From b6e6393c7f5e8b85c2cb2ab6659bbf3293abe374 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Wed, 5 Nov 2025 13:27:41 -0500 Subject: [PATCH 01/52] Add files via upload New pisn model classes and Jupyter workbook --- doc/source/nb/Wright_2017.ipynb | 247 ++++++++++++++++++++++++++++++++ 1 file changed, 247 insertions(+) create mode 100644 doc/source/nb/Wright_2017.ipynb diff --git a/doc/source/nb/Wright_2017.ipynb b/doc/source/nb/Wright_2017.ipynb new file mode 100644 index 000000000..3dd0d7b0d --- /dev/null +++ b/doc/source/nb/Wright_2017.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wright_2017 PISN models\n", + "\n", + "The four PISN models described in the paper `Neutrino signal from pair-instability supernovae' by Wright et al., Phys. Rev. D96 (2017) 103008 `" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "mpl.rc('font', size=14)\n", + "\n", + "import numpy as np\n", + "\n", + "from astropy import units as u \n", + "\n", + "from snewpy.models.pisn import Wright_2017\n", + "from snewpy.flavor import ThreeFlavor\n", + "from snewpy.neutrino import MixingParameters\n", + "from snewpy.flavor_transformation import AdiabaticMSW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A Function which plots the Initial and Oscillated Spectra using the Adiabatic MSW Flavor Transformation and the Normal mass ordering\n", + "\n", + "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_spectra(model, time=1*u.s):\n", + " # Adiabatic MSW effect. NMO is used by default.\n", + " xform_nmo = AdiabaticMSW(MixingParameters())\n", + "\n", + " # Energy array and time to compute spectra.\n", + " # Note that any convenient units can be used and the calculation will remain internally consistent.\n", + " E = np.linspace(0,100,201) * u.MeV\n", + "\n", + " ispec = model.get_initial_spectra(time, E)\n", + " ospec_nmo = model.get_transformed_spectra(time, E, xform_nmo)\n", + "\n", + " fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n", + "\n", + " for i, spec in enumerate([ispec, ospec_nmo]):\n", + " ax = axes[i]\n", + " for flavor in ThreeFlavor:\n", + " ax.plot(E, spec[flavor].array.squeeze(),\n", + " label=flavor.to_tex(),\n", + " color='C0' if flavor.is_electron else 'C1',\n", + " ls='-' if flavor.is_neutrino else ':', lw=2,\n", + " alpha=0.7)\n", + "\n", + " ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n", + " title='Initial Spectra: $t = ${:.1f}'.format(time) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(time))\n", + " ax.grid()\n", + " ax.legend(loc='upper right', ncol=2, fontsize=16)\n", + "\n", + " ax = axes[0]\n", + " ax.set(ylabel=r'neutrino spectra [erg$^{-1}$ s$^{-1}$]')\n", + "\n", + " fig.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Look at what models are available" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'progenitor_mass': , 'eos': ['SFHo', 'Helm']}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Wright_2017.param" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Go through the models, plot the original spectra and transformed spectra at $t=1$ s.\n", + "\n", + "### 150$M_{\\odot}$ Progenitor, Helm EOS" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pisn1 = Wright_2017(progenitor_mass=150*u.solMass,eos='Helm')\n", + "fig = plot_spectra(pisn1, time=1*u.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 150$M_\\odot$ Progenitor, SFHo EOS" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pisn2 = Wright_2017(progenitor_mass=150*u.solMass,eos='SFHo')\n", + "fig = plot_spectra(pisn2, time=1*u.s)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "### 250$M_\\odot$ Progenitor, Helm EOS" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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nN/jgGDlyZIvKvvnmmxvdfuedd/LjH/+Yt956q0XlSfJkZ2c3ut22450MHcdpk3qsW7eOyy67jFAoxEUXXcStt97KoYceSn5+Pl6vF8dxEk+WUrG6mcfjYdSoUYwaNYorr7yS0tJSrrzySl588UVeffVV3nnnHcaPH9/gvJqEXGNq9tU8EQYoKSkBYNu2bWzbtm2/9ap5QlPzxKWgoOCAX1NLNfW7AO62T01gddRRRyW13PaosLAQiP+eGGOwLKvBMXVXFao5fn969uzJu+++y/Tp03nppZd4+eWXefnllwHo1q0bt99+O3fddVe7WSpZRNqHmh4XLendvGrVqnrnQsvfg9riM685qf7Ma20sUBNPHEjscTAUE7XPmEjxUH2tiYdAMVFnpESTi9asWcPEiRPZvHkzkydP5txzz2XHjh387W9/Y968eSxcuJBjjz026de1LAuPx5NIakjn9cwzzxAKhTj22GN5+umnG/xO7Nq1q03rk5+fz2OPPcbLL7+M4zhNBlXbt29vsoyafTVPhKF2SMFtt93Gr3/96wOuT15eHgB79uw54HOSyc32+fDDD4GWPcF79NFHefTRR1t1vRdeeOGAhgSnwvDhw4H4E7wNGzYwYMCABsfULEOdkZHR6P6mHHLIITzxxBPEYjGWL1/Ov//9b1555RUWLlzIPffcQ3l5OT/72c+S80JEpEM48cQT+e1vf8unn37K1q1bD2gqhprl6GuG4NdoyXtQR//Ma20sUHdIW1Oa29daionqcysmak08BOkZE6UyHgLFRJ2NEk0uuvzyy9m2bRvz5s1j0qRJie333nsvRx99NNdeey0rVqxI+nVfeOEFysvLufDCC5NetrijsScOB2Lt2rVAPDBtLPH49ttvH1S9WiM/P59u3bqxffv2Jrvurly5stHtsViML774AoARI0YkttfMQbB06dIW1eWII45g7ty5Lb4PrW2PfbnVPlVVVXz55ZcAjB49+oDP27BhQ4vvcY26XbHb2pgxY8jIyCAYDLJ48WK+853vNDimpgfouHHjWpWk93g8jB07lrFjx3Lbbbfx0EMP8f/+3//jkUceUVAlIvWceeaZFBQUsGfPHmbNmsVPf/rTZo+fP39+YmjUJZdc0ugxB/Ie1NrPvGQ52M+8/X32tjYWOPTQQwH49NNPmzymqbjkYCkmquVGTNTaeAjSMyZqi3gIFBN1FurS4pJly5bx9ttvc8UVV9RLMkF8/p5rr72Wjz/+ODEeOlk2btzIrbfeSmZmJvfff39Syxb31IyRbunkmDXnbdmypcE+Ywz/93//d/CVq6O4uHi/wwK/+OILduzYATQ9l9Xs2bMbnRjzueeeY+vWrfj9fk477bTE9m9961t4vV7ee+895s2bd8D1rZkM89///jdLliw54PNa2x5NldNW7VNj9erVOI5D9+7dW9QtOh0nvoR4V/0zzzwTgD/84Q8N9odCIR5//HEgPrlqMtRMpLpnz54WTaYpIh1fTk4O3//+9wH4xS9+weLFi5s8dtu2bVx//fUATJo0qUGPpqY09h7U2s+8ZDnYz7z9ffa2Nhb4+te/DsC//vWvRpM6//nPf1o1P5NiopZxIyZqbTwE6RkTuREPgWKijkqJJpe88847QDxAmDZtWoOvmokHk7ky3O7duznzzDPZsWMHf/zjHxPdIyX9DRkyBIgHQS0xYcIEIN7L7e9//3tie3l5OVdddRXvv/9+8ipJvNvzyJEj+c1vfsOmTZvq7TPGMG/ePM455xyMMfTr148zzjij0XLKy8u55JJLEvMMQPxJ1m233QbEV2Dp3r17Yt/AgQO5/fbbAbjooot44okniEaj9crctm0bjzzyCDNnzkxsO+KII7j88ssBOO+88/j73/+emCAToLKykscee4wPPvigXlmtbY99HUz7vPPOOwwcOJCBAwc2uNf7UzNHQmlpaaMBXTp68MEHGThwYJMTn/7oRz/C4/GwdOlSfvCDHyTmd6iqquKaa65h7dq1DBgwgKuvvvqAr3n33XfzyCOPNBjWsGfPHmbMmAHEJz5tyWSaItI5/O///i+nn346kUiEr3/96zz44IP1JkCOxWK88sorHH/88axbt44+ffrw5z//uV4ZLX0Pau1nXrIcbEyyv8/e1sYCJ598MscffzzGGC677LJEzxqIx+lTpkxpdCXh/VFM1DJuxEQdMR6C5mOiVMRDoJioUzKSMjNmzDCAeeyxxxrs+8lPfmKA/X49/vjjjZa9detWA5gJEyYcUF12795txowZYyzLMr///e8P4lXJgZgwYYIBzNSpU+ttHzBggAHMm2++2eh5b775pgHMgAEDmiyzsd+nF154IfE7M3jwYHPSSSeZCRMmmBkzZjR7fiwWMxMnTkycO3DgQDN27FiTlZVlbNs2TzzxRGLf2rVrW1SnxsyaNave73evXr3M2LFjzZFHHmkKCwsT23v06GHef//9BufX3L8ZM2aYzMxMk5GRYcaOHWuGDh2aOHfcuHGmrKyswbnRaNRcf/31ieNyc3PNUUcdZY455hjTt2/fxPYrrrii3nmVlZXmzDPPTOzv1q2bOeaYY8zgwYON1+s1gHnxxRdb3B77+10w5uDap+Z3qam2a04sFjOHHXaYAUx2drY55phjzAknnGBuvPHGFpWTbBs2bDBdu3ZNfOXm5hrA2LZdb/vNN9/c4NypU6c2+bdV4/e//72xbdsApqioyIwdO9bk5eUZwBQUFJgPP/ywRfU955xzEm3Qv39/M27cODNy5EgTCAQMYHJycszixYtbehtEpJMIBoPmiiuuSLyPBAIBc/jhh5uxY8fW+8w8+uijzYYNGxqc35r3oNZ85jUVCzQXI6QiJjmQz97WxgJr1qwxffr0MYDxeDzmyCOPNIcffrixLMsMHjzY3HLLLY2e1xzFRO0/Jmqv8ZAxqY2Jkh0PGaOYqDNSoimFmks0PfTQQwYwv/3tb1tVdksSTbt27TJjxowxgHn44YdbdT1pmbZONBljzKOPPmqOPfZYk5ubayzLahAgNHV+ZWWl+cEPfmAGDRpkfD6f6datm/nGN75hFi1aZIwxSU00RSIRs3jxYnPfffeZiRMnmkMOOcRkZ2cbv99vevToYU455RTzf//3f2bPnj2Nnl/3/n388cfmggsuMN27dzd+v98MGzbMTJs2zVRVVTVbh7feestccsklZsCAASYQCJi8vDwzYsQIc95555k5c+aYnTt3NjjHcRzzzDPPmK997WumW7duxufzmZ49e5oTTjjB/PznPzfFxcUNztlfexxIUGVM69vnYBJNxhjz1VdfmUsuucT06dPHeDweA7geWK1du7bRhPy+X40F+geSaDLGmKVLl5pzzz3XdOvWzfj9ftO/f39zww03mI0bN7a4vu+//7659957zYknnmj69u1r/H6/yczMNMOHDzc333xzq9pFRDqfd99911x//fVm+PDhJicnxwQCAdOvXz9z/vnnm+eee844jtPoea19D2rpZ16yEk3GHFxMYsz+P3trtCYW2LZtm7nxxhtNnz59Ep8PN998s9m1a1fiM6YliSbFRLX3qj3HRO0xHjIm9TFRMuMhYxQTdUaWMXX6PEpSzZw5k7vvvpvHHnuMKVOm1Nv33//+l2OPPZZLLrmEv/71ry0ue9u2bfTq1YsJEyawaNGiJo/bvXs3p59+OsuWLeO3v/0tN998c4uvJdJeDBw4kPXr1/Pmm28yceJEt6sjIiIi4grFRCLSnmmOJpeMGzcusTzns88+22C/4ziJWf1ba/fu3Zx22mksW7aM3/zmN0oyiYiIiIiIiEhKed2uQEcze/bsxEoMH3/8cWJbTa+jc889l3PPPReAp59+mlNOOYWLL76YBx98kLFjx5KRkcGGDRv4z3/+w86dO+stb/n5558nJuWrWbmhZhJCgKKionorLnzrW99i+fLlHHrooezevZtp06Y1qO9tt91GQUFBEu+AiIiIiIiIiHRWSjQl2ZIlSxqs+rF06VKWLl0KxLu51iSaBg0axLJly/jVr37FSy+9xKOPPorH46FXr16cfPLJXHDBBfXK2bZtW4Oyt2/fntg2YMCAeommdevWAfFk1PTp0xut75QpU5RoEhEREREREZGk0BxNIpI2NB+BiIiIiGIiEWnflGgSEREREREREZGk0GTgIiIiIiIiIiKSFJqjKQkcx2HLli3k5uZiWZbb1REREZE0YoyhvLyc3r17Y9upewaoeEVERERaqyXxihJNSbBlyxb69evndjVEREQkjW3cuJG+ffumrHzFKyIiInKwDiReUaIpCXJzc4H4Dc/Ly0t6+ZFIhPnz5zN58mR8Pl/Sy5f9Uxu4S/ffXbr/7lMbuCvV97+srIx+/fol4olUUbzSsen+u09t4C7df/epDdzVnuIVJZqSoKb7eV5eXsoCt6ysLPLy8vQH6xK1gbt0/92l++8+tYG72ur+p3o4m+KVjk33331qA3fp/rtPbeCu9hSvaDJwERERERERERFJCiWaREREREREREQkKZRoEhERERERERGRpFCiSUREREREREREkkKJJhERERERERERSQolmkREREREREREJCmUaBIRERERERERkaTwul0BERE5ONFolGg0mrLyI5EIXq+XYDBILBZL2XWkaWoDd7Xk/nu9XrxehVciIvtSvNLxqQ3cdaD337ZtfD4flmWlrC6KhERE0lRVVRXFxcVUVlam9DrGGHr27MnGjRtT+oEkTVMbuKul9z87O5uioiKysrLaoHYiIu2b4pXOQ23grpbcf5/PR25uLkVFRXg8nqTXRYkmEZE0FA6H2bhxIz6fj169ehEIBFL2ge44DhUVFeTk5GDbGnHtBrWBuw70/htjCIVC7N69m40bNzJo0CD8fn8b1lREpH1RvNK5qA3cdSD33xhDLBajoqKCPXv2UF1dTb9+/ZKebFKiSUQkDe3YsQOPx8OAAQNS8hSiLsdxCIfDZGRkKGhwidrAXS25/5mZmeTm5rJ27Vp27NhB375926iWIiLtj+KVzkVt4K6W3P+cnBzy8/PZsGEDxcXF9OjRI6l1UeuLiKQZYwxVVVXk5+enPGgTkZbzeDzk5+dTVVWFMcbt6oiIuELxikj7lpmZSV5eHuXl5UmPV5RoSlPGGAWvIp1UJBIhFouRmZnpdlVEpAmZmZnEYjEikYjbVRERcYXiFZH2Lzc3l0gkkvR4RUPn0lAoGuPnr39BRSjCXV87lKKcgNtVEpE25DgOgJ4OirRjNX+fNX+vIiKdjeIVkfYvVfGKejSloaWri1m/q5JdFWF+v2iN29UREZdoNQ+R9kt/nyIicXo/FGm/UvX3qURTGioPRhPfry1O7TKhIiIiIiIiIiIHSommNFSYpaWSRURERERERKT9UaIpDfm9ajYRERERERERaX+UsUhDzj6rzcUcrT4nIiIiIiIiIu5ToikN7ZNnYk9V2J2KiIiIiIiIiIjU4XW7AtJy+/Zg2lUZpmtOwKXaiIiItE+tXUnF7PtER0RERCRFOmK8okRTGtp36FxxRYhhPXJdqo2IiEj71J4DMBERERHomPFK2g2de/LJJ7n++us5+uijCQQCWJbF448/flBlRiIRRo8ejWVZHHroocmpaArtm2jaVaGhcyIiIiIiIiLivrTr0XTvvfeyfv16ioqK6NWrF+vXrz/oMu+//35Wr16dhNq1Dcep//OuipA7FRERERERERERqSPtejTNnj2bdevWsXPnTm644YaDLu/DDz9kxowZzJgxIwm1axsNejRVqkeTiEhjsrKysCyL5cuX19seDAbp3r07gUCA7du3u1M5aTP6PRARkfZMn1MCHev3IO0STaeffjoDBgxISlnhcJgpU6Ywfvx4br755qSU2RYaztGkRJOISGN69eoFwObNm+ttz8jI4KqrriIcDjN37lw3qiZtSL8HIiLSnulzSqBj/R6kXaIpmaZNm8aqVauYM2dOq2d6d8M+i86xuzLUIScQExE5WH369AEafmADjBs3DoB33323TeskbU+/ByIi0p7pc0qgY/0edNpE03vvvccDDzzA9OnTGTZsmNvVaZF9ezRFY4bS6ohLtRERab969+4NNP6B3a1bNwA2bdrUpnWStqffAxERac/0OSXQsX4P0m4y8GQIhUJMmTKFMWPG8L3vfa9V54dCtRNwl5WVAfHV6yKR5Cd8asqs+XfVtjI+3rwHsOiZF6Brjp/te6rI9qVPr6x0s28bSNvS/a8vEolgjMFxHJx9VwdIgZoekzXXTCc1H9ibNm1qUPfi4mIAAoFAu39d6dwG7cHB/h605v47joMxhkgkgsfjafbYVL23uR2vSNvS/Xef2qA+xSsHTvGKQMeKVzploum+++5j1apVfPDBB/u9mY2ZMWMG06dPb7B9/vz5ZGVlJaOKjVqwYAEAn22wqArGO6OVONXEquCVhdsYkpeyS8teNW0g7tD9j/N6vfTs2ZOKigrC4cbnaPvFP7+iLBhr45odvLwMD98/fXDSyuvSpQsAGzZsSPxHdo2//e1vAIwZM6bBvvaqvLy8xef4Vs/Dt2Y+AMGx1+IUHZrYZ1XuJHPJTACivY4ifOSl9c4NvPsbPHs2AFB1xi/r7fNuWIL/sxcBCB3xP8R6H127Mxoka+EPAYgVDSM09vr65X44B8/OT+PlnjId/Dktfl0tkazfg5bc/3A4THV1NYsXLyYajTZ7bFVV1QGX2xJuxyviDt1/96kN4vYXr6RrrAKKV/ZH8UrrdKR4pdMlmj788EN+9atfcd9993HEEUe0qoy7776bO+64I/FzWVkZ/fr1Y/LkyeTlJT/bE4lEWLBgAZMmTcLn87F64Wo+2LMRAG+mj+5dsxh6eG++fnjPpF9b4vZtA2lbuv/1BYNBNm7cSE5ODhkZGY0f49hU7ufD4oAZiEajeL1eSHHHSb9jJ/V9dPDgeBC4Y8eOeuWuWbOGv/3tb3g8Hq6++uqUvHcnkzGG8vJycnNzWz6noA+saDzg8GYGoO5rtapq93liZOxzHywThr37G9wjv6f23ICvfrkRb2KfjwiBfc+1o4n9eTnZkJHa+3+wvwetuf/BYJDMzExOPvnkJv9Oa6TqPxzcjlekben+u09tUN/+4pWkxiqgeKUdULxycDpSvNLpEk0rVqwgFosxbdo0pk2b1mD/F198gWVZ5Ofns2fPnkbLCAQCBAKBBtt9Pl9KP1Rqyu9flIPP4yEac6gOO9iWTWnI0QdaG0h1G0vzdP/jYrEYlmVh2za23fhUe/mZfpIXZRnCYQu/35fEMhuXn+lr8jW1Rt++fYH4WPeach3H4cYbbyQYDHLrrbcyaNCgxPGxWIyHHnqIRx99lNWrV9OvXz9uv/12brzxxqTVqTVquj/XtHuL+LMhq2v8fK8f6p7v8dbuC+TU3weQmV+7f999/szafb6M+ufansQ+MvIanhvIrT3X42143SRr6e/Bvlpz/23bxrKsA3rfStX7mtvxirhD9999aoO4/cUryY1VQPGK4hVQvALtI17pdImmYcOGcfXVVze6b86cOeTn53PBBRektEv5wTIGfB6LaAyie8dUFpeH9n+iiHQaP/rmYUkry3EcysrKyMvLS2pQ1RZqVu/YtWsXoVCIQCDAD3/4QxYuXMioUaOYMWNG4lhjDBdffDHz58/nnnvu4eijj2bRokXcdNNNdO3alYsuusitl3FwRpwV/2pMTnc475Gmz51wV9P7Bk+MfzXGl9l8ucd9t+l9KdCS34NgMEhOTg5/+MMf6sULb7zxBv/zP//Dpk2bEuWJiEjrJTNWAcUrileaoHjFFR060VRcXExxcTFFRUUUFRUBcPzxx3P88cc3evycOXPo2bMns2fPbstqtljMGLpk+4lmGHweCwPsqlSiSURkXzWTKgJs2bKFJUuWMHPmTAYMGMArr7xS76HCH/7wB1588UXefPNNTjrpJABOO+00Vq5cyZw5c9I3cJMW/R58/PHHxGIxRo8eXa+MTz75hK5duyrJJCIiSad4RaBjxSvpleoFZs+ezZQpU5gyZQrPP/98g20vvfRS4thZs2YxYsQIZs2a5VJtU8MxhqKcAD3zM+iaE8C2LKrC6TmRnohIKmVmZlJYWAjAgw8+yJVXXkmvXr1YsGBBontyjZkzZ3Luuedy3HHHEY1GE1+HHXYY69atc6H2kiwt+T1YtmwZXq+Xww8/vN72jz/+uEEwJyIikgyKVwQ6VrySdj2alixZwp///Od625YuXcrSpUsBGDhwIOeee64LNWs7W/ZUs6mkGsuCwiwfWX4vMce4XS0RkXapd+/elJSU8NBDD9GrVy8WLlzI0KFD6x3z5Zdfsn79etavX9/o+PNjjjmmraorKXIgvwcQD9xGjBjRYG6jjz/+uMPHFyIi4h7FKwIdJ15Ju0TT448/zuOPP35AxzY14XdTjEmPZM3uijC7KuJD5bL8HrL8EFWiSUSkUX369GHlypUMHjyYefPmMWTIkAbHbNmyBYC5c+fSr1+/BvsLCgpSXU1JsQP5PQBYvnw5Y8aMqbetoqKCdevWMWrUqLaoqoiIdEKKVwQ6TrySdokmic/RVMNjWzjGEIsp0SQi0ph58+bt95iaMfGZmZkcffTRqa6SuOBAfg8cx2HFihVceOGF9bYvXboUY0y7CNxERKRjUrwi0HHiFSWa0lD/Lln07ZLFpt1VrN9VRZdsP30LM92ulohI2ho6dCgnnHAC1157Lffeey+HHnoopaWlfP7552zfvp1f/vKXbldR2sAXX3xBVVUVXm/98Oh3v/sdfr+fESNGuFQzERERxSsSlw7xihJNaSjg9ZAbiDedbVlEYg7GgOMYbNtyuXYiIunHsizmzp3Lvffey89+9jO2bdtGUVERo0eP5qabbnK7etJGli9fDsQn4CwoKCArK4unn36aBQsWEAgEWLhwIWeeeaa7lRQRkU5L8YpAesQrSjSloZgxeD0WXo9NXoYXz97kUtQx+JVoEhFple7du/PHP/7R7WqIi5YtW8bAgQO5/fbbuf322zHGcPXVV/PLX/6Su+66i7feesv1wE1ERDo3xSuSDvGKEk1pyDEG27IY2TuPXgUZbN0TBCDqOPixXa6diIhIelq2bBljxozhlltu4ZZbbklsdxyHSy+9lLy8PBdrJyIiIpIe8YqyEmmoIhilMhSlKhzFY9X2YNLKcyIiIq330UcfNVjBRURERKQ9SYd4RT2a0tCanRWs3lEBQL8uWYntWnlORESkdTZt2sTOnTvbfeAmIiIinVe6xCtKNKWhWJ2eS35Pbac09WgSERFpnb59+2KMPkdFRESk/UqXeEVD59JQYbafotwAfq/N6p0VrC2upDocq5eAEhERERERERFpa+rRlIZ65GawuyCMz2NRXBGirDpCYZaPSMxxu2oiIiIiIiIi0ompR1MacvZ2lfPaNvbeycAjMaOhcyIiIiIiIiLiKvVoSkOxvYmmvAwvp43owX/X7sJrW8Qc9WgSEREREREREfco0ZSGaub+8npseuQF8O2dEFw9mkRERERERETETRo6l4Y+21LGZ9vK+GJbOcFILLE9GlOiSURERERERETcox5Naag6EiMcccACj20ltmvVORERERERERFxkxJNaci2rXiCyYKd5SFKqyNYQFRzNImIiIiIiIiIi5RoSkPDe+SSG/AS8Nm8t66EdcWVeD2Whs6JiIiIiIiIiKs0R1MacvbOBm5ZFt69Q+cco6FzIiIiIiIiIuIu9WhKQzX5JI9lMXZgIcUVIWzL0qpzIiIiIiIiIuIq9WhKQ87ehJJtwdgBhfTIy6BbbkA9mkRERERERETEVerRlIa2llazqzJMedCDRe2qc5GYJgMXEYH40OLWMEYJexEREWkbileko1KiKQ1tKwuyuyKMz2tj13lvUo8mEZE4BWAiIiLS3ilekY5KQ+fSUN33I5+ntgk1R5OIiIiIiIiIuEk9mtJQvy5ZFGT6KMj28erHW/h4cynGwOkjerhdNRERERERERHpxNSjKQ1l+T3kZfrolpuB48QnBzfGENYcTSIi9WRlZWFZFsuXL6+3PRgM0r17dwKBANu3b3enciIiIiIoXpGOR4mmNFQzdM62oCDLT8DnIcPnIapEk4hIPb169QJg8+bN9bZnZGRw1VVXEQ6HmTt3rhtVkzakAF5ERNozxSsCHSteUaIpDdVM+u2xLM4Z3ZtDe+YyvGcuAZ/H5ZqJiLQvffr0ARoGbgDjxo0D4N13323TOknbUwAvIiLtmeIVgY4VryjRlIaCkRihqEMo6uCps+ycejSJiNTXu3dvoPHArVu3bgBs2rSpTeskbU8BvIiItGeKVwQ6VryiRFMaWrWjgs+3lvHu2t1adU5EpBnNfWDv3r0biD8lko5NAbyIiLRnilcEOla8olXn0owx8Ym/IT5Hk9dT26MppkSTiNQxb+U25q+Mj+O+9uRBHNozL7FvZ3mIma9/DsBRAwq49NgB9c59aOEq1u+qAuAXFxxRb9/S1cXM/TD+AXjJsf0YO6BLYl8wEuOHL34CwPCeOVx38iH1zp2zZC2fbikDYPo5I8kJpPZjqOYDe8uWLQ32vfzyywCMHz8+pXUQ9ymAFxFpn5IVq/zyolH19qUqVvlo4x62lQU5Y2TP1r/oRiheEehY8YoSTWnGGMjL9BGNOfTIy+DzrWVsKqnGYBjSPcft6olIOxKMxNhTFQYgGqufiDbGJPZVhWINzi0PRhL79xWK1pYbitYfsmsMiX0VwWiDcytD0cR+x6Q+Od7UB/aaNWt4+umn8Xg8fOc730l5PVzzxt1QvcftWrRcZgF8bUbSilMALyLSPqVbrBKOOQQjDetysBSvKF6BjhWvKNGUZhxj6FOQCcCwnrlsLKlmV0UIaPyNUkQ6rwyfh4IsP1C/9yOAZVmJfVmBhgsJ5Gb4Evv3FfDWlhvw1h+Bbe1dDRMgJ6PhR0x2wJvYb1tWg/3J1lgXZMdxuP766wkGg9x6660MGDCgqdPTX/UeqN7tdi1c1+kDeBGRdirdYhW/xyYjBQswKV7Zo3iFjhWvpF2i6cknn+Tf//43H3zwAR9//DHhcJjHHnuMKVOmHHAZS5Ys4cUXX2TRokWsW7eOyspKBg4cyDnnnMPdd99NQUFByup/sGJ1suq2Vf+NM+JoMnARqXXGyJ5Ndu3ulhto0M28rltPG5r43tnnveWEIUWcMKSo0fMyfJ5my736xEHNVTnpaj6wd+3aRSgUIhAI8MMf/pCFCxcyatQoZsyo/xQqFovx0EMP8eijj7J69Wr69evH7bffzo033tim9U6azAK3a9A6Sa53SwL4e+65p8HvRV0vvfQS55xzTlLrJyLSWSUrVtlXqmKVUf0KaPrM1lO8UuB2DVpH8UqT0i7RdO+997J+/XqKioro1asX69evb3EZF1xwAcXFxZx44olcfvnlWJbFokWLeOCBB/jb3/7G22+/Tffu3VNQ+4NXt/embVmcMKSIoT1ysLDIz/S5VzERkXao5gMb4t2QlyxZwsyZMxkwYACvvPIKWVlZif3GGC6++GLmz5/PPffcw9FHH82iRYu46aab6Nq1KxdddJEbL+HgJLE7dzprSQB/5ZVXcvbZZwPwm9/8hsWLF/P8889TWVlJdnY2o0ePduMliIhIB6Z4RfEKdKx4Je0STbNnz2bo0KEMGDCAmTNncvfdd7e4jNtvv53LL7+cXr16JbYZY7jpppt45JFHmD59Og8//HAyq500dSf8ti2LbjkBsvzexM8iIlIrMzOTwsJCSkpKePDBB3n44Yfp1asXCxYsoG/fvvWO/cMf/sCLL77Im2++yUknnQTAaaedxsqVK5kzZ056Bm4CtCyAHzp0KEOHxp+ST5s2jdGjRzN+/HjKysrIy8vDtrVgr4iIJJfiFYGOFa+kXbR0+umnH/T41P/93/+tl2SC+Bjg++67D4C33nrroMpPJccY1uysYNWOCpZv2INtW1h7E0xRrTonItJAzYf2Qw89RPfu3Vm4cGHig7mumTNncu6553LccccRjUYTX4cddhjr1q1r41pLMtUE8AAPPvggV155ZZMBfF0rVqzgyCOPbKtqiohIJ6Z4RTpSvJJ2PZpSyeeLDz3zetvvbXEMVIZiGGMoDUYA8NoWkZghGtMcTSIi++rTpw8rV65k8ODBzJs3jyFDhjQ45ssvv2T9+vWsX78+8VlQ1zHHHNMWVZUU6t27NyUlJTz00EP06tWryQC+RnFxMVu3bm13gZuIiHRMilcEOk680n4zKi549NFHAZg8eXKzx4VCIUKhUOLnsrIyACKRCJFIJOn1qikzEokQihhq/mdhKKmopjocpToSpbzak5LrS/02kLan+19fJBLBGIPjOA0m6k4Fs3dyuJprppvXX3+93s+NvYZNmzYB8MILL9CvX78G+wsKClx97eneBu1B7969EwH866+/zpAhQ5q9lytWrABg5MiRrbr/juNgjCESieDxNL9CUare29yMV6Tt6f67T21Qn+KVllG8ItBx4hUlmvZavnw506dPp3v37tx1113NHjtjxgymT5/eYPv8+fPrjZtMtgULFlARge57V5rrTZDHXpzP8nUeYkBpyW5ee21jyq4v8TYQ9+j+x3m9Xnr27ElFRQXhcLjNrlteXt5m12preXl5QPyDediwYY0eU/Mf6W7qyG2Qas8991y9n/fXnl999RUAhYWFifvekvsfDoeprq5m8eLFRKPRZo+tqqo64HJbws14Rdyj++8+tUGc4pXkU7zS8XWUeEWJJmDt2rWcddZZxGIxnnnmGYqKGl8Ks8bdd9/NHXfckfi5rKyMfv36MXny5MQffzJFIhEWLFjApEmT2BN0+Gf5SgBGDizkmAGFPLfxIyIxh4LCHM48U90lU6FuGzTWTVVSS/e/vmAwyMaNG8nJySEjIyPl1zPGUF5eTm5ubmJOuI5mzJgxnHDCCdx+++3cc889HHrooZSWlvLFF1+wfft2/u///s/V+nWGNmhvalaf/fvf/85JJ51E//79W3T/g8EgmZmZnHzyyfv9O03VfxS4Ga/ovbrt6f67T21Qn+KV5FO8Ivtqr/FKp080rV+/nlNOOYWdO3fyt7/9jVNOOWW/5wQCAQKBQIPtPp8vpR8qPp8PTzSGbcV7NPm9XnoVZtO3MIvKUJSsgFcfaimW6jaW5un+x8ViMSzLwrbtNllRoqbrbc01O6q5c+dy7733MnPmTLZt20ZRURGjR4/mpptucv11d5Y2aE8mT57MhRdeyP/+7//y9a9/nT/96U8tuv+2bWNZ1gG9b6Xqfc3NeEXv1e7R/Xef2iBO8UpqKF6RutprvNKpE03r1q3jlFNOYcuWLTz//POcddZZbldpvxxTu7KcZVn0LcxiWM9ctpcGyfQ3P6ZSRESa1r17d/74xz+6XQ1pJzIyMhLd1x3HaRdDEURERBSvSF3tNV7ptImmdevWMXHiRLZs2cKzzz7LOeec43aVDkgoGqO4IoQFbCutBsBnx7vFxRzTzJkiIiIiIiIiIqnVoRNNxcXFFBcXU1RUVG/epZok0+bNm3n22Wc577zzXKxly4Qjhs0l8QRTQXYFAJ693eKiSjSJiIiIiIiIiIvSLtE0e/ZslixZAsDHH3+c2LZo0SIAzj33XM4991wAZs2axfTp05k6dSrTpk1LlDFx4kTWr1/P+PHjWbFiRWJJwLrqHt+eRGK1yxTaeyf48nri/zqOwRijiddERERERERExBVpl2hasmQJf/7zn+ttW7p0KUuXLgVg4MCBiURTU9avXw/AO++8wzvvvNPoMe010eT1WPTrEl+S+LBeeewoD7JkVTElVWHyM31EYga/V4kmEREREREREWl7aZdoevzxx3n88ccP6Nhp06Y1mjAyJn2HmHlsmy7ZfgD6FmaCgapwlEjUIRozmqdJRERERERERFyjNQfTTN1V52zLwuuxCXg9eD0WHhuijtPM2SIiIiIiIiIiqZN2PZo6u3qJJtuiS7afs0f35sP1JYBWnhMRERERERER9yjRlGYiMSeRTKoZAui1a+dk0spzIiIiIiIiIuIWJZrSTHF5mE82lwJQkOXjwqP74ambaIop0SQiIiIiIiIi7tAcTWkmVmcOJo8n3nz1ezRpjiYRERERERERcYd6NKUZ27bIyYg3W5csHwCfbitna2kQn8fSHE0iIiIiIiIi4holmtJMfqafQ7rlADCmfyEAKzeXsqMsSIbfozmaRERERERERMQ1GjqXZkzdVees+JC5mjmajNEcTSIiIiIiIiLiHiWa0kzdoXE1UzNNOqw7g7tl079LpuZoEhERERERERHXKNGUZuqOjKvpyTSgaw65GT6y/F7N0SQiIiIiIiIirlGiKc1sK6tmbXEl64orWbW9Ath31TklmkRERERERETEHZoMPM1UBKOUVUcA2F0VBuonmtSjSUREJM6yrP0f1Ii68yGKiIiIpFJHjFeUaEozsTq/TDUJpmA0RjASAyAS0xxNIiIi0L4DMBERERHomPGKhs6lmb4FWYzsncdhvfM4emAhAK98tJUvtpXzxfZy9WgSEakjKysLy7JYvnx5ve3BYJDu3bsTCATYvn27O5UTERERQfGKdDxKNKUZywKvx8bnscnweQDwevZ2tTMQiapHk4hIjV69egGwefPmetszMjK46qqrCIfDzJ07142qiYiIiACKV6TjUaIpzdTtseTZO5ZzSLccCrP9dMn2E4mpR5OISI0+ffoADQM3gHHjxgHw7rvvtmmdpO3pSbGIiLRnilcEOla8okRTmqk7Ms7eO0fThOHd6N8li35dsjAo0SQiUqN3795A44Fbt27dANi0aVOb1knanp4Ui4hIe6Z4RaBjxSuaDDzNlFaH2VMVBqzE6nMeuzZfGNUcTSIC8MbdUL0nKUVZGLLCESy/D2jdqhgHLLMAvjYjacU194Rw9+7dQPzDWzq2Pn368NVXX+33SfGNN97Y1lUTEem8khirgOIVSX8dKV5RoinNbCqpZv2uKgDWFlcCtavPAUQ1dE5EIB64Ve9OTlnGYEWjEPXGJ4pLIzVPCLds2dJg38svvwzA+PHj27RO0vb0pFhEpB1KZqwCilck7XWkeEWJpjRTd46mmgRTYjJw1KNJRPbKLEhiYQYTjkBbPSFMoqaeEK5Zs4ann34aj8fDd77znaReU9ofPSkWEWmHkvyZr3hF0l1HileUaEozXXP89C7IxBhDvy5ZACxZVcyqHeU4Bo47pIvLNRSRdiGJ3bmN41BVVkZeXh6WnV5T+zX2ZMhxHK6//nqCwSC33norAwYMAOITLebk5PCHP/yBq6++OnH8K6+8wje/+U02bdqUCADSymevwOevxr8//mboMbJ2X8UOWDA1/n2/Y+Doq+qf+9YDsHtt/PvzHqm/76tF8NGz8e/HToH+x9bui1TDK3fEv+8+Ak64tf65//kdbPs4/v2ZD0AgtzWv7IDpSbGISDuUxFgFFK8oXlG80p4o0ZRmCrP8dMsNANAzP57NLK2OUBWKARCMOK7VTUSkvakJtHbt2kUoFCIQCPDDH/6QhQsXMmrUKGbMqA1yP/74Y2KxGKNHj65XxvLly+natWt6Bm0QD6JqhibEIvX3Gad2X7iy4bnB0qaHNURDdcoN7VOuqd0XKm94briidr9J/eeWnhSLiEh7pngFxSt0rHhFiaY0U3dknGfv0DmfJ56xtywNnRMRqavmyRDEnw4tWbKEmTNnMmDAAF555RWysrIS+5ctW4bX6+Xwww+vV8by5csbBHNpxZcJmXt7u3p89fdZdu0+f3bDczPya/fvyxuoU25gn3Kt2n2NPf3z59Tut1L/1LklT4pFRETamuIVFK/QseIVJZrSjFMnkWTvneTu/LF9WL6hBMuyyM3wNXWqiEink5mZSWFhISUlJTz44IM8/PDD9OrViwULFtC3b996xy5btowRI0YQCNQPQpYvX84555zTltVOrhFnxb8ak9O9YRfzuibc1fS+wRPjX43xZTZf7nHfbXpfCrTkSfE999xT7+d9vfTSS+n9+yAiIu2O4hUUr9Cx4hUlmtKMY+ommuL/+jw21t6kU8zR0DkRkbp69+5NSUkJDz30EL169WLhwoUMHTq0wXHLly9nzJgx9bZVVFTw1VdfpfcTQmnRk+Irr7ySs88+G4Df/OY3LF68mOeff57Kykqys7P1u9BC1eEYPo+F15Ne86WIiLQ1xSvSkeIVfeqnmRWbSuNfm0v5cnt8HKm3zmR3GjonIlJfzdOhwYMHs3jxYkaMGNHgGMdxWLFiBaNGjaq3fenSpRhjGmyX9FLzpBjgwQcf5Morr2zySfHQoUMZP34848ePp6SkhNGjRzN+/HiOOeYYxo8fXy/Ik+ZtKqnie88v587nP6IiFHW7OiIi7ZriFelI8YoSTWkm6hiMMRjHYO9dttPrqV2+MxZToklEpK558+ZhjGHNmjUMGTKk0WO++OILqqqq8Hrrd/R9+OGH8fv9jQZ7kl5qnhI+9NBDdO/evcknxXWtWLGCI488si2q1yE99/4mQhGH8mCUuR9ucrs6IiLtmuIVgY4Tr2joXJoJeC0y/B4wkBWIN9+20mqKK0IYA3uqIvspQURE9rV8+XIg/vSooKCArKwsnn76aebPn09GRgYLFy7ka1/7mruVlIPSp08fVq5cyeDBg5k3b16TQXyN4uJitm7d2u4Ct3TSIy/Ayr3zmQ7vkdoloUVEOgPFKx1fR4lXlGhKM4O75bBlT3Dv9/EZ91ftqGBzSTUAu6tCTZ4rIiKNW7ZsGQMHDuT222/n9ttvxxjD1Vdfza9+9Su+//3vs2jRIgVuaW7evHktOv6TTz4B4IgjjkhFdTqFS48dwKXHpsfqOCIi6UDxSsfXUeKVViWaBg8efNAXvu2227j11lsPupzOpu4UTDWrzgW8nsS2iIbOiYi02LJlyxgzZgy33HILt9xyS7193/1u2644Iu3D1q1bAdJmGWEREen4FK/IvtprvNKqOZrWrVtHSUlJfK6gVnytX7+ePXv2JPmldA6mzqpznr3Lzo3slUf/rlkM6JpFXobPraqJiKStjz76qMEKLtK55ebGh3r95S9/YeXKlS7XRkRERPGKNNRe45VWTwZ+++23s3bt2lZ91U2WtNSTTz7J9ddfz9FHH00gEMCyLB5//PEWl+M4DrNmzeLII48kMzOTbt26cdFFF7Fq1apW160txOp0adrboYleBZl0yw1QkOXH79X87iIiLbFp0yZ27typwE3qOf3007nwwgu56667+PGPf+x2ddKWMYaKUJR1xZX1YhgREWkZxSvSmPYar6TdHE333nsv69evp6ioiF69erF+/fpWlXPDDTfwpz/9icMOO4xbbrmF7du38+yzzzJ//nzefvttDjvssCTXPDnWFVeycXcVWFBaFaF7bgYAXtsmGosRdRyXaygikl769u17UA9ApGPKyMjgueeeA+IPp8rKylyuUfr5z5pdzP73V4mfZ3zrCLrnZbhYIxGR9KV4RRrTXuOVViWaVq1aRZcuXVp90YM5f/bs2QwdOpQBAwYwc+ZM7r777haX8eabb/KnP/2Jk046iQULFhAIBAC4/PLLmTRpEjfeeCNvvfVWq+qXajsrQuyuDANQHY4lttcMo9PTQhEREWkPvthWP9jdWRFSoklERKQTaFWi6ZBDDjmoix7M+aeffvpBXRvgT3/6EwA/+clPEkkmgNNOO40zzjiDN954gy+//JJhw4Yd9LWSrW4eyeuJD5NzHAOWIRpzCEbUo0lERETcV3d9kiE9csgNaB5JERGRziDths4lw6JFi8jOzuaEE05osK8m0fTWW2+1y0TT8B65BCMxMNAzL54k+2xbGe+tLSESdRhQlO1yDUVERETgvDF9mDSiB1HHoW9hluaRFBER6SQ6XaKpsrKSrVu3cvjhh+PxeBrsHzp0KEC7nRTc77XJ9MXrHdj7r9e2ExODa+iciIiItAddsv10yfa7XQ0RERFpY0lNNJWUlPCPf/yDyy+/PJnFJlVpaSkA+fn5je7Py8urd1xjQqEQoVAo8XPNhFuRSIRIJJKsqibUlBmJRIhEYzgmPjwuFo0SsQ2ZXuia7ac8GMVrk5I6dHZ120Danu5/fZFIBGMMjuPgtMECADUTT9ZcU9qe2sBdrbn/juNgjCESiTT6YKuuVL23uRmvSNvT/Xef2qA+xSudj9rAXe0pXklqomnDhg1ceeWV7TrRlAwzZsxg+vTpDbbPnz+frKyslF13wYIFrNpgs6Nq78/z51HTC71LzMZxIFQGr732Wsrq0NktWLDA7Sp0arr/cV6vl549e1JRUUE4HG6z65aXl7fZtaRxagN3teT+h8NhqqurWbx4MdFotNljq6qqDrZqjXIzXhH36P67T20Qp3il81IbuKs9xCstSjRt2LCh2f1btmxpSXGuqOnJ1FSPpZqnfU31eAK4++67ueOOO+qd069fPyZPnpzoEZVMkUiEBQsWMGnSJN59fRV7dlZgAZMmH0WmP96EK974Aru4EoCvf30MVs1YOkmKum3g82ky07am+19fMBhk48aNZGdnk5mZmfLrGWMoLy8nNzdX7y0uURu4qzX3v7q6mszMTE4++WQyMppfaS1VSxG7Ga/4fD42lVQTjMR4feV2yoNRdleG+ek5hyWG/kty6bPSfWqD+hSvdD5qA3cdTLwyYcKEegulNaYl8UqLEk0DBw5stsLGmHb/C5WdnU2vXr1Yu3YtsVisQfewmrmZauZqakwgEGi0EXw+X0o/VHw+H6t2VrJ2ZzyTGMOTuJ7f58W24t2bbI83sSKdJFeq21iap/sfV/Nea4zBtlP/t17T9dayrDa5njSkNnBXa+5/zd9pIBDY7/tWqt7X3IxXfD4ff1v2FZ9trR+UloYNfbL0Pp5K+qx0n9ogTvFK56M2cNfBxCt+vz+p8UqLWr+wsJDf/e53fPrpp41+zZ07tyXFuWbChAlUVlaydOnSBvvmzZuXOKY9cupM9l03l+S1axN8UU0ILtKh+Xw+PB4P1dXVbldFRJpQXV2Nx+Pp1P+xGTP145H8LB9Voea75YtIx6F4RaT9Ky8vT0lyvEU9msaOHcvOnTsZPnx4o/uDwWBiAqr2oLi4mOLiYoqKiigqKkpsv+6663jmmWe49957+ec//4nfH18RZeHChcybN4+TTz6ZYcOGuVXtZnXPy6A6EsNQu+pcVTjK++t2s600SJbfq0STSAdnWRZZWVmUlpbSpUuX/U7cJyJtKxaLUVpaSlZWVrvv6Z1K4wZ2YWDXLCpCMS4b35+AV+9VIp2J4hWR9q26upqysjIKCgqSHq+0KNF04403UllZ2eT+/v3789hjjx10pZoze/ZslixZAsDHH3+c2LZo0SIAzj33XM4991wAZs2axfTp05k6dSrTpk1LlHHKKadwzTXXMHv2bMaMGcM3vvENtm/fzrPPPkteXh6PPPJISl/DweiRl0E46uCxrUTA5hjYXhaiPBgFyyIWU6JJpKPr3r0769atY/369XTp0oVAIJCy/6B1HIdwOEwwGFQ3aJeoDdx1oPffGEMoFGL37t04jkP37t3bsJbtzymHdu7XLyKKVzobtYG7DuT+G2OIxWKUl5dTVlZGIBCo1yknWVqUaDrvvPOa3V9YWMgVV1xxUBXanyVLlvDnP/+53ralS5cmhsENHDgwkWhqzh/+8AeOPPJI/vCHP/DQQw+Rk5PDN7/5TX7605+2295MULtkoV3nDdprW9T8aIwhqqUkRTo8v99P3759KS4uZuvWrSm9ljEmMVFgZ+6d4Sa1gbtaev+zs7Pp2bNnose0iEhnpXilc1EbuKsl99/n81FQUEBRUVFKehu2KNHUHjz++OM8/vjjB3TstGnT6vVkqsu2bW655RZuueWW5FWuDdTMd+CpMydTwGtz3pg+vL+uBMuCmIbOiXQKWVlZ9O/fn2g0ut/lSA9GJBJh8eLFnHzyyZ16vhk3qQ3c1ZL77/V68XrTLrwSEUkZxSudh9rAXQd6/23bxufzpTQZeNCRkMfjYe3atfTv3z8Z9ZH9qMkh1f2dsCyL7IA3kXzSHE0inUuq/8PW4/EQjUbJyMhQ0OAStYG7dP8P3ryV24jEHHICXiYO15A6kc5I8UrHpzZwV3u6/wf9l96eJv/uDD7etIfiijA+j51YihD2WXVOczSJiIiIy2a89hl7qiLkZ/lYV1xJzDH065KlRJOIiEgHp77daSYYcYhEHQzU6+rmqTPZV0RzNImIiIjLdlWGKakME3EcfF6bWDhGJKYYRUREpKNToinNeD0WPo+Nz1N/Fvkte6opqQxjWZbmaBIRERHX5QS8RGMOuQEvlx/XFwvI8mt5cxERkY5OiaY0M6JXHqVVEbpk11/J5p2vdrFhdxU+r62hcyIiIuK6aWePdLsKIiIi4gJ7/4dIe+Ls7a1k7zNDfM1E4MZAVEPnRERERERERMQF6tGUZmpGxdn7pAiPGdiFYCSGbVladU5EREREREREXKFEU5pxTOM9mo7om89nW8sANEeTiIiItCsVoSihSIxIzNAjL1BvQRMRERHpWA460fTDH/6QgoKCJFRFDsTmPdWEIrEGvZZ8ntqATSu6iIiIiJuMMby4bDO2ZVGUE+DtNcV8sa0cgEcuG4vfq0STiIhIR3XQiab7778/GfWQA7S9NEg46hCO1k80eeuMpdNk4CIiIuKmmGN4dcVWAIb0yCHDW7vaXCTm4PdqmlAREZGOKqVD54wxrF69mszMTPr27ZvKS3UaNX2V9u1x7rUtzN5hdVH1aBIREREXxUztQy+vbTGsRy4ZPg8+j9UghhEREZGOJSmJppdffpmXX36ZX/7ylxQWFgKwbt06zjrrLD777DMALr74Yv7yl79g7zuLtbTIoK5ZxBzo2yWz3va5yzazYnMpGKiOxFyqnYiIiEi8p/WdZwwn5hgy/R4O6ZbjdpVERESkjSQl0fT73/+eTZs2JZJMALfddhuffvopp556Krt27eKZZ57h1FNP5eqrr07GJTslYwxZ/niTFeUE6u2zLWDvw8NQVD2aRERExD0e22JErzy3qyEiIiIuSEr3opUrVzJu3LjEz6Wlpbz22mt8+9vf5p///Cf//e9/GTFiBHPmzEnG5TqtuqvJ7dvtvHtugCy/h+yAt8FE4SIiIiJtLlwF7/4Rlj8FRrGJiIhIZ5GURNPOnTvp1atX4uclS5YQjUb5n//5HwB8Ph+TJk1i9erVybhcp1U3f2Tvk2k684jeDO2Ry5DuOXhtTX4gIiIiLluzMP716cuweqHbtREREZE2kpShc3l5eezatSvx86JFi7Btm5NOOimxzefzUVlZmYzLdVrGGKojMSwgHKk/PM7nqU0uadU5ERERcVM05hD8YhG+aAzbstj5/t/5y5q+RKIO/3Nsf83ZJCIi0oElJdF06KGH8o9//IOf/vSneDwennnmGY466qh6czatX7+eHj16JONynVbUcfhyWzkAVeH6E3576vRiijiao0lERETcU1wR5q31XoYHy+mS7cfn28SXoXgMUxGMulw7ERERSaWkDJ279dZb2bJlC3369KFfv35s2bKFG264IbE/FouxZMkSRo0alYzLdVp1OyrZ+wyP83lqmzKmOZpERETERVHHocJTOxm4B6fePhEREem4ktKj6fzzz+fhhx9OTPZ90UUXcdVVVyX2L1y4kKqqKr72ta8l43KdljGGLtl+DNAzL6Pevk+2lLJhdxXGGHZV5LtTQREREREgy+9laLcsCo2fbL+Hwmw/vz93CN6sAqx9VzQRERGRDiUpiSaAG2+8kRtvvLHRfZMnT6akpCRZl+q0jLHo1yULgBG9cuvt27InSEllGIDKkLqki4iIiHu6ZPs5+ZACiGUlttml6yG7sOmTREREpENIytA5aRtOnaWB9x06568zGXg4pi7pIiIi4jJnnwdfJetcqYaIiIi0raT1aJLUM3UTTft0Oz9tRHfe+GQbtmVRkOlv66qJiIiI1OfUX7iEkrXu1ENERETalBJNaaTuZOCefRJNXbIDZPg8ABg0GbiIiIi4rE6PpkjMoXjd53xWtJ3+XbIZ0j3HxYqJiIhIKinRlEbKgxG+2FaOZUGm31Nvn6fOULqoVp0TERERF63eUc66lVsYUFFO15wAPo/N9p3reC68itNHDVSiSUREpAPTHE1pJBIzBCMxqsMxqsL1u6N76yaaNEeTiIiIuKg8GKW0sprKcIxIzKEmTCmKblOcIiIi0sGpR1MaicYcLMvCYOr1YAIoq45SGY4SixnKqrXqnIiIiLjHAB7iD8UsC/xem36FmVw6LEbWIV3drZyIiIiklBJNaSQv08eRffMBOHloUb19n24t46udlTiOITvD09jpIiIiIm3iqP6FHDWqJ2brNgAsLLrmBOiasRu6ZrtcOxEREUklJZrSiFN31bl9ejT5vRY184NHY5qjSURERFzmRLGoH69QudOduoiIiEibSeocTSUlJTzxxBPJLFLqcOpMaWDvs+pcr/xM+hVm0SM/gyy/8ociIiLiMicKxkDd1XBjYdeqIyIiIm0jqYmmDRs2cOWVVyazSKmjXo+mfR4Q9i7I5JBuOfTMyyDLr6FzIiIi4rJgKWxbAdtWYojHMdFwiKqw5pIUERHpyFrU9WXDhg3N7t+yZctBVUaaV1odYUd5CAvYXhZssN/jiWefoo6GzomIiIh7NuyqInf9x2SHw3g8NlbZVlaUZrJn62be9a7mf792qNtVFBERkRRpUaJp4MCBWPsM2arLGNPsfjk4pdURtu6pBmDD7uoG+317uzlpjiYRERFx0+fbyhhWVoYdjZFp2/hC5UAmXqJEos5+zxcREZH01aKhc4WFhfzud7/j008/bfRr7ty5qapnPe+99x5nnnkmhYWFZGdnM27cOJ566qkWlbFnzx5+9KMfceSRR5Kbm0tRURHHHHMMs2bNIhhs2FuoPajbU8nTSELP67H3HudgjJJNIiIi4g7HGEJWRvwHy8JywuRmeOmaYRjcLcfdyomIiEhKtahH09ixY9m5cyfDhw9vdH8wGEx5gmPRokWcccYZ+P1+Lr74YvLz85k7dy6XXnop69at45577tlvGXv27GHs2LF89dVXnHjiiVx//fWEQiFef/11brnlFl588UUWLFiAbSd1CquDVpjpZ0DXbAyGYT3qB2k7yoIs/nInZdURCrJ8OAY86lwmIiIiLhjVr4Ds/AAZZR48tgcsm0OKssAbYMSx/d2unoiIiKRQixJNN954I5WVlU3u79+/P4899thBV6op0WiUa665BsuyWLx4MWPGjAFg6tSpHHfccUydOpULL7yQoUOHNlvOH//4R7766ituv/12fvWrXyW2h8NhTjzxRP71r3+xZMkSTj755JS9ltYI+GwKsnwAdM/LqL/TgkjMIeYYYo4hEnPigZ2IiIhIG+uVnwl+Czw22DZYNkSq4/8aA5pqQUREpMNqUZed8847j8suu6zJ/YWFhVxxxRUHXamm/Otf/2LNmjVccskliSQTQG5uLvfddx/RaPSAEl1fffUVAGeeeWa97X6/n0mTJgGwY8eOJNY8OequOufZZ9m5gMdDpt+D32fj9diaEFxERETc5cT2fmPFE0zhqviP0ZBrVRIREZHUa19jw/Zj0aJFAEyePLnBvpptb7311n7LGTlyJABvvPFGve2RSIR//vOfZGZmctxxxx1kbZOvbu5onzwT+Vk+vnFEL0b0zKNPQSYxTQguIiIibnKi8X+tvYmmyN5e8bGwe3USERGRlGvR0LnGeDwe1q5dS//+qR9vv2rVKoBGh8YVFhZSVFSUOKY511xzDX/5y1/45S9/yfvvv88xxxxDKBTijTfeoKSkhKeeeoo+ffo0eX4oFCIUqn0aV1ZWBsQTVZFIpKUva79qyqwKhghFo1gWhMPRhtcyBsfEV3KpDoXYO8pOkqDmXqeifWX/dP/dpfvvPrWBu1J9/1NVrlvxSiQSIRiJkRmNYCd6Y1tUVZSxI1rBG//8hGvPOCbp1+/s9D7hPrWBu3T/3ac2cFd7ilcsc5Czd9u2zbp169ok0TR58mQWLFjAqlWrGDJkSIP9hxxyCJs2baoXVDWlqqqK66+/nieffDKxzbZtbr75Zu677z6KioqaPHfatGlMnz69wfannnqKrKysA3w1LbdkK7y+OT7v0rgiwzkD6y8P/OYWi1Vl8a5OFw1yKAikrCoiIiKSJFVVVVxyySWUlpaSl5eXtHLdilcA3t5u8d2tPyDPlOPx+sHjx4Qr+NIaxG/9V3HeoYUNemeLiIhI+9WSeOWgezSlo+LiYs455xx27NjBq6++ygknnEAwGOTvf/873/ve93jllVd4//33KSwsbPT8u+++mzvuuCPxc1lZGf369WPy5MlJDRBrRCIRFixYwNDhI/jXznUADB3alzMn1e/ZVfzOBkpXFwNw8sQR9C3MTHpdOquaNpg0aRI+n7qKtTXdf3fp/rtPbeCuVN//mp5GyeZWvDJp0iRKPtyKb4eN7dhkZmXhCWRTXVJNrsfQs0s+kyafQcCnRUuSSe8T7lMbuEv3331qA3e1p3glrRJN+fn5AJSWlja6v6ysLHFMc+644w7efvttPvroI4488shE2ddeey2xWIwbb7yRBx98sNGngACBQIBAoGF3IZ/Pl9I/qNxMH/mZfgxQlBNocK1Pt1WwtTSE17awbI/+uFMg1W0szdP9d5fuv/vUBu5K1f1PVZu6Fa/4fD4GdMkgw2vhjVlYlo1txxctGZJnM/2sYWRkBrC08lxK6H3CfWoDd+n+u09t4K72EK+k1WTgNXMzNTYPU0lJCcXFxY3O37SvV199lS5duiSSTHWdeuqpAHzwwQcHWdvk65GfwaCibAYXZTO8Z8Mnkau2lVNcHqKkKkzUcRopQURERCT1ThnalS5ZPrL9XrweGywb27LwRqvJtGJKMomIiHRgaZVomjBhAgDz589vsK9mW80xzQmHw5SVlREON1z1ZOfOnQCNPgF0m6m36lzDAM3riW9zDES06pyIiIi4xYnWCVz2rjoHEIto1TkREZEOLq0STaeddhqDBw/mqaeeYvny5Ynt5eXl3H///Xi9XqZMmZLYXlxczOeff05xcXG9ck444QSi0Sj3339/ve2hUCix7ZRTTknZ62itmFObPLIbabkzRvZkSPccBhVl1ztWREREpE2ZGLC3d7VVJ9GEA9H9L9oiIiIi6SutEk1er5fZs2fjOA4nnXQS1113HXfeeSejRo1i5cqVTJs2jWHDhiWOnzVrFiNGjGDWrFn1ypk5cya5ubn85Cc/4dhjj+WOO+7gu9/9Locddhjz5s1j7NixXHPNNW398vbLqdOlqbEeTX0KM8kOeMn0eYjENHROREREXBKLQqJDkw3eADHHEApHWLlxJ2VBLX0tIiLSUR10oumHP/whBQUFSajKgTnllFNYsmQJJ554Is899xy/+93v6Nq1K08++SQ//OEPD6iM0aNH88EHH3DllVeybds2Zs2axeOPP052djbTp09n8eLFZGRkpPiVtNzanZWs2VnBVzsr2LC7qsF+b51uTlH1aBIRERGXvLZiI6XVIarCMRzLA54AkZhDRTDMPz/eyLbSoNtVFBERkRQ56FXn9h1+1hbGjRvH66+/vt/jpk2bxrRp0xrdN3ToUB599NEk1yy1SoNRKoJRAKrC0Qb7fZ7aXk5RzdEkIiIiLtlUXE4kGgPjELC84AsAFhYGnwmr57WIiEgHdtCJJmk7sToryXnshkPnyoNRqsJRjIFILNaWVRMRERFJMCaKtXfsnOXxgDcTr8ciy7Y4+ZB8euS1v57jIiIikhxKNKWRQ3vmcXifKgyGw3rmNdj/5uc7WLW9AoBQRE8KRURExB1TxvfDt8oHMYNle8Gbgde28AKHdw9ATvtb3VdERESSIymJpquuumq/x9i2TV5eHsOHD+ess86iT58+ybh0p2JZNT2ZLPxeT4P93jpD50JRJZpERETEHQHbACYevNge8GXW7oxUu1YvERERSb2kJJoef/xxrL2roBnTcG4gy7Lqbb/lllv40Y9+xL333puMy3cadef3thuZxn1Yj1w+2VyKZVn1VqgTERERaVNOFGpiEdsLvqzafZGGC5qIiIhIx3HQq84BrFmzhrPOOosePXowY8YM3nrrLT7//HPeeustfvazn9GjRw/OPvts3n33Xf74xz/Su3dvpk6dyrPPPpuMy3caTp1Mk201nKNp/OCu9C3Mok9BZvwJooiIiIgbnBjsnaMJjw/8tT2aTLiq0QeTIiIi0jEkpUfTs88+y3//+18++ugjunfvntg+bNgwTjrpJKZMmcLo0aN58803ueuuu/j617/OYYcdxu9+9zu+/e1vJ6MKncLO8hC7K8NYFlSGGq4656236pyGzomIiIg7Pt+ym8HRGBbgs73gzSYSM1SHo7y9/Cucbts484hebldTREREUiApPZrmzJnDhRdeWC/JVFfPnj258MIL+dOf/gRAnz59OOuss/joo4+ScflOY92uKjburmLDrip2VYYa7Pd5apsz6uhJoYiIiLhj4cotVIejVEdi8aFze3s0GcBvQkT0QExERKTDSkqPpk2bNhEINL96SEZGBps2bUr83L9/f4LBYDIu32nE6nQz9zQySVN8ovC4aEyJJhEREXGH5dR5IGZ7wZ+dWNSkiz+Glelzr3IiIiKSUknp0dSnTx9efvllQqGGvWwAQqEQL7/8cr2V5nbs2EFhYWEyLt9p9C3MoE9hJr0LMilqZFngd9bs4vNt5Xy2tYztZVrRRURERNwxYVA+GT4PGV47nmjyZuK1bXICXkb3yuCU4Y33ghcREZH0l5RE09VXX83q1auZMGECr776Krt37wZg9+7dvPLKK5x88smsWbOGq666KnHOv//9b0aNGpWMy3caXbL8FOUE6JYboEuWv8H+YCRGKBIjHHUIRtQlXURERNwxskcGAa+N32vHJwP3BmqXzI2oR7uIiEhHlpShc3fddRefffYZTz75JGeffTYAtm3jOPFkhzGGSy+9lB/84AcAbN++nW984xt87WtfS8blO4260y41tqhchs+DbVtYFsQcJZpERETEJbF9hs55A2DZQAyiSjSJiIh0ZElJNHk8Hp544gmuuOIKnnzySVasWEFZWRl5eXmMGjWKSy+9lNNOOy1xfI8ePfj1r3+djEt3Kk69OZoaZpq+fkQv/vX5DgAKs5ufM0tEREQkZaLh2u9rejRZe3s0RRufakFEREQ6hqQkmmqcdtpp9RJKklwxx2CMwbIs7Ea6NHk9Vr1jRURERNxgYmESUYntB08AB5tQJEZZSSkffryVrx/Ry80qioiISIokZY4mj8fDpZdemoyipBn/XVfCik2lfLRpDzvLG3Y799ZbdU5D50RERMQdTyxZRWl1hIpQdG+PJj9YdnweyepqPt1S6nYVRUREJEWSkmjKy8ujX79+yShKmpEYOmcaHzrntWubM6IeTSIiIuICYwweE6nd4PGCNwNr79A5C0O07tA6ERER6VCSMnRu3LhxfPTRR8koSpqR7feSFfCCMQR8ngb7iyuCbC8LYgx0yWm4Kp2IiIhIqjkGemRZeMosPJaVGDqHbZET8JKV7ef64/u6XU0RERFJkaT0aJo+fTr/+te/+POf/5yM4qQJw3rkMLR7DkN75NI1u2EiacueINvK4smmXRWaaFNERETanse2mDS8CzkBL5l+T2IycMvy4LEtfBYU+NXzWkREpKNKSo+m+fPnM3HiRK666ip++9vfMm7cOHr06IG1z4TVlmVx3333JeOSnVLdCb7txobOeWxsy8IxhkhMAZyIiIi4JFZnaJzXX3/VORODmB6IiYiIdFRJSTRNmzYt8f2HH37Ihx9+2OhxSjQdHFMnd9TYqnODumYzvEcuwWiMrho6JyIiIm5x6s7RFNgn0eRAVIkmERGRjiopiaY333wzGcXIfsTqZJoa6dBEfpaPbnkBSqsi+OykjIoUERERabnoPpOBe+KJpphjiEaibNiyi6FdBrlXPxEREUmZpCSaJkyYkIxiZD9W7ahg/a4qLAuiTQyN8+7NQEW16pyIiIi4oDwYYdOGYoaGY3hsC7+nduhcdSRGVSTE8/9dwz2HH+12VUVERCQF1O0ljRSXh9lTFaakMtxojyaIz9MEEIk5bVgzERERkbjqiMPO0grCMSf+4MvjB8uKTwoOWMbBREIYo4diIiIiHVHSEk3RaJRf//rXjBs3jry8PLze2s5Sy5cv57vf/S5ffvllsi7XKdUdOudtZGhczDHEYg6haIzqcKwtqyYiIiICxOMRj4kCYEF82Nzef30em2y/zcQh+a7VT0RERFIrKUPnqqurmTx5Mm+//TZFRUXk5eVRWVmZ2D9o0CAee+wxunTpwk9+8pNkXLJTOqJPLrkZXowBv7dhl6ZdFSHeXrOL6nCMgmxNBi4iIiJtr1uOn279c/Gv9WJZxHs0AXgzCHhtsC1OGJgX7+UkIiIiHU5SejT97Gc/Y+nSpcyYMYNt27ZxzTXX1Nufn5/PhAkTmDdvXjIu12l5bZuA10NWwIvdSI8mn8fG2hu0GWNwNE+TiIiItDGvxybT4+Cxrfgqud6aRNPenk3GgVjYvQqKiIhISiUl0fTss88yceJE7rrrLizLSiQ76ho8eDAbNmxIxuU6rZr5vz1NPAEM+Gx6F2RQkOUj2+/VhOAiIiLiDida+31NjyZfRvxf40A02PZ1EhERkTaRlETThg0bOOaYY5o9Ji8vj9LS0mRcrtOq6aHUSGcmALL8Xo4d1JUBXbPplhsg6mhCcBEREXGBE6n93psZ/9e3918MRKrbvEoiIiLSNpKSaMrNzWXnzp3NHrNmzRq6deuWjMt1Wrsqw5QFI1QEo00e462zHF0kph5NIiIi0rYqQlFKK4NEYya+kIk3vtoc3kyCEYfyYJSnl37Oxt1V7lZUREREUiIpiabx48fzj3/8o8keS5s2beK1117j5JNPTsblOq1VOypYu7OS1TsqmjzG66lt0piGzomIiEgb27C7ivU791AZjhKJOrWrzvkyMBgcY7AiVYSiWiFXRESkI0pKoun73/8+u3fv5vTTT+ftt98mGo33uKmqqmLhwoVMnjyZSCTCHXfckYzLdVq1Q+eaXqXF56ndF41p6JyIiIi0rZhjsM3eGMSywFPToykL24pPEN7VH210Tk8RERFJf95kFHLyySfz8MMPc+utt3LSSScltufm5gLg8Xj43e9+x9ixY5NxuU6rZ34GWYEoOYGmm23J6mK+2lmJz2MRUY8mERERaWPdcgNk5HgIlNl4bBvsvXGLP4uA1ybgtZk0JBe65bhbUREREUmJpPRoArjhhhv46KOPuPnmmznmmGM45JBDGDNmDDfccAPLli3jmmuuSdaleO+99zjzzDMpLCwkOzubcePG8dRTT7W4nPLycqZOncrhhx9OVlYWBQUFHHXUUUyfPj1pdU2mHrkBeuZlMKBrVpPH7K4MUxWKUh2OqUeTiIiItLmeeRl0y/KQ4fPg83hqE02+7NqDopoMXEREpKNKSo+mGiNGjOA3v/lNMotsYNGiRZxxxhn4/X4uvvhi8vPzmTt3Lpdeeinr1q3jnnvuOaByNmzYwKmnnspXX33F6aefzje+8Q1CoRCrV6/mb3/7G1OnTk3p62iNmIn3UPI0N3Ru75J0BoiqR5OIiIi4wdm7cIll1evRlBAJtn2dREREpE0kNdGUatFolGuuuQbLsli8eDFjxowBYOrUqRx33HFMnTqVCy+8kKFDhzZbTiwW44ILLmDLli0sXLiQU045pcF12iMnMd1B04mmC47uw4KV27Esi6hWnRMRERE3OHsn+q6baPLVTTSpR5OIiEhH1aqhc5dccglz585t9UVbe/6//vUv1qxZwyWXXJJIMkF8Lqj77ruPaDTKY489tt9yXnjhBd577z3uvPPOBkkmAK+3febfEj2amkk0Zfq8iURUREPnRERExA01PZqo36MpZiAYibFhx25Wbml8tWIRERFJb61KND3zzDN88sknrb5oa89ftGgRAJMnT26wr2bbW2+9td9ynn32WQAuvPBCNm7cyO9//3tmzpzJ888/T0VFRYvr1VZWbCrlky2lLNtQ0uQx3jqrzsU0dE5ERETa2NtrdrG5pJLyYJRQzIDtie/wBogZi1DUoaSsjK92VrpbUREREUmJVnfdWb58OU888UQy67Jfq1atAmh0aFxhYSFFRUWJY5rz/vvvA7BkyRJuv/12QqFQYl+3bt147rnnmDhxYnIqnUSRmIMxtT2bGuO1a3OHUUc9mkRERKRtBaMOOFEcYzB1ezR5AlhWPE7xmYh6XouIiHRQrU40vfTSS7z88sstPs80kyTZn9LSeBfr/Pz8Rvfn5eWxadOm/ZazY8cOAG655RbuvPNObr75ZjIyMnj66ae58847Offcc/nss8/o1atXo+eHQqF6yamysjIAIpEIkUikRa/pQNSU6fdYOECG127yOuuLy9lZHsQxhorqcErq0xnV3EfdT3fo/rtL9999agN3pfr+p6pct+IVLw4+y2BbFpYFEcdAJIKFF4/HQ5bfw8ACL90O6aLf6STS+4T71Abu0v13n9rAXe0pXmlVoulA5kHan9GjRx90Ga3l7O3pc9ZZZzFz5szE9ltuuYXNmzfz85//nDlz5nDvvfc2ev6MGTOYPn16g+3z588nKyurkTMOnjFQYMcnziyMVfPaa681etz8L22+KosPn1vyzn/Z84WGzyXTggUL3K5Cp6b77y7df/epDdyVqvtfVVWVknLdiFcAKtZ8QBYhvFaMaDjMwnn/xNhe8qo3MC4YwmMihMp28fYi/T6ngt4n3Kc2cJfuv/vUBu5qD/FKqxJNV1xxRWtOO2g1PZlqejbtq6ysrMneTvuWU1xczNlnn91g3ze/+U1+/vOfJ4bXNebuu+/mjjvuqHfdfv36MXnyZPLy8vZ7/ZaKRCK8MX8B3Yq6YdkWQ7vncubkxlfWeyv4Ceu+3AnAkaNH8LWRPZNen84oEomwYMECJk2ahM/nc7s6nY7uv7t0/92nNnBXqu9/TU+jZHMjXlmwYAGTTj+NzLWZEI6BP5uvf+MssGzYtQrvtj9BGPw5mZz59a/HV6WTpND7hPvUBu7S/Xef2sBd7SleaZ/LqzWhZm6mVatWMXbs2Hr7SkpKKC4u5vjjj99vOcOHD6e4uJiCgoIG+2q2VVc3vexuIBAgEAg02O7z+VL2B2UMWLaFbdl4vXaT1xk3qCtri6uwLPB6vPoDT7JUtrHsn+6/u3T/3ac2cFeq7n+q2tSNeAXA57GwMPEkku3B599bh0B2fGJwy8IyDrYNePX7nGx6n3Cf2sBduv/uUxu4qz3EK61adc4tEyZMAOJdvvdVs63mmOaceuqpAHz66acN9tVsGzhwYGurmRJ1F5DzNPP0b2iPHLpk+ynM8ushoYiIiLQ9JwrsDVysOqGmN4DBIuYYwtEoW3Y33kNdRERE0ltaJZpOO+00Bg8ezFNPPcXy5csT28vLy7n//vvxer1MmTIlsb24uJjPP/+c4uLieuVceeWVBAIBfvvb37J58+Z65fzsZz8D4KKLLkrpa2mpsANb9gTZvKeajSVNj42sv+qc5mcSERGRtrViwy4qgxGCkRgRPLU7vAGMZVMRirKrvJoX3v3KvUqKiIhIyqRVosnr9TJ79mwcx+Gkk07iuuuu484772TUqFGsXLmSadOmMWzYsMTxs2bNYsSIEcyaNateOYMGDeIXv/gFO3bsYNSoUVx77bXcfPPNHHnkkSxfvpzrrruO0047ra1fXrOiDhRXhCguD7GtNNTkcV5PbTemaEyJJhEREWlbX20vIxiJEoo6OHVDTW8G1t4HYhYGok1PUyAiIiLpK63maAI45ZRTWLJkCVOnTuW5554jHA4zcuRI7r//fi699NIDLueWW25h4MCB/OIXv+CZZ54hGo0ycuRI7rnnHq699toUvoLWiTm133vspsfE2Va8S7oxhmAk1gY1ExEREallnGg8kQTxOZlqeAJYlge/18b2WIzune1OBUVERCSl0i7RBDBu3Dhef/31/R43bdo0pk2b1uT+b37zm3zzm99MYs1Sx+uBId1zsLA4ok/TK+st+mIHn2yOz3kwun9BG9VOREREJG7i0ELy3veCA1bdyb5tG2wvmT4P+GxOHpTrXiVFREQkZdIy0dQZWUCW34Nt2RRkNT3bu99T++QwpKFzIiIi0sYKMz14LcC2wLNPqOnduwKdcSAabPO6iYiISOolZY6mv//97/s9xnEcfvSjHyXjcp1S3ZRRc0PnuuUFyMnwkpvhxaNV50RERKSt1V11zrPPwzFvRvxf40C06TknRUREJH0lJdF07rnnctNNNxEKNR4wrFu3jpNOOomf/vSnybhcp1R3ATnbajqDNH5QFw7plsPgbjkUZvnboGYiIiIidThRMDVzNO3To8mzt0eT40BMiSYREZGOKCmJpokTJ/LII49w9NFH8/HHH9fb99RTTzF69Gjeeecdvve97yXjcp1SOAZV4RhV4Wizk3x7PbVNGtHQOREREWlju8uqiDkOMcfgWJ76O30ZVIdjlAfDzF70OY6jWEVERKSjSUqiaeHChfzsZz/jyy+/ZNy4cTz00EOUl5dz+eWX853vfIecnBzmz5/PAw88kIzLdUrlUVi9o4JV2ytYuaWsyePqDquL1l2qTkRERKQN/PPjTVSEolSEokRpmGhyMDiOQ6iqgrBiFRERkQ4nKYkmy7L4wQ9+wNKlS+nfvz+33347vXv35q9//Stnn302K1as4LTTTkvGpTqtunFYc3M0+ev0aFLwJiIiIm3NqjMkzmowR1MmFhaWZVHoDRFVjyYREZEOJymJphpHH3003/rWtzDGUFlZSdeuXZkxYwZdunRJ5mU6Jb8HinICdM3x0zM/o8njNpVUsa64kq+KK1m/q6oNaygiIiICQ4t8+L02fo+Nve+qc75Msvwe8jK8XDKmiJyAFkAWERHpaJKWaNq8eTMTJ07kgQce4IgjjuCuu+5iz549HH300fzpT39K1mU6rQwP9C7IoG9hFkO65TR5XEUoSmkwQnl1hNLqSBvWUERERATG9Mok0+ch0+/BgwNrF8PWFfGdvuzaA8MV7lRQREREUiopiaYXX3yRUaNGsXjxYm666Sb++9//MnPmTN566y26d+/ODTfcwLe+9S12796djMt1SvVWnWtm6JzPYydWpdPQOREREWlzsXD8XycGmz+E/zwMX86Lb/Nn1R4Xrmz7uomIiEjKJSXRdP755+PxePjHP/7BQw89RCAQX7r2uOOO46OPPuLiiy/mpZdeYtSoUcm4XKdUdwYDTzOtNqxHLuMGdWFk7zx6NTPETkRERCQlahJNlg3eeEzIjpXxxJO/Tq/ssIb4i4iIdERJSTRNmjSJFStW8I1vfKPBvtzcXP7617/y5z//mbKypldLk+aZuj2arGYmA/fa5GZ48XpsojFNsCkiIiJtLJFosqCgPxx+Ppz8fcACfzZRxxCKOqzdupPtZUFXqyoiIiLJl5RE07x58+jRo0ezx3znO99h+fLlybhcp7QjCF9sq+DzbeV8vrX5hF3NynPhqIbOiYiISNtauHIzFaEowepKCJbAzi8gpyfYNgRyiTmGYCTGluIStpYq0SQiItLRtOlSH4MGDWrLy3UokRiEojEsLML76akU8Hn2nuNgjMFqpgeUiIiISDJVVVURcwxeYyBYBts/AbP34VedycADJkhE80mKiIh0OElJNP34xz8+oOMsy+K+++5LxiU7HQN4bAuw8DYzGXgoGmNXRYiSqjA+j00o6pCxN/EkIiIikmoBK4plgWUMsDdm8frj//oz8Xo9ZFkWw7r6MN1zXauniIiIpEZSEk3Tpk1rdr9lWYmeNUo0tU63TBjZOw/bsjl6YJcmj6sMxVi2YQ9l1RHys3yEY0o0iYiISNuZfGgh3j0+wAeHXwAn3xnfsWU5hCvx2B48GHyeKGT53KyqiIiIpEBSEk1vvvlmo9tLS0v58MMPeeihhzj99NO56aabknG5TsmY2l5MzXRowuuxqBkp5xjN0yQiIiJtLBap/d4bgB2fwr9/CU4U+h0bX40OIBZyp34iIiKSUklJNE2YMKHJfWeffTaXXnopRx11FOeff34yLtcp1U0X2c1kmjJ9Hkb3K2DV9goCXluJJhEREWlTVixa+4PHB/n94kkmgLLNYO3taR3VROAiIiIdUVJWndufoUOHct555zFz5sy2uFyHZOrM/+1pZnJvn8dmTP9CuuUGyMv0KdEkIiIibcsJ137v8UN2EfQ/DoacDr3GYCwLxxii4RClVeGmyxEREZG01CaJJoDu3bvzxRdftNXlOpw9IdheFmJ7WZDdlc0HZX5vbbOGlGgSERGRNvTV9hJCUYdIOAi71sDGd+HE22DctTB0EhFjUR6MsqOsinkfrXe7uiIiIpJkSRk6tz+hUIg33niDgoKCtrhch7QnDNvLglhY7Chvvqt5oE6iST2aREREpC2t21FK90iMLBOEje+AE4H+4+M7fRlYe+dosoxDLKLhcyIiIh1NUhJNTzzxRKPbo9Eomzdv5plnnuHzzz/nlltuScblOqVY3aFzzc0GTm2iyTGGcCyWymqJiIiIJBgD9t75mKya//f4aw/wBLBsDz6PTZbXYlChVp0TERHpaJKSaJoyZQpWI/MGmb0TC1mWxbe//W3N0XQQugZgYNcsLMtmUNfsZo99cdlmPtq0B9uyNHRORERE2tSRvbLI2ugFJwuGfQ2GTqrd6Q3g9Xjx+j3g9zC+X/MxjYiIiKSfpCSaHnvssUa327ZNYWEhRx11FL17907GpTqtDC/k+XzYlk1Btr/ZYy0sMOBgCEXUo0lERETahmVBlwwbj2dvT6beo6H/sfDRs7D5fQhXgb13iL8Tg2jI1fqKiIhI8iUl0XTFFVckoxhphjHU9EFvdtU5gO55ATL8HjyWRXVEPZpERESkDdVddc4biP9bvRv2bIh/b+0dLmcciGqOJhERkY6mTSYDl4NXN11k7yfR9K2j+rK5pDp+njHNHisiIiKSVE6d3tQ18zP5s8H2xv+t3BXfZhyIqUeTiIhIR2Pv/xBpD0Kx+Apy4aiDY5rvpeT3aNU5ERERaXuOgXA4hOM4GMcBrHi37NGXwcV/hW/9EbIKqQzHqAiGeOm9r9yusoiIiCRZq3o0XXXVVa26mGVZzJkzp1XndnYbKy2+qCjHwmJjSTWj+hU2eWzAp0STiIiItL3KKHy+pYRDgmGyCeJZ9DPY+S049rrag7wZxByDMYbde0pcq6uIiIikRqsSTY8//nirLqZEU+vF6uSLfJ7mO6LV69EUU6JJRERE2oZjwDYxEoP8LRs8vvoH+bKwAAN4Y1VtW0ERERFJuVYlmh577DEmTpyY5KpIc7K9hvxMH5ZlkZvRfLOt3lHOxt1VOMawoyy3jWooIiIinZ3Xgi6ZFt6ojTFeKBoOeX3qH+TLJGdvLHP5UUUu1FJERERSqdVD56ZNm8Z9990HwKmnnsqUKVO4/PLLk1o5qdUtE0xeFrZl0ys/s9ljt5eF2F0ZX/FlT3WkLaonIiIiQrYP+uT5sMN+wA8n3g6F/SFYCuuWQLgSQmW1PZ7ClS7WVkRERFKhVYkmr9dLJFKbwFi0aJF6OKWYU2fxOM9+Vp3LCngS3wcjGjonIiIibSix6pxVO2wuWAYfPhH/PhauPTZU0aZVExERkdRrVaKpX79+LF26FMdxsO34fEDWfpIfcnDqJpr2d6tPGlLEsJ652JZFYZY/tRUTERERqcuJxv+1LLD3hpr+7DoH1AlkIurRJCIi0tE0P6t0Ey6++GLefPNNCgsLGTx4MAC//vWvGTx4cLNfhxxySFIq/d5773HmmWdSWFhIdnY248aN46mnnmp1eZFIhNGjR2NZFoceemhS6phsdfsleezmM00FWX6y/F4CXptY3QyViIiISKrV9GiyLLD39rIO5MHxt8LEH0D/44jGDOGow7ptxQQjsabLEhERkbTTqh5NU6dOJSsri9dff50tW7ZgWRbGxJepbc7+9h+IRYsWccYZZ+D3+7n44ovJz89n7ty5XHrppaxbt4577rmnxWXef//9rF69+qDrlkqbKiy2l1ViWRal1RF6FzQ9T5NlWQR8NsFwjHBMwZuIiIi0jR3VsL20koJQED8RPM9PgT5j4ZirYeAJew/6nHDMIRJzWLd1J7mhKBk+T7PlioiISPpoVaLJ5/Nxzz33JJI6tm1z++2386Mf/SipldtXNBrlmmuuwbIsFi9ezJgxY4B44uu4445j6tSpXHjhhQwdOvSAy/zwww+ZMWMGv/rVr7j11ltTVfWDVhWFykgUCwvnABJ2AY9NkBjhqOZoEhERkbYRikE0EsE4MSyisH0lRENw5LdrDwrkJgbPBUyISEy9r0VERDqSVg2d29fUqVPbZDLwf/3rX6xZs4ZLLrkkkWQCyM3N5b777iMajfLYY48dcHnhcJgpU6Ywfvx4br755lRUOWnqpot8+xk6F4zEqApHKQtGKKkMN3usiIiISLLEDNimTm9qC7Bs8Aag5kFZIBef1ybT72F4kZe8zFY99xQREZF2Kimf7FOnTk1GMfu1aNEiACZPntxgX822t95664DLmzZtGqtWreKjjz5q95OZD8419MjMw7JsuuUGmj12R1mIT7aUEQzH6J7f/LEiIiIiyTIgB3rl+bCCWeApgMv+Bpvfh2VPQkY+jLoYgqV4LQtsiy4+B/xKNImIiHQkafXJvmrVKoBGh8YVFhZSVFSUOGZ/3nvvPR544AF+9rOfMWzYsKTWMxUM8bmXbMvC42m+I1rAZye6pEei8bmz2nsiTURERNKfZYFlYvG4w+OHoqGw+BcQqYKStbDu3xCuANuK93CKhtyusoiIiCRZWiWaSktLAcjPz290f15eHps2bdpvOaFQiClTpjBmzBi+973vtbgeoVCIUKg2MCorKwPiq9dFIpEWl7c/kUgEx4BxDI7tEItGae4yAdtwSLdsdpSHyPDZVAXD+L1JGSXZadW0ayraV/ZP999duv/uUxu4K9X3P1XluhGvYAwmFosnkSybqAN2r9FY65aAJwMiQcDCMsSPjVQR0+91Uuh9wn1qA3fp/rtPbeCu9hSvpFWiKVnuu+8+Vq1axQcffIDH0/JVTmbMmMH06dMbbJ8/fz5ZWVnJqGIDxtjsLN4JwD/nz8e/n2pnVFlYQYtQEF55/Q0ytJhLUixYsMDtKnRquv/u0v13n9rAXam6/1VVVSkp1414xcIhWF2FbSKEY9W8+dprZIay8fhOJTtzG7mhrdhOhAGhT7GJUbVrJ2++8hp6HpY8ep9wn9rAXbr/7lMbuKs9xCtplWiq6clU07NpX2VlZU32dqrx4Ycf8qtf/Yr77ruPI444olX1uPvuu7njjjvqXbdfv35MnjyZvLy8VpXZnEgkwl8fWYidmY9tW0w642iy9zOfwcbFawltKAFg4qmH0yXbn/R6dSaRSIQFCxYwadIkfD6f29XpdHT/3aX77z61gbtSff9reholmxvxygt/fxXL68MTjZLlifGN3iWYnkdBzzoxV+VOIn88nVCwkqoYFI04luMP6Zr0+nQ2ep9wn9rAXbr/7lMbuKs9xStplWiqmZtp1apVjB07tt6+kpISiouLOf7445stY8WKFcRiMaZNm8a0adMa7P/iiy+wLIv8/Hz27NnTaBmBQIBAoOEk2z6fL2V/UDuqLcqrglhY+Lw+fL7mmy4z4MO24o8HjeXRH3qSpLKNZf90/92l++8+tYG7UnX/U9WmbsQr2ysdqsNRsmJB/NFK7EU/gUET4II5tQdl5BC1412tvSaGcRz9XieR3ifcpzZwl+6/+9QG7moP8UpaJZomTJjAjBkzmD9/PhdffHG9ffPnz08c05xhw4Zx9dVXN7pvzpw55Ofnc8EFF6SsS3lrOXW+99n7n9i77pxM4ajTzJEiIiIiyWGMg2UM9SIV3z4xlTcD2/bg9VhkeKBLRlvWUERERFItrRJNp512GoMHD+app57i1ltvZfTo0QCUl5dz//334/V6mTJlSuL44uJiiouLKSoqoqioCIDjjz++yV5Pc+bMoWfPnsyePTvVL6XFumUYCjMysbDwevafaFqwchtfbC/HY1mEorE2qKGIiIh0dn2zomT5bexYAMefhX3Et2DgSbB1BZRvhVA5HHYuAb+PQMwLPg8FPZVpEhER6UjSaupFr9fL7NmzcRyHk046ieuuu44777yTUaNGsXLlSqZNm8awYcMSx8+aNYsRI0Ywa9YsF2udHDk+6JLtp1teBra9/2aLOoZYzBB1DCH1aBIREZE20NUfw++x8PkDeLsOgq/NgEPPhC9eh7dnwb9+Aq9+b+/qc4BxIBpqvlARERFJK2nVownglFNOYcmSJUydOpXnnnuOcDjMyJEjuf/++7n00kvdrl7KGBP/12PtvzcTQH6mD7/XxmNbhGNKNImIiEjqWU4snjyyLLDrhJmBXMBAuAJK1pIYW2cciAbdqKqIiIikSNolmgDGjRvH66+/vt/jmprwuymmJpvTDjkGPMABdGYC4Pyj+ibmZtIcTSIiItIWbBOu80OdMHPwRMjtCf/9I/gya/c5MYiFERERkY4jrYbOdWZRA45z4ImwgE+TgYuIiEjbMrEoBjCOg7HrrE7T4zA47Fy4/O9wybPE8gdQFY5RGQrzzxXr3aquiIiIpIASTWniqzKLT7aU8dHG0gM63u9RoklERETa1ue7IpRXR4hVl2K+WgSzjoGPnonvtG3w+uPf+7KIxByiMYftxbtdq6+IiIgknxJNaaKmL5N9YFM04ffWNq0mAxcREZG24EkMndsbucQi8eFx+7B9dVaaC5elvmIiIiLSZtJyjqbOKMNjsANecjJ8+z8YWFtcyZY91cSMYVelJtkUERGR1MvzRPHaFsbxxOdiyukBmQXxndEwhMoBsPxZ5GZ4AYuLR3Vxrb4iIiKSfEo0pYkemZDbNZtuuRn7PxjYXFLFzvL4csF7qiKprJqIiIgIAENywmRVerGsfBgyCc57JL6jfBv84/9B9R7oMggqtmPXrKQbqXStviIiIpJ8SjSliZp5wD0HOHYu01/btNXhhl3WRURERJLN40Rrf6iZjwkgkBv/t2I7VO4E44AxYFkQVqJJRESkI1GiKU3UzNFkWQeWaDr+kCL++Wn8aWFe5oENtxMRERE5GLap04varpNo8mVB98MgUg3BPXuTSwawIFzRxrUUERGRVFKiKU2Ymh5NB5ho6pmfQXYg3rxmP8eKiIiIJEPdRFMYD48vXkN2wMu3j+6H9/SpsHUFhMrgi9eJLXuaGA5bt+2gRyRGhs/jYs1FREQkWZRoShPbqqFsVxXh6IGljequOhfWqnMiIiLSBjaXRzk0HMbrhAh/9jp97N38M+98eudncsqh3aHXkfEDS9YTihkiMYevNm0lozqiRJOIiEgHYe//EGkPqqIWpdURSg5wYm+/p7ZpQ0o0iYiISBuoDkeJxRxsJ0Kgajsjqz8gw1Qx/9NtOE6dh2WZBdR00s5wqqnSfJIiIiIdhhJNacAYkxj+Zh9gizmOQzASozIUZU9VOGV1ExEREalh150MHDCWTcTys6MsxIcbSmp3+HPx2TYZPg9DCy265vgRERGRjkFD59KAMdAny1DULZch3XIP6Jx1u6tYtaMCxzHYB7hSnYiIiMjBGJ4XJrMsgIWPz7tMZq45mTJPIQCfv/0Pxq5ej1VdAn2Owuux8WII+KOQoYVLREREOgolmtJAzBg8Nvg8NpmBA2uygNeT6JIeimjonIiIiKSe34ph2xaW5aU4ox8bnOGJfZW7t1Fd/m+yytbC5g8gFgGPF8JVLtZYREREkk1D59JA3TkNbAuIhuG5KfDk+VC2tdFz8jN99C7IpCg3QHZAk2uKiIhI6tUdOldt6scfQTuLkmoH2PskrKbDdaSqdnldERERSXtKNKWBunNneiwLPn8F1i+BrR/BG3c3ek6XbD8je+fRpyCTXHVHFxERkTZgmTqJpli8F7bHtrAsi08zjuK5vvfAUZfDsDPAE5+XyUTDhMMhV+orIiIiyadEUxoIxWJURaG0OsKe6ghsXR7v1eREYf1S2Lmq0fMC3viTxFA0htGTQhEREUmx6nCUSCxKNFxN14pV9AytIzvgJT/TR9T2szHWFU67D066g7Anm9LqCDtKK3jt/dVuV11ERESSRImmNFAdjrEzaLF+VxWrt1fA+v9AqAyq98S7my+a0eh5fm+8eY2BqKNEk4iIiKTWzqoY0XAIO1LJuD2v8q3Sx8j0eyjMiveuLgtGiMYc8GaAN77NNg6RUIWb1RYREZEkUqIpDUSideZosi0IldfZWQ2fvggb/tvgPL+ntnnDUU0ILiIiIqnlMbHE1EvGQNgKkOXzULA30WQMlAWjYFlYvhw8toXfY+geiDZdqIiIiKQVrTqXBrxei0K/IS8/gwFds6A4Oz6vQSwMZm8C6eXvwi3v1zvv7TXFbC6pxuexCUUdsgMuVF5EREQ6jaJAFDvsw3EcVvsP49OMsWT6PRRkxedjGhr8mNDKjdAlH19WLr6AFyyYMDDT5ZqLiIhIsijRlAZ8tk2eH7rnBuKJpmB3yMiLz9FUvSd+0J4NDc4LRRzCUQcDVIaidMn2t2m9RUREpHMp8EXx48fg55W8y/gqYyRH+70U7k00nVr2MnnvbAe/F4Lle1ebcyBY5m7FRUREJGmUaEoDsToTeduWBeG98xjYXijoB+EqsOz4BOHe2mRSdoYXj8fCY1tUhWNtXW0RERHpZGwTjzdqhs0BZPrsxNC5ajsLwsH40H/LBgxgwf9v787j5Krq/P+/7lJ79Z6ELGQhIQFk3wKyyiDBb0CGGVFBQcIiig46IAMyIyaMoH756TjjMC6Dw/IdBpGvol8cdAIIGIKgyBLCEkhYk0CWTnqt9S7n90d1V3enO0knVHV1d97PxyOPVO499/Spz7l1+5NT556b21qjFouIiEilaY2mMaD/A+NKA02Zvg2NM8CyAANtbw847oJjZ3LQ1Ab226uO7oI3Mo0VERGRPVZ5oAnw7NJAUzLqlgeankqfynsTjoGmfcCJ9SU5WQ00iYiIjBcaaBoDgn5PjHNsq/QtIIDtQN3UvoJbXx9wXCrWN2EtU9CMJhEREakuq9+MJo/SLOvSU+dKr1+LH8ryWV+Cz/w/OPRc8kFp1vVTL7++3TpFRERkbNFA0xjwbnuOd7otXlzfybPvtMGWNaVv/nIdUDe5r+A26zSlY075daagp7mIiIhI9fhByMZsiJftxMlv4eqNV3NkZhmJfk+dA9hccMCNQbwRPwzxgpCOrZsI+32xJiIiImOXBprGAC8sLegd9k4vD/3S0+ZMWJp2XsxAoQvWPDzguGS034wmrdEkIiIiVRSEBscEWJTylViYJ2KKJKMOiYhDLFJKO9uyPbfzJ1tKd/8DiTBL3leuIiIiMh5oMfAxwMIiakM84tBiZ/t22A6EQd+tdJtXDThuQ0eejZ15/NCwqTM/gi0WERGRPY0BYlaAMRYGi7ydJG8nSEQdLMuiMRllY0ee7kwWujdDJE4i4mAi8MFpEdyIs9OfISIiIqOfBprGgInpGFOShkmT0hw+2YK3W0o7WubBtCP7Cm7zaOA3WrvZ0FEaYGrtLoxUc0VERGQPFI84NMcCIlY9ucBm8eRb8a0oC3pmWDcmItRt/DNntP8Xwd0GZ8Jc7DAAx8XxM5SnN4mIiMiYpoGmMSDo99i5RLHfU1ni9bDPCRBJgOVAasKA45pT0fLrjpyeOiciIiLVZZkQrNLspoDSDKVktPR3UzLKWjuJazxoWweFDjAB4EKxu3aNFhERkYrSQNMYEPYbaEp67X07YnWQaIT0pNI6TUFxwHHHzm5h30npUlFXy3GJiIhIdVkmBCDEwlilAaZEz0BTYzLCK3YD6yOzmON24USTQM8sJi87VHUiIiIyBmn0YQwIw77XUb+t7x+xutLf8cbS314Win2J2pSGBFObEqRiLgVfT3IRERGR6rJNaUHvkL71lhKR3oGmKB1uC/+35fO8cfh18BfXE0RSeIEhl+1mU0euJm0WERGRytJA0xjwbkeOLXlY35Yj+t6fS0+YK3T3DSolW/oKb31jwLHpWGnSWqbgj1RzRUREZA+0NVMk7wX4+S6ixTbO3nobqaCjPNDUlIwAEFgubzafAHNOpWBFyRZ9urJ5Xl27oZbNFxERkQrRQNMY0J716PYttmSK1He+Dn4B/DzketZrSrZA6Je2r3t6wLGpnoEmLwgp+uG2VYuIiIhURHfBx5gAO/SImgJHZJfT6BSw7dLtcY3JvrUj27JFsG1w40BpJlQxp3WaRERExoMxOdD09NNPs3DhQpqamkilUsyfP5+777572McvX76cr3zlKxx55JG0tLQQj8fZf//9ufbaa2lvb69ew3eT3+/euXjQLwnrXfx765uQay/NdFr9YHm3MQbHssgVA3LFQLOaREREpGqCIMQmKP/bWBZONF7+d++MJih9iQbgxtPEXId0BOY1j8m0VERERLYx5hYDf+yxxzj99NOJRqOce+65NDQ0cN999/HpT3+at956i7//+7/faR3nnHMOra2tnHDCCXzmM5/Bsiwee+wxbr75Zn7xi1/whz/8gUmTJo3Auxme6c0JpiQMLS1pQjMdgg2AgalHlAo0TIf1fy697lxfPi408ODLG9jaXSQZc+gu+DT1exKdiIiISKVMrY9gORY4abYGSe5t+hwkmsv7GxKlgabDM8s59NU3oQuijguuBZZhekozr0VERMaDMTXQ5Ps+l156KZZlsWzZMg4//HAAFi9ezAc/+EEWL17Mxz/+cebOnbvDeq688ko+85nPMGXKlPI2Ywxf/OIX+eEPf8gNN9zAv/3bv1X1veyKiO0QdUpPbXGDCER6vh3c6wOlv6cdDqt+DZYNTqx8nGNb5XUR/MCQLQbbVi0iIiJSETE7xLfAdqN0U89LyfnsG+ub0eQ6NnVxl+bOzczteBS6cmAMYAALsltq1nYRERGpnDE1R/mRRx7h9ddf51Of+lR5kAmgrq6O66+/Ht/3uf3223daz7XXXjtgkAnAsiyuv/56AH7/+99XtuHvU2j6nhjn+pm+HcmJpb/3PxMSjRCvBzc24Nj9J9fTnIrSlIrSXfBGoLUiIiKyRwp9LAyGvqfOJaLOgCKNyShZO41nbAxW6Uuy3jwnu3WEGywiIiLVMKZmND322GMALFiwYNC+3m3vZ5AoEilN6Xbd0RWWoN9Ak+Nn+3akewaaGmf0JGoh5NoGHLvwkCm0dhcAyBQ0o0lERESqJCh9oWUMBFZpgCm5zUBTUzLKM8kTKFgxLtm3m/j6J2HzqwCEmS1j6xtQERERGdLoGlHZidWrVwMMeWtcU1MTEyZMKJfZHbfddhsw9EBWf4VCgUKhUP53Z2cnAJ7n4XmVnzW0tStPPoDuvIeT24IJPLAcfDsBPT/PjaYh3wH5dvx+bYg7EJrSmgcd2UJV2rcn6I2b4lcbin9tKf61pz6orWrHv1r1jnS+0tbeRjo0BF6eCBmaiuuJOi0DflZD3CZvxfhz8gQWHLof+0Rj5NavIjQef37uVU46Quf47tJ1ovbUB7Wl+Nee+qC2RlO+YhnTb7rMKLdgwQIeeughVq9ezb777jto/5w5c1i3bt2ApGq4nn/+eY4//njS6TQvvfQSEyZM2G7ZJUuWcMMNNwzafvfdd5NMJnf5Z+/Mb9ZaPLGx9B3f885ncAgxWPz68DvKZU58dQnJYisAvzvgZny31I53M/Dfa0vHHtpsOGbSmOluERGRPUI2m+VTn/oUHR0d1NfXV6zekc5XWls38Im1i0mRx8fmeftg/t+UrzC/X+7x3BaLpzdbAJw2LeSU/MPMXPcrDPBE5HiCg86reLtERETk/duVfGVMzWiqljfffJMzzzyTIAi45557djjIBHDddddx1VVXlf/d2dnJ9OnTWbBgQUUTxF4vLV3FExvfJhaNYgelZM2yXRYuXFgu47xxPVYuAxgW7AMcXNq3ri3Hnx54BYA5+05g4bEzKt6+PYHneTz00EOcdtpp5VssZeQo/rWl+Nee+qC2qh3/3plGlTbS+cpzTy/HWtv7LwtidRxz1OH8r4Mml8u0vLmVt594C4B5B+/NgRTJb/g1ADObIuzfL7eRXaPrRO2pD2pL8a899UFtjaZ8ZUwNNDU0NADQ0dEx5P7Ozs5ymeF6++23OeWUU9i8eTO/+MUvOOWUU3Z6TCwWIxaLDdoeiUSq0qGT6hPURwx1cQeTc7ExEE0O/Fm2W1qjCYhsXAFHlL4RfGPLVtZsyuCHhqmNSX3g36dq9bEMj+JfW4p/7akPaqta8a9Wn450vjI5bWNZNgERuqw0a+IHMyUZG/CzJjUkcYB9Cy+RfmcVblM76VgpHT24xQKd3++brhO1pz6oLcW/9tQHtTUa8pUxteZi79pMQ63D1NbWRmtr65DrN23PW2+9xYc+9CHeffdd7r33Xs4888yKtbWSJtXFaIrBvEaPMFpfesLcpAMGFqrbq/S3ZUPne+XNtmWRKwZ4fkhHVvfKioiISHVMSYLjOITROt6IH8TvGv6aZGTgYuAtqSgGi9M77uWgl78Hz98FXq60s9hdg1aLiIhIpY2pgaaTTz4ZgAcffHDQvt5tvWV2pneQaf369fzsZz/jL//yLyvX0ArrXUWrIWzv2xhNDyx06Cch2QLJZkg2lTc3JiNYtoXjWOQ8PXVOREREqiQsAmCMIbBKs5SS0YGT5xuTUSzbImOniXrtkGsvz8jWQJOIiMj4MKYGmk499VRmz57N3XffzfPPP1/e3tXVxTe+8Q1c12XRokXl7a2traxatYrW1tYB9fQfZLrnnnv4q7/6qxF6B7sn6BlpSgf97oncdqBpr4PBKi2uSee75c2H7t3ICfu2cNDUBhqT0Wo3VURERPZUfulhLAbwe1ZnSEQHppqObdGYjPKH9Omsi86BpllAT/7iZUeurSIiIlI1Y2qNJtd1+clPfsLpp5/OiSeeyHnnnUd9fT333Xcfb775JjfeeCPz5s0rl7/lllu44YYbWLx4MUuWLClv/9CHPsTbb7/NscceywsvvMALL7ww6Gf1L19rYdgz0BR29KZiEK8bWGjifn2vM5vKL23boj4eoTPnkyn41W2oiIiI7LEsPw+UZmL3zmhKRAenmi3pKK9mDuOH0VncfO7ROD86Aa+QodDRzutvtXLkrB0/lEVERERGtzE10ARwyimnsHz5chYvXsy9995LsVjkwAMP5Bvf+Aaf/vSnh1XH22+/DcBTTz3FU089NWSZ0TTQ9Ny6dtZnLGbml2ObNrAt6Fg3sFAsDZEUeBnItg3YlexZZNMLQop+SNQdUxPZREREZAx46Z3N7OfnieFxuP84KxLHkowcOqhcSyrKGqDbaaTNj9PoxCn6XXhBkXc3bNBAk4iIyBg35gaaAObPn89vf/vbnZZbsmTJkANGpnfRozEiXwzxDUwwW7AIIaRv4ab+TACFrtJ6B10boK70OOHep7kAZAo+UVe30ImIiEhl+cU8FgYLiJkiEVMgEXUGlWtO9T0Jb0umQFO8Hjo34xCQb980qLyIiIiMLZraMga4toVjQZJC38ZE8+CCXq60PkLow5rflTdv7MyzsTPPhs483bp9TkRERKogSunptgYIscjbKWJDzKJuSZW+8HLDIl2t63HjdaRiLo0xi7+aFxtUXkRERMaWMTmjaU9z4LR6XnzDsNWZSxhsxbEMzD1tcMFkM2S3lF6v/RMcXrqVcPXGbjZ05LFti0xRA00iIiJSeR+YFMdzE2SNwyPpM3ij8YNYljWoXEs6ypz8S5zZ/p/s/aiH4xZLs7JtF7vzHeD4kW+8iIiIVIwGmsaAnrXASYSZ0gvLLt8WN8Csk6DzvdL+ZN+Mp7p4qZvD0NCeLVa7uSIiIrIn8ks5hsEQWBFSsaHTzOZUlJydImKKJLrXQdSB0APHhba3RrDBIiIiUg26dW4M6H3qXDzs99jfZMvggvucCJE4uFHo3ljefOLcicyakGLfSWnyxbDazRUREZE9Udgz0GTAI0JyiCfOAbSkYnQ4zWyMTCfj1EO0rm/tyW0fdiIiIiJjjgaaxoCgJ/mKm1zfxtTEwQUnzOt73fVe+eUHptbRkCh9s5jzgmo1U0RERPZkQbG0QBPgWy6p2OCFwAESUYcw0cR/tXyJ3+x1GRz3N4RODC8wbN34Dl15bwQbLSIiIpWmgaYx4O3WDO0FmFBYC143FLNDDzS17Au9ayFkNpc318Uj5dcdOSVvIiIiUnkbtnZiBXliYZb9c89S72w/52hJRQkth+XOMZg5f0GOKNmiT+eW91jXltvucSIiIjL6aaBpDFjbnqfDs6inC9svgJ8vrWOwLTcKTrz05Ln2teXNE9N9T3DZ2FkYfJyIiIjI+7SloxMXnyge+xZeIR3Z/u36zalSbhKEhg67EcspPYkuEWZo7+oakfaKiIhIdWigaQzoXaPJ7p2Pbg09FR2A3BYodEG+Aza/BkA65mAM5IoB72zNVLu5IiIisgey/Ty9986F2MTi6e2WbU5Hy6+3eBGcRJqYa9MQNcxJ6cElIiIiY5meOjcG7Dc5zYb33iMfxElGLKxE0/YLR9OlW+sAXv8dTJzHKxu6eG1TF/liQFfBIwwNtj34ccMiIiIiu2tmU5zCpjgG+FPqZBLx+HbLtqSiTCm+zfzuR6j/nzwxQnAtsALiVufINVpEREQqTjOaxoBU1GWy20FgRyGSgKaZ2y/cPAecKLhxKJRmL7WkY0SdUlfniiFbs/qmUERERCorGQFjR/DtGC8n55Pst0bktlpSURx8Dso/Tf26x6D7PQh9wEDbmyPWZhEREak8DTSNAaExNNPR8y8LYvXbL7z/GRCvh1gavNJA06S6GDNbkjSno9QnXDZpnSYRERGptNDrvcmfohUlFd3+rf4T6mJ0OM0EuARYYEeg5ym7bNVAk4iIyFimgaYxIDTQEJamkVtQGkjanpZ9+153rAMg4thceNwspjclaUpG2dSVr15jRUREZI9kBX1PmfOsKKnY9ldomNqQoMtu5K4JX+K55jNh1olglVajLG59ewRaKyIiItWigaYxoDvvMzHYgG18MCFEdzDQNGFe3+vO9eWXk+r61knQjCYRERGptEIuixX62MbHDQqkotsfaEpEHSbUxVkX3Zf/Sn8Gc9TFZAKLzpzHS6++iumd3SQiIiJjjgaaxoAV6zo42FtJymQgtxU2v7z9wk2zSuszGQNb34Cw9GjhSfWxchHNaBIREZFK29K6gQQ5UibDmZ13k4zt4Cm5wN5NCQAKXsiWyGQMpfJ1fhudeb/q7RUREZHq0EDTGBAYQyOZvg2pidsvbNvg50sDUl0b4O0nAGhORnFsC2MMGzo10CQiIiKV5YR9Dxsp2AmSO1ijCWB6c7L8+p1iCsst5SrNdgbfD6rWThEREamu7c9pllFjQipK4Lj4OERsoGmfHR8Qq4dMa+n1Cz+DfU6ktbvA2rYsWzNFNnQWMMZgWVbV2y4iIiJ7hnjUxc+XBpfWxeZwRGRnA00J6oI2ZhdWYa8MSMWiYBXBDSBSAOI7PF5ERERGJ81oGgNmtCTpshvJ2WmsRBPMPnnHB+x1MFgW2G5pVhOltRDyXkgQGLJFn/ast+M6RERERHZBQ109WStJzk7zxIRP7vQLrb2bkjT5rZze8TNmrfoPyLZC6EPgQdfGEWq1iIiIVJoGmsaAMDSkyfZtSO+14wNOuBISzZBohKC08HddPEJDwiXi2ri2zaYuLQguIiIiFRT60LOGdzSW2GnxSXUxsrEJ2CbE8bpLG00AGNj6evXaKSIiIlWlgaYxIDCGuMlT/l4wPWnHB0w5pLQgOED72vLmL3xoDh+YUs/MlqQWBBcREZHKCn0MYLCIxXc+0GRZFg0TpvBQ/cdY784grJ/Wt3PDyuq1U0RERKpKA01jQGggQc/AkGWXZivtiG1DQ0+yVuyGtrcBmNrYt+jmxk7NaBIREZHKMUHpSXHGsknFhrcM6N4tdTyTOokfTFrMxiO+TNGKki0GrPjz47RnizuvQEREREYdDTSNAS+u72Ba+B6JsAvynaWBpJ1p2bfv9TtPAqUp6r00o0lEREQqKdf2LnWmm1TQwdzuPw/rmL2bEmBZZJx63orMwyOCF4RMyL/Nm5vaq9tgERERqQoNNI0B3YWACD4OYc/aBcOQaIZ8B2S3wpP/BkBLOoZtl27A26QZTSIiIlJBVuhhY3AISNvDyzP2buqbbb3Ga8FKlmZtJ8MMwWat0yQiIjIWDW9es9RUyu/s+4c9zC6btH/pqS1QXqcpCA3ZYsCmzjwbu/IU/ICYu+NHD4uIiIgMh01Yfu0kG4Z1zN5NpbWc4mGWcO2bxNLNxHKbsG2Lo+xXgQ9Wo6kiIiJSRZrRNAbMn+rSadWRseth8iHDO+jgT0DvY4X9HACObbGlu0BbpkhbxuPVDV1VarGIiIjsaaxkCx1Wmk3OFLKTjxnWMcmoS0s6yie2/JCTX///sLauwQlypQegvP1kVdsrIiIi1aGBpjEg5W0pvbBsSE0Y3kFuFCbsB8lmiNXBltdxbIvZE1MAhMbw3DttVWqxiIiI7HHCALAI7AipRGynxXsdMKWedbHZ1AdbCYr50oxsY2DTS6W/RUREZEzRQNMYUOdt7ftHfHhT0QGYeVxpcArg2TsBuPj4fZg7uY4DJtfxyntdGCVwIiIiUgEmLD11LsQhGR3+rflHz2pmTewg3orOozU+C5wIYCDTCl0bqtNYERERqRoNNI0BybADoDSNfFcGmo74TN/rNb8D4KhZzRwxoxHLstjcVWCjFgUXERGR9ysMwZTWaAqwSUaHvwzo/pPr2FJ/AP+3+XP8qOkrFGedQjGEXKHAC3/4TbVaLCIiIlWigaYxYFLHSuLkccIcBMN86hzA1MOgflrpddcGWPMIAAdP6xusemFde+UaKiIiInukYiGH5WWJkScVdJCKDH9Gk+vYHD6jkU2RaayzprAufQi5YkDRD8mtXqbZ1yIiImOMBprGgGnFN4gZj5gpQG7Lrh084zgodEOuDX5zNQAH791Y3v3i+o4KtlRERET2RLlslogpEjcejWEbqfiuPdj4qJnN5ddPO4fhOKUUdXr3Sto69fASERGRsUQDTaNcGIakyfZtSE/etQoO/Bj4hdJ09va3wSsyua60QOfarVmWvrSRvLcLs6REREREthF6GaA08yjEJhXbtYGmA6bUkYy5YAxvvreZaCRCMmIzxe2k+Y37q9BiERERqRYNNI1yxkCHO5GcFaNoRWHa4btWwYyjIF7X9+9nbsO2LVq7C2zNFNmaKbJ8dWtlGy0iIiJ7lITlU7AS5Kworc5EYu6upZiuY3P49EY+1PVrztp8KyYMiXhdWCaEp38CgV+llouIiEiljcmBpqeffpqFCxfS1NREKpVi/vz53H333btURxiG3HLLLRxyyCEkEgkmTpzIJz7xCVavXl2lVu+eELDdCEWi+E4SJh+665UcfgEkGiHRBM/cjlXo4sS5EwGIuTb/95m1dOW9irZbRERE9hxxPAI7RpEYHbFpWJa1y3WcMHcCb8QOIB12UCh6GCwIPOhYBy//qvKNFhERkaoYcwNNjz32GCeccAKPP/4455xzDpdffjmtra18+tOf5pvf/Oaw6/n85z/PFVdcQRAEXHHFFSxcuJD777+fo48+mpdffrmK72DXBKEhEZZunbMA6qfseiV/cX3fouDZLXD/l/jEUdM5ep9m5u6Vxg8M9/55XcXaLCIiInsYv9C3aLcT3a0q5u1Vx94fOJZnUiexMTKVjYnZGLu0qHhm2b/S2qG1mkRERMaCMTXQ5Ps+l156KZZlsWzZMm699Va+853vsGLFCg488EAWL148rBlJjz76KLfeeisnnngizz77LDfffDN33nknDzzwAJ2dnVx++eUj8G6GJzSGmMmVXlsOxNK7XkkkDmfdAnbPeglvL2fGqp/w7b8+pLyGwh/WtLJynRYGFxERkV0Xell6nw1nObu2PlN/586fweNTL+aRur/iO41fpzvSQjEI8be8xXv/8SnatTC4iIjIqDemBpoeeeQRXn/9dT71qU9x+OF9axXV1dVx/fXX4/s+t99++07rufXWWwG48cYbicVi5e2nnnoqp59+OsuWLeO1116r/BvYDUFoSATdQIhv7d43hEBpbaf5l5Vee3l4eDEN/34k5+3Xdwp88zev8O/LXicI9RhhERERGb5s51Ys4wMhWLufXqZiLp/54Cz+lD6Ftsgk7nXPorsQYhMyo2sF+bvOha6NlWu4iIiIVNyYGmh67LHHAFiwYMGgfb3bfv/73w+rnlQqxfHHHz9o3+mnnz7sekZCe1eOpnALDSZDsvg+F+0+/m8hNQmK3aVVxtvf4bjf/TVXZ77D/K6HCTe/xr8/toZz//1J7nt2Has3dtGZ9/qmwouIiIgMYfObz5M2GRpMhkmdL76vug6b3sjpB5WesvuHuv/FE6nTiIZZ0qaLSa1PE/zgWMJ7LoBn72TLG88R+Hp6roiIyGiy+3Oba6D3tri5c+cO2tfU1MSECRN2eutcJpPhvffe46CDDsJxnEH7e+seLYuC+90b6F1OM2Bwe3eJbcNnH4E7z4T1z4DlYFk2+2efYXrhj5zjh6TJ4GwIMffbbHCnscWOsdmOkPVCGsM2mk0njm3RHZtIzm0CyyLnhfiex4zgLRzbwrOTtCdnltocGrqKAVO8tSQpYNsWm5P7lm/j68r7pPw2JoSbcWybTGQC2dgEAPJeQMEPmVVcg2NDaMfYmpoNlG4p7Mr7TPLWkyaHbVtsTcwicOIAZAo+Mb+Tif4GXMcmF2mkO1ZKWot+SM4LmFF8g4gdYuwIW1L7AmCMoTPv0+JvpMF0Yts2HfG9KTgp5mWzrFn1zzh+lin+OlzbpuDW0ZkorX/l+SFZL2Bv7y1i+GDbtKb3K4e/I+fR5G+m0bTj2DadsckUIo0A5IoBoZdnb/9tHNvGc5J0JGeUzoHQkCn4TPHeIUGxFMPUvmD1xtAj7W+lJdyCY9t0RyaQ6x/Dos8s/3Uc2yZwYrQl9yn3TXfBZy9vPameGG5J7kNol2b5ded94n4HE4ONPTFsoju2VymGXkjOD5hZfB3XNgNi2Ns3E/0N1JlubNuiPT4dz02V+8b1M0z21+M6NgWnns7E1AExnO69SdQKwHJoTc/DGMO8bJZXXvoeLcFmGnpjGJ9CwW0YFEPXtik6KTqS00sxDEIyxYCp3jvEyzGcV/