OpenMC/docs/source/pythonapi/examples/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",
    "\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": [
    "The first thing we want to do is to read an ACE file into memory and instantiate a `IncidentNeutron` object. The easiest way to do this is with the `openmc.data.IncidentNeutron.from_ace(...)` factory method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157.71c>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get filename for Gd-157\n",
    "filename ='/opt/data/ace/nndc/293.6K/Gd_157_293.6K.ace'\n",
    "\n",
    "# Load ACE table into object\n",
    "gd157 = openmc.data.IncidentNeutron.from_ace(filename)\n",
    "gd157"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our ACE table, we can look at its contents. Let's start off by plotting the total cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1682587cf8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEWCAYAAAB1xKBvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16827d39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total = gd157.summed_reactions[1]\n",
    "plt.loglog(total.xs.x, total.xs.y)\n",
    "plt.xlabel('Energy (MeV)')\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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<ACE Reaction: MT=2 (n,elastic)>,\n",
      " <ACE Reaction: MT=16 (n,2n)>,\n",
      " <ACE Reaction: MT=17 (n,3n)>,\n",
      " <ACE Reaction: MT=22 (n,na)>,\n",
      " <ACE Reaction: MT=24 (n,2na)>,\n",
      " <ACE Reaction: MT=28 (n,np)>,\n",
      " <ACE Reaction: MT=41 (n,2np)>,\n",
      " <ACE Reaction: MT=51 (n,n1)>,\n",
      " <ACE Reaction: MT=52 (n,n2)>,\n",
      " <ACE 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 6.400881 MeV\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9zC0aV7vrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1681eae9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n2n = gd157.reactions[16]\n",
    "plt.plot(n2n.xs.x, n2n.xs.y)\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.ylabel('Cross section (b)')\n",
    "plt.xlim((n2n.xs.x[0], n2n.xs.x[-1]))\n",
    "print('Threshold = {} MeV'.format(n2n.threshold))"
   ]
  },
  {
