{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrating your Detector for Energy Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimental Set up\n",
    "\n",
    "| Experimental Item | Description |\n",
    "| -------- | ------- |\n",
    "| Detectors | 2x Scionix 38 B 37 1.5M - E1 PMT |\n",
    "| Detector Bias | 500 Volts (for both) |\n",
    "| Source Type | Spectrum Techniques Cs137 |\n",
    "| Source Activity | 5 uCi |\n",
    "| Source Manufacture Date | May 2017 |\n",
    "| Source Half Life | 30.1 yrs |\n",
    "| Cables | 50 Ohm LEMO - 10ns length |\n",
    "| Vireo Input Termination | 50 Ohm |\n",
    "\n",
    "*add picture of setup*\n",
    "\n",
    "*add Diagram of setup*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calibrating your Detector for Energy Response (Cs137)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up Environment\n",
    "\n",
    "We're going to define some variables up front so they are easier to change later. `TRIGGER_SENSITIVITY` defines how sensitive the trigger is to capture a pulse. `NUMBER_OF_EVENTS` indicates how many events we are going to capture before stopping data collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "## Collecting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skutils is in beta, please contact support@skutek.com with bugs, issues, and questions\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import skutils\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [20, 9]\n",
    "\n",
    "# VIREO_URL = \"192.168.128.154\"\n",
    "VIREO_URL = \"vireo-000019.tek\"\n",
    "# VIREO_URL = \"192.168.7.2\"         # USB IP address by default if using USB - This does not change\n",
    "TRIGGER_SENSITIVITY = 5\n",
    "NUMBER_OF_EVENTS = 10_000\n",
    "EXPERIMENT_NAME = \"Cs137_Energy_Calibration\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Connect to your FemtoDAQ Vireo\n",
    "\n",
    "We can use the `FemtoDAQController` class to control our digitizer remotely in this python script. The FemtoDAQ Controller contains functions to control trigger, data capture, and recording configuration. We will use it here to configure our trigger parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vireo = skutils.FemtoDAQController(VIREO_URL)\n",
    "print(vireo.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Configure Data Recording Parameters\n",
    "\n",
    "Before capturing data, we need to configure the instrument to trigger at the appropriate level \n",
    "\n",
    "\n",
    "{{EXPERIMENT_NAME}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load default trigger settings so we know that we're starting changing settings from a known parameter set\n",
    "vireo.loadDefaultConfig()\n",
    "\n",
    "# Configuring Trigger Settings\n",
    "vireo.setTriggerSensitivity(0, TRIGGER_SENSITIVITY)\n",
    "vireo.setTriggerSensitivity(1, TRIGGER_SENSITIVITY)\n",
    "vireo.setTriggerXPosition(128)\n",
    "vireo.setInvertADCSignal(0, True)\n",
    "vireo.setInvertADCSignal(1, True)\n",
    "vireo.setTriggerAveragingWindow(0, 16)\n",
    "vireo.setTriggerAveragingWindow(1, 16)\n",
    "# Bring baseline to roughly zero - these number will vary depending on your unit.\n",
    "vireo.setDigitalOffset(0, 660)\n",
    "vireo.setDigitalOffset(1, 660)\n",
    "\n",
    "vireo.setHistogramScaling(0, 1)\n",
    "vireo.setHistogramScaling(1, 1)\n",
    "\n",
    "# Enabling Triggers\n",
    "vireo.setEnableTrigger(0, True)\n",
    "vireo.setEnableTrigger(1, True)\n",
    "\n",
    "# Configuring Pulse Windows\n",
    "vireo.setPulseHeightAveragingWindow(8)\n",
    "vireo.setTriggerActiveWindow(32)\n",
    "vireo.setPulseHeightWindow(32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collecting Data without Coincidence Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure Recording on CH0, CH1\n",
    "vireo.configureRecording(\n",
    "    channels_to_record=[0, 1],\n",
    "    number_of_samples_to_capture=512,\n",
    "    file_recording_name_prefix=EXPERIMENT_NAME,\n",
    "    file_recording_format=\"igorph\",\n",
    "    file_recording_data_output=\"pulse_summaries\",\n",
