TrueCapturesComparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from legend_plot_style import LEGENDPlotStyle as lps\n",
    "lps.use(\"legend_talks\")\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import math\n",
    "from collections import Counter\n",
    "from scipy.interpolate import interp1d\n",
    "\n",
    "file_path = '/home/eric/sim/WWLegend/Archive/Neutrontaggerruns/'\n",
    "\"\"\" Amount is required to know how many entries belong to each neutron. \n",
    "    nPMMA is an array where each entry says how many photon timestamp belong to that neutron.\n",
    "    Time for the time entries. Each entry is one photon timestamp.\n",
    "    no is required to match Shower IDs, Identifier for something else.\n",
    "    Read in 2 different setups to compare!\"\"\"\n",
    "nPMMA = np.loadtxt(file_path + \"Warwick with ArgonModerator/Scintillator/ScintWithPMTQE10cmAmount.csv\", dtype=float, delimiter=',', skiprows=5, unpack=True) #first name\n",
    "timePMMA,WavelengthsPMMA = np.loadtxt(file_path + \"Warwick with ArgonModerator/Scintillator/ScintWithPMTQE10cmTimes.csv\", dtype=float, skiprows=5, delimiter=',', unpack=True)\n",
    "\n",
    "nPMMAFoil = np.loadtxt(file_path + \"Warwick with ArgonModerator/Baseline/BaselineWithPMTQEAmount.csv\", dtype=float, delimiter=',', skiprows=5, unpack=True) #second name\n",
    "timePMMAFoil, WavelengthsPMMAFoil = np.loadtxt(file_path + \"Warwick with ArgonModerator/Baseline/BaselineWithPMTQETimes.csv\", dtype=float, skiprows=5, delimiter=',', unpack=True)\n",
    "\n",
    "\"\"\" no carries the id to which shower each neutron belongs. \"\"\"\n",
    "no, x, y, z, ekin, NeutronCaptureTimeDelay = np.loadtxt(file_path + \"Warwick with ArgonModerator/ShowerIDs2.txt\", dtype=float, delimiter=',', unpack=True) #first name\n",
    "identifierPMMA, useless, session, session2, bruh, bruh2 = np.loadtxt(file_path + \"Warwick with ArgonModerator/PMMARun2.txt\", dtype=float, delimiter=',', unpack=True)\n",
    "\n",
    "no2, x2, y2, z2, ekin2, NeutronCaptureTimeDelay2 = np.loadtxt(file_path + \"Warwick with ArgonModerator/ShowerIDs2.txt\", dtype=float, delimiter=',', unpack=True) \n",
    "identifierPMMA2, useless, session, session2, bruh, bruh2 = np.loadtxt(file_path + \"Warwick with ArgonModerator/PMMARun2.txt\", dtype=float, delimiter=',', unpack=True) #second name\n",
    "\n",
    "name1= \"Scintillator\"  # belongs to \"first\" read in setup\n",
    "name2 = \"Baseline\" # belongs to \"second\" read in setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot 2d Histogram with Real Captures in water on the x-Axis and Measure captures or integral light on the y-Axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 4, 0, 1, 34, 46, 1, 286, 34, 0, 0, 626, 0, 1441, 52, 740, 0, 66, 4, 66, 12, 88, 91, 0, 2, 89, 2221, 0, 54, 25, 35, 7, 2, 1, 4, 5, 5, 176, 293, 2, 0, 3, 15, 24, 14, 59, 0, 659, 7, 98, 532, 28, 1366, 21, 0, 42, 54, 50, 55, 23, 4, 15, 14, 34, 17, 24, 2, 678, 0, 0, 0, 81, 209, 688, 160, 1, 293, 7, 19, 607, 44, 90, 21, 3, 18, 0, 0, 58, 126, 2, 0, 174, 92, 0, 0, 500, 15, 278, 113, 2, 2, 1638, 313, 0, 227, 8, 35, 46, 1, 35, 16, 82, 384, 28, 0, 139, 12, 2, 46, 0, 0, 8, 7, 2, 1, 0, 3, 279, 22, 272, 1, 39, 6, 57, 0, 10, 4, 202, 64, 120, 3, 8, 236, 0, 237, 2, 638, 4, 15, 2, 21, 221, 33, 3, 543, 23, 0, 60, 1459, 48, 0, 16, 124, 4, 189, 78, 0, 66, 832, 19, 118, 13, 21, 13, 0, 711, 115, 2, 0, 613, 0, 0, 1918, 1287, 197, 1, 7, 3, 106, 12, 0, 2, 7, 1462, 12, 374, 0, 128, 38, 10, 19, 0, 1901, 46, 0, 238, 96, 533, 1428]\n",
      "209\n",
      "209\n"
     ]
    }
   ],
   "source": [
    "def subarray_it_up(n, time, id):\n",
    "    \"\"\" Takes array of number of timestamps per neutron, the timestamps \n",
    "        and the array of ids of the corresponding Shower of the neutron. \n",
    "        Returns Array where each subarray corresponds to all \n",
    "        Timestamps of one Shower. Size should be amount of Showers.\"\"\"\n",
    "    unique, counts = np.unique(id, return_counts=True)\n",
    "    cum_counts = np.cumsum(counts)\n",
    "    cum_counts = cum_counts.astype(int)\n",
    "    cum_n = np.cumsum(n)\n",
    "    cum_n = cum_n.astype(int)\n",
    "    subarrays = [time[0 if i == 0 else cum_n[(cum_counts[i-1]) - 1]:cum_n[(cum_counts[i])-1]] for i in range(len(counts))]\n",
    "    return subarrays\n",
    "\n",
    "def histlist(AllShowers, window, timeMax):\n",
    "    \"\"\" Takes whole Shower array and divides each Shower \n",
    "        in time windows of size \"window\" up to timeMax.\n",
    "        Returns Array with histogram for each Shower\n",
    "        with window/timeMax bins and max range timeMax.\"\"\"\n",
    "    b = int(timeMax/window)\n",
    "    hists_list = []\n",
    "    for Shower in AllShowers:\n",
    "        x, y = np.histogram(Shower, bins=b, range=(0, timeMax))\n",
    "        hists_list.append(x)\n",
    "    hists_array = np.array(hists_list)\n",
    "    return hists_array\n",
    "\n",
    "def mult_to_phot(mult, Npmt):\n",
    "    \"\"\" Uses surface of Hit area and single PMT area (hardcoded).\n",
