{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from __future__ import division\n",
    "import numpy\n",
    "import scipy\n",
    "import scipy.io as sio\n",
    "from scipy import stats\n",
    "from scipy.optimize import curve_fit\n",
    "from os import listdir\n",
    "import pandas as pd\n",
    "\n",
    "#These are all just bringing in the code modules that we might use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#defining functions to be used later\n",
    "\n",
    "width=4.5\n",
    "\n",
    "def gauss(x, *p):\n",
    "    A, mu, sigma = p\n",
    "    return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))\n",
    "\n",
    "def PlotHist(XD,pltitle='Histogram',nbins=80,plabels=None,xlab=None,plxlim=None,plylim=None,alph=.99, cmap=plt.cm.viridis, \n",
    "             vlx=None,vlabel=None,savname=None):\n",
    "    if len(numpy.shape(XD))==1:\n",
    "        if len(numpy.shape(XD[0])) == 0:   # XD is expected to be an array of Samples, if it is not, then convert to one\n",
    "            print \"Making Data into array of one sample (array of arrays)\"\n",
    "            XD=[XD] \n",
    "    \n",
    "    rows=1\n",
    "    cols=1\n",
    "    scalefac=1/.8\n",
    "    \n",
    "    try: colormap = cmap\n",
    "    except: plt.cm.cool\n",
    "    colors=[colormap(i) for i in numpy.linspace(0, 0.9, len(XD))]\n",
    "    if len(XD) ==1 :\n",
    "        colors=[colormap(i) for i in numpy.linspace(.3, 0.4, len(XD))]        \n",
    "    if len(XD) ==2 :\n",
    "        colors=[colormap(i) for i in numpy.linspace(.25, 0.6, len(XD))]        \n",
    "\n",
    "    f1, axarr = plt.subplots(rows,cols, sharey=False,figsize=(width*cols*scalefac, width*rows*scalefac*(width/6)))\n",
    "    txlm=[min(XD[0]),max(XD[0])]\n",
    "    for ii in range(len(XD)):\n",
    "            thtr=XD[ii]\n",
    "            xlm=[min(thtr), max(thtr)]\n",
    "            if txlm[0] > xlm[0]:\n",
    "                txlm[0]=xlm[0]\n",
    "            if txlm[1] < xlm[1]:\n",
    "                txlm[1]=xlm[1]\n",
    "            if plxlim:\n",
    "                xlm=plxlim\n",
    "            if plabels:\n",
    "                y_val,x_val,_ = axarr.hist(thtr , nbins, range=(xlm[0],xlm[1]), \n",
    "                   color=colors[ii], edgecolor='none',\n",
    "                   alpha=alph-.15*(ii)/len(XD) ,label=plabels[ii] )\n",
    "            else:\n",
    "                y_val,x_val,_ = axarr.hist(thtr , nbins, range=(xlm[0],xlm[1]), \n",
    "                   color=colors[ii], edgecolor='none',\n",
    "                   alpha=alph-.15*(ii)/len(XD) )\n",
    "\n",
    "    if vlx:\n",
    "        axarr.axvline(vlx,color='m',label=vlabel)\n",
    "    axarr.set_xscale('linear')\n",
    "    axarr.set_yscale('linear')\n",
    "    axarr.set_title(pltitle)\n",
    "    axarr.grid(lw=0.1, which='minor', dashes=[4,1])\n",
    "    axarr.grid(lw=0.2, which='major', dashes=[4,1])\n",
    "    if xlab:\n",
    "        axarr.set_xlabel(xlab)  #+'({0:.3e} to {1:.3e})'.format(txlm[0],txlm[1]))\n",
    "    if plxlim:\n",
    "        txlm=plxlim\n",
    "    if plylim:\n",
    "        axarr.set_ylim(plylim[0],plylim[1])\n",
    "    axarr.set_xlim(txlm[0],txlm[1])\n",
    "    if vlabel or plabels:\n",
    "        if len(plabels) > 5 : \n",
    "            axarr.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "        else:\n",
    "            axarr.legend(loc='best')\n",
    "    f1.tight_layout()\n",
    "    if savname:\n",
    "        f1.savefig(savname, dpi=144, facecolor='none')\n",
    "        \n",
    "    return (y_val,x_val)\n",
    "        \n",
    "        \n",
    "def Hist2D(XDAT, YDAT, pltitle='True Position Ba Cryo', plxlabel='True Position X', plylabel='True Position Y', \n",
    "           nbins=100, plxlim=None,plylim=None,\n",
    "           plxscl='linear',plyscl='linear', colmap=plt.cm.viridis,\n",
    "           doProfile=False,profcol='red',\n",
    "           savname=None):\n",
    "\n",
    "    rows=1\n",
    "    cols=1\n",
    "    scalefac=1/.8\n",
    "\n",
    "    try: colormap = colmap\n",
    "    except: colormap = plt.cm.cool_r\n",
    "\n",
    "    if not plxlim:\n",
    "        plxlim=[min(XDAT), max(XDAT)]\n",
    "    if not plylim:\n",
    "        plylim=[min(YDAT), max(YDAT)]\n",
    "\n",