    "    only_record_triggered=True,\n",
    ")\n",
    "\n",
    "# Configuring Coincidence\n",
    "vireo.configureCoincidence(\"multiplicity\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start Waveform Capture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vireo-000019 (http://vireo-000019.tek): collected 6753 out of 10000 events (67.5% complete) events. running time: 2.0sec\n",
      "Vireo-000019 (http://vireo-000019.tek): collected 6753 out of 10000 events (67.5% complete) events. running time: 2.8sec\n",
      "Vireo-000019 (http://vireo-000019.tek): collected 9999 out of 10000 events (100.0% complete) events. running time: 3.8sec\n",
      "Vireo-000019 (http://vireo-000019.tek): Data Collection Complete\n"
     ]
    }
   ],
   "source": [
    "vireo.clearTimestamp()\n",
    "vireo.start(NUMBER_OF_EVENTS)\n",
    "# Wait for data to be collected up to a maximum of 5 minutes\n",
    "timed_out = vireo.waitUntil(nevents=NUMBER_OF_EVENTS, timeout_time=300, print_status=True)\n",
    "vireo.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Download Data Files for Later Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vireo-000019 (http://vireo-000019.tek) Controller : downloaded `Cs137_Energy_Calibration_03.56.54PM_Apr23_2025_seq000001.itx` to '/home/skutek/app-notes/skutils-app-notes/Experiments/Cs137_Energy_Calibration/Cs137_Energy_Calibration_03.56.54PM_Apr23_2025_seq000001.itx'\n"
     ]
    }
   ],
   "source": [
    "files = vireo.downloadLastRunDataFiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Grab Pulse Heights from all events\n",
    "\n",
    "First we are going to grab the pulse heights calculated by our firmware DSP. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Skutils loader to display histogram from downloaded files\n",
    "pulse_heights = []\n",
    "ch0_ph = []\n",
    "ch1_ph = []\n",
    "for filename in files:\n",
    "    loader = skutils.IGORPulseHeightLoader(filename)\n",
    "    # loader = skutils.GretinaLoaderme)\n",
    "    for chan_data in loader.channelByChannel():\n",
    "        if chan_data.channel == 0:\n",
    "            ch0_ph.append(chan_data.pulse_height)\n",
    "        elif chan_data.channel == 1:\n",
    "            ch1_ph.append(chan_data.pulse_height)\n",
    "\n",
    "pulse_heights = np.asarray(pulse_heights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Calculate a histogram using Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_width = 4\n",
    "bin_edges = np.arange(0, 2000, bin_width)\n",
    "hist_bins = (bin_edges[:-1] + bin_edges[1:]) / 2\n",
    "hist0, edges0 = np.histogram(\n",
    "    ch0_ph,\n",
    "    bins=bin_edges,\n",
    ")  # range=(hist_bins.min(), hist_bins.max()))\n",
    "hist1, edges1 = np.histogram(\n",
    "    ch1_ph,\n",
    "    bins=bin_edges,\n",
    ")  # range=(hist_bins.min(), hist_bins.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9989\n",
      "9819\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 2000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].bar(hist_bins, hist0, width=bin_width, label=\"channel 0\")\n",
    "axes[1].bar(hist_bins, hist1, width=bin_width, label=\"channel 1\")\n",
    "axes[0].set_yscale(\"log\")\n",
    "axes[1].set_yscale(\"log\")\n",
    "\n",
    "axes[0].set_xticks(np.arange(hist_bins.min(), hist_bins.max(), step=50))\n",
    "axes[1].set_xticks(np.arange(hist_bins.min(), hist_bins.max(), step=50))\n",
    "axes[0].tick_params(axis=\"x\", labelrotation=45)\n",
    "axes[1].tick_params(axis=\"x\", labelrotation=45)\n",
    "\n",
    "print(np.sum(hist0))\n",
    "print(np.sum(hist1))\n",
    "axes[0].legend()\n",
    "axes[1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A) Define the Noise peaks by hand \n",
    "#### $\\color{red}{\\textbf{Requires User Input}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1366605978.py, line 5)",
     "output_type": "error",
     "traceback": [
      "  \u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[31m    \u001b[39m\u001b[31m**Requires User Input**\u001b[39m\n    ^\n\u001b[31mSyntaxError\u001b[39m\u001b[31m:\u001b[39m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "noise_peak_cutoff_ch0 = 104\n",