    "        Takes the given multiplicity of PMTs + deployed amount of PMTs.\n",
    "        Returns how many photon hits are required to fullfill multiplicity\n",
    "        assuming isotropic hit location. ADJUSTED TO ASSUME PMT QE WAS ALREADY APPLIED\"\"\"\n",
    "    F = 467\n",
    "    Fpmt = 0.05\n",
    "    return (mult * F /( Npmt * Fpmt))\n",
    "\n",
    "def bins_over_thresh(arr, threshold):\n",
    "    \"\"\" Takes single array and a threshold.\n",
    "        Returns how many entries are above threshold\"\"\"\n",
    "    res = np.sum(arr > threshold)\n",
    "    return res\n",
    "\n",
    "def distinguished_neutrons(AllShowers, mult, Npmt, window, timeMax):\n",
    "    \"\"\" Takes whole Shower array, a given multiplicity, \n",
    "        given Amount of PMTs, size of coincidence intervall\n",
    "        (in ns) and maximum time delay (in ns).\n",
    "        Returns an array where each entry is the amount of neutrons\n",
    "        of that Shower that would be detected with these 4 conditions.\n",
    "    \"\"\"\n",
    "    hists_array = histlist(AllShowers, window,timeMax)\n",
    "    threshold = mult_to_phot(mult, Npmt)\n",
    "    results_array = np.array([])\n",
    "    for i in hists_array:\n",
    "        results_array = np.append(results_array, bins_over_thresh(i, threshold))\n",
    "    return results_array\n",
    "\n",
    "def Photons_in_intervall_and_200ns(n,times,startTime,endTime):\n",
    "    \"\"\" Takes the array of photons per Ncapture and counts only those within the time intervall + only those that are within 200ns of the first.\n",
    "    Returns array of photons within intervall per Ncapture. \"\"\"\n",
    "    cum_index = np.cumsum(n)\n",
    "    cum_index = cum_index.astype(int)\n",
    "    # Insert 0 as the first starting index\n",
    "    start_indexes = np.insert(cum_index[:-1], 0, 0)\n",
    "    end_indexes = cum_index\n",
    "\n",
    "    # Initialize an array to store the counts\n",
    "    counts = np.zeros(len(start_indexes), dtype=int)\n",
    "\n",
    "    for i, (start, end) in enumerate(zip(start_indexes, end_indexes)):\n",
    "        # Get the times within the interval\n",
    "        times_within_interval = times[start:end]\n",
    "        if len(times_within_interval) > 0:\n",
    "            first = np.min(times_within_interval)\n",
    "            # Count the values in the time interval\n",
    "            counts[i] = np.sum(((times_within_interval > startTime) & (times_within_interval < endTime)) & (times_within_interval < (first + 200)))\n",
    "    return counts\n",
    "\n",
    "subPMMAFoil = subarray_it_up(nPMMAFoil, timePMMAFoil, no)\n",
    "\n",
    "unique, counts = np.unique(no2, return_counts=True) #Counts should have the number of captures for Each shower.\n",
    "#Now filter out all the water captures\n",
    "cum_counts = np.cumsum(counts)\n",
    "cum_counts = cum_counts.astype(int)\n",
    "counts_only_water = [np.sum(identifierPMMA2[0 if i == 0 else cum_counts[i-1]:cum_counts[i]] != 4) for i in range(len(counts))]\n",
    "\n",
    "\n",
    "\n",
    "neutrons = distinguished_neutrons(AllShowers=subPMMAFoil,mult=6, Npmt=500, window=200, timeMax=200000)\n",
    "Photons_inTime200Setup2 = Photons_in_intervall_and_200ns(nPMMAFoil, timePMMAFoil, 10000, 200000)\n",
    "\n",
    "# Check if the sum of counts matches the length of the second array\n",
    "if counts.sum() != len(Photons_inTime200Setup2):\n",
    "    raise ValueError(\"The sum of 'counts' must be equal to the length of 'second_array'.\")\n",
    "\n",
    "# Initialize the new array to store the sums\n",
    "Photons_inTimeConcenuated = np.empty_like(counts)\n",
    "\n",
    "# Index to keep track of position in the second array\n",
    "index = 0\n",
    "\n",
    "# Populate the new array\n",
    "for i, count in enumerate(counts):\n",
    "    Photons_inTimeConcenuated[i] = Photons_inTime200Setup2[index:index + count].sum()\n",
    "    index += count\n",
    "\n",
    "print(counts_only_water)\n",
    "print(np.size(neutrons))\n",
    "print(np.size(Photons_inTimeConcenuated))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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4uHxj7tq1S2PGjFHDhg21du1ah95DXpKSkjRgwAC1a9dOP/74o1JTU/PcNisrS0uXLlXv3r3Vu3dvnTx50mU5DB48WM2aNdPUqVOVnJyc7/b79u3TRx99pBtuuEHjxo1TZmamw30fP37cXD5//rweeeQRdejQQb/99ptTcZ21ceNGtWzZUrfffrvmzZuntLS0fLdPTU3VvHnz1LdvX7Vs2bJEntO239W+ffvUoUMHPfzwwy55r1FRUapTp47GjBmjnTt35rvtuXPnNH/+fEVERKhbt27avXu3U31v3bpVLVq0UK9evTR//nydP38+3+2zsrK0fv16PfLIIwoNDdWCBQuc6r+wlS5dWl26dDHbhw8f1j///ONwvJ9//tnSnjt3rsOxlixZYmn36tWrwPuePHlS9957r5o2baopU6YU6Hq4Z88evfzyywoJCdHbb7+t7Oxsu3O+GtauXasmTZpowIAB+vPPP/PNMysrS2vWrNGTTz6psLAw/fHHH071bXuex8TEKDw8XE8//bQ5YtbVsGbNGnPZ19dXLVu2LNT+qlSpIi8vL/MvJSXFrv1XrFihBg0a6L777tOKFSuUnp6e7/bp6elavny57rnnHoWFhenvv/92Jn1JUnx8vLl85MgR9enTR127dtX8+fPzzScjI0MLFizQLbfcogceeEBnz551OpeSrLDOj6ysLL399tsKDQ3VG2+8YRkR73ISExM1e/ZstW3bVnfffbeOHTvmcN9xcXHq2LGjIiIi9Ouvv17xficzM1Nr167VoEGDVLduXS1evNjhvgEAAAB3wdSQAAAAAIB8BQcHa/LkyXrwwQclXXygMnjwYG3atEk+Pj5FnF3xkpmZqaFDh2r69OnmOl9fX4WHh6tGjRoKCAhQYmKi4uPjtX37dsu+hw8fNh+CduvWzfLaJ598