{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Tutorial\n",
    "PyGSTi provides support for many different _plots_ you may want to make with your estimates.  In this tutorial, we give examples of how to generate the more commonly-used plots using the functions within `pygsti.report.plotting`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pygsti\n",
    "import pygsti.report.plotting as pplt\n",
    "import json\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "Gather the data we need to create some example plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading tutorial_files/Example_Dataset.txt: 100%\n",
      "Gates     (3):  odict_keys(['Gi', 'Gx', 'Gy'])\n",
      "Fiducials (6):  <map object at 0x1096a0198>\n",
      "Germs     (11):  <map object at 0x1096a0198>\n",
      "Dataset has 2737 gate strings\n"
     ]
    }
   ],
   "source": [
    "#Gate sets\n",
    "gs_target = pygsti.io.load_gateset(\"tutorial_files/Example_Gateset.txt\")\n",
    "gs_mc2gst = pygsti.io.load_gateset(\"tutorial_files/Example_MC2GST_Gateset.txt\")\n",
    "gs_elgst = pygsti.io.load_gateset(\"tutorial_files/Example_eLGST_Gateset.txt\")\n",
    "\n",
    "#Data set\n",
    "ds = pygsti.io.load_dataset(\"tutorial_files/Example_Dataset.txt\", cache=False)\n",
    "\n",
    "#Fiducials, germs, and maximum lengths (necessary to label the plots)\n",
    "fiducialList = pygsti.io.load_gatestring_list(\"tutorial_files/Example_FiducialList.txt\")\n",
    "germList = pygsti.io.load_gatestring_list(\"tutorial_files/Example_GermsList.txt\")\n",
    "maxLengthList = json.load(open(\"tutorial_files/Example_maxLengths.json\",\"r\"))\n",
    "specs = pygsti.construction.build_spam_specs(fiducialGateStrings=fiducialList)\n",
    "strs = pygsti.construction.get_spam_strs(specs)\n",
    "\n",
    "print(\"Gates     (%d): \" % len(gs_target.gates), gs_target.gates.keys())\n",
    "print(\"Fiducials (%d): \" % len(fiducialList), map(str,fiducialList))\n",
    "print(\"Germs     (%d): \" % len(germList),map(str,germList))\n",
    "print(\"Dataset has %d gate strings\" % len(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x1097267b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RIGZMwwgQW7gWEJFdcMsAvEj/5e0MI27qgKnA3ar6Wq6DzJiOeUBQD7SNqufzwK9z7bSm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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096a0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.gate_matrix_boxplot(gs_target['Gx'], mxBasis=\"pp\", mxBasisDims=2, xlabel=\"testX\", ylabel=\"testY\", title=\"mytitle\", boxLabels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10aab04a8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097304a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1C/btg02b4KGHIFIV3dqvvuKybt1ITkqqChvYowfb9u6luKysKqykvJxO999Ph/vuY8i0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109730ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errgen = pygsti.tools.error_generator(gs_mc2gst['Gx'], gs_target['Gx'], \"logG-logT\")\n",
    "hamProjs = pygsti.tools.pauliprod_errgen_projections(errgen, \"hamiltonian\", \"gm\")\n",
    "stoProjs = pygsti.tools.pauliprod_errgen_projections(errgen, \"stochastic\", \"gm\")\n",
    "\n",
    "pplt.pauliprod_projection_boxplot(hamProjs,title=\"Hamiltonian\",boxLabels=True)\n",
    "pplt.pauliprod_projection_boxplot(stoProjs,title=\"Stochastic\",boxLabels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many of the plots in the following examples plot groups of boxes.  Each group corresponds to a particular \"base\" gate string that is sandwiched between sets of fiducial gate strings to create the group.  Here we create a \"gate-string-dictionary\" which associates a base gate string with a (x,y) coordinate pair.  Plotting functions take sets of X-coordinates (`Xs`) and Y-coordinates (`Ys`) to plot, along with a gate string dictionary (`gateStrDict`) which serves as a lookkup table for base gate strings.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Collect data we need for making plots\n",
    "Xs = maxLengthList[1:]; xlbl = r\"$L$ (max length)\"\n",
    "Ys = germList; ylbl = \"Germ\"\n",
    "gateStrDict = { (x,y):pygsti.construction.repeat_with_max_length(y,x,False) for x in Xs for y in Ys }\n",
    "\n",
    "#remove duplicates by replacing duplicate strings with None\n",
    "runningList = []\n",
    "for x in Xs:\n",
    "    for y in Ys:\n",
    "        if gateStrDict[(x,y)] in runningList:\n",
    "            gateStrDict[(x,y)] = None\n",
    "        else: runningList.append( gateStrDict[(x,y)] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## $\\chi^2$ Plots\n",
    "\n",
    "One of the most straightforward ways to evaluate GST estimates is to ask whether the estimates correctly reproduce the observed frequencies of the counts (or \"clicks\") for the experiments. The answer to this question is to compute the $\\chi^{2}$ function for the gateset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10ae76ef0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k9gcX210HAAAswabBxbFHqhIADo8zLrbOtx6LMW/zM2eLMW/zM2eLMW/zM2eL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\nQJOfEaU7HsgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aacd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Demonstrates a blank chi2 boxplot\n",
    "pplt.blank_boxplot( Xs, Ys, gateStrDict, strs, xlbl, ylbl, sumUp=True, ticSize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10e8e62b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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xv2bvWHH34YdXV1KCgoaC7Xhg0b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nsvKisrTc01m/0YLl++nHXe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\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NiwAQ8//LDhWUbvL8nxEAAOHz6Mvn37Ii4uDs8//zyOHj2qewYAbNq0Cb16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\n48dFjymunGWVmJiI3NxcbNy40bBxQ5JkbVg5u3+Z9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jXKGjx4sGi7duzYoWuWszhzjNL7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\nVFZWIjY2Fos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ZQw891PLv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euH1atX69Zfkow4t5G+5iA5j5Jqw5qamstq/u6770Z+fr7T17zktRRArjZOnDiBqVOnXjVn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\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i8OHDfpnzds+N1NRU7NixAzk5OejZs6fVm+Oz1tIHMLvPFh0djU8++QQvvfQSnnrqKUPj\n2LVdlsYxrP9oqlwlJSUsVys2f/585OTkYNu2bejRo4cpMSRz3q7nlxUk+jZ2zo3AwEBEREQAMLe/\nIcmu55ddywVc2O8tLCzEiy++iKVLl1q7YT6wY73RxrA1ERGR5YKCghAXF4ddu3bVv6e1xq5du5CQ\nkGB6/NraWpw9e9b0OGayeh+axa7luhiz8tDqfai1RlVVlelxzOTah3l5efXvaa2Rl5dn+D6UjGVX\ndt2HSUlJyMvLQ05ODnJzc5Gbm4uYmBhMmTIFubm5fjkBamdW1712kJiYiJ07d2L79u3Izs5GdnY2\noqOjMWnSJGRnZ/ttzts5N1JTU5GVlYXt27ejT58+Vm+OIazoA1g5JjKjz2bXdtlqHMO2Xs2Va+TI\nkRZumW/sWi7AOem/bds2bNmyxdT2SzLn7Xp+SZPq21xKucH2q/Wya7kuRmvNPGyFsXjHPxGRzaSm\npmLmzJmIi4tDfHw80tPTUVFRgVmzZhka59FHH0VycjJ69+6N06dPY+PGjdi7dy8yMjIMjQMA5eXl\nOHz4cP1dakePHsWBAwcQHh5uyoBBah+Wl5fjyJEj9eX67rvvcPDgQYSHh+Pyyy83NBYgVy5JknkI\nWHN+nTp1ChkZGXj//feRmZlpaBxAPg/nzJmDO+64A7GxsYiPj8eyZctQUVGBmTNn+nUsKXY9XpLl\ncjgcuOKKKy54r3PnzoY/5rdx+/Xtt9+a1n5JxpJmVbt89OhRHDx4EJ07dzbleEnmfOPcduW80Y9k\nlM5DO/Zt5syZg40bN2LDhg1wOBz1d4N07NgRoaGhhsaSPl6S7bJUbjz++OMYO3YsevXqhdOnTyMz\nMxP79u3D+vXrDY3jYte+jVQeWjmGtUt9KNlWAsDcuXMxa9YsW9XzgFy5JPNw3rx5yMzMxOuvvw6H\nw4Hi4mIAQIcOHQxvvwDZPoDk+WXH/rxk3wawZ2789a9/xZgxY+r7G5s2bTKtv2HX9kuaHeuNxx9/\nHGPGjKnvR2VmZmLv3r3YsGGDoXEA+f6G3eoNTvwTEdnMb37zG5SWlmLhwoUoKirC0KFDsWPHDlx2\n2WWGxikpKcE999yDoqIidOjQAYMHD0ZGRgaSkpIMjQMABQUFuP7666GUglIKCxYsAADMmDEDL730\nkuHxpPZhYWEhxo0bV1+uBx98EAAwbdo0rF692tBYgFy5JEnmISC3D4uLi/HHP/7xvHJlZmaaUi4r\n8vD48eNYtGhR/T7cvn27KXkoGUuKXY+XdLkaM+uO54KCAiQnJ1+0/Xr55Zf9NpY0yXY5JSXlgjyc\nPn264XnInDeGHfs2L7/8MpRSGDdu3Hnv/+1vf8O0adMMjfXxxx+fd7zmz58PALj99ttNO16SfQCp\nMdG9996L4uJitG/fHoMHD8b69esxevRoQ+O42LFvI1lvSI9h7VgfFhQU4IYbbqgv1wMPPADAWa4X\nX3zR0FjAf8qVlpZWX67s7Gy/znlArlyS9fyaNWuglMLEiRPPe/+FF17ArbfeamgsQLYPIBVLul2W\nItm3AeyZGyUlJZg9e/YF/Y3ExERD4wD2bb+kSbbLkv2oxv3eDRs2mNLvtaq/YZd6Q7m+MUFERK2T\nUioWQEFBQQFiY2NNjXXixAlT199Q+/btxWIFBQWJxaqsrBSLFRYWJhZLkmQehoeHi8X6+eefxWKZ\n8a35poSEhIjFktSmjdwvYknWG5LHS/KnKcrKysRide3aVSyWpMBAe34n/MyZM2KxJMfWpaWlYrEk\nf1/ernlYUVEhFkuynpf8mQjJdtl1l6oEyTZFch+eO3dOLNbJkyfFYnXo0EEslmR9KPmIXsmxuWQd\nJdkHqK2tFYt16tQpsVidOnUSiyWppqZGLFZAQIBYLMm+jcPhEIslqfFvfJupS5cuYrHs2p+XJNmP\nkrxW2bFjR7FYwcHBYrEkFRYWIi4uDgDitNaFTX1OrtdPREREREREREREREREREREhuPEPxERERER\nERERERERERERkR/jxD8REREREREREREREREREZEf48Q/ERERERERERERERERERGRH+PEPxERERER\nERERERERERERkR/jxD8REREREREREREREREREZEf48Q/ERERERERERERERERERGRH+PEPxERERER\nERERERERERERkR/jxD8REREREREREREREREREZEfC7R6A4iIyD0//vgjOnbsaGqMPn36mLr+hqqr\nq8ViBQbKNXdBQUFisWpqasRiBQQEiMVq166dWCyttVisTp06icWSzA3JfSiZh5Ik6w3J43X8+HGx\nWD179hSLdfLkSbFYZrf7VqmtrRWL1aaN3HfdJfsbl19+uVgspZRYrDNnzojFCg0NFYsl2X7Zta2U\nHDt06dJFLNapU6fEYkm2KceOHROLJTmGLSsrE4slOSay67hSMpZkH1uyXW7btq1YrH//+99isew6\nNpfMeYfDIRZLsg8guQ8l22XJcYpdSY5TJOt5yX6vZFt5qeMd/0RERERERERERERERERERH6ME/9E\nRERERERERERERERERER+jBP/REREREREREREREREREREfowT/0RERERERERERERERERERH6ME/9E\nRERERERERERERERERER+jBP/REREREREREREREREREREfowT/0RENrJixQpERUWd90pJSTElVn5+\nPiZPnox+/fohNDQUW7ZsMSUOACxduhRJSUno0aMHIiMjceutt+Kbb74xLR4AvPDCC+jfvz8cDgdG\njhyJjz76yPAY+fn5mDRpEiIiIhAcHGzqPnRZuXIlBg4ciHbt2iEhIcGUckmSzEMXidyQjCWZh++9\n9x5uueUW9O3bF0FBQWLHq1+/fggLC8OIESNskfN2Pl4uzz//PPr27YvFixebFkOiPlyzZg1Gjx6N\niIgIREREICUlBbm5uYbHaWjlypUYMGAA2rZta4t63kWiXFa0KZLHS7r9Mrvuzc/Px9SpU+vLlJWV\nZXgMK+Tn52PKlCno378/wsLCRMol2VZK5cakSZMQGRmJkJAQ27SVzz77LMaMGYM+ffogKioK06ZN\nw+HDh02LZ5Xnn38eERERWLJkiWkxJPJQ8nhJ5uH777+PqVOn1rddEnWU3cawLpJ9AKl+lFTd++yz\nz2Ls2LHo27cvBg0ahOnTp5teH0q1X9J9UUl26wO4SJ1f0v1eu/UPJUkeLyvGDhI5b/frh2bH4sQ/\nEZHNDBo0CPv3769/rV+/3pQ45eXliImJwbJly6CUMiWGy969e3H33XfjnXfeQVZWFqqrq3HzzTej\nsrLSlHjr16/H/fffj0WLFqGwsBDR0dEYN24cSktLDY3j2ocrVqwwfR8CwIYNGzB//nykpaXh448/\nRkxMDG666SbDyyVJMg8BudyQjCWZh65Yzz33nNjxmjdvHhYvXoxPPvkEMTExSElJsUXO2/F4uXz6\n6ad44403MHjwYNNiSNWHvXv3RlpaGnbv3o3du3cjKSkJ06dPx6FDhwyN4+KqN9LS0lBQUIDo6Gjc\neOONfp3zgFy5rGpTJI6XdPslUfdWVFQgOjpa7HhJkS6XZFspFct1Li9fvtxWbeW+ffvw3//938jN\nzcXmzZtx7tw5TJo0ybQxkRUOHDhgeh9AKg8lj5dkHpaXlyM6Ohrp6ekcw/rAij6AVD9Kou7dt28f\n7rrrLuTk5ODNN99EdXU1Jk+ebOo1Isn2y259G8CefQBA7vxi/9C/SB4vK3JDsk2x4/VDiVhKa23Y\nyoiIyHhKqVgABZs3b8bVV1/d7GdXrFiB3NxcvP32217F6tOnj1fLhYaGYuPGjZg4caLby5w7d86r\nWABQWlqKyMhI7Ny5EwkJCS1+PiwszKP1jxw5EvHx8Vi+fDkAQGuNvn37Yvbs2ViwYEGzy9bU1HgU\nyyU4OBiZmZke7UNPOz4JCQkYPnw40tPTATjLFRkZiXvvvRfz589vdtmAgACPYvni7NmzXi3nTR4G\nBQV5FMOX3PCUXfPQJSgoCJs2bfIolqd5OGLECAwfPvy8fdinTx/cd999hh8vX3hbH7b243Xs2DGP\n45SXl+Omm27C448/juXLl2PIkCFIS0trcbmePXt6FMeX+vDkyZMexWpswIABWLJkCaZNm9biZzt2\n7OjRuhMSEhAfH49ly5YBcJYrIiICs2fPbrFcbdrIfSe8trbWo8/7Ui5vzy9v2pTAwECPYvhSLk/P\nZV/aFE9j+VL3njlzxqNYLg6HAxs2bMCECRPcXiY0NNSrWN6oqqryarmwsDBs3LjRo3KFhIR4FEOy\nrfQlVnV1tVcxQ0JCkJGR4dG57G196E1befr0aa9iAcDx48cxcOBAbNu2DSNHjmzx8562Kb748ccf\nPV6mvLwc48ePx2OPPYYVK1ZgyJAhWLhwYYvLeTqG9SUPy8rKPIrVkKfHq127dl7F8SYPvR1/tW3b\nFuvXr/eojgoODvYohr+MYSX7Np7yJZa340pv6t7y8nKvYgHO8ysqKgpbt2516/zq1KmTR+v3pd6Q\nvL7h6fklyV/6AJ7WG76cX97mhkS/11/6h5IkxyneXrfxZuzg6XVRX3Le27lou10/9CVWYWEh4uLi\nACBOa13Y1Od4xz8Rkc18//33GDVqFH71q18hNTUVP/30k9WbZLiysjIopdC5c2fD111dXY2CggKM\nHTu2/j2lFJKTk7F//37D40mprq5GYWEhxowZU/+eUgpjx47163JJkswNu+ahpOb24b59+yzcMmrO\nww8/jOTkZIwaNcq0GFbVh7W1tcjMzERlZSWGDRtm+PpdOX+xcvlzzrNcxsWyuv3y5+NlV5LHi7lh\nPDPHRFaQ6gNYlYd2O14S7DqGtaIPYLd+VGNWXSOy0z40k133oV3PL/YPqSnMef+JxYl/IiIbGTp0\nKJ588kmsWbMG//M//4N//vOf+N3vfoeKigqrN80wWmssWLAACQkJuOqqqwxff2lpKWpqatC9e/fz\n3u/WrRv+9a9/GR5Piqtc3bp1O+99fy+XJMncsGseSmpqH3bv3p37sJV666238Pnnn+PBBx80NY50\nffjFF1+gb9++6NGjB+bPn4/XXnsNgwYNMjxOczlfVFRkeDwpLJd5sSTbL9a9rZPk8WJuGEtrjYce\neggjRozAlVdeafXm+Oztt9/GF198gQceeMDUOFblod2OlxS7jmFbQx/A3/tRDZl9frH98p1d96Fd\nzy/2D6kpzHn/ieXZswiJiKhVS0pKqv970KBBiImJQVJSErZt24apU6dauGXGmTNnDr766ivk5uaK\nxtVa2+431gD7lkuS5D7k8fId92HrdOzYMSxevBjr1q3z+FFzRjErNwYNGoQ9e/agrKwMW7ZswT33\n3IOsrCxTJv8vxq45z3IxFpmHudH6zZs3D1999RV27Nhh9ab4zM59ABc7Ha/WwK71Bute78ybNw+H\nDh1Cdna2aFw77UOr2HUfslz+FYt8Z9fj5c85z4l/IiIba9++Pfr164fvv//e6k0xRGpqKnbs2IGc\nnByPf8/ZXV27dkVAQMAF31QsKSm54Nt4/sRVruLi4vPe9/dySZLMDbvmoaSm9mFxcTH3YSt08OBB\nHD9+HOPHj6//3beamhp88MEHWLt2LY4cOWLYIEi6PgwMDERkZCQAICYmBgUFBVi9ejWeeeYZQ+M0\nl/ON75TzJyyXebEk2y/Wva2T5PFibhhn/vz52LlzJ7Zv344ePXpYvTk+++yzz5rtAxw+fNjwPoBk\nHtrteEmy6xi2NfQB/L0f5TJ//nzk5ORg27Ztpp1fbL98Z9d9aNfzi/1Dagpz3n9i8VH/REQ2Vl5e\njh9++AGXXXaZ1Zvis9TUVGRlZWH79u3o06ePaXGCgoIQFxeHXbt21b+ntcauXbswcuRI0+KaLSgo\nCLGxscjLy6t/T2uNvLw8vy6XJMncsGseSmpuHyYkJFi4ZXQxo0ePRk5ODrKzs7Fjxw7s2LED0dHR\nmDx5Mnbs2GHoN5+trg+11qiqqjJ8va6cv1i5/DnnWS7jYlndfvnz8bIryePF3DDG/PnzsW3bNmRl\nZZk6JpKUmJhYPzGenZ2N7OxsREdHY9KkScjOzja8DyCZh3Y8XpKs7rOZxYo+gN36UcB/zq8tW7ZY\ndo3I3/ehFLvuQ7ueX+wfUlOY8/4Ti3f8ExHZyBNPPIExY8agd+/eKCoqwvLlyxEQEICJEycaHqu8\nvBxHjhypvyvj6NGjOHjwIDp37mz4oGvOnDnYuHEjNmzYAIfDUf+tuI4dOyI0NNTQWAAwd+5czJo1\nC3FxcYiPj0d6ejoqKiowa9YsQ+OUl5fj8OHD9fvw22+/xYEDBxAeHm7KwHXu3Lm44447EBsbi2HD\nhmH58uWoqKjAzJkzDY8lRTIPAbnckIwlmYfSOZ+amoqZM2eKHC8pdj1eDofjgkffOxwOdOrUCVFR\nUYbGAuTqw0cffRTJycno3bs3Tp06hYyMDLz//vvIzMw0NI7LnDlz6ssVHx+PZcuW+X09D8iVS7pN\nkTxeku2XVN0rfbykSJdLsq2UzI2G7dfRo0dt0VbOmzcPGRkZeOONN+BwOOrvgu7QoYMpYyIpTfUB\nOnfubEofQCoPJY+XdP+wYR313Xff4eDBgwgPD8fll19uaCzAnmNYQLYPINmPkqp7582bh8zMTLz+\n+usi9SH7Nr6zYx8AsO84xY79Q0mSx8uuY1g7Xz8UiaW15osvvvjiqxW/AMQC0Js3b9aHDx9u9jVh\nwgTdvXt3HRISonv27KknTpyod+/e3eJyrldVVZXbr5ycHK2U0m3atDnvNXPmTLeWLy8vd/t1sTht\n2rTRL774olvL19bWevx6/vnndUREhA4NDdUjRozQH374oVvLVVdXu/3atWtXk/vQneXPnTvn8eu5\n556rL9fw4cP1/v373VpOkmQeSuYG8/Bck7FmzZplWh6+8MIL5+3Djz76yOCM9Z1dj9ePP/7o0ysh\nIUHfeeedbn1Wsj48ceKE26/p06fXx+jWrZu+7rrr9ObNm91evqamxuNX43rjgw8+cGs5SZLlkmxT\nJMsl2aZ4w9u6t7Ky0u3Xzp07L3q8br/9dreWl3TmzBm3X82Vy53lvSHZVnob6+zZs26/cnNzmzyX\n3Vlesq3897//7farqTHRqlWr3Fpe0g8//ODTy9UHcOez3vA2DyWPl2QeVlRUuP3asWPHRWPNmDHD\nreXtOoaV7ANIxpKse3/++We3X82dX+4s7w1v6w3Jvmhr5w99AMnzq7X3e/2hfyhJ8nhJjh0kc57X\nD32PVVBQoAFoALG6mfkkpZ2TSkRE1EoppWIBFGzevBlXX321qbEkv0F87tw5sVhhYWFisWpqasRi\nGfnYy5YEBASIxTp79qxYrKCgILFYkpiH/kWyPpQ8XseOHROL1bNnT7FYJ0+eFIvVsWNHsVht2sj9\nClxtba1YLMnzKzBQ7oF6kueyZKwzZ86IxZK8Q9qMn/doSkhIiFgsSdXV1WKxJOvD06dPi8WSbFN+\n/PFHsViSY9iysjKxWO3atROLJTn+Cg4OFoslOXaQ7NtIkhxXlpeXi8Xq1KmTWCy7nl+SJPsAdr32\n5c9PBmotJMcpkuMvyeuiknPRdr1+WFhYiLi4OACI01oXNvU5udEMERERERERERERERERERERGY4T\n/0RERERERERERERERERERH6ME/9ERERERERERERERERERER+jBP/REREREREREREREREREREfowT\n/0RERERERERERERERERERH6ME/9ERERERERERERERERERER+jBP/REREREREREREREREREREfowT\n/0RERERERERERERERERERH4s0OoNICKiFoUCwJEjR0wPVFpaanoMl3PnzonFCg0NFYtVU1MjFksp\nJRYrICBALFZ1dbVYrMBAe3aFmIf+RbI+lDxekm3KsWPHxGKVl5eLxWrXrp1YrDZt5L4TXltbKxZL\nsj6UrKMkz2XJWFVVVWKxQkJCxGKdPXtWLFZwcLBYLEl2bSsrKirEYrVv314s1r/+9S+xWCUlJWKx\nTp06JRbL4XCIxZIcfwUFBYnFkmyXJfs2kiTLVVlZKRZLsj606/klSbIPIDkmkswNyX6vXUmOUyT7\nopLXRbXWYrHsev3wyy+/dP3Z7GSHktzZRETkOaXU7wCss3o7iIiIiIiIiIiIiIiIyDLTtNavN/WP\nnPgnImrllFJdAKQA+A7AGWu3hoiIiIiIiIiIiIiIiASFAogEsENrfbypD3Hin4iIiIiIiIiIiIiI\niIiIyI/J/WgJERERERERERERERERERERGY4T/0RERERERERERERERERERH6ME/9ERERERERERERE\nRERERER+jBP/RER0yVNKXaaUGq+UWqyU2qaUKlFK1da9XrF6+4iMppSKU0o9opTaoZT6USl1Ril1\nSil1SCn1ilJqlNXbSCRFKfVUgzq/VimVZPU2ERlFKRWklLpTKZWtlPqpQX3/VV19P9LqbSRyhxn9\ndaXUL5VSf1dKfV13XpTV/Z2plPqjUsphdDmI3KGUaq+U+q1S6mml1DtKqW+UUv9WSlUppYqUUruV\nUvOVUuHNrCNQKZWilHpWKfV+3TlzVin1s1KqoK7/00+yXESeUEr1UkotVEp9qJQqVkpVKqV+UEq9\np5RaopQa4sG6wpRS3zZoN741c9uJjOi31OXtJKXUyrrz4ERdPV6qlNqrlEpTSnX3YJvGKaXeUEod\nUUqVNzinNiulfqOUUt6XmC51RlxnVErNbHRtprnX7W5uV6hS6m6l1E6l1D/rtutfSqlCpdRzSqnr\nfS9966O01lZvAxERkaWUUrWN3mrYOK7VWv9ecnuIzKSU2gMgse5/L9YRdA32/hfAnVrrapENI7KA\nUioGwEcAAhq8/Sut9R6LNonIMEqpvgC2ARhc91bjOt9V36/QWs8R2zAiLxjZX1dKtQPwdwC/vsi6\nAOe5oQH8Qmt90NNtJfKVUmosgBxcvK9e/zEApQCma613Nlq+fMOXDQAAIABJREFUK4CvALi+GNBU\nn/8sgAVa6xU+bzSRgZRSdwJ4BkB7NJ2/y7TWqW6u72kAqQ3W9b3Wur8R20p0Mb72W5RS1wDYC6Dt\nRZav/xiAkwDu0lpvbGZdwQBeBzC5iXW5xgTvAZiotT7Z3LYRNWbUdUal1EwAa5pYR2N3aK1fa2G7\nEupi9mtmuz7VWse6Ec+vBFq9AURERK2EqwPwI4AvAaTAvY4Gkb/pCWdu/wRgI5yDux/gnPgcCWAe\ngN4AZtS9N92azSQyV90dDS/BmefFALqD9T7ZhFIqEP+Z9NcADgBIB3AIzovoiXDW920BzFZK/aS1\nfsqizSVyl8/99bq7+LMBJNQtux3APwB8DedTMSMADMN/vhRAZJUfAOQBKIQz54/BmaOXA5gK5wRO\nVwBvKaXitdafNVg2BM5Jfw3gUwBvAfgAQBGAjgBuBDAbQBiAdKVUhdb6ZYlCEbVEKTUHwLNw5u/3\nAFbBmb8n4RynDgJwC4DGE6tNre8XAP4EoBJANYAOxm810UX50m/pAGc/XQN4H0AWgI8BHAdwGZxt\nwJ11n1unlDqptd7RxLqeq/u8hnPc+ySAT+A8H64B8ACc/Z9EOPtEN7ldQiInM64z3gBn36cp/2xu\n4bonDGQDcAA4DWA1gF0A/gVn/6kfgHFwXgeyHd7xT0RElzylVBqcd3x+pLUuUUpFADgKZ6eFd/yT\nrSil3gawFsAmfZGOYN0jQ/fCeUFFA/il1jpfdiuJzNfgouKXAN4E8Gc4c553/JPfU0pNgfOii4az\nTk9qXOcrpWIB7IPzhoB/A+imta6R3lYidxjVX1dKrQRwN4AaAL/XWv9vM59to7V2a2KJyEhKKXWx\nfnqjz/wXnP0XDeBNrfXUBv/WC8ArAB7RWn/UxPLxAHYDCAVQBqCP1rrcoCIQeUUpNRzOSU4FYAuA\n32qtq5r4bEBL/RalVBsAHwL4BYCFcE6URgD4jnf8k5l87bco589x3Qdgkdb6UBOfuRnOdgAAjmit\nB13kM93gnIxVAH4GcI3W+lijz7SH80vCkXXbN0xrXehuWYmMus7Y6I7/flrrH7zcng5wXufpAeAw\ngGSt9Y9NfLbFtsQftbF6A4iIiKymtV6std6mtS6xeluIzKa1vllrndnUxUSt9Qk4v43rMvVinyPy\nZ0qpywEsgXNA+Uc473YgspOEBn8/cbE6v+6CXhacFwI7AbhSaNuIPGZEf73u513+AGfd/0xzk/51\nMTnpT5ZoadK/7jNvwfkUFwVgdKN/+0lrPa6pSf+6z3wIYGXd8h0B2PI3bsnvrIJzvuJ7ALc1NekP\nAG5O1MwBEAvnT188acgWErnB136L1nqf1vq2pib96z7zNoBNcNbjA5RSQy/yseH4zxzgmsaT/nXr\nOQXnk8FcRnqzzXTpaoXXGRfB+RSCcwCmNjXpD7jdlvgdTvwTERERUWPvNPh7gFUbQWSiVXA+OvFV\n3t1PNhXc4O9vm/nckSaWIbKje+C8OF4FTgCRPZyq+2+ol8vvbvA3+/xkKaXUCABD4fxy1lNa60of\n19cXwOK69f1Ra33O960kanVaqsc5JqDW4p0Gf5vW51BKhQKYBWfdv7nRTyFdMjjxT0RERESNNRzo\n2fLbr3TpUkr9BsB4ACcALLB4c4jM0vDuoOYeZeu66KIBfGPe5hC1ClPgzPU8rfXPgPPxnkqpvnUv\nXugmv6GUugL/mST9ysvVhDT4m31+stqvG/yd4fpDKdVFKTVQKdXRw/WtgvO3nV/jF33Jxlqqxz0d\nEzRehsgoUtcZr4fzaXaA86fvAABKqbZ1bUk3E2O3Gpz4JyIiIqLGrmvw95dWbQSR0eouGC6H8yL5\nAq31cYs3icgsb8D5m80KwAN1v3F7HqXUL+D8EowGsE5rfVp2E4nkKKUGAgiv+999dRNJq+H8vdvv\n6l5lSqntSqlEa7aSqHlKqbC6i9apcN45F1j3T+lNL9WsXzb4m31+strwuv9+q7UuVUr9QSl1CEAJ\ngK8B/KyU+lwp9SelVFBzK1JK3QrgRjjr+PmmbjWRtZqtx7XW/xfA+3COCWYppXo2/oxSqj2cP4sB\nOO/832nCdhJd1+Bvd/ocryql/p9SqkopVaKU2qeU+h+lVK8Wlhve4O99SqlfKaXehfMpSV8D+JdS\n6ielVLpSqqtnRfAfnPgnIiIionpKKQXggQZvbbBqW4hMsBRAdwDva63XWL0xRGap+1LL7QDKAYwC\n8JFSaoZSarhSaqxSKg3OSaMgAAUA7rdsY4lkDG7wdxsABwHcBefPvui6VzCAFADvKqXmXbAGIgso\npWYqpWqVUrVw1ulfA3gaQDc48/avWut/eLHeHgDuqPvfEpz/uGgiKwyGM6e/V0qtg/OO/YH4Tx2t\nAVwJ5xddcusmKy+glOpU9xkN4AGtdanAthOJU0rF4D9f4j2otW7qTv3fw/mY/3AAhUqpuUqpXyql\nRiml/gDgUwCRAIoBTOPPYpDRvLzO+EsAPeD8kmM4gHgAfwFwWCn1380s5+rzVwO4BUAugESc35Z0\nB/AnAJ8opYa4XxL/wYl/IiIiImooFc4OtQaQqbX+xOLtITJE3R2c/wfOAeDdFm8Okem01lsAxAH4\nO5yPg14LYB+AHABpcE4gzQGQpLUusWo7iYSEN/j7z3BeSNwGYBicv4/eDcAfAfwbzrvinlJK3SS9\nkURN0Bd5fQogXmv9sJfrXA2gfd26lmitzxqxoUTeqJsUcj3KfxSA2wAcAzAdzvrbAeck0H44czYR\nwMtNrO5pOCd19mqt/27iZhNZpu7niV4GEFD31l+a+qzW+hs4+zsL4fzC4zNwftnrPTi/YNMbzvMm\nVmv9kYmbTZcuT64zHoHzho0pdcvEA7gVzsf218LZb1+llLqzieVdfX4FYBmc13/+AqAvnD+NMQTA\nq3Xb0gvAZqVUW28L1lpx4p+IiIiIAABKqV8C+Gvd/xYBuMfCzSEyTN3jQF+s+99ntdZfWLk9RBLq\n8n4WgP+qe6vxpFF3ADPg/B1EIrtreEEvGM7H2E7UWhdqrau11se11i8CmAjnRUUAeEJ6I4ku4k0A\n19S94uGcEH0Tzi90/UMpNd7TFSql/gJnrmsAeVrrlcZtLpFXHHBO0gDOiZlyAL/UWr+htS7TWldp\nrfMBjIXziS0KwFSl1LUNV6KUSoLzSRb8oi/Z3QtwfsFXA3hVa72thc//F4BpOP9JR65XEIDf1v07\nkaE8vM64SWsdpbV+UGu9WWtdUPfaqLW+FcDNcNbvAJCulOp2kXW4+vxBcM5//15r/YTW+v9prc9p\nrb/SWv8fAC/B2Zb0hw3bC078ExERERHqHm+1Cc7HaFUC+DUfi0g28hc4Hw36PYAlFm8LkemUUg4A\nuwA8CKAzgCcBXAXnxfSOAG4AkA/gWjjvcpjTxKqI7OJM3X9dE0sPaK114w9prd+Hsz+kAAyx6+M/\nyX9orU9qrb+oexVorTdorafC+XMu/eGsw293d31KqWlw9oU0nI9+5kQPtQZnGvytAbyktT7c+ENa\n6zM4/87m37r+qLsD2vVF32Va68/N2FAiqymlHoLzSXYawIcA7m3h888AeAXAFXB+cWwUgHYAwgDE\nAlgD593QTyqlNtY9gYPIZ55eZ9Ran2pufXVfcFkCZz/dAed50JirPXH9BMbrTazuzwCq6v6+tbm4\n/ogT/0RERESXOKVUPwA74JwcOgfgt3UXvon8nlLqCjgnPzWA2VrrSos3iUjCYvzntwx/r7X+s9b6\n67q7HE5rrXcB+BWcj/lUAJYqpa6xcHuJzNbwQmKp1vpgM5/d0eDvYSZtD5FPtNbr4HzsbQCA5+t+\n17xZdU8HeAXOev8YgOu11sWmbiiRG7TWNXBO1rgmHHOa+fguOMes+P/bu/M4yaoqweO/wyIgAkIV\ngyxWDYssiqwissmmgriA0LbQgjSlNtDaHwTHZWba0VFh2h1pG8UNUCzRlrZYRFppQNwQZV9tG6sE\nBGUvQIu1zvxxX5CvwoiMiMyIjIys3/fziU/ciDjvvhNb5ot33r2PZf9G/yOwOXAHZTtImnEi4mjg\nRMo2/i3Aa8b7fVv93T++ij89M/8qM6/IzCWZ+URmXpeZb2Ps4PiDceZH9cEA9zN+kfJ5hnIKmGb1\nbf4ftOskMx8AfkX5v7NtRKzYLnYUrTTsBCRJkjQ8EbEBcDHl3FZLgaMy84LhZiX11fGUaZ1vA54T\nEW9qEbN1rb1vRKxftc/zQAGNqKMoO0T+MzPPahWQmUsj4gOUkf8rUE4L8O4py1CaWndU11lrd4oF\nWHcw6Uh9cS7w15RpbfcHzm4XGBF7UQ4UWBl4ANgvMxcNPkWpa3cAL6i1W8rMxyPiPsopi+p/o99L\n+Rt/MfC6NoOWG1NAr177TXBPZl46mcSlqRARh1Gm+E9gEeXgrQc6LFYfEf2BceL+H+U87KsD86r1\nSBMyyP2MmXlvRNwPzAI2bBFyR5t2K43HVwTWAe6dfIbTg4V/SZKk5VREzKKMptiY8uPxndXoIWkm\nWaW63hT4ZofYYGyHSFK+G7cPKC9pICJiPcqOiwSu6RB+Va295cCSkobv5lq704ie+uNPDyAXqV/q\nO6jntguKiJcC5wGrUkbC7e806JqGbmas8N/t3+n63+hnVdfzqst41mXsd8FllBmQpGkrIl4PnEn5\nvXoXsG9m3t3FoltV138cL746oOYmYGf8TaBJmKL9jH9xuq4at/lxqn9JkqTlUkSsSZn2aivKRvP7\nMvMLw81KGpjs4tIqVhpFT9XanQ72X7nNctKMkpmLgRsoO8z/e4fwTWvt3w8qJ6kP6iPdHm0VEBHb\nAN+nnM95CfDazPzVFOQm9eryWnuTdkERsQYwu7pZ/xvdzfZ+tomVpq2I2Bf4FmW7/n7KSP9FXS7e\n2L7vZgBw43eBvwk0IVOxnzEiZjP2P+CuFiFd/S+pNLb5H+ti9oyRYuFfkiRpORMRqwEXAttTNsY/\nmpmfHG5W0mBk5lGZueJ4F8bOaZjAXtX9K2Wmo/01ih4AHq7au0TEeL/796q1Fw4sI2l6+Lfqes2I\n2GecuINr7Z8MMB9pst5Ya9/Q/GBEbM7Y+XWfAA7OzB9PUW5Sr77LWBH+DePEHUw5iAvgmc9zp+39\napu/sW3/u9r9+/b9mUh9EhG7Agsos9gtBl6Vmbf20EVj+35WRGwxznrWppz+LvE3gSZgCvczHs3Y\n/4AfNT+YmddQToURlNO+tDzvS0RsDGxX5frTAeQ5VBb+JUmSliMRsTLlh+OulA3ckzPzg8PNSppW\nWv4wlEZFZibwPcpneQPgf7eKq3bw/VPtrr6cd1Gaxv6FMs15AJ+uRo0uIyIOpxwQk8AFmemIf025\niDgyIlbpEHM8cEB187fUCqDV43Mo59ddjzJ687DM/PcBpCv1RTWC+V8pf6MPi4i9m2Mi4nnAR6qb\nTwCnT1mC0hSLiO0o2+fPpszq8urMvLbHbs6vtU+u9gc1ryeAUxg7Xcb5zTHSePqxnzEi5laf+fFi\nXsvYqRkfo/3/gI9X13Nr8fV+VgROZaw+/vlech0FUfYJSJK0/IqI3YDNanfNBj7B2FF/X6nHZ+aZ\nU5ed1F8RcQ5lBEUClwDv6rDIE5n5m4E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F38uy57CfiHphedtJ9jUVltbaz+4Q+3ZgZcam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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa03320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tv5vvJ3nrVL/PnA8WHQ8r+1vgEJqnoK5qF37ONUcCFwKHAX8I7Ap8Ockefa1q\nB7VB7y9pfjZzUpKFwI3Ab4BjgKXA3wAT3wp09noP8Fbg7cDzgDOBM5Oc0teqJm8vmgXdJwNPWiiW\n5N3AKTRjfAmwiea4sNtMFjlJE41lT+Bg4L/THN/+mObuw1+cyQKnaMKfzbgkr6P52dwzQ3VN1/b+\nrv0n4AbgB8BRwCBwLrPzfkrb+9l8GDgaOJHmuHABcFGSiR+s1K2UMqe/gG8B/9TxOsDdwJn9rq3C\n2AaAzTR3G+17PdMcw9OAHwKvBL4GnN/vmqY5jg8CX+93HRXHcw3w8a62K4FP97u2aYxlM3B8V9u9\nwBkdr/cGfg38l37XO9WxbKXPi2huIPiMftc73fEAv0dzX6KlwJ3Aaf2udbrjAUaAT/W7tkpjuQN4\nX1fbrcA5U9n3nJ6x6HhY2VfH20rzf2K+PKxsIU2qvL/fheyAjwLXlFL+rd+F7KDXALcmuaI9TTWa\n5KR+F7UDvgksS/L7AEkOAl4KXNfXqipI8mxgMVseFx4EbmZ+HRce6Hch09He7PDTwHmllNX9rmdH\ntGM5FvhRkpXtseFbSV7b79qm6ZvA8UmWACR5BfD7TPFunHM6WDDxw8oWz3w59bR/YS8A/r2U8oN+\n1zMdSd5AM417Vr9rqeAA4G00sy9HA/8CfCTJ5J7jPvt8EPgcsCbJI8B3gAtKKZ/tb1lVLKb54J2P\nx4XdaX52l5dSHup3PdP0HuCRUspF/S6kgkU0s7LvpgnlfwRcDVyV5Mh+FjZNpwKrgbvb48J1wMml\nlBunspNZe4OsHRQmOLc3R3wMeD7Nb5FzTrtw6QLgj0opj/a7ngp2Am4ppXygff29JC+gCRuX9q+s\naXs9zXnUN9CcGz4Y+Kck95ZSPtPXynpnTh8XkuwCfJ5mDG/vcznTkmQIOI1mvch8MP7L+f8qpXyk\n/fPtSf4A+CuatRdzyWk0a/yOozlVdRTwsfa4MOlZ57keLKbzsLJZL8lFwKuBI0sp9/W7nmkaAn4X\n+E47+wLN7NJR7QLB3dvTVnPFfTRJvtNq4IQ+1FLDecDfl1I+377+fpJn0cwuzfVgsY4mROzHlseB\nRcBtfaloB3WEimcCr5zDsxUvozku3PXEYYGdgfOT/HUp5YC+VTY9Y8BjbP3YMKd+KUyyAPgfwGtL\nKSvb5v+T5BDgncCkg8WcPhXS/ib8HWDZeFv7IbaM5lzRnNOGitcCryilrO13PTvgeprV0QcDB7Vf\nt9L8dn+lXyltAAACEUlEQVTQHAsV0FwRcmBX24HAz/pQSw178uTf3jczx48JAKWUO2nCRedxYW+a\n38Tm3HGhI1QcACwrpczVK5GgWVvxQp44JhxEs9D2PJqrreaU9jPo2zz52PBc5t6xYdf2q/u48DhT\nPC7M9RkLmEcPK0vyMWAYOB7YlGR8JmZjKWU2Xrq0TaWUTTRT7L+VZBPwizm6YOvDwI1JzgKuoPmQ\nOonmMtq56BrgfUnuAr4PHErzb+eSvlY1SUn2Ap5DMzMBcEC7APX+UspdNKfh3p/kx8BPaS7/u5tZ\neJnmRGOh+dD9Ak1APw7YteO4cP9sPM04iZ/Nhq7+jwLrSik/mtlKJ2cS4/lH4LNJbqC58u1VND+r\nl/ej3olsbyxJvg78Y5KHaYLRfwbeBPz1lL5Rvy95qXTZzNtpDh6/Bm4CXtTvmqY5js006bD76039\nrq3S+P6NOXq5aVv/q4HbgV/RfBi/pd817cBY9qIJ5XfS3OPhRzT3Stil37VNsv6Xb+Pfyyc7+pxN\n88H8K5pV7c/pd91THQvwH7eybfz1Uf2ufbo/m67+P2EWX246yb9rbwb+b/tvaRQ4rt91T2csNKcL\nPwHc1Y7lB8DpU/0+PoRMkiRVM+fPp0qSpNnDYCFJkqoxWEiSpGoMFpIkqRqDhSRJqsZgIUmSqjFY\nSJKkagwWkiSpGoOFJEmqxmAhSZKqMVhIkqRq/j8Lv8XzC62scAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c149400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compute the chi2 scores for the estimated gateset using the MC2GST algorithm\n",
    "pplt.chi2_boxplot( Xs, Ys, gateStrDict, ds, gs_mc2gst, strs, xlbl, ylbl,\n",
