc5k4Oyespp9iQ97QmrbZTtQ+xv1u4XzCK4bsVnHBI3/Xa7gTNY8Dx+vKXRhKfNDu8HsN7mRKk2XVEuLtjmwIHZreghItJJCp5z2w1YSborIrc/xCpwAOCc2wU4BPhDt7dMeo74nuapUzPbW5zrp9nb6GnPxUlSwtNv78rbMYHzGBaktpHmZrjkEigsjNZ8njo1Oi13nz6WttHU1PrsQ1eUiwu2WV9vbQsPYszlY0dEJIGCbDdAkvPefwyMxsrVfR0Y5b2vDa2yNRZY35mF5klPEd/TvCZSReK66/IjeEnU057jVlctgwdnttwexGqG8FF6G2lsjH3dM2fG3m5oSLxPumJ/zZhhedbBhDrxgxhFRHoQ9TznOO/9Rix9I9F9i4HF8cudcycDJ3vvz+vi5klPkCgvOdd7izOpjbzsXJskpbYWFj6wBKbcSGnjIh7lOA5gXup5zuFBgH36xPY8jx8Pzz+fuOc5fPYh03nsVVVw112xbcuFyh4iIh2k4Ll32g84B8hI8Oyc+xKWGnIs4LDa0pd771dkYvvSxXpKXnJXaeP151LaRhA4r777EUobFlLDXvRnXfLAubDQLr23Wtvnngv77w+vvmrLJ0ywy/DrDnKgg6A10TGR6eOlstKCdLAA/+ST4cAD8/NYFJFeQcGztMk5tyVWGq8eC8g98CvgWefcPsG04ZLj8qmnOZGe8PrnzWPw1T9gcH094CllaeuSdMXFlrcclJWD9oPc+MA4/nayx2Rqf8X3ZF95Ze6/FyIibVDwLO25ENgN2Mt7/xaAc24hsAyYBPxfFtsm0im5krYxfz4s+eMiqN+VUmqoYSSDWM1Q9xEMGmx56nvtBUuWtH5wrgei+X7mQ0R6HQXP0p6vAy8GgTOA9/5d59wLwMkoeJYeLBfSNmpr4YV732HAnMqYwHkft5iSfmvh4V4wqLMn9PyLiKRIwbO0Z2/g4QTLFwPf7Oa2iPQqLfWcX3+fQ3xNpCydY59xO1FyRIV6akVEcpCCZ2nP9sAnCZavBQZ0c1tEMiqbaRst9ZxPOJNdgRpGgCtkVPEySq75PwuanYs+IFytQkREskbBs6Qi0be2S7DM7nBuIpB0vt9hw4Zlok2STV0xA10WZDNtY+VKC5zDBvmPGPKPm6F8bGzgDK1L0YmISFYoeJb2fIL1PscbQOIeabz304HpyTY4duxYRQA9WVfMQJdn5s+HJQ8vA6CUpTzK8dF6zl8fm+XWiYhIWzTDoLRnMZb3HG8UkGDov/R6PXDGvmRKSixVI0jXCK53Za9zS+A8ZQqlLKWGkbH1nIcObd3rHKiq6rqGpaOqCqZMyZ32iIh0I/U893DOuf2AYd77R0KLlwPPZegpHgFudM7t5r1/J/Kcw4FDgZ9k6DmkJwnX7S0shBUrLIjqgb3P3Z22UVsbCZwfnElp46LIAEE4lLnRes4rVyZ+cEGB9fhnu6dfZx5EJM+p57nn+wHwUHiB9/4e7/2RGdr+HVgw/rBz7mTn3Nex6hvvA9My9BzS05xzDpx0kvWQ3nGHBVPqhWxTdOrtKZQunkmN3xOAUSxhDAva30Bzc2709PeiMw8iIh2hnmdpk/f+C+fcUdj03PdiAwWfxqbn/jyrjZPuF+51dM4CunBQl6gHMocHF3ZXtY2Wyhr/eall6m0oYFS/txmzaUFqGykosJ7eYGbBbImfMTDb7RER6WYKnqVd3vsVwPhst0NyQLjXsaDA0jacSx5E5fgp/u5K2ygpsV7n0uem0TL1dvFaSp75K5x/PixdCltsAaeeCqtWwezZ0Qf/5jcwcKDNMpgLP0A0Y6CI5DkFzyKSuvhex6lT2w7qEp3iz7NgK+h1fuWxVXy1uTkyg+Aahn77WNsXiabcznWaMVBE8piCZxFJXXu9jvEpGjl+ir+r0zaCnu3VVcvYtfkdavxIBvER+xTWUHLRNZl9MhER6RYKnkUkPcl6HZOlaOTwKf7uSNtYNWcJTLkRGjYDnsGshqZGmDUr5/aHiIi0T8FzjnHOHZ7mQ4Z0SUNEkkk2AHDGDNi0yWbBC6do5Okp/qC6xuq7H6G0cRGPchwHMI+h1FlZugcfhOuvz3YzRUQkTQqec08liafDTsalub5IxyXrXa6qgrvvjk4fXViYcykaiXRp2sa8eQy++gcM3rwZfDMHMI/jCQ0EfOut6GQomnZbRKTHUPCce55DwbBkQ6Ie5fhlyQYAVlZCY6M9xjk477we0dvcVWkb8+fDkvtqYfPulPrF1DCKQWO+RO0uIyhZ9IQFzmHOKYAWEekhFDznGO99RbbbIHkoUY8ytF6WbABgRQX06WM1n4uLYcKELL2Q7Js/P1JAo7Q0EjiPZBAfs0/RF5R4D1/+cuvgGeCii+xywoTkP146IxPbyuGa3SIi3UXBc45xzg303q/JdjskzySbNS5+2eTJyQcABj2nPagHNdNpG7W10cpzpTee3zL99j4souSlujYeCdx+u13efTc8+6xdz1SN7EzU287xmt0iIt1FwXPu+cg59xw25fbDkQlKRLpWsh7lRMsSDQCsrLQg23u77CH1nDOZthHUcwYoLYWaDV8CbPrtEtoJnMPa+vHS0X2aiXrbqtktIgIoeM5F04CvA38Epjrn5gMPArO89zVZbZn0XslKyqVaZi7H6zl3tZbpt1dHAufI/9RRLGEMC9p+cHy+c3s/XjoiE+9Pnr/HIiIB53vQKdZ84pw7EJsS+xRgBDaIcBkwEwukX8le6zpn7Nixft68edluhmRaD8+HvegiuO22jj02vgcbYOghu1KyeXl0QbJBgcH026++areV8yySFudctfd+bLbbIflDwXMP4JwbBZwGnArsjwXStVhqx4PAc9775uy1MD0KniVXhIPeO++ECy6w6+mkbcyfD0seXgY1NZSeMJyaon0YNAj22QdKdusH9fXQt6/VwA5K04XpM1ikUxQ8S3cryHYDpH3e+yXe+19578uA4cAPgbeBi4CngY+dc3c5507KYjNF8k5tbSRwnjKF0pm/ombSVAZ9vMQC5xKik8Zs2mQPiA+UFTiLiPQ4Cp57GO/9Cu/91EhJu6HAROBF4AxgVhabJtLjlJRYhY2gykZwPZVe55YBgjU1lDa9Tk3zCGioZ58fHkvJJadaigNYb3PwBxYwB3+5oKoKpkyJtldERNqkAYM9mPd+tXNuBTAbOB34apabJNKjdLTaRssAwapllDYvtsCZoLJGLcyaBf/5DzQ0xD4w1yZDUfk5EZG0KXju+b4DTPDe3wT8I9uNEckXg+sWMvj6i6ChgVKaGEpdbEm6+MA5F6n8nIhI2hQ8i0je6sgkKS0zCD62nNKGBmqaRzCI1QxNp5ZzrlD5ORGRtCl4FpG8lW7aRkvgvCySruFKGeRWsU/ftygp+hw+AwoK4Otft9SNeLmUsgHJ63uLiEhSCp5FRFIQEzhf/11qNu8OhQXsc9Y+lFz082jg6VzPCJwDiWaMFBGRpBQ8i0jeCqdt/PGPydM2amsjgTNQuua/Fjj7JkY1LaGkdK/YwDlergbNIiLSIQqeRSRvhdM2vE+cthGs88kncMghULPsK+Cfsqm3/QJYd3RW2i4iItmh4DnHOOcmpPmQPbqkISJ5ZuutWy8LAudVq2DAAKipAX75SwucWWAr3XAD7L47TJzYnc0VEZEsUfCce/6CTb+dKpfm+iKSwBdftF4WHjS4ejWUlsJQno4tSQcwc6YFz97Hpm4oZUNEpNdR8Jx7ZqBgWKRbhHOevW+d89wySDBi1SrA7QSe2AB6/PjodQXMIiK9moLnHOO9/2622yCSL8I5z1tvHZvzvHJlaJBgKcydG+l5/uAVSr5UCM2RjUybppQNEZE8UpDtBoiI5Jr4wLmmxnKeWwYSNjVFe5gnTUpcZUNERHolBc8ikrdKSixVI0jXKCuzAPnjj+12EDgDjBoVN3FKfMCsAFpEJC8obSOHOOd+CPzJe7+pg48fA+zovX8ssy0T6Z3iZxh8/HFYtsyuH3II1PzpGXjlZUadPIIxZ41PvBEREckr6nnOLb8B3nbOXeWc2ymVBzgzzjn3EPAKsG+XtlCklxswAEaMAB58kNK7f8xRr9/EmF9/A6ZPj65UVZW19omISHYpeM4to4FXgSnAcufcHOfcr51zpzrnDnbO7eWc29c5d5Rz7mLn3AzgA+BR4FDgUuCG7DVfpGcJp218/rmVowMYPBhqHnmTVQyKrjxzpl1WVcHRR0NhYezGVGVDRCQvKHjOId77N733XwO+AvwDOAiYDPw/4AVgCTAfeBK4BTgLWA/8D7C79/42731zom2LSGu1tVD90wd53I1j7X0P8+7ZPwWsssagI/dhHxZFS9IF5egqK2HzZhs0WFgIv/mNAmcRkTyinOcc5L2fC8x1zn0POBwLpocBA4GNwMfAQqDSe784aw0V6enuu49Vv7mXZezBRvqxK8t59+yf0v+mX7PPT75KyZevtR7n8eOtqsakSfa4LbawALq4GCoqsvkKRESkmzmvHhPpZmPHjvXz5s3LdjNEYNw4amcvoo6hTOEqJnM9AEM/rG67sgZYj3NFBZSXd0tTRSQx51y1935sttsh+UM9zyKSt54Y+X1enj0XAE8zj3I8/VnHoSvjytIlMnly1zdQRERyjnKeRSR9VVUwZUqPrjpRWwtv7nEinHQKu7KcTxhAf9Yx4rE/MWRIaEXVbxYRkRD1POcw59xdKazWjA0arAH+7b2va2f9jrTjCuBIYCwwBPiF9/7aTD+P9BBBtYkg5/fpp3tc6kJtLSx8YAkDXnyLQ04dTs2/wFHAeB6i5LLZUFQEe+0Fs2Yl3kC2092qqmzgotJGRES6nYLn3PZdIPiWTtT95eOWNzjnfua9/12G23EhFqDPAr6X4W1LTxOuNrF5s93uQQFcEDiv/smNDGpcyaP/3B8YzgDWUMdQ6t6CodRRUjMr8QZyIXDu4T9eRER6MqVt5LbdgYeBNcDPgAqgNHL588jyh7CSdpOAj4DfOudOznA79vbeHwRcluHtSk9UUWFBW2Fhj602Mfidlyhteh18E7uynAOYx2BWZ7tZqUn040VERLqNep5z2ynAYcB+3vsPQ8vfAJ6LTJLyKvC8936qc+5xLH3jUizozgjVjpYY5eXW29kD0wbmz4clS4CBX6G06E5W1w9iEKvZh0U8zNcpY37bG8h2rzNEf7yoVJ6ISFYoeM5tE4F/xgXOLbz37zvn/hlZb2rk9r+BY7uzkZKHyst7VNAMkcD54WVQU0PpCcOpueovuF/+LwB1DAU81YwBImkbxA0fyIXAGXr0jxcRkd5AwXNuGw582s4664BdQ7eXA1t3TXNEeqaWwHnKFEqbXqdm1pdh8mRKi97BNzRmu3np64E/XkREegsFz7ltNdaL3FZB2eOw3OdAf9oIuJ1zx2DTe7dnjve+IoX1Ej3HRKw3PKFhw4Z1ZLMiHVJbG0nVqKmxwLl5BPhGRq15njGbX4J+/aC+njs5L3GPM+ROr7OIiGSdgufcNhP4vnPuPuBq7/2K4A7n3DBgCrAfcHPoMWXAsja2ORcbdNieDWm3NsJ7Px2Ynuz+sWPHKhKRblFbCwsX2vXSE4Zbj7NvZFTxMsaceRG1tVD3wiYAtn4AnrlxLqUsjQ2iFTiLiEiIgufc9r/YgMHvAN92zn2IVdTYEdgJKAQWRNbDOTcUaADuTbZB7/0GYGmXtlokBwSB8+rVUFoKNQf9HpoaGcVSxjx7i6U91EbXHzYMPrnpfvh+WXShAmcREYmj4DmHee/XO+cOAa4EzgF2A4Kch3eAGcAN3vtNkfXrgEOy0VaRXDR4sP1x8EGUNjVGe5SPOAI2b26ZgrvuiYWse+QT+o/+Esyrtj7nodDeDN0iIpJ/FDznOO99PXAdcJ1zbhtgW2C99/6z7mqDc24sNngxqAs+yjn3jcj1RyO92SI5o6UkHZFe58Y9GMRqhgapGA0NLeuWvFdFySVHs2pTBa9UHkTZN4/TYDwREUlKwXMPEgmYuy1oDrkU6/kOfDPyB1bpY3l3N0gkmVaBcw0MKlzHPk2LonnMRUUt69c+Mo+6+i+Db2J945ZU378UissZOpSWnmkREZGAgucewDm3JXAasD/RahrzgYe891909fN777+LTRUuktMSBc4A+6z4DyXDi21EQFGRTTASKC+HogcY3PAJuEIoK2u1XRERkYCC5xznnPsqcA+wPeBCd3ngD865c733/85K40RySEtJOmID51GjIj3I4YA5pOTrYyl59kaorOTRd75B2bkqpSgiIskpeM5hzrkxwINYVY37gWeAOmAocBRwBvD/nHOHeu+rs9ZQkSyLKUl3dhk1jARgVPX9jBnT/mPrisvhuHL6z4XqyP8kpW2IiEgiCp5z20+xHubDvPcvxt33F+fcn4BK4GpgfDe3TSQnzJ9vFeVWr44LnFnCmDLXbrm5kpJokLxqlbI2RESkbQXtryJZdBjwzwSBMwDe+5eA/xdZTyTv1NZabFxWZoEzQClLOYpnGcOC1DZSXAzOQUEBg59/sOsaKyIivYKC59y2HfB+O+uswMrXieSdhQstt7naWeBcw0hWMSj1DRQXR8vWec/Q31wK05NOjikiIqLgOcfVAge2s85YCOpvieSH2lp4/PHo7IFggfMolnA8sxNPre1c9C8QqvcM2ONmzuzi1ouISE+m4Dm3PQoc5Zz7iXOuMHyHc67AOfdD4JjIeiJ5obYW6upCOc5jzwRgEKtjUzXOPDN6PRwwh2+H6j23GK/hAyIikpwGDOa264BTgF8Dk5xzz2O9zEOAr2Cz/q0EfpWl9olkRVkZMDaa41zGfGoZGl3hzDPhvvvsenzgHLZ5czR1wzm4/XaYOLHrGi4iIj2egucc5r1f6Zw7FJgGHAvsErfKk8D3vPdK25C2VVVBZSVUVPTYqadra+3ymWdiUzUGsZpahlrKRXxljbYC50CS+s8iIiKJKHjOcd775cA459xO2AyD22EzDL7qvf8wm22THqKqCo4+OtrL+vTTHQ+gsxiE19VFqmqU2mU1cBTPRvOb09FO+ToREZFkFDz3EJFAWcGypK+y0gLnpia7rKzsWOCbySA8DUGPc824H8CaF2DIEKr//W9q7q1m6NmhWUxSDYi97xU98SIikh0KnnOIc+6uDj7Ue+/Pz2hjpPeoqLBgNwh6Kyo6tp1MBeFpqK2NpGn8+GuUrrEe5pqV23HUyQcy9OWXKfG1bW/A+9jUjSBwzsKPABER6R0UPOeW73bwcR5Q8CyJlZdbgNjZntZMBeEpqq21mf9KS6Hso0epZn/KmA9ASe2rkOrU2fE90ln4ESAiIr2Hgufcsmu2GyC9VHl55wPETAXhKWjpcY4MDKze8avUrNyOodQxlDoYNqzjG+/mHwEiItK7KHjOId7797LdBpE2ZSIIb0dMj3MZVFdDWd2/GbrzgZTUrrTAefnyjj9BN/4IEBGR3kfBs4jkjFY9ztU2/fbQoVDywcttPzidQYDd8CNARER6JwXPIpJ1QW9zqx7nskjg3F5+swYBiohIN9H03CKSVbW1sHCncVS7MqpdGSxc2NLjHATV7Uo0CFBERKQLKHgWkW4X1G7GOep2KmMwqyljvlXTOO9cyjZXcdRRKQbOEB0EWFioQYAiItKllLYhIt2ubqcyoI46xlDDSEpZSjVjAFjFIKispGRyGmkXGgQoIiLdRMGziHSLlhQM51jFcS3LS1naEkC3lKKruDb9J9AgQBER6QYKnkWky9XWwsKFUFcHMIbBrG65bxWDKGVpywQozJ2rIFhERHKWgmcR6TJBbnNdHQwebNUzYD7VjGkJlqsZY73N8TMBioiI5CAFzyLSJebPh4/HfpXVfgC4QkpfmUF1NTDPs2rsuJb1hn5YnfrAQBERkSxTtQ0RyZigp7nWlfBC2SUWOAOlfjE1B0wArG7zPh8+YT3N3itwFhGRHkXBs4hkxPz5lp5R60qYyaksYVTLfY9yPJ/4bVomPOlQwOxc9E9ERCRLlLYhIp3yV3c6I3mDJYzC3Xc/NRzJOvpzKHN5l+EA7Ma7HFnwPCUlf+rYk8QHzM4pR1pERLJCwbOIdMj8+UDZ/jzO93mb3VnPNux71pkA7MpyXmM0+7KIQaxmn4IllDS9n90Gi4iIZIDSNkQkbbW1sGQJPMoJfMjO1FLCviyynuZ778fh2ZdFHMWz7PPhEwqcRUSk11DPs4ikpLYWSnbrB/X13O1+xqyy6/iCMxjKSt5kT+ZwOHuzmBNHgZ/3V5yDkjEZeOJEOc5K2RARkSxR8CwibQpmBqwbXs7KhlIe5QQW+NGMnX87fO97zLkdRlHDkVRy3oe/pqQkNJugiIhIL6PgWUQSagma6+xyVcO2DGYzJ/AEJ/AEjzYfDyVQdtZoxo0bzahR32oJmBU4i4hIb6XgWSSfVVVBZSVUVLRMiR0OmgFqauxycNF6aLDrqxjErn0+hF1h991h5EgYk4kUjUS8j03dyOWUjQT7U0REehcFzyL5qqoKjj4aNm6klqGURKbIjg+aS0sjt++q4qjzd6Vk83Kqiw5mhxefALowaA7L5YA5EOzPzZuhuBiefloBtIhIL6RqGyL5qrKS2o39AahjKGATnNTUWK9zS9BcY7MClpZCSf274D1Dl1cxZkw3Bc49RWWlBc5NTXZZWZntFomISBdQz7O0yTm3J3AJcCSwG/AZ8Arwc+/9a9lsm3RMuGpGHWMooY5VDKIai4TDQfNRR9n1+Bxm5TQnUFFhPc5Bz3NFRbZbJCIiXUDBs7TnOCxwvgeYD/QHrgRecs4d6r2vzmLbpAPqhpdT0lBPLUOpYSQAg1ndcv+qVXD88XY9HCT3+oC5s/nK5eWWqqGcZxGRXk3Bs7TnAeBP3keTTp1zzwDLgR8AE7LULklRS9m4SHC4qmHbaC8zSwGoYSRH8SwlvpbqyM+hoZbJ0fuDZshcvnJ5uYJmEZFeTsGztMl7vzrBsk+dc28CO2WhSZKmujpg3jzqvvUjaGhgME0t961iEMf3nQMvVFNSZsuyEjRnu0pFonxlBcEiIpKAgmdJm3Nue+DLwN3ZbovEcS6mckagZPGTlDS+BM1NVBccQJmbD01NVBcdDJs2MbQ2uolu72nOhSoVylcWEZEUKXiWjrgZcMDURHc65yYCE5M9eNiwYV3TqnwXqYUcVM6oc2Uwz3IwqoecCH3+zVC/AoqK4Nnnoby8JWjOampGLvT6Kl9ZRERSpOA5zzjnjgGeTGHVOd77igSPnwx8Bzjfe/9Wogd676cD05NteOzYsT2gaG/PkWgq7BLqrPe5DKqroezcfWDkjRYc7n0slI+19XIhnzlXen2VrywiIilQ8Jx/5gKlKay3IX6Bc+57wG+An3nv78p0w6RjgklN6ogWXQ4GBIbTMYLgMBfi5Rjq9RURkR5EwXOe8d5vgEiJhTQ4584GbgV+773/dcYbJimpdSXWowwwd25LoFlSAiW+GpyjmjGUMb9nzMoXUK+viIj0EJphUNrlnDsVGxx4p/f+R9luT95yLjoTIEOpPuRSqu9eCFhqRnU11H7oLc85PFgw57qaRUREei71PEubnHOHA38DFgJ/cc4dHLq73nv/anZalicigwDjteQ0r/wP1fvsQ1lZB7ad7fJwIiIiPZCCZ2nPUUBfYH/ghbj73gOGd3eD8kak7BxEK2hAKJ+ZOkoSDK5Lqac5F8rDiYiI9EBK25A2ee+v9d67JH/Ds92+3q4uUrO5jPmWxwwt10vmzrRyc0Pb2UgiicrDiYiISLvU8yySQ2IGBCYyrxriUjQ6lNOcK+XhREREehgFzyK5wrmWcnMJ0zQ+rKYjncwJdXd5uHDudi5XAekp7RQRkaxR8CySRYl6mlsGAwLV83zHBgOmorvKw8UPenQuNwPTntJOERHJKgXPItnSXk9zW+kbIiIikhUKnkW6UZtTaUN0ghOwXs9aREREJIcoeBbpLi09zXXUzauFSA9zTE9z3IDAXjHBifc9I5e4p7RTRESySsGzSBerrYWSnaJBWQl1lIy1fNpqV5YfPc09JRDtKe0UEZGsUfAs0sUWLqQltxlCPc219M6e5u6kWRJFRKSbKXgW6QLh3ObBg2npXY7JaVag3DmZnCXxrLPgscdg7drosi23hC++yExbRUSk11DwLJJBQdC8cCHUhYplVM/zMLaMVQyyBZH0APU0d0KiWRI7EjxvtRVs2NB6+YYNdp8CaBERCVHwLJIBQdBcV2eXgwfTUp+5ujpy3VdTW0vv7XHu7sF2mZglcfjwxIFzoK37REQkLyl4FsmAoJe5pia6rLraLletii7rtT3N2ZhgJBOzJK5Y0fb9W27ZkZaJiEgvpuBZpJPmz48GzaWldllTA0cdZcFybW+toJELOjtL4rBh8N57ie9TzrOIiCRQkO0GiPRU8+db7/KSJbFB89ChdjvoZe61vc25pqoKpkyxy1QtXw677GI95bvsYr3lwZ8CZxERSUA9zyJpCvKbvY/mNUP0el4Gy9meYKQzlTeWL2+9TJOliIhIEgqeRdKUKL85uB7EXHkbQGdLpipvQHbyt0VEpMdQ8CySKueYz37UMArm3R+TqjFqFIwZ0/bDpQtlovKGiIhIChQ8i6RgvtsfzxhqGEkpS2FsGTX3VnPUUXZ/rwuce9rMfZmovCEiIpICBc8ibWjJb6YgOjMgkRkDS3tpekYmZ+7rTp2tvBHIdv62iIjkNAXPIm1oyW9mZMuy4HqvzW9uK384V3ukM90uBcwiIpKEgmeRBILazC31m+fdb6kajGQUSxjjX81e47pasvzhXO2R7qp2BdN2FxXBnDm58VpFRCTrVOdZJKS21v6eecZ6nWPqN39YTem8+3t34AzR/OHrrosNRBP1SOeCrmhXEDgDNDTAIYekVz9aRER6LfU8ixDNba6rs3rNpaV2WV2dp/WbE+UP52pFi4EDoaDAUi0y1a4gcA7rTPk7ERHpNRQ8i5C4dnN1dXTGwKFDbVleBdDxcrGiRVUVXH659ToXFMDUqYnblW5O9JZbtg6gc+XHgoiIZJWCZ8lrrXKbQ2kaRx1lQXNeB8zxMlXRIlOClI3mZhvBuWZN63WqquDII6M95s8+2/5r+OKLzOc8V1XBT34CS5fC+vVQXw/bbgtnnAETJuTWfhURkaQUPEveCVI0gtzm0tLWQTMoaO4RUkklmTHDAlWwyxkzUgtUv/ii/XVSLWlXVQWHHWY95GGffgq33w7Tp8PEidEguqrK2gmw//7w2GPwxhuw115w5ZUKtEVEskjBs+SdujoLjEtKlNvc43VHKklVlQ0YDIuvBQ1tT+NdWdk6cA5rbrYg+vbbYZtt4LPPEq9XUwOPPAK33WbBtoiIdDtV25C8EaRorFoF1a6MalcGY8tacptra5Xb3COVl8PkyckD5wkTrFfaObucMCG17U6fbo+JD5yhdeDcnnTypZMFzoHmZrj4YlX/EBHJEvU8S14Ip2gMPqGsZfkqBnH8WMfQD70C5t7svPPsMllucfyAwunTYdKkzD1/eTn06QONjZnZXlOTqn+IiGSJgmfJCzEpGtAy1XY1Y1rul14ofgKVRL3OiSZZmTmz/W2nM4339OmZC5wDAwdmdnsiIpISpW1Ir1Zba/nM1dV2u7rapteuxfIzhlKXxdblIOeif71BKhOoJFpn/Pi2txsEyt5H/9ry5z+n3/b2JKosIiIiXU49z9KrBQMDIToocOiH91OykwWHJdS1H/jki3QGwPUUqVTjSLROkA4RTt3ozL7o16/jj01GdadFRLJCPc+Sd0pKSL3HUHq2+KnGAaZMiR1sl2w68okTWx8nVVX2+IED7cdFMMK0LdOnw3//m9nXte22yncWEckS5xU8SDcbO3asnzdvXrc/b1DfWZJIlKrRmz4fEuU2pxOABo/fuDF2+ZAh0SkqEz0mUX3nzho8GD7+OLPbFOmhnHPV3vux2W6H5A/1PEubnHPbOOf+4Zx7yzn3hXNunXPuJefcWdluW7oUOLcjPlDubYHztdfaJClt5T+3JciNjrdyZeL1r7oKxo3LfOAMcO65md+miIikRDnP0p5ioBGYAiwH+gLfBu51zg323v8hi22TTOtNATNEZ+q76y6rdtHcDAUFyfOf2xLkRifqeY5/vhdfhAULOtn4NpxyStdtW0RE2qTgWdrkvV8DfCdu8aPOuT2B8wAFz5KbgjSLTZuiPwoKCuCYY6wXOt2c4fBshjfeCGvXxqZsVFXBkUdGpwLvSqrxLCKSNUrbkI5aAzRkuxEiSQVpFkHg7BwUFloZuo4GnsFshmvW2HbDuc4zZnRP4FxYqEobIiJZpJ5nSYlzzgGFwHbAeGAccH5WGyXSlnAJuoICS9loaoLLL4fRozvXc5utOtiFhXDrrep1FhHJIgXPkqpLgJsj1xuAH3jvZ2SxPSJtC6dZrFgBd9xhAXQwWLCjAWh3B87TplmwH54+XEREskbBc55xzh0DPJnCqnO89xWh238HXgQGAV8HbnbONXnvpyV4jonAxGQbHjZsWFptFumw8nL7q6qCe+6xtArnUpvaOhj8t3Kl5TZPmNC9gWtBAdx2m9WbBgXNIiI5QnWe84xzbksgleh1g/d+RRvb+QuWvrG99z6t3Ods1XmWPDd9Olx6qaVu9O2buM5zVRXccAO8+Sa88UZsmbniYuv9PeSQ1J7vzDNh1SrYbz/b3sMPt1/N5MAD4eWX7XphoU3cMnlyqq9QJC+pzrN0N/U85xnv/QZgaQY2NQ84B9gR+CAD25Oeqqqq8ykFwTYGDrTBeKluK9w7DPDEE1ZKLtHEJa++auXqvLd1Lr8czj/flgO88w7Mnp38uTZvtsGGQf50IsOH22X//nD//XZ99uzUpzqvqIB586Il9RYvjk0T2Worm9Vw7VrYsAFGjozNgQ6/FxB7PZispW9fq0AS7DtI3qsebG/dOrssKYErr4xdN/4547cZ34O/7bbwr3/Z6/ra12xfBe/7wIG232pqoLTUfoAEy+OPi7aOmenT4c9/tvaecAI89pjNkjRihP2gGTzYfjytXm1Tp198MVx/ffvvj4gI6nmWDnLO/RM4HhjovU8wc0Ry6nnuRTo7a194G/X10aAxWc9w/OPaKg0XX0buiCOgoRcWiCkoiE7/HbwXhYUWnDY22vX4yV2Kiuxxwb4LetXjg+L4Un8AffrAc89FA+Pwc3of3cfFxXDzzfD976dWhSTZD4xgefi4CF5romNm0SKYNCmlXRfjyisVQPdQ6nmW7qZSddIm59wk59zdzrkznXNHOOdOc849AHwD+FW6gbP0MkE5uI7O2hfeRtCbGx7Ul8rjkgnP/FdZaYFkLtt++449rrnZXl/4vWhoiL0eL7g/fDt+f8eX+gs0NkbXjX/O8HM1NMDMmW2/R2HJOnKC5eHjoq1jZubM1J4v3oMPduxxIpJ3FDxLexZhqRk3ArOxihuDgK9579VNk++CcnCFhR2btS+8jYLIx1GqMwAGj0smPPNfe+vmghNO6NjjnLPXF34viopir8cL7g/fjt/fwfbiq4v06RNdN/45w89VVGRpLu3t92D7yaqYBMvDx0Vbx8z48W0/XzKnndaxx4lI3lHahnQ7pW30Mj0l5zm87vLlsdNnH344fOlL8NJLlhu7YYMFiTvuaG15/327L5OToAweDNttZz3Or79uz9kR06ZFK3KkkvMc9r3v2aVynpWy0YMpbUO6m4Jn6XYKniUnTJ9up/jHj48Gn4Fx4+D552GHHWD9evjss86lfcR/zmaqVnRbebpBANmvn92eNy9xgK7vAOnhFDxLd1PwLN1OwbPknLPOst7QhgbruU5XYSGcdBLMmhVdFv/ZetVVllf71ludamqLcI9zoKoq9VJ6AX0HSA+n4Fm6m0rViUj+ueoq+P3vW6cxpOu446I5uPFpD+PGtV36Ll1nngn33dd6eTi14oYbMvd8IiKSkIJnEckvV13V8SDTOfvbfXebsTAcMI8aZbm6mZQoYA6nm4BN/BLUr06Xep1FRNKm4FlE8ks6JckKC22Am/eWIhGfX1xYmHzClI6KD2iDnuVrroktBTd7dtsTtoTtu6+Vcttrr9YD/kREJC0KnkUkv5x2WvKe53AwWloKS5bE3t+vX+YqbhQUwG23tc5bDgRVKu66K3mt5PjAOdFEI3PnKlgWEckgBc8ikl+C3uNwznNBAZxxRusUiYEDbSrsTEmWtxwv2ex+8YqK7DUUFMAVV1jZt86UDBQRkXYpeBaR/HP99YlLvE2fDj/6EXz+eefzgQsL4Yc/7Fj94GSz+4VdeSWccgpUVrL+yCP5eNAgGoK0jkznXotkQVFRETvssAPbbrtttpsiEkPBs4jkt+nTLZ949erMTOGdaIKWdAUz6G3ebJO1nHuu1WxuaLCg/PnnW3qX1++9Nx999BE7lZSwxRZb4DJVQ1oki7z3bNy4kQ8//BBAAbTkFAXPIpKfqqos9zlcm7kjEqViBIP8Fi+2mQlPOy29HujycpsBLzxz4223JVz1448/ZqeddmLLLbfs6CsQyTnOObbcckt22mknamtrFTxLTinIdgNERLpdkFPc0cB57lxLqfA+GjhXVcGUKdaTffTRcPXVNtX0W29ZkH7VVek9R3k5TJ7cbv5yQ0MDW2yxRcdeR474y1/+gnOu5a+wsJCddtqJb33rW7zxxhttrrvNNtuw7777csstt9AYd+Zg48aNbLfddjjneO211xI+9/r167nmmmsYNWoUW221FQMGDGD06NFMmjSJjz/+uGW9+vp6/vCHP7DvvvuyzTbbsO222zJy5EjOOeccli1b1ubru/baa7N2RqCyshLnHE899VRWnr+ztthii2g6kkiOUM+ziOSfIKc4FX372sC9tgTB+ObNVvEiUfm4Bx/sWP5zCnpLqsY///lPdt55Z5qamnj77be57rrrOProo1m8eDHbbbddwnXXr1/PP//5Ty677DI+/vhjfvnLX7as8+CDD7J+/XoAZsyYwe9///uYbTQ1NXHMMcewfPlyrrrqKvbbbz+++OILXn/9df72t79RW1vLDjvsAMAZZ5zB7NmzufLKKzn44INpamqipqaGf/7znyxZsoQRI0YkfV0XXHABxx9/fKZ2U17pLce29DLee/3pr1v/ysrKvEhWzZ3r/RZbeF9Y6H1RkfcDB3rfr5/3O+/s/YEHej9tWnrb+81vbFvgfUGBbTPaN21/V17ZJS9lyZIlXbLd7nT33Xd7wC9btixm+ZNPPukB/+ijj7a7bkVFhd9mm21ilh177LF+++239wcddJDfcccdfWNjY8z9Tz/9tAf8rFmzErarqanJe+/922+/7QE/derUNtfLRc8++6wH/JNPPpn2YxsbG31DQ0MXtCo97R3jwDyfA99t+sufP6VtiEj+CXKKr7sO5syxwYIbN8L771uOcrLay8kEA/wKC62n+pZb4De/sXzoPfawyhhd1OvcmwV5rqmctj/ggAP47LPPWlItPvzwQ55++mlOP/10LrjgAj766COeeOKJmMesjZQhHDJkSMJtFhQUpLVeMonSNv74xz9SWlrKFltswYABAxg7diwPPfRQy/3ee/7whz+w1157UVxczNChQ7n00ktbetIDjY2NXH/99YwaNYp+/foxePBgjj/+eJYuXRqz3oYNG7j00ksZNGgQgwcP5qyzzmLdunUx6zjn+OlPf8pvf/tbdt11V4qLi1m0aBEA9913H/vuuy/9+vVj0KBBnH322dTFDYwdPnw4Z511Fg888AClpaVstdVWjB07lv/+97+t9smcOXM4+uij2Wabbdhqq60YN24cr7/+epv7USRXKG1DRPJTeXnm6iEnGuAnaWtqaqKxsZGmpibeeecdrr76anbYYQcqKirafey7775LYWEhW2+9NQD33nsvzc3NTJgwgdLSUi677DLuuecevvrVr7Y8ZsyYMfTp04dJkyZxzTXXUFFRwYABA1pte+TIkWy77bb85Cc/oaGhgWOPPZYdd9yxw6/z/vvv54c//CH/+7//y2GHHcbGjRtZuHBhS5AO8NOf/pQpU6ZwySWXcNJJJ7FkyRJ+/vOf89prrzFnzpyWgP30009n1qxZXH755RxzzDFs2rSJ5557jrq6OkaOHNmyvR/84Ad87Wtf469//StvvPEGV155JYWFhdxzzz0xbfvLX/7Cbrvtxo033shWW21FSUkJ06dPZ9KkSXz7299mypQp1NbWcvXVV/PSSy8xf/78ln0O8Pzzz/PGG29w3XXX0a9fP37+85/zta99jeXLl9O/f38A/vOf/3DyySdz4okncl9kzMD111/PYYcdxsKFC/nSl77U4X0r0i2y3fWtv/z7U9qGSOZkLG1j7lxLP5k7NzPbS0OQihH/V1JS4l9++eWE6y5dutQ3NDT4tWvX+ttvv90XFBT4k08+uWW90tJSv9dee7XcPv30033fvn39J598ErO9O+64w2+11VYe8M45P2rUKP+jH/3If/jhhzHrPfLII37QoEEtbdttt938JZdc4mtqatp9fddcc423r1tzySWX+P333z/p+mvWrPF9+/b155xzTszye++91wP+4Ycf9t5H007++Mc/Jt1WkLYxYcKEmOWXXHKJ79u3r29ubm5ZBvihQ4f6DRs2tCxrbGz0O+ywg6+oqIh5/PPPP9/quXfZZRffv39/v3bt2pZlr7zyigf8/fff37Js991390cddVTM9j799FM/cOBA/4Mf/KDVa1Dahv5y7U9pGyIi+S4Y8Pjzn9tlVVVWmvHQQw/xyiuv8PLLLzNr1ixGjRrFV7/6VWoSTPoycuRIioqK2H777bn44os588wzueuuuwB4+eWXqamp4eyzz25Z/5xzzqG+vp5//OMfMdu54IILeP/997nvvvuYOHEizc3N3Hjjjey9994sXry4Zb2TTjqJ5cuX8+CDD3LZZZfRv39/br31Vvbff/+0K1kccMABLFiwgMsuu4ynnnqKDRs2xNz/4osvUl9fz1lnnRWz/PTTT6dPnz7MmTMHgNmzZ+Oc48ILL2z3OU888cSY26NHj6a+vp6PPvooZvnxxx8fU73ljTfe4OOPP+bMM8+MWe8rX/kKu+yyS0tbAuXl5TG996NHjwZgxYoVACxbtoy3336bM888k8bGxpa/LbfckvLycp577rl2X4tItil4FhHJd0H1kaYmu6yszEozvvzlLzN27FgOOOAATj75ZB555BG891x77bWt1g0C7aVLl/LFF18wY8YMtt9+e4CWVISTTjqJdevWsW7dOg444AAGDx7cKk0BYMCAAZx55pncfvvt1NTUMGvWrJYSdmFbbbUVp556KjfddBPV1dXMnTuXwsJCfvKTn6T1OidMmMBtt93GSy+9xLhx49h+++057bTTWL58ORDNsR46dGjM4/r06cPAgQNb7l+zZg3bb799SqUKg30T6Nu3LwCb4irJxD9nsraA5YCvjZu+vr3nCXLSzz//fIqKimL+/v3vf7NmzZp2X4tItinnWUQk34VnNCwutts5YIsttmC33XZj4cKFre778pe/zB577NFq+ebNm3nggQcA2HfffVvdv2rVKt56662Ejw2cfPLJ7LvvvixZsqTN9h188MEcd9xxPP744+29lBjOOSZNmsSkSZP45JNPmD17Nj/84Q/59re/zUsvvdQSgK5cuZK999675XGNjY2sWbOGgQMHAjBo0CDWrl3Lxo0bM1brO35gY7gt8VauXMnYsWPT2n7Q9ilTpnDMMce0ur+4uDit7Ylkg3qeRUTyXbj6yNNP58yAxw0bNvD2228zePDglB/zr3/9i7Vr13LNNdfw7LPPxvwFQfWMGTMAWL16daueV4AvvviC999/v6W39bPPPmtV5QJsgOOyZcsS9sqmasCAAXz729/mW9/6Vku1iYMPPpi+ffu2tDfw97//ncbGRo444ggAjjvuOLz33HnnnR1+/vbstdde7Ljjjq3aMnfuXN57772WtqSzveHDh7N48WLGjh3b6m+fffbJZPNFuoR6nkVEJLPVRzpowYIFrF69Gu89dXV13HLLLaxdu5bLLrss5W3cc889bL311vzoRz+KqQIR+MMf/sCMGTP4xS9+QWVlJRdddBHf/e53Oeyww+jfvz/vvfceN998M2vXruWKK64ALO/3yCOP5Oyzz+boo49mhx12oK6ujjvvvJPXX3+dW2+9Na3XOXHiRLbZZhvKy8vZYYcdePPNN7n33ns57rjjAOvtveKKK5gyZQpbbbVVS973z372M77yla+05C8feeSRjB8/niuuuIL333+fo446ioaGBp577jlOPPHElKqUtKewsJBf/vKXTJo0ibPOOouzzjqLDz/8kJ/+9KeMGDGCc889N63tOef405/+xMknn8zmzZv51re+xaBBg/joo4+YO3cuw4YNa9nvIrlKwbOIiOSEb37zmy3XBw8ezJe//GUef/xxxo0bl9LjV61axWOPPcaECRMSBs5gubYTJ05kzpw5HHzwwVxwwQU888wz3HPPPXzyySf079+fAw44gCeffJKjjjoKgD322IMf//jHPPX/27v3+Kmqev/jr7eCNy6CJqJ57yZyvKT4CzOFvCaWCKhpKFGRZp1z9JhmWEcx43jUSjqWafYrBBQ9hHjylomEl9QKTY8iapoQAioICXJT5HP+WHtgmO/M97uH72W+l/fz8ZjHwN5r7fnMmj3f+ey11157+nSmTZvGkiVL6Nq1KwcddBBTpkzhlFNOqep9Hn744fzqV79i4sSJvP322+y6666ceeaZXH755RvKjB07lp122okbbriB66+/nh133JERI0Zw5ZVXbjKv9G233cZVV13FzTffzLhx49h+++059NBDGTVqVFUx1efss89mu+2245prrmHw4MF07dqVQYMGcfXVV1ds5/oMGjSIhx9+mLFjxzJq1ChWr15N79696d+/P5///OebLG6z5qKIqHUM1sH069cvZs2aVeswzNqFOXPm0KdPn1qHYdZsGtrHJT0ZEdUNvjZrBI95NjMzMzPLycmzmZmZmVlOTp7NzMzMzHJy8mxmZmZmlpOTZzMzMzOznJw8m5mZmZnl5OTZzMzMzCwnJ89mZmZmZjk5eTYzMzMzy8nJs5mZmZlZTk6ezcys5saPH48kJPHSSy/VWT9z5swN66dPnw7AmDFjkMS6des2lFu+fDmXXXYZ++23H126dKFnz57sv//+nHPOObz55pubbKe+x8iRI3PHvmLFCi688EIGDhxI9+7dkcTMmTMb2yRm1kp1qnUAZmZmBd26dWPixIlcccUVmyyfMGEC3bp1Y8WKFRXrvv/++xxzzDHMnTuXiy++mIMOOoiVK1fy3HPPMXnyZBYuXMjBBx/M448/vqHOokWLGDp0KKNHj+akk07asHynnXbKHfNbb73FL3/5Sw4++GCOPfZY7rjjjiresZm1NU6erSqSzgBuBRZExG61jsfM2pehQ4cyadIkvve97yEJgNWrVzN16lSGDRvG+PHjK9Z96KGH+POf/8ydd97J4MGDNyw/6aSTuOSSS1i/fj1bbLEF/fv337Bu7ty5AOyzzz6bLK/GnnvuydKlSwGYPn26k2ezds7DNiw3ST2Aa4HXaxyKmbVTZ511FvPmzePRRx/dsGzatGm8//77DBs2rN66hQS2d+/eZddvsUV1P3mDBg3ikEMOqbN80aJFdOrUiXHjxgFsSPLNrGNw8mzVuBp4Bri/1oGYWdNbuLDWEaRe3COPPJKJEyduWDZhwgSGDBlC165d66178MEH06lTJ8455xymTZvGsmXLGhXLiBEjeOqpp3j++ec3WX7rrbcCcMYZZzRq+2bWNjl5tlwkHQ6cCXyj1rEAIG18mFmTWLSo1hEkI0aMYMqUKaxZs4ZFixYxffp0RowY0WC9ffbZh5/97Ge8/PLLDB06lB133JG+ffty0UUXsXAzjgwGDx5M9+7dN0nkASZOnMhxxx3HzjvvXPU2zaztc/JsDZLUGfg5cE1EvFzreOokzE6gzdqVU089lbVr13LXXXdxyy230Lt3b44++uhcdUeNGsX8+fOZNGkSZ599NuvXr+cHP/gBffv2Zfbs2VXFse222zJs2DBuueUWIgKAZ599lmeeeSZXMm9m7ZOTZ8vjYmBr4Mo8hSWdLWlWpcfixYubN1ozy23hQnjyyfSAjf+u5RCObt26cfLJJzNx4kQmTJjA8OHDqxqv3LNnT4YPH84NN9zAnDlzuPPOOzdMYVetESNGMH/+/A1Tz02cOJFu3bptckGimXUsnm2jg5F0DPBAjqIPRcRASR8GvgMMiYg1eV4jIn5O6qkuq1+/fpErWDNrdrvumh6QkuYy18fVxIgRIzjxxBNZv349kydPbtS2Bg8ezIEHHlhn7HIeAwYMYI899mDSpEkMGDCAyZMnc8opp7Dttts2KiYza7ucPHc8jwF9cpRblT3/FzADeCKbbQNgK0DZ/9dGxOqmDrJeEZsO1Qjn4mbtzbHHHstpp51Gjx496Nu3b646S5YsoWvXrmyzzTabLF+5ciXz58/ngAMOqDoOSQwfPpyf/vSnDBkyhNdee81DNsw6OCfPHUxErAJeqKLKfsCeQLnL1pcBPwbOb3xkVXLCbNbkdtml1hFstOWWW1bd4zxz5kzOPfdcRo4cyRFHHEGPHj2YN28e1113HUuXLuWCCy7YrFhGjBjBlVdeyde+9jV23313BgwYUKfMfffdx8qVK3n22WeBNOf0kiVL6NKlCyeccMJmva6ZtU5Onq0hpwPblCz7NnAIcCrwWotHZGbNojB8o63q378/o0aNYsaMGdx8880sW7aMHj16cOihh/LAAw9w1FFHbdZ29913X/r168esWbMYPXp02Xmdzz33XObNm7fh/2PGjAHS1HuFG7GYWfugcA+eVUnSeOCYzb3DYL9+/WLWrFlNG5RZBzVnzhz69MkzEsusbWpoH5f0ZET0a8GQrIPzbBtmZmZmZjl52IZVLSJG1joGMzMzs1pwz7OZWRvn4XfWXnnfttbIybOZWRvWuXNnVq9u2dkizVrK6tWr6dy5c63DMNuEk2czszasV69eLFiwgFWrVrmXztqNiGDVqlUsWLCAXr161Tocs014zLOZWRvWvXt3ABYuXMh7771X42jMmk7nzp3ZeeedN+zjZq2Fk2czszaue/fuTjDMzFqIh22YmZmZmeXk5NnMzMzMLCcnz2ZmZmZmOTl5NjMzMzPLycmzmZmZmVlOTp7NzMzMzHKSJ9W3liZpMTCvhV7uA8CSFnqtjspt3Pzcxs3PbdwymqOd94yInZp4m2YVOXm2dk3SrIjoV+s42jO3cfNzGzc/t3HLcDtbe+BhG2ZmZmZmOTl5NjMzMzPLycmzmZmZmVlOTp7NzMzMzHJy8mxmZmZmlpOTZzMzMzOznJw8W3v381oH0AG4jZuf27j5uY1bhtvZ2jzP82xmZmZmlpN7ns3MzMzMcnLybGZmZmaWk5NnazckXSDpLkmLJIWkMVXWP1nSXyStkTRP0nclbdlM4bZJkraQNFrS3KydnpE0LGfd8dnnUvoY18xht0qSdpf0a0lvS1ou6Q5Je+Ssu42ka7J9fbWkxyUd2dwxt0WNbOdy+2tIOqiZw25TJO0m6bpsP1yVtdFeOet6X7Y2x8mztSdfBXoBd1ZbUdLxwFTgz8AJwI+B7wL/0YTxtQdXAGOAn5Da6QlgiqRBOesvBg4reVzb9GG2bpK2A2YA+wJfBM4CPgL8XlKXHJv4/6T9/VLgs8Ai4H4ndZtqgnYGGE/dffalJg+2bfswcBqwDHikyrrel63N8QWD1m5I2iIi1kvqBLwHXB4RY3LW/QuwPCIGFC27lJRA7xERrzdHzG2JpF7AfOA/I+KyouUPAjtFxAEN1B8PHBMRuzVroG2ApPOAHwEfi4iXs2V7A38FvhURP6qn7oHA08CXI+JX2bJOwGzgxYg4qZnDbzMa085Z2QDGRsR3mz3YNqzwtzf79yjgJmDviJjbQD3vy9YmuefZ2o3CH+9qSdodOAiYVLJqItCZ1MNqcDywFXXbaRKwf5aUWD4nAU8UEjqAiHgV+AMwOEfd94Dbi+quA24Djpe0ddOH22Y1pp0tp83924v3ZWujnDybQd/s+bnihdmP7CpgvxaPqHXqC6wFXi5ZPjt7ztNOvSQtkbRO0kuSLu6g48r7UrK/ZWbTcDv2BV6NiFVl6m5FOoVuSWPaueBcSWuzsbwzJB3RdOF1eN6XrU3qVOsAzFqBHbLnZWXWLSta39HtAPwj6o71Wlq0vj5PA0+Sfhi3AYYAV5LGoI5qujDbhB0ov78tBXo2om5hvSWNaWdIZ1XuBhYCewIXATMkHRsRM5sqyA7M+7K1SU6erVWSdAzwQI6iD0XEwMa+XPZc7gIAlVnWLmxGG4tGtFFEjCtZdK+kd4DzJV0VEX/Ns512ZHPbslGfQwfUmH32rKL/PiLpf0g92d8HPtUEsXV03petTXLybK3VY0CfHOVKT/dtjvp6OXoUrW9vqm3jpUBPSSrpfe5ZtL5ak4HzgX6ki7g6ikpnNHpSvieu2FKg3FRrjfkc2qvGtHMdEbFC0j3AVxobmAHel62NcvJsrVI2Bu6FFnq5wpjdvsDjhYXZPKXbAc+3UBwtajPaeDawNfAhNh33XBg7ujntVF+vf3s2m41j7YvtR8PtOBsYImm7krGi+wHvUndMekfWmHaupFJvqVXP+7K1Sb5g0Dq8iPg78AwwvGTVmaQrwe9r8aBap9+SftDKtdNz2QWW1foCKRH5cyNja2t+A/SXtE9hQXawdni2rqG6nYFTi+p2Aj4P/C4i1jZ5tG1XY9q5DkndgROBPzZVgB2c92Vrk9zzbO2GpH7AXmw8KNxP0inZv+8t9Gxk8xLvGRHFV3JfAtwt6UbSUIKPk+Z4/rHneE4i4k1J1wKjJa0AniL9yB1FybRfpW0saU/S1H+3kXqTtiZdMDgSuDEiXmmp99FK3AT8M/A/kr5LOoC4gjSP9o2FQlm7vQJ8LyK+BxART0u6HRgnqTPwKnAusDd1D2w6us1uZ0kXAh8Dfs/GCwYvBHrjdq6j6G/tIdnzCZIWA4sj4iHvy9aeOHm29uSfSXcRKziVjT0aewNzs39vScm+HxH3Zn/8LyMldG+Q7i44tvnCbZO+A7wDnEdKIl4ETouIu0rKlbbxCtL4xYuBnUlJzBzgX4HrmznmViciVko6inR3xYmkoQAPAudHxDtFRUVqy9KzhF8i7ZvfJ43Lfwb4TEQ81cyhtymNbOcXSQd4Q4DtgeWk+aG/EhF/aoHw25opJf8vfK8fAgbifdnaEd9h0MzMzMwsJ495NjMzMzPLycmzmZmZmVlOTp7NzMzMzHJy8mxmZmZmlpOTZzMzMzOznJw8m5mZmZnl5OTZOiRJe0kKSeNrGMNHJb0r6aJaxWBtm6S5kubWOo7NIWl89h3cq9axWMMkXSdpmaQP1DoWs1pz8mxWOz8C3gJ+Wm6lpC6Szpc0Q9KbWaL9D0l/kjS2+JbD7VmWYM3cjHqF5CwkXV5PuS8Wlav6dcw6iLGkO4OOqXEcZjXn5NmsBiR9EjgRuK5w2/CS9f1Jdzi7FvgIcC/wA+BmYA3pTn0vSDq4xYJuu9YBX5a0ZYX1X83KtEVHZw+zZhURrwPjgXMk7VHjcMxqysmzWW18A1hPumXwJiTtC9wP7AJ8G9g7IkZGxCURcV5EHElKqO8EurdcyG3W3cBuwGdKV0jqAxwOlN5evE2IiFci4pVax2Edxs1AJ+DsWgdiVktOns1KSNpF0k+z8aTvSlos6Q5Jh1Qov72kcZJek7RG0guSLpC0T7lx1ZK6A6cAj0XE/DKbvI6UFF8VEVdFRJ1e0Yh4NSJOAx4v2m7F8a+SxmSxDCxZHpJmSuot6ReSFkh6X9LIPOuzMp+Q9GtJr2ftNV/SjZJ2LRPHzGybnSRdIumvktZmda6StFVR2ZGSIvvvgKKhFSFpTLn3WcEtwGpSD3OpwrJflKuYfbYXZUNnXivaH36TnR0oV+cISXdl5ddm7fKEpMtKyu0s6QeSXpS0MhuS82I23CTXkJxyn3mh3bLnT2dtvkLSckn3ZAcMuSj5oqTHsve9Jvus7pf0+TLlD5E0VWmY0VpJ8yRdL2mXKl5zZLaNv0lancX9B0lnVihf2Ke2knRp1oZrS793FeoW9u+dJf1S0hvZZ/GYpCOyMl0kXZO9l7WSZks6tcy2Ws2+Iul4SfdKWpJt95XsPfQoU/YASZOzfWltFvNTSn/TOheXjYg/AnNJZ3LUUPuatVedah2AWWsiaW/gUWBXYAYwGdgdOBU4UdKwiLi7qPw2WbmDgb+QErXtge8AR1R4mSOBrbLXKff6x5CGZlzdULwRsTbve6vHDsATwDvAHaQe8TfyrJf0JeAmYC3wG2A+qVd8FPA5Sf0j4u9lXvNWUvvcBywHBgHfAnoBX8rKPA1cDlwGzCOdMi6YWcX7+wcwBfiCpF0iYlEW+9bACOAh4KUKdfuQxno+DNwDLAP2AE4CTpD0uYj4baGwpM9k5ZaT2mMBqf36AF/P3g+StgP+AHwIeIDU8y1gT2Aw8Gvgb1W8x3I+m23rPuAGYD9SOx8qab+IWJJjG2OB0cCrwH8Db5POiBxK+k7cXigo6bPA1Ox9/Jr0mR0CnAsMlnR4RMzN8Zo/A54ntfkiYMcs7omSPhYR/16h3tQsrvtIZ2XezPFaAD1In8UK0vd9B+B04H5JhwE3ZsvuBjoDZwC3S5ofEU8UbadV7CuSLs3qLs1ifhM4ALgQGCTpsIhYnpU9APgjEFkMr5IO3D+cxfBd4L2S9voDMBzoCzyXp4HN2p2I8MOPDvcA9iL9YIwvWX5/tvw7Jcs/SRoX+xbQtWj5v2flJwMqWr47sLjCa/xntnxYmbjOytY9uhnvaS4wt8K6Mdl2B5Ysj+wxAehUpl7F9cBHgXeBl4EPlqw7CngfmFayfGa2vSeBHYqWd8m28z7Qu0wMMzejPcZndY8BPpX9e3TR+tOzZcNJyUKd1yEdCH2gzLZ3AxYCc0qWT822c2CZOh8o+vfnsnLXlim3FdBtcz9zYGS27XXA0SXrrszWfSvn9t8CXgO2a+D9dAWWZJ/fESXlLs5e83cVPp+9SpZ/qEKbPEhK5Er3tcI+9b/lPqsG3l9h/74B2KJoeeF7uJSUrG5TtO6IbF3pvl3zfQX4dFb2MaBHhf3i2qJlP8yWDS6z7Z7FbVK0/LyszteraWs//GhPDw/bMMtI2g04Dvg7Jb2+EfEYG3ulhhat+iKpJ3Z0RERR+fnAuAovVbjYZlGZdYXT269VGX5jvAtcGGWGhzSw/lxST9x5EbGgeEVEzCD1ZH1OUrcy27w4IpYWlV9J6rXfAui3eW+jsoh4FJgDjCo63fxVUu/g1HrqvR1lemgj4jVSj9++Kn/x1Ooydcr19JYr925ErKgUUxVui4gHS5b9PHv+f1Vs5z1SUryJkvczmNRDfHtEPFJS9IekJP/YCm1Vut06Y7gj4l3SrDSdqHyB5L9XaOOGrAIuioj1RctuJR189CTt32uKYnmE9H4OKomxNewr/5o9fzUi/lFSdjzpbM7wnNteVtImBa9nz75o0DosD9sw2+jj2fMjEVF6qhLS8Iwzs3ITlMYufwiYH+VPR9cZlpHZMXteVmZdIbGLMuuay9yIqO8Ud6X1h2XPAyQdWmZ9L2BLUg/1kyXrZpUpXxj/3bO+YBvhF6RE7ihJ80i9dNdFxJr6hm9KOpzU23YY6T1tVVLkg6QDLkgHAEOBP0q6Hfg98IcsgSr2EOk0/beVZky5l3Q6/OmIqJOobqamaONbgH8BZkuaQor78Yh4u6RcYdaXGaUbiIh1kh4mne35OBvbqqwswbyYlCTvAWxbUuSDFar+qb7t1uOl0oOViHhf0htAl4goN3xmAfCJ0oWtYF85jHSwc2q5cdlZPDtJ2jEi3iINuzkPuFPSr4HpWQz1XYRaOOj1fM/WYTl5Ntto++y5XI9w8fIe2XNhpos36hatd3mhl2ebMusWZs+7VajbHF7fzPWFg4CGbvLStXRBaa9YptCzXWlKucaaAPwHaTz2PNKByk31VZA0hNRruIY03vQVYCXpbMNAYABp7lsAIuKObOzvN4EvA+dk23mSdHbigazc8uwisstJY2KPzzaxRNL1wPcrHMBV4x+lC7JEFvK38b+R3vOXSTO/fBtYJ+le4JsR8XJWrtrvTlnZxW9/IiX3jwC/I42zfp+UfH+RovYu0dB+XEnpgUDBugbWbfL72Ur2lR2zuDa54LCMrsBbEfGn7MLI75AuYj4ri+FF4PKImFymbuFgpk5vtVlH4eTZbKPCD2XvCut3KSm3PHveuUL5SssLvbg7lllX6K3uJ2n7Mj189VlP3Z6ugh711Guol7vS+kJs20d2AVJrFhFLJE0DhpA+u8cjoqELnq4gDVvpFxFzildIupGUEJW+zj3APZK6kHonP0sa4nK3pI9HxPNZudeAr2TDSPYjjRP/BnApafhKpQvjWkzWs/lj4MeSepHGjp9Ouliwr6S+kS5arfa7U8kFpO/Fl7JhBhtIOoOUPFeKtSXP1pTTGvaVt0njlHfIG3REPA58NruA9hDSlI7/AtwqaXFETC+pUvi7lfeCTLN2x2OezTb6S/b8KUnlDiw/nT0/BalHiHSV+wdV/hbDn6rwOv+bPe9buiIiXiWdOt2Ghnt0CzNGFCwDdi6dXirT5OOISTNwQOVZRZrKepquN/omUu/fTjTQ65z5MPB8mWRoCyp/vkAaxx0RMyLiAlKP91bACWXKRUTMjojrgGOzxSfniK1FRcSbEXFHpCkSZ5CGLP1Ttrrw3RlYWi/7LhXa6qkGXubD2XO5ceh1ks9WpjXsK08APSX1rTb4iFgbEY9FxKVsHDs9uEzRwt+tp6t9DbP2wsmzWSbr3XmAdHr4/OJ1kj4BfIGUoE4rWjWB9D26snjeU0m7l26jyMzsuezcr6Ren+XAaEnfLJfIS9pD0m1sHHcM6XR3JzZO9VYoO5J0I5Cm9hPS+MprJX20TIxbZaeEG+st0uwlTeH3pIRgCHBbjvJzgY+oaM7q7HO+jNQDuAlJR0sqHaMLG89CrMrK/VOFA65NytWSpK2z96OS5Z1JF87CxjjvJI2FPUN15zQ+H9gHmB7lpy0sNjd7HljymseThtu0ZnOp/b5ybfZ8k8rPs96l+PNRmmd6+9JyFbZd0J80jObhMuvMOgQP2zDb1NdIF+NcI+k40kVXhXme15NOJxdfXHQ1qefndOBjkn5HGv95GunH5eSs3gYR8Vw2pvBoSVuWXvQTES9kycJU0i25z5P0IGk8dBfgQFIyHMBVRVWvIyXOP5N0NOnisANJ0+zdTTol3GSyOL8M/JJ0QdlvSfMldyZd6HUEabq+Oj3sVXoQOF3SXaQLD9cBD0dE1T/e2an931RR5VrSNGZ/kTSVdLBwOCkZuos0jVixHwJ7SZpJSqbeJZ0KP4o0zrqQsB8D/EjSY8ALpFPgu5ES+/XANVW+teawLeksyFxJfyTFvw2px7MP8JtCL2tEvJPtC1OAh7KLC/9Oeu/HkcYjn5PjNa8n7cNTsvZeQOrd/gxpnuk6N2ZpRWq+r0TEg5K+TZqS8K/Z2PRXSWOc9yT13j/KxrttfhM4Lovhb6S53PuSer2XsXF2FiDdCIY0U8uDVQ4pM2tfohXMl+eHHy39oMI8z9m6D5Ju1DCP9IO2hNSzdmiFbfUA/ouU3K4l/cB9k/QjE8C4MnUKc6WeUE+MXUkXbP2e9IP5HmlM45OkH8e9y9T5FClpX0Xqvb6HdIOEMVSe53lmPTE0OMcysD9pzt552ftfSrp5wo3AUSVlZ5LlsGW2MzJ7vZEly3uRpg57g9TjFcCYHJ/x+KzsMTnKlp3nuSiup0kXfy0hnXnYv1ybkg6aJgN/JSUiy7O2GAvsVFSuD/Aj0sHZ4qzd5pIuOPtkFfvxXCrP8zyyQp0GP9OsXGfSjWvuIyXCa7JYnyAdZG5Vps6hWfssJn13/k76Lu1az+ezV8nyT5KGhSwj3bjkUdJB6MByn319+1SO91ixLcq1bUOv2Vr2FdLfgf8m/U16N6v3dLadfkXljgN+RbopzdtZ3C+S/p7tWWa7Z2fv4+TNaW8//GgvD0XU+hoLs/ZJ0ldJPTdfi4gbS9Z1J12N/1hElBtXaGbWqkiaRTqo7xtNN6WiWZvjMc9mjVRhbOHupCvg15GGTGwi0sWGlwEnSWqOi/nMzJqMpJNJw0oudOJsHZ3HPJs13tTsIqonSXPr7kUaX7wdab7WBRXq3Uga8lFpSjszs9ZiW+DfIqJOZ4BZR+NhG2aNJOnrpJsLfIR0seA7pKm7fhIRd9QyNjMzM2taTp7NzMzMzHLymGczMzMzs5ycPJuZmZmZ5eTk2czMzMwsJyfPZmZmZmY5OXk2MzMzM8vJybOZmZmZWU7/Bx4QWIml2n+OAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "from astropy import constants as cs\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['mass_current']),\n", + " np.log10(iso1.secondaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['mass_current']),\n", + " np.log10(iso1.singles[\"L\"]/cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['mass_current']),\n", + " np.log10(iso2.points[\"L\"]/cs.L_sun), \"b+\",\n", + " label=\"MISTv1\", alpha =0.2)\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at solar\" +\n", + " \" metallicity and 1 billion years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which table is causing that line segment at around log10(L/L_sun)=-3?\n", + "Let's find out.\n", + "\n", + "Examining the primaries in the isochrone." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"]/cs.L_sun), \"r.\",\n", + " label=\"BPASS isochrone primaries\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which type of primary star, if any, is it that is causing this strange pattern that is a clump at the bottom of the plot? Let's look at the bad region at around star luminosity = about $10^{-3.2}$ solar luminosities" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "bad_line = iso1.primaries[np.where((np.log10(iso1.primaries[\"L\"]/cs.L_sun)>-3.30) &\n", + " (np.log10(iso1.primaries[\"L\"]/cs.L_sun)<-3.10))[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=827\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", + "
massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
3.22.3883827679197787e+239357.9775720837116611152.3331436118.422415669754056False0.87108101.0117.58935393393404618.04702115688909818.2538464638709217.90854971188045417.791389876956224
2.12.563916806126372e+239906.5856797357036112138.1834153498.521383684299636False0.9351101.0117.5011572325963118.0359291349433118.20320424607129417.91703055652441317.829877846949223
3.72.5608782352407135e+239896.6679683950226120764.4173571248.51964185367448False0.93399101.0117.5025255289210218.03598696065631718.2039344857888217.91675631658854717.829094645531217
4.02.6873048077097104e+2310324.0000981343755761713.5655811318.593080016498233False0.9801101.0117.4502739563048318.0376841821687318.17794383219821717.93210740579169717.86537052007446
2.72.498255942396719e+238423.508142379878344910.1355645738.099560917898952False0.65992101.0117.59875704649794817.89955213930641518.18676042848905517.7210302380163617.542402545101226
3.22.5750812053942883e+239943.1273082105566080491.1992163178.52777983871674False0.93917101.0117.49617761378822718.03575302078810318.2005633466861317.91807145018433217.832784033217216
4.02.696105790173383e+2310354.997538672285736640.8905518298.598288083146574False0.98331101.0117.4469948235373318.03812689757386418.17647592524186417.9334908599028317.86819550392281
3.22.3881902947661124e+239357.3958065972476611707.9865326158.422308768441553False0.87101101.0117.5894611510894418.04704132390912718.25391126763736717.9085477405120317.791353955364297
5.52.5294198888960805e+2313563.7569806171743238468.4039253569.20728513706791False1.27366101.0117.6686058787700118.54419507662386518.53631449645544318.51106347634993218.555369605326103
..........................................
5.52.5633619243366372e+2314006.5765771031643057244.4426305539.263093761022395False1.29075101.0117.68718146500876618.59066474832132718.56841911985710818.56451276240998318.619532313031126
2.32.70260097498769e+2310377.9840765477425718131.8371313888.602141571868392False0.98568101.0117.44460491792933318.03848105799850318.17542137114363517.9345385147032717.87030581577883
2.32.5256428030554265e+239782.886163984226220727.3109927068.49955526901657False0.92114101.0117.51879652766418.03701353766982518.21278255383032317.9139185559360917.820333676471293
3.52.4907660018164023e+239672.2831113291296319717.4863521788.479802314491101False0.90842101.0117.5356375742250718.03865661688515218.22222227033882517.91170632077516317.812211500870816
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "from astropy import constants as cs\n", + "from astropy import units as u\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot((clus_1.star_systems['mass_current']), np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\",label=\"Cluster_w_Binaries made from BPASS\", alpha =1)\n", + "plt.plot((clus_1.companions['mass_current']), np.log10(clus_1.companions[\"L\"]),\n", + " \"r.\", alpha = 1)\n", + "plt.plot((clus_2.star_systems['mass_current']), np.log10(clus_2.star_systems[\"L\"]), \"b.\",\n", + " label=\"Cluster made from MISTv.1\", alpha =0.1)\n", + "plt.plot((clus_2.companions['mass_current']), np.log10(clus_2.companions[\"L\"]), \"b.\",\n", + " label=\"Cluster made from MISTv.1\", alpha = 0.1)\n", + "plt.xlabel(\"Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"Mass-logL of Cluster at solar metallicity and 1 billion years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Where is that hook coming from?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=2>\n", + "\n", + "\n", + "\n", + "
5.0
101.0
" + ], + "text/plain": [ + "\n", + " 5.0\n", + "101.0" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(iso1.primaries[np.where(np.log10(iso1.primaries[\"L\"])<=25)]['phase'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK. It makes sense that our compact remnants are causing this. (Remember, there really is no significant fusion in the insides of neutron stars or white dwarves." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the mass-luminosity relationship of the Cluster_w_Binaries" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log10\n", + " \"\"\"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from astropy import constants as cs\n", + "from astropy import units as u\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['mass_current'], np.log10(clus_1.star_systems[\"L\"]), \"r.\",\n", + " label=\"Cluster_w_Binaries made from BPASS (primary stars and single stars)\",\n", + " alpha =1)\n", + "plt.xlabel(\"log Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"log-Mass-logL of Cluster at 0.1*solar metallicity and 1 billion years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log10\n", + " \"\"\"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "from astropy import constants as cs\n", + "from astropy import units as u\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(clus_1.companions['mass_current'], np.log10(clus_1.companions[\"L\"]), \"r.\",\n", + " label=\"Cluster_w_Binaries made from BPASS (companions)\", alpha =1)\n", + "plt.xlabel(\"log Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L Watts)\")\n", + "plt.title(\"log-Mass-logL of Cluster at 0.1 * solar metallicity\" +\n", + " \" and 1 billion years age\\n\" +\n", + " \"(Companion stars only)\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Aha, it looks like the companions are causing this! and these are probably the 101 phased stars. Otherwise I like the alignment" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uu5qOkUh1x2iyX4KJmFk+/kPzDeBEF9OJ1sxaATfE7RLtaN0zySGTrW8q0VqTRNdM1wTrou9JsnInek+j+wx1ziWqtWgMI4PltBSaL27D/3PlxzZ7ApjZ/fgPklix7+msBMer7j1Ndv2eig/wHnHOXRRXhl7UHDw0lvr8r9bG2mBZn5qlpNdy0BRSRdDc9mPgx2a2J/69vhyfWqMr/td1Kr7FjyqttmtAoiIkfaCeZavDZ1V9fBksByV5fGCwnJPk8UYXNJs/XIf9Un0fGuK7EWr/eVRe1BqO29iir+svzrnrGuiYdT0XdbU2WDZqDbdzbomZvQiMMbO98a1y+wFPptocnEqfvH/hawnOqKkvVGy7Mb7TKvgoOn67PfE1gwuqq452fjTXfKC3mQ1MsEn0C7pBRls2sD2D5Quu6iipg/HVreWcH033Ff619k9wvCMavISVRavZk9X6RWu5+iR4LFEC7Oh7UuWfK+jPkOj1vB8sm2QmCjPrREVux/h+JonsCXye4EOkFYlfT/R/oMpjQZNOonOZShnA9xWK19AfZLXxJb5Ja5glnrWgof5Xo9fIqOC810Vtr+VKnHPznHMP4s93GT7wjqrp/+h9oJuZ7ZtiWWulhrIlU6vPqkBNrzOZN4Pl8fHvX9A953B8P6f343dsSWp4H+r93Rio7edRc/Eh/odWbT7na7remvpcfIEP9PY3s7watq3ODmr+H7o3WI4LbuAHt6Wkxg/JoH/YLfj27ZeCX31VBKkvXo5Z9VCw/LmZ9YjZrjU+H1ErUqsZfAjfT+MPwb7R4+yCH7kT+1zNycJgWRi70sx2xaeBSWQC/rzcbtGOIX6fPlSkmmgs0aahZJ1To33EKjWlmtkQfD+IeFPxX/xHmln8F83VVO2PB/4HxVrgV2ZWZbCImbUys8Ik5asV82k4XsJ3RfiY1P5pFgIDY/+pg/fpV/h+TPEexzdl/DB4D2P3uZ26NaMvDJaFsSvNJ3euMhChqQT9jh7DN7HcGvuY+RkOfoT/sfho1b1r9TzT8NfWMHwH7UrMrLvVPL1iCf7D9bwg0I/uuzM+3U38MXdPEpR1wzcBxXa2X4OvKUn2f/SXYPnPRF8OZtbZzA6NX59MLcuWzMJgWRh37Oo+q2r6vEjI+STjr+G7KVwV9/Cvgc7AhNgBJubnNx4cXEfNUi3fh4b6blxI7T6PmoWgP+BjQL6Z/cLMEtWo7xF8RkfVdL0tpAnPRdD38F78D6D74iq4MLN2se9tNVZR84/9yfia7e/hB1zMcc69Wf0uFVKa8cI597vgjfgVPjP4VPwHZXRasyPx1ewlMftMNbM78VX9M83sP/g28tH46sZ38UPWa/LHYJ9TgU/M7H/4Dt5n4TtX3umcq2lQQjp8hE/1cHpwvt7Fn6vR+OAnUWfNO4HT8J2f9zI/5dZOVCTNPI2KpqaGVoyvibk2+LKL9iO72zn3Lb6fzlx8x+Ld8DkI+1KRK+vs2IM555yZXYLvH/GM+fxT8/Cj147FD5M/IW6fVWZ2JjAJeN/MJuObOXcEz1WA789RmzmSu8Z07m2D/9AdGhyrVVCO76U4SOcv+BxsH5vZM/ig5XD8h8iL+HyJsa9nvvmcaL/DX7tPUpEnb2d8LrX9a/FaoCJ34HVBgP0x/tx8Bx+0NtgIsjr4Kf7X+dVmdhC+1iaaJy8EXO2cW9AAz3MBPjXK78zsjOC+4T+DjscH7guT7eycW2pmj+Gbz2aY2Uv4vmAn4v/P4geHDAUmmdk0fD/IMNADf+23JSa4ds6VmdkHwIjgOebgayFecM596pybbD7X5u3A3ODzbAE+OO6Hr/l5l7j/jWqkXLZq1OWzqqbPi+pciQ/U/2Zmx+ATkh+Cr+2dg88LGqt3sM0i4vqwmtlp+M9FqOgqUGAVcwevdM79pIbyNITaXCMN9d1Yq8+jZuZq/P/rrcCF5gfiLcP3kd4b31fvXPz/BvjrcAlwjpltwQ+gcMCjQX/KdJyLX+Ov25OBOWb2X3wC7D74z6HrqbnZfzL+Nb2IH/y2DZ+R4u3oBsF36X34ROhQi1q86AFSvuFP/t1UZPTegu8k+jI+n1KiTN3n4C/aCL5T5Cz8P3GizPwLSTzjRQd858OZ+F9EkeCY5ybYtj/V5Kmq4fVdRJLcT1Q/q0PC58R/kd8bvK5N+Kbn3+GD1GSvtSs+3UQY3/H5C3yT4sHBc9wVt/3Dwfr+qZa5muc+Af/hXUZFTqT+MY/3wXfQXh28Dx/hk11Wd25iZ7yIkNqMF/3xaVbmBudtXXAeHgVOS/G9jL4nsbdN+DxY7+Ov4yOq2T/ZObqIigztK/EB6ZAaXs+F+GBsE36QxET8h9lM/LRrKV1nce/DY/gPvY34/6kb8EGsI8XcT/W55qspW1f8F9pc/PW7Fh/oH1+b85zC83QPnic6s8ba4H35LdApZrsigqw1cfu3x3+RRrPjz8Pn5KxyDvHNZ7/DB0LRAQmL8Z97oxMce0/8F8sq/A+UKucX34T0FP7/PJqdfwb+gzw/1XNU27JVcz7r8llV7edFDc/XB19zvzR4/YvwM27sXM3/cqIy3ELV//PYW62vrbrc6vI+0DDfjRdRi8+j+Gu7Fq+vkFp+/9X0nPjWwavxAX80j+bX+MDnWuJm28EHfpODbaP/V4Uxj9f7XNT2vOE/L67Gt3RFZ2uZix8wsWcKx90V3+KzDP9jMNk57hY8vin+vNR0s+AA0syZ2WX4C+cHLkn+NGk5zKfMWYZPO1BQ0/YiIpKdgm5Kb+ITfac6yAtIbeCFNKEk/XT64PsfbsNPgyQthJn1MJ/2JHZdG/ycoB3wvzZFRESSiY5wv6e2O6bUJ0+a1DNBUDAN3wTVH9/fqhM+11KT5QeTBnEGcKuZvYFPTr0zvg/rIHzTwt3pK5qIiDRHQZ/r7+C7PI0G/uuc+6C2x1GQ1/w8iu/DdQZ+0EUZfpDDPc65Z9NZMKmTD/D9bo6kIgnoAnzfsTucc6mMfhQRkexyIL6f5zrgafyApVpTnzwRERGRDKQ+eSIiIiIZSEGeiIiISAZSkCfSQpnZRWbmzOyilvDcZvZwsE//mHX9g3UP13VbERFJTEGeSJYws65mdr2ZPWZmn5vZtiBoOjbdZRMRkYan0bUi2aM/FXOzLsZnhe/ZhM9/E/B7/EwddbEEP+tOTdNmiYgIqskTySaL8PMGd3fO9cFPN9dknHNLnXNfOOe21nH/rcH+Sxu6bCIimUhBnkgdmdmBZvZXM/vEzFab2SYzm2tmfzKzbgm2L+/HZmYjzazIzCJmts7MXjKzvZM8z55m9rSZrTGz9WY21cxOqm15nXNrnHOTnXOr6/J6kzGzk4IyrQ/K+B8zG5hguyr97Gr5PEn75JlZLzP7u5ktNLMtZrbCzJ41swMTbFun9yHBcU4IjvNQksfbm9nK4NY+7rFzzezN4HxtMrPZZvbz+O2CbU8zs4lmNic4x2VmNs3MfmRmVT7DY87zADP7oZl9amYbzawoeNzM7HvBe7YieP5vzOxVM/tuiq89z8x+aWbvmVlpcM7DZvZ4Ndexmdk1QVeBTWa2xMzuMbOdgvdtYZL9Uj5XIlKZmmtF6u4yYAzwFvAG0BoYDlwHjDazQ5xzkQT7fQc4FT95+X3APsCJwEFmto9zbmV0wyBYKsYnUn4ZP0vGnsBzwd/pdjo+G/skoAgYhk/kPdLMDnPOfdnYBTCz3fEJp/OAKcATQB/gLOAkMzvDOZdoOsCU34ckXgXmA981sx875+Kbkc/Av29/cs5tjinvg8D38U3mz+JntjkUuA04xsyOc85tiznO7/ETsn+Ab7LeCTga+Ct+0vZkc1n+FRgBvAT8Dz/BOfhE3Dfhk3I/hW/+7hUc6yzgyRpeN/jk3j/Fz6f5DD5p+0DgTOAUMzvcOfdJ3D5/B64Awvh5uLcApwAHA22BKjW8dThXIhLLOaebbrrV4Qb0A1onWH8J4IAb49ZfFKzfBhwT99jtwWM3xK1/LVh/Tdz6U4P1DriojuV/ONj/2Drse1HM838n7rFrgvWTkzxf/5h1/YN1D9dj21eD9T+LW39YcK5XATn1eR+qOQ8/Cba/OsFjRcFjgxI897NAx7jtb0nyXu+R4NitgEeC7Q9Jcu6WALsn2HcVPmjqlOCxXVJ83bsCoQTrh+IDvpfj1o8IyvQl0DVmfTvg7eCxhUmusZTPlW666Vb5puZakTpyzi1yzm1P8NBD+KloRiXZ9d/Ouclx68YHy4OjK8xsN+A4fI1LpYmpnXPP42sQ022Kq1pLdg++hutoM+vXmE8enKPjga+pGFQCgHNuKr5Wb2d8jWO8lN6HGvwL2ARcHleuvYCjgDedc3NiHroGH1x+31Wd0u42fAB2ftzrmB//pM65HfiaOkh+nd3pnFuQ5LGtVNTsxR63ptrL6HbLXYJaaudr76bga3Lbxjz0vWD5W+fc2pjtt+BrFROp9bkSkcrUXCtSR8GX2OXAOfimvp2o3M+1d5JdSxKs+yZYxvblOyBYvpskmCzCBxLpVCXQdM5tN7N3gT3wr2FRIz5/9By94xIP6JgCXBBsNyHusVTfh6Scc6vM7ClgbNA8PTV4aFywvC+6rZl1wtd0rQSuNbNEh9yMH0FMzH7dgevxTckDgM5x+yS7zj5Msv4x4IfALDN7Gv8eFruqzc3VCvqF/gDIB3ah6vfJLkB0kEz5tZzgUO/jg7nYY9fpXIlIZQryROruSXyfvK+A54FS/BcPwLVAso7ha+NXOOe2BV9krWNW7xQslyU5TmmtSts4airbTkkebyjR4ycbcRtd3zXBY2vjVyR5H2pyLzAWH/BPDQYEfA9Yju87GdUNMKAH8KtUDmxmXYGPgN3xQdsEYDU+KOqKr+1Kdp0luz5+jK9p/T6+X91PgW1m9j/g/5xz81Io14/wNYlrgNfxNakb8E2op+EDtNhyJb2Wgx8Fq+JW1/pciUhVCvJE6sDM8vEB3hvAibG1SMGIxxsa4GmiNSvJctnlNsBz1FdNZWvsnHbR4yc7F70auxzOuQ/MbDpwtpldix+I0h24I2iOjIqW4WPn3PAUD38pPsD7tXPultgHzKwAH+QlLVqS8m7HB2h/NbNdgSPwtdFnAfua2b4uZqBIPDNrA/waH0QOd3EpbYJyxVsXLHvifxTFbt8af75i8yfW5VyJSBz1yROpmz2D5QsJmgkPBjo2wHN8HCyPCL4I4xU2wHPUV5Xm4qCsRwR/fhz/eAOLPUeJfrSODJbTG7kc/wA64Gv0xuEDrH/GbuCcKwNm4QOpnVM8bvQ6eybBY/Vuqg/61j3rnDsb37S9B7BfDbvtgq9FnJogwMvBjzCPV/4+JXjsUOIqHOp4rkQkjoI8kbpZGCwLY1cGNSN/b4gncM4txjeF7Q5cHfc8p5L+/njgB1d8J27d1fhg4U3nXGP2x4s9R/3xTeTlzOwQ4Dx8k+KkxiwH8Di+9ukG/PvyeqIBE8Cf8SNKHwqaYisxs25mFhskLQyWhXHbHUDyAQtJmc/dd4zFdXIL+pdGg6kNNRxmebDNgUFQF3uMv+KDwHjR/pA/M7OdYvZpB/wuyfPU9lyJSBw114rUzUfAe8DpZjYV36G8J76p7kt8LrCGcBU+T95dZnY88Am+dmcM8CJwcm0OZmZ/pOJLOFqrcr2ZXRDcf84591wtDvkiMMnMJgHz8H2xTsT3G7uyNmWrhx/g34s/BOeohIo8eTuAixONBG1IzrkNZvYI8KNg1f1JtnvIfILmK4H5ZvYqvj/bzvhg/kj8iN0fBLtMwA+6uMvMRgJz8fnovoNPLZJS8uIYHfFdDBaa2Qf4QTEd8KO498bXTM+u4bXuMLO/4fvyfWZmz+ODsZHB63iTihrU6D5vmdl4fC3nLDN7Bj/C92R8cBzGv1ex+9T2XIlIHAV5InUQdBY/BfgNPqj5Eb5P0QPBus8b6Hnmmtmh+IS4x+JrdD7Fd27vQS2DPHyy2vi0JsfH3F9I5cECNXkWn3bkZ8BJ+C/uZ4Gb4lKHNBrn3FdBH8mf49+LQnwfsFfwKTs+aopy4FPn/Ag/2OOFZBs5564ys5fxwcmx+KbP1fgA5g/AxJhtw2Y2Av/+H4FPl/IFPvB5g9oHeeuBG/FB2GH46yiCH4hxRfAaUvELYAW+z+Dl+EDtdfx78Osk+1wRlP1y/Gtfha9hvRmfty9RqpiUz5WIVGXOJeybKyIitWBmF+Frln7jnPtFmovTYgSzuszB5y08N93lEckk6pMnIlJPwaCP6/CpTRI21WY7M8u1uLl2g3x4dwV/Nna/SZGso+ZaEZE6MrMj8AMtCoEhwD3BYBCp6lrgXDMrwjdp5wLHALvh5w9+Om0lE8lQCvJEROruWHyy3tX4lCkNkR8xU72OH5hzPH7wxDZ8M+3fgLuc+g6JNDj1yRMRERHJQOqTJyIiIpKB1FybIXbZZRfXv3//dBdDREQywLRp01Y653qkuxxSPwryMkT//v0pKSlJdzFERCQDmFmjzlYjTUPNtSIiIiIZSEGeiIiISAZSkCciIiKSgRTkiYiIiGQgBXkiIiIiGUhBnoiIiEgGUgoVERGpt3Xr1rF8+XK2bt2a7qJINdq2bcuuu+5Kly5d0l0UaQIK8tLAzAYBVwEjgQFABPgI+IVz7pN0lk1EpLbWrVvHsmXL6N27Nx07dsTM0l0kScA5x8aNG1myZAmAAr0soOba9DgeH+A9ApwMXAn0AD4wswPTWTARkdpavnw5vXv3plOnTgrwmjEzo1OnTvTu3Zvly5enuzjSBBTkpce/gaHOuT855950zk0CTgA2Atekt2giIrWzdetWOnbsmO5i1MvDDz+MmZXfWrduTe/evTn77LP58ssvk24XCoUYOnQo99xzD9u2bat0zI0bN7LTTjthZnzySeJGmnXr1vGrX/2KffbZh86dO9OtWzeGDBnC5ZdfXikQ27x5M3/5y18YOnQooVCILl26MHjwYL73ve8xd+7cWr3Wjh07qlk9S6i5Ng2ccysTrPvWzOYAvdNQJBGResmUGrynn36a3Xbbje3btzN//nxuu+02jjnmGGbNmsVOO+1UZbt169bx9NNP88Mf/pDly5dz6623lm/z7LPPsm7dOgAmTJjAn/70p0rPtX37do499lgWLlzIjTfeyLBhw1i/fj0zZ87kiSeeIBwOs+uuuwJw7rnn8tprr3HDDTdw6KGHsn37dmbPns3TTz/N559/zsCBA1N+jZnyXkkKnHO6NYMbsDOwHrinLvsfeOCBTkQkHT7//PN0F6He/vWvfznAzZ07t9L6119/3QHuf//7X7XbFRYWulAoVGndcccd53beeWd3yCGHuJ49e7pt27ZVenzy5MkOcM8991zCMm3fvt0559z8+fMd4O66665qt6uNmt4zoMQ1g+9G3ep3U3Nt83E3YMBdiR40s3FmVpLstmLFiiYtrIhINogOTqipefOggw4iEomUN7EuWbKEyZMnc84553DppZeybNkyXn311Ur7rF69GoDc3NyEx2zVqlWtthOJpyujAZjZsWbmUrgVJdn/JuA84Grn3LxE2zjnxjvn8pPdevTo0YivUESkCRQXw+23+2WabN++nW3btrF582Zmz57NzTffzK677kphYWG1+y1YsIDWrVuTk5MDwKOPPsqOHTsYO3YsZ599Nh06dOCRRx6ptM/w4cNp06YNl19+OZMmTWLNmjUJjz148GC6dOnCT3/6UyZOnMiyZcsa5LVK5lOfvIYxFdg7he02xK8wsx8AvwN+7px7qKELJiLSIhQXwzHHwJYt0K4dTJ4MBQVNXozBgwdX+jsvL4///ve/VdKNRIPBSCTCU089xbPPPsvJJ59Mp06dAN8Hb6+99uKQQw4B4LTTTmPSpEmsXbuWrl27AjBgwAD+8Y9/cO2113L66adjZuy9996ceOKJ/PjHPyYvLw+AnJwcJk6cyPe//30uvPDC8n1Hjx7N1VdfXaXMIlGqyWsAzrkNzrkvUrh9HbufmV0I3Av8yTn32/SUXkQaXTOooWr2iop8gLd9u18WFaWlGJMmTeKjjz7iww8/5LnnnmOfffbhxBNPZPbs2ZW2Gzx4MG3btmXnnXfmyiuv5Pzzz+ehh/zv9A8//JDZs2eXB2QA3/ve99i8eTNPPfVUpeNceumlfPPNN0ycOJFx48axY8cO/vjHP7Lvvvsya9as8u1OPvlkFi5cyLPPPssPf/hDunbtyr333ssBBxzAG2+80YhnRFq0dHcKzNYbMAbYBoxviONp4IVIMzV1qnMdOzrXurVfTp2a7hI1uAYZeJHm85RsQMWGDRtc9+7d3dlnn11pu0mTJrmPPvrIffHFF27jxo2V9rnyyisd4D755BO3Zs0at2bNGrdy5UrXo0cPd9hhh9VYlueee861atXKnXHGGdVuV1xc7Dp37uzq8vmvgRfZcVNzbRqY2ZHAE8CnwMNmdmjMw5udcx+np2Qi0uAS1VCloRmy2Sso8E20RUVQWNhszlHHjh0ZMGAAn376aaX1++23H3vuuWeV7bds2cK///1vAIYOHVrl8RUrVjBv3ryE+0adeuqpDB06lM8//7zash166KEcf/zxvPLKK6m8FMlCCvLS42igPXAA8F7cY4uA/k1dIBFpJIWFvo9ZtK9ZDR34s1pBQbMJ7qI2bNjA/Pnz2XfffVPa/sUXX2T16tX86le/qjJYY9myZZxzzjlMmDCBW2+9lZUrV5KTk0OHDh0qbbd+/Xq++eYb9t9/fwAikQjOuYT9AufOnUuvXr3q/gIloynISwPn3C3ALWkuhog0hWZaQyWJzZgxg5UrV+KcY+nSpdxzzz2sXr2aH/7whynt/8gjj5CTk8NPfvKT8pG2sf7yl78wYcIEfv3rX1NUVMQVV1zBRRddxIgRI+jatSuLFi3i7rvvZvXq1Vx33XUAfPnll4wcOZILL7yQY445hl133ZWlS5fywAMPMHPmTO69994GPQeSORTkiYg0tmZYQyWJnXXWWeX3e/TowX777ccrr7zCqFGjatx3xYoVvPzyy4wdOzZhgAdwySWXMG7cON566y0OPfRQLr30UqZMmcIjjzzCmjVr6Nq1KwcddBCvv/46Rx99NAB77rkn119/PW+88QaTJk0qrwEcNmwYTz/9NGeeeWbDvHjJOOb7V0pLl5+f70pKStJdDBHJQrNnz2bvvVPJIiXNRU3vmZlNc87lN2GRpBEohYqISF0pNYqINGNqrhURqYtmkrxXRCQZ1eSJiNRFM0neKyKSjII8EZG6iKZGad1aqVFEpFlSc62ISF0oNYqINHMK8kRE6kqpUUSkGVNzrYiIiEgGUpAnIiIikoEU5ImIiIhkIAV5IiIiIhlIQZ6IiGS9hx9+GDPDzJgzZ06Vx4uKisoff+ONNwC45ZZbMDO2bdtWvt26dev41a9+xT777EPnzp3p1q0bQ4YM4fLLL2f58uWVjlPd7aKLLkq57JFIhJ/85CcUFhbSpUsXzIwi5W0UNLpWRKTWIhEIhdJdCmkMoVCIRx99lNtuu63S+gkTJhAKhYhEIkn33b59O8ceeywLFy7kxhtvZNiwYaxfv56ZM2fyxBNPEA6HGT58OMUx0+AtXbqU008/nZtuuolTTjmlfH2PHj1SLvOqVat46KGHGD58OMcddxzPPvtsLV6xZDIFeSIitaQgL3OdfvrpTJw4kVtvvRUzA2Djxo0888wznHHGGTz88MNJ933rrbf46KOPeO655zj11FPL159yyincfPPN7Nixg1atWnHooYeWP7Zw4UIABgwYUGl9bfTr14/Vq1cD8MYbbyjIk3JqrhWRJlVNRYhI2q+PCy+8kEWLFvHuu++Wr5s0aRLbt2/njDPOqHbfaKCVm5ub8PFWrWr3lXviiSdy4IEHVlm/dOlS2rRpw1133QVQHoyKxFOQJyJNKt1f4nUViUA47G9Qcb+lvp7mKt3ns1+/fhx55JE8+uij5esmTJjAmDFjyMnJqXbf4cOH06ZNGy6//HImTZrEmjVr6lWWsWPHMn36dD7//PNK6x9//HEAzj333HodXzKfgjwRkRSEQpCX529QcV/Ntpln7NixPP3002zatImlS5fyxhtvMHbs2Br3GzBgAP/4xz+YN28ep59+Ot27d2fffffl+uuvJxz9dVALp556Kl26dKkUcAI8+uijHH/88fTs2bPWx5TsoiBPRBpdS64FawllbOma2/Vx1llnsXnzZl588UUee+wxcnNzOeaYY1La99JLL+Wbb75h4sSJjBs3jh07dvDHP/6Rfffdl1mzZtWqHB07duSMM87gsccewzkHwGeffcYnn3ySUtApoiBPRBpdS64FSxRotIRytyTN7foIhUKcdtppPProo0yYMIHzzz+/Vv3punXrxvnnn899993H7Nmzee6558pTq9TW2LFj+eabb8pTojz66KOEQqFKAztEklGQJyJSSwryMt/YsWN56aWX+Oyzz+pda3bqqacydOjQKn3rUnHUUUfRt29fJk6cyI4dO3jiiSc488wz6dixY73KJNlBKVREpEm1hAApEqlcgxdtRgyFWkb5W7Lmcn6PO+44zj77bLp27cq+++6b0j4rV64kJyeHDh06VFq/fv16vvnmG/bff/9al8PMOP/88/n73//OmDFjWLx4sZpqJWUK8kSkSTWXL/HqxAZz4XBFM6I0vuZyfbRu3ZonnniiVvsUFRVxxRVXcNFFFzFixAi6du3KokWLuPvuu1m9ejXXXXddncoyduxYbr/9dn7wgx/Qp08fjjrqqCrbvPzyy6xfv57PPvsM8Dn7Vq5cSefOnRk9enSdnldaPgV5IlIjJf8Vqdmhhx7KpZdeypQpU3jkkUdYs2YNXbt25aCDDuL111/n6KOPrtNxBw8eTH5+PiUlJdx0000J8+JdccUVLFq0qPzvW265BfApYaIJlyX7WHTEjrRs+fn5rqSkJN3FkAyVzbVZCnBrNnv2bPbee+90F0Nqoab3zMymOefym7BI0gg08EJEpBoK8ESkpVJzrYgkpMEHIiItm4I8EUlIgw+kNpxzmkO1hVA3reyh5loREamXtm3bsnHjxnQXQ1K0ceNG2rZtm+5iSBNQkCciNVLzrFRn1113ZcmSJWzYsEG1RM2Yc44NGzawZMkSdt1113QXR5qAmmtFpEYK8qQ6Xbp0ASAcDrN169Y0l0aq07ZtW3r27Fn+nklmU5AnImmXKE1JdNCHAsyWoUuXLgocRJoZNdeKSJOKRCpG6sauS7RdovUiIpIaBXki0qQiEQhmXhIRkUak5loRaVTRptjosqwMVq1KnIevrMzfYpWWQk4O9OqlplsRkdpQkCcijSoayE2dChs2wMaNsGaN/7tTJ9hjD/94fB6+aJNu3qJieLUICguhoKCpii0i0uIpyEsjM+sD/AU4DjDgDeBa59zXaS2YSAMLhaBHD1ixwgd54P+uUUkJnHMMbNkC7drB5MkK9EREUqQ+eWliZp2AKcBg4HvAhcBA4E0z65zOsonUV3RwxZw5vrn1nXd8gLdhA3TvDp9/Drm5MHCgr8FL1AwbCkFo+ls+wNu+3S+Lipr8tYiItFSqyUufy4ABwF7OuXkAZvYpMBe4HPhzGssmUi+xU6LNmeMDutJSv5w/H9q2rbxNsiCPUYfBne0qavIKC5vqJYiItHiqyUufU4D3owEegHNuAfAecGraSiXSwHJyKvrbffaZr80bOBDmzoXp06umU6mkoADuuguOOcYva9NUW1wMt9/ulyIiWUg1eemzL/B8gvWzgLOauCwijSZaS5eb65c5OX50bU6Ov1U7Yra4GK691tfkvfMODBmSWqBXXOwDQ/XlE5Esppq89NkZWJNg/WqgWxOXRaTRxDbJ5uRUrF+2rGq6lComTIBNm2rfJ6+oSH35RCTrqSYvvRLN5G2JNjSzccC4ZAfq27dvQ5VJpFFE++CFw37wRefONdTkFRfDv/4F0QnvW7dO2ievyrRohYW+Bk99+UQkiynIS581+Nq8eN1IUMPnnBsPjE92sPz8/EQBo0j6FBf7GrSY/HaRiB+A0amT36S01C9jB2GUKyqCbdv8fTP4/veTNrlWCfIKCnwTbdzzi4hkEwV56TML3y8v3j7A501cFpGGNX48XH21by5t357IC28S2ecQyspg/XpYudKPut1tN/93z55+t2pr48aOrV0ZCgoU3IlIVlOQlz4vAH80swHOua8AzKw/cDjw03QWrMklqPGRFqy4GK66qqIWbvNmQh9NIXTsIYBvoh0xAl591SdEzs2tOtsFUGNtXPm0aCUlUFxMuKAA8vMT1wqKiGQhBXnp80/gauB5M/s5vn/ebcA3wP3pLFiT0ijIzFNUBDt2VPydpC9dtMm2rkIhCM0shnOOIby5O3ntV+n6ERGJoSAvTZxz683saPy0Zo/iB1xMxk9rVtOYw8yRaBSkvqRbtsJCaN8eNm+GVq3gnnuq9MmbP9832a5c6WfCmD/fN9n26hVTC5fKD4Do9bND14+ISDwFeWkUzFF7RrrLkVYaBZl5qmlmDYVg+HB/PzYJcsLm2qIiHyju2OGXiQK44PoJbd6g60dEJI6CPEkvjYLMTA0x6KF794pm3x07/N+JnmfyZEK6fkREqlCQJ+mnUZBZqcbBER9/XOXvKqlSQNePiEgSmvFCpDmJn281g+dfjY6Crc1I2Eik8cojIpJpVJMnzVsDpFdJWPvTHMUPNLjrrop5W7Nx5PHYsfDQQ7B1K7RtW/s8eSIiWU5BnjSu+gRpsUFPmzZw8cX+i76Wx2kxQV78SONnnsnukccFBVBUROTVqUSGHwX98oGKARvKhyciUj0FedJ46psDLzbo2b4d7r8fHnkkc2u04kcan3EGvPNOdo88LiggVFBANJYLh5OMxBURkSoU5EnjqW8OvGjQs2mTn6TeuZSPUz4bQiAchrKyuDxs9dAotYOJRhoPGaKRxyIiUicK8qTx1DcHXjTomTDB983avj3l48Q25UVrf8LhhgvMGq0JOH6kqEaOVqLmWRGR1CnIk8bTEDnwokHO2LEZW6NVU8DYYvoUNgGdBxGR1JlzLt1lkAaQn5/vSkpK0l2MJpVK8BNtps3Jqby+Lp3245uA63Os+DJW188s2/uhKcgVaXpmNs05l5/uckj9qCZPWqxUv/wHDfLL+gZLiZqApXFFA2sFeSIitadkyNI0xo+HUaP8UohEfKAYTQcSvR+tKazp8WyxdGm6SyAi0nKpJk8a3/jxcPnl/v5rr/nluHF1OlSiUbNQUcsWGyRRUgLFxYQLCiC/YVsd6luzVFOtYLbXGkbf5wUL/ABj5cYTEak9BXnS+J55purfdQzyqgt+ooFBXh6EZhbDWYWEt+xCXruVDZ5IWIFG44kN5Hv0gNJSyM1VgCciUltqrpXGd8YZ1f/dQCo1ZU6Y4FO3EOTWmzChUZ6zIdQUuCiwERGRulBNnjS+aK3dM8/4AK+OtXjxosFPtOYnWuMTDgPvzCdEDiGafyc2BXmVxdbYlZbCwIHZdw5ERBqCgjxpGuPGNVhwFxUKVfTVAh/glZZC7gO/ITSrmBBl/oFWrTS5fQuVk6MAT0SkrhTkSYsX7ZcXDvtAL+/1RyAa4EU3yLAEytmiV690l0BEpOVSnzzJPKefXvnv885LTzmk3lSLJyJSd6rJkxYnWRqVcnfc4ZfPPusDvujfIiIiWUTTmmWIbJzWDKqmUcnGnHIiIg1N05plBtXkSYsXX5M3Z07FXLUK+JoXTVEmItJ01CdPWrRowJCXB3mLiin7033kfFGi4K6ZyrZp2URE0klBnrRo5bVC48fDiBGU/Xk8nHUWFBf79cXFcPvtFX+LiIhkCTXXSotUafBFSQlzLv8TZQxhEbvBFkfpn6aw4rAQpTddS+62MKF2fyE05XmlUkmDmuYbFhGRxqEgT1qk2ABhzhMvkEMkuJVRRg65nSIsmjab3G1h8nYshq2tG3z+WklNdfMNi4hI41FzrbR4OWsWk8dS8ljqAzyWk3fFqXQ6cB9o1w5at/bLwsJ0F1VERKTJqCZPWrxQ7y7l91ewCysOOYXP1hWwYXf47Lcv8Nlnn9LjyL0ZuN8w1DqYXmqeFRFpOgrypOU791zCDz8DO7bTg9Xk3nwyDIH582GPQ/Yl77p9011CCSjIExFpOmqulRYv9NUn5O1YTCk9gB3klU4nLw86d4ayshp3FxERyUgK8qR5qE+qk2eeAeBlTqj0d25uQxVORESk5VFzraRfcTEccwxs2eIHSEyeXKtRsJGTziHy2mfMYChn8SxzjriRnLCaBkVEJLupJk/Sr6jIB3jbt/tlUVHKu06fDi++1o4ijmQjHXmdY3n61RAffFCRm23OHJ+6Q7MtiIhINlGQJ+lXWFi/VCcflQCwlmCU7fvv06lTRT62nBx/XzV7IiKSTdRcK+lXUOCbaCdMqPWuubnACf1YP+FDvqULeYTptn0N3PRTwkN+T1mZD/JERESyjWrypPl45BH45z99/7zYARg1Dcq45lqgNdtpxUY6soZubPhkLp99BgsWQNnb0wnfdDfhF0rUZCsiIllDNXnSPCTql1dQkPKgjM67dqLL8nUM5ku+YC/6dS9j+CiguJjwaReQt3VRnQZ1iIiItFSqyUsTMzvTzJ4xs0VmttHMvjSz280sO3uOxffL697d195NmFDtoIxQyDfZ5vbYQRt2kMsy+vANObbBb1CPQR0iIiItmWry0ucnwNfAzcBi4ADgFmCkmR3mnNuRxrI1vWi/vKIiH+Bde60Pylq3hjbBZZpgUMbSpb5JltJd2EZrPmM/NtCRzssXwpgxMHo0oXabYavmrxURkeyiIC99TnbOrYj5+y0zWw08AhQCU9JSqnQqKPC322+vqH0DuOwy6NvXB2jJmlpHjmT7f1oD0ImN5LAennsOXn2V0F/vglWrqt9fREQkwyjIS5O4AC/qo2DZuynL0uxEm26j/fDGjk0anOXkQI8ewIkH0/o/a+jEer8+WLJpkw/wbrqpacreEIqLfY2mglIREakHBXnNy1HBcnZaS5FusU23NQQ6ZWWwYgXw9ud0JYcPOYhelNKTZUTIIWQboLCQSCS1PHmpbtdo6jn7h4iISJQGXjQTZtYbuBV4wzlXku7ypF1Bga99qyHAidbk9ThyH/qwlK/oT8eguXYpuXDssVBQUCl1SnVpVCKRNM+MoYEiIiLSQFST1wyYWQ7wPLANuDjJNuOAccmO0bdv38YpXDMXncliac5Q2p7WnbL3Z/JV6QL68TWL6EuvKe8QKi6GfhXBYk21dWmtzYtvqtZAERERqSMFeWlmZh2AF4ABwFHOucWJtnPOjQfGJztOfn6+a5wSNn/REba7DNmNrdta8cJ/jV4sAxxzt/WDqx4g598FzJnja/7KyirvHw5X/ru01C9DoTQEe7VoqhYREamOgrw0MrO2wDPAwcCxzrnP0lykFqmsDBYvhvbtYX0oj297G18s2YueLCOHMnI+fpnSa26Gs79Lzpy3ySkoIJyTD1QEcaEQRN4sofS1T8k9fn/IzS9vtg2Fmrh2LzrKWEREpB4U5KWJmbUCHgOOAU5yzr2f5iK1WMOHw/r1fiDtkiWwz5G9YFJH5m7ag/35hEHMo+yVp8l5fSJ5hAm37UfelInlM2pEXightHt7QtdeC5u7w4PtKh4PpH1AhoiISC0pyEufvwNnAb8F1pvZoTGPLU7WbCskTDHSsyfsuScMHAhbt0LP3m2JzN9OZzYQphfL2JWc7QsIsyulO7rBCx/Birbw3QtgyxbCZrBjZ3DbYAsV06rFUKAnIiItiYK89BkdLH8W3GL9Gj/7hcRLkmJk0CDo1ctv8sgjUPCzY1n2/WfJYQMhIuzBAgDyWEqo/TZCpxwERa/D1kWEd+xKXqtlRFrvRMitI9J25/K0K7EjbefO9VOopaWvnoiISC0pyEsT51z/dJehRUqUYiSmxm333X2t3oq8obDvXEpmHUh3VrE7CykjB4DQ5ZdU7NOuHWxuDe3bE7nlb4S2f0MoqCEMURHMhcM+wMvL83+rVk9ERJo7BXnSslSTYiQUgrn/+ZjdZi+nZMFO9PzyHdbQjQX0ZwMdOJYiQpTB8xPhrtvKR7KGXp0Kow7zaVbyKj9dtDavtNQHedGRuGVlCvJERKR5U5AnLUt1KUaKixl+1TEM3NyOGW4ob7vDuJinKeJINtKZpfT0Qd7ChTBoEDzyCJH9Coj0KyDaKhsN4qJNsrG3SKSiJi8+7YqIiEhzoyBPWp5kKUaCptzQjo3swTw+JJ9Sy6N7q3Us3t6TBQygF8t8oDd3Lhx2GKH77yc0zueYDocrgrh4oZDPxxcb3IXDvkYvJyf5fiIiIumiIE8yR2xTbpt2dD37JL7YPAJyctj00LPMYw86UcYwPvOBHsDll8OQISnlpevVq3Ifvbw81eiJiEjzpSBPMkdMU25eYSGj+xXwwQfw/l/eoy/QjTV8ylC+oQ+FvE0ewdQWY8fC3Lk19rFTHzwREWlJFORJZolpys0DxoyBXZZ2YcMH39BjS5hcllJKz8r7zJsHo0YRevXVlJ4iEoH58yumPwN/Pyencm2fiIhIOrVKdwFEGlvPY4ew4ftXs8J2BWvFCnrwGUMIk8sc9vAbvfYa7LabnwEjUv3xQiHYYw8YvrmY3KfvJndxCcOH+7EcCvBERKS5UJAnGW/QIOjX+htwO/jM7UMPVrGKnfmMIbzISUSC/HksWQKHH07kxt9UOUaVwK+kxCdlvvNO+O53fZJmERGRZkRBnmSF4efvy5D2c4BWfNF2Pwb/7CzICTGfAcwNavMi5IBz8I97iVz7i0r7R/PlhcPBYIviYsKbu1O2owNs3uxH9oqIiDQj6pMn2aGggLw3H4cXPuJlN5qXOw5k9+v2I3TrfyimgC8YDDjymU4O65n71/+Su6U9oV07Ehp1GGXdC8jLixldW1BAXvtVCZMyi4iINAfmnEt3GaQB5Ofnu5KSknQXo0V45x348EPo0AG+nvo1Q794GmbOZOMWxyDm0JkN5LKcEBEihKBDR0rvfZbcUUOBmMTIi4oTJ2UWEWnhzGyacy4/3eWQ+lFNnmSdESP8wAmA+5b1ZfD//R+5i0uYf+o1fIoP5PbnE3qyghzWE9q0nNJp08i7eGjlAwUjeSMR0HgLERFpbtQnT7JStOm1Z09Yvx5KV7Vlg3WhG2vowCbmsBclHADga/OeforwCyWEw74WL3YU7dKl9StLTaN5RURE6kJBnmStUAiOO84Heive/pwSN4w1dKMjG5nDnrzH4ZTRiRARcpbPJ+/Ug8j7641V0qSUldWvHAryRESkMSjIk6w2aJC/jRrXnyPbvs8mOrKmTU8OzQuTQxkl5DOVQ8tr9bjzTujfv/JIWyruK2BLoLgYbr9daWZERJqY+uSJABQUMOx/v2fO35dR2mMIa/vtTt/xv2fN191YQzfmsTsn8zJL6cmgRfOhTx+YMAny69YvOZqSJSoaLIZCGZZQubjY5xOMjkKePFmDVEREmoiCPJFA6NhDGL2Pb36dNQve7zeC7379JABfsxsz2J+V7MIyejDi2/cJnXoQ3HADpd+9g7y8qseL77tX6bligrlwmIT7Z4SiIh/gbd/ul0VFCvJERJqIgjyRGNFgKycH3u+7Gy+3/g5s30ZfFvM2I1hCL9qxmRG87ze8805yVnWHB26ocqzqgrxY9e3T16wVFvoavNh8gsVKPSMi0hQU5IkkCDry8uC71/Uj5ztjmPXfeTy3cgSjPvotu68u5lWOZRKn0IkyDuNDej34Gzi4K4wbV/lY/RTAUFDgm2ij5wTUfCsi0kQU5El2q6bP2PDhEBk4nF4nDeftX8KCEfew5B/PcfCSVwmTxywGs5jejGAqoauuAiByzc+JbGkP7R6AJ58kHPTZq66vXU5Ok7zS9AnyCQJ+AEZNzbeq6RMRaRAK8iS71dBnLBqYnX467LGmhM9WPMg8duMq7uNVjgNgAQPotW0ZoR//mNDmjYScg62tCRcXk3dK4oEZWTPwIl6i5ttYGqghItJgFORJdqsp6AiMGAGRW96CLZuYwf48zrkw/EAWTF/JOnJYRTfyN0xjEPP9Dm3awJIlPmhJEqRE+/81m4EXTVGDFt98G/88GqghItJgFORJdqsp6IgRGnUYo+78BRs2doQ27Xk95zh6h/5L38g3fML+fMFeHMB0xvASbN5M6NF74cm/JAxUkg7KSFdTZVPWoMU238ZLMegWEZGaKcgTqS7oiN9u8mTGFBVB9+5w9W/YuNXnEy/gfb5gL8L04VWOZhRTCBGBLcCECUT2K6g2nQqQ3qbK5lKDVougW0REqqcgT6Q2ogHh7bfTb+s8PmZ/Ssktf9jn0xsCwCimECGHUGlpef+7ZP3wgPQGWs2pBi3VoFtERKqlac1EUhE/NVdhIcM7fsEIism1FXxy/PV8whD6spgF7M5jnMPjnEmEEITDlL09nVDI973LW1QMd99N3qJi8vJigrxooNW6ddMHWtEatNtu02AHEZEMYc65dJdBGkB+fr4rKSlJdzEyU7Jm1Jj+c48vKODdp5ew/r3p5K74lK/YnbZsYV9mcTGPUUpPcoflwZAhhP79TyLbO5HXflXVgErpQ0SkGTCzac65us3bKM2GmmtFapKsGTWmWTG/OyxY0JsZbXozo7gPeyx5i6/pyxz2oogj6M5aBs54m9CM/wUH3ZK4SVZNlSIi0kDUXCtSkxSaUQcNgosv9g9t79mH9ezESN5mA514j8NZTC8ihAjTy/fTs/Xp7/smIiIZTTV5IjVJccRnXh5cdRWsXdudJX2PY/lnnRkz/xkW0J9SelFKD4bzqd94z4HwyCM11tqlOv+tiIhIPPXJyxDqk9fAiothwgR/f+zYWjWhhsM+HlywADp8/B77vfQH5m3qRR5h+rGwItDr0AH++lc/522yY71QQt6s19VHT0SalPrkZQYFeRlCQV4DKi72QdWWLf7v9u3hzTdrHeh98IFfdusGHd95hY33PUpHyjiWKYQoA/BNt+efChMnJixH+OgLyNu6SFN8iUiTUpCXGdQnT1qm+JQmDamoCLZurfg7OkCiFvLy4NhjYc89Yc0a6HTaCXQvHMJGOjKXPQgHufUihOCxx+DGG8v3jUR8cBh+oQS2bCG8fVfCm7sTeXVqA7w4ERHJFgrypOWJpjT5xS/8sqEDvcJCaNu24u86DpAIheCww3xN3rx5sOqyn9K9Rxu+YDCTOJXp7F+x8Z13EvnupeV/5uVB3in50K4dea2Xk9d+FaFRh1VKpiwiIlIdDbyQlqexZ4YoKPDHrGOfvFihEJx8MixdCmVlwCsT2f2HF/P6VGMR/QEr37bsqbcIrR1F5F+v+sEWBQXw5JMQ0ycvEtZADBERSY2CPGl5mmIKrgbMVxcK+SbYBQtg1SoYfPe/4A9vE/73E3zOYH7EvYQo8+lVXpsK554Lbz3h9x2ZD6f4bjFNWounpMwiIi2egjxpeVrgJPZ5eVBa6pttX34Z2O9IGBLm7c86Ao4jeZfObOQz9qPH219Qdsr/kXPfn8oDxEjE75+bW3nO23rV6iUL5JLN8CEiIi2KgrxmwsxeAUYBv3XO/Tzd5Wn2WuDMEMOHw8CBMHUqTJnwNX1nF3MIXYFWfEMfBvMlQ5gJQN6Lf4Yuy2DiRCIRHyRGxd6vs+oCucZuDhcRkSahgRfNgJmdCwxNdzmk8YVC0KMHtF+6kLXbOtGT5WymPZ+wP29yJGV0ppSevun2seeJ5I9k7tyK2rvSUn+/3k23iQK5qBRm+BARkeZPQV6amVlX4C/AdWkuijSR4cPh8O90o2vr9cxibw5vN50uQ/bgU4Ywi70ZyDzyWEqIMkLTisj9zdXltXe5uZC3qJjQPbcTeeODuheiukAu2hx+221qqhURacHUXJt+dwKznHNPmNnj6S6MNI1R1w2hR7eL+fqhDZTsfTL5Z+zOjJvm8PEXa9ll4wqGMdPn0AOY9CzhK3MpvfTn5MyZDt/3zayRtg8QmjKxPAir1RRoNfVrbIHN4SIiUpmCvDQysyOAsaipNisNv/gAFnWFt9+G55+HHocOYumB1/PIs7vzzeqnOJmXy0fd5v3jF4S+LCFy8DEVzayucn+5aBNurQI9BXIiIhlLQV6amFlb4H7gj865L1PYfhyQdJLTvn37NmDppKnsuy+sXu372q1b59dFvnsmC+77mJB7GoBSdvUPTPmQshVtmN7mINixFdq0gX2Pg5jceZEIhGYq/YmIiCjIS6cbgY7Ab1PZ2Dk3Hhif7PH8/HxNQtwCDRrkly++6KfHHTnSB2qv5V1I1yUrGcML5LCePJYCEP5sKoMO7gOnncH03BMJjRxanmIFoPTVT+DKCwhtWQXt/kJoyvM+iXJtmnJFRCQjaOBFGphZX+BnwC+A9mbWNRiAQczfrdNWQGlSgwb5WTF694aFC4P8d8MGM2O/71FEIWV0rrzDhx/Co4/C0KGVcuWVlkLunLd9f70dnVi6eefyUbOaDk1EJPsoyEuPAUAHYCKwJuYG8JPg/pD0FE3SYdAgGDXKT5n71VfQqxesHXwY7x35UxbRhzC9CNOLMjr5+7PXknPRmeXpVEIhP/KWggLy2q8ir/VyctpvVfoTEZEspuba9JgBjEyw/k184PcgMK8pCyTpd/LJfvnuu/D11zB6NHz9ZBmfsw/7MptBzCdCDiHKAN90G/rxpYSefKD8GEsH5xP+99tQXEzZ0MOZ3n44TIecnAacKUNERFoEBXlp4JxbCxTFrzczgEXOuSqPSTPSSPO6hkI+0OveHR4PkukMHbkLC6a2Y8H23enFMiKEyoM8gMhT/yPU/0a4447ywC00yM93Gw7DoCC/XjjcQDNliIhIi6EgT6Q2Gnle11AIhgyB/feHtWthz8P2YunlFzNz/gh2jzxOztTXKrYl4nPp3XmnXxET6NVacTFMmODvjx2rUbkiIhlAQV4z4pyzdJdBatAE87rm5flRtm++CSUlsM+xA3jdDWDiLsdz+MYbKfv4WcrIIYcyclhPmF5w56OEFq6q1HQbG/BVCf5iayPBL7ds8ff/9S//5Ar0RERaNAV5IrURnQ4sWpPXSAMbhg/3yzffhM8/hyOOgE8+gfe+cwfs1IVRRT8H8ImSg/QqPPUgHHMwjPPpFJMGebG1ka1bw7BhFQEeNFrwKiIiTUuja0VqownndR04EA4+GLp2hTVroG9f2LwZ5p35M8JX3Jp4pyuvhAsuqP7A8bWRH31U+fFGDF5rpbgYbr/dL0VEpNZUkydSW000HVgoBD17+vuzZvnZMbZs8feX7fILrj96GqEpkyvvtH07PPaYvz9xYuIDR2sjN20C5/ytVSvIz/dViM2hT14j930UEckGqskTacYGDYLjjvMBX1GRj3dCId+Me+Nez7F0yPEARMipvONjjyWvBYvWRl5+ObRv75ts27eHu+6Cf/yjeQRTifo+iohIragmT6SZGzQIrrkGJk3yI267dvUJkNevh7KHnyGy+gMix51enlqlPJfezTdDx46Ja8GitZFjx0JREZGDjiZUcEjzmf6sifo+iohkMtXkibQAeXnQoYNvqi0q8oHYV1/52c2eWnQIHHlk+bYRYqK0jRsrasFi+riVT3NWUAA33URkn0P8vs1l+rMm7PsoIpKpVJMn0kJcconvRvfee7659vDDYfFi+OwzYOwTjJ42DdaXVZ3r9v77fU3YyJHlNWORp94ldEp+Ol5G6pqo76OISKYy51y6yyANID8/35WUlKS7GNIEwmH4wx/8SNtLL4WXX/bz1g4aBHucMQxWLC/fNkTEN9126gQbNlQc48IbCf3990QiUFbmb1E5Of6W6vRnzaaJV0QajJlNc84181+CUhPV5Im0MKEQ7Labr82Ljqt4+22YORNOeHQGQy4cRt6KTyrvtGEDEXIqNeVGm2Z79fL38/LqNv2ZgjwRkeZJQZ5ICxMKwbnn+vvduvllv36wciXMmwdDZsyAgZ0r1dwBhCjztXrt2hE+88xKNXWxffEUtImIZAYNvBBpgaJTn33xhW+ubd8eVqzw9//6V5jz0weS75yTA/n5lQK7aFAXClUEfNUNwohEfK1fOOz/jt5vNgM3REREQZ5ISzV8uM+ht/POvtm2Uyfo3h2mTYM7F53LnFsf90mO461eTeiAPSutig3yoqoL2EIhH2hGm3aj91UDKCLSfKi5VqQFGzECOnf2NXhLlvhZMb7+2gdos/Y7l0Hbz/V55rZuBajol7d8Axx8MOEPP4SSEiieCgWH+Vkv8LVypaWpD74QEZHmR0GeSAs3fDh8/LGfgvbrr33WkVmz4MEH4fXX4Y5VWwgN6gWlpRX98oA5Szoz6OJR8M47FalVXniTuTsfQm6uH7EbiVT00UsW7CkIFBFpntRcK5IBLrkETj7ZB2YzZvhAr2NHX0n31FMQnrbUPxijjBx47TWfMDmYPiz00RRycys3w9ZUm6cgT0SkeVKQJ5IhLrkETjrJ3y8urpgJ7O23fXMuS5f6YbiBHCqS40XIATMoLKwStGkwhYhIy6TmWpEMMmYMvP8+vPEGPPII7L23n/5s1SpYtAgueG0hOT+9GiY9Sw7rCdMLgDI6E2r9DRQUEEJpVEREMoFq8kQyzB13wJVXQt++8MkncMIJvvl29myYOBHKfn8PeQf3ASCPpeSxlBzW+yk0unWDG29k6dKKNCnQwClSYubQFRGRxqOaPJEMdMklfpqzBx6A3XeHwYP9snt3WLYMciZ9QFl+IeGlFfuE6QVrIXTnveS8NpO8j1/y6+swC0ZSxcVwzDHlAz2YPFnz04qINBLV5IlkqBEjfBe8V17xwd6CBTBlCvztbz5h8oJ/FZG3d1fy8JFeCF9NFyEEMz4mnDOIcLjyvLY1JUiuUVGRD/CCgR4UFdX59YmISPUU5IlksFtvhcsuq5gGbehQPyCjSxc/Bdr0iZ/DDTcAftqzaPMtQN76ueT1bUOvrz8AKtKpJFLdY5V07+4TNLdq5WvyoqNDRESkwWVFkGdmH5vZD8xMXckl64wYAT17+mTJ774La9fCunXw4Yfw3HPw6tF3UPqzvybeeft2QscdCsXFlYO4oF9d5I2KALBGxcVw7bW+Fq9VK7jrLjXViog0oqwI8oChwN+BsJk9YGaHprtAIk1p0CA49VTYYw+YM6di3cqV8Pzz8HLHs4j89aHy7SuabnOIkEP4iLMpvekuysog/EIJ4aMvIPLzO1j6ncsIv1BCaanfr9oBGtGm2h07wDk/5FdERBpNtgR5RwATgvvfB94zs0/M7Coz2ymN5RJpMqNG+WbbQYN8ELZuHRxxhH9s5kyYutfFvPP3GURa7VQ+K8ZScn0fvR3byZ1wJwtO/iEUFxPasgp2bKdsSzsoLiY3l/JAL2ny5MJC30TburWaakVEmoA559JdhiYTNNeeD1wKDAccsAl4Gvinc+69NBavXvLz811JSUm6iyEtwJw5frqz997zU9oOGOBz6XXqBOvX+z58Qy4YSt7KTwnTq7yPXphezGd3hrWZTaRNt4oRsk8+Selu+eTk+ACyWsXFvkavsLC8qVY5+USaHzOb5pzLT3c5pH6yKsiLZWZDgXHAecBO+IBvNvBPYIJzbk0ai1drCvKkNsJhH2t98YVvsg2FfF69Dz+Egw+GDh3ggD9fQO7nUyijM2XksJ5OAOzBArBWhC4+k8ipF5B3Sj7hcM3Tn1VXlgZL0SIiDUJBXmbIlubaKpxznzjnrgJ6ARcBU4F9gD8DS8zs0TQWT6RR5eX5uW5POw3at/ezZMya5ae3nTULXnwR3vz+RD474EJ6UcpwZjCMT+nMBj8C1y0h9NBffX4W6h7giYhI48namrxEzGwvYDwwAnDOudZpLlLKVJMndTV9um9F7dbNj7494ghYE9RjL1sGh//7KlbNXUEh71JKT3JZBlQMzggdvA988EGtnjNZyhUFiyLNg2ryMkPW1uTFMrOdzexa4Bn8IA2Ab9NXIpGmM3w4HHcc9OkDZj5p8tq1PuiLRGDeNX/nw4EXUkoPcmJy6YUo8wM0PvzQd8aLmaasppQqoZCvTYw200bvK8ATEWk4WR3kmdnRZvYEsAT4E7659gP8CNze6SybSFMaNAiGDYNzzoH8fFixwgd7Bx3kH5836GSe+97zlHXOJUJOlf0jc5fCYYfBqFGpJ0ZOlea6FRGpk6ybu9bMcoGLgUuA3QED1uCbacc752alsXgiaRMK+cTJkQhs2ACffw6TJkGPHn5QxvSv+7Ny7DscMelaBpe+yUC+Kk+1EiFEiDIir00lMvwoeOutqk+QYGRt9Hmjqoy01Vy3IiJ1lhVBnpkZcBI+dcqJQGt8cPcufjTtU865zekroUjzEQrBIYfAvvv6v9et84MxRo/2zbhLf3IXC95+n0WT/8ou6xcygvcBnzh5LnvCvHXkHngg4fvvh/x8389upg/WIpvbEWp/W6VgrdogL9FctwryRERSkhVBHvA1kIcP7FYBj+Jr7b5Ia6lEmqloX7mzzoKyMvjHP3yAN3q0X//ypkN5vfeh7PL2f9hj1o8oozOl9GQFPdidBZSW5pB76imEDtyLUMmb5cHa0h29CW1ZkHqwFk2gHK3JSzGBsnLviYhkT5DXG3gL3yT7jHNuS5rLI1JZkqbMdIvOjjFypO+j9+absGkTdO0KM2bAir3P5IM9Iqx+4W1G8yq5LKOUnoAffRuaVgQ77UTk4h8RateOZRt7MajdkkrBWnwfvnDYL0MhCBUU+Fq/Wp4bBXkiIlmSQsXMBjrn5tbzGKcCpzrnvt9AxWpQSqHSgrWAfmeRCMydC4sWwf/+B598ArvuCh07+seXL4ebl15Fj7nvkctyH+CV99fzzbi5e3bhg0Ou5pCzd69oxo0LxFJJjJxKAKcEyyL1oxQqmSErRtfWN8ALDAO+1wDHEaksUb+zZiYU8qlWjj0W/vxnuPZaH4cWFsLTT8PYsTCk6O+sGDQCP3lMAvPmsvE///PTbNRDspG7kYgP7qI1gdH7DTrSV0SkBcmW5tpmy8xOBH6Kn0t3BzAHuME5NyWtBZOmU8d+Z+kQrUEbPBg++sjPg9utm6/RmzQJln33bvh8CLu/8Dee2noAo5lMGZ0BKKUnazZ3oOzGX8PmNoSuPSfp8VMRX6MXWzOomjwREQV5aWVmlwP3BLfb8DWrwyCYJFSyQx37naXT8OGwfr1PtdKjBzz5JBx9NOy5J6waPI7Fo8fx9tNL2e3DH9NpzSI6s4lclpFHmBzWU/qrP8IrD8Mff5VSOpVE/fZKS/00bArmREQSU5CXJmbWH7gLuN45d1fMQ6+mozySZgUFLSK4izViRMX9jz+GTp3gm298Ld+KFbAh1ItXvvdvdnv6z/Ra8j4DmccurCSPpeAgNHUaHPYqkdffJ3TsIVWOH19TFw3m4mvpEvXR06ALEREFeen0fXzz7H3pLohIfR1wAOTkwH//65ttwQd9b74JrXteRx/3Hn3Ci/mQA9mDBQCE6QVA2XEXEjr/YJg4Menxo4FcJOJr8KJKS/1t4MCqTbciItkuKwZeNFNHAF8A55jZfDPbZmbzzOyqdBdMpLaGD4deveCaa2DMGL/unXd88NWtG8ztejh/G/g3PmUIZXSijM7lc+DmUAaPPQZduiQdPFHmB+oSCvkmWqhoroWqzbmNTlOtiUgLoJq89MkLbn8AbgbmA2cB95hZG+fcX9NZOJHaig58OPlk6NMHbroJ5s+Hzp39fLirVuUzZ8eePLhwDUO3T2MB/RjC50BQqxeBUM+e5P3iWigsZE73AnKCaXJzcirnz4OKWrzYQC/28SoaKhdhC0h5IyICCvLSqRUQAi5yzj0brJsS9NW7ycz+5mKSGJrZOGBcsoP17du3McvafDXTJMLZLBSCYcPgyit9Pr2ZM2H2bNh7b+h+eFdmDb6BL995nb3WvU8PVpDDBgYxz++8Ebj5Zh88PVJM3jnDgcSjZQcOrAjsahx80ZCBmaZaE5EWQkFe+qwCBgKvx61/DTgB6AWEoyudc+PxM3YklJ+fn/lZreO1lBqVLAxEQyE47zzo3t3PmvH++742bu5cPzXa1/sex+rZ+/Dcizs4gGmUMIzz+E/FAbZsgT/9Ec55vNrngBSbaZPlIqzL+9KCUt6ISHZTkJe6hcDbDXi8WcChCdZbsNzRgM+VmVpCjUpLCUQbyahRvhZut918upW5c+HDD30Ta/9DejOj/a9Y+sw/WeM60pGNjOGl8n1zSopg/HgYNy5papWUB1jEB2bdu9f9fWmBKW9EJDspyEuRc+4R4JEGPOQk4BJgFMRWYTAKWOycK024l1RoCTUqLSEQbWR5ef4WDsPGjbBmDcyb5xMpFxbC1wMu44s73+QxfHLkQ5hevm/48l/DjOWE7vh5+bpESZBrFB+Y1fd9aYEpb0Qk+2RFkGdmY+uyn3NuQkOXJcb/gDeB+81sF+Ar4EzgeODiRnzezNESalRaQiDaRPLy/KCMpUv9/cceg0ce8XPg5p45kk/+M5Ov6c+pTGIfvuQQppNHGP7xC1j3RbUpVlISH5jpfRGRDGcxffszlpntIOmEmol3AZxzrnUjFck/iVkX4HZ8cNcNn1Ll98655B2RksjPz3clJSUNXEJpEFnYJ68m0RkrJkyAJUt8M+6ofb9m2uOz6MAmNtKWW/gdI/ApSiLkEOnWD376U7jggvLjxE5lVmt6X0SSMrNpzrn8dJdD6iebgrytwH8hyNmQAufcLxqtUA1MQZ60RNOn+1jr3/+G9uF5jPnqz3zIcD5hf0bxKn35mqt4oNI+4StuI+/enyc5oog0BAV5mSErmmuBt4AjgdOAXYF/Ak855zals1Ai2W74cD8IY+1aeObh7tzLOHZnIe3Yyn/5DuvozFq68CPGEyLIiPyPeyEUgTvuSOk5Ek17JiKSDbJixgvn3EhgL+CPwJ7Av4ClZna3me2f1sKJZLm8PPjZz+CSa7ux/7E96bjvnrTfb2+OOm1XurTewiyGMJVDmMMeAISIwJ13wiFV57tNpElnwhARaUayIsgDcM7Nc87dCPQBzgY+AK4APjazD83sEjPrnNZCStNKNDWVpqtKm6uugrOv7MWGfvvxRenOTF3Qm16F+1HS/ih+y03cxVVMJ+Y32Ycf+qk1rrgi4ful4E5Esl1W9MlLxsz6AZcCF+GnGCsDTnDOtbhvePXJq6VE+esgq3PaNRfTp8Ndd8G6dX5AxnnnwbrPv6b02ansvOlrTuA1dmchg5hfsVObNvD22+XvVyTi941OeRZVr4EaIllEffIyQ9bU5CXinFsUDK4YBywBcoAe6S2VNIlEedKSzYogTWr4cPj5z+Hss2GXXXw+vb6H9WXUDQewmfa8wij+ylVEyKnYads2P/I2EAr5AC863Vk0V58CPBHJJlkb5JlZnpn93My+wo+67Q5MhJhMrJK5ovnrWreuyJOWaJ2kxaBB/vSffbYP1pYtg3dX7MW+Pz6JzaFdeY/D+QPXVA703n6byHcvJRz2KVqgIlVLbNOtmnFFJFtkVXOtmbUCvoNvoj0BP7r4M/xo20edc9+msXj1oubaOkiUJ02505qdd97xU6K9/LLPqTdgAEydOJf24Xn0YCm/5neVm2779YOFC5kzxweL8aNrw+GKGj4RSUzNtZkhK1KomNnu+CnELgZ6AevxU5T90zn3YTrLJmmUaGoqTVfV7IwY4QO1efNg9myYNg12OmAg3y5fTvG2AUzku1zP3ypSrCxaBG3aUPbTJ+E3Z6iJVkSyVrY0184DbgIW4/vf5TrnLlOAJ9IyhELB6NuzoX9/WLwYTrjlcDq32can7M+LnFB5h+3byfntT+HGGwEfJMY344bDyZtu1aQrIpkgK2ry8NOUbcXX4v0S+KWZ1bSPc871a+yCiUjqLrnEV9StWQOPPw67j9qXz19vz91bcllAX37EeCL4qrsc1hO+81HoPoTQFReUN9HW1FwbiSiBsohkhmwJ8gDaAruluxAiUj/XX++Xn34KCxfCsSd2ZP1z81lHN97gKMbwEgBhepHHUrjxQrj357BwYUo1dKrFE5FMkRXNtc65VnW5pbvcIlJVKAQXXAB77eUHZKxo05vc846ldNh3eKndmTzOmVV3WrQIOneuXEMXl/g62qRbWuofLm/SfeMDJcgWkRYpm2ryRCRDDBrkc+lFIrBiBXTZpy9DT+rLu+8OY9KLe9Bn8WKGMbPyThs2wLBhhJbPKE+GHdncjlD724i88CaRffw0abm5PtDLzYXQ5x8QOmWkEmSLSIuk2ioRaZFCIR/o7babT62yYAH07g2Leo3g9UNv4SlOAyBCDmF6EaYXrFhOuP3uhH/9TyKb2xHZ0Qm2bCH00ZTyhMlQkUg59NEUJcgWkRZLNXki0mLl5cHpp8N//wszZvh1XbrA7G6jeG/QXmya05ExvOj75hH009uyEF79FwCRVqGEia/Lm3SjCbKjNXkx22lwhog0dwryRKRFGzHCt8SuXg1ffOHX9egBi3bqz+unjofJ1zOmbCJ5+M52EXLKR+DStSvhf02CfvmEIhVz25YHbwUFvok2JkF2NLhTkCcizV1WzXiRyTTjhWS7SAT+8Ad45hnIz/f99qJjJQre+S0Xr7uHEGUVSZMJavYO7gMffFDlWMkCuGgKlgadOSM600r37rBqlWZckbTTjBeZQTV5kjk0JVlWC4XgBz+Azz/3f7/yChx2GHz1FRSP+BkLpg7k6DVPcx7/qbzjhx/6nV97rfy6SRbkRSIVo28hSK5cUkJo+luERh1Wt+suGATC5s2wYwe0agXt22uQh4jUm4I8yQzRL0qNgsxqeXlwzTW++faBB3yAN2CAf2wWZ/PsJ73JX/xx+Vy3IYKkeGVlPiI8/3yYOLHKcaMJksEPygAf7A1c/QGhc4LRt3fW8borKvL779jh/96xo/IgD/1wEZE60uhayQzRL0qNgsx6I0bAqFEwZozvm1da6gdj9O0Lm4YeTsmh1/AOhwJUarqNkEP4sSmEDzgJqDz1WbSfXrR5Ni8vCPbefTfhdVerhMrRwR2tgo/jVq383927+x8uv/iFXypPn4jUkmryJDNUMwpSstN558HGjTBpEjz/vA/0OnSAx7v/EPY7kU9n/oExPF8+IKO8v96MpYSHDSNv+YxKx4tEIDSzGF4ogVPyCe1XQGT4UYQSXHe1GpQRO7gjtk9eoh8uqs0TkVpQkCeZIcEoSJFLLoEDDoCPP4Zp0/y6Sy+F4uI92LTpPj67ZTmUFZcHeuVWLPdR4bp1FetKSuCcYwhtbgd/3UJo8mQi+Q103RUUJN5XP1xEpB40ujZDaHStSHLhMNx3H8ye7Ztxu3eHKVN8HJf7p+sYseaF8n564JtuQ5QRadedyIKVfuXdd8Odd1K2owNYa/jxteT83w8qPU9ZGeTkVH7uSilZakuDiSRNNLo2M6gmT0QyXl4enHaaH7S6YAG8+qpPsbJuHcw45s9Mn3o4+4Rf4SoeACr66oW2rCLU2+CGGwgffhZ57VeV16yFj8ovP3ZUsvQqdc6pl6yGT0QkBQryRCQrDB8OAwf6gGvSJPj6az8Yo29fmNXjDF5/vjd54aWM4aWqO995J/SaDHfdVdFnrl/qlRxKnCwi6aAgT0SyRrTpdP/9Yd48mDWrYt2qAYfyXJ872eWD1fRkeaXmW4DQ0i+J/OB6Is9NLg/wyspgzhz/eLSZNhz26xXYiUi6qU9ehlCfPJHaefVVP+q2pMQ34+bmwty5sNtu0Pelv3Aq/2UUU6ru2K4dbN6ccMaL2HWxufVi1auPnkgTUZ+8zKA8eSKSlUaNgjvugO9+1wd4paVw2WWw776w4swf83CbS3mVo6vuuGULmPnosBrRvHqxufViA7xa5dITEakDBXkikrVCITj3XDjhBOjUyQ9k7dvXz5Kx+NBzeWCnn/A4Zybe99SR0L9/leNVJzawi95PVuMnIlJf6pMnIlktLw9Gj4ZNm+DNN32gt3Gjr91btHk0D5X2Y+M3HRjNG5Xy6YUog0VlFbnsSB7kVRf8RQM8NeGKSENTTZ6IZL28PLjqKj917YAB8O23sGgR5OfD5n778MqZj/IvLkjcfLt1qw/0kojW1EUHZEyf7m9lZX5daam/LyLS0BTkiYgExoyBa67xzbft2/s0K+ec4+fAnTP2DzzRZixz2IMIcRmPt26FNm1g/Pgqx4ztm5eTU5HKZdkyH+BBRfA3Z07lZlwRkfpQkCciEiMvDy6+GEaOhI4dffPtvvv65tvFR32PiR3HMZVDqgZ627fD5Zf76TRqEArBHnv4gC8319+GD/cJmjUwQ0QaioI8EZE4eXnwgx/4mj3n4OWX4auvoHdvmH70DTzQ6VpeZDTvcChhcivvvHo1tGqVtFYv0X0RkcagPHkZQnnyRBrHq6/CzJnwxRewdKkfpPH117DrrrD4xr+y3/bpjOa1SoMyynXtCv/7X7VTk8UOvFBuPWkulCcvM2h0rYhINUaNgt13h1694MUXYe1aGDrUt8rOH3cN0x6fQMdvN9GdlVWTJ69dC4cdBv36wcKFCY8fH7xF8+olSrYsIlIbCvJERGowaFDFtGVLl8Irr/i/QyGYdcRYHvr0IHK++ZRObGAYM316lViLFkHr1vCPf8C4cUmfJ5Wp0DRdmoikSn3y0sjMDjez18xsuZmtM7PpZvb9dJdLJK2Ki+H22/2yGcnLg5NP9rejjvL982bN8smTe4/cm7Jjvst/B9/AVA5lDntUPcCOHX5gRocOKT1fskBOyZNFJFWqyUsTM9sfeAN4H7gM2ACcCTxoZu2dc/9IZ/lE0qK4GI45xicXbtcOJk+utj9bU4v2jcvJ8elPpk71t91280HfNMbw5V5j6P/uI4xeNSHx3LebN/tp0YL+0PFBWzhc8VzJKIGyiKRCQV76nAO0Bk52zkXbdl43s6HAWEBBnmSfoiIf4G3f7pdFRc0qyIuK5r376U/h9dd9YPbtt/D978OCBTCj7ff4T9fvMfOB6ziXJxIPyggCvdhBFVX64RUXQ1ERkYOOJrLPIeWrS0v9beBABXoikpyCvPRpB2wFNsatXwt0a/LSiDQHhYUV04S1a+f/bsZGjIDOnX0s9t578O67vkavRw8/AveTg/4M4TxGLnmU4Xxa9QCtWvlm3ERiajVD7W6DF94kss8hlJb6vHqlpZVrATVIQ0TiqU9e+jwcLP9mZnlm1tXMLgOOAf6SvmKJpFFBgW+ive22ZtdUm8zw4XDccT4eW7/ez387Z44ffVtaCv/p8xMmnPkC09m/agJl5ypNiVapVi6uVjP00ZTyx6OBXvT+/PmN+hJFpIVSnrw0MrODgElA72DVVuAK59yDCbYdByQdlte3b98DFy1a1CjlFJHUzJkD994LL73kY7MBA/xo3K1bYWC7uRw9628MYAFjeKnyjq1bw7Ztldcl6Z8YX3sXnf92+PCmeY2SHZQnLzMoyEsTMxsITAZmA3fjm21PBa4ALnLOPVab4ykZskjzEA7Dfff55tsFC+DAA6FTJ1iyBL6dNpuBaz/gJ/yFgXxVNdVK/Odx0CePwsJKtZrhMHz2WdXn7tTJT5emplupLwV5mUFBXpqY2dPAcGCwc25rzPrHgFHArs65JJ11qlKQJ9J8zJnj06v8+c++z14oBMOG+fx6W+fN46jSf9OXrxnDi1UHZaT4mRzNlxdfk6eRt9IQFORlBg28SJ8hwCexAV7gQ+A8YFdINCRPRJq7QYP8LRyGjz+GDz6AadOgSxeg5548veUKdqxewcfsx5U8WHlQRkx6leokC+LmzvX99RTkiYgGXqRPKTDMzNrFrT8E2ASsbvoiiUhDuuoquPJKuPVWOOss6NjRB1+XXNedPnt0Zib7cy+X8yAXVB6U0S7+YyG5UKhiEAbAihV+qYTJIqKavPS5B3gaeNHM7sX3yTsFOBf4i3NuSzoLJyINY/hwn8+uXz+YOdP303vlFcg9oA9zN7WheMlOzGRvwBjBVAYx34/UqGWN3pw5UFYGq1b55tvPPquYczd2OxHJHqrJSxPn3H+AE4H2wAPAM8ARwFXA9Wksmog0sFDIB3uXXgrHH+8r6qZNg10G92LnLsZycvkXY3mRkyqnWjFL6fiRiA/wwKduidbmRefXVa2eSHbSwIsMoYEXIi1DJAJvvOFTAS5fDjvtBL1WzODrFa3JIcJ+zOIEXqcP3zCC9/1OtRiM8eKLMHhw5SbcsjJfo6faPEmVBl5kBjXXiog0oVAIxozx9596ChYtgqUMo2DA5/DB5yxhNx7hfPZkAZ3Z4AdltG7tE++lcOw+fXzz8NKlviYP/DJ2QIaCPZHsoOZaEZE0GDMGLroIzjgD+veH0pX+N/dI3qItO5jGMO7iKl7laD/1WYpNt3vs4YO4nJyKOXbBB3h5eWq+FckmqskTEUmTUaMqBke891RH5s3PZTaD2ZUVtGUH7zKCRfTjaU7hAa5NaTBGNKiLBnORSMU0aOGwfyzaf081eiKZTUGeiEgaRXPq9enTh2+GrufVFzbw1dcDab/6G/bhCzbRgSKO5ZfcxAU8xaBajrqNNs9GIhUBYDhckUxZRDKXgjwRkWZgxAiIDBsMe8GDD8LX03am57dfMICvmMk+/IczWMdOHEwJ56UY6EWFQr6PXrQmD3ztXvQxBXsimUlBnohIMxEKwXnn+cETv/lNHjPf78iydT3Yj8+ZyT48y+n8l+MBONlChFxM57ok89xGRfPlxTbfRv+OPreIZBYFeSIi6VBNUDZiBNx9N9x4YzfmfbCZkqU9yOdjlpLLIvpwO9ezgH5cbL3Ic0v9sY45BrZs8Un4Jk+ucszY5tuoaPOtiGQmja4VEWlq0aDsF7/wy+LiKpsMGuQfvva2XDru3JmZ7ENbtrIrK1jNLjzA9/klNxOxkA8Wt2zxaVa2bPF/V0O1diLZQUGeiEhTSzEoGz4cLrkEjjspRNv2bQiTy1p2ogcrKaMLr3I8V3Enc25+0NfgtW7tl4WF1T69+uGJZAcFeSIiTa2wsFZB2YQJcMvfezN678W0Yjsb6Eghb9OLFUzjUK7nNh7feJKfRiNBU20iCvJEMp+mNcsQmtZMpIWpYaBEItOnw49P/pzN4eXsoA35TGMOg1hHCMc2fsCDXOIebdRiS3bQtGaZQTV5IiLpUFAAN92UcoAHvvn25of24ag+X7Od1kylgN4sAWA+g7iLH/JLu7mxSiwiLYyCPBGRFmTUKLjj67GcdKIBO3iTowBox1YW0Z8HuYRL7a9Mn57ecopI+inIExFpgW596VBGnbsrOzC+YBBbaEt7trCanfkPZ3LpgW+lu4gikmYK8kREWqg7Hh/Aj88I04NllJHDVtowgIVsoSMzGcql9lfmzEl3KUUkXRTkiYi0YP/3nyO590/Qh4VsozWL6MMg5tEax385jZP3msYvf5nuUopIOijIExFpKYqL4fbbqyRPHnXdEG5+YH92Zz5t2MZC+tKHxbRjC8vozaO3zeKdd9JUZhFJGwV5ItJ8JQlqslINs2Rccgnce+zLnMmTdONbwvRiJG+xKyv4lm5cc+Q7XHppmsouImmhIE9EmqcUpv7KKinMkjHi9V/zwPkfcShvsw3jRU5kPZ1pzQ4+ZwgPP7hWTbciWURBnog0T7WcjzXjpTpLxsSJPHG/42yeZBBz2E5rOrKRvnwDtOHl295m7NgmLLeIpI2CPBFpnmo59VfGKyjwU5alMnXZuHFMOH8qx/Mam2jNYnoTphet2c6nDOXRRyMcdFDTFV1E0kPTmmUITWsmGakOU39JjPHjufTyjbzEKaynM9toQy7LWEB/cljHj3/Rk1tvTXchpTnStGaZQUFehlCQJyLJ/N0u49fcxAp6sQuriJDDZtrTgQiHHb0rkyenu4TS3CjIywxqrhURyXBXTf0+J/AKsJXVdGMz7WnHFjYRYsqUdZx6arpLKCKNQUGeiEimKyhgwv1tuIE/syd+CoxW7GB3FmK04Z0XFivQE8lACvJERLLBuHHccX8eN/BnOrCeLbRjNd3IoYwyuvDKC2s06lYkwyjIExHJFuPGccn9IzieV+hIGRvpyFba0o4tbKEDLz+xjEmT0l1IEWkoCvJERLLJuHE8P3UPruIfdKSMTXRgO61pxxbWbOvMT38Kv/1tugspIg1BQZ6ISLYpKOCOqaP4EX+nHRvYRhtasYNd2m6kTRt4+21UoyeSAdqkuwAiIpIGBQXcOhXWnfQcL64pYBm92e/IHowZ4x/eZZf0Fk9E6k9BnohItioo4K7VBQx5EP72N7/qqqvSWyQRaTgK8kREstwll/jl+++ntxwi0rAU5ImICJdcAoMGpbsUItKQNPBCREQAGDEi3SUQkYakIE9EREQkAynIExEREclACvIamJntZmZ3m1mxmW0wM2dm/RNs183MHjCzlWa23szeMLMhaShyvUQi6S6BiIiIJKIgr+HtCZwNrAHeSbSBmRnwAnAC8EPgDKAt8KaZ7dZE5WwQS5emuwQiIiKSiIK8hve2c66nc+5E4Okk25wCHAFc6Jx7wjn3SrCuFXBDE5UzseJiuP12v0xBWVkjl0dERETqRClUGphzbkcKm50ChJ1zb8bs962ZvQicCvyoscpXreJiOOYY2LIF2rWDyZOhoKDKZpFI5WbacNgvQyF/ExERkfRTTV567AvMTLB+FtDXzHKauDxeUZEP8LZv98uiorQUQ0REROpPNXnpsTOwMMH61cGyG9D0DaGFhb4GL1qTV1iYcLPYGrvSUsjLa7ISioiISIoU5KWHAS7J+sQ7mI0DxiV7vG/fvvUvVUGBb6ItKvIBXoKm2ng56alzFBERkRooyEuP1fjavHjdguWa+Aecc+OB8ckOmJ+fnyhorL2CgpSCu6hevRrkWUVERKSBqU9eeszC98uLtw/wtXOuxYxZ1UALERGR5klBXnq8APQ2s6OiK8ysC3By8JiIiIhIvai5thGY2ZnB3QOD5WgzWwGscM69hQ/kioGJZnY9vnn2JnyfvDuburwiIiKSeRTkNY74JMj3Bsu3gELn3A4z+w7wx+CxDvigb6Rz7pumK6aIiIhkKgV5jcA5l3SUbMw2q4HvBzcRERGRBqU+eSIiIiIZSEGeiIg0jFrOfS0ijUvNtSIiUn8pzn0tIk1HQZ6IiNRfsrmvazGDjog0LAV5IiJSf/FzX3fvrpo9kTRTnzwREam/6NzXt93ml6tWJa7ZE5Emo5o8ERFpGPFzX8fW7BUWpq1YItlKQZ6IiDS8aM2e+uSJpI2CPBERaRzxNXuJFBcrEBRpJAryREQkPZR2RaRRaeCFiIikR2zalU2bYMKEdJdIJKMoyBMRkfQoLIQ2QYOSc/DQQ5otQ6QBKcgTEZH0KCiAiy8GM//39u1KtSLSgBTkSXpprkuR7DZ2LHToAK1bK9WKSAPTwAtJH3W6FhGlWhFpNAryJH0SzXWpD3iR7JNKqhURqTU110r6ROe6VDONiIhIg1NNnqSPmmlEREQajYI8SS8104iIiDQKNddK86RRtyIiIvWimjxpfjTqVkREpN5UkyfNT6JRtyJRquUVEUmJavKk+YmOuo3W5GnUrUSplldEJGUK8qT50ahbSUa5FUVEUqYgT5onjbqVRFTLKyKSMgV50vIUF6uWL1upllcS0WeCSEIK8qRlUZ8sUS2vxNJngkhSGl0rLYtG3opILH0miCSlIE9alkTz3Sqlhkj20hzYIkmpuVZalvg+WaCmGpFsVtt+muq/J1lEQZ60PLF9sm6/XSk1RLJdqv001X9Psoyaa6Vli22qadMGvv5azbYikpj670mWUZAnLVu0qeayy8A5+Oc/feB3xRUK9kSkMvXfkyyjIE9avoIC6NvX/zqP/kK//37fLKNAT0Sioj8Kb7tNTbWSFRTkSWaI/kI38387p+YYERHJahp4IZkh+gt9wgT4179g2zY1x4hIZRp4IVlGQZ5kjugIu7FjK6dIUMoEEYHEAy/0mSAZTEFeAzOz3YAbgXxgKNAR2N05tzBmm2OAi4ECIA8IA68Bv3LOLW/qMmec2HQK0V/umzf7ztb33APjxqW3fCKSHtFuHdGaPNX0S4ZTkNfw9gTOBqYB7wDHJ9jmB0AO8BvgK2Ag8GtglJnt75wra6KyZr6iIh/g7djhb1dc4dcr0BPJPrVNnCzSwinIa3hvO+d6ApjZpSQO8q50zq2I+fstM5sDvIUPEB9q/GJmicJCX4O3Y4f/e8cOuPpqGDJEH/Ai2SjVxMkiGUCjaxuYc25HCtusSLD6o2DZu2FLlOUKCnwTbauYS337dj9AQ/PdiohIBlNNXvNxVLCcndZSZKJo0+zVV/sAr21beOCBivvqfC0iIhlINXnNgJmFgLvwAd5zaS1MpigurlxTN24cvPUW/OY3MHq0T7ESzaU3YUJ6yyoiItIIVJOXZmbWBngC30x7uHNuW5LtxgFJRwv07du3cQrYEiXLhRW9RQdfiIiIZDDV5KWRmbUCHgGOBU5zzn2abFvn3HjnXH6yW48ePZqs3M1eTZOQjx0L7dv72THat4cDDlD/PBERyTiqyUuv+4DvAmc65yanuzAZo6ZcWAUF8OabPvjr3h2uvdanWWnVCv7+d6VXERGRjKCavDQxsz8BlwIXO+eeS3NxMksqk5AXFMBNN8GqVRV59LZt84MzVKMnIiIZQDV5jcDMzgzuHhgsR5vZCmCFc+4tM7sRuA6fD2+umR0as/sK59z8Jixu5qnNNGaFhb4GL5pHb/t2jbYVEZGMYM65dJch45hZspP6lnOu0MyKqEiZEu8R59xFtX3O/Px8V1JSUtvdMk9dJiAfP74ivUr79nDXXb6GTxnxRSRLmdk051x+ussh9aOavEbgnLMaHi9soqJkn9pOQF5c7AO6e+7xy2gfvU2b/ONDh8K99yrYExGRFkdBnmSW2kxAnqjWr6jIB3jRGu4ZM+DII+HttxXoiYhIi6KBF5JZUhl0EZWo1i9RULhtW9U0LCIi1YlPyC6SBqrJk8yT6gTkiWr9CgrgvPPgsccqb/vhh/7DWrV5IlKTuvQNFmkEqsmT7JWs1m/iRN9EG+v55/2Htn6Vi0hNakrILtJEVJMn2S1Zrd/vf++Dumj/vNh5blNNzyIi2ak2fYNFGpFSqGQIpVBpBDfeCI8/DuGwz6PXKqj43rED2raFt95SoCciidUmX2czpBQqmUE1eSKJjB8Pd95ZeV00YTLA1q3+8UmTmrZcItIypNo3WKQRqU+eSCLPPFPzNuFw45dDRESkjhTkiSRyxhk1b3PJJY1fDhERkTpSc61IIuPG+eWDD0JJSeWmWjMYMQKGDElP2URERFKgmjyRZMaN8/PYjhsHBx9csd45eOcdpVQREZFmTTV5IskUF8PIkT4NQvwodOdg40afUkWdq0VEpBlSTZ5IMhMmwObNVQO8WP/8p2rzRESkWVKQJxIvOudkaWnN227frmz2IiLSLKm5ViRW7JyTrVv7QRbV1eS1bats9iIi0iypJk9armiNW0M2l8bOObl9O5x6asVMF/GOPLL+s140xmsQERFBNXnSUsXWuLVrB5MnN8wAiOick5s3V6w75RR4/vmKGr3+/WHYMMjNrd9zNdZrEBERQUGetFSxNW5btvi/axMgReeV7N4dVq2qmF+yoMCnTbnySn/s557zTbKtWvm/ARYu9DeA++7zwd6vf12RW6+pXoOIiEg1FORJyxStcYvWgtWmX1y0Bm3zZp/kuFUraN++oiZt1arKyY+3bYPevWHx4sTHKy2Fyy/392sT6NXnNYiIiNRAffKkZSoo8EHZbbfVvpkzWoMWDeR27KioSQMfbLVtW7F9u3Zw3nk1HzeV+W5j1ec1iIiI1EA1edJyRZtXayu23120Ji+2Jq2gwAd8Eyb4v8eO9ev22KMikHvttarHTWW+24Z6DSIiIjUwV116CGkx8vPzXUlJSbqL0XKMH+8DtmHDoGvXij55tdn/9tth5UrYZRe46aba98kTEWmmzGyacy4/3eWQ+lGQlyEU5NWCRrWKiFRLQV5mUJ88yT6JRrWKiIhkGAV5kn26d/czWcT3xRMREckgCvIks9Q0g0RxMVx7rR9w0bq1z4mnploREclAGl0rmaO4GEaO9KNmW7eG//u/qoMqYtOnmPmceCIiIhlIQZ5khmgNXXQ6su3b4c47qyY6VgJiERHJEgrypOWLjpbdtKnqY7GJjqM56SZP9n/XNm2KiEhzFp2uUZ9tElCQJy1ftAk2UTqgRIMrlIBYRDKNUkNJAgrypOWLbYJt0wZGj4bcXDjgAN/nTr9qRSTTJUoNpc+9rKcgT1o+NcGKSLZTf2NJQEGeZAY1wYpINtOPXUlAQZ6IiEgm0I9diaNkyCIiIiIZSEGeiIiISAZSkCciIiKSgRTkNTAz283M7jazYjPbYGbOzPrXsM/9wXYTm6iYIiIikuEU5DW8PYGzgTXAOzVtbGaHAecD6xq5XCIiIpJFFOQ1vLedcz2dcycCT1e3oZm1BcYDv8UHhSIiIiINQkFeA3PO7ajF5tcDrYE/NVJxREREJEspT16amNkewM+Bk5xzW8ws3UUSERGRDKIgL33uA551zr2ZysZmNg4Yl+zxvn37NlS5REREJAMoyEsDM7sAOAgYnOo+zrnx+P57CeXn57sGKJqIiIhkCAV5TczMcoA/A3cAm8ysa/BQK6Bt8Pd659zW9JRQREREMoEGXjS9XYAewO/wI2qjtz5UpF45KW2lExERkYygmrymVwqMTLD+38Bn+HQqM5u0RCIiIpJxFOQ1AjM7M7h7YLAcbWYrgBXOubeAogT7bAKWOeeqPCYiIiJSWwryGkd8EuR7g+VbQGHTFkVERESykTmnQZmZIKgpXJTucqTBLsDKdBeiBdP5qzudu7rTuaufpjh//ZxzPRr5OaSRKciTFs3MSpxz+ekuR0ul81d3Ond1p3NXPzp/kiqNrhURERHJQAryRERERDKQgjwRERGRDKQgT0RERCQDKcgTERERyUAK8kREREQykII8aenGp7sALZzOX93p3NWdzl396PxJSpQnT0RERCQDqSZPREREJAMpyBMRERHJQArypNkxsz5m9h8z+9bM1pnZs2bWN8V9O5jZH8xsqZltNLNiMzuyscvcnNTz/Lkkt2GNXOxmwcx2M7O7g+tmQ/Da+6e4b1Zfe/U8d9l+3Z1pZs+Y2aLg2vnSzG43s1AK+2b1dSfVU5AnzYqZdQKmAIOB7wEXAgOBN82scwqHeBC4DPgl8B1gKfBqFn1Z1Pf8ATwMFMTd5jR4YZunPYGzgTXAO7XcN6uvPep37iC7r7ufANuBm4ETgH8AVwCvm1lN39PZft1JdZxzuunWbG7ANfgPuz1j1u0ObAOuq2HfoYADLo5Z1wb4Engh3a+tuZ+/YFsH/CbdryON569VzP1Lg/PRP4X9dO3V8dwF22f7ddcjwbqxwXk5upr9sv660636m2rypLk5BXjfOTcvusI5twB4Dzg1hX23Ak/G7LsN+DcwyszaN3xxm536nL+s55zbUcdds/7aq8e5y3rOuRUJVn8ULHtXs2vWX3dSPQV50tzsC8xMsH4WsE8K+y5wzm1IsG87fHNSpqvP+Yu6wsw2B/2qppjZiIYrXsbStVd/uu4qOypYzq5mG113Ui0FedLc7Izv0xNvNdCtHvtGH8909Tl/ABOBK4FjgXFAd2CKmRU2UPkyla69+tF1F8PMegO3Am8450qq2VTXnVSrTboLIJJAogzdlsJ+Vo99M0mdz4Fz7sKYP98xs+fxNYO/AY5ogLJlKl179aDrroKZ5QDP4/vRXlzT5ui6k2qoJk+amzUk/vXZjcS/WGOtrmbf6OOZrj7nrwrnXAR4CTionuXKdLr2GlC2Xndm1gF4ARgAjHLOLa5hF113Ui0FedLczML3M4m3D/B5CvvuHqQRid93CzCv6i4Zpz7nL5lktQVSQddew8uq687M2gLPAAcDJzrnPkthN113Ui0FedLcvAAcamYDoiuChKqHB4/VtG9b4KyYfdsA3wVec85tbvDSNj/1OX9VmFkX4CTgg4YqYIbStdeAsu26C3LhPQYcA5zqnHs/xV113Um11CdPmpt/AlcDz5vZz/G/5G8DvgHuj25kZv2A+cCtzrlbAZxzM8zsSeCu4FfxAnxC0d2B85v0VaRPnc+fmf0E2At4EwgD/fBJWnPJnvOHmZ0Z3D0wWI42sxXACufcW7r2kqvLudN1B8Df8YHab4H1ZnZozGOLnXOLdd1JXSjIk2bFObfezI4G/gI8im+ymQxc65wri9nUgNZUrY2+GP9B+RugK/AJcIJzbnojF71ZqOf5+xIYE9x2Atbh8+td4pz7sAmK31w8Hff3vcHyLaAQXXvVqcu503UHo4Plz4JbrF8Dt6DrTurAnMuaLg8iIiIiWUN98kREREQykII8ERERkQykIE9EREQkAynIExEREclACvJEREREMpCCPBEREZEMpCBPREREJAMpyBORFsfMXILbZjNbaGaPmNneKR5narDv6BS2/TLYdli9X4CISBNQMmQRaXHMLPrB9euY1TvhJ3c/DFgPHOGcm1HDcS4C/gVMcs6dXs12RwFFQIlz7qA6F1xEpAkpyBORFica5DnnLMFjd+Pn733EOXdRDcfpCCwFOgO7OeeWJdnuUeACYJxz7p/1K72ISNNQc62IZJrXgmWPmjZ0zm0EJuLn8b4o0TZm1hU4EygDnmiQEoqINAEFeSKSaY4NliUpbj8+WF6S5PELgA7AE865svoUTESkKam5VkRanCR98roABwGHAy8B5znnIike7wN8f76RzrmiuMdmAEOBg5xzqQaOIiJp1ybdBRARqYdfJVj3Ob7WLaUALzAeH+Rdih9gAYCZHYQP8D5WgCciLY1q8kSkxUk08MLMOgP7Ar8HRgK/c879LHisPwn63DnnbonZdynQFshzzq0J1o8HLgOucM7d12gvSESkESjIE5EWp4bRtV2BxUB7YIBz7hszKwTejN82Lki8D7gc+JFz7u6YwK8VPvBb1/CvRESk8WjghYhkFOfcWuBLfHeU4cG6Iuecxd/ido0OwLgsWJ4DhIAnFeCJSEukIE9EMlG3YJnyZ5xzbjowDRhiZtH+eVAR/ImItCgK8kQko5jZacDuwFZgai13jyY6/iNwKPCpc+6DhiudiEjTUZ88EWlxkqRQ6QzsA4wGDLjeOffHWh43BISBnGDVD51z99SzuCIiaaEgT0RanJggL9Z2YAXwIXCPc+71Oh77n/im2o34ARdr61pOEZF0UpAnIiIikoHUJ09EREQkAynIExEREclACvJEREREMpCCPBEREZEMpCBPREREJAMpyBMRERHJQAryRERERDKQgjwRERGRDKQgT0RERCQDKcgTERERyUD/D3bsCaScHVOzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the BPASS Cluster vs MIST Cluster cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['m_ubv_B'] - clus_1.star_systems[\"m_ubv_V\"],\n", + " clus_1.star_systems[\"m_ubv_V\"] - 5 * np.log10(10), \"r.\",label=\"BPASS\")\n", + "plt.plot(clus_2.star_systems['m_ubv_B'] - clus_2.star_systems[\"m_ubv_V\"],\n", + " clus_2.star_systems[\"m_ubv_V\"] - 5 * np.log10(10), \"b+\", label=\"MISTv1\", alpha=0.1)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of clusters at 0.1 * solar metallicity\\n\" +\n", + " \" and 1 billion years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log10\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log10\n", + " import sys\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Abljiu3Gt9CP1C6kYJ74Z7sIPwhXHbQZGqt+oY2G8hesK8ycRORgX4XfDFQW+Q/A/6C24rjI3e//oX3rnvQDXT/cXuCLraDyC+zB8KyKTcf9Ux+ACgbeIbcjmGvPaedyAC7C+EZH/4ertBuMaDC3C9QWu6Xk+FDcq5R3AQhF5HddHuSOuZOQrXIlPOI/jAoGnROREb/9+uK5db+P+pv4exo1CN5WKQWKOwP1tV+D6Xvt8jKsLfVZEXsGNhbBJK4bY/SXuyfc5Efk9rnpqE66B1SHAQbjrtZ7IRJO3UB7F3fQ/9/5um3ElJMfi2qecF2Sfj3E9DN4Xkc9wN/F5qvpWuBOp6pci8nfgT8B33jXKwvWpbgtcr1VHH7wf16L6ClzXzb1ExP/33t7yryLiayz7L1X9PFye4iSav8M1uKe9B0XkZFyjPN84A+W4AZmCNTr2F8v3UVJQ1UVeMPw8sEBE3sdVDTWk4vt5AxV/T3Cft/OBV0XkXVyp6wqvHVmdXwtV3Sgiv8T9f3wqIu/huoO3xP0f5+C6AIbzKe7vfb+IHIQLAlHVewLSvYz73u+C66r5arzeRK298BtBKUyafKIYZ8Bve2NcY41y4JAI8jLOlx/vtQcXyS3DDVTzO6BtiH1HELxfbw6u1fAq3IdxAW5wiQYE6Ufq7dMFF0VuoPIIhL4RpG4ISD81gms4wjvOdtyH4zXgYKLoOxtw3fNDbKs2L0H2ORk3oFIx7gaxFNeFqnWk1znC85xOxUiHvhEIXwNO8EszhCDjDHjbjsUN5rHD+1y8g/snrnINccHbi8AS3M19C66h0r1A+yDH/hOuOsQ3lkR+wPYWuP7rc7zj7cT10X4HVyfYLNJrFG3ewlzPM3GB1FZccPIhrj1N0PPjAuuncKPMlXlpxkVxvstxfda3e+ecBpxZzf9ylWtA5f/xYK+oP1uxvGL4jHTxrt8KXOCwEfe9dGSk/ydE+X0U7LMdxfubSojvAsKMrRHunLjvrHFUHvVyPq7L3AkBaevjqlyW4x6AKr2/eF2LaK8bcCCu1f8q7++4zvssX13dcb31v6JifJGQ909cMKDAg/H6zIp3YJNgInIv7oZwqqp+kOj8GGOMSU5eidNxwAEa4aiY1R7TgoG6JSL7aEAPCK8o60tcJNlFa9hFxBhjTHoSkf646sT3VbUmDRYrSck2AylutogsxRV/bcd1nzoD15jzGgsEjDHGBBKRa3HVSVfgqsfj2kXSSgbqmIjciWsomIurK96Eq5t9SIN35TLGGJPhxI3W2xXXTmK0qv4nrse3YMAYY4zJbKk6zoAxxhhj4sSCAWOMMSbDWTBg0pqITBWRhNSFxXJuEVGv25D/utHe+iGxpjXGmHCsN4ExfrxuO+fg5k44DDeS4SpVjWQ4UWOMSUkWDBhT2S9x86Dvxo0aGHY2yVrQh8jmDwhlLG6o25/jkx1jTCawYMCYysbhhoteoKqldV3FoKqLarj/RtxQtsYYEzFrM2BqlYiMEJHJIrJcRHaKyBYR+UJEfhUi/VSvzruBiNwmIktEpERECkTkryKSFWK/i0RkjneO9SIywX8mx0ip6lxV/VbdLJNxISKNROQeEfnJey/LROTOYO8lWDuAKM8Vss2AN5Pe+yJSJCK7RGSxiDwQbPa/WP8OQY7zknecKjO5edvP87Y/HrC+rYjcLyILvb/pZhH52JvIJ/AYrUTkzyLyiYisFJFSEdkgIm+KyMAQ51XvPXYSkX+JyCoR2SNupkpEpKOIPCQiP4rIdhHZ5P08Ttzsd5G89+NF5BkR+cH73O8Ukfne375xiH06i8i/vc/wThGZKyKXi8gQL8+jg+wT8bUyJhQrGTC17SngB9wkQGuAbNykQhNE5ABVvSPEfv/BzVb2Hm6Sl9NxE410oOpUw38E/o4bwOkFb3kKbojnzXF9N7H5H25Gv1dw1Q/DcJOd5InI2VoHg32IyEjc32I7btaz9biJm0YBZ4nIMaq6KciuEf8dQngSNwPhSNxnINDV3vIZv7x2x02Ek4ub4vl93ERIZ+JmRRypqs/6HaMPbgKgz3CTOxXjZqY7GzhNRM5S1feDnLstbsCvbbiZ38qBdSLSFDeL4L646YXfwk0V2x33t3sFN/BLdUbhZtr70stXY9yMoqOBISJykqr65rBH3FTVX3rv+zPv5064a/hhsBPEcK2MCS5eMx7Zy17BXsC+QdZl4aYg3Y2bi8F/21TcbFxz8JtFEvcFtxQ322Qnv/W5VMxwluu3vh5uvu9qZ86sJv8KrIxxX997WQy08VvfGJjhbbs0yPmmBqwbTQ1mVMPdxEpwN/PeAemf9NI/U5O/QzXXYT6wC2gXsL4H7gb8RZBzlwMXBaxvTcWMbh391rcKPLa3viuwGlgY4u+quOCxQcC2s7xtj4T47LaI8H33xBvYLWD9GO/4Fwasf85b/9eA9f2omPFydE2ulb3sFepl1QSmVqnqsiDrSoEncCVTJ4bYdZSqFvntsx03JWk9IM8v3SW4L+jH1W/ee1UtB/6M+6JMtDGqWuz7Rd38E7d6v/66Ds7/K9w1GqtV2yTcjpsy+FIRaRRk30j/DuE8BTTCTVPs72rcE/c/fStEpB8wGJisqi/5J1ZXcnEnLpga7rd+s7q2EgSkX4l7iu8tIsHmsC8FblLVshD53hnkmKWqujVE+sC0y1U1WKnPo97yFN8Kr9rlYlxJVqX561V1Hi5oqSSWa2VMKFZNYGqV9yU8CnfT7wY0CUjSJcSus4OsK/CWbfzWHe4tpwUmVtXlIlKAezJOpCp5wxXpluG6L9Y23zX6JHCDqhaLyLe46VB7A/MCkkT6dwjnBeAB3M3/YQARaQiMwBXp/88v7VHeslWw+nGgvbfs479SRI7B9QI5CleFEdimoQtVe1jkq+r6IOeYhpuP/hYRORx4F1dtMFf9ivWrIyLNvDydA+yPm4tEAvLkcwDuf2N2iGDjc+CqgHUxXStjgrFgwNQar6HV17ibxnRcvedmXBFzLu5JMdjTqO/JJpDvCa6+3zpf47d1IbKxlsQHA1Xypqp7RKQQd+Oqbb5rtCbEdt/61oEbovg7hKSqW0VkInCNiByvqp/i6t47AY9q5Zk6s73lUO8VSnPfDyJyDq4EYBeujn8Zrm1EOa5dxGCCf87WhsjvFq/h4V24dge+J/iNIvIkcI+q7g6TN1+w8wnQH1dN8l9gA65qDNxTu3+eqvscB1sf9bUyJhQLBkxt+hPuC+sKVR3nv0FELqZqsXEsfA0EOwILgmzvFIdz1FRHAp5KRaQ+7tpsqYPz+65RJ4Jfo84B6WrDU8A1uIaEnxKk4WBAHv6gqo9FeOwxuCL/PFVd6L9BRP6JCwaCCdlw06tiuFJEBOgLnABcB/wFV0USquGrzzBcIDBeVUcE5KkzVaef9X0OQo1rEWx9LNfKmKCszYCpTft5y8lBtoX6go7WN6GO55VM5MTpPDUR7L0OwgXj39bB+X3nGBK4QURa40Zb3IUbZKlWqOp3uKL2c0RkAHAS8FngzRvXuh/c9YnUfsAPQQKBesCxMWYZcC1PVXWBqj5OxdP3LyLME0T+2V+Ea6NwiIi0CLI92PuI5VoZE5QFA6Y25XvLIf4rReQUqtZ/xmoSruj1ehHJ9TtHPeBBkuMzfoeI7K1f9/qY3+/9+u86OP9EKq7RfgHbxgAtgYmqWlLL+XgKV5c/GVd3/nRgAlWdjatSOldEgjauFJGDvW54PvlAL/EbV8J7or8T91QfFRE5yP+z5Mf3dB7JCJH53nJIwLF7An8NTOw1qv0vrrrg/wL26QdcFmSfWK6VMUFZNYGpTU/i+qK/LCKTcY2yDgJOxTUau7CmJ1DVfBG5Bdcw7VsR+S+u+PQUXB34d8AhkR5PRHoDtwSsbiMi4/x+vylY6/UwFgILRMR/nIF9cX3PJ0RxnJh41+gGXA+Ob0Tkf7j668G4RmiLcI08a9vLwCO4hnMbcX37g/klrr79ORH5PTATN3ZEV9zf8iBcvn2N/x7BBRbfep+z3bj+/H1xYwScFWU+TwL+LiJf4q7Neu/cw3DtEB6M4Bhv4bpg/klEDsaVznTD9f9/x/s50C246oibvdKTL3FVOBfgGjH+gqq9Y6K9VsYEl+i+jfZK7xdwNO7LqhjXhe1z3JfaEEL3m9YQxxrh7TMiyLaLcVUGu3A3uonAPuGOF+IcvnyFe+VGeKypXvpGuO5iP+H6iy/Ha0AWZJ+4jzPgt+1kXCPOYi8fS4G/Aa1D5T3av0ME1+QRb98Hq0nXArgNN87BNlwR+k+4G+nVQLMgeZqLazi4EXgNODiaa+e3rQ9uEKvZ3mepBPek/wpwdBTvNQdXcrXKy/8C3IBNDUKdHxcojffOu9N7T5cD53n73FDTa2UvewV7iWrINjTGGBNX4oZaPg44QFWXJDg7KUNE7sXd8E9V1Q8SnR+TfiwYMMbUCXHTQ88E3lfV0xKdn2QkIvuo6uqAdQfjqgxKcSN27gq6szE1YG0GjDG1SkSuxRV/X4Gr8w7sVmcqzBaRpbixCbYDvYAzcA1hr7FAwNQWKxkwxtQqEcnHNWhbjmsj8p/E5ih5iciduDY1ubi2AJtwXQgfUtWpicqXSX8WDBhjjDEZLhn6YBtjjDEmgTK2zUC7du00Nzc30dkwxhiTBubMmbNRVdtXnzI5ZWwwkJuby+zZwSZkM8YYY6IjIisSnYeasGoCY4wxJsNZMGCMMcZkOAsGjDHGmAxnwYAxxhiT4SwYMMYYYzKcBQPGGGNMhsvYroXGmOSyZcsW1q9fz+7duxOdFWP2atiwIR06dKBly5aJzkqtsmDAGJNwW7ZsYd26dXTp0oUmTZogIonOkjGoKjt37mTVqlUAaR0QWDWBMSbh1q9fT5cuXWjatKkFAiZpiAhNmzalS5curF+/PtHZqVUWDBhjEm737t00adIk0dmokXHjxiEie1/169enS5cuXHDBBfz4449h07Zo0YJ+/foxduxYysrKKqXduXMnrVq1QkSYN29e0HNv2bKFO++8k759+9KsWTPatGnDwQcfzMiRIyvdxEpKSnjkkUfo168fLVq0oGXLlvTu3ZvLL7+cJUuW1PgaDBkyhCFDhtT4OME8+uijvPrqq7Vy7Oo0adIk7auvrJrAGJMU0qVE4OWXX6Zr167s2bOHZcuWMWbMGE488UQWLFhAq1atgqbdsmULL7/8Mtdffz3r16/n7rvv3pvm1VdfZcuWLQC88MILPPzww5WOsWfPHk466STy8/MZNWoUhx56KNu3b2f+/Pm8+OKLrF69mg4dOgBw8cUX8+GHH3LzzTczcOBA9uzZw8KFC3n55Zf54Ycf6NWrVy1fndg9+uijHHvssZx77rl1fu50+WyGpaoZ+TriiCPUGJMcfvjhh0Rnocb+/e9/K6BLliyptP6jjz5SQN99991q0w4ZMkRbtGhRad3QoUO1bdu2OmDAAO3YsaOWlZVV2v7xxx8roK+//nrQfO3Zs0dVVZctW6aAPvroo2HT1cTgwYN18ODBNT5OMN27d9dLLrkkrsfctWtXxGmr+4wCszUJ7m2xvqyawBhjapGv0VkkxcxHHnkkW7du3Vu0v2rVKj7++GMuuugirrrqKtatW8cHH3xQaZ+ioiIAOnXqFPSY9erViypdOPPmzeOcc84hOzubJk2acMABB3D//feHTO+rDsnPz6+0fvTo0VWetv/xj3/Qp08fmjRpQps2bcjLy+O1114D3MRyK1asYNKkSXurVkaMGFEpX2effTZt2rShSZMmHHPMMUyfPr3S8UeMGEHXrl2ZMWMGRx99NE2aNOHmm2+u9j1nCgsGjDHpo6AApk93ywTZs2cPZWVllJSUsHDhQm677TY6dOgQUV36Tz/9RP369WnevDkAEyZMoLy8nMsuu4wLLriAxo0bM378+Er7HH744TRo0ICRI0fy2muvUVxcHPTYvXv3pmXLltxyyy1MnDiRdevWRfW+vv76a4466iiWLVvGI488wjvvvMOf/vQnVq5cGdVxgpk0aRI33ngjF198Me+++y6TJk3ivPPO2xvAvPbaa3Tq1IlTTjmFGTNmMGPGDO644w4AvvnmG44++miKiop49tlnmTx5MtnZ2Zx00knMmTOn0nk2b97MRRddxMUXX8x7773HL3/5yxrnPW0kumgiUS+rJjAmecSlmuDnn1WvvFL18svd8uefa37MKPiK/gNf++yzj3799ddB0y5atEh3796tRUVF+vTTT2u9evV02LBhe9P16dNHDzjggL2/X3TRRdqoUSMtLi6udLxnn31WmzVrpoCKiPbt21dvuukmXbVqVaV0b775prZr125v3nr27KnXXXedLly4sNr3N2jQIO3atatu3749ZJrAagLf+/zpp58qpbvzzjvV3X6c6667Tg877LCw5w9VTXDCCSdo7969taSkZO+6srIy7d27d6Vrefnll4etTqmOVRMYYzJbEjxtRyQ/H8rKIDfXLQOKpuvKa6+9xqxZs/j66695/fXX6du3L6effjoLFy6skrZ37940bNiQtm3b8tvf/pZLLrmE559/HnBP4gsXLuTSSy/dm/7yyy+npKSE//3vf5WOc9VVV1FQUMDEiRO5+uqrKS8v56GHHuLAAw9kwYIFe9OdddZZ5Ofn8+qrr3L99dfTunVrnnzySQ477DCmTJkS8j3t2LGDL774gksuuYSmTZvW9BJVceSRRzJ37lyuv/56pkyZwo4dOyLab+fOnUybNo3zzz+fevXqUVZWRllZGarKSSedxGeffVYpfYMGDTjzzDPjnv90YMGAMSa0ggK46y547jm3TOaAIDcXGjRwQUCDBu73BDjooIPIy8vjyCOPZNiwYbz55puoKqNHj66S1hc4LFq0iO3bt/PCCy/Qtm1bgL3VAWeddRabNm1i06ZNHHnkkbRv375KVQFAmzZtuOSSS3j66adZuHAhr7/++t4uh/6aNWvGOeecw2OPPcacOXP48ssvqV+/PrfcckvI91RcXEx5eTldu3atwZUJ7bLLLuOpp55i5syZnHLKKbRt25Zzzz23SluDQEVFRezZs4cxY8bQsGHDSq+xY8fuzbdPhw4dqF+/fq28h1RnXQuNMaH5P23n57tXTk5i8xRKTg7ceafLY25u0uSzSZMm9OzZk++++67KtoMOOoj99tuvyvrS0lJeeuklAPr161dl+4YNG1i6dGnQfX2GDRtGv379+OGHH8Lmb+DAgZx88sm8//77IdO0adOGevXq7R2JL1KNGzcG3PvxV1hYWOl3EWHkyJGMHDmS4uJiPvzwQ2688UYuvPBCZs6cGfL4rVu3pl69elx33XVcdtllQdP4N4zMiC6CMbJgwBgTWpI8bUcsJydpggCfHTt2sGzZMg488MCI93nrrbcoKirizjvvrNLwcN26dVx00UW88MIL3H333WzcuJHmzZvvvfH6bN++nYKCAg455BAAtm7diqpWGVJ3z549LFmyhM6dO4fMT9OmTTn22GOZOHEif/nLXyIeIKp79+4AzJ8/n/333x+AsrIyPvzww5D7tGnTZm8Q8M9//nPv+kaNGrFz585KaZs1a8agQYOYN28ehx9+eEQ9IkxwFgwYY0JL0qftZDZ37lw2btyIqrJmzRrGjh1LUVER119/fcTHGD9+PM2bN+emm27a27PA3yOPPMILL7zAXXfdxdSpU7n22msZMWIEgwYNonXr1qxYsYLHH3+coqIi/vSnPwHw448/cvzxx3PppZdy4okn0qFDB9asWcO//vUv5s+fz5NPPhk2Tw899BCDBw/mqKOO4sYbb6Rr164sX76cuXPn8vjjjwfd58gjj2Tfffflz3/+M+Xl5TRq1Ignn3ySkpKSSumuvvpqWrRowVFHHUWHDh1YvHgxEyZM4OSTT96bpm/fvkyfPp23336bTp060a5dO3Jzc/n73//OcccdxymnnMKVV15J586d2bhxI9988w179uzhgQceiPi6Z7REt2BM1Mt6ExiTPNJp0CH/V/v27fX444/X999/P2jawEGHVFXXr1+vDRo00F//+tchz/XMM88ooJ9++qkWFBToLbfcov3799f27dtrgwYNtF27dnraaafpxx9/vHef4uJiveuuu3TQoEHaqVMnbdCggbZu3VqHDBmiL7/8ckTv8ZtvvtEzzzxTW7VqpY0bN9YDDjhAH3jggb3bgw06NH/+fB08eLA2a9ZMc3Jy9OGHH67Sm2DcuHE6ePBgbd++vWZlZWlubq7ecMMNunnz5r1pFi5cqMcee6w2adJEAb388sv3bvvhhx/0wgsv3Lt/ly5d9KyzztJ33nlnb5rLL79cu3TpEtH7DCbdexOIew+ZJy8vT2fPnp3obBhjgIULF9KnT59EZ8OYkKr7jIrIHFXNq8MsxZVVsBhjjDEZzoIBY4wxJsNZMGCMMcZkOAsGjDHGmAxnwYAxxhiT4SwYMMYYYzKcBQPGGGNMhrNgwBhjjMlwFgwYY4wxGc6CAWOMMSbDWTBgjDFxMG7cOEQEEWHx4sVVtk+dOnXv9ilTpuxdP3r0aESEsrKyveu2bNnCnXfeSd++fWnWrBlt2rTh4IMPZuTIkaxfv77SscK9RowYEXH+t27dyk033cSQIUNo2bIlIsLUqVNrckkq8V2f/Pz8uB3TZ+rUqYwePZry8vK4HztTJDQYEJFTROQTEVkrIiUislJE/icifQPStRGRf4nIRhHZLiJTROTgROXbGGNCadGiBRMmTKiy/oUXXqBFixbV7r9nzx5OOukknnrqKa688krefPNNxo8fz8UXX8yXX37J6tWrOfzww5kxY8be16uvvgrArbfeWmn9HXfcEXG+CwsLef7552nQoAFDhw6N/A0ngalTp3LXXXdZMFADiZ7CuC0wB3gS2AB0A24BvhKRg1V1hYgI8CbQA7geKAZuBT4VkUNVdWVism6MMVWde+65TJw4kbvvvhv39QU7d+5k8uTJDB8+nHHjxoXdf9q0acyaNYvXX3+dYcOG7V1/9tlnc9ttt1FeXk69evUYOHDg3m2+p+2ePXtWWh+N7t27U1RUBMCUKVP2BhiZqqSkhEaNGiU6G3UmoSUDqvqiqv5ZVV9R1WmqOgE4F2gBnOclOxs4FrjUS/++t64ecHNCMm6MSVqFhYk9/6WXXsqKFSv4/PPP96577bXX2LNnD8OHD692f98NuVOnTkG316sX3df26aefzhFHHFFl/Zo1a2jQoAGPPvoowN7AJVbbt2/nlltuYd9996VRo0Z06tSJ4cOHs27dupD7iAijR4+utC4/Px8RqRQ0zZo1i6FDh5KdnU3Tpk3p2bMnv/3tbwFXzXLXXXcB0LBhw71VJD47duxg1KhR9OjRg6ysLHr06MG9995bqRTBV+3y6quv8pvf/Ib27dvTsWPHGl2PVJPokoFgfP/Ku73l2cBqVf3Ul0BVN4vIW8Aw4Pd1nD9jTBIrLITs7MSdv3v37hx33HFMmDCBQYMGAa6K4JxzzqF58+bV7n/44YfToEEDRo4cyZ133smQIUNo06ZNzPm57LLLuPjii/nhhx/o27eiBvY///kPABdffHHMx/YpLS1l6NChzJ07l1tvvZWBAweyefNmPvjgA4qLi2t0Y922bRunnHIK/fv3Z9y4cbRo0YL8/Hy+/PJLAK666ipWrlzJc889x+eff079+vX37ltWVsYpp5zCDz/8wB133MHBBx/MV199xZgxYygqKuLhhx+udK7rr7+e0047jQkTJrBr166Y85yKkiIYEJH6QH2gO/AAsBZ4ydt8IDA/yG4LgMtEpLmqbquTjBpjTAQuu+wybrzxRh577DGKi4uZMmUK7733XkT79uzZk6eeeoobbriBc889FxGhT58+nH766fzxj39kn332iSovw4YNo2XLlkyYMIH7779/7/oJEyZw8sknx+UJeOLEicyYMYM33niDs88+e+/68847L8xekVm0aBHFxcX87W9/45BDDtm73tc4smvXrnTt2hWAAQMG0KBBxW3txRdf5PPPP2fatGkcd9xxAJx44okA3HXXXYwaNYoOHTrsTd+/f3/+9a9/1TjPqShZehPMBEqAxcAhwAmqut7b1hbXTiBQkbcMGjKLyNUiMjvUa8OGDfF+D8aYBCkshMWL3Qsqfk5UlcH5559PSUkJb731FpMmTaJTp057b0KRuOqqqygoKGDixIlcffXVlJeX89BDD3HggQeyYMGCqPLSpEkThg8fzqRJk1BVAL7//nvmzZvHZZddFtWxQvnwww/p1KlTpUAgXnr16kXr1q0ZOXIkEydOpKCgIOJ933//fbp3787RRx9NWVnZ3tfJJ5/M7t27+eqrryqlP+ecc+Kd/ZSRLMHApcBA4JfAFuAjEcn1tgmgQfYJW8Glqs+oal6oV/v27eOZf2NMAmVnw/77uxdU/Jyo6oIWLVrwi1/8ggkTJvDCCy9wySWXRF3X36ZNGy655BKefvppFi5cyOuvv763y2G0LrvsMgoKCvZ2FZwwYQItWrSo1ECxJgoLC+nSpUtcjhWoVatWfPrpp+yzzz789re/pVu3bhx00EFMnjy52n3Xr1/PihUraNiwYaVX//799+bbX+fOnWvlPaSCpKgmUNWF3o8zReQ9IB/Xq+AaXAlA2yC7+UoEgpUaGGNMQl122WWcccYZlJeX8+KLL9b4eMOGDaNfv3788MMPUe87ePBgunXrxsSJExk8eDAvvvgi5513Hk2aNKlxvgDatWvH/PnBanPDa9SoEaWlpZXWBd6gAQ499FAmT55MWVkZs2fP5v777+eCCy5g3rx5HHTQQSGPn52dTY8ePfjf//4XdHtubm6l32vaiDKVJUvJwF6quglYCuznrVqAazcQqC/ws7UXMMb4S2TjQX9Dhw7lggsu4JprruHAA4N9hQW3cePGoI3Xtm/fTkFBQUxPryLCJZdcwiuvvMK7777LypUr41ZFAHDyySezdu1a3nrrraj26969e5Ug4p133gmZvkGDBgwcOJAxY8ZQXl7OwoXuOdLXBXDnzp2V0p966qkUFBTQvHlz8vLyqrzatWsXVX7TWVKUDPgTkY5Ab2CSt+pN4AoRGayq07w0LYGzgP8kJpfGmGSVLMFA/fr1YyoRmDp1Ktdeey0jRoxg0KBBtG7dmhUrVvD4449TVFTEn/70p5jyc9lll3H//fdzzTXXkJOTw+DBg6ukee+999i+fTvff/894MY82LhxI82aNeO0004Leexf/epXPPvss1x88cXceuutDBgwgK1bt/LBBx9www030Lt376D7XXTRRdxzzz3ce++9DBw4kOnTp1e5Zm+//TbPPPMMv/jFL+jRowfbt2/nscceo0WLFhx11FEAe3tJPPzww5x22mnUr1+fvLw8LrnkEv79739z4okncuONN9KvXz9KS0tZtmwZb775Jq+//jpNmzaN6Xqmm4QGAyLyGvAN8B2urcD+wB+BMsDX5+NNYAYwUUT+TMWgQwL8ra7zbIwxtWngwIFcddVVfPLJJ4wfP57i4mJat27NkUceyUcffcQJJ5wQ03F79+5NXl4es2fP5tZbbw1aJH7ttdeyYsWKvb/7xgDo3r172GGEGzZsyIcffshdd93FM888w1133UV2djbHHHMMbdsGq+V1br31VjZt2sTYsWN54IEHOP3005kwYQIDBgzYm6ZXr140adKEMWPGsGbNGlq0aLH3Wvh6EZx55pn89re/5cknn+Tuu+9GVVFVGjZsyAcffMADDzzAM888w08//USzZs3Yd999OeOMM8jKyoryKqYv8bUuTcjJRUYBFwD7AllAATAVuF9V8/3StQUeAn4BNMYFB39S1XmxnjsvL09nz54d6+7GmDhauHAhffr0SXQ2jAmpus+oiMxR1bw6zFJcJbRkQFX/Cvw1gnRFwK+9lzHGGGPiKOkaEBpjjDGmblkwYIxJComssjQmnEz4bFowYIxJuIYNG1bpFmZMsti5cycNGzZMdDZqlQUDxpiE69ChA6tWrWLHjh0Z8RRmUoOqsmPHDlatWlVpDoN0lHTjDBhjMk/Lli0BWL16Nbt3764mtTF1p2HDhnTs2HHvZzRdWTBgjEkKLVu2TPsvXGOSlVUTGGOMMRnOggFjjDEmw1kwYIwxxmQ4CwaMMcaYDGfBgDHGGJPhLBgwxhhjMpwFA8YYY0yGs2DAGGOMyXAWDBhjjDEZzoIBY4wxJsNZMGCMMcZkOAsGTHIqKIDp093SGGNMrbKJikzyKSiAu+6CsjJo0ADuvBNychKdK2OMSVtWMmCST36+CwRyc90yPz/BGTImRlbCZVKElQyY5JOb60oE8vPdMjc3wRkyJgZWwmVSiAUDJvnk5Lgvzvx8FwjYF6hJRf4lXPn57mWfZZOkLBgwySknx744TWqzEi6TQiwYMMaYeCooqCjVshIukyIsGDDGmHgJ1k5g0KBE58qYallvAmOMiRfrCWNSlAUDxhgTL9ZOwKQoqyYwxph4sZ4wJkVZMGCMMfFkPWFMCrJqAmOMMSbDWTBgjDHGZDgLBowxxpgMZ8GAMcYYk+EsGDDGGGMynAUDxhhjTIazYMAYY4zJcBYMGGOMMRnOggFjjDEmw1kwYIwxcVZYmOgcGBMdCwaMMSbOLBgwqcaCAWOMMSbDJTQYEJHzRGSyiKwQkZ0i8qOI3C8iLQLSHSoi74vINhHZIiJvish+icq3McYEKiyExYvdCyp+tlICkwoSPWvhTcDPwG3ASuAwYDRwvIgcrarlItILmA7MBy7B5flO4DMROVRV1yck58YY4yc7273ABQH775/Y/BgTjUQHA2ep6ga/36eJSBEwHhgCfAKMAvYAp6nqJgARmQksxQUTN9dlho0xJpTCwoqAwJhUktBqgoBAwGeWt+ziLQcCM3yBgLffSlxJwTm1mkFjjImCr0rAAgKTapKxAeFgb7nQW+4BSoOkKwH2FZHGdZKrmigogOnT3dIYk/YsGDCpJtHVBJWISBfgbmCKqs72Vv8IHC0iDVV1t5euBXAgIEAbYE2QY10NXB3qXN26dYtz7kMoKIC77oKyMmjQAO68E3Jy6ubcxphaV1hYuZGgrwGhfxsCY5Jd0gQDItIceAMoA67w2/QP4HzgaRH5Cy7PDwPNve3lwY6nqs8Az4Q6X15ensYh29XLz3eBQG6u+zk/34IBY9KINRw06SApqgm8ov43gZ7AKV6bAABU9QvgOuA8XI+DfKA1rpFhKVBUx9mNTm6uKxHIz3fL3NwEZ8gYY4ypLOElAyLSEJgM9AdOUtXvA9Oo6pMi8hywH7BFVQtE5D1gpq/qIGnl5Liqgfx8FwhYqYAxacuqBUyqSmgwICL1gEnAicAZqvpVqLSqWgIs8PY7GDgJuKwu8lljOTkWBBiTASwYMKkq0SUDT+DaA9wLbBeRgX7bVqrqShHpClwLfInrQXAEbpCiV1X1xbrOsDHGGJNuEh0MnOYtb/de/u7CjUa4GxgAjARaAMtwPQ7+UTdZNDEpKLCqEWOMSREJDQZUNTeCNOtwVQImVVh3SmOMSSlJ0ZvApBn/7pRlZe53Y4wxScuCARN/1p3SGGNSSqLbDJh0VF13SmtPYIwxScWCAVM7QnWntPYExhiTdCwYMHXDVxqwbl1ih2e2UgljjKnCggFT+/xLA3btApHo2xPE4yZupRImAoWFNniQyTwWDJjaFzhZ05lnQseOkd/Yq7uJRxoo2KRRJgRfAOCbgdCCAZNpLBgwtS+wd8GAAdHdhMPdxKN52rdeDibA119D//6VgwFjMpEFA6b21XSypnA38Wie9m3SKBPgv/91wUBxsZt++KefoEcP9zNUnp7YmHRmwYCpGzWZrCncTTzap32bNMp4Cgvhs89g5kzYtMm9AOZ9vIF+rVaQfVBnsrO7JDKLxtQZCwZMagh1E7enfROlr7+G77+Hbdtce9b582HtWhg6FFrvXAOfjGX/Zqvgkxq0TzEmxVgwYFJfuKd9+/I2ATZvdjf/jT9tprRQWfujsnF3GzZvhtYbVpFdf1PN26cYk2JsOGKTvnxf3s8955YFBYnOkUkCrVpB16z19Jw7GS0upstnL7Jfqw20aoWrGqDQ1R3s2hW6fYrNuWHSjJUMmPRlXQnTm6/UJysLSksjKv0pLARV6FRvHZ3aFLKnQWP21Msir/NK9t23Pdk7yt04GKpu6b9vq55kW28Uk6YsGDDpy7oSpi9fqc/mzfDdd3DIIe6Rv5qi++xsKCqCbR3bsWlXfQ5o9DP1y0vZ1qwTRUWQvTYfGjWCgQOrBJCFjbuQbe1TTJqyYMCkL2tcmL58pT7Nm1deRlD607YtFPXqTP7Zl9M+awerj+hF5/ZtKSoK8fTvK4Eo3w8GW28Uk54sGDDpzboSpidfqc/mzW65bZsrGYig9Mc3dsB++2WzLSubbdsgL883nkCXSgFkYXE9Csc8ubfR4GJ+B5072/gDJu1YMGBMvFjPhbrjX+oTRZsBH9+wwy1auDjik0+gXz8vUPALILPzp5PdbBXk5rJ43k72r7cU9u9ce+/LmASxYMCYeLBuZ3WvBqU+RUVutMEWLWD5cjfiYMuWIGvXkC1LKwKLSu1Ouli7E5O2LBgwJh6s50JK6dXLtR0oLIR33oGLL4Yejdegj4+lsP4mspuXVAR0XglEdquekGMjEpr0ZMGAMfGQiT0X0qRapHFj74dVq2DrVmgjri2CL6DzXtZEwKQzCwaMiYdM67mQDNUiNQhGlixx1QPgJiqaPRto3oyeC+aTXf6jixCysmw6Y5MxLBgwJl4yqedCoqtFahiM9OrlXgAffAAHHggDChZA/eXQqIk7bkEBhW0GWDBgMoINR2yMiV6iq0XiPDTwfvt5PzRs6MYsaNiwhhk0JrVYyYAxJnqJrhbxBSMLFsDOnVBcDNOnx5SXnj29qoABA+CIIyjZsJldXVpR2H4A4HoaADa2gElroqqJzkNC5OXl6ezZsxOdDWNMrGbOhDFj3DwCS5dGPCRxWAHtEBYvhv33j2emTboSkTmqmpfofMTKSgaMMamptBTatXMTCi1aFNWQxCFlUrsPY/xYmwFjTGryVRVs21Z5Gcf2C1YtYDKFlQwYYypLlfEDajgkcSj+3QktGDCZwoIBYzKd/80fEj9+QDRqoVjfxhYwmciCAWMyRbAn/sD++qeemvHDKhcXJzoHxtQ9CwaMyQShBukJHDwIgo4fkO5Py75ZDAF+/hnatHE/W3dCkyksGDAmzfhuauB3Iws1YmDg4EEDBrhXQAlCTYOBJUsqRvwLlt9E33D9b/rTp1t3QpN5LBgwJoVEcuMMGgyEGjEw1OBBca4aWL48uYMB/5IBsIGGTOaxYMBkpiRvMR/qBhn2xul7T+X7QefOlbeFGzEwRCO8TL1BtmyZ6BwYU/csGDCZJxlm3KtGtE/LhR/OYeltz/Pzjmy2NFsAF5wPbbOZNw+6dXNj72dH2fLe/6Yfy0h8/jMDgpsQCNzwv23b1kKgUYMAz/+8P/1k1QQm81gwkA6S/Ck36SR6xr0oVfuEXlBA9l9vJnvxLAY0aMDiJv2gUS4MOjWhN7XAmQFPOaXy9poEGlXEMcBr3bqGeTEmBVkwkOpS4Ck36SR6xj0//iUA4W76vptl0BvnhAluON7ycvd7WVnc85n01QJxDPD2zmBoTAaJOhgQkd5AN6AdsBNYD3yvqlvinDcTiRR7yk0KgSPX+brUJeC6BY52F/XT8ptvwv33u5n7ysshK4vsw7rBkIMhjjfwmgYDPXvW3rGBuAZ4SR/4GFMLIgoGROQE4ErgJFwQEKhcRL4FXgGeV9WN8cuiCSuJnnJTiu/Gn2KlKlVuVJ984padOsHGjXDUUWQ//QDkdKnzvIUTqicBxOnmm+gplY1JcWGDARE5F7gX2B8QYBXwBrAWKAKa4J4/egOHAnnAXSLyAvAXVV1Xazk3jn0Jxi5BpSqRtNLPzq4oNQg7Vv4JJ8C//w2bN0OjRjByZOZ+BqJsIJkMXRqNSRYhgwER+Qw4FlgI3Aq8pKo/h0mfBRwPXA78CrhIRC5V1TfD7HMecDEuiOgA/Ay8Ctynqlv90h0IjAEGAq2AfOB54B+qGv8K0lRj067GJkGlKpFUB2Rnu22hgoK9zj7btRn45BMXGJx9dq3nP11YMGBMhXAlAy2AX4S7mftT1VLgA+ADEekA3AYcUM1uN+ECgNuAlcBhwGjgeBE5WlXLRWQfYCquVOIGYCNwIvAgLoAYFUn+jKkilUpV3nyTwsnfw9DDyf7VaZW3nX22BQHGmBoJGQyo6mGxHlRV1+Nu3NU5S1U3+P0+TUSKgPHAEOAT4ExcO4VjVNUrUOUTEdkXuAwLBkxNJLhUJfDJ1FeFUFwMmza5Pu+8/x7FTz3EJm1N8StTyG65227+McrUgZSMqU5CuxYGBAI+s7ylrwVUlrcM7K2wCahXC9kyps4E3oACqxCyd61i9ssv06Z0PWTBprLWLJ78PdnHnG03rxjUdCAlY9JVjW+mItJORM4RkVNEpH4c8jTYWy70li/jqgbGikgPEWkpIucAlwIPx+F8xkSmoMDNYlNQUGenzF48g9Y7VlOsrZlfsh89JB8GDqzyhGuMMTURccmAiFwLjABOU9Uib90RwPtAWy/ZbBE5QVW3x5IZEekC3A1MUdXZAKq6TkSOwvVi8A1uqsBoVf1bmGNdDVwdanu3bt1iyaLJVJEM7hTnkSCzs4HFi2mrRRS1aUvXkmK4+Ffsf+2JNT62sWoBY/xFU01wIaC+QMDzINAG+DfQETgDuIYYnthFpDnuhl8GXOG3vj2uh8F24DygEDgB+D8RKVHVvwY7nqo+AzwT6nx5eXkabR5NBquuG2ItjASZvXQmhc9M5unNv6RvgyUc1LecnzoNhGlryD6os93MasiunzEVoqkm6AV85/tFRNrhivSfU9WrVPUsXH3/L6PNhIg0Bt4EegKnqOpKv803A7ne+smqOlVV/4ILRMZ4+TCmdlXXDdE/WCgrqxjVsAYKn3iJ19ceyXeNjqRrs2I2bWtI628/JfvZB8jeUXdVFcaY9BdNyUA2buhhn2O85Wt+66bjqhIiJiINgclAf+AkVf0+IMnBwFJVLQ5Y/zXQENgP16bAmNpTXTfEOI9ZUDjxPeb8dwlvl14G7GBl/Q7QpCetOzeDbett2GljTFxFEwwUUXko4sFAOfCl3zoFGkd6QBGpB0zCjRtwhqp+FSTZWuBoEWkTEBAM8JarIj2fMTUSrhtiJGMWRNGm4K1xG/lf6bUsYn/6MY+X6l9C9vZy6v/wPQP6ltiw08aYuIomGFgInCUitwN7cG0IZgVMUJSLu3lH6gngfNyQx9tFZKDftpVedcHTwCXAhyLyIK7NwBDcgEWvqaqVl5rkEC5YiKJNweTJsLDp4SjLGcRnfM2RHLd9JqeO7Ms5Z/WF3NOtVMAYE1fRtBn4B9AZN1JgAdAJeNK30etWeCwwL4pj+oZSux2YEfC6CsArLRgEbPDy8CZwLq7XwSVRnMuYxImwTUFhIWzZAqX7Hsiqet3ZQkuas43+e76g+6xXYNAgl7COuzgmpQR09TQmXUVcMqCqb4rINVR015ukqhP9kpyEqyL4IIpj5kaY7ivg9EiPa0zSibBNwbJlbibigw6Cd+o1gXI4l9c4m7doX3JArfRaSEl2HYyJq6hGIAzXXU9VP8B1MzTGBIphHoQt2hylnA5s5Cd60D4rC954w81QeOCBdTrTYtJJ0IyTxqSraAYd+gswVVU/C5PmWOAEVb07HpkzJq1EMA9CQQHMn+9+3l3egFZsYT4H0ppN9P/6A2jWDJYudQlatcrchoQJmnHSmHQVTcnAaO8VMhjA9TC4E1efb4yJUk6OqyIAKCOLi5gMlNOT5bBzp5u96JBDYPBgGDYsc5+GU2nGSWNSQLwn+mmA625oTOTSuSFYDd5bacMm7M7uCAjiWzl7NpSUZHYg4JOT4xpUZvp1MCYO4j1r4RHYAEAmGuncEKyG761+wyym5t3EpdOuovWuze4YzZrBkUemzzUKJs5zPBhjqhc2GBCRTwJWjRCRIUGS1gdygO7Ai3HJmckM6dwQrIbvrVkz+GjVwRx45p3sfv0eOtT/xpUK7LOPSxDsppnqN9J0Dg6NSWLVlQwM8ftZcYMK5QZJV44bDOi/wB/jkC+TKdK5IVgM761NG+ja1f3cqhVs3Qo7O/ekzRH7wqoCFww88ohLMGNG5ZsmpP6NNFWCw1QPuowJEDYYUNW9bQpEpBw3bbA1DoyGfWmEl84NwWJ8b59/7noUbN8O9erBs+905rOii7lwT32G75zkGhHedBMcfbSrMvDdNMF1O2ze3C2T9UYaTioEh1Z6YdJQNG0GrgC+ra2MpCX70ohMBF3uUlaU761XLzj0UNi2zfUgLNu1i2ab17CwdD/YUwJ79riEmzbB999D+/YVN83Vq+G77yo+b1lZtfGOalcqBIepUnphTBSi6U0wmOBVBHuJyJki8nyNcpROamFaW5PmCgoY0mgGxxywgf32g+LCcgZkL2eDtGduo/78qD1B1aVdvRo6dKgIMktLXbfD445zy9LSxL6XWCV7L4FUKL0wJkrRlAyMAPJxcwOE0g+4HPh17FlKI/alYaLhlSRll5VxfFkrth5zBwvmNqO4rDlt6m1lSbPD+G7XAOqVQi+WuaDg3Xfh+uvd/rm5rqFBWVlqDEiUqlVoqVB6YUyU4t21sBFuRkMD9qVhouNXkpSdn8/6xZugYTumlwygZcddzCo9Cu3WlC1Ln6RXvZ+gfn3XoPCdd+Caa+r+81aTm3mqV6Glc9WWyUjRBgMaaoOINAKOI7opjNOffWmYSAWUJI26oxGHnw+vvtqIt99uxDXXQKdO/fn6ucZ0W1bG0C2TYc0auPtu193w7LPr7vNW05t5Jta7p2pJiMkI1Y0zsDxg1R9F5IogSesD7XElA0/HKW/GZJYgT/aHN4WWLeGrr1ySQYNg/vxDWNvhbAq/nEP27rVu3uPbb4fDDqu7m0xNb+aZVoWW6iUhJu1VVzJQj4rSAAXEewXaDXwPfAzcE7fcGZNpAp7ss7Pd68QTYckS+OknaNcOPl88kDY7DmbA9g20l92waBFccQXcey8MGFD7+azpzTzTqtAysSTEpJTqxhnI9f3sjTPwiI0zYEzdGzkSnn4aVq6EjRuhpGkbZnY4i10/Kf10Lr3KlsLHH8OcOfDQQ3DllbWboXjczDOpCi3TSkJMyommzcDxuN4Expg61qsXnHoq/PADzJ+9nYPWTWPPjl0s5AA20RRQ18Ng0ya47jooKoI//7l2M5UON/O6qsfPtJIQk3IiDgZUdVptZsSYjBPljWjoUDj8cNg+cykbvlvL4HZzaNZyBa13rqFwd1vA63JYUuLaEBxwgGtUWFdSrYFcXdfjp0PwZNJW1F0LvV4DRwJdcA0Gq1DVF2qYL2PSW4w3ouxsOOmQDbz0jjJ7ex/yGu9geesjKChsgmzfwkj+RTZFsHs3jBkTPBiojZt2KjaQs3p8Y/aKKhgQkV8DfwPahEqCa2howYAx4dTgRtT/kgPY/N4MCgqbsnLPQdCsGV33b0X+1OV8XDYYQTmf12H2bDj3XPjHPyrPahjpTTuaoCHa95MMpQhWj2/MXhEHAyJyKvAvYAFwL/Aw8DrwNW52w5OBl4F3451JY9JOTW5EOTkMnTiCJdPXMv2Htnz/+hLabdxCg32789aP3SmlPt0poD9z4PXXYcECePBBV0oQ6U072if9aN5PspQiWD2+MXtFUzJwI26a4qNVdauIPAzMVdUHgAdE5ErcGAOP10I+jUkvNb0R5eTQ65c5sARadGnJlhVFkN2ODS98A/PnodSjkLYUaRt6LV4Ml1/uehm0agW7dlV/0472ST+a95NMxfNWj28MEF0wcDjwhqpu9Vu3d6IjVX1ORC4FbgdOi1P+jElfcbgR9eoFX3zRnuc+a09BAbRrN5TW2e35vvBg2rGeXizjBh6n3aYiuOEGN4lRy5Zw5pluPIJQ54+l5CLS92PF88YknWiCgWbAGr/fdwEtA9LMxiYpMqZOjRgBZ50Fn34Kb78N9DuU2wZ8zE+3v8vs4u68wRkczA/03zYHpk93EcTaakYND3zSB7dvPIrTqytFSIb2BMZkmGiCgbW4IYd91gAHBKRphRua2BhTh7Kz4fjjYcoUWLcOXv7pcDrlDISthbxddhaz6M8C+nLF1gnwzTdQXAxz54avr/c96ddGHX+oUoRkaU9gTIapV32SvRZQ+eY/HThRRAYBiMhBwAVeOmNMHcvOdmMRDBsGneutZ229zmzr2ptuspLGlPIV/VnMvi7xTz/Bt9+6J/Dq+Nfxl5VFtk84BQWulKGgoPbPZYyJSDQlA+8Bj4rIPqq6GtfF8HxgqogUAW1xXQttbgKT/NK0KHr4cLf8aFJrZr2RzbzNncjOKiC74VbmbevOI/yeE/kUBc6f95Z7Cv/97yuPRxB4beJZx1/dk7/vXAsWwM6dkJUV+7mMMRET1ZCzEldOKNIQd8MvVtVSb91A4P+AfXFDFT+qqh/UTlbjKy8vT2fPnp3obJhEyJCi6MLvVnHHX/YwZVYrspuWMGDXVFj5M8W0YSNtGcELbjyCBg3cxAdXXhn62gQGCLEGU9Onw3PPVfQkuPJKNxWjv5kz3YBJTZq43g9p+vcx6UVE5qhqXqLzEatohiPeDawLWPcVcGa8M2VMrUqmrm21KPuQLox5Dto+4u6rU6dewBMX/4tpj3/Lu7sGM4UT6cf3SBn0uv56t9P++we/Nv51/DUJpiIpZSgtdVMzpvnfx5hkEvVwxMakvAzq2pad7WoAfvjBzXJ84caraN19JfV/XMB3HMxyerIfS3hq5w1wzTVw881Vr01gKUBNgqlIxiPIoL+PMckimhEI84EpwKfAJ6q6JvwexiSpDBt5rn9/91q3Dtavh08+6cqWpvU4dsd7rGYfZnMkr/ALtAzOf/RR+N3voEcP93S+ejU8+2zlUoCa3qyrG48gw/4+xiSDaNoMrAU64OYeAPgR+Bj4BPhUVTfVRgZri7UZMJmosBD+7//g7ddLaLZxBReU/YcV5NCLJUzlOP7MIxzWfCkdWpTAYYeBiKtjOPDAynX8odoMpGnDTGOqk0ltBjqJyIHAicBJwCDgOuC3QLmIzMMLDlKlEaExmSY7G664AkQaMffrrswpPo/D1n/E4du/Y47mUUAO5buyOGLnHDpMnQpt20K3btCsWerMOWCMiVpUbQZUdQFuHIHHRKQekIcLDk4AjgEOw81hYG0RjElS/fvDvvvCW281ZdJzPZi8cTjr2vega/Ea3io7mx1ljRnIDH61YxLtdhSTvW4WHHMMXH99+EGIMqRhpjHpKJpBhypR1XJgG7Ad2Ansxo0zEPMxjTF1IzvbDWN8zQ3NGHJWK3qcfQhT97mYow/bRV/5kZ/pxsucyxucwZLd3dw0yB9+6HYONTCQNfwzJmVF9QQvIt1wJQG+0oCOuADgZ2AyFW0IjDEpYPhwyMlpRZs2rXh7AcxueRr7Zb+EbCxmIx1Ywv6soxP7ly5h+J/+5HZq1QpKSoLf9E891S3DTYJkjEk60fQmWAL09H7diOtV4GsjsKwW8maMqQP9+7vlsGEwd24rPt3nV+ToDLoUzmU1XVhCLxpSQrMt2zn1ppugXz838+Hw4RU3/cCqgwEDKp/EGhYak9SiKRnYF9eT4CPgUeAzVd1RG5kyxtS9UaPg66/hrbea8dVXJ7FtZQ+6rPyWVduERfTiaa5m++aWDP/mQ1cy0LIlnHee2zlcewFrWGhM0osmGLgHON57DQV2i8hMXOnAx8BMVS2LfxaNMXWlf39o08b9/Nln+zJ9R1da6k80376LeRzGKu3M3K0HMoa7YdIk2L4dHnssfHsBa1hoTNKLuLGfqv5FVQfh5ic4CxgLtADuBD4DikTkHRH5U63k1BhTJ3r1gssuc10Qc3o0Ykt2D0o7dGG/jttpSDmfcSwfcoJL/PrrMHiwa1z4m99AXp5b+t/ss7Jg40Y3+ZA1LDQmKUXdBVBVtwPvei9EpC1wKXArcBpwKvD3OObRGFNXvLr9Xrm59BqRQ4sW8Pzzjfjuu0bs6tiAk3c/xxdF+/Mpg9lMSw7le3r9tKwiAOjfH+bOhX32qWhL8OyzbuCinTvhppusVMCYJBRTN0ARaSUiw0TkMeBz3M2/g7e5MIrjnCcik0VkhYjsFJEfReR+EWnhl2aciGiI16JY8m+MCcJXt//cc25ZUMDw4a6Kv08f2Li9OV/2vZKjem5kJy14mzN5mXMppC2ows8/w9tvu4kQfN0NfVUEBx7ohjcuLY08L9Onu6UxptZF05vA16XwROBwXCAhuLEG3sN1KfxYVedFcf6bcN0SbwNW4gYtGg0cLyJHe2MZjAGeDtgvF3gReDOKcxljwglRt9+/P9xzD/z3v1Be3pzv8y/l97+fx9yHPqD5yg3M5nDaUEx/5sCuXTBjhgsoBg2KbewBa3BoTJ2LpprgI29ZAkzHu/kDX6vqnhjPf5aqbvD7fZqIFAHjgSFUdFus1HVRRIZ6P46P8bzGmEBhbty+hoVffAEzZ8JjU/ux7yndaPq/f/P51mPZQFtGc68LCADGj4cuXeDee8NPOuTf5RDcz+vWWYNDY+pYNMHAA7ib/xequiseJw8IBHxmecsuYXa9DJjjDY9sjImHamYL7NXLvQA2b4b8/DY0u+ZyBr3xL95Z3IopnMBmWjHUN+7YfffBwoVu9MJBg6qez78EoKTEVTU0buxKF0RsJENj6lA0ExXdVpsZ8TPYWy4MtlFEjgH2A35fR/kxJnNUN70wbhjjyZNdB4KZxdl07DuKTWU/8ebyTswij1V0YQQTXOLXXoNvvnF1DIEDEflXS3z1lQsAevd26888Ezp2tEGKjKkjSTWhkIh0Ae4GpqhqqPmFL8PNg/BiNce6Grg61PZu3brFmk1jMt7w4bB1q6syWLoUWh/SgwbNm7BtYRlv7j6DY/iSXr7avRUr4OyzXUnBlVdWHMS/WqJNG1cy4CsNsOGMjalToqqJzgMAItIcmArsA/RX1ZVB0jQC1gKfquq5NTlfXl6ezp4dKt4wxkRi8mR4+GFX4t+7N7BrO4tmbeGQkpmcz6sVJQTgbvJPP105IAjWZsBKA0wKEpE5qpqX6HzEKilKBkSkMa5nQE9gcLBAwDMMaI01HDQmKQwf7kYlfvppWLMGOnZsRt5pzVg/vReTCi+mGVs5n9dd4rIyuO46mDULbr+9okrC/8ZvQYAxCZHw6YZFpCFuxsP+wOmq+n2Y5JfjJkl6ty7yZoyp3tChcNZZ0KwZbNvm1un+B7KxxwBernchX3NEReKSEvjnP+H4420MAWOSSEKDARGpB0zCjV0wTFW/CpO2I3Ay8B9V3V1HWTTGRGDECDjqKDdVwYwZbt2uRm2Z1vh0fsvjlQMCgGXLXLfDmrCBiYyJm0SXDDwBnA88BGwXkYF+r64BaS/BVWtYFYExSejuu11PwZ493VABbdpA224t+anJwYytfz0/sm/lHSZMcC0QYxFktERjTOwSHQyc5i1vB2YEvK4KSHs5MF9Vv6m77BljojF0KAwbBvXru/tz48ZQ3qg5n7Yezv3cwhL/gGDHDtfoIJaAwL9bYllZxfDHxpiYRN2AUEQ6AHlAG6B+sDSq+kIkx1LV3EjPq6r9Ik1rjEmcUaPgu+/caIX5+dCpE2zY0JzpLYfxwpYCN/2xz6pVLnqYNSu6xoOxDHNsjAkp4q6FXkO/p3H9/EOVKAigqho0SEgm1rXQmNpTWAh33AFvvOHGIxgwABYtgnYlBfxuw+1c6d/lEODQQ+Hbb0Mf0L8Loi9oCLYuWfjylpXlJmdKxjyauMqkroVjgCtw8wRMAgqAstrIlDEmtWVnw5gxbmTh11+H5cshLw8WLcphvNxM1/WrOMU3bDG4aY9vvz14o8JQExdFMFpiQvjyu3mzKyI55BBo1comXDJJLZpg4JfAYuAwVd1ZS/kxxqSJ7Gx48EFYv951Hli3zpUQzJx5EOMb/B9tVm+umNgI4G9/c6MV3n9/5ZtmiNkU4yqepQy+/DZvXnkZLN/JXLphMko0DQg7AO9aIGCMiVR2NvzlL9C3L+zc6R6W27SBhR2O5+1Ov2EDbSsSl5XBpEluXgL/3gG13T4g3j0TfPndtq3yMjDf1iPCJJFoSgZ+BlrWVkaMMempf383KNG4cRUPwYsWwWvtRrK7TLl/47WVd1iwwM10+Ic/VFQFhJsGuaYiLXmI9CneP7/h2gzURYmHMRGKJhgYB1wnIq1UdXMt5ccYk24KChixbz7bju/D+LfbMXs2nHiiGy9ofNkltGYZo3ioIv2ePW7Cg3nz4Pnna799QCQlD6HaLYQSSX6tR4RJItEEAw8A/YApInIzMEdVt9ROtowxacHvJvq7Bg2QM+9n7EvtmTkTBg2Ct99uwStdb6Dfyu84lQ8r7zttmhuDoCZP6ZGIpOShNp7ia7vEw5goRBMM+IYAFmAKgIgES6eqmhQTIBljDbQSLOAmetERS5mzov3e+3z79lBQ3IVH9n2c7ctGMdw3qRHA7t1w001VpzOO9ik9EtU9ydfWU3yy9ogwGSeam/Z0IDnmOzYmErVx0zDRCbiJZvfryhkl8OOP8PPPrkq9YUOYv3N/Xt/nWg5Z/T29WFax/4oVrghh+vSKv10i6trtKd6kuYiDAVUdUov5MCb+rIFW4gW5iQ7PcQMRPfEELF3qxh/4/nuY2/5k/qH3MnbNRZWPsWIF3HorTJzofk9UXbs9xZs0ZsX5Jn1ZA63kEOQmOmKEW44e7XoWHHwwbNwIb2ddyDFdZ3LxykcqH2PSJLjgAjj77Oqf0uu6aijdz2cyQsTDEacbG444Q9gXZ9L6+mtXizNnDjRr5mY7nD0benXfzr0/nMfQ3e9X3qFjR1i7NvxB67pqKFXOZ/8HtS5thyMWkb/g2gg8oapF3u+RUFUdE5fcGVNTVrRb9yK88fTvDy+8AFdf7ToOrFzpdlmxuhmjWz9M+YbSykMWr1sHRx8NX34Z+tx1XTWUCueztjMmAuGqCUbjgoH/AkXe75FQ3DwGxphME+WNJzvbzXK4Zo37fedOaNsW8rf0ZeY+vyBv9VyyKarYYcYMOP98ePnlquf1DfJTl1VDkY5REK+n8liqvqztjIlAuGDgeG/5c8DvxiQfKwZNDjHcePr3h5NOgilT3J/xsMNcdcGjcj29eq7k4uV/q7zDG2+4fokDBrjfAwOQ3/ymZjMFRvNZChxtMD8fVq+uOD/E96k8ll4N1nbGRCBkMKCq08L9bkzSsGLQ5BHjjecPf3DVBIsWuft8ly6u++GtLf/KivbZ3LJhVEXi3bth2DCYNcv9nQMDkNJS1x0xFrF8lnzbg81UeOqptTNYUTTHiHe3SAu805L1JjCpz4pBk0eMNx7fDIddusCECVBc7O6lTZrA/9rezLEt5nLs8hcrdli3Ds47z0UO0QQg/jcyqJrP6j5LoW6EoWYqhOR4Ko9X2xkLvNOWBQMm9VkxaHKJ8caTnQ1FRbB9u3vAbtkSVq1yD/r39H2Y9+v/z81b4PP113D77XDvvZEFIP43sl27QAQaNapctRCuzUG4G6HvM7h5c8VMha1auaqMAQPS50naAu+0ZcGASX02OlzaeOIJ6NMHHnrIVRscfzx8+y18s7IzZzd9l+e2Xkx7/waF993n+iReeWX1f3f/G9nMmaAKAwe6WRLHjIF27cK3OQh3I6xupsJ0+EwWFLgSmZISC7zTkAUDJj1YF8K08bvfudKBO+90bQj23x+WL4cv6g/h701Gcf/OUZV3uOYaOOigigaFofiXILVq5UoG8vNdF4YmTapvc1BdCVQ6fwb9S0VU4cwzq84ZYVKaBQPGmKQzahS8/76bv2DxYndv/vjjLN7u/FvarC7i5l1/rUhcVuaKEH78sfpphf1LkKDiSf7ZZ6t/2s3kEqjAUpGOHTPr/WcACwaMMUnplVdg5EiYOtV1NWzfHjaVNufxrJvJ39WJJ/ljReKdO12XhFdfDX/QwKd338/77BPZTT6dn/7DsXY5aa9ePA8mIieJyO/jeUxjTGbKzoaLL3b36W3bXNu85s2heE9bpnAK93NT5R1ee801KIxFTo4rfsjEG30kfKUiV15pPQjSVFyDAeAS4JFqUxljTASGD4fJk6FvX9ixw1Xn5+TA2hZ9eJJreJ5LK+9w332xBwQmPAuY0lq8gwFjjImrXr3c7IatWrnSgQ0bXJXBxoY5PMifeIxrK+9w332ut4AxJmIWDBhjkt7QoXDDDdCtmxsiYPNmaNU2i/UNu/EsV1YtITjzzITk05hUZcGAMSYljBoFv/61Gw6gtNSta9qxLRvozFiu5SuOqEi8caOb4bA2FRTA9OluaSJn1y0pWW8CY0zK8I1B8MwzbvybXr1gS9Y+LF3emDHcxqPcTC+WucQzZsCIETBuXPwz4t/vvqTENW6IpN99po/rb8MZJy0rGTDGpJRRo+Dqq10bgjVr3ACE9Vu3ZRkHcD83s4R9KxKPHw9nnBHfDBQUuJkTfUMPT58OY8e6m1y4p13fjfC556pPm678xysoK3O/m6QQtmRARC6L8nj71SAvxhgTkVGj4PPPYdo0VwDQsSNsKOvJm9va8BGDmcvRZPuGLX73XRcQvPNOzU/su6GvWePGSd6925UMbNzoWjWGG6vfxvW38QqSWHXVBOMAjeJ4EmV6Y4yJyVtvwU03uXv82rUwbHgTvn2/mGXrOjCJC/k9T1Ukfvfd+FQZ5Oe7EoEVK1xLRlXo3NkNerRzZ/ibm90IM3sUxyRXXTDwAnZzN8YkqYcecvfgCRPg448h76h9WPhGKRP0UrbRjJE8X1FC8MILcO65cPbZsZ8wN9dNrfjzz1CvnnvSb9vWjZB0xx3RDYecqTfCRI7imOltNsIQ1cy81+fl5ens2bMTnQ1jTBzk5LjSAXBzDpVt30HT8k0cxDymcnpFwlat4IMPqp/UKJynn4Z77nFzLO/Y4UblGzHCbi7JfqOt5caLIjJHVfPidsA6Zg0IjTEpr6AAbr3VTV64axeM/H1TBu23kaaU8kuer0i4eTNceGHNGu+dcQYceyx07+6mQLZAIDUaR1rjxbAsGDDGpIU//AFOPBHq1/ceUM88hJ0devAlx/Jr//YDK1bApZeGPE61cnLgwQfhttvc0j8QyNQ+9Klwo7U2G2GFbDMgIjcCT6jqrlgOLCKHAx1V9b1YM2eMMZHKznazHL7yCnz3nRuHgH0PYd36bbzERfRiGbfykEs8bRqcfz68/HJsJwtW753JfehT4UZrbTbCClcycB+wTERGiUiXSA4mziki8howC+gXj0waY0wkevWCjz6CRo1cQDB3LjRs0ZydNOUB/swxfFSR+JVX4Prr43fyVHg6ri2pMquhTbYUUrhg4GDgW+B+IF9EponIvSJyjogMFJEDRKSfiJwgIr8VkReAlcC7wDHA74C/1fo7MMYYP716wT/+4cYe2L0btm6FRg1hJ834mqMqtyEYOzZ+sxymwtNxbbIbbUqrtjeBiBwNXAcMB7II3tVQvOWPwFPAv1V1axzzGXfWm8CY9DZ2LDz2GCxb5toRdGxXyro1pezLUkbxd0YwoSLxG2/UrMuhT7K3qDe1JtV7E0TctVBEWgDHAccC3YBsYCewHvgOmKqqC2opn3FnwYAx6W/cOFcTsG0btG4N7NxGSUkZTdjB0XzJW5zvEjZoAMuXx/8GbsFBxkj1YCDiiYq8J/13vFdciMh5wMVAHtAB+Bl4FbgvsGRBRAYCo4GBQENgOXCvqr4Ur/wYY9LLiBEuELj1Vm9Fk+Z0IZ/ikizmchiPcy3X85Sr4z/ySJg1K3437UxuUGhSTqK7Ft4E7AFuA07FVTFcC3wkInvzJiJnAJ8Ba4FfAsOAZ4HGdZ1hY0xq+d3vXC+Dtm3dNAINeuTSoJ5QTBue4cqKbofr1sHhh8evW2AmNyg0KSfRUxifpaob/H6fJiJFwHhgCPCJVz3xb+BJVb3BL+2UOsulMSY+ElRs7hu2eNw4WLIEWrTsDJs28SMHsJDerKcdb3O+m3DorLPcxAc1zV+mNyg0KSXiYEBEnq8+FeXAFmAh8LaqrgmXOCAQ8JnlLX3dGc8H2gMPR5hVY0wySnCx+RNPuLEInn7aVR00aNGaHjt+ZNmeHKYyhOt4hCf4I8ybB/vvD3/6E9x7b+wntH7tJoVEUzIwgoqeBBJkuwas3y0i/6eqD0aZp8HecqG3PBYoAg4WkXeBPsAa4F/APaq6J8rjG2MSIQmm8L37bmjWDO67D7ZsgbImB9B8ZxFbaM54LmUBB7i5DHbtcomeecYNsRtrT4NETspjTBSiaTOwL/AGUAj8H64Yv4+3vMNb/xowABgJrAMeEJFhkZ7AG9zobmCKqvqa+u8DNAX+g5tS+SRcNcId4BtOzBiT9JKk2HzUKPjVr1xQsHs3bJG2NKWUcuozh/4M4d2KxBs3wrBhcMIJ4Q+aqcMQm7QRTdfCP+Ia+h2qqquCbM/BDVJ0j6o+6v2+EJihqkMjOH5zYCru5t9fVVd66z8EhgI3qurf/dI/BVwJtFfVzUGOdzVwdajzdevW7YgVK1ZUly1jTDwlUVe7665zgxCuX+9iky5Za1m/ozGg/ILX+Q+/rrxD586wenXVA0Va/ZFE793EX6p3LYymZOBq4OVggQCAqhYAL3vpfL+/DRxe3YFFpDHwJtATOMUXCHgKveVHAbt9iOtieGCI/DyjqnmhXu3bt68uW8aYeEuiUeqeeAImTnQjFZaXw64WnTj0gN0o9ZnCUPozlSXsW7HDmjXQuHHVEQsj6TWQCrP6mYwWTTCQC1R5Ag+wCejh93s+0DzcDiLSEJgM9AdOV9XvA5L4BjIKLMLwtU8oryZPxhgT1NChLiho3Bg2bYJv8tvTuHVLdtCcueRxAeMppG3FDiUlri3B+edXrIuk+sO6GZokF00wsBFXXB/OyVQ8yQO0JkwA4Y0lMAk4ERimql8FSfa6tzw1YP0pwC5gfjV5MsaYkIYPhxtvhJ49oV49N5dBTu/W7KEhS+nN5fyzckAArn7BFxBEMklPkrSXMCaUaNoMPAr8HteQ7zZV/dlvWzfchEYXAY/7xgMQkVlAqaoeE+KYTwHXAPfiqhT8rfRrN/Bv4ELcCITf4BoR/hkYo6qjI3oDAWw4YmOMv8JC6N/fjUosAs2bu8CgAdvZjx/5N1czkDmVd+rRw+0QCWszkNZSvc1ANMFAS+BT4DDcqIGrcD0GOuLGBKgPzAWGqOoWEemMK/5/QVWfDnHMfKB7iFPe5bvRi0gW8Bfgcu98+cATqvqPiDIfhAUDxphAhYVwyCGu2+H27e5ev24dtCxdx4G7v+YEPuPWwE5MnTvDzJm1c4P3BRBZWVBaaoFEEsuYYABARBoBN+Nuyj39Ni0HXgD+pqq74prDWmLBgDEmmK+/hjvugClToGFD6NPH9TAsXbmWLLbxd/7M+XtrLz2NG8N//xufmQ99fI0ON2+G775zUUqrVjbHQZJK9WAgqrkJVLVEVceo6n5AKyAHaKWq+6nq3akSCBhjTCj9+8MHH0BenhuLID8fevcG6diJTfU7MZZreYxrK++0a5cbjyCwp0FN+BodNm9eeWmND00tiHmiIlXdqqqrAmcXNMaYdDBzpgsIdu1y4wlt2wZ7sprzZb0h3M6YigmO/N13n5sqMR58jQ63bau8tMaHphZEVU0AICJNgXNxbQda43oLfAO8pqrb453B2mLVBMaYSFx5JcyYUdERoH17V4LfbvcKfsFrPMkfq+50+unwThxme7c2Aykj1asJom0zcDpuKOC2VJ6HQHHzB1yhqoG9ApKSBQPGmEiNHQt/+YtrWNiiBdSv76rym5dt4HeMZQx3V93pqKPgyy/rPrMmIVI9GIi4mkBEDgdexZUGTAJ+DZzmLSd5618RkSPinktjjEmg3/0OLr7Ytd8rKYEdO9zPOxu1539cyG95pOpOM2ZUHpzImCQWTZuB23ElAINU9TJVHaeqH3jLy3CzCypu/gJjjEkrTzwBN9/