oqefflpZWVnmurp166pu3bqqVKmSUlNTdfjwYW3atMnyADU+Pl633nqr5s2bZymscFRCQoJ69uxpKXzx8/NTeHi4qlevLn9/fx07dkwHDhzQjh07ZBiGud2CBQvUqlUrLV++XLVq1XI4h+3bt6t3797at2+fZb2Pj4+aN2+uatWqKSAgQCdPnlRCQoJl2qy0tDRNnDhRW7du1ezZsx2aKujSw8uzZ8+qZ8+eluK9ihUrqnnz5qpUqZIZu1mzZo68TbtMnjxZI0aMsBwf0sVjr1GjRqpWrZoqVaqkEydO6PDhw/r3338tRWubN29W69atNWnSJD399NOFnu/Vcum7+vfff9W1a1cdPXrUfK1WrVoKCwtTxYoVzetY48aNrxjzwoULGjJkiGbMmJHrtbp16yo0NFTBwcFKSUnRsWPHtHnzZstD6qVLl6pdu3b67bffHCoWmTp1qiIjI5WRkWFZHxgYqKZNmyo4OFhly5ZVcnKyjh49qi1btlj6P3LkiHr37q2xY8dq4sSJdvd/tUREROi3334z24sWLVJYWJjdcS5cuKCFCxda1v3888969tlnHcpr6dKlufIsiE2bNikiIsJSlCFdPEebNGmiGjVqqFy5cjp//rxOnjyprVu36vTp05b3MXr0aP3xxx+aP3++/Pz8HMq/MPzvf//T888/n+v6ExwcrLCwMFWtWlVZWVk6ceKEYmJidOrUKXObuLg49erVS59//rmGDh3qUP+XzvM///xTvXr1shRFNWjQQPXq1VOFChXk7X3xn8Gd+f3Jy9atW83l8PDwQp9GMTIyUpGRkQ7t++qrr+qVV16x/D5LUqVKlRQeHq6KFSuqdOnSSkpK0sGDB7Vt2zbL9SY2NlYdOnTQhx9+qMcff9zh9xAfH6+2bdsqJiZGEREROnTokPlaUFCQmjZtqsqVK6tUqVI6dOiQ4uPjlZCQYIkxc+ZMbdu2TcuWLVPlypUdzqUkK4zzIykpSf379891PZSksLAwhYSEqGLFikpOTtaRI0cUHR1tuef44YcfFB0drUWLFqlOnTp2vZ+lS5eqb9++uQqgy5Qpo/DwcPMe8NJvcHR0tM6dO2dud/DgQfXo0UMvvfSSJkyYYFffAAAAgFsp6iHJAAAAAABFq6DTNPbp08fpqaPs7TOn4j415JAhQ8x14eHhxnfffWekpqZedv/9+/cbzz77bK7pBCtUqGAcOHDA3O6bb74xPDw8DElG6dKljZEjRxp79+69bMwzZ84Yn332mVGlShVLzMDAQOPgwYN2fSY5p4Y8ceKEUbduXXNd586djV9++SXPaaL27NljjBs3zihVqpQll7p16xqJiYl25XLJpk2bjKCgoFzxvvnmmzynxtq/f78xfvz4XNPnde3a1cjKyipQv7bHR926dY3s7GwjIiLCXNe7d2/jr7/+snuKRVd46aWXck2DFB4ebkRFReX5OZ88edL44osvLjul4UsvvXTFPtPS0owDBw7k+mvTpo0Zp0aNGpfd5tLfuXPnXPxJXGQ7NeQjjzxiHD9+3KhZs6Y5PdTw4cONuLg4h2KnpaUZ3bt3t3xepUuXNkaNGmXExsZedp9z584Zc+bMMRo2bGjZz9/f3/jnn3/s6v+LL74wrwWX/vr162esXLkyz2MvPT3dWLBggXHLLbfk+q4nTpxoV/9Xa2pIw8g9vWHPnj0dirNgwYJc79vLy8s4ceKEQ/Fsr60FnbJy48aNuaavbdGihTFz5sw8fx8M4+L1bujQobmmWYyIiDAyMzPz3C8zM/Oy59ykSZMscWbPnp3n+VnQqQYvd/3p16+fsXbt2steX7OysoxVq1YZt912W679vv766wL1aRjWqe9ef/11Y9euXUZgYKAhXZyK9aWXXjIOHTpU4HjOSElJseTz6KOPXpV+HTFu3DhLrp6ensbQoUONjRs35rlPamqqMXPmzFxT6np4eBjffvutXf3b3le8/vrrxp49e4zg4GBzXf/+/Y0///wzz+vZhg0bLPdZtudTfueSYRjG8ePHL3us28a5++678zwnLjeVpS3bOMVpakhXnx+nT582wsPDLX2UK1fOmDhxouXe1VZSUpLx1Vdf5ZoqvVq1anZNe7tmzZpc05G3b9/e+PXXX/O8JmZkZBgLFy40unbtmuu4ee211xz6DAAAAAB3QCEYAAAAAFzjClqUdejQIUsRjo+Pj7F169ZC7TOn4lwI9uWXX5rt0aNH51kgldOqVatyFQk89NBDhmEYxs6dO82HXiEhIQUuHDl58qTRqlUrS8w77rijQPtekrMQ7FLhk6+vrzFlypQCx4mOjrYUkEky7r33XrtyMQzDOHbsWK4Ct+eff95IT08v0P7x8fHGDTfcYNn/rbfeKtC+tsdHQECA8cEHH5ifxbRp0+x+L66S8zzy9vY23njjjQIfe2lpaca4ceNyFZrYU5BhK+cxUxRsC8H69Olj3HnnnYZ0sTBtw4YNTsUeNGiQ5XMKDw834uPjC7RvRkaG8cQTT1j2b9q0qXHhwoUC7b9161bD19fXchwuWLDArvw/++wzw8vLy3Ld2rZtW4H3v5qFYIZhWIrnypQpU+DPytYjjzyS6+G/JOOrr76yO1ZMTIwlxtChQ6+4T0pKihEaGmopnnnjjTcKXIRqGBd/I2yLZSQZn3zyid35O1pUkpevv/7aEq98+fLGkiVLCrz/tGnTLIXQ/v7+xp49ewq0r22/Tz75pNGyZUtDktG4cWOHCz0dFRsba8nnhRdeuKr9F9TChQsteVavXt1Yt25dgffPzMw0xo8fb4kREBBgHDt2rMAxbH8jHn74YaN58+aGJCMoKMhYuHBhgePMnz/fqFChgiWXZ599tsD727KN4UwBl6vi2HJFIZgrz4+srKxcRcVdu3YtcGHt+fPnjdtvv92yf0RERIH2TU1NzXUP99JLL9l1Lf3iiy8MHx8fy/V4zZo1Bd4fAAAAcCcUggEAAADANc6eoqypU6datm3evHmBi04c7dNWcS0E8/DwMIvkHBkpbfny5ZaRfjw9PY19+/YZXbp0MSQZlSpVMvbt22dXzKSkpFyjL2zZsqXA+9s+sL305+PjY/z+++/2vj1j7969uR7a/vnnnwXePzs72+jZs6dl//fee8/uPI4dO2b5THx9fY2EhIQr7pfz+ChXrpzh4eFh/Pzzz3bn4Cp79uwxAgICLMeMvaOzXPLZZ5/lerif16hz+SluhWCXCiwrV67sdHHIrFmzLJ9R+/btCzxqkq1HH33UEqego3J17tzZ3Mfb29vhh9effvqppf9evXoVeN+rXQg2cuRIS3/Lli2za/+srCxLAZXtedynTx+783n//fct+fz0009X3OeVV16x7PPOO+/Y3a9hGMbmzZuN0qVLm3EqV65s96h6riwEi4uLM/z9/c1YwcHBxr///mt3nO+++86S06233lqg/XJejyUZN9xwg3H8+HG7c3DWhg0bLPm8/fbbVz2HK8nOzjbq1Klj5hgYGGjs3r3boVijR4/OVWhUULa/EZeKUitUqGBs377d7jxWrlxpKerx9vY2duzYYXeckl4I5srz47///a8ldr9+/QpcjH9JRkZGrvu5GTNmXHG/KVOmWPZxdOS9H374wRKnRYsWDsUBAAAAijtPAQAAAABQQIMHD1aPHj3M9ubNm/XWW28VYUbFg2EYSkpKUrdu3fTyyy/bvX/nzp01ZMgQs52dna1HHnlEy5cvlyRNnz5d119/vV0xy5Urp8mTJ1vWTZ8+3e7cbE2ePNny/RdU7dq19e2331rWPfPMMzIMo0D7f/bZZ1q4cKHZHjx4sEaMGGF3HsHBwYqKijLb6enp+vDDD+2Ok5ycrJEjR+qOO+6we19Xefjhh3X27Fmz/c477+jBBx90KNZjjz2mCRMmmO2zZ8/qoYcecjrHopacnCxJioqKUt26dR2Oc+TIET322GNmu1q1avrxxx8VGBhod6z3339fN9xwg9n+8MMPlZGRke8+u3fv1ooVK8z28OHD1bZtW7v7lqTIyEi1a9fObP/+++9KSkpyKFZh69Wrl6W9aNEiu/Zfs2aNjh8/LuniNWjw4MHma4sXL9a5c+fsirdkyRJz2dfXV926dct3e8MwNHXqVLPdvHlzjRw50q4+L2nWrJmee+45s33ixAktW7bMoViuMGDAAKWkpEiSvLy8NGvWLDVs2NDuOA888IAGDBhgthctWqTt27fbFSM5OVk+Pj6aNWuWKleubHcOzkpLS7O0g4KCrnoOV7Js2TLt3bvXbL/22msKDQ11KNaECRNUq1Ytsz179uwC/5bbysrKkqenp3766SeFhYXZvf/NN99suf/MzMzU888/b3ecks5V50dMTIzGjx9vths3bqzp06fLx8fHrjje3t6KiopSxYoVzXWTJk264n6zZs0yl0uXLu3wf3v069dPQ4cONdubNm3Sv//+61AsAAAAoDijEAwAAAAAYJcpU6YoICDAbL/66qv6559/ijCj4sHT01OTJ0+Wh4eHQ/s/+eSTlvbixYslST169FDPnj0dinn77berWrVqZvv77793KI4kde3aVZGRkQ7v37NnT0vhRHR0tFatWnXF/TIyMjRx4kSzXa1aNX300UcO59GtWzd16dLFbH/11Ve5HuRfSXBwsF555RWHc3DWkiVLtHr1arPdpk0bS5GII1544QWFh4eb7dWrV2vp0qVOxSwOIiIichUU2et///ufWVQmSZ988omqVKniUKwyZcpo3LhxZvvIkSP68ccf891nzZo1lvbDDz/sUN+XPPLII+ZyVlaWpcCpOOnQoYOl2O7SNbGgfv75Z3O5U6dOat26tUqVKiXpYvHO77//XuBYmZmZ+vPPP812x44dLb+Dl3PgwAHt37/fbD/00EMO/z5I0rBhwyxte/J3pYULF2rdunVm+6mnnrJcU+316quvytPz//+J2pHr+7Bhw9SsWTOHc3BGZmampW1vYczVYPt74eHh4VShb6lSpSzXoBMnTmjTpk0OxXrqqafUqVMnh3MZPny46tSpY7YXLFig+Ph4h+OVVK44P9544w2zaNnDw0PTp09XmTJlHIpVpUoVPf3002Z748aN+vvvv/Pdx7ZYq2PHjk4VXD777LOWtr1FxgAAAIA7oBAMAAAAAGCXmjVr6