OpenMC/examples/jupyter/nuclear-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will go through the salient features of the `openmc.data` package in the Python API. This package enables inspection, analysis, and conversion of nuclear data from ACE files. Most importantly, the package provides a mean to generate HDF5 nuclear data libraries that are used by the transport solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from pprint import pprint\n",
    "import shutil\n",
    "import subprocess\n",
    "import urllib.request\n",
    "\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "import openmc.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical Data\n",
    "\n",
    "Some very helpful physical data is available as part of `openmc.data`: atomic masses, natural abundances, and atomic weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.939608986"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_mass('Fe54')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00015574"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.NATURAL_ABUNDANCE['H2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.011115164862904"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_weight('C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The IncidentNeutron class\n",
    "\n",
    "The most useful class within the `openmc.data` API is `IncidentNeutron`, which stores to continuous-energy incident neutron data. This class has factory methods `from_ace`, `from_endf`, and `from_hdf5` which take a data file on disk and parse it into a hierarchy of classes in memory. To demonstrate this feature, we will download an ACE file (which can be produced with [NJOY 2016](https://github.com/njoy/NJOY2016)) and then load it in using the `IncidentNeutron.from_ace` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = 'https://anl.box.com/shared/static/kxm7s57z3xgfbeq29h54n7q6js8rd11c.ace'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'gd157.ace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load ACE data into object\n",
    "gd157 = openmc.data.IncidentNeutron.from_ace('gd157.ace')\n",
    "gd157"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Python, it's easy to explore (and modify) the nuclear data. Let's start off by reading the total cross section. Reactions are indexed using their \"MT\" number -- a unique identifier for each reaction defined by the ENDF-6 format. The MT number for the total cross section is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Reaction: MT=1 (n,total)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = gd157[1]\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross sections for each reaction can be stored at multiple temperatures. To see what temperatures are available, we can look at the reaction's `xs` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7f60230c5320>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the cross section at a particular energy, 1 eV for example, simply get the cross section at the appropriate temperature and then call it as a function. Note that our nuclear data uses eV as the unit of energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.64747"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs['294K'](1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `xs` attribute can also be called on an array of energies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 142.64747   ,   38.6541761 ,  175.40019642])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs['294K']([1.0, 2.0, 3.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation for each temperature present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': array([  1.00000000e-05,   1.03250000e-05,   1.06500000e-05, ...,\n",
       "          1.95000000e+07,   1.99000000e+07,   2.00000000e+07])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f60205634a8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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bt26w996VPZ0DrR8xEZHyknXnV3dfBOxjZsOAgYQkZZq7P5bv4AqttraW7t27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\n1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60225bc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energies = gd157.energy['294K']\n",
    "total_xs = total.xs['294K'](energies)\n",
    "plt.loglog(energies, total_xs)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reaction Data\n",
    "\n",
    "Most of the interesting data for an `IncidentNeutron` instance is contained within the `reactions` attribute, which is a dictionary mapping MT values to `Reaction` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<Reaction: MT=2 (n,elastic)>,\n",
      " <Reaction: MT=16 (n,2n)>,\n",
      " <Reaction: MT=17 (n,3n)>,\n",
      " <Reaction: MT=22 (n,na)>,\n",
      " <Reaction: MT=24 (n,2na)>,\n",
      " <Reaction: MT=28 (n,np)>,\n",
      " <Reaction: MT=41 (n,2np)>,\n",
      " <Reaction: MT=51 (n,n1)>,\n",
      " <Reaction: MT=52 (n,n2)>,\n",
      " <Reaction: MT=53 (n,n3)>]\n"
     ]
    }
   ],
   "source": [
    "pprint(list(gd157.reactions.values())[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's suppose we want to look more closely at the (n,2n) reaction. This reaction has an energy threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 6400881.0 eV\n"
     ]
    }
   ],
   "source": [
    "n2n = gd157[16]\n",
    "print('Threshold = {} eV'.format(n2n.xs['294K'].x[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (n,2n) cross section, like all basic cross sections, is represented by the `Tabulated1D` class. The energy and cross section values in the table can be directly accessed with the `x` and `y` attributes. Using the `x` and `y` has the nice benefit of automatically acounting for reaction thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7f60230f1128>}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6400881.0, 20000000.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ac4CS3S1/Ad8ljks1M2NGWO/j73+HXXeNnUZEJD+deioMGxaWQfj0U2jQIHai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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60204240b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = n2n.xs['294K']\n",
    "plt.plot(xs.x, xs.y)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')\n",
    "plt.xlim((xs.x[0], xs.x[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7f6022803080>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neutron = n2n.products[0]\n",
    "neutron.distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the neutrons emitted have a correlated angle-energy distribution. Let's look at the `energy_out` attribute to see what the outgoing energy distributions are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.stats.univariate.Tabular at 0x7f60230f1208>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f12b0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f1358>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f14a8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f16a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f1908>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f1c18>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230f1f98>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60228413c8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022841828>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60230d76d8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022841cc0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022849278>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022849828>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022849e48>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602284f4e0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602284fba8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60228562e8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60228569e8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602285c198>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602285c940>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022864160>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022864978>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602286a208>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602286aa90>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022870390>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022870ba8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f60228774a8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f6022877da0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f602287d710>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = neutron.distribution[0]\n",