    "                   scale=1.0, sumUp=False, histogram=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because most of the $\\chi^{2}$ values are close to 0, we conclude this estimated gateset does a good job of reproducing the observed frequencies. Which it should, since the figure of merit we were using in our estimation was the $\\chi^{2}$ function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10d1a94e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1z4j1ouzx7YcffpCaBwAhISHSsrSs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XOQn58vbJzSw5kzZzBp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\nSElJQUFBAfLz89G7d28lGY5Ei3sozZHKWFpaan7MaDS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GgeAFRUVIjmBQUFieYB9oxxZC1d10XzampqRPMAIDAwUDRPeq4ByM+n/P39RfMC\nAgJE80gN6XZTW1srmmeHs2fPiubZMb61bdtWNE/6vCg9fpMa0vVoR7uR7v8tWsh+/6l3796ieQDH\nGyewY21jx7pf0r59+8Qze/XqJZonvT4FZK/5GZmD85uuREREREREREREREREREQmcNOViIiIiIiI\niIiIiIiIiMgEbroSEREREREREREREREREZnATVciIiIiIiIiIiIiIiIiIhO46UpERERERERERERE\nREREZAI3XYmIiIiIiIiIiIiIiIiITOCmKxERERERERERERERERGRCdx0JSKvZGdnIzo6GsHBwUhK\nSsKuXbuU5CxevBixsbEX3YYPH64k61JLly5FTEwMQkJCkJycrKyMALB9+3aMHj0a3bt3R0BAAHJy\ncpRluUmWb8GCBRgyZAi6deuGHj16YMKECTh48KCyPLfs7GxERUUhKCgIAwYMUFpGst6LL76IlJQU\nREREoGfPnpg4caLSdvPaa69h8ODBiImJQUxMDO6++24UFhYqywOAjz/+GKNGjUJERAT8/f2V933p\nsUa6Dt2kzlEA8Mknn2DMmDEN42lubq6yLFLL6e3G3f8jIyPRqlUrx8017MqUbDeNMzm/ad4k69CO\ndQYgV0Y71hl2zm8k+74d4xtZz452Iz2+Sc5tANm5hh3rxQceeADR0dEICgoSW9vYMWck69i1Jnby\n+Pazn/0MCQkJl92ef/55ZZnbt2/HmDFjGs79EvUoeUxXrlyJgQMHonv37ujevTuGDx+OrVu3Ksni\npisRGbZq1SrMnDkTc+fORUlJCeLi4pCamoqysjIleT179sTOnTsbbqtWrVKS05i7jJmZmSguLkZc\nXBxGjBihrIyVlZWIj4/H4sWLoWmakozGpMtXVFSERx99FNu2bcOGDRvgcrkwevRoVFdXK8kDLpQx\nPT0d8+bNw2effYb4+HgMHz5cWRnJekVFRfj5z3+OgoICrF+/Hi6XC/fff7+ydtO1a1c8/fTTKCgo\nQEFBAQYOHIhJkybh73//u5I84N99f8mSJSJ9X3qska5DQP4cVVlZibi4OCxcuFDkmJIavtBu3P1/\n0aJFjpxr2JEp3W7cmZzfNG/SdSh97gdky2jHOsOu+Y1ku7FjfCPr2dFu7BjfpOY2gH3XiqTWi1VV\nVYiLi0NWVpZjjylZz461jdPHtzfffBNbt25tuL300kvQNA133XWXkjxAvv9LH9OuXbsiMzMTH3zw\nAT744AMMGjQIEydOxP79+y3P0nRdt/xFiZxC07QEAMXFxcVISEiw++0oY3QcSEpKQmJiIhYtWtTw\n/IiICEybNg2zZs267vMPHTrkcdbixYuxdetWvP/++4beY2NRUVGGn5OcnIzExERkZWUBuFDG7t27\nY9q0acjIyLju8+vr6w1nugUEBGDdunW45557PH5OixbGPkNjtnwVFRWG8i5VXl6OmJgYbNq0CUlJ\nSR49JzQ01FDGgAED0L9//4vaaXh4OB5//HGP2ilZ79SpU6aeX15ejtjYWGzcuNHjduNyuUxl/uhH\nP8LcuXMxbtw4jx4fFhbmdZa/vz/effddQ33fzDzOm7EGMNf/vanDNm3aGMowe44CgJqaGkOZbiEh\nIVi1ahVGjhxp6HlBQUFe5dHVSc9tvG0zgPftpmXLll5ntmrVCmvXrjXU//38/AxlmJ1reMNsptGL\nC2bbjTcXMzi/af7M1mFtba3X2d6e+42ON2bLeObMGUN5jXmzzjCzdnNnGp3ftG3b1lCG2WMqfV7k\nB9GaBulzhtk8M2s3b+Y2gPz8xsz6zZv1oplzRlBQENasWWN4jurv72/o8WaPqdHrYXR9Zj5EJLUm\nbm7j2+eff24qf/78+di+fTs2bNjg8XN69erldV5wcDBWr15tqB4DAwMNZVhRhz/88IOhzEvFxMTg\nmWeewYQJE6772D179mDIkCEA0FfX9ZJrPZajEhEZ4nK5UFxcjJSUlIb7NE3DsGHDsHPnTiWZR48e\nxW233YYhQ4YgLS0N3333nZIcN3cZhw4d2nCfpmlISUlBUVGR0mwJTaF8p06dgqZpaNeunZLXv1Y7\ndUId+qrTp08rbTeN1dfXY/369aiurka/fv2U5/kK1XVoxzmKmj+2G+vZMdeQzrSj3XB+0/z5Qh3a\nXUbV64wrsXN+43AiQ18AACAASURBVJTxjazXlNqN08Y3p14rsgOPKXnD18Y3l8uFvLw8jBo1SnmW\nFLuPaX19PdatW4fq6mrceuutlr8+N12JyJCysjLU1dWhY8eOF93foUMHHD9+3PK8Pn364Pe//z1W\nrlyJZ599Ft9++y3Gjx+Pqqoqy7PcrlbGjh07orS0VFmuFLvLp+s6nnjiCSQlJZn61NW1XKuMKtop\nqeduNwMGDFDWbgDgq6++QlRUFLp164bZs2fjtddeQ2xsrLI8XyJRh9LnKHIGthvr2THXkM60o91w\nftP8+UId2llGiXXG1TLtmN+oOqY8LzpDU2k3vjK+OeFakR14TMkbvja+ffDBB6ioqDD8Tf6mzK5j\n+re//Q0RERHo1KkTMjIy8Prrr6Nnz56W53j/m1RERI3ouq7kJ4QGDRrU8O+ePXsiPj4egwYNQl5e\nHsaMGWN53rWoKmNTIVW+tLQ07Nu3D/n5+cqzLuX0OnSy9PR07N+/H5s3b1aaExsbiw8++ABnzpxB\nbm4upk6dig0bNnDj1QJSdXgl7PvkDbYb69lxTKUzfaGMZD1fqEOJMtqxzvCl+Y0vtFNfwHZjPV8o\nozQeU/KGU8e3DRs24LbbbkP79u2VZ9lN9THt2bMnPvroI5w+fRo5OTl47LHHkJuba/nGK7/pSkSG\ntG/fHn5+fpd94uzkyZOXfTpFhdatWyMqKgpHjx5VlnG1Mp44cQIdOnRQlivFzvLNnDkTBQUF2Lhx\nIzp16qQs51pllGinZK2MjAwUFBQgJydHabsBLvzNssjISMTFxeHJJ59E79698corryjN9AVSdWj3\nOYqaJ7Yb69kx15DOtKPdcH7T/PlCHdpVRql1RmN2z29UHVOeF52hqbQbXxnfnHCtyA48puQNXxrf\njh07hk8//RT333+/0hxpdh1T9zW/+Ph4/OY3v8GPf/xjLFu2zPIcbrpSk6VpWmtN0+7RNO2/NU3L\n0DTtPzRNa3fJYyI0TUu26z36In9/f/Tt2xfbtm1ruE/XdWzbtg1JSUnK8ysrK/H111/jpptuUpbh\nLmNhYWHDfbquo7CwEMnJzb+52VW+mTNnIi8vDzk5OQgPD1eWA1y7nTqhDn1JRkaGWLu5El3Xcf78\nefFcJ5GsQ7vPUdQ8sd1Yz465hnSmHe2G85vmzxfq0I4ySq4z3JrK/MYp4xtZrym1G6eNb069VmQH\nHlPyhi+Nbxs2bEBYWBhuv/12pTnSmso5Q9d1nDt3zvLX5c8LU5OjaVo0gN8BGAngEwCbARwFEAZg\npaZpXwN4CkA1gHUAcgDssOfd+qYZM2ZgypQp6Nu3LxITE7Fw4UJUVVVhypQplmc9//zzGDp0KLp2\n7YrS0lIsWrQIfn5+yn/Hfvr06XjkkUeQkJCAxMREZGVloaqqCpMnT1aSV1lZiYMHD0LXdQDAoUOH\nsGfPHoSFhSlZxEuXLy0tDWvXrsU777yDkJAQnDhxAgAQGhqKwMBAZZmTJ08WaaekRnp6OtatW4e3\n3noLwcHBytvNb3/724bxpqKiAuvWrcOOHTuwevVqy7PcpPu+dJ50HQKy5yjgwjH9xz/+0XBMjxw5\ngr179yIsLAzdunVTkknW84V2c2n/P3z4sKPmGnZkSrcbgPMbJ5CuQ+lzPyBbRjvWGXbMb6TbjR3j\nG1lPut3YPb6pntsAzr9WdOkc9fDhw9i7dy/atWvnmGNK1rNjbeP08Q24sCH4/vvv495770WLFuq/\nOynd/6WP6XPPPYdhw4aha9euOHv2LNauXYtPPvkE69atszyLm67UpGiaNhXAfABfARig6/oXlzzk\nZU3TUnFhI/YfAPriwqYrCXrooYdQVlaGzMxMlJaWok+fPti8ebOSb58eP34cM2bMwKlTpxAWFoZ+\n/fph7dq1aNeu3fWfbMJDDz2E8vJyzJ07t6GMmzZtUvYN2+LiYgwbNgyapkHTNMyaNQsA8PDDD2P5\n8uWW50mXb8WKFdA0DT/72c8uun/p0qUYN26ckkx3O3366acbyrhlyxal35Ima61cuRKapl32IYvs\n7GyMHTvW8ryTJ09i6tSpOHHiBFq3bo3evXtj9erVGDhwoOVZbrt3776o72dkZAAAJk2apKTvS481\n0nUIyJ6jAKCkpASpqakNx3TOnDkAgAkTJij5mRpSwxfaTXFxMe68884r9v9XX33V8jzpuYYdmdLt\npnEm5zfNl3QdSp/7Adky2rHOsHN+I9Vu7BjfyHp2tRvJ8U1ybgPIzzXsWC8OHz68IW/27NkAgIkT\nJyr7szt2zBnJWnasbZw+vgHAp59+itLSUtx7773KMhorKSm5qP+763HixIlK6lH6mJ44cQL/9V//\nhdLSUoSGhqJ3795Yt24dBg0aZHmW5t65JrKbpmnzcOEbrH8FkKLreuU1HjsGwGoAOoB5uq4/o+g9\nJQAoLi4uRkJCgoqIJkF6HDh06JBoXlRUlGgeANTX14vmSXziqbGKigrRPODCJ8epeTt16pR4psvl\nEs0LCwsTzbNjHifd/9u0aSOaBwA1NTWieUFBQaJ5vkC6b0i3GeDC36OR5OfnJ5pnB03THJ1HzlBb\nWyueKT3enDlzRjRPeu0GAG3bthXNkz4vcnwjb0iv3QD5+Y10X7TjnOHv7y+aJ309zBdUV1eLZzp9\nTfz555+LZ/bq1Us0T9WvfVzLDz/8IJa1Z88eDBkyBAD66rpecq3HclSiJkHTtAdwYcO1AsDoa224\nAoCu62sBrJV4b0RERERERERERERERETXwk1Xsp2maaEAXsWFb63+Qdf1Yx4+dT4AfnySiIiIiIiI\niIiIiIiIbMVNV2oKpgNw/7bOG54+Sdf1XQD2KnlHRERERERERERERERERB7ipis1Bf/fv/7377qu\nHzX43O1WvxkiIiIiIiIiIiIiIiIiI7jpSrbSNO3HAMJx4aeFv/TiJd4CsKXR6/1S07RvNU2rb3Q7\nrmnaPY0e87imabWN/v/vNE3rZLowRERERERERERERERE5JO46Up2+2mjf39r9Mm6ru/Qdf3TRv/9\nsq7r3QDMdL+mruuddF3PafSYxQAeA1AP4D91Xe+i6/px794+ERERERERERERERER+TpuupLdOjb6\n91kLXzcbwCkAXTVNi77C//81gHd0XX/NwkwiIiIiIiIiIiIiIiLyQdx0Jbv5N/q3ZtWL6rp+DsCq\nf/3nlCs8ZCyAZVblERERERERERERERERke/ipivZ7USjf4da/NorcWEjd1LjOzVNCwHQR9f1jy3O\nIyIiIiIiIiIiIiIiIh/ETVey295G/+56rQdqmvaspmkHNE37TtO0Y5fcDmmadk/jx+u6/lcAfwMQ\nrmlaSqP/60EAay0rAREREREREREREREREfm0lna/AfJ5xbjwbdcOAPpc64G6rj8F4ClN09oA+DmA\nPwDQAazSdX38VZ72ZwC/x4WfGN72r/sexpV/cthnVVRUiObFxMSI5lVWVormAUBISIhonnQd3nDD\nDaJ5APDhhx+K5g0ePFg0zxe0adPG7reg3JkzZ0Tz7DimQUFBonmaZtlfH/BYYGCgeCZZq76+XjTv\n7NmzonkA0KpVK9G80FCrf5Tm+uzo/0RG1dXVieb5+fmJ5tlBeq3hC2ONruuieb5wTO0gPd5Iz6fs\naDctWjj7+0jnzp0TzwwICBDPJGtJr/ntUFRUJJqXlJQkmmeHmpoa8cy2bduKZbVu3drjxzr7zEJN\nnn5h5r/yX/8ZqWlarAfPOQ1gTaO79l3j4W8AqAMwWtO01pqmRQKo03X9G+/eMRERERERERERERER\nEdHFuOlKTcEC/Ptvuz5i5Qvrun4cwGYAQQDGApiMC99+JSIiIiIiIiIiIiIiIrIEN13JdrqulwH4\n/wFoAH71r2+jXo+/gYjX/vXajwC4D8A6Y++QiIiIiIiIiIiIiIiI6Oq46UpNgq7ruQCeARAIIFfT\ntPbXecqtBl7+fQDlAPoDKNZ1Xf4HxomIiIiIiIiIiIiIiMixuOlKTYau63Nx4RuvPQAUaZp2x5Ue\np2nazQAyAbzg4eu6ALz1r/9cYf6dEhEREREREREREREREf1bS7vfAFFjuq6v1DRtN4DnABT86985\nAI4AaAPgdgCxAB7Rdb1I07STuPDTwdfzJoBUXdeL1LxzIiIiIiIiIiIiIiIi8lXcdKUmR9f1LwDc\np2naTbiwyRoFIAJAKYDndF3f1+ix8z182Ztx4W+7EhEREREREREREREREVmKPy9MTZau6yd1XV+v\n6/qLuq7/Xtf11xpvuBo0GcDrVr4/X7VgwQIMHjwYXbt2RUxMDMaPH48DBw4oz83OzkZUVBSCgoIw\nYMAA7Nq1S1nWCy+8gDvuuAOdO3dGVFQUxo4d66gyvvDCCxg8eDC6dOmC6OhojBs3TqR8ALB06VLE\nxMQgJCQEycnJlpVx7969+PWvf40HH3wQQ4cOxSeffHLZY1asWIExY8YgNTUVM2fOxD//+U9Lskm9\n7OxsREdHIzg4GElJSUr7v2Teiy++iKFDhyI8PByxsbGYMGECDh48qCTrUpJj6vbt2/HAAw8gOjoa\nQUFByM3NVZblJlm+xpmS7ZSs9fHHH2PUqFGIiIiAv78/cnJylOb9+c9/xpAhQ9CjRw/06NEDP/vZ\nz1BYWKgsz67xxo5+Id3/7RpvpDPJOtLjDeAbfVHVOuNqeEypqbNjrNm+fTtGjx6NyMhItGrVSnmm\nO6979+4ICAgQKSPg7LnG/PnzMWjQIHTq1AmRkZGi18O4lmrenDwnrq+vxyuvvIIxY8ZgyJAhePDB\nB7Fy5UolWZdy8nizfft2jBkzpqHvS1wrAmTGG266kqNomtZb07S9mqb9utF9vQCc1XWdOywW2LFj\nBx599FEUFhbi/fffh8vlwujRo1FdXa0sc9WqVUhPT8e8efPw2WefIT4+HsOHD0dZWZmSvB07duCX\nv/wlPvjgA+Tk5KC2thb33XefY8pYVFTUUIc5OTlwuVwYNWqU0vIBF8o4c+ZMZGZmori4GHFxcRgx\nYoQlZaypqUFMTAx+9atfQdMu/8Xxt99+G++99x7S0tLw0ksvITAwELNmzYLL5TKdTWq5283cuXNR\nUlKCuLg4pKamKuv/knlFRUX4xS9+ga1bt+K9995DbW2t8vEUkB9Tq6qqEBcXh6ysrCv2T6tJl8+d\nKdlOyXqVlZWIj4/HkiVLRNpply5d8NRTT6GgoAAFBQW4/fbbMXnyZPz9739XkmfHeGNHv5Du/3aN\nN9KZZC3p8cZX+qKqdca18nhMqSmTHmsaZy5atEgk0523ePFisTI6fa7hvh724YcfIjc3Fy6XC/fe\ne6/j5oxkLafPid944w289957mDlzJt5++23893//N958802sXbvW8qzGnD7eSF8rAuTGG03XdUtf\nkMhOmqalA5gP4LCu6zH/uu8tAAt1XTf8sQVN0xIAFBcXFyMhIcHaN9uEnD171uvnlpeXIzo6Gps3\nb0ZSUpJHz2ndurWhjAEDBqB///5YtGgRAEDXdYSHh+Pxxx/HrFmzrvv8yspKQ3mXKisrQ1RUFLZs\n2YLk5GSPnhMSEmIow2wZKyoqDOU1VlZW1lCHnpYvODjYcE5ycjISExORlZUF4EIZu3fvjmnTpiEj\nI+O6z//oo488yhk6dCieffZZ3HbbbQ33jRkzBv/xH/+BBx98EMCFNnH//fdjzpw5GDJkyBVfZ/Dg\nwR7lkee8mXMkJSUhMTHxor4RERGBadOmedQ3pPPOnDnjdXZ5eTl69OiBvLw8j8fTNm3aGM4xO96c\nO3fOcKZbUFAQ1qxZg5EjR3r8nFatWhnKMFs+93OMMNtupBYYvqSurs7r5/r7++Pdd9/FPffc4/Fz\nysvLvc5z69WrFzIzMzFu3DiPHm+0bzTmzXgTGhpqKMOK8dto37Ci/zflPLsy6dqkx5sWLYx9dr45\n9sX6+npDeWbXGUbLx2N6/WNqtJ2SZ7wdb7wZawDj7aaxVq1aYe3atYYyzcyJAwICsG7dOsNlbNnS\n2F/ea25zjaqqKlP5ZWVliIyMRH5+vsfXi4KCggxlcC3V/DXHOXFRUZHHWRkZGQgLC8MTTzzRcN+T\nTz6JwMBAPP300x69hqdrrsaa23hTU1PjdXZwcDBWr15t6FoRYHxNbGa8KSkpQb9+/QCgr67rJdd6\nLGdB5DTbAdQC+C3QsAlb5c2GK3nm1KlT0DQN7dq1U/L6LpcLxcXFSElJabhP0zQMGzbM0AnSjNOn\nTzu6jO7yhYWFKctwl3Ho0KEN92mahpSUFOVlPHbsGL7//vuLPjgREhKCm2++GX/729+UZpM51+ob\nO3fubPZ5l1I91gD2jzeq2VE+u9sNNX/19fVYv349qqur3Ys45eyc26jqF9L9v6mNN04Yw8l6vtQX\npdYZPKYca8h3+cJc41JOnDOStXxhTnzLLbeguLgY33zzDQDgwIED+OKLL7zaSPWUL443qkmON9x0\nJUfRdf1TAP8FYKqmaf8H4CYAv7D3XTmXruuYM2cOkpKS0KtXLyUZZWVlqKurQ8eOHS+6v2PHjjh+\n/LiSzMZ0Xcfs2bORlJSEm2++WUmGnWVsXD5VdQhcu4ylpaXKcgHg+++/v+IioV27dvj++++VZpM5\nV2s3HTp0UNI3pPMa03UdTzzxBAYMGGBbX5QYU1Wzo3x2thtq3r766itER0cjPDwcc+bMwcqVKxEb\nG6s8V2K8saNfSPf/pjTeOGUMJ+v5el9Usc7gMVW7diNqyppSX5S6HjZr1iwkJyeLXw/jWqr58IU5\n8cMPP4yUlBSMGzcOgwYNwn/+53/ioYcewp133ml5lpuvjTcSJMcbY7+hQNQM6Lr+JwB/svt9+IK0\ntDTs378f+fn54tm6rov8hMj06dOxb98+bN26VXnWpSTKOGPGDOzfvx8FBQVKc65Gqh6vlk3Nk3S7\nkchLT0/Hvn37sGXLFqU5V2NnX5RgR/mcfkzJvNjYWBQWFuLMmTPIzc3F1KlTsWHDBuUbr3aON77Q\nF32hjNT8+UI7dXqeHZkca4iuzKl90enXw0gtJ50Xt27divz8fDzzzDOIjIzEgQMHkJWVhfbt22PE\niBGW512LU8cbO6koIzddicgr6enpyM/Px+bNm9GpUydlOe3bt4efn99ln6g9ceLEZZ9MsVpaWhry\n8/ORn5/vyDK663DLli1Kywdcu4wdOnRQmh0WFgZd1/HDDz9c9BPKp06dEvlGEXnvau3m5MmTSvqG\ndJ5bRkYG8vPzsWnTJlv7ouoxVYId5bOr3VDz17JlS0RGRgIA4uLi8Nlnn+HVV1/FH/7wB2WZUuON\nHf1Cuv83pfHGKWM4Wc/X+6KKdQaPqdq1G1FT1pT6osT1sC1btqCgoACdO3dWlsO1VPPnC3PipUuX\nYtKkSQ0/ux8dHY1jx47hjTfeULbp6kvjjRTJ8YY/L0xEhqWnpyMvLw+5ubkIDw9XmuXv74++ffti\n27ZtDffpuo5t27YhOTlZWW5aWho2btyIvLw8R5YxPT0dGzduxMaNG5WXD/h3GQsLCxvu03UdhYWF\nSusRADp37oywsDCUlPz7b5xXVlbiq6++wo9//GOl2WTOtfqGir+dIZ0HXNgAkRpPAfvGVCl2lM+O\ndkPOVF9fj3Pnzil7fcnxxo5+Id3/m9p444QxnKznS31Rap3BY8qxhnyXL8w1gAvXw3Jzc7Fp0yZH\nzhnJWr4wJ66pqbnsm5AtWrRAfX295VluvjLeSJIcb/zmzp1r6QsSOcm8efM6A3j00UcfVfrJLrud\nP3/e48fOmDEDa9euxRtvvIFOnTqhsrISlZWV8PPzQ8uWnn15vlWrVobeX2hoKJ566ilERESgVatW\n+M1vfoM9e/Zg+fLlCAkJue7zXS6Xobzp06djzZo1+Mtf/oKOHTs2lLFly5YelzEgIMBQptkyGq3D\nNWvWmKpDf39/j/PcWrdujaeffhrh4eFo1aoVnnrqKezduxevvvqqR2U8evToVf+/6upqHD16FN9/\n/z1ycnJw8803IyAgALW1tQgJCUFdXR3eeustdO/eHS6XC0uWLIHL5cK0adPg5+d3xdd0f/uI7BUa\nGnpRu/nNb36DvXv3etw3pPOMbJikp6djzZo1+POf/3zRWGOkLwYGBnqc52Z2vKmrqzOUV1lZiX37\n9uH48eNYvnw5br31VgQFBeH8+fNo06bNdZ/v6bFwM1s+b5htN07/uR47GP0JefeHcY4fP45XX321\noZ26XC6P2ml1dbWhvN/+9rcICAiAruv47rvvsGzZMqxfvx6ZmZno3r27R69hpG9YMd54M38zO34b\n7RvS/d+u8UY6k65Nerzxpl80t75o9JiaXWfwmF5O+piSZ4zUo9mxxmjepZnueX9gYKCy8c2KMrZo\nYez7SM1truHN9bDVq1fjzTff9Pp6mNHrRVxLNX/NcU787bffepx15MgRbNq0CREREWjZsiVKSkqw\nbNkyDB8+HP369fPoNbz5AENzG29qa2sN5TW+VvSnP/0J/fr1M3StCPDuepG3482xY8fwyiuvAMAr\nc+fOPXbN92XoXRGRz1uxYgU0TcPdd9990f1Lly7F+PHjlWQ+9NBDKCsrw9NPP43S0lL06dMHW7Zs\nwU033aQk709/+hM0TbvsJyJefvllR5TRXb5L6/Cll15SVj7gQhnLy8sxd+7chjJu2rTJkjLu378f\naWlp0DQNmqbhpZdeAgDcddddmD17NsaNG4dz587hxRdfREVFBW655Rb8/ve/92rzmGS5+0ZmZmZD\nu9m8ebOy/i+Z5x5PR44cedH92dnZGDdunOV5btJjanFxMYYPH97QP2fPng0AmDhxonvCainp8jXO\nlGqnZL3du3dj2LBhDe00IyMDADBp0iQsX77c8ryTJ09i6tSpOHHiBFq3bo3evXtj1apVGDhwoOVZ\ngD3jjR39Qrr/2zneSGaStaTHG1/pi6rWGVfL4zGlpk56rAEuzPvvvPPOhsxZs2YBAB5++GG8+uqr\nSvIal7FxnqoyOn2usXz5cmiahtTU1Ivuf/nllzFhwgQlmVxLNX9OnxOnp6fjlVdewYIFC/DDDz+g\nffv2uP/++zFlyhTLsxpz+nhTUlJy0bWiOXPmALhwrWjZsmVKMqXGG83op5aIfImmaQkAiouLi5GQ\nkGD321Hm7NmzonmtW7cWzausrBTNAyD+TYOKigrRvODgYNE8APjoo49E8wYPHiya5wt8Yc5x5swZ\n0TxPP/1nJZU/f3olRr9dZwXptspPZ1vP6DeyzSovLxfNA+T7RmhoqGgewL5BzYP0eGP0W1lWkO6L\nKn+u70rsGGucfkztaKe+QHq88YW+aPRbUs1NVVWVeGZQUJBoHueL5I2ioiLRPF/4ueyamhrxTMk1\ncUlJifubzX11XS+51mM5CyIiIiIiIiIiIiIiIiIiMoGbrkREREREREREREREREREJnDTlYiIiIiI\niIiIiIiIiIjIBG66EhERERERERERERERERGZwE1XIiIiIiIiIiIiIiIiIiITuOlKRERERERERERE\nRERERGQCN12JiIiIiIiIiIiIiIiIiEzgpisRERERERERERERERERkQncdCUiIiIiIiIiIiIiIiIi\nMqGl3W+AiOx3ww03iOadOnVKNK+6ulo0DwBCQkJE8wICAkTzampqRPMAoE+fPuKZZK36+nrxzBYt\nZD9fFhoaKpqn67poHgBomiaaZ8d4ExgYKJ5J1vLz8xPNu/HGG0XzAODs2bOieXaM4efPnxfNCwoK\nEs2TrkMAaN26tXim00mfF3nut15ZWZloHiA/vkm3m9jYWNE8X3H06FHRvOjoaNG8uro60Tw7fP31\n16J5nTp1Es0D5M8ZZD1f6ItJSUl2vwXlvv/+e9G8tm3biuYBsuONkSx+05WIiIiIiIiIiIiIiIiI\nyARuuhIRERERERERERERERERmcBNVyIiIiIiIiIiIiIiIiIiE7jpSkRERERERERERERERERkAjdd\niYiIiIiIiIiIiIiIiIhM4KYrEREREREREREREREREZEJ3HQlIiIiIiIiIiIiIiIiIjKBm65ERERE\nRERERERERERERCZw05WIvJKdnY3o6GgEBwcjKSkJu3btUpLz4osvIiUlBREREejZsycmTpyIgwcP\nKsm6kiVLlqBr166YO3eu8qzs7GxERUUhKCgIAwYMUHZMAWD79u24//77ERUVhcDAQOTk5CjLAoD5\n8+dj0KBB6NSpEyIjIzF27FgcOHBAWZ7d7YastXTpUvTo0QM33HADkpOTlfYNQG58sytPOlN6vNm+\nfTvGjBnTUL7c3FyleW6SYzipIVmHH3/8MUaNGoWIiAj4+/sr7xd2nRclx+9PPvkEY8aMQUxMDEJC\nQhzZ9xcsWIDBgweja9euiImJwfjx45XOp0idpUuXNrRVibmNdB4g1zek1xkAkJWVhcjIyItuKSkp\nyvKWLFmCnj17XnRLTU1VlgcApaWlmDlzJhITExEXF4d77rkHX375pdJMstbixYvRo0ePi27Dhw9X\nnuvk+ZSbXfP+7OxsREZG4plnnlGWIb12c+Naqnmzqy/aca1Iup1KZa5cuRKDBg1qmNekpqZi27Zt\nSrIac+oclZuuRGTYqlWrMHPmTMydOxclJSWIi4tDamoqysrKLM8qKirCz3/+cxQUFGD9+vVwuVy4\n//77UV1dbXnWpT7//HO8+eab6N27t/KsVatWIT09HfPmzcNnn32G+Ph4DB8+XMkxBYDKykrEx8cj\nKysLmqYpyWhsx44d+OUvf4kPP/wQubm5cLlcuPfee5XVo53thqy1evVqZGRkIDMzE7t370Z8fDzu\nvvtuZX1DcnyzI8+OTOnxpqqqCnFxcWJ5gPwYTtaz6zy8ZMkSkXZqx3lRevyurKxEXFwcFi5c6Ni+\nv2PHDjz66KMoLCzE+++/D5fLhdGjR3N+08y4z8OZmZkoLi5GXFwcRowYoXyuIZXnzpTqG9LrDLcf\n/ehHKC4utV7M1AAAIABJREFUxu7du7F7926sW7dOaV7Pnj1RVFTUcHvnnXeUZZ05cwbjxo1DQEAA\nVqxYgU2bNmHOnDkIDQ1Vlklq9OzZE59++il27tyJnTt3YtWqVUrznD6fAuyb9+/Zswdvv/02br75\nZqU50ms3gGspJ7CjL9pxrUi6nUpmdu3aFZmZmSgsLERhYSEGDhyIiRMnYv/+/ZZnuTl5jqrpum7p\nCxI5iaZpCQCKi4uLkZCQYPfbUcboOJCUlITExEQsWrSo4fkRERGYNm0aZs2add3nnz592qv3CQDl\n5eWIjY3Fxo0bkZSU5NFzvFlwV1ZWYvjw4Xj++eeRlZWFn/zkJ4a+7dq5c2dDeQMGDED//v0vOqbh\n4eF4/PHHPTqm58+fN5TXWGBgINasWYN77rnH4+fU1tZ6nQcAZWVliIyMRH5+PpKTkz16jpkyetNu\n2rZt63UeXVldXZ3h5yQnJ6N///5YuHAhgAt9IzIyElOnTkVGRsZ1n9+ihbHPl5kd34ySzrMi0+Vy\neZ3tzXhTX1/vdV5wcDBWr16NkSNHGnpeYGCgocebHcPJfmbr0Jvxzc3f3x/vvvuuoX4BAGfPnvU6\n05vzYuvWrQ1lmB2/Ae/P/SEhIVi1apXhvh8UFGTo8WbbjZk6BC7UY3R0NDZv3qysHun6jJ6nkpOT\nkZiYiKysLAAX2k337t0xbdo0j/uGdJ7R+ZTZvlFVVWUorzFv1hlGL7RlZWUhPz8feXl53rxFAMbG\ntyVLlmDr1q3YsGGD13lG1vzz589v+ACyt2JjY71+Ll3doUOHPH7s4sWLsXXrVrz//vte50VHRxt6\nfHOcT/n5+Rl6vNkyfv3114bygAvXp0aOHInnnnsOixcvxo9//GM8/fTTHj23U6dOhvPcvFm7AUBA\nQIChx3Mt1fTY0ReNMrvWkO773jCb+f3335vK79GjB+bNm4cJEyZ49Hij11Gb2xy1pKQEffv2BYC+\nuq6XXPN9GXpXROTzXC4XiouLL/r5JE3TMGzYMOzcuVN5/unTp6FpGtq1a6c058knn8Sdd96J22+/\nXWkOcO1jWlRUpDzfDlL1aFceWcPlcqGkpARDhw5tuE/TNKSkpCgZb6THNzvGU7vHcCfyxTHcaXyx\nDlWfF6XHbzs0hXZz6tQpzm+aGXe7uVLfUNFupPMaZ9rVN6Tm/UeOHEFiYiIGDhyIX/3qV/juu++U\n5h09ehS33347hg4divT0dBw7dkxZ1gcffICf/OQn+NWvfoWkpCSMGjUKq1evVpZH6hw5cgTJyckY\nMmQI0tLSlLZTu/u+BLvK+NRTTyElJQW33Xabsgy7+EK7Ies1pWtFTpxP1dfX491330V1dTVuvfVW\nJRlOn6Ny05WIDCkrK0NdXR06dux40f0dOnTA8ePHlWbruo4nnngCAwYMQK9evZTlvPfee/jyyy/x\n5JNPKsto7GrHtGPHjsqPqR10XcesWbOQnJys/Kdx3HkS7Yas5+4bHTp0uOh+VeON9Phmx3hq5xju\nVL42hjuRr9WhxHlRevy2g93tRtd1zJkzB0lJSZzfNCPXajelpaXNPu96mRLrRYl1xk9/+lO88MIL\neOONN/Db3/4W33zzDR588EFT39C9lj59+uD555/HihUr8Mwzz+Dbb7/F+PHjleV98803ePvttxEV\nFYUVK1Zg7NixeO6550x905bk9enTB3/4wx+wcuVKPPvss/jmm28wbtw4Ze3G7vOiBDvK+P777+Nv\nf/sbZs+ereT17eYL7Yas11SuFalsp3ZkfvXVV4iIiEDnzp2RkZGB119/HT179lSS5fQ5aktLX42I\nfJau68p/tz89PR379+/H5s2blWV89913yMzMxDvvvAN/f39lOZ6QOKZ2mD59Ovbt24etW7eK5Em0\nG5Il3TecnmdXptPxmDZ/Tq1DO8+LTj2mjUmVMS0tDfv370d+fr7yLFKPcw1rSK0z7rjjjoZ//+hH\nP0J8fDxuu+025Obm4qGHHrI8b+DAgQ3/7tmzJ+Lj43HHHXcgLy8PY8aMsTyvvr4ecXFxmD59OgDg\n5ptvxsGDB/H222/jvvvuszyP1Bg0aFDDv93tdODAgcrazdXw3O+9Y8eO4ZlnnsFf/vIX269PSfOF\ndkPW43zKnNjYWHz00Uc4ffo0cnJy8NhjjyEnJ0fZxuuVOOWYctOViAxp3749/Pz8LvvUycmTJy/7\npIiVMjIyUFBQgLy8PFN/k+J69u7di/LycqSmpjb83Zu6ujrs3LkTK1euxJEjRywfiK92TE+cOKH0\nmNohLS0NW7ZsQUFBgeG/e+sNqXZDarj7xokTJy66X9V4Iz2+2TGe2jWGO5kvjeFO5Ut1KHVelB6/\n7WBnu0lPT0d+fj42b97M+U0zc612c+m3NZpj3vUyVfYN6XVGY6GhoYiKisKRI0dE8lq3bo2oqCiv\n/jakJzp06ICYmJiL7ouJieGHPJo5d7tR1U59YT4lXcYvvvgC5eXlGDly5EXXp/7617/i9ddfx4ED\nB5r9xqQvtBuyXlO5VqSyndqR2bJlS0RGRgIA4uPjUVJSgmXLlmHBggWWZzl9jsqfFyYiQ/z9/dG3\nb19s27at4T5d17Ft2zYkJSUpyczIyEBeXh5ycnIQHh6uJMNt0KBBKCwsREFBAbZu3YqtW7ciPj4e\nDzzwALZu3apkQnutY5qcnGx5nl3S0tKQm5uLTZs2Ka9HQLbdkBr+/v5ISEhAYWFhw326rqOwsFDJ\neCM9vtkxntqR6XS+MoY7ma/UoeR5UXr8toNd7SY9PR15eXnIzc3l/KYZcrebK/UNFe1GOq9xpmTf\nkF5nXKqyshJHjx5VdpHwSnlff/01brrpJiWvn5CQgMOHD1903+HDh9GlSxcleSTD3W5UtVNfmE9J\nl/H222/Hli1bkJeXh02bNmHTpk2Ii4vD6NGjsWnTpma/4Qr4Rrsh6zWla0VOmk9dqr6+HufPn1fy\n2k6fo/KbrkRk2IwZMzBlyhT07dsXiYmJWLhwIaqqqjBlyhTLs9LT07Fu3Tq89dZbCA4ObvgUU2ho\nKAIDAy3PCw4OvuxnE4KDg9GuXTvExsZanueWlpaGyZMnixxT4MKC6x//+EfDpyUPHz6MvXv3ol27\ndkouVEyfPh1r1qzB6tWrERwc3PCpojZt2iipR+l2Q+rMmDEDjzzyCBISEnDrrbdi0aJFqKqqwuTJ\nk5XlSY1vduTZkSk93kjnAfJjOFnPjvPwwYMHG9rpoUOHsGfPHoSFhSlpp3acF6XH70v7/pEjR7B3\n716EhYWhW7duSjKl282MGTOwbt06vPPOOwgJCeH8ppmaPn16Q99ITExEVlaW0r4hnQfI9g3pdQYA\n/M///A+GDRuGrl274vjx41i4cCFatmyp7Kd3n3/+eQwdOhRdu3ZFaWkpFi1aBD8/P4wcOVJJ3pQp\nUzB27Fi8/PLLuPvuu7Fnzx6sWbMGzz77rJI8UuN3v/sdUlJSGtqpu93cc889yjKdPp8CZMt4petT\nQUFBaNu2rbLrU1xLkTfs6IvSaw072qlk5nPPPdcwt6moqMCaNWuwY8cOrF271vIsNyfPUbnpSkSG\nPfTQQygrK0NmZiZKS0vRp08fbN68WcknbVeuXAlN0y5bGGRnZ2Ps2LGW512JxKcH3cf06aefbjim\nW7ZsUfbp5eLiYtx1113QNA2apmH27NkAgIcffhivvPKK5XnLly+HpmlITU296P6XX34ZEyZMsDyv\nKbQbssaDDz6IsrIyzJ07F6WlpYiPj0deXp6yviE5vtmRZ0em9HhTUlKC4cOHN+TNmTMHADBx4kQs\nW7bM8jxAfgwn60nX4e7duzFs2LCGdpqRkQEAmDRpEpYvX255nh3nRenxu6SkBKmpqZf1/QkTJjim\n769YsQKapuHuu+++6P6lS5di/PjxSjLJeg899BDKy8sb+kafPn2wadMmpXMNyTx3plTfkF5nAMDx\n48fx+OOP44cffsCNN96Ifv36Yf369WjXrp2yvLS0NJw6dQphYWHo27cv1qxZoyzvlltuQXZ2NhYs\nWIClS5eiW7du+PWvf61sk5fUOH78OGbMmIEffvgBYWFh6NevH9atW6es3QDOn08B9s/7VV+fkl67\nAfYfUzLPjr5o17UiyXYqmXny5Ek89thjKC0tRWhoKHr37o21a9de9PfBrebkOarm/gQCEV1O07QE\nAMXFxcVISEiw++0oIz0OnD59WjSvurpaNA+A+N8RUvVzD1dTW1srmgfIl7Ft27aieb6grq5OPLNF\nC/4lBau5XC7RvPr6etE8APzGGBlmx/h29uxZ0bzWrVuL5gHy5/6goCDRPOk6BOypR6ez4zwlTXo+\nVVVVJZpXVlYmmgfIj2/Sa36VvxLlyw4dOiSaFx0dLZpnx3zKz89PNE/V32K+Gjv+9ntAQIB4JlnL\njr4oTbrv2+H7778XzbPjOqrkHLWkpAR9+/YFgL66rpdc67G8EklEREREREREREREREREZAI3XYmI\niIiIiIiIiIiIiIiITOCmKxERERERERERERERERGRCdx0JSIiIiIiIiIiIiIiIiIygZuuRERERERE\nREREREREREQmcNOViIiIiIiIiIiIiIiIiMgEbroSEREREREREREREREREZnATVciIiIiIiIiIiIi\nIiIiIhO46UpEREREREREREREREREZEJLu98AEdmvpqZGNK9NmzaieW3bthXNs0Ntba1oXlBQkGge\nAAQHB4tnOl1VVZVonq7ronkA4OfnJ5rXsqXs1OrcuXOieQDQooXsZ/ak80gN6bmGpmmief7+/qJ5\nABAaGiqaV11dLZoHACEhIaJ59fX1onl2zKekSc81XC6XaB4gf+4/ffq0aB4AdOrUSTRPum+Eh4eL\n5gHAsWPHRPO6dOkimmeHiooK0TzpcxQAREdHi2dKkl67AUBdXZ1oXkREhGieHaTXqK1atRLNA+Tb\njfS5X3qdAXDdr0JYWJhonvS1aUB2/WakfGzNREREREREREREREREREQmcNOViIiIiIiIiIiIiIiI\niMgEbroSEREREREREREREREREZnATVciIiIiIiIiIiIiIiIiIhO46UpEREREREREREREREREZAI3\nXYmIiIiIiIiIiIiIiIiITOCmKxERERERERERERERERGRCdx0JVIgOzsbUVFRCAoKwoABA7Br1y7H\n5H3yyScYM2YMYmJiEBISgtzcXGVZjWVnZyM6OhrBwcFISkpSfkzdmU6sx/nz52PQoEHo1KkTIiMj\nMXbsWBw4cEBJ1qWk61G6Du3g5Hbzwgsv4I477kDnzp0RFRUlkrl9+3aMGTOmoZ2qHuO2b9+O0aNH\no3v37ggICEBOTo7SPDuOqfR5Y/v27XjggQcQHR2NoKAg0fMUxxvr2NEX7Wg3S5cubegbycnJytuN\nZJ4d4w0g3xclj6n0OUOaHXONF198EUOHDkV4eDhiY2MxYcIEHDx4UFmeXf3CbcmSJejWrRvmzp2r\nPEt6fLNjnSG9PnWTqkcnz21eeOEFDB48GF26dEF0dDTGjRsndo7idY3mnQdcGN969OiBG264QWx8\nky6j9Lzfjnm4lI8//hijRo1CREQE/P39lc/fVq5ciUGDBiEyMhKRkZFITU3Ftm3blGbaNUe1Y67h\n5L4onSfdbiTzuOlKZLFVq1YhPT0d8+bNw2effYb4+HgMHz4cZWVljsirrKxEXFwcFi5cCE3TlGRc\natWqVZg5cybmzp2LkpISxMXFITU1VVkZ3ZlOrccdO3bgl7/8JT788EPk5ubC5XLh3nvvRXV1teVZ\njUnXo3Qd2sHp7cad+cEHHyAnJwe1tbW47777lGZWVVUhLi4OWVlZImNcZWUl4uPjsXjxYpE8O46p\n9HlDug4BjjcqSNejXe1m5syZyMzMRHFxMeLi4jBixAil50XJPDvGGzvmb5LHVPqcIc2OuUZRURF+\n8YtfYOvWrXjvvfdQW1uL0aNHK8u0o1+4ff7553jrrbfQu3dv5Vl2jW+S6wzp9ambVD06fW5TVFSE\nRx99FIWFhcjJyYHL5cKoUaOUn6N4XaN55wHA6tWrkZGRgczMTOzevRvx8fG4++67HVVGp8/7pbnn\nb0uWLBEpX9euXZGZmYnCwkIUFhZi4MCBmDhxIvbv368s0445qh1zDaf3RTv2GCTbjWSepuu60gCi\n5kzTtAQAxcXFxUhISPDoOQMGDED//v2xaNEiAICu6wgPD8fjjz+OWbNmWf4ercjzdmEREhKCVatW\nYeTIkYaeFxgYaOjxSUlJSExMvKiMERERmDZtmkdl9GYgbW71WFVV5XV2WVkZIiMjkZ+fj+TkZI+e\nExQUZDhHuh6l69AOza3dmJ1zlJWVISoqClu2bPE408/Pz+u84OBgrF692tAY17JlS6/zAgICsG7d\nOtxzzz0eP+fcuXNe5wHeHdMWLbz/zJ435w0zeUFBQVizZo3h81SrVq0MPZ7jzfXLWFNT43W2N33R\nzCLKm3bj7+9vOCc5ORmJiYnIysoCcOGYdu/eHdOmTUNGRobh11OdZ/ZCtDfjTUhIiKEMs+20vr7e\nUJ7ZY2o0rzFvzhmAufOUN6TnGi6Xy+s8ACgvL0ePHj2Ql5eHpKQkj55j5ph60y9Onz5tOKeyshKp\nqan43e9+h6ysLPzkJz8x9C3JTp06Gcoz2zeMjuFm1xlGWZF37Ngxw7lm6rFLly6Gsprj3KaiosLr\n55aVlSE6OhqbN29Wdo6yot009TVxc8yrq6szlJmcnIz+/ftj4cKFDZmRkZGYOnWqR+Ob0fWpHX3R\nbKaZNao383CjazcrGG03bv7+/nj33XcNz9+8Ofc31qNHD8ybNw8TJkzw6PGhoaFeZ3k7RzW67jc7\n1zCa1xz7onRebW2t19nethvJvJKSEvTv3x8A+uq6XnKtx/KbrkQWcrlcKC4uRkpKSsN9mqZh2LBh\nKCoqavZ5drhWGXfu3Cme6cR6PH36NDRNQ7t27ZRlSNej3cdUgt1llGg3TSHT6XhMzbO7L0rwhTJK\ncx/ToUOHNtynaRpSUlKUzjWk8q5E9Xhj1/zNzmPqdL4w15DKe/LJJ3HnnXfi9ttvV5oD2De+NYV1\nhqr1qZtUPfried/dF8PCwpS8Pq9rNP88d2ZJSckVxzenXNfwxf7vZPX19Xj33XdRXV2NW2+91e63\nY5mmNNdwSl9k37cWN12JLFRWVoa6ujp07Njxovs7duyI48ePN/s8O1ytjB06dFBWRl+qR13XMWvW\nLCQnJ+Pmm29WliNdj77cN5zUbi7NnD17NpKSksQynY7H1Bocb5xRRmnXOqalpaXNPu9SEuNNU5q/\nSRxTp7NrrvHEE09gwIAB6NWrl0iexHl4w4YN+PLLL/HEE08oy2isqYxv0usMletTQLYefe2837gv\nqur7vK7R/PMaZ3bo0OGi+510XcPX+r9TffXVV4iIiEDnzp2RkZGB119/HT179rT7bVmmqcw1nNQX\n2fetJfvbQkQ+Std10b9LIJ1nBzvK6MR6nD59Ovbt24etW7cqzbkaJx5Tuzm13djdVp2Ix1Qtjjfk\nDaeeF+0cb5x6TJ3OjjaTnp6Offv2YcuWLSJ5EmX87rvvkJmZibfffturn0G3ktP7osq8plKPTh3f\nZsyYgf3796OgoEA8m9c1mn+eHZm+UEYyJzY2Fh999BFOnz6NnJwcPPbYY8jJyXHUxuuVsC82/zyn\n4KYrkYXat28PPz+/yz5Vc+LEics+KdIc8+xwtTKePHlSWRl9pR7T0tKwZcsWFBQUoHPnzspyAPl6\n9OW+4aR20zgzPz8f+fn5hv+mGF0Zj6l1ON44o4zSrnVML/32RHPMa0xqvGlK8zfVx9Tp7JhrZGRk\nID8/H5s2bRI5L0r1iy+++ALl5eUYMWIEdF0HcOHv0H366ad47bXXcPjwYcsvpDWV8U16naFyfSpd\nj7503k9PT0d+fj62bNliyzmK1zWaT17jzBMnTlx0v5Oua/hS/3eyli1bIjIyEgAQHx+PkpISLFu2\nDAsWLLD3jVmkqcw1nNQX2fetxZ8XJrKQv78/+vbti23btjXcp+s6tm3bhuTk5GafZ4drlTEpKUk8\n0yn1mJaWhtzcXGzatAnh4eFKMhqTrkdf7xtOaTfuzI0bNyIvL08s0+l4TK3F8cYZZZTmPqaFhYUN\n9+m6jsLCQqVzDak8N8nxxq75m/QxdTo75hoZGRnIy8tDbm6uSKZkvxg4cCC2bduG/Px8FBQUoKCg\nAPHx8bj//vtRUFCg5JsLdo1vTWGdoWp9Kl2PvnLeT09Px8aNG7Fx40Zbz1G8rtE88tyZCQkJVxzf\nnHJdw1f6v6+pr6/H+fPn7X4blmlKcw2n9EX2fWvxm65EFktLS8PkyZPRt29fJCYmYuHChaiqqsKU\nKVMckVdZWYl//OMfDZ+wPXLkCPbu3YuwsDB069ZNSeaMGTMwZcoUsTICzq7H6dOnY82aNVi9ejWC\ng4MbPsXUpk0bBAYGWp7nJl2P0nVoB6e3m+nTp2Pt2rVYtWqVWOalY9zhw4exd+9etGvXTsnFmMrK\nShw8eLAh79ChQ9izZw/CwsKU5DWFY6r6vCFdhwDHGxXs6IvS7Wb69Ol45JFHkJCQgMTERGRlZaGq\nqgqTJ092TJ70eCPdTqWPqfQ5Q5odc4309HSsXbsWb7/9NoKDgxu+vRQaGqokU7pfBAcHX/ZTgkFB\nQWjXrh1iY2Mtz3OT7hvS6wzpPDvq0elzmxkzZmDt2rV45513EBISorzvuzN5XaN55wEX6tE9vt16\n661YtGiR0vHNjjI6fd4vTXr+9txzz2HYsGHo2rUrKioqsGbNGuzYsQNr1661PMvNjjmq9FzDF/qi\nHX1fst2I5um6zhtvvF3lBiABgF5cXKwbkZ2drXfv3l0PDAzUBwwYoO/atcvQ840ym1dVVeXxbcuW\nLbqmaXqLFi0uuj388MMev0Z9fb3h2x//+MeLyvjXv/7V4+fadVwl8yorKz2+Xan+WrRoob/yyise\nv4Y3dWhHPUrXoR2aU7upqKgwdLtWpqevUV1dbeiWn59/xdxJkyZ59HyXy2Xotm3btivmTZ482aPn\n23FMjZwzrDhv1NTUGLpdqw49fQ3pvthcmCmjdF+Ubjd1dXVe3S49L3766adev5bqPDvGG+l2Kn1M\npc8ZLpfLq2NqhvRc49SpU4ZuV8t86aWXPH4N6X7xz3/+09QtOTlZ//nPf27oOdJ9Q3qdYUeedD16\no7nNbc6ePevx7Wp9cdmyZR6/hh3txhvN6bqGHXm1tbWGb0uWLGnI7N+/v75z506Pn2tHGaUzpefh\ndjDSXq42f5syZYrHr1FeXu7xbeLEiQ1116FDB33w4MH6+vXrDb2GHXNU6bmGN5pbX5TOs6PdSOZ9\n+umnOgAdQIJ+nT0lTb+wsUREV6BpWgKA4uLiYiQkJNj9dpSprq4WzVP5bcor8YU/+F1VVSWaFxQU\nJJoH+EY9SpNuN3bMOfz8/ETzWraU/RGRc+fOieYBQIsWsn+dQjoPAFq1aiWe6XQ1NTWiedLnDH9/\nf9E8O0jPFwEgJCRENK++vt7ReYD8eUp6ruFyuUTzAPljevr0adE8AOJ//90X5v3Hjh0TzevSpYto\nnh0qKipE86TPUYBv9A1pdXV1onnS61M7SK9R7Vi7Sbcb6XN/aGioaB7gG9cZnK62ttbut6BUSUkJ\n+vfvDwB9dV0vudZj2bqIiIiIiIiIiIiIiIiIiEzgpisRERERERERERERERERkQncdCUiIiIiIiIi\nIiIiIiIiMoGbrkREREREREREREREREREJnDTlYiIiIiIiIiIiIiIiIjIBG66EhERERERERERERER\nERGZwE1XIiIiIiIiIiIiIiIiIiITuOlKRERERERERERERERERGQCN12JiIiIiIiIiIiIiIiIiEzg\npisRERERERERERERERERkQkt7X4DRM1BbW0tamtrRbKOHTsmktNYmzZtRPNatpQdes6cOSOaBwA3\n3nijaJ6u66J5Bw4cEM0DgJ49e4pnOl1QUJBo3t69e0XzAOCWW24RzauvrxfNO378uGgeAMTExIjm\n/fOf/xTNA4CuXbuKZ0qqqakRzwwICBDN0zRNNE+67wNAixayn5+VzgOAqqoq0Tzp86L0nNgO0n3/\n/PnzonmAfBk7deokmmcHXxjDpdeLdXV1onl+fn6ieQCwb98+0bx+/fqJ5gEQuy7lJn2dwY456g03\n3CCad/r0adG8Vq1aiebZlSlNeowLDQ0VzbNjDHc66fEbkF9rOH1tY6R8/KYrERERERERERERERER\nEZEJ3HQlIiIiIiIiIiIiIiIiIjKBm65ERERERERERERERERERCZw05WIiIiIiIiIiIiIiIiIyARu\nuhIRERERERERERERERERmcBNVyIiIiIiIiIiIiIiIiIiE7jpSkRERERERERERERERERkAjddiSy2\nfft2jB49Gt27d0dAQABycnLEsv/4xz+ie/fueOaZZ5RlLFiwAEOGDEG3bt3Qo0cPTJgwAQcPHlSW\nB/z7mEZGRqJVq1bKj+nKlStxxx13ICoqClFRURgxYgS2bdumNBMAsrOzERUVhaCgIAwYMAC7du1S\nkvPCCy/gjjvuQOfOnREVFYWxY8fiwIEDSrIaKy0tRUZGBvr374/4+Hjce++9+PLLL5VmSh1TO0mX\nMTs7G9HR0QgODkZSUpLSvLvvvhs//elPL7s9//zzyjIBYOnSpYiJiUFISAiSk5OVllH6nLF48WLE\nxsZedBs+fLjSTMC+vvjHP/4R4eHhmDdvnvIsJ48327dvx5gxYxr6fm5urkiuZF8EZMe3jz/+GKNG\njUJERAT8/f3F5ouSZfzkk08wZsyYhjpU3W7mz5+PQYMGoVOnToiMjBSb30ge08aZTh5vJM+LL774\nIlJSUhAR8f/Yu/e4qqr8f/yvnSI380J5RRQQGNOCQkVBpRLNaxevk6Jpv89nPjUzWVwEtfqINtU0\npQGIq8eEAAAgAElEQVQ2mnbx0nzKQdBUkKugUyr2UyjtUWp5q2ZUEBw1LibC/v5BhwE1O/ucs9aC\nzev5ePh4zJzOOa+z91nrvdbai3NObwQEBGDWrFmmW9sA8uupikzZ/UL2fHHKlCnw9fWFq6urlHFY\n1TglS11dHd5++208+uijGD58OCZNmoT3339feK7Mdqri2pTs+rZ8+XI88MAD8PT0RN++fTFz5kxT\njf1vvvkmRo4cCS8vL/j7+0u7/qZi3q9iPiWb2fu/qjmxzEyzv4eAmmuMstc1MjK56UrkYJWVlQgK\nCsKKFSugaZq03EOHDmHjxo3o37+/0JzCwkI89dRTyM/Px7Zt21BTU4NJkyahurpaWKblnCYnJ0s5\np56enli8eDHy8/ORn5+PESNGYPbs2fjmm2+EZaakpCA2NhZLly7F559/jqCgIIwZMwZlZWUOz9q3\nbx+efvpp7Nq1C+np6bh27RoeffRRoe/h5cuXMWPGDLRr1w7vv/8+MjMzsXDhQnTo0EFYpsxzqors\nY0xJScH8+fOxZMkSFBcXIzAwEGPHjhWW99FHHzX0w/z8fKxevRqapuGhhx4Skgf85xgTEhJQVFSE\nwMBAjBs3TtgxqhgzAgICsH///oZ/KSkpQvNU9cUvvvgCH330kfBxETB/vamqqkJgYCCSkpKktVPZ\nfVF2fbP0/bfeekv6OZV5jIGBgUhMTJRyjJb5ze7du5GRkYGamho88sgjQuc3ss+pJdPM9Ub2uFhY\nWIjf/e53yMvLw8cff4yamhpMnjzZVGsb2fVURaaqObGs41MxDqsYp2TasGEDPv74YyxYsABpaWmY\nN28e/va3v2HTpk3CMmW3UxXrDNn1bd++fXjqqadQUFCA7du3S7k+JXPsLywsxP/8z/9g586d2Lp1\nK65duyb8+FTO+2XOp2Qze/9XNSeWmWn29xBQM5+Sva6Rlanpuu7QJyQyE03TggEUffbZZwgODjb8\n+Hbt2mHz5s14+OGHrX7M2bNnDedUVlZiwoQJeOWVV7BixQoMGDAAixcvtvrxHTt2NJxpUV5ejr59\n+yIrKwuhoaFWPcbV1dXmPGdnZ6SlpRk6p5cvX7Y5z8Lf3x9Lly7FzJkzrbr/HXfcYej5hw4diiFD\nhiA5ORkAoOs6vLy88OyzzyI+Pv5XH19ZWWkor7GysjL4+PggJycHYWFhVj3mX//6l6GMZcuW4Ysv\nvsD//d//2fISAdRvFBlh7zltCew9RqNzgNDQUISEhDTJ6927N+bNm2dV3uHDhw3lXe/111/Hnj17\nsH37dqsfc8899xjKCAsLQ0hICJKSkgDUH2OfPn0wb948xMXF/erj6+rqDOU1ZsuY8d133xnKWLFi\nBXbu3GnoHF6vb9++hu5vbzs1Wm+A+po4btw4/PnPf0ZSUhLuvvtuJCQkWP14T09PQ3ktrd5cuXLF\n5se6ublh06ZNmDhxoqHHtWvXztD97e2LRhem9tY3e/q+k5MTtmzZYqjvA8Bttxn7+1l7j9GeduPu\n7o6UlBTD7caetWpZWRm8vb2Rm5tr9fzG6BzV3nNqywWUllZvrl27ZvNjbRkXKyoqbM4D6tc2/v7+\n2LFjh9VrG3d3d5vzbFnbtGnTxlCGvfXUFvZmGq1v9vYLozXcEee0pqbGUKaFq6srUlNTDdfTtm3b\n2pQH2DZOGW2njnDw4EGr7xsdHY077rgDL774YsNtCxYsgIuLi9XfkDJo0CBDr88R9dvWmmpLPQXs\nG4dtqW/2zDWA+hru6+uL7Oxsq2t4+/btDWXYO/bbc32qvLwcfn5+yMzMtPr4nJ2dbc6zdd5vNFPF\nfEo2e/u/7PmU0Rpu73toC9ntpqW9h4DxsV/2OkPFusaezOLiYgwcOBAABuq6Xnyr+/KTrkQm8OKL\nL2LUqFEYNmyY9OyLFy9C0zR07txZerYMdXV12LJlC6qrqzF48GAhGTU1NSgqKkJERETDbZqmYdSo\nUSgsLBSS2dilS5eEv4e7du3C3Xffjeeeew5hYWGYNGkSUlNTheWpPqcyyD7GW+Xt37/f4Xk3y8/K\nysKkSZOEZhQVFWHkyJENt2mahoiICNO0G6B+o3bYsGF48MEHERMTgzNnzgjLUtUXX3jhBYwePVrK\nuNga6o1ssvui6vomQ2s4xuuJnt+oOKesN+LJmBfLpGJu05xquMg8s88XzS4wMBAHDhzA999/DwD4\n5ptvcOjQIWFzR9ZvOURfn1I9nzLbGAWoP6cymL3/N7c5sYhMs7+HQPO6xih6jiojk5uuRC3c9u3b\n8fXXX2PBggXSs3Vdx6JFixAaGop+/fpJzxfpyJEj6NOnD3r27In4+Hhs2LAB/v7+QrLKyspQW1uL\nbt26Nbm9W7duOHfunJBMC13XsWDBAoSGhuKuu+4SlvPDDz9g48aN8PHxwdq1a/H444/j5ZdfxrZt\n24TkqTynssg+xl/K69q1q5RzWlBQgB9//NHwX/YZcatzWlJSIixXpnvvvRd/+ctfsG7dOvzpT3/C\nP//5T8ycORNVVVVC8lT0xW3btuGrr77CwoULhTz/9VpDvZFNdl9UXd9kaA3H2Jiu64iPj0dYWJiw\n+Y2Kc8p6I5ZlbTN06FDTrG1UzG2aSw2XPSc203yxNZg7dy5Gjx6NadOmITQ0FE888QRmzJgh7GdM\nWL/F03UdCxcuFHp9SuV8yoxjFNA65qhm7//NaU4sKtPs7yHQvOZTZlhL2f79IkSk3NmzZ7F06VJ8\n+OGHcHJykp4fExODo0ePIjc3V3q2aP7+/vjHP/6BS5cuISMjA3/84x+Rnp4ubOP1ZnRdF/41KVFR\nUTh69Ch27twpNKeurg6BgYGIiooCAPTr1w/ffvstNm7ciEcffVRodmMyzqlqso9RVt62bdswfPhw\n3HnnncKzrmemdhMeHt7wvwMCAhAUFITw8HBkZmZi6tSp0l6HqHN65swZLFmyBB999JGScbExM7Wb\n5sKs9U0lsx6jrPnNzag4p2Z9H2WLjY3FsWPHkJ2drfqlCNca2qnZ88g+eXl5yMnJwSuvvAIfHx98\n8803WL58Obp06YLx48dLex1sN44TExODY8eOKbk+JeN9jI2NxdGjR5GTkyM0p7loDX3D7MfIuYY5\ntIZzKiKTm65ELdiXX36J8vJyTJgwoeG3Nmpra/HZZ59hw4YNOH78uLBCNX/+fOTl5SErKwvdu3cX\nkqFS27Zt4e3tDQAICgpCcXEx1qxZg2XLljk8684770SbNm1u+Mvo0tLSG/76xpFiYmKQm5uL3Nxc\n4e9h165db/gdyL59+yIvL09InqpzKpPsY/ylvPPnzws/p2fPnsVnn32GxMREoTm3Oqddu3YVmq3K\n7bffDh8fH8O/DWst2e3UMi6OHz/+hnFx/fr1OHnypMPHxdZQb2ST3RdV1jdZWsMxWsTExCAnJwd5\neXno0aOHsBwV55T1Rpy4uDjk5eUhMzPTVGsbFXOb5lLDZc+JzTxfNKMVK1bgySefxKhRowDUr03P\nnj2L9evXC9l0Zf0WKzY2Frm5ucjOzhZaw1XNp+Li4pCbm2vK62+tYY5q9v7fnObEojLN/h4CzWs+\nZYa1FL9emKgFGz58eMPEKzs7G9nZ2QgMDMSkSZOQnZ0tdMM1MzMT6enp8PLyEpLR3NTV1eHq1atC\nntvJyQkDBw5Efn5+w226riM/Px9hYWFCMmNiYrBjxw5kZmZKeQ/vu+8+nDp1qsltp06dQs+ePYXk\nqTinssk+xlvlhYaGOjyvsa1bt8LDwwPDhw8XmmM5xoKCgobbdF1HQUGBadrN9SorK/H999+jS5cu\nQp5fdjsdMWIEdu7ciZycnIY/KgkMDMTkyZORm5srZFxsDfVGNtl9UWV9k6U1HCNQP7/JyMhAVlaW\n8PmNinPKeiNGXFycadc2KuY2zamGi8xrTfNFM7py5coN80JN01BXVyckj/VbnNjYWGRmZiIjI8OU\nY79ljJJxfCq0hjmq2ft/c5sTi8g0+3sINK9rjKLnqDIy+UlXIgerrKzE8ePHGz5hc/LkSRw6dAge\nHh4OnyC5ubkhICDghts6d+4s7GtwY2JikJaWhr///e9wd3dHaWkpAKBDhw5wcXERknn9OT116pSw\ncwoAr7zyCiIiIuDp6YmKigqkpaVh3759SE1NdXiWRUxMDObMmYOBAwciJCQEiYmJqKqqwty5cx2e\nFRUVhbS0NKSkpMDNza3hL3w6duwo7D2cO3cuZsyYgTVr1mDcuHE4dOgQUlNT8fLLLwvJA+SeU1Vk\nH2N0dDTmzp0r9Zzquo7t27fjkUcewW23if9bsaioKDz55JMIDg5GSEgIkpKSUFVVhTlz5gjJkzlm\nAMBrr72GkSNHwtPTEyUlJUhOTkabNm2E/lauzHaqYlwEzF9vKisrceLEiSbj8OHDh9G5c2dhF39k\n90XZ9U123wfUHGPjdnP69GkcPnwYHh4e6NWrl8PzoqKikJqaik2bNkmb36gYF1tDvZHZN2JjY7F5\n82Z89NFHcHNzM+XaRnY9VZEpu1+omC/KHodVjFMyjRgxAmvXrkW3bt3g6+uLo0ePCv/pG9ntVMV7\nKLu+RUdHY/PmzVKvT8kc+2NjY5GWloaNGzdKHaNk1xsV8ynZzN7/VbyHsjPN/h4C8o9RxbpGViY3\nXYkcrKioCKNGjYKmadA0DfHx8QCA2bNn47333hOeL/p7z9euXQtN0zBhwoQmt69atQozZswQkllU\nVITRo0ff9Jy+++67Ds8rLS3FH//4R5SUlKBDhw7o378/UlNTm/wWoqNNnz4dZWVlWLx4MUpKSnDv\nvfciJydHyKfP3n//fWiahnHjxjW5ffXq1Zg5c6bD8wDgnnvuwcqVK7Fs2TKsWrUKvXr1wgsvvHBD\nO3IkmedUFdnHaMlLSEhoyMvOzhZ6Tvfv34+SkhJpv/07ffp0lJeXY8mSJQ3HmJWVJewYZY8Z586d\nQ3R0NC5evAgPDw8MGjQIaWlp6Ny5s8OzLFT3RRm/B6L6GEUrLi7GmDFjGtrpwoULAQCzZs3CmjVr\nhGTK7ouy69vBgweb9P24uDgAwBNPPCFsvij7GIuLizF27Ngb2k1kZKSQdvPee+9B0zSMHTu2ye2r\nV69GZGSkw/MANeOi2euN7HFx3bp10DTthj8+WrlyJR5//HGH5wHy1zay66mKTBVzYtnzxcbj8IIF\nCwDUj8PvvPOOkEwV45RM8fHxWL16NV5//XVcuHABXbp0wZQpU/Bf//VfwjJlt1MV16Zk1zfL9anr\nvxJ61apVwq5tyBz7Lcc3ceLEJrevXLlS2PU3VfN+2fMp2cze/1XOiWVlmv09BNRdY5S5rpGVqVl2\ny4noRpqmBQMo+uyzzxAcHCwl8+zZs1JyGuvYsaPUPFdXV6l5ly9flpoHAHfccYfUvMrKSql5//rX\nv6TmAbjh02tkP9lzgMOHD0vNA+o3/GUS9ZVkv0TUb7HeyvW/zyyainrj6ekpPVOmK1euSM9s166d\n1DwZm+2Nye77AKR8+r8xFe1G9jgle44qu52qcO3aNal5FRUVUvMAwN3dXWpemzZtpOapILu+qajh\nNTU1UvPatpX7mQsV7fTgwYNS8wYNGiQ1D5BfU2WPwyrmGu3bt5eaJ/v6lLOzs9Q8FZmcTzlea5hr\nyG43st9DQP7Yb3bFxcUYOHAgAAzUdb34Vvflb7oSEREREREREREREREREdmBm65ERERERERERERE\nRERERHbgpisRERERERERERERERERkR246UpEREREREREREREREREZAduuhIRERERERERERERERER\n2YGbrkREREREREREREREREREduCmKxERERERERERERERERGRHbjpSkRERERERERERERERERkB266\nEhERERERERERERERERHZgZuuRERERERERERERERERER2aKv6BRC1BG3btkXbtnK6i5eXl5Qcla5d\nuyY1z8PDQ2oeAJSWlkrN69q1q9Q8f39/qXkAUF1dLTXP1dVVap4KJ0+elJoXEBAgNQ8AbrtN7t+X\nyc7r0aOH1DwAuHDhgtQ8T09PqXkAUFdXJzVPdrtxcXGRmgcAtbW1UvOqqqqk5rVv315qHiC/nWqa\nJjUPkD8WV1RUSM2rrKyUmgcA3bp1k5p39epVqXlubm5S8wBA13WpebLHDEB+Df/hhx+k5qlYgzs5\nOUnNU1HDZZO9RpXd91Vkym6nKuqb7OsMHTt2lJqnwpkzZ6Tm9ezZU2qeCleuXJGaJ3udoYKzs7Op\n8wDg4sWLUvNUjIudOnWSlmXk+PhJVyIiIiIiIiIiIiIiIiIiO3DTlYiIiIiIiIiIiIiIiIjIDtx0\nJSIiIiIiIiIiIiIiIiKyAzddiYiIiIiIiIiIiIiIiIjswE1XIiIiIiIiIiIiIiIiIiI7cNOViIiI\niIiIiIiIiIiIiMgO3HQlIiIiIiIiIiIiIiIiIrIDN12JBFi5ciV8fHzg6uqKoUOH4sCBA6bKk525\nZ88eTJo0CX369EG7du2Qnp4uLKuxlStXwtfXF25ubggNDRV2jBs2bMCDDz4IPz8/+Pn5YcKECSgo\nKBCS1ZiqdiPjnALA3r17MXXqVPTt2xfu7u7IyMgQlqWSrPdxxYoV8Pf3b/JvzJgxQrIsVL2HZq7h\nb7zxBsLDw9G9e3d4e3vj8ccfx7fffissb926dQgPD4e3tze8vb0xduxY5OfnC8uzUFHfVq1a1dBW\nw8LCpGTKJvO8fvrpp3jsscfQu3dvODk5CR37ly1bhgceeAA9e/aEr68vZsyYIbRfNCa7rcpsp3v2\n7MHUqVMbxn2z1XAV7WbDhg0YOXJkwzg8ceJEKXNGWWSPUUB9O508eTJ8fHzg4uIiZZ2hIlPVuOjn\n54f27dtLHRf/+te/ok+fPnjppZeE5rSGuYbMtZtsb775JkaOHAkvLy/4+/sjMjISx48fF54r85xa\nrqN4e3vD2dlZ6nUUM84XATXjFGD+a36NvfXWW+jVqxeWLFkiJU8mM89Rly9fjgcffBC9evWCn5+f\n8JoqOw+or6lTpkyBr68vXF1dTbe2Wbt2LYYPH47evXujd+/eeOihh7Bz504hWRbr1q3DiBEj0KdP\nH/Tp0wdjxowRngnIGYu56UrkYCkpKYiNjcXSpUvx+eefIygoCGPGjEFZWZkp8lRkVlZWIigoCCtW\nrICmaUIyrpeSkoL58+djyZIlKC4uRmBgIMaOHSvkGHv27In//d//RV5eHvLy8jB8+HDMmTMH33zz\njcOzLFS1G1nnFKhvN4GBgUhMTJTWbmST/T4GBARg//79Df9SUlKE5FioeA/NXsP37duHp59+Grt3\n70ZGRgZqamrwyCOPoLq6Wkiep6cnEhISUFBQgIKCAowYMQKzZs3CsWPHhOQBautbQkICioqKEBgY\niHHjxgnNlE3V2P/WW28J7/+FhYV46qmnUFBQgPT0dNTU1OCxxx4T1i8sVNQbme20qqoKgYGBSEpK\nMmUNV9FuevbsiRdffBG5ubnIzc3F8OHDMXfuXKFzRplkj1HAf2qNzHYqO1PFuLhp0ybExcUhISEB\nBw8eRFBQEMaPHy98XDx06BA2btyI/v37C81pDXMN2Ws32QoLC/E///M/2LlzJ7Zu3Ypr165h0qRJ\nQuuNivVwUFAQkpOTTTkOA3Lni4Cacao1XPOz+OKLL/DRRx8Jr+EqmH2OasnMz8/Htm3bUFNTI7Sm\nys4DzL+28fT0xJIlS7B7927s3r0b4eHhiIyMFHrtxnK9aNeuXdi1axfCw8OlXC+SMRZruq479AmJ\nzETTtGAARUVFRQgODrbqMUOHDsWQIUOQnJwMANB1HV5eXnj22WcRHx/v8NcoO88RmdeuXbM5u127\ndti8eTMefvhhqx/Tpk0bwzmhoaEICQlpcoy9e/fGvHnzrDrG8+fPG85srF+/fkhISMCMGTOsun/X\nrl0NPb+976EtY4e95/TKlSuGMy3c3d2RkpKCiRMnWv0YV1dXm/Nksfd9PHHihNVZK1aswM6dO7F9\n+3abX2/Pnj1tfqwt7yFg/H1saTW8qqrKrvyysjJ4e3sjNzcXYWFhVj3Gnr4IAH5+fli6dCkiIyOt\nur+Hh4eh53fEe1hXV2coMywsDCEhIUhKSmrI7NOnD+bNm4e4uLhfffxttzX/v4O097zW1tbanO3k\n5IQtW7YYGvvtWXCXlZXB19cX2dnZVveL9u3bG86x95zKbqdXr141lNeYm5sbNm3aZLiGu7i4GLq/\nvee0oqLCUF5jtrSbyspKm/Ms7rrrLixevNjqOWO3bt3szjTCnnHKljGqbdu2Nue5uLggNTXVUK2x\nly2Z7dq1M5ThiHHRaA0PCwvDkCFDkJiY2JDp7e2NZ555xqp6c+bMGUN5QH1/mjBhAl555RWsWLEC\nAwYMwOLFi616rJeXl6GsljjXMHqB2N61m4o/er106ZLNjy0vL4efnx8yMzMRGhpq1WM6dOhgKMPe\ncwrYfi3F2dkZaWlphuubk5OTofu3tPkiAPz00082Z9oyTrm5uRnKaInX/Gyt4WPHjsWf//xnJCUl\n4e6777b60672XGeQpaXNUY2OUdcrLy9H3759kZWVZXVNlZ3n7Oxsc56rqytSU1MNrW1sybO33Vy8\neNFwZmO+vr7405/+ZPW1G0fsM/bt2xcvvfSS1ZmdOnUy9Pz2jMXFxcUYNGgQAAzUdb34Vvdt/ld4\niFqQmpoaFBUVISIiouE2TdMwatQoFBYWtvg8VZmy3eoY9+/fLzS7rq4OH3/8Maqrqy2F3OGaW7sR\nfU7NSsX7+N1332HYsGF48MEHERMTY9NiqjlrDTX8epcuXYKmaejcubPwrLq6OmzZsgXV1dUYPHiw\nkAyV9W3kyJFNMiMiIlrFuGiWY2zM0i+MbvgboaresJ2KI6PdNFZXV4etW7cKnTOqJnOMMitV42Jx\ncfFN643Ief+LL76IUaNGYdiwYcIygNYx12iNazfR9aY1nFPV47AKKtuN2a75Pf/88xg9ejSGDx8u\nLEMV1X1D9hwVqN/skzmHk50ng8p2U1dXh82bNwu9dqMiU+ZYbPufgRLRDcrKylBbW3vDX5B369ZN\nyEfjZeepypTtl46xa9euwo7xyJEjmDBhAn766Se0b98e69atg7+/v5Cs5tRuRJ5Ts5P9Pt577734\ny1/+Al9fX5SWlmLFihWYOXMmMjMzDf+1bnPVGmp4Y7quIz4+HmFhYbjrrruE5Rw5cgRjxoxpqG8f\nfPABAgIChGQ1p/rWrVs303zlp+q2KpOu61iwYAFCQ0PRr18/YTnNqd6wndpPVrsB6mvqxIkTG2rq\n2rVrhc0ZVZI1RpmdynHx+m/iETnv3759O77++mspv6/WGuYarW3tpus6Fi1ahKFDhwqr4a3hnLam\n+SIgZ5xqbvVGVOa2bdvw1VdfISsrS8jzq9Za5qiNMxctWiQtU3aeLCrazddff40xY8bgypUraN++\nPf72t78Ju3bTOHPs2LENmSquF4kYi7npSiSBrutSv15Hdp6qTNlEHqO/vz8KCgpw+fJlZGRk4Jln\nnsG2bdukXkRjuzEHUec0PDy84X8HBAQgKCgI4eHhyMzMxNSpUx2e15yYtYZHRUXh6NGj2Llzp9Ac\nf39/fPLJJ7h06RLS09Pxhz/8Aenp6cIn742xvolhxmOMjo7GsWPHkJeXpyTfrPVGJRnHKLPdWOaM\nly5dwo4dOzBv3jxs3brVdBuvssao1spM4+LZs2exdOlSfPjhh4a/CtWRzHROm0ueLLGxsTh69Chy\ncnKkZ5v1nDZm1mNUOU6Zqd6cOXMGCQkJ2Lhxo9IaroLZ5qgWMTExOHr0KHJzc02Zp5rIdhMQEIBP\nP/0Uly5dwvbt2/H73/8eO3bsEHrtJiAg4IbrRRkZGS3+ehE3XYkc6M4770SbNm1QUlLS5PbS0lIh\nv58kO09Vpmy/dIznz58Xdoxt27aFt7c3ACAwMBCff/453n33Xbz++usOz2pO7UbkOTU71X3x9ttv\nh4+PD7777jvhWbK0hhpuERMTg5ycHOTl5aFHjx5CsxrXt6CgIBQXF2PNmjVYvny5w7OaU30rLS01\n/HvbzZXqeiNLbGwscnNzkZOTg+7duwvNak71hu3UPjLbDVBfU/v06QNA/JxRFZljlNmpHBdLS0ub\n3C5q3v/ll1+ivLwcEyZMaPgtsdraWnz22WfYsGEDjh8/7tALaa1hrtGa1m5xcXHIzc1FVlaW0Bre\nGs5pa5kvAvLGqeZWb0TW8HHjxt1Qw9evX49Tp061+E371jJHBYD58+cjLy9PeE1VlSeTinZzs2s3\nq1evxptvvikk72aZRUVF0q8XiRiL+ZuuRA7k5OSEgQMHIj8/v+E2XdeRn59v9Y+VN+c8VZmy3eoY\nZfwAPFD/XfY//fSTkOdubu1G1jk1G9V9sbKyEt9//z26dOkiPEuW1lDDgfqLBBkZGcjKyoKXl5ew\nnF9SV1eHq1evCnlulfWtoKCgSWZBQUGrGBfNcoyxsbHYsWMHduzYIaVfqKo3bKeOJbvd3IzImqqC\n6jHKbFSNi8HBwTetNyLm/cOHD2/YNMvOzkZ2djYCAwMxadIkZGdnO/xifWuYa7SWtVtcXBwyMzOR\nkZEhvN60hnPaGuaLgNxxqrlduxGROWLECOTn5yM3Nxd5eXnIy8tDUFAQJk+ejLy8vBa/4Qq0njnq\n/PnzkZmZifT0dCmZsvNkaw41VcU6Q9d1JdfDHT0W85OuRA4WExODOXPmYODAgQgJCUFiYiKqqqow\nd+5cU+SpyKysrMTx48cb/urt5MmTOHToEDw8PIQNrNHR0Zg7d66UY3z11VcRERGBnj17oqKiAps3\nb0ZhYSFSUlIcnmWhot3IPKdAfbs5ceJEQ7s5ffo0Dh8+DA8PD/Tq1UtIpmwy38fXXnsNI0eOhKen\nJ0pKSpCcnIw2bdrg4YcfdniWhYr30Ow1PCoqCqmpqdi0aRPc3Nwa/sKvY8eOcHFxcXjeyy+/jFHw\niqMAACAASURBVFGjRsHT0xMVFRVITU3Fvn37kJaW5vAsCxX1LSoqCk8++SSCg4MREhKCpKQkVFVV\nYc6cOcIyZTPz2B8dHY20tDT8/e9/h7u7e8Onszp06CCkX1io6P8y2+n1NfzUqVM4fPgwOnfuLGz+\nJvOcqmg3f/7znzFy5MiGOeOWLVuEzxllkj1GAWraqexMVfN+S70ZPHgwkpOThdUbNze3G76Czs3N\nDZ07dxb2tdutYa4he+0mW2xsLNLS0rBx40a4ublJqeEq1sON51KnTp0Sfh3FzPNFQM04ZfZrfjer\n4a6urkJruApmn6PGxMRIzZSdB5h/bfOnP/3phms3e/fuxZYtWxyeZdH4etGPP/6ItLQ07N27F5s3\nbxaWKWss5qYrkYNNnz4dZWVlWLx4MUpKSnDvvfciJydH2KfBZOepyCwqKsKoUaOgaRo0TUN8fDwA\nYPbs2XjvvfeEZFqOMSEhoeEYs7OzhRzj+fPn8cwzz6C0tBS33347+vfvj5SUFIwYMcLhWRYq242M\ncwoAxcXFGDt2bEO7WbhwIQAgMjISa9asEZIpm8z38dy5c4iOjsbFixfh4eGBQYMGIS0tDZ07d3Z4\nloWK99DsNfy9996DpmkYO3Zsk9tXr16NyMhIh+edP38ef/jDH1BSUoIOHTqgf//+SEtLa/IbwY6m\nqr6Vl5djyZIlDZlZWVmm+iS47PN68ODBJmN/XFwcAOCJJ55w+Nj//vvvQ9M0jB8/vsntb7/9NmbO\nnOnQrMZU1BuZ7bS4uBhjxoy5oYbPmjXLFDVcRbs5f/485s2bd8Occfjw4ULyZJM9RgH164yHHnqo\noZ0uWLAAQP0645133jFFpopxcdq0aSgrK2uoN0FBQcjMzJQ2Lor+ZFRrmGvIXrvJtnbtWmiahokT\nJza5feXKlZgxY4aQTNnntKioCKNHj77pdZR3331XSKaZ54uAmnGqNVzzu54ZPt16PbPPUS01dcKE\nCU1uX7VqlZCaKjsPqK+pjdc2lvnbrFmzhM0ZZbab8+fP4/e//33DtZsBAwZgy5YtQq/dlJaWNsns\n378/Nm/eLOV6keixWLPszhPRjTRNCwZQVFRUhODgYNUvxzSuXbsmNa9NmzZS84D6wUom2b/HpmLs\nuHLlitQ8V1dXqXkqnDhxQmpez549peYB5n8fq6qqpGfK7oseHh5S84D6r9GR6bbbzP+LH7W1tVLz\nqqurpea1b99eah4gv52q+ApbkZ80vpmKigqpeZWVlVLzAEj/XT3Z41Tbtub/u/J27dpJz5Rdw8+c\nOSM1T9XPLcgkexNDxabJpUuXpOZ16NBBah4g/1qKk5OT1DzZtQaAsK+x/CVubm5S81SQXcNVXGeQ\nTfYcVfYYpYKzs7Op8wDg4sWLUvNUXCvu1KmTtKzi4mIMGjQIAAbqul58q/ua/woPERERERERERER\nEREREZFA3HQlIiIiIiIiIiIiIiIiIrIDN12JiIiIiIiIiIiIiIiIiOzATVciIiIiIiIiIiIiIiIi\nIjtw05WIiIiIiIiIiIiIiIiIyA7cdCUiIiIiIiIiIiIiIiIisgM3XYmIiIiIiIiIiIiIiIiI7MBN\nVyIiIiIiIiIiIiIiIiIiO3DTlYiIiIiIiIiIiIiIiIjIDtx0JSIiIiIiIiIiIiIiIiKyQ1vVL4CI\nmqqqqpKeWVdXJzXP2dlZal5ZWZnUPABwc3OTmie73chuMwDQvn176ZmyyX4fu3XrJjXv5MmTUvMA\nwM/PT2reuXPnpOZ5eXlJzQOA7777Tmpex44dpeYBwG238e8SWzrZc40rV65IzQPkt9PCwkKpeQBw\n//33S81zd3eXmid7vqiC7Dnj1atXpeYB8uuNCrLrjeyxX8XaRtd16ZkyaZomPbNNmzZS877++mup\neYD8viF7vejk5CQ1DwCqq6ul5skeM3766SepeUDrmN/I1rat3C0cFxcXqXmA/LFY9txGxbgvu964\nurpKzQOA2tpaaVlG2iivKBERERERERERERERERER2YGbrkREREREREREREREREREduCmKxERERER\nERERERERERGRHbjpSkRERERERERERERERERkB266EhERERERERERERERERHZgZuuRERERERERERE\nRERERER24KYrEREREREREREREREREZEduOlKJMDKlSvh4+MDV1dXDB06FAcOHBCW9cYbbyA8PBzd\nu3eHt7c3Hn/8cXz77bfC8pYtW4YHHngAPXv2hK+vL2bMmCE0DwD27NmDSZMmwdvbG87OzkhPTxea\nt379ejzwwAPo27cv+vbti/Hjx6OgoEBYnuxzKrvNAGraDSC3L8rWGvo+AJSWlmLhwoUYPnw4Bg0a\nhMmTJ+Prr78Wlrdnzx5MmTIFvr6+cHV1RUZGhrCspKQk+Pj4NPk3atQoYXmNrVq1Cn379oW7uzvC\nwsKE9o3S0lIsWLAAYWFhCA4OxqRJk4S+h59++ikee+wx9O7dG05OTsLHDIuVK1fC19cXbm5uCA0N\nNVW9sZBdU1etWgU/Pz+0b99eeDuVPdfYs2cPpk6d2tBmRNaaxpmTJ0+Gj48PXFxcHH6Mhw8fxgsv\nvIBp06YhIiICe/fuveE+69atw7Rp0zBu3DjExcXhX//6l0NfAyC3vgFq+r7sY5RFxVyjNaxtADVz\nYll9Y/ny5XjwwQfRq1cv+Pn5ITIyEsePHxeS1ZiKfihzXLTkmbHWAGr6vux1zfX++te/onfv3li6\ndKmwDLPXt3Xr1uH+++9vWLuNGzcO+fn5wvIsZPZ92dcZ3nzzTURERKB3794ICAjArFmzpNRwFWS2\nVRVrDRVzDRXzfrPOpwBg7969mDp1asM5NVu7kXmtiJuuRA6WkpKC2NhYLF26FJ9//jmCgoIwZswY\nlJWVCcnbt28fnn76aezevRsZGRmoqanBI488gurqaiF5hYWFeOqpp1BQUID09HTU1NTgscceE5YH\nAJWVlQgKCkJycjI0TROWY+Hp6YnFixcjLy8PeXl5GDFiBJ544gl88803QvJkn1PZbQZQ025k90XZ\nWkPfv3z5MmbPno127dphzZo12L59O+Li4tChQwdhmVVVVQgMDERSUpKUevOb3/wGBw8exIEDB3Dg\nwAGkpaUJz0xJScH8+fORkJCAoqIiBAYGYty4cUL6xuXLlxEZGYl27drh3XffRXp6uvD30DJmvPXW\nW1LeQ+A/53TJkiUoLi5GYGAgxo4da5p6A8ivqZs2bUJcXBwSEhJw8OBBBAUFYfz48cLyZM81ZNca\n4D/HKCrzypUr8PPzw3PPPXfT59+4cSO2bt2K6OhorFq1Ci4uLoiPj0dNTY3DXoPM+tY4T2bfl32M\nMqmYa7SGtY2KObHMvmF5D/Pz87Ft2zbU1NRg0qRJwtcZsvuh7HHRzLUGkN/3VaxrGvviiy+wceNG\n9O/fX2iO2eub5VpRfn4+8vPzMWLECMyePVvYtSJAft9XcZ3hd7/7HfLy8vDxxx+jpqYGkydPFlrD\nVZDdVmWvNVTONWTO+808nwLqa3hgYCASExNN2W5kXivSdF0XGkDUkmmaFgygqKioCMHBwVY9ZujQ\noRgyZAiSk5MBALquw8vLC88++yzi4+N/9fFVVVX2vGSUlZXB29sbubm5CAsLs+oxdXV1duX5+voi\nOzvb6jxnZ2eb85ydnZGWloaHH37Y6sdcvHjR5jyL3/zmN1iyZAlmzJhh1f1dXV1tzrLlnN52m+1/\nQyO7zVgyjR5j+/btDWXY2xdVsKf/t4S+/9133xnKSExMxKFDh7B+/XobXmE9Pz8/mx/r6uqK1NRU\nTJw40erHnDt3zur7JiUlIS8vDzt27LDl5QEAvLy8DD8mLCwMISEhSEpKAlDfN/r06YN58+YhLi7u\nVx9/7Ngxq7PefPNNfPHFF/jggw8Mv06LgIAAmx/r5OSELVu2GBozAOM1NTQ0FCEhIU3qTe/evTFv\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TdV1sgKatA1Cg6/rfrLjv0wCCAfxF1/UTQl8YkRU0TQsGUFRUVITg4GDVL4eIiIiIiIiI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\n8uXLRTKl8y62d+9e/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8TG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\neOMNjBs3DhERERg0aBB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m4uZs+eLZZnR2ZzczMOHToEXdcBAJ9//jk+/fRT9O3bV8mGgd3lO3z4sNLyeTI/++yzizL3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb45b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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tv5vvJ3nrVL/PnA8WHQ8r+1vgEJqnoK5qF37ONUcCFwKHAX8I7Ap8Ockefa1q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c26ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make another chi2 plot, with different x and y labels\n",
    "pplt.chi2_boxplot( Xs, Ys, gateStrDict, ds, gs_mc2gst, strs, xlbl, ylbl,\n",
    "               scale=1.0, sumUp=False, histogram=True, invert=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10e1acd68>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uuIPjjz+e2rVrk52dzdlnn82iRYs2WeaKK66gRYsW1KxZk4YNG9K9e3cWLlyY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\nMGnSJC6++GKmT5/O/vvvn+EWZEbdunXp06cPOTk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SYlm4kCRJkiRJiWXhQpIkSZIkJVaF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc777f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sum up the chi2 boxes for each (Germ, L) pair\n",
    "pplt.chi2_boxplot( Xs, Ys, gateStrDict, ds, gs_mc2gst, strs, xlbl, ylbl,\n",
    "                scale=1.0, sumUp=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Direct $\\chi^2$ Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Direct LGST precomputation ---\n",
      "--- Computing gateset for string - Iter 01 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 02 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 03 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 04 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 05 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 06 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 07 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 08 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 09 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 10 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 11 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 12 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 13 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 14 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 15 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 16 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 17 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 18 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 19 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 20 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 21 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 22 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 23 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 24 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 25 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 26 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 27 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 28 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 29 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 30 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 31 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 32 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 33 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 34 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 35 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 36 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 37 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 38 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 39 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 40 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 41 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 42 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 43 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 44 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 45 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 46 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 47 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 48 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 49 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 50 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 51 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 52 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 53 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 54 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 55 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 56 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 57 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 58 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 59 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 60 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 61 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 62 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 63 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 64 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 65 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 66 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 67 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 68 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 69 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 70 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 71 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 72 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 73 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 74 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 75 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 76 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 77 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 78 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 79 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 80 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 81 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 82 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 83 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Computing gateset for string - Iter 84 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Direct LSGST precomputation ---\n",
      "--- Computing gateset for string- Iter 01 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 193.951, mu=0\n",
      "--- Outer Iter 1: norm_f = 78.9849, mu=572.356\n",
      "--- Outer Iter 2: norm_f = 78.4328, mu=190.785\n",
      "--- Outer Iter 3: norm_f = 78.4243, mu=63.5951\n",
      "--- Outer Iter 4: norm_f = 78.4242, mu=21.1984\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 78.4242 (192 data params - 56 model params = expected mean of 136; p-value = 0.999981)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 02 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 169.907, mu=0\n",
      "--- Outer Iter 1: norm_f = 72.8692, mu=561.549\n",
      "--- Outer Iter 2: norm_f = 72.5117, mu=187.183\n",
      "--- Outer Iter 3: norm_f = 72.5072, mu=62.3944\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 72.5072 (192 data params - 56 model params = expected mean of 136; p-value = 0.999998)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 03 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 179.404, mu=0\n",
      "--- Outer Iter 1: norm_f = 73.753, mu=563.687\n",
      "--- Outer Iter 2: norm_f = 73.3692, mu=187.896\n",
      "--- Outer Iter 3: norm_f = 73.364, mu=62.6319\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 73.364 (192 data params - 56 model params = expected mean of 136; p-value = 0.999998)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 04 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 181.953, mu=0\n",
      "--- Outer Iter 1: norm_f = 76.6268, mu=560.957\n",
      "--- Outer Iter 2: norm_f = 76.2731, mu=186.986\n",
      "--- Outer Iter 3: norm_f = 76.2685, mu=62.3285\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 76.2685 (192 data params - 56 model params = expected mean of 136; p-value = 0.999992)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 05 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 189.538, mu=0\n",
      "--- Outer Iter 1: norm_f = 85.0724, mu=561.301\n",
      "--- Outer Iter 2: norm_f = 84.6993, mu=187.1\n",
      "--- Outer Iter 3: norm_f = 84.6951, mu=62.3668\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 84.6951 (192 data params - 56 model params = expected mean of 136; p-value = 0.999827)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 06 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 187.571, mu=0\n",
      "--- Outer Iter 1: norm_f = 83.8078, mu=561.192\n",
      "--- Outer Iter 2: norm_f = 83.3305, mu=187.064\n",
      "--- Outer Iter 3: norm_f = 83.3247, mu=62.3546\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 83.3247 (192 data params - 56 model params = expected mean of 136; p-value = 0.999889)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 07 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 187.108, mu=0\n",
      "--- Outer Iter 1: norm_f = 74.5645, mu=567.631\n",
      "--- Outer Iter 2: norm_f = 74.024, mu=189.21\n",
      "--- Outer Iter 3: norm_f = 74.012, mu=63.0701\n",
      "--- Outer Iter 4: norm_f = 74.0119, mu=21.0234\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 74.0119 (192 data params - 56 model params = expected mean of 136; p-value = 0.999997)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 08 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 188.974, mu=0\n",
      "--- Outer Iter 1: norm_f = 89.1284, mu=560.71\n",
      "--- Outer Iter 2: norm_f = 88.7828, mu=186.903\n",
      "--- Outer Iter 3: norm_f = 88.7781, mu=62.3011\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 88.7781 (192 data params - 56 model params = expected mean of 136; p-value = 0.999407)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 09 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 176.244, mu=0\n",
      "--- Outer Iter 1: norm_f = 78.3497, mu=561.034\n",
      "--- Outer Iter 2: norm_f = 78.0235, mu=187.011\n",
      "--- Outer Iter 3: norm_f = 78.0195, mu=62.3371\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 78.0195 (192 data params - 56 model params = expected mean of 136; p-value = 0.999984)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 10 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 175.008, mu=0\n",
      "--- Outer Iter 1: norm_f = 64.0829, mu=578.54\n",
      "--- Outer Iter 2: norm_f = 63.6182, mu=192.847\n",
      "--- Outer Iter 3: norm_f = 63.6116, mu=64.2822\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 63.6116 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 11 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 173.274, mu=0\n",
      "--- Outer Iter 1: norm_f = 71.665, mu=565.637\n",
      "--- Outer Iter 2: norm_f = 71.2296, mu=188.546\n",
      "--- Outer Iter 3: norm_f = 71.2217, mu=62.8486\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 71.2217 (192 data params - 56 model params = expected mean of 136; p-value = 0.999999)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 12 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 167.726, mu=0\n",
      "--- Outer Iter 1: norm_f = 71.8407, mu=560.68\n",
      "--- Outer Iter 2: norm_f = 71.5237, mu=186.893\n",
      "--- Outer Iter 3: norm_f = 71.5194, mu=62.2978\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 71.5194 (192 data params - 56 model params = expected mean of 136; p-value = 0.999999)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 13 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 193.443, mu=0\n",
      "--- Outer Iter 1: norm_f = 86.5298, mu=560.926\n",