    "noise_peak_cutoff_ch1 = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9989\n",
      "9819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6a1f6540d0>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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77BMNGzaMTz/9NDp27JjTHOuy3Xbbxamnnhrbb799RERmsvOWLVvWePI0lxPNfu1rX4vTTjutILI0adIkBg8eHN26dau2vE2bNtGwYcNqeV5++eXo1atX1jMsXLgw2rRpE82aNYshQ4ZEly5d8palSmlpaRx11FGx7bbbRsTqPl5SUhKNGzeO5cuXr7V+LvdRSUlJdOzYMb773e8WRJ6N6TPZLop+lT6TywJtofSbQusz9erVi/79+0e7du0i4j8TzzVv3jwaNGgQK1asyKw7ceLEKCsry/p++uSTT2KrrbaKhg0bxqGHHhpbbLFF3rKsSZ+pWSH0mYjC7DeF0DaF2C5ftPXWW8cRRxwRBxxwQERE1K9fP5Nx3rx5ERHV+m8uclW1xzbbbJP3LFXatm0bJ554YhxyyCHV7reysjL+/e9/11mWquPHlltuWRB5IvSZddE2NSuEdqm6P22ztkJsF8fg2ukztSuEflOIfaYQ2qXq/gqtbVg/Roxs4qqKIpWVldGwYcOIqH6gjoi4+uqr41e/+lWsWrUqZzmmTp0agwcPjs8//zwGDRoUF110USZLRESDBqtrcGueoHz++edzlqc2O+64Y5x44okRsbrNqnK1adOm2gf36667Lt5///2sn+x5++23Y/LkyZksJ5xwQt6yfPzxx/HCCy/EhAkTorKyMnOCu+okWETEokWLMgfviIhLL700+vbtm/mGRLZMnTo1Bg0aFFOnTo2IyJzgXnOEUV1lWVPv3r3jtNNOi4jV+6h+/frRuHHj2GqrrapV9n/961/H7Nmzc3Jy8IMPPogZM2Zk8px66ql5y/NV+0w2vw3xVftMLr+Zke9+U0h9ZtasWfG3v/0t/ud//if+/e9/R8uWLSPiP32msrIyFi1aFEuXLs0cAy+55JL4xje+EfPmzct6n9l///3j//7v/yIiMidxI6LOs3yRPvMfhdRnIgqr3xRS2xRSu3xReXl55t8OHTrEscceGxHVX6vWXC8i4pprrokrrrgi61neeOONuOuuu2LFihXRvn37TJYvjqCuiyyff/55VFZWxvLly2OLLbbIfGD/YrtUfV4oKSmJ0aNHx7Bhw7KeJWL1dduvvPLKWL58ebRu3TqT54ttUxd59JnaaZuaFVK7RGib2hRSuzgG106fqV0h9ZtC6jOF1C4RhdU2bDiFkc1EvXr1qn3IrDpRcemll8bFF18cffv2zXwwzbZXXnkl9t9//9hpp52iadOm0aRJk4j4z0Fp5cqVsWTJkqioqMhcYuGiiy6KsrKy+OSTT3KSqcqbb74ZF154YZxyyilxww03xNtvv13jehUVFZnhbJdeemlceOGFsWjRoqxmmTp1auy5556ZE7lVUkrVTizVRZZXX301ysrK4sQTT4xjjz02dt5557j33ntjwYIFUb9+/UxfKikpiXr16kWLFi3iiiuuiOuvvz6eeOKJzDdUs+GVV16JffbZJ8rKymL33Xevdtuafbousrz99tsxduzYGDVqVNxzzz2xbNmyavdfpaKiIvOiNnr06PjBD34QixcvzlqOKi+//HLstddemUurVWWoqc/kOk82+ky2vg2RjT6TzW9mFFK/KbQ+s88++8T5558fZ511Vuy+++5x3XXXxYcffrhWnykpKYkWLVrEVVddFTfeeGO88MIL0b59+6z2mf322y+OPPLIzLe+qqw5BL4uskToM7UppD4TUVj9ppDappDa5YumT58exx13XBx88MFx+OGHx7PPPps5eVG/fv1Mvi233DJTWLrooovi0ksvjUGDBmU1yyuvvBI77rhjLFq0KBo1ahQR/2mfNR9/XWSZNm1aDBo0KL7xjW/EXnvtFb///e/jo48+ioio1n/atm0bjRs3zmS5/vrr45RTTslqlojVbbPLLrtEo0aNMp8bKisr1zqBUBd59JnaaZuaFVK7RGib2hRSuzgG106fqV0h9ZtC6jOF1C4RhdU2bKS1Zh1hk1VRUZFSSumyyy5LQ4cOTdddd11q3LhxmjJlSs7u85VXXknNmzdPF1xwQbXla06AXFlZmT755JPUqVOn9O6776bLL788tWjRIr3wwgs5y5VSStOnT0+tW7dOAwYMSEOGDEmtW7dO/fr1S7/+9a/XyllWVpZuu+22dOONN+akzaZOnZqaNWu2zsmWVqxYUSdZ5s2bl3r27Jkuuuii9M4776SPPvooHXPMMWnHHXdMl112WZo3b15m3blz56ZevXqlY445JjVq1Ci9+OKLWc0ybdq01LRp03TppZemlFb3lfnz56d33313rXXrIkubNm3St771rXTggQemBg0apCFDhqQJEyZk1lm5cmUqLy9P22+/ffrDH/6Qfvazn+XsOTZ16tTUvHnzdN5559W6zqpVq+okjz6z7jyF0m8Kqc8sWLAg7bHHHmnEiBFp7ty5qaKiIv3kJz9J++67bzrppJPSe++9l1n3888/T7vttlvq379/atSoUZo8eXJWs0yfPj01adIk/fSnP00pre4zH374YZo6depa6+Y6S0r6TG0Kqc+kVFj9ppDappDa5YvefPPN1KpVqzR06NB0wQUXpKOOOiqVlJSkyy67LL3//vvV1r3gggvSWWedlS6//PLUpEmTrL8+1Pb+uLKyMqW0+nlVV1neeeedtMUWW6Rhw4alX/ziF+nss89OW2yxRRo6dOha9/Wzn/0sffe7301jxoxJjRs3zsnr5pe1TV3m0Wdqp21qVkjtkpK2qU0htYtjcO30mdoVUr8ppD5TSO2SUmG1DRtPYWQzdMUVV6SSkpLUunXrnH4I/fjjj1NpaWnq379/Smn1i8W5556bBg4cmHr27Jl+/vOfp3/9618ppZSWL1+evv71r6d+/frl7ATlmsrLy9MJJ5yQzjjjjMyyt956Kx1zzDFpv/32SzfeeGO19b/zne+kNm3apObNm2e9YPPmm2+mxo0bp4svvjiltLoA8vDDD6df/epX6S9/+UtasmRJnWVJafVJja5du661Dy688MK0yy67pGuvvTYtXbo0pZTS66+/nkpKSlLTpk3Tyy+/nNUcn376aerevXvq1atXZtkpp5yS9txzz9SxY8d04IEHppdffjnzopLLLMuWLUvf/va307BhwzLLpkyZkvbaa6/Ur1+/9Kc//ana+gcddFDq3r17atasWU6eYzNmzEjNmjVLF110UUpp9UnSp59+Ov35z39Ozz777Frr5zqPPlOzQuo3hdZn3n///bTtttum//3f/622/Be/+EUqKytLP/rRj9Inn3ySUkpp1qxZqaSkJDVq1Ci98sorWc2xcOHCtP/++6fOnTtnlh177LFpl112Sc2bN0877bRT+uMf/5jpv7nMkpI+sy6F0mdSKrx+UyhtU2jt8kWXXHJJOuSQQ6otu+mmm1K7du3ShRdemObMmZNZft5556WSkpLUvHnzrL8nfeONN1LLli3T0KFDU0qrv7h02223pQsuuCBdcMEF6a233qq2fi6zpJTS9ddfnw488MBqy+6+++606667ppNOOim99tprmeVXXnllKikpSS1btsxJlnfffTdtscUW6Xvf+15KaXXbjB07Np1++unp6KOPTs8++2zmi151kUefqZ22qVmhtEtK2qY2hdYujsG102dqVyj9ptD6TKG0S0qF1zZsPJfS2gz1798/IiL++c9/xl577ZXT+yorK4v58+fHX/7yl/j2t78dr732WvTs2TP69u0bN910U1x33XXx3nvvxSeffBLTp0+P//u//4vJkyfHnnvumdNcjRo1irlz51a7JEj37t3j2muvjZ49e8Yf/vCHeOSRRzLrN23aNJYvXx6TJk2KvffeO2s5Vq1aFTfffHO0aNEic8mfwYMHxyWXXBJXXXVVHHHEEXHKKafEyy+/nPMsVVauXBmrVq3KXLrl888/j4iIsWPHxkEHHRS33npr5pJjW2yxRfzoRz+Kl156aa1LFn1V7dq1iwEDBkTz5s3jpz/9aeyzzz7x8ccfxw9+8IO45ZZbYuXKlTF48OB45513cp6ladOmsWDBgthyyy0jYvXQxz322CP+53/+J1atWhW/+tWv4pVXXsncVllZGR9//HFMmjQp68+xlStXxkUXXRTNmzeP73znOxERceSRR8aPf/zjOPPMM6Nv375x1llnZeYRynWeiNXXUC2UPnPIIYcURJ+JKIx+k1KKFStWFFyfqVevXjRr1ixmz54dEf+5nupZZ50VRx55ZDz11FPx3HPPRcTqYcVXXXVVvPLKK7HrrrtmNUfr1q1j8ODB0aNHjzj55JNjr732is8++yxGjx4dzz33XOywww4xfPjw+Oc//5nzLBGr+8z8+fPzfqypqKiIiy66KJo1a6bP1KDQ+k1JSUk0bdo0723TunXrOPzww6N79+4F0S5fVPXaFPGfNjr77LPjyiuvjJtvvjn+/Oc/Z27v2rVrbLfddvH8889n/T3pCy+8EEuWLImePXvGe++9F3369Im77747nn322Xj66afj61//eowfP75OslRZvHhxLFmyJHNJkO9973sxevTomDhxYtx3332ZS7jutNNOscsuu8TEiRNzkmX69OnRvHnz2GqrrWLKlCnRr1+/ePzxx+Pjjz+OxYsXx7e+9a249dZbM+vnOo8+UzttU7NCaZcIbVObQmuXCMfg2hRKn5k0aVLB9ZmIwug3hdZnIgqjXSIKs23YSPmuzJAbXxyFkCuzZ89OJ510UmratGk6+OCD06effpq57e67705t2rRJjz76aEoppRtuuCFNnz4955lWrVqVVqxYkU455ZR01FFHpeXLl6fKyspMtfadd95JZWVl6Zhjjsn8zf/93/+tNVwzW9588800dOjQtN9++6XOnTunww47LM2YMSMtW7Ysvfjii2nrrbdOJ510Umb9f/zjHznLUmXvvfdOBx10UOb/y5cvz/y+1157pWOPPTbz/88//zzr979m5Xz48OGpQ4cOaeDAgdW+FZJSSjvvvHM6+eSTc5olpZQ+++yzdNBBB6UzzzwzpbS6D61cuTKltHq0xDbbbJN+/OMfp5RWD4u87777arx0U7ZMmTIl9e/fPx1yyCGpZ8+eacCAAemll15K77//fho/fnxq1KhRGjVqVE7zzJ49u9rzda+99spbn5k9e3a1bxufd955ee0zVf138eLF6aCDDko//OEPU0r56TdVw7pffPHF1L9//9S/f/+89ZmlS5dWu4zid77zndSrV6+0cOHClFLKtE1KKR166KGpd+/emf9XXUowm1mWLVuW+f9NN92Udtppp3TIIYekjz76qNq6BxxwQLVvq2U7S0qrvzk/efLktGrVqrz3mVmzZqWZM2em119/Pe995ou+/e1v563P1OSGG27IW7+pqKio9lr53e9+N+2yyy55aZuKiopq2/zlL3+Ztt9++7w9n2pz4403ppYtW2YyrXk8GjNmTGrRokXm/dWHH36YZs2albMsN9xwQ+rUqVPq0qVL+s53vpNmzZqVli9fnj7//PN05plnpjZt2qQPP/ywTrLcf//9qWnTpumll15KKVVvl1tvvTU1atQoTZs2LaW0emTm3Llzc5YlpZTuueeezEjPqtfxqn7yX//1X6lJkyZpxowZdZLnpptuKpg+c+ONNxZMn6nKUyhtU0jPp0LqMykVVr8ppLYppHZxDK5uzfNU+e4zn332Web3QuozKaV07733Fky/+f3vf18wr9v33XdfwbRLSvl/PpEdCiN8ZR999FEaNWpUevLJJ1NK1a+n171798z19ta8LmMufHH7Tz/9dKpfv361y2ZVrfP000+nevXqVRtql8ssb7/9djrxxBPTwIEDM5cXq/Lwww+nkpKStZZny5IlS9LixYvTokWLMsteeuml1L59+3TcccdlllWdZBk+fHgaNGhQnWVJafWQyD/+8Y9rXcNzyJAh6aijjspJlvnz56cZM2akN954I6WU0iOPPJJKSkrSH//4x5RS9RNB99xzT9piiy3SzJkzc5KlKs/rr7+e6QczZsxI3/jGN9LBBx+81v3efPPNacstt8xZAe3DDz9M7dq1S0cccUSaOHFiSimll19+OW255ZZ13mdqypJSStdee22d95mUVrfDt7/97cyb+gcffDBv/ebll19OAwcOzLypnzp1at76zGuvvZYGDhyYnnnmmUzbfPLJJ6lbt27p4IMPrvamNaXVJzkOOOCAaid3c5Gl6pI+KaV05513pocffjhzsrnqvs8555zUt2/frOeoMm3atNS5c+fMPB733ntv3vrMtGnT0jbbbJPOPffclFJKkydPzlufmTVrVrr//vvTH//4x8yHm3z1mdrypJSffjN9+vR04oknpoMOOiidcsop6a9//WuaN29e2m233dJBBx1Up22zZpZTTz01PfTQQymllP785z+nP/7xj3X+fFqX8vLydOCBB6b99tsv80WdquL4xx9/nDp37px53tWFG264Ie27775rXS7h9ddfT+3atUsPP/xwTu9/zffjRxxxROrcuXPmA/maX27o3r17uummm3KaJaXq743vvffe9J3vfKfa63pKqy/XttVWW6Vf/vKXOc+T0urLG/bu3btg+sxNN92Utz5TWVlZbR8tX748b23zxSwp5a9t5s+fX20uvaVLl+a1z3wxT0qrL6uYj7Z56623ql32OZ/Ppy9mSSm/7fKnP/2p2nE2n8fgqjxrfmEoX8fgf/3rX+mEE07IvLdcsmRJ3vpMVZY152nLV59JafWxZcGCBdX20+GHH56XflOVZc0i1t13310Qr9sprf7yXb7f06zp97//fcG0DRtHYYSsWLRo0VoTrn/66aeprKws/c///E/O7/+NN95I119/fZo9e3a15ddff32qV69etQnXU1r9jfwdd9wxJyefasvy/vvvp8ceeyxzAqzqA+sf/vCH1LNnz/Tvf/8761mmT5+eDjnkkNSrV6/UqVOn9Pvf/z6ltPoNx7333pu23HLLdNRRR6UVK1ZkTm6ccMIJ6dhjj00rV66scdKobGZZ80PPF0/0VFZWpqOOOqraJNvZ8tprr6VevXqlXXbZJTVs2DCNGTMmLV++PJ199tmpcePG6ZFHHqm2/l//+te04447VhsRlU1fzFM1qe2//vWv9Ic//GGtPnPzzTenXXbZJWcjaJ566qnUoEGD1KdPn3TSSSdlThLed999aYsttkiDBw+ukz5TU5ZJkyZlblvzjWNKue0zKa0uPDRt2jRdeOGFmWUrV65MZ511VmrcuPFab5Zz2W++mGXNeVX+8Ic/ZJ5PddFnqiYT/8EPfpA++OCDardNnDgxderUKX3rW99Kb775Zub+TzvttHTYYYet9bzPZZaUqr9xrnL88cens88+O1VWVuakzzRr1ix169YtdejQIX388ccppZQ51lSNqKyS6z6zZpaq16iq40zVie266DOvvvpq2nbbbdNee+2VOnTokAYNGpQpUk+cODFts802ddZnasuz5pcVarrPXPWbGTNmpC222CKddtpp6Wc/+1nq379/2m677dKPf/zj9Nxzz6Wdd945feMb36iTtqkpS7du3dJPfvKTWv8ml8+nNb3xxhtpxIgR6fvf/3664YYb0ptvvplSSunJJ59M++yzT+rbt2+aP39+Zv0FCxaknj17rvX6nossr7/+eua2KVOmZF6rqtrjlVdeSTvuuGNO5uqZO3dutfeTVa/T06ZNS9/4xjdSt27dqn3LdenSpalXr145e69eW56UVr9eVfXhqrZ5++2306677pr5slU2vfvuu+m///u/0/Dhw9N9992Xud9HHnkklZWV1WmfqSlLlalTp9Zpn0lpdR/+8Y9/nAYOHJjGjBmTeQ169NFH67xtvphlzW/X1nXbvPPOO2m77bZLo0ePrjY67tFHH037779/nbbLuvKkVPdt8/LLL6dWrVqlX/3qV9WW56PPfDHLmseZum6XV155JW211VbpjDPOSB999FHmPqdNm5bKysrq/BhcW56UVn82r8tjcNVnl5KSkvS73/0uc7/jx4+v8z5TU5Y1b6vrY/C0adPSt7/97bTjjjumwYMHZz5T5uO1+4tZ/vKXv2Ruq+vX7X/9619p5MiR6YQTTkjXXXddmjJlSuY+99133zp/Pn0xz5oFtFdffbVO24bsUhghZy699NLUo0ePalX4XHjrrbdS27ZtU0lJSRo1alRmAtKUVh8cx4wZk0pKStIll1ySXnrppTR//vw0cuTI1L1797W+cZPLLCnVfKL2/PPPT/37919rFMVXNX369NSuXbt03nnnpbvvvjsNHz48NWzYMHOSe+nSpenhhx9O22yzTerZs2caPHhwOvroo1Pz5s2zPpKmtiy1TYi9cuXKdMkll6SOHTuuNdlZtrKcf/75afr06en6669PJSUl6aOPPkofffRROuOMM1LDhg3Trbfemj7++OP0+eefp5EjR6bddtstLViwIKtZ1pWn6nmz5pv7Kj/+8Y/TkCFDqn0bPpvmz5+fvvOd76Rf/vKXaY899kjf+973MiedHnroobTTTjulHXbYIad9prYsxx9/fHr11VdTStXbJpd9JqXVb4ibN2+eGQFXZdWqVenTTz9Nw4YNq7N+U1uWdZ3AzlWfWbJkSTrkkEMyl4ZKafXJ1JdffjnzRnXatGlpp512Sj169Ej77LNPOvzww1OLFi2yPhnzurLU9Dr0+eefp4svvji1b98+JyP2qj50XXTRRemTTz5JO+20U7riiitSSqtPig0dOjQ1bNgw/fKXv8x5n/lilp133jldfvnlmeJ0Ta9Nueoz7733Xtp6663TyJEj05IlS9Jf//rXVFpamp5//vnMOnXVZ9Y3z5py2W+WL1+ejj/++HTOOedUu7/dd989lZSUpOOOOy69+uqrad99903bbbddTtumtiy9evVKJSUlmYkm17wtl8+nNU2fPj21bt06DRgwIA0ZMiS1bt069enTJ911110ppdUjQPfZZ5/UrVu39Pjjj6e///3v6ZJLLkmlpaVZHwFVU5Z+/fql2267rda/ufDCC9Nee+2V9fegr7/+emrUqFE66qijanxP+cILL6TevXunNm3apF/+8pfp3nvvTSNHjkzt2rVL77zzTlazrCtPTe9rqlx88cVp1113XevLRV/Vq6++mrbZZpvUt2/ftP/++6d69eqlsWPHppRWv44/8MADmZOWue4zNWW59tpr1/k3ueozVXnat2+fjjrqqPSDH/wgNWrUKPMFk1WrVqX7778/cwKqLtrmi1mqvixUm1y2zW233ZZKSkpSr1690pVXXpkpRqxatSrdd999dXacqS1P1ZctapOrtqn6osXw4cPXum3VqlXpwQcfrLM+s64stclVu7z//vupS5cua70/r/Lqq6+mAw44oM6OwV+Wpya5OgZXvQ8dMWJEOv/889MBBxyQeT5VVFTU6TG4piwff/zxOr/MkcvjzPTp09MWW2yRhg0blm677bb0jW98I3OFhsrKyjR58uR04IEH1km/qSnL9773vXW2Ta76zPTp01ObNm3Sd7/73XTmmWemzp07p9133z1TAH3ttdfq9PlUU5499tgj3XzzzbX+Ta7ahuxTGCHr7r333jR06NC0xRZbVLscRS4sWbIknXrqqen73/9+GjduXCopKUkXXHBBtRetioqKdOedd6bS0tK09dZbp549e6ZOnTplKs65zrJmcWTNF5Vp06aliy++OLVq1Spzojdb5s+fnw455JBqJzRSSql3797p7LPPrrZs8eLFacSIEen0009PZ511VtbngVmfLGu2y9/+9rc0aNCgVFpamvX+88knn6QDDzwwcw3/qvvu379/mjRpUnr11VfTCy+8kG655ZbUqFGj1K1bt7TrrrumrbbaKid9ubY8AwYMSM8991zmev9V3n777TR69OjUpk2bzLUzs23VqlVp3rx5afvtt08ffvhh+tOf/pT23nvvdNppp6Vvfetb6eijj06LFy9O559/fs76zJdlOeOMM9L++++fhgwZklJKacKECTnrMymtHspdWlqa+vfvn8l17rnnpkMPPTTttNNO6Re/+EV66qmn0k033ZTzflNbloEDB6aePXumn//859W+rfzOO+/ktM8sX748ffOb30wvvfRSWrVqVerfv3/ae++9U4sWLdK+++6bfvOb32TWvemmm9LIkSPTZZddlpMTp7VladmyZdpvv/2qZXn00UdT375909Zbb52TPvPKK6+kxo0bp4suuiiltPp16Kijjkp77rlnZp3Zs2enq666KjVq1Chtt912OesztWXZe++9M+useaIy133ml7/8Zerdu3e14/5hhx2WfvnLX6Y77rgjPfXUU5nlue4zX5bnzjvvTH//+98zy3Pdb1JKqW/fvpkTgVXFzhEjRqQjjzwy7bnnnmncuHEppdWXe8h129SWZciQIWmPPfZI1113XUoppfHjx6d+/frltF2qlJeXpxNOOCGdccYZmWVvvfVWOuaYY9Lee++duVzB66+/no477ri01VZbpe233z7tvPPOWX/ft64s++23X7VLuaaU0nPPPZfOOeec1KZNmzR16tSsZpkzZ07af//9U58+fdKWW26Zvvvd79ZYHFmwYEEaPnx42nHHHdMOO+yQ9t1335zss/XNU2XChAlp2LBhaYsttqj1SzMb67333kvdu3dPI0aMyBzrbr/99tShQ4fMc6eysjJNnTo1HX/88TntM+vKUvUFlDXlss+ktLpI37Vr18x8Uiml9NOf/jT96Ec/qjbC8vXXX0/HHntsTttmXVlqmqso122T0urXz5NPPjldccUVqVOnTum//uu/qo3ofPPNN9P3vve9nLbLl+Wp6YoDuWybN998MzVu3DhdfPHFKaXV80g9/PDD6Ve/+lX605/+lBl9Om3atJwfg9eV5S9/+Uu1+SNSyn2feeSRR9Jhhx2WyXLxxRenwYMHp1NPPTXdc889KaXV71XPOeecnB+D15Xn9NNPT3feeWe1dXN5DH7xxRdTq1atMu9D77333tS6dev0j3/8I7NOXR2DvyzLFwv3ue4zy5YtS4MHD652LuAvf/lLOuKII9LHH3+cOQ7XRb9ZV5a5c+dWez5VVlbmtM989tlnqX///mnEiBGZZVWX195qq63SNddck1nv3HPPzfnzaV15OnTokPniW5Vctg25oTBC1r3yyitp4MCBOTtxu6Zly5alcePGZYah33///TUWR1JKaebMmemZZ55Jjz32WGbirLrK8sWRIzNnzkwDBgxI2223XU4OlnPmzEn77LNPevbZZ1NK/3mRP+WUU9Lxxx+fUkrVJqSvsq5v8eUyS5XKysr01ltvpQsvvDAzSVU2ffrpp+mqq66q9uHz8ssvTyUlJWnXXXdNXbp0SQMGDMjM9XH//fen++67L2ejntaVZ/fdd0+dO3dO/fv3T//4xz/S22+/nQ4//PDUtWvXnL7AVp0cPP7449OECRNSSqtPem255ZapRYsW1U4up5SbPrM+WVq2bJkZ+vzWW2+lESNG5KTPpLS6GHHEEUekvfbaKz300ENpwIABqW/fvuknP/lJ+tGPfpS+9rWvpdNPPz0tWbIkvfLKKzntN+vKMmzYsNStW7d02mmnpffffz9Nnz49531mzpw5aauttkp/+9vf0nnnnZf69++fXnnllfTYY4+lCy64IJWWlmY+DOba+mR58MEHU0qrR8xdeeWVOTuh/MILL6TRo0enlP7zHPnXv/6VWrduvdY3i3LdZ9aV5ZZbbqm2bl30mdtuuy1tt912mQ8uV1xxRSopKUn9+vVLe+21V2rfvv1al+bIpXXl2XvvvVP79u0zx5pc9pvKysq0dOnSdMABB6QTTzwxc3Lpww8/TNtuu2367W9/m0444YR0wAEHZP2+NzbLQQcdlFLK/fPpiw4++OA0dOjQTNaUVn8z9vvf/376xje+kf76179m1p0xY0b66KOP1nofVhdZDjjggMzlMGbNmpWuuOKKtMcee+Rk5NNjjz2Wvve976XJkyen559/PrVt23adxYgPP/ww/fvf/87JZVw3NM9nn32WbrjhhrTffvtl/YtCFRUVaezYsWnAgAFp4cKFmeVVozZq6rO56jMbmuXDDz/MaZ9ZtWpVuu6669IPf/jDavvl9NNPT2VlZWnvvfdOQ4cOrZPn0/pkOfPMMzPPp9mzZ+e0bapMnTo19ejRI1VWVqYxY8akzp07pxtuuCEdfvjhmdfVlHJ/nPmyPEcccUSmoJTLY83KlSvTOeeck9q1a5d5P3XYYYelXXfdNXXt2jXVq1cvHXnkkdWex7lqm/XJ8t3vfjfz2p7rY3BKqycL32+//VJKKfXr1y/17t07/fjHP04HH3xw2m233TIn41PK/TH4y/LsvvvumT6zbNmydOONN+bkGLxkyZLUvHnzzDx7Vfr27Zv69OlT49xoueozG5ol18fglFa/LhxwwAFpzJgxmWXnn39+6tq1a9p6661T7969qxWLc9lvvixLnz590siRI1NKq78YkqvX7ZRWv6/ce++9M58hq0avf/e73019+/ZN++23X3rssccy63/00Uc5fT59WZ79998/81q5ePHinD2fyB2FEXIiF9cAr82ak0KltHoehJKSknT++ednXlBXrlyZs2HN65ul6htGVd+CnzlzZk4zrXmyveqbVpdcckk68cQTq6235geQXF0PfH2zVL3IfHHCxWxavHhx5veqSZDvv//+NH/+/PT000+nvfbaK3MJgbqwrjzPPPNM2nvvvdOYMWPSihUr0t///vecTgC/ppNOOinz5ue0005LW2yxRdppp53SqaeeWm1isVxeQ359slRNtJjLPpPS6g/hJ510UmratGk6+OCDq31j8Pe//31q3bp1zq4pvSFZ7r777tSmTZvMm8Wnnnoqp32msrIyHXvssemss85K3/72tzMFrJRWfwg94YQT0plnnplWrlyZOSmfqz6zPll+8IMf1PjN01yrrKxMCxcuzFx+rqo9cllYXN8sq1atyuQoLy/PeZ9599130/7775+6d++ehgwZkkpKStJDDz2UKisr09y5c9M555yTevfunT755JOc95kNyZOLyyfU5B//+EeqV69eOvDAA9OJJ56Ymjdvnk4//fSU0urLBrRs2TLNmDFjnZdBq6ssLVq0qDZCLddWrVqVVqxYkU455ZR01FFHpeXLl1f7ksc777yTysrK0tFHH535m1y1z/pmOeaYYzI55syZk7O5yubNm1dttNXEiRMzxYg1T8LXdBIqn3mqVFZW5uRypSml9Mwzz2TeQ1SpqKhIXbt2rZaxLmxolnnz5uX0ZPusWbOqvaf7r//6r1S/fv108cUXp5tuuintvffeqU+fPplL3uTyeLO+WaqOxbl8Pq3pkEMOybwmXnvttal58+apdevW1d5j1OVreW15Hn/88ZTS6n308ccf56xt3nzzzTR06NC03377pc6dO6fDDjsszZgxIy1btiy9+OKLaeutt672+S6XfWZ9spx00kmZHLnuM0888UTq06dP+s1vfpMOPvjgzBcxFy5cmClS1HQ54HzmqXoNX758ec5OKq/5nrLqvcuvf/3rtP3222dGhFRUVGT6Si77zPpmqZLLPlNRUZEWLVqU+vfvn4444og0bty4NGrUqNS0adP0u9/9Lj322GNpzJgxaY899kh/+tOf1sqWryxVBepc9Zmq99+dOnXKjExOafVrxE477ZTuvPPOtOuuu2bej1b9Ta5sTJ7y8vKcFj3JPoURNhurVq3KHBSrTjBfcMEF6aOPPkrnnXdeOvLII9OSJUvq5ETul2UZPHhwzibO/qI1X0AvvvjizCV4UkrpqquuSj/72c/q7IPyl2W5/vrr6yxLSqsvafDFIboDBw5M3/72t+ssw/rkGTRoUJ1lqOq3d9xxR7rsssvSD3/4w9SxY8f07rvvpj/96U/pa1/7WjrzzDNrnMQ6H1l+8IMf1Nlz6aOPPkqjRo3KTKC25rGke/fu6fzzz6+THOuTZUOuJ/xVTZ48OTVv3jyVlJSsNQH9T37yk3TggQfWyXG30LLU5I9//GMqKSmpdvmAYszy7rvvpvvvvz9ddtll6aijjqp229ixY9Nuu+1WZ8/rQszzwgsvpBNOOCGdfvrpmUtnpbT6kgY77rhjjSeWN+csXyx8P/3006l+/frVLlVVtc7TTz+d6tWrl9PLTW5ollzNwVXbFwKq3mtNmjSp2kiNFStWpFtuuSX97W9/K5g848aNy0me2rJUHf8rKipSt27dqt33//7v/+akALoxWf72t7/lrCBSW55PP/00nXvuudW+hfv666+nkpKSasvynWXNESx1kaV3796Zyw6ddtppqVWrVqm0tDRde+21a02Anu88ubgiQk1Z3n777XTiiSemgQMHrjXS6eGHH04lJSXpjTfeKJgsuRrN+MUsM2bMSJ06dUo77bRT6tevX7XbPvjgg9SsWbOcjqLemDx33313zrPU9L77s88+S507d07Dhg3Lyf1/1Sy5/Kzwxf00adKkNGDAgPS9730v7bDDDun222/P3DZnzpzUpUuXdPXVVxddlptvvjmVlJSkU089NV1yySWpRYsWmUuXPvjgg6lr167p008/zVmxqNDykFsKI2xW1vy23n333ZcaNmyYdthhh9SgQYM6v77furLk+hrcNWVJaXUx4tBDD00ppTR69OhUUlKSs+vybgpZ1lRRUZE+//zzdMwxx6Qrr7wybzkKJc8zzzyTSkpKUmlpaXrxxRczy//85z+nd999t2izLFq0qNqIuMrKyvTpp5+msrKynH242Jgsv//97+s0y7PPPptKSkrSt7/97WonJM8555x0+umn1+kojULK8kXl5eXpkEMOSccff3xatmxZ3nIUSpZf//rXaeDAgdX68XnnnZcOP/zwtUZgFluemj6Un3/++al3797rnKthc8vyxhtvpOuvv36tiSuvv/76VK9evfTrX/+62vIpU6akHXfcMSejnjaFLF9UdRmro48+Op1yyimpYcOG6e23396s89SUZc0+vHLlyrRkyZLUvXv3NGnSpJRSSqNGjUolJSVZP9FdSFlqy7OmqpHbVZ9hXn311bTHHnvk5HIghZ6l6r3ChRdemP7nf/4nnX322alTp07p3XffTVdddVVq1qxZ+tnPfpaTEcuFlKe2/fT++++nxx57LJOrql//4Q9/SD179szJt6U3hSyPPvpoatCgQWrfvn365z//mVleXl6e+vTpU22k0eaa58ue21V9dNy4celrX/tatc92m3OWdeVZsmRJWrVqVSorK0v3339/ZvmKFSvSwQcfnPliSjYLNoWepaKiIt1xxx1p7733TgMGDMjMK5LS6nn2evXqlbMCVqHlIfcURtjsVFZWZg5Kffr0SW3bts3b9f0KJUtVgeayyy5LQ4cOTdddd11q3LhxTicI3BSyfNHo0aNTly5dapwAMx/ymWfFihXp9ttvz1xTNZ8v9IWUpSaXXnpp6tGjR87motlUsjzzzDOpU6dOaZ999kmnnXZaOvHEE1Pr1q1z9m3pTSXLF1199dWpVatW6eOPP853lLxnmT59emrdunW69tpr01133ZVGjBiR2rRpk7fX7ELLU+XVV19NP/rRj1KrVq3y+gWCus7y1ltvpbZt26aSkpI0atSoat+gX7p0aRozZkwqKSlJl1xySXrppZfS/Pnz08iRI1P37t2z/s3/TSVLTf7xj3+kkpKS1LZt25y81yqkPOuTpeqLJ1UnwS6//PLUvHnzzKU5N8csX5antkvYXHTRRWnfffet0z5cSFlSSum3v/1tKikpSR07dkyTJ0/OLL/mmmty8v68kPJ8WZbaiub9+/fPetF8U8py7733pnr16qX+/fune++9N7311ltp5MiRqVOnTumDDz7IapZCy7MhrwdVlztbczTq5prly/JUVFSkJUuWpH333TeNHj06/fvf/06fffZZGj16dOZqCcWYJaWUPv/887WuVHHWWWelo446Kn3++edZPy9QaHmoGwojbJZWrVqVzjvvvFRSUpLTSfk2tSxVk8q2bt262pvpYs/ywAMPpGHDhqV27drV+WieQs5TSENBCylLlXvvvTcNHTo0bbHFFnnvN4WS5V//+le65JJLUr9+/dIPf/jDvBYiCilLSv/50L5gwYK055571tl8QYWe5e9//3v62te+lnr06JF69+6d99fJQsuzfPny9Kc//Skde+yxRZVlyZIl6dRTT03f//7307hx4zKXJF3zpGhFRUW68847U2lpadp6661Tz549U6dOnbJ+sn1TyFLbCZ/y8vJ05plnppYtW6bp06dnNUuh5dnQLL169Up77713atSoUdbfhxZSlo3JM3369HTJJZekVq1aZf25vqlleeONN9Ill1ySufJALt+PFlKe9cmy5gnAadOmpYsvvji1atUqJxN4b0pZUlp9ab6ysrLUoUOH1LNnz7T99tvn5P15IeXZ0Od2SimdfPLJaYcddkgrVqzI6gnlQsqyIXnuv//+VFJSkrbffvu07777pm233TZv+6lQsqy5L2bMmJHOPffc1LJly5x8aanQ8lB3FEbYLK1atSr95je/qfPLZxV6lsmTJ6eSkpKcfDjelLNMmzYtHX300XU6iey6FFoeavbKK6+kgQMH5uxa9ptqlpRS3iYWr0khZUlp9RvqfFwmqiaFkmX+/Plpzpw5BTNRYaHlWb58eUHsp5TqLsuyZcvSuHHj0n333ZdS+s8H9C8WJFJaPZnqM888kx577LGcXGN/U8lS0wmfF154Ie288845GYFQaHnWN8uqVavS/PnzU+vWrVP9+vVzcjKjkLJsSJ6UVl+W6Igjjkg77rhjTkaFbYpZqi7rlVJuRy0XUp4N2U8zZ85MAwYMSNttt11OPu9uqlk+/fTT9Oabb6aXX345Z3MGFVKeDclS1W8nTZqUk8siF1KWDc3zj3/8I11xxRXptttuy8kXlzbVLIsXL0433XRT+ta3vpWz82qFloe6ozDCZquQhrEVUpZCObmSUmFlyee8AzUptDzUbM25CPKtkLIAfFVffI9w3333pZKSknT++ednPqCuXLkyvf/++7L8/yyffvppSml1QbjqEikLFiwomjzrk2XlypXpk08+SRMmTMjplwkKKcv65lm1alWaO3dumjVrVpo1a1bRZ6kqfFZUVNTZnHaFlGd999O8efPSzJkzc3r829SyrFy5ss5G4xZSnvV9PXjnnXeKKsuX5al6H7FixYqcFdE2xSxffD1YuXJlQbynqcs81A2FEQAAoCCtWrUq8wWTe++9N/PtvY8++iidd9556cgjj0xLliypky+hbEpZBg8enJYtW5bzHIWY58uyHHHEEdW+dV8sWdYnz+DBg9Pnn38uSw3P7ULaT3WZZ1PaT4WUpS5fDwotz/r232LLsj55jjjiiILZT4WUpZjf05B7CiMAAEDBqqyszFwS77777ksNGzZMO+ywQ2rQoEGdX8JgU8mSj/mmCilPbVnq169fMPspH1nWlaeQ9lOhZSm0/VQofbjQ9lMhZdFnZNkU8mwqWYr9PQ25VZJSSgEAAFCgqj6ylJSURN++fWPq1Knx9NNPxy677CJLgWQptDyybBp5ZNk08shS+FkKLY8sm0YeWTadPORIXVZhAAAANsaqVavSeeedl0pKStIrr7wiSwFmKbQ8smwaeWTZNPLIUvhZCi2PLJtGHlk2nTxkX718F2YAAADWx8477xwvvfRS7LrrrvmOIss6FFIeWWpXSHlkqV0h5ZGl8LNEFFYeWWpXSHlkqV2h5SG7XEoLAADYJKSUoqSkJN8xIkKWdSmkPLLUrpDyyFK7QsojS80KKUtEYeWRpXaFlEeW2hVaHrJLYQQAAAAAACgaLqUFAAAAAAAUDYURAAAAAACgaCiMAAAAAAAARUNhBAAAAAAAKBoKIwAAAAAAQNFQGAEAAAAAAIqGwggAAAAAAFA0FEYAAAAAAICioTACAAAAAAAUDYURAAAAAACgaCiMAAAAAAAARUNhBAAAAAAAKBoKIwAAAAAAQNFQGAEAAAAAAIqGwggAAAAAAFA0FEYAAAAAAICioTACAAAAAAAUjQb5DrAxKisrY/bs2dGyZcsoKSnJdxwAAAAAACCPUkrx2WefRadOnaJevXWPCdkkCyOzZ8+Ozp075zsGAAAAAABQQGbNmhXbbLPNOtfZJAsjLVu2jIjVD7BVq1Z5TgMAAAAAAOTT4sWLo3Pnzpn6wbpskoWRqstntWrVSmEEAAAAAACIiFiv6TdMvg4AAAAAABQNhREAAAAAAKBoKIwAAAAAAABFY5OcYwQAAAAAANZHRUVFrFy5Mt8x+IoaNmwY9evXz8q2FEYAAAAAANjspJRizpw5sXDhwnxHIUvatGkTpaWl6zXB+roojAAAAAAAsNmpKoq0b98+mjVr9pVPppM/KaVYtmxZzJs3LyIiOnbs+JW2pzACAAAAAMBmpaKiIlMUadeuXb7jkAVNmzaNiIh58+ZF+/btv9JltUy+DgAAAADAZqVqTpFmzZrlOQnZVLU/v+qcMQojAAAAAABsllw+a/OSrf2pMAIAAAAAABQNhREAAAAAAKBomHwdAAAAAICi0XXk+Dq7r/fGDszett57L7p16xYvv/xy7L777lnbbq707t07dt9997jhhhvyHWUtRozwlXUdOT7zAwAAAAAAX+bjjz+O733ve7H99ttHvXr14txzz62z+1YYAQAAAAAA6lR5eXlstdVWcckll8Ruu+1Wp/etMAIAAAAAAAWgsrIyrr322ujevXs0btw4unTpEldeeWW1dd5999046KCDolmzZrHbbrvFxIkTM7fNnz8/jjvuuNh6662jWbNmscsuu8S9995b7e979+4d55xzTowYMSLatm0bpaWl8dOf/rTaOiUlJfGb3/wmjjjiiGjWrFn06NEjHn744WrrTJs2LQ499NBo0aJFdOjQIU488cT49NNP1/uxdu3aNW688cY46aSTonXr1uv9d9mwwYWRZ599NgYNGhSdOnWKkpKSeOihh2pd98wzz4ySkpK1riG2YMGCOP7446NVq1bRpk2bOO2002LJkiUbGgUAAAAAADYbo0aNirFjx8bo0aPj9ddfj3vuuSc6dOhQbZ2LL744zj///Jg6dWpsv/32cdxxx8WqVasiImL58uWx5557xvjx42PatGkxdOjQOPHEE+OFF16oto0777wzmjdvHs8//3xce+21cfnll8cTTzxRbZ0xY8bE0UcfHa+++mocdthhcfzxx8eCBQsiImLhwoXRp0+f6NWrV7z44osxYcKEmDt3bhx99NE5bJ3s2eDCyNKlS2O33XaLcePGrXO9P//5zzFp0qTo1KnTWrcdf/zxMX369HjiiSfi0UcfjWeffTaGDh26oVEAAAAAAGCz8Nlnn8WNN94Y1157bZx88snxta99Lb75zW/G6aefXm29888/PwYOHBjbb799jBkzJt5///14++23IyJi6623jvPPPz9233332G677eLss8+OAQMGxAMPPFBtG7vuumtcdtll0aNHjzjppJNir732iieffLLaOt///vfjuOOOi+7du8dVV10VS5YsyRRYbr755ujVq1dcddVV0bNnz+jVq1f89re/jaeeeirefPPNHLZSdjTY0D849NBD49BDD13nOh999FGcffbZ8fjjj8fAgQOr3TZjxoyYMGFCTJ48Ofbaa6+IiPjFL34Rhx12WFx//fU1FlIAAAAAAGBzNmPGjCgvL4++ffuuc71dd90183vHjh0jImLevHnRs2fPqKioiKuuuioeeOCB+Oijj2LFihVRXl4ezZo1q3UbVduZN29eres0b948WrVqlVnnlVdeiaeeeipatGixVr533nkntt9++/V4xPmzwYWRL1NZWRknnnhiXHDBBbHzzjuvdfvEiROjTZs2maJIRES/fv2iXr168fzzz8cRRxyx1t+Ul5dHeXl55v+LFy/OdmwAAAAAAMibpk2brtd6DRs2zPxeUlISEavPy0dEXHfddXHjjTf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",
      "text/plain": [
       "<Figure size 2000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoff_bin_ch0 = noise_peak_cutoff_ch0 // bin_width\n",
    "cutoff_bin_ch1 = noise_peak_cutoff_ch1 // bin_width\n",
    "\n",
    "cutoff_hist0 = hist0.copy()\n",
    "cutoff_hist0[:cutoff_bin_ch0] = 0\n",
    "\n",
    "cutoff_hist1 = hist1.copy()\n",
    "cutoff_hist1[:cutoff_bin_ch1] = 0\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].bar(hist_bins, cutoff_hist0, width=bin_width, label=\"channel 0\")\n",