    "dist.energy_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see we have a tabulated outgoing energy distribution for each incoming energy. Note that the same probability distribution classes that we could use to create a source definition are also used within the `openmc.data` package. Let's plot every fifth distribution to get an idea of what they look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ajS0yMpJevXphZmaGs7MzM2bMIC+v5uX15OTkqrZv9TTp0NBQVCoVlpa636BR\nycfHh+bNm9+2Ts+ePXF2dkatbrw/0+VDFnVw/NJx/Jz90FPdsHH2wgUALllaQk4ODgYGFCYXkrEy\ng+bvNkffUvtbm539M9evR9Ox4x93bbblVoZ3mMXU3z7C2mkKI5sqpKcv57xSiMOqTvw0P4SRH2wm\n1d2U5z9LJ/9EPm03tMXQXj6cUZKk2lMUhQULFmj9xdqiRYs7alsIQf/+/Tl58iTPP/883t7eJCQk\n8J///IfffvuN+Ph4zMzMamxn5syZBAQEaJTdPLa4uDj69+9P27ZtWbp0KefPn2fx4sUkJCSwbds2\nncZrYmJCeHg4c+fO1SjPz89n69atmJiY6NTOzcaMGcOcOXPYs2cPvXr1qnY8OTmZ/fv3M336dFSq\nxjtvIQMXHZy4dIKQTiGahRcvgpERl/T1MVIUzPX0OP1eIvrW+ri84KK1ncq9LVZWgVhb970LI789\nCyMLnmr3FF8d/pFXH0qgWbM3yMj4mrS0L7j4WRGfxg9m1eB5zHV/iEULcokJiKH9T+2x6GRRc+OS\nJEk3efTRR/Hz86v3dvfv3090dDTLly9nypQpVeUtW7bk2WefZefOnQwbNqzGdnr16sUTTzxx2zpz\n586lSZMm7Nq1qyoYatasGZMmTWLnzp3079+/xn4GDRrEjz/+yNGjR+nQoUNV+U8//URJSQmPPvoo\nf/75Z43t3CwkJIQ5c+YQFhamNXAJCwurqteYNd6Q6y7KuJ6hmXgOqm6Fziwpwd7QkMKzhWR8m4H7\n6+7om2uPB7OytpCbe+iu7m2pyQTfCZy7co7dybvR17fCze3l8my9rTdhYGzFUyxgjk8Qm1atotD1\nKod6HuJi2MV7PWxJkqQq165dA8DBwUGj3MnJCaBWMxi5ubmUlZVpPXb9+nV27tzJ2LFjNWZwxo0b\nh5mZGRs3btSpj+7du+Ph4VEVSFQKCwvj0UcfxaZiC8LNfv31VwIDAzE3N8fS0pLBgwdz4sQ/j21x\ndXUlMDCQTZs2ab2G8PBwvLy86Ny5c7VjjYkMXHSkcSs0VAUul4qLsTcwIGVxCoYOhjSd2lTr+UKo\nSUp6G2vrPtjY9Gn4Aeuol3svWjRpwX8P/fO0aEXRw87pSXyHJRDwlite+wTDzNZS8t5QSj/5hPj5\n/yPhlQTUpY13jVSSpLvv6tWrZGdna7xycjTvXrxy5Uq1OtpeBQUFVecEBARgZmbGm2++SUREBOnp\n6ezatYviVVK7AAAgAElEQVTZs2fTpUsXnWZBAJ555hksLS0xNjbm4YcfJiZG8yG2R48epbS0FH9/\nf41yAwMDfH19OXTokM7vRVBQEOvXr6/6Ojs7m99+++2WsyHfffcdgwcPxsLCgo8++oj58+cTHx9P\n7969SUlJqaoXGhpKdnY2O3bs0Dj/2LFjHDt2jDFjxug8xvuVDFx0YGFkgafNTan3K7LmXiopwd7A\ngLwjeTQZ2AQ9E+0J5LKyfiIv7zDNm79zF0asO0VReMb3GTad2MS1omuaB5s0wXzFDtq+X0z3/+vH\nQbPnyWq9H76eyHm3J4mZuZTirALtDUuSJN1ACEG/fv2wt7fXeLm6at7I0KlTp2p1bn45ODiwePHi\nqnNsbW3ZuHEjV65coV+/fri6utK3b19cXFz4448/atzPYWhoyMiRI/nss8/YunUrCxcu5NixYwQG\nBnL48OGqehkZGSiKgvMNj3ap5OzsTHp6us7vR0hICMnJyURGRgKwYcMGTExMGDp0aLW6eXl5zJgx\ng0mTJrFt2zamTZvGK6+8wv79+1Gr1RobfUeOHImhoWG12Zx169ahKEqjXyYCucdFJ23t21Zf2rl4\nEXx9uVRSgruREcWZBVj1ttJ6/j+zLQ9jbR14F0ZcO2N9xvLGn2/w/fHvedbvWc2DbdvC+vWYDx7M\nbA9fpoz5gzMXf+D1Nj9Dx1eI/OtjnF3H4tJ+HObm7bV3IElSvcovyedk1skG76e1XWtMDUzrpS1F\nUVi+fDne3t4a5Xp6mh/2wsLCNGZTbsXTU/PDpJ2dHX5+fvTq1Yu2bdsSFxfHhx9+yPjx42tcwune\nvTvdu3ev+nrw4ME8+eST+Pj4MGfOHH755ReAqnEZGVV/jIuxsbFO467Utm1bfHx8CA8Pp0ePHoSH\nhzN8+HCMjY2r1f3999+5evUqQUFBZGdnV5UrikLXrl2JiIioKrO2tmbQoEFs3bqVgoKCqmWyDRs2\nEBAQcMcboe8HMnDRQTv7dtULK5eKSkoIsLCgJLMEQwftd9xcuvQjeXlH8fX9u4FHWjduVm4M8BrA\nqsOrqgcuAIMGwUcfof/qq6xs04bZ3ccwKPUhlqquEnDmWzKMV5ARvRgzs/Y4OATj4BB024dDSpJ0\nZ05mncT///xrrniHYibF4Odcf5tpO3fuXOPm3BsDCF2dPXuWvn37snbtWoYPHw7AkCFDaNasGePH\nj2fHjh0MHDiwVm16eXkxbNgwNm/ejBACRVGqgoCioqJq9QsLC2t9N1BISAhLlixh5syZREZG8sYb\nb2itd+bMGYQQ9O1b/aYORVGwstL80BwaGsrmzZvZsmULQUFB7N27l6SkpAZLCHe3ycBFB23s22gW\nVKT7x8mJS8XFOJTpUXa9DAOH6gnnhFCTnPwONjYDsLauvsv7fjG+43hCfgzhTPYZvG29q1d4+WU4\nfhzl2Wf56M8/sfP0ZNbZs0x77AOmLi4k69wviBf3k1ywkHPn5mFh0aUiiBmFkZH2fT+SJNVNa7vW\nxEyKqbliPfRzt2VlZd1yc+yNzM3NqzbIrlq1iqKiIh5//HGNOpXLLnv37q114ALg5uZGcXExeXl5\nmJub4+zsjBCCjIyManUzMjJo2rR2P+uCg4OZM2cOEydOxM7OjgEDtGdSV6vVKIrC2rVrcXR0rHZc\nX1/zV/ngwYOxsrIiLCyMoKAgwsLC0NfXZ/To0bUa3/1KBi46qDbjUpHuv9TRkZzSUpyula+faptx\nuXRpE3l5x2jZ8v/uxlDrbHjr4VgZWbH68Gree/i96hUUBb76Cs6ehREjeC0qCttWrZh06hRZc+z4\n4JdQksf0wHrQXByWJpBdsImzZ2eTmPgS1tYP4eAQjL39kxgY2N79i5OkB4ypgWm9zoTcTzp37kxy\ncvJt6yiKwltvvcX8+fMByMzMRAhBWVkZBjc8fqWkpASA0tJSre3UJDExEWNjY8zNzQFo3749+vr6\nREdHM3LkSI1+4uLiah0YuLm50bNnT3bt2sW0adNuuRfHy8sLIQT29vY8/PDDNbZbuWfnu+++IzMz\nk02bNtGvX79qd101VjJw0YGD2U3/2BVZc7MdHBBCYFsRuNw84yJEGUlJ72BjMxArq9pPf95NJgYm\nBLUPYvXh1bzT5x3NZHuVDA3hhx+gWzcYPJhnIyNp0q4dQSdOcGVgGf/t0I6kcadJ6uNJ2/BVtO4h\nyMraTGZmOKdPT+XMmeexsXkEe/snsLUdjKFh9U8OkiT9u9Vlj0vLli1Rq9Vs3LiRcePGabSlKIrG\n8lR2djZZWVm4u7tXLe1kZWVhV/H4lkqHDx/mf//7n8YsjqWlJf3792ft2rW8+eabVTM+a9asIS8v\nj1GjRtX6ehcuXEhERMRtg56BAwdiaWnJokWL6NOnT7UZFm3jDw0N5ZtvvmHy5MlkZWURGhpa67Hd\nr2TgooNqG3Mrs+ba2cGlS9hcFpQCBvaagUtm5vfk55+gdev/0hiM9x3PVzFf8ee5PxngdYuHP9rZ\nwc8/Q/fuMHo0I37+me0+Pgw7downHUr56WBHMsadIa5PHB7veuD++jM4O0+gqOgCly5tIjNzPadO\nTQTAwqILdnZDsLUdgplZh/smt40kSfVPCMEvv/xCfHx8tWM9evTAw8MDqNsel/Hjx/Pxxx8zefJk\nYmNjadeuHTExMXzzzTe0b9++at8LwOeff867777LX3/9RWBg+c0So0ePxsTEhB49euDg4MDx48dZ\nuXIl5ubmvP/++xp9LVy4kJ49exIYGMikSZNITU1lyZIlDBw48JZLPbfTu3dvevfufds6FhYWrFix\ngnHjxuHn50dQUBD29vakpKSwbds2evXqxbJlyzTOeeihh3B1dWXLli2YmpoyYsSIWo/tviWEkK9b\nvAA/QAQGBoohQ4aIsLAwIYQQYv16IUD8mZoqiIgQh75KEhFEiLLCMlFJrS4VUVGtxeHDj4nGQq1W\ni9ZftBYhP4TUXHnnTiH09IR44QUhhBDR164Juz17RJuoKJFyPV+cfeOsiFAiRFz/OFF0oUjj1KKi\nTJGRsUocPfqE2L3bXEREICIj3cWpU8+L7OztoqyssCEuT5LuazExMQIQMTEx93oo9W7VqlVCpVLd\n8rV69eo77iM9PV0899xzwsvLSxgbGwsXFxcxZcoUkZ2drVHv7bffFiqVSuzatauq7PPPPxfdunUT\ndnZ2wtDQULi4uIinn35aJCYmau1r7969olevXsLU1FQ4OjqK6dOni9zc3BrHmJSUJFQqlViyZMlt\n640fP15YWlpWK9+1a5d47LHHhI2NjTA1NRXe3t5iwoQJIjY2Vms7r732mlCpVCI4OLjGsdXFrb5n\nw8LCxJAhQ0RgYKAABOAn6vN3c3029qC9KgOXaj9IPvtMCCMjseHCBUFEhDj1/lmx22q3RpULF9aK\niAjE1atRojH54O8PhPF7xuJyweWaK3/1Vfm30OefCyGEOJmXJ9wjI4V7ZKQ4ev26yP49W+xx3CP2\nOO4ROTtztDZRVlYosrO3i1OnnheRke4iIgKxe7e5OHr0CZGe/q0oKsqsz8uTpPvWgxy4SA+mmr5n\nK4/Xd+AiE9DVReWt0KWlGCgKqqwyjY25anUpSUnv0qTJ41hadrmHA629sR3HUlxWzMbjOqSunjQJ\nZs2CGTNg+3ZamZqyt1MnrPT16XHoEAc6CQLiAjBrZ8bhAYc599Y5RJnQaEKlMqJJk4G0bPkF3bol\nERBwGHf31ykqSuPUqQlERjoSG9uL1NQlFBSca6CrliRJkhoLGbjURWXW3Ip0/yVZJRobczMzwyko\nOE3z5m/fuzHWUVOLpgz0GsiquFW6nbB4cXmel1Gj4NgxXI2N2dupE4FWVjx+9Chfl2XR8beONH+3\nOcnvJRPXL46i9Oo5EKB8L5G5uQ/Nms3D338/PXpk0KrVSgwMbDh7di5RUZ4cPOhLUtI75OYerZwV\nkyRJkv5FZOBSFzc+YNHAoDz5nH35jItaXUpy8rvY2g7B0jKghobuT+N9x7Pv/D7dMnPq6UFYGHh4\nwJAhkJmJhb4+Wzp0YLqrKy+cOcP0swm4znXH909fCs4UEN0xmuzt2TU2bWjoiLPzs3To8D969rxE\n27YbMTNrS2rqJ0RH+xAV5U1i4qtcvRqJEPK5SZIkSf8GMnCpixuy5tobGlKcWVw145KZuY6CgoRG\nOdtSaWirodgY27A6brVuJ1hYwP/+BwUFMHw4FBSgpygsbdGCFd7erEhLY+ixY6h6mhMQF4BFgAVH\nHztK4muJqIt0Czj09S1wcHiKtm3D6NnzEh06/IKNzcNcuLCGQ4d6sm+fC6dPTyUn5zfU6pI7uHpJ\nkiTpfiYDl7q4eamoIt1/+d6WBdjaDsPCovEmhzLWNya4fTBrjqyhTF1zBksA3N1h61aIi4OnnwZ1\neUAyxcWFX318iLx6lR6xsaSbl9FhWwc8P/Lk/KfnifaP5nrM9VqNT6Uywtb2MVq1+j969EjH13c3\nDg7B5ORs58iRgezf34xz596isPB8bS9dkiRJus/JwKW2hIDMzH9mXPT1q2ZcMjPXU1iY2KhnWyo9\n0+kZ0q+n8/vZ33U/qUuX8mWjTZtgzpyq4gFNmrDPz498tZqusbHsv34N91fd8Y/xR2WoIqZrDOfe\nOoe6uPbLPYqih7V1b1q0WELXrmfx94/Bzm44588vYf/+5hw79iSXL/8h98NIkiQ9IGTgUlsV6f4r\nAxfnEgNEkcDQwZDr1w9gatoGCwvfez3KO+bv7E87+3a6b9KtNHw4LF0KH30EX35ZVdzGzIwoPz9a\nmprSNy6O8IsXMe9gjl+UH83eaEbKohRiu8aSezS3zmNWFAULCz9atlxO9+5peHsvIz//JIcP9+fA\ngTacP/8ZJSVX6ty+JEmSdO/JwKW2KtL9qx0dyS4pwfFqebZXAwcDiorSMDJyvZejqzeKojDedzw/\nnfyJywWXa3fyjBkwfTo8/zxUPA4ewN7QkJ0dOzLKwYGQ+HjeOncO9BU83vbAL8oPdYmaGP8Ykhcl\noy69s822+vqWuLhMo3PnY/j67sLcvCOJia+wb19TTp2ayPXrh+6ofUmSJOnekIFLbVWk+8+xt0cN\n2F6pCFzsDSguTsfQ8MF5EvIYnzGUqktZf2x97U9esgQGDy6/TfrQP0GCkUrF6tatWejhwYLkZAYd\nOUJWcTEWfhYExATg9rIb5948x6Geh8iLz7vja1AUBWvrQNq120C3bim4u88hO/tXYmL8iI3tzoUL\n31FWVnjH/UiSJEl3hwxcaqtixiXTxgYA64qVB0MHw4oZF5d7NbJ652TuxGPej/Ft3Le1P7nyNuk2\nbeDxxyElpeqQoijMbdaMHT4+xOTm0ikmhn1Xr6IyUuH5vied9nai9Gop0Z2iSf0ktVrSuroyMnKm\nefM36dYtiXbtfkRPz5yTJ8exb58riYmzZYI7SZKkRkAGLrV18SIYGXHJyAgAi8sCFNBvokdxccYD\nFbgABLUL4mD6QbLys2p/splZ+QMZjYzKg5erVzUOD2jShEP+/rgbGREYF8dn588jhMCqmxUBhwJw\nmeZC4quJHHroEPln8uvpikCl0sfefgQdO/5Oly4ncXIaS3r6V0RFeXHkyONkZ29DCB3vppIkSZLu\nKhm41FblrdAl5blCTHLUGNgZUKrOQojSB2qpCKCzS2cADmXUcU+Io2P5Ppfz52HkSCjRzLHiamzM\nX76+THdxYWZCAqNOnOBaaSl6Jnq0WNIC312+FGcUc7DDQZLeSaKssH4DClPTVrRosZQePdJo1Wol\nxcUZHD06mKioFqSkfEhx8aV67U+SJEm6MzJwqa0bks/pKwp6WWUVG3PTAR64GZcWTVpgYWhBbEZs\n3Rtp0wZ+/BF27YLJk8tvKb+BgUrFJy1a8EO7dvyWk0NATAxHcsvvLrLubU3no51xe8mN5PeSifaJ\nJmdnzp1cklZ6emY4Oz+Lv38Mfn77sbIK5Ny5t9i3z5X4+HFcvbpf3lItSXW0evVqVCqV1peenh4H\nDhy44z5WrFjBqFGjaNasGSqVigkTJmitd+HCBV5//XUefvhhLC0tUalU7N69u1Z9paenM2rUKGxs\nbLCysmL48OGcO6d9qfmbb76hbdu2mJiY0LJlS7744gud+rjxPYuMjNRax83NDZVKxdChQ2s1/kuX\nLmFgYMC4ceNuWSc3NxcTExNGjhxZq7bvBv17PYC7SVGUlsAGyp9WqQCtgCAhxFadG7khcLEzMKDk\nUnm6/6Ki8mRnRkYP1oyLSlHRybkTsRfuIHAB6NsXvvkGxo2D5s1h/vxqVZ6wt8fHzIyRx4/TNTaW\nFd7ejHd2Rs9UD89FnjiOceT0tNMcGXAEhyAHvJZ4YeRsdGfjuomiKFhadsXSsiteXp9w4cK3pKev\n4OLF7zA396Vp02k4Ooagp2dWr/1K0oNOURQWLFhA8+bNqx1r0aLFHbf/0UcfkZubS5cuXbhQcROF\nNqdOnWLx4sV4e3vj4+PDvn37atVPXl4effr04fr167zxxhvo6+uzZMkS+vTpQ1xcHDYV+x8Bvvrq\nK6ZOncpTTz3Fyy+/zN9//8306dMpKCjg1Vdf1ak/ExMTwsLC6NGjh0b5rl27SEtLw9jYuFbjB7C3\nt2fAgAFs2bKFwsJCrW388MMPFBcXM3bs2Fq33+Dq81HTjekFmAGZgMlt6vhx8yO7/f2FmDhRvHD6\ntOhw4IA41O+QODbqmEhL+0pERKhEWVmJ1sd7N2Yzf50pWixrUT+NLVggBAjx3//eskp+aal4Nj5e\nEBEhno2PF/mlpVXH1Gq1yFidIfbY7xG7LXeL1M9ThbpUXT9juwW1ukxkZf0ijhwZLCIiFPH33zYi\nMXGOKCxMb9B+pX+XmJgYUe3nzQNi1apVQqVSNei1paSkVP3d3NxcPPPMM1rr5ebmisuXLwshhNi0\naZNQqVRi165dOvfz4YcfVruWkydPCn19fTFv3ryqsoKCAmFnZyeGDh2qcf6YMWOEhYWFuHLlym37\nWbVqlVAURYwcOVI4ODiIsrIyjeOTJk0SnTt3Fh4eHmLIkCE6j7/S2rVrhUqlEhs2bNB6/JFHHhE2\nNjaiuLj4lm3U9D1beRzwE/X4+/vfvFQ0FPhDCFFQq7MqH7B4U7r/oqI0DA0dUakevEksP2c/EnIS\nuFp4tebKNZk3DyZNgokTYccOrVVM9PT4unVrvm3VirDMTPxjYoi5Xv5YAEVRcBrnRJeTXXAIdiDh\nxQRiusZwLfranY/tFhRFha3tY3To8D+6dk3EyWk8aWmfs39/M+Ljx5Obe7TB+pYkSTdubm461TMz\nM8Pa2rrO/fzwww907twZP79/HuvSqlUr+vXrx8aNG6vKIiIiyMnJYdq0aRrnP//88+Tm5rJt27Ya\n+1IUheDgYLKzs/n993+ymJeUlLBp0yZCQkK0LmELIfj0009p3749JiYmODk5MWXKFK5c+ScB54gR\nIzA1NSUsLKza+ZcuXeLPP//kqaeewsDAoMZx3m3/5sBlFOXLRrq7Od2/gUFVuv/i4vQHbn9LJT/n\n8v+gcRfi7rwxRYH//Acee6x8s27srZegxjs7E+Pvj4lKRbfYWN5LSqK04hlIBk0MaPVlKzrt64Qo\nFcR2ieX086cpudKwD1g0MfGgRYsldO9+Hg+PRVy58gfR0T4cPjyQnJzf5D4YSbqNq1evkp2drfHK\nydHcs3blypVqdbS9Cgpq95mzPgghOHLkCAEBAdWOdenShcTERPLyyvNPHarIX+Xv769Rz9/fH5VK\nVXW8Js2bN6dbt26Eh4dXlf3yyy9cu3aNoKAgredMmjSJ2bNn07t3b5YtW8aECRNYt24djz76KGVl\n5Tc4mJqaMmzYMHbs2KER0ACsX78etVpNaGioTmO82+7rwEVRlN6KomxVFCVNURS1oijVdiApivK8\noijnFEUpUBRlv6IonXVo1wLoDvxSU10Nlen+K+4qstc3oCTrxhmXB2t/S6VWdq0w0Te5sw26N9LX\nh/Xr/8nxkpR0y6ptzMzY5+fH6+7uvJWURO+4OM7k/3NrtFU3K/yj/fFa4sXFNRc50PoAF9ZcQKgb\nNoDQ17fC3f0VunY9S5s2aykpucSRIwOJju7IhQurUauLG7R/SWpshBD069cPe3t7jZerq2a28U6d\nOlWrc/PLwcGBxYsX3/VryMnJoaioCGdn52rHKsvS08tv1MjIyEBPTw87OzuNegYGBtja2lbV00VI\nSAg//fQTRUVFAISFhfHQQw/h5ORUre6ePXv45ptvWLNmDStWrGDixIksWrSIH3/8kQMHDvD9999X\n1Q0NDaWoqIhNmzZptBEWFoaLiwuBgYE6j/Fuut/XNcyAOOAb4MebDyqKMhr4BJgEHABmATsURWkp\nhMiqqDMNmEj5Olt3IUQRMAz4TQhRu98ulRu+HB25VFyMc74elJVnzS0qSsfKqsftz2+k9FX6dHTq\neOcbdG9UmeOle/fy2Ze9e6FJE61VDVUqFnh4MKhJE8adPIlvdDQfe3kxpWlTFEVBpa/CbaYbDk85\nkPBSAiefPknaF2m0+LQFVj2s6m/MWqhUBjg6huLgEMKVK3