      "--- Outer Iter 2: norm_f = 86.2048, mu=186.975\n",
      "--- Outer Iter 3: norm_f = 86.2011, mu=62.3251\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 86.2011 (192 data params - 56 model params = expected mean of 136; p-value = 0.999723)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 14 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 200.206, mu=0\n",
      "--- Outer Iter 1: norm_f = 84.0756, mu=561.505\n",
      "--- Outer Iter 2: norm_f = 83.6021, mu=187.168\n",
      "--- Outer Iter 3: norm_f = 83.5969, mu=62.3894\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 83.5969 (192 data params - 56 model params = expected mean of 136; p-value = 0.999879)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 15 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 203.385, mu=0\n",
      "--- Outer Iter 1: norm_f = 93.5035, mu=564.226\n",
      "--- Outer Iter 2: norm_f = 92.963, mu=188.075\n",
      "--- Outer Iter 3: norm_f = 92.9555, mu=62.6918\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 92.9555 (192 data params - 56 model params = expected mean of 136; p-value = 0.998189)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 16 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 183.411, mu=0\n",
      "--- Outer Iter 1: norm_f = 70.4087, mu=572.3\n",
      "--- Outer Iter 2: norm_f = 70.0232, mu=190.767\n",
      "--- Outer Iter 3: norm_f = 70.0157, mu=63.5889\n",
      "--- Outer Iter 4: norm_f = 70.0156, mu=21.1963\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 70.0156 (192 data params - 56 model params = expected mean of 136; p-value = 0.999999)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 17 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 172.803, mu=0\n",
      "--- Outer Iter 1: norm_f = 69.2503, mu=561.186\n",
      "--- Outer Iter 2: norm_f = 68.9196, mu=187.062\n",
      "--- Outer Iter 3: norm_f = 68.9158, mu=62.354\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 68.9158 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 18 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 177.065, mu=0\n",
      "--- Outer Iter 1: norm_f = 76.7361, mu=560.704\n",
      "--- Outer Iter 2: norm_f = 76.4283, mu=186.901\n",
      "--- Outer Iter 3: norm_f = 76.4244, mu=62.3005\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 76.4244 (192 data params - 56 model params = expected mean of 136; p-value = 0.999991)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 19 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 169.884, mu=0\n",
      "--- Outer Iter 1: norm_f = 68.808, mu=572.704\n",
      "--- Outer Iter 2: norm_f = 68.291, mu=190.901\n",
      "--- Outer Iter 3: norm_f = 68.28, mu=63.6338\n",
      "--- Outer Iter 4: norm_f = 68.2799, mu=21.2113\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 68.2799 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 20 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 178.875, mu=0\n",
      "--- Outer Iter 1: norm_f = 74.5477, mu=561.018\n",
      "--- Outer Iter 2: norm_f = 74.223, mu=187.006\n",
      "--- Outer Iter 3: norm_f = 74.2193, mu=62.3353\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 74.2193 (192 data params - 56 model params = expected mean of 136; p-value = 0.999997)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 21 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 188.075, mu=0\n",
      "--- Outer Iter 1: norm_f = 78.567, mu=569.109\n",
      "--- Outer Iter 2: norm_f = 78.082, mu=189.703\n",
      "--- Outer Iter 3: norm_f = 78.0728, mu=63.2343\n",
      "--- Outer Iter 4: norm_f = 78.0727, mu=21.0781\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 78.0727 (192 data params - 56 model params = expected mean of 136; p-value = 0.999983)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 22 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 184.294, mu=0\n",
      "--- Outer Iter 1: norm_f = 86.5632, mu=561.345\n",
      "--- Outer Iter 2: norm_f = 86.2319, mu=187.115\n",
      "--- Outer Iter 3: norm_f = 86.2276, mu=62.3717\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 86.2276 (192 data params - 56 model params = expected mean of 136; p-value = 0.99972)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 23 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 177.897, mu=0\n",
      "--- Outer Iter 1: norm_f = 70.5102, mu=560.646\n",
      "--- Outer Iter 2: norm_f = 70.1649, mu=186.882\n",
      "--- Outer Iter 3: norm_f = 70.1608, mu=62.294\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 70.1608 (192 data params - 56 model params = expected mean of 136; p-value = 0.999999)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 24 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 168.492, mu=0\n",
      "--- Outer Iter 1: norm_f = 68.2857, mu=561.272\n",
      "--- Outer Iter 2: norm_f = 67.952, mu=187.091\n",
      "--- Outer Iter 3: norm_f = 67.9474, mu=62.3636\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 67.9474 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 25 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 172.615, mu=0\n",
      "--- Outer Iter 1: norm_f = 60.5764, mu=585.387\n",
      "--- Outer Iter 2: norm_f = 60.1579, mu=195.129\n",
      "--- Outer Iter 3: norm_f = 60.1494, mu=65.043\n",
      "--- Outer Iter 4: norm_f = 60.1493, mu=21.681\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 60.1493 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 26 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 178.489, mu=0\n",
      "--- Outer Iter 1: norm_f = 79.8012, mu=561.292\n",
      "--- Outer Iter 2: norm_f = 79.4058, mu=187.097\n",
      "--- Outer Iter 3: norm_f = 79.4011, mu=62.3658\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 79.4011 (192 data params - 56 model params = expected mean of 136; p-value = 0.999972)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 27 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 175.594, mu=0\n",
      "--- Outer Iter 1: norm_f = 75.8582, mu=561.037\n",
      "--- Outer Iter 2: norm_f = 75.5103, mu=187.012\n",
      "--- Outer Iter 3: norm_f = 75.5059, mu=62.3374\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 75.5059 (192 data params - 56 model params = expected mean of 136; p-value = 0.999994)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 28 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 177.414, mu=0\n",
      "--- Outer Iter 1: norm_f = 75.2014, mu=560.328\n",
      "--- Outer Iter 2: norm_f = 74.8786, mu=186.776\n",
      "--- Outer Iter 3: norm_f = 74.8743, mu=62.2586\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 74.8743 (192 data params - 56 model params = expected mean of 136; p-value = 0.999995)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 29 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 169.014, mu=0\n",
      "--- Outer Iter 1: norm_f = 67.3174, mu=561.551\n",
      "--- Outer Iter 2: norm_f = 67.0007, mu=187.184\n",
      "--- Outer Iter 3: norm_f = 66.9969, mu=62.3945\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 66.9969 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 30 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 184.287, mu=0\n",
      "--- Outer Iter 1: norm_f = 80.1975, mu=561.678\n",
      "--- Outer Iter 2: norm_f = 79.7757, mu=187.226\n",
      "--- Outer Iter 3: norm_f = 79.7706, mu=62.4087\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 79.7706 (192 data params - 56 model params = expected mean of 136; p-value = 0.999968)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 31 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 189.759, mu=0\n",
      "--- Outer Iter 1: norm_f = 79.3676, mu=561.283\n",
      "--- Outer Iter 2: norm_f = 78.9661, mu=187.094\n",
      "--- Outer Iter 3: norm_f = 78.9615, mu=62.3648\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 78.9615 (192 data params - 56 model params = expected mean of 136; p-value = 0.999976)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 32 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 182.327, mu=0\n",
      "--- Outer Iter 1: norm_f = 66.4596, mu=577.926\n",
      "--- Outer Iter 2: norm_f = 66.0303, mu=192.642\n",
      "--- Outer Iter 3: norm_f = 66.0214, mu=64.214\n",
      "--- Outer Iter 4: norm_f = 66.0212, mu=21.4047\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 66.0212 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 33 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 182.55, mu=0\n",
      "--- Outer Iter 1: norm_f = 82.8134, mu=561.806\n",
      "--- Outer Iter 2: norm_f = 82.4097, mu=187.269\n",
      "--- Outer Iter 3: norm_f = 82.4043, mu=62.4229\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 82.4043 (192 data params - 56 model params = expected mean of 136; p-value = 0.999919)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 34 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 190.285, mu=0\n",
      "--- Outer Iter 1: norm_f = 88.6912, mu=567.362\n",
      "--- Outer Iter 2: norm_f = 88.1986, mu=189.121\n",
      "--- Outer Iter 3: norm_f = 88.1897, mu=63.0402\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 88.1897 (192 data params - 56 model params = expected mean of 136; p-value = 0.999499)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 35 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 176.008, mu=0\n",
      "--- Outer Iter 1: norm_f = 76.3108, mu=560.916\n",
      "--- Outer Iter 2: norm_f = 75.9009, mu=186.972\n",
      "--- Outer Iter 3: norm_f = 75.8958, mu=62.324\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 75.8958 (192 data params - 56 model params = expected mean of 136; p-value = 0.999993)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 36 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 170.704, mu=0\n",
      "--- Outer Iter 1: norm_f = 70.3527, mu=561.586\n",
      "--- Outer Iter 2: norm_f = 69.9484, mu=187.195\n",
      "--- Outer Iter 3: norm_f = 69.9437, mu=62.3984\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 69.9437 (192 data params - 56 model params = expected mean of 136; p-value = 1)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 37 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 193.582, mu=0\n",
      "--- Outer Iter 1: norm_f = 82.1907, mu=560.79\n",
      "--- Outer Iter 2: norm_f = 81.7744, mu=186.93\n",
      "--- Outer Iter 3: norm_f = 81.7697, mu=62.31\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 81.7697 (192 data params - 56 model params = expected mean of 136; p-value = 0.999935)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 38 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 179.742, mu=0\n",
      "--- Outer Iter 1: norm_f = 81.1429, mu=560.67\n",
      "--- Outer Iter 2: norm_f = 80.7425, mu=186.89\n",
      "--- Outer Iter 3: norm_f = 80.737, mu=62.2967\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 80.737 (192 data params - 56 model params = expected mean of 136; p-value = 0.999955)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 39 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 184.337, mu=0\n",
      "--- Outer Iter 1: norm_f = 82.6893, mu=560.395\n",
      "--- Outer Iter 2: norm_f = 82.3213, mu=186.798\n",
      "--- Outer Iter 3: norm_f = 82.3161, mu=62.2661\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 82.3161 (192 data params - 56 model params = expected mean of 136; p-value = 0.999921)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 40 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 176.182, mu=0\n",
      "--- Outer Iter 1: norm_f = 82.7516, mu=561.014\n",
      "--- Outer Iter 2: norm_f = 82.421, mu=187.005\n",
      "--- Outer Iter 3: norm_f = 82.4166, mu=62.3349\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 82.4166 (192 data params - 56 model params = expected mean of 136; p-value = 0.999919)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 41 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 173.058, mu=0\n",
      "--- Outer Iter 1: norm_f = 71.7228, mu=564.057\n",
      "--- Outer Iter 2: norm_f = 71.3374, mu=188.019\n",
      "--- Outer Iter 3: norm_f = 71.3323, mu=62.673\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 71.3323 (192 data params - 56 model params = expected mean of 136; p-value = 0.999999)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 42 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n",
      "  0.664754312975\n",
      "  0.0224691987452\n",
      "  0.0112970484379\n",
      "  \n",
      "  Singular values of target I_tilde (truncating to first 4 of 6) = \n",
      "  3.0\n",
      "  1.0\n",
      "  1.0\n",
      "  1.0\n",
      "  2.46365884775e-16\n",
      "  9.80956846773e-17\n",
      "  \n",
      "--- Minimum Chi^2 GST ---\n",
      "  bulk_evaltree: created initial tree (192 strs) in 0s\n",
      "  bulk_evaltree: split tree (1 subtrees) in 0s\n",
      "Created evaluation tree with 1 subtrees.  Will divide 1 procs into 1 (subtree-processing)\n",
      " groups of ~1 procs each, to distribute over 72 params (taken as 1 param groups of ~72 params).\n",
      "--- Outer Iter 0: norm_f = 200.82, mu=0\n",
      "--- Outer Iter 1: norm_f = 81.2744, mu=561.166\n",
      "--- Outer Iter 2: norm_f = 80.9039, mu=187.055\n",
      "--- Outer Iter 3: norm_f = 80.8997, mu=62.3518\n",
      "  Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n",
      "Sum of Chi^2 = 80.8997 (192 data params - 56 model params = expected mean of 136; p-value = 0.999952)\n",
      "Completed in 0.1s\n",
      "--- Computing gateset for string- Iter 43 of 84 ---: \n",
      "--- LGST ---\n",
      "  Singular values of I_tilde (truncating to first 4 of 6) = \n",
      "  2.99042918449\n",
      "  0.834913690109\n",
      "  0.671124159739\n"
     ]
    }
   ],
   "source": [
    "directLGST = pplt.direct_lgst_gatesets( [gs for gs in gateStrDict.values() if gs is not None],\n",
    "                                ds, specs, gs_target, svdTruncateTo=4, verbosity=2)\n",
    "directLSGST = pplt.direct_mc2gst_gatesets( [gs for gs in gateStrDict.values() if gs is not None],\n",
    "                                ds, specs, gs_target, svdTruncateTo=4, minProbClipForWeighting=1e-2, \n",
    "                                probClipInterval=(-1e6,1e6), verbosity=2)\n",
    "\n",
    "#focusedGS = AT.focused_mc2gst_gatesets( [gs for gs in gateStrDict.values() if gs is not None],\n",
    "#                                ds, specs, gs_mc2gst, minProbClipForWeighting=1e-2, \n",
    "#                                probClipInterval=(-1e6,1e6), verbosity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10dc7da58>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e277470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.direct_chi2_boxplot( Xs, Ys, gateStrDict, ds, directLSGST, strs, xlbl, ylbl,\n",
    "                          scale=1.0, boxLabels=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10d62ac50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pb75hZPPmjO/cmc5xcUzu0YPk+HimbvJGUMWFh/P+4MFc2KoVibGx9EtIYGpy\nMssyM/nRd8G5MyuLTfv3c3vXrnRv0ICOMTE8dNJJHMzJYa0vAZLvb5s2sSc7mwmdO1dvR1TQlPR0\nxiUkMKZRI7pERjK9VSvvvVZCcvGp9HRGxsUx/oQT6BwZyeTmzUmOimJqenpBmdHx8QyJjaVdRARd\nIyN5omVL9ublHTWiYsHevXywfz+PtWhx1Lr1/8zM5IIGDbg2IYF2ERGMjIvjzyecwMMlfAaOp5r4\nfALc3LIl/WJjaR0RwYC4OG5v1YpFe/cWfPaiQ0OZ1rEj1zRrRtPw8KPacTzUxcSFf09W6Wz/zrkX\ngbeArsA9AGZ2AVDfOfdYVR6rmGqLCepmXM65LGC279crAxQZDcyoymP60fmqoLp6vmo4LhGpRX74\n4XvS01MZOHBowWsxMXEkJfVn5crAF7zZ2dl89dWyIvuYGYMGDWPFCm+fNWu+JDc3h0GDCst06NCZ\nFi3aFNT7+ef/wTnH9u0/MHJkN04/vTU33/xrUlOLDkJLSfmY99+fx913T6uyuCsrOzubZatWMfS0\n0wpeMzOGDR5MypdfBtwn5csvGTa46EiREWeeWVD+u82bSd2xg6Gnn16wPS42lv7JyaQsLXr7Tb5d\nmZm8PHcup/TrR2hoaMAym777jvc+/pgzTjmlQjFWh+y8PJbt389Qv29MzYxhDRuSUmxIfr6UffsY\nVuwb1hENG5JSwvD17Lw8ZqSm0jAsjKT69Utsy+7cXBqFhRX83icmhhAznk9LI8859uTkMGvHDobH\nxxNqNbeAV3ZeHssyMxl6wgkFr5kZw5o2JaWk0T07dzLMrzzAiFLKA+zOzsaAhr6LwoSICLrExfHS\n5s0czMkhJy+P6d9+S9PISPrExxfst27PHu5bv55Z/foF1YVdtnMsO3iQobGxBa+ZGcNiYkgp4XaR\nlAMHGOZXHmBEbGyJ5bOdY8bOnTQMDSUpKqrg9bTsbH77ww/8s00bokKO7pWsvDwii72nIkNC+DE7\nm62ljHCrbsHy+dyVnc3L6emcEhdXo5+94oLp/V1V/G9QiquG+n8L7AJuNbOzgfsJfEFSlao7Jqib\ncT2PdyE6xv9FM6sP9HLO/a8ajgk6X5VVV89XTcUlIrVIRkYqZkbjxk2LvJ6Q0JT09MB3kWVmZpCb\nm3vUPo0bNyUjw9tn5840wsPrERNT9J83/3p//PF7cnNzmTHjQe6882n++td57NmziyuvHF4wL0Nm\n5k7+/OfyzQdUAAAgAElEQVSrePjhF6lfP6ZKYj4WGTt3kpubS9NiF4dNmzQhtYR71VN37KBpkyZH\nl/d9k5mWno6ZBS5TrM7b772XmLZtadypEz9s28a/Zx29INUp55xDVKtWdB4wgNMHDmTy7bdXOM6q\nlpGTQ65zNK1Xr8jrTcPDSS1hKHrqkSNHfcPatF49Uotd4L2zaxexKSlELlzIU9u28UGPHjQq4ZvZ\nTYcOMXXbNn7XrHBqp3aRkbzfvTt/3ryZiIULiV+0iB+zsphdw6MIMrKyvD6LjCzyetOICFJLmH8j\n9fDho8tHRpZYPis3l9tXr+bSNm2I8euzD04/neWZmcS+/jpR8+bx5Dff8N7pp9PAd/6O5OZy6eLF\nPJaURMvo6GMJs8pl5OSQCzT1S05BGe+1nJxylX9nzx5iV68mctUqnkpP54OOHYskwa7y3QbSu4Q+\nGREXx/w9e/h43z6cc2w8fJgnfJ/x7TU4P0hNfz5v37yZmIULabx4MT9kZfHvbt2qIKqqUxcTF/5L\nE7YsraCZ/cXMvvFNire92OM7/4n08jnn0oAbgDC8b70fcM5tL16uLGaWZGZbzOwzM4sto3i1xgR1\nMy7n3BJgHdDazIb6bboImFvemHzHD5q4oGrOVwVjglpyvupqXCJSt7z11iv07h3re8SRk1PSH8sO\nq+A3Xs6VvY9/mby8PHJzc7jrrr9yyinDSErqxxNPvMqWLd+weLE3z/Odd17L+edfRp8++aMGanbo\nfknKE3uR8lB2XwUoc9uNN7Lyk0/4YN48QkNCuOK6647a77XnnmPFJ5/wyowZvPPBBzz617+Wu13H\nm6NiQw4D9cmQBg1Y1bs3KUlJnB0fz0Xr1we8L/+nrCxGfvUVv27ShKv9EhdpR45w7aZNXNW0KV8m\nJfHZSScRERISFHNcBFKpPgvwek5eHhelpGDAM8nJRbZdv3w5TSMj+WLIEJYOG8aoli0573//I82X\nALl9zRq6xcVxSZs2BccA73MQrKqi34bExrKqc2dSEhM5OzaWizZvLpg34+n0dPbl5fEnX1IzUF9c\nm5DA7xs35vzvv6feqlUM+uYbLvGNYgmmEQb5jtfn87aWLVnZuzcf9OhBqBlX+M3/EwzqYuJiGd43\nqAb0Kq2gc+4u51wi3pD7x/HudT8B+MQ518F/Ir1i+70GfITXf5VNRV2Ft5LBKUBZkyhWe0y+feti\nXC/66r/S77UrfK9XRLDFVRXnqyIxQe05X3U1LhGpQ4YO/SVvvLHK91hJfHxjnHNkZBS9j3nnzh1H\njajIFx/fmNDQ0ID7JCR4+zRu3Izs7CPs3190mPGuXYX1nnBCcwA6duxasL1Ro8bExzdm27atACxe\n/AkzZz5Gt27hdOsWzsSJv2Hv3t10716P+fNfqHxHVFLjhARCQ0NJKzYSYkdGxlEjJvI1O+EE0vzu\nlQfYkZ5eMGqj2Qkn4JwLXKZYnY3i4zmxQweGnn46rz77LO9++CGLly0rUqZl8+Z0SUzk1xdcwIN3\n3sk9jz5a4xeUjcPCCDUjrdi3sTuys0u8b71ZvXqkFbvA2RHgW96o0FA6REbSLzaWZxMTCTPjuWL3\n5W/LymLI2rWcGhfHjBNPLLJt2vbtNAgN5cF27UiKieHUBg2Y1akTH+3efdQKCMdT44gIr8+KjZbY\nkZV11KiKfM0iI48uH2AURn7S4oeDB/nP4MFFRlt8lJbGu9u3M3vgQAYkJNArPp6pyclEhYby4ubN\nAHyyYwdzfviB8DlzCJ8zh2GffopzjiZvvsnkryqzUFrVaRwWRiiQVmzSzFLfa2Fh5SofFRJCh4gI\n+tWvz7Nt2njvNd9tOJ/s38+iAweIWLWK8JUrSfStftF340au2rq1oI4HW7Rg/0knsbV7d1J79OBk\n3+iMdsVGOxxPNf35bBQezolRUQxt2JBXO3fm3cxMFtfgZ6+4Ope4cN7/CM/7fm1nZonl2GcPMMfv\npVJTu2bWGWiBd7HzR/MtSVlB/wK2AyuAz8poX7XHBHU2rllALnCBmcWaWTsg1zn3Q1nHKibY4qqK\n81XumHxtrC3nq67GJSJ1SHR0fdq06VDwOPHEbjRp0oyUlI8Kyuzfv5dVqxbTu/eggHWEh4fTvXuf\nIvs450hJ+YjkZG+fHj36EBoaxsKFhWW+/34j27ZtpVevgQAkJ5/ie73w27Xdu3eRmZlBy5btAHjt\ntUW88cZK3nxzFW++uYqbbrqXmJg43nhjFcOHX1A1nVIB4eHh9ElK4qP/Fd5t55zjo88+Y9DJJwfc\nZ2Dfvnz0WdH/Fj749FMG9vX++2zfti3NTjihSJm9+/axePlyBvXrV2Jb8iflzCphhQmA3Lw8cnJy\najxxER4SQp+YGD7avbvgNeccH+3ezaC4wHdLDoyNLVIe4IPduxkYW/qgxjy8uQTy/ZSVxZlr13Jy\nTAwzE4/+r/ZgXt5R33aH5I8KqsF+Cw8JoU98PB/5Jcmcc3yUlsaghISA+wxMSChSHuCDtDQG+pXP\nT1p8t38/Hw0eTHyxC+ZDvvdV8W/aQ8wK+mP+oEGsOuusgsc/+vbFzPj8zDO5oVhi6HgLN6NPdDQf\n+V34Ouf4aP9+BpUwt8LA+vWLlAf4YP9+BpZxG0yec2T5+uSvLVuyqkuXgseCjh0x4LW2bbm/efMi\n+5kZzcPDCTPjlcxMBtavT+Nit6ocTzX5+Swuf1LO0socbzV3ZqrX43jfujbx/byjqio2s1DgBeA6\nvG9aXwOeN7Nk51y5b4pyzi2ijGHpxVRbTFB343LOpZrZe8A5eBMhtqAS33IHW1xVcb4qERPUgvNV\nV+MSkbpv7Nhb+Nvf7qNt2xNp2bIdTz55F82atWLo0F8WlBkzZigjRlzIZZddD8BVV43nT38aS/fu\nfejZsx8vvDCFw4cP8n//dyXgTfB50UXX8NBD42nQIJ769WO5776bSE4+haQk72K8XbtEhgz5Bfff\nfzP33juD+vVjefzxP3Piid3o3/8MwJvQ09/q1UsJCQnhxBO7UlPGX3cdY3//e/okJdEvOZkpf/sb\nBw8d4spLLgFgzPXX06pFCx64807AWw1k8C9+wRPPPMO5w4fz6vz5LFu1imenTCmo85Zx47jviSc4\nsX172rVpw10PPkir5s355ciRACxdvpwlK1Zwav/+xDdsyKbvvmPSww+T2KEDA30Jk1fmziU8PJyT\nunUjol49lq5YwR333cfoCy4gJMBEgcfb+BYtGPvNN/SJiaFfbCxTtm3jYF4eVzb1RuCM2bCBVhER\nPNCuHQA3t2jB4DVreOKnnzg3Pp5X09NZtn8/z/qSDwdzc7n/hx/4RUICzcPDycjJYeq2bWw7coSL\nGjcGYPuRI5yxZg3tIiN5pF07dvh9Q5x/P/+58fE8+dNP/GXrVi5p0oS9ubncsXkz7SMj6R1Ts/Oq\njO/cmbFLltAnPp5+jRoxZeNGDubmcqWvj8YsXkyrqCge6NkTgJsTExn83//yxIYNnNu8Oa9u3cqy\nzEye9SXJcvPyuHDhQlbu3s3bp55KtnMFIzQa1atHeEgIAxMSiK9Xj7FLlnBXt25EhYby9+++Y/OB\nA5zruwBvX6xf0rOycM7RJS6OuBpa+cHf+CZNGLt1K32io+kXHc2U9HTvvdaoEQBjtmyhVXg4D7Ro\nAcDNTZoweNMmntixg3Pj4ng1M5NlBw/ybOvWgJfcuj8tjV/ExRW+1zIy2JadzUW+CSpbFUsA1Q8J\nwQEdIiJo4euTnTk5zN29mzNiYjjsHDN37mTenj18VsPJHqiZz+fSfftYsm8fp8bFER8WxqbDh5m0\ndSuJUVFFEiDrDx4kKy+PXTk57M/NZdX+/QAkHafPZ51MXDjnMszsN8CbwM1m9nfn3OYydivvp/tO\nYLFz7jMAM5sPXABMpoovdvxVc0xQd+MC7wL/XLwL0kgg8FdXVUjnqwidL88LHOe4RKR2ufba2zh0\n6CCTJo1j797d9O17Gv/4xwLq+f0h/uOP35OZmVHw+znnXExmZgZPPz2JjIw0unbtxXPPvU+jRoW3\nNtxxxxRCQkK58cZfkZ2dxWmnnX3UyiCPPjqLBx74A+PGnYdZCP37n8Gzzy4ocaWMYHDxqFFk7NrF\npIceIi09nV49evD+a6/RxPfH+I/bthHm9+3pwJNP5tW//52J99/PxAceILFDB96YNYtufpM/3nbT\nTRw8dIhxEyawe+9eThswgAWzZxecg6ioKOa//Tb3PPIIBw4epHnTpowcOpSJf/gD4b6LorCwMB5+\n+mm++e47nHO0bd2am669llt+97vj2Dslu7hJEzJycpi0dStp2dn0ql+f97t3p4mv/T8eOUKY38iH\ngXFxvNq5MxO3bGHili0kRkbyRrdudPN9Cx5qxteHDvHS+vVk5OSQEBbGyTExfN6zJ119Zf6Tmcl3\nhw/z3eHDtPat0JJ/337uqacCcGbDhrzSuTOP/PQTj/70E9EhIQyMjWVB9+5E1HDC5+LWrcnIymLS\n2rWkZWXRq2FD3j/9dJr4bv348dChon3WuDGvDhjAxDVrmLh2LYkxMbxx6ql0a9CgoPzb272pyXp9\n8AFQOD/LJ2ecwelNmpAQEcF7p5/OxDVrGPrpp2Tn5dE9Lo43Tz2Vk4qtIuGvonPiVKeL4+PJyM1l\n0vbtpOXk0Csqivc7dqSJ73P5Y3Z20X6rX59X27Zl4vbtTNy+ncSICN5o355uvn4OBb4+fJiXdu0q\nfK9FR/N5YiJdS7htBwLPD/Hirl3cum0bznfcT088kT5BMMFpTXw+o0JCmL9zJ/ds3cqBvDya16vH\nyPh4JrZuTbjfZ++cr75iq9/Ist4rVxb5DFc3q+kha9XJzO4B7gLWA2c45zJKKXsJ8DLev6OTnXP3\nBigzGO9b0s6+ZQ4xs6Z4E+/FAoOcc4HX4KoiVR2Tr1ydjMuvfDiwDWgEzHTOXVtV7S6LzpfOl1/5\nSsdlZsnAsmXLlpFcbOIuEal533xT0y2ofRIblbwspJRi1KiabkHt4/s2Xypo48aabkHtU8Mjg2qj\n5fv302flSoA+zrnlpZWt+fFq1cg5dw/wG+BEIMV3wXcUM+sK3A08VsL2emZ2Ld63sXvyLxZ9x0jD\nu389DHjVzNpXaRDFVFVMvjJ1Mq4AdWcDr/h+nXlsLa0Yna+f7/kKUHeNxSUiIiIiUpvVyVtF/Dnn\nnjezL4H7gA98z98CNgMNgFOBROAq51yKmaXjN6LIzB4AbgTyxw71MLNbnHNP+rb/H/B7vG9dOwAb\nzGwzMN4593YwxlSX4yrFy8DZzrmUqm956XS+fn7nqxQ1FpeIiIiISG1Vp28VKc7MmuBddLTHuz89\nDVjknAvOBaLLoS7GBFUfl5mNBVo45x6sulZWqh06X+Wrr06er8rGpVtFRIKbbhWpON0qUkm6VaTi\ndKtI5ehWkYrTrSIVVpFbRer8iAt/zrl04PWabkdVqosxQbXENRa4ogrrqxSdr3Krq+crKOISERER\nEalN6vQcF/LzY2bdzGy1mU30e60LsM8591MNNk0CqKvnq67GJSIiIiJSE5S4kLpmJNADuNrvtUl4\n8xVI8Kmr56uuxiUiIiIictwpcSF1zedADvAAgJlNAA4655bWaKukJHX1fNXVuEREREREjjslLqRO\ncc4tBq4Dfm9ma4EmwG9rtlVSkrp6vupqXCIiIiIiNeFnNTmn/Dw4554Dnqvpdkj51NXzVVfjEhER\nERE53jTiQkRERERERESClhIXIiIiIiIiIhK0lLgQERERERERkaClxIWIiIiIiIiIBC0lLkRERERE\nREQkaClxISIiIiIiIiJBS4kLEREREREREQlaSlyIiIiIiIiISNBS4kJEREREREREgpYSFyIiIiIi\nIiIStJS4EBEREREREZGgpcSFiIiIiIiIiAQtJS5EREREREREJGgpcSEiIiIiIiIiQUuJCxERERER\nEREJWkpciIiIiIiIiEjQUuJCRERERERERIKWEhciIiIiIiIiErSUuBARERERERGRoKXEhYiIiIiI\niIgELSUuRERERERERCRoKXEhIiIiIiIiIkFLiQsRERERERERCVpKXIiIiIiIiIhI0Aqr6QaIiIhI\ncPj225puQe0UGVnTLah9Pl2bUNNNqJUGP/RQTTeh9vnVr2q6BbVTXl5Nt6D26dChpltQ++Tmlruo\nRlyIiIiIiIiISNBS4kJEREREREREgpYSFyIiIiIiIiIStJS4EBEREREREZGgpcSFiIiIiIiIiAQt\nJS5EREREREREJGgpcSEiIiIiIiIiQUuJCxEREREREREJWkpciIiIiIiIiEjQUuJCRERERERERIKW\nEhciIiIiIiIiErSUuBARERERERGRoKXEhYiIiIiIiIgELSUuRERERERERCRoKXEhIiIiIiIiIkFL\niQsRERERERERCVpKXIiIiIiIiIhI0FLiQkRERERERESClhIXIiIiIiIiIhK0lLgQERERERERkaCl\nxIWIiIiIiIiIBC0lLkREREREREQkaClxISIiIiIiIiJBS4kLEREREREREQlaSlyIiIiIiIiISNAK\nq+kGiFSGmcUCZwBtgGhgK/Af51ymX5k2QCvn3MIaaWQF1cWYQHFRy+ISEREREQk2GnEhtYqZdTCz\n2UAqcCMQAWwB4oHnzexpM2tgZvWAecCwmmtt+dTFmEBxlRWXmV1rZplmNv14tV1EREREpDbSiAup\nNczs98CjwHpggHNuTbEi083sbOA94FugD/DW8W1lxdTFmEBxUb64bgQaANea2W3Oub3V1GwRERER\nkVpNiQupFcxsMnAXsAQY6pw7EKicc+49M4sBXgPccWxihdXFmEBxVSCuqcAjwHwlLURERERESqZb\nRSTomdmFeBeM+4ELSrpgzOecmwvMPR5tq6y6GBMornzlics593fnXEPn3NVV11IRERERkbpHiQsJ\namYWBzyL9831I8657eXc9VHAqq1hx6AuxgSKK4CgjktEREREpLZQ4kKC3S1AQ9/zWeXdyTm3FFhd\nLS06dnUxJlBcRdSCuEREREREagUlLiTYXeP7udE5t6WC+35e1Y2pInUxJlBcgQSMy8yamFlXMzvd\ndxuKSJ305JOTGDSoBT16RDN27HA2b95U5j6zZk3jjDPa0717FBdeOIDVq5cW2Z6VlcXdd9/AySc3\nJikplhtu+BU7d+4oUiYxMaTIo1OnUN5557UiZd5442XOP78XJ51Un0GDWnD77dewe/euYw/6GD3+\n+CT69m1Bp07RXHpp+frsxRenccop7UlMjOKXvxzAqlVH99mdd95AUlJjunaNZdy4X5GRsSNgXbt3\n76Jfv1a0axfKvn1Fp995/fWXOfvsXnTuXJ++fVtw663B0WczZ07iwgtbMGJENBMmDOfHH8vus9df\nn8bo0e0566worrtuAF9/XbTP3n77WW655UzOPbcBZ54ZwoEDR09FtG9fJvfddxnnntuA886L55FH\nfsOhQ0ffSfivfz3GFVd0ZvjwSC66qDUvv/xg5YOtItPmzaP9RRcRNWQIA377W5auX19q+Tkff0zX\nyy4jasgQksaOZUFKSpHtr3/6KWePH0+T884j5LTTWL3p6HOQdeQINzz+OI3PPZfY4cP51Z13siMz\ns0iZH9LSOPfWW6k/bBjNfvELbnvmGfLy8o494Cow7cAB2qenE5WayoCdO1manV1q+TmHD9PVVz4p\nI4MFWVlFtk/ev5+u6enEpKXRKC2N4bt2seTIkSJllmdnc9auXcSnpdEkLY1xe/ZwoFh/3LJ3L30z\nMohMTSU5I6Nqgq1C0w4epH1GBlE7djBg167y9dvOnUTt2EHSrl1H99uBA3TduZOY9HQapaczPDOT\nJcXqXJ6dzVmZmcSnp9MkPZ1xe/dywBVOO7YrL4+Ru3fTMiODyB07aJORwY379rEvWN5rqam0X7GC\nqMWLGbB2LUv37y+1/JydO+m6ciVRixeTtHo1C4p9rib/+CNdV64kZskSGi1dyvD161lSrM5fbthA\n2+XLiVq8mBbLljFm0ya2+70fJ//4IyGLFhG6aBEhfo/YJUuqLvAyKHEhQcvMugOt8Ybof1WJKl4B\n3ver73dmttnM8vweh82sr297XzM74rftiJl9UiXBFLahSmPy1fk7M/uxWFypZna+X5mbzCzHb/s2\nM2t2TMEUbYPiCuyouHzSfPX9F28ST5E6Z8aMh5k1ayp/+csM5s9fQlRUfa66agRHiv1h7u+dd2bz\n4IMTuPnmybz55gq6dk3iqqtGsGtX4R/j999/C//97ztMnTqPV175jB07tnHDDUfn/x555EUWLUpj\n0aJUUlK2M3z4qIJty5Z9wW23jeXii6/lvffWMXXqXFavXsLEib+t2k6ooGeeeZgXXpjKgw/O4K23\nlhAdXZ/LLy+9z958czb33TeBP/xhMgsWeH12+eVF+2zy5Fv4+ON3mDFjHnPmeH02blzgnOmtt15D\nt269jnp96dIvGD9+LJdcci0ffbSO6dPnsnLlEv70p5rts1deeZjXX5/KhAkz+NvflhAZWZ/bbhtB\ndnbJffbxx7N55pkJXHXVZJ59dgUnnpjErbeOYPfuwj7LyjpE//4jufzyiZgFvuvvL3+5lC1b1vP4\n4x/x0EPvsHr1Zzz++LgiZZ5++iYWLJjJ9dc/waxZG3jggTfp0qVf1QRfSbM/+ogJ06Yx+eqrWfH8\n8ySdeCIjxo8nY/fugOVT1q7l0smTufb881n5wguMOv10Rt1xB+u+/76gzIHDhzm1Z08e/t3vSuyv\nW55+mndSUph33318Nm0a2zIyuHDixILteXl5nHPrreTk5rJoxgxenDiRF959l0n/+EfVdkAlzD50\niAn79jE5JoYVjRuTFBbGiF27yCjhQjflyBEu3b2ba6OjWdm4MaMiIhiVmcm6nJyCMp1DQ5kWF8fa\nhAS+SEigXWgoZ2VmstNX5/bcXIbv2kWnsDCWJCTwXqNGfJWTw5V79hx1vGuioxkdGVk9wR+D2YcP\nM2H/fibXr8+KRo28ftu9u+R+y87m0r17uTYqipWNGjGqXj1G7dlzdL/FxrK2USO+iI/3+m337qL9\ntnu312/x8bzXsCFf5eZy5d7C5GMIMCoigrcaNOCbhARejIvjwyNHuG7fvmrtj/KYnZHBhC1bmNyq\nFSt69iQpOpoR69eTUULCJ2XfPi7dtIlrmzZlZc+ejIqPZ9TGjaw7eLCgTOfISKa1b8/anj35okcP\n2kVEcNb69ez0q3NIXBxzOnViY69ezO/UiW8PH+aib74p2H5r8+ak9unD9j59SPU9ukVFcXFCQvV1\nRjFKXEgw6+33/MeK7uycW+icW+z3+3TnXDtgILAX72J0gnPuS9/2L4EpQB7wENDUOXdm5ZsfUJXG\n5HttunOuFfDH/Hqdc82cc2/5lXkauB4vtqudcy2cc6kVb36JFFcAgeLyaQL8rtKtEqkFXnzxKW64\n4S6GDj2fTp168OijL7FjxzY++ODfJe4zc+YULrlkHBdcMIaOHbvwl79MJzIymrlzZwKwb99e5syZ\nycSJU+jffzDdu/fm4YefZ9myL1i1qui3PrGxDUhIaEJCwgkkJJxAvXr1CratWLGIVq3ac8UVN9Cy\nZVuSkwcxevQ4Vq8+ft8cBTJz5lPcfPNdDB9+Pp0792DKlJdIS9vG+++X3GfPPTeFSy8dx69+NYYT\nT+zCgw9OJyoqmtmzC/vstddmMmnSFAYMGEyPHr157LHn+fLLL1i5smi8s2b9jX379vDb30446jgr\nViyidev2jB17A61ataVv30Fcdtm4o/r9eJs37ynGjLmLQYPOp0OHHtxxx0tkZGzj889L7rM5c6bw\ni1+MY8SIMbRt24Xx46cTERHNggUzC8pceOFNXHLJbXTt2j9gHVu2fM3Spe9z223P0aVLX3r0GMRN\nN/2Vjz/+Fzt3pvrKrOfNN6dz//1vMnDguTRr1pbExN706TO0ajuhgqbMns24X/yCMSNH0qVtW6bf\neivRkZHMfOedgOWfmjOHkQMGMH70aDq3acPka64huVMnps6bV1Dm8hEjuPPKKxnaty/OHb2g1t4D\nB5j5zjtMufFGBvfuTe9OnXj+jjv4Ys0alqxbB8D7ixfz9ZYtvDxpEid17MiI/v35y29+w7T588nx\nu3CtCVMOHmRcdDRjoqLoEhbG9Lg4os2Y6Xdx6O+pgwcZGRHB+Pr16RwWxuTYWJLDw5l6oHBEzuio\nKIZERNAuLIyuYWE8ERvLXudY7buYfDsri3pmTI2LIzEsjD7h4UyPi2NeVhbf+fXHk3FxXBcdTfuw\n4FsscsrBg4yLiirst9hYr98OHQpY/qmDBxlZrx7jo6O9fouJITksjKl+5UdHRjKkXj3ahYZ6/RYT\n4/Wbr0/ePnLE67fY2MJ+i431+i03F4CGISGMi4oiOTyc1qGhnFmvHtdHRfG/MkaDHA9TUlMZ17Qp\nY5o0oUtUFNPbtyc6NJSZ6ekByz+VmsrIhg0Z37w5naOimNy6Ncn16zM1La2gzOjGjRnSoAHtIiPp\nGhXFE23bsjc3l9V+79+bmzenX0wMrSMiGBAby+0tW7Jo3z5yfZ/n6NBQTggPL3hsP3KEdYcOcc0J\nJ1Rvh/hR4kKCWVO/51WWAvVdSN6MN3HidWYW7rc5EbjdOXeHcy4zYAXHplpi8pkG7AZamlmHANu3\nAv9yzr1QxccFxVUhzrmdwItVVZ9IsPnhh+9JT09l0KDCC7TY2DiSkvqzYkVKwH2ys7P56qtlDBxY\nuI+Zccopwwr2Wbv2S3Jzc4qU6dChMy1atDmq3nvuuYGTT27ChRf2Z+7c54tsS04eyPbtP/DppwsA\nyMhI47335nLmmeceW+DHYOtWr89OOaVon/Xu3Z/ly0vuszVrlhXZx8w49dRhBfusXv0lOTk5Rcp0\n7NiZli3bsGxZYb0bN67j6afv48knZxEScvSfh/l99sknXp+lp6fx7rtzGTKk5vps+/bv2bUrleTk\nwu8XkioAACAASURBVNjq14+ja9f+fPVV4D7Lyclm48ZlRfYxM/r0GVbiPoGsW5dCbGw8iYmF+e0+\nfYZhZqxf7+WrU1LepkWLjixc+CaXXNKB0aPb8+ij17JvX3X8eVE+2Tk5LNuwgaF9+xa8ZmYM69uX\nlLVrA+6TsnYtw/zKA4zo14+Ur8o/EHHZhg3k5OYWOW7nNm1o07RpwXEXrVvHSR070rhhw4IyI/r3\nZ8+BA3zlN7rjeMt2jmXZ2Qz1S36aGcPq1SOlpG/BjxxhmF95gBGllM92jhmHDtHQjKT/Z+/Ow6uq\nzr6Pf1eSk5GMJCQCIqQihMEgUSSA1QoKSn1UFAeqCCIVBQWhtVUUpCpVUVEL1qGAQgtSHHEA5Ila\nHzVMAYIVmYeAkEAgCZnIuN4/ziGchAQIZDjh/X2u61yQve+997pXTk6y77322g7nn6VF1uJbJc7f\nNZrlOw84wT6VEmtJKS2tXb+VlNS+344edfabq3BTbb+5/v2uhtFr+8rK+LCoiKt8q27ZsErKy0nJ\nz6dvSEjFMmMM/UJCSK5hNEhybi793OIB+oeG1hhfUl7OmxkZhHl7Ex8UVG3M4dJS/pWZSe/gYLxr\nGEH1jwMH6ODvT6/g4NNJrU6ocCGezL2gUKdPZ7DWvgt8CsQBTwEYY24Ggqy1L9blsaqoz5yKgIWu\nL4dVE3IH8GZdHtON8qolV14i56SDB9MxxhAZGV1peWRkNJmZ1Q+KysrKpKysrNptDh50bpOZmYHD\n4UtwcEiNMQCPPPI0r732b+bO/V8GDLiVyZMfZO7cGRXru3fvxUsv/ZOHH76duDhfEhPPIyQkjMmT\nZ9BYTtZn7rm5O3zY2WdRUWfXZ8XFxTz88BCeeOJFYmJaVXusSy/txSuv/JPRo2/nV7/y5dJLnX32\n9NON12eHDzv7LDy8cv4REdEcPlx9n+XkZFJeXlarbWo6dlhY5SuN3t7ehIREVOxn374dpKfv4j//\neZ+JE//JY4+9y5YtKUyePPi0j1PXMrOzKSsvJzo8vNLy6PBw0g9XP19J+uHDREdEVI6PiKgxvtp9\nHDqEr48PIVVOlNyPm37o0Intch23Nseqa5nl5ZQB0VUKetHe3qTXcMtDenn5acV/fvQowRkZ+Gdk\n8Gp+PssjIohwbXe1nx/p5eW8mJ9PibVklZfzWG4uBuftEJ6uxn7z8qpdv1UT/3lREcEHD+J/8CCv\nFhSwPCzseL/5+jr7raDgeL/l5zv7rcp+huTkEHTgAK0PHSLUGN5uwJPw6mSWllJmLdEOR6Xl0Q4H\n6TUUb9JLSk4r/vOsLIJXrcJ/1SpeTU9neVwcEVVG6fw5LY1mq1YRuWYNe4qK+LhDh2qPWVxezvxD\nh7ivAUdbgAoX4tncZw4LqTHqzP0eOAz80RgzAHiW6k+M61J95zQH5wn2UPeFxpggoJu19v/q4Zig\nvET+v7Z48Xzi44NdrxBKS6v/A8taW+P97zU5nW2qxjz44ES6d08kLi6ekSP/yMiRj/KPf0yrWL91\n60aeeWYsDz/8FJ98spY5c5axd+9Onnji/up2Xy8+/ng+cXHBxMUF06lT4/bZc8/9mfbtO3HjjXdW\nrHP/F5wjMp56aiyPPPIUX3yxlnnzlrFnz04ee6zh+ux//3c+110XzHXXBXP99Q3fZ7Xdj7XllJYW\n8/jj8+jSpRfx8b/m0UdnsW7dV+zdu/UUe2pYFmqVv7W2Tqr5p3vcuvje1DVL7a5oVBd/tZ8fqc2b\nkxwRwQA/Pwa7zf/QyceHd0NDeTk/n8CMDFoeOECsjw8tvLxqvAreFNRJv/n6khoRQXJ4OAN8fRmc\nk1O530JCeLmggMCDB2mZmUmst3e1/fZKcDDrIiL4JDSU7WVlPHKKSTAbS530WWgoqRdfTHKXLgwI\nC2Pw1q0nzJvxaMuWrL/4YpbHxeFtDHdXM8kuwAeHD5NXVsbdUVG1SeOsqXAhnsz9UZLVXwJyMcY8\nbYzZ6pqccX+V1w73CR2PsdZmAKMBH5yjL6Zaa/fXtpHGmHhjzG5jzLfGmFOVaus7p1XARuB8Y4z7\nTbSDgfdPNyfX8ZVXPeclcq7o1+9GPv001fVaT3h4JNZaMjMzKsUdOnSA5s2jq91HeHgk3t7e1W5z\nbBRCZGQMJSXFJzztwj2mOt26XU56+t6KSS7ffPM5EhL6MGLEeC66qAt9+lzDlCmv8/77s084fn25\n9tobWbo0laVLU1my5OR9VlNuERHOPjt4sOZtoqJO3WfJyV/z+eeLiI11EBvrYMiQflhrueSSKKZP\nnwLA668/x2WX9WHkyPF06NCFX//6Gp555nUWLpx9wvHrS+/eNzJrViqzZqXyj3+sJzTU2WdZWZWP\nn5V14IQRFceEhkbi5eVdq22qExERQ3Z25SezlJWVkZubVbGfiIjz8Pb2oVWrX1XEtGkTB0BGRtpp\nH6suRYaF4e3lRUaVpw4cyMo6YbTDMTEREWRUGfFwICvrhFEYJxPTvDnFpaUcya/81BX348Y0b35C\nu44dt6a2NYRILy+8gYwqV+sPlJWdMDrgmBgvr9OKDzCGWB8fevj68nZoKD7ALLd5B+4ICGBfixbs\na9GCQ9HRTG7WjIPl5bTz9q6T3OpTjf1WzaiKY6rtt2riA4wh1tubHg4Hb4eE4GMMs6rMg7EvMpJ9\nkZEciopiclCQs9+q7KeFlxcX+fhwg58fbwQH8/fCwhOO35AifXzwNoaMKgWFA9WMqjgmxuE4rfgA\nLy9i/f3p0awZb8fGOvvsQOXPsAgfHy7096dvaCgL2rfni+xsVlZzy8msAwf4bXg4LWpoU31R4UI8\nWQrOK94GOHGKczfW2iette1x3vrxEs65CVoAX1trY90ndKyy3b+BJJw/C53OsJ3DcT55ojdwqsk8\n6z0nnHMnGCqPHrmb2s+poLwaJi+RJi8wMIg2bWIrXu3bdyIqKoYffkiqiMnNPUJq6kq6d+9V7T4c\nDgedOyeQnHx8G2stP/yQVLFNly4JeHv7VIrZuXML+/alcckliTW2b+PGdYSGhldM0FlYWHDCPA7G\neGGMqXZiwfoQGBjEBRfEVrwuusjZZ99/X7nP1q1bSUJCzX3WtWtCpW2stXz/fVLFNl27JuDj41Mp\nZseOLfzySxqXXuqMefPNDyuKKEuXpvLCC//AGMMHH3zHPfeMBqrvMy+vhu2zgIAgWraMrXi1bduJ\niIgY1q49nlt+/hF+/nklXbpU32c+Pg4uuiih0jbWWtauTapxm+p06pRIXl42W7euq1i2dm0S1tqK\nCT27du1NWVkp+/cfn59hz57NGGOIjr7gtI9Vlxw+PiR06EDSmjUVy6y1JKWk0Ktr12q3SezShaSU\nlErLlq9ZQ2LnztXGVzc6IqFDB3y8vSsdd0taGmkZGRXHTezcmR+3b6/0dJMvV60iNCiITu3anX6S\ndcxhDAkOB0lu8yNYa0kqLqZXDXMiJPr6VooHWF5cTOIpTvTKgeruI43y8iLQGN4rLCTAGK5p5LkY\nTofDGBJ8fKrvtxr6IbFKPwMsLyk5+347evSU/VaG84+9ogb6PKuOw8uLhKAgktyegGKtJenIkRrn\nkkgMDq4UD7A8J4fEU9z2Um7tSXM9Niln1ZhdR4/y9ZEj3NfAoy1AhQvxYNb5l9CxGdXaGmPan8Y2\nOcAit0WbThZvjOkAtMR5cvqHY49GraX3gP3AOuDbU7Sv3nMC5uH8/L3ZGBNsjGkLlFlr95zqWFUo\nr4bJS+ScNGzYOF5//RmSkj5l8+Yf+eMfhxIT05p+/W6siLn77r7885+vV3x9773jee+9t/joo7ls\n376JJ58cxdGjBQwaNAxwTlY5ePAIpk4dz4oV3/Df/6bwpz8NJyGhN/HxzkdMfvXVZyxaNJutWzey\ne/d2/vWvv/PGG39l6NCHK47Tt+8NLFv2IfPnv8GePTtJSfmeZ54ZS3z85URF1dkTlWttxIhxvPba\nMyxf/imbNv3II48M5bzzWnPttcf77I47+jJ37vE+u+++8cyf/xbvvz+Xbds28dhjoygsLGDw4GGA\ns89uv30ETz89nuTkb9iwIYUJE4Zz6aW9iY+/DIA2bdpx0UWdKl7nn98Oay2/+lVHIiIiAejX7waW\nLv2QefPeIC1tJ6tXf89TT43lkksup0WLxuuzW28dx7x5z/DDD5+yY8ePTJ06lKio1vTufbzPxo/v\ny8cfH++z224bz6efvsWyZXPZvXsTL788iqKiAgYMGFYRc/hwBtu2pbJ371astWzfvoFt21IrJta8\n4IKOXHZZf6ZNG8mmTav58cfvee21h+jb906aN3f2R0JCP9q3787zz9/L1q3r2bw5hZdfHsWll15L\n69YXNkwHVWP87bfz1uLFzF2yhE27dzNq2jQKjh5l2HXXATD06ad5/M3jU0eNHTyYJStW8PJ777E5\nLY2nZs0iZfNmxtxy/JG6WUeOkLp1Kz/t3Im1lk27d5O6dWvFiImQoCBGDBzI+Bkz+GbtWlI2bWL4\nX/9K765duSzOOQrl2h496NS2LXc//TQbtm1j2cqVPPmPfzDmlltwNPITM8YHBvJWQQFzCwvZVFrK\nqCNHKLCWYQEBAAzNzuZxtyvTYwMDWVJUxMv5+WwuLeWp3FxSSkoY45rjo8BaJubmsrK4mLSyMtaW\nlHBvTg77ysoY7PZY05n5+awrKWFraSkz8/N5KDeX55o1I8StiLi9tJT1JSXsLyuj0FpSS0pILSmh\ntBFPwI8ZHxjIW4WFx/stN7dyvx05wuNut2eMDQxkSXExLxcUOPstL8/Zb674AmuZmJfHypKS4/12\n5Iiz3/z8KvYzs6DgeL8VFDj7LSioot+WFBXxTmEhP5WWsrusjM+LinggN5c+DgdtGnk0y/jzzuOt\njAzmHjzIpsJCRu3cSUF5OcNchYKh27bxeNrxEVtjY2JYkp3Ny/v3s7mwkKf27CElP58x0c6RXwVl\nZUxMS2Nlbi5pRUWszc/n3u3b2VdSwmDXo0xX5+UxMz2d1Px80oqK+ConhyHbttHe35/EZs0qtW/W\nwYO09PVlgNskug3F856bI1LZSzivkEe5/n28rnZsjPEG3gEewHll/N/AHGNMd2vtaU/XbK1dwSlu\nI6ii3nJytSfdGLMUuB7nxJUtOYMnWCivCvWal8i56ve/f5TCwgKefPJ+jhzJ5tJLr2DWrCWVHku6\nd+9OsrIyK74eOPA2srIyeeWVSWRmZhAX1405c5bRvPnxKzsTJ07H29ubhx66leLiIq64YgBTpsys\nWO/j42DevBk8++wjWGu54IILmTjxFW6//b6KmEGD7iE/P49//nMmzz33B4KDw+jVqy9//ONz9dwr\nJ/fAA84+e+wxZ5/16HEFc+dW7rM9e3Zy+PDxPrvhBmefvfzyJA4ezKBz527Mm1e5zyZNcvbZqFHO\nPrvyygE888xMTqbqVfPBg++hoCCPuXNn8uyzfyAkJIzevfvy5z83bp/deeejHD1awEsv3U9eXjYX\nX3wFzz+/BIfjeJ/t37+TnJzjffab39xGTk4ms2dPIisrgwsv7MYLLywjLOx4ny1e/AbvvjsFYwzG\nGMaNuxKAP/1pDv37O6dbeuKJ+bz66hgmTOiHMV5ceeWtPPTQqxX7MMYwdeqnvPbaQ4wbdyX+/kFc\nfvn1PPBAfc4Bfmq39e1LZk4Ok2bNIuPwYbq1b8+yl18mynU7xt6DB/FxO3lL7NKFBU89xcS33mLi\nW2/R/vzz+eSvf600CmLx998zfOrUiv66c4rzFqPJw4czafhwAKY//DDeM2dy65NPUlRczIDLL2fm\nhOOP3vXy8uKzF17ggZdeotcDDxDk78+w665jyogRDdEtJ3VbQACZ1jIpN5eM8nK6ORwsi4ggynUi\nvLe8HB+3n5lEX18WhIUxMTeXibm5tPfx4ZPwcDq5CjDewKbSUuYWFpJZXk5zLy8uczj4rnlz4tyK\nNKtKSngqL488a+no48PbISEMcZ3EH3PfkSN86zZKofuhQwDsjIpq9JPw2/z9ySwvZ1J+vrPffHxY\nFhZ2vN/KyiqdjCY6HCwICWFifj4T8/Jo7+3NJ6GhlfutrIy5rjktmnt5cZmPD9+Fh1fut9JSnsrP\nr9xvbgWhAGN4++hRxuflUWQt53t7c4ufH38KDGyIbjmp25o3J7OkhEl79pBRUkK3oCCWdexIlGvU\nyd7i4srvteBgFlx4IRP37GFiWhrtAwL4pEMHOrly8TaGTUePMtc1p0Vzh4PLgoL4rnNn4lzvpQAv\nLz48fJin9u4lv7yc8xwOrgsLY2KrVjjcimTWWt49eJDhUVGNMu+MaajhfSJnyhjzW2AxUAh0ttbu\nOkX8hcAWnHPTTLHW/qWGuMlAuLV2nOvr94GbgeettfV6clpfObnF34Lzqv8KnE+B6mWtPVoHTT8p\n5VURf7rvwXKcAztO+peFMaY7kJKSkkL37t3PIAOR07N9e2O3oGlqAqO2Pc6OHY3dgqbpSp/vG7sJ\nTc+ttzZ2C5qmRpzrocmKjW3sFjQ5a/PzSfjxR4AEa+3ak8XqVhHxeNbaz4C/4DxR/cwYE3mKTS47\n1T6NMVfivHr+J7fFo4FszvyWkdNWHzlVsRg4BFwOpDTEyT0oLze1zUtERERERGqgwoU0Cdbap4D7\ngAuBZFfh4QTGmDhgMlDtOExjjK8xZiTOE+Aca23FXD6up4zMw3kL1QJjTL3OBFVXOdWw7xJgvuvL\n2WfX0tpRXqeXl/v7yxhzft21VERERETk3KI5LqTJsNbOMcasAZ4Blrv+/ymwCwgF+gDtgeHW2mRj\nzEHcHmNsjJkKPAQcu4GtizFmnLX2Fdf6QcAYnMP7Y4HNxphdwHjXFXePy+kU/gUMsNYm133LT055\n1ZyXMeY84CvgApzvNYBNxpid1touDZOJiIiIiEjToTkupEkyxkThPElsBziADGCFtbbJPsGhrnMy\nxtwDtLTW/rXuWnlG7VBeZ38szXEhDUJzXJwZzXFRe5rj4sxojoszoDkuzozmuKg9zXFRa7WZ40Ij\nLqRJstYeBD5q7HbUpXrI6R7g7jrc3xlRXiIiIiIicjY0x4VIE2eM6WSM2WCMmei2rCOQa639pRGb\ndlbO1bxERERERKR2VLgQafquA7oA97otm4RzHoam7FzNS0REREREakGFC5Gm7zugFJgKYIyZABRY\na1c3aqvO3rmal4iIiIiI1IIKFyJNnLV2JfAAMMYY818gCvh947bq7J2reYmIiIiISO1ock6Rc4C1\ndhYwq7HbUdfO1bxEREREROT0acSFiIiIiIiIiHgsFS5ERERERERExGOpcCEiIiIiIiIiHkuFCxER\nERERERHxWCpciIiIiIiIiIjHUuFCRERERERERDyWChciIiIiIiIi4rFUuBARERERERERj6XChYiI\niIiIiIh4LBUuRERERERERMRjqXAhIiIiIiIiIh5LhQsRERERERER8VgqXIiIiIiIiIiIx1LhQkRE\nREREREQ8lgoXIiIiIiIiIuKxVLgQEREREREREY+lwoWIiIiIiIiIeCwVLkRERERERETEY6lwISIi\nIiIiIiIeS4ULEREREREREfFYKlyIiIiIiIiIiMdS4UJEREREREREPJYKFyIiIiIiIiLisXwauwEi\nIiLiGX715d8buwlNkh31QGM3ock5f9UHjd2Epqljx8ZuQdNz9Ghjt6Bpioxs7BY0PRERjd2Cpsfb\n+7RDNeJCRERERERERDyWChciIiIiIiIi4rFUuBARERERERERj6XChYiIiIiIiIh4LBUuRERERERE\nRMRjqXAhIiIiIiIiIh5LhQsRERERERER8VgqXIiIiIiIiIiIx1LhQkREREREREQ8lgoXIiIiIiIi\nIuKxVLgQEREREREREY+lwoWIiIiIiIiIeCwVLkRERERERETEY6lwISIiIiIiIiIeS4ULERERERER\nEfFYKlyIiIiIiIiIiMdS4UJEREREREREPJYKFyIiIiIiIiLisVS4EBERERERERGPpcKFiIiIiIiI\niHgsFS5ERERERERExGOpcCEiIiIiIiIiHkuFCxERERERERHxWCpciIiIiIiIiIjHUuFCRERERERE\nRDyWT2M3QORMGGOCgauANkAgkAZ8aa3NcotpA7S21v7QKI2spXMxJ2jaeTXltouIiIiInCs04kKa\nFGNMrDFmIZAOPAT4AbuBcGCOMeY1Y0yoMcYX+ADo13itPT3nYk5Qd3kZY0YaY7KMMW80tbaLiIiI\niMjZ04gLaTKMMWOAacDPQE9r7Y9VQt4wxgwAlgLbgQTg04ZtZe2cizlBnef1EBAKjDTGPGqtPVJP\nzQbO3e+JiIiIiEhTpcKFNAnGmCnAk8AqoK+1Nr+6OGvtUmNMM+DfgG3AJtbauZgT1EteM4AXgA8b\noGhxTn5PRERERESaMt0qIh7PGHMLzpPJPODmmk4mj7HWvg+83xBtO1PnYk5QP3lZa9+y1oZZa++t\nu5ae6Fz9noiIiIiINHUqXIhHM8aEAG/jvKr9grV2/2luOg0w9daws3Au5gRNO6+m3HYRERERkXOd\nChfi6cYBYa7/zzvdjay1q4EN9dKis3cu5gRNO6+m3HYRERERkXOa5rgQTzfC9e8Wa+3uWm77XV03\npo6cizlBPeRljIkCIoEoIMpa+8FZtO9kztXviYiIiIhIk6cRF+KxjDGdgfNxDt//6Qx2MR9Y5ra/\nUcaYXcaYcrfXUWPMpa71lxpjit3WFRtjvq6TZI63oU5zcu3znMzLJcO1v29wToRZ5+rxe7K3yvck\n3Rhzg1vMw8aYUrf1+4wxMWeVjIiHmPmf/9DuyScJGDuWni+8wOpdu04av2jtWuKmTCFg7Fjin32W\nJT9V/lH8aP16BsyYQdSjj+I1ejQb9u6ttH73oUN4jR6N9+jReFV5fbBuXUVc0qZN9H7xRULGj6fV\nY4/x548/pry8vM7yrguTJk2iVauWBAUFcu2117Bt27ZTbjNz5kxiY9sRGBhAYmJPVq9eXWl9UVER\no0ePJioqkpCQYAYPvpUDBw5UitmzZw+//e1AmjUL4rzzYnj00Ucr9U16ejp33fU74uI64uPjzfjx\n4+sm4bM0c8kS2j34IAFDhtDzscdYfYr+WvTDD8SNHUvAkCHET5jAkrVrK63/aOVKBjzzDFH33ovX\n4MFsOMV797pnnsFr8GAWV+nzPZmZDJw6laDf/Y6Y++7j0XnzPOq9NnPBAtr1709AQgI9hwxh9Y9V\nH6BV2aJly4i74QYCEhKIHzSIJf/3fyfETJoxg5a/+Q2Bl17KNffdx7a0tIp1/1m9Gq+uXfG++GK8\nunat9Epx/bwXFRcz/IknuPjmm3F068agsWPrNumzNLOoiHZHjhCQnU3P3FxWl5aeNH5RcTFxrvj4\nI0dYUlJSY+z9BQV4ZWfzWlFRpeVZ5eX8Lj+f0OxswnNyuK+ggHxb/Zzg28rKCM7OJiInp/bJ1aOZ\n2dm027WLgO3b6blnD6uPHj1p/KK8POJ27yZg+3bi09JYkl95yrEphw8Tt3s3zbZvJ2LHDq755RdW\nVbPPz/Pz6blnD4GuuEH7K98FnFRQQO+9ewnZvp1WO3fy58xMymvo24Y2c9cu2n39NQFLl9Lzhx9Y\nnZ190vhF+/cT95//ELB0KfH/938sqfL5PmXrVuL+8x+aLVtGxPLlXLNyJauq7DOrpITfrV9P6Jdf\nEv7ll9y3YQP5bu/xKVu34vXFF3h/8QVebq/gZdX9+V4/VLgQT3aJ2//31hhVA2vtD9balW5fv2Gt\nbQskAkdwnqhOsNauca1fA0wHyoHngGhr7W/OvPnVqtOcXMvOybxcooBRZ9yq01Nf35PWwB+O7dda\nG2Ot/dQt5jXgQZzfl3uttS2ttem1b76IZ1m4Zg0TPviAKQMHsu6xx4hv3Zr+M2aQmZdXbXzyjh0M\nmT2bkX36sP7xx7kpPp6b3nyTjW5/ZOYXFdHnV7/i+ZtuqnZSmTYREaQ/9xz7n3uOdNdrysCBNPPz\n47rOnQHYsHcvA19/nes7d2b944/z3ogRLN6wgT9//HF9dMMZef7555k5cwZvvPEmK1euIigoiAED\n+lNcXFzjNgsXLuQPf5jAU09NYe3adVx8cTwDBvQnMzOzImbcuHF88cXnvP/+B/znP9+yb98+br31\nlor15eXlDBx4PaWlpSQnr+Cdd97l3XffYdKkSRUxRUVFREW1YOLEJ+jWrVv9dEAtLfz+eybMncuU\n225j3bRpxLdtS/9nniHzSPUPoErevJkhr77KyH79WD9tGjf16MFNL7zAxj17KmLyi4ro07Ejz//u\ndxhz8imMpn/6Kd5eXifElZeXc/3UqZSWlbFi6lTeHTOGd77+mkkLF5590nVg4ZIlTJg2jSkPPsi6\nRYuI79CB/vffT2ZWVrXxyevXM+RPf2Lkrbey/v33uenqq7np4YfZuH17Rczzs2YxY/583pw8mVUL\nFhAUGEj/3/+eYtfJeu9LLiH9m2/Y//XXpH/zDenffMN9t9xCu1atSHD9jJaVlRHo78/Yu+7imsTE\n+u+IWlhYXMyEwkKm+PuzLjiYeG9v+ufnk1lDMSq5tJQhBQWM9PVlfXAwNzkc3JSfz8ayshNiPy4u\nZlVZGa2qeb8NKSjg5/Jykpo14/OgIL4tLeX+goIT4kqtZUhBAVf6eNZg+oW5uUw4dIgpERGsO/98\n4v386L9vH5nV9ANAcmEhQ9LTGRkSwvrzz+emoCBu2r+fjW6fgR0cDmZGRfHfNm34vnVr2jocXLtv\nH4fc9vlBXh5DMzIYERLCj23a8EPr1gwJDq5Yv6GoiIH793N9YCDr27ThvZgYFufn8+dDh+qvM07T\nwn37mLBpE1Pat2ddnz7EBwfTf/VqMmv4PZCclcWQ9esZef75rO/Th5uio7lp7Vo25uZWxHQICmJm\n587894or+D4xkbaBgVy7ahWH3PY5ZP16fs7LI6lHDz6/7DK+PXyY+//734r1f4yNJb1vX/b3EKyc\nDgAAIABJREFU7Uu669WpWTNuO++8+uuMKlS4EE8W7fb/3Bqjasl1kjkW56SKDxhjHG6r2wN/ttY+\nbq2t/jf42amXnODczMtaewh4t672V4N6+54AM4FsoJUxJraa9WnAe9bad+r4uCKNZvpXX3F/nz4M\n7dmTjjExvHHnnQT6+jL7hx+qjX/166+5rnNnxvftS4foaKb89rd0P/98ZnzzTUXMXZdfzhPXXUff\nDh2qff6wMYYWwcGVXh+lpnJHQgKBvr4ALExJIb51ayZedx2xkZFcceGFvHDzzcz89lvyq1zlbCyv\nvfYqTzzxJDfccANdunTh3Xfnsm/fPj4+SXHllVem8/vf38/QoUPp2LEjb7zxBoGBgcyePRuAI0eO\nMGfObF5+eTpXXnkll1xyCbNnz+H7779n1apVACxbtoxNmzbxz3/+i65du9K/f3/+8penef31mZS6\nrrhdcMEFTJ8+nbvuuouQkJD674zTMP2zz7j/mmsYetVVdGzVijd+/3vne+2rr6qNf/Xzz7nukksY\nf8MNdGjViim330732FhmLF1aEXPXr3/NE7feSt+LL8ae5Opr6q5dvPL558wePfqEuGXr17Ppl1/4\n19ixdL3gAvp368bTd9zBzKVLKa3hhK0hTZ83j/sHD2bojTfSMTaWNyZNIjAggNkffVRt/Kv//CfX\n9enD+HvuoUO7dkwZM4bunToxY/78SjFPjhrFDVddRZf27Zk7dSr7Dh7k46QkAHx8fGjRvHnFKyI0\nlMVff82IQYMq9hEYEMDMJ55gxC23EN28ef12Qi1NLyrifl9fhvr60tHbmzcCAggEZtdwMvlqURHX\n+fgw3t+fDt7eTAkIoLu3NzOqfNb8Ul7Ow4WFzA8MxKdK4WJTWRnLSkuZFRDApT4+9PLx4W8BAbxX\nUkJ6lYLJxKNHifPyYrDr885TTM/O5v6QEIaGhNDR15c3oqIINIbZNRQXX83J4brAQMaHh9PB15cp\nzZvT3c+PGW6jA+4IDubqwEDaOhzE+frycmQkR8rL2eDq2zJrGZeZyUuRkYwMDeVXDgcdfX25tVmz\nin0szMsj3teXiRERxDocXBEQwAuRkczMySG/kUdGTd+1i/vbtGFo69Z0bNaMN7p0IdDbm9luBVZ3\nr+7axXVRUYyPjaVDs2ZMuegiuoeEMGP38Tuf72jZkqsjI2kbGEhcs2a8HBfHkdJSNriKGz/n5bHs\n4EFmde3KpWFh9AoP52+dO/Pe/v2ku/o10NubFn5+Fa/9RUVszMtjxPnn13+nuKhwIZ7M/cS7Tp/c\nYK19F/gUiAOeAjDG3AwEWWtfrMtjVVFvOcG5mZe1tr7PKOq77ccusQ2rJuQO4M26PKZIYyopKyMl\nLY2+HTtWLDPG0K9jR5J37qx2m+SdO+nnFg/QPy6uxvjTkZKWxvq9exnRq1fFsqLSUvyrXI30dzg4\nWlJCituQ9sayc+dO0tPT6du3b8WykJAQLr/8cpKTk6vdpqSkhJSUlErbGGPo168fK1Y4t1mzZg2l\npaWVYjp06ECbNm0q9rtixQq6du1KZGRkRUz//v3Jycnhp5/O5A66+ldSWkrKjh307dq1Ypkxhn4X\nX0zyli3VbpO8ZQv93OIB+sfHk7x5c62OXVhUxJBXXmHmfffRIjT0hPUrtm6la5s2RLoVePp360ZO\nQQE/1XDy0VBKSkpI2biRvj17ViwzxtCvZ0+S16+vdpvk1FT6ucUD9O/Vi+TUVAB27NlDemYmfS+/\nvGJ9SLNmXN61a0VMVZ98/TWHcnK458YbzzaleldiLSllZfR1HP9zwRhDPx8fkmsaOVBaSj+Ho9Ky\n/lXirbUMLSjgUX9/4ry9q91HuDFc4va51c/HBwOsdNvPVyUlfFBSwszAwDNNsV6UWEtKURF93dpl\njKFfYCDJNdwuknz0KP2q5NH/JPEl1vJmTg5hXl7E+/kBsLaoiH2ugmv3PXtouXMn1+/bV2nURpG1\n+FcpFPkbw1FXmxtLSXk5KTk59HUr3Blj6Ne8Ock13C6SnJVFP7fPboD+kZE1xpeUl/NmWhphDgfx\nrlEoK7KyCHc4uMTt86xfZKTzvVbDfv6xZw8dgoLoFR5emxTPigoX4sncb9Cqj8s7vwcOA380xgwA\nnqX6k8u6VN85wbmbV32p77bPwVkQGeq+0BgTBHSz1p54o7BIE5WZl0eZtUS7DckFiA4OJr2GK2zp\nR46cGB8SUmP86Zj1/fd0ionh8nbtKpb179SJH3bs4L01aygvL+eX7Gye/uILAPZ7wD3h6enpGGOI\njo6utDw6OpqMjOrvIsvMzKSsrOyEbVq0iCY93blNRkYGvr6+J4ySiI4+HpOenl7tcY+t80SZubmU\nlZcTXaVwEB0aSnoNf2inZ2cTHRZWOT4srMb4mjzyzjv0iYvjt5deWvNxqmnXsXWNKTM72/meqTKi\nIbp5c9JrGCafnplZfbzrdqSMQ4ec792TxFQ1+8MP6d+rF62qvO88Uaa1lAHRVU50o728Thj5cEy6\ntaeMf66oCF9gjOuEu7p9tKiyD29jiDCmYj+HyssZXlDAu4GBNDvFrU0NLbOszNlvVYoy0d7epNcw\nP0h6aemJ8T4+pFcpEH2en0/w9u34b9/Oqzk5LG/ZkgjXdjtKSrA458KYFB7O5y1bEu7lxZV795Lt\n2k//wEB+OHqU93JzKbeWX0pLefrwYQD2n2LukvqUWVzs/B1a5T0R7edXMfKhqvTiYqKrjLSpLv7z\nAwcIXrYM/6VLeXXXLpb36EGEa7v0oiJaVNmHtzFEOBzVHre4vJz5+/ZxXwOOtgAVLsSzuT9mstXJ\nAo0xTxtjtromONxf5bXDfVLEY6y1GcBonE/X+RSYaq3dXzXuVIwx8caY3caYb40xwacIr9ec4NzN\nq6m23Vq7CtgInG+M6eu2ajDw/unmJNKUWWtrNZzJWnvK+QVqcrSkhAVr1nBf796Vll8TF8e0QYN4\nYMEC/B5+mI5TpjCwSxcs4O3V8H8OzZ8/n5CQYEJCggkNDaGkhon7zqQvTmeb093vmX4fGouldkPn\natu/i1ev5qv//pfpw4bVtmlAPQy1rCO1/hnl1O+NmmJ+ychg2Q8/cN8tt5y4URNS6/eaW3xKaSmv\nFRUx5wxGSbjvZ2RhIb/z9aW3a1TGyW5v8hSn8945Ib7KsqsDAkht04bk1q0ZEBjI4PT0inkzjpWG\nnoiI4KZmzbjEz4850dEYY1jkmmvpmsBApkVG8sDBg/ht307H3bsZGBTk/H3ggZ95Z/NeO+bq5s1J\nveIKknv1YkBUFIPXrq1x3oxTHfeD9HTyysq4u9VJ/2yucypciCdLwXk13AAnnQnMWvuktbY9zlsk\nXsI5b0EL4Gtrbaz7pIhVtvs3kITzZ6HTGbZzOM6nUvQGTjXpZb3n5Nr2nMyrFjyt7e+69j/Mbdnd\n1P/8HSINKrJZM7yNISO38nQxB/LyiK5hXoSYkJAT43NzTxiFcboWrV1LYUkJd/foccK6cVdfTdZL\nL7Hn2WfJfOEF/ufiiwFoV2WYbUO48cYbWb8+lfXrU1m3bj2RkZFYa8nIyKgUd+DAAVq0qP6qdGRk\nJN7e3idsc/DggYoREzExMRQXF3OkygiWAwcqx1Tdx7Gvq47E8BSRwcF4e3mRUWW0zIGcnBNGVRwT\nExZGRpURDwdyck4YHXEyX//3v+zIyCB06FAct9+O4/bbARg0bRpXP/XU8eNUadexr2tqW0OJDAtz\nvmeqjK44cPhwjfNKxERGnhh/6FBFfMyx9+5JYtzN/ugjIsPCuOGqq84ik4YTaQzeQEaVosCB8nKi\nayh6xhhz0vjvyso4aC3nHzmCIzsbR3Y2u8vLGV9YSKzrZzXGGA5U2UeZtWRZS4xrP1+XlvJiUVHF\nPu4rLCTbWnyzs3mnkefuifT2dvZbldESB8rKThhVcUyMj8+J8dWMwgjw8iLW4aCHvz9vt2iBjzHM\ncvXbea7YOLdbdXyNIdbHhzS30RTjwsLIio1lT9u2ZMbG8j9BQQC0a8QJTiN9fZ2/Q6t87w4UFZ0w\nCuOYGF9fMqoUIKqLD/D2JjYwkB5hYbzdtauzz1y3rsX4+XGgyj7KrCWrpKTa487as4ffRkXRooY2\n1RcVLsRjWWfZeI7ry7bGmPansU0OsMht0aaTxRtjOgAtcZ64/uHYI0Rr6T1gP7AO+PYU7av3nODc\nzasWPK3t84Ay4GZjTLAxpi1QZq1t3JudReqYw9ubhDZtSNp0/EfCWkvSpk30iq1uflpIbNeOpCpz\nDCzftIlEt9s83J3qqtPsH37gfy6+mOZuE7FVFRMaip/DwfzVq2kTHk73Bh7uChAUFERsbGzFq1On\nTsTExJDkmswQnBNrrly5kl5uc3W4czgcJCQkVNrGWktSUhKJic5tEhIS8PHxqRSzZcsW0tLSKvab\nmJjIjz/+WOlJJF9++SWhoaF06nSmte/65fDxISE2liS3x3haa0n68Ud6dehQ7TaJF11UKR5g+YYN\nJNYQX91V4ccGDWLDSy+R+uKLFS+AV4cPZ87o0RXH+TEtrdLTTb5MTSU0MJBOrVvXLtE65nA4SOjU\niaQVKyqWWWtJWrGCXjU8LSYxPp6klZUf/LU8OZnE+HgA2rVuTUxkZKWYI3l5rPzxx2r3+c7HH3PP\njTfiXcPJq6dxGEOCtzdJbqOirLUklZbSq4YcEn18KsUDLC8tJdEVP9ThYENwMKlur5bG8KifH8tc\nJ9CJPj5kW8s6t5PtpNJSLNDDtZ8VzZqx3m0ff/H3JwRIDQ7m5kaeqNNhDAl+fiS5PQXFWktSYSG9\n/P2r3SbR35+kwsJKy5YXFpJYQ/wx5TjnrQBI8PfHzxg2u/V/ibXsKi3lgmqKEjE+PvgZw/zcXNr4\n+NC9gU/G3Tm8vEgIDSXJrQhorSXp0KEa55JIDA8nqcotWcsPHSLxFEXScqDIdctRYng42SUlrHMr\nuCZlZmKBy6vsZ1dBAV8fOtTgt4mAcyi5iCd7CefV8yjXv4/X1Y6NMd7AO8ADOK+a/xuYY4zpbq2t\n+WHbVVhrV3CKWwyqqLec4NzNqzY8re3W2nRjzFLgepwTcrZEoy3kHDW+b1/umTuXhDZt6NG2LdO/\n+oqCkhKGuSb3G/rOO7QOD2eqa1K+sb/5DVdOn87LSUkM7NKFBatXk5KWxtu/+13FPrMKCkg7fJhf\nsrOxwKaMDCzO0RruIzm2HTjAt9u2sXTMmGrb9uLy5Qzo3BkvY/hg3TpeWL6cRffd5zG3Q4wdO45n\nn32GCy+8kLZt2zJp0pO0bt2aG90mMOzXry+DBt3Cgw8+CMAjj4xn2LB7SEhIoEePHkyfPp2CggKG\nuW5lCAkJ4d57RzBhwnjCw8MJDg5m7NiH6d27N5dddhkA1157LZ06dWLo0Lt57rnn2b9/P5MmPcno\n0WNwuF21TE1NxVpLXl4emZkHSU1NxdfXl7i4uIbrJDfjb7iBe2bMICE2lh7t2zP9s88oKCpimOtK\n/tDXXqN18+ZMdb2Xxg4cyJWTJ/Pyp58ysHt3Fnz3HSk7dvD2qONP3c7KyyMtM5NfDh/GWsumffuc\n77WwMKLDwmgRGlrthJznR0ZyQVQUANfGx9OpdWvufu01nr/rLvZnZ/Pke+8xZsAAHB7wuMrxQ4dy\nz8SJJHTqRI+uXZk+bx4FR48y7KabABj62GO0jo5m6rhxAIy96y6uHDaMl999l4G//jULvviClI0b\neXvKlIp9jrv7bp55800ubNOGti1b8uSMGbSOjubGq6+udOykFSvYtW8f9958c7Vt+3n7dopKSjic\nk0NeQQGpriJofJUJfBvaeD8/7ikoIMHHhx7e3kwvKqIAGOYqDgzNz6e1lxdTAwIAGOvnx5V5ebx8\n9CgDHQ4WFBeTUlbG265bQ8K9vKh6GuowhhgvL9q7ihIdvb3p7+PDyMJC/h4QQDHwUGEhdzocFSMu\nOlQpnKwuK8PLmGon+2wM48PCuOfAARL8/Ojh78/07GwKyssZ5vrcHpqRQWsfH6a6RuaMDQ3lyl9+\n4eWsLAYGBbEgN5eUoiLebtECgILycp7NyuJ/goI4z9ubzLIyZuTksK+0lMGuYnWwlxejQkKYfPgw\nrX18uMDHhxeyszFQEQPwYlYWAwIDnb8P8vJ4ITubRTExjf77YHy7dtyTmkpCSAg9wsKYvnMnBWVl\nDHPdljE0NZXW/v5MdRVcx7Zty5UrVvDyjh0MbNGCBfv2kZKTw9tdugBQUFbGs9u28T/R0Zzn50dm\ncTEzdu9m39GjDHY9yrRjs2b0j4pi5I8/8vcuXSguL+ehjRu587zziKlSyJm1dy8t/f0Z4Pq8a0iN\n/+kpchLW2kxjzH3AYmCsMeYta+2uU2zmOMX6Y54AVlprvwUwxnwI3AxMoR5Puus5Jzh386o3DdT2\nd4CBOAsj/kD1l1BFmrjbEhLIzMtj0mefkZGbS7fWrVk2ZgxRrls/9mZn4+P2R3VibCwL7r2XiYsX\nM3HxYtpHRfHJqFF0cns2/OINGxg+bx4G54iLO12P+pw8cCCTrr++Im5OcjLnh4dzTQ0n0ks2bmTq\nsmUUlZYS36oVi0eN4loPGlHw6KOPUlBQwKhR95Odnc0VV1zBF18swdftyunOnTsrjYy47bbbyMzM\nZPLkSWRkZNCtWzeWLl1GlNsfldOnT8fb25vBg2+lqKiIAQMGMGPGzIr1Xl5efPrpZzz44AP07t2L\noKAg7rlnGFPcTkwBune/pOKP+rVr1zJ//nwuuOACtm/fUV9dclK39epF5pEjTFq4kIycHLq1bcuy\nJ54gylVY2Hv4cOX3WocOLBg3jonz5zNxwQLax8TwyZ/+RCe3K4eL16xh+MyZGGMwxnDnK68AMHnw\nYCYNHlxtO6qe6Hh5efHZY4/xwFtv0euJJwjy82PYVVcxxXVbSWO7bcAAMrOzmTRzJhmHDtGtY0eW\nvfkmURERAOzNyKjcb926sWDaNCa+9hoTX3uN9m3a8Mnf/kanX/2qIubRe++loLCQ+6dMITs3lyu6\nd2fJG2/gW+XJGrM/+ojel1xChxpGVF3/4IOk7T8+LdclgwdjjKFsw4Zq4xvKbb6+ZFrLpMJCMqyl\nm7c3y4KCiHIVEPZai4/bbR2JPj4sCAxk4tGjTDx6lPZeXnwSFESnkxQUqjtdnh8YyJjCQvrl5eFl\nDLc6HLzqKo40BbcFB5NZXs6kw4fJKCujm68vy1q2JMrVD3tLSyudjCYGBLAgJoaJhw4x8fBh2jsc\nfBITQyfXZ6C3MWwqLmZubi6ZZWU09/LiMn9/vmvViji3z8kXIyNxGMPQjAwKreVyPz++atWKULf+\nX1JQwNSsLIqsJd7Pj8Xnnce1HvBkltvOO4/M4mImbd1KRlER3UJCWHbZZUS5Cgh7jx6t9OjcxPBw\nFnTrxsQtW5i4ZQvtg4L4JCGBTq7fud7Aprw85v7yC5nFxTT39eWy0FC+S0wkzq2QM79bN8b89BP9\nVq3CC7j1vPN4tcrvR2st7+7dy/DWrRulwGOawiQuIsaYp4AngZ+Bq6y11U9T7Yy9E/gXzjllplhr\n/1JNzJU4r3h3OPa4TWNMNM5JFIOBXtbaNXWdR5U2PEUd5uSKO1fzKsd5V0e9XkKoj7a7xTuAfUAE\nMNtaO7IW7eoOpKSkpNC9e/fT3Uyk9v7+98ZuQZNkRz3Q2E1ocsyHHzR2E5qmRh550CT16dPYLWia\nGmHunybvoosauwVNztqcHBK+/x4gwVq79mSxmuNCmgRr7VPAfcCFQLLrBP0Expg4YDLwYg3rfY0x\nI3FeWc85dnLvOkYGzrkIfIAFxpjqLwfUkbrKyRVzTublimvn9v96vaGurtteZd8lwHzXl7PPrqUi\nIiIiIv//0K0i0mRYa+cYY9YAzwDLXf//FNgFhAJ9gPbAcGttsjHmIG4j74wxU4GHgGPjwLoYY8ZZ\na19xrR8EjMF5BT0W2GyM2QWMt9Z+5ok5neN5nQd8BVzgajvAJmPMTmttl/pod121/ST+BQyw1ibX\nfctFRERERM5NulVEmiRjTBTOE8h2OOcayABWWGvr8skUDepczAmadl513XZjzD1AS2vtX2u5nW4V\nkYahW0XOiG4VqT3dKnKGdKtI7elWkTOjW0VqT7eK1FptbhXRiAtpkqy1B4GPGrsddelczAmadl71\n0PZ7gLvrcH8iIiIiIuc8zXEhIlLHjDGdjDEbjDET3ZZ1BHKttb80YtNERERERJocFS5EROredUAX\n4F63ZZNwzpshIiIiIiK1oMKFiEjd+w4oBaYCGGMmAAXW2tWN2ioRERERkSZIhQsRkTpmrV0JPACM\nMcb8F4gCft+4rRIRERERaZo0OaeISD2w1s4CZjV2O0REREREmjqNuBARERERERERj6XChYiIiIiI\niIh4LBUuRERERERERMRjqXAhIiIiIiIiIh5LhQsRERERERER8VgqXIiIiIiIiIiIx1LhQkRERERE\nREQ8lgoXIiIiIiIiIuKxVLgQEREREREREY+lwoWIiIiIiIiIeCwVLkRERERERETEY6lwISIiIiIi\nIiIeS4ULEREREREREfFYKlyIiIiIiIiIiMdS4UJEREREREREPJYKFyIiIiIiIiLisVS4EBERERER\nERGPpcKFiIiIiIiIiHgsFS5ERERERERExGOpcCEiIiIiIiIiHkuFCxERERERERHxWCpciIiIiIiI\niIjHUuFCRERERERERDyWChciIiIiIiIi4rF8GrsBIiIi4hnW9XygsZvQJF1yIKOxm9D0HD3a2C1o\nmnbtauwWND3XX9/YLWiaNm5s7BY0PTt2NHYLmp5a/C7QiAsRERERERER8VgqXIiIiIiIiIiIx1Lh\nQkREREREREQ8lgoXIiIiIiIiIuKxVLgQEREREREREY+lwoWIiIiIiIiIeCwVLkRERERERETEY6lw\nISIiIiIiIiIeS4ULEREREREREfFYKlyIiIiIiIiIiMdS4UJEREREREREPJYKFyIiIiIiIiLisVS4\nEBERERERERGPpcKFiIiIiIiIiHgsFS5ERERERERExGOpcCEiIiIiIiIiHkuFCxERERERERHxWCpc\niIiIiIiIiIjHUuFCRERERERERDyWChciIiIiIiIi4rFUuBARERERERERj6XChYiIiIiIiIh4LBUu\nRERERERERMRjqXAhIiIiIiIiIh5LhQsRERERERER8Vg+jd0AETk9xphg4CqgDRAIpAFfWmuz3GLa\nAK2ttT80SiNrqSnn1JTbLiIiIiLSlGjEhYiHM8bEGmMWAunAQ4AfsBsIB+YYY14zxoQaY3yBD4B+\njdfa01NXORljRhpjsowxb6jtIiIiIiLnJo24EPFgxpgxwDTgZ6CntfbHKiFvGGMGAEuB7UAC8GnD\ntrJ26jinh4BQYKQx5lFr7ZF6ajbQtNsuIiIiItJUqXAh4qGMMVOAJ4FVQF9rbX51cdbapcaYZsC/\nAduATay1eshpBvAC8GEDFC2abNtFRERERJoy3Soi4oGMMbfgPEnOA26u6ST5GGvt+8D7DdG2M1Uf\nOVlr37LWhllr7627lp6oKbddRERERKSpU+FCxMMYY0KAt3FerX/BWrv/NDedBph6a9hZaMo5NeW2\ni4iIiIicC1S4EPE844Aw1//nne5G1trVwIZ6adHZa8o5NeW2i4iIiIg0eSpciHieEa5/t1hrd9dy\n2+/qujF1pM5zMsZEGWPijDG/dt3KUV+acttFGs3f/z6Ja69tSa9egTzwwDWkpW075TYLF87kt79t\nR2JiAEOH9uSnn1ZXWl9cXMRf/zqaq6+OpE+fYP74x1s5fPhApZifflrNqFH9uPLKcK66KoLRowew\nZcuGSvuYPHk4t912MT16OJgwYVDdJFwHZs6eTbvLLiOgbVt6Xn89q9etO2n8osWLibviCgLatiX+\n6qtZkpR0Qsyk55+nZXw8ge3acc1tt7Ft585K62+85x4uSEggoG1bWsbHM3TMGPZnZFSKWfb11yQO\nHEjIhRfSonNnbh0xgt179px9wnVg5vLltHvkEQKGD6fn5Mms3r79pPGLVq4k7tFHCRg+nPjHH2dJ\namql9R+tXs2A558n6oEH8Lr7bjakpZ2wj6ueeQavu++ueHnffTcPzplTKcZ9/bGYf69YcfYJ15GZ\nn31GuxEjCBg0iJ4TJrB6y5aTxi/67jviRo0iYNAg4seMYcmaNZXWf/TDDwyYNImoIUPwuuEGNlR5\nn2Xl5fHwm2/ScdQogm65hQuGD2fsm29ypKCgUtzqLVvoN3Ei4bffTsQddzBg0qQT9tVYZm7ZQrtP\nPiFg4UJ6LlvG6kOHThq/KC2NuM8+I2DhQuK/+IIl+/ZVrCstL+dP69Zx8Rdf0Ozf/6bVRx9xT3Iy\n+wsLK+1j65Ej3PTtt0R98AGhixZxxfLl/KfKz+e4lBQuXboU//feo/uSJXWXcB2ZefAg7TZuJCA1\nlZ5btrC6yve8qkXZ2cT9/DMBqanEb9rEkiOVpwGbkp5O3M8/02zDBiJ+/JFrtm1jVX7lO3jb/vQT\nXuvXV7y816/nhSr9tqGwkF9v3UpAaioX/PQT0w5U/l3SmGZmZdFu+3YCNm+m565drK7yvqhq0ZEj\nxO3YQcDmzcTv3MmSvLxK66dkZhK3YwfNNm8mYssWrklLY1U1+/w8L4+eu3YR6IobtHdvpfVemzZV\nenlv2sS/jzTcNG0qXIh4EGNMZ+B8nLcl/HQGu5gPLHPb3yhjzF5jTLnbK90Yc4NbzMPGmFK39fuM\nMTFnnczx/ddpTm4yXPv7BudEmHWuKbddpDG9887zLFw4g4kT32Tu3FUEBAQxZkx/SkqKa9xm2bKF\nTJ8+gVGjpjB//jouuiie0aP7k5WVWRHz4ovj+O67z5k27QP+8Y9vOXhwH3/4w/HaX2FhPg89dB0t\nW7Zl3rxVzJ79PUFBwYwZM4CysjIAysrK8PcPZMiQsVx++TX11wm1tPDjj5kwZQpT/vgH/MFcAAAg\nAElEQVRH1i1fTnznzvS/804yazg5Sl6zhiEPPsjI3/2O9f/7v9w0YAA3DR/Oxs2bK2Ke/9vfmDFn\nDm9Om8aqL74gKDCQ/nfcQXHx8e/D1X36sOjtt9ny/fd8OHs223fvZvDIkRXrd6WlcdPw4fT79a9J\nTUriy4ULyTx8mFtGjKCxLVyxggnz5zNl0CDWPfss8W3a0P+FF8jMza02PnnrVoa8/jr/j737Dq+q\nyP84/p40EkIoISF0CBBD01DFIKIUBVx3wbUsossKiOKKDYWfyopgLwiigA1RsSB2QUVksaASEUIv\nAWlBCGkkkIQUUub3Ry6Xm5BQb5Kb9fN6njwk53zPnJlJ7uWe75kzM/qyy1j3xBMM6dqVIdOns2X/\nfmfMkbw8ekVG8szQoeU+62eM4dY+fUiaNYvEmTM5MHMmz95wwwlxb992W4mYId26uaPZ52zB8uXc\n98YbTBk2jLUzZhAVHs6ASZNIPXy4zPiYrVsZ9txzjB4wgHUvvsiQ6GiGPP44W1ySOkfy8ujVvj3P\n3HwzxpzYcwkHD3IgLY1po0axadYs3r73Xr5Zs4ZbXnzxeBm5uQyaPJmWYWH8Nm0avzz3HEEBAQx8\n5BHn67eqLIiP5741a5hy/vmsHTiQqHr1GPD996Tm5pYZH5OSwrBffmF069asGzSIIU2bMmT5crY4\n+ji7sJB16ek80rEjawcO5LNLLmFbRgaDf/yxRDl/+fFHCouK+KFfP9YMHEhU3br85ccfSS513lGt\nWzO0RYuKafw5WJCezn0JCUxp2JC1kZFEBQQwYOdOUgsKyoyPOXKEYXv2MLp+fdZFRjKkTh2G7N7N\nFpf2RtaowaymTdkUGckvERG09PPjip07OehSpjGGxxs1IqljRxI7dOBAhw7cGRrq3J9ZWMiAnTsJ\n9/NjTWQkzzVuzOTEROacIhlVGRZkZHBfcjJTQkJY27IlUf7+DPjjj/L7LCeHYQkJjK5bl3UtWzKk\nVi2G7N/Plrw8Z0yknx+zwsLYFB7OLy1a0NLXlyv++IODLq+rTzIyGJ6QwKi6ddkYHs6KFi0YVrv2\nCed7u1Ejktq0IbFNGw60acOQoCD3d0I5lLgQ8SydXb7fV25UOay1K6y1K11+fsVa2xS4/1iZ1tqG\n1tpFLjEvAv8GioCR1trG1trEs6t+mdzaJhehwJizrtXpqc51F6ky778/g1tueZhLL/0rbdp05NFH\n55GSksD3339e7jHvvTeda665jauuGk54eFsmTnwFf/+afPHFXACysjL44ou53HffdLp2vZS2bTsz\nefKbrF//C5s2/QbA7t1xZGSkM2bMFJo3j6BVq3bceusjpKUlceBA8YCpgICaPPjgLIYMGUVwcFjF\nd8Zpmv7aa9z2z38y/PrraRsRwSvPPkvNgADmzp9fZvyM119nUN++jBszhsg2bZgyYQJdzj+fmXPn\nHo+ZM4eH772Xv15xBR3btWPeSy+RkJTE5y53Ze8ePZoLu3ShWZMmXNS1Kw/ceSe/xsY6LxRjN2yg\nqKiIx/7v/whv0YJOHTty/+23s27z5iq/mJy+eDG39enD8EsuoW3jxrwyciQ1/fyYW+ri75gZS5Yw\n6IILGHfllUQ2bsyUa66hS8uWzPz2W2fMTb168Z8hQ+jXocNJl4Wq6edHaO3aNKhThwZ16lDL3/+E\nmDoBASVi/Hw8YzG/6V98wW0DBzK8Xz/aNmvGK3fcQc0aNZi7dGmZ8TMWLmRQ166Mu/pqIps2ZcqN\nN9KldWtmfvmlM+amPn34z9Ch9OvUCWtP7LkOLVrw0YMPcmX37oQ3bMhlF1zAE//8J4t++42ioiIA\n4vbtIz0riynDhhHRpAntmjXjkRtuIOnQIeJTUiqmM07T9Lg4bmvThuGtWtG2Th1e6d6dmt7ezN21\nq8z4Gdu2MahxY8a1a0dk7dpMueACutSrx0zHyJbavr4s6duXa5o3J6J2bS4MCWFmt27EpqezzzEi\n4WBeHjsyM3mgQwc61K1L66Agnu7UieyCAjYdOuQ81wtdu3J7RAThtWpVfEecoekpKdxWvz7Dg4Np\n6+/PK02bUtPLi7nlJAhmpKQwqHZtxjVoQKS/P1MaNaJLQAAzXX7/Q+vVo29QEC1r1KCdvz/TmjQh\no6iIDaVGENTy8iLUx4cGvr408PUlwOv4Ze+76enkW8sbzZvTzt+f6+vV466QEKZ5wKiL6Wlp3Fa3\nLsPr1KFtjRq8EhZW3GflJBZnpKUxqFYtxgUHE1mjBlNCQ+lSowYz09OdMUNr16ZvYCAt/fxoV6MG\n0xo0KO4zR0Ko0FruSU7m+QYNGF23Lq39/GhbowbXlpG4qHOsXx1ffmUkKiuKEhcinsX1U3TZt4zO\nzizgENDEGNOqjP17gQ+stW+58ZzHVEibrLUHgbfdVV45qnPdRarE/v27OXgwkQsv7OfcVqtWbTp2\n7MGGDTFlHpOfn09cXGyJY4wx9OjRn40bi4/ZsmU1hYUFJWJatoykYcPmznJbtoykTp36fP75G+Tn\n55Obm8Nnn82hVav2NG7csgJa6x75+fnEbthAv0sucW4zxtD/kkuIiY0t85iY1avp37t3iW0DLrvM\nGb8rPp7E5OQSZdYOCqJH587llpmWns57n3zCxd274+3tDUDXCy7Ay8uLN+fPp6ioiMMZGbzz8cdc\n3ru3M6Yq5BcUELtnD/06dHBuM8bQv2NHYn7/vcxjYn7/nf4dO5bYNuD884nZcerHmEp7b8UKQm+/\nnfMfeICHFiwg5+iJo4nuePttQm+/nR6PPMKb5SRTKlt+QQGxO3bQr1Mn5zZjDP07dSImLq7MY2Li\n4ujvEg8woEuXcuNP16EjR6hdsyZejgvKyCZNqB8UxBtLl5JfUEBOXh5zvv2W9s2a0bJBg3M617nI\nLyoiNi2Nfg2PD0Y1xtC/YUNiUlPLPCYmNZX+DUsOXh3QqFG58QCH8vMxQF1fXwDq16hB29q1mbd7\nN9kFBRQUFfHKjh2E+fvTNTj43BtWwfKtJTY7m34ud+SNMfSvVYuYch4XiTlyhP6l7uAPCAoqNz7f\nWl49eJC63t5EBQSU2Pd0UhIhGzfSZds2piYnU+iSUPv1yBF616qFj8tF94CgILbl5XG4ChOy+dYS\nm5tLv5o1nduMMfSvWZOYch4XicnJob9LPMCAwMBy4/Ot5dVDh6jr5UWUI+G6JjeXBMeIji67d9N4\nxw6u/OOPEqM2jrkjKYnQ33+nx549vOmSQKsMnpH6FZFjfF2+d1sK01qbZ4xZANwK3AxMKhUyFHjV\nXecrpULaBM52ubPI0qpz3UWqRGpqIsYY6tcvOZohODiMgwfLHsx16FAqhYWFJ4yACA4OY8+e4kcf\n0tKS8PX1o1atkneA6tc/Xm7NmrV47bXvGTduCK+//igAzZufx6xZS5wXR54oNS2NwsJCwlyGMgOE\nhYayrZw5GxJTUggLCTkhPtFxxzApJQVjTJllJpa6q/jA448zc+5csnNyiO7WjS/fOT4PccvmzVky\nfz7X33ort44fT2FhIdHdurH4/ffPur3ukJqZSWFREWF16pTYHlanDtsOlL34U+Lhw2XGJ57hh+8b\ne/akRUgIjevVY8PevUz44AO2Jyby8d13O2Meu/Za+rZvT00/P77duJF/v/UWR/LyGHvFFWd0LndL\nzcgo7re6dUtsD6tbl237yh5YmJiefmJ8vXokutzRPeN6HD7M4wsWcNvAgc5ttQIC+P7JJxny+OM8\n6hhpdF6TJix59NEqff2m5uVRaC1hpUbVhPn7s62c5/sTc3NPjA8IILGci8m8wkIeWLeOYS1aUMv3\n+EePpX37MmT5coI++ggvxzm/6dOHOn5+59aoSpBaUEAhEFZqpFGYry/bSs3BcExiQUGZ8Yn5+SW2\nfXX4MEPj48kuKqKxry9LW7cm2OW4u0NC6FKzJsHe3qw4coQHDhwgMT+fqU2aOM/TqlQfhjn6PTE/\nnzpVlJRNLSwsu898fNhWTvImsbCwzPjEUo+WfJWVxdCEhOI+8/FhafPmBDvauSs/HwtMOXiQ6Q0a\n0MLXl6lpaVy6dy+/t2pFXUfcYyEh9A0MpKYxfHvkCP9OSuKItYytV889HXAKnvu/uMifk+unyRPH\nZ52bNym++B7uutEYEwh0stb+5ObzHVORbapo1bnuIpVi8eL36dUriF69grjkktoUFOSXE2nLfPb9\nZKw99THFw9KLY/LycpkyZRSdO1/CvHm/8eabK2jTpiN33XUlR4+eeOfI051O+880vqyYCXfcwbpl\ny1j64Yd4e3vzz7FjnfuSUlIYff/9jBg6lNVLlrD888+p4efnEXNclKUi+qy0W/r04fLzz6dD06bc\n0LMn88aM4bPVq9ntkhCaOHgw0RERRLVowfirrmLCVVfx3FdfndF5KlNl9NsxmdnZ/GXKFDq2aMEj\nLnOD5B49yqgXX+SSDh34bdo0Vjz3HB1btODKyZPJyy/vfaXqWHBLnxUUFXHdzz9jgNndu5fY9+9V\nqwjz9+eXyy9n1cCBDGnalKt+/JGkU0zW6MmOv2OffXzfoCDWR0YSExHBwKAgrtuzp8QcEPc0aEDv\nWrXoGBDArSEhPN+4MS+lppJfxmNMrufhDOtWWc74b62M+L41a7K+ZUtiWrRgYGAg1+3f7+yzIkfM\nf+rXZ0hQEJ39/XmzUSMM8JHLnEETQ0KIDgggyt+f8fXrMyE4mOcqcV4QJS5EPIvr8plNThZojHnM\nGPO7YzLNA6W+drlOwAlgrf0N2AI0M8b0c9l1HfDxmVTSGBNljIk3xiw3xpxqVp4Ka9PZqM51F/FE\nl102mA8+WM8HH6xn/vx11KsXgrWWgwdLzuCelpZc7pwSdeuG4O3tTVpayWPS05OdIzfq129Ifv5R\nsrJK3uFMSzses3jxeyQmxjN58lzatetCx44X8vjj77F//25++OELdzXZ7UKCg/H29iap1HP8yamp\nJ4yYOKZhaChJpYadu8Y3DA3FWntaZQbXq0eb8HD6XXIJ819+ma+XLWPlmjVA8UondYKCeGriRKI6\ndKBXjx68M3Mmy376id8cMVUhJCgIby8vkko9952ckUFYGc9lAzSsU6fs+FKjMM5Uj9atscCOUqsW\nlI7Zl5bG0XIm2KssIbVrF/dbqVEmyYcPnzCq4piG9eqdGH/oULnxJ5OVk8OASZOoGxjIpw89VOJx\no/d++IH45GTm3nMPXdq04cLISN67/352JyXxRRWuyBJSowbexpBUakLM5DJGVRzT0N//tOKPJS3+\nyM7m2759S4y2WJaYyNcJCSzo1YuLQkLoVK8eM7t3J8Dbm7c9ZKWVkwnx8cEbSCr1N5+cn+8c3VBa\nQx+f04oP8PKiVY0aXBgYyOvNm+NjDG+c5AK6R2AgBdayx/FIV3nnAcqtW2UI8fYuu88KCggrZxRI\nQ2/v04oP8PKilZ8fFwYE8HqjRvgAbzjeDxs5Ytu5jELxM4ZWvr7sPUnSsEdAAPsKCjh6koSQOylx\nIeJZYim+y2+ATicLtNY+bK2NANoBz1M8H0MD4HtrbSvXCThdvO0o+2aXbf/kzOdbGEHxahsXA31O\nEVvRbTpT1bnuIh4nICCQpk1bOb9atWpP/foN+e2340tzZmVlsGnTSqKiepZZhq+vL23bdi1xjLWW\n335bxgUXFB/Trl1XvL19SsTEx28nMXGvs9zc3ByMKfnRxhiDMQZri/BUvr6+dL3gApb9dHzgm7WW\nZT//TM9yVqKI7tatRDzA0uXLie7aFYDwFi1o2KBBiZiMzExWrl1bbpmAc8LNPMezzdk5OSfMZXFs\n2H5RJX1YLYuvjw9dW7Zk2ebjCz5Za1m2eTM9IyLKPCY6IqJEPMDSTZuIbtOmzPjTvb+5Nj4eAzQ6\nyYX82vh46gUGVvkEnb4+PnRt04Zl69Y5t1lrWbZ+PT3btSvzmOi2bVlWatnYpevWEd22bZnx5d0Z\nzszO5oqHHybAz4+FkybhV+oCMScvD69Sxx57/R6bwLMq+Hp50TU4mGWJxx91s9ayLCmJnqUe1zom\nOiSkRDzA0sREol3ijyUtdmVlsaxvX+qVenQhx/FaLN2bXsZU6WvvdPkaQ9eaNVnmcsfeWsuyrCx6\nBgaWeUx0YGCJeIClWVlEl5rDobQia8k7SZ+szc7GC2jgeP1FBwayPCurxLwX32ZmElmjRpU9JgKO\nPvP3Z5nLYyHWWpZlZ9Oz1Bwex0QHBJSIB1ianU10OfHHFIGzz7r6+1PDGLa5zNWTby178vNpcZJE\nztrcXOp5e1faBJ1KXIh4EFs85vnYYvAtjTFlf/oqecxh4COXTSebLesdoBC42hgTZIxpCRRaa/84\nw6p+ABwA1gLLT1G/im7TmarOdRepFoYNu4c33nicH39cxO+/b2TSpOE0aNCUyy4b7Iy57bZ+fPjh\nbOfPN900jk8/fY0vv5zH7t1xPPHEGHJzs/nb324Giif4HDJkFNOmjWP16h/YsiWWyZNHEBV1MR06\nFA+vvuiiy8nMTOepp+5g9+44du7czOTJI/Dx8aVbt+N5yl27trJt2zoyMtLIyjrM9u3r2b695IVZ\nZRt322289u67zPvwQ+J+/50xEyaQnZPDzf/4BwDDx47loSefdMbfPXo0i7/7jmmvvMK2HTuY/Nxz\nxG7YwNiRI50x94wezeMvvMCib79l49atDL/zTpo2asRgx7wCq9auZdbcuazfvJm9+/bx3c8/M+zf\n/yaiVSuiHcmNv/Tvz6p163hs2jR27N7Nmg0bGHHPPYQ3b07nUhNdVrZxgwbx2vffM++nn4hLSGDM\n3LlkHz3KzY5JS4e/8goPLVjgjL97wAAWr1/PtK+/ZltCApM/+YTYPXtKzDuRfuQI6+Pj2bxvHxaI\nS0hgfXy8c6TGruRkHv/8c9bs3k18aioLY2P51yuvcGm7dnRs1gyAL9euZe6PP7Jl/352JiXx8n//\ny1MLF3LXgAGV1zknMW7IEF5bsoR5y5YR98cfjJk1i+y8PG7u3x+A4c8/z0NvH7+fcfff/sbi2Fim\nffYZ2/btY/J77xG7Ywdjr7rKGZOelcX6XbvYHB+PtZa4fftYv2sXSY55MLJycrj84YfJzstjzl13\ncSgri6T0dJLS051Jics7dyb9yBHuePll4v74g83x8Yx44QV8vb3pc8EFldhDJxrXti2v7dzJvF27\niDt8mDGrVpFdUMDNrYrnOx++YgUPuSSD7o6MZPGBA0zbupVtGRlM3rCB2LQ0xp53HgCFRUVc89NP\nrElL492ePckvKiIpJ4eknBzyHf0RHRJCPT8//hUTw4b0dH7PyGD82rXsycriL02ODwLdmZnJuvR0\nDuTkkFNYyPr0dNanp1NQhcmeY8aFhvLawYPMS0sjLjeXMfv2kV1UxM2OyUWHx8fzUEKCM/7u0FAW\nZ2YyLTmZbbm5TD5wgNjsbMY6RollFxUx8cABVh45wt6jR1mTnc3IvXtJyM/nOkfi8NcjR5iRksKG\nnBx25+XxXloa4xIS+GdwsDMpMaxePfyMYeTevWzJzWVBejovpqZyXxVOAnvMuOBgXjt0iHmHDxOX\nl8eYpKTiPnOMDBuekMBDLiPp7g4OZnFWFtPS0tiWl8fklBRic3Od805kFxUxMSWFlTk57M3PZ01u\nLiMPHCChoIDrHBOhBnl7M6ZuXR5JTWXpkSNsP3qU2xMTMeCM+TIri7mHDrElL4+dR4/ycno6Tx08\nyF2VNL8FaHJOEU/0PMWjAkId/z7kroKttYnGmG+AKymekLMxZ7G6hbX2V07x6EQpFdamM1Wd6y5S\nXdx88wRyc7N54onbyMo6RKdOl/DSS4vx9T1+RzEhYTeHDh1/1OGKK67n0KFUXn55EmlpSZx3Xidm\nzVpCvXrHH2u4777peHl5M2HCtRw9mkfPngN54IFZzv0tW0YyffoiXnttCiNG9MTLy4vIyM7MmrWk\nxGShd911JYmJe50/33BDZ4wxrF5ddbPJXz94MKlpaUx69lmSUlPp1KEDS+bPJ9Rxh3bfgQP4uNyt\nj+7Wjfkvv8zEp59m4tNPExEezhdvvUX7yEhnzISxY8nOyeG28eM5lJHBJT16sPj99/Fz3NkN8Pfn\n06+/ZvLUqRzJyaFRgwYM6tuXiffcg6/jLlufXr14f/Zsnp01i+dmz6ZmQIBzcs4aNWpUYg+d6PqL\nLiI1M5NJn3xC0uHDdGrRgiUTJhDqeFRkX1oaPi6TOkZHRDD/jjuY+NFHTPzoIyIaNuSLe++lvctF\n4MLYWEa8/jqG4jvdN8wq/vt65O9/Z9LVV+Pn7c1/N21ixpIlHMnLo1lwMNf16MHEwceTcr7e3sz8\n9lvuffddrLW0CQvjhZtu4pY+pxrkVzmuv+QSUjMymPTeeyQdOkSn8HCWPPoooY4Lo30HD+Ljctc5\nul075o8fz8R33mHiO+8Q0bgxX/znP7Rv3twZs3DlSka88IJzhMQNzz0HwCM33MCkG24gdscOVjlW\ne2lz663A8Tkfds+ZQ/MGDYhs2pRFDz/MlPnz6Tl+PF5eXnRu1Yoljz5KWCVeHJXl+hYtSM3LY9LG\njSTl5tKpbl2W9OlDqOPRj305OSX/1kJDmd+zJxM3bGDihg1EBAXxRe/etD/Wxzk5fOm4YO/kWJ74\nWH98368fvRs0oH6NGnzTpw8T16+n33ffkV9URIc6dVh46aWc7zK655aVK1nuciHb5ZtvANj9t7/R\nvJyRDZXl+nr1SC0sZNKBAyQVFNApIIAlrVsT6ngv25efX2Jlj+jAQOa3aMHEAweYeOAAETVq8EV4\nOO0d/ewNxOXmMi8tjdSCAur7+NC9Zk1+joignSOmhjF8kJ7OlMRE8qwl3M+P+0JDudclKVHb25sl\nrVszdt8+um3bRoiPD5MbNmRU/fqV1znluL527eI+S0khqbCQTjVqsKRZs+N9VlBQss8CApjfuDET\nU1OZmJJChJ8fXzRpQnvH+7M3EJeXx7zDh0ktLKS+tzfd/f35uUUL2rm8h09t0ABfYxiekECOtfQI\nCOC75s2dyR5fYGZ6OvcmJ2OBNr6+vBAWxi1n8cjY2TJlrbUsIlXLGHMVsBDIATpYa/ecIr4NsJ3i\n+XimWGsfPUnsNRSPCPgV8Ad6Wmtzy4t3l4pqkzGmiOLBERU2tq+q626M6QLExsbG0qVLl7Nogcjp\nWbu2qmtQPXVuXP7cBlKO//63qmtQPVXiRcL/jCpeAafa2rKlqmtQ/eRW+Mfp/zlrcnPpumcPQFdr\n7UknTtKjIiIeyFr7JfAoxYmFL40xZT9EeVz3U+x3tRA4CPQAYisjaQEV3qYKVZ3rLiIiIiJS3Slx\nIeKhrLWTgVuANkCMMebSsuKMMe2AR4Cpp1luPnDs9sPcc6/p6XN3m4wx4S7fN3NfTU9UnesuIiIi\nIlKdaY4LEQ9mrX3TGLMaeBxY6vh+EbAHqAP0AiKAEdbaGGNMCqc3Efp7wEBrbUzF1Lx87miTMaYR\n8B3QguNLb8cZY3ZbaytstrjqXHcRERERkepKiQsRD2et3QgMNsaEUnxhHA40B5KAx621cS6xz51m\nse2At9xc1dN2rm2y1h6guA2VrjrXXURERESkOlLiQqSasNamAJ+5qbh/Af90U1lnzc1tqlTVue4i\nIiIiItWJ5rgQ+R9mjGlvjNlgjJnosq0tkGmt3V+FVRMRERERETktSlyI/G8bBHQERrpsm0TxHA0i\nIiIiIiIeT4kLkf9tPwMFwJMAxpj7gGxr7aoqrZWIiIiIiMhpUuJC5H+YtXYlcDsw1hizCQgFbq3a\nWomIiIiIiJw+Tc4p8j/OWvsG8EZV10NERERERORsaMSFiIiIiIiIiHgsJS5ERERERERExGMpcSEi\nIiIiIiIiHkuJCxERERERERHxWEpciIiIiIiIiIjHUuJCRERERERERDyWEhciIiIiIiIi4rGUuBAR\nERERERERj6XEhYiIiIiIiIh4LCUuRERERERERMRjKXEhIiIiIiIiIh5LiQsRERERERER8VhKXIiI\niIiIiIiIx1LiQkREREREREQ8lhIXIiIiIiIiIuKxlLgQEREREREREY+lxIWIiIiIiIiIeCwlLkRE\nRERERETEYylxISIiIiIiIiIeS4kLEREREREREfFYSlyIiIiIiIiIiMdS4kJEREREREREPJYSFyIi\nIiIiIiLisXyqugIiIiLiGZo2reoaVE8phFV1Faqd0N69q7oK1dOuXVVdg+pn796qrkH1VLduVdeg\n+rnooqquQfWTmAhvvXVaoRpxISIiIiIiIiIeS4kLEREREREREfFYSlyIiIiIiIiIiMdS4kJERERE\nREREPJYSFyIiIiIiIiLisZS4EBERERERERGPpcSFiIiIiIiIiHgsJS5ERERERERExGMpcSEiIiIi\nIiIiHkuJCxERERERERHxWEpciIiIiIiIiIjHUuJCRERERERERDyWEhciIiIiIiIi4rGUuBARERER\nERERj6XEhYiIiIiIiIh4LCUuRERERERERMRjKXEhIiIiIiIiIh5LiQsRERERERER8VhKXIiIiIiI\niIiIx1LiQkREREREREQ8lhIXIiIiIiIiIuKxlLgQEREREREREY+lxIWIiIiIiIiIeCwlLkRERERE\nRETEYylxISIiIiIiIiIeS4kLEREREREREfFYPlVdARE5PcaYIOAyoDlQE9gLfGutTXeJaQ40tdau\nqJJKnqHq3KbqXHcRERERkepEIy5EPJwxppUxZgGQCNwJ1ADigXrAm8aYF40xdYwxfsAnQP+qq+3p\ncVebjDGjjTHpxphXVHcRERERkf9NGnEh4sGMMWOB54CtwEXW2o2lQl4xxgwEvgF2Al2BRZVbyzPj\n5jbdCdQBRhtjJlhrMyqo2kD1rruIiIiISHWlxIWIhzLGTAEeBn4D+llrj5QVZ639xhhTC/gQsJVY\nxTNWAW2aCTwLfFoJSYtqW3cRERERkepMj4qIeCBjzDUUXyRnAVeXd5F8jLX2Y5DcXGoAACAASURB\nVODjyqjb2aqINllrX7PW1rXWjnRfTU9UnesuIiIiIlLdKXEh4mGMMbWB1ym+W/+stfbAaR76HGAq\nrGLnoDq3qTrXXURERETkf4ESFyKe5x6gruP7d073IGvtKmBDhdTo3FXnNlXnuouIiIiIVHtKXIh4\nnlGOf7dba+PP8Nif3V0ZN3F7m4wxocaYdsaY3o5HOSpKda67SJV6+ulJnH9+Y1q0qMm1117Orl07\nTnnMG2/Molu3cJo3D2DQoItYu3ZVif15eXn83//dQdu2IYSHBzFy5LWkpCQ796enpzF06CAuuKAJ\nzZr507lzcx588E6ysjJLlHP06FGefHIiXbu2pFkzf7p1a8UHH7zllnafC0/ts7vuGkFYmBcNG3oT\nFubl/Lr00vPd1/izMOvttwm/+GICIiK4aPBgVq1ff9L4j778knZ9+xIQEUHUgAEs/v77E2ImPf88\njbt1o+Z553H5sGHs2LOnxP6WPXvi1aKF88u7ZUueffnlEjEbtm6l97XXEhARQYvoaJ57xbMWkJr1\n2WeEDx1KwBVXcNHtt7MqLu6k8R/98APthg8n4IoriBo1isUrV5bY/9lPPzFw/HhCBw/Gq08fNuzc\neUIZeUePcscLLxDyt78RNGgQ106aRHK6cwVx3v7mG7z69MG7b1+8+vRxfnn37UvqoUPuafg5mJWQ\nQPiqVQSsWMFF69ezKjPzpPEfpabSLjaWgBUriFqzhsVpaSX2T9m7l3axsdRasYLgX3/l8k2b+M2l\nzPjcXG75/XdarVpFzRUriFi9msnx8eQXFZUo58OUFDqvXUvgihWEr1rF1H373NdoN5i1fz/hv/5K\nwPLlXBQby6qMk0/r9VFyMu1++42A5cuJWrWKxQcPltg/Zc8e2v32G7WWLyf455+5fP16fnMpMz43\nl1vi4mj166/UXL6ciJUrmbx7d4l++/HQIYZs3EjjFSuotXw5XVav5v2kJPc2/BzMio0l/OWXCZg6\nlYvmzWPVgZMP9v0oLo52r79OwNSpRL3xBotdXn8FRUX83/ffc8Ebb1Dr+edpMnMm//rySw5kZZUo\nY/DHH9Ni9mwCpk6l8cyZDF+06ISYDcnJ9H73XQKmTqXF7Nk8V+p9oKIpcSHiQYwxHYBmFD+WsPks\ningfWOJS3hhjzB5jTJHLV64xpptjfzdjzFGXfUeNMSd+ijsH7m6TiyRHeT9QPBGm21XnuotUtRdf\nfIa5c2cydeqrfPPNb9SsGcg//jGAo0ePlnvM558vYPLk+xg/fgrLlq2lffso/vGPARw8mOqM+c9/\n7uG///2KuXM/YeHC5SQmJjBy5PH8n5eXF4MGDeHddxfx66+/89JLb7N8+X8ZP/72Eue65Zbr+Pnn\n75kx401iYrbz6qvzad060v0dcQY8uc+efPJFNm1KZOPGA2zalMj69fuoVy+Yv/3t+orpjNOwYOFC\n7nv8cabcey9rFy8mql07Btx0E6mlLhCPiYmNZdhddzH6hhtY9803DLniCoaMHs2W7dudMc/Mns3M\nt97i1aee4rdFiwisWZMBN91U4ndgjOHx8eNJWrOGxNhYDqxezZ0jRjj3Z2ZlMeCmmwhv1ow1X3/N\ncw89xOTp05kzf37FdcYZWPDdd9w3ezZTRoxg7euvE9WmDQPGjy83ORCzeTPDHnuM0Vddxbo5cxjS\nqxdD/vMftrgkdI7k5NDr/PN55rbbMKbspyTvmTmTr2Ji+OTRR1n+4oskHDzINZMmOfcP7duXxE8/\n5cAnn5D46ackfvopA7p357JOnQipW7fMMivLgpQU7tu9mynNm7O2UyeiAgMZsHkzqfn5ZcbHZGQw\nbNs2RjdsyLpOnRhSvz5Dtm5lS3a2MyYyIIBZrVuzqUsXfrngAlrWqMEVmzZx0FFmXE4OFng9IoIt\nXbowvVUrXklMZGL88Xsoi9PSuGn7dv7dqBGbu3RhduvWTE9IYPYpLnQry4LkZO7bsYMpLVuytmtX\nomrVYsCGDaSW854Wc/gww7ZuZXSjRqzr1o0hISEM2bSJLUeOTy0WGRDArIgINnXvzi+dO9PS358r\n1q8/3m/Z2cX9FhnJlu7dmd66Na8kJDBx925nGSsOHyaqVi0+7dCBjd27M6JhQ4Zv3cpXpZIkVWHB\n1q3c9913TOnVi7UjRhDVoAEDFiwg1eVvx1XM/v0MW7iQ0VFRrBsxgiHnnceQTz9lS2rx/wHZ+fms\nS07mkYsvZu2IEXz297+zLS2NwZ98UqKcvi1a8NGQIWy/9VY+vfpqdh46xHWff+7cn5mXx4AFCwiv\nW5c1N9/Mc336MPnnn5lzimSxOxlrPXoRApE/FWPMTcA8ii+UX7LW3uOmcntQfAEdBNxlrZ3lsu8Z\n4D6KV7h4zlqbXnYpZ33uimpTfeAa4BXAWmu93VFuqXN4RN2NMV2A2NjYWLp06eKOKoiUKSXFfWWd\nf35j7rhjPGPG3AtAZmYGHTqE8dJLbzN4cNkXu4MGXUSXLj144okZAFhr6dSpGbfcchd33jmBzMwM\n2rUL5dVXP+Avf7kagB07tnHxxe1YvPhXunS5sMxy58x5idmzp7JmTfEH/u+++4YxY4axatUu6tSp\n2gsiV57cZ6V9/fXnjBp1LatX76ZJk2Zn3NbQ3D/O+JjSLho8mB6dOjFjyhSguO3NevTgrhEjmHD7\n7SfED73jDrJzclg4d65zW/SQIXTu0IHZTzwBQONu3Rg/Zgz33nILABmZmYR16cLb06Zx/V//CkD4\nxRdz76hR3DWy7LmVX37nHR6eOpXE2Fh8fIoX8Hvw6af54ttv2fLdd+fW6F27zu144KLbb6dH+/bM\nuPNOwNFv113HXddcw4QbbjghfuiUKWTn5bHwySed26L//W86R0Qw+957S8TGJyYSfsMNrJszhwta\nt3ZuzzhyhNAhQ/hg0iSuvuQSALbt3Uu7f/2LX2fP5sJ27U44b+qhQzS57jre/L//Y1j//mff4P/8\n5+yPdbho/Xp61KrFDEebrLU0W7WKuxo3ZkLTpifED42LI7uoiIXt2zu3Ra9fT+fAQGa3aVPmOTIL\nCqjz668s69iRPuUkaqbu28criYns6NYNgBu3baPAWha0beuMmZmQwHP79xPfvftZtxcAn3NffPKi\n2Fh61K7NjIgIwNFvMTHc1bQpE5o3PyF+6ObNxf12/vGRXNFr1tC5Vi1mn3demefILCigzs8/sywq\nij716pUZM3XvXl5JSGDHRReVW9erNmygoZ8fc1z68oydpPzTLmLePHo0asSMyy8HHH02ezZ3de3K\nhDLKH/rFF2Tn57Pw2mud26LnzaNzWBizBwwo8xyrDxygx7x5xN9+O01r1y4zZtHvv3P1p5+SN348\n3l5evLxmDQ//9BOJd96Jj1fx2IcHf/iBL37/nS2jR591e9ckJtL1rbcAulpr15wsViMuRDxLmMv3\nJx+DeAastSuBuymeLPJ2Y4yvy+4I4AFr7UPuTlo4VFSbDgJvu6u8clTnuotUmfj43SQnJ3LJJf2c\n24KCatOlSw9Wr44p85j8/HzWr48tcYwxht69+zuPWbduNQUFBfTufTymTZtImjZtXm65iYkJfPXV\np/TseZlz25Ili4iK6sZLLz1DVFRToqMjmTx5PLm5uefS7HPi6X1W2vz5c+ndu/9ZJS3cIT8/n9iN\nG+l38cXObcYY+vfqRcyasj/7xqxZQ/9evUpsG9C7tzN+V3w8iSkpJcqsHRREj86dTyjz6dmzCYmK\nosuVVzL11VcpLCx07vt1zRp69+jhTFoADLj0Urbt2sXhUwyTr2j5BQXEbt9OP5ckuDGG/l27ErO5\n7IGFMVu20L9r1xLbBnTvXm58WWK3b6egsLDEeSObN6d5gwbllvP2kiUE+vtzTe/ep32eipBfVERs\nVhb9XJIJxhj6161LTDm/z5jMTPqXSj4MqFuXmHIeL8kvKuLVxETq+vgQFRhYbl0OFRYS7PJ3lVdU\nhL9Xycs5fy8v9uXlsbcK38/Apd9ckgnGGPrXq1d+v2Vk0L9U8mHASeLzi4p4NSGhuN9q1Sq3LocK\nCgj29S13P8DhwsJTxlS0/MJCYhMT6deypXObMYb+LVoQs39/mcfE7N9Pf5d4gAHh4eXGAxzKy8MA\ndf39y9yflpPDe1u2cHHTpng7/r5+TUigd7NmzqTFsfNsS0vjcCX9rSlxIeJZXN8x3boihbX2bWAR\n0A6YDGCMuRoItNZOdee5SqnINuW5s7wyVOe6i1SZ5OREjDGEhoaV2B4aGkZycmKZx6SlpVJYWHjS\nY1JSkvDz8yMoqHa5MceMGTOMli0DiYpqSlBQHaZNe925Lz5+FytX/sS2bZt5++3PeeKJGXz55cc8\n+ODYs27zufL0PnOVlJTIsmWLuemms7/Ldq5S09IoLCwkLDS0xPawkBASyxk6lJiSQlhISMn40FAS\nk4vn+0hKTcUYc2JMqTLvHjGCD2bN4ocPP2TMjTfy5MyZ/N9TT538PI6fy6tbZUk9fJjCoiLCSl0c\nhgUHk1jOIzaJaWlnFF9eGX4+PtQudVF+snLeXLyYG/v3p4af32mfpyKkFhRQaC1hpeoR5utLYjmP\niiQePUpYqYvgMD8/Eks9IvFVWhpBMTH4r1jBjIQElnbsWO7F846cHGYmJDCmYUPntgH16vHpwYN8\nd+gQ1lq25+QwzXHBeqCculWW1Pz8svutjH44JvHo0dOK/+rgQYJ++gn/5cuZsX8/S6Oiyu+37Gxm\n7t/PmMaNy63rh8nJrM7MZKRL31aF1Jyc4tdn6ddJYCCJLo/LuEo8cuSM4vMKCnjghx8Y1r49tUr1\n9QM//ECt558nZMYM/sjI4PNrjj9SWN55ju2rDEpciHiWZJfvyx67dW5uBdKA8caYgcATwM0VcB5X\nFd2milSd6y5SaT755H3Cw4MIDw+iVavaFBSU/YHZWlvu8+/lOZ1jyop57LEXWLZsLfPmfUF8/E4e\nfvj4kPaioiK8vLx4+eX36dSpG337DmTKlGl88MFb5OVVTk6xuvWZqw8+eJO6desxaNDgM6pXZTjT\n/jqbvrrnllvo3aMHHSMjufXGG3n+4Yd56a23yD/JheKxR7PP9HdZWSqi386lnJjNm9m6dy+jrrzy\nnM9RUSxndkfDcuLvv2+dOqzv3JmYqCgG1qvHdVu3ljlvxv68PAZt3sw/QkNLXFyPbtiQsY0a8dct\nW/BbsYKe69dzgyOZ5/bnZ93krPqt1La+deuyvls3Yrp0YWBwMNdt3lzmvBn78/IYtHEj/2jQgJGN\nGpVZ/vfp6YyMi2NOZCRtTzLapSqV9bdzNvEFRUVc9/nnGCjzMZIJPXqwbuRIlg4dircx/HPRolOe\nhzOs27lQ4kLEs7gun9nkZIHGmMeMMb8bYxKMMQdKfe0yxvy19DHW2iTgDsCH4tEXT1prz3gGJ2NM\nlDEm3hiz3BgTVJVt+jPVXcRTDRw4mO+/X8/336/nu+/WERwcgrWWlJSSs7SnpiafMDrgmODgELy9\nvU96TIMGDTl69CiZmRnlxhwTGtqA1q3PY8CAv/Lss6/w1lsvk5xcXHZYWCMaNmxCLZehxeed1w5r\nLQkJlTMjf3XrM1fz57/JddcNL/EoRGULCQ7G29ubpFIjGJIPHjxhtMMxDUNDSUpNLRmfmuoctdEw\nNBRr7YkxJykToEenThQUFLDHsZpDmedxTPp3snIqQ0idOnh7eZGUXvLJ0OT09BNGVRzTMDj4jOLL\nK+NoQQEZpe7MllfOnK++olObNnRyzI1QlUJ8fPA2hqRSF8bJ+fknjKo4pqGfH0mlEhDJZYzCCPD2\nppW/PxcGBfF6RAQ+xvBGqdUtEvLy6LtpE71q1+bVMubHeKplS7Kio9nbvTuJPXrQPaj4o03Lch4D\nqCwhvr5l91sZoyqOaejnd1rxAd7etAoI4MLatXk9MrK43xJLjiBLyMuj77p19KpTh1cjy554+cdD\nhxi8aRMzIiK4Mazs99nKFBIQUPz6LP06OXKEsJo1yzymYWDgacUXFBVx3Wef8UdGBt8OHXrCaAuA\n4IAA2tSrR7+WLZk/eDBf79zJyoSEk54HKLdu7qbEhYhniaX4Lr8BOp0s0Fr7sLU2guJHP56neD6G\nBsD31tpW1toy06TW2g+BZRS//tuXFXMaRlC82sbFQJ9TxFZ4m85Qda67iEcKDAykZctWzq/IyPY0\naNCQn35a5ozJzMxgzZqVdO/es8wyfH19iYrqWuIYay0//bTMeUxUVFd8fHxYvvx4zM6d29m3by/d\nukWXW7+iokKMMRw9Wjya4sILLyYxMYFsl1nad+zYhpeXF40bnzjRXkWobn12zC+//MCePTu58cZR\n5RxZOXx9fel6/vks++UX5zZrLct++YWepeZjOCa6S5cS8QBLf/qJaMe8C+HNm9MwNLRETEZmJivX\nri23TIC1mzfj5eVFg/r1nedZvnJliXkvvl2+nMhWrahTzkR4lcXXx4eu553HMpc5O6y1LFuzhp4d\nO5Z5THT79iXiAZbGxhLdoUOZ8WXdfe163nn4eHuXKGf7H3+wNzn5hHKO5OTw0Q8/cMtf/nLa7apI\nvl5edK1Vi2Uuq65Ya1l26BA9y/l9RgcFlYgHWHroENFBJ79fUkTxvBXH7M/Lo8+mTXSvVYu5J0ni\nGGNo5OeHjzG8n5xMdO3ahFTxfA3OfnNJep2y32rXLhEPsDQ9nehTvG6KrD2x39ato3tQEHPLSVr8\nkJ7OVRs38myrVowqZzRGZfP19qZrw4Ysc1mxx1rLsvh4epYxCSxAdJMmJeIBlu7ZQ3ST4/fbjiUt\ndh06xLIbbqDeaSS1Ch39mVdQ4DzP8j/+cG4H+HbPHiKDg6lTSUkyJS5EPIgtHkv6puPHlsaYU95q\nsNYeBj5y2XTSxdiNMZFAY4ovyO8/tjTqGfoAOACsBZafon4V3qYzVJ3rLlJt3HrrPUyf/jhLlixi\ny5aN3HHHcBo1asrAgccfL7jmmn7MnTvb+fOYMeN4553XWLBgHr//Hsf9948hJyeboUNvBoonqxw2\nbBSPPDKOX375gfXrY7nrrhFceOHFztUxli1bzAcfvEVc3Gb++COepUu/YsKE2+nRoxdNmxbPYv/3\nvw8jOLg+d989gu3btxITs5xHH53AsGGjqFGjRuV1Uime3GfHvPfeG3Tp0oPzzjtxFYjKNu6WW3jt\n/feZ9/HHxO3YwZgHHyQ7J4ebr7sOgOH33MNDzzzjjL975EgW//AD015/nW07dzJ52jRiN21i7L/+\n5Yy5Z9QoHn/xRRYtXcrGuDiG33svTRs1YvAVVwDFE2/OeOMNNmzdyu69e3nvs88Y99hj/PPvf3cm\nJYYNGYKfry8j77+fLdu3s2DhQl58803uu/XWSuyd8o27/npeW7SIeUuWEBcfz5hp08jOy+PmgQMB\nGP7kkzz0+vH5Te6+9loWr1zJtA8/ZNvevUx+801it21j7NVXO2PSMzNZv2MHm/fswVpL3N69rN+x\ngyTH/BW1AwMZdeWVjJs1ix/WriV22zZGPPMMF3fseMKKIh989x2FRUUM69cPTzGucWNeS0piXlIS\ncdnZjNm5k+yiIm523KUfvm0bD7lcPN7duDGL09OZtn8/27KzmRwfT2xWFmMd8yxkFxYycc8eVmZm\nsjc3lzVZWYzcvp2Eo0e5zjEq58DRo1y2cSPNa9Tg2ZYtSc7PJ+no0RIjEg7m5/PqgQNsy85mfVYW\nd+/cyScHDzKjVavK65yTGNesGa8dOMC8xETijhxhzPbtZBcWcrPjcZfhW7fykMtKOXc3bcritDSm\n/fFHcb/t3k1sZiZjHRfh2YWFTNy1i5UZGcX9lpnJyLi44n5zjJw6kJfHZevW0dzfn2dbty6z344l\nLe5u0oSrQ0Od+9OreF4QgHHdu/Pa+vXM27iRuIMHGbNkCdn5+dzsWGll+KJFPPTjj874u7t1Y/Gu\nXUz77Te2HTzI5J9+IjYxkbGOZGthURHXfPopa5KSePevfyW/qIikI0dIOnKEfEdyddWBA8yKjWV9\nUhJ7Dx/muz17GLZoERHBwc4EyLD27fHz9mbk11+zJTWVBVu38uLq1dx3YdkrU1WEqhvjJyLleZ7i\nUQGhjn8fclfBxhhv4C3gdopHA3wIvGmM6WKtPe13a2vtr5zi0YlSKqxNZ6o6112kOrnzzgnk5GRz\n//23kZFxiB49LmH+/MX4uQxPjY/fTVra8SH1gwdfz8GDqTz77CRSUpLo0KETCxYsISTk+ASMjz02\nHW9vb0aNupa8vDz69h3I0087V3jG3z+Ad955nUmTxnH0aB6NGzfjqquu4c47/88ZExgYyEcfLeXB\nB+/kiiu6Exxcn8GD/8EDDzxWwb1ycp7cZ1A8AuTrrz/jySdfrMBeOH3X//WvpKanM2naNJJSUujU\noQNL3nmHUMfIh32JiSUeZ4nu2pX5L73ExOeeY+KzzxIRHs4Xc+bQ3mWZxQm33052Tg63PfgghzIy\nuOTCC1k8b57zd1DDz48PFi1iygsvkHf0KOHNmnHf6NHO5VOheCWSJe++y9iHH6bbVVcREhzM5Hvv\nZdTQoZXUMyd3fZ8+pB4+zKS5c0lKT6dTmzYsefZZQh2rYOxLScHH+/gMCdEdOjD/4YeZOGcOE+fM\nIaJpU7544gnau6xksPCXXxjxzDMYYzDGcMNjxa+lR/71LyY5EkPT77gDby8vrn3kEfLy8xl44YXM\nuufEVcbnLl7MNb17U+ckq0RUtutDQ0ktKGDS3r0k5efTKTCQJR06EOoY1bDv6FF8XEaaRNeuzfzI\nSCbGxzMxPp4If3++aN+e9o4h9d7GEJeTw7ytW0ktKKC+jw/da9Xi5wsuoJ0j5tv0dHbl5rIrN5dm\nq1YBx+d7KHRZHeft5GTG79mDpXikx4/nn09XD+m76xs0IDU/n0m7dxf3W61aLLngAkIdr6d9eXkl\n+61OHea3b8/EXbuYuHs3EQEBfHH++bR3zD3hbQxx2dnM27yZ1Pz84n6rXZufO3emnSPm2/R0duXk\nsCsnh2YxxSsnOfvtsssAeDspiZyiIp7au5en9u51nv/SunX5rtNJB9hWuOvbtSM1J4dJP/1EUnY2\nnRo0YMk//kGo4+9iX2ZmiZU9ops0Yf7f/sbE5cuZuHw5EfXq8cU119DekQDbl5nJlzt3AtDpzeJ7\nccfmlvn+hhvo3bw5AT4+fLp9O5N//pkj+fk0qlWLQa1aMbFnT3wd7wW1a9RgyfXXM3bpUrq99RYh\nNWsyuVcvRkVFVVrfmGOTBYmI5zDGXAUsBHKADtbaPaeIbwNsp/i9eYq19tFy4h4B6llr73H8/DFw\nNfCMtbZCL8grsE1FFA+OqLB5qKq67saYLkBsbGwsXVyWkhNxtype8ED+REJz/6jqKlRPLnen5TT9\n5z9VXYPqqQrnsKm2LrqoqmtQ7axJTKTrW28BdLXWlr2WtYMeFRHxQNbaL4FHAX/gS2PMqWbz6n6q\nMo0xl1I8YsD1FtodwCHO/pGR01YRbaos1bnuIiIiIiLVnRIXIh7KWjsZuAVoA8Q4Eg8nMMa0Ax4B\nppaz388YM5riEQOHrbXOmdYcq4y8Q/FjY/ONMeFubUQp7mqTS1y4y/fN3FfTE1XnuouIiIiIVGca\nAyTiway1bxpjVgOPA0sd3y8C9gB1gF5ABDDCWhtjjEnBZblrY8yTwJ3AsXWKOhpj7rHWvuDY/3dg\nLMWPNLQCthlj9gDjHKMMPK5Njno3Ar4DWnB8Gek4Y8xua23Z06L/yesuIiIiIlJdaY4LkWrCGBNK\n8YVxOOALJAG/Wmur7aoV1blNlVl3zXEhlUVzXEhl0RwXZ0lzXJw5zXFxdjTHxZnTHBdn7EzmuNBf\npEg1Ya1NAT6r6nq4U3VuU3Wuu4iIiIhIdaI5LkRERERERETEYylxISIiIiIiIiIeS4kLERERERER\nEfFYSlyIiIiIiIiIiMdS4kJEREREREREPJYSFyIiIiIiIiLisZS4EBERERERERGPpcSFiIiIiIiI\niHgsJS5ERERERERExGMpcSEiIiIiIiIiHkuJCxERERERERHxWEpciIiIiIiIiIjHUuJCRERERERE\nRDyWEhciIiIiIiIi4rGUuBARERERERERj6XEhYiIiIiIiIh4LCUuRERERERERMRjKXEhIiIiIiIi\nIh5LiQsRERERERER8VhKXIiIiIiIiIiIx1LiQkREREREREQ8lhIXIiIiIiIiIuKxlLgQERERERER\nEY+lxIWIiIiIiIiIeCwlLkRERERERETEYylxISIiIiIiIiIeS4kLEREREREREfFYPhVRqDGmOXA9\ncCEQDtQGfE/zcGutbV0R9RIRERERERGR6sWtiQtjjD8wDRjN2Y3mMIB1Z51ERETk9IS+9kRVV6Fa\niuk7saqrUO2EFu2t6ipUT6tXV3UNqp+ioqquQfXUqlVV16D6Wb68qmtQ/WRlnXao2xIXxhgv4Gvg\nUooTECIiIiIiIiIi58SdIy7GAJdxfMTEf4H5wCYgDShw47lERERERERE5E/AnYmLf7p8f5+1drob\nyxYRERERERGRPyF3rirSluLRFpuVtBARERERERERd3Bn4uJYWb+5sUwRERERERER+RNzZ+Ii3vFv\nhSyxKiIiIiIiIiJ/Pu5MXCyneDWRKDeWKSIiIiIiIiJ/Yu5MXLwIFAEXGGN6ubFcEREREREREfmT\nclviwlq7HZhM8aiL940x57mrbBERERERERH5c3LniAustY8DE4FGwBpjzFRjTLQxprY7zyMiIiIi\nIiIifw5un0jTWvuUMSYbmA7c6/jCGHMGRVhN8CkiIiIiIiIi7k1cGGPqAB8AVwD22GZ3nkNERERE\nRERE/jzclrgwxgQA3wGdULJCRERERERERNzAnXNc3AN0dnxfCMwB+gENAF9rrddpfnm7sU4iIiIi\nIiIiUo2581GRf7h8f6O19kM3li0iIiIiIiIif0LuHHFxHsXzWqxS0kJEybyf/AAAIABJREFURERE\nRERE3MGdiYujjn83uLFMEREREREREfkTc2fi4g/Hv/5uLFNERERERERE/sTcmbj4huLVRC50Y5ki\nIiIiIiIi8ifmzsTFK0A2EGGMudqN5YqIiIiIiIjIn5TbEhfW2p3AGIon6HzTGNPHXWWLiIiIiIiI\nyJ+T25ZDNcY0B5YDY4HpwFJjzELgY2ATcJjipMYpWWv3uqteIiIiIiIiIlJ9uS1xAeyhZGLCAIMd\nX2fC4t56icj/s3fnYVVV6wPHv4t5HmUQQRwRR1ScMIeuUORQWZaZlVpdr9lkWfnrZunlVjaZXrt6\n82Zl1nXKtEwb1GjUcCLNIWdzIAZlFGQQOOv3xzngAQ8oeBik9/M8PAf2fvfaa232gbPfvdbaQggh\nhBBCCHGNqosEgcKYfNBmPwshhBBCCCGEEELUmDUTF6e4wqEgQgghhBBCCCGEEFfCaokLrXUra5Ul\nxJ+RUsoduB5oCbhgTAZu1FpnmcW0BIK11j83SCVr6Fpu07VcdyGEEEIIIZoSaz4OVQhRC0qpNkqp\nlUAq8BjgCJwEvDE+oectpZSnUsoBWA3ENFxtr4y12qSUmqiUylJKLbzW6i6EEEIIIYSwDms+VcSj\n7Hut9TlrlStEU6aUehR4AzgA9NNa760UslApdRPwNXAMiATW1W8ta8bKbXoM8AQmKqWm1fXflqb4\n+xBCCCGEEOJaZ805LrIxznGRppQK0VqXWrFsIZocpVQc8AKwHYjWWp+3FKe1/lop5QZ8TCOfR6YO\n2jQfeB1YUw9Jiyb3+xBCCCGEEKIpsOZQkbJExU+StBCiekqpURgvkvOA26q6SC6jtf4E+KQ+6lZb\nddEmrfU7WmsvrfUD1qvppZri70MIIYQQQoimwpqJizTTa1a1UUL8yZmGVS3CeLf+da11yhVu+gaN\n9PHC13KbruW6CyGEEEII8WdgzcTFMdNrkBXLFKIpegLwMn3/0ZVupLXeAeypkxpdvWu5Tddy3YUQ\nQgghhGjyrDnHxRpgIDBIKeWitc63YtlCNCUPml4Pa61P1nDbzdaujJVYvU1KKT+gGeAH+GmtV19F\n/arTFH8fQjSoBVu3MnvzZlLz8ogIDOTfI0bQOzi4yvhV+/YxIz6eE1lZhDVrxqs33sjQsDAASkpL\nmf7NN3x1+DDHs7LwdHQkpm1bXo2Npbm7e3kZs77/ni8OH2Z3SgqOdnZkTp9+yX5OZ2fz0Oef8/3v\nv+Pu6Mi47t159cYbsbFpPA9ZW7RoBuvWvUtubjbdul3HM8+8TXBwu2q3Wb16AcuWzSYjI5X27SN4\n8sl/06lT7/L1Fy4U8dZbU4mPX0lxcRF9+sTy9NP/wcfHvzxm5854Fi2awbFje3Fxceemm+7joYdm\nXXJsli2bzeefLyI19SReXn7cfvvDjBv3d+sehBpYsHo1s1esIDUjg4h27fj3k0/Su2PHKuNXffst\nM957jxMpKYSFhPDqQw8xNCqqQsyMd9/l3XXryM7L47quXXn76adpZ3b+/nLoEM8uXMiOAwews7Pj\n9kGDmPPYY7g6O5fHxO/cyYz33mPvsWO4u7hwX2wssyZNajTn2oItW5j9/fek5uYSERTEv0eOpHfL\nllXGr/r1V2Zs2MCJzEzC/Px4ddgwhpod50/37uW/W7eSmJRERn4+u598km5BFe9lHs/I4Ol169j8\n++8UlZYyNDyct269FX+z93Grl1/mVHZ2+c8KeGXYMKb95S/Wa3wtLUhJYXZyMqnFxUS4uPDv1q3p\nbVb3ylalpzPj9GlOFBUR5uTEq6GhDPX2Ll8fd/o0K9LTOV1UhIONDZGurrzcsiV9zMq89cABdp8/\nz5mSErxtbYnx8uK10FCaOzgAcLKwkNa//FJhvwpI6Nq1QjkNacFvvzF73z5SCwqI8PHh3/360dvP\nr8r4Vb//zoxffuFEXh5hHh682qsXQ0NCACgxGJiemMhXSUkcz83F08GBmKAgXu3Vi+YuLhXK+eL0\naV7cvZs9mZk42dpyffPmrImOLl9v8/77FeKVUiwfPJjRbdpYsfW10xjPNXNHCwro8euv2NvYkNmn\nj3UbXw1r/vX8AEgC3IFXrFiuEE2GUqozEIJxWML+WhSxDNhgVt5DSqkTSimD2VehUqqXaX0vpdQF\ns3UXlFLfWaUxF+tg1TaZSTOV9z3GiTCtri7qbvqdJFX6naQqpW42i3lcKVVitj5ZKRV4VY0RopFY\nuXcvT339NXHR0ex65BEiAgOJXbKE9POWp45JOHWKsR9/zMRevdj9yCOM7NiRkUuX8tuZMwDkFxez\nOyWFmUOGsOvhh/n0nns4lJ7Orf/7X4Vyig0GRnfpwuQqPkQZDAaGffQRJQYDWydNYsmoUXywaxcz\n4uOtewCuwkcfvcYnn8xn2rT/8u6723FycuWJJ2IpLr5Q5TbffLOSf//7Kf761zg++GAX7dpF8OST\nsWRnp5fHzJv3BD///AWzZq1mwYIfSU9P5rnnbi9ff/ToHp5+ejhRUcNYsmQ3//znCjZv/py33362\nwr7mzHmc9evf57HH5rB8+SFee+1zOnWqvw+tla2Mj+epBQuIe+ABdi1eTES7dsROnUq62YWvuYR9\n+xgbF8fEm29m9wcfMHLQIEY+9xy//f57ecxr//sf81ev5r/PPMP2RYtwdXYmdupULhQXA5CSns4N\nTz5JWEgI2xct4uvZs9n/++9MePnl8jL2HD3K8GnTGNavH7sXL2ZFXByfb9nCswvr7cne1Vq5ezdP\nrVtHXGwsu6ZOJSIoiNhFi6p+j544wdilS5nYty+7p05lZJcujPzgA35LTS2POX/hAgNat+a14cMt\njqHMv3CBG995Bxul+H7yZH5+9FGKSkq4ufLFI/DSTTeRNnMmqTNmkDJjBo8NGGDF1tfOyvR0njpx\ngriQEHZ160aEqyuxBw6QbjovKkvIzWXskSNMDAhgd0QEI318GHnwIL/lX7yv28HZmQVt2rCve3e2\ndOlCK0dHbvztNzLMyhzi6cmqDh043KMHa8LDOVZYyJ2HDlXYlwK+7dyZ1F69SO3Vi5RevYh0c6uT\n41BTK48f56kdO4jr0YNdt95KhI8PsRs2kF5YaDE+4cwZxn7/PRM7dGD3yJGMDA1lZHw8v2UZZyLI\nLylhd2YmM03lfRodzaGcHG795psK5aw+cYJxP/7Ig2Fh7L3tNn4eMYKxFhISSwYNIu3uu0m9+25S\nxoxhZGio9Q9CDTXmcw2gRGvGHjnCYE9P6zf+MqyWuNBa5wBjgHPAo0qphUqp+m+REI1bD7Pvk2q6\nsdb6Z631NrOfF2qtWwFRGN97GnhKa73TtH4nMBcwAK8CAVpra9+2sGqbzPgBD9W6VlfG6nU3/U6C\ngafLytVaB2qt15nFvAU8jPH38oDWOkhrnYoQTcDcLVuY1Ls343r0INzPj4W33oqLvT3vV7orWGZe\nQgJDw8KYet11dPDzIy46mp5BQczfuhUADycnNkyYwKjOnWnfrBl9goOZf/PNJCYnk5STU17OzCFD\nmNK/P10DAizuZ8PRoxw8e5ald95J18BAYtu358XoaBZs20ZJaeOYU3zVqnncf/8LDBhwM23bdmHG\njA9JT0/mxx8/q3KblSvncuutkxg6dBytWoUzbdpCnJxcWL/eeEF4/vw51q9/nylT5tKjx2A6dOjB\n9OmL2bv3Z377bTtgTH60axfBhAnTadGiDd27D+SRR17nk0/mU1BgvJg9ceIAn322kNdf/5zrrhtO\n8+ahdOjQg169oqusW12bu3Ilk265hXFDhxIeGsrCZ57BxcmJ97/4wmL8vFWrGNqvH1PHjKFDy5bE\nPfggPcPCmL96dYWYFyZM4OYBA+jSpg0fPv88yenpfPbjjwCs//lnHOztmT91Ku1DQogMD2fhM8+w\n+ocfOP7HH4AxoRLRti3Tx4+nTYsWDIyI4PXJk1mwZg3nCwrq/sBcxtwff2RSv36M69WLcH9/Fo4a\nhYuDA+9v324xft7mzQwND2fq4MF08PcnLjaWnsHBzN+ypTzm3shIno+JIbpdO4uP29py4gQns7JY\nMmYMnQID6RwYyJIxY9iZlMS3R45UiHVzcMDPzQ1/d3f83d1xtre3ZvNrZW5yMpMCAxnn70+4iwsL\n27TBxcaG900J1srmJScz1MuLqUFBdHB2Jq5lS3q6ujI/5eI0WmOaNWOIpyetnJzo6OLCnNatOVda\nyh6zC84pQUH0cXcnxNGRfu7uPNuiBVtzcynVF4+yBnzs7PB3