minuit_fitting_preLL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.7 +/- 1.9\n",
      "19.3 +/- 1.7\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import stats\n",
    "from scipy.integrate import quad\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "import seaborn as sns\n",
    "from scipy.special import factorial\n",
    "import math\n",
    "from iminuit import cost, Minuit \n",
    "import numpy as np\n",
    "\n",
    "sns.set_context('talk', font_scale=1.0)\n",
    "sns.set_palette('colorblind')\n",
    "\n",
    "from depth_helpers import *\n",
    "\n",
    "Cs_line = source_dict['Cs-137']\n",
    "\n",
    "datpath = '/home/cosilab/CalibrationData/CalibratedDATs/'\n",
    "plot_dir = '/home/cosilab/CalibrationData/figures/'\n",
    "AC_illumination_CTD = '/home/cosilab/CalibrationData/CTDs/20231107_Am241_AC_prerad/CTD_parameters.txt'\n",
    "DC_illumination_CTD = '/home/cosilab/CalibrationData/CTDs/20231107_Am241_DC_prerad/CTD_parameters.txt'\n",
    "CTD_sims = '/home/cosilab/COSITrapping/ctd_sims/CTD_HP414183.csv'\n",
    "AC_sim_events = '/home/cosilab/COSITrapping/ctd_sims/am241_interactions_HP414183_ac.csv'\n",
    "DC_sim_events = '/home/cosilab/COSITrapping/ctd_sims/am241_interactions_HP414183_dc.csv'\n",
    "sim_dCCE = '/home/cosilab/COSITrapping/cce_sims/HP414183_dCCE_1000cm.csv'\n",
    "\n",
    "calibrator = DepthCalibrator_Am241(AC_illumination_CTD, DC_illumination_CTD, AC_sim_events, DC_sim_events, CTD_sims)\n",
    "sim_depth, sim_ctd = calibrator.get_simdata()\n",
    "\n",
    "# plt.figure()\n",
    "# plt.hist(calibrator.AC_sim_CTD + np.random.normal(loc=0.0, scale=21.9, size=calibrator.AC_sim_CTD.shape), bins=100, label='Extra noise')\n",
    "# plt.hist(calibrator.DC_sim_CTD + np.random.normal(loc=0.0, scale=19.6, size=calibrator.DC_sim_CTD.shape), bins=100, color='C0')\n",
    "# plt.hist(calibrator.AC_sim_CTD + np.random.normal(loc=0.0, scale=12.0, size=calibrator.AC_sim_CTD.shape), bins=100, histtype='step', color='red', label='Original noise')\n",
    "# plt.hist(calibrator.DC_sim_CTD + np.random.normal(loc=0.0, scale=12.0, size=calibrator.DC_sim_CTD.shape), bins=100, histtype='step', color='red')\n",
    "# plt.legend()\n",
    "# plt.xlabel('CTD (ns)')\n",
    "# plt.ylabel('Counts')\n",
    "# plt.tight_layout()\n",
    "# plt.savefig(plot_dir + 'sim_CTD_noise_comparison.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Look at all Cs137 data prior to Loma Linda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 4.04\n",
      "FWTM = 9.41\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 2.7\n",
      "FWTM = 5.29\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t377dusoAAICIIfGygCNAx8ykJBodUY+kJOnSS91bANbq3p3YQ1iReAHRLitL+vTTSNcCSEwff8yoRoQVaTwQ7crLpcWL3VsA1lq6lNhDWJF4AdGurEyaOtW9BWCt228n9hBWJF4AAAAWIfECAACwCJ3rgWiXnCyNGuXeArDWsGHuR42B4i8zU7LZrKsTYhqJFxDtHA73kkEArPf++1KrVoHLFBcz8hFB41EjEO1cLumBB9xbAEBMI/ECop3LJT34IIkXYJXMTHcr1hdfuL//4gv397VfhYWRrSNiFo8aAQCozWZzPzqsqnJ/73DwKBFhQ4tXAB988IFGjRqltm3bKi0tTa1bt9aYMWO0efPmSFcNAADEIBKvAE6fPq0ePXrod7/7ndasWaOnn35ap06d0tChQ/XRRx9FunpIFKmp0pQp7i0A6xB7MAGPGgO47rrrdN1113ntu/baa9WiRQs9//zzGjBgQIRqhoSSkeFeMgiAtYg9mIAWrwZyOBxKS0tTKv8DglVKS6VbbnFvAViH2IMJYirxOn78uF544QXNmDFDgwcPVmZmpmw2m/r37x/U+evXr9eYMWPUsmVLZWRkqHv37pozZ46cTmfA86qrq1VRUaFDhw7p9ttvl81m09SpU8PxkYD6VVRIS5a4twCsQ+zBBDH1qPHll1/WXXfd1ahzn376ac2YMUOGYahdu3Zq3769du3apXnz5mnFihXauHGjmjdv7vPcb3/721r91QSWrVq10rvvvqsePXo0+nMAAIDEFFMtXjk5ORo5cqRmzZql5cuX65FHHgnqvPz8fM2cOVOStGDBAh06dEhbt27V/v371a9fPxUUFARswXr66ae1ZcsWvf766xowYIBGjx6tf/3rX+H4SAAAIIHEVIvX5MmTNXnyZM/3S5cuDeq8hx56SNXV1Zo0aZKmTZvm2d+2bVu99NJL6t69u1auXKmdO3eqV69edc7v0qWL5+uxY8dqyJAhuvPOO7V9+/ZGfxYgaGlp0ty57i0A6xB7MEFMtXg1RnFxsVatWiVJXklXjS5duujqq6+WJL366qv1vl9Nn7Ldu3eHt6KAP2lp7iWD+OUPWIvYgwniPvHatm2bXC6X0tLSNHDgQJ9lhg4dKknatGlTve9XWVmpjRs36pJLLglrPQG/nE7pmmvcWwDWIfZggph61NgYe/bskSR16NDB7xQQnTt3lqQ6rVg/+tGP1LFjR+Xl5alFixY6cuSIFi9erO3bt2v58uXmVhyoUVUlrVnz9fIlAKxB7MEEcZ94nTp1SpL8jlisfez06dNe+wcNGqQXXnhBf/rTn3T27Fk1b95cV1xxhdatW6dhw4b5fb8FCxZo4cKFQdWvoKAgqHIAACD2xX3iVVZWJkmy2+1+y6R99fy+9LxJ8m6//XbdfvvtDb7msWPHtHXr1gafBwAA4lvcJ17p6emSpPLycr9lXC6XJCkjIyMs12zTpo3y8vI831cFaKbes2dPnYQPccgwpJKSwGX89SNJT5cWLXJvAViH2IMJ4j7xatasmaSvHzn6UnOspmyopk+frunTp3u+t9lsYXlfxLCSEikrK2ARh78Ddrt72RIA1iL2YIK4H9XYtWtXSdKhQ4dU4WfZh3379nmVBaJKcbF02WXuLQDrEHswQdy3eOXl5clut8vlcmnLli0aPHhwnTIbNmyQ5O5Mb4biAEE7ZMgQJmJNNIWFkqNu+5bTValWD65xF8nM/PpAdbW0a5d7C8A6xB5MEPctXllZWbrmmmskyedIw71792rdunWSpPHjx1taNyQoh8PvqyQ1XSWp6RKPpwEgLsV94iVJc+bMkc1m07Jly7Rw4UIZhiHJPfpwwoQJqq6u1rhx49S7d29Trp+VleX3RWsXAACJI6YSr8OHD6tFixae1x133CFJ2rFjh9f+xx57zOu8AQMG6IknnpDk7viem5urvLw8derUSfn5+erWrZsWLVpk+ecBgpKZKa1a5d4CsA6xBxPEVB+vqqoqnTx5ss7+yspKr/0lPobtz5w5Uz179tT8+fO1efNmnThxQrm5uRo/frxmz56trHpGnIWCPl4ISUqKe9kSANYi9mCCmEq8Onbs6HlM2BgjRozQiBEjwlij4Dh8dKSukZQUU42OiIRz56R27aQjR6ScnEjXBkgcxB5MEFOJV6xyBlhgtZrRMghGUVGkawAkJmIPYUbiZQEzH2MCAIDYwXMuAAAAi9DiZQE61yMkDof0ySc+J10FYCJiDyYg8bIAnetRm9NVKaVU1t1f7mcx9aQkqX179xaAdYg9mIDEC7BYqwfXuGenD1ZRkdSkiXT2LCOrACsRezABiZcFGNUIAAAkEi9LMKoR5zs+d5Qc9mS/xzMDHAMAxC4SLyACHPZkOdIIPwBINPzmtwCjGhGS7Gx3H5Ps7EjXBEgsxB5MQOJlAUY1IiTV1dLhw1L37lIyjyAByxB7MAF/9YFo53RKl1/u3gKwDrEHE9DiZQFGNQIAAInEyxKMagQAABKPGoHYQOdeIDKIPYQZLV4WYFQjQpKTI507F+laAImH2IMJaPGygMPh8PtiVCPqVVkprV7t3gKwDrEHE/BXH4h2JSXStde6twCsQ+zBBDxqBAAkDGd5Vb1lMu3JstlsFtQGiYjECwCQMFo/uKbeMkUPj2ZJL5iGnywLMI8XQpKUJF16qXsLwDrEHkxA4mUB5vFCSLKypE8/jXQtgJiVaU9W0cOjA5ZxllfVbQ0j9mAC0ngg2pWXS4sXu7cAGsxms8mRlhL4ZfexFiOxBxOQeFmguLjY76tPnz6Rrh6iXVmZNHWqewvAOsQeTMCjRgs4HA6/x5jHCwCAxMFffQAAAIuQeAHRLjlZGjXKvQVgHWIPJuBRIxDtHA73siUArEXswQS0eAHRzuWSHnjAvQVgHWIPJiDxAqKdyyU9+CC//AGrEXswAY8aLcDM9QAAQCLxsgQz1wMAAIlHjUD0S02VpkxxbwFYh9iDCWjxskBxcbHfY0OGDNH27dutqwxiT0aGe9kSANYi9mACU1u8/ve//+m1117Tm2++qTNnzph5qajmcDj8vpi5HvUqLZVuucW9BWAdYg8mCOmv/scff6zJkydr/vz5dY69/PLL6tixo8aPH6/rr79eHTp00GuvvRbK5YDEVFEhLVni3gKwDrEHE4SUeP31r3/VX/7ylzqtNl988YWmTJmi0tJSGYYhwzBUXFysH/7wh9q3b19IFQYAAIhVISVe//rXvyRJY8eO9dq/cOFClZaWqlevXtq7d68OHz6sYcOGqby8XE899VQolwQAAIhZISVex44dk81mU25urtf+d955RzabTfPmzVPnzp110UUX6cknn5RhGFq3bl1IFQYSTlqaNHeuewvAOsQeTBDSqMaTJ0+qadOmSkn5+m1KS0u1fft2paWladSoUZ79vXr1kt1u18GDB0O5JJB40tLcy5YAsBaxBxOE1OKVkpKic+fOee376KOPVFVVpf79+8tut3sdy8rKUmVlZSiXBBKP0yldc417C8A6xB5MEFLi1bFjR1VVVemjjz7y7HvzzTdls9k0ePBgr7JVVVU6e/asLrzwwlAuCSSeqippzRr3FoB1iD2YIKTE61vf+pYMw9D//d//afPmzXr99de1cOFCSdJ3vvMdr7L/+c9/VFVVpXbt2oVySQAAgJgVUh+ve+65R3/5y1+Un5+vK6+8UpJkGIauvvpqz/c1ajrcDxo0KJRLAgAAxKyQWrwuuugirV+/XsOHD1d6erpat26tqVOnasWKFV7lDMPQc889J8MwNHz48JAqHIucTqffV3V1daSrh2iXni4tWuTeArAOsQcThLxWY+/evbV27dqAZaqrq/Xee+9JcidriSYrKyvSVUAss9vdy5YAsBaxBxNYslBgcnKycnNzlZub6zX1BIAgFBdLl13m3gKwDrEHE4SUeF188cX6xje+EXT5oUOHqnPnzqFcMiYVFxf7ffXp0yfS1UO0q66Wdu1ybwFYh9iDCUJqfjp48KDKysqCLn/kyBEdOnQolEvGJIfD4ffY+etcIgYZhlRSErgM8wABABSGPl4NUVlZSaKB+FNSItGPDwAQBMuyoNLSUp04cULZ2dlWXRKID5mZ0qpV7i0A6xB7MEGDWrwOHTpUZ63F8vJybdiwQYZh+DzHMAydOXNGL774oioqKtSzZ89GVxaIeoWFkp9Hy05XpVo9uEYlKQ1ccDclxb1sCQBrEXswQYMSr+eee06/+tWvvPadPn1aV111Vb3nGoYhm82m6dOnN6iCQExxOPwmXkqpVElqI+YDOndOatdOOnJEyskJrX4AgkfswQQN7uNVu2XLZrP5bemqXSYnJ0eXX365br31Vv3whz9seC2BRFdUFOkaAImJ2EOYNSjxmjt3rubOnev5PikpSa1bt9YXX3wR9ooBAADEm5BGNU6aNElNmzYNU1WA2Od0VUoplb6PlVdZXBsAQLQJKfFaunRpmKoBxIdWD65pXD+uQBwO6ZNP/PcdA2AOYg8mYFItINolJUnt27u3AKxD7MEEYZlAtaioSG+//bZ27typU6dOqaKiwm9Zm82mJUuWhOOyQNQ6PneUHPZkv8czAxyro6hIatJEOnuWkVWAlYg9mCDkxGvp0qWaMWOGimstIuprpGPNCMhYSrxWrFihv/3tb/roo490/PhxtWvXTt/+9rf1y1/+UhdccEGkq4co5rAny5HGgvAAAG8h/WVYvXq1pkyZIsMwlJ6erkGDBqlt27ZKSYmPPziPP/642rZtq1/96lfq0KGDCgoK9MADD2jVqlXatm2bMpnNGAAANEBIGdJjjz0mwzA0aNAgvfHGG2rRokW46hUV3nrrLbVs2dLz/bBhw9SlSxeNHDlSK1as0MSJEyNYOwAAEGtCSrzy8/Nls9m0dOnSuEu6JHklXTUGDBggSTpy5IjV1UEIDMNQSZDTOWTak2Wz2UyuUQNkZ7v7mLDOKWAtYg8mCCnxqqysVFZWlrp06RKu+gR0/PhxrV27Vh999JE+/vhjbdu2TaWlperXr58+/vjjes9fv3695s+fr82bN6u4uFi5ubn63ve+p1mzZskR5HDh9evXS5J69OgR0meBtUrKq5R939+DKlv08Oiv+2cZhlRSEvgEpzPE2tWjulo6fFjq3l1KbkCnfAChIfZggpASr86dO2v37t2qqqpSsgU/lC+//LLuuuuuRp379NNPa8aMGTIMQ+3atVP79u21a9cuzZs3TytWrNDGjRvVvHnzgO9x+vRp3XXXXerZs6e+853vNKoeiH5eE506nXJc0DRidampgy6/nJFVgNWIPZggpMTrxz/+sWbNmqW///3vGjNmTLjq5FdOTo5Gjhyp/v37q3///tqzZ4/uvffees/Lz8/XzJkzJUkLFizQ1KlTZbPZ9MUXX2js2LHKz8/X1KlTtWLFCr/vUVZWphtuuEGnT5/W22+/bUmiCT+CaYU6n6tSmRVlkqT9s0fUmerBmZKm1r/6hySp9YNrPPszK8pULAAAwiOkxGvmzJlasWKFbrvtNnXr1s30R46TJ0/W5MmTPd8HO3P+Qw89pOrqak2aNEnTpk3z7G/btq1eeuklde/eXStXrtTOnTvVq1evOudXVFRo/Pjx2rJli9asWaNLL7005M+CEJSUSFlZDTrFIX2dQD3lo8DJM/W+R6ufPi9naroK547yOVWE01Wpkoffb1C9AACJJaTE66WXXtLEiRP1y1/+Ur1799b48eN1xRVXKLuejoiTJk0K5bINUlxcrFWrVkmSV9JVo0uXLrr66qu1du1avfrqq3USr6qqKk2YMEFr167VW2+9pcGDB1tSb3ytTsd4V6XCvYBHpj1ZRQ+PrnvA6fQkas7UdPdyQA6H5GuOrpRKyaxO+XTuBSKD2EOYhZR43XzzzZ7RX4Zh6MUXX9SLL74Y8BybzWZp4rVt2za5XC6lpaVp4MCBPssMHTpUa9eu1aZNm7z2V1dX66abbtIbb7yh5cuX61vf+pYVVcZ5zu8YX/vxX00rVI1ArVGtvnqE6CnjdEqtWkmSbCUlcjh8JE2Vrrrv5Wd0pGmLYOfkSOfOmfPeAPwj9mCCkBKvDh06RNewex/27NkjyV3X1NRUn2U6d+4sSdq9e7fX/jvuuEMvvviiZs6cqVatWnklZu3atVO7du1MqjWC5WmFqhGgNcpTzleZrxKwYNTuA2aJykrpvfekESOkOJmcGIgJxB5MENJP0sGDB8NUDfOcOnVKkgKOWKw5dvr0aa/977zzjiTp97//vX7/+997HZs7d64eeOABn++3YMECLVy4MKj6FRQUBFUObsfnjpKjoszz+K9w7ig5U9M9yZDlrVFWKCmRrr2WkVWA1Yg9mCDuU/iyMvdINrvd7rdMWlqaJKm0tNRrf2MTy2PHjmnr1q2NOheBOezJciR9/WPrSEuRUr4eodig1qjMTKk4uDGLhmGoMCUt6D5cDVoEG0BsC2Yuv8xM8/qAIqbEfeKVnu5+vFReXu63jMvl7seTkZERlmu2adNGeXl5QZUtKCiok/DFpcZMAVGj1lQQcjp99rtqFJvN/dgxmKJS2Dv0A4gTwXRVKC4O+vcN4lvcJ17NmjWT9PUjR19qjtWUDdX06dM1ffp0z/fOAP8bGjJkiLZv3x6W60a1RkwBUaO+qSD8jkj0I+Zao5KSpEsvdW8BWIfYgwlCSrxqz6kVLJvNpiVLloRy2Qbp2rWrJOnQoUOqqKjw2cF+3759XmXDLauRCUfMiPCyOjabzedIxriRlSV9+mmkawEknkCxF0xXhVojp4EaIf21Wrp0qWw2mwzD8Hn8/BGPhmFYnnjl5eXJbrfL5XJpy5YtPufh2rBhgyRp0KBBltUrrjS0NauwsEFN7j6ngqiRmRn8dWNVebn0/PPSpElSgL6KAMIsUOw1oKsCUFtIidekSZMCTidx9uxZffzxxzpy5IguuOACS5YVOl9WVpauueYavfXWW1q4cGGdxGvv3r1at26dJGn8+PGm1KE4wP+KEuZRY20OR8N+YdU3FUS8KyuTpk6Vvv99Ei/ASsQeTBByi1d9DMPQ0qVL9dOf/lQ5OTl68sknQ7lko8yZM0dvv/22li1bpsGDB3vWajx27JgmTJig6upqjRs3Tr1797a8bnHHR2tWnZnnk+2Sq9Ln6Zn25KifGw4AgMYyvenAZrPpJz/5ic6cOaN77rlH3/zmN3XjjTc26r0OHz6svn37er6vGY24Y8cOtWjRwrP/5z//uX7+8597vh8wYICeeOIJ/exnP9P06dM1b948tWjRQrt27ZLL5VK3bt20aNGiRn7C+sV9H6/afLRmlbgqlf3A3/2c4K3o4dHx3V8LAJDQLBuqccstt8hms+mZZ55p9HtUVVXp5MmTnlfNI7zKykqv/SU+OnrPnDlT//jHPzR69Gg5nU7t2rVLubm5uvfee/Xxxx97JW5AVElOlkaNcm8BWIfYgwksa1rIzs5WTk5OSP2ZOnbs6LcjfzBGjBihESNGNPr8xqKP19eOzx0lx3nTOTjLqwLOPB/Ts86Hg8MhrV4d6VoAiYfYgwksS7xOnTqlM2fOKDMRRqGdxxGgI3lSgs0P47AnB3yUaPk6iLHA5ZJ+/Wtp9mzpq1UWAFiA2IMJLPurP2vWLElSt27drLpk1HA6nX5f1dXVka4eop3LJT34oHsLwHTO8io5XZVyFpXI+fBv3FtXpecVypMXIKQWr+effz7g8bKyMh0+fFivvfaaCgoKPB3tE01Md663YHLUhsw8H3OzzgOIOV4t7zNelX6z0es4g4AQipB+cm6++eaghv7X/O9g0qRJ+r//+79QLgmrhbDUT7DifuZ5AAC+EtJfuw4dOgRMvFJSUtSsWTP17t1bEyZM0NVXXx3K5WIWnesRktRUacoU9xaAKXy2vJeVSXffLc2fL2dSasA+qHXmK5QkV6Vqevg6XZVSSqXnWmGfrzCYpxO1ZWa6Z9+H5UJKvA4ePBimasS3uOlcH8xSPwk4eMJ0GRnS4sWRrgUQ13y2vKdlSYsXuL/2M+lzjZLyKmXf5z1fYWZFmWr+233x/W/K+dUKHHWWPvOcEEIy1NCnE8XFLHkUITzfsYAzQB+omOpc39ClfhAepaXSHXdITz/tTsIAWKN27CWF1uJc+Oykr795yk8hkqGEQOJlgZjuXI/Iq6iQliyRnniCxAuwUu3YSws+8fLMV+h0+k+yfPD7OLKhg5z8PZ1wOqVWrYKvEEwRtsSrvLxc//jHP/Txxx/rxIkTstlsatmypQYMGKCRI0fKzgKjAIAE4Jmv0J7jbsWSO6lq5aOPmKOizNMa1urBNSr56nGk18jJhj5G5OlEVAtL4rVw4ULNmTNHX375pc/jLVq00Lx58zR16tRwXC7m0LkeAOJH0Cts2GxfJ0AplZ6kCokt5MTrF7/4hX772996poy46KKL1K5dO0nSkSNHdPToUf3vf//Trbfeqn379uk3v/lNqJeMOXHTuR6RkZYmzZ3LzNmA1fzEXmNW2PA7X2Gtx5H7Z49Q69/+O/AbMcgp5oX0V//999/X448/LsMwdOONN2rXrl06fPiwPvzwQ3344Yc6fPiwCgoKNH78eBmGoccff1wbNmwIV92BxJCWJj3wAIkXYLUwxl7NqElfrxrnr2PrU81jxEAvpomIaiG1eP3hD3+QJE2ZMkWLFi3yWaZbt2565ZVXNHXqVC1ZskTPPPOMhg4dGsplgcTidEo33CCtXEm/DcBKtWIvMzOTFTYQFiElXh988IGSkpL08MMP11t23rx5+vOf/6x//7ueZtQ4FDfTSSAyqqqkNWvcWwDWqRV7lqyw4XQqs6LM87UqU77+GnEjpJ+iL7/8Uk2aNNGFF15Yb9lWrVqpadOmfjvgxzOmkwAA1MfR4SLPhKv+pqGomXLClNnvYYmQEq/s7GydPXtWZWVlSk8PPFqjtLRURUVFysnJCeWSAAAkrJopJ0JeqDuYVjSWFTJFSIlXr1699M9//lN//vOfddtttwUs++c//1mVlZXq3bt3KJeMSUwngZCkp0uLFrm3AKxjRexlZtY711dtJSlhGmQTzESqzKRvipASrx/96Edav3697r77bqWlpWnKlCk+yy1evFh33323bDabJk6cGMolYxLTSSAkdrt0yy2RrgWQeKyIvVpzfWVmGip87Hq/RZ3lVY2aygLRJaTE6+abb9ayZcv0/vvva9q0afrVr36l4cOH66KLLpLNZtPhw4e1fv16HT16VIZh6KqrrtJNN90UrroDiaG4WLriCmnz5obNXg0gNBbHnukd+Gu1rvnFskKmC+kOJyUl6Y033tDkyZO1cuVKHT58WMuWLfMqUzOx6o033qglS5bQGRBoqOpqadcu9xaAdeIt9mrPpI+ICTm1zsnJ0fLly/XRRx/p5Zdf9qzVKEkXXnih+vfvrx/84AcaMGBAyJWNVUwnAQAApDAukj1gwICETq4CYToJAAAghbhkUHl5uXbu3KnPPvus3rKfffaZdu7cqYqKilAuCSSezExp1SrWXwOsRuzBBCElXn/729/Ut29f/f73v6+37MMPP6y+fftq+fLloVwyJhUXF/t99enTJ9LVQ7RLSZGuuca9BWAdYg8mCCnxWrFihSQFNUXElClTZBhGQiZeDofD74vpJFCvc+eknBz3FoB1iD2YIKQ0/pNPPpGkoCZF7devnyTpP//5TyiXRDgZhlRSErgMa4RFh6KiSNcASEzEHsIspMTriy++UJMmTYLqPJ6dna2mTZvq2LFjoVwS4VRSwrxQABCDnOVVAY+zlmP0CinxstvtKi0tDaqsYRgqLS1VampqKJcEACDh1TeDfchrOcI0IXUw6tSpk8rLy/Xhhx/WW/aDDz6Qy+VSbm5uKJeEWQoL3TMaB3oxsicyHA7pk0+Y+BCwGrEHE4SUDn/rW9/Sjh07NGvWLL333ntK8TPyo7KyUrNnz5bNZtOoUaNCuSTM4nA06peLYRgqqafJu74mcdQjKUlq3969BWCdKIu9THuyih4e7fc4aznGhpASrzvvvFPPPPOMNm7cqJEjR+p3v/ud+vbt61Vm69atuuuuu7Rx40alp6drxowZIVUY0aWkvErZ9/090tWIb0VFUpMm0tmz7hFWAKwRZbFn+lqOsERId7Bdu3ZasGCBbr75Zm3YsEH9+/dX69atlZubK5vNpgMHDqiwsFCGYchms2nhwoXq0KFDuOoeM1gyCAAASGFYMmjixIm64IILdPvtt+vgwYM6duxYnZGLF198sZ555hlde+21oV4uJiXKkkHH546Sw54csExmPccBAFEimOmEMjPdi2+fL5jpioJ5nzgUljbLb3/729q7d6/Wr1+vDz74QMePH5cktWnTRldeeaWGDx/ORKEJwGFPphkcAKJAo/vWuirl6e3bqlX95QsLffcPdjqDO79GcXHCDGII21/J5ORkjRw5UiNHjgzXW8aN4uJiv8eGDBmi7du3W1cZxJ7sbHcfk+zsSNcESCwxHHuN7WSfWVEm/3+xfGhIcgVJYUy84J8jQBZPSyDqVV0tHT4sde8uJfOoFrBMAsZeSUqasu58RZJUOHeU76cYDW3NClerWJwg8QKindMpXX551IysAhJGjMVefdNNBMNrSgqHQ/KVeGVmuh8NBl2xxOm/FQwSLwAA4oBl003YbAnTH8sMPOcCAACwCIkXEAtisHMvEBeIPYQZjxqBaJeTI507F+laAImH2LNOKHOGxRhavIBoV1kprV7t3gKwDrFnnVatpKyswK+GTMgaxWjxAqJdSYl07bUxM7IKiBsJHnv1TcKaaU+WLQ5aoKxG4gUAAOqobxLWoodHhzaKMphpKeJwri8SLwAAYL0EnZaCxMsCzgCdBqurqy2sCWJSUpJ06aXuLQDrJGDs1TcJq9cEq2gUEi8LZGVlRboKiGVZWdKnn0a6FkDiScDYs2wS1gSWOGk8EKvKy6XFi91bANYh9mACEi8LFBcX+3316dMn0tVDtCsrk6ZOdW8BWIfYgwloT7SAI0DnwaQE6jsAAECi468+AACARUi8gGiXnCyNGuXeArAOsQcT8KgRiHYOh3vZEgDWIvZgAlq8gGjnckkPPODeArAOsQcTkHgB0c7lkh58kF/+gNWIPZiAxAsAAMAiJF4AAAAWIfECol1qqjRlinsLwDrEHkzAqEYg2mVkuJctAWAtYg8moMULiHalpdItt7i3AKxD7MEEJF4BHDlyRHfccYcGDRqkzMxM2Ww2HTx4MNLVQqKpqJCWLHFvAViH2IMJSLwC+Pzzz/XKK6+oWbNmGjp0aKSrAwAAYhyJVwDf/OY3VVhYqHfffVff+973Il0dAAAQ40i8AkhK4p8HUSAtTZo7170FYB1iDyaIqczi+PHjeuGFFzRjxgwNHjzY0++qf//+QZ2/fv16jRkzRi1btlRGRoa6d++uOXPmyOl0mlxzIARpae5lS/jlD1iL2IMJYirxevnllzVx4kQ99dRT+uCDD1TagJEmTz/9tEaMGKF33nlH6enp6tGjhw4ePKh58+ZpwIABOnXqlIk1B0LgdErXXOPeArAOsQcTxFTilZOTo5EjR2rWrFlavny5HnnkkaDOy8/P18yZMyVJCxYs0KFDh7R161bt379f/fr1U0FBgaZOnWpizYEQVFVJa9a4twCsQ+zBBDE1gerkyZM1efJkz/dLly4N6ryHHnpI1dXVmjRpkqZNm+bZ37ZtW7300kvq3r27Vq5cqZ07d6pXr17hrjYAAICkGGvxaozi4mKtWrVKkrySrhpdunTR1VdfLUl69dVXLa0bAABILHGfeG3btk0ul0tpaWkaOHCgzzI1c3Rt2rTJyqoBwUlPlxYtcm8BWIfYgwli6lFjY+zZs0eS1KFDB6X6Wei0c+fOkqTdu3fXObZ8+XJJ7n5ikvT3v/9dLVu2VMuWLTVs2DCf77dgwQItXLgwqPoVFBQEVQ4JzG53L1sCwFrEHkwQ94lXzWjF5s2b+y1Tc+z06dN1jp0/ceptt90mSRo2bJj++c9/+ny/Y8eOaevWrY2pLlBXcbF0xRXS5s1SVlakawMkDmIPJoj7xKusrEySZLfb/ZZJ+2qOFl/TUxiG0eBrtmnTRnl5eUGVLSgoaNC0GEhA1dXSrl3uLQDrEHswQdwnXulfPZsvLy/3W8blckmSMjIywnLN6dOna/r06Z7vA03QOmTIEG3fvj0s1wUAANEt7hOvZs2aSVLACVJrjtWUDbcsmqgBAIASYFRj165dJUmHDh1SRUWFzzL79u3zKgtElcxMadUq9xaAdYg9mCDuE6+8vDzZ7Xa5XC5t2bLFZ5kNGzZIkgYNGmRKHYqLi/2++vTpY8o1EUdSUtzLlqTEfQM1EF2IPZgg7hOvrKwsXXPNNZLkc4qHvXv3at26dZKk8ePHW1o3ICjnzkk5Oe4tAOsQezBB3CdekjRnzhzZbDYtW7ZMCxcu9IxUPHbsmCZMmKDq6mqNGzdOvXv3NuX6WVlZfl90rEdQiooiXQMgMRF7CLOYSrwOHz6sFi1aeF533HGHJGnHjh1e+x977DGv8wYMGKAnnnhCknvEYW5urvLy8tSpUyfl5+erW7duWrRokeWfBwAAJJaYenBdVVWlkydP1tlfWVnptb+kpKROmZkzZ6pnz56aP3++Nm/erBMnTig3N1fjx4/X7NmzTR15WFxc7PcY00kAAJA4Yirx6tixY6MmNK0xYsQIjRgxIow1Co7D4fB7LCkpphodEQkOh/TJJ+4tAOsQezBBTCVesSrQBKrVzIiM+iQlSe3bu7cArEPswQQkXhZgAlWEpKhIatJEOnvWPcIKgDWIPZiANB4AAMAitHhZgM71AABAIvGyBJ3rAQCAxKNGIPplZ7v7mGRnR7omQGIh9mACWrwswKhGhKS6Wjp8WOreXUpOjnRtgMRB7MEEJF4WYFQjQuJ0SpdfzsgqwGrEHkzAo0YAAACL0OJlAUY1AgAAicTLEoxqRMjo3AtEBrGHMCPxAqJdTo507lykawEkHmIPJqC5xQJOp9Pvi1GNqFdlpbR6tXsLwDrEHkxAi5cFGNWIkJSUSNdey8gqwGrEHkxAixcAAIBFaPGyAKMaAQCAROJlCUY1IiRJSdKll7q3AKxD7MEEJF5AtMvKkj79NNK1ABIPsQcTkMYD0a68XFq82L0FYB1iDyYg8QKiXVmZNHWqewvAOsQeTMCjRgs4nU6/x5jHCwCAIAT4W+qRmSnZbObXJQQkXhZgHi8AAELUqlX9ZYqLpQAD2qIBjxqBaJecLI0a5d4CsA6xBxPQ4mUB5vFCSBwO97IlAKxF7EVeZqa7FSsQpzO41rAoQYuXBRwOh98X83ihXi6X9MAD7i0A6xB7kWezuRPg+l4xhL/6QLRzuaQHH+SXP2A1Yg8mIPECAACwCIkXAACARUi8gGiXmipNmeLeArAOsQcTMKoRiHYZGe5lSwBYi9iDCWjxsoDT6fT7ivaZ6w3DkNNV6f9VXhXpKsa/0lLpllvcWwDWIfZgAlq8LBDLM9eXlFcp+76/R7oaia2iQlqyRHriCff/wAFYg9iDCWjxAgAAsAgtXhaIl5nrj88dJYfd/9IZmQGOAQAAEi9LOALMqhtLM9c77MlypPEjY7m0NGnuXPcWgHWIPZiAv6JAtEtLcy9bAsBaxB5MEDvNLUCicjqla65xbwFYh9iDCUi8gGhXVSWtWePeArAOsQcTkHgBAABYhMQLAADAIiReQLRLT5cWLXJvAViH2IMJGNUIRDu73b1sCQBrEXswAS1eQLQrLpYuu8y9BWAdYg8mIPECol11tbRrl3sLwDrEHkzAo0YLOAPMAVNtVkAbhlRSErgMc9MAAGApEi8LZGVlWX/RkhIpEtcFAAB+8agRiHaZmdKqVe4tAOsQezABLV4WKA7QMXPIkCHavn27uRUoLJQCLNQtiV8s0Swlxb1sCQBrEXswAYmXBRwBkp6kJAsaHR2O+hMvRK9z56R27aQjR6ScnEjXBkgcxB5MwKNGIBYUFUW6BkBiIvYQZiReAAAAFiHxAgAAsAiJFxDtHA7pk0/opwdYjdiDCUi8gGiXlCS1b+/eArAOsQcT8NMERLuiIqlJEzr5AlYj9mACEi8AAACLkHgBAABYhMQLAADAIiReQLTLzpbOnnVvAViH2IMJSLzqcfjwYY0fP15NmjRRTk6Orr/+ev33v/+NdLWQSKqrpcOH3VsA1iH2YAISrwCcTqeGDx+uzz77TH/5y1+0bNkyff755xo+fHjAha+BsHI6pcsvd28BWIfYgwlYJDuARYsW6cCBA9q9e7cuueQSSVKvXr3UpUsXLViwQHfffXeEawgAAGIJLV4BvPnmm/rGN77hSbokqVOnTho8eLDefPPNCNYMAADEophKvI4fP64XXnhBM2bM0ODBg5WZmSmbzab+/fsHdf769es1ZswYtWzZUhkZGerevbvmzJkjp59m5E8//VSXX355nf2XXXaZdu3aFdJnARqEzr1AZBB7CLOYetT48ssv66677mrUuU8//bRmzJghwzDUrl07tW/fXrt27dK8efO0YsUKbdy4Uc2bN/c65/Tp02rWrFmd92revLlOnz7dqHoADZaTI507F+laAImH2IMJYqrFKycnRyNHjtSsWbO0fPlyPfLII0Gdl5+fr5kzZ0qSFixYoEOHDmnr1q3av3+/+vXrp4KCAk2dOtXnuTabrc4+wzAa/RmABquslFavdm8BWIfYgwliKvGaPHmy/vGPf+jXv/61brzxRrVp0yao8x566CFVV1dr4sSJmjZtmieZatu2rV566SUlJSVp5cqV2rlzp9d5zZo106lTp+q8n7+WMMAUJSXStde6twCsQ+zBBDGVeDVGcXGxVq1aJUmaNm1aneNdunTR1VdfLUl69dVXvY5ddtll+vTTT+ucs2vXLl166aUm1BYAAMSzuE+8tm3bJpfLpbS0NA0cONBnmaFDh0qSNm3a5LV/7Nix2rRpk/bv3+/Zd/DgQf373//W2LFjzas0AACISzHVub4x9uzZI0nq0KGDUlNTfZbp3LmzJGn37t1e+6dOnapnnnlG3/3udzVv3jzZbDbNmTNH7du31/Tp082tOFAjKUm69FL3FoB1iD1TGIahkvKqoMpm2pN99rX2K5jJbjMzpYa8Z5jFfeJV00fr/BGLtdUcO3+kosPh0Lp163TXXXdp4sSJMgxDI0aM0O9//3tlZWX5fb8FCxZo4cKFQdWvoKAgqHJIYFlZko9H3gBMRuyZoqS8Stn3/T2oskUPj5YjrQGpSqtW9ZcpLpYcjuDfM8ziPvEqKyuTJNntdr9l0tLSJEmlpaV1jnXo0EErVqxo0DWPHTumrVu3NugcwK/ycun556VJk6QAP8cAwozYgwniPvFKT0+XJJWXl/st43K5JEkZGRlhuWabNm2Ul5cXVNmCggKfCR/gUVYmTZ0qff/7/PIHrETsme743FFy2JO99jnLq9T6wTXBv0lmprsVKxCnM7jWMAvEfeJVM+2Dr2khatQcC9cUEdOnT/fqA+ZvZnxJGjJkiLZv3x6W6wIAEEsc9uSGPUr0xWaL6KPDhor7xKtr166SpEOHDqmiosJnB/t9+/Z5lQ23QP3BAABA4oj7oRp5eXmy2+1yuVzasmWLzzIbNmyQJA0aNMjKqgHBSU6WRo1ybwFYh9iDCeI+8crKytI111wjST5HGu7du1fr1q2TJI0fP96UOhQXF/t99enTx5RrIo44HO5lS2KoKR2IC8QeTBD3iZckzZkzRzabTcuWLdPChQs9ay0eO3ZMEyZMUHV1tcaNG6fevXtHuKaADy6X9MAD7i0A6xB7MEFMJV6HDx9WixYtPK877rhDkrRjxw6v/Y899pjXeQMGDNATTzwhyd3xPTc3V3l5eerUqZPy8/PVrVs3LVq0yLR6Z2Vl+X3RsR71crmkBx/klz9gNWIPJoipzvVVVVU6efJknf2VlZVe+0t8LGg6c+ZM9ezZU/Pnz9fmzZt14sQJ5ebmavz48Zo9ezYd4AEAgOliKvHq2LGj5zFhY4wYMUIjRowIY42CUxxgfhGmkwAAIHHEVOIVqxwBOmYmsQYY6pOaKk2Z4t4CsA6xBxOQeFkg0ASq1dXVFtYEMSkjQ1q8ONK1ABIPsQcT0NxiATrXIySlpdItt7i3AKxD7MEEJF5AtKuokJYscW8BWIfYgwl41GgBOtcDAABJshmhDBNEyJo3b67Tp08rIyNDPXr0CN8bV1dLNQldnz5SIzvxVxuGth89536bi3KUZLOFp34IXlWVtGOH1Ls3S5cAViL26gjH34T63sOUvzth+pvoT0FBgUpLS9WsWTOdOnUqYFkSrwjLzMxUKf0HAACIeRkZGT7nEq2NR40RduGFF+rEiRNKT09Xp06dLL9+TZYe9hY3mI57F7u4d7GN+xe7zLp3Bw4cUFlZmS688MJ6y5J4RdjBgwcjev1+/fpp69at6tGjh/Lz8yNaFzQM9y52ce9iG/cvdkXDvWNUIwAAgEVIvAAAACxC4gUAAGAREi8AAACLkHgBAABYhMQLAADAIiReAAAAFiHxAgAAsAiJFwAAgEVIvAAAACzCkkEJbtq0aTp27JjatGkT6aqggbh3sYt7F9u4f7ErGu6dzTAMI2JXBwAASCA8agQAALAIiRcAAIBFSLwAAAAsQuKVQL744gs1bdpUNptNNptNBw8eDFh+xYoVGj58uJo1ayaHw6E+ffrot7/9rSoqKgKeV1xcrPvvv1/du3dXRkaGWrZsqTFjxuif//xn+D5MHNu7d68ee+wxjR49WhdddJHsdruaNGmigQMH6je/+Y2Ki4vrfQ/uXfRav369xowZo5YtWyojI0Pdu3fXnDlz5HQ6I121uGUYhj744APNmjVLQ4YM0QUXXKDU1FS1bNlSo0aN0osvvqhA3Z0rKir0+OOPq3fv3nI4HGrevLmuvvpqrVy5st5r79+/X5MnT1a7du2Ulpam9u3ba8qUKTpw4EA4P2LCeffddz1/yzp27Oi3XFTeOwMJ47vf/a4hyfM6cOCA37J33323p1znzp2NXr16GcnJyYYk45vf/KZRVlbm87z//e9/Rrdu3QxJRlpamtG3b1+jXbt2hiTDZrMZf/jDH0z6dPGhsrLS6x61bt3a6N+/v+ffsOZ+HDx40O97cO+i11NPPWXYbDZDktGuXTujb9++RlpamiHJ6NGjh3Hy5MlIVzEurV271iuuLr74YqNfv35G8+bNPfuuu+46n7FRWlpqDBkyxJBkJCcnG7169TI6d+7sOe8Xv/iF3+t+8MEHRlZWliHJaNasmdGvXz+jadOmhiQjOzvb2Lx5s5kfO26dO3fOaN++vece5Obm+iwXrfeOxCtB/O1vfzMkGePGjas38Vq5cqXnj+8bb7zh2V9QUGB06tTJkGT87Gc/83nu2LFjDUlGv379jKNHjxqGYRjV1dXGggULPD/827ZtC/fHixsVFRVGTk6O8fOf/9woKCjwOvbhhx96/v2vuOIKn+dz76LXxx9/bCQlJRk2m81YsGCBUV1dbRiGYRw9etTo16+fIcm44YYbIlzL+PSPf/zD6NSpk/Hkk08ahYWFXseef/55T/Lr6w/xnXfeaUgyOnXqZHz22Wee/W+88YbnvDfffLPOeU6n02jTpo0hyZg8ebJRWlpqGIY7GfjJT37iSb5LSkrC/Gnj309/+lOvv2f+Eq9ovXckXgng5MmTRqtWrYz27dsb//nPf+pNvHr37m1IMn75y1/WOfbee+95/rCfOHHC69jWrVsNSUZSUpKxd+/eOudOnDiRPy71qK6uDtjq8e9//9tz/3wlQdy76FXT4jxp0qQ6x/bs2WMkJSUZkowdO3ZEoHbx7ezZs0Z5ebnf4w8//LAhyWjevLlRVVXl2X/8+HHDbrcbkox169bVOW/OnDmGJCMvL6/Osd/97neGJOOSSy4xXC6X1zGXy+VpeXnqqadC+GSJZ8OGDYbNZjOuv/5647nnnvObeEXzvSPxSgCTJk3yZPYHDhwImHjt2bPHc/zIkSM+3++SSy4xJBmLFy/22n/vvfcakoyRI0f6PG/jxo2GJCM9Pd0oLi4O+XMlqiZNmhiSjJdfftlrP/cuehUVFXn+h71x40afZUaOHGlIMu6//36La4ea/3hIMo4fP+7Zv3DhQs8fYF+OHDniOe/zzz/3OnbllVcakox58+b5PHfevHmGJGPIkCHh+yBxrrS01OjWrZuRnZ1tHDlyJGDiFc33js71cW7NmjV6/vnndeONN+o73/lOveU3bdokSerUqZMuuugin2WGDh3qVfb8c2uOn2/gwIGy2+0qKyvT9u3bg/0IqKWqqsrTQT4zM9PrGPcuem3btk0ul0tpaWkaOHCgzzL+7g3MV1ZW5vk6IyPD83V9cXHRRRepU6dOXmUld5x+/PHHAc+t2f/RRx+pqqoqhNonjl/96lfavXu3HnnkEb+/42pE870j8YpjTqdT06dPV05Ojp566qmgztmzZ48k6ZJLLvFbpnPnzpKk3bt3N+jc1NRUdejQwee5CM4bb7yhkpISpaSkaNCgQV7HuHfRq+bft0OHDkpNTfVZxt+9gfleeuklSVLv3r2Vk5Pj2d/YmDp48KDKy8sDnltznsvl0n//+98Qap8Ytm/frscff1wDBw7UbbfdVm/5aL53rNUYx+69914dPHhQf/jDH9S2bdugzjl16pQkqXnz5n7L1Bw7ffp02M5F/c6dO6d77rlHkjRlyhS1aNHC6zj3Lnrx7xu9tm7dqj/96U+SpFmzZnkda+x9qzkv0Lm193PPA6uqqtKUKVMkSQsXLlRSUv1tRtF872jxilMffvihnnnmGX3jG9/QrbfeGvR5NU3udrvdb5m0tDRJUmlpadjORWBVVVWaMGGCDhw4oI4dO+rRRx+tU4Z7F734941OhYWFuv7661VRUaHrr79eP/jBD7yON/a+1X506e/cmvPOPxd1/fa3v9XWrVv1s5/9TL179w7qnGi+dyReUebWW2/1TArXkNdVV13leY/y8nLdcsstSkpKCvp/BzXS09M97+GPy+WS5N0XItRz40E47p0vhmFo2rRpevfdd9WsWTO99dZbatKkSZ1y3Lvoxb9v9Dl79qxGjx6tQ4cOqV+/flq6dGmdMo29bzXnBTq35rzzz4W3vXv36oEHHlCnTp00d+7coM+L5nvHo8Yo06RJE7Vq1arB59Vu+nz00Ue1a9cuzZo1Sz179mzQ+zRr1kySd3Pr+WqO1ZStfW5JSUmjzo0H4bh3vtx5553685//rOzsbK1evVqXX365z3Lcu+gVyr1B+BUXF+vaa6/Vtm3bdNlll2n16tVefbtqNPa+1f761KlTPrt61H5P7rl/t956q8rKyvTss8/WGVAUSDTfOxKvKPPoo4/6fIzUEPn5+ZKkRYsW6bnnnvM6VnsExoABA5ScnKx77rnH03eoa9eukqTPP//c7/vv27fPq2yNrl276ujRo37Praio0KFDh3yeGw/Cce/Od8899+iZZ55RZmam3n33XQ0YMMBvWe5d9Kr5Nzt06JAqKip8drD3d28QXiUlJbruuuu0adMmde3aVWvXrtUFF1zgs2zXrl3173//u8Ex1bFjR9ntdpWXl+vzzz/3+ce75ry0tDTl5uaG8pHiWn5+vmw2m2666aY6x2oe8x0+fFitW7eWJK1cuVJXXnllVN87HjXGsZMnT6qwsNDr9eWXX3qOf/nllyosLPRa+69mpNyBAwd09OhRn++7YcMGr7I1vvGNb3gdP9+WLVtUXl6u9PR09enTp9GfK1Hcf//9mj9/vtLT0/Xmm29qyJAhActz76JXXl6e7Ha7XC6XtmzZ4rOMv3uD8CkrK9N3v/td/etf/1LHjh313nvvef5g+1ITFxs3bvR5/OjRo551+2rKSlJKSor69esnyX9M1eyv+Q8w/DMMo87fssLCQp07d06SVF1d7dlX83gwqu9do2b/QsyqbwJVwzCMnj171jv7ud1urzP7eX5+flCzn19//fVh+SzxrGYmbbvdbrzzzjtBn8e9i17f+c53gpq5fvv27RGoXfwrLy83vv3tb3uWe9m/f3+95xw/ftxITU2td/bzvn371jk2f/58zwSe58+aX3v28yeffLLxHyrB1TdzfbTeOxKvBBNM4rV8+XLP0jK117H67LPPPOv9zZw50+e5Y8aM8az398UXXxiG8fV6fzabzUhKSjLy8/PD/rniyZNPPmlIMlJSUoyVK1c26FzuXfTasmWLYbPZ6qzV+MUXX3jWahw3blyEaxmfKisrje9973uG5F54fs+ePUGfe/vtt/tc7+/NN9/0rEbw+uuv1zmvuLjYaN26dcD1/tq2bWs4nc7QP2CCCpR4GUb03jsSrwQTTOJlGIYxc+ZMT7nOnTsbvXv3NpKTkz3LJNT8IJ7vxIkTRpcuXTx//Pv27etZRd5ms7EuWT2OHj1q2Gw2Q5LRrFkzY/DgwX5fS5Ys8fke3Lvo9bvf/c5zf9u3b2/07dvX8wegW7duxv/+979IVzEu/fWvf/XERMeOHQPG1datW73OLSkpMQYNGmRI7oXie/fu7WnxkGTcfffdfq+7ceNGw+FweOK5X79+RrNmzQxJRlZWlvHhhx+a/dHjWn2JV7TeOxKvBBNs4mUYhvHKK68Yw4YNM5o0aWJkZGQYPXv2NB599NGAi80ahmGcO3fOuPfee42uXbsaaWlpRvPmzY3Ro0f7bO6Ft9r3p77X3Llz/b4P9y56rV271hg9erTRvHlzIy0tzejatatx7733GkVFRZGuWtyq+QMdzGv9+vV1zne5XMajjz5q9OzZ08jIyDCaNGliDBs2zFi+fHm91/7888+Nm266yWjbtq2RmppqtG3b1rj55puNffv2mfBJE0t9iZdhROe9sxmGYQTTFwwAAAChYVQjAACARUi8AAAALELiBQAAYBESLwAAAIuQeAEAAFiExAsAAMAiJF4AAAAWIfECAACwCIkXAACARUi8AAAALELiBQAAYBESLwA4T1VVlV555RVNmjRJXbt2VdOmTWW323XhhRdqyJAhmj17tj755BNJ0s033yybzdao11VXXeW55lVXXVXneEpKipo1a6aOHTtq1KhR+sUvfqGNGzdG6F8FQDiwSDYA1LJp0ybddNNN2rNnj2dfamqqsrOzdebMGVVXV3v233DDDbrwwgv12muv1Xmfqqoqffnll5KknJwcZWRk1Clz5ZVXauXKlZLcidf777+v1NRUNW/e3FOmqKhIJSUlXuf16NFDzz77rIYNGxbahwVgORIvAPjKW2+9pe9973tyuVy64IILdM899+jGG29Uly5dJLmTqW3btmnFihX64x//qHPnzun06dNq2rRpnfc6ePCgOnXqJEl67rnndPPNNwe8dk3iNWzYMP3zn//0OlZaWuq57uLFi3Xu3DnZbDb98Y9/1K233hqOjw7AIjxqBABJe/fu1Y9//GO5XC5deuml2r59u2bNmuVJuiQpOTlZ/fv3169//WsdOHBA3/3udy2pW0ZGhq688krNnz9fO3fuVK9evWQYhm6//XZt2LDBkjoACA8SLwCQdP/99+vcuXNKT0/Xa6+9pnbt2gUs37x5c73++utq0qSJRTV0y83N1RtvvCGHw6Gqqirde++9ll4fQGhIvAAkvMLCQi1fvlyS9KMf/Uhdu3YN+lybzWZWtfzq2LGj59Hlxo0btX//fsvrAKBxSLwAJLz169d7Os1ff/31Ea5NcK677jrP1++//34EawKgIUi8ACS8Tz/91PN13759I1iT4PXu3dvz9b59+yJYEwANQeIFIOGdPHnS83XtqRyiWe16njp1KoI1AdAQJF4AEh6z6gCwCokXgITXokULz9ex0npUu54XXHBBBGsCoCFIvAAkvMsuu8zz9bZt2yJYk+Dt2LHD83Xnzp0jWBMADUHiBSDhDR8+XElJ7l+Hvpb/iUbvvPOO5+vaaz4CiG4kXgASXqtWrXTjjTdKkv761796rdNYn0j0D/vvf/+rpUuXSpK++c1vqmPHjpbXAUDjkHgBgKR58+YpKytLpaWluuGGG3T06NGA5U+fPq0bb7xRZ8+etaiGbocOHdLYsWPldDqVnJysRx55xNLrAwgNiRcASOratauWLVsmu92uTz/9VH369NGjjz6qzz//3FOmZpHsX/7yl7r44ou1cuVKS+pWVlamDz/8UP/v//0/9erVSzt37lRSUpKeffZZDR482JI6AAiPlEhXAACixbhx47Ru3TrdfPPN+vzzzzVr1izNmjVLdrtdWVlZOnPmjGeGe5vNpgkTJsjhcIS1Dh988IFat27t+d7pdKq4uNirzGWXXaZnn31WQ4cODeu1AZiPxAsAahk8eLA+++wzvfrqq3r77be1efNmnThxQkVFRWrevLm6d++uYcOGaeLEierWrVvYr19RUaHCwkJJUnJysrKyspSbm6suXbooLy9PY8eOpZULiGE2g5kDAQAALEEfLwAAAIuQeAEAAFiExAsAAMAiJF4AAAAWIfECAACwCIkXAACARUi8AAAALELiBQAAYBESLwAAAIuQeAEAAFiExAsAAMAiJF4AAAAWIfECAACwCIkXAACARf4/R3P4UOBHm3kAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 4.41\n",
      "FWTM = 10.71\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 2.77\n",
      "FWTM = 5.37\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 4.21\n",
      "FWTM = 10.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 2.73\n",
      "FWTM = 5.33\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l5eUWtgQAAEQLSZcFMjMzo90EAAAQZVxeBGJFaqo0aZKvBGAN4g4moqfLAi6XK+i2nJwc5eXlWdcYxK70dN9yJACsQ9zBRPR0WcDpdAZ9JLFyPcJVVCTdequvBGAN4g4m4hMfiBWlpdKiRb4SgDWIO5iIpAsAAMACJF0AAAAWIOkCYoXDIc2e7SsBWIO4g4m4exGIFQ6HbzkSANYh7mAierqAWOF2S1dd5SsBWIO4g4lIuoBY4fVKq1b5SgDWIO5gIpIuAAAAC5B0hfDxxx9r5MiRat26tRwOh1q2bKnrrrtOGzdujHbTAABAjCHpCuHMmTPq3r27fvvb32rVqlV6/vnndfr0aQ0ZMkSffvpptJuHRJOWJi1Y4CsBWIO4g4m4ezGEa6+9Vtdee22V566++mo1a9ZML7/8sgYMGBClliEh2e2+5UgAWIe4g4no6aolp9Mph8OhVFach9VcLumSS3wlAGsQdzBRTCVdx48f15///GdNmzZNgwcPVkZGhmw2m/r37x/W/mvXrtV1112n5s2bKz09Xd26ddOsWbPkruFW4PLycpWWlurgwYO6/fbbZbPZNHnyZDPeEhC+8nJpxw5fCcAaxB1MFFOXF1977TXdddddddr3+eef17Rp02QYhtq0aaO2bdtqx44dmjNnjpYtW6b169eradOmAfe95pprtHLlSklSixYt9O6776p79+51fh8AACDxxFRPV3Z2tkaMGKEZM2Zo6dKleuyxx8LaLzc3V9OnT5ckzZs3TwcPHtSWLVu0b98+9evXTzt37gzZc/X8889r06ZN+tvf/qYBAwZo1KhR+te//mXGWwIAAAkipnq6Jk6cqIkTJ/q/X7x4cVj7PfLIIyovL9eECRM0ZcoU//OtW7fWq6++qm7dumn58uXavn27evbsWW3/zp07+78ePXq0cnJydOeddyovL6/O7wWotYwMacUKXwnAGsQdTBRTPV114XK5tGLFCkmqknBV6Ny5s6688kpJ0htvvFHj61WMIdu1a5e5DQVqkpLiW44kJab+VwJiG3EHE8V90rV161Z5PB45HA4NHDgwYJ0hQ4ZIkjZs2FDj65WVlWn9+vW66KKLTG0nEodhGHJ7ygI+KrhLAiw5kp8vZWf7SgDWIO5gorhP3Xfv3i1JateuXdBpHjp16iRJ1XqvfvrTn6pDhw7q27evmjVrpsOHD2vhwoXKy8vT0qVL67fhiFuFJV5lPfBeteczSotVcVP6hY9/IKUGmIyxoKB+GwegOuIOJon7pOv06dOSFPTOxMrbzpw5U+X5QYMG6c9//rP+7//+T2fPnlXTpk112WWXac2aNRo6dGjQ15s3b57mz58fVvt27twZVj0AgDkC9iRXkmFPls1ms6g1SCRxn3QVFxdLkux2e9A6DodDklRUVFTl+dtvv1233357rY957Ngxbdmypdb7IfEcnz1STnuy7xu3W3rO9+WJ2SMlp1OS7wMAgHlaPrwq5PaCR0fJ6Yj7j0dEQdz/VqV9u15WSUlJ0Doej0eSlJ6ebsoxW7Vqpb59+4ZVd+fOndWSPSQOpz35uz/uZd+Fo9ORIp37R9/plD77zJ+MAbAAcQcTxX3S1aRJE0nfXWYMpGJbRd1ITZ06VVOnTvV/H2rG+5ycHKaeQHiSkqS2bX0lgFrJsCer4NFRQbe7S7yBe8CIO5go7pOuLl26SJIOHjyo0tLSgIPp9+7dW6Wu2TIzM+vldZFgCgqkRo2ks2d9d1MBCJvNZqvbJUPiDiaK+9S