spkEuLXVtCdq3h1XN+vISl3AmL1fd6ZVX4Prr6z6zxkQpmmBgEG5ugmCjBKKqM4FXvHTGGJN2Ro2CRx+Frl3d77t3ux5/2ro98xocxcm8UXWnsWPhwWhncjembkUTDLQCqptd42egZezZMcaY5DZ8OIweDW3auAb+y5dDp05Q3KgLn3Iiv+T5qjvdckvdTE5kUymbGDWIIu1q3GRC4eQBa2LPjjHGJD9f78ExY9wIhdu2uUb+ixY1473657DvnjyWcUjFDuXl0LevG984mHgMVRzpVMrGBBFNycC7wAkicouI1PffICL1RORG3JwB78Yzg8YYk4xGjID334d27VyDwh9/hNatYYu2ZjkH0JeA3krbtsEBB1Q9UE2mN/YvCbCZEU0NRBMMjAHW4iYVWioiL4jIX0VkPLAE+Ju3/Z74Z9MYY5JPr17w0UducKLmzV3XwxNOAMhiNd2rNipcvLjqKIWx3sQDg4isLFcisGCBGz85K6vG789kjoiDAVVdCxwDTMFNLvQr3MyBlwI9vPXHqqpVExhjMkavXjBpkpueoLzczWnQqBFspiXvcQb7sLjyFMj33QdnnFHxe6zTGwcGEaWl8JvfwM6d0KQJPPKI681g7QdMBKJpM4Cq5gOniEgX3AiErXAjEH6rqqvinz1jjEl+vXrB+vWuir642M14eOihWSz5bhtF5R25iH/zEcMqdnj3XTj6aDcoUU6Oq9+Pts1AsCAiP98FAiLw1VewaZOry7D2A6YaUQUDPt6N327+xhjjp6AAjj8epk51bQgOHdCcb2Zs5XsO5XA+5xuOrUg8Ywbssw+sXu1u1NHerIMFEatXuxkOt21zjRW7dq2oerBgwIQR80RFxhhjqvr0Uzfb4a5d7uG8SesWbKc58zicHBZVrjJYs8Y9xcc6uVFODgwaVHGjLy11Ax8cc4wbHWnVKpvcyEQkZMmAiATpLBsRVdUrY9zXGGNS3rZtcMopMGeOa1R4xi/aMuX1TRTTnsv5J28TMEzx+PHw4oturOOayM114ySXlcHAgW5QhAEDrFTAVCvkREUiUh7jMVVV61efLLFsoiJjTG3r2xcWLnQNCtu3h5UrS2nAbrJZy1r2C75TlDPJVuE/ZgHUfPwCE5FUn6goXJuBHnWWC2OMSUM//ODGHti2DVauhBYtsti6FdaRw/58y2IOq7qTSM0CAl/7AxuEyEQhZDCgqivqMiPGGJOONm2Cww+Hb791bfqys7MoLISf2I8cFlFA76o71TQggMpdD/PzrRGhCcsaEBpjTC376CNXlQ9QWAgNG0IZzVlJD/blu+A7iVQdoCgasY5fYDJSyDYD6S5ebQYKC12jXWOMCefrr123wx073O+NGrn2gg3YRh8W8B0Dg+941FFuPIJYxGPOAxORVG8zYCUDNVRYmOgcGGNSQf/+8PrrrlQAXCAg4koIvuewqnMZ+MyYAQcfHNtJA7seGhOCBQNxYkGBMaY6Q4e6HoS+gEDVlRBAFks4iF8wKfiO8+fDEUfUVTZNBrJgIAaFhW6+kcWL3e+LF8Ps2RYQGGOqN3w43HIL1PO+fStKCBrxBr8MHRB8840FBKbWWDAQg+xs2H9/9wK37NHD2g4YYyJz991wzTUVv6u6Nn4Ab/BLxnBT8B2/+QbO9xuwyH8KY2NqIKa5CYxTWAg//VTxu6+kIDvbAgNjTHhPPOGWzzzjegCqVvQo/AsP0pF1XM2Eqjv6z0Ro4wiYOLGSgRrIznbzmPuXEOy/vwUCxpjIPPEEnHCC+3nPHqjvN3brb+u/wOeEqBbo1q3qFMb5+bWcW5POLBioIbvxG2Nq4oMPoHt393NZWUVbgj174Nz2s1nCvsF3PO44G0fAxI1VE8SJBQXGmFjl57vZjNesgXK/WWE2bIDhBy/lu+8l+I7PPw/Tptk4AqbGrGQgTiwYMMbUxOOPV5QK+Pv+ezigZYjR4VWrjiNgjQpNDCwYMMaYJDB8uOthIEEKAZZt78bYJiF6GPjzTU703HNuaQGBiZAFA8YYkySeeMINTOTj337gsa4PUrhPwEiEX31V+XdrVGhiZMGAMcYkkQ8+qBh92L/9wJIl8MuDvnMBwOOPu+WAAZV3tsmJTIysAaExxiSZQYNcW4FAH34Ix24fwOefD6i6EVzbgTvvTJ7JiWyipJRhwYAxxiSZJ56AefPgiy+qbvvuO5g82bUxCConJzluvL72CzYoUkqwagJjjElCn38OXbtWXb91K9xwQ51nJ3rWfiGlWDBgjDFJaswYaNas6vp16+DKK+s+P1Gx9gspRVQ10XlIiLy8PJ09O8T84cYYkyQGDICvv666vkMHePLJMNUFySCD2gyIyBxVzUt0PmJlwYAxxiS5Tp1caUCgjh1h7dq6z4+pKtWDAasmMMaYJHfGGdCwYeDaUrZu2smVF2xORJZMmrFgwBhjktxzz0HTpv5rSgHYUVKfyZN3u+J4G4bY1EBCgwEROU9EJovIChHZKSI/isj9ItLCL02uiGiIV+sEZt8YY+rM8OHQpEnV9bvKm3De8DIbhtjUSKJLBm4C9gC3AacCTwHXAh+JSGDe7geOCnhtrbusGmNM4jz3XPCuhmU04LufWyRPNz4roUhJiR506CxV3eD3+zQRKQLGA0OAT/y2LVfVgIG4jTEmc3Tp4oYlhix8VQV7EJasa0nfyX/hh+F3J7Ybnw00lLISGgwEBAI+s7xll7rMizHGJLtPP4XrroOJE2HLliwAevd2247Pa+sGH0hkNz7/gYby893LgoGUkOiSgWAGe8uFAevvF5Gnge3ANOB2VQ0yercxxqSvJ55wL99Uxwv3flO2BgYlJlM+NtBQykqqYEBEugB3A1NU1TcIQAnwT+BDYAPQG9fG4EsR6a+qgUGDMcakvSFD3JDFSSXZJkoyEUuaQYdEpDkwFdgH6K+qK8OkzQEWAG+q6q9CpLkauDrUMbp163bEihUrapRnY4xJpOuuc6UEJvFSfdChpAgGRKQx8C5wKDA4kuJ/EXkX2FdVD4jlnDYCoTHGpKAkHeI41YOBhFcTiEhDYDLQHzgpinYAAiQ+kjHGGFM3rLdCrUn0oEP1gEnAicCwSLsOikg34BhgZi1mzxhjTDKxaZFrTaJLBp4AzgfuBbaLyEC/bStVdaWIPIwLWmbgGhAeANwKlAP31XF+jTHGJIr1Vqg1CW0zICL5QPcQm+9S1dEi8mvcqIT7AS2AjbjBiO5S1R9jPbe1GTDGmBRkbQZqRaIHHcqNIM3zwPO1nxtjjDF1oiY39JycpAoC0kWiqwmMMcZkktpoBJikpQWpxIIBY4wxdSfeQxZbD4O4SPSshcYYYzJJvBsBWg+DuLCSAWOMMXUn3kMWWw+DuLBgwBhjTN2KZyNAmw8hLiwYMMYYk9qsh0GNWZsBY4wxJsNZMGCMMcZkOAsGjDHGZKaCApg+3S0znLUZMMYYk3lsfIJKrGTAGGNM5rHxCSqxYMAYY0zmsfEJKrFqAmOMMZnHxieoxIIBY4wxmcnGJ9jLqgmMMcaYDGfBgDHGGJPhLBgwxhhjMpwFA6bu2UAfxhiTVKwBoalbNtCHMcYkHSsZMHXLBvowxpikY8GAqVs20IcxxiQdqyYwdcsG+jDGmKRjwYCpezbQhzHGJBWrJjDGGGMynAUDxhhjTIazYMAYY4zJcBYMGGOMMRnOGhCa1FNQ4HojZGVBaan1SjDGmBqyYMCkFt8Ihps3w3ffwSGHQKtWNpKhMcbUgFUTmNTiG8GwefPKSxvJ0BhjYmbBgEktvhEMt22rWJaUwLp1NvGRMcbESFQ10XlIiLy8PJ09e3ais2Fi4d9moKAAXnkFGje2iY+MMQkjInNUNS/R+YiVtRkwqcd/BMPSUhcI5Oa6ACE/34IBY4yJklUTmNRmEx8ZY0yNWcmASW2hJj7yVSVYt0NjjKmWBQMm9QVOfOTrflhWZu0IjDEmAlZNYNKPr/thbq51OzTGmAhYMGDST6h2BAUFMH26dUE0xpgAVk1g0k+wdgT+VQclJTB8OAwYYNUHxhiDBQMmXQW2I/BVHbRpA2+/DZs2wfvvW3sCY4zBqglMpvBVHSxc6H7v0gVWrYKZMxOaLWOMSQYJDQZE5DwRmSwiK0Rkp4j8KCL3i0iLMPv8U0RURCbWZV5NivNVHVx1FfTpA9OmwdKlMHmytSEwxmS8RFcT3AT8DNwGrAQOA0YDx4vI0apa7p9YRI4GLgG21HE+TTrwrzr4179cUFBcbKMWGmMyXqKDgbNUdYPf79NEpAgYDwwBPvFtEJGGwDPAvcDIusykSTMDBrj2AsXFNmqhMcaQ4GAgIBDwmeUtuwSs/zNQH3gYCwZMTYQatdAYYzJUoksGghnsLRf6VojIvsD/AWeoaqmIJCRjJo0E9jYI5D+cMVjgYIxJa0kVDIhIF+BuYIqq+s8v/DTwqqp+mpicmYwSOCaBqk2RbIxJa0kTDIhIc+ANoAy4wm/9r4Ajgd5RHu9q4OpQ27t16xZbRk368x/O+Kuv3JgEbduCiDU2NMakpaQIBkSkMfAm0BMYrKorvfXNgb8DfwV2iUhrb5d6QEPv9+2qujvwmKr6DK7BYVB5eXkaz/dg0oj/cMbl5bBggVtfr55rdGiMMWkm4YMOeb0EJgP9gdNV9Xu/ze2A9sB9QLHfKwe4wPv5jDrNsEl/vgaGV14JgwZBdjbsu68bvfDnnxOdO2OMibuElgyISD1gEnAirnHgVwFJ1gLHB9n1JeB7XDfD+bWaSZOZfA0Ms7Jg4kTYuhUaNYIjj3Tb/RsYWrWBMSbFJbqa4AngfNxNfbuIDPTbttKrLpgauJOI7ALWqWqVbcbE1YAB8MILMGuWCwQGDKhoYLh5M+zcCXfc4dYbY0yKSnQwcJq3vN17+bsLNxqhMYk1YEDlm31+vgsEli1zyzFj4KmnrITAGJOyEj3oUG5d7mdMXOTmuhKBzZuhVSto0sR6GRhjUlrCGxAak3JyclzVQJ8+rmFhq1YVgxMVFMD06Tb5kTEmpSS6msCY1DRggKsa8G9E6D9YkQ1QZIxJIRYMGBOrwCGNfYMVtWkDixbBzJkWDBhjUoJVExgTDwUFsG4dFBXB22/D0qUwebJVFxhjUoKVDBhTU/7VA9u3Q5cucNhhbrRCa1hojEkBFgwYU1P+cxls3+56GhQXu3YDvoaFYAMVGWOSlgUDxtSEr3pg1y53o2/VCm66CUpLK9/0baAiY0wSs2DAmFj5Vw+IwJlnuht8sKf+/HxYswa+/x62bIGrroJ//csCAmNMUrAGhMbEyr96oFEj6NgxdPF/VhbMnu0CiM2b3UyIv/udNTA0xiQFCwaMiZX/VMeB7QMClZa62Q/r+f3LLVkC48ZZQGCMSTirJjAmFr7GgL/5TdX2AcHk5kKvXrB8OZSUQP36sHs3TJkCK1bYAEXGmISyYMCYaAUbaRDcMMShgoKcHBg71k2H/J//QHm563lw+OHWBdEYk3AWDBgTLf+2Avn5bqTB99+vfhjinBy49Vb41a9gwgQ3KNG8edC0qWtTYIwxCWLBgDHRCmwrAJWDg+qe8levhvHjXUPCH36AAw900yBffbWrcigsdO0LQvVMMMaYOLNgwJho5eS4p3/fAELgSgYiaUgIMGuWazewe7d7LVwIa9fCNde47cXF0KIFDBwITzxhAYExptZZMGBMLAInKfIPDqq7eR95pGszsGuX611QXu7GKdi1y7Uj2L3blTTMmmWTHRlj6oR1LTQmHnJyYNCgyG7cAwa4xoT9+kFeHrRr5/bbudMFAaounW9pjDG1zEoGjEmEjh3htNOgQwc49FD48EMXDKxd62Y+bNTIdUUE13vBSgeMMbXIggGTfgoKXPE6JFcjPN/YBMXF8Oc/u1IAgOHD4ZNPYNUqV1VQvz40bgzz58OTT7r2CDYOgTGmFlkwYNJLQYG70c6Z437Py4O//S3xN1L/sQkWL3YNCDt1cnMVPP007Njh0u3Z45Y7d7rlF19UTIKU6PdgjElb1mbApJf8fNdlr1kz9/IN6FMbCgrcQEO+4YQDfw+WLxH31F9eXpGuUSPXPsAXCPgrLYVvvnHvwxhjaomVDJj0kpvrphFeutT93qZN9V39YhE4CuFvfgPPPht64KGsLPjuu8rbV692bQW+/Tb0eRo2hNat4eef4/8ejDHGY8GASS85OfDgg1XbDPjq6yPp+heJwFEIZ80KP/BQaSnst5+rDti0yQUCI0ZAnz6uZ0Hz5m6fggJXerBnj+t22KKFK+E48sia59kYY0KwYMCkrlA3+MAxAAoK4OabXVF7mzbxaUOQlQUbN7pxARo0cK/iYtcbIFhpRFYWLFoE69a59gIbN7rRB2+4wQUJZWUuYOjUCZo0cSUNHTu6IOPII11QY4wxtcSCAZOagk0WFOoGP3MmzJjh0pWVVR7IxxdQZGWFn33QP/BYvdoNH6zqugE2awYff+waA3brFnx8gNJS6Nq1YmChRo1c8FBa6vI+cSK8/LL7XcSVHpx+Ovz0E7z5JmzbBsceC9dfbw0JjTFxZ8GASU2BxfThWttv3Ohu2r5gYONGt94XUGze7OrzDznEtTfwDyx83RRfecU1/Csqcl0AN2508we0bOlGDMzOdjfxHj1cMBCYn9xc2GcfN11xebnLh68EISfHlRioVgQl335bMWyxz4wZ7vWf/9QsIIh3lYkxJuVZMGBSU+BkQeEaCbZrB23bVgQD7dq59b6AonnzykvfjdwXLKxcCcuWweDBrmV/WZkLAMDd0Fu1ck/uDRrA+vUuKAichTAnx1VPzJzpAol27SqPgXDCCfDcc67kQNWNNeAbh8Cf70Ye6008mhIVY0zGsGDApKbAyYLC3dAGDICjjqpoM+Crf/cFFJs3u+W2be7G7gssfMFCnz4uGPj++4rW/du2uSf9++5zS99gQs884+r8n33WrQ/XlsHf2WfDpEnwxhsuUPj2WzfGQLD3XZPeEdGUqBhjMoYFAyZ1hbu5Bqb729+qBg7+AUWwNgO+YKG42A1eNHgwTJvmbqY7d8Idd1QEFjk5boyBdu1iv9GefbZ7gXuCf/ddN3hSfn782gxEU6JijMkYohk6GUpeXp7Onj070dkwyS6wfj1cfXuqFMFbmwFj4k5E5qhqXqLzESsLBoyJJ7vRGpORUj0YsGoCY+Ip0qoLY4xJIjY3gTHGGJPhLBgwxhhjMpwFA8YYY0yGs2DAGGOMyXAWDBhjjDEZzoIBY4wxJsNZMGCMMcZkOAsGjDHGmAxnwYAxxhiT4RIaDIjIeSIyWURWiMhOEflRRO4XkRZ+aY4QkfdFZJWI7BKRtSLyrogclci8G2OMMeki0SUDNwF7gNuAU4GngGuBj0TEl7fWwFLgRuAU4Hpv3TQR6V/H+TXGGGPSTqLnJjhLVTf4/T5NRIqA8cAQ4BNV/Rj42H8nEXkf2AhcCnxdR3k1xhhj0lJCSwYCAgGfWd6yS5hdtwMlwO64Z8oYY4zJMIkuGQhmsLdc6L/SqzaoD3QGbvFW/6sO82WMMcakpaQKBkSkC3A3MEVVZwds/h8w3Pt5PXC6qv5Ql/kzxhhj0pGoaqLzAICINAemAvsA/VV1ZcD2nkA2kANcBxwBnBQkaPClvxq4OswpDwB+rHnO92qHa8dgqrJrE55dn9Ds2oRn1ye0ur423VW1fR2eL66SIhgQkcbAu8ChwGBV/b6a9FnAfGC5qp5a+zmsnojMVtW8ROcjGdm1Cc+uT2h2bcKz6xOaXZvoJLprISLSEJgM9McV/YcNBABUtRT4DtivlrNnjDHGpL1EDzpUD5gEnAgMU9WvItyvKZAHLKvF7BljjDEZIdENCJ8AzgfuBbaLyEC/bStVdaWI/BMoAmbj6n+6A7/D9Sq4tI7za4wxxqSdRAcDp3nL272Xv7uA0cBM4CpcY8BmwCpv3ZWRVCkYY4wxJryEBgOqmhtBmueB52s/N8YYY0xmSngDQmOMMcYklgUD8fNMojOQxOzahGfXJzS7NuHZ9QnNrk0UkmKcAWOMMcYkjpUMGGOMMRnOggFjjDEmw1kwEAUReV9EVETuiXK/W739Pq+tvCVapNdGRPJE5BkRWSQiO0TkZxGZJCI96iqviRDNZ0dEGovIgyKyRkR2isgMETmuLvJZF0TkFBH5RETWikiJiKwUkf+JSN8I9u0mIuO9z80OEVksIveISLO6yHttq8m18fbvIyIvi8hG77Pzo4j8obbzXVdqen38jpP238nRSvQ4AylDRC4G+sWwX0/cGArr456pJBHltbkIOBB4DFgAdAHuAGaLyKGqWlA7uUycGD47zwFnAH8GluMm5vpARI5S1bnxz2GdawvMAZ4ENgDdcNOSfyUiB6vqimA7eTf8KUBD3GfmZ+BI3JgkvYALaz/rtS6mawMu0AY+wU34dhWwGXddmtdynutSzNfHJxO+k2Oiqvaq5gW0BtYCFwMK3BPFvh8A/8T9g36e6PeS6GsDtA+yrjtQDtyd6PeTBNenn5fuCr91DXAzbL6Z6PdTi9fpAO993xgmzclempMD1j8AlAFNE/0+Enht6uGC69cSnd9kvD4B6dP6OznWl1UTROZvwAJVfTGanUTkl8DhwK21kqvkENW1UdUNQdatwEX5XeKct2QQ7WfnbGA38F/fClUtA14CThGRRvHPYlIo9Ja7w6TJ8pZbAtZvwt0MJc55ShaRXJshQF/g77Wem+QTyfUBMuY7OSYWDFRDRI4FLgN+G+V+bYBHgJtVtag28pZosV6bIMfpA3QAFsYjX8kixutzIPCTqu4IWL8AdzNMm5k6RaS+iGSJSC/ck9paXNATyhRgCfBXEekrIs1F5ATgD8DTqrq99nNdN2K4Nsd6y8Yi8pWI7BaR9SLymIg0qfUM17EYrk9GfCfXhAUDYXjTK/8TeEhVf4xy9weBxcC4eOcrGdTw2vgfpwHwNK5k4Lk4ZS/hanB92gLFQdYX+W1PFzOBEtz/ySHACaoash5XVXfhbnq+IvGtwMfA27jJy9JJVNcG2Mdb/hf4EBiKK5W6CvhPLeYzUaK9PpDm38k1ZQ0IwxsFNMHNqhgxERmEeyI8XL1KqjQU07UJYixwNHCGqga7CaaqWK+P4Oo/g61PN5cCLYGewE3ARyJyrKrmB0ssIo1xN7sO3r4/A/2Bv+DaDFxbB3muK1FdGyoe7Caq6l+8n6eKSH3gARHpq6o/1GqO61a0n51M+E6uEQsGQhCRbrgWp1cBjQLqahuJSGtgq6ruCbL7P3FPuSu9dOCudX3v952qWlJbea9tNbw2/se5Hzcb5eWq+mFt5beu1fD6FOFaSAdq47c9Laiqr1popoi8B+TjWoZfE2KXK3F14/up6jJv3Wcishl4RkSeVtV5tZjlOhPDtfHVm38UsP5DXAPLQ4G0CQZiuD5p/Z0cD1ZNEFpPoDEwEVds63uBi0SLgYND7NsH96H03+8YYKD3c6o/wdTk2gAgIrfj/nn/oKoTai+rCVGT67MA6CEiTQPW9wVKgaVxz20SUNVNuPcWrk3EwUCxXyDg87W37FMLWUu4CK/NAl/ygPW+EqXyOGcraUR4fdL9O7nGrGQgtLnA8UHWf4r7kn+O0F/MwfZ7FKgPXB9mv1Qxl9ivDSLye+Ae4HZVfbw2Mphgc4n9+ryJ6zd/PjAe9raruBD4MF2fXkSkI9AbmBQm2VqgjYjsp6r+12+At1xVW/lLpAivzXu4OvRTcW0ofE7xlrNrJ3eJF+H1Sffv5JpLdN/GVHsR0Fcc10e+DPhLNftNJc37tEZybXCDDpXjvrwGBrz6Jvo9JPr6eOtfwj2tXAWcCLwC7MLVdyb8fcThOryGGzRoGO5LeiSwCNdFcP8wn51cXLfCxcDl3r5/9tbNBuol+r0l6tp46+/01t8HnIQredsJjEv0+0qG6xPkWGn/nRzNy0oGak5w0aVVuVQV7Nqc6q0/1Xv5m4arE84UoT47V+AaHt6DG7RoHnCqqn5Tp7mrPV8BFwA34rpLFuC+mO/XigZgVa6NquaLyEBgNO7atPP2fQa4V1XToSg8pmvjuRvXw+K3uOqoNbgW9GNqO9N1qCbXx4RhUxgbY4wxGc4iJ2OMMSbDWTBgjDHGZDgLBowxxpgMZ8GAMcYYk+EsGDDGGGMynAUDxhhjTIazYMAYY4zJcBYMGGOMMRnOggGTlkQkV0RURMYlMA8viMh6EWmWqDxEoi6uVW2fIx7Hj1ceReQI7zhX1uQ4xtQlCwaMqQUikgf8CnhAVbf7rd/g3SgifYWaktUkKVWdA7wO3CMizROcHWMiYnMTGFM77sNNoPOUb4VXQvBEQLoGwO246YnvD3Kc92srg35W4aZ43VwH50pm8bwO9wMzgd/jPgvGJDULBoyJMxHZHzdr3L9UdadvvVdCMDogbT9cMPC9qlbaVldUdTdu5reMFs/roKpfi8giYKSI/FVV98TjuMbUFqsmMBlHRC4Qkc9EZLOI7BSR70XkVhFpFCStiMgfROQHEdklIqtEZKyItBKRfBHJD3KKX+NmTvtvBNnJ85ZzYn9HNROqrtx/vffzSyKy0bsOs0XkzDicu56IPOad51URaRywfYCIvCIia0WkVEQKROSfIrJPNcc9ynfMMGkWikiJiLQNfL8B6WK9Di8B3XCBoTFJzYIBk1FE5D7cTboP8B9gLO7GfR/wgYg0DNjlCeBRoBVuqtwXgZOBj4DAtD4nAXtw061W5whvOTviN1H3ugNfA7nABNz1Owh4Q0SOj/Wg3o3/f8D1uOt8nqru8tt+BfAFcBrwKe7vMBu4CpgtIt1CHVtVZwA/AmeKSHaQc/cHegNvqWpRhFmO9jp84S2HRnh8YxJHVe1lr7R74b6wFRjnt+4ob93PQCe/9Q2At7xtt/mtH+St+xFo7bc+C/jM25YfcN5mQBmu2D+SfM70jnN4Ml2rgPUK3Bmw7RRv/buxnANoC0wHyoFRQdLvj2tHsRToErDtBFyw9Vo1f+9bvXW/C3L8J7xtZ9XWdcAFkAp8nej/B3vZq7qXlQyYTPJrb3mPqq71rVTVMuBG3I3pKr/0l3vLe1V1k1/6UtyNJpguQH1gTXWZEZEGwCG4m978yN5C2OP9RkSWi0iZiPwz1LoYrADu8V+hqh/ggqr+MeSzO+6peQBwqar+NUiya3ElL39Q1VUB5/4EeBM4S0RahDnVBNzf9HL/lSKSBVwErAfeiyLrUV0HVd0M7MJVFRiT1KwBockkh3vLTwI3qOpiEVkJ9BCR1t7N/zBv8+dBjvUVrgQgkK9IujiC/BwINAbmeAFGzESkN67nwvle3rYGWxfj4edq8AZwBbjSlmgcAMzAlaCcpqofh0jnO+5gETkyyPYOuKBrf0K0t1DVlSLyMTBURPqq6g/eprNwJROPeIFgpGK5DkVAxyjOYUxCWDBgMkkrbxnqqX0N7imuFbDJL/26wISqukdECoMcw9d7oHGQbYHi2XjwbGC+qr7mWyEiVdbFaFOI9WVE3+5of9yNeC7wTZh0vqDqz9Ucr7p+/ONwdfaXA6O8db6SgvHV7BtoU4j14a5DEyo+E8YkLasmMJnE13+8U4jtnQPSbfGWVZ7sRKQ+FTcsf+u9ZbBtgSJqPCgiXcSNZlgoIptEZLKIdPTbvhj4K9DPa/X+WrB1EeSnLrwF3AYcCnwsIu1CpPP9DVqpqoR5TavmfK/h/o6/EpH6ItIe1yBxnqrOi8P7CUlE6gGtqfhMGJO0LBgwmeRbbzkkcIOI7Ad0BX7yax/gS39skGMNJHjJ2hpgA644vDq+YCBkyYCI9MA9Qa/y8jEEaAc87ZfsWGAx8H+4gObyEOuSgqreD/wRVw3zqX9g48fXE2NQDc+1E9djYR9cL49LcH+3aEsFYnEArqfK3Do4lzE1YsGAySTPe8v/854Qgb1P+Q/h/h+e80v/gre8XURa+aXPIsSocqqquJ4G7bwAI6goGg8+DTynqreq6kJVnQuMAU70S7MF6Al8oaprVXVLiHVJQ1UfxTUSPBCYFmTcgLHAbuARbxCnSkQkS0QiDRTGecvLvFcZMCmGbEdroLf8tA7OZUyNWJsBkzFU9UsR+RtwMzBfRF4BtuOKjQ/CNRR80C/9NBF5BrgaWCAik3E3qLNwxdirca3VA00GhuO6nS0NkZ1qGw96/ehPBgaJyO/9NtUHdvj9fhDuf3luNeuSiqo+LSK7cAHYZyJygqr+7G1bJCK/xgVwC0TkfVxJR0Ncu45BuBKY3hGc5wsRWYprSNkQN7ZAXRTdn4zrAvlGHZzLmBqxYMBkFFUdJSLfAr/DPSU2BJbhitMfDnJjvhY3RO1I4BqgEFcPfRuw0ts30GRco8PLqDoXgU+1VQS4evUtfmn9lQakW+Hf/THEuqSjquNEpARXCuMLCJZ72yaKyDxct8/jcTfX7bgg7BUiG+HRZzyuRMX3c63ySpJ+AbytqgW1fT5jakpcqaYxJhoi0gv3pPqSql4cZPutuKqEw1X128DtEZ7jNFyDu9aqui1MurFAV1X9Rbh1pu6IyPXAY8Bxqjo90fkxpjrWZsCYMESkk9cq3H9dU9zQuOBKCYJ5BDcYzd01OP1XuPEKJojIYSKyr4gMFZEnAvJ0KFWrA4KtM3VARJrgBqWabIGASRUWDBgT3g3ATyIyXkQe8Cax+RE4HTd63cvBdlI3xv6luDH0m8VyYlUtxrVnaIVrhDYX19BxpaqWg5tICdcQca5vv2DrTJ3Kxc1jcVOC82FMxKyawJgwRORE3Jf6objBcspw1QP/AR5VN+2tMcakNAsGjDHGmAxn1QTGGGNMhrNgwBhjjMlwFgwYY4wxGc6CAWOMMSbDWTBgjDHGZDgLBowxxpgMZ8GAMcYYk+EsGDDGGGMy3P8D2a7XUWegHMQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(clus_1.companions['Teff']), np.log10(clus_1.companions[\"L\"]),\n", + " \"r.\",label=\"BPASS cluster\", alpha=0.5)\n", + "plt.plot(np.log10(clus_2.companions['Teff']), np.log10(clus_2.companions[\"L\"]),\n", + " \"b+\", alpha=0.15)\n", + "plt.plot(np.log10(clus_1.star_systems['Teff']), np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", alpha=0.5)\n", + "plt.plot(np.log10(clus_2.star_systems['Teff']), np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b+\", label=\"MISTv1 cluster\", alpha=0.15)\n", + "plt.xlabel(\"log($T_{eff}$ in kelvin)\")\n", + "plt.ylabel(\"log(L in watts)\")\n", + "plt.title(\"HR Diagram of clusters at 0.1 * solar metallicity \\n\" +\n", + " \"and 1 billion years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at which stars are the white dwarves?" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 8, 9, 10, 11, 12, 14,\n", + " 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n", + " 28, 32, 34, 36, 38, 43, 44, 45, 46, 47, 50,\n", + " 52, 53, 54, 61, 62, 63, 64, 66, 67, 69, 70,\n", + " 71, 72, 73, 74, 75, 80, 81, 84, 85, 86, 90,\n", + " 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 104,\n", + " 105, 106, 107, 109, 110, 111, 112, 114, 115, 119, 121,\n", + " 122, 124, 125, 128, 129, 130, 131, 135, 136, 143, 144,\n", + " 145, 146, 147, 148, 149, 152, 153, 154, 155, 156, 157,\n", + " 159, 160, 161, 162, 166, 167, 168, 170, 171, 172, 173,\n", + " 175, 177, 179, 180, 182, 183, 185, 187, 188, 190, 191,\n", + " 192, 193, 194, 195, 197, 198, 199, 206, 207, 208, 209,\n", + " 210, 213, 214, 215, 218, 221, 223, 224, 226, 227, 229,\n", + " 230, 231, 232, 233, 234, 235, 238, 239, 240, 241, 242,\n", + " 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 255,\n", + " 256, 258, 260, 261, 264, 265, 266, 267, 268, 270, 271,\n", + " 272, 273, 274, 275, 276, 301, 335, 369, 373, 387, 393,\n", + " 421, 450, 464, 510, 531, 560, 569, 577, 670, 679, 719,\n", + " 725, 732, 737, 761, 764, 773, 819, 821, 837, 849, 887,\n", + " 921, 947, 953, 959, 972, 988, 1010, 1031, 1038, 1108, 1116,\n", + " 1158, 1175, 1212, 1249, 1263, 1298, 1305, 1307, 1336, 1348, 1392,\n", + " 1445, 1459, 1468, 1472, 1494, 1516, 1519, 1531, 1556, 1571])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.where(clus_1.star_systems['phase'] == 101.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do I see any black holes?" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([126, 204, 205, 216])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 103.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Neutron Stars?" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 7, 42, 55, 78, 83, 89, 127, 132, 176, 178, 186, 196, 237,\n", + " 269, 277])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 102.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding the initial masses of the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1989.0695459244812" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_1.star_systems['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000.1153149538768" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clus_2.star_systems['systemMass'].sum()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/TestPlot_9.2_MIST_0.1.ipynb b/Test_Plots/TestPlot_9.2_MIST_0.1.ipynb new file mode 100755 index 00000000..9d7e45b8 --- /dev/null +++ b/Test_Plots/TestPlot_9.2_MIST_0.1.ipynb @@ -0,0 +1,1555 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing the BPASS isochrone at $10^{9.2}$ Years Age and Comparing with the MIST Model ($0.1Z_{\\odot}$ Metallicity)\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for 9.2 billion years age, 0.1 times solar metallicity, AKs=0.0, and distance of 2000 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram (B-V vs M_V), and the mass luminosity relationship of the cluster. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 24882 logg=5.96\n", + "Changing to logg=5.00 for T= 21594 logg=5.57\n", + "Changing to logg=5.00 for T= 18255 logg=5.15\n", + "Changing to logg=5.00 for T= 24344 logg=7.21\n", + "Changing to logg=5.00 for T= 23742 logg=7.18\n", + "Changing to logg=5.00 for T= 18958 logg=5.15\n", + "Changing to logg=5.00 for T= 24550 logg=7.29\n", + "Changing to logg=5.00 for T= 23791 logg=5.80\n", + "Changing to T= 50000 for T= 67073 logg=5.76\n", + "Changing to logg=5.00 for T= 67073 logg=5.76\n", + "Changing to logg=5.00 for T= 23945 logg=5.82\n", + "Changing to logg=5.00 for T= 33534 logg=6.57\n", + "Changing to logg=5.00 for T= 24339 logg=5.63\n", + "Changing to logg=5.00 for T= 33652 logg=6.22\n", + "Changing to logg=5.00 for T= 20715 logg=7.07\n", + "Changing to logg=5.00 for T= 34515 logg=6.76\n", + "Changing to logg=5.00 for T= 24678 logg=7.23\n", + "Changing to logg=5.00 for T= 26201 logg=5.82\n", + "Changing to logg=5.00 for T= 22989 logg=5.52\n", + "Changing to T= 50000 for T=114496 logg=6.70\n", + "Changing to logg=5.00 for T=114496 logg=6.70\n", + "Changing to logg=5.00 for T= 23312 logg=7.37\n", + "Changing to logg=5.00 for T= 24030 logg=7.31\n", + "Changing to logg=5.00 for T= 25609 logg=5.75\n", + "Changing to logg=5.00 for T= 27463 logg=5.91\n", + "Changing to logg=5.00 for T= 24030 logg=7.35\n", + "Changing to logg=5.00 for T= 24442 logg=7.18\n", + "Changing to logg=5.00 for T= 27434 logg=5.90\n", + "Changing to logg=5.00 for T= 25848 logg=5.76\n", + "Changing to logg=5.00 for T= 31711 logg=6.59\n", + "Changing to logg=5.00 for T= 37476 logg=6.98\n", + "Changing to logg=5.00 for T= 39646 logg=6.99\n", + "Changing to logg=5.00 for T= 21592 logg=5.57\n", + "Changing to logg=5.00 for T= 24351 logg=5.62\n", + "Changing to logg=5.00 for T= 30895 logg=6.47\n", + "Changing to logg=5.00 for T= 17441 logg=5.06\n", + "Changing to logg=5.00 for T= 24415 logg=7.27\n", + "Changing to logg=5.00 for T= 35935 logg=6.68\n", + "Changing to logg=5.00 for T= 40447 logg=7.77\n", + "Changing to logg=5.00 for T= 35551 logg=6.91\n", + "Changing to logg=5.00 for T= 24138 logg=7.28\n", + "Changing to logg=5.00 for T= 22601 logg=7.33\n", + "Changing to logg=5.00 for T= 17862 logg=5.03\n", + "Changing to logg=2.00 for T= 9391 logg=1.87\n", + "Changing to logg=5.00 for T= 20285 logg=5.44\n", + "Changing to logg=5.00 for T= 35681 logg=6.08\n", + "Changing to logg=5.00 for T= 39903 logg=7.00\n", + "Changing to logg=5.00 for T= 32004 logg=6.49\n", + "Changing to logg=5.00 for T= 29347 logg=7.82\n", + "Changing to logg=5.00 for T= 45852 logg=7.73\n", + "Changing to logg=5.00 for T= 23534 logg=7.23\n", + "Changing to logg=5.00 for T= 23944 logg=7.35\n", + "Changing to logg=5.00 for T= 30551 logg=6.30\n", + "Changing to logg=5.00 for T= 33745 logg=6.64\n", + "Changing to logg=5.00 for T= 32667 logg=6.05\n", + "Changing to logg=5.00 for T= 31527 logg=6.41\n", + "Changing to logg=5.00 for T= 33051 logg=6.90\n", + "Changing to logg=5.00 for T= 34912 logg=6.10\n", + "Changing to logg=5.00 for T= 29151 logg=5.95\n", + "Changing to logg=5.00 for T= 27221 logg=6.02\n", + "Changing to logg=5.00 for T= 24462 logg=5.95\n", + "Changing to logg=5.00 for T= 26403 logg=5.88\n", + "Changing to logg=5.00 for T= 27859 logg=6.06\n", + "Changing to logg=5.00 for T= 35305 logg=7.05\n", + "Changing to logg=5.00 for T= 34641 logg=7.04\n", + "Changing to logg=2.50 for T= 14682 logg=2.04\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 26735 logg=5.87\n", + "Changing to logg=5.00 for T= 25841 logg=5.89\n", + "Changing to logg=5.00 for T= 22451 logg=6.92\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 21701 logg=7.25\n", + "Changing to logg=5.00 for T= 27101 logg=5.95\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 26553 logg=5.90\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 24647 logg=6.02\n", + "Changing to logg=5.00 for T= 23856 logg=7.16\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 22478 logg=6.94\n", + "Changing to logg=5.00 for T= 26374 logg=5.88\n", + "Changing to logg=5.00 for T= 42044 logg=7.78\n", + "Changing to logg=2.00 for T= 10637 logg=1.83\n", + "Changing to T= 2300 for T= 1731 logg=4.44\n", + "Changing to logg=5.00 for T= 43656 logg=7.76\n", + "Changing to logg=5.00 for T= 41944 logg=7.78\n", + "Changing to logg=5.00 for T= 24848 logg=7.13\n", + "Changing to logg=5.00 for T= 26437 logg=5.95\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 23744 logg=6.75\n", + "Changing to logg=5.00 for T= 25489 logg=5.85\n", + "Changing to logg=5.00 for T= 24240 logg=7.08\n", + "Changing to T= 50000 for T= 89129 logg=7.12\n", + "Changing to logg=5.00 for T= 89129 logg=7.12\n", + "Changing to T= 2300 for T= 1731 logg=4.44\n", + "Changing to logg=5.00 for T= 25507 logg=5.95\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 22121 logg=5.48\n", + "Changing to logg=5.00 for T= 22602 logg=6.94\n", + "Changing to logg=5.00 for T= 26630 logg=5.89\n", + "Changing to logg=2.50 for T= 11928 logg=1.96\n", + "Changing to logg=5.00 for T= 24630 logg=6.01\n", + "Changing to logg=5.00 for T= 29026 logg=6.07\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=2.00 for T= 11707 logg=1.80\n", + "Changing to logg=5.00 for T= 19124 logg=5.25\n", + "Changing to logg=5.00 for T= 35859 logg=7.05\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 24652 logg=5.81\n", + "Changing to logg=5.00 for T= 24180 logg=5.59\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 22956 logg=6.98\n", + "Changing to logg=2.00 for T= 9032 logg=1.82\n", + "Changing to logg=5.00 for T= 25003 logg=5.75\n", + "Changing to logg=5.00 for T= 22296 logg=7.06\n", + "Changing to T= 50000 for T= 73748 logg=5.80\n", + "Changing to logg=5.00 for T= 73748 logg=5.80\n", + "Changing to logg=5.00 for T= 23957 logg=6.93\n", + "Changing to T= 2300 for T= 1731 logg=4.44\n", + "Changing to logg=5.00 for T= 26658 logg=5.86\n", + "Changing to logg=5.00 for T= 23857 logg=6.80\n", + "Changing to logg=5.00 for T= 23334 logg=7.02\n", + "Changing to T= 2300 for T= 1904 logg=4.44\n", + "Changing to logg=5.00 for T= 43707 logg=7.73\n", + "Changing to logg=5.00 for T= 25536 logg=6.06\n", + "Making photometry for isochrone: log(t) = 9.20 AKs = 0.00 dist = 2000\n", + " Starting at: 2021-08-13 13:57:53.478809 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.180 Msun T = 3463 K m_ubv_U = 26.26\n", + "M = 0.980 Msun T = 6525 K m_ubv_U = 16.05\n", + "M = 2.500 Msun T = 7653 K m_ubv_U = 24.79\n", + "M = 3.200 Msun T = 7964 K m_ubv_U = 24.91\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = 8330 K m_ubv_U = 13.82\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 7452 K m_ubv_U = 25.09\n", + "M = 1.700 Msun T = 15838 K m_ubv_U = 21.39\n", + "M = 2.500 Msun T = 7643 K m_ubv_U = 24.79\n", + "M = 2.700 Msun T = 8076 K m_ubv_U = 25.12\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 3.981 Msun T = nan K m_ubv_U = nan\n", + "M = 1.700 Msun T = 13872 K m_ubv_U = 9.11\n", + "M = 5.000 Msun T = 9914 K m_ubv_U = 24.81\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 1.259 Msun T = 3090 K m_ubv_U = 29.59\n", + "M = 1.700 Msun T = 15821 K m_ubv_U = 21.40\n", + "M = 1.600 Msun T = 4351 K m_ubv_U = 11.59\n", + "M = 0.398 Msun T = nan K m_ubv_U = nan\n", + "M = 2.700 Msun T = 6423 K m_ubv_U = 25.62\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 3.000 Msun T = 7807 K m_ubv_U = 24.96\n", + "M = 2.700 Msun T = 8415 K m_ubv_U = 24.78\n", + "M = 2.500 Msun T = 7654 K m_ubv_U = 24.79\n", + "M = 2.700 Msun T = 8352 K m_ubv_U = 25.05\n", + "M = 3.700 Msun T = 10894 K m_ubv_U = 23.89\n", + "M = 5.000 Msun T = 9554 K m_ubv_U = 24.17\n", + "M = 3.000 Msun T = 7806 K m_ubv_U = 24.96\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.700 Msun T = 7721 K m_ubv_U = 24.99\n", + "M = 1.100 Msun T = 7119 K m_ubv_U = 15.31\n", + "M = 0.794 Msun T = nan K m_ubv_U = nan\n", + "M = 1.600 Msun T = 4352 K m_ubv_U = 11.59\n", + "M = 4.000 Msun T = 6647 K m_ubv_U = 25.69\n", + "M = 0.631 Msun T = 3090 K m_ubv_U = 29.40\n", + "M = 3.500 Msun T = 8232 K m_ubv_U = 24.83\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.400 Msun T = nan K m_ubv_U = nan\n", + "M = 1.100 Msun T = 7122 K m_ubv_U = 15.30\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 1.300 Msun T = 8203 K m_ubv_U = 14.29\n", + "M = 3.200 Msun T = 8462 K m_ubv_U = 24.77\n", + "M = 1.300 Msun T = 8186 K m_ubv_U = 14.28\n", + "M = 1.400 Msun T = 7439 K m_ubv_U = 25.09\n", + "M = 1.500 Msun T = 8186 K m_ubv_U = 13.38\n", + "M = 1.900 Msun T = 8197 K m_ubv_U = 24.38\n", + "M = 1.400 Msun T = 8353 K m_ubv_U = 13.82\n", + "M = 1.000 Msun T = 8008 K m_ubv_U = 13.35\n", + "M = 1.300 Msun T = 8186 K m_ubv_U = 14.28\n", + "M = 3.500 Msun T = 7720 K m_ubv_U = 25.22\n", + "M = 1.250 Msun T = 7745 K m_ubv_U = 14.98\n", + "M = 1.280 Msun T = 7867 K m_ubv_U = 14.92\n", + "M = 4.500 Msun T = 9122 K m_ubv_U = 24.24\n", + "M = 1.500 Msun T = 8003 K m_ubv_U = 13.34\n", + "M = 0.700 Msun T = 4039 K m_ubv_U = 21.14\n", + "M = 2.700 Msun T = 7456 K m_ubv_U = 25.09\n", + "M = 2.160 Msun T = 11886 K m_ubv_U = 12.37\n", + "M = 0.510 Msun T = 4230 K m_ubv_U = 21.93\n", + "M = 0.250 Msun T = 3732 K m_ubv_U = 24.71\n", + "M = 1.350 Msun T = nan K m_ubv_U = nan\n", + "M = 1.200 Msun T = 7750 K m_ubv_U = 14.82\n", + "M = 6.000 Msun T = 6940 K m_ubv_U = 25.54\n", + "M = 1.530 Msun T = 8322 K m_ubv_U = 13.41\n", + "M = 4.500 Msun T = 15639 K m_ubv_U = 9.50\n", + "M = 2.300 Msun T = 7302 K m_ubv_U = 24.94\n", + "M = 4.000 Msun T = 10615 K m_ubv_U = 23.84\n", + "M = 1.360 Msun T = 8930 K m_ubv_U = 14.19\n", + "M = 0.320 Msun T = 3787 K m_ubv_U = 24.13\n", + "M = 3.200 Msun T = 9927 K m_ubv_U = 23.94\n", + "M = 1.620 Msun T = 9464 K m_ubv_U = 13.74\n", + "M = 1.500 Msun T = 7959 K m_ubv_U = 13.33\n", + "M = 1.800 Msun T = 10985 K m_ubv_U = 13.36\n", + "M = 0.540 Msun T = nan K m_ubv_U = nan\n", + "M = 1.250 Msun T = 7735 K m_ubv_U = 14.98\n", + "M = 0.810 Msun T = nan K m_ubv_U = nan\n", + "M = 3.330 Msun T = 12121 K m_ubv_U = 9.94\n", + "M = 4.000 Msun T = 14110 K m_ubv_U = 9.40\n", + "M = 1.800 Msun T = 11016 K m_ubv_U = 13.36\n", + "M = 1.900 Msun T = 8337 K m_ubv_U = 23.93\n", + "M = 1.190 Msun T = nan K m_ubv_U = nan\n", + "M = 0.220 Msun T = 3700 K m_ubv_U = 25.05\n", + "M = 1.600 Msun T = 4338 K m_ubv_U = 11.57\n", + "M = 0.800 Msun T = 4616 K m_ubv_U = 19.38\n", + "M = 0.400 Msun T = 3899 K m_ubv_U = 23.38\n", + "M = 5.000 Msun T = 9191 K m_ubv_U = 24.22\n", + "M = 0.350 Msun T = 3818 K m_ubv_U = 23.88\n", + "M = 1.300 Msun T = 8270 K m_ubv_U = 14.37\n", + "M = 8.500 Msun T = 7081 K m_ubv_U = 24.04\n", + "M = 0.660 Msun T = 3809 K m_ubv_U = 21.90\n", + "M = 2.700 Msun T = 7456 K m_ubv_U = 25.09\n", + "M = 0.390 Msun T = 3880 K m_ubv_U = 23.48\n", + "M = 0.320 Msun T = nan K m_ubv_U = nan\n", + "M = 0.910 Msun T = 5251 K m_ubv_U = 17.83\n", + "M = 1.260 Msun T = nan K m_ubv_U = nan\n", + "M = 0.600 Msun T = 4611 K m_ubv_U = 20.52\n", + "M = 0.570 Msun T = nan K m_ubv_U = nan\n", + "M = 1.260 Msun T = 8137 K m_ubv_U = 14.62\n", + "M = 0.500 Msun T = nan K m_ubv_U = nan\n", + "M = 0.520 Msun T = 3001 K m_ubv_U = 25.65\n", + "M = 0.350 Msun T = nan K m_ubv_U = nan\n", + "Starting filter: ubv,V Elapsed time: 23.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.180 Msun T = 3463 K m_ubv_V = 23.27\n", + "M = 0.980 Msun T = 6525 K m_ubv_V = 15.84\n", + "M = 2.500 Msun T = 7653 K m_ubv_V = 24.93\n", + "M = 3.200 Msun T = 7964 K m_ubv_V = 25.11\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = 8330 K m_ubv_V = 13.64\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 7452 K m_ubv_V = 25.18\n", + "M = 1.700 Msun T = 15838 K m_ubv_V = 22.40\n", + "M = 2.500 Msun T = 7643 K m_ubv_V = 24.93\n", + "M = 2.700 Msun T = 8076 K m_ubv_V = 25.35\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 3.981 Msun T = nan K m_ubv_V = nan\n", + "M = 1.700 Msun T = 13872 K m_ubv_V = 9.78\n", + "M = 5.000 Msun T = 9914 K m_ubv_V = 25.35\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 1.259 Msun T = 3090 K m_ubv_V = 26.02\n", + "M = 1.700 Msun T = 15821 K m_ubv_V = 22.40\n", + "M = 1.600 Msun T = 4351 K m_ubv_V = 9.54\n", + "M = 0.398 Msun T = nan K m_ubv_V = nan\n", + "M = 2.700 Msun T = 6423 K m_ubv_V = 25.42\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 3.000 Msun T = 7807 K m_ubv_V = 25.13\n", + "M = 2.700 Msun T = 8415 K m_ubv_V = 25.08\n", + "M = 2.500 Msun T = 7654 K m_ubv_V = 24.93\n", + "M = 2.700 Msun T = 8352 K m_ubv_V = 25.33\n", + "M = 3.700 Msun T = 10894 K m_ubv_V = 24.54\n", + "M = 5.000 Msun T = 9554 K m_ubv_V = 24.65\n", + "M = 3.000 Msun T = 7806 K m_ubv_V = 25.13\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.700 Msun T = 7721 K m_ubv_V = 25.14\n", + "M = 1.100 Msun T = 7119 K m_ubv_V = 15.14\n", + "M = 0.794 Msun T = nan K m_ubv_V = nan\n", + "M = 1.600 Msun T = 4352 K m_ubv_V = 9.54\n", + "M = 4.000 Msun T = 6647 K m_ubv_V = 25.56\n", + "M = 0.631 Msun T = 3090 K m_ubv_V = 26.02\n", + "M = 3.500 Msun T = 8232 K m_ubv_V = 25.09\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.400 Msun T = nan K m_ubv_V = nan\n", + "M = 1.100 Msun T = 7122 K m_ubv_V = 15.13\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 1.300 Msun T = 8203 K m_ubv_V = 14.11\n", + "M = 3.200 Msun T = 8462 K m_ubv_V = 25.08\n", + "M = 1.300 Msun T = 8186 K m_ubv_V = 14.10\n", + "M = 1.400 Msun T = 7439 K m_ubv_V = 25.18\n", + "M = 1.500 Msun T = 8186 K m_ubv_V = 13.18\n", + "M = 1.900 Msun T = 8197 K m_ubv_V = 24.64\n", + "M = 1.400 Msun T = 8353 K m_ubv_V = 13.64\n", + "M = 1.000 Msun T = 8008 K m_ubv_V = 13.14\n", + "M = 1.300 Msun T = 8186 K m_ubv_V = 14.10\n", + "M = 3.500 Msun T = 7720 K m_ubv_V = 25.38\n", + "M = 1.250 Msun T = 7745 K m_ubv_V = 14.81\n", + "M = 1.280 Msun T = 7867 K m_ubv_V = 14.75\n", + "M = 4.500 Msun T = 9122 K m_ubv_V = 24.66\n", + "M = 1.500 Msun T = 8003 K m_ubv_V = 13.13\n", + "M = 0.700 Msun T = 4039 K m_ubv_V = 19.04\n", + "M = 2.700 Msun T = 7456 K m_ubv_V = 25.18\n", + "M = 2.160 Msun T = 11886 K m_ubv_V = 12.70\n", + "M = 0.510 Msun T = 4230 K m_ubv_V = 20.03\n", + "M = 0.250 Msun T = 3732 K m_ubv_V = 22.19\n", + "M = 1.350 Msun T = nan K m_ubv_V = nan\n", + "M = 1.200 Msun T = 7750 K m_ubv_V = 14.64\n", + "M = 6.000 Msun T = 6940 K m_ubv_V = 25.50\n", + "M = 1.530 Msun T = 8322 K m_ubv_V = 13.23\n", + "M = 4.500 Msun T = 15639 K m_ubv_V = 10.21\n", + "M = 2.300 Msun T = 7302 K m_ubv_V = 24.99\n", + "M = 4.000 Msun T = 10615 K m_ubv_V = 24.47\n", + "M = 1.360 Msun T = 8930 K m_ubv_V = 14.07\n", + "M = 0.320 Msun T = 3787 K m_ubv_V = 21.69\n", + "M = 3.200 Msun T = 9927 K m_ubv_V = 24.48\n", + "M = 1.620 Msun T = 9464 K m_ubv_V = 13.69\n", + "M = 1.500 Msun T = 7959 K m_ubv_V = 13.12\n", + "M = 1.800 Msun T = 10985 K m_ubv_V = 13.56\n", + "M = 0.540 Msun T = nan K m_ubv_V = nan\n", + "M = 1.250 Msun T = 7735 K m_ubv_V = 14.81\n", + "M = 0.810 Msun T = nan K m_ubv_V = nan\n", + "M = 3.330 Msun T = 12121 K m_ubv_V = 10.34\n", + "M = 4.000 Msun T = 14110 K m_ubv_V = 10.01\n", + "M = 1.800 Msun T = 11016 K m_ubv_V = 13.56\n", + "M = 1.900 Msun T = 8337 K m_ubv_V = 24.22\n", + "M = 1.190 Msun T = nan K m_ubv_V = nan\n", + "M = 0.220 Msun T = 3700 K m_ubv_V = 22.46\n", + "M = 1.600 Msun T = 4338 K m_ubv_V = 9.49\n", + "M = 0.800 Msun T = 4616 K m_ubv_V = 17.93\n", + "M = 0.400 Msun T = 3899 K m_ubv_V = 21.10\n", + "M = 5.000 Msun T = 9191 K m_ubv_V = 24.65\n", + "M = 0.350 Msun T = 3818 K m_ubv_V = 21.49\n", + "M = 1.300 Msun T = 8270 K m_ubv_V = 14.19\n", + "M = 8.500 Msun T = 7081 K m_ubv_V = 24.04\n", + "M = 0.660 Msun T = 3809 K m_ubv_V = 19.55\n", + "M = 2.700 Msun T = 7456 K m_ubv_V = 25.18\n", + "M = 0.390 Msun T = 3880 K m_ubv_V = 21.18\n", + "M = 0.320 Msun T = nan K m_ubv_V = nan\n", + "M = 0.910 Msun T = 5251 K m_ubv_V = 16.99\n", + "M = 1.260 Msun T = nan K m_ubv_V = nan\n", + "M = 0.600 Msun T = 4611 K m_ubv_V = 19.02\n", + "M = 0.570 Msun T = nan K m_ubv_V = nan\n", + "M = 1.260 Msun T = 8137 K m_ubv_V = 14.44\n", + "M = 0.500 Msun T = nan K m_ubv_V = nan\n", + "M = 0.520 Msun T = 3001 K m_ubv_V = 22.03\n", + "M = 0.350 Msun T = nan K m_ubv_V = nan\n", + "Starting filter: ubv,B Elapsed time: 45.59 seconds\n", + "Starting synthetic photometry\n", + "M = 0.180 Msun T = 3463 K m_ubv_B = 24.95\n", + "M = 0.980 Msun T = 6525 K m_ubv_B = 16.26\n", + "M = 2.500 Msun T = 7653 K m_ubv_B = 25.30\n", + "M = 3.200 Msun T = 7964 K m_ubv_B = 25.45\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = 8330 K m_ubv_B = 13.77\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 7452 K m_ubv_B = 25.57\n", + "M = 1.700 Msun T = 15838 K m_ubv_B = 22.32\n", + "M = 2.500 Msun T = 7643 K m_ubv_B = 25.30\n", + "M = 2.700 Msun T = 8076 K m_ubv_B = 25.67\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 3.981 Msun T = nan K m_ubv_B = nan\n", + "M = 1.700 Msun T = 13872 K m_ubv_B = 9.65\n", + "M = 5.000 Msun T = 9914 K m_ubv_B = 25.52\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 1.259 Msun T = 3090 K m_ubv_B = 27.90\n", + "M = 1.700 Msun T = 15821 K m_ubv_B = 22.32\n", + "M = 1.600 Msun T = 4351 K m_ubv_B = 10.75\n", + "M = 0.398 Msun T = nan K m_ubv_B = nan\n", + "M = 2.700 Msun T = 6423 K m_ubv_B = 25.96\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 3.000 Msun T = 7807 K m_ubv_B = 25.48\n", + "M = 2.700 Msun T = 8415 K m_ubv_B = 25.37\n", + "M = 2.500 Msun T = 7654 K m_ubv_B = 25.30\n", + "M = 2.700 Msun T = 8352 K m_ubv_B = 25.63\n", + "M = 3.700 Msun T = 10894 K m_ubv_B = 24.65\n", + "M = 5.000 Msun T = 9554 K m_ubv_B = 24.84\n", + "M = 3.000 Msun T = 7806 K m_ubv_B = 25.48\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.700 Msun T = 7721 K m_ubv_B = 25.50\n", + "M = 1.100 Msun T = 7119 K m_ubv_B = 15.45\n", + "M = 0.794 Msun T = nan K m_ubv_B = nan\n", + "M = 1.600 Msun T = 4352 K m_ubv_B = 10.75\n", + "M = 4.000 Msun T = 6647 K m_ubv_B = 26.07\n", + "M = 0.631 Msun T = 3090 K m_ubv_B = 27.86\n", + "M = 3.500 Msun T = 8232 K m_ubv_B = 25.40\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.400 Msun T = nan K m_ubv_B = nan\n", + "M = 1.100 Msun T = 7122 K m_ubv_B = 15.44\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 1.300 Msun T = 8203 K m_ubv_B = 14.27\n", + "M = 3.200 Msun T = 8462 K m_ubv_B = 25.36\n", + "M = 1.300 Msun T = 8186 K m_ubv_B = 14.26\n", + "M = 1.400 Msun T = 7439 K m_ubv_B = 25.58\n", + "M = 1.500 Msun T = 8186 K m_ubv_B = 13.32\n", + "M = 1.900 Msun T = 8197 K m_ubv_B = 24.95\n", + "M = 1.400 Msun T = 8353 K m_ubv_B = 13.77\n", + "M = 1.000 Msun T = 8008 K m_ubv_B = 13.30\n", + "M = 1.300 Msun T = 8186 K m_ubv_B = 14.26\n", + "M = 3.500 Msun T = 7720 K m_ubv_B = 25.74\n", + "M = 1.250 Msun T = 7745 K m_ubv_B = 15.04\n", + "M = 1.280 Msun T = 7867 K m_ubv_B = 14.96\n", + "M = 4.500 Msun T = 9122 K m_ubv_B = 24.89\n", + "M = 1.500 Msun T = 8003 K m_ubv_B = 13.29\n", + "M = 0.700 Msun T = 4039 K m_ubv_B = 20.33\n", + "M = 2.700 Msun T = 7456 K m_ubv_B = 25.57\n", + "M = 2.160 Msun T = 11886 K m_ubv_B = 12.63\n", + "M = 0.510 Msun T = 4230 K m_ubv_B = 21.21\n", + "M = 0.250 Msun T = 3732 K m_ubv_B = 23.68\n", + "M = 1.350 Msun T = nan K m_ubv_B = nan\n", + "M = 1.200 Msun T = 7750 K m_ubv_B = 14.87\n", + "M = 6.000 Msun T = 6940 K m_ubv_B = 25.96\n", + "M = 1.530 Msun T = 8322 K m_ubv_B = 13.36\n", + "M = 4.500 Msun T = 15639 K m_ubv_B = 10.06\n", + "M = 2.300 Msun T = 7302 K m_ubv_B = 25.41\n", + "M = 4.000 Msun T = 10615 K m_ubv_B = 24.59\n", + "M = 1.360 Msun T = 8930 K m_ubv_B = 14.15\n", + "M = 0.320 Msun T = 3787 K m_ubv_B = 23.15\n", + "M = 3.200 Msun T = 9927 K m_ubv_B = 24.65\n", + "M = 1.620 Msun T = 9464 K m_ubv_B = 13.74\n", + "M = 1.500 Msun T = 7959 K m_ubv_B = 13.28\n", + "M = 1.800 Msun T = 10985 K m_ubv_B = 13.52\n", + "M = 0.540 Msun T = nan K m_ubv_B = nan\n", + "M = 1.250 Msun T = 7735 K m_ubv_B = 15.04\n", + "M = 0.810 Msun T = nan K m_ubv_B = nan\n", + "M = 3.330 Msun T = 12121 K m_ubv_B = 10.25\n", + "M = 4.000 Msun T = 14110 K m_ubv_B = 9.88\n", + "M = 1.800 Msun T = 11016 K m_ubv_B = 13.52\n", + "M = 1.900 Msun T = 8337 K m_ubv_B = 24.51\n", + "M = 1.190 Msun T = nan K m_ubv_B = nan\n", + "M = 0.220 Msun T = 3700 K m_ubv_B = 23.98\n", + "M = 1.600 Msun T = 4338 K m_ubv_B = 10.71\n", + "M = 0.800 Msun T = 4616 K m_ubv_B = 18.90\n", + "M = 0.400 Msun T = 3899 K m_ubv_B = 22.48\n", + "M = 5.000 Msun T = 9191 K m_ubv_B = 24.87\n", + "M = 0.350 Msun T = 3818 K m_ubv_B = 22.92\n", + "M = 1.300 Msun T = 8270 K m_ubv_B = 14.35\n", + "M = 8.500 Msun T = 7081 K m_ubv_B = 24.48\n", + "M = 0.660 Msun T = 3809 K m_ubv_B = 20.97\n", + "M = 2.700 Msun T = 7456 K m_ubv_B = 25.57\n", + "M = 0.390 Msun T = 3880 K m_ubv_B = 22.57\n", + "M = 0.320 Msun T = nan K m_ubv_B = nan\n", + "M = 0.910 Msun T = 5251 K m_ubv_B = 17.77\n", + "M = 1.260 Msun T = nan K m_ubv_B = nan\n", + "M = 0.600 Msun T = 4611 K m_ubv_B = 20.01\n", + "M = 0.570 Msun T = nan K m_ubv_B = nan\n", + "M = 1.260 Msun T = 8137 K m_ubv_B = 14.62\n", + "M = 0.500 Msun T = nan K m_ubv_B = nan\n", + "M = 0.520 Msun T = 3001 K m_ubv_B = 23.93\n", + "M = 0.350 Msun T = nan K m_ubv_B = nan\n", + "Starting filter: ubv,R Elapsed time: 67.48 seconds\n", + "Starting synthetic photometry\n", + "M = 0.180 Msun T = 3463 K m_ubv_R = 22.38\n", + "M = 0.980 Msun T = 6525 K m_ubv_R = 15.60\n", + "M = 2.500 Msun T = 7653 K m_ubv_R = 24.71\n", + "M = 3.200 Msun T = 7964 K m_ubv_R = 24.91\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = 8330 K m_ubv_R = 13.57\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 7452 K m_ubv_R = 24.95\n", + "M = 1.700 Msun T = 15838 K m_ubv_R = 22.39\n", + "M = 2.500 Msun T = 7643 K m_ubv_R = 24.71\n", + "M = 2.700 Msun T = 8076 K m_ubv_R = 25.15\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 3.981 Msun T = nan K m_ubv_R = nan\n", + "M = 1.700 Msun T = 13872 K m_ubv_R = 9.81\n", + "M = 5.000 Msun T = 9914 K m_ubv_R = 25.23\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 1.259 Msun T = 3090 K m_ubv_R = 25.01\n", + "M = 1.700 Msun T = 15821 K m_ubv_R = 22.39\n", + "M = 1.600 Msun T = 4351 K m_ubv_R = 8.97\n", + "M = 0.398 Msun T = nan K m_ubv_R = nan\n", + "M = 2.700 Msun T = 6423 K m_ubv_R = 25.11\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 3.000 Msun T = 7807 K m_ubv_R = 24.92\n", + "M = 2.700 Msun T = 8415 K m_ubv_R = 24.90\n", + "M = 2.500 Msun T = 7654 K m_ubv_R = 24.71\n", + "M = 2.700 Msun T = 8352 K m_ubv_R = 25.15\n", + "M = 3.700 Msun T = 10894 K m_ubv_R = 24.45\n", + "M = 5.000 Msun T = 9554 K m_ubv_R = 24.52\n", + "M = 3.000 Msun T = 7806 K m_ubv_R = 24.92\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.700 Msun T = 7721 K m_ubv_R = 24.93\n", + "M = 1.100 Msun T = 7119 K m_ubv_R = 14.95\n", + "M = 0.794 Msun T = nan K m_ubv_R = nan\n", + "M = 1.600 Msun T = 4352 K m_ubv_R = 8.97\n", + "M = 4.000 Msun T = 6647 K m_ubv_R = 25.27\n", + "M = 0.631 Msun T = 3090 K m_ubv_R = 25.01\n", + "M = 3.500 Msun T = 8232 K m_ubv_R = 24.90\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.400 Msun T = nan K m_ubv_R = nan\n", + "M = 1.100 Msun T = 7122 K m_ubv_R = 14.95\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 1.300 Msun T = 8203 K m_ubv_R = 14.03\n", + "M = 3.200 Msun T = 8462 K m_ubv_R = 24.90\n", + "M = 1.300 Msun T = 8186 K m_ubv_R = 14.02\n", + "M = 1.400 Msun T = 7439 K m_ubv_R = 24.95\n", + "M = 1.500 Msun T = 8186 K m_ubv_R = 13.11\n", + "M = 1.900 Msun T = 8197 K m_ubv_R = 24.45\n", + "M = 1.400 Msun T = 8353 K m_ubv_R = 13.58\n", + "M = 1.000 Msun T = 8008 K m_ubv_R = 13.05\n", + "M = 1.300 Msun T = 8186 K m_ubv_R = 14.02\n", + "M = 3.500 Msun T = 7720 K m_ubv_R = 25.16\n", + "M = 1.250 Msun T = 7745 K m_ubv_R = 14.68\n", + "M = 1.280 Msun T = 7867 K m_ubv_R = 14.64\n", + "M = 4.500 Msun T = 9122 K m_ubv_R = 24.51\n", + "M = 1.500 Msun T = 8003 K m_ubv_R = 13.04\n", + "M = 0.700 Msun T = 4039 K m_ubv_R = 18.33\n", + "M = 2.700 Msun T = 7456 K m_ubv_R = 24.95\n", + "M = 2.160 Msun T = 11886 K m_ubv_R = 12.73\n", + "M = 0.510 Msun T = 4230 K m_ubv_R = 19.36\n", + "M = 0.250 Msun T = 3732 K m_ubv_R = 21.37\n", + "M = 1.350 Msun T = nan K m_ubv_R = nan\n", + "M = 1.200 Msun T = 7750 K m_ubv_R = 14.52\n", + "M = 6.000 Msun T = 6940 K m_ubv_R = 25.23\n", + "M = 1.530 Msun T = 8322 K m_ubv_R = 13.16\n", + "M = 4.500 Msun T = 15639 K m_ubv_R = 10.26\n", + "M = 2.300 Msun T = 7302 K m_ubv_R = 24.75\n", + "M = 4.000 Msun T = 10615 K m_ubv_R = 24.37\n", + "M = 1.360 Msun T = 8930 K m_ubv_R = 14.04\n", + "M = 0.320 Msun T = 3787 K m_ubv_R = 20.89\n", + "M = 3.200 Msun T = 9927 K m_ubv_R = 24.36\n", + "M = 1.620 Msun T = 9464 K m_ubv_R = 13.68\n", + "M = 1.500 Msun T = 7959 K m_ubv_R = 13.03\n", + "M = 1.800 Msun T = 10985 K m_ubv_R = 13.58\n", + "M = 0.540 Msun T = nan K m_ubv_R = nan\n", + "M = 1.250 Msun T = 7735 K m_ubv_R = 14.68\n", + "M = 0.810 Msun T = nan K m_ubv_R = nan\n", + "M = 3.330 Msun T = 12121 K m_ubv_R = 10.37\n", + "M = 4.000 Msun T = 14110 K m_ubv_R = 10.05\n", + "M = 1.800 Msun T = 11016 K m_ubv_R = 13.59\n", + "M = 1.900 Msun T = 8337 K m_ubv_R = 24.04\n", + "M = 1.190 Msun T = nan K m_ubv_R = nan\n", + "M = 0.220 Msun T = 3700 K m_ubv_R = 21.64\n", + "M = 1.600 Msun T = 4338 K m_ubv_R = 8.92\n", + "M = 0.800 Msun T = 4616 K m_ubv_R = 17.39\n", + "M = 0.400 Msun T = 3899 K m_ubv_R = 20.33\n", + "M = 5.000 Msun T = 9191 K m_ubv_R = 24.51\n", + "M = 0.350 Msun T = 3818 K m_ubv_R = 20.69\n", + "M = 1.300 Msun T = 8270 K m_ubv_R = 14.12\n", + "M = 8.500 Msun T = 7081 K m_ubv_R = 23.78\n", + "M = 0.660 Msun T = 3809 K m_ubv_R = 18.76\n", + "M = 2.700 Msun T = 7456 K m_ubv_R = 24.95\n", + "M = 0.390 Msun T = 3880 K m_ubv_R = 20.40\n", + "M = 0.320 Msun T = nan K m_ubv_R = nan\n", + "M = 0.910 Msun T = 5251 K m_ubv_R = 16.59\n", + "M = 1.260 Msun T = nan K m_ubv_R = nan\n", + "M = 0.600 Msun T = 4611 K m_ubv_R = 18.47\n", + "M = 0.570 Msun T = nan K m_ubv_R = nan\n", + "M = 1.260 Msun T = 8137 K m_ubv_R = 14.36\n", + "M = 0.500 Msun T = nan K m_ubv_R = nan\n", + "M = 0.520 Msun T = 3001 K m_ubv_R = 20.99\n", + "M = 0.350 Msun T = nan K m_ubv_R = nan\n", + "Starting filter: ubv,I Elapsed time: 89.54 seconds\n", + "Starting synthetic photometry\n", + "M = 0.180 Msun T = 3463 K m_ubv_I = 21.09\n", + "M = 0.980 Msun T = 6525 K m_ubv_I = 15.27\n", + "M = 2.500 Msun T = 7653 K m_ubv_I = 24.47\n", + "M = 3.200 Msun T = 7964 K m_ubv_I = 24.70\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = 8330 K m_ubv_I = 13.49\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 7452 K m_ubv_I = 24.69\n", + "M = 1.700 Msun T = 15838 K m_ubv_I = 22.49\n", + "M = 2.500 Msun T = 7643 K m_ubv_I = 24.47\n", + "M = 2.700 Msun T = 8076 K m_ubv_I = 24.95\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 3.981 Msun T = nan K m_ubv_I = nan\n", + "M = 1.700 Msun T = 13872 K m_ubv_I = 9.90\n", + "M = 5.000 Msun T = 9914 K m_ubv_I = 25.14\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 1.259 Msun T = 3090 K m_ubv_I = 23.31\n", + "M = 1.700 Msun T = 15821 K m_ubv_I = 22.48\n", + "M = 1.600 Msun T = 4351 K m_ubv_I = 8.19\n", + "M = 0.398 Msun T = nan K m_ubv_I = nan\n", + "M = 2.700 Msun T = 6423 K m_ubv_I = 24.73\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 3.000 Msun T = 7807 K m_ubv_I = 24.69\n", + "M = 2.700 Msun T = 8415 K m_ubv_I = 24.72\n", + "M = 2.500 Msun T = 7654 K m_ubv_I = 24.47\n", + "M = 2.700 Msun T = 8352 K m_ubv_I = 24.97\n", + "M = 3.700 Msun T = 10894 K m_ubv_I = 24.41\n", + "M = 5.000 Msun T = 9554 K m_ubv_I = 24.41\n", + "M = 3.000 Msun T = 7806 K m_ubv_I = 24.69\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.700 Msun T = 7721 K m_ubv_I = 24.69\n", + "M = 1.100 Msun T = 7119 K m_ubv_I = 14.71\n", + "M = 0.794 Msun T = nan K m_ubv_I = nan\n", + "M = 1.600 Msun T = 4352 K m_ubv_I = 8.19\n", + "M = 4.000 Msun T = 6647 K m_ubv_I = 24.92\n", + "M = 0.631 Msun T = 3090 K m_ubv_I = 23.32\n", + "M = 3.500 Msun T = 8232 K m_ubv_I = 24.71\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.400 Msun T = nan K m_ubv_I = nan\n", + "M = 1.100 Msun T = 7122 K m_ubv_I = 14.71\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 1.300 Msun T = 8203 K m_ubv_I = 13.93\n", + "M = 3.200 Msun T = 8462 K m_ubv_I = 24.72\n", + "M = 1.300 Msun T = 8186 K m_ubv_I = 13.91\n", + "M = 1.400 Msun T = 7439 K m_ubv_I = 24.69\n", + "M = 1.500 Msun T = 8186 K m_ubv_I = 13.01\n", + "M = 1.900 Msun T = 8197 K m_ubv_I = 24.25\n", + "M = 1.400 Msun T = 8353 K m_ubv_I = 13.49\n", + "M = 1.000 Msun T = 8008 K m_ubv_I = 12.93\n", + "M = 1.300 Msun T = 8186 K m_ubv_I = 13.91\n", + "M = 3.500 Msun T = 7720 K m_ubv_I = 24.93\n", + "M = 1.250 Msun T = 7745 K m_ubv_I = 14.52\n", + "M = 1.280 Msun T = 7867 K m_ubv_I = 14.49\n", + "M = 4.500 Msun T = 9122 K m_ubv_I = 24.38\n", + "M = 1.500 Msun T = 8003 K m_ubv_I = 12.92\n", + "M = 0.700 Msun T = 4039 K m_ubv_I = 17.41\n", + "M = 2.700 Msun T = 7456 K m_ubv_I = 24.69\n", + "M = 2.160 Msun T = 11886 K m_ubv_I = 12.80\n", + "M = 0.510 Msun T = 4230 K m_ubv_I = 18.52\n", + "M = 0.250 Msun T = 3732 K m_ubv_I = 20.28\n", + "M = 1.350 Msun T = nan K m_ubv_I = nan\n", + "M = 1.200 Msun T = 7750 K m_ubv_I = 14.36\n", + "M = 6.000 Msun T = 6940 K m_ubv_I = 24.91\n", + "M = 1.530 Msun T = 8322 K m_ubv_I = 13.08\n", + "M = 4.500 Msun T = 15639 K m_ubv_I = 10.39\n", + "M = 2.300 Msun T = 7302 K m_ubv_I = 24.47\n", + "M = 4.000 Msun T = 10615 K m_ubv_I = 24.32\n", + "M = 1.360 Msun T = 8930 K m_ubv_I = 14.00\n", + "M = 0.320 Msun T = 3787 K m_ubv_I = 19.84\n", + "M = 3.200 Msun T = 9927 K m_ubv_I = 24.28\n", + "M = 1.620 Msun T = 9464 K m_ubv_I = 13.67\n", + "M = 1.500 Msun T = 7959 K m_ubv_I = 12.90\n", + "M = 1.800 Msun T = 10985 K m_ubv_I = 13.63\n", + "M = 0.540 Msun T = nan K m_ubv_I = nan\n", + "M = 1.250 Msun T = 7735 K m_ubv_I = 14.52\n", + "M = 0.810 Msun T = nan K m_ubv_I = nan\n", + "M = 3.330 Msun T = 12121 K m_ubv_I = 10.44\n", + "M = 4.000 Msun T = 14110 K m_ubv_I = 10.15\n", + "M = 1.800 Msun T = 11016 K m_ubv_I = 13.63\n", + "M = 1.900 Msun T = 8337 K m_ubv_I = 23.85\n", + "M = 1.190 Msun T = nan K m_ubv_I = nan\n", + "M = 0.220 Msun T = 3700 K m_ubv_I = 20.52\n", + "M = 1.600 Msun T = 4338 K m_ubv_I = 8.13\n", + "M = 0.800 Msun T = 4616 K m_ubv_I = 16.71\n", + "M = 0.400 Msun T = 3899 K m_ubv_I = 19.34\n", + "M = 5.000 Msun T = 9191 K m_ubv_I = 24.38\n", + "M = 0.350 Msun T = 3818 K m_ubv_I = 19.66\n", + "M = 1.300 Msun T = 8270 K m_ubv_I = 14.02\n", + "M = 8.500 Msun T = 7081 K m_ubv_I = 23.48\n", + "M = 0.660 Msun T = 3809 K m_ubv_I = 17.73\n", + "M = 2.700 Msun T = 7456 K m_ubv_I = 24.69\n", + "M = 0.390 Msun T = 3880 K m_ubv_I = 19.41\n", + "M = 0.320 Msun T = nan K m_ubv_I = nan\n", + "M = 0.910 Msun T = 5251 K m_ubv_I = 16.05\n", + "M = 1.260 Msun T = nan K m_ubv_I = nan\n", + "M = 0.600 Msun T = 4611 K m_ubv_I = 17.78\n", + "M = 0.570 Msun T = nan K m_ubv_I = nan\n", + "M = 1.260 Msun T = 8137 K m_ubv_I = 14.24\n", + "M = 0.500 Msun T = nan K m_ubv_I = nan\n", + "M = 0.520 Msun T = 3001 K m_ubv_I = 19.14\n", + "M = 0.350 Msun T = nan K m_ubv_I = nan\n", + " Time taken: 111.65 seconds\n", + "Isochrone generation took 448.867211 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "iso1=synthetic.Isochrone_Binary(9.2, 0.0, 2000,\n", + " math.log10(0.1), mass_sampling=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For a sanity check, we can see that the primaries, secondaries, and single stars have phasees of 5 or of 101 or 102 or 103 or -99. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x == 102 or\n", + " x == 103) for x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x == 102 or\n", + " x == 103) for x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or\n", + " x == -99 or x==102 or x==103) for x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries and secondaries accounting for NaNs in the columns. (For max we make NaNs the same as - infinity and for median and mean, we do not include them)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294221355125417" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294221355125417" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294186013434684" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's find the mean and median logg values for the single stars, secondary stars, and the primary stars" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.478614555246956" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.829825080157124" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.360931066484793" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.939597169306249" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.079390004361564" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.499390136742458" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median([x for x in iso1.secondaries['logg'] if np.isfinite(x)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make a histogram of log_g values for each table of the isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (secondaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (primaries) of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values single stars of isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For comparison, let's create a MIST v.1. isochrone phot using otherwise same parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 77.300863 s.\n", + "Making photometry for isochrone: log(t) = 9.20 AKs = 0.00 dist = 2000\n", + " Starting at: 2021-08-13 14:01:03.255111 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3386 K m_ubv_U = 28.04\n", + "M = 0.743 Msun T = 5692 K m_ubv_U = 17.86\n", + "M = 1.429 Msun T = 9088 K m_ubv_U = 13.51\n", + "M = 1.488 Msun T = 5358 K m_ubv_U = 13.80\n", + "M = 1.523 Msun T = 4066 K m_ubv_U = 11.90\n", + "M = 1.556 Msun T = 4981 K m_ubv_U = 12.18\n", + "M = 1.559 Msun T = 4269 K m_ubv_U = 11.67\n", + "M = 1.559 Msun T = 3883 K m_ubv_U = 12.10\n", + "M = 1.559 Msun T = 3952 K m_ubv_U = 11.99\n", + "M = 1.559 Msun T = 3806 K m_ubv_U = 12.28\n", + "M = 1.559 Msun T = 3654 K m_ubv_U = 12.69\n", + "M = 1.559 Msun T = 6746 K m_ubv_U = 6.91\n", + "M = 1.559 Msun T = 75219 K m_ubv_U = 10.65\n", + "M = 1.559 Msun T = 76344 K m_ubv_U = 17.61\n", + "Starting filter: ubv,B Elapsed time: 3.18 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3386 K m_ubv_B = 26.51\n", + "M = 0.743 Msun T = 5692 K m_ubv_B = 17.98\n", + "M = 1.429 Msun T = 9088 K m_ubv_B = 13.47\n", + "M = 1.488 Msun T = 5358 K m_ubv_B = 13.80\n", + "M = 1.523 Msun T = 4066 K m_ubv_B = 10.61\n", + "M = 1.556 Msun T = 4981 K m_ubv_B = 12.00\n", + "M = 1.559 Msun T = 4269 K m_ubv_B = 10.71\n", + "M = 1.559 Msun T = 3883 K m_ubv_B = 10.46\n", + "M = 1.559 Msun T = 3952 K m_ubv_B = 10.47\n", + "M = 1.559 Msun T = 3806 K m_ubv_B = 10.50\n", + "M = 1.559 Msun T = 3654 K m_ubv_B = 10.66\n", + "M = 1.559 Msun T = 6746 K m_ubv_B = 7.30\n", + "M = 1.559 Msun T = 75219 K m_ubv_B = 11.81\n", + "M = 1.559 Msun T = 76344 K m_ubv_B = 18.78\n", + "Starting filter: ubv,V Elapsed time: 6.29 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3386 K m_ubv_V = 24.75\n", + "M = 0.743 Msun T = 5692 K m_ubv_V = 17.35\n", + "M = 1.429 Msun T = 9088 K m_ubv_V = 13.41\n", + "M = 1.488 Msun T = 5358 K m_ubv_V = 13.08\n", + "M = 1.523 Msun T = 4066 K m_ubv_V = 9.20\n", + "M = 1.556 Msun T = 4981 K m_ubv_V = 11.15\n", + "M = 1.559 Msun T = 4269 K m_ubv_V = 9.45\n", + "M = 1.559 Msun T = 3883 K m_ubv_V = 8.90\n", + "M = 1.559 Msun T = 3952 K m_ubv_V = 8.96\n", + "M = 1.559 Msun T = 3806 K m_ubv_V = 8.86\n", + "M = 1.559 Msun T = 3654 K m_ubv_V = 8.89\n", + "M = 1.559 Msun T = 6746 K m_ubv_V = 6.81\n", + "M = 1.559 Msun T = 75219 K m_ubv_V = 12.15\n", + "M = 1.559 Msun T = 76344 K m_ubv_V = 19.12\n", + "Starting filter: ubv,R Elapsed time: 9.42 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3386 K m_ubv_R = 23.82\n", + "M = 0.743 Msun T = 5692 K m_ubv_R = 17.01\n", + "M = 1.429 Msun T = 9088 K m_ubv_R = 13.39\n", + "M = 1.488 Msun T = 5358 K m_ubv_R = 12.71\n", + "M = 1.523 Msun T = 4066 K m_ubv_R = 8.53\n", + "M = 1.556 Msun T = 4981 K m_ubv_R = 10.72\n", + "M = 1.559 Msun T = 4269 K m_ubv_R = 8.86\n", + "M = 1.559 Msun T = 3883 K m_ubv_R = 8.15\n", + "M = 1.559 Msun T = 3952 K m_ubv_R = 8.25\n", + "M = 1.559 Msun T = 3806 K m_ubv_R = 8.07\n", + "M = 1.559 Msun T = 3654 K m_ubv_R = 8.02\n", + "M = 1.559 Msun T = 6746 K m_ubv_R = 6.53\n", + "M = 1.559 Msun T = 75219 K m_ubv_R = 12.27\n", + "M = 1.559 Msun T = 76344 K m_ubv_R = 19.24\n", + "Starting filter: ubv,I Elapsed time: 12.47 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3386 K m_ubv_I = 22.45\n", + "M = 0.743 Msun T = 5692 K m_ubv_I = 16.56\n", + "M = 1.429 Msun T = 9088 K m_ubv_I = 13.36\n", + "M = 1.488 Msun T = 5358 K m_ubv_I = 12.20\n", + "M = 1.523 Msun T = 4066 K m_ubv_I = 7.63\n", + "M = 1.556 Msun T = 4981 K m_ubv_I = 10.12\n", + "M = 1.559 Msun T = 4269 K m_ubv_I = 8.05\n", + "M = 1.559 Msun T = 3883 K m_ubv_I = 7.15\n", + "M = 1.559 Msun T = 3952 K m_ubv_I = 7.29\n", + "M = 1.559 Msun T = 3806 K m_ubv_I = 7.03\n", + "M = 1.559 Msun T = 3654 K m_ubv_I = 6.84\n", + "M = 1.559 Msun T = 6746 K m_ubv_I = 6.19\n", + "M = 1.559 Msun T = 75219 K m_ubv_I = 12.56\n", + "M = 1.559 Msun T = 76344 K m_ubv_I = 19.53\n", + " Time taken: 15.52 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(9.2, 0.0, 2000, math.log10(0.1), recomp=True) # New MIST v.