cCj/slWNYwquufv3M6lDB8a1b0+4\nlxcL+/fHxc6O9w8fthg/b/9+hgYHM7VLFzp4ehLXsyc9fX2Zf+AAAB4ODmyIjWVUq1a09/Skj58f\n86OiSMzIIMmUeCs1GHhi2zbe7NOHiR060NbDg3AvL+5o3fqS/Xk6OODn7Iy/6cvB1rbuDsYVaszn\nGsD0kyfp6OzMnb6+dXMAqmG1xIVSahzQDuPjC0uBiUCyUupTpdTzSqlJSqlxV/JlrToJ0QiZf6LO\ntVahpovnKRgT75OVUub/5dsDz2qtn9Na18XkuXXVpgxgibXKq0Kd1N1kAcbHRLdQSlnqd3gKWKG1\n/sDK+xWiwRSXlpKYnEy02Z0tpRQxbduScOqUxW0STp0ipm3bCsti27Uj4fTpKveTXVCAUgovJ6cr\nrtvW06fpGhBAM1fXi/tp356coiL2V/GBsD4lJ/9ORkZqhUSAq6sHnTv3Zd++BIvblJQUc/BgYoVt\nlFL07h1Tvs2BAzspLS2pEBMa2oGAgJbs3WuMKS4uwsGh4rF0cHCiuLiIgwcTAdiyZT0tWrTlp58+\n54472jBqVGteeWUi5841zJzsxSUlJB46RHSvXuXLlFLE9OpFwr59FrdJ2LePGLN4gNg+fUjYb+xw\nd/yPP0jNzCQ6MrJ8vYerK307dSqPKSouxsGu4khrJ1NX6s179pTHODk6XhJTeOECiRbuYNan4tJS\nEpOSiG7fvnyZUoqY9u1JOGl5tGTCiRPEmMUDxIaFVRlvSVFJCUqpCsfO0c4OG6XYbNbjBeDV776j\n2YwZ9Jw7l9nff0+pwXDF+6kLxQYDiefPE212h1kpRYynJwm5lj86JOTmEuPlVWFZrJcXCXl5Ve7j\nv6mpeNnZEVFpyEOZzOJilp49y3UeHpckJm45eJCAHTsYuHcv6zIza9K8OlNsMJCYnk602ZAhpRQx\nQUEkVPE3N+HMGWIqDTGKbdGiyniA7KIiFOBleh/+kpFBsumCvOfatQQtX86wjRvLe22YeyQhAb9l\ny+j7+ecsriKZUp8a+7n2bU4OqzMzWdBAw2msOcfFB1AhyaoAZ+AW09eV0sCH1quWEI2KeULBqulw\nrfUSpdTtwAjgH8B0pdRtgKvWerY191VJXbapSNXtXYO6rvtK4G/ABGBGpZAxwH+tuU8hGlp6fj6l\nWhNQqZtygJsbh9LTLW6TmpdnMT61ig9pRSUlPLtxI2O7dcOt0sVhdVJzcy3up6wOEVdcUt3IyEhF\nKYWPT8UeIz4+AWRkWO6QlZ2djsFQesk23t4BnDxpvEDOzEzDzs4BV1ePS8rNzDSW27dvLB9/PI9N\nm1YQHT2ajIwUFi9+0VQv41275OTjpKSc4LvvPmHmzP9RUlLCvHlP8Pzzd/LWWxW7adeH9OxsSg0G\nAszGcQMEeHtzqIokWWpmJgE+PhXjfXxINV3opWVloZSyHJORAcCQnj15av58Zi9bxpTRo8nLz+fv\nCxeilCLFFBPbty/zVq1ixTffMHrIEFIyMnhxiTEPXxbTUNLPn6/6PXr2rMVtUnNzCag0vj7A3b3K\n96gl/UJDcXVwYNr69cwaNgyDwcCzX36JQWtSzMqZMnAgPVu0wMfFhZ9PnuTZL74gNTeX2TffXE3p\ndSu9pMR4zCr1/Aiwt+dQFT1oUouLL413cCD1QsVhX19kZjLm8GHyDQaCHBzY1KkTPpW2e/bkSean\npJBvMBDl7s56s7lF3GxtmdOqFde5u2OjFJ9kZDDy4EHWhoczotJ5XN/SCwuNx61SgjnA2ZlDZr3l\nzKUWFBBgNldMWXxqFce5qLSUZ3fuZGybNriZjtvx3Fy01sTt2sXcvn0JdXNj9t69DP7qK46MGoWX\n6f/Giz17MiQoCBdbWzYmJ/NwQgLnS0p4tFOnq216rTXmcy2juJj7jxxhWVgYbg3UM8XaMwQpsy9L\ny670S4imyjxl7FFlVO39DcgEnlFK3QS8jPGiuS7VdZvqUl3XfTHGv2kVepIppVyB7lrrn+pgn0I0\nOlrrGv1z1xjvMlVWUlrKnStWoJTiP1a8kGmIDx4bNy4jJsbd9OVBSYnl8ctaa4vHonqX38a83D59\nbuDRR99g9uzJDB7syN13h9O//3C01tjYGD+gGgwGSkouMGPGR3Tt2p8ePQbx97+/R2Lit5w+faS6\nXdWrqs6dKuOv4Nw0P1adWrdmyfTpzFm5EpfoaIJGjqRNUBD+3t7YmibevKF3b9545BEmz56N41/+\nQvjYsQyPikJrXR7T2Ghq9j6o6XnZzNWVVffdx/oDB3B77jm8Z8zgXGEhPVq0qHBH94lBgxjUti1d\nmjfnb/368ebNN/PvzZspbiTDucxZ41wb4unJr927k9C1Kzd5eXHnoUOXzGUwLSiI3RERbOrUCVul\nuM9saI2vvT1PBAXR292dSDc3XgkN5V4/P95ITr6KltWtGh83LJ+bJQYDd377rfH/Qf/+5csNpqEN\nz3fvzsjQUHr4+rJ44EAUsOrEifK46d27E+XvT4SvL8907cq0rl15Y+/eWrWprjWGc23isWPc4+fH\ndR4e5XWqb9bscVHXXbqFaArMH5/ZorpApdSLGO/Ku3Lp3+wCYIr5vAkAWus0pdQjwApgHXC/1jqF\nGlJKRQCfAyeB4Vrr6m6r1Gmbaqox1V1rvV0p9RvQUSkVrbUumwXwTuCTyzZGiGtMMxcXbJUirVIX\n1TPnz19yh7dMoJvbpfEWemGUJS1O5+Tw7QMP1Ki3BUCguzs7THMQlCnbb1V1q0sDB95K5879yn++\ncKEQrTWZmWkVelBkZZ0hLKyHpSLw8mqGjY0tmZlpFZZnZZ0pL8PXN5CSkgucP3+uQq+LrKwzeHtf\n3M9ddz3BXXc9QUZGKu7u3iQn/87bbz9LUFBrUznNsbW1Izj44rCeVq2Md+PS0k4RElJxKEFda+bl\nha2NDWmVun+fycq6pBdGmUAfH9IqdaM/k5VV3sMi0McHrTVplXpmnMnKoofpKTcAY2JiGBMTw9ms\nrPIniby5ciWtzbq4PzF6NE+MHk1qRgbe7u78npLCswsX0rp586tr+FVq5upq+T2al3dJr4oyge7u\npFXqXWHpPXo5MWFhHHn2WTLz87GzscHDyYnmcXG0rqZnQN+WLSkxGDiRmUn7ap5EUZea2dkZj1ml\ni7wzFu50lwm0t7ccX+kJDc62trSxtaWNkxN93N0J++UX3ktL4//MnmLjY2+Pj7097ZydCXd2JiQx\nkW25ufSt4vfV182Nb6ro0VCfmjk5GY9bpYk4zxQUXNILo0ygszNplXoWnLHQC6MsaXH6/Hm+HTq0\nvLcFUP50kY5mwy0cbG1p4+7OqSqGTwD09fPjpV9/5UJpaYPNddGYz7XvcnJYn5VVnhTTWmMAHBIS\neKdtWyb4+1PXrDk55/3W+rJWnYRohBIx3uVXQPfqArXWL2it2wMdgTcxzsfgD3yntW5T1QW+1vpj\nIB7j+7u2/d3ux/i0jeuAy03mWedtqqHGVvclpvInmC27D0n2iibI3taWyKAg4o8fL1+mtSb+2DH6\nV/GoxaiWLYk/dqzCsk3HjhFlevwdXExaHM/KIv7++/Gu9CH2SkSFhLA3La3CkxM2Hj2Kp6Mjnerh\nA1dlzs6utGjRpvyrdetO+PoGsnPnxaecnD9/jv37t9G1a3+LZdjZ2RMeHllhG601O3fGl28THh6J\nra1dhZhTpw6TlnaKrl2jLinT1zcQBwdHNm1aRkBASzp06AlAt27XUVpaQnLy72blHEIpRWBg/c/E\nb29nR2SHDsTv3Fm+TGtNfGIi/bt2tbhNVJcuxCcmVli2aedOojp3BqB1UBCBPj4VYs6dP8+2336j\nf5cul5Tn5+2Ni5MTK775BmcHB27o3fuSmEBfXxwdHFi2aRMtAwLo2aFDrdprLfa2tkQGBxNvdidV\na038kSP0r+KJClGtWhF/9GiFZZuOHCGqivjL3Rf2cXHBw8mJb48c4ez589xiOv6W7EpOxkYp/Bvw\nKRn2psdHxpslA7TWxOfk0L+K5EGUuzvxlZ5usyknh6jLtMMAFOmq72WX9Tspqmbej13nz9O8EUxo\nam9jQ2SzZsSb9f7QWhOfkkL/KiZRjvL3Jz6l4v22TcnJRJn9jS5LWhzPzSV+6FC8KyWxI5s1w9HW\ntsJwlGKDgRN5eYRWc/x3ZWTg7eDQoBN0NuZzbWu3buyOiOBX09c/W7bEw9aWXyMiuK2ehiVZs8eF\nEOIytNZaKbUY+D+glVKqvda62j62WuscpdQq4HXTooPVxSulOgBBGC/In1ZKrSl7ykgNrMDYKyAF\n+PEy9avzNtVQY6v7R8As4DallDvgC5RqraueeVCIa9jU665j/OrVRAYF0Sc4mLlbtpBfXMyEnsYL\n4HGffEKwhwezbrwRgClRUQx+7z3mbNnC8LAwlu/ZQ2JyMotGjgSMM8SPWr6c3SkprL/vPooNhvK7\nxT7OztibPmSezs4ms6CAk6a5D341ffht5+uLq4MDN7ZrRyd/f+775BNei40lJTeXF775hkf79Ssv\no6GNHv0EH3zwEsHB7WjevBXvvPMC/v7BDBx4a3nMY49Fc/31oxg16mEAxoyZyosvjic8PJKOHfuw\ncuVcCgvzGTZsAmCc4HPEiAd5662puLt74+Lizty5j9Ot23UVHmW6bNls+va9CRsbG77/fjVLl77O\nSy+tKu+e3Lt3DGFhPZk16wEef3wuBkMpb775KH363EhwcLv6O0hmpt51F+NffpnIDh3o06kTc1eu\nJL+wkAlDhwIw7sUXCfb3Z9akSQBMufNOBj/6KHNWrGB4//4s37SJxEOHWDRtWnmZT4wezUtLltCu\nRQtaNW/OC+++S7C/P7cOHFges2D1avp37YqbszMbt29n2ttv8/rkyXiYTfw6e9kyburXDxulWP3D\nD7y+bBmr/vnPWgz7sb6pgwYxfsUKIoOD6dOyJXN//NH4HjUlXsYtX06wpyezhg0DYMqAAQx++23m\n/PADwzt2ZPmuXSQmJbHojjvKy8zKz+dUdjZ/5OSggYNnzqAx9tYo68nxwY4ddPT3x8/NjZ9PnOCJ\ntWuZOmhQeU+KrSdPsu3UKf7Sti3ujo78fPIkUz//nPsiI/GsRbLSmqYGBTH+6FEiXV3p4+bGXNM8\nAGV3mccdOUKwgwOzTMmcKUFBDN63jznJyQz39mb52bMk5uWxyDQRcX5pKS8nJXGLjw/NHRxILy5m\nfmoqyRculD+tYUduLtvz8hjg4YG3nR1HCwuZceoU7Z2ciDId0w/PnMHBxoYepnNvdUYGH5w9y3uV\nJjxuKFM7d2b8Tz8R6etLHz8/5u7fT35JCRPaGf9mjPvhB4JdXZllmjR3SufODP7yS+bs28fwkBCW\nHztGYkYGi667DjD9P/j2W3ZnZLD+hhuM/w9MPTR8HB2xt7HB3d6eh8LDmblrF8GuroS6ufH63r0o\n4E7Tk0XWnzrFmcJC+vn54Whry8Y//uCVPXuYVkXSsz411nOtQ6X34I68PGyUomMVE3zWBUlcCFH/\n3sTYK8DP9PqctQpWStlinCh3MsbeAB8Di5VSPbXWlgdQW6C13splhk5UUmdtqqnGVnetdapS6mtg\nGMahJkFIbwvRhI3u2pX0/HxmxMeTlpdH9+bN2TBhAn6mD9ZJOTnYmY3zj2rZkuWjRzN90yamb9pE\ne19f1t5zT3kviKScHNabnsTQfcEC4OKY5+8eeIBBpg+iM+Lj+XD37vJye/7nP2AWY2Njw/p772Xy\nunX0f+cdXO3tmdCzJ3FDhtT1Ibli9947jcLCfF5/fRK5udlERAxkzpyvsLe/2OU3JeV3cnIuTnQa\nHT2a7Ox0Fi2aQWZmGu3bd2fu3A14e1/sVj9lylxsbW2ZPv0OiouL6Nv3Jp56akGFfSckfMWSJbMo\nLi6iXbsIXnvtc/r2vbF8vVKKN95Yx5w5j/HII4NxdnYlKmoYjz5al3M/V290dDTpOTnMeO890jIz\n6d6+PRvmzMHPNFQk6exZ7MySUlFdurD8H/9g+jvvMP2dd2gfEsLaV16hk9ljEqfdcw/5hYVMeuMN\nsvPyGBgRwVezZ+Ngdgd7+4ED/GPxYvLy8wkPDWXRtGmMvfHisQL4ats2Zn30EUXFxUS0a8fnr77K\njX360BiM7t6d9PPnmbFhA2m5uXRv0YINEyfiZ7pDm5SdXfE92qoVy++5h+lffcX0r76ivZ8fa++/\nn06BgeUxn+/fz/0ff1w+Wd3dS5cCMPOGG5hhOjaHzp7l719+SVZBAa28vXnhhhuYYpYQcrSzY8Xu\n3cRt3EhRSQmtfXx4avBgnhw0qO4PymWMbtaM9OJiZpw+TVpxMd1dXNjQqRN+pvMiqagIO7OkVJS7\nO8vDwph+6hTTTReAa8PD6WS6yLNVioMFBXx46BDpJSX42tnR282NzV26lF8IOtvYsCYzk3+cPs15\ng4Hm9vYM9fZmenAw9ma/nxdPn+aUaf/hzs58HBbGbQ3wqEpLRrdpQ3pRETN27SKtoIDuPj5siI3F\nz3QRnGQaNlQmyt+f5ddfz/TERKYnJtLew4O10dF0KntP5+ez3vTEqe5r1wIX51v5buhQBpnOydm9\ne2NvY8O4H3+koLSUvn5+fDt0KJ6m4RP2NjbMP3CAJ7dtQwPtPDz4V9++/LWBe0RB4z7XGprS1XQR\nEULUDaXUCIzzMBQAnbXWJy4T3w44jPHzepzW+p9VxM0EvLXWT5ijdFzqAAAgAElEQVR+/gS4DXhN\na12nyYQ6bJMBY+eIOrslWld1N4sfBawCtgJOQH+tdWF121TavieQmJiYSE/TXWsh6sTLLzd0Da5J\nCUOmN3QVrjlRhi0NXYVr09atDV2Da8+aNQ1dg2tTeHhD1+Dac9CaHYj/HH7JyyPS+CjpSK31L9XF\n1mkKRSnVXSn1qFLqNaXUO0qp9+pyf0JcK7TW64F/YryIXa+UanaZTS4dOFuJUmowxh4D/2e2+BEg\nG+OQkV4WN7SSumhTfamHun8OZAB9gcSaJC2EEEIIIYT4s6uTxIVS6g6l1D6ME9/NA54G/oqFxzIq\npQKUUn8opc4opSQlKv40tNb/wPi+aAckmBIPl1BKdQRmAhb74yqlHJRSEzFeHOdorYvM9pGGcY4F\nO2C5Uqq1pTKsxVptMotrbfZ9SHWxV8vada9UdjGwzPTj+1dXUyGEEEIIIf5crD7HhVJqITCx7MfL\nxZse3xgP3AvcopRqobX+43LbCdEUaK0XK6V2Ai8Bm0zfrwNOAJ7AAKA9xseaJiilzmL2vlJKzQIe\nA8pmxumilHpCa/0v0/rbgUcxDmloAxxSSp0Appp6GTS6Npnq3Rz4Fgjl4qOiDyqlftdaXzq1eyOq\nezWWAjdprROsX3MhhBBCCCGaLqsmLpRSLwF/M/2oga+B74CbqP6xhEswJi4UxgnsFlmzXkI0Zlrr\nvcCtSik/jBfGrYGWQBrwktb6oFnsG5W2fY5qJpPUWq+hASbhvZo2mZalYHzsaL272rpXoyPGiVOF\nEEIIIYQQNWC1CxqlVHug7HlSWcBtWusfTetaUn3i4jsgD3AFrkcSF+JPSGt9Fvi0oethTddym+qg\n7uOB+6xYnhBCCCGEEH8K1pzj4m8YEyEaeLAsaXEltNYG4FeMPS46W7FOQghRr5RSnZRSe5RS082W\nhQO5MgxOCCGEEEKImrNm4iLa9HpUa/1ZLbY/aXoNtlJ9hBCiIQwFugAPmC2bgXHeDCGEEEIIIUQN\nWTNxUTaJ3vZabn/O9OpuneoIIUSD2AyUALMAlFJPAfla6x0NWishhBBCCCGuUdZMXLiaXvNqub2b\n6bXQCnURQogGobXeBkwGHjU9FtqPi5MWCyGEEEIIIWrImk8byAACAf9abh9uej1rneoIIUTD0Fq/\nB7zX0PUQQgghhBCiKbBmj4tjGCfX7FvTDZVSwUAPjENNfrVinYQQQgghhBBCCHENs2biYqPptblS\n6s4abvsyYGv6fpP1qiSEEEIIIYQQQohrmTUTFx9wcX6K+UqpDleykVIqDrgPY2+LLOAjK9ZJCCGE\nEEIIIYQQ1zCrJS601knAbIzDRZoB25VS05VSbU3LyimlfJVSY5VS24HnzVbN0Fqft1adhBBCCCGE\nEEIIcW2z5uScADOBTsDtGJ8S8k/TVzmlVAbgZb7I9LpEa/0fK9dHCCGEEEIIIYQQ1zBrDhVBa62B\n0cBLgAFjUqIsMaFNr95myxVQCsRprR+wZl2EEEIIIYQQQghx7bNq4gJAa23QWs8A2gHzgMNUTFSU\nOQW8DXTQWsdZux5CCCGEEEIIIYS49ll7qEg5rfVJ4EngSaWUD9Ac8ATOA2la69S62rcQQgghhBBC\nCCGahjpLXJjTWmcCmfWxLyGEEEIIIYQQQjQdVh8qIoQQQgghhBBCCGEtddrjQinlBXhcYXiGPApV\nCCGEEEIIIYQQ5mqduFBKTQFcTT9+qrU+YCHsReDhKyzyW+CG2tZHCCGEEEIIIYQQTU+tEhdKqWHA\nXIyPON0LvF5d+BUWO0QpNUJrvb42dRJCCCGEEEIIIUTTU9s5Lp4z+/5BrXXJZeL1FZb7VC3rI4QQ\nQgghhBBCiCaoxj0ulFIhQH+MyYh4rXXiFW7aupp172AcJjJQKdVKa32ipvUSQgghhBBCCCFE01Ob\noSK3mn0//0o30lqfrGqdUuoVjIkLBYyoSblCCCGEEEIIIYRoumozVKS36bUEiLdSPX4AMiqVL4QQ\nQgghhBBCiD+52iQuIkyvv1jr8aVaaw3swdjjors1yhRCCCGEEEIIIcS1rzaJiwCM81v8YeW6JJle\n/axcrhBCCCGEEEIIIa5RtZnjwsv0mn4FsV9eYRxAWe8Nr2qjhBBCCCGEEEII8adRm8RFqenV5XKB\nWuuvgK+usFw306uhFnUSQgghhBBCCCFEE1SboSJlk2g2s2ZFzMrLqDZKCCGEEEIIIYQQfxq1SVyc\nwTiJZk8r1yUS49wZZ6xcrhBCCCGEEEIIIa5RtRkqshVjksFPKdVDa73raiuhlOqBcVJODWy72vKE\nEEIIUQs9rX1P4s8hyumqPwr9CV12xLGwpGXLhq7BtScoqKFrcG3avLmha3Dt6dKloWtw7cnKuuLQ\n2vS4+Nbs+2m12N6S/zP7Pt5KZQohhBBCCCGEEOIaV5vExQbgLMbhIncqpUZcTQWUUrcAd2LsbZEO\nfH015QkhhBBCCCGEEKLpqHHiQmudD7xptv1ypdTNtdm5KWmxFGMSBOBNrXVBbcoSQgghhBBCCCFE\n01ObHhcAbwG/Yuwl4Qp8ppT6SCkVeSUbK6V6KaX+B3xq2l6bynurlvURQgghhBBCCCFEE1SbyTnR\nWhcqpW4FdmB8jKkCxgJjlVK/Y5xg8wCQDZzHmJzwAjoCfYHWpqLKelqcAW7VWhfWsh1CCCGEEEII\nIYRogmqVuADQWp9SSkUBq4EI02IFtOFiYsKSsmSFNr3uAkZprU/Xti5CCCGEEEIIIYRommo7VAQA\nrfVxoB8QB5g/y0RZ3qLC8kzgH0B/rfWJq6mHEEIIIYQQQgghmqZa97goo7UuAuKUUm9gfDrIX4CB\nQEil8kuAU8BPwHfAJ6aJPoUQQgghhBBCCCEsuurERRlTEmKJ6QsApZQ74A7kaq1zrbUvIYQQQggh\nhBBC/DlYLXFhiSlZIQkLIYQQQgghhBBC1MpVzXEhhBBCCCGEEEIIUZckcSGEEEIIIYQQQohGSxIX\nQgghhBBCCCGEaLQkcSGEEEIIIYQQQohGSxIXQgghhBBCCCGEaLQkcSGEEEIIIYQQQohGSxIXQggh\nhBBCCCGEaLQkcSGEEEIIIYQQQohGSxIXQgghhBBCCCGEaLQkcSGEEEIIIYQQQohGSxIXQgghhBBC\nCCGEaLQkcSGEEEIIIYQQQohGSxIXQgghhBBCCCGEaLQkcSGEEEIIIYQQQohGSxIXQgghhBBCCCGE\naLTsGroCQoiLlFLuwPVAS8AFOAVs1FpnmcW0BIK11j83SCVrqCm2CZpuu4QQQgghhGhspMeFEI2A\nUqqNUmolkAo8BjgCJwFvYLFS6i2llKdSygFYDcQ0XG2vTFNsE1ivXUqpiUqpLKXUwvqquxBCCCGE\nENci6XEhRANTSj0KvAEcAPpprfdWClmolLoJ+Bo4BkQC6+q3ljXTFNsEVm/XY4AnMFEpNU1rfa6O\nqi2EEEIIIcQ1TRIXQjQgpVQc8AKwHYjWWp+3FKe1/lop5QZ8DOh6rGKNNcU2QZ20az7wOrBGkhZC\nCCGEEEJUTYaKCNFAlFKjMF4I5wG3VXUhXEZr/QnwSX3UrbaaYpugbtqltX5Ha+2ltX7AejUVQggh\nhBCi6ZHEhRANQCnlASzCeEf+da11yhVu+gag6qxiV6EptgmabruEEEIIIYS4VkjiQoiG8QTgZfr+\noyvdSGu9A9hTJzW6ek2xTdB02yWEEEIIIcQ1Qea4EKJhPGh6Pay1PlnDbTdbuzJW0hTbBHXQLqWU\nH9AM8AP8tNarr6J+QgghhBBCNGnS40KIeqaU6gyEYBx6sL8WRSwDNpiV95BS6oRSymD2VaiU6mVa\n30spdcFs3QWl1HdWaczFOli1TaYyH1JKJVVqV6pS6mazmMeVUiVm65OVUoFX1ZiKdbB6u0zSTOV9\nj3ESTyGalAXr1tH6/vtxHjmSfk8+yY7Dh6uNX/XTT3ScNAnnkSOJeOQRvtqxo8L6T3/+mZteeAG/\nMWOwGT6cPb//XmF9Vm4ujy9cSPjf/obrbbcROn48UxYu5Fx+foU4m+HDK3zZjhjBxz/+aJ1GW8GC\nlStpPWIEzlFR9Bs3jh37q/+zs2rTJjrefjvOUVFE3HUXX23ZcknMjLffJujGG3Hp358bJk/m6KlT\nFdYfOXWKkVOn4jdkCJ6DBjHwgQf4ITGxQoxNZGSFL9tevfh448arb7AVNMZjlpmTw9BHH6VFbCxO\n/frRctgwHnvtNXLPVzs9Ur1a8PXXtH7kEZzvuYd+06ez4+jRauNXJSTQ8ckncb7nHiKeeYavdu2q\nsP7T7du56eWX8XvwQWzuuos9J6vP8w+dNQubu+7i8507La7PzMsj+KGHsL3rrkvexw1lwdGjtP7y\nS5zXrKHft9+yIzOz2vhVSUl03LAB5zVriNi0ia9SLo40LTEY+L89e+i2aRNun35Ki/XrGb9jBykF\nBRXK+CUrixt//BHvtWvx+/xzJiUmcr6kpELME7t30ys+Hqc1a+j5zTfWa7CVLMjOpvXx4zgfOUK/\nU6fYUVhYbfyq3Fw6njiB85EjRJw4wVeV3jdxGRl0PHECtyNH8Dl6lBuSkthe6bgBfJGXR79Tp3Ax\nxd2enFxh/Y7CQmKSkvA+ehSfo0e5KSmJPUVFV99gK1hw7Bitv/4a588+o993313ZubZpE86ffUZE\nfDxfpaaWrysxGPi/ffvo9s03uK1dS4svv2T8zp2Wz7XNm/Fetw6/9euZ9MsvFc61PTk5jN2+nZZf\nfYXL2rV03rSJty7zd8PaJHEhRP3rYfZ9Uk031lr/rLXeZvbzQq11KyAKOIfxIvsprfVO0/qdwFzA\nALwKBGit/1L76ltk1TaZli3UWgcDT5eVq7UO1FqvM4t5C3gYY9se0FoHaa1TsR6rt8vED3io1rUS\nohFb+cMPPPXuu8Tdey+7/v1vItq0Ifb550nPybEYn3DgAGNff52JsbHsnj+fkVFRjHzxRX4zu1g8\nX1jIgE6deO2BB1Dq0qljkjMzScnMZM7Eiex7+22WPPUUXycm8td//euS2CVTp5K2bBmpS5eS8r//\nMTIqynqNvworN2zgqblziXvoIXYtW0ZEWBixjzxCelaWxfiEX39l7PTpTLz9dnYvX87I669n5NSp\n/Hb8eHnMax98wPyVK/nv9Ols//BDXJ2diX30US4UF5fHDH/8cUpLS/l+0SJ+WbqUiLAwhj/+OGcq\nfVBeEhdH2jffkLppEykbNzLyL9b+N1JzjfWY2djYMPL661n3r39x5LPPWBIXxzfbtjF51qy6PSBX\naOXPP/PURx8RN3o0u15/nYjQUGJffpn0c5YfcJVw+DBj33qLidHR7H7jDUb27s3IN97gt6SL/xbP\nFxYyIDyc1+6557KTO81dvx5bG5tq4x58+226t2pV47bVlZWnT/PUnj3Ede7MrpgYIjw9if3pJ9Kr\nuNBNyMhg7LZtTGzdmt033MDIoCBGJiTwm+kY55eWsjsnh5kdO7IrJoZP+/fnUG4ut/78c3kZKQUF\n3PDTT4S5u7N9yBC+HjiQ/efOMaFSYhfgwVatGBMSUjeNvworc3N56uxZ4po1Y1doKBGOjsQmJZFe\nWmoxPqGggLEpKUz09GR3aCgj3dwYmZzMb2bHuYO9PQv8/dnXqhVbQkJoZWfHjX/8QYZZmatzcxmX\nmsqDHh7sDQ3l55YtGevuXr7+vMHA0KQkWtnZsb1lS7aEhOBuY8NNSUmU6oZ90N3KpCSe2ruXuI4d\n2RUdbTzXtmyp/lzbsYOJrVqxOzqakc2bM3Lr1ornWna28VyLjubTfv2M59rWreVlpBQUcMOWLYS5\nubH9L3/h6+uuY39uLhPMErKJWVn4OzqytHdvfouJYXp4OH/fv5//HDtWtwfEjCQuhKh/AWbf51qr\nUNMF8hSME0JOVkrZm61uDzyrtX5Oa235E93VqZM2mSwAsoEWSqk2FtafAlZorT+w8n6h7n5XGcAS\na5UnRGMy97PPmDR0KOOiowkPCWHho4/i4ujI+1XcoZ+3di1De/Vi6u230yE4mLh776Vnu3bMX1ee\no+TeIUN4/u67iY6IQFv4UNk5NJRVzz3HsN69aR0YyPXduvHy+PGs274dg8FQIdbT1RU/T0/8vbzw\n9/LCwd7+kvIawtylS5k0ahTjRowgvHVrFk6fjouTE++vXWsxft7y5Qzt35+p995Lh1atiJs8mZ7h\n4cxfufJizLJlvPDXv3Lz4MF0adeOD//5T5LPnuWz74yd7jKyszl6+jTP3n8/ndu2pW1ICK8+/jj5\nhYXsq3QnzdPdHT9vb/x9fPD38WkUx62xHjMvd3cm3XEHPTt2JCQwkL/07s3Do0fzU6VeCg1l7hdf\nMCkmhnGDBxPeogULJ040vke/s9wZc96XXzK0e3emjhhBh6Ag4kaPpmfr1sz/+uvymHsHDeL5UaOI\n7tq12ueA/3riBP/68kvenzy5yri3N24kJz+fp0aMqH0jrWzukSNMatOGcaGhhHt4sLBnT1zs7Hj/\nxAmL8fOOHGFoYCBTw8Lo4O5OXOfO9PTyYr7pHPGwt2fDwIGMCg6mvbs7fXx8mN+9O4lZWSSZepis\nT0nBwcaG+T160N7dnUhvbxb27MnqP/7geF5e+b7+1b07k9u2pbWra50fh5qam5XFJE9Pxnl4EO7g\nwEJ/f1xsbHi/ikT2vOxshrq6MtXbmw4ODsQ1a0ZPR0fmZ2eXx4zx8GCIiwut7O3p6OjIHH9/zhkM\n5b0lSrXmibNnedPPj4leXrR1cCDcwYE7zBIXBy9cIMtgIM7Xl/YODnR0dGSmry9ppaWcNEtSNoS5\nR44wqXVr47nm7s7CHj1wsbXl/Sp6Mc07doyhAQFMbd/eeK516mQ810wJBQ97ezYMGMCoFi1o7+Zm\n+VxLTcVBKeZ37057Nzfjuda9u/FcM/V4ub9VK/4VEcHAZs1o5erK2JAQ7g8NZU2lnix1SRIXQtQ/\n8097Vn3qhNZ6CbAO6Aj8A0ApdRvgqrWebc19VVKXbSoCyj5VTrAQMgb4rzX3aaau2yVEk1JcUkLi\n0aNEd+9evkwpRUyPHiQcPGhxm4QDB4gxiweI7dmThAMHrqou2Xl5eLi4YGNT8aPOI//5D35jxtD3\niSdY3EiGOxQXF5N48CDRffqUL1NKEdO3Lwl791rcJmHPHmL69q2wLDYqioQ9xjmBjyclkZqRUaFM\nDzc3+nbpUh7j6+VFeKtWfLh+PfkFBZSUlLDwk08I8PUlsmPHCmU/8uqr+A0ZQt9x41hcRWKgPl0L\nx6xM8tmzrPn2W67v1euq2mwNxSUlJB4/TnTXruXLlFLEdO1KQhVDuhIOHybGLB4gNiKiyviqFFy4\nwNi33mLBgw/i7+lpMea3pCReWr2ajx577JL3bkMpNhhIzMoi2t+/fJlSihh/fxIyMixuk5CZSYxZ\nPEBsQAAJ1XT5zy4uRimFl4MDAEUGAw6VjoGTrS0Am6vYb2NSrDWJhYVEu7iUL1NKEePiQoKFoR1g\n7HERYxYPEOviQkIVw0uKtea/2dl42dgQ4egIwC9FRSSbhjj0PHmSoGPHGJaUVLHXhoMDvra2vHfu\nHMVaU2Aw8G5ODp0cHGjVgEnZYoOBxOxsov38ypdd9lzLyLj0XPP3t965lp5eZTk5xcX4mMqoD43j\nL4IQfy5nzL73qIPy/wZkAs8opW4CXsbyBb811XWbFmNMHIwzX6iUcgW6a61/qoN9Qt23S4gmJf3c\nOUoNBgK8vCosD/DyIrWK7vupWVmXxnt7Vxl/RfXIyeGlFSuYNHRoheUv3ncfH//973wzaxZ3DBjA\nw//5T4WeHQ0lPTub0tJSAnx8KiwP8PEhtYoPjakZGZfG+/qSavpwm5aRgVKKAF/fS8s0+wC86e23\n+eXgQdwHDsS5f3/+tWwZX8+fj6fZ3ckXJ0/m41df5ZuFC7kjOpqHX32V+StWXFWbr1ZjP2YAY597\nDtf+/Qm+6SY83dxY9MILtW6vtaTn5hrfo5USBwGenqSa3dU2l5qdbfk9XUV8VZ784AMGhIczIjLS\n4voLJSWMnTeP2ffdR4tKv6eGlF5URKnWBJgujMsEODqSWsUFdWphIQFOThXjnZyqjC8qLeXZffsY\nGxKCm53x2QlD/P1JLSxk9qFDFBsMZF24wN/37kXBJfMTNEbppaWUAgGmC+AyAba2pFYxVCS1tJQA\nu4rPjgiwsyO10rweX+Tl4X7kCE5HjjAvO5tNwcH4mPZzvLgYjXEujBm+vnzRogXetrYMTkoi27Rf\nNxsbvgsO5qNz53A+cgT3o0fZmJ/Ply1aYGNhOGJ9KT/XLJ07VQwVSS0quvpzzc+P1KIiZh8+fPFc\n27/feK5VUc7PGRl8/McfTGrduoatrD1JXAhR/8wfkdmiukCl1ItKqSOmSSdTKn0dN5+osozWOg14\nBONTg9YBs7TWKZXjLkcpFaGUOqmU+lEp5X6Z8Lpu03bgNyBEKRVttupO4JMrbZNp/42mXUL8WWit\nLc5NYa14c7n5+QyfOZMuoaHMHDu2wrrpY8YQ1bEjEW3a8MwddzDtjjt445Ma/QmpV7U6bpeLgQpl\nPvzKKwT4+LDl/ffZ8dFHjLz+ekZMmUKa2YX69L/+laiICCLCwnhm/HimjRvHGx9+WMPW1I/GcswA\n/vX00+xavpy1c+ZwLCmJJ2fXZcfHq1O5jZeNr+Fx/nznTr7dv5+548dXGfPs0qV0Cg7m7gEDyvdR\nVrfGSFOzrphVxZcYDNy5dSsK+E/PnuXLO3l4sKR3b+YcOYLLp58StH49bVxd8XdywrYBL66vljWO\n2xAXF34NDSUhJISbXFy4MyWlfN4Mg+m8ed7Xl5FubvRwcmJxYCAK48SfAIUGAw+mpTHQ2ZntLVvy\nc0gIXRwcGPbHHxRVGl7YGFzJ36lL4i2cIyUGA3du22Y818x6Onby8GBJZCRzjh7FZe1agr780niu\nOTpaPNf25eQwcutW/tGxY4WeSHVNEhdC1L9EjHfyFdC9ukCt9Qta6/YYh368iXHOBX/gO611G/OJ\nKitt9zEQj/E93qmW9bwf4xM1rgMuNwtbnbcJ45wQioq9R+6j5nNFNLZ2CdFkNPPwwNbGhrRKd2LP\n5ORccse2TKC396XxFu7wXom8ggJiX3gBLzc31jz/PLaV7vRV1rdDB5IyMipMvNgQmnl5YWtrS1ql\nrr1nsrIuuftfJtDX99L4zMzy+MBmzdBaX3IxfSYzs7zXQfy2bXy5ZQsrX3uNft260b1DB+Y/+yzO\njo4sqaYnSt+uXUk6c6ZBj9u1cMz8fXwICw3l5sGDWfjcc7z9ySeXlF3fmrm7G9+jleYYOJOTc0kv\njDKBXl6W39NVxFvy3b59HE9Lw3PCBOzvvhv7u+8G4PbZsxkSF2eM2b+fVVu3lq+PefFFNOD34IPE\nrVpVg1ZaVzPTxVtapTveZyzc6S4T6OREWqU71Wcs9MIoS1qcLihg48CB5XfAy4wJCSF5xAiSR4wg\n45ZbmNmpE2eLihrlfBaVNbO1xRZIq9S74kxp6SW9MMoE2tqSVql3xZmSkkt6YTjb2NDGwYE+zs4s\nCgzEDnjPdE43N8V2NBvC4KAUbeztOWUqe2luLieLi3k/MJCeTk70cXZmafPm/F5czNoGfPpP+blW\n+dwpKrqkx0+ZQEfHK4ovS1qcLihg44ABls+1YcNIHjaMjBEjmBkeztkLFy451347d46YzZt5qHVr\n/t6hQ22bWiuSuBCinmnjLYTFph9bKaXaX8E2OYD5f23Lg8VNlFIdgCCMF91Plz0atYZWACnALqDa\n5wXWR5uAj4BS4DallLtSqhVQqrU+fbl9VdLY2iVEk2FvZ0dku3bE795dvkxrTfzu3fSvYvx/VMeO\nxP/6a4Vlm3btIqqK+Kru8ubm53Pj88/j7ODA5zNnXtHkkbuOHcPbza3BJ5q0t7cnMjyc+O3by5dp\nrYnfvp3+3bpZ3CaqW7cK8QCbtm0jyhTfukULAn19K8Scy8tj27599I+IAKDAdCFW+Yja2NiU37W0\nZNfBg3h7eDTocbvWjllpaSlKKYouXLjiNtYFezs7Itu0Id5sHhCtNfH79tG/iouQqLAw4vftq7Bs\n0969RIWFWYy39A79+223sWf2bH59443yL4B5Eyaw+OGHAVjz9NMV1r/70EMoYPOLL/JIbGzNG2sl\n9jY2RHp7E3/m4uhRrTXxZ87Qv4okWZSPT4V4gE1nzhBlNgSmLGlx/Px54gcNwruauQL8HB1xsbNj\nxenTONvackNAQJWxjYW9UkQ6ORFv9jhbrTXx+fn0d3a2uE2Us3OFeIBN+flEVZEgKmMAikzvv0gn\nJxyV4pDZe61Ya04UF5fPX1FgMFxyEaxMX9W9j+uavY0NkV5exJ89W75Ma0382bNVn2u+vhXioYpz\nbds2jufnEz9w4JWda0lJxnPNrEfF/nPnGPLTT9wfGso/O9X2vmjt2V0+RAhRB97EeOffz/T6nLUK\nVkrZAh8AkzHe8f8YWKyU6qm1vuLbY1rrrVxmeEQlddYmU31SlVJfA8MwTsgZRC2ezNHY2iVEUzP1\nttsYP2cOke3b0ycsjLmffUZ+URETbrgBgHGzZxPcrBmzJkwAYMqttzL4//6POWvWMLxPH5Z//z2J\nR4+yaMqU8jKzcnM5dfYsf2RkoLXm4OnTaK0J9PYmwNubvIICbpg+ncLiYpY+8wzZZjPu+3t5oZRi\n/bZtnMnJoV94OI729mz85Rde+fhjpt1xR70en6pMvfdexs+cSWTHjvTp3Jm5S5eSX1jIhFtuAWDc\nCy8Q7O/PrMceA2DK3XczeOJE5vzvfwwfMIDlX39N4oEDFeZReGLsWF567z3ahYTQKiiIF95+m2B/\nf269/nrAeCHv7e7O+JkzeeGvf8XZyYl3Vq/mRHIyIwYOBGD9jz9yJjOTft26GY/b1q28sngx06rp\n8l9fGtsxG24a4vDVli2kZWTQu3Nn3Jyd2XfsGNPmzWNA95/PyyQAACAASURBVO60bN68fg+SBVOH\nD2f8ggVEtmlDn3btmPvFF8b3qKmN4+bPJ9jHh1mmoVZThg1j8MyZzFm/nuE9e7J882YSjx9n0aRJ\n5WVm5eVxKj2dPzIz0cDBP/4wvke9vAjw8sLf09PihJwhzZoRapqIsHWlLudnz51DA+FBQXhUmrCx\nvk1t357xO3YQ6e1NH29v5h45Qn5pKRNMj2wdt307wc7OzDJNYjqlfXsGf/89cw4fZnjz5iw/dYrE\nrCwWmeb3KNWaUQkJ7M7JYf1111FsMJTfNfdxcMDeNFHigqNH6e/ri5udHRvT0pi2dy+vd+2Kh1nS\n8FheHrklJaQUFlJQWsqvpt4xnT08sGvgCU6nenszPjWVSCcn+jg5MTcri3yDgQkexmnDxqWkEGxv\nz6xmzQCY4uXF4NOnmZOVxXBXV5afO0diURGLTImafIOBlzMzucXVleZ2dqSXljI/O5vkkhLudHMD\nwN3Ghoc8Pf+fvTuPr+H6/zj+OoktItaENE3UWoIKoQhVat9KfFWVllK0WmsprfqV6qLrV0tRS2nt\ne+1fRVW/RWOLraW0paVKEiGRDdnm98eN5GYjCLn6fT8fj/sgM5+ZOedk5ubO555zhnEXLuCdLx8P\n5M/PhxcvYoAnUmJauroyKjycgaGhDC5RgiTL4v2LF8lvDI85wrm2bx91ihenXsmSaefaAw8A0Gvf\nPrwLFWJCjRoADK1YkSY//MDE336jvacni//6i+DISGalDDtKsiy67N7NwchI1jdsmP25duJE+nPt\n55/5sEaN1HPtSFQUj23fTpsyZRhWqVLqPpyNwT2b3iC5TYkLkTxgWVa4MaYfsBYYaoyZaVnWnzfY\nLKdfbf0fsNuyrB8AjDFfA52B8dzBm+47XKdrvgLaY0sgFAIa3mw5b9ZdqpfIP8aTjz5KeFQUY+fP\nJzQykloVKrDp7bfxSLlpORMeTj67bsIBvr4sHjWKMfPmMWbePCp7ebFm7FiqlS2bGrN29276fPIJ\nxhiMMXT/8EMAxvXowdgePQj+/Xf2/vYbAJX69QPSxvj+MWcOZUuXJn++fExZt46XZ87EsiwqeXnx\n6fPP069Nm7vVNNf1ZKtWhEdGMvbzzwm9eJFaDz7IpqlT8ShRAoAzoaHp283Pj8XvvceYqVMZM3Uq\nlX18WDNxItUqpD01elTv3sRducIL775LZEwMjWvVYuNnn6X2lChVvDjfTJnCmKlTaT5gAAmJiVSv\nWJG1n3xCjUqVANs39FOWLePliRNt7ebjw6evvEK/zp3vYutkzdHa7KHKtk55LgULMmvVKoZPnMjV\n+Hh8PD3p0qwZr6Yk6/Lakw0bEh4dzdhly2zXaLlybBozBo+Um8kzFy6ku+ENePBBFg8dypglSxiz\neDGV77uPNSNHUs3bOzVm7b599Pn889RvrbtPmgTAuK5dGZtNcjAnY/YdZSaHJ318CI+PZ+yRI4Re\nuUKt4sXZ9MgjeKTcsJ25fDl9m5UqxeL69Rlz5Ahjfv6Zym5urGnYkGrX2jgujvXnbNOP1dqyBUib\ny2FbkyY8mpLM2RMRwZtHjxKTmEjVokWZVacOPezeGwH6BQfzg9037v7ffgvAH+3aUTaPb8KfdHMj\nPCmJseHhhCYlUatgQTZ5e+ORMkzhTGIi+ex60QW4uLD4vvsYEx7OmPBwKufPzxovL6qltLMztkeZ\nzouKIjwpiVLOzjxcsCA7fHzwtbt5/tjDg/zG0CskhMuWRf1ChfjO25tiKe8HVQoUYN399zP+wgUa\nnj6NE1C7UCE2eXtnGpZytz3p7U341auM/eWXtHOtUaP055p9m5UqxeJ69Wzn2pEjVC5ShDUBAWnn\n2uXLaefa1q2A3bn26KM8mpI02hMRwZu//GI719zcmOXvTw8fn9TjrPj7by5cvcrC06dZePp06vIH\nChfm5F36O2qyeh66iNwdxpg3gTeAX4CmlmVl+8whY0x3YCG295vxlmW9lUVME2y9EKpce9ymMaYM\ntokt3YCGlmXty+16ZCjDm+RinTLE5wfOAiWBOZZl9c+tct/InaiXMSYZ24iU6w7EN8b4A8HBwcH4\n203cJZLrNm7M6xLcmzw987oE8r/i99/zugT3nmXL8roE96bDh28cI+ml9IKQnNsfEUGdbdsA6liW\ntf96sZrjQiQPWZb1JtAPqAQEpSQeMjHG+ALjgCynJDfGFDDG9MfWK+DStaRFyjFCsc0PkQ9YbIy5\no88tyq06ZbPvBGBRyo9zbq+kNye362X/ezDG+FwvVkRERETkf5mGiojkMcuyvjTG7APeAbak/H8d\n8CdQDHgEqAz0sSwryBhzHrvek8aYCcBg4Fp/wBrGmGGWZX2asv5fwCBs3/5XAI4bY/4EhluWtd4R\n63QDC4E2lmUF5X7Jry836mWMuQ/4DniAtKe8HTPG/GFZllL1IiIiIiIZKHEh4gAsy/oJ6GSM8cB2\n81seKAuEAu9YlnXMLvajDNu+znXmrrAs62vy4Fq/nTrdgC+2uS7yxO3Wy7Ksc9jqICIiIiIiOaDE\nhYgDsSzrPLAqr8uRm+5AnZ4Feubi/m7JP/F3JSIiIiLiiDTHhYg4JGNMNWPMYWPMGLtlVYFoy7L+\nzsOiiYiIiIjIXaTEhYg4qrZADeA5u2Vjsc0vISIiIiIi/yOUuBARR7UDSAQmABhjRgBxlmXtzdNS\niYiIiIjIXaXEhYg4JMuydgMvAoOMMT8DHsDzeVsqERERERG52zQ5p4g4LMuyZgOz87ocIiIiIiKS\nd9TjQkREREREREQclhIXIiIiIiIiIuKwlLgQEREREREREYelxIWIiIiIiIiIOCwlLkRERERERETE\nYSlxISIiIiIiIiIOS4kLEREREREREXFYSlyIiIiIiIiIiMNS4kJEREREREREHJYSFyIiIiIiIiLi\nsJS4EBERERERERGHpcSFiIiIiIiIiDgsJS5ERERERERExGEpcSEiIiIiIiIiDkuJCxERERERERFx\nWEpciIiIiIiIiIjDUuJCRERERERERByWEhciIiIiIiIi4rCUuBARERERERERh6XEhYiIiIiIiIg4\nLCUuRERERERERMRhKXEhIiIiIiIiIg5LiQsRERERERERcVhKXIiIiIiIiIiIw8qX1wUQEZEcSkqy\nvSRHjv/unNdFuOdUOXMmr4twT9paoG1eF+GeU7NmXpfg3uSxa1deF+HeU7JkXpfg3tSsWV6X4N6z\nbFlel+Dek5iY41D1uBARERERERERh6XEhYiIiIiIiIg4LCUuRERERERERMRhKXEhIiIiIiIiIg5L\niQsRERERERERcVhKXIiIiIiIiIiIw1LiQkREREREREQclhIXIiIiIiIiIuKwlLgQEREREREREYel\nxIWIiIiIiIiIOCwlLkRERERERETEYSlxISIiIiIiIiIOS4kLEREREREREXFYSlyIiIiIiIiIiMNS\n4kJEREREREREHJYSFyIiIiIiIiLisJS4EBERERERERGHpcSFiIiIiIiIiDgsJS5ERERERERExGEp\ncSEiIiIiIiIiDkuJCxERERERERFxWEpciIiIiIiIiIjDUuJCRERERERERByWEhciIiIiIiIi4rCU\nuBARERERERERh5UvrwsgIvc+Y4wb0BQoCxQGTgObLcuKsIspC3hblvVjnhTyJv0T6yQiIiIici9S\njwsRuWXGmArGmKVACDAYKAicAkoAXxpjJhtjihljCgArgRZ5V9qc+SfWSURERETkXqYeFyJyS4wx\ng4CPgF+ABpZl/ZQhZLoxpg3wDXACqAOsu7ulvDn/xDqJiIiIiNzrlLgQkZtmjBkPvAHsAZpblhWb\nVZxlWd8YY4oAywDrLhbxpv0T6yQiIiIi8k+goSIiclOMMV2w3eDHAJ2zu8G/xrKsFcCKu1G2W/VP\nrJOIiIiIyD+FEhcikmPGmKLALGw9DT60LOtcDjf9CDB3rGC34Z9YJxERERGRfxIlLkTkZgwDiqf8\nf35ON7Isay9w+I6U6Pb9E+skIiIiIvKPocSFiNyMvin//mpZ1qmb3HZHbhcml/wT65SlqdOmUb5S\nJVyKFKFBw4bs3bv3uvHLV6zAt0YNXIoUwc/fn40bN2aKGTtuHF4+PhR2c6Nl69b8/vvv6dZHRETw\ndM+eFCtZkhLu7vR7/nliY9NG4pw6dQqn/PnTvZwLFGDPnj25U+lcMnnyWBo39qJWrcI891xLTp36\n/YbbLFw4lebNy+Pn50K3bg346af07R0ff5W33hpIgwbu+Pu7MWTIE1y4EJZpP19//RWdOvnh5+fC\nI4948vbbg1PXTZkyHl9fJ6pVc8bX1yn15e/vdvuVvg1Tt22j/OjRuAwcSIP33mPvn39eN375vn34\njh2Ly8CB+L31Fht/Sj8v7qoDB2gzaRIew4fj9MILHD5zJt36Uxcu4PTCCzi/8AJOGV4rg4MBOHzm\nDD2++IKyr75K4YEDqT5uHJO3bs3VeueGr74aS7duXrRvX5hRo1ry9983PtfWrJnKM8+Up107FwYN\nasCxY+nPtQ0bZjFixGN07FiMli2diI2NyrSPRYsmMHRoIzp0cKVz55KZ1kdFXWT06LZ063Y/bdsW\nonv3snz22WDi4qJvvbK56P33x/LQQ1488EBhnniiJSdP3rjdZs+eSt265Slb1oW2bRtw4ED6drt6\n9SqvvjqQqlXdKV/ejeeee4Lz59Ou0YiIizz1VFtq1rwfH59C1K5dltGjBxMTk75N4uPjmTBhDHXq\nlMPHpxB161ZgyZKvcqXet2rqf/9L+TfewGXoUBp8+OGNr9H9+/EdPx6XoUPxe/ddNh45km79qoMH\naTNlCh6jRuE0cGDW1+jAgTgPHIhThtfKAwdS4/b++SctJk2ixIgRlHzlFdpMmZJpX3ll6pEjlF+8\nGJfZs2mwejV7wzK/X9tbfvIkvsuW4TJ7Nn4rVrDx9OnUdYnJyby6ezc1V6ygyJw53L9gAc9u2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y3Obixay3uV3vvtuDDh1c6d7dG1fXYgwffvu9dG5HWJit3Tw80reBh0cZwsKyboOLF23X6PW2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BHpEpROmzZFugRyCFiatVgpv7TmnCvOsoiIyAEys4uB1yNdDhERERGRYnCJc+6NvBIocCEi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\npGcpcSEiIiIiIiIiPev/AhwFZss68zcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db6e828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.direct_deviation_boxplot(Xs, Ys, gateStrDict, ds, gs_mc2gst, directLSGST, xlbl, ylbl, prec=4,\n",
    "                        scale=1.0, boxLabels=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10c135278>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cbDIs2twJSF89IVXX58Ap7v5EHJ2ooF5umyAiIiIiZZT4FxERERERkcbMq9i+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\nmcRnypXXX0/fww6jdY8edCw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1G+Th1UbdQInWNicODQIWOyBOtQsj9M++IL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d4a7wg5uODg7o0KJF+Wk1aqCuszPa3zXdUiLPlYDhwYwVe/ZgdI8e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\nAPBv1Qpbpk3DtM8/x9vr16OFmxti//lPDP3HPxTPElmH+Rc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BaMC/9EREREREREREREREREREQWjAv/RERERERERERERERERERE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\nj7AyKQlJ//0v7G1tVT3+EnmsJzKLYxtliOo3ImJj0eOll9CwT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I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\nJE2x/wNGJSo+LzRJowAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104a2b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.direct2x_comp_boxplot( Xs, Ys, gateStrDict, ds, directLSGST, strs, xlbl, ylbl,\n",
    "                        scale=1.0, boxLabels=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error Rate Plots (uses Direct-X results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10c06eba8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hqPMvYL4xxxUhz+ZHxLYZf0yGH7vfFWUb4Xv3Dz/37SX3PL0P38xndTB+cJTp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\nad26NR06dOB3v/sdCxYsKMtVKHfjxo3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mS5v4L8T3p6OrGxsdSrV8+lvV69eh7nXvs6d/PbAerXr8+RI0e8lFXB4u4+CdbabO8tUbNmzT98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\nKUt82bJlSUpy7Zv55ptvqFu3L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0TE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\nMzPrAv8PdDUUWm8t1EgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bff2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.small_eigval_err_rate_boxplot(Xs, Ys, gateStrDict, ds, directLSGST, xlbl, ylbl,\n",