7WvTPn0RBspak3htFJ5KLN5Rh6xSIz/De3G8PJ3jqS5HtiOJvQjpbP76TfzsRgE45jk400k4lNAqDgheT9oN/5HWVras7AGHrvUUcG27ZoS8woPRCgJ4YRv5u9/HdxHZu8W09XfOqA87s3hsZ22JKaVzq/e95ri7+RetPZE8NpFNzSkymzxQDLyzLVX1uKoZuiIzEdYwxzMqX47+2/TQyvFMP0/gPO70Z/C01ma08M9yIXbSr3je/1xdB3EgOuEd0FnyneWhJj+BpRdEu3NWcLAbafqfg1IlsMmJXtIv/8F/B0jRh0jegfw2a/8teIjpzHvLDI2thaZn/kb5DBTK69/DqwI++7vk8cNZ19JqS4b9mzTAk3sNWZxF7Be4Shj5fPUXxtGdbqx3EDD48iISGhHaXoJOmOTsJYDt3FkCZ/ExFTJOqA5yTpjkwkdCIUfUOksIVEmCFl5fHcOgpuPflIPcZAIdtNfdhGymSx3BhFJ1G+RmQLPg3FDcRMjrhtKLppMtEWAjuG54fYhTaSQRd15PAiqZ56G0vHZrM0Bq0kTQ7bieA7cTp7rt+5YkC6uJF4mCVhBRQjdWQjTfhOAj80mFw76aCDNDmCSJKikyYbbSnVm8vT6G/ipCBL8UWXjJOkMzYZLJu8F5AobCYRZkhaHsVIHXm3gaKbJjCGINtJOuygznQTukmKTopMrPTQmEzeo8nbQMzke2KYojs6idB2KXohkUIryZ4YFt06CpF6Cm49xpieGLaTNhmMGyvFv+cakS34NBbfI2oKxO2wJ4YTCOxoKYb5raTCbtJWDs9NU3Dr+sUwQ2OwlaTJYjsRPCdBV3xKOYZ1xY3EwhwJ26fo9othEEK+nVTQRZosQSRFwUmXf99nczka/VYSJovruHhOgs7YFLAs8l5AsrCZeJglaRUpRurJuQ14boogNAS5DtJBJ3VkOIEIHasWky3HsEiTt5GYyRF1StfvrtheGMuh4IXECq3l87Do1pGPNFB06wiNoZjtoj7s6IlhnKKTLOcRmYJPU/E9oiZP3Iaim6I7OqH0OfBD3PwWkmGmXwzryUcaMEA+201D0EbKZLDcKJ6dpCvec34XA+oLpfM70ds3kWYCJz4ohn4kRdGtIxdp6umbHI1BKYaO4+I7iW3O703EwmzPeVhPLtKI5yQJQkOYK9VbR2bw+Z0v0OhtIh7miLjbxjDoF8MCx5k4ra99Cy9SiqGX7aQu7CRtujFuYvvntw2em6I7OpHQjlD0QyL5LSTCbtJWnqKbpuA2UIjUl2KY6aYh7I3htteIgIbie8RMvt/5vSvXiC3l83voa0SOhOVv5xrR2XN+D3WN2EzC5HAdp+f83vYa0Xt+D3WN6Oy5Ruwghg4U7ST7B2nefeNf8Xyja8QQ14jQTVJwU2SjE6pwjUgwz0+zcarN3kd/dNd+8VaYZcbQdJUFCxbw0EMPsXr1avbdd99B++fMmcO6desoFArbrePdd99l2rRpHH/88SxfvnzQ/scff5yTTjqJyy67jB//+MdD1lEoFAb8jM7OTqZPn05rayv19fW78c62772VjzP5/o9jYeh2mkh/dVVlKl5xL85zt2G1rwVT+iYw74VEvHbsnlUWuqx66HlqjB8akuSIUXo6XZ44Xs9/NkJjMCakkW6gNCCWtUv/6TKU/rNSTzcOIQDdVh2mJ3kPQkOUAklK8SwSpWAnyvWGBproLP0bm4xdV34LfmhIkyFCqf1ZK0XQ8x+rIDREKJIiD4BHhLydHFBvI51YgMGi264fUG+KLFFKj1LOWQl8K0oYhhgsHHzqKC3Q7uOSs0v/OTIGAmNooKsvhnbDgHq3F8PAGCwT0jBUDHvq7R/DLquu/B+gIDTEKJDYbgxDmnrq7R/D3r6pI4PbE8OMlSLsF8P+fbPrMcwQLfdNksCKlOt18UiTGyKGhsBQjqEBuntiGIYhIRZJsqX/pAN5K45nxcr1Wvg09PTN0DHswtnO+Z0gT5wiAAViFO34LsSwG3cY57dHlPw25/eOYrij89ulSLrn/PaJkNumbxroxO5pY/c252H/8ztvJfB6bssNQoONT335/HbI9cQw6Dn/d3R+J8gR34OvEb31VuUaERosE9BgZQbHUNeIbWJYnWuEjaF1zl8z69zvUGmdnZ1MmDCBjo6OiuYRI5mvvPWL65m56lYsDBuT89jryscrUm9rV57Hn/g9U167i/2yz+EaDwcfsDCAHXrEKeASEGIT4JTPw6DnGmATYlE6v3NWEmPZhMYQNQWieDiEBDh4VrR8PST0SFDAIQAsApzyeRj0XAMcAiwMIQ55K0FoORgDrskTxcMlJMTBsyIUe85DE/okyZfrDXHI9tZrDElTqtfBEOBQsOIElosBnLBAjGJPvTa+FaFg9fyuCgNS5HBMAJZFiE3WSpVeG0Pc5HDxy/UWrRi+FemJYZE4xX4xdMu/q4IwJE12uzGMmTwR/GHE0O6JYbJfDDM4hEPE0BAxBSLDjOG2fZNkOzE04JoC0Z3FsNw3w4+hExaJUeirF7d8nd3VGBataN9yGf1iaIZxfvfGsPf87h/DohUpXw93FsPe89vuea95K064nRiW+ibeU29ActsY9lxnh4phwYoRDCOGYRiS2iaGA/sm31NviN9zHvbG0NI14n2f37t6jcgQx7JdXSNqdI3IEmfLMV9l5od7ls2poF3JV8bUjKbR4lvf+hY33HDDoO0PPvggyWSyoj8r3raKOkp1vuXO5p3f/KZCNadh0peINHcyre1PNGXWkLDbaPK7MSYADFgWlglLKbehvMhnaYqVIQxL/6Ep7Tfb3VceyuyZmhWasLzNmHINYIHZ4bF9+xi0r1RvaMIK1WvK9Zqe/4iVtppde6/b1Ns/hmabn7vzek2/92qg33s1A94rw3qv26s3HFDv0O0dznvtv29gDAfu37V6+42LW9u0F7B2+l4ZcL5gehvYV+1QbWIX2mtMSO8yZ2abesMdHNu/32Do9m4/huGun4fbrdfs4Njt79s2hoPjv6dcI/odXOlrRG8lukbsvN6e/RW9RliwadMmXq7Y7+E+2Wy24nXCCOcrWzwm9OQr6+wZPFPBOMWATbPP4/muj1Hs3kqb5zIn9wKTvXeYzRoms5kUpS8jQqy+fh1wPpQ+Bb3Xl6E+q6bfNcIacA4O/tz0P7a33tBYQ/7O6F9v/2ulGTIXGPhze69bBnC2bdM2n8f+eo9lyDYNvB6yzediwGdupzEceGz/z5y9bRx2+Lt1YAwHXQP61Wv1a5QZMiftH9+BMRz0e7dfvQPerDWcGJpyvfY2vx8H9Ot26t1eDM2AGG5zjm43hmZQmwafh0PEsHx+7ziGZkC92xy7sxju4HPTv15nBzHcNv/btm+2zU92HEN0jRjW+b371wh6YqFrRG2uEQBvvvkGL9U4XxlTM5o+/vGP8/Of/5w///nPHHnkkYP2T5w4Ecuy2LRp03bryGQypNNpDjroIFauXDlo/wMPPMCZZ57J3/3d33HzzTcPWcdIfkMY+gEdHa388Xf/zXHHHUf93gdUtP5hNKC0plMYQDGLKXQTGINnJzBuDAjx/AC/6GPnNhOxATeKnWjCGPBDj3wxxM604lpForZDWDeF3m/aMwUfU8ziFNpIRV3CeD1ES9+m5/0APzDYXeuJR2zsSAKSpSmGgTHkigF2bgtOWCDhOoSpieVHKmeLPqGXw85tJR11CWN1ECv1TSEI8HyDndlAzDG4TgSTLk11NRgyhQA734btZ0lGXMJUC55xeeqPT3HYEfOxwiJ2bgupqIOJJCFRmk7phSEFL8TObCRiB8TcCGF6SjmU3QUfq9CF7XWSiriEiSaIlJLynBcQ+B52ZiOJqIMVSUCiNNXVD0PyXoid3YyLR9wZKobdOIWOUgwTjRBJ9cXQC7Az7xGP2thuXwx9E5b6pn8M6/ai93abTNHHFLLYhbZSDPv1TTmGXe8Si1gDYhhiSrcP5duwgyxJtxRDem5ZKvVNvi+G0TTEG7eJ4QaitiHquoTpKXi+z5NPPcXBhx9NNMjieJ2lvkm2gBvvi2GxgJ3b3BPDvr7pH8OI5REb8jzsH8O+vhlWDLNbcCn09E2/GO7g/O6L4XpiERvXjWFSkwbGcMD5vYMYxurL53cxCCn6fTGMuEOd3x3YfvegGGY9n7BYxM5tJhl1SjFINOP5Pk88+RSHHnE0sUJr6fy2HcL6aeXzO1PwYYfnd78YRpJ953c5hjs6v0f/NaLcN55PWMxX/BrRnSvy1FNPcswHZpJOuLpGbHON6N83VqGr4teI9kyBF557mpM//L9IN+1FpY2HGU3Gy9G9dQPLfv8Yx3/owzROml7R+ncmDEr5iMFgwhDj5ygUCwTZDvDzxBzATYAbBdvB9wKKuS7wC8RMHjdRh7Gj4MYwxpDN5bH8HI6fJR6Pl7ZHe26RLXqYXCdWUCDpGKxYuvRZt128MKSYy4CXJ0qOaDyNcRIQKX1z3Z3LY3nZ0vUjFivV23PtyXkBYb4T/AIJx8eJ1WPcBDgRfBNSyObAzxMJM8TiaYwbL18jMrkifr6Tl555kmOOPgI3XoeJ1oFlkfcDgnw3+HkSVhEnXo9xSu8pMIZcLofl53H9buKJdCm/i/TdykqhE8vPk4o6EElgIimwHQpBgJ/LgJ8nbhVKP9NJlN4Thky2N4bdJOKpbWLoY/I99UYsiKYGxjDbDX6hL4ZusvxZ7o2h42dJxOPgxDCxukExTDohdizdF8MwpJDL9sQwO2QM8bqxvSzJeBTc+MAY9pwvCaeIE2soHetEB/SNVexi5cuvcOT843ET9QNiaPs5kjEX3EQpDpbdE8PunhgWcfv1TYghu00MjRuHaGqIGNoQTZZjWAxCvFwphjHyROI9cXDjpb7J5bG8HI6fIRFPbDeGKScc+vz280TN4PM7k8uDlx0Yw57zuy+G+WGe3/3Ow7wHxa5tYljqm/4xdP08z730KkceezyRWGro89uJ9cWw4GMKO49h3Mr3nN/xvhiW+yZDIp4ceH57/qi8Ruzw/K7QNcKzIjz53Isce+xxhJala8Q21wg3yGw3hpW4Rni4PPncSk5e8FFS9S278dt0x8btjKb+6ydtO9DU1tZGa2srxx133A7rSKVSTJkyhTfffJMgCAat07SjdaB6xWKxAU+r6xWJRIhE3v+6BNtUSpO7F2F6CvV7H1D5+nfeAIglBm0dvAVg/yG37kjzsEodPKL1DvWR9DwPVr7B1NkfGEYfDF3v8Nq064ZX76FVqndk+sbzPKyVbzBj3wOr8hnYE2LYa0e/crZXr+d5ODuJv2L4/uvd0bF1nkds1VtMPezDu/wZUN+8f3Wex8rX3ibdtFdVrkHV+t0+0vlKXSSBlX6FxknTRz5fiZRmPvXpvY129wa83v/nvPLH7qxez/N44a2tNO5/csXi/37aOxZj+H55nscz64o0zT6i3Aejsb3VulbW+r16noe3tkDzrEOH9RnQ+V35ej3Pg9daaZ6+/077QDGsfL2e58GrG0nVt9Q8XxlTT507+eSTgdKU7231busts7N6MpkMTzzxxKB9S5cuHXY9IiIiIiIiIiLSZ0wNNJ166qnMnj2bu+++m+eff768vauri2984xu4rsuiRYvK21tbW1m1ahWtra0D6rnsstLCWF/72tcoFovl7b/73e9YunQpJ510EvPmzUNERERERERERIZvTA00ua7LT37yE8Iw5MQTT+Syyy7j6quv5tBDD+Wll15iyZIlAwaIbrnlFg444ABuueWWAfWccsopXHrppTz++OMcfvjhXHPNNVx44YWcccYZ1NfX88Mf/nCk35qIiIiIiIiIyJg3pgaaoDRItHz5ck444QTuvfdefvCDH9DS0sJdd93FP/zDPwy7nh//+Md8//vfx7Isvv/97/PAAw/w0Y9+lD/96U984AMfqOI7EBEREREREREZn8bUYuC95s+fz29/+9udlluyZAlLliwZcp9t21xxxRVcccUVFW6diIiIiIiIiMieaczNaBIRERERERERkdFJA00iIiIiIiIiIlIRGmgSEREREREREZGK0ECTiIiIiIiIiIhUhAaaRERERERERESkIjTQJCIiIiIiIiIiFaGBJhERERERERERqQgNNImIiIiIiIiISEW4tW7AeGCMAaCzs7Mq9XueRzabpbOzk0gkUpWfITumPqgtxb+2FP/aUx/UVrXj35s/9OYT1aJ8ZXxT/GtPfVBbin/tqQ9qazTlKxpoqoCuri4Apk+fXuOWiIiIyFjV1dVFQ0NDVesH5SsiIiKy+4aTr1im2l+f7QHCMOTdd9+lrq4Oy7IqXn9nZyfTp09n7dq11NfXV7x+2Tn1QW0p/rWl+Nee+qC2qh1/YwxdXV1MnToV267eqgbKV8Y3xb/21Ae1pfjXnvqgtkZTvqIZTRVg2zZ777131X9OfX29PrA1pj6oLcW/thT/2lMf1FY141/NmUy9lK/sGRT/2lMf1JbiX3vqg9oaDfmKFgMXEREREREREZGK0ECTiIiIiIiIiIhUhAaaxoBYLMbixYuJxWK1bsoeS31QW4p/bSn+tac+qC3Ff3gUp9pS/GtPfVBbin/tqQ9qazTFX4uBi4iIiIiIiIhIRWhGk4iIiIiIiIiIVIQGmkREREREREREpCI00CQiIiIiIiIiIhWhgaZR7Omnn2bhwoU0NTWRSqWYP38+d999d62bNa6sX7+ef/7nf2bBggXMmDGDaDTK5MmT+djHPsYf//jHIY/p7OzkqquuYubMmcRiMWbOnMlVV11FZ2fnCLd+fLr55puxLAvLsnjqqaeGLKM+qLxf/vKXnHbaabS0tJBIJNhnn30477zzWLt27YByin3lGWO47777OOWUU5gyZQrJZJL99tuPz33uc7zxxhuDyqsPds9dd93F5z73OY466ihisRiWZXHHHXdst/zuxPnuu+9m/vz5pFIpmpqaWLhwIX/+85+r8G5GF+Ur1aVcZfRRrlI7yldqR/lK9Y2rXMXIqPToo4+aaDRq0um0ufTSS81XvvIVs88++xjA3HTTTbVu3rhx7bXXGsDMmTPHXHzxxearX/2q+djHPmYcxzG2bZuf/exnA8p3d3ebww47zADmtNNOM9dee635yEc+YgBz2GGHme7u7hq9k/Hh5ZdfNrFYzKRSKQOYJ598clAZ9UFlhWFoLrvssvLn4Atf+IK59tprzQUXXGBmzJhhHn/88XJZxb46rrrqKgOYKVOmmM9//vPmmmuuMaeffrqxLMvU1dWZlStXlsuqD3bfzJkzDWAmTJhQfn377bcPWXZ34nzTTTcZwMyYMcNcddVV5rLLLjP19fUmGo2aRx99tLpvroaUr1SfcpXRRblKbShfqT3lK9U3nnIVDTSNQp7nmTlz5phYLGaeffbZ8vbOzk5z4IEHGtd1zWuvvVbDFo4fv/jFL8yyZcsGbV+2bJmJRCKmubnZ5PP58vavf/3rBjDXXHPNgPK927/+9a9Xvc3jle/75uijjzbz5883559//naTN/VBZf3Lv/yLAcwXv/hF4/v+oP2e55VfK/aV99577xnbts2sWbNMR0fHgH3f+973DGAuuuii8jb1we576KGHzFtvvWWMMeZb3/rWDpO3XY3za6+9ZlzXNfPmzTPt7e3l7S+++KJJJpNmzpw5Az5L44XylZGhXGX0UK5SO8pXakv5ysgYT7mKBppGoaVLlw76sPa65557DGCuu+66GrRsz7JgwQIDmKefftoYU/omZerUqSadTg8aIc7lcqapqclMmzbNhGFYi+aOeTfddJOJRqPmxRdfNBdeeOGQyZv6oLKy2axpbm42s2fP3ukvFsW+Op588kkDmE9/+tOD9r322msGMGeccYYxRn1QSTtK3nYnztddd50BzJ133jmovs9//vMGMEuXLq34+6g15Su1p1xlZClXqQ3lK7WnfGXkjfVcRWs0jUKPPfYYAAsWLBi0r3fb73//+5Fs0h4pEokA4LouAKtXr+bdd9/l+OOPJ5VKDSgbj8c56aSTWL9+PWvWrBnxto51L774IjfccANf+9rXOPDAA7dbTn1QWQ899BBbt27l7LPPJggC7rvvPr797W/zox/9aFAMFfvqmDt3LtFolCeeeIKurq4B+37zm98A8Bd/8ReA+mCk7E6cd/R7+/TTTwfG5+9t5Su1p1xl5ChXqR3lK7WnfGV0GQu5iluxmqRiVq9eDZQ+0NtqampiwoQJ5TJSHe+88w4PP/wwkydP5uCDDwZ23C/9t69evXq7ZWQw3/dZtGgRBxxwAF/96ld3WFZ9UFm9C/+5rsuhhx7Kq6++Wt5n2zZXXnkl3/nOdwDFvlpaWlq46aab+Lu/+zsOOOAAzjrrLOrq6li5ciUPP/wwl112GVdccQWgPhgpuxPn1atXk06nmTx58g7LjzfKV2pLucrIUa5SW8pXak/5yugyFnIVDTSNQh0dHQA0NDQMub++vp5169aNZJP2KJ7nccEFF1AoFLj55ptxHAcYXr/0LyfD881vfpMVK1bwxz/+sfzN7PaoDypr06ZNAHz3u9/liCOO4E9/+hMHHHAAzz33HJdddhnf/e53mTNnDpdffrliX0VXX301U6dO5XOf+xw//OEPy9uPO+44zj///PLnQn0wMnYnzh0dHUyaNGnY5ccL5Su1o1xlZClXqS3lK6OD8pXRYyzkKrp1TqSfMAy5+OKLWbZsGZ/97Ge54IILat2kcW3FihXceOONXH311RxxxBG1bs4eJwxDAKLRKL/61a84+uijSafTnHjiifz85z/Htm2++93v1riV49+NN97IokWLuO6661i7di3d3d0sX74c3/c55ZRTuO+++2rdRBEZRZSrjCzlKrWnfGV0UL4iu0IDTaNQ78jk9kYUOzs7tzt6KbvPGMNnP/tZ7rrrLs4//3x+9KMfDdg/nH7pX0527sILL2TOnDksWbJkWOXVB5XVG6ejjjqKqVOnDth34IEHMnv2bF5//XXa29sV+yp55JFHuP766/mbv/kb/v7v/569996bVCrF8ccfz3//93+TSCS48sorAZ3/I2V34tzQ0LBH9ovylZGnXGXkKVepPeUrtad8ZXQZC7mKBppGoR3dI9nW1kZra6vuZ62wMAy55JJLuO222zjvvPO44447sO2BH4+d3bu6s3tlZbAVK1awatUq4vE4lmWV/9x5550AfPCDH8SyLH71q18B6oNK22+//QBobGwccn/v9lwup9hXyQMPPADAKaecMmjfxIkTOfjgg3nnnXcGXPfVB9W1O3GeO3cu3d3dbNiwYVjlxwvlKyNLuUptKFepPeUrtad8ZXQZC7mK1mgahU4++WS+9a1v8eCDD3LuuecO2Pfggw+Wy0hlhGHIpZdeyu23384nP/lJ/vM//7O81kF/c+fOZerUqTzxxBNkMpkBK/zn83mWLVvG1KlT2XfffUey+WPaJZdcMuT2ZcuWsXr1as466ywmTpzIrFmzAPVBpfUmC6+88sqgfZ7nsWbNGlKpFBMnTmTy5MmKfRUUi0UANm/ePOT+3u2xWEzn/wjZnTiffPLJPPnkkzz44IN85jOfGVDf0qVLy2XGG+UrI0e5Su0oV6k95Su1p3xldBkTuYqRUcfzPDN79mwTi8XMc889V97e2dlpDjzwQOO6rnn11Vdr18BxJAgCs2jRIgOYj3/848bzvB2W//rXv24Ac8011wy5/etf/3o1m7vHuPDCCw1gnnzyyUH71AeVtWDBAgOYW2+9dcD2f/zHfzSAOf/888vbFPvK++lPf2oAc+CBB5r29vYB++644w4DmCOPPLK8TX1QGd/61rcMYG6//fYh9+9qnF999VXjuq6ZN2/egH588cUXTTKZNHPmzNnp75exSPnKyFCuMjopVxlZyldqS/nKyBvruYpljDGVG7aSSnn00Uc5/fTTicVinHfeedTX13Pffffx5ptvcuONN/IP//APtW7iuLBkyRJuuOEG0uk0X/7yl3HdwZP8zj77bA477DAAMpkMJ5xwAs8//zynnXYaRx55JCtWrOC3v/0thx12GMuXLx8woiy7Z9GiRdx55508+eSTHHvssQP2qQ8q6/XXX+e4445j06ZNnHHGGey///4899xzPPLII8ycOZOnnnqq/BhUxb7ygiDgwx/+MI899hgTJ07krLPOoqmpiRUrVvDQQw8Ri8V4+OGHOeGEEwD1wfvxk5/8hOXLlwOwcuVKnn32WY4//vjyt31nn302Z599NrB7cb7pppv42te+xowZMzjnnHPIZDL89Kc/JZfLsXTp0iFvNxgPlK9Un3KV0Um5yshSvlJbyldGxrjKVSo2ZCUV98c//tF85CMfMQ0NDSaRSJijjjrK3HXXXbVu1rjS+23Ujv5sO4rc3t5urrzySjN9+nQTiUTM9OnTzZVXXjlodF92346+JTRGfVBp77zzjlm0aJGZPHlyOZ5f/OIXzcaNGweVVewrL5/Pm//9v/+3OeKII0wymTSu65pp06aZT33qU2blypWDyqsPds/OrveLFy8eUH534nzXXXeZo446yiQSCdPQ0GA+8pGPmD/96U9Vfme1p3ylupSrjE7KVUae8pXaUr5SfeMpV9GMJhERERERERERqQg9dU5ERERERERERCpCA00iIiIiIiIiIlIRGmgSEREREREREZGK0ECTiIiIiIiIiIhUhAaaRERERERERESkIjTQJCIiIiIiIiIiFaGBJhERERERERERqQgNNImIiIiIiIiISEVooElERERERERERCpCA00iIiIiIiIiIlIRGmgSEREREREREZGK0ECTiMgO3HHHHViWVf5z7rnn1rpJu2XNmjUD3sesWbNq3SQRERGpEOUrIjKaaKBJRMa1Qw45ZEDCMtSfNWvW7LSev/zLv2Tx4sWcc845A7a/9dZb5XqmTZtGEARDHr9y5cpyuf3333+33st5552HZVncc889Oyy3ZcsWYrEYEyZMoFgsAtDc3MzixYtZvHgxDQ0Nu/XzRUREpDqUryhfERlP3Fo3QESkWvL5PK+88gqTJk3i8ssvH7KM4zjMmTNnp3WdffbZLFq0aLv7Xdfl3XffZenSpSxcuHDQ/v/4j//AdV183x92+7d1ySWXcM8993D77bfv8JvKu+66i2KxyAUXXEA0GgVKiduSJUuA0reeIiIiMjooX1G+IjLeaKBJRMatFStW4Ps+J510UjlpqZbjjjuOFStWcNtttw1K3IrFIv/1X//FwoULuf/++3f7Z5x66qnMmjWLhx9+mLVr1zJ9+vQhy91+++1AKdETERGR0U35ivIVkfFGt86JyLj17LPPAnDUUUdV/WclEgk++clP8utf/5rW1tYB++6//35aW1u56KKLtnv8smXL+OhHP8qECROIxWLMnTuXr33ta2Sz2XIZy7K46KKLCMOQO++8c8h6nnnmGVasWMH8+fM56KCDKvPmREREpGqUryhfERlvNNAkIuPWM888A4xM4gZw8cUXl78N7O+2225j0qRJnHnmmUMe96Mf/YgPfehD/OEPf+DMM8/kS1/6EtOmTeOmm27itNNOK69bAHDRRRdh2zZ33HEHxphBdenbQRERkbFF+YqIjDe6dU5Exq3ebwgfeOABHn/88UH7p0+fXtEE55hjjuHAAw/ktttu48tf/jIA69ev58EHH+Rv//Zvcd3Bl9yXX36ZK664gsMOO4yHH36Y5ubm8r5vf/vbXHfddfzrv/4rX/nKV8ptPu2001i6dCnLli3j5JNPLpcvFArcfffdJJPJMfu0GRERkT2N8hURGW800CQi41KxWOTFF18E4Hvf+96QZS666KKKf5N20UUXcfXVV/PMM89w5JFHcscddxAEARdffPGQ5X/84x/j+z7f//73ByRtANdccw3/9E//xE9/+tNy4galbyKXLl3KbbfdNiBx++Uvf0lbWxsXXngh9fX1FX1fIiIiUnnKV5SviIxHGmgSkXHphRdewPM8PvnJT+708bqVdMEFF3Dddddx2223lRO3Y445hg984ANDln/qqacA+J//+R8efvjhQfsjkQirVq0asO3ss8+mpaWFn//859xyyy3U1dUBpSnvwHaTRBERERldlK+IyHikgSYRGZd6p6EffPDBI/pzJ02axMKFC/npT3/KWWedxZo1a7j66qu3W37r1q0A3HTTTcP+GdFolPPPP59/+Zd/4d577+WSSy5h7dq1/O53v2Pu3LmcdNJJ7/t9iIiISPUpXxGR8UiLgYvIuNS7sOYhhxwy4j/74osvpq2tjUsuuYREIsF555233bK9U8Y7Ozsxxmz3z7Z6p9D3fit4xx13EIahvh0UEREZQ5SviMh4pIEmERmXer8hrEXitnDhQiZPnsz69ev52Mc+tsP1B4455higb0r6cB188MEcffTR/OEPf2DVqlXccccdOI7DhRde+L7aLiIiIiNH+YqIjEcaaBKRccfzPFauXElDQwMzZ84c8Z/vui73338/v/zlL3c6xfwLX/gCrutyxRVXsHbt2kH729vbee6554Y8tvdbwksvvZQ33niDhQsXMmXKlPf/BkRERKTqlK+IyHilNZpEZNx56aWXKBQKNDU1sWTJkiHLJBIJrr322qq14eijj+boo4/eabmDDjqIH/zgB1x++eXst99+LFy4kDlz5tDZ2ckbb7zB73//exYtWsSPfvSjQceed955XHXVVTzxxBMAFX8ijYiIiFSP8hURGa800CQi407vegcbNmzghhtuGLLMBz/4waombrvis5/9LIcddhj/9E//xLJly7j//vtpaGhgxowZXHnlldudXl5fX88555zD//k//4e99tqLM844Y4RbLiIiIrtL+YqIjFeWGWrVNhERAUqLVl500UXcfvvtLFq0qNbNqYhZs2YB8NZbb9W0HSIiIlIZyldEZDTRGk0iIsNw0UUXYVkW5557bq2bslvWrFmDZVlYlsXbb79d6+aIiIhIFShfEZHRQLfOiYjswGGHHcbixYvL/z7ooINq2Jrd19zcPOB9NDY21q4xIiIiUlHKV0RkNNGtcyIiIiIiIiIiUhG6dU5ERERERERERCpCA00iIiIiIiIiIlIRGmgSEREREREREZGK0ECTiIiIiIiIiIhUhAaaRERERERERESkIjTQJCIiIiIiIiIiFaGBJhERERERERERqQgNNImIiIiIiIiISEVooElERERERERERCpCA00iIiIiIiIiIlIR/z8iXCvjhdTm9QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pisn3 = Wright_2017(progenitor_mass=250*u.solMass,eos='Helm')\n", + "fig = plot_spectra(pisn3, time=1*u.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 250$M_\\odot$ Progenitor, SFHo EOS" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pisn4 = Wright_2017(progenitor_mass=250*u.solMass,eos='SFHo')\n", + "fig = plot_spectra(pisn4, time=1*u.s)" + ] + } + ], + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 6085250ea79b45f4ae70ca952fd92e799dbfd9b5 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Wed, 5 Nov 2025 13:28:33 -0500 Subject: [PATCH 02/52] Rename doc/source/nb/Wright_2017.ipynb to doc/source/nb/pisn/Wright_2017.ipynb --- doc/source/nb/{ => pisn}/Wright_2017.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/source/nb/{ => pisn}/Wright_2017.ipynb (100%) diff --git a/doc/source/nb/Wright_2017.ipynb b/doc/source/nb/pisn/Wright_2017.ipynb similarity index 100% rename from doc/source/nb/Wright_2017.ipynb rename to doc/source/nb/pisn/Wright_2017.ipynb From e98787da476477416f950e0c8858eb847c7177ef Mon Sep 17 00:00:00 2001 From: jpkneller Date: Wed, 5 Nov 2025 13:30:16 -0500 Subject: [PATCH 03/52] Add files via upload pisn classes and loaders --- python/snewpy/models/base.py | 20 ++--- python/snewpy/models/model_files.yml | 11 +++ python/snewpy/models/pisn.py | 36 ++++++++ python/snewpy/models/pisn_loaders.py | 124 +++++++++++++++++++++++++++ 4 files changed, 181 insertions(+), 10 deletions(-) create mode 100644 python/snewpy/models/pisn.py create mode 100644 python/snewpy/models/pisn_loaders.py diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index c8b153008..50d62cdd7 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -23,7 +23,7 @@ def _wrapper(self, *arg, **kwargs): init(self, *arg, **kwargs) check(self) return _wrapper - + class SupernovaModel(ABC, LocalFileLoader): """Base class defining an interface to a supernova model.""" @@ -132,10 +132,10 @@ def get_initial_spectra(self, t, E): A container with the information about the initial neutrino spectra """ spectra_dict = self._get_initial_spectra_dict(t, E, flavors=ThreeFlavor) - initial_spectra = flux.Container['1/(MeV*s)'].from_dict(spectra_dict, - time=t, - energy=E, - flavor_scheme=ThreeFlavor) + initial_spectra = flux.Spectrum.from_dict(spectra_dict, + time=t, + energy=E, + flavor_scheme=ThreeFlavor) return initial_spectra def get_transformed_spectra(self, t, E, flavor_xform): @@ -155,8 +155,8 @@ def get_transformed_spectra(self, t, E, flavor_xform): flux.Container A container with the information of the transformed neutrino spectra """ - initialspectra = self.get_initial_spectra(t, E) - transformed_spectra = flavor_xform.apply_to(initialspectra) + initial_spectra = self.get_initial_spectra(t, E) + transformed_spectra = flavor_xform.apply_to(initial_spectra) return transformed_spectra def get_flux (self, t, E, distance, flavor_xform=NoTransformation()): @@ -249,7 +249,7 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): #Reshape the Energy array to shape [1,len(E)] E = np.expand_dims(E, axis=0) - initialspectra = {} + initial_spectra = {} # Estimate L(t), and alpha(t). Express all energies in erg. E = E.to_value('erg') @@ -283,6 +283,6 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): #remove unnecessary dimensions, if E or t was scalar: result = np.squeeze(result) - initialspectra[flavor] = result + initial_spectra[flavor] = result - return initialspectra + return initial_spectra diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml index 3e9b9840d..0d690b3b1 100644 --- a/python/snewpy/models/model_files.yml +++ b/python/snewpy/models/model_files.yml @@ -6,6 +6,7 @@ config: - &snewpy "https://github.com/SNEWS2/snewpy/raw/v{snewpy_version}/models/{model}/{filename}" - &ccsn_repository "https://github.com/SNEWS2/snewpy-models-ccsn/raw/v0.3/models/{model}/{filename}" - &presn_repository "https://github.com/SNEWS2/snewpy-models-presn/raw/v0.2/models/{model}/{filename}" + - &pisn_typeIa_repository "https://github.com/SNEWS2/snewpy-models-pisn_typeIa/raw/v0.1/models/{model}/{filename}" models: ccsn: @@ -73,3 +74,13 @@ models: Yoshida_2016: repository: *presn_repository + + pisn: + Wright_2017: + repository: *pisn_typeIa_repository + + typeIa: + TypeIa: + repository: *pisn_typeIa_repository + + diff --git a/python/snewpy/models/pisn.py b/python/snewpy/models/pisn.py new file mode 100644 index 000000000..f0c3681f6 --- /dev/null +++ b/python/snewpy/models/pisn.py @@ -0,0 +1,36 @@ +# -*- coding: utf-8 -*- +""" +The submodule ``snewpy.models.pisn`` contains models of pair-instability supernova neutrino fluxes, +""" +import logging +import os + +from astropy import units as u + +from snewpy.models import pisn_loaders as loaders +from .base import SupernovaModel + +from snewpy.models.registry_model import RegistryModel, Parameter +from snewpy.models.registry_model import all_models + +@RegistryModel( + progenitor_mass = [150, 250] * u.Msun, + eos = ['SFHo', 'Helm'] +) +class Wright_2017(loaders.Wright_2017): + """PISN model described in the paper `Neutrino signal from pair-instability supernovae' by Wright et al., + Phys. Rev. D96 (2017) 103008 `_ + """ + def _metadata_from_filename(self, filename:str): + metadata = { + 'Progenitor mass': float(filename.split('_')[1].strip('Msun')) * u.Msun, + 'EOS': filename.split('_')[2].strip('EOS=') + } + return metadata + + def __init__(self, progenitor_mass:u.Quantity, eos:str): + filename=f"PISN_{progenitor_mass.to_value('Msun'):.0f}Msun_EOS={eos}_NeutrinoFlux.tar.bz2" + super().