9b9++stvt8ng82rRpU8A669atk+QbOA8AAFAf4j7pyszM1FVXXSVJAe8o3LNnj9asWSNJGjt2bL20weVyBX307t27Xo4JAAAalrhPuiRp1qxZstlsWrJkiebPny/DMCT57jIcN26cysvLdf3116tXr15RbikAAIhXMTWm69ChQ+rTp4//+4q7Drdt26ZmzZr5n7/vvvt03333+b8fMGCAnnnmGd19992aOnWq5syZo2bNmmnHjh3yeDzq2rWrFixYUG/tZkwXTJGV5RtXkpUV7ZYAiYO4g4liqqfL6/Xq1KlT/ofL5Zu/u6ysrMrzhYWF1fadPn263n//fY0aNUput1s7duxQ+/btNXPmTG3evLlK0gaYzjB883B9+8goLVZGaXGV5xTiLldJUnm5dOiQrwRgDeIOJoqpnq4OHTr4Lw3WxfDhwzV8+HATWxSeiuQwEKaMSBCFhdK3PZ5Oyb/cT8VkqGFxu6VLL+UuKsBKxB1MFFM9XQAAALEqpnq6YhVjulCZ++ARtXj6I0m+5X4Czh2UkWFxqwAA9Y2kC7Ca06nC1DT/19WW+wmFwbyA9Yg7mISkywKM6YIpsrOl/PxotwJILMQdTMSYLgs4nc6gjyTW80K4ysqklSt9JQBrEHcwET1dFgi14HU5tyEjXIWF0tVXcxcVYCXiDiYi6bIAA+kBAADXtgAAACxAT5cFGEgPUyQlSRdf7CsBWIO4g4lIuizgdDqDbmMgPcKWmSl9/nm0WwEkFuIOJuITH4gVJSXSwoW+EoA1iDuYiKTLAm63O+iDuxcRtuJiafJkXwnAGsQdTMTlRQtw9yIAAKCnCwAAwAL0dFmAuxdhiuRkaeRIXwnAGsQdTETSZQHuXoQpnE7fciQArEPcwUR84gOxwuORHnrIVwKwBnEHE5F0AbHC45Eefpg//oCViDuYiKQLAADAAozpsoDb7Q66jXm6AABIDCRdFmCeLpgiNVWaNMlXArAGcQcTkXQBsSI93bccCQDrEHcwUb2O6frqq6/05ptv6u2339Y333xTn4dq0FwuV9BH7969o908xIqiIunWW30lAGsQdzBRREnX5s2bNXHiRM2dO7fattdee00dOnTQ2LFjdcMNN6hdu3Z68803IzlczHI6nUEfzNOFsJWWSosW+UoA1iDuYKKIPvH/8pe/6E9/+lO1xOHo0aOaNGmSioqKZBiGDMOQy+XST37yE+3duzeiBgMAAMSiiJKuf/3rX5Kk0aNHV3l+/vz5KioqUs+ePbVnzx4dOnRIQ4cOVUlJiZ577rlIDgkAABCTIkq6jh07JpvNpvbt21d5/h//+IdsNpvmzJmjTp066YILLtCzzz4rwzC0Zs2aiBoMJCyHQ5o921cCsAZxBxNFdPfiqVOn1LhxY6WkfPcyRUVFysvLk8Ph0MiRI/3P9+zZU3a7XQcOHIjkkEDicjh8y5EAsA5xBxNF1NOVkpKi/Pz8Ks99+umn8nq96t+/v+x2e5VtmZmZKisri+SQQOJyu6WrrvKVAKxB3MFEESVdHTp0kNfr1aeffup/7u2335bNZtPgwYOr1PV6vTp79qzOP//8SA4Zk9xud9AHM9IjbF6vtGqVrwRgDeIOJoro8uIPfvAD7dixQ//zP/+j559/XseOHdP8+fMlST/84Q+r1P33v/8tr9erNm3aRHLImMSM9AAAIKKk695779Wf/vQn5ebm6vLLL5ckGYahK6+80v99hYrB9YMGDYrkkAAAADEposuLF1xwgdauXathw4YpLS1NLVu21OTJk7Vs2bIq9QzD0EsvvSTDMDRs2LCIGhyLmJEepkhLkxYs8JUArEHcwUQRr73Yq1cvrV69OmSd8vJyffDBB5J8iVqicTqdQbcxI30cMAypsLDSt4YKS84Z/+F2q+K3wH3utnDZ7b7lSABYh7iDiSxZ8Do5ObnaXF5A3CgslCqN27NJCp5mSxc+/oGUWof/ml0u6bLLpI0bqxwPQD0i7mCiiLpZLrzwQn3ve98Lu/6QIUPUqVOnSA4JJK7ycmnHDl8JwBrEHUwUUU/XgQMHVFxcHHb9w4cP6+DBg5EcEmjYTpyQO8WhFg+vkiTtu3+4nPbkqlUyMiSbTZKUcc42AED8suTyYoWysjLGMCG+OZ1SikOF314+dDbJltNhaZgBABooyz4NioqKdPLkSWVlZVl1SCC+ZGRIK1b4SgDWqCnuwp2pvlIPNxJXrZKugwcPVls7saSkROvWrZNhGAH3MQxD33zzjV555RWVlpaqR48edW4skNBSUnzLkQCwTk1x16JFeK/jcvl6wpHQapV0vfTSS/r1r39d5bkzZ87oiiuuqHFfwzBks9k0derUWjUQwLfy86U2baTDh6Xs7Gi3BkgMxB1MVOvLi5V7tGw2W9Aersp1srOzdemll+q2227TT37yk9q3EoBPQUG0WwAknnPjLiPD13NVE7c7/J4wJIRaJV2zZ8/W7Nmz/d8nJSWpZcuWOnr0qOkNiyfuENf8WfC64Qk4uWkQGfZkMUoDSDA2G5cKUScRDaSfMGGCGjdubFJT4hcLXkfBObPE16jSINfCEq+yHngvrN0KHh0VciJUAAAqRJR0LV682KRmACY7Z5b4Gp048d1/rp4yZZRWn3+uMMUR3buPnE7ps8/4DxuwEnEHEzGBkAVcIa795+TkKC8vz7rGILBK4y6ckgKdMfepbySnU+4Sr1p+O/mppZKSpLZtfSUAaxB3MJEpSVdBQYH+/ve/a/v27Tp9+rRKS0uD1rXZbFq0aJEZh40ZLHgdZZV6saqM13K75WwX/gLsTkeKFM2JTgsKpEaNpLNnuYsKsApxBxNF/AmyePFiTZs2rUpvTqA7GivudEzEpAtR5nT6k65CT5myHvp2vJZhKOPO10PueuLewSETM3eJVyot84/rcnvK5C6nAxkAUF1Enw4rV67UpEmTZBiG0tLSNGjQILVu3VopKXzoIAbYbP7leoKqYRxHy4dXKaO02H85ssXDq2p+TQANmrvy3cslXinF4Ss9ZZK+vWuZ2eVRBxFlR08++aQMw9CgQYP01ltvqVmzZma1Cwgo7OkcPGU13lV4fPbIaotRn6vKgPqKqT/OGWjvDDDoHkDsqjZmc9ob0m/W+78teHQUa6qiTiL6rcnNzZXNZtPixYtJuBC5MKZ5KPSUqUWlP4jB7iis3PsUjNOeXPMfzrJKr/3tYPtgA+0l6cTskVV6xzJqSOpqJSvLN66E9UsBICZFlHSVlZUpMzNTnTt3Nqs9SGRhTPNwbsKTeefrDepyXr0Oti8vlw4dkrp1k5JNTOYAKMOerIJHR1XfUO6Vdu2Wu2MntXzkA+sbhrgS0adDp06dtGvXLnm9XiXzIYAoOHHv4Co9S+4Sry58/APzLvmFu9xH5fr1xe2WLr2Uu6iAemCz2QL3fOcXSn16Sl+djuwAIVYm8as0STPiU0RJ189+9jPNmDFD7733nq677jqz2gRUnay0ErenTBc++LZOvDhBkqrdWRjo0p/bUyal+AbAusNc3seP5T4AmCGcNRhdLv7exLmIkq7p06dr2bJl+sUvfqGuXbtymRHmqTTNQxUpZXLX8nIidxQCABqCiJKuV199VePHj9evfvUr9erVS2PHjtVll12mrBoG+k6YMCGSwyLBFaY4lHnn6zoxe2S1ywHucwbaV9SPGwyiB6xX17gLZ3iC2x1eL1hNIlhvFtaJKOm65ZZb/HOVGIahV155Ra+88krIfWw2W8wkXcuWLdNf//pXffrppzp+/LjatGmja665Rr/61a903nnnRbt5sSWcPwjhjHmQvptfy+msNmg9I8PQiSdvCOtlTL2z0ArZ2VJ+frRbASSWirj7do6uUAJOaVPTP30pVSdXrhgKUeu5wGq73iyXMqMioqSrXbt2cT1B3FNPPaXWrVvr17/+tdq1a6edO3fqoYce0ooVK7R161Zl1Oeg6XhT2z8IdRR0MGw8KCuTPvhAGj5cYgJiwBoVcTdkaI1VC0u8ynrgvVq9fLDJlZkLLD5FdEYPHDhgUjMapnfeeUfNmzf3fz906FB17txZI0aM0LJlyzR+/Pgotg4Jp7BQuvpq7l4ErFQRd5XuXgx2Q06tb9QJV22vFAS5Ecm0S5moM9LoEConXBUGDBggSTp8+LDVzWm4zPqDUBm9iAAaqGoz1gcQzooXknx/G5/zfbnv/uFq+fRH1evU9kpBsBuREHUxlXQdP35cq1ev1qeffqrNmzdr69atKioqUr9+/bR58+Ya91+7dq3mzp2rjRs3yuVyqX379rrppps0Y8YMOcP8BV27dq0kqXv37hG9l7jCHwQAqCKsFS8kqey7Os7S4u+WGHO7v9sW7nhXNHgxlXS99tpruuuuu+q07/PPP69p06bJMAy1adNGbdu21Y4dOzRnzhwtW7ZM69evV9OmTUO+xpkzZ3TXXXepR48e+uEPf1indgB1lpQkXXyxrwRgjW/jLsOREnjG+iDqcqOOs90F380z+FyQSlwpiGkRJV0TJ06s9T42m02LFi2q0/Gys7M1YsQI9e/fX/3799fu3bs1c+bMGvfLzc3V9OnTJUnz5s3T5MmTZbPZdPToUY0ePVq5ubmaPHmyli1bFvQ1iouLdeONN+rMmTP6+9//zgz8wUT4B6GmBa3rbcxELMjMlD7/PNqtABLLt3Fnk9Qg+ue5UhDTIkq6Fi9eLJvNJsMwAm4/985GwzAiSromTpxYJdFbvHhxWPs98sgjKi8v14QJEzRlyhT/861bt9arr76qbt26afny5dq+fbt69uxZbf/S0lKNHTtWmzZt0qpVq3TxxRfXqf0JoYY/COEkVeGMl0hIJSXSyy9LEyZIdnu0WwMkhvqOu0pzeVWeZzDQPIT++ohZESVdEyZMCDllxNmzZ7V582YdPnxY5513XlSWCnK5XFqxYoUkVUm4KnTu3FlXXnmlVq9erTfeeKNa0uX1ejVu3DitXr1a77zzjgYPHmxJu+NVXW6pxreKi6XJk6Uf/5ikC7BKfcdd5aXGUsr8U0a4U9OklABXVL79p7XW83ihQYi4p6smhmFo8eLF+vnPf67s7Gw9++yzkRyy1rZu3SqPxyOHw6GBAwcGrDNkyBCtXr1aGzZsqPJ8eXm5br75Zr311ltaunSpfvCDH1jRZHyrprt/Ym5yUwAIU009/szjFZvq/YzZbDb993//t7755hvde++9+v73v68xY8bU92H9du/eLck3kWtqamrAOp06dZIk7dq1q8rzd9xxh1555RVNnz5dLVq0qJKUtWnTRm3atAn4evPmzdP8+fPDat/OnTvDqhePwkmq+E8OABAvLEuTb731Vv3v//6vXnjhBUuTrtOnfRPahbozsWLbmTNnqjz/j3/8Q5L0u9/9Tr/73e+qbJs9e7YeeuihgK937NgxbdmypY4tbmDMXL7nHGHfUg2f5GRp5EhfCcAaFsZdhj055B2SjHmNfZZ94mVlZSk7O1t5eXlWHVKS765DSbKHuBbvcPjWxioqKqryfF1n3G/VqpX69u0bVt2dO3dWO26DYtHyPQiD0ymtXBntVgCJxcK4s3QZs3D+WWZRbNNZlnSdPn1a33zzjeXrFaal+QYllpSUBK3j8XgkSenp6aYcc+rUqZo6dar/e3eIX+6cnBzLE1HEKI9Hevxx6f77JUcNi+gCMEe8xl04ywGFOwUQiVnYLEu6ZsyYIUnq2rWrVYeUJDVp0kTSd5cZA6nYVlHXbJnx0lPEpHzR5fFIDz8s3X13fP3xBxqyBhp34cxZGPG42HASM5eLecNqIaKk6+WXXw65vbi4WIcOHdKbb76pnTt3+gfVW6lLly6SpIMHD6q0tDTgYPq9e/dWqYsgmJQPABqEcMZ2VbvDsdKcYEGxKHa9iijpuuWWW8LKoismT50wYYL+53/+J5JD1lrfvn1lt9vl8Xi0adOmgPNsrVu3TpI0aNCgemmDK8QvOZcXAQCWqDwnWDAkZvUqoqSrXbt2IZOulJQUNWnSRL169dK4ceN05ZVXRnK4OsnMzNRVV12ld955R/Pnz6+WdO3Zs0dr1qyRJI0dO9by9gFhS02VJk3ylQCs0YDirqa7GyUT7nAMJzFDnUWUdNX17j6rzZo1S3//+9+1ZMkSDR482L/24rFjxzRu3DiVl5fr+uuvV69everl+HEzpgvRlZ4uLVwY7VYAiaUBxZ2ldzeiXiRFuwG1cejQITVr1sz/uOOOOyRJ27Ztq/L8k08+WWW/AQMG6JlnnpHku7Owffv26tu3rzp27Kjc3Fx17dpVCxYssPz9ALVSVCTdequvBGAN4i40t7vmR5D1mRNRTCVdXq9Xp06d8j8qxkqVlZVVeb4wwGSe06dP1/vvv69Ro0bJ7XZrx44dat++vWbOnKnNmzerWbNm9dZul8sV9NG7d+96Oy7iTGmptGiRrwRgDeIutBYtfHM5hnrUNMF2AjGtn7KkpETvv/++Nm/erJMnT8pms6l58+YaMGCARowYEXJy0nB16NDBPyi/LoYPH67hw4dH3A4AABq6mqaVYKk165mSdM2fP1+zZs3S119/HXB7s2bNNGfOHE2ePNmMw8UcxnQBAKxWb4tmc4djnUWcdP3yl7/U008/7e+BuuCCC/wLQR8+fFhHjhzRV199pdtuu0179+7Vb37zm0gPCSQmh0OaPbtBTdAIxL04jrs694Rxh2OdRZR0ffjhh3rqqackSWPGjNEjjzyibt26Vamza9cuzZo1S0uXLtVTTz2la6+9VkOGDInksDEnUebpMgxDhTUEcTizKCMIh0MKssg6gHoSY3FXm0Wz660nDEFF9NP8/e9/L0maNGlS0Lv/unbtqtdff12TJ0/WokWL9MILLyRc0uUM8R9BUlJM3csQUmGJV1kPvBftZsQvt1u68UZp+XL+ywSsEmNxx7QSDVtEZ+bjjz9WUlKSHn300RrrzpkzR3/84x/10UcfRXJIIHF5vdKqVb4SgDXiLO5q0xMG80WUdH399ddq1KiRzj///BrrtmjRQo0bNw462D6eud3uoNvKy8stbIl1js8eKac9OWSdjBq2AwDMRU9YdEX0k8/KytLZs2dVXFystLS0kHWLiopUUFCg7OzsSA4ZkxLx7kWnPZnABgD4LtHWJCPDN0A/zkU0oKhnz57yer364x//WGPdP/7xjyorK6u3pXaAuJeWJi1Y4CsBWIO4ixwTqPpF1BXx05/+VGvXrtU999wjh8OhSZMmBay3cOFC3XPPPbLZbBo/fnwkh4xJiXL3IuqZ3e5bjgSAdYg7ayRIb1hESdctt9yiJUuW6MMPP9SUKVP061//WsOGDdMFF1wgm82mQ4cOae3atTpy5IgMw9AVV1yhm2++2ay2x4xEuXsR9czlki67TNq40fefIYD6R9zVTW0nUA1nIlWXKybuIA0loqQrKSlJb731liZOnKjly5fr0KFDWrJkSZU6FZOmjhkzRosWLWLJAaCuysulHTt8JQBrEHd1wwSqAUU80jk7O1tLly7Vp59+qtdee82/9qIknX/++erfv7/+67/+SwMGDIi4sbEqEe9eBAAgpARcTsi028sGDBiQ0IlVKIl49yIAACElYG9YRAOKSkpKtH37dn3xxRc11v3iiy+0fft2lZaWRnJIIHFlZEgrVvhKANYg7mCiiJKuv/71r+rTp49+97vf1Vj30UcfVZ8+fbR06dJIDhmTXC5X0Efv3r2j3TzEipQU6aqrfCUAayRw3LlLvHJ7yoI+KsZsI3wR/RYtW7ZMksKaBmLSpEl65ZVXtHTpUo0bNy6Sw8Yc7l6EKfLzpTZtpMOHpQScZBiIigSOOxbENl9En/ifffaZJIU14Wm/fv0kSf/+978jOSSQ2AoKot0CIPEQdzBJRCnq0aNH1ahRo7AGimdlZalx48Y6duxYJIcEAAD1hAWx61dESZfdbldRUVFYdQ3DUFFRkVJTUyM5ZExiyggAQCxgQez6FdFPtmPHjtq+fbs++eQTDRo0KGTdjz/+WB6PRxdddFEkh4xJTBkBUzid0mefJdwt1kBUEXcwUURjun7wgx/IMAzNmDFDZWVlQeuVlZXp/vvvl81m08iRIyM5JJC4kpKktm19JQBrEHcwUUS/RXfeeafS0tK0fv16jRgxQlu3bq1WZ8uWLRo+fLjWr18vh8OhadOmRXLImMSUETBFQYHUqBGDegErEXcwUUSXF9u0aaN58+bplltu0bp169S/f3+1bNlS7du3l81m0/79+3XixAkZhiGbzab58+erXbt2ZrU9ZjBlBAAAiHi03Pjx43Xeeefp9ttv14EDB3Ts2LFqdyheeOGFeuGFF3T11VdHejgAAICYZMotCtdcc4327NmjtWvX6uOPP9bx48clSa1atdLll1+uYcOG0aMDAAASmmn3hSYnJ2vEiBEaMWKEWS8JoLKsLOnsWV8JwBrEHUxE91MiMwzJ7a75gYahvFw6dMhXArAGcQcTMQNaIisslJhDLHa43dKll/r+606wNeCAqCHuYCKSLgswIz0AACDpskBMzEh/4kTNMy5nZFjTFgAA4hBJF3ycTpa5iAUM5gWsR9zBJCRdFnC5XEG35eTkKC8vz7rGIHZlZ0v5+dFuBZBYiDuYiLsXLeB0OoM+mL8MYSsrk1au9JUArEHcwUR84gOxorBQuvpqXwnAGsQdTETSBQAAYAGSLgAAAAuQdAGxIilJuvhiXwnAGsQdTMTdi0CsyMyUPv882q0AEgtxBxORugOxoqREWrjQVwKwBnEHE5F0AbGiuFiaPNlXArAGcQcTkXQBAABYgDFdFmDBawAAQNJlgZhY8BoNX3KyNHKkrwRgDeIOJiLpAmKF0+lbjgSAdYg7mIgxXRZwuVxBH71794528xArPB7poYd8JQBrEHcwEUmXBVjwGqbweKSHH+aPP2Al4g4m4hMfAADAAiRdAAAAFiDpAmJFaqo0aZKvBGAN4g4m4u5FIFakp/uWIwFgHeIOJqKnC4gVRUXSrbf6SgDWIO5gIpIuIFaUlkqLFvlKANYg7mAiki4AAAALkHQBAABYgKQLiBUOhzR7tq8EYA3iDiYi6Qrh8OHDuuOOOzRo0CBlZGTIZrPpwIED0W4WEpXD4VuOhD/+gHWIO5iIpCuEL7/8Uq+//rqaNGmiIUOGRLs5SHRut