    "                             scale=1.0, boxLabels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Whack-a-$\\chi^2$-mole Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10d262898>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MNxwdsXTlSpz/+GPszsi483zD0nOKwYBlq1fXZ61ahfP/+hd2b9t25ywLa9RoNGLZ\nihXw79FDyUpJUbK2b79zbXSy7IGwbalDWba8DtWOUR15mG/Tsfz9963OKb1+HZ99+y0KLl1C1muv\nISo0FE6OjqisqoLrXXfdeQXNPo74VnO2bEH2kSM4sHw5vLt3R+mNGyi9cQOd7O3RqRX1XNkjqNVZ\ntdTObRwcLI5SPc5XV6vLWr++vuYXLFCyNm1qOcvCQ7lNRM43RM57O6P1NQ/cHOdXrlTG3s6dsTQ1\nVRl7MzK0/27ehqzyms4W51VXV2PTpvo525//rPRhero+8yg1WWrmh2qpnfeqaZcFQ2+dK1cu4+uv\nv8TFi1/j1VezMWTICFRVVUGSJLi4ND/eO1n+oxTVY5Sa87LaccPCH0fVZW3ZkobevZWsJ59UstLS\n9Kl5NVmFhT/h//2/XQCwKyUl5afmPsdH/RMRERERWZGZM2eiuLgYycuXo7CwEOHh4Tj81lvw0Phx\ngsKzpk1D8X//i+S0NBQWFSH83ntx+MABeLi7a54FAIsXL0ZZWRlmz56FK1euYNiwYXjzzX+gc2fL\nfyjSElF9eOb8eYycOROSJEGSJCStWAEAeOzhh7Fn40ZNs0QTWocPP6xkpaQoWf374/Cbb+qSBQCL\nFil1OGeOUodDhw7DG29oX4cAkJioZMXHz8LVq1cwZMgwvPKKPlkLFypZc+fWt+vQIR2OL8H7SyRR\nYxQgtg7j45WsBQuUOoyJGYb9+/XJEtmHtnZOaQ/Cxo2HHkLxpUtIXrMGhWYzwsPCcPjVV3WZb8yc\nNq0+q6hIyXrlFX2ypk9XamPlSqVd/frh8GuvWf38EBA7RokibH74+ecYGRdXPz/ctg0A8NiECdiz\nZImmWTvfeAOSJGFEUlKj959buBCPPvCAplm1RM5tRJ5TkpKUrLg4Jeu++/QZD0WzxXnvzOnTlXF+\n1ar6sffgQX3GXoFZADB/vtKHiYlKHw4ePAwHDuizv0RmiSSyXW+/fQiJib+vG+/nzPk1AGDBguVY\nsCBZ0yyRY5To7ynXr5chKWkWSkqU7yn79llnliSr+fUsIiISRpKkCAD5+fn5iIiIaO/NISIirdnq\nfPzKFWFRsltXYVkSBO6vH38Ul+Wr398FvIXImldz26lK1dD2LoCWqLmDQS2RP29zsFNxW5xaGt+1\n0RIbLXmUlIjLcnMTlyWS0HOKwDv+1dypppbDjdY/HafNRB5gIgdfgbdMVteIq0OBw7zYgf6DD8Rl\nlZUJiyqNGS0s604P3NBShWUPJWmTJk/71pWaJxmoJXLe64JScWECXa3S9ukDHYWrq7isq1fFZQkc\netGtm7gskU8lEfjjKKHOnz+L0aMjASBSluWzzX2uAz/0iIiIiIiIiIiIiIiIiIiIiO6EF/6JiIiI\niIiIiIiIiIiIiIisGC/8ExERERERERERERERERERWTFe+CciIiIiIiIiIiIiIiIiIrJivPBPRERE\nRERERERERERERERkxXjhn4iIiIiIiIiIiIiIiIiIyIrxwj8REREREREREREREREREZEV44V/IiIi\nIiIiIiIiIiIiIiIiK9apvTeAiIiIiOiXTIYkLEuCLCxLdusqLEuob74RFiUH3S0sS1wViq35iip7\nYVmdBH67dnYWl3XlirgsNzdx+0tkzd+4IS7LqVOlsCyj0UFYVk2NsCh89ZW4LH9/cZXo5CQsCg43\nrgnLqnY2CMsSWYcOP18WllX0s7jiMBqFRcFe3CkF+PlnYVHVUYOFZdlXXBeWBYHHV0WFuCw7G73t\nUuTcxuXY38WFeXmJy/ryS2FRFWN+LSzLzU1YlFCdO4vLcsVVcWGTZgiL+iLjHWFZwVWfCMv6wa2v\nsKzWnr9s9NRDRERERERERERERERERET0y8AL/0RERERERERERERERERERFaMF/6JiIiIiIiIiIiI\niIiIiIisGC/8ExERERERERERERERERERWTFe+CciIiIiIiIiIiIiIiIiIrJivPBPRERERERERERE\nRERERERkxXjhn4iIiIiIiIiIiIiIiIiIyIrxwj8RERERkRVKTk6Gr68PXFyc8cADY/HVV1/pkpOZ\nmYnAoCA4OTsjZvBgnD59WpecWqLaJSLr5OnTmPKnP8H3vvtgd889OPTuu5quvzki+1Akke1KTU1G\nYKAPunZ1xsSJY/H11/plLV+eDD8/HxgMzhg3zrprvqE1a5IRGuoDX19nTJs2Ft98YxvtEmnFimQE\nBfmge3dnTJqkXx1m7tiBwN694WQ0ImboUJw+c0aXnFoia15U1jvvvIrHHx+PwYM9EBJihy+++EiX\nnFqiakOkzF27EBgWBidPT8SMGoXT+fm65omsw5SUZAQE+MDV1RkTJug4Z9u9G4Hh4XDy8UHM2LE4\nffasLjkA8Pe/v4pf/Wo8QkI8YDLZ4ZNP9K15kedlUWy15jN37kRgSAicunVDzPDhup9TVq5MRq9e\nPvDwcMbkyfrWhsg6FDVuiKb3/krbvx9RiYkwPvwwTI88gmmrVuHLH37QNKPWyXPnMCUxEb4TJsBu\n0CAcOn5clxwASHvtNUQtXQrjH/8I0+zZmLZpE7786Sfd8mqtXZuMsDAfBAQ4Y8YMfefztlrzwubz\nzzyDwH794OTlhZgxY3SdA9RKu3ABdu++iwVffqlbhog59sn8fEyJi4PvqFGwCwvDoaNHNc9o6h//\neBW/+c149OvnAT8/O3z6qbbt4oV/IiIiIiIrk56ejszMDOzcmYXc3Dy4uLhg/PhxqKio0DRn3759\nSFq4EKkpKTh39iz69+uHcePHo7i4WNOcWqLaJSqrtKwM4aGhyExJgSRJmq23JSL7UCSR7dqwIR07\nd2YgIyML772nZE2apE/WunXp2L49A9u3Z+HUKSVr4kTrrflaW7akY/fuDGzalIV33lGyZsyw/naJ\ntHFjOrKyMrBtWxZOnMiDs7MLpkzRYZzfvx9JixcjddkynMvLU8b5Bx/UbZwXWfMis65fL0Vk5FAs\nXJiu+3gvqjZE2vfyy0hasgSpTz6Jc++9h/5hYRg3bRqKL13SJU9kbaxfn44dOzKQmZmF999X9pce\n55R9r7yCpGXLkPqXv+DcsWPof++9GDdjhm59WFZWiujooVi2TP+aF3leFsVWa37fgQNIevJJpC5d\ninOnTintio3V7ZyyaVM6du3KwNatWTh2TGnX1KnWPz8UNW6IJmJ/nfzkE8ybPBm5mzbh3VWrUFld\njQeWLcP18nLNMmqVXr+O8OBgZP75z7qPgyc//xzzxo1D7ooVePepp5R2paXhuo41sXVrOvbsycCG\nDVl46y2lDn/1K9a8JYTN5195BUlLlyrnlBMnlDnAQw/pdk4BgNNXr+KZH39E/7vu0i0DEDPHLr1+\nHeF9+iBzyRJhP7cpKytFVNRQPPWUPu2SZFnWfKVERKQdSZIiAOTn5+cjIiKivTeHiIg0pmY67uvr\ng4ULFyExMREAUFJSAi8vE55/fi9mzpzZ7HISLAuLGTwY0VFR2LJly81tleHn74/4efOwePHiFpeV\nYfmXF7XtUkN1H37ztao8u3vuwcEdOzBlzJhWLyMH3W1xjup2ifl+C0Bszav5mUpgoA8SExchPr4+\ny9/fhN2792LGjOazOnWyPMvPzwdJSYuQkFCf5eNjwnPP7cXDDzefZafiV/jV9uGVK5ZnhYb6IC5u\nEebMqc/q08eEzMy9mDat+Sw3N8uzrKHmr1+3fJmgIKUO582rb1fPniY888xeTJ/efLucOlValBMz\ndKgyzm/aBODmOB8UhPi5c7F44cIWl622c7AoC1Bf82qozWrLDWY//HARY8YE4uDBDxEc3O+On/f3\ntzxDdW04WZ6l2rVrFn08ZtQoRA8ciC3r1gG4WYchIYifPRuLExJaXLba2WDx5qmtjZoai6MQEOCD\nBQsWYf78+qwePUx49tmWsxx+vmxRTszYsYiOjMSWtWsB3OzDe+9F/KxZWBwf3+KyRVVdLcpq6Lvv\nLmLgwEAcOfIh+va9c80bjZZnqD0vOzpanqWajda8fYVlJ7CY4cMRPWgQtmzYAOBmu+65B/Fz5mDx\nggUtLltaY/kg1auXDxISFiEurr5dQUEmZGW1PB6qmbOprUM1czbV44blp2XVSkstX0bt/nI59ne1\nm4niq1fh+cgjOLF2LYb27XvnBby8VOXYDRqEgxs2YMrw4a1fqA13SReXlMDziSdwYtkyDO3T546f\nLxrza4szwsJ8MHfuIsyereyva9dK0LevCdu27UVsrLbzeWuoeaHz+YqrFuXEjBmjzAHS0wHcHHv7\n9lXmAPPnt7zwjBkWZQHAz1VViMzLw44+fbDywgUMMBiwqXfvOy73RcY7FmfVsnSOHVz1iaocu7Aw\nHNy6FVNGjmz9trm1YmxpxvffX8TgwYE4fPhDhIbeuV0ff3wWEyZEAkCkLMvNPtaBd/wTEREREVmR\nCxcuoKCgAKNHj657z2g0Ijo6GqdOndIsp7KyEvn5+Y1yJEnCmDFjcOqDDzTLqSWqXaKzRGK7tMsa\nObJx1qBB0cjN1Sdr1KjGWVFR1l3zFy9eQGFhAYYPb5wVGRmN06ett10iffut0ocjRuhbh5WVlcg/\nexajG/xgS5IkjBk1StdxXmTNi8gSSVRtiFRZWYn8Dz/E6BEj6t6TJAljRozAqbw8zfPaow6bnlOi\nonQ4ls+fx+gGF5zq+lDnP9GkN5HnZVFsteYrKyuRf+7cre0aNQqncnM1y6nV3Hg4cGA08vKsf36o\n97ghmsj91dCV0lJIkoRuBst/YaYju1JWBglAN53utr548QLM5gIMG1a/vwwGIyIionHmDGu+NYTO\n5z/88NY5wPDhupxTAGDuF19gsrs7RnXrpsv6qe144Z+IiIiIyIoUFBRAkiSYTKZG75tMJhQWFmiW\nU1xcjOrq6ltzPD1RUKBdTi1R7RKdJRLb1XaFhUqWp+etWVrXva3WfG0feng0zvLwMMFstt52idRc\nHXp6ChznCws1y6llqzUvkqjaEKn40iWlDj08Gr1vC3VYu7+aZnl6antOabYPPTx06UORRJ6XRbHV\nmq87p3h6Ns7RqV3NzTf0GA9F1qGocUM0kfurlizLSHjmGQwNDUWomkfsdFCyLCPhr3/F0OBghPbo\noUuG2SxuPm/rNa/7fL72nHK7sdds1iyn1osFBfjw2jWk9eql+bpJO7zwT0RERETUgWVnZ8NoNMBo\nNMDV1YjKyts/xlmWZSF/j0yrHJHt6mh9qBW2q+3tevHFbLi7G+DuboCHh75ZL7yQDTc3A9zcDOja\n1XZq/sCBbPj7G26+jKiqso12ibRvXzY8PQ3w9DTAZGr/dmmVI7LmRWa9/no2IiIMiIgwIDLSiLNn\n32/T+lrS0WpDJGutw27dDOjWzYDu3dt/f2mV8/LL2QgMNCAw0ICgICNyc/WreZHn5Y7GGmu+NbTK\n2b8/G15eBnh5GeDtbVvzw440bmhF5P5qzpzt2/Hpf/6DF+/wJ+qszZw9e/Dp99/jxXnzNFtn03Fe\nz/m8rdZ8R5uz6ZHz/Y0bSPjyS/zfvffCQc3fMbkDkXNskV59NRvBwQYEBxvQp48Rp0/r3y4Vf9GG\niIiIiIhEiY2NRUxMTN1/37hxA7Iso7CwsNFvxZvNZoSHD9As193dHfb29ihscoeOuajolt/GV0Nk\nu9qrD/XGdrW9XZMnxyIqqj6rvFzJMpu1z5oyJRbR0bZX8xMmxGLgwFuziooKG93hUlxsRliY9bRL\npEmTGtdhbbua1mFRkRn9+wsa55vcNaSGyJoXmTV6dCzCw+uzTCbfNq2vJe1VGyK5d++u1GFRUaP3\nbbkOi4o0nrM114fFxZr04fjxsYiMrG+Xt7d+NS/yvNxebKnmG6o7pzS5w1Srdj34YCwGDbr9fEPr\n8bAjzg+1Hjf0JnJ/3U7cjh1488wZnExPh7cNPY487rnn8Ob58ziZnAzvrl01W2/Tcb625vWYz9tq\nzbfbfL72nHK7sbfJk2XaKr+kBEUVFYjMzYV8871qWcaJy5eR8d13KB81qk2/bCByji3SuHGxiIio\nb5eXl/7t4h3/REREREQdmIuLC4KCgupeoaGh8PLyQk5OTt1nSkpKkJubiyFDhmiW6+DggMjIyEY5\nsiwjJycHQwYPbvP6RbarvfpQb2yX9rUREqJkHT3aOOv06VzExOhTh0eONM7Ky7OumndxcUHPnkF1\nrz59QmEyeeH48cZZ+fm5iIqynnaJ5OLigsDAoLpXSIjSh8eOaV+HDTk4OCAyIgI5R4/WvSfLMnKO\nHtV1nBdZ83pkOTu7wM8vqO7VubNjo3/X8u6q9qoNkRwcHBAZHo6cY8fq3pNlGTnHj2NIdHSb198R\n6rDpOSUvT4djuX9/5Bw/XvdeXR8OGtTm9Tcd5x0d9a15Uefl9mJLNd+Qg4MDIgcMuLVdR49iSINf\n2lOrtePhmTO5iI62/vmh3uOG3kTur6biduzAa7m5OJqWBn8Nfumko4h77jm8lp+Po0uWwN/dXdN1\nNx3ng4ND4enphZMn6/fXtWslOHs2F4MGseZvp13n8+Hht84BTpzQ5JzS0Jju3fFxTAw+jI7G+Zuv\ngUYjHvH2xvmYmDbPB0TOsUVydnZBQEBQ3UvPeVQt+5SUFM1XSkRE2klNTfUGMGvWrFnw9vZu780h\nIqIOoLq6GmvXpiEkJBQVFRWYPz8e5eXl2Lp1K+zt7ZtdztKvE0ajEcuSk+Hv5wdHR0csXboU5z/6\nCLt374aLi8sdlrb8y4vadqmhug8vX251RmlZGT776isUFBUh68UXEdW/P5y6dEFlZSVcDYY7r6Cr\n5XenqG5XB/8OrbZd1dXqstavT0OfPkrWggVK1qZNLWepedphdXU10tPrsxISlKynn9Z+f6ntwxs3\n1GVt3pyG4GAl6y9/UbLWrm05q0sXdVkdvearqixfprq6Ghs21PdhUpLSro0bW26Xg12NRTlGgwHL\nUlLg36OHMs4vX66M81lZdxznZcnycVltzauhNuu//7U86+rVy/j22y/x3Xdf4403shEVNQLV1VUA\nJDg739Xscq6ulmeprg0Hy7NUq6iw6ONGgwHLVq2Cv6+vUocrV+L8xx9jd0YGXJydW1xWdnBs8d9v\nR21tyHKz/9Ri1rp1SlZ5eQUSE+NRUVGOzZtZZYpiAAAgAElEQVRbzrKvsGzwNRoMWJaWpvRh585Y\nuno1zn/yCXZv3XrHPiyrcbIoCwCuXLmMr776Ehcvfo2XX87GffeNQFWVUvMuLs3XvKPlu0v1ebmT\nyOfc2mjN21VbdgIzGo1YtmJF/TklJQXn//Uv7N6+/Y7nlErZ8kGquroaGzcq42F5eQUWLVLatWGD\nPnM2NXWods6matzQ9jTaomaeYt4itfur87f/bnXGnO3bkX38OA48+SS8u3ZF6Y0bKL1xA53s7dGp\nNR10V/PjV1Ol16/jswsXUFBcjKxXXkFU375wcnRUvuu1Zj2XLrU6a86ePcj+5z9xICFBaVd5OUrL\ny9HJzq5V7SoLCmt1Vq3q6mps3ZqG3r2Vmn/qKWV/rVmjz3y+o9e80Pl8dblFOUaDActWr64/p6xa\npYy927bd8ZyC/ftbndPZzg4enTs3emUXFCDIyQmPtOKaxaWJj7Y6q5baObZ7TVGz/9ZUaVkZPvv6\na+VYfuklRIWF1R/Lrfi5zbUulv+C0ZUrl/HNN1/i22+/xsGD2RgypL5dLc2jzOaf8Le/7QKAXSkp\nKT819zk+6p+IiIiIyMosXrwYZWVlmD17Fq5cuYJhw4bhzTf/gc6dO2uaM3PmTBQXFyN5+XIUFhYi\nPDwch996Cx4aPzKulqh2ico68/HHGPnII5AkCZIkISktDQDw2EMPYc/atZrlNCSyD0US2a6kJCUr\nLk7Juu++YTh0SJ+sRYuUrDlzlKyhQ4fhjTest+ZrxccrWQsWzMLVq1cQEzMM+/dbf7tEWrBAade8\neUof3nffMBw8qMM4//DDKL50CckrVijjfP/+OPz3v+s2zouseZFZR44cwlNP/b5+vE/6NQBg7tzl\nmDs3WdMsUbUh0syHHlLqcM0aFJrNCA8Lw+FXX4WHxnc01hJZGwsXKllz59Zn6XFOmTltGor/+18k\np6WhsKgI4ffei8MHDujWh2+9dQjz59fX/KxZSs0vXLgcCxdqW/Miz8ui2GrNz5w+XfnusHKl0q5+\n/XD4tdd0O6ckJirtio9XxsMhQ4bhlVesf34oatwQTcT+2vmPf0CSJIx46qlG7z83fz4eHT1asxwA\nOPPppxg5e3b9uf/ppwEAjz34IPYsX65p1s6cHEgARqxc2ej952bNwqP3369pVq158xbj+vUyLFw4\nCyUlVxAdPQwvvMCat4Sw+fy0afXnlKIi5Zzyyiu6nVMa0vt3qUXMsc988glG/uEP9Rnr1wMAHpsy\nBXtWrdIko6l33jmEBQvq2zV3rtKuxMTlSExse7skWc2vqxIRkTCSJEUAyM/Pz0dERER7bw4REWlM\n5HRcgrgwWfevgO1D+uZrYVly0N3CskTe/Syy5i28Aa9NRN5ZqOZONbWuXBGX5eYmLktkzV+/Li7L\nqZOKW/BUqrYTeQu5OF99JS7L319clpPlN3Wrd+2asKhq51Y8QUcjNZY9UKNNHH5u/ROG2qqoSru/\nE30nRqOwKFVPF1DNRmvevkLcCaxUxZMn1LLVOZvIJ7uUlorLcjn2d3FhXl7isr78UlhU0ZhfC8sS\nOZ8XWfNC5/MVV8WFzZghLOqLjHeEZQVXfSIs6we3vsKyPv74LCZMiASASFmWzzb3OYGnHiIiIiIi\nIiIiIiIiIiIiItIaL/wTERERERERERERERERERFZMV74JyIiIiIiIiIiIiIiIiIismK88E9ERERE\nRERERERERERERGTFeOGfiIiIiIiIiIiIiIiIiIjIivHCPxERERERERERERERERERkRXjhX8iIiIi\nIiIiIiIiIiIiIiIrxgv/REREREREREREREREREREVqxTe28AEbWOJEkGACMA+ANwBvAfAG/Lsny5\nwWf8AfSQZfmf7bKRFrLmNlnzthMRUcci/XxNWJZ8l0FYlgRZWNblK5KwLLegu4Vl2SqRtdG5s7ja\nkKoqhWXJdg7CstzchEXh55/FZRnEDYcoKxOX5dRN3I957GuqhWVdr7AXltW7t7Ao23XXXcKi7CvK\nhWXZdXYUllVt7Cosy61GWBQcKkrFhTm6CIsq7yzupOIIcWNvdWcnYVldhCWJVSPw+BLJ5cjrwrKu\n3j9ZWJars7j5/NVekcKyPN74m7Cs0qm/EZblIO4rEZxKCsWF7dkjLmv3bmFRdhXConDBua+wrMCa\n74RlFdYUtOpzvOOfqIOTJClIkqR9AAoAzAPgCOAigK4AnpMkaaskSa6SJHUG8DKAMe23ta2jVZsk\nSfpfSZIuS5K0k9tOREREREREREREREREv1S845+oA5MkKQ7AegCfAYiRZfnjJh/ZKUnSeABvAfga\nQCQAcb9yqYLGbZoHwBXA/0qStFiW5RKdNhuAdW87ERERERERERERERER2S5e+CfqoCRJSgWwDEAe\ngNGyLN/2eWiyLL8lSdJdAPYDAp+bqoIObcoAsA7AKwIu+lvtthMREREREREREREREZFt46P+iTog\nSZKmQ7nI/DOAac1dZK4ly/IBAAdEbJtaerRJluVdsiy7ybL8B+229FbWvO1ERERERERERERERERk\n+3jhn6iDkSTJCOAZKHeLr5Nl+adWLroegKTbhrWBNbfJmrediIiIiIiIiIiIiIiIfhl44Z+o40kA\n4Hbz//+1tQvJsnwawEe6bFHbWXObrHnbiYiIiIiIiIiIiIiI6BegU3tvABHd4o83//dLWZYvWrjs\ne1pvjEY0b5MkSR4A3AF4APCQZfnlNmxfS6x524mIiIiIiIiIiIiIiOgXgHf8E3UgkiT1BeAH5bHy\nn6hYRTaAww3WN1uSpO8lSapp8CqQJGlyg8/ES5JU1eDff5QkyavNjalfv6ZtaqDw5vqOAdivdvta\nYs3bTkREti1z1y4EhoXBydMTMaNG4XR+vq55ycnJ8PX1gYuLMx54YCy++uorXXIyMzMRGBQEJ2dn\nxAwejNOnT+uSU2vNmmSEhvrA19cZ06aNxTff6NMuQFwfis4SRXRtCKv5HTsQ2Ls3nIxGxAwditNn\nzuiSU8tW63DVqmT07u0Dk8kZsbFj8fXX1l/zAJCWloy+fX3g5+eM6dP1G6NEHl+Z27cjsFcvON11\nF2KGDNH9WF6xIhlBQT7o3t0ZkybpWxu2enyJIrQOd+5EYHAwnNzcEHP//TY19i5fngw/Px8YDM4Y\nN07frJSUZAQE+MDV1RkTJuh4rszKQmDfvnByd0fMyJG6z3tFSk1NRmCgD7p2dcbEifqNUaLHXpF1\naKtZoo4vEdJeeglRSUkw/upXMP32t5i2Zg2+/OEH3XNXr05Gnz4+8PZ2xtSpOs6jBM/n9W5X2qFD\niEpOhvHxx2GaMwfTNm/Glz+19i/fqrdyZTJ69fKBh4czJk+27vn8yQ8+wJRHH4VveDjsvL1x6PDt\nLg9oY2duLvpv3QrX1FS4pqZiyM6deOvLL3XJOpmXhymPPw7f6GjYBQbi0Dvv6JLT0Ntvv4o//nE8\noqM9EBxsh88/1+8By4cPv4rf/W48Bg70wN132+Gzz7TPOpmbiyl/+AN8Bw2CXUAADr39tuYZAC/8\nE3U0Axr8/+8tXViW5X/Kspzb4L93yrLcA8DC2nXKsuwly/LrDT6zFcAcADUA/iDLso8sywXqNv+2\nNG1TAx4AZqveqtax5m0nIiIbte/ll5G0ZAlSn3wS5957D/3DwjBu2jQUX7qkS156ejoyMzOwc2cW\ncnPz4OLigvHjx6GiokLTnH379iFp4UKkpqTg3Nmz6N+vH8aNH4/i4mJNc2pt2ZKO3bszsGlTFt55\nR2nXjBnatwsQ14eis0QRXRvCan7/fiQtXozUZctwLi9PadeDD1p9u0Rnbd6cjl27MrBlSxaOHs2D\ns7MLpk2z7poHgK1b0/HssxnYuDELb7+ttGvmTOsee/ft34+kRYuQunw5zp05g/79+2PcxIm61fzG\njenIysrAtm1ZOHFC6cMpU6y/5jnOtzHrpZeQ9Oc/K2Nvbq4yj5o82SbG3nXr0rF9ewa2b8/CqVNK\n1sSJ+mStX5+OHTsykJmZhfffV46vSZN0GKMOHEDSU08hdckSnHv/ffS/916MmzpVt/0l0oYN6di5\nMwMZGVl47z1lf+nSh4LHXpF1aKtZoo4vUU5++inmTZqE3A0b8O7KlaisqsIDy5fjenm5bplPP52O\nZ57JwObNWcjJUfrwoYesfz4vol0nv/gC8x54ALmpqXj3ySdRWV2NB9LTcV3H+tu0SZnPb92ahWPH\nlONr6lTrrfnSsjKE9+2LzLQ0SJKka5afqyvSx49Hflwc8uPiMCooCLF//Ss+M5s1zyotK0N4aCgy\nV6zQvV21rl8vRWTkUCxalK575vXrpRg4cCgWL9Yvq/T6daU2Vq3StT2SLMu6rZyILCNJUhKA9VDu\nMF8jy/IyjdbrCOAnAK4A7pFl+Zsm/z4ewCOyLD+iRV6TdevSppvrdgRwHYAsy7K9VuttsP4Ose2S\nJEUAyM/Pz0dERIRWm0BERB3FtWsWfTxm1ChEDxyILevWAQBkWYZfSAjiZ8/G4oSEFpeV7zJYvHm+\nvj5YuHAREhMTAQAlJSXw8jLh+ef3YubMmc0uJ8Gy7xkxgwcjOioKW7ZsUbZVluHn74/4efOwePHi\nFpe9fMXyL0yhoT6Ii1uEOXPq29WnjwmZmXsxbVrz7XJzszhKdR+qoXp/ifnerrDwO2hbakOG5Q1T\n3YdVlRblxAwdqrRr0yZlW2UZfkFBiJ87F4sXLmxxWbmTg0VZgHXU4c8/W57Vu7cP4uMXIS6uPqtX\nLxN27tyLhx5qPstg+XComprfy+rbVxmjnnhCade1ayUICTEhI2Mvpk5tvl3du4k7vlBTY1nWkCGI\njo7Gls2b67N69kR8XBwWL1rU4rLXKyz/uhcU5IPExEWYN6++Nnr2NOGZZ/Zi+vTm+7BLF4ujrOL4\nstVxHhZeFIi5/35EDxqELRs31mf16oX4OXOwOCmpxWXlzo4WZQHq95eFhxcAwM/PB0lJi5CQUJ/l\n42PCc8/txcMPa5sVEOCDBQsWYf78+qwePUx49tmWsxwqSi3KiRk5Upn3rl8P4Ob+Cg5G/BNPYPHN\nPm2Wi4tFWW2h5vplYKAyRsXH1/ehv78Ju3fvxYwZzfehY6dqi3LaMvZWw/KxV20dqmENWUKPL8un\nh+q9/vqdP9OM4pISeP72tziRloahoaF3/PzV+yff8TNN9enjg3nzFmHu3Po+7N3bhB07Wv6u5+os\nbj5/tczyHaa6XW/8zeKsWsXXrsFzzhycWLoUQ4OD7/j50qm/sTijVy8fJCQ0ns8HBZmQldXynE3g\nMA8UFqpazM7bGweffx5Txo1r/UJ79qjKqtV95UpsmDgRv4+MvPOH/+d/VGXYBQbi4K5dmDJ2bKuX\n+XdFgKosAPjhh4sYNSoQr732Ifr06XfHz3dqwx+5/+GHi7j//kC88caHCAm5c1Zgp+9U5dgFBODg\nM89gygMPtHqZsx9/jMgHHwSASFmWzza7blVbRER6aXjG1+xruSzL5QD23fzP393mI/8fgCyt8prQ\npU1AXbv0ZM3bTkRENqiyshL5H36I0SNG1L0nSRLGjBiBU3l5mudduHABBQUFGD16dN17RqMR0dHR\nOHXqlGY5lZWVyM/Pb5QjSRLGjBmDUx98oFlOrYsXL6CwsADDhzduV2RkNE6f1q5dgLg+FJ0liuja\nEFrzZ89i9MiRde9JkoQxo0ZZdbtEZ337rXIsjxjROGvgwGjk5VlnzQPKGGU2F+D+++vbZTAYERGh\n7Rgl8viqq/lRoxpnjR6tS803VxuDBkUjN9d6a57jvAZZtxt7R47EqdzbPayvbdqjNkaNapwVFaVf\n1siRt2ZpeXxVVlYi/9y5W+e9Ou0vkZrrQ63HKNFjb3vUoa1m6X18tacrP/8MSZLQ7a67dFl/7Rzg\ndt/1tJwfip7Pi2pXU1dKSyEBuu8vW5vPt4eamhq8eP48yiorMdjPr703h9oRL/wTdSwNn8Fi1Hjd\nz0G5eP1owzclSXIBEC7L8kmN82rp2Sa9WfO2ExGRDSq+dAnV1dUweXg0et/k6YkClb+B3pKCggJI\nkgSTydQ4z2RCYaF2fxmouLhYaVfTHE9PFBRo+ReIFIWFSrs8PBrneXiYYDZrmyeqD0VniSK6NjpE\nzVvxsSw6q/ZY9vRsnOXpab01DwBms5gxSuTxVZfl6al7FiC2Nmz1+BKlQ9ShycSx1wK1x1fTLE9P\nk6b7rG7ee7txQ4dHGIvU3BhlMmnch4LHXluteVs8vtqLLMtI2L0bQ0NCEOrvr0tG7TzqdnMAYfMo\nHc4potrVkCzLSPi//8PQ4GCE+vrqktHcd3Nrn8+L9K+CAhhSUuCYnIw5hw7h1UceQZ8m4z79svDC\nP1HH8lGD/9/i2VSSpJWSJP1bkqQfJUn6qcnrG0mSGj0HSZblPACfAvCTJGl0g396GMABSzZSkqT+\nkiRdlCTphCRJd3pIpm5tUsOat52IiKg5sixr8vfBsrOzYTQaYDQa4OpqRGXl7R91qFXenWiVc+BA\nNvz9DTdfRlQ180h2LfJE9mFH218iseZbZqt1uH9/Nnx8DPDxMcDXV99jWaQDB7IREGBAQIABPXs2\n34eAddWhyKx9+7Lh6WmAp6cBJpPt1HxHG6NEssY6FLm/XnghG25uBri5GdC1q/5Z3boZ0K2bAd27\nt38dWmO9v/hiNtzdDXB3N8DDw3b6UHQd2mpWRzq+9DZnxw58+t13ePEOf2bCEi+9lI0ePQzo0cMA\nP7/270OtcjpCu+Y8/zw+/eEHvDh3rmbr3L8/G15eBnh5GeDt3f77yxb08fDA+fh45D7xBJ6Ijsaj\nL72Ez63wF+Refz0bAwYYbr6MyM9/X7es117LRliYAWFhBvTrZ8SZM/pltYc2/KUDItJBPpS7zD0B\nhLf0wZt/b36ZJEmuAP4XwDoof4t+nyzLzf1xlr0A0qE87j/n5nu/xe0f/9+S3wPwA9ADwEgAh1r4\nrN5tspQ1bzsREf3CuXfvDnt7exQWFTV631xUdMudPGrExsYiJiam7r9v3LgBWZZRWFjY6E4Gs9mM\n8PABbc6r5e7urrSryZ0R5qKiW+6gUGPChFgMHHhru4qKChvdMVFcbEZYWNvaJbIP22t/iaR3bXTI\nmreyY1lk1oMPxmLQoPqs8nIly2xufCwXFZnRr5/11HzTMaq2XU3HqKKito9RDel9fN02q8kPIbXK\nmjQpFlFRt9ah2dy4DouKzOjf33pqnuO8oDo0m61u7J0yJRbR0R0rq6hI43Nl7bz3duNGk6dfdXST\nJzceoxqev4TMN3QaeztiHdpqltbHV3uI27kTb+bn4+TatfDu1k2z9U6c2Hh+2HAOoOf8UO/5fHu1\nq1bc3r1488MPcXLZMnh37arZepvO5xt+N9d6zvZL0cneHkE3j6kIX1/kff89tvzzn9gxdWo7b5ll\nRo+ORf/+9bXh5aXPUyYAYOzYWAwYUJ9lMumX1R54xz9RByLLsgzlkfwA0FOSpHtascxVAC81eOvz\nFj7+VwDVAKZJkmSQJKkngGpZlr+zcFNfBPATgHMATtxh+/Ruk6WseduJiOgXzsHBAZHh4cg5dqzu\nPVmWkXP8OIZER7d5/S4uLggKCqp7hYaGwsvLCzk5OXWfKSkpQW5uLoYMGdLmvFoODg6IjIxslCPL\nMnJycjBk8OA2r9/FxQU9ewbVvfr0CYXJ5IXjxxu3Kz8/F1FRbWuXyD5sr/0lkojaaLeaj4hAztGj\nde/Jsoyco0etrl2iswIDg+petcfysWONs86cyUV0tPXUfNMxKjg4FJ6eXjhxor5d166V4OzZto9R\nDel9fN2SFRGBnCNHGmcdOaJZzTesjZCQ29fG6dO5iImxrprnOK9xVnNjb4ML9mp1hNo4cqRxVl6e\nfllHj96a1dbjqyEHBwdEDhhw67z32DFN9pdITfswJOT2fajFGNWQiLG3vevQVrP0Pr5Ei9u5E6/l\n5eHo6tXw1/gXdyz5rqfl/FDEfL492gUoF/1fy8/H0SVL4O/urum6Wztns7b5fEdSU1OD8urq9t4M\nizk7u8DfP6ju1bmzY6N/1/IJEE2zHB31y2oPvOOfqOPZCOWudI+b//uUViuWZblAkqS3AEwE8P8B\n8IHyFABL1/MB7vDo+yZ0a5OlrHnbiYiIAGBBXBwemz0bkeHhiBo4EJszM1FWVobf/eY3uuTNn5+A\n1atXoVevXujZsyeSk5ehR48eiI2N1TRnQWIiHvvd7xAZGYmoqChs3rxZadfvfqdpTq3ZsxOwceMq\nBAX1gr9/T6xZsww+Pj0wcaK27QLE9aHoLFFE14awmp8/H4/98Y+IHDAAUYMGYfPWrUq7Hn1U05xa\ntlqHc+YkYP36Vbj7buVYXrVKOZYffNB6ax4AZs1KwKZNqxAYqLQrLW0ZvL17YMIE6x17FyQm4rHf\n/x6RERFKzW/ZomQ99pjmWQAQF5eA9HRlnA8I6IkVK5bB17cHJk2y7prnON/GrPh4PPb4443H3uvX\n8bvf/lbzLEDs/oqPT8CaNcp42LNnTyxfrmRNmaJ91rx5CUhLU7ICAnoiJUWfrAVxcXhs1ixlf0VG\nYnNGhrK/dJr3ihQXl4C1a+v7MDVVGaMmT9ZhnBc49oqsQ1vNEnV8iTJnxw68cOIEDi1dChdHRxRe\nvgwAcHVxQZfOnXXJfOKJBGzYUP9db/Vqfb7riZ7Pi2jXnOeewwunTuHQggXK/rp6FQDg6uSk2/6a\nOzcB69bVt2vVKv3mbCKUlpbiq2+/hXJPIfDNxYs4/8kn6ObmBj9fbe8sX/L225jQuzf8XF1xrbwc\nfzt/Hse//RZvjxypaQ4AlJaVNW7Xf/6D859+qrTLx0fzPAC4evUyfvzxPygs/AGyLOObbz6HLMvw\n8PCCu7u2T4aqzSooqM8CZLi7e8HDQ5ssUX3IC/9EHYwsy8WSJD0O5RH08yVJ2iXL8rd3WMzBgojn\nATwI5SJ2FwC6/+qcgDbpxpq3nYiIbNPMhx5C8aVLSF6zBoVmM8LDwnD41VfhofFv4tdavHgxysrK\nMHv2LFy5cgXDhg3Dm2/+A501/tI/c+ZMFBcXI3n5chQWFiI8PByH33oLHjo9yjU+XmnXggWzcPXq\nFcTEDMP+/dq3CxDXh6KzRBFdG8Jq/uGHlWN5xQqlXf374/Df/2717RKdlZCgZM2frxzLgwcPw8sv\nW3fNA8oYdf16GZKSZqGkRBmj9u2z7rF35sMPK1kpKfU1/+abutX8ggVKbcybp9TGffcNw8GD1l/z\nHOfbmDVjRv3YazYrdfj66zYx9i5apGTNmaNkDR06DG+8oU/WwoVK1ty59VmHDukwRk2fruyvVauU\n/dWvHw4fPKjb/hIpKUnpw7g4pQ/vu0+nPhQ89oqsQ1vNEnV8ibLzrbcgSRJGLFnS6P3n4uPx6KhR\numTOn6/0YWJi/fzwwAHrn8+LaNfOI0cgARixenWj95/705/w6LBhmuU0lJiotCs+XmnXkCHD8Mor\n1lvzZ86fx8jp0yFJEiRJQlJKCgDgsZkzsefppzXNKvz5Zzz60kv46do1uHbpgn5eXnj797/HqLvv\n1jQHAM589BFG/vrX9e26WSOPTZ+OPevXa54HADk5h/Dkk7+vy1yw4NcAgLi45YiLS9Y06913D2Hx\n4vqs+fOVrPj45YiP1ybrzEcfYeSvflXfh6tWAQAemzEDezZs0CQDAKTa3ywgoo5FkqQUAMsAfAZg\nhCzLxS189tcA/gblb8qnyrK8ooXPOgD4EUA3AHtkWf5fLbe7JXq0SZKkGihP5bfXfosb5aSgnbZd\nkqQIAPn5+fmIiIhQ2QIiIuqwrl0TFiXfZRCWJUHc94zLV8Q9hs3NTViUUEKfZCfwO6gMcQ2TqiqF\nZcmdbPP3TH/+WVyWQdxwiEuXxGV17ybwZzw1NcKirlfo+nWvkS5dhEUJZavjPCoqhEXJTR45qyeB\nh5fQLIeKUnFhLi7CosrLhUXBsZO4xzdXQ9zYa6uEHl8ip4evvy4s6ur9k4VluTqLm89fLRO3w1zf\n+JuwrNKp4p7IInCYBwoLxWXt2SMu63/+R1jUvysChGV1EnjLe2AnS/+KtnpnP/4YkQ8+CACRsiyf\nbe5zdsK2iIgsIstyCoDHAfQCcEqSpOG3+5wkSSEAlgNo1a8EybJcCSD75n8KPIto3yZJkgIb/H8/\n7bb0Vta87URERERERERERERERGTb+Kh/og5MluXnJEk6A2AVgHdu/v/XAXwLwBXAUAD3APi9LMun\nJEkqAlp1W9PfAIyXZfmUPlvePC3aJEmSN4AjAAKAutsJP5ck6YIsy/dy24mIiIiIiIiIiIiIiOiX\nhBf+iTo4WZY/BhArSZIHlAvLgQD8ARQCWCXL8ucNPtvaP6YSAuB5jTe11draJlmWf4LSBuGseduJ\niIiIiIiIiIiIiIjINvHCP5GVkGW5CMCrGq3uMQC/1WhdqmncJqGseduJiIiIiIiIiIiIiIjItti1\n9wYQkX4kSQqVJOkjSZKWNHivD4Brsiz/0I6bRkREREREREREREREREQa4YV/Its2AcC9AP7Q4L1k\nKH+jnoiIiIiIiIiIiIiIiIhsAC/8E9m29wBUAVgDAJIkJQEok2X5dLtuFRERERERERERERERERFp\nhhf+iWyYLMu5AJ4AECdJ0r8AeAD4U/tuFRERERERERERERERERFpqVN7bwAR6UuW5WcBPNve20FE\nRES3V9nFIC6sSlxUTY0kLOuuu4RFoaREXJZrzWVxYV27issSqKJCXJZjlbiwihoHYVkiGaoE1jzE\n1Xx3t2phWZVV9sKy7OzEZTlVXROWhRpnYVGlN8T1oYuLsChUC5wD2HfuLCyrSuA8yuHbf4sLC7pH\nWFRlZ3GFKPJMKbAMhY7zDj9eFJZV7hUgLKumRlgUnEoKxYWZTMKiyh+YLCyrs8D9JXKgdy3+j7Cs\nyxN/Iyyrk8j9JVDpXeKOL5epU4VlFcqlURIAACAASURBVDkLHHtvCIuC0Sgu6xL8hGVd6VLUqs/x\njn8iIiIiIiIiIiIiIiIiIiIrxgv/REREREREREREREREREREVowX/omIiIiIiIiIiIiIiIiIiKwY\nL/wTERERERERERERERERERFZMV74JyIiIiIiIiIiIiIiIiIismK88E9ERERERERERERERERERGTF\neOGfiIiIiIiIiIiIiIiIiIjIivHCPxERERGRFUpJSUZAgA9cXZ0xYcJYfPXVVzaRlZqajMBAH3Tt\n6oyJE8fi669to12rVyejTx8feHs7Y+rUsfjmG32yMnfvRmB4OJx8fBAzdixOnz2rS45omZmZCAwK\ngpOzM2IGD8bp06d1zRNVh5lZWQjs2xdO7u6IGTkSp/PzdcmpJfL4EtaHtlrz27cjsFcvON11F2KG\nDNG95kWOh8uXJ8PPzwcGgzPGjdMvK3PXLgSGhcHJ0xMxo0bpenyJ3l8rVyajVy8feHg4Y/JkfY9l\nkYTVhuBzit7H18nTpzFl9mz4Dh0Ku+BgHMrJ0XT9zRG1vwCxY5RIycnJ8PX1gYuLMx54wLr78GRe\nHqY8/jh8o6NhFxiIQ++8o+n6myNybrNiRTKCgnzQvbszJk3SPuvkBx9gyqOPwjc8HHbe3jh0+LCm\n6+8IbGl/1crcuROBISFw6tYNMcOH4/SZM5pnnDx9GlP+9Cf43ncf7O65B4fefVfzjNtZsyYZoaE+\n8PV1xrRp+n2HBYBVq5LRu7cPTCZnxMbaztxG7znbyTNnMGXuXPiOGAG7vn1x6MgRTdffnLVrkxEW\n5oOAAGfMmKFfbbzzzqt4/PHxGDzYAyEhdvjii490yamVlpaMvn194OfnjOnT9a15PbN44Z+IiIiI\nyMqsX5+OHTsykJmZhfffz4OzswsmTRqHiooKq87asCEdO3dmICMjC++9lwcXF9to19NPp+OZZzKw\neXMWcnKUrIce0j5r3yuvIGnZMqT+5S84d+wY+t97L8bNmIHiS5c0zRFt3759SFq4EKkpKTh39iz6\n9+uHcePHo7i4WJc8UXW478ABJD31FFKXLMG5999X9tfUqVbfLpFZNlvz+/cjadEipC5fjnNnzqB/\n//4YN3GibrUhcjxcty4d27dnYPv2LJw6pdTGxIk61MbLLyNpyRKkPvkkzr33HvqHhWHctGm61Ibo\n/bVpUzp27crA1q1ZOHZM6cOpU/XZXyIJqw3B5xQRx1fp9esIDwlB5vLlkCRJs/W2RNT+AsSOUSKl\np6cjMzMDO3dmITdX6cPx4623D0vLyhAeGorMFSuE1aHIuc3GjenIysrAtm1ZOHFC6cMpU3Tow759\nkZmWJqwPRbK1/QXcnM8/+SRSly7FuVOnlPlGbKzm55S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anayDh4EPHLlyP5pZdw+dw5hA4bhikxMcjPz9c1p6i4GGFDhmDn2rVQ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\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ZOHH8OOJGjMCU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\nv2bOtP8YJXVemffKK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4r+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Ka60kewH/STuvv5jW0/+sERcfnN+P0gF48L3A7wRakOW4zphkK6b+B5w3Q5WR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10da699e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bt2+pGHjv/KmFbpuqGm41Q5DkSOC7wDOq6pqB9vOBX62qX5lnmxcDF89tlyRJ\nu+x3q+qTC9lgsc5IzAAPAofPaT+Mh89SzFoL/C7wHeC+kVUmSdLS81PAL9C9ly7IopyRAEjyj8A1\nVfW6/nWADcAFVfXfx1qcJEkCFu+MBMC7gI8muY6HLv98LPCRcRYlSZIesmiDRFV9KskE8Ha6Uxzf\nAE6pqn8bb2WSJGnWoj21IUmSFj+ftSFJkpoZJCRJUrMlGSSS/FKSTyf5tyRbknw1ybPHXdcoJDk1\nyT8m+WGSO5NcOu6aRiXJ/km+kWRbkieNu55hSvLvk1yU5F/77+VNSc7t7/K6x0tyRpKbk2ztf16f\nNu6ahinJm5Ncm+TuJJuS/E2SY8Zd16j1x70tybvGXcuwJVme5ONJZvp/k99MsmLcdQ1Tkn2SnDfw\ne+dfkrx1oftZkkECuIzuzpj/AVhB9+TQy5IcNs6ihi3JC4CPAR8Gngj8CrCgG4nsYc4HbgOW4sKe\n44AArwSeQHeV0quB/zbOooZh4AF8bwNOoPv3uLZfTL1UPAt4D3Ai8BvAY4AvJDlwrFWNUB8GX0n3\n/VxSkhwCXAn8CDgFOB74L8DDbwW7Z/sj4FXAH9L9DjobODvJmQvZyZJbbJnkZ4F/A55VVVf2bT8N\n3A38RlX93TjrG5Yk+9LdfOucqvrIeKsZvSTPBf4v4AXAt4CnVNU/jbeq0UryBuDVVfX4cdfyaOzg\nnjC30t0TZkk+gK8PSXcAJ1fVP4y7nmHrf6deB7wGOAe4vqpeP96qhifJO+jurLwkZ7JnJfkcsLGq\nXjnQdgnww6p6ya7uZ8nNSFTV94EbgZckeWyS/eg+2W2i+8FfKlYAywGSrEtye5LPJ3nCmOsauiSH\nAx8EVgF7/uMqd90hwJ3jLuLRGHgA35dm26r79LLUH8B3CN3M2R79/duJ9wKfWyofzObxW8DXk3yq\nP1W1Lskrxl3UCFwFrEzySwBJngw8E/j8QnayaO8j8Sj9R+DTwD3ANroQ8ZtVtWWsVQ3X0XRT4W+j\nmwa/BXgD8OUkv1RVd42zuCH7v4H3VdX1Sf79uIvZHZI8HjgT2NM/5bU8gG+P1s+4vBv4h6r61rjr\nGbYkLwKeAjx13LWM0NF0sy3vpDu9eCJwQZL7quoTY61suN4BHAzcmORBusmFt1TVXy5kJ3vMjESS\n/7Nf1LOjrwcHFje9j+4X1TOBp9GFiv+n/2S7qC3gOGe/d39SVZ+uquuBl9F9CvrPYzuAXbSrx5nk\ntcDPAH8MmVPRAAADGElEQVQ2u+kYy16wBf7czm7z74D/F/ifVfUX46l85MLSXOsC3e+fJwAvGnch\nw5bk5+lC0qqqemDc9YzQPsB1VXVOVX2zqj4IfIguXCwlLwReTPezegLwUuCNSf63hexkj1kj0a99\n+NlHGPavwLOBy4FDqurege3/P+CixX5OdgHH+avA39E9DfWqge3/EfjbqjpndFU+ert4nDcDnwJO\nm9O+L/Bj4OKqetkIyhuaXf1+VtWP+/HLgSuAqxb7se2K/tTGD4EXVNVnB9o/Aiyrqt8ZV22jkORC\numnxZ1XVhnHXM2xJfhu4lO6hirOhfl+6UPggcEDtKW8qO5HkO8AXquoPBtpeTfdp/XFjK2zIkmwA\n/rSq/nyg7S10TwDd5dPke8ypjX7tw/cfadzAKum5P8zb2ANmYBZwnNfRrSg+lu481+wv7V+gO82x\nqC3gOM8C3jLQtJzu6XT/K90zWBa1XT1O+MlMxN8BXwNePsq6dpeqeqD/WV0JfBZ+MvW/ErhgnLUN\nWx8ifht49lIMEb0v0l0hNugjwHrgHUshRPSu5OGn3o5lD/jdukCPZQjvlXtMkFiAq+ku0flokvPo\nFuf9Ad0b7GVjrGuoquqeJH8O/Nckt9H9gJ9N90PxV2Mtboiq6rbB10nupfsk9K9Vdft4qhq+JEcC\nf093Jc7ZwGHd+y1U1dz1BXuaJf8AviTvA6aA5wH3DpxG3VJV942vsuHqZ3m3W/fR/5v8flWtH09V\nI7EGuDLJm+lmRU8EXkF3uetS8jngLUluBf6ZbhH/auCihexkyQWJqvp+kt+kWyDzJbrruf8ZeF5V\n3TDW4obvDcADdPeSOBC4Bvj1JbaodD5L5VPPoOfQLfA6mu7SSHhoHcG+4ypqGPaSB/C9mu579fdz\n2l9G9+9zKVty/x6r6utJfoduMeI5dKdZX7fQRYh7gDOB8+iuwjkMuB14f9+2y/aYNRKSJGnxWfRr\nBiRJ0uJlkJAkSc0MEpIkqZlBQpIkNTNISJKkZgYJSZLUzCAhSZKaGSQkSVIzg4QkSWpmkJAkSc0M\nEpIkqdn/D/dfGCeRV6EuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee8e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "whack = pygsti.objects.GateString( ('Gi',)*256 )\n",
    "fullGatestringList = pygsti.io.load_gatestring_list(\"tutorial_files/Example_LSGSTlist256.txt\")\n",
    "pplt.whack_a_chi2_mole_boxplot( whack, fullGatestringList, Xs, Ys, gateStrDict, ds, gs_mc2gst, strs, xlbl, ylbl,\n",
    "               scale=1.0, sumUp=False, histogram=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10d004cc0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bTulaik/EXdjhPKNdPtZAjd+zEZePNVBztqb/TKMHZc2koSTtZvvBunO0amKm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\nRFT2/wG0YU05i5aGfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d271668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.gof_boxplot_keyplot(strs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10e01c6d8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAM0AAACkCAYAAAA0R6r7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJXJozBI5NGaJHBqzRA6NWSKHxizR/wP0Usnn4IkS5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d08d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.gate_matrix_boxplot(gs_target['Gx'], mxBasis=\"gm\", mxBasisDims=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10c6e7e10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QwS8ATZsi1deX70lbUK9evbB79241PDy8oqIou4hoqKs7kBHRi0R0gIiSnI8/iOiRLM8P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\nZEBLktTQnw9LnJzA8RRyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e02bc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.polar_eigenval_plot(gs_mc2gst['Gx'], gs_target['Gx'], size=(4,4), title=\"Gx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pygsti.report.figure.ReportFigure at 0x10ee29940>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UJIOuJEmSimTQlSRJUpEMupIkSSqSQVeSJElFMuhKkiSpSAZdSZIkFcmgK0mS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\n7CtRFdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e038e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pplt.choi_eigenvalue_barplot([2,4,5,7,-2,-3],[1,2,1,1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}