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: dist/10 pc = 200 for both of our isochrones and clusters" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum logg of the isochrone and the distribution of logg values" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.938784080569296" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([519., 146., 185., 81., 227., 67., 21., 128., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0.]),\n", + " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", + " 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]),\n", + " )" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.xlabel(\"logg in cgs\")\n", + "plt.title(\"Histogram of logg values of stars in the MIST v1 isochrone\")\n", + "plt.hist(np.array([x for x in iso2.points['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding the log_g median and mean for the new isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0655075179623505" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.62397473907989" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso2.points['logg'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we want to make clusters for both of these models. We want to compare stellar mass distributions of these clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/ryotainagaki/Desktop/PyPopStar/spisea/synthetic.py:801: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " compMass = np.array([compMass[x] for x in indices])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Interpolator for: Teff\n", + "Creating Interpolator for: L\n", + "Creating Interpolator for: logg\n", + "Creating Interpolator for: isWR\n", + "Creating Interpolator for: mass_current\n", + "Creating Interpolator for: phase\n", + "Creating Interpolator for: m_ubv_U\n", + "Creating Interpolator for: m_ubv_V\n", + "Creating Interpolator for: m_ubv_B\n", + "Creating Interpolator for: m_ubv_R\n", + "Creating Interpolator for: m_ubv_I\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n", + " result = self.as_array() == other\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5235 stars out of mass range\n", + "Found 5931 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1,\n", + " imf.Kennicutt_1983(multiplicity=\n", + " multiplicity.MultiplicityResolvedDK()),\n", + " 200000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2,\n", + " imf.Kennicutt_1983(multiplicity=\n", + " multiplicity.MultiplicityResolvedDK()),\n", + " 200000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, I plot the mass distribution of all non-compact remnant stars in both the MIST v.1 and the " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "star_systems = clus_1.star_systems\n", + "companions = clus_1.companions\n", + "# prims as in \n", + "prims = [star_systems['mass_current'][x] for x in range(len(star_systems)) if star_systems['phase'][x] == 5]\n", + "companions = [companions['mass_current'][x] for x in range(len(companions)) if companions['phase'][x] == 5]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.39728823833521" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(companions))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "star_systems2 = clus_2.star_systems\n", + "companions2 = clus_2.companions\n", + "plt.hist(np.array(prims+companions), 40, (0, 10),\n", + " density=True, histtype='step', label=\"BPASS\")\n", + "prims2 = [star_systems2['mass_current'][x] for x in range(len(star_systems2))\n", + " if (star_systems2['phase'][x] > -99)]\n", + "companions2 = [companions2['mass_current'][x] for x in range(len(companions2))\n", + " if star_systems2['phase'][x] > -99]\n", + "plt.hist(np.array(prims2+companions2), 40, (0, 10),\n", + " density=True, histtype='step', label=\"MISTv1\")\n", + "plt.xlabel(\"Current mass of the star in solar masses\")\n", + "plt.title(\"Normalized histogram of star masses in \\n\" +\n", + " \"BPASS and MISTv1 clusters (PDF) (binsize = 0.25 solar mass)\")\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "199463.77169444307" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "star_systems2['systemMass'].sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/TestPlot_9.3_MIST_0.1.ipynb b/Test_Plots/TestPlot_9.3_MIST_0.1.ipynb new file mode 100755 index 00000000..a14a1616 --- /dev/null +++ b/Test_Plots/TestPlot_9.3_MIST_0.1.ipynb @@ -0,0 +1,2766 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing BPASS isochrone at $10^{9.3}$ Years of Age and Comparing MIST Models $0.1Z_{\\odot}$\n", + "In this BPASS isochrone and cluster plot, I go over the BPASS isochrone for $10^{9.3}$ years age, a tenth of solar metallicity, AKs=0.0, and distance of 1000 parsecs from Earth. From the isochrone and cluster, we discuss several plots such as the log_g frequency distribution of the isochrone, the color magnitude diagram (B-V vs M_V), and the current-mass luminosity relationship of the cluster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I use the MISTv.1 for comparison since only MISTv.1 and BPASS can account for non-solar metallicities." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## This is for tutorial as well as debugging and demonstration purposes." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /g/lu/models/cdbs/extinction\n", + " warnings.warn('Extinction files not found in %s' % (extdir, ))\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/pysynphot/refs.py:125: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /g/lu/models/cdbs/mtab/*_tmt.fits\n", + " 'no thermal calculations can be performed. ' + str(e))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We found files from which we can create our isochrone\n", + "Evolution Models of potential saved isochrone and desired isochrone\n", + "BPASS\n", + "BPASS\n", + "Atmosphereic Models of potential saved isochrone and desired isochrone\n", + "get_merged_atmosphere\n", + "get_merged_atmosphere\n", + "Reddening Laws of potential saved isochrone and desired isochrone\n", + "N09\n", + "N09\n", + "We (re)compute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: invalid value encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "/u/ryotainagaki/Desktop/PyPopStar/spisea/evolution.py:1807: RuntimeWarning: overflow encountered in power\n", + " (1 / cs.au) * un.m)\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/units/quantity.py:479: RuntimeWarning: divide by zero encountered in true_divide\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 26501 logg=5.93\n", + "Changing to logg=5.00 for T= 20895 logg=7.17\n", + "Changing to logg=2.50 for T= 12169 logg=1.95\n", + "Changing to logg=5.00 for T= 18919 logg=6.90\n", + "Changing to logg=5.00 for T= 21043 logg=7.20\n", + "Changing to logg=5.00 for T= 20406 logg=6.94\n", + "Changing to logg=5.00 for T= 20510 logg=7.13\n", + "Changing to logg=5.00 for T= 23300 logg=6.77\n", + "Changing to logg=5.00 for T= 21555 logg=7.08\n", + "Changing to logg=5.00 for T= 23700 logg=6.80\n", + "Changing to logg=5.00 for T= 19488 logg=6.97\n", + "Changing to T= 50000 for T= 73500 logg=5.90\n", + "Changing to logg=5.00 for T= 73500 logg=5.90\n", + "Changing to logg=5.00 for T= 21446 logg=6.93\n", + "Changing to logg=5.00 for T= 37166 logg=7.13\n", + "Changing to logg=5.00 for T= 20153 logg=6.92\n", + "Changing to logg=5.00 for T= 19976 logg=6.88\n", + "Changing to logg=2.00 for T= 9954 logg=1.62\n", + "Changing to logg=5.00 for T= 20685 logg=7.13\n", + "Changing to logg=5.00 for T= 20567 logg=6.98\n", + "Changing to logg=5.00 for T= 20815 logg=7.18\n", + "Changing to logg=5.00 for T= 21461 logg=7.15\n", + "Changing to logg=5.00 for T= 44356 logg=7.09\n", + "Changing to logg=5.00 for T= 19714 logg=6.96\n", + "Changing to logg=5.00 for T= 20066 logg=7.06\n", + "Changing to logg=5.00 for T= 20435 logg=7.07\n", + "Changing to logg=5.00 for T= 20714 logg=6.86\n", + "Changing to logg=5.00 for T= 20676 logg=6.93\n", + "Changing to logg=5.00 for T= 21459 logg=7.14\n", + "Changing to logg=5.00 for T= 21284 logg=7.20\n", + "Changing to logg=5.00 for T= 21261 logg=7.14\n", + "Changing to logg=5.00 for T= 19956 logg=6.89\n", + "Changing to logg=4.00 for T= 35409 logg=3.94\n", + "Changing to logg=5.00 for T= 18462 logg=6.81\n", + "Changing to logg=5.00 for T= 19952 logg=6.85\n", + "Changing to logg=5.00 for T= 20775 logg=7.05\n", + "Changing to T= 50000 for T= 70513 logg=5.98\n", + "Changing to logg=5.00 for T= 70513 logg=5.98\n", + "Changing to logg=5.00 for T= 19614 logg=6.94\n", + "Changing to logg=5.00 for T= 18710 logg=6.87\n", + "Changing to logg=5.00 for T= 19602 logg=6.96\n", + "Changing to logg=5.00 for T= 20735 logg=7.02\n", + "Changing to logg=5.00 for T= 20872 logg=7.00\n", + "Changing to logg=5.00 for T= 21822 logg=7.09\n", + "Changing to logg=5.00 for T= 22614 logg=7.26\n", + "Changing to logg=5.00 for T= 21031 logg=7.10\n", + "Changing to logg=5.00 for T= 19498 logg=6.97\n", + "Changing to logg=5.00 for T= 20536 logg=7.03\n", + "Changing to logg=5.00 for T= 20912 logg=6.93\n", + "Changing to logg=5.00 for T= 19610 logg=6.86\n", + "Changing to logg=5.00 for T= 19588 logg=6.92\n", + "Changing to logg=5.00 for T= 25718 logg=5.85\n", + "Changing to logg=5.00 for T= 19139 logg=6.85\n", + "Changing to logg=5.00 for T= 28452 logg=5.62\n", + "Changing to logg=5.00 for T= 20672 logg=6.94\n", + "Changing to T= 2300 for T= 923 logg=4.44\n", + "Changing to T= 2300 for T= 1847 logg=4.44\n", + "Changing to T= 2300 for T= 1847 logg=4.44\n", + "Changing to T= 2300 for T= 1385 logg=4.44\n", + "Changing to logg=5.00 for T= 26658 logg=5.98\n", + "Changing to logg=5.00 for T= 21534 logg=7.02\n", + "Changing to T= 2300 for T= 1847 logg=4.44\n", + "Changing to logg=1.50 for T= 8257 logg=1.39\n", + "Changing to logg=5.00 for T= 21461 logg=7.01\n", + "Changing to logg=5.00 for T= 19368 logg=6.97\n", + "Changing to logg=5.00 for T= 19555 logg=6.88\n", + "Changing to logg=5.00 for T= 19829 logg=6.92\n", + "Changing to logg=5.00 for T= 20381 logg=6.80\n", + "Changing to logg=5.00 for T= 26657 logg=5.96\n", + "Changing to T= 2300 for T= 1385 logg=4.44\n", + "Changing to T= 2300 for T= 1385 logg=4.44\n", + "Changing to T= 2300 for T= 1847 logg=4.44\n", + "Changing to logg=5.00 for T= 18965 logg=6.88\n", + "Changing to logg=5.00 for T= 39033 logg=7.06\n", + "Changing to T= 2300 for T= 462 logg=4.44\n", + "Changing to logg=5.00 for T= 21537 logg=7.12\n", + "Changing to T= 2300 for T= 1731 logg=4.44\n", + "Making photometry for isochrone: log(t) = 9.30 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-08-13 11:58:51.121553 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 7184 K m_ubv_U = 23.29\n", + "M = 5.600 Msun T = 11196 K m_ubv_U = 23.56\n", + "M = 2.500 Msun T = 6513 K m_ubv_U = 24.27\n", + "M = 1.900 Msun T = 7208 K m_ubv_U = 23.28\n", + "M = 2.300 Msun T = 7302 K m_ubv_U = 23.87\n", + "M = 1.400 Msun T = 7845 K m_ubv_U = 12.04\n", + "M = 6.000 Msun T = 9065 K m_ubv_U = 23.08\n", + "M = 1.259 Msun T = nan K m_ubv_U = nan\n", + "M = 0.794 Msun T = 3090 K m_ubv_U = 27.96\n", + "M = 3.500 Msun T = 7006 K m_ubv_U = 24.24\n", + "M = 1.400 Msun T = 3090 K m_ubv_U = 27.98\n", + "M = 1.300 Msun T = 7894 K m_ubv_U = 12.64\n", + "M = 1.200 Msun T = 7708 K m_ubv_U = 13.14\n", + "M = 5.000 Msun T = 9201 K m_ubv_U = 23.67\n", + "M = 0.501 Msun T = nan K m_ubv_U = nan\n", + "M = 1.100 Msun T = 7689 K m_ubv_U = 22.87\n", + "M = 1.000 Msun T = 3090 K m_ubv_U = 27.95\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 10.000 Msun T = nan K m_ubv_U = nan\n", + "M = 3.700 Msun T = 7845 K m_ubv_U = 23.94\n", + "M = 1.400 Msun T = 3090 K m_ubv_U = 28.08\n", + "M = 1.200 Msun T = 7701 K m_ubv_U = 13.14\n", + "M = 2.100 Msun T = 7176 K m_ubv_U = 24.17\n", + "M = 0.398 Msun T = nan K m_ubv_U = nan\n", + "M = 0.631 Msun T = 3090 K m_ubv_U = 27.89\n", + "M = 1.000 Msun T = 6624 K m_ubv_U = 14.38\n", + "M = 1.200 Msun T = 7711 K m_ubv_U = 13.15\n", + "M = 4.000 Msun T = 6069 K m_ubv_U = 24.78\n", + "M = 5.500 Msun T = 10998 K m_ubv_U = 23.56\n", + "M = 2.000 Msun T = 6773 K m_ubv_U = 23.70\n", + "M = 1.400 Msun T = 7845 K m_ubv_U = 12.04\n", + "M = 3.200 Msun T = 5883 K m_ubv_U = 24.71\n", + "M = 2.500 Msun T = 6512 K m_ubv_U = 24.27\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 2.000 Msun T = 6779 K m_ubv_U = 23.70\n", + "M = 2.700 Msun T = 6876 K m_ubv_U = 24.07\n", + "M = 1.000 Msun T = nan K m_ubv_U = nan\n", + "M = 1.500 Msun T = 4189 K m_ubv_U = 10.23\n", + "M = 4.000 Msun T = 6101 K m_ubv_U = 24.75\n", + "M = 1.259 Msun T = 3090 K m_ubv_U = 28.01\n", + "M = 1.200 Msun T = 7711 K m_ubv_U = 13.15\n", + "M = 0.631 Msun T = nan K m_ubv_U = nan\n", + "M = 1.300 Msun T = 7894 K m_ubv_U = 12.64\n", + "M = 4.500 Msun T = 8480 K m_ubv_U = 23.18\n", + "M = 1.500 Msun T = 4190 K m_ubv_U = 10.23\n", + "M = 4.000 Msun T = 9482 K m_ubv_U = 22.81\n", + "M = 1.500 Msun T = 4190 K m_ubv_U = 10.23\n", + "M = 6.000 Msun T = 9388 K m_ubv_U = 22.94\n", + "M = 2.300 Msun T = 7086 K m_ubv_U = 24.20\n", + "M = 1.250 Msun T = 7745 K m_ubv_U = 13.47\n", + "M = 0.190 Msun T = 3648 K m_ubv_U = 23.99\n", + "M = 0.460 Msun T = nan K m_ubv_U = nan\n", + "M = 0.280 Msun T = 3757 K m_ubv_U = 22.94\n", + "M = 4.800 Msun T = 16664 K m_ubv_U = 7.45\n", + "M = 2.700 Msun T = 6879 K m_ubv_U = 24.07\n", + "M = 4.000 Msun T = 6171 K m_ubv_U = 24.70\n", + "M = 1.400 Msun T = 8622 K m_ubv_U = 13.01\n", + "M = 0.800 Msun T = 5792 K m_ubv_U = 15.93\n", + "M = 0.130 Msun T = 3424 K m_ubv_U = 25.74\n", + "M = 1.080 Msun T = 7016 K m_ubv_U = 13.96\n", + "M = 0.500 Msun T = 4193 K m_ubv_U = 20.57\n", + "M = 3.700 Msun T = 5666 K m_ubv_U = 25.10\n", + "M = 0.660 Msun T = nan K m_ubv_U = nan\n", + "M = 3.000 Msun T = 7114 K m_ubv_U = 23.95\n", + "M = 2.100 Msun T = 6393 K m_ubv_U = 24.14\n", + "M = 3.500 Msun T = 6267 K m_ubv_U = 24.64\n", + "M = 0.370 Msun T = nan K m_ubv_U = nan\n", + "M = 1.000 Msun T = 6647 K m_ubv_U = 14.31\n", + "M = 0.600 Msun T = 3462 K m_ubv_U = 21.71\n", + "M = 0.840 Msun T = nan K m_ubv_U = nan\n", + "M = 4.500 Msun T = 8452 K m_ubv_U = 23.08\n", + "M = 4.000 Msun T = 6231 K m_ubv_U = 24.66\n", + "M = 0.800 Msun T = 5565 K m_ubv_U = 16.25\n", + "M = 0.360 Msun T = 3832 K m_ubv_U = 22.27\n", + "M = 3.200 Msun T = 13480 K m_ubv_U = 9.28\n", + "M = 3.850 Msun T = 18031 K m_ubv_U = 9.29\n", + "M = 1.200 Msun T = 7521 K m_ubv_U = 13.58\n", + "M = 0.140 Msun T = 3478 K m_ubv_U = 25.31\n", + "M = 2.880 Msun T = 10229 K m_ubv_U = 9.11\n", + "M = 1.500 Msun T = 9385 K m_ubv_U = 12.58\n", + "M = 2.100 Msun T = 6393 K m_ubv_U = 24.14\n", + "M = 1.700 Msun T = 12866 K m_ubv_U = 19.47\n", + "M = 0.200 Msun T = 3668 K m_ubv_U = 23.82\n", + "M = 1.350 Msun T = 8379 K m_ubv_U = 13.11\n", + "M = 2.100 Msun T = 6393 K m_ubv_U = 24.14\n", + "M = 1.200 Msun T = 7746 K m_ubv_U = 13.37\n", + "M = 2.400 Msun T = 12858 K m_ubv_U = 10.78\n", + "M = 3.000 Msun T = 7114 K m_ubv_U = 23.95\n", + "M = 0.360 Msun T = 3832 K m_ubv_U = 22.27\n", + "M = 2.700 Msun T = 6871 K m_ubv_U = 24.07\n", + "M = 0.260 Msun T = 3742 K m_ubv_U = 23.11\n", + "M = 3.150 Msun T = 13065 K m_ubv_U = 8.74\n", + "M = 1.200 Msun T = 7753 K m_ubv_U = 13.37\n", + "M = 2.800 Msun T = 10739 K m_ubv_U = 9.02\n", + "M = 0.450 Msun T = 4028 K m_ubv_U = 21.24\n", + "M = 2.400 Msun T = 11555 K m_ubv_U = 9.71\n", + "M = 0.690 Msun T = nan K m_ubv_U = nan\n", + "Starting filter: ubv,V Elapsed time: 22.19 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 7184 K m_ubv_V = 23.31\n", + "M = 5.600 Msun T = 11196 K m_ubv_V = 24.25\n", + "M = 2.500 Msun T = 6513 K m_ubv_V = 24.09\n", + "M = 1.900 Msun T = 7208 K m_ubv_V = 23.31\n", + "M = 2.300 Msun T = 7302 K m_ubv_V = 23.93\n", + "M = 1.400 Msun T = 7845 K m_ubv_V = 11.82\n", + "M = 6.000 Msun T = 9065 K m_ubv_V = 23.49\n", + "M = 1.259 Msun T = nan K m_ubv_V = nan\n", + "M = 0.794 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 3.500 Msun T = 7006 K m_ubv_V = 24.21\n", + "M = 1.400 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 1.300 Msun T = 7894 K m_ubv_V = 12.44\n", + "M = 1.200 Msun T = 7708 K m_ubv_V = 12.96\n", + "M = 5.000 Msun T = 9201 K m_ubv_V = 24.10\n", + "M = 0.501 Msun T = nan K m_ubv_V = nan\n", + "M = 1.100 Msun T = 7689 K m_ubv_V = 23.01\n", + "M = 1.000 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 10.000 Msun T = nan K m_ubv_V = nan\n", + "M = 3.700 Msun T = 7845 K m_ubv_V = 24.12\n", + "M = 1.400 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 1.200 Msun T = 7701 K m_ubv_V = 12.95\n", + "M = 2.100 Msun T = 7176 K m_ubv_V = 24.19\n", + "M = 0.398 Msun T = nan K m_ubv_V = nan\n", + "M = 0.631 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 1.000 Msun T = 6624 K m_ubv_V = 14.18\n", + "M = 1.200 Msun T = 7711 K m_ubv_V = 12.96\n", + "M = 4.000 Msun T = 6069 K m_ubv_V = 24.45\n", + "M = 5.500 Msun T = 10998 K m_ubv_V = 24.23\n", + "M = 2.000 Msun T = 6773 K m_ubv_V = 23.61\n", + "M = 1.400 Msun T = 7845 K m_ubv_V = 11.82\n", + "M = 3.200 Msun T = 5883 K m_ubv_V = 24.31\n", + "M = 2.500 Msun T = 6512 K m_ubv_V = 24.09\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 2.000 Msun T = 6779 K m_ubv_V = 23.61\n", + "M = 2.700 Msun T = 6876 K m_ubv_V = 24.01\n", + "M = 1.000 Msun T = nan K m_ubv_V = nan\n", + "M = 1.500 Msun T = 4189 K m_ubv_V = 7.79\n", + "M = 4.000 Msun T = 6101 K m_ubv_V = 24.44\n", + "M = 1.259 Msun T = 3090 K m_ubv_V = 24.51\n", + "M = 1.200 Msun T = 7711 K m_ubv_V = 12.96\n", + "M = 0.631 Msun T = nan K m_ubv_V = nan\n", + "M = 1.300 Msun T = 7894 K m_ubv_V = 12.44\n", + "M = 4.500 Msun T = 8480 K m_ubv_V = 23.49\n", + "M = 1.500 Msun T = 4190 K m_ubv_V = 7.79\n", + "M = 4.000 Msun T = 9482 K m_ubv_V = 23.29\n", + "M = 1.500 Msun T = 4190 K m_ubv_V = 7.79\n", + "M = 6.000 Msun T = 9388 K m_ubv_V = 23.40\n", + "M = 2.300 Msun T = 7086 K m_ubv_V = 24.20\n", + "M = 1.250 Msun T = 7745 K m_ubv_V = 13.30\n", + "M = 0.190 Msun T = 3648 K m_ubv_V = 21.32\n", + "M = 0.460 Msun T = nan K m_ubv_V = nan\n", + "M = 0.280 Msun T = 3757 K m_ubv_V = 20.45\n", + "M = 4.800 Msun T = 16664 K m_ubv_V = 8.23\n", + "M = 2.700 Msun T = 6879 K m_ubv_V = 24.01\n", + "M = 4.000 Msun T = 6171 K m_ubv_V = 24.41\n", + "M = 1.400 Msun T = 8622 K m_ubv_V = 12.86\n", + "M = 0.800 Msun T = 5792 K m_ubv_V = 15.48\n", + "M = 0.130 Msun T = 3424 K m_ubv_V = 22.60\n", + "M = 1.080 Msun T = 7016 K m_ubv_V = 13.79\n", + "M = 0.500 Msun T = 4193 K m_ubv_V = 18.63\n", + "M = 3.700 Msun T = 5666 K m_ubv_V = 24.61\n", + "M = 0.660 Msun T = nan K m_ubv_V = nan\n", + "M = 3.000 Msun T = 7114 K m_ubv_V = 23.96\n", + "M = 2.100 Msun T = 6393 K m_ubv_V = 23.92\n", + "M = 3.500 Msun T = 6267 K m_ubv_V = 24.38\n", + "M = 0.370 Msun T = nan K m_ubv_V = nan\n", + "M = 1.000 Msun T = 6647 K m_ubv_V = 14.12\n", + "M = 0.600 Msun T = 3462 K m_ubv_V = 18.92\n", + "M = 0.840 Msun T = nan K m_ubv_V = nan\n", + "M = 4.500 Msun T = 8452 K m_ubv_V = 23.39\n", + "M = 4.000 Msun T = 6231 K m_ubv_V = 24.39\n", + "M = 0.800 Msun T = 5565 K m_ubv_V = 15.65\n", + "M = 0.360 Msun T = 3832 K m_ubv_V = 19.90\n", + "M = 3.200 Msun T = 13480 K m_ubv_V = 9.80\n", + "M = 3.850 Msun T = 18031 K m_ubv_V = 10.11\n", + "M = 1.200 Msun T = 7521 K m_ubv_V = 13.41\n", + "M = 0.140 Msun T = 3478 K m_ubv_V = 22.30\n", + "M = 2.880 Msun T = 10229 K m_ubv_V = 9.25\n", + "M = 1.500 Msun T = 9385 K m_ubv_V = 12.52\n", + "M = 2.100 Msun T = 6393 K m_ubv_V = 23.92\n", + "M = 1.700 Msun T = 12866 K m_ubv_V = 20.30\n", + "M = 0.200 Msun T = 3668 K m_ubv_V = 21.18\n", + "M = 1.350 Msun T = 8379 K m_ubv_V = 12.95\n", + "M = 2.100 Msun T = 6393 K m_ubv_V = 23.92\n", + "M = 1.200 Msun T = 7746 K m_ubv_V = 13.20\n", + "M = 2.400 Msun T = 12858 K m_ubv_V = 11.21\n", + "M = 3.000 Msun T = 7114 K m_ubv_V = 23.96\n", + "M = 0.360 Msun T = 3832 K m_ubv_V = 19.90\n", + "M = 2.700 Msun T = 6871 K m_ubv_V = 24.01\n", + "M = 0.260 Msun T = 3742 K m_ubv_V = 20.60\n", + "M = 3.150 Msun T = 13065 K m_ubv_V = 9.24\n", + "M = 1.200 Msun T = 7753 K m_ubv_V = 13.20\n", + "M = 2.800 Msun T = 10739 K m_ubv_V = 9.25\n", + "M = 0.450 Msun T = 4028 K m_ubv_V = 19.13\n", + "M = 2.400 Msun T = 11555 K m_ubv_V = 10.04\n", + "M = 0.690 Msun T = nan K m_ubv_V = nan\n", + "Starting filter: ubv,B Elapsed time: 44.14 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 7184 K m_ubv_B = 23.74\n", + "M = 5.600 Msun T = 11196 K m_ubv_B = 24.34\n", + "M = 2.500 Msun T = 6513 K m_ubv_B = 24.62\n", + "M = 1.900 Msun T = 7208 K m_ubv_B = 23.73\n", + "M = 2.300 Msun T = 7302 K m_ubv_B = 24.34\n", + "M = 1.400 Msun T = 7845 K m_ubv_B = 12.01\n", + "M = 6.000 Msun T = 9065 K m_ubv_B = 23.72\n", + "M = 1.259 Msun T = nan K m_ubv_B = nan\n", + "M = 0.794 Msun T = 3090 K m_ubv_B = 26.37\n", + "M = 3.500 Msun T = 7006 K m_ubv_B = 24.67\n", + "M = 1.400 Msun T = 3090 K m_ubv_B = 26.37\n", + "M = 1.300 Msun T = 7894 K m_ubv_B = 12.64\n", + "M = 1.200 Msun T = 7708 K m_ubv_B = 13.18\n", + "M = 5.000 Msun T = 9201 K m_ubv_B = 24.32\n", + "M = 0.501 Msun T = nan K m_ubv_B = nan\n", + "M = 1.100 Msun T = 7689 K m_ubv_B = 23.38\n", + "M = 1.000 Msun T = 3090 K m_ubv_B = 26.37\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 10.000 Msun T = nan K m_ubv_B = nan\n", + "M = 3.700 Msun T = 7845 K m_ubv_B = 24.47\n", + "M = 1.400 Msun T = 3090 K m_ubv_B = 26.40\n", + "M = 1.200 Msun T = 7701 K m_ubv_B = 13.18\n", + "M = 2.100 Msun T = 7176 K m_ubv_B = 24.62\n", + "M = 0.398 Msun T = nan K m_ubv_B = nan\n", + "M = 0.631 Msun T = 3090 K m_ubv_B = 26.35\n", + "M = 1.000 Msun T = 6624 K m_ubv_B = 14.58\n", + "M = 1.200 Msun T = 7711 K m_ubv_B = 13.19\n", + "M = 4.000 Msun T = 6069 K m_ubv_B = 25.05\n", + "M = 5.500 Msun T = 10998 K m_ubv_B = 24.33\n", + "M = 2.000 Msun T = 6773 K m_ubv_B = 24.10\n", + "M = 1.400 Msun T = 7845 K m_ubv_B = 12.01\n", + "M = 3.200 Msun T = 5883 K m_ubv_B = 24.95\n", + "M = 2.500 Msun T = 6512 K m_ubv_B = 24.62\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 2.000 Msun T = 6779 K m_ubv_B = 24.10\n", + "M = 2.700 Msun T = 6876 K m_ubv_B = 24.48\n", + "M = 1.000 Msun T = nan K m_ubv_B = nan\n", + "M = 1.500 Msun T = 4189 K m_ubv_B = 9.12\n", + "M = 4.000 Msun T = 6101 K m_ubv_B = 25.04\n", + "M = 1.259 Msun T = 3090 K m_ubv_B = 26.38\n", + "M = 1.200 Msun T = 7711 K m_ubv_B = 13.19\n", + "M = 0.631 Msun T = nan K m_ubv_B = nan\n", + "M = 1.300 Msun T = 7894 K m_ubv_B = 12.64\n", + "M = 4.500 Msun T = 8480 K m_ubv_B = 23.77\n", + "M = 1.500 Msun T = 4190 K m_ubv_B = 9.12\n", + "M = 4.000 Msun T = 9482 K m_ubv_B = 23.49\n", + "M = 1.500 Msun T = 4190 K m_ubv_B = 9.12\n", + "M = 6.000 Msun T = 9388 K m_ubv_B = 23.61\n", + "M = 2.300 Msun T = 7086 K m_ubv_B = 24.64\n", + "M = 1.250 Msun T = 7745 K m_ubv_B = 13.54\n", + "M = 0.190 Msun T = 3648 K m_ubv_B = 22.87\n", + "M = 0.460 Msun T = nan K m_ubv_B = nan\n", + "M = 0.280 Msun T = 3757 K m_ubv_B = 21.93\n", + "M = 4.800 Msun T = 16664 K m_ubv_B = 8.06\n", + "M = 2.700 Msun T = 6879 K m_ubv_B = 24.48\n", + "M = 4.000 Msun T = 6171 K m_ubv_B = 25.00\n", + "M = 1.400 Msun T = 8622 K m_ubv_B = 12.98\n", + "M = 0.800 Msun T = 5792 K m_ubv_B = 16.08\n", + "M = 0.130 Msun T = 3424 K m_ubv_B = 24.32\n", + "M = 1.080 Msun T = 7016 K m_ubv_B = 14.12\n", + "M = 0.500 Msun T = 4193 K m_ubv_B = 19.84\n", + "M = 3.700 Msun T = 5666 K m_ubv_B = 25.30\n", + "M = 0.660 Msun T = nan K m_ubv_B = nan\n", + "M = 3.000 Msun T = 7114 K m_ubv_B = 24.40\n", + "M = 2.100 Msun T = 6393 K m_ubv_B = 24.47\n", + "M = 3.500 Msun T = 6267 K m_ubv_B = 24.96\n", + "M = 0.370 Msun T = nan K m_ubv_B = nan\n", + "M = 1.000 Msun T = 6647 K m_ubv_B = 14.51\n", + "M = 0.600 Msun T = 3462 K m_ubv_B = 20.55\n", + "M = 0.840 Msun T = nan K m_ubv_B = nan\n", + "M = 4.500 Msun T = 8452 K m_ubv_B = 23.67\n", + "M = 4.000 Msun T = 6231 K m_ubv_B = 24.97\n", + "M = 0.800 Msun T = 5565 K m_ubv_B = 16.33\n", + "M = 0.360 Msun T = 3832 K m_ubv_B = 21.33\n", + "M = 3.200 Msun T = 13480 K m_ubv_B = 9.69\n", + "M = 3.850 Msun T = 18031 K m_ubv_B = 9.94\n", + "M = 1.200 Msun T = 7521 K m_ubv_B = 13.67\n", + "M = 0.140 Msun T = 3478 K m_ubv_B = 23.98\n", + "M = 2.880 Msun T = 10229 K m_ubv_B = 9.21\n", + "M = 1.500 Msun T = 9385 K m_ubv_B = 12.57\n", + "M = 2.100 Msun T = 6393 K m_ubv_B = 24.47\n", + "M = 1.700 Msun T = 12866 K m_ubv_B = 20.32\n", + "M = 0.200 Msun T = 3668 K m_ubv_B = 22.72\n", + "M = 1.350 Msun T = 8379 K m_ubv_B = 13.10\n", + "M = 2.100 Msun T = 6393 K m_ubv_B = 24.47\n", + "M = 1.200 Msun T = 7746 K m_ubv_B = 13.42\n", + "M = 2.400 Msun T = 12858 K m_ubv_B = 11.12\n", + "M = 3.000 Msun T = 7114 K m_ubv_B = 24.40\n", + "M = 0.360 Msun T = 3832 K m_ubv_B = 21.33\n", + "M = 2.700 Msun T = 6871 K m_ubv_B = 24.48\n", + "M = 0.260 Msun T = 3742 K m_ubv_B = 22.09\n", + "M = 3.150 Msun T = 13065 K m_ubv_B = 9.13\n", + "M = 1.200 Msun T = 7753 K m_ubv_B = 13.42\n", + "M = 2.800 Msun T = 10739 K m_ubv_B = 9.18\n", + "M = 0.450 Msun T = 4028 K m_ubv_B = 20.42\n", + "M = 2.400 Msun T = 11555 K m_ubv_B = 9.96\n", + "M = 0.690 Msun T = nan K m_ubv_B = nan\n", + "Starting filter: ubv,R Elapsed time: 66.19 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 7184 K m_ubv_R = 23.06\n", + "M = 5.600 Msun T = 11196 K m_ubv_R = 24.17\n", + "M = 2.500 Msun T = 6513 K m_ubv_R = 23.79\n", + "M = 1.900 Msun T = 7208 K m_ubv_R = 23.06\n", + "M = 2.300 Msun T = 7302 K m_ubv_R = 23.69\n", + "M = 1.400 Msun T = 7845 K m_ubv_R = 11.72\n", + "M = 6.000 Msun T = 9065 K m_ubv_R = 23.34\n", + "M = 1.259 Msun T = nan K m_ubv_R = nan\n", + "M = 0.794 Msun T = 3090 K m_ubv_R = 23.51\n", + "M = 3.500 Msun T = 7006 K m_ubv_R = 23.95\n", + "M = 1.400 Msun T = 3090 K m_ubv_R = 23.51\n", + "M = 1.300 Msun T = 7894 K m_ubv_R = 12.34\n", + "M = 1.200 Msun T = 7708 K m_ubv_R = 12.83\n", + "M = 5.000 Msun T = 9201 K m_ubv_R = 23.96\n", + "M = 0.501 Msun T = nan K m_ubv_R = nan\n", + "M = 1.100 Msun T = 7689 K m_ubv_R = 22.80\n", + "M = 1.000 Msun T = 3090 K m_ubv_R = 23.51\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 10.000 Msun T = nan K m_ubv_R = nan\n", + "M = 3.700 Msun T = 7845 K m_ubv_R = 23.91\n", + "M = 1.400 Msun T = 3090 K m_ubv_R = 23.50\n", + "M = 1.200 Msun T = 7701 K m_ubv_R = 12.83\n", + "M = 2.100 Msun T = 7176 K m_ubv_R = 23.94\n", + "M = 0.398 Msun T = nan K m_ubv_R = nan\n", + "M = 0.631 Msun T = 3090 K m_ubv_R = 23.51\n", + "M = 1.000 Msun T = 6624 K m_ubv_R = 13.95\n", + "M = 1.200 Msun T = 7711 K m_ubv_R = 12.84\n", + "M = 4.000 Msun T = 6069 K m_ubv_R = 24.10\n", + "M = 5.500 Msun T = 10998 K m_ubv_R = 24.14\n", + "M = 2.000 Msun T = 6773 K m_ubv_R = 23.33\n", + "M = 1.400 Msun T = 7845 K m_ubv_R = 11.72\n", + "M = 3.200 Msun T = 5883 K m_ubv_R = 23.95\n", + "M = 2.500 Msun T = 6512 K m_ubv_R = 23.79\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 2.000 Msun T = 6779 K m_ubv_R = 23.33\n", + "M = 2.700 Msun T = 6876 K m_ubv_R = 23.73\n", + "M = 1.000 Msun T = nan K m_ubv_R = nan\n", + "M = 1.500 Msun T = 4189 K m_ubv_R = 7.17\n", + "M = 4.000 Msun T = 6101 K m_ubv_R = 24.10\n", + "M = 1.259 Msun T = 3090 K m_ubv_R = 23.51\n", + "M = 1.200 Msun T = 7711 K m_ubv_R = 12.84\n", + "M = 0.631 Msun T = nan K m_ubv_R = nan\n", + "M = 1.300 Msun T = 7894 K m_ubv_R = 12.34\n", + "M = 4.500 Msun T = 8480 K m_ubv_R = 23.31\n", + "M = 1.500 Msun T = 4190 K m_ubv_R = 7.17\n", + "M = 4.000 Msun T = 9482 K m_ubv_R = 23.16\n", + "M = 1.500 Msun T = 4190 K m_ubv_R = 7.17\n", + "M = 6.000 Msun T = 9388 K m_ubv_R = 23.27\n", + "M = 2.300 Msun T = 7086 K m_ubv_R = 23.94\n", + "M = 1.250 Msun T = 7745 K m_ubv_R = 13.18\n", + "M = 0.190 Msun T = 3648 K m_ubv_R = 20.48\n", + "M = 0.460 Msun T = nan K m_ubv_R = nan\n", + "M = 0.280 Msun T = 3757 K m_ubv_R = 19.64\n", + "M = 4.800 Msun T = 16664 K m_ubv_R = 8.29\n", + "M = 2.700 Msun T = 6879 K m_ubv_R = 23.73\n", + "M = 4.000 Msun T = 6171 K m_ubv_R = 24.08\n", + "M = 1.400 Msun T = 8622 K m_ubv_R = 12.81\n", + "M = 0.800 Msun T = 5792 K m_ubv_R = 15.15\n", + "M = 0.130 Msun T = 3424 K m_ubv_R = 21.69\n", + "M = 1.080 Msun T = 7016 K m_ubv_R = 13.59\n", + "M = 0.500 Msun T = 4193 K m_ubv_R = 17.95\n", + "M = 3.700 Msun T = 5666 K m_ubv_R = 24.22\n", + "M = 0.660 Msun T = nan K m_ubv_R = nan\n", + "M = 3.000 Msun T = 7114 K m_ubv_R = 23.70\n", + "M = 2.100 Msun T = 6393 K m_ubv_R = 23.61\n", + "M = 3.500 Msun T = 6267 K m_ubv_R = 24.06\n", + "M = 0.370 Msun T = nan K m_ubv_R = nan\n", + "M = 1.000 Msun T = 6647 K m_ubv_R = 13.89\n", + "M = 0.600 Msun T = 3462 K m_ubv_R = 18.04\n", + "M = 0.840 Msun T = nan K m_ubv_R = nan\n", + "M = 4.500 Msun T = 8452 K m_ubv_R = 23.21\n", + "M = 4.000 Msun T = 6231 K m_ubv_R = 24.07\n", + "M = 0.800 Msun T = 5565 K m_ubv_R = 15.30\n", + "M = 0.360 Msun T = 3832 K m_ubv_R = 19.12\n", + "M = 3.200 Msun T = 13480 K m_ubv_R = 9.84\n", + "M = 3.850 Msun T = 18031 K m_ubv_R = 10.18\n", + "M = 1.200 Msun T = 7521 K m_ubv_R = 13.27\n", + "M = 0.140 Msun T = 3478 K m_ubv_R = 21.41\n", + "M = 2.880 Msun T = 10229 K m_ubv_R = 9.27\n", + "M = 1.500 Msun T = 9385 K m_ubv_R = 12.51\n", + "M = 2.100 Msun T = 6393 K m_ubv_R = 23.61\n", + "M = 1.700 Msun T = 12866 K m_ubv_R = 20.25\n", + "M = 0.200 Msun T = 3668 K m_ubv_R = 20.35\n", + "M = 1.350 Msun T = 8379 K m_ubv_R = 12.88\n", + "M = 2.100 Msun T = 6393 K m_ubv_R = 23.61\n", + "M = 1.200 Msun T = 7746 K m_ubv_R = 13.07\n", + "M = 2.400 Msun T = 12858 K m_ubv_R = 11.25\n", + "M = 3.000 Msun T = 7114 K m_ubv_R = 23.70\n", + "M = 0.360 Msun T = 3832 K m_ubv_R = 19.12\n", + "M = 2.700 Msun T = 6871 K m_ubv_R = 23.73\n", + "M = 0.260 Msun T = 3742 K m_ubv_R = 19.79\n", + "M = 3.150 Msun T = 13065 K m_ubv_R = 9.28\n", + "M = 1.200 Msun T = 7753 K m_ubv_R = 13.07\n", + "M = 2.800 Msun T = 10739 K m_ubv_R = 9.26\n", + "M = 0.450 Msun T = 4028 K m_ubv_R = 18.40\n", + "M = 2.400 Msun T = 11555 K m_ubv_R = 10.06\n", + "M = 0.690 Msun T = nan K m_ubv_R = nan\n", + "Starting filter: ubv,I Elapsed time: 88.47 seconds\n", + "Starting synthetic photometry\n", + "M = 1.880 Msun T = 7184 K m_ubv_I = 22.77\n", + "M = 5.600 Msun T = 11196 K m_ubv_I = 24.14\n", + "M = 2.500 Msun T = 6513 K m_ubv_I = 23.42\n", + "M = 1.900 Msun T = 7208 K m_ubv_I = 22.77\n", + "M = 2.300 Msun T = 7302 K m_ubv_I = 23.41\n", + "M = 1.400 Msun T = 7845 K m_ubv_I = 11.58\n", + "M = 6.000 Msun T = 9065 K m_ubv_I = 23.20\n", + "M = 1.259 Msun T = nan K m_ubv_I = nan\n", + "M = 0.794 Msun T = 3090 K m_ubv_I = 21.81\n", + "M = 3.500 Msun T = 7006 K m_ubv_I = 23.64\n", + "M = 1.400 Msun T = 3090 K m_ubv_I = 21.81\n", + "M = 1.300 Msun T = 7894 K m_ubv_I = 12.21\n", + "M = 1.200 Msun T = 7708 K m_ubv_I = 12.67\n", + "M = 5.000 Msun T = 9201 K m_ubv_I = 23.83\n", + "M = 0.501 Msun T = nan K m_ubv_I = nan\n", + "M = 1.100 Msun T = 7689 K m_ubv_I = 22.56\n", + "M = 1.000 Msun T = 3090 K m_ubv_I = 21.81\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 10.000 Msun T = nan K m_ubv_I = nan\n", + "M = 3.700 Msun T = 7845 K m_ubv_I = 23.68\n", + "M = 1.400 Msun T = 3090 K m_ubv_I = 21.80\n", + "M = 1.200 Msun T = 7701 K m_ubv_I = 12.66\n", + "M = 2.100 Msun T = 7176 K m_ubv_I = 23.65\n", + "M = 0.398 Msun T = nan K m_ubv_I = nan\n", + "M = 0.631 Msun T = 3090 K m_ubv_I = 21.81\n", + "M = 1.000 Msun T = 6624 K m_ubv_I = 13.64\n", + "M = 1.200 Msun T = 7711 K m_ubv_I = 12.68\n", + "M = 4.000 Msun T = 6069 K m_ubv_I = 23.67\n", + "M = 5.500 Msun T = 10998 K m_ubv_I = 24.10\n", + "M = 2.000 Msun T = 6773 K m_ubv_I = 23.00\n", + "M = 1.400 Msun T = 7845 K m_ubv_I = 11.58\n", + "M = 3.200 Msun T = 5883 K m_ubv_I = 23.49\n", + "M = 2.500 Msun T = 6512 K m_ubv_I = 23.43\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 2.000 Msun T = 6779 K m_ubv_I = 23.00\n", + "M = 2.700 Msun T = 6876 K m_ubv_I = 23.41\n", + "M = 1.000 Msun T = nan K m_ubv_I = nan\n", + "M = 1.500 Msun T = 4189 K m_ubv_I = 6.32\n", + "M = 4.000 Msun T = 6101 K m_ubv_I = 23.67\n", + "M = 1.259 Msun T = 3090 K m_ubv_I = 21.81\n", + "M = 1.200 Msun T = 7711 K m_ubv_I = 12.68\n", + "M = 0.631 Msun T = nan K m_ubv_I = nan\n", + "M = 1.300 Msun T = 7894 K m_ubv_I = 12.21\n", + "M = 4.500 Msun T = 8480 K m_ubv_I = 23.14\n", + "M = 1.500 Msun T = 4190 K m_ubv_I = 6.32\n", + "M = 4.000 Msun T = 9482 K m_ubv_I = 23.05\n", + "M = 1.500 Msun T = 4190 K m_ubv_I = 6.32\n", + "M = 6.000 Msun T = 9388 K m_ubv_I = 23.15\n", + "M = 2.300 Msun T = 7086 K m_ubv_I = 23.64\n", + "M = 1.250 Msun T = 7745 K m_ubv_I = 13.02\n", + "M = 0.190 Msun T = 3648 K m_ubv_I = 19.33\n", + "M = 0.460 Msun T = nan K m_ubv_I = nan\n", + "M = 0.280 Msun T = 3757 K m_ubv_I = 18.57\n", + "M = 4.800 Msun T = 16664 K m_ubv_I = 8.43\n", + "M = 2.700 Msun T = 6879 K m_ubv_I = 23.41\n", + "M = 4.000 Msun T = 6171 K m_ubv_I = 23.66\n", + "M = 1.400 Msun T = 8622 K m_ubv_I = 12.74\n", + "M = 0.800 Msun T = 5792 K m_ubv_I = 14.72\n", + "M = 0.130 Msun T = 3424 K m_ubv_I = 20.36\n", + "M = 1.080 Msun T = 7016 K m_ubv_I = 13.34\n", + "M = 0.500 Msun T = 4193 K m_ubv_I = 17.10\n", + "M = 3.700 Msun T = 5666 K m_ubv_I = 23.72\n", + "M = 0.660 Msun T = nan K m_ubv_I = nan\n", + "M = 3.000 Msun T = 7114 K m_ubv_I = 23.41\n", + "M = 2.100 Msun T = 6393 K m_ubv_I = 23.23\n", + "M = 3.500 Msun T = 6267 K m_ubv_I = 23.66\n", + "M = 0.370 Msun T = nan K m_ubv_I = nan\n", + "M = 1.000 Msun T = 6647 K m_ubv_I = 13.58\n", + "M = 0.600 Msun T = 3462 K m_ubv_I = 16.76\n", + "M = 0.840 Msun T = nan K m_ubv_I = nan\n", + "M = 4.500 Msun T = 8452 K m_ubv_I = 23.03\n", + "M = 4.000 Msun T = 6231 K m_ubv_I = 23.66\n", + "M = 0.800 Msun T = 5565 K m_ubv_I = 14.82\n", + "M = 0.360 Msun T = 3832 K m_ubv_I = 18.09\n", + "M = 3.200 Msun T = 13480 K m_ubv_I = 9.94\n", + "M = 3.850 Msun T = 18031 K m_ubv_I = 10.34\n", + "M = 1.200 Msun T = 7521 K m_ubv_I = 13.08\n", + "M = 0.140 Msun T = 3478 K m_ubv_I = 20.13\n", + "M = 2.880 Msun T = 10229 K m_ubv_I = 9.28\n", + "M = 1.500 Msun T = 9385 K m_ubv_I = 12.50\n", + "M = 2.100 Msun T = 6393 K m_ubv_I = 23.23\n", + "M = 1.700 Msun T = 12866 K m_ubv_I = 20.28\n", + "M = 0.200 Msun T = 3668 K m_ubv_I = 19.21\n", + "M = 1.350 Msun T = 8379 K m_ubv_I = 12.79\n", + "M = 2.100 Msun T = 6393 K m_ubv_I = 23.23\n", + "M = 1.200 Msun T = 7746 K m_ubv_I = 12.91\n", + "M = 2.400 Msun T = 12858 K m_ubv_I = 11.34\n", + "M = 3.000 Msun T = 7114 K m_ubv_I = 23.41\n", + "M = 0.360 Msun T = 3832 K m_ubv_I = 18.09\n", + "M = 2.700 Msun T = 6871 K m_ubv_I = 23.41\n", + "M = 0.260 Msun T = 3742 K m_ubv_I = 18.71\n", + "M = 3.150 Msun T = 13065 K m_ubv_I = 9.37\n", + "M = 1.200 Msun T = 7753 K m_ubv_I = 12.91\n", + "M = 2.800 Msun T = 10739 K m_ubv_I = 9.29\n", + "M = 0.450 Msun T = 4028 K m_ubv_I = 17.47\n", + "M = 2.400 Msun T = 11555 K m_ubv_I = 10.12\n", + "M = 0.690 Msun T = nan K m_ubv_I = nan\n", + " Time taken: 111.20 seconds\n", + "Isochrone generation took 401.036019 s.\n" + ] + } + ], + "source": [ + "import spisea\n", + "from spisea import evolution, synthetic\n", + "import math\n", + "# Check if the evolution class works fine\n", + "iso1=synthetic.Isochrone_Binary(9.3, 0.0, 1000, math.log10(1/10), mass_sampling=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a sanity check, I would like to make sure that, for primary stars, we only have (from the isochrone) designated phase 5 stars ( anything that is not a compact remnant), white dwarves (models with phase 101), and non-white dwarf compact remnants are determined to be of phase 102, 103. Also make sure that our secondary stars are non-compact-remnant, white dwarves, or are merged. Merged secondaries have phase of -99." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.all([(x == 5 or x == 101 or x==102 or x==103) for\n", + " x in iso1.primaries['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x==102 or x==103) for\n", + " x in iso1.singles['phase']])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all([(x == 5 or x == 101 or x==102 or x==103 or x==-99) for\n", + " x in iso1.secondaries['phase']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Find the maximum, mean, and median values of logg (cgs) for primaries, single stars, and secondaries. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294221355125417" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso1.singles['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294221355125417" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.primaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9.294186013434684" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(np.nan_to_num(iso1.secondaries['logg'], -np.inf))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.775107941964204" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6.887700577787118" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.438029879311406" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso1.secondaries['logg'][np.where(~np.isnan(iso1.secondaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.9748904281098465" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.singles['logg'][np.where(~np.isnan(iso1.singles['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.079390004361564" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso1.primaries['logg'][np.where(~np.isnan(iso1.primaries['logg']))])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.54017073649751" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median([x for x in iso1.secondaries['logg'] if np.isfinite(x)])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n", + "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n", + "findfont: Generic family 'sans-serif' not found because none of the following families were found: Bitstream Vera Sans\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.title(\"Histogram of logg values (secondaries) of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.secondaries['logg'] if np.isfinite(x)]),\n", + " np.arange(-10, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values (primaries) of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.primaries['logg'] if np.isfinite(x)]),\n", + " np.arange(-10, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'logg in cgs')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.title(\"Histogram of logg values single stars of BPASS isochrone\")\n", + "plt.hist(np.array([x for x in iso1.singles['logg'] if np.isfinite(x)]), np.arange(0, 30, 1))\n", + "plt.xlabel(\"logg in cgs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the CMD of the isochrones at 0.1Z_solar and 10^9.3 years of age for **high** surface graivity (log-g >= 5 cgs) type stars.\n", + "\n", + "Account for distance modulus for dist = 1000 pc " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "indices1= np.where(iso1.singles['logg'] > 5)[0]\n", + "indices2= np.where(iso1.primaries['logg'] > 5)[0]\n", + "indices3= np.where(iso1.secondaries['logg'] > 5)[0]\n", + "plt.plot(iso1.primaries['m_ubv_B'][indices2] - iso1.primaries[\"m_ubv_V\"][indices2],\n", + " iso1.primaries[\"m_ubv_V\"][indices2] - 5 * np.log10(1000/10), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'][indices3] - iso1.secondaries[\"m_ubv_V\"][indices3],\n", + " iso1.secondaries[\"m_ubv_V\"][indices3] - 5 * np.log10(1000/10), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'][indices1] - iso1.singles[\"m_ubv_V\"][indices1],\n", + " iso1.singles[\"m_ubv_V\"][indices1] - 5 * np.log10(1000/10), \"r.\", label=\"BPASS isochrone\")\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar and \" +\n", + " \"10**9.3 years age \\n (logg > 5 stars)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Are we getting white dwarves for the bottom left corner of the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "indices2= np.where((iso1.primaries['logg'] > 5) &\n", + " (iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] > -0.4) &\n", + " (iso1.primaries['m_ubv_B'] - iso1.primaries['m_ubv_V'] < -0.2))[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=1>\n", + "\n", + "\n", + "
5.0
" + ], + "text/plain": [ + "\n", + "5.0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(iso1.primaries[indices2]['phase'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmm, I think those are stars that are going to be white dwarves very soon or may unintentionally be brown dwarves. At least according to HOKI's criterion for what is a white dwarf or not. At the very least, at least the stars we just saw were low mass stars so they should be **LOW** on the CMD" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 80.197346 s.\n", + "Making photometry for isochrone: log(t) = 9.30 AKs = 0.00 dist = 1000\n", + " Starting at: 2021-08-13 12:02:03.503829 Usually takes ~5 minutes\n", + "Starting filter: ubv,U Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3387 K m_ubv_U = 26.52\n", + "M = 0.729 Msun T = 5635 K m_ubv_U = 16.47\n", + "M = 1.309 Msun T = 8707 K m_ubv_U = 12.36\n", + "M = 1.388 Msun T = 5243 K m_ubv_U = 12.16\n", + "M = 1.409 Msun T = 4411 K m_ubv_U = 10.38\n", + "M = 1.432 Msun T = 4912 K m_ubv_U = 10.71\n", + "M = 1.435 Msun T = 4172 K m_ubv_U = 10.35\n", + "M = 1.435 Msun T = 4057 K m_ubv_U = 10.45\n", + "M = 1.435 Msun T = 3763 K m_ubv_U = 11.03\n", + "M = 1.435 Msun T = 3759 K m_ubv_U = 11.06\n", + "M = 1.435 Msun T = 3688 K m_ubv_U = 11.35\n", + "M = 1.435 Msun T = 6591 K m_ubv_U = 5.83\n", + "M = 1.435 Msun T = 64842 K m_ubv_U = 9.11\n", + "M = 1.435 Msun T = 74734 K m_ubv_U = 15.94\n", + "Starting filter: ubv,B Elapsed time: 3.33 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3387 K m_ubv_B = 25.00\n", + "M = 0.729 Msun T = 5635 K m_ubv_B = 16.58\n", + "M = 1.309 Msun T = 8707 K m_ubv_B = 12.31\n", + "M = 1.388 Msun T = 5243 K m_ubv_B = 12.12\n", + "M = 1.409 Msun T = 4411 K m_ubv_B = 9.63\n", + "M = 1.432 Msun T = 4912 K m_ubv_B = 10.49\n", + "M = 1.435 Msun T = 4172 K m_ubv_B = 9.23\n", + "M = 1.435 Msun T = 4057 K m_ubv_B = 9.14\n", + "M = 1.435 Msun T = 3763 K m_ubv_B = 9.23\n", + "M = 1.435 Msun T = 3759 K m_ubv_B = 9.25\n", + "M = 1.435 Msun T = 3688 K m_ubv_B = 9.42\n", + "M = 1.435 Msun T = 6591 K m_ubv_B = 6.19\n", + "M = 1.435 Msun T = 64842 K m_ubv_B = 10.27\n", + "M = 1.435 Msun T = 74734 K m_ubv_B = 17.11\n", + "Starting filter: ubv,V Elapsed time: 6.66 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3387 K m_ubv_V = 23.24\n", + "M = 0.729 Msun T = 5635 K m_ubv_V = 15.92\n", + "M = 1.309 Msun T = 8707 K m_ubv_V = 12.21\n", + "M = 1.388 Msun T = 5243 K m_ubv_V = 11.36\n", + "M = 1.409 Msun T = 4411 K m_ubv_V = 8.46\n", + "M = 1.432 Msun T = 4912 K m_ubv_V = 9.60\n", + "M = 1.435 Msun T = 4172 K m_ubv_V = 7.89\n", + "M = 1.435 Msun T = 4057 K m_ubv_V = 7.71\n", + "M = 1.435 Msun T = 3763 K m_ubv_V = 7.57\n", + "M = 1.435 Msun T = 3759 K m_ubv_V = 7.59\n", + "M = 1.435 Msun T = 3688 K m_ubv_V = 7.70\n", + "M = 1.435 Msun T = 6591 K m_ubv_V = 5.68\n", + "M = 1.435 Msun T = 64842 K m_ubv_V = 10.59\n", + "M = 1.435 Msun T = 74734 K m_ubv_V = 17.44\n", + "Starting filter: ubv,R Elapsed time: 9.94 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3387 K m_ubv_R = 22.31\n", + "M = 0.729 Msun T = 5635 K m_ubv_R = 15.58\n", + "M = 1.309 Msun T = 8707 K m_ubv_R = 12.18\n", + "M = 1.388 Msun T = 5243 K m_ubv_R = 10.97\n", + "M = 1.409 Msun T = 4411 K m_ubv_R = 7.91\n", + "M = 1.432 Msun T = 4912 K m_ubv_R = 9.15\n", + "M = 1.435 Msun T = 4172 K m_ubv_R = 7.26\n", + "M = 1.435 Msun T = 4057 K m_ubv_R = 7.04\n", + "M = 1.435 Msun T = 3763 K m_ubv_R = 6.77\n", + "M = 1.435 Msun T = 3759 K m_ubv_R = 6.78\n", + "M = 1.435 Msun T = 3688 K m_ubv_R = 6.85\n", + "M = 1.435 Msun T = 6591 K m_ubv_R = 5.38\n", + "M = 1.435 Msun T = 64842 K m_ubv_R = 10.71\n", + "M = 1.435 Msun T = 74734 K m_ubv_R = 17.56\n", + "Starting filter: ubv,I Elapsed time: 13.15 seconds\n", + "Starting synthetic photometry\n", + "M = 0.105 Msun T = 3387 K m_ubv_I = 20.94\n", + "M = 0.729 Msun T = 5635 K m_ubv_I = 15.11\n", + "M = 1.309 Msun T = 8707 K m_ubv_I = 12.12\n", + "M = 1.388 Msun T = 5243 K m_ubv_I = 10.43\n", + "M = 1.409 Msun T = 4411 K m_ubv_I = 7.15\n", + "M = 1.432 Msun T = 4912 K m_ubv_I = 8.54\n", + "M = 1.435 Msun T = 4172 K m_ubv_I = 6.41\n", + "M = 1.435 Msun T = 4057 K m_ubv_I = 6.14\n", + "M = 1.435 Msun T = 3763 K m_ubv_I = 5.69\n", + "M = 1.435 Msun T = 3759 K m_ubv_I = 5.70\n", + "M = 1.435 Msun T = 3688 K m_ubv_I = 5.71\n", + "M = 1.435 Msun T = 6591 K m_ubv_I = 5.02\n", + "M = 1.435 Msun T = 64842 K m_ubv_I = 10.99\n", + "M = 1.435 Msun T = 74734 K m_ubv_I = 17.85\n", + " Time taken: 16.36 seconds\n" + ] + } + ], + "source": [ + "iso2=synthetic.IsochronePhot(9.3, 0.0, 1000,\n", + " math.log10(1 / 10), recomp=True)\n", + "# New MISTv.1 isochrone for same metallicity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at the distribution of logg values. for the stars in the Parsec isochrone." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7.902440169584496" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 526.,\n", + " 150., 182., 80., 225., 65., 27., 119., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0.]),\n", + " array([-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2,\n", + " 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n", + " 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n", + " 29]),\n", + " )" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.xlabel(\"logg in cgs\")\n", + "plt.title(\"Histogram of logg values of stars in the Parsec isochrone\")\n", + "plt.hist(np.array([x for x in iso2.points['logg'] if np.isfinite(x)]),\n", + " np.arange(-10, 30, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0208530165838594" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.590379008647578" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(iso2.points['logg'])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import imf\n", + "from spisea.imf import imf, multiplicity\n", + "from spisea import ifmr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make the clusters corresponding to the binary star isochrone and the MISTv.1 isochrone" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/u/ryotainagaki/Desktop/PyPopStar/spisea/synthetic.py:801: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " compMass = np.array([compMass[x] for x in indices])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Interpolator for: Teff\n", + "Creating Interpolator for: L\n", + "Creating Interpolator for: logg\n", + "Creating Interpolator for: isWR\n", + "Creating Interpolator for: mass_current\n", + "Creating Interpolator for: phase\n", + "Creating Interpolator for: m_ubv_U\n", + "Creating Interpolator for: m_ubv_V\n", + "Creating Interpolator for: m_ubv_B\n", + "Creating Interpolator for: m_ubv_R\n", + "Creating Interpolator for: m_ubv_I\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/astropy/table/table.py:3197: FutureWarning: elementwise == comparison failed and returning scalar instead; this will raise an error or perform elementwise comparison in the future.\n", + " result = self.as_array() == other\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 695 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "clus_1=synthetic.Cluster_w_Binaries(iso1,\n", + " imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()),\n", + " 20000, ifmr=ifmr.IFMR_Spera15())\n", + "clus_2=synthetic.ResolvedCluster(iso2,\n", + " imf.IMFSalpeter1955(multiplicity=multiplicity.MultiplicityResolvedDK()),\n", + " 20000, ifmr=ifmr.IFMR_Spera15())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's visualize the isochrone we have created so far with a color magnitude diagram. There we can see the end of main sequence and perhaps the M type" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the Binary Cluster vs Default Settings cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(100), \"r.\")\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(100), \"r.\")\n", + "plt.plot(iso1.singles['m_ubv_B'] - iso1.singles[\"m_ubv_V\"],\n", + " iso1.singles[\"m_ubv_V\"] - 5 * np.log10(100),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(iso2.points['m_ubv_B'] - iso2.points[\"m_ubv_V\"],\n", + " iso2.points[\"m_ubv_V\"] - 5 * np.log10(100), \"b+\",\n", + " label=\"MISTv.1 isochrone\", alpha = 0.2)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do have several outliers but otherwise the pattern does not seem too terribly off from the Parsec isochrone's shape. Now I look at the primary stars and see if there are any problems caused." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the Binary Cluster vs Default Settings cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(iso1.primaries['m_ubv_B'] - iso1.primaries[\"m_ubv_V\"],\n", + " iso1.primaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r+\",\n", + " label=\"BPASS isochrone\", alpha =0.7)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar and \\n\" +\n", + " \"10**9.3 years age (Primary stars only)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.plot(iso1.secondaries['m_ubv_B'] - iso1.secondaries[\"m_ubv_V\"],\n", + " iso1.secondaries[\"m_ubv_V\"] - 5 * np.log10(1000/10), \"r+\",\n", + " label=\"BPASS isochrone\", alpha =0.7)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of Isochrones at 0.1Z_solar and\\n\" +\n", + " \" 10**9.3 years age (secondaries stars only)\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting HR diagrams\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(np.log10(iso1.primaries['Teff']), np.log10(iso1.primaries[\"L\"]),\n", + " \"r.\", alpha =0.3)\n", + "plt.plot(np.log10(iso1.secondaries['Teff']), np.log10(iso1.secondaries[\"L\"]),\n", + " \"r.\", alpha =0.3)\n", + "plt.plot(np.log10(iso1.singles['Teff']), np.log10(iso1.singles[\"L\"]), \"r.\",\n", + " label=\"BPASS isochrone\", alpha =0.3)\n", + "plt.plot(np.log10(iso2.points['Teff']), np.log10(iso2.points[\"L\"]), \"b+\",\n", + " label=\"MISTv.1\", alpha =0.1)\n", + "plt.xlabel(\"log($T_{eff}$ in Kelvin)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"HR Diagram of Isochrones at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A good sign here is that there are plenty of compact remnant (that means good number of stars have gone through their main-sequence and post-main sequence). I also notice stars turning to the red giant branch. This is going to be really important when we decide to use the clusters as reference for finding ages of actual star clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "from astropy import constants as cs\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.secondaries['mass_current']),\n", + " np.log10(iso1.secondaries[\"L\"] / cs.L_sun), \"r.\")\n", + "plt.plot(np.log10(iso1.singles['mass_current']),\n", + " np.log10(iso1.singles[\"L\"] / cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone\")\n", + "plt.plot(np.log10(iso2.points['mass_current']),\n", + " np.log10(iso2.points[\"L\"] / cs.L_sun),\n", + " \"b+\", label=\"MISTv.1\", alpha =0.2)\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which table is causing that line segment at around log10(L/L_sun)=-3?\n", + "Let's find out.\n", + "First I take a look at the shape of the primary stars' plot for log current mass log L." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "plt.plot(np.log10(iso1.primaries['mass_current']),\n", + " np.log10(iso1.primaries[\"L\"] / cs.L_sun),\n", + " \"r.\", label=\"BPASS isochrone (primaries)\")\n", + "plt.xlabel(\"log(Current Mass in solar masses)\")\n", + "plt.ylabel(\"log(L/L_solar)\")\n", + "plt.title(\"log Mass-logL of Isochrones at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which type of secondary star is it that is causing this strange pattern that looks like a line lingering in the bottom of the mass luminosity plot. I will examine the region where luminosity of the star is about $10^{-3.2} L_{\\odot}$" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "bad_line = iso1.primaries[np.where((np.log10(iso1.primaries[\"L\"]/cs.L_sun)<-2.5) &\n", + " (np.log10(iso1.primaries[\"L\"]/cs.L_sun)>=-3.3))[0]]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=419\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", + "
massLTeffRloggisWRmass_currentphasesourcem_ubv_Um_ubv_Vm_ubv_Bm_ubv_Rm_ubv_I
solMassWKmsolMass
float64float64float64float64float64boolfloat64float64int64float64float64float64float64float64
4.51.9481030626200675e+2310122.0854669075675103347.7662708178.722800966601913False1.04101.0122.79862493161156323.36117228868292623.5141937876626123.24930713710100623.172933836205555
1.03.041458738904333e+233090.224276650848468414816.149419254.438067627303133False1.05.0427.94619453376298424.51157493939558326.36637005473067323.5080929840829221.811144934700284
0.5011873.041458738904333e+233090.224276650848468414816.149419254.248557553189591False0.6463835.0327.8280870193922624.50852671602862626.33935753929034723.51142655882651421.81499604741336
0.6309573.041458738904333e+233090.224276650848468414816.149419254.23806739019378False0.6309575.0427.82190992742459224.50835823402293626.33788173602582523.51161138521702321.815209621879195
1.82.143724291205302e+237715.5341882326739213861.1698984767.947053181372584False0.56627101.0122.85445851106060623.00876737447277523.37092667469425322.7925599511703522.55598301458258
1.82.143724291205302e+237715.5341882326739213861.1698984767.947053181372584False0.56627101.0122.85445851106060623.00876737447277523.37092667469425322.7925599511703522.55598301458258
1.72.91673470464279e+238296.2341322545339295562.2638721957.93298604311574False0.55799101.0122.4433460226896822.71979127395169623.019466134284622.53502438968456522.346801955440945
0.6309573.041458738904333e+233090.224276650848468414816.149419254.23806739019378False0.6309575.0427.82190992742459224.50835823402293626.33788173602582523.51161138521702321.815209621879195
1.03.041458738904333e+233090.224276650848468414816.149419254.438067627303133False1.05.0427.94619453376298424.51157493939558326.36637005473067323.5080929840829221.811144934700284
..........................................
