minuit_fitting_postLL1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.8 +/- 1.9\n",
      "19.3 +/- 1.6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cosilab/miniconda3/lib/python3.11/site-packages/numpy/core/fromnumeric.py:3464: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/cosilab/miniconda3/lib/python3.11/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "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": [
    "# Do the same for the data after LL round 1"
   ]
  },
  {
   "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 = 3.42\n",
      "FWTM = 8.37\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 = 19.18\n",
      "FWTM = 27.7\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 = 3.51\n",
      "FWTM = 9.39\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cosilab/miniconda3/lib/python3.11/site-packages/scipy/interpolate/_fitpack2.py:313: UserWarning: \n",
      "The maximal number of iterations maxit (set to 20 by the program)\n",
      "allowed for finding a smoothing spline with fp=s has been reached: s\n",
      "too small.\n",
      "There is an approximation returned but the corresponding weighted sum\n",
      "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n",
      "  warnings.warn(message)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 12.31\n",
      "FWTM = 23.26\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 = 3.46\n",
      "FWTM = 8.75\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 = 16.37\n",
      "FWTM = 24.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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p3ldc1lWBgYF2P3XqeNXXAwCAas3r/lUNCwvT7NmzdfDgQeXk5OjixYv6+OOPdfvtt+vLL7+UpEp1B86YMUMvv/yyAgMDtXz5ckVERGjt2rUaPny4CgoK3H0bZSqee+z48eN2w15xK9y185QBAIDqz+tClz35+flau3atJGnw4MEVOvaVV17Riy++qICAAG3YsEEjRoxQamqqWrZsqeTkZI0YMUKFhYVVUOvSYmNjZbPZlJubq507d5ZZZuvWrZKuDpwHpKvLWmTlFpT7qcr55QAAzvGagfSOvP766/rxxx/VunVrDRo0yOnjXnvtNU2ZMkX+/v5av359SRdlVFSUtmzZojvvvFMffPCBfHx8tGzZMvn4VN0SAkFBQbrrrrv04YcfKikpSd27dy+1//Dhw0pJSZEkDRs2rMrqAe/izDpil2f1Zy1HAPAwr2rpSktL0yeffFJq/qjs7Gy98sormjZtmnx8fLRo0SK7c1z9tzfffFOTJ0+Wv7+/PvzwQ/Xq1avU/tatW2vLli1q3ry5VqxYoUceeaTKWwwSExNlGIaWLVumpKSkkuudPn1aw4cPV1FRkYYMGVLlc4YBAAD38qpffXft2qWJEycqMDBQUVFRqlu3rg4dOqQrV64oICBA77333nXBqTyRkZEKDg7W6tWr1adPnzLLREdHKzU1VT179lR0dLRTM9KfOHGi1LxexW8c7t27V2FhYSXbJ0+erMmTJ5c6Nj4+XnPnztUzzzyjxx57TDNnzlRYWJgOHDig3NxctWnTRm+//bbT94iaKcDmo8uz+pdbJiuv0KlWMACANbwqdPXs2VOPPPKItm/fru+//14FBQVq2bKl7r77bj3zzDOKiIio0PmGDBmio0ePqlGjRuWWi4mJuS4wlaewsFDnzp27bntBQUGp7fbmE0tISFD79u01Z84cpaen6+zZs4qIiNCwYcP0wgsvMKEpZBgG3YUA4GW86qndqVMnvfvuu249p6PAVczZwCVdbUFztRuyT58+dlvfAACA9/GqMV0AAADeitAFAABgAUIXAACABQhdAAAAFiB0AQAAWMCr3l4EUDlZeeUvYxVg83FqDjoAQOURuoBawNEkqSwTBABVj+5FAAAAC/CrLVBDOVoqiGWCAMBahC6ghmKpIACoXngiAwAAjzNNU1fKeenH0QtB3oDQBQAAPO5KXqGCp37s6WpUKQbSAwAAWICWLgAAUK2cmd5PgTYfu/sDytlXnRG6AABAtRJo86mRLwLRvQgAAGCBmhcjAVQYywQBQNUjdAFgmSAAsADdiwAAABbgV1eglmKZIACwFqELqKVYJggArEX3IgAAgAUIXQAAABagbwEAAFS52rCgtSNVGrp++uknpaWlycfHR3fccYcaNmxYlZcDAADVVG1Y0NoRl7oXd+3apTFjxmjOnDnX7Vu5cqUiIyM1bNgw3XfffWrVqpX+/ve/u3I5AAAAr+VSS9df//pXLVmyRK+//nqp7adOndKjjz6q7Ozskm2ZmZl68MEHtX//fkVHR7tyWQAA4MVq6oLWjrjU0vX5559LkgYNGlRqe1JSkrKzs9WhQwcdPnxYJ06c0J133qm8vDz97//+ryuXBAAAXq54QWt7n5q67JhLoev06dMyDEMRERGltn/00UcyDEMzZ85UdHS0mjdvrnnz5sk0TaWkpLhUYQAAAG/kUug6d+6cGjZsqLp1/9NLmZ2drT179sjX11f9+vUr2d6hQwfZbDYdO3bMlUsCAAB4JZdCV926dfXvf/+71LYvv/xShYWFiouLk81mK7UvKChIBQUFrlwSAADAK7kUuiIjI1VYWKgvv/yyZNu6detkGIa6d+9eqmxhYaEuXbqkJk2auHJJAAAAr+RS6PrVr34l0zT15JNPKj09XWvXrlVSUpIk6d577y1V9l//+pcKCwvVokULVy4JAADglVyaMuLZZ5/VkiVLlJGRodtuu03S1Rlne/fuXfLnYsWD67t16+bKJVGLOZrNWKodMxoDALyTS6GrefPmSk1N1aRJk/TFF1+oYcOGGjhwoF599dVS5UzT1HvvvSfTNNWrVy+XKozai9mMAQDezOVlgDp27KjNmzeXW6aoqEj//Oc/JV0NagC8izMtiAE2nxo7tw4AuIMlC177+PhcN5cX4ApHsxlLNXdGY08If+kTh2Uuz+qvQF9LHikA4JVcekK2bt1aTZo00Y4dO5wq36NHD506dUpHjhxx5bJAyWzGAAB4C5f+1Tp27JhycnKcLn/y5EkdP37clUsCsEiAzUeXZ/Uvt0xWXqFTrWAAAIu6F4sVFBSoTh2XZqkAYBHDMGhNBAA3siwBZWdn6+zZswoODrbqkgAAANVGhX6NPX78+HVrJ+bl5Wnr1q0yTbPMY0zT1MWLF/X+++8rPz9f7du3r3RlAQAAvFWFQtd7772nl19+udS2CxcuqGfPng6PNU1ThmHoscceq1AFAQAAaoIKD9i4tkXLMAy7LVzXlqlfv75+8Ytf6PHHH9eDDz5Y8VoCAAB4uQqFrunTp2v69Oklf65Tp47Cw8N16tQpt1cMAACgJnHp1aRRo0apYcOGbqoKAABAzeVS6Fq8eLGbqgEAAFCzMQkPALdwtD4jazMCqO3cErouX76s9evXa9++fTp//rzy8/PtljUMQ++88447LgugGnE0Mz1rMwKo7Vx+Ai5evFgTJkxQZmZmybay3mgsftOR0AUAAGojl0LXpk2b9Oijj8o0Tfn5+albt25q1qyZ6tblt1mgNnC0PiNrMwLAf7iUjl599VWZpqlu3brpH//4h8LCwtxVLwBegPUZAcB5Lq29mJGRIcMwtHjxYgKXh23fvl39+vVTs2bN5Ovrq/DwcA0cOFDp6emerhoAAJCLoaugoEBBQUGKiYlxV31QSRcuXFC7du30xhtv6JNPPtH8+fN1/vx59ejRQ19++aWnqwcAQK3nUr9AdHS0vv32WxUWFsrHx8dddUIlDBgwQAMGDCi17e6771ZYWJiWLl2q+Ph4D9UMAABILrZ0PfTQQ8rPz9fHH3/srvrAjQIDA+Xr66t69ep5uioAANR6LoWuhIQExcfH63/+5390+PBhd9WpXOfPn9fUqVPVqVMnBQUFyWazqXnz5ho6dKhSU1MtqYMjZ86c0fLlyzVhwgR1795dAQEBMgxDcXFxTp8jNTVVAwcOVOPGjeXv76+2bdsqMTFRWVlZ5R5XVFSk/Px8HT9+XOPHj5dhGBo7dqyrtwQAAFzkUvfiihUrNHLkSL344ovq2LGjhg0bpq5duyo4OLjc40aNGlWp6x0+fFg9e/bUqVOnVKdOHUVGRqpBgwY6cuSI1qxZozVr1mjGjBmaNm1apc7vLitXrtTEiRMrffz8+fM1YcIEmaapFi1aqGXLljpw4IBmzpyp5ORkpaWlKTQ0tMxj77nnHm3atEmS1LRpU23YsEHt2rWrdF0AAIB7uBS6Hn744ZJlPUzT1Pvvv6/333+/3GMMw6h06Hr88cd16tQpxcTE6O9//7tuueUWSVJeXp5mzpypGTNm6MUXX9S9996rjh07Vuoa7lC/fn317dtXcXFxiouL06FDhzRlyhSnjs3IyFBCQoIkaeHChRo7dqwMw9CpU6c0aNAgZWRkaOzYsUpOTi7z+Pnz5+vixYs6deqUFi1apP79+2v9+vW644473HV7AACgElwKXa1atbJsLbXLly+XdB++/vrrJYFLkmw2m15++WV9+OGH2rNnjz7++GOnQtelS5eUmJio2bNnKyAgoNyy06ZN06hRo3TTTTc5PO+YMWM0ZsyYkj9XZGHwGTNmqKioSKNGjdK4ceNKtjdr1kwrVqxQ27ZttWbNGu3bt08dOnS47vhr3yQdNGiQbr/9dj399NPas2eP03UAAADu51LoOnbsmJuq4Vhubm7J8kKtW7cus0zr1q21Z8+ectd+vNYzzzyjd999V19//bXWr18vf3//Mss99dRTWrBggdavX6/du3dXWdDMzMzUxo0bJalU4CoWExOj3r17a/PmzVq1alWZoetaxePIkpKSqqS+QEU4WhBbYlFsADWb10wlHRYWppYtW+rEiRPatm2bfvGLX5Tan5OTo127dkmSunbt6tQ5Z8+erfT0dKWkpGjw4MFat26d/Pz8SpVJSEjQggULFBoaqiVLllTpPwi7d+9Wbm6ufH19deutt5ZZpkePHtq8ebN27Njh8HwFBQVKS0vTjTfe6O6qAhXmzHJALIoNoCbzqqfbq6++qgcffFCTJ09WnTp1dO+996p+/fo6ePCgEhMTdfz4cQ0dOlT9+vVz6nyNGzdWSkqKevbsqU8//VT33Xef1q5dK19fX0nSpEmTNG/ePIWEhGjz5s1VPk7s0KFDkq5229qb5iE6OlqS9O2335baPmLECEVGRio2NlZhYWE6efKkFi1apD179mj16tV2r1ne25BFRUUVvQXAJY5aw2gJA+DNvCp0PfDAAwoODtZLL710XfdbWFiYFixYoCeeeKJC52zSpIlSUlJ05513auPGjRo6dKjWrFmjadOmae7cuWrQoIE2bdqkzp07u/NWynT+/HlJsvtm4rX7Lly4UGp7t27dtHz5cr311lu6dOmSQkND1bVr15J7sycoKMgNNQfK5mhBbKn0otiOWsNoCQPgzVx6el07WNxZhmHonXfeqfQ1jxw5ovPnz8swDLVs2VIhISE6cuSIfv75ZyUlJSk2NlbdunWr0DnDw8NLWrw++ugj3XzzzTpy5IiCg4O1ceNGy2Zzz8nJkXT1xQB7ilvhsrOzS20fP368xo8fX3WVAyqBBbEB4D9cehouXrxYhmGUDHD/b//dDWCapkuh68knn9Sf//xndezYUXv37lX79u0lSfn5+ZozZ45eeOEF9e7dW9u2bVNsbGyFzt28eXOlpKSoQ4cOOnLkiCRp3bp1+uUvf1mpulZG8XiyvLw8u2Vyc3Mlye6g/4rKzMy0u+/222/nrUdUOUetYde2hAGAN3MpdI0aNarc8RWXLl3Srl27dPLkSTVq1EgDBw6s9LX27dunv/zlL6pbt66Sk5NLxjZJUr169fT888/rm2++0ZIlSzRt2jRt2LChwtdYtGiRLl68WPLnefPmqXv37pYtoxMSEiLpP92MZSneV1zWVYGBgXb31anj0oIFgFNoDQNQW7jc0uWIaZpavHixnnjiCdWvX1/z5s2r1LXS0tJkmqZiYmJKBa5r3XPPPVqyZIl27txZ4fPPmDFDL7/8sgIDA7Vw4UJNnTpVa9eu1fDhw7Vy5UrVrVv1/ygUzwF2/Phx5efnlxn2ilvhnJkvDAAAVB9V3pRhGIYeeeQRvfLKK1qwYIHdmdQduXz5csn57Cnu5iweG+WsV155RS+++KICAgK0YcMGjRgxQqmpqWrZsqWSk5M1YsQIFRY6nmPIVbGxsbLZbMrNzbUbHLdu3SpJFR63BgAAPMuy/qPf/e53MgxDCxYsqNTxxS07hw4d0nfffVdmmeKJRdu0aeP0eV977TVNmTJF/v7+pZbLiYqK0pYtW9SiRQt98MEHGjlyZJUHr6CgIN11112SVOaEpocPH1ZKSookadiwYVVaFwAA4F6Wha7g4GDVr1+/0gOz77rrLjVt2lQFBQUaNmyYvv7665J9+fn5eu2110q6O0ePHu3UOd98801NnjxZ/v7++vDDD9WrV69S+1u3bq0tW7aoefPmWrFihR555BG7Lw24S2JiogzD0LJly5SUlFRyvdOnT2v48OEqKirSkCFDPLq2JAAAqDjLQtf58+d18eJFp5fo+W8BAQH661//qqCgIO3evVvt27dXZGSkOnfurEaNGmny5MmSpF//+td68sknnTpnZGSkgoODtXbtWvXp06fMMtHR0UpNTVWzZs0UHR3t1MSMJ06cUFhYWMnnqaeekiTt3bu31PZXX331umPj4+M1d+5cSdJjjz2miIgIxcbGKioqShkZGWrTpo3efvttp+4PAABUH5a9MvT8889LqljX33/r3bu39u/frzfffFOffvqpjh49qh9++EGhoaHq3r27Ro8erQceeMDp8w0ZMkRHjx5Vo0aNyi0XExNTEpicUVhYqHPnzl23vaCgoNT2K1eulHl8QkKC2rdvrzlz5ig9PV1nz55VRESEhg0bphdeeIEJTQEA8EIuha6lS5eWuz8nJ0cnTpzQ3//+dx08eLBkUL0rIiIi9MYbb7h0jms5ClzFnA1c0tUWNFe7Ifv06WO39Q0AAHgfl0LXww8/7FR3W3EAGTVqlNNdfwAAADWJS6GrVatW5YauunXrKiQkRB07dtTw4cPVu3dvVy4HoJZjQWwA3syl0HXs2DE3VQMAHGNBbADejKcTAABwiWmauuKgJdpRS3VtQOgCUK2xIDZQ/V3JK1Tw1I89XY1qz22hKy8vT59++ql27dqls2fPyjAMNW7cWPHx8erbt69sNpu7LgWgFmFBbAA1hVueZElJSUpMTNTPP/9c5v6wsDDNnDlTY8eOdcflAABANXVmej8F2nzKLRPgYH9N5XLoeu655/T666+XTAvRvHlztWjRQpJ08uRJ/fDDD/rpp5/0+OOP68iRI5o9e7arlwQAANVUoM2H1mk7XFoG6LPPPtNrr70m0zQ1dOhQHThwQCdOnNAXX3yhL774QidOnNDBgwc1bNgwmaap1157TVu3bnVX3QEAALyGS6HrT3/6kyTp0Ucf1apVq9S2bdvryrRp00YffPCBHn30UZmmqQULFrhySQCwKyuvUFm5BeV+qnrRegCwx6X2v+3bt6tOnTqaNWuWw7IzZ87Uu+++q23btrlySQCwy5m3GJnLC4CnuNTS9fPPP6tBgwZq0qSJw7JNmzZVw4YN7Q62BwAAqMlc+nUvODhYly5dUk5Ojvz8/Motm52drcuXL6t+/fquXBIASnE0j5fEXF4AqgeXWro6dOigwsJCvfvuuw7LvvvuuyooKFDHjh1duSQAlFI8j1e5n1r6ejqA6sWl0DVixAiZpqlJkybpnXfesVtu0aJFmjRpkgzD0MiRI125JAAAgFdyqXvx4Ycf1rJly/TZZ59p3Lhxevnll9WrVy81b95chmHoxIkTSk1N1Q8//CDTNNWzZ0+NHj3aXXUHAADwGi6Frjp16ugf//iHxowZozVr1ujEiRNatmxZqTLFr2cPHTpU77zzjgzDcOWSAAAAXsnl96br16+v1atX68svv9TKlStL1l6UpCZNmiguLk4PPPCA4uPjXa4sAACAt3LbZDXx8fEEKwAAADtcGkifl5enffv26ZtvvnFY9ptvvtG+ffuUn5/vyiUBAAC8kkuh629/+5s6d+6sN99802HZWbNmqXPnzlq9erUrlwQAAPBKLoWu5ORkSXJqGojitRcJXQAAoDZyKXTt379fkpya8LRLly6SpH/961+uXBIAAMAruRS6Tp06pQYNGigoKMhh2eDgYDVs2FCnT5925ZIAAABeyaXQZbPZlJ2d7VRZ0zSVnZ3NPF0AAKBWcil0RUVFKS8vT1988YXDstu3b1dubq4iIiJcuSQAAIBXcil0/epXv5Jpmnr++edVUFBgt1xBQYFeeOEFGYahfv36uXJJAAAAr+RS6Hr66afl5+entLQ09e3bV7t3776uzFdffaU+ffooLS1Nvr6+mjBhgiuXBAAA8EouzUjfokULLVy4UA8//LC2bt2quLg4hYeHKyIiQoZh6OjRo/rxxx9lmqYMw1BSUpJatWrlrroDAAB4DZeXARo5cqQaNWqk8ePH69ixYzp9+vR1byi2bt1aCxYs0N133+3q5QAAALySW9ZevOeee3T48GGlpqZq+/btOnPmjCTphhtu0G233aZevXqpTh2XejIBAAC8mtsWvPbx8VHfvn3Vt29fd50SAACgxqD5CQAAwAKELgAAAAu4rXsRALxBVl5hufsDbD6snAGgShC6ANQq4S99Uu7+y7P6K9CXRyMA96N7EQAAwAL8Ogegxguw+ejyrP5292flFTpsAQMAVxG6ANR4hmHQZQjA43gKAYAbmKapKw4G6RdjsD5QOxG6AMANruQVKnjqx06VZbA+UDsxkB4AAMAC/KoFAG52Zno/Bdp8Sm1jsD68maPuc0fz3+EqQhcAXMMdk6cG2nzoPkSNUpHuc9jHUwEArsHkqa7jpQKgbDw5AABuxUsFNVtZ3efXCihnX23HNx1ArcfkqYDz6D6vPP7WANR6FZk81d6YLwYSl60yLxVUpHtSoosS3oPQBQAV4I4WL3cM1vcWlWkVqeigbboo4S34lgKAxRwFN0djZqSaFcyA2oLQBQAOOBrzVVZ5VzjTmubMYOaqCGXOdP25s6vV3n0yzg7eiNAFAA64Y8Fsdw/W99TUFlbP18SgbdQkfJMBwAKOgpszrWm07gDejdAFANWAM61p7mgtc+fEpc6OPStPWV2RvAmKmorQBQBewh3dnO6cuNQdXX9WvA3qCC8lwCqELgCAV3M1uDHlBKzCtwwAaqnKTFzqDhV5G5QlZVCTELoAoJby1JuBVrwN6ggvJcATCF0AAK/jjuAGWI1vLADUQKwRCVQ/hC4AqIHoOnOdO6fXACRCFwAAZXLn9BqAROgCgBrD6jUiAVQMoauG2L59u37/+99r//79OnfunEJCQhQXF6fExER17drV09UDYAEGl1cdT02vYQWrFzGvzfi/s4a4cOGC2rVrp0cffVTh4eE6e/as3njjDfXo0UPbtm1TfHy8p6sIANWSMy8d1OSFt61exLw2q5nfoFpowIABGjBgQKltd999t8LCwrR06VJCF4AKqy3rIlq1FFFlBttXZDB/Za8B6xC6arDAwED5+vqqXr16nq4KAC9UE7rOrOLM31VlBttXtBXK1QH97ljEHPZ5Teg6duyYoqKinCr78MMP67333qviGtl35swZbd68WV9++aV27dql3bt3Kzs7W126dNGuXbucOkdqaqrmzJmj9PR0ZWZmKiIiQvfff7+ef/55BQYG2j2uqKhIhYWFOn36tGbPni3DMDR27Fh33RoA1Ai8dFC2mtyNWh14zd+sn5+funfvbnd/Tk6OMjIyJEm33XabVdUq08qVKzVx4sRKHz9//nxNmDBBpmmqRYsWatmypQ4cOKCZM2cqOTlZaWlpCg0NLfPYe+65R5s2bZIkNW3aVBs2bFC7du0qXRcAtUttWRfRqqWI3DnY3l4rVE0Z0F8beE3oCg8PV1pamt39S5Ys0cMPPyx/f3/99re/tbBm16tfv7769u2ruLg4xcXF6dChQ5oyZYpTx2ZkZCghIUGStHDhQo0dO1aGYejUqVMaNGiQMjIyNHbsWCUnJ5d5/Pz583Xx4kWdOnVKixYtUv/+/bV+/Xrdcccd7ro9ADUYb0A6r6J/V47GfTkaj0UrlPerMT+9xYsXS5J+/etfq379+k4dc+nSJSUmJmr27NkKCAgot+y0adM0atQo3XTTTQ7PO2bMGI0ZM+a6ujljxowZKioq0qhRozRu3LiS7c2aNdOKFSvUtm1brVmzRvv27VOHDh2uOz4mJqbkvwcNGqTbb79dTz/9tPbs2eN0HQAA7ueoNaqqJlh1NBi/Jr4cUV3ViNB17NgxffbZZ5Kujudy1jPPPKN3331XX3/9tdavXy9/f/8yyz311FNasGCB1q9fr927d1fZmyGZmZnauHGjJJUKXMViYmLUu3dvbd68WatWrSozdF3LMAzFxcUpKSmpSuoLAKj+mBKi+qgRoWvJkiUyTVOtWrVS7969nT5u9uzZSk9PV0pKigYPHqx169bJz8+vVJmEhAQtWLBAoaGhWrJkSZW+irt7927l5ubK19dXt956a5llevTooc2bN2vHjh0Oz1dQUKC0tDTdeOON7q4qAMAJjsZ9MR6rdvH60GWappYuXSpJGjVqlOrUqeP0sY0bN1ZKSop69uypTz/9VPfdd5/Wrl0rX19fSdKkSZM0b948hYSEaPPmzerYsWOV3EOxQ4cOSZJatWpld5qH6OhoSdK3335bavuIESMUGRmp2NhYhYWF6eTJk1q0aJH27Nmj1atX271mVlaW3X1FRUUVvQUAwDWq2xg5R1NCePPLEd6g+nwTKumzzz7Td999J6liXYvFmjRpopSUFN15553auHGjhg4dqjVr1mjatGmaO3euGjRooE2bNqlz585urvn1zp8/L0l230y8dt+FCxdKbe/WrZuWL1+ut956S5cuXVJoaKi6du1acm/2BAUFuaHmAABXWTEZLYPxPcvr/+aLB6n36NGjpBWoosLDw0tavD766CPdfPPNOnLkiIKDg7Vx40bLZnPPycmRJNlsNrtlilvhsrOzS20fP368xo8fX3WVAwBUqaqaGZ+B8tWHV4eurKyskqkTKtPKda3mzZsrJSVFHTp00JEjRyRJ69at0y9/+UtXq+m04vFkeXl5dsvk5uZKkt1B/xWVmZlpd9/tt9/OW48A4EUYH1a9eXXoWrVqlTIzMxUQEKD777/f5fMtWrRIFy9eLPnzvHnz1L17d8uW0QkJCZH0n27GshTvKy7rqvJmt6/I+DgAQMXVlslocZVXh67irsVhw4YpODjYpXPNmDFDL7/8sgIDA7Vw4UJNnTpVa9eu1fDhw7Vy5UrVrVv1f1XFc4AdP35c+fn5ZYa94lY4Z+YLAwBUb1bNjH9tWXiO1zZlHD16VJ9//rkk17sWX3nlFb344osKCAjQhg0bNGLECKWmpqply5ZKTk7WiBEjVFhY9X3isbGxstlsys3N1c6dO8sss3XrVklXB84DAFAc3Jz5VOW0R3DMa0NX8dxckZGR6tmzZ6XP89prr2nKlCny9/cvtVxOVFSUtmzZohYtWuiDDz7QyJEjqzx4BQUF6a677pKkMic0PXz4sFJSUiRdbd0DAADewytD17Vzc40ePbrSyf3NN9/U5MmT5e/vrw8//FC9evUqtb9169basmWLmjdvrhUrVuiRRx6RaZou1788iYmJMgxDy5YtU1JSUsn1Tp8+reHDh6uoqEhDhgyp8jnDAACAe3ll6Prss8909OhRGYah0aNHV/o8kZGRCg4O1tq1a9WnT58yy0RHRys1NVXNmjVTdHS0UwHvxIkTCgsLK/k89dRTkqS9e/eW2v7qq69ed2x8fLzmzp0rSXrssccUERGh2NhYRUVFKSMjQ23atNHbb79d6XsGAACe4ZUD6YsH0N9xxx2Kioqq9HmGDBmio0ePqlGjRuWWi4mJKQlMzigsLNS5c+eu215QUFBq+5UrV8o8PiEhQe3bt9ecOXOUnp6us2fPKiIiQsOGDdMLL7zAhKYAAHghrw1dxcHLVY4CVzFnA5d0tQXN1W7IPn362G19AwAA3scruxcBAAC8DaELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALFDX0xUAipmmqSt5hXb3Z5WzDwCA6o7QhWrjSl6hgqd+7OlqAABQJeheBAAAsAAtXaiWzkzvp0Cbj939AeXsAwCgOiJ0oVoKtPko0JevJwCg5qB7EQAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAAL1PV0BVB7mKapK3mFdvdnlbMPAABvR+iCZa7kFSp46seergYAAB5B9yIAAIAFaOmCR5yZ3k+BNh+7+wPK2QcAgDcidMEjAm0+CvTl6wcAqD3oXgQAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAAsAChCwAAwAKELgAAAAsQugAAACxgmKZperoSqJ5CQ0N14cIF+fv7q127di6fr8g0teeHf0uSOjWvrzqG4fI5AQBwVlX9O3Tw4EFlZ2crJCRE58+ft1uO0AW7AgIClJ2d7elqAADgFfz9/XXlyhW7++taWBd4mSZNmujs2bPy8/NTVFSUW85Z/NuAu1rPUDPxPYGz+K7AGVX9PTl69KhycnLUpEmTcssRumDXsWPH3H7OLl266KuvvlK7du2UkZHh9vOjZuB7AmfxXYEzqsv3hIH0AAAAFiB0AQAAWIDQBQAAYAFCFwAAgAUIXQAAABYgdAEAAFiA0AUAAGABQhcAAIAFCF0AAAAWIHQBAABYgGWAYKlx48bp9OnTuuGGGzxdFVRjfE/gLL4rcEZ1+Z4YpmmaHq0BAABALUD3IgAAgAUIXQAAABYgdAEAAFiA0IVKS01N1cCBA9W4cWP5+/urbdu2SkxMVFZWVrU6JzzLnT/T3//+9zIMo9zPW2+9VQV3gap05swZLV++XBMmTFD37t0VEBAgwzAUFxfn8rl5ptQcVfE9sfqZwtuLqJT58+drwoQJMk1TLVq0UMuWLXXgwAHNnDlTycnJSktLU2hoqMfPCc+qqp9pkyZNFBMTU+Y+T7+dhIpbuXKlJk6c6Pbz8kypWarqeyJZ+EwxgQratWuXWadOHdMwDHPhwoVmUVGRaZqm+cMPP5hdunQxJZm//vWvPX5OeFZV/EynT59uSjJHjx5dBTWGp7zzzjtm3759zeeff95cvXq1+Yc//MGUZHbp0qXS5+SZUvNUxffE6mcKoQsVNnjwYFOSOWrUqOv2HTp0yKxTp44pydy7d69HzwnPqoqfKaGrdnjvvfdc/seUZ0rN547vidXPFMZ0oUIyMzO1ceNGSVcnm/tvMTEx6t27tyRp1apVHjsnPIufKTyJ7x+qK0IXKmT37t3Kzc2Vr6+vbr311jLL9OjRQ5K0Y8cOj50TnlXVP9O9e/fqwQcfVO/evTV48GAlJibq66+/dqnOqDl4pqCirHqmMJAeFXLo0CFJUqtWrVSvXr0yy0RHR0uSvv32W4+dE55V1T/TPXv2aM+ePSV/XrdunWbNmqUJEybo9ddfl4+PT8UrjRqDZwoqyqpnCi1dqJDz589LUrlv/BTvu3DhgsfOCc+qqp9peHi4Jk+erB07duinn35STk6O9u3bp8cff1ymaerNN9/UlClTXKs8vB7PFDjL6mcKLV2okJycHEmSzWazW8bX11eSlJ2d7bFzwrOq6mf6+OOPX7etffv2+stf/qKoqCg999xzmjt3rp544glFRkZWrNKoMXimwFlWP1No6UKF+Pn5SZLy8vLslsnNzZUk+fv7e+yc8CxP/EwnTZqkZs2aqaCgQB9++KFbzgnvxDMF7lAVzxRCFyokJCRE0n+a78tSvK+4rCfOCc/yxM/Ux8dHXbt2lfSfMT2onXimwB2q4plC6EKF3HTTTZKk48ePKz8/v8wyR44cKVXWE+eEZ3nqZ1rcnVRQUOC2c8L78EyBu7j7mULoQoXExsbKZrMpNzdXO3fuLLPM1q1bJUndunXz2DnhWZ76me7fv1+S1KJFC7edE96HZwrcxd3PFEIXKiQoKEh33XWXJCkpKem6/YcPH1ZKSookadiwYR47JzzLEz/Tjz76qGRenX79+rnlnPBOPFPgDlXyTLFk3nvUKDt37jQNw7huTbNTp06VrGk2ZMiQ647r3r27GRERYb7xxhtuOyeqL3d/T/bv32+OGzfO3LNnT6nthYWF5l//+lezfv36piRzwIABVXZPsIazy7vwTKndXP2eeOKZQuhCpbzxxhumYRimJLNly5Zm586dTV9fX1OS2aZNG/Onn3667piIiAhTkjl9+nS3nRPVmzu/J7t37zYlmZLM0NBQs3PnzmZ8fLwZEhJSsr1Hjx7mhQsXrLk5uM3x48fNRo0alXyCgoJMSWbdunVLbf/jH/9Y6jieKbWLu78nnnim0L2ISklISNCnn36q/v37KysrSwcOHFBERISmTJmiXbt2KSwsrFqcE57lzp9pZGSkZs6cqQEDBqhhw4b6v//7P+3Zs0c2m039+/fXsmXLlJqaqoYNG1bdDaFKFBYW6ty5cyWfzMxMSVcHL1+7/cqVKxU6L8+UmsXd3xNPPFMM0zRNt50NAAAAZaKlCwAAwAKELgAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAACxC6AAAALEDoAgAP+ec//ynDMHTPPfd4uipOe/zxx2UYht59911PVwXwOix4DQAeUFRUpNjYWO3du1fp6em69dZbPV0lpxw/flwxMTEKCwvToUOHFBgY6OkqAV6Dli4A8IAlS5Zo7969GjBggNcELklq1aqVHnnkEZ06dUpz5szxdHUAr0JLFwB4QLt27fTNN99o/fr1GjBggKerUyF79uxR586d1ahRI/3www/y9fX1dJUAr0BLFwBYbMuWLfrmm2/UuHFj3XXXXZ6uToV16tRJv/jFL3Tu3DmtXr3a09UBvAahCwDK8Pvf/16GYTj1OXbsWIXO/fbbb0uS7r//ftWtW7fcsidOnNDkyZPVqVMnNWjQQP7+/oqOjtbgwYO1dOlS5eTklCpfXKctW7bo3LlzeuaZZxQdHS1/f39FRERo/Pjx+umnn0rKf//993riiScUFRUlPz8/tWrVSpMmTdLly5fLrdfw4cMlSUlJSRW6d6A2K///dgCopYKCgtS0aVO7+69cueIwmJTFNE1t2rRJktSjR49yyy5btkzjxo0rCVY2m03+/v767rvv9N1332ndunXq0KGDOnXqdN2xx48f18iRI3Xy5EkFBgaqqKhIx48f15/+9CelpKRo+/btOnz4sO655x79/PPPql+/vgoLC3XixAnNnTtX6enp+uyzz+Tj41Nm3e644w5J0rZt23T58mUFBwdX+O8CqG1o6QKAMjz77LM6c+ZMmZ9vv/1WLVq0kCTFxsbqhhtucPq8Bw4c0Llz5ySp3AH0GzZs0OjRo5WTk6Pu3btr69atys7O1sWLF3Xp0iV9/vnnGjt2rGw2W5nHT5gwQWFhYdqxY4cyMzOVmZmpFStWKCAgQAcPHlRiYqLuv/9+dejQQfv379elS5d0+fJlzZ8/Xz4+Ptq2bZvee+89u/Xr0qWL6tatq8LCQm3bts3p+wdqNRMA4LT8/HyzT58+piSzefPm5smTJyt0/DvvvGNKMoODg8u9RlRUlCnJvP32283c3Fynzy/JlGQ2bdrU/Pnnn6/bn5iYWFLmlltuMXNycq4rM3LkSFOS2adPn3Kvdcstt5iSzBdffNHp+gG1GS1dAFABTzzxhP75z38qMDBQ69atU/PmzSt0/KlTpyRJYWFhdsukpqbq6NGjkqQ33njDbmtWecaOHatGjRpdt/3agfvPPPNMmW8eFpfZt29fudcovofiewJQPkIXADjpj3/8oxYtWqQ6depo+fLlio2NrfA5igexh4aG2i2zfft2SVJ4eLji4uIqVVd7XZfXjlOLj48vt8yFCxfKvUbxPVw7MB+AfYQuAHBCcnKyXnjhBUlXw9eQIUMqdZ7iQfHlzW115swZSVJERESlriHJ7sD2a9+WdFSmoKCg3Gv4+/tL0nVvUAIoG6ELABzYuXOnRo4cKdM09bvf/U7PPvtspc9V3OXnqBVJujr9Q3V2/vx5SSqzGxPA9QhdAFCO77//XoMGDVJ2drZ69eqlP//5zy6dr3HjxpL+E1jKUvw2ZPG4ruqq+B6K7wlA+QhdAGDHv//9bw0cOFA//vijbrrpJiUnJ6tevXounfPmm2+WdHUcVGZmZpllbrvtNknSjz/+qF27drl0vapUHArbtWvn4ZoA3oHQBQBlKCgo0G9+8xvt379fjRo10kcffaSQkBCXz3vbbbfJx8dHRUVFdgNVr1691Lp1a0nSxIkTlZeX5/J13e3o0aMlA+jvvPNOD9cG8A6ELgAow4QJE7Rp0ybZbDYlJyfrxhtvdMt5g4OD1aVLF0lSenp6mWV8fHy0YMECGYahtLQ09enTR2lpaSoqKpJ0tQVuy5Yteuihh3TgwAG31KuiiuvetGlTtW3b1iN1ALwNoQsAyvDRRx9Jurpsz29/+1uFh4fb/Zw4caJC5y5et3DdunV2y/Tv31+LFy+Wr6+v0tLS1KNHDwUEBCgkJEQNGjRQr1699P7773usFay47sX3AsAxQhcAlCM/P18//vhjuZ/CwsIKnXP06NHy8/PT9u3byx0sP2rUKH3zzTdKSEjQzTffrLp16yovL0/R0dEaMmSIli1b5pHxVFlZWfrHP/4hSRo3bpzl1we8lWGapunpSgBAbTNmzBi99957eumll/Tiiy96ujoVsnTpUo0ePVq9evVSSkqKp6sDeA1CFwB4wLFjx9S2bVvVr19fR48eVWBgoKer5JSioiJ16NBBX3/9tbZv365u3bp5ukqA16B7EQA8IDIyUk899ZR++ukn/elPf/J0dZy2atUqff3117r//vsJXEAF1XVcBABQFaZOnaqgoCCvaeWSro5xmz59uh555BFPVwXwOnQvAgAAWIDuRQAAAAsQugAAACxA6AIAALAAoQsAAMAChC4AAAALELoAAAAsQOgCAACwAKELAADAAoQuAAAAC/w/jOV2ISRy1U0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mHk/a/2FO4mUCppOIUuvW0qGDln2iBQBgXyReJmA6CQAAQjh5ofTjx6U2baSvvvr2j2+vN7LesDRB4gUAAJLj5IXSq6sDpdud9rcUwyHxMgHTSQAAAInEyxRMJxGlffukgoJktwIAYLaCAunYMUvHAKI+Us+ePRIPHQCA/fj90u7dlo4BJF5IPQMGhJ6/BQBgbV6vdM45lo4BJF4AAAAmIfECAAAwCYPrTcAEqlGy8KBKAEADLB4DSLxMwASqUdq3T3LxrQkAtlNYGJhE1cK41YjU8+5yqaoq2a0AAJitqkpautTSMYDEywQejyfsq3fv3sluXur5/pVSaWmyWwEAMFtpqTRihKVjAPdzTGD3CVQNw1BpRXW9dbwN7AcAwApIvJBwpRXVKvjVP5LdDAAAks763S1IPz26SzboCQQAnCQjQzrrLEvHAHq8YKr9M4bL7cyst07eI5cHVqwHANhLfr706afJbkVCWTelREpyOzPldmWFfzn8csybJ1VUJLupAACzVVRIc+daOgaQeCG1lJdLEyYESgCAvdggBpB4AQAAmIQxXiZgySAAACCReJmCJYOikJkpDR8eKAEA9mKDGEDihdTidgeWiwAApIyGJrnOc2bKEY+n0W0QA0i8TODxeMLuKyoq0qZNm8xrTKrz+aQHH5TuuktyuZLdGgCApFb3Lqt3f8n9I+V2xSGlsEEMYHC9Cdxud9iXHZYMiorPJ917b6AEANiLDWIAPV4AAKCOPGemSu4fGXa/t6K6wZ4w1EXiBQAA6nA4HPG5fYhauM+F1JKdLY0fHygBAPZigxhAKovUkpsbWC4CAGA/NogB9HghtZSVSTfeGCgBAPZigxhA4oXUUlkpzZsXKAEA9mKDGEDiBQAAYBISLwAAAJOQeCG1uFzSjBmWnbEYAFAPG8QAnmpEanG5pHvuSXYrAADJYIMYQI8XUovXK116aaAEANiLDWIAPV4m8NbzDeT3+01sSRqorpaWLQuUAAB7sUEMIPEyQX5+frKbAAAAUgC3GgEAAExCj5cJPB5P2H1FRUXatGmTeY1JdTk50tNPB0oAgL3YIAaQeJnA7XaH3ZeRQadjLU5nYLkIAID92CAGEPWRWjwe6eyzAyUAwF5sEANIvJBa/H5py5ZACQCwFxvEABIvAAAAk5B4AQAAmITEC6klL09asiRQAgDsxQYxgKcakVqysgLLRQAA7McGMYAeL6SW48elwsJACQCwFxvEABIvpJ6SkmS3AACQLBaPASReAAAAJiHxAgAAMAmJF1KL2y198kmgBADYiw1iAIkXUktGhtSuXaAEANiLDWKAdT9ZHHzwwQcaPny42rRpI5fLpVatWunyyy/XmjVrkt006yopkZo0sfzgSgBACDaIASRe9Thy5Ih69Oihxx9/XMuWLdNTTz2lw4cPa/Dgwfroo4+S3TwAAJBmmEC1Hpdddpkuu+yyWttGjBih5s2b69lnn9WAAQOS1DIAAJCO6PGKktvtlsvlUnZ2drKbAgAA0kxaJV779+/X888/r8mTJ2vQoEHKy8uTw+FQ//79Izp+xYoVuvzyy9WiRQvl5uaqe/fumj59urxeb73H+f1+VVZWateuXbr55pvlcDg0YcKEeHwknKygQDp2LFACAOzFBjEgrW41vvTSS7rtttsadexTTz2lyZMnyzAMtW3bVu3atdOWLVs0c+ZMLVy4UKtWrVKzZs1CHvvd735XS5culSS1bNlSb7/9tnr06NHoz4F6+P3S7t1S9+5SZmayWwMAMJMNYkBa9XgVFhZq2LBhmjp1qhYsWKAHHnggouPWr1+vKVOmSJJmz56tXbt2acOGDfryyy/Vr18/FRcX19uD9dRTT2nt2rV67bXXNGDAAI0cOVL/+te/4vGRcDKvVzrnnEAJALAXG8SAtOrxGjdunMaNGxf8ev78+REdd99998nv92vMmDGaOHFicHubNm304osvqnv37lq0aJE+/vhj9ezZs87xXbp0Cf77iiuuUFFRkW699VZt2rSp0Z8FAADYT1r1eDWGx+PRkiVLJKlW0lWjS5cuuuSSSyRJr7zySoPvVzOm7PPPP49vQwEAgOWlVY9XY2zcuFE+n08ul0vnnXdeyDqDBw/W8uXLtXr16gbfr6qqSqtWrdKZZ54Z76amB8OQSktP+NJQaUV17Tper2oWe/D6quT1R/ltZuFBlQCABlg8Blg+8dq6daskqX379mGngOjcubMk1enF+vGPf6yOHTuqb9++at68ufbs2aO5c+dq06ZNWrBgQWIbnqpKS6X8/OCXDkn1rajV8t5lKs3Oifz9Cwul48cb3TwAQBqzQQywfOJ1+PBhSQr7xOKJ+44cOVJr+8CBA/X888/rz3/+s44dO6ZmzZrp/PPP13vvvachQ4aEfb/Zs2drzpw5EbWvuLg4onq2UVUlvfuuNHSolGX5b08AsATvyXc+QshzZsrhcNRfyQYxwJqf6gTl5eWSJKfTGbaOy+WSJJWVldXafvPNN+vmm2+O+pz79u3Thg0boj4u7Rw4IG+WSy3vXSZJ+vKuoXI7az/+eyAvTzrhBy3P2cDjwaWl0ogRgXlcCgvj3mQAQPy1+iYO1Kfk/pFyuxpIO2wQAyyfeOXkBG5zVVRUhK3j8/kkSbm5uXE5Z+vWrdW3b9+I6hYXF9dJ+NKG2y1luYK3Et1NCxv+oQIAwMYsHyWbNm0q6dtbjqHU7KupG6tJkyZp0qRJwa/rmxm/qKiIaSkAAGknz5mpkvtH1lvHW1EdUW+YnVg+8erataskadeuXaqsrAw5wH779u216sZb/gmD0dGAjAzprLMCJQAgZTkcjvjf5bBBDLDuJ/tG37595XQ65fP5tHbt2pB1Vq5cKSkwmB5Jlp8vffpprScnAQA2YYMYYPnEKz8/X5deeqkkhXzScNu2bXrvvfckSaNGjUpIGzweT9hX7969E3LOtFVRIc2dGygBAPZigxhg+cRLkqZPny6Hw6HnnntOc+bMkWEYkgJPH44ePVp+v19XXnmlevXqlZDzu93usK8MC3enNkp5uTRhQqAEANiLDWJAWkX93bt3q3nz5sHXLbfcIknavHlzre0PP/xwreMGDBigxx57TFJg4HuHDh3Ut29fderUSevXr1e3bt309NNPJ6zdXq837Mvv9yfsvAAAILWk1eD66upqHTp0qM72qqqqWttLT1jSpsaUKVN07rnnatasWVqzZo0OHjyoDh06aNSoUbrrrrsSOgCewfUAAEBKs8SrY8eOwduEjTF06FANHTo0ji1C3GVmSsOHB0oAgL3YIAakVeKVrjweT9h9zON1ErdbWro02a0AACSDDWJAWo3xSlcMro+Czyfdc0+gBADYiw1iAFHfBAyuj4LPJ917r6V/6AAAYdggBnCr0QQMrgcAABI9XgAAAKahx8sEDK6PQna2NH58oAQA2IsNYgCJlwncbnfYfQyuP0lubmC5CACA/dggBiQ06n/99dd69dVX9frrr+vo0aOJPBWsoqxMuvHGQAkAsBcbxICYEq9169Zp3LhxmjVrVp19L730kjp27KhRo0bpqquuUvv27fXqq6/Gcrq0xVONUaislObNC5QAAHuxQQyIKfH629/+pr/+9a91bpd99dVXGj9+vMrKymQYhgzDkMfj0Y9+9CNt3749pgano/z8/LAvxncBAGAfMSVe//rXvyRJV1xxRa3tc+bMUVlZmXr27Klt27Zp9+7dGjJkiCoqKvTkk0/GckoAAIC0FVPitW/fPjkcDnXo0KHW9rfeeksOh0MzZ85U586ddfrpp+uJJ56QYRh67733YmpwOvJ4PGFfvXv3TnbzUovLJc2YESgBAPZigxgQ01ONhw4d0imnnKKsrG/fpqysTJs2bZLL5dLw4cOD23v27Cmn06mdO3fGcsq0xFONUXC5AstFAADsxwYxIKaon5WVpePHj9fa9tFHH6m6ulr9+/eX0+mstS8/P19VVVWxnBJW5/VKl14aKAEA9tJQDPB6G34ZhrltjlJMiVfHjh1VXV2tjz76KLjt9ddfl8Ph0KBBg2rVra6u1rFjx3TaaafFckpYXXW1tGxZoAQA2EtDMaBlSyk/v/5Xaam5bY5STLcav/Od72jLli362c9+pqeeekr79u3TnDlzJEnf+973atX9z3/+o+rqarVt2zaWU6Ylbz29N0wnAQCAfcSUeN1xxx3661//qvXr1+uCCy6QJBmGoUsuuST4dY2aAfcDBw6M5ZRpiUWyAQBopLw8qZ6l9yQFbjG2bGlOe2IU063G008/XStWrNDFF1+snJwctWrVShMmTNDChQtr1TMMQ88884wMw9DFF18cU4NhcTk50tNPB0oAgL2EigEOh+R2N/xKEzGv1dirVy8tX7683jp+v1/vvvuupECyZjcskh0FpzOwXAQAwH5sEANMWSQ7MzOzzlxfdsJ0ElHweKTzz5fWrAkMkgQA2IcNYkBMUf+MM87Q//3f/0Vcf/DgwercuXMsp4TV+f3Sli2BEgBgLzaIATH1eO3cuVPl5eUR19+zZ4927doVyykBAADSlqn3uaqqqri1BgAAbMu0LKisrEwHDx5UQUGBWadEOsrLk5YsCZQAAHuxQQyI6lbjrl276qy1WFFRoZUrV8oIM0W/YRg6evSoXnjhBVVWVurcc89tdGPTFROoRiErK7BcBADAfmwQA6JKvJ555hn95je/qbXtyJEjuuiiixo81jAMORwOTZo0KaoGWgETqEbh+HGpbVtpzx6psDDZrQEAmMkGMSDqwfUn9mw5HI6wPV0n1iksLNQ555yjm266ST/60Y+ibyXspaQk2S0AACSLxWNAVInXjBkzNGPGjODXGRkZatWqlb766qu4N8xKmEAVAABIMU4nMWbMGJ1yyilxaop1MYEqAACQYky85s+fH6dmAN9wu6VPPkmrdbcAAHFigxhAdwtSS0aG1K5doAQA2IsNYkBc1mosKSnRm2++qY8//liHDx9WZWVl2LoOh0Pz5s2Lx2lhRSUlUpMm0rFjln2iBQAQhg1iQMyJ1/z58zV58uRaA8hDPelY8wQkiRcAALCrmBKvpUuXavz48TIMQzk5ORo4cKDatGmjrKy4dKQBAABYSkwZ0sMPPyzDMDRw4EAtXrxYzZs3j1e7AAAALCemxGv9+vVyOByaP38+SVc9WDIoCgUFgXv7rOkJAPZjgxgQU+JVVVWl/Px8denSJV7tsSSWDIqC3y/t3i117y5lZia7NQAAM9kgBsT0vGbnzp3l8/lUXV0dr/bA7rxe6ZxzAiUAwF5sEANiSryuu+46VVZW6h//+Ee82mNJHo8n7Kt3797Jbh4AADBJTInXlClTNGDAAP30pz/Vtm3b4tUmy3G73WFfLBkEAIB9xDTG68UXX9T111+vX//61+rVq5dGjRql888/XwUNDIobM2ZMLKeF1Vl4UCUAoAEWjwExJV5jx46Vw+GQFJg09YUXXtALL7xQ7zEOh4PEC+EVFkrHjye7FQCAZLBBDIgp8Wrfvn0w8QLioqpKevddaehQiYl4AcBebBADYvpUO3fujFMzgG+UlkojRlh6nS4AQBg2iAGM7AYAADAJiRcAAIBJSLyQWjIypLPOCpQAAHuxQQyIaYzXuHHjoj7G4XBo3rx5sZwWVpafL336abJbAQBIBhvEgJgSr/nz58vhcMgwjJD7T37i0TAMEi/Ur6JCevZZacwYyelMdmsAAGayQQyIKfEaM2ZMvdNJHDt2TOvWrdOePXt06qmn6vLLL4/ldLCD8nJpwgTpBz+w7A8dACAMG8SAmHu8GmIYhubPn6//9//+nwoLC/XEE0/EckoAAIC0lfDZyRwOh37yk5/o6NGjuuOOO3ThhRfqmmuuSfRpU4q3nlXW/X6/iS2JnmEYKq2o/naDr0rub/7p9VXJ67fmBHcAACSCaVHzxhtv1C9+8Qv9/ve/t13ilZ+fn+wmNFppRbUKfvWP4Nd5leXyfPPvlvcuU2l2TnxPmJkpDR8eKAEA9mKDGGBa4lVQUKDCwkJt2rTJrFMiHbnd0tKlyW4FACAZbBADTEu8Dh8+rKNHjyovL8+sU6YMj8cTdl9RUVHaJKP7ZwyXu7JcejLw9YEZwwM/JN/Ic8bhLxSfT3rwQemuuySXK/b3AwCkDxvEANNmKJs6daokqVu3bmadMmW43e6wr4w0miTO7cyU2/Vtru52ZdV6xWXBdJ9PuvfeQAkAsBcbxICYeryeffbZeveXl5dr9+7devXVV1VcXBwcaA8AAGBHMSVeY8eOjaiXo2aC1TFjxuhnP/tZLKcEAABIWzElXu3bt6838crKylLTpk3Vq1cvjR49Wpdcckksp4MdZGdL48cHSgCAvcQjBtQzhZMkKS9PisfQmEaKKfHauXNnnJoBfCM3V5o7N9mtAAAkQzxiQMuW9e/3eGo9GGa29BnZDXsoK5NuvDFQAgDsxQYxgMQLqaWyUpo3L1ACAOylsTEgLy/QkxXudeBAYtrbCHGbx6uiokLvvPOO1q1bp4MHD8rhcKhFixYaMGCAhg0bJqdFF7sEAABJ5nAk9fZhNOKSeM2ZM0fTp0/X//73v5D7mzdvrpkzZ2rChAnxOB0AAEBaijnx+uUvf6lHH300OGXE6aefrrZt20qS9uzZo7179+rrr7/WTTfdpO3bt+u3v/1trKeElblc0owZlp2xGABQDxvEgJjGeL3//vt65JFHZBiGrrnmGm3ZskW7d+/Whx9+qA8//FC7d+9WcXGxRo0aJcMw9Mgjj2jlypXxajusyOWS7rnH0j90AIAwbBADYkq8/vCHP0iSxo8fr1deeUXdu3evU6dbt256+eWXNX78eBmGod///vexnBJW5/VKl17a8DwsAADrsUEMiCnx+uCDD5SRkaH777+/wbozZ86Uw+HQv//971hOCaurrpaWLQuUAAB7sUEMiCnx+t///qcmTZrotNNOa7Buy5Ytdcopp4QdgA8AAGB1MSVeBQUFKikpUXl5eYN1y8rKVFJSovz8/FhOaaqFCxfqBz/4gTp16qTc3Fx16dJFkydP1qFDh5LdNAAAkIZiSrx69uyp6upq/eUvf2mw7l/+8hdVVVWpV69esZzSVI888oiqqqr0m9/8RkuWLNHtt9+uv//977rgggtUWlqa7OZZU06O9PTTgRIAYC82iAExTSfx4x//WCtWrNDtt98ul8ul8ePHh6w3d+5c3X777XI4HLr++utjOaWp3njjDbVo0SL49ZAhQ9SlSxcNGzZMCxcuTKvPkjaczsByEQAA+7FBDIgp8Ro7dqyee+45vf/++5o4caJ+85vf6OKLL9bpp58uh8Oh3bt3a8WKFdq7d68Mw9BFF12kG264IV5tT7gTk64aAwYMkBSYowwJ4PFI558vrVkjpdFtaQBAHNggBsSUeGVkZGjx4sUaN26cFi1apN27d+u5556rVadmYtVrrrlG8+bNk8PhaPT59u/fr+XLl+ujjz7SunXrtHHjRpWVlalfv35at25dg8evWLFCs2bN0po1a+TxeNShQwdde+21mjp1qtwRLjWwYsUKSVKPHj0a/TlQD79f2rIlUAIA7MUGMSDmmesLCwu1YMECffTRR3rppZeCazVK0mmnnab+/fvrhz/8YbCnKBYvvfSSbrvttkYd+9RTT2ny5MkyDENt27ZVu3bttGXLFs2cOVMLFy7UqlWr1KxZs3rf48iRI7rtttt07rnn6nvf+16j2gEAAOwrbotkDxgwIC7JVX0KCws1bNgw9e/fX/3799fWrVs1bdq0Bo9bv369pkyZIkmaPXu2JkyYIIfDoa+++kpXXHGF1q9frwkTJmjhwoVh36O8vFxXX321jhw5ojfffFOZmZnx+lgAAMAmYkq8Kioq9Nlnn8npdIactf5En332mSoqKtSjRw9lZ2c36nzjxo3TuHHjgl/Pnz8/ouPuu+8++f1+jRkzRhMnTgxub9OmjV588UV1795dixYt0scff6yePXvWOb6yslKjRo3S2rVrtWzZMp111lmNaj8ikJcnLVkSKAEA9mKDGBDTdBJ///vf1adPH/3ud79rsO7999+vPn36aMGCBbGcMmoej0dLliyRpFpJV40uXbrokksukSS98sordfZXV1dr9OjRWr58uV577TUNGjQosQ22u6yswHIRWXHrjAUApAsbxICYEq+aW3ORTKtQs1aj2YnXxo0b5fP55HK5dN5554WsM3jwYEnS6tWra233+/264YYbtHjxYv3973/Xd77znYS31/aOH5cKCwMlAMBebBADYkopP/nkE0mKaFLUfv36SZL+85//xHLKqG3dulWS1L59+7C3ODt37ixJ+vzzz2ttv+WWW/TCCy9oypQpatmyZa3ErG3btmrbtm3I95s9e7bmzJkTUfuKi4sjqmcrJSXJbgEAIM68FQ2vv5hnGHJYPAbElHh99dVXatKkSUTLABUUFOiUU07Rvn37Yjll1A4fPixJ9T6xWLPvyJEjtba/9dZbkqTf/e53dW6nzpgxQ/fcc0/I99u3b582bNjQyBYDAGA9re5d1mCdkqlFimxyp/QVU+LldDpVVlYWUV3DMFRWVtbogfWNVbOOpNPpDFvH5XJJUp3PsnPnzkads3Xr1urbt29EdYuLiyO+hgAAIL3FlHh16tRJH3/8sT788EMNHDiw3roffPCBfD6fzjzzzFhOGbWcb9Z7qqioCFvH5/NJknJzc+NyzkmTJmnSpEkR1e3Xrx+9Yydyu6VPPgmUAIC0lufMVMn9I+ut462o/rY3zJ1n+RgQU+L1ne98R5s3b9bUqVP17rvvKivMUwhVVVW666675HA4NHz48FhOGbWmTZtK+vaWYyg1+2rqxpvX6w27z2/h2XkbJSNDatcuUAIA0prD4ZDbFUWqYYMYENMnu/XWW5WTk6NVq1Zp2LBh2rhxY506GzZs0NChQ7Vq1Sq5XC5Nnjw5llNGrWvXrpKkXbt2qbKyMmSd7du316obb/n5+WFfmzZtSsg501ZJidSkCQPsAcCOSjyWjwEx9Xi1bdtWs2fP1tixY7Vy5Ur1799frVq1UocOHeRwOLRjxw4dOHBAhmHI4XBozpw5at++fbzaHpG+ffvK6XTK5/Np7dq1IefhWrlypSQ1eLsUAAAgFjH35V1//fV644031KFDBxmGoX379mn16tX68MMPtX//fhmGoTPOOENvvfWWrrvuuni0OSr5+fm69NJLJSnkFA/btm3Te++9J0kaNWpUQtrg8XjCvnr37p2QcwIAgNQTl6lhv/vd72rbtm1asWKFPvjgA+3fv19S4Om+Cy64QBdffLEykni/dvr06XrzzTf13HPPadCgQcG1Gvft26fRo0fL7/fryiuvjGg+MgAAgMaK25z8mZmZGjZsmIYNGxavt6xj9+7d6tOnT/DrmqcRN2/erObNmwe333nnnbrzzjuDXw8YMECPPfaYfv7zn2vSpEmaOXOmmjdvri1btsjn86lbt256+umnE9buSOY5wzcKCqRjxwIlAMBeCvItHwPS6rGB6upqHTp0KPjyeDySAk9Nnri9tLS0zrFTpkzRO++8o5EjR8rr9WrLli3q0KGDpk2bpnXr1tVK3JBEfr+0e3egBADYiw1iQFqtQtmxY0cZhtHo44cOHaqhQ4fGsUWRqUkQQykqKuLJxhN5vdI55wT+4iksTHZrAABm8pZaPgakVeKVrtz1TASXzLFvAADAXCReJmACVQAAIJF4mYLB9VGy8KBKAEADLB4DSLyQWgoLpePHk90KAEAyFBRYPgYwwMgETKAahaoqaenSQAkAsJdq68cAerxMwOD6KJSWSiNGWPqJFgBAGKVllo8BRH0AAACTkHgBAACYhFuNJmA6iShkZEhnnRUoAQD2YoMYQOJlAqaTiEJ+vvTpp8luBQAgGdxuy8cA66aUSE8VFdLcuYESAGAvldaPASReJmA6iSiUl0sTJgRKAIC9lPssHwO41WgCppMAAAASPV4AAACmIfFCasnMlIYPD5QAAHuxQQzgVqMJmE4iCm53YLkIAID95OVZPgbQ42WC/Pz8sK9NmzYlu3mpxeeT7rknUAIA7KXC+jGAxAupxeeT7r3X0j90AIAwfBWWjwHcajSBx+MJu6+oqIheLwAAbILEywRMJwEAACRuNSLVZGdL48cHSgCAvdggBtDjhdSSmxtYLgIAYD85OZaPAfR4IbWUlUk33hgoAQD2Ul5u+RhA4mUCr9cb9sU8XieprJTmzQuUAAB7sUEM4FajCfLz85PdBAAAkALo8QIAADAJPV4mYB6vKLhc0owZgRIAYC8up+VjAImXCZjHKwouV2C5CACA/TitHwOI+kgtXq906aWBEgBgL6Wllo8BJF5ILdXV0rJlgRIAYC82iAEkXgAAACYh8QIAADAJiRdSS06O9PTTgRIAYC85LsvHAJ5qRGpxOgPLRQAA7Cfb+jGAHi+kFo9HOvvsQAkAsBev1/IxgB4vE3jreSyWtRpP4vdLW7YESgCAvdggBpB4mYC1GgEAgMStRgAAANPQ42UC1mqMQl6etGRJoAQA2EteruVjAImXCVirMQpZWYHlIgAA9pNp/RhA1LcxwzDk9VXV/6owedmG48elwsJACQCwl5ISy8cAerxsrLSiWgW/+keym1FXSUmyWwAASBaLxwB6vAAAAExCjxckSftnDJfbmVlvnTxnplRlUoMAALAgEi9IktzOTLldKfDt4HZLn3wSKAEA9uLOs3wM4FYjUktGhtSuXaAEANiLDWKAdT8Z0lNJidSkieUHVwIAQijxWD4GkHgBAACYhMQLAADAJCReAAAAJiHxQmopKJCOHQuUAAB7Kci3fAxIgfkDrM/r9Ybd5/f7TWxJGvD7pd27pe7dpcz65xUDAFiMDWIAiZcJ8vPzk92E9OH1SuecE/iLp7Aw2a0BAJjJW2r5GEDiBQAAUoK3slrKckkV1ZIv9FIpec5MORwOk1sWPyReJvB4PGH3FRUVadOmTeY1BgCAFNVq1ofS5Fek364KW6fk/pGpsdJKI6Vvy9OIu56lDzIsPDtvo1l4UCUAwN5IvJBaCgul48eT3QoAgEnynJkquX9kvXW8FdVqde8yk1qUWHS3ILVUVUlLlwZKAIDlORwOuV1ZgVem5P7nu4GyZpsrS26ndZ5wJPFCaiktlUaMCJQAAHuxQQwg8QIAADAJiRcAAIBJSLyQWjIypLPOCpQAAHuxQQzgqUaklvx86dNPk90KAEAy2CAGWDelRHqqqJDmzg2UAAB7sUEMIPFCaikvlyZMCJQAAHuxQQwg8QIAADAJiRcAAIBJSLyQWjIzpeHDAyUAwF5sEANIvOqxZ88e3XLLLRo4cKDy8vLkcDi0c+fOZDfL2tzuwJJB9SwsDgCwKBvEABKvenzxxRd6+eWX1bRpUw0ePDjZzbEHn0+6555ACQCwFxvEABKvelx44YU6cOCA3n77bV177bXJbo49+HzSvfda+ocOABCGDWIAiVc9Miw8cy4AADBfWmUW+/fv1/PPP6/Jkydr0KBBwXFX/fv3j+j4FStW6PLLL1eLFi2Um5ur7t27a/r06fJ6vQluOQAAQJotGfTSSy/ptttua9SxTz31lCZPnizDMNS2bVu1a9dOW7Zs0cyZM7Vw4UKtWrVKzZo1i3OLEbXsbGn8+EAJALAXG8SAtOrxKiws1LBhwzR16lQtWLBADzzwQETHrV+/XlOmTJEkzZ49W7t27dKGDRv05Zdfql+/fiouLtaECRMS2HJELDc3sFxEbm6yWwIAMJsNYkBaJV7jxo3TO++8owcffFDXXHONWrduHdFx9913n/x+v66//npNnDhRDodDktSmTRu9+OKLysjI0KJFi/Txxx8nsvmIRFmZdOONgRIAYC82iAFplXg1hsfj0ZIlSyRJEydOrLO/S5cuuuSSSyRJr7zyiqltQwiVldK8eYESAGAvNogBlk+8Nm7cKJ/PJ5fLpfPOOy9knZo5ulavXm1m0wAAgM2k1eD6xti6daskqX379soOM1ivc+fOkqTPP/+8zr4FCxZICowTk6R//OMfatGihVq0aKEhQ4YkoskAAMCiLJ94HT58WJLqfWKxZt+RI0fq7Dt54tSf/vSnkqQhQ4bon//8Z8j3mz17tubMmRNR+4qLiyOqZxsulzRjRqAEANiLDWKA5ROv8vJySZLT6Qxbx/XNf3BZiMF8hmFEfc59+/Zpw4YNUR8HBX7Y7rkn2a0AACSDDWKA5ROvnJwcSVJFRUXYOr5vlibIjdPjq61bt1bfvn0jqltcXBwy4bMtr1e6+mpp0SJLL5IKAAjBBjHA8olX06ZNJX17yzGUmn01dWM1adIkTZo0Kfh1fTPjFxUVadOmTXE5ryVUV0vLlgVKAIC92CAGWD7x6tq1qyRp165dqqysDDnAfvv27bXqxlt+fn5C3hcAAKQXy08n0bdvXzmdTvl8Pq1duzZknZUrV0qSBg4caGbTAACAzVg+8crPz9ell14qSSGfNNy2bZvee+89SdKoUaMS0gaPxxP21bt374ScM23l5EhPPx0oAQD2YoMYYPnES5KmT58uh8Oh5557TnPmzAk+qbhv3z6NHj1afr9fV155pXr16pWQ87vd7rCvjAxb/BdEzukMLBdRz1OoAACLskEMSKuov3v3bjVv3jz4uuWWWyRJmzdvrrX94YcfrnXcgAED9Nhjj0kKDHzv0KGD+vbtq06dOmn9+vXq1q2bnn766YS12+v1hn35/f6EnTcteTzS2WcHSgCAvdggBqTV4Prq6modOnSozvaqqqpa20tLS+vUmTJlis4991zNmjVLa9as0cGDB9WhQweNGjVKd911V0IHwDO4Pgp+v7RlS6AEANiLDWJAWiVeHTt2bNSEpjWGDh2qoUOHxrFFAAAAkUurxCtdeerpMmUeLwAA7IPEywTuembfZXD9SfLypCVLAiUAwF5sEANIvExQ38z1DK4/SVaW9M30HwAAm7FBDCDxMgGD66Nw/LjUtq20Z49UWJjs1gAAzGSDGMB9LqSekpJktwAAkCwWjwH0eJmAwfUAAEAi8TIFg+sBAIDErUakGrdb+uSTQAkAsBcbxAB6vEzAU41RyMiQ2rULlAAAe7FBDCDxMgFPNUahpERq0kQ6dsyyT7QAAMKwQQywbkoJAACQYujxMgFPNQIAAInEyxQ81QgAACRuNSLVFBQE7u0XFCS7JQAAs9kgBpB4IbX4/dLu3YESAGAvNogB3Go0AdNJRMHrlc45x9JPtAAAwrBBDCDxMgHTSQAAAIlbjQAAAKahx8sETCcRJQsPqgQANMDiMYDEywRMJxGFwkLp+PFktwIAkAw2iAFEfaSWqipp6dJACQCwFxvEABIvpJbSUmnEiEAJALAXG8QAEi8AAACTkHgBAACYhMH1JkjGBKqGYai0orreOt4G9idFRoZ01lmBEgBgLzaIASReJkjGBKqlFdUq+NU/TD9vzPLzpU8/TXYrAADJYIMYYN2UEumpokKaOzdQAgDsxQYxgB4vEyR7AtX9M4bL7cyst05eA/tNU14uTZgg/eAHktOZ7NYAAMxkgxhA4mWCZE+g6nZmyu3ivxoAgGTjViMAAIBJSLyQWjIzpeHDAyUAwF5sEAO4/4TU4nYHlosAANiPDWIAPV5ILT6fdM89gRIAYC82iAEkXkgtPp90772W/qEDAIRhgxhA4gUAAGASxniZIBlLBgEAgNRD4mWCZCwZlLays6Xx4wMlAMBebBADSLyQWnJzA8tFAADsxwYxgDFeJvB4PGFfvXv3TnbzUktZmXTjjYESAGAvNogBJF4mcLvdYV9mLBmUViorpXnzAiUAwF5sEAOI+gAAACZxGIZhJLsRdtasWTMdOXJEubm56tGjR9ze128Y2rT3uCSp9+mFynA44vTGfmnTpsC/e/eW4t1jV10tbd4s9epl6SUjAAAhhIkBMce0BMeu4uJilZWVqWnTpjp8+HC9dUm8kiwvL09lFr6XDQCAXeTm5qq0tLTeOjzVmGSnnXaaDh48qJycHHXq1CnZzUkrNX9hxLu3EJHh+icX1z+5uP7JlWrXf8eOHSovL9dpp53WYF0SryTbuXNnspuQtvr166cNGzaoR48eWr9+fbKbYztc/+Ti+icX1z+50vn6M7geAADAJCReAAAAJiHxAgAAMAmJFwAAgElIvAAAAExC4gUAAGASEi8AAACTkHgBAACYhMQLAADAJCReAAAAJmHJIKStiRMnat++fWrdunWym2JLXP/k4vonF9c/udL5+jsMwzCS3QgAAAA74FYjAACASUi8AAAATELiBQAAYBISL6Scbdu26eGHH9bIkSN1+umny+l0qkmTJjrvvPP029/+Vh6Pp8H3WLhwoS6++GI1bdpUbrdbvXv31qOPPqrKysp6j/N4PLr77rvVvXt35ebmqkWLFrr88sv1z3/+M06fLvWVlZVp8eLFuvvuuzVixAg1b95cDodDDocjomsvcf0TacWKFbr88svVokUL5ebmqnv37po+fbq8Xm+ym5YW9u/fr+eff16TJ0/WoEGDlJeXJ4fDof79+zd4bGVlpR555BH16tVLbrdbzZo10yWXXKJFixY1eOyXX36pcePGqW3btnK5XGrXrp3Gjx+vHTt2xONjpQXDMPTBBx9o6tSpKioq0qmnnqrs7Gy1aNFCw4cP1wsvvKD6hp1b5vobQAqpqqoyJAVfrVq1Mvr372+0bds2uK1z587Gzp07w77H7bffXqtuz549jczMTEOSceGFFxrl5eUhj/v666+Nbt26GZIMl8tl9OnTJ3heh8Nh/OEPf0jUx04pGzdurPV/cOKrpKSkweO5/onz5JNPGg6Hw5BktG3b1ujTp4/hcrkMSUaPHj2MQ4cOJbuJKe/xxx8P+b3dr1+/eo8rKyszioqKDElGZmam0bNnT6Nz587B43/5y1+GPfaDDz4w8vPzDUlG06ZNjX79+hmnnHKKIckoKCgw1qxZE++PmZKWL19e65qfccYZRr9+/YxmzZoFt1122WUhf0dY6fqTeCGlVFZWGoWFhcadd95pFBcX19r34YcfGp06dTIkGeeff37I4xctWhQM3IsXLw5uLy4uDh7785//POSxV1xxRfAX8N69ew3DMAy/32/Mnj07+MO+cePG+HzQFPbJJ58Y5513nvGzn/3MeOaZZ4y333474sSL658469atMzIyMgyHw2HMnj3b8Pv9hmEYxt69e41+/foZkoyrr746ya1MffPmzTOGDRtmTJ061ViwYIHxwAMPRJR43XrrrYYko1OnTsZnn30W3L548eJg8vv666/XOc7r9RqtW7c2JBnjxo0zysrKDMMIJBI/+clPgkl0aWlpfD9oCnrnnXeMTp06GU888YRx4MCBWvueffbZ4HUMlURZ6fqTeCGl+P3+ev9q//e//x1MAkIF4V69ehmSjF//+td19r377rvBpODgwYO19m3YsMGQZGRkZBjbtm2rc+z1119v28C2Y8eOiBMvrn/ifP/73zckGWPGjKmzb+vWrUZGRoYhydi8eXMSWpe+nnnmmQYTr/379xtOp9OQZLz33nt19k+fPt2QZPTt27fOvpoetjPPPNPw+Xy19vl8vmCvzZNPPhn7h0lxx44dMyoqKsLuv//++w1JRrNmzYzq6urgdqtdfxIvpJ0mTZoYkoyXXnqp1vatW7cGE4Q9e/aEPPbMM880JBlz586ttX3atGmGJGPYsGEhj1u1apUhycjJyTE8Hk98PkiaiDTx4vonTklJSfCv+lWrVoWsM2zYMEOScffdd5vcuvQWSeI1Z86cYPAOZc+ePcHv/S+++KLWvgsuuMCQZMycOTPksTNnzjQkGUVFRY3/EBZR8weYJGP//v3B7Va7/gyuR1qprq4ODtDOy8urtW/16tWSpE6dOun0008PefzgwYNr1T352Jr9JzvvvPPkdDpVXl6uTZs2Nbr9Vsb1T5yNGzfK5/PJ5XLpvPPOC1kn3LVF7Br6/jz99NPVqVOnWnWlwO+rdevW1XtszfaPPvpI1dXVcWtzOiovLw/+Ozc3N/hvq11/Ei+klcWLF6u0tFRZWVkaOHBgrX1bt26VJJ155plhj+/cubMk6fPPP4/q2OzsbLVv3z7ksQjg+idOzfVp3769srOzQ9YJd20Ru8Z+b+/cuVMVFRX1HltznM/n03//+9+4tDddvfjii5KkXr16qbCwMLjdatefxAtp4/jx47rjjjskSePHj1fz5s1r7T98+LAkqVmzZmHfo2bfkSNH4nYsArj+icP1Sa7GXv+a4+o79sTtdv6/27Bhg/785z9LkqZOnVprn9WuP4kX0kJ1dbVGjx6tHTt2qGPHjnrooYfq1KnppnY6nWHfx+VySQrMVRWvYxHA9U8crk9yNfb6n3jrLNyxNcedfKydHDhwQFdddZUqKyt11VVX6Yc//GGt/Va7/iReiJubbropONFmNK+LLrqo3vc1DEMTJ07U22+/raZNm+qNN95QkyZN6tTLycmRpGDXcig+n09S7fEDsR6bKhJ1/SNl9+ufSFyf5Grs9a85rr5ja447+Vi7OHbsmEaOHKldu3apX79+mj9/fp06Vrv+WaacBbbQpEkTtWzZMurj6us+lqRbb71Vf/nLX1RQUKClS5fqnHPOCVmvadOmkmp3L5+sZl9N3ROPLS0tbdSxqSJR1z9Sdr/+iRTLtUXsGnv9T/z34cOH1aZNm7DHnVzfDjwej0aMGKGNGzfq7LPP1tKlS2uN7aphtetP4oW4eeihh0LeAozFHXfcod///vfKy8vT22+/rQEDBoSt27VrV0nSF198EbbO9u3ba9U98di9e/eGPbayslK7du0KeWyqSMT1j4bdr38i1XzmXbt2qbKyMuQA+3DXFrHr2rWr/v3vf0f9vd2xY0c5nU5VVFToiy++CBn4a45zuVzq0KFDnFueukpLS3XZZZdp9erV6tq1q5YvX65TTz01ZF2rXX9uNSJl3X333Zo1a5ZycnL0+uuvq6ioqN76NU857tixQ3v37g1ZZ+XKlbXq1vi///u/WvtPtnbtWlVUVCgnJ0e9e/eO5mPYBtc/cfr27Sun0ymfz6e1a9eGrBPu2iJ2Nd+fq1atCrl/7969wTX/aupKUlZWlvr16ycp/Pd2zfYBAwYoMzMzbm1OZeXl5fr+97+vf/3rX+rYsaPeffddtWrVKmx9y11/02YMA6JQM4Ox0+k03nrrrYiPO/fccxucOd3pdNaZOX39+vURzZx+1VVXRf9h0lw0M9dz/RPne9/7XkQz12/atCkJrUtfkc5cn52d3eDM6X369Kmzb9asWcHJP0+etf3EmdOfeOKJ2D9MGqioqDC++93vBpfq+fLLLxs8xmrXn8QLKeeJJ54wJBlZWVnGokWLojp2wYIFwWVpTly367PPPguuFThlypSQx15++eXBX8BfffWVYRjfrhXocDiMjIwMY/369Y3/YGkqmsSL6584a9euNRwOR521Gr/66qvgWo1XXnllkluZfiJJvAzDMG6++eaQawW+/vrrwVUFXnvttTrHeTweo1WrVvWuFdimTRvD6/XG94OloKqqKuPaa681JBmtWrUytm7dGvGxVrr+JF5IKXv37jUcDoehb1aRHzRoUNjXvHnzQr7HlClTgolC586djV69ehmZmZnBZSFqfvBOdvDgQaNLly7BxKFPnz5Gu3btDEmGw+GwxVpqNfr06WOceuqpxqmnnmo0bdo0eD1rtp166qnGFVdcEfJYrn/iPP7448Gfj3bt2hl9+vQJBp1u3boZX3/9dbKbmPJ27dpV6/s4Pz8/+IfeidsfeuihWseVlpYaAwcONPTNgu29evUK9pZIMm6//faw51y1apXhdruDv9f69esX/LnKz883Pvzww0R/7JTwt7/9LXi9OnbsWO/v9w0bNtQ61krXn8QLKeXE3pWGXjNmzAj7Pi+//LIxZMgQo0mTJkZubq5x7rnnGg899FC9C7QahmEcP37cmDZtmtG1a1fD5XIZzZo1M0aOHBmye9vKOnTo0OD1HzJkSNjjuf6Js3z5cmPkyJFGs2bNDJfLZXTt2tWYNm1ag72RCIj0d0yo3y8+n8946KGHjHPPPdfIzc01mjRpYgwZMsRYsGBBg+f94osvjBtuuMFo06aNkZ2dbbRp08YYO3assX379gR8ytRU07sYyWvFihV1jrfK9XcYhmGEHv0FAACAeOKpRgAAAJOQeAEAAJiExAsAAMAkJF4AAAAmIfECAAAwCYkXAACASUi8AAAATELiBQAAYBISLwAAAJOQeAEAAJiExAsAAMAkJF4AcJLq6mq9/PLLGjNmjLp27apTTjlFTqdTp512moqKinTXXXfpk08+kSSNHTtWDoejUa+LLrooeM6LLrqozv6srCw1bdpUHTt21PDhw/XLX/5Sq1atStJVARAPLJINACdYvXq1brjhBm3dujW4LTs7WwUFBTp69Kj8fn9w+9VXX63TTjtNr776ap33qa6u1v/+9z9JUmFhoXJzc+vUueCCC7Ro0SJJgcTr/fffV3Z2tpo1axasU1JSotLS0lrH9ejRQ3/60580ZMiQ2D4sANOReAHAN9544w1de+218vl8OvXUU3XHHXfommuuUZcuXSQFkqmNGzdq4cKF+uMf/6jjx4/ryJEjOuWUU+q8186dO9WpUydJ0jPPPKOxY8fWe+6axGvIkCH65z//WWtfWVlZ8Lxz587V8ePH5XA49Mc//lE33XRTPD46AJNwqxEAJG3btk3XXXedfD6fzjrrLG3atElTp04NJl2SlJmZqf79++vBBx/Ujh079P3vf9+UtuXm5uqCCy7QrFmz9PHHH6tnz54yDEM333yzVq5caUobAMQHiRcASLr77rt1/Phx5eTk6NVXX1Xbtm3rrd+sWTO99tpratKkiUktDOjQoYMWL14st9ut6upqTZs2zdTzA4gNiRcA2ztw4IAWLFggSfrxj3+srl27Rnysw+FIVLPC6tixY/DW5apVq/Tll1+a3gYAjUPiBcD2VqxYERw0f9VVVyW5NZG57LLLgv9+//33k9gSANEg8QJge59++mnw33369EliSyLXq1ev4L+3b9+exJYAiAaJFwDbO3ToUPDfJ07lkMpObOfhw4eT2BIA0SDxAmB7zKoDwCwkXgBsr3nz5sF/p0vv0YntPPXUU5PYEgDRIPECYHtnn3128N8bN25MYksit3nz5uC/O3funMSWAIgGiRcA27v44ouVkRH4dRhq+Z9U9NZbbwX/feKajwBSG4kXANtr2bKlrrnmGknS3/72t1rrNDYkGePD/vvf/2r+/PmSpAsvvFAdO3Y0vQ0AGofECwAkzZw5U/n5+SorK9PVV1+tvXv31lv/yJEjuuaaa3Ts2DGTWhiwa9cuXXHFFfJ6vcrMzNQDDzxg6vkBxIbECwAkde3aVc8995ycTqc+/fRT9e7dWw899JC++OKLYJ2aRbJ//etf64wzztCiRYtMaVt5ebk+/PBD/eIXv1DPnj318ccfKyMjQ3/60580aNAgU9oAID6ykt0AAEgVV155pd577z2NHTtWX3zxhaZOnaqpU6fK6XQqPz9fR48eDc5w73A4NHr0aLnd7ri24YMPPlCrVq2CX3u9Xnk8nlp1zj77bP3pT3/S4MGD43puAIlH4gUAJxg0aJA+++wzvfLKK3rzzTe1Zs0aHTx4UCUlJWrWrJm6d++uIUOG6Prrr1e3bt3ifv7KykodOHBAkpSZman8/Hx16NBBXbp0Ud++fXXFFVfQywWkMYfBzIEAAACmYIwXAACASUi8AAAATELiBQAAYBISLwAAAJOQeAEAAJiExAsAAMAkJF4AAAAmIfECAAAwCYkXAACASUi8AAAATELiBQAAYBISLwAAAJOQeAEAAJiExAsAAMAk/x9iWVKKVTzPXAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AC_list = ['20231030_Cs137_AC_600V.dat', '20231031_Cs137_AC_600V.dat', '20231101_Cs137_AC_600V.dat', '20231102_Cs137_AC_600V_1.dat', '20231102_Cs137_AC_600V_2.dat']\n",
    "DC_list = ['20231030_Cs137_DC_600V_1.dat', '20231030_Cs137_DC_600V_2.dat', '20231031_Cs137_DC_600V.dat', '20231101_Cs137_DC_600V_1.dat', '20231101_Cs137_DC_600V_2.dat', '20231102_Cs137_DC_600V.dat']\n",
    "allpaths = np.concatenate([AC_list, DC_list])\n",
    "    \n",
    "path_lists = [AC_list, DC_list, allpaths]\n",
    "strings = ['allAC_postLL1', 'allDC_postLL1', 'allACandDC_postLL1']\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 + 'postLL1/' + p)\n",
    "    \n",
    "    df = make_df_from_dat(fullpaths, e_min=emin, e_max=emax)\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, bad = 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",
    "            df.loc[df.strip_p.eq(p+1)&df.strip_n.eq(n+1), 'bad'] =  bad\n",
