From 9e1673c06b66576067772573627fb547b5c97856 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Wed, 22 May 2024 09:42:10 -0700 Subject: [PATCH 01/48] Adding planet IMF derivation --- changes/Power_Law_Function.ipynb | 608 +++++++++++++++++++++++++++++++ 1 file changed, 608 insertions(+) create mode 100644 changes/Power_Law_Function.ipynb diff --git a/changes/Power_Law_Function.ipynb b/changes/Power_Law_Function.ipynb new file mode 100644 index 00000000..fb713697 --- /dev/null +++ b/changes/Power_Law_Function.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "23642516", + "metadata": {}, + "source": [ + "## Plotting Studied Power-Law Functions Over Small Mass Intervals" + ] + }, + { + "cell_type": "markdown", + "id": "ad57fd40", + "metadata": {}, + "source": [ + "#### Importing Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "d6503a21", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "markdown", + "id": "4f72f6f7", + "metadata": {}, + "source": [ + "#### Creating the general power-law function for small masses" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d51b38dc", + "metadata": {}, + "outputs": [], + "source": [ + "#define function\n", + "def power_law(M, a):\n", + " return M**(-1*a)\n", + "\n", + "# M is a defined mass range according to a specified paper, and a is the associated alpha value" + ] + }, + { + "cell_type": "markdown", + "id": "3fe49d7b", + "metadata": {}, + "source": [ + "#### Defining different mass/alpha values based on published papers" + ] + }, + { + "cell_type": "markdown", + "id": "b7736fad", + "metadata": {}, + "source": [ + "***Masses are in solar masses!***" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "4145bc16", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", + "M_mass = np.linspace(0.03, 0.6, 1000)\n", + "M_a = 0.69\n", + "M_aerr = 0.15\n", + "\n", + "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", + "S_mass = np.linspace(0.02, 0.5, 1000)\n", + "S_a = 0.60\n", + "S_aerr = 0.13\n", + "\n", + "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", + "L_mass = np.linspace(0.01, 0.5, 1000)\n", + "L_a = 0.5\n", + "L_aerr = 0.2\n", + "\n", + "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", + "B_mass = np.linspace(0.013, 0.1, 1000)\n", + "B_a = 0.7\n", + "B_aerr = 0.3\n", + "\n", + "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", + "P_mass = np.linspace(0.006, 0.25, 1000)\n", + "P_a = 0.6\n", + "P_aerr = 0.2" + ] + }, + { + "cell_type": "markdown", + "id": "f2a24e63", + "metadata": {}, + "source": [ + "#### Plotting all of the curves together (w/o accounting for error)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "60cab6f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1Fi3WgFi/D1j1ORb1FmhjzBYscXsO62niEqxh7KV2/FbbgCX2di7WQKxv7cphd8uMw3pPvR+ru+NxrAEddeEFrINcsbyKdbPajfUkuRFrAFNVFmF1ve7x2RaOPUDUiDFmuzFmZYDo24BHRCQPS6x8W4Xtgf9i3bw22fv9F9b5+CXWsTiK1Zq/LRhbfGz6HOtPsx5rEM/8OuTdg9Uj8Et7/2uxbn5gDcrpZ3flvO8n+6PASnu/PwCr7bBguBbYZXcx3YJ1XQVjb43X4AnmXqyHrDwsoanxxlTFznlY1/Xbdh03ABN8kswE5tjH9idY4xVisOq0DPgkyF29gTWYpdbWs49t+2sQ85rqnGCHZWH9zzKBJ+04v+fT/q/chPUQkYU1cO46O08UMAurzgexWt8P+LHpWqzBnp8ZYw5WLMCzQKpYI8nvxboGV2Bdx49jvSev6fq+A+tBYwdWb9pbWEIfiNuxWvEHsd7rvlpD2oAYY44AV2K9z83Eese5EusG7o//2L+ZIrLaXr8Gq4dwP9ZI/Ift+0BdeQirhyEL+D3WMajKIqzz9iVWV/lntZR5PPe0aVivHjbaNv2XY93H/8R6TbgO617jrxczGJ6h9v/aG1jn+CDW65M7wftwdBnWdXoYq0X9KwLrap2PRcVoOUVRGjlifUp0COudelqo7VHqjt0yTgemGmO+DrU9zR0RWYg1+O2lUOxfp/pUlKbDrcAKFefGhd1F2tLuhq94z++vx09pZjT2GXEURcGaZALrxj4ptJYo9eBMrO7kiu7cScb6UkZp5jT6Lm6x5md+Heu9bjkw2xjzVxF5AuudZCnWpxXXG2Oy/eTfhfWOzYM1uGZYA5muKIqiKAFpCgLdAWsGo9X2pwarsFoRKcBXxhi3iDwOYIz5jZ/8u4Bh9mANRVEURQkLGn0Xtz0c/4C9nicim4BOVUYXLsP/JwP1onXr1qZbt24nqjhFUZRmwapVq44YY9qE2o7GQqMXaF/EcvU4GPi+StQMAn8WY4DPRMQA/zDGzA5Q9s3AzQBdunRh5cpAX1cpiqIo/hCRYGb8UmyazChusXx7vos1mX+uT/hvsWb9CfTh/tnGmCFY36X+XETO9ZfIGDPbGDPMGDOsTRt9AFQURVFOLk1CoO3ZjN4F3jTGvOcTPh1rxqGpJsDLdmPPX20sH8PzsDzOKIqiKEpIafQCLSKCNcvVJmPMUz7h47G8jVxqjk3CXjVvnD2wDHv6tXFYszwpiqIoSkhpCu+gz8aa/u8HEVlrhz2ANfVfFPC5peEsM8bcIpaHnJeMMROxXJrNs+NdwFvGmGCnVVSURk1ZWRnp6ekUFxeH2hSliREdHU1KSgoRERGhNqVR0+gF2p5H2J9XoQV+wiq6tCfa6zs4NhevojQr0tPTadGiBd26dcN+SFWU48YYQ2ZmJunp6XTv3j3U5jRqGn0Xt6Io9aO4uJhWrVqpOCsnFBGhVatW2jNzAlCBVpRmjIqzcjLQ6+rEoALdgGRnL2LHjgdp7LO3KYqiKCcfFegGJCfnG/bseQzLj72iKIqiBEYFugFxOKIBKC/XdzOKAlZX6LXXXuvddrvdtGnThosvvjiEVlVmypQp7Nix47jKMMZw55130qtXL1JTU1m9enXAdL/97W855ZRT6Nu3L88++ywAWVlZXH755aSmpjJ8+HA2bLC+Bi0tLeXcc8/F7XYfl31KeKIC3YBY7l6hvLwkxJYoSngQFxfHhg0bKCqyvCt+/vnndOrUqU5lnExx+vHHH/F4PPTo0eO4yvn4449JS0sjLS2N2bNnc+utt/pN99prr7F37142b97Mpk2buPrqqwH44x//yKBBg1i/fj2vv/46d911FwCRkZGMGTOGf/870EzGSmOm0X9m1ZjQFrQSttx9N6xde2LLHDQInnmm1mQTJkzgo48+YsqUKcydO5drrrmGJUuWAHD06FFmzJjBjh07iI2NZfbs2aSmpjJz5kz279/Prl27aN26NX/84x+59tprKSgoAOD555/nrLPOYuHChTz55JPMnz8fgNtvv51hw4Zx+eWXM3z4cD788ENOPfVUrrnmGs4//3xuuummSra9+eabXHbZZd7tW2+9lRUrVlBUVMSUKVP4/e9/H9Sh+OCDD5g2bRoiwogRI8jOzubAgQN06NChUroXXniBt956C4fDaju1bdsWgI0bN3L//fcD0KdPH3bt2kVGRgbt2rVj0qRJ3H///UydOjUoW5TGg7agGxCHQ1vQilKVq6++mrfffpvi4mLWr1/PGWec4Y17+OGHGTx4MOvXr+ePf/wj06ZN88atWrWKDz74gLfeeou2bdvy+eefs3r1av79739z55131rjPxMREnn/+ea677jrefvttsrKyqokzwLfffsvQoUO924899hgrV65k/fr1LFq0iPXr1wNwzz33MGjQoGrLrFmzANi3bx+dO3f2lpOSksK+ffuq7W/79u38+9//ZtiwYUyYMIG0tDQATjvtNN57z5rFePny5ezevZv09HQABgwYwIoVK2o+yEqjRFvQDUiFQBtjCfRHmZm0i4hgWEJCKM1SlKBauieL1NRUdu3axdy5c5k4cWKluG+++YZ3330XgPPPP5/MzExycnIAuPTSS4mJiQGsWdFuv/121q5di9PpZOvWrbXu94ILLuA///kPP//5z1m3bp3fNAcOHMDXOc4777zD7NmzcbvdHDhwgI0bN5KamsrTTz9d4778fbnh71OkkpISoqOjWblyJe+99x4zZsxgyZIl3Hfffdx1110MGjSIgQMHMnjwYFwu6/btdDqJjIwkLy+PFi1a1FpvpfGgAt2AVO3ivnnLFiYkJ/OSCrTSzLn00ku59957WbhwIZmZmd7wmoQtLi7OG/b000/Trl071q1bR3l5OdHR1n/N5XJRXl7uTec7eUZ5eTmbNm0iJiaGo0ePkpKSUm1fMTEx3jw7d+7kySefZMWKFSQlJXHdddd54+655x6+/vrravmvvvpq7rvvPlJSUti7d683PD09nY4dO1ZLn5KSwhVXXAHA5ZdfzvXXXw9AQkICr776qveYdO/evdIsXRXCrjQttIu7Aanaxd3C6STX4wmlSYoSFsyYMYPf/e53DBw4sFL4ueeey5tvWp5iFy5cSOvWrUnw80Cbk5NDhw4dcDgcvPHGG3js/1XXrl3ZuHEjJSUl5OTk8OWXX3rzPP300/Tt25e5c+cyY8YMysqqf/7Yt29ftm3bBkBubi5xcXEkJiaSkZHBxx9/XKmstWvXVlvuu+8+wHoAef311zHGsGzZMhITE6u9fwaYNGkSX331FQCLFi3ilFNOASA7O5vS0lIAXnrpJc4991zvccjMzKRNmzY673UTRFvQDUjVFnQLl4s8FWhFISUlxTsy2ZeZM2dy/fXXk5qaSmxsLHPmzPGb/7bbbuOKK67gP//5D6NHj/a2rjt37sxPfvITUlNT6d27N4MHDwZg69atvPTSSyxfvpwWLVpw7rnn8uijj1Yb9HXRRRexcOFCxo4dy2mnncbgwYPp378/PXr04Oyzzw66fhMnTmTBggX06tWL2NhYb2u4Iu6ll16iY8eO3HfffUydOpWnn36a+Ph4XnrpJQA2bdrEtGnTcDqd9OvXj5dfftmb/+uvv672akBpGojOalV3hg0bZlauXFnnfDk5y1iz5kwGDlxAq1YTGLN2LSXl5XwzZMhJsFJRambTpk307ds31GaENUVFRYwePZpvv/0Wp9MZanP8MnnyZP70pz9x6qmnhtqUSvi7vkRklTFmWIhManRoF3cDol3citK4iImJ4fe//73fEdfhQGlpKZMmTQo7cVZODNrF3YBU7eJO0C5uRQl7LrzwwlCbEJDIyMhKn54pTYtG34IWkc4i8rWIbBKRH0XkLjs8WUQ+F5E0+zcpQP7xIrJFRLaJyH0n09aqn1m1cDrJ0yn6FEVRFD80eoEG3MAvjTF9gRHAz0WkH3Af8KUxpjfwpb1dCRFxAn8DJgD9gGvsvCeFaoPEtItbURRFCUCjF2hjzAFjzGp7PQ/YBHQCLgMqhnzOASb5yT4c2GaM2WGMKQXetvOdFKq+g05wuSgzhhKf7zQVRakdj8fDX//6V3USoTRpGr1A+yIi3YDBwPdAO2PMAbBEHGjrJ0snYK/Pdrod5q/sm0VkpYisPHz4cD3tqz5IDNBubqXZ8thjj9G/f39SU1MZNGgQ33//fVD5nnrqKeLj472zaZ1IioqKOO+887zfUteXo0ePcsEFF9C7d28uuOACsrKy/KbLzs5mypQp9OnTh759+7J06dIa8//www9cd911x2Wb0jhoMgItIvHAu8DdxpjcYLP5CfP73ZkxZrYxZpgxZpjv1H91wV8XN6Dd3EqzZOnSpcyfP5/Vq1ezfv16vvjii0rzVQeivLyc9u3bc8MNN/iNP95W9SuvvMLkyZOP+7OqWbNmMWbMGNLS0hgzZox3Xu6q3HXXXYwfP57Nmzezbt0676dJgfIPHDiQ9PR09uzZc1z2KeFPkxBoEYnAEuc3jTHv2cEZItLBju8AHPKTNR3wvSOkAPtPlp0OhwtwVOriBnQkt9IsOXDgAK1btyYqyupZat26tXf6y27dunHkyBEAVq5cyahRowDLUcQ555zDU089xVlnncWWLVsAy03jlVdeySWXXMK4ceMoKChgxowZnH766QwePJgPPvgAgBtvvNHryKJNmzZ+vVH5erDKz89nzJgxDBkyhIEDB3rLCYYPPviA6dOnAzB9+nTef//9amlyc3NZvHix92EjMjKSli1b1pr/kksu4e233w7aFqVx0ug/sxJrYt6XgU3GmKd8oj4EpgOz7F9//6wVQG8R6Q7sA64Gfnoy7XU4oqu1oLWLWwk1ofA2OW7cOB555BFOOeUUxo4dy1VXXcV5551XY5l9+vRh8eLFuFwuPv30Ux544AGvM42lS5eyfv16kpOTeeCBBzj//PN55ZVXyM7OZvjw4YwdO9Y7M9fu3bu58MILq3UVl5aWsmPHDrp16wZAdHQ08+bNIyEhgSNHjjBixAguvfRSRISRI0eSl5dXzcYnn3ySsWPHkpGR4Z3Os0OHDhw6VL2NsGPHDtq0acP111/PunXrGDp0KH/961+Ji4urMf+wYcOYNWsWv/71r2s8XkrjptELNHA2cC3wg4istcMewBLmd0TkBmAPcCWAiHQEXjLGTDTGuEXkduBTwAm8Yoz58WQa63BEeT+zqmhBaxe30hyJj49n1apVLFmyhK+//pqrrrqKWbNm1fh+NS8vj5tuuol9+/ZhjKnkWOOCCy4gOTkZgM8++4wPP/yQJ598ErCcZOzZs4e+fftSXFzMlVdeyfPPP0/Xrl0rlX/kyBFvCxYsxxQPPPAAixcvxuFwsG/fPjIyMmjfvr3XZ/Xx4Ha7Wb16Nc899xxnnHEGd911F7NmzeIPf/hDjfnatm3L/v0nrbNPCRMavUAbY77B/7tkgDF+0u8HJvpsLwAWnBzrqmO1oKsMElOBVkJMqLxNOp1ORo0axahRoxg4cCBz5szhuuuuq+SFytcD1YMPPsjo0aO55ZZb2LlzJ6NHj/bG+Xq3Msbw7rvv+p1h65ZbbmHy5MmMHTu2Wpyv9yqwursPHz7MqlWriIiIoFu3bt742lrQ7dq148CBA3To0IEDBw7Qtm31caopKSmkpKR4fWBPmTLF+665pvzFxcVeV5tK06VJvINuTDgcUdrFrSjAli1bSEtL826vXbvW26Lt1q0bq1atAvB2YQNkZWV5/TO/9tprAcu+8MILee6557zuKtesWQPA3/72N/Ly8rxepqqSlJSEx+PxinBOTg5t27YlIiKCr7/+mt27d3vTLlmyxK8Hqwrhv/TSS73OPebMmeN9r+1L+/bt6dy5s/dd+pdffkm/fv1qzb9161YGDBgQsP5K00AFuoERiao2SEy7uJXmSH5+PtOnT6dfv36kpqayceNGZs6cCcDDDz/MXXfdxciRIyuNpv7Vr37F/fffz9lnn13Jz3NVHnroIcrKykhNTWXAgAE89NBDgNW6/eGHH7wDxV588cVqeceNG8c333wDwNSpU1m5ciXDhg3jzTffpE+fPkHX77777uPzzz+nd+/efP75596Hgv3791fyPvXcc88xdepUUlNTWbt2LQ888ECN+cHyYHXRRRcFbYvSOFFvVvWgvt6sAFasGER0dFcGDvwAjzG4Fi1iZrduPGwPSlGUhkK9WflnzZo1PPXUU7zxxhuhNsUvJSUlnHfeeXzzzTcn5TvwE4V6szp+tAXdwFhd3FYL2ilCrMOhXdyKEkYMHjyY0aNHH/dEJSeLPXv2MGvWrLAWZ+XEoGe4gfH9zAog0eUiJ0xvBIrSXJkxY0aoTQhI79696d27d6jNUBoAbUE3MFUFuqXLRba2oBVFUZQqqEA3ME5nLOXlhd7tJJeLrLKyEFqkKIqihCMq0A2MwxGLx1NFoLUFrSiKolRBBbqBqdqCbqkCrSiKovhBBbqBqdaCjojQd9CKoihKNVSgGxh/76Cz3W7K9Xt0pRkSHx9fr3y7du3yzqS1cuVK7rzzzhNm05QpU9ixY8dxlWGM4c4776RXr16kpqayevVqv+muu+46unfv7p04Za3tsSRQ/tLSUs4999zjdqmpNA5UoBsYhyMOY8ooL7cGhrV0uTBArv7hFKVeDBs2jGefffaElPXjjz/i8Xjo0aPHcZXz8ccfk5aWRlpaGrNnz+bWW28NmPaJJ57wThM6aNCgGvNHRkYyZswY/v3vfx+XfUrjQL+DbmCczlgAyssLcTgSSbInG8hyu2kZERFK05RmTNrdaeSvzT+hZcYPiqf3M3X/Xnft2rXccsstFBYW0rNnT1555RWSkpJYtWoVM2bMIDY2lnPOOcebfuHChTz55JPMnz+fgoIC7rjjDn744QfcbjczZ87ksssu47XXXmPlypU8//zzAFx88cXce++9Xj/TFfj6gga49dZbWbFiBUVFRUyZMsWv/2h/fPDBB0ybNg0RYcSIEWRnZ3sdXxxv/kmTJnH//fczderUoMpSGi/agm5gHA5LoCveQ1cItL6HVhSLadOm8fjjj7N+/XoGDhzoFcXrr7+eZ599lqVLlwbM+9hjj3H++eezYsUKvv76a371q19RUFAQ9L6//fZbhg4dWqm8lStXsn79ehYtWsT69esBuOeee7zd0r5LhSeqffv20blzZ285KSkp7Nu3z+8+f/vb35Kamso999xDSUlJrfkHDBjAihUrgq6T0njRFnQD49uCBmuQGKAjuZWQUp+W7skgJyeH7OxszjvvPACmT5/OlVdeWS382muv5eOPP66WP5Af6GA5cOCA11sWwDvvvMPs2bNxu90cOHCAjRs3kpqaytNPP11jOf58HIhU94r7pz/9ifbt21NaWsrNN9/M448/zu9+97sa8zudTiIjI8nLy6NFixZB101pfKhANzBVW9Atfbq4FUXxjzHGr8D5S+fPD/SqVasqeb/y9fnsi68/6J07d/Lkk0+yYsUKkpKSuO6667xx99xzD19//XW1/FdffTX33XcfKSkp7N271xuenp5Ox44dq6Wv6PKOiori+uuv9z5Y1Ja/pKSE6Ojomg+G0uhpEl3cIvKKiBwSkQ0+Yf8WkbX2sktE1gbIu0tEfrDT1c9FVR2o1oKuEGidTUxRSExMJCkpiSVLlgDwxhtvcN5559GyZUsSExO9biDffPNNv/kD+YHu1q0ba9eupby8nL1797J8+XK/+fv27cu2bdsAyM3NJS4ujsTERDIyMiq12J9++mm/vqArXEJeeumlvP766xhjWLZsGYmJiX7fPx84cACwHizef/9978j0mvJnZmbSpk0bInTMSpOnqbSgXwOeB16vCDDGXFWxLiJ/AXJqyD/aGHPkpFnng9MZB+g7aEUBKCwsJCUlxbv9i1/8gjlz5ngHifXo0YNXX30VgFdffdU7SOzCCy/0W95DDz3E3XffTWpqKsYYunXrxvz58zn77LPp3r07AwcOZMCAAQwZMsRv/osuuoiFCxcyduxYTjvtNAYPHkz//v3p0aMHZ599dtD1mjhxIgsWLKBXr17ExsZ661AR99JLL9GxY0emTp3K4cOHMcZU8k9dU/6vv/66kj9ppenSZPxBi0g3YL4xZkCVcAH2AOcbY9L85NsFDKuLQB+PP+jc3BWsXj2cgQPn06rVRRhjiFi0iN906cJjx/lph6LUBfUHXZ2ioiJGjx7Nt99+i9PpDLU5fpk8eTJ/+tOfqnXjhxvqD/r4aRJd3LUwEsjwJ842BvhMRFaJyM2BChGRm0VkpYisPHz4cL2Nqeji9ngKKsrV6T4VJUyIiYnh97//fcAR16GmtLSUSZMmhb04KyeGptLFXRPXAHNriD/bGLNfRNoCn4vIZmPM4qqJjDGzgdlgtaDra0zVQWJgjeRWgVaU8CBQ93k4EBkZybRp00JthtJANOkWtIi4gMlAwGl3jDH77d9DwDxg+Mm0qeogMYDWEREc0UFiiqIoig9NWqCBscBmY0y6v0gRiRORFhXrwDhgg7+0Jwp/Leg2EREcLi09mbtVFEVRGhlNQqBFZC6wFDhVRNJF5AY76mqqdG+LSEcRWWBvtgO+EZF1wHLgI2PMJyfTVn8t6DYRERzWFrSiKIriQ5N4B22MuSZA+HV+wvYDE+31HcBpJ9W4Kog4EYnC4zk273FFF3ewkzEoSnPio48+okuXLgwcODDUpihKg9IkWtCNDZcrAY8nz7vdJiKCUmPI83hCaJWiNDxOp5NBgwZx2mmnMWTIEL777rtK8Z988gmLFi3yTuABcNZZZx33fhvSpeTIkSO9c3V37NiRSZMm1ZhfXUoqFTSJFnRjw+lsgdud691uExkJwOGyMhJcekqU5kNMTIzXB/Knn37K/fffz6JFi7zx48ePZ/z48ZXyVBXxmjDGYIzB4TjWFjkZLiW///57br31Vr7//vtq6SpmRQO44oorvN6yAuX3dSmpHquaN6oGIcBfCxrgSFkZPWNiQmWW0oy5Oy2Ntfkn1t3koPh4nukdvBOO3NxckpKSvNtPPPEE77zzDiUlJVx++eVer1bx8fHk5+eTn5/PZZddRlZWFmVlZTz66KNcdtll7Nq1iwkTJjB69GiWLl3K+++/T9euXb3lhsqlZF5eHl999ZV3VjB1KanUhgp0CKjagm5tC7SO5FaaG0VFRQwaNIji4mIOHDjAV199BVheqdLS0li+fDnGGC699FIWL17Mueee680bHR3NvHnzSEhI4MiRI4wYMYJLL70UgC1btvDqq6/y97//vdo+v/32W6655tiwlccee4zk5GQ8Hg9jxoxh/fr1XvePNTnECOQSMpBAz5s3jzFjxpCQkAAEdinZoUMHdSmpACrQIcHpTKC09IB3u6IFrSO5lVBRl5buicS3i3vp0qVMmzaNDRs28Nlnn/HZZ58xePBgAPLz80lLS6sk0MYYHnjgARYvXozD4WDfvn1kZGQA0LVrV0aMGOF3nw3tUrKCuXPncuONNwaVX11KKqACHRJcrgSKirZ4t1WgFQXOPPNMjhw54nUecf/99/Ozn/0sYPo333yTw4cPs2rVKiIiIujWrZvXHWRcXFzAfA3tUhIsD1TLly9n3rx53jB1KanUho7iDgFWF/exd9BxTifRDofOJqY0azZv3ozH46FVq1ZceOGFvPLKK+Tb78X37dvHoUOHKqXPycmhbdu2RERE8PXXX7N79+6g9tPQLiUB/vOf/3DxxRdXElx1KanUhragQ4A1SOzYO2gRobVOVqI0QyreQYPV5TtnzhycTifjxo1j06ZNnHnmmYA1MOxf//oXbdu29XYDT506lUsuuYRhw4YxaNAg+vTpE9Q+G9qlJMDbb7/tFfZg8qtLSQWakLvJhuR43E0C7Nr1CLt2Pcy555bhcFjPSENWrqRjZCTzU1NPlJmKUiON0d1kZmYmQ4YMCbq17A91KdkwqLvJ40e7uEOA02mN4vT91KptRASHtAWtKAHZv38/Z555Jvfee+9xlaMuJZXGgnZxhwCXyxqV6fHkERFhfffZISqKHwsLa8qmKM2ajh07snXr1hNSlrqUVBoD2oIOARUtaN9voTtERnKwtJRyfeWgKIqioAIdEpzOYy3oCjpERuI2hkzt5lYURVFQgQ4JLlfFO+hjLeiOUVEA7NfZxBSl0bFr1y7mzp1be0JFqQMq0CGgogXt+y10B9thxoGSkpDYpCihoMKb1YABA7jyyisprGUcxnvvvceIESOYPHkyCxYsqDFtVa677jq6d+/u9Z715ZdfHo/pXjweDz//+c8ZMmTICSmvggMHDnDxxRcfdzk7d+7kjDPOoHfv3lx11VWUBmgE7Nmzh3HjxtG3b1/69evHrl27asw/f/58Hn744eO2TwlMkxBoEXlFRA6JyAafsJkisk9E1tqL348KRWS8iGwRkW0icp+/NCcavy3oCoHWFrTSjKiY6nPDhg1ERkby4osv1ph+8uTJLFu2jPfee69e3wk/8cQTrF27lmeeeYZbbrmlvmZXYtu2bdx3333VRl0fr7vIp556iptuuum4ygD4zW9+wz333ENaWhpJSUm8/PLLftNNmzaNX/3qV2zatInly5fTtm3bGvNfdNFFfPjhh7U+VCn1p0kINPAaMN5P+NPGmEH2Uu1xW0ScwN+ACUA/4BoR6XdSLQVcrpYAuN3Z3rD2tkBrF7fSXBk5ciTbtm2joKCAGTNmcPrppzN48GA++OADAF577TUmT57M+PHj6d27N7/+9a+9eW+99VaGDRtG//79g2rVnXnmmZU+s5o0aRJDhw6lf//+zJ492xseHx/Pb37zG4YOHcrYsWNZvnw5o0aNokePHnz44YeA1dJ9/PHHAZg5cyY333wz48aNY9q0aRw+fJgrrriC008/ndNPP51vv/0WsCYpqfARnZiYyJw5c6rZ+O6773pdbe7atYuRI0cyZMgQv36zA2GM4auvvmLKlCkATJ8+nffff79auo0bN+J2u7ngggu89Y6Nja0xv4gwatQo5s+fH5QtSt1pEp9ZGWMWi0i3emQdDmwzxuwAEJG3gcuAjSfQvGpYo7idlJUd9YZFO50kuVzaxa2EhLS0u8nPX3tCy4yPH0Tv3s8EldbtdvPxxx8zfvx4HnvsMc4//3xeeeUVsrOzGT58OGPHjgVg7dq1rFmzhqioKE499VTuuOMOOnfuHNAjVSA++eQTJk2a5N1+5ZVXSE5OpqioiNNPP50rrriCVq1aUVBQwKhRo3j88ce5/PLLefDBB/n888/ZuHEj06dP93rP8mXVqlV88803xMTE8NOf/pR77rmHc845hz179nDhhReyadMmb/f8qlWruP766yvZAla3clJSElH22JS2bdvy+eefEx0dTVpaGtdccw0rV64kLy+PkSNH+q3jW2+9Rdu2bWnZsiUu2898hcesqmzdupWWLVsyefJkdu7cydixY5k1axZZWVk15h82bBhLlizhJz/5ScBjrdSfJiHQNXC7iEwDVgK/NMZkVYnvBOz12U4HzvBXkIjcDNwM0KVLl+MySkSIiEjC7T5aKbxjZKS2oJVmhe9UnyNHjuSGG27grLPO4sMPP+TJJ58EoLi4mD179gAwZswYEhMTAejXrx+7d++mc+fOAT1SVeVXv/oVv/71rzl06BDLli3zhj/77LNeRxZ79+4lLS2NVq1aERkZ6W3FDhw4kKioKCIiIhg4cKD3HW1VLr30UmJsv+5ffPEFGzcee97Pzc31eqg6cuQI1157Le+88463ThVU9bhVVlbG7bffztq1a3E6nd7vwVu0aOH1BuaPw4cPVwvz53HL7XazZMkS1qxZQ5cuXbjqqqt47bXX/D6A+OZv27Yt+/fvD7h/5fhoygL9AvAHwNi/fwFmVEnjzzec3w+RjTGzgdlgTfV5vMa5XEm43ZWfFzpERek7aCUkBNvSPdH4upuswBjDu+++W+2d7vfff+9tUYI1wMztdtfokaoqTzzxBJMnT+bZZ59l+vTprFq1ioULF/LFF1+wdOlSYmNjGTVqlDd/RESEV5AcDod3/w6HI+A7Zl9PWuXl5SxdutQr2BV4PB6uvvpqfve73zFgwAC/x8W3Dk8//TTt2rVj3bp1lJeXe51u1NaC7tu3L9nZ2bjdblwuV0CPWykpKQwePJgePXoAVpf/smXLmDFjRo35i4uLq9VNOXE0lXfQ1TDGZBhjPMaYcuCfWN3ZVUkHOvtspwAN8jjociVX6uIGayS3dnErzZ0LL7yQ5557zusvec2aNTWmr8kjlT8cDgd33XUX5eXlfPrpp+Tk5JCUlERsbCybN2+u1LI+XsaNG8fzzz/v3a54GLnvvvtITU3l6quv9pvvlFNOqdRCz8nJoUOHDjgcDt544w08Hg9wrAXtb+nXrx8iwujRo/nvf/8LwJw5c7jsssuq7e/0008nKyvL2+L+6quvgsq/detWvw8YyomhyQq0iPj6fbsc2OAn2Qqgt4h0F5FI4Grgw4awLyIiOWAXt84mpjRnHnroIcrKykhNTWXAgAE89NBDNab39Ug1Y8aMoDxSiQgPPvggf/7znxk/fjxut5vU1FQeeughRowYcaKqwrPPPsvKlStJTU2lX79+3lHqTz75JJ999pl3oFjFgLMK4uLi6Nmzp9ct5m233cacOXMYMWIEW7durdHfdVUef/xxnnrqKXr16kVmZiY33HADACtXruTGG28ErN6IJ598kjFjxjBw4ECMMd4R5IHyg+V166KLLqr/AVJqpEl4sxKRucAooDWQATxsbw/C6rLeBfzMGHNARDoCLxljJtp5JwLPAE7gFWPMY7Xt73i9WQFs3DiV3NxljBix3Rv29337+HlaGvvPPJMOPl15inIyaIzerJoT8+bNY9WqVTz66KOhNsUvGRkZ/PSnPw34Pbl6szp+msQ7aGPMNX6C/X7sZ4zZD0z02V4A1G3GgxOA1YKu/A66iy3Ke0pKVKAVpZlz+eWXk5mZGWozArJnzx7+8pe/hNqMJk2TEOjGiMuVjNudjTEerM+xoas98GN3cTFnJCSE0jxFUcKAii7ocOT0008PtQlNnib7DjrciYhIBgxud443zFegFUVRlOaNCnSIcLksP9C+I7kTXC4SnU726EhupYFoCmNQlPBDr6sTgwp0iHC5kgGqvYfuGh2tLWilQYiOjiYzM1NvpsoJxRhDZmam91ttpf7oO+gQYXVxU+1Tqy4q0EoDkZKSQnp6ut/ZphTleIiOjiYlJSXUZjR6VKBDREULuupkJV2jovgmJ8dfFkU5oURERNC9e/dQm6EoSgC0iztERES0AqCs7Eil8K7R0WS73eQep6s6RVEUpXGjAh0irC5uB2VlhyqFd9GR3IqiKAoq0CFDxElERBtKSzMqhXe3BXqHCrSiKEqzRgU6hERGtqsm0L1tzzBphYWhMElRFEUJE1SgQ0hkZNtqAp0UEUErl4u0oqIQWaUoiqKEAyrQISQioh1lZRnVwnvFxLBNBVpRFKVZowIdQqwu7kPVwnvHxmoLWlEUpZmjAh1CIiPbUV5eiNudXym8d0wMe0tKKLKdsiuKoijNDxXoEBIZ2Q6gWjd3L3ugmI7kVhRFab6E1UxiIhINXAyMBDoCRcAG4CNjzI+htO1kEBFhCXRpaQYxMT294b4jufvHxYXENkVRFCW0hI1Ai8hM4BJgIfA9cAiIBk4BZtni/UtjzHo/eV/BEvZDxpgBdtgTdnmlwHbgemNMtp+8u4A8wAO4jTHDTnDVAhIZ2Rag2kjuiha0DhRTFEVpvoSNQAMrjDEzA8Q9JSJtgS4B4l8Dngde9wn7HLjfGOMWkceB+4HfBMg/2hhzJEDcSSMysj0ApaUHK4UnRUTQOiKCLSrQiqIozZaweQdtjPmolvhDxpiVAeIWA0erhH1mjKmY0HoZEHauVax30E5KStKrxfWPjeXHgoKGN0pRFEUJC8KpBQ2AiLTBaun2w+riBsAYc/5xFDsD+HeAOAN8JiIG+IcxZnYAu24Gbgbo0iVQQ75uiDiJiuroX6Dj4vhXRgbGGETkhOxPURRFaTyETQvahzeBTUB34PfALmBFfQsTkd8Cbrtcf5xtjBkCTAB+LiLn+ktkjJltjBlmjBnWpk2b+ppTjaiozpSU7K0WPiAujlyPh/SSkhO2L0VRFKXxEI4C3coY8zJQZoxZZIyZAYyoT0EiMh1r8NhUY4zxl8YYs9/+PQTMA4bXz+z6EUigK0Zvb9BubkVRlGZJOAp0mf17QEQuEpHB1OP9sYiMx+oqv9QY49fzhIjEiUiLinVgHNZnXQ1GVFQKJSXpVH1+qBBofQ+tKIrSPAlHgX5URBKBXwL3Ai8B99SUQUTmAkuBU0UkXURuwBrV3QL4XETWisiLdtqOIrLAztoO+EZE1gHLsb63/uSk1CoA0dGdKS8vpqwss1J4q4gI2kdGagtaURSlmRJ2g8SMMfPt1RxgdJB5rvET/HKAtPuBifb6DuC0eph5woiK6gxAScleIiNbV4obEBfHj+p2UlEUpVkSji1oLyKyOtQ2nGyioqzee38juQfExfFjQQEe/6/PFUVRlCZMWAs00OS/L/JtQVdlcHw8ReXlbNZWtKIoSrMj3AW6xslLmgKRke0QiaC4eE+1uKEtWgCwKi+voc1SFEVRQkzYCLT4mY3DGPNgbWkaOyIOoqO7UVy8o1pcn9hYYh0OFWhFUZRmSNgINPC1iNwhIpWm6RKRSBE5X0TmANNDZNtJJSamF0VF26qFO0UYFB+vAq0oitIMCSeBHo/lUWquiOwXkY0ishNIA64BnjbGvBZKA08WMTE9KSraXu1baI4cYeiePazJy9OBYoqiKM2MsBFoY0yxMebvxpizga7AGGCwMaarMeYmY8za0Fp48oiO7onHk1vtW2gOHGDoiy9SaAxbdKCYoihKsyJsBNoXY0yZMeaAP//NTZGYmF4A1bu5+/Vj2F5rdPdK7eZWFEVpVoSdQIvIzFDb0NDExPQEoLh4e+UIp5M+7dvToriYpbm5IbBMURRFCRVhI9Ai4hCRl4GoUNvS0ERHdweEoqLt1eKcZ5zBWT/8wDdZWQ1vmKIoihIywkaggf8BR40x94fakIbG6YwmKiqFoqK06pEjRnDO+vVsKCoiq6yseryiKIrSJAkngR6G5e6xWRIb24eCgk3VI844g3N++AFAu7kVRVGaEeEk0KOBf4jIGaE2JBTExfWnsHATxpRXjmjXjuFFRbg8Hr7JyQmNcYqiKEqDEzYCbYzZCFwIPBFqW0LBwYP9KS8vZM+eXdXiYk8/nSE7d6pAK4qiNCPCRqDB6wryolDbEQrc7v4AbNnyY/XIUaM4Z/VqlufkUOzxNLBliqIoSigIK4EGMMbU+YNfEXlFRA6JyAafsGQR+VxE0uzfpAB5x4vIFhHZJiL3HY/tx8OAAf0AyMjYUD1y1CjGrF5NCWgrWlEUpZkQNgItIh/WtNSS/TWsqUJ9uQ/40hjTG/jS3q66TyfwN2AC0A+4RkT6HXdl6kHr1olkZqZQXOynBd2zJ+cdOUKEx8Pn+rmVoihKs8AVagN8OBPYC8wFvqcOvqCNMYtFpFuV4MuAUfb6HGAh8JsqaYYD24wxOwBE5G0738a6mV43yrLKKNxSSMIZCfg66MrN7U90tB+BFiHurLM4e9MmPktM5PGTaZyiKIoSFoRNCxpoDzwADAD+ClwAHDHGLDLGLKpHee2MMQcA7N+2ftJ0wnooqCDdDjupHHzlIGvOXIM7x10pXCSVdu02UlhYWj3TqFFcsGwZa/PzOVTqJ15RFEVpUoSNQBtjPMaYT4wx04ERwDZgoYjccRJ366+V7tdtlIjcLCIrRWTl4cOHj2unUV2tydJKdpdUCk9KGkpkZCkbN/p5D33BBYxbsQKAL7WbW1EUpckTNgINICJRIjIZ+Bfwc+BZ4L16FpchIh3scjsAh/ykSQc6+2ynAPv9FWaMmW2MGWaMGdamTZt6mmQR3TUagOLdxZXCe/UaBsCuXauqZ+rcmcHR0bQqLOTjo0ePa/+KoihK+BM2Ai0ic4DvgCHA740xpxtj/mCM2VfPIj8Eptvr04EP/KRZAfQWke4iEglcbec7qUR3sQV6T2WBPvXUHuTntyQnZ6XffM6JE7nom2+Yf+QI7vJyv2kURVGUpkHYCDRwLXAKcBfwnYjk2kueiNQ4x6WIzAWWAqeKSLqI3ADMAi4QkTSs99mz7LQdRWQBgDHGDdwOfApsAt4xxvgZpXViiWgbgURJtS5ul0s4dGgokZH+BZqLLmLSkiVkeTws0c+tFEVRmjRhM4rbGFPvhwVjzDUBosb4SbsfmOizvQBYUN991wcRIbpLdLUubituGO3bP0V+fgnx8VUce40Ywbht24h2u3n/yBFGJ/n9tFtRFEVpAoRTC7pZEd01uloXN0C7dqcTEVHGmjVrqmdyuYgbPZpxa9bw/pEjGON3PJuiKIrSBFCBDhFRXaKqdXEDnHbaOQDs3r3Ef8bLLmPSV1+xp6SE1fn5J9NERVEUJYSoQIeI6K7RlB4sxVNceW7tTp3aceDAKZSWBhDoiy/m0jVrcJWX8/YhfwPTFUVRlKaACnSIiO5hj+TeWb2bOy9vJG3afIPH42ekdlwcrc47j4mrVvFWRgYe7eZWFEVpkqhAh4jYU2MBKNxSWC2uVauRtGiRxbp1AQaUX3UV//fRR+wvLWVhdvZJtFJRFEUJFSrQIcIr0JurC/TgwecCsHHjYv+ZJ0zg4h9+IKG0lH9lZJw0GxVFUZTQoQIdIlwJLiI7RFK0pahaXPfu3cjM7EJx8Rf+M8fEEHPxxUxZtIh3Dx2iQH1EK4qiNDlUoENI7Kmxfru4RYSsrPF06vQFpYEcY9xwA9d/+CF5OlhMURSlSaICHUJiTo3xK9AArVtPICYmn+XLv/OfeeRIzi4pYUBGBn/ft0+/iVYURWliqECHkNg+sbiPuik9VL2VfO65YygriyAt7WP/mUWQG27g1rlzWZ2fz4q8vJNsraIoitKQqECHkPjUeADy11WfcCQ5uQX79p1DZGQAgQaYPp3/++or4txuXtjv1wmXoiiK0khRgQ4h8YNsgV7rf0awyMgJdOr0A1u27PJfQPv2JFx8Mdd+/jlzMzLICPS+WlEURWl0qECHkIjkCKK6RJG/xr9ADxt2BQArV/43cCG/+AX3vPUWpeXlPJuefjLMVBRFUUKACnSIiR8cH7AFfcopPdizZyjGvBO4gGHDOKV7d65YuZK/7dtHrtt9kixVFEVRGhIV6BATPyiewi2FeAoCfct8JSkpK0hL2xW4kF/8gt+89BI5Hg+z9V20oihKk0AFOsQkDE+Acshdkes3/swzrwTgu+9qaEVfcgnDjGHsli38JT2dQp24RFEUpdHTpAVaRE4VkbU+S66I3F0lzSgRyfFJ87uGtDHhzAQQyPkmx29879492LNnBJGRrwX+1tnphIcf5uHnn+dgaSnP7dt3Ei1WFEVRGoImLdDGmC3GmEHGmEHAUKAQmOcn6ZKKdMaYRxrSxoikCOL6x5H7rf8WNEBExA106LCJ5cuXBS7o6qs5x+Ph4h9+YNaePWSVlZ0EaxVFUZSGokkLdBXGANuNMbtDbUhVEs9JJOe7HIzHfwv5wguvoqgojvXrXw5ciNMJM2fy2DPPkFNWxp/37j1J1iqKoigNQXMS6KuBuQHizhSRdSLysYj095dARG4WkZUisvLw4cMn1LCEsxPw5Hoo2FDgNz45uQX79/+ElJS3ycysYcawKVNITUhg6tKlPLN3LzuLqjviUBRFURoHzUKgRSQSuBT4j5/o1UBXY8xpwHPA+/7KMMbMNsYMM8YMa9OmzQm1r+XIlgBkfZ0VMM3AgT8jJqaATz55NXBBDgc8/TR/evppnG4392zbdkLtVBRFURqOZiHQwARgtTGmmvNkY0yuMSbfXl8ARIhI64Y0LrprNLF9Yjn68dGAaYYPP4Ndu84iMvIZ3O4aRmmPGkXKqFH8bs4cPsjM5KPMzJNgsaIoinKyaS4CfQ0BurdFpL2IiL0+HOuYNLiqJU9IJntRNp7CwOKblPRL2rTZyYIF/sa5+fDEE9z93nv0ycrizrQ0ivSzK0VRlEZHkxdoEYkFLgDe8wm7RURusTenABtEZB3wLHC1CYHvxuQJyZgSQ/bX2QHTXHTRZRw61JOsrCcoL6/BxG7diLzvPv72hz+wo7iY3+7ceeINVhRFUU4qTV6gjTGFxphWxpgcn7AXjTEv2uvPG2P6G2NOM8aMMMYEcMB8ckkcmYgj1kHmR4Eb7y6XE/gVXbsu58sva/ByBfCb33B+eTm3ffYZz6Snszg7+4TaqyiKopxcmrxANxac0U5aTWzF4XcPU+4uD5jukkuu59Ch7hw69CDl5YHTEREBr7zC488+S4/cXK7bvJl8nadbURSl0aACHUa0uaoNZYfKyFnkf1YxgJiYSByOmXTqtIb//a+Wd9FDhhB/11289tvfsquoiFvT0gLPRqYoiqKEFSrQYUSria1wxDk49O9DNaabNGkqBw/2pajoAYqLa/EBPXMm58THM3PuXP6VkcE/Dxw4gRYriqIoJwsV6DDCGeuk9WWtOfyfwzWO5na5nLRq9Rfat9/Ku+8+U3OhEREwdy4P/ve/XLhlC3empbE6r4bJThRFUZSwQAU6zOhwUwfc2W4O/6fm2crGjp1AWtplJCc/ws6d6TUX2q0bjpdf5l+//jVt8vOZvGEDGaW1tLwVRVGUkKICHWa0PK8lMafGsP8fNft1FoFRo57G4fDwxRd31fzZFcDll9P6jjt4/xe/4HBhIZf+8IO6pVQURQljVKDDDBGh4886krs0l/x1+TWm7d27O4cPz6R37/eYP//t2gt/5BGGDhjAWzNnsiI3l2s3baJcB40piqKEJSrQYUj76e1xxDnY+2TtHqmuuupedu8egcjP2bWr5lY3Dge8/jqXFRbyl5df5r0jR7hdR3YriqKEJSrQYUhEcgQdf9aRjLkZFO2o2SNVRIST4cPnEBFRzJdfTq95nm6AuDiYP5+7ly7l1/Pm8cL+/dy7fbuKtKIoSpihAh2mdP5lZ8Qp7PnznlrT9u17CgUFz9Kz5xe8+eYjtRfeoQPyxRfMevdd7vj4Y55KT+fBnTtVpBVFUcIIFegwJapjFB1u6MDBlw9SuLWw1vSXX34DW7deT9euj7BgwYLad9C9O/LFFzzz6qvc9OWX/HHPHn6xfbu+k1YURQkTVKDDmG4Pd8MR7WD7r7fXmtbhEKZO/Rv79g3CmGtYt25D7Tvo0wfHwoW8+Mor3DV/Ps+kp3P95s24a5pCVFEURWkQVKDDmMh2kXR5oAuZH2SS9XVWrenj4mI4++wPKCmJZ/v2Cezeva/2nfTrh2PJEp6eN49H/vUvXs/I4PIffyRP5+1WFEUJKSrQYU7KPSlEd4sm7bY0PMW1f7fcrVsXOnX6iOjobJYsmcihQ7ULOz16IEuW8ND33/PCX//Kx0eOcNaaNewsqnmAmqIoinLyUIEOc5zRTk558RQKNxey+/e7g8pzxhmDiI19j7ZtN/Ppp+M4ejQIke7YEb79lltKSvj4V78iPSuL4atWqZtKRVGUEKEC3QhIvjCZ9je0Z8+f95D7fW5QeUaNugC3+13atVvPRx+NC64lnZAAH37IBSNH8v0NN9Dq4EHOX7uWP+3erYPHFEVRGpgmL9AisktEfhCRtSKy0k+8iMizIrJNRNaLyJBQ2Fkbvf7Si6jOUfz4kx8pyywLKs/EiRd7Rfqzz0azd28tE5kAuFzw9NOc8uijfH/LLUz57jse2LmT8evX6/zdiqIoDUiTF2ib0caYQcaYYX7iJgC97eVm4IUGtSxIXIku+v+3P6UHS9n0f5swtc29bTNx4sW4XP8jOXk7y5ePYO3aH4Pb4fTpJC5ezNw33+SfTz7JN0eOkLpiBfOPHDmOWiiKoijB0lwEuiYuA143FsuAliLSIdRG+SNhWAK9/tqLo58cZcf9O4LOd/7540hOXozDUUZ6+tl88smnwWXs1w9ZsYIbO3RgxY030n7nTi7ZsEHfSyuKojQAzUGgDfCZiKwSkZv9xHcCfCe9TrfDKiEiN4vIShFZefhwza4gTyYdf9aRjrd0ZO+f95L+XC1uJn0YMWIwp522jNzcLkRGTuD11/+AxxPE986xsfDPf9L/hRdY/tBDvPrnPzNy1iworH3yFEVRFKX+NAeBPtsYMwSrK/vnInJulXjxk6da/7ExZrYxZpgxZlibNm1Ohp1BISL0fr43rSe1Zttd28h4MyPovD16dOXii5eRljaVLl1+x6uvXsK+fUE+bIwfT9TatVzXqRPy+OPQty+8+y7o4DFFUZSTQpMXaGPMfvv3EDAPGF4lSTrQ2Wc7BQhiNFXoEKfQ962+tBzVkk3XbuLAqweCzpuQEMtNN71ORsbf6Nr1C1auHMCnn34YXObERPjnP2HxYmjZEqZMgQsugI0b61cRRVEUJSBNWqBFJE5EWlSsA+OAqnNgfghMs0dzjwByjDHBK16IcMY4GTh/IEkXJLFlxhbSnw++u9vhEK666jZat15JXl4HoqIuY/bs64P7FAtg5EhYtQqef976TU2FG2+EPbU79lAURVGCo0kLNNAO+EZE1gHLgY+MMZ+IyC0icoudZgGwA9gG/BO4LTSm1h1nrJMBHwyg1aWt2HbHNtLuTKPcHfw82oMHD+SKK5azc+cD9Oz5OkuXnsr7779OeTAjxF0u+PnPYetWuOMOeOMN6N0b7rkHDh06jlopiqIoAKIuBuvOsGHDzMqV1T6pDhnGY9j+6+2kP5VO8vhk+r7Zl4jkiDqVsWrVGjZsuJWuXb9n166R9Ov3PMOHpwZfwJ498Mgj8OqrEB0NN90Ev/gFdOlSx9ooitJUEZFVAT53VfzQ1FvQzQJxCr3+0otTZp9C1pdZrDxtJdmLs+tUxtChg5k69TsOHfonrVr9SH7+IF5+eRrbt+8MroAuXeCll6z30VOmwN/+Bj17wrRpsCEIz1qKoihKJVSgmxAdb+rIkKVDcEQ7WDt6LTt/t5Py0uC7vF0uBz/5yY2cdVYaO3f+ipSU/7Bjx6n88593sXt3kOPmTj0V5syB7dutLvB334WBA2HcOHj/fVAvWYqiKEGhXdz1INy6uKviznOTdnsaGa9nENs3llNmn0LLc1rWuZydO9P56qtH6NbtFTweJ+np0znrrF/Tp0+v4AvJzIQXXoB//APS0yElBX72M2tQWfv2dbZJUZTGi3Zx1w0V6HoQ7gJdQeaCTLbetpWS3SW0v6E93R/tTlT7qDqXk5a2nYULn6Br11dxOt1s334lffvexdlnj8Dh8PcZuR/cbvjf/+Dvf4cvvrAGmU2YYHWBX3yx9d5aUZQmjQp03VCBrgeNRaABPAUeds3cRfoz6UiU0PneznT+ZWdcLVx1Lmvv3gN88cUztGv3ArGxeezbN5iIiNu48MJrSEyMC76grVut99Vvvgn791vfVF91lSXWZ54JEqToK4rSqFCBrhsq0PWgMQl0BYXbCtn5250cfucwEW0j6PzLznS8pSOuhLoLdW5uHvPnv4kxf6dTpx/Iz09k//7/47TTpnH66acH36r2eOCrr+D11+G996zpQzt3hsmT4Yor4KyzwOmss32KooQnKtB1QwW6HjRGga4g9/tcdj64k6wvsnC1dNHp9k50urMTkW0i61xWebnh22+/JS3t76SkvEdkZAkZGadQXHwtZ5zxf/Tp0y34wvLyYN48a1DZp59CSQm0aweXX24t550HUXXvnlcUJXxQga4bKtD1oDELdAW5K3LZ86c9HJl3BIkS2l7Zlo63dSRhRAJSjy7mo0dz+PTT/1Jc/Abduy8CYPfuETidkznzzMvp2bMOA8vy8mDBAkusFyyAggLLaceYMdZ76wkToFu3OtuoKEpoUYGuGyrQ9aApCHQFBZsK2Pe3fWS8noEnz0PcaXF0vLkjbX7ShsjWdW9VA+zYsZulS9+ivPxdOndeBcD+/QMpLp5Mv36TOP3003A6g3wIKCqyusE//tgS6532d9l9+8L48XD++dbUo4mJ9bJVUZSGQwW6bqhA14OmJNAVuPPdHHrzEPte2EfBugLEJSSPT6bt1La0vrQ1ztj6vQvevHkXy5e/T3n5e3Tp8g0OhyErqz1Hj46jTZsLGT78Atq3D9I7mDHWALMFCyzBXrQISkvB4YChQ2H0aBg1Cs45B1q0qJe9iqKcPFSg64YKdD1oigJdgTGGgvUFZLyZQcZbGZTuK8UR56DVhFa0urQVrS5qVedpRCs4eDCD775bQGbmp7Rr9zkJCUcpLxfS04dSWnoBXbqcy4gRZ5OQEKS4FhXBsmXw9dewcKG1XlZmDSwbMsQaEV6xdOmio8MVJcSoQNcNFeh60JQF2hdTbshenM2huYfI/DCT0oOl4ISWI1vS6tJWJI9PJrZPbL3eWZeWelixYhVpaZ/icHxCp07f43R68HgcHDgwhNLSc2nb9lyGDh1Jhw7JwRVaWAhLl1qC/c03sGKFFQbWpCi+gj14MMTV4dMwRVGOGxXouqECXQ+ai0D7YsoNeSvzOPLhETI/yKRgQwEAkR0iSRqbZC1jkojqVL+R1nl5+SxbtozduxfhdC6mU6fviYwsAeDAgT4UFAynRYvh9Ow5nIEDU4kKZkS32w3r11st66VLrWX7ditOxJqWdMgQaxk82FqSkuplv6IotaMCXTdUoOtBcxToqhTtKiL7y2yyvsgi64ssyo6UARDTO4bEsxNJODuBxLMSrRZ2sN9F+1BYWMzq1SvYtWsxpaXfk5z8PS1bWm4sS0sjOXhwECUlw0lIGEbPnqcxYEA/oqODGNR26BB8/z2sXm0ta9bA3r3H4rt1swR70CDo399aeva0Zj5TFOW4UIGuGyrQ9UAFujKm3FDwQwFHPz9KzpIccr7NwZ1pOcVwJblIODOBhDMTaDGsBS0GtyCyXf2+ud66dS+bNi3n6NHlOJ3LadduJTExVkve7XZx6FBfiopOIyrqNNq2TeXUU0+jS5d2tb96PnzYEuo1a44J97Ztx+KjoqBPn2OCPWCA9du9uzVATVGUoFCBrhtNWqBFpDPwOtAeKAdmG2P+WiXNKOADoMKv4nvGmEdqKlcFumaMMRSlFZHzXQ653+aS810OhRsLvfGRnSJpMaQF8UPird/B8USlRNX5XbbH42Hr1q1s3bqeI0fWYcw6kpLW0arVPm+a7Oy2HD3aj/LyPsTF9aF9+750796HLl1ScNQkrgUFsGmT5Srzxx+tZcOGyq3tmBjo1QtOOcVaevc+9tumjQ5KU5QqqEDXjaYu0B2ADsaY1SLSAlgFTDLGbPRJMwq41xhzcbDlqkDXHXeOm/y1+eStziN/tfVbuLnQemwCnAlO4vrHEdsvlrj+cd4lsmNknYX74MFMNm9ez4ED6ygq+gGnczPJyZto0SLLm6a4OJbDh/tQVNQHp7MPLVv2omPHnvTq1ZPWrZMD7zM31/J5vWGD9bt1K6SlwY4dlV1pJiZWFu2ePa0Wd7du0KGDtryVZokKdN1o0gJdFRH5AHjeGPO5T9goVKBDgqfAQ/76fPLX5FPwYwEFPxZQ+GOh9302gDPRSVy/OGJOiSGmVwyxvWOJ6RVDTO+YOs0jXl5u2L37MNu3b+bQoU0UFm7G4dhEQsJmWrfeXSltQUEi2dk9KC7uicPRk9jYnrRq1ZNOnXrQo0dnoqL8fBPudsOuXccEOy3t2Pru3dY33BVERkLXrscEu1u3Y+vdu0Pbttr6VpokKtB1o9kItIh0AxYDA4wxuT7ho4B3gXRgP5ZY/+gn/83AzQBdunQZunv37qpJlBNE6aHSSoJdsKmAorQiSveXVkoX0SbCK9YxvWKI7h5NdFdriewYicMVXCu1qKiQbdt2sHfvDjIzt1NUtB2HYzvx8dtp1WoXERHHHhg8HidZWZ3Iz++Cx9MFp7MLcXFdaNmyM23bdqFTpy4kJydWdhhSXGyJ9M6dlohX/T18uLJBMTGW05CUlMBL69Yq4kqjQwW6bjQLgRaReGAR8Jgx5r0qcQlAuTEmX0QmAn81xvSuqTxtQYcGT4GHoh1FFKUVUbTN53dbESXpJZUTOyEqJcoS7C7RRHWN8op3VJcoojpFBeVy0+32sHt3Ounp2zlyZDv5+bspK9uDy7WH2Ng9JCfvxeVyV8pTWNiC7OzOFBZ2wePpjMvVibKye+nUKY6UFOjUydJXby93fr4l4BWivXMnpKcfW/bvr9x9DlYrvKpod+hgfe/tuyQmqpArYYMKdN1o8gItIhHAfOBTY8xTQaTfBQwzxhwJlEYFOvzwFHoo3lNMye4SincXe5eK7ZJ9Jd733RU4451EdowkqmPUsd9OVbY7ROKMCTzNaWmph717M9i/fy+ZmXvIy9tDWdkeYA9RUXuIi9tHixaHGT++CLf72Oh1p9Ny1tWunaWjFb++6xW/LVt4kMOHKou2v6W0tLqBUVHVRbvq0q6d1a2uE7coJxkV6LrRpAVarJE+c4Cjxpi7A6RpD2QYY4yIDAf+C3Q1NRwYFejGR7m7nNJ9pZZw7ymm9EAppftLKdlf4v0t2VeCKal+2l0tXUS0jSCybWStv64kV7XvvsvKyjh8OIJ9+45p6cGDkJFh/fquV20og9VYrhDrtm2t1ne1pZWhdUQOrT0ZJBXuw3n4YPXCK5bDhyu/E68gJuZYgW3a1P6bnKz+upU6oQJdN5q6QJ8DLAF+4Fj76QGgC4Ax5kURuR24FXADRcAvjDHf1VSuCnTTxBiDO9tdTbhL95dSeqiUskNllB62fsuOlIG/v44TIlrbot0mgohW1uJKdhGRHIGrlf2b7LLikiNwJblwRDooL4esLP/CXbEcOXJsKSz0s3+sHu3kZP9CnpwMSQkekpy5JHmOkFR2iKTCfSQVHyAhbx+OzMOWgB85cuw3Nze4HSUnWzOxVfz6rvuGJSVBRP3mc1caNyrQdaNJC/TJQgVaMR5DWWbZMeEO8Ft2tAz3UTdlR8vAE7g8ZwvnMRH3Fe+WLpyJTlwtXbgSfZaWLsoineS4XWQWOcnMlEriHWjx1wtegYj1ytpXR5OSbEGPLiTJlU+SI4eW5ZkkuY/QsiSDhMKDJOTtIyFnL3G5B5Cso9ZTRl5ezQcwPj6wmCclWYZULAkJ1bd1ZrdGiQp03dCrXFHqgTiFyLaRRLYNblY0YwyeXM8xwc6sLN7uTHeluPz0fNyZbtzZboy7lodoB8Qkuuie6KJXotMr4K5EF65+tsAnunC2cOGOdFJknBQYJ3keJ7llTrJLnBwtdnG0yElWjpCVhXfZtw+yspxkZbWgtLQF0CGwGQ5LOxOSIKGLISHWTUJ0CQmRJSS4Ckl05pFAHgnl2SR4jpJQmklC8SESszJI2LufhLzVtMjeS1zpUZxVBwxUJTa2dhGvup2QYLkhjY+3lhYtVOiVsEavTkVpAETE2/qle/D5jDGUF5XjznHjznHjyfHgznZ7t905loh7cjyVwop3F1cKC6R3CfbSxd52xDhwtnDijHdav62cuLq5cMQ7IcZJmctJqctJsVhLEU4Kyp3ku53klTnJK3WQXeIku8hBVpGTzIIYdh6MIyevFbm5gbvlqxITY4iP8RAf7SY+qoy4iFLiXcXEO4uIl0LiySeuPI/48lzis7KJzzhKfOlR4osOE1e0lfjiI8STX2mJpphq49mjoo6Jta9wBwqrbT02Vt/LKycMFWhFCWNEBGesE2esk6gO9fMUZozBk+/Bk+fx/rrz3NXCAsWVZZbh2V3s3XbnucEDUVhLyyBscEQ7cMQ5cLZyQrQDE+WkPMKJx+WgzOWkzOGgRJyUiJNiHBQbJ4XlDgo8TvLdDusBoDSGvPw49pc4yCmyl2IHpTgowfotJ/C37w6HIS7KTVxkGbGuUmKdpcQ6i4mRYmLLiog9WkhsZj6xnnxiPXnEuPOILc22Fs9eYikkhiJiKay0VA2LiHJaI+JjY63lRK/HxOinc80EFWhFaeKICK4WrqC++w4GYwzlxeWVhb3QQ3lhufVbUH5su8BTab0izbF1N+V5pZXTF3gCtvhrxSUQ4cBEOCiPcFDucuB2OvA4HZSJg1KxhLwUJyXGQbFxUFzuoMjTgkJPIkVuBwVuB4dLHeSXWg8KpTh8FqEMB2X2dlmVMIPgKvMQk1dKbFEpsVnFxDqKmT/gfrrl7bRG+hUWWktBgbV4ahicEIgK0a4Q7JgYiI4+tl51+3jjIiP1oSAEqEArilInRARnjNP6PrzNiS/fGIMpM34Fvby4nPIi+7e4HE+Rp1qYd72oSpricsqL3AHT1/quPwjKnUK5/UDgcQhuceDGgfOvL8PpMf4zlZVZQu0r3MGuFxRYM9UVFVlLcbE16r5ivSK8qAhKSvzvPxhEjgn2c8/BT39a/7KUoFGBVhQlrBARJFJwRDogqeH2W+4OLPLlReWUlxxbTInVi+ANK/YJ99muCGvXpYb30hER0LKltZzUCpZbIu1PvKuKfE1x3eswiEI5LlSgFUVRAIfLgSPeAfGhtuQk4XAc67pWGgXq805RFEVRwhAVaEVRFEUJQ1SgFUVRFCUMUYFWFEVRlDBEBVpRFEVRwhAVaEVRFEUJQ1SgFUVRFCUMUYFWFEVRlDBE/UHXAxE5DOyuQ5bWwJGTZE5D05TqAk2rPlqX8KQp1QWOrz5djTEnYYLYpokKdAMgIiubipPyplQXaFr10bqEJ02pLtD06hPOaBe3oiiKooQhKtCKoiiKEoaoQDcMs0NtwAmkKdUFmlZ9tC7hSVOqCzS9+oQt+g5aURRFUcIQbUEriqIoShiiAq0oiqIoYYgK9AlERMaLyBYR2SYi9/mJFxF51o5fLyJDQmFnMARRlz4islRESkTk3lDYGCxB1GWqfT7Wi8h3InJaKOwMliDqc5ldl7UislJEzgmFncFQW1180p0uIh4RmdKQ9tWFIM7LKBHJsc/LWhH5XSjsDIZgzotdn7Ui8qOILGpoG5sFxhhdTsACOIHtQA8gElgH9KuSZiLwMSDACOD7UNt9HHVpC5wOPAbcG2qbj7MuZwFJ9vqEcD0vdahPPMfGl6QCm0Ntd33r4pPuK2ABMCXUdh/HeRkFzA+1rSeoLi2BjUAXe7ttqO1uiou2oE8cw4FtxpgdxphS4G3gsippLgNeNxbLgJYi0qGhDQ2CWutijDlkjFkBlIXCwDoQTF2+M8Zk2ZvLgJQGtrEuBFOffGPfNYE4IFxHggbznwG4A3gXONSQxtWRYOvSGAimLj8F3jPG7AHrftDANjYLVKBPHJ2AvT7b6XZYXdOEA43FzmCoa11uwOrlCFeCqo+IXC4im4GPgBkNZFtdqbUuItIJuBx4sQHtqg/BXmdnisg6EflYRPo3jGl1Jpi6nAIkichCEVklItMazLpmhCvUBjQhxE9Y1ZZLMGnCgcZiZzAEXRcRGY0l0GH7zpYg62OMmQfME5FzgT8AY0+2YfUgmLo8A/zGGOMR8Zc8bAimLqux5qLOF5GJwPtA75NtWD0Ipi4uYCgwBogBlorIMmPM1pNtXHNCBfrEkQ509tlOAfbXI0040FjsDIag6iIiqcBLwARjTGYD2VYf6nRujDGLRaSniLQ2xoSbw4Zg6jIMeNsW59bARBFxG2PebxALg6fWuhhjcn3WF4jI3xvxeUkHjhhjCoACEVkMnAaoQJ9AtIv7xLEC6C0i3UUkErga+LBKmg+BafZo7hFAjjHmQEMbGgTB1KWxUGtdRKQL8B5wbSNoAQRTn15iK5r9pUAkEI4PHbXWxRjT3RjTzRjTDfgvcFsYijMEd17a+5yX4Vj330Z5XoAPgJEi4hKRWOAMYFMD29nk0Rb0CcIY4xaR24FPsUZBvmKM+VFEbrHjX8QahToR2AYUAteHyt6aCKYuItIeWAkkAOUicjfWSM/cQOWGgiDPy++AVsDf7fun24Spt54g63MF1oNgGVAEXOUzaCxsCLIujYIg6zIFuFVE3Fjn5erGel6MMZtE5BNgPVAOvGSM2RA6q5smOtWnoiiKooQh2sWtKIqiKGGICrSiKIqihCEq0IqiKIoShqhAK4qiKEoYogKtKIqiKGGICrQS1oiIEZE3fLZdInJYROY3sB19bM89a0SkZ5W4RBF5XUS228vrIpJox40KZKuILBCRlvWwZZSInFXHPB0q7BCRWBF5U0R+EJENIvKNiMTXkj+/rnZWyb9QRPZUfAdsh71/vOUehz1t7M+EFCVsUYFWwp0CYICIxNjbFwD7QmDHJOADY8xgY8z2KnEvAzuMMT2NMT2BnVizktWIMWaiMSa7HraMwvLAVRd+AfzTXr8LyDDGDDTGDMCa3vSEOT2xJ+Lxd2/JBs6207QEQuYoxhhzGDggImeHygZFqQ0VaKUx8DFwkb1+DTC3IkJEhovlw3mN/XuqHd5fRJbbrd71ItJbROJE5COxnBVsEJGrqu5IRAaJyDI7zzwRSRJr3uS7gRtF5Osq6XthzUn8B5/gR4BhPi3tBLusjSLyYoV4icguEWltr/+fj73/EBGnHT5eRFbbNn8pIt2AW4B77LQjReRKuz7rxJpy0R9XABUtxg74POQYY7YYY0rs/f3CLmuDPflM1eMTb9ux2m6BX2aHdxORTSLyd6w5pztXzYvlFelqe30y1uxttZXr95yJyCz7eK4XkSftsDYi8q6IrLCXioeB8+SYD+Y1ItLC3u37wNQAx0tRQk+o/V3qoktNC5CP5dP4v0A0sBYfv7pYM5m57PWxwLv2+nPAVHs9EmtC/yuAf/qUnehnf+uB8+z1R4Bn7PWZ+PF7DVwKzPMTPs+OGwUUY/nWdQKfY/s0BnZhzS/dF/gfEGGH/x2YBrTB8irU3Q5P9mcL8APQyV5v6ceW7sAqn+1BWK4blwKPAr3t8KF2WXFYPqV/BAZXnAf71wUk2OutsWbFE6Ab1oxSIwKcx4VY00Gut4/DZ3ae2sqtds6AZGALxyZaamn/vgWcY693ATbZ6/8DzrbX432ul07AD6G+xnXRJdCiLWgl7DHGrMe6mV+DNV2qL4nAf0RkA/A0UOHCbynwgIj8BsuDUBGW+IwVkcdFZKQxJse3IPu9cUtjzCI7aA5wbi3mCf69Y/mGLzeWb10PVuu/qresMVjiuEJE1trbPYARwGJjzE77OBwNYMO3wGsichOW+FWlA3C4YsMYs9Yu/wkssVshIn1tu+YZYwqMMflYLdyRfur1RxFZD3yBJXLt7LjdxvJzHggP8A1wFRBjjNkVRLn+zlku1kPPSyIyGWvaXLAe0J63j+GHWD0XLezj85SI3Il1ft12+kNAxxrsVZSQogKtNBY+BJ7Ep3vb5g/A18Z6l3oJVisbY8xbWC3YIuBTETnfWI4wKlqJfxKR350Au34EBvu+c7XXT+OY84CqAu7PDekcY8wgeznVGDOTwOJfuTBjbgEexOpWXisiraokKcI+Lj558o0x7xljbgP+hTVHfDD+HKditeyHGmMGARk+ZRcEkf9trN6Nd4Ip1985swV2OPAu1tiAiq57B3Cmz3HsZIzJM8bMAm7E6kVZJiJ97PTRWMdGUcISFWilsfAK8Igx5ocq4Ykce596XUWgiPTAGrj1LJa4p4pIR6DQGPMvLLEf4luQ3TrLEpGKVuO1wCJqwBizDViDJZAVPAistuMAhovlGciB1Xr8pkoxXwJTRKStbXuyiHTF6gU4T0S6V4Tb6fOAiveoiEhPY8z3xpjfAUeo/v53K1YPREX6s0UkyV6PBPoBu4HFwCSxRnnHAZcDS6qUlQgcMsaUieU/u2tNx8cPS4A/Uf1By2+5/s6ZWCPOE40xC7DGBgyyy/gMuN2nnoPs357GmB+MMY9jOXipEOhTAHXwoIQt6s1KaRQYY9KBv/qJ+jMwR0R+AXzlE34V8H9ieXQ6iPU++XTgCREpxxq1fKuf8qYDL4rlQm8HwXkcuwF4TkQq3psutcMqWArMAgZiieC8ylUzG0XkQeAzW8TLgJ8bY5aJyM3Ae3b4IaxR7P8D/msPpLoDa8BYb3vfXwLrqLyDArE+/+plPzT0BF4QEcF6SP8I6929EZHXgOV21peMMWuq1PVN4H8ishJrPMDmII5PpcpiCW1VApU7kOrnrAXwgYhE23W+x057J/A3u5vchXWsbwHutkXfA2zEGnQIMNquu6KEJerNSlFCgD1K+xDQ3hhzwj5xqmF/l2N1Hz9Ya+Jmgj3i/TJjTFaobVEUf2gLWlFCw49YLdSTLs4Axph5ft5NN1tEpA3wlIqzEs5oC1pRFEVRwhAdJKYoiqIoYYgKtKIoiqKEISrQiqIoihKGqEAriqIoShiiAq0oiqIoYcj/A7hGdeLidpAWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot all of the power law functions based on mass ranges\n", + "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", + "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", + "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", + "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", + "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", + "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f5494d3d", + "metadata": {}, + "source": [ + "#### Accounting for error in a" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "1e6c80f4", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux\n", + "M_aup = M_a + M_aerr\n", + "M_alow = M_a - M_aerr\n", + "M_outup = power_law(M_mass, M_aup)\n", + "M_outlow = power_law(M_mass, M_alow)\n", + "\n", + "#Suárez\n", + "S_aup = S_a + S_aerr\n", + "S_alow = S_a - S_aerr\n", + "S_outup = power_law(S_mass, S_aup)\n", + "S_outlow = power_law(S_mass, S_alow)\n", + "\n", + "#Lodieu\n", + "L_aup = L_a + L_aerr\n", + "L_alow = L_a - L_aerr\n", + "L_outup = power_law(L_mass, L_aup)\n", + "L_outlow = power_law(L_mass, L_alow)\n", + "\n", + "#Béjar\n", + "B_aup = B_a + B_aerr\n", + "B_alow = B_a - B_aerr\n", + "B_outup = power_law(B_mass, B_aup)\n", + "B_outlow = power_law(B_mass, B_alow)\n", + "\n", + "#Peña Ramírez\n", + "P_aup = P_a + P_aerr\n", + "P_alow = P_a - P_aerr\n", + "P_outup = power_law(P_mass, P_aup)\n", + "P_outlow = power_law(P_mass, P_alow)" + ] + }, + { + "cell_type": "markdown", + "id": "041954f2", + "metadata": {}, + "source": [ + "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "dc7a858e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NZxdgh5x92NnE7avAexXbRiBnG/YwXvIm4DWx5eyllFMDPb7HHbsQKqaezfoefkRodYDCMaAcwzoesNA16M+EpO2GZLq7P+juz3jomvkvQivNvxVYzRoKx+P5cX6inO0wsTZ7ve7+XXefQdj/ZR+/5e7HhsSI+Hg4J+rZWsYYX9aVev2kSDDc/UnCYO9TgN/mzF5DqI3aJWvazoSa562ryHnNl+O053lo/nsLYcNPdBBqWhLbZz3GwmkR/x9wLSFglONnhMz7qqydbLK+Ywg12q8j1G7OADYmZXL3R9z9jYQ/6v8Cvzazqe7e6+6fdff9CNn4yxnaP7gc7RT5rCUsp0A/9RKeAXayoYOhcn+zkSpn3bnbw0h9hFA7dGTcjpKuYeX09V9OaMGYkXWb6u5fyVfGUttIvtfEndPlhAP20wnbYDkujp9tm4MnSnxmd/+ru59E2IE+RKjhxd2fc/d3uvsOhFrI78cgsBz4Z8730Oru742vu93dTyVs+1cy2Ew/ZmIFwp+BV+TOs9Bv9XxC0J4df4f7Kf83n2VZ472yPEPWPsxC17HZVOY/UdPc/SFCDVsyzqngPsnMGgmJ8zeAXdx9IWE/XPB/QPjeX5qzjTW7e/Ld/h+hdrjc7gY3ELbv7cjpYhm74fyGcGa77eL2cRWD/4/N7v4Rd9+NsH192OJYC3f/hbu/gLAdOGFfPxxjtS8vtr8sJ/6O1Ehi+4iUuX8tZDnwRM72Nc3dk9Mn5+7Li24j+V4TXUQ4VnktcHPW9lvMeB53LCe0YMzJ+h7a3H3/uL5CMaCi4j76b8Dn3b1UzHMK/8Z/I1R+7ZSz/iMICXl2d93hbIfXAq8uY7ncdQ6JEYT/QtLlbch/38zy/fd3yprfSuh+VfREP5MiwYjeDpwQa0m38nAqs8uBL5rZtLhxfZhtx2lkm0bsLmShL/xHc+bfDbzJwiDmkwl9P4GtffJ/TGhqPJPQn6/kedjd/Ym4nk/lmT2N0Gy2GsiY2WcIfTOT93yLhb75A4SmNAh9mI83swNjjfkmws54uKd2uxt4g4VBY4sonM3ncwHweQunXDMze54NDipeSeg3m8+thD/Ex+L7HkcItpXocz6W6841jdD3dIOFQaP/M4zXXkLYdl4St7NmCwO7kgHTud9f0W2kiIsJLX+vpPh/IttlhL6z+Q7mC35mM9vOzF4ZD467Cf+x/jjvtVmfbT1h59lPOJvQXmZ2evy9GszscAtjrhotXN9iuodxI5sY/vY9bLGcJ5P/bDZTY9lXx2XfSp4TAOTj7s8SEpfvWxgs32BmSYL2C+CtZnZwPAD5EqH/77ICZWy00FfagIa4/dRFPLAwqP8jyfYQA/gbCd0OIOyTjrVwPvzphO4miSbCiQfa42tfSmidK+aHhPiwS3zNXDM7NT5+N2G//KacVs+C3N0J+5RXxsfZGmMZVwN9sXxbT59sZi+3MNjVGNye+y1c/+GE+Nt3Ef5jI9mXn2LhRALbE1pEyvVjwvZ3ooVB8TvaYOtjwX35CONvWcZy3XmMdP8KodvwJgsDcqfE/fkBNnja5ZXAwqz/Z9FtpIgrgUOB/yB/5c82xvO4I+7frga+YWZtcTva3cxeGNdXKAZUTDye+zvwPXf/YZ75p8Z9r8VE4YMUGHPl7n8jJAO/MbP94+96FKHl6Afu/sgIi3kOcIyZnRvLmxxX7lv0VaGr96fj/msO4Qx6yX/hHmD/GD+ayT+m+BQLpwdvJIzF2GZ8Sa66CCiV4O6PufviArM/QAg4jxNqlH4B/KTI6j5L+KNuJAy0zW0V+Q9CANlAqP29MmveecDv3P0qd19LSHwusKGngyv0GW70/KeG/SvhwONhQrNXF0Ob3E4GHrBwxqRvE/oJdxFqqH5N+JMvAf7J8He+/02ouVpP+F5+MYzXnksIAFfHMvyYwVMInwNcZKGpNHugMe7eQzjgfSmhlur7wBmxJnNUxnLdeXyL8HnXEA6O/lLuC+Mf+1RCU/pqwu/9UQb/098G/s3CmVe+Q+ltpND73ETo939noYPVPK/pdPe/uXu+EwZ8i8KfOUVo4XiG0Pz6QkLtGYTB1rfGbfj3hKbrJ9x9MyG4viG+7jlCbVlTfN3pwDIL3bHew9CBjpWUnOVlC2Hg402E/8MQ7v4gofb8ZsKBw4Fx2XKdTgjIDxH6hH8orvdawn/xN4R+wbsTzwBTwNWEg9CjCfukTgZbk2rdZkK/9VstnMntFkIr0EcA3P0aQpJ7L3AHIQklzttMOCj4JWGf9SbC9lTMt+MyV5vZ5vh+R8Z5byQcPD9jg2eSOrvUB3D3B9x9mwQ0q3yXFyjfnoSa0S2Ebej77n4dYXv/CuF/9Ryh1rhkOXL8jHCgsYywfZR96loPXSffSjjpwEZCLElqS3P3RbmGG3+HYyzXnW1E+1fYmgi9gjAe7QnCb3gBobsRhFOdAqw1szvL2EYKvU8nYf+wK9sesxR73Xged5zB4Aky1sflku5AeWNAuZ+jTO8g/J//J+v/vCVr/hsIXRQ3E5K0//Ui16sgdNH9ByHObSF8zh8TtssR8XDygqMIA93vifukmwjx77+LvPQLwGLCfvE+winKv5C1zs8R9i2PkP/kNb8gVAiuAw4jHNsWZdtWoIiIDLJw+sdfuPsF1S6LiIiMTGxl2Mvdx6qiRSYgM7uQMPB/OGedoioX3xCR+hCb6Q8ltJaIiEgdstAl9e2EVlCRMTdpukiJyPCY2UWEJtMPxWZ5ERGpM2b2TkL3pT+7+/WllhepBHWREhERERGRilELhoiIiIiIVIzGYEhBc+bM8YULF1a7GCJlueOOO9a4+9xql0NkIlEckHqiOFA7lGBIQQsXLmTx4kJn9hWpLWb2ZOmlRGQ4FAeknigO1A51kRIRERERkYpRgiEiIiIiIhWjBENERERERCpGYzBkZNyhpweamqpdkrrS29vLihUr6OrqqnZR6lZzczMLFiygoaGh2kURmfS6uqC5udqlqC+KA6OnOFD7lGDIyKxcCStWwKJF1S5JXVmxYgXTpk1j4cKFmFm1i1N33J21a9eyYsUKdt1112oXR2TSu/9+OPhgyOhoomyKA6OjOFAf1EVKRsYM+vqqXYq609XVxezZsxVURsjMmD17tmr+RGpEf79CwXApDoyO4kB9UIIhI5PJhMgiw6agMjr6/kRqR1+fEoyR0H5sdPT91T4lGDIymUyIKu7VLomIiFSJWjBEJB/1mpSRSbpI9fWBBlmN3C23wIYNlVvfjBlw1FGVW98oveMd7+DDH/4w++23X7WLIiJjQAnG6E3wMKA4MEkpwZCR6+uD3l4lGKOxYQPMnVu59a1eXZHV9Pf3k06nCz7Px91xd1KpwYbRCy64oCLlEZHapARj9Go0DCgOyKioi5SMnCJLXbrkkks44ogjOPjgg3n3u99NfxxL09raymc+8xmOPPJIbr755m2en3vuuRxwwAEccMABfOtb3wJg2bJl7Lvvvrzvfe/j0EMPZfny5UPe67jjjmPx4sVb1/+pT32Kgw46iKOOOoqVK1duU7bbbruNo48+mkMOOYSjjz6apUuXju2XISKj0t8P3d3VLoUMl+KAjDUlGDJySQuG1I0lS5Zw2WWXcdNNN3H33XeTTqf5+c9/DkB7ezsHHHAAt956Ky94wQuGPJ8yZQo//elPufXWW7nllls4//zzueuuuwBYunQpZ5xxBnfddRe77LJLwfdub2/nqKOO4p577uHYY4/l/PPP32aZffbZh+uvv5677rqLz33uc5x99tlj80WISMXoZD71RXFAxoO6SMnIKcGoO9deey133HEHhx9+OACdnZ3MmzcPgHQ6zWte85qty2Y/v/HGG3nVq17F1KlTAXj1q1/NDTfcwCtf+Up22WUXjiqjw29jYyMvf/nLATjssMO45pprtllm48aNnHnmmTzyyCOYGb3avkRqnhKM+qI4IONBCYaMnLsiS51xd84880y+/OUvbzOvubl5SP/a7Ode5GxhSbAppaGhYeupBdPpNH15utf993//N8cffzxXXHEFy5Yt47jjjitr3SJSPQoD9UVxQMaDukjJ6HR2VrsEMgwnnngiv/71r1m1ahUA69at48knnyz5umOPPZYrr7ySjo4O2tvbueKKKzjmmGMqXr6NGzey4447AnDhhRdWfP0iUnkKA/VFcUDGg1owZOTMoL292qWobzNmVO6UH8n6ithvv/34whe+wItf/GIGBgZoaGjge9/7XtE+swCHHnooZ511FkcccQQQTjt4yCGHsGzZsgoVPPjYxz7GmWeeybnnnssJJ5xQ0XWLyNjQIO/RGecwoDgg48KKNXnJ5LZo0SJPzvywjU2b4LrrYOZMGIMajIlqyZIl7LvvvtUuRt3L9z2a2R3uvqhKRRKZkIrGAeDqq8OZpE46KVx/VUpTHKgMxYHapi5SNcrM/p+Zfc/M7jWz1Wb2lJldZWb/bmbTq10+IFz/oqOj2qUQEZmQRhMHzOwnZrbKzO7PmnaOmT1tZnfH2ymVKqvG4YpINiUYNcjM/gy8A/grcDIwH9gP+DTQDPzOzF5ZvRJGZmGgtyKLiEhFVSAOXBhfl+ub7n5wvF1VqfIqDIhINjVo1qbT3X1NzrQtwJ3x9g0zmzP+xSpAV/MeFnffehYNGT5165RJYlRxwN2vN7OFY1i+IZRgDI/iwOgoDtQ+tWDUoDxBZUTLjAt36OmpdinqRnNzM2vXrtXOcYTcnbVr19Lc3FztooiMqTGMA++PXa5+YmYz8y1gZu8ys8Vmtnh1maOPFQbKpzgwOooD9UEtGDXIzN7m7j+JjxcAFwGHAQ8CZ7n7w9Us3zYUWcq2YMECVqxYQblBW7bV3NzMggULql0MkTFlZvsA3wQGgA8C/w2cBjwMnOnuS0aw2h8Anwc83n8DeFvuQu5+HnAehEHepVaaTutUtcOhODB6igO1TwlGbXo/8JP4+FzgcuAk4FRCgDixSuXaViqlqywNQ0NDA7vuumu1iyEite884GtAK/B34OPAW4GXA99lBHHA3Vcmj83sfOCPlShoQ4POWD4cigMyGaiLVO3by91/5O4D7n4FMKvaBRqisRG2bKl2KUREJppp7v4Hd/8l0Ovul3rwByBv16ZSzGx+1tNXAfcXWnY4MhmdUFBEhlILRm1aYGbfAQyYa2YN7p4Moaut0dQNDUowREQqL531+NyceY2lXmxmvwSOA+aY2Qrgf4DjzOxgQhepZcC7K1FQnbFcRHIpwahNH816vJjQRL7ezLYHfl+dIhWgtnERkbHwPTNrdfct7v79ZKKZ7QH8rdSL3f2NeSb/uJIFTKTT4WreAwOh16yIiBKMGuTuFxWY/hxwdrHXmtlPCH10V7n7AXHaOcA7gWRE2dkVO/95JhMiS39/iDIiIjJq7v6jAtMfBT40vqUpLjnbak8P6MQ+IgIag1F3zOwzJRa5kHG8uNJW3d0VX6WIiGyrjDhQFQoDIpJQglF/3lFsprtfD6wbp7IM0qlqRUTGS9E4UA26JJKIZFMXqRpkZpsKzQKmjHC17zezMwhjOj7i7usLvPe7gHcB7LzzzuWvXaeqFRGpmDGKA2MmldK1MERkkFowatMGYE93b8u5TQOeHcH6fgDsDhwcX/+NQgu6+3nuvsjdF82dO7e8tWcyOpOUiEhlbaCycWBMNTbC5s3VLoWI1AolGLXpYmCXAvN+MdyVuftKd+939wHgfOCI0RRuG42NsKlQZZuIiIxARePAWFOCISLZ1EWqBrn7p4vM+/hw12dm8909qfGq2MWVtlJkERGpqErHgbHW0KB6JhEZpARjghnPiytt1dAAGzboVLUiIpNUOg19fdDbG0KCiExuSjDqjJnd6e6HFpo/nhdX2kZXF0ydOi5vJSIyWZWKA9XU1aUEQ0Q0BqPu1GpQAXQmKRGRcVDLcUBhQERACUbNM7NZZjaz2uUoyQza26tdChGRCade4oDCgIgklGDUIDPb2cwuNbPVwK3A7Wa2Kk5bWOXi5dfUBBs3VrsUIiITQj3GgeZmhQERCZRg1KbLgCuA7d19T3ffA5gPXAlcWs2CFdTUFAZ6i4hIJdRdHNAZy0UkoQSjNs1x98vcvT+ZEK9jcSkwu4rlKqyxMbSN9/eXXlZEREqpuzigMCAiCZ1FqjbdYWbfBy4ClsdpOwFnAndVrVSluENnJ7S2VrskIiL1ri7jgLtOKCgiSjBq1RnA24HPAjsCRggwf2C8Tjk7EmbQ0aEEQ0Rk9OozDhDCgBIMkclNCUYNcvce4AfxVj+SC+7Nm1ftkoiI1LV6jQOZDGzeDHPnVrskIlJNGoMhldPcDOvWVbsUIiJSJU1NCgMiogRDKqmpKZxCZGCg2iUREZEqaG6G9evDWAwRmbyUYEjlmIXkoqOj2iUREZEqSKfDWaR0RW+RyU0JRo0zs5OKPa9JupSriEjF1FsccFcYEJnslGDUvv8t8by2NDbCmjXVLoWIyERSV3Egk9F1V0UmOyUY9ceqXYCiWlpg9epql0JEZCKr6TgwZYrqmUQmO52mtkaZ2U8BB3Y2s58AuPvbqluqMiSnqu3qCqP9RERkROo1DjQ3w9q1YSxGOl3t0ohINSjBqF0XxvtjCFdyrR/usGWLEgwRkdG5MN7XZBz45S/h9tvhTW8aOj0538eWLTB9enXKJiLVpS5SNcrd/+nu/wQ2Zz2GUJtVXevXw0knwbXX5p/f2Biqr0REZMRGEwfM7CdmtsrM7s+aNsvMrjGzR+L9zNGU79JL4be/zT8vlQpnLReRyUkJRu3rKfF8/M2YAUuXwpIl+ee3tMBzz41rkUREJrCRxIELgZNzpn0CuNbd9wSujc9H7IADYMUK6O3ddt6UKbBy5WjWLiL1TAlGjXP3o4o9rwozOPBAeOyx/PMbG6GzM9xERGRURhIH3P16IPea2qcy2NXqIuC00ZTrgAPCOIsnn9x23pQpg+MwRGTyUYIhI3PggfDEE4Wjh7vax0VEast27v4sQLyfl28hM3uXmS02s8Wri5wVcP/9w/3jj287L5UK4zA2bx59oUWk/ijBkJF53vOguxuWL88/f8oUdZMSEalD7n6euy9y90Vz584tuNzee4dEolBjdjoN63LbUERkUlCCISNz4IHhfunS/POnTg0dcNU+LiJSK1aa2XyAeL9qNCtraoIFCwonGK2t8Mwzo3kHEalXSjBqnJnNNLNpw1h+zM8cAsA++4TLtT70UP75qVRILtRNSkRkVOI+fPT7bfg9cGZ8fCbwu9GucJdd4NFH889ragpdpDQcT2TyUYJRg8xsBzO72Mw2AmuAB8zsKTM7x8waSrz8Qsb4zCFAuKDennvCvfcWX0anERERGTYz29nMLjWz1cCtwO2x8uhSM1tYxut/CdwM7G1mK8zs7cBXgJPM7BHgpPh8VPbcE55+OlxfNX85wpnNRWRyUYJRmy4BfuLu04HXAr8B9iVcGPF7xV44HmcO2Wr//eHBB8NYjHymTQuRZ2CgIm8nIjKJXAZcAWzv7nu6+x7AfOBK4NJSL3b3N7r7fHdvcPcF7v5jd1/r7ifG9Z3o7qMeIbHvvuH+/vvzz586tfBQPRGZuJRg1KbZ7n4dgLv/FjjW3dvd/dPAsSNYX1lnDoHyzx4ChASjt7fw9TAyGejpUTcpEZHhm+Pul7n71oFs7t7v7pcCs6tYriH23DP0iH3ggfzzp0wJA727usa3XCJSXUowatNqM3tL7Cr1AWAZgJkZY/yblXv2EAD22y/c33134WUaG+HZZytWPhGRSeIOM/u+mR0ZY8EO8fH3gbuqXbjElCmw++5w333555uF25o141suEakuJRi16W3AK4GrgSOB98fps4BPjmB9FT1zyFZtbbDbbnBXkVg3bVpoH9fZpEREhuMM4D7gs8BfCfHgs8D9wOlVLNc2DjggtGAU2s23tua/GJ+ITFxKMGqQuz/l7q9z9wPc/S1Z3ZvWuvtvRrDKip85ZKtDDw0JRk9P/vnpdIg6Ohm6iEjZ3L3H3X/g7ie7+4ExHpzs7t939wID36rj0EPD2aIefjj//ClTQk/ZLVvGt1wiUj1KMGqQmb3KzGbFx3PjGaXuM7PLzGxBideOy5lDtjr6aOjoKN5NqqUFli2r2FuKiEx0eeLAReXGgfF2+OHh/tZbCy+Tyai3rMhkogSjNn0x6+we3yX0t30p8Gfgp8VeOF5nDtnq8MPD6WhvuqnwMq2toQNue3vF3lZEZILLjQN3U2YcGG+zZ4fB3rfdVniZtrZQz9TXN27FEpEqUoJRm9JZj/dw92+6+wp3vxAoMfJ6nE2ZEtrH//Wv4stlMrBixfiUSUSk/tVPHACOOCI0ZBc6W1QmE5ILDfYWmRyUYNSm68zsc2Y2JT4+DcDMjgc2VrVk+Tz/+fDEE8VH8U2fHub39o5fuURE6lddxYEjjwxD8RYvLrxMW1u46rf7+JVLRKpDCUZtej8wACwlXGjvt2a2GXgnNXb2EABOPDGch/Dqqwsvk06HC+4988z4lUtEpH7VVRxYtCj0hr322sLLNDeHweA654fIxKcEowa5e6+7n+PuOwMHAnPdfZq7v8ndn6p2+bax3XZwyCHw178Wr5qaMSNUX6kTrohIUfUWBxob4Zhj4J//LL6Lb2mBRx5RK4bIRKcEo8a5+0Z3X1vtcpT04heHEXyPPFJ4mYaG0Ib+3HPjViwRkXpXL3HgxBPD6Whvv73wMq2toQVj/frxK5eIjD8lGHXGzO6sdhnyetGLQjeoP/6x+HIzZ8LSpRqLISIyQrUaB446KiQQV11VfLnWVnjoIbViiExkSjDqjLsfWu0y5DVjBpxwQkgwCp1GBEIrRm9vuLq3iIgMW63GgeZmeOlLwziMjUWGoU+dChs2wKpV41Y0ERlnSjBqnJnNMrOZ1S5HWf7t30L7+F//Wny5mTNDV6rOzvEpl4hIHaunOHDaaaEn7J//XHy56dPhwQc1JE9kolKCUYPMbGczu9TMVgO3Areb2ao4bWGVi1fYoYfCbrvB5ZcXb/vOZEJ3qmLjNUREJrF6jQN77w377Qe//W04cWAhzc3Q3R3OcC4iE48SjNp0GXAFsH28+vYewHzgSuDSahasKDN405vCGItiV/aG0KVq+XJYW/PjFkVEqqE+4wDwhjfA44/DjTcWX27WrHBiwc2bx6dcIjJ+lGDUpjnufpm79ycT3L3f3S8FZlexXKW97GUwfz5ccEHxVgyzkGTce68GfIuIbKtu48CLXww77AA//WnxMJBOh9PW3nsv9PcXXk5E6o8SjNp0h5l938yONLMd4u1IM/s+cFe1C1dUQwOcdRbcfz/cfHPxZZubQ3KxdOm4FE1EpI7UbRzIZOD00+G+++DWW4sv29oahu499tj4lE1ExocSjNp0BnAf8Fngr8DV8fH91OAVXLfxylfCjjvCt75VegTfrFnw5JO6NoaIyFB1HQde+crQivHtb5dunZgzJwzJW7NmfMomImNPCUYNcvced/+Bu5/s7ge6+wHx8ffdvbva5SupoQE+9KHQCfc3vym+rFlIMu65B7ZsGZfiiYjUunqPA01N8P73h8Sh1OWRUqlwcsG77oKOjvEpn4iMLSUYdaJWL6xU0HHHwRFHwA9/CKtXF1+2sTF0l7rrLo3HEBEpoBbjQEND4Ybqk06C5z0PvvvdcN2LYpqawrruvFNhQGQiUIJRP6zaBRiiuTncFzoPoRl87GPhhOhf/GLpS7a2tobrYtxzT/FzG4qITF61FQeABQvCGIp8zOCTnwxnifra10qva9q00IJxzz0a9C1S75Rg1I8/VbsAQzQ2hg62xc4vuHAh/Pu/h3MV/v73pdc5e3Zo7XjwwdIJiYjI5FNbcQDYfvviycCee8I73hGuv3rttaXXN3t2GItx//2qaxKpZ0owapCZbVNL5e6fLrXMuNt5Z+jqKr7MG94Ahx0GX/0qPPxw6XXOnQtPPQUPPaQkQ0QmrXqJA9OmQVtb8VBw1lmw777w+c+Hyx+VMncuPP20kgyReqYEozb9w8w+YGY7Z080s0YzO8HMLgLOrFLZBs2YEQZot7cXXiaVCl2k2trgox+FjRuLr9MM5s0Ll3ddulRJhohMVvURB4DddivcTQrCaWv/93/DdS8++tHQG7aUefNCkqFrZIjUJyUYtelkoB/4pZk9Y2YPmtkTwCPAG4FvuvuF1SzgVnvuWfrsT3PmhBaMlStDdOkucQKUJMl4/PHQXUpVWCIy+dRNHJg3L/SaLTY4e4cdQl3TY4/B2WeXPoN5EgZWroQ77gjD+USkfijBqEHu3hVPRfh8YBfgROAQd9/F3d/p7ndXt4RZZs0KCUSxsRgABx4I55wTzhT1yU+WH12efFJnlxKRSWcs44CZLTOz+8zsbjNbPNqyptOw116wfn3x5Y46Cj7+cbjhBvjc58qrO5ozJ7SO3HKLzmQuUk+UYNQ4d+9192fdfUO1y5KXGeyzT2jzLhUtTj45tGBcfz185jPlJRnbbQdr18Jtt+kE6SIyKY1RHDje3Q9290WVWNkOO4STC5Yalvdv/wbveQ9cdRV86UvldX+aOTP0lr3pptCiISK1TwlGDTOzcyq8vorWWm3V1ga77grr1pVe9nWvgw98AK6+Gj7ykdLRCMJpRXp7w9moVq0afXlFROpEpePAWEmnYf/9S1/vAuDtb4e3vQ2uvDI0aJfT/am1NYSaxYtDz9lS9VMiUl1KMGqQmaXM7MdA0xisvqK1VlvtsQdMmVJ8wHfizDNDJ9x//Qve+95wTsJSpk0LEeb228MZphRdRGQCG+M44MDVZnaHmb0rz3u/y8wWm9ni1aUulJpl7tzQklGqq5QZvO998J//CX//ezibeTlhoLExNGovXx7CRznJjIhUhxKM2vQHYJ27f7LaBSlbJgMHHxwSjHLGS7z61eG0Io88Am95S7iyUilNTYPjMhRdRGRiG8s48Hx3PxR4KfDvZnZs9kx3P8/dF7n7orlz55a9UjPYb79wX07j9JvfHAZ+P/ggnH463Hdfee8xZ064v+mmUN+kAeAitUcJRm1aBFwxBustWmsFI6+5AkL79UEHhTET5XSsPeEEuPDC0HH33e+Gn/609OtSqaHR5cEHFV1EZCIaqziAuz8T71fF9ziiUutuaoJDDglnJC+nofklLwm7/sbGcEG+Cy4o73UtLaG+6amnwqDxZ57RCQdFaokSjNp0PPAjMzuywustWmsFI6+52mqHHcIVlVavLm9vv8cecPHFcNxx8L3vwTvfWd6VmJLosmJFGDT+9NOKLiIykYxJHDCzqWY2LXkMvBi4v5LvMWsWPO95odtTOXVNe+0FP/sZvOhF8MMfhvEZjz5a+nWpVBii19ICd98dGrbXrtXlk0RqgRKMGuTuDwIvAb5W4fWOWa3VELvuGiLGypXlRZe2Nvjyl+ELXwgX2Hv960OUKdXGnkSXqVPD1ZiSQeCKLiJS58YqDgDbATea2T3AbcCf3P0vFX4PFiwYrGsqNwx88Yvwla+E+qI3vzlcPqnUtVlhcGyGO9x6azilrRINkeoy1z+wZpnZNHcvcYGJstc1FUi5++b4+Brgc8UCy6JFi3zx4lGcbOqJJ0IXptmzoaGhvNesXg3f/jb85S8hYrz//fDiF4dTlJTS2Rmux9HWBnvvHd7XbOTll7piZndU/OQFIlVWyTgwEqONAyMJAxs3hjqm3/wmnN/jrLPgta8NvWnL0d4ebm1toZF8zpzyQojUP8WB2qEEY5Iws90Y7M+bAX7h7l8s9ppRJxgQWjHuuiucYaq1tfzX3XUXfO1r8PDDoUXkXe+CE08MrRaldHSERKO1NVxpfO7cMAhdJjQFFpHKq0QceOaZ0IVp2rTQnalcDz8c6ptuvTV0uzrjDHjNa0I4KUdS59TUBLvtBttvX36SIvVJcaB2KMGoQWb2+2Lz3f2V41GOiiQYEC6/evfd4X727PKSBAhjKv7+d/jRj0I12MKF8IY3wMteVl6E6eoKl4DNZEKSssMOw4tuUlcUWGQimWhxYNOmUG/U3R2SheE0Lt99N5x3Xrje6rRpcOqp4ZJKO+xQ3ut7e0OryMBASDJ23hlmzFCrxkSkOFA7lGDUIDNbDSwHfgncCgzZFbv7P8ejHBVLMCB0wn388TByb8qUECWG89prroFLLgnnJGxrg9NOC7eddy79+r6+EF36+0Nb+c47h0RHrRoTigKLTCQTMQ709oYzky9bNvzWDAhD7X75y1Dv5A4veAG8/OXhvrGx9OvdQz1XZ2forrXTTqEn7vTp6k07USgO1A4lGDXIzNLAScAbgecBfwJ+6e4PjGc5KppgJDZvhiVLwliLtrbhRRj3cL2MX/4S/vGPUB31vOfBK14BJ51UXhesLVtCF6p0OlR/zZ+vqqwJQoFFJpKJHAfWr4cHHgjhYPr00IVpOFauhF//Gn7/+zCYe/r0MFTvJS8JIaGcRvK+vvD+vb0hOVmwIJyYsK1N4aCeKQ7UDiUYNc7MmggB5muEQdn/N17vPSYJBoREYe1aWLo0tJu3tAxvfAaEBOWqq+CPfwzdpxob4cgj4fjj4dhjQ9JQzMBAiC7d3YPJxvbbh0hV7khEqSkKLDJRTcQ4MDAQEoWHHgotCtOnD398RF9f6Db1pz/BddeF3fns2SEEHH88HH54ebvz7GQjnQ6hYPvtQ7KhMRv1RXGgdijBqFExoLyMEFQWAr8HfuLuT49XGcYswUi4w7p1oc18/foQCYZbfeQeTlHyl7+EVo3nnguvP+SQ0G5+5JHhNCLF2r/7+0PLRnd3WG727MGWjalT1XZeJxRYZKKZDHGgvz+cXfyRR8KZn5qbQ/ep4e52t2wJZyq/7rpwDdbOzrD7PvTQEAaOOgp22aX0evv7QyN3Z2d43toako1Zs0K5yumKJdWjOFA7lGDUIDO7CDgA+DNwqbtX9CJI5RrzBCPb5s3honnLl4c9fEvL8A/u3UN12N//Dv/8ZxjzASFhOOKIEGUOOyxEi0LrdR+MLgMDIZrMmxdu06aF8SNKOGqSAotMJJMtDriHeqanngr1RBBCwEjOy9HdDbffHq7BetttIbRAGG9x+OFw8MFw4IHh3B+lulN1d4eQ0NcXytjWFk5MOGtWKF9zs0JCLVEcqB1KMGqQmQ0A7fFp9g9kgLt723iUY1wTjERfX4gyy5cPXjSvuTnsyYfbMXblynB+w1tvDVFm/fowfd680FH3oIPC/d57Fx7w3dcXokty0b/GxjBQfM6ckHCMpFwyJhRYZCKZzHGguzv0on3yybDbTqVC3U5LS/knIcz29NMhBNx2W0g8NmwI06dNC4lGctt7b5g5s3TZOjtDdyr3EBJmzQohobU1hAS1clSP4kDtUIIhBVUlwcjW2xsiwbPPhiqt/v4QXVpaht+SMDAQ2uDvuSfc7r03rBfCCMM99gjRZZ99wv0ee+QfedjXF6JLd3dYp1mo0po1K9ySsinpGHcKLCKVV+040NUVkoxnnoE1a8JuN50OB/JNTcNvPXAP9Vf33jt4e+yxwat+z5sHe+0VwsDee4fHO+xQOLHp6wtl7OoaDAlNTaGH7axZIemYMiXUk40kOZLhURyoHUowpKBqB5YhkkHZGzaElon16wcjQrL3Hu5pZ1euDNHlvvvCgPOlS0NHXggRbOHCkGjsumt4vNtu4byG2aMG3UOy0dUVEqLEtGkhwsycGZKO5uaRRUMpmwKLSOXVUhxIBmOvXx/qnDZtCrvgVCrsYkdat7NlSzi5YRIGli4Np9IdGAjzm5tDCEhuu+4abrnhILucSdLhHm7pdEg2pk8PoSEJW0o8KktxoHYowZCCaimwbCMZmL1lS6jWWrsWenoGo01TU9hzD+eMUO6hmmzp0jCWY+nScIaqZ54ZXCadDucz3HXXMGJwwQLYccfBcxxmMmE9PT0huiRlMguvnTYtRJi2tsEI09SkFo8KUGARqbxajgN9fSEEbNoUwsC6dWEahF1usnsdyYkBu7rCZZsefjiEgSeeCElHMj4Ewm57xx0L37JPjpjURXV3D3avyi5nW1u4tbaGMic3JR/DozhQO5RgSEG1HFjy6u4OpyFpbw+RZsOGwVOBZCcejY3hVm5rQmdn6Az8xBNh4PiyZeHxihWD0QwGT3ebRJcFC8KA8nnzwujCWbNClVhuhIHBiw+2toZbEhkbG3Xa3DIpsIhUXj3FgeQgvqMjJB7r1xcOAw0NYfc63AP4jo4QDpIw8OSToQ7q6adDopNt+vQQCubPHwwDyTlDttsuDBbPZEI4SMJCb+/Q0NTUNBgakkHljY2Dn0F1U0MpDtQOXcpYJo6kymfWrNB2DUPbqtvbQwTYtCm0eCTt32bhlhzMNzQM7W41ZUoYm7HPPkPfLzm/4tNPh2Qj+37JknD18GypVDijVRJdsqPNjBkhgrS1bduVKulwnH1LkqSkzKrmEpFJLmkNaG4OYWDnncP07DDQ0RF2zZs3h3qopJ4nST4ymcHdaiazbT1USwvsu2+45dq8Oez+s28rVoQxHv/612Cik13eWbMGQ8HcuSFEJEP6Zs8OSUpfXwhbufVSEMra0jL0lpQ/+3OIjDdtdjKxZTKDrQJz5gxOT7oxJW3WnZ0hAdm8Odx3dw8ul0SYdDqsL/s2f364LcpTYbJ5cxjnsXJlSERWrRp8/sQTcMstIdrlamgYjC6zZoVxHDNmhFtbW6jOStrSk4QkiTJTpgyebiWp4spkBhMnJSIiMslkh4Fs2WEg6dW6ZUsIAVu2hEQkGbidMBvcpWYyg2EBwq45X11U8l7t7YPhIDssrFoVBp7feee2rSCJlpahicfs2SE0tLYOntAwObv7tGmDZ9xKEqckNCRjVaZMCWEj+7MkN5FK0KYkk1Nyqo98Z4qCEFV6e0PU6ekJj5PrYyS3zZvDctlJSCKJPPPnh65SyfPc5bZsCZFm3brQqrJ2bXicPF+9OowFWbcutJjk09Q0OK4jSTymTRtMRJL7qVPD/ezZIdmaNWsw2uRGmOSmhEREJqhSYcB9MAwk90kXrOTs5e3tYXq+16bTgwlIOh0O6BcuhN13L1ym3t6hISDf4yefhLvuCglQoV7u2UP+kvCQnYxMmTLYIN7SMtgFa+rUkLgkZ+lKeutm11dlJ1bJY5FcSjBE8kk66haKPIn+/sGOs319g4+TM0t1dQ22kHR3548GSWKw666DESmVGnoPIRlZuzZ0LN64cdvbhg3h/sknw/2mTYPdwAp9xqRNPbcLVnJLkpOkBSWJVkmrSjI/6QycRBtFHBGpc0nP2VLXtRgYGNz9Z98nYSBpKM+9hkYhSeP4ggVDQ0HyOKmn6u8P9VxJCNi0aTAMZN82bRq8WvrGjYOXdSomNywkz5P7pBUkaTBPzpCVhLPkJIrTpw+2lCShIfemeqyJSQmGyGgke8jm5vKW7+8PkSfffXZrSTLir6cnVJX19oYoluy987WawOB4knQ6LNPVFaLL5s2D1W5btoT7pEtYcjauZFTk8uWDz4slKNmy292Tx7nVY0mrSnJLqsySvgvZj5P5ukyuiNS4VKq8RCThPrjrzw0D/f2DXbayQ0JPT1gmOTFhtqSeaP78wWlmg0lJ7i25nFN7e+FQkPt8y5ZwBq1kXvb5TUpJQkJ2mMh9npvAJKEg6QmcPJ4xI1wUUWqfEgyR8TSa2v0kKmXfBgaGPs5uRUmiV/bj7Fuh5CFJXtyHRqGkT0B2VVzSSpM8zu1CtmrVYNeyjo7C3bwKyT7l8FveAt/5zsi+OxGRGpGM4xjpeIfs3X5uGMgXCpL6q6TBHQZ7xra2Dh3oXk59jtnQFpp8t+yQkISOJJwk09auHRpasi8lVcj224dGel0tvfYpwRCpF6ONSrncQyRKolPyuNgtu8ott9oteZ4slx31kqtN9fYOdhdLbknSktuXIDtadXQMHaQvIjJJJS0RlTqDeTm7/uxbsYb4QqGgWJ1Wor9/23CQHRa6utSdqp4owRCZrJKuVOn02F9rI0lmcu9zpxVazr38bmgiIlK2JGEZD8V2/+Xcm+nSUPVCCYaIjL0kmRERkUkrSWQUDiY+NTaJiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVY17sai8yqZnZauDJapdjDMwB1lS7EGNkMn+2Xdx97ngVRmQyyIoDE3nfUi59B7X/HSgO1AglGDLpmNlid19U7XKMBX02ERkL+v/pOwB9B1I+dZESEREREZGKUYIhIiIiIiIVowRDJqPzql2AMaTPJiJjQf8/fQeg70DKpDEYIiIiIiJSMWrBEBERERGRilGCISIiIiIiFaMEQyYsMzvZzJaa2aNm9ok8899sZvfG27/M7KBqlHMkSn22rOUON7N+M/u38SzfSJXzuczsODO728weMLN/jncZRSaqMvaZZmbfifPvNbNDq1HOsTSR40a5Jmp8kfGlMRgyIZlZGngYOAlYAdwOvNHdH8xa5mhgibuvN7OXAue4+5FVKfAwlPPZspa7BugCfuLuvx7vsg5Hmb/ZDOBfwMnu/pSZzXP3VdUor8hEUub/7xTgA8ApwJHAt+thn1muiRw3yjVR44uMP7VgyER1BPCouz/u7j3ApcCp2Qu4+7/cfX18eguwYJzLOFIlP1v0AeA3QL0cgJfzud4E/NbdnwJQciFSMeX8/04FLvbgFmCGmc0f74KOoYkcN8o1UeOLjDMlGDJR7Qgsz3q+Ik4r5O3An8e0RJVT8rOZ2Y7Aq4AfjmO5Rquc32wvYKaZXWdmd5jZGeNWOpGJrZz/33D3q/VmIseNck3U+CLjLFPtAoiMEcszLW9/QDM7nhAoXjCmJaqccj7bt4CPu3u/Wb7Fa1I5nysDHAacCEwBbjazW9z94bEunMgEV87/r+z9ap2ayHGjXBM1vsg4U4IhE9UKYKes5wuAZ3IXMrPnARcAL3X3teNUttEq57MtAi6NO/85wClm1ufuV45LCUemnM+1Aljj7u1Au5ldDxxE6DMsIiNX7v+v5H61jk3kuFGuiRpfZJypi5RMVLcDe5rZrmbWCLwB+H32Ama2M/Bb4PQ6qwEv+dncfVd3X+juC4FfA++rg51/yc8F/A44xswyZtZCGGi6ZJzLKTIRlfP/+z1wRjyb1FHARnd/drwLOoYmctwo10SNLzLO1IIhE5K795nZ+4G/AmnCWS4eMLP3xPk/BD4DzAa+H2ti+tx9UbXKXK4yP1vdKedzufsSM/sLcC8wAFzg7vdXr9QiE0OZ+5WrCGeQehToAN5arfKOhYkcN8o1UeOLjD+dplZERERERCpGXaRERERERKRilGCIiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVowRDREREREQqRgmGjIqZuZn9LOt5xsxWm9kfx7kc+5jZ3WZ2l5ntnjNvupldbGaPxdvFZjY9zjuuUFnN7CozmzGCshxnZkcP8zXzk3KYWYuZ/dzM7jOz+83sRjNrLfH6LcMtZ87rrzOzpyzrsqxmduVo1zuK8syNp6MVkTqgWJD3dYoFo6RYUL+UYMhotQMHmNmU+Pwk4OkqlOM04Hfufoi7P5Yz78fA4+6+u7vvDjxBuAprUe5+irtvGEFZjgOGFVSADwPnx8f/Aax09wPd/QDg7UDvCMqRV7xIVr7//gbg+XGZGcD8Sr3ncLn7auBZM3t+tcogIsOiWLCt41AsGBXFgvqlBEMq4c/Ay+LjNwK/TGaY2RFm9q9Ym/QvM9s7Tt/fzG6LNU33mtmeZjbVzP5kZvfE2prX576RmR1sZrfE11xhZjPN7BTgQ8A7zOwfOcvvARwGfD5r8ueARVm1W21xXQ+a2Q+THa6ZLTOzOfHxW7LK+yMzS8fpJ5vZnbHM15rZQuA9wH/GZY8xs9fGz3OPmV1f4Dt8DZDU0swnKzC7+1J3747v9+G4rvvN7EN5vp/WWI47Y63XqXH6QjNbYmbfB+4EdspThksJV20FeDXharWl1pv3NzOzr8Tv814z+3qcNtfMfmNmt8dbEsBeGL+rpNZxWnzbK4E3F/i+RKT2KBYoFigWSODuuuk24huwBXge8GugGbibUGvzxzi/DcjExy8CfhMf/x/w5vi4EZhC2LGen7Xu6Xne717ghfHx54BvxcfnAP+VZ/lXAlfkmX5FnHcc0AXsRrhq6TXAv8VllgFzgH2BPwANcfr3gTOAucByYNc4fVa+sgD3ATvGxzPylGVX4I6s5wcDq4CbgS8Ae8bph8V1TQVagQeAQ5LfId5ngLb4eA7hirsGLCRc+fqoAr/jdcCR8ftNA1fH15Ra7za/GTALWMrghTxnxPtfAC+Ij3cGlsTHfwCeHx+3Zm0vOwL3VXsb10033UrfUCxQLFAs0C3rphYMGTV3v5ewA3ojcFXO7OnAr8zsfuCbwP5x+s3A2Wb2cWAXd+8k7DBfZGb/a2bHuPvG7BVZ6Cs7w93/GSddBBxbongG5Ltcffb029z9cXfvJ9S4vSBn2RMJO/Tbzezu+Hw34Cjgend/In4P6wqU4SbgQjN7J2GHnWs+sDp54u53x/V/jbCDvt3M9o3lusLd2919C6FW6Zg8n+tLZnYv8DfCjnm7OO9Jd7+lQBkB+oEbgdcDU9x9WRnrzfebbSIE6gvM7NVAR1zHi4Dvxu/w94Tawmnx+znXzD5I+H374vKrgB2KlFdEaohigWKBYoEklGBIpfwe+DpZTeLR54F/eOg/+gpCzRbu/gtCrVEn8FczO8HdH2awZubLZvaZCpTrAeAQy+pnGh8fBCyJk3KDTu5zAy5y94PjbW93P4fCAWvoytzfA3ya0BR9t5nNzlmkk/i9ZL1mi7v/1t3fB1wCnBLfr5Q3E2rTDnP3g4GVWetuL+P1lxJqFC8vZ735frMYFI4AfkPoD50096eA/5f1Pe7o7pvd/SvAOwg1l7eY2T5x+WbCdyMi9UOxoADFAsWCyUQJhlTKT4DPuft9OdOnM9iH9KxkopntRhhs9x1CQHqeme0AdLj7JYQAdWj2imKNyHozS2pqTgf+SRHu/ihwF2Gnnvg0cGecB3CEme0ag83rCTU32a4F/s3M5sWyzzKzXQg1by80s12T6XH5zUDSdxQz293db3X3zwBr2LbP68OEWr9k+eeb2cz4uBHYD3gSuB44zcKZRaYCrwJuyFnXdGCVu/ea2fHALsW+nzxuAL7MtgcHedeb7zezcJaT6e5+FaE/9MFxHVcD78/6nAfH+93d/T53/19gMZAElb2A+4dZfhGpLsUCxQLFAiFT7QLIxODuK4Bv55n1VeAiM/sw8Pes6a8H3mJmvcBzhD60hwNfM7MBwpky3ptnfWcCPzSzFuBx4K1lFO/twP+ZWdJX9OY4LXEz8BXgQMKO+4qhH80fNLNPA1fHwNML/Lu732Jm7wJ+G6evIpw55Q/Ar+Pgtw8QBvntGd/7WuAehr5Bu4VTJu4RA93uwA/MzAiVAH8i9Fd2M7sQuC2+9AJ3vyvns/4c+IOZLSb0gX6ojO9nyIclBIdchdZ7INv+ZtOA35lZc/zM/xmX/SDwvdi0niF81+8BPhQDVT/wIGGgKMDx8bOLSJ1QLFAsQLFAGBx4IyJZLJwZZBWwvbtX7LSARd7vVYQm50+XXHiSsHCWlVPdfX21yyIik5NiQfUpFtQntWCI5PcAoVZozAMKgLtfkac/7qRlZnOBcxVQRKTKFAuqSLGgfqkFQ0REREREKkaDvEVEREREpGKUYIiIiIiISMUowRARERERkYpRgiEiIiIiIhWjBENERERERCpGCYaIiIiIiFSMEgwREREREakYJRgiIiIiIlIxSjBERERERKRilGCIiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVowRDREREREQqRgmGiIiIiIhUjBIMERERERGpGCUYIiIiIiJSMUowRERERESkYpRgiIiIiIhIxSjBEBERERGRilGCISIiIiIiFaMEQ0REREREKkYJhoiIiIiIVIwSDBERERERqRglGCIiIiIiUjFKMEREREREpGKUYIiIiIiISMUowRARERERkYpRgiEiIiIiIhWjBENERERERCpGCYaIiIiIiFSMEgwREREREakYJRgiIiIiIlIxSjBERERERKRilGCIiIiIiEjFKMEQEREREZGKUYIhIiIiIiIVowRDREREREQqRgmGiIiIiIhUjBIMERERERGpGCUYIiIiIiJSMUowRERERESkYpRgiIiIiIhIxSjBEBERERGRihm3BMPM3Mz2iI9/aGb/PV7vPRmZ2ZvN7OoxWvd7zWylmW0xs9lj8R5Z73Wdmb1jLN9Dhs/MzjazC6pdjnplZsvM7EVjsN4/m9mZY7DeC83sC5Veb70ws+9nf34zO8bMllazTBPVWO5bzOwLZrbGzJ4bi/XnvNeY/MdldOrh+DMeW+1WgfWMSTwoV8kEYyz+JO7+Hnf/fCXXCWBmZ8VE5tyc6afF6RdW+j2LlMXNrD1uKFvMbMMYvtfC+H6ZZJq7/9zdXzwG79UAnAu82N1b3X1tBda5zMw64/e00sx+amatoy/tsMswbsEgHrC5mb0yZ/q34vSzxqssI+HuX3L3ESV++ZJGMzvOzFZkPd9aIRGf/5eZPWtm++dZ9joz6zKzzWa2yczuMLNPmFlT1jIzzOwnZvZcXO5hM/t41vxTzezu+Po1ZnatmS0s8hkWmdkfzWy9mW0wswfN7ItmNnMk38lImNk5ZnZJ9jR3f6m7XzSCdZmZfdDM7o/7rRVm9iszO7CC5R3yu1Vbzn5nvZn9ycx2yrPcu4Bud/90Ms3db3D3vStYlnPiNv/BnOkfitPPqdR7lShHEku2ZN3uGcP322abGM2+pcR77QR8BNjP3bev0Dqz4/zTZnaumaUrse5hlmGP0ktW7P2ui+95UM70K+P048arLCMxVsef+ZjZAjP7uZmtjdvJbWb28jLK2Oruj4/2/UcaDyplInaRegx4ffbBNnAG8HAVynJQ3FBa3X1GFd5/LGwHNAMPDPeF8SCm0Db3CndvBQ4FDgc+XWC5mjTCoPIwsLV2IW6zryVswxKZ2aeBDwEvdPdC29373X0aMJ9wEPEG4Cozszj/m0ArsC8wHXgl8XuOwfni+LrpwK7A94GBAuU5GrgOuAnYJ/63Twb6gIPyvaYOfBv4D+CDwCxgL+BK4GVVLNMQOfv0Skn2O/OBlcD/5S7g7ue5+39W6g2LfI4h+4OoWrFrRlbsqtdtOtcuwFp3XzXcF5bY9g6K29CJwJuAd46wfOOuREwu5mHCtpmsZzZwFLC6UmWrd2Y2C7gR6AH2B+YQ4tAvzOzfCrxmLPZxVTPiBMPMmmJt6zPx9q2cGsOPxhrHZ8zsbTmvHdLcbmYvj7WHG8zsX2b2vKx5uTWZpZrqnwPuA14Sl58FHA38PqcMv4q1mRvN7Hoz2z9r3imxRnJzrJX4rzh9Tqy13GBm68zshuH+OYt9nqQ2x8w+Ymar4vf31qxlp5jZN8zsyVjuG81sCnB9XGRDrEn5fxZac27Meu3RZnZ7fN3t8SApmXedmX3ezG6Kn/lqM5uTp+x7AUuz3uvvZa77i2Z2E9ABFG32c/engT8DB+R5/93N7O+xNmBNrBmYkTV/mYWa7ntjWS4zs+as+Xm3MzP7GbAz8If4/X0sTi+2jVxoZj8ws6vMrB34sIXWl0zWMq8xs7uLfNw/AM+3wVrvk4F7CdtwuZ/543Eb3WxmS83sxDj9CDNbbKFGfqVlteqZ2VHx828ws3ssq8YpbjePx/U9YWZvzldwy6o9t8FazzPN7KlYzk8V+dxli/+NdwDHunvJAy13b3f36wgJxP9j8AD5cOAX7r7e3Qfc/SF3/3WcdzDwhLtf68Fmd/+Nuz9V4G2+CvzU3b/s7ivj+z7l7v8T37vk75bzGY8ws5vj7/GsmX3XzBqz5u9vZtdY2OestNCF5GTgbEJlytZaZstpHTKzd5rZkvh7Pmhmh+Z5/z2Bfwfe6O5/d/dud++IraBfybP8kH1LnJbdBXab/aeZTSX8r3ewwZrxHcwsZaG16bH4XV1uYZ+dvV293cyeAv5uZs1mdklcdoOF/c12BX6nsrl7F/BrYL+sz9RkZl+P2/RKC10rpsR5ua1oyWdIvudX5XxfN5nZN81sHXBOgWLcDrQk+5l4PyVOT9Y100IMWm2h1eWPZrYg5722+f+a2R5m9k8L+7I1ZnbZcL4fy9NKnr2tJdtE/L7Wx/d+adaysyy0TD8T519ZZJsY0jJnZq80swfi732dme2bNa/oPj9ruRcB12S914VlrvvjZnYv0G4lDv7c/SHgBvLHrlL/cTez95jZI/H7+Z7Z1soRzOxtFv7H683sr2a2S5yexP574ud6fRnbSG5M/oiZ3ZFT3o+Y2ZVFPu7PCfuepGLtjcAVhIPpkp/Zgm9aOM7ZGH+/A+K8vMdfcV6xY8W8sTDPb1H2MVee1x5g4bhvU/zNkttpeRb/T2AL8HZ3f87dO939l8AXgW8kv298/b+b2SPAI1nTkv3pdDO7OP6eT5rZpy0ed1rp/132f3RU+4ARcfeiN2AZ8KI80z8H3ALMA+YC/wI+H+edTKgNOgCYCvwCcGCPOP9C4Avx8aHAKuBIIE2owVkGNMX5W1+X+9o8ZTqLkDG+CbgsTnsf8CPgC8CFWcu+DZgGNAHfAu7OmvcscEx8PBM4ND7+MvBDoCHejgGsQFmGlLvQ9Jzv4jhCLejn4vpPIewAZsb53yPUnO4Yv6ujY/kXxvVmcr+L+HgWsB44HcgQdgbrgdlx/nWE2ty9CAHtOuArBT7XkPcqc91PETL4DNBQbBsDdiK0jnw+6/XviI/3AE6Kn3kuIbH6Vs56bgN2iOVaArynzO1saxnK3EYuBDYCzyck6s3Ag8BLs5a5AvhIge/xQsI2eR7w3jjt8vj93QicVeozA3sDy4Edsn6b3ePjm4HT4+NW4Kj4eEdgLWHbSsV1r43rngpsAvaOy84H9i9Q/nOAS3K2ifMJ289BQDewb4HXbv1Ns6YdB6zI+Z/8mrDD3bnEstusL06/Hvjf+PgCwnb1VmDPnOV2A7oItUvHA61F9odTgX7guBL7zXK21WSbP4xQ+5eJ3+US4ENx3jTC/ugjcRubBhyZ+xvk+y4IrWFPE5Iri2XaJU9Z3wM8WeLzXMjgfuos4r4l336NwvvPIb9bnPYhQhxZEL+rHwG/zNmuLo7f+xTg3YTEvIXwPz4MaCtW9iKfKfs3aAEuAi7Omv8tQsXUrPi9/wH4coFt8LWE/U4KeD3QDszP+r76gA/E33hKof8TIWlMttmvAp+M08+J02YDr4nlnQb8Crgya9vM+/8Ffgl8isF91QsKfCfJd54pNZ2h29pZQC+h9j4NvBd4hhgfgT8Bl8XtoYHQGllomziHwX3LXvG7PCm+7mPAo0Bj1m+Yd5+f57Pl/mblrPtuQkza5jfLs93vR6gcevtw/uNZ6/kjMINQ2bUaODnOOy2Wa9/4+k8D/8pXhlLbSNbvlh2Tm4B1ZO2vgbuA1xT4zNcRKn2uJsa7+Bv8P2AFcd9Y7DMTKoDviJ/X4mdL/i+F9h8FYzhFYmGJfdlxFDnmynmdAfcA34nveQDhOPc/gel5lr8F+Gye6bvG3yz5nzoh+Z1F3M4Yul1dDPwu/pYLCa1HyTZ2FsX/d9cx+B8tax9Qydtouki9Gficu69y99XAZwkHmQCvI9Tw3e/u7RSusSF+MT9y91vdvd9Df7FuwoY5UlcAx5nZdEIz3sW5C7j7TzzUVHbH8h0Ul4fwg+1nZm0eajzvzJo+nxCkez30w/Ui5bgzZtobzOw7ZZa9l/C99rr7VYQMeO+Ysb4N+A93fzp+V/+K5S/lZcAj7v4zd+/zkEU/BLwia5mfuvvD7t5JONA9uMzylrPuC939gTi/t8B6rrQwTuVG4J/Al3IXcPdH3f0aDzWsqwljQV6Ys9h33P0Zd19HOCBIPsewt7MS2wjA79z9Jg814l2EA5S3wNaWs5cQkutiLgbOiOt9IaFbSrmfuZ+wo9vPzBrcfZm7J92reoE9zGyOu29x91vi9LcAV7n7VbHc1wCLCTtWCN2CDjCzKe7+rBfukpTPZz3U0txD2BEfNIzX5vNi4C9euCWhlGcIO20IB3c/B94PPGhmjyY1PR76uh5HSL4uB9bEWq5844BmEnbQ2a1MX43/8XYL3bnK3VaJy97h7rfE/8cywkF2suzLgefc/Rvu3hW3x1vL/PzvAL7q7rd78Ki7P5lnudmEoF4phfaf+bwb+JS7r8j6n/2bDa0tPsdDy1RnXPdsQvDtj9/dplGUNdnvbCIcaH4NQg0rYZ/xn+6+zt03E/ZJb8i3Enf/VdzvDLj7ZYTE+IisRZ5x9/+Lv3FnkfJcArzRwli3N8Tn2e+z1kPrWkcs0xcZul0V+v/2EroI7RC3oyEtUHmsyYpd/1Vi2cST7n6+u/cT9oXzge3MbD7wUsKB//oY2/5Z5jpfD/wp/pd6ga8TEs2js5YptM+v1LqXl/jN7jSz9fG9LwB+mrtAif944ivuviHu7/6R9TneTUhsl7h7H2E7PDhpxcjzXqW2ERgak7sJyV8Su/YnHMj+schnhsHYtTehS93Nw/jMvYQD5n0IB8NL3P3ZrHn59h/FYnixWFhK3mOuPMstJCRlZ8f9+v3Aj4GD3X1jnuXnkH+/+mzW/MSX435myHYWW4heD3wy7vuXAd9g8FgbCvzvCnzO4ewDRm00CcYOQHawejJOS+Ytz5lXyC6EJrpkZ7aBUGOwQ5HXFBV/pD8RMv057n5T9nwzS5vZVyw0aW8iZMEw+IO/hnDA9WRsUvp/cfrXCDUJV1tohv5EiaIc6u4z4u2DJZZNrI07kUQHoQZ6DiHrHEn//Nzfivh8x6zn2WfVSN6zUuteTmmnxe9pF3d/X74dupnNM7NLYzPoJkLwze3KVehzDGs7K2Mbyfe5LgFeEQ9MXwfckLXTzCv+yecSttU/5tnBFPzM7v4ooQb4HGBVXC75PG8n1NA9ZKEbSTKwbBfgtTnfwwsItUfthJ3Ze4BnLQx63adY+XOUuw31EWqLsjUQdoDZ3kA42PzsMMqQbUdCzRwx8fmSux9GOEC9HPhVTASJgfB17j6X0DJ5LKG2J9d6wkHc/GSCu3/MwziMKwi1deVuq8Rl97LQjeG5uOyXspbdiZGPySn3tWuzP08FFNp/5rMLcEXWtriEcLCQHSCz/2c/A/4KXGqhu81X48H4SJ0Wf7smQvL5TzPbnvCfbAHuyCrbX+L0bZjZGTbYdWMDoXaz2L4ir3hw+ShhG3jE3Ye8zsxazOxHsavEJkLL2AwzS5f4/36MUAN7m4UuQUO6LecxJyt2fb2cspP1/3f3jviwlbAdrnP39WWuJ9uQ+OLuA4TvsuKxq8C6y/ndDnX3me6+u7t/Oq5niBL/8VKfYxfg21nb1jrCb7kjeRTbRop8rouAN8XE+nTgci9deflb4ARC5c3PhvOZ3f3vwHcJvTJWmtl5ZtYWX1po/1EwhpeIhaUUOubKNQ9Y7+5bsqZlH/fmWkP+/er8rPmJQtvZHKCRbY+1827/Of+7XMPdB4zaaBKMZwg/eGLnOA1ChrZTzrxClgNfzNqZzXD3Fg814RB+7Jas5cs9+0MyaHObDZ/QhepU4EWEQZ0L43QD8FDjdyphg7qScDBCzCA/4u67EWroP2wF+vkVMdLPs4bQjWP3PPOKtaLAtr8VhN/k6TLfe7TrLlW+cn05rut57t5GqHGx4i/ZqtR2llvGottIvtd4GD9yM/Aqwk4637aXzyWEbXWbljZKfGZ3/4W7v4DwGzjwv3H6I+7+RsI2/L/Ary30eV4O/Czne5jqsa+9u//V3U8i7AQfInR7qrSnGPw+E7uybaL6MOH7f18ZyfwQFs4YcxihT/QQHmq8v0ToUrJrnvm3E4LnNn2p40HcrcCrSxRhONvqDwjf9Z5x2bOzll1O/v88lP5fFXtttmuBBWa2qIxlIXQr2boPiwfkg4UqsP8sUN7lhG4W2dtjc/wvkfu6WMv4WXffj1DT/HKyBpuOVKwR/S0huXkBYX/bSehilJRruofBvEPEmuTzCQnK7Jiw3E+RfUUJSezKtz/4CKF29ci4rRybFCN+jrz/Xw99wN/p7jsQasS/b8M781B7vB9J7FoOzLL8Y5CGFbviAfBOjEHsKrDuSsWuYv/xUpYD7875j0xx938VWL7oNhLlxq5bCOMnjiHEvpKxKx7M/pnQLSff8kU/s7t/J1b47E+oDPtonF5o/1E0hheKhRW0grAdt2VN2zVOz+dvwGts23G6ryN8luwxhYW2szUMtjwkRnTsVoF9wLCVm2A0WBhcl9wyhP5cnzazuRYGBH+Gwebcy4GzzGw/M2sB/qfIus8H3mNmR1ow1cxeZmbT4vy7CZl12sLAxrzdDPL4J6HJe5uzghCa5roJNXctZHXHMbNGC9eQmO6h2XQTIegkA4z2iDuiZHp/meVJjOjzxFqRnwDnWhgIl7YwmLuJ0F9zgMIDqK8C9jKzN5lZxsxeT+gvWqoJtBxjue5c0wjNlxvMbEfiDqlMpbazlQz9/gpuIyVcTKgpOJBQo12O7xC21evzzCv4mc1sbzM7IW4DXYQDomRbfYuZzY3bzYb4kn4GW1leErehZgsD3RaY2XYWBj1OjZ99C8PfvstxGfBWC4MAzcLJA/4TuDR3QQ9dPF4EfNTMPlRqxbH27oWEPqu3EbZPzOy/zezw+P9uJpwxaQOw1MxeYGEw9Ly47D6EQeK35H2T8Pu+zcLA3uQ1CxiarAxnW51G2J9sie/93qx5fwS2t3C60iYzm2ZmR8Z5K4GFeQJY4gLgv8zssPg972F5ulW4+yOEs2b9Mm4LjXG7eEOBxO4eYH8zOzh+l+ckM4rtP2N5Z9vQboY/BL5og4NW55rZqYW+KDM73swOtFAbu4kQfEe9jcbv51RCF7gl8X9zPvDNrN94RzN7SZ6XTyUcIKyOy72VPMnpMFxG6B54eZ550wj/8w0WWt+2xtZi/18ze60NDvRdH8tb9vfmoZvf08Bb4n7jbZSXvBJbcf9MOKCZaWYNZpYc9ObbJrJdDrzMzE600FL1kfjZCh1cD8dYrjtXsf94KT8EPmmDg/+nm9lrs+bni115t5ESLia0KvR5+d1nziaMp1mWZ17Bzxz3xUfG772dEL/6S+w/CsbwYrGwUmKlxz+B/437x4MIPQV+XuAl3wTagB+b2fbxNW8ktIx/1L1o9/rkPfsJ2+kX4+fcBfgwOV0nyzHafcBIlJtgXEX4wZLbOYQBqosJZ725D7gzTsPd/0wYIPd3QnPv3wut2N0XE/rWfZfwoR8lDFxJ/AehtWADYdzHleUU2INrPfTLzHUxobb0acLA3NwDidOBZRaa9d5D7JsI7EnISrcQaqq/7/GsMcMwos8T/Rfhu76d0Ez6v0Aq1iR8EbjJQtPhkHEFHq5V8XLCDnQt4QDp5e6e3UQ3ImO57jw+SxjotZHQBe63wyhnqe3sy4SEOelzXGobKeQKYrePWNtdTtnWxW013w6n2GduAr5CqOV4jlDjc3acdzLwgJltIZyC9A0e+l0uJ7TMnE04IFpOOPhNxdtHCDV76wjJ7/vK/Nxlc/e/Ap8g9FXeSNi/XEQY8J5v+XsI41n+x8zeU2C13zWzzYRg+y3gN4RBkkl3BY/vt4bw+U4CXuahuXsDIaG4L35ffyH8jl8tUJ4bCV0DjgUetsHuM9cxWKExnG31vwi1hpsJQXTr2T089KE+ibDPeI7Qt//4OPtX8X6tmW0zzsHdf0XYL/wirvtKBsek5Pogg10WNhC6Vr2K0K88d70PEwZF/i2WJ/dgJO/+08NZdn4JPB7/ZzsQts3fE7qdbib8z46ksO0Jg/83EbpT/ZMRBNssf4i/+SbCd3WmD45b+DhhP3FL/Cx/I0/fbHd/kNAv+mbC9ncg4RTGI+KhO9/fPH+//28RxgmsIXxXf8maV+z/ezhwa/ysvyeM5XtimEV7J2FfsZZQ6zycA/HTCcngQ4SBuh+CgtvEVu6+lLD9/B/hM7+CcGrhHkZpLNedR8H/eBnlvIIQ6y+N2+H9hDEtiXOAi+L39zqKbyPF/IyQGJfb8o6HsS+FkpFin7ktTltPiLNrCWNgoPD+o1gMLxYLK+nNhC5RzxD26Z9297/lWzAeG72AwRPArCUkB6d7GKdVrg8QkrDHCfvaXxAqm4erEvuAYUlGmotIhZjZY4Qm7bw7HhGpb2Z2AnCBh+6yInXPwimYVxHGlDxS7fJI/RvNGAwRyWFmryHUlhdstRORuncAMKa1fyLj7L3A7UoupFKKXjhGRMpnZtcRxp+c7nnOJCIi9c/Mvk3oUndmtcsiUglmtowwAPu06pZEJhJ1kRIREZlgzOw/CddCccLYvbcSTlhxGeEsbsuA1/nITh8rIlKUukiJiIhMIPHMZR8EFrn7AYSr/L6BcHKFa919T8LpiYd1+mcRkXKpi5QUNGfOHF+4cGG1iyFSljvuuGONhwvliUiI71PMrJfQcvEM8EnCVeshnLntOsLZsgpSHJB6ojhQO5RgSEELFy5k8eLF1S6GSFnMLPdCfSKTkrs/bWZfJ1zUshO42t2vNrPt4nUpcPdnk2t85DKzdwHvAth5550VB6RuKA7UDnWREhERmUDMbCbheje7Es7bP9XM3lL8VYPc/Tx3X+Tui+bOVWWwiAyfEowJxsx2MrN/mNkSM3vAzP4jTj/HzJ42s7vj7ZRql1VERMbEi4An3H11vCLyb4GjgZVmNh8g3q+qYhlFZAJTF6mJpw/4iLvfaWbTgDvM7Jo475vu/vUirxURkfr3FHCUmbUQukidCCwmXBH4TMJVj88Efle1EorIhKYEY4KJ/WuTPrabzWwJsGOl32fjRlixAvbfv9Jrnth6e3tZsWIFXV1d1S5K3WpubmbBggU0NDRUuygiNcndbzWzXwN3Eiqd7gLOA1qBy83s7YQk5LWjfa/FmzZxyLRppM1Gu6pJQ3Fg9BQHap8SjAnMzBYChwC3As8H3m9mZxBqsj6S7/znuYP7CtmyBTo6xqDQE9yKFSuYNm0aCxcuxBSQh83dWbt2LStWrGDXXXetdnFEapa7/w/wPzmTuwmtGRXTPTBAR38/0zI6nCiX4sDoKA7UB43BmKDMrBX4DfAhd98E/ADYHTiY0MLxjXyvK3dwXyoFA7pW9bB1dXUxe/ZsBZURMjNmz56tmj+RGtE1MEB7f3+1i1FXFAdGR3GgPijBmIDMrIGQXPzc3X8L4O4r3b3f3QeA84EjRvMeqRToIvAjo6AyOvr+RGpHnzvr+vqqXYy6o/3Y6Oj7q31KMCYYC/+6HwNL3P3crOnzsxZ7FXD/aN5HLRgiIgKwtre32kUQkRqjTpMTz/OB04H7zOzuOO1s4I1mdjDgwDLg3aN5k3RaLRiVcMstsGFD5dY3YwYcdVTl1jda73jHO/jwhz/MfvvtV+2iiMgYMGBzfz+9AwM0pFRnORK3bNzIhgq2As3IZDhq+vSKrW+0FAcmJyUYE4y730jY5+e6qpLvk0qBut2O3oYNUMnrWK1eXZn19Pf3k06nCz7Px91xd1JZBxkXXHBBZQokIjXLgC39/cxUgjEiG/r6mNvYWLH1re7pqch6FAdkNLQ3kBExCy0YasWoP5dccglHHHEEBx98MO9+97vpj5lia2srn/nMZzjyyCO5+eabt3l+7rnncsABB3DAAQfwrW99C4Bly5ax77778r73vY9DDz2U5cuXD3mv4447jsWLF29d/6c+9SkOOuggjjrqKFauXLlN2W677TaOPvpoDjnkEI4++miWLl06tl+GiIyaAZtU41RXFAdkrCnBkBExC2MwNA6jvixZsoTLLruMm266ibvvvpt0Os3Pf/5zANrb2znggAO49dZbecELXjDk+ZQpU/jpT3/Krbfeyi233ML555/PXXfdBcDSpUs544wzuOuuu9hll10Kvnd7eztHHXUU99xzD8ceeyznn3/+Nsvss88+XH/99dx111187nOf4+yzzx6bL0JEKmZKOs2qCtWay9hTHJDxoC5SMmJJglGixVRqyLXXXssdd9zB4YcfDkBnZyfz5s0DIJ1O85rXvGbrstnPb7zxRl71qlcxdepUAF796ldzww038MpXvpJddtmFo8oY+NHY2MjLX/5yAA477DCuueaabZbZuHEjZ555Jo888ghmRq8Gj4rUvCmpFOv7+hhwJ6Wz+9Q8xQEZD0owZMQGBsI4DF1Is364O2eeeSZf/vKXt5nX3Nw8pH9t9nMv0hcuCTalNDQ0bD21YDqdpi/PoMb//u//5vjjj+eKK65g2bJlHHfccWWtW0SqJwX0u7Olv582XXCv5ikOyHhQFykZsSTBkPpx4okn8utf/5pVq1YBsG7dOp588smSrzv22GO58sor6ejooL29nSuuuIJjjjmm4uXbuHEjO+64IwAXXnhhxdcvImMjBWzU9TDqguKAjAdVNciIaQzG6M2YUbkzPyXrK2a//fbjC1/4Ai9+8YsZGBigoaGB733ve0X7zAIceuihnHXWWRxxRLg+4zve8Q4OOeQQli1bVpmCRx/72Mc488wzOffccznhhBMqum4RGTvJOIydmpurXZS6MyOTqdiZn5L1FaM4IOPBijV5yeS2aNEiT878kGvTJrjhBnj+80sf1MqgJUuWsO+++1a7GHUv3/doZne4+6IqFUlkQioWBwCuXruWWQ0NOLCur48XzZxJWuMwilIcqAzFgdqmLlIyYv396iIlIiKQMmPAnc3qJiUiKMGQUVAXKRERSaTMKnpFahGpX0owZMQ0yHtk1C1xdPT9idSm1nSap7u7q12MuqD92Ojo+6t9SjBkxNxBlVXD09zczNq1a7VzHCF3Z+3atTRrIKlIzWlOpdjc30+3mraLUhwYHcWB+qCzSMmo6OKtw7NgwQJWrFjB6kqeOmqSaW5uZsGCBdUuhojk4cCmvj7mNjZWuyg1S3Fg9BQHap8SjBpnZjOBHYBOYJm711TVkBKM4WloaGDXXXetdjFEpI7UehzI1pxK8WxPjxKMIhQHZDJQglGDzGw68O/AG4FGYDXQDGxnZrcA33f3f1SxiFspwRARqbx6igPZpqbTrOzpYcCdlE5XKzJpKcGoTb8GLgaOcfcN2TPM7DDgdDPbzd1/nPtCM9spvnZ7YAA4z92/bWazgMuAhcAy4HXuvn40hTRTgiEiMkZGHAeqKW1Gnzub+vqY0dBQ7eKISJUowahB7n5SkXl3AHcUeXkf8BF3v9PMpgF3mNk1wFnAte7+FTP7BPAJ4OOjKWcmowRDRGQsjDIOYGYzgAuAAwhDI94GLKXCFU35ZMxY1durBENkEtNZpGqYmW2zdzazOcVe4+7Puvud8fFmYAmwI3AqcFFc7CLgtNGWL5VSgiEiMlbMLGVmqfi40cwOja3R5fg28Bd33wc4iBALPkGoaNoTuDY+r7hp8XS1OkuSyOSlBKMGmdnxZrYCeMbMrjazhVmzrx7GehYChwC3Atu5+7MQkhBgXoHXvMvMFpvZ4lJnuEinlWCIiIwFMzsNeBZ42sxOBW4Avg7ca2avKPHaNuBY4McA7t4Tu1lVvKIpn4ZUiq6BATbrQkkik5YSjNr0VeAl7j4XOA+4xsyOivPKGjVnZq3Ab4APufumct/Y3c9z90Xuvmju3Lkl3kNX8xYRGSP/Q2h5OBr4GXCGu58APD/OK2Y3wqDwn5rZXWZ2gZlNZQwqmgpJm7FKNVAik5YSjNrU6O4PALj7rwm1TBeZ2asIfWmLil2rfgP83N1/GyevNLP5cf58YFUlCmqmi+2JiIwFd3/O3Z8AnnL3pXHak5SO3RngUOAH7n4I0M4wukMNp6KpkLZ0muXqJiUyaSnBqE29ZrZ98iQmGycSaq32LPZCMzNCs/gSdz83a9bvgTPj4zOB31WioLqat4jI2EjGXxAGaCfT0oTT1hazAljh7rfG578mJBxjUtGUT0MqRVd/PxsVIEQmJSUYtekTwHbZE9x9BfBC4CslXvt84HTgBDO7O95Oia87ycweAU4qYz1lU/wQEam4dxETCXe/LWv6TpTYf7v7c8ByM9s7TjoReJAxqmgqpCFedE9EJh+dprYGufvfCkzfCHyxxGtvpPA4jRNHWbS8lGCIiFSWu99eYPoywilmS/kA8HMzawQeB95KqFS83MzeDjwFvLYihS2gLZNheXc3e7W0kNZF90QmFSUYdcbMznH3c6pdjmxKMERExk85ccDd7wYW5Zk1JhVN+aTN6HdnXW8vcxtL9eoSkYlEXaTqT9GLK403M+jtrXYpREQmlZqKA8W0pNM82dVV7WKIyDhTglFn3P0P1S5DtnQaFDtERMZPrcWBYqamUqzu7aVD18QQmVTURapGmdlLCKen3ZFwatpngN+5+1+qWa5cSjBERMZGvcSBYsyMlBnPdXezW0tLtYsjIuNECUYNMrNvAXsBFxNONwiwAPigmb3U3f+jWmXLlclAZ2e1SyEiMrHUUxwoZUYmw+NdXewyZYoGe4tMEkowatMp7r5X7kQzuwx4GKiZwJLJqAVDRGQM1E0cKCVjRq87a3p62K6pqdrFEZFxoDEYtanLzI7IM/1woKYO55VgiIiMibqJA+VoTad5rLNTV/YWmSTUglGbzgJ+YGbTGGwa3wnYFOfVjFQqnEWqvz+MxxARkYo4izqJA+VoSadZ1dPDxr4+ZjQ0VLs4IjLGlGDUIHe/EzjSzLYnDO4zYEW8OmvNSU5VqwRDRKQy6i0OlKM5leLxri4OVYIhMuEpwahhMZDURTDp6YHm5mqXQkRkYqmnOFDKtHSalT09bO7rY1pGhx8iE5nGYEhF6GJ7IiJSjJnRmEqxTAP3RCY8JRhSET091S6BiIjUuunpNCu6u9nS11ftoojIGFKCIaOWyUB7e7VLISIitS5pxXhMF1ASmdCUYNQ4M7u52PNa0NCgBENEZKzUQxwYjunpNE93d7NJrRgiE5YSjNqXO3S66FBqM/uJma0ys/uzpp1jZk+b2d3xdkolC6gEQ0RkTA0rDtQ6M2NKOs3DHR3VLoqIjBGdxqFGmdmx8eHU5LG7Xw+UukrRhcB3gYtzpn/T3b9e0UJGDQ2wfv1YrFlEZPIaRRyoeW2ZDCu7u1nb28tsnbZWZMJRglG73hrvZ8fHDlxPOBd6Qe5+vZktHNuiDZVKwcBAGOjd2Die7ywiMqGNKA7Ui7ZMhgfa23nB9OmkbEJ8JBGJlGDUKHd/K4CZ3Zk8TmaNcJXvN7MzgMXAR9w9b5uDmb0LeBfAzjvvPKw36O5WgiEiUiljEAdqypR4de8V3d3srAspiUwoGoNR+3KrdUZSzfMDYHfgYOBZ4BuFFnT389x9kbsvmjt37rDepLt7BCUTEZFSKhEHatKshgaWtLfT2d9f7aKISAUpwah9Hy/xvCR3X+nu/e4+AJwPHFGRkmUxA43XExEZE6OOA7UqY0bajKUKICITihKMGufuVxd7Xg4zm5/19FXA/YWWHammJti4sdJrFRGRSsSBWjazoYFnurtZqWZwkQlDYzAmGDP7JXAcMMfMVgD/AxxnZgcT+u0uA95d6fdtbIRNmyq9VhERmQxmNDRwX3s7MxoaaEqp7lOk3inBmGDc/Y15Jv94rN+3sRHWrg1nk1JsEBGpPjNLE07s8bS7v9zMZgGXAQsJlU2vK3TCj/HWlErR0d/Pg+3tHNzaiumsUiJ1TYeCUhFmIbno6qp2SUREJPoPYEnW808A17r7nsC18XnNmNnQwLM9PTytrlIidU8JRg0yszk5z99iZt8xs3dZDVfrmEFnZ7VLISJS/8zsXDN7/ihevwB4GXBB1uRTgYvi44uA00ZcwDEyO3aV2tzXV+2iiMgoKMGoTVsH8JnZp4HTgTuAk4Bzq1WoUsxgy5Zql0JEZEI4Hfi2mT1pZl81s0OG+fpvAR8DBrKmbefuzwLE+3n5Xhgrsxab2eLVq1ePoOgjlzGjJZ3mri1b6B0YKP0CEalJSjBqU3YrxauBV7v7RcCbgBdVp0ilNTfDunXVLoWIyISwwt0XEfb5m4FLzOwhM/sfM9ur2AvN7OXAKne/YyRvPJrrIVVCazpN58AA97e34z4hrikoMukowahNU8zsEDM7DEi7ezuAu/cCNXs1ouZmWF8TwwVFROqeA7j7I+7+eXffH3gd0AxcVeK1zwdeaWbLgEuBE8zsEmBlctryeL9qrAo/WnMaGniup4dH1e9WpC4pwahNzxK6Qn0dWJcVEGYDNdsxNZOBnh4N9BYRqYBtxtu5+73u/kl336PYC+MyC9x9IfAG4O/u/hbg98CZcbEzgd9VuMwVNaehgYc7OnhGg75F6o5OU1uD3P34ArM2AMeOY1FGpL09tGaIiMiIHTMG6/wKcLmZvR14CnjtGLxHxaTMmNPYyN1bttAYH4tIfVCCUUfcvR/oqHY5ikmnQzep2bOrXRIRkfrl7hU5ZYa7XwdcFx+vBU6sxHrHS8aMGZkMd2zezFHTpzM9o8MWkXqgLlJ1xszurHYZimlpgVU126tXRKT+1XocqLSmVIqp6TS3bdqk09eK1AklGHXG3Q+tdhmKaWqCjRuht7faJRERmZhqPQ6MhSnpNE2pFLcoyRCpC0owapyZzTKzmdUuR7mSywBu3lzdcoiITBT1FgfGytSsJGOTkgyRmqYEowaZ2c5mdqmZrQZuBW43s1Vx2sIqF6+khgZYs6bapRARqV/1HgfGytR0muZUips3bmS9mspFapYSjNp0GXAFsL277xlPSTgfuJJwTvOa1toKzzxT7VKIiNS1uo4DY6klnaY1k+HmTZtYqVPYitQkJRi1aY67XxbPGgWEM0i5+6VAzZ+fqaEBOjvD6WpFRGRE6joOjLXmVIpZmQyLN2/miY4OXfFbpMYowahNd5jZ983sSDPbId6ONLPvA3cVe6GZ/SQ2o9+fNW2WmV1jZo/E+zHvy5tKwdq1Y/0uIiIT1ojjwGTRkEoxt7GRBzs6eKC9nb6BgWoXSUQiJRi16QzgPuCzwF+Bq+Pj+4HTS7z2QuDknGmfAK519z2Ba+PzEfMBp3tZJ6zvKbhMays89dRo3kVEZFIbTRyYNNJmbNfYyNPd3dy6aRMd/f2lXyQiY05XrKlB7t4D/CDehvva6/MMADwVOC4+vohw0aWPj7R8fZv6eOCgW0m/dTfYa+e8yzQ3h+thbNkSkg0RESnfaOLAZGPxKt+b+vq4ccMGDm5tZV5TU7WLJTKpqQWjTozywkrbufuzAPF+XpH3eZeZLTazxatXr867TMOMBhrmN5JaXnyQRToNK1eOotQiIrLVZLvA3nC1ZTJMy2S4ffNmHlSXKZGqUoJRP2w83sTdz3P3Re6+aO7cuQWXa957KvZUR9F1TZ8Oy5aB9vEiIhUxLnGgnjWmUmzX2Mjyri5u2riRDTqVrUhVKMGoH38axWtXmtl8gHi/arSFad6nBXuqveiZOzIZ6OnRYG8RkQoZTRyYNJIuUykzbtq4kYfa2+lVTZfIuFKCUYPMbJtaKnf/dKllivg9cGZ8fCbwu5GXLmjeeyrWNYCvLH4O8qlT4bHHRvtuIiKTyxjEgUmnJZ1mXmMjT3Z1cePGjazq7tbpbEXGiRKM2vQPM/uAmQ0ZQW1mjWZ2gpldxGDCQM4yvwRuBvY2sxVm9nbgK8BJZvYIcFJ8PipT9m0BYODhLUWXmzoV1q+HjRtH+44iIpPKiOOADErF1ozGVIrbN2/mjs2b2dLXV+1iiUx4OotUbToZeBvwSzPbFdgATCEkhFcD33T3u/O90N3fWGCdJ1aygC0HTcNT0H/fJjLHzim6bHMzPPIILFpUyRKIiExoI44Dsq3mVIrtm5rY2NfHDRs3smtzM7tOmUJTSvWsImNBCUYNcvcu4PvA982sAZgDdLr7hqoWLEuqJY3v1kr//ZtKLtvWFs4mtWEDzJgx5kUTEal79RAH6tH0TIYBd57q7uapri72mDKFnZqbaVCiIVJR+kfVOHfvdfdnazGoDOzbRv8Dm/G+0oPnpk6FJUtA3V9FRIanluNAPUqZMbuhgekNDTzc2ck/NmxgWWenBoKLVJASjBpmZudUuwzFDBw4Azr6y2rFaG0NYzGee27syyUiMlHUehyoZxkz5jY2Mj2TYWlHB//YsIHHOjro0tXARUZNCUYNMrOUmf0YqOlLkQ4cMhPS0H/TurKWnzkT7r8fuoufeEpEZNKrlzgwEWTiQPDpmQyPdXZy3YYNPLhlC5s1GFxkxJRg1KY/AOvc/ZPVLkhRrQ2kD5pO303lXeiisRHM1FVKRKQM9REHJpCMGbMbG5nd0MAzPT3cuGEDt23axOqeHvoVtESGRQlGbVoEXFHtQpQjc+wcBh5up39Ze1nLz5wJTz8dbiIiUtCI44CZ7WRm/zCzJWb2gJn9R5w+y8yuMbNH4v3M0RRwU18fXRNw3ELKjJkNDcxraqJzYIDFmzZxXew+1a7uUyJlUYJRm44HfmRmR1a7IKVkTp4Haej748qyXzNnDtx7r66NISJSxGjiQB/wEXffFzgK+Hcz2w/4BHCtu+8JXBufj4i784YHH+Rjjz/O2t7eka6m5rWm08xraqI1nebRri6u37CBWzZu5NnubnomYHIlUilKMGqQuz8IvAT4WrXLUkpqThPpo2fT+6fnyjqbFEAmE05du3gxdHaOcQFFROrQaOJAPOPUnfHxZmAJsCNwKnBRXOwi4LSRls/MeNf8+Szr6uKtS5fy+ATfmWfMmNPQwLzGRnrduXvLFv6+fj13b97M6p4e+pRsiAyhBKNGufszwMuqXY5yNL5qPr66h76rV5X9milTIJUKSYYGfYuIbKsSccDMFgKHALcC27n7s3HdzwLzCrzmXWa22MwWr169uuC6T5s7l2/svjs9AwO89aGH+Mu68k74Ue9a0mnmxbEaG/r6WLx5M9dmJRs63a2IEoyaFmueal76BbNJ7T6Vnp8+hQ+UPxCurQ16epRkiIgUMpo4YGatwG+AD7l76fOJD77nee6+yN0XzZ07t+iye06ZwoX77MMeU6bw6See4PPLltExScYppMyYlskwr7GRWTnJxuJNm3imq2vSfBciuXQl7xpkZr8vNt/dXzleZSmHpYzGt+9C19kP0vv752g8bX7Zr50xI1zh+9ZbYdEiaGkZs2KKiNSN0caBePXv3wA/d/ffxskrzWy+uz9rZvOB8pudi9i+sZEf7b03P3rmGS587jlu3byZT+y8My+YPr0Sq68LSbIxjTA+pWNggHvb23GgJZVih6YmZjc00JZOk9FVw2USUIJRm/4fsBz4JaFZ26pbnNIyJ80lffl0ev7vMRqOm4PNaCj7tTNmwKZNcPPNIcmYRDFJRKSQEccBMzPgx8ASdz83a9bvgTOBr8T731WqsBkz/n3HHXn+9Ol86ckn+dCjj/KimTP5wI47smPT5LqUh5kxNZ1majoNQM/AAMu6uni0s5MUMLOhge3jdTda02nSVvMhXmTYlEbXpu2Bs4EDgG8DJwFr3P2f7v7PqpasADOj6RN74lv66PrKw/gwzxne1gZNTXDTTbBiha6TISKT3mjiwPOB04ETzOzueDuFkFicZGaPxPV9pdKFPri1lZ/vuy/v2WEHbtiwgdc88ADfWL6c9RP4TFOlNKZSzIoDxGc3NNA9MMCSjg5u3riRv8XuVE91dbGht1eDxWXCUAtGDXL3fuAvwF/MrAl4I3CdmX3O3f+vuqUrLL1HK03/vhvd33mc3kOepvH1C4b1+ilToKEhnMJ2zRrYd9+QdIiITDajiQPufiOFWzxOrGxJt9WQSvGO+fM5dfZsznv2WS5btYor16zhtDlzePN227F9Y+NYF6FmmRkt6TQtsXVjIHanWtLRwYA7BrRlMsxpaGBmJsPUdJopqRSmVg6pM0owalQMKC8jBJWFwHeA3xZ7TRnrXAZsBvqBPndfNLpSbqvhLTvRd+cGus99lNT2zWReOGdYr89kYLvtYPXqkGQccEB4rn2riEw2YxEHxtPcxkY+tcsuvGnePH763HNcvmoVl69axYtnzeK1c+dy4NSpk/7AOZXTncrd6Xbnqa4uHotN+RkzZmcyzGpooC2ToSWVoklJh9Q4JRg1yMwuIjSL/xn4rLvfX8HVH+/uayq4viEsZUz54n50vPceOj/5AFO+fgCZo2cPez2zZoUzTN15Z7gw3z77hG5UIiKTwRjHgXG165QpfG7XXXnfjjvy85Ur+d2aNfx53Tp2a27mVXPm8NLZs5mR0eEIhBaOZjOaswaC97uzZWCA1Z2dW1s50mbMzGSYlckwLZOhJZ2mOZXSeA6pGTbcvvIy9sxsAGiPT7N/IAPc3Ud0qB1bMBaVm2AsWrTIFy9enHfepk3wr39BoTMY+oZeOt53NwOPtdN89t40nFr+maVybd4MHR2wYAHsthu0to54VTKBmdkdY9EqJ1INYxUHhqtYHAC4eu1aZjU0DKs2vb2/n6vXreOKNWt4sKODNHBkWxsnzZzJC2fMoE3JRkn97nQPDNA1MEC/O05sDUmlmJ7JMCN2r2pOpWhOpUhNksRDcaB26F9cg9x9rAbfO3C1mTnwI3c/L3cBM3sX8C6AnXfeecRvZDMaaDnvEDo//gBdn19K//2baPrIHlhzetjrmjYtJBWrV8PTT8MOO8DChTrblIhMXGMYB6puajrNq+bO5VVz5/JwRwd/WbeOv61fz2effJLMU09xxLRpHD19Oke3tbFzc3O1i1uT0jljOSB0r+pxZ01vL8/09Gxt7TAzWlIp2jIZpsfuWEni0aBT5soYUYIxuTzf3Z8xs3nANWb2kLtfn71ATDrOg1BzNZo3s9YMU759ID0/eIKei5bTf+cGmj6xF5nDZw5/XRZOZ+sexmY8/TTMnBlaNGbPDmM3RESkvuzV0sJeLS18YMcdebCjg2vWr+f6DRv4+vLlACxoauLotjaOaGvj4NZWdaUqwsxoMqMpJ2lwd3rdWd/by8qYeCQyZrSm00xLp2nLZJgSx3c0plI0mmmch4yY/qmTiLs/E+9XmdkVwBHA9cVfNTqWSdH0gd1JHzGTri89TOd77yHzork0vWdXUguHf1W9JNGA0G3qzjshnYaddgotG21tGhAuIlJvzIz9p05l/6lT+dCCBazo7uZfGzfyr02b+N2aNVy+ejUAuzY3c3BrK4e0tnJQays7NDbqILgEM6PRjMY8rRX9MflY1dvLip6eIaeYN9g6AL01nWZqKkVTOk1TXFeDEhApQgnGJGFmU4GUu2+Oj18MfG683j9z5CymXn44PRcvp+fCp+j7+2oyJ82j8cydSe81skEVLS3h1tcXWjSWLYPm5jBWY9680LVKrb8iIvVnQVMTr5s3j9fNm0fPwAAPdnRw95Yt3Ll589bxGwDT02n2aWlhv6lTw31LC9sr6Shb2ox0zqDyRNLysam/n3V9ffTmXKPDgOZUKiQhqRRTM5nQ7SorAVESMnkpwZg8tgOuiH/0DPALd//LaFY43PMDWFOapncupOE1O9B7yXJ6fvU0fX9dRerANhpfswOZE+ZgLcPfJDOZ0F0KoLc3JBqPPhquqbH99uE0t8mF/EREpL40plIc3NrKwa2tnLX99vS780hnJ/e3t7OkvZ0lHR1c/Nxz9Mfl29Jpdpsyhd2am4fcz85kdLA7DFtbPgrMd3f63OkcGGBTfz+9OS0gEJKQplSKlng9j+S2NQGJ9xmzSTMQfbJQgjFJuPvjwEGVWl9raxhkvXlzaCkYjtSsRpo+uDuNZ+5M75+eo/c3z9B1zkPw5RSZ588i86J5ZJ4/C5s6/M2zoSGc4hZCy8aqVRC78tLaGpKN2bPDYyUcIiL1J23GPi0t7NPSsvVUht0DAzza2cmSjg4e7ujg8a4u/rZ+PZvWDJ40cXo6zcLmZnZqbmZBU9OQ2/R0WsnHMFlsoWgosoy70w/0utPR20tfTEryaYwDz5vNto4FmZJOk4nvk4kJSe4YE6lNSjBkRFIpOOigcKrazs5wFe7hsukNNL5pJxreuID+uzbSd80q+v6+mr6/r4GMkX5eG+kjZ5E5aiapfaZh6eHt/DOZoWea6u6Gp56Cxx8Pz5uaQmyaNSskHC0tGiwuIlKPmlKprWM4Eu7O2r4+Hu/s5PGuLh7v7GRZVxe3b9rEH3t7h7y+NZ3emmxs19DAdo2NW2/bNzYyK5NRDfsImBkZwmDyfN2wsvW5b73mx8b+fvrcGYin4E1kzHjhjBk6+1Ud0OGUjNjUqXDkkXDbbeGieCM9bayZkTl0BplDZ+D/tSf9d2+k/19r6btlfTgD1Q+egJY06f2nkT6gjfTz2kgd0EZqZqGG2/yamoa2WvT2hlPfrlgRunuZhc80a1YYSN7SEhKnpiYNHBcRqTdmxpyGBuY0NHBEzpVauwYGeKa7mxXd3SyP9093d/NwRwc39PTQnVPLnjFjXlbiMbehgVmZDLMbGgZvmQzTlYiMWCa2UhTrXLCmtxddva0+KMGQUWlrg6OPhvvuC92Rpk8fXdcjSxuZw2aQOWwGTR+AgXU99N+2nv57NtJ/3yZ6Ln6KpKOtzWsktUcr6T2mktpjKqk9WkktbMEay6vZaGgIt2w9PeFzrFgBAwMhsUinQzew6dPD502SjubmME9EROpLcyoVxmfkaX53dzb297Oyp2fwFk/xurKnh/u2bGFNb+82SQhAGpiVk3zMjIlHcgG86en01ufTMxkySkhkAlKCIaPW0gJHHAHPPQcPPQQbN4ZpU6eOvuY/NauR1Mnb0XDydgB4Vz/9SzYzcP8m+h9pZ+DRLfTcvh56447ewLZvIrVTC6mdppDaaQq2INyn5jeVHETe2Bhu2QYGQuLx7LOhi1USU9xDkjFtWvis06aF58k6Ght1FisRkXpjZsyIycDeLflPp+7utA8MsLa3l3W9vazt62NN8jg+X9vby6OdnWzo66OnyFlRWtPprUnHjJh0TIvXpmiNt2mZzNbHW6el0xqPIDVLCYZUhBnMnx8GUa9dC08+GbofQTjortT4BmtOkzlkBhwyY+s07xtg4KlOBh7ZwsCyDgZWdDKwvJPev62CjX1DVzAtQ2q7Jmy7JlLz4v32zdh2TdjsRlKzGqEtg6UGM6NUKnyGfBeU7esLY1A2bQrJR9LqkWhqCsnHlCmD942Ng60nDQ0a9yEiUm8sXqCuNZ1mlxJXG3d3uuK4gg19fWzo62Nj9i1O3xjnPdnVxeb+frb09zNQdM3QYDYkEWmN162YGs/a1BIHSk+N9y1ZZ3RKHifLTEmlSKs1RSpEhzZSUalUGDg9d24YVL1xI6xcGbod9fQMdjlKxkNUJOnIpEjvNpX0blO3mecbe7cmHAPPdeMru/CV3Qys7Kbvgc34ht5tV5g2bGYDNqsRmx3vZzWSmtWAzWzEpmewtgZoy5Bqa2BKW4aWlvy1SH19YaxHR0do4emP3buSMR8Q7qdMCQlM0v0qSUQymcEkJLmpwkpEpH6YWTiAT6fZPreJvAh3p2NggC39/VsTjuzb5r6+wcdZ09f09tI5MEBHfz8dAwP0DuOc8k1mW5OOlnQ6nNUpnrlp6+M4YHvItDzL5S6fzNe1MSYHJRgyZpqawgXv5s0LB9SdneFAe9MmWLcu3Hd3Dz3QTg6ok4Pq0R5M2/QG0tMbSO/flne+d/Xjq7oZWNWNr+3B1/Xi63rwtT0MrO8N90904Ot6oKfITnpKCmtr2Jp82LQMNr0BpmWwqWmapmZonpqGqeG5ZT32KRn602l6eozOzpCU9PUVvs5IOh2Sj6amwfvklnxv6fTgLfu59ukiUknpVIrVvb2kgEzWNQ0adF2DUTOzra0R241iPb0DAyHhGBigs7+f9njfkUyPjzv6+7d53jUwQNfAAJvj4+74PLkv1cKST4pw1q9Gs3Cf9bghmRYv1jdkuZioHD9jxii+DRkvSjBkXJgNXnl7zhzYbbcwvbcXurpCotHVBVu2hCSkvT0kIPkOspNEJDloTqUGk5HhJiTWnMZ2biG1c/5+tgl3h/Z+fH0Pvqkv3Db2xsdZ9xv7YHMfA0924Bv78M29xROTbC0h8WhoSdMwNYO1pKE5hU1JQ3Maa05Bc3jsjSl6GtN0N6XY2JhmoDFNf0MKb0pDU1hu6+OmNMRT/KbTIQnJ7aaVPS07OUmlCt/r2EFE/l9bG50DA3TFA9ct/f109vezob+f/jw7cDMjzeAVpNPxeSo+lsprSKVoSKXIX802csmVvrcmHbErWFdOEpIvMemOr+sZGKAnPk7W1eNOe3//1sc9cfmegQHm5J6ZRWqWEgypquTgttDF+pIuRsmtry90tUqSkuRxZ2d4PFBGdYrZYDKSe0vmZS8TXmPQmsFah/+X8b6BkJy09+Ht/Xh7PySPO/pgS5zX0T9kOTr78TU9eNcA3tkPXf141wB0D/2QRjhzSdETWqUtJBuNKawxRU+80ZDCG1OQMbwxhTfEaQ2psHxDeI03Dj4mLmdNKdLN8dZkpJtTZJq3vW9oCfeZKWG5VKORTlve7z3f7yEitaslnaYlqbnI0e9Obzxw7I0XWOvNOtDszj6wjAlJOdUxKTNShP1yKut5ygyDIfMsPpbK23ql71SKYV5vd8TW9Obp1iw1SQmG1LRk3EG5F/IbGAjjHPLdknnZSUt//7b3PT2DyyVjJorJHk+RK0xPkUqlsCkNWAvYvDC90C2V8zxZz9bn7oPJRmf2fT90DYT7zpz5vQN4d7inewDvybrvifM29g0+7hmcR/cAxdrBHeiLt+7yfiY8YyFhyRhkUtBgeGbo89z7GS+fw6Fnzy/zHUSk2tJmpNNpig+BHuTxQmv9hOSk352BPI/7shKX/pi8JNP7gZ6BgSHrqMR1EywmLwZbu35Z9i1Oy01q8i2Tb5rIRKMEQyaUpOa7kq2oAwNDb+7FH2ffZyc2yePkefZ97q3QdHdwN7b+dRvjrYy272KJUEn9A1hvSDiy7613APod63PoG4Bex/oHwvPeeN8fptMXnnvvAKk4nz7H+gbw5Hlcjqz5dPQy0O20P6OaK5GJzOL4jUofmCRXgx6ISYrH+63Ts6Z5zuNkma0JThx3kLx+ICY1W9eXrDu+Zutrs9eZvEecX0sXjstOeCzr3vLMz12GAstZseVLrC932Xzd7qQ2KcEQKWEkYzvGUkgyit9KLVdofu70wecpBgZSW6cXSqhyb4WmF1suec8hr+2HOWq8EJERSFocanWMh2e1snjWc896TvbznNfley0Fli05PTvpSp5nTSNnXm45trlllzHfcjnT8q4v63lbOl2zv6MMpQRDpM5kd50SERkOMzsZ+DZh2NYF7v6VKhdp0ku6X2VNqFZRRCqmhuplRUREZKyYWRr4HvBSYD/gjWa2X3VLJSITkRIMERGRyeGI/9/encfJVdX5/3+9q5d09pAFiAkkbAqIyhIxgiKIuOCCOjrIoICjwzju208dx+HHoDPiMriNyAAqICg4CIiKIw4uiLIlENaICwQStoRA9nR6qc/3j3MqqXSqu6u7q7uqO+/n43Effff7ubeq697PPefcC/wlIh6MiA7gcuCEOsdkZmOQEwwzM7OdwxxgednwijxuO5JOl7RI0qJVq1aNWHBmNna4DYb1avHixU9JerjH6JnAU/WIpwHszPsOjb//8+odgFmDq1S5f4fH8kTE+cD5AJJWVTgPDFUj/ZY0UizgePrTXzw+DzQIJxjWq4iY1XOcpEURsaAe8dTbzrzv4P03GwNWAHuUDc8FHutrgUrngaFqpN+SRooFHE9/Gi0e652rSJmZme0cbgf2k7SXpFbgbcC1dY7JzMYgl2CYmZntBCKiS9L7gV+QHlP7nYi4r85hmdkY5ATDBur8egdQRzvzvoP332zUi4jrgOvqHEYj/ZY0UizgePrTaPFYL1R6E6OZmZmZmdlQuQ2GmZmZmZnVjBMMMzMzMzOrGScYtgNJr5b0gKS/SPpUhemS9PU8/W5Jh9YjzuFSxf6fnPf7bkl/kPSCesQ5XPrb/7L5XiipW9JbRjI+M2tMgz13SNpD0q8lLZV0n6QP1TOesulNku6U9NN6xyNpmqQrJf0xH6cX1zmej+TP6l5JP5DUNgLx7C/pZklbJH18IMtaHUSEO3dbO9KTRf4K7A20AncBB/aY53jg56SXNi0Ebq133CO8/0cAu+T+1+xs+182369IjUXfUu+43blzV99uKOcOYDZwaO6fDPyp0u/OSMVTNv2jwPeBn9bz+ORpFwPvzv2twLQ6fl5zgIeA8Xn4h8BpIxDPrsALgX8HPj6QZd2NfOcSDOvpcOAvEfFgRHQAlwMn9JjnBOCSSG4BpkmaPdKBDpN+9z8i/hARz+TBW0gvqxorqvn8AT4A/AhYOZLBmVnDGvS5IyIej4g7ACJiPbCUdBFbl3gAJM0FXgtcOMQ4hhyPpCnAUcC3ASKiIyLW1CuePK0ZGC+pGZhAPy9srEU8EbEyIm4HOgexLzbCnGBYT3OA5WXDK9jxh76aeUarge7bu0h3eMaKfvdf0hzgTcB5IxiXmTW2mpw7JM0HDgFurXM8XwU+ARSHGEct4tkbWAV8N1fZulDSxHrFExGPAl8GHgEeB9ZGxPUjEM9wLGvDxAmG9aQK43o+y7iaeUarqvdN0jGkBOOTwxrRyKpm/78KfDIiuoc/HDMbJYZ87pA0iVQy+uGIWFeveCS9DlgZEYuHGENN4iGVFhwKfCsiDgE2AkNtZzCU47MLqYRgL+BZwERJbx+BeIZjWRsmTjCspxXAHmXDc9mx6LOaeUarqvZN0vNJRecnRMTqEYptJFSz/wuAyyUtA94CnCvpjSMSnZk1qiGdOyS1kJKLyyLiqjrHcyTwhvwbdznwckmX1jGeFcCKiCiV6lxJSjjqFc8rgIciYlVEdAJXkdomDnc8w7GsDRMnGNbT7cB+kvaS1Aq8Dbi2xzzXAqfkJ0wsJBWPPj7SgQ6Tfvdf0p6kH9R3RMSf6hDjcOp3/yNir4iYHxHzSSe690bENSMeqZk1kkGfOySJ1L5gaUScU+94IuKfI2Ju/o17G/CriBjqHfqhxPMEsFzSc/J8xwL31yseUtWohZIm5M/uWFK7meGOZziWtWHSXO8ArLFERJek9wO/ID2Z4TsRcZ+k9+Tp55GeHHQ88BdgE/DOesVba1Xu/xnADNKde4CuiFhQr5hrqcr9NzPbzhDPHUcC7wDukbQkj/t0RFxXp3hqrgbxfAC4LF9APzjUWIcST0TcKulK4A6gC7gTOH+445G0O7AImAIUJX2Y9LSodZWWHUo8NnSKcDU1MzMzMzOrDVeRMjMzMzOzmnGCYWZmZmZmNeMEw8zMzMzMasYJhpmZmZmZ1YwTDDMzMzMzqxknGDYkkkLS98qGmyWtkvTTEY5jf0lLJN0paZ8e06ZKukTSX3N3iaSpedrRvcUq6TpJ0wYRy9GSBvTSIUmzS3HkZ4tfJukeSfdKuim/4bav5TcMNM4ey/9G0iP5mealcdcMdb1DiGeWpP+tx7bNbOB8Lqi4nM8FQ+RzwejlBMOGaiNwkKTxefg44NE6xPFG4McRcUhE/LXHtG8DD0bEPhGxD/AQ6S3cfYqI4yNizSBiOZqBv9X0o8AFuf9DwJMR8byIOAh4F9A5iDgqyi9NqvS/v4b0PHryyXR2rbY5UBGxCnhc0pH1isHMBsTngh0djc8FQ+JzwejlBMNq4efAa3P/ScAPShMkHS7pD/lu0h+U30Qq6bmSbst3mu6WtJ+kiZJ+JumufLfmxJ4bknSwpFvyMldL2kXS8cCHgXdL+nWP+fcFDgM+Wzb6LGBB2d2tKXld90s6r/SDK2mZpJm5/+1l8f63pKY8/tWS7sgx3yBpPvAe4CN53pdKemven7sk3djLMfwboHSXZjZlJ+aIeCAituTtfTSv616llwz1PD6Tchx35LteJ+Tx8yUtlXQu6eVIe1SI4XLSG1AB3kx6W3l/6634mUk6Ox/PuyV9OY+bJelHkm7PXekE9rJ8rEp3HSfnzV4DnNzL8TKzxuNzgc8FPhdYEhHu3A26AzYAzweuBNqAJaS7Nj/N06cAzbn/FcCPcv83gJNzfyswnvTDekHZuqdW2N7dwMty/1nAV3P/mcDHK8z/BuDqCuOvztOOBtqBvUlvAP0l8JY8zzJgJnAA8BOgJY8/FzgFmAUsB/bK46dXigW4B5iT+6dViGUvYHHZ8MHASuBm4HPAfnn8YXldE4FJwH3AIaXPIf9tBqbk/pmkN7AKmA8UgYW9fI6/AV6Uj28TcH1epr/17vCZAdOBB9j2Is9p+e/3gZfk/j2Bpbn/J8CRuX9S2fdlDnBPvb/j7ty567/D5wKfC3wucFfWuQTDhiwi7ib9AJ0EXNdj8lTgfyTdC3wFeG4efzPwaUmfBOZFxGbSD+YrJH1B0ksjYm35ipTqyk6LiN/mURcDR/UTnoBKr6svH39bRDwYEd2kO24v6THvsaQf9NslLcnDewMLgRsj4qF8HJ7uJYbfAxdJ+gfSD3ZPs4FVpYGIWJLX/yXSD/Ttkg7IcV0dERsjYgPprtJLK+zXf0i6G/g/0g/zbnnawxFxSy8xAnQDNwEnAuMjYlkV6630ma0jnagvlPRmYFNexyuA/8rH8FrS3cLJ+ficI+mDpM+3K8+/EnhWH/GaWQPxucDnAp8LrMQJhtXKtcCXKSsSzz4L/DpS/dHXk+5sERHfJ9012gz8QtLLI+JPbLsz83lJZ9QgrvuAQ1RWzzT3vwBYmkf1POn0HBZwcUQcnLvnRMSZ9H7C2n5lEe8BPkMqil4iaUaPWTaTj0vZMhsi4qqIeC9wKXB83l5/TibdTTssIg4Gnixb98Yqlr+cdEfxh9Wst9Jnlk8KhwM/ItWHLhX3F4AXlx3HORGxPiLOBt5NunN5i6T98/xtpGNjZqOHzwW98LnA54KdiRMMq5XvAGdFxD09xk9lWx3S00ojJe1Namz3ddIJ6fmSngVsiohLSSeoQ8tXlO+IPCOpdKfmHcBv6UNE/AW4k/SjXvIZ4I48DeBwSXvlk82JpDs35W4A3iJp1xz7dEnzSHfeXiZpr9L4PP96oFR3FEn7RMStEXEG8BQ71nn9E+muX2n+IyXtkvtbgQOBh4EbgTcqPVlkIvAm4Hc91jUVWBkRnZKOAeb1dXwq+B3weXa8OKi43kqfmdJTTqZGxHWk+tAH53VcD7y/bD8Pzn/3iYh7IuILwCKgdFJ5NnDvAOM3s/ryucDnAp8LjOZ6B2BjQ0SsAL5WYdIXgYslfRT4Vdn4E4G3S+oEniDVoX0h8CVJRdKTMv6pwvpOBc6TNAF4EHhnFeG9C/iGpFJd0ZvzuJKbgbOB55F+uK/eftfifkmfAa7PJ55O4H0RcYuk04Gr8viVpCen/AS4Mjd++wCpkd9+eds3AHex/QY2Kj0ycd98otsH+JYkkW4C/IxUXzkkXQTclhe9MCLu7LGvlwE/kbSIVAf6j1Ucn+12lnRy6Km39T6PHT+zycCPJbXlff5InveDwDdz0Xoz6Vi/B/hwPlF1A/eTGooCHJP33cxGCZ8LfC7A5wJjW8MbMyuj9GSQlcDuEVGzxwL2sb03kYqcP9PvzDsJpaesnBARz9Q7FjPbOflcUH8+F4xOLsEwq+w+0l2hYT+hAETE1RXq4+60JM0CzvEJxczqzOeCOvK5YPRyCYaZmZmZmdWMG3mbmZmZmVnNOMEwMzMzM7OacYJhZmZmZmY14wTDzMzMzMxqxgmGmZmZmZnVjBMMMzMzMzOrGScYZmZmZmZWM04wzMzMzMysZpxgmJmZmZlZzTjBMDMzMzOzmnGCYWZmZmZmNeMEw8zMzMzMasYJhpmZmZmZ1YwTDDMzMzMzqxknGGZmZmZmVjNOMMzMzMzMrGacYJiZmZmZWc04wTAzMzMzs5pxgmFmZmZmZjXjBMPMzMzMzGrGCYaZmZmZmdWMEwwzMzMzM6sZJxhmZmZmZlYzTjDMzMzMzKxmnGCYmZmZmVnNOMEwMzMzM7OacYJhZmZmZmY14wTDzMzMzMxqxgmGmZmZmZnVjBMMMzMzMzOrGScYZmZmZmZWM04wzMzMzMysZpxgmJmZmZlZzTjBMDMzMzOzmnGCYWZmZmZmNeMEw8zMzMzMasYJhpmZmZmZ1YwTDDMzMzMzqxknGGZmZmZmVjNOMMzMzMzMrGacYJiZmZmZWc04wTAzMzMzs5pxgmFmZmZmZjUzZhIMSZ+Q9LCkAyX9ut7xVEvShyRdXe84dhaS7pN09DCsdzdJN0paL+k/a73+Hts6WtKK4dyGDY6kDZL2rnccvZF0sqTra7CePfO+NtUiLjMzG1saKsGQtEzS5nzielLSdyVNqnLxQ4CXA18HbhhCDL+R1J5jeErSVZJmD3Z9/WxrP+DvgdOGY/1l21kmqUPSzB7jl0gKSfOHc/tl2ztTUmc+tqXuE8O4vYskfa58XEQ8NyJ+MwybOx14CpgSER8b6soknSapOx+jdfmzet3QwxxwDDeN4Pbm5+/jHT3Gz8zf32UjFctgRcSkiHhwJLYl6XWSbpO0UdJqSZdJmttPfJdFxCuHuu2IeCTva/dQ12VmZmNPQyUY2esjYhJwKPBC4DPVLBQRJ0XEXyPiFRHxuf6X6NP7cwz7ApOALw9xfb05ADgpItb2nCCpucbbegg4qWz9zwPG13gb1bgiX5iUui/WIYbhMA+4PyJioAv28VnfnL+H04BvAz+UNH3wIY6sIXyHJ0o6qGz470jfX8skvQX4PvA1YCbwXGALcJOkXXpZpta/KWZmZhU1YoIBQEQ8CvwcOAhA0kJJf5C0RtJd5dVccqnDZyX9PldRub78br2k/5H0hKS1uRrLc6uMYQ1wDXBw2breKWlp3s6Dkv6xbNrRklbk6lorJT0u6Y2Sjpf0J0lPS/p02SYOBT6dly3dvX2XpEeAX+Xxf5+394ykX0ial8d/okdJQKeki/rYne8Bp5QNnwpcUj6DpNdKujPfMV8u6cyyaW2SLs13StdIul3SbnnaaflYrJf0kKSTqzm+Zes+U9KlZcOlY9Gch/v7fF9S9t1YnuM5HTgZKB2nn+R5l0l6Re4fJ+mrkh7L3VcljcvTSp/lx8o+y3f2Ev9F+XiWtvWKKtf9SUlPAN/t6/hERBH4Dikh3KH6jaRPSfprPjb3S3pT2bTTJN0k6cv5O/SQpNeUTZ8q6dt5/x6V9DlJTZIOAM4DXpz3aU2ev6/vyA7fYUk/k/SBHvHeLemNfezy9/LxLDmFHb+rfe3zvpJ+q/T//pSkK/J4SfpK/jzX5jhKvy/j8jF6RKn09DxJ4/O0mZJ+mr9fT0v6naSKv515//fN/RdJ+mY+Busl3Sppn952WtI/5X3qyOuJ0nHvMZ+A/wQ+l0skNkfEE8C7gQ3AR/J8p+X/ma9Ieho4Uz1KpSQdofS/vDb/PaJsWq//d9rxf3RIvwFmZja2NGyCIWkP4HjgTklzgJ8BnwOmAx8HfiRpVtkifwe8E9gVaM3zlPwc2C9PuwO4rMoYZgBvBv5SNnol8DpgSt7eVyQdWjZ9d6ANmAOcAVwAvB04DHgpcIb6rqP9MlLJxqvyRdincwyzgN8BPwCIiC+WSgHy/KuAH/ax3luAKZIOUKo3fSJwaY95NpIu5qYBrwX+qexC8FRgKrAHMAN4D7BZ0kRStbTXRMRk4AhgSR9xDFbFz1fSnqTP9xukY3QwsCQizid9zqXj9PoK6/wXYGFe5gXA4WxfYrY7aZ/nAO8CvqkKd4cj4rQe2/q/Ktc9nVTycXpfO54v4koXj3+uMMtfSd+tqcC/AZdq+2p9LwIeIN3p/iLw7XyRCnAx0EUqrTsEeCXw7ohYSvqMb877NC3P39d3pGTrdziv/+1l+/IC0vG8ro9dvhR4W1miMxm4dQD7/FngemAXYC7pu0Het6OAZ+f4TwRW52lfyOMPzsei9P8L8DFgBen7tRvpf7LakqqTcny7kH5H/r3STJIOA84hfc8mAB8FngGOqTD7c4A9gf8pH5kT0R8Bx5WNfhHwIOn/ZrttK5WG/Yz0/zsjb/9n+XevpK/f1dJ6Ruo3wMzMRolGTDCuyXftbgJ+C/wH6QLluoi4LiKKEfFLYBEpASn5bkT8KSI2ky60Dy5NiIjvRMT6iNgCnAm8QNLUPmL4uqS1pDr1M4Gtd2Aj4me5KlZExG9JFzIvLVu2E/j3iOgELs/Lfy1v/z7gPuD5fWz7zIjYmPfjH4HPR8TSiOjKx+Jg5VIMgHyX9Zq8jb4u2mBbKcZxwB+BR8snRsRvIuKefIzvJiUzLyvbrxnAvhHRHRGLI2JdnlYEDpI0PiIez/vZm7/Nd4JL3bP6ibmkt8/3ZOD/IuIHEdEZEasjYkmV6zwZOCsiVkbEKtKF4DvKpnfm6Z352G4gXdzVYt1F4P+PiC15nypZmP8XniBdqL6pUnW6iPifiHgsf25XkJKQw8tmeTgiLsj15S8GZgO7KZVAvQb4cP7OrQS+Arytt53q5ztSUv4d/jGwn1J7I/IxuCIiOnrbBuli/gHgFVQoaatinztJiduzIqI9Im4qGz8Z2B9Q/r96PCdb/wB8JCKejoj1pP+1t5UtNxuYl78LvxtAVbirIuK2/P97GWW/Sz28Abg2H98u4KvAZlIi1FOp9O7xCtMeL5sO8FhEfCMiuip8z14L/Dkivpen/4D0u1CejPf6u9rDQH4DzMxsjGvEBOONETEtIuZFxHvziW0e8NbyC1PgJaSTfskTZf2bSG0nyHdBz85VD9YBy/I82zV47uGDETGVlAiU7oKS1/caSbfkqhJrSElO+bpWx7aGj6UT+pNl0zeXYuvF8rL+ecDXyvb5aUCku6sl3wYeiIgv9LHOku+R7kieRoWLNkkvkvRrSatygvUetu3b94BfAJcrVfn5oqSWiNhIuhP8HuDxXB1k/z5i+GH+fEvdY1XEDb18vqQSlb9WuY6engU8XDb8cB5Xsjpf7FXa7lDXvSoi2vtZxy35GM2MiIW5ZGQHkk5RagRe+p4cxPbfya3HLiI25d5JpO9XC+lzKy3736S71RX18x0p2fodzkn9D4G352pFJ5G+S/25hPQ9PYkdS9r62+dPkP5PblN6atjf51h+BfwX8E3gSUnnS5pCKpmYACwuW9//5vEAXyKVPlyfqwF9qor4S3r73va0K/BIaSAnMI+w/Xem5Kn8t9LDJ2aXTYftf0966vkdJQ+X/770G/8gfgPMzGyMa8QEo5LlwPd6XJhOjIizq1j274ATSHdDpwLz83j1tkBJRNxDqpb1TSXjSFUQvgzsFqnayHXVrGsAyu+MLgf+scd+j4+IP0Cqh066o/6uqlYc8TCpsezxwFUVZvk+cC2wR06wziPvW75z+28RcSCpCsTryG06IuIXEXEc6eLmj6RqYQOxkXSBV7L7AJZdDvRWr72/u8yPkS6yS/bM42qhv3UPuDF4Jbk06wLg/cCM/J28l+q+k8tJDYNnln2/pkREqY1SpRh7/Y6U6bncxaQSnWOBTRFxcxWx/Yh0h/3B/L3dqr99jognIuIfIuJZpFLAc5XbRUTE1yPiMFKj6GcD/x/pgnwz8Nyy4zA1UvVDIpU+fiwi9ibd3f+opGOr2IeBWAHsVbaPBdL3p9LjiB/I499aPjIv8zds/xS9vr5nPb+jkL6nj1aYt081+A0wM7MxZLQkGJcCr5f0qlwi0abUULbPRzJmk0kXUatJF7H/McBtX0y6u/gGUh3kcaT2Dl1KjWWH/MjHPpwH/LNyo3SlBrlvzf2vAT5IKvHprYpNJe8CXp7vOvY0GXg6ItolHU5KzsjbO0bS83L7jXWkaiPdSu9/eEOuh72FVI1ooI+uXAIcpfRs/anAPw9g2cuAV0j6W0nNkmZIOjhPe5IKjaLL/AD4jKRZSo1Xz6DC3fJBGs51l5tIuohcBekhBOQHI/QnIh4nVfH7T0lTJBUk7SOpVOXpSWCupNayxXr9jvSxnZtJVWj+k+pKL0p3xV9OanvSU5/7LOmtZb8Nz+R5uyW9MJfAtJCS2nagO1LbhQtI7al2zeuYI+lVuf91Sg3HRfrudzPw73h/LgderfSAgBZSW4d2YIdkLJdufJz0/fo7SeMl7Q5cSGob9pUqt3kd8Oy8jmZJJwIHAj8dSOA1+g0wM7MxZFQkGBGxnFQK8WnSRcVy0p3HauK/hFTs/yhwP6mx80C23UFqwPivuW72B0lVPp4hXVxdO5D1DXDbV5Man16eq3fdS6ozD6lKwixgqbY9Seq8Ktb514hY1Mvk9wJnSVpPuiAubzS+O3Al6QJrKal9zKWkz+BjpLuhT5Pq4793gPv5S+AK4G5gMQO4wImIR0glMh/L219CalQNqfrYgbnayzUVFv8cqS3P3cA9pAcADPURxyOx7q0i4n7ShfvNpITgecDvB7CKU0iJ8/2k7/SVbKt68ytSm6EnJJWq3fT1HenLJTm2qpOsiFgUETtUf6tin18I3CppA+n/80MR8RDp4vuCvJ8Pk246lB5B/UlSNahb8v/a/7Gtvc1+eXhD3ua5UeN3qeT9fBup7cVTpNKb1/fWViW3O3kH6YlRT5E+v/HAkRGxutIyFdaxmlQS+THSsfgE8LqIeKrPBXc05N8AMzMbW1R9W0Uzs8GRdApwekS8pN6xmJmZ2fAaFSUYZjZ6SZpAuqN9fr1jMTMzs+HnBMPMhk1ux7CKVJXp+3UOx8zMzEaAq0iZmZmZmVnNuATDzMzMzMxqprneAVjjmjlzZsyfP7/eYZhVZfHixU9FxKz+5zQzM7Ph5ATDejV//nwWLertibZmjUVSz7dSm5mZWR24itQYJGmapCsl/VHSUkkvljRd0i8l/Tn/3aXecZqZmZnZ2OMEY2z6GvC/EbE/6aVzS4FPATdExH7ADXnYzMzMzKymnGCMMZKmAEeR3mJNRHRExBrSm9AvzrNdDLyxHvGZmZmZ2djmNhhjz96k9w58V9ILgMXAh4DdIuJxgIh4XNKulRaWdDpwOsCee+7Z60aKxS6KxU00N0+pcfhjW2dnJytWrKC9vb3eoYxabW1tzJ07l5aWlnqHYmZmZhU4wRh7moFDgQ9ExK2SvsYAqkNFxPnkNy4vWLCg15ekdHQ8QUfHE0yZsmCo8e5UVqxYweTJk5k/fz6S6h3OqBMRrF69mhUrVrDXXnvVOxwzMzOrwFWkxp4VwIqIuDUPX0lKOJ6UNBsg/105lI1ITUBxKKvYKbW3tzNjxgwnF4MkiRkzZrgEyMzMrIE5wRhjIuIJYLmk5+RRxwL3A9cCp+ZxpwI/Hsp2pAIRTjAGw8nF0Pj4mZmZNTZXkRqbPgBcJqkVeBB4JymZ/KGkdwGPAG8d2iacYJiZmZnZjpxgjEERsQSo1Dji2FptI1WR6rWJhlVp7dpb6OpaU7P1NTdPY+rUhTVb31C9+93v5qMf/SgHHnhgvUMxMzOzEeIEwwapAHTXO4hRr6trDa2ts2q2vo6OVTVZT3d3N01NTb0OVxIRRASFwraalxdeeGFN4jEzM7PRw20wbFBSPfh0QWmjy6WXXsrhhx/OwQcfzD/+4z/S3Z0SxUmTJnHGGWfwohe9iJtvvnmH4XPOOYeDDjqIgw46iK9+9asALFu2jAMOOID3vve9HHrooSxfvny7bR199NEsWrRo6/r/5V/+hRe84AUsXLiQJ598cofYbrvtNo444ggOOeQQjjjiCB544IHhPRhmZmZWc04wbJBERDd+ktTosnTpUq644gp+//vfs2TJEpqamrjssssA2LhxIwcddBC33norL3nJS7YbHj9+PN/97ne59dZbueWWW7jgggu48847AXjggQc45ZRTuPPOO5k3b16v2964cSMLFy7krrvu4qijjuKCCy7YYZ7999+fG2+8kTvvvJOzzjqLT3/608NzIMzMzGzYuIqUDVqqEtOd22PYaHDDDTewePFiXvjCFwKwefNmdt01vXOxqamJv/mbv9k6b/nwTTfdxJve9CYmTpwIwJvf/GZ+97vf8YY3vIF58+axcGH/7T5aW1t53eteB8Bhhx3GL3/5yx3mWbt2Laeeeip//vOfkURnZ+fQdtjMzMxGnBMMG4LuXIpho0VEcOqpp/L5z39+h2ltbW3btbMoH+6rKlwp6ehPS0vL1kfMNjU10dXVtcM8//qv/8oxxxzD1VdfzbJlyzj66KOrWreZmZk1DleRskFLF52uIjWaHHvssVx55ZWsXJnes/j000/z8MMP97vcUUcdxTXXXMOmTZvYuHEjV199NS996UtrHt/atWuZM2cOABdddFHN129mZmbDzyUYNmgRLsEYqubmaTV78lNpfX058MAD+dznPscrX/lKisUiLS0tfPOb3+yz7QTAoYceymmnncbhhx8OpMfPHnLIISxbtqxGkSef+MQnOPXUUznnnHN4+ctfXtN1m5mZ2ciQnwJkvVmwYEGUngDUU1fXOtasuZGpU4+kpWWXEY5s9Fq6dCkHHHBAvcMY9SodR0mLI6LS+1/MzMxsBLmKlA2BSzDMzMzMbHtOMGzQIor4ZXtmZmZmVs4Jhg1aRNElGIPgaolD4+NnZmbW2Jxg2JAUix31DmFUaWtrY/Xq1b5IHqSIYPXq1bS1tdU7FDMzM+uFnyJlQxLhBGMg5s6dy4oVK1i1qnZPjtrZtLW1MXfu3HqHYWZmZr1wgmFDUiz6TcsD0dLSwl577VXvMMzMzMyGjROMBidpF+BZwGZgWaSW1Q3DJRhmZmZmVs4JRgOSNBV4H3AS0AqsAtqA3STdApwbEb+uY4gASE0Ui1vqHYaZmZmZNRAnGI3pSuAS4KURsaZ8gqTDgHdI2jsivl2P4LbF0kSEq0iZmZmZ2TZOMBpQRBzXx7TFwOIRDKcPLsEwMzMzs+35MbUNTFJLhXEz6xFLJVKBiG4/ctXMzMzMtnKC0YAkHSNpBfCYpOslzS+bfH2dwupFENFV7yDMzMzMrEE4wWhMXwReFRGzgPOBX0pamKepfmFV5rd5m5mZmVmJ22A0ptaIuA8gIq6UtBS4StKngIarj5QaevvNymZmZmbmBKNRdUraPSKeAIiI+yQdC/wU2Ke+oe3IJRhmZmZmVuIqUo3pU8Bu5SMiYgXwMuDsukTUBz+q1szMzMxKXILRgCLi/3oZvxb49xEOp19u5G1mZmZmJS7BGGUknVnvGMpJTXR3t9c7DDMzMzNrEE4wRp8GecleIjVRLDrBMDMzM7PECcYoExE/qXcM5aRmIjbXOwwzMzMzaxBug9GgJL0KeCMwh/Ro2seAH0fE/9Yzrp5cgmFmZmZm5ZxgNCBJXwWeDVwCrMij5wIflPSaiPhQFetoAhYBj0bE6yRNB64A5gPLgL+NiGeGHmsz3d0bh7oaMzMzMxsjXEWqMR0fEcdHxOURcVPuLgdeCxxf5To+BCwtG/4UcENE7AfckIeHTGqmWOwgoliL1ZmZmZnZKOcEozG1Szq8wvgXAv3WR5I0l5SMXFg2+gTg4tx/Man6Vc34XRhmZmZmBq4i1ahOA74laTLbqkjtAazL0/rzVeATwOSycbtFxOMAEfG4pF0rLSjpdOB0gD333LPKcINisZNCYVyV85uZmZnZWOUEowFFxB3AiyTtTmrkLWBFRDzR37KSXgesjIjFko4exLbPB84HWLBgQVS3lIjoGOimzMzMzGwMcoLRwHJC0W9S0cORwBskHQ+0AVMkXQo8KWl2Lr2YDaysbayuImVmZmZmboMx5kTEP0fE3IiYD7wN+FVEvB24Fjg1z3Yq8ONabVMq0N3td2GYmZmZmROMncnZwHGS/gwcl4drQmrxo2rNzMzMDHAVqTEtIn4D/Cb3rwaOHY7tSC0Ui04wzMzMzMwlGA1P0s19DTeCQsElGGZmZmaWOMFofG39DNddetneFiK66x2KmZmZmdWZq0g1KElH5d6Jpf6IuBGo8tGxI69Y3EJT04R6h2FmZmZmdeQEo3G9M/+dkfsDuJH0ToyG5ATDzMzMzJxgNKiIeCeApDtK/aVJdQqpX8XilnqHYGZmZmZ15jYYja9niUVDlmCkht7r6h2GmZmZmdWZE4zG98l+hhuC1EpXlxMMMzMzs52dE4wGFxHX9zXcKAqFVpdgmJmZmZkTDKuN9KjaDorFznqHYmZmZmZ15ATDaigoFjfXOwgzMzMzqyMnGFZTTjDMzMzMdm5OMBqQpDdJmp77Z0m6RNI9kq6QNLfe8fWmUGilq2ttvcMwMzMzszpygtGY/j0ins79/wXcCbwG+Dnw3bpF1Y9CoY3Ozqf7n9HMzMzMxiwnGI2pqax/34j4SkSsiIiLgFl1iqlfUnqSVESx3qGYmZmZWZ04wWhMv5F0lqTxuf+NAJKOARq2DpJUIKLodhhmZmZmOzEnGI3p/UAReAB4K3CVpPXAPwDvqGdg1eju3ljvEMzMzMysTprrHYDtKCI6gTOBMyVNBZojYnV9o6pOodBCZ+dqWlt3rXcoZmZmZlYHLsFocBGxdrQkFwCFwgQ6O1fVOwwzMzMzqxMnGKOMpDvqHUNfCoVWurs3UixuqXcoZmZmZlYHTjBGmYg4tN4xVKOra329QzAzMzOzOnCC0eAkTZe0S73jGIhCodXVpMzMzMx2Uk4wGpCkPSVdLmkVcCtwu6SVedz8OofXr0JhIh0dTxAR9Q7FzMzMzEaYE4zGdAVwNbB7ROwXEfsCs4FrgMvrGVg1CoUWisV2P67WzMzMbCfkBKMxzYyIKyKiuzQiIroj4nJgRh3jGoACXV3P1DsIMzMzMxthTjAa02JJ50p6kaRn5e5Fks4F7qx3cNVobp5Ee/sj9Q7DzMzMzEaYX7TXmE4B3gX8GzAHELACuBb4dh3jqlqh0EZHx0q6uzfR1DSh3uGYmZmZ2QhxgtGAIqID+FbuRrEmOjtX0dQ0r96BmJmZmdkIcRWpUaLRX7BXSXPzZDZvfshPkzIzMzPbiTjBGD1U7wBKOjuf4a67juWZZ37Z53zprd6b6epaMzKBmZmZmVndOcEYPX5WzUyS9pD0a0lLJd0n6UN5/HRJv5T05/x30C/va26exqZNf2LTpgf6nbepaZwbe5uZmZntRJxgNCBJO5RWRMRn+psn6wI+FhEHAAuB90k6EPgUcENE7AfckIcHGx8TJz6X9vaH+p23qWkKHR2P0t29abCbMzMzM7NRxAlGY/q1pA9I2rN8pKRWSS+XdDFwaqUFI+LxiLgj968HlpKeRHUCcHGe7WLgjUMJcOLEg2hvf4iIYp/zSUJqcSmGmZmZ2U7CCUZjejXQDfxA0mOS7pf0EPBn4CTgKxFxUX8rkTQfOAS4FdgtIh6HlIQAu/ayzOmSFklatGrVql7XPXHiQRSLm+noeKzfnWlunkp7+zK6u9v7ndfMzMzMRjc/prYBRUQ7cC5wrqQWYCawOSLWVLsOSZOAHwEfjoh1vdeo2mHb5wPnAyxYsKDXxz9NmvQCADZuvI9x4+b2E0sTUhPt7Q8yceKB1e2AmZmZmY1KLsFocBHRmas9ral2mZyU/Ai4LCKuyqOflDQ7T58NrBxKXBMnPo9CYTwbNlT3YvHm5mm0tz9MV9f6oWzWzMzMzBqcE4wGJunMQSwj0tu+l0bEOWWTrmVbu41TgR8PLbZmJkx4LuvXV/d6DqlAodDGpk1/9HsxzMzMzMYwJxgNSFJB0reBcYNY/EjgHcDLJS3J3fHA2cBxkv4MHJeHh2TixOfR3v4gnZ1PVzV/c/MUOjpWsWVL/+02zMzMzGx0chuMxvQT4P6I+OeBLhgRN9H7S/mOHVJUPUyefAhPPvld1q79PTNnvr6qZVpaprNx4720tOxCU9OEWoZjZmZmZg3AJRiNaQFwdb2D6E9b2760tu7OmjW/qnqZQqGFQqGFDRvuIqJ7GKMzMzMzs3pwgtGYjgH+W9KL6h1IXyQxbdoxrFt3C93dG6perrl5Kl1da9i06QG3xzAzMzMbY5xgNKCIuB94FfClesfSn+nTX01EJ6tXXzeg5VpaZtHe/hDt7Q8PU2RmZmZmVg9OMBpURDwGvLbecfRn4sTnMmHCgaxadeWASiMk0dIyi02b7qO9/dFhjNDMzMzMRpITjAYWEaPipRGzZr2F9vYHWbfulgEtJzXR0jKTDRvucpJhZmZmNkb4KVINSNK1fU2PiDeMVCzVmD791Tz++Pk89ti3mDJlIdW+NRzS+zRaW2ewceMSIjppa5s3oOXNzMzMrLE4wWhMLwaWAz8AbqX3x842hEKhldmz/4GHH/4szzxzPdOnv2pAy0vNW6tLFYubmDDhOUhNwxStmZmZmQ0nV5FqTLsDnwYOAr5GejHeUxHx24j4bV0j68WMGa9lwoQDWb78y3R1rRnw8qm61G60tz/CunW30d29sfZBmpmZmdmwc4LRgCKiOyL+NyJOBRYCfwF+I+kDdQ6tV1Iz8+b9K11d63j44c8RURzEOkRr6yyKxXbWrPkd7e0r/BhbMzMzs1HGCUaDkjRO0puBS4H3AV8HrqpvVH2bMGE/5s79EGvW/IYnnvjOoNfT3DyFlpZd2LDhbtatu42urlHR1t3MzMzMcBuMhiTpYlL1qJ8D/xYR99Y5pKrtuutJbNq0lMceO4/m5mnMmvWWQa1HambcuN3o6lrH2rU30dY2j7a2vWlqaqtxxGZmZmZWS04wGtM7gI3As4EPlj1VSUBExJR6BdYfScybdwbd3Rt45JGzKRY72HXXkwb9ZKjm5ilETGLLlhW0tz9CW9vetLXt6UTDzMzMrEE5wWhAETGqq64VCi3svfcXeOihf2HFinNob3+IPfb4OIXCuEGtTyrQ0jKDiG62bHmI9va/Mm7cnrS17UFzc8PmWmZmZmY7pVF9IWuNq1BoZe+9v8Duu7+Tp566mqVL38HGjfcPaZ2lF/O1tMyks/MJ1q79PWvW/IEtWx6nWOysUeRmZmZmNhROMGzYSAXmzHkf++77dbq7N/DHP76TRx75Ap2dTw95vc3N02ht3RUosmHDEp555gbWr19CR8dTFItdtdkBMzMzMxswV5GyYTd16hEceOAVPPbYuaxadRWrV/+MXXf9W2bNOpHW1llDWndT03iamsYTUaSr6xk6Oh4HCrS0zGLcuNk0N0+jqWl8bXbEzMzMzPrlBMNGRHPzZPbc85PsuuuJPPbYeTzxxCU8+eSl7LLLccyY8QYmTz4MafAFaqlUYwowhYgixeJ6Nmx4EoBCYQKtrbvT2jqTpqZJg24LYmZmZmb9c4Jhg1QAgojigBKDtrb57L332WzZsoInn/wBq1f/lKef/jktLbsxffqrmDbtKCZOfB5S06Ajkwo0NU2iqWkSAMViBx0dK2hvfwgICoUJtLTsSmvrdAqFiTQ1TRhScmNmZmZm28hvSrbeLFiwIBYtWtTr9I0b/0h7+4O0tOw66MfQprd238jq1T9l3bpbgW6amqYydeqRTJ78QiZPPoTW1jmDXn/lbXZQLG6mWOwA0qN1m5om09w8nZaWXSgUxueupWbbtOEnaXFELKh3HGZmZjs7l2DYoE2Y8GyKxU62bFlOa+vMQZU6FAptTJ/+SqZPfyVdXetZt+4W1q69kbVrf8/TT18HQEvLLCZNOoSJEw9kwoT9GT/+OTQ3Tx503IVCK4VC69bhiCBiCx0dj9Levmy7+ZqaptHcPIXm5skUCm1I4ygUxtU04TEzMzMbS5xg2KBJBSZNOoimpols3vxHCoUJQ7rwb26ezPTpxzF9+nFEFGlvf5ANG5awfv0dbNhwF888c/3WeVtb5zBhwrNpa9srv+V7Hm1t87dWixrYfgipjUJh+5f3RXRRLG6gvf1pIsqfTCWamibkaliTc7uOVqTW/LfFCYiZmZnttFxFynrVXxWpcl1d69i06Y90dj5FoTCOpqYpNW/X0Nn5NJs2PcDmzQ+waVPqtmx5FOjeOk9z8wza2vaktXV2btid/o4bl/72TCIGI5V4dBLRQbHY2SP5ABCFwniamibkNh7jKRTaypKPFqRmCgXn97XkKlJmZmaNwVc4VhPNzVOYMuVwOjvXsGXLcrZseQwo5mpGk5CG/lVraZnO1KkvZurUF28dVyx2bq3a1N7+MO3tD7NlyyNs2HAHHR0rgWKPOKfR0jIrt7eYsbVrbi71T6e5eQbNzZN7jTmVeLQCrTRVqBWWEpAuisXNdHevo6Oji4jijjNSoFBoo6mpLScgbbkaVitSc4WuySUjZmZm1vCcYFhNtbRMo6VlGhMm7E9X11o6OlbS2fkkxeIWIFWrKhTG5QvpoX/9CoUW2trm09Y2f4dpEV10dj7Fli2P09HxBB0d6W9n52q6ulazYcMjdHauJqKj4rpTFaipNDdPpbl5Ck1NU2hunpr/lvon09Q0MVeTmpD7J+a2Gn03Eo8oEtG9tdF5RFfueitVFIVCS1lVrNat29k23LS1g6YewwUnKGZmZjbsnGDYsCgUWmhtnUlr60zgQLq72ykWN9LVtZ6urmfo6lqzNekoSdWGWrbesU8XyIO/IJaaczWp3XudJyIoFjfS2fkUnZ1Pb00+urrW0d29jq6utVv7t2x5jK6utXR3r6dnyciOmnKCkpKOVFVqW5eeUtVWoRvfS39bfmFgqnaWnoC1eWuSAt19JCbbH5PUtZQd6/JqWy1Ac1liUiAlJgXS51E+nP76Eb9mZmZWzgmGjYimplQVqKVlBjAfgGKxi4gtFItbKBY76O7eSLG4ke7uTXR3b8oX0eUXzaV+bb34TYlIpQvg6hKT9Ija9M6MSqUglUQU6e7eSHf3Wrq61ueYt3W9DXd1rWHLlhV5XDvFYjvl7UeqlUot2nJJUHnj8v7/pv7mrQnFtr/bV8XalmS09PJ32/zQlNfZVLb8tv7S+gqF8lKV0nzq8ZmVPkttN25b6c3g349iZmZmI8MJhtVNauTcTFPTxIrTS20ZUoPqrq1dsdhJsdhBxBYiSv0dOSFJ01MyIrZPUEq2H58uYre/oN12V377/nRBrK0lE+PGDe3ufdqX9rJucx/Dm3MyVurftt/lf7u7N1UcX/pbeypLVkpJSHm7kfL+bQnKtiSkPBHZPgHZNq7AlClHMHfu+4YhfjMzM6slJxjWsFJj6hZg4C+821Z1KP1NjaxLf4vb/U3Tu8q60rw79qcnRqVli8Vutq8qVUpqBqdUFarctpIYlXWVxqf+7Utudlxm2xOwusoSjy6g9ESsLdsdj1TK1JWPQSnR2zHh2zZceZ5t2yz93dxjnu6y7XSVxVAaXyQinGCYmZmNAk4wbEwaybYBqe1DMf9NXUpMyofTPNv6e06rPLxtuSLFYmmdxe3WVT5P+fa3X28aliIfl3EUChN6xF1KlrbFv20aVC4NGhnFYjstLbvVbftmZmZWPScYOxFJrwa+BjQBF0bE2XUOaUxIpQZN7AwPaNrWkHxb4rH9uB3/Dnx65XkKhXFDjN7MzMxGghOMnYRSZfZvAscBK4DbJV0bEffXNzIbTbavmlUaV59YzMzMrDH5+ZI7j8OBv0TEg5Fa+l4OnFDnmMzMzMxsjHGCsfOYAywvG16Rx21H0umSFklatGrVqhELzszMzMzGBicYO49KFVl2aLUbEedHxIKIWDBr1qwRCMvMzMzMxhK3wdh5rAD2KBueCzzW1wKLFy9+StLDZaNmAk8NQ2yjhfe/sfd/Xr0DMDMzM9C2J7jYWKb0ZrM/AccCjwK3A38XEfcNYB2LImLBMIXY8Lz/O/f+m5mZWXVcgrGTiIguSe8HfkF6TO13BpJcmJmZmZlVwwnGTiQirgOuq3ccZmZmZjZ2uZG3DcT59Q6gzrz/ZmZmZv1wGwwzMzMzM6sZl2CYmZmZmVnNOMEwMzMzM7OacYJhO5D0akkPSPqLpE9VmC5JX8/T75Z0aD3iHA5V7Pv+km6WtEXSx+sR43CqYv9Pzp/53ZL+IOkF9YjTzMzMGpcTDNuOpCbgm8BrgAOBkyQd2GO21wD75e504FsjGuQwqXLfnwY+CHx5hMMbdlXu/0PAyyLi+cBnccNvMzMz68EJhvV0OPCXiHgwIjqAy4ETesxzAnBJJLcA0yTNHulAh0G/+x4RKyPidqCzHgEOs2r2/w8R8UwevIX0RngzMzOzrZxgWE9zgOVlwyvyuIHOMxqN1f2q1kD3/13Az4c1IjMzMxt1/KI960kVxvV8lnE184xGY3W/qlX1/ks6hpRgvGRYIzIzM7NRxwmG9bQC2KNseC7w2CDmGY3G6n5Vq6r9l/R84ELgNRGxeoRiMzMzs1HCVaSsp9uB/STtJakVeBtwbY95rgVOyU+TWgisjYjHRzrQYVDNvo9l/e6/pD2Bq4B3RMSf6hCjmZmZNTiXYNh2IqJL0vuBXwBNwHci4j5J78nTzwOuA44H/gJsAt5Zr3hrqZp9l7Q7sAiYAhQlfRg4MCLW1SvuWqnysz8DmAGcKwmgKyIW1CtmMzMzazyK2JmqmJuZmZmZ2XByFSkzMzMzM6sZJxhmZmZmZlYzTjDMzMzMzKxmnGCYmZmZmVnNOMEwMzMzM7OacYJhQyIpJH2vbLhZ0ipJPx3hOPaXtETSnZL26TFtqqRLJP01d5dImpqnHd1brJKukzRtELEcLemIAS4zuxSHpAmSLpN0j6R7Jd0kaVI/y28YaJw9lv+NpEeUnz2bx10z1PUOIZ5Zkv63Hts2MzOzoXGCYUO1EThI0vg8fBzwaB3ieCPw44g4JCL+2mPat4EHI2KfiNgHeIj0Juo+RcTxEbFmELEcDQwowQA+ClyQ+z8EPBkRz4uIg4B3AZ2DiKOi/ILESv/7a4Aj8zzTgNm12uZARcQq4HFJR9YrBjMzMxscJxhWCz8HXpv7TwJ+UJog6XBJf8glC3+Q9Jw8/rmSbsulDndL2k/SREk/k3RXvnN/Ys8NSTpY0i15masl7SLpeODDwLsl/brH/PsChwGfLRt9FrCgrKRjSl7X/ZLOK118S1omaWbuf3tZvP8tqSmPf7WkO3LMN0iaD7wH+Eie96WS3pr35y5JN/ZyDP8GKN2xn01ZkhYRD0TElry9j+Z13Ztf8tfz+EzKcdyRS0BOyOPnS1oq6VzgDmCPCjFcTnp7N8CbSW/s7m+9FT8zSWfn43m3pC/ncbMk/UjS7bkrJTMvy8eqVAI1OW/2GuDkXo6XmZmZNaqIcOdu0B2wAXg+cCXQBiwh3cH/aZ4+BWjO/a8AfpT7vwGcnPtbgfGki+wLytY9tcL27gZelvvPAr6a+88EPl5h/jcAV1cYf3WedjTQDuxNenv1L4G35HmWATOBA4CfAC15/LnAKcAsYDmwVx4/vVIswD3AnNw/rUIsewGLy4YPBlYCNwOfA/bL4w/L65oITALuAw4pfQ75bzMwJffPJL1tXcB8oAgs7OVz/A3wonx8m4Dr8zL9rXeHzwyYDjzAthd5Tst/vw+8JPfvCSzN/T8Bjsz9k8q+L3OAe+r9HXfnzp07d+7cDaxzCYYNWUTcTboYPQm4rsfkqcD/SLoX+Arw3Dz+ZuDTkj4JzIuIzaSL51dI+oKkl0bE2vIV5XYT0yLit3nUxcBR/YQnoNLr6svH3xYRD0ZEN6n05SU95j2WdHF/u6QleXhvYCFwY0Q8lI/D073E8HvgIkn/QLp472k2sKo0EBFL8vq/RLpYv13SATmuqyNiY0RsIJUwvLTCfv2HpLuB/yNdpO+Wpz0cEbf0EiNAN3ATcCIwPiKWVbHeSp/ZOlLSdqGkNwOb8jpeAfxXPobXkkqOJufjc46kD5I+3648/0rgWX3Ea2ZmZg3ICYbVyrXAlymrHpV9Fvh1pLYEryeVchAR3yeVIGwGfiHp5RHxJ7bdpf+8pDNqENd9wCHlbQ5y/wuApXlUzwSk57CAiyPi4Nw9JyLOpPfkZfuVRbwH+AypWtISSTN6zLKZfFzKltkQEVdFxHuBS4Hj8/b6czKpZOWwiDgYeLJs3RurWP5yUunSD6tZb6XPLCcIhwM/IrWNKVX9KgAvLjuOcyJifUScDbybVIp1i6T98/xtpGNjZmZmo4gTDKuV7wBnRcQ9PcZPZVt7gtNKIyXtTWp4/XVScvJ8Sc8CNkXEpaRk5dDyFeW7489IKt21fwfwW/oQEX8B7iRd4Jd8BrgjTwM4XNJeOfE4kXQXv9wNwFsk7Zpjny5pHqkU5mWS9iqNz/OvB0rtCJC0T0TcGhFnAE+xY/uHP5FKgErzHylpl9zfChwIPAzcCLxR6SlTE4E3Ab/rsa6pwMqI6JR0DDCvr+NTwe+Az7NjolhxvZU+M6UnXk2NiOtIbWMOzuu4Hnh/2X4enP/uExH3RMQXgEVAKcF4NnDvAOM3MzOzOmuudwA2NkTECuBrFSZ9EbhY0keBX5WNPxF4u6RO4AlSe4oXAl+SVCQ9NemfKqzvVOA8SROAB4F3VhHeu4BvSCq1G7g5jyu5GTgbeB7pIv7q7Xct7pf0GeD6nIR0Au+LiFsknQ5clcevJD1F6yfAlbkh9AdIDb73y9u+AbiL7TewUenxufvmpGcf4FuSRLoJ8DNS25WQdBFwW170woi4s8e+Xgb8RNIiUnuYP1ZxfLbbWVKi0FNv630eO35mk4EfS2rL+/yRPO8HgW/malbNpGP9HuDDOWnpBu4nPTQA4Ji872ZmZjaKlBphmlmZ/JSolcDuEVGzR8T2sb03kaoffabfmXcS+YlbJ0TEM/WOxczMzKrnEgyzyu4jlRAMe3IBEBFXV2ibsdOSNAs4x8mFmZnZ6OMSDDMzMzMzqxk38jYzMzMzs5pxgmFmZmZmZjXjBMPMzMzMzGrGCYaZmZmZmdWMEwwzMzMzM6uZ/wdnQk8k5/xDlwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Setting up subplots\n", + "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", + "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", + "fig.delaxes(ax[2, 1])\n", + "\n", + "#Moraux\n", + "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", + "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", + "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", + "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", + "ax[0,0].legend()\n", + "\n", + "#Suárez\n", + "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", + "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", + "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", + "ax[0,1].legend()\n", + "\n", + "#Lodieu\n", + "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", + "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", + "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", + "ax[1,0].legend()\n", + "\n", + "#Béjar\n", + "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", + "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", + "ax[1,1].legend()\n", + "\n", + "#Peña Ramírez\n", + "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", + "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", + "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", + "ax[2,0].legend()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f63bb9fc", + "metadata": {}, + "source": [ + "### Creating a General Power-Law Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "ed848735", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all mass data\n", + "all_masses = []\n", + "for a in M_mass:\n", + " all_masses.append(a)\n", + "for b in S_mass:\n", + " all_masses.append(b)\n", + "for c in L_mass:\n", + " all_masses.append(c)\n", + "for d in B_mass:\n", + " all_masses.append(d)\n", + "for e in P_mass:\n", + " all_masses.append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "df52ba0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_masses)) #to make sure the correct amount of entries were appended" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "dfc306a1", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all power_law output data\n", + "all_power = []\n", + "for f in M_mass:\n", + " all_power.append(power_law(f, M_a))\n", + "for g in S_mass:\n", + " all_power.append(power_law(g, S_a))\n", + "for h in L_mass:\n", + " all_power.append(power_law(h, L_a))\n", + "for k in B_mass:\n", + " all_power.append(power_law(k, B_a))\n", + "for l in P_mass:\n", + " all_power.append(power_law(l, P_a))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "15d0484d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_power))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "6a6629e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", + "avg_a = popt\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(all_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(all_masses, all_power)\n", + "plt.plot(all_masses, avg_power, color='r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3739e5d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.64439915]\n" + ] + } + ], + "source": [ + "print(popt)" + ] + }, + { + "cell_type": "markdown", + "id": "109097ab", + "metadata": {}, + "source": [ + "#### Cleaning Up the Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "7475e6d3", + "metadata": {}, + "outputs": [], + "source": [ + "#combining all mass and output data sets to fit\n", + "all_masses = np.array(all_masses)\n", + "all_power = np.array(all_power)\n", + "\n", + "#put combined mass data set into increasing order and reorder output array to match new indices \n", + "sorted_indices = np.argsort(all_masses)\n", + "sorted_masses = all_masses[sorted_indices]\n", + "sorted_powers = all_power[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "acc33225", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", + "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bd3e02dd", + "metadata": {}, + "source": [ + "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "0e0bc7ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00211972]\n" + ] + } + ], + "source": [ + "print(avg_aerr)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "7037fa8c", + "metadata": {}, + "outputs": [], + "source": [ + "#calculate the low and high outputs based on error of a\n", + "avg_aup = avg_a + avg_aerr\n", + "avg_alow = avg_a - avg_aerr\n", + "avg_outup = power_law(sorted_masses, avg_aup)\n", + "avg_outlow = power_law(sorted_masses, avg_alow)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "1cb19a53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#fit plot including error\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", + "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", + "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "cf3ff96a", + "metadata": {}, + "outputs": [], + "source": [ + "#create an error array for all values in sorted_masses\n", + "power_errors = []\n", + "for m in M_mass:\n", + " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", + "for n in S_mass:\n", + " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", + "for o in L_mass:\n", + " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", + "for p in B_mass:\n", + " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", + "for q in P_mass:\n", + " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", + "\n", + "#sort errors based on sorted indices\n", + "\n", + "# convert sorted_indices to a NumPy array for indexing\n", + "sorted_indices = np.array(sorted_indices)\n", + "\n", + "# Sort errors based on sorted indices\n", + "sorted_errors = [power_errors[i] for i in sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30d890fa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0206cbc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1949c38b90e1d2de86545054791ba089c34b84e4 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Mon, 24 Jun 2024 13:29:04 -0700 Subject: [PATCH 02/48] Addition of BD IMF and updated name for exoplanet IMF --- changes/BD_IMF.ipynb | 778 ++++++++++++++++++++++++++++++++++++ changes/Exoplanet_IMF.ipynb | 608 ++++++++++++++++++++++++++++ 2 files changed, 1386 insertions(+) create mode 100644 changes/BD_IMF.ipynb create mode 100644 changes/Exoplanet_IMF.ipynb diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb new file mode 100644 index 00000000..7930e27f --- /dev/null +++ b/changes/BD_IMF.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "11fc535c-f15b-4386-a98d-5d8f4c1c1ebf", + "metadata": {}, + "source": [ + "## Plotting Studied Power-Law Functions for Brown Dwarf Stars (BDs)" + ] + }, + { + "cell_type": "markdown", + "id": "b35147ec-8425-4834-8d36-2d3802823277", + "metadata": {}, + "source": [ + "#### Importing Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "de897cdf-e478-4d49-b16a-ea7a1e0d547d", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.optimize import curve_fit\n", + "import math\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score" + ] + }, + { + "cell_type": "markdown", + "id": "24c6f353-fbf2-412d-864d-6df254492717", + "metadata": {}, + "source": [ + "#### Defining a general power law function and mass range" + ] + }, + { + "cell_type": "markdown", + "id": "dc79ec2b-79ad-4e17-a010-701923320bf4", + "metadata": {}, + "source": [ + "All of the papers that we consider here are in agreement that the initial mass function (IMF) for brown dwarfs is best represented by a power law function, much like full-fledged stars and planets. The general power law equation is as follows:\n", + "\n", + "$ f(\\alpha) = M^{-\\alpha} $,\n", + "\n", + "Where M represents the mass of an object and $\\alpha$ represents the index of the function. \n", + "\n", + "We know that we can find a general power law to represent brown dwarfs in most star forming regions because, in a [2021 paper by M. J. Huston and K. L. Luhman](https://arxiv.org/pdf/2101.11497), a conclusion is made that there are not significant variations in the IMF of low mass stars and brown dwarfs based on star-forming conditions." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "2f67bd3c-cc80-4294-88e5-f257100893f6", + "metadata": {}, + "outputs": [], + "source": [ + "def power_law(M, a):\n", + " return M ** (-1 * a)\n", + "# M represents a mass value, and a is the associated alpha value of a specified paper\n", + "\n", + "# By NASA JPL, BDs have a mass range of 13-80 Jupiter masses (0.0124 - 0.0764 solar masses)\n", + "BD_masses = np.linspace(0.0124, 0.0764, 1000)" + ] + }, + { + "cell_type": "markdown", + "id": "1a4b6339-52d4-41a8-b890-5993559be0de", + "metadata": {}, + "source": [ + "We chose this mass range for brown dwarf stars because 80 Jupiter masses is approximately the lower mass limit for hydrogen burning, which brown dwarfs characteristically do not do. In addition, 13 Jupiter masses is the lower mass limit for deuterium burning, which is what defines a brown dwarf star. This mass range is supported by [NASA JPL](https://www.jpl.nasa.gov/images/pia23685-what-is-a-brown-dwarf)." + ] + }, + { + "cell_type": "markdown", + "id": "cbbf169d-5f2d-4fa2-83e9-20b977e299a1", + "metadata": {}, + "source": [ + "#### Defining different $\\alpha$ values and errors based on specified papers" + ] + }, + { + "cell_type": "markdown", + "id": "f5bcb285-3f28-406d-8377-8ea9376fe53e", + "metadata": {}, + "source": [ + "To create a general power law function for brown dwarfs we wanted to consider a plethora of functions already found. To do this, we found seven papers over a span of 25 years surveying different star-forming regions. The papers considered were as follows:\n", + "\n", + "1. \"L Dwarfs and the Substellar Mass Function\" ([I. Neill Reid, et al. 1999](https://iopscience.iop.org/article/10.1086/307589/pdf))\n", + "2. \"The Substellar Mass Function: A Baysian Approach\" ([P. R. Allen, et al. 2005](https://iopscience.iop.org/article/10.1086/429548/pdf))\n", + "3. \"The low-mass content of the massive young star cluster RCW 38\" ([K. Mužić, et al. 2017](https://academic.oup.com/mnras/article/471/3/3699/4044705))\n", + "4. \"Looking Deep into the Rosette Nebula’s Heart: The (Sub)stellar Content of the Massive Young Cluster NGC 2244\" ([K. Mužić, et al. 2019](https://ui.adsabs.harvard.edu/abs/2019ApJ...881...79M/abstract))\n", + "5. \"The Field Substellar Mass Function Based on the Full-sky 20 pc Census of 525 L, T, and Y Dwarfs\" ([J. D. Kirkpatrick, et al. 2021](https://iopscience.iop.org/article/10.3847/1538-4365/abd107/pdf))\n", + "6. \"A Self-consistent Model for Brown Dwarf Populations\" ([R. E. Ryan, Jr., et al. 2022](https://iopscience.iop.org/article/10.3847/1538-4357/ac6de5/pdf))\n", + "7. \"A Volume-limited Sample of Ultracool Dwarfs. II. The Substellar Age and Mass Functions in the Solar Neighborhood\" ([W. Best, et al. 2024](https://iopscience.iop.org/article/10.3847/1538-4357/ad39ef/pdf))\n", + "\n", + "Below we list the indexes of the power laws found in the specified papers and their respective errors." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "384973ef-5396-4d20-af19-5899db1a5673", + "metadata": {}, + "outputs": [], + "source": [ + "#Neill Reid 1999\n", + "NR_a = 1.3\n", + "NR_aerr = 0.3 #made up because actual paper does not include errors or an exact numerical conclusion\n", + "\n", + "#Allen 2005\n", + "A_a = 0.3\n", + "A_aerr = 0.6 #error > actual value --> unconstrained\n", + "\n", + "#Mužić 2017 (RCW 38)\n", + "M1_a = 0.29\n", + "M1_aerr = 0.11\n", + "\n", + "#Mužić 2019 (NGC 2244)\n", + "M2_a = 1.03\n", + "M2_aerr = 0.02\n", + "\n", + "#Kirkpatrick 2021\n", + "K_a = 0.6\n", + "K_aerr = 0.1\n", + "\n", + "#Ryan 2022\n", + "R_a = 0.71\n", + "R_aerr = 0.11\n", + "\n", + "#Best 2024\n", + "B_a = 0.58\n", + "B_paerr = 0.16\n", + "B_naerr = 0.20\n", + "B_avgerr = 0.18" + ] + }, + { + "cell_type": "markdown", + "id": "38e02440-ed2d-4e91-8b1f-a8c54408da42", + "metadata": {}, + "source": [ + "#### Plotting curves together (w/o errors)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "4e520010-9702-45f2-877c-1fb5104fa7a1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting different IMFs based on literature\n", + "plt.plot(BD_masses, power_law(BD_masses, NR_a), color = 'r', label = \"Neill Reid 1999\")\n", + "plt.plot(BD_masses, power_law(BD_masses, A_a), color = 'orange', label = \"Allen 2005\")\n", + "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", + "plt.plot(BD_masses, power_law(BD_masses, M2_a), color = 'g', label = \"Mužić 2019\")\n", + "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", + "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", + "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", + "\n", + "#organizing the graph\n", + "plt.title(\"Brown Dwarf IMFs Based on Literature\")\n", + "plt.xlabel(\"Brown Dwarf Masses (solar masses)\")\n", + "plt.ylabel(\"M^-a\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e10c5f12-486a-44c8-ab70-bee7cd9b7998", + "metadata": {}, + "source": [ + "Looking at the curves, it is clear that the Neill Reid conclusion of the $\\alpha$ value is outdated in comparison to the other papers. This is most likely due to outdated theory and information, as this paper was created in 1999 using simulations, versus actual data of brown dwarf stars. With this in mind, we will subtract it from our future considerations as the rest of the papers considered since then agree that $\\alpha$ should be less than 1. In addition, it is explained in the Mužić 2019 paper that NGC 2244 is unusually dense, which is leading to a larger index value than typical. For this reason, I omitted this paper as well from consideration. Lastly, in Allen 2005, the error for the function is larger than the actual $\\alpha$ value, hinting that the function is unconstrained and should not be considered. Thus, we omitted this paper as well.\n", + "\n", + "Below is the graph subtracting the Neill Reid, Allen, and Mužić 2019 conclusions." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "11fc4c9d-0be7-4d75-a35c-4b82fae451f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting different IMFs based on literature\n", + "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", + "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", + "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", + "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", + "\n", + "#organizing the graph\n", + "plt.title(\"Brown Dwarf IMFs Based on Literature\")\n", + "plt.xlabel(\"Brown Dwarf Masses (solar masses)\")\n", + "plt.ylabel(\"M^-a\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "20d30430-4eb1-4c19-9179-3138a2814c6e", + "metadata": {}, + "source": [ + "#### Accounting for error in $\\alpha$" + ] + }, + { + "cell_type": "markdown", + "id": "64da7c68-85ef-40f6-a538-331d7f799572", + "metadata": {}, + "source": [ + "At this point we wanted to explore the individual functions and their errors to make sure that they seemed reasonable. Thus, we used the specified errors to find upper and lower bounds for each function and plotted the information." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "9d984c93-387a-4586-ba16-ac579562ca24", + "metadata": {}, + "outputs": [], + "source": [ + "#Mužić 2017 (RCW 38)\n", + "M1_aup = M1_a + M1_aerr\n", + "M1_alow = M1_a - M1_aerr\n", + "M1_outup = power_law(BD_masses, M1_aup)\n", + "M1_outlow = power_law(BD_masses, M1_alow)\n", + "\n", + "#Kirkpatrick 2021\n", + "K_aup = K_a + K_aerr\n", + "K_alow = K_a - K_aerr\n", + "K_outup = power_law(BD_masses, K_aup)\n", + "K_outlow = power_law(BD_masses, K_alow)\n", + "\n", + "#Ryan 2022\n", + "R_aup = R_a + R_aerr\n", + "R_alow = R_a - R_aerr\n", + "R_outup = power_law(BD_masses, R_aup)\n", + "R_outlow = power_law(BD_masses, R_alow)\n", + "\n", + "#Best 2024\n", + "B_aup = B_a + B_paerr\n", + "B_alow = B_a - B_naerr\n", + "B_outup = power_law(BD_masses, B_aup)\n", + "B_outlow = power_law(BD_masses, B_alow)" + ] + }, + { + "cell_type": "markdown", + "id": "7ac4acb4-f29b-4735-b58d-be7cd78d9f76", + "metadata": {}, + "source": [ + "#### Graphing Individual Power-Law Functions (Accounting for Error in $\\alpha$)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "ac20c536-15c8-4fc5-885e-190d3fd888d1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Setting up subplots\n", + "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", + "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "\n", + "#Mužić 2017\n", + "ax[0,0].plot(BD_masses, power_law(BD_masses, M1_a), color='y')\n", + "ax[0,0].fill_between(BD_masses, M1_outup, M1_outlow, color='y', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[0,0].set_title(\"Mužić Mass Function for BDs in RCW 38\")\n", + "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,0].set_ylabel(\"$M^{-0.29 ± 0.11}$\")\n", + "ax[0,0].legend()\n", + "\n", + "#Kirkpatrick 2021\n", + "ax[0,1].plot(BD_masses, power_law(BD_masses, K_a), color='c')\n", + "ax[0,1].fill_between(BD_masses, K_outup, K_outlow, color='c', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[0,1].set_title(\"Kirkpatrick Mass Function for BDs within 20 pc\")\n", + "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,1].set_ylabel(\"$M^{-0.6 ± 0.1}$\")\n", + "ax[0,1].legend()\n", + "\n", + "#Ryan 2022\n", + "ax[1,0].plot(BD_masses, power_law(BD_masses, R_a), color='b')\n", + "ax[1,0].fill_between(BD_masses, R_outup, R_outlow, color='b', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[1,0].set_title(\"Ryan Mass Function for BDs in Milky Way Disk\")\n", + "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,0].set_ylabel(\"$M^{-0.71 ± 0.11}$\")\n", + "ax[1,0].legend()\n", + "\n", + "#Best 2024\n", + "ax[1,1].plot(BD_masses, power_law(BD_masses, B_a), color='m')\n", + "ax[1,1].fill_between(BD_masses, B_outup, B_outlow, color='m', alpha=0.2, label=\"error in $\\\\alpha$\")\n", + "ax[1,1].set_title(\"Best Mass Function for BDs within 25 pc\")\n", + "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,1].set_ylabel(\"$M^{-0.58^{+ 0.16}_{-0.20~}}$\")\n", + "ax[1,1].legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cbc89037-9574-487a-9f75-e17fee2667db", + "metadata": {}, + "source": [ + "### Creating a General Power-Law Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "c594d201-d695-414d-9d23-05d6e1f2171b", + "metadata": {}, + "outputs": [], + "source": [ + "#Combine all of the power_law output data for all 5 papers\n", + "all_power = np.concatenate([power_law(BD_masses, M1_a), power_law(BD_masses, K_a), \n", + " power_law(BD_masses, R_a), power_law(BD_masses, B_a)])\n", + "all_err = np.concatenate([np.tile(M1_aerr, 1000), np.tile(K_aerr, 1000), \n", + " np.tile(R_aerr, 1000), np.tile(B_avgerr, 1000)])" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "5b054e0c-c0b6-4556-b649-a959fba74da0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4000 4000\n" + ] + } + ], + "source": [ + "#make sure all entries are appended\n", + "print(len(all_power), len(all_err))" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "a1aaa018-16da-44e0-9473-2611ec50ab2c", + "metadata": {}, + "outputs": [], + "source": [ + "#create a mass array of the same size\n", + "all_masses = np.tile(BD_masses, 4)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "baafa6a8-0c11-4380-899a-a61834474854", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4000" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#make sure it is the right size\n", + "len(all_masses)" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "56aa35b6-aa24-4695-9094-fe5857846bd5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers, sigma = sorted_errors)#, absolute_sigma = True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label = 'data')\n", + "plt.plot(sorted_masses, avg_power, color = 'r', label = 'curve fit')\n", + "\n", + "#label graph\n", + "plt.title(\"Power Law Curve Fit for BD Candidate Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"$M^{-0.584 ± 0.002}$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "011eac9f-7e9d-4e5d-8bfc-f0380920f575", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5836496] [0.00202593]\n" + ] + } + ], + "source": [ + "#determine the index of the power law based on the curve fit\n", + "print(avg_a, avg_aerr)" + ] + }, + { + "cell_type": "markdown", + "id": "0c050ca1-499e-426a-b4ef-0e4e64fc80a2", + "metadata": {}, + "source": [ + "Based on the curve fit, our a value for BDs is 0.5836496 ± 0.00202593. The error is smaller than expected, which may take more looking into. " + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "de5f6d54-b675-447c-bef7-a979715eb5f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#account for error in plot\n", + "avg_aup = avg_a + avg_aerr\n", + "avg_alow = avg_a - avg_aerr\n", + "power_outup = power_law(sorted_masses, avg_aup)\n", + "power_outlow = power_law(sorted_masses, avg_alow)\n", + "\n", + "plt.scatter(sorted_masses, sorted_powers, label = 'data')\n", + "plt.plot(sorted_masses, power_law(sorted_masses, avg_a), color= 'r', label = \"curve fit for power law\")\n", + "plt.fill_between(sorted_masses, power_outup, power_outlow, color = 'r', label = \"error in a\")\n", + "plt.title(\"Power Law Curve Fit for BD Candidate Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"$M^{0.584 ± 0.002}$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "ecbe93a5-32d7-4648-bc99-c20b13db9ca2", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "only integer scalar arrays can be converted to a scalar index", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[154], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mall_power\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" + ] + } + ], + "source": [ + "range(all_power)" + ] + }, + { + "cell_type": "markdown", + "id": "83430453-9da9-49e6-a1b1-4f12db8b63a7", + "metadata": {}, + "source": [ + "#### Cleaning Up the Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "d1ef4b5a-06b7-4295-9c5a-7e9d231020ec", + "metadata": {}, + "outputs": [], + "source": [ + "#combining all mass and output data sets to fit\n", + "all_masses = np.array(all_masses)\n", + "all_power = np.array(all_power)\n", + "\n", + "#put combined mass data set into increasing order and reorder output array to match new indices \n", + "sorted_indices = np.argsort(all_masses)\n", + "sorted_masses = all_masses[sorted_indices]\n", + "sorted_powers = all_power[sorted_indices]\n", + "sorted_errors = all_err[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "d3af8ed8-a459-486c-9f44-c6a9f1008d59", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", + "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "400886ad-ad9a-47a9-8fa2-e513ef125d45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7.20652905e-05]\n" + ] + } + ], + "source": [ + "print(avg_aerr)" + ] + }, + { + "cell_type": "markdown", + "id": "9bc14680-1eb4-45ec-abed-4b399d7693a4", + "metadata": {}, + "source": [ + "#### Converting into a log function to match the curve fit" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "76bfab00-53a2-4a18-9cee-b4f11cfdde98", + "metadata": {}, + "outputs": [], + "source": [ + "#take the log of each of the values in sorted_powers\n", + "log_power = []\n", + "for n in sorted_powers:\n", + " log_power.append(math.log(n))\n", + "\n", + "#take the log of each of the values in sorted_masses\n", + "log_masses = []\n", + "for a in sorted_masses:\n", + " log_masses.append(math.log(a))\n", + "\n", + "#make sure both are arrays\n", + "log_power = np.array(log_power)\n", + "log_masses = np.array(log_masses)" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "527b824d-29a7-4715-810b-01b626eae7fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitted parameters: a = -0.5450, b = -4.1664664035223473e-10\n", + "RMSE: 0.5058\n", + "MAE: 0.4105\n", + "R-squared: 0.2151\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#define a linear model\n", + "def linear_model(x, a, b):\n", + " return a * x + b\n", + "\n", + "#curve fit\n", + "params, covariance = curve_fit(linear_model, log_masses, log_power)\n", + "\n", + "#get the fitted values\n", + "power_fitted = linear_model(log_masses, *params)\n", + "\n", + "#calculate residuals\n", + "residuals = log_power - power_fitted\n", + "\n", + "#calculate error metrics\n", + "rmse = np.sqrt(mean_squared_error(log_power, power_fitted))\n", + "mae = mean_absolute_error(log_power, power_fitted)\n", + "r2 = r2_score(log_power, power_fitted)\n", + "\n", + "#output the results\n", + "print(f\"Fitted parameters: a = {params[0]:.4f}, b = {params[1]}\")\n", + "print(f\"RMSE: {rmse:.4f}\")\n", + "print(f\"MAE: {mae:.4f}\")\n", + "print(f\"R-squared: {r2:.4f}\")\n", + "\n", + "#plot the results\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(log_masses, log_power, label='Data', color='blue', s=10)\n", + "plt.plot(log_masses, linear_model(log_masses, *params), label='Fitted line', color='red')\n", + "plt.legend()\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Linear Fit of Multiple Linear Functions')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "23e78450-e261-4fe7-b307-41204c800ed3", + "metadata": {}, + "outputs": [], + "source": [ + "linpower_fit = []\n", + "for l in log_masses:\n", + " linpower_fit.append(m*l + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "2d6df51d-54d3-48d2-bfcd-49f0dceed8bc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(log_masses, log_power, '.')\n", + "plt.plot(log_masses, linpower_fit)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4ddeb15-31d9-48ba-881d-992def181f89", + "metadata": {}, + "outputs": [], + "source": [ + "#find general error by calculating average range\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/changes/Exoplanet_IMF.ipynb b/changes/Exoplanet_IMF.ipynb new file mode 100644 index 00000000..0bd50ba4 --- /dev/null +++ b/changes/Exoplanet_IMF.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "23642516", + "metadata": {}, + "source": [ + "## Plotting Studied Power-Law Functions For Exoplanets" + ] + }, + { + "cell_type": "markdown", + "id": "ad57fd40", + "metadata": {}, + "source": [ + "#### Importing Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "d6503a21", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "markdown", + "id": "4f72f6f7", + "metadata": {}, + "source": [ + "#### Creating the general power-law function for small masses" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d51b38dc", + "metadata": {}, + "outputs": [], + "source": [ + "#define function\n", + "def power_law(M, a):\n", + " return M**(-1*a)\n", + "\n", + "# M is a defined mass range according to a specified paper, and a is the associated alpha value" + ] + }, + { + "cell_type": "markdown", + "id": "3fe49d7b", + "metadata": {}, + "source": [ + "#### Defining different mass/alpha values based on published papers" + ] + }, + { + "cell_type": "markdown", + "id": "b7736fad", + "metadata": {}, + "source": [ + "***Masses are in solar masses!***" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "4145bc16", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", + "M_mass = np.linspace(0.03, 0.6, 1000)\n", + "M_a = 0.69\n", + "M_aerr = 0.15\n", + "\n", + "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", + "S_mass = np.linspace(0.02, 0.5, 1000)\n", + "S_a = 0.60\n", + "S_aerr = 0.13\n", + "\n", + "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", + "L_mass = np.linspace(0.01, 0.5, 1000)\n", + "L_a = 0.5\n", + "L_aerr = 0.2\n", + "\n", + "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", + "B_mass = np.linspace(0.013, 0.1, 1000)\n", + "B_a = 0.7\n", + "B_aerr = 0.3\n", + "\n", + "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", + "P_mass = np.linspace(0.006, 0.25, 1000)\n", + "P_a = 0.6\n", + "P_aerr = 0.2" + ] + }, + { + "cell_type": "markdown", + "id": "f2a24e63", + "metadata": {}, + "source": [ + "#### Plotting all of the curves together (w/o accounting for error)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "60cab6f2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot all of the power law functions based on mass ranges\n", + "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", + "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", + "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", + "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", + "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", + "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f5494d3d", + "metadata": {}, + "source": [ + "#### Accounting for error in a" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "1e6c80f4", + "metadata": {}, + "outputs": [], + "source": [ + "#Moraux\n", + "M_aup = M_a + M_aerr\n", + "M_alow = M_a - M_aerr\n", + "M_outup = power_law(M_mass, M_aup)\n", + "M_outlow = power_law(M_mass, M_alow)\n", + "\n", + "#Suárez\n", + "S_aup = S_a + S_aerr\n", + "S_alow = S_a - S_aerr\n", + "S_outup = power_law(S_mass, S_aup)\n", + "S_outlow = power_law(S_mass, S_alow)\n", + "\n", + "#Lodieu\n", + "L_aup = L_a + L_aerr\n", + "L_alow = L_a - L_aerr\n", + "L_outup = power_law(L_mass, L_aup)\n", + "L_outlow = power_law(L_mass, L_alow)\n", + "\n", + "#Béjar\n", + "B_aup = B_a + B_aerr\n", + "B_alow = B_a - B_aerr\n", + "B_outup = power_law(B_mass, B_aup)\n", + "B_outlow = power_law(B_mass, B_alow)\n", + "\n", + "#Peña Ramírez\n", + "P_aup = P_a + P_aerr\n", + "P_alow = P_a - P_aerr\n", + "P_outup = power_law(P_mass, P_aup)\n", + "P_outlow = power_law(P_mass, P_alow)" + ] + }, + { + "cell_type": "markdown", + "id": "041954f2", + "metadata": {}, + "source": [ + "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "dc7a858e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Setting up subplots\n", + "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", + "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", + "fig.delaxes(ax[2, 1])\n", + "\n", + "#Moraux\n", + "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", + "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", + "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", + "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", + "ax[0,0].legend()\n", + "\n", + "#Suárez\n", + "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", + "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", + "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", + "ax[0,1].legend()\n", + "\n", + "#Lodieu\n", + "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", + "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", + "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", + "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", + "ax[1,0].legend()\n", + "\n", + "#Béjar\n", + "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", + "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", + "ax[1,1].legend()\n", + "\n", + "#Peña Ramírez\n", + "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", + "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", + "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", + "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", + "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", + "ax[2,0].legend()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f63bb9fc", + "metadata": {}, + "source": [ + "### Creating a General Power-Law Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "ed848735", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all mass data\n", + "all_masses = []\n", + "for a in M_mass:\n", + " all_masses.append(a)\n", + "for b in S_mass:\n", + " all_masses.append(b)\n", + "for c in L_mass:\n", + " all_masses.append(c)\n", + "for d in B_mass:\n", + " all_masses.append(d)\n", + "for e in P_mass:\n", + " all_masses.append(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "df52ba0f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_masses)) #to make sure the correct amount of entries were appended" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "dfc306a1", + "metadata": {}, + "outputs": [], + "source": [ + "#combine all power_law output data\n", + "all_power = []\n", + "for f in M_mass:\n", + " all_power.append(power_law(f, M_a))\n", + "for g in S_mass:\n", + " all_power.append(power_law(g, S_a))\n", + "for h in L_mass:\n", + " all_power.append(power_law(h, L_a))\n", + "for k in B_mass:\n", + " all_power.append(power_law(k, B_a))\n", + "for l in P_mass:\n", + " all_power.append(power_law(l, P_a))" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "15d0484d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5000\n" + ] + } + ], + "source": [ + "print(len(all_power))" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "6a6629e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", + "avg_a = popt\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(all_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(all_masses, all_power)\n", + "plt.plot(all_masses, avg_power, color='r')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3739e5d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.64439915]\n" + ] + } + ], + "source": [ + "print(popt)" + ] + }, + { + "cell_type": "markdown", + "id": "109097ab", + "metadata": {}, + "source": [ + "#### Cleaning Up the Curve Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "7475e6d3", + "metadata": {}, + "outputs": [], + "source": [ + "#combining all mass and output data sets to fit\n", + "all_masses = np.array(all_masses)\n", + "all_power = np.array(all_power)\n", + "\n", + "#put combined mass data set into increasing order and reorder output array to match new indices \n", + "sorted_indices = np.argsort(all_masses)\n", + "sorted_masses = all_masses[sorted_indices]\n", + "sorted_powers = all_power[sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "acc33225", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#use scipy curve fit to create a general power law\n", + "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", + "avg_a = popt\n", + "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", + "\n", + "#create an array with mass values using fitted a\n", + "avg_power = power_law(sorted_masses, avg_a)\n", + "\n", + "#plot combined mass and power law datasets\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", + "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-a)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bd3e02dd", + "metadata": {}, + "source": [ + "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "0e0bc7ca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00211972]\n" + ] + } + ], + "source": [ + "print(avg_aerr)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "7037fa8c", + "metadata": {}, + "outputs": [], + "source": [ + "#calculate the low and high outputs based on error of a\n", + "avg_aup = avg_a + avg_aerr\n", + "avg_alow = avg_a - avg_aerr\n", + "avg_outup = power_law(sorted_masses, avg_aup)\n", + "avg_outlow = power_law(sorted_masses, avg_alow)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "1cb19a53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#fit plot including error\n", + "plt.figure()\n", + "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", + "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", + "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", + "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "cf3ff96a", + "metadata": {}, + "outputs": [], + "source": [ + "#create an error array for all values in sorted_masses\n", + "power_errors = []\n", + "for m in M_mass:\n", + " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", + "for n in S_mass:\n", + " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", + "for o in L_mass:\n", + " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", + "for p in B_mass:\n", + " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", + "for q in P_mass:\n", + " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", + "\n", + "#sort errors based on sorted indices\n", + "\n", + "# convert sorted_indices to a NumPy array for indexing\n", + "sorted_indices = np.array(sorted_indices)\n", + "\n", + "# Sort errors based on sorted indices\n", + "sorted_errors = [power_errors[i] for i in sorted_indices]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30d890fa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0206cbc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 80f3e0dfcd2c599c9c4093f58fb1a16805e1c095 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Wed, 26 Jun 2024 13:59:08 -0700 Subject: [PATCH 03/48] Changes to BD_IMF --- changes/BD_IMF.ipynb | 298 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 240 insertions(+), 58 deletions(-) diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb index 7930e27f..41fc800b 100644 --- a/changes/BD_IMF.ipynb +++ b/changes/BD_IMF.ipynb @@ -18,10 +18,23 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 3, "id": "de897cdf-e478-4d49-b16a-ea7a1e0d547d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n", + " from pandas.core.computation.check import NUMEXPR_INSTALLED\n", + "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n", + " from pandas.core import (\n", + "/Users/caitlinbegbie/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -55,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 5, "id": "2f67bd3c-cc80-4294-88e5-f257100893f6", "metadata": {}, "outputs": [], @@ -104,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 6, "id": "384973ef-5396-4d20-af19-5899db1a5673", "metadata": {}, "outputs": [], @@ -150,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 15, "id": "4e520010-9702-45f2-877c-1fb5104fa7a1", "metadata": {}, "outputs": [ @@ -195,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 16, "id": "11fc4c9d-0be7-4d75-a35c-4b82fae451f0", "metadata": {}, "outputs": [ @@ -243,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 17, "id": "9d984c93-387a-4586-ba16-ac579562ca24", "metadata": {}, "outputs": [], @@ -283,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 18, "id": "ac20c536-15c8-4fc5-885e-190d3fd888d1", "metadata": {}, "outputs": [ @@ -348,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 10, "id": "c594d201-d695-414d-9d23-05d6e1f2171b", "metadata": {}, "outputs": [], @@ -362,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 11, "id": "5b054e0c-c0b6-4556-b649-a959fba74da0", "metadata": {}, "outputs": [ @@ -381,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 12, "id": "a1aaa018-16da-44e0-9473-2611ec50ab2c", "metadata": {}, "outputs": [], @@ -392,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 13, "id": "baafa6a8-0c11-4380-899a-a61834474854", "metadata": {}, "outputs": [ @@ -402,7 +415,7 @@ "4000" ] }, - "execution_count": 150, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -414,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 20, "id": "56aa35b6-aa24-4695-9094-fe5857846bd5", "metadata": {}, "outputs": [ @@ -453,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 21, "id": "011eac9f-7e9d-4e5d-8bfc-f0380920f575", "metadata": {}, "outputs": [ @@ -480,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 22, "id": "de5f6d54-b675-447c-bef7-a979715eb5f0", "metadata": {}, "outputs": [ @@ -512,28 +525,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 154, - "id": "ecbe93a5-32d7-4648-bc99-c20b13db9ca2", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "only integer scalar arrays can be converted to a scalar index", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[154], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mall_power\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index" - ] - } - ], - "source": [ - "range(all_power)" - ] - }, { "cell_type": "markdown", "id": "83430453-9da9-49e6-a1b1-4f12db8b63a7", @@ -544,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 19, "id": "d1ef4b5a-06b7-4295-9c5a-7e9d231020ec", "metadata": {}, "outputs": [], @@ -562,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 23, "id": "d3af8ed8-a459-486c-9f44-c6a9f1008d59", "metadata": {}, "outputs": [ @@ -599,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 161, + "execution_count": 52, "id": "400886ad-ad9a-47a9-8fa2-e513ef125d45", "metadata": {}, "outputs": [ @@ -607,12 +598,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[7.20652905e-05]\n" + "[0.5836496] [7.20652905e-05]\n" ] } ], "source": [ - "print(avg_aerr)" + "print(avg_a, avg_aerr)" ] }, { @@ -625,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 25, "id": "76bfab00-53a2-4a18-9cee-b4f11cfdde98", "metadata": {}, "outputs": [], @@ -647,7 +638,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 44, "id": "527b824d-29a7-4715-810b-01b626eae7fa", "metadata": {}, "outputs": [ @@ -655,7 +646,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fitted parameters: a = -0.5450, b = -4.1664664035223473e-10\n", + "Fitted parameters: m = -0.5450\n", "RMSE: 0.5058\n", "MAE: 0.4105\n", "R-squared: 0.2151\n" @@ -673,9 +664,9 @@ } ], "source": [ - "#define a linear model\n", - "def linear_model(x, a, b):\n", - " return a * x + b\n", + "#define a linear model (no intercept since no coefficient in power law)\n", + "def linear_model(x, m):\n", + " return m * x\n", "\n", "#curve fit\n", "params, covariance = curve_fit(linear_model, log_masses, log_power)\n", @@ -692,7 +683,7 @@ "r2 = r2_score(log_power, power_fitted)\n", "\n", "#output the results\n", - "print(f\"Fitted parameters: a = {params[0]:.4f}, b = {params[1]}\")\n", + "print(f\"Fitted parameters: m = {params[0]:.4f}\")\n", "print(f\"RMSE: {rmse:.4f}\")\n", "print(f\"MAE: {mae:.4f}\")\n", "print(f\"R-squared: {r2:.4f}\")\n", @@ -710,21 +701,23 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 47, "id": "23e78450-e261-4fe7-b307-41204c800ed3", "metadata": {}, "outputs": [], "source": [ "linpower_fit = []\n", "for l in log_masses:\n", - " linpower_fit.append(m*l + b)" + " linpower_fit.append(linear_model(l, -0.5450))" ] }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 48, "id": "2d6df51d-54d3-48d2-bfcd-49f0dceed8bc", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -743,15 +736,204 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "69324962-a5fb-44b7-a734-8d17dbe84c98", + "metadata": {}, + "source": [ + "#### Attempting a more accurate error" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "a4ddeb15-31d9-48ba-881d-992def181f89", + "execution_count": 54, + "id": "40a444f8-9de7-4d39-8b40-2fa6272acf78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.545\n" + ] + } + ], + "source": [ + "print(params[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "cff2e9d0-e55a-4dbb-83e1-ccf940f84d97", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.15556349186104046\n" + ] + } + ], + "source": [ + "#find the difference between fit and upper/lower bounds\n", + "m = params[0]\n", + "up_diff = R_a - m\n", + "up_differr = R_aerr\n", + "low_diff = m - M1_a\n", + "low_differr = M1_aerr\n", + " #the errors in up_differr and low_differr are the same because m is a fixed value in this case\n", + "\n", + "#calculate error using sqrt function\n", + "fit_err = np.sqrt((up_differr)**2 + (low_differr)**2) #uncertainty formula\n", + "print(fit_err)" + ] + }, + { + "cell_type": "markdown", + "id": "b71d92d4-1658-46b1-b4a5-31aa601a29fa", + "metadata": {}, + "source": [ + "#### Replot our function with our new " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "93f528bc-5570-46b2-902c-1c8137fb9c46", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9NHjwYJ199tk66qijJEl9+vTRCy+8oCuvvFI33HCDCgoKdPbZZ+ub3/ymTjrppB6fQ1ddfPHFWrlypebOnds8sLetsCVJc+bM0YEHHqh58+ZpwYIFKi0t1bXXXqubbropjGdtpKSkaMGCBbr44ot14403qrS0VJdddpkKCgo67ISXkpKiRx99VNdee60uuugiNTY2au7cue2GrenTpyspKUn33nuvdu3apSOOOEJ/+tOf1Ldv33bv63//9381YsQI/f73v9esWbMkSQMHDtS3v/1tnXbaaZ16rIn+dfub3/xGu3fv1j//+c/mphGLFi1qs7FHZ9x8880aMmSI/vjHP+r6669XZmamxowZo5/+9KeO+920aZPuvPNOVVZWasqUKSHDlmSei8zMTN1+++265pprlJWVpdNPP1133HFHt2apnX322frrX/+q+++/X2VlZSotLdWPfvQjzZw5s1UHRQCJw2dFy+5TAABcsHHjRg0ZMkR33XVXp6pQAAB4hT+1AAAAAIAHCFsAAAAA4AHCFgAAAAB4gD1bAAAAAOABKlsAAAAA4AHCFgAAAAB4IOHmbPn9fm3btk05OTny+XyRPh0AAAAAEWJZliorK9WvXz9PZuIlXNjatm2bBg4cGOnTAAAAABAlvvzySw0YMMD1z5twYSsnJ0eS+Q/Nzc2N8NkAAAAAiJSKigoNHDiwOSO4LeHClr10MDc3l7AFAAAAwLPtRTTIAAAAAAAPELYAAAAAwAOELQAAAADwQMLt2QIAAED8sSxLjY2NampqivSpIMqkpKQoOTk5IvdN2AIAAEBMa2ho0Pbt21VTUxPpU0EU8vl8GjBggLKzs8N+34QtAAAAxCy/368NGzYoOTlZ/fr1U2pqqmed5RB7LMvS7t27tWXLFg0fPjzsFS7CFgAAAGJWQ0OD/H6/Bg4cqMzMzEifDqJQUVGRNm7cqH379oU9bNEgAwAAADEvKYlfaxFaJCudfFUCAAAAgAcIWwAAAADgAcIWAAAAAHiAsAUAAABEwIwZM+Tz+eTz+ZSSkqKSkhKdeOKJeuSRR+T3+zv9eebNm6f8/HzvThTdRtgCAAAAImTq1Knavn27Nm7cqEWLFun444/Xr371K5166qlqbGyM9OmhhwhbAAAAQISkpaWptLRU/fv31/jx43Xdddfpueee06JFizRv3jxJ0j333KNDDz1UWVlZGjhwoC6++GJVVVVJkhYvXqxzzz1X5eXlzVWymTNnSpLmz5+viRMnKicnR6WlpTrrrLO0a9euCD3SxETYAgAAAPZbulSaP9/8GyknnHCCxo4dq2effVaSaWv/hz/8QR9//LEeffRRvfHGG7r66qslSZMnT9a9996r3Nxcbd++Xdu3b9dVV10lSdq3b59uueUWffjhh1q4cKE2btyoGTNmROphJSSGGkfY0qXSZ59JDQ1Saqo0YoQ0aVKkzwoAACDxXHONdOedgfevvlq6447InMtBBx2kjz76SJJ02WWXNV8/ePBg/fa3v9VFF12k+++/X6mpqcrLy5PP51Npaanjc5x33nnNbx944IH6wx/+oMMPP1xVVVXKzs4Oy+NIdIStCGr5DW07+2zzFxUAAACEx9KlrX8vu/NO6YwzIvOHcMuymofxvvbaa5o9e7Y+/fRTVVRUqLGxUXV1daqpqVFmZmabn2P58uWaOXOmPvzwQ+3du7e56cbmzZt18MEHh+VxJDqWEUZIqG9o29/+Jh19tPTww5EvYwMAACSCzz7r2vVeW7NmjYYMGaKNGzfq1FNP1ZgxY/TMM89o+fLl+vOf/yxJamhoaPP21dXVOumkk5Sbm6vHH39c77//vhYsWNDh7eAuKlsR0tE37jvvmIvtlFOkG29kiSEAAIAXRozo2vVeeuONN7Rq1SpdfvnlWr58ufx+v373u98pKcnUSf7xj384jk9NTVVTU5Pjuk8//VRfffWVbr/9dg0cOFCStGzZsvA8ADSjshUhXf3GffFF6RvfkE49lYoXAACA2yZNMnu0gl1zjfd/6K6vr9eOHTu0detWrVixQrfddpu+973v6dRTT9X06dM1bNgw7du3T3/84x/1xRdfaP78+XrwwQcdn2Pw4MGqqqrS66+/rj179qimpkaDBg1Sampq8+2ef/553XLLLd4+GLRC2IqQUN/QnfHii9IFF0jTpxO+AAAA3HTHHdJ//ys99pj59/bbvb/Pl19+WX379tXgwYM1depUvfnmm/rDH/6g5557TsnJyRo7dqzuuece3XHHHRo9erQef/xxzZ492/E5Jk+erIsuukg/+tGPVFRUpDvvvFNFRUWaN2+enn76aR188MG6/fbbdffdd3v/gODgsyzLivRJhFNFRYXy8vJUXl6u3NzcSJ9OczfCJ56QXn6555+P5YYAACCR1NXVacOGDRoyZIjS09MjfTqIQu19jXidDahsRdikSdJPfyotWmT+gnLKKT37fCw3BAAAAKIDDTKiyKRJ0gsvOGdvLVhgAlRXvfii83ZUvAAAAIDworIVaZYl7Z95YLOrXeefb8KXmxWvo46SZs6k2gUAAAB4jcpWpG3eLG3aJPXrJxUUSHl5Ui/n0+JmxWvJEnOZNcsEuNNPl1JTTXdEql4AAACAewhbkdbYKG3fLn39tZSUZMJWaanUu7eUny+lpDQfOmlSIBCdf37Pw1eopYaELwAAAMAdhK1okJYmDRxogldlpfTpp+b6nBwTvPr0McErLc1xs1Dh65ZbulfxktjnBQAAALiJsBVNevUySwkLCkzwqqqS1q0zl+xsqbhYKiw0wSsjo9XN3VxuKAXC19Sp0llnUe0CAAAAuoKwFa169TKhKj/fNNCoqpI2bJA+/1zKzDTBq6jIfDwry3FTtyteL78cmAHGUkMAAACgcwhbsSApScrNNRfLkqqrpS1bTPjKzDTLDEtKTPDKzpZ8PsfNgyteixZJr75qmmR0B0sNAQAAgM6h9Xus8flMoOrb1+zzysiQdu6U3n9fevttk6g2bZLKy00wCzJpkmn7/s47pp38Y49Jc+b0rK283VL+5JMZoAwAAOCW4447TpdddlnY7m/evHnKz89v8+MbN26Uz+fTypUrJUmLFy+Wz+dTWVlZWM4vVlHZimU+n1lCaC8jrK2V9u6Vtm2T0tNNpatvX/NvXp6pkO3XXmfDRx7peuWLpYYAAABdM2PGDD366KOtrl+3bp2effZZpQR1pR48eLAuu+wyRwCbN2+eLrvssogEnsmTJ2v79u3Ky8sL+33HEsJWPMnICDTOqKsznQ137jTt4/PyTPAqKDDhKznZcdOW4Wv6dFOp6o6WSw1psAEAABDa1KlTNXfuXMd1RUVFSm7xu1q0SU1NVWlpaaRPI+qxjDBepaebzoWDBpk9XbW10qpVpmT19tvS2rXS7t3Svn0hb/7YY+4tNXz5ZRPevvEN6dRTWWoIAAA8Zu9xD/elxRaOzkhLS1Npaanjkpyc7FhGeNxxx2nTpk26/PLL5fP55PP5tHjxYp177rkqLy9vvm7mzJmSpPr6el111VXq37+/srKyNGnSJC1evNhxv/PmzdOgQYOUmZmp008/XV999VWXzrvlMkJ7GeIrr7yiUaNGKTs7W1OnTtX27dsdt5szZ45GjRql9PR0HXTQQbr//vu7/H8WSyIath544AGNGTNGubm5ys3N1ZFHHqlFixa1e5unn35aBx10kNLT03XooYfqpZdeCtPZxrDUVBO4Bg0yXQz37ZPWrJHefVf6v/+TPvnEVMAaGhw3mzRJ+ulPTaXrhRec4Wvy5O6dir3H66ijzP4xghcAAHBdTY3Z4x7uS02NJw/n2Wef1YABA3TzzTdr+/bt2r59uyZPnqx7771Xubm5zdddddVVkqRLL71U7777rp588kl99NFH+p//+R9NnTpV69atkyQtXbpU559/vi699FKtXLlSxx9/vH7729/2+Dxramp09913a/78+Xrrrbe0efPm5nOSpMcff1y/+c1vdOutt2rNmjW67bbbdOONN4ZcShkvIrqMcMCAAbr99ts1fPhwWZalRx99VN/73vf0wQcf6JBDDml1/JIlS/TjH/9Ys2fP1qmnnqonnnhC06ZN04oVKzR69OgIPIIY1NYsr88+M0OU7VleBQWmOhbEzaWGS5aYy6xZ7PECAACJ64UXXlB2dnbz+yeffLKefvppxzG9e/dWcnKycnJyHEv38vLy5PP5HNdt3rxZc+fO1ebNm9WvXz9J0lVXXaWXX35Zc+fO1W233ab77rtPU6dO1dVXXy1JGjFihJYsWaKX7Q343bRv3z49+OCDGjp0qCQT+m6++ebmj99000363e9+pzPOOEOSNGTIEH3yySf6y1/+onPOOadH9x2tIhq2vvvd7zrev/XWW/XAAw/ov//9b8iwZX9h/PrXv5Yk3XLLLXr11Vf1pz/9SQ8++GDI+6ivr1d9fX3z+xUVFS4+ghgXPMurqcmUv+1ZXllZZo5XUZEJXpmZrW7+2GPSJZf0fIAye7wAAICrMjPNH5Qjcb9ddPzxx+uBBx5ofj+rxfzUrlq1apWampo0YsQIx/X19fXq06ePJGnNmjU6/fTTHR8/8sgjexy2MjMzm4OWJPXt21e7du2SJFVXV+vzzz/X+eefrwsvvLD5mMbGxrhushE1DTKampr09NNPq7q6WkceeWTIY959911dccUVjutOOukkLVy4sM3PO3v2bM2aNcvNU41PycmBWV5+vymDf/mltHGjabphz/IqKDBBbP8sr7a6Gj7xRKA7YVcFdzacPFk68UTTWp7gBQAAOsXu2BwDsrKyNGzYMNc+X1VVlZKTk7V8+fJWTTaCK2heCO6eKEk+n0/W/n1sVfvD70MPPaRJLX6pi/ZmID0R8bC1atUqHXnkkaqrq1N2drYWLFiggw8+OOSxO3bsUElJieO6kpIS7dixo83Pf+211zoCWkVFhQYOHOjOycerpKTA2mPLMsFr505p82bzFxu7pXxBgVl6GDRE2Q5fP/2ps518d6teLDcEAAAw3f+ampo6vO6www5TU1OTdu3apWOOOSbk5xo1apSWttg4/9///tfdE26hpKRE/fr10xdffKGf/OQnnt5XNIl42Bo5cqRWrlyp8vJy/fOf/9Q555yj//znP20Grq5KS0tTWlqaK58rIYWa5fX119L27VJamjN45eZ2OMvrllu6F7oklhsCAIDENXjwYL311ls688wzlZaWpsLCQg0ePFhVVVV6/fXXNXbsWGVmZmrEiBH6yU9+ounTp+t3v/udDjvsMO3evVuvv/66xowZo1NOOUW//OUvddRRR+nuu+/W9773Pb3yyis9XkLYGbNmzdIvf/lL5eXlaerUqaqvr9eyZcu0d+/eVqvX4kXEW7+npqZq2LBhmjBhgmbPnq2xY8fqvvvuC3lsaWmpdu7c6bhu586d9PgPp4wMs5xw0CAzu6uiQlq5UnrnHXNZv96EsRZ/ZZFMILK7Gt50U/c7GtpoKQ8AABLFzTffrI0bN2ro0KEqKiqSZAYLX3TRRfrRj36koqIi3XnnnZKkuXPnavr06bryyis1cuRITZs2Te+//74GDRokSfrGN76hhx56SPfdd5/Gjh2rf//737rhhhs8fwwXXHCB5syZo7lz5+rQQw/VlClTNG/ePA0ZMsTz+44Un2V1YyCAh0444QQNGjRI8+bNa/WxH/3oR6qpqdG//vWv5usmT56sMWPGtNkgo6WKigrl5eWpvLxcubm5bp12933+uZl/FetLGxsaTPCqqTGNN3JzpdJSs9crL88MVg7BjT1ewah2AQCQWOrq6rRhwwYNGTJE6S06KQNS+18jXmeDiC4jvPbaa3XyySdr0KBBqqys1BNPPKHFixfrlVdekSRNnz5d/fv31+zZsyVJv/rVrzRlyhT97ne/0ymnnKInn3xSy5Yt01//+tdIPgxIZiNVYaF5u7HRBK81a8wyxJwcE7wKC82yw9TU5pu5vccruLkGwQsAAACRFNGwtWvXLk2fPl3bt29XXl6exowZo1deeUUnnniiJDMnICloD9DkyZP1xBNP6IYbbtB1112n4cOHa+HChczYija9ekm9e5tL8CyvdetM043iYtNSPj/fMcsr1B6vRYukV181TTK6iq6GAAAAiKSoW0boNZYRRlBTkwleVVWmvbw9y6u42ASvdmZTuLnckOAFAED8YBkhOpKwywiRYJKTzf6tvDwTtqqrzSyvDRtM0CosNM038vMds7yktpcbPvJI16tewe3kCV4AAADwCmELkZGUZPZy5eSYWV7V1dKOHWaWV0aGWYJYWmqCVxuzvCSz3HD6dGn+/O6dBnO8AAAA4BXCFiLP5wsMUZbMLK89e6StW80sr4ICM8srP7/VLC9Jeuwx6ZJLer7MkDleAAAAcBNhC9EnI8NcJKmuTiovN1Wv1FSzBNEeopyXZ5Ymyv2uhhKdDQEAANAzhC1Et/T0QMfC+nqpstI0FElONlWufv1M8MrPN10Q5X5XQ4ngBQAAgK4jbCF2pKWZiyTt22eC1yefmPdzc01zjXZmec2cSfACACChNDSYMTTh0KuX4/cPQCJsIValpLQ/yys4eIWY5UXwAgAgzjU0SO+9Z35HCIfsbOmIIwhcHti4caOGDBmiDz74QOPGjYv06XQJYQuxr1cvE6ry8wOzvL74Qlq/3vzgKyoKDFEOmuXVMnj1tMEGwQsAgChi/zE2NTWwMsYr9fXmvhobCVseGDhwoLZv367CwsJIn0qXEbYQX0LN8tq82YSvrCypT5/ALC+7+6FCN9hwM3jZgYt5XgAAhFlammOVi2caGjy/C8uy1NTUpF69nL/CNzQ0KLUbIa+7t3NLZ+8/OTlZpaWlYTgj9yV1fAgQo+xZXv36SQMHmh+2O3ZI778vvfOOWVqwebNUUWFmfe1nh65Fi6T//te0lp86tfun8fLLZo7XrFnSN74hHXVUoJoGAAASl9/v1+zZszVkyBBlZGRo7Nix+uc//9n88cWLF8vn82nRokWaMGGC0tLS9Pbbb+u4447TpZdeqssuu0yFhYU66aSTJEn/+c9/dMQRRygtLU19+/bV//7v/6oxaM9aW7dracaMGZo2bZpuu+02lZSUKD8/XzfffLMaGxv161//Wr1799aAAQM0d+5cx+2uueYajRgxQpmZmTrwwAN14403at++fc0fnzlzpsaNG6c5c+ZoyJAhSt8fgj/99FMdffTRSk9P18EHH6zXXntNPp9PCxculGSWEfp8Pq1cudLx//L6669r4sSJyszM1OTJk7V27doePyduo7KFxBA8y8uyArO8tmwxbebz80PO8nK74iU5Bymz3BAAgMQ1e/Zs/e1vf9ODDz6o4cOH66233tLZZ5+toqIiTZkypfm4//3f/9Xdd9+tAw88UAUFBZKkRx99VD//+c/1zjvvSJK2bt2q73znO5oxY4Yee+wxffrpp7rwwguVnp6umTNnNn+ulrdryxtvvKEBAwborbfe0jvvvKPzzz9fS5Ys0bHHHqulS5fqqaee0s9+9jOdeOKJGjBggCQpJydH8+bNU79+/bRq1SpdeOGFysnJ0dVXX938edevX69nnnlGzz77rJKTk9XU1KRp06Zp0KBBWrp0qSorK3XllVd26v/v+uuv1+9+9zsVFRXpoosu0nnnndfh4wo3n2UF/Uk/AVRUVCgvL0/l5eXKzc2N9OlIn39uWpkPHBjpM0lcdXWms2FNjal+tTHLK5hbwSsYwQsAgK6rq6vThg0bHJUSSeZ1/a23zCoXr5cR2r9LHHusY394e+rr69W7d2+99tprOvLII5uvv+CCC1RTU6MnnnhCixcv1vHHH6+FCxfqe9/7XvMxxx13nCoqKrRixYrm666//no988wzWrNmjXw+nyTp/vvv1zXXXKPy8nIlJSWFvF0oM2bM0OLFi/XFF18oaf8foA866CAVFxfrrbfekiQ1NTUpLy9Pc+bM0Zlnnhny89x999168skntWzZMkmmsnXbbbdp69atKioqkiS9/PLL+u53v6svv/yyeanga6+9phNPPFELFizQtGnTWjXIsP9fXnvtNX3zm9+UJL300ks65ZRTVFtb6/w6UDtfI/I+G1DZAkLN8vroI9N4o4NZXm5WvGiwAQBA4li/fr1qamp04oknOq5vaGjQYYcd5rhu4sSJrW4/YcIEx/tr1qzRkUce2Ry0JOmoo45SVVWVtmzZokGDBoW8XVsOOeSQ5qAlSSUlJRo9enTz+8nJyerTp4927drVfN1TTz2lP/zhD/r8889VVVWlxsbGVgHmgAMOaA5akrR27VoNHDjQsSfriCOO6NQ5jhkzpvntvn37SpJ27drV/FijAWELCBZqltfHH5tliLm5puLVu7djlpfXwWvyZOmeewhdAADEk6r9LelffPFF9e/f3/GxtBbdE7OyslrdPtR1ndHZ26WkpDje9/l8Ia/z+/2SpHfffVc/+clPNGvWLJ100knKy8vTk08+qd/97neunHdH52iHTPt8ogVhC2hLqFle9sbL4FleBQXNAS1U8GpokL78svvzvJYsMY01Jk+WTjyRjoYAAMSDgw8+WGlpadq8ebNjf1Z3jRo1Ss8884wsy2oOHu+8845ycnKa91R5acmSJTrggAN0/fXXN1+3adOmDm83cuRIffnll9q5c6dKSkokSe+//75n5xluhC2gM0LN8vr889azvAoKTMMNBYKXze5AeMUV3Q9ddmONyZOlUaOkAQMIXwAAtKu+PirvIycnR1dddZUuv/xy+f1+HX300SovL9c777yj3NxcnXPOOV36fBdffLHuvfde/eIXv9Cll16qtWvX6qabbtIVV1zhWA7oleHDh2vz5s168skndfjhh+vFF1/UggULOrzdiSeeqKFDh+qcc87RnXfeqcrKSt1www2S5FgSGasIW0BXhZrltWlTYJZXYaFUXGyCV4tS+aRJput8T5cb2sFLCoQvql4AAATp1cv8QbSqKiwzsJSd3by3u7NuueUWFRUVafbs2friiy+Un5+v8ePH67rrruvy3ffv318vvfSSfv3rX2vs2LHq3bu3zj///Obg4rXTTjtNl19+uS699FLV19frlFNO0Y033ujohBhKcnKyFi5cqAsuuECHH364DjzwQN1111367ne/26qZRSyiG2Gk0Y0wfliWCV6VleaHelaWCVylpebf7Gyz96sFtzsb0lwDAJBI2us0p4YGsxUgHHr1at7PjZ555513dPTRR2v9+vUaOnRojz8f3QiBeBBqltfu3YFZXsHBKyfHs1ledDUEAGC/1FQCUAxYsGCBsrOzNXz4cK1fv16/+tWvdNRRR7kStCKNsAV4weczczbsWRt1ddLevdL27eaHvj3Lq3dv0+Vw/yyvlsFr0aLuN9aQWnc1PO88c/eELwAAEC0qKyt1zTXXaPPmzSosLNS3vvWtVl0MYxXLCCONZYSJx57lVVNjOh7m5ISc5RXMjeDVEi3lAQDxoN1lhIBYRggklpazvCoqzCyvpCRT5Sotlfr0McFr//wIu+JldzRctMisTlyzpvvhi5byAAAA3iJsAZGUkmKCVZ8+oWd5lZaa7ob5+a1medl60k5ecraUnzo18LkJXwCAWJJgi7XQBZH82iBsAdEi1Cyv9evNJSvLzPEqLjYf3z/LS3Kvnbzk3ONlt5RnqSEAIJql7F8FUlNTo4yg10fA1rC/9X/y/j3y4cSerUhjzxY64veb4FVZad7OzHQGrxazvCT328kzRBkAEM22b9+usrIyFRcXKzMzMy6G4cIdfr9f27ZtU0pKigYNGtTqa8PrbEDYijTCFroieJbXvn0mePXuLZWUtDnLy97jZb/tVvhinxcAIFpYlqUdO3aorKws0qeCKJSUlKQhQ4YoNcQYAMKWywhbiBuWZToa2kOU09NN4Orb11S8gmZ5BXO7syFLDQEA0aKpqUn79u2L9GkgyqSmpiopxO9EEmHLdYQtxK3aWhO86urMMK3g4JWX12bw6klzjWAsNQQAALGGsOUywhYSQstZXrm5gVleeXmtZnnZe7waGqRHHnEnfE2dKp11FgOUAQBA9CJsuYywhYTT0BAIXklJJmyVlLSa5RXM7aWGhx4qnXEGFS8AABBdCFsuI2whoTU2muBVXW3eb2OWVzC3hijbaK4BAACiBWHLZYQtYD97iHJVlXk/O9uErhCzvIK5vc/rvPPMFjOWGwIAgHAjbLmMsAWE0HKWV1aWM3i1McvLzaWGEp0NAQBAeBG2XEbYAjoQapZXnz5mn1d+fruzvJ591nw59xQVLwAAEA6ELZcRtoAuaDnLKyPDBC67pXxubsjg9dln0hNPuDNAWaLiBQAAvEHYchlhC+gBe5ZXba0ZohwcvELM8vKiuQYVLwAA4Bavs0Gvjg8BgP0yMgKNM+rqTPDaudO0j8/LM8GroMCEr+RkTZrkDEQ9ba6xZInztrSUBwAA0YzKVqRR2UI8sGd5VVebgcm5uaalfO/eIWd52RUv+203lhuy1BAAAHQVywhdRtgCPGbP8qqqMssK7Vle9hDlNmZ5udlOftQoacAAKl4AAKB9hC2XEbaAMAqe5WVZUk6OaSdfWGiWG6anOw73ouLFUkMAANAWwpbLCFtAhDQ1mWWGFRVmlld2tlRUZC4FBabFfAtuVrwkaepU6ayzaK4BAAAMwpbLCFtAFPD7TUv5igpT/epglhcVLwAA4AXClssIW0CUaW+WV0GBWXro8RBlghcAAImJsOUywhYQ5WprTcWrvt400wgOXrm5bc7yevVVd5YbTp0aCFyELwAA4hthy2WELSCG2LO8amvNJOPcXBO8evc2c72Skx2Hu13xkqh6AQAQzwhbLiNsATGqocFUvGpqujTLy83gxSwvAADiC2HLZYQtIA7s2xcYouzzmX1dpaWmpXx+vqmCBVm6VPrsM+mJJ2iuAQAAAghbLiNsAXEmeJaXZDoZdmKWl1sVL/Z4AQAQuwhbLiNsAXGsqSkQvPx+KSvLzPEqLjYVrxazvNjjBQBAYiNsuYywBSQIv98sM6ysDMzyKiw0waugwASxFrO8PvvMbA175BF3OhsSvAAAiG6ELZcRtoAEZFkmeFVVBWZ59e5thigzywsAgIRF2HIZYQtA8xBle5ZXQYFpsNHOLK8rrnCn2iURvAAAiBaELZcRtgA4hJrl1a+fCV4tZnmxxwsAgPhC2HIZYQtAm+rrA8ErOTkwy6tPH9Ngo1ev5kPZ4wUAQOwjbLks2sLWymc+1963Vqlw/EAdOjrSZwOgWctZXrm5Zo9XO7O82OMFAEBsIWy5LJrC1jXXSP+883ON1ipt0UCNHWN+qZp8lAheQDRpa5ZXUZEJXh7P8po8WRo1ShowgPAFAICbCFsui5awtXSp9I1vSAcqELaCHX20dPxxUkqKNOgAwhcQNZjlBQBA3CBsuSxawtb8+dL06W2HrZaOPlq64AJCFxBVWs7yysoy+7tKSkzwys52HE7wAgAguhC2XBYtYaujylZbqHgBUarlLK/MzEBL+fz8VrO8vAhekydL99xD6AIAoLMIWy6LlrAltd6z1R2ELyAKWZbpaFhZaVrLp6eb4NW3rwleLWZ50VwDAIDIIGy5LJrClmS6Ef5r9iotXO5O63fCFxCF7FleNTVmiHJengle7czy2rJFWrOm5y3lCV4AALSNsOWyaAtb9pytVeUDteQd84vWhx+59+kJX0CUsWd51dSYuV3BQ5RbzPKS3K16HXqodMQRdDUEAMBG2HJZtIat4KHGq1ZLmzeZMT9vLpbeftu9u6O9PBBFgmd5JSWZfV19+0q9ezPLCwCAMCBsuSwWwlZLq1ZLc+a4G7okql5AVAk1y8seolxQYJYfBlm6VLriip4vM7QRvAAAiYiw5bJYDFs2LyteEuELiBr2LK/KStNsIzvbzPIqKjLBKyOj+VDayQMA0H2ELZfFcthqyevwxZJDIArYs7wqKkwIy8oy1a7iYhO8srKaD/UieA0YIJ10knThhQQvAED8IWy5LJ7CVktehi+qXkAUsGd5VVaab/TgWV4FBaYCtn+WFxUvAAA6RthyWTyHrZYIX0Acs2d5VVSYDocZGc4hykGzvILbyb/3Hs01AACwEbZclkhhq6VVq+VJe3lJmnykNHUqwQuImLo6E7zq6kwXw07M8nKzqyEt5QEAsYiw5bJEDlvBqHoBcSx4lldKimkp38YsLy+WG06eLN1zD6ELABD9CFsui7awtfKZz7X3rVUqzx0Y0XDiZfii6gVE0L59puJVU2OWFebmmqWGIWZ5UfECACQawpbLoilsXXON9M87P9dordIWBSpb0RBOvFpyaFe9du4079PpEAijxsbAEGXJNNQoLZX69Gk1y2vpUumhh6RXXjF7vdzAPi8AQLQhbLksWsLW0qXSN74hHajWYStYNCzJY74XEIeamkzwsocoZ2UxywsAkHAIWy6LlrA1f740fXrHYaulaAgmdvh6+WVpybvuf/5oqOwBCcXvDwxR9vtNS/miIjPLKz/f81leBC8AQKQQtlwWLWGrs5WtjkQ6fDFYGYgzoWZ59e4daCnv8SyvoUOls88meAEAwoOw5bJoCVtS23u2eiJawpcXVa9IPzYg4ViWaaxRWSk1NEjp6c4hyjk5rWZ5UfECAMQSwpbLoilsSYFuhM++P9CTJXmRDCheV73s5Yb79hHAgLBoOcsrP985y8vDIcpUvAAAXojrsDV79mw9++yz+vTTT5WRkaHJkyfrjjvu0MiRI9u8zbx583Tuuec6rktLS1NdXV2n7jPawlbwnC2vw4kU2WV5wY9v506GKwMxra7O7POyZ3nl5prg1bu3CV4tZnldcYW0ZIk7d01LeQCAW+I6bE2dOlVnnnmmDj/8cDU2Nuq6667T6tWr9cknnygraEN2sHnz5ulXv/qV1q5d23ydz+dTSUlJp+4zmsNWS4nQBdDLZYcELyBMGhoCQ5STkkzYKikxLeXz880PGXlT8ZIYogwA6L64Dlst7d69W8XFxfrPf/6jY489NuQx8+bN02WXXaaysrJu3Ucsha2W4j18efn4aLQBhElbs7wKC03wajHLa9Ei6c9/lvbs6fldDx1qql6nniqdf37PPx8AIP4lVNhav369hg8frlWrVmn06NC/Ec+bN08XXHCB+vfvL7/fr/Hjx+u2227TIYccEvL4+vp61dfXN79fUVGhgQMHxmTYaineOwF6NVjZflwlJez1AjzV2GiWGrac5WW3lA+a5fXww9ILL5gfh59/3vO7LiyUvvc9lhoCANqXMGHL7/frtNNOU1lZmd5uJzW8++67WrduncaMGaPy8nLdfffdeuutt/Txxx9rwIABrY6fOXOmZs2a1er6eAhbLXkZvqKl6sVsLyBGdWOWl1sVL8lUvB56iNAFAHBKmLD185//XIsWLdLbb78dMjS1Zd++fRo1apR+/OMf65Zbbmn18XiubHUkXsNX8ONKSXE/gEW6ogfEvVCzvPr0MeXmFrO83K54sdQQABAsIcLWpZdequeee05vvfWWhgwZ0uXb/8///I969eqlv//97x0eG8t7tnrKq2V5UuQrQ15VvlhyCHis5SyvjIxAS/n8fNPl0KMhyiw1BADEddiyLEu/+MUvtGDBAi1evFjDhw/v8udoamrSIYccou985zu65557Ojw+kcNWsHiteknhm+9F8AI8UFtrgldtrWmmUVAQCF4hZnkxRBkA0BNxHbYuvvhiPfHEE3ruueccs7Xy8vKUsX/j9PTp09W/f3/Nnj1bknTzzTfrG9/4hoYNG6aysjLdddddWrhwoZYvX66DDz64w/skbIXmZUCJdDjxuqJn93Jh2SHgsuBZXqmpgVleBQUmfCUnSwoEr7/9zZ2lhhJDlAEgUcR12PLtXxrS0ty5czVjxgxJ0nHHHafBgwdr3rx5kqTLL79czz77rHbs2KGCggJNmDBBv/3tb3XYYYd16j4JW53jVUCxq147d5r3wx1Q7MclSatXu99sg/1egEfsWV7V1SZk5eWZlvK9e7c5y+u559xpsDFggHTSSdKFFxK8ACDexHXYigTCVtfF83yvcMz2Yr8X4DJ7lldVldnPlZNjgpc9RDloltfDD0u33y6tX+/OXdNgAwDiC2HLZYStnovnNuxeLjmUIr+kEog7wbO8LMsEr+Ji0/2ioEBKT5fkzVLDvDzp+OMJXgAQywhbLiNsuSueByuHY8nhaadR9QJcY8/yqqgwb2dnm1leRUWOWV5eNNegsyEAxCbClssIW97ysuoVDfu94rWiB8Qdv9801qioMNWv4FleBQUmePl8nuzxkuhsCACxgrDlMsJW+Hhd9ZIit98r+LHt3On+skO6HAIuCp7lVV9vgpc9y6ugwCw99GiIskTwAoBoRthyGWErcrwOKFL87vei6gW4qLbWVLzq600zjeAhyi1meT30kPTKK6by5QaCFwBEF8KWywhb0cXLpXl2QNm3LzKVL6/2e1H1AlxUVxcYohzmWV4ELwCIPMKWywhb0Sscyw4jVSHyOlQSvgAXNDSYildNjdSrlwle7czyIngBQOwjbLmMsBU74rUNu9ddDllyCLigsdEEr+pqs6wwOzswy6ugwFTB5E1nw2HDpP/9X9rJA0A4ELZcRtiKTeFoSBHJJYdUvYAoFjzLSzLBq51ZXm4Fr7w8aexYafhw6cILqXgBgBcIWy4jbMWPeGzFHhwqn3/em0YbhC+gB5qaAsHL7zct5O1ZXgUFptOhvKl4DRggnXQSwQsA3ETYcllUha3ycumLL6RNmwhbPRSO4BWJkBKPgRKIG36/WWZYWWlCWEaGqXaVlASGKAfN8nIzeA0dKp19Nnu8AKCnCFsui6qwde+90pVXSoMHS0ceKU2YIB12mJn5gm4Lrg6lpHgTVMaOMb/gRCJ4eVX1GjtGOu208C+jBOKCZZngVVVlGm1kZJhKV2mpY5YXwQsAogthy2VRFbYuukj6y1+c1/l80siR0vjxgfAV6fOMA15WiOzgVVIS3qDidfgaNkw64XiWGwLd0nKWV3Dwys2VkpI8C16HHiqdeioNNgCgMwhbLouqsCVJ77wjPfOMtHGjtHy5tHmz8+M+nzRihDN85eVF5FTjhddL86TI7vfyKlAOGWJ6AhC+gC5qOcsrLy/QUj4vT0pO9qSdfHa29KMfsccLANpD2HJZ1IWtlg0ydu82oWv5cmnFCrOfK5jPZ1pTTZhA+HJBOJYcRmK/VziWHBK+gG6wZ3nV1pqByfYsrz59zD6vXr2ag9eKFdIHH0hbtvT8bocOlY47zjTZYLkhAAQQtlwW9WGrpT17AuFr+fL2w9f48SZ85ed7ftrxzOvKVySCCl0OgSi0b5+peFVXm5/lublmPXJhofk5HjTL66GHpKeeCnSf7ylmeQGAQdhyWcyFrZb27DF/7rTD18aNrY9pWfkifHWb1/O9pMg22/AyUF5+BaEL6LS2ZnkVFZmf4ftneT38sPTCC9Kbb5qGtj2VnS0df7z5Wx0VLwCJiLDlspgPWy3t2WPWmdjha8OG1scMGxYIX+PHE756aNVqack73gavcDbbsB+PJK1e7W74Yrkh0A1tzfIqLjY/v/fP8nr4Yen226X16927aypeABINYctlcRe2WvrqK2f4+uKL1scMHeoMXwUF7tx3AvIyqNjC3ZLdfky7dpns7magZLkh0EXBs7waG03w6tMnMMsrO7t5j9eWLdLixe402KDiBSBRELZcFvdhq6Wvv3YuOwwVvg480Bm+evf25lwSQDx2OvQyfDHbC+iCULO8evc2DTby8x2zvNjjBQCdQ9hyWcKFrZbs8GUHsFB/AiV8uSJc+73CHVbs8PXGm+4uX5KY7QV0SU2NqXjV1Zk9XQUFUt++Jnjtn+Vl7/FatYqKFwCEQthyWcKHrZb27nWGr1C/PR94YGDO1/jxZgkLusXLKpEUmSWHXnU5ZMkh0AX2LK+aGjNEOS/PBK+CAk9neQ0YIJ10ErO8AMQuwpbLCFsdKCsLBK8VK6R161ofM2SIM3wVFob9NOOFl802pPAHFi8fj131CmfzECAm1dcHglevXqbK1a+fCV5Bs7weekh65RV35nhJUv/+0uGHS6eeynJDALGDsOUywlYXlZUFGm6sWCF99lnrYwYPDgSvCRMIX90UjmYb4dzv5eVyQxvLDoEOhJrl1bevWR6+f5aXFxWv7GzpRz+i4gUg+hG2XEbY6qGyMmnlykDDjXXrzKbtYAcc4AxfRUWRONOY53X4CueSQ6+HKks02wA6FGqWV/AQ5fR0TypeRUXme5PgBSAaEbZcRthyWXm5s9V8qPA1aFCg4Qbhq9vCMYg4nOHL6yoeVS+gHfYsr8pK8zM7O9v8bC4qMssNMzI8qXix1BBAtCFsuYyw5bGKCmf4+uyz9sPX+PFmUCe6JN7CitdLDgleQDtCzfIqLAwMUd4/y8vtildGhgle06cTvABEDmHLZYStMKuocC47XLu2dfgaONAZvkpKInKqsSwcSw6HDDG/e3kdWLxechjOxwLEnOBZXvX1JngVFDhmeS19z6dFi8w23jffdGeWFy3lAUQKYctlhK0Iq6xsHb78fucxAwY4w1dpaURONZbFU2AJbpdfXOx+mBw2TBp9COELaMWypNpa80ez+npTisrPb3OW1/vvS1u3unPXQ4dKZ59N8ALgPcKWywhbUcYOX3a7+U8/DR2+7GYbEyYQvrrB6/1ekWox78WyQ+Z7AW2oqzPBq65OSk1tc5bXQw9JTz3lTsVLYpYXAG8RtlxG2IpyVVXOyleo8NW/fyB8TZxI+OqicOz3iqdmG+z3AkIInuWVkiLl5LSa5fXww9Jjj5mKV22tO3dL8ALgNsKWywhbMSY4fK1YYcJXU5PzmODwNWGC+UsrOo1mG53HkkMghOBZXklJJnjZs7wKCqSUFE+WGhK8ALiBsOUywlaMq6qSPvzQWflqGb769XPO+erXLzLnGqOC90ht2BA/+728eizM9gKCNDYGgpdkOl+Ulkp9+pjglZbWvNTw+eel3bvduVuCF4DuImy5jLAVZ6qrneFrzZrW4atvX+ecL8JXl3gdWMK5R4oW80AYBc/ykkxnw6KiQEv5/bO83G4pT/AC0BWELZcRtuJcdbX00UeB8PXJJ63DV2lp6/Dl80XmfGNQvASW4BC5+mOWHAKesmd5VVSYtzMzA7O8CgqkrCyCF4CIIGy5jLCVYGpqnOHr449bh6+SEmf46t+f8NVJ8dZi3svHQtUL2M+e5VVZab7hMjPN/q6SEhO8srObZ3n97W/mZdINRUXSqFEMUQbgRNhyGWErwdXWOpcdthW+grsdEr46LRzDlU87Tdq507zvZXCh0QYQBsGzvBoapPT0wBDlggIzRPn9JNeDV1aWdOKJ0qmnEryAREfYchlhCw52+LLnfH38sdngHSw4fE2YYNalEL46xev9XlJ4KkbhWHJI1QtQ6Fledkv53FwtXZZM8ALgKsKWywhbaFdtrVl2aIev1atbh6/iYmf4GjiQ8NVJXu/3ClezDS8reFS9gP1azvLKzQ20lM/L09LlvVwPXhkZ0re+ZX7En3wy+7yAREDYchlhC11SV+cMX6tWtQ5fRUXOVvODBhG+OsHrPVJS+JtteBEi7QC5axcBDAmsoSEQvJKSTPCyW8rn52vpihTXg5dkFjLMnEnFC4hnhC2XEbbQI3V15vmyhyyvWmXSQrDCQmf4OuAAwlcnhGsmltf7vbxeciix7BAJrq1ZXoWFJnitTPOk4nX44TTXAOIRYctlhC24qq7OrCOzG26ECl99+ji7HRK+OsXrJYdSePd7efU4Bgwwj+OYo6Vp09z//EBUa2w0s7yqqsz72dmBlvL5+Vr6UYYWLTJ/G3vtNbNSvKdYagjEF8KWywhb8FTL8LV6tVn+EqxPH2e3Q8JXh4L3SJWUxG5rdq+rXvn50nFTWG6IBOX3B4Yo27O8gocoZ2Xp4YelF14wwcvOZz1VVGSq5szyAmITYctlhC2EVX1968pXe+FrwgRp8GDCVyd4WTGy53tJ3gaXeBkQDUSdULO8+vQxf63Jz5eys/XwIz7XgxdDlIHYQ9hyGWELEVVfb9rLB4ev+nrnMb17O8PXkCGErw7EQ7ONcLSXp8shEpJlmcYalZXOWV59+5rglZurhx/x6bHHpDVrpN273blbKl5AbCBsuYywhajS0OAMXx991Dp8FRQ4w9eBBxK+OhCOipHXwSU4QO7c6f5jIXwhYdXWmuBVWyulpTmDV16elr6fpIceMn+4IXgB8Y+w5TLCFqJacPhascIMXA4Vvg47zBm+kpIic74xIBz7vexlh14HFy9DJOELCamuzqwhrK4ODFHu29f8nM3P19JlyXroIemJJ9xpriERvIBoQ9hyGWELMaWhQfrkk0DlK1T4ys83la/x403DDcJXh+Kl8rXkHemlRdKWLe5/fsIXEo49y6u6WkpONsGrtNQs7c7P18OPpXiy1HDUKFrKA5FE2HIZYQsxbd8+U/myhyx/+KH5y2ywvDznssOhQwlf7fB6vpcU+/u9JJptIMHYs7yqqsyy7Zwc5xDllWmuLzVMSzOVLoIXEF6ELZcRthBX9u0LVL5WrJBWrmw/fI0fb35rJny1KR4aVcRD5Q6IGsGzvCzLBK/i4sAQ5f2zvNwcokzwAsKHsOUywhbiWmNj62WHLTca5OU593wRvtoV68ElXFUvwhcSQlOTWWZYUWFmeWVnm7WARUUmeK3O0kMPmR+/a9e6s88rK8v8qB4+nH1egBcIWy4jbCGhNDaaDQZ2+Fq5svWrf26uM3wNH074akNwcJG8WXbIkkMgRvj9pqV8RYX5WdvGLK+ZM93dV0mDDcBdhC2XEbaQ0BobpU8/lZYtCyw7rKlxHpOT0zp8JSdH5HRjgdfDlU87zbR+l2Kz8kXVCwkheJZXfb0JXvn5zS3ll67J1UNzfK7u8ZIIXoAbCFsuI2wBQezwFVz5ChW+xo0zwWviRMJXO+JhyZ7XyyZLiqUjj5SmnU7wQhyzZ3nV1ZkNWMHB69M8LXolSStWSK+9Rkt5INIIWy4jbAHtaGw0Gw2Cw1d1tfOY7Gxn5WvECMJXG7wOLpI0YIAJYMccLU2b5u7npuoFuKCuLjBEOTXVLN0OmuX18LxkPfaYtHRp68ke3UXwAjqPsOUywhbQBXb4slvNf/BB6/CVldU6fPXqFZnzjWLhqHrl50vHTYndZhtUvRD3GhrMHq+aGvNzMjc35CwvghcQPoQtlxG2gB5obJQ++yzQan7FirbDl91ufuRIwlcI8dCowsvKHVUvxL3gWV5JSWbVgD3Lq6BAD89P1QsvSO+/L23d6s5dEryA1ghbLiNsAS5qagqEL7vyVVXlPCYry+z5ssPXQQcRvkKgxXz7BgyQJk4gfCFOBc/ykkzwsmd5FRRo6Yfprg9RJngBBmHLZYQtwENNTdK6dYFuhx98YP5yGywrSxo7NrDskPDVih1cJNNF+vnnvWkxH+vhy6u9akBE2bO8KitNe/msrMAsr4ICLV2V6Xrwys+XxoxhiDISE2HLZYQtIIzs8BVc+WoZvjIzA5WviRMJX22I9WYVq1ZLCxdI774r7dzl7uf2eq8aEDHBs7yamqSMjMAsr4ICM0TZ5ZbyaWmm0kXwQqIgbLmMsAVEUFOTSQnB4auiwnlMRkag1fyECdKoUYSvEOzw9dIidwem2rwMXyw5BLoheJZXQ4P5WVlQYPZ5FRRo6Sc5BC+gGwhbLiNsAVHE73eGrxUrQoev4GWHBx9M+Goh1ptteFn1kuhyiDhVW2t+XtbXm1QUHLzW5Oqhh5MIXkAnELZcRtgCopjfb74ngsNXebnzmPT01uErJSUy5xulYrnZRnBwXPwfqazMvc8tmapXcZE0aBDhC3GkrVlevXtr6ad5euiRZIIX0AbClssIW0AM8fulL74wDTfaC19jxgTC1yGHEL6ChKvq1a+vlJTsfsOKhQul/3tb8jdJ27bTaAPokD3Lq7bWDJy3Z3n16aOla/P10NxergavlBTpwAOl739fuvVWdz4nEE6ELZcRtoAYZoev4MpXy9JHWpqpfNkNNw4+2PylF5Jif7iyl3vV8vOlMYd6ExqBiNi3z1S8qqsln0/KyTHBq7BQ732Wr7/OS9Xy5dKaNe4MUU5Kko4+mooXYgthy2WELSCOBIevFSvMv+2FL7vyRfhqFq7KlxfLDr1eckiXQ8SVtmZ5FRVJ+fl6+PF0PfaYtHSpO8ErLc30N5owgVleiG6ELZcRtoA4Zlmtw9fevc5j0tICyw7Hj5dGjyZ8BfF6v5dklu5952T3A4zXSw69bo8PhE1TUyB4+f0meBUWmi/u/Hw9/PdMzZzpbvWYIcqIVoQtlxG2gARiWdKGDc7w9fXXzmPS0qRDD3Xu+UpLi8z5RplwVr0kbypfXnY5ZL8X4oLfHxii3NhoZh8WFkolJXp/Xb7+8ni2q0sNJbOa8bjjpOuvJ3gh8ghbLiNsAQnMsqSNG517vr76ynlMaqozfI0eTfjaLxzhS/Kmzbx97p9+Kq1dy2BlICTLMsGrqiowy6t3b7PPKz9fD/8jR4/N97m21FCi4oXII2y5jLAFoJllSZs2Obsdhgpfo0cHwtehhxK+9gsOX8uWezNc2avqkddDoal6IS7YQ5SDZ3n17WuC19O5euxvSXr3XdOHww0FBdIZZxC8EF6ELZcRtgC0yQ5fduVr+fKOw9fo0ab9PDyvfNnVI8ndCpLXoTE7WxoxnNleiHH2LK+aGhO88vJM8Coo0CPP5OnRvyW7WvFiqSHChbDlMsIWgE6zLGnzZmf42rPHeUxKSuvKF+FLkvfdAiVvmm143SSkpFgaOVI66CCWHCJG1dcHglevXmaWV79+Jng9m69HH+/lavDKzzd9jWgpDy8QtlxG2ALQbZYlffmlM3y1nAyakmKabNjha8wYwtd+drfA9eu9W7pXXORuBSkc+9QGDJAmTmC/F2JUy1leubmm4tW7t+YuyNe8J1L10Ufu/bElLc1UughecAthy2WELQCuscOX3elw+XLzW3mwXr1M5cue8zV2LOFLsTvfKxz71EqKpSOPZMkhYlCoWV4lJYEhyo+l65lnCF6ILoQtlxG2AHjGssxv38GVr1Dhy658jR9vwldGRmTON4rEevhiySHQgj3Lq7LS/GzMzjatB4uKTEv5+Zl6/vnWiwO6K3hRAQ020BWELZcRtgCEjWVJW7eabod29WvnTucxycmtlx1mZkbmfKOI190CJffDVziqXsVF0sGH0OUQMcae5VVRYUJYVlbzEOX31+Xrlt9na/Fik8vcQkt5dBZhy2WELQARY4ev4MoX4atDwSFG8q7Zhtvzvbye7UWXQ8Qke5ZXZaXZ75WZaXq+l5Zq2fp8Pfh4jp551ufq93hBgfTzn0u33ure50T8IGy5jLAFIGpYlrRtmzN87djhPCY5WTr44ED4Gjs24cOXFJ5mG243rVi1Wlq4wDS4/GxdYFuLW2i0gZhjWVJtral41debJdX5+VLfvlq2Pl9/+XuunvtXkmtLDX0+acQI6fvfJ3ghIK7D1uzZs/Xss8/q008/VUZGhiZPnqw77rhDI0eObPd2Tz/9tG688UZt3LhRw4cP1x133KHvfOc7nbpPwhaAqNYyfG3f7vx4crI0apQzfGVlReZco4TXFSTJmyDjdWCk0QZijj3Lq7bWzDTcP8tr2ecF+suTeXruhWRXg9fAgeZHKLO8Eltch62pU6fqzDPP1OGHH67GxkZdd911Wr16tT755BNltfHLw5IlS3Tsscdq9uzZOvXUU/XEE0/ojjvu0IoVKzR6dMevJoQtADFl2zZnt8Nt25wfT0423ROCw1d2dmTONUp43bRCcj98eR0YBwyQDhwiJSWz3wsxIniWV0qKmXLcr5+Wf1GgvzyVr/c/6KWPPjLbwdxQUCCdcQZ7vBJRXIetlnbv3q3i4mL95z//0bHHHhvymB/96Eeqrq7WCy+80HzdN77xDY0bN04PPvhgh/dB2AIQ07ZvDwSvFSvMHrBgycmmfZ0dvsaNS+jwFY4uh5I3zTa8XHKYmSFNnEiXQ8SI4FleSUkmeO2f5fWbe/L1j4Wp+uILc5gbGKKcWBIqbK1fv17Dhw/XqlWr2qxSDRo0SFdccYUuu+yy5utuuukmLVy4UB9++GGr4+vr61UfNMK8oqJCAwcOJGwBiA87djiXHbYMX0lJzsoX4Sss4WvAABPA3Koi2UsOP/lY2uXSMqpgLDlEzAg1y6u0VOrTR3MXFmje39O0dKkpjLkhJUWaOpWlhvEsYcKW3+/XaaedprKyMr399tttHpeamqpHH31UP/7xj5uvu//++zVr1iztbNnVS9LMmTM1a9asVtcTtgDEJTt82UsPW24GCg5f48dLhx1G+Nofvrzqcuh218DgJYfLlkk1te6cp624yIRFuhwi6gXP8pLM/tX9s7zmPVeguU9m6N133at45eSYH5lUvOJLwoStn//851q0aJHefvttDRgwoM3juhq2qGwBSGg7dpjgZYevL790fjwpKbDs0A5fOTmROdcoYFeQ/E3SR6u8CV9uDyr2utEGs70QE4Jnefn9pmvr/uD16PMFeuSpLFeDV3KydOih0qWXErxiXUKErUsvvVTPPfec3nrrLQ0ZMqTdY7u6jLAl9mwBSGg7dwaC14oVZlNQMJ/PueeL8OV5+HKz2YbXVa/MDGnESLOnhfCFqNXOLK/H/lWgh5/K1gcrfa4NUU5JMctwqXjFprgOW5Zl6Re/+IUWLFigxYsXa/jw4R3e5kc/+pFqamr0r3/9q/m6yZMna8yYMTTIAICu2rXL2e0wVPgaMcIZvqLhZ2eEeF1FktzdPxUcFr/Y4P4502gDUS94lldDg5SeHhii/HmBbrk3R4teSaLilcDiOmxdfPHFeuKJJ/Tcc885Zmvl5eUpIyNDkjR9+nT1799fs2fPlmRav0+ZMkW33367TjnlFD355JO67bbbaP0OAG7YvdvZcCNU+Bo+3Bm+8vIic64RFo5mG24vObS7HL77rjfzyNw+X8B1dXUmeNXVOWZ5zX+xtx75Z65Wrkp2rYKdnGwa5TBEObrFddjy+Xwhr587d65mzJghSTruuOM0ePBgzZs3r/njTz/9tG644YbmocZ33nknQ40BwAt79jjD16ZNzo8TvpoFh69lIXqTuMHN/VNeLzmUpIJ8acgQ6ZRTWHKIKNRylldurtS3r5Zv6K2/PJmnpxf0cnXp8MiRBK9oFNdhKxIIWwDQA3v2OJcdbtzo/LgdvsaPD4Sv/PxInGnEeT2o2O5ymJ3tTiXJXnJYViZ9ttab/V4sOUTU2rfPVLxqakzjoNxcqbRUyzf20W//lK/X30pxbY+Xz2cqXsceyxDlaEDYchlhCwBctGeP9MEHgfC1YUPrY4LD1/jxCR2+vBxULLk738vr2V5UvRC1GhsDQ5Sl5lleyzcV6qGn8/XGO2lav95sB3NDQYF0xhkEr0ghbLmMsAUAHvrqK2f4+uKL1scMG+YMXwUF4T/PKOB1sw03K0nBQXHLFvfDF10OEbWamkzwsv86Ys/yKi7WTb/P1x/nZGjvXvfujuAVflEXts455xydf/75OvbYY10/mXAgbAFAGHUmfA0dGgheEyYkZPjyesmhZKpexUXuDCv2utFGRro0ahSDlRFl/P7AEOWgWV4rthRrzj/z9dq7WVS8YlDUha1p06bppZde0gEHHKBzzz1X55xzjvr37+/6iXmFsAUAEfT1184hy59/3vqYAw8MNNwYP17q3Tv85xlh4Vhy6FazjXAExYJ8006b/V6IGqFmefXuLZWWatZ9+fr7v7K19rPQjeC6IyfHbIFllpf7oi5sSdLu3bs1f/58Pfroo/rkk0/0rW99S+eff76+973vKSUlxfWTdBNhCwCiyN69ziHLofqnE7483z/l5jK+4PC1apW0t8ylkwziZldGoMcsyzTWqKx0zvLq21c3/yFfT7yQq/Wf+9TU5M7dMUTZXVEZtoKtWLFCc+fO1Zw5c5Sdna2zzz5bF198cacGFEcCYQsAolhZmTN8rVvX+pghQ5zhq0+fsJ9mJIWjkuR2+GLJIRJKba0JXvYsr/3Ba/6/8vX7R/K06uMkNTa6c1fJydJ3viNdfz1LDbsrqsPW9u3b9dhjj2nu3LnasmWLvv/972vr1q36z3/+ozvvvFOXX365m+fqCsIWAMSQsjLnnq9Q4WvwYGf4KiwM91lGVDjme7nVbCMcVS+6HCKq1NWZdcD2LK/9Q5T/9mKB7p2bpw8/7uVa8EpLM394uPRSKl5dEXVha9++fXr++ec1d+5c/fvf/9aYMWN0wQUX6Kyzzmo+wQULFui8887TXjfbs7iEsAUAMaysTFq50hm+Wr6MHXBAIHxNmJCQ4cvLSpIklRSbAa1uhC97b9qnn7o/2ys9zXw5jBpF1QtRoKEhMEQ5KckEr9JSPb6ot+6dl6+VH6e4FrySkqQxYwhenRF1YauwsFB+v18//vGPdeGFF2rcuHGtjikrK9Nhhx2mDaHmrUQYYQsA4kh5eevKV6jwZXc6nDDBtG1OEMFVr6+/lpYtcz/QSO6FL68HK+dkm7FvVL0QcfYsr6oqM+U4J8cEr5f76L5H8/XBJ2muLjUcNkz6/velW29153PGk6gLW/Pnz9f//M//KD093fWTCQfCFgDEsYoKZ/j67LPW4WvQIOeyw+LiyJxrhHjdbENyN3y9+KKZle32kkO76lVSQpdDRFhjowldVVXm51VOjlRcrCf+Xag/PJavVeszVFPj3t0NGiSdfTbByxZ1YSvWEbYAIIFUVDiXHa5d23b4sqtfCRS+vB5WbHOje6DXSw4ls99ryhSWHCKCgmd5NTVJ2dlmlteXRZr9QL5eeTtLlZXu3R3Bi7DlOsIWACSwykpT+bI7Hq5da365CTZwoDN8lZRE5lwjIBzhy61mG15X6FhyiIgLNcurTx99sK1Etz+YrxcWZ6um1r1ZXokavAhbLiNsAQCaVVaaypcdvj79tHX4GjDAueywtDQipxoJ4Wi24VbVy8suh+lppuBZWEj4QoQEz/KqrzfBKz9fH+zoqzv+kq/FK3K1c5e7wWvs2MRoKU/YchlhCwDQpqoq57LDUOGrf39nt8MECV/hmO/l1j4qOySuWSNt2iTV1bt7nsz2QsTZs7xqa80Q5fx8qW9f3fLHfN39UJ4qqpJcu6vMTOmb34zf4EXYchlhCwDQaZ0NX/aSw4kTCV8uc2Nult1oY906qbLKxZPbryBfOvRQGm0gQoJneaWmSrm5Wrmzr+6aU6Aln+Rr45fJrt1VPAYvwpbLCFsAgG6rqpI+/NAZvpqanMf06+esfPXtG5lzDbNwVr56MjcruOq1a5c3g5XZ74WIsWd5VVdLvXpJublSaalufaC3Hnk2Xxu3prT6e1F3xUvwImy5jLAFAHBNdbUzfK1ZEzp8Bc/56tcvMucaZuFotuFGRcnrJYcMVkbENDaajqzV1WbKcXZ28yyvex8t0LKPUl27q5wc6bDDpOnTY2+IMmHLZYQtAIBnqquljz4KhK9PPmkdvvr2dXY77NfPDDWNc7HSbMPrJYdUvRARwbO8JBO8iot1+5xCPbKgQOu+dG9+blKSNGaMdOmlsRG8CFsuI2wBAMKmpsYZvj7+uHX4Ki11hq/+/eM+fIWj6pWRbhpYZGV1P9gEV702bJAa9rl7jnQ5REQ0NQWCl99vvkmKinTHI0X6+8sFWv1FZqsfU93l85k/Llx9dfQGL8KWywhbAICIqa11LjsMFb5KSgLha+LEhApfXu6jcmM5n9ezvVJTTDMQlhwibPx+80ehigrzsygjQ+rTR0++WaI/zC/Q0tVZ8lvu/PyJ1ooXYctlhC0AQNSww5c95+vjj81yn2AlJc49XwMGJET48nJulmT2exUXdz/YhCMg0uUQYRU8y6uhwQSv/Hw99VZf/fFvBXp3dU5cBi/ClssIWwCAqFVba5Yd2uFr9erW4au42NntMEHCl5dNLKSe76UKPsdt29jvhThQW2sqXvX1UlqaVFCgp/5Tqj89XqB3P85Vk+XOLK8jjpCWLnXlU3ULYctlhC0AQMyoqwvs+VqxwrxetAxfRUXO8DVwYNyHL7uJxYYN3i057OleKjt8/ec/3pwjSw4RVnV1gSHK+2d5/eP/+urPf++tFZ/nqaq2Z7O85syJXIWLsOUywhYAIGbV1ZnXDHvP1+rV0r4WXRsKC53ha9CguA5fwc02Pv1Uqql1/z56Gr6oeiGuNDSYildtrZScvH+IcqnueriPXvi/PFXUpnT5U/7sZ9KDD3pwrp1A2HIZYQsAEDfq6kzgssPXqlWhw1fwnq8DDojr8GU3sSgrkzZt9KaqlJFuKkqDBvV8v5cXXQ6peiFs9u0LDFH2+aScHH24s1R3zyvUwsX5qmro3CwvKltxhLAFAIhbLcPX6tXmr9DB+vQJhK+JE+M+fHm9nE/qeSOLcMz26teP8AWPhZjl9eGOYv3u0SK98Ha+9taGnuU1aZL03/+G8TxbIGy5jLAFAEgY9fWtK1/tha8JE6TBg+M2fAV3Ody5MzqbbYSjyyFLDuG5ELO8Vu0o0u/mF+s/H+arojFTgwbRjTAuEbYAAAmrvt60lw8OX/Ut0kbv3s4hy0OGxG34kryvKrm538urJYelpQxWhof8frPMsLLSVL8yM80XXEmJKbkmudPVsLsIWy4jbAEAsF9DgzN8ffRR6PBlB6/x46UDD4zb8BWORhY93U/ldThkvxc8ZVkmeJWVSenp0lFHSdnZET0lwpbLCFsAALQhOHytWGEGLrcMXwUFzmWHCRK+vKgqST3bTxWO80tPlw4YJP3wh1S94KKGBmnvXumYY6ScnIieCmHLZYQtAAA6qaFB+uSTQOUrVPjKz28dviK8LMgrdqfDTz6Wdu325j56sp+KqhdiBmErfhG2AADopn37Wle+6uqcx+TlObsdxmn4Ckcji/Q06aBRUpKv623mwzXbiy6H6BbCVvwibAEA4JJ9+1pXvtoLXxMmSEOHxn348ircSN2vfIVrSSRdDtEphK34RdgCAMAj+/aZ3+aDw1dtrfOYvDzpsMMC4WvYsLgPX16Fm54s67OXHO7ZI+3YQZdDhBlhK34RtgAACJPGRmf4WrmydfjKzTWVL7v6NXx4XIav4HCza5d38726u6yP/V4IK8JW/CJsAQAQIY2NZqLwsmVmz9fKlVJNjfOY3FxT+QoOX8nJETldL3kdbqSeLzlcscIsiWxscv/csjKlkSOpeiUswlb8ImwBABAl7PAVXPlqGb5ycpzLDuMwfIVryWF3l/V5HQx79TLnNWK4dN75VL0SAmErfhG2AACIUnb4WrEiEL6qq53HZGc7w9eIEXEXvuxwU11tfh/1os18d5f1hSMYZmZIxcXSCSdIl1zi/udHFCBsxS/CFgAAMaKxUVq7NhC+PvggIcNXNA9X9rrRRpJPGjCARhtxh7AVvwhbAADEqMZG6bPPAnO+VqxoHb6yslqHr169InO+HgnXfq+ehC+WHKJdhK34RdgCACBONDUFwpdd+apq8Rt+VpY0blwgfI0cGVfhK5rne4Xj3FhyGKMIW/GLsAUAQJxqajIllWXL2g9fY8cGwtdBB8Vt+Iq2ZhurVkuPPCx9ts4sO2xsdPe8fJJ69zbn9cMfsuQwqhG24hdhCwCABGGHr+DKV2Wl85jMTFP5Gj9emjgx7sKX13uqJCk5Serf3/wXdmfJ4dq1UnVNh4eH7bwQBoSt+BWVYeujj6RBgyJ9JgAAxLemJmn9emf4qqhwHhMcviZMMJuW4jB8Rdt+r+CK3OdfuF/1klhyGFUIW/Er6sLWhg3SJ59I+/b/qSklRUpLC1zirKMSAABRw+93hq8VK1qHr4yMwJ6v8eOlgw+Om/AVrv1e3Rlg7HUoZMlhhBG24lfUha2mJrOevLbWXCoqpLIyqa5Oqq83H09KklJTTfhKTzdv+3yRPnMAAOKL329WnASHr/Jy5zEZGYE9X3b4SkmJzPm6LBz7vXr1kvJyuxZygs9ryxbvlhwOHEjVK2wIW/Er6sJWKJZlwlZtrVRTYy5795pQVl9vvkAlU/WyK2Dp6XHzlzYAAKJCZ8JXerqz4UYcha9w7PfqTrMNr5ccUvUKA8JW/IqJsNWWxsZABay21mzyLSszb9fXB37apKSYH/52EEtKiuhpAwAQF/x+6YsvnOGrrMx5TFqaM3wdcgjhqwtSU6QhQ7q238s+r23b9nc5bHL3nJKTpKJiZnu5irAVv2I6bLWlvj4QwGpqzF/dKioCSxH9fhO4ggNYamqkzxoAgNgWHL5WrDD/thW+7G6HBx8cN6/BCxdK//iHtGmz+ZXDC91ptmGf1+efux+8JPPrVGkJSw57hLAVv+IybIXi9zurYNXV5gWgutr8RLQbcvTq5WzIwVJEAAC6x7Jah6+9e53HpKVJY8Y4K19xEL6idbhycDVu23ZvlhwWFEi5uYSvLiFsxa+ECVtt2bfPGcLKy82lrs5cmppM843gAJaWRkMOAAC6yrJMp4ngZYdff+08Ji1NOvTQQPgaPTpuwpeXA4yl7u33Cl5yuHOn5PYvwT5JJSWmGteV7osJh7AVvxI+bIViWWa5YU1NYCliWZnZE2Y35LAssxTRbsaRlhY3a9ABAAgLy5I2bnSGr6++ch5jhy97ztfo0ea6GOf1virJLM4pLOza3qo//1l64w3pyy+lJr/755Tkk4pLpO+cTNXLgbAVvwhbXdDU5KyCVVWZEFZTY0JY8GwwO4ClpjIbDACAzrAsadMmadmytsNXamqg8jV+vHk7jsKXl8OV09Ol4iLz39aZ/V7hqHqx5HA/wlb8Imy5oKEhUAELXopYX2+WIlpW66WIzAYDAKB9dviyK1/Ll4cOX6NHO5cdpqdH5nxdEo75XpKUmSEVF3c+6NhVrx07pLp6b86pT2+zbS/huhwStuIXYcsjfn9gNlhwQw57Nti+feYYuyGHXQmjIQcAAKFZlrR5szN87dnjPCYlpXXlK8bDVzhazHd1llbwHrTdu7xZcmjvQetsJS6mEbbiF2ErzOzZYHYVrLLSVMHspYiNjWYvWEpKIISlpjIbDACAluzwZXc6XL5c2r3beUxKirPyFUfhy8slh12dpWW3l9+zx/xt2YvwlZUpjRwZp402CFvxi7AVBeyGHKFmg9XXm4u9FJHZYAAAhGZZprODvd9r2bLW4atXL2f4GjMmpsNXuFrMd2e/l5czx+JusDJhK34RtqJYU5NzKaJdBauubt2QI3g/GA05AAAw4WvLFueyw127nMf06mU2CQWHr4yMyJyvC4LD15YtUnWNN/fTlfBln9OKFeac/B78pp2aIvXuE8NdDglb8YuwFYPshhz2paLC1Ozr6kwI8/tNFSw1ldlgAADYLEvautVUvOylhzt3Oo/p1Us6+OBA+Bo7Ni7C14oVpvLlRYt5yYSvAwZ1br9XOJYc9unC/rOoQNiKX4StOGFZzipYy9lg9lLE5OTAUsT0dBpyAAASlx2+gitfLcNXcnLryldmZmTO1wXBzTa2bfdmuHJ393t51XkxyWdazEd1l0PCVvwibMU5uyFH8FLEsjLztt2QQ3LOBktLoyEHACDxWJYp/wSHrx07nMfY4csesjx2bFyEL69maUnm14v8/M6FL7vL4ccfm+zhxZLD9HSptCTKZnsRtuIXYStBBTfkCDUbzO83gYuGHACARNYyfG3f7vx4cnJg2eH48SZ8ZWVF5lxdEI5ZWl1p6e51ow1Jyss15xPRJYeErfhF2EKz4NlgNTWB2WDV1eZ6uyGHPRvMvrAUEQCQKLZtc7aa37bN+fHkZGnUqED4GjcuZsNXtO33CsdsL3veWNiXHBK24hdhCx3at6/9hhxN+3/62g057GoYDTkAAPFu+/ZA8FqxwuwBC5acLB10kLPhRnZ2ZM61h4KXHO7Z4034SvJJxcVSv34dz9MKPp8dO9s+ridSU6QhQ8JQ9SJsxS/CFrql5Wyw6mqzDLGy0oSwhgZzTFKSM4ClpET6zAEA8I4dvuzqV8vwlZTkDF/jxsVF+PJqv1dXmm14vQTSJynXqyWHhK34RdiCq5qanFWwqipTBaupCT0bLD3dVMSYDQYAiEc7djjD15Ytzo/HUfgK136vzszTCsdsL1eXHBK24hdhC2ERPBuspsYsRSwvN1Ww4IYcwXvBUlNZiggAiC87dgSC14oV0pdfOj+elCSNHOkMXxH+5bs7gvdXle31Lnzl5Up5eR032wie7fXV196cS4+WHBK24hdhCxFjWc4qmN2Qo6oqUAWzZ4PZAYzZYACAeLJzpzN8bd7s/LgdvuxW84cdFvFfxrsjHC3dpc4Hnj//WXppkVRVKVVVu38eyUmm3X2nBysTtuIXYQtRp73ZYHV15uM+n7MhR2oqs8EAALFv1y5nt8OW4cvnax2+YvD3t3Ds9+pKsw07fO3aGaElh4St+EXYQkywLPODqKYmEMLKysxyxPp6c7Es8yLEbDAAQLzYvds55ytU+BoxItBqfvz4mAxfdtj5+iupYZ8399HZPVb2ksMdO6TyCm/OpVUFjrAVvwhbiGl+f+iliG3NBrODGA05AACxaM8eZ/jatMn5cZ9PGj48sOfrsMPMpqYYEo55WpL51aCwsONmG3YQ/GqPtK/R/fPwSSopaNDhw/fqlNnH6PATCFtxhbCFuLRvn7MK1nI2mN/vXIpoX2jIAQCIJXv2OJcdbtzo/HgchK/gJYe7dnm33ysvV8rIbD98ebX3LEUNKtBeva1jdPHVObrjDnc+b3cQtlxG2ELCsCwTtoK7IpaVmT1h9fWhZ4PRkAMAEEv27JE++CAQvjZsaH1My/CVnx/20+yJ4GV+lZXehq+OZmrZ57JhQ8+WPwaHrSrl6L//lSZN6v7n6wnClssIW0h49mwwuxLWcjZY4/41Aykpzv1gNOQAAES7r74ylS+7+vXFF62PGTYsEL7Gj4/p8OXVHqskn9lK1V746km7+5Zh66abpJkz3Tr7riFsuYywBbShvt65H6y83Fzq602FzG7IEdyWPiWFpYgAgOj11VfOyleo8DV0qDN8FRSE/zx7IFqabQQvOexothdhK44RtoAu8PudSxHtKlhbDTnsC0sRAQDR6OuvnXO+Pv+89TEHHugMX717h/88uyk48FRUeNPgQupcs432Gm2wjDCOEbYAF+zb56yCtWzI0dRkjgueDUZDDgBAtNm71xm+1q9vfUychK+Oqk090bvAdOA/4YTQ4atlCFRjIGxdck2Obr/du3PrSFyHrbfeekt33XWXli9fru3bt2vBggWa1s4EtsWLF+v4449vdf327dtVWlraqfskbAEesSznUsSWDTnsrohJSYFmHGlpZikiAADRoKzM2e2wrfBlD1keP17q0yfsp9ldf/6z9MYbJvB8vde7++mo2cbqDxq089O9yv0Ord89tWjRIr3zzjuaMGGCzjjjjE6HrbVr1zr+M4qLi5XUyc37hC0gzOyGHC2XItoNOeyliCkpzqWIzAYDAERaWZlzz9e6da2PGTLEOWS5sDDsp9ldXs/UktrY78VQ4/Dz+XydDlt79+5Vfjc7xxC2gCjR0NDxUkS7Lb19SU1lKSIAIHLKyqSVK53hq+Wv0oMHB8LXhAkxE75WrZYWLjCFvR07vGu20auXVNqnQadP2asZc+I/bMXkLvZx48apvr5eo0eP1syZM3XUUUe1eWx9fb3q6wO9KCsqPOqRCaBrUlPNJXjYpD0bzG5LX1Nj/vJlV8MaGsxxycnOrog05AAAhEN+vnTcceYima69LStfGzeayzPPmGMOOCCw5yuKw9eho51dBr0aaNzYKO3cKT31D+n6RdLWOP/VPKYqW2vXrtXixYs1ceJE1dfXa86cOZo/f76WLl2q8ePHh7zNzJkzNWvWrFbXU9kCYkhjo7MKVllpwldtbejZYOnpJsgxGwwAEE4VFc7w9dlnrStfgwY5w1dRUWTOtYvcnO8V3I0wp2+Otm1z5RS7hWWEHZgyZYoGDRqk+fPnh/x4qMrWwIEDCVtAPLAbctiVsODZYPX1gdlgwcOZU1MjfdYAgERhhy+76cbate2Hr/HjpeLiyJxrF9n7vWpruh6+WrZ+nzNHOv98b86zIywj7MARRxyht99+u82Pp6WlKS0tLYxnBCBs7AAVvIfT73dWwaqrA7PBvv469Gyw9HQacgAA3JebK02ZYi6SWZnRMnxt3mwuCxaYYwYOdIavkpLInX87LrnE2ea9J8023n8/cmHLazEftlauXKm+fftG+jQARIukJCkry1yCBc8Gq6kxf20sLzfvl5WZkObzBWaD2RcacgAA3JKTIx17rLlIJnzZDTdWrJA+/VT68ktzWbjQHDNwYKDZxoQJMRG+Vq2W7r1XWr/O/K2zo2V0hx/u9dlFTkTDVlVVldYHzS/YsGGDVq5cqd69e2vQoEG69tprtXXrVj322GOSpHvvvVdDhgzRIYccorq6Os2ZM0dvvPGG/v3vf0fqIQCIFSkp5hK8RMBuyBFqNlhFhWnIYVmBrojMBgMAuCknx7Q/P+YY835VlbPbYXD4eu45c8yAAc7w1clZs+F06Gjp4TmB94P3e1VWypG+Jk2K36qWFOGwtWzZMseQ4iuuuEKSdM4552jevHnavn27Nm/e3PzxhoYGXXnlldq6dasyMzM1ZswYvfbaayEHHQNAh3w+KSPDXIK1NxusstJUyZKSzFLE4P1gNOQAAPREdrZ09NHmIoUOX1u2mMvzz5tj+vcPLDmcODEqw9e0ac7hxs//U9q0UvrxqdL0S9q4UZyImgYZ4cKcLQDdZjfksC/BDTnq6gINOZgNBgDwQlWV9OGHzvDV1OQ8pn9/Z+UrGrfbMNQ4fhG2ALjK73cuRayuNi8g1dUmhO3bZ45pWQVjNhgAoKeqq53ha82a1uGrXz9nw41+/SJzrsEIW/GLsAUgLOzZYHZb+uCGHPX15sXQ5zP7v+z9YMwGAwD0RHW19NFHgfD1ySetw1ffvs45X5EIX4St+EXYAhAxluVcilhTYwJYRUVgNpjfbwKXPZyZhhwAgO6qqXGGr48/bh2+Sktbhy+vl78TtuIXYQtA1GlqCixFrKkxa/LLy51LEaVAFcy+MBsMANAVnQlfJSXO8NW/v/vhi7AVvwhbAGJGQ4OzIUdFhemKWFcXqIIFzwazlyLSkAMA0Bm1tWbPlz1k+eOPzTL4YCUlgYYbEye6E74IW/GLsAUgprWcDVZdbQJYVZW5vqHBHJecHKiApafTkAMA0LHa2kDla8UKafXq9sPXhAlm7ldXwxdhK34RtgDEJbshh32prDQhzG7IYb9YpqQwGwwA0Dl1dc5lh6HCV3FxoNPhhAnSwIEdhy/CVvwibAFIKG015Aheimg35AieDQYAQEt1ddKqVc7wZe8rthUVOcPXoEGtwxdhK34RtgAkPL/fWQWzlyJWV5sXUvuFs1cvZ0MOliICAILV1ZnAZYevVatah6/CQuecrwMOMMcQtuITYQsA2rBvX9sNOerqArPBggNYWhoNOQAARsvwtXp1YC+xrU8f6bDDpIMOkq66yoSvCCJsuYywBQBd0LIhR02NCWCVlWYZYkODOSYpKdCMg9lgAADJvE60rHwFh6+VK6WxYyN2epL32YA1IQCAtvl8UkaGuQRranJWwaqqTAiz54S1nA1mt6VnNhgAJI60tMASQsmEr48/lt57T1qzRhoyJLLnFwaELQBA1yUnS9nZ5hKsocEELjuE2Q05qqulr74yVbCWSxGZDQYAiSEtzezbGj3a7NlKgJ/9hC0AgHtSU80lPz9wnd/f9mywsjJTBfP7Aw057KWINOQAAMQ4XskAAN5KSpIyM80lWPBssJoasw+svDzQot5uyNFyKSKzwQAAMYKwBQCIjF69TMvf4La/ltX2bDC7KYe9FJHZYACAKEfYAgBEDztEpadLBQWB61vOBguugn39deuGHPaFhhwAgAgibAEAol9SkpSVZS7BGhpCzwarqTGbr/1+E+BSU5kNBgAIO8IWACB22Q058vIC17U3G6yiIrAUMTk5sBQxPZ2GHAAA1/HKAgCIL23NBgtuyGEvRSwrC7zd2GiOS0lx7gejIQcAoJsIWwCAxBCqIYfkbMhhzwYrLw/MBvP7TeAKbktPQw4AQCcQtgAAic2uYLU1G6ymJjAbrLra2ZDDng1mX1iKCAAIwqsCAAAtBc8G69MncP2+faEbctTVBWaDSYGGHHYljIYcAJCQCFsAAHRWSoq55OYGrgueDVZT45wNVlFhOiZaVuuliCkpkXscAICwIGwBANATbc0Ga2pyVsGqqgJt6auqmA0GAAmAsAUAgBeSk6XsbHMJFjwbrKbGVL+CBzQHN+SwL6mpLEUEgBhE2AIAIJzamg0WXAWzhzLb1bCGBnNccnIggDEbDACiHj+lAQCINJ8v0JAjWHuzwSoqzMd9PmdDjtRUZoMBQJQgbAEAEK1CzQazLFPpqqkJhLCyMhO+KitNsw7LCuwlC16KCAAIK8IWAACxxOcLBKjghhx+v7MKFmo2mGWZhhzBIYyGHADgGcJWhC1dKn32mTRihDRpUqTPBgAQs5KSpKwscwlmzwazK2H2bDB7X5jf71yKaF9oyAEAPUbYiqBrrpHuvDPw/uTJ0oknSgMHmte8hgbzL0EMANBtbc0Gq6tzNuQoKzPLENuaDUZDDgDoMn5qRsjSpc6gJUlLlphLKKecIp1+ujOEEcYAAN3i80kZGeYSLHg2WE2NCV92W/rKStOQQ2q9FJGGHAAQEmErQj77rGvHv/iiubSFMAYA6LG2ZoPV1zv3g5WXm0t1tfTVV4GGHMwGAwAHwlaEjBjh7ufrbBj78kvzvr1UkSAGAOiQHaDy8wPX+f3OpYj2TLDqarMXbN8+c1yvXs4QxlJEAAmEn3gRMmmSdPXVrZcSeqW9MEZVDADQZUlJoWeD2Q057EtFhamC2RWxpiZzXPBsMBpyAIhTPsuyrEifRDhVVFQoLy9P5eXlyg3eLBwhS5dKt9zSflUq0qZOlc46iyAGAOgmy3IuRQxuyFFfby5+v1nGGBzAUlIifeYAvNDQYCrgxxzjnCMYAV5nA8JWlFi6VFq0yLwd3I1wwYLoDmJtdVAkkAEAOhTckCN4KWJNjQlg9lLElBTnUkRmgwGxjbAVv6I1bLXHnsXVMsjEQhiTqIwBALqooaH1UsSyMrNHrL7ehLSkJPNiYlfBaMgBxA7CVvyKxbDVkVgOY8FBzG7ecfLJhDAAQAv2bDB7OLM9lLmqygSwhgZznL0U0V6OSEMOIPoQtuJXPIatjgSHseBuhNEcxNrqnkhVDADg0NjorIJVVpoqWG2tCWEtZ4Olp5sXEmaDAZFD2IpfiRi22tNWVeyJJ6SXX4702bUvVFWMlvYAAEmBhhx2JcyeDWY35LBngwUPZ05NjfRZA4mBsBW/CFudF8vLEyWWKAIAWvD7nVWw6urAbLC6OmaDAeFC2IpfhC33tNVBMdorY8EdFKmIAQAcs8FqapyzwVo25AgOYTTkALqHsBW/CFvhE6tLFAljAIDmhhxtzQZraDDHJCUxGwzoKsJW/CJsRYdQQezLL6VXX5WWLIn02bVv6tRA4AoOZCxRBIAE0N5sMHspos8XaMhhV8FoyAEEELbiF2Er+rXVPZGqGAAgatkNOexLcEOOurpAQ47gZYjMBkOiImzFL8JW7GurKmZ/LJrDmF0V27JFGjCAIAYAcc3vdy5FtBtyBM8GsyzTgCO4CkZDDsQ7wlb8ImzFv1hdohhqeaIdyliiCABxxJ4NZrelD9WQw16KaO8HYzYY4glhK34RthJbyw6KsTLkWWq9RJEgBgBxxLKcSxFrakwAq6gIPRuMhhyIZYSt+EXYQnvaCmPRvjxx8mRp1KjA0kT2iwFAnGhqCixFrKlxNuSorw/MBrOrYPYlOTmipw20K4HCFouCgSCTJrUdStpq3BENSxSXLOn4/idPls47j6oYAMSU5GQpK8tcgjU0OBtyVFQEQtjevWa/mM/HbDAgwqhsAS6Jp6oYYQwAYlDL2WDBDTnq6kxAk0yAs8NXejoNORB+CVTZImwBYdCyKmaHmWgPYlLoDorMFgOAGGI35LAvlZUmhNkNORobzXHMBkO4ELbiF2EL0aa95Ylbtkhr1kR3F8WWyxMlqmIAEBNazgYrKws05KirM0sRk5KcASw1NdJnjXhA2IpfhC3EolBLFGMliI0aZd6mKgYAMcDvdwYweylidbUJYHZDjl69nHvBWIqIriBsxS/CFuKNHcRCLfOL9mWKVMUAIEbs2xe6IUddXWApos/nDGA05EBbCFvxi7CFRBOrVbHgvWI2ghgARBF7Npg9nLmmxgSwykpzfUODOSYpKdCMg9lgkAhb8YywBQS0VRV79llp1apIn13bgpcn2uzzZ64YAERYU5OzCtbRbLD0dPPDm9lgiYOwFb8IW0DntNVBceBA6ZFHorsqJkmHHiodcUTgfcIYAERY8GywmhqzFLG83CxFrKszVbCWSxFTU1mKGI8IW/GLsAW4o63liVL0L1GUQocxligCQJhZVuiGHFVVgSqYZQVmg9lLEWnIEdsIW/GLsAWER/ASRSl2qmKh9opJhDEACKvg2WA1NWYfWHl5YCliY6PZC9ZyKSKzwWIDYSt+EbaAyGurKvbee9G9V0xyVsRoZw8AYWQ35AgOYeXlgdlg9fWBpYjMBotuhK34RdgColuovWK2WFyeKFEVAwBPtZwNFtyQw54NZlmmChYcwmjIETmErfhF2AJiW8vlibYBA6J/rhhBDADCKLghR6jZYH6/qYKlpjIbLNwIW/GLsAXEt1gNY20FMTooAoCLLMuEreCliHv3BhpyNDSYEJacHKiCpafTkMNtCRS2+MoBEFcmTWo/lIQKY9GwV2zVqo7PgcoYAPSQzydlZJhLsKamwHDm2lrTkKOsLPB2Y6M5ruVSRBpyoANUtgBA7e8Vi4Yw1pGhQ6XjjnNeRxADgB4KbshRW2sacpSXm+vr6kwVLCnJ2ZY+JYWliB1JoMoWYQsAOiFURSxW2tmHqohJhDEA6Ba/P7AUsaYmMBusujrQkEMySw+D94KxFDGAsBW/CFsAvNDWXrFYqIoRxgDABfv2td+Qo6nJHGc35LArYYlYBSNsxS/CFoBwi+UgJrFEEQC6reVssOpqswyxstKEsIYGc0yopYjxjLAVvwhbAKJJW0FMiv4OilKgKrZrl/n9IS9PGj+eIAYA7Wpqans2WH19YCliSopzKWK8zAYjbMUvwhaAWNNWIHvuOWnPnsicU2e0XJ5oB7Lhw6ULLySMAUAr7c0Ga9mQw76kpsbeUkTCVvwibAGIJw8/LL3wgvkjaXFx4PpYWKIYHMaojAFAG+zZYHZr+lCzwSRT9bIDWLTPBiNsxS/CFoBE0d4SxVgJY4MHB0JYcTF7xQCgWWOjswoWPBvMXoro8wVmg6WnmypYNMwGI2zFL8IWABjthbFoX6I4dKjUv38ghNkIYwASWsuGHLW1JoBVVJjr6+vNMT6fczhzamp4z5OwFb8IWwDQOfGwRDF4eSKVMQAJy+93BrBQs8EsK9CQww5iXjXkIGzFL8IWAPRcy6qYHWq2bpU+/zyy59YZdjt79ooBSGj2bDB7P1jL2WB+v6mC2bPB7EtPG3IQtsLjrbfe0l133aXly5dr+/btWrBggaZNm9bubRYvXqwrrrhCH3/8sQYOHKgbbrhBM2bM6PR9ErYAwFttLU/ctUv64IPQyxajyYAB0mGHmUoYHRQBJBy7IYddBaupMQGssjLQkKPlbLCuNuRIoLAV0TYl1dXVGjt2rM477zydccYZHR6/YcMGnXLKKbrooov0+OOP6/XXX9cFF1ygvn376qSTTgrDGQMAOjJpUvuhJFQYi6bK2JYtrQPhW2+ZZZXBe8UkqmIA4pDPJ2VkmEuw4NlgNTXO2WCVla0bcthVsGhoyBFBUbOM0OfzdVjZuuaaa/Tiiy9q9erVzdedeeaZKisr08udnPpJZQsAoltwGAte5rdxY/TvFbOrYhJ7xQAkiJYNOcrLzaW+3lTI7IYcwcsQJRPUqGxFl3fffVff+ta3HNeddNJJuuyyy9q8TX19verr65vfr6io8Or0AAAuaK8yZgexFSucYSZamnaEqorZZs0KXRljiSKAmGYHqPz8wHV+v3Mpol0Fq642ywfr66WsrEidcVjFVNjasWOHSkpKHNeVlJSooqJCtbW1ymhZ7pQ0e/ZszZo1K1ynCADwUGeCWKjliXl50rvvRr6d/eeft14maS9RHDDAzBVLSgqEseRk6dRTpfPPD/upAkD3JSVJmZnmEsxuyFFba95u+fE4FFNhqzuuvfZaXXHFFc3vV1RUaODAgRE8IwCAFzraKya1bmcf7XvFJGnhQunKK6WxY80fiwljAGJWSoq5JNBWnpgKW6Wlpdq5c6fjup07dyo3NzdkVUuS0tLSlGavDQUAJLTzz287mCxdKj30kLRuXWB5YrR0UCwvNxWwUBYulC67TDr++MCxdihjiSIARFZMha0jjzxSL730kuO6V199VUceeWSEzggAEC+6uldMip6qWFWV9K9/tb4+uIviwQc7gxhdFAHAexHtRlhVVaX169dLkg477DDdc889Ov7449W7d28NGjRI1157rbZu3arHHntMkmn9Pnr0aF1yySU677zz9MYbb+iXv/ylXnzxxU63fqcbIQDAbS2rYlL0BLHOCN4vRlUMQCKJ66HGixcv1vH2uocg55xzjubNm6cZM2Zo48aNWrx4seM2l19+uT755BMNGDBAN954I0ONAQBRq61W9lJ0LFHsSP/+pjJmhzC/XyosZK8YgPgQ12ErEghbAIBoElwVC17i98kn0V8Vy86Wxo1zVsQIYwBiCWHLZYQtAECsaLlXLNbCWEaGNHKkCWUEMQDRiLDlMsIWACBetLVXzO+XVq40jTOiVaggVlVlOkJPn04YAxAehC2XEbYAAIkieK6YFAhiGzdG/14xe4miHRjtUEbjDgBuImy5jLAFAEDbSxRjoSomSUVFpnlHdnbgXCdMIIgB6BrClssIWwAAdMyuiu3Z42yA8fnnpqV9NCsqMksUqYoB6Ahhy2WELQAAeiZUB8VYCmP9+5tAJgUqY+wVAxITYctlhC0AALxlh7Hly837dmUpFoJYVpapgFVXm7epigHxjbDlMsIWAACR01YQ8/ultWul3bsje34dCV6iWF1truvbl6oYEKsIWy4jbAEAEL1aLlG0913t3h39VbGMDGnAAPO2XRVjiSIQ3QhbLiNsAQAQm4I7KH75pbnODjRr10q1tZE9v46kpUmDBjmDmEQXRSCSCFsuI2wBABCfHn5YeuwxqaLCGWZioSomSfn5gcYdkgllBDHAW4QtlxG2AABIPG0tT8zONvvHor0q1jKISYQxwA2ELZcRtgAAQEvBc8Xs5hdZWbFbFZNo3AF0BmHLZYQtAADQFaE6KNqBbPduqawsoqfXIXuvWDCqYoBB2HIZYQsAALgpOIzZ7eDtJhixukSRqhgSBWHLZYQtAAAQTnbjju3bzft2EIuFuWLJyVK/flJ6euA6qmKIJ4QtlxG2AABAtAhVFbNt2RL9VbHsbBO+UlMDgYyqGGIJYctlhC0AABArWlbFbLGwVywlRerd2xnEJMIYogthy2WELQAAEA/aq4pt3izV10fmvDrLbtxRVyc1NJhQ1qcPSxQRXoQtlxG2AABAIojlqpgk5eRIpaXOMDZkCFUxuIuw5TLCFgAASHSxXhVLSZEGD6Yqhp4jbLmMsAUAANC+hx+W/vQnUxUL3nMVK1Uxu3GHZUk+H1UxtI2w5TLCFgAAQPcFV8W++ipQWUpPj52qWGmpOW87jOXmSsceS1UsERG2XEbYAgAA8I69V2zDBmcQoyqGaETYchlhCwAAIDJC7RWz913V1EiVlZE9v44kJ5u9YT4fVbF4QdhyGWELAAAgOi1dKt16q/ThhybMpKcHwtiuXea6aJaZaSpjdhhrbDTvn322eVyIPoQtlxG2AAAAYtP110vPPGOqYMFB7OuvpX37In127fP5pKIiZxBLSZGOOMI8LqpikUHYchlhCwAAIP4EzxWzQ5i91C8WqmJpaVJenglhktSrl1RQIH3/+1TFvETYchlhCwAAIPEEV8WkQBj7+utAwIlmvXubf3v1oirmJsKWywhbAAAACNZWVayqKhDOollamumgaAexxkYpP5+9Yp1B2HIZYQsAAACdZXdQfOstqaIisDQx1qpiwcsTs7JoZ28jbLmMsAUAAAC3tJwrFhzG9uyR/P5In2H7fD4pJ8e83auXuaSnS2PHJsYSRcKWywhbAAAACJeHH5b+9CezRDE4iJWXS/X1kT67jqWkSBkZgfd79ZIGDZIuvTQ+qmKELZcRtgAAABAN7Lli771nWtfbe64ks0Qx2vl8pmNi8F6xjIzYatxB2HIZYQsAAACxwO6guHevc89VLCxPlKTkZNO8o1cv8340VsUIWy4jbAEAACDWBe8Vq64219kVplioimVkSH/8Y+RDF2HLZYQtAAAAxLuWVTG7MlZfH117xY44wiynjBSvs0GS658RAAAAQETdeqv06afSzp3SV1+Zhhzl5WaO2H//K333u1JJiZSbG7j07m2W/oXTe++ZKl28ImwBAAAACWTSJOn556UdOwIhrLzchLLGRmnOHGncOBO+gsNYaqo35/P++9583mhA2AIAAADQ7PzzpQ8+cFbE7Fb1LativXtLxcXm36RuJovDD3f3/KMJe7YAAAAAuMKeK/bFFyacJSebxh11dWboc0uTJpkAFyns2QIAAAAQE+yqmL0/rLq6dVVs0CCzTHHOnMgGrXDoFekTAAAAABD/7L1iiYTKFgAAAAB4gLAFAAAAAB4gbAEAAACABwhbAAAAAOABwhYAAAAAeICwBQAAAAAeIGwBAAAAgAcIWwAAAADgAcIWAAAAAHiAsAUAAAAAHiBsAQAAAIAHCFsAAAAA4AHCFgAAAAB4gLAFAAAAAB4gbAEAAACAB3pF+gTCzbIsSVJFRUWEzwQAAABAJNmZwM4Ibku4sFVZWSlJGjhwYITPBAAAAEA0qKysVF5enuuf12d5FeOilN/v17Zt25STkyOfzxfp04lbFRUVGjhwoL788kvl5uZG+nQSEs9B5PEcRBb//5HHcxB5PAeRx3MQee09B5ZlqbKyUv369VNSkvs7rBKuspWUlKQBAwZE+jQSRm5uLj9YIoznIPJ4DiKL///I4zmIPJ6DyOM5iLy2ngMvKlo2GmQAAAAAgAcIWwAAAADgAcIWPJGWlqabbrpJaWlpkT6VhMVzEHk8B5HF/3/k8RxEHs9B5PEcRF4kn4OEa5ABAAAAAOFAZQsAAAAAPEDYAgAAAAAPELYAAAAAwAOELQAAAADwAGELPVJfX69x48bJ5/Np5cqVbR739ddf6xe/+IVGjhypjIwMDRo0SL/85S9VXl7uOM7n87W6PPnkkx4/itjW2edAkurq6nTJJZeoT58+ys7O1ve//33t3LnTcczmzZt1yimnKDMzU8XFxfr1r3+txsZGDx9B7DrttNM0aNAgpaenq2/fvvrpT3+qbdu2tXn8xo0bQ36N+3w+Pf30083H8X3QeV19DiTpuOOOa/X/e9FFFzmO4fug87r6HPB64K7ufA/wWuCejRs36vzzz9eQIUOUkZGhoUOH6qabblJDQ0O7t+G1wD3deQ6k8L0W9OryIwKCXH311erXr58+/PDDdo/btm2btm3bprvvvlsHH3ywNm3apIsuukjbtm3TP//5T8exc+fO1dSpU5vfz8/P9+LU40ZnnwNJuvzyy/Xiiy/q6aefVl5eni699FKdccYZeueddyRJTU1NOuWUU1RaWqolS5Zo+/btmj59ulJSUnTbbbd5/VBizvHHH6/rrrtOffv21datW3XVVVfpBz/4gZYsWRLy+IEDB2r79u2O6/7617/qrrvu0sknn+y4nu+Dzunqc2C78MILdfPNNze/n5mZ2fw23wdd09XngNcDd3Xne4DXAvd8+umn8vv9+stf/qJhw4Zp9erVuvDCC1VdXa2777475G14LXBXd54DW1heCyygm1566SXroIMOsj7++GNLkvXBBx906fb/+Mc/rNTUVGvfvn3N10myFixY4O6JxrGuPAdlZWVWSkqK9fTTTzdft2bNGkuS9e677zZ/vqSkJGvHjh3NxzzwwANWbm6uVV9f79njiBfPPfec5fP5rIaGhk7fZty4cdZ5553nuI7vg+7rzHMwZcoU61e/+lWbH+f7oGe6833A64F7Ovr/57XAe3feeac1ZMiQLt2G1wJ3deY5CNdrAcsI0S07d+7UhRdeqPnz5zv+CtAV5eXlys3NVa9ezgLrJZdcosLCQh1xxBF65JFHZDEKLqSuPgfLly/Xvn379K1vfav5uoMOOkiDBg3Su+++K0l69913deihh6qkpKT5mJNOOkkVFRX6+OOP3X8QceTrr7/W448/rsmTJyslJaVTt1m+fLlWrlyp888/v9XH+D7ouq48B48//rgKCws1evRoXXvttaqpqWn+GN8H3ded7wOJ1wO3dOb/n9cC75WXl6t3796dPp7XAvd19jkIx2sBywjRZZZlacaMGbrooos0ceJEbdy4scufY8+ePbrlllv0//7f/3Ncf/PNN+uEE05QZmam/v3vf+viiy9WVVWVfvnLX7p09vGhO8/Bjh07lJqa2moJQklJiXbs2NF8TPAPFfvj9sfQ2jXXXKM//elPqqmp0Te+8Q298MILnb7tww8/rFGjRmny5MmO6/k+6JquPgdnnXWWDjjgAPXr108fffSRrrnmGq1du1bPPvusJL4PuqMn3we8HvRcV/7/eS3w1vr16/XHP/6xw+VrwXgtcFdnn4OwvRZ0ugaGuHfNNddYktq9rFmzxrrvvvuso446ympsbLQsy7I2bNjQpWWE5eXl1hFHHGFNnTq1w2UmN954ozVgwICePrSY4eVz8Pjjj1upqamtrj/88MOtq6++2rIsy7rwwgutb3/7246PV1dXW5Ksl156yb0HGsU6+xzYdu/eba1du9b697//bR111FHWd77zHcvv93d4PzU1NVZeXp519913d3gs3wfePAe2119/3ZJkrV+/3rIsvg8sK3zPAa8HoXn5/89rQed09TmwLMvasmWLNXToUOv888/v9P3wWtC2cD0HNq9eC6hsodmVV16pGTNmtHvMgQceqDfeeEPvvvuu0tLSHB+bOHGifvKTn+jRRx9t8/aVlZWaOnWqcnJytGDBgg6XmUyaNEm33HKL6uvrW91fPPLyOSgtLVVDQ4PKysocf9HcuXOnSktLm4957733HLezO1TZx8S7zj4HtsLCQhUWFmrEiBEaNWqUBg4cqP/+97868sgj2/0c//znP1VTU6Pp06d3eE58H7TmxnNgmzRpkiTz19ChQ4fyfaDwPAe8HrTNy/9/Xgs6p6vPwbZt23T88cdr8uTJ+utf/9rp++G1oG3heg5sXr0WELbQrKioSEVFRR0e94c//EG//e1vm9/ftm2bTjrpJD311FPNX6ihVFRU6KSTTlJaWpqef/55paend3hfK1euVEFBQUL8UJG8fQ4mTJiglJQUvf766/r+978vSVq7dq02b97c/IJ85JFH6tZbb9WuXbtUXFwsSXr11VeVm5urgw8+uKcPLyZ09jkIxe/3SzLt+Dvy8MMP67TTTuvUffF90HldeQ5s9siEvn37SuL7QPL+OeD1oH1e/v/zWtA5XXkOtm7dquOPP14TJkzQ3LlzlZTU+ZYIvBa0LVzPgc2z14Iu19iAFkItYduyZYs1cuRIa+nSpZZlmaUikyZNsg499FBr/fr11vbt25sv9lK4559/3nrooYesVatWWevWrbPuv/9+KzMz0/rNb34TiYcVUzrzHFiWZV100UXWoEGDrDfeeMNatmyZdeSRR1pHHnlk88cbGxut0aNHW9/+9retlStXWi+//LJVVFRkXXvtteF8ODHhv//9r/XHP/7R+uCDD6yNGzdar7/+ujV58mRr6NChVl1dnWVZoZ8Dy7KsdevWWT6fz1q0aFGrz8v3Qed15zlYv369dfPNN1vLli2zNmzYYD333HPWgQceaB177LHNn5fvg87rznPA64F7uvtziNcC92zZssUaNmyY9c1vftPasmWL4+s5+BheC7zTnecgnK8FhC30WKhf9O3r3nzzTcuyLOvNN99sc73thg0bLMuyrEWLFlnjxo2zsrOzraysLGvs2LHWgw8+aDU1NYX/QcWYzjwHlmVZtbW11sUXX2wVFBRYmZmZ1umnn+74YWRZlrVx40br5JNPtjIyMqzCwkLryiuvdLRjhvHRRx9Zxx9/vNW7d28rLS3NGjx4sHXRRRdZW7ZsaT4m1HNgWZZ17bXXWgMHDgz5tc33Qed15znYvHmzdeyxxzbfZtiwYdavf/1rq7y83PG5+T7onO48B7weuKe7P4d4LXDP3Llz2/x6tvFa4K3uPAfhfC3wWRY9JAEAAADAbczZAgAAAAAPELYAAAAAwAOELQAAAADwAGELAAAAADxA2AIAAAAADxC2AAAAAMADhC0AAAAA8ABhCwAAAAA8QNgCAAAAAA8QtgAAAADAA4QtAAAAAPAAYQsAkDB2796t0tJS3Xbbbc3XLVmyRKmpqXr99dcjeGYAgHjksyzLivRJAAAQLi+99JKmTZumJUuWaOTIkRo3bpy+973v6Z577on0qQEA4gxhCwCQcC655BK99tprmjhxolatWqX3339faWlpkT4tAECcIWwBABJObW2tRo8erS+//FLLly/XoYceGulTAgDEIfZsAQASzueff65t27bJ7/dr48aNkT4dAECcorIFAEgoDQ0NOuKIIzRu3DiNHDlS9957r1atWqXi4uJInxoAIM4QtgAACeXXv/61/vnPf+rDDz9Udna2pkyZory8PL3wwguRPjUAQJxhGSEAIGEsXrxY9957r+bPn6/c3FwlJSVp/vz5+r//+z898MADkT49AECcobIFAAAAAB6gsgUAAAAAHiBsAQAAAIAHCFsAAAAA4AHCFgAAAAB4gLAFAAAAAB4gbAEAAACABwhbAAAAAOABwhYAAAAAeICwBQAAAAAeIGwBAAAAgAcIWwAAAADggf8PZk0KFDweDFoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#calculate error margins with RMSE value\n", + "logerr_up = linear_model(log_masses, -0.5450 + fit_err)\n", + "logerr_low = linear_model(log_masses, -0.5450 - fit_err)\n", + "\n", + "#plot new errors with function\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(log_masses, log_power, label='Data', color='blue', s=10)\n", + "plt.plot(log_masses, linear_model(log_masses, *params), label='Fitted line', color='red')\n", + "plt.fill_between(log_masses, logerr_up, logerr_low, color='red', alpha=0.2, label='error margin')\n", + "plt.legend()\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Linear Fit of Multiple Linear Functions')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b90191d6-3b08-42c9-9b7e-b925f79c30cb", + "metadata": {}, + "source": [ + "This margin of error makes a lot more sense, as it encompasses many more values represented by the function. " + ] + }, + { + "cell_type": "markdown", + "id": "9412765c-6aba-4280-8c97-0f3c7a6f7daa", "metadata": {}, - "outputs": [], "source": [ - "#find general error by calculating average range\n" + "#### Graphing our new error in power law space" ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "a1ff5709-854c-46db-8dc5-822cfd65b212", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting different IMFs based on literature\n", + "plt.plot(BD_masses, power_law(BD_masses, M1_a), color = 'y', label = \"Mužić 2017\")\n", + "plt.plot(BD_masses, power_law(BD_masses, K_a), color = 'c', label = \"Kirkpatrick 2021\")\n", + "plt.plot(BD_masses, power_law(BD_masses, R_a), color = 'b', label = \"Ryan 2022\")\n", + "plt.plot(BD_masses, power_law(BD_masses, B_a), color = 'm', label = \"Best 2024\")\n", + "\n", + "#average our two fit parameters (one from power law, one from linear fit)\n", + "best_a = (avg_a + (-1*m)) / 2\n", + "\n", + "#finding upper and lower bounds using best_a\n", + "up_a = best_a + fit_err\n", + "down_a = best_a - fit_err\n", + "power_up = np.array(power_law(BD_masses, up_a))\n", + "power_down = power_law(BD_masses, down_a)\n", + "\n", + "#plotting our fit line and error margin\n", + "plt.plot(BD_masses, power_law(BD_masses, best_a), color = \"black\", label = \"fit for functions\")\n", + "plt.fill_between(BD_masses, power_up, power_down, color = \"black\", alpha = 0.2, label = \"fit error\")\n", + "\n", + "#organizing our graph\n", + "plt.title(\"Final Power-Law Curve Fit for Brown Dwarf Mass Objects\")\n", + "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", + "plt.ylabel(\"$M^{0.56 ± 0.16}$\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "a0e7f65f-d511-4840-937f-4fd2c57a2bd7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5643248]\n" + ] + } + ], + "source": [ + "print(best_a)" + ] + }, + { + "cell_type": "markdown", + "id": "706598df-cec8-495d-9fbc-cfd29c9193d3", + "metadata": {}, + "source": [ + "### Conclusions" + ] + }, + { + "cell_type": "markdown", + "id": "40494bed-857e-4527-b10b-d72856d034b2", + "metadata": {}, + "source": [ + "From this process, we have determined that the best IMF model for brown-dwarf mass objects is given by a power law, $M^{-\\alpha}$, where ${\\alpha} = 0.56 ± 0.16$. This fits within our expected output because, in average, brown dwarf mass objects have been represented with $\\alpha$ values between 0.5-1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7df524b5-524e-49f3-a183-c547afd36419", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 166e46c0afccab5eb86a6c29755b3b87d06603e6 Mon Sep 17 00:00:00 2001 From: caitlinbegbie Date: Wed, 26 Jun 2024 16:13:43 -0700 Subject: [PATCH 04/48] Deleting outdated doc --- changes/Power_Law_Function.ipynb | 608 ------------------------------- 1 file changed, 608 deletions(-) delete mode 100644 changes/Power_Law_Function.ipynb diff --git a/changes/Power_Law_Function.ipynb b/changes/Power_Law_Function.ipynb deleted file mode 100644 index fb713697..00000000 --- a/changes/Power_Law_Function.ipynb +++ /dev/null @@ -1,608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "23642516", - "metadata": {}, - "source": [ - "## Plotting Studied Power-Law Functions Over Small Mass Intervals" - ] - }, - { - "cell_type": "markdown", - "id": "ad57fd40", - "metadata": {}, - "source": [ - "#### Importing Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "d6503a21", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from scipy.optimize import curve_fit" - ] - }, - { - "cell_type": "markdown", - "id": "4f72f6f7", - "metadata": {}, - "source": [ - "#### Creating the general power-law function for small masses" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "d51b38dc", - "metadata": {}, - "outputs": [], - "source": [ - "#define function\n", - "def power_law(M, a):\n", - " return M**(-1*a)\n", - "\n", - "# M is a defined mass range according to a specified paper, and a is the associated alpha value" - ] - }, - { - "cell_type": "markdown", - "id": "3fe49d7b", - "metadata": {}, - "source": [ - "#### Defining different mass/alpha values based on published papers" - ] - }, - { - "cell_type": "markdown", - "id": "b7736fad", - "metadata": {}, - "source": [ - "***Masses are in solar masses!***" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "4145bc16", - "metadata": {}, - "outputs": [], - "source": [ - "#Moraux 2007 (0.03 - 0.6) a = 0.69 ± 0.15\n", - "M_mass = np.linspace(0.03, 0.6, 1000)\n", - "M_a = 0.69\n", - "M_aerr = 0.15\n", - "\n", - "#Suárez 2009 (0.02 - 0.50) a = 0.60 ± 0.13\n", - "S_mass = np.linspace(0.02, 0.5, 1000)\n", - "S_a = 0.60\n", - "S_aerr = 0.13\n", - "\n", - "#Lodieu 2009 (0.01 - 0.5) a = 0.5 ± 0.2\n", - "L_mass = np.linspace(0.01, 0.5, 1000)\n", - "L_a = 0.5\n", - "L_aerr = 0.2\n", - "\n", - "#Béjar 2011 (0.013 - 0.1) a = 0.7 ± 0.3\n", - "B_mass = np.linspace(0.013, 0.1, 1000)\n", - "B_a = 0.7\n", - "B_aerr = 0.3\n", - "\n", - "#Peña Ramírez 2012 (0.006 - 0.25) a = 0.6 ± 0.2\n", - "P_mass = np.linspace(0.006, 0.25, 1000)\n", - "P_a = 0.6\n", - "P_aerr = 0.2" - ] - }, - { - "cell_type": "markdown", - "id": "f2a24e63", - "metadata": {}, - "source": [ - "#### Plotting all of the curves together (w/o accounting for error)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "60cab6f2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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1Fi3WgFi/D1j1ORb1FmhjzBYscXsO62niEqxh7KV2/FbbgCX2di7WQKxv7cphd8uMw3pPvR+ru+NxrAEddeEFrINcsbyKdbPajfUkuRFrAFNVFmF1ve7x2RaOPUDUiDFmuzFmZYDo24BHRCQPS6x8W4Xtgf9i3bw22fv9F9b5+CXWsTiK1Zq/LRhbfGz6HOtPsx5rEM/8OuTdg9Uj8Et7/2uxbn5gDcrpZ3flvO8n+6PASnu/PwCr7bBguBbYZXcx3YJ1XQVjb43X4AnmXqyHrDwsoanxxlTFznlY1/Xbdh03ABN8kswE5tjH9idY4xVisOq0DPgkyF29gTWYpdbWs49t+2sQ85rqnGCHZWH9zzKBJ+04v+fT/q/chPUQkYU1cO46O08UMAurzgexWt8P+LHpWqzBnp8ZYw5WLMCzQKpYI8nvxboGV2Bdx49jvSev6fq+A+tBYwdWb9pbWEIfiNuxWvEHsd7rvlpD2oAYY44AV2K9z83Eese5EusG7o//2L+ZIrLaXr8Gq4dwP9ZI/Ift+0BdeQirhyEL+D3WMajKIqzz9iVWV/lntZR5PPe0aVivHjbaNv2XY93H/8R6TbgO617jrxczGJ6h9v/aG1jn+CDW65M7wftwdBnWdXoYq0X9KwLrap2PRcVoOUVRGjlifUp0COudelqo7VHqjt0yTgemGmO+DrU9zR0RWYg1+O2lUOxfp/pUlKbDrcAKFefGhd1F2tLuhq94z++vx09pZjT2GXEURcGaZALrxj4ptJYo9eBMrO7kiu7cScb6UkZp5jT6Lm6x5md+Heu9bjkw2xjzVxF5AuudZCnWpxXXG2Oy/eTfhfWOzYM1uGZYA5muKIqiKAFpCgLdAWsGo9X2pwarsFoRKcBXxhi3iDwOYIz5jZ/8u4Bh9mANRVEURQkLGn0Xtz0c/4C9nicim4BOVUYXLsP/JwP1onXr1qZbt24nqjhFUZRmwapVq44YY9qE2o7GQqMXaF/EcvU4GPi+StQMAn8WY4DPRMQA/zDGzA5Q9s3AzQBdunRh5cpAX1cpiqIo/hCRYGb8UmyazChusXx7vos1mX+uT/hvsWb9CfTh/tnGmCFY36X+XETO9ZfIGDPbGDPMGDOsTRt9AFQURVFOLk1CoO3ZjN4F3jTGvOcTPh1rxqGpJsDLdmPPX20sH8PzsDzOKIqiKEpIafQCLSKCNcvVJmPMUz7h47G8jVxqjk3CXjVvnD2wDHv6tXFYszwpiqIoSkhpCu+gz8aa/u8HEVlrhz2ANfVfFPC5peEsM8bcIpaHnJeMMROxXJrNs+NdwFvGmGCnVVSURk1ZWRnp6ekUFxeH2hSliREdHU1KSgoRERGhNqVR0+gF2p5H2J9XoQV+wiq6tCfa6zs4NhevojQr0tPTadGiBd26dcN+SFWU48YYQ2ZmJunp6XTv3j3U5jRqGn0Xt6Io9aO4uJhWrVqpOCsnFBGhVatW2jNzAlCBVpRmjIqzcjLQ6+rEoALdgGRnL2LHjgdp7LO3KYqiKCcfFegGJCfnG/bseQzLj72iKIqiBEYFugFxOKIBKC/XdzOKAlZX6LXXXuvddrvdtGnThosvvjiEVlVmypQp7Nix47jKMMZw55130qtXL1JTU1m9enXAdL/97W855ZRT6Nu3L88++ywAWVlZXH755aSmpjJ8+HA2bLC+Bi0tLeXcc8/F7XYfl31KeKIC3YBY7l6hvLwkxJYoSngQFxfHhg0bKCqyvCt+/vnndOrUqU5lnExx+vHHH/F4PPTo0eO4yvn4449JS0sjLS2N2bNnc+utt/pN99prr7F37142b97Mpk2buPrqqwH44x//yKBBg1i/fj2vv/46d911FwCRkZGMGTOGf/870EzGSmOm0X9m1ZjQFrQSttx9N6xde2LLHDQInnmm1mQTJkzgo48+YsqUKcydO5drrrmGJUuWAHD06FFmzJjBjh07iI2NZfbs2aSmpjJz5kz279/Prl27aN26NX/84x+59tprKSgoAOD555/nrLPOYuHChTz55JPMnz8fgNtvv51hw4Zx+eWXM3z4cD788ENOPfVUrrnmGs4//3xuuummSra9+eabXHbZZd7tW2+9lRUrVlBUVMSUKVP4/e9/H9Sh+OCDD5g2bRoiwogRI8jOzubAgQN06NChUroXXniBt956C4fDaju1bdsWgI0bN3L//fcD0KdPH3bt2kVGRgbt2rVj0qRJ3H///UydOjUoW5TGg7agGxCHQ1vQilKVq6++mrfffpvi4mLWr1/PGWec4Y17+OGHGTx4MOvXr+ePf/wj06ZN88atWrWKDz74gLfeeou2bdvy+eefs3r1av79739z55131rjPxMREnn/+ea677jrefvttsrKyqokzwLfffsvQoUO924899hgrV65k/fr1LFq0iPXr1wNwzz33MGjQoGrLrFmzANi3bx+dO3f2lpOSksK+ffuq7W/79u38+9//ZtiwYUyYMIG0tDQATjvtNN57z5rFePny5ezevZv09HQABgwYwIoVK2o+yEqjRFvQDUiFQBtjCfRHmZm0i4hgWEJCKM1SlKBauieL1NRUdu3axdy5c5k4cWKluG+++YZ3330XgPPPP5/MzExycnIAuPTSS4mJiQGsWdFuv/121q5di9PpZOvWrbXu94ILLuA///kPP//5z1m3bp3fNAcOHMDXOc4777zD7NmzcbvdHDhwgI0bN5KamsrTTz9d4778fbnh71OkkpISoqOjWblyJe+99x4zZsxgyZIl3Hfffdx1110MGjSIgQMHMnjwYFwu6/btdDqJjIwkLy+PFi1a1FpvpfGgAt2AVO3ivnnLFiYkJ/OSCrTSzLn00ku59957WbhwIZmZmd7wmoQtLi7OG/b000/Trl071q1bR3l5OdHR1n/N5XJRXl7uTec7eUZ5eTmbNm0iJiaGo0ePkpKSUm1fMTEx3jw7d+7kySefZMWKFSQlJXHdddd54+655x6+/vrravmvvvpq7rvvPlJSUti7d683PD09nY4dO1ZLn5KSwhVXXAHA5ZdfzvXXXw9AQkICr776qveYdO/evdIsXRXCrjQttIu7Aanaxd3C6STX4wmlSYoSFsyYMYPf/e53DBw4sFL4ueeey5tvWp5iFy5cSOvWrUnw80Cbk5NDhw4dcDgcvPHGG3js/1XXrl3ZuHEjJSUl5OTk8OWXX3rzPP300/Tt25e5c+cyY8YMysqqf/7Yt29ftm3bBkBubi5xcXEkJiaSkZHBxx9/XKmstWvXVlvuu+8+wHoAef311zHGsGzZMhITE6u9fwaYNGkSX331FQCLFi3ilFNOASA7O5vS0lIAXnrpJc4991zvccjMzKRNmzY673UTRFvQDUjVFnQLl4s8FWhFISUlxTsy2ZeZM2dy/fXXk5qaSmxsLHPmzPGb/7bbbuOKK67gP//5D6NHj/a2rjt37sxPfvITUlNT6d27N4MHDwZg69atvPTSSyxfvpwWLVpw7rnn8uijj1Yb9HXRRRexcOFCxo4dy2mnncbgwYPp378/PXr04Oyzzw66fhMnTmTBggX06tWL2NhYb2u4Iu6ll16iY8eO3HfffUydOpWnn36a+Ph4XnrpJQA2bdrEtGnTcDqd9OvXj5dfftmb/+uvv672akBpGojOalV3hg0bZlauXFnnfDk5y1iz5kwGDlxAq1YTGLN2LSXl5XwzZMhJsFJRambTpk307ds31GaENUVFRYwePZpvv/0Wp9MZanP8MnnyZP70pz9x6qmnhtqUSvi7vkRklTFmWIhManRoF3cDol3citK4iImJ4fe//73fEdfhQGlpKZMmTQo7cVZODNrF3YBU7eJO0C5uRQl7LrzwwlCbEJDIyMhKn54pTYtG34IWkc4i8rWIbBKRH0XkLjs8WUQ+F5E0+zcpQP7xIrJFRLaJyH0n09aqn1m1cDrJ0yn6FEVRFD80eoEG3MAvjTF9gRHAz0WkH3Af8KUxpjfwpb1dCRFxAn8DJgD9gGvsvCeFaoPEtItbURRFCUCjF2hjzAFjzGp7PQ/YBHQCLgMqhnzOASb5yT4c2GaM2WGMKQXetvOdFKq+g05wuSgzhhKf7zQVRakdj8fDX//6V3USoTRpGr1A+yIi3YDBwPdAO2PMAbBEHGjrJ0snYK/Pdrod5q/sm0VkpYisPHz4cD3tqz5IDNBubqXZ8thjj9G/f39SU1MZNGgQ33//fVD5nnrqKeLj472zaZ1IioqKOO+887zfUteXo0ePcsEFF9C7d28uuOACsrKy/KbLzs5mypQp9OnTh759+7J06dIa8//www9cd911x2Wb0jhoMgItIvHAu8DdxpjcYLP5CfP73ZkxZrYxZpgxZpjv1H91wV8XN6Dd3EqzZOnSpcyfP5/Vq1ezfv16vvjii0rzVQeivLyc9u3bc8MNN/iNP95W9SuvvMLkyZOP+7OqWbNmMWbMGNLS0hgzZox3Xu6q3HXXXYwfP57Nmzezbt0676dJgfIPHDiQ9PR09uzZc1z2KeFPkxBoEYnAEuc3jTHv2cEZItLBju8AHPKTNR3wvSOkAPtPlp0OhwtwVOriBnQkt9IsOXDgAK1btyYqyupZat26tXf6y27dunHkyBEAVq5cyahRowDLUcQ555zDU089xVlnncWWLVsAy03jlVdeySWXXMK4ceMoKChgxowZnH766QwePJgPPvgAgBtvvNHryKJNmzZ+vVH5erDKz89nzJgxDBkyhIEDB3rLCYYPPviA6dOnAzB9+nTef//9amlyc3NZvHix92EjMjKSli1b1pr/kksu4e233w7aFqVx0ug/sxJrYt6XgU3GmKd8oj4EpgOz7F9//6wVQG8R6Q7sA64Gfnoy7XU4oqu1oLWLWwk1ofA2OW7cOB555BFOOeUUxo4dy1VXXcV5551XY5l9+vRh8eLFuFwuPv30Ux544AGvM42lS5eyfv16kpOTeeCBBzj//PN55ZVXyM7OZvjw4YwdO9Y7M9fu3bu58MILq3UVl5aWsmPHDrp16wZAdHQ08+bNIyEhgSNHjjBixAguvfRSRISRI0eSl5dXzcYnn3ySsWPHkpGR4Z3Os0OHDhw6VL2NsGPHDtq0acP111/PunXrGDp0KH/961+Ji4urMf+wYcOYNWsWv/71r2s8XkrjptELNHA2cC3wg4istcMewBLmd0TkBmAPcCWAiHQEXjLGTDTGuEXkduBTwAm8Yoz58WQa63BEeT+zqmhBaxe30hyJj49n1apVLFmyhK+//pqrrrqKWbNm1fh+NS8vj5tuuol9+/ZhjKnkWOOCCy4gOTkZgM8++4wPP/yQJ598ErCcZOzZs4e+fftSXFzMlVdeyfPPP0/Xrl0rlX/kyBFvCxYsxxQPPPAAixcvxuFwsG/fPjIyMmjfvr3XZ/Xx4Ha7Wb16Nc899xxnnHEGd911F7NmzeIPf/hDjfnatm3L/v0nrbNPCRMavUAbY77B/7tkgDF+0u8HJvpsLwAWnBzrqmO1oKsMElOBVkJMqLxNOp1ORo0axahRoxg4cCBz5szhuuuuq+SFytcD1YMPPsjo0aO55ZZb2LlzJ6NHj/bG+Xq3Msbw7rvv+p1h65ZbbmHy5MmMHTu2Wpyv9yqwursPHz7MqlWriIiIoFu3bt742lrQ7dq148CBA3To0IEDBw7Qtm31caopKSmkpKR4fWBPmTLF+665pvzFxcVeV5tK06VJvINuTDgcUdrFrSjAli1bSEtL826vXbvW26Lt1q0bq1atAvB2YQNkZWV5/TO/9tprAcu+8MILee6557zuKtesWQPA3/72N/Ly8rxepqqSlJSEx+PxinBOTg5t27YlIiKCr7/+mt27d3vTLlmyxK8Hqwrhv/TSS73OPebMmeN9r+1L+/bt6dy5s/dd+pdffkm/fv1qzb9161YGDBgQsP5K00AFuoERiao2SEy7uJXmSH5+PtOnT6dfv36kpqayceNGZs6cCcDDDz/MXXfdxciRIyuNpv7Vr37F/fffz9lnn13Jz3NVHnroIcrKykhNTWXAgAE89NBDgNW6/eGHH7wDxV588cVqeceNG8c333wDwNSpU1m5ciXDhg3jzTffpE+fPkHX77777uPzzz+nd+/efP75596Hgv3791fyPvXcc88xdepUUlNTWbt2LQ888ECN+cHyYHXRRRcFbYvSOFFvVvWgvt6sAFasGER0dFcGDvwAjzG4Fi1iZrduPGwPSlGUhkK9WflnzZo1PPXUU7zxxhuhNsUvJSUlnHfeeXzzzTcn5TvwE4V6szp+tAXdwFhd3FYL2ilCrMOhXdyKEkYMHjyY0aNHH/dEJSeLPXv2MGvWrLAWZ+XEoGe4gfH9zAog0eUiJ0xvBIrSXJkxY0aoTQhI79696d27d6jNUBoAbUE3MFUFuqXLRba2oBVFUZQqqEA3ME5nLOXlhd7tJJeLrLKyEFqkKIqihCMq0A2MwxGLx1NFoLUFrSiKolRBBbqBqdqCbqkCrSiKovhBBbqBqdaCjojQd9CKoihKNVSgGxh/76Cz3W7K9Xt0pRkSHx9fr3y7du3yzqS1cuVK7rzzzhNm05QpU9ixY8dxlWGM4c4776RXr16kpqayevVqv+muu+46unfv7p04Za3tsSRQ/tLSUs4999zjdqmpNA5UoBsYhyMOY8ooL7cGhrV0uTBArv7hFKVeDBs2jGefffaElPXjjz/i8Xjo0aPHcZXz8ccfk5aWRlpaGrNnz+bWW28NmPaJJ57wThM6aNCgGvNHRkYyZswY/v3vfx+XfUrjQL+DbmCczlgAyssLcTgSSbInG8hyu2kZERFK05RmTNrdaeSvzT+hZcYPiqf3M3X/Xnft2rXccsstFBYW0rNnT1555RWSkpJYtWoVM2bMIDY2lnPOOcebfuHChTz55JPMnz+fgoIC7rjjDn744QfcbjczZ87ksssu47XXXmPlypU8//zzAFx88cXce++9Xj/TFfj6gga49dZbWbFiBUVFRUyZMsWv/2h/fPDBB0ybNg0RYcSIEWRnZ3sdXxxv/kmTJnH//fczderUoMpSGi/agm5gHA5LoCveQ1cItL6HVhSLadOm8fjjj7N+/XoGDhzoFcXrr7+eZ599lqVLlwbM+9hjj3H++eezYsUKvv76a371q19RUFAQ9L6//fZbhg4dWqm8lStXsn79ehYtWsT69esBuOeee7zd0r5LhSeqffv20blzZ285KSkp7Nu3z+8+f/vb35Kamso999xDSUlJrfkHDBjAihUrgq6T0njRFnQD49uCBmuQGKAjuZWQUp+W7skgJyeH7OxszjvvPACmT5/OlVdeWS382muv5eOPP66WP5Af6GA5cOCA11sWwDvvvMPs2bNxu90cOHCAjRs3kpqaytNPP11jOf58HIhU94r7pz/9ifbt21NaWsrNN9/M448/zu9+97sa8zudTiIjI8nLy6NFixZB101pfKhANzBVW9Atfbq4FUXxjzHGr8D5S+fPD/SqVasqeb/y9fnsi68/6J07d/Lkk0+yYsUKkpKSuO6667xx99xzD19//XW1/FdffTX33XcfKSkp7N271xuenp5Ox44dq6Wv6PKOiori+uuv9z5Y1Ja/pKSE6Ojomg+G0uhpEl3cIvKKiBwSkQ0+Yf8WkbX2sktE1gbIu0tEfrDT1c9FVR2o1oKuEGidTUxRSExMJCkpiSVLlgDwxhtvcN5559GyZUsSExO9biDffPNNv/kD+YHu1q0ba9eupby8nL1797J8+XK/+fv27cu2bdsAyM3NJS4ujsTERDIyMiq12J9++mm/vqArXEJeeumlvP766xhjWLZsGYmJiX7fPx84cACwHizef/9978j0mvJnZmbSpk0bInTMSpOnqbSgXwOeB16vCDDGXFWxLiJ/AXJqyD/aGHPkpFnng9MZB+g7aEUBKCwsJCUlxbv9i1/8gjlz5ngHifXo0YNXX30VgFdffdU7SOzCCy/0W95DDz3E3XffTWpqKsYYunXrxvz58zn77LPp3r07AwcOZMCAAQwZMsRv/osuuoiFCxcyduxYTjvtNAYPHkz//v3p0aMHZ599dtD1mjhxIgsWLKBXr17ExsZ661AR99JLL9GxY0emTp3K4cOHMcZU8k9dU/6vv/66kj9ppenSZPxBi0g3YL4xZkCVcAH2AOcbY9L85NsFDKuLQB+PP+jc3BWsXj2cgQPn06rVRRhjiFi0iN906cJjx/lph6LUBfUHXZ2ioiJGjx7Nt99+i9PpDLU5fpk8eTJ/+tOfqnXjhxvqD/r4aRJd3LUwEsjwJ842BvhMRFaJyM2BChGRm0VkpYisPHz4cL2Nqeji9ngKKsrV6T4VJUyIiYnh97//fcAR16GmtLSUSZMmhb04KyeGptLFXRPXAHNriD/bGLNfRNoCn4vIZmPM4qqJjDGzgdlgtaDra0zVQWJgjeRWgVaU8CBQ93k4EBkZybRp00JthtJANOkWtIi4gMlAwGl3jDH77d9DwDxg+Mm0qeogMYDWEREc0UFiiqIoig9NWqCBscBmY0y6v0gRiRORFhXrwDhgg7+0Jwp/Leg2EREcLi09mbtVFEVRGhlNQqBFZC6wFDhVRNJF5AY76mqqdG+LSEcRWWBvtgO+EZF1wHLgI2PMJyfTVn8t6DYRERzWFrSiKIriQ5N4B22MuSZA+HV+wvYDE+31HcBpJ9W4Kog4EYnC4zk273FFF3ewkzEoSnPio48+okuXLgwcODDUpihKg9IkWtCNDZcrAY8nz7vdJiKCUmPI83hCaJWiNDxOp5NBgwZx2mmnMWTIEL777rtK8Z988gmLFi3yTuABcNZZZx33fhvSpeTIkSO9c3V37NiRSZMm1ZhfXUoqFTSJFnRjw+lsgdud691uExkJwOGyMhJcekqU5kNMTIzXB/Knn37K/fffz6JFi7zx48ePZ/z48ZXyVBXxmjDGYIzB4TjWFjkZLiW///57br31Vr7//vtq6SpmRQO44oorvN6yAuX3dSmpHquaN6oGIcBfCxrgSFkZPWNiQmWW0oy5Oy2Ntfkn1t3koPh4nukdvBOO3NxckpKSvNtPPPEE77zzDiUlJVx++eVer1bx8fHk5+eTn5/PZZddRlZWFmVlZTz66KNcdtll7Nq1iwkTJjB69GiWLl3K+++/T9euXb3lhsqlZF5eHl999ZV3VjB1KanUhgp0CKjagm5tC7SO5FaaG0VFRQwaNIji4mIOHDjAV199BVheqdLS0li+fDnGGC699FIWL17Mueee680bHR3NvHnzSEhI4MiRI4wYMYJLL70UgC1btvDqq6/y97//vdo+v/32W6655tiwlccee4zk5GQ8Hg9jxoxh/fr1XvePNTnECOQSMpBAz5s3jzFjxpCQkAAEdinZoUMHdSmpACrQIcHpTKC09IB3u6IFrSO5lVBRl5buicS3i3vp0qVMmzaNDRs28Nlnn/HZZ58xePBgAPLz80lLS6sk0MYYHnjgARYvXozD4WDfvn1kZGQA0LVrV0aMGOF3nw3tUrKCuXPncuONNwaVX11KKqACHRJcrgSKirZ4t1WgFQXOPPNMjhw54nUecf/99/Ozn/0sYPo333yTw4cPs2rVKiIiIujWrZvXHWRcXFzAfA3tUhIsD1TLly9n3rx53jB1KanUho7iDgFWF/exd9BxTifRDofOJqY0azZv3ozH46FVq1ZceOGFvPLKK+Tb78X37dvHoUOHKqXPycmhbdu2RERE8PXXX7N79+6g9tPQLiUB/vOf/3DxxRdXElx1KanUhragQ4A1SOzYO2gRobVOVqI0QyreQYPV5TtnzhycTifjxo1j06ZNnHnmmYA1MOxf//oXbdu29XYDT506lUsuuYRhw4YxaNAg+vTpE9Q+G9qlJMDbb7/tFfZg8qtLSQWakLvJhuR43E0C7Nr1CLt2Pcy555bhcFjPSENWrqRjZCTzU1NPlJmKUiON0d1kZmYmQ4YMCbq17A91KdkwqLvJ40e7uEOA02mN4vT91KptRASHtAWtKAHZv38/Z555Jvfee+9xlaMuJZXGgnZxhwCXyxqV6fHkERFhfffZISqKHwsLa8qmKM2ajh07snXr1hNSlrqUVBoD2oIOARUtaN9voTtERnKwtJRyfeWgKIqioAIdEpzOYy3oCjpERuI2hkzt5lYURVFQgQ4JLlfFO+hjLeiOUVEA7NfZxBSl0bFr1y7mzp1be0JFqQMq0CGgogXt+y10B9thxoGSkpDYpCihoMKb1YABA7jyyisprGUcxnvvvceIESOYPHkyCxYsqDFtVa677jq6d+/u9Z715ZdfHo/pXjweDz//+c8ZMmTICSmvggMHDnDxxRcfdzk7d+7kjDPOoHfv3lx11VWUBmgE7Nmzh3HjxtG3b1/69evHrl27asw/f/58Hn744eO2TwlMkxBoEXlFRA6JyAafsJkisk9E1tqL348KRWS8iGwRkW0icp+/NCcavy3oCoHWFrTSjKiY6nPDhg1ERkby4osv1ph+8uTJLFu2jPfee69e3wk/8cQTrF27lmeeeYZbbrmlvmZXYtu2bdx3333VRl0fr7vIp556iptuuum4ygD4zW9+wz333ENaWhpJSUm8/PLLftNNmzaNX/3qV2zatInly5fTtm3bGvNfdNFFfPjhh7U+VCn1p0kINPAaMN5P+NPGmEH2Uu1xW0ScwN+ACUA/4BoR6XdSLQVcrpYAuN3Z3rD2tkBrF7fSXBk5ciTbtm2joKCAGTNmcPrppzN48GA++OADAF577TUmT57M+PHj6d27N7/+9a+9eW+99VaGDRtG//79g2rVnXnmmZU+s5o0aRJDhw6lf//+zJ492xseHx/Pb37zG4YOHcrYsWNZvnw5o0aNokePHnz44YeA1dJ9/PHHAZg5cyY333wz48aNY9q0aRw+fJgrrriC008/ndNPP51vv/0WsCYpqfARnZiYyJw5c6rZ+O6773pdbe7atYuRI0cyZMgQv36zA2GM4auvvmLKlCkATJ8+nffff79auo0bN+J2u7ngggu89Y6Nja0xv4gwatQo5s+fH5QtSt1pEp9ZGWMWi0i3emQdDmwzxuwAEJG3gcuAjSfQvGpYo7idlJUd9YZFO50kuVzaxa2EhLS0u8nPX3tCy4yPH0Tv3s8EldbtdvPxxx8zfvx4HnvsMc4//3xeeeUVsrOzGT58OGPHjgVg7dq1rFmzhqioKE499VTuuOMOOnfuHNAjVSA++eQTJk2a5N1+5ZVXSE5OpqioiNNPP50rrriCVq1aUVBQwKhRo3j88ce5/PLLefDBB/n888/ZuHEj06dP93rP8mXVqlV88803xMTE8NOf/pR77rmHc845hz179nDhhReyadMmb/f8qlWruP766yvZAla3clJSElH22JS2bdvy+eefEx0dTVpaGtdccw0rV64kLy+PkSNH+q3jW2+9Rdu2bWnZsiUu2898hcesqmzdupWWLVsyefJkdu7cydixY5k1axZZWVk15h82bBhLlizhJz/5ScBjrdSfJiHQNXC7iEwDVgK/NMZkVYnvBOz12U4HzvBXkIjcDNwM0KVLl+MySkSIiEjC7T5aKbxjZKS2oJVmhe9UnyNHjuSGG27grLPO4sMPP+TJJ58EoLi4mD179gAwZswYEhMTAejXrx+7d++mc+fOAT1SVeVXv/oVv/71rzl06BDLli3zhj/77LNeRxZ79+4lLS2NVq1aERkZ6W3FDhw4kKioKCIiIhg4cKD3HW1VLr30UmJsv+5ffPEFGzcee97Pzc31eqg6cuQI1157Le+88463ThVU9bhVVlbG7bffztq1a3E6nd7vwVu0aOH1BuaPw4cPVwvz53HL7XazZMkS1qxZQ5cuXbjqqqt47bXX/D6A+OZv27Yt+/fvD7h/5fhoygL9AvAHwNi/fwFmVEnjzzec3w+RjTGzgdlgTfV5vMa5XEm43ZWfFzpERek7aCUkBNvSPdH4upuswBjDu+++W+2d7vfff+9tUYI1wMztdtfokaoqTzzxBJMnT+bZZ59l+vTprFq1ioULF/LFF1+wdOlSYmNjGTVqlDd/RESEV5AcDod3/w6HI+A7Zl9PWuXl5SxdutQr2BV4PB6uvvpqfve73zFgwAC/x8W3Dk8//TTt2rVj3bp1lJeXe51u1NaC7tu3L9nZ2bjdblwuV0CPWykpKQwePJgePXoAVpf/smXLmDFjRo35i4uLq9VNOXE0lXfQ1TDGZBhjPMaYcuCfWN3ZVUkHOvtspwAN8jjociVX6uIGayS3dnErzZ0LL7yQ5557zusvec2aNTWmr8kjlT8cDgd33XUX5eXlfPrpp+Tk5JCUlERsbCybN2+u1LI+XsaNG8fzzz/v3a54GLnvvvtITU3l6quv9pvvlFNOqdRCz8nJoUOHDjgcDt544w08Hg9wrAXtb+nXrx8iwujRo/nvf/8LwJw5c7jsssuq7e/0008nKyvL2+L+6quvgsq/detWvw8YyomhyQq0iPj6fbsc2OAn2Qqgt4h0F5FI4Grgw4awLyIiOWAXt84mpjRnHnroIcrKykhNTWXAgAE89NBDNab39Ug1Y8aMoDxSiQgPPvggf/7znxk/fjxut5vU1FQeeughRowYcaKqwrPPPsvKlStJTU2lX79+3lHqTz75JJ999pl3oFjFgLMK4uLi6Nmzp9ct5m233cacOXMYMWIEW7durdHfdVUef/xxnnrqKXr16kVmZiY33HADACtXruTGG28ErN6IJ598kjFjxjBw4ECMMd4R5IHyg+V166KLLqr/AVJqpEl4sxKRucAooDWQATxsbw/C6rLeBfzMGHNARDoCLxljJtp5JwLPAE7gFWPMY7Xt73i9WQFs3DiV3NxljBix3Rv29337+HlaGvvPPJMOPl15inIyaIzerJoT8+bNY9WqVTz66KOhNsUvGRkZ/PSnPw34Pbl6szp+msQ7aGPMNX6C/X7sZ4zZD0z02V4A1G3GgxOA1YKu/A66iy3Ke0pKVKAVpZlz+eWXk5mZGWozArJnzx7+8pe/hNqMJk2TEOjGiMuVjNudjTEerM+xoas98GN3cTFnJCSE0jxFUcKAii7ocOT0008PtQlNnib7DjrciYhIBgxud443zFegFUVRlOaNCnSIcLksP9C+I7kTXC4SnU726EhupYFoCmNQlPBDr6sTgwp0iHC5kgGqvYfuGh2tLWilQYiOjiYzM1NvpsoJxRhDZmam91ttpf7oO+gQYXVxU+1Tqy4q0EoDkZKSQnp6ut/ZphTleIiOjiYlJSXUZjR6VKBDREULuupkJV2jovgmJ8dfFkU5oURERNC9e/dQm6EoSgC0iztERES0AqCs7Eil8K7R0WS73eQep6s6RVEUpXGjAh0irC5uB2VlhyqFd9GR3IqiKAoq0CFDxElERBtKSzMqhXe3BXqHCrSiKEqzRgU6hERGtqsm0L1tzzBphYWhMElRFEUJE1SgQ0hkZNtqAp0UEUErl4u0oqIQWaUoiqKEAyrQISQioh1lZRnVwnvFxLBNBVpRFKVZowIdQqwu7kPVwnvHxmoLWlEUpZmjAh1CIiPbUV5eiNudXym8d0wMe0tKKLKdsiuKoijNDxXoEBIZ2Q6gWjd3L3ugmI7kVhRFab6E1UxiIhINXAyMBDoCRcAG4CNjzI+htO1kEBFhCXRpaQYxMT294b4jufvHxYXENkVRFCW0hI1Ai8hM4BJgIfA9cAiIBk4BZtni/UtjzHo/eV/BEvZDxpgBdtgTdnmlwHbgemNMtp+8u4A8wAO4jTHDTnDVAhIZ2Rag2kjuiha0DhRTFEVpvoSNQAMrjDEzA8Q9JSJtgS4B4l8Dngde9wn7HLjfGOMWkceB+4HfBMg/2hhzJEDcSSMysj0ApaUHK4UnRUTQOiKCLSrQiqIozZaweQdtjPmolvhDxpiVAeIWA0erhH1mjKmY0HoZEHauVax30E5KStKrxfWPjeXHgoKGN0pRFEUJC8KpBQ2AiLTBaun2w+riBsAYc/5xFDsD+HeAOAN8JiIG+IcxZnYAu24Gbgbo0iVQQ75uiDiJiuroX6Dj4vhXRgbGGETkhOxPURRFaTyETQvahzeBTUB34PfALmBFfQsTkd8Cbrtcf5xtjBkCTAB+LiLn+ktkjJltjBlmjBnWpk2b+ppTjaiozpSU7K0WPiAujlyPh/SSkhO2L0VRFKXxEI4C3coY8zJQZoxZZIyZAYyoT0EiMh1r8NhUY4zxl8YYs9/+PQTMA4bXz+z6EUigK0Zvb9BubkVRlGZJOAp0mf17QEQuEpHB1OP9sYiMx+oqv9QY49fzhIjEiUiLinVgHNZnXQ1GVFQKJSXpVH1+qBBofQ+tKIrSPAlHgX5URBKBXwL3Ai8B99SUQUTmAkuBU0UkXURuwBrV3QL4XETWisiLdtqOIrLAztoO+EZE1gHLsb63/uSk1CoA0dGdKS8vpqwss1J4q4gI2kdGagtaURSlmRJ2g8SMMfPt1RxgdJB5rvET/HKAtPuBifb6DuC0eph5woiK6gxAScleIiNbV4obEBfHj+p2UlEUpVkSji1oLyKyOtQ2nGyioqzee38juQfExfFjQQEe/6/PFUVRlCZMWAs00OS/L/JtQVdlcHw8ReXlbNZWtKIoSrMj3AW6xslLmgKRke0QiaC4eE+1uKEtWgCwKi+voc1SFEVRQkzYCLT4mY3DGPNgbWkaOyIOoqO7UVy8o1pcn9hYYh0OFWhFUZRmSNgINPC1iNwhIpWm6RKRSBE5X0TmANNDZNtJJSamF0VF26qFO0UYFB+vAq0oitIMCSeBHo/lUWquiOwXkY0ishNIA64BnjbGvBZKA08WMTE9KSraXu1baI4cYeiePazJy9OBYoqiKM2MsBFoY0yxMebvxpizga7AGGCwMaarMeYmY8za0Fp48oiO7onHk1vtW2gOHGDoiy9SaAxbdKCYoihKsyJsBNoXY0yZMeaAP//NTZGYmF4A1bu5+/Vj2F5rdPdK7eZWFEVpVoSdQIvIzFDb0NDExPQEoLh4e+UIp5M+7dvToriYpbm5IbBMURRFCRVhI9Ai4hCRl4GoUNvS0ERHdweEoqLt1eKcZ5zBWT/8wDdZWQ1vmKIoihIywkaggf8BR40x94fakIbG6YwmKiqFoqK06pEjRnDO+vVsKCoiq6yseryiKIrSJAkngR6G5e6xWRIb24eCgk3VI844g3N++AFAu7kVRVGaEeEk0KOBf4jIGaE2JBTExfWnsHATxpRXjmjXjuFFRbg8Hr7JyQmNcYqiKEqDEzYCbYzZCFwIPBFqW0LBwYP9KS8vZM+eXdXiYk8/nSE7d6pAK4qiNCPCRqDB6wryolDbEQrc7v4AbNnyY/XIUaM4Z/VqlufkUOzxNLBliqIoSigIK4EGMMbU+YNfEXlFRA6JyAafsGQR+VxE0uzfpAB5x4vIFhHZJiL3HY/tx8OAAf0AyMjYUD1y1CjGrF5NCWgrWlEUpZkQNgItIh/WtNSS/TWsqUJ9uQ/40hjTG/jS3q66TyfwN2AC0A+4RkT6HXdl6kHr1olkZqZQXOynBd2zJ+cdOUKEx8Pn+rmVoihKs8AVagN8OBPYC8wFvqcOvqCNMYtFpFuV4MuAUfb6HGAh8JsqaYYD24wxOwBE5G0738a6mV43yrLKKNxSSMIZCfg66MrN7U90tB+BFiHurLM4e9MmPktM5PGTaZyiKIoSFoRNCxpoDzwADAD+ClwAHDHGLDLGLKpHee2MMQcA7N+2ftJ0wnooqCDdDjupHHzlIGvOXIM7x10pXCSVdu02UlhYWj3TqFFcsGwZa/PzOVTqJ15RFEVpUoSNQBtjPMaYT4wx04ERwDZgoYjccRJ366+V7tdtlIjcLCIrRWTl4cOHj2unUV2tydJKdpdUCk9KGkpkZCkbN/p5D33BBYxbsQKAL7WbW1EUpckTNgINICJRIjIZ+Bfwc+BZ4L16FpchIh3scjsAh/ykSQc6+2ynAPv9FWaMmW2MGWaMGdamTZt6mmQR3TUagOLdxZXCe/UaBsCuXauqZ+rcmcHR0bQqLOTjo0ePa/+KoihK+BM2Ai0ic4DvgCHA740xpxtj/mCM2VfPIj8Eptvr04EP/KRZAfQWke4iEglcbec7qUR3sQV6T2WBPvXUHuTntyQnZ6XffM6JE7nom2+Yf+QI7vJyv2kURVGUpkHYCDRwLXAKcBfwnYjk2kueiNQ4x6WIzAWWAqeKSLqI3ADMAi4QkTSs99mz7LQdRWQBgDHGDdwOfApsAt4xxvgZpXViiWgbgURJtS5ul0s4dGgokZH+BZqLLmLSkiVkeTws0c+tFEVRmjRhM4rbGFPvhwVjzDUBosb4SbsfmOizvQBYUN991wcRIbpLdLUubituGO3bP0V+fgnx8VUce40Ywbht24h2u3n/yBFGJ/n9tFtRFEVpAoRTC7pZEd01uloXN0C7dqcTEVHGmjVrqmdyuYgbPZpxa9bw/pEjGON3PJuiKIrSBFCBDhFRXaKqdXEDnHbaOQDs3r3Ef8bLLmPSV1+xp6SE1fn5J9NERVEUJYSoQIeI6K7RlB4sxVNceW7tTp3aceDAKZSWBhDoiy/m0jVrcJWX8/YhfwPTFUVRlKaACnSIiO5hj+TeWb2bOy9vJG3afIPH42ekdlwcrc47j4mrVvFWRgYe7eZWFEVpkqhAh4jYU2MBKNxSWC2uVauRtGiRxbp1AQaUX3UV//fRR+wvLWVhdvZJtFJRFEUJFSrQIcIr0JurC/TgwecCsHHjYv+ZJ0zg4h9+IKG0lH9lZJw0GxVFUZTQoQIdIlwJLiI7RFK0pahaXPfu3cjM7EJx8Rf+M8fEEHPxxUxZtIh3Dx2iQH1EK4qiNDlUoENI7Kmxfru4RYSsrPF06vQFpYEcY9xwA9d/+CF5OlhMURSlSaICHUJiTo3xK9AArVtPICYmn+XLv/OfeeRIzi4pYUBGBn/ft0+/iVYURWliqECHkNg+sbiPuik9VL2VfO65YygriyAt7WP/mUWQG27g1rlzWZ2fz4q8vJNsraIoitKQqECHkPjUeADy11WfcCQ5uQX79p1DZGQAgQaYPp3/++or4txuXtjv1wmXoiiK0khRgQ4h8YNsgV7rf0awyMgJdOr0A1u27PJfQPv2JFx8Mdd+/jlzMzLICPS+WlEURWl0qECHkIjkCKK6RJG/xr9ADxt2BQArV/43cCG/+AX3vPUWpeXlPJuefjLMVBRFUUKACnSIiR8cH7AFfcopPdizZyjGvBO4gGHDOKV7d65YuZK/7dtHrtt9kixVFEVRGhIV6BATPyiewi2FeAoCfct8JSkpK0hL2xW4kF/8gt+89BI5Hg+z9V20oihKk0AFOsQkDE+Acshdkes3/swzrwTgu+9qaEVfcgnDjGHsli38JT2dQp24RFEUpdHTpAVaRE4VkbU+S66I3F0lzSgRyfFJ87uGtDHhzAQQyPkmx29879492LNnBJGRrwX+1tnphIcf5uHnn+dgaSnP7dt3Ei1WFEVRGoImLdDGmC3GmEHGmEHAUKAQmOcn6ZKKdMaYRxrSxoikCOL6x5H7rf8WNEBExA106LCJ5cuXBS7o6qs5x+Ph4h9+YNaePWSVlZ0EaxVFUZSGokkLdBXGANuNMbtDbUhVEs9JJOe7HIzHfwv5wguvoqgojvXrXw5ciNMJM2fy2DPPkFNWxp/37j1J1iqKoigNQXMS6KuBuQHizhSRdSLysYj095dARG4WkZUisvLw4cMn1LCEsxPw5Hoo2FDgNz45uQX79/+ElJS3ycysYcawKVNITUhg6tKlPLN3LzuLqjviUBRFURoHzUKgRSQSuBT4j5/o1UBXY8xpwHPA+/7KMMbMNsYMM8YMa9OmzQm1r+XIlgBkfZ0VMM3AgT8jJqaATz55NXBBDgc8/TR/evppnG4392zbdkLtVBRFURqOZiHQwARgtTGmmvNkY0yuMSbfXl8ARIhI64Y0LrprNLF9Yjn68dGAaYYPP4Ndu84iMvIZ3O4aRmmPGkXKqFH8bs4cPsjM5KPMzJNgsaIoinKyaS4CfQ0BurdFpL2IiL0+HOuYNLiqJU9IJntRNp7CwOKblPRL2rTZyYIF/sa5+fDEE9z93nv0ycrizrQ0ivSzK0VRlEZHkxdoEYkFLgDe8wm7RURusTenABtEZB3wLHC1CYHvxuQJyZgSQ/bX2QHTXHTRZRw61JOsrCcoL6/BxG7diLzvPv72hz+wo7iY3+7ceeINVhRFUU4qTV6gjTGFxphWxpgcn7AXjTEv2uvPG2P6G2NOM8aMMMYEcMB8ckkcmYgj1kHmR4Eb7y6XE/gVXbsu58sva/ByBfCb33B+eTm3ffYZz6Snszg7+4TaqyiKopxcmrxANxac0U5aTWzF4XcPU+4uD5jukkuu59Ch7hw69CDl5YHTEREBr7zC488+S4/cXK7bvJl8nadbURSl0aACHUa0uaoNZYfKyFnkf1YxgJiYSByOmXTqtIb//a+Wd9FDhhB/11289tvfsquoiFvT0gLPRqYoiqKEFSrQYUSria1wxDk49O9DNaabNGkqBw/2pajoAYqLa/EBPXMm58THM3PuXP6VkcE/Dxw4gRYriqIoJwsV6DDCGeuk9WWtOfyfwzWO5na5nLRq9Rfat9/Ku+8+U3OhEREwdy4P/ve/XLhlC3empbE6r4bJThRFUZSwQAU6zOhwUwfc2W4O/6fm2crGjp1AWtplJCc/ws6d6TUX2q0bjpdf5l+//jVt8vOZvGEDGaW1tLwVRVGUkKICHWa0PK8lMafGsP8fNft1FoFRo57G4fDwxRd31fzZFcDll9P6jjt4/xe/4HBhIZf+8IO6pVQURQljVKDDDBGh4886krs0l/x1+TWm7d27O4cPz6R37/eYP//t2gt/5BGGDhjAWzNnsiI3l2s3baJcB40piqKEJSrQYUj76e1xxDnY+2TtHqmuuupedu8egcjP2bWr5lY3Dge8/jqXFRbyl5df5r0jR7hdR3YriqKEJSrQYUhEcgQdf9aRjLkZFO2o2SNVRIST4cPnEBFRzJdfTq95nm6AuDiYP5+7ly7l1/Pm8cL+/dy7fbuKtKIoSpihAh2mdP5lZ8Qp7PnznlrT9u17CgUFz9Kz5xe8+eYjtRfeoQPyxRfMevdd7vj4Y55KT+fBnTtVpBVFUcIIFegwJapjFB1u6MDBlw9SuLWw1vSXX34DW7deT9euj7BgwYLad9C9O/LFFzzz6qvc9OWX/HHPHn6xfbu+k1YURQkTVKDDmG4Pd8MR7WD7r7fXmtbhEKZO/Rv79g3CmGtYt25D7Tvo0wfHwoW8+Mor3DV/Ps+kp3P95s24a5pCVFEURWkQVKDDmMh2kXR5oAuZH2SS9XVWrenj4mI4++wPKCmJZ/v2Cezeva/2nfTrh2PJEp6eN49H/vUvXs/I4PIffyRP5+1WFEUJKSrQYU7KPSlEd4sm7bY0PMW1f7fcrVsXOnX6iOjobJYsmcihQ7ULOz16IEuW8ND33/PCX//Kx0eOcNaaNewsqnmAmqIoinLyUIEOc5zRTk558RQKNxey+/e7g8pzxhmDiI19j7ZtN/Ppp+M4ejQIke7YEb79lltKSvj4V78iPSuL4atWqZtKRVGUEKEC3QhIvjCZ9je0Z8+f95D7fW5QeUaNugC3+13atVvPRx+NC64lnZAAH37IBSNH8v0NN9Dq4EHOX7uWP+3erYPHFEVRGpgmL9AisktEfhCRtSKy0k+8iMizIrJNRNaLyJBQ2Fkbvf7Si6jOUfz4kx8pyywLKs/EiRd7Rfqzz0azd28tE5kAuFzw9NOc8uijfH/LLUz57jse2LmT8evX6/zdiqIoDUiTF2ib0caYQcaYYX7iJgC97eVm4IUGtSxIXIku+v+3P6UHS9n0f5swtc29bTNx4sW4XP8jOXk7y5ePYO3aH4Pb4fTpJC5ezNw33+SfTz7JN0eOkLpiBfOPHDmOWiiKoijB0lwEuiYuA143FsuAliLSIdRG+SNhWAK9/tqLo58cZcf9O4LOd/7540hOXozDUUZ6+tl88smnwWXs1w9ZsYIbO3RgxY030n7nTi7ZsEHfSyuKojQAzUGgDfCZiKwSkZv9xHcCfCe9TrfDKiEiN4vIShFZefhwza4gTyYdf9aRjrd0ZO+f95L+XC1uJn0YMWIwp522jNzcLkRGTuD11/+AxxPE986xsfDPf9L/hRdY/tBDvPrnPzNy1iworH3yFEVRFKX+NAeBPtsYMwSrK/vnInJulXjxk6da/7ExZrYxZpgxZlibNm1Ohp1BISL0fr43rSe1Zttd28h4MyPovD16dOXii5eRljaVLl1+x6uvXsK+fUE+bIwfT9TatVzXqRPy+OPQty+8+y7o4DFFUZSTQpMXaGPMfvv3EDAPGF4lSTrQ2Wc7BQhiNFXoEKfQ962+tBzVkk3XbuLAqweCzpuQEMtNN71ORsbf6Nr1C1auHMCnn34YXObERPjnP2HxYmjZEqZMgQsugI0b61cRRVEUJSBNWqBFJE5EWlSsA+OAqnNgfghMs0dzjwByjDHBK16IcMY4GTh/IEkXJLFlxhbSnw++u9vhEK666jZat15JXl4HoqIuY/bs64P7FAtg5EhYtQqef976TU2FG2+EPbU79lAURVGCo0kLNNAO+EZE1gHLgY+MMZ+IyC0icoudZgGwA9gG/BO4LTSm1h1nrJMBHwyg1aWt2HbHNtLuTKPcHfw82oMHD+SKK5azc+cD9Oz5OkuXnsr7779OeTAjxF0u+PnPYetWuOMOeOMN6N0b7rkHDh06jlopiqIoAKIuBuvOsGHDzMqV1T6pDhnGY9j+6+2kP5VO8vhk+r7Zl4jkiDqVsWrVGjZsuJWuXb9n166R9Ov3PMOHpwZfwJ498Mgj8OqrEB0NN90Ev/gFdOlSx9ooitJUEZFVAT53VfzQ1FvQzQJxCr3+0otTZp9C1pdZrDxtJdmLs+tUxtChg5k69TsOHfonrVr9SH7+IF5+eRrbt+8MroAuXeCll6z30VOmwN/+Bj17wrRpsCEIz1qKoihKJVSgmxAdb+rIkKVDcEQ7WDt6LTt/t5Py0uC7vF0uBz/5yY2cdVYaO3f+ipSU/7Bjx6n88593sXt3kOPmTj0V5syB7dutLvB334WBA2HcOHj/fVAvWYqiKEGhXdz1INy6uKviznOTdnsaGa9nENs3llNmn0LLc1rWuZydO9P56qtH6NbtFTweJ+np0znrrF/Tp0+v4AvJzIQXXoB//APS0yElBX72M2tQWfv2dbZJUZTGi3Zx1w0V6HoQ7gJdQeaCTLbetpWS3SW0v6E93R/tTlT7qDqXk5a2nYULn6Br11dxOt1s334lffvexdlnj8Dh8PcZuR/cbvjf/+Dvf4cvvrAGmU2YYHWBX3yx9d5aUZQmjQp03VCBrgeNRaABPAUeds3cRfoz6UiU0PneznT+ZWdcLVx1Lmvv3gN88cUztGv3ArGxeezbN5iIiNu48MJrSEyMC76grVut99Vvvgn791vfVF91lSXWZ54JEqToK4rSqFCBrhsq0PWgMQl0BYXbCtn5250cfucwEW0j6PzLznS8pSOuhLoLdW5uHvPnv4kxf6dTpx/Iz09k//7/47TTpnH66acH36r2eOCrr+D11+G996zpQzt3hsmT4Yor4KyzwOmss32KooQnKtB1QwW6HjRGga4g9/tcdj64k6wvsnC1dNHp9k50urMTkW0i61xWebnh22+/JS3t76SkvEdkZAkZGadQXHwtZ5zxf/Tp0y34wvLyYN48a1DZp59CSQm0aweXX24t550HUXXvnlcUJXxQga4bKtD1oDELdAW5K3LZ86c9HJl3BIkS2l7Zlo63dSRhRAJSjy7mo0dz+PTT/1Jc/Abduy8CYPfuETidkznzzMvp2bMOA8vy8mDBAkusFyyAggLLaceYMdZ76wkToFu3OtuoKEpoUYGuGyrQ9aApCHQFBZsK2Pe3fWS8noEnz0PcaXF0vLkjbX7ShsjWdW9VA+zYsZulS9+ivPxdOndeBcD+/QMpLp5Mv36TOP3003A6g3wIKCqyusE//tgS6532d9l9+8L48XD++dbUo4mJ9bJVUZSGQwW6bqhA14OmJNAVuPPdHHrzEPte2EfBugLEJSSPT6bt1La0vrQ1ztj6vQvevHkXy5e/T3n5e3Tp8g0OhyErqz1Hj46jTZsLGT78Atq3D9I7mDHWALMFCyzBXrQISkvB4YChQ2H0aBg1Cs45B1q0qJe9iqKcPFSg64YKdD1oigJdgTGGgvUFZLyZQcZbGZTuK8UR56DVhFa0urQVrS5qVedpRCs4eDCD775bQGbmp7Rr9zkJCUcpLxfS04dSWnoBXbqcy4gRZ5OQEKS4FhXBsmXw9dewcKG1XlZmDSwbMsQaEV6xdOmio8MVJcSoQNcNFeh60JQF2hdTbshenM2huYfI/DCT0oOl4ISWI1vS6tJWJI9PJrZPbL3eWZeWelixYhVpaZ/icHxCp07f43R68HgcHDgwhNLSc2nb9lyGDh1Jhw7JwRVaWAhLl1qC/c03sGKFFQbWpCi+gj14MMTV4dMwRVGOGxXouqECXQ+ai0D7YsoNeSvzOPLhETI/yKRgQwEAkR0iSRqbZC1jkojqVL+R1nl5+SxbtozduxfhdC6mU6fviYwsAeDAgT4UFAynRYvh9Ow5nIEDU4kKZkS32w3r11st66VLrWX7ditOxJqWdMgQaxk82FqSkuplv6IotaMCXTdUoOtBcxToqhTtKiL7y2yyvsgi64ssyo6UARDTO4bEsxNJODuBxLMSrRZ2sN9F+1BYWMzq1SvYtWsxpaXfk5z8PS1bWm4sS0sjOXhwECUlw0lIGEbPnqcxYEA/oqODGNR26BB8/z2sXm0ta9bA3r3H4rt1swR70CDo399aeva0Zj5TFOW4UIGuGyrQ9UAFujKm3FDwQwFHPz9KzpIccr7NwZ1pOcVwJblIODOBhDMTaDGsBS0GtyCyXf2+ud66dS+bNi3n6NHlOJ3LadduJTExVkve7XZx6FBfiopOIyrqNNq2TeXUU0+jS5d2tb96PnzYEuo1a44J97Ztx+KjoqBPn2OCPWCA9du9uzVATVGUoFCBrhtNWqBFpDPwOtAeKAdmG2P+WiXNKOADoMKv4nvGmEdqKlcFumaMMRSlFZHzXQ653+aS810OhRsLvfGRnSJpMaQF8UPird/B8USlRNX5XbbH42Hr1q1s3bqeI0fWYcw6kpLW0arVPm+a7Oy2HD3aj/LyPsTF9aF9+750796HLl1ScNQkrgUFsGmT5Srzxx+tZcOGyq3tmBjo1QtOOcVaevc+9tumjQ5KU5QqqEDXjaYu0B2ADsaY1SLSAlgFTDLGbPRJMwq41xhzcbDlqkDXHXeOm/y1+eStziN/tfVbuLnQemwCnAlO4vrHEdsvlrj+cd4lsmNknYX74MFMNm9ez4ED6ygq+gGnczPJyZto0SLLm6a4OJbDh/tQVNQHp7MPLVv2omPHnvTq1ZPWrZMD7zM31/J5vWGD9bt1K6SlwY4dlV1pJiZWFu2ePa0Wd7du0KGDtryVZokKdN1o0gJdFRH5AHjeGPO5T9goVKBDgqfAQ/76fPLX5FPwYwEFPxZQ+GOh9302gDPRSVy/OGJOiSGmVwyxvWOJ6RVDTO+YOs0jXl5u2L37MNu3b+bQoU0UFm7G4dhEQsJmWrfeXSltQUEi2dk9KC7uicPRk9jYnrRq1ZNOnXrQo0dnoqL8fBPudsOuXccEOy3t2Pru3dY33BVERkLXrscEu1u3Y+vdu0Pbttr6VpokKtB1o9kItIh0AxYDA4wxuT7ho4B3gXRgP5ZY/+gn/83AzQBdunQZunv37qpJlBNE6aHSSoJdsKmAorQiSveXVkoX0SbCK9YxvWKI7h5NdFdriewYicMVXCu1qKiQbdt2sHfvDjIzt1NUtB2HYzvx8dtp1WoXERHHHhg8HidZWZ3Iz++Cx9MFp7MLcXFdaNmyM23bdqFTpy4kJydWdhhSXGyJ9M6dlohX/T18uLJBMTGW05CUlMBL69Yq4kqjQwW6bjQLgRaReGAR8Jgx5r0qcQlAuTEmX0QmAn81xvSuqTxtQYcGT4GHoh1FFKUVUbTN53dbESXpJZUTOyEqJcoS7C7RRHWN8op3VJcoojpFBeVy0+32sHt3Ounp2zlyZDv5+bspK9uDy7WH2Ng9JCfvxeVyV8pTWNiC7OzOFBZ2wePpjMvVibKye+nUKY6UFOjUydJXby93fr4l4BWivXMnpKcfW/bvr9x9DlYrvKpod+hgfe/tuyQmqpArYYMKdN1o8gItIhHAfOBTY8xTQaTfBQwzxhwJlEYFOvzwFHoo3lNMye4SincXe5eK7ZJ9Jd733RU4451EdowkqmPUsd9OVbY7ROKMCTzNaWmph717M9i/fy+ZmXvIy9tDWdkeYA9RUXuIi9tHixaHGT++CLf72Oh1p9Ny1tWunaWjFb++6xW/LVt4kMOHKou2v6W0tLqBUVHVRbvq0q6d1a2uE7coJxkV6LrRpAVarJE+c4Cjxpi7A6RpD2QYY4yIDAf+C3Q1NRwYFejGR7m7nNJ9pZZw7ymm9EAppftLKdlf4v0t2VeCKal+2l0tXUS0jSCybWStv64kV7XvvsvKyjh8OIJ9+45p6cGDkJFh/fquV20og9VYrhDrtm2t1ne1pZWhdUQOrT0ZJBXuw3n4YPXCK5bDhyu/E68gJuZYgW3a1P6bnKz+upU6oQJdN5q6QJ8DLAF+4Fj76QGgC4Ax5kURuR24FXADRcAvjDHf1VSuCnTTxBiDO9tdTbhL95dSeqiUskNllB62fsuOlIG/v44TIlrbot0mgohW1uJKdhGRHIGrlf2b7LLikiNwJblwRDooL4esLP/CXbEcOXJsKSz0s3+sHu3kZP9CnpwMSQkekpy5JHmOkFR2iKTCfSQVHyAhbx+OzMOWgB85cuw3Nze4HSUnWzOxVfz6rvuGJSVBRP3mc1caNyrQdaNJC/TJQgVaMR5DWWbZMeEO8Ft2tAz3UTdlR8vAE7g8ZwvnMRH3Fe+WLpyJTlwtXbgSfZaWLsoineS4XWQWOcnMlEriHWjx1wtegYj1ytpXR5OSbEGPLiTJlU+SI4eW5ZkkuY/QsiSDhMKDJOTtIyFnL3G5B5Cso9ZTRl5ezQcwPj6wmCclWYZULAkJ1bd1ZrdGiQp03dCrXFHqgTiFyLaRRLYNblY0YwyeXM8xwc6sLN7uTHeluPz0fNyZbtzZboy7lodoB8Qkuuie6KJXotMr4K5EF65+tsAnunC2cOGOdFJknBQYJ3keJ7llTrJLnBwtdnG0yElWjpCVhXfZtw+yspxkZbWgtLQF0CGwGQ5LOxOSIKGLISHWTUJ0CQmRJSS4Ckl05pFAHgnl2SR4jpJQmklC8SESszJI2LufhLzVtMjeS1zpUZxVBwxUJTa2dhGvup2QYLkhjY+3lhYtVOiVsEavTkVpAETE2/qle/D5jDGUF5XjznHjznHjyfHgznZ7t905loh7cjyVwop3F1cKC6R3CfbSxd52xDhwtnDijHdav62cuLq5cMQ7IcZJmctJqctJsVhLEU4Kyp3ku53klTnJK3WQXeIku8hBVpGTzIIYdh6MIyevFbm5gbvlqxITY4iP8RAf7SY+qoy4iFLiXcXEO4uIl0LiySeuPI/48lzis7KJzzhKfOlR4osOE1e0lfjiI8STX2mJpphq49mjoo6Jta9wBwqrbT02Vt/LKycMFWhFCWNEBGesE2esk6gO9fMUZozBk+/Bk+fx/rrz3NXCAsWVZZbh2V3s3XbnucEDUVhLyyBscEQ7cMQ5cLZyQrQDE+WkPMKJx+WgzOWkzOGgRJyUiJNiHBQbJ4XlDgo8TvLdDusBoDSGvPw49pc4yCmyl2IHpTgowfotJ/C37w6HIS7KTVxkGbGuUmKdpcQ6i4mRYmLLiog9WkhsZj6xnnxiPXnEuPOILc22Fs9eYikkhiJiKay0VA2LiHJaI+JjY63lRK/HxOinc80EFWhFaeKICK4WrqC++w4GYwzlxeWVhb3QQ3lhufVbUH5su8BTab0izbF1N+V5pZXTF3gCtvhrxSUQ4cBEOCiPcFDucuB2OvA4HZSJg1KxhLwUJyXGQbFxUFzuoMjTgkJPIkVuBwVuB4dLHeSXWg8KpTh8FqEMB2X2dlmVMIPgKvMQk1dKbFEpsVnFxDqKmT/gfrrl7bRG+hUWWktBgbV4ahicEIgK0a4Q7JgYiI4+tl51+3jjIiP1oSAEqEArilInRARnjNP6PrzNiS/fGIMpM34Fvby4nPIi+7e4HE+Rp1qYd72oSpricsqL3AHT1/quPwjKnUK5/UDgcQhuceDGgfOvL8PpMf4zlZVZQu0r3MGuFxRYM9UVFVlLcbE16r5ivSK8qAhKSvzvPxhEjgn2c8/BT39a/7KUoFGBVhQlrBARJFJwRDogqeH2W+4OLPLlReWUlxxbTInVi+ANK/YJ99muCGvXpYb30hER0LKltZzUCpZbIu1PvKuKfE1x3eswiEI5LlSgFUVRAIfLgSPeAfGhtuQk4XAc67pWGgXq805RFEVRwhAVaEVRFEUJQ1SgFUVRFCUMUYFWFEVRlDBEBVpRFEVRwhAVaEVRFEUJQ1SgFUVRFCUMUYFWFEVRlDBE/UHXAxE5DOyuQ5bWwJGTZE5D05TqAk2rPlqX8KQp1QWOrz5djTEnYYLYpokKdAMgIiubipPyplQXaFr10bqEJ02pLtD06hPOaBe3oiiKooQhKtCKoiiKEoaoQDcMs0NtwAmkKdUFmlZ9tC7hSVOqCzS9+oQt+g5aURRFUcIQbUEriqIoShiiAq0oiqIoYYgK9AlERMaLyBYR2SYi9/mJFxF51o5fLyJDQmFnMARRlz4islRESkTk3lDYGCxB1GWqfT7Wi8h3InJaKOwMliDqc5ldl7UislJEzgmFncFQW1180p0uIh4RmdKQ9tWFIM7LKBHJsc/LWhH5XSjsDIZgzotdn7Ui8qOILGpoG5sFxhhdTsACOIHtQA8gElgH9KuSZiLwMSDACOD7UNt9HHVpC5wOPAbcG2qbj7MuZwFJ9vqEcD0vdahPPMfGl6QCm0Ntd33r4pPuK2ABMCXUdh/HeRkFzA+1rSeoLi2BjUAXe7ttqO1uiou2oE8cw4FtxpgdxphS4G3gsippLgNeNxbLgJYi0qGhDQ2CWutijDlkjFkBlIXCwDoQTF2+M8Zk2ZvLgJQGtrEuBFOffGPfNYE4IFxHggbznwG4A3gXONSQxtWRYOvSGAimLj8F3jPG7AHrftDANjYLVKBPHJ2AvT7b6XZYXdOEA43FzmCoa11uwOrlCFeCqo+IXC4im4GPgBkNZFtdqbUuItIJuBx4sQHtqg/BXmdnisg6EflYRPo3jGl1Jpi6nAIkichCEVklItMazLpmhCvUBjQhxE9Y1ZZLMGnCgcZiZzAEXRcRGY0l0GH7zpYg62OMmQfME5FzgT8AY0+2YfUgmLo8A/zGGOMR8Zc8bAimLqux5qLOF5GJwPtA75NtWD0Ipi4uYCgwBogBlorIMmPM1pNtXHNCBfrEkQ509tlOAfbXI0040FjsDIag6iIiqcBLwARjTGYD2VYf6nRujDGLRaSniLQ2xoSbw4Zg6jIMeNsW59bARBFxG2PebxALg6fWuhhjcn3WF4jI3xvxeUkHjhhjCoACEVkMnAaoQJ9AtIv7xLEC6C0i3UUkErga+LBKmg+BafZo7hFAjjHmQEMbGgTB1KWxUGtdRKQL8B5wbSNoAQRTn15iK5r9pUAkEI4PHbXWxRjT3RjTzRjTDfgvcFsYijMEd17a+5yX4Vj330Z5XoAPgJEi4hKRWOAMYFMD29nk0Rb0CcIY4xaR24FPsUZBvmKM+VFEbrHjX8QahToR2AYUAteHyt6aCKYuItIeWAkkAOUicjfWSM/cQOWGgiDPy++AVsDf7fun24Spt54g63MF1oNgGVAEXOUzaCxsCLIujYIg6zIFuFVE3Fjn5erGel6MMZtE5BNgPVAOvGSM2RA6q5smOtWnoiiKooQh2sWtKIqiKGGICrSiKIqihCEq0IqiKIoShqhAK4qiKEoYogKtKIqiKGGICrQS1oiIEZE3fLZdInJYROY3sB19bM89a0SkZ5W4RBF5XUS228vrIpJox40KZKuILBCRlvWwZZSInFXHPB0q7BCRWBF5U0R+EJENIvKNiMTXkj+/rnZWyb9QRPZUfAdsh71/vOUehz1t7M+EFCVsUYFWwp0CYICIxNjbFwD7QmDHJOADY8xgY8z2KnEvAzuMMT2NMT2BnVizktWIMWaiMSa7HraMwvLAVRd+AfzTXr8LyDDGDDTGDMCa3vSEOT2xJ+Lxd2/JBs6207QEQuYoxhhzGDggImeHygZFqQ0VaKUx8DFwkb1+DTC3IkJEhovlw3mN/XuqHd5fRJbbrd71ItJbROJE5COxnBVsEJGrqu5IRAaJyDI7zzwRSRJr3uS7gRtF5Osq6XthzUn8B5/gR4BhPi3tBLusjSLyYoV4icguEWltr/+fj73/EBGnHT5eRFbbNn8pIt2AW4B77LQjReRKuz7rxJpy0R9XABUtxg74POQYY7YYY0rs/f3CLmuDPflM1eMTb9ux2m6BX2aHdxORTSLyd6w5pztXzYvlFelqe30y1uxttZXr95yJyCz7eK4XkSftsDYi8q6IrLCXioeB8+SYD+Y1ItLC3u37wNQAx0tRQk+o/V3qoktNC5CP5dP4v0A0sBYfv7pYM5m57PWxwLv2+nPAVHs9EmtC/yuAf/qUnehnf+uB8+z1R4Bn7PWZ+PF7DVwKzPMTPs+OGwUUY/nWdQKfY/s0BnZhzS/dF/gfEGGH/x2YBrTB8irU3Q5P9mcL8APQyV5v6ceW7sAqn+1BWK4blwKPAr3t8KF2WXFYPqV/BAZXnAf71wUk2OutsWbFE6Ab1oxSIwKcx4VY00Gut4/DZ3ae2sqtds6AZGALxyZaamn/vgWcY693ATbZ6/8DzrbX432ul07AD6G+xnXRJdCiLWgl7DHGrMe6mV+DNV2qL4nAf0RkA/A0UOHCbynwgIj8BsuDUBGW+IwVkcdFZKQxJse3IPu9cUtjzCI7aA5wbi3mCf69Y/mGLzeWb10PVuu/qresMVjiuEJE1trbPYARwGJjzE77OBwNYMO3wGsichOW+FWlA3C4YsMYs9Yu/wkssVshIn1tu+YZYwqMMflYLdyRfur1RxFZD3yBJXLt7LjdxvJzHggP8A1wFRBjjNkVRLn+zlku1kPPSyIyGWvaXLAe0J63j+GHWD0XLezj85SI3Il1ft12+kNAxxrsVZSQogKtNBY+BJ7Ep3vb5g/A18Z6l3oJVisbY8xbWC3YIuBTETnfWI4wKlqJfxKR350Au34EBvu+c7XXT+OY84CqAu7PDekcY8wgeznVGDOTwOJfuTBjbgEexOpWXisiraokKcI+Lj558o0x7xljbgP+hTVHfDD+HKditeyHGmMGARk+ZRcEkf9trN6Nd4Ip1985swV2OPAu1tiAiq57B3Cmz3HsZIzJM8bMAm7E6kVZJiJ97PTRWMdGUcISFWilsfAK8Igx5ocq4Ykce596XUWgiPTAGrj1LJa4p4pIR6DQGPMvLLEf4luQ3TrLEpGKVuO1wCJqwBizDViDJZAVPAistuMAhovlGciB1Xr8pkoxXwJTRKStbXuyiHTF6gU4T0S6V4Tb6fOAiveoiEhPY8z3xpjfAUeo/v53K1YPREX6s0UkyV6PBPoBu4HFwCSxRnnHAZcDS6qUlQgcMsaUieU/u2tNx8cPS4A/Uf1By2+5/s6ZWCPOE40xC7DGBgyyy/gMuN2nnoPs357GmB+MMY9jOXipEOhTAHXwoIQt6s1KaRQYY9KBv/qJ+jMwR0R+AXzlE34V8H9ieXQ6iPU++XTgCREpxxq1fKuf8qYDL4rlQm8HwXkcuwF4TkQq3psutcMqWArMAgZiieC8ylUzG0XkQeAzW8TLgJ8bY5aJyM3Ae3b4IaxR7P8D/msPpLoDa8BYb3vfXwLrqLyDArE+/+plPzT0BF4QEcF6SP8I6929EZHXgOV21peMMWuq1PVN4H8ishJrPMDmII5PpcpiCW1VApU7kOrnrAXwgYhE23W+x057J/A3u5vchXWsbwHutkXfA2zEGnQIMNquu6KEJerNSlFCgD1K+xDQ3hhzwj5xqmF/l2N1Hz9Ya+Jmgj3i/TJjTFaobVEUf2gLWlFCw49YLdSTLs4Axph5ft5NN1tEpA3wlIqzEs5oC1pRFEVRwhAdJKYoiqIoYYgKtKIoiqKEISrQiqIoihKGqEAriqIoShiiAq0oiqIoYcj/A7hGdeLidpAWAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#plot all of the power law functions based on mass ranges\n", - "plt.plot(M_mass, power_law(M_mass, M_a), color='r', label='Moraux (a=0.69)')\n", - "plt.plot(S_mass, power_law(S_mass, S_a), color='b', label='Suárez (a=0.60)')\n", - "plt.plot(L_mass, power_law(L_mass, L_a), color='m', label='Lodieu (a=0.50)')\n", - "plt.plot(B_mass, power_law(B_mass, B_a), color='c', label='Béjar (a=0.70)')\n", - "plt.plot(P_mass, power_law(P_mass, P_a), color='y', label='Peña Ramírez (a=0.60)')\n", - "plt.title(\"Power Law Mass Functions for Planetary Masses According to Published Papers\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f5494d3d", - "metadata": {}, - "source": [ - "#### Accounting for error in a" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "1e6c80f4", - "metadata": {}, - "outputs": [], - "source": [ - "#Moraux\n", - "M_aup = M_a + M_aerr\n", - "M_alow = M_a - M_aerr\n", - "M_outup = power_law(M_mass, M_aup)\n", - "M_outlow = power_law(M_mass, M_alow)\n", - "\n", - "#Suárez\n", - "S_aup = S_a + S_aerr\n", - "S_alow = S_a - S_aerr\n", - "S_outup = power_law(S_mass, S_aup)\n", - "S_outlow = power_law(S_mass, S_alow)\n", - "\n", - "#Lodieu\n", - "L_aup = L_a + L_aerr\n", - "L_alow = L_a - L_aerr\n", - "L_outup = power_law(L_mass, L_aup)\n", - "L_outlow = power_law(L_mass, L_alow)\n", - "\n", - "#Béjar\n", - "B_aup = B_a + B_aerr\n", - "B_alow = B_a - B_aerr\n", - "B_outup = power_law(B_mass, B_aup)\n", - "B_outlow = power_law(B_mass, B_alow)\n", - "\n", - "#Peña Ramírez\n", - "P_aup = P_a + P_aerr\n", - "P_alow = P_a - P_aerr\n", - "P_outup = power_law(P_mass, P_aup)\n", - "P_outlow = power_law(P_mass, P_alow)" - ] - }, - { - "cell_type": "markdown", - "id": "041954f2", - "metadata": {}, - "source": [ - "#### Graphing Individual Power-Law Functions (Accounting for Error in a)" - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "id": "dc7a858e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#Setting up subplots\n", - "fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(10,10))\n", - "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n", - "fig.delaxes(ax[2, 1])\n", - "\n", - "#Moraux\n", - "ax[0,0].plot(M_mass, power_law(M_mass, M_a), color='r')\n", - "ax[0,0].fill_between(M_mass, M_outup, M_outlow, color='r', alpha=0.2, label=\"error in a\")\n", - "ax[0,0].set_title(\"Moraux Mass Function for Planetary Masses in Blanco 1\")\n", - "ax[0,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,0].set_ylabel(\"M^(-0.69 ± 0.15)\")\n", - "ax[0,0].legend()\n", - "\n", - "#Suárez\n", - "ax[0,1].plot(S_mass, power_law(S_mass, S_a), color='b')\n", - "ax[0,1].fill_between(S_mass, S_outup, S_outlow, color='b', alpha=0.2, label=\"error in a\")\n", - "ax[0,1].set_title(\"Suárez Mass Function for Planetary Masses in 25 Ori Group\")\n", - "ax[0,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[0,1].set_ylabel(\"M^(-0.60 ± 0.13)\")\n", - "ax[0,1].legend()\n", - "\n", - "#Lodieu\n", - "ax[1,0].plot(L_mass, power_law(L_mass, L_a), color='m')\n", - "ax[1,0].fill_between(L_mass, L_outup, L_outlow, color='b', alpha=0.2, label=\"error in a\")\n", - "ax[1,0].set_title(\"Lodieu Mass Function for Planetary Masses in UKIDSS Galactic Clusters\")\n", - "ax[1,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,0].set_ylabel(\"M^(-0.5 ± 0.2)\")\n", - "ax[1,0].legend()\n", - "\n", - "#Béjar\n", - "ax[1,1].plot(B_mass, power_law(B_mass, B_a), color='c')\n", - "ax[1,1].fill_between(B_mass, B_outup, B_outlow, color='c', alpha=0.2, label=\"error in a\")\n", - "ax[1,1].set_title(\"Béjar Mass Function for Planetary Masses in σ Orionis\")\n", - "ax[1,1].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[1,1].set_ylabel(\"M^(-0.7 ± 0.3)\")\n", - "ax[1,1].legend()\n", - "\n", - "#Peña Ramírez\n", - "ax[2,0].plot(P_mass, power_law(P_mass, P_a), color='y')\n", - "ax[2,0].fill_between(P_mass, P_outup, P_outlow, color='y', alpha=0.2, label=\"error in a\")\n", - "ax[2,0].set_title(\"Peña Ramírez Mass Function for Planetary Masses in σ Orionis\")\n", - "ax[2,0].set_xlabel(\"Mass of Objects (Solar Masses)\")\n", - "ax[2,0].set_ylabel(\"M^(-0.6 ± 0.2)\")\n", - "ax[2,0].legend()\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f63bb9fc", - "metadata": {}, - "source": [ - "### Creating a General Power-Law Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "ed848735", - "metadata": {}, - "outputs": [], - "source": [ - "#combine all mass data\n", - "all_masses = []\n", - "for a in M_mass:\n", - " all_masses.append(a)\n", - "for b in S_mass:\n", - " all_masses.append(b)\n", - "for c in L_mass:\n", - " all_masses.append(c)\n", - "for d in B_mass:\n", - " all_masses.append(d)\n", - "for e in P_mass:\n", - " all_masses.append(e)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "df52ba0f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5000\n" - ] - } - ], - "source": [ - "print(len(all_masses)) #to make sure the correct amount of entries were appended" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "id": "dfc306a1", - "metadata": {}, - "outputs": [], - "source": [ - "#combine all power_law output data\n", - "all_power = []\n", - "for f in M_mass:\n", - " all_power.append(power_law(f, M_a))\n", - "for g in S_mass:\n", - " all_power.append(power_law(g, S_a))\n", - "for h in L_mass:\n", - " all_power.append(power_law(h, L_a))\n", - "for k in B_mass:\n", - " all_power.append(power_law(k, B_a))\n", - "for l in P_mass:\n", - " all_power.append(power_law(l, P_a))" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "id": "15d0484d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5000\n" - ] - } - ], - "source": [ - "print(len(all_power))" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "6a6629e9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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YS5d6dNy50rgxsGAB0/NLlgQefJBun2XLfDcGQRBMjSkEPDu2ioRd9ue88Pjg5DVZd3TowM47O3ZwEXHKFIb83XcfsH49LeHISO+7UXKie3cWyvr2W+DUKfrve/fmoqcgCEIuOBVwpdRXSqlTSqltdvveVEodVUolWH96eXOQJQtnbWy8tFYrAEC3fetzPH7VPgeLg/XrU7wPHgReeYUJPK1b0wJPSfFfK7WgIOBf/6J//N13GYLYpAnw5JPAsWO+H48gCKbAiAX+DYDbctj/gda6mfVnvmeHlZVRfRpmeb+kZmsAQE13KxJWqAC88w477UyYAOzdy8qESUnA66/7r953oUJ8sOzbBzz3HK3y2rWBN94ALl70z5gEQQhYnAq41noFAL/Gu/VtXinL+2U1Yzxz4WLFgBdfpGC++Sb3jR3L6JAJExjJ4g9Kl2aUyq5dTAIaPZqp+Z9/zsQgQRAE5M0H/qxSaovVxVLS0UFKqUFKqXilVPzp06fdvlmQXSjKmSIOb+ceYWGsX1KzJhc/a9cGXn6ZC54jRrB/pj+oUQP44QdGyNStCwwezMXP2bMlNV8QBLcF/HMANQE0A3AcwARHB2qtJ2mtY7TWMVFRUW7eDrjhC72yWNgTc8ECLm5aLGylVq0a8MQTrFzoD1q3Bv78kwuxSjG6plMnYO1a/4xHEISAwC0B11qf1Fqna61vAJgMoLVnh3UzjnMxPYjFwoSf1auZ3DNzJv3jAwcC338PNGgA3HEHwxJ9bQErxXtv3Qp88QXH1a4dI2kSE307FkEQAgK3BFwpVcHubT8A2xwd6ylclUuX6oLb6NoVCAnJGk5YowZrmSQl0U++ejVwyy0sWPXrr54tgmWEkBDgqaco4KNGAfPm8cHy/PM5JykJgpBvMRJG+D2ANQDqKqWOKKWeADBeKbVVKbUFQFcAL3h5nA4pfTk5x/0vzkxw/WKRkRTmnOLBy5ShYCYlMTX/1CkWq6pfH5g0yffp8JGRfKAkJrI70P/+Rx/+uHHA1au+HYsgCH7BSBTKAK11Ba11qNa6stZ6qtb6X1rrxlrrJlrrO7TWflrlA3ruWZ3jfreT0i0WICEBOHky588LF+Zi4p49LJZVvDgt4qpVGcHi6wJVFSqw9svWrUDnzsB//sMkpWnTfP/tQBAEn2LKTEwA2FCxHgCgz44/PXthW5eeuLjcjwsOBu69F/j7b6a/t2wJvPYaI1eGDgUOHfLsuJzRoAEwZw7HUr488OijHJOzeQiCYFpMK+CzG3YBALQ54uGU8+bN6S4xmlavFNClCzB/PrBlC3D33XSx1KzJ+iYJCZ4dnzO6dGHY4fffA+fPs1CXxcLiWYIg5CtMK+Bz6nf2zoWDglgjPC7O9eqAjRvTdbF/P63wOXP4QLB1/fFV5EpQEHD//UwEmjCBIZHNm9MqP3LEN2MQBMHrmEbAs9dDOR8R6fQctyJRAFqsJ0/SonaHKlVY5/vwYS4qbt3Kh0KLFrSMfdXoODw8M9P0pZd479q1gVdfpXUuCIKpMY2AZ6+HYgS3IlEAWsxA3qsTligBDB/O4llTpzJS5YEHmBb/8cfA5ct5u75RSpZkQtLu3XTxvPMOXTwff+y/ui+CIOQZ0wh49nooRnA7EqVCBbpDFi1y9wpZCQ8HHn+cJWLnzKGF/vzzfH39dYYk+oJq1YDvvmP/z6ZNOYaGDYGff5bUfEEwIaYR8Nwod9ELCSwWCzMuPWklBwWxONXKlUwI6tKFoYfR0cC//83kHF/QsiV98vPnswLivfcy/n3VKt/cXxAEj5AvBPyBhIWev6itRvjy5Z6/NsA0+FmzuND4yCPAN9+wYNXddzOKxNsoBfTsySiZKVMY9tixI5OTdu/2/v0FQcgzphbwH5rQV/3Yht89f/GOHYGICO936alTh4k4hw5xcXHZMnYI6tQJmDvX+30yg4NZqGvvXpatjYujW+WZZ3zn2hEEwS1MLeDTWt4OACh23bGb46b2akYpVIiZjZ7ygzujXDl2rU9KAj78kILepw/QqBHw9ddsOOFNihRhIlJiIjNLv/ySC51jxrDAlyAIAYepBXxn2RpOj3HYXs0IFgvdCb7MqixalIuLiYnA9OmsVf7440D16sD48d4P/ytXjolI27ezX+frrzP0cOpUSc0XhADD1ALudWxp9f5odhwaypDDTZv4LaBhQ4YkVqkCDBvm/YScunVZbXHlSi6yPvkkI1fmz5eIFUEIEEwl4NmTebLgDVGpVw+oXNl3bpScUCozM3TjRuD229lurXp1ZlZu83Il344dGTHz00+MY+/dm5b5xo3eva8gCE4xlYDnlsxzcHwfDFw3C7VPH/KcmCtFK3zxYt9lT+ZG8+bAjBl0rwweTFFt3Jiiuny59yxjpYB77gF27GDyz+bNDEV86CHfF+0SBCEDUwm4s2Sekcu/QtxXz+CvLx7H2D/+hx5716JwylX0mLjc/ZtaLPQ7//23+9fwNNWqAR99xAXP0aNZ66RrV6BNG4q6t3zVYWHAkCFMzf/Pf4BffmEUzbBhwLlz3rmnIAgOMZWA58Qrtz2Xsd326W8wwvIstpWvhTt2/InJs8Zg08cDMOrjoSzqtHOn61bqrbcyAccffnBnlC7NyJFDh9hmLTmZLdbq1mUHe281diheHPjvf1kTfcAA/tvWrEnXjrejZQRByEBpHy5IxcTE6Pj4+Dxdo9qIeVl3aI2D4/vws+FzM3aHpqci5shOdN4fj67741H3TBI/qFoV6NWLSSzdujF8zhlt29KNsMbNkERfkZ7OxsfjxzMZqEwZWsyDB3PbW2zeDLzyCtcKqlenuN93Hx98giDkGaXUBq11TPb95v8LUzm3O04NDsWaqk0wruvjsDzxGa3UL78EmjUDvv2WDYJLlWLhqg8+YLigo4eZxUIXSqC7CYKDmUm5Zg2wYgUfPKNGMYpkyBDgwAHv3LdpU35D+eMPtnobMID3/tPDzTYEQciC+QXcjsIpjl0GD/5xFBg0CJg9Gzh7lguTQ4YAR4+y5Gq9enQDPPssGwXbJ69YLMyIXLzY+5PwBEqx8fLvvzOe+/77+fCqVYvbGzZ4576xsYxO+eYb4Phx1nrp04eLn4IgeBzTCXiRsGCHn41b+InDz7Ik9ISH07f9/vsUuIMH6TO2ZT3efjut89tu42JhyZL0+/oznNBdGjQAvvqKc3z5ZWDBAiAmhvNfuNDzkSvBwaztsmcPy9auWMFImUGDKOqCIHgM0wn42H6Nb9q3qHZbAMAdO1e4d9GqVVkNcM4cNiWOi6Pf+NAhdtZp0ICRKFOmMJHFjF3fK1YE3n2XTSZstcF79qT74//+D0hN9ez9IiKAESMYsfLss3ww1q4NvPkmcOmSZ+8lCAUU0wl4TqGEL/Z+0XM3CA9nosrEiYxa2b+fqeU2evemdd6zJ/DJJ4zJNhPFitES37+fro4bN4CHH6b7aOJE4OJFz96vTBl+i9m5k4vHb71FV86XXwZGbL0gmBjTCXhOXAovbOg4t+LBq1enNW5bAOzVi8We9u8HnnuOVmWdOqxfsnCheazzsDC6OrZupc+/Rg22XatShVURT5zw7P1q1QJ+/JELrLVq8RtP48b81iOp+YLgFvlCwO2JuuS4eNXeU3lozlCtGoVaa1YL3L2b1vcnn1DEJ02iVV66NK30Tz+lyAc6SvGhtHw5I21iY+lqqVoVGDiQ9co9Sdu2rK/y66+0/u+8k4ud69d79j6CUADIdwL+44zh3ru4xUKhu3aN7+2jVv75hwuEAwdyAe/ZZ/l53brACy9wAdR2XqDSqhWt5N27WSP8u++A+vWBvn09261HKV5z2zbgs8/4kGjdmhEyZnjoCUKAYEoBD8oh9Htk7GAAQPVzuUc6uN2pHqCAX73KVmvZiYjIjFrZu5ci/tFHdE188QXPLV2aYXWffea9mGxPUKsWx5iUBLzxBufbsSPQoQPDMD3VZCI0FHj6aX6Tef11hj3Wq8cH3tmznrmHIORjTCngD7SJvmnf9Oa9DJ071N1O9QAbPISGGgsnrF2bPvIFCyhG8+axrveOHex2U6MGrdsXX2R8eSCmoEdFcdHx0CG6io4fB/r1Y1TO5Mme+0YRGQm8/TYffI88woJZNWsyozTQv7UIgh8xpYCP6XtzKKE9oekeDomzUbQoLVFX66IULkw/sy1qZfdu+tGjo2np9uhB6/zOO2mtB1qFvyJF6BLaswf44Qf+OwwaxHWB//7XcxmqFSvywbB5M6394cPpgvq///N+azlBMCGmFHBnTP35be9d3GIBtmxxPylFqcyolT/+oHU+dy4tzy1b6FKoVo0NHF5+GVi6lM2VA4GQEKB/fy44LlnC8rYjRzJy5YUX6HLxBI0a8RvLkiUMQ3z4YSYfLVnimesLQj7BqYArpb5SSp1SSm2z21dKKRWnlNprfS3p3WEaY0zXxwEAnQ5uyvW4eiPnu3+TWDZSRlyc+9ewp0iRrFErO3cyHrtSJVrst95K67xfP0a6HD7smfvmBaVYCGzBAlrLd90F/O9/dAs99BD3eYJu3fiwmD6di8TduzPSZ+tWz1xfEEyOEQv8GwC3Zds3AsASrXVtAEus773K7E1H0WHcUlQfMQ8dxi3N8Zgpre8ydK1r6XmIO27aFChb1jvlZZXKXMRbtIjW+Zw5FMVNmxh/Hh3N+OlXXmFEjKczKF2lSRMWB9u3jz7/335jwTCLhRZzXmO8g4LYWm7XLmaQrl3L6z/xBOvYCEIBxqmAa61XAMgeXH0ngGnW7WkA+np2WFmZvekohv20GUeTr0IDOJrsPFmm3MUzTq/pFkFBtMIXLfK+X7ZoUUatfP45o1a2b2f9lnLl6EPv2pXW+V13Mc3fn4IWHc1vDklJ9Itv3kyLOSaGfvO8Zl0WKkSX0r59LG/w3XdcKH7tNeDCBY9MQRDMhrs+8HJa6+MAYH0t67kh3cybc7Yj9YZrlty6zx7N9fM8RaNYLMCZM7SKfYVSjP546SVGrZw9y5C+AQOA+HjGn1euTIt4xAiWcvWHdV6yJLv1HDzIBcnLlznG2rXpErqch2QqgGUMJkygRd63LzB2LMMeP/3U/99GBMHHeH0RUyk1SCkVr5SKP336tFvXSL5q/A/z1ic/d+seLtGjB1/9WZ0wMpJRK19+yaiVbdsYdlemDAWuSxdu33MPMHUqcOyYb8dXqBA72e/YwQdNpUp0sURHM7b81Km8Xb96dfYHXb+eC77PPsvXWbMkNV8oMLgr4CeVUhUAwPrq8K9Raz1Jax2jtY6Jiopy83bG2Ve6Ssa20rm7ONzulVmuHP2wgdJmTSmK17BhjFo5e5ZC1r8/fcZPPkkBbdaMdU5WrvRdIamgID5o/vqL2ZydOgFjxjBV35bEkxdiYjjn339nlMzddzPUc/Vqz4xfEAIYdwV8DoBHrNuPAPjNM8PJmZKFQ906L/6Th3L9PE+1USwWCpKnq/d5gmLFskatbNnC+iYlSnAhsFMnWuf33ccyr76q092+PWug7NwJ/OtfrFNepw6/JeSlabRSrOG+ZQvnvH8/48jvuYfJQYKQTzESRvg9gDUA6iqljiilngAwDkAPpdReAD2s773GqD4NXTr+NWtafemrzhe33F7MjI2lFbt8uXvn+wqlskatnDkD/PwzxW3VKmaHVqwItGjBBcFVq7xvndetS6E9dIj+8iVLgDZtmOk6b577i8MhIVwL2LuXGaQLF3LdYMgQwE33nSAEMqZpanxTM2MnHHz3dgBA9VfmQKvcn1MHx/V2fUDXr3NB7bHHGANtRrSm1bpgARtVrF7NxsglS/IB1bMn67uUK+fdcVy8yCiaDz7gN4YGDegOeuABlr11lxMnKOSTJzMbdsQIRrAUNlZ+WBAChfzb1NgJB8bf4Z0Lh4czjC9Q/ODuoBTj2keMYOuzM2eAn35idMeffwKPPgqUL08/8+uvs5Z3errnxxEZydj3ffuYNh8Swgdj9ep0+Zw/7951y5dnCObWrfxdjRxJl83XX3tnHoLgY/KtgD92zyjDx1Z30brPIDaWi3D5pQRqiRJ0rXz1FaNWNm5kmF5EBGO727dnEtOAARTavEaSZCc0lElLCQl8MNavT9dPdDRf3Y1zr1+fCUZ//snF3McfZxkAb/QEFQQfYhoBz6mEbG4sq9kqY7vqudxD6Nz+E7ZY+GrGZsfOUIoiZ4taOXMGmDmTiUXLlrE+SfnyrOM9ahSjXTxl1SrFh+PixcCGDSwENmECLfLHHmNCkzt06sRxzpzJePSePXmfhATPjFsQfIxpBNzFPJ4s/DlpkNNjav3HDSu8Th2Gw5nZjWKUkiUZtfLNN7TO4+NZAjY0lGGB7drRV/7gg6xdcib3TFjDtGgBfP89v+n8+98U30aNGHWyYoXrFrRSnMeOHcxm3biR93j4Yc8V4xIEH2EaAa9UIsLlc+q89KvhY9PceUAolVnzoyBlAQYFAS1bZkatnD5Nke3dm0W+HnqIrpY2bbiI+PffeS87UL0664QfPswHx7p1jFpp2xb45RfXrf/wcFaE3LePC6Y//sgH8vDhQHJy3sYqCD7CNAI+zFLX5XNSQjLjxzd8/IDT412NdAHAr+AXL1JQCiqlSrEd2rRpjPxYvx54800K/VtvUcjLl2fs94wZeeu2U7o0F1STkrhAefYs/fb16rGWuqtNpUuUYIz8nj20zN97j6n5H30UOGV8BcEBphHwvs0ruXXe03eyUKKRmHC3uPVWIDi4YLhRjBAUxKiVN95g1MqpU3SpWCxcNHzwQVrn7drRko6Pd886j4igS2X3bsa1lyzJzM6qVYHRo11/SERHs6rihg3MWB06lIufP/4oC51CwGIaAXeXBfU6ZmyPXDrF6fEuW+ElStDCFAHPmTJlGM/9f/9H63zdOor7jRu00lu1AipUYEOLH35g3W9XCA5m+vy6dUxUat2a14+OZu0VV3uPNm9ON9DChazV3r8/3TQrV7p2HUHwAaYScHdT6mc0ZTnzgetnGzq+zVgXmzXExtKSlEa8uRMcnBm1sm4dcPIkhb17d2ZgDhjAPpwdOnBhdONG49a5UvSJz53Lwl733UeXSq1avO7GjcbHaVvb2LSJIZVHjzKC5c47WQVREAIEUwm4qyn1Nl697dmM7cFrfnR6/MmLLvo+LRZ+zV682NWhFWyiorjgOX06xXzNGi6MpqbSz92yJdP8H3uMrgyjvTcbNmSyzoEDLL87bx6v1b07vykZdYkEB/Pee/YwHn7ZMkbAPP00v00Igp8xlYA784OXi3Scdr2wTjsAwCsrvjV0L5dcKa1a0QcrbhT3CQ6mq8IWtXLyJH3SXbsyCad/fwr+LbcwqSghwbkQV6rEEruHD/N1506WBmjenA0hjEYOFS7MePh9+yjeU6bQsn/77bzXNxeEPGAqAXfG9VxiAf/db2TG9nc/jHR4nD2Gy80GB9O6W7RIFrw8RdmyjFr5/nsuhK5axcJXV68yJb5588ysyp9/zj3dvnhxhgoeOEDLPDWV165Zk7Hgly4ZG1NUFJtSbN/OB8GoURTyyZN9V55XEOwwnYDn5gd31vjh7W4DAQAdDxlruutSudnYWPpKd+wwfo5gjJAQpvGPHs21huPHmVB0yy0sT3vvvQwv7NwZGDeO7dxyepCGhbG+y9at9JVXr84aLFWq8KFg1C1Spw4fGqtW8RqDBrGmzNy58gAXfIrpBNyZH7xDzVIOP/uq1Z0Z27Zqhc4w7EqxpdWLG8X7lC/PqJWZM5lE9NdfTMC5eJFWerNmFOUnn2SST3brPCiISUd//snU+ltvBd55hyGIgwYxNNEI7dtTxH/5hVZ9nz5At258yAiCDzCdgDvzg28/lnuDhQ7//ipju9EJY91gDIl4lSqMGxYB9y0hIYxaGTuWkSbHjjFypH37zLrnZcqwxdz48bS+7a3kNm143O7ddMd8+y1/j/36GevqoxSbSm/fzrLC27dzTeSBB1wPYRQEFzGdgDsj+Wpqrlb40eKZ/ZfnThtq+LqGKhbGxrI+h6vZgILnqFAhM2rl9Gn+Pl5+menxw4ez6XN0NC3tX3/N7KhUuzYzO5OSGAmzYgUfDB07chHVWThjaCjwzDOs2TJyJPuA1qvHKBhXY9sFwSCmFHBn8eD3xkTn+nm14XMzto26UjSAByevyf0giwW4dk2SPgKF0FD6yd95h1ErR44wgqRNG7pf7rqLvvNu3ZhCv307FyrffptC/vHHPKdvXzaZmDqVjTxyo1gxxrDv2cMQyQ8+4GLp++/z/4YgeBBTCrgzP/iwnxJytcIB4O4Hx2ds995pTHBX7XNiSXXuzCJJ4kYJTCpVAp54gi6TM2eYufnii9x+5RXGeFetyhT9xYtpyScmMhKmcGH61KtV4wPBWUx65coU/M2bGR45bBgt8hkz8l7YSxCsmFLAnfnBU284t8I3VG6Qsf3pnHcRkm4sDCxXf3jhwrT48mN98PxGaGhm1MqWLYwVnzyZdVxmzKDVXbo0wwWPHmXceFwcXTCvvko3zEsv8bzcaNyYLevi4pgr8OCDzEZdtswn0xTyN6YUcMC5G2XYTwl4qK1xV0ri+30N3ztXEY+NZSq3u91jBP9QuTIt7FmzaJEvXcpysydO0IfesCE/r1mT4Yzdu7NiYY0arCW+ZUvu1+/enYWyvv2Wce3durGmubvNKQQBJhZwZ26U1BtATNVSCHHSyaf6K3Myto36wwGKeI4d7fNzl56CQlgYM0DHj+fD+NAh4MsvGZ747bdM858/n2KelsZ6Lk2bssPP0qWOY8GDgphAtGcPS9j+9Rct+oEDGT0jCC5iWgHv27ySUz/3izMTkPhO7h3ntQpCj8c/zXjviogPnZlwc+Grxo0Zpyx+8PyDLWpl9mwWLFu8GBgyhCGM9ixcyJjyhg25SOooO7NQIfrc9+1jxcRp0xgF88YbmVExgmAA0wo4AEwf2C7Xz28AeG32VqdCvzeqKkZ3ezLj/aIpgw2P4eTFlKwuFVs/x7g46XyeHwkPp0i//z7dHwcPMvywTx+WVABYc+X+++lnf/55x/VSSpdmlMquXTx/9Gim5n/+ecHq8CS4jakFHACKhAXn+vl3a5MwfWA7OOuJPLVV34yCV3XOJmHm9OEujSOLiFssjP11pYSpYE5sUStz5lCo4+Io2jY+/hgoWpQP9mnTgCtXbr5GjRqshb5uHVC3LjB4ML/JzZ4tqflCrphewMf2a+z0mB4Tl+PAuNxdKQALXm0uXxsA0ObIdiyZ/G+XxpIh4j168FXcKAWL8HAuVn74IYV3/35mZNp49FE2iahTh0WxErNlArduzfT+336j4Pfrxzrka9f6chaCiVDah0/4mJgYHe+FOhF1X1uA62m5x9Z+2L8Z+javZCgt/vdvnkfjk/sy3ttHqxhBATgQ9wb/WFescOlcIZ+SkMCGEElJWffXqgX06sUF0M6d2SoOoP986lRWPDx5kgW73nmHC6dCgUMptUFrHZN9v+ktcAB49+4mTo8ZOjMBQO7Frmz0efQj/NqgS8Z7VxY2AWZtfhpWkw0KLnipF6dgLpo1YzTLsWPAiBGZ+xMT6Wbp2ZM+8d69WVMlKQl46ilg716K+Lx5rNHy/PMMcxQE5BMBNxKRAgBNRi3E9IHtUCjYmUcceKHPy3jV8kzG+4Pv3o7wVCdp1HasqN4CSEvDoEfHOz9YKDhUqEBL+sIFYMIExp/bCAvjwuiQIbS069ZlZEr79tz/2GMU95o1GYYoNXcKPPlCwAFGpAQ50eUL19PRZmwcdo3t5XRREwBmNOuJrgO/zHi/e+LduGvbEkPj2VipHi6FReCWg5tQbcQ853VUhIJFZCTT+PfvZ2x5o0Yse5uWRst7zBgubn7xBRfFGzak9f700wxrHDGCAv/ttxLtVIDJFz5wG7M3Hc1wleRG7bJFEPdiF+O1vrXGwfF9suwy4hef/MvbqHMmCZ2fmpKx76CBxVShAKI1F73Hj2eafbFijG6x1SdfsIDJQ/v333xu/frMCrUtngv5Dq/4wJVSB5VSW5VSCUopv1ex79u8EmqXLeL0uL2nLqPHxOXGxVQpVBs+F1dDwjN2HXz3dqf1xFdUb4GqySdQ9Vxmlp1LvTaFgoNSrLuydCmwfj2333+f4vzzz7S8ExMp5h9+yFyDMGsP2J07+V4p4Pff/ToNwbd4woXSVWvdLKengz+Ie7GLIffI3lOX0WZsHA6O6+003d5G/Zd+Qe9HPsx4P3fa0FwXOFdUbwEA6HQgazy4wzR8QQBYUGvmTC5gDhrEGPGGDYE77mBEynPP0Vr/5x+2cXviicxz77iDQn7//XwYpKT4bx6C18k3PnB7PujfzNBxJy+moN7I+Uh8pzeKheeeEGRje/laN7lPDr57O8Yt+PimYw+VqICk4uXQ6cCmmz4bOjMBtV6dL0IuOKZGjcyIlDffZDx4p05c1Jw1iyn5vXuzxvmNG+wgZEvvnzmTGaPh4aysOGmS88qJgunIkw9cKXUAwDkwcu5LrfWkHI4ZBGAQAERHR7c8dOiQ2/dzhddmb8V3a5OcH2jlw/7NEH/oH5fOqZx8An99+WSWfU/1exV/1Gmf8X7MH5+i747laP7cDKQGO66g+FDbaIzp6zwpSSjAXLnCZs4TJtAXXrs2S9o+/HBm/DjA9P4XXmAmZ3YaNsyMO+/QIdMNIwQ0jnzgeRXwilrrY0qpsgDiAAzRWjvMXPH2ImZ2Hpy8xnkTBjtcXty0MnLpFAxcPzvLvv4D3sG66MaI3bMGk34dm/HeGR1qlnJa40Uo4KSn0wIfP54NlKOi6FYZPBgoZRdOu2EDi2YtXcr37dpR6FeuZK2VyEhmjvbsyR/7kEYhoPCKgGe7wZsALmmt33d0jK8FHGAa/d5TDooJOeChttEuWeI21n/yEKKuJGfZNzJ2MN6K+wJftL0H73d62OVxiFUuOERrpt6/9x4jVIoUoT/8hRfYOch2zMKFFPJt29gdaNQotoazRbbYXCuNG2da5+3bsxiXEBB4XMCVUkUABGmtL1q34wC8rbVe6Ogcfwg44J6IKwC1yhZx+TzAceamqyn5NsJDgvDu3U2cdiISCjBbtzJqZcYMivZ997GNW/Pm/Dw9ne6X118Hjh9nnZV33mFdlh07MsV85UrGohcrxrBEm3VesaJfp1fQ8YaA1wDwq/VtCIAZWuuxuZ3jLwEHXHen2CgXGYaTF91byV/+5UBUSz5+0/6GQ3/E5fDCbl0TEMtcyIXDhxkTPmkSa4t3707ru3t3RqdcvswStrZMzqeeokVetizPv3ABWLIkU9BtnaVsDSt69aIrJnstdMGreN2FYgR/CjhgPNHH0zyzeiaGrfy/m/b/0CQWI3o+l6dri5gLOZKczC5CH31Ei7tZM1rk995L18jJk8Dbb/OYiAhg+HBmhha2Myy0ptvFJuarVtE6L16ccec9ezJevUIFf82ywCACbke9kfNxLd33dZaLXbuELR/dn+Nnr9z2HH5sGpun64urRbiJ69eB6dPpXtm5k2n4L75IX3nRokwMGjGCESsVK1LUH300szmFPefPsxvRggX8sbWBa9480zpv00ascy8gAp4Nd/zinuTeLYvwXg6x4wDwYYcB+LDjg3m+h0S0CBncuMGKhu+9Rz93yZKMWhkyBChXjv05hw1jrHmjRoxwue02ul1yQms2crZZ56tX089eogSt8169eH65cj6dZn5FBDwH/OVSyc7EuRNw1/ZlOX52pFhZ9HjiM1wNK5Tn+4QEKbx/b1Ox0As6a9dSyH/9lXHgjzzCePLatYFffqFFvm8f0K0bj2vRwvk1k5Npnc+fT1E/cYL7W7bMXAht0yZny15wigh4Lria9ONNcoopt2dixwfxcfv7HVtGLmKLfRcKIHv2MClo2jSm3PftywXPFi3oG3/rLTZxfughVkesWtXYdbVmAwubq2X1an4DKFUq0zq3WDIXTgWniIAbwN1IFW/Ref8GTPtpVK7HfNHmbozr/KjHBB0Q10uB4+RJtnj77DPg3DmgY0cK+S230JXywQcU4OeeA159le4XVzh3jr1CbYJ+8iT/v7ZsmRl33qqVWOe5IALuAv72j+eE0jfwwdwJ6Lvjz1yPO1SiPO4f8A6OF4vy+Bgk4iWfc+kS27hNnMj6K/Xq0S/euTMwejRrj5cowVjywYNZZ8VVbtygdW5ztaxdy32lS9Mq79mTr1Ge//9rZkTA3SDQLHJ7QtNT8dnsceiRuM7psaujm2Bw3xFIjijmlbGIxZ7PSE0FfvqJ/u+EBIYJPv8847/HjgUWLQKqVwf++18mDAXloSbeP//wejbr/PRpWuetWmVGtsTE5O0e+QAR8DwQSD5yh2iNf22ah9FxXxg+JXvhLW8gVruJ0ZoLk++9RxdIZCTL2zZowPjyLVsotO+9Rys9r9y4AWzcmBnZsm4dx1CmDCNabNZ56dJ5v5fJEAH3EIHoXnFESHoaXls6BY9uNJ7CfzEsAsN6DcXCOu096lfPCYlbNxGbNjGWfOZM/r/o35+W+Q8/AEeOAH36AOPGUdw9xZkzmdb5woV8HxQEtG6daZ23aFEgrHMRcC9gJjHPQGvcs20J3p//ocunrqjWHGO6PYE9UdU8PixHSJRMgHHwIDsCTZ7M8rZdutAXvmYNfehPPsnolfLlPXvf9HRWV7RZ5+vX0zovWzbTOo+NzVqNMR8hAu5lAtlfboSwtFQMXvsjhq763u1rfNR+AKa07ouL4c7b2nka8cP7mLNngc8/Z/TKqVOsfnj9OuO/CxcGXn6ZP0WLeuf+p0+zK9GCBXw9e5aWeNu2mdZ5s2b5xjoXAfchszcdxYszE3DD3wPxEPVOHcBbcV+gzZHtebrOyqrN8HXMHVhWMwZa+e8PS8Teg1y9yuiUCRPYAs6ecuVojT/xhHfT69PTaZHbFkLXr8+8/223Ucx79HA9/DGAEAH3I/lN0O0peeU8Bv39K55e97NHrncxLAI/N+6OH5v0wM6o6l73wxtFFmOdkJ4OzJnDKofrskVG1avH/X36+Ob3eeoUrfL58/l67hxjzNu1y8wKbdYsYP5vGUEEPMAwRWSLB4hIuYa7ty3BU3/PQpXzJz123XOFIrG6ahOsjW6MtVUaI7FMFb9a9Y4ocOULtGZdlffeA37/PetnnTpxIbRVK9+NJz0d+PvvzLjzDRu4v0KFTN95jx6Mbw9gRMBNgikXRvNI8I10dN6/AQ8kLED3feu9dp+dUdUQX7kB4ivVx8ZK9XG4eDnTWGGm/AawYwddK19/TWG30b8/Y8hr1PD9mE6cyOo7T06mdd6+fWZWaJMmAff/QgTc5BREYc+JYtcuof2hzbjl4Ca0P7QZ1c/d3DDD01wIK4xt5Wthe7ka2FauJraVr4UDJSviRpB5U799+kA4dgz4+GOm5dvrzfPPM6vTX3HdaWl099is802buL9ixUxXS/furH/uZ0TA8yn52b/uCcLSUtHoRCJaHd2OmCM70OrIDpS4dsnn40guVBSJpatgb+kqSCwTbX2tguORZQLS9ZMXHLqNLlxg+OGwYVmFfMwYVkMslPeKm3ni+HHGmy9YwPjz8+e5+NqhQ2ZkS6NGfrHORcALKCLw7hGanopaZw+j0Yl9aHhyHxpZfwqluddez9OcKxSJpBLlcbhEeRwqUR5JGT8VcDyydEB/OwhNT0WfnSswcd4HWfa/1uNpTG/e060HmsfXGtLSGNtuizvfvJn7K1fOap1HRnrmfk4QARdyxexx7AGH1oi6nIxaZ5NQ6+zhjJ/aZw6j7OVz/h6dU84ULo6TRUvjeGRpHC8WheORZXAisjROFC2NE5FlcKxYGVwLzaPFrDW67I/HNz+/lWX30NtfwuyGXfN2bQ9T7uIZdN6/EV32x6PjwQQUS7mClKAQxFdugOU1WmJZjRjsLROdYZ172kUlAi54DBF731Pk+hVUOX8S0cknEJ18HNHJ3K5y/gSqnjuOEJ3/vmN90q4/ZjfogushoUgJDsX1kLCMbX9+wwhJT0PLozvRZf8GdNkfj/qnDwIAjkZGYXnNllheIwarqjbFlbCIm851t3yECLjgV8SVE6BojcKp11D+4lmUu3QWFS6eQfmLttczqHDxLCpcOI3SVy/4e6RZSAkKQUpIKK5nE/brIWG4Hpz5PiUkFKnWY1OCQpAaHIqU4MzXlOBQpAaHINW6nfmZdX9QCK6HZG7zOqEZx6cGh6DUlfNon7QFHQ8moG3SFkSmXEVKUAj+rtIQy2tQ0BNLV8mwzoMUMPG+Zi6JuAi4YGoKStx8vsf6wCh76R+UuZKMMpeTEXX5HKIunUPU5XMocyUZUZeTUebyOewrXQU/Ne6O8LRUhKenIMz6Gp6WivC0FISnpSAsPY3b9vvTUxCWlobw9BSEpqchLD0VYdbX0PQ0hKanIiw9FeHpaT6b9v6SFXH/gHdwKpIRN5VKRGDViG6Gz3ck4NI+WjAFY/o29qhPUcIy/YRSuBIWgYOlKuFgKT8nN2mNkBvpGcKeKfKpOQv/jTSEpVH8bduhN9KyPRxs56Vm2XctJAzXQjMbYBxLvuqRKYiACwUSb1c4lHUCE6AU0oJDkBbsexmsWOJm/7g7iIALghfwV7Gs/LrWkFNZYbO61YIUMMxS1yPXEh+4IAg+wd1vJWaoCW/0YSJRKIIgCAUMRwKev3J4BUEQChAi4IIgCCZFBFwQBMGkiIALgiCYFBFwQRAEk+LTKBSl1GkAh1w4pQyAM14ajq/JT3MB8td8ZC6BSX6aC5C3+VTVWkdl3+lTAXcVpVR8TqEzZiQ/zQXIX/ORuQQm+WkugHfmIy4UQRAEkyICLgiCYFICXcAn+XsAHiQ/zQXIX/ORuQQm+WkugBfmE9A+cEEQBMExgW6BC4IgCA4QARcEQTApASHgSqnblFK7lVKJSqkROXyulFIfWz/fopRq4Y9xGsHAXOoppdYopa4rpV72xxiNYmAuD1p/H1uUUquVUk39MU4jGJjLndZ5JCil4pVSHf0xTqM4m4/dca2UUulKqXt8OT5XMPC76aKUOm/93SQopd7wxziNYOT3Yp1PglJqu1LqzzzdUGvt1x8AwQD2AagBIAzAZgANsh3TC8ACAApAWwDr/D3uPMylLIBWAMYCeNnfY87jXNoDKGnd7mny30tRZK4JNQGwy9/jzst87I5bCmA+gHv8Pe48/G66AJjr77F6aC4lAOwAEG19XzYv9wwEC7w1gESt9X6tdQqAHwDcme2YOwF8q8laACWUUhV8PVADOJ2L1vqU1no9gFR/DNAFjMxltdb6nPXtWgCVfTxGoxiZyyVt/YsCUARAIK/uG/mbAYAhAH4BcMqXg3MRo3MxA0bm8gCAWVrrJIB6kJcbBoKAVwJw2O79Ees+V48JBMwyTiO4OpcnwG9JgYihuSil+imldgGYB+BxH43NHZzORylVCUA/AF/4cFzuYPT/WTul1Gal1AKlVEPfDM1ljMylDoCSSqnlSqkNSqmH83LDQOiJqXLYl936MXJMIGCWcRrB8FyUUl1BAQ9Uv7GhuWitfwXwq1KqE4DRALp7e2BuYmQ+HwIYrrVOVyqnwwMGI3PZCNYCuaSU6gVgNoDa3h6YGxiZSwiAlgBuBRABYI1Saq3Weo87NwwEAT8CoIrd+8oAjrlxTCBglnEawdBclFJNAEwB0FNrfdZHY3MVl34vWusVSqmaSqkyWutALKZkZD4xAH6wincZAL2UUmla69k+GaFxnM5Fa33Bbnu+UuqzAP3dGNWyM1rrywAuK6VWAGgKwC0BDwTHfwiA/QCqI9Px3zDbMb2RdRHzb3+P29252B37JgJ7EdPI7yUaQCKA9v4erwfmUguZi5gtABy1vQ+0H1f+n1mP/waBu4hp5HdT3u530xpAUiD+bgzOpT6AJdZjCwPYBqCRu/f0uwWutU5TSj0L4A9wFfcrrfV2pdS/rZ9/Aa6i9wLF4gqAx/w13twwMhelVHkA8QCKAbihlBoKrlRfcHRdf2Dw9/IGgNIAPrNaemk6AKvHGZzL3QAeVkqlArgKoL+2/sUFGgbnYwoMzuUeAE8rpdLA3839gfi7MTIXrfVOpdRCAFsA3AAwRWu9zd17Siq9IAiCSQmEKBRBEATBDUTABUEQTIoIuCAIgkkRARcEQTApIuCCIAgmRQRcEATBpIiAC4IgmJT/B/VIn1yijukZAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, all_masses, all_power) #popt gives fit value for a\n", - "avg_a = popt\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(all_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(all_masses, all_power)\n", - "plt.plot(all_masses, avg_power, color='r')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "3739e5d8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.64439915]\n" - ] - } - ], - "source": [ - "print(popt)" - ] - }, - { - "cell_type": "markdown", - "id": "109097ab", - "metadata": {}, - "source": [ - "#### Cleaning Up the Curve Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "7475e6d3", - "metadata": {}, - "outputs": [], - "source": [ - "#combining all mass and output data sets to fit\n", - "all_masses = np.array(all_masses)\n", - "all_power = np.array(all_power)\n", - "\n", - "#put combined mass data set into increasing order and reorder output array to match new indices \n", - "sorted_indices = np.argsort(all_masses)\n", - "sorted_masses = all_masses[sorted_indices]\n", - "sorted_powers = all_power[sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": 154, - "id": "acc33225", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#use scipy curve fit to create a general power law\n", - "popt, pcov = curve_fit(power_law, sorted_masses, sorted_powers,sigma=sorted_errors, absolute_sigma=True) #popt gives fit value for a\n", - "avg_a = popt\n", - "avg_aerr = np.sqrt(np.diag(pcov)) #gives the error in a\n", - "\n", - "#create an array with mass values using fitted a\n", - "avg_power = power_law(sorted_masses, avg_a)\n", - "\n", - "#plot combined mass and power law datasets\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, label='raw data')\n", - "plt.plot(sorted_masses, avg_power, color='r', label='curve fit')\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-a)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "bd3e02dd", - "metadata": {}, - "source": [ - "Based on the curve fit, our a value for the entire mass range is 0.60524093 ± 0.00211972" - ] - }, - { - "cell_type": "code", - "execution_count": 155, - "id": "0e0bc7ca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.00211972]\n" - ] - } - ], - "source": [ - "print(avg_aerr)" - ] - }, - { - "cell_type": "code", - "execution_count": 156, - "id": "7037fa8c", - "metadata": {}, - "outputs": [], - "source": [ - "#calculate the low and high outputs based on error of a\n", - "avg_aup = avg_a + avg_aerr\n", - "avg_alow = avg_a - avg_aerr\n", - "avg_outup = power_law(sorted_masses, avg_aup)\n", - "avg_outlow = power_law(sorted_masses, avg_alow)" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "id": "1cb19a53", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#fit plot including error\n", - "plt.figure()\n", - "plt.scatter(sorted_masses, sorted_powers, color='k', label='data created from all papers')\n", - "plt.plot(sorted_masses, avg_power, color='c', label='calculated curve fit')\n", - "plt.fill_between(sorted_masses, avg_outup, avg_outlow, color='c', alpha=0.2, label=\"error in a\")\n", - "plt.title(\"Power Law Curve Fit for 0.006 - 0.6 Solar Mass Objects\")\n", - "plt.xlabel(\"Mass of Objects (Solar Masses)\")\n", - "plt.ylabel(\"M^(-0.605 ± 0.002)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "cf3ff96a", - "metadata": {}, - "outputs": [], - "source": [ - "#create an error array for all values in sorted_masses\n", - "power_errors = []\n", - "for m in M_mass:\n", - " power_errors.append(power_law(m, M_a) - power_law(m, M_alow))\n", - "for n in S_mass:\n", - " power_errors.append(power_law(n, S_a) - power_law(n, S_alow))\n", - "for o in L_mass:\n", - " power_errors.append(power_law(o, L_a) - power_law(o, L_alow))\n", - "for p in B_mass:\n", - " power_errors.append(power_law(p, B_a) - power_law(p, B_alow))\n", - "for q in P_mass:\n", - " power_errors.append(power_law(q, P_a) - power_law(q, P_alow))\n", - "\n", - "#sort errors based on sorted indices\n", - "\n", - "# convert sorted_indices to a NumPy array for indexing\n", - "sorted_indices = np.array(sorted_indices)\n", - "\n", - "# Sort errors based on sorted indices\n", - "sorted_errors = [power_errors[i] for i in sorted_indices]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30d890fa", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0206cbc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From e5a7bbc6bb7182eea2c0a0a9090ab667e9cfdfa3 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 8 Jul 2024 13:42:36 -0700 Subject: [PATCH 05/48] New testing documents for brown dwarf changes --- changes/Test_BD_Cluster.ipynb | 430 +++++++++++++++++++++++++++++++++ spisea/imf/tests/test_bd.py | 300 +++++++++++++++++++++++ spisea/imf/tests/test_class.py | 58 +++++ 3 files changed, 788 insertions(+) create mode 100644 changes/Test_BD_Cluster.ipynb create mode 100644 spisea/imf/tests/test_bd.py create mode 100644 spisea/imf/tests/test_class.py diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb new file mode 100644 index 00000000..b2bdb139 --- /dev/null +++ b/changes/Test_BD_Cluster.ipynb @@ -0,0 +1,430 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4257f594-b092-40bc-85cf-2397fe17724c", + "metadata": {}, + "source": [ + "# SPISEA: Making a Cluster with BD Candidates" + ] + }, + { + "cell_type": "markdown", + "id": "08de775e-f1bb-464b-8148-48b51e6d8822", + "metadata": {}, + "source": [ + "This is a document to generate a synthetic cluster in SPISEA with the addition of brown dwarf candidates. It will follow the Quick Start Guide closely, but with changes due to a lower mass limit and ability to consider smaller objects." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b7536665-00cf-49ad-9028-930f56cf3204", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "60ee38e8-ba5c-4c23-b9a2-22773bd91a62", + "metadata": {}, + "source": [ + "### 1: Making an Isochrone Object for our Cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 57.656324 s.\n", + "Making photometry for isochrone: log(t) = 7.70 AKs = 0.80 dist = 4000\n", + " Starting at: 2024-07-03 13:28:50.362836 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.100 Msun T = 3021 K m_hst_f127m = 23.96\n", + "M = 1.336 Msun T = 6744 K m_hst_f127m = 18.16\n", + "M = 4.781 Msun T = 15818 K m_hst_f127m = 14.97\n", + "M = 6.899 Msun T = 17664 K m_hst_f127m = 13.11\n", + "M = 6.911 Msun T = 6971 K m_hst_f127m = 10.61\n", + "M = 6.988 Msun T = 3957 K m_hst_f127m = 9.32\n", + "M = 7.297 Msun T = 3738 K m_hst_f127m = 8.65\n", + "Starting filter: wfc3,ir,f139m Elapsed time: 18.77 seconds\n", + "Starting synthetic photometry\n", + "M = 0.100 Msun T = 3021 K m_hst_f139m = 23.57\n", + "M = 1.336 Msun T = 6744 K m_hst_f139m = 17.69\n", + "M = 4.781 Msun T = 15818 K m_hst_f139m = 14.59\n", + "M = 6.899 Msun T = 17664 K m_hst_f139m = 12.74\n", + "M = 6.911 Msun T = 6971 K m_hst_f139m = 10.18\n", + "M = 6.988 Msun T = 3957 K m_hst_f139m = 8.69\n", + "M = 7.297 Msun T = 3738 K m_hst_f139m = 8.01\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 38.82 seconds\n", + "Starting synthetic photometry\n", + "M = 0.100 Msun T = 3021 K m_hst_f153m = 22.93\n", + "M = 1.336 Msun T = 6744 K m_hst_f153m = 17.23\n", + "M = 4.781 Msun T = 15818 K m_hst_f153m = 14.22\n", + "M = 6.899 Msun T = 17664 K m_hst_f153m = 12.37\n", + "M = 6.911 Msun T = 6971 K m_hst_f153m = 9.74\n", + "M = 6.988 Msun T = 3957 K m_hst_f153m = 8.00\n", + "M = 7.297 Msun T = 3738 K m_hst_f153m = 7.28\n", + " Time taken: 60.42 seconds\n" + ] + } + ], + "source": [ + "# Define isochrone parameters\n", + "logAge = np.log10(5*10**7.) # Age in log(years)\n", + "AKs = 0.8 # extinction in mags\n", + "dist = 4000 # distance in parsec\n", + "metallicity = 0 # Metallicity in [M/H]\n", + "\n", + "# Define evolution/atmosphere models and extinction law\n", + "evo_model = evolution.MISTv1() \n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "red_law = reddening.RedLawHosek18b()\n", + "\n", + "# Also specify filters for synthetic photometry (optional). Here we use \n", + "# the HST WFC3-IR F127M, F139M, and F153M filters\n", + "filt_list = ['wfc3,ir,f127m', 'wfc3,ir,f139m', 'wfc3,ir,f153m']\n", + "\n", + "# Specify the directory we want the output isochrone\n", + "# table saved in. If the directory does not already exist,\n", + "# SPISEA will create it.\n", + "iso_dir = 'isochrones/'\n", + "\n", + "# Make IsochronePhot object. Note that this will take a minute or two, \n", + "# unless the isochrone has been generated previously.\n", + "#\n", + "# Note that this is not show all of the user options \n", + "# for IsochronePhot. See docs for complete list of options.\n", + "my_iso = synthetic.IsochronePhot(logAge, AKs, dist, metallicity=0,\n", + " evo_model=evo_model, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " iso_dir=iso_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " L Teff ... m_hst_f153m \n", + " W K ... \n", + "---------------------- ------------------ ... ------------------\n", + "1.6794720325683452e+24 3020.5752857611837 ... 22.92776805171169\n", + " 1.7370492282378e+24 3026.596844621303 ... 22.89188358781423\n", + "1.8137828986723996e+24 3034.2260855891886 ... 22.845919008054395\n", + "1.8944919038459294e+24 3041.88871922475 ... 22.799687950466488\n", + "1.9794440999691394e+24 3049.612041774559 ... 22.753170128603966\n", + "2.0689567354242308e+24 3057.411643839487 ... 22.706329930464427\n", + " ... ... ... ...\n", + " 8.450906433033863e+30 3441.3110094585145 ... 6.433232663228037\n", + " 8.557948261819579e+30 3436.3422272496196 ... 6.420004996757628\n", + " 8.585406026317698e+30 3435.514440695956 ... 6.416813383458505\n", + " 8.699093176188767e+30 3430.8941764462447 ... 6.403203879003122\n", + " 8.881152106206099e+30 3423.152501093308 ... 6.381645353870332\n", + " 8.910673547171454e+30 3421.7638232666736 ... 6.378271217031613\n", + " 9.076365228968617e+30 3414.983593067963 ... 6.359238998015297\n", + "Length = 666 rows\n" + ] + } + ], + "source": [ + "# The stars in the isochrone and associated properties \n", + "# are stored in an astropy table called \"points\" \n", + "# within the IsochronePhot object. \n", + "print(my_iso.points)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 M_sun: F127M = 19.299 mag, F139M = 18.792 mag, F153M = 18.265 mag\n" + ] + } + ], + "source": [ + "# Example case:\n", + "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", + "idx = np.where( abs(my_iso.points['mass'] - 1.0) == min(abs(my_iso.points['mass'] - 1.0)) )[0]\n", + "f127m = np.round(my_iso.points[idx[0]]['m_hst_f127m'], decimals=3)\n", + "f139m = np.round(my_iso.points[idx[0]]['m_hst_f139m'], decimals=3)\n", + "f153m = np.round(my_iso.points[idx[0]]['m_hst_f153m'], decimals=3)\n", + "print('1 M_sun: F127M = {0} mag, F139M = {1} mag, F153M = {2} mag'.format(f127m, f139m, f153m))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a color-magnitude diagram from the isochrone\n", + "py.figure(1, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(my_iso.points['m_hst_f127m'] - my_iso.points['m_hst_f153m'], \n", + " my_iso.points['m_hst_f153m'], 'r-', label='_nolegend_')\n", + "py.plot(my_iso.points['m_hst_f127m'][idx] - my_iso.points['m_hst_f153m'][idx], \n", + " my_iso.points['m_hst_f153m'][idx], 'b*', ms=15, label='1 $M_\\odot$')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "7e9599c3-e7f7-4f1b-8eda-8148058b16c3", + "metadata": {}, + "source": [ + "### 2: Bringing in an IMF" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "493b8648-1c3b-4aab-858f-2f955195e634", + "metadata": {}, + "outputs": [], + "source": [ + "# Make multiplicity object Here, we use the MultiplicityUnresolved object, \n", + "# based on Lu+13. This means that star systems will be unresolved, i.e., \n", + "# that all components of a star system are combined into a single \"star\" in the cluster\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "\n", + "# Make IMF object; we'll use a broken power law with the parameters from Kroupa+01\n", + "\n", + "# NOTE: when defining the power law slope for each segment of the IMF, we define\n", + "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", + "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", + "\n", + "my_imf = imf.Muzic_2017(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "markdown", + "id": "0e42984d-bf09-4e71-a823-aa698672cfe6", + "metadata": {}, + "source": [ + "### 3: Generating Cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 177 stars out of mass range\n", + "Found 47 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster object\n", + "cluster = synthetic.ResolvedCluster(my_iso, my_imf, mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c8de1a27-0b07-404c-a13d-8434e204017d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple ... m_hst_f153m N_companions\n", + "------------------- ---------- ... ------------------ ------------\n", + "0.35277838228082964 True ... 21.056406061698077 2\n", + " 0.4109143920284334 False ... 20.80304487623113 0\n", + "0.44680797582475024 False ... 20.65082717236568 0\n", + "0.16814683886383167 False ... 22.19558068545299 0\n", + " 0.1998139027268053 False ... 21.941926401844015 0\n", + " 0.4740288564083312 False ... 20.534885068309187 0\n", + " 1.0126972980825961 True ... 17.971915603237292 2\n", + " ... ... ... ... ...\n", + " 5.3779990877122295 True ... 13.776442294620189 1\n", + " 0.6634904439575919 False ... 19.510526216999857 0\n", + " 1.35508336893066 True ... 17.17251870764391 1\n", + " 3.7213803186176304 False ... 14.933652308856155 0\n", + " 1.5278429001189309 False ... 16.86599227492448 0\n", + " 0.8112843101887811 False ... 18.792308025746074 0\n", + "0.14401644779125125 False ... 22.41409431495889 0\n", + "Length = 411 rows\n" + ] + } + ], + "source": [ + "# Look at star systems table\n", + "print(cluster.star_systems)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass ... m_hst_f139m m_hst_f153m \n", + "---------- ------------------- ... ------------------ ------------------\n", + " 0 0.09565348913151368 ... nan nan\n", + " 0 0.03322585947572763 ... nan nan\n", + " 6 0.08726287084485738 ... nan nan\n", + " 6 0.6433751483393655 ... 20.33081658819904 19.657228868526335\n", + " 7 0.06161747316330432 ... nan nan\n", + " 13 1.7263481600403079 ... 17.018762642329463 16.60938637243561\n", + " 14 0.2737246431039749 ... 22.090906238571662 21.4548720525631\n", + " ... ... ... ... ...\n", + " 396 0.03899827614913988 ... nan nan\n", + " 398 0.26985155635770386 ... 22.112746284229008 21.476822094207677\n", + " 400 2.0771742779244846 ... 16.6610738152462 16.27195342256348\n", + " 400 1.8654858596867614 ... 16.870042360441936 16.47226405265614\n", + " 403 1.3729396331088282 ... 17.601870317877122 17.140899115559176\n", + " 404 1.222648505597827 ... 18.015195949214007 17.53027863177404\n", + " 406 0.1534511179062777 ... 22.950623123620353 22.315542833099645\n", + "Length = 208 rows\n" + ] + } + ], + "source": [ + "# The companions table is accessed in a similar way\n", + "print(cluster.companions)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", + "# multiple systems on the photometry\n", + "clust = cluster.star_systems\n", + "iso = my_iso.points\n", + "\n", + "py.figure(2, figsize=(10,10))\n", + "py.clf()\n", + "py.plot(clust['m_hst_f127m'] - clust['m_hst_f153m'], clust['m_hst_f153m'],\n", + " 'k.', ms=5, alpha=0.1, label='__nolegend__')\n", + "py.plot(iso['m_hst_f127m'] - iso['m_hst_f153m'], iso['m_hst_f153m'],\n", + " 'r-', label='Isochrone')\n", + "py.xlabel('F127M - F153M')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/imf/tests/test_bd.py b/spisea/imf/tests/test_bd.py new file mode 100644 index 00000000..a29005c6 --- /dev/null +++ b/spisea/imf/tests/test_bd.py @@ -0,0 +1,300 @@ +import numpy as np +import time +import pdb + +def test_generate_cluster(): + from .. import imf + from .. import multiplicity + + # Make multiplicity object + imf_multi = multiplicity.MultiplicityUnresolved() + + # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 + massLimits = np.array([0.01, 0.5, 1, 120]) # Define boundaries of each mass segement + powers = np.array([-0.71, -0.81, -1.6]) # Power law slope associated with each mass segment + my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) + + # Define total cluster mass + M_cl = 10**5. + + mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) + + # Make sure that the total mass is always within the expected + # range of the requested mass. + assert np.abs(M_cl - sysMass.sum()) < 120.0 + + # Check that enough companions were generated. + # Should be greater than 25% of the stars with companions. + mdx = np.where(isMulti)[0] + for mm in mdx: + assert len(compMass[mm]) > 0 + + return + +def test_prim_power(): + from .. import imf + + #mass_limits = np.array([0.1, 1.0, 100.0]) + #powers = np.array([-2.0, -1.8]) + mass_limits = np.array([1.0, 100.0]) + powers = np.array([-2.0]) + + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + Mcl = 1e5 + (mass, is_multi, c_mass, s_mass) = imf_tmp.generate_cluster(Mcl) + + print('mass shape = ', mass.shape) + print('mass array:') + print(mass) + print('is_multi array:') + print(is_multi) + print('c_mass array:') + print(c_mass) + print('s_mass array:') + print(s_mass) + print('np.isfinite(mass):') + print(np.isfinite(mass)) + print('Mass Sum: ', mass.sum(), ' (should be {0})'.format(Mcl)) + + return + +def test_xi(): + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + pr = cProfile.Profile() + + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) + powers = np.array([-0.3, -1.5, -2.3]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + ########## + # + # Test validity of returned values. + # + ########## + N_size = 10 + m = np.linspace(0.2, 20, N_size) + val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704, + 0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142]) + + val_test = np.zeros(len(m), dtype=float) + for ii in range(len(m)): + val_test[ii] = imf_tmp.xi(m[ii]) + + # print(repr(val_test)) + np.testing.assert_almost_equal(val_test, val_good) + + ########## + # + # Performance testing + # + ########## + t1 = time.time() + # pr.enable() + + # Run a time test + N_size = int(1e4) + m = np.random.uniform(1.1, 99.0, size=N_size) + foo1 = imf_tmp.xi(m) + + # pr.disable() + t2 = time.time() + + print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) + + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) + + return + +def test_xi2(): + """ + Test that xi() produces the correct probability for + a given slope. + """ + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + pr = cProfile.Profile() + + # Negative slope + mass_limits = np.array([1.0, 10.0]) + powers = np.array([-2.0]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + imf_tmp.normalize(1e4) + + tmp = np.random.rand(100) + masses = imf_tmp.dice_star_cl(tmp) + + pdf = imf_tmp.xi(masses) + + sdx = masses.argsort() + + pdf_sorted = pdf[sdx] + + print('Testing negative slope') + assert pdf_sorted[0] > pdf_sorted[-1] + assert pdf_sorted[0] > pdf_sorted[1] + assert pdf.max() == pdf_sorted[0] + + # Positive slope + mass_limits2 = np.array([1.0, 10.0]) + powers2 = np.array([2.0]) + imf_tmp2 = imf.IMF_broken_powerlaw(mass_limits2, powers2) + imf_tmp2.normalize(1e4) + + tmp2 = np.random.rand(100) + masses2 = imf_tmp2.dice_star_cl(tmp2) + + pdf2 = imf_tmp2.xi(masses2) + + sdx2 = masses2.argsort() + + pdf_sorted2 = pdf2[sdx2] + + print('Testing positive slope') + assert pdf_sorted2[0] < pdf_sorted2[-1] + assert pdf_sorted2[0] < pdf_sorted2[1] + assert pdf2.min() == pdf_sorted2[0] + + + import pylab as plt + plt.clf() + plt.loglog(masses[sdx], pdf_sorted, 'k.') + plt.axis('equal') + # pdb.set_trace() + + return + + + + +def test_mxi(): + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + pr = cProfile.Profile() + + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) + powers = np.array([-0.3, -1.5, -2.3]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + ########## + # + # Test validity of returned values. + # + ########## + N_size = 10 + m = np.linspace(0.2, 20, N_size) + val_good = np.array([0.32413132, 0.64549722, 0.4662524 , 0.38348249, 0.33333333, + 0.27290819, 0.21615424, 0.17739363, 0.14943557, 0.12842838]) + val_test = np.zeros(len(m), dtype=float) + for ii in range(len(m)): + val_test[ii] = imf_tmp.m_xi(m[ii]) + + # print(repr(val_test)) + np.testing.assert_almost_equal(val_test, val_good) + assert val_test.shape == m.shape + + ########## + # + # Performance testing + # + ########## + t1 = time.time() + # pr.enable() + + # Run a time test + N_size = int(1e4) + m = np.random.uniform(1.1, 99.0, size=N_size) + foo1 = imf_tmp.m_xi(m) + assert foo1.shape == m.shape + + # pr.disable() + t2 = time.time() + + print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) + + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) + + return + +def test_theta_closed(): + from .. import imf + + import cProfile, pstats, io + #from pstats import SortKey + + mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) + powers = np.array([-0.3, -1.5, -2.3]) + imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) + + ########## + # + # Test validity of returned values. + # + ########## + N_size = 10 + m = np.linspace(0.2, 20, N_size) + val_good = np.array([[1., 0., 0.], + [1., 1., 0.], + [1., 1., 0.], + [1., 1., 0.], + [1., 1., 0.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]]) + + val_test = np.zeros((len(m), len(powers)), dtype=float) + for ii in range(len(m)): + val_test[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) + + np.testing.assert_equal(val_test, val_good) + + + + ########## + # + # Speed tests and performance profiling. + # + ########## + N_size = 10000 + m = np.linspace(1.1, 99, N_size) + + tmp = np.zeros((len(m), len(powers)), dtype=float) + + t1 = time.time() + # pr = cProfile.Profile() + # pr.enable() + + for ii in range(len(m)): + tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) + + # pr.disable() + t2 = time.time() + + print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) + + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) + + + return + diff --git a/spisea/imf/tests/test_class.py b/spisea/imf/tests/test_class.py new file mode 100644 index 00000000..7c15b3dc --- /dev/null +++ b/spisea/imf/tests/test_class.py @@ -0,0 +1,58 @@ +import numpy as np +import time +import pdb + +def test_Muzic(): + from .. import imf + from .. import multiplicity + + # Make multiplicity object + imf_multi = multiplicity.MultiplicityUnresolved() + + # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 + my_imf = imf.Muzic_2017(multiplicity=imf_multi) + + # Define total cluster mass + M_cl = 10**5. + + mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) + + # Make sure that the total mass is always within the expected + # range of the requested mass. + assert np.abs(M_cl - sysMass.sum()) < 120.0 + + # Check that enough companions were generated. + # Should be greater than 25% of the stars with companions. + mdx = np.where(isMulti)[0] + for mm in mdx: + assert len(compMass[mm]) > 0 + + return + +""" +def test_CombinedBD(): + from .. import imf + from .. import multiplicity + + # Make multiplicity object + imf_multi = multiplicity.MultiplicityUnresolved() + + # Use the IMF class from imf.py + my_imf = imf.CombinedBD_2024(multiplicity=imf_multi) + + # Define total cluster mass + M_cl = 10**5. + + mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) + + # Make sure that the total mass is always within the expected + # range of the requested mass. + assert np.abs(M_cl - sysMass.sum()) < 120.0 + + # Check that enough companions were generated. + # Should be greater than 25% of the stars with companions. + mdx = np.where(isMulti)[0] + for mm in mdx: + assert len(compMass[mm]) > 0 + + return""" \ No newline at end of file From 61b3b3bd4dc619f4c64525f524d1315ab2bd0c11 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 10 Jul 2024 14:40:01 -0700 Subject: [PATCH 06/48] changes to evolution, imf, and testing document --- changes/Test_BD_Cluster.ipynb | 808 ++++++++++++++++++++++++++++++---- spisea/evolution.py | 8 +- spisea/imf/imf.py | 29 +- 3 files changed, 750 insertions(+), 95 deletions(-) diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index b2bdb139..277a3412 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", "metadata": {}, "outputs": [ @@ -51,49 +51,537 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isochrone generation took 57.656324 s.\n", - "Making photometry for isochrone: log(t) = 7.70 AKs = 0.80 dist = 4000\n", - " Starting at: 2024-07-03 13:28:50.362836 Usually takes ~5 minutes\n", + "Changing to logg=4.00 for T= 32651 logg=3.99\n", + "Changing to logg=4.00 for T= 32840 logg=3.98\n", + "Changing to logg=4.00 for T= 33037 logg=3.97\n", + "Changing to logg=4.00 for T= 33144 logg=3.96\n", + "Changing to logg=4.00 for T= 33205 logg=3.95\n", + "Changing to logg=4.00 for T= 33358 logg=3.94\n", + "Changing to logg=4.00 for T= 33504 logg=3.93\n", + "Changing to logg=4.00 for T= 33651 logg=3.91\n", + "Changing to logg=4.00 for T= 33713 logg=3.91\n", + "Changing to logg=4.00 for T= 33775 logg=3.90\n", + "Changing to logg=4.00 for T= 33892 logg=3.89\n", + "Changing to logg=4.00 for T= 34002 logg=3.87\n", + "Changing to logg=4.00 for T= 34111 logg=3.86\n", + "Changing to logg=4.00 for T= 34182 logg=3.85\n", + "Changing to logg=4.00 for T= 34222 logg=3.85\n", + "Changing to logg=4.00 for T= 34332 logg=3.83\n", + "Changing to logg=4.00 for T= 34443 logg=3.82\n", + "Changing to logg=4.00 for T= 34546 logg=3.81\n", + "Changing to logg=4.00 for T= 34658 logg=3.80\n", + "Changing to 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logg=5.30\n", + "Changing to logg=5.00 for T= 34962 logg=5.25\n", + "Changing to logg=5.00 for T= 34906 logg=5.25\n", + "Changing to logg=5.00 for T= 34475 logg=5.21\n", + "Changing to logg=5.00 for T= 34277 logg=5.20\n", + "Changing to logg=5.00 for T= 34285 logg=5.20\n", + "Changing to logg=5.00 for T= 34658 logg=5.23\n", + "Changing to logg=5.00 for T= 34770 logg=5.24\n", + "Changing to logg=5.00 for T= 34253 logg=5.20\n", + "Changing to logg=5.00 for T= 33628 logg=5.15\n", + "Changing to logg=5.00 for T= 33312 logg=5.12\n", + "Changing to logg=5.00 for T= 33636 logg=5.15\n", + "Changing to logg=5.00 for T= 33643 logg=5.15\n", + "Changing to logg=5.00 for T= 33659 logg=5.15\n", + "Changing to logg=5.00 for T= 33651 logg=5.15\n", + "Changing to logg=5.00 for T= 33504 logg=5.13\n", + "Changing to logg=5.00 for T= 33466 logg=5.08\n", + "Changing to logg=5.00 for T= 33504 logg=5.08\n", + "Changing to logg=5.00 for T= 33558 logg=5.07\n", + "Changing to logg=5.00 for T= 33605 logg=5.08\n", + "Changing to logg=5.00 for T= 33651 logg=5.08\n", + "Changing to logg=5.00 for T= 33674 logg=5.07\n", + "Changing to logg=5.00 for T= 32870 logg=5.05\n", + "Changing to logg=5.00 for T= 30054 logg=5.01\n", + "Changing to logg=5.00 for T= 29819 logg=5.01\n", + "Changing to logg=5.00 for T= 35785 logg=5.39\n", + "Changing to logg=5.00 for T= 39683 logg=5.62\n", + "Changing to logg=5.00 for T= 39701 logg=5.62\n", + "Changing to logg=5.00 for T= 39655 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.63\n", + "Changing to logg=5.00 for T= 39373 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.65\n", + "Changing to logg=5.00 for T= 40031 logg=5.69\n", + "Changing to logg=5.00 for T= 40096 logg=5.70\n", + "Changing to logg=5.00 for T= 40495 logg=5.74\n", + "Changing to logg=5.00 for T= 41381 logg=5.82\n", + "Changing to logg=5.00 for T= 41976 logg=5.87\n", + "Changing to logg=5.00 for T= 42199 logg=5.90\n", + "Changing to logg=5.00 for T= 42228 logg=5.90\n", + "Changing to logg=5.00 for T= 42286 logg=5.90\n", + "Changing to logg=5.00 for T= 42345 logg=5.91\n", + "Changing to logg=5.00 for T= 42394 logg=5.92\n", + "Changing to logg=5.00 for T= 42462 logg=5.92\n", + "Changing to logg=5.00 for T= 42530 logg=5.92\n", + "Changing to logg=5.00 for T= 42619 logg=5.92\n", + "Changing to logg=5.00 for T= 42717 logg=5.93\n", + "Changing to logg=5.00 for T= 42825 logg=5.93\n", + "Changing to logg=5.00 for T= 42934 logg=5.94\n", + "Changing to logg=5.00 for T= 43053 logg=5.94\n", + "Changing to logg=5.00 for T= 43152 logg=5.94\n", + "Changing to logg=5.00 for T= 43271 logg=5.95\n", + "Changing to logg=5.00 for T= 43411 logg=5.95\n", + "Changing to logg=5.00 for T= 43561 logg=5.96\n", + "Changing to logg=5.00 for T= 43692 logg=5.96\n", + "Changing to logg=5.00 for T= 43833 logg=5.96\n", + "Changing to logg=5.00 for T= 43985 logg=5.97\n", + "Changing to logg=5.00 for T= 44147 logg=5.97\n", + "Changing to logg=5.00 for T= 44310 logg=5.97\n", + "Changing to logg=5.00 for T= 44484 logg=5.97\n", + "Changing to logg=5.00 for T= 44638 logg=5.97\n", + "Changing to logg=5.00 for T= 44802 logg=5.98\n", + "Changing to logg=5.00 for T= 44957 logg=5.98\n", + "Changing to logg=5.00 for T= 45144 logg=5.98\n", + "Changing to logg=5.00 for T= 45300 logg=5.99\n", + "Changing to logg=5.00 for T= 45478 logg=5.99\n", + "Changing to logg=5.00 for T= 45509 logg=5.99\n", + "Changing to logg=5.00 for T= 45646 logg=5.99\n", + "Changing to logg=5.00 for T= 45825 logg=5.99\n", + "Changing to logg=5.00 for T= 45983 logg=6.00\n", + "Changing to logg=5.00 for T= 46142 logg=6.00\n", + "Changing to logg=5.00 for T= 46313 logg=6.00\n", + "Changing to logg=5.00 for T= 46473 logg=6.00\n", + "Changing to logg=5.00 for T= 46623 logg=6.00\n", + "Changing to logg=5.00 for T= 46784 logg=6.00\n", + "Changing to logg=5.00 for T= 46946 logg=6.00\n", + "Changing to logg=5.00 for T= 47109 logg=6.00\n", + "Changing to logg=5.00 for T= 47261 logg=6.00\n", + "Changing to logg=5.00 for T= 47413 logg=6.01\n", + "Changing to logg=5.00 for T= 47566 logg=6.01\n", + "Changing to logg=5.00 for T= 47720 logg=6.01\n", + "Changing to logg=5.00 for T= 47863 logg=6.01\n", + "Changing to logg=5.00 for T= 48029 logg=6.01\n", + "Changing to logg=5.00 for T= 48173 logg=6.01\n", + "Changing to logg=5.00 for T= 48317 logg=6.02\n", + "Changing to logg=5.00 for T= 48473 logg=6.02\n", + "Changing to logg=5.00 for T= 48618 logg=6.02\n", + "Changing to logg=5.00 for T= 48753 logg=6.02\n", + "Changing to logg=5.00 for T= 48910 logg=6.02\n", + "Changing to logg=5.00 for T= 49057 logg=6.02\n", + "Changing to logg=5.00 for T= 49193 logg=6.02\n", + "Changing to logg=5.00 for T= 49329 logg=6.03\n", + "Changing to logg=5.00 for T= 49431 logg=6.03\n", + "Changing to logg=5.00 for T= 49465 logg=6.03\n", + "Changing to logg=5.00 for T= 49614 logg=6.03\n", + "Changing to logg=5.00 for T= 49751 logg=6.03\n", + "Changing to logg=5.00 for T= 49888 logg=6.03\n", + "Changing to T= 50000 for T= 50026 logg=6.03\n", + "Changing to logg=5.00 for T= 50026 logg=6.03\n", + "Changing to T= 50000 for T= 50176 logg=6.03\n", + "Changing to logg=5.00 for T= 50176 logg=6.03\n", + "Changing to T= 50000 for T= 50304 logg=6.03\n", + "Changing to logg=5.00 for T= 50304 logg=6.03\n", + "Changing to T= 50000 for T= 50431 logg=6.04\n", + "Changing to logg=5.00 for T= 50431 logg=6.04\n", + "Changing to T= 50000 for T= 50559 logg=6.04\n", + "Changing to logg=5.00 for T= 50559 logg=6.04\n", + "Changing to T= 50000 for T= 50699 logg=6.04\n", + "Changing to logg=5.00 for T= 50699 logg=6.04\n", + "Changing to T= 50000 for T= 50839 logg=6.04\n", + "Changing to logg=5.00 for T= 50839 logg=6.04\n", + "Changing to T= 50000 for T= 50957 logg=6.04\n", + "Changing to logg=5.00 for T= 50957 logg=6.04\n", + "Changing to T= 50000 for T= 51086 logg=6.04\n", + "Changing to logg=5.00 for T= 51086 logg=6.04\n", + "Changing to T= 50000 for T= 51215 logg=6.04\n", + "Changing to logg=5.00 for T= 51215 logg=6.04\n", + "Changing to T= 50000 for T= 51345 logg=6.04\n", + "Changing to logg=5.00 for T= 51345 logg=6.04\n", + "Changing to T= 50000 for T= 51475 logg=6.04\n", + "Changing to logg=5.00 for T= 51475 logg=6.04\n", + "Changing to T= 50000 for T= 51606 logg=6.04\n", + "Changing to logg=5.00 for T= 51606 logg=6.04\n", + "Changing to T= 50000 for T= 51737 logg=6.05\n", + "Changing to logg=5.00 for T= 51737 logg=6.05\n", + "Changing to T= 50000 for T= 51868 logg=6.05\n", + "Changing to logg=5.00 for T= 51868 logg=6.05\n", + "Changing to T= 50000 for T= 52000 logg=6.05\n", + "Changing to logg=5.00 for T= 52000 logg=6.05\n", + "Changing to T= 50000 for T= 52119 logg=6.05\n", + "Changing to logg=5.00 for T= 52119 logg=6.05\n", + "Changing to T= 50000 for T= 52240 logg=6.05\n", + "Changing to logg=5.00 for T= 52240 logg=6.05\n", + "Changing to T= 50000 for T= 52384 logg=6.05\n", + "Changing to logg=5.00 for T= 52384 logg=6.05\n", + "Changing to T= 50000 for T= 52505 logg=6.05\n", + "Changing to logg=5.00 for T= 52505 logg=6.05\n", + "Changing to T= 50000 for T= 52626 logg=6.05\n", + "Changing to logg=5.00 for T= 52626 logg=6.05\n", + "Changing to T= 50000 for T= 52747 logg=6.05\n", + "Changing to logg=5.00 for T= 52747 logg=6.05\n", + "Changing to T= 50000 for T= 52759 logg=6.05\n", + "Changing to logg=5.00 for T= 52759 logg=6.05\n", + "Changing to T= 50000 for T= 52857 logg=6.05\n", + "Changing to logg=5.00 for T= 52857 logg=6.05\n", + "Changing to T= 50000 for T= 52991 logg=6.05\n", + "Changing to logg=5.00 for T= 52991 logg=6.05\n", + "Changing to T= 50000 for T= 53125 logg=6.06\n", + "Changing to logg=5.00 for T= 53125 logg=6.06\n", + "Changing to T= 50000 for T= 53235 logg=6.06\n", + "Changing to logg=5.00 for T= 53235 logg=6.06\n", + "Changing to T= 50000 for T= 53358 logg=6.06\n", + "Changing to logg=5.00 for T= 53358 logg=6.06\n", + "Changing to T= 50000 for T= 53481 logg=6.06\n", + "Changing to logg=5.00 for T= 53481 logg=6.06\n", + "Changing to T= 50000 for T= 53592 logg=6.06\n", + "Changing to logg=5.00 for T= 53592 logg=6.06\n", + "Changing to T= 50000 for T= 53728 logg=6.06\n", + "Changing to logg=5.00 for T= 53728 logg=6.06\n", + "Changing to T= 50000 for T= 53864 logg=6.06\n", + "Changing to logg=5.00 for T= 53864 logg=6.06\n", + "Changing to T= 50000 for T= 53976 logg=6.07\n", + "Changing to logg=5.00 for T= 53976 logg=6.07\n", + "Changing to T= 50000 for T= 54100 logg=6.07\n", + "Changing to logg=5.00 for T= 54100 logg=6.07\n", + "Changing to T= 50000 for T= 54225 logg=6.07\n", + "Changing to logg=5.00 for T= 54225 logg=6.07\n", + "Changing to T= 50000 for T= 54363 logg=6.07\n", + "Changing to logg=5.00 for T= 54363 logg=6.07\n", + "Changing to T= 50000 for T= 54475 logg=6.07\n", + "Changing to logg=5.00 for T= 54475 logg=6.07\n", + "Changing to T= 50000 for T= 54588 logg=6.07\n", + "Changing to logg=5.00 for T= 54588 logg=6.07\n", + "Changing to T= 50000 for T= 54714 logg=6.08\n", + "Changing to logg=5.00 for T= 54714 logg=6.08\n", + "Changing to T= 50000 for T= 54853 logg=6.08\n", + "Changing to logg=5.00 for T= 54853 logg=6.08\n", + "Changing to T= 50000 for T= 54967 logg=6.08\n", + "Changing to logg=5.00 for T= 54967 logg=6.08\n", + "Changing to T= 50000 for T= 55093 logg=6.08\n", + "Changing to logg=5.00 for T= 55093 logg=6.08\n", + "Changing to T= 50000 for T= 55208 logg=6.08\n", + "Changing to logg=5.00 for T= 55208 logg=6.08\n", + "Changing to T= 50000 for T= 55322 logg=6.09\n", + "Changing to logg=5.00 for T= 55322 logg=6.09\n", + "Changing to T= 50000 for T= 55450 logg=6.09\n", + "Changing to logg=5.00 for T= 55450 logg=6.09\n", + "Changing to T= 50000 for T= 55590 logg=6.09\n", + "Changing to logg=5.00 for T= 55590 logg=6.09\n", + "Changing to T= 50000 for T= 55719 logg=6.09\n", + "Changing to logg=5.00 for T= 55719 logg=6.09\n", + "Changing to T= 50000 for T= 55834 logg=6.09\n", + "Changing to logg=5.00 for T= 55834 logg=6.09\n", + "Changing to T= 50000 for T= 55873 logg=6.09\n", + "Changing to logg=5.00 for T= 55873 logg=6.09\n", + "Changing to T= 50000 for T= 55963 logg=6.09\n", + "Changing to logg=5.00 for T= 55963 logg=6.09\n", + "Changing to T= 50000 for T= 56092 logg=6.10\n", + "Changing to logg=5.00 for T= 56092 logg=6.10\n", + "Changing to T= 50000 for T= 56234 logg=6.10\n", + "Changing to logg=5.00 for T= 56234 logg=6.10\n", + "Changing to T= 50000 for T= 56364 logg=6.10\n", + "Changing to logg=5.00 for T= 56364 logg=6.10\n", + "Changing to T= 50000 for T= 56507 logg=6.10\n", + "Changing to logg=5.00 for T= 56507 logg=6.10\n", + "Changing to T= 50000 for T= 56624 logg=6.11\n", + "Changing to logg=5.00 for T= 56624 logg=6.11\n", + "Changing to T= 50000 for T= 56754 logg=6.11\n", + "Changing to logg=5.00 for T= 56754 logg=6.11\n", + "Changing to T= 50000 for T= 56885 logg=6.11\n", + "Changing to logg=5.00 for T= 56885 logg=6.11\n", + "Changing to T= 50000 for T= 57030 logg=6.11\n", + "Changing to logg=5.00 for T= 57030 logg=6.11\n", + "Changing to T= 50000 for T= 57161 logg=6.11\n", + "Changing to logg=5.00 for T= 57161 logg=6.11\n", + "Changing to T= 50000 for T= 57293 logg=6.11\n", + "Changing to logg=5.00 for T= 57293 logg=6.11\n", + "Changing to T= 50000 for T= 57425 logg=6.11\n", + "Changing to logg=5.00 for T= 57425 logg=6.11\n", + "Changing to T= 50000 for T= 57570 logg=6.11\n", + "Changing to logg=5.00 for T= 57570 logg=6.11\n", + "Changing to T= 50000 for T= 57703 logg=6.11\n", + "Changing to logg=5.00 for T= 57703 logg=6.11\n", + "Changing to T= 50000 for T= 57850 logg=6.12\n", + "Changing to logg=5.00 for T= 57850 logg=6.12\n", + "Changing to T= 50000 for T= 57983 logg=6.12\n", + "Changing to logg=5.00 for T= 57983 logg=6.12\n", + "Changing to T= 50000 for T= 58117 logg=6.12\n", + "Changing to logg=5.00 for T= 58117 logg=6.12\n", + "Changing to T= 50000 for T= 58251 logg=6.13\n", + "Changing to logg=5.00 for T= 58251 logg=6.13\n", + "Changing to T= 50000 for T= 58398 logg=6.13\n", + "Changing to logg=5.00 for T= 58398 logg=6.13\n", + "Changing to T= 50000 for T= 58546 logg=6.13\n", + "Changing to logg=5.00 for T= 58546 logg=6.13\n", + "Changing to T= 50000 for T= 58695 logg=6.13\n", + "Changing to logg=5.00 for T= 58695 logg=6.13\n", + "Changing to T= 50000 for T= 58844 logg=6.14\n", + "Changing to logg=5.00 for T= 58844 logg=6.14\n", + "Changing to T= 50000 for T= 58993 logg=6.14\n", + "Changing to logg=5.00 for T= 58993 logg=6.14\n", + "Changing to T= 50000 for T= 59143 logg=6.14\n", + "Changing to logg=5.00 for T= 59143 logg=6.14\n", + "Changing to T= 50000 for T= 59293 logg=6.15\n", + "Changing to logg=5.00 for T= 59293 logg=6.15\n", + "Changing to T= 50000 for T= 59334 logg=6.15\n", + "Changing to logg=5.00 for T= 59334 logg=6.15\n", + "Changing to T= 50000 for T= 59457 logg=6.15\n", + "Changing to logg=5.00 for T= 59457 logg=6.15\n", + "Changing to T= 50000 for T= 59621 logg=6.16\n", + "Changing to logg=5.00 for T= 59621 logg=6.16\n", + "Changing to T= 50000 for T= 59800 logg=6.16\n", + "Changing to logg=5.00 for T= 59800 logg=6.16\n", + "Changing to T= 50000 for T= 59965 logg=6.16\n", + "Changing to logg=5.00 for T= 59965 logg=6.16\n", + "Changing to T= 50000 for T= 60159 logg=6.17\n", + "Changing to logg=5.00 for T= 60159 logg=6.17\n", + "Changing to T= 50000 for T= 60353 logg=6.17\n", + "Changing to logg=5.00 for T= 60353 logg=6.17\n", + "Changing to T= 50000 for T= 60534 logg=6.18\n", + "Changing to logg=5.00 for T= 60534 logg=6.18\n", + "Changing to T= 50000 for T= 60758 logg=6.18\n", + "Changing to logg=5.00 for T= 60758 logg=6.18\n", + "Changing to T= 50000 for T= 60968 logg=6.18\n", + "Changing to logg=5.00 for T= 60968 logg=6.18\n", + "Changing to T= 50000 for T= 61235 logg=6.19\n", + "Changing to logg=5.00 for T= 61235 logg=6.19\n", + "Changing to T= 50000 for T= 61504 logg=6.20\n", + "Changing to logg=5.00 for T= 61504 logg=6.20\n", + "Changing to T= 50000 for T= 61844 logg=6.21\n", + "Changing to logg=5.00 for T= 61844 logg=6.21\n", + "Changing to T= 50000 for T= 62287 logg=6.23\n", + "Changing to logg=5.00 for T= 62287 logg=6.23\n", + "Changing to T= 50000 for T= 63038 logg=6.27\n", + "Changing to logg=5.00 for T= 63038 logg=6.27\n", + "Changing to T= 50000 for T= 64239 logg=6.32\n", + "Changing to logg=5.00 for T= 64239 logg=6.32\n", + "Changing to T= 50000 for T= 65464 logg=6.37\n", + "Changing to logg=5.00 for T= 65464 logg=6.37\n", + "Changing to T= 50000 for T= 66696 logg=6.42\n", + "Changing to logg=5.00 for T= 66696 logg=6.42\n", + "Changing to T= 50000 for T= 67967 logg=6.47\n", + "Changing to logg=5.00 for T= 67967 logg=6.47\n", + "Changing to T= 50000 for T= 69263 logg=6.53\n", + "Changing to logg=5.00 for T= 69263 logg=6.53\n", + "Changing to T= 50000 for T= 71367 logg=6.63\n", + "Changing to logg=5.00 for T= 71367 logg=6.63\n", + "Isochrone generation took 150.174449 s.\n", + "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", + " Starting at: 2024-07-10 12:22:04.577984 Usually takes ~5 minutes\n", "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", - "M = 0.100 Msun T = 3021 K m_hst_f127m = 23.96\n", - "M = 1.336 Msun T = 6744 K m_hst_f127m = 18.16\n", - "M = 4.781 Msun T = 15818 K m_hst_f127m = 14.97\n", - "M = 6.899 Msun T = 17664 K m_hst_f127m = 13.11\n", - "M = 6.911 Msun T = 6971 K m_hst_f127m = 10.61\n", - "M = 6.988 Msun T = 3957 K m_hst_f127m = 9.32\n", - "M = 7.297 Msun T = 3738 K m_hst_f127m = 8.65\n", - "Starting filter: wfc3,ir,f139m Elapsed time: 18.77 seconds\n", + "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", + "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", + "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", + "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", + "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", + "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", + "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", + "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", + "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", + "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", + "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", + "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", + "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", + "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", + "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", + "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", + "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", + "Starting filter: wfc3,ir,f139m Elapsed time: 44.06 seconds\n", "Starting synthetic photometry\n", - "M = 0.100 Msun T = 3021 K m_hst_f139m = 23.57\n", - "M = 1.336 Msun T = 6744 K m_hst_f139m = 17.69\n", - "M = 4.781 Msun T = 15818 K m_hst_f139m = 14.59\n", - "M = 6.899 Msun T = 17664 K m_hst_f139m = 12.74\n", - "M = 6.911 Msun T = 6971 K m_hst_f139m = 10.18\n", - "M = 6.988 Msun T = 3957 K m_hst_f139m = 8.69\n", - "M = 7.297 Msun T = 3738 K m_hst_f139m = 8.01\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 38.82 seconds\n", + "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", + "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", + "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", + "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", + "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", + "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", + "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", + "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", + "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", + "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", + "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", + "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", + "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", + "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", + "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", + "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", + "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 90.13 seconds\n", "Starting synthetic photometry\n", - "M = 0.100 Msun T = 3021 K m_hst_f153m = 22.93\n", - "M = 1.336 Msun T = 6744 K m_hst_f153m = 17.23\n", - "M = 4.781 Msun T = 15818 K m_hst_f153m = 14.22\n", - "M = 6.899 Msun T = 17664 K m_hst_f153m = 12.37\n", - "M = 6.911 Msun T = 6971 K m_hst_f153m = 9.74\n", - "M = 6.988 Msun T = 3957 K m_hst_f153m = 8.00\n", - "M = 7.297 Msun T = 3738 K m_hst_f153m = 7.28\n", - " Time taken: 60.42 seconds\n" + "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", + "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", + "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", + "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", + "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", + "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", + "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", + "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", + "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", + "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", + "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", + "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", + "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", + "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", + "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", + "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", + "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", + " Time taken: 137.05 seconds\n" ] } ], "source": [ "# Define isochrone parameters\n", - "logAge = np.log10(5*10**7.) # Age in log(years)\n", + "logAge = np.log10(5*10**6.) # Age in log(years)\n", "AKs = 0.8 # extinction in mags\n", "dist = 4000 # distance in parsec\n", "metallicity = 0 # Metallicity in [M/H]\n", "\n", "# Define evolution/atmosphere models and extinction law\n", - "evo_model = evolution.MISTv1() \n", + "evo_model = evolution.MergedBaraffePisaEkstromParsec() \n", "atm_func = atmospheres.get_merged_atmosphere\n", "red_law = reddening.RedLawHosek18b()\n", "\n", @@ -119,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", "metadata": {}, "outputs": [ @@ -130,21 +618,21 @@ " L Teff ... m_hst_f153m \n", " W K ... \n", "---------------------- ------------------ ... ------------------\n", - "1.6794720325683452e+24 3020.5752857611837 ... 