    "axes[1].bar(hist_bins, cutoff_hist1, width=bin_width, label=\"channel 1\")\n",
    "# axes[0].set_yscale('log')\n",
    "# axes[1].set_yscale('log')\n",
    "\n",
    "axes[0].set_xticks(np.arange(hist_bins.min(), hist_bins.max(), step=50))\n",
    "axes[1].set_xticks(np.arange(hist_bins.min(), hist_bins.max(), step=50))\n",
    "axes[0].tick_params(axis=\"x\", labelrotation=45)\n",
    "axes[1].tick_params(axis=\"x\", labelrotation=45)\n",
    "\n",
    "print(np.sum(hist0))\n",
    "print(np.sum(hist1))\n",
    "axes[0].legend()\n",
    "axes[1].legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Finding our spectral peaks\n",
    "\n",
    "mention the two peaks of Cs137\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A) Define a two gaussian fitting function\n",
    "\n",
    "**Add More**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian(hist_bins, amplitude, mean, sigma):\n",
    "    result = amplitude / (sigma * 2 * np.pi) * np.exp(-0.5) * ((hist_bins - mean) / sigma) ** 2\n",
    "    return result\n",
    "\n",
    "\n",
    "def two_gaussians(\n",
    "    hist_bins,  # raw channel histogram\n",
    "    amplitudeA,\n",
    "    meanA,\n",
    "    sigmaA,  # fitting parameters for our first gaussian\n",
    "    amplitudeB,\n",
    "    meanB,\n",
    "    sigmaB,  # fitting parameters for our second gaussian\n",
    "):\n",
    "    gaussianA = gaussian(hist_bins, amplitudeA, meanA, sigmaA)\n",
    "    gaussianB = gaussian(hist_bins, amplitudeB, meanB, sigmaB)\n",
    "    return gaussianA + gaussianB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A) Fit our histogram with two gaussians\n",
    "\n",
    "**Add More**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.36134454e-01,  2.69540747e+03,  1.38796595e+01, -3.08962083e+01,\n",
       "        1.37838597e+03, -3.41140442e+01])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch0_2gauss_params, ch0_2gauss_covariance = scipy.optimize.curve_fit(two_gaussians, hist_bins, hist0, maxfev=100000)\n",
    "ch1_2gauss_params, ch1_2gauss_covariance = scipy.optimize.curve_fit(two_gaussians, hist_bins, hist1, maxfev=100000)\n",
    "ch0_2gauss_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch0_gaussA_amp, ch0_gaussA_mean, ch0_gaussA_sigma = ch0_2gauss_params[\n",
    "    0:3\n",
    "]  # grab the optimal parametets for one peak of ch0. amplitudeA, meanA, sigmaA\n",
    "ch0_gaussB_amp, ch0_gaussB_mean, ch0_gaussB_sigma = ch0_2gauss_params[\n",
    "    3:6\n",
    "]  # grab the optimal parametets for the other peak of ch0. amplitudeB, meanB, sigmaB\n",
    "\n",
    "\n",
    "ch0_gaussA = gaussian(hist_bins, ch0_gaussA_amp, ch0_gaussA_mean, ch0_gaussA_sigma)\n",
    "ch0_gaussB = gaussian(hist_bins, ch0_gaussB_amp, ch0_gaussB_mean, ch0_gaussB_sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9989\n",
      "9819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6a1f4fd2a0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig,(ch0_ax,ch1_ax) = plt.subplots(2)\n",
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].bar(hist_bins, cutoff_hist0, width=bin_width, label=\"channel 0\")\n",
    "axes[1].bar(hist_bins, cutoff_hist1, width=bin_width, label=\"channel 1\")\n",
    "# axes[0].set_yscale('log')\n",
    "# axes[1].set_yscale('log')\n",
    "\n",
    "axes[0].set_xticks(np.arange(hist_bins.min(), hist_bins.max(), step=50))\n",
    "axes[1].set_xticks(np.arange(hist_bins.min(), hist_bins.max(), step=50))\n",
    "axes[0].tick_params(axis=\"x\", labelrotation=45)\n",
    "axes[1].tick_params(axis=\"x\", labelrotation=45)\n",
    "\n",
    "print(np.sum(hist0))\n",
    "print(np.sum(hist1))\n",
    "\n",
    "axes[0].plot(hist_bins, ch0_gaussA, \"g\", label=\"GaussianA\")\n",
    "axes[0].fill_between(hist_bins, ch0_gaussA.min(), ch0_gaussA, facecolor=\"green\", alpha=0.5)\n",
    "axes[0].plot(hist_bins, ch0_gaussB, \"y\", label=\"GaussianB\")\n",
    "axes[0].fill_between(hist_bins, ch0_gaussB.min(), ch0_gaussB, facecolor=\"yellow\", alpha=0.5)\n",
    "\n",
    "\n",
    "axes[0].legend()\n",
    "axes[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}