1.72.91673470464279e+238296.2341322545339295562.2638721957.93298604311574False0.55799101.0122.4433460226896822.71979127395169623.019466134284622.53502438968456522.346801955440945
1.72.8937894508033503e+238260.455577028919339307.2695843767.9254817087518346False0.5536101.0122.4557452314004822.72519919038432323.02845288416037722.5386378332557122.347657813347325
1.43.041458738904333e+233090.224276650848468414816.149419254.487285649973315False1.125.0327.97909241787509824.51236800120853226.37349686463579323.5072288867149121.810146985372228
1.72.91673470464279e+238296.2341322545339295562.2638721957.93298604311574False0.55799101.0122.4433460226896822.71979127395169623.019466134284622.53502438968456522.346801955440945
1.258933.041458738904333e+233090.224276650848468414816.149419254.538069210104485False1.258935.0428.01319377274119724.5132326574502326.3813251759363323.50588868320391421.80830799795702
1.82.143724291205302e+237715.5341882326739213861.1698984767.947053181372584False0.56627101.0122.85445851106060623.00876737447277523.37092667469425322.7925599511703522.55598301458258
0.7943283.041458738904333e+233090.224276650848468414816.149419254.241157485960881False0.63546245.0327.8237258698514224.50840786118676826.3383162561984823.51155693748914321.815146704711537
0.5011873.041458738904333e+233090.224276650848468414816.149419254.138067424857716False0.5011875.0427.76472524484032824.50675345054327226.3239130926853623.5133748679431921.8172476830086
1.02.126727902925366e+237681.800072975079258042.3741657457.939437901071772False0.56178101.0122.86861757570327623.015220648846423.38131945787739622.79702308404495522.557382424879002
0.6309573.041458738904333e+233090.224276650848468414816.149419254.4100965278622555False0.93762445.0427.9279331792258824.51112449237518526.36234056715422723.50858436598961621.811712488422508
" + ], + "text/plain": [ + "\n", + " mass L ... m_ubv_R m_ubv_I \n", + "solMass W ... \n", + "float64 float64 ... float64 float64 \n", + "-------- ---------------------- ... ------------------ ------------------\n", + " 4.5 1.9481030626200675e+23 ... 23.249307137101006 23.172933836205555\n", + " 1.0 3.041458738904333e+23 ... 23.50809298408292 21.811144934700284\n", + "0.501187 3.041458738904333e+23 ... 23.511426558826514 21.81499604741336\n", + "0.630957 3.041458738904333e+23 ... 23.511611385217023 21.815209621879195\n", + " 1.8 2.143724291205302e+23 ... 22.79255995117035 22.55598301458258\n", + " 1.8 2.143724291205302e+23 ... 22.79255995117035 22.55598301458258\n", + " 1.7 2.91673470464279e+23 ... 22.535024389684565 22.346801955440945\n", + "0.630957 3.041458738904333e+23 ... 23.511611385217023 21.815209621879195\n", + " 1.0 3.041458738904333e+23 ... 23.50809298408292 21.811144934700284\n", + " ... ... ... ... ...\n", + " 1.7 2.91673470464279e+23 ... 22.535024389684565 22.346801955440945\n", + " 1.7 2.8937894508033503e+23 ... 22.53863783325571 22.347657813347325\n", + " 1.4 3.041458738904333e+23 ... 23.50722888671491 21.810146985372228\n", + " 1.7 2.91673470464279e+23 ... 22.535024389684565 22.346801955440945\n", + " 1.25893 3.041458738904333e+23 ... 23.505888683203914 21.80830799795702\n", + " 1.8 2.143724291205302e+23 ... 22.79255995117035 22.55598301458258\n", + "0.794328 3.041458738904333e+23 ... 23.511556937489143 21.815146704711537\n", + "0.501187 3.041458738904333e+23 ... 23.51337486794319 21.8172476830086\n", + " 1.0 2.126727902925366e+23 ... 22.797023084044955 22.557382424879002\n", + "0.630957 3.041458738904333e+23 ... 23.508584365989616 21.811712488422508" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bad_line" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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00vs9Y3+vGrY4Zlp4MYj29xX2L3kcFSusRlr+i90E/ofVy5uDXWQALib+GKyK8NjcOMn8FgnL1YqgRe5L2PF7qaq+Wt1tquoSEdkNKzY7HKufCTBfRO4DbowEBhsDC1X115jtrBGR+VhAVdn3uBCrv7QIu378iAUEitXv3I743y7t47sa16rFSTYZd6yH+yWufnddnJNpHZ+qmrSrL7XsmZnB6wq7GdKgS5bg9RwR2RmrrH8Y1vhjEXa83oCdo7/EHTuVCa6nPwJ3BpkDo7DA74IUN3EfMAT7c/ihiDTBrq0/Y3XTQ62w4Lgddi+tVNAdzWdAd+xa/ghWPWkNdp5eRPLrULJj4xLsOP0tVg/vSmCNiLyCVSv5PoV0pXuOJb23BJkJicdC2vsqXXXeaENVVwIfi8hBWN2p+0XkLVWtsS48RKQBlusEZd0rJHMEFuyNUNWhCdvpyIY7PvyH2j7J9pJNB9af/HFuwCp09kvMYRCRf2EX0UwIL3wdsOKGRB0TlqsNbUWkYUzQF1Zqjn72pVhgdLpu2G3PSdhFKSpcN9nv1iHJ9EwI03qEqr5Q0xsXkX5YsDcGKxZeHZnXALi8Bj/uu+C5V5L5YW7l1Br8zHKCbpyewhrHPKyq/6ipbavqLOB3QeODrbEK5+djuTYNsNanYL9paxFplBjcBOlrSyWNKILlrsNudjsmBh9BDlPSpKb8pcrUxbUq6X6hbs7J77BcnF5Y69D1gv3dHQtA4kpQKiwNS1iuWyXz52EBTrkSMhEZjAUHn6XyOZUI/+QMSnUFVf1ERCYAx4vIxVh/fm2weu3RADS8Zn2hqjuSmjOw/Xudqg6LzgiO5bjSwvVJS5LetViwdmfQ6HJPrBTjOKCPiPSpKKe7iudYNFYod5wEOaxtsJKAUFX2VVoyNrRasMP+D8uqreli3aFYUd5sKulvCbvYg1W0TRR34ZqC1V/YVkSKYubvmWIa49LxTcwFtEE1tlkTvgieByXOCP6JbU9Zs/HashHQP2Z6mKYvItPS/T0nJJsXnJSZ3PeJwlbVA2pp++G+eyHmJrsLGxaNQ1lxUoUjD8QIz8v9g2N8veC82gM7z2qzD7ywRe57WEu6Gqdmslo/g/sFk4+MLPIFdh0emLhuMK0hZcdoMm2xnI8PY25EhZRVW6gpdXGtmoDtl7jtDaqhz6hIWIXlwJh5A7Gi1A+rUhxeQ84Mnh+rgW2FxaPptvy8H2tpPQQrzlWsDvt6QdH1ZCyoap3idtO9hqdFVX9W1WdV9Xjsd+6J9XdYkaqcY+F9Ke4Y3o2EDLcq7qu0ZHos3bux7M6hQceT1SIiG4nImVhTaMU6TV1ZyWrFwfOghG31AG5JXDj49/JfLLv2moR1tsMO/qooBrYQkbAoOuya4FoshyBTRmLF278Xkc0T5t2AFW2MrIML381BsQGwvigu3P//iSxXHDwPiq4sIgcQX7fyQ+zf/ECxnvWjLqD26++l43msWOJ8STK0kojsHtQFqYri4HlQwjY3wc6pOGGxRJd0PkhVp2PF7d3YsFud67A/go9oZEQCEWkkIr3FRuioFhH5PVYXpxg4pirFYhVsexuJH1YrzEVeHpn2cPB8c/R3C17/NXj770o+8udgmzsFN59wG42wXI22qac+JcXU/rUqPKdvCqoBhZ8TPe9r09NYXaoTg5zv8PMLsL4AwQKeWiMiDaK/Z2T6GVjr7okkBHwisnFwjnRMmL5r3HUh2P6dwdvKui9L9DiWK3U5Foi9GZzXiW7H6ng+HGQSJKahVVDXNVQcPA9KWG4HrNFLWkSkiYjsIwmV4oLzIwyslm+4ZjlVOcceCZ6vFpFotaPGWGZXnHT3VVoy2g+fqi4Xkb9iQ9Vcjx3EqToyclFtjt1wBmBFjL8AZ2ukJ/MKvIi1xLxURPpiUXkX4FDsBIi7kV2J5QxcLiK7YkFDR+B4rIL2kVh9lnT8A6vX+IWIPIMFWXtgF9AXqdrwc9UW1Lu8GLvhTxCrrDsPO8F3x3I8r6jlZMzG6kZ8LdbPVSOs76OOWCvscZFl78NauD4V7MefsH9vB2Kt3U6IblhVVUR+h9XJeEZEwn74tgP2xfrhivuXX+dUdbWIHI31hfWyiHyIXfSXY0ML7oxVcO5I5RewOJ9hjQuODrb9PhakHIQFxXHVLj4KPuvi4GYc1le5O4UK0edh585dIrIPlku8K9YP4VSshXjUpsEyP2CB4noiciRlOWdhkd/uIjI8eD1fVf8YLLsbdr6F3/l8ia8gHRqrqmMr+S5R+wK3B/twCnaz2AyrPhK2igRAVR8P/mgcj1Uof46yOkHdgSdVtcJcHFVdJyJ3YdelSSLyPHbTGIzd0N4JXteUurhWjcLO1cOx8/55ys77z0jzj1g6xwesr4d5Jhb4jRWRJ7B6ZIdjDfeeJrW+26qjGTBXRN7Erklg97hdsD9+R8XkxB+FBcsjsJKu0FVYn7LvUlb3rDN2brfEzsOb00lccP8egXWGDNZSOW65h8VGHDkPmC4irwdpaI0d4wODNIej6jyCNdi4Iyi6noZV8TiUYNSRdNKJlUyMAYpF5BPs+lGA5bhvhZVoVFhCVZVzTFXfFZEHsNzPyZFz5TAsRikhIU6owr5KTypNeQmacFeyTDEVd8vSLcl6BdhNeR2wbQppGR6mJ3isxcrKp2OVRS8AWidZdygxXUhgB/5jQTpWYNmql2MBsRLT/B+7+YzAgp/oSBvHButcnLD82BT24dBgO8uwf5ejsU5GhxHTNUiytEX2e3GSeZWmJWad/bEcmUVYJf7vsQ6DW6a6n6vyoPxIG/cGv9Eq7MafbKSN/lhW/SKsNdv72MV+EDFd5gTrREfaKKWOR9qI2U5Fad0Ey/35GrtwL8Uuik8DpxLp86sK+7s1FjQXY0X107F/o82SHVNYQPwRZV0tJT3fY9btjF3AZmP1wn7A/jFvcA5H9mNcGoZR/rqQ+CiOLDu0kmUTHxv8BpV8p62wf+rjsevDqmDfPQ30j1m+AXaBHx/8nsuxemPnk/pIGxth9Ve/wa5Hc7C+SLumc0ym8R2HUnPXqg3SF0wPRyUKR5QpxrrrSXukjXSOj4T19sD+xC8K9uskrAHABn1V1vQDC3D/TdloLcuwflz/QqTPxpjfJe5acwh2j5uKBRursT8iY7CApErXDOyPsWLBS4XbwAK2l4LP/TU4Rj/FckwT+3jdGuvq5Ofge3+OldDEHrfJjqHIfrwcq6v4I3Zdm4dVFzkHaJzid03rHIuc25dgf/xWBfvpXux+Voq13K/WvkrnIcHGXQ0RkZuwDjQPVNXXM50e55xzrjaIyFDsD9uNmjCCkUsuqMI2FXhCVdMp2ayWTNfhy1nR+iuRaX2xXKeFxPTr5pxzzuWDoOXqpVhjj9ji3PpORDrENEprhg1EAZYzXmcyPZZuLhsvIt9jxWrLsDoGhxAMhK6VNxZxzjnncoqI7InV4R6EFePfo9YNkdvQxcBJIjIWq7bSARsQYDOsiPmpukyMB3xV9y+sXthJ2Mgci7HK9H/X9Cp4O+eyXFB01S2FRSeq6nO1mhjnMmtfrEX2QqwblprsnzPfvInVc9wfqx+9BivKvQu4Q+u4Tp3X4XPOuUoE/9D3SmHRDTpwd865bOABn3POOedcnvNGG84555xzec4DPuecc865POcBn6txIjJWRLyuQBYTkaEiokFjhLr4vG7B5w1Pcz0/lrJcNv9GIjI8OO66ZTotzmWaB3wub/jFPffU5W8mIsOCz9JgSKhky+0VWa44YV63uOnBvAIR+aOIfCIiv4jIryIyW0Q+F5F7RGSvYLmhke2n9KiFfbGLiNwsIq+KyJzgc7xrDefymHfL4mrDEGwoLudCP2FDjlU2vm5dWAMcJyIXqerimPlnBsukfH0UG1D9XWBHbBikZ7BxhdtjfXSehY1Z+i42JNl1CZvohg3N+AM2TFNtOxm4CBti69sgnVXl57tzOcADPlfjVPXHTKfBZRe1Qd6nZDodgZewPjRPwca1XE9EWgHHAC9iA9Gn6mIs2HsDOExVf43Z7lYAqjoRC/qi8wdhAV+xqg5L43Orajg2FvhkVf21OrmIfr47lxu8SNfVuLg6PZXVGQvmjY287y4ii0VkoYh0TVi2uYh8KyJrI8Vkit0wAWbEFcmJyE4icqeIfBlsd6WITBOR24IbcmKa1qdZRA4RkQ9FZJmILBKRp4PxEOO+S0cRuVdEioNivXki8qyI7BSzbGMRuVBEJgTbXR6s97yI7BuzfO+gGHSmiKwSkbki8riIbJkkLZuLyFPBtpcF3+GQuGWTEZGzg/1wZsL03wbTl4tIk4R5nwb7t2nwfoM6fKn8ZpFlNxKRPwW/16rg+98iIo3T+S6B14BZWE5eot8ABViHsunoHzzfnxjsAajqIlX9MM1tpkREpgTHWdsk868M9uv5kfRMVNUv4tJahc+PO99FRE4Ljrd5wbEwU0ReF5ETYraxk4g8IyI/B7/vDyJyn4h0TPKZzUTkChEZLyKlIrI0uCbcJSKxuZXBcTwpSMtcEXlARDaOWa44eLQQkduD16tFZFhkmX1E5LXIdWSqiPw1yfbGBvs/rWM43XPducp4Dp/LSqo6Q0TOwIaeGSUiA1V1TTD7PqA3MExVwzGLr8NybbYD7sRGPiHyDHaDPworVhsDNMRyZS4FDhKRXVW1NCY5RwMHYeMejgW2x3KBBotIf1X9LlxQRLoD7wOdgLeBUUBn4DjgEBE5RlVfimx7ODZay9fAI8CKYN09gQODdIbbPhB4FmiE5UB9jw3Rc3Sw7cGqOiGy/BbAR0AbbBificDmwHPB+1S9FTzvQ/lAaO/guSmwe7BvCG56OwLvqeqKCrabym8WehwYEKR7CXAw1sP/JsDpqX8VANYCDwN/EZF+qjo+Mu9MoJjIfk/RguC5V5rr1YQRwP9hx9HdMfOHAL8CT9Rhmm4CrgJmAE9iRfkdgZ2xc+G/4YIicihWBC7A01ix9k7AucARIrKHqhZHlm8FvIMdN99hv+WvQE/gt9g5MjchPX8DDsDOmzeAwdhvvTllx3FUY+z8bR0svyT4LojI2cD92JCaTwE/Y8OMXQEcFqR3ccw2Uz6G0z3XnUuJqvrDHzX6wG78mjBtKKDA0CTrKDA2Zvp9wbybg/dDgvfvAA0Slh0ezOuW5DO6Ag1jpv8uWO+KJGlW4NCEeRcF099KmP56MP3qhOn9sXphC4DCYNrGwDpgfJJ0tYm8bgUsAuYDWycs1wdYCkxImP5GkJaLEqYfEflesb9HTFp+wG5sEplWggWDa4EbYrb/58i0bsG04Wn+ZmOD+Z8DrSPTm2M3wbVAhxS/w7BgW2cAXYJ1/xWZv1v422F/hhUrYiXmeyROPzSYvio4Zg8BOqZxzgwiyTmQwrqbBt9lfMy8nYPtPlPJNhSYle5nR3+jhGkLsFzUZjHLt428LgyO6bXAgITlrgjS9UbC9MeD6fez4TWgCNg45vj6EegSmb4RMC6Yt0vCNoqD6WOA5gnzuga/8RKgd8K88Fr1QHWOYapwrvvDH6k8vEjXZbtLgS+BK0TkAuyiOg84RVXXpbMhVf1BVdfGzHoYu4AfkGTVt7V8rhzAPcB0YG8JipxFZDNszMQfsRyF6Gd/iOX2tcb+pYPdBAS7gWzwXVR1QeTtEKzS/7Wq+k3CcpOxnLcdRGTrSFr2w3Il7klY/nkslzMdbwPtsMHSCT6nI5YjMwHL/QuFr9+i5lyhqgvDN6q6DHgMq5bSL92NqdU7ewMb2Lx5MPlM7Ob7nyps7yXsT8AKLGfqJaBErJXuYyIyMN1tpvHZP2H7eicR6ZMwOywyT9oquRatxvZnOao6P/L2CCwH+r+q+l7Cordhwdd+ItIFQEQ2AU7ABqL/Y+I1QFVLVTWuYdD1GqlrqFZaEP7OuyRJ/x+C4yzqVCz37x5VTayTejVQCvwmsYpDINVjOK1z3blUecDnspqqrsQu8Muw4qpmwBBVLUl3WyLSSEQuEJH3g7o3a4O6R+uAFlhOSZwNgqMgcHw/eLtDwvN7ao0UEr0dXU5Vl2DFNf2BiSLyFxEZLCJxLR53D563E+tepNyDsqLErRLS8n6SIHdszLSKhGkPg7mwGOytYN7OIlIUmbcU+DTNz6jI+JhpM4PnDepfpuhBLEfoRBFpgR1nL1fl2AJQ1buw4vgjsYD/zWD7JwPvisj1VUxnKoYHz2GAR1A37ETsD9IrtfjZcR7DckMni3X/cmBc/Tas6B/Kjq/1gqBsXPA2PJ53xu5b42KCsYqke/ysBL6KmV5RehcBX2B1QHtXIw3pnuvOpcTr8LlcMBW7+PYHvsFyZqriv1gdvv8Bz2PdZ6wK5l0MxP0rhw3rA4XmBM8bJzzPTrJ8OL1lZNoJWNHVyZR11bFSRJ7GcjDCz24TPMc1NIgqTEhLZWlPVbQe3z+C51mqOlVE3sLqIu0lIp9hxU6vaFmdy2rT+DpR4fYbVnGzL2D74QysrlRz0m+sUY6qLseOredhfdB1JlZH8c8iMlpVv6jOZyQxGsulPlVErgqC/EOx4+aOmvwtUnQJlgP+W+DK4LFGRF7Bcs6+D5ZL95wJn39KMz2LY6ZVdPz8rKoaM70q5ziQ1jGc7rnuXEo8h8/VlbDoZYM/GSLSspJ1r8SCvflYMHFVuh8uIv2wYG8MVvfmdFW9Sq0LjOuxYppkkvVR1iF4/iXhuUPMsmBFoNHlUNUVqjpMVXth9cpOxXIOT8WKS0lYZztVlQoeIxKWryztKQlyvb7DgromWJ2zMAh8H6s0vy9lOYAb5IBkmyAIGo7V3bsaq3OWTmOWVD7jV1W9FyvOB2ssUOPUGsc8iR1j+wWTM1acq6prVfVOVd0OOwaPwYLSw4HXIkWe6Z4zi4PnZLnxNSUu2IumI+VzvArSPdedS4kHfK6uLAqeO8fMS1oHS0T6YwHZd8A2wfN1IrJnzOJh0WXcP/bNg+cXYopbd8FamiazV0y6GmItacGKcaLPe4pIXO55eLOPbV2nqjNV9TGsLuG0YDvhv/2Pg+cBFaQzKpqWuP0xKMXtRL2F5Sqci+VgvAXrc7U+xoK9aFFvKir6zerCQ9jNfTPg4STF3zUhbP0ttbR9iBTrinXRchDwlVq/fxmjqj+r6rOqejz2R6Andi5D2XE6KHG94BwKz7HwnPkU+/M4MFL3si5VlN6WWAv+lVhn1lWV7rnuXEo84HN1ZTx2oT45WkdNRFqT0MAhMq8VljOyFjgxKN48ASsGGRUJhkJhI4cuMZsrDp4HJXzGJiR0vhtj76DriKgLsBvXO6r6A4CqzsLqbXXDioijn7MrVmy7CMvpQETaBdMTNcfqfq3Bcs7AKpgvBq4VkQ0qmYtIA7HOe0lIS/cgrdFljyAmiE1BmGt3VcL78PU2WA7OAqyhTSoq+s1qnapOx7q/OQq4q6rbEZFzRGS3JPN6Y12RACQ2TKgxqvoB9kfhCCwob0TdjNpRjog0Cfqpk4TpjbBGSwDLg+fngIVY45nE/Xcx0AMYEza4UNV5WPcyHYG/i0i5e5iIFCapK1hTRmKNUX4vIpsnzLsBqws8UlVXbbBm6tI6151Lldfhc3VCVWeLyGNYx7YTReRl7OJ4MFYxe4eY1R7GAoELw1wKVf1SRP6AtTz9DxZghN4CLgMeDOrALQUWq+o9wGfAB8DRIvIhVgzZHssF+Q7rYiSZF4HRIjIa60ZhuyDdC4HzEpY9J/icW0VkfyzQDfvhWwecrmV9/W0KfCwi32I5GDODfXIoVmR0V7isqi4QkWOxYPHjoN7c5GCbXbCK3m2wCuOh87F++O4I0vIlltN5VPCdDqvgO8d5J/i8TYApQevQ0FtYtyftgKeT1H+KU9FvVidUtap1QqMOBO4X6zT6A+y3bIINq3YAFnzdpao12ZAlziNY4PFn7A/D43ELBUHolQmTW0mkY2ysDul80tcUqzpRLCKfYF36FGBFzVthuezfAqjqUhH5Ldaf3bsi8hTWyn0nrMX7HODshO1fgP25OAcYJCKvY3+MumP7+nDSb5SUElUtFpGLsT+JE0TkSaxRzF7YOTgFq5Nbnc+oyrnuXOWS9dfiD39U9UFMv1zB9CbArVhdqV+x4Okqyvo8GxtZ9vfBtOeTfMazwfxLEqZfihWnrCKhvzQsd+E+LLdvJVap/P+wlr/FbNi32tBgG0OxIOwjrLXwYqyj2F5J0rYp1kfYD8H3nI/lZOycsFxL4C9Y7thPQZpnB/vvJCJ93kXW6YYFu9OC77AEu8k8ChwZs/zmWF3AxUHaP8L6iFv/3dL8bT8P1rs3YXojLFhT4Nwk6d6gH74UfrPYYynx90kx7cOC5c9IYdl0++HrBfwBqwP4fbCvV2HBy7PYcGsVfd4gqtgPX8J2wv4FFXgxhc+r6NGtKud7cCxcHuyLH4PjdB5WVHkO0DhmGztjAc487Jz5ETuHOiX5zOZYvcuvsNzCUqxB1x3AJpHlhif7LpF9MCxhenHi7xuz7v5Y47FFwe/8PVZS0bKy/ZPqMUya57o//FHZQ1RT/SPuXGpE5GNgB1VN1uo1J4gNA/cfLFdueGZT45xzzlWd1+FzNSpoINADy8VzzjnnXBbwgM/VmKBT0NcJ6nFlNjXOOeecC3mjDVeT/oLVvfk7cG2G0+Kcc865gNfhc84555zLc16k65xzzjmX57xI19Wptm3bardu3TKdDOecy7jPP/98vqq2y3Q6XP3gAZ+rU926dWP8+PGZToZzzmWciPyQ6TS4+sOLdJ1zzjnn8pwHfM4555xzec4DPuecc865POcBn3POOedcnvOAzznnnHMuz3nA55xzzkWUlmY6Bc7VPA/4nHPOuUBJCUycCFOnZjolztUsD/icc865wNix8Oab8NRTmU6JczXLAz7nclBpaVmxU9yzz8v+edmYpnyeV5nSUrjtNvj0U3j1VfjwQ8vt8+Jdly884HOuhmX6xubzcmNeNqYpn+dVpKQEHngAbrgBXnoJvv4avvwS7rwTnnzSi3ddfvCAz7lK5NrNyzmXnjlz4Nln4ZdfYPp0WLkSFi60XL6PP4YZMzKdQueqz8fSdfVaaSkUFZV/hg2npTOvNtM6fbrdnJYtg+XL4ZNPbF6zZvY+js/LnnkAkyZlV5ryeV7bttC8OSxdCoWFdn4mnqP//jfce++GuXgrVkCDBtC6NfTtG79953KJB3z1lIgcAFwBbA20AuYBHwLDVPWbyHKtgFuBI4GmwEfAJao6qa7TXF1VDepSsXRp+cf06TB/fvyy1b15FRba+/btoVcvK47q1Kn8M2w4zedl17xsTFM+z4szYQI8+CB88UX8/JkzoVUr+2N11FHxyziXKzzgq79aA58D92HBXhfgSuBjEemrqj+IiAAvAN2B3wOLgKuAd0Rke1WdlZmkpy5ZcBdOq2i9OXMseJs7Nz5XJhqcNWtWloOzbBlsv71to7ZvbM65qlu2LPkfM4CCAjj+eA/2XH7wgK+eUtVRwKjoNBH5FJgCHAvcBhwO7AnsrarvBMt8BMwALgcurMs0pyoxyAtfh1LJjQsDuMJCy1ULAzhILTiLfnZtiH6f8HXis8/L/nnZmKZ8nZfo3nutOHf69Pj5BQVwwAHQpo0V9/bqFb+cc7lCVDXTaXBZQkTaYrl9F6nqXSLyb+BAVd00YbkRwCBV7ZruZ/Tr10/Hjx9fMwmOiAZ20eBr6tSy+jtz55bPkWvevHZy42qjXmDiPOdc1U2YAPffDw89lHyZNm0sd2+//Wovh09EPlfVfrWzdefK8xy+ek5EGgINga7AX4E5wBPB7D7A1zGrTQaGiEihqi6tk4QmERcchcWwc+aULbc0SGX//hvm1FWWG5cLuRXOudS98w6MGlXxMqtWwWefQZ8+dZMm52qbB3zuE2Cn4PX3WPHtz8H71kBxzDoLg+dWQMYCvrDbkrggr3lze162rHzjhsTgrjaCM+dc9nr9dRg3zq4NyTRuDIcdBoceCiefXHdpc642ecDnfgO0AHoAfwTeFJE9VbUYECCuzF+SbUxEzgLOSja/S5cu1UpsqLQUZs9OLcgLVTWoc87lh9JSGD/eAr44TZta3b3OneHxx+s2bc7VNg/46jlV/TZ4+YmIvIrl6F0JnIPl5LWOWa1V8LwoZnsPAA8k+7x+/fpVu9JoSUlZkNezpz3PmWP19JIFedHXHsg5Vz+9+CI88wwsXhw/f8AAWLMGjj66TpPlXJ3wkTbceqq6GCvW3TyYNBmrx5doa+DHTNTfiwZ7HTqUvS4sLOufrqiofHDnAZ5zbupUeOwxmDjR3jdpUjavSRM491wr7v3d7+D88zOSROdqlQd8bj0RaQ/0BsKOCl4ANhWRvSLLtAAOC+bVqXCkCSgf7BUVQceOHuQ555IbORJ+/BFUrTPlVatg443t9fPPw8UX23JeZ8/lKy/SradEZDQwAfgKWAL0Ai4B1mB98IEFdR8BI0XkMso6Xhbgb3WZ3rDO3vLlVowbzeXz4M45V5HHH4dvvrH+Nhs1skZeDRvCrrtafd8DDsh0Cp2rfR7w1V8fA8cDfwAaAzOBscDNQYMNVHWdiBwK/B0bkaMACwAHq+rMukpo2Bp37lxo1658sJdsyCTnnAtNmQJffQWLFoEIrF5dNrbuNddkOnXO1Q0P+OopVb0FuCWF5RYCvw0eGTV/fllL3PBi7ZxzFXnvPViyxHL22ra1P44tWsDpp1vjDB9Bw9UXXofPZbUwdw9sdIx58yzYi9bZc865ZObPt66a1q61OnsisM029th++0ynzrm64wGfyxnLl5eNb+ucc5V57z344gvrhmWzzey5bVs46CB7+J9GV594ka7LGStWlBXpOudcZXr2tLp78+fb9aN9exs9w4M9Vx95wOeyWrSLlaZNLXfPG2o45ypTUgKffAIvvABt2sDKldboq2tXrwPs6icP+FxWi9bha9sWZsyw116HzzmXTGmpteZfuNDq7rVsafX4Dj/ccvf8T6Orjzzgc1ktDOrCoK9dO2tlFx1VwznnombPtq5YXnvN+tybN8+CvqZNM50y5zLHG224nBHW31u+3C7izjkXZ+5c+PJLG5mnUSO7ZrRubQGf/0l09ZUHfC7rhfX4Cgvtn3qYyxct7nXOObAxc2fOtEfz5hbstW0LAwfayBoe8Ln6ygM+lzMKC61bFvBcPudcvKVLrUj3p5/sfaNGNipPs2Ye7Ln6zQM+lxPCXL727cty+cILu+fyOefAcve++ML63wO7Nmy8MXTpAt27e8Dn6jcP+FxOiebygefyOefKW7kSFiyw1wUFNppGp07eYbtzHvC5nJGYy9ehg033XD7nXEkJTJ4MEyfa+3XrrA5fr17Qp493xeKcB3wupxQVWR98zZpZP1sdOngDDuectcj95hsr1m3QwOr5tm9vrXM7dsx06pzLPO+Hz+WcoiIrpgkDvObNy4/I4ZyrX0pKLMCbP98aacyfD926wR572MgazjkP+HKGiBQBewBdgLbACuBnYKKqTs5k2jJl6dKyOnzTplkdHR+Bw7n6Z+lSmDXLrgOrVkHjxtYVS6dOVgrg1wTnPODLaiLSFDgJ+B2wC2VF8BI8a7DcfOAZ4H5VnVTX6cyUsBJ2GPQtXWq5fn5xd67+KC21ah1Tp9rrVaugVStrldu2rV8PnAt5wJeFRGQj4ELgaqAVsBL4GPgMmAMsBJoCbYDewG7AOcDZIjIG+IOqfp2BpNeZ6EV8xgzo29fq9M2ZY9O8grZz9cO0afDVV2WNNX79FVq0sJy9nj094HMu5AFfdpoCdAdeA0YAz6vqqopWEJFewFBgCPCFiPxOVR+p7YRmUnghjzbg8KDPufojrMe7eLHV3Zs71xpqbLUVbLZZRpPmXNbxVrrZ6RtgR1U9RFWfrCzYA1DVqar6J6AHcBmWA5j3iorsXzyUBX1gLfa81a5z+W32bOto+Z137P3atfbcvr0V6fqfPufKeA5fFlLVw6ux7q/AHTWXmuwXXtTD3L0OHWDSJLsZgBfpOJfvVq+2Or0bb2wt+Lfd1rticS6R5/C5vBC2xgML+nzoNefy24QJMH48fPyxjagxdWrZMGrNm/t571wiz+HLYSLSGzgIWA48oaq/ZDhJGVVUVL6rFvCWu87lsxkzLNBbtw422cTq8bVqZX/+vDjXufI8hy8HiMhfRGS2iLSOTNsX+AL4O3AfMEFE2mQqjdkgHIWjsLD80Gtz5ljHrM65/FBSAsuWwZIlFuAtXgybbw4DBkC/fv4Hz7k4HvDlhoOAKaq6MDLtZqwfvmuB+7FWvRdlIG1ZJW7oNfCgz7l8snQpfPopfPcdrFgBDRtaZ8stW2Y6Zc5lLw/4ckM34NvwjYhsCuwE3KeqN6rqBcDbwJEZSV2WSdZy14M+53JfSQlMngx33w2LFllXLG3aWI5f69aWw+85fM5tyAO+3NAK62w5tAeWu/dSZNrn2LBrjg0bcXjQ51x+WLrUcvXmzbPi3DVrrAuWXXaBPn082HMuGW+0kRvmAZtG3g8GVgOfRKY1xgP4cuK6a/GOmZ3LXRMmwEcfwQcfgAgUF9uzSFlXLB7wORfPA77cMBE4XES2wYZZOwF4X1VXRJbpBsyu+6Rlt2RB3/TpdmPwm4NzueO++2DMGFiwwN5/8w00bWrdsIC3yHeuIh7w5Ya/Ae8AX0am3Ra+EJECYBDwSt0mKzd4x8zO5b7SUjjvPDtfv/nGcvm22w523hn2289G1/Bz2bnkPODLAar6nogcCpyJ1d17TFVfjSzSHygGRmcgeTkhMeiLdswMfqNwLttNm2ZDqH3zDbRoYXX3Nt7YWud27eqNNZyrjAd8OUJVXwNeSzLvbWCHuk1R7vGOmZ3LTSUlNmbu55/b+0WLLOgrLITOnTObNudyhVfyzwEi8rCIVDi+rogcKiIP11WacpF3zOxcbpo0Cb7+2sbM7dLFcvf694du3eCww+xc9j9tzlXMA77cMBTYvpJltgNOq/WU5DjvmNm53PLee/DUU7BwoVXFmDLFOlru3t1y+cIcew/4nKuYB3z5owmwNtOJyAXeMbNzuaGkBEaNsty98P2qVbDRRqBqdfecc6nxgC93aLIZItIEGAjMqbvk5DbvmNm57FZSAp98Ah9/bI0zWre27lg23xy23x622MLGzvXiXOdS4402spSI/C9h0iUicnrMog2BdlgO3z9rPWF5xDtmdi47lZTA6NE2hNrMmRbQvfMOrF0LfftaPb7Fi60411vnOpcaz+HLXg0ACR4aeZ34WA1MAm4BLstISnNYspy+6dOt9a5zru5Nn27PffrA/PkwYwa0bWs5em3bWrAXnqvOudR4Dl+WUtVu4WsRWQf8Q1Wvz1yK8pd3zOxc9lm5En75xV7/+is0aWI5eiLW916/fp6751w6PODLDYOBGZlORD7zjpmdyx4zZ1qfe0uW2Ptmzez1mjXWFUv79la068MjOpc6D/hyw7XAcOCRZAuIyKnAb1V177pKVL7xjpmdyw433ABTp5a9nzHDHoWFMHAgtGpVVtfWz03nUuMBX24YBIytZJmuwF61npI8Ft44Zs+2m0vfvt6Iw7lM+PTTsjq0m24K555r4+V+/DEUFNjoGl6c61x6PODLH02BNZlORK4LbyBz53rLXecyJbGodqedrM+9b76Bli2tH00vznUuPd5KN3fE9sMnpitwMDCzbpOUn7xjZueyR9eusNlmNpYuQNOmdi56K3rn0uMBX5YSkXUislZEwtEzhoXvow8sV+9/2NBrT2QqvfnGO2Z2Lju88YY1opozp6w41zmXPi/SzV7jKMvVGwj8CBTHLLcWWAC8BTxUJymrJ7xjZucyq6TEGk7Nm2dFuXPnQvPmdi76+edcejzgy1KqOih8HfTD9x/vh6/uedDnXOaEdfR++MGee/f2odScqyov0s0N3YE7a3KDInKsiDwjIj+IyAoR+U5EbhaRooTl+ojIsyJSIiLLRGSyiPxBROrNnwUv3nUuM0pL7Tz75ht736aN199zrqrqzU07l6nqD7Ww2T9ixcR/AmYBOwDDgMEi0l9V14lIJ6w7mJ+Ai4H5wD7ArcAmwBW1kK6s5Dl9ztWtkhI7v5Yts7p7ixdbXT4vznWuajzgyyEi0hELuDYFmsQsoqp6Q4qbO0xV50XevysiC4ERWL9/bwOHAm2BPVQ17Ab1bRHpCQyhHgV8kDzomz7du4hwrqaF59P06TbM2tZbe3Guc9XhAV+OEJHrgCsp/5sJZQ07wtcpBXwJwV7os+B50+C5cfC8JGG5xdTT6gA+7q5zdSMszv3qK3vfrJmPruFcddTLm3auEZFTgD8D7wHHYsHdCOBk4EFgHdYlS3WHVQtH6vg2eH4KK8a9R0S6i0gLETkK+A1wWzU/K2cl1umLjrvrdYucq77S0rIhDhcvtud27Xx0Deeqw3P4csO5WD27A1V1jYgAFKvqE8ATIjIaeBkYVdUPEJFNgeuBMao6HkBV54rI7sDzWF9/YLmIw1T1b1X+NnnAx911rnYVFtrzqlXW+XJYnOvnl3NV4wFfbugLjFLV6NBpDcMXqvq6iLwOXAa8mO7GRaQQC+rWAKdHprcDngWWYTmLC7BcxGtEZJWq3hKzrbOAs5J9VpcuXdJNXlbycXedqz1hce4PP1jAF46uAR7wOVdVHvDlhkZYsBVaAWycsMzXwDnpblhECoAXgB7AXqo6KzL7cqAb0FVVFwXTxopIQ+AGEfm3qs6Pbk9VHwAeSPZ5/fr1ix0iLhf5uLvOlVdaGp/LXdUgraQEmjTx0TWcqwke8OWG2UDHyPsfgW0TltkUy6FLmYg0Ap4BdgH2VdVJCYv0Bb6PBHuhT7EgdHOsjl+9FY676921uHwVrZeaGMzFvQ+P/cRtxK0XFwiG9feWLbPWuWCja3j9PeeqxwO+3PAFFnyF3gbOEpHfYEWug4BjgA9S3aCINAAew7p5OURVP45ZbA7QX0RaJQR9uwbPP6X8DfKY99Hn8k00p2727LL6dInBXFxwFyfZeskCv8JCqypRUGAPr7/nXPV5wJcbXgLuE5HuqjoD+CtwAjA8eACsBq5JY5v3AscBNwHLRGS3yLxZQdHuP4FTgDdE5FasWHkQ1mnzaFWdWdUvlG886HP5IBy7trCw7NgN3yezdKkFZ7OCyiCLF8Mvv9jr9u3t2F+4EHr1svfRbUUDwTD4mzbNxs79/nub3qqV199zriaIat5UqapXRKQ78AegJ1AM3BdTJFvR+sVA1ySzr1PVYcFyuwF/wUbiaBF81ijgNlVdkW66+/Xrp+PHj093tZwRjg4AZUFf+NqDPpfNSkrg1Vdhs82ge3eYPLls+sqVFsStWmWP1auhUSOrX9ekCWy8MXTsaI0rAPr0sUDwhx8s2AM7F+bNs+U32QRatrTgcJtt7PPCwHLyZFgRXFlmz4ZddrFAsWPH/Av4RORzVe2X6XS4+sFz+LKUiFwNvAV8qqrrEucHOX0XVHX7qtotxeU+Bg6u6ufUN57T53LVf/4D330H3bpZbl1Yf27lSgu2dtnF3vfsWbZOdGzp6LTCQnskG4P6iy8sONxsM5sW/gfs3Lks2GvTxgI+r7/nXM3wgC973YD1i7dURN7Fgr+3VPXrzCbLVcaDPpdLSkrgxhvhww9hiy0sNy08VgcOtIArGqxVJAzKEoOz6HodOsAOO1gQN368BXgrV8KPP8L771sOYNu28NlnsPnmnjvuXE3xgC973QgMxlrQHgocAiAiP2ONNsIA8IeMpdAl5UGfywUlJXDnnTB6NLRubcOXjR0LPXpYnbuePa2oNRrAVdTlSrKGFXFj4BYVQe/eG+YChufIsmWw/faes+dcTfGAL0up6l8ARKQZMABrTbs3sD1wEnBiMH8GQfAHvJ3YL57LHA/6XLb75BP4/HML6jbf3HLcttgCttsOBg0qC+CS5dylGowlHuvhemEgGDckYc+eHuw5V5M84MtyqroceD14ICItsZay+2IBYG+gO3AGNqZuo0yk08WrLOjzriZcpvz73/Daa2X15CZNsuLUbt2sLl1d/CFJFgjGzXPOVU+DTCfApUdVF6vqc6p6AdAHOAobZUPw3zMrdeqUvPL67Nn2Oi6Hw7na8t57MGaMtaJt3tzq0K1caUW6LVrAV1/VbXqKiuw8CZ892HOu5nkOX44RkW2wnL19gIFYVykClADvZDBprgLJcvqWLi3r5BY8t8/VjQEDrH7chx/CrbdaVyubbw5bbQXFxVbMu/nm0L+/H5PO5QsP+LKciPSkLMAbBLTDAry5wGtYkPeOqk7LVBpdasKgb/p0Kz5r187eT5tmFdTbt7f3foN1daGoCA44wDo4/vFHC/BWrLAWtG+/Dc8/b/P2288acDjncpsHfFlKRB7GgrzNsABvHvAuZQHelAwmz1VRWGwVjhe6dGn5It5p07wbCle3tt0Wzj7binR/+sn+fIR/Rlq1smPUOZf7PODLXkOxRhhPAbeo6heZTY6rKXE5eN6K12VKWLz75JOwZImNfgEwYYKNmLFsWSZT55yrKV7JP3t9j/0+xwPjROQVEblMRPqJiGQ4ba6awta50eGiEht0lJRkLn2ufikqguOPhxNOgD32sKHTomPe+rHoXO7zHL4spaq9RKQTVqy7D9YJ84GAAktEZBxWvPu2qtZxmzpXE6I5fdHcPc/pc5kQHo+tWlnL3YICmDjRpjVv7sehc7nOc/iymKqWqOqjqjpUVbsCvYDzgDeA3YDbgS9EZJ6IPCUi52Yyva7qKuq6xXNXXF1ZtgwWLYLu3e394sUweTLMm5fRZDnnaoAHfDlEVb9X1X+p6gmq2h7YDrgU65LlGODujCbQVYsHfS7TBgyA3Xe3x88/22gbAB98YH33Oedylxfp5iARaY911RI+uoWzMpUmVzN8ODaXaVtsYc9dulh3LX362POnn1rXQd5Fi3O5yQO+HBAZTi0M8LYKZwXPUygbT9flOA/6XCYVFdkxN3iwFeeCjb4xfbqNtRttaOScyx0e8GUpEdkfa6yxN7A9VvweBng/YsHd28BbqjonE2l0tceDPpdJnTpZh8tz51rQ16eP9dH35ZfQpg307evHoHO5xgO+7PUa1iI37HT5HYIgT1WnZzJhrm6kEvSVlnpui6sdvXpZ0PfMM1ac26uX9dM3fjzMmmXduPix51zu8IAve70MjMECvEmZTozLjIqCvnDEDvAbr6sdAwZYlyzvvGONOPbe26YvWACzZ/tx51wu8YAvS6nqYZlOg8sOyYK+cHi2kN98XW3YcUd7fvVVG1t3993t/YwZdvxtsYUfe87lAg/4nMsB0aBv+nTLdQmLdufOtfc+Bq+rLTvuaH30zZ9v78Oxdn3YNedyh/fD51yO6NTJclN69rRhr8J++nr2tGfvr8/VpgEDrPEGWEfMHTrA8uVWtBtWLXDOZS8P+JzLIeEYvIWFPgavq3u9etmx1qyZHWvt2lmx7rRpHvQ5l+084HMux4RBX9hfGnjQ5+pOUZF1wBw1b54HfM5lOw/4nMtBYe6eD8fm6lpRkXW+XFhYVrQLfsw5l+084HMux3nQ5+paWK1g+XKYNMmKdufNg7Fj/ZhzLlt5wOdcHqgo6CstLXs4V1M6dYJddy0L9vr2telLl/qx5lw28m5ZcoiIbAL0A1oBDeOWUdVH6jRRLmtU1l9fYaHN9z7TXE0Jj7l58+xYa9PGugkK+bHmXPbwgC8HiEgj4J/AEJLnygo2FJsHfPVYXNAH5TtoBr8Ru5o3b549z5xp/fVFW5I75zLPA77ccANwOjAdeAyYCazJaIpc1urUyW60s2eXddIMFvTNmGFdavTs6Z00u5oRPd7Gj4fevS34C4t2PehzLjt4wJcbTgamAjuo6opMJ8Zlv/AmG+ayTJtm7/v2Lcv9Aw/6XM1asaIsp2/GDCve3X57D/qcywbeaCM3bAK84sGeS0e0v76w/p634nW1IeyqZYcd7H27dvYIh2JzzmWeB3y54UegRaYT4XJPmLPSsWPlrXidq46iIhv6r1mzsv75VqzwUTicyxYe8OWG4cBBIrJxphPiclNRUfKuW0pLfTxUVzPCUTjC/vnatIEpU2DiRM9Ndi7TPODLDX8F3gfGiMhgEfHcPlclyYK+pUvLgj4P/Fx1dOwIffpYkS5Y0OecyzwP+HLDauBYYCdgDLBIRNbGPLzlrqtUp05W9FZYWNZ4o0MHC/qmTfPcPlc9YZ3RZcvg++8t8Js5Ez75xHP5nMskb6WbG97D+thzrkaEdftmz7bnaEfNYW5fdDnn0hF21bJ8udXn865anMs8D/hygKoOynQaXP6J3nSnTy8bExXKutTw/vpcdbRrZ3X4QpMnW9C3xRYe9DlX17xI17l6LOy2ZfvtoXv3sul9+1qHzd6K11VV2Gq3d297366dteAFP56cywQP+Jxz6/tRi+uvz1vxuqpK7KolzEGePt2PJ+fqmhfpZiER+QtWZ+9eVV0YvE+FquoNtZg0l8fCIrbS0vixeL1en6uq5s2tE+Yw6Js506b7KBzO1R1R9bYA2UZE1mEB31aqOjV4nwpV1Ya1mLRq69evn44fPz7TyXCVCHP1li4t340L2Huv1+fSVVJSvp7ovHlWjaBjx/ob9InI56raL9PpcPWD5/Blp8HB848J752rExW14g0Dv7D+n3OpKCoqq8MX8la7ztUdD/iykKq+W9F75+pC9CYcLeINXy9dasv4DdulIhyFY+5cK9INu2r54QfrqLlXr0yn0Ln85o02nHNJhbl4yUbnCFvwegV8l4pevazeXufOFuz17Wu5fnPn+jHkXG3zgM85V6lwSLZly6weVmjaNGtx6a14XaqKiqx/R7A/EO3aWQfNfgw5V7u8SNc5l5Jw9ITSUsvdS2zQMW2aN+hwqQnr882bVzYt7OzbW+46Vzs8h885l7KwiLewML7PvjlzfLxUV7nEzr7btSvL6fNcPudqhwd89ZSIHCsiz4jIDyKyQkS+E5GbRWSD/9YispuIvCYii0VkmYhMEpETM5Ful3lh0BftTiMu6PMbt6tIeAyFOX3+p8G52uUBX/31R2At8CfgQOB+4FzgTRFZf1yIyCHAOGAOcDJwBPAgUFDXCXbZIwz6kjXomDOnrEsXD/xcMmHL3eXLy/romzcPPvkEpk7NdOqcyy9ehy9PiMi+wNaqeleKqxymqpEaNLwrIguBEcAg4O0gt+8/wH2qenFk2TE1kGSXJ8I6e4ldt0Rb8YLXy3LxevWy6gFz5pS13J00yY6fkhKvE+pcTfEcvvxxCvCPVBdOCPZCnwXPmwbPxwHtgNuqlzSX77wVr6uO8PiBspa7YMeOF+86VzM8h89F7RU8fxs87wksBPqKyCvAVsBs4CHgRlVdW/dJdNnKW/G66khsuRuOubt8uY/q4lxN8Bw+B4CIbApcD4xR1XCw205AM+BxYDiwL1bk+2fg7xlIpsty3orXVVVREQwYYEW6od697dlziJ2rPs/hc4hIIfA8sAY4PTKrAdY442pVvT2YNlZE2gDni8gwVf0lYVtnAWcl+6wuXbrUaNpd9glzYqIteOPG4vWcPhcnPG4S++gLc4z9uHGuajzgq+dEpAB4AegB7KWqsyKzFwTPbyas9gZwDtAH+DA6Q1UfAB5I9nn9+vXT6qbZZb9o8Vs00Iu+ji7jxXUuVFRkuXxLl1qgF9bng7KGQH68OJc+L9Ktx0SkEfAMsAtwsKpOSlhkcvCcGKRJ8LyuFpPn8kRihfzwdWlpWVGdF9e5qE6d4vvoW7rU6oI659LnOXxZSkSGpLnK5mluvwHwGLAPcIiqfhyz2HPADVg/fV9Hph8ArEyY5lxScV23gN3AozznxoXCMXcnTSrrow9gyhR73mILP16cS4cHfNlrOBvmrFVE0lz+XqzblZuAZSKyW2TeLFWdpapfi8hw4PogQJyANdw4A7hBVZcmbtS5ZMJWvLNnW3cbzZvb9LDorlkzu8F7HS0Xih4z4Z+DxIYcfrw4lxoP+LLXI6QXwKXroOD56uARdR0wLHh9NvAT8HugPVAMXKqqd9Zi2lyeCnNkCgvtdVg817evN+hw8aJd/USFXf/4seJcajzgy1KqOrSWt98txeV+Ba4JHs5VW7QVb2Gh3bS9QYerSBjUhbnB4R+EefN8NA7nUuWNNpxzdS4M4jp29AYdLjVhQw4oP5rLpEkwYYL37+hcZTyHzzmXMdERFLxBh6tM2GVLWPQf7bJlzhzP6XOuIp7Dl4VE5A9B/3hVXX9HETmo8iWdyw6dOlmry8JCa9AR3tCXLrUcnIkTPQfHmWg3P9EuW8LiXedcPA/4stP/AdNF5IpgyLNKiTlAREYDnwHb1WoKnathRUVWZNezpwV/ob59rUWvD8vmQtGgL1q8+8knVrzr1QCc25AX6WanvsDtwM3AjSLyIfA+MB6YDSzChjxrA/QGdsP60+uAjY5xAfCvuk+2c9WTSoMOsBu+j7hQv0X7dgQr3g2HY/Njw7kNecCXhVR1KnCoiPQHzgeOAQYQ301LOOrFd8AtwH9U1f/fupwVbdBRWpp8WLYwF8dv7PVXNOibN6+sTt/06eXnO+c84Mtqqvoh8KGInAMMBPYEumA5eyuAn4GvgLGqOjnphpzLQckadMyZU75fNg/46reiIjsmli8vC/pmzrT30WPIufrOA74cEOTYvRw8nKtXork44QgdYTHe3Ln27Df2+quoCHbc0f4MhPX5eve24G/27LJlnKvvPOBzzmW9cIitMGevsNCCvp49bb4X77rwtw/r8YF10jx3Lmy/vR8bznnA55zLCdEbdrRultfTclDWR184GkdYn2/ePG/E4Rx4wJcTROThFBZbBywBvgVeUtXZtZsq5+pe4pBr3v2Giwpbb8+da4Fe3772PH26F/s75wFfbhhKWQtdiZmvCdNXi8g1qnprbSfMuboW7brFuURFRdC+PUyebHX62rWDKVNsXs+eniPs6i/veDk39ASex/rYuwYYBGwVPP85mD4a2BU4G5gL/FVEjshAWp2rMx70uTi9esGuu5b1zde7d9k8zxV29ZXn8OWGI7F++LZX1Z8i078DxonII8AXwHuqeoeIvIYV7V6ABYrOOVevFBVZfb6wEcfy5fbe/yS4+spz+HLDWcBTCcHeeqo6E3gqWC58/xKwY52l0Dnnskg4VF+Yy9eunQV8s2d7Lp+rnzzgyw3dgF8qWWYx0D3yvhgorJ3kOOdcbujQAZo1K3sf9t3oXH3jAV9umA/sV8ky+2N1+UItqTxIdM65vBW2zG3f3nL5OnSwol3P5XP1kQd8ueEZYEcRGSkiXaIzRKSLiDwGbA88HZm1EzCt7pLonHPZJyzabdbMOutu185y+UpLPehz9Ys32sgNf8EabZwMnCAiP2EtcdsDmwINgYnBcohIR2A18GgmEuucc9mmffuy8ZeXL7fgzxtwuPrEA74coKpLRKQ/cDlwGtADCHP6/gc8AvxNVVcGy88G+mcirc45l23CwG727LJROH74wYp4o/Ody2ce8OUIVV0F3ADcICJFQAtgiap6oYRzzqWgsLCsAceKFd5Ni6tfvA5fDlLVUlX9yYM955xLTTSwmzIF2rSB8eNh2jQoKclcupyrK57Dl0NEpBlwNLADZa1wJwCjVXVZBpPmnHNZr6jIhlcDq8c3YwYMGuS5fK5+8IAvR4jIwcAIoDXlx81V4B8icrqqvpSRxDnnXA4IA7v27S13r7jY3oetdT3wc/nMA74cICI7As9irXEfA94GZgMdgb2Bk4CnRWQPVf08Ywl1zrksV1pa1lp30iRrrRvygM/lMw/4csPVWE7eAFX9OGHecBG5FxgL/Ak4po7TVuNWrlzJvHnzWLlyJWvWrMl0cpyr9xo1asQmm2xCixYtMp2Uapszx+rwrVgBP/8Mr74K3YMxijp1ymzanKtNHvDlhgHYWLqJwR4AqvqJiDwNHFC3yap5v/zyC3PnzqVdu3Z06NCBjTbaCBGpfEXnXK1QVVasWMFPP9lQ3rke9BUWWoMNgHXrYP586NfPpjuXzzzgyw0bAzMrWeZHrKuWnDZ//nw222wzmkUHv3TOZYyI0KxZMzbddFNKSkpyPuCbPBk+Dv46N25sdfkaN4bddoNevTKbNudqk3fLkhtKgF0qWaYfVq8vp/366680bdo008molmHDhiEitV4cPXToULp161bpct26dUNE1j8KCwvZdtttufvuu1HVpMs2aNCAzp07c+yxxzJlypQNtnvjjTciIhx99NFJP/vdd9/lwAMPpFOnThQUFLDZZptx4IEH8thjj5VbbuLEiRxzzDF06dKFJk2a0LFjRwYPHsxdd92V2s6owNixYxERxo4dW+1tJZo4cSLDhg1j4cKFNb7tOHHfZdCgQQwaNKjWP7tp06asXr261j+ntu26K2y3HbRoAWvWWOfLLVpY1ywTJmQ6dc7VHg/4csMrwN4icqWINIzOEJEGIvIHYN9guZznRbg174ADDuCjjz7io48+4tlnn2XQoEFceOGF3H777UmXff/997n++uv59NNPGTBgAD///HO55R555BEAXn75ZRYsWLDBdp577jkGDx5MQUEB99xzD6+99ho33XQTbdu25ZVXyg7Vzz77jN1224358+fzt7/9jddff51bb72VLbfcktGjR9fwnqhZEydO5LrrrquzgC/Offfdx3333Vfrn5Mv52WnTnDYYXD66bDFFjbt9NPhqKPK3juXj7xINzfcABwJ3AScLSLvYbl5HYA9gW7AHODGDKXPZbm2bduy2267rX+///77M2HCBJ588kn+8Ic/JF22f//+9OjRg0GDBjFy5EguvfRSAD788EOmTZvGwQcfzCuvvMKoUaO44IILym3n9ttvZ4cddmD06NHlgoXTTjuNdevWrX9/991307JlS9544w2aNGmyfvqpp55abrn6YvXq1WnVXd16661rOUX5p7TUGm9stRV8+6231HX1g+fw5QBVnQPsAYwBugKnApcBvwG6B9P3DMbQdVni22+/ZfDgwTRr1oyOHTvyl7/8pVwAs3LlSi655BK22WYbCgsL6dChA4cddlhs8elbb73FjjvuSEFBAT179uRf//pXtdPXokWLlIrodt55ZwC+//779dNGjBhBw4YNefDBB+ncufP63L6ohQsXsskmm8QGLg0aNCi3XKtWrcoFe3HLJbNmzRpuueUWtt56awoKCmjXrh0HHnhg7H4MdevWjaFDh24wXUQYNmzY+vdTp07lqKOOYpNNNqGgoIAuXbpw3HHHsWbNGoYPH87pp58OwBZbbLG+KLw46NxtzZo13HzzzfTu3ZsmTZrQqVMn/vCHP7By5cr12y8uLkZEuO+++7j88svp1KkTTZo0YfHixZV+71BikW5Y7PvCCy9wwQUX0LZtW9q1a8epp566wXZTSWM+O+ggC/CWebf1rh7wHL4coarFwAEisik20sbG2EgbX6jqT5lMm4t35JFH8tvf/parrrqK119/nRtuuIEGDRqsDyhWrVpFaWkp11xzDR07dmThwoXcd9997LbbbkyZMoUOwcju3377LQcffDD9+vXjiSeeYNWqVQwbNoylS5fSsGHDClJQRlXX1yksLS3lxRdf5M033+TGGyvPFJ4xYwYALVu2BCxQffLJJ9lvv/3o1KkTp556KjfffDPffvstW2211fr1dtllF0aMGME111zD8ccfT9++fWODv1122YWXX36Zc845h9/+9rfsuOOObLRR6pemE088keeee46LL76Yfffdl5UrVzJu3Dhmz55N7969U95OnEMPPZSWLVty//3307ZtW3766SdeeeUV1q1bxyGHHMI111zDjTfeyFNPPcVmm20GQMeOHQHLoXzxxRe54oor6N+/P99++y1//vOfKS4u5plnnin3OTfddBM777wzDzzwAGvXrqWgoKBa6Qa46KKLOPTQQ3n88cf57rvvuPzyy2nYsCEjRoxYv0w6acxH7drBZptZS93mzTOdGudqmar6wx919thpp520It98802F89OycKHq1Kn2XIeuvfZaBfTmm28uN/2MM87QwsJCXbRoUex6a9as0WXLlmlhYaHefvvt66effPLJ2qZNG126dOn6aT/++KM2atRIu3btWml6unbtqlg/juUeZ555pq5bt26DZU8++WRdvXq1rlq1SidNmqT9+/fXBg0a6Oeff66qqk888YQC+vjjj6uq6pQpUxTQK664oty25s6dqwMGDFj/eS1atNAjjjhC//vf/5Zbbvny5XrkkUeuX65p06a633776QMPPKBr1qyp8Lu99dZbCuidd96ZdJl33nlHAX3nnXfKfc/TTjttg2UBvfbaa1VVdd68eQro888/n3Tb//nPfxTQadOmlZs+btw4BXTEiBHlpo8cOVIB/eKLL1RVdcaMGQroDjvssMFvkep32WuvvXSvvfbaYJkhQ4aUW/f888/XJk2arP+cVNMYVaPnZwYtWaL600/2uPFG1WeftddLltRtOoDxmgXXZX/Uj4cX6br8tGgRPPUUjBljz4sW1XkSjj/++HLvTzzxRJYuXcrXX3+9ftqTTz7JrrvuSsuWLdloo41o3rw5S5cu5bvvvlu/zEcffcTBBx9M80gWROfOndljjz1STstBBx3EZ599xmeffca7777LrbfeyhNPPLFBvTuAxx9/nEaNGtGkSRP69u1LSUkJTz31FDvuuCNgxbktWrTgyCOPBGDLLbdk1113ZeTIkeWKrDfZZBPGjRvHp59+yvXXX8+AAQMYM2YMJ5xwAmeeeeb65Zo2bcro0aOZPHkyt956KwcddBDjx4/nrLPO4uCDD0a1fEviqDfeeAMRKbe9mtKmTRt69OjBlVdeyYMPPsi0adNSXve1116jcePGHHPMMaxZs2b9Y//99wdg3Lhx5ZY/8sgja7xRxCGHHFLufd++fVm1ahVz586tUhrzSVFRWV29IOMasLp94TBrzuUbL9LNQiLycBVXVVX9XY0mJlfNnw9r11p5zaxZ9r5VqzpNQvv27WPfhx3Yvvjii5xwwgmcdtppXHvttbRt25YGDRpw8MEHl6tDNXv27A22FW4vLG6tTOvWrenXr9/69wMHDkRVufzyyzn//PPLVfw/6KCDuP7662nYsCGdOnUq99lz5szhjTfe4Pjjj2fVqlWsWrUKgGOOOYbLL7+ct956i/3226/cZ++8887r6wEuXryYY489loceeoiLLrqIbbbZZv1yW2+99fp0rFy5kjPPPJORI0fy8ssvc+ihh8Z+rwULFtC6deta6cpHRHjzzTcZNmwYV111FQsWLKB79+5cdtllnHvuuRWu+/PPP/Prr79SmKQ338RWzWExcE1q3bp1ufdhHcnw2Eo3jflq883tEuFcvvOALzsNreJ6CnjAB9C2LTRsaFfyhg3tfR2bO3cuPXr0KPceYNNNNwXgiSeeYPPNN2f48OHrl1m9evUGXXx07Nhx/bqJ26+OPn36ADBp0qRyAV9icBg1cuRI1q5dy6hRoxg1atQG80eMGLFBwBfVsmVLLrzwQt566y2++eabcgFfVEFBAZdddhkjR47km2++SRrwtW3bloULF7JixYq0gr6CggJ+/fXXctPiulbp0aMHjzzyCKrKl19+yT333MN5551Ht27dOOigg5Juv02bNhQUFPDee+/Fzu+UMIZXJro8STeN+ap7d4i0R3Iub3mRbnbqXsVHj7iN1UutWsFxx8G++9pzHefugRXXRj3xxBMUFhauD3KWL1++QeOERx99lLVr15abtvvuu/PKK6+wLNKUcObMmXzwwQfVSt9XX30FQLt27VJe55FHHqFr16688847GzwOPPBARo8eTWlQJjZzZvzgMGHr2TBXa1aS7JXE5eLsv//+qCoPPfRQyt8BoGvXruWK1gFeeumlpMuLCNtvv/36fgvDdcNcsxUrVpRb/sADD2TlypX88ssv9OvXb4NHNgRTuZDGulBYCAUFsHRpplPiXO3yHL4spKo/ZDoNeaFVq4wEeqEHH3yQdevWsfPOO/P666/z0EMPMWzYsPWtXQ888ECee+45LrnkEg499FA+//xz7rrrrvXzQ9dccw1PPfUU+++/P5dddhm//vor1157bWwxbzLz58/n42A8qRUrVvDJJ59w0003sd122zFw4MCUtjFhwgQmTZrEsGHDYkd2WLlyJa+99hpPP/00p59+OgcffDCbbLIJJ5xwAltuuSUrVqxg3Lhx3Hbbbey+++7r6yCec845zJ07l9/85jdss802rF27ls8++4y//e1v9OzZk6OOOippmgYPHswxxxzDpZdeysyZM9l7771ZvXo148aN45BDDkk6AsWJJ57Ib3/72/X7/ssvvyyX0woWEF900UWccMIJbL755qxdu5bhw4ez0UYbsffeewNlfeDde++9nHbaaTRq1Ihtt92WQYMGcdJJJ3Hsscdy6aWXsssuu9CgQQOKi4t55ZVXuOWWW+iV4XG8ciGNdaV167KAz/vhc3kr061G/FG/HnXaSjdDwla6kyZN0kGDBmlBQYG2b99er7nmGl27du365dauXatXX321duzYUZs2baoDBw7UCRMmxLYgffPNN3X77bfXxo0ba/fu3fWf//ynnnbaaVVqpdukSRPt1auXXnbZZbpgwYINlj3llFNit3PhhReqiGhxcXHs/LVr12rnzp3Xtxh94okn9LjjjtMePXpo06ZNtaCgQLfaaiu96qqrdEmkOeRrr72mQ4YM0V69emlhYaE2btxYe/Tooeeff77OmTOn0u+3evVqvfHGG3WLLbbQRo0aadu2bfWggw7SKVOmqGp8y9a1a9fqddddp126dNGmTZvq/vvvr99//325Vrpz587VIUOG6BZbbKFNmzbVVq1a6cCBA/W1114r9/nDhg3TTp06aYMGDRTQGTNmrP+MO+64Q7fddltt0qSJtmjRQrfddlu97LLLdPHixapa1kr3wQcfrPR7JvsuyVrpvvnmm+XWDVsUh+lLNY1R+XB+xhk3rqzVbl3CW+n6ow4fopq8BZxzNa1fv346fvz4pPMT+3JzzmWPfDs/w1a506dDz55lz9FWvLVJRD5X1fgKs87VMC/Sdc45Vy+FQV379uWHVwu7ZvHiXZdPPOBzzjlXb4W5eSUlMGlS3eXuOVfXvJWuc865equ01IK9sNHGxIn23jtgdvnGAz7nnHP1XmEhNGsGy5d7Fy0uP3mRrnPOuXorLL4tLbVgr107mDvXAsDofOdynefw1VMicqyIPCMiP4jIChH5TkRuFpGklzcR+ZeIqIiMrMu0OudcXWjWzJ49l8/lIw/46q8/AmuBPwEHAvcD5wJvisgGx4WI9AdOAZbUZSKdc662hQ012reHefMsl2/GjLJuW5zLB16kW38dpqrzIu/fFZGFwAhgEPB2OENEGgEPADcBZ9dlIp1zrq6E9fgAFiywXD4v0nX5wnP46qmEYC/0WfC8acL0y4CGwG21mijnnMuQxFy+Nm2sLp/n8rl84Tl8Lmqv4PnbcIKI9ASuAQ5R1V9FJCMJc8652lZaarl6y5dbTt/UqdC8uc3znD6X6zyHzwEgIpsC1wNjVDU69tk/gWdV9Z3MpCw3DR8+HBFBRJg6deoG88eOHbt+/pgxYwAYNmwYIsKaNWvWL7dkyRKuvfZatt56a5o3b06rVq3o27cvZ599Nj///HO57VT0GDp0aJW/y6xZs/j973/P7rvvTrNmzRARiouLq7w957JVp06wxRbQp48V6W62WVlrXc/lc7nOc/gcIlIIPA+sAU6PTD8V2Bnonca2zgLOSja/S5cuVU9oDioqKuLRRx/lhhtuKDf9kUceoaioiNIK7iJr165l3333pbi4mCuuuILtt9+eZcuW8fXXXzNq1ChKSkrYcccd+eijj9avM3v2bI4++miuuuoqDj/88PXT27VrV+Xv8P333/Pkk0+y0047MWDAAN54440qb8u5XFBYCE2b2uu5c+3Zc/hcrvOAr54TkQLgBaAHsJeqzgqmFwK3A7cAK0WkZbBKA6BR8H6Zqq6Obk9VH8AaeMTq16+f1vR3yGZHH300I0eO5PrrrycsDl+xYgXPPPMMxxxzDMOHD0+67rvvvstnn33Gc889xxFHHLF++uGHH86f/vQn1q1bR4MGDdhtt93Wzwtz3nr06FFuenUMHDiQucFd76GHHvKAz9ULXbtaXb7lyy3o69gx0ylyrnq8SLceC1rfPgPsAhysqpMis9sC7YD/AxZFHp2B44PXh9RpgnPQb37zG3744Qfef//99dNGjx7N2rVrOeaYYypcd+HChQB06NAhdn6DBumdvgcffDA77bTTBtNnz57NRhttxB133FEjn+NcLgsbb3ToUNYR8/z53njD5T6/ktdTQV97jwH7AEeo6scJi8wBBsc85gJjgtfvk+UyfYHu2rUrAwcO5NFHH10/7ZFHHuGoo46iMKwclMSOO+7IRhttxNlnn83o0aNZtGhRtdIyZMgQJkyYwDfffFNu+uOPPw7ASSedVK3tO5dPwo6X582DFStg+nSYPTvz1xTnqsoDvvrrXuA44O/AMhHZLfLYTFVXqurYxAewEpgbvJ+fyS+Qimy4OA8ZMoSnnnqKlStXMnv2bMaMGcOQIUMqXa9Hjx7cf//9fP/99xx99NG0adOGPn36cNlll1FSUpJ2Oo444ghatGhRLvgEePTRR9l///1p37592tt0Lh8VFVkRbp8+9r5NG2utW8l/NOeymgd89ddBwfPVwEcJjzMylah8dNxxx7Fq1SpefPFFHnvsMTp06MA+++yT0rpnnHEGM2fOZOTIkZx11lmsW7eOv//97/Tp04fJkyenlY6mTZtyzDHH8Nhjj6FqVSknTZrEl19+mVIA6lx9E+2I2blc5wFfPaWq3VRVkjyGVbLeqXWY1LSVlkJJiT2g7HWmcvuKioo48sgjefTRR3nkkUc45ZRT0qoX16pVK0455RT++c9/8u233/Lcc8+t764lXUOGDGHmzJmMHTsWsNy9oqKico1CnHPlO2Jevtzq9IHX5XO5y1vpurwTXqjBAr1OnTKbHrBA65BDDmHdunWMGjWqWts64ogj2G677Taoi5eKvfbaiy5dujBy5Ej22msvRo0axbHHHkvTsA8K59x60WsJZMe1xLmq8oDPuTqw3377cfzxx9OyZUv6hBWDKjF//nwKCwspKCgoN33ZsmXMnDmTbbfdNu10iAinnHIK9957L0cddRSzZs3y4lznKtGxo+fqudznRbour2VLZ6kNGzZk1KhR3H///SmvM3bsWDp37sxll13GCy+8wLhx43j00UcZPHgwCxcu5NJLL61SWoYMGcKSJUs455xz6Ny5M3vttdf6eY888ggbbbQR7777brl1nn76aZ5++mk+//xzAF599VWefvrpDZZzLh8l5vQ5l4s8h8/ltVy+SO+2226cccYZvP3224wYMYJFixbRsmVLdt55Z95880323nvvKm23d+/e9OvXj/Hjx3PVVVcRHR953bp1rF27dn2jjtBxxx1X7v15550HWBFxWB/QuXyWy9cS5wAk8cLuXG3q16+fjh8/Pun8b7/9lq222qoOU+ScS5WfnzVLRD5X1X6ZToerH7xI1znnnHMuz3mRrnPOOVdVkSoReImZy2Kew+eyjlczcC77+HkZIxrsxb13Lot4wOeySuPGjVmxYkWmk+GcS7BixQoaNWqU6WQ456rIAz6XVdq2bcusWbNYuHAhq1ev9lwF5zJMVVm+fDk//fQTm2yySaaT45yrIq/D57LKxhtvTJMmTZg3bx4LFixgzZo1mU6Sc/Veo0aNaN++PS1atMh0UrKLqtfhcznDAz6XdQoKCujcuXOmk+Gcc5XzIM/lCC/Sdc4555zLcx7wOeecc87lOQ/4nHPOOefynAd8zjnnnHN5zgM+55xzzrk85wGfc84551yeE+/Y1tUlEZkH/JDpdKSoLTA/04nIY75/a5/v49pV3f3bVVXb1VRinKuIB3zOJSEi41W1X6bTka98/9Y+38e1y/evyyVepOucc845l+c84HPOOeecy3M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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting HR diagrams\n", + "import matplotlib.pyplot as plt\n", + "bad_line2 = bad_line[np.where(bad_line['phase']==5)[0]]\n", + "plt.plot(np.log10(bad_line2['Teff']),\n", + " np.log10(bad_line2[\"L\"]), \"r.\",label=\"bad BPASS cluster line\", alpha =0.3)\n", + "plt.plot(np.log10(iso2.points['Teff']),\n", + " np.log10(iso2.points[\"L\"]), \"b+\", label=\"MISTv.1\", alpha =0.1)\n", + "plt.xlabel(\"log(T in Kelvin)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"HR Diagram of 'bad line' at 0.1Z_solar and\" +\n", + " \" 10**9.3 years age \\n juxtaposed with MIST v1 isochrone\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What sorts of characteristics (logg, Teff, L, mass) can be causing it? Let's find out" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='L' dtype='float64' unit='W' length=36>\n", + "
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In future versions of the stellar evolution model, I may obtain an additional program from the BPASS creators to match the types of compact remnants from the NEWSECMODS." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log10\n", + " \"\"\"\n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in log10\n", + " \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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x5njkz0IKEjryLGIJa0f+7F9E6Pt5r3VMMUH9bTPGnIYMgLkMGSkPUGOMeQB5/tZ2etJCNN8x9/crmmds1NcqHN0VTP9ELs5fEPV3R2eNjTEzkQuyCDjXuYHddUlIwGIA1toGxMR7m5Nj42TkR+NqxKozx6/tC8ALTl6O45D8Ql8DnjfGzLDWfmKtvc3ZXzS4X9zhYdaPCGpX7UyHhWkfbvmhEm0/u8tK5EYebYyZbP2CSbuJa4ULdx/mEtTnaO6LLjj4422tre60ZRChfjy64KuIWLrduQ8PYoyZjQimnmI9Ek/RwTJmjElx+tGKL7i1x3EsAc8g/16/YK1d3lP7dvZ1vvMwOgb50/ZNJHBzn7V2ET30fejO75Z/V7s6l6BjDUVGTK5FYg1rgtZ/Npr9hcE933C/Q+GuV49gra0zxuwARhljRlgnL5IfE53phjDbLyaC4HnH8mC6aPMhEmIQgDHG/RO2oqvjRMBLyB/4uUhsVCT8A/kjeB3yh6NDsLeD+1n+xlr7nQj3fQuQAZzqXMuDGGN+jAimUIS9l621XuB/gP8xxpQg4vM6ZDReHoGDWTrQje9Yd56x3blWIelWaRRrbSVyA4xGfOJdjXpxR849639BHI5FPsTOjrfdWvswEtG+ETjJcXEEt6uz1r7uXJSf48sT0l1cq9nc4BXOw+ckZ/aDoPaznQ87mJNCLDtknB/XUuSHaGKIJqc60w9CrIvmOA2IWAYJvOsU56HWGV5n2iHZnfPly+uiP13dF+0E/sP2x01MF6nAOhTc+/+JEOtOCbON6xKIODO3g+t+PTvEupMRl8wy20sj5JwRcc8iouQea+0/u9ikW1hrm6y1y6y1P0GEBvh+8NcjowGnG2PyQ2we6ffhkH63omQC8nv8agixNNpZf6h8iu+6hLLyzO2BY3RFZ/fnOUFt+hTn9+oLyO/GIz2wS9eV1BrpBtbafciz9ThjzInIiOAtdBzB+B7Sz2h+v0qAA8FiySHc71DEWGs3WWv/7uyrlvACLLhPEOF3zPlzuxl51o0Lsb9Qz9juXKuQHEotuVsQZXhW8Bc8BFuc6Vz/hc6/qj8ENzbGDDHGHBdiP4OQvA+tOP58Y8zpznD3YFylGW1siz9PI66TzzrmTH++jfyILXLdDY5ZeDFyE1zn39hJf9Dj8Ut+/AP5V/W/xq/8hTGmEJ+4+UcPHOcWJNjyKmPM/4a69saYQmPM7xCfdGd8ivxjuMi5F9ztMxCXb/B+I74vHPYTPvPw75EAz98YyVwefCyP4/7pCbY407lBx5iBjG4MhZsFe2yUx3ocGdl5pfPvzT1WOuJyAPhTUD9yjTFTjDEjOAScGJwHEBfCc4SoCHCI+58T5kEf8F134iceRkzsAdZvI6WdvoV89l2JuS3OdG7QPkL+bh0i7rFOCvr+ZiEhEIdcPcF5ID2MfFdu81/n3CtXHeoxIuDPzvRmfzHrPPy+jliw7+/NDhhjBpmgEkFOHM2fEAvWn4Lj4owxI5zvSG7Q8lNC/Tl27rObndkXouyi+/18FLmH7wuOibXW7kU+y5nGmFudP/Ad+mCMGe+3aAsw2ASVrTLGfAX50xkVxpjxTkxUMPmIG60hxLpgtjjTuUH77uw79iCiXe52fnPcbcYgz+UAunmtQtLtL6EjEiKNS1iBjM65xBizDAnMGob8o1iPLzbGZRTwjjFmHfIvcDviuzwfMRv/zk+k/RoYZ6Rm2BbkgXkMMjR8K4fwT8FaW2uM+TIyLPpNI7lAtjn7PxPxtV4XtNnXnXP9ozHmXHx5mC5F3BQX4XNFBfMjE74G3++stZ39I/4Vcj0vAtYYY15ErAmXIwHMv7TWhgqIiwpr7R4jNZKeBr4HfNEY418a5TDk5k9Dcgl1tq8WY8xvEUG3yhjzFHJPzkPuiUO5L0D89FcaCRR/HxFUS6y1S6y1nzqf7T+Aj40xL+Nk+EVEyhxkiGpUtaXC8CASK3WvMeZUxBo20en3k0i8QjD/dbb5q5H4rFqg0vqVjQiFExh/DSKcFhtjHkFE/4XAZGf5o0GbXYw8pB5A3JsHMcb8CN81mO5Mv2SMcf/JvWV9uVu+i8QytDvn+BO/37NQLAwTuBmO7wJnOt/1zcg1OQK5773IkHmXHyGf4TeMxFO9gS8PUzbwDWttWRfHi/Z3q9tYa3c7n9WVyICGVxGX9Dwk6HY1vut/KNyE5Bj6tiOS3DxMn0FGo10Yzc6ivD+w1i4zxvwfMirrQ+fe9jjHHwx8M8p7ojucCvzNGLMI32/IuYhYegH5XQvmbiQg/ksExr0+A1QaY9519pWCBCWf7bz/f35xtBFhrX3bGLMGCfxvIfwf3W8gvyN3IIHibyExPCOR3+FZiIXKvc/vRYTRW8aY/yCuqpmIVeZx5LsbDUcBTxmJF16LfB+GIM+gVHwxTZ3Rne/YL5Fny5XAZL/vyhVIrrn5dHzGRnutQmMjG94XdihoiLbh8jANRrJjb0F+AEoRt1kmHYeP5yGBZK8jgY9NSNDeYuek/HMvXIG4BDciP6DVyIf3M2BIJH129rMgVL+ddbOQYeb7EEG2DfkX0CEbtNN+CvIgrERclsuRIaPfc44xP6j9YgKHpIZ6zY/gHNKRH8S1iLqvQW7AkJlMiTKtQNC2HiRdwYvOZ9PsHO8jxDoUnEtkISGGySNWsR8594N7bX95qPeF034o8C98JQosHfPsTHX6thVf5tu1SMzAaT14vQ5HXFV7nXvifSS2aRwh8oY423wHCSBtctpsieJ4Jzqfjde5Fz5CYg1C5TtZ0Ekfuro3F/q1XdhF2+DX3Civ4ZmIsPsE+bGvQ35UfwcUhWifh/xob3SuYSWS2qNDWgLC52GK+Herq9+RCM4vE/nd2uQcazvyL7sg1L0Xrs9+6zv0z1k+HHkI78OX6XtBV/sLc4yI74+g7b6IPCzd9A9vIjUto/5udeM6u5UYtvvdF28616BDOpige3tB0PIbEZG1FbFwNiG/YY/hl4m8G3280TneY120c7N3L8OXX2wb8ofr23TMTXc+Eo5Q45z3q/jiQEOdX8h7yFk32vkuvI0vULscid0KmTk8zH6i+o5Z33f7d4igakK8Fd9F3HgWuPdQr1Wol5vMS+kDjDEPI6O7pthDD5hWFEVRBiDGmIWIqDzDWvvffu5O3OBY1+8DrreRZfKPbv8qmHoWx5891Fq7O2j56chQ4fXW2lB+X0VRFCXBcWJxNiJu5yOsPqQ7YIwZaQPTLLjX7W3ExTzOhi4jdEgcciCh0gEPsN0Y8wZiJmxF4izmIS6nr/dj3xRFUZQYxBjzOcRdeCUSA3qriqWwPOEE6r+PuBbHIe7GTCT/U4+LJVALU4/jjL64Fwk6H418gBVIMNovbNcJPhUlYTChM++G4mlr7epe7Iqi9CvOYIaTkdiq31hr7+3XDsUwxpgbkBxPE5GA71okrc/vrbVP9tpxVTApitJfGGMi/QH6ku28jJGiKEqvooJJURRFURSlCzSGqY8pLCy048aN6+9uKIqiKAOA999/v8JaO6S/+5EIqGDqY8aNG8fKlR2KiyuKoihK1BhjwhYoVnqWQymNoiiKoiiKkhCoYFIURVEURekCFUyKoiiKoihdkJCCyRhzljHmdWPMbmNMkzGm3BjzH2PM4UHt8o0xfzPGVBhj6owxi4wxU/ur34qiKIqi9A8JKZiQYn/vI4X4zgR+jGTjfscYUwRgpMz6s0jV6W8ClyIVmN8wxozuj04riqIoitI/JOQoOWvtv4F/+y8zxryHlDK5DPg1cCFwElKx/g2nzXKgDPgB8K2+7LOiKIqiKP1HolqYQrHfmbY40wuBna5YArDWVgHPARf1cd8URVEURelHElowGWOSjTEeY8xE4C/AbuARZ/URwNoQm30MjDXGZPVRNxVFURRF6WcS0iXnx7vAMc77TYj7ba8zPxjYEmKbA840Hyn4F4Ax5lrg2nAHHDt2bHf7qiiKoihKP5HogunzQA4wAfge8Jox5iRr7RbAAKEK7ZnOdmitvQ+4L9z6mTNnavE+RVEURYkzEtolZ61dZ6191wkCPx3IAn7krD6AWJmCyXem3j7ooqIoiqIoMUBCCyZ/rLWViFuuxFn0MRLHFMzhwDZrbQd3nKIoiqIoAxMVTA7GmGHAFKDUWfQsMMoYc4pfmxzgAmedoiiKoigJQkLGMBljngI+AD4EqoFJwP8ArUgOJhBRtBx4yBjzfcQF92MkhumXfd1nRVEURVH6j4QUTMA7wBXAdwEPsB1YDNztBHxjrW03xpwP/Ar4I5COCKhTrbXb+6HPiqIoiqL0E8ZaHbTVl8ycOdOuXLmyv7uhKIqi9AdeL1RUQGEh5Od33b4LjDHvW2tn9kDPlC5IVAuToiiKovQtXi889hi0tUFyMlx+eY+IJqVv0KBvJaZpbobaWpkqiqLENRUVIpZGj5ZpRUV/90iJArUwKTFLczNs3QrWgjFQVAQeT3/3SlEUpZsUFoplqbxcpoWF/d0jJQpUMCkxS3OziCWPRyzZtbUwOFQqUUVRlHggP1/ccD0Yw6T0HSqYlND0cGBid/B4oL4ePvkEUlNlPitLrUzKACMGvmtKH5Kfr59znKKCKRHp6gc6hgITW1rEypSZCbaqkubX1uBp2g8zZsD48f3SJ0XpMWLou6YoSueoYEo0IvmB9g9MLC+X+X74EW9uhowMGDIE6kp30vrqQ3g+fBRsExQXw733qmhS4psY+a4pitI1KpgSjUh+oPsjMDGE1cvjAfP+CjxL3idt315K9i7Bs3OLqKhPP4VVq1QwKfGNBgErStygginRiOQHuq8DE8NZvR57jJb/+SMt7WkMaq3BM7EVqqqgpgbS06GhoXf7pSjB9HS8kQYBK0rcoIIp0Yj0B7ovAxODrV5LlsDq1dT+/Sn21owkIxP2thUytmYng0FyDDQ3w549fdM/RYHeizfSIGBFiQtUMCUisfYD7Vq9li+Hjz4Sd1tVFXgNLa2jsfXttGakw5QpUP4RpKVJNHhNTX/3XIkVQll+etoa5C/sN2yAlSth5szY+i4pitJrqGBS+p/8fBFDf/iDPOT27oVRo/DU1mAGDaJ+0GDSrr4Cz+R9sOJVsTBlZ8P06f3dcyUWCGX5gZ63BrnCfsMGiZ8DKCvTkW2KkiCoYFL6H68Xli0TITRlivyT93oht4ARU4swX/4ydvhweP0+mDRJYpfmzoWTT+7vnivR0hs5h0INZICeH33murPd4tmTJunINkVJIFQwKf3L6tWwcCE0NUFlpSybMAHOPx+OOpEKz8m0Z+WStPZTJq5bBxMnwr59cO65+pCKN3orBijcQIbeGH2Wny9uuLIyHdnWG2gSTyWGUcGk9A3BP4RerwR3//rXUF0NgwbB7Nnyr/3002HECEgvZERVLmlp0ORtlbilhjaJYcrJ6e8zSly8XigtlffFxR0fbOEeer2VcyjcQIbeGn2mI9t6B03iqcQ4KpiU3if4h3DePPjXv+DZZ2H7dsmrtHs3JCXBkUfC++9Dejoem0rarMuwnjzShubiSWmTQO/MTCgo6O+zSky8XrEIrlghLtSZM2HBgsBA63APvd7MORRqIENvDm6ItYETAwFN4qnEOCqYlN6nokKETlaWTJcuhf/+V1IDNDdDYyOkpIhVqaYGWluhpARPeTlFmftoHpmHp6URz+yZso/aWmmj9ByRukIqKsQimJsr81VVgQ+2zh56apmJP/rSRaZJPJUYRwWTcmhE8oOakiKjiurrfQ/bigoRPpmZ4l47/HCxMCUlybzzo+kZWYgnCxhZKCPj2tpkqj+m0RPOlRaNK6SwUD7P8nJxjU6aFPhZdPXQ87fMaLxKbNPXLjIV1EqMo4JJ6T5d/aC6D+ht22DsWHj3XVi7FlJT5QcxNRWGDYOhQ2HqVNnm6qshL6/jj6b+mB4aritt5UqpZjxrls+VFq0rJDNTBG5yMsyfH9g20s9J41Vin/5wkamrU4lhVDAp3aezRH5eL/zxj/DOOxLrsns3fPyxBG67jB8vo93KyqCoCC64wLffUA9b/THtPhUV4j5zg+Wrq30PwGhcIRUVUpbmpJOkvesaDbYWdfU5hXsYq9UpdlAXmaIEoIJJ6T7hEvnNmyfzS5bIg7muTlIGtLX53G7Z2SKS/vUvcfFUVsK0afDee2p16IruiIrCQnGFbtwoFiZ/V1o01rtQD9HuWIt6aj9K76FWXUUJQAWT0n1CJfJbvhxuu01E0ZYt8hCsrJQHYFubWJuSkmDyZJm3FoYMEUvF6tXhXQBqeRC6Kyry88UFN2eOzAenA4jUehfqIbpxY/Sum57aj9K7qFVXUQ6igkk5NPwT+S1fLg/z5maxKrW2ihsIxBWXnCxiKSsLNm2SdALJyVIKJT1dSp28915HF4BaHnwcSlyJ+1kdKsEP0e66bnpqP4qiKH2ACibl0HFrwf3977Bnj5QuCTXs33XH1dWJwAIJ+B4zBr73PRFMRUUdLUman8VHLIqKnnLdqAtIUZQYRgWTcuisXg0//KGMgGtoEDdbKDIzZZ0rqJKTxUU3frysg9AugMJCydX0/vsShxMLIqG/iFVREa3rpqxMrIwlJfL5d3c/iqIofYQKJuXQKCuDe++VjN3t7SKCQlmX3MK6dXWSasBasTbV1IhVaunS0GU2cPJbNqXiaUvG0/tn1PscajxWvIuKsjK49Va5T1JS4M47A0WTEn9ojKGSAKhgUrqP1wt//St8+qn8WLa0iGhKSRHh1NTka5uTI8unToUDByS2KTlZiuledplsG8LV1twMW9/ejl1XhUnPoCipHk+8ueT8HyaVlfDQQ2JRy84eePFYkTw4N20SsTRxogR6b9qkgsmfeBMfGmOoJAgqmJTuU1oquZV27/ZZC5qbxXLU1hbYdvBgeeXkwIwZ0n7ECHm5AeHBrjavl+aPyrDPv0LWf5+hti2D5mFteC4+r+/OsTuEE0hJSbBvH2zeLLXwJkwYWPFYZWWRicGSErlXNm6UaUlJ3/c1VolH8aExhkqCoIJJ6T7r14t1qa0tUCAlJfnSBwweLCLq2GPhuOPg1FPhgQfENVdUBJ/7nIin4H/T7oNjyz7q12ymObeItIwUPHlVIjpiFf8HXmNjoEDKzpY2BQWwfz8MHz5w4rG8XhFLH3/ctRgcP17ccKFimBKdeBQfsTgQQVF6ARVMSvdYsgT+938lJUBDgyxzRVNjo5Q9MUZ+QGfMgGuvlWllJezY4Utm+bnPyQ+sf40zgJUrafbWsatwKinZO2itbmdc6nY8+SNi2yLh/8D74AN57wqk3FzJOTVkiIilq6+O/YdhpFRUiGUpUjE4frwKpVDEo/iI1YEIitLDqGBKdLoTL7F6NXz3u2IhaGiQuKVgUlJ8teKOPFJSB+TnwxtviFg67DB5KKxaBVu3+mqcHXGEPHjb22l+ZzV2SCuDj5tMbeFZkLwHTju2/x+0wdfMf97/gZeTI6/2dp9AClUnbyBQWCgWtAkTRBieeGJ/9yg+iVfxEe8DERQlAlQwJTLdiZdwi7ju3SvCJxyNjeKK83ol43dKirz/4AMpxrtzpwT95uXBhx/6apzt3i0PikmT8JgVmMYGapuzMN4DeEZYSWxZVNR/P87u+VdViTCYPx9eey3wGvo/8CB0IeGBhCsY580T69LSpeKu3bQpPmJwYg0VH4oSk6hgSmS6Ey9RUSFuF49HXG7hci7l54tgGjlSLCyrVok4KiuTmnE7d8JFF4lQSk2VmnP+FqYNG/BkJFM0bzLN60rxJLfhGTe9/+M6SkvFGpaTI0HLRUUdr+HEiR3LjsQT0Vgdg0X30UdL1vb+iMGJt9FliqLEFSqYEpnuxEukpIgbbuRIsSCEo61NRMWIEeJye+89STNQUQGjRono+vhjEUqZmfCVr0h7N4aptBRycvDUHsAzJEOWxUpchysSrfW52GKlb4dKtFbHYNEN/RODE4+jyxRFiStUMCUykcZLeL0iYNavF/fT5s2wbp08mNrbO1qZMjIkSeWJJ4qVyVoRQq+8IlapffskcLugwPegHTNGLDMuM2fKNp25tnqKaCwTxcUwa5YIvUmTJJB9xoyBY9mI1uoYLLqLiwM/t766HvE4ukxRlLhCBVOi01W8hNcLd90FL74objQQEWRMx1xLLikpMGgQHHWUPMDWrJFt09Lg7LPlgXbyyWKh6sIS0dwMzbXgGZaPpzeEUmmpxNykp0dmmcjPhwULBm5cUrRWx3CiuzeuR2fCNh5HlymKEleoYEpEorGoLFki+XUqKkKPhvPHGF86gfJy+OQTCf6eP1+CgXNyJEnlyJFdW2a8Xpr//QRb92Zgq6oxxRMomjcJz+QeGiHnBm+vWycuw8suk3xQkVgmBnJQbndGafXF9ejK5Ravo8sURYkbVDAlGtHGemzf7it54mKMTF1XnDFiVfJ4xLLU3CxuuaFDpVZca2tHF1tXloiKCpqbLE3WQ9qi12h6fyjNS1vw/OKWnkkrUFoKb78tlq8dO+Dxx8X6pZaJ8AKoP4OqI3G5DWQhqyhKv5PU3x1Q+hj/B09bm8x3xvjxPoHkYm1g3JL7vqHBZ4lqbIS33pLRcSmOLs/P7ziCLByOi2XXqt1sbhjOriFHQWtL54Hm0dLUJKJx1CgZ7TZnjj5ww+EK7UWLZOr1dn8/GzdGvr3b3q1PqC43RVH6CbUwJRrRxHqsXg2//71Yi4JJShJh5KYWaG+X/YEEcA8dCmPHSmB3a2vX/Qq2XuTnw2mnUVj1IaZiHXbPWhhiey7Ld3ExHH88vPOOxC8ddphvhJ7SkZ4Iqo7Wuhncft680GV0/NurS05RlF4iIQWTMeYy4LPATGAosA14Evi5tbbGr9104BfASUA7sBj4jrW2B80cfUy4WI/gh01ZGdxzD3z0UegEla6Lzl8wWSuWgIwMKXuybZvsM6WL2yzUgxTg9dep2J5FU+tQ2vMGU3RYkwzjj4SuHp75+XDDDXDWWTJfXDxwHrK9IRx6Iqg6WtEV3L61NXAkpT+aVkBRlF4mIQUT8D1EJN0ElAMzgNuAU40xJ1hr240xE4GlwFrgKuRa/RRYYoyZbq3d2y897wmCYz1C/ZN/6CGf66SpKfy+rBVrU2oqZGVJrNLnPieB1EOHQm1t1xamUA9SgLY2CockUZGRTFNJCduoIWtnRdej5SJ9eObnS38HEr0lHHoiqLoz0RVK5EUj0jStgKIovUyiCqYLrLX+Je/fNMYcAB4A5gKvAz8E2oBzrLWVAMaYd4FNiOD6QV92uFcJfths2uQradLc7Hv4hkojYK1YlKyVorKHHw7jxolgqq2V+mJdWSPCPBg9aQbTUEd7cgp5poqU1CSacwrxRHs+ifTwjObco7VEHWpQdWfWzVAiLxqRpmkFFEXpZRJSMAWJJZcVznSUMz0eWO6KJWe7cmPMWuBiBpJgCn7YlJTArl1iGUpJkWm4nEsQmJdp40b41a8kuWN9PVxyibTZuLHzh97RR8vUzzXm+eyllGytgNPTSaGVtKH5eIblRX8+ifTwjPTc+8uFFUp0dSbyIhVpmlZAUZReJiEFUxhOcabrnGkb0ByiXRNQbIxJt9Y29knPepvghw3IA6yqKrLtrZXA6epqEUnt7TItLJT8S8HFaTtzBxYX+xJKAlnFxRw+App37cczwuLp0rwU4nwS6eEZ6blXVEjKh6wsmfanFa6nBK6mFVAUpRdRwQQYY0YBdwCLrLUrncXrgROMManW2hanXTZwBGCAfGBXf/S3V/B/2DzzjBSYDeeGc0lyslKkporrLTNTUguUl8OePb7SIaGsB647qKoqcL2beXvlyoPFeD2ZmXgizcQd6nzile4Gb0dy7ikpkvLBtSK6lsD+oDORpyPfFEWJERJeMBljsoBngFbgS36rfgtcDvzZGPMT5Fr9Gshy1odMe22MuRa4Ntzxxo4d2wO97kXKyuD//T8RPJ2JpZQUsSqlpEjqgBEjxJXX0CCj6oyRXEzgsx40NopAKivzWZ3cNhs2iFVqxw6pVZeWBmlpNO+soDl3CJ6ZE/Hs2Z448Ui97TJrbRUxm5UVWWB+bxNK5OnIN0VRYoiETlxpjEkHngUmAGdZa8vdddbat4GvA5chI+m2AHlIYHgzcCDUPq2191lrZ4Z7DRkypFfP6ZDZtEkeUGlpgctTUmDwYAnwzs6W8iazZsG0aVJoNysLZs8WQVNZKaLJ45FyKJdfLm0BVqyQEXg1NWJVSk+XmnP19SK2/vxn6cPatTTv8bIxeTKfHBjGmqc3U1u6p+sUBQOFaBOMRkthoXyO7e2RBeb3B719DRRFUaIgQZ4+HTHGpAJPAMcCZ1hrPwpuY639ozHm70AJUG2t3W6MeQl413XTDThKSkQsGeNzySUliXusoUGmxshr8GB5mBUWSpLLtWvFLTd9urQ98khfEHduroij0aNFHNXX+2JWcnJku08/lWDz4cPh6KOpnX0u62pmsveVD7CbN8Oweo56/Fk8X/3CwLc09Hbgen/FeUXjYovmGqjrTlGUXiYhBZMxJgl4GDgdOM9a+064ttbaJuBjZ7upwBnAF/qin/3C+PFw001w442wfr0IHzeLt7Uiptxl69aJpSg1VcTQ1Kny0Bo7VmrKXX116Jw62dkSM+NmbQZ46SVx19XXw+7d4PFQWzyNtY+kUH8gm+TUCRwxqIlmbx2eRHDLRSpoDkUo9HWcV7QutmiugbruFEXpZRJSMAF/QOKTfgbUGWOO91tX7qQPGA18DViGjIw7Bkl0+aS19t993eE+ZcQIyYCdng5r1vhimdxyKG1tYmHyeqWdxyPrWlvF9TZnTsfM2f4PPzdVgf9D8OqrZUQdiDVqyhRoa2PIuHQat7VQV1WNqa/Hk58Xm+6j3qArQRNvQqE7+bEiEXWJnHdLUZQ+I1EF0znO9Gbn5c/tSNbvFuA44DogGyhFRtL9tm+62I+48S3NzT63XFubzHs8sqyxUaYffwxnnw3z53de5wt8y0M95MePF6vWQw+Jey47m8ETBjO+Loe6wlkk7dnN1JmH4Tl6nD4MXSIVCrHiruotN2Mi591SFKXPSEjBZK0dF0GbPYj7LTHJzJSyIXv3+kayNTRI3FJLiwino46CCROgqMhX480NzO0s/0+4h3xeHpzjaNniYrLy8zk5H3bvzsWYXDzD8I1RHMhEKnAiEQqxZIXqrbipRM67pShKn5GQgknpgooKccfNnStWpdWrJVappgb27ZORcuPHS1D3tm2SJmDrVtm2q3xJ4R7yoRJYIiPe339f9Jk7EC9rIIumaAROJEIh1txVvRU3FS95t3rT2hcrlkRFGaCoYFI6UlgoVqXnnpNYpZISCQDPzJRCvO3tklZgyhTJwTRpkqgat23wgzn4hzzUQz7Eg702NZ+XXpJDDx0qGq22doALpmgFTldCoSfdVfpADiTa69Gb1r5YsiQqygBFBZMSSFmZ5EEaOlTEUH4+3H+/5FaqrfXlZ9q+XdIADBkiD+PUVMm9tGFDYF4f/x/yxkZfQPjEib5jer0yQq6xMeDBvnu3DMTbuVNyWQ4ePADEUlcP2WhqwUXysO4pd1WiP5CDr3d3rkdvWvtizZKoKAMQFUyKj7Iy+OEPRRzt3Cmut717fUkoQQK7k5NFvYAIpZEjReykp8tItxkzfPssLZV9jR4Nb70l9eZGjPA9YPwfPCCj7JwRdpWlElPe0gK2sZ4J298h64F1cOKJkusp3ojkIRuJwPF6YeFCEZm5ubBgQWC5meDtesJdlcgP5FCfW3euR28Gp2vgu6L0OiqYFB+rVoloMga2bJFl1oobLiNDXHLG+EbQvfKKBHy3tko8U2EhbNwoVqhNm2DePKkLt2GDuOzS0kRc7dwpxxozpmMtudzcgw+eujp5pbRU0frBR9ilt8KgNRJo/uCD8SeaIn3IdiVwSkul1l5Ojlxv12rXmxagRH4gh/rcunM9ejM4XQPfFaXXUcGk+MjLkwfDvn0iiNrbRSQNGiRCJjlZxNNxx/nSCxxxhIift96SXEzV1XDmmZJ88tFHxTx0wQWSz6mpCd54Q5Z9/LHsxy3gG+LB4/HAIFOJZ+27NDfW4KFJFNTmzXK8eBNMPSk6rA2c9naKgUR+IIf63Lp7PXozOD1eAt8VJU5RwaT4mDFDRMi774rVyE0pkJ4On/+8PDC8XrEyFRSIdejjj6VtZqYEGFVXizVp1SoRYLt3w3nniVWopASWLBHLyHvvSfv2dnHD5eZ2ePAM8XgZVfkpLdVbSaWOITgpC+rqxDoVb/SU6CgulmtWXS0B986Iwl5PMeD/QE6kAPBwn5sKFEVJKFQwKT4qKyUGqb1drEHGiAUoOVkEkdcr6+rrRTCNGCHtZ82CF14Q0QO+2nDt7bKPnTvhmmtEQG3aJOkJUlIkiDw7u2NWcCfwPKsynfFDm6hmNzl4yaJe1ns8cPLJfX11eoZIsnd3JUTy8yVuKbhdX6UY6I0A8HDnHSvCTMWRoiQ8KpgSjVCjfdz5TZskVumYY2DRIl8R3pYWePxxGDVKRNGQIfKgHDFC3HdZWSKa9uwRa1R7uwipqip5OBcXixXK/5+6fy05/wfRkiVw222QmUld2xj2VF6NScunoSmdOjKlzYQJ8f3w6kwcRJODKVTAeCQpBjZsENGb0s2vf08HgIc7774emRcr4kxRlJhEBVMiEfwAmjcPXnvNN3/ssfL+0099sTFJSRLDlJUlYqmtTaxHbW1w330ijnbtgm9+E958U7bZtg0++1l49VXJBj5smM9F1NlDffVqGaW3fTvk59OcP5KcsdnkNmVTtWYXzTgj9ebO7c2r1HuUlYmrct06GWUYLAJcIZKXJzFfb7wBp57asxmx583zlZ957bXuiZCeDgAPJ8D6cmReoqdNUBSlS1QwJRLBD6BNmwLnBw2Cr30Nnn5aHtr//KfECyUni+VpyhTZz9VXy4P/gw/gsMNk282b5cFZUiKCp7kZLrwwdCHeULhD5WtrRYR5veRnV5A6ZiSVlVV4+JR8KqUvJSU9cz06s7b1ZELBigq5jr/6lczv2wfXXiuWO38R4CYMfeIJEZ2bNkkGdTdtQE/0Y/t2+cxHjhQXbGcipLM0BT0ZAB5OgPXlyLxETpugKEpEqGBKJIIfQCUlYvXwfyClpIjLbccOWZacLOKnshKmTZPYIf+g1/Jy2Wb6dAnkrqyUGnSRCiWXigqJiyookPmsLLJ+8h3mpBdyYP8+0lmNJ8n6RtUdKl1Z23oqLsc9xvr14gY77DBxXX74oS8Vg0t+vly3sjKJA0tLk8DunnJ51dTAO++I9XDjRvmcOkuM2ZnFpSdjejoLqu6rkXmJnDZBUZSIUMGUSIR6APnPg4gGr1diiWpqpOBucrKMmHvpJRFNbvvvfU/caNOny6uoqPsPt8JCcd2dfrrEPy1YQPPY6ax7FFavKqCSKznQnskV7c/j2b//0K6D1yt5jGpqZJRZKGtbT1gY3KSdkyeLEFy/Xtydo0fD/PkyKjH4GMXFUgNm5UqxNk2a1HMur6wsn+u1pkbEWbhz7GuLSzgB1lfB1omcNkFRlIhQwZRoBD+A/Oc3bhTrx+LFYmFqbBTrUlKSWCXWrIFf/lKETWqqxMGkp4tlqagouoebW4KlpEQEghtf47es9kMvpe828swnxTQynlUcRTHbmF1R0f3z97e2rFoly7KzQ1vbDoWyMnjySQmw3rgRDj9cgun37BHh4p5zMO4IuDlzZD4aK104XOuJOzrRWgnYLy4O73ZLRIuLjoRTFKUTVDAlKqEelCkpYhWprRVB1NgoD0tj5CG7e7cEdh9/vAiqoiIZHbdzp2w3c2Zkx169Gn78Y4lVys2Fe+6RmCnXJVZWJuLphbdZ/uZs9rSNANqpIocXOZPZkwu6f96u5WTSJJmfNk36Ha2FobN4J69XAqs3b/aNKjzsMDkvr1eC5B96CL7xjfCiKdJrGWkaglCjEyG8200tLoqiKAGoYEpEQsWngAiWkSNFOGVnizsuLU3EUlKSWCXq6yVdwL59ErS8ejWMHSsxN51ZQ/yDn++5B9auFTGxd69YeqZODRmQvqMqAzBAMpBEWf4xcMnR3T93f8tJdrZPLEHkFoau4nsqKsT6VlAg7sVx48T99vHHMl9QIOt7azh+KEKd28aNnbvd1OKiKIpyEBVMiUio+BQQl01RkcTXpKVJrbi9eyXwOCdHXkOHiktpyBCxLrkxTFVV4a1M/m6wRYt8BX2tFdGUlxcyIH3F4w2UewcBFhFNbYyZlCHtu0tPWE78h/+vX9/xvAsLRYwNHy6WugsuEBfc1Vf7hvRnZ/fecPxISUS3m6IoSjdRwZSIhHpQVlaKpae1VSxMX/6yCKOKCkkfMGeOrJsxQ0qn1NWJWEhOlhgma0UchLIyVVSIWCovF3GRmioP+qQkEWhuPE+QkFli8mgjFRFLlhxquSTzVSgdGrnLKhSHajlxh/8//7yc9yuvyHL33P3zHY0ZI/mUWltl/Te+0fvD8SNF3W6KoigRo4IpEQn1oKyoEDGUlSUxTDk5YgUBsSZlZMj8qFEiAqZOlYDmoUPhxRelzccfh7YypaTIqLu1a+HAAXH7DR8O554r1pH9+31xNRMnHtysqtF9gDcAcJJZxqyK16H6wj64SJ3gDv+vrpZzcQsKjxzpc4u555OXB889JxY4d73fOR5yPw5V8KjbTVEUJSJUMCUqwQ9K143kWn5ArCStrR0DhT/4QCxSI0bI6LK33hIXXlNT6GPt3y8WGXf/WVmS6Xr0aDnW0qUy2i4oDqeqKnA3Htsgx62p6f55dyc5Zahtiovl/HfulKD4yZMDE0H6lyEJtb6n+q2CR1EUpU9QwaQIrrVi1SqJM3rzTRE4rmgKV+S1slLcau7Is+LiwP2uXg3/+Y9YYzIzRTzMmAHXXy+ZxauqYMWKkHE45eWBuypPGS8B021t3TvH7pS/CLeNe71KS8UaV1kZ6Bbran00fS4tDSsqFUVRlL5BBZMSyNtvy3D4qipxmz30kM9a4i8W3FIir70mIqa+XoLF/R/kq1fDF78ouYcaG2Vo/dy58K1viavKze4dJg4nWFsUptWKO2vGjO6dW3eCpN34q6wsmfpv4w7/Ly7uWGKltFTaFBd3XB8prljbtUssVeeff2hWKkVRFKXbqGBSfAQPh3dHgpWUBD6oXfdQVZXPslReLpYof159VR72qakihvLy4KqrZBpcliTYioU0/e+r9bS2t5BCC1cVvixB0+PHh+5/V+627gRJp6QEBsNfcknHNv5usbIy+OtfReB4PDKScMGC7sUt+eeMWr9eXiNH9t9otmjdmb1Rm09RFKWfUMGk+HDjmCZMkOH+zc0SyL1pk1h1qqpEELgJJhsbZbtQAsTrhS1bZB/19SKaxo3zWVv8LT2trSEFxbnnwjfnfMDGN/cykU2cW/UMfDxJ6tkFE4m7LZogaddKtG2bxB8NHSrB8MGiMHibhx6SOnE1NXKuoWrBRSokXIFXWSnCK9r6fD1JtO7M7rg/FUVRYhgVTIoP//Ik9fUSezRkiCQ4/OADycwNYoVyrUqzZom4Cn74V1TIw/3MM0V4TJoEP/iBr00Elp7aWqir85BtaqnzDKa2NYPB4QK+e7L2mdcLCxdKPbemJhkBd9hhUhLG7Wso0eNa6IYPl6zoe/fKaMJgIRlNsslYGfYf7fXt61p0iqIovYwKJsWHG5NUUyMiyRUnmzfLA2//fjjhBFnmip1wFo+UFBFdU6fK6+qrfa40/4DoTtiyBXakTyA7s5wdTalsGT6NsWeeGbpxsLstJUXOwV9oRCpWKipE8LS3y+i/mho5F//rFMql6GZIP+wwsUidcUbHArvRColYGQUXrTtTk2IqijLAUMGk+HADnDdvFuuI604bNEisJtXVMn/NNSFjjg7iCq/MTGnvL5b8+eADEQ8ffBA2jqneZELhBOqrm+HY42VEXij8rTEpKT63ob8wilSspKSI67GsTCxM48bB7Nm+OC7w7WfDBl9gfGMjHHWUrA9XKqYrIRFclDhWiNbaFUvWMUVRlB5ABZPio7BQBM7+/ZJjKDdXzDwpKbJs1CgJYO7sQV5WJikJ9uyRkimhgsEhULz4iw4/gTNyJIzO8lLTVMXorEZGVq/rvMiva40JVyPNPzdSfb2cVyhaW+G446TI8LZtIvzctADutWhslH3X18t6N0Hl7t1SmHjGDBGCwVaszoREWRnceqvPWnXnnbEnmqIRPrFiHVMURekBVDApPvLzxRr017+KO6qhQQKJiovFanT99SKCwlFWJnFKVVUiKiAw7scff0uLKzpCWH7SW2sxdTtJS2qHrdvFytUV4aw4/iVLMjPFCuUKGjcmKSVF+p+UJHmPDj880OXmWq5A4rcKCmSZm6By2DDYulXSELS1hbZihRMSmzb5AuA3bpT5WBJMiqIoCYwKJiWQvDwJ9K6ulteWLTI83lpZHxwX5OL1SomUTZvEfdfeLtm8w8UJhXKhBQmcut1eUsu3MbhlFzUMoq4lNbJz6MyK47r9ggsPu8WBV62SUXENDR1jkIItV7m5Imj8E1S2t8v51NZGX2C3pMQXe5WSIvOKoihKTKCCSQmkokIsKyUl8N//isDweGS6eLGUAmlsDBzi7gZBb9wo79PTJY3AYYfJPsOJLH9LSwiBk7VvO1RXUU0Wpq2VrEFJIkqg66H54aw4oaxPrnswK0usXZ9+KvNvvy2Cyd/6FM5y5Z/A0r+UTDQuqfHjxQ0XizFMiqIoCY4KJiUQf0GRmSkP7kGDpGiuxxO6mKwrOI44Aj76SPYxbZo88N3RZMEiy58w4mdQRjujkvdS3VJFTlslg6p3iqBZuRJeeUVESU6OxFVFKkxCWZ/8g7lbW8W6NGKEnH9pqS84vZMkmwf3fagxO+PHdy2UNCGkoihKn6OCSQkk2FX29NPimktJEQERqpisO0JsxQpxwxUXw+c+J8LCzRYeLLI6G+oPUFFBbbOHnfXZtLW1UZuUQ217OvzrX+IKe+MNOPJInxALFwge7hz9j+8/ou9734OXXxbXmluEeM8ecUkaEzbJZgd6S9RoQkhFUZR+QQWT0hF/QbFgge/BD6GLyebni2ipqvIJKdcK445KCxZZ/ske/eOC/Cw69R9ZbPZosit3Utc2iPoqJ9h62DARMI2Nkvpg27auM2CHEjBer1iramp8iTgzMyWGq6pK2tTXw/PP+4K+L7igazHUm6JGE0IqiqL0CyqYlM4JdjOFKjZbViaiJTU1UEgBHH20WKiCRZZLcEwRHBQEmVt3YVJTqEnOJdm0kZmbLILGWnFb5eTIiLSPP5b+hBMm4axY/oHeIEHa4IvhKi+XnFSjRomLbtcumX/vvc7FUG+KGk0IqSiK0i+oYFI6Ek1AtX/uoPZ2ST0wY4as8xcp8+eHjv0JjikCsTCVl5M1eBATjxtM64FVpKRYsrJT4JRTZKj/6adL+ZKMDMl9lJkZXpiEEjBVVSKARo6U5UOGyH4LCgIFyfTpMvpvyxaJ5crLExddZ2KoN0WNJoRUFEXpF1QwKYFE605atUq2OewwEQhtbaGTR3YW+xNsxXIEgSe5kOLUvaSWttCSkoYnd5RYe9w8RWPGSJ23/fsllUE4YRKqbMrSpbB2LTzzjIigdetg3z6JWzrqKLFepaSIRck9zrBhYtnatKlzMdRToiaccNWEkIqiKH2OCiYlkGjcSV6vT2js2SNiws0d1F0ri59IyBqUz9Cphqr1hzO0bT9Zk44Wd6C7/+xsmDBBxNLVV0dexNZNnTB9uqQQSE2VdAkNDfDJJ2J9Sk2F1aslhmnfPl85mNbWyMTQoYoaDe5WFEWJKVQwKYF0JnSCLR6u8LjwQolhuvLKjgV2I7WyeL0S8L10qewzOZnmcy5na2U+NUeeRaWt5rD5WXjc/USy/+D++rdJTpbYqowMEV67donVqrlZrEuffipi6bDDpK7eRx/JqLxQ++oNNLhbURQlplDBpAQSToiEsnikpIhLzh1BFpw/KFJh4e5782YJ4L7oIqiqYvfra/nok+l4UpNo3g3TR22nJAvfPjvbf1mZrwRKdnaghcY9x9JSsSStWSN9d2vGvfWWiKnUVBEr48dLDJZ/1u/eRoO7FUVRYoqEFEzGmMuAzwIzgaHANuBJ4OfW2hq/dkcAdwLHA7nAFuAfwG+ttSEqyg4QQgkR1+KRlwfr14vYyM0VEZGVJaVA9u/vXobrigoZrbZvn1h6HnsMcnOpG5XPp6/nQlMrHDhA3ZbnYOs4SXXgbheuTMtDD4n4KigQt12whcbNzg0i9iZPlvOyVgK829pk2emnyzn2ddZtDe5WFEWJKRJSMAHfQ0TSTUA5MAO4DTjVGHOCtbbdGDMSWAzsAL4NVACnA/+LiKwf9nmv+xM3OeXzz4uoWLpUrC7Z2SIukpIC3GmdxtwEu8oKC8X9VVcnAdfJyTB4MHWDx7NrbzK2qh5jM6nbUyexUsHZt4MTYa5cKe8LCjoGhAcfu7hYRsq5uaM+/BC2b5fzyMmBsWP7r0SJBncriqLEDIkqmC6w1u7zm3/TGHMAeACYC7wOnA8UAidaazc47V43xhQDXyBeBFNPZZx2k1NWV0uSR1dguFaQqirJ9N1VzE2wa88tNXLBBbI+M/Nghu3GdbtorxtEhm2goSWJxrpWX/bt4GSXIH1bs0bSG6xfLxYi/4DwcIHU8+aJa3HjRp/gy8uT/SiKoigKCSqYgsSSywpnOsqZepxpdVC7SiCpF7rV8xzKSKtgoeX1yvJQWb7d9UuXiuUnJye8Rcc/mHnDBnGdufE6V1/tc+kB6Q+sIyU/j5a6VFIaGkmfMg6u+aKIGSdXE42NUlfOHd2Wng6XXirHnjZN3G6dZRWvroZFi0QcbdsmaQu8XnEzHn+8b1ReJNco2muqKIqixA0JKZjCcIozXedMHwN+CvzeGPN9YD/ikvs8cHvfd68bdHekVSgr0GuvyTzArFnhS5FYG34/bqB4RYW44OrrxaIUJldT3uzDGP/QVlraKkhNbSSvZIiIJf/4nqoqcRPm5EBamsRBbdggmbn9xRIEBlK7Qmv5ckl8edhh0mbUKMm7dMYZnQd5RytGNU2AoihKXKOCCTDGjALuABZZa1cCWGv3GGNmA88Am52mFrjNWvvL/ulplHR3pFWw0Nq0KXA+Nzd0ULh/SRH/bNquC8+NPXIL3V5wgZQZCdO/tMJ8mvIbqDyQQt6wVNIGV/pEn79lKzdX3GnWilXorLN8lqGNGwMtYf5C6/HHRWR5PLKfadPgkku6rksX6hp1JUb7Ik2AWrAURVF6jYQXTMaYLEQUtQJf8ls+BBk5VwdchliYTgNuMcY0WWvvCbO/a4Frwx1v7NixPdf5rujuSKtgoVVSIsP0OxNe4bJpb9gg8USzZkm7tjZfodtBgzrt35YtsKYsm/ZKS1KNZctxWRSnpIgISknxue8WLJD4KvAJpaCcTgctOu6rrExyLW3YIO646dMlOWWkAd7RitHeThOgFixFUZReJaEFkzEmHXgWmACcYq0t91v9A2AcUGStdQJ4WGyMSQbuNMb83VpbEbxPa+19wH3hjjlz5kwbbl2v0J2RVqGEVlfCK1w27fPPF8E0Z46IGTf2yD8GCqS9ux+HrR97qapoIyMpiYYGw9bcI8U16BbMnTHDl2PJTRHgCoedO0UMXXCBWLdci45rhdm+XYRSWpq459LSxN13KNeoJ9tHiya6VBRF6VUSVjAZY1KBJ4BjgTOstR8FNZkKbPITSy7vAalACZJqYGASLLQiEV7+4iclRURRZaUM23fdXMGioZMEk54WL3UN+dSSimlvw1O5C4a3SUB2a6tM29oCxYGb0yknR9IULF8uVqPCwkArzPbt0NQk+8jOFsEUrciIVoz2RJqAcG43TXSpKIrSqySkYDLGJAEPI0Hc51lr3wnRbDdwgjEmP0g0HedMd/RyN+OPcCkD/B/u/qKhiwSTbRn5WAu2pRFope2d92FovQii9nZJlpmdLeJs40YRSB9+KKPe2tokZ9OIERIIXlkpOZk2b4ahQ6UkyuGHS3xWWpoU1o11kdGZ200TXSqKovQqCSmYgD8AlwM/A+qMMcf7rSt3XHN/Bq4CXjXG/C8SwzQXSXr5lLV2e992OQ4IdgsFjXoL2T4zM3SCSWBffT5JyV4yWmpoJpW69z6GsVVS0+2oo8QlB/D00yKenn9e4qI2bpTjtrfLfvfuhXvuEZGxeLEEh0+aBLfdJn2EyAK9+5uu3G6a6FJRFKXXSFTBdI4zvdl5+XM7MhLuHWPMHOAnwG+BHKQ0yh3Ar/uon/GFmw38/fdl5FqwxcYtsAsiUAoLxUI0YUJggkngwAEZ9V/faGgjjeHsZgKbYGeLjGRbs0YEUmurxCtNnCjvBw8WQdTcLOJi/34RTvv2SRR5S4tYpGpqpL0b+9Sb9NToNXW7KYqi9BsJKZisteMibPcOcG7v9mYAYkzgvCuU3AST1sqouQULwrqR7r9fPGuQRhOpHM8yjmU17B8Ozz0nQqiiAqZMkYSThYUihGpr5fgeDxQVyT5375a2lZUiojIzA/NF9SY9OXpN3W6Koij9RkIKJqULumsRCZWLCUQw7NolNd6GDJGYoepqWT9xYshj3BOQtMHwKUUMpgrqc8Sd1tICzz4rFq2xY+HMM+H662H1ahkdV1QEW7fKcZub5eWWO0lPlzQCnWXx7il6evSaut0URVH6BRVMSiBdWUQ6E1OuS86/PIorGCZNgo8+EtdYdrbMh3Epvfkm7NtX78yJtcqLc6yUFEhNhbVrZVpbCyefDKeeKv0pKpL+u6kHmprkeElJvm0PPxy++c2+ER7qRlMURRkQqGBSAunMIhIqBYC7jb8Q8Hd3uYKhshJOPFGCtXNyAoOsg0TY0//2Amm4YgmgmE/lTXu7iJ7BgyW3k9crmb2DR4u98Yb0Y/t2sS5NnCgiadSo7gd4d8fypm40RVGUAYEKJiWQUBYRNwbpySdlWL6bAsAtdeJao44+OtAlV1oqwd/z5knMEXQUKyEsWkverEcEk48LWCZvDhyAl18WAeb1imvN3Y+/aMrLE+tSXp7ENB15pFiWOhu11xmHEoukbjRFUZS4RwWTEkiwRQR8MUgbNogAcVMAQKA1CjoWt21pEYtQZqaIqQ8+CBQbISxa5RUd3VYNJPn65450a22VbZ5/XoTZggW+bN5r1ogVrKJCElcWFR2aO0wzaSuKoiQ0KpiUjvhbRDZu9MUgrV8voqOoSFIA5OUFljopLpaXW3rkj3+UfezZA8ccA6ef3lFshLBoNdS2EmxhqmCEiK7DD5fCuXv3SlzS1q1iZWpqEhfdzJm+4PPPflaE07HH+mKcuovGIimKoiQ0KpiUzvGPQZo1y1cTzhUfoeJz8vNFMG3eLKPhmppkZNyYMWL18RcbQRatZx+vpKZxcFAnWpnIesnOfdJJks07OVncbG+9JYHk7sg7tzCv2+cJEw5dLIXoZ8j99VS+JUVRFCXmUMGkdE5XQiFcfE5OjqQQSEqSnEhHHgnTpokFqJN93Pu7A0ipPjfg2zKWUq7mKXHz1dVJ240bRZBVVcmypCR4/XVx/+XkwPz5Hcuy9MS1CLevnsy3pCiKosQcKpiUrgkWCl1ZUrxO6b1Zs8QllpYmbrxQYimIj7YM8puzQAP/j+8yll1QB3z6qViaRo0S65HHI8fZuRPeflvilRobfe65aDgUC5Fb9DcrS6Ya46QoijKgUMGkREc4S4o7kq66WkRSerqMpvuf/+mYRqCTfVfXNgcsSqaSC3nDt2DPHhFJycky+i09XaZbt4q1qbRULFs9dV6RkpIieZ9aW+X9JZdE3wdFURQlZlHBpHSNv+Ul1GgxgIULJZN3VZWImEsvFatPW1voVAIhLDm7391KC6MDDp1Lk28mMxPq68X9lpEheZZGj5b9TJ4s0wMHJEA92izehzoKrrVVigFnZUkyTbeor6IoijIgUMGkdE6w5WXevI6jxSoqRCilpcmrpkasTKWlEpC9dm3gkP+FC6V9bq4v1iglhQ3PfcJgBrMfn1tuNst9fTn5ZLHeHHecjM6rqICGBskgnpwsxz7qKLjmmujdYYc6Cs4tJNzW1jGwXVEURYl7VDApoXGtQFVVgbE5ra2+IPCUFN/ULVfS3i7xRUVFUmh32zbYtMkXU1RaKpaonBz4+GMRVGPGyH7aJzB8ZAr7dzYCKeSxm5/yW1+fvv99WL5c+tHQIMuSkyXFwIgRMhrvjDMkjilaDjUjt2b0VhRFGdCoYFI64m9VOnBArEVuLbZLLvGJAX/L0+zZImSGDhVX2aBBsHu3WH2amiS2ycUtndLYKAJr9Gior6e5roEDFZZM6jC0cRd3MIuPpO3gwZImAMQdd8wx4p7buVP6O2iQ9CXa8/QXOIeakVszeiuKogxYVDApHfGP59m7Vyw2EyYExuaUlopYmTxZhExOjrRxBdTo0ZIN3FoJygZJBVBQIKPaqqulTWamuMGys3lv6Lnsas5CUgq0UUWur09TpsB774nF6oIL5Njt7XLc/HyxajU3+0qmRBBg3sHV2NNpCBRFUZQBQ1wIJmNMNnAiMBYoBBqAvcBqa+3H/dm3AUlhoVh/3n9fXG3Dh4s4cWNzvF5YulRccO+/D8cfH5jlu7BQhEtSki8IfPlyyRSenCxxS25tuYKCg0Ll1YvzgXpEMCXzKnO4iT9IuzlzJD5p+3axeDU2yrannipWsMGDZQTdu+/KiDk3ZgpCB5n7i8ING6SosBvHpDmUFEVRlCBiVjAZYzKAzwJfAY4Ft5iYX0ZDaVcBPAH8yVr7UV/3c0BjjFiAgpNAbtwoAiovT9xudXUikNwh9aWlEr/U3i5i68ABcddNmybWpP37A4v2OgLlzTe9+JdE+ZBp8mboUBFfO3dKaZS6OhFLH34o76dMEUvTnj1iuSov98VMhUsX4B/kXV8v56l14hRFUZQwxJxgMsakAN8CbgbygUbgHWAFsBs4AGQABcAU4HjgeuA6Y8wi4LvW2rX90PWBg1uLraREBERrK0yc6FtfWCgio7ZWgrszMsRCk5kJ77wjMUterwilKVMkjikpyTcCDToM4f/bE/kE149rdzXyt74l/amvhxNPFEtVXh6sWAFlZWJtGj9eCgQXFPhipNxzCZUuwD9IOyUFXntN68QpiqIoYYk5wQR8CowHXgYeAJ6x1jZ1toExZhKwAPgCsMoY8xVr7YO93dEBS1dD7PPzpfiuK5JcC01Wlrjg3EzfBw6I1WfkSLjiCgnMTkkRC1NjY8D+77jD3bk5eJi5vCFJKrduFdfbzp2SHHLlShFQu3fLCLudOyUuauxY6cPUqb48TO5Ivvr60HXsOquJpyiKoigOsSiYPgEutdauiXQDa+0G4CZjzG3ADYgFSukukQyRHz8evvENn4Xm6afF2tPe7nPN5efDEUeIdccVS67ISkoSkeMktdy+fR8czL9kgUZ+xx0wbJjESe3eLaJn/XqxIHk80ofcXLEgbdwogun88yWBpJvz6bXXfKLOf4RfqHNWoaQoiqKEIeYEk7X2wkPYthm4t+d6k8BEIiDcNq5FKSNDAsDr6iRPkhsYvnu3jGx77jnJvVRQICPqcnMhP5/553qBQUE7r5b6ccNnSRxURobsMylJ3HHNzRLbNHSoiCpXUK1ZI4IJfO64SZN8rkVFURRF6QYxJ5iUOCG4XIp/zNPkyWIVWrxYgrArK8XSAyKW9u8XEeSMuHvmpVYkfsngxPJzFY9J+/JyEUrudm4izTFj4Gtfk3gpkOXu1I1TOtTs3YqiKIriELeCyRgzBTgHGYf+iLW2qp+7lDiEK5eyYYOImh07fJalvDwRTIsXi2ts2jQRS1dfDfn5LFq4ERgWdIAqHuIHkr07M1OsVm+8IdalUaPExTd/vsQqgRxr2zYZ1Tdpkk8YafZtRVEUpYeIecFkjPkJ8DXgCGvtAWfZGcBzgMdp9gNjzLHW2v391M2BT2cFeFtbRTQ99JC4xZYtkwDrpiaxBjU1SRtrJabp1FMPipebfluI3Ia+YO9CnI8xO1vE1UcfSZB4To6IotZWOc5jj4kgWrBA0ghAx0K/GpukKIqi9AAxL5gQK9KnrlhyuBvx3fwUGI4Eet8I/KTvu5cAdFWANyVF6sVlZkpw91tvybLBg0Ws7N0rQdppaRKY7SdgVqx23XHguuO+zp9ktqpKths3TsRWY6PEMh13nC8uqaJCUh7MnNmnl0RRFEVJLOJBMI0DnnJnjDGjgGOA/7PW3uUsmwLMRwVT7xDKouS6uurqxLIEMoJt8mQJ6B45UlxoTU0ybWmRpJMFBTKirbCQb91YSUd3XB238UcRW21tIppKSyXfU1ERfO5z8OmnkcUlhcrwrSiKoijdIB4EUz6SrNLlRMQU8bzfsveB6/qyUwlFcPC0m9soJUVGvq1aJZafoiKJN7r6ahFV27fDkiUiVjZtgiOPlOBvp6jvff+82jmAzx1XgpNNoq1NRsK5+Z3GjIGvflUsSVOndi2EwmX4VhRFUZRuEA+CaR8wym/+VKAFeNdvmQdf6RSlp/EPnnYtSm5uo4YGX+mTmhq45hrJjwQiaNaskaK51kqdt4ICcad98glNZPodxAKt/I2bxA03ZYokpMzJkW0LC33JKCOJSwqX4VtRFEVRukE8CKbVwIXGmCORMimfAd6y1jb4tRkH7Or7riUQrth45JFAi1J9vbjPhgyREWz79/sEU36+FMwtKxOrUHW1tC8v57LnL6Rj7qUmTmEFpA4SgePu45hjRIj5C56u3G2aUkBRFEXpQeJBMP0SeANcXw0Av3bfGGPSgbnAi33brQQgWJS4IsbfonTddeKW27kTNm+G3/8eLr4YTj5Z2q5ZIyLqlVfEsnTFFXDgAE9scVIC+LnjZrNY3ng8ko5g1CgRZVdd5RNhbr+6crdpSgFFURSlB4l5wWStXWqMOR+4BvHbPGytfcmvyQnAFvwCw5UeIJQoca00gwf7xMwHH4i42bhR8i2tWSPLPvMZETmrVkkA+MqVIrT+8x9WrzXAdL+DWaCOxVwlLrj6esnptGeP5G1yXXEukbrbNKWAoiiK0kPEvGACsNa+jBTjDbXudWBG3/ZoAONalaqqfKJkwwYRPDNnSkB3Q4PUjAOJZRo+XBJTer0SoJ2WJrmY/v532U9zswislhYYNIgFr34fSMffugR1eGiBwaOgtlb2mZvrqwvnj7rbFEVRlD4m5gWTMeYfwNPW2mc7aXM+cIm19st917MBiL9VqbFRlm3YIFYikFikefMkXqm6WkbJNTZKGZSsLMnw3dwsIqmuDnbtkuSTzc3iZmtogBEjWMORfgf1K4WSlCT79HhkOnKkry6cP+puUxRFUfqYmBdMwALE5RZWMAFHAV8EVDAdCqWlInJGjhRBNGeOiCfwJYrctCmwbtywYRLL1NoqVqf2dkleOXGiCKyWFhE/I0ZASwuLzFwIGB0HUCelUK77moilyZNlvzNmBMYu+aPuNkVRFKUPiQfBFAlpQFt/dyKu8Xph6VIpQ/LCCyJwmpokSDs7W8RRY6PEF23fLtPsbBE1H38s2bwzMmDoUIk9clMCuPXdDj8cBg/mqz85ieBSKElUypvdu8XFd+ON4YWSoiiKovQD8SKYbLgVxpg04GRgd991ZwBSUSGWoxNOEOuSxyOj3p57TuKW3JFu//ynxBgddhh861sibK6+WgK6DxwQt1x6uoinE04QITZ+vMwfOMBWRvodVD7W63lYXHpFRRIz1draP9dAURRFUcIQk4LJGLM5aNH/GGO+FKJpMjAEsTD9udc7NpBxA6mtFcHT1ibWosxMETC5ueJ627dP3G4bN/pyLo0fDz/5iVib3npLrEkvvSRiqaRELE0vvMAT26bRMfdSHX/gdkjKkRF2Y8dqELeiKIoSc8SkYEKydrtWJYv4b0yIdi3AR8B/gbv6pmsDFDeQurRURM6774pVqL5eYpDy8iQou65OYpTS0wO3r6z0WYna2nzrKytl++HDuf7DHzuNfR+lx3XHjR4tx50zR2OTFEVRlJgjJgWTtXac+94Y0w78xlp7R//1KIH44AMRPElJ4l4bNUrqv11+uWTbBrEwDRvmy49UVga33uorlXLGGVLaZORIWLtW9vHee1QwxO9Aooe/yd8kDUFqqoit4JxLiqIoihIDxKRgCuJUoKy/O5EQuAkhU1Lg7bcljik5Wdxy7hD+Sy6RtsXFPkvQqlXiqrNWgr0feURceM8/L/sqLeWlnVPo6I6r4Vf8ErIKJCYqM3j0nKIoiqLEBvEgmH4KLAQeDNfAGHM18GVr7Wl91akBSWGhjIR7/XVxpQ0aJGkGhg8XV9wjj4ioSUqSwHCXt9+GLVskxik5WSxQ9fWyTVoa7NnDVXvudRr73HGp1MqbSZPgwgvlmFokV1EURYlB4kEwzQW3yFhYioBTIt2hMeYy4LPATGAosA14Evi5tbbGabMQye0UivXW2imRHi9uyM+XGKI9e8TlduCABH5Pmwb/+Y+MmsvKkhQCL78sVqWkJBFKEybAp5+KuGprk2VtbZLEsqkJLyP8DiTuuC/yiMRGZWaKZSo7WwO+FUVRlJgkHgRTJGQA0YxF/x4ikm4CypHSKrcBpxpjTrDWtgN30nHk3Tjg33SeRDN+8XplOmyYZPPev1+sTKtWScbvjAxJXNnaKvXkKivFklRfL5alCRNgzBgJ+P7kE9lHSwt3fXwmoUbH/ZVbIGmwtJs2TUqvqHVJURRFiUHiRTCFzMNkjDHAWOBcYHsU+7vAWrvPb/5NY8wB4AHEovW6tbYUKA063jzn7QNRHCs+8C+LAnCKY7BbsUKsQB98IMkrjRFLUG2tuO9SU+XV1CRCKiND4pemTYM33oDUVH5T81nnIP7JKqtkXy0tUjqlpETFkqIoihKzJPV3B0JhjGk3xrQZY9zs3be58/4vxKq0GZgOPBLp/oPEkssKZzqqk02/ALxvrf040mPFDW7A9+jRYiHKzZXA7uRkcZfV10vckjGy/vLLYf58GDdORsMdeeTBAG+2bxeX3OjRcMwxHGCw34FE+36dB8SylJ8vuZdee81n4VIURVGUGCNWLUxL8FmVTkbcZ1tCtGsD9iN5mP52iMd0Y6DWhVppjDkRKAG+dYjHiU3cxJXl5TJ1Y4mOPhq2bRNB5FqDamqk3tuVV8KSJfDUUzJCrrlZRNX+/SKEPB5ue7oYyAo6WB2/4y448WIJDN+7V1x8GvCtKIqixCgxKZistXPd904epvt7Mw+TMWYUcAewyFq7MkyzLyCJMv/dxb6uBa4Nt37s2LHd7Wbv4iaudNMHgM9F19go1qDcXBkNl5sLixaJyNm8WdxzbgHe/HxZX1QEDQ3c/tIVzgH8R8dVSbspU2DZMgkWr6mREiuKoiiKEoPEpGAKYjy46aB7HmNMFvAM4t4LVX7FrVd3BfC8tbais/1Za+8D7gu3fubMmWHr4vU7+fk+C8/GjT4XXXm5jJ477DCxKNXWisj5zW+kfVqaxDCNGCHxTgUFYmUyBsj1O4Cc+s/5OXzxi+LmGzxYEltOmaI15BRFUZSYJSZjmPyx1m611lb1xr6NMenIiLcJwFnW2vIwTS8C8hiIwd7hCHbRFRfDqaeKGHIL7e7fLyPoli6VOnAffQTznLj4lhbu/YMl1Oi473G/WK0WLJD6c0cfLSPzNKWAoiiKEqPEg4UJAGPMCOB0JCg7LUQTa629M4r9pQJPAMcCZ1hrP+qk+ReBCuDFyHsc5/jXlquulmlBAZx4opRMcdu4I+SOOEKCtvftk7xK27dzS8svnZ35lwGsdCaV4rb7xjd8bkCNX1IURVFilLgQTMaY24EfEdhfgy8w3H0fkWAyxiQBDyMC7Dxr7TudtB0GnAn80VrbEn3v4xSvV0TSK69ITiU3bcCUKSJuMjJkNFxbmwSDe70ilKZPh+eeg9pa6ijw26F8VL/hLtl+6FARShMnqlBSFEVRYp6YF0zGmKuAW4HXgT8gVqGFwKtIzqSvAI8Bf4lit38ALgd+BtQZY473W1ce5Jq7CrlOieOOc3My7doFy5eLFamuTuYzMiQ26eyzJbh78mRxxeXkwAknSKLLo4/my7cXEsod920ehOFHSjoCdcEpiqIocULMCybga0g27rOtta2Sq5It1tpHgEeMMU8BL9DF6LUgznGmNzsvf25Hsn67fBFYa639oBt9j0/cnEwjR4pIcgvrJiXJCyQYvLFRcjRt2iRtf/97OO442L6d+9u+5+zM545Lo1ICxG+4QVISqGVJURRFiRPiQTBNBf5trfUfQpXsvrHWvmKMeQX4PvBcJDu01o6L9ODW2qMibTtgSEkR0VRXJ7Xhxo4Vq1JtrYxqmzBBgrVnzJBs3suXS/25zZvh+OMl1ikgzEzccTfyF/jSl1QsKYqiKHFHPAimVCQ5pUsDgWPVAdYC1/dZjwYyXq9k3c7MhIYGmDVLhJAxYj066ywZMecKHldMNTdDezvs3cuZ/55PKHfcPfwafrBZxZKiKIoSd8SDYNoFAaXutwHTgtqMIrriu0o4XHfcpEmSUmDWLN86f6HkkpIilqe2NkkNUFTEa9uKnZU+d1wO+yVbeF5er5+CoiiKovQ08SCYViFuOZfXgWuNMZ8HnkQCvy8F3u77rg1AQuVfCmURckfRPfmkBIInJ8tIusceAy7zayjuuHu4HQ4/XMufKIqiKHFJPAim54E/GmPGW2vLgF8An0FGyi102rQAt/RL7wYawSVSwomlxx6TgO8NG6TN7t2waxfXrLqMUO6463kEsr7ccWSc16t5mBRFUZSYJ+YFk7V2IT5hhLV2uzFmFvBdoBgpyvvHLhJPKpESSsC4y1JSJBfT9u0ilkaPlhIqQ4bI6Ldly/gbn3V25J+sskbSDlx1VaAo8nph4UKoqpIUBQsWqGhSFEVRYpKYFEzGmJuB/wLvWWvbg9c7lqZv9HnHBjqu5aitTVxsl18uyx97TIrjrloleZdWrpSUAtnZMlLuyCPhd7+T7N1k+e1Q3HHf5m8SMD5+fODxSktlXzk5IrzmzIGZM/viTBVFURQlKmK1ltydSEyS1xjzrDHmRmPMkf3dqbjG6xVR4vWGb+MGfI8eLVNX0NTUyEg4r1dqyG3eLCkHDhyQBJSPPgpLlvB7riKUO+433C3CKlRxXWsDp4qiKIoSg8SkhQm4CzgVqfN2PnAegDFmLxL0/V/gv9barf3Ww3gilOUolOvLP+C7sVGK6ra3wzvvSED3vn0ikrZtE+tSezv84x/wwQfQ2sq3+KmzI393nIWsLLEiBccvFRfLKLzqahmVV1yMoiiKosQiMSmYrLU/ATDGZAJzkJpvpwHTgc8CVzrry3DEE/C6tbaiP/ob8/hbjsrLw49U8w/4rqqCFStkm337oL4eLrwQXnxRLEzJySKeVqwQCxRgyfPbmViMLuAJuOwyyd8UfMz8fIlb0qBvRVEUJcaJScHkYq2tB15xXhhj8pA0AmcgAmoKMB74KtCOJLlUgglOFdBZDbf8fHl5vWI5Ki8Xa1JSkoio4cOlXlxVlRTdbWgA4BHOIZQ77llugPcOhxtv7Px4iqIoihLDxGoMU0istZXW2qettd8AjgAuRrJ8G+LsXPoU13J0xhnh3XEubqwTwLx5kowSpNxJebm45pKTRSz5xSRdw6+cdyZwf8nJIrY2b+6581EURVGUPiamLUzBOIHfpyEuupOBHOQJvRN4ox+7FvtEYsnxj3VqbJRl1dWSa+nYYyWW6cABWedYllxqGeI3J+642SyWorzGaIZvRVEUJa6JacFkjCnGJ5DmAkMQgbQHeBkRSW9Yazf2Vx8HDF6vb0TcpEnw/vsijIYOhf374a9/hS1bxLLU3BzBDutYxhVQlSNB34MH9/YZKIqiKEqvEZOCyRjzD0QkjUYE0j7gTXwC6dN+7N7Aw00guWWLWJPq68XdtmaNuNRKS6VNS4tYn4IYwxt0jF9yGOQs37wZpk/vpRNQFEVRlN4lJgUTsAAJ4n4MuMdau6p/uzPAKS2FZctgxw4RRvn5EtydkSHiyVrweGQaQjCVc4Tzzhe/lMcOSE2V1AMpKeqSUxRFUeKaWA2U3oT07QpgiTHmRWPM940xM40xpottle5QXQ1794oVaft2EUoZGeJ+S0kRwRRCLHXEAi2s4CKJXxo+HEaN6pjlW1EURVHiiJi0MFlrJxljRiJuudORJJZnI0/jamPMEsQ997q19sP+6+kAwM38PWGC1IfLzoZBg2gu20FzWjae1iQ8mZkyQq69Q5UaLuNeOrrjmilJ2QnTzpH4peZmiYNS0aQoiqLEKTEpmACstTuBfzovjDEl+BJYzgUuAKwx5gCwGBFPf+qXzsYrZWXw0EOQmSkJKs84A9LSaLapbG0chs3MxCxdSlF7NZ6k0MbIJ/iM887f8FclhXZ37w4seRKqsK+iKIqixAExK5iCsdZuQlx1fwEwxkxFxNOXgUuRnEwqmCLF6xWx9PHHkmNpwgQRObm5NDelYh9+g6w9pdR6PDSPn4ynbH2YHSX7vRdxdAu/g127YOtWGXVXWChuvUjKsyiKoihKDBI3gsnFGDMMEUrua5y7qr/6FJdUVIhlqaBA3GXDh0stt/x8PHu8mMYGauskhZJnSK7UgquuDtjFMmYA6UE7ruNO/h98Mlpmc3Ml2eXmzZGVZ1EURVGUGCTmBZNfORRXIB3mrnKmn+KrJ6dESmGhxCtNmCBi6eqrDwoYz8erKFr7As2ZeXiad+NZvvdgvTh/TuNhxMIUIrv3vn0SQN7eDnv2SKLLSMuzKIqiKEqMEZOCyRhzJoEFd5PwPZW34RTbBf5rrd3dH32Me/wL7frHFHm98NxzeDasxZOcLEJp0KAOmb0BmvAXPeKOm8oymU1LE7Hk8Ujgd0YGnHuuxjApiqIocUlMCiYki7fFl7TyDRyRZK0t7c+ODShClUupqBCxM2qUuODa2qTQbnB2b2NcjeRHHR8yH9IHyXbGSB05j0csWVpoV1EURYlTYlUwvQAsQgTSR/3dmYSisFAK7o4cCenpUFkpryC+bH9GyOzeSUkwdqxsc8wxMGuWCKdBYTKBK4qiKEocEJOCyVp7QX/3IWHJz4cFC2DOHHjzTakvF4L7+bzzzj9+qUFyLWVmSpbvkhIYMkRjlhRFUZS4JyYFk9L/NGfk0ry/CU+LwRO8MikJ2tP8Fohv7gb+ARMnShmU1la44AIYM0ZjlhRFUZS4RwWTEojXS/O/n2DrNoN97UNM4xCKaMBDi69NTg5UBm9Yxx+4HYZ9UVx6w4eLtam1tQ87ryiKoii9gwomxYfXCytX0lxZj61sJWvbOmoxNOMJEEzDKl8iZPxSQYHUoRs5Ek49Ff71L0kpMGwY3HCDWpkURVGUuEUFkyJ4vZKJu6YGz/JlmDWV1HpbMFg8BI6Q20ux884Xv5TDDkk9sGePpBP48EN45hkZcdfUBLNnw2mn9eEJKYqiKErPEbpAmJJ4VFRIKoBJk/CMKKAocx8jk3ZTxLZAd1wHLNDCci4TwbRrlwgkEOGUlSXTECPtFEVRFCVeUMGkCIWFvkzcHg+eHWVktXg7iKXv8QM6uuOaORwnPZa1MHOmjLLLy4MdO2Q6Y0YfnISiKIqi9A7qklME/8zfNTUinkLwa77hvAsqh5KUJNakIUMkq3dVlbjnampERFVV9W7/FUVRFKUXiRvBZIwZCswE8pECZh2w1j7Yp50aaLiZuCdPprktmWYG4aE5yMrkX2xX0gnMZrEkphwyBKZMEWvSCy9IpvDCQhFhb70F06f34ckoiqIoSs8R84LJGJMK/Bn4AuFdiAZ5eqtg6gGaR45ja8oELJUYbBdxTHUs4woYOVkCu2+8UdIJjBkjMVG7don1acyYPj0HRVEURelJYl4wAXcCXwJKgYeB7YAm9zlUvN7AQrheL6xaBevX0/z6e9jGZrKoo5ZBB9MKjGQJIdMJGAP19ZI+wC2BUlQkLrrqapkWFfXp6SmKoihKTxIPgulzwAZghrW2ob87MyBwUwi0tUms0rx5kjPpySdh1y48TRbTOIhaBgWkFdjFFGcH/vFLB3xvk5J8JVA2b5Z9FxTIKLnNm9UlpyiKosQt8SCYhgJ/VLHUg7gpBEaPllFxmzZJgLYxkJODZ+9eitjvWJaCY5hcJH7pfn4siSonTYIrrvAlp2xogN27nTIq7TKvKIqiKHFKPAimbUBOf3diQOGfQiA5WYrkDhsmo9kaGyWtAC0BQulWvklHd1wdC3gKPv8j2ecgv/UZGVIeJTlZxFlGRp+cmqIoiqL0BvEgmBYCXzfG5FprdWx6T+CfQqCuTixM550nQdvl5bB4Mdx/f8Amd/Fd511QOoELLhCxlJ3tc8cBTJgA6emSxDI9XeYVRVEUJU6JB8H0C+AoYJEx5gfA+9ba6n7uU/yTny/Zt3/1KymQ29YGV18tbrodO2gmNcgll+m3sbjjJrISrrtO3HFu8LjLoEFwyimwf7/EMQ0KESyuKIqiKHFCPAgm1y9kgEUAxphQ7ay1NqLzMcZcBnwWyes0FHH7PQn83FpbE9T2eOA24HggFdgM/Mxa+0i0JxJzbNokYmn0aPjvf0U8VVfTvHErWxmLxRxMK9CROjZwLvz3f+CEEzoW1q2rgzffFAtTWhp84Qt9ckqKoiiK0hvEg2BaimvS6Dm+h4ikm4ByYAYiik41xpxgrW0HMMacBzwF/AsZrdcMHE5g9sb4paQEUlJg3TqxMOXlQUUFzW3JNOEhjWaa8HAUTxE2ncBLL8H553csrLt5MzQ3S+xSY6OOklMURVHimpgXTNbaub2w2wustfv85t80xhwAHgDmAq8bY7KB+5ERet/2a7uoF/rTP4wfD3feCUuXwjvvSPxSQwM0N7KLEbRjSMLyKcc6G/hb9lrFcrRvn2wXTHKyuPzcUXJhSq0oiqIoSjwQ84KpNwgSSy4rnOkoZ3o5MAT4dZ90qr/IyxMLUHGxCKCmJiivZETbroMWpkDE2Hcxj4sYSk2F3NyO+83OloSVjY2QmSnziqIoihKnhCs1koic4kzXOdOTkKyMU40xHxljWo0x240xPzXGDBxziZuTadIkET5paXja6kmjGYshzUlaGUgdT/ItmDYNDjsstBiqqZFXU5PvvaIoiqLEKTFnYTLG/AQxY/zBWnvAmY8Ea629s5vHHAXcASyy1q50Fo9Ehob9CynP8j5wBnArkAf8T3eOFXP452RqbYUlS/DQQhHbaMbDCN4iZPzS8OES/zR4MKxZIwV3/QO/q6okr1NKigiyKs0IoSiKosQvMSeYkOBrCzyKWHhui3A7iwibqDDGZAHPIPXpvuS3KgkJ7r7ZWvt/zrLFxpgCJC/UbaHyQhljrgWuDXe8sWPHRtvF3sU/J9OiRVL7zY/agx5K//ilFgnmnjABzj5bYpUqKgIFU26uCDFjZBrKbacoiqIocUIsCqZTnem2oPkexxiTDjwLTABOsdb6Ry/vd6avBW32KnA9cASwLHif1tr7gPvCHXPmzJk9PeLv0MnPl9ezz4K1NJN6MK1AINL1r3K/uOEGDxaxlJwcmLQSYNQoGRVnrYimUaNQFEVRlHgl5gSTtfbNzuZ7CmNMKvAEcCxwhrX2o6AmH7tdCN7Umbb3Rr/6lfp6AJrxYDFkUReiUR1/5RYoukCyfI8Z0zFpJUgQ+cSJUFoq74uLe7//iqIoitJLJGTQtzEmCXgYOB24yFr7TohmTzvTs4OWnwU0Amt7rYP9RW0tAB6aaSGFybxCyPilWbNEAM2YIaIoWCwBbN0qLr6PP5bp1q2923dFURRF6UVizsLUR/wBSRvwM6DOyebtUm6tLbfWrjXGLATucATWB0jQ91eBO621tX3d6V7F65U4pKSkg7az0PFLiCsu2AUXzFtvibuusFD2+9ZbmrhSURRFiVsSVTCd40xvdl7+3I4v0Pw6YAfwTWAYsAX4jrX2t73fxT6mokISTKal0dyQRCqtBHoj5f1ZPAuXXQYtLR0Dvf0ZM0b25+53zJhePwVFURRF6S0SUjBZa8dF2K4ZuMV5DUxcy1JdHbz3HjQ04CEVg6WDZYk6Xuar8MplUj+uMyvTtGkwdCjs2CEB39Om9eZZKIqiKEqvkpCCSXHweuGxxyRP0kcfwe7dAHho4XJ+RciSeWeeCSNGwJw54a1LAK+9JvFL7e3imnvtNbg2bLYFRVEURYlpEjLoW3Fws3zn5Yl1qdYXlrWGE513QVamtDSpQdfVqLe33hK3Hcj0rbd6rNuKoiiK0tcMCMFkjDnDGPOt/u5H3OFm+X7zTRnF1hyqDAq48UsjWQfHHQcLFnRuXQIYPVpyMLW1yXT06B7tuqIoiqL0JQNCMAFXAb/p707EHfn5MG8e7N0roiaps9uhjh2cAo88Ii62rjjuOBlNl5kp0+OO66leK4qiKEqfozFMic7+/VJDLiNDYpqAl5hDyPglY6ChAVatErdcZ2Rnw8iREsOUlBS6QK+iKIqixAkqmBTIyoIjjoADB6CxkXPb/wok0yF+qaAAmprEjdcVOTkwebIU321tlXlFURRFiVNUMCU6BQUwbpxYjkAsQgz2a+DmYtonQiknJzJrUXGxpB6oqpLCu1oaRVEURYljVDAlMl6vDPcvKBC3WUqKYxEKbliH5QhIHgnDhkVmLcrPh/nzYdMmKCnpOkhcURRFUWKYgRL0rXSH0lLYuVNijYyR4rttbeHb19VFllIAfGJs0yaZOvFRiqIoihKPxKSFyRjzhSg3KemVjgxkvF5YuhQ2bIAPPpDEle3tTLIvErLg7ogR4oo75ZTIrEUVFVBTI/FRNTWdl1FRFEVRlBgnJgUTsJDAQmZdYaJsr1RUQHo6nHoq/OUvsG8ftLezkWOcBkEB3/v2wfDhkZc4SUmBd94Rq1VmJlxySY92X1EURVH6klgVTA+iAqh3cZNWlpb6UgskJ0MIj9wslkiephEjJCN4UVHX1qL9+2Wb3FxJiLl/f9epCBRFURQlRolJwWStXdDffRjw5OfD5ZfDG2/AsmXiomtqCiGYmniWr4HHAyedJDFO6l5TFEVREoyYFExKH5GfLy65Dz6APXv4ffnZBMYviZEvKy8Nxo2SDN8FBWKd6oqCArFaVVRIrbqCgl44AUVRFEXpG3SUXKLjDv+/+GK+Wf9dZ6HxmxoOtDtutZYWKaUSiXVp/36xWBkj0/37e6f/iqIoitIHxJxgMsZ81xgToi5HxNsfbYw5pyf7NGDxemHjRrH+DBsGZPitdEPIKqlszpRcSqmpkiYgkhQBO3bAunWwbZtMd+zohRNQFEVRlL4h5gQT8HOg1BjzQ2PMqEg2MMJZxpingBXAUb3aw4GA1wuPPQaLFsG//iXCpgNN/IT7yMtqFQtRWRl8+KFs15VoamuTNASDB8u0s/xOiqIoihLjxGIM01Tg/4C7gbuMMcuAt4CVwC7Ai1SGLQCmAMcDpwPDgf3AN4C/9H2344yKChExeXnwwgvibutAC1MG72fwpCFw1XxJETBpEpSXdx34PWGCuOMqK6Ww74QJvXMeiqIoitIHxJxgstZuAM43xpwAfB24FJhD6DQDbrDNeuAe4H5rbU2fdDTecdMKbNgA6ekUvfATOiasTCYnvZXmSUfC2WdLxu7yctmuq8Dv1lbJIN7UBGlpMq8oiqIocUrMCSYXa+0yYJkx5nrgZOAkYCxiWWoA9gIfAouttR/3W0fjFTetQGkp5OSw7Qk3R5J/wkpLan6O1I5rbZX2FRUilroK/K6uhr17pUZdVZXMK4qiKEqcErOCycWxGL3gvJSeJD8fZs6U2nDf8bcAiTEvm3JGpVWQlZ4lmbvz8zX/kqIoipKQxGLQt9LXhBRBdewcdikTs3bjyfLAQw9J0Hek5ORI3NLEiTLNyemx7iqKoihKXxPzFial/8iq3Q17UiTOqaVFRNM3vhGZlam4GE48UdxxubkyryiKoihxSswLJmPMPyJo1g5UA+uA5621u3q3VwMAr/dgPJIZDB0DvhF33f79sH07HHGEFNGNtCyKmxBz0ybJ4aSuPEVRFCWOiXnBBCzAN0LOhFhvg5a3GGNusdb+b293LG5xczC1tcmINy4G0uhweXfuhLFjYcwYGD5c8ilFUhbFPcbTT4uFae1aWLBARZOiKIoSt8RDDFMx8AySY+kWYC5wmDO91Vn+FHAccB2wB/iFMeaifuhr3+Bm6I4k43Yo3BxMo0dDTXAWBtGmF/MIjBsHU6fCkUfKqkjLooCMvlu5EvbskWlpaff6qiiKoigxQDxYmOYjeZimW2v962usB5YYYx4EVgFLrbX3GmNeRlxz30CE1sAi2Dp0+eXRW27cHEyrV4vLrAN1PMm3oHa2ZOp2k1X651Lyc+mFPb61gVNFURRFiVPiwcJ0LfBYkFg6iLV2O/CY086dfx44us962JdUVMCWLfDppzKtqIh+H/n5cOyxsGIFd701k5DxS8XFEqzd0NAxWaV/WZVwZVKKi2HWLHHlzZqlQd+KoihKXBMPFqZxQFUXbSqB8X7zW4Cs3ulOP7Nrl4xWa2mRYrhnny1D96Nl3z7IzOTWzRc7C4Lil9z9n3GGxDC5YmnjRolLcl164cqk5OdL3FKkiS4VRVEUJYaJB8FUAcwDftxJmzORWCaXPLoWWfHJhx+KaywrC+rqZP7kk6PfT0kJtLcjwd4urutsn1iWBg+GGTNE7Pi7AhsbpVlXZVI00aWiKIoyQIgHwfQE8C1jzEPATdbabe4KY8xYpEjvdOD/+W1zDLCxLzvZZ+TnS322lhYRPN0VJHl5YjnqQB2WI6AxG044wbd//0Dx8nJxs+XmqvVIURRFSQjiQTD9BAn6/hzwGWPMDmQk3DBgFJAMrHbaYYwZAbQA/+yPzvY6kyfLqLXKShE9kyd3bz8VFSFGyDlkZcGgQZCe7lvmBoq7VqXiYhVKiqIoSsIQ84LJWlttjDkB+AHwRWACUoQXYDPwIPBLa22j034XcEJ/9LVPSEmB2lqx9tTWynx3KCzE/PXbhAz4bm+Xorm5ub5lbrFejUlSFEVREpCYF0wA1tom4E7gTmNMNpADVDuFeROLffsChcy+fd3bT34+4KYJCAr4HjVKXG+jRnXcRoWSoiiKkoDEhWDyxxFJiSeUXJqbYc0anxWoubl7++mQCkACvsewVmKbTjtNUwEoiqIoikPcCCZjTCZwCTAD3yi4D4CnrLV1/di1vmXfPhgxQtxiFRXdszC5I964OGhFHdvm3gETJ8PnPqfWJEVRFEVxiAvBZIw5F3gAGEyg/8gCvzHGfMla+3y/dK6vmT5dgq537JBiuNOnR7+PigrMdScRMn4pN1fyOvln9VYURVGUBCfmM30bY44GnkSsSg8DXwbOcaYPO8sfN8Yc009d7Ftyc2HoUMjIkKl/PFOkFBYCQ5yZoPil5cvFAhVpkV1FURRFSQDiwcJ0M2JJmmOtfSdo3UJjzB+AxcBNwKV93Le+Z9UqccPl5cl01SoYP77LzQLIzwf8XXl+CSvT0qScibrjFEVRFOUg8SCY5iC15ILFEgDW2neNMY8DZ/Vtt/qJ5GTYvx8OHJCitsnJPbTjOmzqdKgZFL0AUxRFUZQBTjwIplxgexdttiGpBgY+2dmSVLKxURJLZmdHvQtjIGT80uTJMHIkeDyH3E1FURRFGUjEfAwTsBM4tos2M4Fdke7QGHOZMeYJY8xWY0yDMWa9MeZuJ8eT22acMcaGeeV171R6gJoaeTU3+95HjeuOC4pf2rtXRFhJyaH2UlEURVEGFPEgmF4ETjPG/MgYE+B/MsYkGWO+C5zhtIuU7wFtSNzT2cCfgK8Brxljgq/J3cDsoFf/5YGqqpL8S5mZMq061BrDfvFLo0fDpZeqS05RFEVRgogHl9ydwHzgZ8B1xpiliDVpOHASMA7YDdwVxT4vsNb6Rz2/aYw5gKQumAu87rduc7j4qX5h9GgYMkTEUnu7zB8yTsHd0RfCnDk9sD9FURRFGVjEvGCy1u42xpwI/AWYBxQFNXkNuN6pIRfpPkNle1zhTEeFWBc7zJghVqA9e2DYMJmPmn1IDJOfdem44+DYY2X0naIoiqIoAcS8YAKw1m4BzjLGjEIyfecimb5XWWt39NBhTnGm64KW322M+TNQB7wJ3Gyt/aiHjhk9+flwww2HVATX2sMx5hMkF9M+bMZMsFPFYlVRoSkFFEVRFCWIuBBMLo446imBdBBHiN0BLLLWrnQWNyFWrVcRk8wUJOZpmTHmWGttsLBy93UtcG24Y40dO/bQO9wDRXDtQ6tEeDU0SA3effskXYEmrFQURVGUDsSVYOoNjDFZwDOIbPiSu9xx8V3v13SpMeZl4GMkmebVofZnrb0PuC/c8WbOnGnDreszvF545BGoqxOrEkBKCixYoNYlRVEURQlBzAkmY8w/urmptdZ+JcpjpQPPAhOAU6y15V0cYLsx5i1gVjf7GBtUVEBqqiS9THIGBV52Wei6dF7vIbn/FEVRFGUgEHOCCVjQze0sELFgMsakAk8gOZ7OiCIuyeCLlo5PCgth3DgYPBja2mR6xhkd23m98LOfwaZNkpvp5ptVNCmKoigJSSwKpl5PAuTkWnoYOB04L9K0AcaYscCJwFO92L3eJz8fvvlNKa1SVyej7XJyRCD5C6KXXoK//U3cdosXw9FHw+c+12/dVhRFUZT+IuYEk7V2ax8c5g/A5UhupzpjzPF+68qtteXGmF8jiT2XI0Hfk4EfA+3Az/ugj73L+PHwk59AaSksXSriaP9+iWNyXXPvvCOZxI0RcfXOOyqYFEVRlIQk5gRTH3GOM73ZeflzO3AbEtz9NcRFmA1UIAktb7fWru+TXvY2+fmQmwu1tfDf/4o42rwZfvtbEVT+QeEg84qiKIqSgCSkYLLWjougzT+A7gagxwder5RW2bFDxNLw4TJabtMmEUwHDgS2D55XFEVRlAQhIQWTgoilxx6ToO/CQhg6FNLSpEadW3w3KaisXvC8oiiKoiQIKpgSlYoKsSplZYlr7vvfF/FUUuIrvltQELhN8LyiKIqiJAgqmBKVlBRYtQpaW+X9JZeIUCorg9deE+FUFFS2L3heURRFURIEFUyJhtcrI+O2bYOxYyE9XUbBtbbC6tUyci4lRVxzo0bJ1Fppk5ra371XFEVRlH5BBVMi4fXCwoWwYoWMjCsvh5EjJTbplFPgX/8SMTVqlLRvavKNkrMW8vL6q+eKoiiK0q+oYEokKiqgulpSCbgCyBixKN1/P6xfL+urquDII2HMGIlxam8XUZWe3t9noCiKoij9gg57SiQKCyWjd1UVtLSICGpqErdbaSls2SL15dLTJabJGMm9VFsL9fUirBRFURQlAdEnYCLgX0B3wQKYM0csSa+/DmvWwL59YnEaMkRE0uDBIp4++AAaGnwxTmvX9veZKIqiKEq/oIJpoOOfbyk5GS6/HGbOhI0bJVbpiCPg3XdFDCUlSbvJkyWFQFubiKX0dLFI7dnT32ejKIqiKP2CuuQGOhUVInxGj5ZpRYUsLywUAbVzp8xPnQpz58Lhh8O0aSKeJk2SZJbGwKBB4qZTFEVRlARELUwDHVcYlZfLtLBQlufnw7x58NBDEsP0yitiRaqulnil446DL31JrE1LlsA558B55/XvuSiKoihKP6GCaaCTny9uODeGKT/ft661VZYlJcm0uVksSYMGybrqanjrLYljeuUVOO00XxZwRVEURUkgVDAlAvn5gUIJfIV3DxwQYZSeLu63ujqxNOXkiFXq009l3fbtkhlcBZOiKIqSgKhgSkTcQPCaGsnuXVwMxx4Ls2fL+pwcWfbii7Bjh1ig2tvF0qQoiqIoCYgKpkTEDQTPypK4pvHjRRCNGQMTJ/raZWTISLq0NMnXlJHRf31WFEVRlH5ER8klIm4geG2tJKOsrQ0MCHeZMUPE1KBBMp0xo3/6qyiKoij9jFqYEhH/QPBLLvEFfwfHOeXliatuzx4YNkxrySmKoigJiwqmRCVUIHgwFRXiqisslGlFRdfbKIqiKMoARAWTEp66OnjkEXHZZWXBBRf0d48URVEUpV/QGKZExOuV0iheb+fLli+X1AI1NTJdvjx8W0VRFEUZwKiFKdEIVVsOOi7Lz4e9eyWZZXKyrNu7N/T26qZTFEVRBjhqYUoUXKtQaWnH2nKh6s15vb5ivE1NEsM0YkT42nSKoiiKMoBRC1Mi4G8VamyUZcG15fzrzaWkSPuPP5Z1xvjEU7jadIqiKIoygFHBlAj4W4XKy2HWrI5tjj5apsXFvvb19VImBWS6cWPntekURVEUZYCigikRCLYKFRTA009LLbnUVMjMlHpxyckimNz2rrstNVUEVHW1zEeSkkBRFEVRBhAaw5QI5OfDvHlQUiLT/fth5UpJSPnOOzL1j0lyrUjnnSfuONfK5NaaUxRFUZQEQy1MiYDXC6+9JoKorEyEk7WyLi1N4pOCY5Ly86W2XG6uZAJPSRFLU7THVdedoiiKMgBQwZQIBMcw5eRIHFN1NUyaBPPnhy6Psm+fjJBLSpLpvn2RH1PTDyiKoigDCBVMiUBhoYyOe/99sRgVFMCcObKuuDi8kMnMFLHU3i7TzMzIjxks0rSsiqIoihLHqGBKJIyRkW9PPx0Y5B2M60prapL4JddKlJwc+bE0/YCiKIoygFDBlAhUVIhAKimBDz6Q0XElJaEtP/6utJdeElcdyHTz5siPqekHFEVRlAGECqZEwN/ak5Mjy8JZfvxdafX14o5zqayM7riafkBRFEUZIKhgSgT8rT0pKZJWoLraJ5788RdXmZnixjNGRtXl5fV51xVFURQlFlDBlCi4lp7HHoOaGli1CmbMEBed/wg2f3HV1ARvvOFLKzBhQv/1X1EURVH6ERVMiYTrbsvKEhGUlRWYrNLFdaXt3RtYGmXLln7ptqIoiqL0N5rpO5Fw3W21tWIxqq3tfATbunWdzyuKoihKgqAWpkTC3912ySWhk1X6k57e+byiKIqiJAgqmBKNaEauDRvW+byiKIqiJAgqmAYyh1rLbcwYqTXnP9+Xx1cURVGUGEEF00AlVC03CBQwXQmaGTNg5EjJx5SZKfOHcnwVTYqiKEqckpCCyRhzGfBZYCYwFNgGPAn83FpbE2abvwDXAg9ba6/uq752G/8ElBs2SHqArVslEWV9PVxwAbz3XueCxg0MdxNYpkRxu1RUSPqCrCyZai05RVEUJY5JSMEEfA8RSTcB5cAM4DbgVGPMCdbadv/GxpgTgKuA6j7uZ/dxR8Rt2CA5l/btg02bJPlkba0krxwzBiZNCl8c9403JLt3UpJM33gDTj45suOnpMhx3RxOl1zSwyeoKIqiKH1HogqmC6y1+/zm3zTGHAAeAOYCr7srjDGpwH3Az4Dr+rKTh4Q7Im7lSplPTobFi0UoHX44FBSI5aiz4rg7d0r+paQksTDt3Bn58VtbxYWXlSUCza1JpyiKoihxSEIKpiCx5LLCmY4KWv59IBn4NfEkmEBE08yZ4np75hloaBD32OzZMuJt3rzOUwuMHClB365gGjky8mMXFkJ2trj8srPD53pSFEVRlDggIQVTGE5xpgezMxpjioFbgPOstc3GmH7p2CGRnw+HHQbLlsl082Zxw110UdcxRRddBI8/LiIrO1vmozmum/NJR8kpiqIocY4KJsAYMwq4A1hkrV3pt+rPwJPW2jf6p2c9xIwZIljcgrqnnBKZgJk+Hf7wBxFbJ5wg89EQTc4nRVEURYlhEl4wGWOygGeAVuBLfsuvBmYBU6Lc37XIaLqQjB07tnsdPRTGj4c775Sg75ISmY8ErxeWLoXdu2U6daoKIEVRFCUhSWjBZIxJB54FJgCnWGvLneVZwP8B9wCNxpg8Z5MkINWZr7PWtgTv01p7HxIkHpKZM2fanjyHiBk/PnKh5LJqFfznP77UA7Nnw2mn9U7/FEVRFCWGSdjiu87otyeAY4FzrbUf+a0uBIYAPwe8fq8xwBXO+/P6tMP9wfr1UFYG27fLdP36/u6RoiiKovQLCWlhMsYkAQ8DpyMB3e8ENdkNnBpi00eAj5AUA2t7tZOxQEUF1NWBtWCMzCuKoihKApKQggn4A3A5InzqjDHH+60rd1xzi4M3MsY0AnustR3WxR2rV8tr+vTwwdw7dkg6ARDRtGNH3/RNURRFUWKMRBVM5zjTm52XP7cjWb8HLqtXw3XX+bJw/+UvPtHkX1+uoSFwu927Zb0GfiuKoigJRkIKJmvtuL7cLuZYvVrEUkmJjJxzLU3BBXODa8ft3y/rtZCuoiiKkmAkbNB3QjN9uoihTZtk6lqX/Av2trV1jFlKTQ29XFEURVEGOAlpYUp4pk8XN1xwDJNbsNetL9fUFLjd3r3h684piqIoygBGBVOiEirYO7icyQMPBK73eNQdpyiKoiQkKpiUQPzLmWRlBa4bMkTFkqIoipKQaAyTEp4RIzqfVxRFUZQEQQWTEp7m5s7nFUVRFCVBUMGkhKe1tfN5RVEURUkQVDAp4RkzpvN5RVEURUkQVDAp4WlpkakxgfOKoiiKkmCoYFLCk5wsU2sD5yPF64WNG2WqKIqiKHGMse7DUOkTZs6caVeuXNnf3YiMzMzAenIZGVBfH9m2wWVWNH+ToihKj2OMed9aO7O/+5EIqIVJCU9w8d3g+c7oqsyKoiiKosQRmrhSCeTRR+HZZ2HmTLEMtbX51gUX4+2M4DIrWk5FURRFiWNUMCk+Hn0UvvAFybf0r39BenqgYJoyxffe6/WVUAnlagsus6LuOEVRFCWOUcGk+Pjb3wKTUzY2Bq7Py5NppPFJ/mVWFEVRFCWO0RgmxUdNTefrXZdcpPFJOkpOURRFGSCohUnx4eZbCoc7ojKS+CQdJacoiqIMIFQwKT66Ekzjx8s0OD6pshJWroSSEl8bfytUebnMq2BSFEVR4hQVTIqPE06A5cvDr1+zxvfejU8qK4Nbb5U6cykpcOedIpp0lJyiKIoygFDBpPgYMQJSU8Uy1N7ecf3WrR2XbdokYmniRIlX2rRJBJOOklMURVEGEBr0rfioqZF6caHEEsC8eR2XlZSIZWnjRpmWlPjWVVbCli0yVRRFUZQ4Rkuj9DExXRolKckX2O0ycqQIqXPPhUceCb1dWZlYlvxjmMK56hRFUZQeQ0uj9B3qklN8hBLPCxeGtiz5M358RzEUzlWnKIqiKHGIuuSUzlmxIjCPUqS5lTpz1SmKoihKnKEWJsXH00/D/PmBy15/XYK2L79c5iPNrTR+vLjhgl11iqIoihKHqIVJ8XHyyXDccYHLsrN92bwjzfDtMn68uPNULCmKoihxjgomxUd+PowdK++Tk2W6Y4cvj5LmVlIURVESFHXJKYGMGSPTtjaZHnmkzx1XUSEWo9bWyHIrhRo9pyiKoihxiKYV6GNiOq0AhC6PcuBA9HXhNK1AWKqrq9m7dy8tLS393RVFUWKQlJQU0tPTGTJkCOnp6Z221bQCfYdamJSu6U5dOE0rEJLq6mr27NnDqFGjyMjIwHRVv09RlITCWktrayu1tbVs27aNYcOGkZub29/dUlDBpERCd2KXNK1ASPbu3cuoUaPIzMzs764oihKDGGNITU0lPz+ftLQ0du/erYIpRtCgbyWQ008PnD/5ZF9duDPO8MUz+edicnMzlZX5lo8fD9deC9Ony1StSwC0tLSQkZHR393oEZYvX84VV1zByJEj8Xg8FBQUMG/ePB544AHanBi4hQsXYoxhy5YtPX78xYsXc9ttt9EerpTPAGLLli0YY1i4cGGP7fPnP/85Y8eOJSUlhenTp/fYfnsaY0zAKy8vj2OPPZZ//etfnbZNSUlh/PjxfOlLX6K8vLxD269+9asYY/jOd74T9thPP/00J598MkOHDiUjI4OioiLmz5/Pyy+/HNDuzTff5Oyzz2bkyJGkp6czevRozj77bB5++OFDOveMjAyampoOaR9Kz6GCSQlk167A+U8/FQGUny+WpdJSyf797LPw+9/D6tUS3/TssxKz9OyzMr96Nfy//wdLl8q0rKwfTiY2GQhuuHvvvZcTTzyRAwcOcM8997Bo0SL+8Y9/MGnSJL72ta/x/PPP93ofFi9ezO23354Qgqmnee+997j55pu58sorWbJkCf/85z/7u0udsmDBApYvX87y5cv597//TXFxMVdddRWPP/542LaLFy/mu9/9Ls8++yynn346DQ0NB9s0NDTw2GOPAfDwww/T2traYT+/+93vuPjii5k4cSJ///vfeeGFF7jlllsAeP311w+2e/rppzn11FNJT0/n97//PS+//DI/+9nPKCws5MUXXzyk8x4IvxUDCXXJKYEEm34zM335lh57TATVRx9BXh7U1sL+/TKyLitLYpaysmDdOnj+eVi/HiZMELG0apVamQYIS5Ys4Tvf+Q7f+MY3+N3vfhew7qKLLuI73/kOdXV1/dS7Q6OpqYm0tLT+7kavs27dOgCuv/56JkyYELZdW1sb1lpSUvr3UTFq1CiOP/74g/NnnXUWb7/9No899hiXXXZZ2LYnnXQS2dnZLFiwgJdeeolLLrkEgKeeeorq6mrOPfdcXnzxRV5++WXOP//8gP386le/Yv78+fz9738/uOy0007jmmuuCRDp//d//8eMGTN46qmnAgTOF7/4RRXzAwy1MCmBBD8samrEsuQGfk+aBE1NIpwKCuS1f7+IorY2sSw9+CAsWyaB3qtXQ1WVL6+TEvf84he/YPDgwfzyl78Mub64uJhp06aF3d4Yw2233RawLJTLacWKFcybN4+CggIyMzOZMGECN9xwAwC33XYbt99+OwCpqakH3TAu9fX1/PCHP2T8+PF4PB7Gjx/Pz372s4AH2OLFizHG8OSTT3LNNdcwZMgQhg0bFrbfrntx2bJlXHHFFWRnZzNs2DDuvvtuAF5++WVmzJjBoEGDmDVrFu+//37A9q+++irnnnsuI0aMIDMzkyOPPJJf//rXB92X/n2/4YYbKCgoICsriwsvvDCkSwnEFXT66aeTnZ3NoEGDOOuss1i7dm3YcwCYO3cuCxYsAOSz8v88jDHcfPPN/OIXvzh47T766CMAHnroIY466ijS09MpLCzk85//PLuCLNLjxo3j6quv5p///CeTJ08mIyODOXPmsHHjRurq6rjuuusoKChg2LBhfPe73w1p2YmEpKQksrKyIhppOmvWLAA2bdp0cNkDDzxAfn4+CxcuJCMjgwcffLDDdgcOHGD48OFhj+/fbujQoSGtQf7tlPhHLUxKIIsXB87v3w9PPgmXXCKip7ISjj8e6upELCUliYCqrYWiIti9G1JTISdHtt2/HxoaYMkSXzyUEre0tbWxePFi5s+f3+Vw50OhtraWs846i2OPPZaFCxeSnZ3Nli1bWLZsGSDxJ+Xl5fz973/nrbfeItlPkLe2tnLWWWfxySefcOuttzJ16lTeeecd7rzzTg4cOMCvf/3rgGN985vf5JxzzuGf//wnjY2NXfbti1/8Il/4whe49tpreeyxx7jpppuorKzkxRdf5OabbyYrK4sf/OAHzJ8/n9LSUjweDwCbN2/m9NNP55vf/Cbp6emsXLmS2267jX379vGLX/zi4P6vu+46Hn30UX76058ya9YsXnvtNT73uc916McLL7zARRddxHnnncdDDz0EwD333MOcOXP48MMPGePmVAvij3/8Iw899BB33303Tz75JCNGjGD06NEH1y9cuJAJEybwq1/9ikGDBjFy5Ejuu+8+rrvuOj7zmc9w9913s3PnTm666SbeffddPvjgA7Kysg5uv2TJEkpLS7nnnntobm7m29/+NpdeeikTJkygpKSERx55hCVLlnDXXXdRXFx8UAR3hjtyDMDr9bJw4ULWrVvHT3/60y63LXPCAfLy8gDYuXMnixYt4tprr2XIkCHMnz+fJ598Eq/XS77f79Oxxx7LAw88wIQJE7jooouYNGlSyP277W655RauuOIKpk6dqq60gYq1Vl99+DrmmGNsTAMdX9OmyboDB6zdsEGm7vunn7b2/POtvewyaw8/3NpTT7U2Pd3a1FTZdtgwa8eOtfaKK6x99VXZLoH55JNPemZH/p9FH7J7924L2B/96EcRtb///vstYMvKyg4uA+xPf/rTgHZlZWUWsPfff7+11toVK1ZYwK5Zsybsvn/6059awLa0tAQsf/DBBy1g33zzzYDld911l01NTbV79uyx1lr7xhtvWMDOnz8/qnO5/fbbDy5raWmxQ4YMsSkpKXbz5s0Hlz/zzDMWsIsXLw65r/b2dtvS0mLvuusum5eXZ9va2qy11n766ac2KSnJ3n333QHtr7/++oDrY621xcXF9rTTTgtoV1VVZQsKCuyNN97Y6bn89a9/7fC5WCufzYgRI2x9ff3BZa2trXbo0KF27ty5AW2XLl1qAfvb3/724LKioiKbn59vKysrDy777W9/awH7la98JWD7GTNmdNhnKIAOr6SkJHvHHXeEbHvTTTfZlpYW29DQYJcvX26nTJliMzMz7Y4dO6y11v7iF7+wgF22bJm11tqXX37ZAvZPf/pTwL7Wr19vp06devCYBQUF9sorr7SvvPJKQLs9e/bYOXPmHGyXk5NjL7roIvvoo492eW6R0NVvBrDSxsCzLRFeai9UuqamRqb5+ZJXKT/fFwT+7rvijluyBLZuFTdcWpq8kpIkYPzAAbE8ffihxEG5o+uU7uH1ynVctGjAXs+JEyeSl5fHddddx0MPPcT27dsj3vbll1+mqKiIE044gdbW1oOvM888k5aWFt55552A9hdffHFUfTvnnHMOvk9JSaGkpIRJkyYx3i9Gb8qUKQAB/d61axfXXXcdRUVFeDweUlNTueWWW6isrGTv3r0AvPvuu7S3t3PFFVcEHPPKK68MmN+4cSOlpaVcddVVAeeYmZnJ7NmzWbJkSVTn5M/ZZ58dMJJz/fr17N27l6uuuiqg3UknnURRURFvvvlmwPLZs2cHDIN3r8VZZ50V0G7KlCkRf65f/vKXWbFiBStWrOD111/n5ptv5o477uB///d/O7T9+c9/TmpqKhkZGcyePZvU1FRefPFFRo4cCcCDDz7IxIkTmT17NgBnnHEGI0eO7OCWmzRpEqtWreLNN9/k5ptvZvr06Tz11FOcddZZ3HXXXQfbDR06lCVLlvDee+9xxx13MGfOHBYtWsRnPvMZrrnmmojOT4kP1CWnBGJtx2zfBQWh27rB4K2t4pJrboa9e8VFl5Ym+Zeys0VYpafDyJHi0istleDySMqrKB3pTiLRHqKgoICMjAy2bt3aq8fJzc3ljTfe4M477+SGG26gpqaGI444gttvv51LL72002337t3L1q1bSU1NDbl+//79AfMjRoyIqm/5Qdfa4/GEXAYcdPG1t7dz4YUXsnPnTm677TamTJlCRkYGTz/9ND/72c8OtnNjgoJjqYLnXYH1la98ha985Ssd+jjWrQnZDYKvx4EDB0IuBxg+fPjB9S7hrkWo5ZG4QN1jz5zpS2Z96qmnsn//fm699Va++tWvBuz7y1/+Ml/72tdISUlhzJgxFPj9fq1YsYJPPvmEH/7wh1RWVh5cfskll/D73/+eDRs2BLjekpOTOfnkkzn55JMBceedffbZ3H777Xz9618POO6sWbMOxktVVlZy2WWX8be//Y0bb7yRI488MqLzVGIbFUxKR6ZNE2uQS3OzWDEqKgJFTkoKrF0LbuClMb73zc0ivtrbRRylpcGGDRLbtHSpCKhIy6wogfRjEeSUlBTmzp3La6+91u0RZWlpaTQ3NwcsCxYxANOnT+eJJ56gtbWVlStXcvfdd3PFFVewZs2aTh9ABQUFjB8/nv/85z8h148bNy5gvi/iTUpLS1m5ciX//Oc/ufrqqw8uf+655wLauaJkz549AaPX9uzZE9DOFQF33303Z5xxRofjuSKlOwRfj8GDBwOwe/fuDm13794dIGT6kiOOOOL/t3fn4VFV5wPHv282ICQKhB1NAgiIomIbAYuyiIhg2X5qpAKCgoKUqsVawSiggoCyVKpWAQUMIHVBtIAbymYBBbW2IljKJoKAssgqScj7++PciZPJJJNAyCTwfp7nPpOce+695965ybxztsvx48f573//S/PmzXPSA4MrfzNnzgRcX69x48blWf/yyy/nqj0KVLt2bfr378+9997Lxo0badasWdB8lSpV4p577uHDDz/k66+/toDpDGFNciavwIkVfU1pgU1Ae/e6WqO4OLdNRITr8B0Z6YKpqCi3LiLC5bv6areUL++mJdi8GZYsOSOblE6rwIlESzjgHDp0KHv37uWBBx4Iun7Lli382z/gDpCUlJRnJNfChQvzzR8VFUWLFi14/PHHyc7OzhkS7wvW/OfXAdektH37duLi4khJScmzVC3BANPn6NGjALlqvTIzM/NMbNi8eXMiIiLyBHtz587N9XujRo1ITk5m3bp1Qc+xoFGKRdWoUSNq1KiRpwwrV65k27ZttG7dutiOVRS+e6xatWqFyp+RkcHcuXNp3rw5S5YsybM0bdqU9PR0XLcg8m0u3LBhA0DOCLpQ+Ypag2lKr7OyhklEbgJ+B6QA1YFvgXnAE6p6yMvza2A0cAmQABwAPgceV9VVYSh2yVm3Lm/agAEwZYprfvM1xa1YATt2uLR69eDnn13w89NPLkg6eNAFVXv2QOPGrn9T+/Yu3yuvuKa5Tz918zYNGmQ1TUXh60cWBq1atWLixIkMGTKE9evX07dvXxITE9m/fz8ffvgh06ZNY86cOfl+aPfo0YNRo0YxevRoWrRowYoVK3jllVdy5VmwYAFTpkyhW7du1K1blyNHjjB58mTi4+Nz+p5cdNFFAEyYMIGOHTsSGRlJSkoKPXv2ZPr06bRr147777+fyy67jIyMDDZt2sTbb7/N/PnzS/zRNI0bNyYpKYm0tDQiIyOJjo5m0qRJefI1atSIW2+9leHDh5OdnZ0zSi5wAkQR4dlnn6Vr165kZGSQmppK1apV2b17NytXriQxMbHAGayLIjIykscee4wBAwbQq1cvevXqxY4dO0hLS6NBgwbcfvvtxXKcguzYsSOn79mhQ4dYunQp06ZNo1OnTgXOI+VvwYIF7N27lwkTJtCmTZs86wcMGMDdd9/N0qVLadu2LU2aNKFt27Z0796dunXrcvDgQRYtWsTzzz9PampqTrNnp06dqF69OrfccguNGjXi2LFjLF++nAkTJnDllVfSsmXLYrsOJszC3es8HAuwGngV6Am0Bu7DBUSrgQgvTzvgGaCHl+dmYCWQATQ72WOX+lFyqhp0pByonnOO6h13uJFZa9aojhih+sgjqjVrqtar50bTTZqk2qeP6hVXqJYrp1qtmnvt3l31uefcdjNnqrZooVq3rmr9+qrXXKP6xhtnxQi6YhslVwr885//1Jtuuklr1qypUVFRWrlyZW3fvr2mp6fnjPoKNkru2LFjes8992jNmjU1Li5OU1NT9ZNPPsk1CmzDhg2ampqqycnJWq5cOa1atap27NhRV69enbOfrKwsHTRokFarVk1FRN2/s1+OMWLECG3UqJHGxMRo5cqVNSUlRUeMGJEzqs43Su6DDz4o1Pn6zmXjxo250lu3bq0tW7bMleYb9Td16tSctC+++EJbtmypFSpU0Dp16ugjjzwSdLTakSNHdODAgVq5cmWtWLGidu7cWT/++OM8o+RUVVeuXKk33HCDVqpUScuVK6dJSUl6yy235IwAy09Bo+TS0tKCbpOenq6XXnqpxsTEaJUqVbRXr166c+fOXHmSkpK0Z8+eudLyu859+vTROnXqFFhOX5n8l9jYWL344ot19OjReuTIkUKXv0uXLhofH59nG58DBw5ohQoVtE+fPqqq+re//U07d+6siYmJWq5cOY2NjdWmTZvquHHj9Pjx4znbzZ07V2+++WatV6+eVqhQQcuXL6+NGzfWYcOG6cGDB0OeXyg2Sq70LOKu99lFRKqp6g8BabcBM4F2qvpRPtvFAz8CU1T1Dydz7JSUFF27du3JbFpyQvXpaNECLroIfvgBvv0Wtm+H+vVdv6UhQ9y6d96B8eNdWlYWpKS4JqQTJ+DoUXj/fVfTBK6JrksXV0t1hvdpWr9+PY0bNw53MYwxZUSo/xki8pmqhqcj2VnmrGySCwyWPGu81zoFbHoEOA6Enl62LKtRAwI6meayerVbzj/fderOzHQzf59zjpvZ+8gR1yR34YWuY3LlynDZZS7A2rPH9XGqVcuNphP5Zf2BAyU64ssYY4wprLMyYMqHr+fiev9EEYkAIoFawFAveVoJlqvkrV8P3siYAm3f7gKj2FgXNFWs6GqLIiLc3E0XXOACouhoN9t3lSpuTqZDh6BaNbjnHjftwJdfws6d8Pnnblbx+Hjo1w9uuOH0n6sxxhhTCBYwASJSB3gMWKyqge1lrwK+iV/2AJ1U9esC9nUXcFd+609lfpQS8+OP0KSJmzIglOxsNwfT4cOuVmrdOvjVr1wz3KZN7jUz0014uWmTGzVXrRrUqeMCo++/d53HP/vMNe/5fPABzJ1rQZMxxphS4awPmEQkDngLyAKCDff4MzAOOB/4PbBARK4NElgBoKpTgCn5HS8lJaX0dxqrWtXVCJ2Mo0fh44/dNAI//OCmFjhxwgVSNWq44OngQfjiCzfqLr/jZGS4KQcsYDLGGFMKnNUBk4iUB94G6gGtVTXPI8FVdTOwGVgjIguAr4BRwPUlWdYSVbmyaz7btOnk9+Gb5M73NPJPPy3a9jEx0LbtyR/fGGOMKUZn7cSVIhINvAE0wzWz/SfUNqqaAfwbuOA0Fy/8Tpw4te2zs09uu8qV4brrrDnOGGNMqXJW1jB5Hbln4+ZaukFVV4fYxLddLG6yy29OY/FKh5tvdp2wS0psLNx1l+sI7vcQU2OMMaY0OCsDJuBZ3ESUo4EjItLCb913qvqdiLwA7APW4uZeSgIG40bL9S7h8pa8od6AwPHj3XxJFSu6KQFOh+uug3HjoGnT07N/Y4wx5hSdrU1yHb3XNGBVwNLfW/cJbqqBKcB7wAjgf0CKqq4o0dKGy9ChbsTc4cO/TDJZnC6+GBYsgPfes2DJGGNMqXZW1jCpanIh8rwEvHT6S1MG/OtfbmRbcRCBzp1hxgyboNIYY0yZcbbWMJnC2rIFJk1ys3OfqhtucJ3B33rLgqUzwKpVq0hNTaV27drExMSQkJBA+/btmTlzJie8QQMzZsxARNi6dWuxH3/p0qWMHDmS7JMdYFCGbN26FRFhxowZxbbPJ554gsTERKKiomhaimt4RQQR4aGHHsqzTlWpV68eIkKvXr1y0n3Xa9q03HMMz58/n1atWlG9enUqVKhAUlIS3bp149133wUgOTk553gFLadi4sSJdO7cmVq1aiEijBw58pT2Z0qOBUwmf1u2wNNPuykCzj8/97rISGjY0HXWrlQp9L7uvts1v5kzwl/+8hdatmzJvn37GDduHIsXL+all16iYcOG3H333Swogfd66dKlPProo2dFwFTcPv30U9LS0ujRowfLly8nPT093EUqUHx8PLNnzybw2acrVqxg69atVKxYMeQ+Jk+eTPfu3WnQoAEvvvgiCxcu5OGHHwbgo4/c40PffPNNVq1albNcfvnlXHrppbnSVq1adUrnMnXqVPbs2UO3bt1OaT+m5J2VTXKmEPbvh1mz3OzbBw64PkxJSe5xJ61bwwsvuGfGRUVB48awdaubtTtQ3bpw6aXwwAMlfQbmNFm+fDlDhgxh8ODBTJ48Ode6rl27MmTIEI4cORKm0p2a48ePU65cuXAX47Rbv949AWrgwIHUq1cv33wnTpxAVYmKCu9HRffu3Zk1axbLli2jTZs2Oekvv/wyrVu3ZsuWLSH3MX78eLp168aLL76Yk3bNNddw55135gTdl19+ea5tzjnnHLKysmjRogXFZd26dURERJCVlcXzzz9fbPs1p5/VMJngfvzR1R7VquWCovh4uPZaFzytXu1m7I6NhfPOg9694e234c47f9m+fn3YvNkFVpMm2VQBZ5CxY8dSpUoVnnzyyaDr69evz6WXXprv9sGaIYI1Oa1Zs4b27duTkJBAbGws9erVY9CgQQCMHDmSRx99FIDo6Og8TSVHjx7lwQcfpG7dusTExFC3bl1Gjx6dqzZq6dKliAjz5s3jzjvvpFq1atSoUSPfcvuaF1euXElqairx8fHUqFGDMWPGAPDuu+9y+eWXU7FiRa644go+++yzXNu///77dOrUiVq1ahEbG0uTJk2YMGFCTvOlf9kHDRpEQkICcXFxdOnShe++yzOnLgDLli2jXbt2xMfHU7FiRTp06MBXIR5p1KZNG/r27Qu498r//RAR0tLSGDt2bM61+89/3BR1s2bN4rLLLqN8+fJUrVqV3r17833Al6Tk5GR69epFeno6jRo1okKFClx99dVs3LiRI0eOMGDAABISEqhRowb3338/Wb6JbUM4//zzad26da6asJ9//pnXX3+d2267rVD72LdvHzVr1gy6LiKiaB+Fr776KiLCv//97zzrOnbsWGATZ1GPZUoPe+dMcFWruiCpZk33HLj69V3T3LFjbnqBiAj3oN369eGKKyAlxT3qRNUt//ufC5Lat7dg6Qxy4sQJli5dynXXXUf58uVP23EOHz5Mhw4diIyMZMaMGSxatIjhw4fnfMD279+ffv36AfDxxx/nairJysqiQ4cOTJs2jXvvvZd33nmH/v378/jjj/NAkJrOP/zhD6gq6enpheoj1KdPHy655BLefPNNunXrxkMPPcSDDz7IAw88wIMPPsjf//53jhw5Qrdu3cjIyMjZbvPmzbRr146XXnqJhQsX0qdPH0aOHElaWlqu/Q8YMIBp06YxZMgQ5s2bR6NGjbj11lvzlGPhwoW0a9eOuLg4Zs2axZw5czh06BBXX30127dvz7f8zz33HMOGDQNg3rx5rFq1iv79++esnzFjBgsXLmT8+PEsXLiQ2rVrM2XKFHr37k3jxo2ZN28eY8eO5b333qN169YcPnw41/6XL1/Oc889x7hx45g5cyabNm3ixhtvpGfPnsTHxzN37lzuuusuJk6cyJQp+T5FKo/bbruN119/nWPe45Tmz59PZmYmN910U6G2b9asGTNnzuSpp57iv//9b6GPG0yXLl0499xzmTVrVq703bt3s3jxYnr3PvNnnjkrqaotJbj8+te/1jJj82bVxx5THT9e9eGHVe+9VzUpSbVmTdWEBNWWLVWHDVPdty/cJS0zvv7662LZz/HjqocOudeStGvXLgV06NChhco/ffp0BXTLli05aYCOGDEiV74tW7YooNOnT1dV1TVr1iigX375Zb77HjFihAKamZmZK/3ll19WQJctW5YrfdSoURodHa27d+9WVdUlS5YooN26dSvSuTz66KM5aZmZmVqtWjWNiorSzZs356S/9dZbCujSpUuD7is7O1szMzN11KhRWqlSJT1x4oSqqm7YsEEjIiJ0zJgxufIPHDgw1/VRVa1fv75ec801ufL99NNPmpCQoPfee2+B5zJ16tQ874uqe29q1aqlR48ezUnLysrS6tWra5s2bXLlXbFihQL69NNP56QlJSVp5cqV9cCBAzlpTz/9tALar1+/XNtffvnlefYZDKBpaWl66NAhjY2N1VdeeUVVVTt27Ki33nprznF79uyZs43vfpo6dWpO2jfffKOXXHKJAgpoQkKC9ujRQ9977718j926dWtt2bJl0HX9+/fXOnXq5Lx3qqqTJk3SyMhI3blzZ8jzyszMDPq3ECjU/wxgrZaCz7azYbEaJpO/rCxX01S7NqxfD6tWuYfixsZCtWqudunOO23EWwnLyIBt22DnTvfqV4lxxmjQoAGVKlViwIABzJo1q8Aak0DvvvsuSUlJ/OY3vyErKytnue6668jMzGT16twT+3fv3r1IZevYsWPOz1FRUVxwwQU0bNiQun41qRdeeCFArnJ///33DBgwgKSkJGJiYoiOjubhhx/mwIED7PEmhf3kk0/Izs4mNTU11zF79OiR6/eNGzeyadMmevbsmescY2NjufLKK1m+fHmRzsnf9ddfT4UKFXJ+/+abb9izZw89e/bMle+qq64iKSmJZcuW5Uq/8sorOffcc/Nciw4dOuTKd+GFFxbpfY2Li6N79+6kp6eza9cu3n///UI3xwE0bNiQL774gmXLlpGWlkbTpk1588036dChA6NGjSr0fnx69+7Njh07cjqMA6Snp3PttddSq1atIu/PlH4WMJn8Va3qRsN98w0cPw6JiW7G75gYF0T98Y/W3BYGGRmu1TMuzr2WZMCUkJBAhQoV2LZt22k9zrnnnsuSJUuoXbs2gwYNIjExkSZNmvDGG2+E3HbPnj1s27aN6OjoXEuzZs0A2Lt3b678Rf1wqxzwBSEmJiZoGrh+NgDZ2dl06dKFBQsW8PDDD/PRRx+xZs2anOY4Xz5fn6DAvlSBv/sCrH79+uU5zwULFuQ5x6IIvB779u0Lmg5Qs2bNnPU++V2LYOk/F3FC3Ntuu43333+fSZMmUb16da699toibR8ZGUmrVq0YNWoUixcvZvPmzVxyySU8+uij7N+/v0j7uvrqq0lOTs7pV7V+/Xo+//xza447g9koOZO/ypXdM+U2bYLoaFi3zo2Iq10bBg602bnDJCbGzf95+LB79T6PSkRUVBRt2rThgw8+OOkRZeXKlcvVtwfyBjEATZs25Y033iArK4u1a9cyZswYUlNT+fLLL2nSpEm++09ISKBu3bq8+uqrQdcnJyfn+v1U59UpjE2bNrF27VrS09NzzRf0j3/8I1c+X1Cye/fuXKPXdu/enStfQkICAGPGjAkaNMScwk0ReD2qVKkCwK5du/Lk3bVrFykpKSd9rKK69tprqV69OuPHj2fIkCFEnuL8cLVr16Z///7ce++9bNy4MSeoLgzf3E9/+ctf+Nvf/kZ6enpOLZg5M1kNkylY5cquQ/egQTBkCAwfDmPGWLAURjExboaH2rXda0kGTABDhw5l7969QTtQA2zZsiXo6CGfpKSkPCO5Fi5cmG/+qKgoWrRoweOPP052dnbOkHhfsObrBOxz/fXXs337duLi4khJScmzVK1atVDnWZyOHj0KuBF9PpmZmcyePTtXvubNmxMREZEn2Js7d26u3xs1akRycjLr1q0Leo4FjVIsqkaNGlGjRo08ZVi5ciXbtm2jdevWxXasUCIiInjkkUfo3Lkzd9xxR5G2za/5b8OGDQD5jqArSO/evTl8+DDz5s1j9uzZ3HjjjcTGxhZ5P6ZssBomUzi+wMmUCjExJR8o+bRq1YqJEycyZMgQ1q9fT9++fUlMTGT//v18+OGHTJs2jTlz5uT7od2jRw9GjRrF6NGjadGiBStWrOCVV17JlWfBggVMmTKFbt26UbduXY4cOcLkyZOJj4/nyiuvBOCiiy4CYMKECXTs2JHIyEhSUlLo2bMn06dPp127dtx///1cdtllZGRksGnTJt5++23mz59f4h9qjRs3JikpibS0NCIjI4mOjmbSpEl58vlGxA0fPpzs7GyuuOIKPvjgAxYtWpQrn4jw7LPP0rVrVzIyMkhNTaVq1ars3r2blStXkpiYyJAhQ4ql7JGRkTz22GMMGDCAXr160atXL3bs2EFaWhoNGjTg9ttvL5bjFNbAgQMZOHBgkbdr0qQJbdu2pXv37tStW5eDBw+yaNEinn/+eVJTU0lMTCzyPhs2bEjz5s0ZOnQoO3bsyNMcFxUVRZ8+fXLN/bR27Vq2bt2aM8XF119/zeuvvw5Ap06dLOAqxSxgMsYU2X333UezZs2YNGkSf/rTn/jxxx+Jj48nJSWFF154gc6dO+e77bBhwzhw4ADPPPMMY8eOpVOnTqSnp9O8efOcPA0aNKBChQo8/vjjfP/998THx+cED+eddx4Av/3tbxk0aBDPPfccjz32WM5IlujoaN577z3Gjh3LlClT2LJlCxUrVqR+/frccMMNp9RcdbJiYmKYP38+gwcP5rbbbqNKlSrccccdJCYmcqf//GXACy+8QFxcHOPHjycjI4NrrrmGOXPmcNVVV+XK16lTJ5YvX87o0aPp378/x44do2bNmrRo0YJbbrmlWMt/1113ERsby1NPPUXXrl2Ji4ujU6dOPPnkk8TFxRXrsU6XcePG5UxPsXv3biIjI2nYsCFjx47lvvvuO+n99u7dm8GDB1OnTh3atm2ba92JEyfyzLP1zDPPMHPmzJzfX3vtNV577TXA1c4GNhmb0kPcqERTUlJSUnTt2rXhLoYJk/Xr19O4ceNwF8MYU0aE+p8hIp+pqlX/lwDrw2SMMcYYE4IFTMYYY4wxIVjAZEwJs2ZwY0xh2P+K0sUCJmNKUHR0dJ5h8MYYE8yxY8dOaq4zc3pYwGRMCapevTo7duzg6NGj9u3RGJOHqpKZmcm+ffv47rvvciYpNeFn0woYU4LOOeccAHbu3ElmZmaYS2OMKY2ioqIoX748iYmJlC9fPtzFMR4LmIwpYeecc05O4GSMMaZssCY5Y4wxxpgQLGAyxhhjjAnBAiZjjDHGmBAsYDLGGGOMCcECJmOMMcaYECxgMsYYY4wJQWzyvJIlIj8A24phV1WBH4thP+YXdk2Ll13P4mfXtHidCdczSVWrhbsQZwMLmMooEVmrqinhLseZxK5p8bLrWfzsmhYvu56mKKxJzhhjjDEmBAuYjDHGGGNCsIDJGGOMMSYEC5iMMcYYY0KwgMkYY4wxJgQLmIwxxhhjQrCAqeyaEu4CnIHsmhYvu57Fz65p8bLraQrN5mEyxhhjjAnBapiMMcYYY0KwgMkYY4wxJgQLmMoQETlfRF4XkZ9E5KCIzBORxHCXq6wSkZtE5A0R2SYix0TkGxEZIyLx4S7bmUJE3hURFZFR4S5LWSYinURkuYgc9v7214rINeEuV1kkIi1F5H0R2eNdy89F5I5wl8uUfhYwlREiEgt8BFwI9AF6Aw2AJSJSMZxlK8P+BJwAHgKuB/4G3A18ICL2t3GKROR3wGXhLkdZJyIDgLeAz4DuwM3Aa0BsOMtVFonIpcBiIBq4E7gRWAO8KCJ3h7NspvSzTt9lhIjcC0wEGqnq/7y0usBG4M+qOjGc5SuLRKSaqv4QkHYbMBNop6ofhadkZZ+IVAI2AH8E5gCjVfXhsBaqDBKRZGA9MExV/xLe0pR9IvIE7otSFVU97Je+GlBVvTJshTOlnn2LLju6AKt9wRKAqm4B/gl0DVupyrDAYMmzxnutU5JlOQM9CaxT1VfCXZAy7g4gG3g+3AU5Q8QAmcCxgPQD2OehCcFukLLjYuCrIOnrgItKuCxnstbe6/qwlqIME5GrgNuAQeEuyxngKlxNXQ8R2SQiWSLyPxH5fbgLVkbN8F4ni0htEakkIncC7YBJ4SuWKQuiwl0AU2hVgP1B0vcBlUu4LGckEakDPAYsVtW14S5PWSQi0cALwHhV/Sbc5TkD1PaWp3B97Tbh+jA9IyJRqvp0OAtX1qjqVyLSBniTXwL6TGCgqs4NV7lM2WABU9kSrMOZlHgpzkAiEofrWJsF3B7m4pRlDwIVgNHhLsgZIgKIB/qq6jwv7SOvb9MwEZms1hG10ESkAfAGrmZ+IK5privwvIj8rKqzw1k+U7pZwFR27MfVMgWqTPCaJ1NIIlIeeBuoB7RW1e/CXKQyyZviIg3oD5QTkXJ+q8t5HcEPqeqJcJSvjNqLGw37QUD6+7iRnbWAnSVdqDLsCVyN0m9VNdNL+1BEEoCnReQVVc0OX/FMaWZ9mMqOdbh+TIEuAr4u4bKcMbwmpDeAZkAnVf1PmItUltUDygOzcEG8bwE3Mmk/cEl4ilZmrcsn3VezbB/uRXMJ8KVfsOTzKZAAVC/5IpmywgKmsuNtoIWI1PMleNXyLb11poi8uZZm4zp8dlXV1WEuUln3L6BtkAVcENUW+F/QLU1+3vReOwSkdwC+U9VdJVyesm4X0FREYgLSmwM/4/qEGhOUNcmVHVOBwcBbIvIwrj/T48B2XCdbU3TP4jrQjgaOiEgLv3XfWdNc0ajqAWBpYLqIAGxT1TzrTEiLgCXACyJSFdgM3ARch/W1OxnP4Cb9/IeIPIfrw9QF+B0wSVUzwlk4U7rZxJVliNdHZBLQHlcl/yFwn6puDWe5yioR2Qok5bP6UVUdWXKlOXOJiGITV540ETkHGIMLlCrjphkYq6pzwlqwMkpEOuIGJ1yMa0LeBEwBXrD+daYgFjAZY4wxxoRgfZiMMcYYY0KwgMkYY4wxJgQLmIwxxhhjQrCAyRhjjDEmBAuYjDHGGGNCsIDJGGOMMSYEC5iMMWEhIm1EREVkZLjLcjK8si8NdzmMMSXDAiZz2ojIhSLyVxH5SkR+EpEMEdkpIgtFpJ/30Nszmoj09T5Y+57Etuot2SJSv4B8S/zyFvk4xhhjQrOAyZwWIjIc9+DQwcAhYCYwHngHuBCYBnwctgKWHVm4Wd37BVspIg2A1l6+suZToDHucRXGGFOq2bPkTLETkYeAR3HPubtZVT8Jkue3wP0lXbYyaDfwPXC7iAxX1cDAqD8uoFoAdCvhsp0SVT2Ke8yHMcaUelbDZIqViCQDI4FMoFOwYAlAVRcA1/ttV2B/FhHZ6j37zT8tp7lLRK4XkaVe058WZr2XJ0pEBonIahE5KCJHReQLERksIhEBx0v29jfD+3muiPwoIj+LyFovCPTPvxSY7v063a/ZTL3rVFhTgZpA4P6jgT7ASlxtXrDr9msReVpEvhSRfV5ZN4rIBBGpHCR/jIjcIyKfi8h+73psFZG3ROTagLxXi8g/ROQ7ETkuIru86ziiMCeV33vuvU/qvTcPeeU9LiLbRWRckCfNF3SMGiIyXkS+EZEjInLA+3mGiNQLyBshIgNFZI2IHPbyrxGRuwPvhQKOV1tEhovIP73r4WuGniMijYPk97+nGorI30Vkj9cM2ybEsfzv7/YissIr9w8iMl1EKnn5LheRBd77eVhE3g52/5WWe0VEYkVkmIj8y3sPDovIKhH5XZC8IiJ9RGSld94/e/fJeyJyS0HXz5iishomU9xuB6KBuar6VUEZVfV4MR3zJlzw9Q7wPJBcmPXiAo5/AB2Ab4A5wM9AW+CvQHOgd5DjJeGakzYD6UAV4BbgLRG5VlWXePlmAAeArsBbwL/89nGgCOf3CjARV5s03y+9C1ADGApckM+2dwLdgWXAYiAS+BUwBOgoIs1V9ZBf/hm4J7d/BbyMe5p7beAq3DVcDCAi1wMLgYPA28AO3HVoDAzC1TCeqjnA1bj37SDQCfgzUB13nxVIRGKBfwL1gQ9w77Xg3r+uwOu499AnHbgVVzM6DVDctXsOd/49C1HmVrj3YwnwBnAYaIC7B7uISEtV/TLIdvWBT4D/ArOBCt45F0YXXDC9AHd//wboC9QVkaG4h3SvAF4ELgE6A/VF5BJVzfbbT9jvFS/I+wi4HPgceAn3xb4DMEdELg54iPNoYBiwBXgV+AmoBVwB3Az8vZDX0JjQVNUWW4ptwf1zVqB/Ebdr4203Mp/1W4GtAWl9vW2ygeuDbBNq/Uhv/V+BSL/0SNyHiwJd/dKTvTQFRgTsq4OXviifMvQ9iWupwHfez9Nw/ZTO81v/Lu4DIhYYFew4uOAgMsi++3n5H/RLO9e7Vmvz2SbB7+c3vO0vC5Kv6qm858BSL/0zoIpfekXgf8AJoGYh9t/Z28+kIOtigHi/33/n5f0ciAs45lpv3a1B3p+lAWnV/ffrl34ZLnh6JyDd/556ooj3h+/eygJa+6VH4AJEBfYBPQO2y3Nvl5Z7BReEKfDngPTy3v2eDTT1S98LfAfEnux9aIsthV2sSc4Ut1re63cleMy3VPXdoqz3mlgGA7uAP6rqCd867+f7cf+4g9UqbMMFKPht8x7wLdDspM4gtKm4QO4OABFJAtoDs9X1BQpKVbf5n5ufl3Df+Dv4Z8fVwBzHfTAF7mtvkP0cC5Lvx/xPo0geVNV9fvs9gqt9iQBSirCfYGXM0Ny1JXd4r0NV9XDAMR/0fu0f6kCquidgv770L3E1J229ms1Auzn5WrlXVHWZ37GycbVlAF+p6uyA/C97r00DyhjWe0VEEoBewFpVfTIg38+490FwtYD+MnFBdL77NqY4WJOcKW7ivWqBuYrXpyexviGQAGwEHhaRIFk4hms2CPSvfD5YtgNXhijLSVHVT0TkP8AdIjIK9+EdgQuk8uV9OA8AegAX4WoG/L8o1fE7xkER+QeuZuZfIvIGrinnkyBB2Wzg/4BPROTvuCaof6pqcQbKa4Okbfde8/SpCWIZrvlnqIj8CliEa6IL9v79CvfBvzSf/ZzANROFJCI3AANxQV1V8v6frYrryO/vSz35Jupg12mn9/pZkHU7vNfz/BNLwb1yBe5LQX59GX2Bpv/f5GzgD8A6EXkN916tUtWfgmxvzCmxgMkUt524aQPOC5WxGO06ifUJ3msDoKBOynFB0g7kkzeL0zuQYiowGdc/5HbgM1X9IsQ2f8f1S9mM60e1C1crAHAfUC4g/y24b/K38kuNx88i8jrwJ1XdDaCq8+SXkY534D5oEZHPgGGq+sFJnmMOVT0QJNk3SjCyENsfFJEWuPPowi81JD+KyHPAKFXN9NLOBfapakaQ/WSJyI+45rYCicg9wNPAflyz2LfAUdwXiG64prnAaw6h7+GCBAsOsgqxLrCmK9z3iu9v8gpvyY//3+QfgU3efod6S5aILALuV9X/FbAfY4rEAiZT3D4GrgHa4fpKFJavWj+/e/Jcgv/zh9C1WcHW+/b1pqr+X4jtS4t0YBzwAu7b/mMFZRaRFNwH4GLciMVMv3URuA7UuajqMVzfrpEicj6uE3NfXFNJMq4Tti/vQmChiFTEdZD/LXA3sEBELlfVr0/yPIuNV4vRT1wV4kW4e/P3wHBccPuIl/UnoIqIRPtfJ3AjKXG1QgV2wvbyPYoLNH6lqt8HrC+o9rEka2TzKCX3iu9vcpKqDilMub2awqeBp0WkOq7DeQ9ch++LvU7ixTW4xJzlrA+TKW7TcX0KbhSRiwrKKCL+31j3e6/nB8l3AVCpuAro2YCrKWqRT5+S4uJr+glZIxKKV+PyOq727ghu9FxBfCPn3g4MAnB9rSqEON52r/9LB1zT5VVeP5PAfEdU9SPvQ+4JXIfqjiHKVqLUWaeqf8X1/YLc81Z9gft/2CrI5q1w79/nIQ5TFXefrgwSLMXhmv1Kq9Jwr3yK++J0deB2heH1H5unqqm4/mL1gSYnsy9jgrGAyRQrVd2K+9YZg/tGGbRzrjfU+B2/pA24b/BdvW+KvnwVcM1QxV3OLNzouFrAZO84gWWsFSroKwRf59fEU9yPz8O4moAOwToXB9jqvbbxT/Su77OBmUWkmog0D7KfikA8riknw8vbLtg1w01zAK4ZKqxEpIkEn+8qWBlf8l7HeNMR+PYRC4z1fg1VY7rH2+evvQDJt49oXC1I1cKXvsRt9V7b+CeW5L2iqntwfZJSROQRr8Yu8Lj1RaSu93M5b98SkCcaN21Bzr6NKQ7WJGeKnao+4f2zGwGsEZGVuI6ph3H/JFvh+g6t9dsmU0SexjWRfCEib+Luz/a4flE7KX6P4/qUDAQ6i8hHuA6x1b3ytQTSgFNpWlqF+6d9n4hUwY2GAvjryXRMVdVvcf1iCmMNrpPz/3nvwce4698RN+9U4DWtA6wWkfW42pTtwDm45pOawGS/IG0CkCxucs6tuA/HX+OavLYBc4t6bqfBtcBE79w34AKa83BzMGUDT/kyquocEekKpOI6EM/nl35HdYFXg4w2y0VVs0VkMq4fzX9E5C3cF4e2uA/wJd7PpVFpuVcG4/72HgN6i8jHuL+Z2rjO3lfgpoDYgqv1WgxsFZFPvH2Vx/3PaIyrLVt/itfFmF+cjrkKbLFFVcH90/orbmK7g7h/lN/japb6AeUC8gvuw2aTl/db4EncPENbyX8epr75HL/A9X7H7I2bP2qfd9wduA+Mh4Dz/fIme/ubkc++lro/qTzp1+MCp8P8MudOciGuX848TIXIm988TFVwEy9uxU3KuQnXFJLnmuKak4bjmjN24Dr8fu+d1+8A8cubimsS3Oid10HvfR4NVCtkmdtQwDxMJ/ueBtx/E3GB+Q/e+WzFNWv+Jkj+CNxEimtxQe5R3Ciz3wMR+bw/SwPSonATPX6NG2W5C9f3LIlf5hhK9stf4D0V4vzyvRb5XduCjlla7hVckDkYN4P9T96+v8X9jd6HN8cTrtP6n3H/T771yvwDsBr3JSimqNfUFlsKWkQ1rH0NjTHGGGNKPevDZIwxxhgTggVMxhhjjDEhWMBkjDHGGBOCBUzGGGOMMSFYwGSMMcYYE4IFTMYYY4wxIVjAZIwxxhgTggVMxhhjjDEhWMBkjDHGGBOCBUzGGGOMMSH8P89JiFpL0/IgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "\n", + "plt.plot((clus_1.star_systems['mass_current']),\n", + " np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"Cluster made from BPASS\", alpha =0.3)\n", + "plt.plot((clus_1.companions['mass_current']),\n", + " np.log10(clus_1.companions[\"L\"]), \"r.\", alpha = 0.3)\n", + "plt.plot((clus_2.star_systems['mass_current']),\n", + " np.log10(clus_2.star_systems[\"L\"]),\n", + " \"b.\", label=\"Cluster made from MISTv.1\", alpha =0.1)\n", + "plt.plot((clus_2.companions['mass_current']),\n", + " np.log10(clus_2.companions[\"L\"]), \"b.\", alpha = 0.1)\n", + "plt.xlabel(\"Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"Mass-logL of Cluster at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Where is that hook coming from?" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='phase' dtype='float64' length=2>\n", + "\n", + "\n", + "\n", + "
5.0
101.0
" + ], + "text/plain": [ + "\n", + " 5.0\n", + "101.0" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(iso1.primaries[np.where(np.log10(iso1.primaries[\"L\"]) <= 25)]['phase'])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2281" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(iso1.primaries[np.where((np.log10(iso1.primaries[\"L\"]) <= 25) &\n", + " (iso1.primaries[\"phase\"] > 5))])" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2281" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(iso1.primaries[np.where((np.log10(iso1.primaries[\"L\"])<=25) &\n", + " (iso1.primaries[\"phase\"]==101))])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK. It makes sense that our compact remnants are causing this. (Remember, there really is no significant fusion in the insides of neutron stars or white dwarves." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log10\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['mass_current'], np.log10(clus_1.star_systems[\"L\"]),\n", + " \"r.\", label=\"Synthetic Cluster made from BPASS (Star Systems)\", alpha =1)\n", + "plt.xlabel(\"log Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"log-Mass-logL of Cluster at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in log10\n", + " \n", + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the mass-luminosity relationship\n", + "plt.figure(figsize=(8, 6))\n", + "plt.plot(clus_1.companions['mass_current'], np.log10(clus_1.companions[\"L\"]),\n", + " \"r.\", label=\"Synthetic Cluster made from BPASS (Companions)\", alpha =1)\n", + "plt.xlabel(\"log Current Mass in solar masses\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"log-Mass-logL of Cluster at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.legend()\n", + "# Rough pattern seems to fit. What's that line?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now go back to the shape of the clusters' Color magnitude diagrams." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='log_a' dtype='float64' length=2678>\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", + "\n", + "\n", + "
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48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Taking a look at the Binary Cluster vs Default Settings cluster Observer's HR Diagram\n", + "# Remember to use a distance modulus!\n", + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(clus_1.star_systems['m_ubv_B'] - clus_1.star_systems[\"m_ubv_V\"],\n", + " clus_1.star_systems[\"m_ubv_V\"] - 5 * np.log10(100),\n", + " \"r.\",label=\"BPASS\", alpha =0.3)\n", + "plt.plot(clus_2.star_systems['m_ubv_B'] - clus_2.star_systems[\"m_ubv_V\"],\n", + " clus_2.star_systems[\"m_ubv_V\"] - 5 * np.log10(100), \"b+\",\n", + " label=\"MISTv.1\", alpha=0.1)\n", + "plt.xlabel(\"B-V\")\n", + "plt.ylabel(\"M_V\")\n", + "plt.title(\"Color magnitude Diagram of clusters at 0.1Z_solar and 10**9.3 years age\")\n", + "plt.gca().invert_yaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in log10\n", + " \n", + "/opt/anaconda3/envs/astroconda/lib/python3.7/site-packages/ipykernel_launcher.py:7: RuntimeWarning: divide by zero encountered in log10\n", + " import sys\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (7.5, 7.5))\n", + "plt.plot(np.log10(clus_1.companions['Teff']), np.log10(clus_1.companions[\"L\"]),\n", + " \"r.\",label=\"BPASS cluster\", alpha=0.5)\n", + "plt.plot(np.log10(clus_2.companions['Teff']),\n", + " np.log10(clus_2.companions[\"L\"]), \"b+\", alpha=0.15)\n", + "plt.plot(np.log10(clus_1.star_systems['Teff']),\n", + " np.log10(clus_1.star_systems[\"L\"]), \"r.\", alpha=0.5)\n", + "plt.plot(np.log10(clus_2.star_systems['Teff']),\n", + " np.log10(clus_2.star_systems[\"L\"]), \"b+\",\n", + " label=\"MISTv.1 cluster\", alpha=0.15)\n", + "plt.xlabel(\"log($T_{eff}$ in Kelvin)\")\n", + "plt.ylabel(\"log(L in Watts)\")\n", + "plt.title(\"HR Diagram of clusters at 0.1Z_solar \\n and 10**9.3 years age\")\n", + "plt.gca().invert_xaxis()\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If I look at the HR Diagram, I do see deviation with respects to the MIST line for some of the brighter stars, but I see an overall good trend with the main-sequence turnoff. Also, I see the compact remnants where they should be given my setup." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking at which stars are the white dwarves?" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 3, ..., 14920, 14930, 14943])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.where(clus_1.star_systems['phase'] == 101.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do I see any black holes?" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 40, 110, 125, 145, 157, 168, 190, 250, 298, 366, 394,\n", + " 485, 551, 614, 620, 622, 623, 643, 718, 766, 888, 913,\n", + " 954, 980, 990, 1022, 1089, 1127, 1203, 1241, 1260, 1311, 1315,\n", + " 1357, 1372, 1602, 1603, 1675, 1686, 1699, 1702, 1718, 1728, 1730,\n", + " 1749, 1787, 1833, 1841, 1860, 1867, 1868, 2037, 2067, 2070, 2106,\n", + " 2161, 2183, 2192, 2276, 2349, 2350, 2391, 2406, 2411, 2462, 2475,\n", + " 2492, 2563, 2614, 2623, 2647])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 103.0)[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Neutron Stars?" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 7, 54, 56, 81, 82, 92, 99, 100, 107, 123, 130,\n", + " 144, 151, 159, 176, 181, 196, 207, 220, 245, 259, 261,\n", + " 273, 303, 314, 351, 368, 373, 383, 393, 433, 435, 438,\n", + " 443, 454, 461, 478, 497, 499, 518, 532, 535, 540, 585,\n", + " 587, 604, 631, 647, 651, 678, 690, 734, 735, 738, 799,\n", + " 820, 823, 839, 840, 848, 849, 851, 857, 912, 920, 928,\n", + " 947, 1028, 1029, 1031, 1058, 1059, 1095, 1188, 1191, 1211, 1224,\n", + " 1225, 1227, 1229, 1244, 1257, 1259, 1266, 1280, 1303, 1309, 1310,\n", + " 1335, 1340, 1342, 1362, 1364, 1381, 1385, 1392, 1395, 1402, 1434,\n", + " 1442, 1446, 1457, 1484, 1485, 1498, 1509, 1523, 1554, 1557, 1563,\n", + " 1580, 1612, 1641, 1646, 1652, 1653, 1656, 1674, 1681, 1700, 1705,\n", + " 1713, 1725, 1739, 1751, 1764, 1782, 1810, 1818, 1847, 1884, 1894,\n", + " 1909, 1920, 1934, 1936, 1942, 1955, 1961, 1981, 1986, 2014, 2015,\n", + " 2021, 2045, 2085, 2096, 2111, 2131, 2149, 2172, 2174, 2180, 2182,\n", + " 2253, 2263, 2283, 2293, 2345, 2351, 2363, 2392, 2393, 2401, 2410,\n", + " 2414, 2423, 2427, 2443, 2464, 2467, 2478, 2497, 2501, 2510, 2515,\n", + " 2526, 2615, 2621, 2624])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.where(clus_1.star_systems['phase'] == 102.0)[0]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Test_Plots/iso_7.00_0.00_01000_m10.fits b/Test_Plots/iso_7.00_0.00_01000_m10.fits new file mode 100755 index 00000000..067b0cbb --- /dev/null +++ b/Test_Plots/iso_7.00_0.00_01000_m10.fits @@ -0,0 +1,264 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 97 / length of dimension 1 NAXIS2 = 662 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 13 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_ubv_U ' TFORM9 = 'D ' TTYPE10 = 'm_ubv_B ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_R ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_I ' TFORM13 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' LOGAGE = 7.0 AKS = 0.0 DISTANCE= 1000 METAL_IN= -1.0 HIERARCH METAL_ACT = -0.9761970853273753 WAVEMIN = 3000 WAVEMAX = 52000 END EIҩ@xyA㽬?7+@)i6F?5@7>M$@6 ܝ@4ixL<@3Ƿ5@2pCT2[vEl@ \A4?q܈t@~F?qQ;d@72F@6 @4MA@3$@2gj |EZ6O@(An/?Ot[@}^8uF?OrmDl@7%5u@6 I>%\@4sH @3|P\@2]Z"E'-&l@EANa?,5@}9FF?,p@7ǚ =@5X&@4h".d@3{@2Si]'E,@N9A`Ow? 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P?egFCgq@1<@FwK݅/@?@!> ?"OF9 +E)@79;aB[=Б`;@4h?M%?.BV F@3 Ng@@[TZ@&@g@R^'?NۿF%@kB[S*@4hKL?:#F@3ׅQ@@Gֽn@kɁ@a{2ȵ@@?- \ No newline at end of file diff --git a/Test_Plots/iso_7.00_0.00_01000_m10_IBprimary.fits b/Test_Plots/iso_7.00_0.00_01000_m10_IBprimary.fits new file mode 100755 index 00000000..8f734e90 --- /dev/null +++ b/Test_Plots/iso_7.00_0.00_01000_m10_IBprimary.fits @@ -0,0 +1,842 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 105 / length of dimension 1 NAXIS2 = 4968 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 14 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'L ' TFORM2 = 'D ' TUNIT2 = 'W ' TTYPE3 = 'Teff ' TFORM3 = 'D ' TUNIT3 = 'K ' TTYPE4 = 'R ' TFORM4 = 'D ' TUNIT4 = 'm ' TTYPE5 = 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+9?sN?ə'@ 1wς@7|X@ MئNn@ {@2?Fw9c@nM +pA!O@ 0RT^>F@1Ӯh]@F@V*G@u@_@G@HT@\~{yE V@͑/ AН@@(Q2F@@F@$ +K@%|R@%m@&L@&I +@4?eӀFe>x@VAB_t3=:=tF@3`sW@F@+C@2rzR@!@/Uu?tBƫ@.ݍjOFdeo @y,AZ=1@1Z6F@-^J82@F@xQ@~I@wc@#[m@-l<@&Hx=nFHh@GYA]kT@sT@&Fd@F@YVg@%@?Z@"_G@ F^@(?qkpFQ$0`z@lvCA +*@.F@'W@F@.r@{@Bb@uz#)@y2Bv@0OYFm_@݀P@A 4h@F@/P3:~@F@?_7#@քw@љv@Dlt@HH]@̿d|F,p)S @kCA &w@x+F@̧s>@F@c^m=@!{iLf@ $@@!|f@"$HCh@5?A F@_?Bi^?f@ ?$'hEtT@_A%Y[ݖ@;VF@ @F@$?@%CQ@%Y :}@%l@&Rv@$ffffff?OgvFC9[@OvڇA 5@%gZF@$d1@F@f6i@he9u@֦Ju@ /W@ !@(?`FQ$0`z@lvCA +*@.F@'W@F@.r@{@Bb@uz#)@y2Bv@.?bxFg72ϓ@ݐdYA!@?@SF@- k@F@f8u@@R|X"@ۼ:@hT1@mg#@'fffffgƧS(UFOcN@-jWAa\w@qs"F@'`y+@F@3]@;Ψ@P@g0"%@N]x@3?5/k=F^ң^@R .B,>:֭S?S#JF@2yU@F@ LQ@ʕPu!@@ƹ@R;A@? s\F&Ay0X@tuiAjCld'@/rF@z@F@@pc@!co@!ab[S@" :2@"}( @0{ήFn I@^aAS8=S@{W`F@/1e@F@;@@_@DD_@/|ji@2X@!?'lF4/0@ھ2Aݬ<|@ݓF@ 0j+@F@;e[@ mttx@ zjN;@!1+ס(@!LW@!?߮WW@F4/0@ھ2Aݬ<|@ݓF@ 0j+@F@;e[@ mttx@ zjN;@!1+ס(@!LW@2}OFu9ɓAo@٬0?rB5u@ ^F@/X- @F@Eź@Njv l@8@|d5@i +@+ۏg^F[Do%@T!gvAiq,@{rZF@*f@F@ȡ@!Ői@n%@})j@0Y9@.鵍bFfq\Y@ž|A@B +F@.xg@F@ d"@+[@g Z)@ЕC@ʂ@1ojFssaz+@8KA䁿@ W:AF@0KCW@F@n*U@;@|W@=EK@z$ \ No newline at end of file diff --git a/Test_Plots/iso_7.00_0.00_01000_m10_IBsingle.fits b/Test_Plots/iso_7.00_0.00_01000_m10_IBsingle.fits new file mode 100755 index 00000000..ab6c9f39 --- /dev/null +++ b/Test_Plots/iso_7.00_0.00_01000_m10_IBsingle.fits @@ -0,0 +1,33 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 105 / length of dimension 1 NAXIS2 = 94 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 14 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'L ' TFORM2 = 'D ' TUNIT2 = 'W ' TTYPE3 = 'Teff ' TFORM3 = 'D ' TUNIT3 = 'K ' TTYPE4 = 'R ' TFORM4 = 'D ' TUNIT4 = 'm ' TTYPE5 = 'logg ' TFORM5 = 'D ' TUNIT5 = '' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'source ' TFORM9 = 'K ' TTYPE10 = 'm_ubv_U ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_B ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_R ' TFORM13 = 'D ' TTYPE14 = 'm_ubv_I ' TFORM14 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'BPASS ' LOGAGE = 7.0 AKS = 0.0 DISTANCE= 1000 METAL_IN= -1.0 HIERARCH METAL_ACT = -1.0 WAVEMIN = 3000 WAVEMAX = 52000 END @#F@YBH@XYj@!SyJ y@ 0@! a#@!TZj@ EZ_ @Ш6fyAǟr@o 8F@ >(@@#0{@$;!@$gӢ @$}g@%%g@333333Fo@YAے0@zWF@2Q@@ ;>[@"cE<@!R;@"?Pc@"? 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I^5?F@Q_@Эw@7a +@HF@ɹU?,ZKF6|,-@—BqNe@GG%5?nF@GE84@s&i@㬅@o=5_@!yd?USbF0fX5@Z-3B}\`I@Y`E?FsF@Yb}@L;7@@x=@W?^NoFҁ>@3o(B5MI@h^_ܴv?L_F@hXy=@4P$@qD@>Ť?y@bN*?itWF.'@>]M@iB @rQ?0 (F@rW@ܔჳ@ r@eCg@?1) Fx@NmRB!JmQ @uȠBU:?C,zxF@u\(@HunR@dK)@YZ+@~6?RsFZ)@<[_B"Gб@vdӏ?(\)F@v_ح@@'BWC@Sk@UZp?YW FU@,YB" !@vv jG?"`F@vz@ߐ`@}]@_f@+$)c?iFMl.mx@W ٧B#@v;?>BZc F@vFs@A@Df@pKy@uE>?tTwFVs@K ~_B$}@v #6?7KƧF@v}Vl@[Ҟ-@Y@P@L?V|EF"^}@49hB$<|#7@v_?ߤ?F@v!.I@T%B@>@&=@vժ?|FAO@2=B$RW@w!P?a@OF@wkP@\x @ +h@;>@3e +N?Λ6K\Fd@YB%=S)v@x +߬H?*0U2F@xF]d@6to@z,p@RJ@tn`?ij F\]@7XuB&X@xbbh?@NUF@xl"h @ro@S׷gg@g`;@0|y?TnJYNFN7@q4IgB',vx@xПf?fAF@xF@y&@3@B?O}ħ?YzSF YO@M~٨B)}C@y^_Y?ڹYF@yXbM@yy@! c@rsYF?:,? }F!>@+ ( B+J@z 2 ?zGF@z@Њr +@CnL@m?#աC?|#1F#N@'6kB,e@z[d?Ϫ͞F@z)_@<83@;@5ܞ?"qڅ?w>0 +F%6@pi +B.*@{L4?4֡aF@{A@F5~@Y+@=}h?w*?%EF'3F@ B0a*=@|W?'/WF@|PH@Wi@ @]k~?U?nk +UfF),@Dl1#B1sg6C@}@Ԁ?.HF@}K]@ut!@vb@l7 ?"B ?=^F,s<~@ɄB2@}:?0 )F@}v@u[S@SH@Їh][?t?VF/8 .w̶@0yjB3MK&@~.?k?EF@~($ x@{y#@\Ҿ-@Rk?po\?Sh.F1!@xB5@~7?ZF@~6z@@;?..@^ӏ?8f0?ױF2:}͗@6uSB6x{dA+@ ?Xe+F@@?\@ ~O@ U?f +ڤ?lF4@{"'B7H@0?癙F@$tS@)@ e[-@pHn?wC#?ʿ1F6y0M@ B9yE?@_fZ?b}F@@'G9@ Uw@W}?z?@uzF8MH&@nsgxB;!,l@O͜?u%F@NU@Zh@ @Tn?\`i?o0F:M@:?aeF@C@˂\{@ 5@2/?edBEF@ +^@ B@\-0@C=?N;5F@:S@j@V H?ЖF@Zc@ +@$@6#q?󥥌Bp >^k \ No newline at end of file diff --git a/Test_Plots/iso_8.20_0.00_00100_p00_IBprimary.fits b/Test_Plots/iso_8.20_0.00_00100_p00_IBprimary.fits new file mode 100755 index 00000000..cd43a595 --- /dev/null +++ b/Test_Plots/iso_8.20_0.00_00100_p00_IBprimary.fits @@ -0,0 +1,1072 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 105 / length of dimension 1 NAXIS2 = 2645 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 14 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'L ' TFORM2 = 'D ' TUNIT2 = 'W ' TTYPE3 = 'Teff ' TFORM3 = 'D ' TUNIT3 = 'K ' TTYPE4 = 'R ' TFORM4 = 'D ' TUNIT4 = 'm ' TTYPE5 = 'logg ' TFORM5 = 'D ' TUNIT5 = '' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'source ' TFORM9 = 'K ' TTYPE10 = 'm_ubv_U ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_B ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_R ' TFORM13 = 'D ' TTYPE14 = 'm_ubv_I ' TFORM14 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'BPASS ' LOGAGE = 8.199999999999999 AKS = 0.0 DISTANCE= 100 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.0 WAVEMIN = 3000 WAVEMAX = 52000 END @E+%d@ڥ|AX9/-@ F?쿱[W?@Y@@--@/ќFd@/˂i@0s@0D0zJ@FF))zU@k'{PsA鼵1_{v@ ,:5&F@`@@ + I^@2X@ b\am@l7__=@ӴHx?$ȟ@F?$ȟ@@@ '76F@ '76@Y@Ek5Y @ēoYA‰e@i F@@@uM#@@PÆ@!lI@!>@E#zͤ#r@AgZ1AY"Q"Q@ HfF?Կ @Y@@. +Uf@0^@/ETE@0.c,s@0hP&@ F +Ӷ @Ɖ6VAa]j@ {5MF@q!@@o8@ ~@Nd@ n'@ +N=@E,+8@ٹAZy=@ hF?LA@Y@@-o@/z'@/]xT=@/P}@0+P@Ek5Y @ēoYA‰e@i F@@@uM#@@PÆ@!lI@!>@FF))zU@k'{PsA鼵1_{v@ ,:5&F@`@@ + I^@2X@ b\am@l7__=@ӴHx@E$^@PztAX0@ jF? ~@Y@@-}0@06U@/fS@0-g{ +@0h/ob@EҎM)@ď33uA%4WF@FF@>(@@G{@ľ,@u@'@b;,@E&+Bb@lOAvBτ@萶kT?@N@@##O@!UsH@ I@!}Z@"$NF@EݐP @ B`nAڂ͐բ@dX,F@9@@ Go@>~2U@i@Y}aWq@ED@ E4'[@~r@AS +?t@!j3OF?ǙXd@Y@@-TC@/c/m@/~ob8@0Ğ"@0[KjSd@ E_@ʙUA +:?@ F@ +j@@`;@w7ʸ@ #'M@@>{?ffffffD<'@exH +ZAr,_@ lF?1Hwh@@8;H{hb@5̄@7ѶB@4bJ@18"?E=@a tAQncD@cgF@ 2/Y@@@N7@@:w@n0W@N ~@EݐP @ B`nAڂ͐բ@dX,F@9@@ Go@>~2U@i@Y}aWq@ED@<JwF@(:@Y@Ee@KAscNo@ʝnF@ v5C@@eڷ=@d.M@y +@#,@״Z5@ E_@ʙUA +:?@ F@ +j@@`;@w7ʸ@ #'M@@>{@ ExGB,@ĭ]A܅7}:@@8(>F@ Ev @@2Y@Vp@di_K@tT.]@ƞպ@EBe\@*5DMA0M",@gKSF@ck@@ d&$@YȒP@r(~f@wbX3`@u7@Ek5Y @ēoYA‰e@i F@@@uM#@@PÆ@!lI@!>?ffffffEB3@As)m@ P13F?ffffff@@]}V]@By@r@s +@<c@E+wA@yAFOJ@"_{ F?[BF@Y@@/~:@0|;@0dx@1D7@1T{|?zSHF?zSH@@E( @WpXAO.c@!EF?hM@Y@@.lL @0cLx@0L(L + @0Ώ@0)p@FF))zU@k'{PsA鼵1_{v@ ,:5&F@`@@ + I^@2X@ b\am@l7__=@ӴHx@Ezݔ@nARt,@jC:bF@LPr@@!ȱu@Rx WB@:mT @m@ܿ@ Fx^ |@Κ{1A:#F@D?F@pϿ@@ԪM5@ /șA@ +ifz@ hW5@ .@E%1@Cc6ACg@jF@ hH@@[rDE@T@N5Ԇ@"%H@H]@ ExGB,@ĭ]A܅7}:@@8(>F@ Ev @@2Y@Vp@di_K@tT.]@ƞպ@ EjV_0k@\BW¨A5.8]@~uU@T@s&@飝Qw@ E3 +@4 LAVjz*@!F?Zx@Y@@-6 @//T@/OKG@/1ل@0;گ? 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@ RG@ `U@-bD!g@?Q?G%SPEF^R@z꩝AK#҆@(Q +F?Q@F@e@0PXi@V@D5a@bn&>@?F7F + ߕ`@cEY)AФ~@]F@(Ah@F@ +޷%<<@)@6@̨M@6Y@?ϐE,O@O<wA՞}@ w$F@@F@'ٺx@5Ml@e/ \ No newline at end of file diff --git a/Test_Plots/iso_8.20_0.00_00100_p00_IBsingle.fits b/Test_Plots/iso_8.20_0.00_00100_p00_IBsingle.fits new file mode 100755 index 00000000..65c28189 --- /dev/null +++ b/Test_Plots/iso_8.20_0.00_00100_p00_IBsingle.fits @@ -0,0 +1,43 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 105 / length of dimension 1 NAXIS2 = 96 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 14 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'L ' TFORM2 = 'D ' TUNIT2 = 'W ' TTYPE3 = 'Teff ' TFORM3 = 'D ' TUNIT3 = 'K ' TTYPE4 = 'R ' TFORM4 = 'D ' TUNIT4 = 'm ' TTYPE5 = 'logg ' TFORM5 = 'D ' TUNIT5 = '' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'source ' TFORM9 = 'K ' TTYPE10 = 'm_ubv_U ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_B ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_R ' TFORM13 = 'D ' TTYPE14 = 'm_ubv_I ' TFORM14 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'BPASS ' LOGAGE = 8.199999999999999 AKS = 0.0 DISTANCE= 100 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.0 WAVEMIN = 3000 WAVEMAX = 52000 END ? 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V@Y@@3$@5 STŮ@5Df*@5Ve5@5nhE;R@AbcUd2? hq@kJF? e @Y@@4P@5ܒ]:k@5L,0E@@5]U|?@vF? ].u,@Y@@4)wf@56ey@5k~Pm@5z @5SE4^xp@:yKAb7Fv?@&lF? tG@Y@@42ɽ@5>@5rc2f@5O@5"{5XE3.ysK@cAb1{?GjK@P03F?f@Y@@4;bK@5FQx#@5yJ@5ʘr@5a \ No newline at end of file diff --git a/Test_Plots/iso_9.20_0.00_02000_m10_IBprimary.fits b/Test_Plots/iso_9.20_0.00_02000_m10_IBprimary.fits new file mode 100755 index 00000000..a1e0d620 --- /dev/null +++ b/Test_Plots/iso_9.20_0.00_02000_m10_IBprimary.fits @@ -0,0 +1,818 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 105 / length of dimension 1 NAXIS2 = 4902 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 14 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'L ' TFORM2 = 'D ' TUNIT2 = 'W ' TTYPE3 = 'Teff ' TFORM3 = 'D ' TUNIT3 = 'K ' TTYPE4 = 'R ' TFORM4 = 'D ' TUNIT4 = 'm ' TTYPE5 = 'logg ' TFORM5 = 'D ' TUNIT5 = '' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'source ' TFORM9 = 'K ' TTYPE10 = 'm_ubv_U ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_B ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_R ' TFORM13 = 'D ' TTYPE14 = 'm_ubv_I ' TFORM14 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'BPASS ' LOGAGE = 9.199999999999999 AKS = 0.0 DISTANCE= 2000 METAL_IN= -1.0 HIERARCH METAL_ACT = -1.0 WAVEMIN = 3000 WAVEMAX = 52000 END @D՞ @ BA]q/ @ mSFF?vtc4@Y@@83gm>@8 +7R@9L30@8O@8w?P?F?@@ D5W@ɇdA_ڂH@ 5o;aF?;wF{@Y@@9}%#@9[@xN@9ݪrD@9mG@8|3o?$ȟ@F?Ta=2@@ DJ@E=,AY89ʴ@ FҾF?*c@Y@@8ܖ@9[1@9r"Y@8 J@8,y?zSHF?zSH@@Dn@qAS|Tw7 @!q}?DžF? +=p@Y@@7ᨲ%@8ʙB1@8j@8|1{>@8t~ m?0̼,F?0̼,@@D{'@w3AWPa>H@! +axHF?VϪ@Y@@8~RJx@9Di@9]3 +˹@8B r@8KKt? 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Qr@@D=:?@A]r @ l>zF?tqp@Y@@8Y @ DSZY@sAXoM@ "F?@Y@@8j@9M։@9e{25@8,8Ը@8D@ LYt[F@ ?5@@ffffffD`yT@B/AW?/0@ ŵMF?y2@Y@@8T.j@9l@@9a6 @8yd@8 s@ DP@8}A][0@ azF? >@Y@@9N’5~@9E'M@9BV `@9ޞ +m@8W6?ffffffEjgt@E$Aj#E@Z F?ffffff@@+Ǖ@+EM@+L4;X@+%ǧDx)@*c@ Ds9'@(^2!AWr\@!QlYџF?c^t*@Y@@99CY#@9aIDy@9ڲl@9*=R@8펊J@3ݗ+kF@6K:@Y \ No newline at end of file diff --git a/Test_Plots/iso_9.20_0.00_02000_m10_IBsecondary.fits b/Test_Plots/iso_9.20_0.00_02000_m10_IBsecondary.fits new file mode 100755 index 00000000..19154b03 --- /dev/null +++ b/Test_Plots/iso_9.20_0.00_02000_m10_IBsecondary.fits @@ -0,0 +1,1697 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 114 / length of dimension 1 NAXIS2 = 4902 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 16 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'log_a ' TFORM2 = 'D ' TUNIT2 = '' TTYPE3 = 'L ' TFORM3 = 'D ' TUNIT3 = 'W ' TTYPE4 = 'Teff ' TFORM4 = 'D ' TUNIT4 = 'K ' TTYPE5 = 'R ' TFORM5 = 'D ' TUNIT5 = 'm ' TTYPE6 = 'logg ' TFORM6 = 'D ' TUNIT6 = '' TTYPE7 = 'isWR ' TFORM7 = 'L ' TTYPE8 = 'mass_current' TFORM8 = 'D ' TUNIT8 = 'solMass ' TTYPE9 = 'phase ' TFORM9 = 'D ' TTYPE10 = 'merged ' TFORM10 = 'L ' TTYPE11 = 'source ' TFORM11 = 'K ' TTYPE12 = 'm_ubv_U ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_V ' TFORM13 = 'D ' TTYPE14 = 'm_ubv_B ' TFORM14 = 'D ' TTYPE15 = 'm_ubv_R ' TFORM15 = 'D ' TTYPE16 = 'm_ubv_I ' TFORM16 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'BPASS ' LOGAGE = 9.199999999999999 AKS = 0.0 DISTANCE= 2000 METAL_IN= -1.0 HIERARCH METAL_ACT = -1.0 WAVEMIN = 3000 WAVEMAX = 52000 END ??Ui8/E/Y@@ھA}Z@F?62+@F@-Tc@-s@.~@-^#@- ;A?nTE~@5cAI^@@J'F?@F@*]|Ny@*;K?p@*@* +*@)W'<?fffffgNdCES@.sAcE@/V0F?$-fj@F@,}ζ@,rFM@,s5.@,򻛼@,r(#*@ ?֯~DhV@צ*AXV9:@ s F?8@Y@F@8*w@9-T@9e:L@8-ӗ@8ؖ @zG|?s"`E^Xx/c@ʇ\ANx@8vUy@8jaƎ@TF0DB j\@- AX*zg@ 5KF?-w1@Y@F@8/*@8Ӕ,@8grv>@8{l0o@8[!?zG?A٬{KpE?\z@4)Adž4KN@mF?$/@F@,C\q@,&=cU@,WC@,}ڤ@,^@6@ ?SQ&pDi 63@AXP@ ڇF?OQCr@Y@F@7/(@8~>D@8qg@8][`@8CJ{??ݦB4[E[+|@J0eAFUH@~DF?@F@2sB@1i~@2fS:?A@1;T.<@0v?`?(\)?iqE[ƼĄ@K: AȄϺo@>OIF?(\)@F@-<D{@,؅@-=Q;@,B0;@,|W­@Q?syE5@˯Ih1AϢt?ha@?\F@Q@F@'|@)'@(䙰T@)6d,K@)oժ+@?E~TG$@VAq*:#@>J2F@@F@*oץk@*Q>@*m +@*˦:@*fh?GzHa*FXT?=p +>??EǾZ"@ºvABvi@[ xUCF@@F@) @)C(@) x @)u +(@)N?GzH@EG.@j="Ad@7SF?f^@F@/_JɆk@/8h* @0.7@. gy@.VQ:@ ?J?fDs#@#G`LAY0@ w#F?Pܜ@Y@F@8MOpC@9-@9r-|@8%ʜ@8y? +=p ?zZL FXT??/Eb@qÄn @8F?@F@,޲@,*B]@,~<@,v]@+>Z?333333?*<*E8\|@ n݇(Aln@wF?333333@F@78=@4m') @6 ̒@3 8@2DnɅ? 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+=p @9mFXT?(\)?ѧxFXT@?gEEGpl@ʪ=GAκu;@mZF@_a(@F@(Y'@)b }@), +Zg!@)w l@)g/@?&B[`D5'@6;{A^G@ Xl4F?Y@Y@F@8Pj]@8@O@9YH +@8~/6@8x>^#S?zGAñ?FXT?zpE{@bAeR]@=t6oF?K@F@+ / )@+@{H@+:J=@+Gن@+W"?Q?[L E1pIzg@>&Au@灺F?Q@F@8M56B@5$CF@6sт@41xy@2%?fffffg5Fz+FXT@?^w FD_=@ǪAF|@"LxBF?K]c@Y@F@8¡]+@9Qӊ@9n@9w\r@9xGE \ No newline at end of file diff --git a/Test_Plots/iso_9.20_0.00_02000_m10_IBsingle.fits b/Test_Plots/iso_9.20_0.00_02000_m10_IBsingle.fits new file mode 100755 index 00000000..92163150 --- /dev/null +++ b/Test_Plots/iso_9.20_0.00_02000_m10_IBsingle.fits @@ -0,0 +1,55 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 105 / length of dimension 1 NAXIS2 = 118 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 14 / number of table fields TTYPE1 = 'mass ' TFORM1 = 'D ' TUNIT1 = 'solMass ' TTYPE2 = 'L ' TFORM2 = 'D ' TUNIT2 = 'W ' TTYPE3 = 'Teff ' TFORM3 = 'D ' TUNIT3 = 'K ' TTYPE4 = 'R ' TFORM4 = 'D ' TUNIT4 = 'm ' TTYPE5 = 'logg ' TFORM5 = 'D ' TUNIT5 = '' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'source ' TFORM9 = 'K ' TTYPE10 = 'm_ubv_U ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_B ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_R ' TFORM13 = 'D ' TTYPE14 = 'm_ubv_I ' TFORM14 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'BPASS ' LOGAGE = 9.199999999999999 AKS = 0.0 DISTANCE= 2000 METAL_IN= -1.0 HIERARCH METAL_ACT = -1.0 WAVEMIN = 3000 WAVEMAX = 52000 END ? 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Trying to encompass +# all possible cases, even if there is a bit of redundancy and excess. +# I get this list of possible intial primary and secondary +# star masses from the BPASS v2.2.1 README files +mass_list = [round(0.1, 1)] + [round(0.12 + x * 0.020, 2) for x in range(95)] +mass_list = mass_list + [round(2.05 + x * 0.05, 2) for x in range(20)] +mass_list = mass_list + [round(3.1 + 0.1 * x, 1) for x in range(70)] +mass_list = (mass_list + [11 + x for x in range(90)] + + [120, 400, 500] + [125 + 25 * x for x in range(8)]) + + +def convert(x): + """ + Helper function to use for possible mass list. If the + number can be represented by an integer, + it turns into an integer. + This is to make sure that I am really matching masses. + """ + if (x % 1 == 0.0): + return int(x) + else: + return x + + +mass_list = list(map(convert, mass_list)) +period_list = ['0', '0.2', '0.4', '0.6', '0.8', '1', '1.2', '1.4', + '1.6', '1.8', '2', '2.2', '2.4', '2.6', '2.8', '3', + '3.2', '3.4', '3.6', '3.8', '4'] +combos = [] +for y in period_list: + for z in possible_secondary_q: + combos.append((z, y)) + + +def assign_props(dictionary, input_str, y): + dictionary[input_str] = y + return input_str + + +# Source: https://stackoverflow.com/questions/44369504/ +# how-to-convert-entire-dataframe-values-to-float-in-pandas +vals = hoki.dummy_dict.values() +vals = [hoki.dummy_dict[keyword] for keyword in ['timestep', 'age', + 'log(R1)', 'log(T1)', + 'log(L1)', 'M1', 'X', + 'P_bin', 'log(a)', 'M2', + 'log(R2)', 'log(T2)', + 'log(L2)']] +cols_to_keep = ["col" + str(v + 1) for v in vals] +# According to the BPASS v2.2.1 manual, much of +# columns 50 and onward +# are basically spectra and atmosphere related models. +# They will be calculated later. +# I have only kept the column numbers corresponding to +# Source for inverse mapping: https://stackoverflow.com/questions/483666/ +# reverse-invert-a-dictionary-mapping +# I create a mapping from column NUMBER to +# column name +invmap = {u: v for v, u in hoki.dummy_dict.items()} +# Accounting for zero indexing, write out a list +# of column names to assign to each BPASS dat file +# column name. +# Keep in mind the non-zero indexing of the data +# table itself. +lisnp = [invmap[int(x[3:]) - 1] for x in cols_to_keep] + + +def find_mergers(star_table, initial_mass): + """ + given star_table, an Astropy Table of primary masses of a + NEWBINMODS stellar model + and age, and given the initial mass of the stellar model, + sorts the star_table by age and + returns the index at which the primary star's mass increases. + If the mass of the primary star ends up being greater than the + initial mass at the very first + age represented by the star_table, then 0 is designated as + the index where the merger occurs. + If the merger does not occur then the length of the star_table + is returned. (cannot be a valid index + in a table). This is to ensure that all rows corresponding + to positions after the returned index of the + star_table are merger. + """ + + if (star_table['M1'][0] > initial_mass + 0.01): + return 0 + # returns index where the merger is completed + # assuming that f is a merger model. + # We need to make sure that Mass of primary is increasing as age increases. + # I make sure that age is actually increasing and + # that the mass of the primary + # is increasing by at least 0.01 solar masses. + # Per Dr. Jan Edlridge, the increase in mass is supposed to be + # an indicator of a merger happening + val = np.where((np.diff(star_table['M1']) > 0.01) & + (np.diff(star_table['age']) >= 0))[0] + if (len(val)): + return val[-1] + 1 + return len(star_table) + + +def reformatter(destination, metallicity): + """I will expedite the process of writing and saving files by + making the BPASS stellar input files binary fits tables. + This function reads every stellar model of the specified metallicities + as an astropy table. Deletes photometry related columns of each stellar + model and renames the columns to a human readable name as prescribed by + the hoki.dummy_dict. Saves the reformatted BPASS stellar evolution model + as a FITS formatted file for the sake of speed of processing later on. + + Parameters + ---------- + destination: String + describes the absolute path to the directory of BPASS models. + Has a dash at the end + metallicity: List of strings + represents the metallicities of the BPASS input files that are + to be processed. + + Remarks: Run this on Terminal not on Ipython. This function goes + through tens of thousands of files full of data; expect it to take + a long time to complete during your setup if you choose to use this + via Jupyter Notebook. Using the reformatter on the the + terminal will help save time. + Cluster sub-class that produces a *resolved* stellar cluster. + A table is output with the synthetic photometry and intrinsic + properties of the individual stars (or stellar systems, if + mutliplicity is used in the IMF object). + + If multiplicity is used, than a second table is produced that + contains the properties of the companion stars independent of their + primary stars. + + Parameters + ----------- + iso: isochrone object + SPISEA isochrone object + + imf: imf object + SPISEA IMF object + + cluster_mass: float + Total initial mass of the cluster, in M_sun + + ifmr: ifmr object or None + If ifmr object is defined, will create compact remnants + produced by the cluster at the given isochrone age. Otherwise, + no compact remnants are produced. + + seed: int + If set to non-None, all random sampling will be seeded with the + specified seed, forcing identical output. + Default None + + vebose: boolean + True for verbose output. + """ + # I will use the Python HashSet since (assuming that strings + # have a good HashCode, adding to the HashSet should be fast in + # normal case. + if (not os.path.isdir(destination)): + os.makedirs(destination) + setofAll = set() + for x in metallicity: + # Generate all possible BPASS models with the given metallicities. + # Get a set of all single star models. + a = set(glob.glob("{}/NEWSINMODS/{}/*".format(hoki.MODELS_PATH, x))) + # Get a set of all Quasi-Chemicall Homogeneous Evolution (Binary) + # Models. + d = set(glob.glob("{}/NEWSINMODS/{}hmg/*".format(hoki.MODELS_PATH, x))) + # Get a set of all regular binary and merged binary models. + b = set(glob.glob(("{}/NEWBINMODS/" + + "NEWBINMODS/{}/*").format(hoki.MODELS_PATH, x))) + # Get a set of all models where the primary is a compact remnant. + c = set(glob.glob(("{}/NEWBINMODS/" + + "NEWSECMODS/{}_2/*").format(hoki.MODELS_PATH, x))) + setofAll = setofAll.union(a, b, c, d) + if not os.path.isdir("{}/{}/".format(destination, x)): + os.makedirs("{}/{}/".format(destination, x)) + # Creates container directory for stellar models grouped by metallicity + # and whether they are single or binary related + # the latter included mergers, QHE Models + # Single star models that are not the hmg models + # go into the /sin + # directory. + new_sin_to_met = len(hoki.MODELS_PATH) + len("/NEWSINMODS/") + new_sin_and_met = len(hoki.MODELS_PATH) + len("/NEWSINMODS/zxxx/") + new_hmg_and_met = len(hoki.MODELS_PATH) + len("/NEWSINMODS/zxxxhmg/") + new_bin_to_met = len(hoki.MODELS_PATH) + len("/NEBINMODS/NEWBINMODS/") + new_sec_to_met = len(hoki.MODELS_PATH) + len("/NEBINMODS/NEWSECMODS/") + new_bin_and_met = len(hoki.MODELS_PATH) + \ + len("/NEBINMODS/NEWBINMODS/zxxx/") + new_sec_and_met = len(hoki.MODELS_PATH) + \ + len("/NEBINMODS/NEWBINMODS/zxxx_2/") + for x in setofAll: + astroTable = Table.read(x, format='ascii') + # Drop all of the columns that are not defined by the + # HOKI package provided mapping of column name to + # column number. + # We will be recalculating the photometry related stuff + # Using zero indexing for list of columns, + # All columns (except column number 12, 13, 36) + # up to 48 inclusive will be kept + # when we create the isochrone. + astroTable.keep_columns(cols_to_keep) + astroTable.rename_columns(astroTable.colnames, lisnp) + astroTable.sort("age") + # Will remove photometry related columns + # Merger Related indicates whether the star is + # a model with a merger in it. + # Checking if the model is a single star model. + sin_segment = x[new_sin_to_met:new_sin_to_met + 4] + # SEE IF THE FILE IS IN THE NEWSINMODS directory + if (x[len(hoki.MODELS_PATH) + 1:len(hoki.MODELS_PATH) + + len("NEWSINMODS") + 1] == "NEWSINMODS" and x[new_sin_to_met + + 4:new_sin_to_met + + 7] != "hmg"): + + astroTable.write(destination + + "/" + sin_segment + "/Fits" + + "Models{}sin.fits".format( + x[new_sin_and_met:]), + format='fits', overwrite=True) + else: + # Checking if the model is a HMG Binary model + # (Still for some reason in SINMODS) + if x[len(hoki.MODELS_PATH) + 1:len(hoki.MODELS_PATH) + 1 + + len("NEWSINMODS")] == "NEWSINMODS": + + astroTable.write(destination + "/" + + sin_segment + "/Fits" + + "Models{}hmg.fits".format(x[new_hmg_and_met:]), + format='fits', overwrite=True) + continue + # Models that count as secondary star with compact remnants + # go into the /bin subdirectory of + # the destination. + if x[len(hoki.MODELS_PATH) + 1:len(hoki.MODELS_PATH) + 1 + + len("NEWBINMODS/XXXXXXXXXX")] == "NEWBINMODS/NEWSECMODS": + + astroTable.write(destination + + "/" + x[new_sec_to_met: + (new_sec_and_met) - 2] + + "/FitsModels{}sec.fits".format(x[new_sec_and_met + 1:]), + format='fits', overwrite=True) + else: + # Models that count as secondary star with compact remnants + # go into the / subdirectory of the destinatuion. + + astroTable.write(destination + '/' + + x[new_bin_to_met: new_bin_and_met] + + "/FitsModels{}bin.fits".format(x[new_bin_and_met + 1:]), + format='fits', overwrite=True) + + +def extractor(age, metallicity, input_dir, bpass_evo_dir, + margin): + """ + The following extracts the preprocessed BPASS + isochrone file + from the pool of listed files. From the subdirectory + of the input directory, the function inspects all of the files + and sees if there are rows in the files that correspond to + the specified age (log age to be precise). + Checks for merger related (models_type=0) models + and mark them using the functions merger_related and + find_mergers respectively. + + + + parameters + ---------- + age: float or double + The age in years of the isochrone desired + metallicity: string + The BPASS metallicity of the isochrone desired + input_dir: string + The absolute path to the directory with the reformatted BPASS stellar + models. The path should be to a folder whose name was recently + passed into reformatter. + bpass_evo_dir: string + The absolute path to the directory where the outputs of this program, + preprocessed isochrone files for BPASS, will + be stored in subdirectories + named after their metallicities. + margin: float or double + Contains the margin of error of base-10 logAge that will be + tolerated for rows of stellar model that are included in + the isochrone. + + Output: + Saves into the specified destination + directory a reformatted, FITS format version + of a preprocessed BPASS isochrone of the specified age + and metallicity whose individual + rows correspond to stars of age closest to + the specified age and within the specified + margin of error of log(age of the star in years) + + Parameters added to the table: + ‘mass’ - The column corresponding to the initial mass + of the primary/single star. + ‘mass2’ - The column corresponding to the initial mass of + the secondary. Set to nan if the system is not a binary + ‘log_P’ - The column corresponding to the log(Period in days) of + the binary. Set to nan if the system is not a binary + ‘Single’ - The column that tells whether the stellar system is single + ‘Merged?’ - Whether the binary star has been merged. + + """ + # Set of files that exist and have been caught by the looping. + caught_no = set() + # Mapping of file name to a tuple of properties + # (primary star mass,(secondary star mass, initial logP for bin.fits types) + names_to_prop = {} + # Find all filenames of reformatted BPASS models from reformatter + for x in mass_list: + # Find all NEWBINMODS systems of the specified metallicity + for y in combos: + st = "{}/{}/FitsModelssneplot-{}-{}-{}-{}bin.fits" + st = st.format(input_dir,metallicity, + metallicity, + str(x), y[0], + y[1]) + names_to_prop[st] = (str(x), y[0], y[1]) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all single star systems of the specified metallicity + st = "{}/{}/FitsModelssneplot-{}-{}sin.fits" + st = st.format(input_dir, metallicity, + metallicity, str(x)) + names_to_prop[st] = (str(x), ) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all Binary QHE systems of the specified metallicity + st = "{}/{}/FitsModelssneplot-{}-{}hmg.fits" + st = st.format(input_dir, metallicity, + metallicity, str(x)) + names_to_prop[st] = (str(x), ) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all NEWSINMODS systems of the specified metallicity + st = "{}/{}/FitsModelssneplot_2-{}-{}-*sec.fits" + st = st.format(input_dir, metallicity, + metallicity, + str(x)) + li = glob.glob(str(st)) + sec_files = set(li) + caught_no = caught_no.union(sec_files) + [assign_props(names_to_prop, name, (x,)) for name in sec_files] + len_of_heading = len("{}/{}/FitsModelssneplot_2-{}-".format(input_dir, metallicity, metallicity)) + bigOne = None + initial = True + initlMass = np.nan + initlMass2 = np.nan + indicesOfInterest = None + entries = glob.glob(str("{}/{}/*".format(input_dir, metallicity))) + suffix_len = len("xxx.fits") # 8 + for x in entries: + # If I want to see the name of the file, uncomment below. + # print(x) + # x is the name of the reformatted stellar evolution file. + # Rest carries the initial mass of the system and for NEWBINMODS + # systems carries the secondary and log_P in days + rest = names_to_prop[x] + # The following conditional would have been that x is a path to a file, + # but this seems to be always true. + # as I am getting the entries from globs and + # prior checks as to whether they are existing files. + # We only want to consider the + org = Table.read(x, format='fits') + indicesOfInterest = np.where(np.abs(np.log10(org['age']) - + age) <= margin)[0] + f = org[indicesOfInterest] # f stands for frame in DataFrame + # which is how AstroPy was thought of. + indicesOfInterest = np.array(indicesOfInterest) + # If no stars have a log10(age) within margin of + # the given lage in log-10 years, we + # must skip over to the next model. + if (len(f) != 0): + # Find the star with age closest to the input log(Age of the star) + filterDown = np.where(np.abs(f['age'] - 10 ** age) == + np.min(np.abs(f['age'] - 10 ** age)))[0] + f = f[filterDown] + indicesOfInterest = np.array([indicesOfInterest[filterDown][0]]) + if len(f) != 0: + f = f[[0]] + f['source'] = 0 + indexlen = 1 + if (x[-suffix_len:-5] == 'bin'): + f['single'] = np.repeat(False, indexlen) + initlMass = float(rest[0]) + f['mass'] = np.repeat(initlMass, indexlen) + f['mass2'] = np.repeat(initlMass * float(rest[1]), + indexlen) + f['initl_logP'] = np.repeat(float(rest[2]), indexlen) + # Now, for binaries, I check whether a model is + # a merger model + # is the same for all rows of the model + merge_pt = find_mergers(org[['age', 'M1']], initlMass) + # Find whether we should treat our model as + # one big star or still two stars + # will still keep initial parameters + # for the sake of working + # with the Duchene-Krauss distributions + f['mergered?'] = indicesOfInterest >= merge_pt + # Mark the problem-rows + f['source'] = 1 + elif (x[-suffix_len:-5] == 'sec'): + # Recall that the ending of the file + # is going to be XXX.fits + # The XXX can be sec, hmg, bin, sin. + # I will explot the pattern that the log_P is + # going to be either 9 characters, + # 8 characters, or 7 characters + # long. + f['single'] = np.repeat(False, indexlen) + initlMass = float(rest[0]) + # See if the number would begin + # right after a dash in index -18. + # 8 characters given for the xxx.fits + # 9 spaces for the decimal number. + # We want the dash before that as our cue. + if (x[-10 - 1 * suffix_len] == "-"): + # Here the decimal is number is 9 + # characters long and + # xxx.fits has length of 8 + log_P_in_days = float(x[-9 - suffix_len: + -1 * suffix_len]) + # Accounting for the dashes, find the + # sandwiched initial + # mass of the "remnant primary" + # sandwiched between initial secondary star mass + # and the initial logP + initlMass2 = float(x[(len_of_heading + + len(str(rest[0]) + "-")): + -10 - 1 * suffix_len]) + f['source'] = 2 + else: + # See if the number begins right + # after a dash in index -17 + # 8 for the xxx.fits and 8 for + # the log_P and we want + # the preceding dash. + + if (x[-9 - 1 * suffix_len] == "-"): + + # Here the decimal is number is 8 + # characters long and + # xxx.fits has length of 8. + log_P_in_days = float(x[-8 - suffix_len: + -1 * suffix_len]) + # Accounting for the dashes, find + # the sandwiched initial mass of the + # "remnant primary" + # sandwiched between initial secondary star + # mass and the initial logP + initlMass2 = float(x[(len_of_heading + + len(str(rest[0]) + + "-")): + -9 - 1 * suffix_len]) + # Assume that the number would begin + # at index -16. (From my observation, we only + # have a few choices for number of digits of + # the log_period + f['source'] = 3 + else: + # Here the decimal is number is 7 + # characters long and + # xxx.fits has length of 8 + log_P_in_days = float(x[-7 - suffix_len: + -1 * suffix_len]) + # Accounting for the dashes on both + # ends of the "remnant primary" mass, find the + # sandwiched initial mass of the remnant primary + # sandwiched between initial secondary star mass + # and the initial logP + initlMass2 = float(x[(len_of_heading + + len(str(rest[0]) + + "-")): + -8 - 1 * suffix_len]) + f['source'] = 4 + f['mass'] = np.repeat(initlMass2, indexlen) + f['mass2'] = np.repeat(initlMass, indexlen) + temp1 = f['M2'] + temp2 = f['log(T2)'] + temp3 = f['log(R2)'] + temp4 = f['log(L2)'] + f['M2'] = f['M1'] + f['log(R2)'] = f['log(R1)'] + f['log(T2)'] = f['log(T1)'] + f['log(L2)'] = f['log(L1)'] + f['M1'] = temp1 + f['log(R1)'] = temp3 + f['log(T1)'] = temp2 + f['log(L1)'] = temp4 + f['initl_logP'] = np.repeat(log_P_in_days, indexlen) + f['mergered?'] = np.repeat(False, indexlen) + f['single'] = np.repeat(False, indexlen) + elif (x[-1 * suffix_len: -5] == 'hmg'): + f['single'] = np.repeat(True, indexlen) + initlMass = float(rest[0]) + # To be consistent with obtaining initial mass + # (or whichever value is closest) for the HMG Model). + # Per Eldridge the QHE models are single star models. + initlMass2 = np.nan + P_in_days = np.nan + f['mass'] = np.repeat(initlMass, indexlen) + f['mass2'] = np.repeat(initlMass2, indexlen) + f['initl_logP'] = np.repeat(P_in_days, indexlen) + f['mergered?'] = np.repeat(False, indexlen) + f['source'] = 5 + else: + f['single'] = np.repeat(True, indexlen) + # Single stars do not have companions. + initlMass = rest[0] + initlMass2 = np.nan + P_in_days = np.nan + f['mass'] = np.repeat(initlMass, indexlen) + f['mass2'] = np.repeat(initlMass2, indexlen) + f['initl_logP'] = np.repeat(np.nan, indexlen) + f['mergered?'] = np.repeat(False, indexlen) + f['source'] = 6 + if initial: + initial = False + bigOne = f.to_pandas() + else: + bigOne = pd.concat([f.to_pandas(), bigOne]) + if not isinstance(bigOne, type(None)) and not (bigOne['age'].empty): + bigOne = bigOne.apply(pd.to_numeric, errors='coerce') + reduced = Table.from_pandas(bigOne) + if not os.path.isdir('{}/iso/{}/'.format(bpass_evo_dir, metallicity)): + os.makedirs('{}/iso/{}/'.format(bpass_evo_dir, metallicity)) + reduced.write("{}iso/{}/iso{}.fits".format(bpass_evo_dir, + metallicity, str(age)), + format='fits', + overwrite=True) diff --git a/spisea/Sanity_Checks_Logg.ipynb b/spisea/Sanity_Checks_Logg.ipynb new file mode 100644 index 00000000..ea42280c --- /dev/null +++ b/spisea/Sanity_Checks_Logg.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "a0fe5409-e821-4b35-b1c7-9d989c521e03", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import astropy.constants as cs\n", + "import astropy.units as un\n", + "\n", + "R1 = 1.0\n" + ] + }, + { + "cell_type": "markdown", + "id": "af3a1b29-e189-4396-b09d-c85846c45c57", + "metadata": {}, + "source": [ + "First Star: The Sun!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "138c0605-c54a-45f3-be27-139f67eecd09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$4.4380676 \\; \\mathrm{}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log10((1)* cs.GM_sun /\n", + " (((10 ** (0) *\n", + " cs.R_sun) ** 2)) *\n", + " un.s * un.s/un.cm)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f6c7ec80-a162-46c5-ac1b-871f239442a7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.437750562820388" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log10(274*10**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "feeb3bad-a051-4cd1-9935-a6bd7be8508e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$4.3043234 \\; \\mathrm{}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log10((1.100)* cs.GM_sun /\n", + " (((10 ** (np.log10(1.2234)) *\n", + " cs.R_sun) ** 2)) *\n", + " un.s * un.s/un.cm)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "f856df29-4a00-49fb-9fc4-7ffa3ae033ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$4.5234521 \\; \\mathrm{}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.log10((0.907)* cs.GM_sun /\n", + " (((10 ** (np.log10(0.8632)) *\n", + " cs.R_sun) ** 2)) *\n", + " un.s * un.s/un.cm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59d92ab4-a301-4750-b34d-6bc30a419a68", + "metadata": {}, + "outputs": [], + "source": [ + "np.log10((0.907)* cs.GM_sun /\n", + " (((10 ** (np.log10(0.8632)) *\n", + " cs.R_sun) ** 2)) *\n", + " un.s * un.s/un.cm)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "astroconda", + "language": "python", + "name": "astroconda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/evolution.py b/spisea/evolution.py index e3ddf23c..4c6806ca 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -10,9 +10,27 @@ from scipy import interpolate import pylab as py from spisea.utils import objects +from astropy import constants as cs +from astropy import units +import spisea.IsochroneMakerReformattedVersionNew +from spisea.IsochroneMakerReformattedVersionNew import extractor, reformatter +un = units logger = logging.getLogger('evolution') +# Plug in some rough bounds for parameters such that +# if the parameter is greater than the specified value +# that parameter is almost certainly bogus! (Best used with good physical +# sense) +# User is more than welcome to modify these bounds! +upper_logg = 20 # in CGS +upper_Teff = 10 ** 8.0 # in Kelvin +upper_mass = 10 ** 4.0 # in solar masses +upper_log_a = 10 # in log - solar radii +upper_L = 10 ** 9 # in solar luminosity + + + # Fetch root directory of evolution models. try: models_dir = os.environ['SPISEA_MODELS'] @@ -25,18 +43,14 @@ class StellarEvolution(object): """ Base Stellar evolution class. - Parameters ---------- model_dir: path Directory path to evolution model files - age_list: list List of ages - mass_list: list List of masses - z_list: list List of metallicities """ @@ -112,9 +126,7 @@ class Ekstrom12(StellarEvolution): """ Evolution models from `Ekstrom et al. 2012 `_. - Downloaded from `website `_. - Parameters ---------- rot: boolean, optional @@ -201,11 +213,9 @@ def format_isochrones(input_iso_dir): r""" Parse iso.fits (filename hardcoded) file downloaded from Ekstrom+12 models, create individual isochrone files for the different ages. - input_iso_directory should lead to Ekstrom2012/iso/ directory, where iso.fits file should be located. - Creates two new directories, rot and norot, which contain their respective isochrones. """ @@ -260,7 +270,6 @@ def create_iso(fileList, ageList, rot=True): Given a set of isochrone files downloaded from the server, put in correct iso.fits format for parse_iso code. - fileList: list of downloaded isochrone files (could be one) ageList: list of lists of ages associated with each file in filelist. @@ -325,13 +334,10 @@ class Parsec(StellarEvolution): Evolution models from `Bressan et al. 2012 `_, version 1.2s. - Downloaded from `here _` - Notes ----- Evolution model parameters used in download: - * n_Reimers parameter (mass loss on RGB) = 0.2 * photometric system: HST/WFC3 IR channel * bolometric corrections OBC from Girardi+08, based on ATLAS9 ODFNEW models @@ -426,7 +432,6 @@ def format_isochrones(input_iso_dir, metallicity_list): input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity subdirectories already exist with isochrone files downloaded in them (isochrones files expected to start with "output*") - metallicity_list format: absolute (vs. relative to solar), z + : e.g. Z = 0.014 --> z014 """ @@ -478,11 +483,9 @@ class Pisa(StellarEvolution): `Tognelli et al. 2011 `_. Downloaded `online `_ - Notes ------ Parameters used in download: - * Y = middle value of 3 provided (changes for different metallicities) * mixing length = 1.68 * Deuterium fraction: 2*10^-5 for Z = 0.015, 0.03; 4*10^-4 for 0.005 @@ -563,14 +566,12 @@ def format_isochrones(input_iso_dir, metallicity_list): r""" Rename the isochrone files extracted from Pisa (Tognelli+11) to fit naming/directory scheme - input_iso_dir: points to Pisa2011/iso directory. Individual metallicity directories with the downloaded isochrones are expected to already exist there metallicity_list is the list of metallicities on which function is to be run. - format for metallicity_list : absolute (vs. relative to sun) 'z' + , e.g Z = 0.015 --> z015. """ @@ -617,7 +618,6 @@ def make_isochrone_grid(metallicity=0.015): Create isochrone grid of given metallicity with time sampling = 0.01 in logAge (hardcoded). This interpolates the downloaded isochrones when necessary. Builds upon the online iscohrone grid. - Note: format of metallicity is important. After decimal point, must match the format of the metallcity directory (i.e., 0.015 matches directory z015, while 0.0150 would not) @@ -642,7 +642,6 @@ class Baraffe15(StellarEvolution): """ Evolution models published in `Baraffe et al. 2015 `_. - Downloaded from `BHAC15 site `_. """ def __init__(self): @@ -718,7 +717,6 @@ def tracks_to_isochrones(self, tracksFile): Create isochrones at desired age sampling (6.0 < logAge < 8.0, steps of 0.01; hardcoded) from the Baraffe+15 tracks downloaded online. - tracksFile: tracks.dat file downloaded from Baraffe+15, with format modified to be read in python @@ -930,9 +928,7 @@ class MISTv1(StellarEvolution): """ Define intrinsic properties for the MIST v1 stellar models. - Models originally downloaded from `online server `_. - Parameters ---------- version: '1.0' or '1.2', optional @@ -1070,15 +1066,12 @@ def format_isochrones(self, input_iso_dir, metallicity_list): Parse isochrone file downloaded from MIST web server, create individual isochrone files for the different ages. Assumes all files start with MIST_iso* - Parameters: ----------- input_iso_dir: path Points to MISTv1//iso directory. - metallicity_list: array List of metallicity directories to check (i.e. z015 is solar) - """ # Store current directory for later start_dir = os.getcwd() @@ -1130,25 +1123,19 @@ def format_isochrones(self, input_iso_dir, metallicity_list): class MergedBaraffePisaEkstromParsec(StellarEvolution): """ This is a combination of several different evolution models: - * Baraffe (`Baraffe et al. 2015 `_) * Pisa (`Tognelli et al. 2011 `_) * Geneva (`Ekstrom et al. 2012 `_) * Parsec (version 1.2s, `Bressan+12 `_) - The model used depends on the age of the population and what stellar masses are being modeled: - For logAge < 7.4: - * Baraffe: 0.08 - 0.4 M_sun * Baraffe/Pisa transition: 0.4 - 0.5 M_sun * Pisa: 0.5 M_sun to the highest mass in Pisa isochrone (typically 5 - 7 Msun) * Geneva: Highest mass of Pisa models to 120 M_sun - For logAge > 7.4: - * Parsec v1.2s: full mass range Parameters @@ -1235,7 +1222,6 @@ class MergedPisaEkstromParsec(StellarEvolution): """ Same as MergedBaraffePisaEkstromParsec, but without the Baraffe models. - Parameters ---------- rot: boolean, optional @@ -1308,20 +1294,16 @@ def isochrone(self, age=1.e8, metallicity=0.0): class MergedSiessGenevaPadova(StellarEvolution): """ This is a combination of several different evolution models. - The model used depends on the age of the population and what stellar masses are being modeled: - * Siess (`Siess et al. 2000 `_) * Geneva (`Meynet & Maeder 2003 `_) * Padova (`Marigo et al. 2008 `_) - For logAge < 7.4: * Siess: 0.1 - 7 M_sun * Siess/Geneva transition: 7 - 9 M_sun * Geneva: > 9 M_sun - For logAge > 7.4: * Padova: full mass range @@ -1415,7 +1397,6 @@ def make_isochrone_pisa_interp(log_age, metallicity=0.015, """ Read in a set of isochrones and generate an isochrone at log_age that is well sampled at the full range of masses. - Puts isochrones is Pisa2011/iso// """ # If logage > 8.0, quit immediately...grid doesn't go that high @@ -1615,6 +1596,282 @@ def get_orig_pisa_isochrones(metallicity=0.015): return data -class Isochrone(object): - def __init__(self, log_age): - self.log_age = log_age +# ============================== # +# BPASS v2.2 models +# ============================== # +# These models were published by Stanway and Elridge et. al in 2018 +# The code is based from the Merged Evolution model methods outlined in the +# existing code. +# After all, I want some degree of consistency with regards to +# Our original isochrone files are the stellar models (single, binary, and +# secondary) listed in the BPASS input files from one of the stellar models +# for ages 10**6.0, 10**6.1, ... , 10**11.0 years. + + +class BPASS(StellarEvolution): + """ These models were published by Stanway and Elridge et. al in 2018 + Some of the code is based from the Merged Evolution model methods + by Hosek et. al. After all, I want some degree of consistency with + regards to how isochrones are made.Also, I do not want to reinvent + the wheel twice. + If one has not checked the IsochroneMakerReformatted.py file, each + isochrone file is made of star from stellar model that are closest + to specified input age and are within the specified margin of error + for log(Age in years). This is to be doubly sure that I am getting + the same age. + Brief description: Creates a BPASS evolution object and + specifies valid primary masses, + secondary masses, metallicities, ages. + """ + def __init__(self): + """ + Note: All of the possible mass list items include possible mass for + single star, binary primary and secondary. + Recall that BPASS age bins + are 10**6.0, 10**6.1, ... , 10**11.0 yrs """ + self.age_list = [round(6.0 + x * 0.1, 1) for x in range(0, 51)] + self.z_list = [ + 10 ** -5, + 10 ** -4, + 10 ** -3, + 0.002, + 0.003, + 0.004, + 0.006, + 0.008, + 0.010, + 0.014, + 0.020, + 0.030, + 0.040, + ] + self.models_dir = models_dir + "/BPASS/v2.2/" + self.z_solar = 0.020 + # The possible Metallicities of a BPASS Stellar model + self.z_file_map = { + 10 ** -5: 'zem5/', + 10 ** -4: 'zem4/', + 0.00300: 'z003/', + 0.00200: 'z002/', + 0.00300: 'z003/', + 0.00400: 'z004/', + 0.00600: 'z006/', + 0.00800: 'z008/', + 0.01000: 'z010/', + 0.01400: 'z014/', + 0.02000: 'z020/', + 0.03000: 'z030/', + 0.04000: 'z040/', + } + def run_formatter(self, destination="/g/lu/scratch/ryotainagaki/" + + "BPASS_tester_newReformatTest/", + metallicity=["zem5", "zem4", "z001", "z002", "z003", "z004", + "z006", "z008", "z010", "z014", "z030", "z040"]): + """ + Reformat all BPASS stellar system models in HOKI.MODELS_PATH + using the reformatter function into fits tables with the following + naming convention: + Fitsmodels.fits + The files will be stored in destination/ directory + """ + reformatter(destination, metallicity) + return + + def run_extractor(self, source="/g/lu/scratch/ryotainagaki/" + + "BPASS_tester_newReformatTest/", + metallicity=["zem5", "zem4", "z001", "z002", "z003", + "z004", "z006", "z008", "z010", "z014", + "z030", "z040"], + destination=models_dir + "/BPASS/v2.2/", + times=51): + """ + Uses the reformatted BPASS stellar system model files in source directory + to create isochrones for log10(Age) = 6.0, 6.1, ... , 6.0 + 0.1*(x-times) + and for every metallicity. Stores those isochrones into the + destination/iso/ directory. + """ + for met in metallicity: + for x in range(times): + extractor(round(6.0 + 0.1 * x,1), met, source, + destination, 0.05) + + def isochrone(self, age=1 * 10 ** 8.0, metallicity=0.0): + """ + This function adds several necessary + columns (e.g. isWR, logg, and + whether a star is WR) to the existing + isochrone files' tables. + Important note on convention: I will specify + the phase of the star as follows: + White dwarf -> 101 + All other stars (no neutron stars or black holes) -> -1 + Just to make sure, I do have word that neutron stars + and black holes are NOT INCLUDED in the BPASS + evolution grids. + If you are REALLY curious about black holes, try using TUI + with BPASS. + + Parameters + ---------- + dir_in: string + The string representation of the absolute path to the directory + of preprocessed isochrones.This is for customization/Testing purposes. + For folks do not want to store in the models directory. + age: float or double + The age of the isochrone in years + metallicity: float or double + The log10(Z/Z_solar) of the star + (HERE THIS IS IN terms of Z which is the initial + metallicity mass fraction from the formula introduced + in Grevesse & Noels 1993.) + Z_solar = 0.020 + Output + ------ + A more informative version of the preprocessed isochrone. + New Columns: + isWR: Boolean + whether a single or primary is a WR star + isWR2: Boolean + phase: Integer (101 if white dwarf, 12 otherwise) + indicates whether a star is a white dwarf + logg: float or double + the log10(surface gravity of the primary/single in m/s^2) + logg2: float or double + the log10(surface gravity of the secondary in m/s^2) + set to N.A. when the system is nonbinary + log_ai: float or double + the log10(separation of the primary and secondary in AU) + set to N.A. if the system is not a binary system + More clearly named are the columns for the + current masses of both stars. + 'mass_current' column stands for the current + mass of the primary star + 'mass_current2' column stands for the current + mass of the secondary star + """ + oldmetallicity = metallicity + # The following metallicity fraction as how BPASS is organized. + # Borrow from matt hosek's code: + # Look for the closest valid age to the input age + metallicity = self.z_solar * 10 ** metallicity + log_age = math.log10(age) + if log_age < np.min(self.age_list) or log_age > np.max(self.age_list): + logger.error('Requested age {0} is out of bounds.'.format(log_age)) + if (metallicity < np.min(self.z_list) or + metallicity > np.max(self.z_list)): + logger.error('Requested metallicity {0} is out of bounds.'. + format(metallicity)) + iso_file = 'iso' + str(round(log_age, 1)) + '.fits' + # Find the closest valid metallicity to the given metallicity + closest_metallicity = min([x for x in self.z_file_map], + key=lambda x: abs(metallicity - x)) + z_dir = self.z_file_map[closest_metallicity] + # Use the full path to the desired isochrone file. + + full_iso_file = self.models_dir + 'iso/' + z_dir + iso_file + iso = Table.read(full_iso_file, format='fits') + # Create columns for isWR, logg, + # Phase. I will need to find how + # phase for individual stars will be added + # then I will fill it in with appropriate values. + isWR = Column(np.repeat(False, len(iso)), name='isWR') + isWR2 = Column(np.repeat(False, len(iso)), name='isWR2') + colG = Column(np.repeat(np.nan, len(iso)), name='logg') + colP = Column(np.repeat(np.nan, len(iso)), name='phase') + colP2 = Column(np.repeat(np.nan, len(iso)), name='phase2') + collog_a = Column(np.repeat(np.nan, len(iso)), name='log_ai') + iso.add_column(colG) + iso.add_column(isWR) + iso.add_column(colP) + iso.add_column(colP2) + iso.add_column(isWR2) + iso.add_column(collog_a) + # We may as well delete a for loop here + # Convert to CGS in the next two lines. + iso['logg'] = np.log10((iso['M1'] * cs.GM_sun / + (((10 ** iso['log(R1)']) * + cs.R_sun) ** 2)) * + un.s * un.s/un.cm) + iso['logg2'] = np.log10((iso['M2'] * cs.GM_sun / + (((10 ** iso['log(R2)']) * + cs.R_sun) ** 2)) * + un.s * un.s/un.cm) + # Apply Kepler's Third law to find the log of initial separation of the + # binary system. + iso['log_ai'] = np.log10((cs.GM_sun * (un.s ** 2) / (un.m ** 3)) ** + (1 / 3) * + ((iso['mass'] + iso['mass2']) * + ((24 * (60 ** 2) * (10 ** + iso['initl_logP'])) ** + 2) / (4 * (np.pi) ** 2)) ** (1 / 3) * + (1 / cs.au) * un.m) + iso.rename_column('M1', 'mass_current') + iso['age'] = np.log10(iso['age']) + iso['phase'] = 5 + iso['phase2'] = 5 + # We may also want to consider which secondary stars are invalid + # i.e. have some infinite current mass, surface gravity, temperature + # or log(L2) + # We shouldn't worry much about replacing Temperature and Luminosity + # type parameters. Even if dealing with compact remnants, + # there should not be infinite parameters. + problematic_secsM = np.where(iso['M2'] > upper_mass) + iso['M2'][problematic_secsM] = np.nan + iso['phase2'][problematic_secsM] = -99 + problematic_secs_lg = np.where(iso['logg2'] > upper_logg) + iso['logg2'][problematic_secs_lg] = np.nan + iso['phase2'][problematic_secs_lg] = -99 + problematic_secs_T = np.where((10 ** iso['log(T2)'] > upper_Teff)) + iso['log(T2)'][problematic_secs_T] = np.nan + iso['phase2'][problematic_secs_T] = -99 + problematic_secs_L = np.where((10 ** iso['log(L2)'] > upper_L)) + iso['log(L2)'][problematic_secs_L] = np.nan + iso['phase2'][problematic_secs_L] = -99 + + # Now deal with primary stars + problematic_primsM = np.where(iso['mass_current'] > upper_mass) + iso['mass_current'][problematic_primsM] = np.nan + iso['phase'][problematic_primsM] = -99 + problematic_prims_lg = np.where(iso['logg'] > upper_logg) + iso['logg'][problematic_prims_lg] = np.nan + iso['phase'][problematic_prims_lg] = -99 + problematic_prims_T = np.where((10 ** iso['log(T1)'] > upper_Teff)) + iso['log(T1)'][problematic_prims_T] = np.nan + iso['phase'][problematic_prims_T] = -99 + problematic_prims_L = np.where((10 ** iso['log(L1)'] > upper_L)) + iso['log(L1)'][problematic_prims_L] = np.nan + iso['phase'][problematic_prims_L] = -99 + problematic_log_a = np.where(np.abs(iso['log(a)']) > upper_log_a) + iso['log(a)'][problematic_prims_L] = np.nan + + # Using Stanway and Elridge Criterion for calculating whether + # a star is a WR star or not for using the PotsDam Atmospheres + # Decided to get rid of loops in order to take fuller advantage + # of the C-based numpy! + iso['isWR'] = (iso['X'] < 0.40) & (iso['log(T1)'] >= 4.45) + iso['isWR2'] = (iso['X'] < 0.40) & (iso['log(T2)'] >= 4.45) + iso['phase'][np.where((iso['logg'] > 6.9) & (iso['log(L1)'] < -1) & + (iso['mass_current'] < 1.4))[0]] = 101 + iso['phase'][np.where(((iso['source'] == 2) | (iso['source']==3) | + (iso['source'] == 4)) & + (np.round(iso['mass_current'], 1) == 1.4))[0]] = 102 + iso['phase'][np.where(((iso['source'] == 2) | (iso['source'] == 3) | + (iso['source'] == 4)) & + (np.round(iso['mass_current']) > 3.0))[0]] = 103 + iso['phase2'][np.where((iso['logg2'] > 6.9) & + (iso['log(L2)'] < -1) & + (iso['M2'] < 1.4))[0]] = 101 + # Changing column name to + # Making sure that names of the columns are consistent with + # general format of isochrone. + iso.rename_column('log(T1)', 'logT') + iso.rename_column('M2', 'mass_current2') + iso.rename_column('log(L1)', 'logL') + iso.rename_column('age', 'logAge') + iso['logT'][np.where(iso['phase'] >= 102)] = np.nan + iso['logL'][np.where(iso['phase'] >= 102)] = np.nan + iso.meta['log_age'] = age + iso.meta['metallicity_in'] = oldmetallicity + iso.meta['metallicity_act'] = np.log10(closest_metallicity / + self.z_solar) + return iso \ No newline at end of file diff --git a/spisea/iso_7.00_0.70_00100_p00.fits b/spisea/iso_7.00_0.70_00100_p00.fits new file mode 100644 index 00000000..b1e49e7a --- /dev/null +++ b/spisea/iso_7.00_0.70_00100_p00.fits @@ -0,0 +1,208 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 97 / length of dimension 1 NAXIS2 = 554 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 13 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_ubv_U ' TFORM9 = 'D ' TTYPE10 = 'm_ubv_B ' TFORM10 = 'D ' TTYPE11 = 'm_ubv_V ' TFORM11 = 'D ' TTYPE12 = 'm_ubv_R ' TFORM12 = 'D ' TTYPE13 = 'm_ubv_I ' TFORM13 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' LOGAGE = 7.0 AKS = 0.7 DISTANCE= 100 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.02380291467262473 WAVEMIN = 3000 WAVEMAX = 52000 END E[&r@A-??2zV@'rWF?0lwr@=XI@=@;jU`@6RfU@1|XE2E @U AuT?@ɞ F?aS1@=LJc[–@=ާ@;[ B@6Ǡ/@1oQE(@̪|quABa?'@TF?z@=Lj]@=rUp@;t@6U@!@1c@i13E|/'@8zAޏ ?_!@c[F?\k@=+@=[OCp&r@;\@6pw@1VVIiE.;iG@_A\~>0?1~ @C&F?1tj@=r@=Pp_@;Pw}@6ӦS@1TEyhH@-+F"AnyTc?\ޤ;@F?\Ywx@=r@= @; e@6z#m@17P E4f<@7)UjdAH&?)t@oQF?)]pE.ZH@uhYL%A[oTWl? ͞p@<ɤF? 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Repair for zeros since we are plotting in semi-log-y + d.eN = np.ma.masked_where(d.N <= 0, d.eN) + d.N = np.ma.masked_where(d.N <= 0, d.N) + + d.eN_ext = np.ma.masked_where(d.N_ext <= 0, d.eN_ext) + d.N_ext = np.ma.masked_where(d.N_ext <= 0, d.N_ext) + + d.eN_ext_cmp_sp = np.ma.masked_where(d.N_ext_cmp_sp <= + 0, d.eN_ext_cmp_sp) + d.N_ext_cmp_sp = np.ma.masked_where(d.N_ext_cmp_sp <= + 0, d.N_ext_cmp_sp) + + d.eN_ext_cmp_sp_im = np.ma.masked_where(d.N_ext_cmp_sp_im <= + 0, d.eN_ext_cmp_sp_im) + d.N_ext_cmp_sp_im = np.ma.masked_where(d.N_ext_cmp_sp_im <= + 0, d.N_ext_cmp_sp_im) + + d.eKLF = np.ma.masked_where(d.KLF <= 0, d.eKLF) + d.KLF = np.ma.masked_where(d.KLF <= 0, d.KLF) + + d.eKLF_ext = np.ma.masked_where(d.KLF_ext <= 0, + d.eKLF_ext) + d.KLF_ext = np.ma.masked_where(d.KLF_ext <= 0, + d.KLF_ext) + + d.eKLF_ext_cmp_sp = np.ma.masked_where(d.KLF_ext_cmp_sp <= 0, + d.eKLF_ext_cmp_sp) + d.KLF_ext_cmp_sp = np.ma.masked_where(d.KLF_ext_cmp_sp <= 0, + d.KLF_ext_cmp_sp) + + d.eKLF_ext_cmp_sp_im = (np.ma. + masked_where(d.KLF_ext_cmp_sp_im <= + 0, d.eKLF_ext_cmp_sp_im)) + d.KLF_ext_cmp_sp_im = (np.ma. + masked_where(d.KLF_ext_cmp_sp_im <= + 0, d.KLF_ext_cmp_sp_im)) + + + _in.close() + + return d \ No newline at end of file diff --git a/spisea/synthetic.py b/spisea/synthetic.py index e6249bca..e9e7a48c 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -12,8 +12,8 @@ from pysynphot import ObsBandpass from pysynphot import observation as obs import pysynphot -from astropy import constants, units -from astropy.table import Table, Column, MaskedColumn +from astropy import units +from astropy.table import Table, Column, MaskedColumn, hstack, vstack import pickle import time, datetime import math @@ -24,17 +24,26 @@ import matplotlib.pyplot as plt import time import warnings -import pdb from scipy.spatial import cKDTree as KDTree import inspect import astropy.modeling +from astropy import constants +import pdb +import copy +from scipy.interpolate import LinearNDInterpolator +cs = constants +un = units +c = constants default_evo_model = evolution.MISTv1() default_red_law = reddening.RedLawNishiyama09() default_atm_func = atm.get_merged_atmosphere default_wd_atm_func = atm.get_wd_atmosphere + def Vega(): + + # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 # These parameters are defined in Girardi+02 vega = atm.get_kurucz_atmosphere(temperature=9550, @@ -53,7 +62,14 @@ def Vega(): vega = Vega() +class BypasserContainer(): + description = "This is so vectorization doesn't think that I'm an array" + + + class Cluster(object): + + """ Base class to create a cluster with user-specified isochrone, imf, ifmr, and total mass. @@ -82,8 +98,12 @@ class Cluster(object): vebose: boolean True for verbose output. """ + + def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, seed=None): + + self.verbose = verbose self.iso = iso self.imf = imf @@ -93,13 +113,884 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, return + +class Cluster_w_Binaries(Cluster): + """ + Cluster sub-class that produces a *resolved* stellar cluster with + binary evolution accounted for. + A table is output with the synthetic photometry and intrinsic + properties of the individual stars (or stellar systems, if + mutliplicity is used in the IMF object). + + A second table is produced that + contains the properties of the companion stars independent of their + primary stars. + + Parameters + ----------- + iso: isochrone_binary object + SPISEA binary_isochrone object + imf: imf object + SPISEA IMF object + cluster_mass: float + Total initial mass of the cluster, in M_sun + ifmr: ifmr object or None + If ifmr object is defined, will create compact remnants + produced by the cluster at the given isochrone age. Otherwise, + no compact remnants are produced. + seed: int + If set to non-None, all random sampling will be seeded with the + specified seed, forcing identical output. + Default None + vebose: boolean + True for verbose output. + + ==================== + + """ + def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=False, + seed=None, tests='mass_current'): + Cluster.__init__(self, iso, imf, cluster_mass, + ifmr=ifmr, verbose=verbose, + seed=seed) + # Provide a user warning is random seed is set + if seed is not None: + print('WARNING: random seed set to %i' % seed) + t1 = time.time() + self.iso = iso + self.verbose = verbose + ##### + # Sample the IMF to build up our cluster mass. + # NOTE: I have adjustment factor in place + # in order to account for the amount of stars and star mass that + # may be weeded out due to making initial masses come from the + # isochrone + ##### + + mass, isMulti, compMass, sysMass = imf.generate_cluster(cluster_mass,seed=seed) + self.filt_names = self.iso.filt_names_table + # Below: INTENDED cluster mass! + self.cluster_mass = cluster_mass + # A little sanity check inserted to make sure that IMF generated + # cluster mass is close to the desired cluster mass + np_min_mass = np.min([sysMass.sum(), cluster_mass]) + ##### + single_star_systems = self.make_singles_systems_table(isMulti, sysMass) + if self.imf.make_multiples: + # Temporary companion mass finder: + # Double systems is a bit of a misnomer + # Correct term would be "multi_star_system" + companions, double_systems = \ + self.make_primaries_and_companions(sysMass, compMass) + self.star_systems = vstack([double_systems, single_star_systems]) + self.companions = companions + + else: + self.star_systems = single_star_systems + self.intended_primaries_mass = 0 + self.intended_companions_mass = 0 + self.intended_singles_mass = sysMass.sum() + return + + def set_columns_of_table(self, t, N_systems, multi=False): + """ + adds columns for basic stellar parameters to table t with their + descriptions (e.g. metallicity or Teff) + + Parameters + ----------- + t: Astropy Table + table containing entries for initial masses + of the primary, comapnions, or single-system stars + N_systems: integer + length of AstroPy Table + multi: boolean + whether t is the table representing + the primary stars of multi-star systems + """ + t.add_column(Column(np.zeros(N_systems, dtype=float), + name='Teff')) + t.add_column(Column(np.empty(N_systems, dtype=float), + name='L')) + t.add_column(Column(np.empty(N_systems, dtype=float), + name='logg')) + t.add_column(Column(np.repeat(0.0, N_systems), + name='isWR')) + t.add_column(Column(np.empty(N_systems, dtype=float), + name='mass_current')) + t.add_column(Column(np.empty(N_systems, dtype=float), + name='phase')) + t.add_column(Column(np.repeat(False, N_systems), + name='IFMR_it')) + # To ADD: + # t.add_column(Column(np.repeat(False, N_systems), + #. name='out_of_range')) + t.add_column(Column(np.repeat(self.iso.metallicity, + N_systems), + name='metallicity')) + t.add_column(Column(np.repeat(multi, N_systems), + name='isMultiple')) + t.add_column(Column(np.repeat(0.0, N_systems), + name='merged')) + # Add the filter columns to the table. They are empty so far. + # Keep track of the filter names in : filt_names + for filt in self.filt_names: + t.add_column(Column(np.zeros(N_systems, + dtype=float), + name=filt)) + if multi: + t.add_column(Column(np.arange(N_systems), + name='designation')) + + return None + + def applying_IFMR_stars(self, stars): + """ + Applies the IFMR (if there's any given) to the + a table of stars. Specifically, when stars have phase of 5 but + have an effective temperature of 0 or some non-finite quantity, + or have logg >= 6.9 or have non-physical temperatures + (and are not merged with the primary) + This is in order to make sure there are no compact stars masquerading + as non-compact stars. + + Output: None + + Parameters + ----------- + stars: + Astropy table of stars (either of single stars, primary stars, + or companions) + """ + star_systems_phase_non_nan = np.nan_to_num(stars['phase'], + nan=-99) + bad = np.where((star_systems_phase_non_nan > 5) & + (star_systems_phase_non_nan < 101) & + (star_systems_phase_non_nan != -99)) + # Print warning, if desired + if self.verbose: + for ii in range(len(bad[0])): + print('WARNING: changing phase ' + + '{0} to 5'.format(star_systems_phase_non_nan[bad[0] + [ii]])) + stars['phase'][bad] = 5 + # Also dealing with interpolation screwing up phases less than 5 + bad2 = np.where((star_systems_phase_non_nan < 5)) + # Print warning, if desired + if self.verbose: + for ii in range(len(bad[0])): + print('WARNING: changing phase ' + + '{0} to 5'.format(star_systems_phase_non_nan[bad[0] + [ii]])) + stars['phase'][bad2] = -99 + #### + # The IFMR is applied on stars with phase 5 (which are therefore not + # secondary stars that were merged with the system primary) and have + # have nonphysical effective temperature (Teff = 0 or some finite value) + # and/or have surface gravity of at least 6.9 cgs. + ##### + + cdx_rem = np.where((stars['merged']==0) & (stars['phase'] == 5) & ((((stars['Teff'] == 0) | + (~np.isfinite(stars['Teff'])) | + (stars['logg'] >= 6.9))) | + (stars['IFMR_it'])))[0] + if self.ifmr: + # Identify compact objects as those with Teff = 0. + # Conditions the star has to be not merged and the star has to be + # Calculate remnant mass and ID for compact + # objects; update remnant_id and + # remnant_mass arrays accordingly + cond = ('metallicity_array' in + inspect.getfullargspec(self.ifmr.generate_death_mass).args) + if cond: + r_mass_tmp, r_id_tmp = \ + self.ifmr.generate_death_mass(mass_array=stars + ['mass'][cdx_rem], + metallicity_array=stars + ['metallicity'][cdx_rem]) + else: + r_mass_tmp, r_id_tmp = \ + self.ifmr.generate_death_mass(mass_array=stars + ['mass'][cdx_rem]) + # Drop remnants where it is not relevant + # (e.g. not a compact object or + # outside mass range IFMR is defined for) + good = np.where(r_id_tmp > 0) + cdx_rem_good = cdx_rem[good] + stars['mass_current'][cdx_rem_good] = r_mass_tmp[good] + # when we have somee undefined or 0 temperature, + # shouldn't we practically have no light coming + # out of it (not even blackbody radiation)? + stars['L'][cdx_rem] = 0.0 + stars['phase'][cdx_rem_good] = r_id_tmp[good] + for filt in self.filt_names: + stars[filt][cdx_rem] = \ + np.full(len(cdx_rem), np.nan) + else: + # If the IFMR doesn't exist, then we may need to + # get rid of the stars that would warrant usage of the IFMR. + stars.remove_rows(cdx_rem) + return None + + def generate_2body_parameters(self, star_systemsPrime, companions): + """ + Creates columns of table corresponding to log-separation, eccentricity, + inclination, argument of periapsis, longitude of ascending node associated with + each companion of each primary star of non-single-star system. + + Parameters + ----------- + star_systemsPrime: AstroPy Table + table of the primary stars of the cluster + companions. Used for calculating log-separation when + multiplicity type allows it. + companions: Astropy table of the companion stars of the cluster + Generate the log_separation, e, i, omegas, + (2-body problem parameters) using the given + multiplicity. + """ + + N_comp_tot = len(companions) + inst = isinstance(self.imf._multi_props, + multiplicity.MultiplicityResolvedDK) + if inst: + companions.add_column(Column(np.zeros(N_comp_tot, dtype=float), + name='log_a')) + companions.add_column(Column(np.zeros(N_comp_tot, dtype=float), + name='e')) + companions.add_column(Column(np.zeros(N_comp_tot, dtype=float), + name='i', description='degrees')) + companions.add_column(Column(np.zeros(N_comp_tot, dtype=float), + name='Omega')) + companions.add_column(Column(np.zeros(N_comp_tot, dtype=float), + name='omega')) + for ii in range(len(companions)): + # If the mass of the star is less than 0.1 I will + # then let the log_a be 0. + # Do not want to trigger an error but I will try + # NOT to cause and instead make + # log_a = np.nan. That will be our indicator that + # I could not calculate the log_a using + # Duchene and Krauss and that we will + # not use the log(a) + try: + companions['log_a'][ii] = \ + self.imf._multi_props.log_semimajoraxis(star_systemsPrime + ['mass'] + [companions + ['system_idx'] + [ii]]) + except ValueError: + # Indicator that we can't use the Multiplicity for log_a + companions['log_a'][ii] = np.nan + continue + companions['e'] = \ + self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) + props = self.imf._multi_props + companions['i'], companions['Omega'], companions['omega'] = \ + props.random_keplarian_parameters(np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot)) + else: + # Indicator that we can't use the Multiplicity for log_a + companions['log_a'] = np.nan + companions['e'] = np.nan + companions['i'], companions['Omega'], companions['omega'] = \ + np.nan, np.nan, np.nan + return None + + def finding_secondary_stars(self, star_systemsPrime, companions): + """ + Given the table so far of the primary stars and of the companions + we try to find the secondary of the primary star. Secondary star + is the companion star with a) greatest gravitational pull on the primary + (if isochrone provides primary) or b) greatest mass. + + Output: compMass_IDXs: a hashmap linking the star system to the next companion + star in compMass array (generated by IMF) that needs to be evaluated in + filling_in_primaries_and_companions + max_log_gs: hashmap linking star system to a 2-element list such that the first + element of the list is the GM_secondary star/(separation)^2 and Mass of the secondary star + + Parameters + ----------- + + star_systemsPrime: AstroPy Table + table of primary stars (with companions) + companions: AstroPy Table + table of companions of all primary stars listed in star_systemsPrime + """ + compMass_IDXs = {} + max_log_gs = {} + + for x in range(len(star_systemsPrime)): + # subtbl stands for sub-table. (of companions whose primary star + # is at index x. + subtbl = companions[np.where(companions['system_idx'] == x)[0]] + subtbl2 = copy.deepcopy(subtbl) + max_log_gs[x] = [0, 0] + # handling nan'ed separations. We generally don't want to + # consider for minimum distance and maximum gravitational + # influence to the primary + subtbl2['log_a'][np.where(~np.isfinite(subtbl['log_a']))] = np.inf + max_log_gs[x][0] = np.max(subtbl2['mass']/(10 ** subtbl2['log_a']) ** 2) + max_log_gs[x][1] = np.max(subtbl2['mass']) + + # Indicate where in the compMass array for star system + # number x we are in + # when we start looking at the companion + compMass_IDXs[x] = 0 + # This is where the matching of the primary and the secondary begin + # I will be tracking the number of bad star systems throguh the + # variable rejected_system and rejected_companion + return compMass_IDXs, max_log_gs + + def filling_in_primaries_and_companions(self, star_systemsPrime, + companions, + min_log_gs, + error_check='mass_current'): + """ + Utilizes linear interpolation to try to find the luminosity, current mass, + photometry, phase, surface gravity, and temperature of the cluster. + + Output: rejected_system[:, 0], rejected_system[:, 1]: NumPy table + The first two being the imf generated masses of primary stars + and secondary stars such that when mass_current + is interpolated over the primary star's initial mass and the secondary star's + initial mass, the primary star's or secondary star's error_check parameter is NOT + interpolated correctly (i.e. spits out an NaN). This does not include + stars that interpolation predicts to be the + + good_system is the NumPy table where each row contains the primary-secondary star initial + mass pair (IMF generated) of star systems not included in rejected_system + + Parameters + ----------- + star_systems: AstroPy Table + table containing information so far + about IMF generated multi-star systems and their primary stars + companions: AstroPy Table + table containing information so far about IMF generated companions of + each of the star systems represented in star_systems + min_log_gs: dictionary + maps from index of primary star in star_systems to a 2-element list containing, + respectively, maximum gravitational pull between primary and companion that exists in + the star system of the primary star AND the maximum mass of a companion of the primary + star + error_check: String + name of parameter used to check whether interpolation was correctly done. + + """ + + # Case where multiplicity DK is used. + li = list(zip(self.iso.primaries['mass'], self.iso.secondaries['mass'], + self.iso.secondaries['log_a'])) + li = np.array(li) + li2 = list(zip(self.iso.primaries['mass'], + self.iso.secondaries['mass'])) + li2 = np.array(li2) + interp_keys = ['Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase'] + self.filt_names + iso_interpsP = {} + iso_interpsS = {} + interp_creation_time = [] + inst = isinstance(self.imf._multi_props, + multiplicity.MultiplicityResolvedDK) + if not inst: + for ikey in interp_keys: + print("Creating Interpolator for: " + ikey) + iso_interpsP[ikey] = LinearNDInterpolator(li2, self.iso.primaries[ikey]) + iso_interpsS[ikey] = LinearNDInterpolator(li2, self.iso.secondaries[ikey]) + iso_interpsS['log_a'] = LinearNDInterpolator(li2, self.iso.secondaries['log_a']) + iso_interpsS['merged'] = LinearNDInterpolator(li2, self.iso.secondaries['merged']) + else: + for ikey in interp_keys: + print("Creating Interpolator for: " + ikey) + iso_interpsP[ikey] = LinearNDInterpolator(li, self.iso.primaries[ikey]) + iso_interpsS[ikey] = LinearNDInterpolator(li, self.iso.secondaries[ikey]) + iso_interpsS['merged'] = LinearNDInterpolator(li, self.iso.secondaries['merged']) + # Dealing with single stars and higher order companions + iso_interps3 = {} + interp_creation_time3 = [] + for ikey in interp_keys: + iso_interps3[ikey] = interpolate.interp1d(self.iso.singles['mass'], + self.iso.singles[ikey], + kind='linear', bounds_error=False, + fill_value=np.nan) + # Now time to introduce it to the IMF generated stars + rejected_system = [] + good_system = [] + if inst: + for sys in range(len(star_systemsPrime)): + # segundaria: index of the secondary + comps_of_sys= np.where(companions['system_idx'] == star_systemsPrime[sys]['designation'])[0] + segundaria = np.where((companions['system_idx'] == star_systemsPrime[sys]['designation']) & + np.isclose(companions['mass'] / + (10 ** (2 * companions['log_a'])), + min_log_gs[sys][0], rtol=1e-2))[0][0] + for k in interp_keys: + # Run the interpolator on all companions. Then, rerun the interpolator on the + # secondary star. Although it may seem redundant. Note that there is ony one + # secondary star and that this prevents me from having to do some really + # crafty and nasty logic trick. + companions[k][comps_of_sys] = np.round(iso_interps3[k](companions['mass'][comps_of_sys])) + put_in_table = iso_interpsP[k](star_systemsPrime[sys]['mass'], + companions['mass'][segundaria], + companions['log_a'][segundaria]) + put_in_table2 = iso_interpsS[k](star_systemsPrime[sys]['mass'], + companions['mass'][segundaria], + companions['log_a'][segundaria]) + if (k == 'phase' or k == 'isWR'): + star_systemsPrime[k][sys] = np.round(put_in_table) + companions[k][segundaria] = np.round(put_in_table2) + else: + star_systemsPrime[k][sys] = put_in_table + companions[k][segundaria] = put_in_table2 + + companions['merged'][segundaria] = np.round(iso_interpsS['merged'] + (star_systemsPrime[sys]['mass'], + companions['mass'][segundaria], + companions['log_a'][segundaria])) + companions['the_secondary_star?'][segundaria] = True + if (error_check == 'Teff' or error_check == 'L'): + # We need to make sure that the primary did not interpolate + # correctly or the secondary did not interpolate correctly + # Consider where the star system did not interpolate correctly. + # Note that current parameters can be validly equal to NaN if they are of + # a merged secondary + cond_bad = (np.isnan(companions[error_check][segundaria]) and + (companions['phase'][segundaria] < 101) and + (companions['merged'][segundaria] == 0)) + cond_bad = cond_bad or ((np.isnan(star_systemsPrime[error_check][sys]) and + (star_systemsPrime['phase'][sys] < 101))) + else: + # Consider where the star system did not interpolate correctly. + # Note that current parameters can be validly equal to NaN if they are of + # a merged secondary + cond_bad = ((np.isnan(companions[error_check][segundaria]) and + (companions['merged'][segundaria] == 0)) or + np.isnan(star_systemsPrime[error_check][sys])) + if cond_bad: + rejected_system.append([star_systemsPrime['mass'][sys], + companions['mass'][segundaria]]) + else: + good_system.append([star_systemsPrime['mass'][sys], + companions['mass'][segundaria]]) + else: + # Case where the multiplicity does not create the log- + for sys in range(len(star_systemsPrime)): + # segundaria: index of the secondary + comps_of_sys= np.where(companions['system_idx'] == star_systemsPrime[sys]['designation'])[0] + segundaria = np.where((companions['system_idx'] == star_systemsPrime[sys]['designation']) & + np.isclose(companions['mass'], + min_log_gs[sys][1], rtol=1e-2))[0][0] + for k in interp_keys: + companions[k][comps_of_sys] = iso_interps3[k](companions['mass'][comps_of_sys]) + put_in_table = iso_interpsP[k](star_systemsPrime[sys]['mass'], + companions['mass'][segundaria]) + put_in_table2 = iso_interpsS[k](star_systemsPrime[sys]['mass'], + companions['mass'][segundaria]) + # we want to make sure that the phase and status of whether a star + # isWR are integers, not some wacky float + if k == 'phase' or k == 'isWR': + put_in_table = np.round(put_in_table) + put_in_table2 = np.round(put_in_table2) + star_systemsPrime[k][sys] = put_in_table + companions[k][sys] = put_in_table2 + companions['log_a'][segundaria] = iso_interpsS['log_a'](star_systemsPrime[sys]['mass'], + companions['mass'][segundaria]) + # Check if the secondary star is merged. + companions['merged'][segundaria] = np.round(iso_interpsS['merged'] + (star_systemsPrime[sys]['mass'], + companions['mass'][segundaria])) + # Kind of obvious, but we want to mark the secondary star as the + # secondary; otherwise, the star is just another companion. + companions['the_secondary_star?'][segundaria] = True + # Consider stars that I suspect are NOT properly inteprolated + if (error_check == 'Teff' or error_check == 'L'): + # We need to make sure that the primary did not interpolate + # correctly or the secondary did not interpolate correctly + # Consider where the star system did not interpolate correctly. + # Note that current parameters can be validly equal to NaN if they are of + # a merged secondary + cond_bad = (np.isnan(companions[error_check][segundaria]) and + (companions['phase'][segundaria] < 101) and + (companions['merged'][segundaria] == 0)) + cond_bad = cond_bad or (np.isnan(star_systemsPrime[error_check][sys]) and + (star_systemsPrime['phase'][sys] < 101)) + else: + cond_bad = ((np.isnan(companions[error_check][segundaria]) and + (companions['merged'][segundaria] == 0)) or + np.isnan(star_systemsPrime[error_check][sys])) + if cond_bad: + # If not already, note that current parameters can be validly equal to + # NaN if they are of a merged secondary + rejected_system.append([star_systemsPrime['mass'][sys], + companions['mass'][segundaria]]) + else: + good_system.append([star_systemsPrime['mass'][sys], + companions['mass'][segundaria]]) + star_systemsPrime['metallicity'] = np.ones(len(star_systemsPrime)) * self.iso.metallicity + companions['metallicity'] = np.ones(len(companions)) * self.iso.metallicity + rejected_system = np.array(rejected_system) + good_system = np.array(good_system) + return rejected_system[:, 0], rejected_system[:, 1], good_system + + def adding_up_photometry(self, star_systemsPrime, companions): + """ + Adds up initial masses of secondaries to initial mass + of the primary to create the systemMass values. + Also adding up the photometries (kind of hard and ugly) + to find each star systems' aggregate photometry. + + Parameters + ----------- + star_systemsPrime: AstroPy table + table representing non-single star systems/primaries with some essential data already + inputted through filling_in_primaries_and_companions + companions: AstroPy table + table containing companion stars of + systems represented in star_systemsPrime + filled in partially by filling_in_primaries_and_companons + """ + + for x in range(len(star_systemsPrime)): + # Find all companions of the star system + sub_tbl = companions[np.where(companions['system_idx'] == x)[0]] + sum_of_comp = sub_tbl['mass'].sum() + # Obtain the system mass. + star_systemsPrime['systemMass'][x] = sum_of_comp + \ + star_systemsPrime['mass'][x] + if np.isnan(star_systemsPrime['phase'][x]): + star_systems_phase_non_nan = -99 + else: + star_systems_phase_non_nan = star_systemsPrime['phase'][x] + cond = ((int(star_systems_phase_non_nan) > 5) and + (int(star_systems_phase_non_nan) < 101) and + (int(star_systems_phase_non_nan) != -99)) + if (cond): + print("Changing phase of primaries") + if (self.verbose): + print('WARNING: changing phase' + + ' {0} to 5'.format(star_systems_phase_non_nan)) + star_systemsPrime['phase'][x] = 5 + for filt in self.filt_names: + # Magnitude of primary star (initially) + mag_s = star_systemsPrime[filt][x] + # Companion stars corresponding to the primary star + # Note that I try tio make sure that + comps = companions[np.where((companions['system_idx'] == x) & + np.isfinite(companions[filt]))[0]] + # trying to obtain as many finite magnitudes as I can + if (not len(comps[filt])): + mag_c = np.nan + else: + # Finding the sum of fluxes and then taking magnitude. + mag_c = comps[filt] + # Add companion flux to system flux. + f1 = 10 ** (-1 * mag_s / 2.5) + f2 = 10 ** (-1 * mag_c / 2.5) + + f1 = np.nan_to_num(f1) + f2 = np.nan_to_num(f2) + f2 = np.sum(f2) + # Good and bad systems + + # If *both* objects are dark, then keep the magnitude + # as np.nan. Otherwise, add fluxes together + if (f1 != 0 or f2 != 0): + star_systemsPrime[filt][x] = -2.5 * np.log10(f1 + f2) + else: + star_systemsPrime[filt][x] = np.nan + + + def make_singles_systems_table(self, isMulti, sysMass): + """ + Makes the part of the star_systems table corresponding to single-star systems + and obtains synthetic photometry for each single star system via interpolation. + + Output: A part of what becomes the self.star_systems table that contains all + information (mass, luminosity, photometry) regarding single star systems. + Data values are matched from rows of the self.isochrone + based on the row's initial primary mass. + + Parameters + ----------- + isMulti: NumPy Array of booleans + table containing whether a star system is a + multi-star system. (output of the IMF) + sysMass: NumPy Array of floats + System masses of all stellar systems (generated by IMF) + """ + # We will be only looking for stars that are not multiple systems + sysMass = sysMass[np.where(~ isMulti)[0]] + N_systems = len(sysMass) + star_systems = Table([sysMass, sysMass], + names=['mass', 'systemMass']) + self.set_columns_of_table(star_systems, N_systems) + # Set up for interpolation + interp_keys = (['Teff', 'L', 'logg', 'isWR', 'mass_current', 'phase'] + + self.filt_names) + iso_interps3 = {} + for ikey in interp_keys: + iso_interps3[ikey] = interpolate.interp1d(self.iso.singles['mass'], + self.iso.singles[ikey], + kind='linear', + bounds_error=False, + fill_value=np.nan) + # Add columns for the Teff, L, logg, isWR, + # mass_current, phase for the primary stars. + star_systems['Teff'] = iso_interps3['Teff'](star_systems['mass']) + star_systems['L'] = iso_interps3['L'](star_systems['mass']) + star_systems['logg'] = iso_interps3['logg'](star_systems['mass']) + star_systems['isWR'] = np.round(iso_interps3['isWR'](star_systems['mass'])) + star_systems['mass_current'] = iso_interps3['mass_current'](star_systems['mass']) + star_systems['phase'] = np.round(iso_interps3['phase'](star_systems['mass'])) + star_systems['metallicity'] = np.ones(N_systems) * \ + self.iso.metallicity + # Remove star systems that are outside of max mass of isochrone + # Still inquiring about the secondary stars + # dummy variable exists as there is no companions table passed into + # this verison of _remove_bad_systems + star_systems, dummy = self._remove_bad_systems(star_systems) + self.applying_IFMR_stars(star_systems) + star_systems.remove_columns(['IFMR_it']) + for filt in self.filt_names: + star_systems[filt] = iso_interps3[filt](star_systems['mass']) + # star_systems.remove_columns(['touchedP']) + return star_systems + + def make_primaries_and_companions(self, star_systems, compMass, test='mass_current'): + """ + + Makes the primary stars and companions for non-single star systems of + the star_systems table and get synthetic + photometry for each single star system. Creates remnants when necessary. + Given an initial primary star mass, initial companion mass, and log + separation (last one is included if applicable) between the star and + the companion, we designate the star with the secondary as the star with + least separation from the primary. If there are multiple stars with the + same value for separation, the star with the most mass is selected out of + the set of companions with the same minimum separation and is designated + as the secondary. + + Output: Creates tables star_systemsPrime and companions, + which contain data regarding + star systems with multiple stars + and the companions. + + Parameters + ----------- + star_systems: numpy array + array of all star_system masses (generated by IMF) + + compMass: numpy array/iterable + list of lists of masses of companions of each star system + + test: String + name of stellar parameter used to check for failure of interpolation + """ + + # Obtain the indices of the systems corresponding to each companion + # make star_systems contain only multi-star systems + indices = [x for x in range(len(compMass)) if len(compMass[x])] + # We only consider star systems with at least one companion + star_systems = star_systems[indices] + # For each star system, the total mass of companions + compMass_sum = np.array([sum(compMass[x]) for x in indices]) + # Converting companion masses 2D list into array. + compMass = np.array([compMass[x] for x in indices]) + # Make star_systems array only contain the masses of the primary stars + star_systems = star_systems - compMass_sum + # Number of multi-body star systems + N_systems = len(star_systems) + ##### + # MULTIPLICITY + # Make a second table containing all the companion-star masses. + # This table will be much longer... here are the arrays: + # sysIndex - the index of the system this star belongs too + # mass - the mass of this individual star. + N_companions = np.array([len(star_masses) for star_masses in compMass]) + N_comp_tot = N_companions.sum() + system_index = np.repeat(range(len(indices)), N_companions) + # From now on, we will try to use the + # new indices (i.e. what the 0-index system is + # after we get rid of the single systems from the + # star systems table) as much as possible. + # Shows which star system number (index of primary star) + # each companion star corresponds to + companions = Table([system_index], names=['system_idx']) + star_systemsPrime = Table([star_systems, np.zeros(N_systems, + dtype=float)], + names=['mass', 'systemMass']) + # Create a table for primary stars + N_systems = len(indices) + # Add columns for the Teff, L, logg, isWR, + # mass_current, phase for the primary stars. + self.set_columns_of_table(star_systemsPrime, N_systems, multi=True) + + # The following column indicates whether a star system + # will have to be deleted due to lack of good match. + star_systemsPrime.add_column(Column(np.repeat(False, N_systems), + name='bad_system')) + # Designation is really the index of the star, + # but this may not be equal to + # the 0, 1.., len -1 index iterable of the + # cluster once we kill off the bad systems (systems whose + # primary-secondary pair could not be found + + # Add columns for the Teff, L, logg, isWR mass_current, + # phase, and filters for the companion stars. + companions.add_column(Column(np.zeros(N_comp_tot, dtype=float), + name='mass')) + self.set_columns_of_table(companions, N_comp_tot) + + # Marks whether the system was unmatchable and + # its row must be deleted. + companions.add_column(Column(np.repeat(False, N_comp_tot), + name='bad_system')) + companions.add_column(Column(np.repeat(False, N_comp_tot), + name='the_secondary_star?')) + for ind in range(len(star_systemsPrime)): + (companions['mass'][np.where + (companions['system_idx'] == + ind)]) = compMass[ind] + + self.generate_2body_parameters(star_systemsPrime, companions) + # Finding the secondary star for each primary star through finding the companion + # that exerts strongest gravitational pull on the primary or finding the companion + # with the highest mass. + compMass_IDXs, max_log_gs = (self.finding_secondary_stars(star_systemsPrime, + companions)) + + self.rejected_prims, self.rejected_sec, self.good_systems = \ + self.filling_in_primaries_and_companions(star_systemsPrime, + companions, + max_log_gs, + error_check=test) + star_systemsPrime, companions = self._remove_bad_systems(star_systemsPrime, companions) + # run removal of bad systems on companion stars + companions, dummy = self._remove_bad_systems(companions) + + # We don't need to worry about stars that did not get matched + # to a close enough equivalent in the KD Tree. + # Why? We've moved on to using the + + for x in range(len(companions)): + # Relabeling: new system_index of the companion star + # is going to be the position on the table of the companion's + # primary star. + companions['system_idx'][x] = \ + np.where(star_systemsPrime['designation'] == + companions['system_idx'][x])[0] + ##### + # Make Remnants for non-merged stars with nan + # and for stars with 0 Kelvin Teff + ##### + # Identify compact objects as those with Teff = 0 or + # the secondary stars that are non-merged and have a non- + # finite temperature + self.applying_IFMR_stars(companions) + self.applying_IFMR_stars(star_systemsPrime) + # The compact remnants have photometric + # fluxes of nan now. So now we can procede + # with the photometry. + self.adding_up_photometry(star_systemsPrime, companions) + companions_phase_non_nan = np.nan_to_num(companions['phase'], + nan=-99) + bad = np.where((companions_phase_non_nan > 5) & + (companions_phase_non_nan < 101) & + (companions_phase_non_nan != -99)) + if self.verbose: + print("Running the changing phase on companions") + for ii in range(len(bad[0])): + print('WARNING: changing phase {0} to 5'.format(companions_phase_non_nan[bad[0][ii]])) + companions['phase'][bad] = 5 + # Get rid of the columns designation and and bad_system + star_systemsPrime.remove_columns(['bad_system', 'designation', 'IFMR_it']) + companions.remove_columns(['bad_system', 'IFMR_it']) + # Following is commented out as I will need to rethink deletion. + # if self.verbose: + #. print("{} non-single star systems".format(str(rejected_prims)) + + #. " had to be deleted" + + #. " before IFMR application") + #. print("{} companions".format(str(rejected_companions)) + + #. " had to be deleted before" + + #. " IFMR was applied") + # For testing purposes we can make rejected_system and rejected_comapnions + # be returned too. + return companions, star_systemsPrime + + def _remove_bad_systems(self, star_systems, companions=None): + """ + Helper function to remove stars with masses outside the isochrone + mass range from the cluster. These stars are identified by having + a Teff = 0, as set up by _make_star_systems_table_interp. + If self.ifmr == None, then both high and low-mass bad systems are + removed. If self.ifmr != None, then we will save the high mass systems + since they will be plugged into an ifmr later. + + Unlike this function's equivalent in the ResolvedCluster, I directly use the minimum + and maximum mass of the isochrone to see whether a star is outside of + the isochrone mass range. + + Parameters + ----------- + star_systems: AstroPy Table + Table for the single stars, primary stars, or secondary stars in the cluster. + companions: AstroPy Table + Table for the companion stars of the synthetic star cluster. + (equals None iff star_systems table is not the table for primary stars of + non-single-star systems) + + """ + N_systems = len(star_systems) + + # Get rid of the bad ones + # Convert nan_to_num to avoid errors on greater than, less than comparisons + star_systems_mass_non_nan = np.nan_to_num(star_systems['mass'], nan=0) + star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=0) + if self.ifmr == None: + # Trim out bad systems; specifically, stars with masses outside those provided + # by the model isochrone (except for compact objects, which is where I check + # phases and allow stars with 101 + phase to not get filtered out. + idx = np.where(((star_systems_mass_non_nan >= self.iso.min_mass) & + (star_systems_mass_non_nan <= self.iso.max_mass)) | + (star_systems_phase_non_nan >= 101))[0] + # As in the old _remove_bad_systems we make sure that the for the + # very massive stars, the log_g is applied + else: + # We want to trim out bad systems; specifically, stars with masses outside + # those provided by the model isochrone (except for compact objects, + # which is where I check phases and allow stars with 101 + phase to not get + # filtered out. + idx = np.where((star_systems_mass_non_nan >= self.iso.min_mass)| + (star_systems_phase_non_nan >= 101))[0] + + if len(idx) != N_systems and self.verbose: + print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) + star_systems = star_systems[idx] + # In the case IFMR!=None, we must designate stars with + # mass above max_mass of isochrone to be inputted into the + # IFMR. + to_still_apply_IFMR = np.where(star_systems['mass'] > self.iso.max_mass)[0] + if to_still_apply_IFMR and self.ifmr != None: + star_systems['IFMR_it'][to_still_apply_IFMR] = True + N_systems = len(star_systems) + + if companions!