    "    f2, axarr = plt.subplots(rows,cols, sharey=False,figsize=(width*cols*scalefac, width*rows*scalefac*(width/7)))\n",
    "    if plyscl=='log':\n",
    "        TYDAT=numpy.log10(YDAT)\n",
    "        tplylim=[numpy.log10(plylim[0]),numpy.log10(plylim[1])]\n",
    "        plylabel=plylabel+' log scale [$10^x$]'\n",
    "    else: \n",
    "        TYDAT=YDAT\n",
    "        tplylim=plylim\n",
    "    h1=axarr.hist2d( XDAT, TYDAT,\n",
    "                     bins=nbins, norm=mpl.colors.LogNorm(), \n",
    "                     alpha=0.95, cmap=colormap,\n",
    "                     range=numpy.array([(plxlim[0], plxlim[1]),(tplylim[0], tplylim[1])]) )\n",
    "\n",
    "    f2.colorbar(h1[3],ax=axarr,label='Events per bin \\n({0:.2e},{1:.2e} bin width)'.format( (plxlim[1]-plxlim[0])/nbins, (tplylim[1]-tplylim[0])/nbins ))\n",
    "\n",
    "    axarr.set_xscale(plxscl)\n",
    "    axarr.set_yscale('linear')\n",
    "    axarr.set_title(pltitle)\n",
    "    axarr.grid(lw=0.1, which='minor', dashes=[4,1])\n",
    "    axarr.grid(lw=0.2, which='major', dashes=[4,1])\n",
    "    axarr.set_ylabel(plylabel)\n",
    "    axarr.set_xlabel(plxlabel)\n",
    "\n",
    "    if doProfile:\n",
    "        Profile(XDAT, TYDAT, nbins, plxlim[0] , plxlim[1], axarr, color=profcol, showxer=True  )\n",
    "    axarr.set_ylim(tplylim)\n",
    "\n",
    "    f2.tight_layout()\n",
    "    if savname:\n",
    "            f2.savefig(savname, dpi=144, facecolor='none', bbox_inches='tight')\n",
    "\n",
    "def mat_to_pandas(mat):\n",
    "    \"\"\"read in struct mat file and return pandas dataframe\n",
    "    (Thanks to dunovank on github:\n",
    "    https://gist.github.com/dunovank/a80f82ad9111d9bb03ed8f641eb49014)\n",
    "    \"\"\"\n",
    "\n",
    "    restruct = {k:v for k, v in mat.items() if k[0] != '_'}\n",
    "    if 'D' in restruct.keys():\n",
    "        struct_data = restruct[\"D\"]\n",
    "        data_dict = {n: struct_data[n][0, 0].flatten() for n in struct_data.dtype.names}\n",
    "    else:\n",
    "        mat = restruct\n",
    "        data_dict = {k: numpy.hstack(mat[k]) if len(mat[k])>0 else mat[k] for k in numpy.sort(mat.keys())}\n",
    "\n",
    "    #data_dict = {k: np.hstack(mat[k]) if len(mat[k])>0 else mat[k] for k in np.sort(mat.keys())}\n",
    "    idx=numpy.arange(len(data_dict[data_dict.keys()[0]]))\n",
    "    datadf = pd.DataFrame(data_dict, index=idx)\n",
    "\n",
    "    return datadf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#setting up dataframe from data using .txt files\n",
    "\n",
    "#file to be read\n",
    "filename='PATH/TO/FILE'\n",
    "\n",
    "#If your output file doesn't start with a header, use these lines (and comment out the one below)\n",
    "colnames = ['EV', 'DT', 'TS', 'P', 'Type', 'E1',\n",
    "            'D3', 'YLD3', 'PX3', 'PY3', 'PZ3', 'X3', 'Y3', 'Z3', 'T3',\n",
    "            'PX1', 'PY1', 'PZ1', 'X1', 'Y1', 'Z1', 'T1']\n",
    "superPD = pd.read_csv(filename, delimiter='\\t', names=colnames, usecols=['EV','DT','D3','X3','Y3'])\n",
    "\n",
    "#If your output file does start with a header, use these lines (and comment out the above lines)\n",
    "#superPD = pd.read_csv(filename, delimiter='\\t', skiprows=2, header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "369294.196311\n",
      "CPU times: user 8 ms, sys: 2 ms, total: 10 ms\n",
      "Wall time: 5.78 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#The above line will print out the time it took for this cell to be executed (prints after the other things)\n",
    "SumEVs = superPD.groupby('EV').sum()['D3']     #orders the dataframe by event number and sums up the deposited energy\n",
    "print(SumEVs.max())                #says what the maximum deposited energy was"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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Soew+s89OnnOb3fO4e84+O3mOWaeXGZ85cybW5atBERERkdRRgyIiIiKpowZF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\nIqmjBkVERERSRw2KiIiIpM7/BYtkzI10yq5+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba41206e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_min = 0\n",
    "plot_max = 400\n",
    "\n",
    "(counts1, bins1) = PlotHist([SumEVs/1e3],nbins=80,\n",
    "                   plxlim=[plot_min,plot_max],\n",
    "                   xlab='Recoil Energy [keV] ',\n",