3TVVb4SgDWIO5iIpCuE73//+zpx4oTeffdd3XTTTdFuDhKd1yutWuUrAViDuIOJSLpCYDFqAABglpjKKo4fP64///nPmjZtmgYPHuwfZ9W/f/+w9l+7dq2uu+46NW/eXOnp6erWrZtmzZolN93GAACgnsXUMkCvvfaa7rrrrjrt+/zzz2vatGkyDENt2rRR27ZttWPHDs2ZM0fLli3T+vXr1bRpU5NbDJgoLU1asMBXArAGcQcTxVRPV3Z2tkaMGKEZM2Zo6dKleuyxx8LaLzc3V9OnT5ckzZs3TwcPHtSWLVu0b98+9evXTzt37tTkyZPrseWACex233Ikdnu0WwIkDuIOJoqppGvixIl6//339fjjj2vMmDFq1apVWPs98sgjKi8v1/jx4zVlyhTZbDZJUuvWrfXqq68qKSlJy5cv1/bt2+uz+UBkXC7pkkt8JQBrEHcwUUwlXXXhcrm0YsUKSdKUKVOqbe/cubOuvPJKSdIbb7xhaduAWikvl3bs8JUArEHcwURxn3Rt3bpVHo9HDodDAwcODFinYg6uDRs2WNk0AACQQGJqIH1d7N69W5LUrl07paamBqzTqVMnSdKuXbuqbVu6dKkk37gwSXrvvffUvHlzNW/eXEOHDq2PJgMAgHOFM9NARob07RCihijuk67Tp09LUsg7Eyu2nTlzptq2cydF/cUvfiFJGjp0qP75z38GfL158+Zp/vz5YbVv586dYdUDlJEhrVjhKwFYg7hrOFq0qLmOyyU5nfXfljqK+6SruLhYkmQPceeJ49s1tYqKiqptMwyj1sc8duyYtmzZUuv9gJBSUnzLkQCwDnEHE8V90pX27dwqJSUlQet4PB5JUnp6uinHbNWqlfr27ev/3hti+Yjdu3cHTPYaIsMwVFgS/L24Q2yDCfLzpTZtpMOHpezsaLcGSAzEXXRlZNR856jbHV4vWAMQ90lXkyZNJH13mTGQim0VdSM1depUTZ061f+9rQFfX66NwhKvsh54L9rNSGwFBdFuAZB4iLvosdka9OXC2or7uxe7dOkiSTp48KBKS0sD1tm7d2+VugAAAGaL+56uvn37ym63y+PxaNOmTRo8eHC1OuvWrZMkDRo0qF7a4ArRNZqTk6O8vLx6OW59Oj57pJz25KDbM0JsAwAgEcV9T1dmZqau+nYQZKA7Cvfs2aM1a9ZIksaOHWtp22KZ054spyMl6CNeLqk2KE6n9NlncdXVDjR4xB1MFPdJlyTNmjVLNptNS5Ys0fz58/13JB47dkzjxo1TeXm5rr/+evXq1atejp+ZmRn0EYu9XIiSpCSpbVtfCcAaxB1MFFO/RYcOHVKzZs38jzvuuEOStG3btirPP/nkk1X2GzBggJ555hlJvkHu7du3V9++fdWxY0fl5uaqa9euWrBggeXvB6iVggKpUSMG9QJWIu5gopga0+X1enXq1Klqz5eVlVV5vrCwsFqd6dOnq0ePHpo7d642btyokydPqn379ho7dqzuv/9+ZWZm1lu743FMFwAAqJ2YSro6dOhQp8lKKwwfPlzDhw83sUXhcYYYC5BElzUAAAkhppKuWOUOsV5UOSvXAwCQEEi6LFCfly6RQLKypLNnfSUAaxB3MBHXtoBYUV4uHTrkKwFYg7iDiUi6LOByuYI+evfuHe3mIVa43dKll/pKANYg7mAiLi9agIH0AACApMsCURlIbxhSgKkzquA/NwAALEPSZYGoDKQvLJQYwB9/GMwLWI+4g0lIuoBYkZ0t5edHuxVAYiHuYCIGFFkg6gPpT5yQXK7Qj4yM+m8HIlNWJq1c6SsBWIO4g4no6bJA1AfSO52+B2JbYaF09dW+OYOys6PdGiAxEHcwET1dAAAAFqCnywIsAwQAAEi6LMAyQDBFUpJ08cW+EoA1iDuYiKQLiBWZmdLnn0e7FUBiIe5gIlJ3C0T97kXEh5ISaeFCXwnAGsQdTETSZQGn0xn0wTJACFtxsTR5sq8EYA3iDibiEx8AAMACJF0AAAAWYCC9BZgyAqZITpZGjvSVAKxB3MFEJF0WYMoImMLp9C1HAsA6xB1MxOVFIFZ4PNJDD/lKANYg7mAiki4LMGUETOHxSA8/zB9/wErEHUzE5UULRH3BawAAEHV84gMAAFiApAuIFamp0qRJvhKANYg7mIjLi0CsSE/3LUcCwDrEHUxETxcQK4qKpFtv9ZUArEHcwUQkXUCsKC2VFi3ylQCsQdzBRFxetAAz0gMA4o27xBtye4Y9WTabzaLWxAaSLgswIz0AIN60fHhVyO0Fj46S00GaURmXF4FY4XBIs2f7SgDWIO5gIlJQC7hcrqDbcnJylJeXZ11jELscDt9yJACsQ9xVkWFPVsGjo4Jud5d4a+wBS2T0dFnA6XQGfTAjPcLmdktXXeUrAViDuKvCZrPJ6UgJ/rAnR7uJDRqf+ECs8HqlVat8JQBrEHcwEUkXAACABUi6AAAALEDSBcSKtDRpwQJfCcAaxB1MxN2LQKyw233LkQCwDnEHE9HTBcQKl0u65BJfCcAaxB1MRNIFxIrycmnHDl8JwBrEHUxE0gUAAGABki4AAAALMJDeAu4QMxmX02WNcGVkSCtW+EoA1iDuYCKSLgtkZmZGuwmIBykpvuVIAFiHuIOJuLwIxIr8fCk721cCsAZxBxPR02UBV4hbjXNycpSXl2ddYxDbCgqi3QIg8RB3MAlJlwWcTmfQbUlJdDYCAJAI+MQHAACwAEkXECucTumzz3wlAGsQdzARSRcQK5KSpLZtfSUAaxB3MBG/RUCsKCiQGjViUC9gJeIOJiLpAgAAsABJFwAAgAVIugAAACxA0gXEiqws6exZXwnAGsQdTETSVYNDhw5p7NixatSokbKzs3XDDTfoP//5T7SbhURUXi4dOuQrAViDuIOJSLpCcLvdGjZsmL744gv96U9/0pIlS/Tll19q2LBhIZf2AeqF2y1deqmvBGAN4g4mYhmgEBYsWKD9+/dr165duuiiiyRJPXv2VOfOnTVv3jzdc889UW4hAACIFfR0hfD222/re9/7nj/hkqSOHTtq8ODBevvtt6PYMgAAEGtiKuk6fvy4/vznP2vatGkaPHiwMjIyZLPZ1L9//7D2X7t2ra677jo1b95c6enp6tatm2bNmiV3kG7jzz//XJdeemm15y+55BLt2LEjovcC1AmDeQHrEXcwSUxdXnzttdd011131Wnf559/XtOmTZNhGGrTpo3atm2rHTt2aM6cOVq2bJnWr1+vpk2bVtnnzJkzatKkSbXXatq0qc6cOVOndgB1lp0t5edHuxVAYiHuYKKY6unKzs7WiBEjNGPGDC1dulSPPfZYWPvl5uZq+vTpkqR58+bp4MGD2rJli/bt26d+/fpp586dmjx5csB9bTZbtecMw6jzewDqrKxMWrnSVwKwBnEHE8VU0jVx4kS9//77evzxxzVmzBi1atUqrP0eeeQRlZeXa/z48ZoyZYo/kWrdurVeffVVJSUlafny5dq+fXuV/Zo0aaLTp09Xe71gPWBAvSoslK6+2lcCsAZxBxPFVNJVFy6XSytWrJAkTZkypdr2zp0768orr5QkvfHGG1W2XXLJJfr888+r7bNjxw5dfPHF9dBaAAAQr+I+6dq6das8Ho8cDocGDhwYsM6QIUMkSRs2bKjy/OjRo7Vhwwbt27fP/9yBAwf00UcfafTo0fXXaAAAEHdiaiB9XezevVuS1K5dO6Wmpgas06lTJ0nSrl27qjw/efJkvfDCC/rRj36kOXPmyGazadasWWrbtq2mTp1avw0HzpWUJF18sa8EYA3iznSGYaiwxBtW3Qx7csCx1UGFM4ltRoZUm9c0UdwnXRVjss69M7Gyim3n3pHodDq1Zs0a3XXXXRo/frwMw9Dw4cP1u9/9TpmZmUFfb968eZo/f35Y7du5c2dY9QBlZkoBLncDqEfEnekKS7zKeuC9sOoWPDpKTkctUpUWLWqu43JJTmf4r2miuE+6iouLJUl2uz1oHYfDIUkqKiqqtq1du3ZatmxZrY557NgxbdmypVb7ADUqKZFeflmaMEEK8fsMwETEHUwU90lXWlqaJKmkpCRoHY/HI0lKT0835ZitWrVS3759/d97vcG7UXfv3h0w2QOqKS6WJk+Wfvxj/vgDViHu6tXx2SPltCdXec5d4lXLh1eF/yIZGb7eq1Dc7vB6wepZ3CddFVM7BJr6oULFNrOmgZg6dWqVMV+1uh4NAECCcNqTa3f5MBCbLWqXC2sr7kcGdunSRZJ08OBBlZaWBqyzd+/eKnUBAADMFvdJV9++fWW32+XxeLRp06aAddatWydJGjRoUL20weVyBX307t27Xo6JOJScLI0c6SsBWIO4g4niPunKzMzUVVddJUkB7yjcs2eP1qxZI0kaO3aspW0DasXp9C1HEiPd6EBcIO5gorhPuiRp1qxZstlsWrJkiebPn+9fO/HYsWMaN26cysvLdf3116tXr171cvzMzMygj7y8vHo5JuKQxyM99JCvBGAN4g4miqmk69ChQ2rWrJn/cccdd0iStm3bVuX5J598ssp+AwYM0DPPPCPJN8i9ffv26tu3rzp27Kjc3Fx17dpVCxYssPz9ALXi8UgPP8wff8BKxB1MFFN3L3q9Xp06dara82VlZVWeLwywMOn06dPVo0cPzZ07Vxs3btTJkyfVvn17jR07Vvfff3/IyU4j5QpxK2tOTg69XQAAJICYSro6dOjgvzRYF8OHD9fw4cNNbFF4nCHGAiSxtAQAAAkhppKuWOUOsRZUeXm5hS1BTEtNlSZN8pUArEHcwUQkXRaoz0uXSCDp6dLChdFuBZBYiDuYiGtbQKwoKpJuvdVXArAGcQcTkXRZgMlRYYrSUmnRIl8JwBrEHUzE5UULMJAeAADYjEhuB0TEmjZtqjNnzig9PV3du3c374XLy6WKqSh695ZMSO7KDUN5R/J9L3lBtpJYyNtaXq+0bZvUqxdLkgBWIe5qJZzPiZrq1MtnTT18Jla2c+dOFRUVqUmTJjp9+nTQeiRdUZaRkaEixgoAABDz0tPTA84VWoHLi1F2/vnn6+TJk0pLS1PHjh2j1o6KLN30HjfUO85d7OLcxSbOW+yqr3O3f/9+FRcX6/zzzw9Zj6Qryg4cOBDtJkiS+vXrpy1btqh79+7Kzc2NdnNQC5y72MW5i02ct9gV7XPHKG4AAAALkHQBAABYgKQLAADAAiRdAAAAFiDpAgAAsABJFwAAgAVIugAAACxA0gUAAGABki4AAAALkHQBAABYgGWAIEmaMmWKjh07platWkW7Kaglzl3s4tzFJs5b7Ir2ubMZhmFE5cgAAAAJhMuLAAAAFiDpAgAAsABJFwAAgAVIuhLQ0aNH1bhxY9lsNtlsNh04cCBk/WXLlmnYsGFq0qSJnE6nevfuraefflqlpaUh93O5XHrwwQfVrVs3paenq3nz5rruuuv0z3/+07w3E8f27NmjJ598UqNGjdIFF1wgu92uRo0aaeDAgfrNb34jl8tV42tw7hqmtWvX6rrrrlPz5s2Vnp6ubt26adasWXK73dFuWtwyDEMff/yxZsyYoZycHJ133nlKTU1V8+bNNXLkSL3yyisKNcS5tLRUTz31lHr16iWn06mmTZvqyiuv1PLly2s89r59+zRx4kS1adNGDodDbdu21aRJk7R//34z32JCeffdd/2fYR06dAhar8GdNwMJ50c/+pEhyf/Yv39/0Lr33HOPv16nTp2Mnj17GsnJyYYk4/vf/75RXFwccL+vvvrK6Nq1qyHJcDgcRp8+fYw2bdoYkgybzWb8/ve/r6d3Fx/KysqqnKOWLVsa/fv39/8MK87HgQMHgr4G565heu655wybzWZIMtq0aWP06dPHcDgchiSje/fuxqlTp6LdxLi0evXqKjF14YUXGv369TOaNm3qf+7aa68NGBdFRUVGTk6OIclITk42evbsaXTq1Mm/3y9/+cugx/3444+NzMxMQ5LRpEkTo1+/fkbjxo0NSUZWVpaxcePG+nzbcSk/P99o27at/+ffvn37gPUa4nkj6Uowf/3rXw1JxvXXX19j0rV8+XL/B+9bb73lf37nzp1Gx44dDUnG3XffHXDf0aNHG5KMfv36GUeOHDEMwzDKy8uNefPm+QNg69atZr+9uFFaWmpkZ2cb9913n7Fz584q2z755BP/z/+yyy4LuD/nrmHavHmzkZSUZNhsNmPevHlGeXm5YRiGceTIEaNfv36GJOPGG2+Mcivj0/vvv2907NjRePbZZ40TJ05U2fbyyy/7E99AH8R33nmnIcno2LGj8cUXX/iff+utt/z7vf3229X2c7vdRqtWrQxJxsSJE42ioiLDMHzJwH//93/7E+/CwkKT3218+/nPf17lcyxY0tUQzxtJVwI5deqU0aJFC6Nt27bGv//97xqTrl69ehmSjF/96lfVtn3wwQf+D/WTJ09W2bZlyxZDkpGUlGTs2bOn2r7jx4/nw6UG5eXlIXs8PvroI//5C5QAce4apope5gkTJlTbtnv3biMpKcmQZGzbti0KrYtvZ8+eNUpKSoJuf/TRRw1JRtOmTQ2v1+t//vjx44bdbjckGWvWrKm236xZswxJRt++fatt++1vf2tIMi666CLD4/FU2ebxePy9Ls8991wE7yyxrFu3zrDZbMYNN9xgvPTSS0GTroZ63ki6EsiECRP8mf3+/ftDJl27d+/2bz98+HDA17vooosMScbChQurPD9z5kxDkjFixIiA+61fv96QZKSlpRkulyvi95WoGjVqZEgyXnvttSrPc+4apoKCAv9/1+vXrw9YZ8SIEYYk48EHH7S4daj4h0OScfz4cf/z8+fP938AB3L48GH/fl9++WWVbZdffrkhyZgzZ07AfefMmWNIMnJycsx7I3GsqKjI6Nq1q5GVlWUcPnw4ZNLVUM8bA+kTxKpVq/Tyyy9rzJgx+uEPf1hj/Q0bNkiSOnbsqAsuuCBgnSFDhlSpe+6+FdvPNXDgQNntdhUXFysvLy/ct4BKvF6vfzB8RkZGlW2cu4Zp69at8ng8cjgcGjhwYMA6wc4L6l9xcbH/6/T0dP/XNcXEBRdcoI4dO1apK/lidPPmzSH3rXj+008/ldfrjaD1ieHXv/61du3apcceeyzo37YKDfW8kXQlALfbralTpyo7O1vPPfdcWPvs3r1bknTRRRcFrdOpUydJ0q5du2q1b2pqqtq1axdwX4TnrbfeUmFhoVJSUjRo0KAq2zh3DVPFz7Zdu3ZKTU0NWCfYeUH9e/XVVyVJvXr1UnZ2tv/5usbTgQMHVFJSEnLfiv08Ho/+85//RND6+JeXl6ennnpKAwcO1C9+8Ysa6zfU88baiwlg5syZOnDggH7/+9+rdevWYe1z+vRpSVLTpk2D1qnYdubMGdP2Rc3y8/N17733SpImTZqkZs2aVdnOuWuY+Nk2XFu2bNH//d//SZJmzJhRZVtdz1vFfqH2rfw85zw4r9erSZMmSZLmz5+vpKSa+4sa6nmjpyvOffLJJ3rhhRf0ve99T7fddlvY+1V0tdvt9qB1HA6HJKmoqMi0fRGa1+vVuHHjtH//fnXo0EFPPPFEtTqcu4aJn23DdOLECd1www0qLS3VDTfcoP/6r/+qsr2u563y5cpg+1bsd+6+qOrpp5/Wli1bdPfdd6tXr15h7dNQzxtJVwN12223+Sd+q83jiiuu8L9GSUmJbr31ViUlJYX930GFtLQ0/2sE4/F4JFUd/xDpvvHAjHMXiGEYmjJlit599101adJE77zzjho1alStHueuYeJn2/CcPXtWo0aN0sGDB9WvXz8tXry4Wp26nreK/ULtW7HfufviO3v27NFDDz2kjh07avbs2WHv11DPG5cXG6hGjRqpRYsWtd6vcrfnE088oR07dmjGjBnq0aNHrV6nSZMmkqp2tZ6rYltF3cr7FhYW1mnfeGDGuQvkzjvv1B//+EdlZWVp5cqVuvTSSwPW49w1TJGcF5jP5XLp6quv1tatW3XJJZdo5cqVVcZyVajreav89enTpwMO7aj8mpzzwG677TYVFxfrxRdfrHbTUCgN9byRdDVQTzzxRMBLR7WRm5srSVqwYIFeeumlKtsq33ExYMAAJScn69577/WPFerSpYsk6csvvwz6+nv37q1St0KXLl105MiRoPuWlpbq4MGDAfeNB2acu3Pde++9euGFF5SRkaF3331XAwYMCFqXc9cwVfy8Dh48qNLS0oCD6YOdF5irsLBQ1157rTZs2KAuXbpo9erVOu+88wLW7dKliz766KNax1OHDh1kt9tVUlKiL7/8MuCHd8V+DodD7du3j+Qtxa3c3FzZbDbdfPPN1bZVXNo7dOiQWrZsKUlavny5Lr/88gZ73ri8mABOnTqlEydOVHl8/fXX/u1ff/21Tpw4UWUtv4o74vbv368jR44EfN1169ZVqVvhe9/7XpXt59q0aZNKSkqUlpam3r171/l9JYoHH3xQc+fOVVpamt5++23l5OSErM+5a5j69u0ru90uj8ejTZs2BawT7LzAPMXFxfrRj36kf/3rX+rQoYM++OAD/wd2IBUxsX79+oDbjxw54l+Lr6KuJKWkpKhfv36SgsdTxfMV//giMMMwqn2GnThxQvn5+ZKk8vJy/3MVlwQb7Hmr9cxeiAs1TY5qGIbRo0ePGmc1t9vt1WY1z83NDWtW8xtuuMGU9xLPKmbJttvtxj/+8Y+w9+PcNUw//OEPw5qRPi8vLwqti38lJSXGNddc41/GZd++fTXuc/z4cSM1NbXGmc379OlTbdvcuXP9E3SeOxt+5ZnNn3322bq/qQRW04z0DfG8kXQlqHCSrqVLl/qXi6m8PtUXX3zhX79v+vTpAfe97rrr/Ov3HT161DCM79bvs9lsRlJSkpGbm2v6+4onzz77rCHJSElJMZYvX16rfTl3DdOmTZsMm81Wbe3Fo0eP+tdevP7666PcyvhUVlZm3HTTTYbkW0B+9+7dYe97++23B1zD7+233/avMvC3v/2t2n4ul8to2bJlyDX8Wrdubbjd7sjfYAIKlXQZRsM8byRdCSqcpMswDGP69On+ep06dTJ69eplJCcn+5dAqPhlPNfJkyeNzp07+z/4+/Tp418V3mazsdZYDY4