p133jHb6enpGjJkiLKysoowq6LXu3dvh0ZEuaRZs2aWkYIuGTVqlMMxPT091a9fP7N96NAhHTt2zKFYb775psN5XJKzeCrnKGGXM2vWLB08eNBsjx8/3uGHj5c8/vjj5nJSUpLWr19v1/7Dhw9X2bJlncrBGe+9956l/emnnzpVYCJdHKXjs88+y7cfdzR27Fin9k9JSdEXX3xhtlu3bu30SHD33Xefypcvb7av9BA6IyNDYWFh5l/9+vWd6j9n4UVsbKxT8QqLj49PruLRU6dOFXj/uXPnmsudOnVS6dKl1bp1a3OdbaHYlaxbt84yAl9ERMQV90lKSrJ8b82bNy9wf5dTu3Zt1axZ02wX1ff27rvvmsv+/v6WwkZH1K5dW7fddpvZtrcow8PDQ6NHj3Yqh2vBpeOwffv2luuPI1xxDfHz89OLL77oVB7e3t566aWXLOtmzJjhVMySxhXnR0JCgqVg+Z577nG6sCwyMtJy33Kl89722l+pUiWn+m7cuLHlHs72HhMAAAAoKSgEAwAAAADY7dFHH9Utt9xittevX18iikacce+99zodo1WrVpZ2jRo11LlzZ6di5iw+2Lp1q90xatasqZYtWzqVhyS1b99etWvXNttz5szJNZpKTrbFSWXKlHF6NCRJuUavsR0tpSAeeOABp3NwVHx8vP744w+z3alTJzVt2tQlsdu0aaM2bdqY7d9//10JCQkuiV0Urr/+erVv396pGDNnzrRMnWhbROgoX19fS15XOv6GDh2q7du3m39+fn5O9W87SqAkc/rE4sh2NLfs7OwCj14WExNjmQrvUuGKbQHLggULrjgt5yU5+y3IKHNNmjSxfG8333xzgfrKj+13VxTfW1xcnGVKyv79+7tkOkbb+4n4+HgdOXKkwPt26NDB7qmTrzXjx483j8O//vrL6XiuuIYEBwe75Ni5++67LddECsGsXHF+TJkyxfI/e7jidzA4ONgyJeiVfgdtp5J0xX3J+vXrFR0drejoaD3zzDNOxwMAAACKGwrBAAAAAAB28/Dw0JQpUyz/R/3LL7+sXbt2FWFWRat69epOx7jxxhst7S5dulimzHJFzB07dtgdw9kcbN15553mcmJiomW6n5xSUlIso3VFREQ4PRqYdHE0CduRdTZv3mzX/rZTUV1ty5cvl2EYZvvRRx91aXzb6T8Nw9Dy5ctdGv9qql27ttMjpdkWvfj4+Oj22293Ni1JsoymEhsbq3PnzrkkbkHkLCSznfayuOnZs6ddo8ZcYjvaV61atcwC1K5du5rrk5KStGLFigLFs50mNTQ01OlR2Rxl+90Vxfdmez5IFwvBXCHn6EL2XJOL8np8rSpO15CyZctaRpT7999/LcW71zpXnB+2532VKlXUsWNHp2NK1vP+Sue8bdHYunXrFBcX51TfjRo1UtOmTdW0aVPL/SAAAABQUlAIBgAAAABwSJ06dTRx4kSznZaWpiFDhig7O7sIs3Jv1113naXtilG4qlatamknJiY6HdMZtiO/SNKWLVvy3HbNmjWWEcPatWvnsjxspxY6efKky+IWtpyjufTo0cOl8Xv27Jlvf9ealStXmssNGzZUUFCQS+LmnNqqKI9B28LC4qZatWqWEe8WL15coP1sp4W86667zOUOHTpYRiEqyPSQ586d099//222CzIt5NVQFN+b7fkgue6aXJzOB9ivqK8hOe8rHBn5FJd37tw5bdy40Wy3bt3aZf9zgO15f/r06XyPI9tRd7OzszVo0CCdP3/eJXkAAAAAJRGFYAAAAAAAhz311FOWKc7WrFmjyZMnF2FG7s3f39/SDgwMdDpmQECApX327FmnYzrDdlQHSYqOjs5zW9viC0lq3Lixy/Kw/WyLujjOHmvXrjWX69WrZ5kuyRWCg4MtI4isWbPGpfHdyf79+y1T1BXW8Se51zF4tdkWXh04cEA7d+7Md/uEhARLgWm/fv3MZS8vL0th2C+//HLFIpaVK1dappAsyLSQJZXtNbl69eoqX768S+JyPsAZOa/N+RWYwz6bNm2yFOQX1u9gdnZ2viPLDRw40NL3mjVr1K5dO23YsMFl+QAAAAAliXdRJwAAAAAAcF+enp6aOnWqwsPDlZaWJkkaN26c+vTpo9DQ0CLOzv3kLNpyhZzFZWfOnHF5H/YICQmRv7+/UlJSJEnx8fF5bnvs2DFL+9Zbby2UnIr6M7HH4cOHzeXw8PBC6SM8PFx79+6VJEsh1LUm5/E3Y8YMzZgxo1D6ctUxmJ6eruTkZF24cMEl8YqDXr166Y033jDbixcvVoMGDfLc3nY0sOrVq6tt27aW1++++259/vnnki6eT+vXr1fr1q3zjGc7LWSZMmXUuXNnO9/BlWVmZurMmTNKTU3NtzCtqL9X23Pi8OHDTk+9mhd3uiaXNGlpaUpOTrYUP+Z09OjRq5jRleUsTsrvvgL2yfk7OHHiRMtowK505syZPEfd9PHx0ezZs9WtWzfzPmjr1q266aab1KVLFw0aNEh9+/Z12aidAAAAgLujEAwAAAAA4JQbbrhBr776qkaNGiVJOn/+vIYOHarly5cX2kPiksrLy6vQY+b3cPdq8PDwUOXKlc1CsPwe+J86deqq5OQu05lmZGRYRnRz9Whgl4t75swZZWZmytv72vsnpKt1/EmOHYO7du3S3LlztXnzZkVHR+vAgQNmQW5J0rp1a1WsWNH8PhYtWqThw4fnub3tdI933nlnrt+hzp07q1KlSub0g3Pnzs23EGzJkiXmcteuXVWqVCmH3sclJ06c0E8//aT169crOjpacXFx5vWwOEtLS7tqU7G5yzVZyv0bm56eXkSZ2G/Tpk1asGCBoqOjFR0drSNHjrhV/pdUqlRJnp6e5nFDIaHrFKffwYYNG2r16tUaMmSIli9fbq5fvny5li9fLh8fH910003q1KmTOnXqpA4dOqhMmTKFnTYAAABQLDE1JAAAAADAaSNGjNBNN91ktleuXKlPPvmkCDNCcWY78ll+UwGdPn36aqTjNnJ+Hq6ali2nnAVm1+r3UFzf94wZM3TTTTepQYMGGjNmjGbPnq24uLgSWQQmXRx5skePHmZ7xYoVeRa0njx5UqtXrzbbttNCXuLl5aU777zTbNsWjuV0/Phxbdu2zWzbTlNpr3Xr1qlPnz6qXr26IiMjNXXqVEVHR7tFEZhUfM+Hola6dGlLOykpqWgSKaDMzEx98sknCgsLU8uWLfXyyy9r7ty52rdvn1sWgV1iO/ppfvcVsE9xO+9r1aqlZcuW6fvvv1fLli0tr2VkZGj16tWaOHGievTooaCgIHXs2FHvvvuuDhw4UEQZAwAAAEXj2vvfOQEAAAAALufl5aWoqCg1a9bMfJA4ZswY9erVS7Vq1Sra5FDs2BaC5Tdyh+1D6Ro1amjt2rWFko+7jHaVmppqaTs7MlFechY2XK1RgIqbnEURs2fPzjXNoKtUrlz5itv8888/GjZsWKGdB8VZr169zGk5U1JStHbtWt188825tps3b56ysrIkXRwl6HLbSBenh5wyZYqkiyOr7dy587LTTS5btswyVWOvXr3szj0xMVHPPPOMvv3223ynfSzucp4Pzz33nEaMGFEofQUGBhZK3MJQrlw5S7s4F4KtWrVKjz32mP7999+iTsXlAgMDzfsJRgRznZzn/dq1a1WjRo1C6atatWoF3vaee+7RPffco3Xr1un777/XTz/9pH379lm2ycjI0KpVq7Rq1SqNGjVKt9xyi0aPHq3u3bu7OnUAAACg2HGPf+kEAAAAABR7jRo10vjx4/Xiiy9KuviwftiwYVq8eHERZ4bixtPz/wcoz68wwnbKraysrEJ7+Ogu/Pz8LO3CGgEqZ8HZtTq1Us4p38qVK1dkx+DixYvVv3//XAUOAQEB6t+/vzp16qQWLVqoSpUqCgoKko+PT77x3G3a3h49elimflu8ePFli7xsR/e644478pxut0uXLpbpJn/++WeNHTs213a200I2btxYNWvWtCvvhIQERUREaMeOHZb1Xl5euu2223TbbbepVatWuv766xUUFJTrHM+pc+fOWrlypV05uErOz7J06dLX/DVZkqpXr25pX82p9Ozx7bffasiQIblG06tUqZLuu+8+tW/fXk2bNlXlypUVFBSU71TVCQkJql27dmGn7DB3LrgsbnIeBxUqVChW533r1q3VunVrTZo0SXFxcfrrr7/0119/afny5ZbCMMMwtHTpUi1dulQdOnTQlClTLlv8CwAAAJQUTA0JAAAAAHCZ0aNHq1mzZmZ7yZIl5qgrwCW2xSz5jfwSFBRkLhfnUVaulgoVKljaiYmJhdJPzrg5+71W2B5/UtEdg3///bd69eplOW/8/f311ltv6ejRo5o6daoGDhyoxo0bq3LlylcsAnNHFStWVOvWrc32okWLcm1z7tw5S+HW5aaFvMTb21t33HGH2Z47d+5lt1u6dKm5bO9oYImJierUqZOlCMzDw0NDhw7VgQMHNH/+fD311FNq3bq1qlWrdsUisKJWXM6H4sbf31+VKlUy29HR0YXeZ0pKihISEsy/zMzMfLf/8ccf9dBDD1mKwCpXrqwpU6bo8OHD+vDDD3XfffepQYMGqlixYr5FYMVVQe8rYB93Ou/r1aunIUOGKCoqSgkJCdq7d68mTZpkmbpeujgyXtOmTTVz5swiyhQAAAAofBSCAQAAAABcxtvbW1FRUZZChOeff14HDx50SXxfX19L+0oPP1E8nT171lzOOa2WrYoVK5rLqampunDhQqHmVdz5+PhYptU8ffp0ofRjO6JNYGCg20yd6Wq2x59UeIV3+UlJSdGDDz5oKeBo1KiRtm3bplGjRl1To7XZFmJt3Lgx1/excOFCc5S8oKAgde3aNd94d999t7m8YcMGHTp0yPL6nj17lJCQYLYjIiLsyjcyMlL79+8324GBgfrjjz/05Zdf2jUFWnEREBBg+W0vivOhuGrSpIm5vHXr1kL/rYqKilLt2rXNv+PHj+e57aFDhzRs2DDLuo4dO2rHjh165JFHSkThqGEYSklJMdv53VcUFtvPsSTdmxaH30FH1a5dW88995zWrVun1atXq2PHjuZrFy5c0IABAzRnzpwizBAAAAAoPBSCAQAAAABcKjw8XGPGjDHbZ86cyfUQ0lG2RTDSxRFg4F4Mw7A8tM452oQt21FWJGnnzp2FlZbbsC0giYmJKZQ+tm7dai5XrVq1UPpwB8Xh+Pvmm2+0d+9es12nTh2tWrVKtWrVuuq5FDXbQqzs7GwtW7bM8rrttJB9+vS5YoHLLbfcovLly0u6eF365ZdfLK/bji4WFBSkdu3aFTjXHTt2aPbs2Wa7VKlSWrJkibp3717gGMWR7TnB9fj/tW3b1lxOT0/Xpk2bCrW/kydPWtr+/v55bjt58mRL8U6bNm20ePHiXAU+7uzo0aPmtLFS/vcVhcX2/rQk3ZsWh99BV2jXrp1WrFhhmQI4Oztbjz/+eL6FlAAAAIC7ohAMAAAAAOByL774oho3bmy2f//9d3399ddOx8053Y/tCBBwD3v37rV8b/Xq1ctz2/DwcEv777//LrS83EWbNm3M5bi4OJePCnby5Ent2bPnsv1da0JDQy0FFkVx/E2bNs3S/uabb8zipWtNs2bNLIWQttNDZmRk6LfffjPb+U0LeYmPj49lekjbQjLJOi3krbfeatfIeDm/t/Hjx6tVq1YF3r+4sr0mb9u2TefPny/CbIqPHj16WNrfffddofZ35MgRc7lcuXJ5ToVoGIa++eYbs+3j46OZM2eqVKlShZrf1WZbvCzlf19RWGy/g5J0b1qS7sM8PT01ceJEDR482Fx36tQpffXVV0WYFQAAAFA4KAQDAAAAALicr6+voqKi5OXlZa577rnndPjwYafiVqhQwdI+cOCAU/Fw9eV8YNuyZcs8t+3UqZOlvWLFisJIya3YTm1kGIalGMYVcsaz7e9a4+Xlpfbt25vt6OhoJScnX9UcbM+Xli1b2jUqVUnUs2dPc3n58uXm8qpVq5SUlCTp4uhIOQtz8mI7PeSKFSssBRy21xvbaSkLYsuWLeayr6+vIiMj7dq/uLK9JmdlZemvv/4qwmyKj3bt2ql69epm+7vvvivUIrk///zTXA4LC8tzu2PHjlmKxm6//fYSOZpgdHS0pd2iRYurnoPt/WlJujetUqWK6tevb7b//PNPy+hr7ujdd9+1TDU/d+7coksGAAAAKCQUggEAAAAACkXLli01cuRIs52UlOT0w/CAgABdf/31ZnvHjh1OxbukJI3eUNz9+uuvlnZ+D2yrVq1qeQA5d+5cyxRX16IuXbpY2lOmTHFp/M8//9zS7ty5s0vjuxvb93/hwgXNmDHjqvV98uRJpaWlmW1XjSi1f/9+l8QpCrYFWbYj4tlOE9mrVy+VLl26QPG6detmTiOXmZmpNWvWSJJ27dqlEydOSLo4ioxtAVpBHDx40FwODQ3NVcTsCMMwLHGLQs7rwdSpU4smkWLGy8tLDz/8sNlOTk52ySiol3Pw4EHt2rXLbOcsmM65ra2Seg2xHc3P29s71yhWV4PtKLh79uxRRkaG0zGLy72p7Xl/+PBh/fHHH1e1/5UrV2rFihVasWKFS6amrFChgjp06GC24+PjnY4JAAAAFDcUggEAAAAACs2ECRMshTy//vqr01MmNW3a1FzeunWr0w/bkpOTtXDhQqdilHQnT550SQFWenq6ZeSF+vXrKzQ0NN99Bg0aZC6npaVd84UHoaGh6tatm9levny5/vnnH5fE3rJli2WkmW7duqlu3bouie2uHnzwQcuUgJ999pkMwyiSXMqVK+eSOO+//75L4hSF7t27y8fHx2yvX79eknV0sIJMC3mJj4+Pbr/9drO9cuVKSdLq1avNdS1btlTlypUdztlV39u8efMs07bay9PT+s/AjhzHrVu3VoMGDcz23LlzLSNOXcueeeYZSwHiuHHjdPToUZf388EHH1ja/fv3L/C+xekakp6e7oJMLhZdbdiwwWx36dKlwIWgkuTh4WEuO3Ntt703zczMtIwK6KiZM2c6HcMVbO/DJOmTTz65qv33799fXbp0UZcuXfSf//zHJTGDg4PN5bNnz7okJgAAAFCcUAgGAAAAACg0pUuX1tSpUy0PoJ955hkdP37c4ZitW7c2l5OTky0jwTji/fffL9QpnEqCc+fO6a233nI6zhdffGFO3yZJgwcPvuI+kZGR8vf3N9uvv/56oTxcdycjRowwlw3D0BNPPOF0cVJ2drYef/zxPPu5VtWsWVP33nuv2Y6Jick1alphqVChguXaeejQIadjRkdH6+OPP3Y6TlEJCAiwjOSyfv16nTt3ziwI8/PzU0REhF0xbaeHvFQIuWrVKnOdvdNCSrIUjrniezt79qyeffZZp2LYXkeli9d1e3l4eFhG+kxPT9dzzz3nVF4lRdWqVfXMM8+Y7aSkJA0fPtylfRw+fNhy/rZt21bNmzfPc/ucBYyuOBbnz5/vkqn0jhw5otmzZzsdZ+LEiZZ2Qe4rbNmeF46cE5fY3ptK0k8//eRwLOni1LSXRigsam3atLFMkzx//nz9/vvvV61/2+lMXTEimCTt27fPXK5WrZpLYgIAAADFCYVgAAAAAIBC1a5dOz399NNm+9SpU7ke3Nnj/vvvt4zg4ExRxr///qu3337b4f2vJe+9957WrVvn8P7JycmaMGGC2fbx8bFMpZWXoKAgS4FSUlKSnnrqKZeMynTs2DGnYxSF2267zTLF159//un0CB2TJk3S33//bbZbtmyp2267zamYJcWoUaPk5eVltseOHasDBw44HfdKx5+np6caNWpktpcsWaKsrCyH+zt8+LDuvvvuXKMoZmdnOxyzKNgWZq1fv16rV68231OPHj1UtmxZu+J1797dHClp/fr1SktLs4wIZm9hmWSdJu7AgQNOTWOckZGhAQMGKCEhwbLe3u+tfPnylrajx/BDDz2kmjVrmu3vv/8+15S/jkhJSXGqEKc4eOmllyxFKz/88IMmTZrkktipqam66667lJqaaq670r1UjRo1LKOAOTul3z///HPZQitHryFPP/20U4XdW7ZssUzBWaFCBd155512xbA9L5y5rrdt29by3X/zzTcO/08G586d05NPPulwLoXhhRdesLSfeOIJJScnOx23IPdhffr0MZd3795tuVdxxJ49eywxmjRp4lQ8AAAAoDiiEAwAAAAAUOjeeOMNyxSAzjw8ql27tm6++Waz/fPPPzv0cPPw4cPq27evUlNTXTZdUkmWmZmpvn37KjY21u59MzIydM899+jkyZPmupEjRxZ4FIYJEyYoLCzMbP/4449OjUKTmZmpp59+Wm3atHHL0eA8PDz0zTffqEyZMua6Z555RnPmzHEo3rfffqtRo0aZbT8/P33zzTeWgstrWZMmTfTyyy+b7aSkJHXr1s2pkQ3/+OMPNWzYUL/99lu+2916663m8pEjR/Tll1861N+OHTvUpUsX7dmzR9dff71lpDF3mxbLtjBr/fr1WrFihdm2Z1rIS3x9fdW3b19JF0e4+vXXX83rXJUqVdSiRQu7Y9p+b5L06quv2h1Duvhbedddd2nevHny8fHRddddZ75m7/fWsGFDS9u22M0epUqV0vTp0y3H0H333edwPOliYUbbtm0txcLuqGzZspo5c6Zl+tLnn3/e6akUk5KS1K9fP0sxdmRkpDp37pzvfl5eXuratavZXr9+vcPFYGvWrFHXrl118uRJS8GT5Pg15NixY+rTp49lpFB79r3rrrssRWivvfaaXdNCStbzIiYmxuH34uHhoYEDB5rtQ4cO6bXXXrM7Tnp6uh588EH9+++/xereNCIiQkOGDDHb8fHx6tWrl1PFm9OmTVPdunUVExOT73YDBw60fK+PPfaYpSDSHunp6RoyZIjlfya4//77HYoFAAAAFGcUggEAAAAACl2ZMmX05Zdfuqyw5NVXX7XEuvfee+0arWrt2rVq06aN9uzZo6CgII0bN84leZVUffr0UUhIiI4fP662bdtesXjFVlJSku6//34tWrTIXFerVi2NHz++wDH8/Pw0a9Ysy4PADz74QEOHDrW7qHDJkiVq166dPvzwQyUkJDj0oLY4qF+/vqW4ICsrSw8++KDefvttZWZmFihGZmamJk6cqMGDB1seir733ntq0KCBy3N2Z+PGjbMUoMbGxqpTp07asGGDXXEOHTqkJ598UhEREUpMTNTw4cOVlpaW5/aPPfaYZTSy5557zlL4dCXp6en63//+p1atWik2NlalSpXS999/r4CAAHMbV4zqcjU1bNhQtWvXliSdOHFCU6ZMkXSxoMt25Bh72E4PaXtt6tmzp0O/W7fddpuZoyTNmjXL7tEn58+fr2bNmmn+/PmSpLffflstW7Y0Xz9z5oxd8apWraqQkBCz/fPPP2v//v12xbikc+fOGjt2rNk+f/68IiIi9N1339kV5/z583r33XfVsmVLbd++Xe+//77++ecfh3IqLtq0aaMpU6aYx41hGBoxYoQefPBBSzF0QS1evFjh4eFauHChua5jx4764IMPCrT/E088YWkPGDDArs84JSVF48ePV6dOnXTs2DFVqFBBP/74o2UbR64hgYGBCgwM1MaNG9WuXTu7coqLi1OPHj0UHx9vrmvVqpUiIyPtzqNNmzbmclpamr744gu7Y1wyfPhwBQcHm+233npLn376aYH3P3jwoHr06KFffvlFkvTOO+84nEthmDx5surXr2+2V69ere7du9v9PwjExsbq/vvv16BBg5SSkqLHH38831FeQ0JCLNflmJgYdezYUXFxcXb1e+LECfXq1cucAliSwsPDddddd9kVBwAAAHAHFIIBAAAAAK6Kzp07O/SQ7nJuvvlmy3SBycnJuvnmm/XCCy/kOc1Qdna2Vq5cqQcffFDt27fXgQMH5OXlpRkzZhR4ZKprVZMmTTRnzhyVLVtWp0+fVq9evdSnTx/98ccfeU5Vd/r0aU2ZMkVhYWGWh8alSpXSt99+Kz8/P7tyaNy4sWbNmmUZaWXq1Klq2LChJk+erMOHD+e5b3x8vD799FN17NhR3bt3txTvHDlyxK48ipNHH31U//nPf8x2RkaGRo8erVatWmnatGl5PpxPTk7WtGnT1KJFC40bN85SOPbcc89Zzi1c5OnpqR9++MEyVePOnTvVpk0bRUZG6u+//87zQXZKSormzZunYcOGKTQ0VJ988ok5ik16erpOnDiRZ7833HCDhg0bZrZTU1PVvXt