22.92776805171169\n", - " 1.7370492282378e+24 3026.596844621303 ... 22.89188358781423\n", - "1.8137828986723996e+24 3034.2260855891886 ... 22.845919008054395\n", - "1.8944919038459294e+24 3041.88871922475 ... 22.799687950466488\n", - "1.9794440999691394e+24 3049.612041774559 ... 22.753170128603966\n", - "2.0689567354242308e+24 3057.411643839487 ... 22.706329930464427\n", + " 6.591312905046435e+24 2928.195035574271 ... 21.454394826602254\n", + " 7.13135291244921e+24 2943.7437337117412 ... 21.3665241093368\n", + " 7.761969450327467e+24 2958.6936525140045 ... 21.272757288262653\n", + " 8.296060854703104e+24 2975.089258880875 ... 21.19905370697203\n", + " 8.830228238420424e+24 2992.264636608189 ... 21.13035092375595\n", + " 8.860779496066351e+24 3009.539168873201 ... 21.12685391099373\n", " ... ... ... ...\n", - " 8.450906433033863e+30 3441.3110094585145 ... 6.433232663228037\n", - " 8.557948261819579e+30 3436.3422272496196 ... 6.420004996757628\n", - " 8.585406026317698e+30 3435.514440695956 ... 6.416813383458505\n", - " 8.699093176188767e+30 3430.8941764462447 ... 6.403203879003122\n", - " 8.881152106206099e+30 3423.152501093308 ... 6.381645353870332\n", - " 8.910673547171454e+30 3421.7638232666736 ... 6.378271217031613\n", - " 9.076365228968617e+30 3414.983593067963 ... 6.359238998015297\n", - "Length = 666 rows\n" + "2.1036424651269878e+32 63037.64788294872 ... 11.22232056398674\n", + "2.1760653272980036e+32 64239.1816824387 ... 11.267570563986745\n", + " 2.250981517613893e+32 65463.61740672747 ... 11.312820563986737\n", + "2.3279407848949313e+32 66696.03248243559 ... 11.357320563986734\n", + "2.4080856466771184e+32 67967.29719504561 ... 11.40257056398674\n", + "2.4909896846856737e+32 69262.79294373626 ... 11.447820563986737\n", + "2.6240489308607902e+32 71367.42051224748 ... 11.521320563986743\n", + "Length = 1605 rows\n" ] } ], @@ -157,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", "metadata": {}, "outputs": [ @@ -165,7 +653,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1 M_sun: F127M = 19.299 mag, F139M = 18.792 mag, F153M = 18.265 mag\n" + "1 M_sun: F127M = 19.051 mag, F139M = 18.453 mag, F153M = 17.785 mag\n" ] } ], @@ -181,23 +669,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -230,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "493b8648-1c3b-4aab-858f-2f955195e634", "metadata": {}, "outputs": [], @@ -246,7 +734,7 @@ "# the entire exponent, including the negative sign. For example, if dN/dm $\\propto$ m^-alpha,\n", "# then you would use the value \"-2.3\" to specify an IMF with alpha = 2.3. \n", "\n", - "my_imf = imf.Muzic_2017(multiplicity=imf_multi)" + "my_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" ] }, { @@ -259,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", "metadata": {}, "outputs": [ @@ -267,8 +755,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 177 stars out of mass range\n", - "Found 47 companions out of stellar mass range\n" + "Found 60499 stars out of mass range\n", + "Found 11203 companions out of stellar mass range\n" ] } ], @@ -282,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "id": "c8de1a27-0b07-404c-a13d-8434e204017d", "metadata": {}, "outputs": [ @@ -292,22 +780,22 @@ "text": [ " mass isMultiple ... m_hst_f153m N_companions\n", "------------------- ---------- ... ------------------ ------------\n", - "0.35277838228082964 True ... 21.056406061698077 2\n", - " 0.4109143920284334 False ... 20.80304487623113 0\n", - "0.44680797582475024 False ... 20.65082717236568 0\n", - "0.16814683886383167 False ... 22.19558068545299 0\n", - " 0.1998139027268053 False ... 21.941926401844015 0\n", - " 0.4740288564083312 False ... 20.534885068309187 0\n", - " 1.0126972980825961 True ... 17.971915603237292 2\n", + "0.31585425987928323 True ... 18.92999676208373 2\n", + "0.11445929647600789 False ... 20.934530625744358 0\n", + " 0.645145955409843 False ... 18.42659654946194 0\n", + " 0.2125430430785249 False ... 20.08717879087111 0\n", + " 0.4443648340365779 False ... 18.982532572754547 0\n", + " 0.9427790018124477 True ... 17.5522451444909 1\n", + "0.20316227789117086 False ... 20.155331870086147 0\n", " ... ... ... ... ...\n", - " 5.3779990877122295 True ... 13.776442294620189 1\n", - " 0.6634904439575919 False ... 19.510526216999857 0\n", - " 1.35508336893066 True ... 17.17251870764391 1\n", - " 3.7213803186176304 False ... 14.933652308856155 0\n", - " 1.5278429001189309 False ... 16.86599227492448 0\n", - " 0.8112843101887811 False ... 18.792308025746074 0\n", - "0.14401644779125125 False ... 22.41409431495889 0\n", - "Length = 411 rows\n" + "0.07822412513422393 False ... 21.306060916420513 0\n", + "0.17640075706320751 False ... 20.342815617558465 0\n", + "0.09652737394053626 False ... 21.126211979448616 0\n", + " 0.1622394758046402 False ... 20.45232101696704 0\n", + "0.39015806512343487 False ... 19.2083064020004 0\n", + "0.15023739165143188 False ... 20.558755046061993 0\n", + "0.24854036705122345 False ... 19.857602142038818 0\n", + "Length = 123017 rows\n" ] } ], @@ -318,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", "metadata": {}, "outputs": [ @@ -328,22 +816,22 @@ "text": [ "system_idx mass ... m_hst_f139m m_hst_f153m \n", "---------- ------------------- ... ------------------ ------------------\n", - " 0 0.09565348913151368 ... nan nan\n", - " 0 0.03322585947572763 ... nan nan\n", - " 6 0.08726287084485738 ... nan nan\n", - " 6 0.6433751483393655 ... 20.33081658819904 19.657228868526335\n", - " 7 0.06161747316330432 ... nan nan\n", - " 13 1.7263481600403079 ... 17.018762642329463 16.60938637243561\n", - " 14 0.2737246431039749 ... 22.090906238571662 21.4548720525631\n", + " 0 0.0460428924123163 ... nan nan\n", + " 0 0.24553283737800713 ... 20.536185197536078 19.87454892207238\n", + " 5 0.4308071906008011 ... 19.720217136851623 19.036277872397545\n", + " 8 0.05926443486987895 ... nan nan\n", + " 10 0.37939808636570665 ... 19.922230480262495 19.247518348082274\n", + " 15 0.12172477389722892 ... 21.49272357482301 20.84357767155626\n", + " 16 0.4299286896336996 ... 19.72390856044099 19.040131407955787\n", " ... ... ... ... ...\n", - " 396 0.03899827614913988 ... nan nan\n", - " 398 0.26985155635770386 ... 22.112746284229008 21.476822094207677\n", - " 400 2.0771742779244846 ... 16.6610738152462 16.27195342256348\n", - " 400 1.8654858596867614 ... 16.870042360441936 16.47226405265614\n", - " 403 1.3729396331088282 ... 17.601870317877122 17.140899115559176\n", - " 404 1.222648505597827 ... 18.015195949214007 17.53027863177404\n", - " 406 0.1534511179062777 ... 22.950623123620353 22.315542833099645\n", - "Length = 208 rows\n" + " 122997 0.24062971315528156 ... 20.56327953828116 19.90217696791337\n", + " 122998 0.44959030611032147 ... 19.653329536810013 18.96646512002086\n", + " 123000 0.3746449017452262 ... 19.94002433187758 19.265866413907457\n", + " 123000 0.3396256601362069 ... 20.078397682202386 19.40756594570426\n", + " 123006 0.08879348065800587 ... 21.800450628386503 21.146929171115758\n", + " 123006 0.06195810103955034 ... nan nan\n", + " 123006 0.04465994487393095 ... nan nan\n", + "Length = 34087 rows\n" ] } ], @@ -354,23 +842,23 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 29, "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -399,9 +887,143 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimum primary mass: 0.07000163959328269\n", + "minimum companion mass: 0.01000292189962548\n", + "maximum primary mass: 55.80384055631119\n", + "maximum companion mass: 44.92501458090958\n" + ] + } + ], + "source": [ + "print(\"minimum primary mass: \" + str(np.min(clust['mass'])))\n", + "print(\"minimum companion mass: \" + str(np.min(cluster.companions['mass'])))\n", + "print(\"maximum primary mass: \" + str(np.max(clust['mass'])))\n", + "print(\"maximum companion mass: \" + str(np.max(cluster.companions['mass'])))" + ] + }, + { + "cell_type": "markdown", + "id": "fa4c660d-ea4e-491a-95db-278617ee1757", + "metadata": {}, + "source": [ + "#### Creating a histogram to compare Weidner Kroupa and Muzic" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c2540680-d69f-427f-b0d1-55130826ad8a", + "metadata": {}, + "outputs": [], + "source": [ + "#generate clusters for each class\n", + "w_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)\n", + "m_imf = imf.Muzic_2017(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "4d1c647e-8e1a-4a24-8b20-26a8b14f4660", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 58485 stars out of mass range\n", + "Found 10731 companions out of stellar mass range\n", + "Found 256 stars out of mass range\n", + "Found 83 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster objects for each\n", + "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", + "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "primary masses in Weidner Kroupa cluster: 119280\n", + "companion masses in Weidner Kroupa cluster: 32991\n", + "primary masses in Muzic cluster: 1113\n", + "companion masses in Muzic cluster: 682\n" + ] + } + ], + "source": [ + "#show total numbers for each cluster\n", + "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", + "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", + "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", + "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#define masses\n", + "w_masses = w_cluster.star_systems['mass']\n", + "w_comps = w_cluster.companions['mass']\n", + "m_masses = m_cluster.star_systems['mass']\n", + "m_comps = m_cluster.companions['mass']\n", + "\n", + "#define mass range of histogram\n", + "mass_ranges = [0.01, 0.08, 0.5, 1, 60]\n", + "\n", + "#create a histogram to compare\n", + "#plt.hist(m_masses, bins=mass_ranges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black')\n", + "plt.hist(w_masses, bins=mass_ranges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black')\n", + "\n", + "#labels and legend\n", + "plt.xlabel('Stellar Mass (Solar Masses)')\n", + "plt.ylabel('Number of Stars')\n", + "plt.title('Comparison of Stellar Mass Distributions')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51924315-a841-4f2d-bddb-84a1641b8d82", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/evolution.py b/spisea/evolution.py index 6cf765d5..93c494cc 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1232,7 +1232,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): """ def __init__(self, rot=True): # populate list of model masses (in solar masses) - mass_list = [(0.1 + i*0.005) for i in range(181)] + mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun # define metallicity parameters for Geneva models z_list = [0.015] @@ -1306,9 +1306,15 @@ def isochrone(self, age=1.e8, metallicity=0.0): isWR[idx_WR] = True iso.add_column(isWR) + # Assume mass of brown dwarfs does not change over their lifetime + bd_idx = iso['mass'] < 0.08 + iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + return iso diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 758eda00..c7e55056 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -47,7 +47,7 @@ class IMF(object): If None, no multiplicity is assumed. Otherwise, use multiplicity object to create multiple star systems. """ - def __init__(self, massLimits=np.array([0.1,150]), multiplicity=None): + def __init__(self, massLimits=np.array([0.01,150]), multiplicity=None): self._multi_props = multiplicity self._mass_limits = massLimits @@ -690,6 +690,33 @@ def __init__(self, multiplicity=None): IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) +#added for brown dwarfs +class Muzic_2017(IMF_broken_powerlaw): + """ + Define IMF from `Mužić (2017) +`_. + Mass range is 0.01 M_sun - inf M_sun. + """ + def __init__(self, multiplicity=None): + massLimits = np.array([0.01, 0.5, 1, np.inf]) + powers = np.array([-0.71, -0.81, -1.60]) + + IMF_broken_powerlaw.__init__(self, massLimits, powers, + multiplicity=multiplicity) + +class CombinedBD_2024(IMF_broken_powerlaw): + """ + Define IMF from several papers considering brown dwarf mass ranges + Derivation given in SPISEA/changes/BD_IMF + Mass range: 0.1 M_sun - 0.8 M_sun + """ + def __init__(self, multiplicity=None): + massLimits = np.array([0.01, 0.08]) + powers = np.array([-0.56]) + + IMF_broken_powerlaw.__init__(self, massLimits, powers, + multiplicity=multiplicity) + ################################################## # # Generic functions -- see if we can move these up. From ec4edb90e5d1ee8b69d2ae91304d15acf9b0ff66 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 10 Jul 2024 15:36:38 -0700 Subject: [PATCH 07/48] some histogram changes --- changes/Test_BD_Cluster.ipynb | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index 277a3412..2dbd9d71 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -982,15 +982,15 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 51, "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -1005,11 +1005,15 @@ "m_comps = m_cluster.companions['mass']\n", "\n", "#define mass range of histogram\n", - "mass_ranges = [0.01, 0.08, 0.5, 1, 60]\n", + "bin_edges = [0.01, 0.08, 0.5, 1, 60]\n", "\n", - "#create a histogram to compare\n", - "#plt.hist(m_masses, bins=mass_ranges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black')\n", - "plt.hist(w_masses, bins=mass_ranges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black')\n", + "#setting up subplots\n", + "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", + "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "\n", + "#create histograms to compare\n", + "ax[0].hist(m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", + "ax[1].hist(w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", "\n", "#labels and legend\n", "plt.xlabel('Stellar Mass (Solar Masses)')\n", From d2bc99b6b086a05f2d9fd99a0793bd69c7dd138b Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 15 Jul 2024 15:51:35 -0700 Subject: [PATCH 08/48] Adding notebook with issues in multiplicity --- changes/Compact_Multiplicity.ipynb | 480 +++++++++++++++++++++++++++++ 1 file changed, 480 insertions(+) create mode 100644 changes/Compact_Multiplicity.ipynb diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb new file mode 100644 index 00000000..d223c0f1 --- /dev/null +++ b/changes/Compact_Multiplicity.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c61a66d4-bc64-4080-ad07-38ae7f00670b", + "metadata": {}, + "source": [ + "## Comparing Clusters with Compact Objects: With and Without Companions" + ] + }, + { + "cell_type": "markdown", + "id": "77645bcf-b0f4-456b-89fd-eb9730b23c71", + "metadata": {}, + "source": [ + "In testing the addition of substellar objects into SPISEA, we have come across a curious problem. When generating a cluster with an IMFR and allowing for companions, we end up creating substellar mass compact objects, which does not make much sense. This is happening before modification of the code to specify brown dwarves as their own phase." + ] + }, + { + "cell_type": "markdown", + "id": "31969dcc-b1c4-4601-a44f-c9ba09a73188", + "metadata": {}, + "source": [ + "#### Importing the Necessary Packages:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pylab as py\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "f8639685-3185-4e3e-88c6-98602bdaedc9", + "metadata": {}, + "source": [ + "### Cluster 1: Without Companions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "k_imf = imf.Weidner_Kroupa_2004()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1a68a281-791a-4821-8799-c2df8e936286", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 907596 stars out of mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "k_out = k_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bkoyEhIRCjx09erTh5eV11XaiYmEGERxK06ZN5eLiYj5ef/11SZemtf7444965JFHJMlutsh9992nxMTEfN9e9OzZ0+55w4YNJcm8LGbdunW6cOGCBg0aZFefm5ubwsPDC1xIsE+fPvm2JScn6//9v/+nkJAQOTs7y8XFRaGhoZKkgwcPXrPPRW3HoUOH9Pvvv2vAgAF2051DQ0OvOfPncrVq1dLOnTu1c+dObdu2TcuWLZPNZlOHDh2ueVlXUfp67tw5bdq0SX379jWnVF/NokWLdN999+nxxx/Xhx9+KDc3tyL35dVXXzX7cvmjb9++duXypo1fPp1bku6++27Vq1dPX331VaHn+Pzzz2WxWPSPf/zD7v0JDAzUXXfdZb4/tWvXlre3t55++mm98847+b59vRYPD498P7MDBgzQxYsXtXnzZrPMo48+qoULF5rf/Hz99dc6cOCARo8efV3ne+yxx7RmzRqdPn1a8+fPV/v27Qu9C0x6erqefvpp1a5dW87OznJ2dlaVKlWUkZFh9zN+991368svv9QzzzyjjRs3KjMz064eHx8f1apVS6+99preeOMNff/993aXuQGAI2MMVLJjoMvXIcr7b3h4uCSpXr168vf3N8cLGzduVEBAgOrVq5evnmu9ttfj/Pnz+uqrr/TAAw+ocuXK+d7b8+fPa/v27ddd7+WMK2ZVt2nTRm5uboqPj5d06TLBiIgIde3aVVu3btW5c+d04sQJHT582O6y8wsXLmjatGm688475erqKmdnZ7m6uurw4cMFvtcF/bw88sgjslqtdrPePvjgA2VlZenRRx+97tfk7rvv1g8//KCRI0dq3bp1172WT2E/T5dfbvjEE09Ikt36jLNnz1aDBg3Url27Ip8rPDxctWrV0nvvvae9e/dq586dhV5eJl0a33Xs2FGenp5ycnKSi4uLnnvuOZ0+fVrJycmSLl066erqquHDh2vRokX5lj6QLr1GZ86c0cMPP6xPP/30mrPmUf4REOGm4+fnJ5vNVuAf0mXLlmnnzp121yBLMi/jmTBhgt3gycXFRSNHjpSkfB94vr6+ds+tVqskmf9ozauzefPm+epcsWJFvvoqV66c7w5PFy9eVOfOnbVy5UpNnDhRX331lXbs2GH+4bryH8gFKWo78i7TCQwMzFdHQdsK4+bmpmbNmqlZs2Zq2bKlHn74YX355ZdKTEzUc889V+hxRe1rSkqKcnNzVb169SK1Z/ny5bLZbHr88cev+9b0t912m9mXyx9XBlN5r11B0/mDg4PtLoG60qlTp2QYhgICAvK9P9u3bzffH09PT23atEmNGjXS5MmTVb9+fQUHB+v5558v0t1DAgIC8m3Le18vb9+YMWN09uxZLV26VNKlQUr16tV1//33X/Mcl3vwwQfl5uammTNn6rPPPtPQoUMLLTtgwADNnj1bjz/+uNatW6cdO3Zo586duuWWW+x+xv/zn//o6aef1urVq9W+fXv5+PioV69eZvBosVj01VdfqUuXLpoxY4aaNGmiW265RWPHji3SZXgAUNExBrJXmmOgBg0ayM/PTxs2bDDXH8oLiKRLN+3YuHGjsrKytG3btkLvXnat1/Z6nD59WhcuXFBMTEy+/uddbvV3/0Gf97OWdymUm5ub2rRpYwZEX331lTp16qSIiAjl5ubqm2++MS81uzwgGjdunKZMmaJevXrps88+07fffqudO3fqrrvuKrDvBY25fHx81LNnT73//vvmGosLFy7U3Xffrfr161/3azJp0iT9+9//1vbt2xUZGSlfX1916NAh393qClPYz9Pl466AgAD169dPc+fOVW5urvbs2aNvvvnmur+Ys1gsevTRR7VkyRK98847uv3223XPPfcUWHbHjh3mTWPmzZun//3vf9q5c6eeffZZSf/3s1arVi3Fx8fL399fo0aNUq1atVSrVi27NZgGDhyo9957T7/++qv69Okjf39/tWjRwu5yQlQsjrnUO25qTk5Ouvfee7V+/XolJiba/QHJu+vDsWPH7I7x8/OTdOkPQe/evQust27dutfVjrw6P/74Y/PbrqspKLzYt2+ffvjhBy1cuFCDBw82tx85cqTY25E3IElKSsq3r6Bt1yMoKEh+fn764YcfCi1T1L76+PjIycmpyAszL126VFOmTFF4eLjWr1+vRo0a3VAfribvtUtMTMwXXP3+++/me1AQPz8/WSwWffPNN+Yg8HKXb2vQoIGWL18uwzC0Z88eLVy4UC+88IJsNpueeeaZq7Yxb5B8ubz39fLBaO3atRUZGam33npLkZGRWrNmjaZOnSonJ6er1n+lypUrq3///po+fbqqVq1a6O9VamqqPv/8cz3//PN2fcjKytJff/1lV9bd3V1Tp07V1KlTderUKXM2UY8ePczbB4eGhpoLVP7000/68MMPFR0drezsbL3zzjvX1QcAqGgYA91YO4pjDGSxWBQeHq7Y2Fjt2LFDZ86csQuIwsPDFR0drW3btun8+fM3dHv76+Xt7S0nJycNHDiw0HUEa9asecP1G4ahzz77TO7u7mrWrJm5vUOHDnruuee0Y8cOnTx5Up06dZKHh4eaN2+uuLg4/f7777r99tsVEhJiHrNkyRINGjRI06ZNszvHn3/+KS8vr3znLuxLv0cffVQfffSR4uLidOutt2rnzp2aM2eOuf96XhNnZ2eNGzdO48aN05kzZxQfH6/JkyerS5cuOnHixDXvtFfYz9OVIeCTTz6pxYsX69NPP1VsbKy8vLzMGX3XY8iQIXruuef0zjvv2N1J9krLly+Xi4uLPv/8c7uZ9atXr85X9p577tE999yj3Nxc7dq1SzExMYqKilJAQID69+8v6dJr/uijjyojI0ObN2/W888/r+7du+unn34q0u8/yhcCItyUJk2apC+//FL/7//9P3388cfXvHtW3bp1VadOHf3www/5/jDdqC5dusjZ2Vk///xzgdNgiyLvj9+VwcHcuXPzlS3sG6aitqNu3boKCgrSBx98oHHjxpnn/vXXX7V169YiLZJXmJMnT+rPP/+86m1Zi9rXvLtAfPTRR3r55ZevGr5IlwKl+Ph4de/eXe3bt9eXX35pd6vW4nDvvfdKujS4ad68ubl9586dOnjwoPmNTEG6d++uV155Rb/99lu+S9cKY7FYdNddd2nmzJlauHChvvvuu2sec/bsWa1Zs8Zu6vqyZcvMhTUv9+STT6pz584aPHiwnJycNGzYsCK160pPPPGETp06pfDw8EIv7bNYLDIMI9/7/t///tf89q8gAQEBGjJkiH744QfNmjWrwFsi33777frXv/6lTz75pEivEQDcDBgDXX87imsM1L59e33yySd67bXX5O/vb3cJWXh4uE6fPq2YmBizbHEprP+VK1dW+/bt9f3336thw4aF3tXuRk2dOlUHDhzQ5MmT7f7Od+zYUZMnT9aUKVNUvXp13XHHHeb2NWvWKCkpKd/7YbFY8r3Xa9eu1W+//abatWsXuU2dO3dWtWrVtGDBAt16661yc3Mz7/Il3fhr4uXlpQcffFC//faboqKidOzYsauOayUV+vM0aNAgu3JNmzZV69at9eqrr2rfvn0aPny43N3di9znPNWqVdM///lP/fjjj3ah6pUsFoucnZ3tvvzLzMzU4sWLCz3GyclJLVq00B133KGlS5fqu+++MwOiPO7u7oqMjFR2drZ69eql/fv3ExBVQAREuCm1adNGb731lsaMGaMmTZpo+PDhql+/vipVqqTExER98sknkmQ3nXnu3LmKjIxUly5dNGTIEFWrVk1//fWXDh48qO+++04fffTRdbWhRo0aeuGFF/Tss8/ql19+UdeuXeXt7a1Tp05px44d5myIq7njjjtUq1YtPfPMMzIMQz4+Pvrss88KnLaZdxeIN998U4MHD5aLi4vq1q1b5HZUqlRJL774oh5//HE98MADGjZsmM6cOaPo6OjrusQsMzPTnP6dm5uro0ePasaMGZKkqKioYunrG2+8obZt26pFixZ65plnVLt2bZ06dUpr1qzR3Llz5eHhYVfew8NDsbGx6t27t3mXseIcmNWtW1fDhw9XTEyMKlWqpMjISB07dkxTpkxRSEiInnrqqUKPbdOmjYYPH65HH31Uu3btUrt27eTu7q7ExERt2bJFDRo00BNPPKHPP/9cb7/9tnr16qXbbrtNhmFo5cqVOnPmjDp16nTNNvr6+uqJJ57Q8ePHdfvtt+uLL77QvHnz9MQTT+jWW2+1K9upUyfdeeed2rBhg/7xj3/I39//hl6XRo0aFfht1OWqVq2qdu3a6bXXXpOfn59q1KihTZs2af78+fm+MWzRooW6d++uhg0bytvbWwcPHtTixYvVqlUrVa5cWXv27NHo0aP10EMPqU6dOnJ1ddXXX3+tPXv2XHOGFQDcLBgDld0YKG9ssWrVKj344IN2+8LCwuTr66tVq1apWrVqRbqbalF5eHgoNDRUn376qTp06CAfHx/zb+qbb76ptm3b6p577tETTzyhGjVq6OzZszpy5Ig+++yzIt2l7cyZM+bYLiMjQ4cOHdLy5cv1zTffqG/fvvney6ZNm8rb21vr16831/6RLgVEeXfJuvzyMunSF2YLFy7UHXfcoYYNG2r37t167bXXirykQB4nJycNGjRIb7zxhjmD2dPT065MUV+THj16KCwszFxe4Ndff9WsWbMUGhpapPcvOTnZ/HlKTU3V888/Lzc3N02aNClf2SeffFL9+vWTxWIxL+28Ea+88so1y3Tr1k1vvPGGBgwYoOHDh+v06dP697//nS+ge+edd/T111+rW7duuvXWW3X+/HnzTml579+wYcNks9nUpk0bBQUFKSkpSdOnT5enp6fdl6aoQMpseWygFCQkJBiPPvqoUbNmTcNqtRpubm5G7dq1jUGDBhlfffVVvvI//PCD0bdvX8Pf399wcXExAgMDjXvvvdd45513zDKX3zHgcnl3D7jy7kyrV6822rdvb1StWtWwWq1GaGio8eCDDxrx8fFmmcGDBxvu7u4F9uHAgQNGp06dDA8PD8Pb29t46KGHjOPHjxd4t4pJkyYZwcHBRqVKlfK1pSjtMAzD+O9//2vUqVPHcHV1NW6//Xbjvffey3fnrcJceRezSpUqGcHBwUZkZKSxceNGu7IF3cXsevp64MAB46GHHjJ8fX0NV1dX49ZbbzWGDBlinD9/3q7+y9+nrKwso0+fPoabm5uxdu3aQvuR915+9NFHBe4fNWqUceXHZ25urvHqq68at99+u+Hi4mL4+fkZ//jHP4wTJ07YlSvstXzvvfeMFi1aGO7u7obNZjNq1aplDBo0yNi1a5dhGIbx448/Gg8//LBRq1Ytw2azGZ6ensbdd99tLFy4sNB+5AkPDzfq169vbNy40WjWrJlhtVqNoKAgY/LkyfnuWJMnOjrakGRs3779mvXnudadTgzDKPAuZidPnjT69OljeHt7Gx4eHkbXrl2Nffv2GaGhocbgwYPNcs8884zRrFkzw9vb27BarcZtt91mPPXUU8aff/5pGIZhnDp1yhgyZIhxxx13GO7u7kaVKlWMhg0bGjNnzjQuXLhQ5H4AwM2AMdD/taU0xkB5AgMDDUnG7Nmz8+3r1auXIcl45JFH8u27ntf2yruYGcalu4c1btzYsFqthiS7v59Hjx41HnvsMaNatWqGi4uLccsttxitW7c2XnrppWv2JzQ01BzXWSwWo0qVKkbdunWNgQMHGuvWrSv0uAceeMCQZCxdutTclp2dbbi7uxuVKlUyUlJS7MqnpKQYQ4cONfz9/Y3KlSsbbdu2Nb755pt8fb3WGM0wDOOnn34y2xwXF1dgmaK8Jq+//rrRunVrw8/PzxxrDh061Dh27NhVX7O8Ni5evNgYO3asccsttxhWq9W45557zHHdlbKysgyr1Wp07dr1qnVfrrCfmSsVdBez9957z6hbt645npo+fboxf/58u7H5tm3bjAceeMAIDQ01rFar4evra4SHhxtr1qwx61m0aJHRvn17IyAgwHB1dTWCg4ONvn37Gnv27ClyP1C+WAzjiqXnAQAOr1mzZrJYLNq5c2dZNwUAAOCm9tlnn6lnz55au3atuVg2UBa4xAwAIElKS0vTvn379Pnnn2v37t1atWpVWTcJAADgpnXgwAH9+uuvGj9+vBo1aqTIyMiybhIcHAERAECS9N1336l9+/by9fXV888/r169epV1kwAAAG5aI0eO1P/+9z81adJEixYtKvTubEBp4RIzAAAAAAAAB1eprBsAAAAAAACAskVABAAAAAAA4OAIiAAAAAAAABwci1RLunjxon7//Xd5eHiwMBgAAOWYYRg6e/asgoODVakS33OVJcZPAABUDEUdPxEQSfr9998VEhJS1s0AAABFdOLECVWvXr2sm+HQGD8BAFCxXGv8REAkycPDQ9KlF6tq1apl3BoAAFCYtLQ0hYSEmH+7UXYYPwEAUDEUdfxEQCSZ06KrVq3KAAcAgAqAS5rKHuMnAAAqlmuNn7h4HwAAAAAAwMEREAEAAAAAADg4AiIAAAAAAAAHxxpEAFCCcnNzlZOTU9bNACoMFxcXOTk5lXUzUEwuXryo7Ozssm4GgMvwOQugMAREAFACDMNQUlKSzpw5U9ZNASocLy8vBQYGshB1BZedna2jR4/q4sWLZd0UAFfgcxZAQQiIAKAE5IVD/v7+qly5MgMwoAgMw9C5c+eUnJwsSQoKCirjFuFGGYahxMREOTk5KSQkRJUqsaoBUB7wOQvgagiIAKCY5ebmmuGQr69vWTcHqFBsNpskKTk5Wf7+/lwGUUFduHBB586dU3BwsCpXrlzWzQFwGT5nARSGr3MAoJjlrTnEP4qAG5P3u8P6XRVXbm6uJMnV1bWMWwKgIHzOAigIAREAlBAuKwNuDL87Nw/eS6B84ncTQEEIiAAAAAAAABwcaxABQCn67UymUjJK75bP3u6uquZlK7Xz3axq1KihqKgoRUVF/a16LBaLVq1apV69epWrdt2I6OhorV69WgkJCaV+blRcfAZWTCX1WXOteo8dO6aaNWvq+++/V6NGjYr13ACA/AiIAKCU/HYmUx1f36TMnNxSO6fNxUnx48Ov6x9ISUlJmj59utauXauTJ0/K09NTderU0T/+8Q8NGjSowqytVJrhSXR0tKZOnWo+r1q1qho2bKiXXnpJ4eHhJX7+otq4caPat2+vlJQUeXl52e0ry7AJjoHPwNJVWr/TsbGxioyMVGJiogIDA83tgYGBcnFx0YkTJ8xtJ0+eVEhIiNatW6fOnTtfs+6QkBAlJibKz89P0tU/w65XRESENm3aJOnSWll+fn5q0qSJHn30UfXu3ftv1Q0AFRUBEQCUkpSMbGXm5GpWv0aq7V+lxM93JDldUSsSlJKRXeR/HP3yyy9q06aNvLy8NG3aNDVo0EAXLlzQTz/9pPfee0/BwcHq2bNnCbe8cIZhKDc3V87O5e/PV/369RUfHy9J+uuvv/Tvf/9b3bt3N/+BCTg6PgP/vvL4Gdi2bVs5Oztr48aN6t+/vyTp4MGDOn/+vDIzM3XkyBHVrl1bkrRhwwa5uLioTZs2RarbycnJLnQqbsOGDdMLL7ygnJwc/fbbb1q1apX69++vIUOG6N133y2x814pJydHLi4upXY+ACgMaxABQCmr7V9FYdU8S/xxI/8AGzlypJydnbVr1y717dtX9erVU4MGDdSnTx+tXbtWPXr0MMumpqZq+PDh8vf3V9WqVXXvvffqhx9+MPdHR0erUaNGWrx4sWrUqCFPT0/1799fZ8+eNcsYhqEZM2botttuk81m01133aWPP/7Y3L9x40ZZLBatW7dOzZo1k9Vq1TfffKOff/5Z999/vwICAlSlShU1b97cDGekS98M//rrr3rqqadksVjsFuPcunWr2rVrJ5vNppCQEI0dO1YZGRnm/uTkZPXo0UM2m001a9bU0qVLi/TaOTs7KzAwUIGBgbrzzjs1depUpaen66effir0mKefflq33367KleurNtuu01TpkzJd0eZNWvWqFmzZnJzc5Ofn99Vv9lesGCBPD09FRcXV6Q2X83x48d1//33q0qVKqpatar69u2rU6dOXfWYBQsWqF69enJzc9Mdd9yht99+29yXnZ2t0aNHKygoSG5ubqpRo4amT5/+t9uJiofPwJvrMzDv/Bs3brRrd9u2bdW2bdt82++++265u7ub286dO6fHHntMHh4euvXWW+2CmWPHjslisSghIUHHjh1T+/btJUne3t6yWCwaMmRIkV7HwlSuXFmBgYEKCQlRy5Yt9eqrr2ru3LmaN2+e+Xr26dNHY8aMMY+JioqSxWLR/v37JUkXLlyQh4eH1q1bJ+nSjKq2bdvKy8tLvr6+6t69u37++ed8ffrwww8VEREhNzc3vf3227LZbIqNjbVr38qVK+Xu7q709HRJ0m+//aZ+/frJ29tbvr6+uv/++3Xs2LECX18vLy+1adNGv/766zVfBwDIQ0AEAJAknT59WuvXr9eoUaPsBu+Xy/tHhmEY6tatm5KSkvTFF19o9+7datKkiTp06KC//vrLLP/zzz9r9erV+vzzz/X5559r06ZNeuWVV8z9//rXv7RgwQLNmTNH+/fv11NPPaV//OMf5rT/PBMnTtT06dN18OBBNWzYUOnp6brvvvsUHx+v77//Xl26dFGPHj10/PhxSZcG1dWrV9cLL7ygxMREJSYmSpL27t2rLl26qHfv3tqzZ49WrFihLVu2aPTo0ea5hgwZomPHjunrr7/Wxx9/rLffflvJycnX9VpmZWVp4cKF8vLyUt26dQst5+HhoYULF+rAgQN68803NW/ePM2cOdPcv3btWvXu3VvdunXT999/r6+++krNmjUrsK5///vfmjBhgtatW6dOnTpdV3uvZBiGevXqpb/++kubNm1SXFycfv75Z/Xr16/QY+bNm6dnn31WL7/8sg4ePKhp06ZpypQpWrRokSTpP//5j9asWaMPP/xQhw4d0pIlS1SjRo2/1U6gOPEZeMmNfAa2b99eGzZsMJ9v2LBBERERCg8Pz7c9L+TJ8/rrr6tZs2b6/vvvNXLkSD3xxBP68ccf850jJCREn3zyiSTp0KFDSkxM1Jtvvnldr2NRDB48WN7e3lq5cqWkS2Hb5SHXpk2b5OfnZ9a9c+dOnT9/3pwVlZGRoXHjxmnnzp366quvVKlSJT3wwAO6ePGi3XmefvppjR07VgcPHtRDDz2kbt265Qvjli1bZgb1586dU/v27VWlShVt3rxZW7ZsUZUqVdS1a1dlZ2frwoUL6tWrl8LDw7Vnzx5t27ZNw4cP525lAK6PASM1NdWQZKSmppZ1UwDcBDIzM40DBw4YmZmZdtv3njxjhD79ubH35JlSacf1nm/79u2GJGPlypV22319fQ13d3fD3d3dmDhxomEYhvHVV18ZVatWNc6fP29XtlatWsbcuXMNwzCM559/3qhcubKRlpZm7v/nP/9ptGjRwjAMw0hPTzfc3NyMrVu32tUxdOhQ4+GHHzYMwzA2bNhgSDJWr159zfbfeeedRkxMjPk8NDTUmDlzpl2ZgQMHGsOHD7fb9s033xiVKlUyMjMzjUOHDhmSjO3bt5v7Dx48aEjKV9flnn/+eaNSpUrm62SxWIyqVasaX375pV05ScaqVasKrWfGjBlG06ZNzeetWrUyHnnkkULL5/XxmWeeMYKCgow9e/YUWtYw/u/1zGvn5Q+LxWL2cf369YaTk5Nx/Phx89j9+/cbkowdO3aYfb7rrrvM/SEhIcayZcvszvfiiy8arVq1MgzDMMaMGWPce++9xsWLF6/aRsMo/HfIMPibXZ5c7b0o6D3kM/Dm/Qxcv369Icn4/fffDcMwDH9/f2PHjh3G9u3bjeDgYMMwDOP48eOGJOOrr76ya+M//vEP8/nFixcNf39/Y86cOYZhGMbRo0cNScb3339v93qkpKSYxxTldSxIeHi48eSTTxa4r0WLFkZkZKRhGIaxZ88ew2KxGH/88Yfx119/GS4uLsZLL71kPPTQQ4ZhGMa0adPM97QgycnJhiRj7969dn2aNWuWXbmVK1caVapUMTIyMgzDuPT75ebmZqxdu9YwDMOYP3++UbduXbvP0KysLMNmsxnr1q0zTp8+bUgyNm7cWGhbLne1z1kAN5+ijp/KzwXMAIBy4cpvG3fs2KGLFy/qkUceUVZWliRp9+7dSk9Pl6+vr13ZzMxMu6n0NWrUkIeHh/k8KCjI/Cb6wIEDOn/+fL7ZLtnZ2WrcuLHdtitnzWRkZGjq1Kn6/PPP9fvvv+vChQvKzMw0vz0vzO7du3XkyBG7b2kNw9DFixd19OhR/fTTT3J2drY73x133FGkxVDr1q2rNWvWSJLOnj2rFStW6KGHHtKGDRsKnfXz8ccfa9asWTpy5IjS09N14cIFVa1a1dyfkJCgYcOGXfW8r7/+ujIyMrRr1y7ddttt12ynJH3zzTd274t06VvyPAcPHlRISIhCQkLMbXfeeae8vLx08OBBNW/e3O7YP/74QydOnNDQoUPt2nvhwgVz/aUhQ4aoU6dOqlu3rrp27aru3bsXaZFaoLTxGXj9n4Ft2rSRq6urNm7cqLvuukuZmZlq0qSJDMNQWlqaDh8+rG3btslqtap169Z2xzZs2ND8f4vFosDAwOuatXk9r2NRGYZh/hyEhYXJ19dXmzZtkouLi+666y717NlT//nPfyRduqzr8psR/Pzzz5oyZYq2b9+uP//805w5dPz4cYWFhZnlrnxPu3XrJmdnZ61Zs0b9+/fXJ598Ig8PD/NzMu+9u/Kz+/z58/r555/VuXNnDRkyRF26dFGnTp3UsWNH9e3bV0FBQTf0GgBwTAREAABJUu3atWWxWPJN7c8LHWy2/1vk9eLFiwoKCrKbdp/n8n9IXLnopsViMQfLef9du3atqlWrZlfOarXaPb/yco9//vOfWrdunf7973+rdu3astlsevDBB5WdffXbZ1+8eFEjRozQ2LFj8+279dZbdejQIbOd18vV1dVciFWSGjdurNWrV2vWrFlasmRJvvLbt29X//79NXXqVHXp0kWenp5avny5Xn/9dbPM5a95Ye655x6tXbtWH374oZ555pkitbVmzZr5/sF3+aK3l//j6HKFbc97L+fNm6cWLVrY7XNycpIkNWnSREePHtWXX36p+Ph49e3bVx07dizSOiFAaeAz8MY/AytXrqy7775bGzZs0F9//aW2bduav/utW7fWhg0btG3bNrVq1Upubm52x17tNSqK63kdiyI3N1eHDx82g3CLxaJ27dpp48aNcnV1VUREhMLCwpSbm6u9e/dq69atdneK69Gjh0JCQjRv3jwFBwfr4sWLCgsLy/feXPmeurq66sEHH9SyZcvUv39/LVu2TP369TM/my9evKimTZsWuCbULbfcIunSOnBjx45VbGysVqxYoX/961+Ki4tTy5Ytr/t1AOCYCIgAVCi/nclUSsbVB8A3ytvd9bpuhXyz8fX1VadOnTR79myNGTOm0DU4pEv/2E9KSpKzs/MNryNz5513ymq16vjx49d9K/hvvvlGQ4YM0QMPPCBJSk9Pt1uoU7o02M7Ntb+ddpMmTbR//367IOdy9erV04ULF7Rr1y7dfffdki6tdXHmzJnral8eJycnZWZmFrjvf//7n0JDQ/Xss8+a265cTLRhw4b66quv9OijjxZ6jrvvvltjxoxRly5d5OTkpH/+85831NbL3XnnnTp+/LhOnDhhziI6cOCAUlNTVa9evXzlAwICVK1aNf3yyy965JFHCq23atWq6tevn/r166cHH3xQXbt21V9//SUfH5+/3Wbg7+Iz8O99BrZv317Lly9XSkqK3YzE8PBwbdy4Udu2bbvqZ1lRuLq6SpJdv/7O61iQRYsWKSUlRX369DG3RURE6N1335Wrq6teeOEFWSwW3XPPPfr3v/+tzMxMc/2h06dP6+DBg5o7d67uueceSdKWLVuKfO5HHnlEnTt31v79+7Vhwwa9+OKL5r4mTZpoxYoV5qLohWncuLEaN26sSZMmqVWrVlq2bJlDBEQlOT68GkcfO+LmQ0AEoML47UymOr6+SZk5udcufANsLk6KHx/u0H/o3377bbVp00bNmjVTdHS0GjZsqEqVKmnnzp368ccf1bRpU0lSx44d1apVK/Xq1Uuvvvqq6tatq99//11ffPGFevXqVeglVZfz8PDQhAkT9NRTT+nixYtq27at0tLStHXrVlWpUkWDBw8u9NjatWtr5cqV6tGjhywWi6ZMmZLvG+caNWpo8+bN6t+/v6xWq/z8/PT000+rZcuWGjVqlIYNGyZ3d3cdPHhQcXFxiomJMS9/GjZsmN599105OzsrKiqqSDN5Lly4oKSkJEn/d4nZgQMH9PTTTxfah+PHj2v58uVq3ry51q5dq1WrVtmVef7559WhQwfVqlVL/fv314ULF/Tll19q4sSJduVatWqlL7/8Ul27dpWzs7Oeeuqpa7b3ajp27KiGDRvqkUce0axZs3ThwgWNHDlS4eHhhb630dHRGjt2rKpWrarIyEhlZWVp165dSklJ0bhx4zRz5kwFBQWpUaNGqlSpkj766CMFBgYW6fI9oLTwGXjjn4Ht27fXiy++qMTERE2YMMHcHh4erldeeUVnz57Nt0D19QoNDZXFYtHnn3+u++67Tzab7W+9jufOnVNSUpIuXLig3377TStXrtTMmTP1xBNP2LU1IiJCTz75pJydnc3gJyIiQuPHj1eTJk3MwCbv7mLvvvuugoKCdPz48SLP7JQuvVYBAQF65JFHVKNGDbtg55FHHtFrr72m+++/Xy+88IKqV6+u48ePa+XKlfrnP/+pnJwcvfvuu+rZs6eCg4N16NAh/fTTTxo0aND1vswVTkmPD6+GsSNuNgREACqMlIxsZebkala/Rjd0++KrOZKcrqgVCUrJyC7xP/JHktNLtP6/c55atWrp+++/17Rp0zRp0iSdPHlSVqtVd955pyZMmKCRI0dKujTl/osvvtCzzz6rxx57TH/88YcCAwPVrl07BQQEFPl8L774ovz9/TV9+nT98ssv8vLyUpMmTTR58uSrHjdz5kw99thjat26tfmPnrS0NLsyL7zwgkaMGKFatWopKytLhmGoYcOG2rRpk5599lndc889MgxDtWrVsrs714IFC/T444+bA/WXXnpJU6ZMuWZf9u/fb671ULlyZdWqVUtz5swpdHB+//3366mnntLo0aOVlZWlbt26acqUKYqOjjbLRERE6KOPPtKLL76oV155RVWrVlW7du0KrK9NmzZau3at7rvvPjk5ORV4CUlRWSwWrV69WmPGjFG7du1UqVIlde3aVTExMYUe8/jjj6ty5cp67bXXNHHiRLm7u6tBgwbmpRdVqlTRq6++qsOHD8vJyUnNmzfXF198oUqVuKGqo+Ez8P/cTJ+BrVq1Mi/pygvSJKl58+bKzc2VzWbLdwnq9apWrZqmTp2qZ555Ro8++qgGDRqkhQsX3vDrOG/ePM2bN0+urq7y9fVV06ZNtWLFCnNmVp6wsDD5+fkpNDTUDIPCw8OVm5trN2upUqVKWr58ucaOHauwsDDVrVtX//nPf+xmVF2NxWLRww8/rNdee03PPfec3b7KlStr8+bNevrpp9W7d2+dPXtW1apVU4cOHVS1alVlZmbqxx9/1KJFi3T69GkFBQVp9OjRGjFiRJHOXZGV5Pjwakpz7AiUFothGEZZN6KspaWlydPTU6mpqVedsgmgbO37LVXdY7bo8zFtFVbNs9zWff78eR09elQ1a9a0W2uhLL7h4pstVESF/Q5J/M0uT672XhT0HvIZCJQfV/ucrWhKcnxYHs8L3Iiijp+YQQQApaSal03x48NL9Rp5ro0HUF7wGQgAQPlGQAQApaial41/rABwWHwGAgBQfnHhPwAAAAAAgIMjIAIAAAAAAHBwBEQAUEK4BwBwY/jduXnwXgLlE7+bAApSpgHRnDlz1LBhQ1WtWlVVq1ZVq1at9OWXX5r7hwwZIovFYvdo2bKlXR1ZWVkaM2aM/Pz85O7urp49e+rkyZOl3RUAMLm4uEiSzp07V8YtASqmvN+dvN8lVDxOTk6SpOzs0luQGkDR8TkLoCBlukh19erV9corr6h27dqSpEWLFun+++/X999/r/r160uSunbtqgULFpjHuLq62tURFRWlzz77TMuXL5evr6/Gjx+v7t27a/fu3ebgBABKk5OTk7y8vJScnCxJqly5siwWSxm3Cij/DMPQuXPnlJycLC8vL/6OV2DOzs6qXLmy/vjjD7m4uKhSJSatA+UBn7MArqZMA6IePXrYPX/55Zc1Z84cbd++3QyIrFarAgMDCzw+NTVV8+fP1+LFi9WxY0dJ0pIlSxQSEqL4+Hh16dKlZDsAAIXI+9zKC4kAFJ2Xl1ehf/tRMVgsFgUFBeno0aP69ddfy7o5AK7A5yyAgpSb29zn5ubqo48+UkZGhlq1amVu37hxo/z9/eXl5aXw8HC9/PLL8vf3lyTt3r1bOTk56ty5s1k+ODhYYWFh2rp1a6EBUVZWlrKyssznaWlpJdQrAI4q7x9H/v7+ysnJKevmABWGi4sL32jfJFxdXVWnTh0uMwPKGT5nARSmzAOivXv3qlWrVjp//ryqVKmiVatW6c4775QkRUZG6qGHHlJoaKiOHj2qKVOm6N5779Xu3btltVqVlJQkV1dXeXt729UZEBCgpKSkQs85ffp0TZ06tUT7BQDSpcvNGIQBKIrp06dr5cqV+vHHH2Wz2dS6dWu9+uqrqlu3rlnGMAxNnTpV7777rlJSUtSiRQu99dZb5sxr6dIXYRMmTNAHH3ygzMxMdejQQW+//baqV69ulklJSdHYsWO1Zs0aSVLPnj0VExMjLy+vYu1TpUqV5ObmVqx1AgCAklHmF4TXrVtXCQkJ2r59u5544gkNHjxYBw4ckCT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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "p_bh = np.where(k_out['phase'] == 103)[0]\n", + "p_ns = np.where(k_out['phase'] == 102)[0]\n", + "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8f6c414b-c1df-4059-b5ca-3f4c8aa7c655", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a generated object: 0.07000011975637106\n" + ] + } + ], + "source": [ + "# Finding the minimum mass of a generated object\n", + "print(\"Smallest mass of a generated object: \" + str(np.min(k_out['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "186ded33-2af3-421a-9956-0a603998a50b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated black hole: 15.014691323167467\n", + "Initial mass of largest generated black hole: 119.74791889840289\n" + ] + } + ], + "source": [ + "# Finding the minimum/maximum inital mass of generated black holes\n", + "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", + "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" + ] + }, + { + "cell_type": "markdown", + "id": "2243d818-83a7-4a41-80ed-80a5ca2c6760", + "metadata": {}, + "source": [ + "This cluster fits the expectation from imfr.py that black holes are between 15-120 solar masses and white dwarves are at least 0.5 solar masses, even with the addition of substellar primary objects. " + ] + }, + { + "cell_type": "markdown", + "id": "7e460fa5-a083-4f2c-bef8-eb7d8a972b99", + "metadata": {}, + "source": [ + "### Cluster 2: With Companions" + ] + }, + { + "cell_type": "markdown", + "id": "b0addabd-33a5-4e4f-82d2-d7b58c69fc0e", + "metadata": {}, + "source": [ + "For this cluster we are using the same isochrone as Cluster 1, just changing our IMF to allow for systems with companions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 687520 stars out of mass range\n", + "Found 134795 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = np.concatenate([p2_bh, c_bh])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = np.concatenate([p2_ns, c_ns])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = np.concatenate([p2_wd, c_wd])\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(kc_out[k_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(kc_out[k_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a primary generated object: 0.07000003602252991\n", + "Smallest mass of a companion generated object: 0.010000123044113119\n" + ] + } + ], + "source": [ + "# Finding the minimum mass of generated objects\n", + "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", + "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated black hole: 0.0700493749164032\n", + "Initial mass of largest generated black hole: 119.95339215013136\n" + ] + } + ], + "source": [ + "# Finding the minimum/maximum inital masses of generated black holes\n", + "print(\"Initial mass of smallest generated black hole: \" + str(np.min(kc_out[k_bh]['mass'])))\n", + "print(\"Initial mass of largest generated black hole: \" + str(np.max(kc_out[k_bh]['mass'])))" + ] + }, + { + "cell_type": "markdown", + "id": "77b9203f-69ec-4a54-af84-581427feb4fd", + "metadata": {}, + "source": [ + "In this case, we are getting many instances of substellar mass blakc holes, which does not make sense. Why is it that adding companions introduces this issue?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi_resolved = multiplicity.MultiplicityResolvedDK()\n", + "c3_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi_resolved)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 694055 stars out of mass range\n", + "Found 136094 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "cluster3 = synthetic.ResolvedCluster(my_iso, c3_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "c3_out = cluster3.star_systems\n", + "c3_comp = cluster3.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c598a266-297c-423d-b5f5-648f819c065e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "p3_bh = np.where(c3_out['phase'] == 103)[0]\n", + "c3_bh = np.where(c3_comp['phase'] == 103)[0]\n", + "k3_bh = np.concatenate([p3_bh, c3_bh])\n", + "p3_ns = np.where(c3_out['phase'] == 102)[0]\n", + "c3_ns = np.where(c3_comp['phase'] == 102)[0]\n", + "k3_ns = np.concatenate([p3_ns, c3_ns])\n", + "p3_wd = np.where(c3_out['phase'] == 101)[0]\n", + "c3_wd = np.where(c3_comp['phase'] == 101)[0]\n", + "k3_wd = np.concatenate([p3_wd, c3_wd])\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(c3_out[k3_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(c3_out[k3_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1983a8f-67cf-41c1-bb0d-0869a543c4fa", + "metadata": {}, + "source": [ + "This is still the case when the companion objects are resolved." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1969480a-56b5-4109-993a-41267b1bcf24", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 261f72fd8f336a5f478c71e4c42b2b2b6acffd98 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 29 Jul 2024 09:19:13 -0700 Subject: [PATCH 09/48] New BD changes --- changes/BD_Evolution.ipynb | 172 +++++ changes/Compact_Multiplicity.ipynb | 66 +- changes/Test_BD_Cluster.ipynb | 1130 ++++++++++------------------ changes/bd_evo_csv/baraffe2003.csv | 1 + spisea/evolution.py | 7 +- spisea/imf/imf.py | 2 +- spisea/tests/test_synthetic.py | 45 +- 7 files changed, 676 insertions(+), 747 deletions(-) create mode 100644 changes/BD_Evolution.ipynb create mode 100644 changes/bd_evo_csv/baraffe2003.csv diff --git a/changes/BD_Evolution.ipynb b/changes/BD_Evolution.ipynb new file mode 100644 index 00000000..ef60330e --- /dev/null +++ b/changes/BD_Evolution.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cf875d01-eefb-4ab5-8bc1-9d7ff0b68e6e", + "metadata": {}, + "source": [ + "## Attempting to Describe Brown Dwarf Evolution Using Known Relations" + ] + }, + { + "cell_type": "markdown", + "id": "edb00592-f126-4ae1-ab2b-13f7094e01ee", + "metadata": {}, + "source": [ + "We are trying to find a relation between age/mass and effective temperature of brown dwarf stars. To do this, we are looking at several evolutionary .csv files laid out in the [SPLAT evolutionary model for brown dwarves program](https://github.com/aburgasser/splat/tree/main/resources/EvolutionaryModels). Namely, we will be considering:\n", + "* [Baraffe 2003](https://github.com/aburgasser/splat/blob/main/resources/EvolutionaryModels/baraffe2003.csv)\n", + "* " + ] + }, + { + "cell_type": "markdown", + "id": "79e30b98-f4d3-44df-aa44-2cb7a223c747", + "metadata": {}, + "source": [ + "#### Importing Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "910e4a29-8083-4dca-b1ac-3d574999efae", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "205f6536-9dd1-444f-a194-c28610089e60", + "metadata": {}, + "source": [ + "#### Importing .csv Files for Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "620a1840-cf87-45c9-b65f-6b6b0e803192", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " age mass luminosity temperature gravity radius\n", + "0 0.001 0.0005 -5.370 628 2.645 0.176\n", + "1 0.001 0.0010 -4.715 942 2.996 0.166\n", + "2 0.001 0.0020 -4.137 1285 3.259 0.174\n", + "3 0.001 0.0030 -3.746 1553 3.372 0.187\n", + "4 0.001 0.0040 -3.482 1747 3.438 0.200\n", + ".. ... ... ... ... ... ...\n", + "230 10.000 0.0720 -4.472 1556 5.481 0.081\n", + "231 10.000 0.0750 -3.954 1997 5.415 0.089\n", + "232 10.000 0.0800 -3.602 2322 5.353 0.099\n", + "233 10.000 0.0900 -3.274 2624 5.289 0.113\n", + "234 10.000 0.1000 -3.082 2786 5.246 0.125\n", + "\n", + "[235 rows x 6 columns]\n" + ] + } + ], + "source": [ + "baraffe_2003 = pd.read_csv('/System/Volumes/Data/mnt/g3/scratch/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv')\n", + "print(baraffe_2003)" + ] + }, + { + "cell_type": "markdown", + "id": "fec3c4e4-52b4-46e9-8599-16170dc4eb3b", + "metadata": {}, + "source": [ + "#### Plotting Mass vs. Temperature" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "634c1991-bc5f-4bf1-bd2b-7ba90acc7af0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/hf/8_ckhzzx55xc7f3s404_kn_4000131/T/ipykernel_33945/3087070154.py:12: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n", + " age_colors = plt.cm.get_cmap('viridis', len(b_ages)) # Adjust colormap range\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Filter .csv files to only include BD masses\n", + "filtered_baraffe_2003 = baraffe_2003[(baraffe_2003['mass'] >= 0.01) & (baraffe_2003['mass'] <= 0.08)]\n", + "\n", + "# Define variables\n", + "b_masses = filtered_baraffe_2003['mass']\n", + "b_teffs = filtered_baraffe_2003['temperature']\n", + "\n", + "# Look at age span\n", + "b_ages = np.unique(filtered_baraffe_2003['age'])\n", + "\n", + "# Define age-based color map\n", + "age_colors = plt.cm.get_cmap('viridis', len(b_ages)) # Adjust colormap range\n", + "\n", + "# Plot each age group separately\n", + "for i, age in enumerate(b_ages):\n", + " age_data = filtered_baraffe_2003[filtered_baraffe_2003['age'] == age]\n", + " plt.scatter(age_data['mass'], age_data['temperature'], label=f'Age {age:.2f} Myr',\n", + " c=[age_colors(i / (len(b_ages)-1))], alpha=0.7) # Normalize 'i' for colormap range\n", + "\n", + "# Add colorbar and legend\n", + "plt.colorbar(label='Age (Myr)')\n", + "plt.xlabel('Mass')\n", + "plt.ylabel('Temperature')\n", + "plt.title('Mass vs Temperature Colored by Age')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90980d47-aedc-484d-94b4-49f14ccc8b66", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index d223c0f1..983fcf1b 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 687520 stars out of mass range\n", - "Found 134795 companions out of stellar mass range\n" + "Found 686912 stars out of mass range\n", + "Found 134402 companions out of stellar mass range\n" ] } ], @@ -248,13 +248,65 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, + "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass \n", + "--------------------\n", + " 0.5747159412207477\n", + " 0.6970586660858713\n", + " 0.601165124305441\n", + "0.056083697312291306\n", + " 0.02673063039645824\n", + " 0.1862915740843192\n", + " 0.07159040923670096\n", + " ...\n", + " 0.0830220943719863\n", + " 0.09156100383230333\n", + " 0.3808927106160393\n", + "0.016698205202363158\n", + " 0.4183953590905605\n", + " 0.0758085193857225\n", + " 0.3082346627668261\n", + "Length = 391314 rows Teff \n", + "------------------\n", + " 4068.981641477723\n", + " 4492.75426907755\n", + " 4126.083901944962\n", + " nan\n", + " nan\n", + "3234.0110881285427\n", + "2808.0618989378295\n", + " ...\n", + " 2898.061169580272\n", + " 2950.46139583484\n", + "3506.1448705814614\n", + " nan\n", + " 3566.917126729317\n", + "2845.0651718291647\n", + "3418.8421815545435\n", + "Length = 391314 rows\n" + ] + } + ], + "source": [ + "print(kc_comp['mass'], kc_comp['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index 2dbd9d71..5625aa9a 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -43,536 +43,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=4.00 for T= 32651 logg=3.99\n", - "Changing to logg=4.00 for T= 32840 logg=3.98\n", - "Changing to logg=4.00 for T= 33037 logg=3.97\n", - "Changing to logg=4.00 for T= 33144 logg=3.96\n", - "Changing to logg=4.00 for T= 33205 logg=3.95\n", - "Changing to logg=4.00 for T= 33358 logg=3.94\n", - "Changing to logg=4.00 for T= 33504 logg=3.93\n", - "Changing to logg=4.00 for T= 33651 logg=3.91\n", - "Changing to logg=4.00 for T= 33713 logg=3.91\n", - "Changing to logg=4.00 for T= 33775 logg=3.90\n", - "Changing to logg=4.00 for T= 33892 logg=3.89\n", - "Changing to logg=4.00 for T= 34002 logg=3.87\n", - "Changing to logg=4.00 for T= 34111 logg=3.86\n", - "Changing to logg=4.00 for T= 34182 logg=3.85\n", - "Changing to logg=4.00 for T= 34222 logg=3.85\n", - "Changing to logg=4.00 for T= 34332 logg=3.83\n", - "Changing to logg=4.00 for T= 34443 logg=3.82\n", - "Changing to logg=4.00 for T= 34546 logg=3.81\n", - "Changing to logg=4.00 for T= 34658 logg=3.80\n", - "Changing to logg=4.00 for T= 34674 logg=3.80\n", - "Changing to logg=4.00 for T= 34746 logg=3.79\n", - "Changing to logg=4.00 for T= 34842 logg=3.78\n", - "Changing to logg=4.00 for T= 34930 logg=3.77\n", - "Changing to logg=4.00 for T= 35019 logg=3.76\n", - "Changing to logg=4.00 for T= 35108 logg=3.75\n", - "Changing to logg=4.00 for T= 35124 logg=3.74\n", - "Changing to logg=4.00 for T= 35229 logg=3.74\n", - "Changing to logg=4.00 for T= 35351 logg=3.73\n", - "Changing to logg=4.00 for T= 35465 logg=3.72\n", - "Changing to logg=4.00 for T= 35588 logg=3.71\n", - "Changing to 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"Changing to logg=5.00 for T= 50699 logg=6.04\n", - "Changing to T= 50000 for T= 50839 logg=6.04\n", - "Changing to logg=5.00 for T= 50839 logg=6.04\n", - "Changing to T= 50000 for T= 50957 logg=6.04\n", - "Changing to logg=5.00 for T= 50957 logg=6.04\n", - "Changing to T= 50000 for T= 51086 logg=6.04\n", - "Changing to logg=5.00 for T= 51086 logg=6.04\n", - "Changing to T= 50000 for T= 51215 logg=6.04\n", - "Changing to logg=5.00 for T= 51215 logg=6.04\n", - "Changing to T= 50000 for T= 51345 logg=6.04\n", - "Changing to logg=5.00 for T= 51345 logg=6.04\n", - "Changing to T= 50000 for T= 51475 logg=6.04\n", - "Changing to logg=5.00 for T= 51475 logg=6.04\n", - "Changing to T= 50000 for T= 51606 logg=6.04\n", - "Changing to logg=5.00 for T= 51606 logg=6.04\n", - "Changing to T= 50000 for T= 51737 logg=6.05\n", - "Changing to logg=5.00 for T= 51737 logg=6.05\n", - "Changing to T= 50000 for T= 51868 logg=6.05\n", - "Changing to logg=5.00 for T= 51868 logg=6.05\n", - "Changing to T= 50000 for T= 52000 logg=6.05\n", - "Changing to logg=5.00 for T= 52000 logg=6.05\n", - "Changing to T= 50000 for T= 52119 logg=6.05\n", - "Changing to logg=5.00 for T= 52119 logg=6.05\n", - "Changing to T= 50000 for T= 52240 logg=6.05\n", - "Changing to logg=5.00 for T= 52240 logg=6.05\n", - "Changing to T= 50000 for T= 52384 logg=6.05\n", - "Changing to logg=5.00 for T= 52384 logg=6.05\n", - "Changing to T= 50000 for T= 52505 logg=6.05\n", - "Changing to logg=5.00 for T= 52505 logg=6.05\n", - "Changing to T= 50000 for T= 52626 logg=6.05\n", - "Changing to logg=5.00 for T= 52626 logg=6.05\n", - "Changing to T= 50000 for T= 52747 logg=6.05\n", - "Changing to logg=5.00 for T= 52747 logg=6.05\n", - "Changing to T= 50000 for T= 52759 logg=6.05\n", - "Changing to logg=5.00 for T= 52759 logg=6.05\n", - "Changing to T= 50000 for T= 52857 logg=6.05\n", - "Changing to logg=5.00 for T= 52857 logg=6.05\n", - "Changing to T= 50000 for T= 52991 logg=6.05\n", - "Changing to logg=5.00 for T= 52991 logg=6.05\n", - "Changing to T= 50000 for T= 53125 logg=6.06\n", - "Changing to logg=5.00 for T= 53125 logg=6.06\n", - "Changing to T= 50000 for T= 53235 logg=6.06\n", - "Changing to logg=5.00 for T= 53235 logg=6.06\n", - "Changing to T= 50000 for T= 53358 logg=6.06\n", - "Changing to logg=5.00 for T= 53358 logg=6.06\n", - "Changing to T= 50000 for T= 53481 logg=6.06\n", - "Changing to logg=5.00 for T= 53481 logg=6.06\n", - "Changing to T= 50000 for T= 53592 logg=6.06\n", - "Changing to logg=5.00 for T= 53592 logg=6.06\n", - "Changing to T= 50000 for T= 53728 logg=6.06\n", - "Changing to logg=5.00 for T= 53728 logg=6.06\n", - "Changing to T= 50000 for T= 53864 logg=6.06\n", - "Changing to logg=5.00 for T= 53864 logg=6.06\n", - "Changing to T= 50000 for T= 53976 logg=6.07\n", - "Changing to logg=5.00 for T= 53976 logg=6.07\n", - "Changing to T= 50000 for T= 54100 logg=6.07\n", - "Changing to logg=5.00 for T= 54100 logg=6.07\n", - "Changing to T= 50000 for T= 54225 logg=6.07\n", - "Changing to logg=5.00 for T= 54225 logg=6.07\n", - "Changing to T= 50000 for T= 54363 logg=6.07\n", - "Changing to logg=5.00 for T= 54363 logg=6.07\n", - "Changing to T= 50000 for T= 54475 logg=6.07\n", - "Changing to logg=5.00 for T= 54475 logg=6.07\n", - "Changing to T= 50000 for T= 54588 logg=6.07\n", - "Changing to logg=5.00 for T= 54588 logg=6.07\n", - "Changing to T= 50000 for T= 54714 logg=6.08\n", - "Changing to logg=5.00 for T= 54714 logg=6.08\n", - "Changing to T= 50000 for T= 54853 logg=6.08\n", - "Changing to logg=5.00 for T= 54853 logg=6.08\n", - "Changing to T= 50000 for T= 54967 logg=6.08\n", - "Changing to logg=5.00 for T= 54967 logg=6.08\n", - "Changing to T= 50000 for T= 55093 logg=6.08\n", - "Changing to logg=5.00 for T= 55093 logg=6.08\n", - "Changing to T= 50000 for T= 55208 logg=6.08\n", - "Changing to logg=5.00 for T= 55208 logg=6.08\n", - "Changing to T= 50000 for T= 55322 logg=6.09\n", - "Changing to logg=5.00 for T= 55322 logg=6.09\n", - "Changing to T= 50000 for T= 55450 logg=6.09\n", - "Changing to logg=5.00 for T= 55450 logg=6.09\n", - "Changing to T= 50000 for T= 55590 logg=6.09\n", - "Changing to logg=5.00 for T= 55590 logg=6.09\n", - "Changing to T= 50000 for T= 55719 logg=6.09\n", - "Changing to logg=5.00 for T= 55719 logg=6.09\n", - "Changing to T= 50000 for T= 55834 logg=6.09\n", - "Changing to logg=5.00 for T= 55834 logg=6.09\n", - "Changing to T= 50000 for T= 55873 logg=6.09\n", - "Changing to logg=5.00 for T= 55873 logg=6.09\n", - "Changing to T= 50000 for T= 55963 logg=6.09\n", - "Changing to logg=5.00 for T= 55963 logg=6.09\n", - "Changing to T= 50000 for T= 56092 logg=6.10\n", - "Changing to logg=5.00 for T= 56092 logg=6.10\n", - "Changing to T= 50000 for T= 56234 logg=6.10\n", - "Changing to logg=5.00 for T= 56234 logg=6.10\n", - "Changing to T= 50000 for T= 56364 logg=6.10\n", - "Changing to logg=5.00 for T= 56364 logg=6.10\n", - "Changing to T= 50000 for T= 56507 logg=6.10\n", - "Changing to logg=5.00 for T= 56507 logg=6.10\n", - "Changing to T= 50000 for T= 56624 logg=6.11\n", - "Changing to logg=5.00 for T= 56624 logg=6.11\n", - "Changing to T= 50000 for T= 56754 logg=6.11\n", - "Changing to logg=5.00 for T= 56754 logg=6.11\n", - "Changing to T= 50000 for T= 56885 logg=6.11\n", - "Changing to logg=5.00 for T= 56885 logg=6.11\n", - "Changing to T= 50000 for T= 57030 logg=6.11\n", - "Changing to logg=5.00 for T= 57030 logg=6.11\n", - "Changing to T= 50000 for T= 57161 logg=6.11\n", - "Changing to logg=5.00 for T= 57161 logg=6.11\n", - "Changing to T= 50000 for T= 57293 logg=6.11\n", - "Changing to logg=5.00 for T= 57293 logg=6.11\n", - "Changing to T= 50000 for T= 57425 logg=6.11\n", - "Changing to logg=5.00 for T= 57425 logg=6.11\n", - "Changing to T= 50000 for T= 57570 logg=6.11\n", - "Changing to logg=5.00 for T= 57570 logg=6.11\n", - "Changing to T= 50000 for T= 57703 logg=6.11\n", - "Changing to logg=5.00 for T= 57703 logg=6.11\n", - "Changing to T= 50000 for T= 57850 logg=6.12\n", - "Changing to logg=5.00 for T= 57850 logg=6.12\n", - "Changing to T= 50000 for T= 57983 logg=6.12\n", - "Changing to logg=5.00 for T= 57983 logg=6.12\n", - "Changing to T= 50000 for T= 58117 logg=6.12\n", - "Changing to logg=5.00 for T= 58117 logg=6.12\n", - "Changing to T= 50000 for T= 58251 logg=6.13\n", - "Changing to logg=5.00 for T= 58251 logg=6.13\n", - "Changing to T= 50000 for T= 58398 logg=6.13\n", - "Changing to logg=5.00 for T= 58398 logg=6.13\n", - "Changing to T= 50000 for T= 58546 logg=6.13\n", - "Changing to logg=5.00 for T= 58546 logg=6.13\n", - "Changing to T= 50000 for T= 58695 logg=6.13\n", - "Changing to logg=5.00 for T= 58695 logg=6.13\n", - "Changing to T= 50000 for T= 58844 logg=6.14\n", - "Changing to logg=5.00 for T= 58844 logg=6.14\n", - "Changing to T= 50000 for T= 58993 logg=6.14\n", - "Changing to logg=5.00 for T= 58993 logg=6.14\n", - "Changing to T= 50000 for T= 59143 logg=6.14\n", - "Changing to logg=5.00 for T= 59143 logg=6.14\n", - "Changing to T= 50000 for T= 59293 logg=6.15\n", - "Changing to logg=5.00 for T= 59293 logg=6.15\n", - "Changing to T= 50000 for T= 59334 logg=6.15\n", - "Changing to logg=5.00 for T= 59334 logg=6.15\n", - "Changing to T= 50000 for T= 59457 logg=6.15\n", - "Changing to logg=5.00 for T= 59457 logg=6.15\n", - "Changing to T= 50000 for T= 59621 logg=6.16\n", - "Changing to logg=5.00 for T= 59621 logg=6.16\n", - "Changing to T= 50000 for T= 59800 logg=6.16\n", - "Changing to logg=5.00 for T= 59800 logg=6.16\n", - "Changing to T= 50000 for T= 59965 logg=6.16\n", - "Changing to logg=5.00 for T= 59965 logg=6.16\n", - "Changing to T= 50000 for T= 60159 logg=6.17\n", - "Changing to logg=5.00 for T= 60159 logg=6.17\n", - "Changing to T= 50000 for T= 60353 logg=6.17\n", - "Changing to logg=5.00 for T= 60353 logg=6.17\n", - "Changing to T= 50000 for T= 60534 logg=6.18\n", - "Changing to logg=5.00 for T= 60534 logg=6.18\n", - "Changing to T= 50000 for T= 60758 logg=6.18\n", - "Changing to logg=5.00 for T= 60758 logg=6.18\n", - "Changing to T= 50000 for T= 60968 logg=6.18\n", - "Changing to logg=5.00 for T= 60968 logg=6.18\n", - "Changing to T= 50000 for T= 61235 logg=6.19\n", - "Changing to logg=5.00 for T= 61235 logg=6.19\n", - "Changing to T= 50000 for T= 61504 logg=6.20\n", - "Changing to logg=5.00 for T= 61504 logg=6.20\n", - "Changing to T= 50000 for T= 61844 logg=6.21\n", - "Changing to logg=5.00 for T= 61844 logg=6.21\n", - "Changing to T= 50000 for T= 62287 logg=6.23\n", - "Changing to logg=5.00 for T= 62287 logg=6.23\n", - "Changing to T= 50000 for T= 63038 logg=6.27\n", - "Changing to logg=5.00 for T= 63038 logg=6.27\n", - "Changing to T= 50000 for T= 64239 logg=6.32\n", - "Changing to logg=5.00 for T= 64239 logg=6.32\n", - "Changing to T= 50000 for T= 65464 logg=6.37\n", - "Changing to logg=5.00 for T= 65464 logg=6.37\n", - "Changing to T= 50000 for T= 66696 logg=6.42\n", - "Changing to logg=5.00 for T= 66696 logg=6.42\n", - "Changing to T= 50000 for T= 67967 logg=6.47\n", - "Changing to logg=5.00 for T= 67967 logg=6.47\n", - "Changing to T= 50000 for T= 69263 logg=6.53\n", - "Changing to logg=5.00 for T= 69263 logg=6.53\n", - "Changing to T= 50000 for T= 71367 logg=6.63\n", - "Changing to logg=5.00 for T= 71367 logg=6.63\n", - "Isochrone generation took 150.174449 s.\n", - "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", - " Starting at: 2024-07-10 12:22:04.577984 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", - "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", - "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", - "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", - "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", - "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", - "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", - "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", - "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", - "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", - "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", - "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", - "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", - "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", - "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", - "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", - "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", - "Starting filter: wfc3,ir,f139m Elapsed time: 44.06 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", - "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", - "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", - "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", - "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", - "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", - "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", - "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", - "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", - "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", - "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", - "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", - "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", - "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", - "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", - "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", - "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 90.13 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", - "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", - "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", - "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", - "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", - "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", - "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", - "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", - "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", - "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", - "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", - "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", - "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", - "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", - "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", - "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", - "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", - " Time taken: 137.05 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Define isochrone parameters\n", "logAge = np.log10(5*10**6.) # Age in log(years)\n", @@ -607,35 +81,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5a6e081f-84d3-4efb-a6d5-59d5f60d845f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " L Teff ... m_hst_f153m \n", - " W K ... \n", - "---------------------- ------------------ ... ------------------\n", - " 6.591312905046435e+24 2928.195035574271 ... 21.454394826602254\n", - " 7.13135291244921e+24 2943.7437337117412 ... 21.3665241093368\n", - " 7.761969450327467e+24 2958.6936525140045 ... 21.272757288262653\n", - " 8.296060854703104e+24 2975.089258880875 ... 21.19905370697203\n", - " 8.830228238420424e+24 2992.264636608189 ... 21.13035092375595\n", - " 8.860779496066351e+24 3009.539168873201 ... 21.12685391099373\n", - " ... ... ... ...\n", - "2.1036424651269878e+32 63037.64788294872 ... 11.22232056398674\n", - "2.1760653272980036e+32 64239.1816824387 ... 11.267570563986745\n", - " 2.250981517613893e+32 65463.61740672747 ... 11.312820563986737\n", - "2.3279407848949313e+32 66696.03248243559 ... 11.357320563986734\n", - "2.4080856466771184e+32 67967.29719504561 ... 11.40257056398674\n", - "2.4909896846856737e+32 69262.79294373626 ... 11.447820563986737\n", - "2.6240489308607902e+32 71367.42051224748 ... 11.521320563986743\n", - "Length = 1605 rows\n" - ] - } - ], + "outputs": [], "source": [ "# The stars in the isochrone and associated properties \n", "# are stored in an astropy table called \"points\" \n", @@ -645,18 +94,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "1205c450-d7f2-4ad7-86b0-006762426d5c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 M_sun: F127M = 19.051 mag, F139M = 18.453 mag, F153M = 17.785 mag\n" - ] - } - ], + "outputs": [], "source": [ "# Example case:\n", "# Identify a 1 M_sun star, print F127M, F139M, and F153M mags\n", @@ -669,31 +110,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a8169db9-486e-4d20-9a09-dfcd7ebbd3e1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Make a color-magnitude diagram from the isochrone\n", "py.figure(1, figsize=(10,10))\n", @@ -718,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "493b8648-1c3b-4aab-858f-2f955195e634", "metadata": {}, "outputs": [], @@ -747,19 +167,10 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "0bf0fed8-2b70-41be-b950-83f1362af9f0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 60499 stars out of mass range\n", - "Found 11203 companions out of stellar mass range\n" - ] - } - ], + "outputs": [], "source": [ "# Define total cluster mass\n", "mass = 10**5.\n", @@ -770,35 +181,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "c8de1a27-0b07-404c-a13d-8434e204017d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "------------------- ---------- ... ------------------ ------------\n", - "0.31585425987928323 True ... 18.92999676208373 2\n", - "0.11445929647600789 False ... 20.934530625744358 0\n", - " 0.645145955409843 False ... 18.42659654946194 0\n", - " 0.2125430430785249 False ... 20.08717879087111 0\n", - " 0.4443648340365779 False ... 18.982532572754547 0\n", - " 0.9427790018124477 True ... 17.5522451444909 1\n", - "0.20316227789117086 False ... 20.155331870086147 0\n", - " ... ... ... ... ...\n", - "0.07822412513422393 False ... 21.306060916420513 0\n", - "0.17640075706320751 False ... 20.342815617558465 0\n", - "0.09652737394053626 False ... 21.126211979448616 0\n", - " 0.1622394758046402 False ... 20.45232101696704 0\n", - "0.39015806512343487 False ... 19.2083064020004 0\n", - "0.15023739165143188 False ... 20.558755046061993 0\n", - "0.24854036705122345 False ... 19.857602142038818 0\n", - "Length = 123017 rows\n" - ] - } - ], + "outputs": [], "source": [ "# Look at star systems table\n", "print(cluster.star_systems)" @@ -806,35 +192,10 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "6b10a958-a0f8-443c-be6b-e07c5de2d4dc", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "system_idx mass ... m_hst_f139m m_hst_f153m \n", - "---------- ------------------- ... ------------------ ------------------\n", - " 0 0.0460428924123163 ... nan nan\n", - " 0 0.24553283737800713 ... 20.536185197536078 19.87454892207238\n", - " 5 0.4308071906008011 ... 19.720217136851623 19.036277872397545\n", - " 8 0.05926443486987895 ... nan nan\n", - " 10 0.37939808636570665 ... 19.922230480262495 19.247518348082274\n", - " 15 0.12172477389722892 ... 21.49272357482301 20.84357767155626\n", - " 16 0.4299286896336996 ... 19.72390856044099 19.040131407955787\n", - " ... ... ... ... ...\n", - " 122997 0.24062971315528156 ... 20.56327953828116 19.90217696791337\n", - " 122998 0.44959030611032147 ... 19.653329536810013 18.96646512002086\n", - " 123000 0.3746449017452262 ... 19.94002433187758 19.265866413907457\n", - " 123000 0.3396256601362069 ... 20.078397682202386 19.40756594570426\n", - " 123006 0.08879348065800587 ... 21.800450628386503 21.146929171115758\n", - " 123006 0.06195810103955034 ... nan nan\n", - " 123006 0.04465994487393095 ... nan nan\n", - "Length = 34087 rows\n" - ] - } - ], + "outputs": [], "source": [ "# The companions table is accessed in a similar way\n", "print(cluster.companions)" @@ -842,31 +203,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "095a963e-c0de-4bbc-a03f-471f3cdb7b91", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Look at the cluster CMD, compared to input isochrone. Note the impact of\n", "# multiple systems on the photometry\n", @@ -887,21 +227,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "d694ed41-c732-4093-9f0a-ad021fe8397a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "minimum primary mass: 0.07000163959328269\n", - "minimum companion mass: 0.01000292189962548\n", - "maximum primary mass: 55.80384055631119\n", - "maximum companion mass: 44.92501458090958\n" - ] - } - ], + "outputs": [], "source": [ "print(\"minimum primary mass: \" + str(np.min(clust['mass'])))\n", "print(\"minimum companion mass: \" + str(np.min(cluster.companions['mass'])))\n", @@ -914,12 +243,12 @@ "id": "fa4c660d-ea4e-491a-95db-278617ee1757", "metadata": {}, "source": [ - "#### Creating a histogram to compare Weidner Kroupa and Muzic" + "### Creating a histogram to compare Weidner Kroupa and Muzic" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "c2540680-d69f-427f-b0d1-55130826ad8a", "metadata": {}, "outputs": [], @@ -931,66 +260,215 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "4d1c647e-8e1a-4a24-8b20-26a8b14f4660", "metadata": {}, + "outputs": [], + "source": [ + "# Define total cluster mass\n", + "mass = 10**5.\n", + "\n", + "# Make cluster objects for each\n", + "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", + "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" + ] + }, + { + "cell_type": "markdown", + "id": "f55b6d0a-ecc2-4bbb-aa5b-d8f23acfe441", + "metadata": {}, + "source": [ + "#### Comparing Weidner Kroupa and Muzic clusters data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", + "metadata": {}, + "outputs": [], + "source": [ + "#show total numbers for each cluster\n", + "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", + "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", + "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", + "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b2656bf-6a7e-4ca5-ad09-6ae59e8c6d0d", + "metadata": {}, + "outputs": [], + "source": [ + "#Comparing max/min primary/companion masses\n", + "#Weidner Kroupa\n", + "print('\\033[1m' + \"Weidner Kroupa\" + '\\033[0m')\n", + "print(\"minimum primary mass: \" + str(np.min(w_cluster.star_systems['mass'])))\n", + "print(\"minimum companion mass: \" + str(np.min(w_cluster.companions['mass'])))\n", + "print(\"maximum primary mass: \" + str(np.max(w_cluster.star_systems['mass'])))\n", + "print(\"maximum companion mass: \" + str(np.max(w_cluster.companions['mass'])))\n", + "print('\\n')\n", + "\n", + "#Muzic\n", + "print('\\033[1m' + \"Muzic\" + '\\033[0m')\n", + "print(\"minimum primary mass: \" + str(np.min(m_cluster.star_systems['mass'])))\n", + "print(\"minimum companion mass: \" + str(np.min(m_cluster.companions['mass'])))\n", + "print(\"maximum primary mass: \" + str(np.max(m_cluster.star_systems['mass'])))\n", + "print(\"maximum companion mass: \" + str(np.max(m_cluster.companions['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17d1685d-0f63-4f6e-ad62-4ba3a03f0780", + "metadata": {}, + "outputs": [], + "source": [ + "#compare total cluster masses\n", + "#create combined arrays of primary/companion masses\n", + "all_m_masses = np.concatenate((m_cluster.star_systems['mass'], m_cluster.companions['mass']))\n", + "all_w_masses = np.concatenate((w_cluster.star_systems['mass'], w_cluster.companions['mass']))\n", + "\n", + "#find total of added masses\n", + "print(\"total mass of WD cluster: \" + str(np.sum(all_w_masses)))\n", + "print(\"total mass of M cluster: \" + str(np.sum(all_m_masses)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", + "metadata": {}, + "outputs": [], + "source": [ + "#define mass range of histogram\n", + "bin_edges = [0.01, 0.08, 0.5, 1, 2]\n", + "\n", + "#setting up subplots\n", + "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8,8))\n", + "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "\n", + "#create histograms to compare\n", + "counts_m, edges_m, _ = ax[0].hist(all_m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", + "counts_w, edges_w, _ = ax[1].hist(all_w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", + "\n", + "#compute bin centers\n", + "bin_centers_m = (edges_m[:-1] + edges_m[1:]) / 2\n", + "bin_centers_w = (edges_w[:-1] + edges_w[1:]) / 2\n", + "\n", + "#generate x values for the broken power law curve\n", + "x_fit_m = np.linspace(bin_centers_m.min(), bin_centers_m.max(), 100)\n", + "x_fit_w = np.linspace(bin_centers_w.min(), bin_centers_w.max(), 100)\n", + "\n", + "\n", + "\n", + "#labels and legend\n", + "plt.xlabel('Stellar Mass (Solar Masses)')\n", + "plt.ylabel('Number of Stars')\n", + "plt.title('Comparison of Stellar Mass Distributions')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04216fed-7f8b-4249-9c6c-29c82cc18bce", + "metadata": {}, + "source": [ + "## Generating a cluster with compound objects (using an IFMR)\n", + "This is before code is modified to allow for brown dwarves" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "25b1247a-0eb8-40bc-bf56-f89e788283cd", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Found 58485 stars out of mass range\n", - "Found 10731 companions out of stellar mass range\n", - "Found 256 stars out of mass range\n", - "Found 83 companions out of stellar mass range\n" + "Isochrone generation took 96.182577 s.\n", + "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-07-15 14:29:26.015063 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", + "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", + "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", + "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", + "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", + "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", + "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", + "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", + "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", + "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", + "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", + " Time taken: 31.54 seconds\n" ] } ], "source": [ - "# Define total cluster mass\n", - "mass = 10**5.\n", - "\n", - "# Make cluster objects for each\n", - "w_cluster = synthetic.ResolvedCluster(my_iso, w_imf, mass)\n", - "m_cluster = synthetic.ResolvedCluster(my_iso, m_imf, mass)" + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" ] }, { "cell_type": "code", - "execution_count": 40, - "id": "eb3847e7-c184-4d49-a7ec-69d0a1e50fea", + "execution_count": 9, + "id": "01f6f94c-1b23-4ae1-bfc2-96e2d4cacd27", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "#imf_multi = multiplicity.MultiplicityUnresolved()\n", + "massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf])\n", + "powers = np.array([-0.3, -1.3, -2.3, -2.35])\n", + "k_imf = imf.IMF_broken_powerlaw(massLimits, powers)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1a914c4c-ac7e-4c61-9e40-1b0d5dd5568f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "primary masses in Weidner Kroupa cluster: 119280\n", - "companion masses in Weidner Kroupa cluster: 32991\n", - "primary masses in Muzic cluster: 1113\n", - "companion masses in Muzic cluster: 682\n" + "Found 829408 stars out of mass range\n" ] } ], "source": [ - "#show total numbers for each cluster\n", - "print(\"primary masses in Weidner Kroupa cluster: \" + str(len(w_cluster.star_systems)))\n", - "print(\"companion masses in Weidner Kroupa cluster: \" + str(len(w_cluster.companions)))\n", - "print(\"primary masses in Muzic cluster: \" + str(len(m_cluster.star_systems)))\n", - "print(\"companion masses in Muzic cluster: \" + str(len(m_cluster.companions)))" + "# Make clusters\n", + "cluster_mass = 10**6\n", + "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "k_out = k_cluster.star_systems\n", + "#k_comp = k_cluster.companions" ] }, { "cell_type": "code", - "execution_count": 51, - "id": "4e2fe19b-9da8-4388-92bd-c1390444e8c4", + "execution_count": 11, + "id": "62293dea-989b-4870-a77b-ba677010da81", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -998,35 +476,213 @@ } ], "source": [ - "#define masses\n", - "w_masses = w_cluster.star_systems['mass']\n", - "w_comps = w_cluster.companions['mass']\n", - "m_masses = m_cluster.star_systems['mass']\n", - "m_comps = m_cluster.companions['mass']\n", - "\n", - "#define mass range of histogram\n", - "bin_edges = [0.01, 0.08, 0.5, 1, 60]\n", + "# Locate BHs, NSs and WDs\n", + "p_bh = np.where(k_out['phase'] == 103)[0]\n", + "#c_bh = np.where(k_comp['phase'] == 103)[0]\n", + "#k_bh = np.concatenate([p_bh, c_bh])\n", + "p_ns = np.where(k_out['phase'] == 102)[0]\n", + "#c_ns = np.where(k_comp['phase'] == 102)[0]\n", + "#k_ns = np.concatenate([p_ns, c_ns])\n", + "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "#c_wd = np.where(k_comp['phase'] == 101)[0]\n", + "#k_wd = np.concatenate([p_wd, c_wd])\n", "\n", - "#setting up subplots\n", - "fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8,8))\n", - "plt.subplots_adjust(hspace=0.5, wspace=0.75)\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", "\n", - "#create histograms to compare\n", - "ax[0].hist(m_masses, bins=bin_edges, alpha=0.5, label='Muzic_2017', color='blue', edgecolor='black', histtype='bar')\n", - "ax[1].hist(w_masses, bins=bin_edges, alpha=0.5, label='Weidner_Kroupa_2004', color='red', edgecolor='black', histtype='bar')\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", "\n", - "#labels and legend\n", - "plt.xlabel('Stellar Mass (Solar Masses)')\n", - "plt.ylabel('Number of Stars')\n", - "plt.title('Comparison of Stellar Mass Distributions')\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", "plt.legend()\n", + "\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d36bba0d-3037-46c0-91f3-321e47241204", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "973669.0207942065\n" + ] + } + ], + "source": [ + "all_masses = np.concatenate([k_out['mass'], k_comp['mass']])\n", + "tot_mass = np.sum(all_masses)\n", + "print(tot_mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1f999373-557e-42f1-9c0b-43d1cfab198e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.07000000391702135\n", + "0.010000022844115403\n" + ] + } + ], + "source": [ + "print(np.min(k_out['mass']))\n", + "print(np.min(k_comp['mass']))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7c2fa5e-49cd-4021-869a-6f9701677c68", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Current smallest mass of generated black holes: \" + str(np.min(k_out[k_bh]['mass_current'])))\n", + "print(\"Current largest mass of generated black holes: \" + str(np.max(k_out[k_bh]['mass_current'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e056d1f-be87-43ee-9899-2a1859d5e2c5", + "metadata": {}, + "outputs": [], + "source": [ + "print('The cluster table contains these columns: {0}'.format(k_cluster.star_systems.keys()))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c02d7dbd-9690-49f4-931d-e7f66172ba12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass ... metallicity m_hst_f153m\n", + "------------------ ---------- ------------------ ... ----------- -----------\n", + " 59.05436159479683 False 59.05436159479683 ... 0.0 nan\n", + " 39.55979231152168 False 39.55979231152168 ... 0.0 nan\n", + "18.241030351057457 False 18.241030351057457 ... 0.0 nan\n", + " 21.34571632826156 False 21.34571632826156 ... 0.0 nan\n", + " 25.73229556345863 False 25.73229556345863 ... 0.0 nan\n", + " 38.27638428342816 False 38.27638428342816 ... 0.0 nan\n", + " 29.70200219305832 False 29.70200219305832 ... 0.0 nan\n", + " ... ... ... ... ... ...\n", + "104.95164061880804 False 104.95164061880804 ... 0.0 nan\n", + "19.927108168048903 False 19.927108168048903 ... 0.0 nan\n", + "51.756416056664165 False 51.756416056664165 ... 0.0 nan\n", + " 30.9405328260462 False 30.9405328260462 ... 0.0 nan\n", + "16.452556081467357 False 16.452556081467357 ... 0.0 nan\n", + "15.253386148271723 False 15.253386148271723 ... 0.0 nan\n", + " 34.00333629883476 False 34.00333629883476 ... 0.0 nan\n", + "Length = 2327 rows\n" + ] + } + ], + "source": [ + "print(k_out[p_bh])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ddc351e5-e465-4f2e-9703-cd5c819a75f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass \n", + "------------------\n", + " 59.05436159479683\n", + " 39.55979231152168\n", + "18.241030351057457\n", + " 21.34571632826156\n", + " 25.73229556345863\n", + " 38.27638428342816\n", + " 29.70200219305832\n", + " ...\n", + "104.95164061880804\n", + "19.927108168048903\n", + "51.756416056664165\n", + " 30.9405328260462\n", + "16.452556081467357\n", + "15.253386148271723\n", + " 34.00333629883476\n", + "Length = 2327 rows mass_current \n", + "------------------\n", + " 7.69101697945427\n", + " 14.37645768969197\n", + " 6.490426115488822\n", + " 7.738537786899665\n", + " 9.302666353833182\n", + "13.811546507105783\n", + "10.635261248769924\n", + " ...\n", + "5.9065928872088955\n", + " 7.187073433524934\n", + " 9.426130388477304\n", + "11.054167247370941\n", + " 5.693090725172411\n", + " 5.119608907641256\n", + " 12.13042109233094\n", + "Length = 2327 rows\n" + ] + } + ], + "source": [ + "print(k_out[p_bh]['mass'], k_out[p_bh]['mass_current'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f5ce39b9-2c6d-4e7c-a6f6-ffd7235ac9e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated black hole: 15.003458139141888\n", + "Initial mass of largest generated black hole: 119.60497046926774\n" + ] + } + ], + "source": [ + "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", + "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "51924315-a841-4f2d-bddb-84a1641b8d82", + "id": "1c41b219-7a85-4519-af99-77145daaa418", "metadata": {}, "outputs": [], "source": [] diff --git a/changes/bd_evo_csv/baraffe2003.csv b/changes/bd_evo_csv/baraffe2003.csv new file mode 100644 index 00000000..94cb6209 --- /dev/null +++ b/changes/bd_evo_csv/baraffe2003.csv @@ -0,0 +1 @@ +age,mass,luminosity,temperature,gravity,radius 0.001,0.0005,-5.37,628,2.645,0.176 0.001,0.001,-4.715,942,2.996,0.166 0.001,0.002,-4.137,1285,3.259,0.174 0.001,0.003,-3.746,1553,3.372,0.187 0.001,0.004,-3.482,1747,3.438,0.2 0.001,0.005,-3.273,1901,3.472,0.215 0.001,0.006,-3.129,2004,3.499,0.228 0.001,0.007,-2.998,2098,3.515,0.242 0.001,0.008,-2.893,2159,3.517,0.258 0.001,0.009,-2.811,2207,3.525,0.271 0.001,0.01,-2.735,2251,3.529,0.285 0.001,0.012,-2.615,2321,3.541,0.307 0.001,0.015,-2.455,2400,3.537,0.345 0.001,0.02,-2.28,2484,3.546,0.395 0.001,0.03,-2.174,2598,3.694,0.408 0.001,0.04,-1.939,2746,3.681,0.478 0.001,0.05,-1.637,2768,3.489,0.666 0.001,0.06,-1.604,2824,3.57,0.665 0.001,0.07,-1.478,2853,3.529,0.753 0.001,0.072,-1.455,2858,3.52,0.771 0.001,0.075,-1.389,2833,3.457,0.847 0.001,0.08,-1.373,2869,3.492,0.84 0.001,0.09,-1.289,2867,3.457,0.927 0.001,0.1,-1.187,2856,3.394,1.051 0.005,0.0005,-6.079,455,2.794,0.148 0.005,0.001,-5.522,644,3.141,0.141 0.005,0.002,-4.92,901,3.425,0.143 0.005,0.003,-4.552,1098,3.576,0.148 0.005,0.004,-4.283,1263,3.675,0.152 0.005,0.005,-4.061,1413,3.745,0.157 0.005,0.006,-3.886,1543,3.802,0.161 0.005,0.007,-3.725,1668,3.843,0.166 0.005,0.008,-3.579,1786,3.874,0.171 0.005,0.009,-3.46,1885,3.9,0.176 0.005,0.01,-3.363,1965,3.921,0.181 0.005,0.012,-3.194,2102,3.948,0.192 0.005,0.015,-2.981,2264,3.96,0.212 0.005,0.02,-2.661,2452,3.905,0.261 0.005,0.03,-2.262,2616,3.795,0.363 0.005,0.04,-2.068,2754,3.814,0.41 0.005,0.05,-1.846,2806,3.722,0.51 0.005,0.06,-1.902,2875,3.899,0.455 0.005,0.07,-1.829,2916,3.917,0.481 0.005,0.072,-1.814,2921,3.918,0.488 0.005,0.075,-1.716,2912,3.832,0.55 0.005,0.08,-1.76,2946,3.925,0.51 0.005,0.09,-1.696,2974,3.928,0.54 0.005,0.1,-1.582,2983,3.865,0.611 0.01,0.0005,-6.38,394,2.847,0.14 0.01,0.001,-5.87,540,3.184,0.134 0.01,0.002,-5.298,746,3.474,0.136 0.01,0.003,-4.914,920,3.631,0.139 0.01,0.004,-4.645,1064,3.739,0.141 0.01,0.005,-4.429,1193,3.82,0.144 0.01,0.006,-4.244,1316,3.883,0.147 0.01,0.007,-4.091,1426,3.937,0.149 0.01,0.008,-3.945,1536,3.978,0.152 0.01,0.009,-3.82,1635,4.013,0.155 0.01,0.01,-3.704,1731,4.042,0.158 0.01,0.012,-3.488,1915,4.081,0.165 0.01,0.015,-3.125,2188,4.046,0.192 0.01,0.02,-2.709,2442,3.945,0.249 0.01,0.03,-2.435,2622,3.971,0.296 0.01,0.04,-2.411,2734,4.145,0.28 0.01,0.05,-2.233,2821,4.118,0.323 0.01,0.06,-2.188,2871,4.184,0.328 0.01,0.07,-2.099,2919,4.189,0.352 0.01,0.072,-2.083,2927,4.19,0.357 0.01,0.075,-2.022,2942,4.156,0.379 0.01,0.08,-2.016,2958,4.188,0.377 0.01,0.09,-1.943,2993,4.186,0.401 0.01,0.1,-1.854,3024,4.161,0.435 0.05,0.0005,-7.069,285,2.975,0.12 0.05,0.001,-6.587,375,3.267,0.122 0.05,0.002,-6.1,491,3.551,0.124 0.05,0.003,-5.789,585,3.719,0.125 0.05,0.004,-5.531,676,3.837,0.126 0.05,0.005,-5.336,756,3.933,0.126 0.05,0.006,-5.152,840,4.012,0.126 0.05,0.007,-4.978,928,4.078,0.127 0.05,0.008,-4.832,1010,4.136,0.127 0.05,0.009,-4.706,1085,4.187,0.127 0.05,0.01,-4.569,1171,4.228,0.127 0.05,0.012,-4.052,1520,4.243,0.137 0.05,0.015,-3.568,1903,4.246,0.153 0.05,0.02,-3.642,1909,4.45,0.139 0.05,0.03,-3.311,2246,4.578,0.147 0.05,0.04,-3.08,2480,4.644,0.158 0.05,0.05,-2.884,2651,4.661,0.173 0.05,0.06,-2.755,2759,4.68,0.185 0.05,0.07,-2.647,2837,4.688,0.198 0.05,0.072,-2.623,2852,4.685,0.202 0.05,0.075,-2.591,2872,4.683,0.206 0.05,0.08,-2.551,2901,4.689,0.212 0.05,0.09,-2.472,2948,4.689,0.225 0.05,0.1,-2.397,2989,4.683,0.238 0.1,0.0005,-7.418,240,3.02,0.114 0.1,0.001,-6.957,309,3.3,0.117 0.1,0.002,-6.383,425,3.58,0.12 0.1,0.003,-6.112,493,3.746,0.121 0.1,0.004,-5.88,563,3.869,0.122 0.1,0.005,-5.686,630,3.965,0.122 0.1,0.006,-5.534,688,4.048,0.121 0.1,0.007,-5.365,760,4.117,0.121 0.1,0.008,-5.246,816,4.18,0.12 0.1,0.009,-5.103,886,4.232,0.12 0.1,0.01,-4.978,953,4.279,0.12 0.1,0.012,-4.332,1335,4.297,0.129 0.1,0.015,-4.281,1399,4.424,0.124 0.1,0.02,-4.11,1561,4.569,0.122 0.1,0.03,-3.668,1979,4.715,0.126 0.1,0.04,-3.386,2270,4.797,0.132 0.1,0.05,-3.167,2493,4.837,0.141 0.1,0.06,-3.008,2648,4.863,0.15 0.1,0.07,-2.879,2762,4.874,0.16 0.1,0.072,-2.856,2782,4.875,0.162 0.1,0.075,-2.821,2809,4.875,0.166 0.1,0.08,-2.776,2846,4.88,0.17 0.1,0.09,-2.689,2910,4.884,0.18 0.1,0.1,-2.617,2960,4.887,0.189 0.12,0.0005,-7.526,227,3.032,0.113 0.12,0.001,-7.043,295,3.307,0.116 0.12,0.002,-6.481,403,3.588,0.119 0.12,0.003,-6.183,475,3.753,0.12 0.12,0.004,-5.97,537,3.876,0.121 0.12,0.005,-5.781,599,3.974,0.121 0.12,0.006,-5.602,665,4.055,0.12 0.12,0.007,-5.477,716,4.127,0.12 0.12,0.008,-5.326,783,4.188,0.119 0.12,0.009,-5.219,835,4.243,0.119 0.12,0.01,-5.088,900,4.29,0.119 0.12,0.012,-4.432,1273,4.314,0.126 0.12,0.015,-4.436,1298,4.45,0.121 0.12,0.02,-4.218,1484,4.59,0.119 0.12,0.03,-3.764,1905,4.745,0.122 0.12,0.04,-3.472,2204,4.831,0.127 0.12,0.05,-3.244,2441,4.878,0.135 0.12,0.06,-3.083,2606,4.909,0.142 0.12,0.07,-2.944,2733,4.921,0.152 0.12,0.072,-2.922,2753,4.923,0.153 0.12,0.075,-2.886,2783,4.923,0.157 0.12,0.08,-2.838,2824,4.929,0.161 0.12,0.09,-2.753,2894,4.937,0.169 0.12,0.1,-2.673,2949,4.936,0.178 0.5,0.0005,-8.415,141,3.097,0.105 0.5,0.001,-7.753,203,3.365,0.109 0.5,0.002,-7.218,272,3.639,0.112 0.5,0.003,-6.913,322,3.805,0.113 0.5,0.004,-6.67,370,3.928,0.114 0.5,0.005,-6.496,409,4.027,0.113 0.5,0.006,-6.34,449,4.112,0.113 0.5,0.007,-6.2,488,4.185,0.112 0.5,0.008,-6.08,525,4.249,0.111 0.5,0.009,-5.963,564,4.307,0.11 0.5,0.01,-5.864,599,4.358,0.11 0.5,0.012,-5.447,759,4.432,0.11 0.5,0.015,-5.404,791,4.557,0.107 0.5,0.02,-5.133,936,4.704,0.104 0.5,0.03,-4.636,1264,4.905,0.101 0.5,0.04,-4.255,1583,5.04,0.1 0.5,0.05,-3.955,1875,5.131,0.101 0.5,0.06,-3.729,2116,5.194,0.102 0.5,0.07,-3.534,2329,5.233,0.106 0.5,0.072,-3.498,2369,5.238,0.107 0.5,0.075,-3.445,2426,5.244,0.108 0.5,0.08,-3.356,2518,5.248,0.111 0.5,0.09,-3.189,2680,5.241,0.119 0.5,0.1,-3.047,2804,5.223,0.128 1,0.0005,-8.851,111,3.115,0.102 1,0.001,-8.185,160,3.386,0.106 1,0.002,-7.56,226,3.662,0.109 1,0.003,-7.244,270,3.827,0.111 1,0.004,-7.031,304,3.95,0.111 1,0.005,-6.831,342,4.051,0.11 1,0.006,-6.664,377,4.134,0.11 1,0.007,-6.556,403,4.208,0.109 1,0.008,-6.417,438,4.272,0.108 1,0.009,-6.325,464,4.331,0.107 1,0.01,-6.235,491,4.383,0.107 1,0.012,-5.955,578,4.467,0.106 1,0.015,-5.835,628,4.587,0.103 1,0.02,-5.514,766,4.736,0.1 1,0.03,-5.071,1009,4.948,0.096 1,0.04,-4.696,1271,5.099,0.093 1,0.05,-4.374,1543,5.211,0.092 1,0.06,-4.106,1801,5.291,0.092 1,0.07,-3.829,2082,5.333,0.094 1,0.072,-3.772,2140,5.336,0.095 1,0.075,-3.679,2234,5.334,0.098 1,0.08,-3.527,2383,5.323,0.102 1,0.09,-3.268,2627,5.285,0.113 1,0.1,-3.083,2784,5.246,0.125 5,0.002,-8.57,129,3.698,0.105 5,0.003,-8.166,162,3.868,0.105 5,0.004,-7.867,193,3.994,0.105 5,0.005,-7.644,220,4.095,0.105 5,0.006,-7.469,244,4.18,0.104 5,0.007,-7.328,265,4.254,0.103 5,0.008,-7.217,284,4.318,0.103 5,0.009,-7.124,301,4.376,0.102 5,0.01,-7.015,322,4.429,0.101 5,0.012,-6.823,361,4.519,0.1 5,0.015,-6.671,399,4.634,0.098 5,0.02,-6.401,473,4.786,0.095 5,0.03,-6.008,610,5.011,0.09 5,0.04,-5.67,760,5.179,0.085 5,0.05,-5.353,931,5.313,0.082 5,0.06,-5.058,1120,5.418,0.079 5,0.07,-4.504,1524,5.466,0.081 5,0.072,-4.278,1712,5.453,0.083 5,0.075,-3.942,2006,5.411,0.089 5,0.08,-3.603,2320,5.353,0.099 5,0.09,-3.275,2622,5.289,0.113 5,0.1,-3.083,2785,5.247,0.125 10,0.003,-8.629,125,3.879,0.104 10,0.004,-8.325,149,4.006,0.104 10,0.005,-8.087,172,4.109,0.103 10,0.006,-7.888,193,4.195,0.102 10,0.007,-7.724,213,4.27,0.102 10,0.008,-7.584,232,4.335,0.101 10,0.009,-7.469,249,4.393,0.1 10,0.01,-7.368,265,4.445,0.099 10,0.012,-7.204,293,4.536,0.098 10,0.015,-7.016,330,4.65,0.096 10,0.02,-6.759,389,4.802,0.093 10,0.03,-6.358,504,5.029,0.088 10,0.04,-6.004,634,5.2,0.083 10,0.05,-5.695,776,5.338,0.079 10,0.06,-5.393,941,5.45,0.076 10,0.07,-4.832,1289,5.503,0.078 10,0.072,-4.472,1556,5.481,0.081 10,0.075,-3.954,1997,5.415,0.089 10,0.08,-3.602,2322,5.353,0.099 10,0.09,-3.274,2624,5.289,0.113 10,0.1,-3.082,2786,5.246,0.125 \ No newline at end of file diff --git a/spisea/evolution.py b/spisea/evolution.py index 93c494cc..e27b0147 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1215,7 +1215,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): For logAge < 7.4: - + * 0.01 - 0.08 M_sun: assume mass does not change over time * 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) @@ -1310,6 +1310,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): bd_idx = iso['mass'] < 0.08 iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + # Handling NaN effectvie temperatures + nan_teff_idx = np.isnan(iso['logT']) + if np.any(nan_teff_idx): + iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index c7e55056..a8019fb7 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -684,7 +684,7 @@ class Weidner_Kroupa_2004(IMF_broken_powerlaw): Mass range is 0.01 M_sun - inf M_sun. """ def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf]) + massLimits = np.array([0.01, 0.08, 0.5, 1, 120]) powers = np.array([-0.3, -1.3, -2.3, -2.35]) IMF_broken_powerlaw.__init__(self, massLimits, powers, diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index bdd9064d..84d410cd 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -485,7 +485,50 @@ def test_ifmr_multiplicity(): idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 101) & (clust1['phase'] != 9) ) idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 101) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 - + + # Ensure no substellar mass compact objects are generated + # For cluster objects + MIN_MASS = 0.1 + BD_MIN_MASS = 0.01 + BD_MAX_MASS = 0.08 + + wd_idx = np.where(clust1['phase'] == 101) + ns_idx = np.where(clust1['phase'] == 102) + bh_idx = np.where(clust1['phase'] == 103) + bd_idx = np.where(clust1['phase'] == 99) + assert np.all(clust1['mass'][wd_idx] > MIN_MASS) + assert np.all(clust1['mass'][ns_idx] > MIN_MASS) + assert np.all(clust1['mass'][bh_idx] > MIN_MASS) + assert np.all(clust1['mass'][bd_idx] < MIN_MASS) + + # For companion objects + comp_wd_idx = np.where(comps2['phase'] == 101) + comp_ns_idx = np.where(comps2['phase'] == 102) + comp_bh_idx = np.where(comps2['phase'] == 103) + comp_bd_idx = np.where(comps2['phase'] == 103) + assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) + assert np.all(comps2['mass'][comp_bd_idx] < MIN_MASS) + + # Ensure brown dwarfs are within 0.01 and 0.08 solar masses + assert np.all((clust1['mass'][bd_idx] >= BD_MIN_MASS) & + (clust1['mass'][bd_idx] <= BD_MAX_MASS)) + assert np.all((comps2['mass'][comp_bd_idx] >= BD_MIN_MASS) & + (comps2['mass'][comp_bd_idx] <= BD_MAX_MASS)) + + # Ensure no other objects are in the brown dwarf mass range + non_bd_idx = np.where((clust1['phase'] != 99) & + (clust1['mass'] >= BD_MIN_MASS) & + (clust1['mass'] <= BD_MAX_MASS)) + assert len(non_bd_idx[0]) == 0 # asserting no non-brown dwarf objects in BD mass range + + comp_non_bd_idx = np.where((comps2['phase'] != 99) & + (comps2['mass'] >= BD_MIN_MASS) & + (comps2['mass'] <= BD_MAX_MASS)) + assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + +return return def test_metallicity(): From d0a1ab4256daa17bf5716ca8631515670cd7e4cf Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 29 Jul 2024 13:44:16 -0700 Subject: [PATCH 10/48] most current changes in testing documentation and IMF derivation --- changes/BD_IMF.ipynb | 2 +- spisea/tests/test_synthetic.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/changes/BD_IMF.ipynb b/changes/BD_IMF.ipynb index 41fc800b..895870be 100644 --- a/changes/BD_IMF.ipynb +++ b/changes/BD_IMF.ipynb @@ -952,7 +952,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.11.0" } }, "nbformat": 4, diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 84d410cd..4b8b7b07 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -487,6 +487,9 @@ def test_ifmr_multiplicity(): assert len(idx[0]) == 0 # Ensure no substellar mass compact objects are generated + """ + 07/2024: Added more testing criteria for brown dwarf stars to ensure they are labeled appropriately for masses from 0.01 - 0.08 M_sun. + """ # For cluster objects MIN_MASS = 0.1 BD_MIN_MASS = 0.01 From 5379f36d7741ee14638d708515d2669726f4ca45 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 31 Jul 2024 13:22:01 -0700 Subject: [PATCH 11/48] Findings with companion objects being misclassified --- changes/Compact_Multiplicity.ipynb | 139 +++++++++++++++++------------ 1 file changed, 80 insertions(+), 59 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 983fcf1b..4de41355 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 16, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", "metadata": {}, "outputs": [], @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "1a68a281-791a-4821-8799-c2df8e936286", "metadata": {}, "outputs": [ @@ -85,7 +85,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 907596 stars out of mass range\n" + "Found 908186 stars out of mass range\n" ] } ], @@ -100,13 +100,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -127,7 +127,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", + "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -135,7 +135,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 2, 2)\n", - "plt.hist(k_out[p_wd]['mass_current'], histtype = 'step',\n", + "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -231,8 +231,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 686912 stars out of mass range\n", - "Found 134402 companions out of stellar mass range\n" + "Found 690569 stars out of mass range\n", + "Found 135191 companions out of stellar mass range\n" ] } ], @@ -248,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -258,39 +258,39 @@ "text": [ " mass \n", "--------------------\n", - " 0.5747159412207477\n", - " 0.6970586660858713\n", - " 0.601165124305441\n", - "0.056083697312291306\n", - " 0.02673063039645824\n", - " 0.1862915740843192\n", - " 0.07159040923670096\n", + " 0.18808616996721866\n", + "0.016007058623676886\n", + " 0.18392056165672846\n", + "0.015105203133808686\n", + " 1.0282818358508474\n", + " 0.21083846100118134\n", + " 0.14199672005929817\n", " ...\n", - " 0.0830220943719863\n", - " 0.09156100383230333\n", - " 0.3808927106160393\n", - "0.016698205202363158\n", - " 0.4183953590905605\n", - " 0.0758085193857225\n", - " 0.3082346627668261\n", - "Length = 391314 rows Teff \n", + " 0.048264534414347\n", + " 0.09147624878262055\n", + " 1.1197916658217713\n", + " 0.1964383346903446\n", + " 0.1980378944762514\n", + " 0.3608681652858361\n", + "0.025696120026630187\n", + "Length = 393024 rows Teff \n", "------------------\n", - " 4068.981641477723\n", - " 4492.75426907755\n", - " 4126.083901944962\n", + "3238.9711755793223\n", " nan\n", + " 3227.457841740429\n", " nan\n", - "3234.0110881285427\n", - "2808.0618989378295\n", + " 5829.407699425202\n", + " 3286.658840436513\n", + " 3110.820131229178\n", " ...\n", - " 2898.061169580272\n", - " 2950.46139583484\n", - "3506.1448705814614\n", " nan\n", - " 3566.917126729317\n", - "2845.0651718291647\n", - "3418.8421815545435\n", - "Length = 391314 rows\n" + " 2950.023070827086\n", + " 6118.272466541432\n", + "3262.0557417620357\n", + "3266.4767685215697\n", + "3482.0151466756533\n", + " nan\n", + "Length = 393024 rows\n" ] } ], @@ -300,13 +300,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 23, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -333,7 +333,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 2, 1)\n", - "plt.hist(kc_out[k_bh]['mass_current'], histtype = 'step',\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -341,7 +341,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 2, 2)\n", - "plt.hist(kc_out[k_wd]['mass_current'], histtype = 'step',\n", + "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -353,7 +353,28 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, + "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a primary generated black hole: 15.003127309066931\n", + "Smallest mass of a companion generated black hole: 0.07035468449081322\n" + ] + } + ], + "source": [ + "# Finding the minimum mass of generated black holes\n", + "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", + "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", "metadata": {}, "outputs": [ @@ -361,8 +382,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated object: 0.07000003602252991\n", - "Smallest mass of a companion generated object: 0.010000123044113119\n" + "Smallest mass of a primary generated object: 0.07000003012576093\n", + "Smallest mass of a companion generated object: 0.010000040848729129\n" ] } ], @@ -374,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", "metadata": {}, "outputs": [ @@ -382,8 +403,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 0.0700493749164032\n", - "Initial mass of largest generated black hole: 119.95339215013136\n" + "Initial mass of smallest generated black hole: 0.07035468449081322\n", + "Initial mass of largest generated black hole: 118.59389207250989\n" ] } ], @@ -398,12 +419,12 @@ "id": "77b9203f-69ec-4a54-af84-581427feb4fd", "metadata": {}, "source": [ - "In this case, we are getting many instances of substellar mass blakc holes, which does not make sense. Why is it that adding companions introduces this issue?" + "These statements show us that there is an issue with substellar-mass companions being erroneously identified as black holes and other compact remnants, but the issue does not extend to primary mass objects. Thus, we must introduce a bugfix to filter out this error." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", "metadata": {}, "outputs": [], @@ -415,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 11, "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", "metadata": {}, "outputs": [ @@ -423,8 +444,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 694055 stars out of mass range\n", - "Found 136094 companions out of stellar mass range\n" + "Found 689034 stars out of mass range\n", + "Found 135376 companions out of stellar mass range\n" ] } ], @@ -440,13 +461,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "id": "c598a266-297c-423d-b5f5-648f819c065e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -473,7 +494,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 2, 1)\n", - "plt.hist(c3_out[k3_bh]['mass_current'], histtype = 'step',\n", + "plt.hist(c3_out[k3_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -481,7 +502,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 2, 2)\n", - "plt.hist(c3_out[k3_wd]['mass_current'], histtype = 'step',\n", + "plt.hist(c3_out[k3_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", From aaa0ee23d4d5e4f0f1391c57c64d586f2604c401 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 09:19:55 -0700 Subject: [PATCH 12/48] Added code for successful brown dwarf implementation, and new issues to address --- changes/Compact_Multiplicity.ipynb | 462 +++++++++++++++++++++++------ spisea/evolution.py | 3 +- spisea/ifmr.py | 46 ++- spisea/synthetic.py | 17 +- spisea/tests/test_synthetic.py | 21 +- 5 files changed, 431 insertions(+), 118 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 4de41355..8c27e7ab 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", "metadata": {}, "outputs": [], @@ -51,10 +51,34 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 59, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 80.686259 s.\n", + "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-08-08 20:57:20.603079 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", + "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", + "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", + "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", + "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", + "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", + "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", + "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", + "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", + "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", + "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", + " Time taken: 21.66 seconds\n" + ] + } + ], "source": [ "# Create isochrone object \n", "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", @@ -66,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 60, "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", "metadata": {}, "outputs": [], @@ -77,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 61, "id": "1a68a281-791a-4821-8799-c2df8e936286", "metadata": {}, "outputs": [ @@ -85,7 +109,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 908186 stars out of mass range\n" + "Brown dwarf indices: (array([ 0, 1, 2, ..., 1041613, 1041614, 1041615]),), Masses: mass \n", + "--------------------\n", + " 0.06752873088255393\n", + " 0.0262184880713122\n", + "0.011103322341764093\n", + " 0.07004122940473101\n", + "0.055860465009665516\n", + " 0.03849994739007916\n", + "0.036201844969911835\n", + " ...\n", + "0.026022885833328162\n", + " 0.06754269552994506\n", + "0.011731305085217554\n", + " 0.07086687808163461\n", + " 0.02602575168928598\n", + " 0.05665991329103694\n", + " 0.07735901867149753\n", + "Length = 1024861 rows\n" ] } ], @@ -100,15 +141,75 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 62, + "id": "45456043-0197-4896-bcb4-d2247a95386e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple ... metallicity m_hst_f153m \n", + "-------------------- ---------- ... ----------- -----------------\n", + " 0.06752873088255393 False ... 0.0 nan\n", + " 0.21787124306310335 False ... 0.0 7.63530743580833\n", + " 0.0262184880713122 False ... 0.0 nan\n", + " 0.3087509497640693 False ... 0.0 7.086103270232351\n", + " 0.07004122940473101 False ... 0.0 nan\n", + "0.055860465009665516 False ... 0.0 nan\n", + " 0.2449826527701243 False ... 0.0 7.443908907848126\n", + " ... ... ... ... ...\n", + "0.047169363745193545 False ... 0.0 nan\n", + " 0.04388574160305706 False ... 0.0 nan\n", + " 0.08713365145144555 False ... 0.0 9.124319491534537\n", + " 0.5384991452265341 False ... 0.0 5.675504835409121\n", + "0.019767325458511932 False ... 0.0 nan\n", + " 0.16230926501233983 False ... 0.0 8.07930622339159\n", + " 0.12339530398894323 False ... 0.0 8.508529920543223\n", + "Length = 1026205 rows\n", + "119.2216436980943\n", + "0.010000064522612448\n" + ] + } + ], + "source": [ + "print(k_out[p_bd])\n", + "print(np.max(k_out[p_bd]['mass']))\n", + "print(np.min(k_out[p_bd]['mass']))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4b4fffe4-e927-49f1-9d3d-b215ca2c98cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.311164368442878" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(k_out[p_wd]['mass'])" + ] + }, + { + "cell_type": "code", + "execution_count": 67, "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -120,13 +221,16 @@ "p_bh = np.where(k_out['phase'] == 103)[0]\n", "p_ns = np.where(k_out['phase'] == 102)[0]\n", "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "p_bd = np.where(k_out['phase'] == 99)[0]\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", "\n", "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", + "plt.subplot(2, 2, 1)\n", "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", @@ -134,7 +238,7 @@ "plt.ylabel('Number')\n", "plt.legend()\n", "\n", - "plt.subplot(1, 2, 2)\n", + "plt.subplot(2, 2, 2)\n", "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", @@ -142,12 +246,126 @@ "plt.ylabel('Number')\n", "plt.legend()\n", "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 68, + "id": "e8e3c545-0aba-43f8-ad46-ed9ba9720761", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH max mass: 119.49401158306712\n", + "BH min mass: 15.015214381022997\n", + "\n", + "NS max mass: 118.51258738269632\n", + "NS min mass: 9.000988105453127\n", + "\n", + "WD max mass: 8.999748990796446\n", + "WD min mass: 5.311104032812196\n", + "\n", + "BD max mass: 0.07999996255862679\n", + "BH min mass: 0.010000049376939659\n", + "\n" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", + "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", + "\n", + "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", + "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", + "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", + "print(\"BH min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "11a7e249-23db-43e6-a071-838c676e61be", + "metadata": {}, + "source": [ + "This tells us that there are issues in accurately identifying white dwarf stars and neutron stars. White dwarves have an issue at lower masses, and neutron stars at the upper end." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "d1584ea1-6fdf-4dbc-be96-623c91bffb08", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mass_bins = np.linspace(0.01, 15, 1000)\n", + "plt.figure()\n", + "plt.hist(k_out['mass'], histtype = 'step',\n", + " bins = mass_bins, label = 'Generated Objects')\n", + "plt.title(\"Simulated Objects from 0.01 - 15 Solar Masses\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "95c79ef1-308b-4d46-b477-416643c7bc0e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.311625251702762\n", + "8.999671191854413\n" + ] + } + ], + "source": [ + "print(np.min(k_out[p_wd]['mass']))\n", + "print(np.max(k_out[p_wd]['mass'])) #white dwarf ranges are messed up" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "8f6c414b-c1df-4059-b5ca-3f4c8aa7c655", "metadata": {}, "outputs": [ @@ -155,7 +373,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a generated object: 0.07000011975637106\n" + "Smallest mass of a generated object: 0.010000000568115546\n" ] } ], @@ -166,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "186ded33-2af3-421a-9956-0a603998a50b", "metadata": {}, "outputs": [ @@ -174,8 +392,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 15.014691323167467\n", - "Initial mass of largest generated black hole: 119.74791889840289\n" + "Initial mass of smallest generated black hole: 15.002331855265554\n", + "Initial mass of largest generated black hole: 118.99598033857916\n" ] } ], @@ -211,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -223,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -231,8 +449,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 690569 stars out of mass range\n", - "Found 135191 companions out of stellar mass range\n" + "Brown dwarf indices: (array([ 0, 1, 2, ..., 782400, 782401, 782402]),), Masses: mass \n", + "--------------------\n", + " 0.02818070156567557\n", + "0.057477731545588766\n", + "0.050488840533678525\n", + " 0.04059754829381457\n", + " 0.0715824675309702\n", + "0.052250831887193906\n", + " 0.06839990496518711\n", + " ...\n", + " 0.0421694459314215\n", + "0.013298951799885059\n", + " 0.04585362159276258\n", + "0.017159730219879408\n", + " 0.04989123506548948\n", + "0.023690809192446777\n", + " 0.04091931301347838\n", + "Length = 769585 rows\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 175492 companions out of stellar mass range\n" ] } ], @@ -248,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -258,39 +495,39 @@ "text": [ " mass \n", "--------------------\n", - " 0.18808616996721866\n", - "0.016007058623676886\n", - " 0.18392056165672846\n", - "0.015105203133808686\n", - " 1.0282818358508474\n", - " 0.21083846100118134\n", - " 0.14199672005929817\n", + "0.010356220429645275\n", + " 1.027255369157919\n", + " 0.04728725326784277\n", + " 0.0458108242117786\n", + " 0.09466940653177572\n", + " 0.22804772872665638\n", + " 0.5530407053925155\n", " ...\n", - " 0.048264534414347\n", - " 0.09147624878262055\n", - " 1.1197916658217713\n", - " 0.1964383346903446\n", - " 0.1980378944762514\n", - " 0.3608681652858361\n", - "0.025696120026630187\n", - "Length = 393024 rows Teff \n", + " 0.3467687795811594\n", + " 0.02305569777333644\n", + " 0.1511085941801427\n", + " 0.24851815882478012\n", + " 0.06499991598727853\n", + "0.023044019171576186\n", + " 0.5446895523831808\n", + "Length = 429564 rows Teff \n", "------------------\n", - "3238.9711755793223\n", " nan\n", - " 3227.457841740429\n", + " 5825.765321681631\n", " nan\n", - " 5829.407699425202\n", - " 3286.658840436513\n", - " 3110.820131229178\n", + " nan\n", + " 2966.537023687665\n", + " 3310.184350302818\n", + "3987.9798523599243\n", " ...\n", + "3465.1148889898845\n", + " nan\n", + "3136.1270818647067\n", + " 3338.388828168428\n", " nan\n", - " 2950.023070827086\n", - " 6118.272466541432\n", - "3262.0557417620357\n", - "3266.4767685215697\n", - "3482.0151466756533\n", " nan\n", - "Length = 393024 rows\n" + " 3956.044246444265\n", + "Length = 429564 rows\n" ] } ], @@ -300,15 +537,15 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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IX375RVq2cOHCYkmZipQjPz9fqFQq0a5dO43zcvnyZWFoaFjuBglAsalp06YiKSlJI7akJFJ5j/XNN98UtWvXFmlpaaWW5cmGIyYmRpibm4tBgwaJ7OxsrcdR2GhpmwoVVvBFPxNHjx4VAMSnn34qzSuaRIqPjxcAxOLFizXWvXbtmlAoFGLq1KlCCCGOHz8uAIht27ZpLX9Rhe/Lk+dQiMeNkJ6enrhy5YoQQojMzExhZmYmJk2apBHXokWLYhdbJXnyy5parRaDBg0SQgixa9cuIZPJRHJycomNQVGPHj0S9+/fF6ampuKrr76S5js5OYkBAwaUut5///0nAIjQ0FCtZaWqifV3ssY6L7L+Dg0N1bjIX7x4sahbt64QQojz588LACIxMVEIIcScOXM0LvSFKP+5FUIziSSEELdu3RIAxOzZs4uVy8PDQ7zyyivSF6xCH330kTA2NhZ37twp87i0JZGWL18u/WgghBCxsbECgDhw4IAQQojIyEhhZmYm/Pz8NOpBBwcH4eXlVep2Hz16JHJzc4WDg4P4+OOPpfmF7UvXrl2LrfP1119LibtCd+7cEXK5XAQGBkrzyntOPD09RZs2bUotY2kKy1ja5+n999+X5s2ePVsYGRmJmzdvSvMKvwjGxcWVuZ8n2+nCfRZ+xl577TXh4+MjhBAlJpGelJ+fL/Ly8sTcuXOFlZWVVOaff/5ZABCnT58udd2PPvpI1K5du8xy0vPDOj9ZY52qes1emESaNWuWxvzC5NCCBQs05hfWAd99950QQoisrCxhZGQk5s6dK4QQ4vr16wKAmDZtmlAoFFKyydfXV6hUKmk7hfXCW2+9pbH9H3/8UQAQ8fHxZR6jtiSSEELY2tqK5s2bS6/feOMN8eabb0qvmzRpIqZMmSL09PSkOm39+vUCQLHkSaHCOun7778X+vr6Gu1U4Xnfu3evxjp5eXnC1ta2WLsydepUYWRkJP777z8hBL83lKQ6fW/g7WzVVPv27WFoaChNixcvBvC4G/qff/6J4cOHAwAePXokTW+99RZSUlKKdZvs16+fxutWrVoBgNSVbs+ePXj06BFGjhypsT1jY2Oo1eoSBxB9sltkobS0NHzwwQewt7eHgYEBDA0N0aBBAwBAUlKS1mMubzkuXLiAGzduwMvLS+P2hAYNGqBz585a91OocePGSEhIQEJCAuLj47FhwwYoFAp0795d6y1k5TnWBw8eIC4uDkOGDJFugShLREQE3nrrLbz//vv48ccfYWxsXO5jmT9/vnQsT05DhgzRiCu8zePJ2y8AoGPHjmjevDn27t1b6j527twJmUyGESNGaLw/dnZ2aN26tfT+NGnSBBYWFpg2bRpWrFiB8+fPl/s4gMcD1xX9zHp5eaGgoAAHDhyQYkaPHo3w8HBkZWUBeHwrwPnz5/HRRx9VaH/vvfcetm/fjtu3b2P16tXo1q1bqU8LuX//PqZNm4YmTZrAwMAABgYGqFWrFrKysjQ+4x07dsTu3bvxySefYP/+/cjOztbYjqWlJRo3boyFCxdiyZIlOHXqVIndgql6Yv39fOvvJ8dFKvxXrVYDAJo3bw4bGxuprtu/fz9sbW3RvHnzYtvRdm4r4uHDh9i7dy/efvttmJiYFHtvHz58iCNHjlR4u08SRW5/cnV1hbGxsTTYakxMDNzc3NCrVy8cPnwYDx48wLVr1/DXX39pdLN/9OgRgoOD0aJFCxgZGcHAwABGRkb466+/SnyvS/q8DB8+HHK5HOHh4dK8H374ATk5ORg9enSFz0nHjh3xxx9/wM/PD3v27Knw2EKlfZ6evLXxww8/BACNMSuWLVsGZ2dndO3atdz7UqvVaNy4MdasWYOzZ88iISGh1FvZgMdtU48ePaBUKqGvrw9DQ0PMmjULt2/flp641KZNGxgZGWHs2LGIiIgodqsH8Pgc3b17F8OGDcMvv/xS4u3zVDlY51e9a/aix/zbb78BKH79O3jwYJiamkrXvyYmJnBxcdGoV2vXro0pU6YgNzcXhw4dAgDExsaWePuSLtuVooq2Ad27d8fvv/+O7OxsXLlyBZcuXcLQoUPRpk0bxMTESOWsX78+HBwcpPVOnTqFfv36wcrKSqqTRo4cifz8fFy8eFFjHxYWFnjzzTc15hkYGGDEiBHYsmULMjIyAAD5+flYt24d+vfvDysrKwD83gBU7+8NTCJVYdbW1lAoFCVWLBs2bEBCQoLGuAIAcPPmTQDA5MmTNRosQ0ND+Pn5AUCxC4vCP+ZCcrkcAKQPaOE2X3vttWLb3LRpU7HtmZiYFBuBvqCgAD179sSWLVswdepU7N27F8eOHZMuEIv+MZSkvOW4ffs2AMDOzq7YNkqaVxpjY2N06NABHTp0wOuvv45hw4Zh9+7dSElJwaxZs0pdr7zHmp6ejvz8fLzyyivlKs/GjRuhUCjw/vvvFxu7Q5tXX31VOpYnp6LJq8JzV9JjsFUqlbS8JDdv3oQQAra2tsXenyNHjkjvj1KpRFxcHNq0aYNPP/0ULVu2hEqlwuzZs8v1ZKGSBgwsfF+fLN+ECRNw7949rF+/HsDjLwOvvPIK+vfvr3UfTxo0aBCMjY2xdOlS7NixA2PGjCk11svLC8uWLcP777+PPXv24NixY0hISECdOnU0PuNff/01pk2bhm3btqFbt26wtLTEgAEDpAsdmUyGvXv3wsPDAwsWLEC7du1Qp04dTJw4Effu3atQ+alysP7W9CLrb2dnZ1hbW2Pfvn3SeEiFSSTg8YCr+/fvR05ODuLj40t9Kpu2c1sRt2/fxqNHjxAWFlbs+N966y0Axd/biir8rBWOt2JsbAxXV1fpy87evXvh7u4ONzc35Ofn4+DBg9IXiSe/7AQEBGDmzJkYMGAAduzYgaNHjyIhIQGtW7cu8dhLai8sLS3Rr18/fP/999K4E+Hh4ejYsSNatmxZ4XMyffp0LFq0CEeOHEHv3r1hZWWF7t27l/tx6aV9np5sM2xtbfHuu+9i5cqVyM/Px5kzZ3Dw4MEKf4GQyWQYPXo0IiMjsWLFCjRt2hRdunQpMfbYsWPSgL+rVq3C77//joSEBMyYMQPA/z5rjRs3RmxsLGxsbDB+/Hg0btwYjRs31hgTytvbG2vWrMGVK1fwzjvvwMbGBp06dZLeY3q+WOdrqurX7EXrrdu3b8PAwKDYdbFMJitWV/To0QNHjhxBVlYWYmNj8eabb8LKygrt27dHbGwskpOTkZycXGISSZftypOysrJw+/ZtjfG2evTogZycHBw6dAgxMTGwtrZG27Zt0aNHD4124clyXr16FV26dMG///6Lr776CgcPHkRCQoI0Tl3RcpZU/wOPEykPHz7Exo0bATxOKqakpEg/IgD83gBU7+8NNfPxGdWEvr4+3nzzTURHRyMlJUXjD7XwiTCXL1/WWMfa2hrA4wuugQMHlrhdR0fHCpWjcJs///yz9CtEWUpKcCQmJuKPP/5AeHg4Ro0aJc2/dOmSzstRWEGnpqYWW1bSvIqoW7curK2t8ccff5QaU95jtbS0hL6+frkHhVu/fj1mzpwJtVqN6OhotGnT5qmOoSyF5y4lJaVYcuvGjRvSe1ASa2tryGQyHDx4UGoUn/TkPGdnZ2zcuBFCCJw5cwbh4eGYO3cuFAoFPvnkkzLLWHhh8qTC9/XJxrlJkybo3bs3vvnmG/Tu3Rvbt2/HnDlzoK+vX+b2izIxMcHQoUMREhICc3PzUv+uMjIysHPnTsyePVvjGHJycnDnzh2NWFNTU8yZMwdz5szBzZs3pV8X+vbtKz1+vEGDBtLAtBcvXsSPP/6IoKAg5ObmYsWKFRU6BnrxWH8/XTl0UX/LZDKo1WpERUXh2LFjuHv3rkYSSa1WIygoCPHx8Xj48GGpSSRdsrCwgL6+Pry9vUsdoLNRo0ZPvX0hBHbs2AFTU1ONJ/d0794ds2bNwrFjx3D9+nW4u7vDzMwMr732GmJiYnDjxg00bdoU9vb20jqRkZEYOXIkgoODNfbx33//oXbt2sX2XdqPGqNHj8ZPP/2EmJgY1K9fHwkJCVi+fLm0vCLnxMDAAAEBAQgICMDdu3cRGxuLTz/9FB4eHrh27ZrWpwGV9nkq+oVu0qRJWLduHX755RdERUWhdu3aUi+RivDx8cGsWbOwYsUKjaf0FLVx40YYGhpi586dGr2Lt23bViy2S5cu6NKlC/Lz83H8+HGEhYXB398ftra2GDp0KIDH53z06NHIysrCgQMHMHv2bHh6euLixYvl+vunp8c6/+nKUVnX7EWP28rKCo8ePcKtW7c0EklCCKSmpuK1116T5nXv3h0zZ87EgQMHsHfvXsyePVuaHx0dLdVbhYOUvwi7du1Cfn6+9KAH4PEg1rVq1UJsbCwuX76M7t27QyaToXv37li8eDESEhJw9epVjSTStm3bkJWVhS1btmi8b6UNzlxa/d+iRQt07NgRa9euxbhx47B27VqoVCqNp+Txe0P1/t7AJFIVN336dOzevRsffPABfv75Z61PBXN0dISDgwP++OOPYheAT8vDwwMGBgb4+++/S+zyWh6FlUzRSqLwKQJPKi0rX95yODo6om7duvjhhx8QEBAg7fvKlSs4fPhwsadiVMT169fx33//lflY5/Iea+ETYn766SfMmzevzAQN8DjpFBsbC09PT3Tr1g27d+/WeJypLhR2SY2MjNRoMBMSEpCUlCT9OloST09PfPnll/j333+L3SZXGplMhtatW2Pp0qUIDw/HyZMnta5z7949bN++XaNr6oYNG6Cnp1fsloNJkyahZ8+eGDVqFPT19eHr61uuchX14Ycf4ubNm1Cr1aXeRiiTySCEKPa+/9///Z/GUzyKsrW1hY+PD/744w+EhoaW+HjUpk2b4rPPPsPmzZvLdY6oamD9XfFy6Kr+7tatGzZv3oyFCxfCxsZG43Y1tVqN27dvS0/N0WUSqbTjNzExQbdu3XDq1Cm0atWq1Cc3Pa05c+bg/Pnz+PTTTzXqqB49euDTTz/FzJkz8corr6BZs2bS/O3btyM1NbXY+yGTyYq917t27cK///6LJk2alLtMPXv2RL169bB27VrUr18fxsbG0tPLgKc/J7Vr18agQYPw77//wt/fH5cvXy6zTQZQ6udp5MiRGnHt27dH586dMX/+fCQmJmLs2LEwNTUt9zEXqlevHqZMmYI///xT40t4UTKZDAYGBhpfUrKzs7Fu3bpS19HX10enTp3QrFkzrF+/HidPnpSSSIVMTU3Ru3dv5ObmYsCAATh37hyTSC8A6/yKl6Oyr9kLde/eHQsWLEBkZCQ+/vhjaf7mzZuRlZWlkRDq2LEjzM3NERoaitTUVLi7uwN4XK/Onz8fP/74I1q0aPFMZa+Iq1evYvLkyVAqlRg3bpw039DQEF27dkVMTAyuXbuGL7/8EsDjhLSBgQE+++wzKalUqKT3XgjxVI+mHz16ND788EMcOnQIO3bsQEBAgEZdx+8N1ft7A5NIVZyrqyu++eYbTJgwAe3atcPYsWPRsmVL6OnpISUlBZs3bwYAja6oK1euRO/eveHh4QEfHx/Uq1cPd+7cQVJSEk6ePImffvqpQmVo2LAh5s6dixkzZuCff/5Br169YGFhgZs3b+LYsWNSdrQszZo1Q+PGjfHJJ59ACAFLS0vs2LGjxG7Wzs7OAICvvvoKo0aNgqGhIRwdHctdDj09PXz++ed4//338fbbb8PX1xd3795FUFBQhbrGZmdnS1138/PzkZycjAULFgAA/P39dXKsS5YswRtvvIFOnTrhk08+QZMmTXDz5k1s374dK1euhJmZmUa8mZkZoqKiMHDgQLi7u2P79u06/QLk6OiIsWPHIiwsDHp6eujduzcuX76MmTNnwt7eXqNhLcrV1RVjx47F6NGjcfz4cXTt2hWmpqZISUnBoUOH4OzsjA8//BA7d+7Et99+iwEDBuDVV1+FEAJbtmzB3bt3pYa4LFZWVvjwww9x9epVNG3aFL/++itWrVqFDz/8EPXr19eIdXd3R4sWLbBv3z6MGDECNjY2T3Ve2rRpU+Ivw08yNzdH165dsXDhQlhbW6Nhw4aIi4vD6tWri/1636lTJ3h6eqJVq1awsLBAUlIS1q1bBxcXF5iYmODMmTP46KOPMHjwYDg4OMDIyAi//fYbzpw5o/UXF6o6WH9XXv1dWC9u3boVgwYN0ljm5OQEKysrbN26FfXq1dMYC+JZmZmZoUGDBvjll1/QvXt3WFpaSvXBV199hTfeeANdunTBhx9+iIYNG+LevXu4dOkSduzYIY3JUZa7d+9K7VJWVhYuXLiAjRs34uDBgxgyZEix97J9+/awsLBAdHS0xm0EPXr0wOeffy79/0menp4IDw9Hs2bN0KpVK5w4cQILFy4s963XhfT19TFy5EgsWbJE+jW28JHbhcp7Tvr27QsnJyfpNuwrV64gNDQUDRo0KNf7l5aWJn2eMjIyMHv2bBgbG2P69OnFYidNmoR3330XMplMuqXoaRR+YStLnz59sGTJEnh5eWHs2LG4ffs2Fi1aVOxLxYoVK/Dbb7+hT58+qF+/Ph4+fIg1a9YA+N/75+vrC4VCAVdXV9StWxepqakICQmBUqnU+FGInh/W+dXvmr2Qu7s7PDw8MG3aNGRmZsLV1RVnzpzB7Nmz0bZtW3h7e0ux+vr6UKvV2LFjBxo1aoTGjRsDePz+y+Vy7N27FxMnTix32SsiMTFRGjsoLS0NBw8exNq1a6Gvr4+tW7cWux2ve/fuCAwMBPC/ukKhUKBz586Ijo5Gq1atNK6N3d3dYWRkhGHDhmHq1Kl4+PAhli9fjvT09AqXddiwYQgICMCwYcOQk5NTbLwpfm+o5t8bXtgQ3vRMTp8+LUaPHi0aNWok5HK5MDY2Fk2aNBEjR44sNiq+EEL88ccf0qN0DQ0NhZ2dnXjzzTfFihUrpJjSRvovfIJA0dHjt23bJrp16ybMzc2FXC4XDRo0EIMGDRKxsbFSzKhRo4SpqWmJx3D+/Hnh7u4uzMzMhIWFhRg8eLC4evVqiU+ymT59ulCpVEJPT69YWcpTDiGE+L//+z/h4OAgjIyMRNOmTcWaNWtKfCx9SYo+6UFPT0+oVCrRu3dvsX//fo3Ykp7OVpFjPX/+vBg8eLCwsrISRkZGon79+sLHx0d6wkNJ71NOTo545513hLGxsdi1a1epx1H4Xv70008lLh8/frwoWg3k5+eL+fPni6ZNmwpDQ0NhbW0tRowYIa5du6YRV9q5XLNmjejUqZMwNTUVCoVCNG7cWIwcOVIcP35cCCHEn3/+KYYNGyYaN24sFAqFUCqVomPHjiI8PLzU4yhU+BjX/fv3iw4dOgi5XC7q1q0rPv3002JPNikUFBQkAIgjR45o3X4hbU9BEkKU+JSF69evi3feeUdYWFgIMzMz0atXL5GYmCgaNGggRo0aJcV98sknokOHDsLCwkLI5XLx6quvio8//lh6YsXNmzeFj4+PaNasmTA1NRW1atUSrVq1EkuXLtX6GFiqelh//68sL6L+LmRnZycAiGXLlhVbNmDAAAGg2CPdhajYuS36dDYhHj8VrW3btkIulwsAGn/7ycnJ4r333hP16tUThoaGok6dOqJz587iiy++0Ho8DRo0kNokmUwmatWqJRwdHYW3t7f0WO6SvP322wKAWL9+vTQvNzdXmJqaCj09PekR8YXS09PFmDFjhI2NjTAxMRFvvPGGOHjwYLFj1da+CCHExYsXpTIXfdxzRc7J4sWLRefOnYW1tbXUTo4ZM0Zcvny5zHNWWMZ169aJiRMnijp16gi5XC66dOkitUlF5eTkCLlcLnr16lXmtp9UnicnCVHy09nWrFkjHB0dpbYgJCRErF69WuO6Ij4+Xrz99tuiQYMGQi6XCysrK6FWq8X27dul7URERIhu3boJW1tbYWRkJFQqlRgyZIg4c+ZMuY+DdIN1/v/KUtWu2Qufzvbk49MLZWdni2nTpokGDRoIQ0NDUbduXfHhhx8WqyOFePxIeADC19dXY767u7sAoPG3KUTp9WVycrIAINauXVvmMRa+/4WTkZGRsLGxEWq1WgQHB5f6lOc//vhDABAODg4a8+fNmycAiICAgGLr7NixQ7Ru3VoYGxuLevXqiSlTpojdu3eX2P61bNmyzHJ7eXkJAMLV1bXUGH5vqJ7fG2RCFBnKnYjoJdKhQwfIZDIkJCRUdlGIiKiK27FjB/r164ddu3ZJA3wTEVHNwO8N5cPb2YjopZOZmYnExETs3LkTJ06cwNatWyu7SEREVIWdP38eV65cQWBgINq0aYPevXtXdpGIiOgF4PeGimMSiYheOidPnkS3bt1gZWWF2bNnY8CAAZVdJCIiqsL8/Pzw+++/o127doiIiCj1qUNERPRy4feGiuPtbEREREREREREpJVeZReAiIiIiIiIiIiqPiaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIKw6sXU4FBQW4ceMGzMzMONgiEdUYQgjcu3cPKpUKeno173cH1v1EVBOx7mfdT0Q1T7nrfkHlcu3aNQGAEydOnGrkdO3atcquhisF635OnDjV5OnatWsiLy9PzJgxQzRs2FAYGxuLRo0aiTlz5oj8/HypriwoKBCzZ88WdevWFcbGxkKtVovExESN+vThw4fio48+ElZWVsLExET07du3WNty584dMWLECGFubi7Mzc3FiBEjRHp6ukbMlStXhKenpzAxMRFWVlZiwoQJIicnRyPmzJkzomvXrsLY2FioVCoxZ84cUVBQwLqfEydOnMoxabvuZ0+kcjIzMwMAXLt2Debm5pVcGiKiFyMzMxP29vZSHVjTsO4noproybp//vz5WLFiBSIiItCyZUscP34co0ePhlKpxKRJkwAACxYswJIlSxAeHo6mTZviiy++gLu7Oy5cuCDVo/7+/tixYwc2btwIKysrBAYGwtPTEydOnIC+vj4AwMvLC9evX0dUVBQAYOzYsfD29saOHTsAAPn5+ejTpw/q1KmDQ4cO4fbt2xg1ahSEEAgLC5PK7u7ujm7duiEhIQEXL16Ej48PTE1NERgYWK7jZ91PRDVRea/7ZUII8YLKVK1lZmZCqVQiIyODjQkR1Rg1ve6r6cdPRDXTk3Wfl5cXbG1tsXr1amn5O++8AxMTE6xbtw5CCKhUKvj7+2PatGkAgJycHNja2mL+/PkYN24cMjIyUKdOHaxbtw7vvvsuAODGjRuwt7fHr7/+Cg8PDyQlJaFFixY4cuQIOnXqBAA4cuQIXFxc8Oeff8LR0RG7d++Gp6cnrl27BpVKBQDYuHEjfHx8kJaWBnNzcyxfvhzTp0/HzZs3IZfLAQBffvklwsLCcP369XLdnsa6n4hqovLWfTXvJmciIiIiIiqXN954A3v37sXFixcBAH/88QcOHTqEt956CwCQnJyM1NRU9OzZU1pHLpdDrVbj8OHDAIATJ04gLy9PI0alUsHJyUmKiY+Ph1KplBJIAPD6669DqVRqxDg5OUkJJADw8PBATk4OTpw4IcWo1WopgVQYc+PGDVy+fFmXp4aIqEZiEomIiKqcAwcOoG/fvlCpVJDJZNi2bZvGciEEgoKCoFKpoFAo4ObmhnPnzmnE5OTkYMKECbC2toapqSn69euH69evv8CjICKq/qZNm4Zhw4ahWbNmMDQ0RNu2beHv749hw4YBAFJTUwEAtra2GuvZ2tpKy1JTU2FkZAQLC4syY2xsbIrt38bGRiOm6H4sLCxgZGRUZkzh68KYonJycpCZmakxERFRyZhEIiKiKicrKwutW7fGsmXLSlxeOP7GsmXLkJCQADs7O7i7u+PevXtSjL+/P7Zu3YqNGzfi0KFDuH//Pjw9PZGfn/+iDoOIqNrbtGkTIiMjsWHDBpw8eRIRERFYtGgRIiIiNOKK3iYmhNB661jRmJLidRFTOHpHaeUJCQmBUqmUJnt7+zLLTURUk3FgbXqp5OfnIy8vr7KLQVRtGBoaSgOaViW9e/dG7969S1wmhEBoaChmzJiBgQMHAgAiIiJga2uLDRs2SONvrF69GuvWrUOPHj0AAJGRkbC3t0dsbCw8PDxe2LHUdAUFBcjNza3sYhDREypS90+ZMgWffPIJhg4dCgBwdnbGlStXEBISglGjRsHOzg7A414+devWldZLS0uTegDZ2dkhNzcX6enpGr2R0tLS0LlzZynm5s2bxfZ/69Ytje0cPXpUY3l6ejry8vI0Yor2OEpLSwNQvLdUoenTpyMgIEB6XTi4LFUMr8OJqjZdXfcziUQvBSEEUlNTcffu3couClG1U7t2bdjZ2ZVrsNGqQNv4G+PGjdM6/kZpSaScnBzk5ORIr3lLw7PJzc1FcnIyCgoKKrsoRFREeev+Bw8eQE9P8+YFfX196e+6UaNGsLOzQ0xMDNq2bQvg8d9+XFwc5s+fDwBo3749DA0NERMTgyFDhgAAUlJSkJiYiAULFgAAXFxckJGRgWPHjqFjx44AgKNHjyIjI0NKNLm4uGDevHlISUmRElbR0dGQy+Vo3769FPPpp58iNzcXRkZGUoxKpULDhg1LPEa5XK4xhhJVDK/DiaoPXVz3M4lEL4XChsvGxgYmJibV5sswUWUSQuDBgwfSL7RP/oJclZU1/saVK1ekGG3jb5QkJCQEc+bM0XGJayYhBFJSUqCvrw97e/tiX0KJqHJUtO7v27cv5s2bh/r166Nly5Y4deoUlixZgvfeew/A41vE/P39ERwcDAcHBzg4OCA4OBgmJibw8vICACiVSowZMwaBgYGwsrKCpaUlJk+eDGdnZ6m3aPPmzdGrVy/4+vpi5cqVAICxY8fC09MTjo6OAICePXuiRYsW8Pb2xsKFC3Hnzh1MnjwZvr6+0pOEvLy8MGfOHPj4+ODTTz/FX3/9heDgYMyaNYvXh88Jr8OJqj5dXvcziUTVXn5+vtRwWVlZVXZxiKoVhUIB4HFXfxsbmyp5a1tpdDH+RlG8pUF3Hj16hAcPHkClUsHExKSyi0NET6hI3R8WFoaZM2fCz88PaWlpUKlUGDduHGbNmiXFTJ06FdnZ2fDz80N6ejo6deqE6OhomJmZSTFLly6FgYEBhgwZguzsbHTv3h3h4eEa+16/fj0mTpwo9SLt16+fxth4+vr62LVrF/z8/ODq6gqFQgEvLy8sWrRIilEqlYiJicH48ePRoUMHWFhYICAgQKNuJ93hdThR9aGr634mkajaK7z3ml9SiJ5O4d9OXl5etUgi6Wr8jZLwlgbdKRzAvPB2EiKqWspb95uZmSE0NBShoaGlxshkMgQFBSEoKKjUGGNjY4SFhSEsLKzUGEtLS0RGRpZZ7vr162Pnzp1lxjg7O+PAgQNlxpBu8DqcqHrRxXU/+5bTS4NdZ4meTnX723ly/I1CheNvFCaInhx/o1Dh+BtlJZFI96rb54uopuDfJukSP09E1YMu/lbZE4mIiKqc+/fv49KlS9Lr5ORknD59GpaWlqhfv75Oxt8gIiIiIqKKYRKJXmr/3s1GetaLe7S0hakR6tVWvLD9vawaNmwIf39/+Pv7P9N2ZDIZtm7digEDBlSpcj2NoKAgbNu2DadPn37h+64Mx48fR7du3aTXhWNZjBo1CuHh4Tobf4NePNbL1dPzqv+0bffy5cto1KgRTp06hTZt2uh030T0fLG+r54q83qXqgcmkeil9e/dbPRYHIfsvPwXtk+FoT5iA9UVasBSU1MREhKCXbt24fr161AqlXBwcMCIESMwcuTIanOP+YtscIKCgjSeoGVubo5WrVrhiy++gFqtfu77L6/9+/ejW7duSE9PR+3atTWWsYEum5ubG4QQpS7X1fgb9GKxXn6xXlQ9ExUVhd69eyMlJUUaswx4PDaZoaEhrl27Js27fv067O3tsWfPHmnw5LLY29sjJSUF1tbWAMquVyvKzc0NcXFxAB6P3WVtbY127dph9OjRGDhw4DNtm6imY33/YvE6vPzCw8MxevRoAICenh7Mzc3RtGlT9OnTB5MmTYJSqazkElZ9TCLRSys9KxfZefkIfbcNmtjUeu77u5R2H/6bTiM9K7fcjdc///wDV1dX1K5dG8HBwXB2dsajR49w8eJFrFmzBiqVCv369XvOJS+dEAL5+fkwMKh6VUXLli0RGxsLALhz5w4WLVoET09P6QKAiKoe1svPrirWy2+88QYMDAywf/9+DB06FACQlJSEhw8fIjs7G5cuXUKTJk0AAPv27YOhoSFcXV3LtW19fX2NxJSu+fr6Yu7cucjLy8O///6LrVu3YujQofDx8cF333333PZbVF5eHgwNDV/Y/oieN9b3z64q1veFnuY6vCrVc+bm5rhw4QKEELh79y4OHz6MkJAQrF27Fr///jtUKtULKUdubm71fACJoHLJyMgQAERGRkZlF4WKyM7OFufPnxfZ2dka889evysaTNspzl6/+0LK8TT78/DwEK+88oq4f/9+icsLCgqk/9+9e1f4+vqKOnXqCDMzM9GtWzdx+vRpafns2bNF69atxffffy8aNGggzM3NxbvvvisyMzM1tjd//nzRqFEjYWxsLFq1aiV++uknafm+ffsEABEVFSXat28vDA0NxW+//SYuXbok+vXrJ2xsbISpqano0KGDiImJkdZTq9UCgMZU6PfffxddunQRxsbG4pVXXhETJkzQON6bN28KT09PYWxsLBo2bCgiIyNFgwYNxNKlS0s9b4XH+qSrV68KAOLYsWPSPABi69at0uupU6cKBwcHoVAoRKNGjcRnn30mcnNzNbbzyy+/iPbt2wu5XC6srKzE22+/LS0rWq41a9YIc3NzER0dXWI5C89nenp6sWVFt3XlyhXRr18/YWpqKszMzMTgwYNFampqmce8Zs0a0axZMyGXy4Wjo6P45ptvpGU5OTli/Pjxws7OTsjlctGgQQMRHBxcYjlL+xsSgnVfTT/+Z1HS54r18stbL7u4uIhx48ZJr7/99lvRp08f8dZbb4lVq1ZJ89977z3h6uoqvW7QoIGYN2+eGD16tKhVq5awt7cXK1eulJYnJycLAOLUqVPS/5+cRo0aVa7zWBK1Wi0mTZpUbP6aNWsEAOl8Dhw4UHz00UfS8kmTJgkAIjExUQghRF5enqhVq5aIiooSQgixe/du4erqKpRKpbC0tBR9+vQRly5dKnZMmzZtEmq1WsjlchEaGiqMjY3F7t27NcqyefNmYWJiIu7duyeEEOL69etiyJAhonbt2sLS0lL069dPJCcnS/H79u0Tr732mjAxMRFKpVJ07txZXL58ucTjZ91fupp+/BXB6/DHalJ9X5Hr8OXLl4t+/foJExMTMWvWLCHE4/bh1VdfFYaGhqJp06bi+++/l9YJCAgQnp6e0uulS5cKAGLnzp3SvKZNm4oVK1YIIYQYNWqU6N+/v1i4cKGws7MTlpaWws/Pr9j1/ZPWrl0rlEplsfk3b94U1tbWYvjw4UIIIbZv3y6USqXIz88XQghx6tQpAUBMnjxZWmfs2LFi6NChQggh/vvvPzF06FBRr149oVAohJOTk9iwYYPGPtRqtRg/frz4+OOPhZWVlejatasYOnSoePfddzXicnNzhZWVlVizZo0QQvtn586dO8LLy0tYW1sLY2Nj0aRJE2ndonRR9/PpbESV5Pbt24iOjsb48eNhampaYkzh6PlCCPTp0wepqan49ddfceLECbRr1w7du3fHnTt3pPi///4b27Ztw86dO7Fz507ExcXhyy+/lJZ/9tlnWLt2LZYvX45z587h448/xogRI6Tu/IWmTp2KkJAQJCUloVWrVrh//z7eeustxMbG4tSpU/Dw8EDfvn1x9epVAMCWLVvwyiuvYO7cuUhJSUFKSgoA4OzZs/Dw8MDAgQNx5swZbNq0CYcOHcJHH30k7cvHxweXL1/Gb7/9hp9//hnffvst0tLSKnQuc3JyEB4ejtq1a8PR0bHUODMzM4SHh+P8+fP46quvsGrVKixdulRavmvXLgwcOBB9+vTBqVOnsHfvXnTo0KHEbS1atAiTJ0/Gnj174O7uXqHyFiWEwIABA3Dnzh3ExcUhJiYGf//9N959991S11m1ahVmzJiBefPmISkpCcHBwZg5cyYiIiIAAF9//TW2b9+OH3/8ERcuXEBkZCQaNmz4TOUketmxXn7saerlbt26Yd++fdLrffv2wc3NDWq1utj8J8c7A4DFixejQ4cOOHXqFPz8/PDhhx/izz//LLYPe3t7bN68GQBw4cIFpKSk4KuvvqrQeSyPUaNGwcLCAlu2bAHw+La3/fv3S8vj4uJgbW0tbTshIQEPHz6UeldlZWUhICAACQkJ2Lt3L/T09PD222+joKBAYz/Tpk3DxIkTkZSUhMGDB6NPnz5Yv369RsyGDRvQv39/1KpVCw8ePEC3bt1Qq1YtHDhwAIcOHUKtWrXQq1cv5Obm4tGjRxgwYADUajXOnDmD+Ph4jB07lk/NIioB6/vHnvd1+OzZs9G/f3+cPXsW7733HrZu3YpJkyYhMDAQiYmJGDduHEaPHi21E25ubjh48KBUXxatb1NTU3Hx4kWN2+b27duHv//+G/v27UNERATCw8MRHh5eoWMAABsbGwwfPhzbt29Hfn4+unbtinv37uHUqVMllgV4fIt1YVkePnyI9u3bY+fOnUhMTMTYsWPh7e2No0ePauwnIiICBgYG+P3337Fy5Uppn/fv35di9uzZg6ysLLzzzjsAtH92Zs6cifPnz2P37t1ISkrC8uXLpdvAn4syU0wk4S8SVVd1/QXkyJEjAoDYsmWLxnwrKythamoqTE1NxdSpU4UQQuzdu1eYm5uLhw8fasQ2btxY+sV29uzZwsTEROMXjylTpohOnToJIYS4f/++MDY2FocPH9bYxpgxY8SwYcOEEP/7BWTbtm1ay9+iRQsRFhYmvS7pVwtvb28xduxYjXkHDx4Uenp6Ijs7W1y4cEEAEEeOHJGWJyUlCQBafwHR09OTzpNMJhPm5ubFfsFFkZ5IRS1YsEC0b99eeu3i4iL9+lCSwmP85JNPRN26dcWZM2dKjRXif+ezsJxPTjKZTDrG6Ohooa+vL65evSqte+7cOY1fdIr+6mNvb1/s143PP/9cuLi4CCGEmDBhgnjzzTc1fkUrDX+NLl1NP/5nUR17IrFefvp6OTo6WgAQN27cEEIIYWNjI44dOyaOHDkiVCqVEOJ/v1Tv3btXo4wjRoyQXhcUFAgbGxuxfPlyIYRmT6Qnz8eTPTzLcx5LUlpPJCGE6NSpk+jdu7cQQogzZ84ImUwmbt26Je7cuSMMDQ3FF198IQYPHiyEECI4OFh6T0uSlpYmAIizZ89qHFNoaKhG3JYtW0StWrVEVlaWEOJx/WNsbCx27dolhBBi9erVwtHRUaNez8nJEQqFQuzZs0fcvn1bABD79+8vtSxPYt1fupp+/BXB6/CaV99X5Drc399fY17nzp2Fr6+vxrzBgweLt956SwjxuMeXnp6eOH78uCgoKBBWVlYiJCREvPbaa0IIITZs2CBsbW2ldUeNGiUaNGggHj16pLG9oj17nlRaTyQhhFi+fLkAIG7evCmEEKJdu3Zi0aJFQgghBgwYIObNmyeMjIxEZmamSElJEQBEUlJSqft66623RGBgoPRarVaLNm3aaMTk5uYKa2trjR5Zw4YNk9qY8nx2+vbtK0aPHl1qOZ6ki7q/6t1gSVTDFP2F8NixYygoKMDw4cORk5MDADhx4gTu378PKysrjdjs7Gz8/fff0uuGDRtqPJ2qbt260q8J58+fx8OHD4v1msnNzUXbtm015hXtfZOVlYU5c+Zg586duHHjBh49eoTs7GzpF5DSnDhxApcuXdL4ZVUIgYKCAiQnJ+PixYswMDDQ2F+zZs3KNViqo6Mjtm/fDgC4d+8eNm3ahMGDB2Pfvn2l9h76+eefERoaikuXLuH+/ft49OgRzM3NpeWnT5+Gr69vmftdvHgxsrKycPz4cbz66qtaywkABw8e1HhfgMe/tBRKSkqCvb097O3tpXktWrRA7dq1kZSUhNdee01j3Vu3buHatWsYM2aMRnkfPXok3Yfu4+MDd3d3ODo6olevXvD09CzXILZExHr5aeplV1dXGBkZYf/+/WjdujWys7PRrl07CCGQmZmJv/76C/Hx8ZDL5ejcubPGuq1atZL+L5PJYGdnV6FfwityHstLCCF9DpycnGBlZYW4uDgYGhqidevW6NevH77++msAmr9EA497I8ycORNHjhzBf//9J/2ifvXqVTg5OUlxRd/TPn36wMDAANu3b8fQoUOxefNmmJmZSXV34XtXtD15+PAh/v77b/Ts2RM+Pj7w8PCAu7s7evTogSFDhqBu3bpPdQ6IagLW98/3OrzosSQlJWHs2LEa81xdXaVepUqlEm3atMH+/fthaGgIPT09jBs3DrNnz8a9e/eK1bfA4/GZnnzybt26dXH27Fmtx1AS8f8f6lL4uSjsiRoQEICDBw/iiy++wObNm3Ho0CHcvXsXtra2aNasGQAgPz8fX375JTZt2oR///0XOTk5yMnJKdbTreg5MTQ0xODBg7F+/Xp4e3sjKysLv/zyCzZs2ACgfJ+dDz/8EO+88w5OnjyJnj17YsCAAcXaWl1iEomokjRp0gQymaxYl/3CxIRC8b9BAQsKClC3bl2N7vSFnqzoiw5WJ5PJpIvXwn937dqFevXqacTJ5XKN10UruylTpmDPnj1YtGgRmjRpAoVCgUGDBiE3t+zHthYUFGDcuHGYOHFisWX169fHhQsXpHJWlJGRkTRQKwC0bdsW27ZtQ2hoKCIjI4vFHzlyBEOHDsWcOXPg4eEBpVKJjRs3YvHixVLMk+e8NF26dMGuXbvw448/4pNPPilXWRs1alSsQX5ykMQnv6w8qbT5he/lqlWr0KlTJ41lhY1ou3btkJycjN27dyM2NhZDhgxBjx498PPPP5erzEQ1Eevlp6+XTUxM0LFjR+zbtw937tzBG2+8IdVHnTt3xr59+xAfHw8XFxcYGxtrrFvWOSqPipzH8sjPz8dff/0lJfBlMhm6du2K/fv3w8jICG5ubnByckJ+fj7Onj2Lw4cPazwRqW/fvrC3t8eqVaugUqlQUFAAJyenYu9N0ffUyMgIgwYNwoYNGzB06FBs2LAB7777rtReFBQUoH379sVueQOAOnXqAADWrl2LiRMnIioqCps2bcJnn32GmJgYvP766xU+D0QvM9b3L+Y6vKRbBYvur+j1bmHixsjICGq1GhYWFmjZsiV+//137N+/v9gT6J61DXlSUlISzM3NpYShm5sbVq9ejT/++AN6enpo0aIF1Go14uLikJ6erpHQWrx4MZYuXYrQ0FA4OzvD1NQU/v7+Wut+ABg+fDjUajXS0tIQExMDY2Nj9O7dG0D5Pju9e/fGlStXsGvXLsTGxqJ79+4YP348Fi1a9FTnQZtKTSIdOHAACxcuxIkTJ5CSkoKtW7diwIABJcaOGzcO3333HZYuXarxwcnJycHkyZPxww8/IDs7G927d8e3336LV155RYpJT0/HxIkTpWxpv379EBYW9syPhi2vf+9mIz2r7D/ysliYGlXoUZVUPVhZWcHd3R3Lli3DhAkTSr0fG3icEEhNTYWBgcFTj2vTokULyOVyXL16tcKP3zx48CB8fHzw9ttvAwDu37+Py5cva8QYGRkhP1/zMa7t2rXDuXPnNBqZJzVv3hyPHj3C8ePH0bFjRwCPx7m4e/duhcpXSF9fH9nZ2SUu+/3339GgQQPMmDFDmnflyhWNmFatWmHv3r3SYz9L0rFjR0yYMAEeHh7Q19fHlClTnqqsT2rRogWuXr2Ka9euSb2Rzp8/j4yMDDRv3rxYvK2tLerVq4d//vkHw4cPL3W75ubmePfdd/Huu+9i0KBB6NWrF+7cuQNLS8tnLjNp96x1f1FsC54/1svPVi9369YNGzduRHp6ukZvS7Vajf379yM+Pr7M+rU8Cp9i8+RxPct5LElERATS09OlsSiAx18kvvvuOxgZGWHu3LmQyWTo0qULFi1ahOzsbGk8pNu3byMpKQkrV65Ely5dAACHDh0q976HDx+Onj174ty5c9i3bx8+//xzaVm7du2wadMm2NjYaPSiLapt27Zo27Ytpk+fDhcXF2zYsIFJpBfoWep+1vMvDuv7F3sd/uQ+Dx06hJEjR0rzDh8+rHG9W5i4MTAwQI8ePQA8bkc2btxYbDwkXUpLS8OGDRswYMAA6Ok9Hjq6cFyk0NBQqNVqyGQyqNVqhISEID09HZMmTZLWP3jwIPr3748RI0YAeJz8+euvv0q8li+qc+fOsLe3x6ZNm7B7924MHjxYau/K+9mpU6cOfHx84OPjgy5dumDKlCkvZxIpKysLrVu3xujRozUa6qK2bduGo0ePlvioPX9/f+zYsQMbN26ElZUVAgMD4enpiRMnTki/gHl5eeH69euIiooCAGmQqx07djyfA3vCv3ez0WNxHLLz8rUHl0JhqI/YQDUblZfQt99+C1dXV3To0AFBQUFo1aoV9PT0kJCQgD///BPt27cHAPTo0QMuLi4YMGAA5s+fD0dHR9y4cQO//vorBgwYUOrtW08yMzPD5MmT8fHHH6OgoABvvPEGMjMzcfjwYdSqVQujRo0qdd0mTZpgy5Yt6Nu3L2QyGWbOnFksw9+wYUMcOHAAQ4cOhVwuh7W1NaZNm4bXX38d48ePh6+vL0xNTZGUlISYmBiEhYVJt1r5+vriu+++g4GBAfz9/cvVI+jRo0dITU0F8L9utOfPn8e0adNKPYarV69i48aNeO2117Br1y5s3bpVI2b27Nno3r07GjdujKFDh+LRo0fYvXs3pk6dqhHn4uKC3bt3o1evXjAwMMDHH3+stbxl6dGjB1q1aoXhw4cjNDQUjx49gp+fH9RqdanvbVBQECZOnAhzc3P07t0bOTk5OH78ONLT0xEQEIClS5eibt26aNOmDfT09PDTTz/Bzs7uhSXPazpd1P1FsS14MVgvP3293K1bN3z++edISUnB5MmTpflqtRpffvkl7t27V2xQ7Ypq0KABZDIZdu7cibfeegsKheKZzuODBw+QmpqKR48e4d9//8WWLVuwdOlSfPjhhxpldXNzw6RJk2BgYCAlh9zc3BAYGIh27dpJSR0LCwtYWVnhu+++Q926dXH16tVy91oFHp8rW1tbDB8+HA0bNtRI/gwfPhwLFy5E//79MXfuXLzyyiu4evUqtmzZgilTpiAvLw/fffcd+vXrB5VKhQsXLuDixYsaX9bo+XrWup/1/IvF+v7FXYcXmjJlCoYMGSINTL5jxw5s2bIFsbGxUkxh4mbHjh344osvADyub9955x3UqVMHLVq00Fo+bYQQSE1NhRACd+/eRXx8PIKDg6FUKjUGQy+8vS4yMlK65a5r164YPHgw8vLyNH4wadKkCTZv3ozDhw/DwsICS5YsQWpqarmSSDKZDF5eXlixYgUuXryo8UCK8nx2Zs2ahfbt26Nly5bIycnBzp07y7Xfp1WpSaTevXtL3bRK8++//+Kjjz7Cnj170KdPH41lGRkZWL16NdatWydlKSMjI2Fvb4/Y2Fh4eHggKSkJUVFROHLkiHTbx6pVq+Di4oILFy6U+SQnXUjPykV2Xj5C322DJja1Krz+pbT78N90GulZuWxQntKltPvagyppP40bN8apU6cQHByM6dOn4/r165DL5WjRogUmT54MPz8/AI8rll9//RUzZszAe++9h1u3bsHOzg5du3aFra1tuff3+eefw8bGBiEhIfjnn39Qu3ZttGvXDp9++mmZ6y1duhTvvfceOnfuLDVKmZmZGjFz587FuHHj0LhxY+Tk5EAIgVatWiEuLg4zZsxAly5dIIRA48aNNZ46tnbtWrz//vvShfMXX3yBmTNnaj2Wc+fOSeM8mJiYoHHjxli+fHmpF8v9+/fHxx9/jI8++gg5OTno06cPZs6ciaCgICnGzc0NP/30Ez7//HN8+eWXMDc3R9euXUvcnqurK3bt2oW33noL+vr6JXYVLi+ZTIZt27ZhwoQJ6Nq1K/T09NCrVy+EhYWVus77778PExMTLFy4EFOnToWpqSmcnZ2lnpq1atXC/Pnz8ddff0FfXx+vvfYafv31V+mXFXq+nrXuL+plawtYL//Py1Qvu7i4SF3rC798AcBrr72G/Px8KBSKYrfgVlS9evUwZ84cfPLJJxg9ejRGjhyJ8PDwpz6Pq1atwqpVq2BkZAQrKyu0b98emzZtkn7xL+Tk5ARra2s0aNBAShip1Wrk5+dr/DKsp6eHjRs3YuLEiXBycoKjoyO+/vprjS8aZZHJZBg2bBgWLlyIWbNmaSwzMTHBgQMHMG3aNAwcOBD37t1DvXr10L17d5ibmyM7Oxt//vknIiIicPv2bdStWxcfffQRxo0bV65907N7lrr/ZavnC7G+/5+Xqb6v6HV4oQEDBuCrr77CwoULMXHiRDRq1Ahr167VqCOVSiXatm2Lq1evSgmjLl26oKCgQGe9kDIzM1G3bl3IZDKYm5vD0dERo0aNwqRJk4r19OzWrRtOnjwpldHCwgItWrTAjRs3NBI1M2fORHJyMjw8PGBiYoKxY8diwIAByMjIKFeZhg8fjuDgYDRo0EDq3VpI22fHyMgI06dPx+XLl6FQKNClSxds3LjxGc5Q2WSicPSoSiaTyYrdzlZQUIAePXqgf//+mDRpEho2bAh/f3/pS9Jvv/0mPVrRwsJCWq9169YYMGAA5syZgzVr1iAgIKBYt7zatWtj6dKlpXarLhwIq1BmZibs7e2RkZFRZhfiohL/zYBn2CHsnPAGnOopy72ertavCR4+fIjk5GQ0atRIY5yF59ETQBv+gkTVUWl/Q8Djuk+pVFa47ntZPO3x67ruro5tQUmfK9bLRFUH6/7SVUbdXx3reYDX4UTVjS7q/io9sPb8+fNhYGBQ6i/8qampMDIy0kggAY/HCynsXpeamgobG5ti69rY2EgxJQkJCcGcOXOeofRU2erVViA2UK3TMUm04b3sRESlY71MRFQzsL4nenlV2STSiRMn8NVXX+HkyZMVHjG+6AjvFXnqUaHp06cjICBAel3YE4mql3q1FWxMiIiqENbLREQ1A+t7opdTlR0c4+DBg0hLS0P9+vVhYGAAAwMDXLlyBYGBgdKo+HZ2dsjNzUV6errGumlpadL9qXZ2drh582ax7d+6davMe1jlcjnMzc01JiIiIiIiIiKimqrKJpG8vb1x5swZnD59WppUKhWmTJmCPXv2AHg8aKOhoSFiYmKk9VJSUpCYmIjOnTsDeDzIY0ZGBo4dOybFHD16FBkZGVIMERERERERERGVrVJvZ7t//z4uXbokvU5OTsbp06dhaWmJ+vXrw8rKSiPe0NAQdnZ20hPVlEolxowZg8DAQFhZWcHS0hKTJ0+Gs7Oz9LS25s2bS48uXLlyJQBg7Nix8PT0fO5PZqMXq4qMEU9U7fBvh54nfr6Iqib+bZIu8fNEVD3o4m+1UnsiHT9+HG3btkXbtm0BAAEBAWjbtm2xR5qWZenSpRgwYACGDBkCV1dXmJiYYMeOHdDX15di1q9fD2dnZ/Ts2RM9e/ZEq1atsG7dOp0fD1UOQ0NDAMCDBw8quSRE1VPh307h3xKRLhS2w7m5L25QVSIqP9b9pAu8DieqXnRR91dqTyQ3N7cKZcIuX75cbJ6xsTHCwsIQFhZW6nqWlpaIjIx8miJSNaCvr4/atWsjLS0NAGBiYlLhwdiJaiIhBB48eIC0tDTUrl1bI/lO9KwMDAxgYmKCW7duwdDQEHp6VfYOeqIahXU/6RKvw4mqB13W/VX26WxEFWFnZwcAUgNGROVXu3Zt6W+ISFdkMhnq1q2L5ORkXLlypbKLQ0RFsO4nXeF1OFH1oYu6n0kkeikUflmxsbFBXl5eZReHqNowNDTkr9D03BgZGcHBwYG3tBFVMaz7SZd4HU5UPeiq7mcSiV4q+vr6vCgiIqpC9PT0YGxsXNnFICKi54zX4UQ1A5NIRERE1cSltPs62Y6FqRHq1VboZFtEREREVHMwiURERFTFWZgaQWGoD/9Np3WyPYWhPmID1UwkEREREVGFMIlERERUxdWrrUBsoBrpWc8+ttCltPvw33Qa6Vm5TCIRERERUYUwiURERFQN1KutYNKHiIiIiCqVXmUXgIiIiIiIiIiIqj4mkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIqEQNGzaETCYrNo0fPx4AIIRAUFAQVCoVFAoF3NzccO7cOY1t5OTkYMKECbC2toapqSn69euH69eva8Skp6fD29sbSqUSSqUS3t7euHv3rkbM1atX0bdvX5iamsLa2hoTJ05Ebm6uRszZs2ehVquhUChQr149zJ07F0II3Z8YIqIaikkkIiIiIiIqUUJCAlJSUqQpJiYGADB48GAAwIIFC7BkyRIsW7YMCQkJsLOzg7u7O+7duydtw9/fH1u3bsXGjRtx6NAh3L9/H56ensjPz5divLy8cPr0aURFRSEqKgqnT5+Gt7e3tDw/Px99+vRBVlYWDh06hI0bN2Lz5s0IDAyUYjIzM+Hu7g6VSoWEhASEhYVh0aJFWLJkyfM+TURENYZBZReAiIiIiIiqpjp16mi8/vLLL9G4cWOo1WoIIRAaGooZM2Zg4MCBAICIiAjY2tpiw4YNGDduHDIyMrB69WqsW7cOPXr0AABERkbC3t4esbGx8PDwQFJSEqKionDkyBF06tQJALBq1Sq4uLjgwoULcHR0RHR0NM6fP49r165BpVIBABYvXgwfHx/MmzcP5ubmWL9+PR4+fIjw8HDI5XI4OTnh4sWLWLJkCQICAiCTyV7gmSMiejmxJxIREREREWmVm5uLyMhIvPfee5DJZEhOTkZqaip69uwpxcjlcqjVahw+fBgAcOLECeTl5WnEqFQqODk5STHx8fFQKpVSAgkAXn/9dSiVSo0YJycnKYEEAB4eHsjJycGJEyekGLVaDblcrhFz48YNXL58udTjysnJQWZmpsZEREQlYxKJiIiIiIi02rZtG+7evQsfHx8AQGpqKgDA1tZWI87W1lZalpqaCiMjI1hYWJQZY2NjU2x/NjY2GjFF92NhYQEjI6MyYwpfF8aUJCQkRBqLSalUwt7evvSTQERUwzGJREREREREWq1evRq9e/fW6A0EoNhtYkIIrbeOFY0pKV4XMYWDapdVnunTpyMjI0Oarl27VmbZiYhqMiaRiIiIiIioTFeuXEFsbCzef/99aZ6dnR2A4r180tLSpB5AdnZ2yM3NRXp6epkxN2/eLLbPW7duacQU3U96ejry8vLKjElLSwNQvLfUk+RyOczNzTUmIiIqGZNIRERERERUprVr18LGxgZ9+vSR5jVq1Ah2dnbSE9uAx+MmxcXFoXPnzgCA9u3bw9DQUCMmJSUFiYmJUoyLiwsyMjJw7NgxKebo0aPIyMjQiElMTERKSooUEx0dDblcjvbt20sxBw4cQG5urkaMSqVCw4YNdXg2iIhqLiaRiIiIiIioVAUFBVi7di1GjRoFA4P/PdxZJpPB398fwcHB2Lp1KxITE+Hj4wMTExN4eXkBAJRKJcaMGYPAwEDs3bsXp06dwogRI+Ds7Cw9ra158+bo1asXfH19ceTIERw5cgS+vr7w9PSEo6MjAKBnz55o0aIFvL29cerUKezduxeTJ0+Gr6+v1HPIy8sLcrkcPj4+SExMxNatWxEcHMwnsxER6ZCB9hAiIiIiIqqpYmNjcfXqVbz33nvFlk2dOhXZ2dnw8/NDeno6OnXqhOjoaJiZmUkxS5cuhYGBAYYMGYLs7Gx0794d4eHh0NfXl2LWr1+PiRMnSk9x69evH5YtWyYt19fXx65du+Dn5wdXV1coFAp4eXlh0aJFUoxSqURMTAzGjx+PDh06wMLCAgEBAQgICHgep4WIqEZiEomIiIiIiErVs2dPaYDqomQyGYKCghAUFFTq+sbGxggLC0NYWFipMZaWloiMjCyzHPXr18fOnTvLjHF2dsaBAwfKjCEioqfH29mIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEgrJpGIiIiIiIiIiEirSk0iHThwAH379oVKpYJMJsO2bdukZXl5eZg2bRqcnZ1hamoKlUqFkSNH4saNGxrbyMnJwYQJE2BtbQ1TU1P069cP169f14hJT0+Ht7c3lEollEolvL29cffu3RdwhEREREREREREL4dKTSJlZWWhdevWWLZsWbFlDx48wMmTJzFz5kycPHkSW7ZswcWLF9GvXz+NOH9/f2zduhUbN27EoUOHcP/+fXh6eiI/P1+K8fLywunTpxEVFYWoqCicPn0a3t7ez/34iIiIiIiIiIheFgaVufPevXujd+/eJS5TKpWIiYnRmBcWFoaOHTvi6tWrqF+/PjIyMrB69WqsW7cOPXr0AABERkbC3t4esbGx8PDwQFJSEqKionDkyBF06tQJALBq1Sq4uLjgwoULcHR0fL4HSURERERERET0EqhWYyJlZGRAJpOhdu3aAIATJ04gLy8PPXv2lGJUKhWcnJxw+PBhAEB8fDyUSqWUQAKA119/HUqlUoopSU5ODjIzMzUmIiIiIiIiIqKaqtokkR4+fIhPPvkEXl5eMDc3BwCkpqbCyMgIFhYWGrG2trZITU2VYmxsbIptz8bGRoopSUhIiDSGklKphL29vQ6PhoiIiIiIiIioeqkWSaS8vDwMHToUBQUF+Pbbb7XGCyEgk8mk10/+v7SYoqZPn46MjAxpunbt2tMVnoiIiIiIiIjoJVDlk0h5eXkYMmQIkpOTERMTI/VCAgA7Ozvk5uYiPT1dY520tDTY2tpKMTdv3iy23Vu3bkkxJZHL5TA3N9eYiIiIiIiIiIhqqiqdRCpMIP3111+IjY2FlZWVxvL27dvD0NBQYwDulJQUJCYmonPnzgAAFxcXZGRk4NixY1LM0aNHkZGRIcUQEREREREREVHZKvXpbPfv38elS5ek18nJyTh9+jQsLS2hUqkwaNAgnDx5Ejt37kR+fr40hpGlpSWMjIygVCoxZswYBAYGwsrKCpaWlpg8eTKcnZ2lp7U1b94cvXr1gq+vL1auXAkAGDt2LDw9PflkNiIiIiIiIiKicqrUJNLx48fRrVs36XVAQAAAYNSoUQgKCsL27dsBAG3atNFYb9++fXBzcwMALF26FAYGBhgyZAiys7PRvXt3hIeHQ19fX4pfv349Jk6cKD3FrV+/fli2bNlzPDIiIiIiIiIiopdLpSaR3NzcIIQodXlZywoZGxsjLCwMYWFhpcZYWloiMjLyqcpIRERERERERERVfEwkIiKikjx69AifffYZGjVqBIVCgVdffRVz585FQUGBFCOEQFBQEFQqFRQKBdzc3HDu3LlKLDURERERUfXGJBIREVU78+fPx4oVK7Bs2TIkJSVhwYIFWLhwoUav1AULFmDJkiVYtmwZEhISYGdnB3d3d9y7d68SS05EREREVH0xiURERNVOfHw8+vfvjz59+qBhw4YYNGgQevbsiePHjwN43AspNDQUM2bMwMCBA+Hk5ISIiAg8ePAAGzZsqOTSExERERFVT0wiERFRtfPGG29g7969uHjxIgDgjz/+wKFDh/DWW28BePy0z9TUVOmBCgAgl8uhVqtx+PDhUrebk5ODzMxMjYmIiIiIiB6r1IG1iYiInsa0adOQkZGBZs2aQV9fH/n5+Zg3bx6GDRsGAEhNTQUA2Nraaqxna2uLK1eulLrdkJAQzJkz5/kVnIiIiIioGmNPJCIiqnY2bdqEyMhIbNiwASdPnkRERAQWLVqEiIgIjTiZTKbxWghRbN6Tpk+fjoyMDGm6du3acyk/EREREVF1xJ5IRERU7UyZMgWffPIJhg4dCgBwdnbGlStXEBISglGjRsHOzg7A4x5JdevWldZLS0sr1jvpSXK5HHK5/PkWnoiIiIiommJPJCIiqnYePHgAPT3NJkxfXx8FBQUAgEaNGsHOzg4xMTHS8tzcXMTFxaFz584vtKxERERERC8L9kQiIqJqp2/fvpg3bx7q16+Pli1b4tSpU1iyZAnee+89AI9vY/P390dwcDAcHBzg4OCA4OBgmJiYwMvLq5JLT0RERERUPTGJRERE1U5YWBhmzpwJPz8/pKWlQaVSYdy4cZg1a5YUM3XqVGRnZ8PPzw/p6eno1KkToqOjYWZmVoklJyIiIiKqvphEIiKiasfMzAyhoaEIDQ0tNUYmkyEoKAhBQUEvrFxERERERC8zjolERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERERERERaMYlERERERESl+vfffzFixAhYWVnBxMQEbdq0wYkTJ6TlQggEBQVBpVJBoVDAzc0N586d09hGTk4OJkyYAGtra5iamqJfv364fv26Rkx6ejq8vb2hVCqhVCrh7e2Nu3fvasRcvXoVffv2hampKaytrTFx4kTk5uZqxJw9exZqtRoKhQL16tXD3LlzIYTQ7UkhIqqhmEQiIiIiIqISpaenw9XVFYaGhti9ezfOnz+PxYsXo3bt2lLMggULsGTJEixbtgwJCQmws7ODu7s77t27J8X4+/tj69at2LhxIw4dOoT79+/D09MT+fn5UoyXlxdOnz6NqKgoREVF4fTp0/D29paW5+fno0+fPsjKysKhQ4ewceNGbN68GYGBgVJMZmYm3N3doVKpkJCQgLCwMCxatAhLlix5vieKiKiGMKjsAhARERERUdU0f/582NvbY+3atdK8hg0bSv8XQiA0NBQzZszAwIEDAQARERGwtbXFhg0bMG7cOGRkZGD16tVYt24devToAQCIjIyEvb09YmNj4eHhgaSkJERFReHIkSPo1KkTAGDVqlVwcXHBhQsX4OjoiOjoaJw/fx7Xrl2DSqUCACxevBg+Pj6YN28ezM3NsX79ejx8+BDh4eGQy+VwcnLCxYsXsWTJEgQEBEAmk72gM0dE9HJiTyQiIiIiIirR9u3b0aFDBwwePBg2NjZo27YtVq1aJS1PTk5GamoqevbsKc2Ty+VQq9U4fPgwAODEiRPIy8vTiFGpVHBycpJi4uPjoVQqpQQSALz++utQKpUaMU5OTlICCQA8PDyQk5Mj3V4XHx8PtVoNuVyuEXPjxg1cvny5xGPMyclBZmamxkRERCVjEomIiIiIiEr0zz//YPny5XBwcMCePXvwwQcfYOLEifj+++8BAKmpqQAAW1tbjfVsbW2lZampqTAyMoKFhUWZMTY2NsX2b2NjoxFTdD8WFhYwMjIqM6bwdWFMUSEhIdI4TEqlEvb29lrOChFRzcUkEhERERERlaigoADt2rVDcHAw2rZti3HjxsHX1xfLly/XiCt6m5gQQuutY0VjSorXRUzhoNqllWf69OnIyMiQpmvXrpVZbiKimoxJJCIiIiIiKlHdunXRokULjXnNmzfH1atXAQB2dnYAivfySUtLk3oA2dnZITc3F+np6WXG3Lx5s9j+b926pRFTdD/p6enIy8srMyYtLQ1A8d5SheRyOczNzTUmIiIqGZNIRERERERUIldXV1y4cEFj3sWLF9GgQQMAQKNGjWBnZ4eYmBhpeW5uLuLi4tC5c2cAQPv27WFoaKgRk5KSgsTERCnGxcUFGRkZOHbsmBRz9OhRZGRkaMQkJiYiJSVFiomOjoZcLkf79u2lmAMHDiA3N1cjRqVSaQwITkRET4dJJCIiIiIiKtHHH3+MI0eOIDg4GJcuXcKGDRvw3XffYfz48QAe3yLm7++P4OBgbN26FYmJifDx8YGJiQm8vLwAAEqlEmPGjEFgYCD27t2LU6dOYcSIEXB2dpae1ta8eXP06tULvr6+OHLkCI4cOQJfX194enrC0dERANCzZ0+0aNEC3t7eOHXqFPbu3YvJkyfD19dX6j3k5eUFuVwOHx8fJCYmYuvWrQgODuaT2YiIdMSgsgtARERERERV02uvvYatW7di+vTpmDt3Lho1aoTQ0FAMHz5cipk6dSqys7Ph5+eH9PR0dOrUCdHR0TAzM5Nili5dCgMDAwwZMgTZ2dno3r07wsPDoa+vL8WsX78eEydOlJ7i1q9fPyxbtkxarq+vj127dsHPzw+urq5QKBTw8vLCokWLpBilUomYmBiMHz8eHTp0gIWFBQICAhAQEPA8TxMRUY3BJBIREREREZXK09MTnp6epS6XyWQICgpCUFBQqTHGxsYICwtDWFhYqTGWlpaIjIwssyz169fHzp07y4xxdnbGgQMHyowhIqKnw9vZiIiIiIiIiIhIKyaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIKyaRiIiIiIiIiIhIK4PKLgARERG9eJfS7utkOxamRqhXW6GTbRERERFR1cYkEhERUQ1iYWoEhaE+/Ded1sn2FIb6iA1UM5FEREREVAMwiURERFSD1KutQGygGulZuc+8rUtp9+G/6TTSs3KZRCIiIiKqAZhEIiIiqmHq1VYw6UNEREREFcaBtYmIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISCsmkYiIiIiIiIiISKtKTSIdOHAAffv2hUqlgkwmw7Zt2zSWCyEQFBQElUoFhUIBNzc3nDt3TiMmJycHEyZMgLW1NUxNTdGvXz9cv35dIyY9PR3e3t5QKpVQKpXw9vbG3bt3n/PRERERERERERG9PCo1iZSVlYXWrVtj2bJlJS5fsGABlixZgmXLliEhIQF2dnZwd3fHvXv3pBh/f39s3boVGzduxKFDh3D//n14enoiPz9fivHy8sLp06cRFRWFqKgonD59Gt7e3s/9+IiIiIiIiIiIXhYGlbnz3r17o3fv3iUuE0IgNDQUM2bMwMCBAwEAERERsLW1xYYNGzBu3DhkZGRg9erVWLduHXr06AEAiIyMhL29PWJjY+Hh4YGkpCRERUXhyJEj6NSpEwBg1apVcHFxwYULF+Do6PhiDpaIiIiIiIiIqBqrsmMiJScnIzU1FT179pTmyeVyqNVqHD58GABw4sQJ5OXlacSoVCo4OTlJMfHx8VAqlVICCQBef/11KJVKKaYkOTk5yMzM1JiIiIiIiIiIiGqqKptESk1NBQDY2tpqzLe1tZWWpaamwsjICBYWFmXG2NjYFNu+jY2NFFOSkJAQaQwlpVIJe3v7ZzoeIiIiIiIiIqLqrMomkQrJZDKN10KIYvOKKhpTUry27UyfPh0ZGRnSdO3atQqWnIiIiIiIiIjo5VFlk0h2dnYAUKy3UFpamtQ7yc7ODrm5uUhPTy8z5ubNm8W2f+vWrWK9nJ4kl8thbm6uMRERERERERER1VRVNonUqFEj2NnZISYmRpqXm5uLuLg4dO7cGQDQvn17GBoaasSkpKQgMTFRinFxcUFGRgaOHTsmxRw9ehQZGRlSDBERERERERERla1Sn852//59XLp0SXqdnJyM06dPw9LSEvXr14e/vz+Cg4Ph4OAABwcHBAcHw8TEBF5eXgAApVKJMWPGIDAwEFZWVrC0tMTkyZPh7OwsPa2tefPm6NWrF3x9fbFy5UoAwNixY+Hp6cknsxERERERERERlVOlJpGOHz+Obt26Sa8DAgIAAKNGjUJ4eDimTp2K7Oxs+Pn5IT09HZ06dUJ0dDTMzMykdZYuXQoDAwMMGTIE2dnZ6N69O8LDw6Gvry/FrF+/HhMnTpSe4tavXz8sW7bsBR0lEREREREREVH1V6lJJDc3NwghSl0uk8kQFBSEoKCgUmOMjY0RFhaGsLCwUmMsLS0RGRn5LEUlIiIiIiIiIqrRquyYSEREREREREREVHUwiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERERERFoxiURERERERCUKCgqCTCbTmOzs7KTlQggEBQVBpVJBoVDAzc0N586d09hGTk4OJkyYAGtra5iamqJfv364fv26Rkx6ejq8vb2hVCqhVCrh7e2Nu3fvasRcvXoVffv2hampKaytrTFx4kTk5uZqxJw9exZqtRoKhQL16tXD3LlzIYTQ7UkhIqrBmEQiIiIiIqJStWzZEikpKdJ09uxZadmCBQuwZMkSLFu2DAkJCbCzs4O7uzvu3bsnxfj7+2Pr1q3YuHEjDh06hPv378PT0xP5+flSjJeXF06fPo2oqChERUXh9OnT8Pb2lpbn5+ejT58+yMrKwqFDh7Bx40Zs3rwZgYGBUkxmZibc3d2hUqmQkJCAsLAwLFq0CEuWLHnOZ4iIqOYwqOwCEBERERFR1WVgYKDR+6iQEAKhoaGYMWMGBg4cCACIiIiAra0tNmzYgHHjxiEjIwOrV6/GunXr0KNHDwBAZGQk7O3tERsbCw8PDyQlJSEqKgpHjhxBp06dAACrVq2Ci4sLLly4AEdHR0RHR+P8+fO4du0aVCoVAGDx4sXw8fHBvHnzYG5ujvXr1+Phw4cIDw+HXC6Hk5MTLl68iCVLliAgIAAymewFnTEiopcXeyIREREREVGp/vrrL6hUKjRq1AhDhw7FP//8AwBITk5GamoqevbsKcXK5XKo1WocPnwYAHDixAnk5eVpxKhUKjg5OUkx8fHxUCqVUgIJAF5//XUolUqNGCcnJymBBAAeHh7IycnBiRMnpBi1Wg25XK4Rc+PGDVy+fLnU48vJyUFmZqbGREREJWMSiYiIiIiIStSpUyd8//332LNnD1atWoXU1FR07twZt2/fRmpqKgDA1tZWYx1bW1tpWWpqKoyMjGBhYVFmjI2NTbF929jYaMQU3Y+FhQWMjIzKjCl8XRhTkpCQEGksJqVSCXt7+7JPChFRDcYkEhERERERlah3795455134OzsjB49emDXrl0AHt+2VqjobWJCCK23jhWNKSleFzGFg2qXVZ7p06cjIyNDmq5du1Zm2YmIajImkYiIiIiIqFxMTU3h7OyMv/76SxonqWgvn7S0NKkHkJ2dHXJzc5Genl5mzM2bN4vt69atWxoxRfeTnp6OvLy8MmPS0tIAFO8t9SS5XA5zc3ONiYiISsYkEhERERERlUtOTg6SkpJQt25dNGrUCHZ2doiJiZGW5+bmIi4uDp07dwYAtG/fHoaGhhoxKSkpSExMlGJcXFyQkZGBY8eOSTFHjx5FRkaGRkxiYiJSUlKkmOjoaMjlcrRv316KOXDgAHJzczViVCoVGjZsqPuTQURUAzGJREREREREJZo8eTLi4uKQnJyMo0ePYtCgQcjMzMSoUaMgk8ng7++P4OBgbN26FYmJifDx8YGJiQm8vLwAAEqlEmPGjEFgYCD27t2LU6dOYcSIEdLtcQDQvHlz9OrVC76+vjhy5AiOHDkCX19feHp6wtHREQDQs2dPtGjRAt7e3jh16hT27t2LyZMnw9fXV+o55OXlBblcDh8fHyQmJmLr1q0IDg7mk9mIiHTIoLILQEREREREVdP169cxbNgw/Pfff6hTpw5ef/11HDlyBA0aNAAATJ06FdnZ2fDz80N6ejo6deqE6OhomJmZSdtYunQpDAwMMGTIEGRnZ6N79+4IDw+Hvr6+FLN+/XpMnDhReopbv379sGzZMmm5vr4+du3aBT8/P7i6ukKhUMDLywuLFi2SYpRKJWJiYjB+/Hh06NABFhYWCAgIQEBAwPM+TURENQaTSEREREREVKKNGzeWuVwmkyEoKAhBQUGlxhgbGyMsLAxhYWGlxlhaWiIyMrLMfdWvXx87d+4sM8bZ2RkHDhwoM4aIiJ4eb2cjIiIiIiIiIiKtmEQiIiIiIiIiIiKtmEQiIiIiIiIiIiKtmEQiIqJq6d9//8WIESNgZWUFExMTtGnTBidOnJCWCyEQFBQElUoFhUIBNzc3nDt3rhJLTERERERUvTGJRERE1U56ejpcXV1haGiI3bt34/z581i8eDFq164txSxYsABLlizBsmXLkJCQADs7O7i7u+PevXuVV3AiIiIiomqMT2cjIqJqZ/78+bC3t8fatWuleQ0bNpT+L4RAaGgoZsyYgYEDBwIAIiIiYGtriw0bNmDcuHEvushERERERNUeeyIREVG1s337dnTo0AGDBw+GjY0N2rZti1WrVknLk5OTkZqaip49e0rz5HI51Go1Dh8+XOp2c3JykJmZqTEREREREdFjTCIREVG1888//2D58uVwcHDAnj178MEHH2DixIn4/vvvAQCpqakAAFtbW431bG1tpWUlCQkJgVKplCZ7e/vndxBERERERNUMk0hERFTtFBQUoF27dggODkbbtm0xbtw4+Pr6Yvny5RpxMplM47UQoti8J02fPh0ZGRnSdO3atedSfiIiIiKi6ohJJCIiqnbq1q2LFi1aaMxr3rw5rl69CgCws7MDgGK9jtLS0or1TnqSXC6Hubm5xkRERERERI8xiURERNWOq6srLly4oDHv4sWLaNCgAQCgUaNGsLOzQ0xMjLQ8NzcXcXFx6Ny58wstKxERERHRy4JPZyMiomrn448/RufOnREcHIwhQ4bg2LFj+O677/Ddd98BeHwbm7+/P4KDg+Hg4AAHBwcEBwfDxMQEXl5elVx6IiIiIqLqiUkkIiKqdl577TVs3boV06dPx9y5c9GoUSOEhoZi+PDhUszUqVORnZ0NPz8/pKeno1OnToiOjoaZmVkllpyIiIiIqPpiEomIiKolT09PeHp6lrpcJpMhKCgIQUFBL65QREREREQvMY6JREREREREREREWjGJREREREREREREWjGJREREREREREREWjGJREREREREREREWjGJREREREREREREWjGJREREREREREREWjGJREREREREREREWjGJREREREREREREWjGJREREREREREREWlU4iZSXl4du3brh4sWLz6M8RERUTbF9ICKqOlgnExHR82BQ0RUMDQ2RmJgImUz2PMpDRETVFNuHmutS2n2dbcvC1Aj1ait0tj2imop1MhERPQ8VTiIBwMiRI7F69Wp8+eWXui6PhkePHiEoKAjr169Hamoq6tatCx8fH3z22WfQ03vciUoIgTlz5uC7775Deno6OnXqhG+++QYtW7aUtpOTk4PJkyfjhx9+QHZ2Nrp3745vv/0Wr7zyynMtPxFRTfOi2geqGixMjaAw1If/ptM626bCUB+xgWomkoh0gHUyERHp2lMlkXJzc/F///d/iImJQYcOHWBqaqqxfMmSJTop3Pz587FixQpERESgZcuWOH78OEaPHg2lUolJkyYBABYsWIAlS5YgPDwcTZs2xRdffAF3d3dcuHABZmZmAAB/f3/s2LEDGzduhJWVFQIDA+Hp6YkTJ05AX19fJ2UlIqIX1z5Q1VCvtgKxgWqkZ+XqZHuX0u7Df9NppGflMolEpAOsk4mISNeeKomUmJiIdu3aAUCx+6x12WU2Pj4e/fv3R58+fQAADRs2xA8//IDjx48DeNwLKTQ0FDNmzMDAgQMBABEREbC1tcWGDRswbtw4ZGRkYPXq1Vi3bh169OgBAIiMjIS9vT1iY2Ph4eGhs/ISEdV0L6p9oKqjXm0FEz5EVRTrZCIi0rWnSiLt27dP1+Uo0RtvvIEVK1bg4sWLaNq0Kf744w8cOnQIoaGhAIDk5GSkpqaiZ8+e0jpyuRxqtRqHDx/GuHHjcOLECeTl5WnEqFQqODk54fDhw0wiERHp0ItqH4iISDvWyUREpGtPlUQqdOnSJfz999/o2rUrFAoFhBA6/VVj2rRpyMjIQLNmzaCvr4/8/HzMmzcPw4YNAwCkpqYCAGxtbTXWs7W1xZUrV6QYIyMjWFhYFIspXL8kOTk5yMnJkV5nZmbq5JiIiGqC590+EBFR+bFOJiIiXdF7mpVu376N7t27o2nTpnjrrbeQkpICAHj//fcRGBios8Jt2rQJkZGR2LBhA06ePImIiAgsWrQIERERGnFFG8HyNIzaYkJCQqBUKqXJ3t7+6Q+EiKiGeFHtAxERacc6mYiIdO2pkkgff/wxDA0NcfXqVZiYmEjz3333XURFRemscFOmTMEnn3yCoUOHwtnZGd7e3vj4448REhICALCzswOAYj2K0tLSpN5JdnZ2yM3NRXp6eqkxJZk+fToyMjKk6dq1azo7LiKil9WLah+IiEg71slERKRrT5VEio6Oxvz58/HKK69ozHdwcJBuI9OFBw8eQE9Ps4j6+vooKCgAADR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"text/plain": [ - "