+RmvoJJ0+O5+zZObi4vEjTppMxMNB+\nXZJUZ/n5cPJkw/fTujWYmtZLU4qisHz5cry9vTXK9fT0NL4OCwvTaTbF09OzXsZVG5XjMjKqflNA\n5QbXyjoFBQUYGhpqbcfY2LhWM0ajRo1i5syZ/PzzzwwcOJCff/75lncnbdq0CWtra/r160d2dnZV\neadOnTA3NyciIqJqpuaRRx7B3t6esLAwnnvuOQCSkpKIioritdde03l8d9t9HbgIIbYD2wEURVG0\nVJkFfCWEWFNRZwrwODAB+KiijeXA8pvOGwV8VesBVT6nyMGBrIwMnK6WT1iVLxU9uDMuAH5OfvyV\n/Ff9NurgANu3Q48eMHQo7NwJt9kh393KiriAAF5JTGTamTNszc7mm1ataFrxQ8TIxYh2G9px5YUr\nJMxM4FDPQzgEOeD5gSfGzWq/8742FEXBxqYvNjZ9ycuL5/z5JSQlvUNy8ns4Oz+Lq+tMTEzu/g9a\n6QF18iTctATRIGJi4Ia9HHfq5r0h2nTv3r3e+qtvJiYmAFUzHzcqLCzUqGNiYkJxsfbPxoWFhVX1\ndGFnZ0f//v0JCwsjLy8PtVp9y9uUz5w5w5UrV3BwcKh2TFEUMjMzq77W09Nj9OjRrFixgoyMDJyd\nnVm3bh2KohASEqLz+O62+zpwuR1FUQwAf2BRZZkQQiiKspPyZaBbnWcJdAaeqHWnFYHLFTs7yjIy\nsK14wKK+vaAkI+uB3eMC5ftcvoz5krziPMwM6/FWYG/v8pmXvn1hzBjYuBFUt17BNNPTY0XLlgy1\ntWXCqVN0OHiQL1u25Kkb/pNa97bG/6A/F9Zc4NyccxxofQC3V9xwm+2GvnnDf8ubmbWhVauVeHi8\nR1ractLS/kNa2n+wt38SN7dXsLTs0uBjkB5wrVuXBxV3o5+7LCsrq2ofxu2Ym5tjZnZ30xI0adIE\nIyMjMjIyqh2rLGvatPwDrLOzM2VlZWRlZWksF5WUlJCdnV1VT1chISFMnDiRjIwMHnvsMSwsLLTW\nU6vVODo6EhYWpnXPnb29vcbXY8aM4YsvviA8PJyXXnqJ9evX07ZtW3x8fGo1vrup0QYugB2gB1y8\nqfwi5flZtBJCXAOqL1DexqxZs7CysoKzZ0GlInfcOPDzw9rrWQQgrLMg48HL4XIjP2c/1ELNkYtH\n6O5Wz5+IunYt3/MyYgS89BIsXVq+ifc2HrO15WhAAFPPnGHUiROEZmXxmbc3thU74BWVgvN4Z+xH\n2pPyQQopi1PI+CYDz/c9cRzriKK6ffv1wdDQEQ+Pd3B3n82FC6s5f34JsbFdsbLqjZvbq9jaPo6i\n3NfbzKT7lalpvc6E3E86d+5McnLybesoisJbb73FfC35oBqSoih06NCB6OjoaseioqLw9PSsCqZ8\nfX0RQhAdHc2jjz5aVe/gwYOo1Wp8fX1r1feIESOYPHkyUVFRbNhw6/tKvLy8+OOPP+jRo4fWJa2b\ndenSBS8vL8LCwujfvz/Hjx/n/fffr9XYAMLDwzU2EEP5ZuyG0JgDl7tm6dKl5dObc+dCbi5/r1tH\nRFwc5ocEuQYKZUblU2+Ghg/ujEs7h3YYqAyIzYit/8AFypeKvvgCpk0Dd/fyAKYGdoaGbGzblnUX\nL/JiQgK/HzjAMm9vRtnbU7myqG+uj+d7njg/58zZ189ycvxJzn9+nhaftsC6V91viawNPT1TXFym\n0rTpJLKytpKauphjx4ZiYtIKN7eXcXQci55ewy5lSVJjcT/tcUlNTSU/P59Wrf75LDxy5EjmzJlD\nbGxs1bLXqVOn+PPPPzX2hTz88MM0adKEFStWaAQuK1aswMzMjMcff7xWYzEzM+PLL78kKSmJIUOG\n3LLeqFGjWL58Oe+++y4LFy7UOFZWVkZubm75B/EbhIaG8u677/LWW2+hUqkIDg6u1dgAgoODq50X\nGxtb7a6q+tCYA5csoAxwvKncEbh12sQ7cUPWXADjnDKK7Mv3t8CDPeNiqGdIB8cO9XdnkTZTp5Y/\nRfqVV6BFi/JgpgaKojDGyYl+Nja8eOYMQSdOsM7WluXe3rjesF/GpLkJ7daX739JnJVIXO847EfZ\n4/mBJyYeuq813wlF0cPefgT29iO4ejWS1NSPOX16MufOvYGLy4u4uEzFwMD2roxFku42IQS//PIL\n8fHx1Y716NEDDw8PoO57XH7++WcOHz6MEIKSkhIOHz5c9Yt72LBhtG/fvqrue++9h6IoHD9+HCEE\na9as4e+//wZg3rx5VfXGjh3L7t27UVekYQCYNm0aK1euZNCgQbzyyivo6+uzdOlSnJ2deemGD1zG\nxsYsWLCAF154gVGjRjFw4EB2795NWFgYixYt0imXzM1LPbpksQ0MDGTy5Ml88MEHxMXF8cgjj2Bg\nYCwTMu4AACAASURBVMDp06fZtGkTy5Yt44knNHdKjBkzhnfffZctW7bQq1cv3N3da+znnqrPbHYN\n+QLUwNCbyvYDn93wtQKkAq/WU5+amXMHDRJi6FDxZVqaUEVEiJNTTomDvgdFauqnYtcuY6FWN2wG\n13vtuS3PiY4rOjZsJ2VlQjz5pBBmZkIcOlTr03/MzBTOe/cKi927xfLz50WZln8TdVl59t29TfeK\nvwz/EgmvJIjinFtnh2xIeXmnxalTU8WuXcZi1y4TcerU8yI/P+GejEW6t/4NmXNv9Vq9evUd9zF+\n/Hid21cURWs9PT09jXp9+vSpViaEEGlpaWLUqFHC2tpaWFpaimHDhonExESt4/r6669FmzZthLGx\nsfD29hbLli3T6Xp0zTbs4eFRLTtvZb+dO3cWZmZmwsrKSnTs2FHMmTNHXLhwQWs7Xbp0ESqVSnz1\n1Vc6jU+Ie5c5VxH/z96Zx1VdpX/8fe5luZd930FAQcSNRRCtzNK0ptQcV1D7lVOWU1lmU5OVmmVp\nOVmNo7ZY2iioqS1j20xu1SiiCO6ICyog+6IIynbP748LjAjIInCv+n2/Xt/X697zPd9zPhfuvd/n\nPuc5z2PECbOEEJZAN/QGyX7gBWA7UCilTBdCjAdWAU/xv+3QY4EgKeUNl/UVQoQBiYMGDcLW1pbo\nw4eJHjqUt2bP5u+ZmWxdYkt1STVWy9aSl7eZqKiTNzqlUbN873Jm/DSDkldK0Jh04NJGWRkMGqT3\ncCUkQCM5E65HcWUlL58+zSdZWdxpa8ungYEENRLEV11aTfr76ZxbdA6VuQrfOb54TPdAZdb5cScV\nFXmcP7+czMylVFYW4Oz8R7y9/6IE8t5G1LrVExMTm915o6BgDDT1nq2Nd7lw4UJtActwKWW7ueuN\nPTKwH5AEJKK32v6G3oB5A0BKuQF4EZhf068PMLw9jJarWbJkCd999x3RlZX1suZene7/Vl4mqiXM\nPYwqXRWHcw937EQWFvDdd/pMxaNGQSszZNqZmvJx9+7sCAkhp6KCvvv28daZM1Rc5e4FUFuq8X3d\nl/4n++M81pmTL5wkITiB3I25nZ4B18zMGV/fOURFnSUwcBmXLiWzf39/kpLuJj9/C1Lqmh9EQUFB\nwQiIjo7mu+++Y8mSJR0yvlEbLlLKnVJKlZRSfc0x9ao+y6SUvlJKrZRygJSyYbh3+4jRewBqs+aa\nmd0W6f6vpo9rH9RC3bFxLrV4eOiNlyNH4NFHQdf6G/fddnYc6NePWd7ezDtzhn6JiSRcbFjTyNzN\nnO4fdyfiYAQW3S04Ou4oSXckcWF3x0TEXw+1WouHx5NERqbQs+cmpKzg8OER7N3bm6ysL9DpGuaP\nUFBQULidMGrDxagoKoIaj0tdgcW8Wz/d/9VoTbX0cO7ROYYL6Ld7rlmjz+3yxhttGkKrVvO2vz/7\nwsMxE4Ko/ft5JjWVC1VVDfpa9rSkz/d96PtLX3SXdSQNTOLI+CNcPtX5NVH0gbx/JDR0FyEhv6HV\nduP48anEx/tx7twiKiuLmx9EQUFB4RZEMVxawMyZMxk5dixxULdU5IIJVYVVmDibUF5+e3hcAMLd\nwzvPcAF9bpd33oH586GRKqYtJcTamviwMJZ068bqnByCEhJYn9v4kpD9EHvCE8MJWh3EhV0XSOiR\nwMmZJ6ks7NgCjo0hhMDO7k569/6WiIhjODo+SFraHOLjvTl5chZXrqR3uiYFBQWF6xEXF8fIkSOZ\nOXNmh4yvGC4tYMmSJXw3Zw7RUGe4eFzS/+nULlfQ6UpvC48L6ONcDuYcpLK6E2/iL78MjzwCU6fC\n7t1tHsZEpeI5Ly+ORUQw0MaGiUeP8sDBg5xqJIZGqARuj7jRP7U/vvN8yfosiz1d95D+fjq6csPE\nm1haBtG9+6dERZ3F03MG2dmfs2ePP8eOTeHSpYMG0aSgoKBwLbd1jItRUVNgUbq6kl9ZicvFmsJg\nTvoiVreLxyXMPYzy6nKO5TfMxdBhCAGffAIREfDww9BMVs3m8NJo2NSrF//q1YtjZWX02ruXBWfP\nNgjeBVBbqOkyuwv9T/bHZaILp146RUKPBHI3dH4Aby3m5m74+y8gKiqdrl0XU1z8K/v29eXgwT9Q\nXLzTYLoUFBQUOgPFcGkpOTmg0XDBwoJKKXGqCTGQtvqsubfDriKAvq59EYjOXS4CMDeHr7/WV5V+\n6CFoJMi2tTzk5MTRyEhmeHoyNy2NkH372FnceOyImasZgcsDiTgYgWVPS45OOErSwCQu7Or8AN5a\nTEys8PJ6jv79TxIU9E/KyzNITh7M/v1R5OVtRsrm670oKCgo3GwohksLmDlzJiOXLiXOyqoua65d\nzf1NZ6HfeX27LBVZm1sT6BjY+YYLgJOTviBjejqMH68Plr5BLNVqFnXtSlK/ftiZmDA4OZnHUlLI\nb6Kqq2WwJb3/1Zu+W/uiK9eRdEcSR8YZJoC3FpXKFDe3yfTrd4DevX9ArbbgyJExJCQEc/78p1RX\nXzGYNgUFhdsPJcbFCFiyZAnfDRpEdNeudYaLZZFEZaGikixMTOxRqzsnbbwxEOYeZhjDBSA4GDZt\ngq1b9XWN2mlZpLeVFb+HhvJxYCDf5OcTlJDAF1lZTS672N9rT/i+mgDe3TUBvC8YJoC3FiEEjo4P\nEBKynbCweCwte5Ga+iR79vhx9uxCZSeSgoJCp6DEuBgL2dl1W6EBNAU6zFzMbpscLlcT5h5GcnYy\n1ToDLUUMGQKffaY/2lDFtClUQjDNw4OUyEjud3Bg6vHjDE5O5lhpaaP96wXwzvUl61PDB/DWYmPT\nn169NhEZmYKj4wjOnJlLfLwPp079hfLyTINqU1BQULgRFMOlpVxVYFEAJvnVmLqY3jY5XK4mzD2M\n0spSThSeMJyI//s/mDcPXn31hrZJN4armRlrgoP5T58+ZNVk3n09LY3L1Y0bamoLNV1evSqA9y+n\nSOiZQN7mPIMHylpYBNK9+ydERZ3B0/Npzp//hPh4P1JSplJammJQbQoKCgptQTFcWspVWXMdTEyo\nqks+d/t5XELdQgEMt1xUy5w5egPmscdg5852H36ogwMH+/XjFR8f3j13jt579/KfwsIm+18dwGsR\naMGRMUdIHpxMSWJJu2trLebm7vj7v8OAAen4+S2gsPAn9u4N5vDh0Vy4EG9oeQoKCgotRjFcWsDM\n559nZGYmcenp+uRzZmZU5lXWpPu//Twu9lp7/Oz8DG+41G6TvvNOfaK6lPb3IGjUat7w8+NgRATe\nGg3DDh4k5uhRssubTr1v2dOSPj/0oc9PfagsqCSxXyLH/u8YVzIMHyRrYmKDj89fiIpKo3v3Tykt\nPUpS0gCSkgZTUPCjwT1ECgoKNz9KcK4RsOSNN/hOSqIffJC8mnT/FbkVmDirKS/Puu08LqBfLkrM\nSjS0DDAz0wfrenjAAw/oPWMdQHcLC7b17cvqoCD+U1REUEICH58/j+46N3qH4Q70S+5H4MeBFP5U\nSEJgAmlz06i61LDcQGejUpnj7v4nIiOP0rPnJnS6yxw69Af27QshJycWnc7wGhVuHVavXo1KpWr0\nUKvVJCQk3PAcy5cvZ/z48XTp0gWVSsXUqVMb7ffbb78xatQofHx80Gq1uLu788ADD7Br164WzfPG\nG280+josLCwa7b9y5UqCg4PRarUEBgaydOnSFs1z9d+sKW3e3t6oVCpGjhzZojFrycvLw9TUlEce\neaTJPpcuXUKr1TJ27NhWjQ0dH5xr0iGj3mrULg/Uqwx9CbXHRaD6tsnhcjVh7mEs+u8idFKHShjY\n/rWzg++/h6goGDECtm/X53tpZ4QQPOLmxoOOjrx86hRPpaayJieHTwID6dHEfCoTFR7TPHCZ6MK5\nd85xbtE5sj7Nwm+BH27/54ZQiXbX2RpqayI5OY2muHgn584t5NixSaSlvYqX1yzc3aeiVjf+hayg\n0BqEELz55pv4+vo2ONetW7cbHv/dd9/l0qVLREZGkl2TMLQxUlNTUavVTJ8+HTc3N4qKilizZg2D\nBg3ihx9+YNiwYc3OJYRgxYoVWF71uVer1Q36ffzxx0yfPp1x48Yxa9YsfvvtN2bMmMHly5f5y1/+\n0qLXpdVqiY2NZeDAgfXad+7cSWZmJhqNpkXjXI2zszP33Xcf3377LVeuXGl0jE2bNlFRUcGUKVNa\nPX6HI6VUjiYOIAyQiStWSAlSpqbKkL175dPJx+R2tsu0dT/L7duRFy7slbcbP574UTIPebLgpKGl\n/I/ERCktLaUcNUrKqqoOn25HUZEMiI+XZjt2yPlpabK8urrZa8rSyuSRiUfkdrbLvaF7ZeG2wg7X\n2VouXkySR45Ey+3bVfL3351kWtp8WVGRb2hZtzyJiYkSkImJiYaW0u6sWrVKqlSqDn1t586dq3ts\nZWUlH3vssRZfW1ZWJt3c3OQDDzzQbN958+ZJlUolCwoKrtvv8uXL0snJSY4cObJe++TJk6W1tbUs\nLi6+7vWrVq2SQgg5duxY6eLiIquv+X6ZNm2ajIiIkH5+fnLEiBHN6r6WNWvWSJVKJdevX9/o+WHD\nhkl7e3tZUVHR5BjNvWdrzwNhsh3vzcpSUUu42uNSUYFHSY1l7ZAP3D7p/q/GaAJ0ryYsDNavh3/9\nC154ocOnu9vOjoP9+vGitzfzz54lbN8+dl+4fiZdra+W4LhgQneFojJXceDeAxwaeYjSlMa3XBsC\na+sQgoNj6d//JM7OEzh37m127/bhxInnuHz5jKHlKSg0ire3d5uv1Wq1ODs7U9xE5uzG0Ol0lJQ0\nHXi/fft2CgsL+fOf/1yv/emnn+bSpUt8//33zc4hhCA6OpqCggL+85//1LVXVlayceNGYmJiGo1L\nk1LywQcf0KtXL7RaLW5ubjz11FP1Xt/o0aOxsLAgtpFdmXl5eWzbto1x48ZhamrarM7ORjFcWkJB\nAWg0yJrMuS7Feve+ziYPUGNm5mJYfQbA1coVT2tP4zJcAB58EP7xD/joI/jwww6fTqNWs8Dfn8Tw\ncCzUau5ISmLGiROUVF0/RsR2gC2hu0IJXhdM6aFS9vbaS+rTqVTkNZ6x1xBotX4EBi4lKuoc3t4v\nkpOzhj17unH06CRKSpINLU/hJuTChQsUFBTUOwqv2alXXFzcoE9jx+VGiqO2hpKSEgoKCjh+/Diz\nZ8/myJEjDB06tEXXSinx9/fH1tYWa2trpkyZQm5ubr0+SUlJAISHh9drDw8PR6VS1Z1vDl9fX6Ki\nooiLi6tr++GHH7h48SITJ05s9Jpp06bx8ssvc9ddd/HRRx8xdepU1q5dy/333091TVoHCwsLRo0a\nxc8//9zAYFu3bh06nY5Jkya1SGNno8S4tICZGzZgKwQPx8ZS4eWF40W94VKtzcGs2g0hGq5t3g6E\nuYexP9vIDBeAp56CU6dg5kzw9YVRozp8yj5WVuwOC+PvGRm8mpbGN/n5LAsI4CEnpyavEULgMsEF\nx1GOZC7N5OxbZ8lZk0OX2V3wfM4TtcY43ldmZs74+b2Bj89LZGV9TkbG+yQmhmJvfx/e3i9hbz8E\nIQwbq6Ng/EgpGTJkSIN2jUZDWVlZ3fPQ0FDONlNIVQjB3LlzmTNnTpv1jB8/np9//hkAMzMznnzy\nSV577bVmr7O3t+fZZ59lwIABmJub89tvv7F06VL27t3Lvn37sLKyAiArKwu1Wo3TNd8BpqamODo6\ncv78+RZrjYmJYfbs2ZSXl2Nubk5sbCx33303bm5uDfr+/vvvrFy5kri4OCZMmFDXfs899zB8+HC+\n+uqrOoNn0qRJxMbGsnHjRh5//PG6vrGxsXh6ejJo0KAWa7yauLg44uLiuNCMB7qtKIZLC1gSEkJY\nbi6nxoyBPXuwLdK3V5vk3JaBubWEu4ezdO9SpJTGd+NatAjS0iA6Wp/jJSKiw6dUC8Hz3t487OTE\nU6mpjDh8mAnOznwYEICrmVnT12nU+Lzog9ujbpx98yxpr6WRuSwT/3f8cZnoYvAA3lrUaku8vJ7F\nw2M6eXkbSU9/l4MH78PKKhRv77/g7DwOlUr5SukMyqqrSbnqZt9RBFlYYNFI0GlbEEKwbNkyAgIC\n6rVfG9QaGxvbIm+Kv7//DelZtGgRL774Iunp6axevZqKigoqKysxu85nFWDGjBn1no8ePZqIiAgm\nTZrEsmXLeOmllwC4fPlyk2NpNJpWeYzGjx/P888/z5YtWxg+fDhbtmxpcnfSxo0bsbOzY8iQIRQU\nFNS1h4aGYmVlxfbt2+sMl2HDhuHs7ExsbGyd4XLmzBn27NlT9zraQnR0NNHR0ezfv7+Bx6k9UL5l\nWkJhYV18C4BVoY4KOxMqqm6/5HNXE+YeRn5ZPhkXM/C2bfv6coegUsE//wn33quvJh0fD35+nTK1\nr1bLj336EJuby3MnThCckMDSgAAmurhc18AzczIj4MMAPJ/x5PTLpzk26RgZH2TQdXFX7AbZdYr2\nlqBSmeDqOhEXlwkUF2/j3Ll3OXYshrS02Xh5vVCzE6n9d3Up/I+UsjLCEzs+HUFieDhh1tbtNl5E\nRARhYWHX7TNgwIB2m+969OnTp+7xpEmTCAsL47HHHmPDhg2tHis6OppZs2bxyy+/1N3wtVotFU0U\na71y5Qpabcvr2zk5OTF06FBiY2MpLS1Fp9M1uU35xIkTFBcX4+LSMIRBCFFvSUutVjNhwgSWL19O\nVlYW7u7urF27FiEEMTExLdbX2SiGS0soLISePesKLGoKdciadP+2tncZWJzhCHYOBuB4wXHjM1wA\ntFr47jv9NukHH4T//hfs7TtlaiEEk1xdGWZvzzMnThBz7Bhf5eWxPDDwut4XAIsAC3pt7kXxb8Wc\nmnWK5LuTcXrYCf9F/lgEGs/WZCEE9vZDsLcfQklJMunpizl5ciZnzszDw2M6np7PYG7e0JWtcOME\nWViQ2AG/ZBubp7PJz8+vi8O4HlZWVvW2I98IpqamjBw5kkWLFtUtx7QWb2/vevE67u7uVFdXk5+f\nX2+5qLKykoKCAjw8Wuetj4mJ4YknniArK4sHHngA6yYMSp1Oh6urK7GxsY0G7jo7O9d7PnnyZJYu\nXUpcXBwvvPAC69atIzg4uJ5hZ2wohktLyM/XF1isMVzU+dWYuZhRehum+78aJwv9h7HocpGBlVwH\nZ2f48UcYMADGjIGfftInreus6c3MWN+zJ+Nyc/lzK7wvAHZ32REWH0bu+lxOv3KahOAEPJ7woMuc\nLpi7t/6LtSPR70Rag7//AjIyPiAz80PS09/D1TUGL68XsLLqbWiJtxQWanW7ekKMiYiIiE6JcbmW\nsrIypJSUlJS0yXA5c+ZMPW9SSEgIUkr27dvH/fffX9e+d+9edDodISEhrRp/9OjRPPnkk+zZs4f1\n69c32a9r165s3bqVgQMHtuh1REZG0rVrV2JjYxk6dChHjhzhnXYsXtsRKIZLSygqqlsqsjcxoTq/\nChN3HVVVBbdduv+rsTG3QS3UFF5uun6PURAYCN98A0OHwhNPwKpV+nIBnchYFxfutrPj6Rrvy8a8\nPJa1wPsiVALXaFecRjtx/h/nObvgLNlfZuP1vBfef/HG1M64tipqNF3o1m0Jvr7zOH/+UzIzPyQ7\nexX29vfh5fUCDg7DjS8eSsGo6OgYl7y8vAZeh+LiYjZt2oSPj08970h6ejplZWV07969ru1aDwrA\nsmXLyMvL44EHHqhru/fee3FwcGD58uX1DJfly5djaWnJgw8+2CrdlpaWrFixgjNnzjBixIgm+40f\nP55ly5Yxf/58FixYUO9cdXU1ly5dwtbWtl77pEmTmD9/PnPnzkWlUhEdHd0qbZ2NYri0hKqqellz\nK3Ir0Abro6VvZ4+LEAJ7rb3xGy4Ad92lN1hiYsDfH+bO7XQJzmZmbOjZk69qvC89ExL4R2Ag452d\nm72ZqzVqvGd54/YnN9LfTSdjSQbnV5zH5xUfPJ8xnh1ItZiY2OLj8yJeXs/VBPL+jUOHHsDCIhhv\n7xdwcZmEWt36jJ8KNy9SSn744QeOHTvW4NzAgQPxq4lBa2uMy5YtWzhw4ABSSiorKzlw4EDdjXvk\nyJH07q33+j3wwAN4eXnRv39/XFxcOHv2LKtWrSIrK6tBfMuUKVP49ddf0el0dW1dunRhwoQJ9O7d\nG41Gw2+//cb69esJCwtj2rRpdf00Gg1vvvkmzzzzDOPHj2f48OH8+uuvxMbG8vbbb2Nn13zc2rVL\nPS3JYjto0CCefPJJFi5cSHJyMsOGDcPU1JTU1FQ2btzIRx99xB//+Md610yePJn58+fz7bffcued\nd+Lj49PsPAalPbPZ3WoHtZlzQcrffpNTjh6Vd+7fL3d57ZJHF6+X27cjL1063GjGwNuFwL8Hyhd/\nftHQMlrOggX6LMirVhlURm55uRx3+LBk+3Y55tAhmVNe3qrrr2RekSlPpsjt6u1yl9cueX7leVld\n2XzmXkOh0+lkUdGv8uDBUXL7diF//91ZpqXNk+XluYaWZjTcDplzmzpWr159w3M8+uijLRp/2bJl\nctCgQdLFxUWamZlJV1dX+fDDD8v//ve/DcYcPHiwVKvV9dqmTZsme/XqJW1tbaW5ubkMDAyUs2fP\nlpcuXWpU12effSZ79OghNRqNDAgIkB999FGLXk9Lsw37+fk1yM5bO29ERIS0tLSUtra2sm/fvvKV\nV16R2dnZjY4TGRkpVSqV/Pjjj1ukT0rDZc4VspHgHQU9QogwIHEQYHvvvZwdNoyuf/gDz/UrxOXT\no+T4/Jk77ijC1NR4dnx0NgNWDqCHUw8+H/W5oaW0DCn1y0WrV+trGt15p0HlbMjN5ekTJ5BS8nH3\n7oy5xoXdHGWpZaS9nkbehjwseljg97YfTqOcjHo5pqzsBBkZH5Kd/QVSVuPm9gheXs9hadnT0NIM\nSu3W0cTExGZ33igoGANNvWevzuPy66+/AoRLKdst6VerMucKITYKIe4Xxvyt2AEsAb77+mtMhwzB\no9wEWSHBPg+VSouJiW2z19/KOGgdbo6lolqEgOXL4Y47YOxYyMw0qJzxLi4ciYhgkJ0dY48c4U8p\nKVxqJuvu1VgEWtBzfU/C9oZh7mnOkdFHSLojieKdLU9d3tlYWAQQGLiUAQPS8fWdS0HBFvbu7cWB\nA/eRn/8vpGx+R4mCgoLx0tHVoVub8t8e+B44J4SYL4S4sQxANwvm5mBtTV5lJe4X9X8ynXUe5uae\nRv3LtjO46QwXAFNTfU0jU1P9TqPycoPKcTEzY1PPnqzs3p31ubmE7NvHnosXWzWGTT8b+v6nL33+\n0wddhY7kwckkD0mm+FfjNWBMTR3o0uUVoqLO0KNHLFVVJRw+PJI9ewJJT/+AqqqOybqpoKBwc9Mq\nw0VKOQTwB1YCk4ETQohtQogYIYRx7c9sTxwckEBuZSUuNYZLtXnObb2jqBYHzU1ouAC4usKmTZCU\nBNdkwjQEQgimuruT3K8fjqam3LF/P2+eOUPVVUGBLcFhqAPhCeH0/LonlQWVJN9t/AaMSmWGq2s0\n4eHxhIXFY2MzgNOnX2LXLk9SU5+hrOy4oSUqKCgYEa0usiilPCulnCel9AfuA84DnwJZQoh/CCE6\nPitSZ+PoSGl1NVd0OhxrCixWqXNu6x1FtdyUHpdaIiP1y0affKI/jIBuFhb8HhrK7C5dmHfmDIOT\nk0lrZTE5oRI4P+xMv/39bioDBsDGpj/BwWuIijqLt/cs8vK+IiEhiIMHH6Cg4EekbJ0hp6CgcOtx\nQ9WhpZTbpJSTATfgFWAisKc9hBkVjo51WXNtiySooFJ3eyefq6XWcLlpg7ynToXp0+GZZ/RlAYwA\nU5WK+X5+7AwJIbOigr779rEmO7vVf+Ob2YAxN3fHz+8NBgw4R1DQaioqcjl06A8kJPQgI2MpVVUl\nhpaooKBgIG7IcAEQQvgBLwKzAVvglxsd0+i4ynCxLJKYOJlQXnFeWSpCb7iUV5dzuerGSswblA8+\n0BdhHDMGsrMNraaOO+3sSO7Xj4ednJiSkkLMsWMU17wPW8PNbMCoVOa4uT1CePg+QkN/x8oqhJMn\nn2f3bg9SU5+mtPSIoSUqKCh0Mm0yXIQQGiHEZCHENuAE8Aj6uBc/KeX917/6JsTB4X91igp0mHWp\nQKcrUzwu6A0X4OZdLgJ9CYCNG/VbpceNgyYKoxkCWxMTvuzRg7gePfixoIA++/YR38ZS8U0ZMEmD\nkyj4ocCovWZCCGxt76Bnz/VERZ3By2smeXmb2Lu3F0lJg8nN3YBOZzz/NwUFhY6jtduhI4UQK4As\n9HEt2cD9gL+Ucr6UMr0DNBoeR8e6ytAmBdWoffW1eRSPyy1iuAC4u+uNlz17YNYsQ6tpwERXVw5G\nROBlbs6g5GT+npHRZkPjWgNGd1nHoQcPsbf3XrJXZ6OrMO44Eo3GCz+/+QwYcI7g4HWA5OjRCcTH\ndyEtbQ5XrmQYWqKCgkIH0lqPSzzQH3gd8JBSxkgpf5HG/FOtPXB0JLeyElu1mqq8SlQ++pu04nG5\nhQwXgIED4aOPYOlSfXkAI8NHo2FnSAhPe3oy4+RJYo4da1XOl2upNWDC4sMI2RmC1k9LyqMpxPvH\nc27xOaoutn3szkClMsPFZQKhoTvp1+8QTk6jychYQny8L4cPj6GoaKtRe5EUFBTaRmtrFfVrz+x3\nNwsz164l86ef0AweTGXu/Zi66W/SZmbuBlZmeG4pwwXgySdh3z546ino2VMf+2JEmKpULOnWjQE2\nNvzp+HEi9+9nU8+e9LC0bPOYQgjsBtlhN8iO0qOlpC9OJ212GmffPIvHUx54PeeFuYdxZzuwsupF\nYOAy/P0XkpPzTzIzl3HgwFC02u54ev4ZV9dHbusM1woKncnVmXM7gtbmcakzWoQQdwkh1gghdgsh\nPGvapgghDJtDvQNYMm8edy5dStcHH6QitwKc8jExcVSKxAF2Gv3N4JYxXITQe1xCQ2HYMPj9VZAA\nsgAAIABJREFUd0MrapTxLi7sDQtDABGJiazLyWmXcS2DLQn6PIiotCg8nvLg/IrzxPvGkzI1hdKj\npe0yR0diYmKDp+fTREQcJiRkB1ZWfTh1aha7d3tw7NijXLiwS/HCKCh0MMaWORcAIcQY4GfgMhAK\n1P4cs0W/u+jWomZXkbPahMr8SqRdPubmSnwLgFqlxk5jd+sYLgAaDfz0E4SEwH33wb/+ZWhFjRJk\nacmesDBGOTkRfewYM06coKKVCeuawtzTnK6LujIgfQB+b/tR+O9C9vbcy8E/HKTg+wKkzrhv/kII\n7OzupmfPDURFnaNLl9e4cOFXkpLuYO/eXmRkfEhl5S30nlVQuI1o63bo14CnpJRPAFfvz/wv+orK\ntxYWFuRVVuJ12RR0oLNQks9djaPW8dYyXABsbeHHH+HBB2H0aPjcOItIWpmYsKZHD5YFBLDi/Hnu\nTk4m/cqVdhvfxMYEnxd9iDodRdDqICpyKzj00CH2BOwh/W/pVBa2fnt2Z2Nu7k6XLrPp3/8kffr8\nG0vLYE6depFduzw4enQyxcW/Kl6YDmb16tWoVKpGD7VaTUJCwg3PsXz5csaPH0+XLl1QqVRMnTr1\nuv1/+eUXhgwZgp2dHTY2NvTr14+vvvqqRXOlpKRw//33Y21tjaOjI4888gj5+fkN+kkpeffdd/H3\n90er1dK3b1/WrVvXojneeOONur9PZiM11UpKStBqtahUKma0Mvt3UlISKpWKOXPmNNnn5MmTqFQq\nXnzxxVaN3Rm0Nsallu7Ar420XwBuvYVkIcirqMD9Qm26/1wszfoaWJTxcFNnz70eGo2+ptEzz8Cf\n/gQ5OfDXv+qXk4wIIQTTPT0Jt7Zm3JEjhCUmEtejB0MdHNptDpWZCrdH3HCd4kpJQgmZSzM5Pfs0\naa+l4TLJBc+nPbEOtW63+ToCIVQ4ONyHg8N9VFTkkJ29mqysT0lOXotW2x0Pjydwdf0/zMycDC31\nlkQIwZtvvomvr2+Dc926dbvh8d99910uXbpEZGQk2c3kY/riiy94/PHHGTZsGO+88w5qtZrjx4+T\nnt78xtjMzEzuuusu7O3tWbhwISUlJbz33nscPnyYhIQETEz+d1udPXs2ixYt4sknn6Rfv358++23\nxMTEoFKpGD9+fItel0ajIS4uroEBsXnzZoQQbaqXFxoaSlBQEHFxccyfP7/RPmvXrkUIwZQpU1o9\nfocjpWz1AZwGhtY8LkG/HRr0+VyOtmVMYzzQe49kYmKitNy5U36y/rjcznb5+04Pefr061JBz/B/\nDpdj1o8xtIyOQ6eTct48KUHK556Tsrra0IqaJL+iQg5PTpaq7dvlh+npUqfTddhc5Tnl8syCM3KX\n9y65ne0ycWCizF6bLavLjffvcy06XbUsLNwqjxyZKHfsMJM7dpjJw4cnyIKCf0udrqpTtSQmJsra\n75tbjVWrVkmVStWhr+3cuXN1j62srORjjz3WaL8zZ85ICwsLOXPmzDbNM336dGlpaSkzMjLq2n75\n5RcphJCffvppXVtmZqY0MzOTM2bMqHf9oEGDpI+PT7OfzXnz5kmVSiXHjh0rw8LCGpwfNmyYHDdu\nnBRCyGeffbbVr+Ott96SKpVK7tmzp9HzQUFBMjg4+LpjNPeerT0PhMl2vDe3danoU+BDIUT/GlEe\nQohJwGJgeVuNKGPlcnU1pTodDsWAqppKnVJg8WpuWY9LLULA3Ln6ukYffQSTJxtVkrqrcTQ15fs+\nfZjp5cVzJ0/yVGoqle0U93ItZi5mdJndhf6n+9Nzc09UWhXHJh1jt89u0l5P40p6+y1ZdRRCqLC3\nv5fg4DgGDMjE3/8dLl1K5uDBYeze3YVTp/5KaelRQ8tUaAHe3t4t6rd8+XJ0Oh1vvPEGAKWlrQs6\n37x5Mw899BCenv8LFxgyZAiBgYFs2LChru2bb76hqqqK6dOn17t++vTpZGRksHv37hbNFxMTQ1JS\nEqmpqXVtOTk5bNu2jZiYmEavqaioYO7cuQQEBKDRaPDx8eHll1+m4qrvrUmTJiGlJDY2tsH1+/fv\n5/jx40yePLlFGjubthouC4FYYCtghX7Z6DPgYynl39tJm9FQXJMrw6YIcLkAVCsxLldxyxsutTz1\nFGzYoK8q/dBDUGKc9XLUQrC4WzdWdu/OF9nZDDt4kII2lApoKSoTFc6jnQn5JYSIIxE4j3Um44MM\n4rvEc/APB8nbnIeu0riT2gGYmTnh7f0CkZHHCAuLx8lpFFlZn7B3b0/27etHRsZHVFTkGVrmTc2F\nCxcoKCiodxQW1v/uKC4ubtCnseNyK4uP1rJ161aCgoL4/vvv8fb2rotTmTNnTrOxTufPnyc3N5d+\n/fo1OBcZGUlSUlLd8+TkZCwtLQkKCmrQT0pZr+/1GDRoEF5eXvUMjHXr1mFtbc2DDz7YoL+UkhEj\nRvD+++8zatQoli5dyujRo1myZAkTJ06s6+fr68vAgQPZsGFDg9ddu0wUHR3dIo2dTZsMlxov0ALA\nAegFRAHOUsrX21OcsVBU86VvVSgxDdDXdlF2Ff2P28ZwARg7Vr/jKD4ehgwxWuMFYKq7O1v79uVw\naSn9ExM51spflm3BMtiSwKWBDDg/gO6fdqeysJIjY46w22s3p14+RVlqWYdruFGEENjY9Ccw8B8M\nHJhFz56b0Wi8OXXqRXbv9uDQoZHk5m5Epys3tNSbCiklQ4YMwdnZud7h5eVVr19oaGiDPtceLi4u\nvPfee23SceLECc6dO8fUqVN5/PHH2bRpE3/4wx946623eO211657bVZWFgDu7g1zeLm7u1NYWEhl\nzf0iKysLV1fXRvuB3ghqCUIIJk6cSFxcXF1bbGwsY8aMwdTUtEH/tWvXsm3bNv7973+zePFiHn/8\ncT788EOWLl3Kt99+S/xVxWQnTZpETk4OW7durWuTUrJhwwYGDBjQaDySMdCq4FwhxDngu5pjm5Sy\nArjl/ahFVVWgUmFeVE1FlyIqATMzxeNSy21luADccw/s3Al33w2PPqovFWBkAbu13GVnR0JYGCMO\nHSJq/37WBwdzv6Njh89rYm2C+5/ccf+TO5cOXSJrZRZZn2aR/m46tnfb4v64O85jnFFr1R2u5UZQ\nqcxxdh6Ns/NoKiryyctbT3b2lxw9Og4TEztcXCbi6voINjZRbQqSbCvVZdWUpXS8EWgRZIHaon3+\nR0IIli1bRkBAQL12tbr++LGxsS3ypvj7+7dJx6VLl5BSsmjRorqA19GjR1NQUMCHH37I7NmzsWwi\noWOtLnPzhgkZNRpNXR9TU1MuX77cbL+WEhMTw+LFi0lMTMTOzo69e/eycOHCRvtu3LiRHj16EBgY\nSEFBQV37Pffcg5SS7du3ExUVBcCECRN4/vnniY2NZejQoQDs2LGDzMxMXn311Rbr62xau6toCjAS\n+AfgLIT4Gb0R872U0rjLzN4ARVVVYGaGSb6Oqu6FgBozMxdDyzIaHLQOlFaWUl5VjrmJcWdYbTdC\nQ+HLL/VbpRcuhFdeMbSiJvHTatkVFkbM0aM8eOgQ73frxgxPz0670Vr1tiLggwD8F/qT/3U+WZ9l\nkTIlhZPPnsR1sivuj7tj1deqU7TcCGZmTnh6Po2n59OUlqaQk/NPcnL+yfnzK9Bo/HBy+iPOzmOw\nsemPEG1dhW8ZZSllJIYndugcAOGJ4ViHtd9usYiICMLCrp8xY8CAAe02X2NotVrKysrqLZuAPmna\nzz//TFJSEnfe2XgeVa1WC0B5eUNv25WaNAS1fbRabYv6tYSQkBCCgoKIjY3F1tYWd3d37rnnnkb7\nnjhxgpSUFJydnRucE0KQm5tb99zBwYHhw4fz9ddfs2LFCszMzIiNjcXU1JRx48a1WF9n0yrDRUq5\nE9gJzBJC9ERvxDwLrBRC7KLGGyOlPN3uSg1IUVUV1lot1XmViEGFmJu7d/gX081Ebdr/oitFuFm5\nGVhNJ/Lww/D66/Dqq3pD5n7jLYxuY2LCt71789fTp3n+5EmOlJayNCAAM1XnvY/VGjWu0a64Rrty\n+dRlslZmkf1FNplLM7EKt8J9qjsu0S6Y2jd0fxsblpZB+PsvwM/vTYqLd5CX9xU5OWvIyPgbZmYe\nODmNxtl5DLa2d6FStTXrRNNYBFkQnhje7uM2Nk9nk5+fT3V1dbP9rKysmvSMXA8PDw9OnjzZYBnH\nxcUFKSVFRUVNXlu7zFO7ZHQ1WVlZODg41C3fuLu7s2PHjkb71epoDTExMSxfvhxra2smTJjQZD+d\nTkfv3r1ZsmRJozE71wYxT548mS1btrBlyxZGjBjB5s2bGT58OI6d4JltK23+REkpjwBHgHeEEG7A\nCPSGzNtCiNPAy1LK79tHpmEprKzE2dSUitwKhEOeskx0DVfXK7qtDBeAefNg/36IjtbXOOra1dCK\nmkQtBO917UqwhQVPpqaSWlbGpl69cGxknbyj0XbV4v+2P77zfSn8oZCsz7M4MeMEJ184ifNoZ9ym\numE/xB6hMs4luFpqdyXZ299LQMBSLlzYRV7eJvLzN3P+/D8wNXXCyelhnJz+iL39EFQqs3aZV22h\nbldPiDERERHB2bNnr9tHCMHcuXOvm0CtKcLDwzl58iSZmZn1YjgyMzMRQjTqqajFw8MDZ2dn9u3b\n1+BcQkICISEhdc9DQkJYuXIlKSkp9QJ04+PjEULU69sSYmJimDNnDtnZ2U3uJgLo2rUrBw8ebNIj\ncy0jR47E2tqa2NhYTExMKCoqYtKkSa3S1tm0y08BKWU2+i3SnwohLIDhwC0TufbzW29RrtXyY8a9\nPGCjpPu/lluu0GJrUKlgzRqIjNR7YHbvBivjXvZ4zN2dAK2W0UeOcMf+/fzUpw++rXBbtycqExVO\nI51wGulEeXY5OWtyyP48m4PDDmLuY47bo264PeqG1s8w+lqDEGrs7O7Czu4uunVbQknJXvLyNpOf\nv4msrM9Qq21xchqBk9MYHByGoVZ3vjfjZqCjY1wmTJjAunXrWLlyJW+++SagD0j94osvcHBwIDz8\nf56s06dPN5hrzJgxfPnll2RmZtZtid66dSupqanMmjWrrt+oUaOYOXMmy5Yt46OPPqprX7FiBZ6e\nngwcOLBVuv39/fnwww+5fPlyo7uaahk/fjw//PADn376KU888US9c1euXEGn02Fh8b/3nkajYfTo\n0axfv57S0lKsrKwYOXJkq7RdS0cXWWyT4SKE2AmsBL6SUtZ7h0kpy4Cv20Gb0RD44os4BPRgcHgh\n1RbrMTfvbWhJRsVtbbgA2NnBN99A//76DLvr1hltsG4td9rZsTs0lOEHDzIgKYkfevcm1Nqwv+DN\n3czxedEH71neXNxzkezPs8lYksHZ+Wexu8cOt6luOP/Rud2CRTsS/c6kSGxsIvH3f4fS0kN1npic\nnDUIYY6t7Z04ONyHvf19WFm17tf3zYiUkh9++IFjx441ODdw4ED8/PyAtse4bNmyhQMHDiClpLKy\nkgMHDrBgwQJAb0T06tWr7vGQIUN45513yMvLo2/fvnz99dfs2rWLTz75pN5OnXvvvReVSlVnwIA+\nG+7GjRsZPHgwzz33HCUlJSxevJi+ffvy6KOP1vXz9PTk+eefZ/HixVRUVBAREcHXX3/Nf//7X2Jj\nY9sUY/bss88222fKlCls2LCB6dOns337du644w6qq6s5duwYX331Ff/+978bxBlNnjyZL7/8kp9/\n/pnJkye3Kv6mMaKjo4mOjmb//v31DMF2oy1Z64APgFz0Kf4/BaLaMyuesRzUZM7ttWaNfHrnYbmd\n7fLXbfbyzJm3pcL/qKiqkMxDfpH0haGlGJZNm/TZdRctMrSSFpNdXi777dsnrX/9Vf6noMDQchpQ\ndalKZn2ZJZMGJ+k/fza/yuN/Pi4vJl00tLQ2U1qaIs+de18eOPCA3LnTQm7fjvz9dye5YcN9t3zm\n3KaO1atX3/Acjz76aIvHLy0tlTNnzpQeHh5So9HIvn37yri4uAZj+vr6Sn9//wbtR48elffff7+0\nsrKSDg4O8pFHHpG5ubmN6lq4cKH08/OTGo1G9u7du9F5GqM2c25BM59LlUrVIDt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LAAAg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\nNnoOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60203a2748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for e_in, e_out_dist in zip(dist.energy[::5], dist.energy_out[::5]):\n",