3jR07Nt8pteLi4vT666+rfv36eu6553Tu3DmVKlVKP/30k9q0aaOgoCBzW3crBJOso4JdKq7p2rWrwyPo3HrrrQoMDJR08Xu9xHYaSnv4+PjkmrZv9OjR6tevn7Zt25bnfpcK29q2bas+ffqYhS4vvviinn32Wcv3lp2dbffoRQ888IC5fP78efXt21d79uyxK8YlEyZM0H333We2z5w5owEDBujWW2/Vr7/+qgsXLlx2v6ysLK1evVrjxo1TnTp19J///MccEcrT01MHDx50KJ/iZODAgfr0008tRZwzZsxQaGioRo8efcWip/j4eH311Vdq0aKFbr31VkvBXvfu3fXbb7/J19e3QLl07drVMs3vyZMn1bp1a7399tt5nvuGYWjr1q0aO3asQkND9dprrykzM1NBQUFatGiRmjZtaimQdOQaUr58eU2fPl1eXl7asWOHmjZtqscee0wbN27Mc5+9e/fqlVdeUXh4uLZu3WquDwoKyjVKXUHZnhPSxaLfGTNm2B1HkipWrKiPPvrIbBuGoSeeeEL9+/fP9zvfsWOHxowZo4YNG5qFvmPHjlXPnj0dyqOwlC1bVvPnz1f16tXNdWvXrlV4eLhGjx6d77Xt9OnT+v7773X//ferUaNGmjVrlvna2bNnr3gt+89//mMp2Nq0aZMaNWqkgQMHaunSpfkWwO/fv19vvPGG6tevryVLlpjrAwICNG3aNMt9JQAAAFBSeBj5/e8WAAAAAIAS7+uvv9bgwYPNdlRUlAYNGlQofaWkpKhx48bat2+fZb0jfV64cEH33HOP5s2bZ1nv4eGhZs2aKSQkRIGBgTp79qyOHj2qrVu3Wqaw8fX11fTp03Xvvffq22+/1UMPPWS+VpD/VM75uS1fvvyK0zRdyYoVK9SlSxez7arvwvaB7cCBA/X1119fcZ/OnTtr5cqVkqQvvvhCw4YN04oVK3TnnXdapnEqW7asmjRpouuuu84sFDt8+LBiYmKUkZFhiVm6dGnNnj3b4VF7pIujo9x7771KTEy0rPf09FSjRo103XXXKTg4WCkpKTp+/LgOHTqkhISEXHE8PT314osv6uWXX77iw+NBgwZp2rRpZru4/VPKO++8ozFjxlimyJIuHuNhYWGqVq2aKlasqNOnT+vIkSPavn270tPTLdt6eHjo9ddf1wsvvOBwHrbHTEhIyGU/98JWq1Yt8/rSqVMnu0bPKoiTJ0+qf//+5vu0Vb16ddWtW1fVqlWTp6enjh8/ruPHj2vXrl25Pm9Jat++vb7//nvLdH+Xc+HCBXXp0kVr1661rPf09NSNN96ounXryt/fX+fOndOpU6cUExOjU6dOWbatUaOGfvzxR910002SpA4dOphT+ZUvX16nT5++4nsvrOuTI3777bdcRVpffvmlhg4d6nDMAQMGWEa08vb21qlTp8wCMUeMHDlSkyZNyrW+Vq1aatKkicqXL68LFy4oKSlJO3bsyPXbWLp0aX300Ufm+3rxxRf1xhtvmK9v2bJF4eHhBc7n2LFjCgsLsxwfPj4+6tChg66//nqdP39ee/fu1VNPPVWg7zYrK0sjRozQ5MmTc70WGBioRo0aqVq1avL399eJEyd0/Phx7dmz57KFQzVq1NDMmTPVoUOHK/bryG9aUVi4cKEGDhx42WLPqlWrKiwsTFWqVJGPj4+SkpKUmJio2NjYyxa0e3t7a8KECRozZozdBU+nT59W69attXv3bst6Hx8f836pbNmyOnPmjI4fP66YmJhcI841atRIc+fOVb169SRd/L4OHTokSWratKmio6OvmMflfiO+//57DRw40FI4WKlSJd14440KDg6Wr6+vjh8/rv3792vHjh25YgYFBWnBggVq166dXZ+JrZznvnRx1M0WLVrIw8NDR48eVZkyZXLdb+blv//972V/S+vWrasGDRqoYsWKSk1NNa/XOY+PZ599VpMmTdKhQ4dUs2ZNc31B7zML+/yIj49X3759tX379lyv1apVS7Vr11a1atWUmZmp48eP69ixY4qNjb3s/zhwxx13KCoqylLkmpeMjAwNGjTosoV6ZcqUUVhYmGrWrKmAgAClp6crMTFR//zzjw4cOJBr+woVKmjhwoXmbyIAAABQ4hgAAAAAgGtaVFSUIcn8i4qKKtT+Fi1aZOnPmT4zMjKMp556yvD09MwVM7+/Bg0aGOvWrTPjfPPNN5bXCyLn57Z8+XKH3oOt5cuXF8p3YRtz4MCBBdqnU6dO5j5//PGHuT42Ntbo2LGjXZ/3pc9848aNLnk/+/fvN/r06WN3Dpf+OnXqZGzevLnA/Q0cONDu4+NqW7t2rREeHu7Q5xEWFmb8+eefTudge8yEhIQ4/6YcEBISYvmeC0NmZqbx1ltvGf7+/g593lWrVjU+/fRTIysrq8B9nj171rj//vvt7svb29t44oknjKSkJEu8YcOGWbbbv3//FXMorOuTI86fP2/4+fmZuXh5eRknTpxwKubcuXMt769z585O55mdnW289dZbhpeXl93fXffu3Y3Y2FhLvO+++86yzfTp0+3O6ffff7d8dpf7Gzx4sF0x582bZzn37Pnz8/MzRo4caSQnJxe4P0d+04rKsWPHjCFDhjh0DEgyPDw8jD59+hgbNmxwOo+uXbs69P2MHz/eSEtLs8Tr3r27uU3p0qWNCxcuXDGHvH4jNm3aZDRt2tTu3Fq2bGns2rXLqc/FMAwjMTHRaN68eb59lSpVykhPTy9wzC+//NIIDAy06/0EBQUZ06ZNM2McOHDA8npB7zOvxvlx/vx54/nnnzd8fX0dOq5DQ0ON2bNnO9T3jBkzjEqVKjnUrySjX79+xqFDh1z8iQAAAADFC1NDAgAAAACuqu7duzs1aostb29vffjhh9q8ebP69eunsmXL5rmth4eH2rRpo6lTpyomJoZRAOwQEhJiLterV08rV67UL7/8ou7du19xSp2mTZvq888/V0xMjFq0aOGSfGrWrKl58+Zp3bp1GjhwYIGmgwsODtbQoUO1Zs0arVixQs2aNXNJLsVFmzZtFB0drblz56p3794qXbp0vtv7+vqqR48emj17tmJiYtSxY8erlKn78/Ly0qhRo5SQkKDXX39dDRo0uOI+pUqV0i233KKpU6cqPj5ekZGRdo3q4+/vrxkzZmjRokW69dZb5e3tne/2tWrV0ogRI7R79259/PHHuc6RJk2aWNqXG+GsOPPz87OMTtapUydVqlTJqZg9evRQQECA2badftJRHh4eGjVqlLZt26YhQ4bI398/3+2DgoI0YMAArVmzRosWLTJHX7rEFd9bjx499Ndff6l9+/Z5bpPftKOX06dPH8XGxmrq1Knq2LGjZUrEy/Hw8FB4eLgmTpyo+Ph4vfvuu06NvFacBQcH66uvvlJsbKzGjh2b6zu9HE9PTzVr1kwjR47Uzp07NW/ePLVs2dLpPBYtWqQ5c+aobdu2llGjLqdhw4Z6+eWXtW/fPk2YMEGlSpWyvG57LKalpWn9+vUO59a8eXNt2rRJM2fOVKdOnfI9fi7dy3377bdat26dbrjhBof7vSQoKEgrV67U008/neeUmz4+PpcdWSovQ4cOVVxcnIYPH66qVavmu23dunU1YcIE7dmzRw8//LB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      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins = 100\n",
    "plt.scatter(counts_only_water, neutrons)\n",
    "\n",
    "plt.xlabel('Number of real water Captures')\n",
    "plt.ylabel('Number of measured Captures')\n",
    "plt.title('Event identification')\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.vlines(x=50, ymin=0, ymax=800, color = lps.colors[\"darkblue\"], label=\"50 Neutron Threshold\")\n",
    "plt.hlines(y=15, xmin=0, xmax=5000, color = lps.colors[\"red\"], label=\"15 Neutrons cut\")\n",
    "plt.grid()\n",
    "plt.legend(loc= \"upper left\")\n",
    "\n",
    "#plt.savefig(\"/home/eric/sim/WWLegend/Plots/EventIdentification/MeasuredNeutrons.