cOWLYbDZD8q1wP3jw4KCPRYsWBXwNzl3D9Nvf/tZ/btu2bWv06dPH/wHQtWtX46uvvop2E+PSX/7yF388dOjQIWRMbdmypcq+hYWFxqBBgwzJt+B7r169/L0dkox77rkn6HHXr19vOJ1Ofyz369fPaNKkiSHJyMzMND755JP6futxq6akqyGeN5KuBBVu0mUYhvH6668bQ4cONRo1amSkp6cbPXr0MJ544omQi8cahmHk5+cbM2fONLp06WI4HA6jadOmxqhRowJ29aKqyuenpsfs2bODvg7nrmFavXq1MWrUKKNp06aGw+EwunTpYsycOdMoKCiIdtPiVsUHdDiPtWvXVtvf4/EYTzzxhNGjRw8jPT3daNSokTF06FBj6dKlNR77yy+/NG6++WajdevWRmpqqtG6dWvjlltuMfbu3VsP7zRx1JR0GUbDO282wzCMcMZ+AQAAoO64exEAAMACJF0AAAAWIOkCAACwAEkXAACABUi6AAAALEDSBQAAYAGSLgAAAAuQdAEAAFiApAsAAMACJF0AAAAWIOkCAACwAEkXAJzD6/Xq9ddf14QJE9SlSxc1btxYdrtd559/vnJycnT//ffrs88+kyTdcsststlsdXpcccUV/mNeccUV1banpKSoSZMm6tChg0aOHKlf/vKXWr9+fZR+KgAixYLXAFDJhg0bdPPNN2v37t3+51JTU5WVlaVvvvlG5eXl/udvvPFGnX/++XrzzTervY7X69XXX38tScrOzlZ6enq1OpdffrmWL18uyZd0ffjhh0pNTVXTpk39dQoKClRYWFhlv+7du+vFF1/U0KFDI3uzACxF0gUA33rnnXd00003yePx6LzzztO9996rMWPGqHPnzpJ8idTWrVu1bNky/eEPf1B+fr7OnDmjxo0bV3utAwcOqGPHjpKkl156SbfcckvIY1ckXUOHDtU///nPKtuKior8x124cKHy8/Nls9n0hz/8QbfddpsZbx2ABbi8CACS9uzZo5/97GfyeDy6+OKLlZeXpxkzZvgTLklKTk5W//799fjjj2v//v360Y9+ZEnb0tPTdfnll2vu3Lnavn27evbsKcMwdPvtt2vdunWWtAFA5Ei6AEDSgw8+qPz8fKWlpenNN99UmzZtQtZv2rSp/va3v6lRo0YWtdCnffv2euutt+R0OuX1ejVz5kxLjw+g7ki6ACS8EydOaOnSpZKkn/70p+rSpUvY+9pstvpqVlAdOnTwX65cv3699u3bZ3kbANQeSReAhLd27Vr/APkbbrghyq0Jz7XXXuv/+sMPP4xiSwCEi6QLQML7/PPP/V/36dMnii0JX69evfxf7927N4otARAuki4ACe/UqVP+rytP19CQVW7n6dOno9gSAOEi6QKQ8Jg5B4AVSLoAJLxmzZr5v46VXqPK7TzvvPOi2BIA4SLpApDwLrnkEv/XW7dujWJLwrdt2zb/1506dYpiSwCEi6QLQMIbNmyYkpJ8fw4DLenTEP3jH//wf115DUcADRdJF4CE16JFC40ZM0aS9Je//KXKuos1icZ4sP/85z9avHixJOn73/++OnToYHkbANQeSRcASJozZ44yMzNVVFSkG2+8UUeOHAlZ/8yZMxozZozOnj1rUQt9Dh48qNGjR8vtdis5OVmPPfaYpccHUHckXQAgqUuXLlqyZInsdrs+//xz9e7dW0888YS+/PJLf52KBa9/9atf6cILL9Ty5cstaVtxcbE++eQT/e///q969uyp7du3KykpSS+++KIGDx5sSRsARC4l2g0AgIbi+uuv15o1a3TLLbfoyy+/1IwZMzRjxgzZ7XZlZmbqm2++8c9cb7PZNG7cODmdTlPb8PHHH6tly5b+791ut1wuV5U6l1xyiV588UUNGTLE1GMDqF8kXQBQyeDBg/XFF1/ojTfe0N///ndt3LhRJ0+eVEFBgZo2bapu3bpp6NChGj9+vLp27Wr68UtLS3XixAlJUnJysjIzM9W+fXt17txZffv21ejRo+ndAmKUzWBWQAAAgHrHmC4AAAALkHQBAABYgKQLAADAAiRdAAAAFiDpAgAAsABJFwAAgAVIugAAACxA0gUAAGABki4AAAALkHQBAABYgKQLAADAAiRdAAAAFiDpAgAAsABJFwAAgAX+H4HIeLH7G8voAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AC_list = ['20231003_Cs137_AC_600V_1.dat', '20231003_Cs137_AC_600V_2.dat', '20231011_Cs137_AC_600V.dat', '20231020_Cs137_AC_600V.dat']\n",
    "DC_list = ['20230927_Cs137_DC_600V.dat', '20231006_Cs137_DC_600V_1.dat', '20231006_Cs137_DC_600V_2.dat', '20231023_Cs137_DC_600V.dat']\n",
    "allpaths = np.concatenate([AC_list, DC_list])\n",
    "    \n",
    "path_lists = [AC_list, DC_list, allpaths]\n",
    "strings = ['allAC_preLL', 'allDC_preLL', 'allACandDC_preLL']\n",
    "\n",
    "emin = 600.\n",
    "emax = 670.\n",
    "\n",
    "df_list = []\n",
    "    \n",
    "for i in range(len(path_lists)):\n",
    "    \n",
    "    ## Read in the dat files\n",
    "    fullpaths = []\n",
    "    for p in path_lists[i]:\n",
    "        fullpaths.append(datpath + 'preLL/' + p)\n",
    "    \n",
    "    df = make_df_from_dat(fullpaths, e_min=emin, e_max=emax)\n",
    "\n",
    "    plt.figure()\n",
    "    hist,binedges,_  = plt.hist(df['energy_p'], bins=100)\n",
    "    plt.yscale('log')\n",
    "    plt.xlabel('Energy (keV)')\n",
    "    plt.ylabel('Counts')\n",
    "    plt.show()\n",
    "    \n",
    "    fwhm_spline = UnivariateSpline(binedges[:-1], hist-0.5*np.max(hist))\n",
    "    fwtm_spline = UnivariateSpline(binedges[:-1], hist-0.1*np.max(hist))\n",
    "    fwhm = fwhm_spline.roots()[-1]-fwhm_spline.roots()[0]\n",
    "    fwtm = fwtm_spline.roots()[-1]-fwtm_spline.roots()[0]\n",
    "    print('FWHM = ' + str(round(fwhm, 2)))\n",
    "    print('FWTM = ' + str(round(fwtm, 2)))\n",
    "\n",
    "    plt.figure()\n",
    "    hist,binedges,_  = plt.hist(df['energy_n'], bins=100)\n",
    "    plt.yscale('log')\n",
    "    plt.xlabel('Energy (keV)')\n",
    "    plt.ylabel('Counts')\n",
    "    plt.show()\n",
    "    \n",
    "    fwhm_spline = UnivariateSpline(binedges[:-1], hist-0.5*np.max(hist))\n",
    "    fwtm_spline = UnivariateSpline(binedges[:-1], hist-0.1*np.max(hist))\n",
    "    fwhm = fwhm_spline.roots()[-1]-fwhm_spline.roots()[0]\n",
    "    fwtm = fwtm_spline.roots()[-1]-fwtm_spline.roots()[0]\n",
    "    print('FWHM = ' + str(round(fwhm, 2)))\n",
    "    print('FWTM = ' + str(round(fwtm, 2)))\n",
    "\n",
    "    ### Run depth calibration\n",
    "\n",
    "    ctd_obs_map = [[[] for p in range(37)] for n in range(37)]\n",
    "    ctd_stretch_map = [[[] for p in range(37)] for n in range(37)]\n",
    "    ctd_obs_flat = []\n",
    "    ctd_stretch_flat = []\n",
    "\n",
    "    for p in range(37):\n",
    "        for n in range(37):\n",
    "            ctd_obs, ctd_stretch, depth, depth_err = calibrator.depth_from_timing_prob(p+1, n+1, df.loc[df.strip_p.eq(p+1)&df.strip_n.eq(n+1), 'time_p'], \\\n",
    "                                                                                       df.loc[df.strip_p.eq(p+1)&df.strip_n.eq(n+1), 'time_n'], \\\n",
    "                                                                                      df.loc[df.strip_p.eq(p+1)&df.strip_n.eq(n+1), 'energy_p'])\n",
    "            df.loc[df.strip_p.eq(p+1)&df.strip_n.eq(n+1), 'z'] =  depth\n",
    "            df.loc[df.strip_p.eq(p+1)&df.strip_n.eq(n+1), 'z_err'] =  depth_err\n",
    "            ctd_obs_map[p][n] = ctd_obs\n",
    "            ctd_stretch_map[p][n] = ctd_stretch\n",
    "            ctd_obs_flat.append(ctd_obs)\n",
    "            ctd_stretch_flat.append(ctd_stretch)\n",
    "\n",
    "    ctd_obs_flat = np.concatenate(ctd_obs_flat)\n",
    "    ctd_stretch_flat = np.concatenate(ctd_stretch_flat)\n",
    "    df_list.append(df)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.hist(df[\"z\"], bins=50, histtype = \"step\", range=(calibrator.zmin,calibrator.zmax))\n",
    "    plt.yscale('log')\n",
    "    plt.xlabel(\"z (cm)\")\n",
    "    plt.ylabel(\"counts\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(plot_dir + 'Cs137_depth_' + strings[i] + '_prob.pdf')\n",
    "\n",
    "    plt.figure()\n",
    "    plt.hist(ctd_obs_flat, bins=50, histtype=\"step\", label=\"Observed CTD\")\n",
    "    plt.hist(ctd_stretch_flat, bins=50, histtype=\"step\", color='red', label=\"stretched and shifted\")\n",
    "    plt.axvline(np.max(sim_ctd), lw=0.8, color='red', ls='dashed', zorder=0)\n",
    "    plt.axvline(np.min(sim_ctd), lw=0.8, color='red', ls='dashed', zorder=0)\n",
    "    plt.yscale('log')\n",
    "    plt.xlabel('CTD')\n",
    "    plt.ylabel('counts')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(plot_dir + \"Cs137_CTD_\" + strings[i] + \".pdf\")\n",
    "\n",
    "z_list, e_signal_preLL, h_signal_preLL = make_depthplot(df_list[-1], strings[-1], zmin=calibrator.zmin, zmax=calibrator.zmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 4.21\n",
      "FWTM = 10.03\n",
      "FWHM = 3.56\n",
      "FWTM = 8.35\n",
      "FWHM = 2.73\n",
      "FWTM = 5.33\n",
      "FWHM = 2.69\n",
      "FWTM = 5.23\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>det</th>\n",
       "      <th>strip_p</th>\n",
       "      <th>energy_p</th>\n",
       "      <th>time_p</th>\n",
       "      <th>strip_n</th>\n",
       "      <th>energy_n</th>\n",
       "      <th>time_n</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>z_err</th>\n",
       "      <th>z_binned</th>\n",
       "      <th>depth_corrected_energy_p</th>\n",
       "      <th>depth_corrected_energy_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>660.205139</td>\n",
       "      <td>240.0</td>\n",
       "      <td>17</td>\n",
       "      <td>658.675337</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.234105</td>\n",
       "      <td>0.033038</td>\n",
       "      <td>(0.201, 0.252]</td>\n",
       "      <td>661.797604</td>\n",
       "      <td>658.589894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>655.807642</td>\n",
       "      <td>285.0</td>\n",
       "      <td>34</td>\n",
       "      <td>660.789426</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.081396</td>\n",
       "      <td>0.043679</td>\n",
       "      <td>(0.0503, 0.101]</td>\n",
       "      <td>657.731553</td>\n",
       "      <td>660.760191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>663.441611</td>\n",
       "      <td>185.0</td>\n",
       "      <td>14</td>\n",
       "      <td>666.980549</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.826595</td>\n",
       "      <td>0.042963</td>\n",
       "      <td>(0.805, 0.856]</td>\n",
       "      <td>663.750922</td>\n",
       "      <td>667.115494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>313</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>662.525549</td>\n",
       "      <td>155.0</td>\n",
       "      <td>29</td>\n",
       "      <td>658.234542</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.437274</td>\n",
       "      <td>0.054104</td>\n",
       "      <td>(1.409, 1.46]</td>\n",
       "      <td>661.870671</td>\n",