__init__(filename, self.metadata) + + + diff --git a/python/snewpy/models/pisn_loaders.py b/python/snewpy/models/pisn_loaders.py new file mode 100644 index 000000000..cee133f77 --- /dev/null +++ b/python/snewpy/models/pisn_loaders.py @@ -0,0 +1,124 @@ +# -*- coding: utf-8 -*- +""" +The submodule ``snewpy.models.pisn_loaders`` contains classes to load pair-instability supernova +models from files stored on disk. +""" +import logging +import os +import numpy as np +import pandas as pd +import tarfile + +from astropy import units as u +from astropy.table import Table + +from scipy import interpolate + +from snewpy.models.base import SupernovaModel +from snewpy.flavor import ThreeFlavor +from snewpy.flux import Spectrum +from snewpy import _model_downloader + + +class Wright_2017(SupernovaModel): + """PISN model described in the paper `Neutrino signal from pair-instability supernovae' by Wright et al., + Phys. Rev. D96 (2017) 103008 ` + """ + + def __init__(self, filename, metadata={}): + """ + Parameters + ---------- + tarfilename: str + Absolute or relative path to tar archive + """ + # Open the requested filename using the model downloader. + datafile = self.request_file(filename) + + tf = tarfile.open(datafile) + + # Find the "NoOsc" files. + datafiles = sorted([f.name for f in tf if '.dat' in f.name]) + nooscfiles = [df for df in datafiles if 'NoOsc' in df] + nooscfiles.sort(key=len) + + # Loop through the NoOsc files and pull out the number fluxes. + self.time = [] + self.energy = None + self.initial_spectra = {} + self.interpolation = {} + + self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', + ThreeFlavor.NU_E_BAR: 'aNuE', + ThreeFlavor.NU_MU: 'NuMu', + ThreeFlavor.NU_MU_BAR: 'aNuMu', + ThreeFlavor.NU_TAU: 'NuTau', + ThreeFlavor.NU_TAU_BAR: 'aNuTau'} + + for nooscfile in nooscfiles: + with tf.extractfile(nooscfile) as f: + logging.debug('Reading {}'.format(nooscfile)) + meta = f.readline() + metatext = meta.decode('utf-8') + t = float(metatext.split('TBinMid=')[-1].split('sec')[0]) + dt = float(metatext.split('tBinWidth=')[-1].split('s')[0]) + dE = float(metatext.split('eBinWidth=')[-1].split('MeV')[0]) + + data = Table.read(f, format='ascii.commented_header', header_start=-1) + data.meta['t'] = t + data.meta['dt'] = dt + data.meta['dE'] = dE + + self.time.append(t) + + if self.energy is None: + self.energy = (data['E(GeV)'].data*1000).tolist() + + for flavor in ThreeFlavor: + key = self._flavorkeys[flavor] + # convert from flux back to initial spectra: number per /s/erg + spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/dE + if flavor in self.initial_spectra: + self.initial_spectra[flavor].append(spectrum) + else: + self.initial_spectra[flavor] = [spectrum] + + for flavor in ThreeFlavor: + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') + + self.time *= u.s + self.energy *= u.MeV + + self.filename = os.path.basename(filename) + + def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): + """Get neutrino spectra/luminosity curves after oscillation. + + Parameters + ---------- + t : astropy.Quantity + Time to evaluate initial spectra. + E : astropy.Quantity or ndarray of astropy.Quantity + Energies to evaluate the initial spectra. + flavors: iterable of snewpy.neutrino.Flavor + Return spectra for these flavors only (default: all) + + Returns + ------- + spectra : dict + Dictionary of model spectra, keyed by neutrino flavor. + """ + #convert input arguments to 1D arrays + t = u.Quantity(t, ndmin=1) + E = u.Quantity(E, ndmin=1) + + initial_spectra = {} + for flavor in ThreeFlavor: + initial_spectra[flavor] = self.interpolation[flavor]((t, E)) / (u.MeV * u.s) + + return initial_spectra + + + + + From 9cdad25964dd47b77280b122cf09ae94e5e3d955 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 6 Nov 2025 09:43:38 -0500 Subject: [PATCH 04/52] TypeIa SN models --- python/snewpy/models/typeIa.py | 43 ++++++++ python/snewpy/models/typeIa_loaders.py | 135 +++++++++++++++++++++++++ 2 files changed, 178 insertions(+) create mode 100644 python/snewpy/models/typeIa.py create mode 100644 python/snewpy/models/typeIa_loaders.py diff --git a/python/snewpy/models/typeIa.py b/python/snewpy/models/typeIa.py new file mode 100644 index 000000000..3db3e3262 --- /dev/null +++ b/python/snewpy/models/typeIa.py @@ -0,0 +1,43 @@ +# -*- coding: utf-8 -*- +""" +The submodule ``snewpy.models.typeIa`` contains models of neutrino fluxes from Type Ia supernovae +""" +import logging +import os + +from astropy import units as u + +from snewpy.models import typeIa_loaders as loaders +from .base import SupernovaModel + +from snewpy.models.registry_model import RegistryModel, Parameter +from snewpy.models.registry_model import all_models + +@RegistryModel( + mechanism = ['GCD', 'DDT'], +) +class TypeIa(loaders.TypeIa): + """The DDT model is described in 'Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario', by Warren P. Wright et al., + [Phys. Rev. D94 (2016) 025026](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.94.025026), [arXiv:1605.01408](https://arxiv.org/abs/1605.01408). + There are 30 snapshots in time and for each snapshot there are 8 lines of sight through the explosion. Flavor transformation due to MSW effects and decoherence + are included plus, the no-oscillations case. Self-interaction effects do not occur in Type Ia models. Earth matter effects are not included. Both mass orderings + are considered. The format of each data file is the SNOwGLoBES format. + + The GCD model is described in 'Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario' by Warren P. Wright et al., + [Phys. Rev. D95 (2017) 043006](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.95.043006), [arXiv:1609.07403](https://arxiv.org/abs/1609.07403). + There are 64 snapshots in time and for each snapshot there are 10 lines of sight through the explosion. Flavor transformation due to MSW effects and decoherence are + included, plus the no-oscillations case. Self-interaction effects do not occur in Type Ia models. Earth matter effects are not included. Both mass orderings are + considered. The format of each data file is the SNOwGLoBES format. Flavor transformation due to MSW effects and decoherence are included. Self-interaction effects + do not occur in Type Ia models. Earth matter effects are not included. + """ + def _metadata_from_filename(self, filename:str): + metadata = { + 'mechanism': filename.split('_')[0] + } + return metadata + + def __init__(self, mechanism:str): + filename=f"{mechanism}_NeutrinoFlux.tar.bz2" + super().__init__(filename) + + diff --git a/python/snewpy/models/typeIa_loaders.py b/python/snewpy/models/typeIa_loaders.py new file mode 100644 index 000000000..b3e2c475d --- /dev/null +++ b/python/snewpy/models/typeIa_loaders.py @@ -0,0 +1,135 @@ +# -*- coding: utf-8 -*- +""" +The submodule ``snewpy.models.typeIa_loaders`` contains classes to load type Ia supernova +models from files stored on disk. +""" + +# -*- coding: utf-8 -*- +""" +The submodule ``snewpy.models.pisn_loaders`` contains classes to load pair-instability supernova +models from files stored on disk. +""" +import logging +import os +import numpy as np +import pandas as pd +import tarfile + +from astropy import units as u +from astropy.table import Table + +from scipy import interpolate + +from snewpy.models.base import SupernovaModel +from snewpy.flavor import ThreeFlavor +from snewpy.flux import Spectrum +from snewpy import _model_downloader + +class TypeIa(SupernovaModel): + """ + The DDT model is described in "Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario", + Warren P. Wright, Gautam Nagaraj, James P. Kneller, Kate Scholberg, Ivo R. Seitenzahl, Phys. Rev. D94 (2016) 025026, arXiv:1605.01408. + There are 30 snapshots in time and for each snapshot there are 8 lines of sight through the explosion. The format of each data file is the SNOwGLoBES format. + + The GCD model is described in "Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario" + Warren P. Wright, James P. Kneller, Sebastian T. Ohlmann, Friedrich K. Röpke, Kate Scholberg, Ivo R. Seitenzahl, Phys. Rev. D95 (2017) 043006, arXiv:1609.07403. + There are 64 snapshots in time and for each snapshot there are 10 lines of sight through the explosion. The format of each data file is the SNOwGLoBES format. + """ + + def __init__(self, filename, metadata={}): + """ + Parameters + ---------- + tarfilename: str + Absolute or relative path to tar archive + """ + # Open the requested filename using the model downloader. + datafile = self.request_file(filename) + + tf = tarfile.open(datafile) + + # Find the "NoOsc" files. + datafiles = sorted([f.name for f in tf if '.dat' in f.name]) + nooscfiles = [df for df in datafiles if 'NoOsc' in df] + nooscfiles.sort(key=len) + + # Loop through the NoOsc files and pull out the number fluxes. + self.time = [] + self.energy = None + self.initial_spectra = {} + self.interpolation = {} + + self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', + ThreeFlavor.NU_E_BAR: 'aNuE', + ThreeFlavor.NU_MU: 'NuMu', + ThreeFlavor.NU_MU_BAR: 'aNuMu', + ThreeFlavor.NU_TAU: 'NuTau', + ThreeFlavor.NU_TAU_BAR: 'aNuTau'} + + for nooscfile in nooscfiles: + with tf.extractfile(nooscfile) as f: + logging.debug('Reading {}'.format(nooscfile)) + meta = f.readline() + metatext = meta.decode('utf-8') + t = float(metatext.split('TBinMid=')[-1].split('sec')[0]) + dt = float(metatext.split('tBinWidth=')[-1].split('s')[0]) + dE = float(metatext.split('eBinWidth=')[-1].split('MeV')[0]) + + data = Table.read(f, format='ascii.commented_header', header_start=-1) + data.meta['t'] = t + data.meta['dt'] = dt + data.meta['dE'] = dE + + self.time.append(t) + + if self.energy is None: + self.energy = (data['E(GeV)'].data*1000).tolist() + + for flavor in ThreeFlavor: + key = self._flavorkeys[flavor] + # convert from flux back to initial spectra: number per /s/erg + spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/dE + if flavor in self.initial_spectra: + self.initial_spectra[flavor].append(spectrum) + else: + self.initial_spectra[flavor] = [spectrum] + + for flavor in ThreeFlavor: + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') + + self.time *= u.s + self.energy *= u.MeV + + self.filename = os.path.basename(filename) + + def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): + """Get neutrino spectra/luminosity curves after oscillation. + + Parameters + ---------- + t : astropy.Quantity + Time to evaluate initial spectra. + E : astropy.Quantity or ndarray of astropy.Quantity + Energies to evaluate the initial spectra. + flavors: iterable of snewpy.neutrino.Flavor + Return spectra for these flavors only (default: all) + + Returns + ------- + spectra : dict + Dictionary of model spectra, keyed by neutrino flavor. + """ + #convert input arguments to 1D arrays + t = u.Quantity(t, ndmin=1) + E = u.Quantity(E, ndmin=1) + + initial_spectra = {} + for flavor in ThreeFlavor: + initial_spectra[flavor] = self.interpolation[flavor]((t, E)) / (u.MeV * u.s) + + return initial_spectra + + + + + From 4b97bcd4abe2dfaab8436c5628eade999525d1ae Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 6 Nov 2025 09:52:12 -0500 Subject: [PATCH 05/52] Add files via upload --- python/snewpy/models/typeIa.py | 11 +++-------- python/snewpy/models/typeIa_loaders.py | 13 ++++++------- 2 files changed, 9 insertions(+), 15 deletions(-) diff --git a/python/snewpy/models/typeIa.py b/python/snewpy/models/typeIa.py index 3db3e3262..2499dd258 100644 --- a/python/snewpy/models/typeIa.py +++ b/python/snewpy/models/typeIa.py @@ -19,16 +19,11 @@ class TypeIa(loaders.TypeIa): """The DDT model is described in 'Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario', by Warren P. Wright et al., [Phys. Rev. D94 (2016) 025026](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.94.025026), [arXiv:1605.01408](https://arxiv.org/abs/1605.01408). - There are 30 snapshots in time and for each snapshot there are 8 lines of sight through the explosion. Flavor transformation due to MSW effects and decoherence - are included plus, the no-oscillations case. Self-interaction effects do not occur in Type Ia models. Earth matter effects are not included. Both mass orderings - are considered. The format of each data file is the SNOwGLoBES format. + There are 30 snapshots in time and the format of each data file is the SNOwGLoBES format. The GCD model is described in 'Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario' by Warren P. Wright et al., [Phys. Rev. D95 (2017) 043006](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.95.043006), [arXiv:1609.07403](https://arxiv.org/abs/1609.07403). - There are 64 snapshots in time and for each snapshot there are 10 lines of sight through the explosion. Flavor transformation due to MSW effects and decoherence are - included, plus the no-oscillations case. Self-interaction effects do not occur in Type Ia models. Earth matter effects are not included. Both mass orderings are - considered. The format of each data file is the SNOwGLoBES format. Flavor transformation due to MSW effects and decoherence are included. Self-interaction effects - do not occur in Type Ia models. Earth matter effects are not included. + There are 64 snapshots in time and the format of each data file is the SNOwGLoBES format. """ def _metadata_from_filename(self, filename:str): metadata = { @@ -37,7 +32,7 @@ def _metadata_from_filename(self, filename:str): return metadata def __init__(self, mechanism:str): - filename=f"{mechanism}_NeutrinoFlux.tar.bz2" + filename = f"{mechanism}_NeutrinoFlux.tar.bz2" super().__init__(filename) diff --git a/python/snewpy/models/typeIa_loaders.py b/python/snewpy/models/typeIa_loaders.py index b3e2c475d..dcc4c76c6 100644 --- a/python/snewpy/models/typeIa_loaders.py +++ b/python/snewpy/models/typeIa_loaders.py @@ -26,14 +26,13 @@ from snewpy import _model_downloader class TypeIa(SupernovaModel): - """ - The DDT model is described in "Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario", - Warren P. Wright, Gautam Nagaraj, James P. Kneller, Kate Scholberg, Ivo R. Seitenzahl, Phys. Rev. D94 (2016) 025026, arXiv:1605.01408. - There are 30 snapshots in time and for each snapshot there are 8 lines of sight through the explosion. The format of each data file is the SNOwGLoBES format. + """The DDT model is described in 'Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario', by Warren P. Wright et al., + [Phys. Rev. D94 (2016) 025026](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.94.025026), [arXiv:1605.01408](https://arxiv.org/abs/1605.01408). + There are 30 snapshots in time and the format of each data file is the SNOwGLoBES format. - The GCD model is described in "Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario" - Warren P. Wright, James P. Kneller, Sebastian T. Ohlmann, Friedrich K. Röpke, Kate Scholberg, Ivo R. Seitenzahl, Phys. Rev. D95 (2017) 043006, arXiv:1609.07403. - There are 64 snapshots in time and for each snapshot there are 10 lines of sight through the explosion. The format of each data file is the SNOwGLoBES format. + The GCD model is described in 'Neutrinos from type Ia supernovae: The gravitationally confined detonation scenario' by Warren P. Wright et al., + [Phys. Rev. D95 (2017) 043006](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.95.043006), [arXiv:1609.07403](https://arxiv.org/abs/1609.07403). + There are 64 snapshots in time and the format of each data file is the SNOwGLoBES format. """ def __init__(self, filename, metadata={}): From e3d6d4709c850cab540e04169a44cfb8a3bfb1fd Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 6 Nov 2025 09:53:00 -0500 Subject: [PATCH 06/52] Add files via upload --- doc/source/nb/TypeIa.ipynb | 196 +++++++++++++++++++++++++++++++++++++ 1 file changed, 196 insertions(+) create mode 100644 doc/source/nb/TypeIa.ipynb diff --git a/doc/source/nb/TypeIa.ipynb b/doc/source/nb/TypeIa.ipynb new file mode 100644 index 000000000..11ff4eb8f --- /dev/null +++ b/doc/source/nb/TypeIa.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# TypeIa SN models\n", + "\n", + "### The two TypeIa supernova models described in the papers Warren P. Wright et al., Phys. Rev. D94 (2016) 025026, and Warren P. Wright et al.,Phys. Rev. D95 (2017) 043006" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "mpl.rc('font', size=14)\n", + "\n", + "import numpy as np\n", + "\n", + "from astropy import units as u \n", + "\n", + "from snewpy.models.typeIa import TypeIa\n", + "from snewpy.flavor import ThreeFlavor\n", + "from snewpy.neutrino import MixingParameters\n", + "from snewpy.flavor_transformation import AdiabaticMSW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## A Function which plots the Initial and Oscillated Spectra using the Adiabatic MSW Flavor Transformation and the Normal mass ordering\n", + "\n", + "Plot the neutrino spectra at the source and after the requested flavor transformation has been applied." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_spectra(model, time=1*u.s):\n", + " # Adiabatic MSW effect. NMO is used by default.\n", + " xform_nmo = AdiabaticMSW(MixingParameters())\n", + "\n", + " # Energy array and time to compute spectra.\n", + " # Note that any convenient units can be used and the calculation will remain internally consistent.\n", + " E = np.linspace(0,100,201) * u.MeV\n", + "\n", + " ispec = model.get_initial_spectra(time, E)\n", + " ospec_nmo = model.get_transformed_spectra(time, E, xform_nmo)\n", + "\n", + " fig, axes = plt.subplots(1,2, figsize=(12,5), sharex=True, sharey=True, tight_layout=True)\n", + "\n", + " for i, spec in enumerate([ispec, ospec_nmo]):\n", + " ax = axes[i]\n", + " for flavor in ThreeFlavor:\n", + " ax.plot(E, spec[flavor].array.squeeze(),\n", + " label=flavor.to_tex(),\n", + " color='C0' if flavor.is_electron else 'C1',\n", + " ls='-' if flavor.is_neutrino else ':', lw=2,\n", + " alpha=0.7)\n", + "\n", + " ax.set(xlabel=r'$E$ [{}]'.format(E.unit),\n", + " title='Initial Spectra: $t = ${:.1f}'.format(time) if i==0 else 'Oscillated Spectra: $t = ${:.1f}'.format(time))\n", + " ax.grid()\n", + " ax.legend(loc='upper right', ncol=2, fontsize=16)\n", + "\n", + " ax = axes[0]\n", + " ax.set(ylabel=r'neutrino spectra [erg$^{-1}$ s$^{-1}$]')\n", + "\n", + " fig.