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do same but on y axis integral light"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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kMvL5fAqHwwoEAm3uuB8KhRSJRAq6d2sjJ4vJas2lrgsAAAAAAKCUKrIR7Mknn3S6BBxl5cqVecfnnHOOrdc/55xzco1gK1asaHXNvHnztGbNGknSqlWrdMEFF3T6Pl/72teOOff3v/9dl19+eaevBfdZtmyZJGnChAkOVwIAACoduQIAyk8sFrO0LhqNFrURzDRNJRIJxWIxpdNp1dbWKhQKyTCMNhu32uPxeGQYhgzDsPweK2Ml29PWyMliKmQUZiUjUwAAADuQKQAAcI+KbARD+dm2bVvecU1Nja3XHzRoUJv3atHY2GjrPYGjbd682ekSAACAS5ArAKD8pNNpS+symUzRashms62Op4xEIvL7/aqvr5fX6y3a/Vu0jJVsa1TmZZddpkOHDmnJkiXHfK69kZPF1FHNTtVVbGQKAABgBzIFAADuQSMYbJHNZvOOBwwYYOv1TzzxxNzr9957T83NzceMBZ01a5ZmzZrV6Ws/+eSTuuKKKyRJ//d//6ePfvSjhZQKF5s2bZrTJQAAAJcgVwBA+amtrbW0zufzFeX+pmm22cQkHR5LGQwGlUgkStLM1NFYSdM0NWfOHD3yyCPKZrPyer2aPn26vvnNb6pHjx5Fr68rNbutCUwiUwAAAHuQKQAAcA8awWCLHTt25B337dvX1uv369cv7zibzRbtB68AAAAAALiV3WMHK0lHzx4KhRSJRDq8TjgcLkp9iUSiw1GMqVRKyWSyU2MeC9HWWMnWdi7bsmWL7rjjDv3tb38r2c5lrenKKEwAAAAAAAC3oBEMtmhubi7p/d57772S3g+QpKamJkn2jz4FAADdD7kCgBPKZeygE6w8u2EY8vv97TZjBQIBBQIBbd/xru01xmIxS+ui0aijTU7ltnNZd0emAAAAdiBTAADgHq5sBDt48KDWrl2rrVu3aufOndq5c6cOHDhg6z1uuOEGW68Hd2sJ0J21detWmytBIZ599llJbJEMAAAKR64AUGrduXmnM89eX1/f5tpAIKBYLFa0P590Om1pXSaTKcr9rSrHncu6MzIFAACwA5kCAAD3cE0j2Ouvv67f/OY3SiaTWrVqlfbt21fU+9EIhs4YPHiw0yXABmPHjnW6BAAA4BLkCgCl1p2bdzr77IlEQslkUtFoVJlMRj6fT+FwWIFAoKhNcrW1tZbW+Xy+otVgRaXsXNZdkCkAAIAdyBQAALhHD6cLKNSGDRv04Q9/WKNHj9acOXP00ksvqbm5WaZp2v4hKfe/gNNWrFihhoaG3FjOnTt3qqGhQatXr86tWbJkiRYtWpQ73rRpkxoaGrRp06bcuUWLFmnJkiW549WrV6uhoUE7d+6UdHjsZ0NDg1asWJFbs2zZMjU0NOSOm5qa1NDQoHXr1uXOJZNJJZPJ3PG6devU0NCQtztaQ0ODli1bVjHPtH79eq1fv95Vz+TGvyeeiWfimXgmnolnqoRnkqSVK1e66pnc+PfEM/FMbnqmWCyms88+W3fffbfOPvvs3JpvfetbmjFjRu746aefrphnkqz9PS1ZskR333137pe0+vTpo7vvvltTp07Nrbnuuuv0+uuvS5I8Ho/GjRunKVOm6Kc//akefvhhGYaRaxZrTyHPFAqFJKnDv6dwOOzo196AAQN09913q0+fPpIO//Lb3XffLb/fn1szY8YMnX766bljt/3zVE7PNHLkSI0cOdJVz9SCZ+KZeCaeiWfimXim0j3T1q1bNXLkSFc9kxv/nngmnoln4pl4psp5JidVdCPYwoULNWbMGMXj8VyDVjF/M5MmMAAAAAAAUImsjh1s+aGXm7zzzjuW1h04cKDIlbTPMIy8ZqrWBAIBBQKBElXUun79+llad/zxxxe5EgAAAAAAABzNY1Zod1MikdBHP/pR7du3L9f8ZZqm+vfvr8svv1znnHOOTj31VPXr10/9+/dXjx729rxddtlltl6vlKZPn6758+fnju34Epg1a5a+973v5Y7Xr1+vESNGFHzdUlz/ySef1BVXXCFJ+r//+z999KMfteW6R7KzQbGxsVE1NTW2XQ/WtXTwOv1DdwAAUPnIFQBKra6uTpFIpMN1M2fO1MMPP1yCikqnGM++LbtL4yffnndu+eL7NNBrrUmqLdlsVsFgsNVRloFAQLFYTF6vt6B7FCoej2vSpEmW1jEasvjIFAAAwA5kCgAA7NPU1JTbmb5FKfs8qkpyF5u9++67+vSnP51rAjNNU4MHD9a9996ra6+9Vr169XK6RFSQyy+/vOi7vTU2NnbpfVu3btWZZ55pczUAAAAAgO4mFApZaoYKh8MlqKa0KunZvV6vEomEksmkotGoMpmMfD6fwuGwAoFAUXfCt6pl57LWmtValMPOZQAAAAAAAN1RRTaCzZkzR42Njbkffk2YMEFPPPGEBg0a5HBlQOvYwcsd+CE2AACwC7kCQKl15+adSnt2j8cjwzDKdjctj8ej+vr6DncuK4emte6gXL5uAQBAZSNTAADgHvbOSyyR//7v/87tBObz+WgCAwAAAAAAaEdL847f72/1825u3unOz14sLTuXxeNxzZw5U1OnTtXMmTMVj8cVj8cdH18JAAAAAADQXVXcjmBr1qzRhg0b5PF45PF49N3vfpcmMAAlsW7dOknSyJEjHa4EAABUOnIFACdUwtjBYunOz14s5b5zWXdBpgAAAHYgUwAA4B4V1wj26quvSpJM09Txxx+vT3/60w5XBKC7WLlypSS+EQIAAIUjVwBwSndu3unOzw73IlMAAAA7kCkAAHCPimsEa2pqknT4h3fnn3++evfu7XBFALqLiy++2OkSAACAS5ArAACAHcgUAADADmQKAADco+IawXbs2JF7PWzYMOcKAWw2e/Zs3XPPPXnnTNN0qBq0pqamxukSAACAS5ArAACAHcgUAADADmQKAADco4fTBXRWnz59Wn0NZ/Xs2bOo1z+6IeqEE04o6v2csH//fu3ZsyfvY+/evU6XBQAAAACAKxw6dEj33nuvRo8erSFDhmj06NG69957dejQIadLAwAAAAAAAGxRcTuC1dbW5l4fuTsYnHXiiSfmHb/77ru2Xn/Xrl3t3s8Nqqurjxl1apomzWBlpKGhQZI0bdo0R+sAAACVj1wBAPYxTVOJREKxWEzpdFq1tbUKhUIyDEMej0eStH79ep1//vnKZrO59zU2Nupb3/qWfvjDH+qll17SKaec4tQjAF1GpgAAAHYgUwAA4B4V1wg2bty43OsNGzY4WAmOdHRjlt1Neu+8807udc+ePV25I9idd96pO++8M+9cU1OTBg8e7FBFONrQoUOdLgEAALgEuQIA7JHNZhUMBpVKpfLORyIR+f1+1dfXa8CAAcc0gR19jfPPP19bt25Vjx4Vt3k+ujkyBQAAsAOZAgAA96i4RrBRo0bp5JNP1ltvvaVVq1Zp79696tWrl9NldXsDBw7MO25sbLT1+kde733ve5+t1wasmjBhgtMlAAAAlyBXAEDhTNNstQmsRSqVUjAYlGEYbTaBtchms/rRj36k2267rRilAkVDpgAAAHYgUwAA4B4V+WuO119/vSTp4MGDWrhwocPVQJLGjh2bd/zyyy/bev1//OMfbd4LAAAAAAB0P4lEos0msBapVEoPPvigpev99re/taMsAAAAAAAAwDEV2Qj29a9/XX379pUk3X///Q5XA0ny+XwaPnx47vjFF1+07dqNjY16++23c8cf/OAHbbs20BkrVqzQihUrnC4DAAC4ALkCAAoXi8UsrWtqarK0rqNdw4rBNE3F43HV1dVp6tSpqqurUzwel2maJa8FlYlMAQAA7ECmAADAPSqyEWzQoEH6wQ9+INM09corr+hnP/uZ0yVB0oUXXph7/dRTT2n79u22XPcPf/hD3vGHPvQhW64LdNb69eu1fv16p8sAAAAuQK4AgMKl02lL64477jhL67xebyHldFo2m5VhGJo0aZIikYieeOIJRSIRTZo0ydI4S0AiUwAAAHuQKQAAcI+KbASTpC984Qu67rrrZJqmvvnNb2rRokVOl9TthUKh3Ovm5mYtWLDAluseOZph4MCBuuKKK2y5LtBZkydP1uTJk50uAwAAuAC5AgAKV1tba2ndmWeeaWndjTfeWEg5nWKapoLBYJujLVOplILBIDuDoUNkCgAAYAcyBQAA7lGxjWCSNH/+fF1zzTU6cOCAPv7xjysSiThdUrf2sY99TMOGDcsd//jHP9aePXsKumY8HtcLL7yQO77xxht1wgknFHRNoKt69uypnj17Ol0GAABwAXIFgGLqLuMGj/yFtPbcfffdHe725fV6deutt9pRliWJRKLNJrAWqVRKyWSyRBWhUpEpAACAHcgUAAC4R0U3glVXVysWi+nuu++Wx+PRzTffrMmTJ2vlypVOl9YtVVVV6fOf/3zueMOGDfrud7/b5es1NzfrC1/4Qu74uOOO00033VRQjeVs9uzZ6tOnT97H8OHDnS4LR9i5c6d27tzpdBkAAMAFyBUAiqWjcYPbt293TZOYYRjy+/3trgkEArryyiv10ksvtdkM5vV69dJLL6lHj9L9mCwWi1laF41Gi1wJKh2ZAgAA2IFMAQCAe1Q5deOOflDXWUOGDNHGjRv1t7/9TX/729903nnnqV+/frbeo4XH4+E3MtvwjW98QwsWLNBrr70m6fCuYGeddZamT5/eqescPHhQ119/vd54443cudtvv12nnnqqneWWlf379xe8gxqKq+W3tadNm+ZsIQAAoOKRKwAUg5Vxg6eeeqqy2Wze+UgkIr/fr/r6+g53zionHo9H9fX1bT5zIBBQLBaTx+PRKaecoq1bt+pHP/qRfvvb3yqbzcrr9erGG2/Urbfe2ukmsNYa55566il9/OqPyOPxdPj+dDpt6T6ZTKZTdaH7IVMAAAA7kCkAAHAPxxrBnnzySUs/GOsMj8eT+0Hcyy+/bOu1W5imaXvdbtKzZ0/Nnz9fF154oQ4cOCDTNDVjxgxls1l95StfsfSD1W3btqmurk4LFy7MnRszZkxBu4tVgurqavXu3TvvnGma2rt3r0MV4WijRo1yugQAAOAS5AoAxWBl3ODRTWAtUqmUgsGgEolERf3cw+v1KpFIKJlMKhqNKpPJyOfzKRwOKxAI5D1Ljx49dNttt+m2224r6J7ZbFbB4LWS8n9ZLRQK6fJLL7LUUFdbW2vpXj6fr6tlopsgUwAAADuQKQAAcA+P6dDe/z169KioHyy2aGkEO3jwoNOldNn06dM1f/783HExvgTmzp2rm2++Oe/c+eefrzvuuEOTJ09Wnz59jnnP5s2bFYvFNHv2bDU1NeXO19TUaMmSJd0yhDY1NWnw4MF55xobG1VTU+NQRQAAAACAclVXV6dIJFLQNeLxuAzDsKki9zFNU4Zh6Mmnn9PJ59+Y97m3XvqtDh14T36/v8OGung8rkmTJnV4P/4+AAAAAAAAKovTfR6O7Qh28sknV2QjGKy56aabVFVVpVtuuUX79++XJL300ksKhULq1auXxo4dK5/Pp/79+6upqUmbNm3SqlWrjmlKGz58uP785z93yyYwAAAAAAA6w+q4wfZEo1Eaj9rRsutaj6oT2lyTSqWUTCbb/XM0DEMTJ07UkiVL2lxz6aWXKhAIdFiTaZpKJBKKxWJKp9Oqra1VKBSSYRj87A0AAAAAAKCbcawR7M0333Tq1iiRGTNmaNy4cZoxY4ZWrlyZO79371698MILHb7/+uuv189//nMNHDiwmGUClrX8gH7ixIkOVwIAACoduQJAMVgdN9ieTCZjQyXuFYvFLK2z0lBnR5PW4TGVwWNGgkYiEfn9fktjKlHZyBQAAMAOZAoAANyjh9MFwN3Gjx+vV155RbFYTJMmTVJVVfu9hwMGDNCNN96ol19+WQsWLKAJDGVl165d2rVrl9NlAAAAFyBXACiGUChU8DV8Pp8NlbiX1V3XOmqoSyQSevrpp9td8/TTTyuZTLb5edM0W20Ca5FKpRQMBo/ZfR3uQqYAAAB2IFMAAOAeju0IBuc88sgjeuSRR0p2vx49eigYDCoYDGr37t1avny53njjDW3btk379u1T79695fP5dOaZZ2rs2LE67rjjSlYb0BlTpkxxugQAAOAS5AoAxWAYhvx+f5uNQVaEw+GC63DzqEKru6511FBnx85iLWMq22NlTCUqG5kCAADYgUwBAIB70AiGkurTp48uu+wyXXbZZU6XAgAAAACAq3g8HtXX17e5S5TX61U2m23z/YFAQIFAoKAa3D6qMBQKKRKJdLiuo4Y6O3YWs3NMJQAAAAAAANyB0ZAAYNGmTZu0adMmp8sAAAAuQK4AUCxer1eJRELxeFwzZ87U1KlTNXPmTMXjcf3rX/+S3+9v9X2BQECxWKygHbu6w6jCll3X2mOloc6OncXsGlOJykamAAAAdiBTAADgHuwIBgAWvfjii5KkYcOGOVwJAACodOQKoHJU4phDj8cjwzBa3QUqkUgomUwqGo0qk8nI5/MpHA4rEAgU/DzdYVRhy65rH7/mWm3Yc+znrTbU2bGzmF1jKlHZyBQAAMAOZAoAANzDY1byr2ECLjJ79mzdc889eedM09TevXvzzjU2NqqmpqaUpeH/1/LbMHwjBAAACkWuACpDW2MOJblizKHd6urqLDU3zZw5Uw8//HAJKiqerdt36oIpd+Sd+8m3PqJpH5tiqaHONE0ZhtFu41wgEFA8Hm/zevF4XJMmTerwXvF4vGIb79AxMgUAALADmQIAAPs0NTVp8ODBeedK2efBaEigTOzfv1979uzJ+zi6CQzOGjZsGN8EAQAAW5ArgPLXHcYc2q07jSpsrTnr0ksvPea8aZqKx+Oqq6vT1KlTVVdXp3g8Lkmqr68vaFSnXWMqUdnIFAAAwA5kCgAA3IPRkECZqK6uVu/evfPOtbYjGAAAAACg+LrDmEO7MaowX1s7ykUikdyOcoWM6mwZU9lWw6LVMZUAAAAAAABwj4ocDfn973/f6RJUXV2t973vfRo4cKBOPvlknX/++TruuOOcLgsu4/SWgci3aNEiSdKUKVMcrgQAAFQ6cgVQ/rrTmEO7dKdRhduyuzR+8u1555Yvvk8Dvf0kWRv96Pf7lUgkCm7UMk2zy81kqHxkCgAAYAcyBQAA9nG6z6MidwSbNWtW2f0gq3fv3rrooov0uc99TsFgsOzqA1C4fv36OV0CAABwCXIFUP6605hDu7SMKmyv+am7jCos5Y5yHo9HhmFUfHMduoZMAQAA7ECmAADAPSqyEUw6/NuObbGjCevo67d1zZZ1u3fvViKRUCKR0Omnn66f/exnln4LFkDlmDhxotMlAAAAlyBXAOWPMYedx6jCf4vFYpbWRaNRGrhQEDIFAACwA5kCAAD3qMhGsB/+8IdKp9NKp9PavHmz0um01q1bp0OHDklqv0msM1p+MGmapuVrmqaptWvX6iMf+Yjuvvtu3X777R2/CQAAAACATjJNU4lEQrFYTOl0WrW1tQqFQjIMo1s02hRbKBSyNBoyHA6XoJrK4fV6lUgkuv2oQnaUAwAAAAAAgBMqDqej0wABAABJREFUshHs1ltvzb1ubm7Wrbfeql/96lcyTVOjRo3SPffco/POO6/L13/nnXf0/e9/XwsXLpTH49Hw4cM1f/58DR8+PLfmwIEDymaz2rZtm15++WU988wzisfjOnDggDwejw4ePKhvf/vbOnTokL71rW8V9LwAysPq1aslSaNHj3a4EgAAUOnIFShUNpttddelSCQiv9+v+vp6eb1eh6pzB8Ycdp0doworvdGRHeVQKmQKAABgBzIFAADuUZGNYC127typqVOn6plnnpFpmvryl7+sH/7whzr++OMLvnZ9fb1+97vfqa6uTm+99ZY++clP6u9//7tOP/30Y9ZeddVVkqS3335bs2fP1ty5c+XxeGSapv7zP/9TH/zgB+X3+wuuCYCz1q5dK4lvhAAAQOHIFSiEaZptjt6TpFQqpWAwqEQiURENM+WKMYfOKVajYymby9hRDqVCpgAAAHYgUwAA4B4e0645iiW2f/9+XXnllXr66afl8Xj04x//WF/96ldtv088HtfUqVO1b98+DR8+XC+//HKHP2xsaGjQddddp/3798s0TZ1xxhl67bXXbK8N7tfU1KTBgwfnnWtsbFRNTY1DFXVvO3fulCT179/f4UoAAEClI1egEPF4XJMmTbK0rpAdmXCYaZrdfsxhKZmmKcMw2t2Jze/3d7rRsa3mspbrdba5bFt2l8ZPvj3v3PLF92mgt58ka88RCAQUj8f5OkJByBQAAMAOZAoAAOzjdJ9Hj5LcpQjuuuuuXBPYN77xjaI0gUnSlVdeqR/96EeSpLfeeku33HJLh++ZNm2aHnjgAbX02K1du1YNDQ1FqQ9A6fTv359vggAAgC3IFShELBaztC4ajRa5ku6hZczhww8/rD/96U96+OGHK2Y8YSVKJBLtNk9Jh3e9SyaTlq9pdRc9O39XsmVHubZ2iGdHOdilVJnCNE3F43HV1dVp6tSpqqurUzwet/WfGwAA4Bx+TgEAgHtUZCPY66+/rh//+MfyeDw644wzdM899xT1fl/84hd1/vnnyzRNxWIxPf/88x2+53Of+5wuuuii3LGVcQAAyltzc7Oam5udLgMAALgAuQKFSKfTltZlMpkiVwLYrxiNjsVoLrPC6/UqkUgoHo9r5syZmjp1qmbOnKl4PK54PN6l8ZbA0UqRKbLZrAzD0KRJkxSJRPTEE08oEolo0qRJMgxD2Wy2qPcHAADFx88pAABwj4psBJszZ44OHjwoSbrttttUVVVV9Hveeuutudc///nPLb3nm9/8pqTDvzH3zDPP6NChQ0WpDUBpLF68WIsXL3a6DAAA4ALkChSitrbW0jqfz9fle7DzC5xSjEZHJ3fRY0c5FFuxM4UTO+oBAIDS4+cUAAC4R/E7qGx24MAB/f73v5ckVVVVKRwOl+S+V199tY4//njt27dPTzzxhPbv36/q6up233PllVeqqqpKBw4c0LvvvqtXXnlF559/fknqReWZPXv2Mbvb8UO08nLKKac4XQIAAHAJcgUKEQqFLO063dXvl7PZbKv/0T8Sicjv96u+vr7LOxmZpqlEIqFYLKZ0Oq3a2lqFQiEaY5BTjEZHdtGDmxU7U3RmRz3DMIpaCwAAKB5+TgEAgHtU3I5g//jHP7Rjxw55PB6NGTNGvXr1Ksl9TzjhBJ199tmSpD179ujll1/u8D29e/fW2LFjc8fr168vWn2ofPv379eePXvyPvbu3et0WTjCuHHjNG7cOKfLAPD/sXfvcU7Vd/7H32cAEXCQiDMmoCB4K4qgLeiqxUvOEQuVlv01SbGtly7O9Ndat65t11r01+622PpzrXW9rDK6altXTaJSq1LNxQviBawXVgW0BRUhYQYYud85vz/4TZaRmSEznOTknLyejwePJplvct6Rgb6Z+cz3CwA+QK/AgbAsS+FwuMs1pmnKNM1uv3Ypd37haDEUIxqNFrWuO4OO5dhFD3BLqTuFmzvqAQCA8uHrFAAA+IfnBsHefffdwu3hw4eX9dpHH3104fbixYuLes7eX2xcu3at05HgI3369FH//v3b/SrXoCMAAAAA7zAMQ8lkstNhMNM0lUgkerTDVnd2fukOjhZDsUox6FiK4TKgWrCjHgAAAAAA3uK5QbDVq1cXbg8cOLCs166trS3cbmlpKeo5hx12WOE2P92MrsyYMUObNm1q9+ujjz5yOxb2Mn/+fM2fP9/tGAAAwAfoFThQgUBA6XRaqVRKDQ0NmjJlihoaGpRKpZRKpXp8dGOpdn4p1YAZ/KcUg46l3EUPcFupOwU76gEAUB34OgUAAP7R2+0A3bV169bC7b2Hwsph3bp1hdvbtm0r6jm7d+8u3K6p8dzcHYC9rFy50u0IAADAJ+gVcIJhGLIsS5ZlOfaapdr5pTsDZk6+H3hT26BjJpNRPB5XPp9XMBhULBaTaZrd3u2ubbi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",
      "text/plain": [
       "<Figure size 2400x1350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins = 100\n",
    "plt.scatter(counts_only_water, Photons_inTimeConcenuated)\n",
    "\n",
    "plt.xlabel('Number of real water Captures')\n",
    "plt.ylabel('Integral light')\n",
    "plt.title('Event identification')\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.vlines(x=50, ymin=0, ymax=6e5, color = lps.colors[\"darkblue\"], label=\"50 Neutron Threshold\")\n",
    "plt.hlines(y=120*15, xmin=0, xmax=5000, color = lps.colors[\"red\"], label=\"rough cut\")\n",
    "plt.grid()\n",
    "plt.legend(loc= \"upper left\")\n",
    "\n",
    "#\n",
    "#plt.savefig(\"/home/eric/sim/WWLegend/Plots/EventIdentification/IntegralLight.png\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}