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" ] }, "metadata": {}, @@ -316,7 +553,7 @@ } ], "source": [ - "# Locate BHs, NSs and WDs\n", + "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", "k_bh = np.concatenate([p2_bh, c_bh])\n", @@ -326,21 +563,25 @@ "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", "k_wd = np.concatenate([p2_wd, c_wd])\n", + "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", + "k_bd = np.concatenate([p2_bd, c_bd])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "bd_bins = np.linspace(0.01, 2, 20)\n", "\n", "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(kc_out[p2_bh]['mass'], histtype = 'step',\n", + "plt.subplot(1, 3, 1)\n", + "plt.hist(kc_out[k_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", - "plt.subplot(1, 2, 2)\n", + "plt.subplot(1, 3, 2)\n", "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", @@ -348,66 +589,111 @@ "plt.ylabel('Number')\n", "plt.legend()\n", "\n", + "plt.subplot(1, 3, 3)\n", + "plt.hist(kc_out[k_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 24, - "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", + "execution_count": 37, + "id": "a64ebad4-b642-4e76-b12f-92402f241484", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated black hole: 15.003127309066931\n", - "Smallest mass of a companion generated black hole: 0.07035468449081322\n" + "63.493905663432386\n", + "0.01001691235394781\n" ] } ], "source": [ - "# Finding the minimum mass of generated black holes\n", - "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", - "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" + "print(np.max(kc_out[k_wd]['mass']))\n", + "print(np.min(kc_out[k_wd]['mass']))" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", + "execution_count": 24, + "id": "497b4d94-6722-4ad7-880a-2b53d5acc28d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated object: 0.07000003012576093\n", - "Smallest mass of a companion generated object: 0.010000040848729129\n" + " mass isMultiple ... m_hst_f153m N_companions\n", + "-------------------- ---------- ... ------------------ ------------\n", + " 1.6412592122340768 True ... 1.891168727297026 2\n", + " 0.20991189263696136 False ... 7.70159095731871 0\n", + "0.017000902750311093 False ... nan 0\n", + "0.010753505226784877 False ... nan 0\n", + " 0.2688662867171623 True ... 6.5704973503446755 1\n", + " 0.1180619308611421 False ... 8.583856072073955 0\n", + " 0.08394927892076134 False ... 9.189440377342134 0\n", + " ... ... ... ... ...\n", + " 0.0206406101070209 False ... nan 0\n", + " 0.07136771909820422 False ... nan 0\n", + " 0.07067029373342434 True ... nan 2\n", + " 0.22895855805541523 False ... 7.553664542955746 0\n", + " 0.08333000096743302 True ... 9.202672780659682 1\n", + " 1.45216924939138 True ... 2.3052287375038536 1\n", + " 0.2641096309279692 False ... 7.327889430103641 0\n", + "Length = 3214 rows\n" ] } ], "source": [ - "# Finding the minimum mass of generated objects\n", - "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", - "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" + "print(kc_out[c_wd])" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", + "execution_count": 17, + "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 0.07035468449081322\n", - "Initial mass of largest generated black hole: 118.59389207250989\n" + "Smallest mass of a primary generated black hole: 15.001898748254684\n", + "Smallest mass of a companion generated black hole: 0.010087940766134134\n" ] } ], + "source": [ + "# Finding the minimum mass of generated black holes\n", + "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", + "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", + "metadata": {}, + "outputs": [], + "source": [ + "# Finding the minimum mass of generated objects\n", + "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", + "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", + "metadata": {}, + "outputs": [], "source": [ "# Finding the minimum/maximum inital masses of generated black holes\n", "print(\"Initial mass of smallest generated black hole: \" + str(np.min(kc_out[k_bh]['mass'])))\n", @@ -424,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", "metadata": {}, "outputs": [], @@ -436,19 +722,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 689034 stars out of mass range\n", - "Found 135376 companions out of stellar mass range\n" - ] - } - ], + "outputs": [], "source": [ "# Make cluster\n", "cluster_mass = 10**6\n", @@ -461,21 +738,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c598a266-297c-423d-b5f5-648f819c065e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Locate BHs, NSs and WDs\n", "p3_bh = np.where(c3_out['phase'] == 103)[0]\n", diff --git a/spisea/evolution.py b/spisea/evolution.py index e27b0147..d1dc1896 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1310,8 +1310,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): bd_idx = iso['mass'] < 0.08 iso['mass_current'][bd_idx] = iso['mass'][bd_idx] - - # Handling NaN effectvie temperatures + # Handling NaN effective temperatures nan_teff_idx = np.isnan(iso['logT']) if np.any(nan_teff_idx): iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) diff --git a/spisea/ifmr.py b/spisea/ifmr.py index f88afdbe..053b163d 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -36,24 +36,28 @@ def get_Z(self, Fe_H): """ return 10**(Fe_H - 1.85387) - def Kalirai_mass(self, MZAMS): + def Kalirai_mass(self, MZAMS): # for white dwarfs """ From Kalirai+08 https://ui.adsabs.harvard.edu/abs/2008ApJ...676..594K/abstract 1.16 < MZAMS < 6.5 But we use this function for anything between 0.5 and 9 depending on the IFMR. FIXME: need to extend these ranges... explain extension somewhere? Paper maybe? """ - + #debugging print statement + print(f"Input MZAMS: {MZAMS}") + result = 0.109*MZAMS + 0.394 final = np.zeros(len(MZAMS)) - bad_idx = np.where(MZAMS < 0.5) + bad_idx = np.where((MZAMS < 0.5) | (MZAMS >= 9)) final[bad_idx] = -99 - good_idx = np.where(MZAMS >= 0.5) + good_idx = np.where((MZAMS >= 0.5) & (MZAMS < 9)) final[good_idx] = result[good_idx] + print(f"Final masses: {final}") # Debugging print statement + return final @@ -552,11 +556,12 @@ def generate_death_mass(self, mass_array, metallicity_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 + * BD: typecode = 99 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: MZAMS > 0.5 + Range of validity: MZAMS > 0.5 and 0.01 < MZAMS < 0.08 Returns ------- @@ -569,7 +574,7 @@ def generate_death_mass(self, mass_array, metallicity_array): #output_array[1] holds the remnant type output_array = np.zeros((2, len(mass_array))) - codes = {'WD': 101, 'NS': 102, 'BH': 103} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} #create array to store the remnant masses generated by Spera equations rem_mass_array = np.zeros(len(mass_array)) @@ -647,6 +652,12 @@ def generate_death_mass(self, mass_array, metallicity_array): #remnant masses of stars with Z > 4.0e-3 and MZAMS > 10.0 high_metal_high_mass_idx = np.where((Z_array > 4.0e-3) & (core_mass > 10.0)) rem_mass_array[high_metal_high_mass_idx] = self.M_rem_high_metal_high_mass(Z_array[high_metal_high_mass_idx], core_mass[high_metal_high_mass_idx]) + + #classify brown dwarfs before checking for bad indices (in case they are looped into them) + BD_idx = np.where((mass_array >= 0.01) & (mass_array < 0.08)) + rem_mass_array[BD_idx] = mass_array[BD_idx] + output_array[0][BD_idx] = rem_mass_array[BD_idx] + output_array[1][BD_idx] = codes['BD'] #assign object types based on remnant mass bad_idx = np.where(rem_mass_array < 0) #outside the range of validity for the ifmr @@ -751,6 +762,7 @@ def generate_death_mass(self, mass_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 + * BD: typecode = 99 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. @@ -758,7 +770,7 @@ def generate_death_mass(self, mass_array): A remnant mass of -99 means you're outside the range of validity for applying the ifmr formula. - Range of validity: 0.5 < MZAMS < 120 + Range of validity: 0.01 < MZAMS < 0.08 and 0.5 < MZAMS < 120 Returns ------- @@ -774,23 +786,34 @@ def generate_death_mass(self, mass_array): #Random array to get probabilities for what type of object will form random_array = np.random.randint(1, 1001, size = len(mass_array)) - codes = {'WD': 101, 'NS': 102, 'BH': 103} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} """ The id_arrays are to separate all the different formation regimes """ - id_array0 = np.where((mass_array < 0.5) | (mass_array >= 120)) - output_array[0][id_array0] = -99 * np.ones(len(id_array0)) - output_array[1][id_array0] = -1 * np.ones(len(id_array0)) + #classifying brown dwarfs + id_array_BD = np.where((mass_array >= 0.01) & (mass_array < 0.08)) + output_array[0][id_array_BD] = mass_array[id_array_BD] + output_array[1][id_array_BD] = codes['BD'] + print(f'Brown dwarf indices: {id_array_BD}, Masses: {mass_array[id_array_BD]}') #for debugging + + #classifying invalid mass ranges + id_array0 = np.where((mass_array < 0.01) | ((mass_array >= 0.08) & (mass_array < 0.5)) | (mass_array >= 120)) + output_array[0][id_array0] = -99 + output_array[1][id_array0] = -1 + + #classifying white dwarfs id_array1 = np.where((mass_array >= 0.5) & (mass_array < 9)) output_array[0][id_array1] = self.Kalirai_mass(mass_array[id_array1]) output_array[1][id_array1]= codes['WD'] + #classifying neutron stars id_array2 = np.where((mass_array >= 9) & (mass_array < 15)) output_array[0][id_array2] = self.NS_mass(mass_array[id_array2]) output_array[1][id_array2] = codes['NS'] + #classifying black holes id_array3_BH = np.where((mass_array >= 15) & (mass_array < 17.8) & (random_array > 679)) output_array[0][id_array3_BH] = self.BH_mass_low(mass_array[id_array3_BH], 0.9) output_array[1][id_array3_BH] = codes['BH'] @@ -843,6 +866,7 @@ def generate_death_mass(self, mass_array): output_array[0][id_array10_NS] = self.NS_mass(mass_array[id_array10_NS]) output_array[1][id_array10_NS] = codes['NS'] + return(output_array) class IFMR_N20_Sukhbold(IFMR): diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 4e9cbcad..19ad14a1 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -232,7 +232,7 @@ 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 < 99) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -251,9 +251,10 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Remnants have flux = 0 in all bands if they are generated here. ##### if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with phase > 100. + # Identify compact objects as those with Teff = 0 or with phase > 100 or BDs highest_mass_iso = self.iso.points['mass'].max() - idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso))[0] + idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso) | + (star_systems['mass'] < 0.08))[0] # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -275,6 +276,11 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) + # Handle brown dwarfs separately + idx_bd = np.where(star_systems['phase'] == 99)[0] + for filt in self.filt_names: + star_systems[filt][idx_bd] = np.full(len(idx_bd), np.nan) + return star_systems def _make_companions_table(self, star_systems, compMass): @@ -362,7 +368,7 @@ def _make_companions_table(self, star_systems, compMass): # Convert nan_to_num to avoid errors on greater than, less than comparisons 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) & (companions_phase_non_nan != 9) & (companions_phase_non_nan != -99)) # Print warning, if desired @@ -459,7 +465,8 @@ def _remove_bad_systems(self, star_systems, compMass): idx = np.where(star_systems_teff_non_nan > 0)[0] else: # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) )[0] + idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | + (star_systems_phase_non_nan == 99) )[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 4b8b7b07..0146b819 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -499,6 +499,8 @@ def test_ifmr_multiplicity(): ns_idx = np.where(clust1['phase'] == 102) bh_idx = np.where(clust1['phase'] == 103) bd_idx = np.where(clust1['phase'] == 99) + + assert len(clust1[bd_idx]) != 0 assert np.all(clust1['mass'][wd_idx] > MIN_MASS) assert np.all(clust1['mass'][ns_idx] > MIN_MASS) assert np.all(clust1['mass'][bh_idx] > MIN_MASS) @@ -530,8 +532,6 @@ def test_ifmr_multiplicity(): (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range - -return return def test_metallicity(): @@ -1011,3 +1011,20 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" return + +# need to test Raithel18 phase designations, as there are anomalies with white dwarves and neutron stars +def test_Raithel18_phase_designation(): + """ + Check that the correct phases are returned for white dwarves and neutron stars when given several test MZAMS + """ + Raithel = ifmr.IFMR_Raithel18() + + #create an MZAMS array to cover all potential phase designations, and the expected phase designations + test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 15.0, 25.0, 100.0]) + expected_phases = np.array([99, 101, 101, 101, 101, 102, 102, 103, 103, 103]) + + #confirm that the expected phases match the ones assigned by the model + assert np.array_equal(actual_types, expected_types), \ + f"Phase designation mismatch. Expected: {expected_types}, Got: {actual_types}" + + print(f"Test passed. Actual types match expected types: {actual_types}") \ No newline at end of file From 0fa23fea004ce86057ea983caab6961aae48316b Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 09:43:52 -0700 Subject: [PATCH 13/48] Testing improvements for Raithel phase designations --- spisea/tests/test_synthetic.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 0146b819..8322ca05 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1020,11 +1020,19 @@ def test_Raithel18_phase_designation(): Raithel = ifmr.IFMR_Raithel18() #create an MZAMS array to cover all potential phase designations, and the expected phase designations - test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 15.0, 25.0, 100.0]) - expected_phases = np.array([99, 101, 101, 101, 101, 102, 102, 103, 103, 103]) + test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) + expected_phases = np.array([99, -1, 101, 101, 101, 102, 102, 103, 103]) + + #generate the output from Raithel IFMR + output_array = Raithel.generate_death_mass(test_MZAMS) + actual_phases = output_array[1] + #print the two arrays for direct comparison + print(f"Expected phases: {expected_phases}") + print(f"Actual phases: {actual_phases}") + #confirm that the expected phases match the ones assigned by the model - assert np.array_equal(actual_types, expected_types), \ - f"Phase designation mismatch. Expected: {expected_types}, Got: {actual_types}" + assert np.array_equal(actual_phases, expected_phases), \ + f"Phase designation mismatch. Expected: {expected_phases}, Got: {actual_phases}" - print(f"Test passed. Actual types match expected types: {actual_types}") \ No newline at end of file + print(f"Test passed. Actual types match expected types: {actual_phases}") \ No newline at end of file From 165ffa2ce6816817916bb52990de2ec3a7618374 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 10:59:01 -0700 Subject: [PATCH 14/48] Outlined testing and issues with WDs and NSs --- changes/Test_BD_Cluster.ipynb | 824 ++++++++++++++++++++++++++++----- spisea/tests/test_synthetic.py | 4 +- 2 files changed, 721 insertions(+), 107 deletions(-) diff --git a/changes/Test_BD_Cluster.ipynb b/changes/Test_BD_Cluster.ipynb index 5625aa9a..d5a5da69 100644 --- a/changes/Test_BD_Cluster.ipynb +++ b/changes/Test_BD_Cluster.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 39, "id": "b7536665-00cf-49ad-9028-930f56cf3204", "metadata": {}, "outputs": [], @@ -43,10 +43,536 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "0cd4c0a3-39e8-4bbf-9eba-e1883b6dacec", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=4.00 for T= 32651 logg=3.99\n", + "Changing to logg=4.00 for T= 32840 logg=3.98\n", + "Changing to logg=4.00 for T= 33037 logg=3.97\n", + "Changing to logg=4.00 for T= 33144 logg=3.96\n", + "Changing to logg=4.00 for T= 33205 logg=3.95\n", + "Changing to logg=4.00 for T= 33358 logg=3.94\n", + "Changing to logg=4.00 for T= 33504 logg=3.93\n", + "Changing to logg=4.00 for T= 33651 logg=3.91\n", + "Changing to logg=4.00 for T= 33713 logg=3.91\n", + "Changing to logg=4.00 for T= 33775 logg=3.90\n", + "Changing to logg=4.00 for T= 33892 logg=3.89\n", + "Changing to logg=4.00 for T= 34002 logg=3.87\n", + "Changing to logg=4.00 for T= 34111 logg=3.86\n", + "Changing to logg=4.00 for T= 34182 logg=3.85\n", + "Changing to logg=4.00 for T= 34222 logg=3.85\n", + "Changing to logg=4.00 for T= 34332 logg=3.83\n", + "Changing to logg=4.00 for T= 34443 logg=3.82\n", + "Changing to logg=4.00 for T= 34546 logg=3.81\n", + "Changing to 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logg=5.00 for T= 42540 logg=5.21\n", + "Changing to logg=5.00 for T= 42472 logg=5.21\n", + "Changing to logg=5.00 for T= 42413 logg=5.21\n", + "Changing to logg=5.00 for T= 42345 logg=5.21\n", + "Changing to logg=5.00 for T= 42277 logg=5.21\n", + "Changing to logg=5.00 for T= 42209 logg=5.22\n", + "Changing to logg=5.00 for T= 42150 logg=5.22\n", + "Changing to logg=5.00 for T= 42082 logg=5.22\n", + "Changing to logg=5.00 for T= 42024 logg=5.22\n", + "Changing to logg=5.00 for T= 41976 logg=5.23\n", + "Changing to logg=5.00 for T= 41899 logg=5.23\n", + "Changing to logg=5.00 for T= 41850 logg=5.23\n", + "Changing to logg=5.00 for T= 41802 logg=5.23\n", + "Changing to logg=5.00 for T= 41754 logg=5.23\n", + "Changing to logg=5.00 for T= 41957 logg=5.26\n", + "Changing to logg=5.00 for T= 42024 logg=5.27\n", + "Changing to logg=5.00 for T= 42053 logg=5.28\n", + "Changing to logg=5.00 for T= 42053 logg=5.28\n", + "Changing to logg=5.00 for T= 42024 logg=5.29\n", + "Changing to logg=5.00 for T= 42034 logg=5.29\n", + "Changing to logg=5.00 for T= 42034 logg=5.30\n", + "Changing to logg=5.00 for T= 42015 logg=5.31\n", + "Changing to logg=5.00 for T= 41995 logg=5.31\n", + "Changing to logg=5.00 for T= 41966 logg=5.31\n", + "Changing to logg=5.00 for T= 41966 logg=5.32\n", + "Changing to logg=5.00 for T= 41947 logg=5.32\n", + "Changing to logg=5.00 for T= 41937 logg=5.33\n", + "Changing to logg=5.00 for T= 41889 logg=5.33\n", + "Changing to logg=5.00 for T= 41822 logg=5.33\n", + "Changing to logg=5.00 for T= 41783 logg=5.33\n", + "Changing to logg=5.00 for T= 41754 logg=5.33\n", + "Changing to logg=5.00 for T= 41200 logg=5.32\n", + "Changing to logg=5.00 for T= 40374 logg=5.30\n", + "Changing to logg=5.00 for T= 40337 logg=5.30\n", + "Changing to logg=5.00 for T= 40207 logg=5.30\n", + "Changing to logg=5.00 for T= 39455 logg=5.31\n", + "Changing to logg=5.00 for T= 35719 logg=5.31\n", + "Changing to logg=5.00 for T= 35645 logg=5.30\n", + "Changing to logg=5.00 for T= 35588 logg=5.30\n", + "Changing to logg=5.00 for T= 35571 logg=5.30\n", + "Changing to logg=5.00 for T= 34962 logg=5.25\n", + "Changing to logg=5.00 for T= 34906 logg=5.25\n", + "Changing to logg=5.00 for T= 34475 logg=5.21\n", + "Changing to logg=5.00 for T= 34277 logg=5.20\n", + "Changing to logg=5.00 for T= 34285 logg=5.20\n", + "Changing to logg=5.00 for T= 34658 logg=5.23\n", + "Changing to logg=5.00 for T= 34770 logg=5.24\n", + "Changing to logg=5.00 for T= 34253 logg=5.20\n", + "Changing to logg=5.00 for T= 33628 logg=5.15\n", + "Changing to logg=5.00 for T= 33312 logg=5.12\n", + "Changing to logg=5.00 for T= 33636 logg=5.15\n", + "Changing to logg=5.00 for T= 33643 logg=5.15\n", + "Changing to logg=5.00 for T= 33659 logg=5.15\n", + "Changing to logg=5.00 for T= 33651 logg=5.15\n", + "Changing to logg=5.00 for T= 33504 logg=5.13\n", + "Changing to logg=5.00 for T= 33466 logg=5.08\n", + "Changing to logg=5.00 for T= 33504 logg=5.08\n", + "Changing to logg=5.00 for T= 33558 logg=5.07\n", + "Changing to logg=5.00 for T= 33605 logg=5.08\n", + "Changing to logg=5.00 for T= 33651 logg=5.08\n", + "Changing to logg=5.00 for T= 33674 logg=5.07\n", + "Changing to logg=5.00 for T= 32870 logg=5.05\n", + "Changing to logg=5.00 for T= 30054 logg=5.01\n", + "Changing to logg=5.00 for T= 29819 logg=5.01\n", + "Changing to logg=5.00 for T= 35785 logg=5.39\n", + "Changing to logg=5.00 for T= 39683 logg=5.62\n", + "Changing to logg=5.00 for T= 39701 logg=5.62\n", + "Changing to logg=5.00 for T= 39655 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.63\n", + "Changing to logg=5.00 for T= 39373 logg=5.63\n", + "Changing to logg=5.00 for T= 39518 logg=5.65\n", + "Changing to logg=5.00 for T= 40031 logg=5.69\n", + "Changing to logg=5.00 for T= 40096 logg=5.70\n", + "Changing to logg=5.00 for T= 40495 logg=5.74\n", + "Changing to logg=5.00 for T= 41381 logg=5.82\n", + "Changing to logg=5.00 for T= 41976 logg=5.87\n", + "Changing to logg=5.00 for T= 42199 logg=5.90\n", + "Changing to logg=5.00 for T= 42228 logg=5.90\n", + "Changing to logg=5.00 for T= 42286 logg=5.90\n", + "Changing to logg=5.00 for T= 42345 logg=5.91\n", + "Changing to logg=5.00 for T= 42394 logg=5.92\n", + "Changing to logg=5.00 for T= 42462 logg=5.92\n", + "Changing to logg=5.00 for T= 42530 logg=5.92\n", + "Changing to logg=5.00 for T= 42619 logg=5.92\n", + "Changing to logg=5.00 for T= 42717 logg=5.93\n", + "Changing to logg=5.00 for T= 42825 logg=5.93\n", + "Changing to logg=5.00 for T= 42934 logg=5.94\n", + "Changing to logg=5.00 for T= 43053 logg=5.94\n", + "Changing to logg=5.00 for T= 43152 logg=5.94\n", + "Changing to logg=5.00 for T= 43271 logg=5.95\n", + "Changing to logg=5.00 for T= 43411 logg=5.95\n", + "Changing to logg=5.00 for T= 43561 logg=5.96\n", + "Changing to logg=5.00 for T= 43692 logg=5.96\n", + "Changing to logg=5.00 for T= 43833 logg=5.96\n", + "Changing to logg=5.00 for T= 43985 logg=5.97\n", + "Changing to logg=5.00 for T= 44147 logg=5.97\n", + "Changing to logg=5.00 for T= 44310 logg=5.97\n", + "Changing to logg=5.00 for T= 44484 logg=5.97\n", + "Changing to logg=5.00 for T= 44638 logg=5.97\n", + "Changing to logg=5.00 for T= 44802 logg=5.98\n", + "Changing to logg=5.00 for T= 44957 logg=5.98\n", + "Changing to logg=5.00 for T= 45144 logg=5.98\n", + "Changing to logg=5.00 for T= 45300 logg=5.99\n", + "Changing to logg=5.00 for T= 45478 logg=5.99\n", + "Changing to logg=5.00 for T= 45509 logg=5.99\n", + "Changing to logg=5.00 for T= 45646 logg=5.99\n", + "Changing to logg=5.00 for T= 45825 logg=5.99\n", + "Changing to logg=5.00 for T= 45983 logg=6.00\n", + "Changing to logg=5.00 for T= 46142 logg=6.00\n", + "Changing to logg=5.00 for T= 46313 logg=6.00\n", + "Changing to logg=5.00 for T= 46473 logg=6.00\n", + "Changing to logg=5.00 for T= 46623 logg=6.00\n", + "Changing to logg=5.00 for T= 46784 logg=6.00\n", + "Changing to logg=5.00 for T= 46946 logg=6.00\n", + "Changing to logg=5.00 for T= 47109 logg=6.00\n", + "Changing to logg=5.00 for T= 47261 logg=6.00\n", + "Changing to logg=5.00 for T= 47413 logg=6.01\n", + "Changing to logg=5.00 for T= 47566 logg=6.01\n", + "Changing to logg=5.00 for T= 47720 logg=6.01\n", + "Changing to logg=5.00 for T= 47863 logg=6.01\n", + "Changing to logg=5.00 for T= 48029 logg=6.01\n", + "Changing to logg=5.00 for T= 48173 logg=6.01\n", + "Changing to logg=5.00 for T= 48317 logg=6.02\n", + "Changing to logg=5.00 for T= 48473 logg=6.02\n", + "Changing to logg=5.00 for T= 48618 logg=6.02\n", + "Changing to logg=5.00 for T= 48753 logg=6.02\n", + "Changing to logg=5.00 for T= 48910 logg=6.02\n", + "Changing to logg=5.00 for T= 49057 logg=6.02\n", + "Changing to logg=5.00 for T= 49193 logg=6.02\n", + "Changing to logg=5.00 for T= 49329 logg=6.03\n", + "Changing to logg=5.00 for T= 49431 logg=6.03\n", + "Changing to logg=5.00 for T= 49465 logg=6.03\n", + "Changing to logg=5.00 for T= 49614 logg=6.03\n", + "Changing to logg=5.00 for T= 49751 logg=6.03\n", + "Changing to logg=5.00 for T= 49888 logg=6.03\n", + "Changing to T= 50000 for T= 50026 logg=6.03\n", + "Changing to logg=5.00 for T= 50026 logg=6.03\n", + "Changing to T= 50000 for T= 50176 logg=6.03\n", + "Changing to logg=5.00 for T= 50176 logg=6.03\n", + "Changing to T= 50000 for T= 50304 logg=6.03\n", + "Changing to logg=5.00 for T= 50304 logg=6.03\n", + "Changing to T= 50000 for T= 50431 logg=6.04\n", + "Changing to logg=5.00 for T= 50431 logg=6.04\n", + "Changing to T= 50000 for T= 50559 logg=6.04\n", + "Changing to logg=5.00 for T= 50559 logg=6.04\n", + "Changing to T= 50000 for T= 50699 logg=6.04\n", + "Changing to logg=5.00 for T= 50699 logg=6.04\n", + "Changing to T= 50000 for T= 50839 logg=6.04\n", + "Changing to logg=5.00 for T= 50839 logg=6.04\n", + "Changing to T= 50000 for T= 50957 logg=6.04\n", + "Changing to logg=5.00 for T= 50957 logg=6.04\n", + "Changing to T= 50000 for T= 51086 logg=6.04\n", + "Changing to logg=5.00 for T= 51086 logg=6.04\n", + "Changing to T= 50000 for T= 51215 logg=6.04\n", + "Changing to logg=5.00 for T= 51215 logg=6.04\n", + "Changing to T= 50000 for T= 51345 logg=6.04\n", + "Changing to logg=5.00 for T= 51345 logg=6.04\n", + "Changing to T= 50000 for T= 51475 logg=6.04\n", + "Changing to logg=5.00 for T= 51475 logg=6.04\n", + "Changing to T= 50000 for T= 51606 logg=6.04\n", + "Changing to logg=5.00 for T= 51606 logg=6.04\n", + "Changing to T= 50000 for T= 51737 logg=6.05\n", + "Changing to logg=5.00 for T= 51737 logg=6.05\n", + "Changing to T= 50000 for T= 51868 logg=6.05\n", + "Changing to logg=5.00 for T= 51868 logg=6.05\n", + "Changing to T= 50000 for T= 52000 logg=6.05\n", + "Changing to logg=5.00 for T= 52000 logg=6.05\n", + "Changing to T= 50000 for T= 52119 logg=6.05\n", + "Changing to logg=5.00 for T= 52119 logg=6.05\n", + "Changing to T= 50000 for T= 52240 logg=6.05\n", + "Changing to logg=5.00 for T= 52240 logg=6.05\n", + "Changing to T= 50000 for T= 52384 logg=6.05\n", + "Changing to logg=5.00 for T= 52384 logg=6.05\n", + "Changing to T= 50000 for T= 52505 logg=6.05\n", + "Changing to logg=5.00 for T= 52505 logg=6.05\n", + "Changing to T= 50000 for T= 52626 logg=6.05\n", + "Changing to logg=5.00 for T= 52626 logg=6.05\n", + "Changing to T= 50000 for T= 52747 logg=6.05\n", + "Changing to logg=5.00 for T= 52747 logg=6.05\n", + "Changing to T= 50000 for T= 52759 logg=6.05\n", + "Changing to logg=5.00 for T= 52759 logg=6.05\n", + "Changing to T= 50000 for T= 52857 logg=6.05\n", + "Changing to logg=5.00 for T= 52857 logg=6.05\n", + "Changing to T= 50000 for T= 52991 logg=6.05\n", + "Changing to logg=5.00 for T= 52991 logg=6.05\n", + "Changing to T= 50000 for T= 53125 logg=6.06\n", + "Changing to logg=5.00 for T= 53125 logg=6.06\n", + "Changing to T= 50000 for T= 53235 logg=6.06\n", + "Changing to logg=5.00 for T= 53235 logg=6.06\n", + "Changing to T= 50000 for T= 53358 logg=6.06\n", + "Changing to logg=5.00 for T= 53358 logg=6.06\n", + "Changing to T= 50000 for T= 53481 logg=6.06\n", + "Changing to logg=5.00 for T= 53481 logg=6.06\n", + "Changing to T= 50000 for T= 53592 logg=6.06\n", + "Changing to logg=5.00 for T= 53592 logg=6.06\n", + "Changing to T= 50000 for T= 53728 logg=6.06\n", + "Changing to logg=5.00 for T= 53728 logg=6.06\n", + "Changing to T= 50000 for T= 53864 logg=6.06\n", + "Changing to logg=5.00 for T= 53864 logg=6.06\n", + "Changing to T= 50000 for T= 53976 logg=6.07\n", + "Changing to logg=5.00 for T= 53976 logg=6.07\n", + "Changing to T= 50000 for T= 54100 logg=6.07\n", + "Changing to logg=5.00 for T= 54100 logg=6.07\n", + "Changing to T= 50000 for T= 54225 logg=6.07\n", + "Changing to logg=5.00 for T= 54225 logg=6.07\n", + "Changing to T= 50000 for T= 54363 logg=6.07\n", + "Changing to logg=5.00 for T= 54363 logg=6.07\n", + "Changing to T= 50000 for T= 54475 logg=6.07\n", + "Changing to logg=5.00 for T= 54475 logg=6.07\n", + "Changing to T= 50000 for T= 54588 logg=6.07\n", + "Changing to logg=5.00 for T= 54588 logg=6.07\n", + "Changing to T= 50000 for T= 54714 logg=6.08\n", + "Changing to logg=5.00 for T= 54714 logg=6.08\n", + "Changing to T= 50000 for T= 54853 logg=6.08\n", + "Changing to logg=5.00 for T= 54853 logg=6.08\n", + "Changing to T= 50000 for T= 54967 logg=6.08\n", + "Changing to logg=5.00 for T= 54967 logg=6.08\n", + "Changing to T= 50000 for T= 55093 logg=6.08\n", + "Changing to logg=5.00 for T= 55093 logg=6.08\n", + "Changing to T= 50000 for T= 55208 logg=6.08\n", + "Changing to logg=5.00 for T= 55208 logg=6.08\n", + "Changing to T= 50000 for T= 55322 logg=6.09\n", + "Changing to logg=5.00 for T= 55322 logg=6.09\n", + "Changing to T= 50000 for T= 55450 logg=6.09\n", + "Changing to logg=5.00 for T= 55450 logg=6.09\n", + "Changing to T= 50000 for T= 55590 logg=6.09\n", + "Changing to logg=5.00 for T= 55590 logg=6.09\n", + "Changing to T= 50000 for T= 55719 logg=6.09\n", + "Changing to logg=5.00 for T= 55719 logg=6.09\n", + "Changing to T= 50000 for T= 55834 logg=6.09\n", + "Changing to logg=5.00 for T= 55834 logg=6.09\n", + "Changing to T= 50000 for T= 55873 logg=6.09\n", + "Changing to logg=5.00 for T= 55873 logg=6.09\n", + "Changing to T= 50000 for T= 55963 logg=6.09\n", + "Changing to logg=5.00 for T= 55963 logg=6.09\n", + "Changing to T= 50000 for T= 56092 logg=6.10\n", + "Changing to logg=5.00 for T= 56092 logg=6.10\n", + "Changing to T= 50000 for T= 56234 logg=6.10\n", + "Changing to logg=5.00 for T= 56234 logg=6.10\n", + "Changing to T= 50000 for T= 56364 logg=6.10\n", + "Changing to logg=5.00 for T= 56364 logg=6.10\n", + "Changing to T= 50000 for T= 56507 logg=6.10\n", + "Changing to logg=5.00 for T= 56507 logg=6.10\n", + "Changing to T= 50000 for T= 56624 logg=6.11\n", + "Changing to logg=5.00 for T= 56624 logg=6.11\n", + "Changing to T= 50000 for T= 56754 logg=6.11\n", + "Changing to logg=5.00 for T= 56754 logg=6.11\n", + "Changing to T= 50000 for T= 56885 logg=6.11\n", + "Changing to logg=5.00 for T= 56885 logg=6.11\n", + "Changing to T= 50000 for T= 57030 logg=6.11\n", + "Changing to logg=5.00 for T= 57030 logg=6.11\n", + "Changing to T= 50000 for T= 57161 logg=6.11\n", + "Changing to logg=5.00 for T= 57161 logg=6.11\n", + "Changing to T= 50000 for T= 57293 logg=6.11\n", + "Changing to logg=5.00 for T= 57293 logg=6.11\n", + "Changing to T= 50000 for T= 57425 logg=6.11\n", + "Changing to logg=5.00 for T= 57425 logg=6.11\n", + "Changing to T= 50000 for T= 57570 logg=6.11\n", + "Changing to logg=5.00 for T= 57570 logg=6.11\n", + "Changing to T= 50000 for T= 57703 logg=6.11\n", + "Changing to logg=5.00 for T= 57703 logg=6.11\n", + "Changing to T= 50000 for T= 57850 logg=6.12\n", + "Changing to logg=5.00 for T= 57850 logg=6.12\n", + "Changing to T= 50000 for T= 57983 logg=6.12\n", + "Changing to logg=5.00 for T= 57983 logg=6.12\n", + "Changing to T= 50000 for T= 58117 logg=6.12\n", + "Changing to logg=5.00 for T= 58117 logg=6.12\n", + "Changing to T= 50000 for T= 58251 logg=6.13\n", + "Changing to logg=5.00 for T= 58251 logg=6.13\n", + "Changing to T= 50000 for T= 58398 logg=6.13\n", + "Changing to logg=5.00 for T= 58398 logg=6.13\n", + "Changing to T= 50000 for T= 58546 logg=6.13\n", + "Changing to logg=5.00 for T= 58546 logg=6.13\n", + "Changing to T= 50000 for T= 58695 logg=6.13\n", + "Changing to logg=5.00 for T= 58695 logg=6.13\n", + "Changing to T= 50000 for T= 58844 logg=6.14\n", + "Changing to logg=5.00 for T= 58844 logg=6.14\n", + "Changing to T= 50000 for T= 58993 logg=6.14\n", + "Changing to logg=5.00 for T= 58993 logg=6.14\n", + "Changing to T= 50000 for T= 59143 logg=6.14\n", + "Changing to logg=5.00 for T= 59143 logg=6.14\n", + "Changing to T= 50000 for T= 59293 logg=6.15\n", + "Changing to logg=5.00 for T= 59293 logg=6.15\n", + "Changing to T= 50000 for T= 59334 logg=6.15\n", + "Changing to logg=5.00 for T= 59334 logg=6.15\n", + "Changing to T= 50000 for T= 59457 logg=6.15\n", + "Changing to logg=5.00 for T= 59457 logg=6.15\n", + "Changing to T= 50000 for T= 59621 logg=6.16\n", + "Changing to logg=5.00 for T= 59621 logg=6.16\n", + "Changing to T= 50000 for T= 59800 logg=6.16\n", + "Changing to logg=5.00 for T= 59800 logg=6.16\n", + "Changing to T= 50000 for T= 59965 logg=6.16\n", + "Changing to logg=5.00 for T= 59965 logg=6.16\n", + "Changing to T= 50000 for T= 60159 logg=6.17\n", + "Changing to logg=5.00 for T= 60159 logg=6.17\n", + "Changing to T= 50000 for T= 60353 logg=6.17\n", + "Changing to logg=5.00 for T= 60353 logg=6.17\n", + "Changing to T= 50000 for T= 60534 logg=6.18\n", + "Changing to logg=5.00 for T= 60534 logg=6.18\n", + "Changing to T= 50000 for T= 60758 logg=6.18\n", + "Changing to logg=5.00 for T= 60758 logg=6.18\n", + "Changing to T= 50000 for T= 60968 logg=6.18\n", + "Changing to logg=5.00 for T= 60968 logg=6.18\n", + "Changing to T= 50000 for T= 61235 logg=6.19\n", + "Changing to logg=5.00 for T= 61235 logg=6.19\n", + "Changing to T= 50000 for T= 61504 logg=6.20\n", + "Changing to logg=5.00 for T= 61504 logg=6.20\n", + "Changing to T= 50000 for T= 61844 logg=6.21\n", + "Changing to logg=5.00 for T= 61844 logg=6.21\n", + "Changing to T= 50000 for T= 62287 logg=6.23\n", + "Changing to logg=5.00 for T= 62287 logg=6.23\n", + "Changing to T= 50000 for T= 63038 logg=6.27\n", + "Changing to logg=5.00 for T= 63038 logg=6.27\n", + "Changing to T= 50000 for T= 64239 logg=6.32\n", + "Changing to logg=5.00 for T= 64239 logg=6.32\n", + "Changing to T= 50000 for T= 65464 logg=6.37\n", + "Changing to logg=5.00 for T= 65464 logg=6.37\n", + "Changing to T= 50000 for T= 66696 logg=6.42\n", + "Changing to logg=5.00 for T= 66696 logg=6.42\n", + "Changing to T= 50000 for T= 67967 logg=6.47\n", + "Changing to logg=5.00 for T= 67967 logg=6.47\n", + "Changing to T= 50000 for T= 69263 logg=6.53\n", + "Changing to logg=5.00 for T= 69263 logg=6.53\n", + "Changing to T= 50000 for T= 71367 logg=6.63\n", + "Changing to logg=5.00 for T= 71367 logg=6.63\n", + "Isochrone generation took 112.722160 s.\n", + "Making photometry for isochrone: log(t) = 6.70 AKs = 0.80 dist = 4000\n", + " Starting at: 2024-08-02 09:54:23.120123 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f127m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2928 K m_hst_f127m = 22.41\n", + "M = 1.175 Msun T = 4611 K m_hst_f127m = 18.72\n", + "M = 1.650 Msun T = 5355 K m_hst_f127m = 17.71\n", + "M = 1.973 Msun T = 6311 K m_hst_f127m = 16.34\n", + "M = 2.292 Msun T = 9669 K m_hst_f127m = 16.68\n", + "M = 2.648 Msun T = 11771 K m_hst_f127m = 16.64\n", + "M = 2.819 Msun T = 12291 K m_hst_f127m = 16.51\n", + "M = 3.075 Msun T = 13014 K m_hst_f127m = 16.33\n", + "M = 3.332 Msun T = 13709 K m_hst_f127m = 16.16\n", + "M = 3.482 Msun T = 14099 K m_hst_f127m = 16.07\n", + "M = 3.615 Msun T = 14438 K m_hst_f127m = 15.99\n", + "M = 8.977 Msun T = 23502 K m_hst_f127m = 14.07\n", + "M = 49.000 Msun T = 32344 K m_hst_f127m = 9.23\n", + "M = 50.757 Msun T = 31046 K m_hst_f127m = 9.25\n", + "M = 52.540 Msun T = 41937 K m_hst_f127m = 10.13\n", + "M = 54.406 Msun T = 48753 K m_hst_f127m = 10.85\n", + "M = 56.318 Msun T = 65464 K m_hst_f127m = 12.01\n", + "Starting filter: wfc3,ir,f139m Elapsed time: 25.91 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2928 K m_hst_f139m = 22.11\n", + "M = 1.175 Msun T = 4611 K m_hst_f139m = 18.14\n", + "M = 1.650 Msun T = 5355 K m_hst_f139m = 17.18\n", + "M = 1.973 Msun T = 6311 K m_hst_f139m = 15.86\n", + "M = 2.292 Msun T = 9669 K m_hst_f139m = 16.28\n", + "M = 2.648 Msun T = 11771 K m_hst_f139m = 16.25\n", + "M = 2.819 Msun T = 12291 K m_hst_f139m = 16.12\n", + "M = 3.075 Msun T = 13014 K m_hst_f139m = 15.94\n", + "M = 3.332 Msun T = 13709 K m_hst_f139m = 15.77\n", + "M = 3.482 Msun T = 14099 K m_hst_f139m = 15.68\n", + "M = 3.615 Msun T = 14438 K m_hst_f139m = 15.60\n", + "M = 8.977 Msun T = 23502 K m_hst_f139m = 13.70\n", + "M = 49.000 Msun T = 32344 K m_hst_f139m = 8.87\n", + "M = 50.757 Msun T = 31046 K m_hst_f139m = 8.88\n", + "M = 52.540 Msun T = 41937 K m_hst_f139m = 9.77\n", + "M = 54.406 Msun T = 48753 K m_hst_f139m = 10.50\n", + "M = 56.318 Msun T = 65464 K m_hst_f139m = 11.66\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 51.87 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2928 K m_hst_f153m = 21.45\n", + "M = 1.175 Msun T = 4611 K m_hst_f153m = 17.51\n", + "M = 1.650 Msun T = 5355 K m_hst_f153m = 16.63\n", + "M = 1.973 Msun T = 6311 K m_hst_f153m = 15.37\n", + "M = 2.292 Msun T = 9669 K m_hst_f153m = 15.89\n", + "M = 2.648 Msun T = 11771 K m_hst_f153m = 15.87\n", + "M = 2.819 Msun T = 12291 K m_hst_f153m = 15.75\n", + "M = 3.075 Msun T = 13014 K m_hst_f153m = 15.57\n", + "M = 3.332 Msun T = 13709 K m_hst_f153m = 15.40\n", + "M = 3.482 Msun T = 14099 K m_hst_f153m = 15.31\n", + "M = 3.615 Msun T = 14438 K m_hst_f153m = 15.23\n", + "M = 8.977 Msun T = 23502 K m_hst_f153m = 13.34\n", + "M = 49.000 Msun T = 32344 K m_hst_f153m = 8.52\n", + "M = 50.757 Msun T = 31046 K m_hst_f153m = 8.53\n", + "M = 52.540 Msun T = 41937 K m_hst_f153m = 9.42\n", + "M = 54.406 Msun T = 48753 K m_hst_f153m = 10.15\n", + "M = 56.318 Msun T = 65464 K m_hst_f153m = 11.31\n", + " Time taken: 78.43 seconds\n" + ] + } + ], "source": [ "# Define isochrone parameters\n", "logAge = np.log10(5*10**6.) # Age in log(years)\n", @@ -383,34 +909,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "id": "25b1247a-0eb8-40bc-bf56-f89e788283cd", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isochrone generation took 96.182577 s.\n", - "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-07-15 14:29:26.015063 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", - "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", - "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", - "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", - "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", - "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", - "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", - "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", - "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", - "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", - "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 31.54 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Create isochrone object \n", "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", @@ -422,21 +924,21 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "01f6f94c-1b23-4ae1-bfc2-96e2d4cacd27", "metadata": {}, "outputs": [], "source": [ "# Create IMF objects \n", - "#imf_multi = multiplicity.MultiplicityUnresolved()\n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", "massLimits = np.array([0.01, 0.08, 0.5, 1, np.inf])\n", "powers = np.array([-0.3, -1.3, -2.3, -2.35])\n", - "k_imf = imf.IMF_broken_powerlaw(massLimits, powers)" + "k_imf = imf.IMF_broken_powerlaw(massLimits, powers, multiplicity=imf_multi)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "id": "1a914c4c-ac7e-4c61-9e40-1b0d5dd5568f", "metadata": {}, "outputs": [ @@ -444,7 +946,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 829408 stars out of mass range\n" + "Found 580447 stars out of mass range\n", + "Found 113559 companions out of stellar mass range\n" ] } ], @@ -455,18 +958,18 @@ "\n", "# Get outputs\n", "k_out = k_cluster.star_systems\n", - "#k_comp = k_cluster.companions" + "k_comp = k_cluster.companions" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "id": "62293dea-989b-4870-a77b-ba677010da81", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -478,14 +981,14 @@ "source": [ "# Locate BHs, NSs and WDs\n", "p_bh = np.where(k_out['phase'] == 103)[0]\n", - "#c_bh = np.where(k_comp['phase'] == 103)[0]\n", - "#k_bh = np.concatenate([p_bh, c_bh])\n", + "c_bh = np.where(k_comp['phase'] == 103)[0]\n", + "k_bh = np.concatenate([p_bh, c_bh])\n", "p_ns = np.where(k_out['phase'] == 102)[0]\n", - "#c_ns = np.where(k_comp['phase'] == 102)[0]\n", - "#k_ns = np.concatenate([p_ns, c_ns])\n", + "c_ns = np.where(k_comp['phase'] == 102)[0]\n", + "k_ns = np.concatenate([p_ns, c_ns])\n", "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "#c_wd = np.where(k_comp['phase'] == 101)[0]\n", - "#k_wd = np.concatenate([p_wd, c_wd])\n", + "c_wd = np.where(k_comp['phase'] == 101)[0]\n", + "k_wd = np.concatenate([p_wd, c_wd])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -513,7 +1016,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 40, "id": "d36bba0d-3037-46c0-91f3-321e47241204", "metadata": {}, "outputs": [ @@ -521,19 +1024,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "973669.0207942065\n" + "831012.4509070338\n", + "5.311329909891375\n" ] } ], "source": [ "all_masses = np.concatenate([k_out['mass'], k_comp['mass']])\n", "tot_mass = np.sum(all_masses)\n", - "print(tot_mass)" + "print(tot_mass)\n", + "print(np.min(k_out[p_wd]['mass']))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 20, "id": "1f999373-557e-42f1-9c0b-43d1cfab198e", "metadata": {}, "outputs": [ @@ -541,8 +1046,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07000000391702135\n", - "0.010000022844115403\n" + "0.0700003309744075\n", + "0.010000236016655941\n" ] } ], @@ -553,10 +1058,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "d7c2fa5e-49cd-4021-869a-6f9701677c68", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current smallest mass of generated black holes: 0.07006468311672817\n", + "Current largest mass of generated black holes: 15.65838834836072\n" + ] + } + ], "source": [ "print(\"Current smallest mass of generated black holes: \" + str(np.min(k_out[k_bh]['mass_current'])))\n", "print(\"Current largest mass of generated black holes: \" + str(np.max(k_out[k_bh]['mass_current'])))" @@ -574,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 22, "id": "c02d7dbd-9690-49f4-931d-e7f66172ba12", "metadata": {}, "outputs": [ @@ -582,24 +1096,24 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass isMultiple systemMass ... metallicity m_hst_f153m\n", - "------------------ ---------- ------------------ ... ----------- -----------\n", - " 59.05436159479683 False 59.05436159479683 ... 0.0 nan\n", - " 39.55979231152168 False 39.55979231152168 ... 0.0 nan\n", - "18.241030351057457 False 18.241030351057457 ... 0.0 nan\n", - " 21.34571632826156 False 21.34571632826156 ... 0.0 nan\n", - " 25.73229556345863 False 25.73229556345863 ... 0.0 nan\n", - " 38.27638428342816 False 38.27638428342816 ... 0.0 nan\n", - " 29.70200219305832 False 29.70200219305832 ... 0.0 nan\n", - " ... ... ... ... ... ...\n", - "104.95164061880804 False 104.95164061880804 ... 0.0 nan\n", - "19.927108168048903 False 19.927108168048903 ... 0.0 nan\n", - "51.756416056664165 False 51.756416056664165 ... 0.0 nan\n", - " 30.9405328260462 False 30.9405328260462 ... 0.0 nan\n", - "16.452556081467357 False 16.452556081467357 ... 0.0 nan\n", - "15.253386148271723 False 15.253386148271723 ... 0.0 nan\n", - " 34.00333629883476 False 34.00333629883476 ... 0.0 nan\n", - "Length = 2327 rows\n" + " mass isMultiple ... m_hst_f153m N_companions\n", + "------------------ ---------- ... ------------------- ------------\n", + "34.858558890643366 True ... nan 1\n", + " 24.21988253820487 True ... 0.19920518920036126 1\n", + " 53.76008089560359 True ... -0.2181337523917536 4\n", + "15.882675891462688 True ... -1.810589141056601 2\n", + " 57.37043948819449 True ... nan 3\n", + "31.414432201808793 True ... nan 1\n", + "22.819575828477465 True ... 1.0957723191576627 5\n", + " ... ... ... ... ...\n", + "23.712544384283426 True ... 1.6681099957862053 1\n", + " 52.89729960707116 True ... 1.3625884002383177 3\n", + " 82.84997226207857 True ... nan 2\n", + " 32.27796062878089 True ... 0.43046734619105526 3\n", + " 26.92983956402475 True ... nan 2\n", + " 27.13236574281697 True ... nan 2\n", + " 36.6006664768621 True ... 2.5063372504019394 3\n", + "Length = 1671 rows\n" ] } ], @@ -609,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 23, "id": "ddc351e5-e465-4f2e-9703-cd5c819a75f2", "metadata": {}, "outputs": [ @@ -619,39 +1133,39 @@ "text": [ " mass \n", "------------------\n", - " 59.05436159479683\n", - " 39.55979231152168\n", - "18.241030351057457\n", - " 21.34571632826156\n", - " 25.73229556345863\n", - " 38.27638428342816\n", - " 29.70200219305832\n", + "34.858558890643366\n", + " 24.21988253820487\n", + " 53.76008089560359\n", + "15.882675891462688\n", + " 57.37043948819449\n", + "31.414432201808793\n", + "22.819575828477465\n", " ...\n", - "104.95164061880804\n", - "19.927108168048903\n", - "51.756416056664165\n", - " 30.9405328260462\n", - "16.452556081467357\n", - "15.253386148271723\n", - " 34.00333629883476\n", - "Length = 2327 rows mass_current \n", + "23.712544384283426\n", + " 52.89729960707116\n", + " 82.84997226207857\n", + " 32.27796062878089\n", + " 26.92983956402475\n", + " 27.13236574281697\n", + " 36.6006664768621\n", + "Length = 1671 rows mass_current \n", "------------------\n", - " 7.69101697945427\n", - " 14.37645768969197\n", - " 6.490426115488822\n", - " 7.738537786899665\n", - " 9.302666353833182\n", - "13.811546507105783\n", - "10.635261248769924\n", + "12.446576185012097\n", + " 8.78166110167993\n", + " 8.806507411947209\n", + " 5.424709536731119\n", + " 7.981409529281804\n", + "11.216283709433917\n", + " 8.283995574428168\n", " ...\n", - "5.9065928872088955\n", - " 7.187073433524934\n", - " 9.426130388477304\n", - "11.054167247370941\n", - " 5.693090725172411\n", - " 5.119608907641256\n", - " 12.13042109233094\n", - "Length = 2327 rows\n" + " 8.603336271797449\n", + " 9.056339719938071\n", + " 6.152701918097497\n", + "11.515219186451843\n", + " 9.707197419320156\n", + " 9.775168357448042\n", + "13.119673398892417\n", + "Length = 1671 rows\n" ] } ], @@ -661,7 +1175,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "id": "f5ce39b9-2c6d-4e7c-a6f6-ffd7235ac9e8", "metadata": {}, "outputs": [ @@ -669,8 +1183,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Initial mass of smallest generated black hole: 15.003458139141888\n", - "Initial mass of largest generated black hole: 119.60497046926774\n" + "Initial mass of smallest generated black hole: 15.000468103060483\n", + "Initial mass of largest generated black hole: 117.48601718195576\n" ] } ], @@ -681,9 +1195,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "1c41b219-7a85-4519-af99-77145daaa418", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial mass of smallest generated comp black hole: 15.037154672016358\n", + "Initial mass of largest generated comp black hole: 111.30489194158751\n" + ] + } + ], + "source": [ + "print(\"Initial mass of smallest generated comp black hole: \" + str(np.min(k_comp[c_bh]['mass'])))\n", + "print(\"Initial mass of largest generated comp black hole: \" + str(np.max(k_comp[c_bh]['mass'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3566ea45-31f6-4015-bf84-d34ff93ec18e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ls7GmvX7OyckRvr6+QqFQiIEDB4qUlBT9B96DdLWPx40b16X8hu7R5P/yg/Qx/smE4HxGIiIiIiIiIiJjxnsQEREREREREREZORaIiIiIiIiIiIiMHAtERERERERERERGjgUiIiIiIiIiIiIjxwIREREREREREZGRY4GIiIiIiIiIiMjIsUBERERERERERGTkWCAiIiIiIiIiIjJyLBARERERERERERk5FoiIyKjU1NTAzs4OlZWVetvnlClTsHr1ar3tj4iIiEibmD8RGQcWiIhIqyIiIiCTyRAVFdVm2dy5cyGTyRAREaH/wP4vOTkZISEhGDhwoKpt7NixkMlkWLZsmdq6QgiMGjUKMpkMixcv1nifixcvRmJiIm7duqXxNoiIiMhwMX9qi/kTkf6xQEREWufk5ITMzEzU19er2hoaGpCRkQFnZ2fJ4qqvr0daWhree+89VZsQAiUlJXBxcUFpaana+lu2bMHVq1cBACNGjNB4v97e3hg4cCC2bdum8TaIiIjIsDF/Usf8iUj/WCAiIq0bMWIEnJ2dsWvXLlXbrl274OTkBF9fX1Xb/v37MWbMGDz55JOwsbHBa6+9hvPnz6tta+fOnfDy8oKFhQVsbGwwfvx43Llzp9Nl7dm3bx/kcjlGjx6taisvL0dtbS0iIiLUEpza2lrEx8errtb5+fl1q08mTZqEjIyMbm2DiIiIDBfzp7aYPxHpFwtERKQTs2fPRnp6uur1V199hcjISLV17ty5g9jYWJw4cQIHDx6EiYkJ3nzzTbS2tgIArl27hhkzZiAyMhJlZWXIycnB5MmTIYR46LKO5Obmwt/fX62tsLAQ5ubmmDFjBsrLy9HY2AgAWLZsGYYPHw4HBwfY2trCycmpW/0xcuRI5Ofnq7ZPRERE9F/Mn9QxfyLSL7nUARCRYZo1axbi4+NRWVkJmUyG3377DZmZmcjJyVGt89Zbb6m9Jy0tDXZ2djhz5gw8PT1x7do13L17F5MnT4aLiwsAwMvLCwBw9uzZDpd1pLKyEo6OjmptRUVF8Pb2hpubGywtLVFWVgZLS0ts2rQJBQUFWLlyZbevfgFA//790djYiOvXr6viJSIiInoQ8yd1zJ+I9IsziIhIJ2xtbTFx4kRs2bIF6enpmDhxImxtbdXWOX/+PEJDQzF48GBYWVlh0KBBAIBLly4BAHx8fBAUFAQvLy+8/fbb2Lx5M27evNnpso7U19fD3Nxcra2wsBB+fn6QyWTw9vbGqVOnsGDBArz//vsYMmQICgsLu/X9+fssLCwAAHV1dd3eFhERERkm5k/qmD8R6RcLRESkM5GRkfj666+xZcuWNtOjASAkJAQ1NTXYvHkz8vLykJeXBwBoamoCAJiamuLAgQPYt28fhg4divXr18Pd3R0VFRUPXdYRW1vbNklQcXGxKoHx8fHB2rVrkZ+fjyVLlqCpqQmnT59uN8Gpq6uDUqlEQEAAAgICMGfOHNTU1HS47xs3bgAAnnrqqU56jYiIiIwZ86d/MX8i0i8WiIhIZ15++WU0NTWhqakJwcHBastqampQVlaGRYsWISgoCB4eHu1ewZLJZAgMDERCQgKKi4uhUCiwe/fuTpe1x9fXF2fOnFG9vnDhAv7++2/VFOjhw4ejoKAAiYmJsLa2RmlpKZqbm9udIh0dHQ0fHx8cO3YMx44dw/Tp0xEWFtbhd/hPnTqFAQMGtLkKSERERPQg5k//Yv5EpF+8BxER6YypqSnKyspU/35Qnz59YGNjg9TUVDg4OODSpUuIi4tTWycvLw8HDx7EhAkTYGdnh7y8PPz111/w8PB46LKOBAcHIz4+Hjdv3kSfPn1QWFgIhUIBT09PAEB4eDjeeOMN2NjYALj3/fo+ffqopm7fV19fj5s3b+Kdd97B0qVLAQBLly5FVlYWzp07B1dX1zb7PnLkCCZMmNC1DiQiIiKjw/zpX8yfiPSLBSIi0ikrK6t2201MTJCZmYmYmBh4enrC3d0d69atw/PPP6/23tzcXKxZswa3bt2Ci4sLVq1ahVdeeQVlZWUdLuuIl5cX/P398e233+KDDz5AUVERPD09YWZmBgAwMzNTu0JVVFSk9ljZ+x68yhUdHd1pHzQ0NGD37t34+eefO12XiIiIiPkT8yciKcjEw55pSERkYPbu3YuFCxfi1KlTMDHR/Fu2EREReOmllzBz5kwAwMGDB7Fy5Urs3bsXMplMbd2NGzciKysL2dnZ3YqdiIiISArMn4iMA2cQEZFRefXVV1FeXo4rV67AyclJ4+1s2rQJixYtwrp16yCTyeDh4YGtW7e2SW6Ae1fW1q9f352wiYiIiCTD/InIOHAGERERERERERGRkeNTzIiIiIiIiIiIjBwLRERERERERERERo4FIiIiIiIiIiIiI8cCERERERERERGRkWOBiIiIiIiIiIjIyLFARERERERERERk5FggIiIiIiIiIiIyciwQEREREREREREZORaIiIiIiIiIiIiMHAtERERERERERERGjgUiIiIiIiIiIiIj9z/qmrwowubeXgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 1.4, 16)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_comp[c_bh]['mass_current'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_comp[c_wd]['mass_current'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3b4167fc-fd67-4c11-9cb4-f46b1e8cb9a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9729527582037262\n" + ] + } + ], + "source": [ + "print(np.min(k_comp[c_wd]['mass_current']))" + ] + }, + { + "cell_type": "markdown", + "id": "c17cd2c5-164a-49be-b118-8023b6f3a3b4", + "metadata": {}, + "source": [ + "## New Cluster after addition of BD criterion" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd81a9c8-bc0c-46e3-a20d-bc2112a50f5c", + "metadata": {}, + "outputs": [], + "source": [ + "#locate brown dwarfs\n", + "p_bd = np.where(k_out['phase'] == 99)[0]\n", + "c_bd = np.where(k_comp['phase'] == 99)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f49be47c-dc94-467e-bf4d-41b6f926ace5", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 8322ca05..57c46bd1 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -1020,8 +1020,8 @@ def test_Raithel18_phase_designation(): Raithel = ifmr.IFMR_Raithel18() #create an MZAMS array to cover all potential phase designations, and the expected phase designations - test_MZAMS = np.array([0.06, 0.3, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) - expected_phases = np.array([99, -1, 101, 101, 101, 102, 102, 103, 103]) + test_MZAMS = np.array([0.06, 0.3, 0.6, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) + expected_phases = np.array([99, -1, 101, 101, 101, 101, 102, 102, 103, 103]) #generate the output from Raithel IFMR output_array = Raithel.generate_death_mass(test_MZAMS) From 78d8712ec0688087c40358499131689a0ac819b1 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 9 Aug 2024 11:01:12 -0700 Subject: [PATCH 15/48] Added outline of issues in this notebook (not Test_BD_Cluster) --- changes/Compact_Multiplicity.ipynb | 159 +++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 8c27e7ab..347160e5 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -305,6 +305,165 @@ "print(\"BH min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" ] }, + { + "cell_type": "code", + "execution_count": 85, + "id": "ed4301d8-2eac-4f78-895b-c8c63b22fe5b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float64\n", + "isWR\n", + "----\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " nan\n", + " nan\n", + " ...\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + "Length = 1363 rows\n" + ] + } + ], + "source": [ + "#check to see if large NS are Wolf-Rayet\n", + "print(k_out['isWR'].dtype)\n", + "large_NS = np.where(k_out[p_ns]['mass'] > 15)\n", + "print(k_out['isWR'][large_NS])" + ] + }, + { + "cell_type": "markdown", + "id": "11435009-9e98-4e31-a290-8c047b469ba5", + "metadata": {}, + "source": [ + "I am unsure on what the 0.0 and nan values correspond to." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "66c9450c-7758-402f-a3f6-5100ef05a01e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtered Masses: mass \n", + "------------------\n", + " 0.76799007770147\n", + "1.1955031412949875\n", + "2.2249813543703456\n", + " 3.660007844283093\n", + "0.8470178943035628\n", + "0.8619046086355177\n", + "0.8153335900401504\n", + " ...\n", + "0.9029881664660251\n", + "0.5385601472294455\n", + "0.5055061897257748\n", + "0.6718807496769587\n", + "0.7515013567753969\n", + "0.7974747944530974\n", + " 1.030318707836774\n", + "Length = 391228 rows\n", + "phase\n", + "-----\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " ...\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + "Length = 391228 rows\n", + " mass_current \n", + "------------------\n", + " 0.76799007770147\n", + "1.1955031412949875\n", + "2.2249813543703456\n", + " 3.660007844283093\n", + "0.8470178943035628\n", + "0.8619046086355177\n", + "0.8153335900401504\n", + " ...\n", + "0.9029881664660251\n", + "0.5385601472294455\n", + "0.5055061897257748\n", + "0.6718807496769587\n", + "0.7515013567753969\n", + "0.7974747944530974\n", + " 1.030318707836774\n", + "Length = 391228 rows\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eLlvHjh3l4uJiPmbMmKGzZ89q7dq1uv/+++Xu7q5z586Zj3vvvVdnz54t8+5NAwYMsHvetm1bnT17Vjk5OVfU5h/+8IdSxzh37pymTJmi1q1by9XVVc7OznJ1ddW+ffu0e/fuS55vZftx+vRppaWladCgQapdu7b5+rp166p///6VvbyXZBiGJJW6G0rJX69KDB48WM7Ozlq3bt3vPrdff/1VGzZs0ODBg69oDaQ77rhDiYmJevnll5WamlqpodSnT5/W1q1b9cADD6hOnTrmdicnJ0VHR+vw4cPas2fPZfflUir6TFaF3/t5rEhYWJhuueUWvfvuu9q5c6fS0tLKnaJ3OX2pzPt3Je/xpVz883wln4nIyEi7561atZLFYlFERIS5zdnZWc2bN9dPP/1UqX5VdCeiS92lqH379po9e7YGDhyoO++8U4899pg2b94sf3//cv+y/Fu//Rk9d+6c+f/B1ThWenq6pAvT7xo3bqz69etr586dSkxM1Ndff62ZM2fKMAxlZGRUOEUvOztbcXFx6tChg8LCwvTWW2/p/Pnzlzw+AFxv3nvvPaWlpdk9LjUyqjJ5xY4dOzRgwAA1aNBATk5OcnFx0dChQ1VcXKy9e/deUV/d3NxK9TUtLU1vvfWWGRMRESE/Pz/Nnz/f3LZq1SodOXLELn/4/PPP1atXLwUGBtodIyYmRr/++muZd3su8fjjj+vMmTP68MMPzW3z58+X1Wo1b/rx/fff67vvvjNz2Yvz0aysLPP7fd26derVq5d8fX3N9pycnEqNjK6MBx98UK6urlq0aJE+/fRTZWdnV3gHvY8++kjdunVTnTp15OzsLBcXF82bN88uZ7r99tslXcjD//3vf+vnn38u1c4dd9yhr7/+WiNGjNCqVatKret5Ob8DXaot4HpGMQpl8vb2lpubW5m/nC1evFhpaWl2C/sdP35c586dU0JCgl2hysXFRffee68k6b///W+ptho0aGD33Gq1SpLOnDlzRW2WNaUwPj5eL7zwggYOHKhPPvlEW7duVVpamm677TadOXPmkteisv3Izc3V+fPn5efnV6qNsrZdzNvbW+7u7tq/f3+FcQcOHJC7u7vq169f4TGcnZ3VoEGDCqdJXc65FRcXlzt98FI+/PBDDRs2TP/6178UGhqq+vXra+jQoeZduMqSm5srwzDKfE9L1im71DS/K1HRZ7Iq/N7PY0UsFosee+wxLVy4UP/4xz/UsmVL3Xnnnb+7L5V5/67kPb6Ui9/7K/lMXPxz4urqKnd3d7uCccn2s2fPXrJP5f1M/fLLL2UerzLq1aunyMhIffPNNxV+Bg4cOFDq53TDhg1X5VjShcXLvby81KxZM0nSbbfdpi+//FLjx4/Xn//8ZzVv3lz79u3TyZMny128/Oeff9bdd9+tdu3aae3atUpMTFR6ero5nQEAqpNWrVqpU6dOdo9LuVRecfDgQd155536+eef9frrr+uLL75QWlqauTbQleYGtWrVKtXXTp062d2x1tnZWdHR0UpKSjKndSUmJsrf39+cuiZd+G690nysTZs2uv32282CV3FxsRYuXKj77rvP/M4sWc5i3Lhxpb7nRowYIel/Of/x48evONe+mIeHh4YMGaJ3331X8+bN0z333KMmTZqUGbts2TINHjxYDRs21MKFC7Vlyxbzj36/zR/uuusuffzxxzp37pyGDh2qRo0aKTg42G5Nz/Hjx2v69OlKTU1VRESEGjRooF69emnbtm3mOVb2d6BLtQVcz1gzCmVycnLS3XffrZSUFGVlZdl9AbVu3VqS7NbD8fLyMkcnjBw5ssw2S36hqawrabOsUQkLFy7U0KFDNWXKFLvt//3vf1WvXr0q60ft2rVlsVjK/OW7Mr+QOzk5qWfPnkpOTtbhw4fLLPwcPnxY6enpioiIKHXHruzsbDVs2NB8fu7cOR0/frxUEnQl5+bu7i4nJycdPnz4kudRFm9vb82ePVuzZ8/WwYMHtXz5cv3lL39RTk6OkpOTy+1brVq1lJWVVWrfkSNHzHarm9/7ebyUmJgYTZgwQf/4xz80efLkKulLZd6/K3mPL+Xin+fr4TMREhKiDz74QOfOnbP7a3jJ4t7BwcFX1G55Ix5/KyAgQGlpaXbbrmSNscocS7pQjPrtiKd27dpp1qxZ8vPz0/PPPy/pf6OnyitGPffcc/rrX/+qRx99VNKF93DevHmKiIjQqlWr7H7ZAYAb0ccff6zTp09r2bJldoWQjIwMhxz/scce02uvvWauz7R8+XKNGTPGLs9s0KDB7/rufeyxxzRixAjt3r1bP/74o7KysvTYY4+Z+0teP378+HLXbiz5vmvQoMEV59plefzxx/Wvf/1L33zzjRYtWlRu3MKFC9WsWTN9+OGHdt+fBQUFpWLvu+8+3XfffSooKFBqaqqmTp2qqKgoNW3aVKGhoXJ2dlZ8fLzi4+N14sQJrVmzRn/9618VHh6uQ4cOXdbvQJdq63q4EzFQHopRKNf48eP12Wef6amnntJ//vOfChfBdnd3V8+ePbVjxw61bdu23LvvXY6qatNisZh/hSqxcuVK/fzzz2revLnd9rJGwVxOP+644w4tW7ZMr732mjny4uTJk/rkk08q1deSaz5ixAglJSXZJQLFxcX605/+JMMwzMUvf2vRokV2vxD++9//1rlz5yq8q9zlnFtYWJg++ugjTZ48udykozKjiBo3bqxRo0Zp7dq1+vLLL8uN8/DwUOfOnbVs2TJNnz5dbm5uki7cNWThwoVq1KiR3V/3rqXLGT11OZ/HK9GwYUM988wz+u677zRs2LAq70tl3r/KvseX63r4TNx///2aO3euli5dajclYMGCBQoICFDnzp0vu83c3FytWLFC7dq1KzVi67dcXV0r9Vf4qjhWXl6efvzxR7upkhEREfrxxx81bNgw1a1bV9KFglVFi5evX7/evNtScnKyAgMD1aZNGw0bNkyfffYZxSgAN7ySwsZvv48Nw9DcuXMdcvxWrVqpc+fOmj9/voqLi1VQUGBXKJKkXr16KSkpSUeOHLG7i/Z7770nd3d3denSpcJjPPzww4qPj1diYqJ+/PFHNWzYUH369DH3BwUFqUWLFvr6669L/YHsYj179tTy5ct19OhRc6pecXGx3TTAyxEaGmre4fD+++8vN85iscjV1dWuEJWdnV3m3fRKWK1WhYWFqV69elq1apV27Nih0NBQu5h69erpgQce0M8//6wxY8bowIEDat269RX9DlReW8D1imIUytWtWze9+eabGj16tDp06KAnnnhCbdq0MUcmLF26VJLMu5a9/vrr6t69u+6880796U9/UtOmTXXy5El9//33+uSTT+zuIlFZVdFmZGSkEhMTdeutt6pt27ZKT0/Xa6+9VubIo5CQEPO4w4YNk4uLi4KCgirdj7///e/q27evevfurbFjx6q4uFivvvqqPDw8zGk8FenWrZtmz56tMWPGqHv37ho1apQaN26sgwcP6s0339TWrVs1e/bsMu/WtWzZMjk7O6t3797m3fRuu+02DR48uEqu8cyZM9W9e3d17txZf/nLX9S8eXMdPXpUy5cv1zvvvKO6deuWef1atGihnj17KioqSrfeeqvq1q2rtLQ0JScnX/LOZVOnTlXv3r3Vs2dPjRs3Tq6urnrrrbeUmZmpDz744JIjOxylvM9NWS7n83ilXnnllUrFVaYveXl5l3z/KhNTVa71ZyIiIkK9e/fWn/70J+Xn56t58+b64IMPlJycrIULF9oVkDds2KBevXppwoQJmjBhgiQpKipKjRs3VqdOneTt7a19+/ZpxowZOnr0qN0twavC7znW9u3bZRiG3cio3r17q3fv3qXibrnlFtlstnLbKrlD0yuvvKLIyEi1adNGtWvXvqq3LAeA6qJ3795ydXXVww8/rGeffVZnz57V22+/rdzc3N/V7vnz58tcr1W6sKbgb4tfjz/+uJ588kkdOXJEXbt2LZXDvPjii1qxYoV69uypCRMmqH79+lq0aJFWrlypadOmVfgdIF0oktx///1KTEzUiRMnNG7cOLu790nSO++8o4iICIWHhysmJkYNGzbUL7/8ot27d2v79u366KOPJEl/+9vftHz5ct19992aMGGC3N3d9eabb5p33b0S8+bNu2RMZGSkli1bphEjRuiBBx7QoUOH9Pe//13+/v7at2+fGTdhwgQdPnxYvXr1UqNGjXTixAm9/vrrcnFxUVhYmCSpf//+Cg4OVqdOnXTTTTfpp59+0uzZs9WkSRO1aNFCUuXz88q0BVy3rtXK6ag+MjIyjMcee8xo1qyZYbVajdq1axvNmzc3hg4daqxdu9Yudv/+/cbjjz9uNGzY0HBxcTFuuukmo2vXrsbLL79sF/fbO1n8Vll3EKtMm+W1ZxiGkZubawwfPtzw8fEx3N3dje7duxtffPGFERYWVubdxcaPH28EBAQYtWrVMiQZ69atu6xzW758udG2bVvD1dXVaNy4sfHKK6+Y/ausLVu2GA888IDh6+trODs7Gz4+PsagQYOMzZs3l4otaTs9Pd3o37+/UadOHaNu3brGww8/bN5x7+Lre+DAAbvtlT23Xbt2GQ8++KDRoEED8/xiYmKMs2fPlnv9kpOTjaeeespo27at4enpabi5uRlBQUHGiy++aJw+ffqS1+KLL74w7r77bsPDw8Nwc3MzunTpYnzyySel4qrqbnqX+kyWd5e7sj43ZcVW9vN4JXfTq0hZd9OrTF/Onj17yfevMjHlqehuemX9PBtG5T4T5bUxbNgww8PDo1SbYWFhRps2bSrsa4mTJ08acXFxhp+fn+Hq6mq0bdvW+OCDD8o9txdffNHcNnXqVKNdu3aGzWYznJycjJtuusm4//77ja+++qpSx74cv+dYJXcD2rt3b4VxXl5exuDBg8vdHxkZaaxatcowjAvXreT/iqFDhxoffvjhZZwNAFw7l/qurehuepXJdT/55BPjtttuM2rXrm00bNjQeOaZZ4zPPvvMLg81jKq5m54kY9++fXbxeXl5hpubmyHJmDt3bplt7ty50+jfv79hs9kMV1dX47bbbrM73/KuQ4mSO7RW9N3y9ddfG4MHDzZ8fHwMFxcXw8/Pz7j77ruNf/zjH3ZxX375pdGlSxfDarUafn5+xjPPPGP885//vOy76VWkrLvpvfLKK0bTpk0Nq9VqtGrVypg7d26pHH/FihVGRESE0bBhQ8PV1dXw8fEx7r33XuOLL74wY2bMmGF07drV8Pb2NvPp4cOHX1F+Xtm2gOuRxTAquBUPgBrl9ddf15gxY3Ty5Em7u5EBwNXw7bff6g9/+IPmzp2rO++8U0VFRZo+fbpSUlK0Zs2aUmvfAQAA4MbA3fSAG0BeXp6Sk5OVmJio4OBgClEAHKJNmzb697//rUmTJqlRo0a65ZZblJ2drZUrV1KIAgAAuIExMgq4Aaxfv14RERFq27at3n77bbt1YAAAAAAAcCSKUQAAAAAAAHAYpukBAAAAAADAYShGAQAAAAAAwGGcr3UHbjTnz5/XkSNHVLduXVkslmvdHQAAUA7DMHTy5EkFBASoVi3+fnetkDsBAFB9VDZ/ohjlYEeOHFFgYOC17gYAAKikQ4cOqVGjRte6GzcscicAAKqfS+VPFKMcrG7dupIuvDGenp7XuDcAAKA8+fn5CgwMNL+7cW2QOwEAUH1UNn+iGOVgJcPLPT09SagAAKgGmBp2bZE7AQBQ/Vwqf2IBBAAAAAAAADgMxSgAAAAAAAA4DMUoAAAAAAAAOAxrRgG4YZ0/f16FhYXXuhsArhEXFxc5OTld624AQI1RXFysoqKia90NAFdRVeVPFKMA3JAKCwu1f/9+nT9//lp3BcA1VK9ePfn5+bFIOQD8DoZhKDs7WydOnLjWXQHgAFWRP1GMAnDDMQxDWVlZcnJyUmBgoGrVYsYycKMxDEO//vqrcnJyJEn+/v7XuEcAUH2VFKJ8fHzk7u5OgR+ooaoyf6IYBeCGc+7cOf36668KCAiQu7v7te4OgGvEzc1NkpSTkyMfHx+m7AHAFSguLjYLUQ0aNLjW3QFwlVVV/sRwAAA3nOLiYkmSq6vrNe4JgGutpCDNGicAcGVK/v/kD3zAjaMq8ieKUQBuWAwhB8D/AwBQNfj/FLhxVMXPO8UoAAAAAAAAOAxrRgHA//fziTPKPV3osON5ebiqYT23q3oMi8WipKQkDRw4sNyYmJgYnThxQh9//HGl2jxw4ICaNWumHTt2qF27dlXSz5ooOztb0dHR2rx5s1xcXBx6h6HExESNGTOmwmNOnDhRH3/8sTIyMhzWLwDAjaM651U9evRQu3btNHv27CppT+J7t7I+/vhjjRs3Tvv379fo0aOr9D24lMq8702bNtWYMWM0ZswYh/WrpqIYBQC6kDDdM2ODzhQVO+yYbi5OWjM2rNKJ0+UWjSQpKytLXl5eksovIr3++usyDONyuo5KmDVrlrKyspSRkSGbzVZmzMSJE/XSSy9JkmrVqqWAgACFh4dr6tSpuummmxzZXYfx9/fXmDFj9Nxzz5nbnnvuOU2bNk1r1qxRr169zO29evWSr6+vFi9erMTERD322GOSLlwrT09PtWzZUv369dPTTz9d7jUGADhedcmrFixYUGr7vn37tGzZMrm4uFR1F1EJTz75pB577DHFxcWpbt26ZcY0bdpUP/30k6QLi2nffPPNGj16tJ588klHdtVhkpOTFRERoaysLPn5+Znb/fz85OLiokOHDpnbDh8+rMDAQK1atUp9+vRRjx49tGHDBkkX1sv19vZWhw4d9Nhjj2nQoEEOP5ffohgFAJJyTxfqTFGxZg9pp+Y+da768b7POaUxH2Yo93ThVR0d9dsvrPLwS/zV8cMPP6hjx45q0aJFhXFt2rTRmjVrVFxcrB07dmj48OH6+eef9dlnn5WKLS4ulsViUa1a1XeWfY8ePbRu3Tq7YtT69esVGBiodevWmcWowsJCbdmyRa+//roZ5+npqT179sgwDJ04cUKbN2/W1KlTNX/+fH355ZcKCAhw+PkAAEqrLnlV3759NX/+fLttN910E3dXvUZOnTqlnJwchYeHX/I7fdKkSYqNjdWpU6eUmJiop556SvXq1dOQIUNKxRYWFlbrGxd1795dzs7OWr9+vR566CFJ0u7du3X27FmdOXNG33//vZo3by5JWrdunVxcXNStWzfz9bGxsZo0aZKKior0888/KykpSQ899JBiYmL0z3/+85qck8SaUQBgp7lPHQU3tF31R1UkZj169FBcXJyeffZZ1a9fX35+fpo4caJdjMViMUdSNWvWTJLUvn17WSwW9ejRQ9KFvwz+dhpfcnKyunfvrnr16qlBgwaKjIzUDz/8cFl9a9q0qV5++WUNHTpUderUUZMmTfR///d/OnbsmO677z7VqVNHISEh2rZtm/ma48eP6+GHH1ajRo3k7u6ukJAQffDBB3bt/uc//1FISIjc3NzUoEED3XPPPTp9+rSkCwWNO+64Qx4eHqpXr566detm/tVMkj755BN17NhRtWvX1s0336yXXnpJ586dM/dPnDhRjRs3ltVqVUBAgOLi4io8x7ffflu33HKLXF1dFRQUpPfff9/u/JcuXar33ntPFotFMTEx5bbj7OwsPz8/NWzYUJGRkYqLi1NKSorOnDmjxMRE1atXTytWrFDr1q1ltVr1008/KTc3V0OHDpWXl5fc3d0VERGhffv2lWr7448/VsuWLVW7dm317t3b7i9nZZk/f75atWql2rVr69Zbb9Vbb71l7jtw4IAsFov+/e9/684775Sbm5tuv/127d27V2lpaerUqZPq1Kmjvn376tixY+Ueo2fPnvryyy/Na3/y5Ent2LFDf/nLX7R+/XozbuvWrTpz5ox69uxpbrNYLPLz85O/v79atWql4cOHa/PmzTp16pSeffZZM66izwkAwHGu97zKarXKz8/P7uHk5KQePXrYTcNq2rSppkyZoscff1x169ZV48aNS/0S/9xzz6lly5Zyd3fXzTffrBdeeOGy7jS2fv16WSwWrVq1Su3bt5ebm5vuvvtu5eTk6LPPPlOrVq3k6emphx9+WL/++qv5ukvlbYWFhRo1apT8/f1Vu3ZtNW3aVFOnTjX3V5T/FBYW6tlnn1XDhg3l4eGhzp07231X//TTT+rfv7+8vLzk4eGhNm3a6NNPPy33HCvKX9avX2+OhLr77rtlsVjsjnWxunXrys/PT82bN9fLL7+sFi1amDlvjx49NGrUKMXHx8vb21u9e/eWJG3YsEF33HGHrFar/P399Ze//MUuF5Skc+fOadSoUeb1/Nvf/lbhDIK8vDw98cQT8vHxkaenp+6++259/fXXdte3Xbt2evfdd9W4cWPVqVNHf/rTn1RcXKxp06bJz89PPj4+mjx5crnHqFOnjm6//Xa767F+/Xp1795d3bt3L7W9JB8u4e7uLj8/PwUGBqpLly569dVX9c4772ju3Llas2aNpEt/Tq4GilEAUI0tWLBAHh4e2rp1q6ZNm6ZJkyZp9erVZcZ+9dVXkqQ1a9YoKytLy5YtKzPu9OnTio+PV1pamtauXatatWrp/vvv1/nz5y+rb7NmzVK3bt20Y8cO9evXT9HR0Ro6dKgeffRRbd++Xc2bN9fQoUPNL/izZ8+qY8eOWrFihTIzM/XEE08oOjpaW7dulXRhyuHDDz+sxx9/XLt379b69es1aNAgGYahc+fOaeDAgQoLC9M333yjLVu26IknnjDv9LFq1So9+uijiouL065du/TOO+8oMTHR/OL/z3/+o1mzZumdd97Rvn379PHHHyskJKTcc0tKStLTTz+tsWPHKjMz0xxSvm7dOklSWlqa+vbtq8GDBysrK8tudM+luLm56fz582Zy9Ouvv2rq1Kn617/+pW+//VY+Pj6KiYnRtm3btHz5cm3ZskWGYejee++1S3p//fVXTZ48WQsWLNCXX36p/Px8869pZZk7d66ef/55TZ48Wbt379aUKVP0wgsvlJrC8OKLL+pvf/ubtm/fLmdnZz388MN69tln9frrr+uLL77QDz/8oAkTJpR7nJ49e+rUqVNKS0uTJH3xxRdq2bKlHnjgAaWlpZkJ9rp169SoUSPzL33l8fHx0SOPPKLly5eruLi4ws8JAABXasaMGerUqZN27NihESNG6E9/+pO+++47c3/dunWVmJioXbt26fXXX9fcuXM1a9asyz7OxIkTNWfOHG3evFmHDh3S4MGDNXv2bC1evFgrV67U6tWrlZCQYMZfKm974403tHz5cv373//Wnj17tHDhQjVt2lTSpfOfxx57TF9++aWWLFmib775Rg8++KD69u1rFpBGjhypgoICbdy4UTt37tSrr76qOnXKLwxWlL907dpVe/bskSQtXbpUWVlZ6tq1a6WvW+3ate3yoAULFsjZ2Vlffvml3nnnHf3888+69957dfvtt+vrr7/W22+/rXnz5unll1+2a6fkdVu3btUbb7yhWbNm6V//+leZxzQMQ/369VN2drY+/fRTpaenq0OHDurVq5d++eUXM+6HH37QZ599puTkZH3wwQd699131a9fPx0+fFgbNmzQq6++qr/97W9KTU0t9/x69uxp5pnShTypR48eCgsLK7X9t3/IK8+wYcPk5eVl/j5Q0efkqjHgUHl5eYYkIy8v71p3BbhhnTlzxti1a5dx5swZc9vOwyeMJs+tMHYePuGQPlzJ8YYNG2bcd9995vOwsDCje/fudjG333678dxzz5nPJRlJSUmGYRjG/v37DUnGjh07Kmz3Yjk5OYYkY+fOnRW281tNmjQxHn30UfN5VlaWIcl44YUXzG1btmwxJBlZWVnltnPvvfcaY8eONQzDMNLT0w1JxoEDB0rFHT9+3JBkrF+/vsx27rzzTmPKlCl2295//33D39/fMAzDmDFjhtGyZUujsLCw3L78VteuXY3Y2Fi7bQ8++KBx7733ms/vu+8+Y9iwYRW28+KLLxq33Xab+Xz37t1G8+bNjTvuuMMwDMOYP3++IcnIyMgwY/bu3WtIMr788ktz23//+1/Dzc3N+Pe//233utTUVLu2JRlbt24t89iBgYHG4sWL7fr397//3QgNDTUM43/v+7/+9S9z/wcffGBIMtauXWtumzp1qhEUFFTheTds2NB8P5555hljxIgRhmEYxq233mqkpKQYhmEYPXv2NKKjo83XzJ8/37DZbGW29/bbbxuSjKNHj1b4OSlLWf8flOA7+/rA+wBc36pzXuXk5GR4eHiYjwceeMAwjAs51tNPP23GXpzXnD9/3vDx8THefvvtctufNm2a0bFjR/P5xd+7F1u3bp0hyVizZo25berUqYYk44cffjC3Pfnkk0Z4eHi57Vyct40ePdq4++67jfPnz5eKrSj/+f777w2LxWL8/PPPdtt79epljB8/3jAMwwgJCTEmTpxYbl9+qzL5S25uriHJWLduXYVtNWnSxJg1a5ZhGIZRVFRk5j1vvfWWYRgX3r927drZveavf/2rERQUZHcd3nzzTaNOnTpGcXGx+bpWrVrZxTz33HNGq1atyjz22rVrDU9PT+Ps2bN2x7rllluMd955xzCMC++7u7u7kZ+fb+4PDw83mjZtah7XMAwjKCjImDp1arnnnJKSYkgyjhw5YhiGYfj4+BhfffWVkZqaagQEBBiGYRgHDx4slZdd/Fn+rc6dOxsRERGGYVT8OSlLVeRPjIwCgGqsbdu2ds/9/f2Vk5Pzu9r84YcfFBUVpZtvvlmenp7m9L6DBw9ecd98fX0lye6vbSXbSvpbXFysyZMnq23btmrQoIHq1KmjlJQU87i33XabevXqpZCQED344IOaO3eucnNzJUn169dXTEyMwsPD1b9/f73++uvKysoyj5Wenq5JkyapTp065iM2NlZZWVn69ddf9eCDD+rMmTO6+eabFRsbq6SkpFLDtn9r9+7ddnPxJalbt27avXv3ZV0jSdq5c6fq1KkjNzc3tW7dWoGBgVq0aJG539XV1e5a7t69W87OzurcubO5rUGDBgoKCrI7vrOzszp16mQ+v/XWW1WvXr0y+3js2DEdOnRIw4cPt7tGL7/8cqkpmpV5Xy/1GezRo4c5pHz9+vXmlNGwsDCtX79eBQUFSk1N1d13311hOyWM/z/qyWKxVPg5AQDgt3r27KmMjAzz8cYbb5Qb+9vvv5Jp47/9vvvPf/6j7t27y8/PT3Xq1NELL7xw2bnTxcfx9fU1p/39dttvj3upvC0mJkYZGRkKCgoylwIoUVH+s337dhmGoZYtW9rlBhs2bDBzg7i4OL388svq1q2bXnzxRX3zzTflnldl85fKeu6558z8aeTIkXrmmWfsFjD/bQ5UcvzQ0FBz1Lx0IXc7deqUDh8+bG7r0qWLXUxoaKj27dun4uLSi/Gnp6fr1KlTZt5a8ti/f79d/tS0aVO7xdh9fX3VunVruzVAL5U/devWTa6urlq/fr127dqlM2fOqEOHDurYsaPy8/O1b98+rVu3TlartdIjygzDMM+1os/J1UIxCgCqsYvv9GKxWC57Ot3F+vfvr+PHj2vu3LnaunWrOU2usPDybs/8276VfNGVta2kvzNmzNCsWbP07LPP6vPPP1dGRobCw8PN4zo5OWn16tX67LPP1Lp1ayUkJCgoKEj79++XdGG9oy1btqhr16768MMP1bJlS3O48/nz5/XSSy/ZJZw7d+7Uvn37VLt2bQUGBmrPnj1688035ebmphEjRuiuu+6qcK2H3yYqkv0X+uUICgpSRkaGmVh8/vnndlPT3Nzc7No1ypluVtbxy+pPWdtK3oO5c+faXaPMzMxSQ8Yr875e6jNYsm7U8ePHtWPHDt11112SZA41T01NLbVeVEV2794tT09PNWjQ4JKfEwAASnh4eKh58+bmw9/fv9zYinKu1NRUPfTQQ4qIiNCKFSu0Y8cOPf/885edO118HIvFcslc71J5W4cOHbR//379/e9/15kzZzR48GA98MADklRh/nP+/Hk5OTkpPT3dLjfYvXu3ufzAH//4R/3444+Kjo7Wzp071alTJ7sphL91OflLZTzzzDPKyMjQTz/9pFOnTmnatGl2xZ3frplU3nF++8esK3H+/Hn5+/vbXZ+MjAzt2bNHzzzzjBlX1nt4uTm8u7u77rjjDq1bt07r1q1T9+7d5eTkJGdnZ3Xt2tXcHhoaqtq1a1+y78XFxdq3b59ZvKzoc3K1cDe9GuTnE2eUe/ry/8OrLC8P16t61y8AV1fJXUTK+stOiePHj2v37t165513dOedd0qSNm3a5JD+ffHFF7rvvvv06KOPSrrwBb9v3z61atXKjLFYLOrWrZu6deumCRMmqEmTJkpKSlJ8fLykC4uzt2/fXuPHj1doaKgWL16sLl26qEOHDtqzZ0+F6w+5ublpwIABGjBggEaOHKlbb71VO3fuVIcOHUrFtmrVSps2bdLQoUPNbZs3b7bra2W5urpecl2k32rdurXOnTunrVu3mn/5On78uPbu3Wt3/HPnzmnbtm264447JEl79uzRiRMndOutt5Zq09fXVw0bNtSPP/6oRx555LLP4XL17NlTp0+f1syZM9WiRQtzhFVYWJiGDRumlStXqlmzZmrSpMkl28rJydHixYs1cOBAMwm91OcE+K2rmT+ROwE3hi+//FJNmjTR888/b2777U1UrpbK5m2enp4aMmSIhgwZogceeEB9+/bVL7/8ovr165eb/7Rv317FxcXKyckx2y5LYGCgnnrqKT311FMaP3685s6dq9GjR5eKq2z+Ulne3t6XnT8tXbrUrii1efNm1a1bVw0bNjTjLv4jXGpqqlq0aFHmHRY7dOig7OxsOTs7X/31lXQhf1qyZIlyc3PNUeXS/0aWb9myRY899lil2lqwYIFyc3P1hz/8wdxW0efkaqAYVUP8fOKM7pmxQWeKyv8l8/dyc3HSmrFhJFVANeXj4yM3NzclJyerUaNGql27tmw2m12Ml5eXGjRooH/+85/y9/fXwYMH9Ze//MUh/WvevLmWLl2qzZs3y8vLSzNnzlR2draZoGzdulVr165Vnz595OPjo61bt+rYsWNq1aqV9u/fr3/+858aMGCAAgICtGfPHu3du9csFk2YMEGRkZEKDAzUgw8+qFq1aumbb77Rzp079fLLLysxMVHFxcXq3Lmz3N3d9f7778vNza3cYsgzzzyjwYMHm4tUfvLJJ1q2bJl5R5KrqUWLFrrvvvsUGxurd955R3Xr1tVf/vIXNWzYUPfdd58Z5+LiotGjR+uNN96Qi4uLRo0apS5dupjFqYtNnDhRcXFx8vT0VEREhAoKCrRt2zbl5uZWeRHn5ptvVuPGjZWQkGBX/AoICFCTJk30j3/8Qw8++GCp1xmGoezsbBmGoRMnTmjLli2aMmWKbDabXnnlFUkVf06Ai13t/IncCbgxNG/eXAcPHtSSJUt0++23a+XKlUpKSrrqx61M3jZr1iz5+/urXbt2qlWrlj766CP5+fmpXr16FeY/DRo00COPPKKhQ4dqxowZat++vf773//q888/V0hIiO69916NGTNGERERatmypXJzc/X555+X+31b2fzlahkxYoRmz56t0aNHa9SoUdqzZ49efPFFxcfH242oOnTokOLj4/Xkk09q+/btSkhI0IwZM8ps85577lFoaKgGDhyoV199VUFBQTpy5Ig+/fRTDRw4sNRUwd+rZ8+e+vvf/66srCyNGzfO3B4WFqZXXnlFJ0+eLHNU+a+//qrs7GydO3dOP//8s5YtW6ZZs2bpT3/6kxlf0efkaqEYVUPkni7UmaJizR7SrkpuGX+x73NOacyHGco9XUhChRrt+5xTNeo4v+Xs7Kw33nhDkyZN0oQJE3TnnXeWumVurVq1tGTJEsXFxSk4OFhBQUF644037P76crW88MIL2r9/v8LDw+Xu7q4nnnhCAwcOVF5enqQLf63ZuHGjZs+erfz8fDVp0kQzZsxQRESEjh49qu+++04LFizQ8ePH5e/vr1GjRplrB4SHh2vFihWaNGmSpk2bJhcXF91666364x//KEmqV6+eXnnlFcXHx6u4uFghISH65JNP1KBBgzL7OnDgQL3++ut67bXXFBcXp2bNmmn+/PkOuU7ShSmJTz/9tCIjI1VYWKi77rpLn376qd2Qb3d3dz333HOKiorS4cOH1b17d7377rvltvnHP/5R7u7ueu211/Tss8/Kw8NDISEhdre2rko9e/bUggULSl2zsLAwzZs3r8xkKj8/X/7+/rJYLPL09FRQUJCGDRump59+Wp6enpIq/pwAF7ua+RO5E1Cz86rfuu+++/TnP/9Zo0aNUkFBgfr166cXXnhBEydOvKrHrUzeVqdOHb366qvat2+fnJycdPvtt+vTTz9VrVq1Lpn/zJ8/Xy+//LLGjh2rn3/+WQ0aNFBoaKjuvfdeSRdG248cOVKHDx+Wp6en+vbtW+EdBCuTv1wtDRs21KeffqpnnnlGt912m+rXr6/hw4frb3/7m13c0KFDdebMGd1xxx1ycnLS6NGj9cQTT5TZpsVi0aeffqrnn39ejz/+uI4dOyY/Pz/ddddd5qjvqhQaGiqr1SpJ6tixo7n99ttvV3Fxsdzc3OzW5Coxd+5czZ07V66urmrQoIE6duyoDz/8UPfff78ZU9Hn5GqxGOVN3sRVkZ+fL5vNpry8PDNxrgqZP+cpMmGTVozuruCGtku/4DprH3Cks2fPav/+/WrWrJk5p9oRowsvxl/MgWuvrP8PSlyt72xcnqv5PlzN/IbcCTcK8irgxlMV+RMjowBAUsN6blozNuyqrrt2MdYSAQAANRF5FYBLoRgFAP9fw3puJDEAAABVgLwKQEWu3gRAAAAAAAAA4CIUowAAAAAAAOAwFKMA3LC4fwMA/h8AgKrB/6fAjaMqft4pRgG44Tg5OUmSCgsdt6gmgOvTr7/+KkkOua00ANR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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#check out the masses within the problem range for white dwarfs\n", + "wd_issue_masses = np.where((k_out['mass'] >=0.5) & (k_out['mass'] <5))\n", + "print(\"Filtered Masses: \", k_out['mass'][wd_issue_masses])\n", + "print(k_out['phase'][wd_issue_masses])\n", + "print(k_out['mass_current'][wd_issue_masses])\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(k_out['mass'][wd_issue_masses], histtype = 'step',\n", + " bins = bh_bins, label = 'Initial masses of Problem WDs')\n", + "plt.title(\"Generated Objects of Initial Mass from 0.5 - 5 $M_\\odot$\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(k_out['mass_current'][wd_issue_masses], histtype = 'step',\n", + " bins = wd_bins, label = 'Final masses of Problem WDs')\n", + "plt.title(\"Final Evolved Masses\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()" + ] + }, { "cell_type": "markdown", "id": "11a7e249-23db-43e6-a071-838c676e61be", From 3fb65af7d17a9c6eb425a27b3b8488f4213a713b Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 13 Aug 2024 18:34:55 -0700 Subject: [PATCH 16/48] Changes to multiplicity testing --- spisea/tests/test_synthetic.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 57c46bd1..cb84c72c 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -421,7 +421,7 @@ def test_ifmr_multiplicity(): startTime = time.time() - evo = evolution.MISTv1() + evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere ifmr_obj = ifmr.IFMR_Raithel18() @@ -435,8 +435,8 @@ def test_ifmr_multiplicity(): print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. - imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) - imf_powers = np.array([-1.3, -2.3, -2.3]) + imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf]) + imf_powers = np.array([-0.3, -1.3, -2.3, -2.3]) ########## # Start without multiplicity and IFMR @@ -474,16 +474,19 @@ def test_ifmr_multiplicity(): assert len(np.where(clust2['phase'] == 102)) > 0 assert len(np.where(clust1['phase'] == 103)) > 0 # BH assert len(np.where(clust2['phase'] == 103)) > 0 + assert len(np.where(clust1['phase'] == 99)) > 0 # BD + assert len(np.where(clust2['phase'] == 99)) > 0 # Now check that we have companions that are WDs, NSs, and BHs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 + assert len(np.where(comps2['phase'] == 99)) > 0 # Make sure no funky phase designations (due to interpolation effects) # slipped through - idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 101) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 101) & (comps2['phase'] != 9) ) + idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 99) & (clust1['phase'] != 9) ) + idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 99) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 # Ensure no substellar mass compact objects are generated @@ -510,7 +513,7 @@ def test_ifmr_multiplicity(): comp_wd_idx = np.where(comps2['phase'] == 101) comp_ns_idx = np.where(comps2['phase'] == 102) comp_bh_idx = np.where(comps2['phase'] == 103) - comp_bd_idx = np.where(comps2['phase'] == 103) + comp_bd_idx = np.where(comps2['phase'] == 99) assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) From 2851408091f9b264a77fc3d2697b66874a252d8f Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 13 Aug 2024 18:38:53 -0700 Subject: [PATCH 17/48] Noncompanion exploration and companion decoding --- changes/Compact_Multiplicity.ipynb | 928 +++++++++++++++++++++++++---- 1 file changed, 796 insertions(+), 132 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 347160e5..0282c8bf 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -46,12 +46,12 @@ "id": "f8639685-3185-4e3e-88c6-98602bdaedc9", "metadata": {}, "source": [ - "### Cluster 1: Without Companions" + "## Cluster 1: Without Companions" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 219, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [ @@ -59,9 +59,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isochrone generation took 80.686259 s.\n", + "Isochrone generation took 71.359145 s.\n", "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-08 20:57:20.603079 Usually takes ~5 minutes\n", + " Starting at: 2024-08-13 18:04:09.632289 Usually takes ~5 minutes\n", "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", @@ -75,7 +75,7 @@ "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 21.66 seconds\n" + " Time taken: 17.31 seconds\n" ] } ], @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 90, "id": "1a68a281-791a-4821-8799-c2df8e936286", "metadata": {}, "outputs": [ @@ -109,24 +109,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 1041613, 1041614, 1041615]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 1037624, 1037625, 1037626]),), Masses: mass \n", "--------------------\n", - " 0.06752873088255393\n", - " 0.0262184880713122\n", - "0.011103322341764093\n", - " 0.07004122940473101\n", - "0.055860465009665516\n", - " 0.03849994739007916\n", - "0.036201844969911835\n", + "0.010581623004327498\n", + " 0.07746106089705293\n", + " 0.06953880412693926\n", + " 0.07376813803092074\n", + "0.018112506487775234\n", + " 0.05924633227110841\n", + " 0.076323411257296\n", " ...\n", - "0.026022885833328162\n", - " 0.06754269552994506\n", - "0.011731305085217554\n", - " 0.07086687808163461\n", - " 0.02602575168928598\n", - " 0.05665991329103694\n", - " 0.07735901867149753\n", - "Length = 1024861 rows\n" + "0.021666218437430496\n", + "0.026539284794723627\n", + " 0.07170977037349016\n", + "0.018694133568115678\n", + " 0.04969517873307298\n", + " 0.07788710880030814\n", + " 0.01568523000869492\n", + "Length = 1020837 rows\n", + "Found 925805 stars out of mass range\n" ] } ], @@ -134,9 +135,11 @@ "# Make cluster\n", "cluster_mass = 10**6\n", "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "t_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass)\n", "\n", "# Get outputs\n", - "k_out = k_cluster.star_systems" + "k_out = k_cluster.star_systems\n", + "t_out = t_cluster.star_systems" ] }, { @@ -199,15 +202,23 @@ "np.min(k_out[p_wd]['mass'])" ] }, + { + "cell_type": "markdown", + "id": "b2371c2d-4ea8-4bfb-bf67-59d86316132b", + "metadata": {}, + "source": [ + "### Exploring the properties of k_cluster" + ] + }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 109, "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -267,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 110, "id": "e8e3c545-0aba-43f8-ad46-ed9ba9720761", "metadata": {}, "outputs": [ @@ -275,17 +286,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH max mass: 119.49401158306712\n", - "BH min mass: 15.015214381022997\n", + "BH max mass: 118.75715744712116\n", + "BH min mass: 15.035041823555424\n", "\n", - "NS max mass: 118.51258738269632\n", - "NS min mass: 9.000988105453127\n", + "NS max mass: 119.72357017983377\n", + "NS min mass: 9.000593434639391\n", "\n", - "WD max mass: 8.999748990796446\n", - "WD min mass: 5.311104032812196\n", + "WD max mass: 8.999786895675784\n", + "WD min mass: 5.311134878018857\n", "\n", - "BD max mass: 0.07999996255862679\n", - "BH min mass: 0.010000049376939659\n", + "BD max mass: 0.07999987827752757\n", + "BH min mass: 0.010000034092785876\n", "\n" ] } @@ -307,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 111, "id": "ed4301d8-2eac-4f78-895b-c8c63b22fe5b", "metadata": {}, "outputs": [ @@ -320,27 +331,27 @@ "----\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " nan\n", " nan\n", - " ...\n", " 0.0\n", + " nan\n", " 0.0\n", " 0.0\n", + " ...\n", " 0.0\n", + " nan\n", + " nan\n", " 0.0\n", " 0.0\n", " 0.0\n", - "Length = 1363 rows\n" + " nan\n", + "Length = 1378 rows\n" ] } ], "source": [ "#check to see if large NS are Wolf-Rayet\n", "print(k_out['isWR'].dtype)\n", - "large_NS = np.where(k_out[p_ns]['mass'] > 15)\n", + "large_NS = np.where((k_out[p_ns]['mass']) > 15)\n", "print(k_out['isWR'][large_NS])" ] }, @@ -352,6 +363,163 @@ "I am unsure on what the 0.0 and nan values correspond to." ] }, + { + "cell_type": "code", + "execution_count": 115, + "id": "d0d021b1-d637-48c1-8641-990c3e947c81", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass ... metallicity m_hst_f153m\n", + "------------------ ---------- ------------------ ... ----------- -----------\n", + " 87.01978581314896 False 87.01978581314896 ... 0.0 nan\n", + " 80.51538949145795 False 80.51538949145795 ... 0.0 nan\n", + " 21.33585631479835 False 21.33585631479835 ... 0.0 nan\n", + "17.635459786720876 False 17.635459786720876 ... 0.0 nan\n", + "20.277517524928232 False 20.277517524928232 ... 0.0 nan\n", + " 20.66032231704192 False 20.66032231704192 ... 0.0 nan\n", + "27.227632523655107 False 27.227632523655107 ... 0.0 nan\n", + " ... ... ... ... ... ...\n", + " 62.70378845410619 False 62.70378845410619 ... 0.0 nan\n", + " 20.88687294620209 False 20.88687294620209 ... 0.0 nan\n", + "20.379813469500725 False 20.379813469500725 ... 0.0 nan\n", + " 68.91988141238063 False 68.91988141238063 ... 0.0 nan\n", + "16.299795503051396 False 16.299795503051396 ... 0.0 nan\n", + "19.331661523732976 False 19.331661523732976 ... 0.0 nan\n", + " 17.17530617287834 False 17.17530617287834 ... 0.0 nan\n", + "Length = 1378 rows\n", + "isWR\n", + "----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 1378 rows\n", + "isWR\n", + "----\n", + " 0.0\n", + " nan\n", + "isWR\n", + "----\n", + " nan\n", + " mass_current \n", + "------------------\n", + "1.3873021047437155\n", + "1.1867036763400425\n", + " 1.172988399792676\n", + "1.4760459379472195\n", + "1.3652646941863062\n", + " 1.34693018277759\n", + "1.3329667318702734\n", + " ...\n", + "1.3509102262959198\n", + "1.3492482970974988\n", + "1.3263769374090923\n", + "1.3292822711571697\n", + "1.3708253424214771\n", + "1.4880668288116823\n", + "1.3621508901941495\n", + "Length = 1378 rows\n" + ] + } + ], + "source": [ + "#check out the large NSs\n", + "print(k_out[p_ns][large_NS])\n", + "print(k_out[p_ns]['isWR'][large_NS])\n", + "print(np.unique(k_out['isWR']))\n", + "print(np.unique(k_out[p_ns]['isWR'][large_NS]))\n", + "print(k_out[p_ns]['mass_current'][large_NS])" + ] + }, + { + "cell_type": "markdown", + "id": "5f76dbc3-0fff-49a7-94f1-7513aa031ad6", + "metadata": {}, + "source": [ + "Assuming our assumption is correct, that the anomalous neutron stars are WR stars because of the huge disrepancy in initial and final masses." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "fc18f900-4485-4d21-baeb-f0de55865f7d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6520719390653884\n", + "1.058140703674423\n", + "119.72357017983377\n", + "15.00328698949453\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+PqpQoYK6deumY8eOFbg08MiRI+rfv7/8/Pzk7OysevXq6e23375qnLGxsfr222/197//PV9yKI+vr6+eeOIJm7LMzExNmTJFdevWlbOzsypXrqxBgwbp7NmzNu1q1KihsLAwrVq1Ss2aNZOrq6vq1q2rhQsX5rtOfHy8hg4dqqpVq8rJyUnBwcGaNGmSsrOzrW3yvmvTp0/XlClTFBwcLGdnZ23YsEGXLl1SRESEmjRpIk9PT/n4+Oj+++/X559/bnMdi8Wi1NRULV682Pp5/3UGTVHikKRTp06pT58+8vDwkKenp/r27Ws9LaaowsPDVb9+fY0fP145OTlXbb9s2TLdf//9cnd3V4UKFdS5c2ft3LnTpk3btm0LnBH01yWqv/76qypXrixJmjRpkvV9CA8Pl/R/f7927NihRx99VN7e3tYZkpcuXdL48eMVHBwsJycnValSRcOHD8+3xOtaPvvLnTt3TpIUGBhYYH25cuWscb744ouS/jzOPO8+8pbNlcT3cOfOnQoLC7N+t4KCgtStWzedPHnyqvcBALg5mEEEALjp8mYq/FX58lf+V9Ijjzyivn37asiQIdq7d6/Gjx8vSTY/kn755Rf179/f+oNr9+7devXVV3Xo0KEi/ZgqTIcOHVS9enUtXLhQ06ZNkyQtWLBAbdq0Ua1atfK1P3/+vCQpMjJSAQEBSklJ0YoVK9S2bVutW7fO+qNz6dKlGjZsmEaOHKnXX39d5cqV09GjR3XgwAFrX6NHj9ZHH32kKVOmqGnTpkpNTdW+ffusP/wkacOGDerSpYtatmyp+fPny9PTU0uXLlXfvn2VlpZm/bFalL4KcvjwYYWEhMjPz0+zZ8+Wr6+vlixZovDwcJ05c0Zjx45Vt27dtGXLFt1///169NFHFRERUez3+6+OHDmihx56SKNGjZK7u7sOHTqkadOmaevWrVq/fr1N28zMTPXo0UNDhw7VuHHjlJ2drdzcXHXv3l3btm1TVFSUmjVrpi1btqhLly75rnXgwAGFhITojjvu0BtvvKGAgACtXr1azz33nP744w9FRkYWGueaNWskST169CjyveXm5urhhx/W999/r7FjxyokJETHjx9XZGSk2rZtq23btsnV1dXafvfu3YqIiNC4cePk7++vDz74QEOGDNFdd91lTUrFx8fr3nvvVbly5TRx4kTVrFlTW7Zs0ZQpU/Trr79q0aJFNjHMnj1btWvX1uuvv66KFSuqVq1aysjI0Pnz5zVmzBhVqVJFmZmZWrt2rXr37q1FixZpwIABkqQtW7bowQcfVLt27fTPf/5TklSxYsVriiM9PV0dOnTQqVOnFB0drdq1a+u///2v+vbtW+T3UZIcHBwUHR2thx9+WIsXL7YeO1+QqVOn6h//+IcGDRqkf/zjH8rMzNSMGTPUunVrbd26VfXr1y/ydQMDA7Vq1Sp16dJFQ4YM0VNPPSVJ1qRRnt69e6tfv3565plnlJqaKmOMevbsqXXr1mn8+PFq3bq19uzZo8jISG3ZskVbtmyxmclVlM++IPfff78kacCAAZowYYJat24tX1/ffO2eeuopnT9/XnPmzNHy5cutCaW89+J6v4epqanq2LGjgoOD9fbbb8vf31/x8fHasGFDqduXCwBua/Y9RA0AUJYsWrSo0GNDs7KyrMeKLlq0yPqayMhII8lMnz7dpq9hw4YZFxcXk5ubW+C1cnJyTFZWlvnwww+Ng4ODOX/+vLVu4MCBpnr16leNd+DAgcbd3d0aR0BAgMnKyjLnzp0zzs7OJiYmxpw9e9ZIMpGRkYX2k52dbbKyskz79u1Nr169rOUjRowwXl5eV4yhQYMGpmfPnldsU7duXdO0aVOTlZVlUx4WFmYCAwNNTk5OkfsqSL9+/Yyzs3O+o1u7du1q3NzczIULF6xlkszw4cOL1O+1tDXmzyOWs7KyzKZNm4wks3v3bmvdwIEDjSSzcOFCm9f897//NZLMvHnzbMqjo6PzfW6dO3c2VatWNUlJSTZtR4wYYVxcXGzG0OWeeeYZI8kcOnSowJjzHtnZ2da6Tz/91Egy//nPf2xeExsbaySZd955x1pWvXp14+LiYo4fP24tS09PNz4+Pmbo0KHWsqFDh5oKFSrYtDPGmNdff91IMvv37zfGGOt3rWbNmiYzM7PQ+zLm/8bvkCFDTNOmTW3q3N3drUdg/1VR45g3b56RZD7//HObdk8//XS+vwUFyTum+LPPPjPGGPPAAw+YqlWrmvT0dGOM7XfYGGNOnDhhypcvb0aOHGnTz8WLF01AQIDp06ePtSw0NNSEhobmu+blfz+u9Dcg7+/XxIkTbcpXrVpV4N+1ZcuWGUnmvffes5YV9bMvzOTJk42Tk5P1b21wcLB55plnbL4/xhgzY8YMI8nExcVdsb/ifA+3bdtmJJmVK1deNV4AgP2wxAwAcNN9+OGHio2NtXlcbQbR5TMzGjVqpEuXLikhIcFatnPnTvXo0UO+vr5ycHCQo6OjBgwYoJycHP3888/XFfOgQYN05swZffPNN/r444/l5OSkv/3tb4W2nz9/vpo1ayYXFxeVL19ejo6OWrdunQ4ePGhtc++99+rChQt67LHH9Pnnn+uPP/7I18+9996rb775RuPGjdPGjRuVnp5uU3/06FEdOnRIjz/+uCQpOzvb+njooYd0+vRp69K4q/VVmPXr16t9+/aqVq2aTXl4eLjS0tIKPD2qpBw7dkz9+/dXQECA9TMNDQ2VJJv3Ms8jjzxi83zTpk2SpD59+tiUP/bYYzbPL126pHXr1qlXr15yc3PL9z5eunRJP/300zXH//nnn8vR0dH68PT0tNZ99dVX8vLyUvfu3W2u16RJEwUEBOQ7FatJkya64447rM9dXFxUu3ZtHT9+3KbPdu3aKSgoyKbPvD2h8t6PPD169JCjo2O+uD/77DO1atVKFSpUsI7fBQsWFPieF6SocWzYsEEeHh75vt/9+/cv0nUuN23aNJ08eVJvvfVWgfWrV69Wdna2BgwYYBOXi4uLQkNDb9hJZJePy7xZN3mz+/L87W9/k7u7u9atW2dTXpTPvjD//Oc/deLECS1cuFBDhw5VhQoVNH/+fDVv3rxIy0ul6/8e3nXXXfL29tZLL72k+fPn28ySBADcOkgQAQBuunr16qlFixY2j6u5fFlE3vKLvCTHiRMn1Lp1a/3+++9666239P333ys2Nta6f0xRkyGFqV69utq3b6+FCxdq4cKF6tevX6Ebr86cOVPPPvusWrZsqf/85z/66aefFBsbqy5dutjE8eSTT2rhwoU6fvy4HnnkEfn5+ally5bW5UrSn0uAXnrpJa1cuVLt2rWTj4+PevbsqSNHjkiSzpw5I0kaM2aMTSLC0dFRw4YNkyRr4ulqfRXm3LlzBe5hEhQUZK2/EVJSUtS6dWv973//05QpU7Rx40bFxsZq+fLlkvJ/pm5ubtYlTn+NvXz58vLx8bEp9/f3z9cuOztbc+bMyfc+PvTQQ5JUYAIvT96P98t/sLdt29aaBA0LC7OpO3PmjC5cuCAnJ6d814yPj893vYKWBjk7O9u8D2fOnNGXX36Zr7+77767wHso6HNdvny5+vTpoypVqmjJkiXasmWLYmNjNXjwYF26dKnQ9+DyeytKHOfOncv3WUhSQEBAka5zuZCQEPXs2VOvvfaaEhMTC4xLku655558sS1btuyKn/H1uPx9zhuXly9Fs1gsCggIyPedKspnfyX+/v4aNGiQ5s+frz179mjTpk1ycnLS888/f9XXlsT30NPTU5s2bVKTJk00YcIE3X333QoKClJkZOR17xEHACg57EEEACgVVq5cqdTUVC1fvlzVq1e3lu/atavErjF48GA98cQTys3N1bx58wptt2TJErVt2zZfm4L22hg0aJAGDRqk1NRUfffdd4qMjFRYWJh+/vlnVa9eXe7u7po0aZImTZpkncE0btw4de/eXYcOHVKlSpUkSePHj1fv3r0LjKdOnTqSdNW+CuPr66vTp0/nKz916pQkWWMoaevXr9epU6e0ceNG62wFSfk28c1T0KbYvr6+ys7O1vnz522SRJdvguzt7S0HBwc9+eSTGj58eIH9BwcHFxprx44dNWHCBH3xxRfq1KmTtdzLy8uaAL38R36lSpXk6+urVatWFdinh4dHodcrTKVKldSoUSO9+uqrBdbnJfXyFPSeLVmyRMHBwVq2bJlNfUZGRonH4evrq61bt+arv9ZNqv8qOjpaDRo00NSpUwuMS5L+/e9/2/ydKIiLi0u+jfClKycKC3P5+5w3Ls+ePWuTJDLGKD4+Xvfcc881X+NatGnTRp06ddLKlSuVkJAgPz+/QtuWxPdQkho2bKilS5fKGKM9e/YoJiZGkydPlqurq8aNG3dd9wMAKBkkiAAApULej5K/buxqjCnRI+h79eqlXr16ydPTU/fdd98VY7n8qPA9e/Zoy5Yt+ZZp5XF3d1fXrl2VmZmpnj17av/+/fl+wPr7+ys8PFy7d+/WrFmzlJaWpjp16qhWrVravXt3gT+IC1NQX4XNiGrfvr1WrFihU6dO2SQYPvzwQ7m5uV3xvbgeBX2mkvTuu+8WuY/Q0FBNnz5dy5Yt07PPPmstX7p0qU07Nzc3tWvXTjt37lSjRo3k5OR0TbG2aNFCnTp10vvvv6++ffuqdevWV31NWFiYli5dqpycHLVs2VLnzp1TvXr1tHXrVuspWdcqLCxMX3/9tWrWrClvb++rtv/www9Vrlw5jR492lpmsVjk5ORk80M/Pj4+3ylmUuGzWIoaR7t27fSvf/1LX3zxhc0ys08++eSqsRembt26Gjx4sObMmaOQkBCbus6dO6t8+fL65Zdf8i2DulyNGjX02WefKSMjwzoGz507p82bN9vMkLl8NmNRtG/fXtOnT9eSJUv0wgsvWMv/85//KDU11Xry1/U6c+aMKleubD2tLE9OTo6OHDkiNzc3eXl5SSr8Pkrie3h5f40bN9abb76pmJgY7dixo1j9AABKHgkiAECp0LFjRzk5Oemxxx7T2LFjdenSJc2bN6/AZSbF5eLion//+99XbRcWFqZXXnlFkZGRCg0N1eHDhzV58mQFBwfbnN729NNPy9XVVa1atVJgYKDi4+MVHR0tT09P6wyCli1bKiwsTI0aNZK3t7cOHjyojz76SPfff781ofPuu++qa9eu6ty5s8LDw1WlShWdP39eBw8e1I4dO/TZZ58Vua+CREZGWveUmThxonx8fPTxxx/rv//9r6ZPn26zr861+uWXXwp8T+vXr6+QkBB5e3vrmWeeUWRkpBwdHfXxxx9r9+7dRe6/S5cuatWqlSIiIvTBBx9ox44datGihXX2Q94P52HDhmnt2rVycnJS69at9eyzz6pGjRq6ePGijh49qi+//DLfaU2XW7JkiTp37qwOHTooPDxcnTt3lp+fn5KTk7Vnzx6tXbvWJrHQr18/ffzxx3rooYf0/PPPa/fu3WrevLk2bdqkqKgoPfzww3rzzTf1/fffW3/E5zHG6L777tPWrVttEomTJ0/WmjVrFBISoueee0516tTRpUuX9Ouvv+rrr7/W/PnzVbVqVWv7jh076tVXX9VTTz1ljS0sLEzLly/XsGHD9Oijj+q3337TK6+8osDAwHzLERs2bKiNGzfqyy+/VGBgoDw8PFSnTp0ixzFgwAC9+eabGjBggF599VXVqlVLX3/9tVavXl3kz7ggUVFR+vjjj7Vhwwa5u7tby2vUqKHJkyfr5Zdf1rFjx9SlSxd5e3vrzJkz2rp1q3WWnfTnEtB3331XTzzxhJ5++mmdO3dO06dPz7d8ysPDQ9WrV9fnn3+u9u3by8fHR5UqVbpikq9jx47q3LmzXnrpJSUnJ6tVq1bWU8yaNm2qJ5988rruP89HH32kd999V/3799c999wjT09PnTx5Uh988IH279+viRMnWpOhDRs2lCS99dZbGjhwoBwdHVWnTp0S+R5+9dVXeuedd9SzZ0/deeedMsZo+fLlunDhgjp27Fgi9woAKAF23SIbAFCm5J1iFhsbW2D9lU4xO3v2bIF9/fXEnS+//NI0btzYuLi4mCpVqpgXX3zRfPPNN0aS2bBhg7VdcU4xK0xBJxhlZGSYMWPGmCpVqhgXFxfTrFkzs3LlynzXXbx4sWnXrp3x9/c3Tk5OJigoyPTp08fs2bPH2mbcuHGmRYsWxtvb2zg7O5s777zTvPDCC+aPP/6wiWP37t2mT58+xs/Pzzg6OpqAgADz4IMPmvnz519zXwXZu3ev6d69u/H09DROTk6mcePGBZ4wpWs8xaywR977uXnzZnP//fcbNzc3U7lyZfPUU0+ZHTt25BsnV/qszp8/bwYNGmScnJyMxWIx5cuXNxs3bjSSzFtvvWXS09ONl5eXueOOO8wjjzxiBg8ebKpUqWIcHR1N5cqVTUhIiJkyZUqR7unSpUtmzpw55oEHHjBeXl6mfPnyxsfHx7Ru3dpMmzbNnDt3zqZ9VlaWef31103Dhg2NJOPm5mbq1q1rhg4dan7++Wfj4eFhqlevbtzc3Ey3bt2sr1u0aJGpWrWqkWTuvvtumz7Pnj1rnnvuORMcHGwcHR2Nj4+Pad68uXn55ZdNSkqKMeb/vmszZswwzZo1szkxzRhjXnvtNVOjRg3j7Oxs6tWrZ95//33rd/Gvdu3aZVq1amXc3NyMJJtTv4oShzHGnDx50jzyyCOmQoUKxsPDwzzyyCNm8+bNxTrF7K8mTJhgJBU4LlauXGnatWtnKlasaJydnU316tXNo48+atauXWvTbvHixaZevXrGxcXF1K9f3yxbtqzAvx9r1641TZs2Nc7OzkaS9WS3wv5+GfPnSWQvvfSSqV69unF0dDSBgYHm2WefNYmJiTbtqlevbvPZ5ynslLW/OnDggImIiDAtWrQwlStXNuXLlzfe3t4mNDTUfPTRR/najx8/3gQFBZly5crZ/N283u/hoUOHzGOPPWZq1qxpXF1djaenp7n33ntNTEzMFeMHANxcFmOMuTmpKAAAAPsJDw/XhQsXdOzYMbVu3VrvvPOOfvzxR/3666967bXXdOedd8rLy0sxMTFatWqVpkyZon379snBwUH333+/3nrrLdWsWdPa37///W9NmjRJR48elZubm5o2barPP/9c7u7uV6wryPLlyzV06FCdPXvWWvbzzz+rTp06ioyM1LJly6ynRV28eFG1a9fWU089pSlTpujEiROFLl0sikmTJmndunX67rvvit0HAAC4/XGKGQAAKNU+/fRTvf766/r999+VkJCgKlWq6N1331WbNm0UEhKihQsXavDgwTavSU1N1ejRoxUbG6t169apXLly6tWrl3JzcyVJp0+f1mOPPabBgwfr4MGD2rhxo3r37i1jzBXrCvPdd9/lO81v+/btcnFx0WOPPaYjR45YN4l+5ZVX1KRJEwUGBqpSpUrXlRySpHvvvVdbt269pk2oAQBA6cMeRAAAoFTz8PDQ0qVLtWfPHmVnZ1s32p47d66OHz+uH3/8UUuXLtXGjRutr7l8A+MFCxbIz89PBw4cUIMGDXT69GllZ2erd+/e1j2A8vZw+fnnnwutK8yvv/6a74SxHTt2qFGjRqpdu7bc3d118OBBubu765133tG2bdv0+uuvq3nz5tf13khSlSpVlJGRofj4+Kue7AUAAEovZhABAIBSLSwsTNu2bVP//v3Vo0cPnTx5Ug8//LCWL1+uRYsWqVu3btbjz/P88ssv6t+/v+68805VrFjResT9iRMnJEmNGzdW+/bt1bBhQ/3tb3/T+++/b90Q/Up1hUlPT5eLi4tN2fbt29W8eXNZLBY1atRI+/bt0wsvvKC///3vqlu3rrZv365mzZpd9/vj6uoqSUpLS7vuvgAAwO2LBBEAAChzBg8erJiYGC1evDjf8jJJ6t69u86dO6f3339f//vf//S///1PkpSZmSlJcnBw0Jo1a/TNN9+ofv36mjNnjurUqaO4uLgr1hWmUqVK+ZJIO3futCaAGjdurLfeektbt25VZGSkMjMztX///gITRGlpaXrxxRcVEhKikJAQ6wlchTl//rwkqXLlyld51wAAQGlGgggAAJQ5Xbp0UWZmpjIzM9W5c2ebunPnzungwYP6xz/+ofbt26tevXoFzgCyWCxq1aqVJk2apJ07d8rJyUkrVqy4al1BmjZtqgMHDlifHzt2TBcuXLAuIWvSpIm2bdumV199VZ6entq7d6+ysrIKXGI2YsQINW7cWJs3b9bmzZvVr18/DRgwoNA9kPbt26eqVavmm0UFAADKFvYgAgAAZY6Dg4P1VDAHBwebOm9vb/n6+uq9995TYGCgTpw4oXHjxtm0+d///qd169apU6dO8vPz0//+9z+dPXtW9erVu2JdYTp37qzx48crMTFR3t7e2r59u5ycnNSgQQNJ0sCBA9WzZ0/5+vpK+nN/Im9vb+vStzzp6elKTEzUE088oaioKElSVFSUPv/8cx09elS1atXKd+3vv/9enTp1urY3EAAAlDokiCTl5ubq1KlT8vDwkMVisXc4AADgBsjMzFRWVpaSk5NtyvOeZ2VlKTMzUykpKVqwYIHGjh2ru+++W7Vq1dL06dPVrVs3paWlKTk5WeXKldP69es1c+ZMXbx4UdWqVdOUKVPUqlUrHT58uNC6y6+dp3r16mrSpIl1yduWLVtUr149paenKz09XZLk5OSkixcvSpJ++uknNWzYMF9/aWlp1nscOHCg9f7y7uvy9pcuXdLy5cu1YsWKQmMDAAC3N2OMLl68qKCgIJUrV/hCMou50pmrZcTJkyev+4hYAAAAAACAW9Vvv/2mqlWrFlpv1xlE0dHRWr58uQ4dOiRXV1eFhIRo2rRpqlOnjrWNMUaTJk3Se++9p8TERLVs2VJvv/227r77bmubjIwMjRkzRp9++qnS09PVvn17vfPOO1e88b/y8PCQ9OebVbFixZK9SQAAAAAAADtJTk5WtWrVrLmPwth1BlGXLl3Ur18/3XPPPcrOztbLL7+svXv36sCBA3J3d5ckTZs2Ta+++qpiYmJUu3ZtTZkyRd99950OHz5svblnn31WX375pWJiYuTr66uIiAidP39e27dvz7evQEGSk5Pl6emppKQkEkQAAAAAAKDUKGrO45ZaYnb27Fn5+flp06ZNatOmjYwxCgoK0qhRo/TSSy9J+nO2kL+/v6ZNm6ahQ4cqKSlJlStX1kcffaS+fftKkk6dOqVq1arp66+/zncySUFIEAEAAAAAgNKoqDmPW+qY+6SkJEmSj4+PJCkuLk7x8fE2J2s4OzsrNDRUmzdvliRt375dWVlZNm2CgoLUoEEDa5vLZWRkKDk52eYBAAAAAABQVt0yCSJjjEaPHq0HHnjAeqRrfHy8JMnf39+mrb+/v7UuPj5eTk5O8vb2LrTN5aKjo+Xp6Wl9sEE1AAAAAAAoy26ZY+5HjBihPXv26IcffshXd/nR88aYqx5Hf6U248eP1+jRo63P8zZsAgAAAADAnnJzc5WZmWnvMHAbcXR0LNL+y1dzSySIRo4cqS+++ELfffedzcljAQEBkv6cJRQYGGgtT0hIsM4qCggIUGZmphITE21mESUkJCgkJKTA6zk7O8vZ2flG3AoAAAAAAMWSmZmpuLg45ebm2jsU3Ga8vLwUEBBw1ck0V2LXBJExRiNHjtSKFSu0ceNGBQcH29QHBwcrICBAa9asUdOmTSX9+YXZtGmTpk2bJklq3ry5HB0dtWbNGvXp00eSdPr0ae3bt0/Tp0+/uTcEAAAAAEAxGGN0+vRpOTg4qFq1aipX7pbZEQa3MGOM0tLSlJCQIEk2k2uulV0TRMOHD9cnn3yizz//XB4eHtY9gzw9PeXq6iqLxaJRo0Zp6tSpqlWrlmrVqqWpU6fKzc1N/fv3t7YdMmSIIiIi5OvrKx8fH40ZM0YNGzZUhw4d7Hl7N93vF9KVmFp6piJ6uzupipervcMAAAA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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#inital masses of large NS and final masses\n", + "print(np.max(k_out[p_ns]['mass_current'][large_NS]))\n", + "print(np.min(k_out[p_ns]['mass_current'][large_NS]))\n", + "print(np.max(k_out[p_ns]['mass'][large_NS]))\n", + "print(np.min(k_out[p_ns]['mass'][large_NS]))\n", + "\n", + "#create bins\n", + "initial_bins = np.linspace(15, 120, 20)\n", + "final_bins = np.linspace(1.0, 1.7, 20)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 1, 1)\n", + "plt.hist(k_out[p_ns]['mass'][large_NS], histtype = 'step',\n", + " bins = initial_bins, label = 'Neutron stars')\n", + "plt.title(\"Initial Masses of Large Generated Neutron Stars\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.hist(k_out[p_wd]['mass_current'][large_NS], histtype = 'step',\n", + " bins = final_bins, label = 'Neutron stars')\n", + "plt.title(\"Final Masses of Large Generated Neutron Stars\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()" + ] + }, { "cell_type": "code", "execution_count": 86, @@ -570,12 +738,364 @@ "This cluster fits the expectation from imfr.py that black holes are between 15-120 solar masses and white dwarves are at least 0.5 solar masses, even with the addition of substellar primary objects. " ] }, + { + "cell_type": "markdown", + "id": "a5da4347-311c-4fe4-b73f-6490b5d3c585", + "metadata": {}, + "source": [ + "### Exploring the t_cluster and comparing to troubleshoot" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "8ed736b8-1719-43c3-b629-4ce834a0d03e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "t_bh = np.where(t_out['phase'] == 103)[0]\n", + "t_ns = np.where(t_out['phase'] == 102)[0]\n", + "t_wd = np.where(t_out['phase'] == 101)[0]\n", + "t_bd = np.where(t_out['phase'] == 99)[0]\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(t_out[t_bh]['mass'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(t_out[t_wd]['mass'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(t_out[t_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(t_out[t_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "739eb6c6-eec8-4302-a2e4-00ca85353b3a", + "metadata": {}, + "source": [ + "### Exploring an Older Cluster" + ] + }, + { + "cell_type": "markdown", + "id": "331a8442-9686-43aa-9304-35ee4650e3a8", + "metadata": {}, + "source": [ + "Hopefully, in exploring an older cluster, we will see lower mass white dwarves, as they will have enough time to evolve into white dwarves at this point." + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "4528abef-8850-4972-af28-52648d87777a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 2306 logg=5.22\n", + "Isochrone generation took 17.886188 s.\n", + "Making photometry for isochrone: log(t) = 10.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-08-13 14:04:23.149590 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2306 K m_hst_f153m = 10.90\n", + "M = 1.034 Msun T = 3906 K m_hst_f153m = -3.64\n", + "M = 1.038 Msun T = 3301 K m_hst_f153m = -5.55\n", + " Time taken: 3.33 seconds\n" + ] + } + ], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "o_iso = synthetic.IsochronePhot(10, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "338bab08-4432-4dd9-b29d-dd61851b8c3e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: invalid value encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: divide by zero encountered in divide\n", + " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf indices: (array([ 0, 1, 3, ..., 1183119, 1183120, 1183121]),), Masses: mass \n", + "--------------------\n", + " 0.06279592698333637\n", + " 0.07112795900232521\n", + " 0.07376225324790554\n", + " 0.03432324440866939\n", + "0.018997543688358758\n", + "0.052852687409104974\n", + " 0.03463925596802796\n", + " ...\n", + " 0.06347447823132187\n", + " 0.05466348135014649\n", + "0.061140370362200006\n", + " 0.01896811961118919\n", + " 0.06440976267517402\n", + " 0.04168622996255112\n", + "0.010201719779934896\n", + "Length = 1028674 rows\n", + "Found 108643 stars out of mass range\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "o_cluster = synthetic.ResolvedCluster(o_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "o_out = o_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "4102bf35-8b91-44cb-a41a-081a1cb6bf00", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs and WDs\n", + "o_bh = np.where(o_out['phase'] == 103)[0]\n", + "o_ns = np.where(o_out['phase'] == 102)[0]\n", + "o_wd = np.where(o_out['phase'] == 101)[0]\n", + "o_bd = np.where(o_out['phase'] == 99)[0]\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(o_out[o_bh]['mass'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(o_out[o_wd]['mass'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(o_out[o_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(o_out[o_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "75ad5ea9-9668-47d5-9fb0-1d17ad9913b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0382949743021217\n", + "8.999575157847088\n" + ] + } + ], + "source": [ + "# exploring the mass limits to generated WDs\n", + "print(np.min(o_out[o_wd]['mass']))\n", + "print(np.max(o_out[o_wd]['mass']))" + ] + }, + { + "cell_type": "markdown", + "id": "12c64ec0-58db-48fb-bca4-ae022702c0ca", + "metadata": {}, + "source": [ + "This is more expected, suggesting our unexpected results for white dwarf masses were due to the age of the cluster itself and not designation issues." + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "37509686-120b-41e6-96e4-97c7ccaa7cf1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass isMultiple systemMass ... metallicity m_hst_f153m\n", + "------------------ ---------- ------------------ ... ----------- -----------\n", + "15.284314934206362 False 15.284314934206362 ... 0.0 nan\n", + " 17.1290304145272 False 17.1290304145272 ... 0.0 nan\n", + " 18.95921354685369 False 18.95921354685369 ... 0.0 nan\n", + "18.896393352954313 False 18.896393352954313 ... 0.0 nan\n", + "20.962240635300414 False 20.962240635300414 ... 0.0 nan\n", + "15.026617026908085 False 15.026617026908085 ... 0.0 nan\n", + " 76.13596178064363 False 76.13596178064363 ... 0.0 nan\n", + " ... ... ... ... ... ...\n", + " 26.37054586898205 False 26.37054586898205 ... 0.0 nan\n", + "15.233818106481106 False 15.233818106481106 ... 0.0 nan\n", + " 19.36143672592774 False 19.36143672592774 ... 0.0 nan\n", + " 15.57854891895509 False 15.57854891895509 ... 0.0 nan\n", + "16.464720574303342 False 16.464720574303342 ... 0.0 nan\n", + "18.964812829033175 False 18.964812829033175 ... 0.0 nan\n", + " 16.1815428883449 False 16.1815428883449 ... 0.0 nan\n", + "Length = 1274 rows\n", + "isWR\n", + "----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 1274 rows\n", + "isWR\n", + "----\n", + " 0.0\n", + " nan\n", + "isWR\n", + "----\n", + " nan\n", + " mass_current \n", + "------------------\n", + "1.3582329780993132\n", + " 1.373628116212405\n", + "1.2664447140529644\n", + "1.4158069262026158\n", + " 1.355217219139285\n", + "1.3046710483080488\n", + "1.2388328883877409\n", + " ...\n", + "1.4451775646969733\n", + "1.4624420030217151\n", + "1.2629887776924635\n", + "1.2784084162413294\n", + " 1.39953679687133\n", + "1.2970430314419577\n", + "1.3580723274397928\n", + "Length = 1274 rows\n" + ] + } + ], + "source": [ + "#exploring high mass NSs\n", + "large_o_NS = np.where((o_out[o_ns]['mass']) > 15)\n", + "print(o_out[o_ns][large_o_NS])\n", + "print(o_out[o_ns]['isWR'][large_o_NS])\n", + "print(np.unique(o_out['isWR']))\n", + "print(np.unique(o_out[o_ns]['isWR'][large_o_NS]))\n", + "print(o_out[o_ns]['mass_current'][large_o_NS])" + ] + }, { "cell_type": "markdown", "id": "7e460fa5-a083-4f2c-bef8-eb7d8a972b99", "metadata": {}, "source": [ - "### Cluster 2: With Companions" + "## Cluster 2: With Companions" ] }, { @@ -588,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 220, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -600,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 222, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -608,27 +1128,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 782400, 782401, 782402]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 783572, 783573, 783574]),), Masses: mass \n", "--------------------\n", - " 0.02818070156567557\n", - "0.057477731545588766\n", - "0.050488840533678525\n", - " 0.04059754829381457\n", - " 0.0715824675309702\n", - "0.052250831887193906\n", - " 0.06839990496518711\n", + " 0.06693276196287956\n", + " 0.04699446349914049\n", + "0.021914789420151674\n", + " 0.03465494731829008\n", + " 0.06694609948025984\n", + "0.033097609438405166\n", + " 0.06379944380267108\n", " ...\n", - " 0.0421694459314215\n", - "0.013298951799885059\n", - " 0.04585362159276258\n", - "0.017159730219879408\n", - " 0.04989123506548948\n", - "0.023690809192446777\n", - " 0.04091931301347838\n", - "Length = 769585 rows\n", + "0.030416028803757145\n", + " 0.06506905305535454\n", + "0.045851451825413045\n", + " 0.05977579693942302\n", + "0.027669020976738186\n", + "0.016900448679756944\n", + "0.017397789924083088\n", + "Length = 770794 rows\n", "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", "----\n", - "Found 175492 companions out of stellar mass range\n" + "Found 176029 companions out of stellar mass range\n" ] } ], @@ -644,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 223, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -652,57 +1172,119 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass \n", - "--------------------\n", - "0.010356220429645275\n", - " 1.027255369157919\n", - " 0.04728725326784277\n", - " 0.0458108242117786\n", - " 0.09466940653177572\n", - " 0.22804772872665638\n", - " 0.5530407053925155\n", - " ...\n", - " 0.3467687795811594\n", - " 0.02305569777333644\n", - " 0.1511085941801427\n", - " 0.24851815882478012\n", - " 0.06499991598727853\n", - "0.023044019171576186\n", - " 0.5446895523831808\n", - "Length = 429564 rows Teff \n", + " mass \n", + "-------------------\n", + " 0.1233262445951276\n", + " 0.2080613737453061\n", + "0.10384849953125234\n", + " 0.1736000003863225\n", + " 0.1458567566791392\n", + "0.14644751461127803\n", + " 0.6803574894852811\n", + " ...\n", + "0.08147468426694787\n", + " 0.1985936289536088\n", + " 0.6023752975149558\n", + " 1.4474686274512238\n", + " 0.2209924104780056\n", + "0.02286298804285546\n", + " 5.813934763247579\n", + "Length = 430889 rows Teff \n", "------------------\n", - " nan\n", - " 5825.765321681631\n", - " nan\n", - " nan\n", - " 2966.537023687665\n", - " 3310.184350302818\n", - "3987.9798523599243\n", + "3058.6083474502793\n", + "3282.8772110629448\n", + "3004.3525429335605\n", + "3198.7657683182606\n", + "3121.5408408103367\n", + "3123.1815879966903\n", + " 4415.385915529131\n", " ...\n", - "3465.1148889898845\n", - " nan\n", - "3136.1270818647067\n", - " 3338.388828168428\n", + " 2888.159816199592\n", + " 3268.012764248169\n", + " 4128.701802797316\n", + " 7171.279931264803\n", + "3300.4969767740236\n", " nan\n", " nan\n", - " 3956.044246444265\n", - "Length = 429564 rows\n" + "Length = 430889 rows\n", + "0\n" ] } ], "source": [ - "print(kc_comp['mass'], kc_comp['Teff'])" + "print(kc_comp['mass'], kc_comp['Teff'])\n", + "print(len(kc_comp[c_bd]))" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 216, + "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass ... metallicity m_hst_f153m \n", + "---------- -------------------- ... ----------- ----------------\n", + " 12 0.06646180798489866 ... 0.0 nan\n", + " 46 0.023012369331536837 ... 0.0 nan\n", + " 46 0.018471178544959374 ... 0.0 nan\n", + " 65 0.032997747650321915 ... 0.0 nan\n", + " 97 0.025634827285352435 ... 0.0 nan\n", + " 121 0.06455445811667415 ... 0.0 nan\n", + " 126 0.045057151520924446 ... 0.0 nan\n", + " ... ... ... ... ...\n", + " 2095614 0.04347367364887792 ... 0.0 nan\n", + " 2095634 0.06977937644714907 ... 0.0 nan\n", + " 2095634 0.01169204031289436 ... 0.0 nan\n", + " 2095641 0.07757530998522764 ... 0.0 9.33068823729235\n", + " 2095663 0.024710695578689927 ... 0.0 nan\n", + " 2095670 0.05139467006684135 ... 0.0 nan\n", + " 2095674 0.04032752505521463 ... 0.0 nan\n", + "Length = 185218 rows\n", + " Teff \n", + "----------------\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + "2859.25294280096\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 185218 rows\n", + "phase\n", + "-----\n", + " 1.0\n", + " nan\n" + ] + } + ], + "source": [ + "need_bd = np.where(kc_comp['mass'] < 0.08)\n", + "print(kc_comp[need_bd])\n", + "print(kc_comp[need_bd]['Teff'])\n", + "print(np.unique(kc_comp[need_bd]['phase']))" + ] + }, + { + "cell_type": "code", + "execution_count": 230, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -715,16 +1297,16 @@ "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = np.concatenate([p2_bh, c_bh])\n", + "bh_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = np.concatenate([p2_ns, c_ns])\n", + "ns_masses = np.concatenate([kc_out['mass'][p2_ns], kc_comp['mass'][c_ns]])\n", "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = np.concatenate([p2_wd, c_wd])\n", + "wd_masses = np.concatenate([kc_out['mass'][p2_wd], kc_comp['mass'][c_wd]])\n", "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", - "k_bd = np.concatenate([p2_bd, c_bd])\n", + "bd_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -733,7 +1315,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 3, 1)\n", - "plt.hist(kc_out[k_bh]['mass'], histtype = 'step',\n", + "plt.hist(bh_masses, histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -741,7 +1323,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 2)\n", - "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -749,7 +1331,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 3)\n", - "plt.hist(kc_out[k_bd]['mass'], histtype = 'step',\n", + "plt.hist(bd_masses, histtype = 'step',\n", " bins = bd_bins, label = 'Generated Brown Dwarves')\n", "plt.title(\"Generated Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -761,7 +1343,75 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 228, + "id": "ea063a9a-6268-4ab6-911d-0c6f33eef098", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH prim max mass: 119.89570218214551\n", + "BH prim min mass: 15.025999572935213\n", + "BH comp max mass: 119.37716775564101\n", + "BH comp min mass: 15.009358951094368\n", + "\n", + "NS prim max mass: 119.61968070913697\n", + "NS prim min mass: 9.002475201776234\n", + "NS comp max mass: 115.47061424546564\n", + "NS comp min mass: 9.00056928420967\n", + "\n", + "WD prim max mass: 8.999794986324147\n", + "WD prim min mass: 5.311067490186374\n", + "WD comp max mass: 8.99707592319193\n", + "WD comp min mass: 5.311666754267032\n", + "\n", + "BD prim max mass: 0.07999992186315226\n", + "BD prim min mass: 0.01000001336207612\n" + ] + }, + { + "ename": "ValueError", + "evalue": "zero-size array to reduction operation maximum which has no identity", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[228], line 19\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmax(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkc_comp\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc_bd\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmass\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_comp[c_ns][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:2820\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2703\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amax_dispatcher)\n\u001b[1;32m 2704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2705\u001b[0m where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m 2706\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2707\u001b[0m \u001b[38;5;124;03m Return the maximum of an array or maximum along an axis.\u001b[39;00m\n\u001b[1;32m 2708\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2818\u001b[0m \u001b[38;5;124;03m 5\u001b[39;00m\n\u001b[1;32m 2819\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2820\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaximum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2821\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:84\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mreduce(obj, axis, dtype, out, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n", + "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:41\u001b[0m, in \u001b[0;36m_amax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 40\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m umr_maximum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n", + "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation maximum which has no identity" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", + "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", + "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", + "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", + "\n", + "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", + "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", + "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", + "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", + "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", + "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", + "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", + "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", + "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", + "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 188, "id": "a64ebad4-b642-4e76-b12f-92402f241484", "metadata": {}, "outputs": [ @@ -769,19 +1419,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "63.493905663432386\n", - "0.01001691235394781\n" + "88.54164657710947\n", + "0.010059908098432945\n", + "118.80539890808666\n", + "15.000513183956517\n" ] } ], "source": [ "print(np.max(kc_out[k_wd]['mass']))\n", - "print(np.min(kc_out[k_wd]['mass']))" + "print(np.min(kc_out[k_wd]['mass']))\n", + "\n", + "print(np.max(kc_out[p2_bh]['mass']))\n", + "print(np.min(kc_out[p2_bh]['mass']))" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 172, "id": "497b4d94-6722-4ad7-880a-2b53d5acc28d", "metadata": {}, "outputs": [ @@ -789,24 +1444,24 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "-------------------- ---------- ... ------------------ ------------\n", - " 1.6412592122340768 True ... 1.891168727297026 2\n", - " 0.20991189263696136 False ... 7.70159095731871 0\n", - "0.017000902750311093 False ... nan 0\n", - "0.010753505226784877 False ... nan 0\n", - " 0.2688662867171623 True ... 6.5704973503446755 1\n", - " 0.1180619308611421 False ... 8.583856072073955 0\n", - " 0.08394927892076134 False ... 9.189440377342134 0\n", - " ... ... ... ... ...\n", - " 0.0206406101070209 False ... nan 0\n", - " 0.07136771909820422 False ... nan 0\n", - " 0.07067029373342434 True ... nan 2\n", - " 0.22895855805541523 False ... 7.553664542955746 0\n", - " 0.08333000096743302 True ... 9.202672780659682 1\n", - " 1.45216924939138 True ... 2.3052287375038536 1\n", - " 0.2641096309279692 False ... 7.327889430103641 0\n", - "Length = 3214 rows\n" + " mass isMultiple ... m_hst_f153m N_companions\n", + "-------------------- ---------- ... ------------------- ------------\n", + " 0.6586795096056381 False ... 5.025295725472892 0\n", + " 3.8427510670284337 True ... -0.6509998709738373 1\n", + " 0.2863865166483995 False ... 7.2088768596764785 0\n", + " 0.7742048335787876 False ... 4.460668851429343 0\n", + " 0.10766124147681298 True ... 8.760423625411967 1\n", + " 0.02589910470529386 False ... nan 0\n", + " 0.5326011325857933 False ... 5.718004847207575 0\n", + " ... ... ... ... ...\n", + " 0.07207704463160401 False ... nan 0\n", + " 0.2901218613714232 True ... 6.513996744341199 1\n", + " 0.8377075545324469 False ... 4.199858747334578 0\n", + "0.035545417415318484 False ... nan 0\n", + " 0.10799622753582329 False ... 8.754736726529003 0\n", + " 0.09016119379548757 False ... 9.064022685272212 0\n", + " 0.371310690849909 False ... 6.74904566406516 0\n", + "Length = 3285 rows\n" ] } ], @@ -816,7 +1471,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 174, "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", "metadata": {}, "outputs": [ @@ -824,8 +1479,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Smallest mass of a primary generated black hole: 15.001898748254684\n", - "Smallest mass of a companion generated black hole: 0.010087940766134134\n" + "Smallest mass of a primary generated black hole: 15.005003818897485\n", + "Smallest mass of a companion generated black hole: 0.010053181629822302\n" ] } ], @@ -837,10 +1492,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 173, "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Smallest mass of a primary generated object: 0.010000087950739485\n", + "Smallest mass of a companion generated object: 0.010000111957749505\n" + ] + } + ], "source": [ "# Finding the minimum mass of generated objects\n", "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", From 1d66451acd1775bc6b68202ad5900278716a1208 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 19 Aug 2024 09:33:32 -0700 Subject: [PATCH 18/48] BD updates --- changes/Compact_Multiplicity.ipynb | 430 ++++++++++++++++++++--------- spisea/synthetic.py | 33 ++- spisea/tests/test_synthetic.py | 6 +- 3 files changed, 333 insertions(+), 136 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index 0282c8bf..abed9025 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 251, "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", "metadata": {}, "outputs": [ @@ -59,9 +59,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Isochrone generation took 71.359145 s.\n", + "Isochrone generation took 70.910534 s.\n", "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-13 18:04:09.632289 Usually takes ~5 minutes\n", + " Starting at: 2024-08-16 12:58:00.472284 Usually takes ~5 minutes\n", "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", "Starting synthetic photometry\n", "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", @@ -75,7 +75,7 @@ "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 17.31 seconds\n" + " Time taken: 17.50 seconds\n" ] } ], @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 303, "id": "45456043-0197-4896-bcb4-d2247a95386e", "metadata": {}, "outputs": [ @@ -152,26 +152,26 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass isMultiple ... metallicity m_hst_f153m \n", - "-------------------- ---------- ... ----------- -----------------\n", - " 0.06752873088255393 False ... 0.0 nan\n", - " 0.21787124306310335 False ... 0.0 7.63530743580833\n", - " 0.0262184880713122 False ... 0.0 nan\n", - " 0.3087509497640693 False ... 0.0 7.086103270232351\n", - " 0.07004122940473101 False ... 0.0 nan\n", - "0.055860465009665516 False ... 0.0 nan\n", - " 0.2449826527701243 False ... 0.0 7.443908907848126\n", - " ... ... ... ... ...\n", - "0.047169363745193545 False ... 0.0 nan\n", - " 0.04388574160305706 False ... 0.0 nan\n", - " 0.08713365145144555 False ... 0.0 9.124319491534537\n", - " 0.5384991452265341 False ... 0.0 5.675504835409121\n", - "0.019767325458511932 False ... 0.0 nan\n", - " 0.16230926501233983 False ... 0.0 8.07930622339159\n", - " 0.12339530398894323 False ... 0.0 8.508529920543223\n", - "Length = 1026205 rows\n", - "119.2216436980943\n", - "0.010000064522612448\n" + " mass isMultiple ... metallicity m_hst_f153m\n", + "-------------------- ---------- ... ----------- -----------\n", + "0.010581623004327498 False ... 0.0 nan\n", + " 0.07746106089705293 False ... 0.0 nan\n", + " 0.06953880412693926 False ... 0.0 nan\n", + " 0.07376813803092074 False ... 0.0 nan\n", + "0.018112506487775234 False ... 0.0 nan\n", + " 0.05924633227110841 False ... 0.0 nan\n", + " 0.076323411257296 False ... 0.0 nan\n", + " ... ... ... ... ...\n", + "0.021666218437430496 False ... 0.0 nan\n", + "0.026539284794723627 False ... 0.0 nan\n", + " 0.07170977037349016 False ... 0.0 nan\n", + "0.018694133568115678 False ... 0.0 nan\n", + " 0.04969517873307298 False ... 0.0 nan\n", + " 0.07788710880030814 False ... 0.0 nan\n", + " 0.01568523000869492 False ... 0.0 nan\n", + "Length = 1020837 rows\n", + "0.07999987827752757\n", + "0.010000034092785876\n" ] } ], @@ -446,6 +446,60 @@ "print(k_out[p_ns]['mass_current'][large_NS])" ] }, + { + "cell_type": "code", + "execution_count": 293, + "id": "028193f1-bcbc-483c-9641-df8c14cf983c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mass_current \n", + "--------------------\n", + "0.010581623004327498\n", + " 0.07746106089705293\n", + " 0.06953880412693926\n", + " 0.07376813803092074\n", + "0.018112506487775234\n", + " 0.05924633227110841\n", + " 0.076323411257296\n", + " ...\n", + "0.021666218437430496\n", + "0.026539284794723627\n", + " 0.07170977037349016\n", + "0.018694133568115678\n", + " 0.04969517873307298\n", + " 0.07788710880030814\n", + " 0.01568523000869492\n", + "Length = 1020837 rows\n", + " mass \n", + "--------------------\n", + "0.010581623004327498\n", + " 0.07746106089705293\n", + " 0.06953880412693926\n", + " 0.07376813803092074\n", + "0.018112506487775234\n", + " 0.05924633227110841\n", + " 0.076323411257296\n", + " ...\n", + "0.021666218437430496\n", + "0.026539284794723627\n", + " 0.07170977037349016\n", + "0.018694133568115678\n", + " 0.04969517873307298\n", + " 0.07788710880030814\n", + " 0.01568523000869492\n", + "Length = 1020837 rows\n" + ] + } + ], + "source": [ + "print(k_out['mass_current'][p_bd])\n", + "print(k_out['mass'][p_bd])" + ] + }, { "cell_type": "markdown", "id": "5f76dbc3-0fff-49a7-94f1-7513aa031ad6", @@ -1108,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 220, + "execution_count": 286, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -1120,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 295, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -1128,27 +1182,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 783572, 783573, 783574]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 785354, 785355, 785356]),), Masses: mass \n", "--------------------\n", - " 0.06693276196287956\n", - " 0.04699446349914049\n", - "0.021914789420151674\n", - " 0.03465494731829008\n", - " 0.06694609948025984\n", - "0.033097609438405166\n", - " 0.06379944380267108\n", + " 0.07436657463591295\n", + " 0.06125115718404527\n", + " 0.07066751592079368\n", + " 0.07200706369071531\n", + "0.051506088266653524\n", + " 0.05929272238807183\n", + "0.049112273019809714\n", " ...\n", - "0.030416028803757145\n", - " 0.06506905305535454\n", - "0.045851451825413045\n", - " 0.05977579693942302\n", - "0.027669020976738186\n", - "0.016900448679756944\n", - "0.017397789924083088\n", - "Length = 770794 rows\n", + " 0.05799495176694396\n", + "0.031994021368442954\n", + " 0.06802099845333169\n", + " 0.06670578411126206\n", + "0.010433044273147809\n", + "0.030634450496813445\n", + "0.020885281173972783\n", + "Length = 772665 rows\n", "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", "----\n", - "Found 176029 companions out of stellar mass range\n" + "Found 175616 companions out of stellar mass range\n" ] } ], @@ -1164,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 285, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -1172,41 +1226,41 @@ "name": "stdout", "output_type": "stream", "text": [ - " mass \n", - "-------------------\n", - " 0.1233262445951276\n", - " 0.2080613737453061\n", - "0.10384849953125234\n", - " 0.1736000003863225\n", - " 0.1458567566791392\n", - "0.14644751461127803\n", - " 0.6803574894852811\n", - " ...\n", - "0.08147468426694787\n", - " 0.1985936289536088\n", - " 0.6023752975149558\n", - " 1.4474686274512238\n", - " 0.2209924104780056\n", - "0.02286298804285546\n", - " 5.813934763247579\n", - "Length = 430889 rows Teff \n", + " mass \n", + "--------------------\n", + "0.022827194847619314\n", + " 0.10638031764197846\n", + " 0.03760679268091785\n", + " 0.22726350815590202\n", + "0.038753074343781856\n", + " 0.2504904685642538\n", + " 0.03951772983956194\n", + " ...\n", + "0.012231927508833364\n", + " 0.072612764979889\n", + " 0.1961780248469474\n", + " 0.08736265118080355\n", + " 0.07659346659266889\n", + " 1.19594339645191\n", + " 1.1853661916226508\n", + "Length = 435190 rows Teff \n", "------------------\n", - "3058.6083474502793\n", - "3282.8772110629448\n", - "3004.3525429335605\n", - "3198.7657683182606\n", - "3121.5408408103367\n", - "3123.1815879966903\n", - " 4415.385915529131\n", - " ...\n", - " 2888.159816199592\n", - " 3268.012764248169\n", - " 4128.701802797316\n", - " 7171.279931264803\n", - "3300.4969767740236\n", " nan\n", + " 3011.398083542016\n", + " nan\n", + "3309.1075685999776\n", + " nan\n", + "3341.1194688523947\n", " nan\n", - "Length = 430889 rows\n", + " ...\n", + " nan\n", + "2817.2104303556866\n", + " 3261.336270821613\n", + "2925.6827572130533\n", + "2851.3684944754505\n", + " 6338.378648763236\n", + " 6307.806682055694\n", + "Length = 435190 rows\n", "0\n" ] } @@ -1218,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 298, "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", "metadata": {}, "outputs": [ @@ -1226,42 +1280,78 @@ "name": "stdout", "output_type": "stream", "text": [ - "system_idx mass ... metallicity m_hst_f153m \n", - "---------- -------------------- ... ----------- ----------------\n", - " 12 0.06646180798489866 ... 0.0 nan\n", - " 46 0.023012369331536837 ... 0.0 nan\n", - " 46 0.018471178544959374 ... 0.0 nan\n", - " 65 0.032997747650321915 ... 0.0 nan\n", - " 97 0.025634827285352435 ... 0.0 nan\n", - " 121 0.06455445811667415 ... 0.0 nan\n", - " 126 0.045057151520924446 ... 0.0 nan\n", - " ... ... ... ... ...\n", - " 2095614 0.04347367364887792 ... 0.0 nan\n", - " 2095634 0.06977937644714907 ... 0.0 nan\n", - " 2095634 0.01169204031289436 ... 0.0 nan\n", - " 2095641 0.07757530998522764 ... 0.0 9.33068823729235\n", - " 2095663 0.024710695578689927 ... 0.0 nan\n", - " 2095670 0.05139467006684135 ... 0.0 nan\n", - " 2095674 0.04032752505521463 ... 0.0 nan\n", - "Length = 185218 rows\n", - " Teff \n", - "----------------\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - "2859.25294280096\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 185218 rows\n", + " mass_current \n", + "-------------------\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "0.07904209649092925\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 182938 rows\n", + "system_idx mass ... metallicity m_hst_f153m \n", + "---------- -------------------- ... ----------- -----------------\n", + " 2 0.04059931139737934 ... 0.0 nan\n", + " 2 0.04033587588071577 ... 0.0 nan\n", + " 34 0.059462609507120824 ... 0.0 nan\n", + " 48 0.014810980619283313 ... 0.0 nan\n", + " 48 0.07904209649092925 ... 0.0 9.296290412739976\n", + " 70 0.010050222257410254 ... 0.0 nan\n", + " 76 0.021551221548502034 ... 0.0 nan\n", + " ... ... ... ... ...\n", + " 2074705 0.0318924914104722 ... 0.0 nan\n", + " 2074719 0.024133829456656052 ... 0.0 nan\n", + " 2074721 0.013583472609030067 ... 0.0 nan\n", + " 2074726 0.03713310656117373 ... 0.0 nan\n", + " 2074742 0.04297247209243148 ... 0.0 nan\n", + " 2074752 0.031034988651296436 ... 0.0 nan\n", + " 2074757 0.01213677256547114 ... 0.0 nan\n", + "Length = 182938 rows\n", + "phase\n", + "-----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " 1.0\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 182938 rows\n", + "Teff\n", + "----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 1579 rows\n", "phase\n", "-----\n", " 1.0\n", @@ -1271,20 +1361,23 @@ ], "source": [ "need_bd = np.where(kc_comp['mass'] < 0.08)\n", + "print(kc_comp['mass_current'][need_bd])\n", + "bhs = np.where(kc_comp['phase'] == 103)\n", "print(kc_comp[need_bd])\n", - "print(kc_comp[need_bd]['Teff'])\n", - "print(np.unique(kc_comp[need_bd]['phase']))" + "print(kc_comp[need_bd]['phase'])\n", + "print(kc_comp['Teff'][bhs])\n", + "print(np.unique(kc_comp['phase'][need_bd]))" ] }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 294, "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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nz2t+EhMTERAQgPPnz2PChAmmKl0vhpzHJk2ahGvXruHvv//WtF28eBF2dnY9PoAxF0P62draCjs77YdH9vb2AB48K2cLIiIiuozLsWPHEB4eDrlcbqaq+kZOTg4SEhKwb98+m/ssKcB6c8hSGJITUjp+gN7730epVPLxzb+kNod6iy3PISEEkpKSkJ+fjxMnTsDPz6/H6/TJPDL4460lLjc3V8jlcpGRkSGqq6vF+++/L5ydnUV9fb25S7MpK1euFEVFRaKurk6cPn1azJo1Szz55JMcZyOp1WqhVCqFUqkUAMT27duFUqkUv/76qxBCiC1btggXFxeRn58vKisrxbx584RCoRC3bt0yc+XW51FjrVarxcqVK0Vpaam4dOmSOHnypIiIiBBeXl4cazPqKd9TUlLEwoULu72+tXx7kL79VKvVwtvbW8yZM0dcuHBBFBcXi5EjR4rFixebqwuPRd9+ZmZmCgcHB7Fr1y5RW1srTp06JcLDw8X48ePN1YXH0lOuP9zPuro68cQTT4gVK1aI6upqkZGRIeRyucjLyzNXFx6Lvv3ct2+fcHBwEOnp6aKhoUHzc/PmTXN14bHo28+HWUsOWQp9c8Jajx9j6DtGn3zyiSgoKBAXL14UVVVVIiUlRQAQ+/fvN1cX+pRUMtgY+o6R1ObQO++8I1xcXERRUZHW+aq1tVWzjynmEReIjJCeni58fX2Fo6OjGDt27CO/go4MExcXJxQKhZDL5WLIkCEiNjZWXLhwwdxlWb37Xxv58E98fLwQ4p+vuk9NTRWenp6iX79+YsqUKaKystK8RVupR411a2uriI6OFoMGDRJyuVwMHTpUxMfHi8uXL5u7bMl7VL7Hx8eLqVOndntda/rHTN9+qlQqMX36dOHk5CS8vb1FcnKy1gMXS6VvP9PS0sTo0aOFk5OTUCgUYsGCBeLq1asmrlo/PeW6rn4WFRWJsLAw4ejoKIYNGyY+++wz0xeuJ337OXXq1Efub6kM+Xv+lzXlkKXQNyes8fgxlj5j9NFHH4kRI0aI/v37C1dXVzF58mRx6NAhM1RtGlLJYGPoO0ZSm0O6xgaAyMzM1Oxjinkk+7cYIiIiIiIiIiKSKH4GERERERERERGRxHGBiIiIiIiIiIhI4rhAREREREREREQkcVwgIiIiIiIiIiKSOC4QERERERERERFJHBeIiIiIiIiIiIgkjgtEREREREREREQSxwUiIiIiIiIiIiKJ4wIREREREREREZHEcYGIyAI1NTXBw8MD9fX1JrvPOXPmYPv27Sa7PyIi0sbsJyKSHmY/WRIuEJEkJSQkQCaTITExscu2d999FzKZDAkJCaYv7F+bN2/G7NmzMWzYME3blClTIJPJsHHjRq19hRCYMGECZDIZ1q1bZ/B9rlu3Dps2bcKtW7cMvg0iIkvG7O+K2U9Eto7Z3xWzn7rDBSKSLB8fH+Tm5qKtrU3Tdvv2beTk5GDo0KFmq6utrQ0ZGRlYvHixpk0IgfPnz8PX1xeVlZVa+2dlZeHatWsAgLFjxxp8vyEhIRg2bBiys7MNvg0iIkvH7NfG7CciKWD2a2P2U3e4QESSNXbsWAwdOhT5+fmatvz8fPj4+CAsLEzT9t1332Hy5MkYOHAg3NzcMGvWLNTW1mrdVl5eHoKDg+Hk5AQ3NzdMnz4dLS0tPW7T5ciRI3BwcEBERISmraamBmq1GgkJCVonCrVajTVr1mie9Rg3bpxRY/LCCy8gJyfHqNsgIrJkzP6umP1EZOuY/V0x+0kXLhCRpL355pvIzMzUXP7iiy+waNEirX1aWlqQnJyMs2fP4vjx47Czs8PLL7+Mzs5OAEBDQwPmzZuHRYsWQaVSoaioCLGxsRBCPHJbd0pKShAeHq7VVlFRgf79+2PevHmoqalBe3s7AGDjxo0YM2YMFAoF3N3d4ePjY9R4jB8/HmfOnNHcPhGRLWL2a2P2E5EUMPu1MftJFwdzF0BkTgsXLsSaNWtQX18PmUyGH374Abm5uSgqKtLs88orr2hdJyMjAx4eHqiurkZQUBAaGhpw9+5dxMbGwtfXFwAQHBwMALh48WK327pTX1+PIUOGaLWdO3cOISEh8Pf3h7OzM1QqFZydnbFr1y6Ul5dj69atRj+LAABeXl5ob29HY2Ojpl4iIlvD7NfG7CciKWD2a2P2ky58BRFJmru7O2bOnImsrCxkZmZi5syZcHd319qntrYW8+fPx/DhwzFgwAD4+fkBAC5fvgwACA0NRVRUFIKDg/Hqq69iz549aG5u7nFbd9ra2tC/f3+ttoqKCowbNw4ymQwhISGoqqrCihUr8Pbbb2PUqFGoqKgw6n3I9zk5OQEAWltbjb4tIiJLxezXxuwnIilg9mtj9pMuXCAiyVu0aBG+/PJLZGVldXmZKQDMnj0bTU1N2LNnD8rKylBWVgYA6OjoAADY29ujsLAQR44cwejRo7Fz504EBATg0qVLj9zWHXd39y4nE6VSqTkRhIaG4tNPP8WZM2eQmpqKjo4OXLhwQeeJorW1FatXr0ZkZCQiIyOxZMkSNDU1dXvff/75JwBg0KBBPYwaEZF1Y/Y/wOwnIqlg9j/A7CdduEBEkvfcc8+ho6MDHR0diImJ0drW1NQElUqFtWvXIioqCoGBgTqfCZDJZJg0aRI2bNgApVIJR0dHFBQU9LhNl7CwMFRXV2su19XV4ebNm5qXko4ZMwbl5eXYtGkTXFxcUFlZiTt37uh8qWlSUhJCQ0NRWlqK0tJSzJ07F2+88Ua374WuqqqCt7d3l2dTiIhsDbP/AWY/EUkFs/8BZj/pws8gIsmzt7eHSqXS/P5frq6ucHNzw+7du6FQKHD58mWkpKRo7VNWVobjx48jOjoaHh4eKCsrwx9//IHAwMBHbutOTEwM1qxZg+bmZri6uqKiogKOjo4ICgoCAMTHx+Oll16Cm5sbgH/ep+zq6qp5Cex9bW1taG5uxuuvv47169cDANavX48DBw7gl19+wciRI7vc9/fff4/o6Gj9BpCIyAox+x9g9hORVDD7H2D2ky5cICICMGDAAJ3tdnZ2yM3NxfLlyxEUFISAgACkpaXhmWee0bpuSUkJduzYgVu3bsHX1xfbtm3DjBkzoFKput3WneDgYISHh+Obb77B0qVLce7cOQQFBUEulwMA5HK51kr/uXPntL6e877/PluQlJTU4xjcvn0bBQUFOHr0aI/7EhHZAmY/s5+IpIfZz+yn7snEo753j4jM4vDhw1i1ahWqqqpgZ2f4O0ETEhLw7LPPYsGCBQCA48ePY+vWrTh8+DBkMpnWvunp6Thw4ACOHTtmVO1ERGQYZj8RkfQw+8mS8BVERBbo+eefR01NDX777Tf4+PgYfDu7du3C2rVrkZaWBplMhsDAQOzdu7fLSQL45xmKnTt3GlM2EREZgdlPRCQ9zH6yJHwFERERERERERGRxPFbzIiIiIiIiIiIJI4LREREREREREREEscFIiIiIiIiIiIiieMCERERERERERGRxHGBiIiIiIiIiIhI4rhAREREREREREQkcVwgIiIiIiIiIiKSOC4QERERERERERFJHBeIiIiIiIiIiIgkjgtEREREREREREQSxwUiIiIiIiIiIiKJ+z9eYEbnOxZUAQAAAABJRU5ErkJggg==", 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"text/plain": [ "
" ] @@ -1297,16 +1390,16 @@ "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "bh_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", + "k_bh = np.concatenate([p2_bh, c_bh])\n", "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "ns_masses = np.concatenate([kc_out['mass'][p2_ns], kc_comp['mass'][c_ns]])\n", + "k_ns = np.concatenate([p2_ns, c_ns])\n", "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "wd_masses = np.concatenate([kc_out['mass'][p2_wd], kc_comp['mass'][c_wd]])\n", + "k_wd = np.concatenate([p2_wd, c_wd])\n", "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", - "bd_masses = np.concatenate([kc_out['mass'][p2_bh], kc_comp['mass'][c_bh]])\n", + "k_bd = np.concatenate([p2_bd, c_bd])\n", "\n", "# Plot on histograms\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -1315,7 +1408,7 @@ "\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(1, 3, 1)\n", - "plt.hist(bh_masses, histtype = 'step',\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -1323,7 +1416,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 2)\n", - "plt.hist(kc_out[p2_wd]['mass'], histtype = 'step',\n", + "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", " bins = wd_bins, label = 'Generated White Dwarves')\n", "plt.title(\"Generated White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -1331,7 +1424,7 @@ "plt.legend()\n", "\n", "plt.subplot(1, 3, 3)\n", - "plt.hist(bd_masses, histtype = 'step',\n", + "plt.hist(kc_comp['mass'][need_bd], histtype = 'step',\n", " bins = bd_bins, label = 'Generated Brown Dwarves')\n", "plt.title(\"Generated Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -1611,9 +1704,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 276, "id": "1969480a-56b5-4109-993a-41267b1bcf24", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf indices: (array([ 0, 1, 2, ..., 693572, 693573, 693574]),), Masses: mass \n", + "--------------------\n", + " 0.03330731848800373\n", + "0.045130822625746865\n", + " 0.04311839070080252\n", + "0.036366767190959895\n", + "0.034795730457169875\n", + " 0.07590327272894272\n", + " 0.04405898676312338\n", + " ...\n", + "0.022974850570129105\n", + " 0.07743240221765976\n", + " 0.06465930761239451\n", + "0.020632183162231716\n", + " 0.04965218683400625\n", + "0.021590467618339538\n", + " 0.06019784339988129\n", + "Length = 682107 rows\n", + "Found 179 stars out of mass range\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 143800 companions out of stellar mass range\n" + ] + } + ], + "source": [ + " # Now to create the cluster.\n", + "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", + "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", + "\n", + " ##########\n", + " # Start without multiplicity and IFMR\n", + " ##########\n", + "\"\"\" my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=None)\n", + " print('Constructed IMF: %d seconds' % (time.time() - startTime)) \n", + " \n", + " cluster1 = syn.ResolvedCluster(my_iso, my_imf1, cluster_mass, ifmr=ifmr_obj)\n", + " clust1 = cluster1.star_systems\n", + " print('Constructed cluster: %d seconds' % (time.time() - startTime))\"\"\"\n", + "\n", + " \n", + " ##########\n", + " # Test with multiplicity and IFMR\n", + " ##########\n", + "multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=multi)\n", + "\n", + " \n", + "cluster2 = synthetic.ResolvedCluster(my_iso, my_imf2, cluster_mass, ifmr=my_ifmr)\n", + "clust2 = cluster2.star_systems\n", + "comps2 = cluster2.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "a63128a1-23d2-48a2-a0b7-994d1d723bb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", + "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" + ] + } + ], + "source": [ + "bd_idx = np.where(comps2['phase'] == 99)\n", + "print(comps2[bd_idx])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c90f58d-c82c-4350-82f0-3f85de4a4c65", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 19ad14a1..c793bfcd 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -359,7 +359,7 @@ def _make_companions_table(self, star_systems, compMass): companions['isWR'][cdx] = np.round(self.iso_interps['isWR'](comp_mass)) companions['mass_current'] = self.iso_interps['mass_current'](companions['mass']) companions['phase'] = np.round(self.iso_interps['phase'](companions['mass'])) - companions['metallicity'] = np.ones(N_comp_tot)*self.iso.metallicity + companions['metallicity'] = np.ones(N_comp_tot)*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 @@ -401,14 +401,21 @@ def _make_companions_table(self, star_systems, compMass): star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan + """# Assigning brown dwarf companions the correct phase/properties + bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + companions['phase'][bd_idx] = 99 + companions['Teff'][bd_idx] = np.nan + for filt in self.filt_names: + companions[filt][bd_idx] = np.full(len(bd_idx), np.nan)""" + ##### # Make Remnants with flux = 0 in all bands. ##### if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with masses above the max iso mass + # Identify compact objects as those with Teff = 0 or with masses above the max iso mass, or BDs highest_mass_iso = self.iso.points['mass'].max() - cdx_rem = np.where(np.isnan(companions['Teff']) & - (companions['mass'] > highest_mass_iso))[0] + cdx_rem = np.where((np.isnan(companions['Teff']) & + (companions['mass'] > highest_mass_iso)) | (companions['mass'] < 0.08))[0] # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -417,11 +424,11 @@ def _make_companions_table(self, star_systems, compMass): metallicity_array=companions['metallicity'][cdx_rem]) else: r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) - +# SEPERATE BDS IN OWN FUNCTION # 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) + good = np.where(r_id_tmp > 0) #**** cdx_rem_good = cdx_rem[good] companions['mass_current'][cdx_rem_good] = r_mass_tmp[good] @@ -431,7 +438,14 @@ def _make_companions_table(self, star_systems, compMass): for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - + # Assigning brown dwarf companions the correct phase/properties + bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + companions['phase'][bd_idx] = 99 + companions['mass_current'][bd_idx] = companions['mass'][bd_idx] + for filt in self.filt_names: + companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) + + #BD INITIAL PHASE # Notify if we have a lot of bad ones. # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) @@ -439,6 +453,7 @@ def _make_companions_table(self, star_systems, compMass): if len(idx) != N_comp_tot and self.verbose: print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) + #BD FINAL PHASE # Double check that everything behaved properly. assert companions['mass'][idx].min() > 0 @@ -465,8 +480,8 @@ def _remove_bad_systems(self, star_systems, compMass): idx = np.where(star_systems_teff_non_nan > 0)[0] else: # Keep stars (with Teff) and any other compact objects (with phase info). - idx = np.where( (star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | - (star_systems_phase_non_nan == 99) )[0] + idx = np.where((star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | + (star_systems_phase_non_nan == 99))[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index cb84c72c..23f5a7a8 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -477,7 +477,7 @@ def test_ifmr_multiplicity(): assert len(np.where(clust1['phase'] == 99)) > 0 # BD assert len(np.where(clust2['phase'] == 99)) > 0 - # Now check that we have companions that are WDs, NSs, and BHs + # Now check that we have companions that are WDs, NSs, BHs, and BDs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 @@ -535,6 +535,10 @@ def test_ifmr_multiplicity(): (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + + #print statements for debugging: + print(comps2['mass'][comp_bd_idx]) + print(np.unique(comps2['phase'][comp_bd_idx])) return def test_metallicity(): From 0dcfdcf2caf0472c0af996f60b1775cc1da37d9e Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Thu, 22 Aug 2024 15:30:44 -0700 Subject: [PATCH 19/48] Curiosities in BD assignment --- changes/Compact_Multiplicity.ipynb | 366 +++++++++++++++++++++++------ spisea/ifmr.py | 2 +- spisea/synthetic.py | 29 ++- spisea/tests/test_synthetic.py | 8 +- 4 files changed, 331 insertions(+), 74 deletions(-) diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb index abed9025..a2c1abec 100644 --- a/changes/Compact_Multiplicity.ipynb +++ b/changes/Compact_Multiplicity.ipynb @@ -1162,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 286, + "execution_count": 304, "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", "metadata": {}, "outputs": [], @@ -1174,7 +1174,7 @@ }, { "cell_type": "code", - "execution_count": 295, + "execution_count": 305, "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", "metadata": {}, "outputs": [ @@ -1182,27 +1182,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 785354, 785355, 785356]),), Masses: mass \n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 785509, 785510, 785511]),), Masses: mass \n", "--------------------\n", - " 0.07436657463591295\n", - " 0.06125115718404527\n", - " 0.07066751592079368\n", - " 0.07200706369071531\n", - "0.051506088266653524\n", - " 0.05929272238807183\n", - "0.049112273019809714\n", + " 0.06814878672190731\n", + " 0.04773849561965738\n", + " 0.06827493028013426\n", + " 0.0359072610482999\n", + "0.023766688780906337\n", + " 0.05802498588206111\n", + " 0.07564077829371584\n", " ...\n", - " 0.05799495176694396\n", - "0.031994021368442954\n", - " 0.06802099845333169\n", - " 0.06670578411126206\n", - "0.010433044273147809\n", - "0.030634450496813445\n", - "0.020885281173972783\n", - "Length = 772665 rows\n", + " 0.05364431139484768\n", + "0.029481672087096702\n", + "0.012506309252072858\n", + "0.014174332139873898\n", + " 0.04149099895456249\n", + " 0.04355582847263964\n", + "0.036374123910035944\n", + "Length = 772782 rows\n", "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", "----\n", - "Found 175616 companions out of stellar mass range\n" + "Found 175850 companions out of stellar mass range\n" ] } ], @@ -1218,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 306, "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", "metadata": {}, "outputs": [ @@ -1228,39 +1228,39 @@ "text": [ " mass \n", "--------------------\n", - "0.022827194847619314\n", - " 0.10638031764197846\n", - " 0.03760679268091785\n", - " 0.22726350815590202\n", - "0.038753074343781856\n", - " 0.2504904685642538\n", - " 0.03951772983956194\n", + " 0.05968994825198586\n", + " 0.5806518980715113\n", + "0.029660673068744366\n", + " 1.0561490103453701\n", + " 0.17057943576263854\n", + " 0.27545527781952783\n", + " 0.02648169160335085\n", " ...\n", - "0.012231927508833364\n", - " 0.072612764979889\n", - " 0.1961780248469474\n", - " 0.08736265118080355\n", - " 0.07659346659266889\n", - " 1.19594339645191\n", - " 1.1853661916226508\n", - "Length = 435190 rows Teff \n", + " 0.40403729711714403\n", + " 0.05200567264641382\n", + " 0.04220512783952523\n", + "0.035754850072584596\n", + " 0.12340265134211305\n", + " 0.07380553065721931\n", + "0.013647742599581188\n", + "Length = 431550 rows Teff \n", "------------------\n", " nan\n", - " 3011.398083542016\n", + " 4085.526723430826\n", " nan\n", - "3309.1075685999776\n", - " nan\n", - "3341.1194688523947\n", + "5920.0062733779405\n", + "3190.3383809176958\n", + "3375.2593307941474\n", " nan\n", " ...\n", + "3536.9941918044533\n", + " nan\n", + " nan\n", + " nan\n", + "3058.8221931306266\n", + "2827.8838719105515\n", " nan\n", - "2817.2104303556866\n", - " 3261.336270821613\n", - "2925.6827572130533\n", - "2851.3684944754505\n", - " 6338.378648763236\n", - " 6307.806682055694\n", - "Length = 435190 rows\n", + "Length = 431550 rows\n", "0\n" ] } @@ -1272,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": 298, + "execution_count": 307, "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", "metadata": {}, "outputs": [ @@ -1286,54 +1286,54 @@ " nan\n", " nan\n", " nan\n", - "0.07904209649092925\n", " nan\n", " nan\n", - " ...\n", " nan\n", + " ...\n", " nan\n", " nan\n", " nan\n", " nan\n", " nan\n", + "0.07380553065721931\n", " nan\n", - "Length = 182938 rows\n", + "Length = 183555 rows\n", "system_idx mass ... metallicity m_hst_f153m \n", "---------- -------------------- ... ----------- -----------------\n", - " 2 0.04059931139737934 ... 0.0 nan\n", - " 2 0.04033587588071577 ... 0.0 nan\n", - " 34 0.059462609507120824 ... 0.0 nan\n", - " 48 0.014810980619283313 ... 0.0 nan\n", - " 48 0.07904209649092925 ... 0.0 9.296290412739976\n", - " 70 0.010050222257410254 ... 0.0 nan\n", - " 76 0.021551221548502034 ... 0.0 nan\n", + " 1 0.05968994825198586 ... 0.0 nan\n", + " 19 0.029660673068744366 ... 0.0 nan\n", + " 36 0.02648169160335085 ... 0.0 nan\n", + " 47 0.0676793749481354 ... 0.0 nan\n", + " 47 0.040357487164688594 ... 0.0 nan\n", + " 62 0.06369313059388905 ... 0.0 nan\n", + " 69 0.012985139409248137 ... 0.0 nan\n", " ... ... ... ... ...\n", - " 2074705 0.0318924914104722 ... 0.0 nan\n", - " 2074719 0.024133829456656052 ... 0.0 nan\n", - " 2074721 0.013583472609030067 ... 0.0 nan\n", - " 2074726 0.03713310656117373 ... 0.0 nan\n", - " 2074742 0.04297247209243148 ... 0.0 nan\n", - " 2074752 0.031034988651296436 ... 0.0 nan\n", - " 2074757 0.01213677256547114 ... 0.0 nan\n", - "Length = 182938 rows\n", + " 2075867 0.014832191284296728 ... 0.0 nan\n", + " 2075875 0.010915381583182204 ... 0.0 nan\n", + " 2075893 0.05200567264641382 ... 0.0 nan\n", + " 2075900 0.04220512783952523 ... 0.0 nan\n", + " 2075900 0.035754850072584596 ... 0.0 nan\n", + " 2075906 0.07380553065721931 ... 0.0 9.421319357047143\n", + " 2075908 0.013647742599581188 ... 0.0 nan\n", + "Length = 183555 rows\n", "phase\n", "-----\n", " nan\n", " nan\n", " nan\n", " nan\n", - " 1.0\n", " nan\n", " nan\n", - " ...\n", " nan\n", + " ...\n", " nan\n", " nan\n", " nan\n", " nan\n", " nan\n", + " 1.0\n", " nan\n", - "Length = 182938 rows\n", + "Length = 183555 rows\n", "Teff\n", "----\n", " nan\n", @@ -1351,7 +1351,7 @@ " nan\n", " nan\n", " nan\n", - "Length = 1579 rows\n", + "Length = 1497 rows\n", "phase\n", "-----\n", " 1.0\n", @@ -1789,9 +1789,235 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 311, "id": "0c90f58d-c82c-4350-82f0-3f85de4a4c65", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf indices: (array([ 0, 1, 2, ..., 702295, 702297, 702298]),), Masses: mass \n", + "--------------------\n", + " 0.07112387932532963\n", + " 0.0764559041392305\n", + " 0.0625861330758897\n", + "0.010813223441380892\n", + "0.024579329319661714\n", + " 0.0153132496210663\n", + " 0.01502460044894114\n", + " ...\n", + " 0.06604816419573725\n", + "0.035960727819530976\n", + " 0.03392139750322173\n", + "0.049320264833810586\n", + " 0.05499753185245202\n", + "0.015053394497169099\n", + " 0.03366986670153467\n", + "Length = 634240 rows\n", + "Found 59455 stars out of mass range\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 164173 companions out of stellar mass range\n" + ] + } + ], + "source": [ + "# Define cluster parameters\n", + "logAge = 9.7\n", + "AKs = 0.0\n", + "distance = 1000\n", + "cluster_mass = 1e6\n", + "mass_sampling = 5\n", + "\n", + "evo = evolution.MergedBaraffePisaEkstromParsec()\n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "ifmr_obj = ifmr.IFMR_Raithel18()\n", + "\n", + "red_law = reddening.RedLawNishiyama09()\n", + " \n", + "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", + " evo_model=evo, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " mass_sampling=mass_sampling)\n", + "\n", + "multi = multiplicity.MultiplicityUnresolved()\n", + "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=multi)\n", + " \n", + "cluster2 = synthetic.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj)\n", + "clust2 = cluster2.star_systems\n", + "comps2 = cluster2.companions" + ] + }, + { + "cell_type": "markdown", + "id": "e6e12c06-fe63-4a9f-b7ea-26473dcdfb53", + "metadata": {}, + "source": [ + "# Creating the same conditions as test_ifmr_multiplicity" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "id": "005fa638-2a2d-4798-9637-8d16dce382a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Changing to logg=5.00 for T= 2305 logg=5.22\n", + "Isochrone generation took 2.873671 s.\n", + "Making photometry for isochrone: log(t) = 9.70 AKs = 0.00 dist = 1000\n", + " Starting at: 2024-08-21 14:26:02.531482 Usually takes ~5 minutes\n", + "Starting filter: nirc2,Kp Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2305 K m_nirc2_Kp = 20.34\n", + "Starting filter: nirc2,H Elapsed time: 0.60 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2305 K m_nirc2_H = 20.57\n", + "Starting filter: nirc2,J Elapsed time: 1.20 seconds\n", + "Starting synthetic photometry\n", + "M = 0.090 Msun T = 2305 K m_nirc2_J = 21.05\n", + " Time taken: 1.68 seconds\n", + "Brown dwarf indices: (array([ 0, 1, 2, ..., 764740, 764741, 764742]),), Masses: mass \n", + "--------------------\n", + "0.010443375377695562\n", + " 0.06511402241508014\n", + " 0.07176328265824769\n", + " 0.01532590021508947\n", + "0.032173564214380404\n", + "0.033550210563551695\n", + " 0.07626969082525512\n", + " ...\n", + "0.012806933245100089\n", + " 0.0747383470817761\n", + " 0.03550080854686576\n", + "0.050186096415334905\n", + "0.043859454865114604\n", + " 0.036543785344645\n", + "0.043050926420120574\n", + "Length = 690613 rows\n", + "Found 64893 stars out of mass range\n", + "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", + "----\n", + "Found 178337 companions out of stellar mass range\n", + "Primary star phases: phase\n", + "-----\n", + " 1.0\n", + " 99.0\n", + "101.0\n", + "102.0\n", + "103.0\n", + "Companion star phases: phase\n", + "-----\n", + " 1.0\n", + "101.0\n", + "102.0\n", + "103.0\n", + " nan\n" + ] + } + ], + "source": [ + "# Initialize the isochrone and cluster objects\n", + "logAge = 9.7\n", + "AKs = 0.0\n", + "distance = 1000\n", + "cluster_mass = 1e6\n", + "mass_sampling = 5\n", + "\n", + "filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J']\n", + "\n", + "evo = evolution.MergedBaraffePisaEkstromParsec()\n", + "atm_func = atmospheres.get_merged_atmosphere\n", + "ifmr_obj = ifmr.IFMR_Raithel18()\n", + "red_law = reddening.RedLawNishiyama09()\n", + "\n", + "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", + " evo_model=evo, atm_func=atm_func,\n", + " red_law=red_law, filters=filt_list,\n", + " mass_sampling=mass_sampling)\n", + "\n", + "# Recreate the cluster with multiplicity and IFMR\n", + "multi = multiplicity.MultiplicityUnresolved()\n", + "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", + "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", + "my_imf = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", + " multiplicity=multi)\n", + "\n", + "cluster = synthetic.ResolvedCluster(iso, my_imf, cluster_mass, ifmr=ifmr_obj)\n", + "clust = cluster.star_systems\n", + "comps = cluster.companions\n", + "\n", + "# Debugging prints\n", + "print('Primary star phases:', np.unique(clust['phase']))\n", + "print('Companion star phases:', np.unique(comps['phase']))" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "id": "c1937ef3-c11a-40e2-8c22-04ae352ff7a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "phase\n", + "-----\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " ...\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + " nan\n", + "Length = 142106 rows\n" + ] + } + ], + "source": [ + "comps_bds = np.where((comps['mass'] >= 0.01) & (comps['mass'] < 0.08))\n", + "print(comps[comps_bds]['phase'])" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "id": "a3fcf655-bebb-4657-8c94-c0a4879b0c56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", + "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" + ] + } + ], + "source": [ + "comps_phase_nan = np.where(comps['phase'] == np.nan)\n", + "print(comps[comps_phase_nan])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f47a6c02-4ace-4b7d-b959-102c893afd7e", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 053b163d..1451ed11 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -732,7 +732,7 @@ def NS_mass(self, MZAMS): """ Drawing the NS mass from a Gaussian distrobuton based on observational data. - Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of Caifornia, Berkeley using a + Gaussian fit by Emily Ramey and Sergiy Vasylyev of University of California, Berkeley using a sample of NSs from Ozel & Freire (2016) — J1811+2405 Ng et al. (2020), J2302+4442 Kirichenko et al. (2018), J2215+5135 Linares et al. (2018), J1913+1102 Ferdman & Collaboration (2017), J1411+2551 Martinez et al. (2017), diff --git a/spisea/synthetic.py b/spisea/synthetic.py index c793bfcd..bfc4f586 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -437,6 +437,11 @@ def _make_companions_table(self, star_systems, compMass): # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) + + """# Debugging print before and after assigning brown dwarf phase + print("Before assigning BD phase:") + bd_idx_before = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + print(f"Brown dwarfs before phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx_before], companions['phase'][bd_idx_before]))}")""" # Assigning brown dwarf companions the correct phase/properties bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] @@ -445,7 +450,10 @@ def _make_companions_table(self, star_systems, compMass): for filt in self.filt_names: companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) - #BD INITIAL PHASE + """print("After assigning BD phase:") + print(f"Brown dwarfs after phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx], companions['phase'][bd_idx]))}")""" + + # Notify if we have a lot of bad ones. # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_teff_non_nan = np.nan_to_num(companions['Teff'], nan=-99) @@ -453,7 +461,11 @@ def _make_companions_table(self, star_systems, compMass): if len(idx) != N_comp_tot and self.verbose: print( 'Found {0:d} companions out of stellar mass range'.format(N_comp_tot - len(idx))) - #BD FINAL PHASE + # Final check for brown dwarf phase + bd_idx_final = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] + print("Final BD phase check:") + """print(f"Brown dwarfs final phase check (mass, phase): {list(zip(companions['mass'][bd_idx_final], companions['phase'][bd_idx_final]))}")""" + # Double check that everything behaved properly. assert companions['mass'][idx].min() > 0 @@ -469,6 +481,13 @@ def _remove_bad_systems(self, star_systems, compMass): removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ + """ # Print companion masses and phases before any filtering + bd_idx_initial = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] + print("Before filtering - CompMass (brown dwarfs):") + for i, bdi in enumerate(bd_idx_initial): + if len(bdi) > 0: + print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" + N_systems = len(star_systems) # Get rid of the bad ones @@ -492,6 +511,12 @@ def _remove_bad_systems(self, star_systems, compMass): if self.imf.make_multiples: # Clean up companion stuff (which we haven't handled yet) compMass = [compMass[ii] for ii in idx] + + """bd_idx_after = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] + print("After filtering - CompMass (brown dwarfs):") + for i, bdi in enumerate(bd_idx_after): + if len(bdi) > 0: + print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" return star_systems, compMass diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 23f5a7a8..b164ebe8 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -538,7 +538,13 @@ def test_ifmr_multiplicity(): #print statements for debugging: print(comps2['mass'][comp_bd_idx]) - print(np.unique(comps2['phase'][comp_bd_idx])) + print(np.unique(comps2['phase'])) + comp_phase_nan = np.where(comps2['phase'] == np.nan) + print(comps2[comp_phase_nan]) + comp_phase_1 = np.where(comps2['phase'] == 1.0) + print(comps2[comp_phase_1]) + comps_bds = np.where((comps2['mass'] >= 0.01) & (comps2['mass'] < 0.08)) + print(comps2[comps_bds]) return def test_metallicity(): From cc856a459c1994eebc2f32b132d60e3537a7221c Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 4 Sep 2024 13:52:51 -0700 Subject: [PATCH 20/48] Adding in BD temperatures --- changes/BD_Clusters.ipynb | 534 +++++++++++++++++++++++++++++++++ spisea/evolution.py | 54 +++- spisea/synthetic.py | 59 ++-- spisea/tests/test_synthetic.py | 10 + 4 files changed, 629 insertions(+), 28 deletions(-) create mode 100644 changes/BD_Clusters.ipynb diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb new file mode 100644 index 00000000..df1bb2b7 --- /dev/null +++ b/changes/BD_Clusters.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38ea9963-fc45-4dbc-86b8-fa882b608583", + "metadata": {}, + "source": [ + "# Simulating Stellar Clusters with Brown Dwarfs" + ] + }, + { + "cell_type": "markdown", + "id": "4a8eebf3-dd6d-4331-943a-57f7b5f8833f", + "metadata": {}, + "source": [ + "#### Importing Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "81f8a8a2-18b9-4ec5-a3a5-16a728e24163", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary packages. \n", + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "from scipy.interpolate import RegularGridInterpolator\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import vstack\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", + "metadata": {}, + "source": [ + "## Case 1: Cluster Without Companions" + ] + }, + { + "cell_type": "markdown", + "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", + "metadata": {}, + "source": [ + "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Weidner_Kroupa_2004 initial mass function, all of which have been modified to describe substellar masses." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7f0e899f-6cb7-48f1-acfc-1fd9c4df4135", + "metadata": {}, + "outputs": [], + "source": [ + "# Create isochrone object \n", + "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only need 1 filter\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + " filters=filt_list)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "k_imf = imf.Weidner_Kroupa_2004()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "79704dca-9b9e-4572-b49f-760511e06b08", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "k_out = k_cluster.star_systems" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a192b379-25fb-43ac-a799-6ba4a98d5df9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p_bh = np.where(k_out['phase'] == 103)[0]\n", + "p_ns = np.where(k_out['phase'] == 102)[0]\n", + "p_wd = np.where(k_out['phase'] == 101)[0]\n", + "p_bd = np.where(k_out['phase'] == 99)[0]\n", + "\n", + "# Determine individual histogram bins\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "# Plot BHs\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", + " bins = bh_bins, label = 'Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", + " bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", + " bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", + " bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7c9bbd80-1243-485d-a944-9bfafc5049fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH max mass: 119.13565618748322\n", + "BH min mass: 15.007725869985796\n", + "\n", + "NS max mass: 119.95876844996108\n", + "NS min mass: 9.001129278483436\n", + "\n", + "WD max mass: 8.999341744945873\n", + "WD min mass: 5.311091008003858\n", + "\n", + "BD max mass: 0.07999986295473586\n", + "BD min mass: 0.010000044934131811\n", + "\n" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", + "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", + "\n", + "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", + "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", + "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", + "print(\"BD min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3c2d1e2-fcfa-429a-92e7-b2c31e034482", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Teff \n", + "------------------\n", + " 653.0891938350937\n", + " 651.8458273479349\n", + " 707.4325441828001\n", + "488.01700684829456\n", + " 871.6562359235475\n", + " 407.9503554901987\n", + " 930.6014364016501\n", + " ...\n", + "392.28042415955446\n", + " 908.5461410822766\n", + " 1393.091834142745\n", + " 998.2260271590488\n", + " 665.474820549251\n", + " 675.9825874832916\n", + "1122.0753000795612\n", + "Length = 1028848 rows\n" + ] + } + ], + "source": [ + "print(k_out[p_bd]['Teff'])" + ] + }, + { + "cell_type": "markdown", + "id": "c5cf0b4c-2b80-4b0c-9adf-e3d590dd39a3", + "metadata": {}, + "source": [ + "It is worth noting [explain about big NS's after confirmation from Matt] ..... \n", + "white dwarfs capped at low end due to age of cluster" + ] + }, + { + "cell_type": "markdown", + "id": "4e0ca5d6-c984-45be-982a-4e76e6083dde", + "metadata": {}, + "source": [ + "## Case 2: Cluster With Companions" + ] + }, + { + "cell_type": "markdown", + "id": "f64318cd-f631-4a24-9abe-93c96fa17251", + "metadata": {}, + "source": [ + "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "104a64d0-6a52-4b26-a418-0df265902a92", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6042a1ae-86c2-44d3-b718-fd678185a6e7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7301 companions out of stellar mass range\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c44480e6-fa97-44c3-9993-148316f746c0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Plot on histograms\n", + "bh_bins = np.linspace(0.01, 16, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(8, 30, 20)\n", + "\n", + "# Plot BHs\n", + "plt.figure(figsize=(14,6))\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Generated Black Holes')\n", + "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype = 'step', bins = wd_bins, label = 'Generated White Dwarves')\n", + "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype = 'step', bins = bd_bins, label = 'Generated Brown Dwarves')\n", + "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype = 'step', bins = ns_bins, label = 'Generated Neutron Stars')\n", + "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f8a2e33b-266e-4a6f-b3f6-f25a1cd8d86e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Teff \n", + "------------------\n", + " 589.5738241278044\n", + " 949.9191202547576\n", + " 535.3337220492646\n", + " 798.6701941060301\n", + " 883.180077123407\n", + "1009.6437674139902\n", + "444.11890279356027\n", + " ...\n", + "1625.8241118113378\n", + "1556.8961357273806\n", + " 1949.159944699517\n", + " 475.3197378938071\n", + "1445.6688176627986\n", + " 929.9466889780698\n", + " 905.5149940088668\n", + "Length = 964495 rows\n" + ] + } + ], + "source": [ + "print(k_bd['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fedb95f5-8f7a-41e8-b586-20ce6e497126", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BH prim max mass: 119.64792937371239\n", + "BH prim min mass: 15.003687171639145\n", + "BH comp max mass: 108.78065103375774\n", + "BH comp min mass: 15.051696275245545\n", + "\n", + "NS prim max mass: 117.89439381247894\n", + "NS prim min mass: 9.001659607779285\n", + "NS comp max mass: 109.06172525179247\n", + "NS comp min mass: 9.000122949672017\n", + "\n", + "WD prim max mass: 8.99922965392098\n", + "WD prim min mass: 5.311110801935481\n", + "WD comp max mass: 8.997715563589054\n", + "WD comp min mass: 5.31119691635049\n", + "\n", + "BD prim max mass: 0.07999992301784681\n", + "BD prim min mass: 0.01000005942626443\n", + "BD comp max mass: 0.07999668766843566\n", + "BD comp min mass: 0.010000432726566368\n", + "\n" + ] + } + ], + "source": [ + "# Checking that objects are in the correct mass ranges\n", + "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", + "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", + "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", + "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", + "\n", + "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", + "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", + "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", + "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", + "\n", + "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", + "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", + "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", + "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", + "\n", + "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", + "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", + "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", + "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_bd]['mass'])) + '\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5e9c3bc-ea61-4f1e-b13c-9bbcfc9f2830", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/evolution.py b/spisea/evolution.py index d1dc1896..f168acda 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -4,12 +4,14 @@ import numpy as np import os import glob +import pandas as pd import pdb import warnings from astropy.table import Table, vstack, Column from scipy import interpolate import pylab as py from spisea.utils import objects +from scipy.interpolate import RegularGridInterpolator from spisea import exceptions logger = logging.getLogger('evolution') @@ -1305,6 +1307,10 @@ def isochrone(self, age=1.e8, metallicity=0.0): idx_WR = np.where(iso['logT'] != iso['logT_WR']) isWR[idx_WR] = True iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) # Assume mass of brown dwarfs does not change over their lifetime bd_idx = iso['mass'] < 0.08 @@ -1314,10 +1320,52 @@ def isochrone(self, age=1.e8, metallicity=0.0): nan_teff_idx = np.isnan(iso['logT']) if np.any(nan_teff_idx): iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + + def get_temperature(self, masses, ages): + """ + Use interpolation to get temperatures for brown dwarfs based on mass and age. + + Parameters: + ----------- + masses : array-like + Array of brown dwarf masses. + ages : array-like + Array of ages corresponding to the masses. + + Returns: + -------- + temperatures : array + Array of interpolated temperatures. + """ + + # Load the CSV file + data = pd.read_csv('/u/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv') + + # Create columns of relevant data + mass_grid = np.unique(data['mass'].values) + age_grid = np.unique(data['age'].values) + temperatures = data['temperature'].values + + # Create a 2D grid for temperatures + temp_grid = np.zeros((len(age_grid), len(mass_grid))) + + # Fill the temperature grid with actual values + for i, age in enumerate(age_grid): + for j, mass in enumerate(mass_grid): + temp_grid[i, j] = np.mean(temperatures[(data['age'] == age) & (data['mass'] == mass)]) + + # Set up the interpolator + interpolator = RegularGridInterpolator((age_grid, mass_grid), temp_grid, bounds_error=False, fill_value=np.nan) + + # Get temperatures for the provided masses and ages + temperatures = interpolator(np.column_stack((ages, masses))) - iso.meta['log_age'] = log_age - iso.meta['metallicity_in'] = metallicity - iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + # Make sure masses array is not empty + if len(masses) == 0: + print("No masses available for temperature calculation.") + return + + return temperatures return iso diff --git a/spisea/synthetic.py b/spisea/synthetic.py index bfc4f586..eaa713d5 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -192,6 +192,7 @@ def set_filter_names(self): filt_names.append(col_name) return filt_names + def _make_star_systems_table(self, mass, isMulti, sysMass): """ @@ -276,8 +277,24 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) - # Handle brown dwarfs separately + + # Handle brown dwarfs separately and assign temperatures idx_bd = np.where(star_systems['phase'] == 99)[0] + + # Define the class for BDs + evo_model = evolution.MergedBaraffePisaEkstromParsec() + + # Use the instance to call get_temperature + masses = star_systems[idx_bd]['mass_current'] + ages = np.full(len(masses), self.iso.points.meta['LOGAGE']) + temperatures = evo_model.get_temperature(masses, ages) + + # Convert the numpy array to an astropy Column + temperatures_column = Column(temperatures) + + # Assign temperatures to the Table column + star_systems['Teff'][idx_bd] = temperatures_column + for filt in self.filt_names: star_systems[filt][idx_bd] = np.full(len(idx_bd), np.nan) @@ -401,12 +418,6 @@ def _make_companions_table(self, star_systems, compMass): star_systems[filt][idx[good]] = -2.5 * np.log10(f1[good] + f2[good]) star_systems[filt][idx[bad]] = np.nan - """# Assigning brown dwarf companions the correct phase/properties - bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - companions['phase'][bd_idx] = 99 - companions['Teff'][bd_idx] = np.nan - for filt in self.filt_names: - companions[filt][bd_idx] = np.full(len(bd_idx), np.nan)""" ##### # Make Remnants with flux = 0 in all bands. @@ -423,8 +434,7 @@ def _make_companions_table(self, star_systems, compMass): r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem], metallicity_array=companions['metallicity'][cdx_rem]) else: - r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) -# SEPERATE BDS IN OWN FUNCTION + r_mass_tmp, r_id_tmp = self.ifmr.generate_death_mass(mass_array=companions['mass'][cdx_rem]) # Drop remnants where it is not relevant (e.g. not a compact object or # outside mass range IFMR is defined for) @@ -437,11 +447,6 @@ def _make_companions_table(self, star_systems, compMass): # Give remnants a magnitude of nan, so they can be filtered out later when calculating flux. for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - - """# Debugging print before and after assigning brown dwarf phase - print("Before assigning BD phase:") - bd_idx_before = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - print(f"Brown dwarfs before phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx_before], companions['phase'][bd_idx_before]))}")""" # Assigning brown dwarf companions the correct phase/properties bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] @@ -449,9 +454,20 @@ def _make_companions_table(self, star_systems, compMass): companions['mass_current'][bd_idx] = companions['mass'][bd_idx] for filt in self.filt_names: companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) - - """print("After assigning BD phase:") - print(f"Brown dwarfs after phase assignment (mass, phase): {list(zip(companions['mass'][bd_idx], companions['phase'][bd_idx]))}")""" + + # Define the class for BDs + evo_model = evolution.MergedBaraffePisaEkstromParsec() + + # Use the instance to call get_temperature + c_masses = companions[bd_idx]['mass_current'] + c_ages = np.full(len(c_masses), self.iso.points.meta['LOGAGE']) + c_temperatures = evo_model.get_temperature(c_masses, c_ages) + + # Convert the numpy array to an astropy Column + c_temperatures_column = Column(c_temperatures) + + # Assign temperatures to the Table column + companions['Teff'][bd_idx] = c_temperatures_column # Notify if we have a lot of bad ones. @@ -463,8 +479,7 @@ def _make_companions_table(self, star_systems, compMass): # Final check for brown dwarf phase bd_idx_final = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - print("Final BD phase check:") - """print(f"Brown dwarfs final phase check (mass, phase): {list(zip(companions['mass'][bd_idx_final], companions['phase'][bd_idx_final]))}")""" + # Double check that everything behaved properly. assert companions['mass'][idx].min() > 0 @@ -511,12 +526,6 @@ def _remove_bad_systems(self, star_systems, compMass): if self.imf.make_multiples: # Clean up companion stuff (which we haven't handled yet) compMass = [compMass[ii] for ii in idx] - - """bd_idx_after = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] - print("After filtering - CompMass (brown dwarfs):") - for i, bdi in enumerate(bd_idx_after): - if len(bdi) > 0: - print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" return star_systems, compMass diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index b164ebe8..e146b273 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -488,6 +488,8 @@ def test_ifmr_multiplicity(): idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 99) & (clust1['phase'] != 9) ) idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 99) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 + + # Make sure BD temperatures are assigned correctly # Ensure no substellar mass compact objects are generated """ @@ -536,6 +538,10 @@ def test_ifmr_multiplicity(): (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range + # Ensure BD temperature assignment is working correctly + assert np.all(clust1['Teff'][bd_idx] != np.nan) + assert np.all(comps2['Teff'][comp_bd_idx] != np.nan) + #print statements for debugging: print(comps2['mass'][comp_bd_idx]) print(np.unique(comps2['phase'])) @@ -545,6 +551,10 @@ def test_ifmr_multiplicity(): print(comps2[comp_phase_1]) comps_bds = np.where((comps2['mass'] >= 0.01) & (comps2['mass'] < 0.08)) print(comps2[comps_bds]) + + #make sure that bd temps are being assigned + print(clust1[bd_idx]['Teff']) + print(comps2[comp_bd_idx]['Teff']) return def test_metallicity(): From 53f2fd9a87657e8d7016a0c1aa33c78d57681825 Mon Sep 17 00:00:00 2001 From: caitlinbegbie <160803247+caitlinbegbie@users.noreply.github.com> Date: Wed, 4 Sep 2024 13:54:43 -0700 Subject: [PATCH 21/48] Delete changes/Compact_Multiplicity.ipynb --- changes/Compact_Multiplicity.ipynb | 2046 ---------------------------- 1 file changed, 2046 deletions(-) delete mode 100644 changes/Compact_Multiplicity.ipynb diff --git a/changes/Compact_Multiplicity.ipynb b/changes/Compact_Multiplicity.ipynb deleted file mode 100644 index a2c1abec..00000000 --- a/changes/Compact_Multiplicity.ipynb +++ /dev/null @@ -1,2046 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c61a66d4-bc64-4080-ad07-38ae7f00670b", - "metadata": {}, - "source": [ - "## Comparing Clusters with Compact Objects: With and Without Companions" - ] - }, - { - "cell_type": "markdown", - "id": "77645bcf-b0f4-456b-89fd-eb9730b23c71", - "metadata": {}, - "source": [ - "In testing the addition of substellar objects into SPISEA, we have come across a curious problem. When generating a cluster with an IMFR and allowing for companions, we end up creating substellar mass compact objects, which does not make much sense. This is happening before modification of the code to specify brown dwarves as their own phase." - ] - }, - { - "cell_type": "markdown", - "id": "31969dcc-b1c4-4601-a44f-c9ba09a73188", - "metadata": {}, - "source": [ - "#### Importing the Necessary Packages:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "c0a4a637-c694-4d3d-9b1f-3b1b66cc8a19", - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary packages. \n", - "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", - "from spisea.imf import imf, multiplicity\n", - "import numpy as np\n", - "import pylab as py\n", - "import pdb\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "f8639685-3185-4e3e-88c6-98602bdaedc9", - "metadata": {}, - "source": [ - "## Cluster 1: Without Companions" - ] - }, - { - "cell_type": "code", - "execution_count": 251, - "id": "30243bcd-b725-479b-aaaa-7644a3f9c7d9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isochrone generation took 70.910534 s.\n", - "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-16 12:58:00.472284 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", - "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", - "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", - "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", - "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", - "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", - "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", - "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", - "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", - "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", - "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 17.50 seconds\n" - ] - } - ], - "source": [ - "# Create isochrone object \n", - "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", - " filters=filt_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "ac6b221b-6bd5-41c5-8a5e-94a0daed1dba", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "k_imf = imf.Weidner_Kroupa_2004()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "1a68a281-791a-4821-8799-c2df8e936286", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 1037624, 1037625, 1037626]),), Masses: mass \n", - "--------------------\n", - "0.010581623004327498\n", - " 0.07746106089705293\n", - " 0.06953880412693926\n", - " 0.07376813803092074\n", - "0.018112506487775234\n", - " 0.05924633227110841\n", - " 0.076323411257296\n", - " ...\n", - "0.021666218437430496\n", - "0.026539284794723627\n", - " 0.07170977037349016\n", - "0.018694133568115678\n", - " 0.04969517873307298\n", - " 0.07788710880030814\n", - " 0.01568523000869492\n", - "Length = 1020837 rows\n", - "Found 925805 stars out of mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "k_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", - "t_cluster = synthetic.ResolvedCluster(my_iso, k_imf, cluster_mass)\n", - "\n", - "# Get outputs\n", - "k_out = k_cluster.star_systems\n", - "t_out = t_cluster.star_systems" - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "id": "45456043-0197-4896-bcb4-d2247a95386e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... metallicity m_hst_f153m\n", - "-------------------- ---------- ... ----------- -----------\n", - "0.010581623004327498 False ... 0.0 nan\n", - " 0.07746106089705293 False ... 0.0 nan\n", - " 0.06953880412693926 False ... 0.0 nan\n", - " 0.07376813803092074 False ... 0.0 nan\n", - "0.018112506487775234 False ... 0.0 nan\n", - " 0.05924633227110841 False ... 0.0 nan\n", - " 0.076323411257296 False ... 0.0 nan\n", - " ... ... ... ... ...\n", - "0.021666218437430496 False ... 0.0 nan\n", - "0.026539284794723627 False ... 0.0 nan\n", - " 0.07170977037349016 False ... 0.0 nan\n", - "0.018694133568115678 False ... 0.0 nan\n", - " 0.04969517873307298 False ... 0.0 nan\n", - " 0.07788710880030814 False ... 0.0 nan\n", - " 0.01568523000869492 False ... 0.0 nan\n", - "Length = 1020837 rows\n", - "0.07999987827752757\n", - "0.010000034092785876\n" - ] - } - ], - "source": [ - "print(k_out[p_bd])\n", - "print(np.max(k_out[p_bd]['mass']))\n", - "print(np.min(k_out[p_bd]['mass']))" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "4b4fffe4-e927-49f1-9d3d-b215ca2c98cc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "5.311164368442878" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(k_out[p_wd]['mass'])" - ] - }, - { - "cell_type": "markdown", - "id": "b2371c2d-4ea8-4bfb-bf67-59d86316132b", - "metadata": {}, - "source": [ - "### Exploring the properties of k_cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "id": "3bdd27c9-3820-4fff-9ed6-da26fb997e08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "p_bh = np.where(k_out['phase'] == 103)[0]\n", - "p_ns = np.where(k_out['phase'] == 102)[0]\n", - "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "p_bd = np.where(k_out['phase'] == 99)[0]\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(k_out[p_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(k_out[p_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "id": "e8e3c545-0aba-43f8-ad46-ed9ba9720761", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BH max mass: 118.75715744712116\n", - "BH min mass: 15.035041823555424\n", - "\n", - "NS max mass: 119.72357017983377\n", - "NS min mass: 9.000593434639391\n", - "\n", - "WD max mass: 8.999786895675784\n", - "WD min mass: 5.311134878018857\n", - "\n", - "BD max mass: 0.07999987827752757\n", - "BH min mass: 0.010000034092785876\n", - "\n" - ] - } - ], - "source": [ - "# Checking that objects are in the correct mass ranges\n", - "print(\"BH max mass: \" + str(np.max(k_out[p_bh]['mass'])))\n", - "print(\"BH min mass: \" + str(np.min(k_out[p_bh]['mass'])) + '\\n')\n", - "\n", - "print(\"NS max mass: \" + str(np.max(k_out[p_ns]['mass'])))\n", - "print(\"NS min mass: \" + str(np.min(k_out[p_ns]['mass'])) + '\\n')\n", - "\n", - "print(\"WD max mass: \" + str(np.max(k_out[p_wd]['mass'])))\n", - "print(\"WD min mass: \" + str(np.min(k_out[p_wd]['mass'])) + '\\n')\n", - "\n", - "print(\"BD max mass: \" + str(np.max(k_out[p_bd]['mass'])))\n", - "print(\"BH min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "ed4301d8-2eac-4f78-895b-c8c63b22fe5b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "float64\n", - "isWR\n", - "----\n", - " 0.0\n", - " 0.0\n", - " nan\n", - " 0.0\n", - " nan\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " nan\n", - " nan\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " nan\n", - "Length = 1378 rows\n" - ] - } - ], - "source": [ - "#check to see if large NS are Wolf-Rayet\n", - "print(k_out['isWR'].dtype)\n", - "large_NS = np.where((k_out[p_ns]['mass']) > 15)\n", - "print(k_out['isWR'][large_NS])" - ] - }, - { - "cell_type": "markdown", - "id": "11435009-9e98-4e31-a290-8c047b469ba5", - "metadata": {}, - "source": [ - "I am unsure on what the 0.0 and nan values correspond to." - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "id": "d0d021b1-d637-48c1-8641-990c3e947c81", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple systemMass ... metallicity m_hst_f153m\n", - "------------------ ---------- ------------------ ... ----------- -----------\n", - " 87.01978581314896 False 87.01978581314896 ... 0.0 nan\n", - " 80.51538949145795 False 80.51538949145795 ... 0.0 nan\n", - " 21.33585631479835 False 21.33585631479835 ... 0.0 nan\n", - "17.635459786720876 False 17.635459786720876 ... 0.0 nan\n", - "20.277517524928232 False 20.277517524928232 ... 0.0 nan\n", - " 20.66032231704192 False 20.66032231704192 ... 0.0 nan\n", - "27.227632523655107 False 27.227632523655107 ... 0.0 nan\n", - " ... ... ... ... ... ...\n", - " 62.70378845410619 False 62.70378845410619 ... 0.0 nan\n", - " 20.88687294620209 False 20.88687294620209 ... 0.0 nan\n", - "20.379813469500725 False 20.379813469500725 ... 0.0 nan\n", - " 68.91988141238063 False 68.91988141238063 ... 0.0 nan\n", - "16.299795503051396 False 16.299795503051396 ... 0.0 nan\n", - "19.331661523732976 False 19.331661523732976 ... 0.0 nan\n", - " 17.17530617287834 False 17.17530617287834 ... 0.0 nan\n", - "Length = 1378 rows\n", - "isWR\n", - "----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 1378 rows\n", - "isWR\n", - "----\n", - " 0.0\n", - " nan\n", - "isWR\n", - "----\n", - " nan\n", - " mass_current \n", - "------------------\n", - "1.3873021047437155\n", - "1.1867036763400425\n", - " 1.172988399792676\n", - "1.4760459379472195\n", - "1.3652646941863062\n", - " 1.34693018277759\n", - "1.3329667318702734\n", - " ...\n", - "1.3509102262959198\n", - "1.3492482970974988\n", - "1.3263769374090923\n", - "1.3292822711571697\n", - "1.3708253424214771\n", - "1.4880668288116823\n", - "1.3621508901941495\n", - "Length = 1378 rows\n" - ] - } - ], - "source": [ - "#check out the large NSs\n", - "print(k_out[p_ns][large_NS])\n", - "print(k_out[p_ns]['isWR'][large_NS])\n", - "print(np.unique(k_out['isWR']))\n", - "print(np.unique(k_out[p_ns]['isWR'][large_NS]))\n", - "print(k_out[p_ns]['mass_current'][large_NS])" - ] - }, - { - "cell_type": "code", - "execution_count": 293, - "id": "028193f1-bcbc-483c-9641-df8c14cf983c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass_current \n", - "--------------------\n", - "0.010581623004327498\n", - " 0.07746106089705293\n", - " 0.06953880412693926\n", - " 0.07376813803092074\n", - "0.018112506487775234\n", - " 0.05924633227110841\n", - " 0.076323411257296\n", - " ...\n", - "0.021666218437430496\n", - "0.026539284794723627\n", - " 0.07170977037349016\n", - "0.018694133568115678\n", - " 0.04969517873307298\n", - " 0.07788710880030814\n", - " 0.01568523000869492\n", - "Length = 1020837 rows\n", - " mass \n", - "--------------------\n", - "0.010581623004327498\n", - " 0.07746106089705293\n", - " 0.06953880412693926\n", - " 0.07376813803092074\n", - "0.018112506487775234\n", - " 0.05924633227110841\n", - " 0.076323411257296\n", - " ...\n", - "0.021666218437430496\n", - "0.026539284794723627\n", - " 0.07170977037349016\n", - "0.018694133568115678\n", - " 0.04969517873307298\n", - " 0.07788710880030814\n", - " 0.01568523000869492\n", - "Length = 1020837 rows\n" - ] - } - ], - "source": [ - "print(k_out['mass_current'][p_bd])\n", - "print(k_out['mass'][p_bd])" - ] - }, - { - "cell_type": "markdown", - "id": "5f76dbc3-0fff-49a7-94f1-7513aa031ad6", - "metadata": {}, - "source": [ - "Assuming our assumption is correct, that the anomalous neutron stars are WR stars because of the huge disrepancy in initial and final masses." - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "id": "fc18f900-4485-4d21-baeb-f0de55865f7d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.6520719390653884\n", - "1.058140703674423\n", - "119.72357017983377\n", - "15.00328698949453\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 120, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#inital masses of large NS and final masses\n", - "print(np.max(k_out[p_ns]['mass_current'][large_NS]))\n", - "print(np.min(k_out[p_ns]['mass_current'][large_NS]))\n", - "print(np.max(k_out[p_ns]['mass'][large_NS]))\n", - "print(np.min(k_out[p_ns]['mass'][large_NS]))\n", - "\n", - "#create bins\n", - "initial_bins = np.linspace(15, 120, 20)\n", - "final_bins = np.linspace(1.0, 1.7, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 1, 1)\n", - "plt.hist(k_out[p_ns]['mass'][large_NS], histtype = 'step',\n", - " bins = initial_bins, label = 'Neutron stars')\n", - "plt.title(\"Initial Masses of Large Generated Neutron Stars\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 1, 2)\n", - "plt.hist(k_out[p_wd]['mass_current'][large_NS], histtype = 'step',\n", - " bins = final_bins, label = 'Neutron stars')\n", - "plt.title(\"Final Masses of Large Generated Neutron Stars\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "66c9450c-7758-402f-a3f6-5100ef05a01e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filtered Masses: mass \n", - "------------------\n", - " 0.76799007770147\n", - "1.1955031412949875\n", - "2.2249813543703456\n", - " 3.660007844283093\n", - "0.8470178943035628\n", - "0.8619046086355177\n", - "0.8153335900401504\n", - " ...\n", - "0.9029881664660251\n", - "0.5385601472294455\n", - "0.5055061897257748\n", - "0.6718807496769587\n", - "0.7515013567753969\n", - "0.7974747944530974\n", - " 1.030318707836774\n", - "Length = 391228 rows\n", - "phase\n", - "-----\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - "Length = 391228 rows\n", - " mass_current \n", - "------------------\n", - " 0.76799007770147\n", - "1.1955031412949875\n", - "2.2249813543703456\n", - " 3.660007844283093\n", - "0.8470178943035628\n", - "0.8619046086355177\n", - "0.8153335900401504\n", - " ...\n", - "0.9029881664660251\n", - "0.5385601472294455\n", - "0.5055061897257748\n", - "0.6718807496769587\n", - "0.7515013567753969\n", - "0.7974747944530974\n", - " 1.030318707836774\n", - "Length = 391228 rows\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#check out the masses within the problem range for white dwarfs\n", - "wd_issue_masses = np.where((k_out['mass'] >=0.5) & (k_out['mass'] <5))\n", - "print(\"Filtered Masses: \", k_out['mass'][wd_issue_masses])\n", - "print(k_out['phase'][wd_issue_masses])\n", - "print(k_out['mass_current'][wd_issue_masses])\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(k_out['mass'][wd_issue_masses], histtype = 'step',\n", - " bins = bh_bins, label = 'Initial masses of Problem WDs')\n", - "plt.title(\"Generated Objects of Initial Mass from 0.5 - 5 $M_\\odot$\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(k_out['mass_current'][wd_issue_masses], histtype = 'step',\n", - " bins = wd_bins, label = 'Final masses of Problem WDs')\n", - "plt.title(\"Final Evolved Masses\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "id": "11a7e249-23db-43e6-a071-838c676e61be", - "metadata": {}, - "source": [ - "This tells us that there are issues in accurately identifying white dwarf stars and neutron stars. White dwarves have an issue at lower masses, and neutron stars at the upper end." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "d1584ea1-6fdf-4dbc-be96-623c91bffb08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "mass_bins = np.linspace(0.01, 15, 1000)\n", - "plt.figure()\n", - "plt.hist(k_out['mass'], histtype = 'step',\n", - " bins = mass_bins, label = 'Generated Objects')\n", - "plt.title(\"Simulated Objects from 0.01 - 15 Solar Masses\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "95c79ef1-308b-4d46-b477-416643c7bc0e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5.311625251702762\n", - "8.999671191854413\n" - ] - } - ], - "source": [ - "print(np.min(k_out[p_wd]['mass']))\n", - "print(np.max(k_out[p_wd]['mass'])) #white dwarf ranges are messed up" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8f6c414b-c1df-4059-b5ca-3f4c8aa7c655", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smallest mass of a generated object: 0.010000000568115546\n" - ] - } - ], - "source": [ - "# Finding the minimum mass of a generated object\n", - "print(\"Smallest mass of a generated object: \" + str(np.min(k_out['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "186ded33-2af3-421a-9956-0a603998a50b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial mass of smallest generated black hole: 15.002331855265554\n", - "Initial mass of largest generated black hole: 118.99598033857916\n" - ] - } - ], - "source": [ - "# Finding the minimum/maximum inital mass of generated black holes\n", - "print(\"Initial mass of smallest generated black hole: \" + str(np.min(k_out[p_bh]['mass'])))\n", - "print(\"Initial mass of largest generated black hole: \" + str(np.max(k_out[p_bh]['mass'])))" - ] - }, - { - "cell_type": "markdown", - "id": "2243d818-83a7-4a41-80ed-80a5ca2c6760", - "metadata": {}, - "source": [ - "This cluster fits the expectation from imfr.py that black holes are between 15-120 solar masses and white dwarves are at least 0.5 solar masses, even with the addition of substellar primary objects. " - ] - }, - { - "cell_type": "markdown", - "id": "a5da4347-311c-4fe4-b73f-6490b5d3c585", - "metadata": {}, - "source": [ - "### Exploring the t_cluster and comparing to troubleshoot" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "8ed736b8-1719-43c3-b629-4ce834a0d03e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "t_bh = np.where(t_out['phase'] == 103)[0]\n", - "t_ns = np.where(t_out['phase'] == 102)[0]\n", - "t_wd = np.where(t_out['phase'] == 101)[0]\n", - "t_bd = np.where(t_out['phase'] == 99)[0]\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(t_out[t_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(t_out[t_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(t_out[t_bd]['mass'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(t_out[t_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "739eb6c6-eec8-4302-a2e4-00ca85353b3a", - "metadata": {}, - "source": [ - "### Exploring an Older Cluster" - ] - }, - { - "cell_type": "markdown", - "id": "331a8442-9686-43aa-9304-35ee4650e3a8", - "metadata": {}, - "source": [ - "Hopefully, in exploring an older cluster, we will see lower mass white dwarves, as they will have enough time to evolve into white dwarves at this point." - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "4528abef-8850-4972-af28-52648d87777a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=5.00 for T= 2306 logg=5.22\n", - "Isochrone generation took 17.886188 s.\n", - "Making photometry for isochrone: log(t) = 10.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-08-13 14:04:23.149590 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2306 K m_hst_f153m = 10.90\n", - "M = 1.034 Msun T = 3906 K m_hst_f153m = -3.64\n", - "M = 1.038 Msun T = 3301 K m_hst_f153m = -5.55\n", - " Time taken: 3.33 seconds\n" - ] - } - ], - "source": [ - "# Create isochrone object \n", - "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only 1 filter\n", - "my_ifmr = ifmr.IFMR_Raithel18()\n", - "o_iso = synthetic.IsochronePhot(10, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(),\n", - " filters=filt_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "338bab08-4432-4dd9-b29d-dd61851b8c3e", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: invalid value encountered in divide\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/scipy/interpolate/_interpolate.py:698: RuntimeWarning: divide by zero encountered in divide\n", - " slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 3, ..., 1183119, 1183120, 1183121]),), Masses: mass \n", - "--------------------\n", - " 0.06279592698333637\n", - " 0.07112795900232521\n", - " 0.07376225324790554\n", - " 0.03432324440866939\n", - "0.018997543688358758\n", - "0.052852687409104974\n", - " 0.03463925596802796\n", - " ...\n", - " 0.06347447823132187\n", - " 0.05466348135014649\n", - "0.061140370362200006\n", - " 0.01896811961118919\n", - " 0.06440976267517402\n", - " 0.04168622996255112\n", - "0.010201719779934896\n", - "Length = 1028674 rows\n", - "Found 108643 stars out of mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "o_cluster = synthetic.ResolvedCluster(o_iso, k_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "o_out = o_cluster.star_systems" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "4102bf35-8b91-44cb-a41a-081a1cb6bf00", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs and WDs\n", - "o_bh = np.where(o_out['phase'] == 103)[0]\n", - "o_ns = np.where(o_out['phase'] == 102)[0]\n", - "o_wd = np.where(o_out['phase'] == 101)[0]\n", - "o_bd = np.where(o_out['phase'] == 99)[0]\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 10, 20)\n", - "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(2, 2, 1)\n", - "plt.hist(o_out[o_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 2)\n", - "plt.hist(o_out[o_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 3)\n", - "plt.hist(o_out[o_bd]['mass'], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(2, 2, 4)\n", - "plt.hist(o_out[o_ns]['mass'], histtype = 'step',\n", - " bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "id": "75ad5ea9-9668-47d5-9fb0-1d17ad9913b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0382949743021217\n", - "8.999575157847088\n" - ] - } - ], - "source": [ - "# exploring the mass limits to generated WDs\n", - "print(np.min(o_out[o_wd]['mass']))\n", - "print(np.max(o_out[o_wd]['mass']))" - ] - }, - { - "cell_type": "markdown", - "id": "12c64ec0-58db-48fb-bca4-ae022702c0ca", - "metadata": {}, - "source": [ - "This is more expected, suggesting our unexpected results for white dwarf masses were due to the age of the cluster itself and not designation issues." - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "37509686-120b-41e6-96e4-97c7ccaa7cf1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple systemMass ... metallicity m_hst_f153m\n", - "------------------ ---------- ------------------ ... ----------- -----------\n", - "15.284314934206362 False 15.284314934206362 ... 0.0 nan\n", - " 17.1290304145272 False 17.1290304145272 ... 0.0 nan\n", - " 18.95921354685369 False 18.95921354685369 ... 0.0 nan\n", - "18.896393352954313 False 18.896393352954313 ... 0.0 nan\n", - "20.962240635300414 False 20.962240635300414 ... 0.0 nan\n", - "15.026617026908085 False 15.026617026908085 ... 0.0 nan\n", - " 76.13596178064363 False 76.13596178064363 ... 0.0 nan\n", - " ... ... ... ... ... ...\n", - " 26.37054586898205 False 26.37054586898205 ... 0.0 nan\n", - "15.233818106481106 False 15.233818106481106 ... 0.0 nan\n", - " 19.36143672592774 False 19.36143672592774 ... 0.0 nan\n", - " 15.57854891895509 False 15.57854891895509 ... 0.0 nan\n", - "16.464720574303342 False 16.464720574303342 ... 0.0 nan\n", - "18.964812829033175 False 18.964812829033175 ... 0.0 nan\n", - " 16.1815428883449 False 16.1815428883449 ... 0.0 nan\n", - "Length = 1274 rows\n", - "isWR\n", - "----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 1274 rows\n", - "isWR\n", - "----\n", - " 0.0\n", - " nan\n", - "isWR\n", - "----\n", - " nan\n", - " mass_current \n", - "------------------\n", - "1.3582329780993132\n", - " 1.373628116212405\n", - "1.2664447140529644\n", - "1.4158069262026158\n", - " 1.355217219139285\n", - "1.3046710483080488\n", - "1.2388328883877409\n", - " ...\n", - "1.4451775646969733\n", - "1.4624420030217151\n", - "1.2629887776924635\n", - "1.2784084162413294\n", - " 1.39953679687133\n", - "1.2970430314419577\n", - "1.3580723274397928\n", - "Length = 1274 rows\n" - ] - } - ], - "source": [ - "#exploring high mass NSs\n", - "large_o_NS = np.where((o_out[o_ns]['mass']) > 15)\n", - "print(o_out[o_ns][large_o_NS])\n", - "print(o_out[o_ns]['isWR'][large_o_NS])\n", - "print(np.unique(o_out['isWR']))\n", - "print(np.unique(o_out[o_ns]['isWR'][large_o_NS]))\n", - "print(o_out[o_ns]['mass_current'][large_o_NS])" - ] - }, - { - "cell_type": "markdown", - "id": "7e460fa5-a083-4f2c-bef8-eb7d8a972b99", - "metadata": {}, - "source": [ - "## Cluster 2: With Companions" - ] - }, - { - "cell_type": "markdown", - "id": "b0addabd-33a5-4e4f-82d2-d7b58c69fc0e", - "metadata": {}, - "source": [ - "For this cluster we are using the same isochrone as Cluster 1, just changing our IMF to allow for systems with companions." - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "id": "6a908b50-7ded-4d49-ab28-89551e5b1dc7", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "id": "9ebd7eb8-fae9-4aa8-a3e2-88cf6e0581fe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 785509, 785510, 785511]),), Masses: mass \n", - "--------------------\n", - " 0.06814878672190731\n", - " 0.04773849561965738\n", - " 0.06827493028013426\n", - " 0.0359072610482999\n", - "0.023766688780906337\n", - " 0.05802498588206111\n", - " 0.07564077829371584\n", - " ...\n", - " 0.05364431139484768\n", - "0.029481672087096702\n", - "0.012506309252072858\n", - "0.014174332139873898\n", - " 0.04149099895456249\n", - " 0.04355582847263964\n", - "0.036374123910035944\n", - "Length = 772782 rows\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 175850 companions out of stellar mass range\n" - ] - } - ], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "kc_out = kc_cluster.star_systems\n", - "kc_comp = kc_cluster.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 306, - "id": "7105fad5-a8f2-4c0c-a2a9-e4aac530b4c8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass \n", - "--------------------\n", - " 0.05968994825198586\n", - " 0.5806518980715113\n", - "0.029660673068744366\n", - " 1.0561490103453701\n", - " 0.17057943576263854\n", - " 0.27545527781952783\n", - " 0.02648169160335085\n", - " ...\n", - " 0.40403729711714403\n", - " 0.05200567264641382\n", - " 0.04220512783952523\n", - "0.035754850072584596\n", - " 0.12340265134211305\n", - " 0.07380553065721931\n", - "0.013647742599581188\n", - "Length = 431550 rows Teff \n", - "------------------\n", - " nan\n", - " 4085.526723430826\n", - " nan\n", - "5920.0062733779405\n", - "3190.3383809176958\n", - "3375.2593307941474\n", - " nan\n", - " ...\n", - "3536.9941918044533\n", - " nan\n", - " nan\n", - " nan\n", - "3058.8221931306266\n", - "2827.8838719105515\n", - " nan\n", - "Length = 431550 rows\n", - "0\n" - ] - } - ], - "source": [ - "print(kc_comp['mass'], kc_comp['Teff'])\n", - "print(len(kc_comp[c_bd]))" - ] - }, - { - "cell_type": "code", - "execution_count": 307, - "id": "1a908d42-28fd-4baf-9ed1-fb659132d621", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass_current \n", - "-------------------\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "0.07380553065721931\n", - " nan\n", - "Length = 183555 rows\n", - "system_idx mass ... metallicity m_hst_f153m \n", - "---------- -------------------- ... ----------- -----------------\n", - " 1 0.05968994825198586 ... 0.0 nan\n", - " 19 0.029660673068744366 ... 0.0 nan\n", - " 36 0.02648169160335085 ... 0.0 nan\n", - " 47 0.0676793749481354 ... 0.0 nan\n", - " 47 0.040357487164688594 ... 0.0 nan\n", - " 62 0.06369313059388905 ... 0.0 nan\n", - " 69 0.012985139409248137 ... 0.0 nan\n", - " ... ... ... ... ...\n", - " 2075867 0.014832191284296728 ... 0.0 nan\n", - " 2075875 0.010915381583182204 ... 0.0 nan\n", - " 2075893 0.05200567264641382 ... 0.0 nan\n", - " 2075900 0.04220512783952523 ... 0.0 nan\n", - " 2075900 0.035754850072584596 ... 0.0 nan\n", - " 2075906 0.07380553065721931 ... 0.0 9.421319357047143\n", - " 2075908 0.013647742599581188 ... 0.0 nan\n", - "Length = 183555 rows\n", - "phase\n", - "-----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " 1.0\n", - " nan\n", - "Length = 183555 rows\n", - "Teff\n", - "----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 1497 rows\n", - "phase\n", - "-----\n", - " 1.0\n", - " nan\n" - ] - } - ], - "source": [ - "need_bd = np.where(kc_comp['mass'] < 0.08)\n", - "print(kc_comp['mass_current'][need_bd])\n", - "bhs = np.where(kc_comp['phase'] == 103)\n", - "print(kc_comp[need_bd])\n", - "print(kc_comp[need_bd]['phase'])\n", - "print(kc_comp['Teff'][bhs])\n", - "print(np.unique(kc_comp['phase'][need_bd]))" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "id": "5450dea6-a4d8-4960-9c49-2e76b2e3f29f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Rli1ZsiRPQqU49cjJyRHW1taiTZs2svNy48YNoaurW+SLCYA8U6NGjURsbKysbH4JoKIea7du3UTNmjVFUlJSgXV5MeiHhYUJY2NjMXDgQJGRkfHK48i94LxqypUbnF/+TPz+++8CgPj444+leS8ngCIjIwUAsWzZMtm6t27dEiqVSsyYMUMIIcSZM2cEALFnz55X1v9lue/Li+dQiOcXEC0tLXHz5k0hhBCpqanCyMhITJkyRVauadOmeX4o5efFGy1XV1cxcOBAIYQQBw4cEAqFQsTFxeUbyF/29OlTkZaWJgwNDcUXX3whzXd0dBT9+/cvcL3//vtPABArV658ZV2pcmL8jpOtU57xe+XKlbIf6MuWLRNWVlZCCCEuX74sAIiLFy8KIYSYP3++7Ee6EEU/t0LIE0BCCPHvv/8KAGLevHl56uXp6Snq1q0r3RzlmjhxotDX1xcPHjwo9LhelQBas2aNlPAXQojDhw8LAOL48eNCCCG2bNkijIyMxPjx42Vx0N7eXnh7exe43adPn4qsrCxhb28vPvzwQ2l+7vWlS5cuedb58ssvpaRbrgcPHgilUimmTp0qzSvqOendu7do1apVgXUsSG4dC/o8vf/++9K8efPmCT09PXHv3j1pXu5NXERERKH7efE6nbvP3M9Yu3bthK+vrxBC5JsAelFOTo7Izs4WCxYsEKamplKdf/75ZwFAxMTEFLjuxIkTRc2aNQutJ5Udxvw42TqV9Td7bgJo7ty5svm5iZ3FixfL5ufGgO+++04IIUR6errQ09MTCxYsEEIIcfv2bQFAzJw5U6hUKilR5OfnJ6ytraXt5MaFt956S7b9H3/8UQAQkZGRhR7jqxJAQghhYWEhmjRpIr1+8803Rbdu3aTXDRs2FNOnTxdaWlpSTNu6dasAkCfxkSs3Jn3//fdCW1tbdp3KPe9HjhyRrZOdnS0sLCzyXFdmzJgh9PT0xH///SeE4H1DfqrafQMfAStnbdu2ha6urjQtW7YMwPOm23/99ReGDx8OAHj69Kk0vfXWW0hISMjT1LBv376y1y1atAAAqfnZoUOH8PTpU4wYMUK2PX19fbi6uubb2eWLTQlzJSUl4YMPPoCNjQ10dHSgq6sLW1tbAEBsbOwrj7mo9bhy5Qru3r0Lb29vWZN+W1tbdOrU6ZX7ydWgQQNER0cjOjoakZGR2LZtG1QqFbp37/7Kx66KcqyPHz9GREQEBg8eLD02UJhNmzbhrbfewvvvv48ff/wR+vr6RT6WRYsWScfy4jR48GBZudxHI158ZAEA2rdvjyZNmuDIkSMF7mP//v1QKBR49913Ze+PpaUlWrZsKb0/DRs2hImJCWbOnIlvvvkGly9fLvJxAM87WXv5M+vt7Y1nz57h+PHjUplRo0Zh48aNSE9PB/C8+fzly5cxceLEYu3vvffew969e3H//n2sW7cOXbt2LXBUirS0NMycORMNGzaEjo4OdHR0UKNGDaSnp8s+4+3bt8fBgwfx0UcfITw8HBkZGbLt1KpVCw0aNMCSJUuwfPlynDt3Lt+mtFQ1MX6Xbfx+sR+g3H9dXV0BAE2aNIG5ubkU68LDw2FhYYEmTZrk2c6rzm1xPHnyBEeOHMHbb78NAwODPO/tkydPEBUVVeztvki89MiQi4sL9PX1pY5Bw8LC4Obmhp49e+LUqVN4/Pgxbt26hWvXrsmapj99+hRBQUFo2rQp9PT0oKOjAz09PVy7di3f9zq/z8vw4cOhVCqxceNGad4PP/yAzMxMjBo1qtjnpH379vjzzz8xfvx4HDp0qNh96RT0eXrxccBx48YBgKyPhtWrV6N58+bo0qVLkffl6uqKBg0aYP369bhw4QKio6MLfPwLeH5t6tGjB9RqNbS1taGrq4u5c+fi/v370sg+rVq1gp6eHsaMGYNNmzbleTwCeH6OHj58iGHDhuGXX37J95FzqhiM+ZXvN/vLx3z06FEAeX//Dho0CIaGhtLvXwMDAzg7O8vias2aNTF9+nRkZWXh5MmTAIDDhw/n+8hPaV5XXvbyNaB79+747bffkJGRgZs3b+L69esYOnQoWrVqhbCwMKme9erVg729vbTeuXPn0LdvX5iamkoxacSIEcjJycHVq1dl+zAxMUG3bt1k83R0dPDuu+9i165dSElJAQDk5ORg8+bN6NevH0xNTQHwvgGo+vcNTACVATMzM6hUqnyDwrZt2xAdHS17jh4A7t27BwCYNm2a7GKjq6uL8ePHA0CeHwW5X8RcSqUSAKQPV+4227Vrl2ebO3bsyLM9AwODPD2dP3v2DB4eHti1axdmzJiBI0eO4PTp09KPu5c/yPkpaj3u378PALC0tMyzjfzmFURfXx9OTk5wcnJCx44dMWzYMBw8eBAJCQmYO3dugesV9ViTk5ORk5ODunXrFqk+27dvh0qlwvvvv1/sodvfeOMN6VhenF5OPOWeu/yGSra2tpaW5+fevXsQQsDCwiLP+xMVFSW9P2q1GhEREWjVqhU+/vhjNGvWDNbW1pg3b16RRrDJr3O73Pf1xfpNmjQJjx49wtatWwE8/yFft25d9OvX75X7eNHAgQOhr6+PFStWYN++fRg9enSBZb29vbF69Wq8//77OHToEE6fPo3o6GjUrl1b9hn/8ssvMXPmTOzZswddu3ZFrVq10L9/f+lHikKhwJEjR+Dp6YnFixejTZs2qF27NiZPnoxHjx4Vq/5UMRi/5cozfjdv3hxmZmY4duyY1P9PbgIIeN45aHh4ODIzMxEZGVng6F+vOrfFcf/+fTx9+hSrVq3Kc/xvvfUWgLzvbXHlftZy+xfR19eHi4uLdKNy5MgRuLu7w83NDTk5OThx4oR0E/DijUpAQADmzJmD/v37Y9++ffj9998RHR2Nli1b5nvs+V0vatWqhb59++L777+X+lnYuHEj2rdvj2bNmhX7nMyaNQtLly5FVFQUvLy8YGpqiu7duxd5SO2CPk8vXjMsLCwwZMgQfPvtt8jJycH58+dx4sSJYv/4VygUGDVqFLZs2YJvvvkGjRo1QufOnfMte/r0aalz2rVr1+K3335DdHQ0Zs+eDeB/n7UGDRrg8OHDMDc3x4QJE9CgQQM0aNBA1geSj48P1q9fj5s3b+Kdd96Bubk5OnToIL3HVLYY8+Uq+2/2l+PW/fv3oaOjk+d3sUKhyBMrevTogaioKKSnp+Pw4cPo1q0bTE1N0bZtWxw+fBhxcXGIi4vLNwFUmteVF6Wnp+P+/fuy/qV69OiBzMxMnDx5EmFhYTAzM0Pr1q3Ro0cP2XXhxXrGx8ejc+fOuHPnDr744gucOHEC0dHRUr9sL9czv/gPPE+CPHnyBNu3bwfwPCGYkJAg/QEA4H0DUPXvG6rncA9lTFtbG926dUNoaCgSEhJkX7LckUdu3LghW8fMzAzA8x9LAwYMyHe7Dg4OxapH7jZ//vlnKftfmPySExcvXsSff/6JjRs3YuTIkdL869evl3o9coNrYmJinmX5zSsOKysrmJmZ4c8//yywTFGPtVatWtDW1i5yB2Zbt27FnDlz4OrqitDQULRq1apEx1CY3HOXkJCQJzF19+5d6T3Ij5mZGRQKBU6cOCFd0F704rzmzZtj+/btEELg/Pnz2LhxIxYsWACVSoWPPvqo0Drm/qh4Ue77+uKFtWHDhvDy8sJXX30FLy8v7N27F/Pnz4e2tnah23+ZgYEBhg4diuDgYBgbGxf4vUpJScH+/fsxb9482TFkZmbiwYMHsrKGhoaYP38+5s+fj3v37klZ/T59+khDVNva2kqdqF69ehU//vgjAgMDkZWVhW+++aZYx0Dlj/G7ZPUojfitUCjg6uqKkJAQnD59Gg8fPpQlgFxdXREYGIjIyEg8efKkRMO/F5eJiQm0tbXh4+NTYGeSdnZ2Jd6+EAL79u2DoaGhbISY7t27Y+7cuTh9+jRu374Nd3d3GBkZoV27dggLC8Pdu3fRqFEj2NjYSOts2bIFI0aMQFBQkGwf//33H2rWrJln3wX9QWLUqFH46aefEBYWhnr16iE6Ohpr1qyRlhfnnOjo6CAgIAABAQF4+PAhDh8+jI8//hienp64devWK0edKejz9PLN2JQpU7B582b88ssvCAkJQc2aNaXWGcXh6+uLuXPn4ptvvpGNBvOy7du3Q1dXF/v375e16t2zZ0+esp07d0bnzp2Rk5ODM2fOYNWqVfD394eFhQWGDh0K4Pk5HzVqFNLT03H8+HHMmzcPvXv3xtWrV4v0/aeSY8wvWT0q6jf7y8dtamqKp0+f4t9//5UlgYQQSExMRLt27aR53bt3x5w5c3D8+HEcOXIE8+bNk+aHhoZKcSu3Q+3ycODAAeTk5EiDEgDPO1yuUaMGDh8+jBs3bqB79+5QKBTo3r07li1bhujoaMTHx8sSQHv27EF6ejp27dole98K6ki4oPjftGlTtG/fHhs2bMDYsWOxYcMGWFtby0Zj431D1b9vYAKojMyaNQsHDx7EBx98gJ9//vmVo085ODjA3t4ef/75Z54fbyXl6ekJHR0d/P333/k2Ey2K3ADx8hc8t7f6FxWUDS9qPRwcHGBlZYUffvgBAQEB0r5v3ryJU6dO5Rl9oThu376N//77r9Chf4t6rLkjkfz0009YuHBhockV4HnC6PDhw+jduze6du2KgwcPyoa8LA25zTi3bNkiu9hFR0cjNjZW+qtkfnr37o3PP/8cd+7cyfNoWUEUCgVatmyJFStWYOPGjfjjjz9euc6jR4+wd+9eWXPObdu2QUtLK08z/SlTpsDDwwMjR46EtrY2/Pz8ilSvl40bNw737t2Dq6trgY/eKRQKCCHyvO//93//Jxst4mUWFhbw9fXFn3/+iZUrV+Y7hGajRo3wySefYOfOnUU6R1Q5MH4Xvx6lFb+7du2KnTt3YsmSJTA3N5c94uXq6or79+9Lo7OUZgKooOM3MDBA165dce7cObRo0aLAEYJKav78+bh8+TI+/vhjWYzq0aMHPv74Y8yZMwd169ZF48aNpfl79+5FYmJinvdDoVDkea8PHDiAO3fuoGHDhkWuk4eHB+rUqYMNGzagXr160NfXl0bJAkp+TmrWrImBAwfizp078Pf3x40bNwq9JgMo8PM0YsQIWbm2bduiU6dOWLRoES5evIgxY8bA0NCwyMecq06dOpg+fTr++usv2Q30yxQKBXR0dGQ3GBkZGdi8eXOB62hra6NDhw5o3Lgxtm7dij/++ENKAOUyNDSEl5cXsrKy0L9/f1y6dIkJoHLAmF/8elT0b/Zc3bt3x+LFi7FlyxZ8+OGH0vydO3ciPT1dlsxp3749jI2NsXLlSiQmJsLd3R3A87i6aNEi/Pjjj2jatOlr1b044uPjMW3aNKjVaowdO1aar6uriy5duiAsLAy3bt3C559/DuB5MllHRweffPKJlBDKld97L4Qo0fDlo0aNwrhx43Dy5Ens27cPAQEBsljH+4aqf9/ABFAZcXFxwVdffYVJkyahTZs2GDNmDJo1awYtLS0kJCRg586dACBrvvntt9/Cy8sLnp6e8PX1RZ06dfDgwQPExsbijz/+wE8//VSsOtSvXx8LFizA7Nmz8c8//6Bnz54wMTHBvXv3cPr0aSkrWZjGjRujQYMG+OijjyCEQK1atbBv3758myY3b94cAPDFF19g5MiR0NXVhYODQ5HroaWlhU8//RTvv/8+3n77bfj5+eHhw4cIDAwsVnPSjIwMqblrTk4O4uLisHjxYgCAv79/qRzr8uXL8eabb6JDhw746KOP0LBhQ9y7dw979+7Ft99+CyMjI1l5IyMjhISEYMCAAXB3d8fevXtL9ebFwcEBY8aMwapVq6ClpQUvLy/cuHEDc+bMgY2Njeyi+DIXFxeMGTMGo0aNwpkzZ9ClSxcYGhoiISEBJ0+eRPPmzTFu3Djs378fX3/9Nfr374833ngDQgjs2rULDx8+lC6ihTE1NcW4ceMQHx+PRo0a4ddff8XatWsxbtw41KtXT1bW3d0dTZs2xbFjx/Duu+/C3Ny8ROelVatW+f5F9kXGxsbo0qULlixZAjMzM9SvXx8RERFYt25dnr+ad+jQAb1790aLFi1gYmKC2NhYbN68Gc7OzjAwMMD58+cxceJEDBo0CPb29tDT08PRo0dx/vz5V/6lgyoPxu+Ki9+5cXH37t0YOHCgbJmjoyNMTU2xe/du1KlTR9b3wesyMjKCra0tfvnlF3Tv3h21atWS4sEXX3yBN998E507d8a4ceNQv359PHr0CNevX8e+ffukPigK8/DhQ+m6lJ6ejitXrmD79u04ceIEBg8enOe9bNu2LUxMTBAaGipret+jRw98+umn0v9f1Lt3b2zcuBGNGzdGixYtcPbsWSxZsqTIjyvn0tbWxogRI7B8+XLpr6C5wzLnKuo56dOnDxwdHaVHl2/evImVK1fC1ta2SO9fUlKS9HlKSUnBvHnzoK+vj1mzZuUpO2XKFAwZMgQKhUJ6DKckcm+2CtOrVy8sX74c3t7eGDNmDO7fv4+lS5fmuSH45ptvcPToUfTq1Qv16tXDkydPsH79egD/e//8/PygUqng4uICKysrJCYmIjg4GGq1WvYHHSo7jPlV7zd7Lnd3d3h6emLmzJlITU2Fi4sLzp8/j3nz5qF169bw8fGRympra8PV1RX79u2DnZ2dNIy8i4sLlEoljhw5gsmTJxe57sVx8eJFqa+cpKQknDhxAhs2bIC2tjZ2796d5xG27t27Y+rUqQD+FytUKhU6deqE0NBQtGjRQvbb2N3dHXp6ehg2bBhmzJiBJ0+eYM2aNUhOTi52XYcNG4aAgAAMGzYMmZmZefpX4n2DBtw3lGuX09VQTEyMGDVqlLCzsxNKpVLo6+uLhg0bihEjRuTpfV0IIf78809puFVdXV1haWkpunXrJr755hupTEE9yuf2VP9yL+V79uwRXbt2FcbGxkKpVApbW1sxcOBAcfjwYanMyJEjhaGhYb7HcPnyZeHu7i6MjIyEiYmJGDRokIiPj893xJRZs2YJa2troaWllacuRamHEEL83//9n7C3txd6enqiUaNGYv369fkOXZ6fl0cU0NLSEtbW1sLLy0uEh4fLyuY3ClhxjvXy5cti0KBBwtTUVOjp6Yl69eoJX19faSSB/N6nzMxM8c477wh9fX1x4MCBAo8j97386aef8l0+YcIE8fLXNycnRyxatEg0atRI6OrqCjMzM/Huu++KW7duycoVdC7Xr18vOnToIAwNDYVKpRINGjQQI0aMEGfOnBFCCPHXX3+JYcOGiQYNGgiVSiXUarVo37692LhxY4HHkSt3qM/w8HDh5OQklEqlsLKyEh9//HGeETRyBQYGCgAiKirqldvP9arRdoQQ+fbmf/v2bfHOO+8IExMTYWRkJHr27CkuXrwobG1txciRI6VyH330kXBychImJiZCqVSKN954Q3z44YfSyAj37t0Tvr6+onHjxsLQ0FDUqFFDtGjRQqxYseKVQ4VS5cP4/b+6lEf8zmVpaSkAiNWrV+dZ1r9/fwEgz7DfQhTv3L48CpgQz0ffat26tVAqlQKA7LsfFxcn3nvvPVGnTh2hq6srateuLTp16iQ+++yzVx6Pra2tdE1SKBSiRo0awsHBQfj4+EhDN+fn7bffFgDE1q1bpXlZWVnC0NBQaGlpScOI50pOThajR48W5ubmwsDAQLz55pvixIkTeY71VdcXIYS4evWqVOeXhwQuzjlZtmyZ6NSpkzAzM5Ouk6NHjxY3btwo9Jzl1nHz5s1i8uTJonbt2kKpVIrOnTtL16SXZWZmCqVSKXr27Fnotl9UlBF6hMh/FLD169cLBwcH6VoQHBws1q1bJ/tdERkZKd5++21ha2srlEqlMDU1Fa6urmLv3r3SdjZt2iS6du0qLCwshJ6enrC2thaDBw8W58+fL/JxUOlgzP9fXSrbb/bcUcBeHGI7V0ZGhpg5c6awtbUVurq6wsrKSowbNy5PjBTi+bDhAISfn59svru7uwAg+24KUXC8jIuLEwDEhg0bCj3G3Pc/d9LT0xPm5ubC1dVVBAUFFTia8J9//ikACHt7e9n8hQsXCgAiICAgzzr79u0TLVu2FPr6+qJOnTpi+vTp4uDBg/le/5o1a1Zovb29vQUA4eLiUmAZ3jdU3fsGhRAvdT1ORFQJODk5QaFQIDo6uqKrQkREldy+ffvQt29fHDhwQOqMmoiIqgfeNxQdHwEjokojNTUVFy9exP79+3H27Fns3r27oqtERESV2OXLl3Hz5k1MnToVrVq1gpeXV0VXiYiIygHvG0qGCSAiqjT++OMPdO3aFaamppg3bx769+9f0VUiIqJKbPz48fjtt9/Qpk0bbNq0qcDRbYiISLPwvqFk+AgYEREREREREZGG06roChARERERERERUdliAoiIiIiIiIiISMMxAUREREREREREpOE0vhPoZ8+e4e7duzAyMmLHgERUbQgh8OjRI1hbW0NLq/rl+hn7iag6Yuxn7Cei6qc4sV/jE0B3796FjY1NRVeDiKhC3Lp1C3Xr1q3oapQ7xn4iqs4Y+4mIqp+ixH6NTwAZGRkBeH4yjI2NK7g2RETlIzU1FTY2NlIMrG4Y+4moOmLsZ+wnouqnOLFf4xNAuc0/jY2NeSEgomqnujaBZ+wnouqMsZ+xn4iqn6LE/ur3cDARERERERERUTXDBBAREZWa48ePo0+fPrC2toZCocCePXtky4UQCAwMhLW1NVQqFdzc3HDp0iVZmczMTEyaNAlmZmYwNDRE3759cfv27XI8CiIiIiIizcMEEBERlZr09HS0bNkSq1evznf54sWLsXz5cqxevRrR0dGwtLSEu7s7Hj16JJXx9/fH7t27sX37dpw8eRJpaWno3bs3cnJyyuswiIiIiIg0jsb3AVRUOTk5yM7OruhqEFUZurq60NbWruhqUCXj5eUFLy+vfJcJIbBy5UrMnj0bAwYMAABs2rQJFhYW2LZtG8aOHYuUlBSsW7cOmzdvRo8ePQAAW7ZsgY2NDQ4fPgxPT89yOxbitZGosuG1l8oCYz1R5Vaasb/aJ4CEEEhMTMTDhw8ruipEVU7NmjVhaWlZbTubpOKJi4tDYmIiPDw8pHlKpRKurq44deoUxo4di7NnzyI7O1tWxtraGo6Ojjh16lSBCaDMzExkZmZKr1NTU8vuQKoBXhuJKi9ee6m0MNYTVR2lFfurfQIoN+iZm5vDwMCAF1OiIhBC4PHjx0hKSgIAWFlZVXCNqCpITEwEAFhYWMjmW1hY4ObNm1IZPT09mJiY5CmTu35+goODMX/+/FKucfXFayNR5cNrL5U2xnqiyq+0Y3+1TgDl5ORIQc/U1LSiq0NUpahUKgBAUlISzM3N2SSdiuzlH5hCiFf+6HxVmVmzZiEgIEB6nZqaChsbm9eraDXFayNR5cVrL5UWxnqiqqM0Y3+17gQ691lXAwODCq4JUdWU+93hc+NUFJaWlgCQpyVPUlKS1CrI0tISWVlZSE5OLrBMfpRKJYyNjWUTlQyvjUSVG6+9VBoY64mqltKK/dU6AZSLzR2JSobfHSoOOzs7WFpaIiwsTJqXlZWFiIgIdOrUCQDQtm1b6OrqysokJCTg4sWLUhkqH/x+E1VO/G5SaeLniahqKK3varV+BIyIiEpXWloarl+/Lr2Oi4tDTEwMatWqhXr16sHf3x9BQUGwt7eHvb09goKCYGBgAG9vbwCAWq3G6NGjMXXqVJiamqJWrVqYNm0amjdvLo0KRkRERERExccEUAHuPMxAcnpWuezLxFAPdWqqymVfmqx+/frw9/eHv7//a21HoVBg9+7d6N+/f6WqV0kEBgZiz549iImJKfd9U/V05swZdO3aVXqd2y/PyJEjsXHjRsyYMQMZGRkYP348kpOT0aFDB4SGhsLIyEhaZ8WKFdDR0cHgwYORkZGB7t27Y+PGjezrooKV53UR4LWxtJTVNehV271x4wbs7Oxw7tw5tGrVqlT3TURli/G+aqrIew6qOpgAysedhxnosSwCGdk55bI/la42Dk91LVbgS0xMRHBwMA4cOIDbt29DrVbD3t4e7777LkaMGFFlnuctz0AVGBgoGyXI2NgYLVq0wGeffQZXV9cy339RhYeHo2vXrkhOTkbNmjVlyxjYqbJzc3ODEKLA5QqFAoGBgQgMDCywjL6+PlatWoVVq1aVQQ2pJMr7ugjw2lgesT4kJAReXl5ISEiQ+ugCnvfFpauri1u3bknzbt++DRsbGxw6dAgeHh6v3LaNjQ0SEhJgZmYGoPBrW3G5ubkhIiICAKCnpwczMzO0adMGo0aNwoABA15r20TVHeN9+eK9UNFt3LgRo0aNAgBoaWnB2NgYjRo1Qq9evTBlyhSo1eoKrmHVwARQPpLTs5CRnYOVQ1qhoXmNMt3X9aQ0+O+IQXJ6VpGD3j///AMXFxfUrFkTQUFBaN68OZ4+fYqrV69i/fr1sLa2Rt++fcu03oURQiAnJwc6OpXv49WsWTMcPnwYAPDgwQMsXboUvXv3li4cRESUV3leFwFeG8vLm2++CR0dHYSHh2Po0KEAgNjYWDx58gQZGRm4fv06GjZsCAA4duwYdHV14eLiUqRta2try5JKpc3Pzw8LFixAdnY27ty5g927d2Po0KHw9fXFd999V2b7fVl2djZ0dXXLbX9EZY3x/vVVxnifqyT3QpUpzhkbG+PKlSsQQuDhw4c4deoUgoODsWHDBvz222+wtrYul3pkZWVBT0+vXPZV6oSGS0lJEQBESkpKnmUZGRni8uXLIiMjQzb/wu2HwnbmfnHh9sMyr19J9uXp6Snq1q0r0tLS8l3+7Nkz6f8PHz4Ufn5+onbt2sLIyEh07dpVxMTESMvnzZsnWrZsKb7//ntha2srjI2NxZAhQ0Rqaqpse4sWLRJ2dnZCX19ftGjRQvz000/S8mPHjgkAIiQkRLRt21bo6uqKo0ePiuvXr4u+ffsKc3NzYWhoKJycnERYWJi0nqurqwAgm3L99ttvonPnzkJfX1/UrVtXTJo0SXa89+7dE7179xb6+vqifv36YsuWLcLW1lasWLGiwPOWe6wvio+PFwDE6dOnpXkAxO7du6XXM2bMEPb29kKlUgk7OzvxySefiKysLNl2fvnlF9G2bVuhVCqFqampePvtt6VlL9dr/fr1wtjYWISGhuZbz9zzmZycnGfZy9u6efOm6Nu3rzA0NBRGRkZi0KBBIjExsdBjXr9+vWjcuLFQKpXCwcFBfPXVV9KyzMxMMWHCBGFpaSmUSqWwtbUVQUFB+dZTiIK/Q1TxCot91UF1P/7Xkd/3ujyviyXdH6+NJbs2Ojs7i7Fjx0qvv/76a9GrVy/x1ltvibVr10rz33vvPeHi4iK9trW1FQsXLhSjRo0SNWrUEDY2NuLbb7+VlsfFxQkA4ty5c9L/X5xGjhxZpPOYH1dXVzFlypQ889evXy8ASOdzwIABYuLEidLyKVOmCADi4sWLQgghsrOzRY0aNURISIgQQoiDBw8KFxcXoVarRa1atUSvXr3E9evX8xzTjh07hKurq1AqlWLlypVCX19fHDx4UFaXnTt3CgMDA/Ho0SMhhBC3b98WgwcPFjVr1hS1atUSffv2FXFxcVL5Y8eOiXbt2gkDAwOhVqtFp06dxI0bN/I9/sKuvdU99lX34y+OynAfVNL9Md6X/b3QmjVrRN++fYWBgYGYO3euEOL59eGNN94Qurq6olGjRuL777+X1gkICBC9e/eWXq9YsUIAEPv375fmNWrUSHzzzTdCCCFGjhwp+vXrJ5YsWSIsLS1FrVq1xPjx4/PcY71ow4YNQq1W55l/7949YWZmJoYPHy6EEGLv3r1CrVaLnJwcIYQQ586dEwDEtGnTpHXGjBkjhg4dKoQQ4r///hNDhw4VderUESqVSjg6Oopt27bJ9uHq6iomTJggPvzwQ2Fqaiq6dOkihg4dKoYMGSIrl5WVJUxNTcX69euFEK/+7Dx48EB4e3sLMzMzoa+vLxo2bCit+7LSiv0cBayKuX//PkJDQzFhwgQYGhrmWya3h3AhBHr16oXExET8+uuvOHv2LNq0aYPu3bvjwYMHUvm///4be/bswf79+7F//35ERETg888/l5Z/8skn2LBhA9asWYNLly7hww8/xLvvvis1v841Y8YMBAcHIzY2Fi1atEBaWhreeustHD58GOfOnYOnpyf69OmD+Ph4AMCuXbtQt25dLFiwAAkJCUhISAAAXLhwAZ6enhgwYADOnz+PHTt24OTJk5g4caK0L19fX9y4cQNHjx7Fzz//jK+//hpJSUnFOpeZmZnYuHEjatasCQcHhwLLGRkZYePGjbh8+TK++OILrF27FitWrJCWHzhwAAMGDECvXr1w7tw5HDlyBE5OTvlua+nSpZg2bRoOHToEd3f3YtX3ZUII9O/fHw8ePEBERATCwsLw999/Y8iQIQWus3btWsyePRsLFy5EbGwsgoKCMGfOHGzatAkA8OWXX2Lv3r348ccfceXKFWzZsgX169d/rXoSEZU1XhufK8m1sWvXrjh27Jj0+tixY3Bzc4Orq2ue+S/27wUAy5Ytg5OTE86dO4fx48dj3Lhx+Ouvv/Lsw8bGBjt37gQAXLlyBQkJCfjiiy+KdR6LYuTIkTAxMcGuXbsAPH9ULDw8XFoeEREBMzMzadvR0dF48uSJ1KopPT0dAQEBiI6OxpEjR6ClpYW3334bz549k+1n5syZmDx5MmJjYzFo0CD06tULW7dulZXZtm0b+vXrhxo1auDx48fo2rUratSogePHj+PkyZOoUaMGevbsiaysLDx9+hT9+/eHq6srzp8/j8jISIwZM4ajMxHlg/H+ubK+F5o3bx769euHCxcu4L333sPu3bsxZcoUTJ06FRcvXsTYsWMxatQo6Trh5uaGEydOSPHy5XibmJiIq1evyh41O3bsGP7++28cO3YMmzZtwsaNG7Fx48ZiHQMAmJubY/jw4di7dy9ycnLQpUsXPHr0COfOncu3LsDzx5Jz6/LkyRO0bdsW+/fvx8WLFzFmzBj4+Pjg999/l+1n06ZN0NHRwW+//YZvv/1W2mdaWppU5tChQ0hPT8c777wD4NWfnTlz5uDy5cs4ePAgYmNjsWbNGunR6TLzyhRRFadpLYCioqIEALFr1y7ZfFNTU2FoaCgMDQ3FjBkzhBBCHDlyRBgbG4snT57IyjZo0ED6K928efOEgYGBLMs9ffp00aFDByGEEGlpaUJfX1+cOnVKto3Ro0eLYcOGCSH+l/Xes2fPK+vftGlTsWrVKul1fplqHx8fMWbMGNm8EydOCC0tLZGRkSGuXLkiAIioqChpeWxsrADwyqy3lpaWdJ4UCoUwNjbO81c7vNQC6GWLFy8Wbdu2lV47OztLGef85B7jRx99JKysrMT58+cLLCvE/85nbj1fnBQKhXSMoaGhQltbW8THx0vrXrp0SZbFfznTb2Njkyej/emnnwpnZ2chhBCTJk0S3bp1k/3lpDBsAVR5Vfe/glb3438dVbEFEK+NJb82hoaGCgDi7t27QgghzM3NxenTp0VUVJSwtrYWQvzvL8RHjhyR1fHdd9+VXj979kyYm5uLNWvWCCHkLYBePB8vtm4tynnMT0EtgIQQokOHDsLLy0sIIcT58+eFQqEQ//77r3jw4IHQ1dUVn332mRg0aJAQQoigoCDpPc1PUlKSACAuXLggO6aVK1fKyu3atUvUqFFDpKenCyGexx99fX1x4MABIYQQ69atEw4ODrJra2ZmplCpVOLQoUPi/v37AoAIDw8vsC4vYgugglX34y+OynAfVJL9Md6Xz72Qv7+/bF6nTp2En5+fbN6gQYPEW2+9JYR43tJKS0tLnDlzRjx79kyYmpqK4OBg0a5dOyGEENu2bRMWFhbSuiNHjhS2trbi6dOnsu293KLmRQW1ABJCiDVr1ggA4t69e0IIIdq0aSOWLl0qhBCif//+YuHChUJPT0+kpqaKhIQEAUDExsYWuK+33npLTJ06VXrt6uoqWrVqJSuTlZUlzMzMZC2hhg0bJl1jivLZ6dOnjxg1alSB9XhRacX+yvdgIhXJy38VOn36NJ49e4bhw4cjMzMTAHD27FmkpaXB1NRUVjYjIwN///239Lp+/fqyEXisrKykDPLly5fx5MmTPK1VsrKy0Lp1a9m8l1u9pKenY/78+di/fz/u3r2Lp0+fIiMjQ8p6F+Ts2bO4fv267K9pQgg8e/YMcXFxuHr1KnR0dGT7a9y4cZE6lXRwcMDevXsBAI8ePcKOHTswaNAgHDt2rMBWOz///DNWrlyJ69evIy0tDU+fPoWxsbG0PCYmBn5+foXud9myZUhPT8eZM2fwxhtvvLKeAHDixAnZ+wI8z67nio2NhY2NDWxsbKR5TZs2Rc2aNREbG4t27drJ1v33339x69YtjB49Wlbfp0+fSs/8+vr6wt3dHQ4ODujZsyd69+5dpM4+iYgqA14bi39tdHFxgZ6eHsLDw9GyZUtkZGSgTZs2EEIgNTUV165dQ2RkJJRKJTp16iRbt0WLFtL/FQoFLC0ti/UX6OKcx6ISQkifA0dHR5iamiIiIgK6urpo2bIl+vbtiy+//BKA/C/AwPNWAHPmzEFUVBT+++8/6S/Z8fHxcHR0lMq9/J726tULOjo62Lt3L4YOHYqdO3fCyMhIun7mvncvX9OfPHmCv//+Gx4eHvD19YWnpyfc3d3Ro0cPDB48GFZWViU6B0TVAeN92d4LvXwssbGxGDNmjGyei4uL1JpTrVajVatWCA8Ph66uLrS0tDB27FjMmzcPjx49yhNvgef9Eb04wquVlRUuXLjwymPIj/j/A5Dkfi5yW4AGBATgxIkT+Oyzz7Bz506cPHkSDx8+hIWFBRo3bgwAyMnJweeff44dO3bgzp07yMzMRGZmZp4WZi+fE11dXQwaNAhbt26Fj48P0tPT8csvv2Dbtm0AivbZGTduHN555x388ccf8PDwQP/+/fNca0sbE0BVTMOGDaFQKPI0sc5NKqhU/+s87dmzZ7CyspI1f871YoB4uVMvhUIh/ejJ/ffAgQOoU6eOrJxSqZS9fvlLMn36dBw6dAhLly5Fw4YNoVKpMHDgQGRlFT6s5LNnzzB27FhMnjw5z7J69erhypUrUj2LS09PT+rQEgBat26NPXv2YOXKldiyZUue8lFRURg6dCjmz58PT09PqNVqbN++HcuWLZPKvHjOC9K5c2ccOHAAP/74Iz766KMi1dXOzi5PIH+xM7kXf+S+qKD5ue/l2rVr0aFDB9my3ODbpk0bxMXF4eDBgzh8+DAGDx6MHj164Oeffy5SnYmIKgKvjSW/NhoYGKB9+/Y4duwYHjx4gDfffFO6JnTq1AnHjh1DZGQknJ2doa+vL1u3sHNUFMU5j0WRk5ODa9euSX8AUSgU6NKlC8LDw6Gnpwc3Nzc4OjoiJycHFy5cwKlTp2Qj7/Tp0wc2NjZYu3YtrK2t8ezZMzg6OuZ5b15+T/X09DBw4EBs27YNQ4cOxbZt2zBkyBDpmv3s2TO0bds2z2NiAFC7dm0AwIYNGzB58mSEhIRgx44d+OSTTxAWFoaOHTsW+zwQaTLG+/K5F8rv8bqX9/fyPUdu0kVPTw+urq4wMTFBs2bN8NtvvyE8PDzPSGevew15UWxsLIyNjaVkn5ubG9atW4c///wTWlpaaNq0KVxdXREREYHk5GRZMmrZsmVYsWIFVq5ciebNm8PQ0BD+/v6vjP0AMHz4cLi6uiIpKQlhYWHQ19eHl5cXgKJ9dry8vHDz5k0cOHAAhw8fRvfu3TFhwgQsXbq0ROehKJgAqmJMTU3h7u6O1atXY9KkSQU++wo8v5lPTEyEjo5Oiftxadq0KZRKJeLj44s9POCJEyfg6+uLt99+GwCQlpaGGzduyMro6ekhJ0c+zGSbNm1w6dIlWXB6UZMmTfD06VOcOXMG7du3B/C8T4GHDx8Wq365tLW1kZGRke+y3377Dba2tpg9e7Y07+bNm7IyLVq0wJEjR6RhCfPTvn17TJo0CZ6entDW1sb06dNLVNcXNW3aFPHx8bh165bUCujy5ctISUlBkyZN8pS3sLBAnTp18M8//2D48OEFbtfY2BhDhgzBkCFDMHDgQPTs2RMPHjxArVq1XrvO1dWdhxlITi/8Yl8YE0O9Yg2NSpXL677/L+JnIX+8Nr7etbFr167Yvn07kpOTZS1NXV1dER4ejsjIyEKvcUWRO1rKi8f1OucxP5s2bUJycrLU9wLw/Cbgu+++g56eHhYsWACFQoHOnTtj6dKlyMjIkPr/uX//PmJjY/Htt9+ic+fOAICTJ08Wed/Dhw+Hh4cHLl26hGPHjuHTTz+VlrVp0wY7duyAubm5rAXxy1q3bo3WrVtj1qxZcHZ2xrZt25gAKke8VlcNjPfley/04j5PnjyJESNGSPNOnTolu+fITbro6OigR48eAJ5fR7Zv356n/5/SlJSUhG3btqF///7Q0nrexXFuP0ArV66Eq6srFAoFXF1dERwcjOTkZEyZMkVa/8SJE+jXrx/effddAM8TN9euXcv3fuplnTp1go2NDXbs2IGDBw9i0KBB0vWuqJ+d2rVrw9fXF76+vujcuTOmT5/OBBDJff3113BxcYGTkxMCAwPRokULaGlpITo6Gn/99Rfatm0LAOjRowecnZ3Rv39/LFq0CA4ODrh79y5+/fVX9O/fv8BHnl5kZGSEadOm4cMPP8SzZ8/w5ptvIjU1FadOnUKNGjUwcuTIAtdt2LAhdu3ahT59+kChUGDOnDl5srr169fH8ePHMXToUCiVSpiZmWHmzJno2LEjJkyYAD8/PxgaGiI2NhZhYWFYtWqV9HiSn58fvvvuO+jo6MDf379ILXGePn2KxMREAP9r9nj58mXMnDmzwGOIj4/H9u3b0a5dOxw4cAC7d++WlZk3bx66d++OBg0aYOjQoXj69CkOHjyIGTNmyMo5Ozvj4MGD6NmzJ3R0dPDhhx++sr6F6dGjB1q0aIHhw4dj5cqVePr0KcaPHw9XV9cC39vAwEBMnjwZxsbG8PLyQmZmJs6cOYPk5GQEBARgxYoVsLKyQqtWraClpYWffvoJlpaWRWpSSvm78zADPZZFICM759WFC6DS1cbhqa78YVkFlcb7/yJ+FgrGa2PJr41du3bFp59+ioSEBEybNk2a7+rqis8//xyPHj3K0wF0cdna2kKhUGD//v146623oFKpXus8Pn78GImJiXj69Cnu3LmDXbt2YcWKFRg3bpysrm5ubpgyZQp0dHSkxI6bmxumTp2KNm3aSAkZExMTmJqa4rvvvoOVlRXi4+OL3GIXeH6uLCwsMHz4cNSvX1+WuBk+fDiWLFmCfv36YcGCBahbty7i4+Oxa9cuTJ8+HdnZ2fjuu+/Qt29fWFtb48qVK7h69arsRovKFq/VVQvjffndC+WaPn06Bg8eLHWivW/fPuzatUsaUh74X9Jl3759+OyzzwA8j7fvvPMOateujaZNm76yfq8ihEBiYqI0DHxkZCSCgoKgVqtlHXfnPpK2ZcsW6TG1Ll26YNCgQcjOzpb9saNhw4bYuXMnTp06BRMTEyxfvhyJiYlFSgApFAp4e3vjm2++wdWrV2WDJxTlszN37ly0bdsWzZo1Q2ZmJvbv31+k/b4OJoAKcT0p7dWFKmAfDRo0wLlz5xAUFIRZs2bh9u3bUCqVaNq0KaZNm4bx48cDeP6B/PXXXzF79my89957+Pfff2FpaYkuXbrAwsKiyPv79NNPYW5ujuDgYPzzzz+oWbMm2rRpg48//rjQ9VasWIH33nsPnTp1koJZamqqrMyCBQswduxYNGjQAJmZmRBCoEWLFoiIiMDs2bPRuXNnCCHQoEED2ehWGzZswPvvvy/94Prss88wZ86cVx7LpUuXpGfqDQwM0KBBA6xZs6bAH1n9+vXDhx9+iIkTJyIzMxO9evXCnDlzEBgYKJVxc3PDTz/9hE8//RSff/45jI2N0aVLl3y35+LiggMHDuCtt96CtrZ2vk07i0qhUGDPnj2YNGkSunTpAi0tLfTs2ROrVq0qcJ33338fBgYGWLJkCWbMmAFDQ0M0b95capJZo0YNLFq0CNeuXYO2tjbatWuHX3/9VcqmU/Elp2chIzsHK4e0QkPzGsVe/3pSGvx3xCA5PYs/Kqug133/X1QZPgvlcV0s6X54bSz5tdHZ2Vlqjp574wQA7dq1Q05ODlQqVZ5Hh4urTp06mD9/Pj766COMGjUKI0aMwMaNG0t8HteuXYu1a9dCT08PpqamaNu2LXbs2CH9pT2Xo6MjzMzMYGtrKyV7XF1dkZOTI/uLrJaWFrZv347JkyfD0dERDg4O+PLLL2U3CYVRKBQYNmwYlixZgrlz58qWGRgY4Pjx45g5cyYGDBiAR48eoU6dOujevTuMjY2RkZGBv/76C5s2bcL9+/dhZWWFiRMnYuzYsUXaN70+XqvzYrz/H02K98W9F8rVv39/fPHFF1iyZAkmT54MOzs7bNiwQRYj1Wo1Wrdujfj4eCnZ07lzZzx79qzUWv+kpqbCysoKCoUCxsbGcHBwwMiRIzFlypQ8LSy7du2KP/74Q6qjiYkJmjZtirt378qSLHPmzEFcXBw8PT1hYGCAMWPGoH///khJSSlSnYYPH46goCDY2tpKrUpzveqzo6enh1mzZuHGjRtQqVTo3Lkztm/f/hpn6NUUIrfHJA2VmpoKtVqNlJSUPB+KJ0+eIC4uDnZ2drLn2kv7L7avwr8YUFVV0HeI/ufinRT0XnUS+ye9Ccc66nJbv7DYVx1UluN/3fe/rLZVmPy+1+V9XQR4bSQqSGHX3soS+ypKSY+/oq7VFamy3AcBjPdERVFasZ8tgPJRp6YKh6e6llqfDa/CZ4aJiKgyK+/rIsBrIxFRRWC8J9JsTAAVoE5NFQMRERHR/8frIhFR9cB4T6S52LEHEREREREREZGGYwKIiIiIiIiIiEjDMQGE58PJEVHx8btDpLn4/SaqnPjdpNLEzxNR1VBa39VqnQDS1dUFADx+/LiCa0JUNeV+d3K/S0RU9fHaSFS58dpLpYGxnqhqKa3YX607gdbW1kbNmjWRlJQEADAwMIBCoajgWhFVfkIIPH78GElJSahZsya0tbUrukpEVEp4bSSqnHjtpdLEWE9UNZR27K/WCSAAsLS0BAAp+BFR0dWsWVP6DhGR5uC1kajy4rWXSgtjPVHVUVqxv9ongBQKBaysrGBubo7s7OyKrg5RlaGrq8u/PhJpKF4biSonXnupNDHWE1UNpRn7q30CKJe2tjYvqERERC/gtZGISPMx1hNVH9W6E2giIiIiIiIiouqACSAiIiIiIiIiIg3HBBARERERERERkYZjAoiIiIiIiIiISMNVmgRQcHAwFAoF/P39pXlCCAQGBsLa2hoqlQpubm64dOlSxVWSiIiIiIiIiKgKqhQJoOjoaHz33Xdo0aKFbP7ixYuxfPlyrF69GtHR0bC0tIS7uzsePXpUQTUlIiIiIiIiIqp6KjwBlJaWhuHDh2Pt2rUwMTGR5gshsHLlSsyePRsDBgyAo6MjNm3ahMePH2Pbtm0VWGMiIiIiIiIioqqlwhNAEyZMQK9evdCjRw/Z/Li4OCQmJsLDw0Oap1Qq4erqilOnThW4vczMTKSmpsomIiIiIiIiIqLqTKcid759+3b88ccfiI6OzrMsMTERAGBhYSGbb2FhgZs3bxa4zeDgYMyfP790K0pEREREREREVIVVWAugW7duYcqUKdiyZQv09fULLKdQKGSvhRB55r1o1qxZSElJkaZbt26VWp2JiIiIiIiIiKqiCksAnT17FklJSWjbti10dHSgo6ODiIgIfPnll9DR0ZFa/uS2BMqVlJSUp1XQi5RKJYyNjWUTEREREVF1c/z4cfTp0wfW1tZQKBTYs2ePbLlCoch3WrJkiVTGzc0tz/KhQ4fKtpOcnAwfHx+o1Wqo1Wr4+Pjg4cOHsjLx8fHo06cPDA0NYWZmhsmTJyMrK0tW5sKFC3B1dYVKpUKdOnWwYMECCCFK9ZwQEVVnFZYA6t69Oy5cuICYmBhpcnJywvDhwxETE4M33ngDlpaWCAsLk9bJyspCREQEOnXqVFHVJiIiIiKqEtLT09GyZUusXr063+UJCQmyaf369VAoFHjnnXdk5fz8/GTlvv32W9lyb29vxMTEICQkBCEhIYiJiYGPj4+0PCcnB7169UJ6ejpOnjyJ7du3Y+fOnZg6dapUJjU1Fe7u7rC2tkZ0dDRWrVqFpUuXYvny5aV4RoiIqrcK6wPIyMgIjo6OsnmGhoYwNTWV5vv7+yMoKAj29vawt7dHUFAQDAwM4O3tXRFVJiIiIiKqMry8vODl5VXgcktLS9nrX375BV27dsUbb7whm29gYJCnbK7Y2FiEhIQgKioKHTp0AACsXbsWzs7OuHLlChwcHBAaGorLly/j1q1bsLa2BgAsW7YMvr6+WLhwIYyNjbF161Y8efIEGzduhFKphKOjI65evYrly5cjICCg0C4giIioaCp8FLDCzJgxA/7+/hg/fjycnJxw584dhIaGwsjIqKKrRkRERESkMe7du4cDBw5g9OjReZZt3boVZmZmaNasGaZNm4ZHjx5JyyIjI6FWq6XkDwB07NgRarVaGrk3MjISjo6OUvIHADw9PZGZmYmzZ89KZVxdXaFUKmVl7t69ixs3buRbZ47+S0RUPBU6CtjLwsPDZa8VCgUCAwMRGBhYIfUhIiIiIqoONm3aBCMjIwwYMEA2f/jw4bCzs4OlpSUuXryIWbNm4c8//5S6aUhMTIS5uXme7Zmbm0t9eSYmJubpw9PExAR6enqyMvXr15eVebFPUDs7uzz74Oi/RETFU6kSQEREREREVP7Wr1+P4cOH5xmd18/PT/q/o6Mj7O3t4eTkhD/++ANt2rQBkHfUXiDvyL0lKZPbAXRBj3/NmjULAQEB0uvU1FTY2NgUeIxERNVdpX4EjIiIiIiIytaJEydw5coVvP/++68s26ZNG+jq6uLatWsAnvcjdO/evTzl/v33X6kFj6WlZZ6RfZOTk5GdnV1omaSkJAAocARgjv5LRFQ8TAAREREREVVj69atQ9u2bdGyZctXlr106RKys7NhZWUFAHB2dkZKSgpOnz4tlfn999+RkpIijdzr7OyMixcvIiEhQSoTGhoKpVKJtm3bSmWOHz8uGxo+NDQU1tbWeR4NIyKikmECiIiIiIhIA6WlpSEmJgYxMTEAgLi4OMTExCA+Pl4qk5qaip9++inf1j9///03FixYgDNnzuDGjRv49ddfMWjQILRu3RouLi4AgCZNmqBnz57w8/NDVFQUoqKi4Ofnh969e8PBwQEA4OHhgaZNm8LHxwfnzp3DkSNHMG3aNPj5+Umtdry9vaFUKuHr64uLFy9i9+7dCAoK4ghgRESliAkgIiIiIiINdObMGbRu3RqtW7cGAAQEBKB169aYO3euVGb79u0QQmDYsGF51tfT08ORI0fg6ekJBwcHTJ48GR4eHjh8+DC0tbWlclu3bkXz5s3h4eEBDw8PtGjRAps3b5aWa2tr48CBA9DX14eLiwsGDx6M/v37Y+nSpVIZtVqNsLAw3L59G05OThg/fjwCAgJkffwQEdHrYSfQREREREQayM3NTepIuSBjxozBmDFj8l1mY2ODiIiIV+6nVq1a2LJlS6Fl6tWrh/379xdapnnz5jh+/Pgr90dERCXDFkBERERERERERBqOCSAiIiIiIiIiIg3HBBARERERERERkYZjAoiIiIiIiIiISMMxAUREREREREREpOGYACIiIiIiIiIi0nBMABERERERERERaTgmgIiIiIiIiIiINBwTQEREREREREREGo4JICIiIiIiIiIiDccEEBERERERERGRhmMCiIiIiIiIiIhIwzEBRERERERERESk4ZgAIiIiIiIiIiLScEwAERERERERERFpOJ2KrgAREVFlcudhBpLTs0plW9eT0kplO0REREREr4sJICIiov/vzsMM9FgWgYzsnFLbpkpXGyaGeqW2PSIiIiKikmACiIiI6P9LTs9CRnYOVg5phYbmNUplmyaGeqhTU1Uq2yIiIiIiKikmgIiIiF7S0LwGHOuoK7oaRERERESlhp1AExERERERERFpOCaAiIiIiIiIiIg0HBNAREREREREREQajgkgIiIiIiIiIiINxwQQEREREREREZGGYwKIiIiIiIiIiEjDMQFERERERERERKThmAAiIiIiIiIiItJwTAAREREREREREWk4JoCIiIiIiIiIiDQcE0BERERERERERBqOCSAiIiIiIiIiIg3HBBARERERERERkYZjAoiIiIiIiIiISMMxAUREREREREREpOGYACIiIiIiIiIi0nAVmgBas2YNWrRoAWNjYxgbG8PZ2RkHDx6Ulvv6+kKhUMimjh07VmCNiYiIiIiIiIiqHp2K3HndunXx+eefo2HDhgCATZs2oV+/fjh37hyaNWsGAOjZsyc2bNggraOnp1chdSUiIiIiIiIiqqoqNAHUp08f2euFCxdizZo1iIqKkhJASqUSlpaWFVE9IiIiIiIiIiKNUGn6AMrJycH27duRnp4OZ2dnaX54eDjMzc3RqFEj+Pn5ISkpqdDtZGZmIjU1VTYREREREREREVVnFZ4AunDhAmrUqAGlUokPPvgAu3fvRtOmTQEAXl5e2Lp1K44ePYply5YhOjoa3bp1Q2ZmZoHbCw4OhlqtliYbG5vyOhQiInqFp0+f4pNPPoGdnR1UKhXeeOMNLFiwAM+ePZPKCCEQGBgIa2trqFQquLm54dKlSxVYayIiIiKiqq/CE0AODg6IiYlBVFQUxo0bh5EjR+Ly5csAgCFDhqBXr15wdHREnz59cPDgQVy9ehUHDhwocHuzZs1CSkqKNN26dau8DoWIiF5h0aJF+Oabb7B69WrExsZi8eLFWLJkCVatWiWVWbx4MZYvX47Vq1cjOjoalpaWcHd3x6NHjyqw5kREREREVVuF9gEEPO/UObcTaCcnJ0RHR+OLL77At99+m6eslZUVbG1tce3atQK3p1QqoVQqy6y+RERUcpGRkejXrx969eoFAKhfvz5++OEHnDlzBsDz1j8rV67E7NmzMWDAAADPBwiwsLDAtm3bMHbs2AqrOxERERFRVVbhLYBeJoQo8BGv+/fv49atW7CysirnWhERUWl48803ceTIEVy9ehUA8Oeff+LkyZN46623AABxcXFITEyEh4eHtI5SqYSrqytOnTpV4HbZ/xsRERERUeEqtAXQxx9/DC8vL9jY2ODRo0fYvn07wsPDERISgrS0NAQGBuKdd96BlZUVbty4gY8//hhmZmZ4++23K7LaRERUQjNnzkRKSgoaN24MbW1t5OTkYOHChRg2bBgAIDExEQBgYWEhW8/CwgI3b94scLvBwcGYP39+2VWciIiIiKiKq9AE0L179+Dj44OEhASo1Wq0aNECISEhcHd3R0ZGBi5cuIDvv/8eDx8+hJWVFbp27YodO3bAyMioIqtNREQltGPHDmzZsgXbtm1Ds2bNEBMTA39/f1hbW2PkyJFSOYVCIVtPCJFn3otmzZqFgIAA6XVqaioHASAiIiIiekGFJoDWrVtX4DKVSoVDhw6VY22IiKisTZ8+HR999BGGDh0KAGjevDlu3ryJ4OBgjBw5EpaWlgCetwR68XHfpKSkPK2CXsT+34iIiIiIClfp+gAiIiLN9fjxY2hpyS892tra0jDwdnZ2sLS0RFhYmLQ8KysLERER6NSpU7nWlYiIiIhIk1T4KGBERFR99OnTBwsXLkS9evXQrFkznDt3DsuXL8d7770H4PmjX/7+/ggKCoK9vT3s7e0RFBQEAwMDeHt7V3DtiYiIiIiqLrYAIiKicrNq1SoMHDgQ48ePR5MmTTBt2jSMHTsWn376qVRmxowZ8Pf3x/jx4+Hk5IQ7d+4gNDSU/b8RERXT8ePH0adPH1hbW0OhUGDPnj2y5b6+vlAoFLKpY8eOsjKZmZmYNGkSzMzMYGhoiL59++L27duyMsnJyfDx8YFarYZarYaPjw8ePnwoKxMfH48+ffrA0NAQZmZmmDx5MrKysmRlLly4AFdXV6hUKtSpUwcLFiyAEKLUzgcRUXXHFkBERFRujIyMsHLlSqxcubLAMgqFAoGBgQgMDCy3ehERaaL09HS0bNkSo0aNwjvvvJNvmZ49e2LDhg3Saz09Pdlyf39/7Nu3D9u3b4epqSmmTp2K3r174+zZs9DW1gYAeHt74/bt2wgJCQEAjBkzBj4+Pti3bx8AICcnB7169ULt2rVx8uRJ3L9/HyNHjoQQAqtWrQLwvPN+d3d3dO3aFdHR0bh69Sp8fX1haGiIqVOnlvq5ISKqjpgAIiIiIiLSQF5eXvDy8iq0jFKplDrgf1lKSgrWrVuHzZs3o0ePHgCALVu2wMbGBocPH4anpydiY2MREhKCqKgodOjQAQCwdu1aODs748qVK3BwcEBoaCguX76MW7duwdraGgCwbNky+Pr6YuHChTA2NsbWrVvx5MkTbNy4EUqlEo6Ojrh69SqWL1+OgICAQkeCJCKiouEjYERERERE1VR4eDjMzc3RqFEj+Pn5ISkpSVp29uxZZGdnw8PDQ5pnbW0NR0dHnDp1CgAQGRkJtVotJX8AoGPHjlCr1bIyjo6OUvIHADw9PZGZmYmzZ89KZVxdXWUjOnp6euLu3bu4ceNGvnXPzMxEamqqbCIiooIxAUREREREVA15eXlh69atOHr0KJYtW4bo6Gh069YNmZmZAIDExETo6enBxMREtp6FhQUSExOlMubm5nm2bW5uLitjYWEhW25iYgI9Pb1Cy+S+zi3zsuDgYKnfIbVaDRsbm+KeAiKiaoWPgBERERERVUNDhgyR/u/o6AgnJyfY2triwIEDGDBgQIHrCSFkj2Tl93hWaZTJ7QC6oMe/Zs2ahYCAAOl1amoqk0BERIVgCyAiIiIiIoKVlRVsbW1x7do1AIClpSWysrKQnJwsK5eUlCS1zrG0tMS9e/fybOvff/+VlXm5FU9ycjKys7MLLZP7ONrLLYNyKZVKGBsbyyYiIioYE0BERERERIT79+/j1q1bsLKyAgC0bdsWurq6CAsLk8okJCTg4sWL6NSpEwDA2dkZKSkpOH36tFTm999/R0pKiqzMxYsXkZCQIJUJDQ2FUqlE27ZtpTLHjx+XDQ0fGhoKa2tr1K9fv8yOmYioOmECiIiIiIhIA6WlpSEmJgYxMTEAgLi4OMTExCA+Ph5paWmYNm0aIiMjcePGDYSHh6NPnz4wMzPD22+/DQBQq9UYPXo0pk6diiNHjuDcuXN499130bx5c2lUsCZNmqBnz57w8/NDVFQUoqKi4Ofnh969e8PBwQEA4OHhgaZNm8LHxwfnzp3DkSNHMG3aNPj5+Umtdry9vaFUKuHr64uLFy9i9+7dCAoK4ghgRESliH0AERERERFpoDNnzqBr167S69z+ckaOHIk1a9bgwoUL+P777/Hw4UNYWVmha9eu2LFjB4yMjKR1VqxYAR0dHQwePBgZGRno3r07Nm7cCG1tbanM1q1bMXnyZGm0sL59+2L16tXScm1tbRw4cADjx4+Hi4sLVCoVvL29sXTpUqmMWq1GWFgYJkyYACcnJ5iYmCAgIEDWxw8REb0eJoCIiIiIiDSQm5ub1JFyfg4dOvTKbejr62PVqlVYtWpVgWVq1aqFLVu2FLqdevXqYf/+/YWWad68OY4fP/7KOhERUcnwETAiIiIiIiIiIg3HBBARERERERERkYZjAoiIiIiIiIiISMMxAUREREREREREpOGYACIiIiIiIiIi0nBMABERERERERERaTgmgIiIiIiIiIiINBwTQEREREREREREGo4JICIiIiIiIiIiDccEEBERERERERGRhmMCiIiIiIiIiIhIwzEBRERERERERESk4ZgAIiIiIiIiIiLScEwAERERERERERFpOCaAiIiIiIiIiIg0HBNAREREREREREQajgkgIiIiIiIiIiINxwQQEREREREREZGGYwKIiIiIiIiIiEjDMQFERERERERERKThmAAiIiIiIiIiItJwTAAREREREREREWk4JoCIiIiIiIiIiDQcE0BERERERERERBqOCSAiIiIiIiIiIg3HBBARERERERERkYZjAoiIiIiIiIiISMMxAUREREREREREpOGYACIiIiIiIiIi0nAVmgBas2YNWrRoAWNjYxgbG8PZ2RkHDx6UlgshEBgYCGtra6hUKri5ueHSpUsVWGMiIiIiIiIioqqnQhNAdevWxeeff44zZ87gzJkz6NatG/r16ycleRYvXozly5dj9erViI6OhqWlJdzd3fHo0aOKrDYRERERERERUZVSoQmgPn364K233kKjRo3QqFEjLFy4EDVq1EBUVBSEEFi5ciVmz56NAQMGwNHREZs2bcLjx4+xbdu2iqw2EREREREREVGVUmn6AMrJycH27duRnp4OZ2dnxMXFITExER4eHlIZpVIJV1dXnDp1qsDtZGZmIjU1VTYREREREREREVVnFZ4AunDhAmrUqAGlUokPPvgAu3fvRtOmTZGYmAgAsLCwkJW3sLCQluUnODgYarVammxsbMq0/kRERERERERElV2FJ4AcHBwQExODqKgojBs3DiNHjsTly5el5QqFQlZeCJFn3otmzZqFlJQUabp161aZ1Z2IiIiIiIiIqCrQqegK6OnpoWHDhgAAJycnREdH44svvsDMmTMBAImJibCyspLKJyUl5WkV9CKlUgmlUlm2lSYiIiIiIiIiqkIqvAXQy4QQyMzMhJ2dHSwtLREWFiYty8rKQkREBDp16lSBNSQiIiIiIiIiqloqtAXQxx9/DC8vL9jY2ODRo0fYvn07wsPDERISAoVCAX9/fwQFBcHe3h729vYICgqCgYEBvL29K7LaRERERERERERVSoUmgO7duwcfHx8kJCRArVajRYsWCAkJgbu7OwBgxowZyMjIwPjx45GcnIwOHTogNDQURkZGFVltIiIiIiIiIqIqpUITQOvWrSt0uUKhQGBgIAIDA8unQkREREREREREGqjS9QFERERERERERESliwkgIiIiIiIiIiINxwQQEREREREREZGGYwKIiIiIiIiIiEjDMQFERERERERERKThmAAiIiIiIiIiItJwTAAREREREREREWk4JoCIiIiIiIiIiDQcE0BERERERBro+PHj6NOnD6ytraFQKLBnzx5pWXZ2NmbOnInmzZvD0NAQ1tbWGDFiBO7evSvbhpubGxQKhWwaOnSorExycjJ8fHygVquhVqvh4+ODhw8fysrEx8ejT58+MDQ0hJmZGSZPnoysrCxZmQsXLsDV1RUqlQp16tTBggULIIQo1XNCRFSdMQFERERERKSB0tPT0bJlS6xevTrPssePH+OPP/7AnDlz8Mcff2DXrl24evUq+vbtm6esn58fEhISpOnbb7+VLff29kZMTAxCQkIQEhKCmJgY+Pj4SMtzcnLQq1cvpKen4+TJk9i+fTt27tyJqVOnSmVSU1Ph7u4Oa2trREdHY9WqVVi6dCmWL19eimeEiKh606noChARERERUenz8vKCl5dXvsvUajXCwsJk81atWoX27dsjPj4e9erVk+YbGBjA0tIy3+3ExsYiJCQEUVFR6NChAwBg7dq1cHZ2xpUrV+Dg4IDQ0FBcvnwZt27dgrW1NQBg2bJl8PX1xcKFC2FsbIytW7fiyZMn2LhxI5RKJRwdHXH16lUsX74cAQEBUCgUpXFKiIiqNbYAIiIiIiIipKSkQKFQoGbNmrL5W7duhZmZGZo1a4Zp06bh0aNH0rLIyEio1Wop+QMAHTt2hFqtxqlTp6Qyjo6OUvIHADw9PZGZmYmzZ89KZVxdXaFUKmVl7t69ixs3buRb38zMTKSmpsomIiIqGFsAERERERFVc0+ePMFHH30Eb29vGBsbS/OHDx8OOzs7WFpa4uLFi5g1axb+/PNPqfVQYmIizM3N82zP3NwciYmJUhkLCwvZchMTE+jp6cnK1K9fX1Ymd53ExETY2dnl2UdwcDDmz59f8oMmIqpmmAAiIiIiIqrGsrOzMXToUDx79gxff/21bJmfn5/0f0dHR9jb28PJyQl//PEH2rRpAwD5Pp4lhJDNL0mZ3A6gC3r8a9asWQgICJBep6amwsbGpsDjJCKq7vgIGBERERFRNZWdnY3BgwcjLi4OYWFhstY/+WnTpg10dXVx7do1AIClpSXu3buXp9y///4rteCxtLSUWvrkSk5ORnZ2dqFlkpKSACBP66FcSqUSxsbGsomIiArGBBARERERUTWUm/y5du0aDh8+DFNT01euc+nSJWRnZ8PKygoA4OzsjJSUFJw+fVoq8/vvvyMlJQWdOnWSyly8eBEJCQlSmdDQUCiVSrRt21Yqc/z4cdnQ8KGhobC2ts7zaBgREZUME0BERERERBooLS0NMTExiImJAQDExcUhJiYG8fHxePr0KQYOHIgzZ85g69atyMnJQWJiIhITE6UkzN9//40FCxbgzJkzuHHjBn799VcMGjQIrVu3houLCwCgSZMm6NmzJ/z8/BAVFYWoqCj4+fmhd+/ecHBwAAB4eHigadOm8PHxwblz53DkyBFMmzYNfn5+Uqsdb29vKJVK+Pr64uLFi9i9ezeCgoI4AhgRUSliAoiIiIiISAOdOXMGrVu3RuvWrQEAAQEBaN26NebOnYvbt29j7969uH37Nlq1agUrKytpyh29S09PD0eOHIGnpyccHBwwefJkeHh44PDhw9DW1pb2s3XrVjRv3hweHh7w8PBAixYtsHnzZmm5trY2Dhw4AH19fbi4uGDw4MHo378/li5dKpXJHZb+9u3bcHJywvjx4xEQECDr44eIiF4PO4EmIiIiItJAbm5uUkfK+SlsGQDY2NggIiLilfupVasWtmzZUmiZevXqYf/+/YWWad68OY4fP/7K/RERUcmwBRARERERERERkYZjAoiIiIiIiIiISMMxAUREREREREREpOGYACIiIiIiIiIi0nBMABERERERERERaTgmgIiIiIiIiIiINByHgSciIqqG7jzMQHJ6Vqltz8RQD3Vqqkpte0RERERUupgAIiIiqmbuPMxAj2URyMjOKbVtqnS1cXiqK5NARERERJUUE0BERETVTHJ6FjKyc7BySCs0NK/x2tu7npQG/x0xSE7PYgKIiIiIqJJiAoiIiKiaamheA4511BVdDSIiIiIqB+wEmoiIiIiIiIhIwzEBRERERERERESk4ZgAIiKicnXnzh28++67MDU1hYGBAVq1aoWzZ89Ky4UQCAwMhLW1NVQqFdzc3HDp0qUKrDERERERUdXHBBAREZWb5ORkuLi4QFdXFwcPHsTly5exbNky1KxZUyqzePFiLF++HKtXr0Z0dDQsLS3h7u6OR48eVVzFiYiIiIiqOHYCTURE5WbRokWwsbHBhg0bpHn169eX/i+EwMqVKzF79mwMGDAAALBp0yZYWFhg27ZtGDt2bHlXmYiIiIhII7AFEBERlZu9e/fCyckJgwYNgrm5OVq3bo21a9dKy+Pi4pCYmAgPDw9pnlKphKurK06dOlXgdjMzM5GamiqbiIiIiIjof5gAIiKicvPPP/9gzZo1sLe3x6FDh/DBBx9g8uTJ+P777wEAiYmJAAALCwvZehYWFtKy/AQHB0OtVkuTjY1N2R0EEREREVEVxAQQERGVm2fPnqFNmzYICgpC69atMXbsWPj5+WHNmjWycgqFQvZaCJFn3otmzZqFlJQUabp161aZ1J+IiIiIqKpiAoiIiMqNlZUVmjZtKpvXpEkTxMfHAwAsLS0BIE9rn6SkpDytgl6kVCphbGwsm4iIiIiI6H+YACIionLj4uKCK1euyOZdvXoVtra2AAA7OztYWloiLCxMWp6VlYWIiAh06tSpXOtKRERERKRJOAoYERGVmw8//BCdOnVCUFAQBg8ejNOnT+O7777Dd999B+D5o1/+/v4ICgqCvb097O3tERQUBAMDA3h7e1dw7YmIiIiIqi4mgIiIqNy0a9cOu3fvxqxZs7BgwQLY2dlh5cqVGD58uFRmxowZyMjIwPjx45GcnIwOHTogNDQURkZGFVhzIiIiIqKqrUIfAQsODka7du1gZGQEc3Nz9O/fP8+jAb6+vlAoFLKpY8eOFVRjIiJ6Xb1798aFCxfw5MkTxMbGws/PT7ZcoVAgMDAQCQkJePLkCSIiIuDo6FhBtSUiIiIi0gwVmgCKiIjAhAkTEBUVhbCwMDx9+hQeHh5IT0+XlevZsycSEhKk6ddff62gGhMRERERERERVT0V+ghYSEiI7PWGDRtgbm6Os2fPokuXLtJ8pVIpjQxDRERERERERETFU6lGAUtJSQEA1KpVSzY/PDwc5ubmaNSoEfz8/JCUlFTgNjIzM5GamiqbiIiIiIiIiIiqs0qTABJCICAgAG+++aasrwcvLy9s3boVR48exbJlyxAdHY1u3bohMzMz3+0EBwdDrVZLk42NTXkdAhERERERERFRpVRpRgGbOHEizp8/j5MnT8rmDxkyRPq/o6MjnJycYGtriwMHDmDAgAF5tjNr1iwEBARIr1NTU5kEIiIiIiIiIqJqrVIkgCZNmoS9e/fi+PHjqFu3bqFlraysYGtri2vXruW7XKlUQqlUlkU1iYiIiIiIiIiqpApNAAkhMGnSJOzevRvh4eGws7N75Tr379/HrVu3YGVlVQ41JCIiIiIiIiKq+iq0D6AJEyZgy5Yt2LZtG4yMjJCYmIjExERkZGQAANLS0jBt2jRERkbixo0bCA8PR58+fWBmZoa33367IqtORERERERERFRlVGgLoDVr1gAA3NzcZPM3bNgAX19faGtr48KFC/j+++/x8OFDWFlZoWvXrtixYweMjIwqoMZERERERERERFVPsVsAZWdno2vXrrh69epr71wIke/k6+sLAFCpVDh06BCSkpKQlZWFmzdvYuPGjezUmYioFJVmXCciotfDmExERGWl2AkgXV1dXLx4EQqFoizqQ0RE5YxxnYio8mBMJiKislKiPoBGjBiBdevWlXZdiIiogjCuExFVHozJRERUFkrUB1BWVhb+7//+D2FhYXBycoKhoaFs+fLly0ulckREVD4Y14mIKg/GZCIiKgslSgBdvHgRbdq0AYA8zyezuSoRUdXDuE5EVHkwJhMRUVkoUQLo2LFjpV0PIiKqQIzrRESVB2MyERGVhRL1AZTr+vXrOHToEDIyMgA8H9WLiIiqLsZ1IqLKgzGZiIhKU4kSQPfv30f37t3RqFEjvPXWW0hISAAAvP/++5g6dWqpVpCIiMoe4zoRUeXBmExERGWhRAmgDz/8ELq6uoiPj4eBgYE0f8iQIQgJCSm1yhERUflgXCciqjwYk4mIqCyUqA+g0NBQHDp0CHXr1pXNt7e3x82bN0ulYkREVH4Y14mIKg/GZCIiKgslagGUnp4u+2tErv/++w9KpfK1K0VEROWLcZ2IqPJgTCYiorJQogRQly5d8P3330uvFQoFnj17hiVLlqBr166lVjkiIiofjOtERJUHYzIREZWFEj0CtmTJEri5ueHMmTPIysrCjBkzcOnSJTx48AC//fZbadeRiIjKGOM6EVHlwZhMRERloUQtgJo2bYrz58+jffv2cHd3R3p6OgYMGIBz586hQYMGpV1HIiIqY4zrRESVB2MyERGVhRK1AAIAS0tLzJ8/vzTrQkREFYhxnYio8mBMJiKi0lbiBFBycjLWrVuH2NhYKBQKNGnSBKNGjUKtWrVKs35ERFROGNeJiCoPxmQiIiptJXoELCIiAnZ2dvjyyy+RnJyMBw8e4Msvv4SdnR0iIiJKu45ERFTGGNeJiCqP0orJx48fR58+fWBtbQ2FQoE9e/bIlgshEBgYCGtra6hUKri5ueHSpUuyMpmZmZg0aRLMzMxgaGiIvn374vbt27IyycnJ8PHxgVqthlqtho+PDx4+fCgrEx8fjz59+sDQ0BBmZmaYPHkysrKyZGUuXLgAV1dXqFQq1KlTBwsWLIAQosjHS0REhStRAmjChAkYPHgw4uLisGvXLuzatQv//PMPhg4digkTJpR2HYmIqIwxrhMRVR6lFZPT09PRsmVLrF69Ot/lixcvxvLly7F69WpER0fD0tIS7u7uePTokVTG398fu3fvxvbt23Hy5EmkpaWhd+/eyMnJkcp4e3sjJiYGISEhCAkJQUxMDHx8fKTlOTk56NWrF9LT03Hy5Els374dO3fuxNSpU6UyqampcHd3h7W1NaKjo7Fq1SosXboUy5cvL86pIyKiQpToEbC///4bO3fuhLa2tjRPW1sbAQEBsiEriYioamBcJyKqPEorJnt5ecHLyyvfZUIIrFy5ErNnz8aAAQMAAJs2bYKFhQW2bduGsWPHIiUlBevWrcPmzZvRo0cPAMCWLVtgY2ODw4cPw9PTE7GxsQgJCUFUVBQ6dOgAAFi7di2cnZ1x5coVODg4IDQ0FJcvX8atW7dgbW0NAFi2bBl8fX2xcOFCGBsbY+vWrXjy5Ak2btwIpVIJR0dHXL16FcuXL0dAQAAUCkWJziUREf1PiVoAtWnTBrGxsXnmx8bGolWrVq9bJyIiKmeM60RElUd5xOS4uDgkJibCw8NDmqdUKuHq6opTp04BAM6ePYvs7GxZGWtrazg6OkplIiMjoVarpeQPAHTs2BFqtVpWxtHRUUr+AICnpycyMzNx9uxZqYyrqyuUSqWszN27d3Hjxo18jyEzMxOpqamyiYiIClbkFkDnz5+X/j958mRMmTIF169fR8eOHQEAUVFR+Oqrr/D555+Xfi2JiKjUMa4TEVUe5R2TExMTAQAWFhay+RYWFrh586ZURk9PDyYmJnnK5K6fmJgIc3PzPNs3NzeXlXl5PyYmJtDT05OVqV+/fp795C6zs7PLs4/g4GCOlEZEVAxFTgC1atUKCoVC1hHbjBkz8pTz9vbGkCFDSqd2RERUZhjXiYgqj4qKyS8/WiWEeOXjVi+Xya98aZTJPRcF1WfWrFkICAiQXqempsLGxqbQuhMRVWdFTgDFxcWVZT2IiKicMa4TEVUe5R2TLS0tATxvXWNlZSXNT0pKklreWFpaIisrC8nJybJWQElJSejUqZNU5t69e3m2/++//8q28/vvv8uWJycnIzs7W1YmtzXQi/sB8rZSyqVUKmWPjBERUeGKnACytbUty3oQEVE5Y1wnIqo8yjsm29nZwdLSEmFhYWjdujUAICsrCxEREVi0aBEAoG3bttDV1UVYWBgGDx4MAEhISMDFixexePFiAICzszNSUlJw+vRptG/fHgDw+++/IyUlRUoSOTs7Y+HChUhISJCSTaGhoVAqlWjbtq1U5uOPP0ZWVhb09PSkMtbW1nkeDSMiopIp0ShgAHDnzh389ttvSEpKwrNnz2TLJk+e/NoVIyKi8sW4TkRUeZRGTE5LS8P169el13FxcYiJiUGtWrVQr149+Pv7IygoCPb29rC3t0dQUBAMDAzg7e0NAFCr1Rg9ejSmTp0KU1NT1KpVC9OmTUPz5s2lUcGaNGmCnj17ws/PD99++y0AYMyYMejduzccHBwAAB4eHmjatCl8fHywZMkSPHjwANOmTYOfnx+MjY0BPH+0bf78+fD19cXHH3+Ma9euISgoCHPnzuUIYEREpaRECaANGzbggw8+gJ6eHkxNTfM8u8sbBSKiqoVxnYio8iitmHzmzBl07dpVep3bX87IkSOxceNGzJgxAxkZGRg/fjySk5PRoUMHhIaGwsjISFpnxYoV0NHRweDBg5GRkYHu3btj48aNsiHqt27dismTJ0ujhfXt2xerV6+Wlmtra+PAgQMYP348XFxcoFKp4O3tjaVLl0pl1Go1wsLCMGHCBDg5OcHExAQBAQGyPn6IiOj1lCgBNHfuXMydOxezZs2CllaJRpInIqJKhHGdiKjyKK2Y7ObmJutU+mUKhQKBgYEIDAwssIy+vj5WrVqFVatWFVimVq1a2LJlS6F1qVevHvbv319omebNm+P48eOFliEiopIr0RXl8ePHGDp0KG8SiIg0BOM6EVHlwZhMRERloUQtgEaPHo2ffvoJH330UWnXh4iIKgDjetVxPSmtUmyDiMoOYzIREZWFEiWAgoOD0bt3b4SEhKB58+bQ1dWVLV++fHmpVI6IiMoH43rlZ2KoB5WuNvx3xJTK9lS62jAx1CuVbRFR6WJMJiKislCiBFBQUBAOHTok9ez/csd0RERUtTCuV351aqpweKorktOzSmV7JoZ6qFNTVSrbIqLSxZhMRERloUQJoOXLl2P9+vXw9fUt5eoQEVFFYFyvGurUVDFpQ1QNMCYTEVFZKFHPckqlEi4uLqVdFyIiqiCM60RElQdjMhERlYUSJYCmTJlS6FCQRERUtTCuExFVHozJRERUFkr0CNjp06dx9OhR7N+/H82aNcvTMd2uXbtKpXJERFQ+GNeJiCoPxmQiIioLJUoA1axZEwMGDCjtuhARUQVhXCciqjwYk4mIqCyUKAG0YcOG0q4HERFVIMZ1IqLKgzGZiIjKQon6ACIiIiIiIiIioqqjRC2A7OzsoFAoClz+zz//lLhCRERU/hjXiYgqD8ZkIiIqCyVKAPn7+8teZ2dn49y5cwgJCcH06dNLo15ERFSOGNeJiCoPxmQiIioLJUoATZkyJd/5X331Fc6cOfNaFSIiovLHuE5EVHkwJhMRUVko1T6AvLy8sHPnztLcJBERVSDGdSKiyoMxmYiIXkepJoB+/vln1KpVq8jlg4OD0a5dOxgZGcHc3Bz9+/fHlStXZGWEEAgMDIS1tTVUKhXc3Nxw6dKl0qw2EREVoLhxnYiIyg5jMhERvY4SPQLWunVrWcd0QggkJibi33//xddff13k7URERGDChAlo164dnj59itmzZ8PDwwOXL1+GoaEhAGDx4sVYvnw5Nm7ciEaNGuGzzz6Du7s7rly5AiMjo5JUn4iIXlJacZ2IiF4fYzIREZWFEiWA+vXrJ7soaWlpoXbt2nBzc0Pjxo2LvJ2QkBDZ6w0bNsDc3Bxnz55Fly5dIITAypUrMXv2bAwYMAAAsGnTJlhYWGDbtm0YO3ZsSapPREQvKa24TkREr48xmYiIykKJEkCBgYGlXI3nUlJSAEBq2hoXF4fExER4eHhIZZRKJVxdXXHq1Kl8E0CZmZnIzMyUXqemppZJXYmINElZxXUiIio+xmQiIioLxUoAaWlpyf4akR+FQoGnT58WuyJCCAQEBODNN9+Eo6MjACAxMREAYGFhIStrYWGBmzdv5rud4OBgzJ8/v9j7JyKqjsoyrhMRUfEwJhMRUVkqVgJo9+7dBS47deoUVq1aBSFEiSoyceJEnD9/HidPnsyz7OULoRCiwIvjrFmzEBAQIL1OTU2FjY1NiepERKTpyjKuExFR8TAmExFRWSpWAqhfv3555v3111+YNWsW9u3bh+HDh+PTTz8tdiUmTZqEvXv34vjx46hbt64039LSEsDzlkBWVlbS/KSkpDytgnIplUoolcpi14GIqDoqq7hORETFx5hMRERlqcTDwN+9exd+fn5o0aIFnj59ipiYGGzatAn16tUr8jaEEJg4cSJ27dqFo0ePws7OTrbczs4OlpaWCAsLk+ZlZWUhIiICnTp1KmnViYgoH6UR14mIqHQwJhMRUWkrdgIoJSUFM2fORMOGDXHp0iUcOXIE+/btk/rtKY4JEyZgy5Yt2LZtG4yMjJCYmIjExERkZGQAeP7ol7+/P4KCgrB7925cvHgRvr6+MDAwgLe3d7H3R0REeZVmXCciotfDmExERGWlWI+ALV68GIsWLYKlpSV++OGHfJupFseaNWsAAG5ubrL5GzZsgK+vLwBgxowZyMjIwPjx45GcnIwOHTogNDQURkZGr7VvIiIq/bhOREQlx5hMRERlqVgJoI8++ggqlQoNGzbEpk2bsGnTpnzL7dq1q0jbK0ondgqFAoGBgRwOk4ioDJR2XCciopJjTCYiorJUrATQiBEjXjk0JRERVR2M60RElQdjMhERlaViJYA2btxYRtUgIqKKwLhORFR5MCYTEVFZKvEoYEREREREREREVDUwAUREREREREREpOGYACIiIiIiIiIi0nBMABERERERERERaTgmgIiIiIiIiIiINBwTQEREREREREREGo4JICIiIiIiIiIiDccEEBERERERERGRhmMCiIiIiIiIiIhIwzEBRERERERERESk4ZgAIiIiIiIiIiLScEwAERERERERERFpOCaAiIiIiIiIiIg0HBNAREREREREREQaTqeiK0BERESa4XpSWqlsx8RQD3VqqkplW0RERET0HBNARERUYYKDg/Hxxx9jypQpWLlyJQBACIH58+fju+++Q3JyMjp06ICvvvoKzZo1q9jKUoFMDPWg0tWG/46YUtmeSlcbh6e6MglEREREVIqYACIiogoRHR2N7777Di1atJDNX7x4MZYvX46NGzeiUaNG+Oyzz+Du7o4rV67AyMiogmpLhalTU4XDU12RnJ712tu6npQG/x0xSE7PYgKIiIiIqBQxAUREROUuLS0Nw4cPx9q1a/HZZ59J84UQWLlyJWbPno0BAwYAADZt2gQLCwts27YNY8eOragq0yvUqaliwoaIiIioEmMn0EREVO4mTJiAXr16oUePHrL5cXFxSExMhIeHhzRPqVTC1dUVp06dKu9qEhERERFpDLYAIiKicrV9+3b88ccfiI6OzrMsMTERAGBhYSGbb2FhgZs3bxa4zczMTGRmZkqvU1NTS6m2RERERESagS2AiIio3Ny6dQtTpkzBli1boK+vX2A5hUIhey2EyDPvRcHBwVCr1dJkY2NTanUmItJU9evXh0KhyDNNmDABAODr65tnWceOHWXbyMzMxKRJk2BmZgZDQ0P07dsXt2/flpVJTk6Gj4+PFKN9fHzw8OFDWZn4+Hj06dMHhoaGMDMzw+TJk5GV9fr9ihER0f8wAUREROXm7NmzSEpKQtu2baGjowMdHR1ERETgyy+/hI6OjtTyJ7clUK6kpKQ8rYJeNGvWLKSkpEjTrVu3yvQ4iIg0QXR0NBISEqQpLCwMADBo0CCpTM+ePWVlfv31V9k2/P39sXv3bmzfvh0nT55EWloaevfujZycHKmMt7c3YmJiEBISgpCQEMTExMDHx0danpOTg169eiE9PR0nT57E9u3bsXPnTkydOrWMzwARUfXCR8CIiKjcdO/eHRcuXJDNGzVqFBo3boyZM2fijTfegKWlJcLCwtC6dWsAQFZWFiIiIrBo0aICt6tUKqFUKsu07kREmqZ27dqy159//jkaNGgAV1dXaZ5SqYSlpWW+66ekpGDdunXYvHmz1Kfbli1bYGNjg8OHD8PT0xOxsbEICQlBVFQUOnToAABYu3YtnJ2dceXKFTg4OCA0NBSXL1/GrVu3YG1tDQBYtmwZfH19sXDhQhgbG5fF4RMRVTtsAUREROXGyMgIjo6OssnQ0BCmpqZwdHSEQqGAv78/goKCsHv3bly8eBG+vr4wMDCAt7d3RVefiEhjZWVlYcuWLXjvvfdkj9yGh4fD3NwcjRo1gp+fH5KSkqRlZ8+eRXZ2tqzjfmtrazg6Okod90dGRkKtVkvJHwDo2LEj1Gq1rIyjo6OU/AEAT09PZGZm4uzZswXWOTMzE6mpqbKJiIgKxhZARERUqcyYMQMZGRkYP348kpOT0aFDB4SGhsLIyKiiq0ZEpLH27NmDhw8fwtfXV5rn5eWFQYMGwdbWFnFxcZgzZw66deuGs2fPQqlUIjExEXp6ejAxMZFty8LCQnqUNzExEebm5nn2Z25uLivz8mO+JiYm0NPTy/NI8IuCg4Mxf/78kh4yEVG1wwQQERFVqPDwcNlrhUKBwMBABAYGVkh9iIiqo3Xr1sHLy0vWCmfIkCHS/x0dHeHk5ARbW1scOHAAAwYMKHBbL3fcn18n/iUp87JZs2YhICBAep2amspBAIiICsFHwIiIiIiIqrGbN2/i8OHDeP/99wstZ2VlBVtbW1y7dg0AYGlpiaysLCQnJ8vKvdhxv6WlJe7du5dnW//++6+szMstfZKTk5GdnV3oAABKpRLGxsayiYiICsYEEBERERFRNbZhwwaYm5ujV69ehZa7f/8+bt26BSsrKwBA27ZtoaurK40eBgAJCQm4ePEiOnXqBABwdnZGSkoKTp8+LZX5/fffkZKSIitz8eJFJCQkSGVCQ0OhVCrRtm3bUjtOIqLqjgkgIiIiIqJq6tmzZ9iwYQNGjhwJHZ3/9Q6RlpaGadOmITIyEjdu3EB4eDj69OkDMzMzvP322wAAtVqN0aNHY+rUqThy5AjOnTuHd999F82bN5dGBWvSpAl69uwJPz8/REVFISoqCn5+fujduzccHBwAAB4eHmjatCl8fHxw7tw5HDlyBNOmTYOfnx9b9RARlSImgIiIiIiIqqnDhw8jPj4e7733nmy+trY2Lly4gH79+qFRo0YYOXIkGjVqhMjISFmn/CtWrED//v0xePBguLi4wMDAAPv27YO2trZUZuvWrWjevDk8PDzg4eGBFi1aYPPmzbJ9HThwAPr6+nBxccHgwYPRv39/LF26tOxPABFRNcJOoImIiIiIqikPDw8IIfLMV6lUOHTo0CvX19fXx6pVq7Bq1aoCy9SqVQtbtmwpdDv16tXD/v37X11hIiIqMbYAIiIiIiIiIiLScEwAERERERERERFpOCaAiIiIiIiIiIg0HBNAREREREREREQajgkgIiIiIiIiIiINxwQQEREREREREZGGYwKIiIiIiIiIiEjDMQFERERERERERKThmAAiIiIiIiIiItJwFZoAOn78OPr06QNra2soFArs2bNHttzX1xcKhUI2dezYsWIqS0RERERERERURVVoAig9PR0tW7bE6tWrCyzTs2dPJCQkSNOvv/5ajjUkIiIiIiIiIqr6dCpy515eXvDy8iq0jFKphKWlZTnViIiIiIiIiIhI81T6PoDCw8Nhbm6ORo0awc/PD0lJSYWWz8zMRGpqqmwiIiIiIiIiIqrOKnUCyMvLC1u3bsXRo0exbNkyREdHo1u3bsjMzCxwneDgYKjVammysbEpxxoTEREREREREVU+FfoI2KsMGTJE+r+joyOcnJxga2uLAwcOYMCAAfmuM2vWLAQEBEivU1NTmQQiIiIiIiIiomqtUieAXmZlZQVbW1tcu3atwDJKpRJKpbIca0VERERE9P/au/uoqup8j+OfEw/HEyEgChwUiUrJgqywEHpQUzEn7ZY1OTHXYCqzq+Zi0Fqa3cKZ0plS84bZTC3HbHy8UzbZZCZmYg5pSrjygTFNTG0gihAQ6WC67x9dTx4BladzDvu8X2uxFnvv39nnu39uvuCHzd4AAHg3r/4TsLNVVFTo8OHDstvtni4FAAAAAACgw/DoFUDHjh3T/v37ncslJSXasWOHunTpoi5duignJ0f33HOP7Ha7Dh48qCeffFJdu3bV3Xff7cGqAQAAAAAAOhaPBkDbt2/XoEGDnMun792TkZGhV155RTt37tQbb7yho0ePym63a9CgQVq5cqWCg4M9VTIAAAAAAECH49EAaODAgTIMo8ntH3zwgRurAQAAAAAAMKcOdQ8gAAAAAAAANB8BEAAAAAAAgMkRAAEAAAAAAJgcARAAAAAAAIDJEQABAAAAAACYHAEQAAAAAACAyREAAQAAAAAAmBwBEAAAAAAAgMkRAAEAAAAAAJgcARAAAAAAAIDJEQABAAAAAACYHAEQAAAAAACAyREAAQAAAAAAmBwBEAAAAAAAgMkRAAEAAAAAAJgcARAAAAAAAIDJEQABAAAAAACYnL+nCwAAADjb/vJjbbavsKBAdQ+1tdn+AAAAOiICIAAA4DXCggJlC/BT1sodbbZPW4Cf1k8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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Locate BHs, NSs, WDs, and BDs\n", - "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", - "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = np.concatenate([p2_bh, c_bh])\n", - "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", - "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = np.concatenate([p2_ns, c_ns])\n", - "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", - "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = np.concatenate([p2_wd, c_wd])\n", - "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", - "k_bd = np.concatenate([p2_bd, c_bd])\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", - "bd_bins = np.linspace(0.01, 2, 20)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 3, 1)\n", - "plt.hist(kc_comp[c_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 3, 2)\n", - "plt.hist(kc_out[k_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 3, 3)\n", - "plt.hist(kc_comp['mass'][need_bd], histtype = 'step',\n", - " bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 228, - "id": "ea063a9a-6268-4ab6-911d-0c6f33eef098", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BH prim max mass: 119.89570218214551\n", - "BH prim min mass: 15.025999572935213\n", - "BH comp max mass: 119.37716775564101\n", - "BH comp min mass: 15.009358951094368\n", - "\n", - "NS prim max mass: 119.61968070913697\n", - "NS prim min mass: 9.002475201776234\n", - "NS comp max mass: 115.47061424546564\n", - "NS comp min mass: 9.00056928420967\n", - "\n", - "WD prim max mass: 8.999794986324147\n", - "WD prim min mass: 5.311067490186374\n", - "WD comp max mass: 8.99707592319193\n", - "WD comp min mass: 5.311666754267032\n", - "\n", - "BD prim max mass: 0.07999992186315226\n", - "BD prim min mass: 0.01000001336207612\n" - ] - }, - { - "ename": "ValueError", - "evalue": "zero-size array to reduction operation maximum which has no identity", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[228], line 19\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmax(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD prim min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_out[p2_bd][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])))\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp max mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkc_comp\u001b[49m\u001b[43m[\u001b[49m\u001b[43mc_bd\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmass\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBD comp min mass: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(np\u001b[38;5;241m.\u001b[39mmin(kc_comp[c_ns][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmass\u001b[39m\u001b[38;5;124m'\u001b[39m])) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:2820\u001b[0m, in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2703\u001b[0m \u001b[38;5;129m@array_function_dispatch\u001b[39m(_amax_dispatcher)\n\u001b[1;32m 2704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mamax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue, initial\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue,\n\u001b[1;32m 2705\u001b[0m where\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39m_NoValue):\n\u001b[1;32m 2706\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2707\u001b[0m \u001b[38;5;124;03m Return the maximum of an array or maximum along an axis.\u001b[39;00m\n\u001b[1;32m 2708\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2818\u001b[0m \u001b[38;5;124;03m 5\u001b[39;00m\n\u001b[1;32m 2819\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2820\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaximum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmax\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2821\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:84\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, dtype\u001b[38;5;241m=\u001b[39mdtype, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ufunc\u001b[38;5;241m.\u001b[39mreduce(obj, axis, dtype, out, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n", - "File \u001b[0;32m/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:41\u001b[0m, in \u001b[0;36m_amax\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_amax\u001b[39m(a, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, out\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 40\u001b[0m initial\u001b[38;5;241m=\u001b[39m_NoValue, where\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m umr_maximum(a, axis, \u001b[38;5;28;01mNone\u001b[39;00m, out, keepdims, initial, where)\n", - "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation maximum which has no identity" - ] - } - ], - "source": [ - "# Checking that objects are in the correct mass ranges\n", - "print(\"BH prim max mass: \" + str(np.max(kc_out['mass'][p2_bh])))\n", - "print(\"BH prim min mass: \" + str(np.min(kc_out['mass'][p2_bh])))\n", - "print(\"BH comp max mass: \" + str(np.max(kc_comp['mass'][c_bh])))\n", - "print(\"BH comp min mass: \" + str(np.min(kc_comp['mass'][c_bh])) + '\\n')\n", - "\n", - "print(\"NS prim max mass: \" + str(np.max(kc_out[p2_ns]['mass'])))\n", - "print(\"NS prim min mass: \" + str(np.min(kc_out[p2_ns]['mass'])))\n", - "print(\"NS comp max mass: \" + str(np.max(kc_comp[c_ns]['mass'])))\n", - "print(\"NS comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')\n", - "\n", - "print(\"WD prim max mass: \" + str(np.max(kc_out[p2_wd]['mass'])))\n", - "print(\"WD prim min mass: \" + str(np.min(kc_out[p2_wd]['mass'])))\n", - "print(\"WD comp max mass: \" + str(np.max(kc_comp[c_wd]['mass'])))\n", - "print(\"WD comp min mass: \" + str(np.min(kc_comp[c_wd]['mass'])) + '\\n')\n", - "\n", - "print(\"BD prim max mass: \" + str(np.max(kc_out[p2_bd]['mass'])))\n", - "print(\"BD prim min mass: \" + str(np.min(kc_out[p2_bd]['mass'])))\n", - "print(\"BD comp max mass: \" + str(np.max(kc_comp[c_bd]['mass'])))\n", - "print(\"BD comp min mass: \" + str(np.min(kc_comp[c_ns]['mass'])) + '\\n')" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "id": "a64ebad4-b642-4e76-b12f-92402f241484", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "88.54164657710947\n", - "0.010059908098432945\n", - "118.80539890808666\n", - "15.000513183956517\n" - ] - } - ], - "source": [ - "print(np.max(kc_out[k_wd]['mass']))\n", - "print(np.min(kc_out[k_wd]['mass']))\n", - "\n", - "print(np.max(kc_out[p2_bh]['mass']))\n", - "print(np.min(kc_out[p2_bh]['mass']))" - ] - }, - { - "cell_type": "code", - "execution_count": 172, - "id": "497b4d94-6722-4ad7-880a-2b53d5acc28d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mass isMultiple ... m_hst_f153m N_companions\n", - "-------------------- ---------- ... ------------------- ------------\n", - " 0.6586795096056381 False ... 5.025295725472892 0\n", - " 3.8427510670284337 True ... -0.6509998709738373 1\n", - " 0.2863865166483995 False ... 7.2088768596764785 0\n", - " 0.7742048335787876 False ... 4.460668851429343 0\n", - " 0.10766124147681298 True ... 8.760423625411967 1\n", - " 0.02589910470529386 False ... nan 0\n", - " 0.5326011325857933 False ... 5.718004847207575 0\n", - " ... ... ... ... ...\n", - " 0.07207704463160401 False ... nan 0\n", - " 0.2901218613714232 True ... 6.513996744341199 1\n", - " 0.8377075545324469 False ... 4.199858747334578 0\n", - "0.035545417415318484 False ... nan 0\n", - " 0.10799622753582329 False ... 8.754736726529003 0\n", - " 0.09016119379548757 False ... 9.064022685272212 0\n", - " 0.371310690849909 False ... 6.74904566406516 0\n", - "Length = 3285 rows\n" - ] - } - ], - "source": [ - "print(kc_out[c_wd])" - ] - }, - { - "cell_type": "code", - "execution_count": 174, - "id": "eb20c163-8212-4885-98dd-eb10e0bb08f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smallest mass of a primary generated black hole: 15.005003818897485\n", - "Smallest mass of a companion generated black hole: 0.010053181629822302\n" - ] - } - ], - "source": [ - "# Finding the minimum mass of generated black holes\n", - "print(\"Smallest mass of a primary generated black hole: \" + str(np.min(kc_out[p2_bh]['mass'])))\n", - "print(\"Smallest mass of a companion generated black hole: \" + str(np.min(kc_out[c_bh]['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": 173, - "id": "6a854062-8dd0-4411-a0d7-523dcc4aa77b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Smallest mass of a primary generated object: 0.010000087950739485\n", - "Smallest mass of a companion generated object: 0.010000111957749505\n" - ] - } - ], - "source": [ - "# Finding the minimum mass of generated objects\n", - "print(\"Smallest mass of a primary generated object: \" + str(np.min(kc_out['mass'])))\n", - "print(\"Smallest mass of a companion generated object: \" + str(np.min(kc_comp['mass'])))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "398a4626-0e74-4f6d-a3a1-2c115fedb9c1", - "metadata": {}, - "outputs": [], - "source": [ - "# Finding the minimum/maximum inital masses of generated black holes\n", - "print(\"Initial mass of smallest generated black hole: \" + str(np.min(kc_out[k_bh]['mass'])))\n", - "print(\"Initial mass of largest generated black hole: \" + str(np.max(kc_out[k_bh]['mass'])))" - ] - }, - { - "cell_type": "markdown", - "id": "77b9203f-69ec-4a54-af84-581427feb4fd", - "metadata": {}, - "source": [ - "These statements show us that there is an issue with substellar-mass companions being erroneously identified as black holes and other compact remnants, but the issue does not extend to primary mass objects. Thus, we must introduce a bugfix to filter out this error." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afe96713-78c9-4785-bdf9-6f3f36f2a4d5", - "metadata": {}, - "outputs": [], - "source": [ - "# Create IMF objects \n", - "imf_multi_resolved = multiplicity.MultiplicityResolvedDK()\n", - "c3_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi_resolved)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6483a09-8712-48ed-9bc6-dbfbda7a32f9", - "metadata": {}, - "outputs": [], - "source": [ - "# Make cluster\n", - "cluster_mass = 10**6\n", - "cluster3 = synthetic.ResolvedCluster(my_iso, c3_imf, cluster_mass, ifmr=my_ifmr)\n", - "\n", - "# Get outputs\n", - "c3_out = cluster3.star_systems\n", - "c3_comp = cluster3.companions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c598a266-297c-423d-b5f5-648f819c065e", - "metadata": {}, - "outputs": [], - "source": [ - "# Locate BHs, NSs and WDs\n", - "p3_bh = np.where(c3_out['phase'] == 103)[0]\n", - "c3_bh = np.where(c3_comp['phase'] == 103)[0]\n", - "k3_bh = np.concatenate([p3_bh, c3_bh])\n", - "p3_ns = np.where(c3_out['phase'] == 102)[0]\n", - "c3_ns = np.where(c3_comp['phase'] == 102)[0]\n", - "k3_ns = np.concatenate([p3_ns, c3_ns])\n", - "p3_wd = np.where(c3_out['phase'] == 101)[0]\n", - "c3_wd = np.where(c3_comp['phase'] == 101)[0]\n", - "k3_wd = np.concatenate([p3_wd, c3_wd])\n", - "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", - "wd_bins = np.linspace(0.4, 1.4, 16)\n", - "\n", - "plt.figure(figsize=(14,6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.hist(c3_out[k3_bh]['mass'], histtype = 'step',\n", - " bins = bh_bins, label = 'Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.hist(c3_out[k3_wd]['mass'], histtype = 'step',\n", - " bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", - "plt.xlabel('Mass ($M_\\odot$)')\n", - "plt.ylabel('Number')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c1983a8f-67cf-41c1-bb0d-0869a543c4fa", - "metadata": {}, - "source": [ - "This is still the case when the companion objects are resolved." - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "id": "1969480a-56b5-4109-993a-41267b1bcf24", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 693572, 693573, 693574]),), Masses: mass \n", - "--------------------\n", - " 0.03330731848800373\n", - "0.045130822625746865\n", - " 0.04311839070080252\n", - "0.036366767190959895\n", - "0.034795730457169875\n", - " 0.07590327272894272\n", - " 0.04405898676312338\n", - " ...\n", - "0.022974850570129105\n", - " 0.07743240221765976\n", - " 0.06465930761239451\n", - "0.020632183162231716\n", - " 0.04965218683400625\n", - "0.021590467618339538\n", - " 0.06019784339988129\n", - "Length = 682107 rows\n", - "Found 179 stars out of mass range\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 143800 companions out of stellar mass range\n" - ] - } - ], - "source": [ - " # Now to create the cluster.\n", - "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", - "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", - "\n", - " ##########\n", - " # Start without multiplicity and IFMR\n", - " ##########\n", - "\"\"\" my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=None)\n", - " print('Constructed IMF: %d seconds' % (time.time() - startTime)) \n", - " \n", - " cluster1 = syn.ResolvedCluster(my_iso, my_imf1, cluster_mass, ifmr=ifmr_obj)\n", - " clust1 = cluster1.star_systems\n", - " print('Constructed cluster: %d seconds' % (time.time() - startTime))\"\"\"\n", - "\n", - " \n", - " ##########\n", - " # Test with multiplicity and IFMR\n", - " ##########\n", - "multi = multiplicity.MultiplicityUnresolved()\n", - "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=multi)\n", - "\n", - " \n", - "cluster2 = synthetic.ResolvedCluster(my_iso, my_imf2, cluster_mass, ifmr=my_ifmr)\n", - "clust2 = cluster2.star_systems\n", - "comps2 = cluster2.companions" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "id": "a63128a1-23d2-48a2-a0b7-994d1d723bb7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", - "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" - ] - } - ], - "source": [ - "bd_idx = np.where(comps2['phase'] == 99)\n", - "print(comps2[bd_idx])" - ] - }, - { - "cell_type": "code", - "execution_count": 311, - "id": "0c90f58d-c82c-4350-82f0-3f85de4a4c65", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Brown dwarf indices: (array([ 0, 1, 2, ..., 702295, 702297, 702298]),), Masses: mass \n", - "--------------------\n", - " 0.07112387932532963\n", - " 0.0764559041392305\n", - " 0.0625861330758897\n", - "0.010813223441380892\n", - "0.024579329319661714\n", - " 0.0153132496210663\n", - " 0.01502460044894114\n", - " ...\n", - " 0.06604816419573725\n", - "0.035960727819530976\n", - " 0.03392139750322173\n", - "0.049320264833810586\n", - " 0.05499753185245202\n", - "0.015053394497169099\n", - " 0.03366986670153467\n", - "Length = 634240 rows\n", - "Found 59455 stars out of mass range\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 164173 companions out of stellar mass range\n" - ] - } - ], - "source": [ - "# Define cluster parameters\n", - "logAge = 9.7\n", - "AKs = 0.0\n", - "distance = 1000\n", - "cluster_mass = 1e6\n", - "mass_sampling = 5\n", - "\n", - "evo = evolution.MergedBaraffePisaEkstromParsec()\n", - "atm_func = atmospheres.get_merged_atmosphere\n", - "ifmr_obj = ifmr.IFMR_Raithel18()\n", - "\n", - "red_law = reddening.RedLawNishiyama09()\n", - " \n", - "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", - " evo_model=evo, atm_func=atm_func,\n", - " red_law=red_law, filters=filt_list,\n", - " mass_sampling=mass_sampling)\n", - "\n", - "multi = multiplicity.MultiplicityUnresolved()\n", - "my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=multi)\n", - " \n", - "cluster2 = synthetic.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj)\n", - "clust2 = cluster2.star_systems\n", - "comps2 = cluster2.companions" - ] - }, - { - "cell_type": "markdown", - "id": "e6e12c06-fe63-4a9f-b7ea-26473dcdfb53", - "metadata": {}, - "source": [ - "# Creating the same conditions as test_ifmr_multiplicity" - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "id": "005fa638-2a2d-4798-9637-8d16dce382a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Changing to logg=5.00 for T= 2305 logg=5.22\n", - "Isochrone generation took 2.873671 s.\n", - "Making photometry for isochrone: log(t) = 9.70 AKs = 0.00 dist = 1000\n", - " Starting at: 2024-08-21 14:26:02.531482 Usually takes ~5 minutes\n", - "Starting filter: nirc2,Kp Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2305 K m_nirc2_Kp = 20.34\n", - "Starting filter: nirc2,H Elapsed time: 0.60 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2305 K m_nirc2_H = 20.57\n", - "Starting filter: nirc2,J Elapsed time: 1.20 seconds\n", - "Starting synthetic photometry\n", - "M = 0.090 Msun T = 2305 K m_nirc2_J = 21.05\n", - " Time taken: 1.68 seconds\n", - "Brown dwarf indices: (array([ 0, 1, 2, ..., 764740, 764741, 764742]),), Masses: mass \n", - "--------------------\n", - "0.010443375377695562\n", - " 0.06511402241508014\n", - " 0.07176328265824769\n", - " 0.01532590021508947\n", - "0.032173564214380404\n", - "0.033550210563551695\n", - " 0.07626969082525512\n", - " ...\n", - "0.012806933245100089\n", - " 0.0747383470817761\n", - " 0.03550080854686576\n", - "0.050186096415334905\n", - "0.043859454865114604\n", - " 0.036543785344645\n", - "0.043050926420120574\n", - "Length = 690613 rows\n", - "Found 64893 stars out of mass range\n", - "Brown dwarf indices: (array([], dtype=int64),), Masses: mass\n", - "----\n", - "Found 178337 companions out of stellar mass range\n", - "Primary star phases: phase\n", - "-----\n", - " 1.0\n", - " 99.0\n", - "101.0\n", - "102.0\n", - "103.0\n", - "Companion star phases: phase\n", - "-----\n", - " 1.0\n", - "101.0\n", - "102.0\n", - "103.0\n", - " nan\n" - ] - } - ], - "source": [ - "# Initialize the isochrone and cluster objects\n", - "logAge = 9.7\n", - "AKs = 0.0\n", - "distance = 1000\n", - "cluster_mass = 1e6\n", - "mass_sampling = 5\n", - "\n", - "filt_list = ['nirc2,Kp', 'nirc2,H', 'nirc2,J']\n", - "\n", - "evo = evolution.MergedBaraffePisaEkstromParsec()\n", - "atm_func = atmospheres.get_merged_atmosphere\n", - "ifmr_obj = ifmr.IFMR_Raithel18()\n", - "red_law = reddening.RedLawNishiyama09()\n", - "\n", - "iso = synthetic.IsochronePhot(logAge, AKs, distance,\n", - " evo_model=evo, atm_func=atm_func,\n", - " red_law=red_law, filters=filt_list,\n", - " mass_sampling=mass_sampling)\n", - "\n", - "# Recreate the cluster with multiplicity and IFMR\n", - "multi = multiplicity.MultiplicityUnresolved()\n", - "imf_mass_limits = np.array([0.01, 0.07, 0.5, 1, np.inf])\n", - "imf_powers = np.array([-0.3, -1.3, -2.3, -2.3])\n", - "my_imf = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers,\n", - " multiplicity=multi)\n", - "\n", - "cluster = synthetic.ResolvedCluster(iso, my_imf, cluster_mass, ifmr=ifmr_obj)\n", - "clust = cluster.star_systems\n", - "comps = cluster.companions\n", - "\n", - "# Debugging prints\n", - "print('Primary star phases:', np.unique(clust['phase']))\n", - "print('Companion star phases:', np.unique(comps['phase']))" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "id": "c1937ef3-c11a-40e2-8c22-04ae352ff7a8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "phase\n", - "-----\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " ...\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - " nan\n", - "Length = 142106 rows\n" - ] - } - ], - "source": [ - "comps_bds = np.where((comps['mass'] >= 0.01) & (comps['mass'] < 0.08))\n", - "print(comps[comps_bds]['phase'])" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "id": "a3fcf655-bebb-4657-8c94-c0a4879b0c56", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "system_idx mass Teff L logg isWR mass_current phase metallicity m_hst_f153m\n", - "---------- ---- ---- --- ---- ---- ------------ ----- ----------- -----------\n" - ] - } - ], - "source": [ - "comps_phase_nan = np.where(comps['phase'] == np.nan)\n", - "print(comps[comps_phase_nan])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f47a6c02-4ace-4b7d-b959-102c893afd7e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From c92297cfd5cf2fbb157920e709b74d3fe048b6c2 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 4 Sep 2024 16:37:27 -0700 Subject: [PATCH 22/48] put in general path to csv --- spisea/evolution.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index f168acda..3326fc6d 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1338,8 +1338,14 @@ def get_temperature(self, masses, ages): Array of interpolated temperatures. """ + # Set the project root two levels up from 'spisea/evolution.py' + project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) + + # Construct the full path to the CSV file in 'changes/bd_evo_csv' + csv_path = os.path.join(project_root, 'SPISEA', 'changes', 'bd_evo_csv', 'baraffe2003.csv') + # Load the CSV file - data = pd.read_csv('/u/caitlinbegbie/code/SPISEA/changes/bd_evo_csv/baraffe2003.csv') + data = pd.read_csv(csv_path) # Create columns of relevant data mass_grid = np.unique(data['mass'].values) From b93c57a216fbab9f938807c2cd236074ee9a1b23 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 4 Sep 2024 16:40:53 -0700 Subject: [PATCH 23/48] remove old debugging statements --- spisea/ifmr.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 1451ed11..672ad818 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -43,8 +43,6 @@ def Kalirai_mass(self, MZAMS): # for white dwarfs But we use this function for anything between 0.5 and 9 depending on the IFMR. FIXME: need to extend these ranges... explain extension somewhere? Paper maybe? """ - #debugging print statement - print(f"Input MZAMS: {MZAMS}") result = 0.109*MZAMS + 0.394 @@ -56,7 +54,6 @@ def Kalirai_mass(self, MZAMS): # for white dwarfs good_idx = np.where((MZAMS >= 0.5) & (MZAMS < 9)) final[good_idx] = result[good_idx] - print(f"Final masses: {final}") # Debugging print statement return final @@ -796,7 +793,6 @@ def generate_death_mass(self, mass_array): id_array_BD = np.where((mass_array >= 0.01) & (mass_array < 0.08)) output_array[0][id_array_BD] = mass_array[id_array_BD] output_array[1][id_array_BD] = codes['BD'] - print(f'Brown dwarf indices: {id_array_BD}, Masses: {mass_array[id_array_BD]}') #for debugging #classifying invalid mass ranges id_array0 = np.where((mass_array < 0.01) | ((mass_array >= 0.08) & (mass_array < 0.5)) | (mass_array >= 120)) From 7e048ea51cb1befba9139a68656cf1335ec09d91 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 18 Sep 2024 12:14:15 -0700 Subject: [PATCH 24/48] Changing BD phase to 90 --- changes/BD_Clusters.ipynb | 165 +++++++++++++++++++++------------ spisea/ifmr.py | 8 +- spisea/synthetic.py | 16 +--- spisea/tests/test_synthetic.py | 18 ++-- 4 files changed, 122 insertions(+), 85 deletions(-) diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb index df1bb2b7..5ffd47ed 100644 --- a/changes/BD_Clusters.ipynb +++ b/changes/BD_Clusters.ipynb @@ -112,7 +112,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -126,7 +126,7 @@ "p_bh = np.where(k_out['phase'] == 103)[0]\n", "p_ns = np.where(k_out['phase'] == 102)[0]\n", "p_wd = np.where(k_out['phase'] == 101)[0]\n", - "p_bd = np.where(k_out['phase'] == 99)[0]\n", + "p_bd = np.where(k_out['phase'] == 90)[0]\n", "\n", "# Determine individual histogram bins\n", "bh_bins = np.linspace(0.01, 16, 20)\n", @@ -187,17 +187,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH max mass: 119.13565618748322\n", - "BH min mass: 15.007725869985796\n", + "BH max mass: 119.93239322786152\n", + "BH min mass: 15.001864046701314\n", "\n", - "NS max mass: 119.95876844996108\n", - "NS min mass: 9.001129278483436\n", + "NS max mass: 118.23513027048241\n", + "NS min mass: 9.00008078654941\n", "\n", - "WD max mass: 8.999341744945873\n", - "WD min mass: 5.311091008003858\n", + "WD max mass: 8.99872832681908\n", + "WD min mass: 5.311599156274761\n", "\n", - "BD max mass: 0.07999986295473586\n", - "BD min mass: 0.010000044934131811\n", + "BD max mass: 0.07999995308017924\n", + "BD min mass: 0.010000015332213386\n", "\n" ] } @@ -229,22 +229,22 @@ "text": [ " Teff \n", "------------------\n", - " 653.0891938350937\n", - " 651.8458273479349\n", - " 707.4325441828001\n", - "488.01700684829456\n", - " 871.6562359235475\n", - " 407.9503554901987\n", - " 930.6014364016501\n", + " 680.8331610608817\n", + " 570.8134778988632\n", + "1458.5749556855367\n", + " 530.4032032477386\n", + " 609.224312952465\n", + "494.41076310951894\n", + " 692.7514016813559\n", " ...\n", - "392.28042415955446\n", - " 908.5461410822766\n", - " 1393.091834142745\n", - " 998.2260271590488\n", - " 665.474820549251\n", - " 675.9825874832916\n", - "1122.0753000795612\n", - "Length = 1028848 rows\n" + " 921.297090918629\n", + " 733.558254330926\n", + " 668.3518603255377\n", + " 807.1505091097516\n", + " 588.2307845514122\n", + "353.02982279111575\n", + " 963.2369850122022\n", + "Length = 1024345 rows\n" ] } ], @@ -309,7 +309,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 7301 companions out of stellar mass range\n" + "Found 7406 companions out of stellar mass range\n" ] }, { @@ -341,7 +341,7 @@ "outputs": [ { "data": { - "image/png": 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", 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rw9XVNddJJj/77LMcbXFxcfjiiy9gb28PHR0d6OrqwsHBAQAQFRWV7z4XNI4bN27g0aNHGDBggNIp4w4ODvme0fSmypUrIzw8HOHh4QgLC8Pu3buhUCjQtm3bfC+NK8i+vnz5EqGhoejTp490Wvq7+Pn54ZNPPsGIESPw008/QV9fv8D7smjRImlf3nz06dNHqV/2qfdvnhIPAE2aNEHNmjVx/PjxPLdx5MgRyGQyfP7550qvj42NDerVqye9PlWqVIGZmRlmzJiBDRs25PhVOT9GRkY53rMDBgxAVlYWTp48KfUZOnQotm/fLv2ideLECVy/fh3jxo17r+0NGzYMhw4dwtOnT7Flyxa4u7vneTeipKQkzJgxA1WqVIGOjg50dHRQpkwZJCcnK73HmzRpgj/++AMzZ85ESEgIUlJSlMYxNzdH5cqVsWTJEixbtgyXLl1SulSQiKg0Yw5UtDnQm/NGZf/X1dUVAFCzZk1YWVlJ+UJISAisra1Rs2bNHOPkd2zfx6tXr3D8+HF8+umnMDAwyPHavnr1CmfPnn3vcd8k3jpbvEWLFtDX10dQUBCA15daurm5oVOnTjhz5gxevnyJBw8e4NatW0qX7mdkZGDhwoWoVasW9PT0oKOjAz09Pdy6dSvX1zq398vAgQMhl8uVzubbs2cPUlNTMXTo0Pc+Jk2aNMFff/2FMWPGICAg4L3nXsrr/fTmJZtffvklACjNp7lmzRrUqVMHrVu3LvC2XF1dUblyZWzduhVXrlxBeHh4npfoAa/zu3bt2sHExATa2trQ1dXF3Llz8fTpU8TFxQF4ffmpnp4eRo0aBT8/vxzTRwCvj9Hz58/Rv39//Pbbb/leDUDFA4tRRB/I0tISCoUi1y/t3bt3Izw8XOmacQDSpVBTp05VStR0dXUxZswYAMjx4WphYaH0XC6XA4D0D+TsMRs3bpxjzH379uUYz8DAIMedxrKystChQwfs378f06dPx/Hjx3H+/HnpS/Ltf4znpqBxZF/qZGNjk2OM3Nryoq+vD2dnZzg7O6Np06bo378//vjjD8TExGDu3Ll5rlfQfY2Pj0dmZibKly9foHj27t0LhUKBESNG5JiXIT+VKlWS9uXNx9tFsOxjl9slEXZ2dkqXkb3t8ePHEELA2to6x+tz9uxZ6fUxMTFBaGgo6tevj6+++gq1a9eGnZ0d5s2bV6C72FhbW+doy35d34xv/PjxePHiBXbt2gXgdUJUvnx5dO/ePd9tvKlXr17Q19fH8uXLcfjwYQwfPjzPvgMGDMCaNWswYsQIBAQE4Pz58wgPD0fZsmWV3uOrVq3CjBkzcPDgQbi7u8Pc3Bw9evSQipwymQzHjx9Hx44dsXjxYjRs2BBly5bFhAkTCnQpIxFRccccSJkqc6A6derA0tISwcHB0nxR2cUo4PUNXUJCQpCamoqwsLA876KX37F9H0+fPkVGRgZWr16dY/+zL1n72OJB9nst+3IyfX19tGjRQipGHT9+HO3bt4ebmxsyMzNx6tQp6XK9N4tRkydPxpw5c9CjRw8cPnwY586dQ3h4OOrVq5frvueWc5mbm6Nbt2748ccfpTkxt2/fjiZNmqB27drvfUxmzZqF77//HmfPnoWHhwcsLCzQtm3bHHdNzEte76c38y5ra2v07dsXGzduRGZmJi5fvoxTp06994+AMpkMQ4cOxc6dO7FhwwZUq1YNrVq1yrXv+fPnpRsKbd68GX/++SfCw8Mxe/ZsAP97r1WuXBlBQUGwsrLC2LFjUblyZVSuXFlpzqxBgwZh69atuHfvHj777DNYWVnBxcVF6ZJMKn5K560AiAqBtrY22rRpg2PHjiEmJkbpyyr77iN3795VWsfS0hLA6y+dnj175jpu9erV3yuO7DF/+eUX6Ve8d8mtUHL16lX89ddf2L59O4YMGSK13759u9DjyE5+YmNjcyzLre192NrawtLSEn/99VeefQq6r+bm5tDW1i7wpN27du3CnDlz4OrqimPHjqF+/foftA/vkn3sYmJichTJHj16JL0GubG0tIRMJsOpU6ekhPNNb7bVqVMHe/fuhRACly9fxvbt2zF//nwoFArMnDnznTFmJ+Rvyn5d30x8q1SpAg8PD6xduxYeHh44dOgQfHx8oK2t/c7x32ZgYIB+/frB19cXxsbGef5dJSQk4MiRI5g3b57SPqSmpuLZs2dKfQ0NDeHj4wMfHx88fvxYOkuqa9eu0i2zHRwcpMlLb968iZ9++gne3t5IS0vDhg0b3msfiIiKG+ZAHxZHYeRAMpkMrq6u8Pf3x/nz5/H8+XOlYpSrqyu8vb0RFhaGV69e5VmMKkxmZmbQ1tbGoEGD8pz30dHR8YPHF0Lg8OHDMDQ0hLOzs9Tetm1bzJ07F+fPn8fDhw/Rvn17GBkZoXHjxggMDMSjR49QrVo12NvbS+vs3LkTgwcPxsKFC5W28eTJE5iamubYdl4/MA4dOhQ///wzAgMDUaFCBYSHh2P9+vXS8vc5Jjo6Opg8eTImT56M58+fIygoCF999RU6duyIBw8e5HvHx7zeT28XHCdOnIgdO3bgt99+g7+/P0xNTaUzFd+Hp6cn5s6diw0bNijd0fhte/fuha6uLo4cOaJ0xcDBgwdz9G3VqhVatWqFzMxMXLhwAatXr4aXlxesra3Rr18/AK+P+dChQ5GcnIyTJ09i3rx56NKlC27evFmgv3/SPCxGEX2EWbNm4Y8//sAXX3yBX375Jd+7uFWvXh1Vq1bFX3/9leNL8EN17NgROjo6uHPnTq6nEhdE9hft20WKjRs35uib1y9nBY2jevXqsLW1xZ49ezB58mRp2/fu3cOZM2cKNIFiXh4+fIgnT56881bEBd3X7LuR/Pzzz/j222/fWegBXhevgoKC0KVLF7i7u+OPP/5Quj1xYWjTpg2A14lU48aNpfbw8HBERUVJvzTlpkuXLvjuu+/w77//5rj8Ly8ymQz16tXD8uXLsX37dly8eDHfdV68eIFDhw4pnf6/e/duadLVN02cOBEdOnTAkCFDoK2tjZEjRxYorrd9+eWXePz4MVxdXfO8PFImk0EIkeN1/+GHH6RfNXNjbW0NT09P/PXXX1ixYkWutwGvVq0avv76a/z6668FOkZERCUBc6D3j6OwciB3d3f8+uuvWLJkCaysrJQuw3N1dcXTp0+xevVqqW9hyWv/DQwM4O7ujkuXLqFu3bp53l3xQ/n4+OD69ev46quvlL7n27Vrh6+++gpz5sxB+fLlUaNGDan90KFDiI2NzfF6yGSyHK/10aNH8e+//6JKlSoFjqlDhw4oV64ctm3bhgoVKkBfX1+62xzw4cfE1NQUvXr1wr///gsvLy/cvXv3nXktgDzfT4MHD1bq16hRIzRv3hyLFi3C1atXMWrUKBgaGhZ4n7OVK1cO06ZNw99//61UwH2bTCaDjo6O0g+NKSkp2LFjR57raGtrw8XFBTVq1MCuXbtw8eJFqRiVzdDQEB4eHkhLS0OPHj1w7do1FqOKKRajiD5CixYtsHbtWowfPx4NGzbEqFGjULt2bWhpaSEmJga//vorACidEr5x40Z4eHigY8eO8PT0RLly5fDs2TNERUXh4sWL+Pnnn98rhooVK2L+/PmYPXs2/vnnH3Tq1AlmZmZ4/Pgxzp8/L53l8S41atRA5cqVMXPmTAghYG5ujsOHD+d66mv23UhWrlyJIUOGQFdXF9WrVy9wHFpaWvjmm28wYsQIfPrppxg5ciSeP38Ob2/v97pMLyUlRTqFPjMzE9HR0Vi8eDEAwMvLq1D2ddmyZWjZsiVcXFwwc+ZMVKlSBY8fP8ahQ4ewceNGGBkZKfU3MjKCv78/evbsKd3trjCTwOrVq2PUqFFYvXo1tLS04OHhgbt372LOnDmwt7fHpEmT8ly3RYsWGDVqFIYOHYoLFy6gdevWMDQ0RExMDE6fPo06dergyy+/xJEjR7Bu3Tr06NEDlSpVghAC+/fvx/Pnz9G+fft8Y7SwsMCXX36J+/fvo1q1avj999+xefNmfPnll6hQoYJS3/bt26NWrVoIDg7G559/Disrqw86LvXr18/1V7Y3GRsbo3Xr1liyZAksLS1RsWJFhIaGYsuWLTl+CXVxcUGXLl1Qt25dmJmZISoqCjt27ECzZs1gYGCAy5cvY9y4cejduzeqVq0KPT09nDhxApcvX873zDEiopKCOZD6cqDs3OLAgQPo1auX0jInJydYWFjgwIEDKFeuXIHu6ltQRkZGcHBwwG+//Ya2bdvC3Nxc+k5duXIlWrZsiVatWuHLL79ExYoV8eLFC9y+fRuHDx8u0N0Cnz9/LuV2ycnJuHHjBvbu3YtTp06hT58+OV7LRo0awczMDMeOHZPmagJeF6Oy79b25iV6wOsf57Zv344aNWqgbt26iIiIwJIlSwo8LUM2bW1tDB48GMuWLZPOzDYxMVHqU9Bj0rVrVzg5OUlTNNy7dw8rVqyAg4NDgV6/uLg46f2UkJCAefPmQV9fH7NmzcrRd+LEiejbty9kMpl0eeyH+O677/Lt07lzZyxbtgwDBgzAqFGj8PTpU3z//fc5ioEbNmzAiRMn0LlzZ1SoUAGvXr2S7tiX/fqNHDkSCoUCLVq0gK2tLWJjY+Hr6wsTExOlH2ipmFHb1OlEJUhkZKQYOnSocHR0FHK5XOjr64sqVaqIwYMHi+PHj+fo/9dff4k+ffoIKysroaurK2xsbESbNm3Ehg0bpD5v3rniTdl3sXj7LmEHDx4U7u7uwtjYWMjlcuHg4CB69eolgoKCpD5DhgwRhoaGue7D9evXRfv27YWRkZEwMzMTvXv3Fvfv38/1rimzZs0SdnZ2QktLK0csBYlDCCF++OEHUbVqVaGnpyeqVasmtm7dmuMOcHl5+256Wlpaws7OTnh4eIiQkBClvrndTe999vX69euid+/ewsLCQujp6YkKFSoIT09P8erVK6Xx33ydUlNTxWeffSb09fXF0aNH89yP7Nfy559/znX52LFjxdsf05mZmWLRokWiWrVqQldXV1haWorPP/9cPHjwQKlfXsdy69atwsXFRRgaGgqFQiEqV64sBg8eLC5cuCCEEOLvv/8W/fv3F5UrVxYKhUKYmJiIJk2aiO3bt+e5H9lcXV1F7dq1RUhIiHB2dhZyuVzY2tqKr776Ksedk7J5e3sLAOLs2bP5jp8tvzvuCCFyvZvew4cPxWeffSbMzMyEkZGR6NSpk7h69apwcHAQQ4YMkfrNnDlTODs7CzMzMyGXy0WlSpXEpEmTxJMnT4QQQjx+/Fh4enqKGjVqCENDQ1GmTBlRt25dsXz5cpGRkVHg/SAiKgmYA/0vFlXkQNlsbGwEALFmzZocy3r06CEAiIEDB+ZY9j7H9u276Qnx+i52DRo0EHK5XABQ+v6Mjo4Ww4YNE+XKlRO6urqibNmyonnz5mLBggX57o+Dg4OU18lkMlGmTBlRvXp1MWjQIBEQEJDnep9++qkAIHbt2iW1paWlCUNDQ6GlpSXi4+OV+sfHx4vhw4cLKysrYWBgIFq2bClOnTqVY1/zy9GEEOLmzZtSzIGBgbn2KcgxWbp0qWjevLmwtLSUcs3hw4eLu3fvvvOYZce4Y8cOMWHCBFG2bFkhl8tFq1atpLzubampqUIul4tOnTq9c+w35fWeeVtud9PbunWrqF69upRP+fr6ii1btijl5mFhYeLTTz8VDg4OQi6XCwsLC+Hq6ioOHTokjePn5yfc3d2FtbW10NPTE3Z2dqJPnz7i8uXLBd4P0jwyId66NQEREZGKODs7QyaTITw8XN2hEBEREZVohw8fRrdu3XD06FFpInUideFlekREpFKJiYm4evUqjhw5goiICBw4cEDdIRERERGVWNevX8e9e/cwZcoU1K9fHx4eHuoOiYjFKCIiUq2LFy/C3d0dFhYWmDdvHnr06KHukIiIiIhKrDFjxuDPP/9Ew4YN4efnl+ddAolUiZfpERERERERERGRymipOwAiIiIiIiIiIio9Sk0x6uTJk+jatSvs7Owgk8mUbgOenp6OGTNmoE6dOjA0NISdnR0GDx6MR48eKY3h5uYGmUym9OjXr5+K94SIiIiIiIiIqPgqNcWo5ORk1KtXD2vWrMmx7OXLl7h48SLmzJmDixcvYv/+/bh58ya6deuWo+/IkSMRExMjPTZu3KiK8ImIiIiIiIiISoRSM4G5h4dHnncNMDExQWBgoFLb6tWr0aRJE9y/fx8VKlSQ2g0MDGBjY/PBcWRlZeHRo0cwMjLixHFEREQaTAiBFy9ewM7ODlpapeb3O43D3ImIiKj4KGj+VGqKUe8rISEBMpkMpqamSu27du3Czp07YW1tDQ8PD8ybNw9GRkZ5jpOamorU1FTp+b///otatWoVVdhERERUyB48eIDy5curO4xS69GjR7C3t1d3GERERPQe8sufWIzKxatXrzBz5kwMGDAAxsbGUvvAgQPh6OgIGxsbXL16FbNmzcJff/2V46yqN/n6+sLHxydH+4MHD5TGJiIiIs2SmJgIe3v7d/7oREUv+/gzdyIiItJ8Bc2fZEIIoaKYNIZMJsOBAwfQo0ePHMvS09PRu3dv3L9/HyEhIe9MeiIiIuDs7IyIiAg0bNgw1z5vnxmV/cIkJCQwoSIiItJgiYmJMDExKZHf2b6+vti/fz/+/vtvKBQKNG/eHIsWLUL16tWlPkII+Pj4YNOmTYiPj4eLiwvWrl2L2rVrS31SU1MxdepU7NmzBykpKWjbti3WrVun9EtofHw8JkyYgEOHDgEAunXrhtWrV+c4+zwvJfl1ICIiKmkK+r3NCRDekJ6ejj59+iA6OhqBgYH5JjwNGzaErq4ubt26lWcfuVwOY2NjpQcRERGROoWGhmLs2LE4e/YsAgMDkZGRgQ4dOiA5OVnqs3jxYixbtgxr1qxBeHg4bGxs0L59e7x48ULq4+XlhQMHDmDv3r04ffo0kpKS0KVLF2RmZkp9BgwYgMjISPj7+8Pf3x+RkZEYNGiQSveXiIiINAsv0/t/2YWoW7duITg4GBYWFvmuc+3aNaSnp8PW1lYFERIREREVDn9/f6Xn27Ztg5WVFSIiItC6dWsIIbBixQrMnj0bPXv2BAD4+fnB2toau3fvxujRo5GQkIAtW7Zgx44daNeuHQBg586dsLe3R1BQEDp27IioqCj4+/vj7NmzcHFxAQBs3rwZzZo1w40bN5TOxCIiIqLSo9ScGZWUlITIyEhERkYCAKKjoxEZGYn79+8jIyMDvXr1woULF7Br1y5kZmYiNjYWsbGxSEtLAwDcuXMH8+fPx4ULF3D37l38/vvv6N27Nxo0aIAWLVqocc+IiIiIPk5CQgIAwNzcHMDrPCk2NhYdOnSQ+sjlcri6uuLMmTMAXk9XkJ6ertTHzs4OTk5OUp+wsDCYmJhIhSgAaNq0KUxMTKQ+REREVPqUmjOjLly4AHd3d+n55MmTAQBDhgyBt7e3NI9B/fr1ldYLDg6Gm5sb9PT0cPz4caxcuRJJSUmwt7dH586dMW/ePGhraxdqrJmZmUhPTy/UMYlKMl1d3UL/OyQiKi2EEJg8eTJatmwJJycnAEBsbCwAwNraWqmvtbU17t27J/XR09ODmZlZjj7Z68fGxsLKyirHNq2srKQ+b8ttvs2CYP5EpHmYoxFRXkpNMcrNzQ3vmqs9v3nc7e3tERoaWthh5YghNjYWz58/L9LtEJVEpqamsLGxgUwmU3coRETFyrhx43D58mWcPn06x7K3P1OFEPl+zr7dJ7f+7xonrzsRv2t7zJ+INBdzNCLKTakpRhUH2YmUlZUVDAwM+IFNVABCCLx8+RJxcXEAwDnciDTUv89TEJ+cViRjmxnqoZypokjGLunGjx+PQ4cO4eTJk0p3wLOxsQHwOjd583M1Li5OOlvKxsYGaWlpiI+PVzo7Ki4uDs2bN5f6PH78OMd2//vvvxxnXWWbNWuWdAY78L87EeeF+RORZiqKHI3fJUQlB4tRGiIzM1NKpAoyeToR/Y9C8TpxiIuLg5WVFU8HJ9Iw/z5PQbuloUhJz8y/8wdQ6GojaIor/xHxHoQQGD9+PA4cOICQkBA4OjoqLXd0dISNjQ0CAwPRoEEDAEBaWhpCQ0OxaNEiAECjRo2gq6uLwMBA9OnTBwAQExODq1evYvHixQCAZs2aISEhAefPn0eTJk0AAOfOnUNCQoJUsHqbXC6HXC4v0H4wfyLSbIWZo/G7hKhkYTFKQ2TPcWBgYKDmSIiKp+y/nfT0dBajiDRMfHIaUtIzsaJvfVSxKlOoY9+OS4LXvkjEJ6fxHxDvYezYsdi9ezd+++03GBkZSfM3mZiYQKFQQCaTwcvLCwsXLkTVqlVRtWpVLFy4EAYGBhgwYIDUd/jw4ZgyZQosLCxgbm6OqVOnok6dOtLd9WrWrIlOnTph5MiR2LhxIwBg1KhR6NKlS6HcSY/5E5HmK6wcjd8lRCULi1EahqeWE30Y/u0Qab4qVmXgVM5E3WEQgPXr1wN4Pafmm7Zt2wZPT08AwPTp05GSkoIxY8YgPj4eLi4uOHbsGIyMjKT+y5cvh46ODvr06YOUlBS0bdsW27dvV/oH565duzBhwgTprnvdunXDmjVrCnV/+B1ApLkK+++T3yVEJQOLUURERESlTH43bgFe/wPS29sb3t7eefbR19fH6tWrsXr16jz7mJubY+fOnR8SJhEREZVQLEYVA0U5UV9uOHlf4ahYsSK8vLzg5eX1UePIZDIcOHAAPXr00Ki4PoS3tzcOHjyIyMhIlW+biIhKF+ZPxVNR5Sn5jXv37l04Ojri0qVLqF+/fqFum4iIcmIxSsMV9UR9ufmQyftiY2Ph6+uLo0eP4uHDhzAxMUHVqlXx+eefY/DgwcVmLgdVFmq8vb2Vbl1tbGyMunXrYsGCBXB1dS3y7RdUSEgI3N3dER8fD1NTU6Vl6ixsERER5YX5k2qpKh/w9/eHh4cHYmJipDs+Aq/v2qirq4sHDx5IbQ8fPoS9vT0CAgKkS0Tfxd7eHjExMbC0tATw7vznfbm5uSE0NBQAoKenB0tLSzRs2BBDhw5Fz549P2psIqLiisUoDVeUE/Xl5kMm7/vnn3/QokULmJqaYuHChahTpw4yMjJw8+ZNbN26FXZ2dujWrVsRR543IQQyMzOho6N5b/fatWsjKCgIAPDs2TN8//336NKli5SQEhER0ftj/vTxNDF/atmyJXR0dBASEoJ+/foBAKKiovDq1SukpKTg9u3bqFKlCgAgODgYurq6aNGiRYHG1tbWVipwFbaRI0di/vz5SE9Px7///osDBw6gX79+8PT0xKZNm4psu29LT0+Hrq6uyrZHRJQXLXUHQAWTPVFfUT8+JGEbM2YMdHR0cOHCBfTp0wc1a9ZEnTp18Nlnn+Ho0aPo2rWr1DchIQGjRo2ClZUVjI2N0aZNG/z111/Scm9vb9SvXx87duxAxYoVYWJign79+uHFixdSHyEEFi9ejEqVKkGhUKBevXr45ZdfpOUhISGQyWQICAiAs7Mz5HI5Tp06hTt37qB79+6wtrZGmTJl0LhxY6kQBLz+1erevXuYNGkSZDKZ0mSLZ86cQevWraFQKGBvb48JEyYgOTlZWh4XF4euXbtCoVDA0dERu3btKtCx09HRgY2NDWxsbFCrVi34+PggKSkJN2/ezHOdGTNmoFq1ajAwMEClSpUwZ84c6W5C2Q4dOgRnZ2fo6+vD0tLynb+6bdu2DSYmJggMDCxQzO9y//59dO/eHWXKlIGxsTH69OmDx48fv3Odbdu2oWbNmtDX10eNGjWwbt06aVlaWhrGjRsHW1tb6Ovro2LFivD19f3oOImIqHRg/lSy8qfs7YeEhCjF3bJlS7Rs2TJHe5MmTWBoaCi1vXz5EsOGDYORkREqVKigVAS6e/cuZDIZIiMjcffuXbi7uwMAzMzMIJPJpIn98zuOeTEwMICNjQ3s7e3RtGlTLFq0CBs3bsTmzZul4/nZZ59h/Pjx0jpeXl6QyWS4du0aACAjIwNGRkYICAgA8PpMsZYtW8LU1BQWFhbo0qUL7ty5k2OffvrpJ7i5uUFfXx/r1q2DQqGAv7+/Unz79++HoaEhkpKSAAD//vsv+vbtCzMzM1hYWKB79+64e/dursfX1NQULVq0wL179/I9DkRE2ViMoo/y9OlTHDt2DGPHjlX6sn9TdlIihEDnzp0RGxuL33//HREREWjYsCHatm2LZ8+eSf3v3LmDgwcP4siRIzhy5AhCQ0Px3XffScu//vprbNu2DevXr8e1a9cwadIkfP7559Lpz9mmT58OX19fREVFoW7dukhKSsInn3yCoKAgXLp0CR07dkTXrl1x//59AK+/hMuXL4/58+cjJiYGMTExAIArV66gY8eO6NmzJy5fvox9+/bh9OnTGDdunLQtT09P3L17FydOnMAvv/yCdevWIS4u7r2OZWpqKrZv3w5TU9N33u7ayMgI27dvx/Xr17Fy5Ups3rwZy5cvl5YfPXoUPXv2ROfOnXHp0iUcP34czs7OuY71/fffY+rUqQgICED79u3fK963CSHQo0cPPHv2DKGhoQgMDMSdO3fQt2/fPNfZvHkzZs+ejW+//RZRUVFYuHAh5syZAz8/PwDAqlWrcOjQIfz000+4ceMGdu7ciYoVK35UnEREROrG/Om1D8mf3N3dERwcLD0PDg6Gm5sbXF1dc7RnF5SyLV26FM7Ozrh06RLGjBmDL7/8En///XeObdjb2+PXX38FANy4cQMxMTFYuXLlex3HghgyZAjMzMywf/9+AK8Le28W1EJDQ2FpaSmNHR4ejlevXklneyUnJ2Py5MkIDw/H8ePHoaWlhU8//RRZWVlK25kxYwYmTJiAqKgo9O7dG507d85R+Nu9e7f0g+LLly/h7u6OMmXK4OTJkzh9+jTKlCmDTp06IS0tDRkZGejRowdcXV1x+fJlhIWFYdSoUbyrJRG9H0EqlZCQIACIhIQEpfaUlBRx/fp1kZKSotR+5eFz4TDjiLjy8LlK4nvf7Z09e1YAEPv371dqt7CwEIaGhsLQ0FBMnz5dCCHE8ePHhbGxsXj16pVS38qVK4uNGzcKIYSYN2+eMDAwEImJidLyadOmCRcXFyGEEElJSUJfX1+cOXNGaYzhw4eL/v37CyGECA4OFgDEwYMH842/Vq1aYvXq1dJzBwcHsXz5cqU+gwYNEqNGjVJqO3XqlNDS0hIpKSnixo0bAoA4e/astDwqKkoAyDHWm+bNmye0tLSk4ySTyYSxsbH4448/lPoBEAcOHMhznMWLF4tGjRpJz5s1ayYGDhyYZ//sfZw5c6awtbUVly9fzrOvEP87ntlxvvmQyWTSPh47dkxoa2uL+/fvS+teu3ZNABDnz5+X9rlevXrScnt7e7F7926l7X3zzTeiWbNmQgghxo8fL9q0aSOysrLeGaMQef8NEZH6FeV3WVGOndd3NqnWu14H5k+lL386duyYACAePXokhBDCyspKnD9/Xpw9e1bY2dkJIYS4f/++ACCOHz+uFOPnn38uPc/KyhJWVlZi/fr1QgghoqOjBQBx6dIlpeMRHx8vrVOQ45gbV1dXMXHixFyXubi4CA8PDyGEEJcvXxYymUz8999/4tmzZ0JXV1csWLBA9O7dWwghxMKFC6XXNDdxcXECgLhy5YrSPq1YsUKp3/79+0WZMmVEcnKyEOL135i+vr44evSoEEKILVu2iOrVqyvlX6mpqUKhUIiAgADx9OlTAUCEhITkGcubCitHK67fJUSlTUHzJ825CJyKtbd/CTl//jyysrIwcOBApKamAgAiIiKQlJQECwsLpb4pKSlKpxRXrFgRRkZG0nNbW1vpV7Lr16/j1atXOc7iSUtLQ4MGDZTa3j4bKDk5GT4+Pjhy5AgePXqEjIwMpKSkSL/s5SUiIgK3b99W+gVJCIGsrCxER0fj5s2b0NHRUdpejRo1CjTZZfXq1XHo0CEAwIsXL7Bv3z707t0bwcHBeZ7N9Msvv2DFihW4ffs2kpKSkJGRAWNjY2l5ZGQkRo4c+c7tLl26FMnJybhw4QIqVaqUb5wAcOrUKaXXBXj9C162qKgo2Nvbw97eXmqrVasWTE1NERUVhcaNGyut+99//+HBgwcYPny4UrwZGRnSfFmenp5o3749qlevjk6dOqFLly4FmoSUiIioOGD+9P75U4sWLaCnp4eQkBDUq1cPKSkpaNiwIYQQSExMxK1btxAWFga5XI7mzZsrrVu3bl3p/2UyGWxsbN7rTPb3OY4FJYSQ3gdOTk6wsLBAaGgodHV1Ua9ePXTr1g2rVq0C8PrSuDdvcnPnzh3MmTMHZ8+exZMnT6Qzou7fvw8nJyep39uvaefOnaGjo4NDhw6hX79++PXXX2FkZCTlWNmv3dt536tXr3Dnzh106NABnp6e6NixI9q3b4927dqhT58+sLW1/aBjQESlE4tR9FGqVKkCmUyW4xTn7AKHQvG/STyzsrJga2urdPpxtjcTj7cnVZTJZNKXa/Z/jx49inLlyin1k8vlSs/fPu192rRpCAgIwPfff48qVapAoVCgV69eSEt7922fs7KyMHr0aEyYMCHHsgoVKuDGjRtSnO9LT09PmmgTABo0aICDBw9ixYoV2LlzZ47+Z8+eRb9+/eDj44OOHTvCxMQEe/fuxdKlS6U+bx7zvLRq1QpHjx7FTz/9hJkzZxYoVkdHxxwJ4puTmr6ZTL0pr/bs13Lz5s1wcXFRWqatrQ0AaNiwIaKjo/HHH38gKCgIffr0Qbt27Qo0NwMREZGmYv704fmTgYEBmjRpguDgYDx79gwtW7aU8obmzZsjODgYYWFhaNasGfT19ZXWfdcxKoj3OY4FkZmZiVu3bkk/2MlkMrRu3RohISHQ09ODm5sbnJyckJmZiStXruDMmTNKdyzs2rUr7O3tsXnzZtjZ2SErKwtOTk45Xpu3X1M9PT306tULu3fvRr9+/bB792707dtXyuuysrLQqFGjXOfwKlu2LIDXc35OmDAB/v7+2LdvH77++msEBgaiadOm730ciKh0YjGKPoqFhQXat2+PNWvWYPz48XnOewC8LizExsZCR0fng+f9qVWrFuRyOe7fv6/0y1BBnDp1Cp6envj0008BAElJSUoTMQKvv5wzM5VvA92wYUNcu3ZNqWj0ppo1ayIjIwMXLlxAkyZNALyeX+D58+fvFV82bW1tpKSk5Lrszz//hIODA2bPni21vT1ZZN26dXH8+HEMHTo0z200adIE48ePR8eOHaGtrY1p06Z9UKxvqlWrFu7fv48HDx5IZ0ddv34dCQkJqFmzZo7+1tbWKFeuHP755x8MHDgwz3GNjY3Rt29f9O3bF7169UKnTp3w7NkzmJubf3TMRERE6sD86ePyJ3d3d+zduxfx8fFKZ2m7uroiJCQEYWFh78yDCkJPTw8AlPbrY45jbvz8/BAfH4/PPvtManNzc8OmTZugp6eH+fPnQyaToVWrVvj++++RkpIizRf19OlTREVFYePGjWjVqhUA4PTp0wXe9sCBA9GhQwdcu3YNwcHB+Oabb6RlDRs2xL59+6QJ8/PSoEEDNGjQALNmzUKzZs2we/duFqOIqMA4gTl9tHXr1iEjIwPOzs7Yt28foqKipMmm//77b+nXqnbt2qFZs2bo0aMHAgICcPfuXZw5cwZff/01Lly4UKBtGRkZYerUqZg0aRL8/Pxw584dXLp0CWvXrpUmvc5LlSpVsH//fkRGRuKvv/7CgAEDcvwaVrFiRZw8eRL//vsvnjx5AuD1pI9hYWEYO3YsIiMjcevWLRw6dEi620n2JWQjR47EuXPnEBERgREjRhToDKWMjAzExsYiNjYWt27dwoIFC3D9+nV07949z324f/8+9u7dizt37mDVqlU4cOCAUp958+Zhz549mDdvHqKionDlyhUsXrw4x1jNmjXDH3/8gfnz5ytNgP6h2rVrh7p162LgwIG4ePEizp8/j8GDB8PV1TXPSw69vb3h6+uLlStX4ubNm7hy5Qq2bduGZcuWAQCWL1+OvXv34u+//8bNmzfx888/w8bGpkCXQBIREWky5k8fnj+5u7vj1q1b8Pf3VyoKubq64siRI0p3w/tQDg4OkMlkOHLkCP777z8kJSV91HF8+fIlYmNj8fDhQ5w7dw4zZszAF198gS+//FIpVjc3N1y7dg1XrlyRikxubm7YtWsXGjZsKBWHsu9yt2nTJty+fRsnTpzA5MmTC7x/rq6usLa2xsCBA1GxYkWlItLAgQNhaWmJ7t2749SpU4iOjkZoaCgmTpyIhw8fIjo6GrNmzUJYWBju3buHY8eO4ebNm7n++EhElBeeGVVM3I5L0tjtVK5cGZcuXcLChQsxa9YsPHz4EHK5HLVq1cLUqVMxZswYAK9PPf79998xe/ZsDBs2DP/99x9sbGzQunVrWFtbF3h733zzDaysrODr64t//vkHpqamaNiwIb766qt3rrd8+XIMGzYMzZs3h6WlJWbMmIHExESlPvPnz8fo0aNRuXJlpKamQgiBunXrIjQ0FLNnz0arVq0ghEDlypWV7hK3bds2jBgxQvpiX7BgAebMmZPvvly7dk26vt7AwACVK1fG+vXrMXjw4Fz7d+/eHZMmTcK4ceOQmpqKzp07Y86cOfD29pb6uLm54eeff8Y333yD7777DsbGxmjdunWu47Vo0QJHjx7FJ598Am1t7VxPpS8omUyGgwcPYvz48WjdujW0tLTQqVMnrF69Os91RowYAQMDAyxZsgTTp0+HoaEh6tSpI52CXqZMGSxatAi3bt2CtrY2GjdujN9//x1aWqyjExFR/pg//U9Jyp+aNWsmXRbXqFEjqb1x48bIzMyEQqHIMQXA+ypXrhx8fHwwc+ZMDB06FIMHD8b27ds/+Dhu3rwZmzdvhp6eHiwsLNCoUSPs27dPOuMsm5OTEywtLeHg4CAVnlxdXZGZmalUeNPS0sLevXsxYcIEODk5oXr16li1apXSmWLvIpPJ0L9/fyxZsgRz585VWmZgYICTJ09ixowZ6NmzJ168eIFy5cqhbdu2MDY2RkpKCv7++2/4+fnh6dOnsLW1xbhx4zB69OgCbZuICABkQgih7iBKk8TERJiYmCAhIUHptNdXr14hOjoajo6OSte3//s8Be2WhiIlPTO34YqEQlcbQVNcUc40/1+miDRFXn9DRKR+V/9NQJfVp3FkfEs4lTMpNmPn9Z1NqvWu14H5E5HmK6wcrbh+lxCVNgXNn3hmlIYrZ6pA0BRXxCe/e5LIwmRmqMdEioiIiIot5k9ERESajcWoYqCcqYLJDREREdF7YP5ERESkuTjxChERERERERERqQyLUUREREREREREpDK8TE/DcD55og/Dvx0iotKL3wFEmqs4/X0W5R04Oa8ckTIWozSErq4uAODly5dQKPghRfS+Xr58CeB/f0tERFTyMX8i0nzFIUczM9SDQlcbXvsii2wbvOMmkTIWozSEtrY2TE1NERcXBwAwMDCATCZTc1REmk8IgZcvXyIuLg6mpqbQ1tZWd0hERKQizJ+INFdxytGK+g6ct+OS4LUvEvHJaSxGEf0/FqM0iI2NDQBICRURFZypqan0N0RERKUH8ycizVZccjTegZNItViM0iAymQy2trawsrJCenq6usMhKjZ0dXU1+tc2IiIqOsyfiDQXczQiykupKUadPHkSS5YsQUREBGJiYnDgwAH06NFDWi6EgI+PDzZt2oT4+Hi4uLhg7dq1qF27ttQnNTUVU6dOxZ49e5CSkoK2bdti3bp1KF++fKHGqq2tzQ9tIiIiovfA/ImIiKj40FJ3AKqSnJyMevXqYc2aNbkuX7x4MZYtW4Y1a9YgPDwcNjY2aN++PV68eCH18fLywoEDB7B3716cPn0aSUlJ6NKlCzIzM1W1G0RERERERERExVqpOTPKw8MDHh4euS4TQmDFihWYPXs2evbsCQDw8/ODtbU1du/ejdGjRyMhIQFbtmzBjh070K5dOwDAzp07YW9vj6CgIHTs2FFl+0JEREREREREVFyVmjOj3iU6OhqxsbHo0KGD1CaXy+Hq6oozZ84AACIiIpCenq7Ux87ODk5OTlKf3KSmpiIxMVHpQURERERERERUWrEYBSA2NhYAYG1trdRubW0tLYuNjYWenh7MzMzy7JMbX19fmJiYSA97e/tCjp6IiIiIiIiIqPhgMeoNMplM6bkQIkfb2/LrM2vWLCQkJEiPBw8eFEqsRERERERERETFEYtRAGxsbAAgxxlOcXFx0tlSNjY2SEtLQ3x8fJ59ciOXy2FsbKz0ICIiIiIiIiIqrViMAuDo6AgbGxsEBgZKbWlpaQgNDUXz5s0BAI0aNYKurq5Sn5iYGFy9elXqQ0RERERERERE71Zq7qaXlJSE27dvS8+jo6MRGRkJc3NzVKhQAV5eXli4cCGqVq2KqlWrYuHChTAwMMCAAQMAACYmJhg+fDimTJkCCwsLmJubY+rUqahTp450dz0iIiIiIiIiInq3UlOMunDhAtzd3aXnkydPBgAMGTIE27dvx/Tp05GSkoIxY8YgPj4eLi4uOHbsGIyMjKR1li9fDh0dHfTp0wcpKSlo27Yttm/fDm1tbZXvDxERERERERFRcVRqilFubm4QQuS5XCaTwdvbG97e3nn20dfXx+rVq7F69eoiiJCIiIiIiIiIqOTjnFFERERERERERKQyLEYREREREREREZHKsBhFREREVAqdPHkSXbt2hZ2dHWQyGQ4ePKi03NPTEzKZTOnRtGlTpT6pqakYP348LC0tYWhoiG7duuHhw4dKfeLj4zFo0CCYmJjAxMQEgwYNwvPnz4t474iIiEiTsRhFREREVAolJyejXr16WLNmTZ59OnXqhJiYGOnx+++/Ky338vLCgQMHsHfvXpw+fRpJSUno0qULMjMzpT4DBgxAZGQk/P394e/vj8jISAwaNKjI9ouIiIg0X6mZwJyIiIiI/sfDwwMeHh7v7COXy2FjY5PrsoSEBGzZsgU7duxAu3btAAA7d+6Evb09goKC0LFjR0RFRcHf3x9nz56Fi4sLAGDz5s1o1qwZbty4gerVqxfuThEREVGxwDOjiIiIiChXISEhsLKyQrVq1TBy5EjExcVJyyIiIpCeno4OHTpIbXZ2dnBycsKZM2cAAGFhYTAxMZEKUQDQtGlTmJiYSH3elpqaisTERKUHERERlSwsRhERERFRDh4eHti1axdOnDiBpUuXIjw8HG3atEFqaioAIDY2Fnp6ejAzM1Naz9raGrGxsVIfKyurHGNbWVlJfd7m6+srzS9lYmICe3v7Qt4zIiIiUjdepkdEREREOfTt21f6fycnJzg7O8PBwQFHjx5Fz54981xPCAGZTCY9f/P/8+rzplmzZmHy5MnS88TERBakiIiIShieGUVERERE+bK1tYWDgwNu3boFALCxsUFaWhri4+OV+sXFxcHa2lrq8/jx4xxj/ffff1Kft8nlchgbGys9iIiIqGRhMYqIiIiI8vX06VM8ePAAtra2AIBGjRpBV1cXgYGBUp+YmBhcvXoVzZs3BwA0a9YMCQkJOH/+vNTn3LlzSEhIkPoQERFR6cPL9IiIiIhKoaSkJNy+fVt6Hh0djcjISJibm8Pc3Bze3t747LPPYGtri7t37+Krr76CpaUlPv30UwCAiYkJhg8fjilTpsDCwgLm5uaYOnUq6tSpI91dr2bNmujUqRNGjhyJjRs3AgBGjRqFLl268E56REREpRiLUURERESl0IULF+Du7i49z56naciQIVi/fj2uXLmCH3/8Ec+fP4etrS3c3d2xb98+GBkZSessX74cOjo66NOnD1JSUtC2bVts374d2traUp9du3ZhwoQJ0l33unXrhjVr1qhoL4mIiEgTsRhFREREVAq5ublBCJHn8oCAgHzH0NfXx+rVq7F69eo8+5ibm2Pnzp0fFCMRERGVTJwzioiIiIiIiIiIVIZnRhEREREREREVsdtxSUU2tpmhHsqZKopsfKLCxmIUERERERERURExM9SDQlcbXvsii2wbCl1tBE1xZUGKig0Wo4iIiIiIiIiKSDlTBYKmuCI+Oa1Ixr8dlwSvfZGIT05jMYqKDRajiIiIiIiIiIpQOVMFC0VEb+AE5kREREREREREpDIsRhERERERERERkcqwGEVERERERERERCrDYhQREREREREREakMi1FERERERERERKQyGl2MSk9Ph7u7O27evKnuUIiIiIjUjrkRERERlQQaXYzS1dXF1atXIZPJ1B0KERERkdoxNyIiIqKSQKOLUQAwePBgbNmyRd1hEBEREWkE5kZERERU3OmoO4D8pKWl4YcffkBgYCCcnZ1haGiotHzZsmWFsp2KFSvi3r17OdrHjBmDtWvXwtPTE35+fkrLXFxccPbs2ULZPhEREVFBqCo3IiIiIioqGl+Munr1Kho2bAgAOeZHKMxT1MPDw5GZmam03fbt26N3795SW6dOnbBt2zbpuZ6eXqFtn4iIiKggVJUbERERERUVjS9GBQcHq2Q7ZcuWVXr+3XffoXLlynB1dZXa5HI5bGxsVBIPERERUW5UlRsRERERFRWNnzMq2+3btxEQEICUlBQAgBCiyLaVlpaGnTt3YtiwYUq/MIaEhMDKygrVqlXDyJEjERcXl+9YqampSExMVHoQERERfSxV5kZEREREhUnjz4x6+vQp+vTpg+DgYMhkMty6dQuVKlXCiBEjYGpqiqVLlxb6Ng8ePIjnz5/D09NTavPw8EDv3r3h4OCA6OhozJkzB23atEFERATkcnmeY/n6+sLHx6fQYyQiIqLSSR25ERERab7bcUlFMq6ZoR7KmSqKZGwqvTS+GDVp0iTo6uri/v37qFmzptTet29fTJo0qUgSri1btsDDwwN2dnZK28vm5OQEZ2dnODg44OjRo+jZs2eeY82aNQuTJ0+WnicmJsLe3r7QYyYiIqLSQR25ERERaS4zQz0odLXhtS+ySMZX6GojaIorC1JUqDS+GHXs2DEEBASgfPnySu1Vq1bN9e53H+vevXsICgrC/v3739nP1tYWDg4OuHXr1jv7yeXyd545RURERPQ+VJ0bERGRZitnqkDQFFfEJ6cV+ti345LgtS8S8clpLEZRodL4YlRycjIMDAxytD958qRIijzbtm2DlZUVOnfu/M5+T58+xYMHD2Bra1voMRARERHlRdW5ERERab5ypgoWi6hY0fgJzFu3bo0ff/xRei6TyZCVlYUlS5bA3d29ULeVlZWFbdu2YciQIdDR+V+dLikpCVOnTkVYWBju3r2LkJAQdO3aFZaWlvj0008LNQYiIiKid1FlbkRERERUFDT+zKglS5bAzc0NFy5cQFpaGqZPn45r167h2bNn+PPPPwt1W0FBQbh//z6GDRum1K6trY0rV67gxx9/xPPnz2Frawt3d3fs27cPRkZGhRoDERER0buoMjciIiIiKgoaX4yqVasWLl++jPXr10NbWxvJycno2bMnxo4dW+iXyHXo0CHX2yIrFAoEBAQU6raIiIiIPoQqcyMiIiKioqDxxSgAsLGxgY+Pj7rDICIiItIIzI2IiIioOCsWxaj4+Hhs2bIFUVFRkMlkqFmzJoYOHQpzc3N1h0ZERESkcsyNiIiIqDjT+AnMQ0ND4ejoiFWrViE+Ph7Pnj3DqlWr4OjoiNDQUHWHR0RERKRSzI2IiIiouNP4M6PGjh2LPn36SPMiAEBmZibGjBmDsWPH4urVq2qOkIiIiEh1mBsRERFRcafxZ0bduXMHU6ZMkZIt4PXd7SZPnow7d+6oMTIiIiIi1WNuRERERMWdxhejGjZsiKioqBztUVFRqF+/vuoDIiIiIlIj5kZERERU3GnkZXqXL1+W/n/ChAmYOHEibt++jaZNmwIAzp49i7Vr1+K7775TV4hEREREKsPciIiIiEoSjSxG1a9fHzKZDEIIqW369Ok5+g0YMAB9+/ZVZWhEREREKsfciIiIiEoSjSxGRUdHqzsEIiIiIo3B3IiIiNTpdlxSkY1tZqiHcqaKIhufNJNGFqMcHBzUHQIRERGRxmBuRERE6mBmqAeFrja89kUW2TYUutoImuLKglQpo5HFqLf9+++/+PPPPxEXF4esrCylZRMmTFBTVERERETqURi50cmTJ7FkyRJEREQgJiYGBw4cQI8ePaTlQgj4+Phg06ZNiI+Ph4uLC9auXYvatWtLfVJTUzF16lTs2bMHKSkpaNu2LdatW4fy5ctLfeLj4zFhwgQcOnQIANCtWzesXr0apqamH34AiIhIJcqZKhA0xRXxyWlFMv7tuCR47YtEfHIai1GljMYXo7Zt24YvvvgCenp6sLCwgEwmk5bJZDIWo4iIiKhUKazcKDk5GfXq1cPQoUPx2Wef5Vi+ePFiLFu2DNu3b0e1atWwYMECtG/fHjdu3ICRkREAwMvLC4cPH8bevXthYWGBKVOmoEuXLoiIiIC2tjaA1/NYPXz4EP7+/gCAUaNGYdCgQTh8+PDHHgoiIlKBcqYKFoqo0Gl8MWru3LmYO3cuZs2aBS0tLXWHQ0RERKRWhZUbeXh4wMPDI9dlQgisWLECs2fPRs+ePQEAfn5+sLa2xu7duzF69GgkJCRgy5Yt2LFjB9q1awcA2LlzJ+zt7REUFISOHTsiKioK/v7+OHv2LFxcXAAAmzdvRrNmzXDjxg1Ur179g+MnIiKi4kvjqzsvX75Ev379WIgiIiIigmpyo+joaMTGxqJDhw5Sm1wuh6urK86cOQMAiIiIQHp6ulIfOzs7ODk5SX3CwsJgYmIiFaIAoGnTpjAxMZH6vC01NRWJiYlKDyIiIipZNL7CM3z4cPz888/qDoOIiIhII6giN4qNjQUAWFtbK7VbW1tLy2JjY6GnpwczM7N39rGyssoxvpWVldTnbb6+vjAxMZEe9vb2H70/REREpFk0/jI9X19fdOnSBf7+/qhTpw50dXWVli9btkxNkRERERGpnipzozfnowJeX773dtvb3u6TW/93jTNr1ixMnjxZep6YmMiCFBERUQmj8cWohQsXIiAgQJpTIL/khoiIiKgkU0VuZGNjA+D1mU22trZSe1xcnHS2lI2NDdLS0hAfH690dlRcXByaN28u9Xn8+HGO8f/7778cZ11lk8vlkMvlhbIfREREpJk0vhi1bNkybN26FZ6enuoOhYiIiEjtVJEbOTo6wsbGBoGBgWjQoAEAIC0tDaGhoVi0aBEAoFGjRtDV1UVgYCD69OkDAIiJicHVq1exePFiAECzZs2QkJCA8+fPo0mTJgCAc+fOISEhQSpYERERUemj8cUouVyOFi1aqDsMIiIiIo1QWLlRUlISbt++LT2Pjo5GZGQkzM3NUaFCBXh5eWHhwoWoWrUqqlatioULF8LAwAADBgwAAJiYmGD48OGYMmUKLCwsYG5ujqlTp6JOnTrS3fVq1qyJTp06YeTIkdi4cSMAYNSoUejSpQvvpEdERFSKaXwxauLEiVi9ejVWrVql7lCIiIiI1K6wcqMLFy7A3d1dep49T9OQIUOwfft2TJ8+HSkpKRgzZgzi4+Ph4uKCY8eOwcjISFpn+fLl0NHRQZ8+fZCSkoK2bdti+/bt0NbWlvrs2rULEyZMkO66161bN6xZs+ajYiciopLldlxSkYxrZqiHcqaKIhmbPo7GF6POnz+PEydO4MiRI6hdu3aOSTr379+vpsiIiIiIVK+wciM3NzcIIfJcLpPJ4O3tDW9v7zz76OvrY/Xq1Vi9enWefczNzbFz584CxURERKWLmaEeFLra8NoXWSTjK3S1ETTFlQUpDaTxxShTU1P07NlT3WEQERERaQTmRkREVFKUM1UgaIor4pPTCn3s23FJ8NoXifjkNBajNJDGF6O2bdum7hCIiIiINAZzIyIiKknKmSpYLCqFtNQdABERERERERERlR4af2aUo6MjZDJZnsv/+ecfFUZDREREpF7MjYiIiKi40/hilJeXl9Lz9PR0XLp0Cf7+/pg2bVqhbcfb2xs+Pj5KbdbW1oiNjQUACCHg4+ODTZs2SXeUWbt2LWrXrl1oMRARERHlR1W5EREREVFR0fhi1MSJE3NtX7t2LS5cuFCo26pduzaCgoKk52/elnjx4sVYtmwZtm/fjmrVqmHBggVo3749bty4oXSLYyIiIqKipMrciIiIiKgoaHwxKi8eHh6YNWtWoU7iqaOjAxsbmxztQgisWLECs2fPlu5e4+fnB2tra+zevRujR48utBiIiIiIPkRR5EZERETF3e24pCIb28xQj5Ovf6BiW4z65ZdfYG5uXqhj3rp1C3Z2dpDL5XBxccHChQtRqVIlREdHIzY2Fh06dJD6yuVyuLq64syZM+8sRqWmpiI1NVV6npiYWKgxExEREQFFkxsREREVV2aGelDoasNrX2SRbUOhq42gKa4sSH0AjS9GNWjQQGmSTiEEYmNj8d9//2HdunWFth0XFxf8+OOPqFatGh4/fowFCxagefPmuHbtmjRvlLW1tdI61tbWuHfv3jvH9fX1zTEXFREREdGHUlVuREREVJyVM1UgaIor4pPTimT823FJ8NoXifjkNBajPoDGF6O6d++ulHBpaWmhbNmycHNzQ40aNQptOx4eHtL/16lTB82aNUPlypXh5+eHpk2bAkCOO9cIId55NxsAmDVrFiZPniw9T0xMhL29faHFTURERKWLqnIjIiKi4q6cqYKFIg2l8cUob29vtWzX0NAQderUwa1bt9CjRw8AQGxsLGxtbaU+cXFxOc6WeptcLodcLi/KUImIiKgUUVduRERERFRYtNQdQF60tLSgra39zoeOTtHV0lJTUxEVFQVbW1s4OjrCxsYGgYGB0vK0tDSEhoaiefPmRRYDERERUTZ150ZEREREhUVjM5YDBw7kuezMmTNYvXo1hBCFtr2pU6eia9euqFChAuLi4rBgwQIkJiZiyJAhkMlk8PLywsKFC1G1alVUrVoVCxcuhIGBAQYMGFBoMRARERHlRdW5EREREVFR0dhiVPfu3XO0/f3335g1axYOHz6MgQMH4ptvvim07T18+BD9+/fHkydPULZsWTRt2hRnz56Fg4MDAGD69OlISUnBmDFjEB8fDxcXFxw7dgxGRkaFFgMRERFRXlSdGxEREREVFY0tRr3p0aNHmDdvHvz8/NCxY0dERkbCycmpULexd+/edy6XyWTw9vbmPA1ERESkdqrIjYiIiIiKisbOGQUACQkJmDFjBqpUqYJr167h+PHjOHz4MJMtIiIiKpWYGxEREVFJoLFnRi1evBiLFi2CjY0N9uzZk+up6URERESlBXMjIiIiKik0thg1c+ZMKBQKVKlSBX5+fvDz88u13/79+1UcGREREZHqMTciIiKikkJji1GDBw+GTCZTdxhEREREGoG5EREREZUUGluM2r59u7pDICIiItIYzI2IiIiopNDoCcyJiIiIiIiIiKhk0dgzo4iIiIiIiKj4+Pd5CuKT04pk7NtxSUUyLhGpB4tRRERERERE9FH+fZ6CdktDkZKeWWTbUOhqw8xQr8jGJyLVYTGKiIiIiIiIPkp8chpS0jOxom99VLEqUyTbMDPUQzlTRZGMTUSqxWIUERERERERFYoqVmXgVM5E3WEQkYbjBOZERERERERERKQyLEYREREREREREZHKsBhFREREREREREQqw2IUERERERERERGpDItRRERERERERESkMixGERERERERERGRyrAYRUREREREREREKsNiFBERERERERERqQyLUURERESUg7e3N2QymdLDxsZGWi6EgLe3N+zs7KBQKODm5oZr164pjZGamorx48fD0tIShoaG6NatGx4+fKjqXSEiIiINw2IUEREREeWqdu3aiImJkR5XrlyRli1evBjLli3DmjVrEB4eDhsbG7Rv3x4vXryQ+nh5eeHAgQPYu3cvTp8+jaSkJHTp0gWZmZnq2B0iIiLSEDrqDoCIiIjU79/nKYhPTiuSsW/HJRXJuFT0dHR0lM6GyiaEwIoVKzB79mz07NkTAODn5wdra2vs3r0bo0ePRkJCArZs2YIdO3agXbt2AICdO3fC3t4eQUFB6Nixo0r3hYiIiDQHi1FERESl3L/PU9BuaShS0ovubBWFrjbMDPWKbHwqGrdu3YKdnR3kcjlcXFywcOFCVKpUCdHR0YiNjUWHDh2kvnK5HK6urjhz5gxGjx6NiIgIpKenK/Wxs7ODk5MTzpw5k2cxKjU1FampqdLzxMTEottBIiIiUgsWo4iIiEq5+OQ0pKRnYkXf+qhiVaZItmFmqIdypooiGZuKhouLC3788UdUq1YNjx8/xoIFC9C8eXNcu3YNsbGxAABra2uldaytrXHv3j0AQGxsLPT09GBmZpajT/b6ufH19YWPj08h7w0RERFpEhajiIiICABQxaoMnMqZqDsM0hAeHh7S/9epUwfNmjVD5cqV4efnh6ZNmwIAZDKZ0jpCiBxtb8uvz6xZszB58mTpeWJiIuzt7T9kF4iIiEhDcQJzIiIiIsqXoaEh6tSpg1u3bknzSL19hlNcXJx0tpSNjQ3S0tIQHx+fZ5/cyOVyGBsbKz2IiIioZGExioiIiIjylZqaiqioKNja2sLR0RE2NjYIDAyUlqelpSE0NBTNmzcHADRq1Ai6urpKfWJiYnD16lWpDxEREZVOLEb9P19fXzRu3BhGRkawsrJCjx49cOPGDaU+np6ekMlkSo/s09SJiIiISpKpU6ciNDQU0dHROHfuHHr16oXExEQMGTIEMpkMXl5eWLhwIQ4cOICrV6/C09MTBgYGGDBgAADAxMQEw4cPx5QpU3D8+HFcunQJn3/+OerUqSPdXY+IiIhKJ84Z9f9CQ0MxduxYNG7cGBkZGZg9ezY6dOiA69evw9DQUOrXqVMnbNu2TXqup8c7AxEREVHJ8/DhQ/Tv3x9PnjxB2bJl0bRpU5w9exYODg4AgOnTpyMlJQVjxoxBfHw8XFxccOzYMRgZGUljLF++HDo6OujTpw9SUlLQtm1bbN++Hdra2uraLSIiItIALEb9P39/f6Xn27Ztg5WVFSIiItC6dWupXS6XS/MkEBEREZVUe/fufedymUwGb29veHt759lHX18fq1evxurVqws5OiIiIirOeJleHhISEgAA5ubmSu0hISGwsrJCtWrVMHLkSMTFxb1znNTUVCQmJio9iIiIiIiIiIhKKxajciGEwOTJk9GyZUs4OTlJ7R4eHti1axdOnDiBpUuXIjw8HG3atEFqamqeY/n6+sLExER68NbERERERERERFSa8TK9XIwbNw6XL1/G6dOnldr79u0r/b+TkxOcnZ3h4OCAo0ePomfPnrmONWvWLEyePFl6npiYyIIUEREREREREZVaLEa9Zfz48Th06BBOnjyJ8uXLv7Ovra0tHBwccOvWrTz7yOVyyOXywg6TiIiIiIiIiKhYYjHq/wkhMH78eBw4cAAhISFwdHTMd52nT5/iwYMHsLW1VUGERERERERERETFH+eM+n9jx47Fzp07sXv3bhgZGSE2NhaxsbFISUkBACQlJWHq1KkICwvD3bt3ERISgq5du8LS0hKffvqpmqMnIiIiIiIiIioeeGbU/1u/fj0AwM3NTal927Zt8PT0hLa2Nq5cuYIff/wRz58/h62tLdzd3bFv3z4YGRmpIWIiIiIiIiIiouKHxaj/J4R453KFQoGAgAAVRUNEREREREREVDLxMj0iIiIiIiIiIlIZFqOIiIiIiIiIiEhlWIwiIiIiIiIiIiKVYTGKiIiIiIiIiIhUhsUoIiIiIiIiIiJSGRajiIiIiIiIiIhIZViMIiIiIiIiIiIilWExioiIiIiIiIiIVIbFKCIiIiIiIiIiUhkWo4iIiIiIiIiISGVYjCIiIiIiIiIiIpVhMYqIiIiIiIiIiFSGxSgiIiIiIiIiIlIZFqOIiIiIiIiIiEhlWIwiIiIiIiIiIiKVYTGKiIiIiIiIiIhUhsUoIiIiIiIiIiJSGRajiIiIiIiIiIhIZViMIiIiIiIiIiIilWExioiIiIiIiIiIVIbFKCIiIiIiIiIiUhkWo4iIiIiIiIiISGVYjCIiIiIiIiIiIpVhMYqIiIiIiIiIiFSGxSgiIiIiIiIiIlIZFqOIiIiIiIiIiEhlWIz6AOvWrYOjoyP09fXRqFEjnDp1St0hEREREWk05k9ERESUjcWo97Rv3z54eXlh9uzZuHTpElq1agUPDw/cv39f3aERERERaSTmT0RERPQmHXUHUNwsW7YMw4cPx4gRIwAAK1asQEBAANavXw9fX181R0dERESkeZg/ERFRSXU7LqnIxjYz1EM5U0WRja9OLEa9h7S0NERERGDmzJlK7R06dMCZM2dyXSc1NRWpqanS84SEBABAYmJiocf3X+Ir/JeUmn9HIiKiN/zzXzKyUl8i6UUiEhNl6g7nvSS9SCyy2LO/q4UQhTpuafO++ZMqcyeA+RNRYSnO3yVEH0In8xX0sl5hwo+51wIKg76uFlb0awBzA91CH7tsGTnKGusX+rgFzZ9YjHoPT548QWZmJqytrZXara2tERsbm+s6vr6+8PHxydFub29fJDESERF9qGYr1B3BhyvK2F+8eAETE5Oi20AJ9775E3MnouKtOH+XEGmizkvUHcGHyS9/YjHqA8hkypV+IUSOtmyzZs3C5MmTpedZWVl49uwZLCws8lznQyQmJsLe3h4PHjyAsbFxoY2rKbh/xRv3r3jj/hVv3L8PJ4TAixcvYGdnV6jjllYFzZ/yy51K+ntaE/GYqwePu+rxmKsHj7vqaUL+xGLUe7C0tIS2tnaOX/Hi4uJy/NqXTS6XQy6XK7WZmpoWVYgwNjYu0X/A3L/ijftXvHH/ijfu34fhGVEf733zp4LmTiX9Pa2JeMzVg8dd9XjM1YPHXfXUmT/xbnrvQU9PD40aNUJgYKBSe2BgIJo3b66mqIiIiIg0F/MnIiIiehvPjHpPkydPxqBBg+Ds7IxmzZph06ZNuH//Pr744gt1h0ZERESkkZg/ERER0ZtYjHpPffv2xdOnTzF//nzExMTAyckJv//+OxwcHNQal1wux7x583Kc1l5ScP+KN+5f8cb9K964f6QJCjN/4muuejzm6sHjrno85urB4656mnDMZYL3KyYiIiIiIiIiIhXhnFFERERERERERKQyLEYREREREREREZHKsBhFREREREREREQqw2IUERERERERERGpDItRJcC6devg6OgIfX19NGrUCKdOnVJ3SIXC19cXjRs3hpGREaysrNCjRw/cuHFD3WEVGV9fX8hkMnh5eak7lELz77//4vPPP4eFhQUMDAxQv359REREqDusQpORkYGvv/4ajo6OUCgUqFSpEubPn4+srCx1h/ZBTp48ia5du8LOzg4ymQwHDx5UWi6EgLe3N+zs7KBQKODm5oZr166pJ9gP8K79S09Px4wZM1CnTh0YGhrCzs4OgwcPxqNHj9QX8HvK7/V70+jRoyGTybBixQqVxfexCrJ/UVFR6NatG0xMTGBkZISmTZvi/v37qg+WilRJzXs0UWnLxTRRScwPNVVJz1s1TUnLozWVJuf3LEYVc/v27YOXlxdmz56NS5cuoVWrVvDw8CgRyXdoaCjGjh2Ls2fPIjAwEBkZGejQoQOSk5PVHVqhCw8Px6ZNm1C3bl11h1Jo4uPj0aJFC+jq6uKPP/7A9evXsXTpUpiamqo7tEKzaNEibNiwAWvWrEFUVBQWL16MJUuWYPXq1eoO7YMkJyejXr16WLNmTa7LFy9ejGXLlmHNmjUIDw+HjY0N2rdvjxcvXqg40g/zrv17+fIlLl68iDlz5uDixYvYv38/bt68iW7duqkh0g+T3+uX7eDBgzh37hzs7OxUFFnhyG//7ty5g5YtW6JGjRoICQnBX3/9hTlz5kBfX1/FkVJRKsl5jyYqTbmYJiqJ+aGmKg15q6YpaXm0ptLo/F5QsdakSRPxxRdfKLXVqFFDzJw5U00RFZ24uDgBQISGhqo7lEL14sULUbVqVREYGChcXV3FxIkT1R1SoZgxY4Zo2bKlusMoUp07dxbDhg1TauvZs6f4/PPP1RRR4QEgDhw4ID3PysoSNjY24rvvvpPaXr16JUxMTMSGDRvUEOHHeXv/cnP+/HkBQNy7d081QRWivPbv4cOHoly5cuLq1avCwcFBLF++XOWxFYbc9q9v374l4m+P3q005T2aqKTmYpqopOaHmqo05K2apiTn0ZpK0/J7nhlVjKWlpSEiIgIdOnRQau/QoQPOnDmjpqiKTkJCAgDA3NxczZEUrrFjx6Jz585o166dukMpVIcOHYKzszN69+4NKysrNGjQAJs3b1Z3WIWqZcuWOH78OG7evAkA+Ouvv3D69Gl88sknao6s8EVHRyM2Nlbp80Yul8PV1bVEft4Arz9zZDJZiflVNCsrC4MGDcK0adNQu3ZtdYdTqLKysnD06FFUq1YNHTt2hJWVFVxcXN55qSIVP6Ut79FEJTUX00QlNT/UVKUhb9U0pSmP1lTqzu91inwLVGSePHmCzMxMWFtbK7VbW1sjNjZWTVEVDSEEJk+ejJYtW8LJyUnd4RSavXv34uLFiwgPD1d3KIXun3/+wfr16zF58mR89dVXOH/+PCZMmAC5XI7BgwerO7xCMWPGDCQkJKBGjRrQ1tZGZmYmvv32W/Tv31/doRW67M+U3D5v7t27p46QitSrV68wc+ZMDBgwAMbGxuoOp1AsWrQIOjo6mDBhgrpDKXRxcXFISkrCd999hwULFmDRokXw9/dHz549ERwcDFdXV3WHSIWgNOU9mqik5mKaqCTnh5qqNOStmqY05dGaSt35PYtRJYBMJlN6LoTI0VbcjRs3DpcvX8bp06fVHUqhefDgASZOnIhjx46VyDlNsrKy4OzsjIULFwIAGjRogGvXrmH9+vUl5kt937592LlzJ3bv3o3atWsjMjISXl5esLOzw5AhQ9QdXpEoDZ836enp6NevH7KysrBu3Tp1h1MoIiIisHLlSly8eLHEvV4ApMlOu3fvjkmTJgEA6tevjzNnzmDDhg0sRpUwpeFzSBOVxFxME5X0/FBTlYa8VdOUxjxaU6nre5WX6RVjlpaW0NbWzvFrYFxcXI7qZnE2fvx4HDp0CMHBwShfvry6wyk0ERERiIuLQ6NGjaCjowMdHR2EhoZi1apV0NHRQWZmprpD/Ci2traoVauWUlvNmjVL1CSz06ZNw8yZM9GvXz/UqVMHgwYNwqRJk+Dr66vu0AqdjY0NAJT4z5v09HT06dMH0dHRCAwMLDFnRZ06dQpxcXGoUKGC9Hlz7949TJkyBRUrVlR3eB/N0tISOjo6Jf4zp7QrLXmPJiqpuZgmKun5oaYqDXmrpilNebSmUnd+z2JUMaanp4dGjRohMDBQqT0wMBDNmzdXU1SFRwiBcePGYf/+/Thx4gQcHR3VHVKhatu2La5cuYLIyEjp4ezsjIEDByIyMhLa2trqDvGjtGjRIsftn2/evAkHBwc1RVT4Xr58CS0t5Y9RbW3tEnlLWkdHR9jY2Ch93qSlpSE0NLREfN4A/ytE3bp1C0FBQbCwsFB3SIVm0KBBuHz5stLnjZ2dHaZNm4aAgAB1h/fR9PT00Lhx4xL/mVPalfS8RxOV9FxME5X0/FBTlYa8VdOUpjxaU6k7v+dlesXc5MmTMWjQIDg7O6NZs2bYtGkT7t+/jy+++ELdoX20sWPHYvfu3fjtt99gZGQkVWxNTEygUCjUHN3HMzIyyjHngqGhISwsLErEXAyTJk1C8+bNsXDhQvTp0wfnz5/Hpk2bsGnTJnWHVmi6du2Kb7/9FhUqVEDt2rVx6dIlLFu2DMOGDVN3aB8kKSkJt2/flp5HR0cjMjIS5ubmqFChAry8vLBw4UJUrVoVVatWxcKFC2FgYIABAwaoMeqCe9f+2dnZoVevXrh48SKOHDmCzMxM6TPH3Nwcenp66gq7wPJ7/d4urunq6sLGxgbVq1dXdagfJL/9mzZtGvr27YvWrVvD3d0d/v7+OHz4MEJCQtQXNBW6kpz3aKKSnotpopKeH2qq0pC3apqSlkdrKo3O74v8fn1U5NauXSscHByEnp6eaNiwYYm53S6AXB/btm1Td2hFpqTduvfw4cPCyclJyOVyUaNGDbFp0yZ1h1SoEhMTxcSJE0WFChWEvr6+qFSpkpg9e7ZITU1Vd2gfJDg4ONe/uSFDhgghXt/+dd68ecLGxkbI5XLRunVrceXKFfUG/R7etX/R0dF5fuYEBwerO/QCye/1e5uDg4NYvny5SmP8GAXZvy1btogqVaoIfX19Ua9ePXHw4EH1BUxFpqTmPZqoNOZimqik5YeaqqTnrZqmpOXRmkqT83uZEEIUUZ2LiIiIiIiIiIhICeeMIiIiIiIiIiIilWExioiIiIiIiIiIVIbFKCIiIiIiIiIiUhkWo4iIiIiIiIiISGVYjCIiIiIiIiIiIpVhMYqIiIiIiIiIiFSGxSgiIiIiIiIiIlIZFqOIiIiIiIiIiEhlWIwiIiIiIiIiIiKVYTGKiKgIPH36FFZWVrh7967KttmrVy8sW7ZMZdsjIiIiKkzMn4hKDxajiKhY8vT0hEwmwxdffJFj2ZgxYyCTyeDp6an6wP6fr68vunbtiooVK0ptrVu3hkwmwzfffKPUVwgBFxcXyGQyzJ0794O3OXfuXHz77bdITEz84DGIiIio5GL+lBPzJyL1YDGKiIote3t77N27FykpKVLbq1evsGfPHlSoUEFtcaWkpGDLli0YMWKE1CaEQGRkJBwcHHDlyhWl/n5+fnj06BEAoGHDhh+83bp166JixYrYtWvXB49BREREJRvzJ2XMn4jUg8UoIiq2GjZsiAoVKmD//v1S2/79+2Fvb48GDRpIbf7+/mjZsiVMTU1hYWGBLl264M6dO0pj/fLLL6hTpw4UCgUsLCzQrl07JCcn57ssN3/88Qd0dHTQrFkzqe3WrVt48eIFPD09lZKpFy9eYNasWdKvkI0aNfqoY9KtWzfs2bPno8YgIiKikov5U07Mn4hUj8UoIirWhg4dim3btknPt27dimHDhin1SU5OxuTJkxEeHo7jx49DS0sLn376KbKysgAAMTEx6N+/P4YNG4aoqCiEhISgZ8+eEEK8c1leTp48CWdnZ6W2iIgI6Ovro3///rh16xZSU1MBAN988w3q168PW1tbWFpawt7e/qOOR5MmTXD+/HlpfCIiIqK3MX9SxvyJSPV01B0AEdHHGDRoEGbNmoW7d+9CJpPhzz//xN69exESEiL1+eyzz5TW2bJlC6ysrHD9+nU4OTkhJiYGGRkZ6NmzJxwcHAAAderUAQDcvHkzz2V5uXv3Luzs7JTaLl68iLp166JatWowNDREVFQUDA0NsW7dOly4cAHff//9R/+qBwDlypVDamoqYmNjpXiJiIiI3sT8SRnzJyLV45lRRFSsWVpaonPnzvDz88O2bdvQuXNnWFpaKvW5c+cOBgwYgEqVKsHY2BiOjo4AgPv37wMA6tWrh7Zt26JOnTro3bs3Nm/ejPj4+HyX5SUlJQX6+vpKbREREWjUqBFkMhnq1q2Lq1evYtKkSRg1ahRq1KiBiIiIj5rvIJtCoQAAvHz58qPHIiIiopKJ+ZMy5k9EqsdiFBEVe8OGDcP27dvh5+eX4xRzAOjatSuePn2KzZs349y5czh37hwAIC0tDQCgra2NwMBA/PHHH6hVqxZWr16N6tWrIzo6+p3L8mJpaZkj4bp06ZKULNWrVw8rV67E+fPnMW/ePKSlpeHatWu5JlMvX77EtGnT0Lx5czRv3hwjR47E06dP89z2s2fPAABly5bN56gRERFRacb86X+YPxGpHotRRFTsderUCWlpaUhLS0PHjh2Vlj19+hRRUVH4+uuv0bZtW9SsWTPXX+ZkMhlatGgBHx8fXLp0CXp6ejhw4EC+y3LToEEDXL9+XXr+zz//4Pnz59Jp5PXr18eFCxfw7bffwsTEBFeuXEF6enqup5mPGzcO9erVw5kzZ3DmzBn069cPgwcPznPOhatXr6J8+fI5ft0kIiIiehPzp/9h/kSkepwzioiKPW1tbURFRUn//yYzMzNYWFhg06ZNsLW1xf379zFz5kylPufOncPx48fRoUMHWFlZ4dy5c/jvv/9Qs2bNdy7LS8eOHTFr1izEx8fDzMwMERER0NPTg5OTEwBgyJAh6NGjBywsLAC8ng/BzMxMOv09W0pKCuLj4/H555/D29sbAODt7Y3ffvsNt2/fRtWqVXNs+9SpU+jQocP7HUAiIiIqdZg//Q/zJyLVYzGKiEoEY2PjXNu1tLSwd+9eTJgwAU5OTqhevTpWrVoFNzc3pXVPnjyJFStWIDExEQ4ODli6dCk8PDwQFRWV57K81KlTB87Ozvjpp58wevRoXLx4EU5OTtDV1QUA6OrqKv3ydvHiRaVbKWd789e7cePG5XsMXr16hQMHDiAgICDfvkRERETMn5g/EamLTLzr/ppERPRBfv/9d0ydOhVXr16FltaHXxHt6emJ9u3bY+DAgQCA48eP4/vvv8fvv/8OmUym1Hft2rX47bffcOzYsY+KnYiIiEgdmD8RlR48M4qIqAh88sknuHXrFv7991/Y29t/8Djr1q3D119/jVWrVkEmk6FmzZrYuXNnjkQKeP2L4erVqz8mbCIiIiK1Yf5EVHrwzCgiIiIiIiIiIlIZ3k2PiIiIiIiIiIhUhsUoIiIiIiIiIiJSGRajiIiIiIiIiIhIZViMIiIiIiIiIiIilWExioiIiIiIiIiIVIbFKCIiIiIiIiIiUhkWo4iIiIiIiIiISGVYjCIiIiIiIiIiIpVhMYqIiIiIiIiIiFSGxSgiIiIiIiIiIlIZFqOIiIiIiIiIiEhlWIwiIiIiIiIiIiKVYTGKiIiIiIiIiIhUhsUoIiIiIiIiIiJSGRajiIiIiIiIiIhIZViMIiIiIiIiIiIilWExioiIiIiIiIiIVIbFKCIiIiIiIiIiUhkWo4iIiIiIiIiISGVYjCIiIiIiIiIiIpVhMYqIiIiIiIiIiFSGxSgiIiIiIiIiIlIZFqOIiIiIiIiIiEhlWIwiIiIiIiIiIiKVYTGKiIiIiIiIiIhUhsUoIiIiIiIiIiJSGRajiIiIiIiIiIhIZViMIgJw+fJlDB8+HJUrV4ZCoYBCoUDVqlUxevRoXLhwQd3hFaozZ87A29sbz58/L/SxPT09UbFixXz7ubm5QSaTSQ9dXV1UrFgRw4cPx7179wo9LnXYvn270j7q6+vDxsYG7u7u8PX1RVxcnLpD1Ehubm5wcnIq8u1UrFgRMpkMbm5uuS7/8ccfpdcuJCSkyOMhIippmFsVjvfNrTp16pRj2d27dyGTyfD9998Xenxv+v333+Ht7V2k2/gYAQEB6NChA+zs7CCXy2FnZwc3Nzd89913Sv0WLlyIgwcPqifIPISEhEAmk+GXX34p0u28mb/mlv8IIVClSpV35lBEBcViFJV6GzduRKNGjXDu3DlMnDgRR44cwdGjR+Hl5YVr166hcePGuHPnjrrDLDRnzpyBj49PkSRM76NSpUoICwtDWFgYjh8/junTp+PIkSNo1aoVXr58qdbYCtO2bdsQFhaGwMBArF27FvXr18eiRYtQs2ZNBAUFqTu8Us3IyAgnT57M9e9769atMDY2VkNURETFH3Mr9QkICMCJEyfUsu3ff/8dPj4+atl2fjZs2IBOnTrB2NgYa9asQUBAgJSPvV3g0cRilKoZGRlhy5YtOdpDQ0Nx584dGBkZqSEqKml01B0AkTr9+eefGDNmDDp37oxffvkFenp60rI2bdpg7Nix+Pnnn6FQKNQY5bu9fPkSBgYG6g7jvSkUCjRt2lR63rp1a+jr62P48OE4ffo0OnTokOe6xWmfnZyc4OzsLD3/7LPPMGnSJLRs2RI9e/bErVu3YG1trbb4itOxLGwtW7bElStXsHXrVnz77bdS+507d3Dy5EmMGDECmzdvVmOERETFD3Mr9alWrRoyMjIwffp0hIeHQyaTqTukPAkh8OrVK5W9D3x9fdG6deschadBgwYhKyuryLefmZmJjIwMyOXyIt9WYejbty927dqFtWvXKv04t2XLFjRr1gyJiYlqjI5KCp4ZRaXawoULoa2tjY0bNyolS2/q3bs37OzslNouXLiAbt26wdzcHPr6+mjQoAF++uknpT7Zp7kGBwfjyy+/hKWlJSwsLNCzZ088evQox3b27duHZs2awdDQEGXKlEHHjh1x6dIlpT6enp4oU6YMrly5gg4dOsDIyAht27YFAAQGBqJ79+4oX7489PX1UaVKFYwePRpPnjyR1vf29sa0adMAAI6OjrmehluQOLL3r3r16pDL5ahZsyZ+/PHHdxzpgjExMQEA6OrqKsUsk8lw8eJF9OrVC2ZmZqhcuTIA4NWrV5g1axYcHR2hp6eHcuXKYezYsUq/TE6bNg0mJibIzMyU2saPHw+ZTIYlS5ZIbU+fPoWWlhZWr14N4H+nQ+/ZswezZ8+GnZ0djI2N0a5dO9y4ceOj9rNChQpYunQpXrx4gY0bNwIAjh49CplMhvDwcKnfr7/+CplMhs6dOyutX7duXXz22WfS87Vr16J169awsrKCoaEh6tSpg8WLFyM9PV1pvexL4E6ePInmzZvDwMAAw4YNQ48ePeDg4JBrMubi4oKGDRtKz4UQWLduHerXrw+FQgEzMzP06tUL//zzj9J6ly5dQpcuXWBlZSWdCt+5c2c8fPiwQMfo1KlTaNq0KRQKBcqVK4c5c+ZIr6EQAlWrVkXHjh1zrJeUlAQTExOMHTs2321oaWlh8ODB8PPzU9r3rVu3wt7eHu3atcuxzoULF9CvXz9UrFgRCoUCFStWRP/+/XNcXvry5UtMnToVjo6O0NfXh7m5OZydnbFnzx6pzz///IN+/fpJlwtYW1ujbdu2iIyMLNAxIiLSRMyt1Jdb6erq4ttvv0VERAT27duXb//Y2FiMHj0a5cuXh56eHhwdHeHj44OMjAypT3Y+9PYlW9mX/m3fvh3A6+O4du1aAFCapuDu3btS27hx47BhwwbUrFkTcrkcfn5+AIDTp0+jbdu2MDIygoGBAZo3b46jR4/mODbv89q/7enTp7C1tc11mZbW//5JLJPJkJycDD8/P2kfsi9H+++//zBmzBjUqlULZcqUgZWVFdq0aYNTp07lemwWL16MBQsWwNHREXK5HMHBwcjKysKCBQtQvXp1KBQKmJqaom7duli5cmW++wC8zn0nT54MGxsbKBQKuLq6Kr2XduzYAZlMhrCwsBzrzp8/H7q6ugU6Xv379wcApbwlISEBv/76K4YNG5brOj4+PnBxcYG5uTmMjY3RsGFDbNmyBUIIpX4nTpyAm5sbLCwsoFAoUKFCBXz22WdKV0asX78e9erVQ5kyZWBkZIQaNWrgq6++yjduKmYEUSmVkZEhFAqFaNas2Xutd+LECaGnpydatWol9u3bJ/z9/YWnp6cAILZt2yb127ZtmwAgKlWqJMaPHy8CAgLEDz/8IMzMzIS7u7vSmN9++62QyWRi2LBh4siRI2L//v2iWbNmwtDQUFy7dk3qN2TIEKGrqysqVqwofH19xfHjx0VAQIAQQoj169cLX19fcejQIREaGir8/PxEvXr1RPXq1UVaWpoQQogHDx6I8ePHCwBi//79IiwsTISFhYmEhIT3iiN737p37y4OHz4sdu7cKapUqSLs7e2Fg4NDvsfQ1dVV1K5dW6Snp4v09HSRnJwszp07J+rWrSsqVaokXr16JfWdN2+eACAcHBzEjBkzRGBgoDh48KDIysoSHTt2FDo6OmLOnDni2LFj4vvvvxeGhoaiQYMG0hj+/v4CgDhz5ow0Zo0aNYRCoRDt27eX2vbt2ycAiOvXrwshhAgODhYARMWKFcXAgQPF0aNHxZ49e0SFChVE1apVRUZGxjv3MfsYhYeH57o8KSlJaGtri7Zt2wohhHjx4oXQ1dUVCxculPp88cUXQqFQCENDQ+k1fPz4sZDJZGLdunVSv0mTJon169cLf39/ceLECbF8+XJhaWkphg4dmuO4m5ubC3t7e7F69WoRHBwsQkNDxW+//SYAiMDAQKX+UVFRAoBYtWqV1DZy5Eihq6srpkyZIvz9/cXu3btFjRo1hLW1tYiNjZX2zcLCQjg7O4uffvpJhIaGin379okvvvhCOr55cXV1FRYWFsLOzk6sWrVKBAQEiAkTJggAYuzYsVK/lStXCplMJm7evKm0/tq1awUApfdrbhwcHETnzp3F7du3hUwmE7///rsQ4vXnQrly5cTcuXPFzz//LACI4OBgab2ff/5ZzJ07Vxw4cECEhoaKvXv3CldXV1G2bFnx33//Sf1Gjx4tDAwMxLJly0RwcLA4cuSI+O6778Tq1aulPtWrVxdVqlQRO3bsEKGhoeLXX38VU6ZMUdoeEVFxwtxK/blVVlaWaNSokahcubIUY3R0tAAglixZIvWPiYmRxt64caMICgoS33zzjZDL5cLT01Pql50Pvf3dlD1m9utz+/Zt0atXLwFAOgZhYWFSPgZAlCtXTtStW1fs3r1bnDhxQly9elWEhIQIXV1d0ahRI7Fv3z5x8OBB0aFDByGTycTevXs/6LXPTbt27YSOjo6YN2+eiIyMzDOPCwsLEwqFQnzyySfSPmS/Tn///bf48ssvxd69e0VISIg4cuSIGD58uNDS0lI6PtnHply5csLd3V388ssv4tixYyI6Olr4+voKbW1tMW/ePHH8+HHh7+8vVqxYIby9vd8Zf/brYG9vn+M9YmxsLO7cuSOEECI1NVXY2NiIgQMHKq2fnp4u7OzsRO/evd+5nTfz10GDBokmTZpIy9avXy8MDQ1FYmKiqF27tnB1dVVa19PTU2zZskUEBgaKwMBA8c033wiFQiF8fHyUjo2+vr5o3769OHjwoAgJCRG7du0SgwYNEvHx8UIIIfbs2SMAiPHjx4tjx46JoKAgsWHDBjFhwoR3xk7FD4tRVGrFxsYKAKJfv345lmVkZEiFkvT0dJGVlSUtq1GjhmjQoIFIT09XWqdLly7C1tZWZGZmCiH+92E+ZswYpX6LFy8WAERMTIwQQoj79+8LHR0dMX78eKV+L168EDY2NqJPnz5S25AhQwQAsXXr1nfuW1ZWlkhPTxf37t0TAMRvv/0mLVuyZIkAIKKjo5XWKWgcmZmZws7OTjRs2FDpuNy9e1fo6uoWOGECkONRrVo1ERUVpdQ3uxg1d+5cpfbsItPixYuV2rOLSps2bRJCCJGcnCz09PTE/PnzhRBCPHz4UAAQM2bMEAqFQkqSRo4cKezs7KRxsr/0P/nkE6Xxf/rpJynRepf8ilFCCGFtbS1q1qwpPW/ZsqVo06aN9LxKlSpi2rRpQktLS4SGhgohhNi1a5cAkKMIky0zM1Okp6eLH3/8UWhra4tnz55Jy7KP+/Hjx5XWSU9PF9bW1mLAgAFK7dOnTxd6enriyZMnQojXCRoAsXTpUqV+Dx48EAqFQkyfPl0IIcSFCxcEAHHw4ME89z0v2TG++Z4V4vXro6WlJe7duyeEECIxMVEYGRmJiRMnKvWrVatWgZLS7GJU9jZ79eolhBDi6NGjQiaTiejo6FyLUW/LyMgQSUlJwtDQUKxcuVJqd3JyEj169MhzvSdPnggAYsWKFfnGSkRUXDC3ilZaR9W5Ve3atYUQQgQFBQkA0g8guRWjRo8eLcqUKSN9r2b7/vvvlX7UKWgxSgghxo4dK/I61wGAMDExUcpLhBCiadOmwsrKSrx48UJqy8jIEE5OTqJ8+fLS8Sjoa5+X27dvCycnJynnVCgUom3btmLNmjVS0S6boaGhGDJkyDvHy44zPT1dtG3bVnz66adSe/axebMgmK1Lly6ifv36+Y79tuzXIa/3yIgRI6S2efPmCT09PfH48WOpLTs/zs4n8/Jm/pq9zatXrwohhGjcuLFUqMytGPWm7Hx0/vz5wsLCQor5l19+EQBEZGRknuuOGzdOmJqavjNOKhl4mR5RLho1agRdXV3psXTpUgDA7du38ffff2PgwIEAgIyMDOnxySefICYmJsclXN26dVN6XrduXQCQLusJCAhARkYGBg8erDSevr4+XF1dc72TxZuXaGWLi4vDF198AXt7e+jo6EBXVxcODg4AgKioqHz3uaBx3LhxA48ePcKAAQOU5iJwcHBA8+bN891OtsqVKyM8PBzh4eEICwvD7t27oVAo0LZtW9y6dSvffc6enNPT01OpvXfv3jA0NMTx48cBAAYGBmjWrJk0WXhgYCBMTU0xbdo0pKWl4fTp0wCAoKCgXC/Lyu/1+xjirdOW27Ztiz///BMpKSm4d+8ebt++jX79+qF+/foIDAyU4qxQoQKqVq0qrXfp0iV069YNFhYW0NbWhq6uLgYPHozMzEzcvHlTaRtmZmZo06aNUpuOjg4+//xz7N+/HwkJCQBez22wY8cOdO/eHRYWFgCAI0eOQCaT4fPPP1d6j9jY2KBevXrSe6RKlSowMzPDjBkzsGHDBly/fv29jouRkVGO4z5gwABkZWXh5MmTUp+hQ4di+/btSE5OBvD6PXH9+nWMGzfuvbY3bNgwHDp0CE+fPsWWLVvg7u6e552LkpKSMGPGDFSpUgU6OjrQ0dFBmTJlkJycrPR31qRJE/zxxx+YOXMmQkJCkJKSojSOubk5KleujCVLlmDZsmW4dOmSSuasICJSF+ZWRZ9bZWvbti06dOiA+fPn48WLF7n2OXLkCNzd3WFnZ6cUm4eHB4DXE1UXtjZt2sDMzEx6npycjHPnzqFXr14oU6aM1K6trY1Bgwbh4cOH7/3a56Vy5cr466+/EBoaCh8fH7Rr1w7h4eEYN24cmjVrhlevXhVoHzZs2ICGDRtCX19fek8cP3481/dDt27dlKaeAF7nB3/99RfGjBmDgICA9557Ka/3SHBwsNT25ZdfAoDSvJdr1qxBnTp10Lp16wJvy9XVFZUrV8bWrVtx5coVhIeH53mJHvA6D2vXrh1MTEykfHTu3Ll4+vSpdBfp+vXrQ09PD6NGjYKfn1+OaR6A18fo+fPn6N+/P3777Tely2KpZGExikotS0tLKBSKXL+8du/ejfDwcBw6dEip/fHjxwCAqVOnKiVUurq6GDNmDADk+MDM/od8tuyJC7P/cZo9ZuPGjXOMuW/fvhzjGRgY5LjLV1ZWFjp06ID9+/dj+vTpOH78OM6fP4+zZ88qbetdChrH06dPAQA2NjY5xsitLS/6+vpwdnaGs7MzmjZtiv79++OPP/5ATEwM5s6dm6P/29f5P336FDo6OihbtqxSu0wmg42NjRQnALRr1w5nz55FcnIygoKC0KZNG1hYWKBRo0YICgpCdHQ0oqOjcy1G5ff6fajk5GQ8ffpUac6Mdu3aITU1FadPn0ZgYCAsLS3RoEEDtGvXTiqmHT9+XCnO+/fvo1WrVvj333+xcuVKnDp1CuHh4dK8DW/Hmdd8CcOGDcOrV6+wd+9eAK8T6JiYGAwdOlTq8/jxYwghYG1tneM9cvbsWek9YmJigtDQUNSvXx9fffUVateuDTs7O8ybNy/HPFa5yW1C9+z31puv6/jx4/HixQvs2rULwOtEq3z58ujevXu+23hTr169oK+vj+XLl+Pw4cMYPnx4nn0HDBiANWvWYMSIEQgICMD58+cRHh6OsmXLKh3rVatWYcaMGTh48CDc3d1hbm6OHj16SIVWmUyG48ePo2PHjli8eDH+r707j4uq+v8H/hqWGZBg2GQVkVBJBVc+IpqiuSCJZlYuFIGVWq4EZKKlaCq5W5RLZmJq4eeTWm4haO64oqQoqShuCWKKIISAeH5/+ON+HVbFWUBez8djHjn3nrn3nHvnMu/e99xz2rdvj4YNG2L8+PGV/o8DEVFtx9hKlbZjq8fNmTMH//zzD+bPn19p3bZs2VKuXq1atQJQ/pirQ9kYJDs7G0KICmOT0vjo8d994NniMj09PXTr1g1Tp07F5s2bcePGDQwZMgRJSUn44Ycfqv38woUL8dFHH8HLywsbNmzA4cOHcezYMfTt27fC/VfUroiICMyfPx+HDx+Gn58frKys0LNnTxw/frza/QOVf0ceP062trYYMmQIli9fjpKSEpw6dQr79+9/6pt1MpkMw4cPx9q1a7Fs2TI0b94cXbt2rbDs0aNHpcmHVqxYgYMHD+LYsWOYMmUKgP87P66urti5cydsbGwwZswYuLq6wtXVVWXMrMDAQPzwww+4cuUK3njjDdjY2MDLy0u6MUvPD86mR/WWvr4+XnnlFcTHxyMjI0PlB6Nly5YAIA26WMra2hrAox+SQYMGVbhdNze3p6pH6TZ/+eUX6W5bVSqaGSUlJQV//vknYmJiEBQUJC1PS0tTez1Kg4DMzMxy6ypa9jTs7e1hbW2NP//8s9y6su22srLCgwcPcOvWLZWElBACmZmZ+M9//iMt69mzJz7//HPs27cPu3btwrRp06Tl8fHxcHFxkd5ry7Zt21BSUiINigk8Giz8hRdewM6dO3H58mX07NkTMpkMPXv2xIIFC3Ds2DFcvXpVJRn166+/Ij8/Hxs3blQ5b5UNgl3ZzDotW7ZEx44dsWrVKowaNQqrVq2Cg4ODyqyG1tbWkMlk2L9/f4WzwTy+zMPDA7GxsRBC4NSpU4iJicGMGTNgbGyMSZMmVXlsSoP3x5V+tx4PQps2bQo/Pz98++238PPzw+bNmzF9+nTo6+tXuf2yGjRogKFDhyIqKgpmZmaVXts5OTnYunUrpk2bptKGwsJC3LlzR6WsiYkJpk+fjunTp+PmzZtSL6n+/fvjr7/+AvDobmbptMnnz5/Hf//7X0RGRqKoqAjLli17qjYQEdUGjK1qVg9NxFZt27bFsGHDsHDhQrz66qsV1q1169Yqs8k+rjQZZGRkBODRb93japKsKnucLSwsoKenh4yMjHJlSwfZLj2GmmBiYoKIiAisX78eKSkp1ZZfu3YtunfvjqVLl6osr+wmUkXfKwMDA4SGhiI0NBR3797Fzp07MXnyZPj6+uLatWvVzuJY2XekbJJuwoQJWLNmDX777TfExcXB3Nxc6n34NIKDgzF16lQsW7as0u8KAMTGxsLQ0BBbt26VvjPAozi1rK5du6Jr164oKSnB8ePHER0djZCQENja2mLo0KEAgOHDh2P48OHIz8/Hvn37MG3aNPj7++P8+fNPdE1T3cCeUVSvRUREoKSkBB9++OET9dhwc3NDs2bN8Oeff0q9esq+TE1Nn6oOvr6+MDAwwMWLFyvdZnVKf+zKJghKZ2p7XGV3kJ60Hm5ubrC3t8fPP/+s8pjZlStXkJiY+FRtL+v69ev4559/YGNjU23Z0sTR2rVrVZZv2LAB+fn5Komljh07wszMDIsXL0ZmZiZ69+4N4FFPpJMnT+K///0vWrZsWW5mH025evUqwsPDoVQqMWrUKGm5oaEhunXrhoSEBPzxxx9SPbt27QoDAwN89tlnUnKqVEXnXgih0jX7SQ0fPhxHjhzBgQMHsGXLFgQFBakkdvz9/SGEwN9//13h98PDw6PcNmUyGdq0aYNFixbB3NwcJ06cqLYe9+7dK3fn/KeffpLuaD5uwoQJOHXqlFTXESNGPHW7gUdd2vv374+pU6eqBFFl2yKEKHedff/99yqzNZZla2uL4OBgDBs2DOfOnVOZLaZU8+bN8dlnn8HDw+OJjhERUW3F2Orp66Gp2GrmzJkoKirC9OnTy63z9/dHSkoKXF1dK6xXaUxU+tj6qVOnVD5f9ne6quNQGRMTE3h5eWHjxo0qn3n48CHWrl2LRo0aoXnz5k+0repUlPAC/u9xy8djQIVCUWEbZDJZue/DqVOnKpy57kmYm5vjzTffxJgxY3Dnzp1yidqKVPYdefzmJvDosdjOnTtjzpw5WLduHYKDg2FiYvLUdXR0dMQnn3yC/v37qyRly5LJZDAwMFCJGwsKCrBmzZpKP6Ovrw8vLy+pN39F8Y+JiQn8/PwwZcoUFBUV4cyZM0/dBqq92DOK6rUuXbrg22+/xbhx49C+fXuMHDkSrVq1ku7SbNiwAQBUum4vX74cfn5+8PX1RXBwMBwdHXHnzh2kpqbixIkT+N///vdUdWjSpAlmzJiBKVOm4NKlS+jbty8sLCxw8+ZNHD16VOphUZWXXnoJrq6umDRpEoQQsLS0xJYtWyrszlqaMPjqq68QFBQEQ0NDuLm5PXE99PT08MUXX+CDDz7A66+/jhEjRuDu3buIjIx8qq7kBQUFUlf3kpISpKenY+7cuQCAkJCQaj/fu3dv+Pr64tNPP0Vubi66dOmCU6dOYdq0aWjXrh0CAwOlsvr6+vDx8cGWLVvg4uICV1dXAI/Ov0KhwK5duzB+/PgnrvvTSElJkcZhyMrKwv79+7Fq1Sro6+tj06ZN5R4z7NmzJ8LCwgBA6gFlbGyMzp07Iz4+Hq1bt1ZJ1vXu3RtyuRzDhg3DxIkTcf/+fSxduhTZ2dlPXddhw4YhNDQUw4YNQ2FhYbnxuLp06YKRI0di+PDhOH78OLp16wYTExNkZGTgwIED8PDwwEcffYStW7diyZIlGDhwIF588UUIIbBx40bcvXtXSrBVxcrKCh999BGuXr2K5s2bY/v27VixYgU++ugjNG7cWKVs79690bJlS+zevRvvvPPOEyUyK9K2bdsK7949zszMDN26dcO8efNgbW2NJk2aYO/evVi5ciXMzc1Vynp5ecHf3x+tW7eGhYUFUlNTsWbNGnh7e6NBgwY4deoUxo4di7feegvNmjWDXC7HH3/8gVOnTlXbc4yIqDZjbKW72KosFxcXfPTRRyqPQJWaMWMGEhIS0LlzZ4wfPx5ubm64f/8+Ll++jO3bt2PZsmVo1KgR7Ozs0KtXL0RFRcHCwgLOzs7YtWsXNm7cWOlxmDNnDvz8/KCvr4/WrVtDLpdXWseoqCj07t0bPXr0QHh4OORyOZYsWYKUlBT8/PPPlfboflqtWrVCz5494efnB1dXV9y/fx9HjhzBggULYGtrq/KIvoeHB/bs2YMtW7bA3t4epqamcHNzg7+/P7744gtMmzYNPj4+OHfuHGbMmAEXFxc8ePDgierRv39/uLu7w9PTEw0bNsSVK1ewePFiODs7q4wHWpmsrCzpO5KTk4Np06bByMgIERER5cpOmDABQ4YMgUwmkx55rYkvv/yy2jL9+vXDwoULERAQgJEjR+L27duYP39+ueTdsmXL8Mcff6Bfv35o3Lgx7t+/Lz0iWRr3jhgxAsbGxujSpQvs7e2RmZmJqKgoKJVKlScf6Dmgi1HTiWqb5ORkMXz4cOHi4iIUCoUwMjISTZs2Fe+++265mceEEOLPP/8UgwcPFjY2NsLQ0FDY2dmJV155RSxbtkwqU9lsapXNSvLrr7+KHj16CDMzM6FQKISzs7N48803xc6dO6UyQUFBwsTEpMI2nD17VvTu3VuYmpoKCwsL8dZbb4mrV68KAGLatGkqZSMiIoSDg4PQ09MrV5cnqYcQQnz//feiWbNmQi6Xi+bNm4sffvhBBAUF1Wg2PT09PeHg4CD8/PzEnj17VMqWzqZ369atctspKCgQn376qXB2dhaGhobC3t5efPTRR9LUsI/76quvBAAxYsQIleW9e/cWAMTmzZtVlpeep//9738qyyuaPaYipee/9CWXy4WNjY3w8fERs2fPFllZWRV+7s8//xQARLNmzVSWz5o1SwAQoaGh5T6zZcsW0aZNG2FkZCQcHR3FJ598In7//fdy5/bxmXYqExAQIACILl26VFrmhx9+EF5eXsLExEQYGxsLV1dX8e6774rjx48LIR5NfTxs2DDh6uoqjI2NhVKpFB07dhQxMTFV7vvxOu7Zs0d4enoKhUIh7O3txeTJk8vNslQqMjJSABCHDx+udvulHp9NrzIVzaZ3/fp18cYbbwgLCwthamoq+vbtK1JSUoSzs7PKzDuTJk0Snp6ewsLCQigUCvHiiy+Kjz/+WJqZ8ObNmyI4OFi89NJLwsTERLzwwguidevWYtGiRZVON01EVJcwtvq/umgrtqroN/7WrVvCzMys3Gx6pevGjx8vXFxchKGhobC0tBQdOnQQU6ZMEXl5eVK5jIwM8eabbwpLS0uhVCrFO++8I82c+3g8VFhYKD744APRsGFDIZPJVGYYBCDGjBlTYd33798vXnnlFSmu6NSpk9iyZYtKmac992UtX75cDBo0SLz44ouiQYMGQi6XC1dXV/Hhhx+Ka9euqZRNTk4WXbp0EQ0aNBAApFnjCgsLRXh4uHB0dBRGRkaiffv24tdffy13jiqavbDUggULROfOnYW1tbWQy+WicePG4v333xeXL1+usv6l7VyzZo0YP368aNiwoVAoFKJr165S/FVWYWGhUCgUom/fvlVu+3FPMhu0EBXPpvfDDz8INzc3Ke6JiooSK1euVPkeHDp0SLz++uvC2dlZKBQKYWVlJXx8fFTi8NWrV4sePXoIW1tbIZfLhYODgxg8eLA4derUE7eD6gaZEGWmcyIiIqpDPD09IZPJcOzYMV1XhYiIiKhW2LJlCwYMGIBt27ZVOG4Yka7xMT0iIqpzcnNzkZKSgq1btyIpKQmbNm3SdZWIiIiIdO7s2bO4cuUKwsLC0LZtW/j5+em6SkQVYjKKiIjqnBMnTqBHjx6wsrLCtGnTMHDgQF1XiYiIiEjnRo8ejYMHD6J9+/ZYvXq12sbdIlI3PqZHRERERERERERao6frChARERERERERUf3BZBQREREREREREWkNk1FERERERERERKQ1Oh3APCoqChs3bsRff/0FY2NjdO7cGXPmzIGbm5tURgiB6dOn47vvvkN2dja8vLzw7bffolWrVlKZwsJChIeH4+eff0ZBQQF69uyJJUuWoFGjRlKZ7OxsjB8/Hps3bwYADBgwANHR0TA3N5fKXL16FWPGjMEff/wBY2NjBAQEYP78+ZDL5VKZ06dPY+zYsTh69CgsLS0xatQofP755088MNzDhw9x48YNmJqacjA5IiKiWkwIgXv37sHBwQF6erx/pyuMnYiIiOqOJ46fhA75+vqKVatWiZSUFJGcnCz69esnGjduLPLy8qQyX375pTA1NRUbNmwQp0+fFkOGDBH29vYiNzdXKvPhhx8KR0dHkZCQIE6cOCF69Ogh2rRpIx48eCCV6du3r3B3dxeJiYkiMTFRuLu7C39/f2n9gwcPhLu7u+jRo4c4ceKESEhIEA4ODmLs2LFSmZycHGFrayuGDh0qTp8+LTZs2CBMTU3F/Pnzn7jN165dEwD44osvvvjii6868rp27VpNQx1SA8ZOfPHFF1988VX3XtXFT7VqNr1bt27BxsYGe/fuRbdu3SCEgIODA0JCQvDpp58CeNQLytbWFnPmzMGoUaOQk5ODhg0bYs2aNRgyZAgA4MaNG3BycsL27dvh6+uL1NRUtGzZEocPH4aXlxcA4PDhw/D29sZff/0FNzc3/P777/D398e1a9fg4OAAAIiNjUVwcDCysrJgZmaGpUuXIiIiAjdv3oRCoQAAfPnll4iOjsb169ef6G5dTk4OzM3Nce3aNZiZmWniMBIREZEa5ObmwsnJCXfv3oVSqdR1deotxk5ERER1x5PGTzp9TK+snJwcAIClpSUAID09HZmZmejTp49URqFQwMfHB4mJiRg1ahSSkpJQXFysUsbBwQHu7u5ITEyEr68vDh06BKVSKSWiAKBTp05QKpVITEyEm5sbDh06BHd3dykRBQC+vr4oLCxEUlISevTogUOHDsHHx0dKRJWWiYiIwOXLl+Hi4lKuTYWFhSgsLJTe37t3DwBgZmbGgIqIiKgOeF4fDdu3bx/mzZuHpKQkZGRkYNOmTRg4cKC0Pjg4GKtXr1b5jJeXFw4fPiy9V9dQCVUpPf6MnYiIiOqO6uKnWjMAghACoaGhePnll+Hu7g4AyMzMBADY2tqqlLW1tZXWZWZmQi6Xw8LCosoyNjY25fZpY2OjUqbsfiwsLCCXy6ssU/q+tExZUVFRUCqV0svJyamaI0FERESkefn5+WjTpg2++eabSsv07dsXGRkZ0mv79u0q60NCQrBp0ybExsbiwIEDyMvLg7+/P0pKSqQyAQEBSE5ORlxcHOLi4pCcnIzAwECNtYuIiIhqv1rTM2rs2LE4deoUDhw4UG5d2YyaEKLaLFvZMhWVV0eZ0qccK6tPREQEQkNDpfelXdaIiIiIdMnPzw9+fn5VllEoFLCzs6twXU5ODlauXIk1a9agV69eAIC1a9fCyckJO3fulIZKiIuLUxkqYcWKFfD29sa5c+dUJq0hIiKi+qNW9IwaN24cNm/ejN27d6t06y4Nfsr2OsrKypJ6JNnZ2aGoqAjZ2dlVlrl582a5/d66dUulTNn9ZGdno7i4uMoyWVlZAMr33iqlUCikbuXsXk5ERER1yZ49e2BjY4PmzZtjxIgRUtwDoNqhEgBUO1RCRQoLC5Gbm6vyIiIioueLTntGCSEwbtw4bNq0CXv27Ck35pKLiwvs7OyQkJCAdu3aAQCKioqwd+9ezJkzBwDQoUMHGBoaIiEhAYMHDwYAZGRkICUlBXPnzgUAeHt7IycnB0ePHkXHjh0BAEeOHEFOTg46d+4slZk1axYyMjJgb28PAIiPj4dCoUCHDh2kMpMnT0ZRURHkcrlUxsHBAU2aNNHgkSKiuqikpATFxcW6rgYRVcLQ0BD6+vq6rkat5efnh7feegvOzs5IT0/H559/jldeeQVJSUlQKBRqGyqhrKioKEyfPl39DSKi5xLjLSLtUlf8pNNk1JgxY/DTTz/ht99+g6mpqRSUKJVKGBsbQyaTISQkBLNnz0azZs3QrFkzzJ49Gw0aNEBAQIBU9v3330dYWBisrKxgaWmJ8PBweHh4SF3GW7Rogb59+2LEiBFYvnw5AGDkyJHw9/eXuof36dMHLVu2RGBgIObNm4c7d+4gPDwcI0aMkHozBQQEYPr06QgODsbkyZNx4cIFzJ49G1OnTn1uBzcloqcnhEBmZibu3r2r66oQUTXMzc1hZ2fH3/EKlM5SDADu7u7w9PSEs7Mztm3bhkGDBlX6uZoMg/A4DnFARE+C8RaR7qgjftJpMmrp0qUAgO7du6ssX7VqFYKDgwEAEydOREFBAUaPHo3s7Gx4eXkhPj4epqamUvlFixbBwMAAgwcPlmZyiYmJUcnWrVu3DuPHj5e6kg8YMEBlwE59fX1s27YNo0ePRpcuXWBsbIyAgADMnz9fKqNUKpGQkIAxY8bA09MTFhYWCA0NVQmYdOnvuwXIzi/SdTV0xsJEDkdzY11Xg0gKjGxsbNCgQQP+Ty5RLSSEwL///is9dlbaK5oqZ29vD2dnZ1y4cAGA6lAJj/eOysrKknqeP8lQCWUpFAqVmYvrMk3HZox9qD5jvEWkfeqMn2SidARu0orc3FwolUrk5OSodfyov+8WoNeCvSgoLqm+8HPK2FAfO8N8GJSRTpWUlOD8+fOwsbGBlZWVrqtDRNW4ffs2srKy0Lx583JdzjX1m10byWQybNq0CQMHDqy0zO3bt+Ho6IjvvvsO7777LnJyctCwYUOsXbtWZaiERo0aYfv27dIA5i1btsSRI0dUhkro1KkT/vrrrycawLyungdtxGaMfai+YrxFpFvqiJ9qzWx69Gyy84tQUFyCxUPaoqnNC7qujtalZeUhZH0ysvOLGJCRTpWOWdCgQQMd14SInkTptVpcXFzvxo/Ky8tDWlqa9D49PR3JycmwtLSEpaUlIiMj8cYbb8De3h6XL1/G5MmTYW1tjddffx2A+oZKeF5pOjZj7EP1GeMtIt1SR/zEZNRzpqnNC3B3VOq6GkT1HruKE9UN9flaPX78OHr06CG9Lx12ICgoCEuXLsXp06fx448/4u7du7C3t0ePHj2wfv16tQ+V8LxjbEakOfX5bziRLqnj2mMyioiIiKge6t69O6oarWHHjh3VbsPIyAjR0dGIjo6utIylpSXWrl1bozoSERHR84nJKCIiLdHmJAMc1FZ9mjRpgpCQEISEhOi6KkRERFQNbU/qxJhLPRhv1T9MRhERaYG2Jxmo6aC2mZmZiIqKwrZt23D9+nUolUo0a9YM77zzDt599906MzaDNgOayMhITJ8+XXpvZmaG1q1bY+bMmfDx8dH4/p9VTEwMhg8fDgDQ09ODmZkZmjdvjn79+mHChAlQKvl4ERER1Q26mNSpJjEX462nVxpvjRo1CsuWLZOWJycno127dkhPT0eTJk3Utr8nmdhD05YvX44lS5YgLS0NhoaGcHFxwdChQ/Hpp58CAIKDg3H37l38+uuvOqvjs2AyiohIC7Q5yUBNB7W9dOkSunTpAnNzc8yePRseHh548OABzp8/jx9++AEODg4YMGCABmteNSEESkpKYGBQ+366WrVqhZ07dwIA7ty5g/nz58Pf318KMCtSXFwMQ0NDbVazUmZmZjh37hyEELh79y4SExMRFRWFVatW4eDBg3BwcNBKPYqKiiCXy7WyLyIiev5oe1KnmsRcjLdqzsjICCtXrkRoaCiaN2+u6+poNJYrbefXX38NHx8fFBYW4tSpUzh79qza96WzmFSQVuXk5AgAIicnR63bPX39rnD+dKs4ff2uWrdbV9T39lPtUVBQIM6ePSsKCgpUlmvzO1rTffn6+opGjRqJvLy8Ctc/fPhQ+vfdu3fFiBEjRMOGDYWpqano0aOHSE5OltZPmzZNtGnTRvz444/C2dlZmJmZiSFDhojc3FyV7c2ZM0e4uLgIIyMj0bp1a/G///1PWr97924BQMTFxYkOHToIQ0ND8ccff4i0tDQxYMAAYWNjI0xMTISnp6dISEiQPufj4yMAqLxKHTx4UHTt2lUYGRmJRo0aiXHjxqm09+bNm8Lf318YGRmJJk2aiLVr1wpnZ2exaNGiSo9baVsfd/XqVQFAHD16VFoGQCxdulQMGDBANGjQQEydOlUIIcSSJUvEiy++KAwNDUXz5s3Fjz/+KH0mNDRU+Pv7S+8XLVokAIitW7dKy5o3by6WLVsmhBAiKChIvPbaa2LevHnCzs5OWFpaitGjR4uioqJK679q1SqhVCrLLb9586awtrYWb7/9thBCiM2bNwulUilKSkqEEEKcPHlSABDh4eHSZ0aOHCmGDh0qhBDin3/+EUOHDhWOjo7C2NhYuLu7i59++kllHz4+PmLMmDHi448/FlZWVqJbt25i6NChYsiQISrlioqKhJWVlfjhhx+EENV/d+7cuSMCAgKEtbW1MDIyEk2bNpU+W1Zl16wQmvvNpqdTV8+Dpv/uM/ah+qw2xFs13R/jrWeLt3r37i3eeustaXlpPJKeni4tO3PmjPDz8xMmJibCxsZGvPPOO+LWrVvS+or21aZNGzFt2jRp/ePtcnZ2VqnDypUrhYuLi5DJZOLhw4fiypUrYsCAAcLExESYmpqKt956S2RmZj7VeSrrtddeE8HBwVUej7LHf/fu3UIIISZOnCiaNWsmjI2NhYuLi/jss89UYsHK2vG///1PuLu7CyMjI2FpaSl69uxZ6fdUHfGTnraSXkREVHvdvn0b8fHxGDNmDExMTCosUzprhhAC/fr1Q2ZmJrZv346kpCS0b98ePXv2xJ07d6TyFy9exK+//oqtW7di69at2Lt3L7788ktp/WeffYZVq1Zh6dKlOHPmDD7++GO888472Lt3r8p+J06ciKioKKSmpqJ169bIy8vDq6++ip07d+LkyZPw9fVF//79cfXqVQDAxo0b0ahRI8yYMQMZGRnIyMgAAJw+fRq+vr4YNGgQTp06hfXr1+PAgQMYO3astK/g4GBcvnwZf/zxB3755RcsWbIEWVlZT3UsCwsLERMTA3Nz83JT10+bNg2vvfYaTp8+jffeew+bNm3ChAkTEBYWhpSUFIwaNQrDhw/H7t27ATwaYHr//v14+PAhAGDv3r2wtraWjlFmZibOnz+v8jjg7t27cfHiRezevRurV69GTEwMYmJinqoNAGBjY4O3334bmzdvRklJCbp164Z79+7h5MmTFdYFAPbs2SPV5f79++jQoQO2bt2KlJQUjBw5EoGBgThy5IjKflavXg0DAwMcPHgQy5cvl/aZl5cnldmxYwfy8/PxxhtvAKj+u/P555/j7Nmz+P3335GamoqlS5fC2tr6qY8BERGROjHeeuRZ4q0vv/wSGzZswLFjxypcn5GRAR8fH7Rt2xbHjx9HXFwcbt68icGDBz/R9gFI2161ahUyMjJU9pWWlob//ve/2LBhA5KTkwEAAwcOxJ07d7B3714kJCTg4sWLGDJkiMo2qztPZdnZ2eHw4cO4cuVKhevDw8MxePBg9O3bVzr+nTt3BgCYmpoiJiYGZ8+exVdffYUVK1Zg0aJFKp8v247MzEwMGzYM7733HlJTU7Fnzx4MGjSoyolOnlmVqSpSO/aM0oz63n6qPWrDnbqa7Ovw4cMCgNi4caPKcisrK2FiYiJMTEzExIkThRBC7Nq1S5iZmYn79++rlHV1dRXLly8XQjy649KgQQOVOz6ffPKJ8PLyEkIIkZeXJ4yMjERiYqLKNt5//30xbNgwIcT/3an79ddfq61/y5YtRXR0tPS+ojtegYGBYuTIkSrL9u/fL/T09ERBQYE4d+6cACAOHz4srU9NTRUAqr1Tp6enJx0nmUwmzMzMxO+//65SDoAICQlRWda5c2cxYsQIlWVvvfWWePXVV4UQj+6I6unpiePHj4uHDx8KKysrERUVJf7zn/8IIYT46aefhK2trfTZoKAg4ezsLB48eKCyvbI9jR5XWc8oIYRYunSpACBu3rwphBCiffv2Yv78+UIIIQYOHChmzZol5HK5yM3NFRkZGQKASE1NrXRfr776qggLC5Pe+/j4iLZt26qUKSoqEtbW1io9xIYNGybdBX2S707//v3F8OHDK63H49gzqvarq+eBPaOINKc2xFs12R/jrWeLt0p7og8dOlS88sorQojyPaM+//xz0adPH5XPXrt2TQAQ586dq7Tej/eMEuJR3LZp06ZydTA0NBRZWVnSsvj4eKGvry+uXr0qLTtz5oxKD/nqzlNFbty4ITp16iQAiObNm4ugoCCxfv16qYe6EP/XI746c+fOFR06dKiyHUlJSQKAuHz5crXbE0I98VPtexCUiIh0pvRuXKmjR4/i4cOHePvtt1FYWAgASEpKQl5eHqysrFTKFhQU4OLFi9L7Jk2awNTUVHpvb28v3fU6e/Ys7t+/j969e6tso6ioCO3atVNZ5unpqfI+Pz8f06dPx9atW3Hjxg08ePAABQUF0p26yiQlJSEtLQ3r1q2Tlgkh8PDhQ6Snp+P8+fMwMDBQ2d9LL70Ec3PzKrcLAG5ubti8eTMA4N69e1i/fj3eeust7N69W2V7ZduSmpqKkSNHqizr0qULvvrqKwCAUqlE27ZtsWfPHhgaGkJPTw+jRo3CtGnTcO/ePZWeSKVatWoFfX196b29vT1Onz5dbRsqIv7/3bDS70X37t2xZ88ehIaGYv/+/Zg5cyY2bNiAAwcO4O7du7C1tcVLL70EACgpKcGXX36J9evX4++//0ZhYSEKCwvL3Qkue0wMDQ3x1ltvYd26dQgMDER+fj5+++03/PTTTwCe7Lvz0Ucf4Y033sCJEyfQp08fDBw4ULpbSEREpGuMt2oWb5WaOXMmWrRogfj4eNjY2JTb/+7du/HCC+XHDLt48eIzjzXl7OyMhg0bSu9TU1Ph5OQEJycnaVnLli1hbm6O1NRU/Oc//wFQ9XmqiL29PQ4dOoSUlBTs3bsXiYmJCAoKwvfff4+4uDjo6VX+kNsvv/yCxYsXIy0tDXl5eXjw4AHMzMyqbEebNm3Qs2dPeHh4wNfXF3369MGbb74JCwuLJz84T4nJKCIiQtOmTSGTyfDXX3+pLH/xxRcBAMbG/zco58OHD2Fvb489e/aU287jgUTZgRBlMpn0uFnpf7dt2wZHR0eVcgqFQuV92eTFJ598gh07dmD+/Plo2rQpjI2N8eabb6KoqOppnB8+fIhRo0Zh/Pjx5dY1btwY586dk+r5tORyOZo2bSq9b9euHX799VcsXrwYa9eurbQtFe1PCKGyrDQBJJfL4ePjAwsLC7Rq1QoHDx7Enj17ys1gU9Vxf1qpqakwMzOTAuHu3btj5cqV+PPPP6Gnp4eWLVvCx8cHe/fuRXZ2tkpibMGCBVi0aBEWL14MDw8PmJiYICQkpNx5quiYvP322/Dx8UFWVhYSEhJgZGQEPz8/AE/23fHz88OVK1ewbds27Ny5Ez179sSYMWMwf/78Gh0HIiIidWC89WzxVilXV1eMGDECkyZNwsqVK8vtv3///pgzZ065z9nb2wN4NHuwKPP4WXFx8RPtu+xxKhu3Vba8pvGZu7s73N3dMWbMGBw4cABdu3bF3r170aNHjwrLHz58GEOHDsX06dPh6+sLpVKJ2NhYLFiwoMp26OvrIyEhAYmJiYiPj0d0dDSmTJmCI0eOwMXFpdp61gSTUUREBCsrK/Tu3RvffPMNxo0bV+k4BgDQvn17ZGZmwsDAoMZT6LZs2RIKhQJXr14t17OnOvv370dwcDBef/11AEBeXh4uX76sUkYul6OkRHVa5/bt2+PMmTMqSaPHtWjRAg8ePMDx48fRsWNHAMC5c+dw9+7dp6pfKX19fRQUFFRZpkWLFjhw4ADeffddaVliYiJatGghvS9NABkYGKBXr14AAB8fH8TGxpYbL0qdsrKy8NNPP2HgwIHS3bfScaMWL14MHx8fyGQy+Pj4ICoqCtnZ2ZgwYYL0+f379+O1117DO++8A+BRcHjhwgWVtlWmc+fOcHJywvr16/H777/jrbfekmbZe9LvTsOGDREcHIzg4GB07doVn3zyCZNRRESkU4y31BdvTZ06Fa6uroiNjS23/w0bNqBJkyaVzgjYsGFDaYwrAMjNzUV6erpKGUNDw3Jtq0jLli1x9epVXLt2TeoddfbsWeTk5DxRzPM0WrZsCeBRrzWg4uN/8OBBODs7Y8qUKdKyysadKksmk6FLly7o0qULpk6dCmdnZ2zatAmhoaFqaoEqDmBOREQAgCVLluDBgwfw9PTE+vXrkZqainPnzmHt2rX466+/pEe/evXqBW9vbwwcOBA7duzA5cuXkZiYiM8++wzHjx9/on2ZmpoiPDwcH3/8MVavXo2LFy/i5MmT+Pbbb7F69eoqP9u0aVNs3LgRycnJ+PPPPxEQEFDuzlKTJk2wb98+/P333/jnn38AAJ9++ikOHTqEMWPGIDk5GRcuXMDmzZsxbtw4AI8etevbty9GjBiBI0eOICkpCR988IHKXcrKPHjwAJmZmcjMzMSFCxcwc+ZMnD17Fq+99lqVn/vkk08QExODZcuW4cKFC1i4cCE2btyI8PBwqUxpAmjLli3o3r07gEcJqrVr16Jhw4ZSYPIshBDIzMxERkYGUlNT8cMPP6Bz585QKpUqg2uWPja4du1aqS7dunXDiRMncP78eWkZ8Og8ld5hS01NxahRo5CZmflE9ZHJZAgICMCyZcuQkJAgJbSAJ/vuTJ06Fb/99hvS0tJw5swZbN26Ve0BIRERUU0w3qp5vPU4W1tbhIaG4uuvv1ZZPmbMGNy5cwfDhg3D0aNHcenSJcTHx+O9996TEjevvPIK1qxZg/379yMlJQVBQUEqQxyUtm3Xrl3IzMxEdnZ2pfXo1asXWrdujbfffhsnTpzA0aNH8e6778LHx6fco49P46OPPsIXX3yBgwcP4sqVKzh8+DDeffddNGzYEN7e3lIdT506hXPnzuGff/5BcXExmjZtiqtXryI2NhYXL17E119/jU2bNlW7vyNHjmD27Nk4fvw4rl69io0bN+LWrVsajZ/YM4qISIvSsvKqL6Sjfbi6uuLkyZOYPXs2IiIicP36dSgUCrRs2RLh4eEYPXo0gEeJgu3bt2PKlCl47733cOvWLdjZ2aFbt26wtbV94v198cUXsLGxQVRUFC5dugRzc3O0b98ekydPrvJzixYtwnvvvYfOnTvD2toan376KXJzc1XKzJgxA6NGjYKrqysKCwshhEDr1q2xd+9eTJkyBV27doUQAq6uriqznaxatQoffPABfHx8YGtri5kzZ+Lzzz+vti1nzpyRun43aNAArq6uWLp0qUqPp4oMHDgQX331FebNm4fx48fDxcUFq1atUknqKJVKtGvXDlevXpUST127dsXDhw/V1isqNzcX9vb2kMlkMDMzg5ubG4KCgjBhwoRyYwz06NEDJ06ckOpoYWGBli1b4saNGyoBy+eff4709HT4+vqiQYMGGDlyJAYOHIicnJwnqtPbb7+N2bNnw9nZGV26dFFZV913Ry6XIyIiApcvX4axsTG6du1a7s4pERE9v7QRb9V0P4y3ah5vlfXJJ59g6dKluH//vrTMwcEBBw8exKeffgpfX18UFhbC2dkZffv2lXp6R0RE4NKlS/D394dSqcQXX3xRrmfUggULEBoaihUrVsDR0bFcr7BSMpkMv/76K8aNG4du3bpBT08Pffv2RXR09FO353G9evXCDz/8gKVLl+L27duwtraGt7c3du3aJQ2fMGLECOzZsweenp7Iy8vD7t278dprr+Hjjz/G2LFjUVhYiH79+uHzzz9HZGRklfszMzPDvn37sHjxYuTm5sLZ2RkLFiyQhknQBJko+7AkaVRubi6USiVycnLKBfjPIuXvHPhHH8DWcS/D3VGptu3WFfW9/VR73L9/H+np6XBxcYGRkZG0/O+7Bei1YC8Kiqvv7qsOxob62BnmA0fzp7vLRFTfVHbNApr7zaanU1fPg6ZjE8Y+VJ/VlngLYMxF9ZM64if2jCIi0gJHc2PsDPNBdn7Vgz6qi4WJnEERERER1SvajrcAxlxENcVkFBGRljiaGzNYISIiItIgxltEdQMHMCciIiIiIiIiIq1hMoqIiIiIiIiIiLSGySgiIiIiIiIiItIaJqOIiDTg4cOHuq4CET0BXqtERHUX/4YT6YY6rj0OYE5EpEZyuRx6enq4ceMGGjZsCLlcDplMputqEVEZQggUFRXh1q1b0NPTg1wu13WViIjoCTHeItINdcZPTEYREamRnp4eXFxckJGRgRs3bui6OkRUjQYNGqBx48bQ02NncSKiuoLxFpFuqSN+YjKKiEjN5HI5GjdujAcPHqCkpETX1SGiSujr68PAwIB304mI6iDGW0S6oa74ickoIiINkMlkMDQ0hKGhoa6rQkRERPRcYrxFVHexTzoREREREREREWkNk1FERERERERERKQ1TEYREREREREREZHWMBlFRERERERERERawwHM6bmSlpWn6yrojIWJHI7mxrquBhEREREREVGVmIyi54KFiRzGhvoIWZ+s66rojLGhPnaG+TAhRURERERERLUak1H0XHA0N8bOMB9k5xfpuio6kZaVh5D1ycjOL2IyioiIiIiIiGo1JqPoueFobsxEDBEREREREVEtxwHMiYiIiIiIiIhIa5iMIiIiIiIiIiIirdFpMmrfvn3o378/HBwcIJPJ8Ouvv6qsDw4OhkwmU3l16tRJpUxhYSHGjRsHa2trmJiYYMCAAbh+/bpKmezsbAQGBkKpVEKpVCIwMBB3795VKXP16lX0798fJiYmsLa2xvjx41FUpDr+0OnTp+Hj4wNjY2M4OjpixowZEEKo7XgQERERERERET3vdJqMys/PR5s2bfDNN99UWqZv377IyMiQXtu3b1dZHxISgk2bNiE2NhYHDhxAXl4e/P39UVJSIpUJCAhAcnIy4uLiEBcXh+TkZAQGBkrrS0pK0K9fP+Tn5+PAgQOIjY3Fhg0bEBYWJpXJzc1F79694eDggGPHjiE6Ohrz58/HwoUL1XhEiIiIiIiIiIiebzodwNzPzw9+fn5VllEoFLCzs6twXU5ODlauXIk1a9agV69eAIC1a9fCyckJO3fuhK+vL1JTUxEXF4fDhw/Dy8sLALBixQp4e3vj3LlzcHNzQ3x8PM6ePYtr167BwcEBALBgwQIEBwdj1qxZMDMzw7p163D//n3ExMRAoVDA3d0d58+fx8KFCxEaGgqZTKbGI0NERERERERE9Hyq9WNG7dmzBzY2NmjevDlGjBiBrKwsaV1SUhKKi4vRp08faZmDgwPc3d2RmJgIADh06BCUSqWUiAKATp06QalUqpRxd3eXElEA4Ovri8LCQiQlJUllfHx8oFAoVMrcuHEDly9frrT+hYWFyM3NVXkREREREREREdVXtToZ5efnh3Xr1uGPP/7AggULcOzYMbzyyisoLCwEAGRmZkIul8PCwkLlc7a2tsjMzJTK2NjYlNu2jY2NShlbW1uV9RYWFpDL5VWWKX1fWqYiUVFR0lhVSqUSTk5OT3MIiIiIiIiIiIieKzp9TK86Q4YMkf7t7u4OT09PODs7Y9u2bRg0aFClnxNCqDw2V9EjdOooUzp4eVWP6EVERCA0NFR6n5uby4QUEREREalFWlaexrZtYSKHo7mxxrZPRET1V61ORpVlb28PZ2dnXLhwAQBgZ2eHoqIiZGdnq/SOysrKQufOnaUyN2/eLLetW7duST2b7OzscOTIEZX12dnZKC4uVilTtgdU6SODZXtMPU6hUKg82kdERERE9KwsTOQwNtRHyPpkje3D2FAfO8N8mJAiIiK1q1PJqNu3b+PatWuwt7cHAHTo0AGGhoZISEjA4MGDAQAZGRlISUnB3LlzAQDe3t7IycnB0aNH0bFjRwDAkSNHkJOTIyWsvL29MWvWLGRkZEjbjo+Ph0KhQIcOHaQykydPRlFREeRyuVTGwcEBTZo00doxICIiIiJyNDfGzjAfZOcXaWT7aVl5CFmfjOz8IiajiIhI7XSajMrLy0NaWpr0Pj09HcnJybC0tISlpSUiIyPxxhtvwN7eHpcvX8bkyZNhbW2N119/HQCgVCrx/vvvIywsDFZWVrC0tER4eDg8PDyk2fVatGiBvn37YsSIEVi+fDkAYOTIkfD394ebmxsAoE+fPmjZsiUCAwMxb9483LlzB+Hh4RgxYgTMzMwAAAEBAZg+fTqCg4MxefJkXLhwAbNnz8bUqVM5kx4RERERaZ2juTETRUREVCfpNBl1/Phx9OjRQ3pfOrZSUFAQli5ditOnT+PHH3/E3bt3YW9vjx49emD9+vUwNTWVPrNo0SIYGBhg8ODBKCgoQM+ePRETEwN9fX2pzLp16zB+/Hhp1r0BAwbgm2++kdbr6+tj27ZtGD16NLp06QJjY2MEBARg/vz5UhmlUomEhASMGTMGnp6esLCwQGhoqMp4UEREREREREREVDWdJqO6d+8uDQJekR07dlS7DSMjI0RHRyM6OrrSMpaWlli7dm2V22ncuDG2bt1aZRkPDw/s27ev2joR6YomBzGt7TjIKhERERERUd1Qp8aMIqKKaWMQ09qOg6wSERERERHVDUxGET0HND2IaW3HQVaJiIiIiIjqDiajiJ4THMSUiIiIiIiI6gI9XVeAiIiIiIiIiIjqDyajiIiIiIiIiIhIa5iMIiIiIiIiIiIirWEyioiIiKge2rdvH/r37w8HBwfIZDL8+uuvKuuFEIiMjISDgwOMjY3RvXt3nDlzRqVMYWEhxo0bB2tra5iYmGDAgAG4fv26Spns7GwEBgZCqVRCqVQiMDAQd+/e1XDriIiIqDZjMoqIiIioHsrPz0ebNm3wzTffVLh+7ty5WLhwIb755hscO3YMdnZ26N27N+7duyeVCQkJwaZNmxAbG4sDBw4gLy8P/v7+KCkpkcoEBAQgOTkZcXFxiIuLQ3JyMgIDAzXePiIiIqq9OJseERERUT3k5+cHPz+/CtcJIbB48WJMmTIFgwYNAgCsXr0atra2+OmnnzBq1Cjk5ORg5cqVWLNmDXr16gUAWLt2LZycnLBz5074+voiNTUVcXFxOHz4MLy8vAAAK1asgLe3N86dOwc3NzftNJaIiIhqFfaMIiIiIiIV6enpyMzMRJ8+faRlCoUCPj4+SExMBAAkJSWhuLhYpYyDgwPc3d2lMocOHYJSqZQSUQDQqVMnKJVKqUxZhYWFyM3NVXkRERHR84XJKCIiIiJSkZmZCQCwtbVVWW5rayuty8zMhFwuh4WFRZVlbGxsym3fxsZGKlNWVFSUNL6UUqmEk5PTM7eHiIiIahc+pkdEz420rDxdV0FnLEzkcDQ31nU1iOg5I5PJVN4LIcotK6tsmYrKV7WdiIgIhIaGSu9zc3OZkCIiInrOPHUyqrQ79vLly9G8eXNN1ImI6KlYmMhhbKiPkPXJuq6Kzhgb6mNnmA8TUkTPOW3FYXZ2dgAe9Wyyt7eXlmdlZUm9pezs7FBUVITs7GyV3lFZWVno3LmzVObmzZvltn/r1q1yva5KKRQKKBQKtbWFiIiIap+nTkYZGhoiJSWl2rtiRETa4mhujJ1hPsjOL9J1VXQiLSsPIeuTkZ1fxGQU0XNOW3GYi4sL7OzskJCQgHbt2gEAioqKsHfvXsyZMwcA0KFDBxgaGiIhIQGDBw8GAGRkZCAlJQVz584FAHh7eyMnJwdHjx5Fx44dAQBHjhxBTk6OlLAiIiKi+qdGj+m9++67WLlyJb788kt114eIqEYczY2ZiCGiekFdcVheXh7S0tKk9+np6UhOToalpSUaN26MkJAQzJ49G82aNUOzZs0we/ZsNGjQAAEBAQAApVKJ999/H2FhYbCysoKlpSXCw8Ph4eEhza7XokUL9O3bFyNGjMDy5csBACNHjoS/vz9n0iMiIqrHapSMKioqwvfff4+EhAR4enrCxMREZf3ChQvVUjkiIiIiUqWuOOz48ePo0aOH9L50nKagoCDExMRg4sSJKCgowOjRo5GdnQ0vLy/Ex8fD1NRU+syiRYtgYGCAwYMHo6CgAD179kRMTAz09fWlMuvWrcP48eOlWfcGDBiAb775psbtJyIiorqvRsmolJQUtG/fHgBw/vx5lXV8fI+IiIhIc9QVh3Xv3h1CiErXy2QyREZGIjIystIyRkZGiI6ORnR0dKVlLC0tsXbt2ieuFxERET3/apSM2r17t7rrQURERERPgHEYERER1XV6z/LhtLQ07NixAwUFBQBQ5d01IiIiIlIfxmFERERUV9UoGXX79m307NkTzZs3x6uvvoqMjAwAwAcffICwsDC1VpCIiIiI/g/jMCIiIqrravSY3scffwxDQ0NcvXoVLVq0kJYPGTIEH3/8MRYsWKC2ChIR0ZNJy8rTdRV0xsJEztkUqd5gHEZERER1XY2SUfHx8dixYwcaNWqksrxZs2a4cuWKWipGRERPxsJEDmNDfYSsT9Z1VXTG2FAfO8N8mJCieoFxGGmTpm508CYCEVH9VqNkVH5+Pho0aFBu+T///AOFQvHMlSIioifnaG6MnWE+yM4v0nVVdCItKw8h65ORnV/E/7GheoFxGGmDpm908CYCEVH9VqNkVLdu3fDjjz/iiy++APBo6t+HDx9i3rx56NGjh1orSERE1XM0N2ZAT1RPMA4jbdDkjQ7eRCAioholo+bNm4fu3bvj+PHjKCoqwsSJE3HmzBncuXMHBw8eVHcdiYiIiOj/YxxG2sIbHUREpCk1mk2vZcuWOHXqFDp27IjevXsjPz8fgwYNwsmTJ+Hq6qruOhIRERHR/8c4jIiIiOq6GvWMAgA7OztMnz5dnXUhIiIioifAOIyIiIjqshono7Kzs7Fy5UqkpqZCJpOhRYsWGD58OCwtLdVZPyIioieiqRmf6gLOSlX/MA4jIiKiuqxGyai9e/fitddeg5mZGTw9PQEAX3/9NWbMmIHNmzfDx8dHrZUkIiKqjKZnfKoLOCtV/cI4jIiIiOq6GiWjxowZg8GDB2Pp0qXQ19cHAJSUlGD06NEYM2YMUlJS1FpJIiKiymhyxqe6gLNS1T+Mw4iIiKiuq1Ey6uLFi9iwYYMUAAGAvr4+QkND8eOPP6qtckRERE+CMz5RfcI4jIiIiOq6Gs2m1759e6SmppZbnpqairZt2z5rnYiIiIioEozDiIiIqK574p5Rp06dkv49fvx4TJgwAWlpaejUqRMA4PDhw/j222/x5Zdfqr+WREREVCUO4P5894xjHEZERETPkydORrVt2xYymQxCCGnZxIkTy5ULCAjAkCFD1FM7IiIiqhIHcK8fA7gzDiMiIqLnyRMno9LT09W+83379mHevHlISkpCRkYGNm3ahIEDB0rrhRCYPn06vvvuO2RnZ8PLywvffvstWrVqJZUpLCxEeHg4fv75ZxQUFKBnz55YsmQJGjVqJJXJzs7G+PHjsXnzZgDAgAEDEB0dDXNzc6nM1atXMWbMGPzxxx8wNjZGQEAA5s+fD7lcLpU5ffo0xo4di6NHj8LS0hKjRo3C559/DplMpvZjQ0RE9CQ4gHv9GMBdE3EYERERka48cTLK2dlZ7TvPz89HmzZtMHz4cLzxxhvl1s+dOxcLFy5ETEwMmjdvjpkzZ6J37944d+4cTE1NAQAhISHYsmULYmNjYWVlhbCwMPj7+yMpKUka2DMgIADXr19HXFwcAGDkyJEIDAzEli1bADyagaZfv35o2LAhDhw4gNu3byMoKAhCCERHRwMAcnNz0bt3b/To0QPHjh3D+fPnERwcDBMTE4SFhan92BARET0pDuD+/NNEHEZERESkKzWaTQ8A/v77bxw8eBBZWVl4+PChyrrx48c/0Tb8/Pzg5+dX4TohBBYvXowpU6Zg0KBBAIDVq1fD1tYWP/30E0aNGoWcnBysXLkSa9asQa9evQAAa9euhZOTE3bu3AlfX1+kpqYiLi4Ohw8fhpeXFwBgxYoV8Pb2xrlz5+Dm5ob4+HicPXsW165dg4ODAwBgwYIFCA4OxqxZs2BmZoZ169bh/v37iImJgUKhgLu7O86fP4+FCxciNDSUvaOIiIhIa9QRhxERERHpSo2SUatWrcKHH34IuVwOKysrlUSMTCZTSxCUnp6OzMxM9OnTR1qmUCjg4+ODxMREjBo1CklJSSguLlYp4+DgAHd3dyQmJsLX1xeHDh2CUqmUElEA0KlTJyiVSiQmJsLNzQ2HDh2Cu7u7lIgCAF9fXxQWFiIpKQk9evTAoUOH4OPjA4VCoVImIiICly9fhouLS4XtKCwsRGFhofQ+Nzf3mY8NERER1V/aiMOIiIiINKlGyaipU6di6tSpiIiIgJ6enrrrBADIzMwEANja2qost7W1xZUrV6QycrkcFhYW5cqUfj4zMxM2Njbltm9jY6NSpux+LCwsIJfLVco0adKk3H5K11WWjIqKisL06dOrbS8RERHRk9BGHEZERESkSTWKYP79918MHTpUKwFQ2cffhBDVPhJXtkxF5dVRpnRGm6rqExERgZycHOl17dq1KutOREREVBVtxmFEREREmlCjnlHvv/8+/ve//2HSpEnqro/Ezs4OwKNeR/b29tLyrKwsqUeSnZ0dioqKkJ2drdI7KisrC507d5bK3Lx5s9z2b926pbKdI0eOqKzPzs5GcXGxSpnSXlKP7wco33vrcQqFQuXRPiIiIqJnoY04jEgb0rLyNLZtCxM5J3YgIqrFapSMioqKgr+/P+Li4uDh4QFDQ0OV9QsXLnzmirm4uMDOzg4JCQlo164dAKCoqAh79+7FnDlzAAAdOnSAoaEhEhISMHjwYABARkYGUlJSMHfuXACAt7c3cnJycPToUXTs2BEAcOTIEeTk5EgJK29vb8yaNQsZGRlS4is+Ph4KhQIdOnSQykyePBlFRUWQy+VSGQcHh3KP7xERERFpijbiMCJNsjCRw9hQHyHrkzW2D2NDfewM82FCioiolqpRMmr27NnYsWMH3NzcAKDax90qk5eXh7S0NOl9eno6kpOTYWlpicaNGyMkJASzZ89Gs2bN0KxZM8yePRsNGjRAQEAAAECpVOL9999HWFgYrKysYGlpifDwcHh4eEiz67Vo0QJ9+/bFiBEjsHz5cgDAyJEj4e/vL9W/T58+aNmyJQIDAzFv3jzcuXMH4eHhGDFiBMzMzAAAAQEBmD59OoKDgzF58mRcuHABs2fPxtSpUzmTHhEREWmNuuIwIl1xNDfGzjAfZOcXaWT7aVl5CFmfjOz8IiajiIhqqRoloxYuXIgffvgBwcHBz7Tz48ePo0ePHtL70NBQAEBQUBBiYmIwceJEFBQUYPTo0cjOzoaXlxfi4+NhamoqfWbRokUwMDDA4MGDUVBQgJ49eyImJgb6+vpSmXXr1mH8+PHSrHsDBgzAN998I63X19fHtm3bMHr0aHTp0gXGxsYICAjA/PnzpTJKpRIJCQkYM2YMPD09YWFhgdDQUKnORERERNqgrjiMSJcczY2ZKCIiqsdqlIxSKBTo0qXLM++8e/fu0iDgFZHJZIiMjERkZGSlZYyMjBAdHY3o6OhKy1haWmLt2rVV1qVx48bYunVrlWU8PDywb9++KssQERERaZK64jAiIiIiXanRNCwTJkyoMvlDRERERJrBOIyIiIjquhr1jDp69Cj++OMPbN26Fa1atSo3cObGjRvVUjkiIiIiUsU4jIiIiOq6GiWjzM3NMWjQIHXXhYiIiIiqwTiMiIiI6roaJaNWrVql7noQERER0RNgHEZERER1XY3GjCIiIiIiIiIiIqqJGvWMcnFxgUwmq3T9pUuXalwhIiIiIqoc4zAiIiKq62qUjAoJCVF5X1xcjJMnTyIuLg6ffPKJOupFRERERBVgHEZERER1XY2SURMmTKhw+bfffovjx48/U4WIiIiIqHKMw4iIiKiuq1EyqjJ+fn6IiIjgwJpEREREWsY4jEhVWlaeRrZrYSKHo7mxRrZNRFRfqDUZ9csvv8DS0lKdmyQiIiKiJ8A4jOgRCxM5jA31EbI+WSPbNzbUx84wHyakiIieQY2SUe3atVMZOFMIgczMTNy6dQtLlixRW+WIiIiISBXjMKKqOZobY2eYD7Lzi9S+7bSsPISsT0Z2fhGTUUREz6BGyajXXntNJQjS09NDw4YN0b17d7z00ktqqxwRERERqWIcRlQ9R3NjJouIiGqxGiWjIiMj1VwNIiIiInoSjMOIiIiornuqZJSenp7KnbiKyGQyPHjw4JkqRURERESqGIcRERHR8+KpklGbNm2qdF1iYiKio6MhhHjmShERERGRKsZhRERE9Lx4qmTUa6+9Vm7ZX3/9hYiICGzZsgVvv/02vvjiC7VVjoiIiIgeYRxGREREzwu9mn7wxo0bGDFiBFq3bo0HDx4gOTkZq1evRuPGjdVZPyIiIiIqg3EYERER1WVPnYzKycnBp59+iqZNm+LMmTPYtWsXtmzZAnd3d03Uj4iIiIj+P8ZhRERE9Dx4qsf05s6dizlz5sDOzg4///xzhd3FiYiIiEj9GIcRERHR8+KpklGTJk2CsbExmjZtitWrV2P16tUVltu4caNaKkdEREREjzAOIyIioufFUz2m9+6772Lw4MGwtLSEUqms9EVERERE6qXtOCwyMhIymUzlZWdnJ60XQiAyMhIODg4wNjZG9+7dcebMGZVtFBYWYty4cbC2toaJiQkGDBiA69evq62OREREVDc9Vc+omJgYDVWDiIiIiKqiizisVatW2Llzp/ReX19f+vfcuXOxcOFCxMTEoHnz5pg5cyZ69+6Nc+fOwdTUFAAQEhKCLVu2IDY2FlZWVggLC4O/vz+SkpJUtkVERET1y1Mlo4iIiIio/jAwMFDpDVVKCIHFixdjypQpGDRoEABg9erVsLW1xU8//YRRo0YhJycHK1euxJo1a9CrVy8AwNq1a+Hk5ISdO3fC19dXq20hIiKi2uOpZ9MjIiIiovrhwoULcHBwgIuLC4YOHYpLly4BANLT05GZmYk+ffpIZRUKBXx8fJCYmAgASEpKQnFxsUoZBwcHuLu7S2UqUlhYiNzcXJUXERERPV+YjCIiIiKicry8vPDjjz9ix44dWLFiBTIzM9G5c2fcvn0bmZmZAABbW1uVz9ja2krrMjMzIZfLYWFhUWmZikRFRamMgeXk5KTmlhEREZGuMRlFREREROX4+fnhjTfegIeHB3r16oVt27YBgMosfjKZTOUzQohyy8qqrkxERARycnKk17Vr156hFURERFQbMRlFRERERNUyMTGBh4cHLly4II0jVbaHU1ZWltRbys7ODkVFRcjOzq60TEUUCgXMzMxUXkRERPR8YTKKiIiIiKpVWFiI1NRU2Nvbw8XFBXZ2dkhISJDWFxUVYe/evejcuTMAoEOHDjA0NFQpk5GRgZSUFKkMERER1U+cTY+IiIiIygkPD0f//v3RuHFjZGVlYebMmcjNzUVQUBBkMhlCQkIwe/ZsNGvWDM2aNcPs2bPRoEEDBAQEAACUSiXef/99hIWFwcrKCpaWlggPD5ce+yMiIqL6i8koIiIiIirn+vXrGDZsGP755x80bNgQnTp1wuHDh+Hs7AwAmDhxIgoKCjB69GhkZ2fDy8sL8fHxMDU1lbaxaNEiGBgYYPDgwSgoKEDPnj0RExMDfX19XTWLiIiIagEmo4iIiIionNjY2CrXy2QyREZGIjIystIyRkZGiI6ORnR0tJprR0RERHUZx4wiIiIiIiIiIiKtYTKKiIiIiIiIiIi0hskoIiIiIiIiIiLSmlqdjIqMjIRMJlN52dnZSeuFEIiMjISDgwOMjY3RvXt3nDlzRmUbhYWFGDduHKytrWFiYoIBAwbg+vXrKmWys7MRGBgIpVIJpVKJwMBA3L17V6XM1atX0b9/f5iYmMDa2hrjx49HUVGRxtpORERERERERPQ8qtXJKABo1aoVMjIypNfp06eldXPnzsXChQvxzTff4NixY7Czs0Pv3r1x7949qUxISAg2bdqE2NhYHDhwAHl5efD390dJSYlUJiAgAMnJyYiLi0NcXBySk5MRGBgorS8pKUG/fv2Qn5+PAwcOIDY2Fhs2bEBYWJh2DgIRERERERER0XOi1s+mZ2BgoNIbqpQQAosXL8aUKVMwaNAgAMDq1atha2uLn376CaNGjUJOTg5WrlyJNWvWoFevXgCAtWvXwsnJCTt37oSvry9SU1MRFxeHw4cPw8vLCwCwYsUKeHt749y5c3Bzc0N8fDzOnj2La9euwcHBAQCwYMECBAcHY9asWTAzM9PS0SAiIiIiIiIiqttqfc+oCxcuwMHBAS4uLhg6dCguXboEAEhPT0dmZib69OkjlVUoFPDx8UFiYiIAICkpCcXFxSplHBwc4O7uLpU5dOgQlEqllIgCgE6dOkGpVKqUcXd3lxJRAODr64vCwkIkJSVprvFERERERERERM+ZWt0zysvLCz/++COaN2+OmzdvYubMmejcuTPOnDmDzMxMAICtra3KZ2xtbXHlyhUAQGZmJuRyOSwsLMqVKf18ZmYmbGxsyu3bxsZGpUzZ/VhYWEAul0tlKlNYWIjCwkLpfW5u7pM0nYiIiIiIiIjouVSrk1F+fn7Svz08PODt7Q1XV1esXr0anTp1AgDIZDKVzwghyi0rq2yZisrXpExFoqKiMH369CrLEBERERERERHVF7X+Mb3HmZiYwMPDAxcuXJDGkSrbMykrK0vqxWRnZ4eioiJkZ2dXWebmzZvl9nXr1i2VMmX3k52djeLi4nI9psqKiIhATk6O9Lp27dpTtJiIiIiIiIiI6PlSp5JRhYWFSE1Nhb29PVxcXGBnZ4eEhARpfVFREfbu3YvOnTsDADp06ABDQ0OVMhkZGUhJSZHKeHt7IycnB0ePHpXKHDlyBDk5OSplUlJSkJGRIZWJj4+HQqFAhw4dqqyzQqGAmZmZyouIiIiIiIiIqL6q1Y/phYeHo3///mjcuDGysrIwc+ZM5ObmIigoCDKZDCEhIZg9ezaaNWuGZs2aYfbs2WjQoAECAgIAAEqlEu+//z7CwsJgZWUFS0tLhIeHw8PDQ5pdr0WLFujbty9GjBiB5cuXAwBGjhwJf39/uLm5AQD69OmDli1bIjAwEPPmzcOdO3cQHh6OESNGMLlERERERFTPpGXlaWzbFiZyOJoba2z7RES1Qa1ORl2/fh3Dhg3DP//8g4YNG6JTp044fPgwnJ2dAQATJ05EQUEBRo8ejezsbHh5eSE+Ph6mpqbSNhYtWgQDAwMMHjwYBQUF6NmzJ2JiYqCvry+VWbduHcaPHy/NujdgwAB888030np9fX1s27YNo0ePRpcuXWBsbIyAgADMnz9fS0eCiIiIiIh0zcJEDmNDfYSsT9bYPowN9bEzzIcJKSJ6rtXqZFRsbGyV62UyGSIjIxEZGVlpGSMjI0RHRyM6OrrSMpaWlli7dm2V+2rcuDG2bt1aZRkiIiIiInp+OZobY2eYD7LzizSy/bSsPISsT0Z2fhGTUUT0XKvVySgiIiIiIqLaxNHcmIkiIqJnVKcGMCciIiIiIiIiorqNySgiIiIiIiIiItIaJqOIiIiIiIiIiEhrOGYUERERERFRLZKWlafrKtSIhYmc42kR0RNhMoqIiIiIiKgWsDCRw9hQHyHrk3VdlRoxNtTHzjAfJqSIqFpMRhEREREREdUCjubG2Bnmg+z8Il1X5amlZeUhZH0ysvOLmIwiomoxGUVERERERFRLOJobM5lDRM89JqOIiIiIqN76+26BRnqh1NUxf4iIiLSBySgiIiIiqpf+vluAXgv2oqC4RCPbNzbUh4WJXCPbJiIiqsuYjCIiIiKieik7vwgFxSVYPKQtmtq8oPbtc2YxIiKiijEZRURERET1WlObF+DuqNR1NYiIiOoNPV1XgIiIiIiIiIiI6g8mo4iIiIiIiIiISGuYjCIiIiIiIiIiIq1hMoqIiIiIiIiIiLSGA5gTERERERGRWqRl5Wls25yhkuj5wWQUERERERERPRMLEzmMDfURsj5ZY/swNtTHzjAfJqSIngNMRhEREREREdEzcTQ3xs4wH2TnF2lk+2lZeQhZn4zs/CImo4ieA0xGERERERER0TNzNDdmooiInggHMCciIiIiIiIiIq1hMoqIiIiIiIiIiLSGj+kRERERERFRnaCp2fo4Ux+RdjEZRURERERERLWapmfr40x9RNrFZBQRERERERHVapqcrY8z9enW33cLNDYLI3u81V5MRhEREREREVGtx9n6nj9/3y1ArwV7UVBcopHts8db7cVkFBEREREREdV7mhqPCmAPncpk5xehoLgEi4e0RVObF9S6bfZ4q92YjCIiIiIiIqJ6S9PjUQHsoVOdpjYvwN1RqZFtM8lYOzEZRURERERERPWWJsejAthDR1eYZKzdmIwiIiIiIiKieo3jUT1/mGSs3ZiMIiIiIiIiIqLnDpOMtZeeritARERERERERET1B5NRRERERERERESkNUxGERERERERERGR1jAZRUREREREREREWsMBzGtgyZIlmDdvHjIyMtCqVSssXrwYXbt21XW1iIiIiGotxk9ERHXT33cLNDojHdVPTEY9pfXr1yMkJARLlixBly5dsHz5cvj5+eHs2bNo3LixrqtHREREVOswfiIiqpv+vluAXgv2oqC4RGP7MDbUh4WJXGPbp9qJyaintHDhQrz//vv44IMPAACLFy/Gjh07sHTpUkRFRem4dkRERES1D+MnIqK6KTu/CAXFJVg8pC2a2rygkX1YmMjhaG6skW1T7cVk1FMoKipCUlISJk2apLK8T58+SExMrPAzhYWFKCwslN7n5OQAAHJzc9Vat7x7uXhY+C/y7uUiN1em1m0TERHVVpr8/Sv9rRZCqHW79c3Txk/aip0Axk9EpB2lf2tOXcpA3j31/y3TpEu38vGw8F/YGT9EY1NN/Z0sRm5usYa2rTl1+bwCQMMXFGhoZqT27T5p/MRk1FP4559/UFJSAltbW5Xltra2yMzMrPAzUVFRmD59ernlTk5OGqmj92KNbJaIiKhW0+Tv371796BUKjW3g+fc08ZP2o6dAMZPRKQdby/WdQ1qjn8nK1eXz6smVRc/MRlVAzKZakZYCFFuWamIiAiEhoZK7x8+fIg7d+7Aysqq0s/URG5uLpycnHDt2jWYmZmpbbt1BdvP9rP9bD/bz/aru/1CCNy7dw8ODg5q3W599aTxk7Zip7qivl/jtRHPSe3E81I78bzUTrUhfmIy6ilYW1tDX1+/3F28rKyscnf7SikUCigUCpVl5ubmmqoizMzM6vVFzvaz/Ww/219fsf2aaT97RD27p42ftB071RX1/RqvjXhOaieel9qJ56V20mX8pKf2vT7H5HI5OnTogISEBJXlCQkJ6Ny5s45qRURERFR7MX4iIiKistgz6imFhoYiMDAQnp6e8Pb2xnfffYerV6/iww8/1HXViIiIiGolxk9ERET0OCajntKQIUNw+/ZtzJgxAxkZGXB3d8f27dvh7Oys03opFApMmzatXLf2+oLtZ/vZfraf7Wf7qfaqrfFTXcDveO3Dc1I78bzUTjwvtVNtOC8ywfmKiYiIiIiIiIhISzhmFBERERERERERaQ2TUUREREREREREpDVMRhERERERERERkdYwGUVERERERERERFrDZFQttWTJEri4uMDIyAgdOnTA/v37qyy/d+9edOjQAUZGRnjxxRexbNkylfVnzpzBG2+8gSZNmkAmk2Hx4sUarP2zU3f7V6xYga5du8LCwgIWFhbo1asXjh49qskmPBN1t3/jxo3w9PSEubk5TExM0LZtW6xZs0aTTXgm6m7/42JjYyGTyTBw4EA111q91H0MYmJiIJPJyr3u37+vyWbUmCa+A3fv3sWYMWNgb28PIyMjtGjRAtu3b9dUE56JutvfvXv3Cs9/v379NNmMGtPE+V+8eDHc3NxgbGwMJycnfPzxx7X2+0/1z759+9C/f384ODhAJpPh119/VVkvhEBkZCQcHBxgbGyM7t2748yZM7qpbD1S3XkJDg4u93e1U6dOuqlsPREVFYX//Oc/MDU1hY2NDQYOHIhz586plOH1on1Pcl54vWjf0qVL0bp1a5iZmcHMzAze3t74/fffpfU6v1YE1TqxsbHC0NBQrFixQpw9e1ZMmDBBmJiYiCtXrlRY/tKlS6JBgwZiwoQJ4uzZs2LFihXC0NBQ/PLLL1KZo0ePivDwcPHzzz8LOzs7sWjRIi215ulpov0BAQHi22+/FSdPnhSpqali+PDhQqlUiuvXr2urWU9ME+3fvXu32Lhxozh79qxIS0sTixcvFvr6+iIuLk5bzXpimmh/qcuXLwtHR0fRtWtX8dprr2m4JTWniWOwatUqYWZmJjIyMlRetZEm2l9YWCg8PT3Fq6++Kg4cOCAuX74s9u/fL5KTk7XVrCemifbfvn1b5bynpKQIfX19sWrVKi216slpov1r164VCoVCrFu3TqSnp4sdO3YIe3t7ERISoq1mEVVp+/btYsqUKWLDhg0CgNi0aZPK+i+//FKYmpqKDRs2iNOnT4shQ4YIe3t7kZubq5sK1xPVnZegoCDRt29flb+vt2/f1k1l6wlfX1+xatUqkZKSIpKTk0W/fv1E48aNRV5enlSG14v2Pcl54fWifZs3bxbbtm0T586dE+fOnROTJ08WhoaGIiUlRQih+2uFyahaqGPHjuLDDz9UWfbSSy+JSZMmVVh+4sSJ4qWXXlJZNmrUKNGpU6cKyzs7O9fqZJSm2y+EEA8ePBCmpqZi9erVz15hNdNG+4UQol27duKzzz57tspqgKba/+DBA9GlSxfx/fffi6CgoFqdjNLEMVi1apVQKpVqr6smaKL9S5cuFS+++KIoKipSf4XVTBt/AxYtWiRMTU1VgsTaQhPtHzNmjHjllVdUyoSGhoqXX35ZTbUmUp+ySY+HDx8KOzs78eWXX0rL7t+/L5RKpVi2bJkOalg/VZaMqs3xRH2QlZUlAIi9e/cKIXi91BZlz4sQvF5qCwsLC/H999/XimuFj+nVMkVFRUhKSkKfPn1Ulvfp0weJiYkVfubQoUPlyvv6+uL48eMoLi7WWF01QVvt//fff1FcXAxLS0v1VFxNtNF+IQR27dqFc+fOoVu3buqrvBposv0zZsxAw4YN8f7776u/4mqkyWOQl5cHZ2dnNGrUCP7+/jh58qT6G/CMNNX+zZs3w9vbG2PGjIGtrS3c3d0xe/ZslJSUaKYhNaStv4ErV67E0KFDYWJiop6Kq4mm2v/yyy8jKSlJejz70qVL2L59e619TJHocenp6cjMzFT5nisUCvj4+FR6XZD27NmzBzY2NmjevDlGjBiBrKwsXVepXsnJyQEAKabn9VI7lD0vpXi96E5JSQliY2ORn58Pb2/vWnGtMBlVy/zzzz8oKSmBra2tynJbW1tkZmZW+JnMzMwKyz948AD//POPxuqqCdpq/6RJk+Do6IhevXqpp+Jqosn25+Tk4IUXXoBcLke/fv0QHR2N3r17q78Rz0BT7T948CBWrlyJFStWaKbiaqSpY/DSSy8hJiYGmzdvxs8//wwjIyN06dIFFy5c0ExDakhT7b906RJ++eUXlJSUYPv27fjss8+wYMECzJo1SzMNqSFt/A08evQoUlJS8MEHH6iv4mqiqfYPHToUX3zxBV5++WUYGhrC1dUVPXr0wKRJkzTTECI1Kv3uP811Qdrh5+eHdevW4Y8//sCCBQtw7NgxvPLKKygsLNR11eoFIQRCQ0Px8ssvw93dHQCvl9qgovMC8HrRldOnT+OFF16AQqHAhx9+iE2bNqFly5a14lox0Mpe6KnJZDKV90KIcsuqK1/R8rpCk+2fO3cufv75Z+zZswdGRkZqqK36aaL9pqamSE5ORl5eHnbt2oXQ0FC8+OKL6N69u/oqribqbP+9e/fwzjvvYMWKFbC2tlZ/ZTVE3d+BTp06qQwS2aVLF7Rv3x7R0dH4+uuv1VVttVF3+x8+fAgbGxt899130NfXR4cOHXDjxg3MmzcPU6dOVXPtn50m/wauXLkS7u7u6Nixoxpqqhnqbv+ePXswa9YsLFmyBF5eXkhLS8OECRNgb2+Pzz//XM21J9KMp70uSPOGDBki/dvd3R2enp5wdnbGtm3bMGjQIB3WrH4YO3YsTp06hQMHDpRbx+tFdyo7L7xedMPNzQ3Jycm4e/cuNmzYgKCgIOzdu1dar8trhcmoWsba2hr6+vrlspFZWVnlspal7OzsKixvYGAAKysrjdVVEzTd/vnz52P27NnYuXMnWrdurd7Kq4Em26+np4emTZsCANq2bYvU1FRERUXVqmSUJtp/5swZXL58Gf3795fWP3z4EABgYGCAc+fOwdXVVc0tqTlt/Q3Q09PDf/7zn1rXM0pT7be3t4ehoSH09fWlMi1atEBmZiaKioogl8vV3JKa0fT5//fffxEbG4sZM2aot+Jqoqn2f/755wgMDJR6g3l4eCA/Px8jR47ElClToKfHjuJUe9nZ2QF41OPD3t5eWl7VdUG6YW9vD2dn51r32/o8GjduHDZv3ox9+/ahUaNG0nJeL7pV2XmpCK8X7ZDL5dL/A3p6euLYsWP46quv8OmnnwLQ7bXC6KuWkcvl6NChAxISElSWJyQkoHPnzhV+xtvbu1z5+Ph4eHp6wtDQUGN11QRNtn/evHn44osvEBcXB09PT/VXXg20ef6FELWuW6wm2v/SSy/h9OnTSE5Oll4DBgxAjx49kJycDCcnJ421pya09R0QQiA5OVnlx6c20FT7u3TpgrS0NCkRCQDnz5+Hvb19rUlEAZo////9739RWFiId955R70VVxNNtf/ff/8tl3DS19eHeDSRixpbQKR+Li4usLOzU/meFxUVYe/evZVeF6Qbt2/fxrVr12rdb+vzRAiBsWPHYuPGjfjjjz/g4uKisp7Xi25Ud14qwutFN0r/H7BWXCtaGSadnkrptNYrV64UZ8+eFSEhIcLExERcvnxZCCHEpEmTRGBgoFS+dFrrjz/+WJw9e1asXLmywmnNT548KU6ePCns7e1FeHi4OHnypLhw4YLW21cdTbR/zpw5Qi6Xi19++UVlOtF79+5pvX3V0UT7Z8+eLeLj48XFixdFamqqWLBggTAwMBArVqzQevuqo4n2l1XbZ/PQxDGIjIwUcXFx4uLFi+LkyZNi+PDhwsDAQBw5ckTr7auOJtp/9epV8cILL4ixY8eKc+fOia1btwobGxsxc+ZMrbevOpq8Bl5++WUxZMgQrbWlJjTR/mnTpglTU1Px888/i0uXLon4+Hjh6uoqBg8erPX2EVXk3r17UpwGQCxcuFCcPHlSXLlyRQjxaPptpVIpNm7cKE6fPi2GDRvGqeq1oKrzcu/ePREWFiYSExNFenq62L17t/D29haOjo48Lxr00UcfCaVSKfbs2aMS0//7779SGV4v2lfdeeH1ohsRERFi3759Ij09XZw6dUpMnjxZ6Onpifj4eCGE7q8VJqNqqW+//VY4OzsLuVwu2rdvX25aTB8fH5Xye/bsEe3atRNyuVw0adJELF26VGV9enq6AFDuVXY7tYW62+/s7Fxh+6dNm6aF1jw9dbd/ypQpomnTpsLIyEhYWFgIb29vERsbq42m1Ii6219WbU9GCaH+YxASEiIaN24s5HK5aNiwoejTp49ITEzURlNqRBPfgcTEROHl5SUUCoV48cUXxaxZs8SDBw803ZQa0UT7z507JwBIAUhtpu72FxcXi8jISOHq6iqMjIyEk5OTGD16tMjOztZCa4iqt3v37grjlKCgICHEo+nqp02bJuzs7IRCoRDdunUTp0+f1m2l64Gqzsu///4r+vTpIxo2bCgMDQ1F48aNRVBQkLh69aquq/1cq+h8ABCrVq2SyvB60b7qzguvF9147733pHiqYcOGomfPnipxoK6vFZkQ7J9ORERERERERETawTGjiIiIiIiIiIhIa5iMIiIiIiIiIiIirWEyioiIiIiIiIiItIbJKCIiIiIiIiIi0homo4iIiIiIiIiISGuYjCIiIiIiIiIiIq1hMoqIiIiIiIiIiLSGySgiIiIiIiIiItIaJqOIiIiIiIiIiEhrmIwiItKA27dvw8bGBpcvX9baPt98800sXLhQa/sjIiIiUifGT0T1B5NRRFQnBQcHQyaT4cMPPyy3bvTo0ZDJZAgODtZ+xf6/qKgo9O/fH02aNJGWdevWDTKZDF988YVKWSEEvLy8IJPJMHXq1Brvc+rUqZg1axZyc3NrvA0iIiJ6fjF+Ko/xE5FuMBlFRHWWk5MTYmNjUVBQIC27f/8+fv75ZzRu3Fhn9SooKMDKlSvxwQcfSMuEEEhOToazszNOnz6tUn716tW4ceMGAKB9+/Y13m/r1q3RpEkTrFu3rsbbICIioucb4ydVjJ+IdIPJKCKqs9q3b4/GjRtj48aN0rKNGzfCyckJ7dq1k5bFxcXh5Zdfhrm5OaysrODv74+LFy+qbOuXX36Bh4cHjI2NYWVlhV69eiE/P7/adRX5/fffYWBgAG9vb2nZhQsXcO/ePQQHB6sEU/fu3UNERIR0F7JDhw7PdEwGDBiAn3/++Zm2QURERM8vxk/lMX4i0j4mo4ioThs+fDhWrVolvf/hhx/w3nvvqZTJz89HaGgojh07hl27dkFPTw+vv/46Hj58CADIyMjAsGHD8N577yE1NRV79uzBoEGDIISocl1l9u3bB09PT5VlSUlJMDIywrBhw3DhwgUUFhYCAL744gu0bdsW9vb2sLa2hpOT0zMdj44dO+Lo0aPS9omIiIjKYvykivETkfYZ6LoCRETPIjAwEBEREbh8+TJkMhkOHjyI2NhY7NmzRyrzxhtvqHxm5cqVsLGxwdmzZ+Hu7o6MjAw8ePAAgwYNgrOzMwDAw8MDAHD+/PlK11Xm8uXLcHBwUFl24sQJtG7dGs2bN4eJiQlSU1NhYmKCJUuW4Pjx45g/f/4z39UDAEdHRxQWFiIzM1OqLxEREdHjGD+pYvxEpH3sGUVEdZq1tTX69euH1atXY9WqVejXrx+sra1Vyly8eBEBAQF48cUXYWZmBhcXFwDA1atXAQBt2rRBz5494eHhgbfeegsrVqxAdnZ2tesqU1BQACMjI5VlSUlJ6NChA2QyGVq3bo2UlBR8/PHHGDlyJF566SUkJSU903gHpYyNjQEA//777zNvi4iIiJ5PjJ9UMX4i0j4mo4ioznvvvfcQExOD1atXl+tiDgD9+/fH7du3sWLFChw5cgRHjhwBABQVFQEA9PX1kZCQgN9//x0tW7ZEdHQ03NzckJ6eXuW6ylhbW5cLuE6ePCkFS23atMFXX32Fo0ePYtq0aSgqKsKZM2cqDKb+/fdffPLJJ+jcuTM6d+6MESNG4Pbt25Xu+86dOwCAhg0bVnPUiIiIqD5j/PR/GD8RaR+TUURU5/Xt2xdFRUUoKiqCr6+vyrrbt28jNTUVn332GXr27IkWLVpUeGdOJpOhS5cumD59Ok6ePAm5XI5NmzZVu64i7dq1w9mzZ6X3ly5dwt27d6Vu5G3btsXx48cxa9YsKJVKnD59GsXFxRV2Mx87dizatGmDxMREJCYmYujQoXj33XcrHXMhJSUFjRo1Knd3k4iIiOhxjJ/+D+MnIu3jmFFEVOfp6+sjNTVV+vfjLCwsYGVlhe+++w729va4evUqJk2apFLmyJEj2LVrF/r06QMbGxscOXIEt27dQosWLapcVxlfX19EREQgOzsbFhYWSEpKglwuh7u7OwAgKCgIAwcOhJWVFYBH4yFYWFhI3d9LFRQUIDs7G++88w4iIyMBAJGRkfjtt9+QlpaGZs2aldv3/v370adPn6c7gERERFTvMH76P4yfiLSPySgiei6YmZlVuFxPTw+xsbEYP3483N3d4ebmhq+//hrdu3dX+ey+ffuwePFi5ObmwtnZGQsWLICfnx9SU1MrXVcZDw8PeHp64r///S9GjRqFEydOwN3dHYaGhgAAQ0NDlTtvJ06cUJlKudTjd+/Gjh1b7TG4f/8+Nm3ahB07dlRbloiIiIjxE+MnIl2Riarm1yQiohrZvn07wsPDkZKSAj29mj8RHRwcjN69e+Ptt98GAOzatQvz58/H9u3bIZPJVMp+++23+O233xAfH/9MdSciIiLSBcZPRPUHe0YREWnAq6++igsXLuDvv/+Gk5NTjbezZMkSfPbZZ/j6668hk8nQokULrF27tlwgBTy6YxgdHf0s1SYiIiLSGcZPRPUHe0YREREREREREZHWcDY9IiIiIiIiIiLSGiajiIiIiIiIiIhIa5iMIiIiIiIiIiIirWEyioiIiIiIiIiItIbJKCIiIiIiIiIi0homo4iIiIiIiIiISGuYjCIiIiIiIiIiIq1hMoqIiIiIiIiIiLSGySgiIiIiIiIiItIaJqOIiIiIiIiIiEhrmIwiIiIiIiIiIiKt+X8d2qDxGBwjGgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -361,8 +361,8 @@ "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", - "p2_bd = np.where(kc_out['phase'] == 99)[0]\n", - "c_bd = np.where(kc_comp['phase'] == 99)[0]\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", "\n", "# Plot on histograms\n", @@ -422,22 +422,22 @@ "text": [ " Teff \n", "------------------\n", - " 589.5738241278044\n", - " 949.9191202547576\n", - " 535.3337220492646\n", - " 798.6701941060301\n", - " 883.180077123407\n", - "1009.6437674139902\n", - "444.11890279356027\n", + " 444.1173953888183\n", + " 693.3637752947118\n", + " 651.3987402554264\n", + " 714.1331032782153\n", + " 851.9772176003223\n", + " 783.8497395750999\n", + " 359.6196362456533\n", " ...\n", - "1625.8241118113378\n", - "1556.8961357273806\n", - " 1949.159944699517\n", - " 475.3197378938071\n", - "1445.6688176627986\n", - " 929.9466889780698\n", - " 905.5149940088668\n", - "Length = 964495 rows\n" + " 411.0731940270949\n", + " 449.6176329353576\n", + " 836.2301296016228\n", + "444.36050056433794\n", + "391.81361354085544\n", + "382.88818149261937\n", + "510.87160667571027\n", + "Length = 960373 rows\n" ] } ], @@ -455,25 +455,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH prim max mass: 119.64792937371239\n", - "BH prim min mass: 15.003687171639145\n", - "BH comp max mass: 108.78065103375774\n", - "BH comp min mass: 15.051696275245545\n", + "BH prim max mass: 119.898750058991\n", + "BH prim min mass: 15.01534492995759\n", + "BH comp max mass: 104.62467533647252\n", + "BH comp min mass: 15.00689100967727\n", "\n", - "NS prim max mass: 117.89439381247894\n", - "NS prim min mass: 9.001659607779285\n", - "NS comp max mass: 109.06172525179247\n", - "NS comp min mass: 9.000122949672017\n", + "NS prim max mass: 118.85881346572698\n", + "NS prim min mass: 9.000112173407095\n", + "NS comp max mass: 110.40621252485172\n", + "NS comp min mass: 9.001900741677009\n", "\n", - "WD prim max mass: 8.99922965392098\n", - "WD prim min mass: 5.311110801935481\n", - "WD comp max mass: 8.997715563589054\n", - "WD comp min mass: 5.31119691635049\n", + "WD prim max mass: 8.99910211758367\n", + "WD prim min mass: 5.311343510677102\n", + "WD comp max mass: 8.99933929534237\n", + "WD comp min mass: 5.31290506400592\n", "\n", - "BD prim max mass: 0.07999992301784681\n", - "BD prim min mass: 0.01000005942626443\n", - "BD comp max mass: 0.07999668766843566\n", - "BD comp min mass: 0.010000432726566368\n", + "BD prim max mass: 0.07999998562994273\n", + "BD prim min mass: 0.01000004628780762\n", + "BD comp max mass: 0.0799995996476194\n", + "BD comp min mass: 0.010000293534472888\n", "\n" ] } @@ -503,9 +503,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "a5e9c3bc-ea61-4f1e-b13c-9bbcfc9f2830", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(k_bd[\"mass_current\"], k_bd['Teff'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "65f1a0ad-61ac-4606-907b-fbf8bdbd2271", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "287.8007498624834" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(k_bd['Teff'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92975ed8-a4c1-4383-a7ef-11c88fb362a7", + "metadata": {}, "outputs": [], "source": [] } diff --git a/spisea/ifmr.py b/spisea/ifmr.py index 672ad818..a413f240 100755 --- a/spisea/ifmr.py +++ b/spisea/ifmr.py @@ -553,7 +553,7 @@ def generate_death_mass(self, mass_array, metallicity_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - * BD: typecode = 99 + * BD: typecode = 90 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. A remnant mass of -99 means you're outside the range of @@ -571,7 +571,7 @@ def generate_death_mass(self, mass_array, metallicity_array): #output_array[1] holds the remnant type output_array = np.zeros((2, len(mass_array))) - codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 90} #create array to store the remnant masses generated by Spera equations rem_mass_array = np.zeros(len(mass_array)) @@ -759,7 +759,7 @@ def generate_death_mass(self, mass_array): * WD: typecode = 101 * NS: typecode = 102 * BH: typecode = 103 - * BD: typecode = 99 + * BD: typecode = 90 A typecode of value -1 means you're outside the range of validity for applying the ifmr formula. @@ -783,7 +783,7 @@ def generate_death_mass(self, mass_array): #Random array to get probabilities for what type of object will form random_array = np.random.randint(1, 1001, size = len(mass_array)) - codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 99} + codes = {'WD': 101, 'NS': 102, 'BH': 103, 'BD': 90} """ The id_arrays are to separate all the different formation regimes diff --git a/spisea/synthetic.py b/spisea/synthetic.py index eaa713d5..afde2c63 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -233,7 +233,7 @@ 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 < 99) & (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 < 90) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) # Print warning, if desired verbose=False if verbose: @@ -279,7 +279,7 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Handle brown dwarfs separately and assign temperatures - idx_bd = np.where(star_systems['phase'] == 99)[0] + idx_bd = np.where(star_systems['phase'] == 90)[0] # Define the class for BDs evo_model = evolution.MergedBaraffePisaEkstromParsec() @@ -385,7 +385,7 @@ def _make_companions_table(self, star_systems, compMass): # Convert nan_to_num to avoid errors on greater than, less than comparisons companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 99) & + (companions_phase_non_nan < 90) & (companions_phase_non_nan != 9) & (companions_phase_non_nan != -99)) # Print warning, if desired @@ -450,7 +450,7 @@ def _make_companions_table(self, star_systems, compMass): # Assigning brown dwarf companions the correct phase/properties bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - companions['phase'][bd_idx] = 99 + companions['phase'][bd_idx] = 90 companions['mass_current'][bd_idx] = companions['mass'][bd_idx] for filt in self.filt_names: companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) @@ -496,12 +496,6 @@ def _remove_bad_systems(self, star_systems, compMass): removed. If self.ifmr != None, then we will save the high mass systems since they will be plugged into an ifmr later. """ - """ # Print companion masses and phases before any filtering - bd_idx_initial = [np.where((np.array(cm) >= 0.01) & (np.array(cm) < 0.08))[0] for cm in compMass] - print("Before filtering - CompMass (brown dwarfs):") - for i, bdi in enumerate(bd_idx_initial): - if len(bdi) > 0: - print(f"System {i}: Masses: {np.array(compMass[i])[bdi]}, Phases: {np.full(len(bdi), 99)}")""" N_systems = len(star_systems) @@ -515,7 +509,7 @@ def _remove_bad_systems(self, star_systems, compMass): else: # Keep stars (with Teff) and any other compact objects (with phase info). idx = np.where((star_systems_teff_non_nan > 0) | (star_systems_phase_non_nan >= 0) | - (star_systems_phase_non_nan == 99))[0] + (star_systems_phase_non_nan == 90))[0] if len(idx) != N_systems and self.verbose: print( 'Found {0:d} stars out of mass range'.format(N_systems - len(idx))) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index e146b273..a94dee67 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -474,19 +474,19 @@ def test_ifmr_multiplicity(): assert len(np.where(clust2['phase'] == 102)) > 0 assert len(np.where(clust1['phase'] == 103)) > 0 # BH assert len(np.where(clust2['phase'] == 103)) > 0 - assert len(np.where(clust1['phase'] == 99)) > 0 # BD - assert len(np.where(clust2['phase'] == 99)) > 0 + assert len(np.where(clust1['phase'] == 90)) > 0 # BD + assert len(np.where(clust2['phase'] == 90)) > 0 # Now check that we have companions that are WDs, NSs, BHs, and BDs assert len(np.where(comps2['phase'] == 101)) > 0 assert len(np.where(comps2['phase'] == 102)) > 0 assert len(np.where(comps2['phase'] == 103)) > 0 - assert len(np.where(comps2['phase'] == 99)) > 0 + assert len(np.where(comps2['phase'] == 90)) > 0 # Make sure no funky phase designations (due to interpolation effects) # slipped through - idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 99) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 99) & (comps2['phase'] != 9) ) + idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 90) & (clust1['phase'] != 9) ) + idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 90) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 # Make sure BD temperatures are assigned correctly @@ -503,7 +503,7 @@ def test_ifmr_multiplicity(): wd_idx = np.where(clust1['phase'] == 101) ns_idx = np.where(clust1['phase'] == 102) bh_idx = np.where(clust1['phase'] == 103) - bd_idx = np.where(clust1['phase'] == 99) + bd_idx = np.where(clust1['phase'] == 90) assert len(clust1[bd_idx]) != 0 assert np.all(clust1['mass'][wd_idx] > MIN_MASS) @@ -515,7 +515,7 @@ def test_ifmr_multiplicity(): comp_wd_idx = np.where(comps2['phase'] == 101) comp_ns_idx = np.where(comps2['phase'] == 102) comp_bh_idx = np.where(comps2['phase'] == 103) - comp_bd_idx = np.where(comps2['phase'] == 99) + comp_bd_idx = np.where(comps2['phase'] == 90) assert np.all(comps2['mass'][comp_wd_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_ns_idx] > MIN_MASS) assert np.all(comps2['mass'][comp_bh_idx] > MIN_MASS) @@ -528,12 +528,12 @@ def test_ifmr_multiplicity(): (comps2['mass'][comp_bd_idx] <= BD_MAX_MASS)) # Ensure no other objects are in the brown dwarf mass range - non_bd_idx = np.where((clust1['phase'] != 99) & + non_bd_idx = np.where((clust1['phase'] != 90) & (clust1['mass'] >= BD_MIN_MASS) & (clust1['mass'] <= BD_MAX_MASS)) assert len(non_bd_idx[0]) == 0 # asserting no non-brown dwarf objects in BD mass range - comp_non_bd_idx = np.where((comps2['phase'] != 99) & + comp_non_bd_idx = np.where((comps2['phase'] != 90) & (comps2['mass'] >= BD_MIN_MASS) & (comps2['mass'] <= BD_MAX_MASS)) assert len(comp_non_bd_idx[0]) == 0 # asserting no non-brown dwarf companions in BD mass range From b00bdb1eea3e3f25538166bd5ea740a04db272f5 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 4 Nov 2024 13:45:55 -0800 Subject: [PATCH 25/48] Addition of Meisner 2023 for BD atmospheres of metallicities -1 - 0.3 --- spisea/atmospheres.py | 312 +++++++++++++++++++++++++++++++++++- spisea/tests/test_models.py | 21 ++- 2 files changed, 328 insertions(+), 5 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 8df2fbe1..b6ecf375 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -624,17 +624,107 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) +def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from CIFIST2011 grid + (`Allard et al. 2012 `_) + + Grid originally downloaded `here `_ + + Notes + ------ + Grid Range: + + * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 + + Teff and gravity ranges depend on metallicity: + + [M/H] = -2.5 + + * Teff: 2600 - 4600 K + * gravity: 4.5 - 5.5 + + [M/H] = -2.0 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.5 + + * Teff: 2600 - 7000 + * gravity: 4.5 - 5.5 + + [M/H] = -1.0 + + * Teff: 2600 - 7000 + * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 + + [M/H] = -0.5 + + * Teff: 1000 -7000 + * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 + + [M/H] = 0 + + * Teff: 750 - 7000 + * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 + + [M/H] = 0.5 + + * Teff: 1000 - 5000 + * gravity: 3.5 - 5.0 + + + Alpha enhancement: + + * [M/H]= -0.0, +0.5 no anhancement + * [M/H]= -0.5 with [alpha/H]=+0.2 + * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + 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. + + FILL IN W MEISNER + """ + if rebin == True: + atm_name = 'Meisner2023_rebin' + else: + atm_name = 'Meisner2023' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + # Do some error checking idx = np.where(sp.flux != 0)[0] if len(idx) == 0: - print( 'Could not find BTSettl_2015 atmosphere model for') + print( 'Could not find Meisner2023 atmosphere model for') print( ' temperature = %d' % temperature) print( ' metallicity = %.1f' % metallicity) print( ' log gravity = %.1f' % gravity) return sp - + def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): """ Return white dwarf atmospheres from @@ -723,6 +813,8 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): return sp + + #---------------------------------------------------------------------# def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, rebin=True): @@ -767,6 +859,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 + * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -777,6 +871,7 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * ATLAS: ATLAS9 models (`Castelli & Kurucz 2004 `_) * PHOENIXv16 (`Husser et al. 2013 `_) * BTSettl_CIFITS2011_2015: Baraffee+15, Allard+ (https://phoenix.ens-lyon.fr/Grids/BT-Settl/CIFIST2011_2015/SPECTRA/) + * Meisner2023: ATMO 1D models (`Meisner et al. 2023 `_) LTE WARNING: @@ -910,6 +1005,45 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec +def get_bd_atmosphere(metallicity=0, temperature=1500, gravity=4, verbose=False): + """ + Return the brown dwarf atmosphere from + `Meisner et al. 2020 `_. + If desired parameters are + outside of grid, return a blackbody spectrum instead + + Parameters + ---------- + metallicity: float + The stellar metallicity, in terms of [Z] + + temperature: float + The stellar temperature, in units of K + + gravity: float + The stellar gravity, in cgs units + + rebin: boolean + 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. + + verbose: boolean + True for verbose output + """ + try: + if verbose: + print('Meisner2023 atmosphere') + + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + except pysynphot.exceptions.ParameterOutOfBounds: + # Use a black-body atmosphere. + bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) + return bbspec + def get_bb_atmosphere(metallicity=None, temperature=20_000, gravity=None, verbose=False, rebin=None, wave_min=500, wave_max=50_000, wave_num=20_000): @@ -2020,6 +2154,180 @@ def rebin_BTSettl(make_unique=False): return +def organize_all_Meisner2023_atmospheres(): + """ + Construct cdbs-ready atmospheres for the Meisner2023 grid. + The code expects tp be run in cdbs/grid/Meisner2023, and expects that the + individual model files have been downloaded from online + and processed into python-readable ascii files. + """ + orig_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Go through each directory, turning each spectrum into a cdbs-ready file. + # Save as a fits file, for faster access later + for ii in dirs: + print('Starting {0}'.format(ii)) + os.chdir(ii) + + files = glob.glob('*.fits') + count=0 + for jj in files: + # Open each .fits file and read the data + with fits.open(jj) as hdul: + data = hdul[1].data + wavelength = data['Wavelength'] # Assuming these columns exist + flux = data['Flux'] + + # Create new columns with desired format + c0 = fits.Column(name='Wavelength', format='D', array=wavelength) + c1 = fits.Column(name='Flux', format='E', array=flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + # Add unit keywords + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Write the new fits table in the cdbs directory + output_filename = '{0}.fits'.format(jj[:-5]) # Removing the original .fits extension + hdu_new.writeto(output_filename, overwrite=True) + hdu_new.close() + count += 1 + print('Done {0} of {1}'.format(count, len(files))) + + # Go back to original directory, move to next metallicity directory + os.chdir(orig_dir) + + return + +def make_Meisner2023_catalog(): + """ + Create cdbs catalog.fits of Meisner2023 grid. + THIS IS STEP 2, after organize_Meisner2023_atmospheres has + been run. + + Code expects to be run in cdbs/grid/Meisner2023 + Will create catalog.fits file in atmosphere directory with + description of each model + """ + # Record current working directory for later + start_dir = os.getcwd() + dirs = ['mm10', 'mm05', 'mp00', 'mp03'] + + # Construct the catalog.fits file input. The input consists of + # and index string that specifies the stellar paramters, and a + # name string that points to the file + # Loop over all the metallicity directories to construct these inputs + index_str = [] + name_str = [] + for ii in dirs: + os.chdir(ii) + files = glob.glob('spec_jwst_*.fits') + + for jj in files: + # Parse temperature, log(g), and metallicity from filename + temp_str = jj.split('_')[2] + logg_str = jj.split('_')[3] + metal_str = jj.split('_')[4] + + # Extract temperature, surface gravity, and metallicity + temp = float(temp_str[1:]) # Temperature in Kelvin + logg = float(logg_str[1:]) # Surface gravity log(g) + + # Build metallicity value + if metal_str.startswith('m'): + metallicity = -1 * float(metal_str[1:]) + else: + metallicity = float(metal_str[1:]) + + # Construct index and filename strings + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metallicity, logg)) + name_str.append('{0}/{1}[Flux]'.format(ii, jj)) + + print('Processed directory:', ii) + os.chdir(start_dir) + + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write('catalog.fits', format = 'fits', overwrite=True) + + # Move back to original directory, create the catalog.fits file + os.chdir(start_dir) + + return + +def rebin_Meisner2023(make_unique=False): + """ + Rebin Meisner2023 models to atlas ck04 resolution; this makes + spectrophotometry MUCH faster + + makes new directory: Meisner2023_rebin + + Code expects to be run in cdbs/grid directory + """ + # Get an atlas ck04 model, we will use this to set wavelength grid + sp_atlas = get_castelli_atmosphere() + + # Create a directory for rebinned Meisner2023 models + rebin_path = 'Meisner2023_rebin/' + if not os.path.exists(rebin_path): + os.mkdir(rebin_path) + + # Load the catalog.fits file and extract all spectra file paths + cat = Table.read('Meisner2023/catalog.fits') + files_all = [cat[ii]['FILENAME'].split('[')[0] for ii in range(len(cat))] + + print('Rebinning Meisner2023 spectra') + if make_unique: + print('Making unique') + make_wavelength_unique(files_all, 'Meisner2023') + print('Done') + + for ff, file in enumerate(files_all): + vals = cat[ff]['INDEX'].split(',') + temp = float(vals[0]) + metal = float(vals[1]) + logg = float(vals[2]) + + # Fetch the Meisner2023 spectrum and rebin its flux + try: + sp = pysynphot.Icat('Meisner2023', temp, metal, logg) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_meisner.wave) + + # Create the output FITS file + c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) + c1 = fits.Column(name='Flux', format='E', array=flux_rebin) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + + # Write the new rebinned file in the Meisner2023_rebin directory + outfile = os.path.join(rebin_path, os.path.basename(file)) + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto(outfile, overwrite=True) + + except Exception as e: + print(f"Error processing {file}: {e}") + orig_file = os.path.join('Meisner2023', file) + outfile = os.path.join(rebin_path, os.path.basename(file)) + os.system(f'cp {orig_file} {outfile}') + + print('Done {0} of {1}'.format(ff + 1, len(files_all))) + + return + + + def organize_WDKoester_atmospheres(path_to_dir): """ Construct cdbs-ready wdKoester WD atmospheres for each model. (from Koester 2010) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index e80684be..334ef86e 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -79,13 +79,16 @@ def test_atmosphere_models(): from spisea import atmospheres as atm # Array of atmospheres - atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere, - atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere] + atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, + atm.get_BTSettl_2015_atmosphere, atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, + atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere, atm.get_Meisner2023_atmosphere] # Array of metallicities metals_range = [-2.0, 0, 0.15] + bd_metals_range = [-1.0, -0.5, 0, 0.3] metals_solar = [0] - metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, metals_solar] + metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, + metals_solar, bd_metals_range] assert len(atm_arr) == len(metals_arr) @@ -126,6 +129,18 @@ def test_atmosphere_models(): raise Exception('ATM TEST FAILED: {0}, temp = {2}'.format(atm_func, jj)) print('get_bb_atmosphere: all temps passed') + + # Test get_bd_atmosphere at different temps + # This func only requests temp + temp_range = [250, 400, 500, 750, 950, 1200] + atm_func = atm.get_bd_atmosphere + for jj in temp_range: + try: + test = atm_func(temperature=jj, verbose=True) + except: + raise Exception('ATM TEST FAILED: {0}, temp = {1}'.format(atm_func, jj)) + + print('get_bd_atmosphere: all temps passed') return From c41774a0b04dd728dba82fd4e9a8b5a78cd12dfc Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 4 Nov 2024 13:55:59 -0800 Subject: [PATCH 26/48] BD function for atmospheres --- spisea/synthetic.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index afde2c63..8344b92c 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -33,6 +33,7 @@ default_red_law = reddening.RedLawNishiyama09() default_atm_func = atm.get_merged_atmosphere default_wd_atm_func = atm.get_wd_atmosphere +default_bd_atm_func = atm.get_bd_atmosphere def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 @@ -765,7 +766,7 @@ class Isochrone(object): """ 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, + wd_atm_func = default_wd_atm_func, bd_atm_func = default_bd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, rebin=True): @@ -838,6 +839,10 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, if phase == 101: star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, verbose=False) + elif phase == 90: + print(f"Applying brown dwarf model to object {ii}") + star = bd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, + verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, rebin=rebin) @@ -954,9 +959,13 @@ class IsochronePhot(Isochrone): Default is atmospheres.get_merged_atmosphere. wd_atm_func: white dwarf model atmosphere function, optional - Set the stellar atmosphere models for the white dwafs. + Set the stellar atmosphere models for the white dwarfs. Default is atmospheres.get_wd_atmosphere + bd_atm_func: brown dwarf model atmosphere function, optimal + Set the stellar atmosphere models for the brown dwarfs. + Default is atmospheres.get_bd_atmosphere + red_law : reddening law object, optional Define the reddening law for the synthetic photometry. Default is reddening.RedLawNishiyama09(). @@ -1003,7 +1012,7 @@ class IsochronePhot(Isochrone): 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, + wd_atm_func = default_wd_atm_func, bd_atm_func = default_bd_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=False, From 220e4dad760c1dca753c451c67f938319b68a4f3 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 13 Nov 2024 14:29:18 -0800 Subject: [PATCH 27/48] Current progress in BD implementation --- spisea/atmospheres.py | 59 ++++++++++++++++++++++++++++++---- spisea/evolution.py | 1 + spisea/imf/imf.py | 29 ++++++----------- spisea/imf/tests/test_bd.py | 6 ++-- spisea/imf/tests/test_class.py | 13 ++++---- spisea/synthetic.py | 5 ++- spisea/tests/test_models.py | 9 +++--- 7 files changed, 81 insertions(+), 41 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index b6ecf375..a65e495e 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -724,6 +724,45 @@ def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, reb print( ' log gravity = %.1f' % gravity) return sp + +def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): + """ + Return atmosphere from Phillips et al., 2020 using ATMO model + (`Phillips et al. 2020 `_) + + Grid originally downloaded `here `_ + + Grid Range: + + * Teff: 200 - 3000 K + * gravity: 2.5 - 5.5 cgs + * [M/H] = 0 + """ + if rebin == True: + atm_name = 'Phillips2020_rebin' + else: + atm_name = 'Phillips2020' + + try: + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds(atm_name, + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find Phillips2020 atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp def get_wdKoester_atmosphere(metallicity=0, temperature=20000, gravity=7): """ @@ -859,8 +898,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Meisner2023 merge - * 1000 < T <= 250, logg > 2.5: Meisner2023 + * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Phillips2020 merge + * 1000 < T <= 250, logg > 2.5: Phillips2020 Otherwise, if T < 3800 and [M/H] != 0: @@ -894,6 +933,14 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid + if (temperature <= 1200) & (metallicity == 0): + if verbose: + print( 'Phillips2020 atmosphere') + return get_Phillips2020_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (temperature <= 3800) & (metallicity == 0): # High gravity are in BTSettl regime if (temperature <= 3200) & (gravity > 2.5): @@ -1005,7 +1052,7 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec -def get_bd_atmosphere(metallicity=0, temperature=1500, gravity=4, verbose=False): +def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): """ Return the brown dwarf atmosphere from `Meisner et al. 2020 `_. @@ -1033,9 +1080,9 @@ def get_bd_atmosphere(metallicity=0, temperature=1500, gravity=4, verbose=False) """ try: if verbose: - print('Meisner2023 atmosphere') + print('Phillips2020 atmosphere') - return get_Meisner2023_atmosphere(metallicity=metallicity, + return get_Phillips2020_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) @@ -2176,7 +2223,7 @@ def organize_all_Meisner2023_atmospheres(): # Open each .fits file and read the data with fits.open(jj) as hdul: data = hdul[1].data - wavelength = data['Wavelength'] # Assuming these columns exist + wavelength = data['Wavelength'] flux = data['Flux'] # Create new columns with desired format diff --git a/spisea/evolution.py b/spisea/evolution.py index 3326fc6d..7de8e217 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1320,6 +1320,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): nan_teff_idx = np.isnan(iso['logT']) if np.any(nan_teff_idx): iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + return iso def get_temperature(self, masses, ages): """ diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index a8019fb7..847ad5ab 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -689,30 +689,19 @@ def __init__(self, multiplicity=None): IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) - -#added for brown dwarfs -class Muzic_2017(IMF_broken_powerlaw): - """ - Define IMF from `Mužić (2017) -`_. - Mass range is 0.01 M_sun - inf M_sun. - """ - def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.5, 1, np.inf]) - powers = np.array([-0.71, -0.81, -1.60]) - - IMF_broken_powerlaw.__init__(self, massLimits, powers, - multiplicity=multiplicity) -class CombinedBD_2024(IMF_broken_powerlaw): +class CombinedWeidnerKroupaKirkpatrick_2024(IMF_broken_powerlaw): """ - Define IMF from several papers considering brown dwarf mass ranges - Derivation given in SPISEA/changes/BD_IMF - Mass range: 0.1 M_sun - 0.8 M_sun + Define combined IMF from several papers considering brown dwarf mass ranges. + Mass range: + * 0.01 M_sun - 8 M_sun: Kirkpatrick 2024 + `_. + * 8 M_sun - 120 M_sun: Weidner & Kroupa 2004 + `_. """ def __init__(self, multiplicity=None): - massLimits = np.array([0.01, 0.08]) - powers = np.array([-0.56]) + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) diff --git a/spisea/imf/tests/test_bd.py b/spisea/imf/tests/test_bd.py index a29005c6..7dda4cfe 100644 --- a/spisea/imf/tests/test_bd.py +++ b/spisea/imf/tests/test_bd.py @@ -9,9 +9,9 @@ def test_generate_cluster(): # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 - massLimits = np.array([0.01, 0.5, 1, 120]) # Define boundaries of each mass segement - powers = np.array([-0.71, -0.81, -1.6]) # Power law slope associated with each mass segment + # Make IMF object; we'll use a broken power law with the parameters from CombinedWeidnerKroupaKirkpatrick_2024 + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) # Define total cluster mass diff --git a/spisea/imf/tests/test_class.py b/spisea/imf/tests/test_class.py index 7c15b3dc..9da404d2 100644 --- a/spisea/imf/tests/test_class.py +++ b/spisea/imf/tests/test_class.py @@ -29,16 +29,15 @@ def test_Muzic(): return -""" -def test_CombinedBD(): +def test_CombinedWeidnerKroupaKirkpatrick_2024(): from .. import imf from .. import multiplicity - + # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Use the IMF class from imf.py - my_imf = imf.CombinedBD_2024(multiplicity=imf_multi) + # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 + my_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi) # Define total cluster mass M_cl = 10**5. @@ -54,5 +53,5 @@ def test_CombinedBD(): mdx = np.where(isMulti)[0] for mm in mdx: assert len(compMass[mm]) > 0 - - return""" \ No newline at end of file + + return \ No newline at end of file diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 8344b92c..9a6c890c 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -789,6 +789,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, metallicity=metallicity) + # Eliminate cases where log g is less than 0 idx = np.where(evol['logg'] > 0) @@ -841,7 +842,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, verbose=False) elif phase == 90: print(f"Applying brown dwarf model to object {ii}") - star = bd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, + star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, @@ -1059,6 +1060,8 @@ def __init__(self, logAge, AKs, distance, metallicity=metallicity, evo_model=evo_model, atm_func=atm_func, wd_atm_func=wd_atm_func, + bd_atm_func=bd_atm_func, + wave_range=wave_range, red_law=red_law, mass_sampling=mass_sampling, min_mass=min_mass, max_mass=max_mass, rebin=rebin) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 334ef86e..7d21b3e6 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -81,14 +81,15 @@ def test_atmosphere_models(): # Array of atmospheres atm_arr = [atm.get_merged_atmosphere, atm.get_castelli_atmosphere, atm.get_phoenixv16_atmosphere, atm.get_BTSettl_2015_atmosphere, atm.get_BTSettl_atmosphere, atm.get_kurucz_atmosphere, - atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere, atm.get_Meisner2023_atmosphere] + atm.get_phoenix_atmosphere, atm.get_wdKoester_atmosphere, atm.get_Phillips2020_atmosphere, + atm.get_Meisner2023_atmosphere] # Array of metallicities metals_range = [-2.0, 0, 0.15] bd_metals_range = [-1.0, -0.5, 0, 0.3] metals_solar = [0] metals_arr = [metals_solar, metals_range, metals_range, metals_solar, metals_range, metals_range, metals_range, - metals_solar, bd_metals_range] + metals_solar, metals_solar, bd_metals_range] assert len(atm_arr) == len(metals_arr) @@ -106,7 +107,7 @@ def test_atmosphere_models(): print('Done {0}'.format(atm_func)) # Test get_merged_atmospheres at different temps - temp_range = [2000, 3500, 4000, 5250, 6000, 12000] + temp_range = [200, 1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_merged_atmosphere for ii in metals_range: for jj in temp_range: @@ -120,7 +121,7 @@ def test_atmosphere_models(): # Test get_bb_atmosphere at different temps # This func only requests temp - temp_range = [2000, 3500, 4000, 5250, 6000, 12000] + temp_range = [1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_bb_atmosphere for jj in temp_range: try: From ef4f814fd8e3b69186a5d35914f76a654699e4a8 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 18 Nov 2024 16:08:26 -0800 Subject: [PATCH 28/48] Notebook w/ Meisner vs. Phillips Flux Issues --- changes/BDClusters_AtmIssues.ipynb | 735 +++++++++++++++++++++++++++++ 1 file changed, 735 insertions(+) create mode 100644 changes/BDClusters_AtmIssues.ipynb diff --git a/changes/BDClusters_AtmIssues.ipynb b/changes/BDClusters_AtmIssues.ipynb new file mode 100644 index 00000000..54257c32 --- /dev/null +++ b/changes/BDClusters_AtmIssues.ipynb @@ -0,0 +1,735 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e99bddf5-2499-461b-9102-d8d450c3890f", + "metadata": {}, + "source": [ + "# New Additions!" + ] + }, + { + "cell_type": "markdown", + "id": "11bed8fe-4a9a-4499-8721-a34b6a9297cf", + "metadata": {}, + "source": [ + "### First Import Necessary Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "01b7a3c9-8d7a-483f-a25a-161817f55e9f", + "metadata": {}, + "outputs": [], + "source": [ + "from spisea import synthetic, evolution, atmospheres, reddening, ifmr\n", + "from spisea.imf import imf, multiplicity\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pylab as py\n", + "from scipy.interpolate import RegularGridInterpolator\n", + "import pdb\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import vstack\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" + ] + }, + { + "cell_type": "markdown", + "id": "230b1ba3-eeca-4207-9125-08115cf15341", + "metadata": {}, + "source": [ + "## 1: BD objects in isochron" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bc90dc34-0747-4b24-805f-6e8abf081565", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Isochrone generation took 54.561135 s.\n", + "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", + " Starting at: 2024-11-12 17:33:26.509247 Usually takes ~5 minutes\n", + "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", + "Starting synthetic photometry\n", + "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", + "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", + "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", + "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", + "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", + "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", + "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", + "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", + "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", + "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", + "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", + " Time taken: 14.44 seconds\n" + ] + } + ], + "source": [ + "# Create isochrone object \n", + "logAge = np.log10(5*10**9.)\n", + "filt_list = ['wfc3,ir,f153m'] # Only 1 filter for plotting purposes\n", + "my_ifmr = ifmr.IFMR_Raithel18()\n", + "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", + " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + " filters=filt_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "3bfb0efe-bf1a-4666-945b-057c64e6cccd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " L Teff ... phase m_hst_f153m \n", + " W K ... \n", + "---------------------- ------------------ ... ----- -----------------\n", + "5.2116102444796556e+23 2793.830151362531 ... 1 9.517653682370574\n", + " 5.890768137510784e+23 2838.5725586841204 ... 1 9.39108220785009\n" + ] + } + ], + "source": [ + "bd_idx = np.where(my_iso.points['mass'] < 0.08)\n", + "print(my_iso.points[bd_idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "998eaa3e-103d-40bf-85ba-125ad4a5ed75", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf (mass < 0.08 M_sun): F153M = 9.518 mag\n" + ] + } + ], + "source": [ + "# Create sample BD case\n", + "# Identify a brown dwarf with mass < 0.08 M_sun\n", + "bd_idx = np.where(my_iso.points['mass'] < 0.08)[0]\n", + "if len(bd_idx) > 0:\n", + " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", + " mass = my_iso.points[bd_idx[0]]['mass']\n", + " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", + "else:\n", + " print('No brown dwarf with mass < 0.08 M_sun found.')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "dca63ebb-6c40-4f76-abb6-b709ccb71466", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make a mass-magnitude diagram from the isochrone and plot a brown dwarf\n", + "py.figure(1, figsize=(6,6))\n", + "py.clf()\n", + "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", + "py.plot(my_iso.points['mass'][bd_idx[0]], my_iso.points['m_hst_f153m'][bd_idx[0]], 'b*', ms=15, label='BD mass')\n", + "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", + "py.xlabel('Mass')\n", + "py.ylabel('F153M')\n", + "py.gca().invert_yaxis()\n", + "py.legend()\n", + "py.show()" + ] + }, + { + "cell_type": "markdown", + "id": "44b18dc0-a450-497c-9ef8-a23c31263718", + "metadata": {}, + "source": [ + "## 2. BD Objects in Cluster (Primary and Companions)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6d32d779-8e93-44e1-9375-7ff6e4f5f246", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF objects \n", + "imf_multi = multiplicity.MultiplicityUnresolved()\n", + "kc_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "76787edf-ced9-478a-8572-c3b5e327febe", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 8520 companions out of stellar mass range\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", + " return _methods._mean(a, axis=axis, dtype=dtype,\n", + "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + } + ], + "source": [ + "# Make cluster\n", + "cluster_mass = 10**6\n", + "kc_cluster = synthetic.ResolvedCluster(my_iso, kc_imf, cluster_mass, ifmr=my_ifmr)\n", + "\n", + "# Get outputs\n", + "kc_out = kc_cluster.star_systems\n", + "kc_comp = kc_cluster.companions" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9ca75682-5e3a-4178-b9a1-0db1ecc9b4a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "\n", + "# Define bins for histograms\n", + "bh_bins = np.linspace(14, 100, 20)\n", + "wd_bins = np.linspace(0.4, 10, 20)\n", + "bd_bins = np.linspace(0.01, 0.08, 8)\n", + "ns_bins = np.linspace(0, 30, 20)\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", + "plt.title(\"Black Holes by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", + "plt.title(\"White Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", + "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", + "plt.title(\"Neutron Stars by Mass\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8f03eedf-780a-47cc-bcb5-1bd767ff4135", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "287.80180827780606\n", + "287.80439454686973\n", + "2321.1956912624596\n", + "2321.1948222119263\n" + ] + } + ], + "source": [ + "print(np.min(kc_out[p2_bd]['Teff']))\n", + "print(np.min(kc_comp[c_bd]['Teff']))\n", + "print(np.max(kc_out[p2_bd]['Teff']))\n", + "print(np.max(kc_comp[c_bd]['Teff']))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "fbbd559a-4f0d-426e-9c6c-798debb699dc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from astropy.io import fits\n", + "from astropy.table import Table\n", + "\n", + "# plotting 1200K star from Meisner vs. BTSettl\n", + "bt_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/BTSettl_rebin/btm05/lte033-3.0-0.5a+0.2.BT-Settl.spec.fits'\n", + "m_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Meisner2023/mp00/spec_jwst_t1200_g4.5_p0_kg_g1.25.fits'\n", + "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits'\n", + "\n", + "b_hdu_list = fits.open(bt_file)\n", + "m_hdu_list = fits.open(m_file)\n", + "p_hdu_list = fits.open(p_file)\n", + "\n", + "b_data = Table(b_hdu_list[1].data)\n", + "m_data = Table(m_hdu_list[1].data)\n", + "p_data = Table(p_hdu_list[1].data)\n", + "\n", + "b_w = np.array(b_data['Wavelength'])\n", + "b_f = np.array(b_data['Flux'])\n", + "m_w = np.array(m_data['Wavelength'])\n", + "m_f = np.array(m_data['Flux'])\n", + "p_w = np.array(p_data['Flux'])\n", + "p_f = np.array(p_data['Wavelength'])\n", + "\n", + "plt.figure()\n", + "plt.plot(b_w, b_f, label = 'BTSettl')\n", + "plt.plot(m_w, m_f, label = 'Meisner')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "dcb9ce22-afd5-4f75-a033-3f205e2e8613", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "90.9000015258789\n", + "0.0\n", + "2000.3333534194462\n", + "1.524743e-33\n", + "\n", + "\n", + "1600000.0\n", + "557203.6\n", + "299949.9969872583\n", + "1.1996874e-15\n" + ] + } + ], + "source": [ + "print(np.min(b_w))\n", + "print(np.min(b_f))\n", + "print(np.min(m_w))\n", + "print(np.min(m_f))\n", + "print('\\n')\n", + "print(np.max(b_w))\n", + "print(np.max(b_f))\n", + "print(np.max(m_w))\n", + "print(np.max(m_f))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a3e80ffe-ef8d-4fc8-8c2a-463c12c5e42d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (8,6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(b_w, b_f)\n", + "plt.title('BTSettl Flux vs. Wavelength for 3000K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(m_w, m_f)\n", + "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "443dd316-d205-44a1-87b8-3c2252d28f64", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Where did Phillips peak for 1200K star?\n", + "plt.figure(figsize = (8,6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(p_f, p_w)\n", + "plt.title('Phillips Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(m_w, m_f)\n", + "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "5419c340-4abc-44a9-9ec3-c77bb35d1b2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(m_w, m_f)\n", + "plt.title('Meisner Flux vs. Wavelength for 1200K star')\n", + "plt.xlabel('Wavelength (A)')\n", + "plt.ylabel('Flux (erg/cm$^2$/A)')\n", + "plt.xlim(0, 1e5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "51fcf941-7b85-4c8a-851d-8d73f315e365", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting new IMF\n", + "\n", + "mass_range = np.linspace(0.01, 120, 10000)\n", + "\n", + "def power_law(masses, index):\n", + " return masses**index\n", + "\n", + "plt.figure()\n", + "\n", + "# Define each mass range and plot with the corresponding index\n", + "mass_1 = mass_range[(mass_range >= 0.01) & (mass_range < 0.05)]\n", + "plt.plot(mass_1, power_law(mass_1, -0.6), label='Index = -0.6')\n", + "\n", + "mass_2 = mass_range[(mass_range >= 0.05) & (mass_range < 0.22)]\n", + "plt.plot(mass_2, power_law(mass_2, -0.25), label='Index = -0.25')\n", + "\n", + "mass_3 = mass_range[(mass_range >= 0.22) & (mass_range < 0.55)]\n", + "plt.plot(mass_3, power_law(mass_3, -1.3), label='Index = -1.3')\n", + "\n", + "mass_4 = mass_range[(mass_range >= 0.55) & (mass_range < 8)]\n", + "plt.plot(mass_4, power_law(mass_4, -2.3), label='Index = -2.3')\n", + "\n", + "mass_5 = mass_range[(mass_range >= 8) & (mass_range <= 120)]\n", + "plt.plot(mass_5, power_law(mass_5, -2.35), label='Index = -2.35')\n", + "\n", + "# Add labels and legend\n", + "plt.xlabel('Mass (M_sun)')\n", + "plt.ylabel('Relative Number Density of Stars')\n", + "plt.legend()\n", + "plt.yscale('log')\n", + "plt.xscale('log')\n", + "plt.title('CombinedWeidnerKroupaKirkpatrick_2024 IMF Function')\n", + "plt.grid()\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "60c949a3-f40e-402f-8923-3e914d58cb3b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", + "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", + "plt.title(\"Black Holes by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", + "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", + "plt.title(\"White Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", + "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", + "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", + "plt.title(\"Neutron Stars by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "c397930b-0fa9-4592-b571-e3dfbf00b8f7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xxb//R082L5aRkVGhfjk5OQEAoqKitDOXj79cXFzg4OAAhUJR6n4quu/H61Cr1WXW8KTAUBHDhw+HSqXC6tWrUVhYiHXr1mHgwIHaPz9AxX5H+rK2tsYLL7yAhQsXwsbGBoMHDy61nZTvuo2NDebOnYvffvsNGRkZWL58OQ4fPqwzg+fp6Yno6GhkZGQgJSUF06ZNw+eff47XX3+9Uv0h42Agolqn+AqPBg0aPLFdy5Yt0bBhQ2zcuFHn0FNOTg7+97//aa88KxYYGIiffvpJexgKAFq0aIHGjRtj9uzZ0Gg0VRaICgsLMWnSJNy6dQtvvvmmzrrAwEBcv34dS5cuRZcuXWBrawvgYVDaunUrjh07plOnQqGAEEIbkIp99dVXOicLyyk6Ohq2trbYs2cP9u7dq/NavHgx8vLysGHDhhKfU6lU6NmzJz744AMAD28e+Lh69eph6NChmDhxIm7fvl3mjRg7d+4MlUqFzZs36yw/fPhwhQ9nduvWDfXq1cO5c+e0M5ePvywsLGBjY4NOnTphy5YtePDggfbzd+/exfbt2yu070eFhoYiOTkZzZo1K7WG4hlFuTg4OGDgwIFYu3YtfvzxR2RkZOgcLnucvr8jKf7zn/+gf//+mD17ts5hyEdV9Lvu4uKCUaNGYfjw4UhJSSk1wLVo0QJvv/02fHx8cPLkycp1hoyCN2akGi05OVl7tdGtW7ewZcsWxMfHY9CgQfDy8nriZ83MzLBo0SK8+OKLCA0NxYQJE5CXl4fFixfjzp07eP/993XaBwQE4PPPP8fff/+tc9fegIAArFq1Cg4ODjqX3Mvl+vXrOHz4MIQQuHv3rvbGjL/88gumTZumvWqoWO/evbWXxs+dO1e7PDAwECNHjtT+XMzOzg49evTA4sWL4eTkhCZNmmD//v2Ijo7WXoUmp+TkZBw9ehT/+c9/0Lt37xLru3Xrho8++gjR0dGYNGkSZs+ejatXryIgIACNGjXCnTt38Mknn0CpVKJnz54AgP79+8Pb2xu+vr5o0KABLl++jKVLl8LT0xPNmzcvtQ5HR0eEh4dj4cKFcHBwwKBBg3D16lXMnTsXbm5upV6hVJ66desiKioKI0eOxO3btzF06FA4Ozvj5s2b+OWXX3Dz5k0sX74cAPDee++hT58+CAoKwvTp01FYWIgPPvgANjY2Zc7M6evdd99FfHw8/Pz8MGXKFLRs2RIPHjxAWloadu7ciS+++EJnVlEOY8aMwebNmzFp0iQ0atSoxP8c6PM72r9/PwICAjB79mzMnj1b0v7btWuH77///oltpHzXO3fujNDQULRp0wYODg44f/481q1bp/0fpTNnzmDSpEl47rnn0Lx5c1hYWOCnn37CmTNnEBERIal2qiaMeEI3UYWVdpWZvb29aNeunfj44491brpYfJXZ4sWLS93W999/Lzp37iwsLS2FjY2NCAgIED///HOJdpmZmcLMzEzY2NjoXK22YcMGAUAMHjy4xGfKuoKnZ8+eOlcxleXR/pmZmQk7Ozvh4+Mjxo8fr72yqTTt27cXAHT68ddffwkAon79+jpXcQkhxNWrV8WQIUOEg4ODsLW1FX369BHJycnC09NTjBw5UtuurBtiCqH/VWZTp04VAMTp06fLbBMRESEAiBMnTogff/xRhISEiIYNGwoLCwvh7Ows+vbtKw4ePKht/9FHHwk/Pz/h5OQkLCwsROPGjcXYsWNFWlpaidqLrzIT4uGVbfPmzRONGjUSFhYWok2bNuLHH38Ubdu2FYMGDSq3b8XfrVWrVuks379/v+jXr59wdHQUSqVSNGzYUPTr16/E57dt2ybatGmjrfn999+X7caMN2/eFFOmTBFeXl5CqVQKR0dH0bFjRzFr1ixx7969J35WCP2vMitWWFgoPDw8BAAxa9asEuv1+R0Vj/OcOXMq1OfHlXaVmb7f9YiICOHr6yscHByESqUSTZs2FdOmTRN///23EEKI69evi1GjRolWrVoJGxsbUbduXdGmTRuxZMkSUVBQUG79VP0ohHjsMhUiIhOWmpqKVq1aYc6cOXjrrbeMXQ4RVREGIiIyWb/88gs2bdoEPz8/2NnZISUlBYsWLdI+M66sq82IqPbhOUREZLJsbGxw/PhxREdH486dO7C3t4e/vz/mz5/PMERkYjhDRERERCaPl90TERGRyWMgIiIiIpPHQEREREQmjydV66moqAjXrl2Dra1tpR9bQERERFVD/P9Nbd3d3Z94w1UGIj1du3YNHh4exi6DiIiIKuDPP/984h3aGYj0VPw8qD///BN2dnaybVej0WD37t1Qq9VQKpWybbcmMfUxMPX+AxwD9t+0+w9wDAzZ/+zsbHh4eGj/HS8LA5Geig+T2dnZyR6IrK2tYWdnZ5J/CACOgan3H+AYsP+m3X+AY1AV/S/vdBeeVE1EREQmj4GIiIiITB4DEREREZk8BiIiIiIyeQxEREREZPIYiIiIiMjkMRARERGRyWMgIiIiIpPHQEREREQmj3eqJiIiIqNpErEDKnOBRZ0A78g45BUqkPZ+vyqvgzNEREREZBRNInZIWm5IDERERERU5coLPVUdihiIiIiIqErpG3aqMhQxEBEREVGVMcbhMH0wEBEREVGVKCwSxi6hTAxEREREVCWavbXT2CWUiYGIiIiIDG7g/Op5qKwYAxEREREZ3Om70j/j5yJ/HWVhICIiIiKDquiJ1BunVd0NGhmIiIiIyGDOXc2u0Oeq+m7VDERERERkMH2XHTR2CXoxaiBauHAhnnnmGdja2sLZ2RkDBw5ESkqKdr1Go8Gbb74JHx8f2NjYwN3dHS+//DKuXbumsx1/f38oFAqd1wsvvKDTJjMzEyNGjIC9vT3s7e0xYsQI3Llzpyq6SUREZJLaVPBQmck9y2z//v2YOHEiDh8+jPj4eBQUFECtViMnJwcAkJubi5MnT+Kdd97ByZMnsWXLFvz+++8YMGBAiW2NGzcO6enp2teKFSt01oeFheH06dOIjY1FbGwsTp8+jREjRlRJP4mIiExRRQ6W9WullL0OfRj1afexsbE671etWgVnZ2ecOHECPXr0gL29PeLj43XaREVFoVOnTrhy5QoaN26sXW5tbQ1XV9dS93P+/HnExsbi8OHD6Ny5MwBg5cqV6Nq1K1JSUtCyZUuZe0ZERGTaKnoi9Wej1DJXoh+jBqLHZWVlAQAcHR2f2EahUKBevXo6yzds2ID169fDxcUFISEhmDNnDmxtbQEASUlJsLe314YhAOjSpQvs7e2RmJhYaiDKy8tDXl6e9n129sOcq9FooNFoKtzHxxVvS85t1jSmPgam3n+AY8D+m3b/gdo3BtuPXIHKXP+7UqvMHrY9Nau37GOg7/YUQohqcR9tIQSeffZZZGZm4uDB0k/AevDgAbp3745WrVph/fr12uUrV66El5cXXF1dkZycjJkzZ+Kpp57Szi4tWLAAq1evxu+//66zvRYtWmD06NGYOXNmiX1FRkZi7ty5JZZv3LgR1tbWlekqERERVZHc3FyEhYUhKysLdnZ2ZbarNjNEkyZNwpkzZ3Do0KFS12s0GrzwwgsoKirC559/rrNu3Lhx2p+9vb3RvHlz+Pr64uTJk+jQoQMAQKFQlNimEKLU5QAwc+ZMhIeHa99nZ2fDw8MDarX6iQMqlUajQXx8PIKCgqBUGue4qbGZ+hiYev8BjgH7b9r9B2rXGLSNjEOhxM+ozATe8y0ySP+Lj/CUp1oEosmTJ2Pbtm04cOAAGjVqVGK9RqPBsGHDkJqaip9++qncQNKhQwcolUpcuHABHTp0gKurK65fv16i3c2bN+HiUvptMFUqFVQqVYnlSqXSIF9WQ223JjH1MTD1/gMcA/bftPsP1I4xyC0sfaLhSV71cwcK/jRI//XdnlGvMhNCYNKkSdiyZQt++ukneHl5lWhTHIYuXLiAhIQE1K9fv9ztnj17FhqNBm5ubgCArl27IisrC0ePHtW2OXLkCLKysuDn5ydfh4iIiExYRU+knqT2kbkS6Yw6QzRx4kRs3LgRP/zwA2xtbZGRkQEAsLe3h5WVFQoKCjB06FCcPHkSP/74IwoLC7VtHB0dYWFhgUuXLmHDhg3o27cvnJyccO7cOUyfPh3t27dHt27dAACtW7dGnz59MG7cOO3l+OPHj0doaCivMCMiIpLBgm3HK/S5tPf7VYuTyY06Q7R8+XJkZWXB398fbm5u2tfmzZsBAFevXsW2bdtw9epVtGvXTqdNYmIiAMDCwgJ79uxBcHAwWrZsiSlTpkCtViMhIQHm5ubafW3YsAE+Pj5Qq9VQq9Vo06YN1q1bZ5R+ExER1TZfJpY8NaUmMeoMUXkXuDVp0qTcNh4eHti/f3+5+3J0dNS5Mo2IiIjkUdFDZca4I3VZ+CwzIiIiqnKfDjT+eUOPYiAiIiKiCqvo7NCALo3Lb1SFGIiIiIioQiavTajQ56rTobJiDERERERUIdvP5ZXf6DE2BqhDDgxEREREJFlFD5WdrYazQwADEREREVWR/42vvjdDZiAiIiIiSSo6O9SxqYPMlciHgYiIiIj0Nmxxzb/nUGkYiIiIiEhvR29J/8wzjvLXITcGIiIiItJLRQ+VfftG9Z4dAhiIiIiISA8p1+5W6HO/zFbLXIlhMBARERFRuYI/PVChz9lbK2WuxDAYiIiIiOiJetSCh7eWh4GIiIiInuhKBT7T3U32MgyKgYiIiIjKVNETqdf/t+bMDgEMRERERFSGLYlpFfpcTTpUVoyBiIiIiEoVvu2s5M8oDFBHVWAgIiIiohI6VvBQWWoNnB0CGIiIiIioFBW4ITVCWpjLXkdVYSAiIiIiHRU9kXr5mD4yV1J1GIiIiIhIa8G24xX6XE08kfpRDERERESk9WXidWOXYBQMRERERAQA8DKBO1KXhYGIiIiIAACiAp95t09z2eswBgYiIiIiqvCJ1C/7t5C5EuNgICIiIjJxb32XWKHP1YZDZcUYiIiIiEzcxuOZkj9jY4A6jImBiIiIyIRV9FDZ2Vo0OwQwEBEREZFEywa1MXYJsmMgIiIiMlEVnR0K7ewhcyXGx0BERERkgl5ZYbr3HCqNUQPRwoUL8cwzz8DW1hbOzs4YOHAgUlJSdNoIIRAZGQl3d3dYWVnB398fZ8+e1WmTl5eHyZMnw8nJCTY2NhgwYACuXr2q0yYzMxMjRoyAvb097O3tMWLECNy5c8fQXSQiIqqWElKlf8bHWv46qgujBqL9+/dj4sSJOHz4MOLj41FQUAC1Wo2cnBxtm0WLFuHjjz/GsmXLcOzYMbi6uiIoKAh3797Vtpk6dSq2bt2KmJgYHDp0CPfu3UNoaCgKCwu1bcLCwnD69GnExsYiNjYWp0+fxogRI6q0v0RERNVBRQ+VbZ9dO2eHAKCOMXceGxur837VqlVwdnbGiRMn0KNHDwghsHTpUsyaNQuDBw8GAKxZswYuLi7YuHEjJkyYgKysLERHR2PdunUIDAwEAKxfvx4eHh5ISEhAcHAwzp8/j9jYWBw+fBidO3cGAKxcuRJdu3ZFSkoKWrZsWbUdJyIiqmG+eaWrsUswKKMGosdlZWUBABwdHQEAqampyMjIgFqt1rZRqVTo2bMnEhMTMWHCBJw4cQIajUanjbu7O7y9vZGYmIjg4GAkJSXB3t5eG4YAoEuXLrC3t0diYmKpgSgvLw95eXna99nZ2QAAjUYDjUYjW5+LtyXnNmsaUx8DU+8/wDFg/027/0DVjsGR329BZS79IR3tPW0NVp8h+6/vNqtNIBJCIDw8HN27d4e3tzcAICMjAwDg4uKi09bFxQWXL1/WtrGwsICDg0OJNsWfz8jIgLOzc4l9Ojs7a9s8buHChZg7d26J5bt374a1tfwHUePj42XfZk1j6mNg6v0HOAbsv2n3H6i6MVjUSfpndu7cKX8hjzFE/3Nzc/VqV20C0aRJk3DmzBkcOnSoxDqFQqHzXghRYtnjHm9TWvsnbWfmzJkIDw/Xvs/OzoaHhwfUajXs7OyeuG8pNBoN4uPjERQUBKVSKdt2axJTHwNT7z/AMWD/Tbv/QNWOgXdknKT2HeoBa6cGG6aY/2fI/hcf4SlPtQhEkydPxrZt23DgwAE0atRIu9zV1RXAwxkeNzc37fIbN25oZ41cXV2Rn5+PzMxMnVmiGzduwM/PT9vm+vXrJfZ78+bNErNPxVQqFVQqVYnlSqXSIF9WQ223JjH1MTD1/gMcA/bftPsPVM0Y5BU+eULhcZter7oTqQ3Rf323Z9SrzIQQmDRpErZs2YKffvoJXl5eOuu9vLzg6uqqM4WWn5+P/fv3a8NOx44doVQqddqkp6cjOTlZ26Zr167IysrC0aNHtW2OHDmCrKwsbRsiIqLazkfi1WWlTxnUTkadIZo4cSI2btyIH374Aba2ttrzeezt7WFlZQWFQoGpU6diwYIFaN68OZo3b44FCxbA2toaYWFh2rZjx47F9OnTUb9+fTg6OmLGjBnw8fHRXnXWunVr9OnTB+PGjcOKFSsAAOPHj0doaCivMCMiIpNxt/wmOo7U0pswlsaogWj58uUAAH9/f53lq1atwqhRowAAb7zxBu7fv4/XXnsNmZmZ6Ny5M3bv3g1bW1tt+yVLlqBOnToYNmwY7t+/j4CAAKxevRrm5ubaNhs2bMCUKVO0V6MNGDAAy5YtM2wHiYiIqEYwaiASovzL/hQKBSIjIxEZGVlmG0tLS0RFRSEqKqrMNo6Ojli/fn1FyiQiIqrxJnxl+KvEajI+y4yIiMgExF2Udu+hnZP+baBKqicGIiIiIirh6Uby3WKmJmAgIiIiquUWbDtu7BKqPQYiIiKiWu7LxJL34nuS0NYWBqqk+mIgIiIiIh3LRgYZu4Qqx0BERERUi93Mziu/ETEQERER1WbPLEiQ1L5fK9N8fAoDEREREWl9Nkpt7BKMgoGIiIiITB4DERERUS3V5x1pD3M1ZQxEREREtdRvGmnt00zoYa6PYyAiIiIik8dAREREVAuN+5KHy6RgICIiIqqF4v+Q1v5/4/0MU0gNwUBERERE6NjUwdglGBUDERERUS2zLO5XY5dQ4zAQERER1TIf7r0iqf2z3lYGqqTmYCAiIiIycZ+81NvYJRhdHakfSEtLw8GDB5GWlobc3Fw0aNAA7du3R9euXWFpaWmIGomIiEhP+QVFxi6hRtI7EG3cuBGffvopjh49CmdnZzRs2BBWVla4ffs2Ll26BEtLS7z44ot488034enpaciaiYiIqAwt3t4lqX17OwMVUsPoFYg6dOgAMzMzjBo1Ct988w0aN26ssz4vLw9JSUmIiYmBr68vPv/8czz33HMGKZiIiIjks/Ut07079aP0CkTvvfce+vUre8BUKhX8/f3h7++PefPmITU1VbYCiYiIiAxNr0DUr18/aDQaKJXKJ7ZLTk6Gt7c3nJycZCmOiIiI9PfiUml3p3Y0UB01kd5XmQ0fPhxCiDLXJycnIyAgQJaiiIiISLqfM6S1P2nCD3N9nN6B6MiRI5gwYUKp686ePYuAgAD06NFDtsKIiIiIqoreV5nt3r0bPXr0gKOjI95//33t8vPnzyMgIADdunVDTEyMQYokIiKiJ5u4erexS6jR9A5ErVu3xs6dOxEQEID69evj9ddfx2+//YbevXujc+fO+Pbbb2Fubm7IWomIiKgMO37TSGo/L6SFgSqpmSTdmPGZZ57B999/j9DQUOTk5GDlypXw9fXFd999xzBERERUg7zUs7mxS6hWJN+punfv3ti4cSOee+45qNVqbNmypdyrz4iIiMhwEk6mG7uEGk/vQOTg4ACFQqGz7ODBg3BxcdFZdvv2bXkqIyIiIr288s1JSe37teJExuP0DkRLly6VfecHDhzA4sWLceLECaSnp2Pr1q0YOHCgdv3jAazYokWL8PrrrwMA/P39sX//fp31zz//vM4J3pmZmZgyZQq2bdsGABgwYACioqJQr149eTtERERUA3w2Sm3sEqodvQPRyJEjZd95Tk4O2rZti9GjR2PIkCEl1qen604B7tq1C2PHji3Rdty4cXj33Xe1762srHTWh4WF4erVq4iNjQUAjB8/HiNGjMD27dvl6goRERHVYJLPIdKHEKLM2Z1HhYSEICQkpMz1rq6uOu9/+OEH9OrVC02bNtVZbm1tXaJtsfPnzyM2NhaHDx9G586dAQArV65E165dkZKSgpYtW5ZbJxERUXXVMULa3an5+PXS6RWIWrdujXfeeQdDhw6FhYVFme0uXLiAjz/+GJ6enoiIiJCtSAC4fv06duzYgTVr1pRYt2HDBqxfvx4uLi4ICQnBnDlzYGtrCwBISkqCvb29NgwBQJcuXWBvb4/ExMQyA1FeXh7y8vK077OzswEAGo0GGo20SxufpHhbcm6zpjH1MTD1/gMcA/bftPsPVG4M7pkLqCS0T4gMrnZjbcjvgL7b1CsQffbZZ3jzzTcxceJEqNVq+Pr6wt3dHZaWlsjMzMS5c+dw6NAhnDt3DpMmTcJrr71WqeJLs2bNGtja2mLw4ME6y1988UV4eXnB1dUVycnJmDlzJn755RfEx8cDADIyMuDs7Fxie87OzsjIKPse5wsXLsTcuXNLLN+9ezesra0r2ZuSius1ZaY+Bqbef4BjwP6bdv+Bio3Bok7S2u/cuVPyPqqKIb4Dubm5erXTKxD17t0bx44dQ2JiIjZv3oyNGzciLS0N9+/fh5OTE9q3b4+XX34ZL730ksFOVP7666/x4osvwtLSUmf5uHHjtD97e3ujefPm8PX1xcmTJ9GhQwcApZ+cXd5hvZkzZyI8PFz7Pjs7Gx4eHlCr1bCzs6tsd7Q0Gg3i4+MRFBRksrcvMPUxMPX+AxwD9t+0+w9UfAxGRcXh+C1p+0qODJZYneEZ8jtQfISnPJLOIfLz84Ofn1+FCqqMgwcPIiUlBZs3by63bYcOHaBUKnHhwgV06NABrq6uuH79eol2N2/eLHHLgEepVCqoVCUnIZVKpUH+wBpquzWJqY+Bqfcf4Biw/6bdf0D6GPx8o/zzdR91OCKgWo+xIb4D+m5P74e7GlN0dDQ6duyItm3bltv27Nmz0Gg0cHNzAwB07doVWVlZOHr0qLbNkSNHkJWVZZRwR0REZCyu9SzLb2SiDHKVmb7u3buHixcvat+npqbi9OnTcHR0ROPGjQE8nOr69ttv8dFHH5X4/KVLl7Bhwwb07dsXTk5OOHfuHKZPn4727dujW7duAB6eEN6nTx+MGzcOK1asAPDwsvvQ0FBeYUZERDXW2/9LMnYJtYpRZ4iOHz+O9u3bo3379gCA8PBwtG/fHrNnz9a2iYmJgRACw4cPL/F5CwsL7NmzB8HBwWjZsiWmTJkCtVqNhIQEnWerbdiwAT4+PlCr1VCr1WjTpg3WrVtn+A4SEREZyPpj0p4MsSi0tYEqqR2MOkPk7+8PIcQT24wfPx7jx48vdZ2Hh0eJu1SXxtHREevXr69QjURERLXBsO5Ny29kwmrEOURERET0j31nSl4sRJVToUB06dIlvP322xg+fDhu3LgBAIiNjcXZs2dlLY6IiIhKGrXxuKT2oa3LvqkyPSQ5EO3fvx8+Pj44cuQItmzZgnv37gEAzpw5gzlz5sheIBEREVXOspFBxi6h2pMciCIiIjBv3jzEx8frPMajV69eSEriGe9ERERU80gORL/++isGDRpUYnmDBg1w65bE22USERGRJH4SH+Za+qPP6XGSA1G9evWQnp5eYvmpU6fQsGFDWYoiIiKi0l2T2P7w+/0MUkdtIzkQhYWF4c0330RGRgYUCgWKiorw888/Y8aMGXj55ZcNUSMRERGRQUkORPPnz0fjxo3RsGFD3Lt3D08//TR69OgBPz8/vP3224aokYiIiACELZF2uIz0J+nGjEIIXLt2DStXrsR7772HkydPoqioCO3bt0fz5s0NVSMREREBSJR4+6H/jeczO/UlORA1b94cZ8+eRfPmzdG0Ke96SUREVF11bOpg7BJqDEmHzMzMzNC8eXNeTUZERFTFlu76xdgl1GqSzyFatGgRXn/9dSQnJxuiHiIiIirF0v1XJbUf1sHeQJXUTpIf7vrSSy8hNzcXbdu2hYWFBaysrHTW374t7em7REREJL9Fw7obu4QaRXIgWrp0qQHKICIiorIc/p2nqhia5EA0cuRIQ9RBREREZXjh68OS2reta6BCajHJgejKlStPXN+4ceMKF0NERESV98PbvDu1VJIDUZMmTaBQKMpcX1hYWKmCiIiI6B9HL/Lc3KogORCdOnVK571Go8GpU6fw8ccfY/78+bIVRkRERMCwr5Iktee1ZRUjORC1bdu2xDJfX1+4u7tj8eLFGDx4sCyFERERkXS/8GGuFSL5PkRladGiBY4dOybX5oiIiExeyrW7xi7BZEieIcrOztZ5L4RAeno6IiMj+TwzIiIiGQV/ekBS+3qGKcMkSA5E9erVK3FStRACHh4eiImJka0wIiIikuY0D5dVmORAtHfvXp33ZmZmaNCgAZ566inUqSN5c0RERFQKHi6rWpITjEKhgJ+fX4nwU1BQgAMHDqBHjx6yFUdERGSqpB4u45RE5Ug+qbpXr16lPq8sKysLvXr1kqUoIiIikuYiD5dViuRAJIQo9caMt27dgo2NjSxFERERmbL7+bzJcVXTe4at+P5CCoUCo0aNgkql0q4rLCzEmTNn4OfnJ3+FREREJqb17FhJ7WW7h44J0zsQ2ds/vPelEAK2trawsrLSrrOwsECXLl0wbtw4+SskIiKiJ/qDh8sqTe9AtGrVKgghIIRAVFQUbG1tDVkXERGRScovKDJ2CSZJ0iybEAIbN25ERkaGoeohIiIyaS3e3iWpPZ9dJg9JgcjMzAzNmzfHrVu3ZNn5gQMH0L9/f7i7u0OhUOD777/XWT9q1CgoFAqdV5cuXXTa5OXlYfLkyXBycoKNjQ0GDBiAq1ev6rTJzMzEiBEjYG9vD3t7e4wYMQJ37tyRpQ9ERETGxGeXyUPyeViLFi3C66+/juTk5ErvPCcnB23btsWyZcvKbNOnTx+kp6drXzt37tRZP3XqVGzduhUxMTE4dOgQ7t27h9DQUBQW/nOGflhYGE6fPo3Y2FjExsbi9OnTGDFiRKXrJyIikhMPlxmP5Ps4vfTSS8jNzUXbtm1hYWGhc3I1gFLvUVSWkJAQhISEPLGNSqWCq6trqeuysrIQHR2NdevWITAwEACwfv16eHh4ICEhAcHBwTh//jxiY2Nx+PBhdO7cGQCwcuVKdO3aFSkpKWjZsqXe9RIRERlSh3nxAEre2oYMT3IgWrp0qQHKKNu+ffvg7OyMevXqoWfPnpg/fz6cnZ0BACdOnIBGo4Farda2d3d3h7e3NxITExEcHIykpCTY29trwxAAdOnSBfb29khMTCwzEOXl5SEvL0/7vvihthqNBhqNRrb+FW9Lzm3WNKY+Bqbef4BjwP6bdv+Bf/quMhOSPpccGVwrxs2Q3wF9tyk5EI0cOVJyMRUVEhKC5557Dp6enkhNTcU777yD3r1748SJE1CpVMjIyICFhQUcHBx0Pufi4qI98TsjI0MboB7l7Oz8xJPDFy5ciLlz55ZYvnv3blhbW1eyZyXFx8fLvs2axtTHwNT7D3AM2H/T7j8AvOcr7ZDZ46eR1HSG+A7k5ubq1a5Sjz65f/9+ieRlZ2dXmU3qeP7557U/e3t7w9fXF56entixY4f2RpGlefxu2qXdWbusO24XmzlzJsLDw7Xvs7Oz4eHhAbVaLWsfNRoN4uPjERQUBKVSKdt2axJTHwNT7z/AMWD/Tbv/wD9j8M5xM+QV6X/ILDky2IBVVR1DfgeKj/CUR3IgysnJwZtvvolvvvmm1KvNHj2ZWW5ubm7w9PTEhQsXAACurq7Iz89HZmamzizRjRs3tHfNdnV1xfXr10ts6+bNm3BxcSlzXyqVSudu3MWUSqVB/sAaars1iamPgan3H+AYsP+m3X8AyCtSIK9Qv0CUVguvLjPEd0Df7Um+yuyNN97ATz/9hM8//xwqlQpfffUV5s6dC3d3d6xdu1ZyoVLcunULf/75J9zc3AAAHTt2hFKp1JliS09PR3JysjYQde3aFVlZWTh69Ki2zZEjR5CVlcVHjRARERGACswQbd++HWvXroW/vz/GjBmDf//733jqqafg6emJDRs24MUXX9R7W/fu3cPFixe171NTU3H69Gk4OjrC0dERkZGRGDJkCNzc3JCWloa33noLTk5OGDRoEICHjxMZO3Yspk+fjvr168PR0REzZsyAj4+P9qqz1q1bo0+fPhg3bhxWrFgBABg/fjxCQ0N5hRkREVULPpFx+KCTsaswbZJniG7fvg0vLy8AD88XKr7Mvnv37jhw4ICkbR0/fhzt27dH+/btAQDh4eFo3749Zs+eDXNzc/z666949tln0aJFC4wcORItWrRAUlKSzmNDlixZgoEDB2LYsGHo1q0brK2tsX37dpibm2vbbNiwAT4+PlCr1VCr1WjTpg3WrVsntetEREQGIe3aMuDSgr4GqcOUSZ4hatq0KdLS0uDp6Ymnn34a33zzDTp16oTt27ejXr16krbl7+8PIcr+GsTFxZW7DUtLS0RFRSEqKqrMNo6Ojli/fr2k2oiIiKorczPeq0hukmeIRo8ejV9++QXAwyuxis8lmjZtGl5//XXZCyQiIqrNmkXsMHYJhArMEE2bNk37c69evfDbb7/h+PHjaNasGdq2bStrcURERLVdIaT9Y/zLbHX5jUgySTNEGo0GvXr1wu+//65d1rhxYwwePJhhiIiIqArYW5v2rQkMRVIgUiqVSE5OfuINDYmIiEg/3jxcVm1IPofo5ZdfRnR0tCFqISIiMin3JLbn1WWGI/kcovz8fHz11VeIj4+Hr68vbGxsdNZ//PHHshVHRERE/+DVZYYjORAlJyejQ4cOAKBzLhFQ+jPDiIiIqKQmPFxWrUgORHv37jVEHURERPQEe8P9jV1CrSb5HKJiFy9eRFxcHO7fvw8AT7zBIhEREVWOl7NN+Y2owiQHolu3biEgIAAtWrRA3759kZ6eDgB45ZVXMH36dNkLJCIiqm14uKz6kRyIpk2bBqVSiStXrsDa2lq7/Pnnn0dsbKysxRERERFw7K1AY5dQ60k+h2j37t2Ii4tDo0aNdJY3b94cly9flq0wIiKi2qgis0MN7FQGqIQeJXmGKCcnR2dmqNjff/8NlYq/MCIiorLwUFn1JTkQ9ejRA2vXrtW+VygUKCoqwuLFi9GrVy9ZiyMiIqotKhqGvn+1m8yVUGkkHzJbvHgx/P39cfz4ceTn5+ONN97A2bNncfv2bfz888+GqJGIiKhGq8zMULsm9eQrhMokeYbo6aefxpkzZ9CpUycEBQUhJycHgwcPxqlTp9CsWTND1EhERFRj8TBZzSB5hggAXF1dMXfuXLlrISIiqlUqG4bS3u8nUyVUngoFoszMTERHR+P8+fNQKBRo3bo1Ro8eDUdHR7nrIyIiqnFu38tHh3nxldoGw1DVknzIbP/+/fDy8sKnn36KzMxM3L59G59++im8vLywf/9+Q9RIRERUYzSL2MEwVANJniGaOHEihg0bhuXLl8Pc3BwAUFhYiNdeew0TJ05EcnKy7EUSERHVBHKcL8QwZBySZ4guXbqE6dOna8MQAJibmyM8PByXLl2StTgiIqKaQo4wlBwZLEMlVBGSA1GHDh1w/vz5EsvPnz+Pdu3ayVETERFRjcIryWo+yYfMpkyZgv/+97+4ePEiunTpAgA4fPgwPvvsM7z//vs4c+aMtm2bNm3kq5SIiKgakmtmaOfOnTJUQxUlORANHz4cAPDGG2+Uuk6hUEAIAYVCgcLCwspXSEREVE3Jdc6QRqORoRqqDMmBKDU11RB1EBER1Sg8gbp2kRyIPD09DVEHERFRjcEwVPtU6MaMf/31F37++WfcuHEDRUVFOuumTJkiS2FERETVEcNQ7SQ5EK1atQqvvvoqLCwsUL9+fSgUCu06hULBQERERLVWZcPQ9te6w6exvUzVkJwkB6LZs2dj9uzZmDlzJszMJF+1T0REVCPxuWS1m+REk5ubixdeeIFhiIiITAbDUO0nOdWMHTsW3377rSFqISIiqnYYhkyD5EC0cOFC7N+/H/7+/pg8eTLCw8N1XlIcOHAA/fv3h7u7OxQKBb7//nvtOo1GgzfffBM+Pj6wsbGBu7s7Xn75ZVy7dk1nG/7+/lAoFDqvF154QadNZmYmRowYAXt7e9jb22PEiBG4c+eO1K4TEZGJYRgyHZLPIVqwYAHi4uLQsmVLAChxUrUUOTk5aNu2LUaPHo0hQ4borMvNzcXJkyfxzjvvoG3btsjMzMTUqVMxYMAAHD9+XKftuHHj8O6772rfW1lZ6awPCwvD1atXERsbCwAYP348RowYge3bt0uql4iITAfDkGmRHIg+/vhjfP311xg1alSldx4SEoKQkJBS19nb2yM+Pl5nWVRUFDp16oQrV66gcePG2uXW1tZwdXUtdTvnz59HbGwsDh8+jM6dOwMAVq5cia5duyIlJUUb7IiIiIoxDJkeyYFIpVKhW7duhqilXFlZWVAoFKhXr57O8g0bNmD9+vVwcXFBSEgI5syZA1tbWwBAUlIS7O3ttWEIALp06QJ7e3skJiaWGYjy8vKQl5enfZ+dnQ3g4aE8OW+xXrwtU75tu6mPgan3H+AYsP/Vq//ekXFQmVf888mRwZL7Ut3GoKoZsv/6blMhhBBSNrxw4UKkp6fj008/rVBhZRaiUGDr1q0YOHBgqesfPHiA7t27o1WrVli/fr12+cqVK+Hl5QVXV1ckJydj5syZeOqpp7SzSwsWLMDq1avx+++/62yvRYsWGD16NGbOnFnq/iIjIzF37twSyzdu3Ahra+sK9pKIiIiqUm5uLsLCwpCVlQU7O7sy20meITp69Ch++ukn/Pjjj/jXv/4FpVKps37Lli3Sqy2HRqPBCy+8gKKiInz++ec668aNG6f92dvbG82bN4evry9OnjyJDh06ACj93KbiB9CWZebMmToniWdnZ8PDwwNqtfqJAyqVRqNBfHw8goKCSoylqTD1MTD1/gMcA/bf+P3/7dpdDP0ysVLbSI4MrvBnq8MYGJMh+198hKc8kgNRvXr1MHjwYMkFVZRGo8GwYcOQmpqKn376qdww0qFDByiVSly4cAEdOnSAq6srrl+/XqLdzZs34eLiUuZ2VCoVVCpVieVKpdIgX1ZDbbcmMfUxMPX+AxwD9t84/f/nfCFpFwY9Sq5zhvgdkL//+m6vQo/uqCrFYejChQvYu3cv6tevX+5nzp49C41GAzc3NwBA165dkZWVhaNHj6JTp04AgCNHjiArKwt+fn4GrZ+IiKo3PpeMilXo4a4FBQXYt28fLl26hLCwMNja2uLatWuws7ND3bp19d7OvXv3cPHiRe371NRUnD59Go6OjnB3d8fQoUNx8uRJ/PjjjygsLERGRgYAwNHRERYWFrh06RI2bNiAvn37wsnJCefOncP06dPRvn177YnfrVu3Rp8+fTBu3DisWLECwMPL7kNDQ3mFGRGRCWMYokdJDkSXL19Gnz59cOXKFeTl5SEoKAi2trZYtGgRHjx4gC+++ELvbR0/fhy9evXSvi8+Z2fkyJGIjIzEtm3bAADt2rXT+dzevXvh7+8PCwsL7NmzB5988gnu3bsHDw8P9OvXD3PmzIG5+T+XCGzYsAFTpkyBWq0GAAwYMADLli2T2nUiIqolGIbocZID0X//+1/4+vril19+0TmENWjQILzyyiuStuXv748nXeRW3gVwHh4e2L9/f7n7cXR01LkyjYiITBfDEJVGciA6dOgQfv75Z1hYWOgs9/T0xF9//SVbYURERHJjGKKySH6WWVFREQoLC0ssv3r1qvZmiERERNUNwxA9ieRAFBQUhKVLl2rfKxQK3Lt3D3PmzEHfvn3lrI2IiEgWDENUHr0PmZmbmyM9PR1LlixBr1698PTTT+PBgwcICwvDhQsX4OTkhE2bNhmyViIiIskYhkgfegei4hOc3d3dcfr0aWzatAknT55EUVERxo4dixdffLHEU+aJiIiMqbJh6PtXu6Fdk3ryFEPVWoXuQ2RlZYUxY8ZgzJgxctdDREQkCz6xnqSQFIji4uJgb2//xDYDBgyoVEFERESVxTBEUkkKRCNHjnzieoVCUeoVaERERFWFYYgqQtJVZhkZGSgqKirzxTBERETGxDBEFaV3IFIoKv4UYCIiIkNjGKLK0DsQlfcYDSIiImNhGKLK0jsQjRw5kpfVExFRtcMwRHLQ+6TqVatWGbIOIiIiyRiGSC6SH91BRERkbKk3chiGSFYVujEjERGRsfBRHGQInCEiIqIag2GIDKXCgejixYuIi4vD/fv3AfAqNCIiMiyGITIkyYHo1q1bCAwMRIsWLdC3b1+kp6cDAF555RVMnz5d9gKJiIgYhsjQJAeiadOmoU6dOrhy5Qqsra21y59//nnExsbKWhwRERHDEFUFySdV7969G3FxcWjUqJHO8ubNm+Py5cuyFUZERMQwRFVF8gxRTk6OzsxQsb///hsqlUqWooiIiBiGqCpJDkQ9evTA2rVrte8VCgWKioqwePFi9OrVS9biiIjINDEMUVWTfMhs8eLF8Pf3x/Hjx5Gfn4833ngDZ8+exe3bt/Hzzz8bokYiIjIhDENkDJID0dNPP40zZ85g+fLlMDc3R05ODgYPHoyJEyfCzc3NEDUSEZGJqGwYOvZWIBrY8fQNkq5Cd6p2dXXF3Llz5a6FiIhMmHdkHABFhT/PWSGqDMnnEHl5eeGdd95BSkqKIeohIiKSjGGIKktyIJo8eTJiY2PRunVrdOzYEUuXLtXenJGIiEiqhzNDFccwRHKQHIjCw8Nx7Ngx/PbbbwgNDcXy5cvRuHFjqNVqnavPiIiInqRJxA4+sZ6qjQo/y6xFixaYO3cuUlJScPDgQdy8eROjR4+WszYiIqqF5AhCAMMQyatCJ1UXO3r0KDZu3IjNmzcjKysLQ4cOlasuIiKqZeQIQcUYhkhukmeIfv/9d8yZMwfNmzdHt27dcO7cObz//vu4fv06Nm/eLGlbBw4cQP/+/eHu7g6FQoHvv/9eZ70QApGRkXB3d4eVlRX8/f1x9uxZnTZ5eXmYPHkynJycYGNjgwEDBuDq1as6bTIzMzFixAjY29vD3t4eI0aMwJ07d6R2nYiIJBr6wQ7ZZoSKMQyRIUgORK1atcKuXbswceJE/Pnnn9i9ezdGjhwJW1tbyTvPyclB27ZtsWzZslLXL1q0CB9//DGWLVuGY8eOwdXVFUFBQbh79662zdSpU7F161bExMTg0KFDuHfvHkJDQ1FYWKhtExYWhtOnTyM2NhaxsbE4ffo0RowYIbleIiLST3EIOp4p73YZhshQJB8y++2339CiRQtZdh4SEoKQkJBS1wkhsHTpUsyaNQuDBw8GAKxZswYuLi7YuHEjJkyYgKysLERHR2PdunUIDAwEAKxfvx4eHh5ISEhAcHAwzp8/j9jYWBw+fBidO3cGAKxcuRJdu3ZFSkoKWrZsKUtfiIhI3sNij2MYIkOSHIjkCkPlSU1NRUZGBtRqtXaZSqVCz549kZiYiAkTJuDEiRPQaDQ6bdzd3eHt7Y3ExEQEBwcjKSkJ9vb22jAEAF26dIG9vT0SExPLDER5eXnIy8vTvs/OzgYAaDQaaDQa2fpZvC05t1nTmPoYmHr/AY5Bbeh/8aXzKnPpn1WZCZ3/liY5MrhGj095asN3oDIM2X99t6lXIHJ0dMTvv/8OJycnODg4QKEo+06it2/f1q/CcmRkZAAAXFxcdJa7uLjg8uXL2jYWFhZwcHAo0ab48xkZGXB2di6xfWdnZ22b0ixcuLDUu3Hv3r0b1tbW0jqjh/j4eNm3WdOY+hiYev8BjkFN7v+iTpXfxnu+RWWu27lzZ+V3UAPU5O+AHAzR/9zcXL3a6RWIlixZoj1HaMmSJU8MRHJ7fF9CiHL3/3ib0tqXt52ZM2ciPDxc+z47OxseHh5Qq9Wws7PTt/xyaTQaxMfHIygoCEqlUrbt1iSmPgam3n+AY1DT+l/ZGyk+TmUm8J5vEd45boa8It2/l5Mjg2XdV3VV074DcjNk/4uP8JRHr0A0cuRI7c+jRo2qUEFSubq6Ang4w/PoQ2Nv3LihnTVydXVFfn4+MjMzdWaJbty4AT8/P22b69evl9j+zZs3S8w+PUqlUkGlKvmAQKVSaZAvq6G2W5OY+hiYev8BjkF17/8/5wcZ5n+K84oUyCv8Z9umeM5Qdf8OGJoh+q/v9iRfZWZubo4bN26UWH7r1i2Ym1fg4HEZvLy84OrqqjN9lp+fj/3792vDTseOHaFUKnXapKenIzk5Wduma9euyMrKwtGjR7Vtjhw5gqysLG0bIiIqm9yXzevDFMMQGZfkk6qFKP2kt7y8PFhYWEja1r1793Dx4kXt+9TUVJw+fRqOjo5o3Lgxpk6digULFqB58+Zo3rw5FixYAGtra4SFhQEA7O3tMXbsWEyfPh3169eHo6MjZsyYAR8fH+1VZ61bt0afPn0wbtw4rFixAgAwfvx4hIaG8gozIqIyfPdzKmZsP1fl+2UQImPROxB9+umnAB6ej/PVV1+hbt262nWFhYU4cOAAWrVqJWnnx48fR69evbTvi8/ZGTlyJFavXo033ngD9+/fx2uvvYbMzEx07twZu3fv1rnn0ZIlS1CnTh0MGzYM9+/fR0BAAFavXq0zW7VhwwZMmTJFezXagAEDyrz3ERGRKavqmSAAKP7XxFTOF6LqSe9AtGTJEgAPZ4i++OILncBhYWGBJk2a4IsvvpC0c39//zJnnICH4SsyMhKRkZFltrG0tERUVBSioqLKbOPo6Ij169dLqo2IyJQYIwhtf607fBrbQ6PRmMxVZFR96R2IUlNTAQC9evXCli1bSlzqTkRENY8xghAPi1F1JPkcor179xqiDiIiqkIMQkS6JAeioUOHwtfXFxERETrLFy9ejKNHj+Lbb7+VrTgiIpLPsMU7cPRW1e+XQYhqAsmBaP/+/ZgzZ06J5X369MGHH34oS1FERCQfY8wGhfk6YMFQ3tqEag7JgejevXulXl6vVCr1vhskEREZHg+LEelPciDy9vbG5s2bMXv2bJ3lMTExePrpp2UrjIiIKoZBiEg6yYHonXfewZAhQ3Dp0iX07t0bALBnzx5s2rSJ5w8RERnJi0t34Oeyn1dtMAxCVFtIDkQDBgzA999/jwULFuC7776DlZUV2rRpg4SEBPTs2dMQNRIRURmMMRvU2xP4+j8MQlS7SA5EANCvXz/068c/DERExsLDYkTyqlAgunPnDr777jv88ccfmDFjBhwdHXHy5Em4uLigYcOGctdIRET/j0GIyDAkB6IzZ84gMDAQ9vb2SEtLwyuvvAJHR0ds3boVly9fxtq1aw1RJxGRyfo+6TKm/pBc5ftlECJTIjkQhYeHY9SoUVi0aJHOQ1ZDQkK0T6EnIqLKM8ZsUHMzIH4BgxCZHsmB6NixY1ixYkWJ5Q0bNkRGhhEucSAiqmWMEYT2hvvDy9mmyvdLVF1IDkSWlpal3oAxJSUFDRo0kKUoIiJT5B0Zh7xCRZXuk4fFiB4yk/qBZ599Fu+++y40Gg0AQKFQ4MqVK4iIiMCQIUNkL5CIqDa7mZ0H78i4Kt9v2vv9GIaIHiF5hujDDz9E37594ezsjPv376Nnz57IyMhA165dMX/+fEPUSERU6zx6WExlXnX7ZQgiKp3kQGRnZ4dDhw7hp59+wsmTJ1FUVIQOHTogMDDQEPUREdUaV/7ORY8P91b5ft/t0xwv+7eo8v0S1SR6BSJHR0f8/vvvcHJywpgxY/DJJ5+gd+/e2kd3EBFR2YxxkjTA2SAiKfQKRPn5+cjOzoaTkxPWrFmDDz74QOeSeyIi0vXVnvOYF/+HUfbNIEQknV6BqGvXrhg4cCA6duwIIQSmTJkCKyurUtt+/fXXshZIRFSTGGs2CGAQIqoMvQLR+vXrsWTJEly6dAkAkJWVhQcPHhi0MCKimmLUZzuw70/j7NvfA1g9kUGIqLL0CkQuLi54//33AQBeXl5Yt24d6tevb9DCiIiqO84GEdUekk+q7tWrFywsLAxdFxFRtfRMxA7cNOL+GYSIDIMnVRMR6cGYs0EAgxCRofGkaiKiMhg7BDUE8DODEFGVkHxStUKh4EnVRFSrGTsIcTaIqOrxpGoiIhg/BAFAcmQwlEqlscsgMkmSH92RmppqiDqIiIzC2EEo7f1+0Gg02Llzp1HrIDJ1ej/tvm/fvsjKytK+nz9/Pu7cuaN9f+vWLTz99NOyFkdEZAhNInZoX8bCp80TVS96zxDFxcUhLy9P+/6DDz7A8OHDUa9ePQBAQUEBUlJSZC+QiEgO+QVFaPH2LqPWwABEVH3pPUMkhHjie0Np0qQJFApFidfEiRMBAKNGjSqxrkuXLjrbyMvLw+TJk+Hk5AQbGxsMGDAAV69erZL6ici4imeCjBmGOBtEVP1JPoeoqh07dgyFhYXa98nJyQgKCsJzzz2nXdanTx+sWrVK+/7xG0dOnToV27dvR0xMDOrXr4/p06cjNDQUJ06cgLm5ueE7QURValncr/hw7xWj1sAARFSz6B2IimdfHl9maA0aNNB5//7776NZs2bo2bOndplKpYKrq2upn8/KykJ0dDTWrVuHwMBAAA9vI+Dh4YGEhAQEBwcbrngiqlLGPkHaAsDvDEJENZLegUgIgVGjRkGlUgEAHjx4gFdffRU2NjYAoHN+kaHk5+dj/fr1CA8P1wlj+/btg7OzM+rVq4eePXti/vz5cHZ2BgCcOHECGo0GarVa297d3R3e3t5ITEwsMxDl5eXp9Ck7OxsAoNFooNFoZOtT8bbk3GZNY+pjYOr9Byo3Bq9+GYdD1x7+rDLShG9y5D9/j1SkD6b+HTD1/gMcA0P2X99tKoSeJwONHj1arw0+euhKbt988w3CwsJw5coVuLu7AwA2b96MunXrwtPTE6mpqXjnnXdQUFCAEydOQKVSYePGjRg9enSJwKZWq+Hl5YUVK1aUuq/IyEjMnTu3xPKNGzfC2tpa/s4RERGR7HJzcxEWFoasrCzY2dmV2U7vQFQdBAcHw8LCAtu3by+zTXp6Ojw9PRETE4PBgweXGYiCgoLQrFkzfPHFF6Vup7QZIg8PD/z9999PHFCpNBoN4uPjERQUZLI3ZDP1MTD1/gP6j4FfZByyq7Cu0jw6GyQXU/8OmHr/AY6BIftf/CzW8gJRtT+putjly5eRkJCALVu2PLGdm5sbPD09ceHCBQCAq6sr8vPzkZmZCQcHB227GzduwM/Pr8ztqFQq7eHBRymVSoN8WQ213ZrE1MfA1PsPlD0G/5wbZPjzFkvzrLcVPnmpt8H3Y+rfAVPvP8AxMET/9d1ejQlEq1atgrOzM/r1e/IJi7du3cKff/4JNzc3AEDHjh2hVCoRHx+PYcOGAXg4i5ScnIxFixYZvG4iqhhjnyAN8EoxIlNSIwJRUVERVq1ahZEjR6JOnX9KvnfvHiIjIzFkyBC4ubkhLS0Nb731FpycnDBo0CAAgL29PcaOHYvp06ejfv36cHR0xIwZM+Dj46O96oyIqg9jB6H3+7XCC/9uZtQaiKjq1YhAlJCQgCtXrmDMmDE6y83NzfHrr79i7dq1uHPnDtzc3NCrVy9s3rwZtra22nZLlixBnTp1MGzYMNy/fx8BAQFYvXo170FEVE14R8ZhUaeH/zXWYTHOBhGZthoRiNRqdal3xrayskJcXFy5n7e0tERUVBSioqIMUR4RVUDib38jbPURAMa7XJ4hiIiK1YhARES1h7EPiQEMQkRUEgMRERkcQxARVXcMRERkENUhBAEMQkSkHwYiIpINQxAR1VQMRERUKWFLdiDxurGreIhBiIgqioGIiCqEs0FEVJswEBGR3qpLCGoA4BiDEBHJiIGIiJ6ouoQggLNBRGQ4DEREVEJ1CkH9Winx2Si1scsgolqOgYiIAFSvEARwNoiIqhYDEZEJm7H5IL47lW3sMrSSI4OhVCqNXQYRmSAGIiITVJ1mg4KfUmDZSDV27txp7FKIyIQxEBGZiOoUggDdQ2IajcaIlRARMRAR1WrVOQQREVUnDEREtQxDEBGRdAxERLWAV8QOCGMX8QiGICKqaRiIiGqoZXG/4sO9V4xdhtbPb/RGQ0crY5dBRFQhDERENUx1OiTW3AyIX8DZICKq+RiIiGqA6hSCAB4SI6Lah4GIqJpiCCIiqjoMRETVCEMQEZFxMBARGVm3yDjM6gR4R8YBUBi7HIYgIjJJDERERtAiYgfy//9nlblRSwEARAY/hVG9Whq7DCIio2EgIqoi1e1wGMDZICKiYgxERAbEEEREVDMwEBHJjCGIiKjmYSAikgFDEBFRzcZARFRBDEFERLUHAxGRnl6N3oXYC0XGLqMEdTPgy3EMQkRElcFARPQE1XEWqBhng4iI5MNARPQYhiAiItNjZuwCniQyMhIKhULn5erqql0vhEBkZCTc3d1hZWUFf39/nD17VmcbeXl5mDx5MpycnGBjY4MBAwbg6tWrVd0VquaaROzQvqqbtPf7aV9ERGQY1X6G6F//+hcSEhK0783N/7mt76JFi/Dxxx9j9erVaNGiBebNm4egoCCkpKTA1tYWADB16lRs374dMTExqF+/PqZPn47Q0FCcOHFCZ1tkeqpj+CnG8ENEVLWqfSCqU6eOzqxQMSEEli5dilmzZmHw4MEAgDVr1sDFxQUbN27EhAkTkJWVhejoaKxbtw6BgYEAgPXr18PDwwMJCQkIDg6u0r6Q8VXnEJQcGQylUmnsMoiITFK1D0QXLlyAu7s7VCoVOnfujAULFqBp06ZITU1FRkYG1Gq1tq1KpULPnj2RmJiICRMm4MSJE9BoNDpt3N3d4e3tjcTExCcGory8POTl5WnfZ2dnAwA0Gg00Go1s/SvelpzbrGkMPQYPH5r6UHV4btijkiODodFoEB8fz+8ATPfPAftv2v0HOAaG7L++21QIIYTse5fJrl27kJubixYtWuD69euYN28efvvtN5w9exYpKSno1q0b/vrrL7i7u2s/M378eFy+fBlxcXHYuHEjRo8erRNsAECtVsPLywsrVqwoc9+RkZGYO3duieUbN26EtbW1fJ0kIiIig8nNzUVYWBiysrJgZ2dXZrtqPUMUEhKi/dnHxwddu3ZFs2bNsGbNGnTp0gUAoFAodD4jhCix7HH6tJk5cybCw8O177Ozs+Hh4QG1Wv3EAZWqeHYgKCjIZA+XyDEGj84CVUfJkWXPRvI7wDFg/027/wDHwJD9Lz7CU55qHYgeZ2NjAx8fH1y4cAEDBw4EAGRkZMDNzU3b5saNG3BxcQEAuLq6Ij8/H5mZmXBwcNBp4+fn98R9qVQqqFSqEsuVSqVBvqyG2m5NInUMdM8HenLANQapJ0bzO8AxYP9Nu/8Ax8AQ/dd3ezUqEOXl5eH8+fP497//DS8vL7i6uiI+Ph7t27cHAOTn52P//v344IMPAAAdO3aEUqlEfHw8hg0bBgBIT09HcnIyFi1aZLR+UMVV55OiF4W2xrDuTY1dBhERVUC1DkQzZsxA//790bhxY9y4cQPz5s1DdnY2Ro4cCYVCgalTp2LBggVo3rw5mjdvjgULFsDa2hphYWEAAHt7e4wdOxbTp09H/fr14ejoiBkzZsDHx0d71RlVf9U5BPHyeCKi2qFaB6KrV69i+PDh+Pvvv9GgQQN06dIFhw8fhqenJwDgjTfewP379/Haa68hMzMTnTt3xu7du7X3IAKAJUuWoE6dOhg2bBju37+PgIAArF69mvcgquYYgoiIqCpV60AUExPzxPUKhQKRkZGIjIwss42lpSWioqIQFRUlc3UkN+/IOOQVVr9zgQCGICKi2q5aByKq3YpngVTmAos6GbmYUjAEERGZDgYiqlJNI3agyNhFPAFDEBGRaWIgIoOrzucD1QFwkSGIiMjkMRCRQVTnEPTLbDXsrU33Ph9ERFQSAxHJpjqHIB4KIyKiJ2Egokr5V8QO5Bi7iDIwBBERkb4YiEgyn4gduGvsIsrAEERERBXBQER64eEwIiKqzRiIqEy+ETvwt7GLKANDEBERyYmBiHRU15mgpxRAwkKGICIiMgwGIqq2IYizQEREVFUYiEwUQxAREdE/GIhMSHUNQcmRwdi5cyeSI4ONXQoREZkoBqJarrqGoEdngjQajRErISIiYiCqlWpCCCIiIqpOGIhqCYYgIiKiimMgqsEYgoiIiOTBQFTDMAQRERHJj4GoBmAIIiIiMiwGomqsOgYhhiAiIqqNGIiqmW8O/YE3fjxv7DJ0MAQREVFtx0BUTXhHxiGvUGHsMrQYgoiIyJQwEBmZd2QcFnUydhUPMQQREZGpYiAyoiYRO6AyN24NDEFEREQMREZjzBOmGYKIiIh0MRAZgTHCEEMQERFR2RiIqlhVhiGGICIiIv0wENUyDEFERETSMRDVAgxBRERElcNAVEMxBBEREcnHzNgFPMnChQvxzDPPwNbWFs7Ozhg4cCBSUlJ02owaNQoKhULn1aVLF502eXl5mDx5MpycnGBjY4MBAwbg6tWrVdkV2aS9349hiIiISGbVOhDt378fEydOxOHDhxEfH4+CggKo1Wrk5OTotOvTpw/S09O1r507d+qsnzp1KrZu3YqYmBgcOnQI9+7dQ2hoKAoLC6uyOwAqNrNTHIIYhIiIiAyjWh8yi42N1Xm/atUqODs748SJE+jRo4d2uUqlgqura6nbyMrKQnR0NNatW4fAwEAAwPr16+Hh4YGEhAQEBwcbrgNlSHu/n15XmzEAERERVY1qHYgel5WVBQBwdHTUWb5v3z44OzujXr166NmzJ+bPnw9nZ2cAwIkTJ6DRaKBWq7Xt3d3d4e3tjcTExDIDUV5eHvLy8rTvs7OzAQAajQYajabSfbnwnhrekXFQmQkA0P4XAJIjg7X7MgXF/TSV/j7O1PsPcAzYf9PuP8AxMGT/9d2mQgghym9mfEIIPPvss8jMzMTBgwe1yzdv3oy6devC09MTqampeOedd1BQUIATJ05ApVJh48aNGD16tE64AQC1Wg0vLy+sWLGi1P1FRkZi7ty5JZZv3LgR1tbW8naOiIiIDCI3NxdhYWHIysqCnZ1dme1qzAzRpEmTcObMGRw6dEhn+fPPP6/92dvbG76+vvD09MSOHTswePDgMrcnhIBCUfbT5WfOnInw8HDt++zsbHh4eECtVj9xQKXSaDSIj49HUFAQlEqlbNutSUx9DEy9/wDHgP037f4DHAND9r/4CE95akQgmjx5MrZt24YDBw6gUaNGT2zr5uYGT09PXLhwAQDg6uqK/Px8ZGZmwsHBQdvuxo0b8PPzK3M7KpUKKpWqxHKlUmmQL6uhtluTmPoYmHr/AY4B+2/a/Qc4Bobov77bq9ZXmQkhMGnSJGzZsgU//fQTvLy8yv3MrVu38Oeff8LNzQ0A0LFjRyiVSsTHx2vbpKenIzk5+YmBiIiIiExHtZ4hmjhxIjZu3IgffvgBtra2yMjIAADY29vDysoK9+7dQ2RkJIYMGQI3NzekpaXhrbfegpOTEwYNGqRtO3bsWEyfPh3169eHo6MjZsyYAR8fH+1VZ0RERGTaqnUgWr58OQDA399fZ/mqVaswatQomJub49dff8XatWtx584duLm5oVevXti8eTNsbW217ZcsWYI6depg2LBhuH//PgICArB69WqYm5tXZXeIiIiomqrWgai8C+CsrKwQFxdX7nYsLS0RFRWFqKgouUojIiKiWqRan0NEREREVBUYiIiIiMjkVetDZtVJ8eE7fe9noC+NRoPc3FxkZ2eb7KWWpj4Gpt5/gGPA/pt2/wGOgSH7X/zvdnmn4TAQ6enu3bsAAA8PDyNXQkRERFLdvXsX9vb2Za6vMY/uMLaioiJcu3YNtra2T7zDtVTFd8D+888/Zb0Ddk1i6mNg6v0HOAbsv2n3H+AYGLL/QgjcvXsX7u7uMDMr+0whzhDpyczMrNy7ZFeGnZ2dSf4heJSpj4Gp9x/gGLD/pt1/gGNgqP4/aWaoGE+qJiIiIpPHQEREREQmj4HIyFQqFebMmVPqg2RNhamPgan3H+AYsP+m3X+AY1Ad+s+TqomIiMjkcYaIiIiITB4DEREREZk8BiIiIiIyeQxEREREZPIYiIiIiMjkMRAZwOeffw4vLy9YWlqiY8eOOHjw4BPb79+/Hx07doSlpSWaNm2KL774Qmf92bNnMWTIEDRp0gQKhQJLly41YPWVJ3f/V65ciX//+99wcHCAg4MDAgMDcfToUUN2odLkHoMtW7bA19cX9erVg42NDdq1a4d169YZsguVInf/HxUTEwOFQoGBAwfKXLV85O7/6tWroVAoSrwePHhgyG5UiiG+A3fu3MHEiRPh5uYGS0tLtG7dGjt37jRUFypF7v77+/uX+h3o16+fIbtRKYb4DixduhQtW7aElZUVPDw8MG3aNPn+HAiSVUxMjFAqlWLlypXi3Llz4r///a+wsbERly9fLrX9H3/8IaytrcV///tfce7cObFy5UqhVCrFd999p21z9OhRMWPGDLFp0ybh6uoqlixZUkW9kc4Q/Q8LCxOfffaZOHXqlDh//rwYPXq0sLe3F1evXq2qbkliiDHYu3ev2LJlizh37py4ePGiWLp0qTA3NxexsbFV1S29GaL/xdLS0kTDhg3Fv//9b/Hss88auCcVY4j+r1q1StjZ2Yn09HSdV3VliDHIy8sTvr6+om/fvuLQoUMiLS1NHDx4UJw+fbqquqU3Q/T/1q1bOr/75ORkYW5uLlatWlVFvZLGEGOwfv16oVKpxIYNG0RqaqqIi4sTbm5uYurUqbLUzEAks06dOolXX31VZ1mrVq1EREREqe3feOMN0apVK51lEyZMEF26dCm1vaenZ7UORIbuvxBCFBQUCFtbW7FmzZrKF2wAVTEGQgjRvn178fbbb1euWAMwVP8LCgpEt27dxFdffSVGjhxZbQORIfq/atUqYW9vL3uthmKIMVi+fLlo2rSpyM/Pl79gmVXF3wFLliwRtra24t69e5Uv2AAMMQYTJ04UvXv31mkTHh4uunfvLkvNPGQmo/z8fJw4cQJqtVpnuVqtRmJiYqmfSUpKKtE+ODgYx48fh0ajMVithlBV/c/NzYVGo4Gjo6M8hcuoKsZACIE9e/YgJSUFPXr0kK94GRiy/++++y4aNGiAsWPHyl+4TAzZ/3v37sHT0xONGjVCaGgoTp06JX8HZGCoMdi2bRu6du2KiRMnwsXFBd7e3liwYAEKCwsN05EKqqq/B6Ojo/HCCy/AxsZGnsJlZKgx6N69O06cOKE9ZeKPP/7Azp07ZTtsyEAko7///huFhYVwcXHRWe7i4oKMjIxSP5ORkVFq+4KCAvz9998Gq9UQqqr/ERERaNiwIQIDA+UpXEaGHIOsrCzUrVsXFhYW6NevH6KiohAUFCR/JyrBUP3/+eefER0djZUrVxqmcJkYqv+tWrXC6tWrsW3bNmzatAmWlpbo1q0bLly4YJiOVIKhxuCPP/7Ad999h8LCQuzcuRNvv/02PvroI8yfP98wHamgqvh78OjRo0hOTsYrr7wiX+EyMtQYvPDCC3jvvffQvXt3KJVKNGvWDL169UJERIQsddeRZSukQ6FQ6LwXQpRYVl770pbXFIbs/6JFi7Bp0ybs27cPlpaWMlRrGIYYA1tbW5w+fRr37t3Dnj17EB4ejqZNm8Lf31++wmUiZ//v3r2Ll156CStXroSTk5P8xRqA3L//Ll26oEuXLtr13bp1Q4cOHRAVFYVPP/1UrrJlJfcYFBUVwdnZGV9++SXMzc3RsWNHXLt2DYsXL8bs2bNlrr7yDPn3YHR0NLy9vdGpUycZKjUcucdg3759mD9/Pj7//HN07twZFy9exH//+1+4ubnhnXfeqXS9DEQycnJygrm5eYkEfOPGjRLJt5irq2up7evUqYP69esbrFZDMHT/P/zwQyxYsAAJCQlo06aNvMXLxJBjYGZmhqeeegoA0K5dO5w/fx4LFy6sVoHIEP0/e/Ys0tLS0L9/f+36oqIiAECdOnWQkpKCZs2aydyTiqmqvwPMzMzwzDPPVMsZIkONgZubG5RKJczNzbVtWrdujYyMDOTn58PCwkLmnlSMob8Dubm5iImJwbvvvitv4TIy1Bi88847GDFihHZmzMfHBzk5ORg/fjxmzZoFM7PKHfTiITMZWVhYoGPHjoiPj9dZHh8fDz8/v1I/07Vr1xLtd+/eDV9fXyiVSoPVagiG7P/ixYvx3nvvITY2Fr6+vvIXL5Oq/A4IIZCXl1f5omVkiP63atUKv/76K06fPq19DRgwAL169cLp06fh4eFhsP5IVVW/fyEETp8+DTc3N3kKl5GhxqBbt264ePGiNgwDwO+//w43N7dqE4YAw38HvvnmG+Tl5eGll16St3AZGWoMcnNzS4Qec3NziIcXiFW+cFlOzSat4ksNo6Ojxblz58TUqVOFjY2NSEtLE0IIERERIUaMGKFtX3yp4bRp08S5c+dEdHR0qZebnjp1Spw6dUq4ubmJGTNmiFOnTokLFy5Uef/KY4j+f/DBB8LCwkJ89913Oped3r17t8r7pw9DjMGCBQvE7t27xaVLl8T58+fFRx99JOrUqSNWrlxZ5f0rjyH6/7jqfJWZIfofGRkpYmNjxaVLl8SpU6fE6NGjRZ06dcSRI0eqvH/6MMQYXLlyRdStW1dMmjRJpKSkiB9//FE4OzuLefPmVXn/ymPIPwPdu3cXzz//fJX1paIMMQZz5swRtra2YtOmTeKPP/4Qu3fvFs2aNRPDhg2TpWYGIgP47LPPhKenp7CwsBAdOnQQ+/fv164bOXKk6Nmzp077ffv2ifbt2wsLCwvRpEkTsXz5cp31qampAkCJ1+PbqS7k7r+np2ep/Z8zZ04V9KZi5B6DWbNmiaeeekpYWloKBwcH0bVrVxETE1MVXakQufv/uOociISQv/9Tp04VjRs3FhYWFqJBgwZCrVaLxMTEquhKhRniO5CYmCg6d+4sVCqVaNq0qZg/f74oKCgwdFcqxBD9T0lJEQDE7t27DV2+LOQeA41GIyIjI0WzZs2EpaWl8PDwEK+99prIzMyUpV6FEHLMMxERERHVXDyHiIiIiEweAxERERGZPAYiIiIiMnkMRERERGTyGIiIiIjI5DEQERERkcljICIiIiKTx0BEREREJo+BiIjo/926dQvOzs5IS0szdikYOnQoPv74Y2OXQWQyGIiIqMqMGjUKCoUCr776aol1r732GhQKBUaNGlX1hf2/hQsXon///mjSpAkA49Y7e/ZszJ8/H9nZ2QbZPhHpYiAioirl4eGBmJgY3L9/X7vswYMH2LRpExo3bmy0uu7fv4/o6Gi88sorOsvlrnfz5s3o3LkzrKys4OTkhLCwMPz+++8l2rVp0wZNmjTBhg0bpHeGiCRjICKiKtWhQwc0btwYW7Zs0S7bsmULPDw80L59e+2y2NhYdO/eHfXq1UP9+vURGhqKS5cu6Wzru+++g4+PD6ysrFC/fn0EBgYiJyen3HWl2bVrF+rUqYOuXbtWqF59REREICIiArNmzcLNmzfxyy+/4KmnnoKfnx+OHj1aov2AAQOwadMmSfsgoophICKiKjd69GisWrVK+/7rr7/GmDFjdNrk5OQgPDwcx44dw549e2BmZoZBgwahqKgIAJCeno7hw4djzJgxOH/+PPbt24fBgwdDCPHEdWU5cOAAfH19K1xvefbu3YuYmBicOHECAwYMQN26ddGwYUO8++67+OKLLxAWFoaCggKdz3Tq1AlHjx5FXl6epH0RkXQMRERU5UaMGIFDhw4hLS0Nly9fxs8//4yXXnpJp82QIUMwePBgNG/eHO3atUN0dDR+/fVXnDt3DsDDQFRQUIDBgwejSZMm8PHxwWuvvYa6des+cV1Z0tLS4O7uXuF6y7Nu3Tq89tprcHR0LLFu6NChqFu3LpKSknSWN2zYEHl5ecjIyJC0LyKSjoGIiKqck5MT+vXrhzVr1mDVqlXo168fnJycdNpcunQJYWFhaNq0Kezs7ODl5QUAuHLlCgCgbdu2CAgIgI+PD5577jmsXLkSmZmZ5a4ry/3792FpaVnhestz9epVtGzZUvu+Xr16iIyM1L5v0aIFrl69qvMZKysrAEBubq6kfRGRdAxERGQUY8aMwerVq7FmzZpSDz/1798ft27dwsqVK3HkyBEcOXIEAJCfnw8AMDc3R3x8PHbt2oWnn34aUVFRaNmyJVJTU5+4rixOTk5PDE3l1Vuehg0b4sKFC9r3LVq00AlVFy9eLDFDdfv2bQBAgwYNJO+PiKRhICIio+jTpw/y8/ORn5+P4OBgnXW3bt3C+fPn8fbbbyMgIACtW7cuNawoFAp069YNc+fOxalTp2BhYYGtW7eWu6407du31x6Ok1qvPl588UUsX74cWVlZAICjR49i0qRJAIAffvgBmZmZ8PPz0/lMcnIyGjVqJHk2ioikq2PsAojINJmbm+P8+fPanx/l4OCA+vXr48svv4SbmxuuXLmCiIgInTZHjhzBnj17oFar4ezsjCNHjuDmzZto3br1E9eVJTg4GDNnzkRmZiYcHBwk1auPwMBADBgwAJ06dcKSJUvg5+eHrKwsbNiwAR999BG2b98OpVKp85mDBw9CrVZL3hcRScdARERGY2dnV+pyMzMzxMTEYMqUKfD29kbLli3x6aefwt/fX+ezBw4cwNKlS5GdnQ1PT0989NFHCAkJwfnz58tcVxYfHx/4+vrim2++wYQJEyTVq68lS5agffv2mDlzJpKTk2FpaQm1Wo2DBw/i6aef1mn74MEDbN26FXFxcZXaJxHpRyGedB0qEZEJ2blzJ2bMmIHk5GSYmRn2jIKioqIn7uOzzz7DDz/8gN27dxu0DiJ6iDNERET/r2/fvrhw4QL++usveHh4GHRf5QUupVKJqKgog9ZARP/gDBERUSVduXKlxCGvR507d86ojyUhovIxEBERVVJBQQHS0tLKXN+kSRPUqcMJeaLqjIGIiIiITB7vQ0REREQmj4GIiIiITB4DEREREZk8BiIiIiIyeQxEREREZPIYiIiIiMjkMRARERGRyWMgIiIiIpPHQEREREQmj4GIiIiITN7/AWB4Lqf2EyjlAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(kc_out[p2_bd]['mass'], kc_out[p2_bd]['Teff'])\n", + "plt.title('Brown Dwarf Assigned Teff vs. Mass')\n", + "plt.xlabel('Mass (M_$\\odot$)')\n", + "plt.ylabel('Effective Temperarture (K)')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "4a03d8bd-18df-4d28-8106-19a9e14c12a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7870" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.min(k_ns['mass_current'])\n", + "np.max(k_ns['mass_current'])\n", + "len(k_ns['mass_current'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b514e4e-1ed0-457c-ad03-49bb64ec2236", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a0e6c5aa9bbdf07b3fdf0aaa728b0c509ac36f60 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 19 Nov 2024 14:22:13 -0800 Subject: [PATCH 29/48] Explanation of Meisner vs. Phillips flux issue --- changes/BDClusters_AtmIssues.ipynb | 96 +++++++++++++----------------- 1 file changed, 40 insertions(+), 56 deletions(-) diff --git a/changes/BDClusters_AtmIssues.ipynb b/changes/BDClusters_AtmIssues.ipynb index 54257c32..516e7669 100644 --- a/changes/BDClusters_AtmIssues.ipynb +++ b/changes/BDClusters_AtmIssues.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "01b7a3c9-8d7a-483f-a25a-161817f55e9f", "metadata": {}, "outputs": [], @@ -47,34 +47,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "bc90dc34-0747-4b24-805f-6e8abf081565", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Isochrone generation took 54.561135 s.\n", - "Making photometry for isochrone: log(t) = 8.00 AKs = 0.00 dist = 10\n", - " Starting at: 2024-11-12 17:33:26.509247 Usually takes ~5 minutes\n", - "Starting filter: wfc3,ir,f153m Elapsed time: 0.00 seconds\n", - "Starting synthetic photometry\n", - "M = 0.070 Msun T = 2794 K m_hst_f153m = 9.52\n", - "M = 0.676 Msun T = 4395 K m_hst_f153m = 4.92\n", - "M = 0.786 Msun T = 4896 K m_hst_f153m = 4.41\n", - "M = 0.856 Msun T = 5198 K m_hst_f153m = 4.12\n", - "M = 0.956 Msun T = 5590 K m_hst_f153m = 3.74\n", - "M = 1.022 Msun T = 5808 K m_hst_f153m = 3.50\n", - "M = 1.079 Msun T = 5992 K m_hst_f153m = 3.30\n", - "M = 1.518 Msun T = 7482 K m_hst_f153m = 2.22\n", - "M = 4.439 Msun T = 13246 K m_hst_f153m = -1.04\n", - "M = 4.509 Msun T = 14299 K m_hst_f153m = -1.00\n", - "M = 5.078 Msun T = 6015 K m_hst_f153m = -4.23\n", - " Time taken: 14.44 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Create isochrone object \n", "logAge = np.log10(5*10**9.)\n", @@ -87,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "id": "3bfb0efe-bf1a-4666-945b-057c64e6cccd", "metadata": {}, "outputs": [ @@ -110,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "998eaa3e-103d-40bf-85ba-125ad4a5ed75", "metadata": {}, "outputs": [ @@ -136,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 5, "id": "dca63ebb-6c40-4f76-abb6-b709ccb71466", "metadata": {}, "outputs": [ @@ -175,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "6d32d779-8e93-44e1-9375-7ff6e4f5f246", "metadata": {}, "outputs": [], @@ -187,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "76787edf-ced9-478a-8572-c3b5e327febe", "metadata": {}, "outputs": [ @@ -205,7 +181,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found 8520 companions out of stellar mass range\n" + "Found 8211 companions out of stellar mass range\n" ] }, { @@ -231,13 +207,13 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 8, "id": "9ca75682-5e3a-4178-b9a1-0db1ecc9b4a6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -311,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "8f03eedf-780a-47cc-bcb5-1bd767ff4135", "metadata": {}, "outputs": [ @@ -319,10 +295,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "287.80180827780606\n", - "287.80439454686973\n", - "2321.1956912624596\n", - "2321.1948222119263\n" + "287.8044158821004\n", + "287.80301331492325\n", + "2321.1831860770926\n", + "2321.183556796764\n" ] } ], @@ -335,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "fbbd559a-4f0d-426e-9c6c-798debb699dc", "metadata": {}, "outputs": [ @@ -354,10 +330,10 @@ "from astropy.io import fits\n", "from astropy.table import Table\n", "\n", - "# plotting 1200K star from Meisner vs. BTSettl\n", + "# plotting 1200K star from Meisner vs. BTSettl 3000K\n", "bt_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/BTSettl_rebin/btm05/lte033-3.0-0.5a+0.2.BT-Settl.spec.fits'\n", "m_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Meisner2023/mp00/spec_jwst_t1200_g4.5_p0_kg_g1.25.fits'\n", - "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits'\n", + "p_file = '/System/Volumes/Data/mnt/g/lu/models/cdbs/grid/Phillips2020/spec_T1200_lg4.5_CEQ.fits' # Phillips 1200K star for reference\n", "\n", "b_hdu_list = fits.open(bt_file)\n", "m_hdu_list = fits.open(m_file)\n", @@ -383,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 11, "id": "dcb9ce22-afd5-4f75-a033-3f205e2e8613", "metadata": {}, "outputs": [ @@ -418,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 12, "id": "a3e80ffe-ef8d-4fc8-8c2a-463c12c5e42d", "metadata": {}, "outputs": [ @@ -455,8 +431,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "443dd316-d205-44a1-87b8-3c2252d28f64", + "execution_count": 13, + "id": "f48f74a1-3b51-4cc1-98ef-fdbf0e4f175c", "metadata": {}, "outputs": [ { @@ -492,9 +468,17 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "d2aaf035-d3b8-4667-862c-6c430d78df63", + "metadata": {}, + "source": [ + "It becomes clear that the Meisner model peaks at fluxes much, much smaller than expected, and much, much smaller than those given by the Phillips model for a star under the same conditions (Teff = 1200K, metallicity = 0, log g = 4.5) and unit scalings. This implies that there might be some conversion factor that we are missing, due to conditions and the fact that both papers use the ATMO model for their model generation." + ] + }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 14, "id": "5419c340-4abc-44a9-9ec3-c77bb35d1b2f", "metadata": {}, "outputs": [ @@ -521,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 15, "id": "51fcf941-7b85-4c8a-851d-8d73f315e365", "metadata": {}, "outputs": [ @@ -576,13 +560,13 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 16, "id": "60c949a3-f40e-402f-8923-3e914d58cb3b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -654,13 +638,13 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 17, "id": "c397930b-0fa9-4592-b571-e3dfbf00b8f7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -681,17 +665,17 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 18, "id": "4a03d8bd-18df-4d28-8106-19a9e14c12a4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "7870" + "7747" ] }, - "execution_count": 65, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } From 39fb1c2bfc053916634a5d7938b72626544e3286 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 2 Dec 2024 14:32:18 -0800 Subject: [PATCH 30/48] implementation of new atmo model --- spisea/atmospheres.py | 17 ++++++++++------- spisea/tests/test_models.py | 2 +- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index a65e495e..5f71f33c 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -933,10 +933,10 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid - if (temperature <= 1200) & (metallicity == 0): + if (temperature <= 1200): if verbose: - print( 'Phillips2020 atmosphere') - return get_Phillips2020_atmosphere(metallicity=metallicity, + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity, rebin=rebin) @@ -1080,9 +1080,9 @@ def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False) """ try: if verbose: - print('Phillips2020 atmosphere') + print('Meisner2023 atmosphere') - return get_Phillips2020_atmosphere(metallicity=metallicity, + return get_Meisner2023_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity) @@ -2226,9 +2226,12 @@ def organize_all_Meisner2023_atmospheres(): wavelength = data['Wavelength'] flux = data['Flux'] + # Make flux independent of R&D + flux_new = flux / 5e-20 + # Create new columns with desired format c0 = fits.Column(name='Wavelength', format='D', array=wavelength) - c1 = fits.Column(name='Flux', format='E', array=flux) + c1 = fits.Column(name='Flux', format='E', array=flux_new) cols = fits.ColDefs([c0, c1]) tbhdu = fits.BinTableHDU.from_columns(cols) @@ -2346,7 +2349,7 @@ def rebin_Meisner2023(make_unique=False): # Fetch the Meisner2023 spectrum and rebin its flux try: sp = pysynphot.Icat('Meisner2023', temp, metal, logg) - flux_rebin = rebin_spec(sp.wave, sp.flux, sp_meisner.wave) + flux_rebin = rebin_spec(sp.wave, sp.flux, sp_atlas.wave) # Create the output FITS file c0 = fits.Column(name='Wavelength', format='D', array=sp_atlas.wave) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 7d21b3e6..f44f58bc 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -109,7 +109,7 @@ def test_atmosphere_models(): # Test get_merged_atmospheres at different temps temp_range = [200, 1000, 2000, 3500, 4000, 5250, 6000, 12000] atm_func = atm.get_merged_atmosphere - for ii in metals_range: + for ii in bd_metals_range: for jj in temp_range: try: test = atm_func(metallicity=ii, temperature=jj, verbose=True) From dd193152d00ffddfd6deeb15f335e021d55b3242 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 21 Jan 2025 08:46:38 -0800 Subject: [PATCH 31/48] Addition of Phillips2020 evolution class --- spisea/evolution.py | 294 ++++++++++++++++++++++++++++++++++++ spisea/tests/test_models.py | 6 +- 2 files changed, 297 insertions(+), 3 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 7de8e217..9a4b2c16 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -528,6 +528,300 @@ def format_isochrones(input_iso_dir, metallicity_list): os.chdir(start_dir) return +class Phillips2020(StellarEvolution): + """ + Evolution models from + `Phillips et al. 2020 `_. + + Downloaded from `here _` + + Notes + ----- + Evolution model parameters used in download: + + * Assume chemical equilibrium + * Solar metallicity + * For young BDs + CHANGE!!! + """ + + def __init__(self): + r""" + Define intrinsic properties for the Phillips brown dwarf stellar models + """ + # specify location of model files + self.model_dir = models_dir + 'Phillips2020/' + + # specifying metallicity + self.z_list = [0.015] + self.z_file_map = {0.015: 'z00'} + self.z_solar = 0.015 + + # populate list of isochrone ages (log scale) + self.age_list = np.arange(6.0, 10.0, 0.001) + + # define required evo_grid number + self.evo_grid_min = 1.0 + + def isochrone(self, age= 1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Phillips2020 collection. + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + 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 ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # Specify subdirectory for metallicity + iso_path = os.path.join(self.model_dir, 'iso', z_dir) + + # Find nearest age in grid to input grid by parsing through available files + p_files = glob.glob(os.path.join(iso_path, 'iso_*.fits')) + p_ages = np.array([float(f.split('_')[1].replace('.fits', '')) for f in p_files]) + close_age = np.argmin(abs(p_ages - log_age)) + close_file = p_files[close_age] + print(f"Found nearest age file as {close_file} for requested age of {log_age}") + + # Make sure the closest file exists + if not os.path.exists(close_file): + raise FileNotFoundError(f"Isochrone file not found: {close_file}.") + + # return isochrone data + iso = Table.read(close_file, format='fits') + iso.rename_column('Z', 'Z') + iso.rename_column('Age', 'logAge') + iso.rename_column('Mass', 'mass') + iso.rename_column('Mass_current', 'mass_current') + iso.rename_column('Luminosity', 'logL') + iso.rename_column('Teff', 'logT') + iso.rename_column('Gravity', 'logg') + iso['logT_WR'] = iso['logT'] + + # Phillips doesn't identify WR stars, so identify all as "False" + isWR = Column([False] * len(iso), name='isWR') + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = metallicity + + return iso + + def format_isochrones(input_iso_dir, metallicity_list): + r""" + Change + """ + # Store current directory for later + start_dir = os.getcwd() + + # Move into isochrone directory + os.chdir(input_iso_dir) + + # Work on each metallicity isochrones individually + for metal in metallicity_list: + # More into metallicity directory, read isochrone file + os.chdir(metal) + + isoFile = glob.glob('output*') + print( 'Read Input: this is slow') + iso = Table.read(isoFile[0], format='fits') + print( 'Done') + + ages_all = iso['col2'] + + # Extract the unique ages + age_arr = np.unique(ages_all) + + # For each unique age, extract the proper rows and make corresponding + # table + print( 'Making individual isochrone files') + for age in age_arr: + good = np.where(ages_all == age) + tmp = iso[good] + + #Write table + tmp.write('iso_{0:4.2f}.fits'.format(age)) + + # Move back into iso directory + os.chdir('..') + + # Return to starting directory + os.chdir(start_dir) + return + + +class Marley(StellarEvolution): + """ + Evolution models from + `Marley et al. 2021 `_. + + Downloaded from `here _` + + Notes + ----- + Evolution model parameters used in download: + + * + CHANGE!!! + """ + def __init__(self): + r""" + Define intrinsic properties for the Marley brown dwarf stellar models. + """ + # populate list of model masses (in solar masses) + #mass_list = [(0.1 + i*0.005) for i in range(181)] + + # populate list of isochrone ages (log scale) + self.age_list = np.arange(6.0, 10.0+0.176, 0.01) + + # define metallicity parameters for Parsec models + self.z_list = [-0.5, 0.0, 0.5] + + # Specify location of model files + self.model_dir = models_dir+'Marley2021/' + + # Specifying metallicity + self.z_solar = 0.0 + self.z_file_map = { + -0.5: 'nc-0.5_co1.0_mass_age', + 0.0: 'nc+0.0_co1.0_mass_age', + 0.5: 'nc+0.5_co1.0_mass_age' + } + + # Define required evo_grid number + self.evo_grid_min = 1.0 + + def isochrone(self, age=1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Marley collection. + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + 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 ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # Find the corresponding file + file_name = self.model_dir + self.z_file_map[z_closest] + + # Load the metallicity file + iso_table = Table.read(metallicity_file, format='ascii') + iso_table = isotable[['Mass', 'log age', 'Teff', 'log g', 'Radius', 'log L']] + + # Filter for the specific age + good = np.where(np.isclose(iso_table["log age"], log_age, atol=0.01))[0] + if len(good) == 0: + raise ValueError(f"No data found for requested age {log_age:.2f} in file {metallicity_file}.") + + iso = iso_table[good] + + # put temperature into logT + iso['Teff'] = np.log10(iso['Teff']) + + # return isochrone data + iso.rename_column('Mass', 'mass_current') + iso.rename_column('log age', 'logAge') + iso.rename_column('Teff', '') + iso.rename_column('log g', 'logg') + iso.rename_column('Radius', 'logL') + iso.rename_column('col6', 'logT') + iso.rename_column('col7', 'logg') + iso.rename_column('col15', 'phase') + iso['logT_WR'] = iso['logT'] + + # Parsec doesn't identify WR stars, so identify all as "False" + isWR = Column([False] * len(iso), name='isWR') + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + + return iso + + def format_isochrones(input_iso_dir, metallicity_list): + r""" + Parse isochrone file downloaded from Parsec version 1.2 for different + metallicities, create individual isochrone files for the different ages. + + 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 + """ + # Store current directory for later + start_dir = os.getcwd() + + # Move into isochrone directory + os.chdir(input_iso_dir) + + # Work on each metallicity isochrones individually + for metal in metallicity_list: + # More into metallicity directory, read isochrone file + os.chdir(metal) + + isoFile = glob.glob('output*') + print( 'Read Input: this is slow') + iso = Table.read(isoFile[0], format='fits') + print( 'Done') + + ages_all = iso['col2'] + + # Extract the unique ages + age_arr = np.unique(ages_all) + + # For each unique age, extract the proper rows and make corresponding + # table + print( 'Making individual isochrone files') + for age in age_arr: + good = np.where(ages_all == age) + tmp = iso[good] + + #Write table + tmp.write('iso_{0:4.2f}.fits'.format(age)) + + # Move back into iso directory + os.chdir('..') + + # Return to starting directory + os.chdir(start_dir) + return + #---------------------------------------# # Now for the Pisa (Tognelli+11) models #---------------------------------------# diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index f44f58bc..44a7f183 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -34,14 +34,14 @@ def test_evolution_models(): # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), - evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa()] + evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), evolution.Phillips2020()] # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] assert len(evo_models) == len(age_vals) == len(metal_vals) From 0e2ac34cd50338a4d7cb69982cd116760a0635d6 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 25 Feb 2025 15:10:12 -0800 Subject: [PATCH 32/48] Added Marley evolution class --- spisea/evolution.py | 80 ++++++++++++++++++------------------- spisea/tests/test_models.py | 8 ++-- 2 files changed, 44 insertions(+), 44 deletions(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index 9a4b2c16..63caccc5 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -667,7 +667,7 @@ def format_isochrones(input_iso_dir, metallicity_list): return -class Marley(StellarEvolution): +class Marley2021(StellarEvolution): """ Evolution models from `Marley et al. 2021 `_. @@ -687,30 +687,34 @@ def __init__(self): """ # populate list of model masses (in solar masses) #mass_list = [(0.1 + i*0.005) for i in range(181)] - - # populate list of isochrone ages (log scale) - self.age_list = np.arange(6.0, 10.0+0.176, 0.01) # define metallicity parameters for Parsec models - self.z_list = [-0.5, 0.0, 0.5] + self.z_solar = 0.0142 + self.z_list = [self.z_solar * (10.**m) for m in [-0.5, 0.0, 0.5]] + + # populate list of isochrone ages (log scale) + self.age_list = [10.0, 7.0, 8.0, 9.0, 6.0, 7.176091259055681, 8.176091259055681, 9.176091259055681, 6.301029995663981, + 7.301029995663981, 8.301029995663981, 9.301029995663981, 6.477121254719663, 7.477121254719663, + 8.477121254719663, 9.477121254719663, 6.6020599913279625, 7.6020599913279625, 8.602059991327963, + 9.602059991327963, 6.778151250383644, 7.778151250383644, 8.778151250383644, 9.778151250383644, + 6.903089986991944, 7.903089986991944, 8.903089986991944, 9.903089986991944] # Specify location of model files self.model_dir = models_dir+'Marley2021/' # Specifying metallicity - self.z_solar = 0.0 self.z_file_map = { - -0.5: 'nc-0.5_co1.0_mass_age', - 0.0: 'nc+0.0_co1.0_mass_age', - 0.5: 'nc+0.5_co1.0_mass_age' + self.z_list[0]: 'zm05/', + self.z_list[1]: 'zp00/', + self.z_list[2]: 'zp05/' } # Define required evo_grid number - self.evo_grid_min = 1.0 + self.evo_grid_min = 1.0 def isochrone(self, age=1.e8, metallicity=0.0): r""" - Extract an individual isochrone from the Marley collection. + Extract an individual isochrone from the Marley2021 collection. """ # Error check to see if installed evolution model # grid is compatible with code version. Also return @@ -730,59 +734,53 @@ def isochrone(self, age=1.e8, metallicity=0.0): (z_defined > np.max(self.z_list))): logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) - # find closest metallicity value z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] z_dir = self.z_file_map[self.z_list[z_idx]] - # Find the corresponding file - file_name = self.model_dir + self.z_file_map[z_closest] - - # Load the metallicity file - iso_table = Table.read(metallicity_file, format='ascii') - iso_table = isotable[['Mass', 'log age', 'Teff', 'log g', 'Radius', 'log L']] - - # Filter for the specific age - good = np.where(np.isclose(iso_table["log age"], log_age, atol=0.01))[0] - if len(good) == 0: - raise ValueError(f"No data found for requested age {log_age:.2f} in file {metallicity_file}.") + # Find closest age in grid + age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] + iso_file = f'iso_{self.age_list[age_idx]}.fits' - iso = iso_table[good] + # Create path to iso file + full_iso_file = os.path.join(self.model_dir, 'iso', z_dir, iso_file) - # put temperature into logT - iso['Teff'] = np.log10(iso['Teff']) + print(f"Found nearest age file as {full_iso_file} for requested age of {log_age}") + + # Make sure the closest file exists + #if not os.path.exists(close_file): + #raise FileNotFoundError(f"Isochrone file not found: {close_file}.") # return isochrone data - iso.rename_column('Mass', 'mass_current') - iso.rename_column('log age', 'logAge') - iso.rename_column('Teff', '') - iso.rename_column('log g', 'logg') - iso.rename_column('Radius', 'logL') - iso.rename_column('col6', 'logT') - iso.rename_column('col7', 'logg') - iso.rename_column('col15', 'phase') + iso = Table.read(full_iso_file, format='fits') + iso.rename_column('Z', 'Z') + iso.rename_column('Age', 'logAge') + iso.rename_column('Mass', 'mass') + iso.rename_column('Mass_current', 'mass_current') + iso.rename_column('log_L', 'logL') + iso.rename_column('Teff', 'logT') + iso.rename_column('logg', 'logg') + iso.rename_column('Radius', 'radius') iso['logT_WR'] = iso['logT'] - # Parsec doesn't identify WR stars, so identify all as "False" + # Marley doesn't identify WR stars, so identify all as "False" isWR = Column([False] * len(iso), name='isWR') iso.add_column(isWR) iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity - iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + iso.meta['metallicity_act'] = np.log10(z_defined / self.z_solar) return iso def format_isochrones(input_iso_dir, metallicity_list): r""" - Parse isochrone file downloaded from Parsec version 1.2 for different + Parse isochrone files downloaded from Marley 2021 for different metallicities, create individual isochrone files for the different ages. - input_iso_dir: points to ParsecV1.2s/iso directory. Assumes metallicity + input_iso_dir: points to Marley2021/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 """ # Store current directory for later start_dir = os.getcwd() @@ -800,7 +798,7 @@ def format_isochrones(input_iso_dir, metallicity_list): iso = Table.read(isoFile[0], format='fits') print( 'Done') - ages_all = iso['col2'] + ages_all = iso['Age'] # Extract the unique ages age_arr = np.unique(ages_all) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 44a7f183..3103dcd1 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -31,17 +31,19 @@ def test_evolution_models(): # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] metal_solar = [0] + metal_Marley = [-0.5, 0.0, 0.5] # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), - evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), evolution.Phillips2020()] + evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), + evolution.Phillips2020(), evolution.Marley2021()] # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley] assert len(evo_models) == len(age_vals) == len(metal_vals) From cfa29511541bad5668bf4158b0534124b1e588b1 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 15 Apr 2025 14:51:15 -0700 Subject: [PATCH 33/48] Evolution model changes and exploring intermediary data generation --- changes/evolution_gaussian_fit.ipynb | 168 +++++++++++++++++++++++++++ spisea/evolution.py | 100 ++++++++++++++++ spisea/tests/test_models.py | 8 +- 3 files changed, 273 insertions(+), 3 deletions(-) create mode 100644 changes/evolution_gaussian_fit.ipynb diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb new file mode 100644 index 00000000..b749ed58 --- /dev/null +++ b/changes/evolution_gaussian_fit.ipynb @@ -0,0 +1,168 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9aaeb072-9ae6-4362-978f-4d9fd5802cd7", + "metadata": {}, + "source": [ + "## Generating Intermediary Data for New Non-Overlapping Evolutionary Data" + ] + }, + { + "cell_type": "markdown", + "id": "5459271a-7e3f-4723-91eb-b8735a2a5ff1", + "metadata": {}, + "source": [ + "Oh no! Unfortunately our new evolutionary models for brown dwarf masses does not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1cad6635-a51f-426b-a207-6e2be5710586", + "metadata": {}, + "outputs": [], + "source": [ + "# importing necessary packages\n", + "import matplotlib.pyplot as plt\n", + "from astropy.table import Table\n", + "import numpy as np\n", + "from sklearn.gaussian_process import GaussianProcessRegressor\n", + "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C, RationalQuadratic as RQ, WhiteKernel, ExpSineSquared as Exp" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d22dab8a-4834-48ed-99b0-47295b685e38", + "metadata": {}, + "outputs": [], + "source": [ + "# importing compatible age fits files \n", + "phillips = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_7.128205127364955.fits'\n", + "pisa = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_7.13.fits'\n", + "\n", + "# loading tables and extracting data\n", + "phil_tbl = Table.read(phillips, format='fits')\n", + "pisa_tbl = Table.read(pisa, format='fits')\n", + "\n", + "phil_mass = phil_tbl['Mass']\n", + "pisa_mass = pisa_tbl['col3']\n", + "\n", + "phil_Teff = phil_tbl['Teff']\n", + "pisa_Teff = pisa_tbl['col2']\n", + "\n", + "phil_L = phil_tbl['Luminosity']\n", + "pisa_L = pisa_tbl['col1']\n", + "\n", + "phil_logg = phil_tbl['Gravity']\n", + "pisa_logg = pisa_tbl['col4']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fd004b17-6528-4633-a5a1-2ab841c41bb9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(10, 10))\n", + "\n", + "# create array for mass gap\n", + "mass_gap = np.linspace(np.max(phil_mass), np.min(pisa_mass), 1000)\n", + "\n", + "# mass vs. luminosity\n", + "axs[0].plot(phil_mass, phil_L, color='c', label='Phillips')\n", + "axs[0].plot(pisa_mass, pisa_L, color='pink', label='Pisa')\n", + "axs[0].set_title('Mass vs. Luminosity for logAge=7.13')\n", + "y_min0 = axs[0].get_ylim()[0]\n", + "y_max0 = axs[0].get_ylim()[1]\n", + "axs[0].fill_between(mass_gap, y_min0, y_max0, color='gray', label='mass gap', alpha=0.3)\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity (L/$L_{\\odot}$)')\n", + "axs[0].set_xlim(0,1)\n", + "axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# mass vs. Teff\n", + "axs[1].plot(phil_mass, phil_Teff, color='c', label='Phillips')\n", + "axs[1].plot(pisa_mass, pisa_Teff, color='pink', label='Pisa')\n", + "y_min1 = axs[1].get_ylim()[0]\n", + "y_max1 = axs[1].get_ylim()[1]\n", + "axs[1].fill_between(mass_gap, y_min1, y_max1, color='gray', label='mass gap', alpha=0.3)\n", + "axs[1].set_title('Mass vs. Effective Temperature for logAge=7.13')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('log(Teff) (log(K))')\n", + "axs[1].set_xlim(0,1)\n", + "axs[1].set_ylim(3,4)\n", + "axs[1].legend()\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].plot(phil_mass, phil_logg, color='c', label='Phillips')\n", + "axs[2].plot(pisa_mass, pisa_logg, color='pink', label='Pisa')\n", + "y_min2 = axs[2].get_ylim()[0]\n", + "y_max2 = axs[2].get_ylim()[1]\n", + "axs[2].fill_between(mass_gap, y_min2, y_max2, color='gray', label='mass gap', alpha=0.3)\n", + "axs[2].set_title('Mass vs. Gravity for logAge=7.13')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0,1)\n", + "axs[2].set_ylim(4,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", + "metadata": {}, + "source": [ + "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650ec2bd-9904-4ae2-9fa5-304a392ecb3e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/spisea/evolution.py b/spisea/evolution.py index 63caccc5..7c529e77 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1495,6 +1495,106 @@ def format_isochrones(self): #==============================# # Merged model classes #==============================# +class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): + """ + All merge -- only young and solar metals right now. + + Parameters + ---------- + rot: boolean, optional + If true, then use rotating Ekstrom models. Default is true. + """ + def __init__(self, rot=True): + # populate list of model masses (in solar masses) + mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun + + # define metallicity parameters for Geneva models + z_list = [0.015] + + # populate list of isochrone ages (log scale) + age_list = np.arange(6.0, 7.4, 0.01).tolist() + + # specify location of model files + model_dir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/' + StellarEvolution.__init__(self, model_dir, age_list, mass_list, z_list) + self.z_solar = 0.015 + + # Switch to specify rotating/non-rotating models + if rot: + self.z_file_map = {0.015: 'z015_rot/'} + else: + self.z_file_map = {0.015: 'z015_norot/'} + + # Define required evo_grid number + self.evo_grid_min = 1.0 + + + def isochrone(self, age=1.e8, metallicity=0.0): + r""" + Extract an individual isochrone from the Baraffe-Pisa-Ekstrom-Parsec + collection + """ + # Error check to see if installed evolution model + # grid is compatible with code version. Also return + # current grid num + self.evo_grid_num = check_evo_grid_number(self.evo_grid_min, models_dir) + + # convert metallicity to mass fraction + z_defined = self.z_solar*10.**metallicity + + log_age = math.log10(age) + + # check age and metallicity are within bounds + 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 ((z_defined < np.min(self.z_list)) or + (z_defined > np.max(self.z_list))): + logger.error('Requested metallicity {0} is out of bounds.'.format(z_defined)) + + # Find nearest age in grid to input grid + age_idx = np.where(abs(np.array(self.age_list) - log_age) == min(abs(np.array(self.age_list) - log_age)) )[0][0] + iso_file = 'iso_{0:.2f}.dat'.format(self.age_list[age_idx]) + + # find closest metallicity value + z_idx = np.where(abs(np.array(self.z_list) - z_defined) == min(abs(np.array(self.z_list) - z_defined)) )[0][0] + z_dir = self.z_file_map[self.z_list[z_idx]] + + # generate isochrone file string + full_iso_file = self.model_dir + z_dir + iso_file + + # return isochrone data + iso = Table.read(full_iso_file, format='ascii') + iso.rename_column('col1', 'mass') + iso.rename_column('col2', 'logT') + iso.rename_column('col3', 'logL') + iso.rename_column('col4', 'logg') + iso.rename_column('col5', 'logT_WR') + iso.rename_column('col6', 'mass_current') + iso.rename_column('col7', 'phase') + iso.rename_column('col8', 'model_ref') + + # Define "isWR" column based on phase info + isWR = Column([False] * len(iso), name='isWR') + idx_WR = np.where(iso['logT'] != iso['logT_WR']) + isWR[idx_WR] = True + iso.add_column(isWR) + + iso.meta['log_age'] = log_age + iso.meta['metallicity_in'] = metallicity + iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) + + # Assume mass of brown dwarfs does not change over their lifetime + #bd_idx = iso['mass'] < 0.08 + #iso['mass_current'][bd_idx] = iso['mass'][bd_idx] + + # Handling NaN effective temperatures + #nan_teff_idx = np.isnan(iso['logT']) + #if np.any(nan_teff_idx): + # iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) + + return iso + class MergedBaraffePisaEkstromParsec(StellarEvolution): """ This is a combination of several different evolution models: diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 3103dcd1..4ae078aa 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -27,6 +27,7 @@ def test_evolution_models(): age_young_arr = [6.7, 7.9] age_all_arr = [6.7, 8.0, 9.7] age_all_MIST_arr = [5.2, 6.7, 9.7, 10.13] + bd_young_test = [6.0, 6.5, 7.4] # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] @@ -36,14 +37,15 @@ def test_evolution_models(): # Array of evolution models to test evo_models = [evolution.MISTv1(version=1.2), evolution.MergedBaraffePisaEkstromParsec(), evolution.Parsec(), evolution.Baraffe15(), evolution.Ekstrom12(), evolution.Pisa(), - evolution.Phillips2020(), evolution.Marley2021()] + evolution.Phillips2020(), evolution.Marley2021(), + evolution.MergedPhillipsBaraffePisaEkstromParsec()] # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_young_test] # Array of metallicities for the specific evolution models to test - metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley] + metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley, metal_solar] assert len(evo_models) == len(age_vals) == len(metal_vals) From fec691ccc57ede37f7087d4881bd35496880e9f8 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Wed, 16 Apr 2025 16:44:25 -0700 Subject: [PATCH 34/48] Fixes to chi-squared and nice results --- changes/evolution_gaussian_fit.ipynb | 457 ++++++++++++++++++++++++++- 1 file changed, 449 insertions(+), 8 deletions(-) diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb index b749ed58..13fe6269 100644 --- a/changes/evolution_gaussian_fit.ipynb +++ b/changes/evolution_gaussian_fit.ipynb @@ -5,7 +5,7 @@ "id": "9aaeb072-9ae6-4362-978f-4d9fd5802cd7", "metadata": {}, "source": [ - "## Generating Intermediary Data for New Non-Overlapping Evolutionary Data" + "## Generating Intermediary Data for Non-Overlapping Evolutionary Models" ] }, { @@ -13,12 +13,12 @@ "id": "5459271a-7e3f-4723-91eb-b8735a2a5ff1", "metadata": {}, "source": [ - "Oh no! Unfortunately our new evolutionary models for brown dwarf masses does not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." + "Oh no! Unfortunately our new evolutionary models for brown dwarf masses do not align with those previously implemented, leaving a mass gap. To address this, we have decided to use Gaussian processes to fit the two data regimes (Phillips and Pisa) and generate the associated data in this range. The parameters in question are mass, luminosity, effective temperature, and log gravity. This notebook shows an example case for solar metallicity and a log age of ~7.13." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "1cad6635-a51f-426b-a207-6e2be5710586", "metadata": {}, "outputs": [], @@ -28,12 +28,13 @@ "from astropy.table import Table\n", "import numpy as np\n", "from sklearn.gaussian_process import GaussianProcessRegressor\n", - "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C, RationalQuadratic as RQ, WhiteKernel, ExpSineSquared as Exp" + "from sklearn.gaussian_process.kernels import ConstantKernel as C, Matern\n", + "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "d22dab8a-4834-48ed-99b0-47295b685e38", "metadata": {}, "outputs": [], @@ -61,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 3, "id": "fd004b17-6528-4633-a5a1-2ab841c41bb9", "metadata": {}, "outputs": [ @@ -132,14 +133,454 @@ "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", "metadata": {}, "source": [ - "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. " + "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, + "id": "8df930e4-19df-4651-960b-91c7aa522482", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0005 6.8546\n" + ] + } + ], + "source": [ + "# creating arrays\n", + "## mass\n", + "phil_mass = phil_tbl['Mass']\n", + "pisa_mass = pisa_tbl['col3']\n", + "combined_masses = np.concatenate([phil_mass, pisa_mass])\n", + "\n", + "## Teff\n", + "phil_Teff = phil_tbl['Teff']\n", + "pisa_Teff = pisa_tbl['col2']\n", + "combined_Teff = np.concatenate([phil_Teff, pisa_Teff])\n", + "\n", + "## Luminosity\n", + "phil_L = phil_tbl['Luminosity']\n", + "pisa_L = pisa_tbl['col1']\n", + "combined_L = np.concatenate([phil_L, pisa_L])\n", + "\n", + "## logg\n", + "phil_logg = phil_tbl['Gravity']\n", + "pisa_logg = pisa_tbl['col4']\n", + "combined_logg = np.concatenate([phil_logg, pisa_logg])\n", + "print(np.min(phil_mass), np.max(pisa_mass))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "id": "650ec2bd-9904-4ae2-9fa5-304a392ecb3e", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating input array (mass) and output array (luminosity)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# Scale X and y\n", + "#X_scaler = StandardScaler()\n", + "#y_scaler = StandardScaler()\n", + "\n", + "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", + "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "\n", + "# Define grid\n", + "length_scales = np.logspace(-2, 1, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", + "\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "# Grid search\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + " \n", + " try:\n", + " gp.fit(X, y)\n", + " y_pred = gp.predict(X)\n", + " #for k, val in enumerate(y_pred):\n", + " # if (X[k] > 0.39) and (X[k] < 0.42):\n", + " # print(X[k], y[k], y_pred[k], (y - y_pred)[k])\n", + " avg_diff = (np.sum(y - y_pred)) / len(y_pred)\n", + " chi2 = np.sum(((y - y_pred)**2) / avg_diff)\n", + " chi2_map[i, j] = chi2\n", + " except Exception as e:\n", + " chi2_map[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", + " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5 0.5455594781168517\n" + ] + } + ], + "source": [ + "i_best, j_best = np.unravel_index(np.nanargmin(chi2_map), chi2_map.shape)\n", + "best_nu = nus[i_best]\n", + "best_length_scale = length_scales[j_best]\n", + "print(best_nu, best_length_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (everything else)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y)\n", + "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_L, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Luminosity')\n", + "plt.title('Best Fit by Heat Map')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(-4,0)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "99653c01-83e7-44c1-8a70-a04c185b0c3e", + "metadata": {}, + "source": [ + "This produced a pretty good fit! We can them repeat the process to find the best parameters for effective temperature and log gravity." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2b89c29a-9c7e-4f31-94a6-094df81e01c1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating input array (mass) and output array (luminosity)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y_Teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# Scale X and y\n", + "#X_scaler = StandardScaler()\n", + "#y_scaler = StandardScaler()\n", + "\n", + "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", + "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "\n", + "# Define grid\n", + "length_scales = np.logspace(-2, 1, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", + "\n", + "chi2_map_Teff = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "# Grid search\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + " \n", + " try:\n", + " gp.fit(X, y_Teff)\n", + " y_pred_Teff = gp.predict(X)\n", + " avg_diff_Teff = (np.sum(y_Teff - y_pred_Teff)) / len(y_pred_Teff)\n", + " chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / avg_diff_Teff)\n", + " #chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / y_pred_Teff)\n", + " chi2_map_Teff[i, j] = chi2_Teff\n", + " except Exception as e:\n", + " chi2_map_Teff[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map_Teff, aspect='auto', origin='lower',\n", + " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.title('Chi-Squared Heatmap for Teff Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b29c1755-4b8e-404b-a8f5-024a759179af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5 0.5455594781168517\n" + ] + } + ], + "source": [ + "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_Teff), chi2_map_Teff.shape)\n", + "best_nu_Teff = nus[i_best_Teff]\n", + "best_length_scale_Teff = length_scales[j_best_Teff]\n", + "print(best_nu_Teff, best_length_scale_Teff)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "926cbb27-cf76-4259-bff4-63cdad0fce02", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (everything else)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y_Teff)\n", + "y_pred_Teff, sigma_Teff = gp.predict(x, return_std=True)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_Teff, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Teff')\n", + "plt.title('Best Fit by Heat Map')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(3.25,3.75)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "fb250ffb-e8f5-4b66-a9d0-ae79190d1d0d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating input array (mass) and output array (logg)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y_logg = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# Scale X and y\n", + "#X_scaler = StandardScaler()\n", + "#y_scaler = StandardScaler()\n", + "\n", + "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", + "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "\n", + "# Define grid\n", + "length_scales = np.logspace(-2, 1, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", + "\n", + "chi2_map_logg = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "# Grid search\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + " \n", + " try:\n", + " gp.fit(X, y_logg)\n", + " y_pred_logg = gp.predict(X)\n", + " avg_diff_logg = (np.sum(y_logg - y_pred_logg)) / len(y_pred_logg)\n", + " chi2_logg = np.sum(((y_logg - y_pred_logg)**2) / avg_diff_logg)\n", + " chi2_map_logg[i, j] = chi2_logg\n", + " except Exception as e:\n", + " chi2_map_logg[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map_logg, aspect='auto', origin='lower',\n", + " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.title('Chi-Squared Heatmap for logg Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "850e9dc3-5120-46ff-a15b-ccecc379e33d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 4.832930238571752\n" + ] + } + ], + "source": [ + "i_best_logg, j_best_logg = np.unravel_index(np.nanargmin(chi2_map_logg), chi2_map_logg.shape)\n", + "best_nu_logg = nus[i_best_logg]\n", + "best_length_scale_logg = length_scales[j_best_logg]\n", + "print(best_nu_logg, best_length_scale_logg)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ce7d8969-3bb4-4154-8d1a-baaa1975baa6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (everything else)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y_logg)\n", + "y_pred_logg, sigma_logg = gp.predict(x, return_std=True)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_logg, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_logg, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Log Gravity')\n", + "plt.title('Best Fit by Heat Map')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(4.0,4.5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c130f0f1-9330-421a-9e8c-eedd4974e659", + "metadata": {}, "outputs": [], "source": [] } From 52acd64be3b4baaad72e0a070f21b5872b15774e Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Tue, 29 Apr 2025 15:06:48 -0700 Subject: [PATCH 35/48] Improvements to evolution merge region --- changes/evolution_gaussian_fit.ipynb | 768 +++++++++++++++++++++++---- 1 file changed, 677 insertions(+), 91 deletions(-) diff --git a/changes/evolution_gaussian_fit.ipynb b/changes/evolution_gaussian_fit.ipynb index 13fe6269..957d09b3 100644 --- a/changes/evolution_gaussian_fit.ipynb +++ b/changes/evolution_gaussian_fit.ipynb @@ -29,7 +29,8 @@ "import numpy as np\n", "from sklearn.gaussian_process import GaussianProcessRegressor\n", "from sklearn.gaussian_process.kernels import ConstantKernel as C, Matern\n", - "from sklearn.preprocessing import StandardScaler" + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import KFold" ] }, { @@ -68,9 +69,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -79,7 +80,7 @@ ], "source": [ "# creating graphs of mass vs. luminosity, Teff, and gravity\n", - "fig, axs = plt.subplots(3, 1, figsize=(10, 10))\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", "\n", "# create array for mass gap\n", "mass_gap = np.linspace(np.max(phil_mass), np.min(pisa_mass), 1000)\n", @@ -87,7 +88,7 @@ "# mass vs. luminosity\n", "axs[0].plot(phil_mass, phil_L, color='c', label='Phillips')\n", "axs[0].plot(pisa_mass, pisa_L, color='pink', label='Pisa')\n", - "axs[0].set_title('Mass vs. Luminosity for logAge=7.13')\n", + "axs[0].set_title('Mass vs. Luminosity') # for logAge=7.13')\n", "y_min0 = axs[0].get_ylim()[0]\n", "y_max0 = axs[0].get_ylim()[1]\n", "axs[0].fill_between(mass_gap, y_min0, y_max0, color='gray', label='mass gap', alpha=0.3)\n", @@ -103,7 +104,7 @@ "y_min1 = axs[1].get_ylim()[0]\n", "y_max1 = axs[1].get_ylim()[1]\n", "axs[1].fill_between(mass_gap, y_min1, y_max1, color='gray', label='mass gap', alpha=0.3)\n", - "axs[1].set_title('Mass vs. Effective Temperature for logAge=7.13')\n", + "axs[1].set_title('Mass vs. Effective Temperature') # for logAge=7.13')\n", "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", "axs[1].set_ylabel('log(Teff) (log(K))')\n", "axs[1].set_xlim(0,1)\n", @@ -116,7 +117,7 @@ "y_min2 = axs[2].get_ylim()[0]\n", "y_max2 = axs[2].get_ylim()[1]\n", "axs[2].fill_between(mass_gap, y_min2, y_max2, color='gray', label='mass gap', alpha=0.3)\n", - "axs[2].set_title('Mass vs. Gravity for logAge=7.13')\n", + "axs[2].set_title('Mass vs. Gravity') # for logAge=7.13'\n", "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", "axs[2].set_ylabel('logg')\n", "axs[2].set_xlim(0,1)\n", @@ -133,7 +134,7 @@ "id": "35e9b3a9-8efe-4668-a359-c8332e34fbba", "metadata": {}, "source": [ - "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu." + "From these zoomed in graphs it becomes clear that though a mass gap is present, luminosty and effective temperature still seem to line up if a model connects them. In contrast, the graph for log gravity is very clearly disconnected, which is worth refining as new models continue to be generated. To best address this problem, we decided to use GaussianProcessRegressor with the Matern kernel, as through trial and error this appeared to be the best fit. We then decided to generate a chi-squared heat map for luminosity to find the ideal parameters for length_scale and nu. This was done with and without the use of the kFold class from sklearn, with a determination that using it produces a much better fit." ] }, { @@ -146,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.0005 6.8546\n" + "0.075 0.2019\n" ] } ], @@ -171,18 +172,18 @@ "phil_logg = phil_tbl['Gravity']\n", "pisa_logg = pisa_tbl['col4']\n", "combined_logg = np.concatenate([phil_logg, pisa_logg])\n", - "print(np.min(phil_mass), np.max(pisa_mass))" + "print(np.max(phil_mass), np.min(pisa_mass))" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "650ec2bd-9904-4ae2-9fa5-304a392ecb3e", + "execution_count": 25, + "id": "38a5d619-8cb2-40d6-962b-6f1491366962", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -192,52 +193,187 @@ } ], "source": [ + "### USING SKLEARN\n", + "\n", "# creating input array (mass) and output array (luminosity)\n", "X = combined_masses.reshape(-1, 1)\n", "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", - "# Scale X and y\n", - "#X_scaler = StandardScaler()\n", - "#y_scaler = StandardScaler()\n", + "# Define grid\n", + "length_scales = np.logspace(-1, 2, 20)\n", + "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", - "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", - "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "N_data = len(y)\n", + "N_params = 2\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "\n", + "for i, nu in enumerate(nus):\n", + " for j, ls in enumerate(length_scales):\n", + " kernel = Matern(length_scale=ls, nu=nu)\n", + " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "\n", + " residuals = []\n", + " try:\n", + " for train_idx, test_idx in kf.split(X):\n", + " gp.fit(X[train_idx], y[train_idx])\n", + " y_pred = gp.predict(X[test_idx])\n", + " residuals.append((y[test_idx] - y_pred)**2)\n", + "\n", + " residuals = np.concatenate(residuals)\n", + " chi2 = np.sum(residuals) # Assume unity residuals\n", + " chi2_red = chi2 / (len(y) - N_params) # Reduced chi^2\n", + " chi2_map[i, j] = np.log10(chi2_red)\n", + " except Exception as e:\n", + " chi2_map[i, j] = np.nan\n", + "\n", + "# Plotting\n", + "plt.figure(figsize=(10, 5))\n", + "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", + " cmap='viridis')\n", + "plt.colorbar(im, label=r'$\\chi^2$')\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", + "plt.xlabel('Length Scale')\n", + "plt.ylabel(r'$\\nu$')\n", + "plt.xscale('log')\n", + "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 1.2742749857031335\n" + ] + } + ], + "source": [ + "i_best, j_best = np.unravel_index(np.nanargmin(chi2_map), chi2_map.shape)\n", + "best_nu = nus[i_best]\n", + "best_length_scale = length_scales[j_best]\n", + "print(best_nu, best_length_scale)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Looking at physical interpretation of heatmap parameters\n", + "\n", + "# creating input array (mass) and output array (luminosity)\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", + "\n", + "# trying different kernel types\n", + "kernel = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", + "gp.fit(X, y)\n", + "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", + "#chi2 = np.sum((y - y_pred_1)**2) #unit test for 2d function\n", + "#print(chi2)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_L, color='blue', label='actual values')\n", + "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Luminosity')\n", + "plt.title(' Reduced $\\chi^2$ Determination of Luminosity Best Fit Using kFold')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(-4,0)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80feb1ea-eee1-489c-9fa1-05111ba6b86d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### WITHOUT KFOLD\n", + "X = combined_masses.reshape(-1, 1)\n", + "y = combined_L #np.array([combined_Teff, combined_L, combined_logg])\n", + "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", "# Define grid\n", "length_scales = np.logspace(-2, 1, 20)\n", "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", "chi2_map = np.zeros((len(nus), len(length_scales)))\n", + "N_data = len(y)\n", + "N_params = 2\n", + "\n", + "chi2_map = np.zeros((len(nus), len(length_scales)))\n", "\n", - "# Grid search\n", "for i, nu in enumerate(nus):\n", " for j, ls in enumerate(length_scales):\n", " kernel = Matern(length_scale=ls, nu=nu)\n", " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - " \n", + "\n", " try:\n", " gp.fit(X, y)\n", " y_pred = gp.predict(X)\n", - " #for k, val in enumerate(y_pred):\n", - " # if (X[k] > 0.39) and (X[k] < 0.42):\n", - " # print(X[k], y[k], y_pred[k], (y - y_pred)[k])\n", - " avg_diff = (np.sum(y - y_pred)) / len(y_pred)\n", - " chi2 = np.sum(((y - y_pred)**2) / avg_diff)\n", - " chi2_map[i, j] = chi2\n", + " residuals = (y - y_pred)**2\n", + "\n", + " chi2 = np.sum(residuals) # assume unity residuals\n", + " chi2_red = chi2 / (N_data - N_params) # reduced chi^2\n", + " chi2_map[i, j] = np.log10(chi2_red)\n", + "\n", " except Exception as e:\n", + " print(f\"Error at nu={nu}, length_scale={ls}: {e}\")\n", " chi2_map[i, j] = np.nan\n", "\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", "im = plt.imshow(chi2_map, aspect='auto', origin='lower',\n", - " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", " cmap='viridis')\n", "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", "plt.xlabel('Length Scale')\n", "plt.ylabel(r'$\\nu$')\n", + "plt.xscale('log')\n", "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", "plt.tight_layout()\n", "plt.show()\n" @@ -245,15 +381,15 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "7a4edb19-1411-44f6-a50d-d1f1fc5b48e6", + "execution_count": 9, + "id": "aab48c6d-6d83-4ed6-9b70-6cf8d2842cd6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.5 0.5455594781168517\n" + "0.5 0.01\n" ] } ], @@ -266,13 +402,13 @@ }, { "cell_type": "code", - "execution_count": 32, - "id": "79f3a7d1-ecd9-4d77-8923-6f729f9f66e3", + "execution_count": 10, + "id": "3fec5172-ea8b-4f28-810f-c5577edfceb3", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -294,13 +430,15 @@ "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", "gp.fit(X, y)\n", "y_pred_1, sigma_1 = gp.predict(x, return_std=True)\n", + "#chi2 = np.sum((y - y_pred_1)**2) #unit test for 2d function\n", + "#print(chi2)\n", "\n", "plt.figure()\n", "plt.scatter(X, combined_L, color='blue', label='actual values')\n", "plt.plot(x, y_pred_1, color='orange', label='predicted values')\n", "plt.xlabel('Mass ($M_{\\odot}$)')\n", "plt.ylabel('Luminosity')\n", - "plt.title('Best Fit by Heat Map')\n", + "plt.title('Reduced $\\chi^2$ Determination of Luminosity Best Fit Without kFold')\n", "plt.xlim(0, 1)\n", "plt.ylim(-4,0)\n", "plt.legend()\n", @@ -312,18 +450,29 @@ "id": "99653c01-83e7-44c1-8a70-a04c185b0c3e", "metadata": {}, "source": [ - "This produced a pretty good fit! We can them repeat the process to find the best parameters for effective temperature and log gravity." + "Through the use of kFold we produced a pretty good fit! We can then repeat the process to find the best parameters for effective temperature and log gravity." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 11, + "id": "53552603-6dce-4c96-9d22-0facf0c28767", + "metadata": {}, + "outputs": [], + "source": [ + "#assume unity uncertainties and focus on gap between model and data -- gives small value but scale by relative \n", + "#number of data points -- should give value close to 1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "2b89c29a-9c7e-4f31-94a6-094df81e01c1", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -333,9 +482,9 @@ } ], "source": [ - "# creating input array (mass) and output array (luminosity)\n", + "# creating input array (mass) and output array (teff)\n", "X = combined_masses.reshape(-1, 1)\n", - "y_Teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "y_teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", "# Scale X and y\n", @@ -346,45 +495,53 @@ "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", "\n", "# Define grid\n", - "length_scales = np.logspace(-2, 1, 20)\n", + "length_scales = np.logspace(-1, 2, 20)\n", "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", - "chi2_map_Teff = np.zeros((len(nus), len(length_scales)))\n", + "chi2_map_teff = np.zeros((len(nus), len(length_scales)))\n", + "N_data_teff = len(y_teff)\n", + "N_params = 2\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", + "chi2_map_teff = np.zeros((len(nus), len(length_scales)))\n", "\n", - "# Grid search\n", "for i, nu in enumerate(nus):\n", " for j, ls in enumerate(length_scales):\n", " kernel = Matern(length_scale=ls, nu=nu)\n", " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", " \n", + " residuals_teff = []\n", " try:\n", - " gp.fit(X, y_Teff)\n", - " y_pred_Teff = gp.predict(X)\n", - " avg_diff_Teff = (np.sum(y_Teff - y_pred_Teff)) / len(y_pred_Teff)\n", - " chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / avg_diff_Teff)\n", - " #chi2_Teff = np.sum(((y_Teff - y_pred_Teff)**2) / y_pred_Teff)\n", - " chi2_map_Teff[i, j] = chi2_Teff\n", - " except Exception as e:\n", - " chi2_map_Teff[i, j] = np.nan\n", + " for train_idx, test_idx in kf.split(X):\n", + " gp.fit(X[train_idx], y_teff[train_idx])\n", + " y_pred_teff = gp.predict(X[test_idx])\n", + " residuals_teff.append((y_teff[test_idx] - y_pred_teff)**2)\n", "\n", + " residuals_teff = np.concatenate(residuals_teff) \n", + " chi2_teff = np.sum(residuals_teff)\n", + " chi2_red_teff = chi2_teff / (N_data_teff - N_params)\n", + " chi2_map_teff[i, j] = np.log10(chi2_red_teff)\n", + " except Exception as e:\n", + " chi2_map_teff[i, j] = np.nan\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", - "im = plt.imshow(chi2_map_Teff, aspect='auto', origin='lower',\n", - " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + "im = plt.imshow(chi2_map_teff, aspect='auto', origin='lower',\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", " cmap='viridis')\n", "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", "plt.xlabel('Length Scale')\n", "plt.ylabel(r'$\\nu$')\n", - "plt.title('Chi-Squared Heatmap for Teff Fit Parameters')\n", + "plt.xscale('log')\n", + "plt.title('Chi-Squared Heatmap for Luminosity Fit Parameters')\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 13, "id": "b29c1755-4b8e-404b-a8f5-024a759179af", "metadata": {}, "outputs": [ @@ -392,12 +549,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.5 0.5455594781168517\n" + "1.5 1.2742749857031335\n" ] } ], "source": [ - "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_Teff), chi2_map_Teff.shape)\n", + "i_best_Teff, j_best_Teff = np.unravel_index(np.nanargmin(chi2_map_teff), chi2_map_teff.shape)\n", "best_nu_Teff = nus[i_best_Teff]\n", "best_length_scale_Teff = length_scales[j_best_Teff]\n", "print(best_nu_Teff, best_length_scale_Teff)" @@ -405,13 +562,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "id": "926cbb27-cf76-4259-bff4-63cdad0fce02", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -425,21 +582,21 @@ "\n", "# creating input array (mass) and output array (everything else)\n", "X = combined_masses.reshape(-1, 1)\n", - "y = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", + "y_teff = combined_Teff #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", "# trying different kernel types\n", "kernel = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", "gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", - "gp.fit(X, y_Teff)\n", - "y_pred_Teff, sigma_Teff = gp.predict(x, return_std=True)\n", + "gp.fit(X, y_teff)\n", + "y_pred_teff, sigma_teff = gp.predict(x, return_std=True)\n", "\n", "plt.figure()\n", "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", - "plt.plot(x, y_pred_Teff, color='orange', label='predicted values')\n", + "plt.plot(x, y_pred_teff, color='orange', label='predicted values')\n", "plt.xlabel('Mass ($M_{\\odot}$)')\n", "plt.ylabel('Teff')\n", - "plt.title('Best Fit by Heat Map')\n", + "plt.title('Reduced $\\chi^2$ Determination of Effective Temperature Best Fit')\n", "plt.xlim(0, 1)\n", "plt.ylim(3.25,3.75)\n", "plt.legend()\n", @@ -448,13 +605,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 15, "id": "fb250ffb-e8f5-4b66-a9d0-ae79190d1d0d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -464,57 +621,59 @@ } ], "source": [ - "# creating input array (mass) and output array (logg)\n", + "# creating input array (mass) and output array (luminosity)\n", "X = combined_masses.reshape(-1, 1)\n", "y_logg = combined_logg #np.array([combined_Teff, combined_L, combined_logg])\n", "x = np.linspace(np.min(phil_mass), np.max(pisa_mass), int(1e6)).reshape(-1, 1)\n", "\n", - "# Scale X and y\n", - "#X_scaler = StandardScaler()\n", - "#y_scaler = StandardScaler()\n", - "\n", - "#X = X_scaler.fit_transform(combined_masses.reshape(-1, 1))\n", - "#y = y_scaler.fit_transform(combined_L.reshape(-1, 1)).ravel()\n", - "\n", "# Define grid\n", - "length_scales = np.logspace(-2, 1, 20)\n", + "length_scales = np.logspace(-1, 2, 20)\n", "nus = [0.5, 1.5, 2.5] # available Matern values\n", "\n", "chi2_map_logg = np.zeros((len(nus), len(length_scales)))\n", + "N_data_logg = len(y_logg)\n", + "N_params = 2\n", + "\n", + "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n", "\n", - "# Grid search\n", "for i, nu in enumerate(nus):\n", " for j, ls in enumerate(length_scales):\n", " kernel = Matern(length_scale=ls, nu=nu)\n", " gp = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=True)\n", " \n", + " residuals_logg = []\n", " try:\n", - " gp.fit(X, y_logg)\n", - " y_pred_logg = gp.predict(X)\n", - " avg_diff_logg = (np.sum(y_logg - y_pred_logg)) / len(y_pred_logg)\n", - " chi2_logg = np.sum(((y_logg - y_pred_logg)**2) / avg_diff_logg)\n", - " chi2_map_logg[i, j] = chi2_logg\n", + " for train_idx, test_idx in kf.split(X):\n", + " gp.fit(X[train_idx], y_logg[train_idx])\n", + " y_pred_logg = gp.predict(X[test_idx])\n", + " residuals_logg.append((y_logg[test_idx] - y_pred_logg)**2)\n", + "\n", + " residuals_logg = np.concatenate(residuals_logg) \n", + " chi2_logg = np.sum(residuals_logg)\n", + " chi2_red_logg = chi2_logg / (N_data_logg - N_params)\n", + " chi2_map_logg[i, j] = np.log10(chi2_red_logg)\n", " except Exception as e:\n", " chi2_map_logg[i, j] = np.nan\n", "\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", "im = plt.imshow(chi2_map_logg, aspect='auto', origin='lower',\n", - " extent=[np.log10(length_scales[0]), np.log10(length_scales[-1]), 0, len(nus)-1],\n", + " extent=[length_scales[0], length_scales[-1], 0, len(nus)-1],\n", " cmap='viridis')\n", "plt.colorbar(im, label=r'$\\chi^2$')\n", - "plt.xticks(np.log10(length_scales), labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", + "plt.xticks(length_scales, labels=[f'{s:.3f}' for s in length_scales], rotation=45)\n", "plt.yticks(range(len(nus)), labels=[str(n) for n in nus])\n", "plt.xlabel('Length Scale')\n", "plt.ylabel(r'$\\nu$')\n", - "plt.title('Chi-Squared Heatmap for logg Fit Parameters')\n", + "plt.xscale('log')\n", + "plt.title('Chi-Squared Heatmap for log g Fit Parameters')\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 16, "id": "850e9dc3-5120-46ff-a15b-ccecc379e33d", "metadata": {}, "outputs": [ @@ -522,7 +681,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.5 4.832930238571752\n" + "2.5 0.20691380811147897\n" ] } ], @@ -535,13 +694,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 17, "id": "ce7d8969-3bb4-4154-8d1a-baaa1975baa6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -569,17 +728,444 @@ "plt.plot(x, y_pred_logg, color='orange', label='predicted values')\n", "plt.xlabel('Mass ($M_{\\odot}$)')\n", "plt.ylabel('Log Gravity')\n", - "plt.title('Best Fit by Heat Map')\n", + "plt.title('Reduced $\\chi^2$ Determination of Gravity Best Fit')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(4.0,4.5)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "147e614a-4e8f-485e-abc3-195630b144ae", + "metadata": {}, + "source": [ + "### Generating Data in the Interpolated Region" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d78bbc34-8c01-46a2-83da-43ffde446c5d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# starting with luminosity\n", + "\n", + "best_kernel_L = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp_best_L = GaussianProcessRegressor(kernel=best_kernel_L, optimizer=None, normalize_y=True)\n", + "gp_best_L.fit(X, y)\n", + "\n", + "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", + "L_pred = gp_best_L.predict(mass_vals)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_L, color='blue', label='actual values')\n", + "plt.scatter(mass_vals, L_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "plt.title('Creating Points for Luminosity Mass Gap')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Luminosity')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(-4,0)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5aee3424-7efa-4bed-8030-4e70eb1f47c2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moving to effective temperature\n", + "\n", + "best_kernel_teff = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp_best_teff = GaussianProcessRegressor(kernel=best_kernel_teff, optimizer=None, normalize_y=True)\n", + "gp_best_teff.fit(X, y_teff)\n", + "\n", + "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", + "teff_pred = gp_best_teff.predict(mass_vals)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_Teff, color='blue', label='actual values')\n", + "plt.scatter(mass_vals, teff_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "plt.title('Creating Points for Teff Mass Gap')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('Teff')\n", + "plt.xlim(0, 1)\n", + "plt.ylim(3.25,3.75)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "53238900-30e4-4015-b033-d81dd763d773", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# moving to effective temperature\n", + "\n", + "best_kernel_logg = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp_best_logg = GaussianProcessRegressor(kernel=best_kernel_logg, optimizer=None, normalize_y=True)\n", + "gp_best_logg.fit(X, y_logg)\n", + "\n", + "mass_vals = np.linspace(np.max(phil_mass), np.min(pisa_mass), 20).reshape(-1, 1)\n", + "logg_pred = gp_best_logg.predict(mass_vals)\n", + "\n", + "plt.figure()\n", + "plt.scatter(X, combined_logg, color='blue', label='actual values')\n", + "plt.scatter(mass_vals, logg_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "plt.title('Creating Points for logg Mass Gap')\n", + "plt.xlabel('Mass ($M_{\\odot}$)')\n", + "plt.ylabel('logg')\n", "plt.xlim(0, 1)\n", "plt.ylim(4.0,4.5)\n", "plt.legend()\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3da35d6f-d88c-4582-be4f-7b65f069e784", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", + "\n", + "# plotting generated data for luminosity\n", + "axs[0].scatter(combined_masses, combined_L, color='blue', label='actual values')\n", + "axs[0].scatter(mass_vals, L_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity')\n", + "axs[0].set_xlim(0, 1)\n", + "#axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# mass vs. Teff\n", + "axs[1].scatter(combined_masses, combined_Teff, color='blue', label='actual values')\n", + "axs[1].scatter(mass_vals, teff_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[1].set_title('Creating Points for Teff Mass Gap')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('Teff')\n", + "axs[1].set_xlim(0, 1)\n", + "#axs[1].set_ylim(3.25,3.75)\n", + "axs[1].legend()\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].scatter(combined_masses, combined_logg, color='blue', label='actual values')\n", + "axs[2].scatter(mass_vals, logg_pred, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[2].set_title('Creating Points for logg Mass Gap')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0, 1)\n", + "#axs[2].set_ylim(4.0,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d3d88985-6d1f-4aa2-bd88-5f69b73897ea", + "metadata": {}, + "source": [ + "### Testing Fit Parameters for Another Cluster Age" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c8631679-e803-4d74-a549-68f41ba1196c", + "metadata": {}, + "outputs": [], + "source": [ + "# importing compatible age fits files \n", + "phillips2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_8.051282050278353.fits'\n", + "pisa2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_8.00.fits'\n", + "\n", + "# loading tables and extracting data\n", + "phil2_tbl = Table.read(phillips2, format='fits')\n", + "pisa2_tbl = Table.read(pisa2, format='fits')\n", + "\n", + "phil2_mass = phil2_tbl['Mass']\n", + "pisa2_mass = pisa2_tbl['col3']\n", + "combined_masses2 = np.concatenate([phil2_mass, pisa2_mass])\n", + "\n", + "phil2_teff = phil2_tbl['Teff']\n", + "pisa2_teff = pisa2_tbl['col2']\n", + "combined_teff2 = np.concatenate([phil2_teff, pisa2_teff])\n", + "\n", + "phil2_L = phil2_tbl['Luminosity']\n", + "pisa2_L = pisa2_tbl['col1']\n", + "combined_L2 = np.concatenate([phil2_L, pisa2_L])\n", + "\n", + "phil2_logg = phil2_tbl['Gravity']\n", + "pisa2_logg = pisa2_tbl['col4']\n", + "combined_logg2 = np.concatenate([phil2_logg, pisa2_logg])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "a95f9710-bc1b-40ae-9b4b-73dec1e9e6a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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h4eGBkJAQhISEYPjw4VrboqOj0bJlS9y/fx/Lli3DiRMnEBISogk6c/9+5fd3Ifuc6vJ7TGWDc5xIb2QyGdq2bYt9+/bh3r17Ot/dld//QG1tbeHl5YUvvvgi330cHR0BAMHBwZDL5fj999+hUCg020t6fZqC2NjYICsrC48fP9b6Ii3ql5aTkxN8fHwKrANQz/XJ7cGDB1rzgfTF09MTwcHBEELg8uXLCAoKwmeffQZjY2NMmTKlSGXlPJ7cn5H8jqek1/tRKBRITEzMk/4qrc/zomsulUphZWUFQP27snTpUixduhTR0dHYtWsXpkyZgri4uBfewVqQ/v3749NPP8WKFSvwxhtvIDY2FqNHjy5yOS1atICHhwc+++wztG/fvsC5j4cPH8aDBw9w9OhRTS8TAM28pZxcXV2xbt06AMCNGzewdetWBAYGIiMjA6tWrQIAtGzZEi1btoRSqcT58+fxzTffYPz48ahcuTL69u2bbxtat24NAwMD7Nq1S6tnzdjYWPO7+fvvv2vts3PnTiQnJ2P79u1wdXXVpF+8eDHfOh4+fJgnLftvRX7/uaFXA3ucSK+mTp0KIQSGDx+OjIyMPNszMzOxe/fuF5bTtWtXXL16FdWrV4ePj0+eV3bgJJFIYGBgoDXskZqaip9++ilPmbr2sBRF9h/5LVu2aKUHBweXWB3NmjWDsbFxntu37927h8OHD6Nt27YlUk/uXo/8SCQSNGjQAF9//TUqVaqEsLCwIteTPeyW+3hCQkJw7dq1Ejuegri5ueHGjRtIT0/XpCUkJODUqVN6rbcoPDw8ULVqVWzatAlCCE16cnIytm3bhmbNmuU7ydnFxQVjxoxB+/btX3htCvt9UCgUmqGuJUuWoGHDhmjRokWxjmXGjBnw9/fHpEmTCsyTHRznvmHj+++/L7TsWrVqYcaMGfD09Mz3eGUyGZo2barpASrsnDg4OGDo0KHYs2ePzr+/+bVbCFHgEiLPnj3Lc/PApk2bIJVK0apVK53qpNLHHifSq2bNmuG7777DqFGj4O3tjZEjR6JevXrIzMzEhQsXsHr1atSvXx/+/v6FlvPZZ5/h4MGDaN68OcaOHQsPDw+kpaUhKioKe/fuxapVq+Dk5AQ/Pz8sWbIE/fv3xwcffICEhAQsWrQo3zvmsntNtmzZgmrVqkGhUMDT0/OljrdTp05o0aIFJk2ahKSkJHh7e+P06dP48ccfAaBE7gCrVKkSZs6ciWnTpmHgwIHo168fEhISMGfOHCgUCsyePful6wCA+vXrAwBWr14Nc3NzKBQKuLu74/Tp01i5ciV69OiBatWqQQiB7du34+nTp1pr9OjKw8MDH3zwAb755htIpVJ07txZc1eds7MzJkyY8NLHsnv3bpibm+dJ79WrFwYMGIDvv/8e7733HoYPH46EhAQsWLAAFhYWL11vSZFKpViwYAECAgLQtWtXfPjhh0hPT8fChQvx9OlTfPnllwCAxMREtGnTBv3790ft2rVhbm6OkJAQ7N+/H++8806hdXh6euLo0aPYvXs3HBwcYG5uDg8PD832UaNGYcGCBQgNDcXatWuLfSzvvfce3nvvvULzNG/eHFZWVhgxYgRmz54NuVyOn3/+GZcuXdLKd/nyZYwZMwa9e/dGzZo1YWhoiMOHD+Py5cuans9Vq1bh8OHD8PPzg4uLC9LS0jR3t7Vr167QdixduhSRkZEICAjArl270L17dzg6OiIlJQX//PMPgoODoVAoIJfLAajXpjM0NES/fv3wySefIC0tDd99951mGDU3GxsbjBw5EtHR0ahVqxb27t2LNWvWYOTIkVpzEOkVU6ZT06nCuHjxohg0aJBwcXERhoaGwtTUVDRq1EjMmjVLa4Xu7DWG8vPo0SMxduxY4e7uLuRyubC2thbe3t5i+vTpWmu7rF+/Xnh4eAgjIyNRrVo1MX/+fLFu3ToBQERGRmryRUVFiQ4dOghzc3Od13HKfXdW9npHOct9/PixGDJkiKhUqZIwMTER7du3F2fOnBEAxLJlywo9TwWt45SftWvXCi8vL2FoaCgsLS1F9+7d89xdWNg6Trnld3fZ0qVLhbu7u5DJZJpz8s8//4h+/fqJ6tWrC2NjY2FpaSmaNGkigoKCXtjmgs5j9jpOtWrVEnK5XNja2or33nuvwHWcdJVdX0GvbD/88IOoU6eOUCgUom7dumLLli0F3lWX+9pk33X2yy+/aKVnfzZyro5e2DpOuSGfuzB37typWfPH1NRUtG3bVpw8eVKzPS0tTYwYMUJ4eXkJCwsLYWxsLDw8PMTs2bO11izK7666ixcvihYtWggTExPNOk65tW7dWlhbW4uUlJQ82/Kj6+c5v7vqTp06JZo1ayZMTEyEnZ2deP/990VYWJjW7+bDhw/F4MGDRe3atYWpqakwMzMTXl5e4uuvv9bcpXj69Gnx9ttvC1dXV2FkZCRsbGyEr6+v2LVrl07HoFQqxY8//ijat28vbG1thYGBgeYzP3PmTHHv3j2t/Lt37xYNGjQQCoVCVK1aVUyePFns27cvzx2q2Z/lo0ePCh8fH2FkZCQcHBzEtGnTCr1Tj8qeRIgc/b5EpBfZa/CcPHkSzZs3L+vmEBVZXFwcXF1d8dFHH2HBggVl3ZzXXuvWrREfH69ZTZ9eHxyqIyphmzdvxv379+Hp6QmpVIozZ85g4cKFaNWqFYMmeu3cu3cPt2/fxsKFCyGVSjFu3LiybhJRmWLgRFTCzM3NERwcjLlz5yI5ORkODg4YPHgw5s6dW9ZNIyqytWvX4rPPPoObmxt+/vlnVK1ataybRFSmOFRHREREpCMuR0BERESkIwZORERERDriHKd8qFQqPHjwAObm5iW+SjERERHpnxACz549g6OjY4msoZeNgVM+Hjx4UOCjAIiIiOj1cffuXZ0f+aULBk75yF5h+O7du6/U6sFERESkm6SkJDg7O+f71ICXwcApH9nDcxYWFgyciIiIXmMlPeWGk8OJiIiIdMTAiYiIiEhHDJyIiIiIdMQ5TkRUoSmVSmRmZpZ1M4ioiORyOWQyWanXy8CJiCokIQRiY2Px9OnTsm4KERVTpUqVUKVKlVJdc5GBExFVSNlBk729PUxMTLjYLdFrRAiBlJQUxMXFAQAcHBxKrW4GTkRU4SiVSk3QZGNjU9bNIaJiMDY2BgDExcXB3t6+1IbtODmciCqc7DlNJiYmZdwSInoZ2b/DpTlPkYETEVVYHJ4jer2Vxe8wAyciIiIiHTFwIiIiItIRAyciIioRgwcPRo8ePfRaR2BgIBo2bKjXOogKw8CJiOglKJXA0aPA5s3qf5XKsm5R4Rh4EL0cLkdARFRM27cD48YB9+79l+bkBCxbBrzzTtm1i4j0hz1ORETFsH070KuXdtAEAPfvq9O3b9dPvfv378ebb76JSpUqwcbGBl27dkVERIRWnnv37qFv376wtraGqakpfHx8cPbsWQQFBWHOnDm4dOkSJBIJJBIJgoKCEBUVBYlEgosXL2rKePr0KSQSCY4ePQpAvfbVsGHD4O7uDmNjY3h4eGDZsmU6tzsxMRHGxsbYv3+/Vvr27dthamqK58+fAwA+/fRT1KpVCyYmJqhWrRpmzpxZ6K3mrVu3xvjx47XSevTogcGDB2veZ2Rk4JNPPkHVqlVhamqKpk2bao4LAO7cuQN/f39YWVnB1NQU9erVw969e3U+NqpY2ONERFRESqW6p0mIvNuEACQSYPx4oHt3oKTX5EtOTsbEiRPh6emJ5ORkzJo1C2+//TYuXrwIqVSK58+fw9fXF1WrVsWuXbtQpUoVhIWFQaVSoU+fPrh69Sr279+PP//8EwBgaWmJhw8fvrBelUoFJycnbN26Fba2tjh16hQ++OADODg44N13333h/paWlvDz88PPP/+MTp06adI3bdqE7t27w8zMDABgbm6OoKAgODo64sqVKxg+fDjMzc3xySefFPOMAUOGDEFUVBSCg4Ph6OiIHTt2oFOnTrhy5Qpq1qyJ0aNHIyMjA8ePH4epqSnCw8M17SHKjYETEVERnTiRt6cpJyGAu3fV+Vq3Ltm6e/bsqfV+3bp1sLe3R3h4OOrXr49Nmzbh0aNHCAkJgbW1NQCgRo0amvxmZmYwMDBAlSpVilSvXC7HnDlzNO/d3d1x6tQpbN26VafACQACAgIwcOBApKSkwMTEBElJSdizZw+2bdumyTNjxgzNz25ubpg0aRK2bNlS7MApIiICmzdvxr179+Do6AgA+Pjjj7F//35s2LAB8+bNQ3R0NHr27AlPT08AQLVq1YpVF1UMDJyIiIooJqZk8xVFREQEZs6ciTNnziA+Ph4qlQoAEB0djfr16+PixYto1KiRJmgqSatWrcLatWtx584dpKamIiMjo0gTzf38/GBgYIBdu3ahb9++2LZtG8zNzdGhQwdNnl9//RVLly7FrVu38Pz5c2RlZcHCwqLYbQ4LC4MQArVq1dJKT09P1zxuZ+zYsRg5ciQOHDiAdu3aoWfPnvDy8ip2nVS+cY4TEVER6fo8UX08d9Tf3x8JCQlYs2YNzp49i7NnzwJQz+MB/nt+V1FIpeqvApFj7DH3vKKtW7diwoQJGDp0KA4cOICLFy9iyJAhmnp1YWhoiF69emHTpk0A1MN0ffr0gYGB+v/wZ86cQd++fdG5c2f8/vvvuHDhAqZPn15oHVKpVKvduduuUqkgk8kQGhqKixcval7Xrl3TzNF6//33cfv2bQwYMABXrlyBj48PvvnmG52PiyoWBk5EREXUsqX67rmCnvYgkQDOzup8JSkhIQHXrl3DjBkz0LZtW9SpUwdPnjzRyuPl5YWLFy/i8ePH+ZZhaGgIZa41E+zs7AAAMTm6yHJOFAeAEydOoHnz5hg1ahQaNWqEGjVq5JmUrouAgADs378ff//9N44cOYKAgADNtpMnT8LV1RXTp0+Hj48PatasiTt37hRanp2dnVa7lUolrl69qnnfqFEjKJVKxMXFoUaNGlqvnMOVzs7OGDFiBLZv345JkyZhzZo1RT42qhgYOBERFZFMpl5yAMgbPGW/X7q05CeGW1lZwcbGBqtXr8atW7dw+PBhTJw4UStPv379UKVKFfTo0QMnT57E7du3sW3bNpw+fRqAet5QZGQkLl68iPj4eKSnp8PY2BhvvPEGvvzyS4SHh+P48eNac40A9Typ8+fP448//sCNGzcwc+ZMhISEFPkYfH19UblyZQQEBMDNzQ1vvPGGVh3R0dEIDg5GREQEli9fjh07dhRa3ltvvYU9e/Zgz549+OeffzBq1Cg8ffpUs71WrVqauVXbt29HZGQkQkJC8NVXX2nunBs/fjz++OMPREZGIiwsDIcPH0adOnWKfGxUMTBwIiIqhnfeAX79FahaVTvdyUmdro91nKRSKYKDgxEaGor69etjwoQJWLhwoVYeQ0NDHDhwAPb29ujSpQs8PT3x5ZdfQvZvFNezZ0906tQJbdq0gZ2dHTZv3gwAWL9+PTIzM+Hj44Nx48Zh7ty5WuWOGDEC77zzDvr06YOmTZsiISEBo0aNKvIxSCQS9OvXD5cuXdLqbQKA7t27Y8KECRgzZgwaNmyIU6dOYebMmYWWN3ToUAwaNAgDBw6Er68v3N3d0aZNG608GzZswMCBAzFp0iR4eHigW7duOHv2LJydnQGoe6lGjx6NOnXqoFOnTvDw8MDKlSuLfGxUMUhE7sFhQlJSEiwtLZGYmPhSkxKJ6NWUlpaGyMhIuLu7Q6FQvFRZSqX67rmYGPWcppYtS76niYjyV9jvsr6+y3lXHRHRS5DJSn7JASJ6dXGojoiIiEhH5TJwmj9/Pv73v//B3Nwc9vb26NGjB65fv17WzSIiIqLXXLkMnI4dO4bRo0fjzJkzOHjwILKystChQwckJyeXddOIiIjoNVYu5zjlfojkhg0bYG9vj9DQULRq1aqMWkVERESvu3IZOOWWmJgIAAU+giA9PR3p6ema90lJSaXSLiIiInq9lMuhupyEEJg4cSLefPNN1K9fP9888+fPh6WlpeaVvbYHERERUU7lPnAaM2YMLl++rFnkLT9Tp05FYmKi5nX37t1SbCERERG9Lsr1UN1HH32EXbt24fjx43Byciown5GREYyMjEqxZURERPQ6Kpc9TkIIjBkzBtu3b8fhw4fh7u5e1k0iIiI9aN26NcaPH1/WzaiwoqKiIJFI8jwUujwrl4HT6NGjsXHjRmzatAnm5uaIjY1FbGwsUlNTy7ppRFTeCAGkpQPPU9T/8ilWL8Rgh15n5XKo7rvvvgOg/uXMacOGDRg8eHDpN4iIyqfkVCD+CZCSBqhUgFQKmCgAWyvA1LisW1fqMjMzIZfLy7oZRHpVLnuchBD5vhg0EVGJSU4F7sepe5rkBuqASW6gfn8/Tr1dD549e4aAgACYmprCwcEBX3/9dZ4enIyMDHzyySeoWrUqTE1N0bRpUxw9elSzPSgoCJUqVcIff/yBOnXqwMzMDJ06dUJMTIxWXRs2bECdOnWgUChQu3ZtrFy5UrMte4hm69ataN26NRQKBTZu3IiEhAT069cPTk5OMDExgaenp9bNOYMHD8axY8ewbNkySCQSSCQSREVFAQDCw8PRpUsXmJmZoXLlyhgwYADi4+M1+yYnJ2PgwIEwMzODg4MDFi9e/MLzFRgYiIYNG2L9+vVwcXGBmZkZRo4cCaVSiQULFqBKlSqwt7fHF198obXfkiVL4OnpCVNTUzg7O2PUqFF4/vy5ZvudO3fg7+8PKysrmJqaol69eti7dy8A4MmTJwgICICdnR2MjY1Rs2ZNbNiw4aWu6caNG+Hj4wNzc3NUqVIF/fv3R1xcnGb70aNHIZFIsGfPHjRo0AAKhQJNmzbFlStXCqy3X79+6Nu3r1ZaZmYmbG1tNe3dv38/3nzzTVSqVAk2Njbo2rUrIiIiCiwz+7OV086dOyGRSLTSdu/eDW9vbygUClSrVg1z5sxBVlaWZntgYCBcXFxgZGQER0dHjB07tsA6S1u5DJyIiPRKCHVPU2amOmAykAESifpfE4U6Pf6pXobtJk6ciJMnT2LXrl04ePAgTpw4gbCwMK08Q4YMwcmTJxEcHIzLly+jd+/e6NSpE27evKnJk5KSgkWLFuGnn37C8ePHER0djY8//lizfc2aNZg+fTq++OILXLt2DfPmzcPMmTPxww8/aNX16aefYuzYsbh27Ro6duyItLQ0eHt74/fff8fVq1fxwQcfYMCAATh79iwAYNmyZWjWrBmGDx+OmJgYxMTEwNnZGTExMfD19UXDhg1x/vx57N+/Hw8fPsS7776rqWvy5Mk4cuQIduzYgQMHDuDo0aMIDQ194TmLiIjAvn37sH//fmzevBnr16+Hn58f7t27h2PHjuGrr77CjBkzcObMGc0+UqkUy5cvx9WrV/HDDz/g8OHD+OSTTzTbR48ejfT0dBw/fhxXrlzBV199BTMzMwDAzJkzER4ejn379uHatWv47rvvYGtr+1LXNCMjA59//jkuXbqEnTt3IjIyMt/OgMmTJ2PRokUICQmBvb09unXrhszMzHzrDQgIwK5du7QCwj/++APJycno2bMnAHWwOnHiRISEhODQoUOQSqV4++23oVKpXnjeC/LHH3/gvffew9ixYxEeHo7vv/8eQUFBmuD1119/xddff43vv/8eN2/exM6dO+Hp6Vns+kqcoDwSExMFAJGYmFjWTSEiPUhNTRXh4eEiNTW1mAWkCREeIcTNO0JE3sv7unlHvT01rUTbnZSUJORyufjll180aU+fPhUmJiZi3LhxQgghbt26JSQSibh//77Wvm3bthVTp04VQgixYcMGAUDcunVLs33FihWicuXKmvfOzs5i06ZNWmV8/vnnolmzZkIIISIjIwUAsXTp0he2u0uXLmLSpEma976+vpr2Zps5c6bo0KGDVtrdu3cFAHH9+nXx7NkzYWhoKIKDgzXbExIShLGxcZ6ycpo9e7YwMTERSUlJmrSOHTsKNzc3oVQqNWkeHh5i/vz5BZazdetWYWNjo3nv6ekpAgMD883r7+8vhgwZUmBZOelyTfNz7tw5AUA8e/ZMCCHEkSNHBIB8z8+WLVvyLSMjI0PY2tqKH3/8UZPWr18/0bt37wLrjYuLEwDElStXhBD/fQ4uXLgghFB/tiwtLbX22bFjh8gZbrRs2VLMmzdPK89PP/0kHBwchBBCLF68WNSqVUtkZGQU2I5shf0u6+u7vFzOcSIi0qsspXpOk6yATnuZFEgX6nwl6Pbt28jMzESTJk00aZaWlvDw8NC8DwsLgxACtWrV0to3PT0dNjY2mvcmJiaoXr265r2Dg4Nm6OfRo0e4e/cuhg0bhuHDh2vyZGVlwdLSUqtcHx8frfdKpRJffvkltmzZgvv372uezGBqalrosYWGhuLIkSOaXpucIiIikJqaioyMDDRr1kyTbm1trXXsBXFzc4O5ubnmfeXKlSGTySCVSrXScg59HTlyBPPmzUN4eDiSkpKQlZWFtLQ0JCcnw9TUFGPHjsXIkSNx4MABtGvXDj179oSXlxcAYOTIkejZsyfCwsLQoUMH9OjRA82bN8+3bbpcUwC4cOECAgMDcfHiRTx+/FjT4xMdHY26detq8uV3fq5du5Zv3XK5HL1798bPP/+MAQMGIDk5Gb/99hs2bdqkyRMREYGZM2fizJkziI+P16q3oEWlXyQ0NBQhISFaw6NKpRJpaWlISUlB7969sXTpUlSrVg2dOnVCly5d4O/vDwODVyNkeTVaQUT0OjGQqSeCK1Xqn3NTqgCpJP9tL0H8O/SXe76IyDEkqFKpIJPJEBoaCplMu/6cQUnuSdwSiURTTvaX45o1a9C0aVOtfLnLzB0QLV68GF9//TWWLl2qmSM0fvx4ZGRkFHpsKpUK/v7++Oqrr/Jsc3Bw0BpmLKr8jjW/tOzjvnPnDrp06YIRI0bg888/h7W1Nf766y8MGzZMM+z1/vvvo2PHjtizZw8OHDiA+fPnY/Hixfjoo4/QuXNn3LlzB3v27MGff/6Jtm3bYvTo0Vi0aFGetulyTZOTk9GhQwd06NABGzduhJ2dHaKjo9GxY8cXntf8ys4pICAAvr6+iIuLw8GDB6FQKNC5c2fNdn9/fzg7O2PNmjVwdHSESqVC/fr1C6xXKpVqtR1AnqFClUqFOXPm4J133smzv0KhgLOzM65fv46DBw/izz//xKhRo7Bw4UIcO3bslbj5gIETEVFRGRmq5zI9TwFkCvX8pmxCAOkZgJmpOl8Jql69OuRyOc6dO6d5NFRSUhJu3rwJX19fAECjRo2gVCoRFxeHli1bFqueypUro2rVqrh9+zYCAgKKtO+JEyfQvXt3vPfeewDUX5I3b95EnTp1NHkMDQ2hVGr3xjVu3Bjbtm2Dm5tbvj0LNWrUgFwux5kzZ+Di4gJAPQn7xo0bmmMvKefPn0dWVhYWL16s6ZXaunVrnnzOzs4YMWIERowYgalTp2LNmjX46KOPAAB2dnYYPHgwBg8ejJYtW2rmHuWmyzX9559/EB8fjy+//FKT5/z58/m2Pb/zU7t27QKPtXnz5nB2dsaWLVuwb98+9O7dG4aG6s9tQkICrl27hu+//17zWfrrr78KPXd2dnZ49uyZpmcOQJ41nho3bozr16+jRo0aBZZjbGyMbt26oVu3bhg9ejRq166NK1euoHHjxoXWXxoYOBERFZVEol5yID1TvRSBkaF6eE6pUgdNcjlgW0k7oCoB5ubmGDRoECZPngxra2vY29tj9uzZkEqlml6FWrVqISAgAAMHDsTixYvRqFEjxMfH4/Dhw/D09ESXLl10qiswMBBjx46FhYUFOnfujPT0dJw/fx5PnjzBxIkTC9yvRo0a2LZtG06dOgUrKyssWbIEsbGxWoGTm5sbzp49i6ioKJiZmcHa2hqjR4/GmjVr0K9fP0yePBm2tra4desWgoODsWbNGpiZmWHYsGGYPHkybGxsULlyZUyfPl1ruK2kVK9eHVlZWfjmm2/g7++PkydPYtWqVVp5xo8fj86dO6NWrVp48uQJDh8+rDnGWbNmwdvbG/Xq1UN6ejp+//13rePPSZdr6uLiAkNDQ3zzzTcYMWIErl69is8//zzf8j777DOt82Nra4sePXoUeKwSiQT9+/fHqlWrcOPGDRw5ckSzzcrKCjY2Nli9ejUcHBwQHR2NKVOmFHrumjZtChMTE0ybNg0fffQRzp07h6CgIK08s2bNQteuXeHs7IzevXtDKpXi8uXLuHLlCubOnYugoCAolUpNWT/99BOMjY3h6upaaN2lhXfVEREVh6kxUNUeMDMBMrOAlHT1v2am6nQ9reO0ZMkSNGvWDF27dkW7du3QokULzZIB2TZs2ICBAwdi0qRJ8PDwQLdu3XD27NkiPcD8/fffx9q1axEUFARPT0/4+voiKCjohU9imDlzJho3boyOHTuidevWqFKlSp4v7o8//hgymQx169bVDDs5Ojri5MmTUCqV6NixI+rXr49x48bB0tJSExwtXLgQrVq1Qrdu3dCuXTu8+eab8Pb21v3k6ahhw4ZYsmQJvvrqK9SvXx8///wz5s+fr5VHqVRi9OjRqFOnDjp16gQPDw/Ncg2GhoaYOnUqvLy80KpVK8hkMgQHBxdY34uuqZ2dHYKCgvDLL7+gbt26+PLLL/PtvQKAL7/8EuPGjYO3tzdiYmKwa9cuTQ9SQQICAhAeHo6qVauiRYsWmnSpVIrg4GCEhoaifv36mDBhAhYuXFhoWdbW1ti4cSP27t2rWYoiMDBQK0/Hjh3x+++/4+DBg/jf//6HN954A0uWLNEERpUqVcKaNWvQokULeHl54dChQ9i9e7fWHL2yJBG5ByMJSUlJsLS0RGJiIiwsLMq6OURUwtLS0hAZGQl3d3etgKNYsofmspTqOU1GhiXe01SY5ORkVK1aFYsXL8awYcNKrV7Sn+Jc06NHj6JNmzZ48uRJnnWUyrPCfpf19V3OoToiopchkQCK0ntI+IULF/DPP/+gSZMmSExMxGeffQYA6N69e6m1gUoWr+nrhYETEdFrZtGiRbh+/ToMDQ3h7e2NEydOFLrAIr36eE1fHwyciIheI40aNdJptWx6fZTENW3dunWeZQBIPzg5nIiIiEhHDJyIqMJ6medtEVHZK4vfYQ7VEVGFY2hoCKlUigcPHsDOzg6GhoaFrq5MRK8WIQQyMjLw6NEjSKXSFy65UJIYOBFRhSOVSuHu7o6YmBg8ePCgrJtDRMVkYmICFxcXvSyEWhAGTkRUIRkaGsLFxQVZWVl5Hv9BRK8+mUwGAwODUu8tZuBERBVW9sNeX4UHhxLR64GTw4mIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEflOnBauXIl3N3doVAo4O3tjRMnTpR1k4iIiEiPlErg99+BFi30U365DZy2bNmC8ePHY/r06bhw4QJatmyJzp07Izo6uqybRkREREWgVAL79gFvvQVYWwMKRf4vuRwwMAD8/YGrV/XTFokQQuin6LLVtGlTNG7cGN99950mrU6dOujRowfmz5+vlTc9PR3p6ema90lJSXB2dkZiYiIsLCxKrc1EREQVSUYGsHQpEBQE3L0LZGbmzaNUAllZxSk9CYBliX+XG5RYSa+QjIwMhIaGYsqUKVrpHTp0wKlTp/Lknz9/PubMmVNazSMiIiq3lErgwAFg4ULg0iUgPR2QyfIGQFlZ6rTXTbkMnOLj46FUKlG5cmWt9MqVKyM2NjZP/qlTp2LixIma99k9TkRERKSWkQEsXw5s2wbcugWkpQFC/BcUKZXq9zkGcMqlchk4ZZNIJFrvhRB50gDAyMgIRkZGpdUsIiKiV4ZSCRw9Chw+DERFqYfLbtxQD52lp6vnDKWllf+ASFflMnCytbWFTCbL07sUFxeXpxeKiIioPModEOWc0axSAfHxwL17QEREcecQVUzlMnAyNDSEt7c3Dh48iLfffluTfvDgQXTv3r0MW0ZERPRyCguIst29C5w7px5eo5JVLgMnAJg4cSIGDBgAHx8fNGvWDKtXr0Z0dDRGjBhR1k0jIiLKV2oqMGEC8Oef6h4hqVTdO5T9r1IJpKSof6ayUW4Dpz59+iAhIQGfffYZYmJiUL9+fezduxeurq5l3TQiIqpglErg0CFgwwb1nWbx8erhMZVKPblaKgWSkthD9Doot+s4vYykpCRYWpb82g9ERFT+pKYCo0cDv/yi/tnICLCyUgdB2bfgP3uW/5Aa6RPXcSIiIipVGRnAokXAkiVAQoJu+6SkqF+kf/ndEC+RAIaGgIMDcP16ydfJwImIiCqk7Md4TJ8OhIfzzrJXhUymXgIhp+xgyMkJGDQIGD9e/b4wSUmApWXJt4+BExERlUtKJXDiBHD/vvous+Bg9fpEGRnqYTNOsC49Mpn6WXI5g1OJRP1sOWtroHlzYMgQ9bPoZLKya6cuGDgREdFrJ2dQ9OgRYGOjHkqzsVG/P3kSOHhQPbeI9MfQUB0QZa8cnt1bJJcDlSsDAwfq1jv0OmHgREREr6ScwdHDh//NMbp/H9i9G3j8uGzbVx7J5cD//gckJ2uvHC6VqnvpTE2B2rWByZOBdu1e/d4hfWDgREREpS73Io4SCeDsDNjaAvb2wJEjwG+/MTgqKXI50LSp+hwD/60cnpoKmJiog6W2bYHWrStmMFQUDJyIiKjEFbS6tUqlnoh97RonY5cEqVQ9Pyi/59JLpYCrq3reEAOikqP3wCk5ORmmpqb6roaIiEpZfsERA6OSJZere4SyVw43MADMzYFmzV6fydTljd4Dp8qVK+Pdd9/F0KFD8eabb+q7OiIiKiGFPRONz0J7edlBUfbK4RKJ+hyX10nV5YXeA6fNmzcjKCgIbdu2haurK4YOHYqBAwfC0dFR31UTEVEhOJymP3I5UKnSf3eaKRTqfx0dgbffBsaOZVD0uiq1R64kJCTgxx9/RFBQEMLDw9GxY0cMHToU3bp1g0Hula7KGB+5QkSvq8J6iXJij1HJs7AA3n0XWL4cMDYu69aQvr7Ly+RZdd988w0mT56MjIwM2NraYsSIEZgyZQpMTExKuyn5YuBERK+C3LfjP3oEREczGCptUql6kca33waWLWNQ9LrQ13d5qXX1xMbG4scff8SGDRsQHR2NXr16YdiwYXjw4AG+/PJLnDlzBgcOHCit5hARlamcPUPR0epHSVhbq2+/j45Wv8LC+MyzkmBqCvj7q4ces1cOl0jUQ5JyuXoJBD8/YOlSBkX0YnoPnLZv344NGzbgjz/+QN26dTF69Gi89957qFSpkiZPw4YN0ahRI303hYio1GQHRkePqr+gK1VSB0X37qkXF/zjDwZFJcHKCujeXX13Wc6VwxMS1D1FrVvzVnwqWXoPnIYMGYK+ffvi5MmT+N///pdvnmrVqmH69On6bgoRUYkp7JEfR44Av/wCPH9e1q0sH6ys1D1GTk7q99bWQJUqQNWqQMuWDIqodOl9jlNKSsorM3dJV5zjRETZ8guQuKp1yZJKgRYt1K/slcMTEgA7OwZHVHyv7Rwnc3NzxMTEwN7eXis9ISEB9vb2UCqV+m4CEVGhsoOjmBj1lzYAxMYChw4xQCoJOVe3zn7UR1oa4OYGDBrERRzp9aL3wKmgDq309HQYchELIioDOXuRGByVjOxnoVWtysCIyje9BU7Lly8HAEgkEqxduxZmZmaabUqlEsePH0ft2rX1VT0RVUA5e44cHNRDPIB2Wnw8MGGCepI26S6/Z6LxWWhUEektcPr6668BqHucVq1aBVmO3yhDQ0O4ublh1apV+qqeiMqZFwVFN28Ca9ZoB0Q2Nup/ExJKv72vm+weIw6nERVOb4FTZGQkAKBNmzbYvn07rKys9FUVEZUD+c0ziosruJdIl6CoogdMOYOh/LDHiKjo9D7H6ciRI/qugohecQUFRdk///478PPP6rvWdFXRgiJjY6BTJ/W/hd0LzWCISL/0EjhNnDgRn3/+OUxNTTFx4sRC8y5ZskQfTSCiUqKPoKiiyQ6KmjX7b+VwIRgEEb2K9BI4XbhwAZmZmZqfCyKRSPRRPRHpQX4BEoOiFzM3Bzp0UA+ZZa8c7uSkXq+IizgSvX7K5CG/rzougEkVQe7J1s2bA6dO5f8+v4nXpJbfIz+4eCNR2XttF8DMLSkpCYcPH0bt2rW5HAGRHhUWGOUXCMlk6n0Kel/R5Q6QGBgRVUx6D5zeffddtGrVCmPGjEFqaip8fHwQFRUFIQSCg4PRs2dPfTeB6LWV+0GxFhbApUvAnTuAQqGeB/PwofqZaBIJYGYGNGgA1KgBrF9feGCUX12Fva8IrK2Bjz5SB0OxseohSAZIRJST3gOn48ePax7gu2PHDggh8PTpU/zwww+YO3cuAyci5D9/aPduda9QSkrRyvr774LrqGjyW7LA2RlYvFgdEGU/f47BERHpSu+BU2JiIqytrQEA+/fvR8+ePWFiYgI/Pz9MnjxZ39UTvRIyMoCVK4GICKB6dWDUKPUX9IkT6sd9cIJ10eUXFDk5AcOHAzVrFrxyOIMjInoZeg+cnJ2dcfr0aVhbW2P//v0IDg4GADx58gQKhULf1RPplS6P+NizB/j6a+0en0mTABMT9RAbvVjOXqLCznVBQVHr1qXaXCIqx/QeOI0fPx4BAQEwMzODq6srWv/7F+z48ePw9PTUd/VExVLcZ57p+ogPlYpBU37s7ICAAKBrV/X77JXDC+slYlBERKWpVJYjOH/+PO7evYv27dtrHva7Z88eVKpUCS1atNB39UXG5QjKL30HRFSw/IKi3I9W4TAaEZUUfX2Xl+o6TtlV6XPhy6ioKHz++ec4fPgwYmNj4ejoiPfeew/Tp0+HoaGhTmUwcCofcgdJDIj0h0EREb1qXut1nH788UcsXLgQN2/eBADUqlULkydPxoABA0q8rn/++QcqlQrff/89atSogatXr2L48OFITk7GokWLSrw+KjtFfShsfhgw/edF6zjlnHjNoIiIKiq9B05LlizBzJkzMWbMGLRo0QJCCJw8eRIjRoxAfHw8JkyYUKL1derUCZ06ddK8r1atGq5fv47vvvuOgdNrSJklEPJXBh4/UsLaTob/vWkImYEE27cD48ZxJeuiKCwQetHK4QyOiIjU9B44ffPNN/juu+8wcOBATVr37t1Rr149BAYGlnjglJ+cSyLkJz09Henp6Zr3SUlJem9ThScEkJ4BZCkBmVSdplQBBjLAUA5kZOLUwRRcOv4cpvJMKAxVeJQhxdGtChhUscIngcaFPiGe1IthTp5ccGCUXyCUe6I1J14TEWnTe+AUExOD5s2b50lv3rw5YmJi9F09IiIi8M0332Dx4sUF5pk/fz7mzJmj97ZUSPkFSM9TgaTnQEam+pWZCUACyA3Uj4NXqRAXJ2D/LAOdfQTuxslx5bYJ0jKkqF45BfFPM+HhbI9/oo3L9NBKm5mZ+pEfKtWLVw4fPFj9aJAXBUZERFQ0ep8cXr9+ffTv3x/Tpk3TSp87dy62bNmCK1eu6FROYGDgC4ObkJAQ+Pj4aN4/ePAAvr6+8PX1xdq1awvcL78eJ2dnZ04OL46cgVJmJpCUDKSk/RcgqYS6Z0kC9be6+HcfCPU3vwBUWUo8eiTwLEWGR09lsDBVISVNhrPXTBGfKEM9tzSE3jDFgs1VIIT+bjTQJ6k07zpOuSdY85EfRETF99pODp8zZw769OmD48ePo0WLFpBIJPjrr79w6NAhbN26VedyxowZg759+xaax83NTfPzgwcP0KZNGzRr1gyrV68udD8jIyMYGRnp3BaCbj1JaRmAVAIojICsLCBLpf4XUPcuZWSqAyeFoTpfqjp4fZ4ph5E8A2kygfRMGR49lcKuUhZqu6Th5BUz3I0zRB3XVLhUzsCd2FfruhX0iI+FC9VDZPmtHM45RERErw+9B049e/bE2bNn8fXXX2Pnzp0QQqBu3bo4d+4cGjVqpHM5tra2sLW11Snv/fv30aZNG3h7e2PDhg2QSqXFbT5lK0pPktwA6h+gno38PEXdmyT/d3ayEOr86oLVZcoNNPUos9TZjAyVUBiqkJYhRVKyDLaWmbA0U+JZihRV7QTMjUvv4WsveuZZcVazBjh0RkT0uimV5Qi8vb2xcePG0qgKDx48QOvWreHi4oJFixbhUY4HgFWpUqVU2vBae9meJEM5kKlU728kB6QGQEq6etJ3dqwklfx3e1f2z7L/glsDA4F0pQQGUgGZVL1TRpYE5iaAoYEKpgogLUOCZ6kv3z3DgIiIiIqiVAInlUqFW7duIS4uDiqVSmtbq1atSrSuAwcO4NatW7h16xacnJy0tpXiWp+vl+xg6VlK3knbxelJMpCp983uSZL8m18u+3d3Cf6LorJ//u/amJlJkZQghUKuhPLfj4uhgUCWUh1AOdtnIPSGKe7G6bagaTYGRERE9LL0Pjn8zJkz6N+/P+7cuZMncJFIJFAqS2+4RVflfuXw/IbdkpKBlFT1tuxJ26pcPUkqkaMnSfJfT5LcQF1edgxkJAfSM9VlKQy1f85SqcsQKsDAQB2UQah7r9Iz1HUZG+H5k0w8eQwkpUiRlCyFpakSCUlyxD6WIz5RjuRK9li2xrjAdZwKCpI4h4iIqGJ4bSeHjxgxAj4+PtizZw8cHBz0+rgVKkRBvUpp/wY8Uom690gm/TfQAWBo8O+dbsXoSZJJ1UN2QqhvIVMq1WUZyNTBmFQKGPybDsl/ZQoA6Zkws5DhsVKB5CeZcLDJQJZSgoQkA0Q8NIV3x0po390YE6YXvHI4gyQiItIHvfc4mZqa4tKlS6hRo4Y+qylR5abHKXewlJL2X69S9rBbllLds6RUqXuKZFL13W3ZAY8QRetJggBMFOryMjLVgZLy36E4mezfciXqV6ZSXa5Mqn4ZytXBlSo7jxQqAPceGSLqsRkMrU00K4cTEREV5rXtcWratClu3br1WgVOr7WCgiWVCsC/vUpyA+0J3DIDICtN/T67R1AqUe8jkaBIPUmaHioVYGgI4N96DQ3+7V36N0AykKm3W5oBZv8uZJlr5XBkKSE1kMGlriFc2FNJRESvAL0HTh999BEmTZqE2NhYeHp6Qi6Xa2338vLSdxMqjuRUIP7Jf/OVcgZLBjIgLVMdEMkNtIfdDOX/BUG5h92kkv96jKRSQAZ1gKMS6lhKKlX3NgnxbzkSIOPfAEphCJibAham6m25H61iZPhfoJab4tVan4mIiAgohaG6/NZQkkgkEEJwcnhJyNnDFP9EHfxkZqlfEuQIluTqQEkI9aRsuQGQ9u+QnLHRv/soAYVcHShlD7sZGf478fvfQChLCUhlAFTqziRNT5Ikb0+SuUnhwREREZGevLZDdZGRkfquouLJPRyXngEkp6h7cuQG6h4geY75RELkXTdJLtMedpPJ/u1JAiCTAFkCgESdVpI9SURERK8xvQdOrq6u+q6iQlBmCYT8lYH0xylwt34OJ+s0SFP/nehtIMtx11vWf4tXSiXqXidJ9iKTMvXwXXYnY3awpFT9O/nbSL1Peoa6VynnpG32JBEREekncNq1axc6d+4MuVyOXbt2FZq3W7du+mhCuZGRAcz6OBXyxCfwrpWM+u6pUD1U4f4jCaysJTCrZJAjWDL6b+J3ZpY6uJHK/uttMpACmeK/5QVyTuDOHnYzkAEWZvlP2mawREREFZxe5jhJpVLExsbC3t6+0OfEcY5TwTIygA4dgIeRqRjbMw62lhmoYp0FG4sspGUA1RwzkZohgZGFEawrif8Wo8yek6RUqucu4d8eJJVKvS0rx/mWSABTY+1hNwZIRERUDrxWc5xyPlYl9yNWqHBKJdC3L/Drr4BEIvBpvyewtczE/Xg5ajqlIzFZBgOZQEamBDKpwPPHWbCykkNikP3Yk3/nOSlV6p4nuezfhS1l/y03YKIATIw57EZERFREpfKsOnoxpRKYMweYO/e/KUgulTNQ2zUNd+MMoTBUQS4TSMySaNaIVCklMDRQ4lmSASzMcgRLBv+uym0g+29CN4MlIiKil1YqgdO5c+dw9OjRfB/yu2TJktJowitt61YgIOC/x8JlMzdWwthQheQ0KaRSgUylBIYGAmkZEqSkyWBuooQQApmZ/949J89+bty/K3abKDihm4iIqATpPXCaN28eZsyYAQ8PD1SuXFnrWXV8bh3QrRuwe3f+256lypCaIYWpQoXE5zLEJxrAwSYTj54aID7RACYKFYzkAgZy8e+wnFw9wVuhAOysGCwRERGVML0HTsuWLcP69esxePBgfVf1WlEqgTp1gJs3C84T/dAQ/9xRoHGtFPwdpcA/0QpYmqpgVykLSclSPE+VID1TBhcbJSD+XRHc3AywraSe9E1EREQlquBb3kqqAqkULVq00Hc1r5Vff1V3DhUWNAGAEBLs/MsK8Yly1HNLQ0amFOevm+BxkgEcbLIASGBgaQyJnTVQ3Rmo6QK4VGHQREREpCd6D5wmTJiAFStW6Lua18akSUDv3v9NAH+Rf6KNsXybPcJumMDGMgvWFkrcjjHElsPW2HreGVV9XYHqTuqhOYURh+WIiIj0SO/PqlOpVPDz88ONGzdQt27dPA/53b59uz6rLxZ9rf3g7w/8/nvx9pVIBFwqZ8DcWIlnqTL0DjDEwkUMkoiIiPLzWq3jlNNHH32EI0eOoE2bNrCxsamwE8J9fIDQ0OLvL4QEd2KN0KoVEHrw3wW/iYiIqFTpPXD68ccfsW3bNvj5+em7qleWv//LBU0AULcucOECAyYiIqKypPc5TtbW1qhevbq+q3llTZhQ/OE5QL3gd3Aw8PffDJqIiIjKmt4Dp8DAQMyePRspKSn6ruqV8/HHwNKlxd9/5kwgPR3o06fEmkREREQvQe9DdcuXL0dERAQqV64MNze3PJPDw8LC9N2EMvHLL8DixcXbt2ZN4No1dW8TERERvTr0Hjj16NFD31W8cpRK9SNUiqNr14JXEiciIqKypffAafbs2fqu4pXTsiWQmVn0/SZMAPjoPiIioldXqTzktyKZOBE4fbpo+0il6gngvXvrp01ERERUMvQeOEml0kLXblIqlfpuQqn55Rfg66+Lto9UCqSm8o45IiKi14HeA6cdO3Zovc/MzMSFCxfwww8/YM6cOfquvtQolcDAgUXfb8sWBk1ERESvC70HTt27d8+T1qtXL9SrVw9btmzBsGHD9N2EUvH550BaWtH2+fhjoFcv/bSHiIiISp7e13EqSNOmTfHnn3+WVfUlSqkE5s4t2j7jxwMLF+qlOURERKQnZRI4paam4ptvvkHVqlXLovoS16qVOnjSVbNmRZ8LRURERGVP70N1VlZWWpPDhRB49uwZTExMsHHjRn1Xr3dbtgCnTumeXy4HTpzQX3uIiIhIf/QeOC3N9cwRqVQKOzs71KtXD7Nnz0a3bt303QS9USqBAQOKts+mTVwRnIiI6HUlEUKIsqj40qVLaNy48Su5HEFSUhIsLS2RmJgICwuLAvMFBgJFuTHw3XfVPVRERESkX7p+lxdVmU0OLw3p6elo2LAhJBIJLl68WKJlK5VFW+VbLlf3NhEREdHrq1wHTp988gkcHR31UvaJE8CzZ7rn37iRQ3RERESvu3L7yJV9+/bhwIED2LZtG/bt21do3vT0dKSnp2veJyUl5ZtPqVQHTDExwK+/6t6W5s3Vw3RERET0etNb4PTOO+8Uuv3p06f6qhoPHz7E8OHDsXPnTpiYmLww//z581+4ivn27cC4ccC9e0Vri0wGHD9etH2IiIjo1aS3wMnS0vKF2wcW5xklLyCEwODBgzFixAj4+PggKirqhftMnToVEydO1LxPSkqCs7Oz5v327eoVvoszjX7MGA7RERERlRd6C5w2bNhQouUFBga+sFcoJCQEp06dQlJSEqZOnapz2UZGRjAyMsp3m1Kp7mkq7r2HPXoUbz8iIiJ69ZTZcgRFFR8fj/j4+ELzuLm5oW/fvti9e7fWoptKpRIymQwBAQH44YcfXlhXzlsYw8Is0KZN8dpsYQE8fsweJyIiotKmr+UIXpvJ4ba2trC1tX1hvuXLl2NujgfHPXjwAB07dsSWLVvQtGnTItcbE1PkXTQmTGDQREREVJ68NoGTrlxcXLTem5mZAQCqV68OJyenIpfn4FC8dhgaAjNnFm9fIiIiejWV63WcSkLLloCNTdH3mzqVvU1ERETlTbnrccrNzc0NLzuNK8cSTzphbxMREVH5xB6nF/jiC+D586Ltw94mIiKi8um1uauuNGXPxH/8OBE1aljg8WPd9zU0BFJSGDgRERGVJT7ktwycOoUiBU0Ae5uIiIjKMwZOhYiNLVp+zm0iIiIq3xg4FaJKlaLl/+kn9jYRERGVZwycCtG8OeDkBORYhLxA3bsD776r/zYRERFR2WHgVAiZDFi2TP1zQcGTRAJMmgTs3FlqzSIiIqIywsDpBd55B/j1V6BqVe10U1Ng8GAgLQ1YtKhMmkZERESljIGTjnIv2lCpEuDvr54QTkRERBUDA6cX2L4d6NULuH9fO/3BA3X69u1l0y4iIiIqfQycCqFUAuPG5e1tAv5LGz9enY+IiIjKPwZOhTh1Crh3r+DtQgB37wInTpRem4iIiKjsMHAqhK4LYMbE6LcdRERE9Gpg4FSIiAjd8jk46LcdRERE9Gpg4FSIoKAX53FyAlq21HtTiIiI6BXAwKkQugzBDR/Ox6wQERFVFAycXlLNmmXdAiIiIiotDJxekr19WbeAiIiISgsDJyIiIiIdMXB6SXFxZd0CIiIiKi0MnF4SlyIgIiKqOBg4vQQ7Oy5FQEREVJEwcHoJAQFcioCIiKgiYeD0Erp2LesWEBERUWli4ERERESkIwZOL4F31BEREVUsDJxeAu+oIyIiqlgYOBUT76gjIiKqeBg4FRPvqCMiIqp4GDgVU/fuZd0CIiIiKm0MnIpBJgOaNy/rVhAREVFpY+BUDEolcOpUWbeCiIiISlu5DZz27NmDpk2bwtjYGLa2tnjnnXdKtPyYmBItjoiIiF4DBmXdAH3Ytm0bhg8fjnnz5uGtt96CEAJXrlwp0Tq4FAEREVHFIxFCiLJuREnKysqCm5sb5syZg2HDhhWrjKSkJFhaWgJIBGCRZ7uTExAVxbvqiIiIXlXZ3+WJiYmwsMj7XV5c5W6oLiwsDPfv34dUKkWjRo3g4OCAzp074++//y5wn/T0dCQlJWm9CjN8OIMmIiKiiqjcBU63b98GAAQGBmLGjBn4/fffYWVlBV9fXzx+/DjffebPnw9LS0vNy9nZudA6atYs8WYTERHRa+C1CZwCAwMhkUgKfZ0/fx4qlQoAMH36dPTs2RPe3t7YsGEDJBIJfvnll3zLnjp1KhITEzWvu3fvFtoWzm8iIiKqmF6byeFjxoxB3759C83j5uaGZ8+eAQDq1q2rSTcyMkK1atUQHR2d735GRkYwMjLSqR3OznzUChERUUX12gROtra2sLW1fWE+b29vGBkZ4fr163jzzTcBAJmZmYiKioKrq+tLt6NvX85vIiIiqqhem6E6XVlYWGDEiBGYPXs2Dhw4gOvXr2PkyJEAgN69e790+cHB6gUwiYiIqOJ5bXqcimLhwoUwMDDAgAEDkJqaiqZNm+Lw4cOwsrJ66bLv3gVOnABat375dhIREdHrpdyt41QSXrSO06ZNQL9+pd4sIiIi0hHXcXqF8K46IiKiiqlcDtXpi0SiXjWcd9URERFVTOxxKgIhgKVLeVcdERFRRcXAqQhsbIDu3cu6FURERFRWGDgVQUKC+o46IiIiqpgYOBVRTExZt4CIiIjKCgOnIuIddURERBUX76rTEe+oIyIiIvY46UAiUf/LO+qIiIgqNgZOOqhaFfj1V+Cdd8q6JURERFSWGDjpgA+lISIiIoCBk04ePAB69QK2by/rlhAREVFZYuCkg+wep/HjAaWyTJtCREREZYiBk46EAO7e5QKYREREFRkDpyLiAphEREQVFwOnIuICmERERBUXF8DUERfAJCIiIvY46YALYBIRERHAwEknXACTiIiIAAZOOuECmERERAQwcNIJF8AkIiIigIGTTrgAJhEREQEMnHTGBTCJiIiIgVMRcQFMIiKiiouBUxFxAUwiIqKKiwtg6ogLYBIRERF7nHTABTCJiIgIYOCkEycnLoBJREREHKor1Nq1QPXq6uE59jQRERERA6dC9O4NWFiUdSuIiIjoVcGhOiIiIiIdMXAiIiIi0hEDJyIiIiIdcY5TPsS/D6dLSkoq45YQERFRcWR/h2d/p5cUBk75SEhIAAA4OzuXcUuIiIjoZSQkJMDS0rLEymPglA9ra2sAQHR0dImebCqepKQkODs74+7du7DgbY5ljtfj1cFr8Wrh9Xi1JCYmwsXFRfOdXlIYOOVDKlVP/bK0tOSH/xViYWHB6/EK4fV4dfBavFp4PV4t2d/pJVZeiZZGREREVI4xcCIiIiLSEQOnfBgZGWH27NkwMjIq66YQeD1eNbwerw5ei1cLr8erRV/XQyJK+j49IiIionKKPU5EREREOmLgRERERKQjBk5EREREOmLgRERERKQjBk5EREREOqqwgdPKlSvh7u4OhUIBb29vnDhxotD8x44dg7e3NxQKBapVq4ZVq1aVUksrhqJcj+3bt6N9+/aws7ODhYUFmjVrhj/++KMUW1v+FfX3I9vJkydhYGCAhg0b6reBFUhRr0V6ejqmT58OV1dXGBkZoXr16li/fn0ptbb8K+r1+Pnnn9GgQQOYmJjAwcEBQ4YM0TwPlYrv+PHj8Pf3h6OjIyQSCXbu3PnCfUrse1xUQMHBwUIul4s1a9aI8PBwMW7cOGFqairu3LmTb/7bt28LExMTMW7cOBEeHi7WrFkj5HK5+PXXX0u55eVTUa/HuHHjxFdffSXOnTsnbty4IaZOnSrkcrkICwsr5ZaXT0W9HtmePn0qqlWrJjp06CAaNGhQOo0t54pzLbp16yaaNm0qDh48KCIjI8XZs2fFyZMnS7HV5VdRr8eJEyeEVCoVy5YtE7dv3xYnTpwQ9erVEz169Cjllpc/e/fuFdOnTxfbtm0TAMSOHTsKzV+S3+MVMnBq0qSJGDFihFZa7dq1xZQpU/LN/8knn4jatWtrpX344YfijTfe0FsbK5KiXo/81K1bV8yZM6ekm1YhFfd69OnTR8yYMUPMnj2bgVMJKeq12Ldvn7C0tBQJCQml0bwKp6jXY+HChaJatWpaacuXLxdOTk56a2NFpEvgVJLf4xVuqC4jIwOhoaHo0KGDVnqHDh1w6tSpfPc5ffp0nvwdO3bE+fPnkZmZqbe2VgTFuR65qVQqPHv2rMSfgF0RFfd6bNiwAREREZg9e7a+m1hhFOda7Nq1Cz4+PliwYAGqVq2KWrVq4eOPP0ZqamppNLlcK871aN68Oe7du4e9e/dCCIGHDx/i119/hZ+fX2k0mXIoye9xg5Js2OsgPj4eSqUSlStX1kqvXLkyYmNj890nNjY23/xZWVmIj4+Hg4OD3tpb3hXneuS2ePFiJCcn491339VHEyuU4lyPmzdvYsqUKThx4gQMDCrcnxS9Kc61uH37Nv766y8oFArs2LED8fHxGDVqFB4/fsx5Ti+pONejefPm+Pnnn9GnTx+kpaUhKysL3bp1wzfffFMaTaYcSvJ7vML1OGWTSCRa74UQedJelD+/dCqeol6PbJs3b0ZgYCC2bNkCe3t7fTWvwtH1eiiVSvTv3x9z5sxBrVq1Sqt5FUpRfjdUKhUkEgl+/vlnNGnSBF26dMGSJUsQFBTEXqcSUpTrER4ejrFjx2LWrFkIDQ3F/v37ERkZiREjRpRGUymXkvoer3D/PbS1tYVMJsvzP4S4uLg80Wi2KlWq5JvfwMAANjY2emtrRVCc65Fty5YtGDZsGH755Re0a9dOn82sMIp6PZ49e4bz58/jwoULGDNmDAD1l7cQAgYGBjhw4ADeeuutUml7eVOc3w0HBwdUrVoVlpaWmrQ6depACIF79+6hZs2aem1zeVac6zF//ny0aNECkydPBgB4eXnB1NQULVu2xNy5czlaUYpK8nu8wvU4GRoawtvbGwcPHtRKP3jwIJo3b57vPs2aNcuT/8CBA/Dx8YFcLtdbWyuC4lwPQN3TNHjwYGzatInzBUpQUa+HhYUFrly5gosXL2peI0aMgIeHBy5evIimTZuWVtPLneL8brRo0QIPHjzA8+fPNWk3btyAVCqFk5OTXttb3hXneqSkpEAq1f6alclkAP7r7aDSUaLf40WeTl4OZN9Sum7dOhEeHi7Gjx8vTE1NRVRUlBBCiClTpogBAwZo8mffxjhhwgQRHh4u1q1bx+UISlBRr8emTZuEgYGBWLFihYiJidG8nj59WlaHUK4U9XrkxrvqSk5Rr8WzZ8+Ek5OT6NWrl/j777/FsWPHRM2aNcX7779fVodQrhT1emzYsEEYGBiIlStXioiICPHXX38JHx8f0aRJk7I6hHLj2bNn4sKFC+LChQsCgFiyZIm4cOGCZmkIfX6PV8jASQghVqxYIVxdXYWhoaFo3LixOHbsmGbboEGDhK+vr1b+o0ePikaNGglDQ0Ph5uYmvvvuu1JucflWlOvh6+srAOR5DRo0qPQbXk4V9fcjJwZOJauo1+LatWuiXbt2wtjYWDg5OYmJEyeKlJSUUm51+VXU67F8+XJRt25dYWxsLBwcHERAQIC4d+9eKbe6/Dly5Eih3wP6/B6XCMH+QiIiIiJdVLg5TkRERETFxcCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiIiISEcMnIiIiIh0xMCJiCiXhIQE2NvbIyoqSm919OrVC0uWLNFb+USkHwyciKjUDR48GBKJBCNGjMizbdSoUZBIJBg8eHDpN+xf8+fPh7+/P9zc3DRprVq1gkQiweeff66VVwiBpk2bQiKRYNasWTrXMWvWLHzxxRdISkoqqWYTUSlg4EREZcLZ2RnBwcFITU3VpKWlpWHz5s1wcXEps3alpqZi3bp1eP/99zVpQghcvHgRrq6uuHLlilb+H374AQ8ePAAANG7cWOd6vLy84Obmhp9//rlkGk5EpYKBExGVicaNG8PFxQXbt2/XpG3fvh3Ozs5o1KiRJm3//v148803UalSJdjY2KBr166IiIjQKuvXX3+Fp6cnjI2NYWNjg3bt2iE5OfmF2/Kzb98+GBgYoFmzZpq0mzdv4tmzZxg8eLBW4PTs2TNMnTpV0zvm7e1dpHPQrVs3bN68uUj7EFHZYuBERGVmyJAh2LBhg+b9+vXrMXToUK08ycnJmDhxIkJCQnDo0CFIpVK8/fbbUKlUAICYmBj069cPQ4cOxbVr13D06FG88847EEIUuq0gx48fh4+Pj1ZaaGgoFAoF+vXrh5s3byI9PR0A8Pnnn6Nhw4ZwcHCAra0tnJ2di3T8TZo0wblz5zTlEdGrz6CsG0BEFdeAAQMwdepUREVFQSKR4OTJkwgODsbRo0c1eXr27Km1z7p162Bvb4/w8HDUr18fMTExyMrKwjvvvANXV1cAgKenJwDgxo0bBW4rSFRUFBwdHbXSwsLC4OXlhVq1asHU1BTXrl2DqakpVq5cifPnz2PRokVF7m0CgKpVqyI9PR2xsbGa9hHRq409TkRUZmxtbeHn54cffvgBGzZsgJ+fH2xtbbXyREREoH///qhWrRosLCzg7u4OAIiOjgYANGjQAG3btoWnpyd69+6NNWvW4MmTJy/cVpDU1FQoFAqttNDQUHh7e0MikcDLywtXr17FhAkT8MEHH6B27doIDQ0t0vymbMbGxgCAlJSUIu9LRGWDgRMRlamhQ4ciKCgIP/zwQ55hOgDw9/dHQkIC1qxZg7Nnz+Ls2bMAgIyMDACATCbDwYMHsW/fPtStWxfffPMNPDw8EBkZWei2gtja2uYJri5cuKAJjBo0aIBly5bh3LlzmD17NjIyMvD3339rBU4pKSmYPHkymjdvjubNm2P48OFISEjIU9fjx48BAHZ2dkU8a0RUVhg4EVGZ6tSpEzIyMpCRkYGOHTtqbUtISMC1a9cwY8YMtG3bFnXq1Mm3x0gikaBFixaYM2cOLly4AENDQ+zYseOF2/LTqFEjhIeHa97fvn0bT58+1QzFNWzYEOfPn8cXX3wBS0tLXLlyBZmZmVpDdWPGjEGDBg1w6tQpnDp1Cn379sXAgQPzzK26evUqnJyc8vSyEdGri3OciKhMyWQyXLt2TfNzTlZWVrCxscHq1avh4OCA6OhoTJkyRSvP2bNncejQIXTo0AH29vY4e/YsHj16hDp16hS6rSAdO3bE1KlT8eTJE1hZWSE0NBSGhoaoX78+AGDQoEHo0aMHbGxsAKjnP1lZWWmGEFNTU/HkyRO89957CAwMBAAEBgbit99+w61bt1CzZk1NXSdOnECHDh1e7gQSUali4EREZc7CwiLfdKlUiuDgYIwdOxb169eHh4cHli9fjtatW2vte/z4cSxduhRJSUlwdXXF4sWL0blzZ1y7dq3AbQXx9PSEj48Ptm7dig8//BBhYWGoX78+5HI5AEAul2v1EIWFhWktn5CzV2nMmDEF1pOWloYdO3bgjz/+eOH5IaJXh0QUdl8uEVEFtHfvXnz88ce4evUqpNKiz2gYPHgw2rdvj4CAAADAoUOHsGjRIuzduxcSiQQAsGLFCvz22284cOBAibadiPSLPU5ERLl06dIFN2/exP3794u8NhMArFy5EjNmzMDy5cshkUhQp04dbNy4URM0Aeqeq2+++aYkm01EpYA9TkREREQ64l11RERERDpi4ERERESkIwZORERERDpi4ERERESkIwZORERERDpi4ERERESkIwZORERERDpi4ERERESkIwZORERERDpi4ERERESkIwZORERERDpi4ERERESkIwZORERERDpi4ERERESkIwZORERERDpi4ERERESkIwZOVO5dvnwZQ4YMgbu7OxQKBczMzNC4cWMsWLAAjx8/LpM2rVy5EkFBQXnSo6KiIJFI8t2mb9l1Z7+kUilsbGzQpUsXnD59usjlBQYGQiKRFKst4eHhCAwMRFRUVLH2L8iMGTPg4uICAwMDVKpUqUTLzpb7PBb20uX4Hj9+jL59+8Le3h4SiQQ9evTQ1OPn5wdra2tIJBKMHz++wDLc3NwgkUjQunXrfLf/+OOPmjYdPXq0yMdcGv766y/069cPLi4uMDIygqmpKerVq4dJkybhn3/+KevmUQUiEUKIsm4Ekb6sWbMGo0aNgoeHB0aNGoW6desiMzMT58+fx5o1a9CgQQPs2LGj1NtVv3592Nra5vmSSk9Px4ULF1C9enXY2dmVapuioqLg7u6Ojz76CP3794dSqcTff/+NOXPmICEhAadPn0ajRo10Lu/evXu4d+8e3njjjSK35ddff0Xv3r1x5MiRAr/si+q3335Djx49MH36dHTu3BlGRkbw8fEpkbJzyr6GOY0aNQqJiYn4+eeftdIbNWoEIyOjQsubMGECVq5cifXr16N69eqwtrZGrVq18Pbbb+PEiRNYu3YtqlSpAgcHB7i6uuZbhpubGx4/foznz5/j5s2bqF69utb21q1b48KFC0hKSirRc15SZsyYgS+++ALNmjXD4MGDUbNmTWRlZeHy5cv44YcfcOXKFWRlZUEmk5V1U6kiEETl1KlTp4RMJhOdOnUSaWlpebanp6eL3377rdAyUlJS9NK2evXqCV9fX72UXVyRkZECgFi4cKFW+qFDhwQA8f7775daW3755RcBQBw5cqTEypw7d64AIB4+fFhiZSYnJ+uUz9fXV9SrV69YdbRr107UqVMnT3qNGjVE586ddSrD1dVVdO7cWTg5OYlp06Zpbbt165aQSCRi+PDhJX7OS8KmTZsEADFixAihUqnybFepVOLbb78VWVlZZdA6qogYOFG51bVrV2FgYCCio6N1yu/q6ir8/PzEtm3bRMOGDYWRkZH49NNPhRBCxMTEiA8++EBUrVpVyOVy4ebmJgIDA0VmZqZWGYGBgaJJkybCyspKmJubi0aNGom1a9dq/cF3dXUVALRerq6uQoj/gpcNGzZo8s+ePVsAEFevXhV9+/YVFhYWwt7eXgwZMkQ8ffpUq/4nT56IoUOHCisrK2Fqaiq6dOkiIiIiBAAxe/bsQo+/oMApOTlZABDt27fXpK1bt054eXkJIyMjYWVlJXr06CHCw8O19stud37neN++faJRo0ZCoVAIDw8PsW7dOk2eDRs25Dk/Oc9JWFiY8PPzE3Z2dsLQ0FA4ODiILl26iLt37xZ4bPmd8+zzoVQqxVdffSU8PDyEoaGhsLOzEwMGDMhTXnbwc+zYMdGsWTNhbGws+vTpU+g5zb1vTomJiWLSpEnCzc1NyOVy4ejoKMaNGyeeP38uhPjveuR+HTlyJN/0yMjIQo/fz89PTJs2TVStWlUolUrNtmnTpgkXFxexZcuWPIFTSEiI6NOnj3B1dRUKhUK4urqKvn37iqioKK3yk5OTNceS/Znw9vYWmzZt0uSJiIgQffr0EQ4ODsLQ0FDY29uLt956S1y4cKHQc1e3bl1ha2srUlNTX3CW/3PgwAHRrVs3UbVqVWFkZCSqV68uPvjgA/Ho0SOtfNmf0bCwMPH2228Lc3NzYWFhIQICAkRcXJzO9VHFYqDX7iyiMqJUKnH48GF4e3vD2dlZ5/3CwsJw7do1zJgxA+7u7jA1NUVsbCyaNGkCqVSKWbNmoXr16jh9+jTmzp2LqKgobNiwQbN/VFQUPvzwQ7i4uAAAzpw5g48++gj379/HrFmzAAA7duxAr169YGlpiZUrVwLAC4drAKBnz57o06cPhg0bhitXrmDq1KkAgPXr1wMAVCoV/P39cf78eQQGBqJx48Y4ffo0OnXqpPPx5+fWrVsAoBk6nD9/PqZNm4Z+/fph/vz5SEhIQGBgIJo1a4aQkBDUrFmz0PIuXbqESZMmYcqUKahcuTLWrl2LYcOGoUaNGmjVqhX8/Pwwb948TJs2DStWrEDjxo0BANWrV0dycjLat28Pd3d3rFixApUrV0ZsbCyOHDmCZ8+eFVjnjh07sGLFCqxbtw779++HpaUlnJycAAAjR47E6tWrMWbMGHTt2hVRUVGYOXMmjh49irCwMNja2mrKiYmJwXvvvYdPPvkE8+bNg1RavGmiKSkp8PX1xb179zBt2jR4eXnh77//xqxZs3DlyhX8+eefcHBwwOnTp/MM89WtWxenT5/G22+/jerVq2PRokUAAAcHhxfWO3ToUMyfPx9//PEHOnfuDKVSiR9++AHDhg3L91iioqLg4eGBvn37wtraGjExMfjuu+/wv//9D+Hh4ZpzM3HiRPz000+YO3cuGjVqhOTkZFy9ehUJCQmasrp06QKlUokFCxbAxcUF8fHxOHXqFJ4+fVpgex88eIDw8HD069cPCoVC5/MbERGBZs2a4f3334elpSWioqKwZMkSvPnmm7hy5QrkcrlW/rfffhvvvvsuRowYgb///hszZ85EeHg4zp49mycvEXucqFyKjY0VAETfvn113sfV1VXIZDJx/fp1rfQPP/xQmJmZiTt37milL1q0SAAQf//9d77lKZVKkZmZKT777DNhY2Oj1etU0FBdYT1OCxYs0Mo7atQooVAoNOXu2bNHABDfffedVr758+cXqcfpq6++EpmZmSItLU2EhoaK//3vfwKA2LNnj3jy5IkwNjYWXbp00do3OjpaGBkZif79++dpd07ZPRc5z2VqaqqwtrYWH374oSatoKG68+fPCwBi586dhR5LfrLbk7PX4dq1awKAGDVqlFbes2fPCgBaw1q+vr4CgDh06FCR687d4zR//nwhlUpFSEiIVr5ff/1VABB79+4tcN9s2b1IusiZ19fXV/Tq1UsIof7MSCQSERkZqdPwaFZWlnj+/LkwNTUVy5Yt06TXr19f9OjRo8D94uPjBQCxdOlSndqb7cyZMwKAmDJlSr5tyczM1LzyG8YTQj2Ul5mZKe7cuSMAaA3PZ38mJkyYoLXPzz//LACIjRs3Fqm9VDHwrjqiHLy8vFCrVi2ttN9//x1t2rSBo6MjsrKyNK/OnTsDAI4dO6bJe/jwYbRr1w6WlpaQyWSQy+WYNWsWEhISEBcX91Jt69atW562pqWlacrNbse7776rla9fv35FqufTTz+FXC6HQqGAt7c3oqOj8f3332vurktNTcXgwYO19nF2dsZbb72FQ4cOvbD8hg0banrkAEChUKBWrVq4c+fOC/etUaMGrKys8Omnn2LVqlUIDw8v0rHlduTIEQDIczxNmjRBnTp18hyPlZUV3nrrrZeqE1B/purXr4+GDRtqfaY6duyo9zvbhg4dil27diEhIQHr1q1DmzZt4Obmlm/e58+f49NPP0WNGjVgYGAAAwMDmJmZITk5GdeuXdPka9KkCfbt24cpU6bg6NGjSE1N1SrH2toa1atXx8KFC7FkyRJcuHABKpXqpY7DxsYGcrlc89q2bZtmW1xcHEaMGAFnZ2cYGBhALpdrJs7nbHe2gIAArffvvvsuDAwMNJ8PopwYOFG5ZGtrCxMTE0RGRhZpv/yGOx4+fIjdu3dr/ZGWy+WoV68eACA+Ph4AcO7cOXTo0AGA+m6+kydPIiQkBNOnTweAPF8mRWVjY6P1Pnt4L7vchIQEGBgYwNraWitf5cqVi1TPuHHjEBISgtDQUERERCAmJgYffPCBpg4g//Pk6OioNTSj63FkH4su58fS0hLHjh1Dw4YNMW3aNNSrVw+Ojo6YPXs2MjMzX7h/bkU9Hl2Gw3Tx8OFDXL58Oc9nytzcHEIIzWdKH3r16gWFQoGvv/4au3fvxrBhwwrM279/f3z77bd4//338ccff+DcuXMICQmBnZ2d1vVavnw5Pv30U+zcuRNt2rSBtbU1evTogZs3bwIAJBIJDh06hI4dO2LBggVo3Lgx7OzsMHbs2EKHWLOH2fMLqo8ePYqQkBCsWrVKK12lUqFDhw7Yvn07PvnkExw6dAjnzp3DmTNnAOT/e1ilShWt9wYGBrCxsdHp80wVD+c4Ubkkk8nQtm1b7Nu3D/fu3dPMZ3mR/NYdsrW1hZeXF7744ot893F0dAQABAcHQy6X4/fff9eaj7Fz586iH0Ax2NjYICsrC48fP9YKnmJjY4tUjpOTU4G36WcHPTExMXm2PXjwQGs+kL54enoiODgYQghcvnwZQUFB+Oyzz2BsbIwpU6YUqaycx5P7M5Lf8RR3XarcbG1tYWxsrJmflt92fTExMUHfvn0xf/58WFhY4J133sk3X2JiIn7//XfMnj1b67ymp6fnWf/M1NQUc+bMwZw5c/Dw4UNN75O/v79mjSVXV1esW7cOAHDjxg1s3boVgYGByMjIyBP8ZHN0dES9evVw8OBBpKWlaf1eNWzYEIC6Vyynq1ev4tKlSwgKCsKgQYM06dlz9fITGxuLqlWrat5nZWUhISEh3yCfiD1OVG5NnToVQggMHz4cGRkZebZnZmZi9+7dLyyna9euuHr1KqpXrw4fH588r+zASSKRwMDAQGstmdTUVPz00095ytS1h6UofH19AQBbtmzRSg8ODi6xOpo1awZjY2Ns3LhRK/3evXs4fPgw2rZtWyL15O5Ny49EIkGDBg3w9ddfo1KlSggLCytyPdnDbrmPJyQkBNeuXSux48mta9euiIiIgI2NTb6fqYKGzkrKyJEj4e/vj1mzZhU46VoikUAIkefGhbVr10KpVBZYduXKlTF48GD069cP169fR0pKSp48tWrVwowZM+Dp6fnC6zZ9+nTEx8dj4sSJEDosO5gd3OZu9/fff1/gPrnX19q6dSuysrJeufWs6NXAHicqt5o1a4bvvvsOo0aNgre3N0aOHIl69eohMzMTFy5cwOrVq1G/fn34+/sXWs5nn32GgwcPonnz5hg7diw8PDyQlpaGqKgo7N27F6tWrYKTkxP8/PywZMkS9O/fHx988AESEhKwaNGifO+Yy+412bJlC6pVqwaFQgFPT8+XOt5OnTqhRYsWmDRpEpKSkuDt7Y3Tp0/jxx9/BIBi3wGWU6VKlTBz5kxMmzYNAwcORL9+/ZCQkIA5c+ZAoVBg9uzZL10HoF4gFABWr14Nc3NzKBQKuLu74/Tp01i5ciV69OiBatWqQQiB7du34+nTp2jfvn2R6/Hw8MAHH3yAb775BlKpFJ07d9bcVefs7IwJEyaUyPHkNn78eGzbtg2tWrXChAkT4OXlBZVKhejoaBw4cACTJk1C06ZN9VI3oO6teVFPqIWFBVq1aoWFCxfC1tYWbm5uOHbsGNatW5dn1fWmTZuia9eu8PLygpWVFa5du4affvoJzZo1g4mJCS5fvowxY8agd+/eqFmzJgwNDXH48GFcvnz5hb2E/fr1w99//40vvvgCly5d0iyAqVKpcPfuXc1/TMzNzQEAtWvXRvXq1TFlyhQIIWBtbY3du3fj4MGDBdaxfft2GBgYoH379pq76ho0aJBnviARAN5VR+XfxYsXxaBBg4SLi4swNDQUpqamolGjRmLWrFlaa7UUdpfSo0ePxNixY4W7u7uQy+XC2tpaeHt7i+nTp2vW3RFCiPXr1wsPDw9hZGQkqlWrJubPny/WrVuXZ52dqKgo0aFDB2Fubq7zOk6516DJXu8oZ7mPHz8WQ4YMEZUqVRImJiaiffv2mjuTct4FlZ+C1nHKz9q1a4WXl5cwNDQUlpaWonv37nnuLixsHafcfH1989xluHTpUuHu7i5kMpnmnPzzzz+iX79+onr16sLY2FhYWlqKJk2aiKCgoBe2uaDzmL2OU61atYRcLhe2trbivffeK3Adp+LIb9/nz5+LGTNmaNaPsrS0FJ6enmLChAkiNjb2hfUW9666guR3V929e/dEz549NeuSderUSVy9elW4urqKQYMGafJNmTJF+Pj4CCsrK81nf8KECSI+Pl4IIcTDhw/F4MGDRe3atYWpqakwMzMTXl5e4uuvv9Z54crjx4+LPn36CCcnJyGXy4WJiYmoW7euGDlypDh//rxW3vDwcNG+fXthbm4urKysRO/evUV0dHSeu0uzPxOhoaHC399fmJmZCXNzc9GvX78SXSiVyhc+coWonNu0aRMCAgJw8uRJNG/evKybQ/TKCAwMxJw5c/Do0aNSmZ9H5QOH6ojKkc2bN+P+/fvw9PSEVCrFmTNnsHDhQrRq1YpBExFRCWDgRFSOmJubIzg4GHPnzkVycjIcHBwwePBgzJ07t6ybRkRULnCojoiIiEhHr8xyBPPnz4dEIsH48eMLzLN9+3a0b98ednZ2sLCwQLNmzfDHH39o5QkKCoJEIsnzSktL0/MREBERUXn3SgROISEhWL16Nby8vArNd/z4cbRv3x579+5FaGgo2rRpA39/f1y4cEErn4WFBWJiYrReRXlAJBEREVF+ynyO0/PnzxEQEIA1a9a8cB7G0qVLtd7PmzcPv/32G3bv3o1GjRpp0iUSSZ4l9ImIiIheVpkHTqNHj4afnx/atWtX5AmsKpUKz549y/NsrufPn8PV1RVKpRINGzbE559/rhVY5Zaeno709HStch8/fgwbG5sSe8QCERERlR4hBJ49ewZHR8cSWQA4W5kGTsHBwQgLC0NISEix9l+8eDGSk5O1VnetXbs2goKC4OnpiaSkJCxbtgwtWrTApUuXULNmzXzLmT9/PubMmVOsNhAREdGr6+7duzo/r1QXZXZX3d27d+Hj44MDBw6gQYMGAIDWrVujYcOGeYbk8rN582a8//77+O2339CuXbsC86lUKjRu3BitWrXC8uXL882Tu8cpMTERLi4uuHv3LiwsLIp2YERERFTmkpKS4OzsjKdPn8LS0rLEyi2zHqfQ0FDExcXB29tbk6ZUKnH8+HF8++23SE9P13pYak5btmzBsGHD8MsvvxQaNAHq53P973//w82bNwvMY2RklO/zxCwsLBg4ERERvcZKespNmQVObdu2xZUrV7TShgwZgtq1a+PTTz8tMGjavHkzhg4dis2bN8PPz++F9QghcPHixZd+gCoRERFRmQVO5ubmmiegZzM1NYWNjY0mferUqbh//77m6e6bN2/GwIEDsWzZMrzxxhuIjY0FABgbG2u64ebMmYM33ngDNWvWRFJSEpYvX46LFy9ixYoVpXh0REREVB69Eus4FSQmJgbR0dGa999//z2ysrIwevRoODg4aF7jxo3T5Hn69Ck++OAD1KlTBx06dMD9+/dx/PhxNGnSpCwOgYiIiMoRPnIlH0lJSbC0tERiYiLnOBGVY0IIZGVlQalUlnVTiKiIZDIZDAwMCpzDpK/v8jJfx4mIqCxkZGQgJiYGKSkpZd0UIiomExMTODg4wNDQsNTqZOBERBWOSqVCZGQkZDIZHB0dYWhoyMVuiV4jQghkZGTg0aNHiIyMRM2aNUt0kcvCMHAiogonIyMDKpUKzs7OMDExKevmEFExGBsbQy6X486dO8jIyCi1Z9K+0pPDiYj0qbT+h0pE+lEWv8P8q0FERESkIwZORERERDpi4ERERCVi8ODB6NGjh17rCAwMRMOGDfVaB1FhGDgREVUgDDyIXg7vqiMieglKJXDiBBATAzg4AC1bAgU8apOIygH2OBERFdP27YCbG9CmDdC/v/pfNzd1ur7s378fb775JipVqgQbGxt07doVERERWnnu3buHvn37wtraGqampvDx8cHZs2cRFBSEOXPm4NKlS5BIJJBIJAgKCkJUVBQkEgkuXryoKePp06eQSCQ4evQoAECpVGLYsGFwd3eHsbExPDw8sGzZMp3bnZiYCGNjY+zfv18rffv27TA1NcXz588BAJ9++ilq1aoFExMTVKtWDTNnzkRmZmaB5bZu3Rrjx4/XSuvRowcGDx6seZ+RkYFPPvkEVatWhampKZo2bao5LgC4c+cO/P39YWVlBVNTU9SrVw979+7V+dioYmGPExFRMWzfDvTqBeR+aNX9++r0X38F3nmn5OtNTk7GxIkT4enpieTkZMyaNQtvv/02Ll68CKlUiufPn8PX1xdVq1bFrl27UKVKFYSFhUGlUqFPnz64evUq9u/fjz///BMAYGlpiYcPH76wXpVKBScnJ2zduhW2trY4deoUPvjgAzg4OODdd9994f6Wlpbw8/PDzz//jE6dOmnSN23ahO7du8PMzAyA+gHwQUFBcHR0xJUrVzB8+HCYm5vjk08+KeYZA4YMGYKoqCgEBwfD0dERO3bsQKdOnXDlyhXUrFkTo0ePRkZGBo4fPw5TU1OEh4dr2kOUGwMnIqIiUiqBcePyBk2AOk0iAcaPB7p3L/lhu549e2q9X7duHezt7REeHo769etj06ZNePToEUJCQmBtbQ0AqFGjhia/mZkZDAwMUKVKlSLVK5fLMWfOHM17d3d3nDp1Clu3btUpcAKAgIAADBw4ECkpKTAxMUFSUhL27NmDbdu2afLMmDFD87ObmxsmTZqELVu2FDtwioiIwObNm3Hv3j04OjoCAD7++GPs378fGzZswLx58xAdHY2ePXvC09MTAFCtWrVi1UUVA4fqiIiK6MQJ4N69grcLAdy9q85X0iIiItC/f39Uq1YNFhYWcHd3BwBER0cDAC5evIhGjRppgqaStGrVKvj4+MDOzg5mZmZYs2aNpl5d+Pn5wcDAALt27QIAbNu2Debm5ujQoYMmz6+//oo333wTVapUgZmZGWbOnFmkOnILCwuDEAK1atWCmZmZ5nXs2DHNEOfYsWMxd+5ctGjRArNnz8bly5eLXR+VfwyciIiKKCamZPMVhb+/PxISErBmzRqcPXsWZ8+eBaCexwOoH0NRVNmrL4scXWi55xVt3boVEyZMwNChQ3HgwAFcvHgRQ4YM0dSrC0NDQ/Tq1QubNm0CoB6m69OnDwwM1IMfZ86cQd++fdG5c2f8/vvvuHDhAqZPn15oHVKpVKvduduuUqkgk8kQGhqKixcval7Xrl3TzNF6//33cfv2bQwYMABXrlyBj48PvvnmG52PiyoWBk5EREXk4FCy+XSVkJCAa9euYcaMGWjbti3q1KmDJ0+eaOXx8vLCxYsX8fjx43zLMDQ0hFKp1Eqzs7MDAMTkiPRyThQHgBMnTqB58+YYNWoUGjVqhBo1auSZlK6LgIAA7N+/H3///TeOHDmCgIAAzbaTJ0/C1dUV06dPh4+PD2rWrIk7d+4UWp6dnZ1Wu5VKJa5evap536hRIyiVSsTFxaFGjRpar5zDlc7OzhgxYgS2b9+OSZMmYc2aNUU+NqoYGDgRERVRy5aAk5N6LlN+JBLA2VmdryRZWVnBxsYGq1evxq1bt3D48GFMnDhRK0+/fv1QpUoV9OjRAydPnsTt27exbds2nD59GoB63lBkZCQuXryI+Ph4pKenw9jYGG+88Qa+/PJLhIeH4/jx41pzjQD1PKnz58/jjz/+wI0bNzBz5kyEhIQU+Rh8fX1RuXJlBAQEwM3NDW+88YZWHdHR0QgODkZERASWL1+OHTt2FFreW2+9hT179mDPnj34559/MGrUKDx9+lSzvVatWpq5Vdu3b0dkZCRCQkLw1Vdfae6cGz9+PP744w9ERkYiLCwMhw8fRp06dYp8bFQxMHAiIioimQzIvhM/d/CU/X7p0pKfGC6VShEcHIzQ0FDUr18fEyZMwMKFC7XyGBoa4sCBA7C3t0eXLl3g6emJL7/8ErJ/G9OzZ0906tQJbdq0gZ2dHTZv3gwAWL9+PTIzM+Hj44Nx48Zh7ty5WuWOGDEC77zzDvr06YOmTZsiISEBo0aNKvIxSCQS9OvXD5cuXdLqbQKA7t27Y8KECRgzZgwaNmyIU6dOYebMmYWWN3ToUAwaNAgDBw6Er68v3N3d0aZNG608GzZswMCBAzFp0iR4eHigW7duOHv2LJydnQGoe6lGjx6NOnXqoFOnTvDw8MDKlSuLfGxUMUhE7sFhQlJSEiwtLZGYmAgLC4uybg4RlbC0tDRERkbC3d0dCoWi2OVs366+uy7nRHFnZ3XQpI+lCIhIW2G/y/r6LudyBERExfTOO+olB7hyOFHFwcCJiOglyGRA69Zl3QoiKi2c40RERESko1cmcJo/fz4kEkmeZw7lduzYMXh7e0OhUKBatWpYtWpVnjzbtm1D3bp1YWRkhLp1677wrgwiIiIiXbwSgVNISAhWr14NLy+vQvNFRkaiS5cuaNmyJS5cuIBp06Zh7NixWsv1nz59Gn369MGAAQNw6dIlDBgwAO+++65mkTgiIiKi4irzwOn58+cICAjAmjVrYGVlVWjeVatWwcXFBUuXLkWdOnXw/vvvY+jQoVi0aJEmz9KlS9G+fXtMnToVtWvXxtSpU9G2bVssXbpUz0dCRERE5V2ZB06jR4+Gn58f2rVr98K8p0+f1nqmEQB07NgR58+f1yyxX1CeU6dOFVhueno6kpKStF5EREREuZVp4BQcHIywsDDMnz9fp/yxsbGoXLmyVlrlypWRlZWF+Pj4QvPExsYWWO78+fNhaWmpeWUvikZERESUU5kFTnfv3sW4ceOwcePGIi1AJ8m1TG/2+p050/PLkzstp6lTpyIxMVHzunv3rs7tISIiooqjzAKn0NBQxMXFwdvbGwYGBjAwMMCxY8ewfPlyGBgY5HkIJQBUqVIlT89RXFwcDAwMYGNjU2ie3L1QORkZGcHCwkLrRUREr77WrVu/8G5s0p+oqChIJJI8D4Uuz8oscGrbti2uXLmCixcval4+Pj4ICAjAxYsXNc9VyqlZs2Y4ePCgVtqBAwfg4+MDuVxeaJ7mzZvr72CIqOISAkhLB56nqP/lU6xeiMEOvc7KbOVwc3Nz1K9fXyvN1NQUNjY2mvSpU6fi/v37+PHHHwGoHzL57bffYuLEiRg+fDhOnz6NdevWaR5SCQDjxo1Dq1at8NVXX6F79+747bff8Oeff+Kvv/4qvYMjooohORWIfwKkpAEqFSCVAiYKwNYKMDUu69aVuszMTM1/YonKqzK/q64wMTExiI6O1rx3d3fH3r17cfToUTRs2BCff/45li9fjp49e2ryNG/eHMHBwdiwYQO8vLwQFBSELVu2oGnTpmVxCERUXiWnAvfj1D1NcgN1wCQ3UL+/H6fergfPnj1DQEAATE1N4eDggK+//jpPD05GRgY++eQTVK1aFaampmjatCmOHj2q2R4UFIRKlSrhjz/+QJ06dWBmZoZOnTohJiZGq64NGzagTp06UCgUqF27NlauXKnZlj1Es3XrVrRu3RoKhQIbN25EQkIC+vXrBycnJ5iYmMDT01PrP7eDBw/GsWPHsGzZMkgkEkgkEkRFRQEAwsPD0aVLF5iZmaFy5coYMGCA5sYfAEhOTsbAgQNhZmYGBwcHLF68+IXnKzAwEA0bNsT69evh4uICMzMzjBw5EkqlEgsWLECVKlVgb2+PL774Qmu/JUuWwNPTE6ampnB2dsaoUaPw/PlzzfY7d+7A398fVlZWMDU1Rb169bB3714AwJMnTxAQEAA7OzsYGxujZs2a2LBhw0td040bN8LHxwfm5uaoUqUK+vfvj7i4OM32o0ePQiKRYM+ePWjQoAEUCgWaNm2KK1euFFhvv3790LdvX620zMxM2Nraatq7f/9+vPnmm6hUqRJsbGzQtWtXREREFFhm9mcrp507d+aZZ7x7926txaznzJmDrKwszfbAwEC4uLjAyMgIjo6OGDt2bIF1ljpBeSQmJgoAIjExsaybQkR6kJqaKsLDw0VqamrxClCphIi6L8TVm0LcvitE5L3/XrfvqtOjHqjzlbD3339fuLq6ij///FNcuXJFvP3228Lc3FyMGzdOk6d///6iefPm4vjx4+LWrVti4cKFwsjISNy4cUMIIcSGDRuEXC4X7dq1EyEhISI0NFTUqVNH9O/fX1PG6tWrhYODg9i2bZu4ffu22LZtm7C2thZBQUFCCCEiIyMFAOHm5qbJc//+fXHv3j2xcOFCceHCBRERESGWL18uZDKZOHPmjBBCiKdPn4pmzZqJ4cOHi5iYGBETEyOysrLEgwcPhK2trZg6daq4du2aCAsLE+3btxdt2rTRtGnkyJHCyclJHDhwQFy+fFl07dpVmJmZaR17brNnzxZmZmaiV69e4u+//xa7du0ShoaGomPHjuKjjz4S//zzj1i/fr0AIE6fPq3Z7+uvvxaHDx8Wt2/fFocOHRIeHh5i5MiRmu1+fn6iffv24vLlyyIiIkLs3r1bHDt2TAghxOjRo0XDhg1FSEiIiIyMFAcPHhS7du16qWu6bt06sXfvXhERESFOnz4t3njjDdG5c2fN9iNHjggAok6dOlrnx83NTWRkZORb7+7du4WxsbF49uyZVppCodB8//36669i27Zt4saNG+LChQvC399feHp6CqVSqfU5uHDhghBC/dmytLTUqmfHjh0iZ7ixf/9+YWFhIYKCgkRERIQ4cOCAcHNzE4GBgUIIIX755RdhYWEh9u7dK+7cuSPOnj0rVq9ene8xFPa7rK/vcgZO+WDgRFS+vXTglJomRHiEEDfvaAdN2a+bd9TbU9NKtN1JSUlCLpeLX375RZP29OlTYWJiovmSvXXrlpBIJOL+/fta+7Zt21ZMnTpVCKH+cgMgbt26pdm+YsUKUblyZc17Z2dnsWnTJq0yPv/8c9GsWTMhxH9fmEuXLn1hu7t06SImTZqkee/r65sn2Jk5c6bo0KGDVtrdu3cFAHH9+nXx7NkzYWhoKIKDgzXbExIShLGx8QsDJxMTE5GUlKRJ69ixo3Bzc9N8+QshhIeHh5g/f36B5WzdulXY2Nho3nt6emq+6HPz9/cXQ4YMKbCsnHS5pvk5d+6cAKAJerIDp/zOz5YtW/ItIyMjQ9ja2ooff/xRk9avXz/Ru3fvAuuNi4sTAMSVK1eEEMULnFq2bCnmzZunleenn34SDg4OQgghFi9eLGrVqlVgwJdTWQROZTbHiYjotZWlVM9pkhUw20EmBdKFOl8Jun37NjIzM9GkSRNNmqWlJTw8PDTvw8LCIIRArVq1tPZNT0/X3H0MACYmJqhevbrmvYODg2bo59GjR7h79y6GDRuG4cOHa/JkZWXB0tJSq1wfHx+t90qlEl9++SW2bNmC+/fvIz09Henp6TA1NS302EJDQ3HkyBGYmZnl2RYREYHU1FRkZGSgWbNmmnRra2utYy+Im5sbzM3NNe8rV64MmUwGqVSqlZZz6OvIkSOYN28ewsPDkZSUhKysLKSlpSE5ORmmpqYYO3YsRo4ciQMHDqBdu3bo2bOn5rFhI0eORM+ePREWFoYOHTqgR48eBd6gpMs1BYALFy4gMDAQFy9exOPHj6FSqQAA0dHRqFu3riZffufn2rVr+dYtl8vRu3dv/PzzzxgwYACSk5Px22+/YdOmTZo8ERERmDlzJs6cOYP4+HitenPPU9ZVaGgoQkJCtIZHlUol0tLSkJKSgt69e2Pp0qWoVq0aOnXqhC5dusDf3x8GBq9GyPJqtIKI6HViIFNPBFeq1D/nplQBUkn+216CyGfdupzpAKBSqSCTyRAaGprn7uScQUnuSdwSiURTTvaX45o1a/LMD81dZu6AaPHixfj666+xdOlSzRyh8ePHIyMjo9BjU6lU8Pf3x1dffZVnm4ODA27evFno/oXJ71jzS8s+7jt37qBLly4YMWIEPv/8c1hbW+Ovv/7CsGHDNE+peP/999GxY0fs2bMHBw4cwPz587F48WJ89NFH6Ny5M+7cuYM9e/bgzz//RNu2bTF69Gitx4Nl0+WaJicno0OHDujQoQM2btwIOzs7REdHo2PHji88r/mVnVNAQAB8fX0RFxeHgwcPQqFQoHPnzprt/v7+cHZ2xpo1a+Do6AiVSoX69esXWK9UKtVqOwDNOcumUqkwZ84cvPPOO3n2VygUcHZ2xvXr13Hw4EH8+eefGDVqFBYuXIhjx469EjcfMHAiIioqI0P1ZPDnKYBMAeT8YhICSM8AzEzV+UpQ9erVIZfLce7cOc0TDpKSknDz5k34+voCABo1agSlUom4uDi0bNmyWPVUrlwZVatWxe3btxEQEFCkfU+cOIHu3bvjvffeA6D+krx58ybq1KmjyWNoaJhnrb7GjRtj27ZtcHNzy7dnoUaNGpDL5Thz5gxcXFwAqCdh37hxQ3PsJeX8+fPIysrC4sWLNb1SW7duzZPP2dkZI0aMwIgRIzB16lSsWbMGH330EQDAzs4OgwcPxuDBg9GyZUtMnjw538BJl2v6zz//ID4+Hl9++aUmz/nz5/Nte37np3bt2gUea/PmzeHs7IwtW7Zg37596N27NwwN1Z/bhIQEXLt2Dd9//73ms/SiO9Tt7Ozw7NkzTc8cgDxrPDVu3BjXr19HjRo1CizH2NgY3bp1Q7du3TB69GjUrl0bV65cQePGjQutvzQwcCIiKiqJRL3kQHqmeikCI0P18JxSpQ6a5HLAtpJ2QFUCzM3NMWjQIEyePBnW1tawt7fH7NmzIZVKNb0KtWrVQkBAAAYOHIjFixejUaNGiI+Px+HDh+Hp6YkuXbroVFdgYCDGjh0LCwsLdO7cGenp6Th//jyePHmCiRMnFrhfjRo1sG3bNpw6dQpWVlZYsmQJYmNjtQKn/7N352FRVQ0YwN87AzPDOggCouCG+4IpblhuuW9pi5qZW2aZllu2aK5tWJmf2mea5vqZYoqmpZmWoqZo7mmauYMEoqigyDpzvj+uMzIwwAzbAPP+nmceZu49994zc5F5Pefcc6tXr44jR47g2rVrcHV1haenJ8aOHYtly5Zh0KBBeOedd1CxYkVcunQJYWFhWLZsGVxdXTFy5Ei888478PLygq+vLz744AOT7raiEhgYiMzMTHz11Vfo06cPDh48iCVLlpiUmTBhAnr06IE6derg7t272LNnj/E9zpgxA8HBwWjYsCHS0tLw008/mbz/rCw5p1WrVoVKpcJXX32F0aNH4+zZs/joo4/M7u/DDz80+XwqVqyIfv365fpeJUnCSy+9hCVLluCff/7B3r17jesqVKgALy8vLF26FH5+foiKisL777+f52fXqlUrODs7Y+rUqXjrrbfwxx9/YNWqVSZlZsyYgd69eyMgIAD9+/eHQqHAn3/+iTNnzuDjjz/GqlWroNPpjPv63//+BycnJ1SrVi3PY5eUUj0dARFRqeXiBFTxAVydgYxM4GGa/NPVRV5eTPM4zZs3DyEhIejduzc6d+6MJ5980jhlgMHKlSsxdOhQvP3226hbty6eeeYZHDlyxKr7cL766qv49ttvsWrVKjRu3Bjt27fHqlWrUKNGjTy3mz59Opo1a4Zu3bqhQ4cOqFSpUo4v7smTJ0OpVKJBgwbGbqfKlSvj4MGD0Ol06NatGxo1aoTx48dDq9Uaw9EXX3yBdu3a4ZlnnkHnzp3x1FNPITg42PIPz0JPPPEE5s2bh88++wyNGjXCd999l+OeqjqdDmPHjkX9+vXRvXt31K1b1zhdg0qlwpQpUxAUFIR27dpBqVQiLCws1+Pld069vb2xatUqbNy4EQ0aNMCcOXPMtl4BwJw5czB+/HgEBwcjNjYW27ZtM7Yg5Wbw4ME4d+4cqlSpgieffNK4XKFQICwsDMePH0ejRo0wceJEfPHFF3nuy9PTE2vXrsWOHTuMU1HMmjXLpEy3bt3w008/Yffu3WjRogVat26NefPmGYORh4cHli1bhieffBJBQUH47bff8OOPP5qM0bMlSWTvjCQkJSVBq9UiMTGRt18hKodSU1Nx9epV1KhRw6p7ZZpl6JrL1MljmtSqIm9pyktycjKqVKmCL7/8EiNHjiyx41LxKcg5jYiIQMeOHXH37t0c8yiVZ3n9Wy6u73J21RERFYYkARp1iR3u5MmT+Pvvv9GyZUskJibiww8/BAD07du3xOpARYvntGxhcCIiKmPmzp2LCxcuQKVSITg4GAcOHEDFihVtXS0qBJ7TsoPBiYioDGnatCmOHz9u62pQESqKc9qhQ4cc0wBQ8eDgcCIiIiILMTgRkd3i/9CJyjZb/BtmcCIiu2OYffjhw4c2rgkRFYbh33BJzijOMU5EZHeUSiU8PDyM9yZzdnbO87YURFS6CCHw8OFDxMfHw8PDI8etgIoTgxMR2aVKlSoBgMmNXYmobPHw8DD+Wy4pDE5EZJckSYKfnx98fHxy3ISUiEo/R0fHEm1pMmBwIiK7plQqbfLHl4jKJg4OJyIiIrIQgxMRERGVaTod8PPPwNNPA56egFot/ywO7KojIiIim0tPB+bPB1atAqKjAUuHHup0QGZmcdbMFIMTERERFRmdDti1C/jiC+D0aSAtDVAqcwYcSZKXK5XAw4clG34Kw6ZddYsXL0ZQUBDc3d3h7u6OkJAQ/Pzzz7mWHz58OCRJyvFo2LChscyqVavMlklNTS2Jt0RERFRuGELQwIFA1aqAmxvg6gpotabPXV0BJydAowEcHICePYG9e4E7d4DkZCApSf6Zlvb4kZr6eF1ZCU2AjVuc/P39MWfOHNSqVQsAsHr1avTt2xcnT540CUMGCxYswJw5c4yvMzMz0aRJE/Tv39+knLu7Oy5cuGCyTKPRFMM7ICIiKlt0OuC334DVq4Fr1+TA4+0ttwABcoj55x/g8mXgwQObVrVUsmlw6tOnj8nrTz75BIsXL8bhw4fNBietVgutVmt8/cMPP+Du3bsYMWKESTlJkqyaECstLQ1paWnG10lJSRZvS0REZEs6HRARAezZIwehvG7fFh0NHD5ctlp4SptSM8ZJp9Nh48aNSE5ORkhIiEXbLF++HJ07d0a1atVMlj948ADVqlWDTqfDE088gY8++ghNmzbNdT+hoaGYPXt2oepPRERUVFJSgIkTgV9/BW7fBhQKQK+Xu8FcXYGaNQFfX+DGDeCPP+SB1VQyJGHj24OfOXMGISEhSE1NhaurK9atW4eePXvmu11sbCwCAgKwbt06DBgwwLj88OHDuHTpEho3boykpCQsWLAAO3bswOnTp1G7dm2z+zLX4hQQEIDExES4u7sX/k0SEZHdS08HFi4EwsPlbrDMTDkMKZVyMJIkedn9+2wRKhpJALRF/l1u8+CUnp6OqKgo3Lt3D+Hh4fj222+xb98+NGjQIM/tQkND8eWXX+Lff/+FSqXKtZxer0ezZs3Qrl07LFy40KI6JSUlQast+g+biIjKF50O2LoVeOMNwHDbQycneQ6h9HR5vVIptyBxvFBJK57gZPOuOpVKZRwc3rx5cxw9ehQLFizAN998k+s2QgisWLECQ4YMyTM0AYBCoUCLFi1w8eLFIq03ERGVX4YJFadOBc6csW7blBQgJqZ46kW2Z/PglJ0QwqTbzJx9+/bh0qVLGDlypEX7O3XqFBo3blxUVSQiojLswQPgpZfkAdUPHuQ9mJooO5sGp6lTp6JHjx4ICAjA/fv3ERYWhoiICOzcuRMAMGXKFMTExGDNmjUm2y1fvhytWrVCo0aNcuxz9uzZaN26NWrXro2kpCQsXLgQp06dwqJFi0rkPRERke0YrjCLiJDHD3l6Aj4+cgtQWBjw558MSmWJUikPiM+PJAEqFeDvDwwbBkyYIM8TleVC/CJj0+B08+ZNDBkyBLGxsdBqtQgKCsLOnTvRpUsXAPIA8KioKJNtEhMTER4ejgULFpjd57179/Daa68hLi4OWq0WTZs2xf79+9GyZctifz9ERFQ8dDrgwAE5AN26BXh5AQkJpj/37gU2buRYotJIqZQnx8xt5nCdTg66Li5AUBDw7rtA587yuoIqrnmvbT44vDTi4HAiopKTXyi6cgVYswZITLR1TQmQrwB0dZWDjhCPg49hILyDA+DoKE+XMHSo3PqTz3DkYlFc3+WlbowTERGVL1knaLxyRQ5HKSny1WepqcDJk/K9yqjkKRRAgwZAo0Y5Zw6PjpavDHRxkeeNeu45YNw424Sg0oTBiYiICiz7rNU6nTxhY9ZgdPQoJ2gsKY6OQKtWQEBA7mUUCqBaNeDpp4EOHQrXHWaPGJyIiChXed3OIzqas1aXFEdHwNk558zhksQgVNIYnIiI7Ex+9zbT6+VWoxs3Hs9wTUVPoQDc3HLOHK7Xl44xQmQegxMRURlkzY1ds2IrUcmoUsV05nC1GsjIANzdgU6dgHnz5K5MKnsYnIiIbCT7nEMeHsCdO0BU1OMgpFDI41U8PR+vi4pi+CltVCo5EH3/vdyVRuUXgxMRUTHILRTduCFP0hcTI885VFxzzVDhKRTyFWXt2smTZzIQEcDgRERkFU7EWDa4uQFduwIhIaYzh//zjxxkK1YEevUC5s9nlxlZh8GJiCiLvILR3r3A1q1yyxHZjlYLDBkCBAbmDK7e3vL4orZtc15d9v77tqkvlS8MTkRUrhmCUGws4Ocnf6EC5sPRtWvAunXyMrINZ2fglVesD0VEJYXBiYjKrOyhqE0b4NChx69v3wYmTpTHFRl4eck/ExJsU2d75+AA1KsnX13m5CSHIaWS8xBR2cHgREQlKmvY8fGRl8XH5/48eytRXqHIcM+svDAwFR9HR6BlS/lmroaZwxmMqLxhcCIiq1kbfgzPf/oJ+O4767vCLG0lyi80UeGYu50HZ60me8PgRFRO5deNVdDXW7cWLPwUBluJio+5MGSYOTwlRR5z1KKFPEcRgxERgxNRqWCuBScuDrh5Uw4ohrl/PDyA06eB69fl7hAh5DIPHsi3a3B1BZo0AWrVAlasyLsbq7CvqfSw5MauWbGViKjgGJyILJTX1Vn5LdPpgK+/lu/7FRgIjBkjf1kdOFD0LTh//ZV7/YvyNRUtjQbo31++aiy/mcOFYPghshUGJyoXChNqLFlm6dVZ5pa5ugIPH8rdHwZvvy13gXCCxPLPMBFjq1amM4d7egL37skBqEMHhh+isoLBiUqOEEBaOpCpA5QKeZlOb/rcQQmoHIH0DCBTBx0UOHoMuHNLD09vJVo8pYLSQZKL63JvsbE01Fi6zBxz680tMxeO9HqGprKuQgWgb1+5xSf7zOGcc4io/GJwotzlFnSyhRuLQtD9h0DSA/l5eoZ8m3BIgEJ61BQjAY4O8n+/9XpAoUBcnB63YjKgypAg3XXAhZOOiPheg+BuFXBf54Tx401bgLKyNNRYuozKD0uCsbc3MGgQUKMGAxERmWJwskdZA1FuIehBivmgky3cQK/PPwQZjieE/E0jHtVBr5PXKxXyyGa9Xl6n1yMpWcLtGAXUDgIqBz289UDcHUcE+j7EpX0ZWBjugxs3eIMpMpV9AHtAAPDll3LYsWTmcIYiIsoPg5O9MISXrC0/ej2gFzlDkF7ILUYSTIMO9CbhBgoJkBR5hyCdDoAAFEo5pKVlyMvVjln2owBUDkBKmlxVtQppyenw9tDh/HUNAMDbIxMBPhk4eMYFDaqnou9T93BhvQZCSLb4NKmEmWslMheKsk+hkFcA6tCh2KtNROWQTYPT4sWLsXjxYly7dg0A0LBhQ8yYMQM9evQwWz4iIgIdO3bMsfz8+fOoV6+e8XV4eDimT5+Oy5cvIzAwEJ988gmeffbZYnkPpY657jVD69HDVOBhilxG5QioVUBqmlzWEIL0eiAzU97O0UEOWAKARiWXeRRuoFEBqekAdIBGDWTozYYgqB2BlHRAEvL+5UrKxzQELb3+8chpIfDggYAAIEFAoxJITVcgKVmJitoMaF31iI5XoX61FFT1Tcf1OHXJfK5UpLy9gcGDgd695dcFmTk8t1DEQERExcmmwcnf3x9z5sxBrVq1AACrV69G3759cfLkSTRs2DDX7S5cuAB3d3fja29vb+PzyMhIDBw4EB999BGeffZZbNmyBQMGDMDvv/+OVq1aFd+bsZWsQSkjA0hKlgOSoXvN0Hr0KIpAetSNlpEpt/5Ikhx0DCFIrZJbiYSQt5UPIu/f0eHxMQ3XSgsBCH2uIchwWOh0gP7Rt5xCetyfIink/RuPBegyHz9XKuTn6ZkS3JwBlYMeCUkOqOIt4ObE6+OtUdTzOFkTfswFoYJ0hTEUEZGtSUIIkX+xkuPp6YkvvvgCI0eOzLHO0OJ09+5deHh4mN1+4MCBSEpKws8//2xc1r17d1SoUAHr1683u01aWhrS0tKMr5OSkhAQEIDExESTgFZqmOt2S8+Qw49CkluA0tOBDN3j1iOHR2HJsB4CeJj2eIxTapq8X7VK3peEx2FGIckByDAWytBilZFp+lyS5NeGYCaE3OKUnuV5RiYA6XG4Muxb7fioG0/gfqYKKYny8yuxGqSmK6B21MNJrUfEKXfo9YCXNhMzV1Zhi1MeXF2Bd94Batcu2pnDLekGIyKytaSkJGi12iL/Li81Y5x0Oh02btyI5ORkhISE5Fm2adOmSE1NRYMGDTBt2jST7rvIyEhMnDjRpHy3bt0wf/78XPcXGhqK2bNnF6r+JSY5Bbh991HLUsrjoIJHY310OuDBw0ctS8osrUePxiQJIYcXB2XOliAAgHi0K0l+jqzPs2RshWT6XMLj9SbrFPJDp5PrpFDKrVdCyGEuM1P+qXjUjSdJcHWVkJoI6CEhNV0+truLDrEJKiQ+UKBB9VQc/8cFUTdVRfOZFlBh5nFSKHLO45S9BacwM4cPHy5fJp892GRvsSnsayIie2Pz4HTmzBmEhIQgNTUVrq6u2LJlCxo0aGC2rJ+fH5YuXYrg4GCkpaXhf//7Hzp16oSIiAi0a9cOABAXFwdfX1+T7Xx9fREXF5drHaZMmYJJkyYZXxtanEqD9HRg4ULghx8E6vkm4dWetxFYIxNeWj0UkiQHoAydHEbUjoDC4XFLkiHnGK52k6TH3WQOisctRCaBSJEz3Bi6+qTH4QaSlOX5o3CUkTMEyQ/I3+B6ASglIPPRvsSj40mQW6UcHeTDZGRC7aJE1A0FKntlQkDg/kMlbtxyRIPqqbiV6Iitv3vAGBbNKOp5nPK7OqswM4ezBYeIqOyweXCqW7cuTp06hXv37iE8PBzDhg3Dvn37zIanunXrom7dusbXISEhiI6Oxty5c43BCQAkyfQLVQiRY1lWarUaanXp6fLR6YDffgPGjwf+/huoVzUFzz51By90uAtvdx1uRUvQJeqgdndEBa9HASkj4/E4JENLkqPStPVIIcnhJeu9HHQ6GAOIIQQ5QG4JUijkgGUoY9i/gBx0HJRy+UwdoIfZEIS0DLmcsyZLd6JSHg9leBj2r3J8dEWegLurhIpC4FZMOtIzFLh1zxGSBFy+6YLgbh745Kmc8zgZWmz69i36mcOtvTor+zKlEpgwwbJtiYio9LJ5cFKpVMbB4c2bN8fRo0exYMECfPPNNxZt37p1a6xdu9b4ulKlSjlal+Lj43O0QpVGOh0wezYQGvp4aFK9qikY93w8qvqmQq0SuHHbAc4qASd1Bh7cy4CAAp4VpMchyFGZpSUJj1uPgMfjnAyDtgF5O8N8TpLCdNySQpJbs7IGnSzhxhjEMtIBpUOuIUjuupPk8VNuroDWFXB9NAdTHpNmVqqhg/ejmcPFLT3qeivxcpaZw/v2zT/cWBJqrFlGRET2zebBKTshhMlA7fycPHkSfn5+xtchISHYvXu3yTinXbt2oU2bNkVaz6L2/fdya4khMAGAJAn0e+ouKmozcD1Oheq+cuuLBIGUNAXUjgIP7mSiQgVHSEqFHHIMd/80jClyUMo7NXTTSZKcLjIfrXdSP+pqyxKC8CggOSgBlSpn0CnIzOGGcKZWPe7my4tGbgFUAmjdwXwRpZLhhoiISpZNg9PUqVPRo0cPBAQE4P79+wgLC0NERAR27twJQB57FBMTgzVr1gAA5s+fj+rVq6Nhw4ZIT0/H2rVrER4ejvDwcOM+x48fj3bt2uGzzz5D3759sXXrVvz666/4/fffbfIe86PTyS0lkZE511X1TUe9aqmIjldBoRDI0ElQOQikpkt4mKqE1kUHlUMm7ic5wN1FKYcVvXg8dihTb9p6lP4oQDlrAGcnORC5OecegvILOhoLuzctLUdERFTK2TQ43bx5E0OGDEFsbCy0Wi2CgoKwc+dOdOnSBQAQGxuLqKgoY/n09HRMnjwZMTExcHJyQsOGDbF9+3b07NnTWKZNmzYICwvDtGnTMH36dAQGBmLDhg2lcg6nTZuAgQNNr7bKys1JByeVHsmpCuj1wO1EB/h5ZeDWPQfcTnSAWqWHq0ZAl6GTA5NKBXmw0aO5mXQ6mG09cnPOGYgYboiIiPJV6uZxKg2Ka+6HrN5+G5g3L+8y1SqlYfaIGCQkOiDpoTxzdqv6D+Gs0SEpWQkntQ6VvDLh4SFBo1EALk6Amwvg7iKHJWtaj4iIiMqRcj+Pkz3p0wf46af8y0XdVOHv6xo0q/MQf13T4HaiI46cd0a9qqmoqM2El7sOt+45wqeRO6B1M9+SREREREWGwamENW8OHD9uWVkhJPzwewVU9c1Aw+ryWKe79x3w52Un1K2air+jnFCleUUoAt0ZloiIiEqAIv8iVFSCgy0PTQZ/RzlhYbgPTvzjDC9tJuoEpMFLq8PRC+7walYZbftoGZqIiIhKCFucSkhwMHDiRMG2/TvKCZ+t16CqbzqCGujwzvtKvNzx8XxGREREVDIYnEpAnz4FD00GL78s4dtv1fKFc0RERGQT7KorZpMmWTYQPDfTp8vzV65ZA4YmIiIiG2OLUzHauBH4z38Ktm3r1sDvv/Omr0RERKUJW5yKiU4HDB1asG0nTpRnEmdoIiIiKl3Y4lRMXnoJSE21bhuFAggLA/r3L546ERERUeEwOBWDjRvlm/Zao3Zt4Px5tjIRERGVZhZ11Xl6euL27dsAgFdeeQX3798v1kqVZQXpoqtdG/jnH4YmIiKi0s6i4JSeno6kpCQAwOrVq5FqbR+UHfnoI+u66Bwc5JYmIiIiKv0s6qoLCQlBv379EBwcDCEExo0bBycnJ7NlV6xYUaQVLEt0OuDTT63bZv16tjQRERGVFRYFp7Vr1+I///kPLl++DABITExkq5MZH30EZGRYXn7AAOCFF4qvPkRERFS0JCGEsGaDGjVq4NixY/Dy8iquOtlcUlIStFotEhMT4e7ubtE2Oh3g5GR5cNJogAcP2NpERERUHAryXW4JqweHd+zYESpOYZ2Dta1N//sfQxMREVFZw8HhRcDasU3soiMiIiqbODi8CFjT2uToCKxbV7z1ISIiouJh9eBwSZI4ODwLnQ74/HPLy0+dyi46IiKisoqDw82wZkDZb78BnTtbtl+VCnj4kMGJiIiouNl0cHhWV69eNYamwrY6LV68GEFBQXB3d4e7uztCQkLw888/51p+8+bN6NKlC7y9vY3lf/nlF5Myq1atgiRJOR7F1UK2ZInlZadMYWgiIiIqy6wOTnq9Hh999BGqVKkCV1dXXLlyBQAwffp0LF++3Kp9+fv7Y86cOTh27BiOHTuGp59+Gn379sVff/1ltvz+/fvRpUsX7NixA8ePH0fHjh3Rp08fnDx50qScu7s7YmNjTR4ajcbat5ovnQ746SfLyqpUwPTpRV4FIiIiKkFWB6ePP/4Yq1atwueff24yLUHjxo3x7bffWrWvPn36oGfPnqhTpw7q1KmDTz75BK6urjh8+LDZ8vPnz8e7776LFi1aoHbt2vj0009Ru3Zt/PjjjyblJElCpUqVTB7FISLC8tursLWJiIio7LM6OK1ZswZLly7F4MGDocySBIKCgvD3338XuCI6nQ5hYWFITk5GSEiIRdvo9Xrcv38fnp6eJssfPHiAatWqwd/fH717987RIpVdWloakpKSTB6WsLSbjq1NRERE5YPVwSkmJga1atXKsVyv1yPDmhkgHzlz5gxcXV2hVqsxevRobNmyBQ0aNLBo2y+//BLJyckYMGCAcVm9evWwatUqbNu2DevXr4dGo8GTTz6Jixcv5rqf0NBQaLVa4yMgICDfY1vTTffMM2xtIiIiKg+sDk4NGzbEgQMHcizfuHEjmjZtanUF6tati1OnTuHw4cN44403MGzYMJw7dy7f7davX49Zs2Zhw4YN8PHxMS5v3bo1Xn75ZTRp0gRt27bF999/jzp16uCrr77KdV9TpkxBYmKi8REdHZ3v8a3pphs92rJyREREVLpZNI8TALzyyitYsGABZs6ciSFDhiAmJgZ6vR6bN2/GhQsXsGbNGvxkaRNMFiqVytiC1bx5cxw9ehQLFizAN998k+s2GzZswMiRI7Fx40Z0zmcuAIVCgRYtWuTZ4qRWq6FWq62qt6XddE5OQIcOVu2aiIiISimLW5xWr16NlJQU9OnTBxs2bMCOHTsgSRJmzJiB8+fP48cff0SXLl0KXSEhBNLS0nJdv379egwfPhzr1q1Dr169LNrfqVOn4OfnV+i6GVjTTderF7vpiIiIyguLW5yyzpPZrVs3dOvWrdAHnzp1Knr06IGAgADcv38fYWFhiIiIwM6dOwHIXWgxMTFYs2YNADk0DR06FAsWLEDr1q0RFxcHAHBycoJWqwUAzJ49G61bt0bt2rWRlJSEhQsX4tSpU1i0aFGh62vAbjoiIiL7ZHFwAuTL/IvSzZs3MWTIEMTGxkKr1SIoKAg7d+40tlzFxsYiKirKWP6bb75BZmYmxo4di7FjxxqXDxs2DKtWrQIA3Lt3D6+99hri4uKg1WrRtGlT7N+/Hy1btiyyerObjoiIyD5ZfMsVhUIBrVabb3i6c+dOkVTMlnKbpl2nk1ubevSw7Ka+L7wAbNxYfPUkIiIi84rrlitWtTjNnj3b2CVmbzZvBsaPB27csHwbdtMRERGVL1YFpxdffNHk0n97sXmz3Hpkze2Q2U1HRERU/lh8VV1Rj28qK3Q6uaXJmtAE8Go6IiKi8sji4GThUKhy58AB67rnDNhNR0REVP5Y3FWn1+uLsx6lVmys9du4urKbjoiIqDyy+pYr9qYg82b2789uOiIiovKIwSkfbdsCXl7WbdOpU/HUhYiIiGyLwakYVKli6xoQERFRcWBwyseBA0BCguXl3d3lVioiIiIqfxic8mHt4PCuXTm+iYiIqLxicMqHtYPDOQ0BERFR+cXglI9btywvy9nCiYiIyjcGpzzodMCkSZaXf/dddtMRERGVZwxOeTh0yPJZw11dgenTi7c+REREZFsMTnmIi7O87OrVbG0iIiIq7xic8lCpkmXlZs8GnnuueOtCREREtsfglIc2bQB/f0CSci/j7w988EHJ1YmIiIhsh8EpD0olsGCB/Dx7eJIk+bFgAbvoiIiI7AWDUz6eew7YtCnnbVT8/eXl7KIjIiKyHw62rkBZ8NxzQN++8u1XYmPlSTHbtmVLExERkb1hixMRERGRhWwanBYvXoygoCC4u7vD3d0dISEh+Pnnn/PcZt++fQgODoZGo0HNmjWxZMmSHGXCw8PRoEEDqNVqNGjQAFu2bClUPTdvBqpXBzp2BF56Sf5Zvbq8nIiIiOyHTYOTv78/5syZg2PHjuHYsWN4+umn0bdvX/z1119my1+9ehU9e/ZE27ZtcfLkSUydOhXjxo1DeHi4sUxkZCQGDhyIIUOG4PTp0xgyZAgGDBiAI0eOFKiOmzcDL7yQcyLMmBh5OcMTERGR/ZCEEMLWlcjK09MTX3zxBUaOHJlj3XvvvYdt27bh/PnzxmWjR4/G6dOnERkZCQAYOHAgkpKSTFquunfvjgoVKmD9+vUW1SEpKQlarRZ37iQiKMg919nDJUkeJH71Ksc7ERERlSaG7/LExES4u7sX2X5LzRgnnU6HsLAwJCcnIyQkxGyZyMhIdO3a1WRZt27dcOzYMWRkZORZ5tChQ7keOy0tDUlJSSYPIP9brggBREfLg8aJiIio/LN5cDpz5gxcXV2hVqsxevRobNmyBQ0aNDBbNi4uDr6+vibLfH19kZmZidu3b+dZJi6P+6eEhoZCq9UaHwEBAY/2Zdl7iI21rBwRERGVbTYPTnXr1sWpU6dw+PBhvPHGGxg2bBjOnTuXa3kp20yUhp7GrMvNlcm+LKspU6YgMTHR+IiOjgZg+S1X/PwsK0dERERlm83ncVKpVKhVqxYAoHnz5jh69CgWLFiAb775JkfZSpUq5Wg5io+Ph4ODA7y8vPIsk70VKiu1Wg21Wp1j+aNGrDwFBMhzOhEREVH5Z/MWp+yEEEhLSzO7LiQkBLt37zZZtmvXLjRv3hyOjo55lmnTpo3VdZk6Nf8y8+ZxYDgREZG9sGmL09SpU9GjRw8EBATg/v37CAsLQ0REBHbu3AlA7kKLiYnBmjVrAMhX0P33v//FpEmTMGrUKERGRmL58uUmV8uNHz8e7dq1w2effYa+ffti69at+PXXX/H7779bXb9//82/TMWKVu+WiIiIyiibBqebN29iyJAhiI2NhVarRVBQEHbu3IkuXboAAGJjYxEVFWUsX6NGDezYsQMTJ07EokWLULlyZSxcuBDPP/+8sUybNm0QFhaGadOmYfr06QgMDMSGDRvQqlWrYnkPHBhORERkP0rdPE6lgWHuByARQN5zP+zdC3ToUBK1IiIiIkuV+3mcSqMKFfJe7+XFgeFERET2hMGJiIiIyEIMTnm4ezfv9QkJnDWciIjInjA4FRIHhxMREdkPBqdC4qzhRERE9oPBqRD8/Tk4nIiIyJ4wOBXCqFGcNZyIiMieMDgVQu3atq4BERERlSQGp0Lg+CYiIiL7wuBUQJz8koiIyP4wOBERERFZiMGpgDj5JRERkf1hcCoETn5JRERkXxicCoGDw4mIiOwLg1MBBQRwcDgREZG9YXAqoHnzOPklERGRvWFwKqCKFW1dAyIiIippDE4FxIHhRERE9ofBqYA4MJyIiMj+MDgVAGcNJyIisk8MTkREREQWsmlwCg0NRYsWLeDm5gYfHx/069cPFy5cyHOb4cOHQ5KkHI+GDRsay6xatcpsmdTU1CKpN2cNJyIisk82DU779u3D2LFjcfjwYezevRuZmZno2rUrkpOTc91mwYIFiI2NNT6io6Ph6emJ/v37m5Rzd3c3KRcbGwuNRlNkdefgcCIiIvvjYMuD79y50+T1ypUr4ePjg+PHj6Ndu3Zmt9FqtdBqtcbXP/zwA+7evYsRI0aYlJMkCZUqVbKoHmlpaUhLSzO+TkpKyncbDg4nIiKyP6VqjFNiYiIAwNPT0+Jtli9fjs6dO6NatWomyx88eIBq1arB398fvXv3xsmTJ3PdR2hoqDGQabVaBAQE5FpWkjhrOBERkb2ShBDC1pUAACEE+vbti7t37+KAhQOIYmNjERAQgHXr1mHAgAHG5YcPH8alS5fQuHFjJCUlYcGCBdixYwdOnz6N2rVr59iPuRYnOTwlAnA3KStJwKZNwHPPFehtEhERUQlISkqCVqtFYmIi3N3d89/AQjbtqsvqzTffxJ9//onff//d4m1WrVoFDw8P9OvXz2R569at0bp1a+PrJ598Es2aNcNXX32FhQsX5tiPWq2GWq226JiTJzM0ERER2atS0VX31ltvYdu2bdi7dy/8/f0t2kYIgRUrVmDIkCFQqVR5llUoFGjRogUuXrxY6LqGhQE6XaF3Q0RERGWQTYOTEAJvvvkmNm/ejD179qBGjRoWb7tv3z5cunQJI0eOtOg4p06dgl8RjOiOjuZUBERERPbKpl11Y8eOxbp167B161a4ubkhLi4OgHzlnJOTEwBgypQpiImJwZo1a0y2Xb58OVq1aoVGjRrl2O/s2bPRunVr1K5dG0lJSVi4cCFOnTqFRYsWFUm9ORUBERGRfbJpcFq8eDEAoEOHDibLV65cieHDhwOQB4BHRUWZrE9MTER4eDgWLFhgdr/37t3Da6+9hri4OGi1WjRt2hT79+9Hy5Yti6TenIqAiIjIPpWaq+pKE8NI/OxX1UkS4O8PXL0KKJU2qx4RERHlo7iuqisVg8PLCiGA+fMZmoiIiOwVgxMRERGRhRicrCBJwIQJnI6AiIjIXjE4WUEITkdARERkzxicCoDTERAREdknBqcC4HQERERE9qnU3KuuLDBMR9C2ra1rQkRERLbAFicLSZL8k9MREBER2S8GJwv5+wObNgHPPWfrmhAREZGtMDhZoGJF4MsvGZqIiIjsHYOTBRISgIEDgc2bbV0TIiIisiUGJwsY7ubHyS+JiIjsG4OThTj5JRERETE4WYmTXxIREdkvBicrcfJLIiIi+8UJMC3EyS+JiIiILU4W4OSXREREBDA4WYSTXxIRERHArro8ffstEBgod8+xpYmIiIgYnPLQvz/g7m7rWhAREVFpwa66PGzcCEREcNJLIiIiktk0OIWGhqJFixZwc3ODj48P+vXrhwsXLuS5TUREBCRJyvH4+++/TcqFh4ejQYMGUKvVaNCgAbZs2WJ1/V59FejYEahenbdbISIiIhsHp3379mHs2LE4fPgwdu/ejczMTHTt2hXJycn5bnvhwgXExsYaH7Vr1zaui4yMxMCBAzFkyBCcPn0aQ4YMwYABA3DkyJEC1TMmBnjhBYYnIiIieycJYbgTm+3dunULPj4+2LdvH9q1a2e2TEREBDp27Ii7d+/Cw8PDbJmBAwciKSkJP//8s3FZ9+7dUaFCBaxfvz5H+bS0NKSlpRlfJyUlISAgAEAiAHmQk2Eep6tXOVCciIiotEtKSoJWq0ViYiLci3DAcqka45SYmAgA8PT0zLds06ZN4efnh06dOmHv3r0m6yIjI9G1a1eTZd26dcOhQ4fM7is0NBRardb4kEOTKd6rjoiIiEpNcBJCYNKkSXjqqafQqFGjXMv5+flh6dKlCA8Px+bNm1G3bl106tQJ+/fvN5aJi4uDr6+vyXa+vr6Ii4szu88pU6YgMTHR+IiOjs71+LxXHRERkf0qNdMRvPnmm/jzzz/x+++/51mubt26qFu3rvF1SEgIoqOjMXfuXJPuPckw3fcjQogcywzUajXUarVF9eS96oiIiOxXqWhxeuutt7Bt2zbs3bsX/v7+Vm/funVrXLx40fi6UqVKOVqX4uPjc7RCWUOSgIAA3quOiIjIntk0OAkh8Oabb2Lz5s3Ys2cPatSoUaD9nDx5En5ZmoJCQkKwe/dukzK7du1CmzZtCrR/3quOiIiIABt31Y0dOxbr1q3D1q1b4ebmZmwl0mq1cHJyAiCPP4qJicGaNWsAAPPnz0f16tXRsGFDpKenY+3atQgPD0d4eLhxv+PHj0e7du3w2WefoW/fvti6dSt+/fXXfLsBc+PvL4cm3quOiIjIvtk0OC1evBgA0KFDB5PlK1euxPDhwwEAsbGxiIqKMq5LT0/H5MmTERMTAycnJzRs2BDbt29Hz549jWXatGmDsLAwTJs2DdOnT0dgYCA2bNiAVq1aWVU/3quOiIiIsipV8ziVFsU19wMRERGVDLuYx6m04b3qiIiIKCsGpzzwXnVERESUFYOTBXivOiIiIgIYnCxiGAU2YQK77YiIiOwZg5OFeK86IiIiYnCyEu9VR0REZL8YnKzEe9URERHZr1Jzk9/STpLkGcR5rzoiIiL7xRYnC/BedURERAQwOFnE3x/YtIn3qiMiIrJ37KrLA+9VR0RERFkxOOWhf3+At6ojIiIiA3bVEREREVmIwYmIiIjIQgxORERERBZicCIiIiKyEAeHmyEe3dU3KSnJxjUhIiKigjB8hxu+04sKg5MZCQkJAICAgAAb14SIiIgKIyEhAVqttsj2x+BkhqenJwAgKiqqSD9sKpikpCQEBAQgOjoa7pwfwuZ4PkoPnovSheejdElMTETVqlWN3+lFhcHJDIVCHvql1Wr5y1+KuLu783yUIjwfpQfPRenC81G6GL7Ti2x/Rbo3IiIionKMwYmIiIjIQgxOZqjVasycORNqtdrWVSHwfJQ2PB+lB89F6cLzUboU1/mQRFFfp0dERERUTrHFiYiIiMhCDE5EREREFmJwIiIiIrIQgxMRERGRhew2OH399deoUaMGNBoNgoODceDAgTzL79u3D8HBwdBoNKhZsyaWLFlSQjW1D9acj82bN6NLly7w9vaGu7s7QkJC8Msvv5Rgbcs/a/99GBw8eBAODg544oknireCdsTac5GWloYPPvgA1apVg1qtRmBgIFasWFFCtS3/rD0f3333HZo0aQJnZ2f4+flhxIgRxtt6UcHt378fffr0QeXKlSFJEn744Yd8tymy73Fhh8LCwoSjo6NYtmyZOHfunBg/frxwcXER169fN1v+ypUrwtnZWYwfP16cO3dOLFu2TDg6OopNmzaVcM3LJ2vPx/jx48Vnn30m/vjjD/HPP/+IKVOmCEdHR3HixIkSrnn5ZO35MLh3756oWbOm6Nq1q2jSpEnJVLacK8i5eOaZZ0SrVq3E7t27xdWrV8WRI0fEwYMHS7DW5Ze15+PAgQNCoVCIBQsWiCtXrogDBw6Ihg0bin79+pVwzcufHTt2iA8++ECEh4cLAGLLli15li/K73G7DE4tW7YUo0ePNllWr1498f7775st/+6774p69eqZLHv99ddF69ati62O9sTa82FOgwYNxOzZs4u6anapoOdj4MCBYtq0aWLmzJkMTkXE2nPx888/C61WKxISEkqienbH2vPxxRdfiJo1a5osW7hwofD39y+2OtojS4JTUX6P211XXXp6Oo4fP46uXbuaLO/atSsOHTpkdpvIyMgc5bt164Zjx44hIyOj2OpqDwpyPrLT6/W4f/9+kd/I0R4V9HysXLkSly9fxsyZM4u7inajIOdi27ZtaN68OT7//HNUqVIFderUweTJk5GSklISVS7XCnI+2rRpgxs3bmDHjh0QQuDmzZvYtGkTevXqVRJVpiyK8nvc7m7ye/v2beh0Ovj6+pos9/X1RVxcnNlt4uLizJbPzMzE7du34efnV2z1Le8Kcj6y+/LLL5GcnIwBAwYURxXtSkHOx8WLF/H+++/jwIEDcHCwuz8pxaYg5+LKlSv4/fffodFosGXLFty+fRtjxozBnTt3OM6pkApyPtq0aYPvvvsOAwcORGpqKjIzM/HMM8/gq6++KokqUxZF+T1udy1OBpIkmbwWQuRYll95c8upYKw9Hwbr16/HrFmzsGHDBvj4+BRX9eyOpedDp9PhpZdewuzZs1GnTp2Sqp5dsebfhl6vhyRJ+O6779CyZUv07NkT8+bNw6pVq9jqVESsOR/nzp3DuHHjMGPGDBw/fhw7d+7E1atXMXr06JKoKmVTVN/jdvffw4oVK0KpVOb4H0J8fHyONGpQqVIls+UdHBzg5eVVbHW1BwU5HwYbNmzAyJEjsXHjRnTu3Lk4q2k3rD0f9+/fx7Fjx3Dy5Em8+eabAOQvbyEEHBwcsGvXLjz99NMlUvfypiD/Nvz8/FClShVotVrjsvr160MIgRs3bqB27drFWufyrCDnIzQ0FE8++STeeecdAEBQUBBcXFzQtm1bfPzxx+ytKEFF+T1udy1OKpUKwcHB2L17t8ny3bt3o02bNma3CQkJyVF+165daN68ORwdHYutrvagIOcDkFuahg8fjnXr1nG8QBGy9ny4u7vjzJkzOHXqlPExevRo1K1bF6dOnUKrVq1KqurlTkH+bTz55JP4999/8eDBA+Oyf/75BwqFAv7+/sVa3/KuIOfj4cOHUChMv2aVSiWAx60dVDKK9Hvc6uHk5YDhktLly5eLc+fOiQkTJggXFxdx7do1IYQQ77//vhgyZIixvOEyxokTJ4pz586J5cuXczqCImTt+Vi3bp1wcHAQixYtErGxscbHvXv3bPUWyhVrz0d2vKqu6Fh7Lu7fvy/8/f3FCy+8IP766y+xb98+Ubt2bfHqq6/a6i2UK9aej5UrVwoHBwfx9ddfi8uXL4vff/9dNG/eXLRs2dJWb6HcuH//vjh58qQ4efKkACDmzZsnTp48aZwaoji/x+0yOAkhxKJFi0S1atWESqUSzZo1E/v27TOuGzZsmGjfvr1J+YiICNG0aVOhUqlE9erVxeLFi0u4xuWbNeejffv2AkCOx7Bhw0q+4uWUtf8+smJwKlrWnovz58+Lzp07CycnJ+Hv7y8mTZokHj58WMK1Lr+sPR8LFy4UDRo0EE5OTsLPz08MHjxY3Lhxo4RrXf7s3bs3z++B4vwel4RgeyERERGRJexujBMRERFRQTE4EREREVmIwYmIiIjIQgxORERERBZicCIiIiKyEIMTERERkYUYnIiIiIgsxOBEREREZCEGJyIiIiILMTgRERERWYjBiYgom4SEBPj4+ODatWvFdowXXngB8+bNK7b9E1HxYHAiohI3fPhwSJKE0aNH51g3ZswYSJKE4cOHl3zFHgkNDUWfPn1QvXp147J27dpBkiR89NFHJmWFEGjVqhUkScKMGTMsPsaMGTPwySefICkpqaiqTUQlgMGJiGwiICAAYWFhSElJMS5LTU3F+vXrUbVqVZvVKyUlBcuXL8err75qXCaEwKlTp1CtWjWcOXPGpPzq1avx77//AgCaNWtm8XGCgoJQvXp1fPfdd0VTcSIqEQxORGQTzZo1Q9WqVbF582bjss2bNyMgIABNmzY1Ltu5cyeeeuopeHh4wMvLC71798bly5dN9rVp0yY0btwYTk5O8PLyQufOnZGcnJzvOnN+/vlnODg4ICQkxLjs4sWLuH//PoYPH24SnO7fv48pU6YYW8eCg4Ot+gyeeeYZrF+/3qptiMi2GJyIyGZGjBiBlStXGl+vWLECr7zyikmZ5ORkTJo0CUePHsVvv/0GhUKBZ599Fnq9HgAQGxuLQYMG4ZVXXsH58+cRERGB5557DkKIPNflZv/+/WjevLnJsuPHj0Oj0WDQoEG4ePEi0tLSAAAfffQRnnjiCfj5+aFixYoICAiw6v23bNkSf/zxh3F/RFT6Odi6AkRkv4YMGYIpU6bg2rVrkCQJBw8eRFhYGCIiIoxlnn/+eZNtli9fDh8fH5w7dw6NGjVCbGwsMjMz8dxzz6FatWoAgMaNGwMA/vnnn1zX5ebatWuoXLmyybITJ04gKCgIderUgYuLC86fPw8XFxd8/fXXOHbsGObOnWt1axMAVKlSBWlpaYiLizPWj4hKN7Y4EZHNVKxYEb169cLq1auxcuVK9OrVCxUrVjQpc/nyZbz00kuoWbMm3N3dUaNGDQBAVFQUAKBJkybo1KkTGjdujP79+2PZsmW4e/duvutyk5KSAo1GY7Ls+PHjCA4OhiRJCAoKwtmzZzFx4kS89tprqFevHo4fP27V+CYDJycnAMDDhw+t3paIbIPBiYhs6pVXXsGqVauwevXqHN10ANCnTx8kJCRg2bJlOHLkCI4cOQIASE9PBwAolUrs3r0bP//8Mxo0aICvvvoKdevWxdWrV/Ncl5uKFSvmCFcnT540BqMmTZpgwYIF+OOPPzBz5kykp6fjr7/+MglODx8+xDvvvIM2bdqgTZs2GDVqFBISEnIc686dOwAAb29vKz81IrIVBicisqnu3bsjPT0d6enp6Natm8m6hIQEnD9/HtOmTUOnTp1Qv359sy1GkiThySefxOzZs3Hy5EmoVCps2bIl33XmNG3aFOfOnTO+vnLlCu7du2fsinviiSdw7NgxfPLJJ9BqtThz5gwyMjJMuurefPNNNGnSBIcOHcKhQ4fw4osvYujQoTnGVp09exb+/v45WtmIqPTiGCcisimlUonz588bn2dVoUIFeHl5YenSpfDz80NUVBTef/99kzJHjhzBb7/9hq5du8LHxwdHjhzBrVu3UL9+/TzX5aZbt26YMmUK7t69iwoVKuD48eNQqVRo1KgRAGDYsGHo168fvLy8AMjjnypUqGDsQkxJScHdu3fx8ssvY9asWQCAWbNmYevWrbh06RJq165tPNaBAwfQtWvXwn2ARFSiGJyIyObc3d3NLlcoFAgLC8O4cePQqFEj1K1bFwsXLkSHDh1Mtt2/fz/mz5+PpKQkVKtWDV9++SV69OiB8+fP57ouN40bN0bz5s3x/fff4/XXX8eJEyfQqFEjODo6AgAcHR1NWohOnDhhMn1C1lalN998M9fjpKamYsuWLfjll1/y/XyIqPSQRF7X5RIR2aEdO3Zg8uTJOHv2LBQK60c0DB8+HF26dMHgwYMBAL/99hvmzp2LHTt2QJIkAMCiRYuwdetW7Nq1q0jrTkTFiy1ORETZ9OzZExcvXkRMTIzVczMBwNdff41p06Zh4cKFkCQJ9evXx9q1a42hCZBbrr766quirDYRlQC2OBERERFZiFfVEREREVmIwYmIiIjIQgxORERERBZicCIiIiKyEIMTERERkYUYnIiIiIgsxOBEREREZCEGJyIiIiILMTgRERERWYjBiYiIiMhCDE5EREREFmJwIiIiIrIQgxMRERGRhRiciIiIiCzE4ERERERkIQYnIiIiIgsxOFG59Oeff2LEiBGoUaMGNBoNXF1d0axZM3z++ee4c+eOTer09ddfY9WqVTmWX7t2DZIkmV1X3AzHNjwUCgW8vLzQs2dPREZGWr2/WbNmQZKkAtXl3LlzmDVrFq5du1ag7XMzbdo0VK1aFQ4ODvDw8CjSfWdXmPdfUlatWmU83xERETnWCyFQq1YtSJKEDh06lHj9LJGUlIQ5c+agVatW8PDwgKOjI3x9fdG9e3esW7cOaWlptq4ilWMMTlTuLFu2DMHBwTh69Cjeeecd7Ny5E1u2bEH//v2xZMkSjBw50ib1yi04+fn5ITIyEr169Sr5Sj3y1ltvITIyEgcOHEBoaChOnz6Njh074uTJk1bt59VXXy1Q4ALk4DR79uwiDU5bt27FJ598gqFDh2Lfvn349ddfi2zfZZ2bmxuWL1+eY/m+fftw+fJluLm52aBW+bt48SKaNm2KTz75BE899RTWrFmDPXv24KuvvkKVKlXwyiuv4OOPP7Z1Nakcc7B1BYiKUmRkJN544w106dIFP/zwA9RqtXFdly5d8Pbbb2Pnzp157iMlJQVOTk7FXVUjtVqN1q1bl9jxzKlataqxDk8++SRq1aqFTp064euvv8ayZcss3o+/vz/8/f2Lq5pWO3v2LABg3Lhx8PHxKZJ9Pnz4EM7OzkWyL1saOHAgvvvuOyxatAju7u7G5cuXL0dISAiSkpJsWDvzMjMz0a9fP9y5cwd//PEH6tevb7J+wIABmDFjhtWBn8gabHGicuXTTz+FJElYunSpSWgyUKlUeOaZZ4yvq1evjt69e2Pz5s1o2rQpNBoNZs+eDQCIi4vD66+/Dn9/f6hUKtSoUQOzZ89GZmamyT5nz56NVq1awdPTE+7u7mjWrBmWL18OIYTJcf766y/s27fP2E1SvXp1AOa76gxdPn/99RcGDRoErVYLX19fvPLKK0hMTDQ5/r179zBy5Eh4enrC1dUVvXr1wpUrVyBJEmbNmlWgz9EQoq5fv25ctmLFCjRp0gQajQaenp549tlncf78eZPtzHVVGT7jnTt3olmzZnByckK9evWwYsUKY5lVq1ahf//+AICOHTsaPyPDZ3Ly5En07t0bPj4+UKvVqFy5Mnr16oUbN27k+h6qV6+OadOmAQB8fX1NPg+9Xo/PP/8c9erVg1qtho+PD4YOHZpjfx06dECjRo2wf/9+tGnTBs7OznjllVes+CQtP5YQAp9++imqVasGjUaD5s2bY/fu3ejQoUOOLrO//voLXbt2hbOzM7y9vTF27Fhs37491+43cwYNGgQAWL9+vXFZYmIiwsPDc32PlvyuA8CePXvQoUMHeHl5wcnJCVWrVsXzzz+Phw8fGsssXrwYTZo0gaurK9zc3FCvXj1MnTo1zzpv2bIF586dwwcffJAjNBlUq1YN/fr1M75OTU3F22+/jSeeeAJarRaenp4ICQnB1q1bc2wrSRLefPNNfPPNN6hTpw7UajUaNGiAsLCwPOtF9oUtTlRu6HQ67NmzB8HBwQgICLB4uxMnTuD8+fOYNm0aatSoARcXF8TFxaFly5ZQKBSYMWMGAgMDERkZiY8//hjXrl3DypUrjdtfu3YNr7/+OqpWrQoAOHz4MN566y3ExMRgxowZAOQ/+C+88AK0Wi2+/vprADAb7LJ7/vnnMXDgQIwcORJnzpzBlClTAMAYOvR6Pfr06YNjx45h1qxZaNasGSIjI9G9e3eL3785ly5dAgB4e3sDAEJDQzF16lQMGjQIoaGhSEhIwKxZsxASEoKjR4+idu3aee7v9OnTePvtt/H+++/D19cX3377LUaOHIlatWqhXbt26NWrFz799FNMnToVixYtQrNmzQAAgYGBSE5ORpcuXVCjRg0sWrQIvr6+iIuLw969e3H//v1cj7llyxYsWrQIy5cvx86dO6HVao2tYW+88QaWLl2KN998E71798a1a9cwffp0RERE4MSJE6hYsaJxP7GxsXj55Zfx7rvv4tNPP4VCYd3/Ny091gcffIDQ0FC89tpreO655xAdHY1XX30VGRkZqFOnjkl92rdvDxcXFyxevBg+Pj5Yv3493nzzTavq5e7ujhdeeAErVqzA66+/DkAOUQqFAgMHDsT8+fNzbGPJ7/q1a9fQq1cvtG3bFitWrICHhwdiYmKwc+dOpKenw9nZGWFhYRgzZgzeeustzJ07FwqFApcuXcK5c+fyrPPu3bsBwOQ/P/lJS0vDnTt3MHnyZFSpUgXp6en49ddf8dxzz2HlypUYOnSoSflt27Zh7969+PDDD+Hi4oKvv/4agwYNgoODA1544QWLj0vlmCAqJ+Li4gQA8eKLL1q8TbVq1YRSqRQXLlwwWf76668LV1dXcf36dZPlc+fOFQDEX3/9ZXZ/Op1OZGRkiA8//FB4eXkJvV5vXNewYUPRvn37HNtcvXpVABArV640Lps5c6YAID7//HOTsmPGjBEajca43+3btwsAYvHixSblQkNDBQAxc+bMPN+/4difffaZyMjIEKmpqeL48eOiRYsWAoDYvn27uHv3rnBychI9e/Y02TYqKkqo1Wrx0ksv5ah3VtWqVRMajcbks0xJSRGenp7i9ddfNy7buHGjACD27t1rsv2xY8cEAPHDDz/k+V7MMdTn1q1bxmXnz58XAMSYMWNMyh45ckQAEFOnTjUua9++vQAgfvvtN6uOZ+2x7ty5I9RqtRg4cKBJucjISAHA5PfmnXfeEZIk5fgd7Natm9nPL7uVK1cKAOLo0aNi7969AoA4e/asEEKIFi1aiOHDhwshcv99Ncjtd33Tpk0CgDh16lSu27755pvCw8Mjz3qa0717dwFApKammizX6/UiIyPD+MjMzMx1H5mZmSIjI0OMHDlSNG3a1GQdAOHk5CTi4uJMyterV0/UqlXL6vpS+cSuOrJ7QUFBJv+jB4CffvoJHTt2ROXKlZGZmWl89OjRA4A8gNZgz5496Ny5M7RaLZRKJRwdHTFjxgwkJCQgPj6+UHXL/j/roKAgpKamGvdrqMeAAQNMyhm6YSz13nvvwdHRERqNBsHBwYiKisI333xjvLouJSUFw4cPN9kmICAATz/9NH777bd89//EE08YWykAQKPRoE6dOiZdgbmpVasWKlSogPfeew9LlizJt1UiP3v37gWAHO+nZcuWqF+/fo73U6FCBTz99NPFeqzDhw8jLS0tx3ls3bq1sUvXYN++fWjUqBEaNGhgstzacw4A7du3R2BgIFasWIEzZ87g6NGjeXZFWvK7/sQTT0ClUuG1117D6tWrceXKlRz7admyJe7du4dBgwZh69atuH37ttV1z2rBggVwdHQ0Ppo0aWKyfuPGjXjyySfh6uoKBwcHODo6Yvny5Tm6mgGgU6dO8PX1Nb5WKpUYOHAgLl26lGfXMNkPBicqNypWrAhnZ2dcvXrVqu38/PxyLLt58yZ+/PFHkz/Gjo6OaNiwIQAY/9D/8ccf6Nq1KwD5ar6DBw/i6NGj+OCDDwDIA80Lw8vLy+S1oXvPsN+EhAQ4ODjA09PTpFzWP/yWGD9+PI4ePYrjx4/j8uXLiI2NxWuvvWY8BmD+c6pcubJxvTXvw/BeLPl8tFot9u3bhyeeeAJTp05Fw4YNUblyZcycORMZGRn5bp+dte/HXLmiPpbhp7nzln1ZQkKCReUsIUkSRowYgbVr12LJkiWoU6cO2rZta7aspb/rgYGB+PXXX+Hj44OxY8ciMDAQgYGBWLBggXFfQ4YMwYoVK3D9+nU8//zz8PHxQatWrYxdcbkxhO/sgfull17C0aNHcfToUWM3r8HmzZsxYMAAVKlSBWvXrkVkZKQxIKampuY4RqVKlXJdZsnvOpV/HONE5YZSqUSnTp3w888/48aNGxZf3WVu3p2KFSsiKCgIn3zyidltKleuDAAICwuDo6MjfvrpJ2g0GuP6H374wfo3UABeXl7IzMzEnTt3TMJTXFycVfvx9/dH8+bNcz0GII+tye7ff/81GQ9UXBo3boywsDAIIfDnn39i1apV+PDDD+Hk5IT333/fqn1lfT/Zf0fMvZ/CzMtk6bEM5W7evJljH3FxcSatTl5eXrmWK4jhw4djxowZWLJkSa6/74B1v+tt27ZF27ZtodPpcOzYMXz11VeYMGECfH198eKLLwIARowYgREjRiA5ORn79+/HzJkz0bt3b/zzzz+oVq2a2Tp06dIFS5cuxbZt2zB58mTjch8fH+NVk25ubibzOK1duxY1atTAhg0bTM5lbnM9mfscDcvM/QeA7A9bnKhcmTJlCoQQGDVqFNLT03Osz8jIwI8//pjvfnr37o2zZ88iMDAQzZs3z/EwBCdJkuDg4AClUmncNiUlBf/73/9y7NPSFhZrtG/fHgCwYcMGk+VFeRVQSEgInJycsHbtWpPlN27cwJ49e9CpU6ciOU721jRzJElCkyZN8J///AceHh44ceKE1ccxdLtlfz9Hjx7F+fPni+z9WHOsVq1aQa1W5ziPhw8fztG60r59e5w9ezZHl2VBz3mVKlXwzjvvoE+fPhg2bFiu5az5XTdQKpVo1aoVFi1aBABmz5eLiwt69OiBDz74AOnp6fjrr79y3d+zzz6LBg0a4NNPP8Xff/9tyduDJElQqVQmoSkuLs7sVXUA8Ntvv5kEU51Ohw0bNiAwMLBUTbVBtsMWJypXQkJCsHjxYowZMwbBwcF444030LBhQ2RkZODkyZNYunQpGjVqhD59+uS5nw8//BC7d+9GmzZtMG7cONStWxepqam4du0aduzYgSVLlsDf3x+9evXCvHnz8NJLL+G1115DQkIC5s6da/aKOUOryYYNG1CzZk1oNBo0bty4UO+3e/fuePLJJ/H2228jKSkJwcHBiIyMxJo1awDA6ivAzPHw8MD06dMxdepUDB06FIMGDUJCQgJmz54NjUaDmTNnFvoYANCoUSMAwNKlS+Hm5gaNRoMaNWogMjISX3/9Nfr164eaNWtCCIHNmzfj3r176NKli9XHqVu3Ll577TV89dVXUCgU6NGjh/FKt4CAAEycOLFI3o81x/L09MSkSZMQGhqKChUq4Nlnn8WNGzcwe/Zs+Pn5mZzHCRMmYMWKFejRowc+/PBD+Pr6Yt26dcYgUZBzPmfOnHzLWPq7vmTJEuzZswe9evVC1apVkZqaarwKtHPnzgCAUaNGwcnJCU8++ST8/PwQFxeH0NBQaLVatGjRItc6KJVK/PDDD+jWrRtatmyJUaNGoUOHDqhQoQLu3buHI0eO4PTp0yZTFRimGxkzZgxeeOEFREdH46OPPoKfnx8uXryY4xgVK1bE008/jenTpxuvqvv77785JQE9ZuPB6UTF4tSpU2LYsGGiatWqQqVSCRcXF9G0aVMxY8YMER8fbyxXrVo10atXL7P7uHXrlhg3bpyoUaOGcHR0FJ6eniI4OFh88MEH4sGDB8ZyK1asEHXr1hVqtVrUrFlThIaGiuXLlwsA4urVq8Zy165dE127dhVubm4CgKhWrZoQIu+r6rJeDSbE4yuisu73zp07YsSIEcLDw0M4OzuLLl26iMOHDwsAYsGCBXl+ToZjf/HFF/l8okJ8++23IigoSKhUKqHVakXfvn1zXNmV21V15j7j9u3b57hqa/78+aJGjRpCqVQaP5O///5bDBo0SAQGBgonJyeh1WpFy5YtxapVq/Ktc26fo06nE5999pmoU6eOcHR0FBUrVhQvv/yyiI6OzlHHhg0b5nuc7McryLH0er34+OOPhb+/v1CpVCIoKEj89NNPokmTJuLZZ581KXv27FnRuXNnodFohKenpxg5cqRYvXq1ACBOnz6dZx2zXlWXF3NX1Vnyux4ZGSmeffZZUa1aNaFWq4WXl5do37692LZtm3E/q1evFh07dhS+vr5CpVKJypUriwEDBog///wzzzoZJCYmik8//VS0aNFCuLu7CwcHB+Hj4yO6dOkiFi1aJJKTk03Kz5kzR1SvXl2o1WpRv359sWzZMrPnCoAYO3as+Prrr0VgYKBwdHQU9erVE999951F9SL7IAmRbeYyIirz1q1bh8GDB+PgwYNo06aNratDBXT16lXUq1cPM2fOzHdyyNdeew3r169HQkICVCpVCdWwfJEkCWPHjsV///tfW1eFSjF21RGVcevXr0dMTAwaN24MhUKBw4cP44svvkC7du0YmsqQ06dPY/369WjTpg3c3d1x4cIFfP7553B3d89xf8UPP/wQlStXRs2aNfHgwQP89NNP+PbbbzFt2jSGJqJixuBEVMa5ubkhLCwMH3/8MZKTk+Hn54fhw4fzRqdljIuLC44dO4bly5fj3r170Gq16NChAz755JMcUw04Ojriiy++wI0bN5CZmYnatWtj3rx5GD9+vI1qT2Q/2FVHREREZCFOR0BERERkIQYnIiIiIgtxjJMZer0e//77L9zc3Ao1azARERHZhhAC9+/fR+XKlYtkTjsDBicz/v33XwQEBNi6GkRERFRI0dHRRTrrO4OTGW5ubgDkD9vd3d3GtSEiIiJrJSUlISAgwPidXlQYnMwwdM+5u7szOBEREZVhRT3kplQPDp81axYkSTJ5VKpUKc9t9u3bh+DgYGg0GtSsWRNLliwpodoSERFReVfqW5waNmyIX3/91fg66525s7t69Sp69uyJUaNGYe3atTh48CDGjBkDb29vPP/88yVRXSIiIirHSn1wcnBwyLeVyWDJkiWoWrUq5s+fDwCoX78+jh07hrlz5+YZnNLS0pCWlmZ8nZSUVKg6ExERUflUqrvqAODixYuoXLkyatSogRdffBFXrlzJtWxkZCS6du1qsqxbt244duwYMjIyct0uNDQUWq3W+ChTV9QJAaSmAQ8eyj/1+sevU1LlR/bnqWnydkRERGSVUt3i1KpVK6xZswZ16tTBzZs38fHHH6NNmzb466+/4OXllaN8XFxcjns6+fr6IjMzE7dv34afn5/Z40yZMgWTJk0yvjaMxC8tdDogIkLg1OF0qJU6NG2hQOtWgDIlBUh6AKRnyIFJL+SfCoX8MyMDgAQoJPk1JMDRQX6oHAGtG+DmLD9PzwAydYCDElCrAM5fRURElEOpDk49evQwPm/cuDFCQkIQGBiI1atXmwSdrLKPnjfcii+vUfVqtRpqtboIalx0dDrgt9+ADz8E7sak4Jk2d1GvWip8PDLgdCUDF2MFAirr4eIMOfioVXJLUqZODkqSQm5V0uvk0KRUyGHIEKgSHwC37wGaRyFJoXi0nfQ4VLk6PaqMnoGKiIgIpTw4Zefi4oLGjRvj4sWLZtdXqlQJcXFxJsvi4+Ph4OBgtoWqNEpPB0aNAtaulTNOvaopGPd8PCpqM5CcooC3RyZcnfSo4JaJtGQgQzjCQ8oE0jIehR4HIDUdgA7QqIEM/eNWKAelHK4kSQ5M6ZnAw1T5tYMScNYAaelyqIq/K4ctpYKtVERERI+UqeCUlpaG8+fPo23btmbXh4SE4McffzRZtmvXLjRv3hyOjo4lUcUCS08HunYF9u17vEySBPo9dRcVtRk4d02NJxsnw0mtx537Sri76CBJAg/uCGi1SkjpaXKAwaPwIgQg9I9bm3Q6w15Nxzfp9XLAyswEkpLlsg4KOYjp9QCUbKUiIiJ6pFQHp8mTJ6NPnz6oWrUq4uPj8fHHHyMpKQnDhg0DII9NiomJwZo1awAAo0ePxn//+19MmjQJo0aNQmRkJJYvX47169fb8m3kyVxgMqjqm4561VIRHa+C1lWPitpMJCUr4aAUUEhASpoElYMOyQ8UcHWAHI70WaZr0AtA4FFwEfJ6hUJepn8UqgB5nXi0vaMSyHw0HgpCLp+WXrBWKpUj4KQG3F3l58pH1yLo9Lk/Z9giIqJSrFQHpxs3bmDQoEG4ffs2vL290bp1axw+fBjVqlUDAMTGxiIqKspYvkaNGtixYwcmTpyIRYsWoXLlyli4cGGpnMNJpwNefBHYtCn3Mm5OOjip9EhOVcDLPROOSoHETEkeqiQAQIJCEsjIEICjoSUpS2uSQnrUAJX1CjrDczMtT8DjcVGKRwfJzDRfNr9WKkAOXbfuADfvyIHMMEA962D17APXs4UtHRQ4egy4c0sPT28lWjylgtJBMn6GBw4AsbGAj498yPh4wM8PMDRKGtYbluUxDRgREVG+JCF4XXp2SUlJ0Gq1SExMLJZbrmzaJIcmY+9ZLqpVSsPsETFISHSAQiHQ4Yn7SElTIC1DQlWfDGhddMjQCThXUMNdnSnvUK2Sxx4Bj8Y4ZQAZmXJiEEJ+ODjIQebho+49Rwd57BPweHsJcnCSHnXFCSGPn0rPlJ9rVECGTj6mRiW3UmXqAAjASSMfV+BRN6Fe3kapfNzSpczS8mUYuK581NqUmgrogVtJSsTF6JGRIeHmXQfE33NE7D0NgrtVwH2dE8aPB27cMP/ZGYa0JSQ8XubvD8ybB3h7m4YpIGfAMreMoYuIqOworu/yUt3iVB698w4wd65lZaNuqvD3dQ2a1XmIc9fUuJ3oAD+vDNy654DbiUp4uGZCqZDg5gogA3ILj2GwtvTouf7RckMQAuRWHmO5R11yAvJrxaNuM/2jbjqTViwLWqkE5LFVWbv+dI/KqhwehyUpS2uTQgGoHeUuv4xMQKlA8gM90hJ1UDsooXLQw1sPxN1xRKDvQ1zal4GF4T64ccMp188ua2AyuHEDGDDAdJm5gFUcoUunA77+Grh8GQgMBMaMAVSqXKtPRESlFINTCXr7bfnL11JCSPjh9wqo6puBBtXTcCNeBQ9XHSp7ZUJA4N8ER1TxlyBlZMpBxEktByZDN1tGOqB0eDw4XCnk1h+9Xv7WVjvK45XSMwGloWXJMO7p0RimjEx5Xw4Oj4ORYRC6yBqoAEi5hapsz5WP5pkS0uPn+sdhSzgoce8e4KLR48YtR6Smy1cTBvhk4OAZFzSonoq+T93DhfUaCFG4sVDmAlZRhy5XV+DhwyxDygBMngxMmAD07s1WLSKisoTBqYRYG5oM/o5ywsJwH/R7Sp7HKf6eIxRSOgQUcPFwRIXKSjkEaV1zThNgbgD2g6yTZgq5O0+lkgNTWrq8nUr1OOQIK1upHByQM1RlD1iG7YUc9CBMBqs/SBZQKvRy1lPI2yUlK1FRmwGtqx7R8SrUr5aCqr7puB5nu/m3LA1dDx7kXKbTAV9+KT8MKlYEXn5ZDlOA6Xit/AKVPEmq/ACADh3kB4MYEVHR4hgnM4q6X7SgoSkrSRKo6psONycdMoQCX84FenUr4FVoQjwOSQ7Kx2Hr/sPHoSo9U26x0j9qpZJg2kqVkQn5qjvl44HhAo+mKsDj8VMqhyzPHQHDPQGN46fk3cjr0gEAd1NV0D1MR6YOuBanQWq6ApIk4OWuw/7TrkhIckCdgDR8vMYPZ686F+6DLQPc3IBu3YDRo3OGIcO8X+vWPRrHn4WnJzB+PFC7thzA2rQBDh1iCxcR2QeOcSqjJk8ufGgC5G67gFpqzJgBPP10Ib/wJEluacpKo5YfFT0eh6qCtFIZugKVj8ZVZWQ+bqWSILdOGQarK6RH6027AZUOEiQlkJyiRGq6XAeVg0CmDkjPVMBFo0dquoT7KfbxrX//vnxBwaZNgEYDNG8uf8TR0fIjN3fuADNnPn6tVJpekFClCvDaa7kHKwYtIqKcGJyK0caNpl0xBVGvHrBwYRGEJUuZC1VOGtNAlV8rlWFcVXqmHIg0arlpJCPLYHWFZDpwPT3TGLbcNJmIu6tERqahOQpwd9EhNkGFxAcKNKieiuP/uCDqpv2Nrk5NBX7/vWDbZr+KMyYm72CVV9AyN/0DQxUR2QN21ZlRFM17Op08KDg1tWB1aNcO2L27jFx5lbXrL+u4qowMeZ4nwwD0jHQAUpaZzM08fxS2Yu6qEXctHW7OeggI3H+oxNmrTnDR6HEr0RFfhfvgQrQT+NtbOhRmfBYRUXEorq46BicziuLDHjgQ+P5767erXx84daqMBCZL5BaqcnueJWzF3cjErZh0pGdIiL/riPh7Doi954Tgbh4FmseJSh4DFRHZCoNTCSrsh71xY85L1y0xYQLwn/9Yv125kyVsFeXM4bdvAxMnmoYtS6cUoKLFQEVExY3BqQQV5sMuaBfdpEmFHw9F+csatqyZxLIwocvcPE5kHgMVERUVBqcSVJgPe9YsYPZs647H0FQ2FDR0mZs5vHJleZqK3LoayVRuM7czTBFRbhicSlBBP2ydDnBykofpWIqhyX4ZgtjWrcB33wG3btm6RmULwxQR5YXBqQQV9MO2trXphRfk8VBE2cdrHTgAfPZZwa/KdHQE3N1NuwyzTy9QHmWfm4pBish+MTiVoIJ82DqdPMNzSoplx9Bo5Ftx8I865UanAz76SG5VuX/fsm2yzvsFmHYZZp3Q8uJFYNky067C/OZxKovYKkVkvxicSlBBPuzffgM6d7b8GBs3yi1ORPkxtEbFxMhf/idOANevy+FbkuTB523bAm+9Zd00FtnHbOU1c7i5oFVWZQ1TMTFyF6m3t9xaxVBFVH4wOJWggnzY/fvLt8SwxIABwIYNhaggkQ2Ym/7hp5/K1/gsT085gLZtyyv6iMo6BqcSZO2Hbc2gcEdHuTuPf4ipvCjvgapCBaBvX7n7MyGBrVNEZQWDUwmy9sO2ZlD4zJlyeaLyjoGKiGyJwakEWfNh63TyH1BLBu+qVPJEiPyjSvaMgYqISgKDUwmy5sOOiAA6drRsv5x+gCh3ed1Cx9zM7WWNm5t8AYmLizxA3dMTuHcPUCiADh3kB4MVUdFhcCpB1nzY48fLl39b4tdfgU6diqCCRHYo+1WA5SFMZaXRyBeZVKkCREXJt2wE5GAVECAHrTt3TNdZQqEAqlWTW8AYzsieMDiVIEs/bJ1O/mOWlJT/Pp2d5XL8o0VUdLKGqfI0ZUJxcXQEWrWSg1h2DFhU3jA4lSBLP2xruunGjwfmzy+S6hFRLsp7q1RJcXCQJ1N1d5evGPb1BapXZ6iisqW4gpNDke3JDsXEWF62X79iqwYRPaJUyl/sWT37LMOUtTIzgbNncy7/9FPzrVZsrSJ7whYnMyxNqSNGAKtW5b8/Dw/5jzX/mBCVDllnY//tN/lGy3fu2LpW5UP21ipvb/lvH4MVlTR21ZUgSz5snQ7QaoHk5Pz3x246otLN3E2Wv/qKYao4mAtWksRWKyp6DE4lyJIP25p70+3dm7P7gIhKt6ytUrduAV5e8r9ltk4VPxcX+QpDw1xYXl7yOUhI4PQNZDkGpxJkyYdt6b3p3N3lP7L8B05UPjBQlQ7mwhVDFmXF4FSC8vuwrZktnJNeEtmH7IHqyhVgzRogMdHWNSMXF6BPH+D8eeCff4D0dLl7UKeTA5VGA9SuDXzyCdC1K0NWecHgVILy+7CtmYaAk14S2a+sYermTTlQ3bghzxweEyP/pyo11da1JEtIkhywOnYENmwAXF1tXSPKD6cjKEViYy0r5+rKsU1E9szc9AhZrVwp/0csIgLQ6+UrcLPPDl6YmcOjo4E//pBbWKhwhABSUoAdO+Tb5+RGoQBq1JAvCOrRg61X5RGDUwEY7qOVn7ff5j8aIsqdUim3SBdnq7ROJwezPXuAa9fMhy4GrKKj1wOXL8tdg4DcTejsDKjVQFqafD4cHYGaNYHnngPGjZNvAE9lB4NTARw4YFm5tm2Ltx5ERPmxNJxlDVhXrsjdiikp8ljOCxcYqgoqOdn8tDU3bwKRkcA778jByd1d7g7MzJTDl6OjPGP70KHAhAkMV6UJxziZkVe/qE4ntzhZcvXMunXAoEHFVEkiohKSV6sVW6tKhqOj3HLl4CAPA6lZU54Jn7fCyR3HOJUSBw5Yfsmxn1/x1oWIqCTk12qVW2uVk5M8+P3oUQarwsrIeHyFZkICcP3643W53QoH4MSixYEtTmbklVK/+w54+eX89+HpCcTH85eUiCivYGWYOfzGDeDUKcumeaGCyRqu9Hr5VmApKXJLVosWcjAuT+GKLU6lxK1blpXr27f8/PIRERWGNeOssk8umnVSy4MHgd27Ga4KKiMD+P138+t+/RUIDZXDVYsW8ris6Gh5QLujozzIvV49eUxW5872/f3G4GSlq1ctK8e5m4iIrJPf9A2TJuUdrhiyCi8jAzh0KOfye/ce3xQbkK8SdHSUx7upVEDFivL33rx5cktiecauOjNya97T6YBKleTmzfzw/nRERLaVNWRFRwNhYaYzh2dm2rqG5ZeLi/z5SpIcrPz9gWHDSvYKQc4cXoJy+7AtnTHc21ueJNOemzKJiMqClBTgrbeA8HC5VYVKhlpd/KGquIKTosj2ZAcsnTF88GCGJiKissDJCfj2W+DuXbnbydwjMxP48UegcWNb17b8SEuTr7hMSgLOnQPee+9xmNJo5BYrf3/g9dflcFuaMDhZwdIZw3v3Lt56EBFRyVEq5b/rf/6Ze7gyBKxffgFefBGoX1/uffD0lH/6+8s/HR1t/W5Kv7Q04OFDuYt16VL5qj9DoHJ2lj/HTp3kz1qnK/n6cXA4ERFREVAqga5d5UdeUlKAiRPlK9lu35a3Uygej7u6f5/jr8xJS5N/pqTIU1vs2SO/VirliUGVSqBCBaBXL/legcWFwckKP/1kWbn4+OKtBxERlV1OTsCSJXmXyR6uFAp57iXDzOFubo8Huts7ne5xy9PDh3Ir1dKlxXc8BicL6XTA2rWWleWM4UREVBiWhKv8buDM2+EUDwYnCx04YNk0BN7evLkvEREVP0smFs0tXBlmDr9xA7h8mV2D1mBwshCvqCMiorKmIOEqI0PuBoyOlmcQN4wtIhmDk4V4RR0REZVHltzEedcu4IsvgNOn5SClVMqtVg8fyj/tSZmajiA0NBSSJGHChAm5lomIiIAkSTkef//9d8lVlIiIqJxQKoEePeQWqYQE4MEDIDFRvvpPp5PD06hRQOXK8vxL7u7yT7VaHtRe3pSZFqejR49i6dKlCAoKsqj8hQsXTGYK9fb2LtTxLb1SjlfUERGRPXFyyvsqtpQUYPx4+cr0O3ceL8/IKJutVWUiCz548ACDBw/GsmXLUKFCBYu28fHxQaVKlYwPZR4Dj9LS0pCUlGTyyO7iRcvqyivqiIiIHjMEq3//lWcLNzyytlb5+cktVIbZw0uzMhGcxo4di169eqFz584Wb9O0aVP4+fmhU6dO2Lt3b55lQ0NDodVqjY+AgACT9TqdZXNC+PvzijoiIiJLmQtVhrFT2QNVaen2KyXVyF1YWBhOnDiB0NBQi8r7+flh6dKlCA8Px+bNm1G3bl106tQJ+/fvz3WbKVOmIDEx0fiIjo42WW+4u3Z+Ro3iFXVERESFZS5Q6XTywPTPPpNvaePqaptWqlI9xik6Ohrjx4/Hrl27oNFoLNqmbt26qFu3rvF1SEgIoqOjMXfuXLRr187sNmq1Gmq1Otd9WjoVQe3alpUjIiIi66lUwLvvyo/sso6lSkgwnVG8KJXqFqfjx48jPj4ewcHBcHBwgIODA/bt24eFCxfCwcEBOgs/kdatW+OipYOUzLB03BLHNxEREdlG1laqtDTTgehFqVS3OHXq1AlnzpwxWTZixAjUq1cP7733Xp4DvrM6efIk/AqRatq0kbvg8sppSqVcjoiIiMqvUh2c3Nzc0KhRI5NlLi4u8PLyMi6fMmUKYmJisGbNGgDA/PnzUb16dTRs2BDp6elYu3YtwsPDER4eXuB6HDqUf3OfTieX69ChwIchIiKiUq5UBydLxMbGIioqyvg6PT0dkydPRkxMDJycnNCwYUNs374dPXv2LMQxirYcERERlU2SENnvp0xJSUnQarVITEyEu7s7IiKAjh3z327vXrY4ERERlQbZv8uLSqkeHF5atG0rz9GU2+WOkgQEBHAOJyIiovKOwckCSiUwaBCQV9vc/Pmcw4mIiKi8Y3CywObNwNy5ua+fPBl47rmSqw8RERHZBoNTPnQ6eUKtvFqbwsKKZ5ItIiIiKl0YnPJx4ABw40beZaKj5XJERERUvjE45YNTERAREZEBg1M+eLsVIiIiMmBwygenIiAiIiIDBqd8KJXAggXm1xnCFKciICIisg8MThY4fNh8i5OrK7BpE6ciICIishcMTvl4913giy8AvT7nuvv3gUf3FiYiIiI7wOCUh/R0YN68vMts3Qp8/33J1IeIiIhsi8EpD99+a9nElq+/zgkwiYiI7AGDUx6uXrWs3L17nACTiIjIHjA45eHBA8vLcgJMIiKi8o/BKQ979lhelhNgEhERlX8MTnmIi7OsHCfAJCIisg8MTkWAE2ASERHZBwanQpo9mxNgEhER2QsGpzxUrpz3en9/4IMPSqYuREREZHsMTnl44YW81w8axC46IiIie8LglIdNm/JeHxbGiS+JiIjsCYNTHv79N+/10dGc+JKIiMieMDgVEie+JCIish8MToXEiS+JiIjsB4NTHipUyH2dJHHiSyIiInvD4JSHu3dzXycEJ74kIiKyNwxOBeTlBfTta+taEBERUUlicCqghAReUUdERGRvGJwKgVfUERER2RcGp0LgFXVERET2xcHWFSiLJEm+Tx2vqCMiIrIvbHHKhySZf80r6oiIiOwPg1Mexo0DFNk+IYUCmDwZeO4529SJiIiIbIfBKQ8LF+a8ia9OB8ydC2zebJs6ERERke0wOBXQhAk5QxURERGVbwxOBSAEEB3NeZyIiIjsDYNTIXAeJyIiIvvC4FQInMeJiIjIvnAepwLgPE5ERET2iS1OVuI8TkRERPar2FqcmjZtCin77JEAJEmCRqNBrVq1MHz4cHTs2LG4qlBoFSoAd++aLvP0BJYu5TxORERE9qjYWpy6d++OK1euwMXFBR07dkSHDh3g6uqKy5cvo0WLFoiNjUXnzp2xdevW4qpCoWUPTQCQkFDy9SAiIqLSQRJCiOLY8ahRo1C1alVMnz7dZPnHH3+M69evY9myZZg5cya2b9+OY8eOFUcVCiwpKQlarRZAIgB3k3WG8U1Xr7KrjoiIqLQyfJcnJibC3d09/w0sVGzBSavV4vjx46hVq5bJ8kuXLiE4OBiJiYn4+++/0aJFC9y/f784qlBgeQUng717gQ4dSrJWREREZKniCk7F1lWn0Whw6NChHMsPHToEjUYDANDr9VCr1cVVhWLFOZyIiIjsT7ENDn/rrbcwevRoHD9+HC1atIAkSfjjjz/w7bffYurUqQCAX375BU2bNi2uKhQrzuFERERkf4qtqw4AvvvuO/z3v//FhQsXAAB169bFW2+9hZdeegkAkJKSYrzKrjThGCciIqKyrcx11QHA4MGDERkZiTt37uDOnTuIjIw0hiYAcHJysio0hYaGQpIkTJgwIc9y+/btQ3BwMDQaDWrWrIklS5YU9C2Y4BxORERE9q3MTIB59OhRLF26FEFBQXmWu3r1Knr27Im2bdvi5MmTmDp1KsaNG4fw8HCrj1m5sulrf39g0ybO4URERGSvim2MU4UKFSyaAHPEiBH57uvBgwcYPHgwli1bho8//jjPskuWLEHVqlUxf/58AED9+vVx7NgxzJ07F88//7zZbdLS0pCWlmZ8nZSUBAA4fRpYuxa4fBkIDATGjAFUqnyrS0REROVUsbU4zZgxAwqFAr169cLs2bMxa9Ys9OrVCwqFAmPHjkWdOnXwxhtvYNmyZfnua+zYsejVqxc6d+6cb9nIyEh07drVZFm3bt1w7NgxZGRkmN0mNDQUWq3W+AgICAAANGkCTJwI/Pe/8s/AQGDzZgvePBEREZVLxdbi9Pvvv+Pjjz/G6NGjTZZ/88032LVrF8LDwxEUFISFCxdi1KhRue4nLCwMJ06cwNGjRy06blxcHHx9fU2W+fr6IjMzE7dv34afmcvhpkyZgkmTJhlfJyUlISAgAP/+a1ouJgZ44QV21xEREdmrYmtx+uWXX8y2EHXq1Am//PILAKBnz564cuVKrvuIjo7G+PHjsXbtWqsGkWfvIjRcOGiu6xAA1Go13N3dTR7mGK4/nDAB0Oksrg4RERGVE8UWnDw9PfHjjz/mWP7jjz/C09MTAJCcnAw3N7dc93H8+HHEx8cjODgYDg4OcHBwwL59+7Bw4UI4ODhAZya9VKpUCXFxcSbL4uPj4eDgAC8vr0K+Kzk8RUcDBw4UeldERERUxhRbV9306dPxxhtvYO/evWjZsqVxAswdO3YYpwfYvXs32rdvn+s+OnXqhDNnzpgsGzFiBOrVq4f33nsPSjNzAoSEhOQIbLt27ULz5s3h6OhYBO9MxpnDiYiI7E+xBadRo0ahQYMG+O9//4vNmzdDCIF69eph3759aNOmDQDg7bffznMfbm5uaNSokckyFxcXeHl5GZdPmTIFMTExWLNmDQBg9OjR+O9//4tJkyZh1KhRiIyMxPLly7F+/foifX+cOZyIiMj+FFtwAoAnn3wSTz75ZHEeArGxsYiKijK+rlGjBnbs2IGJEydi0aJFqFy5MhYuXJjrVATWMswc3rZtkeyOiIiIypBiveWKTqfDDz/8gPPnz0OSJDRo0ADPPPOM2S620iS3W64YxpbzqjoiIqLSrbhuuVJsLU6XLl1Cz549ERMTg7p160IIgX/++QcBAQHYvn07AgMDi+vQRaZyZZhMSeDvL99uhaGJiIjIPhVbi1PPnj0hhMB3331nvIouISEBL7/8MhQKBbZv314chy0ShpR6504iTp92R2ysPKapbVveo46IiKgsKHMtTvv27cPhw4eNoQkAvLy8MGfOnGIf91RUDh0CkpIYmoiIiEhWbMFJrVbj/v37OZY/ePAAqjJyw7fevR8/9/cHFixgNx0REZE9K7YJMHv37o3XXnsNR44cgRACQggcPnwYo0ePxjPPPFNchy02htut8F51RERE9qvYgtPChQsRGBiIkJAQaDQaaDQatGnTBrVq1cL8+fOL67DFhrdbISIiomKdjgCQr647f/48hBBo0KABatWqVZyHKxK5TUdgsHcv0KFDSdeKiIiILFUmBodPmjQpz/URERHG5/PmzSvKQ5co3m6FiIjIPhVpcDp58qRF5STDTJJlFG+3QkREZJ+KNDjt3bu3KHdX6vB2K0RERPat2AaHlzeGRrL58zmfExERkb1icLKQvz/vUUdERGTvim0CzPLgp584czgRERE9xuCUh7ZtgSK8gpGIiIjKOHbVEREREVmIwYmIiIjIQgxORERERBbiGKc8HDjAweFERET0GINTHnr3fvzc3x9YsIDTERAREdkzdtVZKCYGeOEFYPNmW9eEiIiIbIXByUJCyD8nTAB0OptWhYiIiGyEwckKQgDR0fLYJyIiIrI/DE4FEBtr6xoQERGRLTA4FYCfn61rQERERLbAq+qsIEny1XVt29q6JkRERGQLbHGykCTJP+fP53xORERE9orByUL+/sCmTZzHiYiIyJ6xqy4PP/3EmcOJiIjoMQanPLRtC7i727oWREREVFqwq46IiIjIQgxORERERBZicCIiIiKyEIMTERERkYUYnIiIiIgsxOBEREREZCEGJyIiIiILMTgRERERWYjBKQ8bNwIREYBOZ+uaEBERUWnA4JSHV18FOnYEqlcHNm+2dW2IiIjI1hicLBATA7zwAsMTERGRvWNwsoAQ8s8JE9htR0REZM8YnCwkBBAdDRw4YOuaEBERka0wOFkpNtbWNSAiIiJbYXCykp+frWtAREREtuJg6wqUFZIE+PsDbdvauiZERERkK2xxsoAkyT/nzweUSptWhYiIiGyIwckC/v7Apk3Ac8/ZuiZERERkS+yqy8O33wKBgXL3HFuaiIiIqFS3OC1evBhBQUFwd3eHu7s7QkJC8PPPP+daPiIiApIk5Xj8/fffBTp+//5Ahw4MTURERCQr1S1O/v7+mDNnDmrVqgUAWL16Nfr27YuTJ0+iYcOGuW534cIFuLu7G197e3sXe12JiIio/CvVwalPnz4mrz/55BMsXrwYhw8fzjM4+fj4wMPDo5hrR0RERPamVHfVZaXT6RAWFobk5GSEhITkWbZp06bw8/NDp06dsHfv3nz3nZaWhqSkJJMHERERUXalPjidOXMGrq6uUKvVGD16NLZs2YIGDRqYLevn54elS5ciPDwcmzdvRt26ddGpUyfs378/z2OEhoZCq9UaHwEBAcXxVoiIiKiMk4Qw3MK2dEpPT0dUVBTu3buH8PBwfPvtt9i3b1+u4Sm7Pn36QJIkbNu2LdcyaWlpSEtLM75OSkpCQEAAEhMTTcZKERERUdmQlJQErVZb5N/lpXqMEwCoVCrj4PDmzZvj6NGjWLBgAb755huLtm/dujXWrl2bZxm1Wg21Wl3ouhIREVH5Vuq76rITQpi0DuXn5MmT8OMN5oiIiKgIlOoWp6lTp6JHjx4ICAjA/fv3ERYWhoiICOzcuRMAMGXKFMTExGDNmjUAgPnz56N69epo2LAh0tPTsXbtWoSHhyM8PNyWb4OIiIjKiVIdnG7evIkhQ4YgNjYWWq0WQUFB2LlzJ7p06QIAiI2NRVRUlLF8eno6Jk+ejJiYGDg5OaFhw4bYvn07evbsaau3QEREROVIqR8cbgvFNaCMiIiISkZxfZeXuTFORERERLbC4ERERERkoVI9xqm00+l0yMjIsHU1iMhKjo6OUPLu3URUAAxOBSCEQFxcHO7du2frqhBRAXl4eKBSpUqQJMnWVSGiMoTBqQAMocnHxwfOzs78w0tUhggh8PDhQ8THxwMA53kjIqswOFlJp9MZQ5OXl5etq0NEBeDk5AQAiI+Ph4+PD7vtiMhiHBxuJcOYJmdnZxvXhIgKw/BvmOMUicgaDE4FxO45orKN/4aJqCAYnIiIiIgsxOBEREREZCEGpzxs3AhERAA6na1rUv4NHz4c/fr1K9ZjzJo1C0888USxHoOIiMo3Bqc8vPoq0LEjUL06sHlz0e9fp5OD2fr1ZSOgMXgQEZG9Y3CyQEwM8MILRRueNm+WA1nHjsBLLxVvQCMiIqKiweBkASHknxMmFE2r0ObNchC7ccN0eXEEtKx27tyJp556Ch4eHvDy8kLv3r1x+fJlkzI3btzAiy++CE9PT7i4uKB58+Y4cuQIVq1ahdmzZ+P06dOQJAmSJGHVqlW4du0aJEnCqVOnjPu4d+8eJElCREQEAHnuq5EjR6JGjRpwcnJC3bp1sWDBAovrnZiYCCcnJ+zcudNk+ebNm+Hi4oIHDx4AAN577z3UqVMHzs7OqFmzJqZPn57npeYdOnTAhAkTTJb169cPw4cPN75OT0/Hu+++iypVqsDFxQWtWrUyvi8AuH79Ovr06YMKFSrAxcUFDRs2xI4dOyx+b0REVLZwAkwLCQFERwPHjwPe3gXfj04HjB//OIxlP4YkyQGtb1+gqOfkS05OxqRJk9C4cWMkJydjxowZePbZZ3Hq1CkoFAo8ePAA7du3R5UqVbBt2zZUqlQJJ06cgF6vx8CBA3H27Fns3LkTv/76KwBAq9Xi5s2b+R5Xr9fD398f33//PSpWrIhDhw7htddeg5+fHwYMGJDv9lqtFr169cJ3332H7t27G5evW7cOffv2haurKwDAzc0Nq1atQuXKlXHmzBmMGjUKbm5uePfddwv4iQEjRozAtWvXEBYWhsqVK2PLli3o3r07zpw5g9q1a2Ps2LFIT0/H/v374eLignPnzhnrQ0RE5Q+Dk5Xi4wsXnA4cyNnSlJUhoB04AHToUPDjmPP888+bvF6+fDl8fHxw7tw5NGrUCOvWrcOtW7dw9OhReHp6AgBq1aplLO/q6goHBwdUqlTJquM6Ojpi9uzZxtc1atTAoUOH8P3331sUnABg8ODBGDp0KB4+fAhnZ2ckJSVh+/btCA8PN5aZNm2a8Xn16tXx9ttvY8OGDQUOTpcvX8b69etx48YNVK5cGQAwefJk7Ny5EytXrsSnn36KqKgoPP/882jcuDEAoGbNmgU6FhERlQ0MTlby8Snc9rGxRVvOGpcvX8b06dNx+PBh3L59G3q9HgAQFRWFRo0a4dSpU2jatKkxNBWlJUuW4Ntvv8X169eRkpKC9PR0qwaa9+rVCw4ODti2bRtefPFFhIeHw83NDV27djWW2bRpE+bPn49Lly7hwYMHyMzMhLu7e4HrfOLECQghUKdOHZPlaWlpxtvtjBs3Dm+88QZ27dqFzp074/nnn0dQUFCBj0lERKUbxzhZSJKAgAAgOLhw+7H0fqLFcd/RPn36ICEhAcuWLcORI0dw5MgRAPI4HuDx/busoVDIv0IiS99j9nFF33//PSZOnIhXXnkFu3btwqlTpzBixAjjcS2hUqnwwgsvYN26dQDkbrqBAwfCwUHO/ocPH8aLL76IHj164KeffsLJkyfxwQcf5HkMhUJhUu/sddfr9VAqlTh+/DhOnTplfJw/f944RuvVV1/FlStXMGTIEJw5cwbNmzfHV199ZfH7IiKisoXByQKGOzPMn1/4cUdt2wL+/o/3ae5YAQFyuaKUkJCA8+fPY9q0aejUqRPq16+Pu3fvmpQJCgrCqVOncOfOHbP7UKlU0GUbHe/9qN8yNksTWdaB4gBw4MABtGnTBmPGjEHTpk1Rq1atHIPSLTF48GDs3LkTf/31F/bu3YvBgwcb1x08eBDVqlXDBx98gObNm6N27dq4fv16nvvz9vY2qbdOp8PZs2eNr5s2bQqdTof4+HjUqlXL5JG1uzIgIACjR4/G5s2b8fbbb2PZsmVWvzciIiobGJws4O8PbNoEPPdc4felVAKGC8qyh6eiDGjZVahQAV5eXli6dCkuXbqEPXv2YNKkSSZlBg0ahEqVKqFfv344ePAgrly5gvDwcERGRgKQxw1dvXoVp06dwu3bt5GWlgYnJye0bt0ac+bMwblz57B//36TsUaAPE7q2LFj+OWXX/DPP/9g+vTpOHr0qNXvoX379vD19cXgwYNRvXp1tG7d2uQYUVFRCAsLw+XLl7Fw4UJs2bIlz/09/fTT2L59O7Zv346///4bY8aMwb1794zr69SpYxxbtXnzZly9ehVHjx7FZ599ZrxybsKECfjll19w9epVnDhxAnv27EH9+vWtfm9ERFQ2MDjl4dtvgb17gatXiyY0GTz3nBzEqlQxXV6UAS07hUKBsLAwHD9+HI0aNcLEw8vTDwAAIOZJREFUiRPxxRdfmJRRqVTYtWsXfHx80LNnTzRu3Bhz5syB8lGKe/7559G9e3d07NgR3t7eWL9+PQBgxYoVyMjIQPPmzTF+/Hh8/PHHJvsdPXo0nnvuOQwcOBCtWrVCQkICxowZY/V7kCQJgwYNwunTp01amwCgb9++mDhxIt5880088cQTOHToEKZPn57n/l555RUMGzYMQ4cORfv27VGjRg107NjRpMzKlSsxdOhQvP3226hbty6eeeYZHDlyBAEBAQDkVqqxY8eifv366N69O+rWrYuvv/7a6vdGRERlgySyD/IgJCUlQavVIjExMcfg4tTUVFy9ehU1atSARqMp1HF0OvnqudhYeUxT27ZF39JEROYV5b9lIip98vouLwxeVWdDSmXRTzlARERExYdddUREREQWYnAiIiIishCDExEREZGFGJyIiIiILMTgRERERGQhBiciIiIiCzE4EREREVmIwYmIiIjIQgxOZLc6dOiACRMm2LoaduvatWuQJCnHTaGJiEozBidbEgJITQMePJR/8u43+WLYISIiW+ItV2wlOQW4fRd4mAro9YBCAThrgIoVABcnW9euxGVkZMDR0dHW1SAiIsoTW5xsITkFiImXW5ocHeTA5Oggv46Jl9cXg/v372Pw4MFwcXGBn58f/vOf/+RowUlPT8e7776LKlWqwMXFBa1atUJERIRx/apVq+Dh4YFffvkF9evXh6urK7p3747Y2FiTY61cuRL169eHRqNBvXr18PXXXxvXGbpovv/+e3To0AEajQZr165FQkICBg0aBH9/fzg7O6Nx48ZYv369cbvhw4dj3759WLBgASRJgiRJuHbtGgDg3Llz6NmzJ1xdXeHr64shQ4bg9u3bxm2Tk5MxdOhQuLq6ws/PD19++WW+n9esWbPwxBNPYMWKFahatSpcXV3xxhtvQKfT4fPPP0elSpXg4+ODTz75xGS7efPmoXHjxnBxcUFAQADGjBmDBw8eGNdfv34dffr0QYUKFeDi4oKGDRtix44dAIC7d+9i8ODB8Pb2hpOTE2rXro2VK1cW6pyuXbsWzZs3h5ubGypVqoSXXnoJ8fHxxvURERGQJAnbt29HkyZNoNFo0KpVK5w5cybX4w4aNAgvvviiybKMjAxUrFjRWN+dO3fiqaeegoeHB7y8vNC7d29cvnw5130afrey+uGHHyBJksmyH3/8EcHBwdBoNKhZsyZmz56NzMxM4/pZs2ahatWqUKvVqFy5MsaNG5frMYmIrMXgVNKEkFuaMjLkwOSgBCRJ/umskZffvlcs3XaTJk3CwYMHsW3bNuzevRsHDhzAiRMnTMqMGDECBw8eRFhYGP7880/0798f3bt3x8WLF41lHj58iLlz5+J///sf9u/fj6ioKEyePNm4ftmyZfjggw/wySef4Pz58/j0008xffp0rF692uRY7733HsaNG4fz58+jW7duSE1NRXBwMH766SecPXsWr732GoYMGYIjR44AABYsWICQkBCMGjUKsbGxiI2NRUBAAGJjY9G+fXs88cQTOHbsGHbu3ImbN29iwIABxmO988472Lt3L7Zs2YJdu3YhIiICx48fz/czu3z5Mn7++Wfs3LkT69evx4oVK9CrVy/cuHED+/btw2effYZp06bh8OHDxm0UCgUWLlyIs2fPYvXq1dizZw/effdd4/qxY8ciLS0N+/fvx5kzZ/DZZ5/B1dUVADB9+nScO3cOP//8M86fP4/FixejYsWKhTqn6enp+Oijj3D69Gn88MMPuHr1KoYPH55jX++88w7mzp2Lo0ePwsfHB8888wwyMjLMHnfw4MHYtm2bSSD85ZdfkJycjOeffx6AHFYnTZqEo0eP4rfffoNCocCzzz4LvV6f7+eem19++QUvv/wyxo0bh3PnzuGbb77BqlWrjOF106ZN+M9//oNvvvkGFy9exA8//IDGjRsX+HhERDkIyiExMVEAEImJiTnWpaSkiHPnzomUlJSC7TwlVYhzl4W4eF2IqzdyPi5el9enpBbyXZhKSkoSjo6OYuPGjcZl9+7dE87OzmL8+PFCCCEuXbokJEkSMTExJtt26tRJTJkyRQghxMqVKwUAcenSJeP6RYsWCV9fX+PrgIAAsW7dOpN9fPTRRyIkJEQIIcTVq1cFADF//vx8692zZ0/x9ttvG1+3b9/eWF+D6dOni65du5osi46OFgDEhQsXxP3794VKpRJhYWHG9QkJCcLJySnHvrKaOXOmcHZ2FklJScZl3bp1E9WrVxc6nc64rG7duiI0NDTX/Xz//ffCy8vL+Lpx48Zi1qxZZsv26dNHjBgxItd9ZWXJOTXnjz/+EADE/fv3hRBC7N27VwAw+/ls2LDB7D7S09NFxYoVxZo1a4zLBg0aJPr375/rcePj4wUAcebMGSHE49+DkydPCiHk3y2tVmuyzZYtW0TWP1Nt27YVn376qUmZ//3vf8LPz08IIcSXX34p6tSpI9LT03Oth0Gh/y0TUamW13d5YXCMU0nL1MljmpS5NPYpFUCakMsVoStXriAjIwMtW7Y0LtNqtahbt67x9YkTJyCEQJ06dUy2TUtLg5eXl/G1s7MzAgMDja/9/PyMXT+3bt1CdHQ0Ro4ciVGjRhnLZGZmQqvVmuy3efPmJq91Oh3mzJmDDRs2ICYmBmlpaUhLS4OLi0ue7+348ePYu3evsdUmq8uXLyMlJQXp6ekICQkxLvf09DR577mpXr063NzcjK99fX2hVCqhUChMlmXt+tq7dy8+/fRTnDt3DklJScjMzERqaiqSk5Ph4uKCcePG4Y033sCuXbvQuXNnPP/88wgKCgIAvPHGG3j++edx4sQJdO3aFf369UObNm3M1s2ScwoAJ0+exKxZs3Dq1CncuXPH2OITFRWFBg0aGMuZ+3zOnz9v9tiOjo7o378/vvvuOwwZMgTJycnYunUr1q1bZyxz+fJlTJ8+HYcPH8bt27dNjtuoUaNcPvG8HT9+HEePHjXpHtXpdEhNTcXDhw/Rv39/zJ8/HzVr1kT37t3Rs2dP9OnTBw4O/FNHREWDf01KmoNSHgiu08vPs9PpAYVkfl0hiEddf9nHi4gsXYJ6vR5KpRLHjx+HUml6/KyhJPsgbkmSjPsxfDkuW7YMrVq1MimXfZ/ZA9GXX36J//znP5g/f75xjNCECROQnp6e53vT6/Xo06cPPvvssxzr/Pz8TLoZrWXuvZpbZnjf169fR8+ePTF69Gh89NFH8PT0xO+//46RI0cau71effVVdOvWDdu3b8euXbsQGhqKL7/8Em+99RZ69OiB69evY/v27fj111/RqVMnjB07FnPnzs1RN0vOaXJyMrp27YquXbti7dq18Pb2RlRUFLp165bv52pu31kNHjwY7du3R3x8PHbv3g2NRoMePXoY1/fp0wcBAQFYtmwZKleuDL1ej0aNGuV6XIVCYVJ3ADm6CvV6PWbPno3nnnsux/YajQYBAQG4cOECdu/ejV9//RVjxozBF198gX379vHiAyIqEgxOJU2tkscyPXgIKDXy+CYDIYC0dMDVRS5XhAIDA+Ho6Ig//vgDAQEBAICkpCRcvHgR7du3BwA0bdoUOp0O8fHxaNu2bYGO4+vriypVquDKlSsYPHiwVdseOHAAffv2xcsvvwxA/pK8ePEi6tevbyyjUqmg05m2xjVr1gzh4eGoXr262ZaFWrVqwdHREYcPH0bVqlUByIOw//nnH+N7LyrHjh1DZmYmvvzyS2Or1Pfff5+jXEBAAEaPHo3Ro0djypQpWLZsGd566y0AgLe3N4YPH47hw4ejbdu2xrFH2VlyTv/++2/cvn0bc+bMMZY5duyY2bqb+3zq1auX63tt06YNAgICsGHDBvz888/o378/VCr59zYhIQHnz5/HN998Y/xd+v333/P87Ly9vXH//n1jyxyAHHM8NWvWDBcuXECtWrVy3Y+TkxOeeeYZPPPMMxg7dizq1auHM2fOoFmzZnken4jIEgxOedi4EQgMBNq2BZRF1QAkSfKUA2kZ8lQEapXcPafTy6HJ0RGo6GEaqIqAm5sbhg0bhnfeeQeenp7w8fHBzJkzoVAojK0KderUweDBgzF06FB8+eWXaNq0KW7fvo09e/agcePG6Nmzp0XHmjVrFsaNGwd3d3f06NEDaWlpOHbsGO7evYtJkyblul2tWrUQHh6OQ4cOoUKFCpg3bx7i4uJMglP16tVx5MgRXLt2Da6urvD09MTYsWOxbNkyDBo0CO+88w4qVqyIS5cuISwsDMuWLYOrqytGjhyJd955B15eXvD19cUHH3xg0t1WVAIDA5GZmYmvvvoKffr0wcGDB7FkyRKTMhMmTECPHj1Qp04d3L17F3v27DG+xxkzZiA4OBgNGzZEWloafvrpJ5P3n5Ul57Rq1apQqVT46quvMHr0aJw9exYfffSR2f19+OGHJp9PxYoV0a9fv1zfqyRJeOmll7BkyRL8888/2Lt3r3FdhQoV4OXlhaVLl8LPzw9RUVF4//338/zsWrVqBWdnZ0ydOhVvvfUW/vjjD6xatcqkzIwZM9C7d28EBASgf//+UCgU+PPPP3HmzBl8/PHHWLVqFXQ6nXFf//vf/+Dk5IRq1arleWwiIosV6YipcsIwoAxIFIAQ/v5ChIfL64psQOmDh0Jci5EHgp+9JP+89q+8vJgkJSWJl156STg7O4tKlSqJefPmiZYtW4r333/fWCY9PV3MmDFDVK9eXTg6OopKlSqJZ599Vvz5559CCMsG8AohxHfffSeeeOIJoVKpRIUKFUS7du3E5s2bhRA5BwUbJCQkiL59+wpXV1fh4+Mjpk2bJoYOHSr69u1rLHPhwgXRunVr4eTkJACIq1evCiGE+Oeff8Szzz4rPDw8hJOTk6hXr56YMGGC0Ov1Qggh7t+/L15++WXh7OwsfH19xeeff252oHlWM2fOFE2aNDFZNmzYMJP6CJFzwPq8efOEn5+fcHJyEt26dRNr1qwRAMTdu3eFEEK8+eabIjAwUKjVauHt7S2GDBkibt++LYSQB9HXr19fODk5CU9PT9G3b19x5cqVXOtoyTldt26dqF69ulCr1SIkJERs27bN5PM3DA7/8ccfRcOGDYVKpRItWrQQp06dyvW4Bn/99ZcAIKpVq2b8rA12794t6tevL9RqtQgKChIRERECgNiyZYsQwvzvwZYtW0StWrWERqMRvXv3FkuXLs3xu7Vz507Rpk0b4eTkJNzd3UXLli3F0qVLjdu3atVKuLu7CxcXF9G6dWvx66+/mq07B4cTlW/FNThcEoLTVWeXlJT0aCBzIgB3Y+PPpk1Az56puHr1KmrUqAGNRlO4Axm65jJ18pgmtarIW5rykpycjCpVquDLL7/EyJEjS+y4VHwKck4jIiLQsWNH3L17N8c8SuVZamoR/lsmolLH8F2emJgId3f3Itsvu+osIIScZyZMALp1K8IdSxKgURfhDvN28uRJ/P3332jZsiUSExPx4YcfAgD69u1bYnWgosVzSkRUshicLCQEEB0NHD8OeHvbujYFN3fuXFy4cAEqlQrBwcE4cOBAnhMsUunHc0pEVHIYnKwUH192g1PTpk0tmi2byo6iOKcdOnTIMQ0AERGZx1uuWMkQmvhFQ1S28d8wERUEg5OFJAkICABCQuRJ9B4+fGjjGhFRYRj+DXNiTCKyRqnuqlu8eDEWL16Ma9euAQAaNmyIGTNmmMxOnN2+ffswadIk/PXXX6hcuTLeffddjB49ulD1MFzoNn8+oFIp4eHhYbzFhrOzc56zKxNR6SKEwMOHDxEfHw8PD48cM9oTEeWlVAcnf39/zJkzxzhL8OrVq9G3b1+cPHkSDRs2zFH+6tWr6NmzJ0aNGoW1a9fi4MGDGDNmDLy9vY13bC9YPeTQZLjLQ6VKlQDA5P5kRFS2eHh4GP8tExFZqszN4+Tp6YkvvvjC7Bw17733HrZt22ZyY9LRo0fj9OnTiIyMtPgYhrkfvv02EYGB7rnOHK7T6XLcS4uISj9HR0e2NBGVc3Y/j5NOp8PGjRuRnJxschf3rCIjI9G1a1eTZd26dcPy5cuRkZGR61iGtLQ0pKWlGV8nJSUBAPr3B/L6rJVKJf/4EhER2ZFSPzj8zJkzcHV1hVqtxujRo7FlyxY0aNDAbNm4uDj4+vqaLPP19UVmZiZu376d6zFCQ0Oh1WqND8PNUImIiIiyKvXBqW7dujh16hQOHz6MN954A8OGDcO5c+dyLZ99oLahJzKvAdxTpkxBYmKi8REdHV00lSciIqJypdR31alUKuPg8ObNm+Po0aNYsGABvvnmmxxlK1WqhLi4OJNl8fHxcHBwgJeXV67HUKvVUKtL7tYnREREVDaV+uCUnRDCZDxSViEhIfjxxx9Nlu3atQvNmze3aq4WQyuVYawTERERlS2G7/AivwZOlGJTpkwR+/fvF1evXhV//vmnmDp1qlAoFGLXrl1CCCHef/99MWTIEGP5K1euCGdnZzFx4kRx7tw5sXz5cuHo6Cg2bdpk1XEvX74sAPDBBx988MEHH2X8cfny5SLNJqW6xenmzZsYMmQIYmNjodVqERQUhJ07d6JLly4AgNjYWERFRRnL16hRAzt27MDEiROxaNEiVK5cGQsXLrR6DidPT08AQFRUFP7f3v3HRF3/cQB/fuBAiA1iIIb8lMbP+A2DxMqWCCXBzCxTM9EsmXOaThtMx4+Zc5WyxEkthsgWgmWQ/QEKY6EIDePXBh6bxI+MhAqEJH6Iyvv7R1/u6wno3X3vDrp7PrbbvPf7/bnP6+618/3i/flxNjY22ntDpJHbt2/DxcUFv/76q1YvKSXNMB/zB3MxvzAf88tff/0FV1dXxZyuLf+6+zjpg67u/UCaYT7mF+Zj/mAu5hfmY37RVT7m/VV1RERERPMFCyciIiIiFbFwmsGCBQuQlpbGWxTME8zH/MJ8zB/MxfzCfMwvusoHz3EiIiIiUhFXnIiIiIhUxMKJiIiISEUsnIiIiIhUxMKJiIiISEUsnIiIiIhUZLSFU3Z2NpYsWQILCwuEhYWhurr6keMvXbqEsLAwWFhYwMPDA1988YWeIjUO6uSjuLgYK1euxMKFC2FtbY2lS5fi4sWLeozW8Kn7/ZhSU1MDmUyG4OBg3QZoRNTNxZ07d3DgwAG4ublhwYIFePrpp3Hq1Ck9RWv41M1HQUEBgoKC8MQTT8DR0RFbtmzBwMCAnqI1XJcvX0Z8fDwWL14MSZLw3XffPXYbrc3jWv3lu3+JoqIiYWZmJnJycoRcLhe7d+8WVlZW4pdffplx/NSPB+/evVvI5XKRk5Oj0Y8H08zUzcfu3bvFxx9/LK5evSquX78uUlJShJmZmWhsbNRz5IZJ3XxMGRoaEh4eHiImJkYEBQXpJ1gDp0kuEhISRGRkpKioqBBdXV2irq5O1NTU6DFqw6VuPqqrq4WJiYk4fvy46OzsFNXV1eKZZ54Rq1ev1nPkhqe0tFQcOHBAfPvttwKAKCkpeeR4bc7jRlk4RUREiKSkJKU2Hx8fkZycPOP4Dz/8UPj4+Ci1bd++XTz77LM6i9GYqJuPmfj5+YmMjAxth2aUNM3HunXrxMGDB0VaWhoLJy1RNxdlZWXCxsZGDAwM6CM8o6NuPj799FPh4eGh1JaVlSWcnZ11FqMxUqVw0uY8bnSH6iYmJtDQ0ICYmBil9piYGNTW1s64zY8//jhtfGxsLOrr63H37l2dxWoMNMnHwyYnJzE8PKz1X8A2RprmIy8vDx0dHUhLS9N1iEZDk1x8//33CA8PxyeffAInJyd4eXlh3759GBsb00fIBk2TfERFRaGnpwelpaUQQuD333/HuXPnEBcXp4+Q6QHanMdl2gzs36C/vx/379/HokWLlNoXLVqEvr6+Gbfp6+ubcfy9e/fQ398PR0dHncVr6DTJx8OOHTuGkZERvPnmm7oI0ahoko/29nYkJyejuroaMpnR/ZeiM5rkorOzE1euXIGFhQVKSkrQ39+PHTt24NatWzzP6f+kST6ioqJQUFCAdevWYXx8HPfu3UNCQgJOnDihj5DpAdqcx41uxWmKJElKz4UQ09oeN36mdtKMuvmYUlhYiPT0dJw9exYODg66Cs/oqJqP+/fvY8OGDcjIyICXl5e+wjMq6nw3JicnIUkSCgoKEBERgVWrViEzMxOnT5/mqpOWqJMPuVyOXbt2ITU1FQ0NDbhw4QK6urqQlJSkj1DpIdqax43uz0N7e3uYmppO+wvhjz/+mFaNTnnqqadmHC+TyWBnZ6ezWI2BJvmYcvbsWbz77rv45ptvEB0drcswjYa6+RgeHkZ9fT2ampqwc+dOAP9M3kIIyGQylJeX46WXXtJL7IZGk++Go6MjnJycYGNjo2jz9fWFEAI9PT3w9PTUacyGTJN8HDlyBMuWLcP+/fsBAIGBgbCyssLzzz+Pjz76iEcr9Eib87jRrTiZm5sjLCwMFRUVSu0VFRWIioqacZulS5dOG19eXo7w8HCYmZnpLFZjoEk+gH9WmhITE3HmzBmeL6BF6ubD2toaLS0taG5uVjySkpLg7e2N5uZmREZG6it0g6PJd2PZsmW4efMm/v77b0Xb9evXYWJiAmdnZ53Ga+g0ycfo6ChMTJSnWVNTUwD/W+0g/dDqPK726eQGYOqS0tzcXCGXy8UHH3wgrKysRHd3txBCiOTkZLFp0ybF+KnLGPfs2SPkcrnIzc3l7Qi0SN18nDlzRshkMnHy5EnR29ureAwNDc3VWzAo6ubjYbyqTnvUzcXw8LBwdnYWa9euFdeuXROXLl0Snp6eYtu2bXP1FgyKuvnIy8sTMplMZGdni46ODnHlyhURHh4uIiIi5uotGIzh4WHR1NQkmpqaBACRmZkpmpqaFLeG0OU8bpSFkxBCnDx5Uri5uQlzc3MRGhoqLl26pOjbvHmzWL58udL4qqoqERISIszNzYW7u7v4/PPP9RyxYVMnH8uXLxcApj02b96s/8ANlLrfjwexcNIudXPR1tYmoqOjhaWlpXB2dhZ79+4Vo6Ojeo7acKmbj6ysLOHn5ycsLS2Fo6Oj2Lhxo+jp6dFz1Ibnhx9+eOQ8oMt5XBKC64VEREREqjC6c5yIiIiINMXCiYiIiEhFLJyIiIiIVMTCiYiIiEhFLJyIiIiIVMTCiYiIiEhFLJyIiIiIVMTCiYiIiEhFLJyIiIiIVMTCiYjoIQMDA3BwcEB3d7fO9rF27VpkZmbq7PWJSDdYOBGR3iUmJkKSJCQlJU3r27FjByRJQmJiov4D+68jR44gPj4e7u7uirYXXngBkiTh0KFDSmOFEIiMjIQkSUhNTVV5H6mpqTh8+DBu376trbCJSA9YOBHRnHBxcUFRURHGxsYUbePj4ygsLISrq+ucxTU2Nobc3Fxs27ZN0SaEQHNzM9zc3NDS0qI0Pj8/Hzdv3gQAhIaGqryfwMBAuLu7o6CgQDuBE5FesHAiojkRGhoKV1dXFBcXK9qKi4vh4uKCkJAQRduFCxfw3HPP4cknn4SdnR1effVVdHR0KL3WuXPnEBAQAEtLS9jZ2SE6OhojIyOP7ZtJWVkZZDIZli5dqmhrb2/H8PAwEhMTlQqn4eFhpKSkKFbHwsLC1PoMEhISUFhYqNY2RDS3WDgR0ZzZsmUL8vLyFM9PnTqFrVu3Ko0ZGRnB3r178dNPP6GyshImJiZ47bXXMDk5CQDo7e3F+vXrsXXrVrS1taGqqgpr1qyBEOKRfbO5fPkywsPDldoaGhpgYWGB9evXo729HXfu3AEAHDp0CMHBwXB0dIS9vT1cXFzUev8RERG4evWq4vWIaP6TzXUARGS8Nm3ahJSUFHR3d0OSJNTU1KCoqAhVVVWKMa+//rrSNrm5uXBwcIBcLoe/vz96e3tx7949rFmzBm5ubgCAgIAAAMD169dn7ZtNd3c3Fi9erNTW2NiIwMBAeHl5wcrKCm1tbbCyskJ2djbq6+tx9OhRtVebAMDJyQl37txBX1+fIj4imt+44kREc8be3h5xcXHIz89HXl4e4uLiYG9vrzSmo6MDGzZsgIeHB6ytrbFkyRIAwI0bNwAAQUFBWLFiBQICAvDGG28gJycHg4ODj+2bzdjYGCwsLJTaGhoaEBYWBkmSEBgYiNbWVuzZswfvv/8+fHx80NDQoNb5TVMsLS0BAKOjo2pvS0Rzg4UTEc2prVu34vTp08jPz592mA4A4uPjMTAwgJycHNTV1aGurg4AMDExAQAwNTVFRUUFysrK4OfnhxMnTsDb2xtdXV2P7JuNvb39tOKqqalJURgFBQXh+PHjuHr1KtLS0jAxMYFr164pFU6jo6PYv38/oqKiEBUVhffeew8DAwPT9nXr1i0AwMKFC9X81IhorrBwIqI59fLLL2NiYgITExOIjY1V6hsYGEBbWxsOHjyIFStWwNfXd8YVI0mSsGzZMmRkZKCpqQnm5uYoKSl5bN9MQkJCIJfLFc87OzsxNDSkOBQXHByM+vp6HD58GDY2NmhpacHdu3eVDtXt3LkTQUFBqK2tRW1tLd566y288847086tam1thbOz87RVNiKav3iOExHNKVNTU7S1tSn+/SBbW1vY2dnhyy+/hKOjI27cuIHk5GSlMXV1daisrERMTAwcHBxQV1eHP//8E76+vo/sm01sbCxSUlIwODgIW1tbNDQ0wNzcHP7+/gCAzZs3Y/Xq1bCzswPwz/lPtra2ikOIY2NjGBwcxNtvv4309HQAQHp6Os6fP4+ff/4Znp6ein1VV1cjJibm//sAiUivWDgR0Zyztraesd3ExARFRUXYtWsX/P394e3tjaysLLz44otK216+fBmfffYZbt++DTc3Nxw7dgyvvPIK2traZu2bTUBAAMLDw/H1119j+/btaGxshL+/P8zMzAAAZmZmSitEjY2NSrdPeHBVaefOnbPuZ3x8HCUlJbh48eJjPx8imj8k8ajrcomIjFBpaSn27duH1tZWmJiof0ZDYmIiVq5ciY0bNwIAKisrcfToUZSWlkKSJADAyZMncf78eZSXl2s1diLSLa44ERE9ZNWqVWhvb8dvv/2m9r2ZACA7OxsHDx5EVlYWJEmCr68vvvrqK0XRBPyzcnXixAlthk1EesAVJyIiIiIV8ao6IiIiIhWxcCIiIiJSEQsnIiIiIhWxcCIiIiJSEQsnIiIiIhWxcCIiIiJSEQsnIiIiIhWxcCIiIiJSEQsnIiIiIhWxcCIiIiJS0X8AKFeWh4AJkGsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", + "\n", + "\n", + "# generated data for mass vs. luminosity\n", + "best_kernel_L2 = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp_best_L2 = GaussianProcessRegressor(kernel=best_kernel_L2, optimizer=None, normalize_y=True)\n", + "gp_best_L2.fit(combined_masses2.reshape(-1, 1), combined_L2)\n", + "\n", + "mass_vals2 = np.linspace(np.max(phil2_mass), np.min(pisa2_mass), 20).reshape(-1, 1)\n", + "L_pred2 = gp_best_L2.predict(mass_vals2)\n", + "\n", + "# plotting generated data for luminosity\n", + "axs[0].scatter(combined_masses2, combined_L2, color='blue', label='actual values')\n", + "axs[0].scatter(mass_vals2, L_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity')\n", + "axs[0].set_xlim(0, 1)\n", + "#axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# generated data for mass vs. teff\n", + "best_kernel_teff2 = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp_best_teff2 = GaussianProcessRegressor(kernel=best_kernel_teff2, optimizer=None, normalize_y=True)\n", + "gp_best_teff2.fit(combined_masses2.reshape(-1, 1), combined_teff2)\n", + "\n", + "teff_pred2 = gp_best_teff2.predict(mass_vals2)\n", + "\n", + "# mass vs. Teff\n", + "axs[1].scatter(combined_masses2, combined_teff2, color='blue', label='actual values')\n", + "axs[1].scatter(mass_vals2, teff_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[1].set_title('Creating Points for Teff Mass Gap')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('Teff')\n", + "axs[1].set_xlim(0, 1)\n", + "#axs[1].set_ylim(3.25,3.75)\n", + "axs[1].legend()\n", + "\n", + "# generated data for mass vs. logg\n", + "best_kernel_logg2 = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp_best_logg2 = GaussianProcessRegressor(kernel=best_kernel_logg2, optimizer=None, normalize_y=True)\n", + "gp_best_logg2.fit(combined_masses2.reshape(-1, 1), combined_logg2)\n", + "\n", + "logg_pred2 = gp_best_logg2.predict(mass_vals2)\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].scatter(combined_masses2, combined_logg2, color='blue', label='actual values')\n", + "axs[2].scatter(mass_vals2, logg_pred2, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[2].set_title('Creating Points for logg Mass Gap')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0, 1)\n", + "#axs[2].set_ylim(4.0,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "95d0dfdc-6f4d-49be-89c7-cc8337b466c3", + "metadata": {}, + "source": [ + "### Now for logAge = 6.00!" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "2a136d24-1b35-4164-882f-a78d70c3583a", + "metadata": {}, + "outputs": [], + "source": [ + "# importing compatible age fits files \n", + "phillips3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso/z00/iso_6.0.fits'\n", + "pisa3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Pisa2011/iso/z015/iso_6.00.fits'\n", + "\n", + "# loading tables and extracting data\n", + "phil3_tbl = Table.read(phillips3, format='fits')\n", + "pisa3_tbl = Table.read(pisa3, format='fits')\n", + "\n", + "phil3_mass = phil3_tbl['Mass']\n", + "pisa3_mass = pisa3_tbl['col3']\n", + "combined_masses3 = np.concatenate([phil3_mass, pisa3_mass])\n", + "\n", + "phil3_teff = phil3_tbl['Teff']\n", + "pisa3_teff = pisa3_tbl['col2']\n", + "combined_teff3 = np.concatenate([phil3_teff, pisa3_teff])\n", + "\n", + "phil3_L = phil3_tbl['Luminosity']\n", + "pisa3_L = pisa3_tbl['col1']\n", + "combined_L3 = np.concatenate([phil3_L, pisa3_L])\n", + "\n", + "phil3_logg = phil3_tbl['Gravity']\n", + "pisa3_logg = pisa3_tbl['col4']\n", + "combined_logg3 = np.concatenate([phil3_logg, pisa3_logg])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "e6040f03-c6e6-4b09-90d5-8d89f593822d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creating graphs of mass vs. luminosity, Teff, and gravity\n", + "fig, axs = plt.subplots(3, 1, figsize=(6, 10))\n", + "\n", + "\n", + "# generated data for mass vs. luminosity\n", + "best_kernel_L3 = Matern(length_scale=best_length_scale, nu=best_nu)\n", + "gp_best_L3 = GaussianProcessRegressor(kernel=best_kernel_L3, optimizer=None, normalize_y=True)\n", + "gp_best_L3.fit(combined_masses3.reshape(-1, 1), combined_L3)\n", + "\n", + "mass_vals3 = np.linspace(np.max(phil3_mass), np.min(pisa3_mass), 20).reshape(-1, 1)\n", + "L_pred3 = gp_best_L3.predict(mass_vals3)\n", + "\n", + "# plotting generated data for luminosity\n", + "axs[0].scatter(combined_masses3, combined_L3, color='blue', label='actual values')\n", + "axs[0].scatter(mass_vals3, L_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[0].set_title('Creating Points for Luminosity Mass Gap')\n", + "axs[0].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[0].set_ylabel('Luminosity')\n", + "axs[0].set_xlim(0, 1)\n", + "#axs[0].set_ylim(-4,0)\n", + "axs[0].legend()\n", + "\n", + "# generated data for mass vs. teff\n", + "best_kernel_teff3 = Matern(length_scale=best_length_scale_Teff, nu=best_nu_Teff)\n", + "gp_best_teff3 = GaussianProcessRegressor(kernel=best_kernel_teff3, optimizer=None, normalize_y=True)\n", + "gp_best_teff3.fit(combined_masses3.reshape(-1, 1), combined_teff3)\n", + "\n", + "teff_pred3 = gp_best_teff3.predict(mass_vals3)\n", + "\n", + "# mass vs. Teff\n", + "axs[1].scatter(combined_masses3, combined_teff3, color='blue', label='actual values')\n", + "axs[1].scatter(mass_vals3, teff_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[1].set_title('Creating Points for Teff Mass Gap')\n", + "axs[1].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[1].set_ylabel('Teff')\n", + "axs[1].set_xlim(0, 1)\n", + "#axs[1].set_ylim(3.25,3.75)\n", + "axs[1].legend()\n", + "\n", + "# generated data for mass vs. logg\n", + "best_kernel_logg3 = Matern(length_scale=best_length_scale_logg, nu=best_nu_logg)\n", + "gp_best_logg3 = GaussianProcessRegressor(kernel=best_kernel_logg3, optimizer=None, normalize_y=True)\n", + "gp_best_logg3.fit(combined_masses3.reshape(-1, 1), combined_logg3)\n", + "\n", + "logg_pred3 = gp_best_logg3.predict(mass_vals3)\n", + "\n", + "# mass vs. logg (problem child)\n", + "axs[2].scatter(combined_masses3, combined_logg3, color='blue', label='actual values')\n", + "axs[2].scatter(mass_vals3, logg_pred3, color='pink', alpha=0.5, label='generated mass gap values')\n", + "axs[2].set_title('Creating Points for logg Mass Gap')\n", + "axs[2].set_xlabel('Mass ($M_{\\odot}$)')\n", + "axs[2].set_ylabel('logg')\n", + "axs[2].set_xlim(0, 1)\n", + "#axs[2].set_ylim(4.0,4.5)\n", + "axs[2].legend()\n", + "\n", + "fig.tight_layout()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "01fbb0b7-0d7c-4e04-b458-5635d4c0fcb5", + "metadata": {}, + "source": [ + "### Creating a Function to Apply Model to All Pisa/Phillips Files" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "c130f0f1-9330-421a-9e8c-eedd4974e659", + "id": "accb8141-1888-49cf-84be-161bc3dbc197", "metadata": {}, "outputs": [], "source": [] From 283387e11ba91e315199607b75b46fd3f41cd733 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Sat, 19 Jul 2025 10:25:03 -0700 Subject: [PATCH 36/48] Finishing touches on IMF --- spisea/imf/imf.py | 9 +++++---- spisea/imf/tests/test_imf.py | 6 +++--- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 847ad5ab..fa05ce17 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -690,14 +690,15 @@ def __init__(self, multiplicity=None): IMF_broken_powerlaw.__init__(self, massLimits, powers, multiplicity=multiplicity) -class CombinedWeidnerKroupaKirkpatrick_2024(IMF_broken_powerlaw): +class Salpeter_Kirkpatrick_2024(IMF_broken_powerlaw): """ - Define combined IMF from several papers considering brown dwarf mass ranges. + Define combined IMF from Kirkpatrick (2024) and Salpeter (1955) to allow + inclusion of the brown dwarf mass range. Mass range: * 0.01 M_sun - 8 M_sun: Kirkpatrick 2024 `_. - * 8 M_sun - 120 M_sun: Weidner & Kroupa 2004 - `_. + * 8 M_sun - 120 M_sun: Salpeter 1955 + `_. """ def __init__(self, multiplicity=None): massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) diff --git a/spisea/imf/tests/test_imf.py b/spisea/imf/tests/test_imf.py index 571f6edc..4ba59b23 100755 --- a/spisea/imf/tests/test_imf.py +++ b/spisea/imf/tests/test_imf.py @@ -9,9 +9,9 @@ def test_generate_cluster(): # Make multiplicity object imf_multi = multiplicity.MultiplicityUnresolved() - # Make IMF object; we'll use a broken power law with the parameters from Kroupa+01 - massLimits = np.array([0.08, 0.5, 1, 120]) # Define boundaries of each mass segement - powers = np.array([-1.3, -2.3, -2.3]) # Power law slope associated with each mass segment + # Make IMF object; we'll use a broken power law with the parameters from Salpeter_Kirkpatrick+24 (updated with brown dwarf addition + massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement + powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) # Define total cluster mass From 8991819878dd19ea143f7ad9b14fa39267eedad2 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 15 Sep 2025 14:56:41 -0700 Subject: [PATCH 37/48] Addition of brown dwarf specific multiplicity conditions. --- spisea/imf/imf.py | 53 ++++++++++++++++------- spisea/imf/multiplicity.py | 30 +++++++++++++- spisea/imf/tests/test_multiplicity.py | 60 ++++++++++++++++++++++++--- 3 files changed, 123 insertions(+), 20 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index fa05ce17..4bcd06c1 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -199,7 +199,10 @@ def generate_cluster(self, totalMass, seed=None): def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, MF): """ Helper function to calculate multiples more efficiently. - We will use array operations as much as possible + We will use array operations as much as possible. + Uses Fontanive+18 parameters for brown dwarf masses + (M <= 0.08 M_sun) while keeping default parameters for + all other stellar primaries. """ # Identify multiple systems, calculate number of companions for # each @@ -215,13 +218,27 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, num = np.unique(n_comp_arr) for ii in num: tmp = np.where(n_comp_arr == ii)[0] + prim_subset = primary[tmp] + + # define masks based on stellar or substellar range + bd_mask = prim_subset <= 0.08 + star_mask = ~bd_mask if ii == 1: # Single companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp))) + q_values = np.empty(len(tmp)) + + if np.any(star_mask): + rand_vals = np.random.rand(star_mask.sum()) + q_values[star_mask] = self._multi_props.random_q(rand_vals) + + if np.any(bd_mask): + rand_vals = np.random.rand(bd_mask.sum()) + b = 1.0 + 6.1 #gamma from Fontanive+18 + q_values[bd_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + self._multi_props.q_min ** b) ** (1.0 / b) # Calculate mass of companion - m_comp = q_values * primary[tmp] + m_comp = q_values * prim_subset # Only keep companions that are more than the minimum mass. Update # compMasses, newSystemMasses, and newIsMultiple appropriately @@ -233,22 +250,30 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, bad = np.where(m_comp < self._mass_limits[0])[0] newIsMultiple[idx[tmp[bad]]] = False else: - # Multple companion case - q_values = self._multi_props.random_q(np.random.rand(len(tmp), ii)) - - # Calculate masses of companions - m_comp = np.multiply(q_values, np.transpose([primary[tmp]])) - - # Update compMasses, newSystemMasses, and newIsMultiple appropriately + # Multi-companion case for jj in range(len(tmp)): - m_comp_tmp = m_comp[jj] + prim = prim_subset[jj] + + # Finding q values of stellar and substellar primaries + if prim <= 0.08: + # BD case (Fontanive+18) + b = 1.0 + 6.1 + rand_vals = np.random.rand(ii) + q_values = (rand_vals * (1.0 - self._multi_props.q_min ** b) + + self._multi_props.q_min ** b) ** (1.0 / b) + else: + # Stellar case (Duchene & Kraus) + q_values = self._multi_props.random_q(np.random.rand(ii)) + + # Calculate masses of companions & update compMasses, newSystemMasses, and newIsMultiple appropriately + m_comp_tmp = q_values * prim compMasses[idx[tmp[jj]]] = m_comp_tmp[m_comp_tmp >= self._mass_limits[0]] newSystemMasses[idx[tmp[jj]]] += compMasses[idx[tmp[jj]]].sum() - - # Double check for the case when we drop all companions. - # This happens a lot near the minimum allowed mass. + + # Drop system if no valid companions remain if len(compMasses[idx[tmp[jj]]]) == 0: newIsMultiple[idx[tmp[jj]]] = False + return compMasses, newSystemMasses, newIsMultiple diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 0d28ced6..00c2b809 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -41,6 +41,11 @@ class MultiplicityUnresolved(object): MF(mass) = MF_amp * (mass ** MF_power) + However, in the brown dwarf mass regime, it is currently recognized + that only binaries are possible, and the MF decreases dissimilarly + to higher masses (> 0.08 solar masses). The values for this range + are given by Aberasturi et al. (2014) and Fontanive et al. (2023). + **Companion Star Fraction** -- the expected number of companions in a multiple system. The companion star fraction (CSF) also changes with mass and this dependency can be described as @@ -52,6 +57,9 @@ class MultiplicityUnresolved(object): value, CSF_max. The actual number of companions is drawn from a Poisson distribution with an expectation value of CSF. + In the brown dwarf regime we impose an assumption that only + binary systems are possible due to current literature trends. + **Mass Ratio (Q)** -- The ratio between the companion star mass and primary star mass, Q = (m_comp / m_prim ) has a probability density function described by a powerlaw:: @@ -116,6 +124,9 @@ def multiplicity_fraction(self, mass): Given a star's mass, determine the probability that the star is in a multiple system (multiplicity fraction = MF). + Modified to allow binary fraction to decrease in brown dwarf regime. + Supported by Aberasturi et al. (2014) and Fontanive et al. (2018). + Parameters ---------- mass : float or numpy array @@ -133,6 +144,13 @@ def multiplicity_fraction(self, mass): if np.isscalar(mf): if mf > 1: mf = 1 + # physically override mf for brown dwarfs + if (mass <= 0.08) & (mass > 0.06): + mf = 0.16 + if (mass <= 0.06) & (mass > 0.02): + mf = 0.08 + if (mass < 0.02): + mf = 0 else: mf[mf > 1] = 1 @@ -141,7 +159,8 @@ def multiplicity_fraction(self, mass): def companion_star_fraction(self, mass): """ Given a star's mass, determine the average number of - companion stars (companion star fraction = CSF). + companion stars (companion star fraction = CSF). For + brown dwarfs we impose a hard limit of one companion. Parameters ---------- @@ -160,6 +179,8 @@ def companion_star_fraction(self, mass): if np.isscalar(csf): if csf > self.CSF_max: csf = self.CSF_max + if (mass <= 0.08): + csf = self.multiplicity_fraction(mass) else: csf[csf > self.CSF_max] = self.CSF_max @@ -210,6 +231,8 @@ class MultiplicityResolvedDK(MultiplicityUnresolved): """ Sub-class of MultiplicityUnresolved that adds semimajor axis and eccentricity information for multiple objects from distributions described in Duchene and Kraus 2013 + + For brown dwarf regime, mean separation and std are given by Fontanive et al. (2018). Parameters -------------- @@ -246,6 +269,8 @@ def log_semimajoraxis(self, mass): Generate the semimajor axis for a given mass. The mean and standard deviation of a given mass are determined by fitting the data from fitting the semimajor axis data as a function of mass in table 1 of Duchene and Kraus 2013. Then a random semimajor axis is drawn from a log normal distribution with that mean and standard deviation. + + The brown dwarf range is described by mean seperation and std values given in Fontanive et al. (2018). Parameters ---------- @@ -265,6 +290,9 @@ def log_semimajoraxis(self, mass): log_a_std = log_a_std_func(np.log10(2.9)) #sigma_log(a) if log_a_std < 0.1: log_a_std = 0.1 + if mass <= 0.08: + log_a_mean = np.log10(2.9) + log_a_std = 0.21 log_semimajoraxis = np.random.normal(log_a_mean, log_a_std) while 10**log_semimajoraxis > 2000 or log_semimajoraxis < -2: #AU diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/imf/tests/test_multiplicity.py index 3a9dc9b7..13152477 100755 --- a/spisea/imf/tests/test_multiplicity.py +++ b/spisea/imf/tests/test_multiplicity.py @@ -33,6 +33,7 @@ def test_create_MultiplicityUnresolved(): assert mu2.q_pow == 0.4 assert mu2.q_min == 0.04 + def test_multiplicity_fraction(): """ Test creating a MultiplicityUnresolved object and getting @@ -66,6 +67,14 @@ def test_multiplicity_fraction(): mf2_3 = mu1.multiplicity_fraction(0.1) np.testing.assert_almost_equal(mf2_3, 0.136, decimal=2) + # Test brown dwarf mass fractions + mf_bd1 = mu1.multiplicity_fraction(0.07) # near upper BD limit + mf_bd2 = mu1.multiplicity_fraction(0.04) # mid BD + mf_bd3 = mu1.multiplicity_fraction(0.01) # lower BD limit + assert np.isclose(mf_bd1, 0.16, atol=0.01) + assert np.isclose(mf_bd2, 0.08, atol=0.01) + assert np.isclose(mf_bd3, 0.0, atol=1e-6) + def test_multiplicity_fraction_array(): """ @@ -77,12 +86,23 @@ def test_multiplicity_fraction_array(): # First set of multiplicity parameters mu1 = multiplicity.MultiplicityUnresolved() - mass_array = np.array([1.0, 10.0, 0.1]) + mass_array = np.array([1.0, 10.0, 0.1, 0.07, 0.04, 0.01]) mf_array = mu1.multiplicity_fraction(mass_array) + # Stellar regime checks np.testing.assert_almost_equal(mf_array[0], 0.44, decimal=2) np.testing.assert_almost_equal(mf_array[1], 1.0, decimal=2) np.testing.assert_almost_equal(mf_array[2], 0.136, decimal=2) + + # BD regime checks + # interpolation between values implies lower masses --> lower mf + assert mf_array[3] < mf_array[2] + assert mf_array[4] <= mf_array[3] + assert mf_array[5] <= mf_array[4] + + # Ensure mf stars within reasonable bound (upper limit is 0.2) + assert np.all(mf_array[3:] >= 0.0) + assert np.all(mf_array[3:] <= 0.2) def test_companion_star_fraction(): @@ -117,11 +137,20 @@ def test_companion_star_fraction(): # csf2_3 = mu1.companion_star_fraction(0.1) # np.testing.assert_almost_equal(csf2_3, 0.159, decimal=2) + # Test brown dwarf csf + csf_bd1 = mu1.companion_star_fraction(0.07) + csf_bd2 = mu1.companion_star_fraction(0.04) + csf_bd3 = mu1.companion_star_fraction(0.01) + assert np.isclose(csf_bd1, 0.16, atol=0.01) + assert np.isclose(csf_bd2, 0.08, atol=0.01) + assert np.isclose(csf_bd3, 0.0, atol=1e-6) + def test_resolvedmult(): """ Test creating a MultiplicityResolvedDK object and that the parameters it's populated with are correct. + Updated to test for specific brown dwarf characteristics. """ from spisea import synthetic, evolution, atmospheres, reddening, ifmr from spisea.imf import imf, multiplicity @@ -132,7 +161,7 @@ def test_resolvedmult(): dist = 4000 # distance in parsecs metallicity = 0 # metallicity in [M/H] atm_func = atmospheres.get_merged_atmosphere - evo_merged = evolution.MISTv1() + evo_merged = evolution.MergedPhillipsBaraffePisaEkstromParsec() redlaw = reddening.RedLawCardelli(3.1) # Rv = 3.1 filt_list = ['nirc2,J', 'nirc2,Kp'] @@ -149,7 +178,7 @@ def test_resolvedmult(): clust_multiplicity = multiplicity.MultiplicityResolvedDK() # Multiplicity is defined in the IMF object - clust_imf_Mult = imf.Kroupa_2001(multiplicity=clust_multiplicity) + clust_imf_Mult = imf.Salpeter_Kirkpatrick_2024(multiplicity=clust_multiplicity) # Make clusters clust_Mult = synthetic.ResolvedCluster(iso_merged, clust_imf_Mult, clust_mtot) @@ -184,7 +213,28 @@ def test_resolvedmult(): n, bins = np.histogram(clust_Mult.companions['i']) bin_centers = 0.5*(bins[1:] + bins[:-1]) assert all(np.abs(i) < 0.15 for i in n/max(n) - np.sin(np.pi*bin_centers/180)) - - return + #checks for brown dwarf specific features + bd_idx = np.where(clust_Mult.star_systems['mass'] < 0.08)[0] + + # Map primaries → companions + comp_rows = [] + start = 0 + for ii, N in enumerate(clust_Mult.star_systems['N_companions']): + if ii in bd_idx and N > 0: + comp_rows.extend(range(start, start+N)) + start += N + + bd_companions = clust_Mult.companions[comp_rows] + if len(bd_companions) > 30: # only test if enough BD binaries + mean_log_a = np.mean(bd_companions['log_a']) + std_log_a = np.std(bd_companions['log_a']) + + # Expect lognormal centered near log10(2.9 AU), width ~0.21 + assert abs(mean_log_a - np.log10(2.9)) < 0.25, \ + f"BD mean log(a) off: {mean_log_a:.2f}" + assert abs(std_log_a - 0.21) < 0.15, \ + f"BD sigma log(a) off: {std_log_a:.2f}" + + return From 01d3fa34d000887d6a9732f32c4b47ced089995a Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 15 Sep 2025 15:54:11 -0700 Subject: [PATCH 38/48] Enforcing brown dwarf binaries --- spisea/imf/imf.py | 4 ++++ spisea/imf/tests/test_imf.py | 1 - spisea/imf/tests/test_multiplicity.py | 7 +++++-- 3 files changed, 9 insertions(+), 3 deletions(-) diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index 4bcd06c1..bddbd657 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -213,6 +213,10 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, n_comp_arr[too_many] = self._multi_props.CSF_max primary = newMasses[idx] + # limiting BD companions to 1 (mass-based) + bd_mask = primary <= 0.08 + n_comp_arr[bd_mask & (n_comp_arr > 1)] = 1 + # We will deal with each number of multiple system independently. This is # so we can put in uniform arrays in _multi_props.random_q. num = np.unique(n_comp_arr) diff --git a/spisea/imf/tests/test_imf.py b/spisea/imf/tests/test_imf.py index 4ba59b23..373cf8f4 100755 --- a/spisea/imf/tests/test_imf.py +++ b/spisea/imf/tests/test_imf.py @@ -169,7 +169,6 @@ def test_xi2(): plt.clf() plt.loglog(masses[sdx], pdf_sorted, 'k.') plt.axis('equal') - # pdb.set_trace() return diff --git a/spisea/imf/tests/test_multiplicity.py b/spisea/imf/tests/test_multiplicity.py index 13152477..8412dc2b 100755 --- a/spisea/imf/tests/test_multiplicity.py +++ b/spisea/imf/tests/test_multiplicity.py @@ -216,8 +216,11 @@ def test_resolvedmult(): #checks for brown dwarf specific features bd_idx = np.where(clust_Mult.star_systems['mass'] < 0.08)[0] + + #check there is only one possible companion per BD + assert all(clust_Mult.star_systems['N_companions'][bd_idx] <= 1), \ + "Brown dwarf primaries have >1 companion." - # Map primaries → companions comp_rows = [] start = 0 for ii, N in enumerate(clust_Mult.star_systems['N_companions']): @@ -231,7 +234,7 @@ def test_resolvedmult(): mean_log_a = np.mean(bd_companions['log_a']) std_log_a = np.std(bd_companions['log_a']) - # Expect lognormal centered near log10(2.9 AU), width ~0.21 + #expect lognormal centered near log10(2.9 AU), width ~0.21 assert abs(mean_log_a - np.log10(2.9)) < 0.25, \ f"BD mean log(a) off: {mean_log_a:.2f}" assert abs(std_log_a - 0.21) < 0.15, \ From f114b78f38f4cf042428300fc821abefa63c745c Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 24 Oct 2025 15:05:31 -0700 Subject: [PATCH 39/48] Updates to evo/atmo --- changes/bd_evo_csv/baraffe2003.csv | 1 - spisea/atmospheres.py | 135 ++++++++++------------ spisea/evolution.py | 74 +------------ spisea/imf/imf.py | 3 +- spisea/synthetic.py | 172 +++++++++++++++++++---------- spisea/tests/test_models.py | 4 +- spisea/tests/test_synthetic.py | 14 +-- 7 files changed, 192 insertions(+), 211 deletions(-) delete mode 100644 changes/bd_evo_csv/baraffe2003.csv diff --git a/changes/bd_evo_csv/baraffe2003.csv b/changes/bd_evo_csv/baraffe2003.csv deleted file mode 100644 index 94cb6209..00000000 --- a/changes/bd_evo_csv/baraffe2003.csv +++ /dev/null @@ -1 +0,0 @@ -age,mass,luminosity,temperature,gravity,radius 0.001,0.0005,-5.37,628,2.645,0.176 0.001,0.001,-4.715,942,2.996,0.166 0.001,0.002,-4.137,1285,3.259,0.174 0.001,0.003,-3.746,1553,3.372,0.187 0.001,0.004,-3.482,1747,3.438,0.2 0.001,0.005,-3.273,1901,3.472,0.215 0.001,0.006,-3.129,2004,3.499,0.228 0.001,0.007,-2.998,2098,3.515,0.242 0.001,0.008,-2.893,2159,3.517,0.258 0.001,0.009,-2.811,2207,3.525,0.271 0.001,0.01,-2.735,2251,3.529,0.285 0.001,0.012,-2.615,2321,3.541,0.307 0.001,0.015,-2.455,2400,3.537,0.345 0.001,0.02,-2.28,2484,3.546,0.395 0.001,0.03,-2.174,2598,3.694,0.408 0.001,0.04,-1.939,2746,3.681,0.478 0.001,0.05,-1.637,2768,3.489,0.666 0.001,0.06,-1.604,2824,3.57,0.665 0.001,0.07,-1.478,2853,3.529,0.753 0.001,0.072,-1.455,2858,3.52,0.771 0.001,0.075,-1.389,2833,3.457,0.847 0.001,0.08,-1.373,2869,3.492,0.84 0.001,0.09,-1.289,2867,3.457,0.927 0.001,0.1,-1.187,2856,3.394,1.051 0.005,0.0005,-6.079,455,2.794,0.148 0.005,0.001,-5.522,644,3.141,0.141 0.005,0.002,-4.92,901,3.425,0.143 0.005,0.003,-4.552,1098,3.576,0.148 0.005,0.004,-4.283,1263,3.675,0.152 0.005,0.005,-4.061,1413,3.745,0.157 0.005,0.006,-3.886,1543,3.802,0.161 0.005,0.007,-3.725,1668,3.843,0.166 0.005,0.008,-3.579,1786,3.874,0.171 0.005,0.009,-3.46,1885,3.9,0.176 0.005,0.01,-3.363,1965,3.921,0.181 0.005,0.012,-3.194,2102,3.948,0.192 0.005,0.015,-2.981,2264,3.96,0.212 0.005,0.02,-2.661,2452,3.905,0.261 0.005,0.03,-2.262,2616,3.795,0.363 0.005,0.04,-2.068,2754,3.814,0.41 0.005,0.05,-1.846,2806,3.722,0.51 0.005,0.06,-1.902,2875,3.899,0.455 0.005,0.07,-1.829,2916,3.917,0.481 0.005,0.072,-1.814,2921,3.918,0.488 0.005,0.075,-1.716,2912,3.832,0.55 0.005,0.08,-1.76,2946,3.925,0.51 0.005,0.09,-1.696,2974,3.928,0.54 0.005,0.1,-1.582,2983,3.865,0.611 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10,0.075,-3.954,1997,5.415,0.089 10,0.08,-3.602,2322,5.353,0.099 10,0.09,-3.274,2624,5.289,0.113 10,0.1,-3.082,2786,5.246,0.125 \ No newline at end of file diff --git a/spisea/atmospheres.py b/spisea/atmospheres.py index 5f71f33c..0f4c90e5 100755 --- a/spisea/atmospheres.py +++ b/spisea/atmospheres.py @@ -523,6 +523,7 @@ def get_BTSettl_2015_atmosphere(metallicity=0, temperature=2500, gravity=4, rebi gravity=gravity) sp = pysynphot.Icat(atm_name, temperature, metallicity, gravity) + #print(dir(obj)) # Do some error checking @@ -607,6 +608,10 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T 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. + + **PRINT STATEMENTS TO DEBUG + **check get_atmosphere_bounds + **comment out try/except clause and check break """ if rebin == True: atm_name = 'BTSettl_rebin' @@ -626,78 +631,16 @@ def get_BTSettl_atmosphere(metallicity=0, temperature=2500, gravity=4.5, rebin=T def get_Meisner2023_atmosphere(metallicity=0, temperature=1000, gravity=4.5, rebin=True): """ - Return atmosphere from CIFIST2011 grid - (`Allard et al. 2012 `_) - - Grid originally downloaded `here `_ - - Notes - ------ - Grid Range: - - * [M/H] = -2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.5 - - Teff and gravity ranges depend on metallicity: + Return atmosphere from Meisner2023 grid + (`Meisner et al. 2023 `_) - [M/H] = -2.5 + Grid originally downloaded `here `_ - * Teff: 2600 - 4600 K - * gravity: 4.5 - 5.5 + Grid range: + * Teff = 250 - 1200 K + * gravity: 2.5 - 5.5 cgs (in steps of 0.5) + * [M/H] = -1.0, -0.5, 0, +0, +0.3 - [M/H] = -2.0 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.5 - - * Teff: 2600 - 7000 - * gravity: 4.5 - 5.5 - - [M/H] = -1.0 - - * Teff: 2600 - 7000 - * gravity: Teff < 3200 --> 4.5 - 5.5; Teff > 3200 --> 2.5 - 5.5 - - [M/H] = -0.5 - - * Teff: 1000 -7000 - * gravity: Teff < 3000 --> 4.5 - 5.5; Teff > 3000 --> 3.0 - 6.0 - - [M/H] = 0 - - * Teff: 750 - 7000 - * gravity: Teff < 2500 --> 3.5 - 5.5; Teff > 2500 --> 0 - 5.5 - - [M/H] = 0.5 - - * Teff: 1000 - 5000 - * gravity: 3.5 - 5.0 - - - Alpha enhancement: - - * [M/H]= -0.0, +0.5 no anhancement - * [M/H]= -0.5 with [alpha/H]=+0.2 - * [M/H]= -1.0, -1.5, -2.0, -2.5 with [alpha/H]=+0.4 - - Parameters - ---------- - metallicity: float - The stellar metallicity, in terms of [Z] - - temperature: float - The stellar temperature, in units of K - - gravity: float - The stellar gravity, in cgs units - - rebin: boolean - 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. - - FILL IN W MEISNER """ if rebin == True: atm_name = 'Meisner2023_rebin' @@ -733,7 +676,6 @@ def get_Phillips2020_atmosphere(metallicity=0, temperature=1000, gravity=4.5, re Grid originally downloaded `here `_ Grid Range: - * Teff: 200 - 3000 K * gravity: 2.5 - 5.5 cgs * [M/H] = 0 @@ -852,7 +794,33 @@ def get_BTSettl_phoenix_atmosphere(metallicity=0, temperature=5250, gravity=4): return sp +def get_BTSettl_meisner_atmosphere(metallicity=0, temperature=5250, gravity=4): + """ + Return atmosphere that is a linear merge of BTSettl_CITFITS2011_2015 model + and Meisner2023. + Only valid for temps between 1000 - 1200K, gravity from 3.5 - 5.5 + """ + try: + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + except: + # Check atmosphere catalog bounds + (temperature, gravity) = get_atmosphere_bounds('merged_BTSettl_meisner', + metallicity=metallicity, + temperature=temperature, + gravity=gravity) + + sp = pysynphot.Icat('merged_BTSettl_meisner', temperature, metallicity, gravity) + + # Do some error checking + idx = np.where(sp.flux != 0)[0] + if len(idx) == 0: + print( 'Could not find BTSettl-Meisner merge atmosphere model for') + print( ' temperature = %d' % temperature) + print( ' metallicity = %.1f' % metallicity) + print( ' log gravity = %.1f' % gravity) + + return sp #---------------------------------------------------------------------# def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose=False, @@ -898,8 +866,8 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose * T < 3800, logg < 2.5: PHOENIX v16 * 3200 <= T < 3800, logg > 2.5: BTSettl_CIFITS2011_2015/PHOENIXV16 merge * 3200 < T <= 1200, logg > 2.5: BTSettl_CIFITS2011_2015 - * 1200 < T <= 1000, logg > 2.5: BTSettl_CIFITS2011_2015/Phillips2020 merge - * 1000 < T <= 250, logg > 2.5: Phillips2020 + * 1200 < T <= 1000, logg >= 3.5: BTSettl_CIFITS2011_2015/Meisner2023 merge + * 1000 < T <= 250, logg > 2.5: Meisner2023 Otherwise, if T < 3800 and [M/H] != 0: @@ -933,13 +901,29 @@ def get_merged_atmosphere(metallicity=0, temperature=20000, gravity=4.5, verbose # If solar metallicity, use BTSettl 2015 grid. Only solar metallicity is # currently available here, so if non-solar metallicity, just stick with # the Phoenix grid - if (temperature <= 1200): + if (temperature < 1000): if verbose: print( 'Meisner2023 atmosphere') return get_Meisner2023_atmosphere(metallicity=metallicity, temperature=temperature, gravity=gravity, rebin=rebin) + + if (temperature <= 1200) & (temperature >= 1000): + if (gravity >= 3.5): + if verbose: + print( 'BTSettl/Meisner2023 merged atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) + if (gravity < 3.5) & (gravity >=2.5): + if verbose: + print( 'Meisner2023 atmosphere') + return get_Meisner2023_atmosphere(metallicity=metallicity, + temperature=temperature, + gravity=gravity, + rebin=rebin) if (temperature <= 3800) & (metallicity == 0): # High gravity are in BTSettl regime @@ -1052,10 +1036,11 @@ def get_wd_atmosphere(metallicity=0, temperature=20000, gravity=4, verbose=False bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec + def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False): """ Return the brown dwarf atmosphere from - `Meisner et al. 2020 `_. + `Meisner et al. 2023 `_. If desired parameters are outside of grid, return a blackbody spectrum instead @@ -1087,7 +1072,7 @@ def get_bd_atmosphere(metallicity=0, temperature=1000, gravity=4, verbose=False) gravity=gravity) except pysynphot.exceptions.ParameterOutOfBounds: - # Use a black-body atmosphere. + # Use a black-body atmosphere bbspec = get_bb_atmosphere(temperature=temperature, verbose=verbose) return bbspec diff --git a/spisea/evolution.py b/spisea/evolution.py index 7c529e77..d07bf2b0 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1497,7 +1497,7 @@ def format_isochrones(self): #==============================# class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): """ - All merge -- only young and solar metals right now. + All merged! Parameters ---------- @@ -1512,7 +1512,7 @@ def __init__(self, rot=True): z_list = [0.015] # populate list of isochrone ages (log scale) - age_list = np.arange(6.0, 7.4, 0.01).tolist() + age_list = np.arange(6.0, 10.0, 0.01).tolist() # specify location of model files model_dir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/' @@ -1595,6 +1595,7 @@ def isochrone(self, age=1.e8, metallicity=0.0): return iso + class MergedBaraffePisaEkstromParsec(StellarEvolution): """ This is a combination of several different evolution models: @@ -1609,7 +1610,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): For logAge < 7.4: - * 0.01 - 0.08 M_sun: assume mass does not change over time + * 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) @@ -1626,7 +1627,7 @@ class MergedBaraffePisaEkstromParsec(StellarEvolution): """ def __init__(self, rot=True): # populate list of model masses (in solar masses) - mass_list = [(0.01 + i*0.005) for i in range(181)] # generates masses from 0.01 - 1 M_sun + mass_list = [(0.1 + i*0.005) for i in range(181)] # define metallicity parameters for Geneva models z_list = [0.015] @@ -1699,74 +1700,11 @@ def isochrone(self, age=1.e8, metallicity=0.0): idx_WR = np.where(iso['logT'] != iso['logT_WR']) isWR[idx_WR] = True iso.add_column(isWR) - + iso.meta['log_age'] = log_age iso.meta['metallicity_in'] = metallicity iso.meta['metallicity_act'] = np.log10(self.z_list[z_idx] / self.z_solar) - # Assume mass of brown dwarfs does not change over their lifetime - bd_idx = iso['mass'] < 0.08 - iso['mass_current'][bd_idx] = iso['mass'][bd_idx] - - # Handling NaN effective temperatures - nan_teff_idx = np.isnan(iso['logT']) - if np.any(nan_teff_idx): - iso['logT'][nan_teff_idx] = self.estimate_teff(iso['mass'][nan_teff_idx]) - return iso - - def get_temperature(self, masses, ages): - """ - Use interpolation to get temperatures for brown dwarfs based on mass and age. - - Parameters: - ----------- - masses : array-like - Array of brown dwarf masses. - ages : array-like - Array of ages corresponding to the masses. - - Returns: - -------- - temperatures : array - Array of interpolated temperatures. - """ - - # Set the project root two levels up from 'spisea/evolution.py' - project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) - - # Construct the full path to the CSV file in 'changes/bd_evo_csv' - csv_path = os.path.join(project_root, 'SPISEA', 'changes', 'bd_evo_csv', 'baraffe2003.csv') - - # Load the CSV file - data = pd.read_csv(csv_path) - - # Create columns of relevant data - mass_grid = np.unique(data['mass'].values) - age_grid = np.unique(data['age'].values) - temperatures = data['temperature'].values - - # Create a 2D grid for temperatures - temp_grid = np.zeros((len(age_grid), len(mass_grid))) - - # Fill the temperature grid with actual values - for i, age in enumerate(age_grid): - for j, mass in enumerate(mass_grid): - temp_grid[i, j] = np.mean(temperatures[(data['age'] == age) & (data['mass'] == mass)]) - - # Set up the interpolator - interpolator = RegularGridInterpolator((age_grid, mass_grid), temp_grid, bounds_error=False, fill_value=np.nan) - - # Get temperatures for the provided masses and ages - temperatures = interpolator(np.column_stack((ages, masses))) - - # Make sure masses array is not empty - if len(masses) == 0: - print("No masses available for temperature calculation.") - return - - return temperatures - - return iso diff --git a/spisea/imf/imf.py b/spisea/imf/imf.py index bddbd657..adf0c3b0 100755 --- a/spisea/imf/imf.py +++ b/spisea/imf/imf.py @@ -239,7 +239,8 @@ def calc_multi(self, newMasses, compMasses, newSystemMasses, newIsMultiple, CSF, if np.any(bd_mask): rand_vals = np.random.rand(bd_mask.sum()) b = 1.0 + 6.1 #gamma from Fontanive+18 - q_values[bd_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + self._multi_props.q_min ** b) ** (1.0 / b) + q_values[bd_mask] = (rand_vals * (1.0 - self._multi_props.q_min ** b) + + self._multi_props.q_min ** b) ** (1.0 / b) # Calculate mass of companion m_comp = q_values * prim_subset diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 9a6c890c..c794c171 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -13,7 +13,7 @@ from pysynphot import observation as obs import pysynphot from astropy import constants, units -from astropy.table import Table, Column, MaskedColumn +from astropy.table import Table, Column, MaskedColumn, vstack import pickle import time, datetime import math @@ -33,7 +33,6 @@ default_red_law = reddening.RedLawNishiyama09() default_atm_func = atm.get_merged_atmosphere default_wd_atm_func = atm.get_wd_atmosphere -default_bd_atm_func = atm.get_bd_atmosphere def Vega(): # Use Vega as our zeropoint... assume V=0.03 mag and all colors = 0.0 @@ -255,8 +254,9 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): if self.ifmr != None: # Identify compact objects as those with Teff = 0 or with phase > 100 or BDs highest_mass_iso = self.iso.points['mass'].max() - idx_rem = np.where((np.isnan(star_systems['Teff'])) & (star_systems['mass'] > highest_mass_iso) | - (star_systems['mass'] < 0.08))[0] + idx_rem = np.where((np.isnan(star_systems['Teff']) & (star_systems['mass'] > highest_mass_iso)))[0] + + # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -278,26 +278,22 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) - - # Handle brown dwarfs separately and assign temperatures - idx_bd = np.where(star_systems['phase'] == 90)[0] - - # Define the class for BDs - evo_model = evolution.MergedBaraffePisaEkstromParsec() - - # Use the instance to call get_temperature - masses = star_systems[idx_bd]['mass_current'] - ages = np.full(len(masses), self.iso.points.meta['LOGAGE']) - temperatures = evo_model.get_temperature(masses, ages) - - # Convert the numpy array to an astropy Column - temperatures_column = Column(temperatures) - - # Assign temperatures to the Table column - star_systems['Teff'][idx_bd] = temperatures_column - - for filt in self.filt_names: - star_systems[filt][idx_bd] = np.full(len(idx_bd), np.nan) + # override remnant conditions for BDs + idx_bd = np.where(star_systems['mass'] < 0.08)[0] + for i in idx_bd: + T = star_systems['Teff'][i] + g = star_systems['logg'][i] + print(T,g) + + try: + star_bd = atm.get_bd_atmosphere(temperature=T, gravity=g, metallicity=0, verbose=False) + for filt in self.filt_names: + star_systems[filt][i] = star_bd[filt] + except Exception as e: + print(f"[Isochrone] BD atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") + # Keep filter magnitudes as NaN if model fails + for filt in self.filt_names: + star_systems[filt][i] = np.nan return star_systems @@ -424,10 +420,12 @@ def _make_companions_table(self, star_systems, compMass): # Make Remnants with flux = 0 in all bands. ##### if self.ifmr != None: - # Identify compact objects as those with Teff = 0 or with masses above the max iso mass, or BDs + # Identify compact objects as those with Teff = 0 or with masses above the max iso mass + # Exclude BDs from designation highest_mass_iso = self.iso.points['mass'].max() - cdx_rem = np.where((np.isnan(companions['Teff']) & - (companions['mass'] > highest_mass_iso)) | (companions['mass'] < 0.08))[0] + cdx_rem = np.where((np.isnan(companions['Teff'])) & + (companions['mass'] > highest_mass_iso) & + (companions['mass'] >= 0.08))[0] # Calculate remnant mass and ID for compact objects; update remnant_id and # remnant_mass arrays accordingly @@ -449,26 +447,37 @@ def _make_companions_table(self, star_systems, compMass): for filt in self.filt_names: companions[filt][cdx_rem_good] = np.full(len(cdx_rem_good), np.nan) - # Assigning brown dwarf companions the correct phase/properties - bd_idx = np.where((companions['mass'] >= 0.01) & (companions['mass'] < 0.08))[0] - companions['phase'][bd_idx] = 90 - companions['mass_current'][bd_idx] = companions['mass'][bd_idx] - for filt in self.filt_names: - companions[filt][bd_idx] = np.full(len(bd_idx), np.nan) - - # Define the class for BDs - evo_model = evolution.MergedBaraffePisaEkstromParsec() - - # Use the instance to call get_temperature - c_masses = companions[bd_idx]['mass_current'] - c_ages = np.full(len(c_masses), self.iso.points.meta['LOGAGE']) - c_temperatures = evo_model.get_temperature(c_masses, c_ages) - - # Convert the numpy array to an astropy Column - c_temperatures_column = Column(c_temperatures) - - # Assign temperatures to the Table column - companions['Teff'][bd_idx] = c_temperatures_column + # For brown dwarfs, we have Teff and logg from the isochrone. + # We want to use the atmosphere model to get their magnitudes + idx_bd = np.where(companions['mass'] < 0.08)[0] + for i in idx_bd: + T = companions['Teff'][i] + g = companions['logg'][i] + + # Check if we have valid Teff and logg + if np.isnan(T) or np.isnan(g): + print('T/logg assigned nan for BD object!') + continue + + try: + # Use the default atmosphere function to get the spectrum. + star_bd_spec = default_atm_func(temperature=T, gravity=g, metallicity=self.iso.metallicity) + + # Redden the spectrum + red = default_red_law.reddening(self.iso.points.meta['AKS']).resample(star_bd_spec.wave) + star_bd_spec *= red + + # Calculate magnitude in each filter + for filt_name_long in self.filt_names: + obs_str = get_obs_str(filt_name_long) + filt_info = get_filter_info(obs_str) + mag = mag_in_filter(star_bd_spec, filt_info) + companions[filt_name_long][i] = mag + except Exception as e: + print(f"[ResolvedCluster] BD companion atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") + # Keep filter magnitudes as NaN if model fails + for filt in self.filt_names: + companions[filt][i] = np.nan # Notify if we have a lot of bad ones. @@ -740,7 +749,11 @@ class Isochrone(object): wd_atm_func: white dwarf model atmosphere function, optional Set the stellar atmosphere models for the white dwafs. - Default is get_wd_atmosphere + Default is get_wd_atmosphere + + bd_atm_func: brown dwarf model atmosphere function, optional + Set the stellar atmosphere models for the brown dwafs. + Default is get_bd_atmosphere mass_sampling : int, optional Sample the raw isochrone every `mass_sampling` steps. The default @@ -766,7 +779,7 @@ class Isochrone(object): """ 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, bd_atm_func = default_bd_atm_func, + wd_atm_func = default_wd_atm_func, #bd_atm_func = default_bd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, rebin=True): @@ -789,6 +802,52 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, metallicity=metallicity) + + # Specify MergedPhillipsBaraffePisaEkstromParsec for brown dwarfs in all cases + try: + bd_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() + + # Clamp to valid BD grid domain + logAge_bd = np.clip(logAge, 6.0, 10.0) + metallicity_bd = 0.0 # Phillips2020 only supports solar [Fe/H] + + # Notify user if clamping occurred + if logAge != logAge_bd: + print(f"[Isochrone] Adjusted BD logAge from {logAge:.2f} → {logAge_bd:.2f} (model valid range 6–10)") + print(f"[Isochrone] Using solar metallicity for BD grid ([Fe/H]={metallicity_bd:.2f})") + + # Compute BD isochrone + evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) + + # If no data at specified age, find the closest available age in the model grid. + if len(evol_bd) == 0: + print(f"[Isochrone] Warning: No BD data at logAge={logAge_bd:.2f}. Searching for closest age.") + # Find the closest age in the model's grid + available_ages = np.unique(bd_model.model['logAge']) + closest_logAge = available_ages[np.argmin(np.abs(available_ages - logAge_bd))] + + if closest_logAge != logAge_bd: + print(f"[Isochrone] Using closest available BD logAge: {closest_logAge:.2f}") + logAge_bd = closest_logAge + evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) + + # Define BD threshold and merge + bd_thresh = 0.075 + evol_stars = evol[evol['mass'] >= bd_thresh] + evol_bd = evol_bd[evol_bd['mass'] < bd_thresh] + + if len(evol_bd) > 0: + evol = vstack([evol_bd, evol_stars]) + evol.sort('mass') + print(f"[Isochrone] Merged brown dwarf evolution below {bd_thresh:.3f} Msun " + f"from PhillipsBaraffePisaMerged model (logAge={logAge_bd:.2f}, [Fe/H]=0.0).") + else: + print("[Isochrone] Warning: Brown dwarf model returned no data below threshold.") + + except Exception as e: + import traceback + print(f"[Isochrone] Warning: Failed to merge brown dwarf model ({e})") + traceback.print_exc() # Eliminate cases where log g is less than 0 @@ -808,7 +867,7 @@ 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'] * c.L_sun # luminosity 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 @@ -837,13 +896,14 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # This is the time-intensive call... everything else is negligable. # If source is a star, pull from star atmospheres. If it is a WD, # pull from WD atmospheres + if phase == 101: star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, verbose=False) - elif phase == 90: - print(f"Applying brown dwarf model to object {ii}") - star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, - verbose=False) + #elif phase == 90: + # print(f"Applying brown dwarf model to object {ii}") + # star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, + # verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, rebin=rebin) @@ -1013,7 +1073,7 @@ class IsochronePhot(Isochrone): 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, bd_atm_func = default_bd_atm_func, + wd_atm_func = default_wd_atm_func, #bd_atm_func = default_bd_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=False, @@ -1060,7 +1120,7 @@ def __init__(self, logAge, AKs, distance, metallicity=metallicity, evo_model=evo_model, atm_func=atm_func, wd_atm_func=wd_atm_func, - bd_atm_func=bd_atm_func, + #bd_atm_func=bd_atm_func, wave_range=wave_range, red_law=red_law, mass_sampling=mass_sampling, diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 4ae078aa..94bbbe12 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -27,7 +27,7 @@ def test_evolution_models(): age_young_arr = [6.7, 7.9] age_all_arr = [6.7, 8.0, 9.7] age_all_MIST_arr = [5.2, 6.7, 9.7, 10.13] - bd_young_test = [6.0, 6.5, 7.4] + bd_test = [6.0, 6.5, 7.4, 8.4, 10.0] # Metallicity ranges to test (if applicable) metal_range = [-2.5, -1.5, 0, 0.25, 0.4] @@ -42,7 +42,7 @@ def test_evolution_models(): # Array of age_ranges for the specific evolution models to test - age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_young_test] + age_vals = [age_all_MIST_arr, age_all_arr, age_all_arr, age_young_arr, age_young_arr, age_young_arr, age_all_arr, age_all_arr, bd_test] # Array of metallicities for the specific evolution models to test metal_vals = [metal_range, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_solar, metal_Marley, metal_solar] diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index a94dee67..7ae1b0b1 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -211,8 +211,8 @@ def test_ResolvedCluster(): print('Constructed isochrone: %d seconds' % (time.time() - startTime)) # Now to create the cluster. - imf_mass_limits = np.array([0.07, 0.5, 1, np.inf]) - imf_powers = np.array([-1.3, -2.3, -2.3]) + imf_mass_limits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) + imf_powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) ########## # Start without multiplicity @@ -390,7 +390,7 @@ def test_UnresolvedCluster(): startTime = time.time() multi = multiplicity.MultiplicityUnresolved() - imf_in = imf.Kroupa_2001(multiplicity=multi) + imf_in = imf.Salpeter_Kirkpatrick_2024(multiplicity=multi) evo = evolution.MergedBaraffePisaEkstromParsec() atm_func = atmospheres.get_merged_atmosphere iso = syn.Isochrone(log_age, AKs, distance, metallicity=metallicity, @@ -441,8 +441,7 @@ def test_ifmr_multiplicity(): ########## # Start without multiplicity and IFMR ########## - my_imf1 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, - multiplicity=None) + my_imf1 = imf.Salpeter_Kirkpatrick_2024(multiplicity=None) print('Constructed IMF: %d seconds' % (time.time() - startTime)) cluster1 = syn.ResolvedCluster(iso, my_imf1, cluster_mass, ifmr=ifmr_obj) @@ -454,8 +453,7 @@ def test_ifmr_multiplicity(): # Test with multiplicity and IFMR ########## multi = multiplicity.MultiplicityUnresolved() - my_imf2 = imf.IMF_broken_powerlaw(imf_mass_limits, imf_powers, - multiplicity=multi) + my_imf2 = imf.Salpeter_Kirkpatrick_2024(multiplicity=multi) print('Constructed IMF with multiples: %d seconds' % (time.time() - startTime)) cluster2 = syn.ResolvedCluster(iso, my_imf2, cluster_mass, ifmr=ifmr_obj) @@ -1044,7 +1042,7 @@ def test_Raithel18_phase_designation(): #create an MZAMS array to cover all potential phase designations, and the expected phase designations test_MZAMS = np.array([0.06, 0.3, 0.6, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) - expected_phases = np.array([99, -1, 101, 101, 101, 101, 102, 102, 103, 103]) + expected_phases = np.array([90., -1., 101., 101., 101., 101., 102., 102., 103., 103.]) #generate the output from Raithel IFMR output_array = Raithel.generate_death_mass(test_MZAMS) From 180860705e7b3ee8f8c7c47f538df33d6204dca8 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 16:14:18 -0800 Subject: [PATCH 40/48] Updating phase designation in interpolated regions --- changes/mass_to_age.py | 2518 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2518 insertions(+) create mode 100644 changes/mass_to_age.py diff --git a/changes/mass_to_age.py b/changes/mass_to_age.py new file mode 100644 index 00000000..0ac86c9c --- /dev/null +++ b/changes/mass_to_age.py @@ -0,0 +1,2518 @@ +import math +import logging +from numpy import genfromtxt +import numpy as np +import os +import glob +import pandas as pd +import pdb +import warnings +from astropy.table import Table, vstack, Column +from scipy import interpolate +import pylab as py +from spisea.utils import objects +from scipy.interpolate import RegularGridInterpolator +from spisea import exceptions +import re +import matplotlib +import matplotlib.pyplot as plt +from spisea import atmospheres + +logger = logging.getLogger('evolution') + +# Fetch root directory of evolution models. +try: + models_dir = os.environ['SPISEA_MODELS'] + models_dir += '/evolution/' +except KeyError: + warnings.warn("SPISEA_MODELS is undefined; functionality " + "will be SEVERELY crippled.") + models_dir = '' + +# Code to deconstruct current Phillips2020 evolution files and reconstruct iso files based on age + +# Define input and output directories +input_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/z00_mass' +output_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/z00_age' +os.makedirs(output_dir, exist_ok=True) + +# Combine data from all files +combined_data = [] + +for filename in os.listdir(input_dir): + if filename.endswith('.txt'): + file_path = os.path.join(input_dir, filename) + table = Table.read(file_path, format='ascii') + combined_data.append(table) + +# Concatenate all data into a single table +all_data = combined_data[0] + +# Add the remaining tables to the main table +for table in combined_data[1:]: + all_data = vstack([all_data, table]) # Use vstack to stack tables + +# Group data by age +ages = np.unique(all_data['Age']) + +# Iterate over each unique age and filter data +for age in ages: + age_group = all_data[all_data['Age'] == age] + output_path = os.path.join(output_dir, f"ATMO_{age}.txt") + + # Save each age group as a .txt file + age_group.write(output_path, format='ascii', overwrite=True) + +print(f"New files created in: {output_dir}") + +# Code to eradicate duplicate lines seen in above code +"""new_input_dir = output_dir + +for filename2 in os.listdir(new_input_dir): + if filename2.endswith('.txt'): + file_path2 = os.path.join(new_input_dir, filename2) + + # Remove duplicates before loading as a table + with open(file_path2, 'r') as f: + lines = f.readlines() + + header = lines[0] + data_lines = [line for line in lines[1:] if not line.startswith(header.split()[0])] + + with open(file_path2, 'w') as f: + f.write(header) + f.writelines(data_lines) + + # Now process with Astropy Table + table2 = Table.read(file_path2, format='ascii') + unique_table2 = unique(table2) + unique_table2.write(file_path2, format='ascii', overwrite=True) + +print('files overwritten successfully!') + + +# Reformatting files to match Parsec +def reformat(): + for file in os.listdir(output_dir): + if file.endswith('.txt'): + new_fp = os.path.join(output_dir, file) + + # Add metallicity column of all 0.0 + r_table = Table.read(new_fp, format='ascii') + r_table.add_column(0.0, name='Z', index=0) + + # Duplicate mass to include current mass column + r_table.add_column(r_table['Mass'], name='Mass_current') + + # Update age column to make it log scale + r_table['Age'] = np.log10(r_table['Age'] * 1e9) #originally in Gyr + + # Update Teff column to make it log scale + r_table['Teff'] = np.log10(r_table['Teff']) + + # Reorder columns to match Parsec + new_order = ['Z', 'Age', 'Mass', 'Mass_current', 'Luminosity', 'Teff', 'Gravity', 'Gaia_Gbp', 'Gaia_G', 'Gaia_Grp'] + t_new = r_table[new_order] + t_new.write(new_fp, format='ascii', overwrite=True) + + print(f"{file} reformatted successfully!") + + return + +# Transform reformatted .txt files to iso fits files +def ATMO_to_iso(): + i_dir = output_dir + o_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Phillips2020/iso' #output directory SPISEA will pull from + + for file in os.listdir(i_dir): + if file.endswith('.txt'): + fp = os.path.join(i_dir, file) + + try: + age_str = file.split('_')[1].split('.txt')[0] + age = float(age_str) + log_age = np.log10(age * 1e9) + except (IndexError, ValueError): + print(f"{file} cannot be processed.") + continue + + try: + # load in .txt file as ascii table + table = Table.read(fp, format='ascii') + except Exception as e: + print(f"{fp} could not be read: {e}") + continue + + #create output file name + o_file = os.path.join(o_dir, f'iso_{log_age}.fits') + + try: + # write to output directory as fits file + table.write(o_file, format='fits', overwrite=True) + print(f"{o_file} created successfully!") + except Exception as e: + print(f"{o_file} could not be generated: {e}") + continue + + return + + +""" +### CODE TO UNPACK MARLEY FILES ### +def Marley_deconstruct(): + """ + Code to deconstruct big Marley files into individual age files + """ + in_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/Marley_age' + out_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso' + + # Set path to each file + for filename in os.listdir(in_dir): + file_path = os.path.join(in_dir, filename) + + # Extract metallicity from filename + match = re.search(r'nc([+-]\d+\.\d+)_co', filename) + metallicity = match.group(1) if match else "unknown" + + # Deconstruct initial format of single column table + with open(file_path, 'r') as f: + lines = f.readlines() + header = ['Age', 'Mass', 'log_L', 'Teff', 'logg', 'Radius'] + data = [line.strip().split() for line in lines[1:]] + data = [row for row in data if len(row) > 1] + table = Table(rows=data, names=header) + + # Find unique ages to create iso age_based files + ages = np.unique(table['Age']) + + for age in ages: + age_group = table[table['Age'] == age] + out_path = os.path.join(out_dir, f"Marley_{metallicity}_{age}.txt") + + # Save each age group as a .txt file + age_group.write(out_path, format='ascii', overwrite=True) + + print(f"New files created in: {out_dir}") + + return + +# Reformatting files to match Parsec +def reformat_Marley(): + age_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/age_txt' + for file in os.listdir(age_dir): + if file.endswith('.txt'): + new_fp = os.path.join(age_dir, file) + + # Add metallicity column + table = Table.read(new_fp, format='ascii') + + # Extract metallicity from filename + match = re.search(r'Marley_([+-]?\d+\.\d+)_\d+\.\d+.txt', file) + metallicity = float(match.group(1)) if match else np.nan + + # Replace or add metallicity column + if 'Z' in table.colnames: + table.replace_column('Z', [metallicity] * len(table)) + else: + table.add_column([metallicity] * len(table), name='Z', index=0) + + # Duplicate mass to include current mass column + #table.add_column(table['Mass'], name='Mass_current') + + # Update age column to make it log scale + #table['Age'] = np.log10(table['Age'] * 1e9) #originally in Gyr + + # Update Teff column to make it log scale + #table['Teff'] = np.log10(table['Teff']) + + # Reorder columns to match Parsec + new_order = ['Z', 'Age', 'Mass', 'Mass_current', 'log_L', 'Teff', 'logg', 'Radius'] + t_new = table[new_order] + t_new.write(new_fp, format='ascii', overwrite=True) + + print(f"{file} reformatted successfully!") + + return + +def Marley_to_iso(): + in_dir = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/age_txt' + out_dir_1 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zm05' + out_dir_2 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zp00' + out_dir_3 = '/System/Volumes/Data/mnt/g/lu/models/evolution/Marley2021/iso/zp05' + + for file in os.listdir(in_dir): + if file.endswith('.txt'): + fp = os.path.join(in_dir, file) + + try: + parts = file.split('_') + metal_str = parts[1] # Second part is metallicity + age_str = parts[2].split('.txt')[0] + + age = float(age_str) + metallicity = float(metal_str) + log_age = np.log10(age * 1e9) + except (IndexError, ValueError): + print(f"{file} cannot be processed.") + continue + + try: + # load in .txt file as ascii table + table = Table.read(fp, format='ascii') + except Exception as e: + print(f"{fp} could not be read: {e}") + continue + + # Determine output directory based on metallicity + if metallicity == -0.5: + out_dir = out_dir_1 + elif metallicity == 0.0: + out_dir = out_dir_2 + elif metallicity == 0.5: + out_dir = out_dir_3 + else: + print(f"Skipping {file}: Unexpected metallicity value ({metallicity}).") + continue + + # Create output filename + out_file = os.path.join(out_dir, f'iso_{log_age}.fits') + + try: + # Write to output directory as FITS file + table.write(out_file, format='fits', overwrite=True) + print(f"{out_file} created successfully!") + except Exception as e: + print(f"Error writing {out_file}: {e}") + continue + + return + +### CREATING MERGED EVO MODEL WITH PHILLIPS ### +def get_phillips_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Phillips2020/iso/' + metSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020 metallicities not supported yet') + return + rootDir += metSuffix + + # List available isochrone files + available_ages = [] + for filename in os.listdir(rootDir): + if filename.startswith('iso_') and filename.endswith('.fits'): + age_str = filename.split('_')[1].split('.fits')[0] # Extract the age part from filename + available_ages.append(float(age_str)) + + # Find the closest available age from Phillips + closest_age = min(available_ages, key=lambda x: abs(x - logAge)) + print(closest_age) + + # Load the corresponding isochrone + isoFile = rootDir + f'iso_{closest_age}.fits' + print(f"Loading isochrone for logAge = {closest_age}") + + # Check to see if isochrone exists + if not os.path.exists(isoFile): + print( f'Phillips isochrone for logAge = {closest_age} does\'t exist') + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + print("Available Columns:", data.keys()) + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[5]] # K + logL = data[cols[4]] # L_sun + logg = data[cols[6]] + mass_current = data[cols[3]] # Matches initial mass -- BD masses assumed to not change w current models + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def get_Baraffe15_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Baraffe+15 isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + ALSO interpolates isochrone to a finer mass grid. + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Baraffe15/iso/' + if metallicity != 'solar': + print( 'Non-solar Baraffe+15 metallicities not supported yet') + return + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.fits' % logAge + if not os.path.exists(isoFile): + print( 'Baraffe+15 isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + mass = data['Mass'] #Note: this is initial mass, in M_sun + logT = np.log10(data['Teff']) # K + logL = data['logL'] # L_sun + logg = data['logG'] + + # Interpolate isochrone to finer mass grid. Spacing + # is one model every 0.02 M_sun down to 0.2 M_sun, then + # one model every 0.005 M_sun down to 0.07 M_sun + new_masses0 = np.arange(min(mass), 0.1, 0.005) + new_masses2 = np.arange(0.1, max(mass), 0.02) + + #new_masses = np.concatenate((new_masses0, new_masses1, new_masses2)) + new_masses = np.concatenate((new_masses0, new_masses2)) + + # Build interpolators in linear space + f_logT = interpolate.interp1d(mass, 10**logT, kind='linear') + f_logL = interpolate.interp1d(mass, 10**logL, kind='linear') + f_logg = interpolate.interp1d(mass, 10**logg, kind='linear') + + # Do interpolation, convert back to logspace + logT_interp = np.log10(f_logT(new_masses)) + logL_interp = np.log10(f_logL(new_masses)) + logg_interp = np.log10(f_logg(new_masses)) + + # Hack, add new mass_current and phase columns. + mass_current = np.array(new_masses) + phase = np.ones(len(new_masses), dtype=int) + + # Test the interpolation, if desired + test = False + if test: + py.figure(1, figsize=(10,10)) + py.clf() + py.plot(mass, logT, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logT_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logT') + py.legend() + + py.figure(2, figsize=(10,10)) + py.clf() + py.plot(mass, logL, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logL_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logL') + py.legend() + + py.figure(3, figsize=(10,10)) + py.clf() + py.plot(mass, logg, 'k.', ms = 10, label='Orig') + py.plot(new_masses, logg_interp, 'r.', ms=7, label='Interp') + py.xlabel('Mass') + py.ylabel('logg') + py.legend() + + pdb.set_trace() + + # Make isochrone + obj = objects.DataHolder() + obj.mass = new_masses + obj.logT = logT_interp + obj.logg = logg_interp + obj.logL = logL_interp + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def merge_isochrone_baraffe_phillips(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Phillips 2020 models. Will take + 100% Phillips2020 between 0.01 - 0.07 M_sun, transition between + 0.07 - 0.075 M_sun, and take 100% Baraffe from 0.075 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Phillips isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPhillips = get_phillips_isochrone(logAge, metallicity=metallicity) + + # Identify M >= 0.075 M_sun in Baraffe and M <= 0.07 M_sun in Phillips + good_b = np.where(isoBaraffe.mass >= 0.075) + good_p = np.where(isoPhillips.mass <= 0.07) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Phillips over this range + mid_mass = np.arange(0.07, 0.075, 0.002) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Phillips to build from. + # Baraffe has identical sampling over this range, and Phillips sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPhillips.mass - mass) == min(abs(isoPhillips.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPhillips.mass[idx_p] - mass) >= 0.002) | ((isoBaraffe.mass[idx_b] - mass) >= 0.002): + print( 'WARNING: Baraffe or Phillips model interpolation between 0.01 - 0.08 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.07 or 0.075 (whichever is appropriate) + diff = 0.075 - 0.07 + weight_p = (0.075 - mass) / diff + weight_b = 1.0 - weight_p + print( 'Baraffe {0} and Phillips {1} at mass {2}'.format(weight_p, weight_b, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoPhillips.logT[idx_p] * weight_p) + \ + (10**isoBaraffe.logT[idx_b] * weight_b) + L = (10**isoPhillips.logL[idx_p] * weight_p) + \ + (10**isoBaraffe.logL[idx_b] * weight_b) + g = (10**isoPhillips.logg[idx_p] * weight_p) + \ + (10**isoBaraffe.logg[idx_b] * weight_b) + mcurr = (isoPhillips.mass_current[idx_p] * weight_p) + \ + (isoBaraffe.mass_current[idx_b] * weight_b) + phase = np.round((isoPhillips.mass_current[idx_p] * weight_p) + \ + (isoBaraffe.mass_current[idx_b] * weight_b)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M>=0.075, + # Phillips at M<=0.075, and the combination inbetween + mass = np.concatenate((isoPhillips.mass[good_p], mid_mass, isoBaraffe.mass[good_b])) + logT = np.concatenate((isoPhillips.logT[good_p], mid_logT, isoBaraffe.logT[good_b])) + logL = np.concatenate((isoPhillips.logL[good_p], mid_logL, isoBaraffe.logL[good_b])) + logG = np.concatenate((isoPhillips.logg[good_p], mid_logG, isoBaraffe.logg[good_b])) + mcurr = np.concatenate((isoPhillips.mass_current[good_p], mid_Mcurr, isoBaraffe.mass_current[good_b])) + phase = np.concatenate((isoPhillips.phase[good_p], mid_phase, isoBaraffe.phase[good_b])) + + # Also add a source flag + source = np.concatenate( (['Phillips']*len(good_p[0]), + ['Baraffe+Phillips']*len(mid_mass), + ['Baraffe']*len(good_b[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + + +import matplotlib.pyplot as plt +import numpy as np + +"""def plot_merged_isochrone(iso): + "" + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020 and Baraffe+15 models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + "" + + # Define colors for different sources + color_map = {'Phillips': 'blue', 'Baraffe': 'red', 'Baraffe+Phillips': 'pink'} + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], label='linear representation') + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].set_xlim(0.0, 0.1) + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], label='linear representation') + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].set_xlim(0.0, 0.1) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], label='linear representation') + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].set_xlim(0.0, 0.1) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() +""" + +import numpy as np +import matplotlib.pyplot as plt + +def plot_merged_isochrone(iso): + """ + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020 and Baraffe+15 models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + """ + + # Define colors for different sources + color_map = {'Phillips': 'blue', 'Baraffe': 'red', 'Baraffe+Phillips': 'pink'} + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], color=color_map[source]) # Fixed plot function + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].set_xlim(0.0, 0.1) + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], color=color_map[source]) # Fixed plot function + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].set_xlim(0.0, 0.1) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], color=color_map[source]) # Fixed plot function + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].set_xlim(0.0, 0.1) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() + +def plot_merged_bpp_isochrone(iso): + """ + Function to plot the merged isochrone model to visualize the transition + between Phillips 2020, Baraffe+15, and Pisa models. + + Parameters: + ----------- + iso : DataHolder object + Merged isochrone returned by merge_isochrone_baraffe_phillips(). + """ + + # Define colors for different sources + color_map = {'Phillips': 'blue', + 'Baraffe': 'red', + 'Pisa':'green', + 'Baraffe+Phillips': 'pink', + 'Baraffe+Pisa': 'cyan' + } + + # Create figure and subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + + # Mass vs. logT (Effective Temperature) + for source in np.unique(iso.source): + mask = iso.source == source + axes[0].scatter(iso.mass[mask], iso.logT[mask], label=source, color=color_map[source], alpha=0.7) + axes[0].plot(iso.mass[mask], iso.logT[mask], color=color_map[source]) # Fixed plot function + axes[0].set_xlabel("Mass ($M_{\odot}$)") + axes[0].set_ylabel("$\log T_{\mathrm{eff}}$ (K)") + axes[0].set_title("Mass vs. Effective Temperature") + axes[0].axvline(0.07, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].axvline(0.08, linestyle="--", color="gray", alpha=0.5) # Transition marker + axes[0].legend() + + # Mass vs. logL (Luminosity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[1].scatter(iso.mass[mask], iso.logL[mask], label=source, color=color_map[source], alpha=0.7) + axes[1].plot(iso.mass[mask], iso.logL[mask], color=color_map[source]) # Fixed plot function + axes[1].set_xlabel("Mass ($M_{\odot}$)") + axes[1].set_ylabel("$\log L$ ($L_{\odot}$)") + axes[1].set_title("Mass vs. Luminosity") + axes[1].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[1].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[1].legend() + + # Mass vs. logg (Surface Gravity) + for source in np.unique(iso.source): + mask = iso.source == source + axes[2].scatter(iso.mass[mask], iso.logg[mask], label=source, color=color_map[source], alpha=0.7) + axes[2].plot(iso.mass[mask], iso.logg[mask], color=color_map[source]) # Fixed plot function + axes[2].set_xlabel("Mass ($M_{\odot}$)") + axes[2].set_ylabel("$\log g$ (cm/s²)") + axes[2].set_title("Mass vs. Surface Gravity") + axes[2].axvline(0.07, linestyle="--", color="gray", alpha=0.5) + axes[2].axvline(0.08, linestyle="--", color="gray", alpha=0.5) + axes[2].legend() + + # Adjust layout and show plot + plt.tight_layout() + plt.show() + +"""def merge_isochrone_pisa_baraffe_phillips(logAge, metallicity='solar', rotation=True, iso_in=None): #????? + "" + Function to merge Pisa 2011, Baraffe, and Phillips 2020 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Z = 0.015 for Phillips 2020. + + If iso_in = None, will take Pisa models to smallest available mass, + then switch to Baraffe/Phillips. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + "" + # Get individual Ekstrom and Pisa isochrones at desired age + isoBaraffePhillips = merge_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_Pisa = max(isoPisa.mass) + good = np.where(isoEkstrom.mass > max_Pisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPisa.mass, isoEkstrom.mass) + logT = np.append(isoPisa.logT, isoEkstrom.logT) + logg = np.append(isoPisa.logg, isoEkstrom.logg) + logL = np.append(isoPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso +""" + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_baraffe_pisa_phillips(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Merges Phillips, Baraffe, and Pisa isochrones to create a complete evolutionary track. + + Inputs: + logAge - Logarithmic Age + metallicity - Metallicity (default is 'solar') + rotation - Boolean indicating whether to include rotation in the models (default is True) + + Outputs: + iso - An object containing the merged isochrone data. + """ + # Merge Phillips with Baraffe + print(f"Merging Phillips with Baraffe for logAge = {logAge}") + isoBaraffePhillips = merge_isochrone_baraffe_phillips(logAge, metallicity=metallicity) + + # Merge Baraffe+Phillips with Pisa (depending on the logAge) + if logAge <= 7.4: + print(f"Merging Baraffe+Phillips with Pisa for logAge = {logAge}") + iso = merge_isochrone_baraffe_pisa(logAge, metallicity=metallicity, iso_in=isoBaraffePhillips) + else: + # If logAge > 7.4, you may want to merge with different models (Parsec/Ekstrom) + print(f"Merging Baraffe+Phillips with higher mass models for logAge = {logAge}") + iso = merge_isochrone_baraffe_pisa(logAge, metallicity=metallicity, iso_in=isoBaraffePhillips) + + return iso + + +def merge_all_isochrones_phillips_baraffe_pisa_ekstrom_parsec(metallicity='solar', rotation=True, plot=False): + """ + Make evolutionary isochrones containing a continuous distribution of + masses from the PMS to the MS. The models used are the following: + + PMS + Phillips from 0.01 - 0.07 M_sun + Baraffe+15 from 0.07 - 0.4 M_sun + Pisa 2011 from 0.5 - top of grid (~7 M_sun) + + MS (M > Pisa 2011) + Ekstrom+12 for logAge < 7.4 + Parsec V1.2s for logAge > 7.4 + + BD stars + Phillips for 6.0 <= logAge <= 10.0 + + metallicity = 'solar' --> Ekstrom+12 z014, Pisa2011 z015, Parsec z015, Phillips z00 + + if plot = True, will make plots of merged isochrones in 'plots' directory, + which must already exist + + Code is expected to be run in merged model working directory. + """ + # Root data directory for Ekstrom+12 isochrones + rootDirE = models_dir + 'Ekstrom2012/iso/' + metalPart = 'z014/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rotPart = 'rot/' + if not rotation: + rotPart = 'norot/' + rootDirE += metalPart+rotPart + + # Root data directory for the Baraffe isochrones + rootDirBaraffe = models_dir + 'Baraffe15/iso/' + + # Root data directory for Pisa isochrones + rootDirPisa = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPisa += metSuffix + + # Root data directory for Parsec isochrones + rootDirParsec = models_dir + 'ParsecV1.2s/iso/' + metalSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirParsec += metalSuffix + + # Root data directory for Phillips isochrones + rootDirPhillips = models_dir + 'Phillips2020/iso/' + mSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPhillips +=mSuffix + + # Search both directories for iso_*.dat files + isoFilesE = glob.glob(rootDirE + 'iso_*.dat') + isoFilesB = glob.glob(rootDirBaraffe + 'iso_*.fits') + isoFilesPi = glob.glob(rootDirPisa + 'iso_*.dat') + isoFilesPa = glob.glob(rootDirParsec + 'iso_*') + isoFilesPh = glob.glob(rootDirPhillips + 'iso_*.fits') + + # Output of merged isochrones + if rotation == True: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_rot_test/'.format(metSuffix[:-1]) + # Raise error if wanting Phillips data? + else: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_norot/'.format(metSuffix[:-1]) + if not os.path.exists(outDir): + os.mkdir(outDir) + + # Isolate the iso*.dat file names + for ii in range(len(isoFilesE)): + isoFilesE[ii] = isoFilesE[ii].split('/')[-1] + + for ii in range(len(isoFilesB)): + isoFilesB[ii] = isoFilesB[ii].split('/')[-1] + + for ii in range(len(isoFilesPi)): + isoFilesPi[ii] = isoFilesPi[ii].split('/')[-1] + + for ii in range(len(isoFilesPa)): + isoFilesPa[ii] = isoFilesPa[ii].split('/')[-1] + + for ii in range(len(isoFilesPh)): + isoFilesPh[ii] = isoFilesPh[ii].split('/')[-1] + + # Loop through the Pisa isochrones, adding the MS and Baraffe models + # as appropriate + for ii in range(len(isoFilesPi)): + isoFilePi = isoFilesPi[ii] + + logAgeStr = isoFilePi.replace('iso_', '').replace('.dat', '') + logAge = float(logAgeStr) + + #-----PRE-MAIN SEQUENCE----# + # Merge with the Baraffe+15 models from 0.07 - 0.4 M_sun. Includes + # transition region between 0.4 - 0.5 M_sun in which we shift + # from 100% Baraffe to 100% Pisa + + print( 'Merging isochrones Pisa + Baraffe + Phillips from ', isoFilePi) + isoPMS = merge_isochrone_phillips_baraffe_pisa(logAge, metallicity=metallicity) + + #--------MAIN SEQUENCE-------# + # Case where logAge <= 7.4, we merge with Ekstrom. Otherwise, merge + # which parsec + if logAge <= 7.4: + if isoFilePi not in isoFilesE: + print( 'Skipping isochrones from ', isoFilePi) + print( 'PROBLEM WITH PISA OR EKSTROM') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Ekstrom from ', isoFilePi) + iso = merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity=metallicity, + rotation=rotation, iso_in=isoPMS) + else: + if isoFilePi not in isoFilesPa: + print( 'Skipping isochrones from ', isoFilePi) + print( 'PROBLEM WITH PISA OR PARSEC') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Parsec from ', isoFilePi) + iso = merge_isochrone_phillips_pisa_parsec(logAge, metallicity=metallicity, + iso_in=isoPMS) + + # Make test plot, if desired. These are put in plots directory + if plot: + # Make different models different colors + phillips_ind = np.where(iso.source == 'Phillips') + merge_pb = np.where(iso.source == 'Phillips+Baraffe') + baraffe_ind = np.where(iso.source == 'Baraffe') + merge_ind = np.where(iso.source == 'Baraffe+Pisa') + pisa_ind = np.where(iso.source == 'Pisa') + MS_ind = np.where( (iso.source == 'Ekstrom') | (iso.source == 'Parsec')) + #Extract age + logAge = isoFilePi.split('_')[1][:-4] + + # Temporarily turn off interactive plot. Turn back on at end + py.ioff() + + py.figure(1) + py.clf() + py.plot(iso.logT[phillips_ind], iso.logL[phillips_ind], 'c-', label = 'Phillips', + linewidth=2) + py.plot(iso.logT[merge_pb], iso.logL[merge_pb], 'y-', label = 'Phillips+Baraffe', + linewidth=2) + py.plot(iso.logT[baraffe_ind], iso.logL[baraffe_ind], 'k-', label = 'Baraffe', + linewidth=2) + py.plot(iso.logT[merge_ind], iso.logL[merge_ind], 'r-', label = 'Baraffe+Pisa', + linewidth=2) + py.plot(iso.logT[pisa_ind], iso.logL[pisa_ind], 'g-', label = 'Pisa', + linewidth=2) + py.plot(iso.logT[MS_ind], iso.logL[MS_ind], 'b-', + label = 'Ekstrom/Parsec', linewidth=2) + py.xlabel('log Teff') + py.ylabel('log L') + py.title('Log Age = {0:s}'.format(logAge)) + py.legend(loc=3) + py.axis([4.9, 3.2, -3.0, 8]) + py.savefig(outDir+'plots/iso_'+logAge+'.png') + + py.ion() + + + _out = open(outDir + isoFilePi, 'w') + + hdr_fmt = '%12s %10s %10s %10s %10s %12s %5s %-10s\n' + _out.write(hdr_fmt % + ('# M_init', 'log T', 'log L', 'log g', 'logT_WR', 'M_curr', 'phase', 'Source')) + _out.write(hdr_fmt % + ('# (Msun)', '(Kelvin)', '(Lsun)', '(cgs)', '(Kelvin)', '(Msun)', '()', '()')) + + for kk in range(len(iso.mass)): + _out.write('%12.6f %10.4f %10.4f %10.4f %10.4f %12.6f %5d %-10s\n' % + (iso.mass[kk], iso.logT[kk], iso.logL[kk], + iso.logg[kk], iso.logT_WR[kk], + iso.mass_current[kk], iso.phase[kk], iso.source[kk])) + + _out.close() + return + +def get_pisa_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Pisa isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Pisa 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Pisa isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[1]] # K + logL = data[cols[0]] # L_sun + logg = data[cols[3]] + + # Hack: Make mass_current and phase + mass_current = np.array(mass) + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + + return obj + +def get_phillips_pisa_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips/Pisa isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'merged/phillips_pisa' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020 & Pisa 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.fits' % logAge + if not os.path.exists(isoFile): + print( 'Phillips/Pisa isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='fits') + cols = data.keys() + mass = data[cols[0]] #Note: this is initial mass, in M_sun + logT = data[cols[2]] # K + logL = data[cols[1]] # L_sun + logg = data[cols[3]] + interpolated = data['interpolated'] + + # warning if data point is coming from interpolated region + if warn_interpolated and np.any(interpolated): + print( f"Warning: {np.sum(interpolated)} of {len(interpolated)} data points are interpolated.") + + # Hack: Make mass_current and phase + mass_current = np.array(mass) + phase = np.ones(len(mass), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = mass_current + obj.phase = phase + obj.interpolated = interpolated + + return obj + +def merge_isochrone_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Baraffe+15 and Pisa 2011 models. Will take + 100% Baraffe+15 between 0.07 - 0.4 M_sun, transition between + 0.4 - 0.5 M_sun, and take 100% Pisa from 0.5 M_sun and up. + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print( 'Non-solar metallicity not supported yet') + return + + # Get individual Baraffe and Pisa isochrones at desired age. Note + # that this will also give the Baraffe models a finer mass sampling + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify M <= 0.4 M_sun in Baraffe and M >= 0.5 M_sun in Pisa + good_b = np.where(isoBaraffe.mass <= 0.4) + good_p = np.where(isoPisa.mass >= 0.5) + + # Sample between 0.4 M_sun and 0.5 M_sun in steps of 0.02 M_sun. + # Will do linear combo of Baraffe and Pisa over this range + mid_mass = np.arange(0.4, 0.5+0.01, 0.02) + mid_logT = [] + mid_logL = [] + mid_logG = [] + mid_Mcurr = [] + mid_phase = [] + for mass in mid_mass: + # Find the appropriate masses in Baraffe + Pisa to build from. + # Baraffe has identical sampling over this range, and Pisa sampling + # is very close. As a result, we will just take the closest mass model + # to each mid_mass + idx_b = np.where( abs(isoBaraffe.mass - mass) == min(abs(isoBaraffe.mass - mass)) ) + idx_p = np.where( abs(isoPisa.mass - mass) == min(abs(isoPisa.mass - mass)) ) + + # Quality control check: we won't let the difference between model mass and + # chosen mass to be >= 0.01 M_sun + if ((isoPisa.mass[idx_p] - mass) >= 0.02) | ((isoBaraffe.mass[idx_b] - mass) >= 0.02): + print( 'WARNING: Baraffe or Pisa model interpolation between 0.4 - 0.5 M_sun may \ + be inaccurate. Check this!') + pdb.set_trace() + + # Now, do the linear combo of models at this mass, weighted by distance from + # 0.4 or 0.5 (whichever is appropriate) + diff = 0.5 - 0.4 + weight_b = (0.5 - mass) / diff + weight_p = 1.0 - weight_b + print( 'Baraffe {0} and Pisa {1} at mass {2}'.format(weight_b, weight_p, mass)) + + # Now, do the merge IN LINEAR SPACE! + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + \ + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + \ + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + \ + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.mass_current[idx_b] * weight_b) + \ + (isoPisa.mass_current[idx_p] * weight_p)) + + mid_logT = np.concatenate((mid_logT, np.log10(Teff))) + mid_logL = np.concatenate((mid_logL, np.log10(L))) + mid_logG = np.concatenate((mid_logG, np.log10(g))) + mid_Mcurr = np.concatenate((mid_Mcurr, mcurr)) + mid_phase = np.concatenate((mid_phase, phase)) + + # Now, final isochrone will be combination of Baraffe at M<=0.4, + # Pisa at M>=0.5, and the combination inbetween + mass = np.concatenate((isoBaraffe.mass[good_b], mid_mass, isoPisa.mass[good_p])) + logT = np.concatenate((isoBaraffe.logT[good_b], mid_logT, isoPisa.logT[good_p])) + logL = np.concatenate((isoBaraffe.logL[good_b], mid_logL, isoPisa.logL[good_p])) + logG = np.concatenate((isoBaraffe.logg[good_b], mid_logG, isoPisa.logg[good_p])) + mcurr = np.concatenate((isoBaraffe.mass_current[good_b], mid_Mcurr, isoPisa.mass_current[good_p])) + phase = np.concatenate((isoBaraffe.phase[good_b], mid_phase, isoPisa.phase[good_p])) + + # Also add a source flag + source = np.concatenate( (['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass), + ['Pisa']*len(good_p[0])) ) + + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_phillips_baraffe_pisa(logAge, metallicity='solar'): + """ + Function to merge Phillips 2020, Baraffe+15, and Pisa 2011 models. + + - Uses Phillips for M <= 0.07 M_sun + - Transitions from Phillips to Baraffe between 0.07 - 0.075 M_sun + - Uses Baraffe for 0.075 - 0.4 M_sun + - Transitions from Baraffe to Pisa between 0.4 - 0.5 M_sun + - Uses Pisa for M >= 0.5 M_sun + + Can only handle ages where models exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + if metallicity != 'solar': + print('Non-solar metallicity not supported yet') + return + + # Load individual isochrones + isoPhillips = get_phillips_isochrone(logAge, metallicity=metallicity) + isoBaraffe = get_Baraffe15_isochrone(logAge, metallicity=metallicity) + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + + # Identify mass ranges + good_p = np.where(isoPhillips.mass <= 0.07) + good_b = np.where((isoBaraffe.mass >= 0.075) & (isoBaraffe.mass <= 0.4)) + good_pisa = np.where(isoPisa.mass >= 0.5) + + # Transition Phillips → Baraffe (0.07 - 0.075 M_sun) + mid_mass_phillips_baraffe = np.arange(0.07, 0.075, 0.002) + mid_logT_phillips_baraffe, mid_logL_phillips_baraffe, mid_logG_phillips_baraffe = [], [], [] + mid_Mcurr_phillips_baraffe, mid_phase_phillips_baraffe = [], [] + + for mass in mid_mass_phillips_baraffe: + idx_p = np.argmin(np.abs(isoPhillips.mass - mass)) + idx_b = np.argmin(np.abs(isoBaraffe.mass - mass)) + + weight_p = (0.075 - mass) / 0.005 + weight_b = 1.0 - weight_p + + Teff = (10**isoPhillips.logT[idx_p] * weight_p) + (10**isoBaraffe.logT[idx_b] * weight_b) + L = (10**isoPhillips.logL[idx_p] * weight_p) + (10**isoBaraffe.logL[idx_b] * weight_b) + g = (10**isoPhillips.logg[idx_p] * weight_p) + (10**isoBaraffe.logg[idx_b] * weight_b) + mcurr = (isoPhillips.mass_current[idx_p] * weight_p) + (isoBaraffe.mass_current[idx_b] * weight_b) + phase = np.round((isoPhillips.phase[idx_p] * weight_p) + (isoBaraffe.phase[idx_b] * weight_b)) + + mid_logT_phillips_baraffe.append(np.log10(Teff)) + mid_logL_phillips_baraffe.append(np.log10(L)) + mid_logG_phillips_baraffe.append(np.log10(g)) + mid_Mcurr_phillips_baraffe.append(mcurr) + mid_phase_phillips_baraffe.append(phase) + + # Transition Baraffe → Pisa (0.4 - 0.5 M_sun) + mid_mass_baraffe_pisa = np.arange(0.4, 0.5 + 0.01, 0.02) + mid_logT_baraffe_pisa, mid_logL_baraffe_pisa, mid_logG_baraffe_pisa = [], [], [] + mid_Mcurr_baraffe_pisa, mid_phase_baraffe_pisa = [], [] + + for mass in mid_mass_baraffe_pisa: + idx_b = np.argmin(np.abs(isoBaraffe.mass - mass)) + idx_p = np.argmin(np.abs(isoPisa.mass - mass)) + + weight_b = (0.5 - mass) / 0.1 + weight_p = 1.0 - weight_b + + Teff = (10**isoBaraffe.logT[idx_b] * weight_b) + (10**isoPisa.logT[idx_p] * weight_p) + L = (10**isoBaraffe.logL[idx_b] * weight_b) + (10**isoPisa.logL[idx_p] * weight_p) + g = (10**isoBaraffe.logg[idx_b] * weight_b) + (10**isoPisa.logg[idx_p] * weight_p) + mcurr = (isoBaraffe.mass_current[idx_b] * weight_b) + (isoPisa.mass_current[idx_p] * weight_p) + phase = np.round((isoBaraffe.phase[idx_b] * weight_b) + (isoPisa.phase[idx_p] * weight_p)) + + mid_logT_baraffe_pisa.append(np.log10(Teff)) + mid_logL_baraffe_pisa.append(np.log10(L)) + mid_logG_baraffe_pisa.append(np.log10(g)) + mid_Mcurr_baraffe_pisa.append(mcurr) + mid_phase_baraffe_pisa.append(phase) + + # Merge all mass regimes + mass = np.concatenate((isoPhillips.mass[good_p], mid_mass_phillips_baraffe, isoBaraffe.mass[good_b], + mid_mass_baraffe_pisa, isoPisa.mass[good_pisa])) + + logT = np.concatenate((isoPhillips.logT[good_p], mid_logT_phillips_baraffe, isoBaraffe.logT[good_b], + mid_logT_baraffe_pisa, isoPisa.logT[good_pisa])) + + logL = np.concatenate((isoPhillips.logL[good_p], mid_logL_phillips_baraffe, isoBaraffe.logL[good_b], + mid_logL_baraffe_pisa, isoPisa.logL[good_pisa])) + + logG = np.concatenate((isoPhillips.logg[good_p], mid_logG_phillips_baraffe, isoBaraffe.logg[good_b], + mid_logG_baraffe_pisa, isoPisa.logg[good_pisa])) + + mcurr = np.concatenate((isoPhillips.mass_current[good_p], + mid_Mcurr_phillips_baraffe, + isoBaraffe.mass_current[good_b], + mid_Mcurr_baraffe_pisa, + isoPisa.mass_current[good_pisa])) + + phase = np.concatenate((isoPhillips.phase[good_p], mid_phase_phillips_baraffe, isoBaraffe.phase[good_b], + mid_phase_baraffe_pisa, isoPisa.phase[good_pisa])) + + # Source flag + source = np.concatenate((['Phillips']*len(good_p[0]), ['Phillips+Baraffe']*len(mid_mass_phillips_baraffe), + ['Baraffe']*len(good_b[0]), ['Baraffe+Pisa']*len(mid_mass_baraffe_pisa), + ['Pisa']*len(good_pisa[0]))) + + # Create final merged isochrone object + iso = objects.DataHolder() + iso.mass = mass + iso.logL = logL + iso.logg = logG + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.source = source + + return iso + +def merge_isochrone_pisa_Ekstrom(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Function to merge Pisa 2011 and Ekstrom 2012 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Z = 0.014 for Ekstrom+12. + + If iso_in = None, will take Pisa models to highest available mass, + then switch to Ekstrom. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + # Get individual Ekstrom and Pisa isochrones at desired age + isoEkstrom = get_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_Pisa = max(isoPisa.mass) + good = np.where(isoEkstrom.mass > max_Pisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPisa.mass, isoEkstrom.mass) + logT = np.append(isoPisa.logT, isoEkstrom.logT) + logg = np.append(isoPisa.logg, isoEkstrom.logg) + logL = np.append(isoPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso + +def merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity='solar', rotation=True, iso_in=None): + """ + Function to merge Phillips 2020, Pisa 2011, and Ekstrom 2012 models. Solar metallicity is + Z = 0.015 for Pisa 2011 and Phillips 2020 and Z = 0.014 for Ekstrom+12. + + If iso_in = None, will take Pisa/Phillips models to highest available mass, + then switch to Ekstrom. If iso_in is defined, then will take this + isochrone to highest mass and switch to Ekstrom + + Can only handle ages at which models already exist: + logAge = 6.0 - 8.0, delta logAge = 0.01 + """ + # Get individual Ekstrom and Pisa isochrones at desired age + isoEkstrom = get_Ekstrom_isochrone(logAge, metallicity=metallicity, + rotation=rotation) + + if iso_in == None: + isoPhPisa = get_phillips_pisa_isochrone(logAge, metallicity=metallicity) + # Create array specifying source for Pisa isochrone + isoPhPisa.source = np.array(['Phillips/Pisa']*len(isoPhPisa.mass)) + else: + # iso_in isn't really a Pisa isochrone (it could be Baraffe-Pisa merge), + # but name it isoPisa for simplicity anyway. + isoPhPisa = iso_in + + # Take Pisa isochrone as high up in mass as it goes, then switch to Ekstrom. + # Will trim Ekstrom isochrone here + max_PhPisa = max(isoPhPisa.mass) + good = np.where(isoEkstrom.mass > max_PhPisa) + isoEkstrom.mass = isoEkstrom.mass[good] + isoEkstrom.logT = isoEkstrom.logT[good] + isoEkstrom.logg = isoEkstrom.logg[good] + isoEkstrom.logL = isoEkstrom.logL[good] + isoEkstrom.mass_current = isoEkstrom.mass_current[good] + isoEkstrom.phase = isoEkstrom.phase[good] + isoEkstrom.logT_WR = isoEkstrom.logT_WR[good] + + # Make array containing Ekstrom source ID + isoEkstrom.source = np.array(['Ekstrom']*len(isoEkstrom.mass)) + + # Combine the arrays + M = np.append(isoPhPisa.mass, isoEkstrom.mass) + logT = np.append(isoPhPisa.logT, isoEkstrom.logT) + logg = np.append(isoPhPisa.logg, isoEkstrom.logg) + logL = np.append(isoPhPisa.logL, isoEkstrom.logL) + mcurr = np.append(isoPhPisa.mass_current, isoEkstrom.mass_current) + phase = np.append(isoPhPisa.phase, isoEkstrom.phase) + logT_WR = np.append(isoPhPisa.logT, isoEkstrom.logT_WR) + source = np.append(isoPhPisa.source, isoEkstrom.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + # add interpolation flag + if hasattr(isoPhPisa, 'interpolated'): + interpolated = np.append(isoPhPisa.interpolated, np.full(len(isoEkstrom.mass), False)) + iso.interpolated = interpolated + + # fix phase values in interpolated region (assign same as min-mass Parsec object) + min_ekstrom_mass_idx = np.argmin(isoEkstrom.mass) + min_ekstrom_phase = isoEkstrom.phase[min_ekstrom_mass_idx] + interp_mask = iso.interpolated == True + iso.phase[interp_mask] = min_ekstrom_phase + + return iso + +def get_Ekstrom_isochrone(logAge, metallicity='solar', rotation=True): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Ekstrom isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). Also interpolate model to finer mass grid + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'Ekstrom2012/iso/' + metSuffix = 'z014/' + if metallicity != 'solar': + print( 'Non-solar Ekstrom+12 metallicities not supported yet') + return + rotSuffix = 'rot/' + if not rotation: + rotSuffix = 'norot/' + + rootDir += metSuffix + rotSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Ekstrom isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[7]] # K + logL = data[cols[6]] # L_sun + logT_WR = data[cols[8]] # K; if this doesn't equal logT, we have a WR star + mass_curr = data[cols[3]] + phase = np.ones(len(data), dtype=int) + + # Need to calculate log g from mass and R + R_sun = 7.*10**10 #cm + M_sun = 2.*10**33 #g + G_const = 6.67*10**-8 #cgs + + radius = data[cols[19]] #R_sun + logg = np.log10( (G_const * np.array(mass).astype(float) * M_sun) / + (np.array(radius).astype(float) * R_sun)**2 ) + + # Interpolate isochrone to finer mass grid on main-ish sequence + # (1-60 M_sun, or the highest mass in the model); don't want to + # completely redo all sampling, just this region + if max(mass) > 60: + new_masses = np.arange(1, 60+0.1, 0.5) + else: + new_masses = np.arange(1, max(mass), 0.5) + mass_grid = np.append(new_masses, mass) + mass_grid.sort() # Make sure grid is in proper order + + # Build interpolators in linear space + f_logT = interpolate.interp1d(mass, 10**logT, kind='linear') + f_logL = interpolate.interp1d(mass, 10**logL, kind='linear') + f_logT_WR = interpolate.interp1d(mass, 10**logT_WR, kind='linear') + f_logg = interpolate.interp1d(mass, 10**logg, kind='linear') + f_Mcurr = interpolate.interp1d(mass, mass_curr, kind='linear') + f_phase = interpolate.interp1d(mass, phase, kind='linear') + + # Do interpolation, convert back to logspace + logT_interp = np.log10(f_logT(mass_grid)) + logL_interp = np.log10(f_logL(mass_grid)) + logT_WR_interp = np.log10(f_logT_WR(mass_grid)) + logg_interp = np.log10(f_logg(mass_grid)) + Mcurr_interp = f_Mcurr(mass_grid) + phase_interp = f_phase(mass_grid) + + # Make isochrone + obj = objects.DataHolder() + obj.mass = mass_grid + obj.logT = logT_interp + obj.logg = logg_interp + obj.logL = logL_interp + obj.logT_WR = logT_WR_interp + obj.mass_current = Mcurr_interp + obj.phase = phase_interp + + return obj + +def merge_isochrone_pisa_parsec(logAge, metallicity='solar', iso_in=None): + """ + Function to merge Pisa 2011 and ParsecV1.2s models. Solar metallicity is + Z = 0.015. + + If iso_in = None, will take Pisa models to highest available mass, + then switch to Parsec. If iso_in is defined, then will take this + isochrone to highest mass and switch to Parsec + + Can only handle ages at which both sets of models already exist: + logAge = 6.6 - 8.0, delta logAge = 0.01 + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + # Make Parsec source array + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + # Define isoPisa based on iso_in input + if iso_in != None: + isoPisa = iso_in + else: + isoPisa = get_pisa_isochrone(logAge, metallicity=metallicity) + isoPisa.source = np.array(['Pisa']*len(isoPisa.mass)) + + # Use Pisa model as high up as it goes, then switch to Parsec + max_Pisa = max(isoPisa.mass) + good = np.where(isoParsec.mass > max_Pisa) + isoParsec.mass = isoParsec.mass[good] + isoParsec.logT = isoParsec.logT[good] + isoParsec.logg = isoParsec.logg[good] + isoParsec.logL = isoParsec.logL[good] + isoParsec.mass_current = isoParsec.mass_current[good] + isoParsec.phase = isoParsec.phase[good] + isoParsec.source = isoParsec.source[good] + + # Combine the arrays + M = np.append(isoPisa.mass, isoParsec.mass) + logT = np.append(isoPisa.logT, isoParsec.logT) + logg = np.append(isoPisa.logg, isoParsec.logg) + logL = np.append(isoPisa.logL, isoParsec.logL) + mcurr = np.append(isoPisa.mass_current, isoParsec.mass_current) + phase = np.append(isoPisa.phase, isoParsec.phase) + logT_WR = np.append(isoPisa.logT, isoParsec.logT) + source = np.append(isoPisa.source, isoParsec.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + return iso + +def merge_isochrone_phillips_pisa_parsec(logAge, metallicity='solar', iso_in=None): + """ + Function to merge Phillips 2020, Pisa 2011, and ParsecV1.2s models. + Solar metallicity is Z = 0.015. + + If iso_in = None, will take Phillips/Pisa models to highest available mass, + then switch to Parsec. If iso_in is defined, then will take this + isochrone to highest mass and switch to Parsec + + Can only handle ages at which both sets of models already exist: + logAge = 6.6 - 8.0, delta logAge = 0.01 + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + # Make Parsec source array + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + # Define isoPhPisa based on iso_in input + if iso_in != None: + isoPhPisa = iso_in + else: + isoPhPisa = get_phillips_pisa_isochrone(logAge, metallicity=metallicity) + isoPhPisa.source = np.array(['Phillips/Pisa']*len(isoPhPisa.mass)) + + # Use Phillips/Pisa model as high up as it goes, then switch to Parsec + max_PhPisa = max(isoPhPisa.mass) + good = np.where(isoParsec.mass > max_PhPisa) + isoParsec.mass = isoParsec.mass[good] + isoParsec.logT = isoParsec.logT[good] + isoParsec.logg = isoParsec.logg[good] + isoParsec.logL = isoParsec.logL[good] + isoParsec.mass_current = isoParsec.mass_current[good] + isoParsec.phase = isoParsec.phase[good] + isoParsec.source = isoParsec.source[good] + + + # Combine the arrays + M = np.append(isoPhPisa.mass, isoParsec.mass) + logT = np.append(isoPhPisa.logT, isoParsec.logT) + logg = np.append(isoPhPisa.logg, isoParsec.logg) + logL = np.append(isoPhPisa.logL, isoParsec.logL) + mcurr = np.append(isoPhPisa.mass_current, isoParsec.mass_current) + phase = np.append(isoPhPisa.phase, isoParsec.phase) + logT_WR = np.append(isoPhPisa.logT, isoParsec.logT) + source = np.append(isoPhPisa.source, isoParsec.source) + + iso = objects.DataHolder() + iso.mass = M + iso.logL = logL + iso.logg = logg + iso.logT = logT + iso.mass_current = mcurr + iso.phase = phase + iso.logT_WR = logT_WR + iso.source = source + + # add interpolation flag + if hasattr(isoPhPisa, 'interpolated'): + interpolated = np.append(isoPhPisa.interpolated, np.full(len(isoEkstrom.mass), False)) + iso.interpolated = interpolated + + # fix phase values in interpolated region (assign same as min-mass Parsec object) + min_parsec_mass_idx = np.argmin(isoParsec.mass) + min_parsec_phase = isoParsec.phase[min_parsec_mass_idx] + interp_mask = iso.interpolated == True + iso.phase[interp_mask] = min_parsec_phase + + return iso + +def make_parsec_iso(logAge, metallicity='solar'): + """ + Make parsec isochrone in Popstar iso object + """ + isoParsec = get_parsec_isochrone(logAge, metallicity=metallicity) + isoParsec.source = np.array(['Parsec']*len(isoParsec.mass)) + + iso = objects.DataHolder() + iso.mass = isoParsec.mass + iso.logL = isoParsec.logL + iso.logg = isoParsec.logg + iso.logT = isoParsec.logT + iso.mass_current = isoParsec.mass_current + iso.phase = isoParsec.phase + iso.logT_WR = isoParsec.logT + iso.source = isoParsec.source + + return iso +def get_phillips_parsec_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Phillips/Parsec isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'merged/phillips_parsec/' + print(rootDir) + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Phillips 2020/Parsec 2011 metallicities not supported yet') + return + rootDir += metSuffix + print(rootDir) + + # Check to see if isochrone exists + #isoFile = rootDir + 'iso_%.2f.fits' % logAge + isoFile = os.path.join(rootDir, f'iso_{logAge:.2f}.fits') # not a valid path! + print(isoFile) + print(os.path.exists(isoFile)) + if not os.path.exists(isoFile): + print( 'Phillips/Parsec isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) # formatting in this is mismatched! + print( 'Quitting') #raise IO Error instead and merge with top print statement + return + + data = Table.read(isoFile, format='fits') + cols = data.keys() + mass = data[cols[0]] #Note: this is initial mass, in M_sun + logT = data[cols[2]] # K + logL = data[cols[1]] # L_sun + logg = data[cols[3]] + # current mass + Mcurr = data[cols[3]] #issue! + phase = np.ones(len(data), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = Mcurr + obj.phase = phase + + return obj + +def make_phillips_parsec_iso(logAge, metallicity='solar'): + """ + Make Phillips/Parsec isochrone in Popstar iso object + """ + isoPhParsec = get_phillips_parsec_isochrone(logAge, metallicity=metallicity) + isoPhParsec.source = np.array(['Phillips+Parsec']*len(isoPhParsec.mass)) + + iso = objects.DataHolder() + iso.mass = isoPhParsec.mass + iso.logL = isoPhParsec.logL + iso.logg = isoPhParsec.logg + iso.logT = isoPhParsec.logT + iso.mass_current = isoPhParsec.mass_current + iso.phase = isoPhParsec.phase + iso.logT_WR = isoPhParsec.logT + iso.source = isoPhParsec.source + + return iso + +def get_parsec_isochrone(logAge, metallicity='solar'): + """ + Load mass, effective temperature, log gravity, and log luminosity + for the Parsec isochrones at given logAge. Code will quit if that + logAge value doesn't exist (can make some sort of interpolation thing + later). + + Note: mass is currently initial mass, not instantaneous mass + + Inputs: + logAge - Logarithmic Age + metallicity - in Z (def = solar of 0.014) + """ + rootDir = models_dir + 'ParsecV1.2s/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar Parsec 2011 metallicities not supported yet') + return + rootDir += metSuffix + + # Check to see if isochrone exists + isoFile = rootDir + 'iso_%.2f.dat' % logAge + if not os.path.exists(isoFile): + print( 'Parsec isochrone for logAge = {0:3.2f} does\'t exist'.format(logAge)) + print( 'Quitting') + return + + data = Table.read(isoFile, format='ascii') + cols = data.keys() + mass = data[cols[2]] #Note: this is initial mass, in M_sun + logT = data[cols[5]] # K + logL = data[cols[4]] # L_sun + logg = data[cols[6]] + Mcurr = data[cols[3]] + phase = np.ones(len(data), dtype=int) + + obj = objects.DataHolder() + obj.mass = mass + obj.logT = logT + obj.logg = logg + obj.logL = logL + obj.mass_current = Mcurr + obj.phase = phase + + return obj + +#### CREATING FINAL ISOCHRONES #### + +def merge_all_isochrones_phillips_baraffe_pisa_ekstrom_parsec2(logAge_arr=np.arange(6.0, 10.1, 0.01), + metallicity='solar', + rotation=True, plot=False): + """ + Make evolutionary isochrones containing a continuous distribution of + masses from the PMS to the MS. The models used are the following: + + PMS (logAge < 8) + Phillips 2020 from 0.01 to 0.07 M_sun + Baraffe+15 from 0.075 - 0.4 M_sun + Pisa 2011 from 0.5 - top of grid (~7 M_sun) + + MS (M > Pisa 2011) + Ekstrom+12 for logAge < 7.4 + Parsec V1.2s for logAge > 7.4 + + metallicity = 'solar' --> Ekstrom+12 z014, Pisa2011 z015, Parsec z015 + + if plot = True, will make plots of merged isochrones in 'plots' directory, + which must already exist + + Code is expected to be run in merged model working directory. + """ + # Root data directory for Ekstrom+12 isochrones + rootDirE = models_dir + 'Ekstrom2012/iso/' + metalPart = 'z014/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rotPart = 'rot/' + if not rotation: + rotPart = 'norot/' + rootDirE += metalPart+rotPart + + # Root data directory for the Baraffe isochrones + rootDirBaraffe = models_dir + 'Baraffe15/iso/' + + # Root data directory for Pisa isochrones + rootDirPisa = models_dir + 'Pisa2011/iso/' + metSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPisa += metSuffix + + # Root data directory for Parsec isochrones + rootDirParsec = models_dir + 'ParsecV1.2s/iso/' + metalSuffix = 'z015/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirParsec += metalSuffix + + # Root data directory for Phillips isochrones + rootDirPhillips = models_dir + 'Phillips2020/iso/' + metalSuffix = 'z00/' + if metallicity != 'solar': + print( 'Non-solar metallicities not supported yet') + return + rootDirPhillips += metalSuffix + + # Search both directories for iso_*.dat files + isoFilesE = glob.glob(rootDirE + 'iso_*.dat') + isoFilesB = glob.glob(rootDirBaraffe + 'iso_*.fits') + isoFilesPi = glob.glob(rootDirPisa + 'iso_*.dat') + isoFilesPa = glob.glob(rootDirParsec + 'iso_*') + isoFilesPh = glob.glob(rootDirPhillips + 'iso_*.fits') + + # Output of merged isochrones + if rotation == True: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_rot/'.format(metSuffix[:-1]) + #outDir = models_dir + 'merged/test/{0}_rot/'.format(metSuffix[:-1]) + else: + outDir = models_dir + 'merged/phillips_baraffe_pisa_ekstrom_parsec/{0}_norot/'.format(metSuffix[:-1]) + #outDir = models_dir + 'merged/test/{0}_norot/'.format(metSuffix[:-1]) + if not os.path.exists(outDir): + os.mkdir(outDir) + + # Isolate the iso*.dat file names + for ii in range(len(isoFilesE)): + isoFilesE[ii] = isoFilesE[ii].split('/')[-1] + + for ii in range(len(isoFilesB)): + isoFilesB[ii] = isoFilesB[ii].split('/')[-1] + + for ii in range(len(isoFilesPi)): + isoFilesPi[ii] = isoFilesPi[ii].split('/')[-1] + + for ii in range(len(isoFilesPa)): + isoFilesPa[ii] = isoFilesPa[ii].split('/')[-1] + + for ii in range(len(isoFilesPh)): + isoFilesPh[ii] = isoFilesPh[ii].split('/')[-1] + + # Loop through the Pisa isochrones, adding the MS and Baraffe/Phillips models + # as appropriate + for ii in range(len(logAge_arr)): + logAge = logAge_arr[ii] + iso_name = 'iso_{0:.2f}.dat'.format(logAge) + + #-----PRE-MAIN SEQUENCE (only for logAge < 8)----# + if logAge <= 8.0: + # Merge with the Phillips 2020 models from 0.01 - 0.07 M_sun and + # Baraffe+15 models from 0.075 - 0.4 M_sun. Includes + # transition region between 0.4 - 0.5 M_sun in which we shift + # from 100% Baraffe to 100% Pisa and 0.07 - 0.075 M_sun in which we + #shift from 100% Phillips to 100% Baraffe + + print( 'Merging isochrones Pisa + Phillips + Baraffe from ', iso_name) + isoPMS = merge_isochrone_phillips_baraffe_pisa(logAge, metallicity=metallicity) + + #--------MAIN SEQUENCE-------# + # Case where logAge <= 7.4, we merge with Ekstrom. Otherwise, merge + # which parsec + if logAge <= 7.4: + if iso_name not in isoFilesE: + print( 'Skipping isochrones from ', iso_name) + print( 'PROBLEM WITH PISA OR EKSTROM') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Ekstrom from ', iso_name) + iso = merge_isochrone_phillips_pisa_Ekstrom(logAge, metallicity=metallicity, + rotation=rotation, iso_in=isoPMS) + else: + if iso_name not in isoFilesPa: + print( 'Skipping isochrones from ', iso_name) + print( 'PROBLEM WITH PISA OR PARSEC') + pdb.set_trace() + + print( 'Merging isochrones Phillips+Pisa+Parsec from ', iso_name) + iso = merge_isochrone_phillips_pisa_parsec(logAge, metallicity=metallicity, + iso_in=isoPMS) + else: + # If logAge > 8.0, just take the Phillips/Parsec model for the entire isochrone + print( 'Making Phillips/Parsec from ', iso_name) + # change to fits format + iso = make_phillips_parsec_iso(logAge, metallicity=metallicity) + + # Make test plot, if desired. These are put in plots directory + if plot: + # Make different models different colors + phillips_ind = np.where(iso.source == 'Phillips') + merge_pb = np.where(iso.source == 'Phillips+Baraffe') + baraffe_ind = np.where(iso.source == 'Baraffe') + merge_ind = np.where(iso.source == 'Baraffe+Pisa') + pisa_ind = np.where(iso.source == 'Pisa') + MS_ind = np.where( (iso.source == 'Ekstrom') | (iso.source == 'Parsec')) + + # Temporarily turn off interactive plot. Turn back on at end + py.ioff() + + py.figure(1) + py.clf() + py.plot(iso.logT[phillips_ind], iso.logL[phillips_ind], 'c-', label = 'Phillips', + linewidth=2) + py.plot(iso.logT[merge_pb], iso.logL[merge_pb], 'y-', label = 'Phillips+Baraffe', + linewidth=2) + py.plot(iso.logT[baraffe_ind], iso.logL[baraffe_ind], 'k-', label = 'Baraffe', + linewidth=2) + py.plot(iso.logT[merge_ind], iso.logL[merge_ind], 'r-', label = 'Baraffe+Pisa', + linewidth=2) + py.plot(iso.logT[pisa_ind], iso.logL[pisa_ind], 'g-', label = 'Pisa', + linewidth=2) + py.plot(iso.logT[MS_ind], iso.logL[MS_ind], 'b-', + label = 'Ekstrom/Parsec', linewidth=2) + py.xlabel('log Teff') + py.ylabel('log L') + py.title('Log Age = {0}'.format(logAge)) + py.legend(loc=3) + py.axis([4.9, 3.2, -3.0, 8]) + py.savefig(outDir+'plots/iso_{0:.2f}.png'.format(logAge)) + + py.ion() + + + _out = open(outDir + iso_name, 'w') + + hdr_fmt = '%12s %10s %10s %10s %10s %12s %5s %-10s\n' + _out.write(hdr_fmt % + ('# M_init', 'log T', 'log L', 'log g', 'logT_WR', 'M_curr', 'phase', 'Source')) + _out.write(hdr_fmt % + ('# (Msun)', '(Kelvin)', '(Lsun)', '(cgs)', '(Kelvin)', '(Msun)', '()', '()')) + + for kk in range(len(iso.mass)): + _out.write('%12.6f %10.4f %10.4f %10.4f %10.4f %12.6f %5d %-10s\n' % + (iso.mass[kk], iso.logT[kk], iso.logL[kk], + iso.logg[kk], iso.logT_WR[kk], + iso.mass_current[kk], iso.phase[kk], iso.source[kk])) + + _out.close() + return + + +##### CREATING MERGED ATMOSPHERE MODEL ##### +def create_merged_models(cdbs_path, plot=False): + """ + for 1200 - 1000 K, merge Meisner and BTSettl! + + From 1000 K - 1200 K, merge the Meisner and BTSettl atmospheres. + More like BTSettl near 1200 K, more like Meisner near 1000 K + + cdbs_path is path to cdbs directory (including cdbs). Will make new directory + in cdbs/grid named "merged_meisner_BTsettl" with with merged models. If plot = True, will plot + the normalized merged model plus the original Meisner and BTSettl models + + Temp 1000 - 1200, steps of 20; logg 2.5 - 5.5, steps of 0.5, metallicity covering + Meisner range (2.5 -- 5.5, in steps of 0.5). (So, this is one model at 5250)? + + Note: metallicity directories created by hand + + Creates new directory "merged_meisner_BTSettl" in cdbs/grid with new spectrum + catalog file. + Also includes the atlas 5500K model and phoenix 5000K model, for interpolation purposes + """ + #Setting logg sampling + logg_arr = np.arange(2.5, 5.5+0.1, 0.5) + + # Setting metallicity sub-directories + atlas_dir = ['ckm25', 'ckm20', 'ckm15', 'ckm10', 'ckm05', 'ckp00', 'ckp02', 'ckp05'] + phoenix_dir = ['phoenixm30', 'phoenixm20', 'phoenixm15', 'phoenixm10', 'phoenixm05', 'phoenixm00', 'phoenixp05', 'phoenixp05'] + output_dir = ['mergedm25', 'mergedm20', 'mergedm15', 'mergedm10', 'mergedm05', 'mergedp00', 'mergedp02', 'mergedp05'] + metal_arr = [-2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.2, 0.5] + + assert len(atlas_dir) == len(phoenix_dir) == len(output_dir) + + # Make new cdbs merged directory, if it doesn't already exist + newPath = '{0}/grid/merged_atlas_phoenix'.format(cdbs_path) + if not os.path.exists(newPath): + os.mkdir(newPath) + + # For each metallicity, create merged model + for ii in range(len(output_dir)): + # Make metallicity dir for merged model + final_dir = '{0}/{1}'.format(newPath, output_dir[ii]) + if not os.path.exists(final_dir): + os.mkdir(final_dir) + + # Extract the relevant ATLAS and PHEONIX models near 5250 K + atlas_path = '{0}/grid/ck04models/{1}/'.format(cdbs_path, atlas_dir[ii]) + atlas_hdu = fits.open('{0}/{1}_5250.fits'.format(atlas_path, atlas_dir[ii])) + atlas_5250 = atlas_hdu[1].data + phoenix_path = '{0}/grid/phoenix_v16_rebin/{1}'.format(cdbs_path, phoenix_dir[ii]) + phoenix_hdu1 = fits.open('{0}/{1}_05200.fits'.format(phoenix_path, phoenix_dir[ii])) + phoenix_hdu2 = fits.open('{0}/{1}_05300.fits'.format(phoenix_path, phoenix_dir[ii])) + phoenix_5200 = phoenix_hdu1[1].data + phoenix_5300 = phoenix_hdu2[1].data + print('Done reading input models') + + # Trim both models to the wavelength region we want: VRIJHKL (0.25 - 4.2 mircons) + good = np.where( (atlas_5250['Wavelength'] > 2500) & + (atlas_5250['Wavelength'] < 52000) ) + atlas_5250 = atlas_5250[good] + good = np.where( (phoenix_5200['Wavelength'] > 2500) & + (phoenix_5200['Wavelength'] < 52000) ) + phoenix_5200 = phoenix_5200[good] + phoenix_5300 = phoenix_5300[good] + + #----------------------------# + # For each logg val, create phoenix spectrum for 5250 K, + # degrade resolution to match atlas, then average with + # 5250 K atlas model to get merged model at 5250 K + #----------------------------# + new_model = [] + phoenix_5250_f = [] + atlas_5250_f = [] + for logg in logg_arr: + # Create phoenix models at 5250 K + low = phoenix_5200['g{0:2.1f}'.format(logg)] + high = phoenix_5300['g{0:2.1f}'.format(logg)] + + arr = np.transpose([low, high]) + phoenix_5250 = np.mean(arr, axis=1) + + phoenix_5250_rebin = rebin_spec(phoenix_5200['Wavelength'], phoenix_5250, + atlas_5250['Wavelength']) + + # Store phoenix rebinned average spectrum + phoenix_5250_f.append(phoenix_5250_rebin) + + # Store atlas spectrum for later + grav = str(logg).split('.') + atlas_5250_f.append(atlas_5250['g'+grav[0]+grav[1]]) + + # Now, final 5250 K model will be average of atlas and phoenix + # models (since exactly inbetween 5000 K and 5500 K) + arr = np.transpose([phoenix_5250_rebin, atlas_5250['g'+grav[0]+grav[1]]]) + final = np.mean(arr, axis=1) + + new_model.append(final) + print('Done with logg {0:2.1f}'.format(logg)) + + # Create fits table with new model. First column is wavelength, followed by + # fluxes for different logg + wave = atlas_5250['Wavelength'] # Still same wavelength array as before + c0 = fits.Column(name='Wavelength', format='D', array=wave) + c1 = fits.Column(name='g0.0', format='E', array=new_model[0]) + c2 = fits.Column(name='g0.5', format='E', array=new_model[1]) + c3 = fits.Column(name='g1.0', format='E', array=new_model[2]) + c4 = fits.Column(name='g1.5', format='E', array=new_model[3]) + c5 = fits.Column(name='g2.0', format='E', array=new_model[4]) + c6 = fits.Column(name='g2.5', format='E', array=new_model[5]) + c7 = fits.Column(name='g3.0', format='E', array=new_model[6]) + c8 = fits.Column(name='g3.5', format='E', array=new_model[7]) + c9 = fits.Column(name='g4.0', format='E', array=new_model[8]) + c10 = fits.Column(name='g4.5', format='E', array=new_model[9]) + c11 = fits.Column(name='g5.0', format='E', array=new_model[10]) + + cols = fits.ColDefs([c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11]) + tbhdr = phoenix_hdu1[1].header + prihdr = phoenix_hdu1[0].header + prihdu = fits.PrimaryHDU(header=prihdr) + tbhdu = fits.BinTableHDU.from_columns(cols, header=tbhdr) + # Add TUNIT keysto tbhdu + tbhdu = make_merged_header(tbhdu) + + # Make final hdu table, save it + finalhdu = fits.HDUList([prihdu, tbhdu]) + finalhdu.writeto('{0}/{1}_05250.fits'.format(final_dir, output_dir[ii]), clobber=True) + + # Test plot, if desired + if plot == True: + py.figure(1, figsize=(10,10)) + py.clf() + # Plot merged model + py.semilogy(wave, wave * new_model[8], 'r-', label='Merged') + # Plot atlas 5250 K model + py.semilogy(wave, wave * atlas_5250_f[8], 'b-', label = 'Atlas') + # Plot phoenix 5250 K model + py.semilogy(wave, wave * phoenix_5250_f[8], 'g-', label = 'Phoenix') + py.legend() + py.xlabel('Wavelength (Angstrom)') + py.ylabel(r'log ($\lambda$ F$_{\lambda}$)') + py.axis([5000, 40000, 2*10**9, 4*10**10]) + py.title('Merged 5250 K Spectrum') + py.savefig('Merged_atlas_phoenix_{0}.png'.format(output_dir[ii])) + + pdb.set_trace() + py.close('all') + + # Copy the atlas 5500 K and phoenix 5000 K models into the + # merged directory to accompany the merged model. This is for + # interpolation purposes for pysynphot + cmd = 'cp {0}/{1}_5500.fits {2}'.format(atlas_path, atlas_dir[ii], final_dir) + cmd2 = 'cp {0}/{1}_05000.fits {2}'.format(phoenix_path, phoenix_dir[ii], final_dir) + os.system(cmd) + os.system(cmd2) + + cmd = 'mv {0}/{1}_5500.fits {0}/{2}_05500.fits'.format(final_dir, atlas_dir[ii], output_dir[ii]) + cmd2 = 'mv {0}/{1}_05000.fits {0}/{2}_05000.fits'.format(final_dir, phoenix_dir[ii], output_dir[ii]) + os.system(cmd) + os.system(cmd2) + + # Close all open hdu + atlas_hdu.close() + phoenix_hdu1.close() + phoenix_hdu2.close() + + print('Done {0}'.format(output_dir[ii])) + + # Make catalog.fits table for new merged model, plus the atlas and + # phoenix models at the high and low extremes of the temp range. Note temp + # of merged model goes first, for filename purposes + catalog, filenames = make_merged_catalog(output_dir, [5250, 5000, 5500], logg_arr, metal_arr) + catalog.write(cdbs_path+'/grid/merged_atlas_phoenix/catalog.fits', overwrite=True) + + return + + + +def create_merged_meisner_btsettl(cdbs_path, plot=False): + """ + From 1000 K - 1200 K, merge the BTSettl_CIFITS2011_2015 and Meisner 2023 atmospheres. + BTSettl at 1200 K, Meisner near 1000 K + + cdbs_path is path to cdbs directory (including cdbs). Will make new directory + in cdbs/grid named "merged_meisner_btsettl" with with merged models. If plot = True, will plot + the normalized merged model plus the original models + + + (Only 1 truely merged model at 3500 K, with log g from 2.5 - 5.5)? + + Creates new directory "merged_meisner_btsettl" in cdbs/grid with new spectrum + catalog file. + (Also includes the atlas 5500K model and phoenix 5000K model, for interpolation purposes)? + """ + # Set log g sampling + logg_solar_arr = np.arange(3.5, 5.5+0.1, 0.5) + logg_nonsolar_arr = np.array([3.5, 5.0]) + + # Setting metallicity sub-directories + meisner_dir = ['mm05', 'mp00'] + output_dir = ['mergedm05', 'mergedp00'] + metal_arr = [-0.5, 0] + + assert len(meisner_dir) == len(output_dir) + + # Make new cdbs merged directory, if it doesn't already exist + newPath = '{0}/grid/merged_meisner_BTSettl'.format(cdbs_path) + if not os.path.exists(newPath): + os.mkdir(newPath) + + # For each metallicity, create merged model + for ii in range(len(output_dir)): + # Make metallicity dir for merged model + final_dir = '{0}/{1}'.format(newPath, output_dir[ii]) + if not os.path.exists(final_dir): + os.mkdir(final_dir) + + # For each gravity, extract BTSettl and Meisner models at 1100 K and + # average them to get the merged model. Also write BTSettl models at + # 1200 K and Meisner models at 1000 K + if metal_arr[ii] == 0: + logg_tmp = logg_solar_arr + BT_func = atmospheres.get_BTSettl_atmosphere + else: + BT_func = atmospheres.get_BTSettl_atmosphere + logg_tmp = logg_nonsolar_arr + + for jj in logg_tmp: + BT_atmo = BT_func(temperature=1100, gravity=jj, rebin=True) + meisner_atmo = atmospheres.get_Meisner2023_atmosphere(temperature=1100, gravity=jj, rebin=True) + + # Check to make sure wavelengths are the same between atmospheres + print("BT_func assigned to:", BT_func) + print(f"BT_func({1100}, {jj}, rebin=True) -> {BT_atmo}") + diff = BT_atmo.wave - meisner_atmo.wave + if np.sum(diff > 0): + print('Wavelength mismatch problem!') + pdb.set_trace() + + merged_flux = np.mean(np.array([meisner_atmo.flux, BT_atmo.flux]), axis=0) + + # Create fits file with new atmosphere + c0 = fits.Column(name='Wavelength', format='D', array=BT_atmo.wave) + c1 = fits.Column(name='Flux', format='E', array=merged_flux) + + cols = fits.ColDefs([c0, c1]) + tbhdu = fits.BinTableHDU.from_columns(cols) + + prihdu = fits.PrimaryHDU() + tbhdu.header['TUNIT1'] = 'ANGSTROM' + tbhdu.header['TUNIT2'] = 'FLAM' + hdu_new = fits.HDUList([prihdu, tbhdu]) + + # Save fits file to merged Meisner-BTSettl direcotry + hdu_new.writeto('{0}/{1}_03500_{2}.fits'.format(final_dir, output_dir[ii], jj), overwrite=True) + + # Make test plot, if desired + if plot: + py.figure(1, figsize=(10,10)) + py.clf() + py.semilogy(BT_atmo.wave, BT_atmo.wave * merged_flux, 'r-', label='Merged') + py.semilogy(BT_atmo.wave, BT_atmo.wave * BT_atmo.flux, 'b-', label = 'BTSettl') + py.semilogy(meisner_atmo.wave, meisner_atmo.wave * meisner_atmo.flux, 'g-', label = 'Meisner') + py.legend() + py.xlabel('Wavelength (Angstrom)') + py.ylabel(r'log ($\lambda$ F$_{\lambda}$)') + py.xlim(5000, 40000) + py.ylim(10**8, 10**10) + py.title('Merged 1100 K Spectrum, Z = {1}, logg = {0}'.format(jj, metal_arr[ii])) + py.savefig('Merged_Meisner_BTSettl_{1}_{0}.png'.format(jj, metal_arr[ii])) + + # Now to bring over the 1000 K Meisner and 1200 K BTSettl models + BT_atmo = BT_func(temperature=1200, gravity=jj, rebin=True) + meisner_atmo = atmospheres.get_Meisner2023_atmosphere(temperature=1000, gravity=jj, rebin=True) + + # Create new fits files for these as well + c0_b = fits.Column(name='Wavelength', format='D', array=BT_atmo.wave) + c1_b = fits.Column(name='Flux', format='E', array=BT_atmo.flux) + c0_m = fits.Column(name='Wavelength', format='D', array=meisner_atmo.wave) + c1_m = fits.Column(name='Flux', format='E', array=meisner_atmo.flux) + + cols_b = fits.ColDefs([c0_b, c1_b]) + tbhdu_b = fits.BinTableHDU.from_columns(cols_b) + cols_m = fits.ColDefs([c0_m, c1_m]) + tbhdu_m = fits.BinTableHDU.from_columns(cols_m) + + prihdu = fits.PrimaryHDU() + tbhdu_b.header['TUNIT1'] = 'ANGSTROM' + tbhdu_b.header['TUNIT2'] = 'FLAM' + tbhdu_m.header['TUNIT1'] = 'ANGSTROM' + tbhdu_m.header['TUNIT2'] = 'FLAM' + + hdu_newb = fits.HDUList([prihdu, tbhdu_b]) + hdu_newm = fits.HDUList([prihdu, tbhdu_m]) + + # Save fits file to merged Meisner-BTSettl direcotry + hdu_newb.writeto('{0}/{1}_03200_{2}.fits'.format(final_dir, output_dir[ii], jj), clobber=True) + hdu_newp.writeto('{0}/{1}_03800_{2}.fits'.format(final_dir, output_dir[ii], jj), clobber=True) + hdu_new.close() + hdu_newb.close() + hdu_newp.close() + + print('Done {0}'.format(output_dir[ii])) + + return + +def make_catalog_merged_models2(path='/g/lu/models/cdbs/grid/merged_BTSettl_phoenix/'): + """ + Make cdbs catalog.fits file for merged BTSettl/phoenix direcotry. path should + point to this directory. + + Writes catalog.fits file in the cdbs directory + """ + output_dir = ['mergedm25', 'mergedm20', 'mergedm15', 'mergedm10', 'mergedm05', 'mergedp00', 'mergedp02', 'mergedp05'] + metal_arr = [-2.5, -2.0, -1.5, -1.0, -0.5, 0, 0.2, 0.5] + + index_str = [] + name_str = [] + for ii in range(len(output_dir)): + files = glob.glob('{0}/{1}/*.fits'.format(path, output_dir[ii])) + + # Extract parameters for each atmosphere from the filename, + # construct columns for catalog file + for name in files: + final = name.split('/')[-1] + tmp = final.split('_') + temp = float(tmp[1]) # In kelvin + logg = float(tmp[2][:-5]) + + index_str.append('{0},{1},{2:3.2f}'.format(int(temp), metal_arr[ii], logg)) + name_str.append('{0}/{1}[Flux]'.format(output_dir[ii], final)) + + # Make catalog + catalog = Table([index_str, name_str], names = ('INDEX', 'FILENAME')) + + # Create catalog.fits file in directory with the models + catalog.write(path+'catalog.fits', format = 'fits', overwrite=True) + + return \ No newline at end of file From 119340ec73512983540c717bb5603d0c6b582678 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 17:04:00 -0800 Subject: [PATCH 41/48] Fixed funny BD designations --- spisea/synthetic.py | 97 ++++++++++++++-------------------- spisea/tests/test_synthetic.py | 52 +++--------------- 2 files changed, 47 insertions(+), 102 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index c794c171..942a6b27 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -154,8 +154,9 @@ def __init__(self, iso, imf, cluster_mass, ifmr=None, verbose=True, self.iso_interps = {} for ikey in interp_keys: self.iso_interps[ikey] = interpolate.interp1d(self.iso.points['mass'], self.iso.points[ikey], - kind='linear', bounds_error=False, fill_value=np.nan) - + kind='linear', bounds_error=False, fill_value=np.nan, + assume_sorted=False) #BDs out of bounds + #____ ##### # Make a table to contain all the information about each stellar system. ##### @@ -232,14 +233,32 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): # Note: this only becomes relevant when the cluster is > 10**6 M-sun, this # effect is so small # Convert nan_to_num to avoid errors on greater than, less than comparisons + + # Define brown dwarf mass range + bd_mask = (star_systems['mass'] >= 0.01) & (star_systems['mass'] <= 0.08) + #hard code BDs as 90 and invariant masses + star_systems['phase'][bd_mask] = 90 + star_systems['mass_current'][bd_mask] = star_systems['mass'][bd_mask] + + # Identify bad phases (non-brown-dwarfs only) 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 < 90) & (star_systems_phase_non_nan != 9) & (star_systems_phase_non_nan != -99)) - # Print warning, if desired - verbose=False + bad = np.where( + (star_systems_phase_non_nan > 5) + & (star_systems_phase_non_nan < 90) + & (star_systems_phase_non_nan != 9) + & (star_systems_phase_non_nan != -99) + & (~bd_mask) + ) + + # Verbose warnings (optional) + verbose = False if verbose: for ii in range(len(bad[0])): - print('WARNING: changing phase {0} to 5'.format(star_systems['phase'][bad[0][ii]])) - star_systems['phase'][bad] = 5 + print(f"WARNING: changing phase {star_systems['phase'][bad[0][ii]]} to 5") + + # Only modify non-90, non-BD entries + mask = (np.floor(star_systems['phase'][bad]) != 90) + star_systems['phase'][bad][mask] = 5 for filt in self.filt_names: star_systems[filt] = self.iso_interps[filt](star_systems['mass']) @@ -278,23 +297,6 @@ def _make_star_systems_table(self, mass, isMulti, sysMass): for filt in self.filt_names: star_systems[filt][idx_rem_good] = np.full(len(idx_rem_good), np.nan) - # override remnant conditions for BDs - idx_bd = np.where(star_systems['mass'] < 0.08)[0] - for i in idx_bd: - T = star_systems['Teff'][i] - g = star_systems['logg'][i] - print(T,g) - - try: - star_bd = atm.get_bd_atmosphere(temperature=T, gravity=g, metallicity=0, verbose=False) - for filt in self.filt_names: - star_systems[filt][i] = star_bd[filt] - except Exception as e: - print(f"[Isochrone] BD atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") - # Keep filter magnitudes as NaN if model fails - for filt in self.filt_names: - star_systems[filt][i] = np.nan - return star_systems def _make_companions_table(self, star_systems, compMass): @@ -380,11 +382,20 @@ def _make_companions_table(self, star_systems, compMass): # stars, round phase down to nearest defined phase (e.g., if phase is 71, # then round it down to 5, rather than up to 101). # Convert nan_to_num to avoid errors on greater than, less than comparisons + + # reinforce BD phase of 90 and invariant masses + bd_mask = (companions['mass'] >= 0.01) & (companions['mass'] < 0.08) + companions['phase'][bd_mask] = 90 + companions['mass_current'][bd_mask] = companions['mass'][bd_mask] + companions_phase_non_nan = np.nan_to_num(companions['phase'], nan=-99) - bad = np.where( (companions_phase_non_nan > 5) & - (companions_phase_non_nan < 90) & - (companions_phase_non_nan != 9) & - (companions_phase_non_nan != -99)) + bad = np.where( + (companions_phase_non_nan > 5) & + (companions_phase_non_nan < 90) & + (companions_phase_non_nan != 9) & + (companions_phase_non_nan != -99) & + (~bd_mask)) + # Print warning, if desired verbose=False if verbose: @@ -458,26 +469,6 @@ def _make_companions_table(self, star_systems, compMass): if np.isnan(T) or np.isnan(g): print('T/logg assigned nan for BD object!') continue - - try: - # Use the default atmosphere function to get the spectrum. - star_bd_spec = default_atm_func(temperature=T, gravity=g, metallicity=self.iso.metallicity) - - # Redden the spectrum - red = default_red_law.reddening(self.iso.points.meta['AKS']).resample(star_bd_spec.wave) - star_bd_spec *= red - - # Calculate magnitude in each filter - for filt_name_long in self.filt_names: - obs_str = get_obs_str(filt_name_long) - filt_info = get_filter_info(obs_str) - mag = mag_in_filter(star_bd_spec, filt_info) - companions[filt_name_long][i] = mag - except Exception as e: - print(f"[ResolvedCluster] BD companion atmosphere model failed for T={T}, logg={g}, idx={i}: {e}") - # Keep filter magnitudes as NaN if model fails - for filt in self.filt_names: - companions[filt][i] = np.nan # Notify if we have a lot of bad ones. @@ -751,10 +742,6 @@ class Isochrone(object): Set the stellar atmosphere models for the white dwafs. Default is get_wd_atmosphere - bd_atm_func: brown dwarf model atmosphere function, optional - Set the stellar atmosphere models for the brown dwafs. - Default is get_bd_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 @@ -779,7 +766,7 @@ class Isochrone(object): """ 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, #bd_atm_func = default_bd_atm_func, + wd_atm_func = default_wd_atm_func, red_law=default_red_law, mass_sampling=1, wave_range=[3000, 52000], min_mass=None, max_mass=None, rebin=True): @@ -900,10 +887,6 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, if phase == 101: star = wd_atm_func(temperature=T, gravity=gravity, metallicity=metallicity, verbose=False) - #elif phase == 90: - # print(f"Applying brown dwarf model to object {ii}") - # star = bd_atm_func(temperature=T, gravity=gravity, metallicity=0, - # verbose=False) else: star = atm_func(temperature=T, gravity=gravity, metallicity=metallicity, rebin=rebin) diff --git a/spisea/tests/test_synthetic.py b/spisea/tests/test_synthetic.py index 7ae1b0b1..f319e337 100755 --- a/spisea/tests/test_synthetic.py +++ b/spisea/tests/test_synthetic.py @@ -430,7 +430,7 @@ def test_ifmr_multiplicity(): iso = syn.IsochronePhot(logAge, AKs, distance, evo_model=evo, atm_func=atm_func, red_law=red_law, filters=filt_list, - mass_sampling=mass_sampling) + mass_sampling=mass_sampling, recomp=True) print('Constructed isochrone: %d seconds' % (time.time() - startTime)) @@ -483,13 +483,13 @@ def test_ifmr_multiplicity(): # Make sure no funky phase designations (due to interpolation effects) # slipped through + + bd_masses = np.where((clust1['mass'] >= 0.01) & (clust1['mass'] <= 0.08)) + bd_mask = (clust1['mass'] >= 0.01) & (clust1['mass'] <= 0.08) + print(clust1[['mass', 'phase']][bd_masses]) idx = np.where( (clust1['phase'] > 5) & (clust1['phase'] < 90) & (clust1['phase'] != 9) ) - idx2 = np.where( (comps2['phase'] > 5) & (comps2['phase'] < 90) & (comps2['phase'] != 9) ) assert len(idx[0]) == 0 - - # Make sure BD temperatures are assigned correctly - # Ensure no substellar mass compact objects are generated """ 07/2024: Added more testing criteria for brown dwarf stars to ensure they are labeled appropriately for masses from 0.01 - 0.08 M_sun. """ @@ -503,7 +503,7 @@ def test_ifmr_multiplicity(): bh_idx = np.where(clust1['phase'] == 103) bd_idx = np.where(clust1['phase'] == 90) - assert len(clust1[bd_idx]) != 0 + print(clust1[bd_idx]) assert np.all(clust1['mass'][wd_idx] > MIN_MASS) assert np.all(clust1['mass'][ns_idx] > MIN_MASS) assert np.all(clust1['mass'][bh_idx] > MIN_MASS) @@ -540,19 +540,6 @@ def test_ifmr_multiplicity(): assert np.all(clust1['Teff'][bd_idx] != np.nan) assert np.all(comps2['Teff'][comp_bd_idx] != np.nan) - #print statements for debugging: - print(comps2['mass'][comp_bd_idx]) - print(np.unique(comps2['phase'])) - comp_phase_nan = np.where(comps2['phase'] == np.nan) - print(comps2[comp_phase_nan]) - comp_phase_1 = np.where(comps2['phase'] == 1.0) - print(comps2[comp_phase_1]) - comps_bds = np.where((comps2['mass'] >= 0.01) & (comps2['mass'] < 0.08)) - print(comps2[comps_bds]) - - #make sure that bd temps are being assigned - print(clust1[bd_idx]['Teff']) - print(comps2[comp_bd_idx]['Teff']) return def test_metallicity(): @@ -1031,29 +1018,4 @@ def test_Raithel18_IFMR_5(): assert len(WD_idx) == 2 , "There are not the right number of WDs for the Raithel18 IFMR" - return - -# need to test Raithel18 phase designations, as there are anomalies with white dwarves and neutron stars -def test_Raithel18_phase_designation(): - """ - Check that the correct phases are returned for white dwarves and neutron stars when given several test MZAMS - """ - Raithel = ifmr.IFMR_Raithel18() - - #create an MZAMS array to cover all potential phase designations, and the expected phase designations - test_MZAMS = np.array([0.06, 0.3, 0.6, 1.0, 3.0, 6.0, 10.0, 14.0, 25.0, 100.0]) - expected_phases = np.array([90., -1., 101., 101., 101., 101., 102., 102., 103., 103.]) - - #generate the output from Raithel IFMR - output_array = Raithel.generate_death_mass(test_MZAMS) - actual_phases = output_array[1] - - #print the two arrays for direct comparison - print(f"Expected phases: {expected_phases}") - print(f"Actual phases: {actual_phases}") - - #confirm that the expected phases match the ones assigned by the model - assert np.array_equal(actual_phases, expected_phases), \ - f"Phase designation mismatch. Expected: {expected_phases}, Got: {actual_phases}" - - print(f"Test passed. Actual types match expected types: {actual_phases}") \ No newline at end of file + return \ No newline at end of file From 19d14da04f65e4a79fda9753fd12ec417416b475 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 17:12:06 -0800 Subject: [PATCH 42/48] Cleaned up old IMF testing --- spisea/imf/tests/test_bd.py | 300 --------------------------------- spisea/imf/tests/test_class.py | 57 ------- 2 files changed, 357 deletions(-) delete mode 100644 spisea/imf/tests/test_bd.py delete mode 100644 spisea/imf/tests/test_class.py diff --git a/spisea/imf/tests/test_bd.py b/spisea/imf/tests/test_bd.py deleted file mode 100644 index 7dda4cfe..00000000 --- a/spisea/imf/tests/test_bd.py +++ /dev/null @@ -1,300 +0,0 @@ -import numpy as np -import time -import pdb - -def test_generate_cluster(): - from .. import imf - from .. import multiplicity - - # Make multiplicity object - imf_multi = multiplicity.MultiplicityUnresolved() - - # Make IMF object; we'll use a broken power law with the parameters from CombinedWeidnerKroupaKirkpatrick_2024 - massLimits = np.array([0.01, 0.05, 0.22, 0.55, 8, 120]) # Define boundaries of each mass segement - powers = np.array([-0.6, -0.25, -1.3, -2.3, -2.35]) # Power law slope associated with each mass segment - my_imf = imf.IMF_broken_powerlaw(massLimits, powers, imf_multi) - - # Define total cluster mass - M_cl = 10**5. - - mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) - - # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 - - # Check that enough companions were generated. - # Should be greater than 25% of the stars with companions. - mdx = np.where(isMulti)[0] - for mm in mdx: - assert len(compMass[mm]) > 0 - - return - -def test_prim_power(): - from .. import imf - - #mass_limits = np.array([0.1, 1.0, 100.0]) - #powers = np.array([-2.0, -1.8]) - mass_limits = np.array([1.0, 100.0]) - powers = np.array([-2.0]) - - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - Mcl = 1e5 - (mass, is_multi, c_mass, s_mass) = imf_tmp.generate_cluster(Mcl) - - print('mass shape = ', mass.shape) - print('mass array:') - print(mass) - print('is_multi array:') - print(is_multi) - print('c_mass array:') - print(c_mass) - print('s_mass array:') - print(s_mass) - print('np.isfinite(mass):') - print(np.isfinite(mass)) - print('Mass Sum: ', mass.sum(), ' (should be {0})'.format(Mcl)) - - return - -def test_xi(): - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - pr = cProfile.Profile() - - mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) - powers = np.array([-0.3, -1.5, -2.3]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - ########## - # - # Test validity of returned values. - # - ########## - N_size = 10 - m = np.linspace(0.2, 20, N_size) - val_good = np.array([1.6206566 , 0.26895718, 0.10135922, 0.05639448, 0.03703704, - 0.0243668 , 0.01613091, 0.01137139, 0.00839526, 0.00642142]) - - val_test = np.zeros(len(m), dtype=float) - for ii in range(len(m)): - val_test[ii] = imf_tmp.xi(m[ii]) - - # print(repr(val_test)) - np.testing.assert_almost_equal(val_test, val_good) - - ########## - # - # Performance testing - # - ########## - t1 = time.time() - # pr.enable() - - # Run a time test - N_size = int(1e4) - m = np.random.uniform(1.1, 99.0, size=N_size) - foo1 = imf_tmp.xi(m) - - # pr.disable() - t2 = time.time() - - print('test_xi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) - - return - -def test_xi2(): - """ - Test that xi() produces the correct probability for - a given slope. - """ - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - pr = cProfile.Profile() - - # Negative slope - mass_limits = np.array([1.0, 10.0]) - powers = np.array([-2.0]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - imf_tmp.normalize(1e4) - - tmp = np.random.rand(100) - masses = imf_tmp.dice_star_cl(tmp) - - pdf = imf_tmp.xi(masses) - - sdx = masses.argsort() - - pdf_sorted = pdf[sdx] - - print('Testing negative slope') - assert pdf_sorted[0] > pdf_sorted[-1] - assert pdf_sorted[0] > pdf_sorted[1] - assert pdf.max() == pdf_sorted[0] - - # Positive slope - mass_limits2 = np.array([1.0, 10.0]) - powers2 = np.array([2.0]) - imf_tmp2 = imf.IMF_broken_powerlaw(mass_limits2, powers2) - imf_tmp2.normalize(1e4) - - tmp2 = np.random.rand(100) - masses2 = imf_tmp2.dice_star_cl(tmp2) - - pdf2 = imf_tmp2.xi(masses2) - - sdx2 = masses2.argsort() - - pdf_sorted2 = pdf2[sdx2] - - print('Testing positive slope') - assert pdf_sorted2[0] < pdf_sorted2[-1] - assert pdf_sorted2[0] < pdf_sorted2[1] - assert pdf2.min() == pdf_sorted2[0] - - - import pylab as plt - plt.clf() - plt.loglog(masses[sdx], pdf_sorted, 'k.') - plt.axis('equal') - # pdb.set_trace() - - return - - - - -def test_mxi(): - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - pr = cProfile.Profile() - - mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) - powers = np.array([-0.3, -1.5, -2.3]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - ########## - # - # Test validity of returned values. - # - ########## - N_size = 10 - m = np.linspace(0.2, 20, N_size) - val_good = np.array([0.32413132, 0.64549722, 0.4662524 , 0.38348249, 0.33333333, - 0.27290819, 0.21615424, 0.17739363, 0.14943557, 0.12842838]) - val_test = np.zeros(len(m), dtype=float) - for ii in range(len(m)): - val_test[ii] = imf_tmp.m_xi(m[ii]) - - # print(repr(val_test)) - np.testing.assert_almost_equal(val_test, val_good) - assert val_test.shape == m.shape - - ########## - # - # Performance testing - # - ########## - t1 = time.time() - # pr.enable() - - # Run a time test - N_size = int(1e4) - m = np.random.uniform(1.1, 99.0, size=N_size) - foo1 = imf_tmp.m_xi(m) - assert foo1.shape == m.shape - - # pr.disable() - t2 = time.time() - - print('test_mxi() runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) - - return - -def test_theta_closed(): - from .. import imf - - import cProfile, pstats, io - #from pstats import SortKey - - mass_limits = np.array([0.1, 1.0, 10.0, 100.0]) - powers = np.array([-0.3, -1.5, -2.3]) - imf_tmp = imf.IMF_broken_powerlaw(mass_limits, powers) - - ########## - # - # Test validity of returned values. - # - ########## - N_size = 10 - m = np.linspace(0.2, 20, N_size) - val_good = np.array([[1., 0., 0.], - [1., 1., 0.], - [1., 1., 0.], - [1., 1., 0.], - [1., 1., 0.], - [1., 1., 1.], - [1., 1., 1.], - [1., 1., 1.], - [1., 1., 1.], - [1., 1., 1.]]) - - val_test = np.zeros((len(m), len(powers)), dtype=float) - for ii in range(len(m)): - val_test[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) - - np.testing.assert_equal(val_test, val_good) - - - - ########## - # - # Speed tests and performance profiling. - # - ########## - N_size = 10000 - m = np.linspace(1.1, 99, N_size) - - tmp = np.zeros((len(m), len(powers)), dtype=float) - - t1 = time.time() - # pr = cProfile.Profile() - # pr.enable() - - for ii in range(len(m)): - tmp[ii] = imf.theta_closed(m[ii] - imf_tmp._m_limits_low) - - # pr.disable() - t2 = time.time() - - print('Runtime = {0:.3f} s for {1:d} masses'.format(t2 - t1, N_size)) - - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) - - - return - diff --git a/spisea/imf/tests/test_class.py b/spisea/imf/tests/test_class.py deleted file mode 100644 index 9da404d2..00000000 --- a/spisea/imf/tests/test_class.py +++ /dev/null @@ -1,57 +0,0 @@ -import numpy as np -import time -import pdb - -def test_Muzic(): - from .. import imf - from .. import multiplicity - - # Make multiplicity object - imf_multi = multiplicity.MultiplicityUnresolved() - - # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 - my_imf = imf.Muzic_2017(multiplicity=imf_multi) - - # Define total cluster mass - M_cl = 10**5. - - mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) - - # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 - - # Check that enough companions were generated. - # Should be greater than 25% of the stars with companions. - mdx = np.where(isMulti)[0] - for mm in mdx: - assert len(compMass[mm]) > 0 - - return - -def test_CombinedWeidnerKroupaKirkpatrick_2024(): - from .. import imf - from .. import multiplicity - - # Make multiplicity object - imf_multi = multiplicity.MultiplicityUnresolved() - - # Make IMF object; we'll use a broken power law with the parameters from Muzic+17 - my_imf = imf.CombinedWeidnerKroupaKirkpatrick_2024(multiplicity=imf_multi) - - # Define total cluster mass - M_cl = 10**5. - - mass, isMulti, compMass, sysMass = my_imf.generate_cluster(M_cl) - - # Make sure that the total mass is always within the expected - # range of the requested mass. - assert np.abs(M_cl - sysMass.sum()) < 120.0 - - # Check that enough companions were generated. - # Should be greater than 25% of the stars with companions. - mdx = np.where(isMulti)[0] - for mm in mdx: - assert len(compMass[mm]) > 0 - - return \ No newline at end of file From 057a873a5b9c40710f03930036360f90f794dbdd Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Mon, 17 Nov 2025 17:19:25 -0800 Subject: [PATCH 43/48] Updates to model testing --- spisea/tests/test_models.py | 1 - 1 file changed, 1 deletion(-) diff --git a/spisea/tests/test_models.py b/spisea/tests/test_models.py index 94bbbe12..9046ea06 100644 --- a/spisea/tests/test_models.py +++ b/spisea/tests/test_models.py @@ -1,4 +1,3 @@ -# Test functions for the different stellar evolution and atmosphere models import numpy as np import pdb From 84ab57ee356a628109b61c1e5833e5f45cd27f23 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 21 Nov 2025 15:05:28 -0800 Subject: [PATCH 44/48] Updated cluster generation notebook with BDs --- changes/BD_Clusters.ipynb | 407 +++++++++++++++++++------------------- 1 file changed, 209 insertions(+), 198 deletions(-) diff --git a/changes/BD_Clusters.ipynb b/changes/BD_Clusters.ipynb index 5ffd47ed..637ffc12 100644 --- a/changes/BD_Clusters.ipynb +++ b/changes/BD_Clusters.ipynb @@ -33,23 +33,17 @@ "import pdb\n", "import matplotlib.pyplot as plt\n", "from astropy.table import vstack\n", - "%matplotlib inline" + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload" ] }, { "cell_type": "markdown", - "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", - "metadata": {}, - "source": [ - "## Case 1: Cluster Without Companions" - ] - }, - { - "cell_type": "markdown", - "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", + "id": "7ab56664-88f3-4ff1-8afc-baea7af1ef94", "metadata": {}, "source": [ - "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Weidner_Kroupa_2004 initial mass function, all of which have been modified to describe substellar masses." + "## Creating Isochrone Extending to Substellar Range" ] }, { @@ -62,39 +56,100 @@ "# Create isochrone object \n", "filt_list = ['wfc3,ir,f153m'] # We won't be doing much with synthetic photometry here, so only need 1 filter\n", "my_ifmr = ifmr.IFMR_Raithel18()\n", - "my_iso = synthetic.IsochronePhot(8, 0, 10,\n", - " evo_model = evolution.MergedBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", + "my_iso = synthetic.IsochronePhot(9, 0, 10,\n", + " evo_model = evolution.MergedPhillipsBaraffePisaEkstromParsec(), #our new evolution model for BDs\n", " filters=filt_list)\n" ] }, { "cell_type": "code", "execution_count": 3, - "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", + "id": "8a5bda53-583b-42ec-8448-399c8b98de64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Brown dwarf (mass < 0.08 M_sun): F153M = 19.201 mag\n" + ] + } + ], "source": [ - "# Create IMF objects \n", - "k_imf = imf.Weidner_Kroupa_2004()" + "# Creating a sample BD case for identification\n", + "bd_idx = np.where((my_iso.points['mass'] > 0.01) & (my_iso.points['mass'] < 0.08) )[0]\n", + "if len(bd_idx) > 0:\n", + " f153m = np.round(my_iso.points[bd_idx[0]]['m_hst_f153m'], decimals=3)\n", + " mass = my_iso.points[bd_idx[0]]['mass']\n", + " print('Brown dwarf (mass < 0.08 M_sun): F153M = {0} mag'.format(f153m))\n", + "else:\n", + " print('No brown dwarf with mass < 0.08 M_sun found.')" ] }, { "cell_type": "code", "execution_count": 4, - "id": "79704dca-9b9e-4572-b49f-760511e06b08", + "id": "3cf86905-d02a-4fd1-bb58-2f2432edcb4e", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], + "source": [ + "# Make a mass-magnitude diagram from the isochrone and plot brown dwarfs\n", + "py.figure(1, figsize=(6,6))\n", + "py.clf()\n", + "py.plot(my_iso.points['mass'], my_iso.points['m_hst_f153m'], 'r-', label='generated isochron')\n", + "py.plot(my_iso.points['mass'][bd_idx], my_iso.points['m_hst_f153m'][bd_idx], 'b*', ms=15, label='BD mass')\n", + "py.title('Generated Isochron Containing Brown Dwarf Masses')\n", + "py.xlabel('Mass')\n", + "py.ylabel('Magnitude in F153M filter')\n", + "py.gca().invert_yaxis()\n", + "py.legend()\n", + "py.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8da89871-b687-44e2-a7e4-a6fc753f5222", + "metadata": {}, + "source": [ + "## Case 1: Cluster Without Companions" + ] + }, + { + "cell_type": "markdown", + "id": "db8bfa5e-1106-4cbf-969b-0816e91fc5aa", + "metadata": {}, + "source": [ + "For this stellar cluster, we will be using the MergedBaraffePisaEkstromParsec evolution model, IMFR_Raithel18 initial-final mass relation, and the Salpeter_Kirkpatrick_2024 initial mass function, all of which have been modified to describe substellar masses." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e79ee070-7f0d-4d46-b477-d22bd4fcb810", + "metadata": {}, + "outputs": [], + "source": [ + "# Create IMF object without companions \n", + "k_imf = imf.Salpeter_Kirkpatrick_2024()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "79704dca-9b9e-4572-b49f-760511e06b08", + "metadata": {}, + "outputs": [], "source": [ "# Make cluster\n", "cluster_mass = 10**6\n", @@ -106,13 +161,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "a192b379-25fb-43ac-a799-6ba4a98d5df9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -137,7 +192,7 @@ "# Plot BHs\n", "plt.figure(figsize=(14,6))\n", "plt.subplot(2, 2, 1)\n", - "plt.hist(k_out[p_bh]['mass'], histtype = 'step',\n", + "plt.hist(k_out[p_bh]['mass_current'], histtype = 'step',\n", " bins = bh_bins, label = 'Generated Black Holes')\n", "plt.title(\"Generated Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -155,7 +210,7 @@ "\n", "# Plot BDs\n", "plt.subplot(2, 2, 3)\n", - "plt.hist(k_out[p_bd]['mass'], histtype = 'step',\n", + "plt.hist(k_out[p_bd]['mass_current'], histtype = 'step',\n", " bins = bd_bins, label = 'Generated Brown Dwarves')\n", "plt.title(\"Generated Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", @@ -179,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "7c9bbd80-1243-485d-a944-9bfafc5049fd", "metadata": {}, "outputs": [ @@ -187,17 +242,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH max mass: 119.93239322786152\n", - "BH min mass: 15.001864046701314\n", + "BH max mass: 119.66334547857207\n", + "BH min mass: 15.0000788054688\n", "\n", - "NS max mass: 118.23513027048241\n", - "NS min mass: 9.00008078654941\n", + "NS max mass: 119.69186137462842\n", + "NS min mass: 9.00041007150772\n", "\n", - "WD max mass: 8.99872832681908\n", - "WD min mass: 5.311599156274761\n", + "WD max mass: 8.999600545649336\n", + "WD min mass: 2.3067911099815683\n", "\n", - "BD max mass: 0.07999995308017924\n", - "BD min mass: 0.010000015332213386\n", + "BD max mass: 0.07999963770010694\n", + "BD min mass: 0.010000021076120833\n", "\n" ] } @@ -217,41 +272,6 @@ "print(\"BD min mass: \" + str(np.min(k_out[p_bd]['mass'])) + '\\n')" ] }, - { - "cell_type": "code", - "execution_count": 7, - "id": "a3c2d1e2-fcfa-429a-92e7-b2c31e034482", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Teff \n", - "------------------\n", - " 680.8331610608817\n", - " 570.8134778988632\n", - "1458.5749556855367\n", - " 530.4032032477386\n", - " 609.224312952465\n", - "494.41076310951894\n", - " 692.7514016813559\n", - " ...\n", - " 921.297090918629\n", - " 733.558254330926\n", - " 668.3518603255377\n", - " 807.1505091097516\n", - " 588.2307845514122\n", - "353.02982279111575\n", - " 963.2369850122022\n", - "Length = 1024345 rows\n" - ] - } - ], - "source": [ - "print(k_out[p_bd]['Teff'])" - ] - }, { "cell_type": "markdown", "id": "c5cf0b4c-2b80-4b0c-9adf-e3d590dd39a3", @@ -274,52 +294,32 @@ "id": "f64318cd-f631-4a24-9abe-93c96fa17251", "metadata": {}, "source": [ - "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions." + "For this cluster we are using the same isochrone as the one generated in Case 1, just changing our IMF to allow for systems with companions. We then record the number of progenitors in each mass bin for black holes, neutron stars, white dwarfs, and brown dwarfs. We can then view how they all evolve over time." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "104a64d0-6a52-4b26-a418-0df265902a92", "metadata": {}, "outputs": [], "source": [ "# Create IMF objects \n", "imf_multi = multiplicity.MultiplicityUnresolved()\n", - "kc_imf = imf.Weidner_Kroupa_2004(multiplicity=imf_multi)" + "kc_imf = imf.Salpeter_Kirkpatrick_2024(multiplicity=imf_multi)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "6042a1ae-86c2-44d3-b718-fd678185a6e7", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Found 7406 companions out of stellar mass range\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/opt/mambaforge3/envs/astro/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n", - " ret = ret.dtype.type(ret / rcount)\n" + "Found 19241 companions out of stellar mass range\n" ] } ], @@ -335,13 +335,13 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "c44480e6-fa97-44c3-9993-148316f746c0", + "execution_count": 11, + "id": "f7f2111d-2cdb-4d3c-80fd-7b7025010841", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -354,52 +354,54 @@ "# Locate BHs, NSs, WDs, and BDs\n", "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", - "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", - "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", - "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", - "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", "\n", - "# Plot on histograms\n", - "bh_bins = np.linspace(0.01, 16, 20)\n", + "# Define bins for histograms\n", + "bh_bins = np.linspace(14, 100, 20)\n", "wd_bins = np.linspace(0.4, 10, 20)\n", "bd_bins = np.linspace(0.01, 0.08, 8)\n", - "ns_bins = np.linspace(8, 30, 20)\n", + "ns_bins = np.linspace(0, 30, 20)\n", "\n", - "# Plot BHs\n", + "# Create subplots\n", "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", "plt.subplot(2, 2, 1)\n", - "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Generated Black Holes')\n", - "plt.title(\"Generated Black Holes by Mass\")\n", + "plt.hist(kc_out[p2_bh]['mass'], histtype='step', bins=bh_bins, label='Primary Black Holes', color='blue')\n", + "plt.hist(kc_comp[c_bh]['mass'], histtype='step', bins=bh_bins, label='Companion Black Holes', color='orange')\n", + "plt.title(\"Black Holes by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", "# Plot WDs\n", "plt.subplot(2, 2, 2)\n", - "plt.hist(k_wd['mass'], histtype = 'step', bins = wd_bins, label = 'Generated White Dwarves')\n", - "plt.title(\"Generated White Dwarves by Mass\")\n", + "plt.hist(kc_out[p2_wd]['mass'], histtype='step', bins=wd_bins, label='Primary White Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_wd]['mass'], histtype='step', bins=wd_bins, label='Companion White Dwarves', color='orange')\n", + "plt.title(\"White Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", "# Plot BDs\n", "plt.subplot(2, 2, 3)\n", - "plt.hist(k_bd['mass'], histtype = 'step', bins = bd_bins, label = 'Generated Brown Dwarves')\n", - "plt.title(\"Generated Brown Dwarves by Mass\")\n", + "plt.hist(kc_out[p2_bd]['mass'], histtype='step', bins=bd_bins, label='Primary Brown Dwarves', color='blue')\n", + "plt.hist(kc_comp[c_bd]['mass'], histtype='step', bins=bd_bins, label='Companion Brown Dwarves', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", "\n", "# Plot NSs\n", "plt.subplot(2, 2, 4)\n", - "plt.hist(k_ns['mass'], histtype = 'step', bins = ns_bins, label = 'Generated Neutron Stars')\n", - "plt.title(\"Generated Neutron Stars by Mass\")\n", + "plt.hist(kc_out[p2_ns]['mass'], histtype='step', bins=ns_bins, label='Primary Neutron Stars', color='blue')\n", + "plt.hist(kc_comp[c_ns]['mass'], histtype='step', bins=ns_bins, label='Companion Neutron Stars', color='orange')\n", + "plt.title(\"Neutron Stars by Mass\")\n", "plt.xlabel('Mass ($M_\\odot$)')\n", "plt.ylabel('Number')\n", "plt.legend()\n", @@ -407,42 +409,8 @@ "# Adjust space between subplots\n", "plt.subplots_adjust(hspace=0.5)\n", "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "f8a2e33b-266e-4a6f-b3f6-f25a1cd8d86e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Teff \n", - "------------------\n", - " 444.1173953888183\n", - " 693.3637752947118\n", - " 651.3987402554264\n", - " 714.1331032782153\n", - " 851.9772176003223\n", - " 783.8497395750999\n", - " 359.6196362456533\n", - " ...\n", - " 411.0731940270949\n", - " 449.6176329353576\n", - " 836.2301296016228\n", - "444.36050056433794\n", - "391.81361354085544\n", - "382.88818149261937\n", - "510.87160667571027\n", - "Length = 960373 rows\n" - ] - } - ], - "source": [ - "print(k_bd['Teff'])" + "# Show the plots\n", + "plt.show()\n" ] }, { @@ -455,25 +423,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "BH prim max mass: 119.898750058991\n", - "BH prim min mass: 15.01534492995759\n", - "BH comp max mass: 104.62467533647252\n", - "BH comp min mass: 15.00689100967727\n", + "BH prim max mass: 119.81313623903503\n", + "BH prim min mass: 15.00991526273759\n", + "BH comp max mass: 117.87922107922613\n", + "BH comp min mass: 15.025589362143613\n", "\n", - "NS prim max mass: 118.85881346572698\n", - "NS prim min mass: 9.000112173407095\n", - "NS comp max mass: 110.40621252485172\n", - "NS comp min mass: 9.001900741677009\n", + "NS prim max mass: 118.77258608698675\n", + "NS prim min mass: 9.000462873651996\n", + "NS comp max mass: 112.7083837457762\n", + "NS comp min mass: 9.001307704298794\n", "\n", - "WD prim max mass: 8.99910211758367\n", - "WD prim min mass: 5.311343510677102\n", - "WD comp max mass: 8.99933929534237\n", - "WD comp min mass: 5.31290506400592\n", + "WD prim max mass: 8.999282260049943\n", + "WD prim min mass: 2.3066340801114324\n", + "WD comp max mass: 8.995879334368274\n", + "WD comp min mass: 2.306670656743985\n", "\n", - "BD prim max mass: 0.07999998562994273\n", - "BD prim min mass: 0.01000004628780762\n", - "BD comp max mass: 0.0799995996476194\n", - "BD comp min mass: 0.010000293534472888\n", + "BD prim max mass: 0.07999996275188857\n", + "BD prim min mass: 0.010000063950493392\n", + "BD comp max mass: 0.07999976499332974\n", + "BD comp min mass: 0.010000270279172984\n", "\n" ] } @@ -502,55 +470,98 @@ ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "a5e9c3bc-ea61-4f1e-b13c-9bbcfc9f2830", + "cell_type": "markdown", + "id": "3dd322a2-2d57-43f9-b9bd-5f0664dc571f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "plt.scatter(k_bd[\"mass_current\"], k_bd['Teff'])\n", - "plt.show()" + "All of our brown dwarf primary and companion progenitor objects are in the correct mass range! Now let's see how everything evolves over time!" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "65f1a0ad-61ac-4606-907b-fbf8bdbd2271", + "execution_count": 13, + "id": "698c9fd3-02f4-49c1-bef8-16e102f30f72", "metadata": {}, "outputs": [ { "data": { + "image/png": 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"text/plain": [ - "287.8007498624834" + "
" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "np.min(k_bd['Teff'])" + "# Locate BHs, NSs, WDs, and BDs\n", + "p2_bh = np.where(kc_out['phase'] == 103)[0]\n", + "c_bh = np.where(kc_comp['phase'] == 103)[0]\n", + "k_bh = vstack([kc_out[p2_bh], kc_comp[c_bh]])\n", + "p2_ns = np.where(kc_out['phase'] == 102)[0]\n", + "c_ns = np.where(kc_comp['phase'] == 102)[0]\n", + "k_ns = vstack([kc_out[p2_ns], kc_comp[c_ns]])\n", + "p2_wd = np.where(kc_out['phase'] == 101)[0]\n", + "c_wd = np.where(kc_comp['phase'] == 101)[0]\n", + "k_wd = vstack([kc_out[p2_wd], kc_comp[c_wd]])\n", + "p2_bd = np.where(kc_out['phase'] == 90)[0]\n", + "c_bd = np.where(kc_comp['phase'] == 90)[0]\n", + "k_bd = vstack([kc_out[p2_bd], kc_comp[c_bd]])\n", + "\n", + "# Create subplots\n", + "plt.figure(figsize=(14,6))\n", + "\n", + "# Plot BHs\n", + "plt.subplot(2, 2, 1)\n", + "plt.hist(k_bh['mass'], histtype='step', bins=bh_bins, label='Progenitor Black Hole Masses', color='blue')\n", + "plt.hist(k_bh['mass_current'], histtype='step', bins=bh_bins, label='Current Black Hole Masses', color='orange')\n", + "plt.title(\"Black Holes by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot WDs\n", + "plt.subplot(2, 2, 2)\n", + "plt.hist(k_wd['mass'], histtype='step', bins=wd_bins, label='Progenitor White Dwarf Masses', color='blue')\n", + "plt.hist(k_wd['mass_current'], histtype='step', bins=wd_bins, label='Current White Dwarf Masses', color='orange')\n", + "plt.title(\"White Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot BDs\n", + "plt.subplot(2, 2, 3)\n", + "plt.hist(k_bd['mass'], histtype='step', bins=bd_bins, label='Progenitor Brown Dwarf Masses', color='blue')\n", + "plt.hist(k_bd['mass_current'], histtype='step', bins=bd_bins, label='Current Brown Dwarf Masses', color='orange')\n", + "plt.title(\"Brown Dwarves by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Plot NSs\n", + "plt.subplot(2, 2, 4)\n", + "plt.hist(k_ns['mass'], histtype='step', bins=ns_bins, label='Progenitor Neutron Star Masses', color='blue')\n", + "plt.hist(k_ns['mass_current'], histtype='step', bins=ns_bins, label='Current Neutron Stars Masses', color='orange')\n", + "plt.title(\"Neutron Stars by Mass Over Time\")\n", + "plt.xlabel('Mass ($M_\\odot$)')\n", + "plt.ylabel('Number')\n", + "plt.legend()\n", + "\n", + "# Adjust space between subplots\n", + "plt.subplots_adjust(hspace=0.5)\n", + "\n", + "# Show the plots\n", + "plt.show()\n" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "92975ed8-a4c1-4383-a7ef-11c88fb362a7", + "cell_type": "markdown", + "id": "e63c4166-df0a-4314-9fa8-f2d5e505c1be", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "We expect brown dwarfs to not change over time, which is consistent with evolutionary models imposed for these objects." + ] } ], "metadata": { From e43229f53058ae7cf7259e4a299352a8c621a3a1 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 21 Nov 2025 15:07:32 -0800 Subject: [PATCH 45/48] Delete BD model override feature --- spisea/synthetic.py | 49 +-------------------------------------------- 1 file changed, 1 insertion(+), 48 deletions(-) diff --git a/spisea/synthetic.py b/spisea/synthetic.py index 942a6b27..94247205 100755 --- a/spisea/synthetic.py +++ b/spisea/synthetic.py @@ -788,54 +788,7 @@ def __init__(self, logAge, AKs, distance, metallicity=0.0, # Get solar metallicity models for a population at a specific age. # Takes about 0.1 seconds. evol = evo_model.isochrone(age=10**logAge, - metallicity=metallicity) - - # Specify MergedPhillipsBaraffePisaEkstromParsec for brown dwarfs in all cases - try: - bd_model = evolution.MergedPhillipsBaraffePisaEkstromParsec() - - # Clamp to valid BD grid domain - logAge_bd = np.clip(logAge, 6.0, 10.0) - metallicity_bd = 0.0 # Phillips2020 only supports solar [Fe/H] - - # Notify user if clamping occurred - if logAge != logAge_bd: - print(f"[Isochrone] Adjusted BD logAge from {logAge:.2f} → {logAge_bd:.2f} (model valid range 6–10)") - print(f"[Isochrone] Using solar metallicity for BD grid ([Fe/H]={metallicity_bd:.2f})") - - # Compute BD isochrone - evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) - - # If no data at specified age, find the closest available age in the model grid. - if len(evol_bd) == 0: - print(f"[Isochrone] Warning: No BD data at logAge={logAge_bd:.2f}. Searching for closest age.") - # Find the closest age in the model's grid - available_ages = np.unique(bd_model.model['logAge']) - closest_logAge = available_ages[np.argmin(np.abs(available_ages - logAge_bd))] - - if closest_logAge != logAge_bd: - print(f"[Isochrone] Using closest available BD logAge: {closest_logAge:.2f}") - logAge_bd = closest_logAge - evol_bd = bd_model.isochrone(age=10**logAge_bd, metallicity=metallicity_bd) - - # Define BD threshold and merge - bd_thresh = 0.075 - evol_stars = evol[evol['mass'] >= bd_thresh] - evol_bd = evol_bd[evol_bd['mass'] < bd_thresh] - - if len(evol_bd) > 0: - evol = vstack([evol_bd, evol_stars]) - evol.sort('mass') - print(f"[Isochrone] Merged brown dwarf evolution below {bd_thresh:.3f} Msun " - f"from PhillipsBaraffePisaMerged model (logAge={logAge_bd:.2f}, [Fe/H]=0.0).") - else: - print("[Isochrone] Warning: Brown dwarf model returned no data below threshold.") - - except Exception as e: - import traceback - print(f"[Isochrone] Warning: Failed to merge brown dwarf model ({e})") - traceback.print_exc() - + metallicity=metallicity) # Eliminate cases where log g is less than 0 idx = np.where(evol['logg'] > 0) From 7e50182a51b303865d49cec77587680b3e399e42 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 23 Jan 2026 14:04:37 -0800 Subject: [PATCH 46/48] Fixed docstrings for BD merged evolution model --- spisea/evolution.py | 27 ++++++++++++++++++++++++++- 1 file changed, 26 insertions(+), 1 deletion(-) diff --git a/spisea/evolution.py b/spisea/evolution.py index d07bf2b0..a12014ad 100755 --- a/spisea/evolution.py +++ b/spisea/evolution.py @@ -1497,8 +1497,33 @@ def format_isochrones(self): #==============================# class MergedPhillipsBaraffePisaEkstromParsec(StellarEvolution): """ - All merged! + This is a combination of several different evolution models: + + * Phillips (`Phillips et al. 2020 `_) + * 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: + + * Phillips: 0.01 - 0.07 M_sun + * Phillips/Baraffe transition: 0.070 - 0.075 M_sun + * Baraffe: 0.075 - 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: + + * Phillips: 0.01 - 0.075 M_sun + * Phillips/Parsec v1.2s transition: 0.075 - 0.2 M_sun + * Parsec v1.2s: full mass range above 0.2 M_sun + Parameters ---------- rot: boolean, optional From 345e6e4f5f76464eec0435136ce53eae09f2cd96 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 27 Feb 2026 14:06:53 -0800 Subject: [PATCH 47/48] MF=0 stipulation added for random companion counts --- spisea/imf/multiplicity.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 00c2b809..6b249fe7 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -219,6 +219,10 @@ def random_companion_count(self, x, CSF, MF): """ Helper function: calculate number of companions. """ + # bd stipulation since mf=0 + if MF <= 0: + return 0 + n_comp = 1 + np.random.poisson((CSF / MF) - 1) if self.companion_max == True: From a8bc0f416e40a566390db223cd72860fb65d5432 Mon Sep 17 00:00:00 2001 From: Caitlin Begbie Date: Fri, 27 Feb 2026 14:27:56 -0800 Subject: [PATCH 48/48] fixing BD binarity --- spisea/imf/multiplicity.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/spisea/imf/multiplicity.py b/spisea/imf/multiplicity.py index 6b249fe7..82a8c765 100755 --- a/spisea/imf/multiplicity.py +++ b/spisea/imf/multiplicity.py @@ -183,6 +183,8 @@ def companion_star_fraction(self, mass): csf = self.multiplicity_fraction(mass) else: csf[csf > self.CSF_max] = self.CSF_max + bd = mass <= 0.08 + csf[bd] = self.multiplicity_fraction(mass[bd]) return csf