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Look at what models are available" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mechanism': ['GCD', 'DDT']}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TypeIa.param" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Go through the models, plot the original spectra and transformed spectra at $t=1$ s.\n", + "\n", + "### The GCD model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sn1 = TypeIa(mechanism='GCD')\n", + "fig = plot_spectra(sn1, time=1*u.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The DDT model" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sn2 = TypeIa(mechanism='DDT')\n", + "fig = plot_spectra(sn2, time=1*u.s)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 96254aaaac8ae9d68ed8d35172d6c5e9770e3116 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 6 Nov 2025 09:53:43 -0500 Subject: [PATCH 07/52] Rename doc/source/nb/TypeIa.ipynb to doc/source/nb/TypeIa/TypeIa.ipynb --- doc/source/nb/{ => TypeIa}/TypeIa.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename doc/source/nb/{ => TypeIa}/TypeIa.ipynb (100%) diff --git a/doc/source/nb/TypeIa.ipynb b/doc/source/nb/TypeIa/TypeIa.ipynb similarity index 100% rename from doc/source/nb/TypeIa.ipynb rename to doc/source/nb/TypeIa/TypeIa.ipynb From 66b07248f9ca801b63c6e6eb0cf2f2ff0d5b19a1 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 10:06:59 -0400 Subject: [PATCH 08/52] Update ccsn.py Moving SNOWGLOBES model class to base.py. Will become parent to TypeIa and Wright_2017 model classes. --- python/snewpy/models/ccsn.py | 77 ------------------------------------ 1 file changed, 77 deletions(-) diff --git a/python/snewpy/models/ccsn.py b/python/snewpy/models/ccsn.py index 0bf0ad9aa..da5acc686 100644 --- a/python/snewpy/models/ccsn.py +++ b/python/snewpy/models/ccsn.py @@ -392,83 +392,6 @@ def __init__(self, *, Bfield:str, direction:str, rotation:int=None, grav:str=Non filename = f'{Bfield}_b12_3d_{rotation}deg_snewpy_{direction}.dat' return super().__init__(filename=filename, metadata=self.metadata) -class SNOwGLoBES: - """A model that does not inherit from SupernovaModel (yet) and imports a group of SNOwGLoBES files.""" - - def __init__(self, tarfilename): - """ - Parameters - ---------- - tarfilename: str - Absolute or relative path to tar archive with SNOwGLoBES files. - """ - self.tfname = tarfilename - tf = tarfile.open(self.tfname) - - # For now just pull out the "NoOsc" files. - datafiles = sorted([f.name for f in tf if '.dat' in f.name]) - noosc = [df for df in datafiles if 'NoOsc' in df] - noosc.sort(key=len) - - # Loop through the noosc files and pull out the number fluxes. - self.time = [] - self.energy = None - self.flux = {} - self.fmin = 1e99 - self.fmax = -1e99 - - for nooscfile in noosc: - with tf.extractfile(nooscfile) as f: - logging.debug('Reading {}'.format(nooscfile)) - meta = f.readline() - metatext = meta.decode('utf-8') - t = float(metatext.split('TBinMid=')[-1].split('sec')[0]) - dt = float(metatext.split('tBinWidth=')[-1].split('s')[0]) - dE = float(metatext.split('eBinWidth=')[-1].split('MeV')[0]) - - data = Table.read(f, format='ascii.commented_header', header_start=-1) - data.meta['t'] = t - data.meta['dt'] = dt - data.meta['dE'] = dE - - self.time.append(t) - if self.energy is None: - self.energy = (data['E(GeV)'].data*1000).tolist() - - for flavor in ['NuE', 'NuMu', 'NuTau', 'aNuE', 'aNuMu', 'aNuTau']: - if flavor in self.flux: - self.flux[flavor].append(data[flavor].data.tolist()) - else: - self.flux[flavor] = [data[flavor].data.tolist()] - - # We now have a table with rows=times and columns=energies. Transpose - # so that rows=energy and cols=time. - for k, v in self.flux.items(): - self.flux[k] = np.transpose(self.flux[k]) - self.fmin = np.minimum(self.fmin, np.min(self.flux[k])) - self.fmax = np.maximum(self.fmax, np.max(self.flux[k])) - - def get_fluence(self, t): - """Return the fluence at a given time t. - - Parameters - ---------- - t : float - Time in seconds. - - Returns - ------- - fluence : dict - A dictionary giving fluence at time t, keyed by flavor. - """ - # Get index of closest element in the array - idx = np.abs(np.asarray(self.time) - t).argmin() - - fluence = {} - for k, fl in self.flux.items(): - fluence[k] = fl[:,idx] - - return fluence class Analytic3Species(PinchedModel): """An analytical model calculating spectra given total luminosity, From f504a575340ffa6e4f51dc9da7c9a9efdfcff6a5 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 10:14:24 -0400 Subject: [PATCH 09/52] Update base.py Moved SNOwGLoBES model class here. It will become the parent for the TypeIa and Wright_2017 model classes. --- python/snewpy/models/base.py | 95 ++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 50d62cdd7..8dce52bbf 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -286,3 +286,98 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): initial_spectra[flavor] = result return initial_spectra + +class SNOwGLoBES(SupernovaModel): + """Subclass for models whose input data is in SNOwBLoBES format""" + def __init__(self, filename, metadata={}): + """ + Parameters + ---------- + tarfilename: str + Absolute or relative path to tar archive + """ + # Open the requested filename using the model downloader. + datafile = self.request_file(filename) + + tf = tarfile.open(datafile) + + # Find the "NoOsc" files. + datafiles = sorted([f.name for f in tf if '.dat' in f.name]) + nooscfiles = [df for df in datafiles if 'NoOsc' in df] + nooscfiles.sort(key=len) + + # Loop through the NoOsc files and pull out the number fluxes. + self.time = [] + self.energy = None + self.initial_spectra = {} + self.interpolation = {} + + self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', + ThreeFlavor.NU_E_BAR: 'aNuE', + ThreeFlavor.NU_MU: 'NuMu', + ThreeFlavor.NU_MU_BAR: 'aNuMu', + ThreeFlavor.NU_TAU: 'NuTau', + ThreeFlavor.NU_TAU_BAR: 'aNuTau'} + + for nooscfile in nooscfiles: + with tf.extractfile(nooscfile) as f: + logging.debug('Reading {}'.format(nooscfile)) + meta = f.readline() + metatext = meta.decode('utf-8') + t = float(metatext.split('TBinMid=')[-1].split('sec')[0]) + dt = float(metatext.split('tBinWidth=')[-1].split('s')[0]) + dE = float(metatext.split('eBinWidth=')[-1].split('MeV')[0]) + + data = Table.read(f, format='ascii.commented_header', header_start=-1) + data.meta['t'] = t + data.meta['dt'] = dt + data.meta['dE'] = dE + + self.time.append(t) + + if self.energy is None: + self.energy = (data['E(GeV)'].data*1000).tolist() + + for flavor in ThreeFlavor: + key = self._flavorkeys[flavor] + # convert from flux back to initial spectra: number per /s/erg + spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/dE + if flavor in self.initial_spectra: + self.initial_spectra[flavor].append(spectrum) + else: + self.initial_spectra[flavor] = [spectrum] + + for flavor in ThreeFlavor: + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') + + self.time *= u.s + self.energy *= u.MeV + + self.filename = os.path.basename(filename) + + def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): + """Get neutrino spectra/luminosity curves after oscillation. + + Parameters + ---------- + t : astropy.Quantity + Time to evaluate initial spectra. + E : astropy.Quantity or ndarray of astropy.Quantity + Energies to evaluate the initial spectra. + flavors: iterable of snewpy.neutrino.Flavor + Return spectra for these flavors only (default: all) + + Returns + ------- + spectra : dict + Dictionary of model spectra, keyed by neutrino flavor. + """ + #convert input arguments to 1D arrays + t = u.Quantity(t, ndmin=1) + E = u.Quantity(E, ndmin=1) + + initial_spectra = {} + for flavor in ThreeFlavor: + initial_spectra[flavor] = self.interpolation[flavor]((t, E)) / (u.MeV * u.s) + + return initial_spectra From 97c67158931bf385502e04b8d4de7ab85307d759 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 10:17:55 -0400 Subject: [PATCH 10/52] Update typeIa_loaders.py --- python/snewpy/models/typeIa_loaders.py | 110 +------------------------ 1 file changed, 3 insertions(+), 107 deletions(-) diff --git a/python/snewpy/models/typeIa_loaders.py b/python/snewpy/models/typeIa_loaders.py index dcc4c76c6..6181f3220 100644 --- a/python/snewpy/models/typeIa_loaders.py +++ b/python/snewpy/models/typeIa_loaders.py @@ -10,22 +10,10 @@ models from files stored on disk. """ import logging -import os -import numpy as np -import pandas as pd -import tarfile -from astropy import units as u -from astropy.table import Table +from snewpy.models.base import SNOwGLoBES -from scipy import interpolate - -from snewpy.models.base import SupernovaModel -from snewpy.flavor import ThreeFlavor -from snewpy.flux import Spectrum -from snewpy import _model_downloader - -class TypeIa(SupernovaModel): +class TypeIa(SNOwGLoBES): """The DDT model is described in 'Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario', by Warren P. Wright et al., [Phys. Rev. D94 (2016) 025026](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.94.025026), [arXiv:1605.01408](https://arxiv.org/abs/1605.01408). There are 30 snapshots in time and the format of each data file is the SNOwGLoBES format. @@ -34,99 +22,7 @@ class TypeIa(SupernovaModel): [Phys. Rev. D95 (2017) 043006](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.95.043006), [arXiv:1609.07403](https://arxiv.org/abs/1609.07403). There are 64 snapshots in time and the format of each data file is the SNOwGLoBES format. """ - - def __init__(self, filename, metadata={}): - """ - Parameters - ---------- - tarfilename: str - Absolute or relative path to tar archive - """ - # Open the requested filename using the model downloader. - datafile = self.request_file(filename) - - tf = tarfile.open(datafile) - - # Find the "NoOsc" files. - datafiles = sorted([f.name for f in tf if '.dat' in f.name]) - nooscfiles = [df for df in datafiles if 'NoOsc' in df] - nooscfiles.sort(key=len) - - # Loop through the NoOsc files and pull out the number fluxes. - self.time = [] - self.energy = None - self.initial_spectra = {} - self.interpolation = {} - - self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', - ThreeFlavor.NU_E_BAR: 'aNuE', - ThreeFlavor.NU_MU: 'NuMu', - ThreeFlavor.NU_MU_BAR: 'aNuMu', - ThreeFlavor.NU_TAU: 'NuTau', - ThreeFlavor.NU_TAU_BAR: 'aNuTau'} - - for nooscfile in nooscfiles: - with tf.extractfile(nooscfile) as f: - logging.debug('Reading {}'.format(nooscfile)) - meta = f.readline() - metatext = meta.decode('utf-8') - t = float(metatext.split('TBinMid=')[-1].split('sec')[0]) - dt = float(metatext.split('tBinWidth=')[-1].split('s')[0]) - dE = float(metatext.split('eBinWidth=')[-1].split('MeV')[0]) - - data = Table.read(f, format='ascii.commented_header', header_start=-1) - data.meta['t'] = t - data.meta['dt'] = dt - data.meta['dE'] = dE - - self.time.append(t) - - if self.energy is None: - self.energy = (data['E(GeV)'].data*1000).tolist() - - for flavor in ThreeFlavor: - key = self._flavorkeys[flavor] - # convert from flux back to initial spectra: number per /s/erg - spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/dE - if flavor in self.initial_spectra: - self.initial_spectra[flavor].append(spectrum) - else: - self.initial_spectra[flavor] = [spectrum] - - for flavor in ThreeFlavor: - self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') - - self.time *= u.s - self.energy *= u.MeV - - self.filename = os.path.basename(filename) - - def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): - """Get neutrino spectra/luminosity curves after oscillation. - - Parameters - ---------- - t : astropy.Quantity - Time to evaluate initial spectra. - E : astropy.Quantity or ndarray of astropy.Quantity - Energies to evaluate the initial spectra. - flavors: iterable of snewpy.neutrino.Flavor - Return spectra for these flavors only (default: all) - - Returns - ------- - spectra : dict - Dictionary of model spectra, keyed by neutrino flavor. - """ - #convert input arguments to 1D arrays - t = u.Quantity(t, ndmin=1) - E = u.Quantity(E, ndmin=1) - - initial_spectra = {} - for flavor in ThreeFlavor: - initial_spectra[flavor] = self.interpolation[flavor]((t, E)) / (u.MeV * u.s) - - return initial_spectra + pass From 06a505aa2bcdc56a47dc88fa7415c603958c0c8c Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 10:18:50 -0400 Subject: [PATCH 11/52] Update typeIa.py --- python/snewpy/models/typeIa.py | 1 - 1 file changed, 1 deletion(-) diff --git a/python/snewpy/models/typeIa.py b/python/snewpy/models/typeIa.py index 2499dd258..6611883ba 100644 --- a/python/snewpy/models/typeIa.py +++ b/python/snewpy/models/typeIa.py @@ -8,7 +8,6 @@ from astropy import units as u from snewpy.models import typeIa_loaders as loaders -from .base import SupernovaModel from snewpy.models.registry_model import RegistryModel, Parameter from snewpy.models.registry_model import all_models From ae38d88c9ea294f38ec6a3e35467525db3a1f2ff Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 10:19:47 -0400 Subject: [PATCH 12/52] Update pisn_loaders.py --- python/snewpy/models/pisn_loaders.py | 114 +-------------------------- 1 file changed, 3 insertions(+), 111 deletions(-) diff --git a/python/snewpy/models/pisn_loaders.py b/python/snewpy/models/pisn_loaders.py index cee133f77..c68235c94 100644 --- a/python/snewpy/models/pisn_loaders.py +++ b/python/snewpy/models/pisn_loaders.py @@ -4,121 +4,13 @@ models from files stored on disk. """ import logging -import os -import numpy as np -import pandas as pd -import tarfile -from astropy import units as u -from astropy.table import Table - -from scipy import interpolate - -from snewpy.models.base import SupernovaModel -from snewpy.flavor import ThreeFlavor -from snewpy.flux import Spectrum +from snewpy.models.base import SNOwGLoBES from snewpy import _model_downloader -class Wright_2017(SupernovaModel): +class Wright_2017(SNOwGLoBES): """PISN model described in the paper `Neutrino signal from pair-instability supernovae' by Wright et al., Phys. Rev. D96 (2017) 103008 ` """ - - def __init__(self, filename, metadata={}): - """ - Parameters - ---------- - tarfilename: str - Absolute or relative path to tar archive - """ - # Open the requested filename using the model downloader. - datafile = self.request_file(filename) - - tf = tarfile.open(datafile) - - # Find the "NoOsc" files. - datafiles = sorted([f.name for f in tf if '.dat' in f.name]) - nooscfiles = [df for df in datafiles if 'NoOsc' in df] - nooscfiles.sort(key=len) - - # Loop through the NoOsc files and pull out the number fluxes. - self.time = [] - self.energy = None - self.initial_spectra = {} - self.interpolation = {} - - self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', - ThreeFlavor.NU_E_BAR: 'aNuE', - ThreeFlavor.NU_MU: 'NuMu', - ThreeFlavor.NU_MU_BAR: 'aNuMu', - ThreeFlavor.NU_TAU: 'NuTau', - ThreeFlavor.NU_TAU_BAR: 'aNuTau'} - - for nooscfile in nooscfiles: - with tf.extractfile(nooscfile) as f: - logging.debug('Reading {}'.format(nooscfile)) - meta = f.readline() - metatext = meta.decode('utf-8') - t = float(metatext.split('TBinMid=')[-1].split('sec')[0]) - dt = float(metatext.split('tBinWidth=')[-1].split('s')[0]) - dE = float(metatext.split('eBinWidth=')[-1].split('MeV')[0]) - - data = Table.read(f, format='ascii.commented_header', header_start=-1) - data.meta['t'] = t - data.meta['dt'] = dt - data.meta['dE'] = dE - - self.time.append(t) - - if self.energy is None: - self.energy = (data['E(GeV)'].data*1000).tolist() - - for flavor in ThreeFlavor: - key = self._flavorkeys[flavor] - # convert from flux back to initial spectra: number per /s/erg - spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/dE - if flavor in self.initial_spectra: - self.initial_spectra[flavor].append(spectrum) - else: - self.initial_spectra[flavor] = [spectrum] - - for flavor in ThreeFlavor: - self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') - - self.time *= u.s - self.energy *= u.MeV - - self.filename = os.path.basename(filename) - - def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): - """Get neutrino spectra/luminosity curves after oscillation. - - Parameters - ---------- - t : astropy.Quantity - Time to evaluate initial spectra. - E : astropy.Quantity or ndarray of astropy.Quantity - Energies to evaluate the initial spectra. - flavors: iterable of snewpy.neutrino.Flavor - Return spectra for these flavors only (default: all) - - Returns - ------- - spectra : dict - Dictionary of model spectra, keyed by neutrino flavor. - """ - #convert input arguments to 1D arrays - t = u.Quantity(t, ndmin=1) - E = u.Quantity(E, ndmin=1) - - initial_spectra = {} - for flavor in ThreeFlavor: - initial_spectra[flavor] = self.interpolation[flavor]((t, E)) / (u.MeV * u.s) - - return initial_spectra - - - - - + pass From 08a9f53f6daaa70d875b3a5c3c2fed6cc5a984ff Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 10:28:11 -0400 Subject: [PATCH 13/52] Update model_files.yml --- python/snewpy/models/model_files.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml index 0d690b3b1..e9df8b4a5 100644 --- a/python/snewpy/models/model_files.yml +++ b/python/snewpy/models/model_files.yml @@ -6,6 +6,7 @@ config: - &snewpy "https://github.com/SNEWS2/snewpy/raw/v{snewpy_version}/models/{model}/{filename}" - &ccsn_repository "https://github.com/SNEWS2/snewpy-models-ccsn/raw/v0.3/models/{model}/{filename}" - &presn_repository "https://github.com/SNEWS2/snewpy-models-presn/raw/v0.2/models/{model}/{filename}" + - &presn_repository_main "https://github.com/SNEWS2/snewpy-models-presn/raw/master/models/{model}/{filename}" - &pisn_typeIa_repository "https://github.com/SNEWS2/snewpy-models-pisn_typeIa/raw/v0.1/models/{model}/{filename}" models: @@ -74,6 +75,9 @@ models: Yoshida_2016: repository: *presn_repository + + Myers_2026: + repository: *presn_repository_main pisn: Wright_2017: From d5ae06439057dde55e8f169c3f230079237c5293 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 11:28:49 -0400 Subject: [PATCH 14/52] Update SNOwGLoBES_models.ipynb --- doc/source/nb/SNOwGLoBES_models.