   "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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=2.0>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7f1682a348d0>]"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.stats.univariate.Tabular at 0x7f1681b23550>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b235f8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b236a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b237f0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b239e8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b23c50>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b23f60>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b29320>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b29710>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b29b70>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b2f080>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b2f550>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b2fac8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b360b8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b366d8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b36d30>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b3c438>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b3cb38>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b43278>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b439e8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b4c1d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b4c9b0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b52208>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b52a58>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b58320>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b58be0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b5f438>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1681b5fcf8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1682a2d630>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f1682a2df60>]"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAERCAYAAABowZDXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4lUXah+856b2SHpIQSkIvEUJoQaUo0nUFQhFBBVkV\ny36grpTFCuvqWhYbIki3g4qC0ktCEAiQBEJJBwIhnZB65vvjJDEH0htJnPu63ivnzMw75Rw4zzsz\nzzw/IaVEoVAoFIrq0NzpDigUCoWiZaAMhkKhUChqhDIYCoVCoagRymAoFAqFokYog6FQKBSKGqEM\nhkKhUChqhDIYCoVCoagRymAoFAqFokY0a4MhhPARQnwmhNhyp/uiUCgUf3WatcGQUsZKKWff6X4o\nFAqFookNhhBilRAiRQhx8pb0kUKIM0KIGCHEgqbsk0KhUChqRlPPMFYDI8onCCE0wAcl6V2AyUII\nv1vuE03TPYVCoVBURpMaDCnlASD9luS+wDkpZbyUshDYBIwFEELYCyFWAj3VzEOhUCjuLIZ3ugOA\nO5BY7n0SOiOClDINmFvVzUIIFW5XoVAo6oCUslarN81607umSClb7TVhwoQ73gc1PjU2Nb7Wd9WF\n5mAwkoG25d57lKTVmCVLlrBnz56G7JNCoVC0Svbs2cOSJUvqdO+dMBgC/U3scKC9EMJLCGEMTAK2\n1qbCJUuWEBwc3HA9VCgUilZKcHBwyzAYQogNwCGgoxAiQQgxU0pZDDwF7AAigU1Syuja1NuaZxj+\n/v53uguNSmseX2seG6jxtVTqM8No0k1vKeWUStK3A9vrWm9dB98S6Ny5853uQqPSmsfXmscGanwt\nleDgYIKDg1m6dGmt720OXlIKhaKJ8Pb2Jj4+vsHqCwkJabC6miOtYXxeXl7ExcU1SF2twmCU7mGo\nfQyFomri4+Pr7CGjaJkIoe85u2fPnjov4bcag6FQKBSK6qnPklRzcKtVKBQKRQugVRiM1uwlpVAo\nFA1Ji/GSaizUkpRCoVDUDLUkpVAoWgXe3t6Ym5tjbW2NlZUV1tbWPP3007WuJzU1lZCQEGxtbXFw\ncGDatGnV3rN37140Gg2LFi3SS9+wYQPe3t5YWVkxYcIEMjIyquy/qakpaWlpeum9evVCo9GQkJBQ\nZR8uXbqEkZERsbGxt+WNHz+e//u//6t2HI1JqzAYaklKoWgdCCH46aefyMrKIjs7m6ysLN57771a\n1zNhwgTc3NxISkri6tWrvPDCC1WWLyoqYv78+QQGBuqlR0ZGMmfOHNavX09KSgpmZmbMnVt5PFQh\nBD4+PmzcuLEs7fTp09y8efM2b6WKcHNz49577+XLL7/US09PT2f79u088sgj1dZRHS0tNEiDo0KD\nKBSth/q6/e7cuZOkpCSWL1+OpaUlBgYG9OjRo8p73n77bUaMGIGfn74Uz4YNGxgzZgwDBgzA3Nyc\nZcuW8e2333Ljxo1K65o2bRpr1qwpe79mzRpmzJihV6agoIAXXngBLy8vXF1defLJJ8nPzwdg+vTp\ntxmMjRs30qVLlwY5TNhiQoMoFApFXTl48CB2dnbY29tjZ2en99re3p5Dhw4BEBoaSseOHZk+fTqO\njo7069ePffv2VVpvfHw8q1evZtGiRbcZq8jISD1j065dO0xMTIiJiam0vsDAQLKzszl79ixarZbN\nmzczdepUvboXLFjA+fPnOXnyJOfPnyc5OZl//etfgG7pKTU1tWw8AOvWrWuQ2UV9UQZDoVDoIUT9\nr/owbtw4PUOwatUqAAYMGEB6ejppaWmkp6frvU5LSyMoKAiApKQkdu7cyT333ENKSgrPPfccY8eO\nvW1foZRnnnmGV199FXNz89vycnJysLGx0UuztrYmOzu7yjGUzjJ27tyJv78/bm5uevmffvop77zz\nDjY2NlhYWLBw4cKyZSxTU1MefPBB1q5dC8C5c+c4duwYkydPrsGn17i0Gi8pddJboWgY7vRB8B9+\n+IGhQ4fW+X4zMzO8vb3LnsgffvhhXnvtNQ4ePMjo0aP1ym7bto3s7GwefPDBCuuytLQkKytLLy0z\nMxMrK6sq+zB16lQGDx5MbGws06dP18u7du0aubm59OnTpyxNq9XqzUBmzJjB2LFjee+99/jyyy8Z\nMWIEjo6O1Y69JqiT3sqtVqFoNVS2h3HgwAHuu+++2zaPpZQIIdi+fTsDBgyge/fu/Pjjj3plKttw\n3rVrF3/88Qeurq6AzhgYGhpy6tQpvvvuO7p06UJERERZ+QsXLlBYWEjHjh2rHEPbtm3x8fFh+/bt\nfP7553p5jo6OmJubExkZWdburQwcOBB7e3u+//571q9fz4oVK6psrzYot1qFQtHqGThwYJnnVPmr\nNG3AgAGAbg8gPT2dL7/8Eq1Wy9dff01ycnJZfnleffVVYmJiiIiIICIigjFjxvDYY4+xevVqQBd8\ncNu2bRw8eJAbN26waNEiJk6ciIWFRbX9/fzzz9m1axdmZmZ66UIIHnvsMebPn8+1a9cASE5OZseO\nHXrlpk2bxoIFC8jMzLxtZnSnUAZDoVA0K0aPHo21tXXZNXHixFrdb2dnx9atW1mxYgW2trYsX76c\nrVu3Ym9vD8DcuXN58sknAbCwsMDJyansMjMzw8LCAltbW0AX4vyjjz5iypQpuLi4cPPmTT788MNK\n2y4/k/Hx8aF3794V5r311lu0b9+ewMBAbG1tGT58+G0b6dOnTycxMZFJkyZhZGRUq8+gsRAtPXKl\nEEK29DFUxYYNG5gypUIZkVZBax5fcxybEEJFq/2LUdl3XpJeKxcFNcNQKBQKRY1oFQZDnfRWKBSK\nmqGCDyovKYVCoagRyktKoVAoFI2OMhgKhUKhqBHKYCgUCoWiRiiDoVAoFIoaoQyGQqFQKGqEMhgK\nhUKhqBGtwmCocxgKReugoSRa33//fdq1a4etrS19+/bl4MGDNWrT2tqakSNH6uW3NonW+pzDQErZ\noi/dEFov69evv9NdaFRa8/ia49ia+/8Xb29vuWvXrnrVERYWJi0sLOTx48ellFKuXLlStmnTRmq1\n2lq3efr0aWllZSUPHDggb9y4IadMmSInTZpUZf/9/PzkBx98UJZ26tQp2alTJ6nRaGR8fHy1/R85\ncqRcunSpXlpaWpo0MTGRkZGR1d5/K5V95yXptfq9bRUzDIVC0XqQ9Yx1FRcXR9euXenZsyegC+J3\n/fp1rl69Wus2W6NEa31QBkOhULQIairRet9991FcXMyRI0fQarWsWrWKnj174uzsXGndISEhODs7\nM3LkSE6ePFmWriRa9WkVoUEUCkXDIZbWU2MVkIvrPksYN24choaGZcJIK1asYNasWWUSrdVRutcw\ncOBAAGxtbdm+fXul5Tds2EDv3r2RUvLuu+8yYsQIzp49i7W1db0lWocMGVKpROupU6fK6l64cCEh\nISG89tprehKtQUFBZRKtW7durXbsjY0yGAqFQo/6/Ng3BPWVaP3ss89YvXo10dHR+Pr68uuvvzJq\n1ChOnDiBi4vLbeX79+9f9nrhwoWsWbOG/fv3M2rUqFYp0Vof1JKUQqFoVlS2n3DgwIEyz6nyV2la\nqSdUREQEo0ePxtfXF4ARI0bg6uqqt8RTFeX1IxpConXChAl6eeUlWtPS0khLSyMjI4PMzMyyMrdK\ntN66B3KnUAZDoVC0CGoq0XrXXXfx008/lbmm7ty5k3PnztG1a9fb6kxMTOTQoUMUFhaSn5/PihUr\nuH79elldSqJVn2ZtMIQQ5kKIL4QQHwshmpd0mUKhaBTqK9E6ffp0Jk2aRHBwMDY2NsyfP59PPvmk\nbFZQXqI1OzubuXPnYm9vj4eHBzt27OCXX37Bzs4OUBKtt9KsJVqFEFOBdCnlT0KITVLKSRWUkc15\nDPWlOcp8NiSteXzNcWxKovWvR4uVaBVCrBJCpAghTt6SPlIIcUYIESOEWFAuywNILHld3GQdVSgU\nCsVtNPWS1GpgRPkEIYQG+KAkvQswWQjhV5KdiM5oANTf10+hUCgUdaZJDYaU8gBwqyN1X+CclDJe\nSlkIbALGluR9BzwohPgQ2NZ0PVUoFArFrTSHcxju/LnsBJCEzoggpcwFHq2ugvKbYv7+/nf8+HxD\nUlXQtNZAax5fax6bomWxYcMGoqKiiI6Orlc9zcFg1JtvvvnmTnehUWluG6cNTWseX3MbW0hIyJ3u\nguIOUNG/w/JeWzWlORiMZKBtufceJWk1ZsmSJQQHBxMcHFyrhtNvppOcnUzazbSyK/1mOr1dexPs\nHVynD1ShUCiaM3v27KmzHMSdMBgC/Q3scKC9EMILuAxMAibXpsLaxnYv1hbzXth7LNu3DFcrV+zN\n7LE3s8fO1A4bExtWHV+FocaQp/s9TUi3EMyMzKqvVKFQKFoApQ/XS5curfW9TWowhBAbgGDAQQiR\nACyWUq4WQjwF7EC3Cb9KSlm/hbYqOJt6lke3PoqBMODIY0dob9/+tjJSSn67+Bv/DfsvL/7+IrN7\nzebpfk/jauXaWN1SKBSKZk+TGgwpZYULulLK7UDl