    "\n",
    "            ctd_obs_map[p][n] = ctd_obs[~bad]\n",
    "            ctd_stretch_map[p][n] = ctd_stretch[~bad]\n",
    "            ctd_obs_flat.append(ctd_obs[~bad])\n",
    "            ctd_stretch_flat.append(ctd_stretch[~bad])\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",
    "    hist,binedges,_  = plt.hist(df['energy_p'][~(df['bad'])], 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'][~(df['bad'])], bins=binedges)\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",
    "    \n",
    "    plt.figure()\n",
    "    plt.hist(df['z'][~(df['bad'])], 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",
    "df = df_list[-1]\n",
    "z_list, e_signal_postLL1, h_signal_postLL1 = make_depthplot(df, strings[-1], zmin=calibrator.zmin, zmax=calibrator.zmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "┌─────────────────────────────────────────────────────────────────────────┐\n",
      "│                                Migrad                                   │\n",
      "├──────────────────────────────────┬──────────────────────────────────────┤\n",
      "│ FCN = 730.4 (chi2/ndof = 18.3)   │              Nfcn = 289              │\n",
      "│ EDM = 7.16e-10 (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   │  664.478  │   0.008   │   -0.008   │   0.008    │         │         │       │\n",
      "│ 1 │ b    │   1.357   │   0.010   │   -0.010   │   0.010    │    0    │         │       │\n",
      "│ 2 │ c    │   9.376   │   0.013   │   -0.012   │   0.013    │    0    │         │       │\n",
      "│ 3 │ ah   │  664.011  │   0.010   │   -0.010   │   0.010    │         │         │       │\n",
      "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n",
      "┌──────────┬───────────────────────┬───────────────────────┬───────────────────────┬───────────────────────┐\n",
      "│          │          ae           │           b           │           c           │          ah           │\n",
      "├──────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┼───────────┬───────────┤\n",
      "│  Error   │  -0.008   │   0.008   │   -0.01   │   0.01    │  -0.012   │   0.013   │   -0.01   │   0.01    │\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 │ 6.48e-05 6.64e-05 2.74e-05 2.41e-05 │\n",
      "│  b │ 6.64e-05 9.53e-05 2.56e-05 2.53e-05 │\n",
      "│  c │ 2.74e-05 2.56e-05 0.000158 0.000108 │\n",
      "│ ah │ 2.41e-05 2.53e-05 0.000108 9.95e-05 │\n",
      "└────┴─────────────────────────────────────┘\n"
     ]
    }
   ],
   "source": [
    "m = fit_CCE(z_list, [e_signal_postLL1[0], e_signal_postLL1[1]], [h_signal_postLL1[0], h_signal_postLL1[1]], sim_dCCE, plot_suffix = 'Cs137allACandDC_postLL1', trim=0.03)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 3.47\n",
      "FWTM = 8.77\n",
      "FWHM = 3.34\n",
      "FWTM = 8.54\n",
      "FWHM = 19.48\n",
      "FWTM = 0.0\n",
      "FWHM = 12.31\n",
      "FWTM = 18.79\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "             ID  det  strip_p    energy_p  time_p  strip_n    energy_n  \\\n",
       "0             5   11       29  634.707619   165.0       30  651.024903   \n",
       "1           226   11       26  645.811275   195.0       31  658.296893   \n",
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       "...         ...  ...      ...         ...     ...      ...         ...   \n",
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       "399053  2841687   11       12  647.530503   175.0       32  101.928716   \n",
       "399054  2841742   11       17  618.459167   205.0       19  229.405370   \n",
       "\n",
       "        time_n              x              y         z     z_err    bad  \\\n",
       "0        270.0  1.634266e+307  1.634266e+307  1.019637  0.046272  False   \n",
       "1        185.0  1.634266e+307  1.634266e+307  0.516903  0.039118  False   \n",
       "2        140.0  1.634266e+307  1.634266e+307  0.375470  0.035870  False   \n",
       "3        185.0  1.634266e+307  1.634266e+307  0.578499  0.039306  False   \n",
       "4        335.0  1.634266e+307  1.634266e+307  1.325537  0.064006  False   \n",
       "...        ...            ...            ...       ...       ...    ...   \n",
       "399050     0.0  1.634266e+307  1.634266e+307  0.029289  0.013004   True   \n",
       "399051     0.0  1.634266e+307  1.634266e+307  0.065197  0.017667   True   \n",
       "399052     0.0  1.634266e+307  1.634266e+307  0.001155  0.014996   True   \n",
       "399053     0.0  1.634266e+307  1.634266e+307  0.012397  0.022718   True   \n",
       "399054   295.0  1.634266e+307  1.634266e+307  0.924316  0.044762  False   \n",
       "\n",
       "               z_binned  depth_corrected_energy_p  depth_corrected_energy_n  \n",
       "0        (1.007, 1.057]                633.260428                660.415419  \n",
       "1        (0.503, 0.554]                644.878745                661.573165  \n",
       "2        (0.352, 0.403]                661.015665                657.571075  \n",
       "3        (0.554, 0.604]                662.830844                660.700137  \n",
       "4        (1.309, 1.359]                657.441262                666.531837  \n",
       "...                 ...                       ...                       ...  \n",
       "399050    (0.0, 0.0503]                638.740649                570.166177  \n",
       "399051  (0.0503, 0.101]                642.087110                 83.415629  \n",
       "399052    (0.0, 0.0503]                639.213029                513.660866  \n",
       "399053    (0.0, 0.0503]                647.627393                101.597104  \n",
       "399054   (0.906, 0.956]                617.070628                232.236310  \n",
       "\n",
       "[399055 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_correction(df, z_list, e_signal_postLL1, h_signal_postLL1, plot_suffix = 'Cs137allACandDC_postLL1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cosilab/COSITrapping/depth_helpers.py:324: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  mean_depth = np.sum(prob_dist*self.sim_depth)/np.sum(prob_dist)\n",
      "/home/cosilab/COSITrapping/depth_helpers.py:330: RuntimeWarning: invalid value encountered in divide\n",
      "  prob_dist = prob_dist/prob_norm\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for p in range(37):\n",
    "    for n in range(37):\n",