    "    plt.semilogy(e_out_dist.x, e_out_dist.p, label='E={:.2f} MeV'.format(e_in/1e6))\n",
    "plt.ylim(ymax=1e-6)\n",
    "plt.legend()\n",
    "plt.xlabel('Outgoing energy (eV)')\n",
    "plt.ylabel('Probability/eV')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also `summed_reactions` attribute for cross sections (like total) which are built from summing up other cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<Reaction: MT=1 (n,total)>, <Reaction: MT=27 (n,absorption)>]\n"
     ]
    }
   ],
   "source": [
    "pprint(list(gd157.summed_reactions.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the cross sections for these reactions are represented by the `Sum` class rather than `Tabulated1D`. They do not support the `x` and `y` attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7f603c930198>}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157[27].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unresolved resonance probability tables\n",
    "\n",
    "We can also look at unresolved resonance probability tables which are stored in a `ProbabilityTables` object. In the following example, we'll create a plot showing what the total cross section probability tables look like as a function of incoming energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f60202807f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QLgBhn6rqxIbFoA2D2aDpKVKUrfHHi3/P793t/OtZ/CpCJSn6k62U+hzwYeCr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\nZmZWSU5SzMzMrJKcpJiZmVkl/R8gvAbpuSeyUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6020421320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cm = matplotlib.cm.Spectral_r\n",
    "\n",
    "# Determine size of probability tables\n",
    "urr = gd157.urr['294K']\n",
    "n_energy = urr.table.shape[0]\n",
    "n_band = urr.table.shape[2]\n",
    "\n",
    "for i in range(n_energy):\n",
    "    # Get bounds on energy\n",
    "    if i > 0:\n",
    "        e_left = urr.energy[i] - 0.5*(urr.energy[i] - urr.energy[i-1])\n",
    "    else:\n",
    "        e_left = urr.energy[i] - 0.5*(urr.energy[i+1] - urr.energy[i])\n",
    "\n",
    "    if i < n_energy - 1:\n",
    "        e_right = urr.energy[i] + 0.5*(urr.energy[i+1] - urr.energy[i])\n",
    "    else:\n",
    "        e_right = urr.energy[i] + 0.5*(urr.energy[i] - urr.energy[i-1])\n",
    "        \n",
    "    for j in range(n_band):\n",
    "        # Determine maximum probability for a single band\n",
    "        max_prob = np.diff(urr.table[i,0,:]).max()\n",
    "        \n",
    "        # Determine bottom of band\n",
    "        if j > 0:\n",
    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
    "            value = (urr.table[i,0,j] - urr.table[i,0,j-1])/max_prob\n",
    "        else:\n",
    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
    "            value = urr.table[i,0,j]/max_prob\n",
    "\n",
    "        # Determine top of band\n",
    "        if j < n_band - 1:\n",
    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
    "        else:\n",
    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
    "    \n",
    "        # Draw rectangle with appropriate color\n",
    "        ax.add_patch(Rectangle((e_left, xs_bottom), e_right - e_left, xs_top - xs_bottom,\n",
    "                     color=cm(value)))\n",
    "\n",
    "# Overlay total cross section\n",
    "ax.plot(gd157.energy['294K'], total.xs['294K'](gd157.energy['294K']), 'k')\n",
    "\n",
    "# Make plot pretty and labeled\n",
    "ax.set_xlim(1.0, 1.0e5)\n",
    "ax.set_ylim(1e-1, 1e4)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Energy (eV)')\n",
    "ax.set_ylabel('Cross section(b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting HDF5 data\n",
    "\n",
    "If you have an instance `IncidentNeutron` that was created from ACE or HDF5 data, you can easily write it to disk using the `export_to_hdf5()` method. This can be used to convert ACE to HDF5 or to take an existing data set and actually modify cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gd157.export_to_hdf5('gd157.h5', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With few exceptions, the HDF5 file encodes the same data as the ACE file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
    "np.all(gd157[16].xs['294K'].y == gd157_reconstructed[16].xs['294K'].y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And one of the best parts of using HDF5 is that it is a widely used format with lots of third-party support. You can use `h5py`, for example, to inspect the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reaction_002, (n,elastic)\n",
      "reaction_016, (n,2n)\n",
      "reaction_017, (n,3n)\n",
      "reaction_022, (n,na)\n",
      "reaction_024, (n,2na)\n",
      "reaction_028, (n,np)\n",
      "reaction_041, (n,2np)\n",
      "reaction_051, (n,n1)\n",
      "reaction_052, (n,n2)\n",
      "reaction_053, (n,n3)\n"
     ]
    }
   ],
   "source": [
    "h5file = h5py.File('gd157.h5', 'r')\n",
    "main_group = h5file['Gd157/reactions']\n",
    "for name, obj in sorted(list(main_group.items()))[:10]:\n",
    "    if 'reaction_' in name:\n",
    "        print('{}, {}'.format(name, obj.attrs['label'].decode()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<HDF5 group \"/Gd157/reactions/reaction_016/294K\" (1 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/product_0\" (2 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/product_1\" (2 members)>]\n"
     ]
    }
   ],
   "source": [
    "n2n_group = main_group['reaction_016']\n",
    "pprint(list(n2n_group.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see that the hierarchy of data within the HDF5 mirrors the hierarchy of Python objects that we manipulated before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   3.02679600e-13,   1.29110100e-02,\n",
       "         6.51111000e-02,   3.92627000e-01,   5.75226800e-01,\n",
       "         6.96960000e-01,   7.39937800e-01,   9.63545000e-01,\n",
       "         1.14213000e+00,   1.30802000e+00,   1.46350000e+00,\n",
       "         1.55760000e+00,   1.64055000e+00,   1.68896000e+00,\n",
       "         1.71140000e+00,   1.73945000e+00,   1.78207000e+00,\n",
       "         1.81665000e+00,   1.84528000e+00,   1.86540900e+00,\n",
       "         1.86724000e+00,   1.88155800e+00,   1.88156000e+00,\n",
       "         1.88180000e+00,   1.89447000e+00,   1.86957000e+00,\n",
       "         1.82120000e+00,   1.71600000e+00,   1.60054000e+00,\n",
       "         1.43162000e+00,   1.28346000e+00,   1.10166000e+00,\n",
       "         1.06530000e+00,   9.30730000e-01,   8.02980000e-01,\n",
       "         7.77740000e-01])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n_group['294K/xs'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with ENDF files\n",
    "\n",
    "In addition to being able to load ACE and HDF5 data, we can also load ENDF data directly into an `IncidentNeutron` instance using the `from_endf()` factory method. Let's download the ENDF/B-VII.1 evaluation for $^{157}$Gd and load it in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download ENDF file\n",
    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/Gd/157'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'gd157.endf')\n",
    "\n",
    "# Load into memory\n",
    "gd157_endf = openmc.data.IncidentNeutron.from_endf(filename)\n",
    "gd157_endf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as before, we can get a reaction by indexing the object directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "elastic = gd157_endf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we look at the cross section now, we see that it isn't represented as tabulated data anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0K': <openmc.data.function.ResonancesWithBackground at 0x7f601ec47ba8>}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If had [Cython](http://cython.org/) installed when you built/installed OpenMC, you should be able to evaluate resonant cross sections from ENDF data directly, i.e., OpenMC will reconstruct resonances behind the scenes for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998.78711745214866"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.xs['0K'](0.0253)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When data is loaded from an ENDF file, there is also a special `resonances` attribute that contains resolved and unresolved resonance region data (from MF=2 in an ENDF file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.resonance.ReichMoore at 0x7f60230e5208>,\n",