4SSroSZLUsXaYt4NfZc3DrzB4iGLmdd3HhpR\n4iQWFgbvvQc+PuDnh/DzY1inQIZNGca56+d4L+w9Aj4NYNvkbfR27V1pGwqFQtHcqc+SVLMODVJT\nSg1GZVxMv8ig1YPYFrONsNlhPNXvqT+NxdatMHo0BASAkRH89BM89hi4uICnJx2eeJH3e73E+/e9\nz8h1I9lxYUel7SgUivrREBKtV65cYezYsbi7u1coi/rVV18xYMAALCwsuPvuu6utryqJ1oKCAh59\n9FFsbGxwc3PjnXfeqbSevXv3otFobgt1cvLkSTQaTY368tZbbzFkyJDb0q9fv46JiQlRUVHV1hEc\nHFxnidZWYzAqs5hfRX5F4GeBPNT5IXbN2IWvve+fmStXwhNP6IzEs8/C4sWwcSMcPw7Z2XDgAHTt\nCnfdxYTrTnz78LdM+24aayPWNs3AFIq/GEIIfvrpJ72ggu+9916t6tBoNNx33318++23FTquODg4\n8Oyzz/Liiy9WW1dkZCRz5sxh/fr1pKSkYGZmxty5c8vyFy9ezIULF0hMTGTXrl0sX778tiCC5WnT\npg2HDx/W0/VYs2YNnTp1qtHYpk6dyuHDh4mPj9dL37hxI927d6/RkQKl6V0BuQW5cs62OdL3v74y\nPDlcP1OrlfLFF6Vs317K8+fLklMLCuQj0dFyalSU/D0tTRaXagBv3y6lk5OU774ro1Iipdc7XvK1\nfa9VqhHckDRHXeiGpDWPrzmOrbL/L80Fb29v+fvvvzdIXUVFRVIIUamO9meffSaHDh1aZR0vvfSS\nDAkJKXt/4cIFaWxsLHNycqSUUrq5ucnffvutLH/RokVy8uTJFda1Z88e6eHhIefOnSs//PBDKaWU\nxcXF0t3dXS5btkyvL9HR0XLYsGHS3t5e+vn5yS1btpTlDR8+XC5btkyv7r59+8r333+/wnYr+85R\nmt46zqSeIXBVIOl56fzx+B8EuAX8mVlQADNmwK5dcOgQlMTM/+X6dXqEh2NnaEiAlRXPnj+Pb1gY\nS+PiiBsyBEJDYc0a/J/+F4cf3smWyC3M/2W+CuSmUDQRNZVobUiqkmjNyMjg8uXLdO/evSy/R48e\nREZGVlqfEILp06ezdq1uleLXX3+lW7duuLr+6VCTm5vL8OHDmTp1KqmpqWzatIl58+Zx5swZQCeu\nVF7z++zZs0RERDB5cq2cS+tEqzAY5ZekTl89zaDVg3iq71NsnLgRG9Ny8orZ2TBqFGRk6AxGmzbk\nFhczLyaGJ2JiWOvvz3/at+cZDw9OBATwtZcfqYWFBPzxB/dkZrLuhx/It7TEddh4Dgz8nF1xu/j0\n2Kd3ZtAKRWMhRP2vejBu3Dg9Q7Bq1SqAMonWtLQ00tPT9V6npaURFBTUEKPXoyqJ1pycHIQQevk1\nkW8NDAwkPT2dmJgY1q5de5si348//oiPjw/Tp09HCEGPHj2YMGECX331FaDT/E5JSSE0NBSAL7/8\nkvvuuw8HB4cajak+S1KtxmCUbnp/f+Z7ZvSYwezes/XXLwsKYPx48PSEb78Fc3OOZGXR6+hRMouK\niAgI4O6SGPjZx7M5Pf402Z4nmD0vl8gbfjzh6sqq69cJmjOHCy+8gOXdI/nJ6yX+ueufhCeH34FR\nKxSNhJT1v+rBDz/8oGcIZs2a1UADqz1VSbRaWloC6OXXRL4VdOJIH3zwAXv27GH8+PF6efHx8YSG\nhmJvb19mODds2MCVK1cAMDMzK9P8BmqtyPeX3/Quz67YXdztc4u3gVYLM2eCpSV8+ilFGg3/iotj\n9KlT/MvHh3WdO2NrZET2H9mcGnuKUw+cwu5uOwZeH4jTFCfin7tIu5GJbIpyZYajM4EdOvDN+vW0\nfeQZvnH8Ow9+9SCpual3ZsAKRSujsmXemkq0NiRVSbTa2tri6uqqlx8REUGXLl2qrXfq1Kn873//\nY9SoUZiamurleXp6EhwcXCbfmp6eTlZWlp5w04wZM9iyZQs7d+4kJyeHBx54oAFGWz3NITRIg3Gz\n8CZHko8wqO0g/YyFCyEuDn77DanRMCkyksziYo4FBOBuYkLW0Szil8aTfTybtgva0nlzZwxMDQBw\nfcQVl+kupG1PI2F5An1fzuf7uc48etc19m3ZwvIpU1g8L4gp30xhe8h2DDQGTT9wheIvQKlEa