    "                   pltitle='True Recoil Energy'\n",
    "                   )\n",
    "\n",
    "#max1 = counts1.max()\n",
    "#max2 = counts2.max()\n",
    "#print (\"Maximum counts: 1st detector-{}, second detector-{}\".format(max1,max2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K/twpFckIG/KFjfzqIyLyFuBPwWrYt4xWNj10R1jMKPEcX79QUvis32ftYPrL\nSb3nS4J97vu4JAk/LZMsrZRycytuQckWRl/yq8QInmZGieesWrUKgIULF5ZZSTx81u+zdjD95cT3\nJcE+931cNML0TTXvEqxpF6cluFAzSjzHp4i8ufBZv8/awfSXE5+XA4PffR+XZIQN+ap5+iYdEalX\n1f78JcNjRolhGIZh5CGpEmFJ8PgwSoDtIvIgcA9wN3C/ao4dYCNgRolhGIZh5MEtCQ67+mbcGCXH\nAq8DjsYFJp0gIv8kMFJU9S9RG6zeiS/DMAzDKBKpOCVhj/GAqv5NVb+nqscBU4A3AO3AmcCf47Rp\nIyWe09zcXG4JBeGzfp+1g+kvJ7XUlltCQfjc93HRCD4lOk58SgBE5EDcSEnqmAjcjpvOiYwZJZ4z\nffr0cksoCJ/1+6wdTH85mZhoKLeEgvC57+OSJEJE13FilIhIJ9CAM0DuBs4HHtUCPKHNKPGc9vZ2\nANra2sqsJJsjbv9e3jL9G7cB8NjHLy61nKJTyX0fBtMfnigBy8IEE0vXXgnB0KLi+7MTB43gUzKO\ndgneigsvPyM49scZKb1xGzSfEsMwDMPIg/mUZKOqh+GMke/jpm2+BzwnIveLyNI4bdpIiWEYhmHk\nweKU5EZVtwO3ich9wP3AW4H3AkcCZ0dtz4wSwzAMw8hDXWJiaF+gusTEEqupDETk/7LXwfXFQBfw\nN+CLuGXBkTGjxDAMwzDyMDA0SN9QuOClA0ODJVZTMVwF3IvbJfgeVX2s0AbNKPGcKVOmlFtCQdQ0\n1ZdbQmx873vTXz581g7+649DFF+RceRTUvRlWGaUeE5LS0u5JRTEhMn+Lo30ve9Nf/nwWTv4rz8O\nquGNjXG4NVDRMKPEc1avXg34uzRvYPPz5ZYQG9/73vSXD5+1g//642BxSsYGM0oMwzAMIw+2+mZs\nMKOkiqi0IEwPnXxW3jKpIEyVpr3S8L1/fNdfSj2Vdq1R8f3ehkUj+JSMo+BpRceMEsMwDMPIgzm6\njg1mlBiGYRhGHszRNRsRaQK+ChwDTCcjSryqLojaphklnuO7F7zP+n3WDqa/nPisHfzXHwcbKcnJ\nNcDrgV8Am4CCzTEzSjzH9y3Efdbvs3Yw/eXEZ+3gv/44KBLaV0THj6PrCcBJqnpfsRo0o8Rz1q5d\nC/i7NC+l30eqpe9N/9jjs3bwX38cNMLqm3FklDyPCy1fNELvEiwid4vIAcU8uWEYhmH4gO0SnJNv\nAN8Wkca5lZEyAAAgAElEQVRiNRhlpOQF4FER+YKq/qxYAgzDMAyj0lENv9R3vDi64jbeWwg8KyJr\ngV3pmaq6KGqDoUdKVPWtwKeBC0RkmYjMiHqyYiEinxKRNSLSJyJ/F5Ej8pQ/WkRWiEi/iDwjIh/I\nyH+xiPw2aDMpIp8t7RUYhmEYPqERRknGUZySW4GLgYuA3wJ/yDgiE8mnRFV/ISJ3AlcDj4vItcDu\njDL5I2YVgIi8G9cJHwceBM4A7hCRA1X1uRzl5wG3A1cCpwDHAteIyEZV/UtQrBFYBdwCXFpK/aXE\n16BE4Ld2KH0AKd/7J6r+MP25M9kNwKREs/f94zOluLeFtF8qNIKxMV6MElX9VrHbjOPouhl4ADgR\neAPDjZKxGLQ6A7haVW8AEJHTgJOADwMX5Ch/OrBaVc8MXj8tIq8J2vkLgKr+E/hn0N75pZVfXKZO\nnVpuCQXhs36ftYP/+utkYrklxMb3vvddfxyqee8bEakjd5yR9WOtJZJRIiIHAj8H5gBvVtU/lUTV\nyOevBRYD30ulqaoGozdHjVDtlcCdGWl34PGISDqNjUXzLyoLPuv3WTv4r7/G48WDvve97/rj4HxK\nwpf1ARF5EXAt8KrMLNwgQ02OOl3Agar6nIg8zyiDEaoaOaBN6He1iHweZwz8HjhRVcuxvet+uE56\nNiP9WeCgEerMGKF8s4hMVNWB4kocWzZs2AD4uzTPZ/0+awf/9fdpDwCTxL+YGb73ve/641ClS4Kv\nx812nEz44GdnADuCvz9fbEFRfmqcDXxAVX9TbBE+s2HDhmGBhKZMmUJLS8uerb3TaWlpobm5OWds\njqlTp9LY2LjnzZ7OtGnTqKuro7OzMyuvp6eHRCKxZ2O7dGbNmkUymWTz5s1Zea2trQwODrJ169as\nvDlz5tDb28u2bduy8ubNm0d3dzddXdlL0xcsWEBXVxfbt2/Pymtra2PLli10d3cPS1+3bh0HHHAA\nmzZtoqenZ1heTU0N8+fPp7Ozk76+vmF5dXV1zJ07l46ODvr7+4fl1dfXM3v2bNavX8/g4OCwvIaG\nBlpbW1mzZg1DQ0PD8pqampg5cyarVq1CM37qNDc3M3369GH9vG7dOsDdc1WlR3eQSZ1MZAK19OrO\nYent7e2x7/mMGTNIJBJs3LgxKy/KPU/ph7G95yLCwoULR73nfclehoa7q5GghsZEE33JHoYYoje5\nt087OjrG5J6nKPR9nt73KcbinqdT6D3fsSP7eQ9zz1O+QHvyEJoSk0e850Co93mu91ExqU/U0VhT\nH6psMlFXUi1F5DBgsao+FbaCqv4819/FIopR8lJVzRxxGGueA4aA/TPS98f5uuRi8wjlu4sxSrJ0\n6VImT56853VtbS1LlizhyCOPLLRpwzAMIwfLli1j+fLlNDQ00NfXRzKZzGkkFZO+oUFkd7ivjL6h\nwfyFKoMncDMQsRGRGuBtwCFpbf5BVXePXGuU9jJ/HVQ6IvJ34B+q+rngtQDrgctV9cIc5b8PnKCq\nh6al3QRMUdUTc5RfA1yqqpfn0bEIWLFixQoWLYq8FLtopH7F+TqM6rP+dO0+riio5L6v9tU3ldz3\nYShEf6neKytXrmTx4sXgfvmvjCxsBFKf9S+67CM0ts0MVae3fRP/+/mfFV1LsRGRNwLfBc4CHiM7\nzkh3rnpp9V8C3IZzk3g6SD4Q2IrzO308qiYfPcUuAa4XkRXsXRLciJsbQ0TOA2apaioWyVXAp4JV\nNdfidjN8B271EEGdWuDFOOeeOqBVRA4FdqrqqrG4KMMwDKNyqUZHV/YuArkrI31ER9cMrgH+Dfyf\nlJ+piOyL+z7+CdkOtHnxzihR1VtEZD/g27hpmH8Bx6tqatJ0Bm51UKr8WhE5Cbfa5rNAB/ARVU1f\nkTMLeJi9Tj5fCo57gDeW8HIKZtq0aeWWUBA+6/dZO/ivf6KEm9+vRHzve9/1xyNKUDRvHF3fUGD9\nw0gzSABU9XkRORt4KE6D3hklAKp6JS4YWq68D+VIuxe3lHik9tYRIbptJVFX541DVU581p+u3bfp\nA6jsvg/Tnynn54aGBu+mz8ay70vRN4Xoj9r3R9z+vfyFgJ72bAfhYlKNwdNU9Z4Cm3gGNzjw74z0\n6UC2h3gIYhslIjKX3MFWHozbphGdlKe+r3PTPuv3WTuY/nLis3bwX38comy0V8kb8onIy4HHVTUZ\n/D0iqvpojvrpa/C/BlwuIucCfw/SXgl8E/hKHH2RjRIRWQz8EngR2WNUYeagDMMwDMMrqsin5F84\nN4ctwd9K7vmmkb7PtzM8nongtmjRtNcAy0aoPypxRkp+CjwFvJfwwVYMwzAMw18i7BJc4d+K83Gr\nY1J/R6VQP5RRiWOUHAS8S1VjzRcZhmEYhm9oBEfXSo7oGvhQZv0doX6hfiijEscoWQHMI6YTi2EY\nhmH4hhJ+AKSyB0qGIyIHAZ9hb/CzJ4ErVPXpkWuVjjhGyYXAxUE8kFzBVp4phjAjHDNmzCi3hILw\nWb/P2sH0lxOftYP/+uNQjatvROTtwM3AP4EHguRXAo+LyHtU9XdjrSmOUfKH4P+byHZ2MUfXMSaR\n8HIl8x581u+zdjD95cRn7eC//lhU51DJBcB5qvrN9EQR+VaQ54VRckj+IsZYkdqgy9eleT7r91k7\nmP5y4rN28F9/HKpxpASYCeQKHHMj8OUx1gLEMErKNc9UDfgW4KlQou5fEgXf+yYq4+3Zicp4u94o\nVFrfRH2WH4qy903umJrFIcKSYI9GSu4GXku2j+hrgP8ZczXEDJ4WBE5Ld4x5AucYU9q9ow3DMAyj\nDFTL6hsReUvay9uA84P4Y+nBz94JnJOnnY/iDJq7VfU6EXk3cC4wEfiFqo5afyTiBE97I/BHXHjZ\n+4Lk44BPi8iJqnp3HCGGYRiGUbEoUB1xSm7NkfbJ4EjnR7gNbbMQkc/jdhe+A1gqIrNwm+NeivMr\n/aKIdKrqT6KKizNScgHwY1X9QobIS3Arc46I0aZhGIZhVCzVEtFVVYvhpfwJ4OOqepOIHA48CJym\nqj8DEJFO4HTcTsGRiGOUvBQ4JUf6VYEIYwyZNWtWuSUURL00lltCbHzve9NfPnzWDv7rj0NjopaG\nmnAbEdYkakuspuwcAPwNQFUfFpEh9k7/ANwDXBSn4ThGyTbgJbjpm3ReGuQZY0gymSy3hAKp4J8U\nefC9701/+fBZO/ivPw69Q7sY2j0YquzA0K78hfymF2hKe70V2JlRJpbPapxhnGuBn4rI50TkiOD4\nPG6Y5to4Ioz4bN68mc2bN5dbRmz6tY9+7Su3jFj43vemv3z4rB381x+H1JLgsEeV8xSwZ4dhVZ2T\nEbL+YGBtnIbjWDLn4KykbwAtQdo2nD/JhXFEGIZhGEbF4+/AbrH5CtAzSv5c4Oo4DceJU5IEzgPO\nE5FpQdrW0WsZhmEYhr9UW/A0EZmA8w+9Q1WfjVJXVe/Lkx87YEysOZ+0E5sxEoFKC2JUasJcb3u7\ni9kzniJDxmG8PTuGP5Q6sF/Y9rcPldilscrCzKvqbhG5igKjtIvIPkBqM6TNqvpCIe2FMkpE5H7g\nRFXdLiIPMEqXq+qrChFkGIZhGJWHBEfYsl7wIHAYsC5fwUyC4GlfAA7KSH8auDi1PDgqYUdK7gEG\n0/72wA4cH7S2tpZbQkH4rN9n7WD6y4nP2sF//bGospGSgCuBS0RkDrCCDD8RVX00VyUR+TIueuvl\nuABqqemf/XHBVH8gIvuqauRlwaGMElX9WtrfX416EqN0DA46W7GhoaHMSuLhs36ftYPpLyc+awf/\n9ceiOo2Sm4P/L09LU9xQj+Kis+bi08CHVPWWjPQngbtF5BHcwpfSGCXpiMgTwGtUtSsjfR/gAVV9\ncdQ2jfhs3ercevbZZ58yK4mHz/p91g6mv5z4rB381x8PCR9m3p/pm/kx600HHhsl/zFgvzgNx3F0\nPXiEevXAwjgiDMMwDKOiqcJdgjNii0ThIeCrIvIRVd2dniEiNbglww/FaTi0USIix6W9PFpEtqe9\nrgGOBdbHEWEYhmEYFU11Tt8gIkuA03CjJkep6rogIOoaVf3DCNU+jfMl2Swi9zLcp+R1OB/U40ao\nOypRRkr+HPyv7J2HIi2tA/h8HBGGYRiGUdFUzy7BexCR04FvA5cBZ7PXh2Q77vs8p1Giqo+KyIHA\n+4BXAguCrM3A14GbVLU7jqYoRkkDbqJsDW4n4PQYJbtVdSiOAMMwDMOoeBSk+kZKPgN8TFVvFZH0\nRSz/JI+TqqruAH4cHEUjtFGiqgPBnzOLKcAojDlz5pRbQkH4rH8stUcNULXxlwfnLZPc5aaCE09N\n4KGTz4qlq5zYs1M+0vWXOrBf2PZXrlzJ4sXLS6rFI2MjLPOBh3OkDzB8w728iMgfgY+q6qZCBIUN\nnvZx4OeqOhD8PSKq+pNCBBnR6O3tBWDixIllVhIPn/X7rB0gOeB2Mk3UFhTYuWz43P8+awf/9cdC\nI6y+8SDMfMAacgdPexNueW8UXoebUSmIsJ9G3wJ+h7OevjVKOcXtFmyMEdu2udDK++67b5mVxMNn\n/T5rB9jd7b5YJkzyM9aEz/3vs3bwX38sqtPR9RLgRyJSj3PPeIWIvBf4GvDRcghKhCmkqjNVdVva\n3yMds0or1yEinxKRNSLSJyJ/F5Ej8pQ/WkRWiEi/iDwjIh/IUeadIvJk0OYjInJC6a7AMAzD8AqN\neHiAql6DW777XaARuAk4HficqmYuaMnHOmBXoZpCGSWjIY6DRSTS/FMB53s3cDFwDnA48Ahwh4jk\nDNQiIvOA24G7gEOBHwDXiMh/pJV5Fe5m/BQ3lPUH4FYRsUBwhmEYRlUaJQCq+ktVfREwCZihqrPj\n7Fujqi9V1Q2F6olslIjIBSLyweDvBPBX4Algo4i8ulBBITgDuFpVb1DVp3Drq3uBD49Q/nRgtaqe\nqapPq+qPgN8G7aT4LPAnVb0kKPNNYCVuLbZhGIYxzmmcUMekCRNDHY0T6sotNxIiMh1YDBwkItMi\n1n2tiNwoIveLSGuQtkREXhNHS5yRkvcA/w7+Pgm37fFhwFXA9+OICIuI1OI67q5UmqoqcCdw1AjV\nXhnkp3NHRvmjQpQxDMMwxim9u3exc9dgqKN3d8GzGGOCiEwWkV8AG3Gb7d6DG2C4Mdg6Jl/9t+O+\nK/uARUDK83kfINaSvjhu99OB1JKfk4BbgkAqO3GjFqVkP1xwl2cz0p8lY/vkNGaMUL5ZRCYGS51H\nKjMjn6ANGzbQ3Ny85/WUKVNoaWlh9erVWWVbWlpobm5m7dq1WXlTp06lsbGRDRuyR7+mTZtGXV0d\nnZ2dWXn77bcfiUSC9vb2rLxZs2aRTCbZvHlzVl5rayuDg4N79rBIZ86cOfT29u5xZktn3rx5dHd3\n09XVlZW3YMECurq62L59e1ZeW1sbW7Zsobt7eDydoaEhFi5cyKZNm+jpGbZBJTU1NcyfP5/Ozk76\n+vqG5dXV1TF37lw6Ojro7+8flldfX8/s2bNZv379no3DUjQ0NNDa2sqaNWsYGhoeWqepqYmZM2ey\natUqNCOedHNzM9OnTx/Wz7t3uyW1zz33XMnv+c7k3n6rFxcyqF97s+rVSyOg9G/Mvnd1U5tJ7h5i\n9wuunzXprr9/4zYGBgbG7J6LSFHuear/29vbx+yepyj0fZ6uPcVo7/MZM2aQSCTYuHFjVl453ueT\nJk1i+/btWX1T6nueTuY9z/U+KiYSIU5J6Hgm5ecanBvEScADQdpRODeHq3GDEKPxdeA0Vb1BRNLL\n3hfkRSaOUbIFN8SzEbds6LNBej1ezaQVh6VLlzJ58uQ9r2tra1myZAlHHnnkmJy/u7ubRKJg16Cy\nsXPnzqwPMF/YuXMn4AxDH0n2uS/vmiY/V9+k+n/KlCllVhIdn7UD7Nixg507d5ZN/7Jly1i+fDkN\nDQ309fWRTCbZsWNHaU9anatvTgaOV9W/paXdISIfY28U99E4CLg3R/oLQKyHQzJ/HeStILIU+ATQ\nCUwF2lS1P1jR8klVLdm3cTB90wu8XVVvS0u/HthHVd+Wo849wApV/UJa2geBS1V13+D1OuBiVb08\nrcy5wFtV9fARtCwCVqxYsYJFixYV4erikfql0tbWVjYNheCz/rHUXorgaanRlPpZU70MnmbPTvmo\nRP0ueNpigMWqurJY7aY+62d++fNMnDM7VJ2BDR1suvCyomspNiKyHjhJVR/LSH85sFxVR71gEVkN\nfFxV7xSRHcChqrpaRN4PfFVVIy8WiTxSoqpni8iTwBzgZlVNjalNAC6M2l7Ec+8SkRXAMcBt4Fb/\nBK8vH6HaA0Dm8t7j2DtUlSqT2cZ/ZJQxIhLmizQ1LXHXjluL3nY6pY46WWpKob8Sv1gMI5Ow7/Xt\nQ9nTUMWkSqdvvgtcIiJLVHUzgIjMwH2XfydE/Z8CPxCRD+PGh2aJyFG4EPVh6mcRK5Sjqt6YIy3y\nEqKYXAJcHxgnD+JW0TQC1wOIyHnALFVNxSK5CviUiJwPXIszPt4BnJjW5g+Au0XkC8AfgffiHGo/\nVvKrMQzDMCqf6ozoejrQBqwPRk0A5uICpU4TkU+kCqpqrimB7+MWzNyF+x6+N6h7kapeEUdQLKNE\nRI4EvoRbeQNuSfBFqvpgnPaioKq3BDFJvo3bJvlfuDmxlCfXDNwoTqr8WhE5CbgU5//SAXxEVe9M\nK/OAiJwCLA2O/8VN3TxR6usxDMMwPKA6fUqiDVFnEKx+XSoiF+KMm0nAE6q6M26bkY0SEXkXLtDY\nH4HUmPKrgftE5BRV/U1cMWFR1SuBK0fI+1COtHtxIx+jtfk7XCh9wzAMw8jGH2MjFKo62rYxUdoZ\nBJ4QkQmquruQtuKMlJwDnK2q56cnishXgHOBkhslxl4WLFhQbgkF0SST8xeqUHzve9NfPnzWDv7r\nj0OV+pTEQkTeBHSq6mNBENWzcSFBZojIJuCHwPkadSUN8YKntZF7ROF3wMIY7RkF0NXVlTOWgC8M\n6gCDOlBuGbHwve9Nf/nwWTv4rz8WVRpmPiaXsXfJ71eAz+GcW0/COcl+HjgzTsNxjJJO3BbFmbw+\nyDPGkO3bt+cMXOULuxhkF4P5C1Ygvve96S8fPmsH//XHwoySdObhNuADOAU4XVUvVdU/q+oPgI8Q\nc5fhONM3l+G2On4ZcH+Q9mrg4ziLyTAMwzCqCpu+GUYXMAtYD0wDMsMePwO0xmk48khJEGDsw8Br\ngeuC4zXAh+IuATIMwzCMykb2LgvOd+DNkuBhiEiNiBwmIvvmKfp74GwRqQH+AHwyiBmW4jO4lbGR\niRun5FfAr+LUrTbOeN03mFIzNVRZ3wN4RSXM9ebaV6RYbRuGEZ0oQQ8nJZrHz3uxCpcEi8hlwGOq\n+rPAwLgHeBXQKyInq+rdI1Q9C7eJ7VO4IKPvBP5DRJ7B+Z22AMfH0RRppERE3iIiPxORXwSh2g3D\nMAyj+tG9Uzj5Dl+MElwg0UeCv98MzAcOxsX1WjpSJVV9AWe8XIzbbmYtLmhaHW7A4qWq+o84gkKP\nlIjIR4Gf4OaQ+oFTRORFqnp2nBMbxcH3EOE+6/dZO5j+cuKzdnAjJOOOKhwpAfYDUttLnwj8RlWf\nEZFrcStqRkRVd+Eipl9VTEFRRko+B5ynqvNU9WCcY+tn89QxSsyWLVvYsmVLuWXExmf9PmsH019O\nfNYOMJDsYyDZV24ZY0rYUZIoDrEVwLPAi4OpmzcBfwnSG4GhcgiKYpQsBK5Je30dMFFEZhZXkhGF\n7u5uuru7yy0jNj7r91k7mP5y4rN2gF3Bv3FH9S0Hvg64BXgcpzy1/cqROH+RERGRQ0Xk6yLyyWDr\nl/S85mC0JTJRHF3rgT3x7FU1KSIDQEOcExuGYRiGLzRMqKWxti5UWZlQW2I1xUFVzxWRx3H7xf1G\ndU8kyyHcZns5EZHjgGW4feImA98WkXeq6n8HRRqAD+BW6kYi6uqbr4tIT9rrOuBLIrInio6qnhVV\nhGEYhmFUMn27dqGD4QI99u/yYxRJRN4P/DrNGEnxK+A9o1Q9F7cJ79nBUuAvA7cFhsmfC9EUxSh5\nEHhFRtpK4PC0134NXBmGYRhGCKo0eNp1wJ+BTAenyUHeSOu9XwIsgT07BV8gIh3Ab0XkPcBDcQWF\nNkpU9ZVxT2KUjuHxavzDZ/0+awfTX0581g4gngYHM7IQcg8mzAZeGKXeAHv3vgFAVW8SkSTwa+CL\ncQXFCp5mVA4LF/q9B6LP+n3WDqa/VIQJPpZOpQUfi6qn1NcbtvzKlStZvHh5pLYjUUVLgkXkYfZe\n0V0isjstuwYXr2S0aZh/AW8AVqQnqurNwXTOz+NqM6PEczZt2gTAzJl+LoLyWb/P2sH0l5P+ZC8A\n9YnGMiuJh899H5cqm765Nfj/MOAO0haxAIO4YGi/G6X+j8m9MS+q+qvAMPlYHGFmlHhOT09P/kIV\njM/6fdYOpr+c7GZ3/kIVjM99XxCVb2yEQlW/BSAia3GOrv0R6/8et//NSPk3ATfF0WZGiWEYhmHk\no4qmb1Ko6s8BRKQOmE5G7DJVXT/WmswoMQzDMIw8VNn0DQAi8iLgWtw+NsOycKZVTcx2fw7MUdU3\nRq0byygRkVfgwswvBE5V1Y3BMqC1qvr3OG0ahmEYRsVShSMlwPXAbuBkYBPFU94JJONUjGyUiMhb\ncEt+fgschYv0Cm7o5324izPGiJqaWIZsxeCzfp+1g+kvJ74vqfW572MTZU8bf4ySw4DFqjpqSPmo\nFBJENc5IyTnAp1X1ZyLyn2npfwO+FleIEY/58+eXW0JB+KzfZ+1g+stJU2JyuSUUhM99XxD+GBth\neQK3U3DFEGVDvhQHA3flSN8O7FuYHCMqnZ2ddHZ2lltGbHzW77N2MP3lpC/ZS1+wLNhHfO772ITd\njM+vTfm+govGerSITA020ttzjFZRRBpE5DUi8uIcefVBCPvIxBkp2YILrLI2I/0oYE0cET5z6b3f\nYdGiRaHKliLIUF/f3u3DfQzalK7fN3zWDtWlv5Ke/TBtt7e3A9DW1lYyHaUkve9L/TkS9t5uH9pW\nUh3V6OjK3l2BMwcaRnV0FZEDgf8C5gIqIn8D3qOqm4Ii+zB6mPoRiWOUXAdcFlhBCkwVkcOBi4AL\nYrRnGIZhGJVNdTq6viFmvfOBx4H/gws3fxlwn4gcXegy4jhGyXeBWuABnJPr33Heu5er6qWFiDEM\nwzCMiqQKjRJVvSdm1VcBx6rqc8BzIvJm4Ergf0TkDUDs6HqRfUpUNamq3wCm4aykNwAzVPXLcUUY\nhmEYRiUj7J3CyXuUW2wEROS1InKjiNwvIq1B2hIRec0o1Rpgb1hidZwOLAPuAQ6Mqyd28DRV7QFW\nxq1vFIe6urpySygIn/X7rB1MfznxWTv4rz8WVThSIiJvB34B/BJYBEwMsvYBzgJOHKHqU7hBiSfT\nE1X108EO2LfF1RQnTsmo2zCq6kgXYZSAuXPnlltCQfis32ftYPrLic/awX/9cWisraUppDEmtbUl\nVlM0vg6cpqo3BAFQU9wX5I3E74H34gyaYQSGSQI4LY6gOEuC12UcG3GB014VvC4ZIrKviPxSRF4Q\nkedF5BoRaQpR79sislFEekXkLyLSlpH/MRH576DdZL6lUJVER0cHHR0d5ZYRG5/1+6wdTH858Vk7\n+K8/Dn2Du+gZGAx19A3uKrfcsBwE3Jsj/QWcA2tOVPW80QYgVPWTqhrHvog+UhLMG2UhIt+j9FNp\nNwH7A8cAdbgQuVfjIsnmRES+AnwaeD9uGfN3gTtE5BBVHQyKNQB/Co7zSqS9JPT3R9rcseLwWb/P\n2sH0lxOftYP/+mNRhdM3wGagjewQH68BVo+5GuKNlIzEdcDHitjeMETkYOB44COq+k9VvR/4DPAe\nEZkxStXPAd9R1dtV9XGccTIL2BONVlUvV9ULgH+USr9hGIbhMdUZPO2nwA9E5Eic6lkiciouxMeP\nC2lYRK4VkSVR6xVzl+BFQCnHrI4CnlfVh9PS7sR15JHAHzIriMh8YAZpgWFUtVtE/hG0d0sJ9WZR\n6iBDlRAMzfCTSgo+FoeoeirpeitJy1hQqutduXIlixeP6vJYEEL4qQCPVt98Hzc4cRfQiJvKGQAu\nUtUrCmx7AfBGEfmiqh4WtlIcR9ebMpOAmcCrKW3wtBm4aLJ7UNUhEekK8kaqo8CzGenPjlInEhs2\nbKC5ea8LypQpU2hpaWH16uyRr5aWFpqbm1m7dm1W3tSpU2lsbGTDhg1ZedOmTaOuri5nWOeenh4S\nicSeCJHpzJo1i2QyyebNm7PyWltbGRwcZOvWrVl5c+bMobe3l23bsiMkzps3j+7ubrq6urLyFixY\nQFdXF9u3b8/Ka2trY8uWLXR3dw9LX7duHQcccACbNm2ip2f40vaamhrmz59PZ2dnVvTRuro65s6d\nS0dHR9ZQcn19PbNnz2b9+vUMDg4Oy2toaKC1tZU1a9YwNDQ0LK+pqYmZM2eyatUqVIf/1Glubmb6\n9OnD+nndOudCNdb3fMaMGSQSCTZu3JiVF+Wep/QDDOkQQ+xmUAey6jXKJHaza1heqh/i3HMRYeHC\nhQXf83T9Ue/5zqTTNIEJ1Cca6UnuQDN+3tZSy8REAzuT3Vnvr0Lvebr2lJaJUk+CBH2aHX6+XhoA\noV97s7SU632+Y8eOrLww9zx1vXvyEJoSk+lL9jK0d5UpAIkgoGiY93mu91HR8WcEJBTqPuiWisiF\nuGmcScATqrqzCG0fDZArDP1oxBkpyTQCk8C/gEtUNfIyIBE5Dxd/fyQUOCRqu2PF0qVLmTx57+Za\ntbW1LFmyhCOPPHJMzl9fX08iUcxZuLFl4sSJ1NfX5y9Ygfi+LNL0lw+ftYP73BkYyDZgx4ply5ax\nfPlyGhoa6OvrI5lM5jSSikoV7hIsIu8D/p+q9uI25ys6qhqpXcn8RThqYZEaYDHwtKq+EFHbSG1O\nBb0OzHIAACAASURBVKbmKbYaWIIbUtpTNtDTD7xDVUeavlkFHKaqj6al3w08rKpnZJR/PfBXYF9V\nHW7OZ7e9CFixYsWK0HvfGEalYlMIo2PTN8WjtNM3iwEWq2rRYmilPuvb3vMFGqfPDlWnd0sH7Tdf\nUnQtxUZEtuIWetwG3AjcoapDo9faU1eAecAGVd0tInXA23CxTpYH0V4jE2mkJJgu+R/cyEVRjBJV\n3Qbk3UlJRB4ApojI4Wl+JcfgRm5yOqiq6hoR2RyUezRopxnng/KjIsgvO+vXu20GfI0b4LN+n7WD\n6S8nPmsH//XHojpX38wE3oSLOXIL0CsivwF+GSwmyYmIHATcAcwBVovIccBvgINx38m9IvIqVf3f\nqILijPs/EQgZU1T1KVwn/FREjhCRVwNXAL9S1T2TqSLylIi8Na3qZcDXReTNIvIy3K6FHaQ5xorI\n/iJyKPAiXIe+XEQOFZF9S39lhTE4OJg1h+4TPuv3WTuY/nLis3bwX38cQoeYjzLNU2ZUdXewMvVU\nXLyxM3CjH/8tIqtGqXo+8AhwGHA78Efc9+q+QAtub7xvxtEUxyg5E7hIRI4NgpnVpR9xRETgFFx4\n2ztxHXEv8ImMMi/ChcgFIFjqewUunsk/cENVJ6TFKAEXee7hoIziYvevBN5ckqswDMMw/KO6lgMP\nI/AruQMXr+t/ccbJSLwKOEdVH8NFfj0Y516xS1UHcKt6XhdHRxxH1zsy/s+kJo6QMKjqdkYJlBaU\nyTq/qp4LnDtKnW8B3ypQnmEYhlGlRBkB8WWkBEBEGnG+IKfiXB02AL8C3jFKtUlAF7h98ESkB9iU\nlr8BF+g0MnGMkhPinMgwDMMwvKUKfUpE5GbgZKAX51PyHVV9IETVjcBcYH3w+kyGh+yYBjwfR1No\no0REvokbnhlphMQoAw0NDeWWUBCVqj/MCoG+pIsn0ZBo9DJ4V3rf+7iio5BnJ+r1HnH790KXfejk\ns/KWsb73jyodKRkC3kWEVTcBd+KmbP4GoKqZ0V+Pw7lARCbKSMk5wFU4i8qoEFpbW8stoSB81t+Q\naCy3hILwue/Bb/0+awf/9ceiCkdKAgfXOPXy7QD8a+DncdqO4ujqUeTc8cOaNWtYs2ZNuWXExmf9\nPckd9CRLHLCphPjc9+C3fp+1g//6Y1Mljq4islxE9kl7/VURmZL2eqqIxA6mpqprVHVT/pLZRPUp\n8aC7xxeZodJ9w2f9mWHJfcPnvge/9fusHfzXH4cqm745HhfkLMVZOJ+S1H4RE4CDRmsgWG37n7h9\n5FLbtmwG7gf+kLHCNTRRjZJnREbvblVtiSPEMAzDMCqW6pq+yZz5iDQTIiJtuBW4s3ChNlL7yx2O\nC7HRISInqGr2pmx5iGqUnEORIrkahmEYhi+IKhJyW5aw5Tzmx8BjwOGZW7IEUdNvwEVNPz5qw1GN\nkptVdUv+YoZhGIZRPdTX1dI0MVx80GRdbYnVFEyucZ8oltSrgVfk2iNOVbtF5BuMsP1LPqIYJVVv\n+vlIU1NTuSUUhM/6J8QK81M5+Nz34Ld+n7WD//rj0D+wi0R/ODeJ/oFdJVZTMAJcLyKprZ7rgauC\nIGgw3N8kF9txEV8fHyF/Hnv9UyIR5VPVVt9UIDNnziy3hILwWX+950uCfe578Fu/z9rBf/2xiLKn\nTeX/hM9crntjjjKjBZS5BrhBRL4D3MVen5L9cVFhv47b3iUyoY0SVY2zT45RYlatcnsmLVy4sMxK\n4lGp+sMEeCpEeyUEkKrUvg9Luv5SB6ObdepT4QuH8LorpO8rIfCe789ObCrf2AiFqn6owPrfDEZV\nvgxczN6eEdwKnPODfeci4/f4s4F67lDls36ftYPpLyc+awf/9cehypYEF4yqng+cLyLzSVsSrKoF\nBbCx0Q/DMAzDyEfYwGmeBFArFkGgtAdw9sTGQtszo8QwDMMw8pAaKQl7jEP+BBS8/4BN3xiGYRhG\nPqoreFopKMpiGDNKPKe5ubncEgrCZ/0+awfTX0581g7+64+DEMGnpKRKqhszSjxn+vTp5ZZQED7r\n91k7mP5y4rN28F9/LFTdEbbs+OMT7F0aHBszSjynvd1tLdDW1lZmJfHwWb/P2sH0lxOftYP/+uNg\nq29GJtgLZxuQDF6LxlyiZY6uhmEYhpEPW32ThYhMFZE7gWeA5UAqqt7PROTiOG3aSMkYUglBjwyj\nGin1e6WS3oul1hLmc2pn0m15MinRXFF9U1IUJBm+7DjhUmA3MJf/396dh9lV1eke/76VpFKVkCJU\nCFQSMhIBRyABaVDRaxSuXsEJh1ahr4oTKooo0A0XEUSERlul7ZYLzoKiPn0ZNN0RREWvSEMCYhRa\nM1dSCYmpDEUNKVLn13+sfcKuU8MZ6kyr8vs8z36Ss/fa67znnBpWrb3W2vBEav8dwBeBi4ut0Bsl\nzjnnXD4++2Y4ZwBnmtlmadDw3r8A80up0BslzjnnXB4+pmRYU4GeYfa3AvuG2Z+XN0oiN3369FpH\nGJOY88ecHTx/LcWcHWASjbWOUH0++2Y4vwbOA/5P8tgkNQCXAL8opUJvlESutbW11hHGJOb8MWcH\nz19LMWcHaFS+O9uPP95TMqxLgJ9LOgloBG4Ank/oKXlJKRX67JvIrVu3jnXr1tU6Rslizh9zdvD8\ntRRzdoBu66Lbumodo/p85s0gZrYaOAb4DXAX4XLOvwEnmtnaUur0nhLnnHMuD+8pGZ6Z7QGuLVd9\n3ihxzjnn8mhqnMiUpkkFld3feHD8apW0CHgpYX2SDLAWuM/M9pZa58HxzjnnnHNj0LfvGSb29hdc\ndjyTNBX4FvDmZJcB24GZQK+ky8zsq6XU7Y2SKjpoFhly0fGF/con9sXHOm47Lm+Zvo6dAOydPaPS\nceqGX74Z5IuE3pEXAX3AdcA64DPA24GbJO0ys9uLrTiqga6SDpN0m6Q9knZJujVpseU772pJHZJ6\nJN2brNOfrvMrkp5Mjm+U9GVJUdwGs7W1NeqR/DHnjzk7eP5aatTkqGewTJzWzMRpzbWOUV2+zHza\nm4CPmdlqM1sDvB+4EMDMvkGYlfOpUiqOrafkduBIYBlh+tG3gJuBd410gqRLgY8Q5lJvAD4LrJD0\nXDPrB2YTWnyfICyTOz+pcxbw1gq9jrKJ/RbiMeePOTt4/lqaSGFjE+rVhCnxNqjG4iDoASnURCA9\nbuTpZF92MbWfATeWUnE0PSWSjgPOBN5rZo+Y2W+BjwJvl9Q2yqkfA64xs58k05fOIzRE3gBgZn80\ns7eY2XIzW29mvwQuB85KFoGpaxs2bGDDhg21jlGymPPHnB08fy312NP02NO1jlGyfU/tZt9Tu2sd\no7oyQMYK3GodtuIeJvxuzfoYsMPMdiSPDyE0VIpW9790U04FdpnZo6l99xE6yk4Z7gRJC4E24OfZ\nfcmo4IeS+kYyHdhrZuP/S8s551x+fvkm7TLgbyVtlbSRMCX4E6njpxHuGly0mC7ftBFG9x5gZgOS\nOpNjI51jwFM5+58a6RxJhwNXEC7h5NXe3j6oG3n69Om0trYOuzBSa2srLS0tw/51N2PGDKZMmUJ7\ne/uQYzNnzqSxsZEtW7YMOdbd3U1DQwNr1qwZcmz27NlkMhm2bds25NicOXPo7+9nx44dQ47NnTuX\nnp4edu7cOeTYggUL2Lt3L52dnUOOLVq0iM7OTnbvHvoX1OLFi9m+fTt79w6eKbZx40bmz5/P1q1b\n6e7uHnRswoQJLFy4kC1bttDb2zvoWGNjI/PmzWPz5s309fUNOtbU1MRRRx3Fpk2b6O8fPFq+ubmZ\nOXPmsH79egYGBgYdmzp1KrNmzWLt2rVYzjLRLS0tHHHEEYPe540bNwLV/8zb2tpoaGigo6NjyLFi\nPvNsfoABG2CA/fTb0NtVTNEh7OeZQcey70Mpn7kkjj766DF/5un81frMs0b7zPttHxOZNGxPSKMm\nM4GJ9GSePZatvxqfedpI3+d9HTuZfOR0Bnr2sb+rd8h5k9sOY//TvQz09A3KD5X/zNNyP/Phvo/K\nyQe6PsvMVkl6AfA6YDJwv5n9KXX8q0Ccs28kXQdcOkoRA55bpSzTgJ8CqwmjiPO69tprmTZt2oHH\nkyZN4txzz+WUU4btvHHOOTdG99xzD8uXL6e5uZne3l4ymQxdXZVeYbaIe98cBF0lZrYVuKXc9Sr3\nr4NqkzQDyDevbB1wLnCjmR0oK2kCYTrSOWZ21zB1LyQs5nKCmT2e2v9L4FEzuyi17xDC4Jwu4Kxk\nEOxouZcAK1euXMmSJUvyxK+c7F8pixcvzlOyPsWcP+bsMDh/jFOC6/X9j31K8Mk/+VzeMtkpwU2z\nZ/Dw6/6h0pEKsmrVKpYuXQqw1MxWlave7M/6pX/zUaa1zCnonK69W1j5u5vKnqXeSbofeLeZbcxb\neAQ17ykxs53A0OsEOSQ9CEyXdGJqXMkyQIQxIsPVvV7StqTc40k9LYQxKAe6lpIekhVAL3B2vgZJ\nPZkxI+51AmLOH3N28Py1FPN0YICJLVNqHaH6ihkrMs47SiSdPcKh04HXSWoHMLO7i6275