       " <openmc.data.resonance.Unresolved at 0x7f601f9850b8>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_endf.resonances.ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that $^{157}$Gd has a resolved resonance region represented in the Reich-Moore format as well as an unresolved resonance region. We can look at the min/max energy of each region by doing the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1e-05, 306.6), (306.6, 54881.1)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(r.energy_min, r.energy_max) for r in gd157_endf.resonances.ranges]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With knowledge of the energy bounds, let's create an array of energies over the entire resolved resonance range and plot the elastic scattering cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f601ebdd748>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Pscdd8CHH1Z2zDRTYiIiknHMMdDcDDGvUSYlqGZTjnN+RE1UxypneykGDMh/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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f601ebc7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-spaced array of energies\n",
    "resolved = gd157_endf.resonances.resolved\n",
    "energies = np.logspace(np.log10(resolved.energy_min),\n",
    "                       np.log10(resolved.energy_max), 1000)\n",
    "\n",
    "# Evaluate elastic scattering xs at energies\n",
    "xs = elastic.xs['0K'](energies)\n",
    "\n",
    "# Plot cross section vs energies\n",
    "plt.loglog(energies, xs)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resonance ranges also have a useful `parameters` attribute that shows the energies and widths for resonances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>energy</th>\n",
       "      <th>L</th>\n",
       "      <th>J</th>\n",
       "      <th>neutronWidth</th>\n",
       "      <th>captureWidth</th>\n",
       "      <th>fissionWidthA</th>\n",
       "      <th>fissionWidthB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0314</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.1072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.8250</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.0970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.2400</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.0910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.7700</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.012800</td>\n",
       "      <td>0.0805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.5600</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.011360</td>\n",
       "      <td>0.0880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.6500</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3300</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.1700</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44.2200</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.0960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
       "0   0.0314  0  2.0      0.000474        0.1072            0.0            0.0\n",
       "1   2.8250  0  2.0      0.000345        0.0970            0.0            0.0\n",
       "2  16.2400  0  1.0      0.000400        0.0910            0.0            0.0\n",
       "3  16.7700  0  2.0      0.012800        0.0805            0.0            0.0\n",
       "4  20.5600  0  2.0      0.011360        0.0880            0.0            0.0\n",
       "5  21.6500  0  2.0      0.000376        0.1140            0.0            0.0\n",
       "6  23.3300  0  1.0      0.000813        0.1210            0.0            0.0\n",
       "7  25.4000  0  2.0      0.001840        0.0850            0.0            0.0\n",
       "8  40.1700  0  1.0      0.001307        0.1100            0.0            0.0\n",
       "9  44.2200  0  2.0      0.008960        0.0960            0.0            0.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolved.parameters.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heavy-nuclide resonance scattering\n",
    "\n",
    "OpenMC has two methods for accounting for resonance upscattering in heavy nuclides, DBRC and ARES. These methods rely on 0 K elastic scattering data being present. If you have an existing ACE/HDF5 dataset and you need to add 0 K elastic scattering data to it, this can be done using the `IncidentNeutron.add_elastic_0K_from_endf()` method. Let's do this with our original `gd157` object that we instantiated from an ACE file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gd157.add_elastic_0K_from_endf('gd157.endf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check to make sure that we have both the room temperature elastic scattering cross section as well as a 0K cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0K': <openmc.data.function.Tabulated1D at 0x7f601eb805c0>,\n",
       " '294K': <openmc.data.function.Tabulated1D at 0x7f60230d7cf8>}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157[2].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating data from NJOY\n",
    "\n",
    "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://njoy.github.io/NJOY2016/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n",
    "\n",
    "Let's use `IncidentNeutron.from_njoy()` to run NJOY to create data for $^2$H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " njoy 2016.12  02Apr17                                       05/01/17 12:33:17\n",
      " *****************************************************************************\n",
      "\n",
      " reconr...                                                                0.0s\n",
      "\n",
      " broadr...                                                                0.0s\n",
      "      300.0 deg                                                           0.0s\n",
      "      400.0 deg                                                           0.2s\n",
      "      500.0 deg                                                           0.3s\n",
      "\n",
      " heatr...                                                                 0.4s\n",
      "\n",
      " purr...                                                                  0.6s\n",
      "\n",
      " mat =  128                                                               0.6s\n",
      "\n",
      " ---message from purr---mat  128 has no resonance parameters\n",
      "                          copy as is to nout\n",
      "\n",
      " acer...                                                                  0.6s\n",
      "\n",
      " acer...                                                                  0.7s\n",
      "\n",
      " acer...                                                                  0.8s\n",
      "                                                                          0.8s\n",
      " *****************************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Download ENDF file\n",
    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/H/2'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'h2.endf')\n",
    "\n",
    "# Run NJOY to create deuterium data\n",
    "h2 = openmc.data.IncidentNeutron.from_njoy('h2.endf', temperatures=[300., 400., 500.], stdout=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use our `h2` object just as we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0K': <openmc.data.function.Tabulated1D at 0x7ffa93130d30>,\n",
       " '300K': <openmc.data.function.Tabulated1D at 0x7ffa919485c0>,\n",
       " '400K': <openmc.data.function.Tabulated1D at 0x7ffa92fcb630>,\n",
       " '500K': <openmc.data.function.Tabulated1D at 0x7ffa91805240>}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2[2].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that 0 K elastic scattering data is automatically added when using `from_njoy()` so that resonance elastic scattering treatments can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Windowed multipole\n",
    "\n",
    "OpenMC can also be used with an experimental format called windowed multipole.  Windowed multipole allows for analytic on-the-fly Doppler broadening of the resolved resonance range.  Windowed multipole data can be downloaded with the `openmc-get-multipole-data` script.  This data can be used in the transport solver, but it can also be used directly in the Python API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = 'https://anl.box.com/shared/static/ulhcoohm12gduwdalknmf8dpnepzkxj0.h5'\n",
    "filename, headers = urllib.request.urlretrieve(url, '092238.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "u238_multipole = openmc.data.WindowedMultipole.from_hdf5('092238.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `WindowedMultipole` object can be called with energy and temperature values.  Calling the object gives a tuple of 3 cross sections: total, radiative capture, and fission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(9.638243132516015),\n",
       " array(0.5053244245010787),\n",
       " array(2.931753364280356e-06))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u238_multipole(1.0, 294)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array can be passed for the energy argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f601eb44f60>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CIKJkVlgIiFQ1iqoPlzCCiQHChpKS8MsXgN7P0+WIKFkVFmp4CDdDWxOXMIIpsJ0o/dDK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\niIiixgBBREREUft/dmr85+/bFVoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f601ea5ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.linspace(5, 25, 1000)\n",