=None: + # Clean up companion stuff (which we haven't handled yet) + # Delete stars that are companions of primaries we deleted + companions = companions[np.where(np.isin(star_systems['designation'], companions['system_idx']))[0]] + # Now get rid of the + + return star_systems, companions + class ResolvedCluster(Cluster): """ Cluster sub-class that produces a *resolved* stellar cluster. A table is output with the synthetic photometry and intrinsic properties of the individual stars (or stellar systems, if mutliplicity is used in the IMF object). - If multiplicity is used, than a second table is produced that contains the properties of the companion stars independent of their primary stars. @@ -111,20 +1002,16 @@ class ResolvedCluster(Cluster): imf: imf object SPISEA IMF object - cluster_mass: float Total initial mass of the cluster, in M_sun - ifmr: ifmr object or None If ifmr object is defined, will create compact remnants produced by the cluster at the given isochrone age. Otherwise, no compact remnants are produced. - seed: int If set to non-None, all random sampling will be seeded with the specified seed, forcing identical output. Default None - vebose: boolean True for verbose output. """ @@ -223,7 +1110,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): star_systems['isWR'] = np.round(self.iso_interps['isWR'](star_systems['mass'])) star_systems['mass_current'] = self.iso_interps['mass_current'](star_systems['mass']) star_systems['phase'] = np.round(self.iso_interps['phase'](star_systems['mass'])) - star_systems['metallicity'] = np.ones(N_systems)*self.iso.metallicity + star_systems['metallicity'] = np.ones(N_systems) * self.iso.metallicity # For a very small fraction of stars, the star phase falls on integers in-between # the ones we have definition for, as a result of the interpolation. For these @@ -233,7 +1120,8 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) + bad = np.where((star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & + (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -317,9 +1205,11 @@ def _make_companions_table(self, star_systems, compMass): for ii in range(len(companions)): companions['log_a'][ii] = self.imf._multi_props.log_semimajoraxis(star_systems['mass'][companions['system_idx'][ii]]) - companions['e'] = self.imf._multi_props.random_e(np.random.rand(N_comp_tot)) - companions['i'], companions['Omega'], companions['omega'] = self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot),np.random.rand(N_comp_tot),np.random.rand(N_comp_tot)) + companions['i'], companions['Omega'], companions['omega'] = \ + self.imf._multi_props.random_keplarian_parameters(np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot), + np.random.rand(N_comp_tot)) # Make an array that maps system index (ii), companion index (cc) to @@ -362,7 +1252,8 @@ def _make_companions_table(self, star_systems, compMass): # then round it down to 5, rather than up to 101). # Convert nan_to_num to avoid errors on greater than, less than comparisons star_systems_phase_non_nan = np.nan_to_num(star_systems['phase'], nan=-99) - bad = np.where( (star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) + bad = np.where((star_systems_phase_non_nan > 5) & (star_systems_phase_non_nan < 101) & + (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -469,9 +1360,10 @@ def _remove_bad_systems(self, star_systems, compMass): compMass = [compMass[ii] for ii in idx] return star_systems, compMass - - + class ResolvedClusterDiffRedden(ResolvedCluster): + + """ Sub-class of ResolvedCluster that applies differential extinction to the synthetic photometry. @@ -510,6 +1402,7 @@ class ResolvedClusterDiffRedden(ResolvedCluster): def __init__(self, iso, imf, cluster_mass, deltaAKs, ifmr=None, verbose=False, seed=None): + ResolvedCluster.__init__(self, iso, imf, cluster_mass, ifmr=ifmr, verbose=verbose, seed=seed) @@ -561,7 +1454,10 @@ def __init__(self, iso, imf, cluster_mass, deltaAKs, #print 'Diff redden: {0}'.format(t2 - t1) return + class UnresolvedCluster(Cluster): + + """ Cluster sub-class that produces an *unresolved* stellar cluster. Output is a combined spectrum that is the sum of the individual @@ -657,7 +1553,10 @@ def __init__(self, iso, imf, cluster_mass, return + class Isochrone(object): + + """ Base Isochrone class. @@ -710,6 +1609,8 @@ class Isochrone(object): resolution as the Castelli+04 atmospheres. Default is False, which is often sufficient synthetic photometry in most cases. """ + + def __init__(self, logAge, AKs, distance, metallicity=0.0, evo_model=default_evo_model, atm_func=default_atm_func, wd_atm_func = default_wd_atm_func, @@ -753,7 +1654,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, evol = evol[::mass_sampling] # Give luminosity, temperature, mass, radius units (astropy units). - L_all = 10**evol['logL'] * c.L_sun # luminsoity in W + L_all = 10**evol['logL'] * constants.L_sun # luminsoity in W T_all = 10**evol['logT'] * units.K R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma_sb * T_all**4)) mass_all = evol['mass'] * units.Msun # masses in solar masses @@ -821,6 +1722,8 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, return def plot_HR_diagram(self, savefile=None): + + """ Make a standard HR diagram for this isochrone. @@ -848,6 +1751,8 @@ def plot_HR_diagram(self, savefile=None): return def plot_mass_luminosity(self, savefile=None): + + """ Make a standard mass-luminosity relation plot for this isochrone. @@ -872,6 +1777,583 @@ def plot_mass_luminosity(self, savefile=None): return + +class Isochrone_Binary(Isochrone): + """ + Base Isochrone class. + + Parameters + ---------- + logAge : float + The age of the isochrone, in log(years) + AKs : float + The total extinction in Ks filter, in magnitudes + distance : float + The distance of the isochrone, in pc + metallicity : float, optional + The metallicity of the isochrone, in [M/H]. + Default is 0. + evo_model: model evolution class, optional + Set the stellar evolution model class. + Default is evolution.MISTv1(). + atm_func: model atmosphere function, optional + Set the stellar atmosphere models for the stars. + Default is get_merged_atmosphere. + wd_atm_func: white dwarf model atmosphere function, optional + Set the stellar atmosphere models for the white dwafs. + Default is get_wd_atmosphere + mass_sampling : int, optional + Sample the raw isochrone every `mass_sampling` steps. The default + is mass_sampling = 0, which is the native isochrone mass sampling + of the evolution model. + wave_range : list, optional + length=2 list with the wavelength min/max of the final spectra. + Units are Angstroms. Default is [3000, 52000]. + min_mass : float or None, optional + If float, defines the minimum mass in the isochrone. + Unit is solar masses. Default is None + max_mass : float or None, optional + If float, defines the maxmimum mass in the isochrone. + Units is solar masses. Default is None. + rebin : boolean, optional + If true, rebins the atmospheres so that they are the same + resolution as the Castelli+04 atmospheres. Default is False, + which is often sufficient synthetic photometry in most cases. + Important Attributes: + Primaries-- AstroPy Table containing the following columns: + ----------------------------------------------------------- + mass -- in units of Solar Masses. Contains the initial mass of the primary + star + log_a -- in cgs. log10(separation of the star with the secondary in AU) + mass_current = in units of solar masses. Contains the initial mass of + the primary star + L -- in Watts. Luminosity of the primary star + Teff -- in Kelvin. Effective temperature of the primary star + R -- in units of solar radii. Radius of the primary star. + phase -- integer indicating whether a secondary star is a + white dwarf (101), neutron star (102), black hole, already merged star, + or is none of the previous (5) + gravity -- log10(acceleration of gravity at the primary star's surface + in m/s^2) + isWR -- boolean indicating whether a primary star is a WR Star + ------------------------------------------------------------ + singles-- AstroPy Table containing the following columns: + ----------------------------------------------------------- + mass -- solar masses. Contains the initial mass + of the single star + mass_current -- in units of solar masses. Contains the + initial mass of the single star + L -- in Watts. Luminosity of the single star + Teff -- in Kelvin. Effective temperature of the single star + R -- in units of solar radii. Radius of the single star. + phase -- integer indicating whether a secondary star is a + white dwarf (101), neutron star (102), black hole, already merged star, + or is none of the previous (5) + gravity -- log10(acceleration of gravity at the single + star's surface in m/s^2) + isWR -- boolean indicating whether a single star is a WR Star + ------------------------------------------------------------ + secondaries -- AstroPy Table containing the following columns: + ----------------------------------------------------------- + mass -- in solar masses. Contains the initial mass + of the secondary star + mass_current -- in solar masses. Contains the initial + mass of the secondary star + L -- Luminosity of the secondary star in Watts + Teff -- in Kelvin. Effective temperature of the single star + R -- in units of solar radii. Radius of the secondary star. + phase -- integer. Indicates whether a secondary star is a + white dwarf (101), neutron star (102), black hole, already merged star, + or is none of the previous (5) + logg -- log10(acceleration of gravity at the + secondary star's surface in m/s^2) + isWR -- boolean. Indicates whether a secondary star is a WR Star + merged -- whether the corresponding primary star (WHICH IS AT THE + SAME INDEX in the primaries table as is the secondary star), + has actually incorporated the secondary. + + If merged is true, the L, T_eff, gravity, mass_current will be set + to np.nan along with any magnitudes. + ------------------------------------------------------------ + """ + + def __init__(self, logAge, AKs, distance, metallicity, + evo_model=evolution.BPASS(), atm_func=default_atm_func, + wd_atm_func=default_wd_atm_func, wave_range=[3000, 52000], + red_law=default_red_law, mass_sampling=1, iso_dir='./', + min_mass=None, max_mass=None, + rebin=True, recomp=True, + filters=['ubv,U', 'ubv,V', 'ubv,B', 'ubv,R', 'ubv,I']): + t1=time.time() + self.metallicity = metallicity + self.logage = logAge + # Accounting for the definition of metallicity of the + # evolution object's + # Isochrone function. + # Changes by Ryota: make atm_func and wd_atm_func instance vars + self.atm_func = atm_func + self.wd_atm_func = wd_atm_func + self.distance = distance + self.wave_range = wave_range + self.AKs = AKs + self.red_law = red_law + self.filters = filters + self.filt_names_table = [] + self.verbose = True + + if not os.path.exists(iso_dir): + os.mkdir(iso_dir) + + # Assert that the wavelength ranges are within the limits of the + # VEGA model (0.1 - 10 microns) + try: + assert wave_range[0] > 1000 + assert wave_range[1] < 100000 + except AssertionError: + print('Desired wavelength range invalid. Limit to 1000 - 10000 A') + return + # See if there is a saved isochrone + if metallicity < 0: + metal_pre = 'm' + else: + metal_pre = 'p' + metal_flag = int(abs(metallicity) * 10) + # The files name minus file type and whether the star is a single + # primary or secondary star + save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_{4}{5:2s}_IB' + self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, + str(distance).zfill(5), metal_pre, + str(metal_flag).zfill(2)) + # Expected filters + self.filters = filters + # Recalculate isochrone if save_file doesn't exist or recomp == True + # otherwise retrieve it! + file_exists = self.check_save_file(evo_model, atm_func, red_law) + if file_exists[0] and not recomp: + print("We won't recompute the Isochrone Object!") + self.recalc = False + self.singles = file_exists[1] + self.primaries = file_exists[2] + self.secondaries = file_exists[3] + self.max_mass = np.max([np.max(self.singles['mass']), + np.max(self.secondaries['mass']), + np.max(self.primaries['mass'])]) + self.min_mass = np.min([np.min(self.singles['mass']), + np.min(self.secondaries['mass']), + np.min(self.primaries['mass'])]) + + return + # Get solar metallicity models for a population at a specific age. + # Takes about 0.1 seconds. + print("We (re)compute") + evol = evo_model.isochrone(age=10 ** logAge, + metallicity=metallicity) + + # Eliminate cases where log g is less than 0 + idx = np.where(evol['logg'] > 0) + evol = evol[idx] + + # Trim to desired mass range + if min_mass: + idx = np.where((evol['mass'] >= min_mass) & (evol['mass2'] >= min_mass)) + evol = evol[idx] + if max_mass: + idx = np.where((evol['mass'] <= max_mass) & (evol['mass2'] <= min_mass)) + evol = evol[idx] + self.max_mass = np.max([np.max(evol['mass']), np.max(evol['mass2'])]) + self.min_mass = np.min([np.min(evol['mass']), np.min(evol['mass2'])]) + # Give luminosity, temperature, mass, radius units (astropy units). + L_all = 10 ** evol['logL'] * constants.L_sun # luminsoity in W + T_all = 10 ** evol['logT'] * units.K + # TO DO: Conditionals to make sure I am using valid values + + R_all = np.sqrt(L_all / (4.0 * math.pi * c.sigma_sb * T_all ** 4)) + # masses in solar masses + mass_all = evol['mass'] * units.Msun + logg_all = evol['logg'] + mass_curr_all = evol['mass_current'] * units.Msun + # We will keep track of the phase of the BPASS primary/ + # single star system + phase_all = evol['phase'] + # We will keep track of the phase of the BPASS secondary + phase_all2 = evol['phase2'] + # We will keep track of whether the star is WR or not + isWR_all = evol['isWR'] + isWR2 = evol['isWR2'] + # Actual temperature of secondary in Kelvins + Tef2 = (10 ** evol['log(T2)']) * units.K + R2 = (10 ** evol['log(R2)']) * constants.R_sun + L2 = (10 ** evol['log(L2)']) * constants.L_sun + singles = Table([mass_all, L_all, T_all, R_all, logg_all, isWR_all, + mass_curr_all, phase_all, evol['single'], evol['source']], + names=['mass', 'L', 'Teff', 'R', 'logg', + 'isWR', 'mass_current', 'phase', 'single', 'source']) + # Also have inserted conversion factor to deal with the units of + # log(a) + primaries = Table([mass_all, + L_all, T_all, R_all, logg_all, isWR_all, + mass_curr_all, phase_all, evol['single'], evol['source']], + names=['mass', 'L', 'Teff', 'R', 'logg', + 'isWR', 'mass_current', 'phase', 'single', 'source']) + # Note that we keep information regarding whether + # a star corresponds to a merger. + # This will help us decide whether we should not account + # for the L2, Tef_2 during atmosphere creation + # Yes I do unit conversions for log(a) column + # as that is in log(a/R_sun) + secondaries = Table([evol['mass2'] * units.Msun, + evol['log(a)'] + np.log10(constants.R_sun/constants.au), + L2, Tef2, R2, + evol['logg2'], isWR2, + evol['mass_current2'] * units.Msun, + phase_all2, + evol['single'], + evol['mergered?'], evol['source']], + names=['mass', 'log_a', 'L', 'Teff', + 'R', 'logg', 'isWR', 'mass_current', + 'phase', 'single', 'merged', 'source']) + # Make sure that we are only looking at stars with companions when + # examining the secondaries and primaries. + secondaries = secondaries[np.where(~secondaries['single'])[0]] + secondaries.remove_column('single') + singles = singles[np.where(singles['single'])[0]] + singles.remove_column('single') + primaries = primaries[np.where(~primaries['single'])[0]] + primaries.remove_column('single') + # Trim down the table by selecting every Nth point where + # N = mass sampling factor. + singles = singles[::mass_sampling] + primaries = primaries[::mass_sampling] + secondaries = secondaries[::mass_sampling] + # Inserting before I forget + # I try to make sure that we have a queue + # of atm_function results to process + # If we have null values + self.spec_list_si = [None for x in range(len(singles))] # For single Stars + self.spec_list2_pri = [None for x in range(len(primaries))] # For primary stars + self.spec_list3_sec = [None for x in range(len(secondaries))] # For secondary stars + # Turns into an attribute since we will access this in another function + self.pairings2 = {"Singles": self.spec_list_si, + "Primaries": self.spec_list2_pri, + "Secondaries": self.spec_list3_sec} + pairings = {"Singles": singles, "Primaries": primaries, + "Secondaries": secondaries} + codes = {"Singles": 0, "Primaries": 1, + "Secondaries": 2} + self.codes2 = {0: singles, 1: primaries, + 2: secondaries} + self.pairings = pairings + self.singles = singles + self.primaries = primaries + self.secondaries = secondaries + # For each temperature extract the synthetic photometry. + for x in pairings: + tab = pairings[x] + atm_list = self.pairings2[x] + # Workaround for a glitch encountered with units not showing up. + # may need to come back and get rid of it since it looks silly. + R_all = tab['R'] * units.m/units.m + gravity_table = tab['logg'] + # a little issue with the compact remnant primaries from + # the secondary star + if x == 'Secondaries': + merged = np.where(tab['merged']) + tab['mass_current'][merged] = np.nan + tab['Teff'][merged] = np.nan + tab['R'][merged] = np.nan + tab['logg'][merged] = np.nan + tab['isWR'][merged] = False + tab['phase'][merged] = -99 + + cond = np.where(np.isfinite(tab['logg']) & (tab['logg'] != 0.0) & + np.isfinite(tab['L']) & (tab['L'] > 0.0) & + np.isfinite(tab['Teff']) & (tab['Teff'] > 0.0) & + np.isfinite(tab['R']) & (tab['R']> 0.0) & + (tab['phase'] <= 101) & (tab['phase'] != -99))[0] + + wrapper = BypasserContainer() + wrapper.wave_range = wave_range + wrapper.distance = distance + wrapper.lis = atm_list + vectorized_atm_maker = np.vectorize(self.atm_generator_to_vectorize) + vectorized_atm_maker(cond, wrapper, wrapper, metallicity, atm_func, + wd_atm_func, red_law, AKs, rebin, codes[x]) + bad_starcond = np.where(~ (np.isfinite(tab['logg']) & + (tab['logg'] != 0.0) & + np.isfinite(tab['L']) & + (tab['L'] > 0.0) & + np.isfinite(tab['Teff']) & + (tab['Teff'] > 0.0) & + np.isfinite(tab['R']) & (tab['R'] > 0.0) & + (tab['phase'] <= 101) & + (tab['phase'] != -99)))[0] + tab['Teff'][bad_starcond] = np.nan + tab['L'][bad_starcond] = np.nan + tab['R'][bad_starcond] = np.nan + tab['logg'][bad_starcond] = np.nan + # I hope to change the next few lines to this as this will make + # more thorough use of numpy and decrease the number of for-loop + # iterations that are used. + + self.singles = singles + self.primaries = primaries + self.secondaries = secondaries + # Append all the meta data to the summary table. + for tab in (singles, primaries, secondaries): + tab.meta['REDLAW'] = red_law.name + tab.meta['ATMFUNC'] = atm_func.__name__ + tab.meta['EVOMODEL'] = 'BPASS' + tab.meta['LOGAGE'] = logAge + tab.meta['AKS'] = AKs + tab.meta['DISTANCE'] = distance + tab.meta['METAL_IN'] = evol.meta['metallicity_in'] + tab.meta['METAL_ACT'] = evol.meta['metallicity_act'] + tab.meta['WAVEMIN'] = wave_range[0] + tab.meta['WAVEMAX'] = wave_range[1] + self.make_photometry() + t2 = time.time() + print('Isochrone generation took {0:f} s.'.format(t2-t1)) + return + + def atm_generator_to_vectorize(self, c_ind, w_wrapper, list_wrapper, met, + atm_func, wd_atm_func, red_law, AKs, rebin, + code): + """ + - Input - + c_ind: + w_wrapper: + list_wrapper: + met: + rebin: + atm_func: atmosphere function inputted into the constructor of + Isochrone_Binary + red_law: Extinction law object inputted into constructor of isochrone + binary + AKs: extinction at the Ks band inputted + """ + tab = self.codes2[code] + wave_range = w_wrapper.wave_range + distance = w_wrapper.distance + atm_list = list_wrapper.lis + R_all = tab['R'] * units.m / units.m + R = float( R_all[c_ind].to('pc') / units.pc) + + if (tab[c_ind]['phase'] < 101): + star = atm_func(temperature=tab['Teff'][c_ind], + gravity=tab['logg'][c_ind], + metallicity=met, + rebin=rebin) + else: + star = wd_atm_func(temperature=tab['Teff'][c_ind], + gravity=tab['logg'][c_ind], + metallicity=met, + verbose=False) + # Trim wavelength range down to + # JHKL range (0.5 - 5.2 microns) + star = spectrum.trimSpectrum(star, wave_range[0], + wave_range[1]) + # Convert into flux observed at Earth (unreddened) + R = float(R_all[c_ind].to("pc") / units.pc) + star *= (R / distance) ** 2 # in erg s^-1 cm^-2 A^-1 + # Redden the spectrum. This doesn't take much time at all. + red = red_law.reddening(AKs).resample(star.wave) + star *= red + # Save the final spectrum to our spec_list for later use. + atm_list[c_ind] = star + return + + def plot_HR_diagram(self, savefile=None): + + + """ + Make a standard HR diagram for this isochrone. + Took the code from the Isochrone base class and + adapted it to work on the isochrone binary object + Parameters + ----------- + savefile: path or None, optional + Path to file plot too, if desired. + Default is None + """ + plt.clf() + plt.loglog(self.singles['Teff'], self.singles['L'], + color='black', linestyle='solid', marker='+') + plt.loglog(self.primaries['Teff'], self.primaries['L'], + color='black', linestyle='solid', marker='+') + plt.loglog(self.secondaries['Teff'], self.secondaries['L'], + color='black', linestyle='solid', marker='+') + plt.gca().invert_xaxis() + plt.xlabel(r'T$_{\mathrm{eff}}$ (K)') + plt.ylabel('Luminosity (erg / s)') + + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' + plt.title(fmt_title.format(self.singles.meta['LOGAGE'], + self.singles.meta['DISTANCE']/1e3, + self.singles.meta['AKS'])) + + if savefile != None: + plt.savefig(savefile) + + return + + def plot_mass_luminosity(self, savefile=None): + + + """ + Make a standard mass-luminosity relation plot for this isochrone. + Took the code from the Isochrone base class and + adapted it to work on the isochrone binary object + Parameters + ----------- + savefile: path or None, optional + Path to file plot too, if desired. + Default is None + """ + plt.clf() + plt.loglog(self.singles['mass'], self.singles['L'], 'k.') + plt.loglog(self.primaries['mass'], self.primaries['L'], 'k.') + plt.loglog(self.secondaries['mass'], self.secondaries['L'], 'k.') + plt.xlabel(r'Mass (M$_\odot$)') + plt.ylabel('Luminosity (erg / s)') + + fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}' + plt.title(fmt_title.format(self.singles.meta['LOGAGE'], + self.singles.meta['DISTANCE']/1e3, + self.singles.meta['AKS'])) + + if savefile != None: + plt.savefig(savefile) + + return + + def make_photometry(self, rebin=True, vega=vega): + """ + Make synthetic photometry for the specified filters. This function + udpates the self.points table to include new columns with the + photometry. + """ + startTime = time.time() + + meta = self.singles.meta + + print('Making photometry for isochrone: log(t) = %.2f AKs = %.2f dist = %d' %(meta['LOGAGE'], meta['AKS'], meta['DISTANCE'])) + print(' Starting at: ', datetime.datetime.now(), + ' Usually takes ~5 minutes') + # npoints = len(self.points) + verbose_fmt = 'M = {0:7.3f} Msun T = {1:5.0f} K m_{2:s} = {3:4.2f}' + + # Loop through the filters, get filter info, make photometry for + # all stars in this filter. + for ii in self.filters: + prt_fmt = 'Starting filter: {0:s} Elapsed time: {1:.2f} seconds' + print(prt_fmt.format(ii, time.time() - startTime)) + filt = get_filter_info(ii, rebin=rebin, vega=vega) + filt_name = get_filter_col_name(ii) + # Make the column to hold magnitudes + # in this filter. Add to points table. + col_name = 'm_' + filt_name + self.filt_names_table.append(col_name) + mag_col = Column(np.zeros(len(self.singles), dtype=float), + name=col_name) + self.singles.add_column(mag_col) + mag_col = Column(np.zeros(len(self.secondaries), dtype=float), + name=col_name) + self.secondaries.add_column(mag_col) + mag_col = Column(np.zeros(len(self.primaries), dtype=float), + name=col_name) + self.primaries.add_column(mag_col) + # Loop through each star in the isochrone and + # do the filter integration + print('Starting synthetic photometry') + for x in self.pairings: + listofStars = self.pairings2[x] + table = self.pairings[x] + length_of_list = len(listofStars) + for ss in range(length_of_list): + star = listofStars[ss] + # Nada used to be a fill in value. + # I thought that None would be more intuitive + # So we will be using None + if star: + # These are already extincted, observed spectra. + star_mag = mag_in_filter(star, filt) + else: + # We want nan to stand in place + # for stars that do not have valid T + # or radius or L. + star_mag = np.nan + table[col_name][ss] = star_mag + if (self.verbose and (ss % 100) == 0): + print(verbose_fmt.format(table['mass'][ss], + table['Teff'][ss], + filt_name, star_mag)) + endTime = time.time() + print(' Time taken: {0:.2f} seconds'.format(endTime - startTime)) + if self.save_file != None: + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + self.singles.write(self.save_file + + "single.fits", overwrite=True) + self.primaries.write(self.save_file + + "primary.fits", overwrite=True) + self.secondaries.write(self.save_file + + "secondary.fits", overwrite=True) + return + + def check_save_file(self, evo_model, atm_func, red_law): + """ + Checks to see if a fits file with name the same as the value of + self.save_file exists. + If the filename exists, check the + meta-data as well. We make sure all three necessary tables + are there. + returns a tuple: + first element: True is file exists, false otherwise + tmp1: single star table (if file exists) + tmp2: primary star table (if file exists) + tmp3: secondary star table (if file exists) + """ + if os.path.exists(self.save_file + "single.fits"): + tmp1 = Table.read(self.save_file + "single.fits") + else: + return (False, None, None, None) + if os.path.exists(self.save_file + "primary.fits"): + tmp2 = Table.read(self.save_file + "primary.fits") + else: + return (False, None, None, None) + if os.path.exists(self.save_file + "secondary.fits"): + tmp3 = Table.read(self.save_file + "secondary.fits") + else: + return (False, None, None, None) + # See if the meta-data matches: evo model, atm_func, redlaw + out_bool1 = ( (tmp1.meta['EVOMODEL'] == type(evo_model).__name__) & + (tmp1.meta['ATMFUNC'] == atm_func.__name__) & + (tmp1.meta['REDLAW'] == red_law.name) ) + out_bool2 = ( (tmp2.meta['EVOMODEL'] == type(evo_model).__name__) & + (tmp2.meta['ATMFUNC'] == atm_func.__name__) & + (tmp2.meta['REDLAW'] == red_law.name) ) + out_bool3 = ( (tmp3.meta['EVOMODEL'] == type(evo_model).__name__) & + (tmp3.meta['ATMFUNC'] == atm_func.__name__) & + (tmp3.meta['REDLAW'] == red_law.name) ) + ret_tuple = ((out_bool1 and out_bool2 and out_bool3), tmp1, tmp2, tmp3) + if ret_tuple[0]: + print("We found files from which we can create our isochrone") + print("Evolution Models of potential saved " + + "isochrone and desired isochrone") + print(tmp1.meta['EVOMODEL'] ) + print(type(evo_model).__name__) + print("Atmosphereic Models of potential saved" + + " isochrone and desired isochrone") + print(tmp1.meta['ATMFUNC']) + print(atm_func.__name__) + print("Reddening Laws of potential saved" + + " isochrone and desired isochrone") + print(tmp1.meta['REDLAW']) + print(red_law.name) + else: + print("Need to Recompute") + return ret_tuple + class IsochronePhot(Isochrone): """ Make an isochrone with synthetic photometry in various filters. @@ -889,7 +2371,7 @@ class IsochronePhot(Isochrone): The distance of the isochrone, in pc metallicity : float, optional - The metallicity of the isochrone, in [M/H]. + The metallicity of the isochrone, in [Z/Z_solar]. Default is 0. evo_model: model evolution class, optional @@ -979,7 +2461,8 @@ def __init__(self, logAge, AKs, distance, else: metal_pre = 'p' metal_flag = int(abs(metallicity)*10) - + + save_file_fmt = '{0}/iso_{1:.2f}_{2:4.2f}_{3:4s}_{4}{5:2s}.fits' self.save_file = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5), metal_pre, str(metal_flag).zfill(2)) self.save_file_legacy = save_file_fmt.format(iso_dir, logAge, AKs, str(distance).zfill(5), metal_pre, str(metal_flag).zfill(2)) @@ -1246,15 +2729,11 @@ def __init__(self, logAge, distance, evo_model=default_evo_model, # Initialize output for stellar spectra self.spec_list = [] - # For each temperature extract the synthetic photometry. for ii in range(len(tab['Teff'])): # Loop is currently taking about 0.11 s per iteration - gravity = float( logg_all[ii] ) - L = float( L_all[ii].cgs / (units.erg / units.s)) # in erg/s - T = float( T_all[ii] / units.K) # in Kelvin - R = float( R_all[ii].to('pc') / units.pc) # in pc + # Get the atmosphere model now. Wavelength is in Angstroms # This is the time-intensive call... everything else is negligable. @@ -1681,24 +3160,12 @@ def match_model_mass(isoMasses,theMass): dm = np.abs(isoMasses - theMass) mdx = dm.argmin() - # Model mass has to be within 2% of the desired mass + # Model mass has to be within 10% of the desired mass if (dm[mdx] / theMass) > 0.1: return None else: return mdx -def match_model_masses(isoMasses, starMasses): - kdt = KDTree( isoMasses.reshape((len(isoMasses), 1)) ) - q_results = kdt.query(starMasses.reshape((len(starMasses), 1)), k=1) - indices = q_results[1] - - dm_frac = np.abs(starMasses - isoMasses[indices]) / starMasses - - idx = np.where(dm_frac > 0.1)[0] - indices[idx] = -1 - - return indices - def get_evo_model_by_string(evo_model_string): return getattr(evolution, evo_model_string) diff --git a/spisea/tests/Comparisons_w_Real_Data.png b/spisea/tests/Comparisons_w_Real_Data.png new file mode 100644 index 00000000..3c82a88c Binary files /dev/null and b/spisea/tests/Comparisons_w_Real_Data.png differ diff --git a/spisea/tests/iso_6.78_2.70_08000_p00.fits b/spisea/tests/iso_6.78_2.70_08000_p00.fits new file mode 100644 index 00000000..4d9a0126 --- /dev/null +++ b/spisea/tests/iso_6.78_2.70_08000_p00.fits @@ -0,0 +1,134 @@ +SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 65 / length of dimension 1 NAXIS2 = 645 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 9 / number of table fields TTYPE1 = 'L ' TFORM1 = 'D ' TUNIT1 = 'W ' TTYPE2 = 'Teff ' TFORM2 = 'D ' TUNIT2 = 'K ' TTYPE3 = 'R ' TFORM3 = 'D ' TUNIT3 = 'm ' TTYPE4 = 'mass ' TFORM4 = 'D ' TUNIT4 = 'solMass ' TTYPE5 = 'logg ' TFORM5 = 'D ' TTYPE6 = 'isWR ' TFORM6 = 'L ' TTYPE7 = 'mass_current' TFORM7 = 'D ' TUNIT7 = 'solMass ' TTYPE8 = 'phase ' TFORM8 = 'D ' TTYPE9 = 'm_nirc2_Kp' TFORM9 = 'D ' REDLAW = 'N09 ' ATMFUNC = 'get_merged_atmosphere' EVOMODEL= 'MISTv1 ' LOGAGE = 6.78 AKS = 2.7 DISTANCE= 8000 METAL_IN= 0.0 HIERARCH METAL_ACT = 0.02380291467262473 WAVEMIN = 3000 WAVEMAX = 52000 END EgtI#@oA1?@! h-F?UB@7|3 $EqN@`.ơmA ]{?Z@#,#F?9q"@7pFEj@V A?is?h@%T9F?xز@7etfE|@׵B@A8?ev@+ݖ\F?؟@7TWE3FH1݊@2#Agg?RWv@-[F?R ҿ@7Hd +:E ir@ _A?S?l@9svF?@7AE"Hp:_@Aݿ>#AY:7O?L{̇@7N(k|F?LzKi@7)E#=3@_JgA#co10?&/.@6 XF?&G@7 lE$ C@5AuD?%4uG@67JF?%3~@6LE&em@y ǞwA'Vs{?50f@7F?5.,!|@6J E'MnN*@A ?Ff#[#@9rDF?Fe2C1L@6m~ܰE)P]@/Awnm?zy}@;EF?zx58ݿ@6ôw"E+2@[yALC?Ȇbi@<7F?Ȇʿ@6UY%rE-6m@#'`A;zm_?ɂ,>@=xIF?ɂP@6p E.eym@=kA:1?u}@>wLAmӶ?U@D=F?T@6&.8pE8}b^@>1XAgI?҄Sr@Ggz!FF?҄Ro@6pE9׸8@_hEƆAPJ;R?''9:@H97*F?'m @6 +zE;@6LZAq?4< =@N7ʟ8F?4:o"@5<߄bE;lQF-@}A?Cա~%@P_F?Ci@5bYE<Aw@=A[?[9$@P^GF?[7r@5^E?ԇQ@M 2F?ԇ׫[S@5HiE=p7LG@ "AU+?J֏@N{0\F?I(Z@5QE>h]@ژ.iA)?J#XZ@PT5DF?Jkl@59|SE@P@'LAvv??Z9%@S~TXF?Z8̿@5ϞWEAVKF@C zAįN?IG@VvF?I@5j ECED!@9rAB"'Ղn?ʼ5=@[sF?ʺ#@5V3EDxN@s 9ՄAÊ2?پ8,@_fR/F?پV@5LEF2 Kw@b[ At?ە@elkF?ەė'X@5C@JEH<4o@M5}SAė?T +q@k t&F?TM@5m鐒EJAX'%;@?AA iG%?Wn @pQF?uȿ@5Z&wELL3@̘[A}U4?[pR@uF?[ @5HexEN&@ٛ +Ax5?Ph &-@{F?Pfο@54QEP}@{@QCVAh*Z6?;.jl@OF?;zܿ@5##$EQL @@tCZxJA  +? 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(around 6 million years of age, solar metallicity, + 8000 parsecs from the Earth, and 2.7 AKs extinction). + For our purposes, we want to make sure that the Observed + and BPASS counterpart's KLF's are no farther apart than 0.25. + We may also inspect the KLF of the BPASS and MIST v.1. cluster + and also the overall initial mass of both BPASS and MIST v1 counterparts + to the MIST cluster """ + + result = load_klf_by_radius(mask_for_log=True) + magBin = result.Kp[1] - result.Kp[0] + idx = np.where(result.Kp < 16)[0] + klf_mag_bins = np.arange(9.0, 17, 1.0) + binsKp = klf_mag_bins + binEdges = binsKp[0:-1] + (binsKp[1:] - binsKp[0:-1]) / 2.0 + + + BPASS_iso = synthetic.Isochrone_Binary(6.78, 2.7, + 8000, 0.0, + filters=['nirc2,Kp']) + # If we want to use control group, let's use multiplicity = None. + custom_IMF = imf.imf.IMF_broken_powerlaw(np.array([1, 150]), + np.array([-1.7]), + multiplicity= + (imf.multiplicity. + MultiplicityResolvedDK())) + BPASS_Cluster = synthetic.Cluster_w_Binaries(BPASS_iso, custom_IMF, + 1000000, + ifmr=ifmr.IFMR_Spera15()) + + area = 116.098 # arcsec^2 + # Think of how many stars per area would there be in a cluster + # similar to BPASS_Cluster + # but with lower mass. We use scaling. + scaleFactorBPS = ((17000 / + BPASS_Cluster.star_systems['systemMass'].sum()) / + area) + MIST_iso = synthetic.IsochronePhot(6.78, 2.7, 8000, 0.0, + filters=['nirc2,Kp']) + MIST_Cluster = synthetic.ResolvedCluster(MIST_iso, custom_IMF, + 1000000, + ifmr=ifmr.IFMR_Spera15()) + totl_mist =(MIST_Cluster.star_systems['m_nirc2_Kp'] + [np.where((MIST_Cluster.star_systems['isWR'] == 0))[0]]) + totl_BPASS = (BPASS_Cluster.star_systems['m_nirc2_Kp'] + [np.where((~BPASS_Cluster.star_systems['isWR']))[0]]) + mist_scale = ((17000 / + MIST_Cluster.star_systems['systemMass'].sum()) / + area) + weightsMST = np.array([1.0 for x in totl_mist]) + weightsMST *= mist_scale + weightsBPS = np.array([1.0 for x in totl_BPASS]) + weightsBPS *= scaleFactorBPS + # Binning the K' magnitudes into their respective bins + # And plotting KLF's of BPASS_Cluster, MIST clusters, and + # Observed KLF + # n, bins, patches are exactly the same objects that are + # described as outputs of matplotlib.hist + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n, bins, patches = py.hist(totl_mist, bins=binEdges, histtype='step', + weights=weightsMST, color='green', label='MISTv.1 Model', + align='mid', linewidth=1.5) + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n2, bins2, patches2 = py.hist(totl_BPASS, bins=binEdges, histtype='step', + weights=weightsBPS, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + py.errorbar(result.Kp[idx], result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', xerr=magBin/2.0, capsize=0, linewidth=2) + py.errorbar(result.Kp[idx] ,result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', yerr=result.eKLF_ext_cmp_sp_im_noWR[idx], + linewidth=2, + label='Observed') + py.xlim(8.5, 15.5) + py.xlabel('Kp magnitude') + py.ylabel("stars / (arcsecond^2 mag)") + py.title("KLF's at Age = %d Myr" % (10**(6.78 - 6)), fontsize=14) + py.legend(loc='upper left', numpoints=1) + py.savefig('Comparisons_w_Real_Data.png') + # Now compare differences in the heights of BPASS cluster KLF and given KLF + for x in range(len(binEdges)): + assert np.abs(result.KLF_ext_cmp_sp_im_noWR[idx][x] - n[x]) == approx(0, abs=0.25) + print("BPASS IMF does not seem too far off when compared to the BPASS IMF") + # Now compare differences in the BPASS and MIST KLF's (or values + # of bins in histograms for both KLF's) + for x in range(len(binEdges)): + assert np.abs(n2[x] - n[x]) == approx(0, abs=0.25) + # Now compare the initial cluster mass of both the BPASS + # and MIST cluster + assert (np.abs(np.log10(BPASS_Cluster()) - + np.log10(MIST_Cluster())) == approx(0, rel=0.2)) + + +def test_second_figure_BPASS(): + """ In this test, I will generate figures comparing the + current mass distributions of noncompact remnant stars + (non-phase = -99 stars) in both BPASS and MIST clusters + of 10^9.2 years of age and 1/10th of solar metallicity. + In the tests I will create and save a figure juxtaposing + the current mass distributions. + + Things I will be testing: + - Will there be no main sequence stars of + > ~ 5 solar masses (thanks Dr. McKee)? + - Will the highest mass stars have + + + """ + # Check if the evolution class works fine + # BPASS Isochrone used to create cluster + iso1 = synthetic.Isochrone_Binary(9.2, 0.5, 2000, + math.log10(0.1), + mass_sampling=1) + # MIST v1 Isochrone used to create cluster + iso2 = synthetic.IsochronePhot(9.2, 0.5, 2000, + math.log10(0.1), + recomp=True) + clus_1 = synthetic.Cluster_w_Binaries(iso1, + imf.imf.IMFSalpeter1955(multiplicity= + imf.multiplicity. + MultiplicityResolvedDK()), + 200000, ifmr=ifmr.IFMR_Spera15()) + clus_2 = synthetic.ResolvedCluster(iso2, + imf.imf.IMFSalpeter1955(multiplicity= + imf.multiplicity. + MultiplicityResolvedDK()), + 200000, ifmr=ifmr.IFMR_Spera15()) + # extract the BPASS isochrone's Primary stars' and companion stars' current mass + star_systems = clus_1.star_systems + companions = clus_1.companions + prims_cm1 = (star_systems['mass_current'] + [np.where(star_systems['phase'] == 5)[0]]) + companions_cm1 = (companions['mass_current'] + [np.where(companions['phase'] == 5)[0]]) + plt.hist(np.hstack((prims_cm1, companions_cm1)), 40, (0, 10), + density=True, histtype='step', label="BPASS") + star_systems2 = clus_2.star_systems + companions2 = clus_2.companions + prims_cm2 = star_systems2['mass_current'][np.where(star_systems2['phase'] < + 101)[0]] + companions_cm2 = companions2['mass_current'][np.where(companions2['phase'] < + 101)[0]] + plt.hist(np.hstack((prims_cm2 , companions_cm2)), 40, (0, 10), + density=True, histtype='step', label="MISTv1") + plt.xlabel("Current mass of the star in solar masses +" + " MISTv1 clusters (PDF) (binsize = 0.25 solar mass)") + plt.xscale('log') + plt.yscale('log') + plt.legend() + py.savefig('Comparing_BPASS_Current_Mass_Dist_w_MIST.png') + +def test_figure_one_BPASS(): + """ In this test, I will be running the plotting code comparing BPASS + and SPISEA codes. The BPASS and SPISEA isochrones are 10^8.2 years old and + have solar metallicity. I will also check if the supposed helium stars seem + to fit the profile of what is associated with that stellar type. In other words, + I verify whether the stars that are in what I think is the helium star area + (4.25= 4.25) & + (np.log10(iso_bpass.primaries['L']) >= 27.5) & + (np.log10(iso_bpass.primaries['L']) <= 29))] + # Do all of the primary stars in that region have initial mass + # >= 2.0 solar mass + for_all = all(He_star_region['mass'] >= 2.0) + assert for_all + # see if any of the secondary stars have become helium stars + He_star_second = iso_bpass.secondaries[np.where((np.log10(iso_bpass.primaries['Teff']) <= 4.75) & + (np.log10(iso_bpass.primaries['Teff']) >= 4.25) & + (np.log10(iso_bpass.primaries['L']) >= 27.5) & + (np.log10(iso_bpass.primaries['L']) <= 29))] + for_all_2 = all(He_star_second['mass'] >= 2.0) + assert for_all_2 + # To do: write subtest for checking the main sequence of the isochrone + +def test_isochrones_dim_stars(): + """ In this test, I will check whether the very dim (in V-band) + stars in the V band filter (bolometric magnitude + is akin to V band magnitude) are low initial mass, low temperature + stars. The isochrone used to generate those stars will be 10^6.6 years old + and have solar metallicity""" + iso1 = synthetic.Isochrone_Binary(6.6, 0, 10, + math.log10(1), + mass_sampling=1) + + # Now, consider very dim to be M_V > 17.5. + # Physical intuition tells us that the low mass stars (e.g. + # stars closer to M spectral type or brown dwarf area on the + # HR diagram. + dim_prims = iso1.primaries[np.where((iso1.primaries['m_ubv_V'] > 17.5))] + dim_singles = iso1.singles[np.where((iso1.singles['m_ubv_V'] > 17.5))] + dim_secondaries = iso1.secondaries[np.where((iso1.secondaries['m_ubv_V'] > 17.5))] + print("Number of primary stars with $M_V < 17.5") + print(len(dim_prims)) + print("Number of single stars with $M_V < 17.5") + print(len(dim_singles)) + print("Number of secondary stars with $M_V < 17.5") + print(len(dim_secondaries)) + assert all(dim_prims['Teff'] < 2000) + assert all(dim_singles['Teff'] < 2000) + assert all(dim_secondaries['Teff'] < 2000) + assert all(dim_prims['mass'] < 1.0) + assert all(dim_singles['mass'] < 1.0) + assert all(dim_secondaries['mass'] < 1.0) + + +def test_cluster_2(): + # Check if the evolution class works fine + # BPASS Isochrone used to create cluster + iso1 = synthetic.Isochrone_Binary(9.2, 0.5, 8000, + math.log10(1), + mass_sampling=1) + # MIST v1 Isochrone used to create cluster + iso2 = synthetic.IsochronePhot(9.2, 0.5, 8000, + math.log10(1), + recomp=True) + clus_1 = synthetic.Cluster_w_Binaries(iso1, + imf.imf.IMFSalpeter1955(multiplicity= + imf.multiplicity. + MultiplicityUnresolved()), + 200000, ifmr=ifmr.IFMR_Spera15()) + clus_2 = synthetic.ResolvedCluster(iso2, + imf.imf.IMFSalpeter1955(multiplicity=imf. multiplicity. + MultiplicityResolvedDK()), + 200000, ifmr=ifmr.IFMR_Spera15()) + assert (np.abs(clus_1.star_systems['systemMass'].sum() - + clus_2.star_systems['systemMass'].sum()) == + approx(0, rel=10**-2)) + # Now trying this with the custom IMF from Figure 3 of my lunch talk. + custom_IMF = imf.imf.IMF_broken_powerlaw(np.array([1, 150]), + np.array([-1.7]), + multiplicity= + (imf.multiplicity. + MultiplicityResolvedDK())) + clus_1 = synthetic.Cluster_w_Binaries(iso1, custom_IMF, + 1000000, ifmr=ifmr.IFMR_Spera15()) + clus_2 = synthetic.ResolvedCluster(iso2, custom_IMF, + 1000000, ifmr=ifmr.IFMR_Spera15()) + assert np.abs(clus_1['systemMass'].sum() - clus_2['systemMass'].sum()) == approx(0, rel=10**-2) + # Now trying this with the Kennicutt 1983 from Figure 3 of my lunch talk. + Kennicutt_IMF = imf.imf.Kennicutt1983(multiplicity = imf.multiplicity. + MultiplicityResolvedDK()) + clus_1 = synthetic.Cluster_w_Binaries(iso1, Kennicutt_IMF, + 1000000, ifmr=ifmr.IFMR_Spera15()) + clus_2 = synthetic.ResolvedCluster(iso2, custom_IMF, + 1000000, ifmr=ifmr.IFMR_Spera15()) + assert np.abs(clus_1['systemMass'].sum() - clus_2['systemMass'].sum()) == approx(0, rel=10**-2) + + +def test_third_figure_revised(): + """ In this test I will try to make sure that + BPASS clusters' KLF's do not deviate too widely + from that of the Observed KLF from the Young Galactic Center Stars + from Lu et al. 2013. (around 6 million years of age, solar metallicity, + 8000 parsecs from the Earth, and 2.7 AKs extinction). + For our purposes, we want to make sure that the Observed + and BPASS counterpart's KLF's are no farther apart than 0.25. + We may also inspect the KLF of the BPASS and MIST v.1. cluster + and also the overall initial mass of both BPASS and MIST v1 counterparts + to the MIST cluster. + Big Change Made: Use of MultiplicityUnresolved (Lu et. al 2013) + Use of Merged Models + IFMR = None is used + """ + + result = load_klf_by_radius(mask_for_log=True) + magBin = result.Kp[1] - result.Kp[0] + idx = np.where(result.Kp < 16)[0] + klf_mag_bins = np.arange(9.0, 17, 1.0) + binsKp = klf_mag_bins + binEdges = binsKp[0:-1] + (binsKp[1:] - binsKp[0:-1]) / 2.0 + + + BPASS_iso = synthetic.Isochrone_Binary(6.78, 2.7, + 8000, 0.0, + filters=['nirc2,Kp']) + # If we want to use control group, let's use multiplicity = None. + custom_IMF = imf.imf.IMF_broken_powerlaw(np.array([1, 150]), + np.array([-1.7]), + multiplicity= + (imf.multiplicity. + MultiplicityUnresolved())) + BPASS_Cluster = synthetic.Cluster_w_Binaries(BPASS_iso, custom_IMF, + 1000000, + ifmr=None) + + area = 150. # arcsec^2 + clus_mass = 1000000 + # Think of how many stars per area would there be in a cluster + # similar to BPASS_Cluster + # but with lower mass. We use scaling. + scaleFactorBPS = ((17000 / + clus_mass) / + area) + Merged_iso = synthetic.IsochronePhot(6.78, 2.7, 8000, 0.0, + evo_model=evolution.MergedBaraffePisaEkstromParsec(), + recomp=False, + filters=['nirc2,Kp']) + Merged_iso = synthetic.IsochronePhot(6.78, 2.7, 8000, 0.0, + recomp=False, + filters=['nirc2,Kp']) + Mist_Cluster = synthetic.ResolvedCluster(MIST_iso, custom_IMF, + 1000000, + ifmr=None) + Merged_Cluster = synthetic.ResolvedCluster(Merged_iso, custom_IMF, + 1000000, + ifmr=None) + totl_mist =(MIST_Cluster.star_systems['m_nirc2_Kp'] + [np.where((MIST_Cluster.star_systems['isWR'] == 0))[0]]) + totl_merged =(Merged_Cluster.star_systems['m_nirc2_Kp'] + [np.where((Merged_Cluster.star_systems['isWR'] == 0))[0]]) + totl_BPASS = (BPASS_Cluster.star_systems['m_nirc2_Kp'] + [np.where((~BPASS_Cluster.star_systems['isWR']))[0]]) + mist_scale = scaleFactorBPS + merged_scale = mist_scale + weightsMST = np.array([1.0 for x in totl_mist]) + weightsMST *= mist_scale + weightsBPS = np.array([1.0 for x in totl_BPASS]) + weightsBPS *= scaleFactorBPS + weightsMerged = np.array([1.0 for x in totl_merged]) + weightsMerged *= merged_scale + # Binning the K' magnitudes into their respective bins + # And plotting KLF's of BPASS_Cluster, MIST clusters, and + # Observed KLF + # n, bins, patches are exactly the same objects that are + # described as outputs of matplotlib.hist + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n, bins, patches = py.hist(totl_mist, bins=binEdges, histtype='step', + weights=weightsMST, color='green', label='Merged Model', + align='mid', linewidth=1.5) + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n2, bins2, patches2 = py.hist(totl_BPASS, bins=binEdges, histtype='step', + weights=weightsBPS, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + n3, bins3, patches3 = py.hist(totl_merged, bins=binEdges, histtype='step', + weights=weightsMerged, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + py.errorbar(result.Kp[idx], result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', xerr=magBin/2.0, capsize=0, linewidth=2) + py.errorbar(result.Kp[idx] ,result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', yerr=result.eKLF_ext_cmp_sp_im_noWR[idx], + linewidth=2, + label='Observed') + py.xlim(8.5, 15.5) + py.xlabel('Kp magnitude') + py.ylabel("stars / (arcsecond^2 mag)") + py.title("KLF's at Age = %d Myr" % (10**(6.78 - 6)), fontsize=14) + py.legend(loc='upper left', numpoints=1) + py.savefig('Comparisons_w_Real_Data.png') + # Now compare differences in the heights of BPASS cluster KLF and given KLF + for x in range(len(binEdges)): + assert np.abs(result.KLF_ext_cmp_sp_im_noWR[idx][x] - n[x]) == approx(0, abs=0.25) + print("BPASS IMF does not seem too far off when compared to the BPASS IMF") + # Now compare differences in the BPASS and MIST KLF's (or values + # of bins in histograms for both KLF's) + for x in range(len(binEdges)): + assert np.abs(n2[x] - n[x]) == approx(0, abs=0.125) + assert np.abs(n2[x] - n3[x]) == approx(0, abs=0.125) + # Now we try to generate a figure closer to the Figure 1 of Lu et al 2013 + # In other words use a Salpeter-style IMF + custom_IMF2 = imf.imf.IMF_broken_powerlaw(np.array([1, 150]), + np.array([-2.35]), + multiplicity= + (imf.multiplicity. + MultiplicityUnresolved())) + BPASS_Cluster = synthetic.Cluster_w_Binaries(BPASS_iso, custom_IMF2, + 1000000, + ifmr=None) + Mist_Cluster = synthetic.ResolvedCluster(MIST_iso, custom_IMF, + 1000000, + ifmr=None) + Merged_Cluster = synthetic.ResolvedCluster(Merged_iso, custom_IMF, + 1000000, + ifmr=None) + totl_mist =(MIST_Cluster.star_systems['m_nirc2_Kp'] + [np.where((MIST_Cluster.star_systems['isWR'] == 0))[0]]) + totl_merged =(Merged_Cluster.star_systems['m_nirc2_Kp'] + [np.where((Merged_Cluster.star_systems['isWR'] == 0))[0]]) + totl_BPASS = (BPASS_Cluster.star_systems['m_nirc2_Kp'] + [np.where((~BPASS_Cluster.star_systems['isWR']))[0]]) + mist_scale = scaleFactorBPS + merged_scale = mist_scale + weightsMST = np.array([1.0 for x in totl_mist]) + weightsMST *= mist_scale + weightsBPS = np.array([1.0 for x in totl_BPASS]) + weightsBPS *= scaleFactorBPS + weightsMerged = np.array([1.0 for x in totl_merged]) + weightsMerged *= merged_scale + # Binning the K' magnitudes into their respective bins + # And plotting KLF's of BPASS_Cluster, MIST clusters, and + # Observed KLF + # n, bins, patches are exactly the same objects that are + # described as outputs of matplotlib.hist + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n, bins, patches = py.hist(totl_mist, bins=binEdges, histtype='step', + weights=weightsMST, color='green', label='Merged Model', + align='mid', linewidth=1.5) + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n2, bins2, patches2 = py.hist(totl_BPASS, bins=binEdges, histtype='step', + weights=weightsBPS, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + n3, bins3, patches3 = py.hist(totl_merged, bins=binEdges, histtype='step', + weights=weightsMerged, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + py.errorbar(result.Kp[idx], result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', xerr=magBin/2.0, capsize=0, linewidth=2) + py.errorbar(result.Kp[idx] ,result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', yerr=result.eKLF_ext_cmp_sp_im_noWR[idx], + linewidth=2, + label='Observed') + py.xlim(8.5, 15.5) + py.xlabel('Kp magnitude') + py.ylabel("stars / (arcsecond^2 mag)") + py.title("KLF's at Age = %d Myr" % (10**(6.78 - 6)), fontsize=14) + py.legend(loc='upper left', numpoints=1) + py.savefig('Comparisons_w_Real_Data.png') + # Now compare differences in the heights of BPASS cluster KLF and given KLF + for x in range(len(binEdges)): + assert np.abs(result.KLF_ext_cmp_sp_im_noWR[idx][x] - n[x]) == approx(0, abs=0.25) + print("BPASS IMF does not seem too far off when compared to the BPASS IMF") + # Now compare differences in the BPASS and MIST KLF's (or values + # of bins in histograms for both KLF's) + +def test_third_figure_w_Tab2Sol1(): + """ In this test I will try to make sure that + BPASS clusters' KLF's do not deviate too widely + from that of the Observed KLF from the Young Galactic Center Stars + from Lu et al. 2013. (around 6 million years of age, solar metallicity, + 8000 parsecs from the Earth, and 2.7 AKs extinction). + For our purposes, we want to make sure that the Observed + and BPASS counterpart's KLF's are no farther apart than 0.25. + We may also inspect the KLF of the BPASS and MIST v.1. cluster + and also the overall initial mass of both BPASS and MIST v1 counterparts + to the MIST cluster. + Big Change Made: Use of MultiplicityUnresolved (Lu et. al 2013) + Use of Merged Models + IFMR = None is used + Using Table 2 Solution 1 from Lu et. al 2013 + """ + + result = load_klf_by_radius(mask_for_log=True) + magBin = result.Kp[1] - result.Kp[0] + idx = np.where(result.Kp < 16)[0] + klf_mag_bins = np.arange(9.0, 17, 1.0) + binsKp = klf_mag_bins + binEdges = binsKp[0:-1] + (binsKp[1:] - binsKp[0:-1]) / 2.0 + + + BPASS_iso = synthetic.Isochrone_Binary(6.62, 2.7, + 8000, 0.0, + filters=['nirc2,Kp']) + # If we want to use control group, let's use multiplicity = None. + custom_IMF = imf.imf.IMF_broken_powerlaw(np.array([1, 150]), + np.array([-1.7]), + multiplicity= + (imf.multiplicity. + MultiplicityUnresolved())) + BPASS_Cluster = synthetic.Cluster_w_Binaries(BPASS_iso, custom_IMF, + 1000000, + ifmr=None) + + area = 150. # arcsec^2 + clus_mass = 1000000 + # Think of how many stars per area would there be in a cluster + # similar to BPASS_Cluster + # but with lower mass. We use scaling. + scaleFactorBPS = ((17000 / + clus_mass) / + area) + Merged_iso = synthetic.IsochronePhot(6.62, 2.7, 8000, 0.0, + evo_model=evolution.MergedBaraffePisaEkstromParsec(), + recomp=False, + filters=['nirc2,Kp']) + Merged_iso = synthetic.IsochronePhot(6.62, 2.7, 8000, 0.0, + recomp=False, + filters=['nirc2,Kp']) + Mist_Cluster = synthetic.ResolvedCluster(MIST_iso, custom_IMF, + 1000000, + ifmr=None) + Merged_Cluster = synthetic.ResolvedCluster(Merged_iso, custom_IMF, + 1000000, + ifmr=None) + totl_mist =(MIST_Cluster.star_systems['m_nirc2_Kp'] + [np.where((MIST_Cluster.star_systems['isWR'] == 0))[0]]) + totl_merged =(Merged_Cluster.star_systems['m_nirc2_Kp'] + [np.where((Merged_Cluster.star_systems['isWR'] == 0))[0]]) + totl_BPASS = (BPASS_Cluster.star_systems['m_nirc2_Kp'] + [np.where((~BPASS_Cluster.star_systems['isWR']))[0]]) + mist_scale = scaleFactorBPS + merged_scale = mist_scale + weightsMST = np.array([1.0 for x in totl_mist]) + weightsMST *= mist_scale + weightsBPS = np.array([1.0 for x in totl_BPASS]) + weightsBPS *= scaleFactorBPS + weightsMerged = np.array([1.0 for x in totl_merged]) + weightsMerged *= merged_scale + # Binning the K' magnitudes into their respective bins + # And plotting KLF's of BPASS_Cluster, MIST clusters, and + # Observed KLF + # n, bins, patches are exactly the same objects that are + # described as outputs of matplotlib.hist + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n, bins, patches = py.hist(totl_mist, bins=binEdges, histtype='step', + weights=weightsMST, color='green', label='Merged Model', + align='mid', linewidth=1.5) + # Right below: BPASS cluster KLF histogram's bin values, bin-edges, and patches + n2, bins2, patches2 = py.hist(totl_BPASS, bins=binEdges, histtype='step', + weights=weightsBPS, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + n3, bins3, patches3 = py.hist(totl_merged, bins=binEdges, histtype='step', + weights=weightsMerged, color='blue', + label='(BPASS Model KLF)', + align='mid', linewidth=1.5) + py.errorbar(result.Kp[idx], result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', xerr=magBin/2.0, capsize=0, linewidth=2) + py.errorbar(result.Kp[idx] ,result.KLF_ext_cmp_sp_im_noWR[idx], + fmt='ro-', yerr=result.eKLF_ext_cmp_sp_im_noWR[idx], + linewidth=2, + label='Observed') + py.xlim(8.5, 15.5) + py.xlabel('Kp magnitude') + py.ylabel("stars / (arcsecond^2 mag)") + py.title("KLF's at Age = %d Myr" % (10**(6.62 - 6)), fontsize=14) + py.legend(loc='upper left', numpoints=1) + py.savefig('Comparisons_w_Real_Data.png') + # Now compare differences in the heights of BPASS cluster KLF and given KLF + for x in range(len(binEdges)): + assert np.abs(result.KLF_ext_cmp_sp_im_noWR[idx][x] - n[x]) == approx(0, abs=0.25) + print("BPASS IMF does not seem too far off when compared to the BPASS IMF") + # Now compare differences in the BPASS and MIST KLF's (or values + # of bins in histograms for both KLF's) + for x in range(len(binEdges)): + assert np.abs(n2[x] - n[x]) == approx(0, abs=0.1) + assert np.abs(n2[x] - n3[x]) == approx(0, abs=0.1) + diff --git a/spisea/tests/test_bpass_assumptions_and_etc.py b/spisea/tests/test_bpass_assumptions_and_etc.py new file mode 100755 index 00000000..1a0a9b3a --- /dev/null +++ b/spisea/tests/test_bpass_assumptions_and_etc.py @@ -0,0 +1,340 @@ +import numpy as np +import pandas as pd +from astropy.io import fits +from astropy.table import Table +import os +import hoki +from hoki import load +import glob +import unittest + +import numpy as np +import pandas as pd +from astropy.io import fits +from astropy.table import Table +import os +import hoki +from hoki import load +import glob +possible_secondary_q = ['0.1', '0.2', '0.3', + '0.4', '0.5', + '0.6', '0.7', '0.8', '0.9'] +# The list encompassing possible primary +# mass for primary and secondary. Trying to encompass +# all possible cases, even if there is a bit of redundancy and excess. +mass_list = [round(0.1, 1)] + [round(0.12 + x * 0.020, 2) for x in range(95)] +mass_list = mass_list + [round(2.05 + x * 0.05, 2) for x in range(20)] +mass_list = mass_list + [round(3.1 + 0.1 * x, 1) for x in range(70)] +mass_list = (mass_list + [11 + x for x in range(90)] + + [120, 400, 500] + [125 + 25 * x for x in range(8)]) + + +def convert(x): + """ + Helper function to use for possible mass list. If the + number can be represented by an integer, + it turns into an integer. + This is to make sure that I am really matching masses. + """ + if (x % 1 == 0.0): + return int(x) + else: + return x + + +mass_list = list(map(convert, mass_list)) +period_list = ['0', '0.2', '0.4', '0.6', '0.8', '1', '1.2', '1.4', + '1.6', '1.8', '2', '2.2', '2.4', '2.6', '2.8', '3', + '3.2', '3.4', '3.6', '3.8', '4'] +combos = [] +for y in period_list: + for z in possible_secondary_q: + combos.append((z, y)) + + +def assign_props(dictionary, input_str, y): + dictionary[input_str] = y + return input_str + +# Did I end up scrambling the Naming Convention? +# Fitsmodels.fits +# May seem like an obvious one, but put it anyway: +# Helps ensure that we are not creating jiberrish file names. +class test_reformatter(unittest.TestCase): + def help_test2(self, destination, metallicity): + x = metallicity + x1 = glob.glob("{}/{}/*sin.fits".format(destination, metallicity)) + x2 = glob.glob("{}/{}/*bin.fits".format(destination, metallicity)) + x3 = glob.glob("{}/{}/*hmg.fits".format(destination, metallicity)) + x4 = glob.glob("{}/{}/*sec.fits".format(destination, metallicity)) + total_set = set(x1+x2+x3+x4) + g = glob.glob("{}/NEWSINMODS/{}/*".format(hoki.MODELS_PATH, x)) + h = glob.glob("{}/NEWSINMODS/{}hmg/*".format(hoki.MODELS_PATH, x)) + j = glob.glob("{}/NEWBINMODS/NEWBINMODS/{}/*".format(hoki.MODELS_PATH, x)) + k = glob.glob("{}/NEWBINMODS/NEWSECMODS/{}_2/*".format(hoki.MODELS_PATH, x)) + for x in total_set: + if (x[-8:]=="sin.fits"): + if (not (os.path.isfile("{}/NEWSINMODS/{}/{}".format(hoki.MODELS_PATH, metallicity, x[len(destination + "/xxx/FitsModelss"):-8])))): + print("ERROR IN ", x) + return + if (x[-8:]=="hmg.fits"): + if not (os.path.isfile("{}/NEWSINMODS/{}hmg/{}".format(hoki.MODELS_PATH, metallicity, x[len(destination + "/xxx/FitsModelss"):-8]))): + print("Error in ", x) + return + if (x[-8:]=="bin.fits"): + if not (os.path.isfile("{}/NEWBINMODS/NEWBINMODS/{}/{}".format(hoki.MODELS_PATH, metallicity, x[len(destination + "/xxx/FitsModelss"):-8]))): + print("Error in ", x) + return + if (x[-8:]=="sec.fits"): + if not (os.path.isfile("{}/NEWBINMODS/NEWSECMODS/{}_2/{}".format(hoki.MODELS_PATH, metallicity, x[len(destination + "/xxx/FitsModelss"):-8]))): + print("Error in ", x) + return + print(len(x1)) + print(len(g)) + print(len(x1)==len(g)) + print(len(x2)) + print(len(j)) + print(len(x2)==len(j)) + print(len(x3)) + print(len(h)) + print((len(x3)==len(h))) + print(len(x4)) + print(len(k)) + print((len(x4)==len(k))) + if (len(x1)==len(g)) and (len(x2)==len(j)) and (len(x3)==len(h)) and (len(x4)==len(k)): + print("Passed! "+metallicity) + return True + else: + print() + print("Failed! "+metallicity) + return False + + def extractor(self, metallicity, input_dir): + """ + Portions of the extractor function meant to test a) + whether all possible stars are covered + by the looping hashmap-parameter matching scheme + for binaries and single stars and b) whether all + possible stars are getting covered by my assumptions + about the length of + """ + # Set of files that exist and have been caught by the looping. + caught_no = set() + # Mapping of file name to a tuple of properties + # (primary star mass,(secondary star mass, initial logP for bin.fits types) + names_to_prop = {} + # Find all filenames of reformatted BPASS models from reformatter + for x in mass_list: + # Find all NEWBINMODS systems of the specified metallicity + for y in combos: + st = "{}/{}/FitsModelssneplot-{}-{}-{}-{}bin.fits" + st = st.format(input_dir,metallicity, + metallicity, + str(x), y[0], + y[1]) + names_to_prop[st] = (str(x), y[0], y[1]) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all single star systems of the specified metallicity + st = "{}/{}/FitsModelssneplot-{}-{}sin.fits" + st = st.format(input_dir, metallicity, + metallicity, str(x)) + names_to_prop[st] = (str(x), ) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all Binary QHE systems of the specified metallicity + st = "{}/{}/FitsModelssneplot-{}-{}hmg.fits" + st = st.format(input_dir, metallicity, + metallicity, str(x)) + names_to_prop[st] = (str(x), ) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all NEWSINMODS systems of the specified metallicity + st = "{}/{}/FitsModelssneplot_2-{}-{}-*sec.fits" + st = st.format(input_dir, metallicity, + metallicity,str(x)) + li = glob.glob(str(st)) + sec_files = set(li) + caught_no = caught_no.union(sec_files) + [assign_props(names_to_prop, name, (x,)) for name in sec_files] + len_of_heading = len("{}/{}/FitsModelssneplot_2-{}-".format(input_dir, metallicity, metallicity)) + entries = glob.glob(str("{}/{}/*".format(input_dir, metallicity))) + suffix_len = len("xxx.fits") # 8 + # Note some of the branching statements have + # if True statements. + # This is redundant, but it would have been + # a real pain to adjust the indentation + # which in Python is really important + # and quite annoying at times. + # Count variable stands for number of sec.fits + # files caught. + count = 0 + for x in entries: + rest = names_to_prop[x] + if (x[-8:-5] == 'sec'): + initlMass = float(rest[0]) + if (x[-10 - 1 * suffix_len] == "-"): + # Here the decimal is number is 9 + # characters long and + # xxx.fits has length of 8 + log_P_in_days = float(x[-9 - suffix_len: + -1 * suffix_len]) + initlMass2 = float(x[(len_of_heading + + len(str(rest[0]) + + "-")): -10 - 1 * suffix_len]) + st = "{}/{}/FitsModelssneplot_2-{}-{}-{}-{}sec.fits" + st = st.format(input_dir, metallicity, + metallicity,rest[0], x[(len_of_heading + + len(str(rest[0]) + + "-")): + -10 - 1 * suffix_len], + x[-9 - suffix_len: -1 * suffix_len]) + if not ((log_P_in_days<0) and (os.path.isfile(st))): + return False + count+=1 + else: + # See if the number begins right + # after a dash in index -17 + # 8 for the xxx.fits and 8 for + # the log_P and we want + # the preceding dash. + if (x[-9 - 1 * suffix_len] == "-"): + # Here the decimal is number is 8 + # characters long and + # xxx.fits has length of 8. + log_P_in_days = float(x[-8 - suffix_len: + -1 * suffix_len]) + # Accounting for the dashes, find + # the sandwiched initial mass of the + # "remnant primary" + # sandwiched between initial secondary star + # mass and the initial logP + initlMass2 = float(x[(len_of_heading + + len(str(rest[0]) + + "-")): + -9 - 1 * suffix_len]) + # Assume that the number would begin + # at index -16. (From my observation, we only + # have a few choices for number of digits of + # the log_period + st = "{}/{}/FitsModelssneplot_2-{}-{}-{}-{}sec.fits" + st = st.format(input_dir, metallicity, + metallicity,rest[0], x[(len_of_heading + + len(str(rest[0]) + + "-")): + -9 - 1 * suffix_len], + x[-8 - suffix_len: -1 * suffix_len]) + if not (os.path.isfile(st)): + return False + count+=1 + elif (x[-8 - 1 * suffix_len] == "-"): + # Here the decimal is number is 7 + # characters long and + # xxx.fits has length of 8 + log_P_in_days = float(x[-7 - suffix_len: + -1 * suffix_len]) + # Accounting for the dashes on both + # ends of the "remnant primary" mass, find the + # sandwiched initial mass of the remnant primary + # sandwiched between initial secondary star mass + # and the initial logP + initlMass2 = float(x[(len_of_heading + + len(str(rest[0]) + + "-")): + -8 - 1 * suffix_len]) + st = "{}/{}/FitsModelssneplot_2-{}-{}-{}-{}sec.fits" + st = st.format(input_dir, metallicity, + metallicity,rest[0], x[(len_of_heading + + len(str(rest[0]) + + "-")): + -8 - 1 * suffix_len], + x[-7 - suffix_len: -1 * suffix_len]) + if not (os.path.isfile(st) and (initlMass2>0)): + return False + count += 1 + return (count == len(glob.glob("{}/{}/FitsModelssneplot_2-{}-*sec.fits" + .format(input_dir, metallicity, + metallicity)))) + def extractor_check_all(self, metallicity, input_dir): + """ + Portions of the extractor function meant to test a) + whether all possible stars are covered + by the looping hashmap-parameter matching scheme + for binaries and single stars and b) whether all + possible stars are getting covered by my assumptions + about the length of + """ + # Set of files that exist and have been caught by the looping. + caught_no = set() + # Mapping of file name to a tuple of properties + # (primary star mass,(secondary star mass, initial logP for bin.fits types) + names_to_prop = {} + # Find all filenames of reformatted BPASS models from reformatter + for x in mass_list: + # Find all NEWBINMODS systems of the specified metallicity + for y in combos: + st = "{}/{}/FitsModelssneplot-{}-{}-{}-{}bin.fits" + st = st.format(input_dir,metallicity, + metallicity, + str(x), y[0], + y[1]) + names_to_prop[st] = (str(x), y[0], y[1]) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all single star systems of the specified metallicity + st = "{}/{}/FitsModelssneplot-{}-{}sin.fits" + st = st.format(input_dir, metallicity, + metallicity, str(x)) + names_to_prop[st] = (str(x), ) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all Binary QHE systems of the specified metallicity + st = "{}/{}/FitsModelssneplot-{}-{}hmg.fits" + st = st.format(input_dir, metallicity, + metallicity, str(x)) + names_to_prop[st] = (str(x), ) + if (os.path.isfile(st)): + caught_no.add(st) + # Find all NEWSINMODS systems of the specified metallicity + st = "{}/{}/FitsModelssneplot_2-{}-{}-*sec.fits" + st = st.format(input_dir, metallicity, + metallicity,str(x)) + li = glob.glob(str(st)) + sec_files = set(li) + caught_no = caught_no.union(sec_files) + [assign_props(names_to_prop, name, (x,)) for name in sec_files] + return caught_no == set(glob.glob("{}/{}*.fits".format(input_dir, metallicity))) + + def test_reformatter_coverage(self, destination="/g/lu/scratch/ryotainagaki/BPASS_tester_newReformatTest/"): + mets = ["zem5", "zem4", "zem3", "z001", + "z002", "z003", "z004", "z006", + "z008", "z010", "z014", "z020", + "z030", "z040"] + for met in mets: + print(met) + self.assertTrue(self.help_test2(destination, met)) + def test_extractor(self, source="/g/lu/scratch/ryotainagaki/BPASS_tester_newReformatTest/"): + print("Testing if all of my assumptions regarding the reading of " + + "NEWSECMODS type reformatted files are correct.") + print("Also testing for coverage of ALL secmods files" + + " in the sense that all of them would be considered" + + " in isochrone file making.") + mets = ["zem5", "zem4", "zem3", "z001", + "z002", "z003", "z004", "z006", + "z008", "z010", "z014", "z020", + "z030", "z040"] + for met in mets: + self.assertTrue(self.extractor(met, source)) + def test_extractor2(self, source="/g/lu/scratch/ryotainagaki/BPASS_tester_newReformatTestIsolated/"): + print("Also testing for whether all files in the input directory will be covered" + + " by the infrastructure/assumptions of the extractor function") + mets = ["zem5", "zem4", "zem3", "z001", + "z002", "z003", "z004", "z006", + "z008", "z010", "z014", "z020", + "z030", "z040"] + for met in mets: + result = self.extractor_check_all(met, source) + print(met+" ", result) + self.assertTrue(result) +if __name__ == '__main__': + unittest.main() diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 27c66ff2..dae7ecb6 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -1,6 +1,7 @@ # Test functions for the different stellar evolution and atmosphere models import pdb + def test_evolution_models(): """ Test to make sure the different evolution models work diff --git a/spisea/untitled.txt b/spisea/untitled.txt new file mode 100644 index 00000000..7068c8b2 --- /dev/null +++ b/spisea/untitled.txt @@ -0,0 +1,2 @@ +from spisea import evolution +