03I\nz8+nqKgIgLy8PPLz8zExMQFAq9VSWFhIYWEhxcXF5OfnY2BggKHh7T+HISEhBAUFcfDgQXr27Hmb\nROu0adN49dVX6dOnD5cvX+bTTz8te/KvCm9vb/bt20e7du1uy3vggQd48cUXWbduHZMmTUJKSURE\nBJaWlvj56ZQfBg0ahI2NDY8//jiTJk2qsO+NQm13yZvbBcjFixfL3bt3y98v/i77f9Zf3xXg3Xel\n9POTMjVVSinlfxIS5F1Hj8q84mKZ9UeWjLg/Qh7yOCSTPkiSRTeLKvQmKE9maKY8NfGU3Oe4X77+\nZKjs/+tueaFLF7loXhf58u8vV3t/bWmOnjYNSWseX3McGy3AS8rc3FxaWVmVXRMmTKh1PUIIqdFo\npEajKXtdyhdffKGXr9Fo5MyZM8vyLS0t5YEDB8reb9y4UbZt21ZaWlrK8ePHy/T09LK8/Px8+eij\nj0pra2vp4uIi33333Ur7tGfPHunp6Vlh3q0eWzExMXLUqFGyTZs20tHRUd5zzz0yIiJC754lS5ZI\njUYjjxw5UuVncet3vnv3brl48eI6eUnd8R/8+l7lP4yXf39ZvvTbS39+Mps3S+nuLmVcnJRSykMZ\nGdLpwAEZm5srb8bdlPsd9sukD5NkcV5xlR94RdyIuSFPTTglt7c/IIM+3C2/GXW//HuIvdx6Zmut\n66qK5vij05C05vE1x7E1d4OhaHgq+87rYjBa1R7G7rjdf+5f7NkDf/+77lCelxepBQVMiori006d\n8DI1JWZuDJ7PeeL+pDsak9p/DOYdzOn6TVd6v96B15YaEmb5D8zsH+H3f07leu71hh2YQqFQNANa\njcHIzs8m4koEQZ5BcPYsPPwwbNoEPXqglZLpZ87wsJMTYxwdubrpKvlJ+Xj+w7Pe7To95ETQ6b5M\n0jrQ7/sxZLi8xW+LZjfAiBQKhaJ50So2vZcsWYJZNzPucr9LF4p861adwSiJzfJWQgJZRUW85uND\nQWoB5589T7et3dAY1cxearX5aLV5GBraVJhv3MaYXlu64frNVcye0HIwwJ0O77xP72efarAxKhQK\nRUOggg8uWUKqVSpDvYfqEhITocT7YG9GBu8lJ7Opc2eMNBouPH8B58nOWPe1rrJOKYtJS/uNM2dm\nceiQK4cPe3L69HiuXfsWrTa/wntcJjoxLGYgzjlZxCzvzI//t6FBx6lQKBT1RZ3D4Jb9i8RE8PTk\nSn4+U6KiWOPnh4epKWk708jYm4H3Mu8K65BSkpkZyrlzz3D4sAcXLy7EwqIzAQEn6d8/EQeHB0hK\neo9Dh9yJiZlLZubh0o33MozsjRjydVe+GbmOglUurB/xI4U5RY08eoVCoWh8WsWSVPrNdGKux9DX\nva8uITGRYg8PpkRHM8vVleH29hTfKCbmiRg6ruyIoaX+sG/ciCIlZT1Xr25ECGOcnSfTs+dezM07\n6pVzdZ2Fq+ssbt6M4+rV9Zw5MxMoxtl5Gs7OUzEz081qerj04Oawa1wZeZL8d335qcNe+m3qhesQ\n+6b4OBQKhaJRaBUzjL3xe+nv2R9jA2NdQmIiK62tkcBib28AYhfHYh1kjcN9+vFWMjL2cvz4YKQs\noEuXb+jbNxpv78W3GYvymJl54+X1Mn37RuPvv57CwmscO9aP48cHcenSJxQWpvPSoJdYceW/PL6l\nO6eCz3BkfARHnomm+GZxI30KCoVC0bi0CoOxK3YXd3uXLEfl5UFmJieEYIqTEwZCkHU0i5QvU2j/\njr4ca3FxHmfPPkGnTp/h67sCK6teFcbPrwwhBNbWfenQ4X3690/G0/MfpKXtIDTUG5usdxjuYsF3\nabt55Z2JZIzcwm/HL7G7RxhZYVnVV65QKBTNjFZhMDaHbcYmrcSDKSkJ3NxILijAzcQEbaGWmMdi\n8P23L8ZtjPXuS0h4AwuLzrRpM67efdBojHF0HEPXrl8TGBiHnd29POwJNtdmE5P1KuPfGMcIq7f5\ndGI2h0ed4PyLF9Dma+vdrkKhUNSGv3y02nzTfGY/UHL2oWTD+1J+Pm7GxiT9JwkjJyOcpzrr3XPj\nRjSXLv2PDh3eb/D+GBnZ4eb2BEP7n+KDRH8uZF0n6vI8iv55jiVGj/PVW8fYefgyob3CyTyc2eDt\nKxQtlaaQaF2wYAFt27bFxsYGHx8f3