       "      <td>658.739274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>661.195999</td>\n",
       "      <td>165.0</td>\n",
       "      <td>32</td>\n",
       "      <td>659.252122</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.450465</td>\n",
       "      <td>0.059556</td>\n",
       "      <td>(1.409, 1.46]</td>\n",
       "      <td>660.528522</td>\n",
       "      <td>659.761460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269464</th>\n",
       "      <td>3233669</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>661.603411</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1</td>\n",
       "      <td>662.397956</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.123342</td>\n",
       "      <td>0.043693</td>\n",
       "      <td>(0.101, 0.151]</td>\n",
       "      <td>663.449921</td>\n",
       "      <td>662.346554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269465</th>\n",
       "      <td>3233730</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>645.373936</td>\n",
       "      <td>275.0</td>\n",
       "      <td>9</td>\n",
       "      <td>657.603027</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.225049</td>\n",
       "      <td>0.034338</td>\n",
       "      <td>(0.201, 0.252]</td>\n",
       "      <td>646.950655</td>\n",
       "      <td>657.519314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269466</th>\n",
       "      <td>3233856</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>658.766425</td>\n",
       "      <td>275.0</td>\n",
       "      <td>14</td>\n",
       "      <td>661.566536</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.129226</td>\n",
       "      <td>0.039415</td>\n",
       "      <td>(0.101, 0.151]</td>\n",
       "      <td>660.591799</td>\n",
       "      <td>661.512516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269467</th>\n",
       "      <td>3233939</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>659.473632</td>\n",
       "      <td>170.0</td>\n",
       "      <td>31</td>\n",
       "      <td>661.643388</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.661387</td>\n",
       "      <td>0.041682</td>\n",
       "      <td>(0.654, 0.705]</td>\n",
       "      <td>660.119235</td>\n",
       "      <td>661.669767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269468</th>\n",
       "      <td>3233955</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>652.366653</td>\n",
       "      <td>300.0</td>\n",
       "      <td>22</td>\n",
       "      <td>660.805175</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.076037</td>\n",
       "      <td>0.043280</td>\n",
       "      <td>(0.0503, 0.101]</td>\n",
       "      <td>654.292331</td>\n",
       "      <td>660.779136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>269469 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  det  strip_p    energy_p  time_p  strip_n    energy_n  \\\n",
       "0            25   11       25  660.205139   240.0       17  658.675337   \n",
       "1            73   11       31  655.807642   285.0       34  660.789426   \n",
       "2           191   11        7  663.441611   185.0       14  666.980549   \n",
       "3           313   11        8  662.525549   155.0       29  658.234542   \n",
       "4           431   11       12  661.195999   165.0       32  659.252122   \n",
       "...         ...  ...      ...         ...     ...      ...         ...   \n",
       "269464  3233669   11       25  661.603411   290.0        1  662.397956   \n",
       "269465  3233730   11       24  645.373936   275.0        9  657.603027   \n",
       "269466  3233856   11       23  658.766425   275.0       14  661.566536   \n",
       "269467  3233939   11       10  659.473632   170.0       31  661.643388   \n",
       "269468  3233955   11        8  652.366653   300.0       22  660.805175   \n",
       "\n",
       "        time_n              x              y         z     z_err  \\\n",
       "0        145.0  1.634266e+307  1.634266e+307  0.234105  0.033038   \n",
       "1        150.0  1.634266e+307  1.634266e+307  0.081396  0.043679   \n",
       "2        245.0  1.634266e+307  1.634266e+307  0.826595  0.042963   \n",
       "3        350.0  1.634266e+307  1.634266e+307  1.437274  0.054104   \n",
       "4        360.0  1.634266e+307  1.634266e+307  1.450465  0.059556   \n",
       "...        ...            ...            ...       ...       ...   \n",
       "269464   150.0  1.634266e+307  1.634266e+307  0.123342  0.043693   \n",
       "269465   155.0  1.634266e+307  1.634266e+307  0.225049  0.034338   \n",
       "269466   145.0  1.634266e+307  1.634266e+307  0.129226  0.039415   \n",
       "269467   210.0  1.634266e+307  1.634266e+307  0.661387  0.041682   \n",
       "269468   150.0  1.634266e+307  1.634266e+307  0.076037  0.043280   \n",
       "\n",
       "               z_binned  depth_corrected_energy_p  depth_corrected_energy_n  \n",
       "0        (0.201, 0.252]                661.797604                658.589894  \n",
       "1       (0.0503, 0.101]                657.731553                660.760191  \n",
       "2        (0.805, 0.856]                663.750922                667.115494  \n",
       "3         (1.409, 1.46]                661.870671                658.739274  \n",
       "4         (1.409, 1.46]                660.528522                659.761460  \n",
       "...                 ...                       ...                       ...  \n",
       "269464   (0.101, 0.151]                663.449921                662.346554  \n",
       "269465   (0.201, 0.252]                646.950655                657.519314  \n",
       "269466   (0.101, 0.151]                660.591799                661.512516  \n",
       "269467   (0.654, 0.705]                660.119235                661.669767  \n",
       "269468  (0.0503, 0.101]                654.292331                660.779136  \n",
       "\n",
       "[269469 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_correction(df_list[-1], z_list, e_signal_preLL, h_signal_preLL, plot_suffix = 'Cs137allACandDC_preLL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 253.9 (chi2/ndof = 4.5)    │              Nfcn = 269              │\n",
      "│ EDM = 9.78e-07 (Goal: 0.0002)    │                                      │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│          Valid Minimum           │        No Parameters at limit        │\n",
      "├──────────────────────────────────┼──────────────────────────────────────┤\n",
      "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
      "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
      "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
      "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
      "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
      "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
      "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
      "│ 0 │ ae   │  662.411  │   0.006   │   -0.006   │   0.006    │         │         │       │\n",
      "│ 1 │ b    │   1.326   │   0.008   │   -0.008   │   0.008    │    0    │         │       │\n",
      "│ 2 │ c    │   0.547   │   0.005   │   -0.005   │   0.005    │    0    │         │       │\n",
      "│ 3 │ ah   │  662.114  │   0.005   │   -0.005   │   0.005    │         │         │       │\n",
      "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n",
      "│          │          ae           │           b           │           c           │          ah           │\n",
      "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n",
      "│  Error   │  -0.006   │   0.006   │  -0.008   │   0.008   │  -0.005   │   0.005   │  -0.005   │   0.005   │\n",
      "│  Valid   │   True    │   True    │   True    │   True    │   True    │   True    │   True    │   True    │\n",
      "│ At Limit │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
      "│ Max FCN  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
      "│ New Min  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
      "└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n",
      "┌────┬─────────────────────────────────────┐\n",
      "│    │       ae        b        c       ah │\n",
      "├────┼─────────────────────────────────────┤\n",
      "│ ae │  4.2e-05 3.75e-05 9.96e-06 1.15e-05 │\n",
      "│  b │ 3.75e-05 6.38e-05 1.29e-05 1.65e-05 │\n",
      "│  c │ 9.96e-06 1.29e-05 2.68e-05 2.18e-05 │\n",
      "│ ah │ 1.15e-05 1.65e-05 2.18e-05 2.64e-05 │\n",
      "└────┴─────────────────────────────────────┘\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th colspan=\"5\" style=\"text-align:center\" title=\"Minimizer\"> Migrad </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Minimum value of function\"> FCN = 253.9 (chi2/ndof = 4.5) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total number of function and (optional) gradient evaluations\"> Nfcn = 269 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:left\" title=\"Estimated distance to minimum and goal\"> EDM = 9.78e-07 (Goal: 0.0002) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center\" title=\"Total run time of algorithms\">  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Valid Minimum </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> No Parameters at limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td colspan=\"2\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below EDM threshold (goal x 10) </td>\n",