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/nb/SNOwGLoBES_models.ipynb b/doc/source/nb/SNOwGLoBES_models.ipynb index 3695c2c56..c6c6af97b 100644 --- a/doc/source/nb/SNOwGLoBES_models.ipynb +++ b/doc/source/nb/SNOwGLoBES_models.ipynb @@ -19,7 +19,7 @@ "metadata": {}, "outputs": [], "source": [ - "from snewpy.models.ccsn import SNOwGLoBES\n", + "from snewpy.models.base import SNOwGLoBES\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", From 411a35b5290d2afcbc2e6fb4f52a29f45f547dfa Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 11:32:01 -0400 Subject: [PATCH 15/52] Update base.py --- python/snewpy/models/base.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 8dce52bbf..3c3cfd931 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -3,13 +3,16 @@ from abc import ABC, abstractmethod import numpy as np + from astropy import units as u from astropy.table import Table from astropy.units import UnitTypeError, get_physical_type from astropy.units.quantity import Quantity from scipy.special import loggamma -from snewpy._model_downloader import LocalFileLoader +import tarfile + +from snewpy._model_downloader import LocalFileLoader from snewpy.flavor import ThreeFlavor from snewpy.flavor_transformation import NoTransformation from functools import wraps From c6dcbf7647bdb3e2862a7e9b343ba8f1f77ad1ac Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 11:36:43 -0400 Subject: [PATCH 16/52] Update base.py Modified SNOWGLOBES class to follow suggestion by Jost --- python/snewpy/models/base.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 3c3cfd931..f64a16d3c 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -375,12 +375,12 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): spectra : dict Dictionary of model spectra, keyed by neutrino flavor. """ - #convert input arguments to 1D arrays - t = u.Quantity(t, ndmin=1) - E = u.Quantity(E, ndmin=1) + t = u.Quantity(t, ndmin=1).to(u.s).value + E = u.Quantity(E, ndmin=1).to(u.MeV).value + tE_grid = np.stack(np.meshgrid(t, E, indexing='ij'), axis=-1) initial_spectra = {} for flavor in ThreeFlavor: - initial_spectra[flavor] = self.interpolation[flavor]((t, E)) / (u.MeV * u.s) + initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.MeV * u.s) return initial_spectra From c3da4d71e264a8a2b928f18252eba8c515074609 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 11:37:50 -0400 Subject: [PATCH 17/52] Update typeIa.py --- python/snewpy/models/typeIa.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/typeIa.py b/python/snewpy/models/typeIa.py index 6611883ba..46432b67a 100644 --- a/python/snewpy/models/typeIa.py +++ b/python/snewpy/models/typeIa.py @@ -15,7 +15,7 @@ @RegistryModel( mechanism = ['GCD', 'DDT'], ) -class TypeIa(loaders.TypeIa): +class Wright_2016(loaders.Wright_2016): """The DDT model is described in 'Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario', by Warren P. Wright et al., [Phys. Rev. D94 (2016) 025026](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.94.025026), [arXiv:1605.01408](https://arxiv.org/abs/1605.01408). There are 30 snapshots in time and the format of each data file is the SNOwGLoBES format. From aefbb76871fc8846ca76c9c1467a4fc4100c50df Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 11:38:17 -0400 Subject: [PATCH 18/52] Update typeIa_loaders.py --- python/snewpy/models/typeIa_loaders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/typeIa_loaders.py b/python/snewpy/models/typeIa_loaders.py index 6181f3220..a83e5e68e 100644 --- a/python/snewpy/models/typeIa_loaders.py +++ b/python/snewpy/models/typeIa_loaders.py @@ -13,7 +13,7 @@ from snewpy.models.base import SNOwGLoBES -class TypeIa(SNOwGLoBES): +class Wright_2016(SNOwGLoBES): """The DDT model is described in 'Neutrinos from type Ia supernovae: The deflagration-to-detonation transition scenario', by Warren P. Wright et al., [Phys. Rev. D94 (2016) 025026](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.94.025026), [arXiv:1605.01408](https://arxiv.org/abs/1605.01408). There are 30 snapshots in time and the format of each data file is the SNOwGLoBES format. From 6df479146c829c42504a3c4eb153a363b545e10f Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 11:41:44 -0400 Subject: [PATCH 19/52] Update TypeIa.ipynb --- doc/source/nb/TypeIa/TypeIa.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/source/nb/TypeIa/TypeIa.ipynb b/doc/source/nb/TypeIa/TypeIa.ipynb index 11ff4eb8f..da73570a8 100644 --- a/doc/source/nb/TypeIa/TypeIa.ipynb +++ b/doc/source/nb/TypeIa/TypeIa.ipynb @@ -25,7 +25,7 @@ "\n", "from astropy import units as u \n", "\n", - "from snewpy.models.typeIa import TypeIa\n", + "from snewpy.models.typeIa import Wright_2016\n", "from snewpy.flavor import ThreeFlavor\n", "from snewpy.neutrino import MixingParameters\n", "from snewpy.flavor_transformation import AdiabaticMSW" @@ -103,7 +103,7 @@ } ], "source": [ - "TypeIa.param" + "Wright_2016.param" ] }, { @@ -132,7 +132,7 @@ } ], "source": [ - "sn1 = TypeIa(mechanism='GCD')\n", + "sn1 = Wright_2016(mechanism='GCD')\n", "fig = plot_spectra(sn1, time=1*u.s)" ] }, @@ -160,7 +160,7 @@ } ], "source": [ - "sn2 = TypeIa(mechanism='DDT')\n", + "sn2 = Wright_2016(mechanism='DDT')\n", "fig = plot_spectra(sn2, time=1*u.s)" ] }, From 40de9b2a24b9ab2c89fc95e3cae8b3118c0543cb Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 12:10:59 -0400 Subject: [PATCH 20/52] Update base.py --- python/snewpy/models/base.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index f64a16d3c..5464ce490 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -12,6 +12,8 @@ import tarfile +import logging + from snewpy._model_downloader import LocalFileLoader from snewpy.flavor import ThreeFlavor from snewpy.flavor_transformation import NoTransformation From 1d27eabbbe20d962f742c4e88bb8b17bac5e5fdd Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 12:11:17 -0400 Subject: [PATCH 21/52] Update typeIa_loaders.py --- python/snewpy/models/typeIa_loaders.py | 1 - 1 file changed, 1 deletion(-) diff --git a/python/snewpy/models/typeIa_loaders.py b/python/snewpy/models/typeIa_loaders.py index a83e5e68e..e4d817f3a 100644 --- a/python/snewpy/models/typeIa_loaders.py +++ b/python/snewpy/models/typeIa_loaders.py @@ -9,7 +9,6 @@ The submodule ``snewpy.models.pisn_loaders`` contains classes to load pair-instability supernova models from files stored on disk. """ -import logging from snewpy.models.base import SNOwGLoBES From eb2623f2b46dbe893673dad4ea50dc86a99529ae Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 12:11:30 -0400 Subject: [PATCH 22/52] Update pisn_loaders.py --- python/snewpy/models/pisn_loaders.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/python/snewpy/models/pisn_loaders.py b/python/snewpy/models/pisn_loaders.py index c68235c94..6e96de261 100644 --- a/python/snewpy/models/pisn_loaders.py +++ b/python/snewpy/models/pisn_loaders.py @@ -3,12 +3,10 @@ The submodule ``snewpy.models.pisn_loaders`` contains classes to load pair-instability supernova models from files stored on disk. """ -import logging from snewpy.models.base import SNOwGLoBES from snewpy import _model_downloader - class Wright_2017(SNOwGLoBES): """PISN model described in the paper `Neutrino signal from pair-instability supernovae' by Wright et al., Phys. Rev. D96 (2017) 103008 ` From 867c829d208627c1223c36430d26d02790bad95c Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 12:34:30 -0400 Subject: [PATCH 23/52] Update base.py --- python/snewpy/models/base.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 5464ce490..82906eae4 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -12,6 +12,8 @@ import tarfile +from scipy import interpolate + import logging from snewpy._model_downloader import LocalFileLoader From 72c842148e9055178e47c5481b743ee43c1e80e6 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 12:45:37 -0400 Subject: [PATCH 24/52] Update model_files.yml --- python/snewpy/models/model_files.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml index 21f8dd2bf..42c032b82 100644 --- a/python/snewpy/models/model_files.yml +++ b/python/snewpy/models/model_files.yml @@ -90,7 +90,7 @@ models: repository: *pisn_typeIa_repository typeIa: - TypeIa: + Wright_2016: repository: *pisn_typeIa_repository From b95458baa89e8e11e6f91efe2ec9651a8abebe9e Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 13:29:02 -0400 Subject: [PATCH 25/52] Update model_files.yml --- python/snewpy/models/model_files.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml index 42c032b82..5e05efb21 100644 --- a/python/snewpy/models/model_files.yml +++ b/python/snewpy/models/model_files.yml @@ -7,7 +7,7 @@ config: - &ccsn_repository "https://github.com/SNEWS2/snewpy-models-ccsn/raw/v0.4/models/{model}/{filename}" - &presn_repository "https://github.com/SNEWS2/snewpy-models-presn/raw/v0.2/models/{model}/{filename}" - &presn_repository_main "https://github.com/SNEWS2/snewpy-models-presn/raw/master/models/{model}/{filename}" - - &pisn_typeIa_repository "https://github.com/SNEWS2/snewpy-models-pisn_typeIa/raw/v0.1/models/{model}/{filename}" + - &pisn_typeIa_repository "https://github.com/SNEWS2/snewpy-models-pisn_typeIa/raw/v0.2/models/{model}/{filename}" models: ccsn: From 47bd360cecbe4f52d336aa7e9bf241724f7684e3 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 14:14:29 -0400 Subject: [PATCH 26/52] Create test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 28 +++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 python/snewpy/test/test_pisn_rates.py diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py new file mode 100644 index 000000000..0439ff29f --- /dev/null +++ b/python/snewpy/test/test_pisn_rates.py @@ -0,0 +1,28 @@ +import astropy.units as u +import numpy as np +from snewpy.models import presn +from snewpy.neutrino import MassHierarchy, MixingParameters +from snewpy.flavor_transformation import AdiabaticMSW +from snewpy.rate_calculator import RateCalculator +import pytest + +pytestmark=pytest.mark.snowglobes + +rc = RateCalculator() + +distance = 1000*u.pc + #SNOwGLoBES detector for water Cerenkov +E = np.linspace(0,20,100)*u.MeV + +@pytest.mark.parametrize('model_class,model_params',[ + (pisn.Wright_2017, {'progenitor_mass': 250*u.Msun, 'eos': 'Helm'}), +]) +@pytest.mark.parametrize('transformation',[AdiabaticMSW(MixingParameters(mh)) for mh in MassHierarchy]) + +def test_pisn_rate(model_class, model_params, transformation, detector): + model = model_class(**model_params) + flux = model.get_flux(T, E, distance=distance, flavor_xform=transformation) + rate = rc.run(flux, detector='wc100kt30prct', detector_effects=False)['ibd'] + #the factor of 0.5 is because the mass of SuperK is 50kt, not 100kt + ibd_events = 0.5 * rate.integrate_or_sum('time').integrate_or_sum('energy').array.squeeze() + assert 1 Date: Mon, 15 Jun 2026 14:16:20 -0400 Subject: [PATCH 27/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index 0439ff29f..f15655286 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -1,6 +1,6 @@ import astropy.units as u import numpy as np -from snewpy.models import presn +from snewpy.models.pisn import Wright_2016 from snewpy.neutrino import MassHierarchy, MixingParameters from snewpy.flavor_transformation import AdiabaticMSW from snewpy.rate_calculator import RateCalculator From 6eec0fc4632022984d2d39316dc2e4245b6be054 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 16:22:52 -0400 Subject: [PATCH 28/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index f15655286..0dab7b7f1 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -1,6 +1,6 @@ import astropy.units as u import numpy as np -from snewpy.models.pisn import Wright_2016 +from snewpy.models.pisn import Wright_2017 from snewpy.neutrino import MassHierarchy, MixingParameters from snewpy.flavor_transformation import AdiabaticMSW from snewpy.rate_calculator import RateCalculator From e3f6e81c3fbcf76bf9940ffc8ee9937ec477d8c3 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 16:37:05 -0400 Subject: [PATCH 29/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index 0dab7b7f1..fa9544e59 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -1,6 +1,6 @@ import astropy.units as u import numpy as np -from snewpy.models.pisn import Wright_2017 +from snewpy.models import pisn from snewpy.neutrino import MassHierarchy, MixingParameters from snewpy.flavor_transformation import AdiabaticMSW from snewpy.rate_calculator import RateCalculator From 40472c134be540117bdc5ac49ef7e083bd0b874d Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 16:45:15 -0400 Subject: [PATCH 30/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index fa9544e59..3e86cc69a 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -21,8 +21,8 @@ def test_pisn_rate(model_class, model_params, transformation, detector): model = model_class(**model_params) - flux = model.get_flux(T, E, distance=distance, flavor_xform=transformation) - rate = rc.run(flux, detector='wc100kt30prct', detector_effects=False)['ibd'] + fluence = model.get_fluence(E, distance=distance, flavor_xform=transformation) + dNdE = rc.run(fluence, detector='wc100kt30prct', detector_effects=False)['ibd'] #the factor of 0.5 is because the mass of SuperK is 50kt, not 100kt - ibd_events = 0.5 * rate.integrate_or_sum('time').integrate_or_sum('energy').array.squeeze() - assert 1 Date: Mon, 15 Jun 2026 17:01:39 -0400 Subject: [PATCH 31/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index 3e86cc69a..796932060 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -19,7 +19,7 @@ ]) @pytest.mark.parametrize('transformation',[AdiabaticMSW(MixingParameters(mh)) for mh in MassHierarchy]) -def test_pisn_rate(model_class, model_params, transformation, detector): +def test_pisn_rate(model_class, model_params, transformation): model = model_class(**model_params) fluence = model.get_fluence(E, distance=distance, flavor_xform=transformation) dNdE = rc.run(fluence, detector='wc100kt30prct', detector_effects=False)['ibd'] From ba5743b16312b2666a965c8e8c119fc7855996a7 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 15 Jun 2026 17:13:10 -0400 Subject: [PATCH 32/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index 796932060..502935748 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -11,8 +11,6 @@ rc = RateCalculator() distance = 1000*u.pc - #SNOwGLoBES detector for water Cerenkov -E = np.linspace(0,20,100)*u.MeV @pytest.mark.parametrize('model_class,model_params',[ (pisn.Wright_2017, {'progenitor_mass': 250*u.Msun, 'eos': 'Helm'}), @@ -21,7 +19,9 @@ def test_pisn_rate(model_class, model_params, transformation): model = model_class(**model_params) - fluence = model.get_fluence(E, distance=distance, flavor_xform=transformation) + times = model.get_time() + energies = np.linspace(0,40,201)< Date: Wed, 17 Jun 2026 16:23:44 -0400 Subject: [PATCH 33/52] Update base.py Fix units in SNOwGLoBES class --- python/snewpy/models/base.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 82906eae4..cabf864d0 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -347,8 +347,10 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: key = self._flavorkeys[flavor] - # convert from flux back to initial spectra: number per /s/erg - spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/dE + # convert from flux back to initial spectra: number per /s/erg + # the (200.*u.keV) factor is a subtle point about the SNOwGLoBES format + # see the comment on line 223 of the pinched.cc code in SNOwGLoBES/fluxes + spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/(200*u.keV) if flavor in self.initial_spectra: self.initial_spectra[flavor].append(spectrum) else: @@ -380,11 +382,11 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): Dictionary of model spectra, keyed by neutrino flavor. """ t = u.Quantity(t, ndmin=1).to(u.s).value - E = u.Quantity(E, ndmin=1).to(u.MeV).value + E = u.Quantity(E, ndmin=1).to(u.erg).value tE_grid = np.stack(np.meshgrid(t, E, indexing='ij'), axis=-1) initial_spectra = {} for flavor in ThreeFlavor: - initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.MeV * u.s) + initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.erg * u.s) return initial_spectra From 61dbfbdcba89caa54633a34dd9193d121aa5cb4f Mon Sep 17 00:00:00 2001 From: jpkneller Date: Wed, 17 Jun 2026 16:32:36 -0400 Subject: [PATCH 34/52] Update base.py --- python/snewpy/models/base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index cabf864d0..69b8b673d 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -356,11 +356,11 @@ def __init__(self, filename, metadata={}): else: self.initial_spectra[flavor] = [spectrum] - for flavor in ThreeFlavor: - self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') - self.time *= u.s self.energy *= u.MeV + + for flavor in ThreeFlavor: + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') self.filename = os.path.basename(filename) From 35280e101493231505589c023e8abcbecb1de088 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Wed, 17 Jun 2026 16:49:36 -0400 Subject: [PATCH 35/52] Update base.py --- python/snewpy/models/base.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 69b8b673d..c16d4beb5 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -356,12 +356,12 @@ def __init__(self, filename, metadata={}): else: self.initial_spectra[flavor] = [spectrum] + for flavor in ThreeFlavor: + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') + self.time *= u.s self.energy *= u.MeV - for flavor in ThreeFlavor: - self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') - self.filename = os.path.basename(filename) def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): @@ -382,7 +382,7 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): Dictionary of model spectra, keyed by neutrino flavor. """ t = u.Quantity(t, ndmin=1).to(u.s).value - E = u.Quantity(E, ndmin=1).to(u.erg).value + E = u.Quantity(E, ndmin=1).to(u.MeV).value tE_grid = np.stack(np.meshgrid(t, E, indexing='ij'), axis=-1) initial_spectra = {} From 46e03da6603275eb7fba587ce37ab053587849d8 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 18 Jun 2026 11:06:33 -0400 Subject: [PATCH 36/52] Update base.py --- python/snewpy/models/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index c16d4beb5..759ac48b8 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -350,7 +350,7 @@ def __init__(self, filename, metadata={}): # convert from flux back to initial spectra: number per /s/erg # the (200.