    "plt.semilogy(E, u238_multipole(E, 293.606)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real advantage to multipole is that it can be used to generate cross sections at any temperature.  For example, this plot shows the Doppler broadening of the 6.67 eV resonance between 0 K and 900 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f601e973780>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EHDAAhg+3V3BnzeqVkB5x5AgULAgffwwdOngnZsvZLfnhlx/Y+9Je0gem905Q\nSRNSSs2DiPiB6VuncyTmCL2r9/ZKvLg4mDzZ9nbw58QBIH9+aNjQJg/e0q9WP36O+ZnJmyd7L6hI\nEil5EEmF4uLjGLJmCM2KN6NUnlJeiblsmf3E7q1P6p72wguwaZPt++ANJXOXpGXplgxaPYjLsZe9\nE1QkifwqeVDNg0jizNo+i31n9tGnhveufh43DkqVsgWHqUHjxnDXXTB+vPdi9qvVj1///JWJURO9\nF1RSLdU8qOZBJNHiTTxlx5blnuB7+K71d16JeeSIbQo1Zgx06uSVkF7x1lswcqR3azjazGnDysMr\n2d9tv2ofxC1U8yAit7Rg9wK2n9pO3xqev8Miwdix9i/Xp5/2WkivaN8eLlyw93R4S+8avTn6x1Gm\nbZnmvaAiLlLyIJKKGGMYuGogDxV8iGr5q3kl5sWLMGECPPus/xdKXi+hcHLsWPDWIm3J3CV5ouQT\nDFkzhNj4WO8EFXGRkgeRVGTR/kVEHov0aq3D55/Db79Bly5eC+lVL71kiyZXr/ZezD41+nDg9wPM\n3OaFKz5FkkDJg0gqkbDqUPmuytQpVOfWb3BLTPjgA/vpvGhRr4T0uvr1bbfM99/3Xszyd5Tn0WKP\nMmj1IOJNvPcCiySSXyUPOm0h8u9WHV7F2p/X0rdmX+xltp63bh1ERdlLsFIrx4FXXoF58+DAAe/F\n7VOjD7tO72LOzjneCyqpik5b6LSFyC3V+7Qep86fYnPHzV5LHh57DPbuhW3bIMCvPoq45sIFuOce\ne2nWe+95L64v/ptK6qPTFiJyUxt+2cDSA0vpU6OP1/6S2bHDXl396qupO3EAyJwZXnwRJk2CP/7w\nXty+Nfqy5cQWvtn7jfeCiiRCKv8tL5I2DFo9iOK5ivN4ice9FvPdd20TJX+9ettVnTvbkyUTvdi/\nqWaBmlTPX52BqwbiD6vEknYoeRDxc1uOb+GrPV/Rq3ovAgMCvRLzl1/gs88gPBzSp5E+RgmJ0ogR\ncNlL3aMdx6Fvjb5sOLqBZQeXeSeoSCIoeRDxc4PXDKZQ9kKElQ7zWsz33oMsWez9D2lJr1622+TU\nqd6LWb9IfSrdWYmBqwZ6L6jILfhV8qDTFiL/tOv0Lr7Y/gWvV3udoMAgr8Q8fdreNtm5M2TL5pWQ\nKcZ990Hz5jBsGMR6qX9TwurDysMrWX3Yi80mxO/ptIVOW4jc1DPznmHpgaUc6HaADOkyeCXmG2/A\nhx/CwYNi3p8+AAAgAElEQVSQK5dXQqYomzdDSAhMn+69eo94E0/5ceW5M9udLGyz0DtBJdXQaQsR\n+Z+Dvx/ks62f8dqDr3ktcTh1yl5+9dJLaTNxAKhQARo1gsGDId5L/ZsCnAD61OjDov2L2HR0k3eC\nivwHJQ8ifmrY2mHkzJST5ys+77WY775rj2X26OG1kClSnz72qOqXX3ovZvOSzSl2ezEGrlbtg/ie\nkgcRP3T0j6N8Ev0J3at2J3NQZq/EPHnSbld06wa33+6VkCnWgw9CgwbQrx/ExXknZmBAIL2r92bB\n7gVsPbHVO0FF/oWSBxE/NHzdcDIHZabz/Z29FnPYMEiXDrp391rIFG3QINi1y9Y+eEurMq0omL0g\ng1cP9l5QkZtQ8iDiZ06eP8n4yPF0e6Abt2W4zSsxDx+2qw7du0POnF4JmeJVrAiPPw79+8OVK96J\nGRQYxBvV3mDW9lnsOr3LO0FFbsKvkgcd1RSB9354j8CAQLpV7ua1mG++Cdmzq9bhem+/bRMrb3ad\nbFe+HXdmu5Mha4Z4L6j4JR3V1FFNEQB+v/g7Bd4vQKdKnRhWb5hXYm7ebD9ljx0LHTt6JaRfadsW\nliyB/fvtHRjeMHrDaLov6s6el/ZQOEdh7wQVv6WjmiJp3AcbP+Bq/FW6V/Ve4cHrr0OxYtC+vddC\n+pX+/W3jrPff917MDiEduD3z7Qxb450EUuR6Sh5E/MSfl//k/fXv83zI8+TNmtcrMRcvtp+qhw61\nxZJyo8KFoWtXGDIEjh/3TszMQZnpUbUHn0R/wi9//OKdoCJ/o+RBxE+M+3Ec566c49UHX/VKvNhY\n6NkTqlWDxx7zSki/1a+fvSCsb1/vxexUqRPZMmTjnbXveC+oyDVKHkT8wMWrFxnxwwieKfcM9wTf\n45WY48bBtm12Od5xvBLSb+XIYbcvJk+G6GjvxMyWIRuvVH6FCVETOH7OS0seItcoeRDxA5M2T+LU\nhVO8Uf0Nr8Q7dcqesHj+eahUySsh/d6LL9qLs7p3B2/Vob9U+SXSB6Zn5A8jvRNQ5BolDyIp3JW4\nK7yz9h3CSodRJGcRr8Ts3duuNgwa5JVwqUJQEAwfDt9/D/PmeSdm9ozZ6Xp/Vz7a9BG/XfjNO0FF\nUPIgkuJNiZ7CL3/8Qu8avb0Sb9MmmDQJBg5Mu5dfJVWjRtCwIbzyCpw/752Yr1R5BYNh1IZR3gko\ngpIHkRTtatxVhqwZQvOSzSmZu6TH48XFQZcuUK6cejokhePYW0dPnrQNpLwhd5bcdKrUidEbRhNz\nKcY7QSXN86vkQR0mJa2ZvnU6h84eom9N75TxjxkDP/4IH30EgYFeCZnqFC5sb90cOdIWnHpDj6o9\nuBR7iTEbx3gnoPgFdZhUh0lJg2LjYynxYQlK5ynN3BZzPR7v0CEoXRqefRY++MDj4VK1y5ft6k2e\nPLBypXdOq3T9tiszt83k0CuHyJo+q+cDit9Qh0mRNGTmtpnsO7OPN2u+6fFYxtjTAjlzwmBd2Jhs\nGTLY1ZvVq2HqVO/EfK3aa8RcjmH8j+O9E1DSNCUPIilQXHwcg1YPonHRxoTk8/xq22efwaJF9v6K\nbNk8Hi5NqF0bWre2jbZOnvR8vPzB+Xmm3DMM/2E4F69e9HxASdOUPIikQLN3zGbX6V1eWXU4dcqe\nDggLg8aNPR4uTRk50m5ZdOninXi9qvfi5PmTTNo8yTsBJc1S8iCSwsSbeAauHkj9IvWpfHdlj8ZK\n2K4wxrsXO6UVefLY7YvZs+GLLzwfr0jOIrQq04pha4dxOfay5wNKmqXkQSSFmbdrHttObqNfzX4e\nj/XppzBnDowfb/+iE/d78klo3hw6d/bO9kWfGn349c9fmRg10fPBJM1S8iCSghhjGLhqIA8XfJhq\n+at5NNbhw/DSS/D00/YvN/GcDz+0qztdu3o+VvFcxWldpjWDVg9S7YN4jJIHkRRk3q55bD6+mX61\nPLvqEB8P7dpBcLCOZXpDnjw2gfjiC/j8c8/H61erHyfPn2Tcj+M8H0zSJCUPIilEXHwcb37/JnUK\n1eGhgg95NNb778OKFTBlik0gxPOeegpatLCdO48c8Wyse3PeS7vy7RiyZgjnrpzzbDBJk5Q8iKQQ\ns7bPYvup7QysPdCjcSIjoVcvCA+3xwnFOxzHHoW97TZo08a2AvekN2u+ydlLZ9V1UjxCyYNIChAb\nH8tbK97i0WKPUuXuKh6LExNjPwGXKQNDhngsjPyLHDlg+nRYuxaGDvVsrALZC9AhpAPvrnuXPy7/\n4dlgkuYoeRBJAT7d8il7z+zl7Yc8d5uSMfD883D6NMyaZbsgivfVrGlXft56C9av92ysPjX6cP7K\ned5fr3O44l5KHkR87HLsZQasHEDzks2pkK+Cx+KMHWsL9iZPtpc3ie+89RZUqgStWsHvv3suzl23\n3UWnSp0Y8cMIzlw847lAkub4VfKgWzUlNZq0eRJHYo4w4KEBHouxebOtcejSBZ54wmNhJJGCgiAi\nwiYO7drZ0y+e8kb1N4iNj2X4uuGeCyIpkm7V1K2akkqdv3Keoh8UpW7hukxrNs0jMc6cgfvvt6cq\n1q2DjBk9EkaS4OuvoUkTW//w+uuei9NraS9GbRjFvm77uDPbnZ4LJCmSbtUUSWXeW/8epy+cpv9D\n/T0yfmwstGxpCyXnzFHikNI8+ij07m1/rFjhuThvVH+DzEGZ6b+iv+eCSJqi5EHER06cO8GwtcPo\n+kBXCufwTBFCr16wfLltTFSwoEdCSDK9/TY89JBN8o4d80yM4IzB9K3Zl0mbJ7Hj1A7PBJE0RcmD\niI+8vfJt0gWko2/Nvh4Zf8YMGD7c/qhTxyMhxA0CA239Q2CgvQfjsofus+pUqRMFggvQa1kvzwSQ\nNEXJg4gP7D69m/GR4+ldvTc5M+V0+/hRUdC+vb234uWX3T68uFmePPDll7Bpk71AyxOlaBnSZWBw\nncEs2L2A1YdXuz+ApClKHkR8oNeyXtx92928VPklt4/9yy8QGgqlS9vbMh3H7SHEA6pUgQkT7FHa\nUaM8E+OpUk9RMV9FXl3yKv5QLC8pl5IHES9bc2QNc3fNZVDtQWRM594Kxj/+gMaNISAAFiyATJnc\nOrx4WNu28Oqr0KMHLFzo/vEDnADerfcuG45uYPaO2e4PIGmGkgcRL4qLj6Pbd92omK8iYWXC3Dp2\nbKy9eOnQIfj2W8iXz63Di5cMGQING9r/lrt2uX/8hws9TOOijXlt6Wu6sluSTMmDiBdNjJrI5uOb\nGdNoDAGO+377GWMbQC1davfOS5d229DiZYGBttj17ruhUSM4ccL9Md5r8B5H/ziqxlGSZEoeRLzk\ntwu/0Xt5b9qVb+f2y6/eeQc+/tjWONSt69ahxQduuw2++QYuXrS9IM65+VbtorcXJbxKOEPWDOFI\njIfvB5dUScmDiJe8+f2bxMbHMrSOe69TnDwZ3ngD+vaF555z69DiQwULwnff2a2LFi3stpQ79a3Z\nl+CMwby25DX3DixpgpIHES+IPh7N+Mjx9K/Vn7xZ87pt3Dlz7E2ZHTvaZkOSupQvb7ehFi+GTp3c\ne4QzW4ZsDKs7jM+3f87KQyvdN7CkCUoeRDws3sTT9duuFM9VnK4PdHXbuEuXQliYbSz04Yc6kpla\n1a8PEyfaH+5OENuUbUOVu6vQbWE3YuPdvLQhqZqSBxEPmxg1kbU/r2VMwzEEBQa5ZcwNG6BpU6hd\nG6ZNs0V2kno98wwMGgT9+8Po0e4bN8AJYEzDMWw7uY1R6z3UXEJSJZ8kD47j3O04zveO42x3HCfa\ncZzmvpiHiKf9+uevvLrkVdpXaM/DhR52y5hbttgq/IQl7fTp3TKspHC9ekHPnrZj6OTJ7hu34p0V\neemBl+i3oh8Hfz/ovoElVfPVykMs8LIxphTQAHjfcRy1s5FUp+u3XcmULhPv1nvXLeNt2WLvqShY\n0F7nnDmzW4YVP+A49lTNiy9Chw72sjN3+b+H/4/bM91O5287q/OkJIpPkgdjzHFjzNZr/3wCOA24\nv8G/iA/N2TmHubvmMqbRGHJkypHs8bZutYlDgQK23iF7djdMUvyK49j6ljZt7I+vvnLPuNkyZOPD\nRh+ycN9CZm6b6Z5BJVXzec2D4zgVgQBjzFFfz0XEXc5cPEPXb7vy2H2P8USJJ5I93tattr6hQAFY\nsgRyJD8XET8VEGC3LR57zBbLLl3qnnGb3NeEJ0s+ycsLX+bMxTPuGVRSLZeTB8dxajiOs8BxnKOO\n48Q7jhN6k2e6OI5z0HGci47jrHcc5/5/GSsnMBV43vWpi6RMxhg6fdOJS7GX+LDRhzjJPAbx0092\nxSF/fps45NQaXZqXLp3tQlmnDjRpAosWuWfcUY+M4krcFV5Z+Ip7BpRUKykrD1mAaKAzcMPmmOM4\nLYARwFtABWALsMhxnFzXPZcemAsMNsZsSMI8RFKkiG0RzNo+i7GNx3LXbXcla6xNm+Dhh+Gee+wn\nTCUOkiB9etvno25de4vqN98kf8x82fIx6pFRfLr1U+bsnJP8ASXVcjl5MMYsNMb0M8bMB272kSoc\nGG+MmWaM2QW8CFwAru99NxVYZoyZ4eocRFKqn2N+psu3XWhVphUtSrdI1lgrVtitimLFYNkyJQ5y\nowwZ7ImbRo2gWTOYPz/5Y7Yt15amxZvS8euOnDjngYs1JFVwa82D4zhBQEVgWcJrxpbuLgWq/u25\nasCTQFPHcTY7jhPlOE4pd85FxNviTTzPzn+WLEFZGNNwTLLG+vpreOQRqFLFdhdUjYP8m/TpYdYs\nWwPRvLlNJpLDcRzGPzoeB4eOX3fU6Qu5qXRuHi8XEAhcn66eAO5L+MYYszYpscPDwwkODv7Ha2Fh\nYYSFufdqY5GkGLZmGMsPLmfx04uTdboiIgLatrVL0TNm2E+XIv8lKMj+f/P00/YejE8+sf+cVHmy\n5GFCkwk0/bwpn0R/wnMVdGmKv4iIiCAiIuIfr8XExLg/kDEmyT+AeCD0b9/nu/Za5eueGwb8kIw4\nIYCJjIw0IinRioMrTMCAANN3Wd9kjTNmjDGOY0y7dsZcveqmyUmaERtrzHPPGQPGjByZ/PGenfes\nyTwos9l5amfyBxOfiYyMNNgaxRCTjL/z//7D3Uc1TwNxwPU3/+QFjrs5lkiKcOLcCVp+2ZJaBWrR\n/6H+SRojPh5eew26doXwcJg0yVbUi7giMNDegfH669C9O/TunbzLtD5o+AEFggvw1BdPcfHqRfdN\nVPyeW5MHY8xVIBKok/CaY8+p1QHWJXf88PBwQkNDb1iSEfGVuPg4Ws1phTGGGU/MIDDA9UsmLl2C\nVq1g+HAYNQpGjLBn+UWSwnFg6FD7/9OQIfbG1bi4pI2VJX0WZj05i71n9vLywpfdO1HxuIiICEJD\nQwkPD3f/4K4uVWCPapYDymO3KF659v09137+KezpirZAcWA88BuQO6nLI2jbQlKonot6moABAWbZ\ngWVJev9vvxlTo4YxGTMaM2eOmycnad6UKcYEBhrz+OPGXLyY9HEmRE4w9MfM2DrDfZMTr/HEtkVS\nFkYrAd9fm4jB9nQAe/TyOWPMrGs9Hd7GbldEAw2MMaeSEEskxZoaPZXhPwzn/QbvU7tQbZfff/Ag\nNGwIp0/D8uVQteqt3yPiimeesUd8n3rKNpSaNw9y53Z9nPYV2rPi0Ao6fNWBUnlKUTZvWfdPVvxK\nUvo8rDTGBBhjAq/78dzfnvnIGFPQGJPJGFPVGPOje6ct4lvrfl7HC1+/QIcKHehWuZvL71+xAu6/\nH2Jj4YcflDiI5zRpYv9/27fP/n+2e7frYziOw8dNPua+2+/jsZmPcfrCabfPU/yLX+2squZBUoLD\nZw/T7PNmVL6rMh82dr399NixUK+evVJ740YoWtRDExW5pnJlWL/e9oSoWhVWrnR9jMxBmZnbYi7n\nrpzjqS+e4mrcVfdPVNzKkzUPjvGDBiCO44QAkZGRkYSEhPh6OpKG/XbhN6pNrsaVuCts6LCB3FkS\nvwZ89Sp06wbjxsFLL9nCyKAgD05W5Dpnz8ITT8Dq1fZET1J6Qaw8tJK6n9blxYov8kGjD9w/SXG7\nqKgoKlasCFDRGBPljjH9auVBxJfOXznPoxGPcubiGRa1WeRS4nDqlF1tmDQJJkyA0aOVOIj3Zc8O\n331nk4a2baFXL9dPYtQqWIsPGn7AmE1jGPnDSM9MVFI8nSQXSYSrcVdpMbsFP534iRXtVlD09sTv\nNfz4o20bfOGCLYysXt2DExW5hfTpbS+IEiVsP4joaNvJ1JUW6C9WepFDZw/RY3EP7sp2V7LvcRH/\no5UHkVuIjY/lmXnPsGj/Iua0mEOlOysl6n3G2C2KatUgTx57Q6YSB0kJHAd69rSrEBs2wAMPwPbt\nro0xuM5gWpdpTdt5bVlxaIVH5ikpl18lDyqYFG+Li4+j3bx2zNo+i4gnIqhfpH6i3nf+vF0W7tQJ\nOnSwe8wFCnh4siIuql/fJrUZM9pL2ObNS/x7A5wAJj82mRr5a9B0ZlO2ntjquYlKkqhgUgWT4gNx\n8XE8t+A5pm+dTsQTETxV6qlEvW/3bluUdvCgrW9o1crDExVJpnPnbE+IOXOgb1/o39+2uk6MPy7/\nwcNTH+bnmJ9Z0W4FJXOX9OhcxXUqmBTxkrj4OJ7/6nmmb53O9GbTE504RETY/g1xcfYTnRIH8QdZ\ns8Ls2TBwIAwebFckTlx/N/K/uC3DbSxus5g7st5BnWl12PPbHs9OVlIEJQ8i17kce5kWs1swbcs0\npjWdRliZW1/5/uef9pNbq1a2Kc/GjVBSH8DEjzgO9OkDS5fa+ofy5W1zqcS4PfPtLG27lBwZc1B7\nam0O/H7Ao3MV31PyIPI3f17+k8YzGvP1nq+Z02IOrcu2vuV7Nm2CChXsku+0afDZZ5AtmxcmK+IB\nDz9sT2CUKGFbWg8aZG99vZU8WfKwrO0yMgdl5qEpD2kFIpXzq+RBBZPiSacvnKbOtDps+nUTi9os\nIvS+0P98Pj4ehg2DBx+E22+3f+AmpemOSEpzxx2wZIldiXjzTWjUyPYquZV82fLx/TPfky1DNmp8\nUoPo49Gen6z8KxVMqmBSPGzvb3t5NOJRzl46y8LWC6mQr8J/Pv/zz/Dss7Zvw+uvw9tvq+mTpE6L\nF0ObNraAcsoUaNDg1u85feE0DT9ryN7f9vJt62958J4HPT5P+XcqmBTxgBWHVlB5YmUA1j639j8T\nB2PsH6ClS8OuXXZ/eMgQJQ6SetWvD1u2QNmy8Mgj8MorcOnSf78nV+ZcLGu7jPJ3lKfep/VYvH+x\ndyYrXqPkQdK0SVGTqPdpPULyhbC+/XruzXnvvz577BiEhtoVh2bNYNs2qO36TdwifidfPttQ6v33\nbeOz+++Hn3767/fcluE2vmv9HbUL1abxjMZMjJroncmKVyh5kDQpNj6WHot60OGrDrSv0J7vWn9H\njkw3789rDMycaVcbNm2C+fPt6kP27N6ds4gvBQTAyy/b3wOOYxOIUaP+u5gyU1Am5raYy/Mhz/P8\nV8/Ta2kv4k0iqi8lxVPyIGnO8XPHqTOtDqM3jmbUI6MY23gsQYE333c4dQqeegrCwuzFVtu22dUH\nkbSqTBl7FLlTJ7uF0aABHDny78+nC0jHh40+ZET9EQxbO4yWs1ty8epF701YPMKvCiZr1qxJcHAw\nYWFhhIXd+uy9yPVWH17NU7OfwsFh1pOzqJ7/5pdNGANTp0KPHvZT1kcf2SRCRP6yeDG0bw8xMTB8\nODz/vP398m/m7pxL6zmtKZm7JHNazCF/cH7vTTYNioiIICIigpiYGFatWgVuLJj0q+RBpy0kqYwx\nvLf+PV5b8hrV81dnZvOZ3JH1jps+u28fdOxoT1K0aQMjR0LuxN++LZKmxMTYS7YmToS6de3X/7rH\nJepYFM0+b8aFqxf4vPnn1C6kwiFP02kLkSQ4fu44jWY0osfiHnSv2p2lbZfeNHG4etWenChTxt5L\nsWgRfPqpEgeR/xIcbO9wWbjQ3utSujSMH29X724mJF8IkS9EUi5vOep9Wo8R60bgDx9i5Z+UPEiq\n9vWeryk7tiybj23mu9bf8U69d0gXkO6G5zZsgIoVbUOcl16yleT1E3eBpohgax9++snWB734ol2F\n2Lfv5s/mypyLhW0W8uqDr9JzSU9aftmSmEsx3p2wJIuSB0mVLl69SNdvu9IkogmV767M1k5beeTe\nR2547vRpeOEFqFoV0qe3leTvvANZsvhg0iJ+LjgYPv7YrtodOGBXIQYNgitXbnw2XUA6htYdyuwn\nZ7Nw30IqjK/Ahl82eH/SkiRKHiTV2Xh0I5UmVGLS5kl82OhDFrRcQJ4sef7xTFwcjB0LxYrBF1/A\n6NGwfr29o0JEkqd+fXu51ssvw1tv2d9Xa9bc/NknSj5BdMdo8mTJQ/VPqjNszTAd5/QDSh4k1bh4\n9SKvLXmNqpOqkildJn58/kc6398Z57ry73XroFIl6NwZHn/c7tN27QrpbtzNEJEkypzZ3v0SFWUv\niqtRwxYi//77jc8WylGI1c+upkfVHryx7A0emf4Ix/485v1JS6IpeZBUYe2RtZQfX57RG0YzqPYg\n1ndYT6k8pf7xzPHj0K4dVKtm+/SvX28rw/PkufmYIpJ8ZcvC2rX2uPPMmVC8OEyffmNBZVBgEEPr\nDmVxm8VsPbGV0mNL8/m2z30zabklv0oedKumXO/8lfO8/N3L1PikBjkz5WRzx828Uf2NfxRFXrwI\nQ4faLYqvvrKV4Bs2QOXKPpy4SBoSGGibSu3cCQ89ZG+frVEDNm++8dl6ReqxrfM26hSqQ8svW9Ji\ndgtOXzjt9TmnBrpVU30e5DrGGObtmsfLC1/m9IXTDKo9iG6VuxEYEPi/Z+Lj7SedXr3g11/tNkW/\nfvb6bBHxneXLoVs3m0y88AIMHHjz35efb/uczt92Jl1AOj5+9GMeK/6Y9yebCqjPgwhw4PcDNIlo\nwuOzHqdM3jJs67yN8Krh/0gc1qyBKlWgdWsICbHFW6NGKXEQSQlq17arDiNHwowZdlVw7FhbyPx3\nLUq3YHvn7VS+qzJNP29K6zmtOXn+pG8mLf+g5EH8xuXYywxcNZBSH5Vi64mtzHlqDl+HfU3hHIX/\n98y+ffDEE3ZJND4eVqyAuXPtH04iknIEBdnTGHv3QtOmdmWwYkWwXZT/ckfWO5jfcj5Tm05l0b5F\nFB9TnMmbJ6uxlI8peRC/8N3e7yg3rhwDVg6g2wPd2NFlB81KNPvfSYrjx21zp5Il7aU9n35qv9aq\n5eOJi8h/ypMHJk2ydUgZMtjfs02bwp49fz3jOA5ty7VlZ5edPFrsUdovaM/DUx9m9+ndvpt4Gqfk\nQVK07Se388j0R2g0oxH5suVjc8fNDKs3jKzpswL22Ffv3lCkiK3gHjDAHr1s08ZeISwi/uGBB+CH\nH+w2RnQ0lCplPxCcOvXXM7mz5GZas2kseXoJv/zxC2XHlWXAigFcjr3su4mnUfrjVVKk0xdO0+Wb\nLpQbV459Z/Yxt8VclrddTuk8pQE4dw4GD4ZChWwtwyuv2I52vXrZ8+Ui4n8CAmx761277O/vTz+F\ne++1/SIu/u0W77qF6/JTp5/oWbUnA1fbrcyv93ytrQwvUvIgKcqVuCu898N73Dv6Xqb/NJ2hdYey\nvfN2mhZviuM4XL4MH3xgVxr694e2bWH/ftsCN0cOX89eRNwhY0Z49VVbw/TMM9C371/9IRKKKjMF\nZWJQnUFseXELhXIUoklEExrPaMye3/b89+DiFkoeJEWIN/HM3DaTUh+VoueSnoSVDmPvS3vp+WBP\nMqTLwOXLtj9DsWJ2laFRI7snOno03HHzm7VFxM/lymV/j2/fbospn34aypeH+fP/ajJVMndJFrdZ\nzNwWc9l5eielPyrN60te58/Lf/p28qmcXyUPahKV+hhj+G7vd1T8uCJhX4ZRPFdxojtGM/bRseTJ\nkodLl2xnuqJFbZOZKlVg2zb45BMoWNDXsxcRbyhWDObMsTURefLYgsoqVWDZMvvzjuPQtHhTdnTe\nwZs13+SDjR9QbEwxpkRPIS4+7r8HT8U82SQKY0yK/wGEACYyMtJI6rH2yFpT85Oahv6Y6pOrm9WH\nV//v5y5cMGbUKGPuvNOYgABjWrc2ZscOH05WRFKMpUuNqVzZGDDm4YeNWbfunz9/+Oxh0+KLFob+\nmLJjy5qFexf6ZqIpRGRkpAEMEGLc9PeyX608SOrw04mfCI0Ipdrkapy9dJZvWn3DqnarqJ6/OufP\n28YxhQpB9+5Qr57tQjd9OpQo4euZi0hKUKeOXYWYP9+exnjwQQgNhS1b7M/nD87PzOYzWd9+Pbdl\nuI1HPnuE+p/WJ/p4tG8nnoooeRCv2XpiK81nNafsuLJsP7Wdzx7/jM0dN9OoaCPOnnUYNMgmDa+/\nDo0b24rrKVPU4ElEbuQ4fyUMM2bYDxnly9stjahrDZgr312ZVe1WMa/FPI7EHCFkfAht57blSMwR\n304+FVDyIB4XfTyaxz9/nHLjyhF1LIqJTSays8tOWpVpxdFfAujRA/Lnh//7P3tF9p49tmnMvff6\neuYiktIlHO/cscN+2NixwxZXNmliG8U5jsNjxR9jW+dtjG08lsX7F1Psg2L0XNyTU+dP3XJ8uTkl\nD+IxUceieGzmY1QYX4GtJ7YyOXQyu7vupn1Ie/buSk+7dlC4MEyebC/JOXwYxo2zqw8iIq4ICrLH\nOnfssNuc+/bZm3MbNrRbHOkC0tGxUkf2vrSXN6q/wceRH1NoVCH6LOvDmYtnfD19v6PkQdxu49GN\nNIloQsWPK7Lz1E6mNp3Krq67aFf+WdavC6JJEyhdGpYutc1fjhyxfRry5vX1zEXE36VLZy/E27bN\n3qr788+2JqJ+fVi9GrKmz0b/h/pz8OWDvPTAS7y/4X0KjSrEgBUD+OPyH76evt9Q8iBuYa4duXxo\nyjqoKK0AABCiSURBVENUnliZvb/t5dNmn7Kjyw7CSrblyy/S8eCDULOm7QQ5ZYr92r07ZMvm69mL\nSGoTGAgtWsDWrTB7Npw4Yf/8qVbNFlrmyHg7Q+oO4UC3A7Sv0J4ha4ZQaFQhhq4Zyvkr5309/RRP\nyYMky9W4q0zfOp1y48rRaEYjLsVeYs5Tc9jeeTsN72rD8HfSUagQtGwJmTLBggXw0092eTF9el/P\nXkRSu4AAe9NudDR8/bVdmWja1N6dMXkyZA/Ky8gGI9nfbT8tS7Wk3/f9KDiqIENWDyHmUoyvp59i\nKXmQJDl/5Tyj1o/i3g/u5em5T3NP8D2seGYFP7T/gWLxzejcKZB77rEtpBs0sBXRy5fbIiZdWCUi\n3uY49hTXqlWwbh3cdx+0b2/rroYPh2zcxYeNP2TvS3tpXqI5/Vf2p8D7BXhz+ZucvnDa19NPcfTH\nuLjk1z9/5c3lb5L//fz0XNKTWgVqsfXFrXzV8hvO76hFgwYOpUvbFYbeve1+46RJULasr2cuImJV\nrQrz5tnjnY88Yv+syp/fXqyX4VIBxj46loMvH6R9hfaMXD+Sgu8XpOfinhz785ivp55iKHmQRNl4\ndCOt57SmwPsFeH/D+zxd9mn2d9vPezWnsXh6Ge67z2b1v/9uK50PH7aX2eTO7euZi4jcXPHi9sPN\nwYPw/PPw4Yc2iXjmGTi+905GNBjB4VcO80qVV5gQNYFCowrR+ZvOHDp7yNdT9zklD/KvrsZdZea2\nmVSdVJXKEyuz/pf1vFvvXX5+5RdaZn+fN7vl5667bNb+wAOwZo09V926teoZRMR/3HUXvPuuPfk1\nZAisXGl7RdSsCasW5mJArYEceeUI/Wr144sdX3Dv6HsJ+zKMqGNRvp66zyh5kBucvnCawasHU2hU\nIcK+DCNzUGbmt5xPVLs9ZN76Cg8/GEzVqvbY04ABdmvis89sFbPj+Hr2IiJJkz079Ohhe0TMnm1f\ne+IJ27Bu4ofBdC7Tm0MvH2LUI6PYeHQjFT+uSJ1pdVi4b2HCPUxphuMP/8KO44QAkTVr1iQ4OJiw\nsDDCwsJ8Pa1UxRjDhqMbGPfjOD7f/jkAbcq0oVvlbgScLsPYsTBtGpw/b7cnOnWyhZAqfhSR1Cwy\nEkaNsj0j0qeHZ5+1Te0KF4ljzs45vLPuHX789UdK5ynN0DpDaVyssa+n/D8RERFEREQQExPDqlWr\nACoaY9yyXOJXyUNkZCQhISG+nk6q8uflP5nx0wzGRY4j+ng0BbMX5IWQFwi773mWfZWLyZNtZXLe\nvNChA7zwgt0TFBFJS44dsx1wx461l3E1aABdukDDhoa1v6zi3XXvElY6jNZlW/t6qjeIioqiYsWK\n4MbkIZ07BhH/s/XEVsZuGsv0n6Zz4eoFHi32KIMeHky2kw34ZHIApWfBhQu2K9usWfDYY6pjEJG0\nK18+u03bqxd8/rktrgwNhQIFHF58sRaftK+VpgrEteichly8epFpW6bx4KQHKTeuHPN3z6d7le5s\nbHWIakfm0z20ITVrBPD99/Zmy8OHYeFCePJJJQ4iIgAZM9rTGBs32h8PPWT72dxzD4wZ4+vZeY9W\nHlI5Y/6/vXsPsqI88zj+fWZkYcFLWA1yVZYwxoDC4AIlEVGioxEXEUHkEiqBci0FtlIJgm5tqUnt\n4oYYLTW6YVcjuExERgOJlwQkKxvDQGEQR+7geokwMLqg3HTCbZ794z2ss8cZoHvOme6Z+X2qumqm\nT7+nn36qz+nn9Nv9trNm5xrmVszlmfXPsO/QPkp6lLDwpl9y2rvDefrhVsx6OYy6NmpUqKaHDtW1\nDCIiJzNgQBhq/8EHw2iV/folHVHjUfHQTH306UeUritlbsVcNny0gS5ndGHKgKn0L5zEfy3qybTp\nod/ukkvCxUDjx0P79klHLSLS9Jx9NsyYkXQUjUvFQzNytOYov337tzxV8RQvbXuJAitgxFdHcGef\nB/hgeQm/mFrIv2yFjh1h4sQwFRcnHbWIiDQ1Kh6agU3/s4l5FfOYv24+VQerKO5YzKzLH6LV1vH8\n6rGz+c5r0LYt3HQTPPoofOMboZtCREQkDh1CmqidB3ayYP0CSteXUlFVQfs27RnX+1sUHZxE+S/7\nce934cgRuOqqMD7DyJFw+ulJRy0iIs2BiocmZP+h/SzavIjSdaW8+t6rtCpsxd8WDeeGM+/l/WXD\neOZHrdm7F/r2hVmzYNw46Nw56ahFRKS5UfGQcoePHWbpfy+ldH0pL2x9gUNHDzHk/CuYXvQEu/8w\nipce+RKLdkNREUybBrfcAhddlHTUIiLSnKl4SKFjNcdY8cEKFm5cSNnGMvZU76FPhz5M6v5Dqv84\njiVzuvH7Kjj/fJg8ORQM/frpuRIiItI4VDykRI3XUP5BOWUby3h+8/NUHayi25ndGNbxVlg/geVP\nXszPdoRuiLFjwzRwoAoGERFpfCoeElTjNazavur/CoadB3bS9YyuDD5rHOwYw8onBjK/soAOHWD0\n6HCGYfBgDeAkIiLJUvHQyGq8htU7VlO2sYznNj1H5YFKOp/emf5tb+bY22NY9dNLeX5PAV27wuhR\n4fbKwYOhsDDpyEVERAIVD43gaM1RVnywgsWbF7N4y2K2799Ox3adKG49mgv/NIbVz3+dFw4UUFQE\nt/1dKBj691eXhIiIpFNixYOZLQKuBH7n7mOSiiNfqo9Us+zdZSzespgXt77Inuo9dGrXha8V3Mh5\n28awZvFlLKkupLgYZs4IBUOvXioYREQk/ZLsPX8YmJjg+nPuk+pPKF1XyqiyUZzzwDmMeHYEr72z\nmosO3cbFq15n18ztLL/zMfz9Icz6p0LeeQfefBPuuQd698594bBgwYLcvmELobxFp5zFo7xFp5yl\nQ2LFg7u/BhxMav25Urm/ksdff5yS+SV0+EkHJi6eyKbtlRTvvZcui7bw7vRNrJl9P0XtBjD3KaOq\nCsrLYfp06NEjv7HpQxaP8hadchaP8hadcpYOuuahAeasmcMdL9/BaQWncWHroVy841G2vXgDW3Z1\noVs3GDEchs8Oz3tv0ybpaEVERHIjcvFgZpcDM4C/AToBN7r7C1nLTAXuBDoCbwF/7+5/bHi46dK6\n8mou2FDK278ZxobP2jNwINw9BYYPhz59dP2CiIg0T3HOPLQDKoCfA4uyXzSzW4AHgduA14HvAUvN\n7AJ3392AWFOnc5ueXHi4JzMegeuvh06dko5IREQk/yIXD+6+BFgCYFbnb+vvAf/m7v+RWeZ24Hpg\nMvDjrGUtM51MG4DNmzdHDTevvvxluO++8PeuXWFKk3379rF27dqkw2hylLfolLN4lLfolLPoah07\nc9aBbu4ev7FZDbW6LcysFfAZMKp2V4aZzQPOcveRteYtA/oQzmR8DNzs7qvrWc944BexAxUREZEJ\n7v5MLt4o1xdMngMUAh9mzf8Q+GrtGe5eEuF9lwITgPeBPzcgPhERkZamDdCdcCzNiSZxt4W77wFy\nUi2JiIi0QCtz+Wa5HudhN3AMODdr/rlAVY7XJSIiIgnIafHg7keAN4Crjs/LXFR5FTmuekRERCQZ\nccZ5aAf05PO7JHqYWV/gY3ffDjwEzDOzN/j8Vs22wLycRCwiIiKJiny3hZldASwHshs+7e6TM8tM\nAWYSuisqCINErWl4uCIiIpK0yN0W7v57dy9w98KsaXKtZf7V3bu7+1+6+6CTFQ5m1tnM5pvZbjP7\nzMzeMrNLTtLmSjN7w8z+bGbbzOzbUbelKYuaMzMbaWavmNlHZrbPzFaa2TWNGXMaxNnXarW9zMyO\nmFmLusk85ufzL8xslpm9n/mMvmtm32mkkBMXM2cTzKzCzD41s51m9nMz+6vGijlpZvaemdXUMf30\nBG1uNrPNZladyfF1jRlzGkTNm5ndamavmdnHmWmZmQ2Iut4kn6oJgJl9CSgHDgHXAl8DpgOfnKBN\nd+Al4D+BvsAjwJNmFuX2zyYrTs6AIcArwHXAJYSzRy9mupxahJh5O972LOBp4Hf5jDFtGpCz54Ch\nwCTgAmAcsDV/kaZHzO+0ywj71xNAL2A0MBD493zHmyL9CY80OD6VEM5wl9W1sJl9nXAX3hNAMfBr\n4Fdm1qtRok2PSHkDriDk7UrgUmA78IqZRRojuUGDROWCmf0IGOTuV0RoMxu4zt371Jq3gDAQ1bA8\nhJkqcXJWz/tsAJ5193/OTWTp1pC8ZfavbUANMMLdT+lsRVMX8/P5TcKXUw9335u34FIqZs6mA7e7\ne1GtedOAme5+Xh7CTD0zexgY5u4X1PP6s0Bbd7+h1rxVwJvuPqWRwkydk+WtjuULCIXtVHcvPdX1\nJH7mARgOrDGzMjP70MzWmtmtJ2lzKV/8BbgUGJSXCNMnTs7+n8xdMGcQRvdsKWLlzcwmAX8N/DDv\nEaZPnJwNB9YAd5nZDjPbamYPmFlLebZsnJytArodP+1uZucSzj68nOdYU8nCaMUTCM9Qqs8gWvZx\n4AtOMW/Z2gGtiHgsSEPx0AO4g3BK8xrgZ8CjZjbxBG06UvcolmeaWeu8RJkucXKWbQZhp6nv1FZz\nFDlvZlYE3E8Y1rWmUaJMlzj7Wg/gcqA3cCPwXcKB8PH8hpoakXPm7iuBbwELzewwsAvYC0zLf7ip\nNBI43lVYn/qOAx3zFVQTcCp5yzYbqCRql6y7JzoR+gX/kDXvEaD8BG22AndlzbuOMEBV66S3KY05\ny1p2PHAAGJr0tqQ5b4Ti+nXgtlrzfgCsTXpb0pqzzOtLgU+B02vNGwkc1eez3ja9Ml/g3wcuIvRb\nvwU8mfT2JJTDJcCvTyHPt2TNuwPYlXT8ac5b1vJ3EwZ37B11XWk487ALyH5c5mbgRP18VdQ9iuV+\ndz+Uw9jSKk7OADCzsYSLsG529+V5iC3NoubtDMLFSI9l7rI4AtwDFJvZYTO7Mm+RpkecfW0XUOnu\nB7PaGNA1t+GlUpyc3Q2scPeH3H2Duy8DpgCTM10YLYaZnQdcTbgQ8kTqOw60yNGMI+Tt+PJ3EoZU\nKHH3jVHXl4bioZysh2Zl/v/TCdqsotYolhnXZOa3BHFyhpmNI/SFjfXwaPWWJmre9hN+BRYT7urp\nC8wBtmT+rvMpsM1MnH2tHOhsZm2z2tQAO3IbXirFyVlbQn5qqyFcNW9fXLxZm0zofvjNSZar6zhQ\nQss5DmQ71bxhZjOBfwSudfc3Y60tBadZ+hNOP/0D8BU+P6U+ttYy9xMGoTr+f/fMMrMJH8opwGHg\n6qS3J8U5G5/J0e2E6vz4dGbS25PmvNXxHvfRsrot4uxr7QgHyoWE2xSHELoa5yS9PSnO2bczbW4n\nXJx7GaHLbGXS29PIuTPC05Nn1fHa08D9tf4flMnZ9zPHgR8QnrrcK+ntSHne7srkaWTWsaBdpHUm\nvdGZjRkGrAM+AzYCk7Nenwu8mjVvCOE5GtXA28DEpLcjzTkjjOtwrI7pqaS3Jc15q6N9iyoe4uaM\nMLbDUuBgppD4MS3geocG5mwqsD6Tsx2ZL/1OSW9LI+etJPO91LOO117N/r4CRhHOBFZn8n1t0tuQ\n9rwB79VzLLg3yjoTH+dBREREmpY0XPMgIiIiTYiKBxEREYlExYOIiIhEouJBREREIlHxICIiIpGo\neBAREZFIVDyIiIhIJCoeREREJBIVDyIiIhKJigcRERGJRMWDiIiIRKLiQURERCL5X9QpE5GsVUy3\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f601eb320f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.linspace(6.1, 7.1, 1000)\n",
    "plt.semilogy(E, u238_multipole(E, 0)[1])\n",
    "plt.semilogy(E, u238_multipole(E, 900)[1])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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
}