       "        <td colspan=\"3\" style=\"text-align:center;background-color:#92CCA6;color:black\"> Below call limit </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Covariance </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\"> Hesse ok </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix accurate?\"> Accurate </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Is covariance matrix positive definite?\"> Pos. def. </td>\n",
       "        <td style=\"text-align:center;background-color:#92CCA6;color:black\" title=\"Was positive definiteness enforced by Minuit?\"> Not forced </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th title=\"Variable name\"> Name </th>\n",
       "        <th title=\"Value of parameter\"> Value </th>\n",
       "        <th title=\"Hesse error\"> Hesse Error </th>\n",
       "        <th title=\"Minos lower error\"> Minos Error- </th>\n",
       "        <th title=\"Minos upper error\"> Minos Error+ </th>\n",
       "        <th title=\"Lower limit of the parameter\"> Limit- </th>\n",
       "        <th title=\"Upper limit of the parameter\"> Limit+ </th>\n",
       "        <th title=\"Is the parameter fixed in the fit\"> Fixed </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 0 </th>\n",
       "        <td> ae </td>\n",
       "        <td> 662.411 </td>\n",
       "        <td> 0.006 </td>\n",
       "        <td> -0.006 </td>\n",
       "        <td> 0.006 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 1 </th>\n",
       "        <td> b </td>\n",
       "        <td> 1.326 </td>\n",
       "        <td> 0.008 </td>\n",
       "        <td> -0.008 </td>\n",
       "        <td> 0.008 </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 2 </th>\n",
       "        <td> c </td>\n",
       "        <td> 0.547 </td>\n",
       "        <td> 0.005 </td>\n",
       "        <td> -0.005 </td>\n",
       "        <td> 0.005 </td>\n",
       "        <td> 0 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> 3 </th>\n",
       "        <td> ah </td>\n",
       "        <td> 662.114 </td>\n",
       "        <td> 0.005 </td>\n",
       "        <td> -0.005 </td>\n",
       "        <td> 0.005 </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "        <td>  </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> ae </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> b </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> c </th>\n",
       "        <th colspan=\"2\" style=\"text-align:center\" title=\"Parameter name\"> ah </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Lower and upper minos error of the parameter\"> Error </th>\n",
       "        <td> -0.006 </td>\n",
       "        <td> 0.006 </td>\n",
       "        <td> -0.008 </td>\n",
       "        <td> 0.008 </td>\n",
       "        <td> -0.005 </td>\n",
       "        <td> 0.005 </td>\n",
       "        <td> -0.005 </td>\n",
       "        <td> 0.005 </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Validity of lower/upper minos error\"> Valid </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> True </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Did scan hit limit of any parameter?\"> At Limit </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"Did scan hit function call limit?\"> Max FCN </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th title=\"New minimum found when doing scan?\"> New Min </th>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "        <td style=\"background-color:#92CCA6;color:black\"> False </td>\n",
       "    </tr>\n",
       "</table><table>\n",
       "    <tr>\n",
       "        <td></td>\n",
       "        <th> ae </th>\n",
       "        <th> b </th>\n",
       "        <th> c </th>\n",
       "        <th> ah </th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> ae </th>\n",
       "        <td> 4.2e-05 </td>\n",
       "        <td style=\"background-color:rgb(250,141,141);color:black\"> 3.75e-05 <strong>(0.724)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,205,205);color:black\"> 9.96e-06 <strong>(0.297)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,198,198);color:black\"> 1.15e-05 <strong>(0.345)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> b </th>\n",
       "        <td style=\"background-color:rgb(250,141,141);color:black\"> 3.75e-05 <strong>(0.724)</strong> </td>\n",
       "        <td> 6.38e-05 </td>\n",
       "        <td style=\"background-color:rgb(250,203,203);color:black\"> 1.29e-05 <strong>(0.313)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,190,190);color:black\"> 1.65e-05 <strong>(0.402)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> c </th>\n",
       "        <td style=\"background-color:rgb(250,205,205);color:black\"> 9.96e-06 <strong>(0.297)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,203,203);color:black\"> 1.29e-05 <strong>(0.313)</strong> </td>\n",
       "        <td> 2.68e-05 </td>\n",
       "        <td style=\"background-color:rgb(250,127,127);color:black\"> 2.18e-05 <strong>(0.819)</strong> </td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th> ah </th>\n",
       "        <td style=\"background-color:rgb(250,198,198);color:black\"> 1.15e-05 <strong>(0.345)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,190,190);color:black\"> 1.65e-05 <strong>(0.402)</strong> </td>\n",
       "        <td style=\"background-color:rgb(250,127,127);color:black\"> 2.18e-05 <strong>(0.819)</strong> </td>\n",
       "        <td> 2.64e-05 </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "┌─────────────────────────────────────────────────────────────────────────┐\n",
       "│                                Migrad                                   │\n",
       "├──────────────────────────────────┬──────────────────────────────────────┤\n",
       "│ FCN = 253.9 (chi2/ndof = 4.5)    │              Nfcn = 269              │\n",
       "│ EDM = 9.78e-07 (Goal: 0.0002)    │                                      │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│          Valid Minimum           │        No Parameters at limit        │\n",
       "├──────────────────────────────────┼──────────────────────────────────────┤\n",
       "│ Below EDM threshold (goal x 10)  │           Below call limit           │\n",
       "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n",
       "│  Covariance   │     Hesse ok     │ Accurate  │  Pos. def.  │ Not forced │\n",
       "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n",
       "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n",
       "│   │ Name │   Value   │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit-  │ Limit+  │ Fixed │\n",
       "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n",
       "│ 0 │ ae   │  662.411  │   0.006   │   -0.006   │   0.006    │         │         │       │\n",
       "│ 1 │ b    │   1.326   │   0.008   │   -0.008   │   0.008    │    0    │         │       │\n",
       "│ 2 │ c    │   0.547   │   0.005   │   -0.005   │   0.005    │    0    │         │       │\n",
       "│ 3 │ ah   │  662.114  │   0.005   │   -0.005   │   0.005    │         │         │       │\n",
       "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
       "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n",
       "│          │          ae           │           b           │           c           │          ah           │\n",
       "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n",
       "│  Error   │  -0.006   │   0.006   │  -0.008   │   0.008   │  -0.005   │   0.005   │  -0.005   │   0.005   │\n",
       "│  Valid   │   True    │   True    │   True    │   True    │   True    │   True    │   True    │   True    │\n",
       "│ At Limit │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "│ Max FCN  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "│ New Min  │   False   │   False   │   False   │   False   │   False   │   False   │   False   │   False   │\n",
       "└──────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘\n",
       "┌────┬─────────────────────────────────────┐\n",
       "│    │       ae        b        c       ah │\n",
       "├────┼─────────────────────────────────────┤\n",
       "│ ae │  4.2e-05 3.75e-05 9.96e-06 1.15e-05 │\n",
       "│  b │ 3.75e-05 6.38e-05 1.29e-05 1.65e-05 │\n",
       "│  c │ 9.96e-06 1.29e-05 2.68e-05 2.18e-05 │\n",
       "│ ah │ 1.15e-05 1.65e-05 2.18e-05 2.64e-05 │\n",
       "└────┴─────────────────────────────────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fit_CCE(z_list, e_signal_preLL, h_signal_preLL, sim_dCCE, plot_suffix = 'Cs137allACandDC_preLL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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