*u.keV) factor is a subtle point about the SNOwGLoBES format # see the comment on line 223 of the pinched.cc code in SNOwGLoBES/fluxes - spectrum = data[key].data.tolist() * (4*np.pi*(10*u.kpc)**2) /dt/(200*u.keV) + spectrum = data[key].data.tolist() * (((4*np.pi*(10*u.kpc)**2) /dt/(200*u.keV)).value) if flavor in self.initial_spectra: self.initial_spectra[flavor].append(spectrum) else: From ad74522425e6caaf1e3e14b99ae5c75a33abb276 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 18 Jun 2026 11:32:03 -0400 Subject: [PATCH 37/52] Update base.py --- python/snewpy/models/base.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 759ac48b8..a845cde1c 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -316,7 +316,7 @@ def __init__(self, filename, metadata={}): # Loop through the NoOsc files and pull out the number fluxes. self.time = [] self.energy = None - self.initial_spectra = {} + self.initial_lnspectra = {} self.interpolation = {} self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', @@ -350,14 +350,14 @@ def __init__(self, filename, metadata={}): # convert from flux back to initial spectra: number per /s/erg # the (200.*u.keV) factor is a subtle point about the SNOwGLoBES format # see the comment on line 223 of the pinched.cc code in SNOwGLoBES/fluxes - spectrum = data[key].data.tolist() * (((4*np.pi*(10*u.kpc)**2) /dt/(200*u.keV)).value) - if flavor in self.initial_spectra: - self.initial_spectra[flavor].append(spectrum) + lnspectrum = np.ln( data[key].data * (((4*np.pi*(10*u.kpc)**2)/dt/(200*u.keV)).value) ).tolist() + if flavor in self.initial_lnspectra: + self.initial_lnspectra[flavor].append(lnspectrum) else: - self.initial_spectra[flavor] = [spectrum] + self.initial_lnspectra[flavor] = [lnspectrum] for flavor in ThreeFlavor: - self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_lnspectra[flavor], method='cubic') self.time *= u.s self.energy *= u.MeV @@ -387,6 +387,6 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): initial_spectra = {} for flavor in ThreeFlavor: - initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.erg * u.s) + initial_spectra[flavor] = np.exp( self.interpolation[flavor](tE_grid) ) / (u.erg * u.s) return initial_spectra From 7949db129319953f7d8d744caacf16ae9affa657 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 18 Jun 2026 11:37:39 -0400 Subject: [PATCH 38/52] Update base.py --- python/snewpy/models/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index a845cde1c..cd51f5325 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -350,7 +350,7 @@ def __init__(self, filename, metadata={}): # convert from flux back to initial spectra: number per /s/erg # the (200.*u.keV) factor is a subtle point about the SNOwGLoBES format # see the comment on line 223 of the pinched.cc code in SNOwGLoBES/fluxes - lnspectrum = np.ln( data[key].data * (((4*np.pi*(10*u.kpc)**2)/dt/(200*u.keV)).value) ).tolist() + lnspectrum = np.log( data[key].data * (((4*np.pi*(10*u.kpc)**2)/dt/(200*u.keV)).value) ).tolist() if flavor in self.initial_lnspectra: self.initial_lnspectra[flavor].append(lnspectrum) else: From a628f160334ff1d8439c2f1c2d83fc0561de883e Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 18 Jun 2026 12:00:44 -0400 Subject: [PATCH 39/52] Update base.py --- python/snewpy/models/base.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index cd51f5325..9a52a86f2 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -347,10 +347,8 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: key = self._flavorkeys[flavor] - # convert from flux back to initial spectra: number per /s/erg - # the (200.*u.keV) factor is a subtle point about the SNOwGLoBES format - # see the comment on line 223 of the pinched.cc code in SNOwGLoBES/fluxes - lnspectrum = np.log( data[key].data * (((4*np.pi*(10*u.kpc)**2)/dt/(200*u.keV)).value) ).tolist() + # convert from flux back to initial spectra: number per /s/erg + lnspectrum = (np.log(data[key].data * (4*np.pi*(10*3.08567758128E+18)**2)/dt/dE )).tolist() if flavor in self.initial_lnspectra: self.initial_lnspectra[flavor].append(lnspectrum) else: @@ -358,10 +356,10 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_lnspectra[flavor], method='cubic') - + self.time *= u.s self.energy *= u.MeV - + self.filename = os.path.basename(filename) def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): From 176c47dbefee30896cd247c13a9869bb9738f724 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 18 Jun 2026 12:10:53 -0400 Subject: [PATCH 40/52] Update base.py --- python/snewpy/models/base.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 9a52a86f2..df19f87be 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -316,7 +316,7 @@ def __init__(self, filename, metadata={}): # Loop through the NoOsc files and pull out the number fluxes. self.time = [] self.energy = None - self.initial_lnspectra = {} + self.initial_spectra = {} self.interpolation = {} self._flavorkeys = {ThreeFlavor.NU_E: 'NuE', @@ -347,15 +347,15 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: key = self._flavorkeys[flavor] - # convert from flux back to initial spectra: number per /s/erg - lnspectrum = (np.log(data[key].data * (4*np.pi*(10*3.08567758128E+18)**2)/dt/dE )).tolist() - if flavor in self.initial_lnspectra: - self.initial_lnspectra[flavor].append(lnspectrum) + # convert from flux back to initial spectra: number per /s/MeV + lnspectrum = (data[key].data * (4*np.pi*(10*3.08567758128E+18)**2)/dt/dE ).tolist() + if flavor in self.initial_spectra: + self.initial_spectra[flavor].append(spectrum) else: - self.initial_lnspectra[flavor] = [lnspectrum] + self.initial_spectra[flavor] = [spectrum] for flavor in ThreeFlavor: - self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_lnspectra[flavor], method='cubic') + self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') self.time *= u.s self.energy *= u.MeV @@ -385,6 +385,6 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): initial_spectra = {} for flavor in ThreeFlavor: - initial_spectra[flavor] = np.exp( self.interpolation[flavor](tE_grid) ) / (u.erg * u.s) + initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.MeV * u.s) return initial_spectra From 472de1fa3e6ac9592b2f99b4d102c8dfc8aeed7e Mon Sep 17 00:00:00 2001 From: jpkneller Date: Thu, 18 Jun 2026 12:36:23 -0400 Subject: [PATCH 41/52] Update base.py --- python/snewpy/models/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index df19f87be..a60de9935 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -348,7 +348,7 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: key = self._flavorkeys[flavor] # convert from flux back to initial spectra: number per /s/MeV - lnspectrum = (data[key].data * (4*np.pi*(10*3.08567758128E+18)**2)/dt/dE ).tolist() + spectrum = (data[key].data * (4*np.pi*(10*3.08567758128E+18)**2)/dt/dE ).tolist() if flavor in self.initial_spectra: self.initial_spectra[flavor].append(spectrum) else: From aef02fa0d48d69635474c4f38dc36b459da9d7ca Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 08:57:30 -0400 Subject: [PATCH 42/52] Update base.py --- python/snewpy/models/base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index a60de9935..709550c73 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -347,8 +347,8 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: key = self._flavorkeys[flavor] - # convert from flux back to initial spectra: number per /s/MeV - spectrum = (data[key].data * (4*np.pi*(10*3.08567758128E+18)**2)/dt/dE ).tolist() + # convert from flux back to initial spectra: number per /s/erg + spectrum = (data[key].data * (4*np.pi*(u.kpc.to(u.cm,10))**2)/dt/(u.MeV.to(u.erg,dE))) ).tolist() if flavor in self.initial_spectra: self.initial_spectra[flavor].append(spectrum) else: @@ -385,6 +385,6 @@ def _get_initial_spectra_dict(self, t, E, flavors=ThreeFlavor): initial_spectra = {} for flavor in ThreeFlavor: - initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.MeV * u.s) + initial_spectra[flavor] = self.interpolation[flavor](tE_grid) / (u.erg * u.s) return initial_spectra From ac7007207e162caa96b390887e6a05176369bc81 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 09:00:58 -0400 Subject: [PATCH 43/52] Update base.py --- python/snewpy/models/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 709550c73..64d0ca472 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -348,7 +348,7 @@ def __init__(self, filename, metadata={}): for flavor in ThreeFlavor: key = self._flavorkeys[flavor] # convert from flux back to initial spectra: number per /s/erg - spectrum = (data[key].data * (4*np.pi*(u.kpc.to(u.cm,10))**2)/dt/(u.MeV.to(u.erg,dE))) ).tolist() + spectrum = (data[key].data * (4*np.pi*(u.kpc.to(u.cm,10))**2)/dt/(u.MeV.to(u.erg,dE)) ).tolist() if flavor in self.initial_spectra: self.initial_spectra[flavor].append(spectrum) else: From 154efe6e20d9fce28e9f690b0e3e23135af34d6d Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 10:17:40 -0400 Subject: [PATCH 44/52] Update base.py --- python/snewpy/models/base.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 64d0ca472..ab57ff7f8 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -352,7 +352,11 @@ def __init__(self, filename, metadata={}): if flavor in self.initial_spectra: self.initial_spectra[flavor].append(spectrum) else: - self.initial_spectra[flavor] = [spectrum] + self.initial_spectra[flavor] = [spectrum] + + # Transpose so that rows=energy and cols=time. + for k, v in self.initialspectra.items(): + self.initial_spectra[k] = np.transpose(self.initial_spectra[k]) for flavor in ThreeFlavor: self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') From 856922b1ff439f90913e0cf3438341f69ba74f2c Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 10:25:26 -0400 Subject: [PATCH 45/52] Update base.py --- python/snewpy/models/base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index ab57ff7f8..6334b04b0 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -355,7 +355,7 @@ def __init__(self, filename, metadata={}): self.initial_spectra[flavor] = [spectrum] # Transpose so that rows=energy and cols=time. - for k, v in self.initialspectra.items(): + for k, v in self.initial_spectra.items(): self.initial_spectra[k] = np.transpose(self.initial_spectra[k]) for flavor in ThreeFlavor: From 71ae985f44fe0b424b0f5b6c04c58112f264f07f Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 10:31:05 -0400 Subject: [PATCH 46/52] Update base.py --- python/snewpy/models/base.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/python/snewpy/models/base.py b/python/snewpy/models/base.py index 6334b04b0..623ab76de 100644 --- a/python/snewpy/models/base.py +++ b/python/snewpy/models/base.py @@ -354,10 +354,6 @@ def __init__(self, filename, metadata={}): else: self.initial_spectra[flavor] = [spectrum] - # Transpose so that rows=energy and cols=time. - for k, v in self.initial_spectra.items(): - self.initial_spectra[k] = np.transpose(self.initial_spectra[k]) - for flavor in ThreeFlavor: self.interpolation[flavor] = interpolate.RegularGridInterpolator((self.time, self.energy), self.initial_spectra[flavor], method='cubic') From e9047712136ba6b46dc27dd5f9218eb240eb63bb Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 12:06:11 -0400 Subject: [PATCH 47/52] Update model_files.yml --- python/snewpy/models/model_files.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/snewpy/models/model_files.yml b/python/snewpy/models/model_files.yml index 5e05efb21..6ded2ea58 100644 --- a/python/snewpy/models/model_files.yml +++ b/python/snewpy/models/model_files.yml @@ -6,7 +6,7 @@ config: - &snewpy "https://github.com/SNEWS2/snewpy/raw/v{snewpy_version}/models/{model}/{filename}" - &ccsn_repository "https://github.com/SNEWS2/snewpy-models-ccsn/raw/v0.4/models/{model}/{filename}" - &presn_repository "https://github.com/SNEWS2/snewpy-models-presn/raw/v0.2/models/{model}/{filename}" - - &presn_repository_main "https://github.com/SNEWS2/snewpy-models-presn/raw/master/models/{model}/{filename}" + - &presn_repository_main "https://github.com/SNEWS2/snewpy-models-presn/raw/main/models/{model}/{filename}" - &pisn_typeIa_repository "https://github.com/SNEWS2/snewpy-models-pisn_typeIa/raw/v0.2/models/{model}/{filename}" models: From f8717882f0811c45985e76f3f7db687cb3a2558d Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 12:23:30 -0400 Subject: [PATCH 48/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index 502935748..9e6aead26 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -13,7 +13,10 @@ distance = 1000*u.pc @pytest.mark.parametrize('model_class,model_params',[ - (pisn.Wright_2017, {'progenitor_mass': 250*u.Msun, 'eos': 'Helm'}), + (pisn.Wright_2017_150SFHo, {'progenitor_mass': 150*u.Msun, 'eos': 'SFHo'}), + (pisn.Wright_2017_150Helm, {'progenitor_mass': 150*u.Msun, 'eos': 'Helm'}), + (pisn.Wright_2017_250SFHo, {'progenitor_mass': 250*u.Msun, 'eos': 'SFHo'}), + (pisn.Wright_2017_250Helm, {'progenitor_mass': 250*u.Msun, 'eos': 'Helm'}) ]) @pytest.mark.parametrize('transformation',[AdiabaticMSW(MixingParameters(mh)) for mh in MassHierarchy]) @@ -25,4 +28,4 @@ def test_pisn_rate(model_class, model_params, transformation): dNdE = rc.run(fluence, detector='wc100kt30prct', detector_effects=False)['ibd'] #the factor of 0.5 is because the mass of SuperK is 50kt, not 100kt Nevents = 0.5 * dNdE.integrate_or_sum('energy').array.squeeze() - assert 1 Date: Fri, 19 Jun 2026 12:55:29 -0400 Subject: [PATCH 49/52] Update test_pisn_rates.py --- python/snewpy/test/test_pisn_rates.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/snewpy/test/test_pisn_rates.py b/python/snewpy/test/test_pisn_rates.py index 9e6aead26..2e721ab82 100644 --- a/python/snewpy/test/test_pisn_rates.py +++ b/python/snewpy/test/test_pisn_rates.py @@ -13,10 +13,10 @@ distance = 1000*u.pc @pytest.mark.parametrize('model_class,model_params',[ - (pisn.Wright_2017_150SFHo, {'progenitor_mass': 150*u.Msun, 'eos': 'SFHo'}), - (pisn.Wright_2017_150Helm, {'progenitor_mass': 150*u.Msun, 'eos': 'Helm'}), - (pisn.Wright_2017_250SFHo, {'progenitor_mass': 250*u.Msun, 'eos': 'SFHo'}), - (pisn.Wright_2017_250Helm, {'progenitor_mass': 250*u.Msun, 'eos': 'Helm'}) + (pisn.Wright_2017, {'progenitor_mass': 150*u.Msun, 'eos': 'SFHo'}), + (pisn.Wright_2017, {'progenitor_mass': 150*u.Msun, 'eos': 'Helm'}), + (pisn.Wright_2017, {'progenitor_mass': 250*u.Msun, 'eos': 'SFHo'}), + (pisn.Wright_2017, {'progenitor_mass': 250*u.Msun, 'eos': 'Helm'}) ]) @pytest.mark.parametrize('transformation',[AdiabaticMSW(MixingParameters(mh)) for mh in MassHierarchy]) From a54745075dea29834a9e35002efdb45a7e62bbac Mon Sep 17 00:00:00 2001 From: jpkneller Date: Fri, 19 Jun 2026 14:24:29 -0400 Subject: [PATCH 50/52] Create test_typeIa_rates.py --- python/snewpy/test/test_typeIa_rates.py | 29 +++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 python/snewpy/test/test_typeIa_rates.py diff --git a/python/snewpy/test/test_typeIa_rates.py b/python/snewpy/test/test_typeIa_rates.py new file mode 100644 index 000000000..3f87990b8 --- /dev/null +++ b/python/snewpy/test/test_typeIa_rates.py @@ -0,0 +1,29 @@ +import astropy.units as u +import numpy as np +from snewpy.models import typeIa +from snewpy.neutrino import MassHierarchy, MixingParameters +from snewpy.flavor_transformation import AdiabaticMSW +from snewpy.rate_calculator import RateCalculator +import pytest + +pytestmark=pytest.mark.snowglobes + +rc = RateCalculator() + +distance = 1*u.kpc + +@pytest.mark.parametrize('model_class,model_params',[ + (typeIa.Wright_2016, {'mechanism': 'GCD'}), + (typeIa.Wright_2016, {'mechanism': 'DDT'}) +]) +@pytest.mark.parametrize('transformation',[AdiabaticMSW(MixingParameters(mh)) for mh in MassHierarchy]) + +def test_typeIa_rate(model_class, model_params, transformation): + model = model_class(**model_params) + times = model.get_time() + energies = np.linspace(0,40,201)< Date: Mon, 10 Aug 2026 09:15:54 -0400 Subject: [PATCH 51/52] Update ccsn_loaders.py --- python/snewpy/models/ccsn_loaders.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/python/snewpy/models/ccsn_loaders.py b/python/snewpy/models/ccsn_loaders.py index f52ee9eec..d45aac107 100644 --- a/python/snewpy/models/ccsn_loaders.py +++ b/python/snewpy/models/ccsn_loaders.py @@ -1069,3 +1069,26 @@ def __init__(self, filename, metadata={}): tf.close() super().__init__(simtab, metadata) + +class Analytic3Species(PinchedModel): + """An analytical model calculating spectra given total luminosity, + average energy, and rms or pinch, for each species. + """ + + param = "There are no repository files available for this class. Use `doc/scripts/Analytic.py` in the SNEWPY GitHub repo to create a custom input file." + + def get_param_combinations(cls): + print(cls.param) + return [] + + def __init__(self, filename): + """ + Parameters + ---------- + filename : str + Absolute or relative path to file with model data. + """ + + simtab = Table.read(filename,format='ascii') + self.filename = filename + super().__init__(simtab, metadata={}) From f22d33200bdc5e9248367419dd103211d21620d2 Mon Sep 17 00:00:00 2001 From: jpkneller Date: Mon, 10 Aug 2026 09:21:37 -0400 Subject: [PATCH 52/52] Update ccsn.py --- python/snewpy/models/ccsn.py | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/python/snewpy/models/ccsn.py b/python/snewpy/models/ccsn.py index 6d5362641..429aa4656 100644 --- a/python/snewpy/models/ccsn.py +++ b/python/snewpy/models/ccsn.py @@ -457,26 +457,3 @@ def __init__(self, *, Bfield:str, direction:str, rotation:int=None, grav:str=Non filename = f'{Bfield}_b12_3d_{rotation}deg_snewpy_{direction}.dat' return super().__init__(filename=filename, metadata=self.metadata) - -class Analytic3Species(PinchedModel): - """An analytical model calculating spectra given total luminosity, - average energy, and rms or pinch, for each species. - """ - - param = "There are no input files available for this class. Use `doc/scripts/Analytic.py` in the SNEWPY GitHub repo to create a custom input file." - - def get_param_combinations(cls): - print(cls.param) - return [] - - def __init__(self, filename): - """ - Parameters - ---------- - filename : str - Absolute or relative path to file with model data. - """ - - simtab = Table.read(filename,format='ascii') - self.filename = filename - super().__init__(simtab, metadata={})