    "        ctd_obs, ctd_stretch, depth, depth_err, bad = calibrator.depth_from_timing_and_energy(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), 'energy_n'], \\\n",
    "                                                                                             sim_dCCE, m.values['ae']/m.values['ah'], m.values['b'], m.values['c'])\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",
    "\n",
    "plt.figure()\n",
    "plt.hist(df['z'][~(df['bad'])], 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] + '_energy.pdf')\n",
    "\n",
    "z_list, e_signal_postLL1, h_signal_postLL1 = make_depthplot(df, 'allACandDC_postLL1_energydepthcal', zmin=calibrator.zmin, zmax=calibrator.zmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FWHM = 3.47\n",
      "FWTM = 8.77\n",
      "FWHM = 3.36\n",
      "FWTM = 8.49\n",
      "FWHM = 19.48\n",
      "FWTM = 0.0\n",
      "FWHM = 4.16\n",
      "FWTM = 9.36\n"
     ]
    },
    {
     "data": {
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       "<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",
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       "      <td>22</td>\n",
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       "      <td>185.0</td>\n",
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       "      <td>1.634266e+307</td>\n",
       "      <td>0.538609</td>\n",
       "      <td>0.038483</td>\n",
       "      <td>False</td>\n",
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       "      <td>355.0</td>\n",
       "      <td>25</td>\n",
       "      <td>571.855481</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>0.048241</td>\n",
       "      <td>0.011919</td>\n",
       "      <td>True</td>\n",
       "      <td>(0.0, 0.0503]</td>\n",
       "      <td>638.768826</td>\n",
       "      <td>570.435803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399051</th>\n",
       "      <td>2841420</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>642.093556</td>\n",
       "      <td>210.0</td>\n",
       "      <td>32</td>\n",
       "      <td>83.610257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399052</th>\n",
       "      <td>2841586</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>639.096258</td>\n",
       "      <td>245.0</td>\n",
       "      <td>32</td>\n",
       "      <td>515.441361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399053</th>\n",
       "      <td>2841687</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>647.530503</td>\n",
       "      <td>175.0</td>\n",
       "      <td>32</td>\n",
       "      <td>101.928716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399054</th>\n",
       "      <td>2841742</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>618.459167</td>\n",
       "      <td>205.0</td>\n",
       "      <td>19</td>\n",
       "      <td>229.405370</td>\n",
       "      <td>295.0</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>1.634266e+307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>399055 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID  det  strip_p    energy_p  time_p  strip_n    energy_n  \\\n",
       "0             5   11       29  634.707619   165.0       30  651.024903   \n",
       "1           226   11       26  645.811275   195.0       31  658.296893   \n",
       "2           237   11       29  661.685139   210.0       21  655.784919   \n",
       "3           242   11       36  663.903327   195.0       22  656.770645   \n",
       "4           267   11        3  658.637600   140.0       22  651.787015   \n",
       "...         ...  ...      ...         ...     ...      ...         ...   \n",
       "399050  2841218   11        4  638.677239   355.0       25  571.855481   \n",
       "399051  2841420   11        1  642.093556   210.0       32   83.610257   \n",
       "399052  2841586   11        6  639.096258   245.0       32  515.441361   \n",
       "399053  2841687   11       12  647.530503   175.0       32  101.928716   \n",
       "399054  2841742   11       17  618.459167   205.0       19  229.405370   \n",
       "\n",
       "        time_n              x              y         z     z_err    bad  \\\n",
       "0        270.0  1.634266e+307  1.634266e+307  0.946927  0.043748  False   \n",
       "1        185.0  1.634266e+307  1.634266e+307  0.475397  0.036407  False   \n",
       "2        140.0  1.634266e+307  1.634266e+307  0.429740  0.035621  False   \n",
       "3        185.0  1.634266e+307  1.634266e+307  0.538609  0.038483  False   \n",
       "4        335.0  1.634266e+307  1.634266e+307  1.249471  0.054020  False   \n",
       "...        ...            ...            ...       ...       ...    ...   \n",
       "399050     0.0  1.634266e+307  1.634266e+307  0.048241  0.011919   True   \n",
       "399051     0.0  1.634266e+307  1.634266e+307       NaN       NaN   True   \n",
       "399052     0.0  1.634266e+307  1.634266e+307       NaN       NaN   True   \n",
       "399053     0.0  1.634266e+307  1.634266e+307       NaN       NaN   True   \n",
       "399054   295.0  1.634266e+307  1.634266e+307       NaN       NaN  False   \n",
       "\n",
       "              z_binned  depth_corrected_energy_p  depth_corrected_energy_n  \n",
       "0       (0.906, 0.956]                633.289976                659.485483  \n",
       "1       (0.453, 0.503]                644.973088                661.193820  \n",
       "2       (0.403, 0.453]                660.919759                658.182205  \n",
       "3       (0.503, 0.554]                662.918781                660.348417  \n",
       "4       (1.208, 1.258]                657.298437                664.876553  \n",
       "...                ...                       ...                       ...  \n",
       "399050   (0.0, 0.0503]                638.768826                570.435803  \n",
       "399051             NaN                       NaN                       NaN  \n",
       "399052             NaN                       NaN                       NaN  \n",
       "399053             NaN                       NaN                       NaN  \n",
       "399054             NaN                       NaN                       NaN  \n",
       "\n",
       "[399055 rows x 16 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_correction(df, z_list, e_signal_postLL1, h_signal_postLL1, plot_suffix = 'Cs137allACandDC_postLL1_energydepthcal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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