o2SQpnZ\nk5JWALdI+hBhSvBNwPfN7MDFVElPApemek6+BFwhaQ1hSvA1wGbCzYOyDZJ7gSbgnYSGT7a6HfU+\n2HXKlLh/OMScfyzZ66Fnop7f+0LenwEL40MmaEJd9TYUkmXfvjA+Z/Lk+mucFNLzUc/5K0VmqMAr\nC4WWi9idhKaXhjl2U/KvAROKrTim2TcA7wCeJMy6+QnwAPCBnDLPAQ7NPjCzGwhv0s2EHpVm4DWp\n3pAlwMnAC4E1QAewNfn3qEq9kHJpb2+v+ACvSoo5f8zZIf78vdZNr3XnL1iHYn/vY89fEiOZFlzA\nNu7bJKwA/h1oM7OG7AYMAC9IHhfdIIGIekoAzGw3oyyUlpQZ8kaY2VXAVSOU/xUltOacc84dPLyn\n5Flm9hpJFwGPSLrAzH5Srrpj6ylxzjnnqs/XKRnEzP4JOBu4XtLNkspyPdgbJc4551w+ZsVtBwEz\neww4idAMe4zhx5gUJarLN84551xNFLF42sHQU5JlZr3AB5MZOf8D+OtY6vNGSeRmzpxZ6whjEnP+\nmLND/Pknq6nWEUoW+3sfe/6SHSQ9IKVIpv8WPQU4lzdKItfY2FjrCGMSc/6Ys0P8+Rsivvoc+3sf\ne/5SNDdOZGpTYa/7mf6D+1erpJOAKWb2QLHnHtzv3DiQvU9Gva1qWaiY88ecHeLP32s9AByiljwl\n60/s733s+UvR17efnomFravZ17e/wmnq3neBYyhhZqs3SlyUil18rFiVXoyrnhb7gjjzxL5MvotM\nMQNY/TLPMmBSKSd6o8Q555zLx5eZL5iZDb2ddYG8UeKcc87lIYpYPO1gb5WMQbwjxZxzzrlq8XVK\nBpF0gaT7JP1Q0rKcY4dLWldKvd5TErm2trZaRxiTmPPHnB08fy3FnB3iz1+S7H1tCi07jkm6ELgO\n+CbhXnPLJV1lZtclRSYA80up2xslkWtoiLuzK+b8MWcHz19LMWeH+POXwu99M8gHgPeZ2e0Akv4V\nuFNSs5ldOZaKD76vrHGmo6ODjo6SxxTVXMz5Y84Onr+WYs4O8ecvmV+6yVoI/Db7wMx+C7wSeL+k\n60Y8qwDeU+Kcc87l41OC0/4KzAU2ZHeY2WpJrwTuB2aXWrH3lDjnnHP5ZIrcxrffAG/K3WlmfyKs\nUfKaUiv2nhJXF3zxq/Iq5P18OrMXgEMaWqJ/P2PP7+qfjykZ5PPA0uEOmNkfkx6TN5dSsTdKnHPO\nuXz88s0BZvY48Pgox1cDq0up2xslkZs9u+RLd3Uh5vwxZwdo0pRaRxiTmN//mLND/PlLU8wg1vHb\nKJE0z8w2FVF+jpltKbS8jymJXCaTIZOJ9wJmzPljzh4Us252/Yn5/Y85O8SfvyRGEYun1TpsRT0s\n6WZJJ49UQNKhkt4naTVFXsbxnpLIbdu2DYj3bp3Z/DGK/b3vs14ADlFJ982quZjf/5izQ/z5S+KL\np2U9D7gcuFdSH7AS6AD6gMOS488HVgGXmNnyYir3RolzzjmXTxEDXcfzmBIz2wl8QtLlwP8CXkpY\nvbWZMFX4NmBFMq6kaN4occ455/LyMSVpZtYL/DjZysYbJc4551w+GQtboWVdSbxR4pxzzuXR1DSJ\n5imNBZXdR5zjtOqBN0oiN2fOnFpHGJNs/hgXv6rn976Q97O3Nwx0bW5urnScihjL+1/rxfpizg71\n/bVfKX29/fTavsLK9vVXOM345VOCI9ff309/f7zfADHnjzk7eP5aijk7xJ+/JD4luCq8pyRyO3bs\nAODQQw+tcZLSxJw/5uzg+Wsp5uwQf/7S+EDXavBGiXPOOZePD3StCm+UOOecc/lYJmyFlnUliWpM\niaTDJN0maY+kXZJulTS1gPOultQhqUfSvZIW5xz/mqQ1yfHtku6UdGzlXolzzrmo+JiSqoiqUQLc\nDjwXWEZYSe504ObRTpB0KfAR4P3Ai4FuYIWk9NyuR4D/DRwHnAEoKaMy53fOORcjs2cv4eTbxvGK\nrpUWzeUbSccBZwJLzezRZN9HgZ9K+qSZjXQTlY8B15jZT5JzzgOeAt4A/BDAzG5Nld8k6QrgMWAB\nsL4CL6ds5s6dW+sIYxJz/pizg+evpZizQ/z5S2JFNDa8UVKymHpKTgV2ZRskifsIHWWnDHeCpIVA\nG/Dz7D4z2ws8lNQ33DlTgfcA64D2siSvoJ6eHnp6emodo2Qx5485O3j+Woo5O8SfvyQFX7rxnpKx\niKanhNC42J7eYWYDkjqTYyOdY4SekbSncs+R9CHgBmAq8CRwhpntzxeqvb2dlpaWA4+nT59Oa2sr\n69atG1K2tbWVlpYWNmzYMOTYjBkzmDJlCu3tQ9tBM2fOpLGxkS1btgw51t3dTUNDAzt37hxybPbs\n2WQymWHvxDtnzhz6+/sPTO1Lmzt3Lj09PcPWuWDBAvbu3UtnZ+eQY4sWLaKzs5Pdu3cPObZ48WK2\nb9/O3r17B+3fuHEj8+fPp6+vj+7u7kHHJkyYwMKFC9myZcuBhb6yGhsbmTdvHps3b6avr2/Qsaam\nJo466ig2bdo0ZC2F5uZm5syZw/r16xkYGBh0bOrUqcyaNYu1a9diOT9UWlpaOOKII1izZs2g7ADH\nH398VT/ztrY2Ghoa6OjoGHKsmM88m3/+/PlV/cwlcfTRR7N169Yxfebp/MV+5l9YeSVQ+Gee/txh\n7N/nq1atOpA9q9DPPJs9K99nvmfPnrJ/n//lL3+hq6trUH6o/GeelvuZD/d9VFbeU1IVNW+USLoO\nuHSUIkYYR1Jp3wN+BswCPgn8SNJpZjbqCkHXXnst06ZNO/B40qRJnHvuuZxyyrCdN84558bonnvu\nYfny5TQ3N9Pb20smk6Grq6uyT2oGmUJn33ijpFTK/eug6gGkGcCMPMXWAecCN5rZgbKSJgB9wDlm\ndtcwdS8E1gInmNnjqf2/BB41s4tGyDQJ2AW818zuGKHMEmDlypUrWbJkSZ74lZP9C27x4sV5Stan\nmPPHnB08fy3FnB3qM/+qVatYunQphHGHq8pVb/Zn/amt53DopJkFnbPnmR082Pnjsmc5GNS8p8TM\ndgJD+w9zSHoQmC7pxNS4kmWEmTIPjVD3eknbknKPJ/W0EMagfHWUp2tI6p1c6Otwzjk3jvnlm6qI\nZqCrmT0JrABukXSypJcANwHfT8+8kfSkpNenTv0ScIWksyS9EPgOsBm4Kym/UNJlkpZImivpNOBH\nQA+wvDqvzjnnXF3zKcFVUfOekiK9A/hnwqybDPBjwpTftOcAB27IYGY3SJpCWM9kOvBr4DWpsSJ9\nwMuSeg4jDIJ9ADjNzP5auZdSHgsWLKh1hDGJOX/M2cHz11LM2SH+/CUxwwpe0dUbJaWKqlFiZruB\nd+UpM2GYfVcBV41QfithIbYoZUe5t7a21jhJaWLOH3N28Py1FHN2iD9/SfzeN1URVaPEDZWdshfr\nD4eY88ecHTx/LcWcHeLPXxIfU1IV3ihxzjnn8rFMEVOC/YZ8pfJGiXOO1x96XlHl79rznQolKU3s\n+V39a2puZEpjYRMy+/sbw13WXNG8UeKcc87l0dfTT8++vvwFgb6BUdfcdKOIZkqwc845VyuWyRS1\nVYuk1yVLYfyXpPdW7YkrxHtKIrdo0aJaRxiTmPPHnB08fy3FnB3iz1+aYtYfqc5A12RV8y8ALwee\nBlZJ+jcz21WVABXgPSWR6+zs5NZbb611jJLFnD/m7OD5aynm7BB//pIUunBaMVOHx+7FwGoz22Zm\nTwM/Bc6o1pNXgjdKIrd7927uuGPY2/NEIeb8MWcHz19LMWeH+POXxCzMqiloq1qjZDaQvq30FmBO\ntZ68ErxR4pxzzuVhZlimwK2ARomkl0m6W9IWSRlJZw9T5sOS1kvqlfQ7SSdX5MXVEW+UOOecc/kU\n3EuSKXSdkqnAY8AFDDMIRdLbCONFPg2cCPweWCHp8FSxDuCo1OM5yb5oeaPEOeecyyO0NQrtLSmg\nPrP/MLMrzewuwl3pc10E3Gxm30luSPtBwo1i35Mq85/A8yXNknQI8D8JN66Nls++KV0TwBNPPFHT\nEO3t7XR1dbFq1aqa5ihVzPljzg6D83/6Fx8v6tx6eM0x5x9PXzv1IvWzuKkS9e9v6qG/YVJhZTM9\n0Fv6c0maBCwFPpfdZ2Ym6T7g1NS+AUkXA78kNGyuj3nmDYAKufblhpL0DuC2Wudwzjk3yDvN7PZy\nVSZpHvAEMKXIU/cBx5jZpgKeIwO8wczuTh7PIgxaPdXMHkqVux443cxOHb6m+HlPSelWAO8ENgCF\nLfPnnHOuUpqABZT58oWZbZL0XODwvIUH+2shDRI3mDdKSmRmO4Gytcadc86N2W8rUWnSuKhmA+Ov\nwABwZM7+I4FtVcxRdT7Q1TnnnKsjZvYMsBJYlt0nScnjijS86oX3lDjnnHNVJmkqsJhnZ94sknQ8\n0Glm7cAXgW9JWkmYZXMRYVzLt2oQt2p8oKtzzjlXZZJeDvyCoWuUfNvM3pOUuQC4hHDZ5jHgo2b2\nSFWDVpk3SpxzzjlXF3xMSZ2TdJik2yTtkbRL0q1Jt1++866W1CGpR9K9khbnHP+apDXJ8e2S7pR0\nbAz5kzq/ktyuu0fSRklfltQSQ/7k+Psk/SKpN1OO7MUuSS3pFZJWSuqT9GdJfzdMmbdIeiKp8/eS\nXjPWnNXKL+l5kn6c1JmRdGGlslco//mSHpDUmWz3VnKZ8Qrkf6Okh5Pvm6clPSrpXZXK78YJM/Ot\njjfg34FVwEnAacCfge/lOedSoBN4HfAC4E5gLdCYKnM+8FJgHnACcBdherPqPT/wfOBHwGuBhcAr\ngP8CfhjR+38hoVv2EsIo+5Yx5nwbYWr6ecBxwM1JhsNHKL+AcKvzG4BjgQ8DzwCvTpU5Ldn3iaTM\n1YS1F55Xgfe5EvlPAq4H3kpY8+HCcueucP7vElbxfBFwDPANYBcwK5L8pwOvT44vTL7mB5Xxzbfc\nreYBfBvlwwk/HDLAial9ZwL7gbZRzusALko9biGsL/jWUc55YfLLcWGk+c9JyjTElB94OeVplPwO\n+HLqsYDNwCUjlL8eeDxn3/eB5anHPwDuzinzIPAv5XqPK5k/59h6KtsoqWj+5HgDsAd4V4z5kzIr\ngc9U6nPwLf7NL9/Ut1OBXWb2aGrffYSBUacMd4KkhUAb8PPsPjPbCzxEanninHOmEu6nsA5oL0vy\noCr5E9OBvWaF3QmrQNXMXzI9uyR1+jktyTrSc/5NcjxtRU75UwsoM2YVzF8VVcw/FZhE6MEom2rl\nl7SM0OPzq7HkdeObN0rqWxuwPb3DzAYIP5TaRjnHgKdy9j+Ve46kD0nqAroIPQBnmNn+MuROZ6lY\n/iyFu2ZeQehyLqeq5C+Dw4EJRT5n2wjlWyRNzlOm3K+jUvmrpVr5rydchsptDIxVxfJLapHUJakf\nuIcwe+T+8sR245E3SmpA0nXJwLuRtgFJx1QhyvcI40lOJ4yV+JGkxnwn1VF+JE0DfgqsBj5T4Dl1\nk9+5Qki6jDA25g1m1l/rPEXoAo4njO+5HPgnSafXNpKrZ754Wm3cCHwzT5l1hOWEj0jvlDQBaGXk\npYa3Ea4HH8ngv2SOBNKXITCzbC/JWkkPEQbRvRG4I4b8CrfqXgHsBt6U9GIUoi7yl1EpS1JvG6H8\nXjPbl6dMuZe5rlT+aqlofkmfJAyIXmZmfxx73CEqlj+5DLQuefi4pOcBfw88MNbQbnzynpIaMLOd\nZvbnPNt+wqDC6ZJOTJ2+jPBL76ER6l5P+IGRXp64hTAGYrTliRuSevN2fddD/qSH5GeEAaRnF/PX\nYz3kLycrbUnqB9PlE2ck+0cr8+qcMmNWwfxVUcn8ki4h9DCcmTO2qWyq/P43UMDPGHcQq/VIW99G\n34DlwCPAycBLCFNfv5tT5kng9anHlwA7gbMIs2ruBP7Cs1NqFwKXAUuAuYSpn3cDOxhhCmCd5Z9G\nmC3wWPJajkxtZZt9U6n8SZkjCd3a5xNm+Lw0eXxYiTnfCvQweErnTmBmcvw6wkqR2fILCL1k1xOm\nbF4A9AOvSpU5lTAFODsl+CrCtNFKTAmuRP5JyXt6AmEsxvXJ46MjyX9p8n6/MedrfGok+S8DXkX4\nHj0OuDj5enp3ufP7Nn62mgfwLc8HFGaVfI8wFXAXcAswJafMAHBezr6rCFNTewiXOBanjs0ijMPY\nmvzQ20hYE+E5keTPTqNNb5nk33n1nj85/ulU5vR23hiyXkBYa6aX8BfrSalj3wTuzyl/OuEv5F5C\no+ncYep8M6HR1Qs8TviLvVJf62XND8wf4T2+P5L864fJPgBcGUn+awiN+G7CJaLfAOdU6uvHt/Gx\n+TLzzjnnnKsLPqbEOeecc3XBGyXOOeecqwveKHHOOedcXfBGiXPOOefqgjdKnHPOOVcXvFHitSU6\nNAAABQlJREFUnHPOubrgjRLnnHPO1QVvlDjnnHOuLnijxDnnnHN1wRslzrm8JG2V9P48Za6TVJGb\nDjrnDg7eKHGuSJIykgaSf3O3AUlX1iDT71IZeiT9QdL5ZXyKFwDfTp5rcvI8Z+SUuQZ4bRmfcwhJ\n8yTtyX1tkl4mqV/Syyr5/M65yvJGiXPFayPc1LAN+DjhZn1HpvbfONxJkiZUMJMBNyU5nk+46/P/\nlXR2WSo322lm+5KHGqFMj5ntLsfzjZJjE+GuxV+QNA9A0lTCDeO+Yma/ruTzO+cqyxslzhXJzLZn\nN0KDxMxsR2p/j6Qzk96EV0t6VNI+YKmk70u6PV2fpH+VtDz1uEHSlZLWS+qWtLLAxkV3kmO9mV0O\nbAJen6r3QknrJO2T9EdJb83Jca2kTZL6kn9vSB1LX75ZT2gE/UfyGv+UlPm8pAdzXsc1krYkdT4i\n6ZWp48cm558l6YHkta6StDTP+/91wh1nv5Hs+gLQD1xewHvknKtj3ihxrrI+R+hNeS7hNu6F+Azw\nZuA9hF6PfwHukPTiIp+7D2gEkPS3wPXAZ5M6vwPcLumU5Pi7gA8A7wYWJ8//pxHqPZnQW/J2Qu/Q\nS5P9lmxZlwIfAj4CvAh4APhptocj5bPA1cDxhIbUbQW8tvOBEyTdlmQ+L9WT45yL1MRaB3BuHDPg\n783sV9kd0rBXPkgdnwpcDJxqZr9Pdn9d0iuA9wP/me9JJTUQflE/B/h8svti4Gtmlu1duF7SacAn\ngbcAc4EtwC/MLANsBh4e4Sl2JP/uTnqLRnIxcI2Z/b/k8SckLQM+CnwqVe46M7svyf4Z4BFJ85JL\nNcMys62Srga+BHzRzB4ZJYdzLhLeU+JcZa0ssvyxQBPwa0ld2Y3QcDg6z7kXJ2X7CL+sP29m30qO\nHQfkzoz5/4QeHIAfADOAdZK+llxSKfnng6SZQGue54TQcPtD6vFWQi/MEXnqnwD8HdANnKZ8rT3n\nXBS8p8S5yurOeZxh6B8Dk1L/P4Twi3oZsDOnXF+e5/o6YZBtr5ltLSakma2XtBg4A3gVcAvwhKRX\nmpmNfvaYPZOOkvybr0F0BaHhcjJhfMnFjDDA2DkXD+8pca66dhBm6KSdkPr/H4D9wDwzW5ezdeSp\ne3dSbrgGyZPAS3L2vYTUuBEz6zOzu83sQkLj5OWEnptc+wmNhxFnE5nZDkKjKvc5T2PksSoFkXQi\n8A/A+Wb2JHAhcLWkY8ZSr3Ou9rynxLnquh/4sKS3Aat4dmDpdgAz2yXpK8A/S2oCHgSmEwaTbjez\nH5T4vP8IfFPSH4BfEQayvpak0SDpvYTGxsOEHpl3Al1Ae25FZjYgaTPwKkmrgD4z2zPMc94IXCFp\nE7Aa+CBwDHBWqkxRl10kTSKsl/IdM1uR5Lld0jnAtyWdVoWeHedchXijxLkqMrO7k6m2XyJctrkF\n+D4wP1XmU5I6CJcoFgK7CGNTPjta1Xme9w5JRxB6GL4KrAXeYWbZgbN7CINPjyU0FB4HXmtm2ctP\nufV/HLiBMLNmLfC8YZ72H4GpwFcI41VWJ3WmGzrD5R7ttVwFHEpYqyTtA0n9fhnHuYjJ/6hwzjnn\nXD3wMSXOOeecqwveKHHOOedcXfBGiXPOOefqgjdKnHPOOVcXvFHinHPOubrgjRLnnHPO1QVvlDjn\nnHOuLnijxDnnnHN1wRslzjnnnKsL3ihxzjnnXF3wRolzzjnn6sJ/A8eLxo7UUtjwAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba3a9a9f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#This is one of the functions defined at the top. Shows where in the detector hits occurred.\n",
    "Hist2D(XDAT=superPD.groupby('EV').mean()['X3'],YDAT=superPD.groupby('EV').mean()['Y3'],nbins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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