nzzzUrr2rt3LwYGBnr9Ka9FoSRaWyCDvQZjqCnZyC4xGMn5\n+bRJ1JKwIoGOH3XUW2qSUktMzON4eS3GxMS90folhOCJwKUsPn6Bvn1j8Ou8FsO/3cejbf4PjwWT\n2fXCGo7P/o1zT5+jSHlSKRRNItE6a9YsoqKiyMzM5NChQ6xbt47vv/++0vrc3d31+jNt2rSyvJYo\n0VofWoXB0AtnnphIftu2ZBUXk73iEp7PeWLmY6ZX/vLlVWi1hbi7z6WxGec3juyCbHbF7cLGpj8d\nun5E/+CrDNxkR6DxYbI/mEVc/wcJfWYBV7ZHN3p/FIrmzq2u6rXFycmJOXPmEBAQUGFdHTt2LNOy\n0Gq1aDQazp8/X6e21q5dy6JFi7C2tsbPz4/HH3+cL774otLyxsbGjBs3jo0bN5a1v3nzZkJCQvTK\nnTlzhuHDh+Pg4IC/v3+ZeJK7uztDhw7Vm+WATkSpNpoYdaVVGoxLPj64GhuTdz4Pm4H6p7Pz868Q\nG/synTp9ghCNH4pcIzQsGLCANw68UZYmrG2x+99h7l3jSLdNE9jQ7lHOjTnDGW0/Dm7oy8Wo18nO\nPoGUao9DoSilISVa33rrLaysrPD09CQ3N5cpU6ZUWvbq1au4urri6+vLc889R25uLsBfUqK1VZzD\n6OrU9c83iYlcGjMGNxMT8pPyMXE30St7/vx8XFwexdKyO03FlG5TWLR7EUeSj/x5VsTCArZtw3vi\nRD55ay+L33yHNxOTWHk8mqSTu7ly7yqk2Q1sbTuQkiKxsxuGsbFTk/VZ8ddF1DFsRHlkiQJmXRg3\nbhyGhoZIKRFCsGLFCmbNmlUm0doQLFiwgAULFhAREcH3339/mwxrKf7+/pw4cQI/Pz/i4+OZPn06\nzz//PCtXrmxQidZSIwT6Eq2AnkTrK6+8wvjx43nyyScJDQ0lMDCw1hKt9aFVGAyNKDdRSkwk2dER\ndyMj8i9nY+z2p2fU9es/k50djp/f503aP2MDY57v/zxvHXyLb/72zZ8Zpqbw3XcYTZ3K6/PmMXTN\nGqaamTI/5W6Cn8vBsOM1rj/wHdeufU1MzDzMzHyxtx+Ond29WFsHYmBg0aTjUPw1qM+PfUPwww8/\nMHTo0CZpq0ePHvzyyy8sWrSIt99++7Z8JycnnJx0D2peXl4sX76c0aNHs3LlSj2JVkdHR6BuEq2r\nV69m/fr1ZXnlJVpBt0RXXFxctndSXqI1MDCQ9evXV7nZ3pBUuSQlhIgSQvxTCOHbJL2pI0uWLPkz\nmFZiIpdsbPDONcLAwgADM92yU3HxDWJinqRjx48wMDBv8j7O7j2b/fH7OZN6Rj/D2Bg2bgRPT4Y9\n9BDHOnTgt3YF/OMzA4z6+WPx1CTM1v6bfj0u0779u4ABsbGLOXjQiT/+COTChf8jNXUbhYUN8+Sl\nUNxpKtvDaCyJ1qKiIi5evFjj8lqtbqm4pUq01settjpxoh7AG8AF4AjwLOBWW9GNxrwoLw6SlSWl\nubn8x7lz8r2fY+SR7n8qUZ0797yMjAypXm2kEVm6Z6mc+f3MijOLi6V86ikpe/WSxSkp8o24OOl0\n4ID8x2ffyqgZUfKg+0F5ZcMVqdVqpZRSFhXlyrS03TI29l/yxIl75b59lvLIkW7y7Nl5MiVls8zP\nv9KEI6s7zVFkqKFojmOjmQsoeXt7y99//73e9eTl5cmcnBwphJBnz56VeXl5UkoptVqt/Pjjj8tU\n88LCwqSrq6v84IMPKqxn9+7dMj4+XkopZUJCggwODpazZs0qy1+4cKEMDg6W6enpMioqSrq4uMgd\nO3ZUWNetinsHDx6Uly9fllLqK+5lZ2dLb29v+eWXX8rCwkJZUFAgw8PDZXR0tF597dq1k97e3vLv\nf/97lZ9FZd85jam4BwQC7wAJwG7gsdo21hiX3ocRGSllp05ySmSk3LL2nIy4TydpmJX1hzxwwEnm\n56dU+cE2Ntdzr0u7N+1kQkZCxQW0WilffllKf38pk5JkaGamdN2xQz58+rSM33NNhvcKl8cGH5PZ\nJ7Nvu7W4uEBmZobJ+PgV8uTJB+T+/bYyLKyzPHt2nrx69WuZn3+tkUdXN5rjj2pD0RzH1hIMRmNK\ntGq1Wjly5Ejp4OAgraysZKdOneSbb76pd295idb//Oc/0t3dXVpYWMi2bdvK+fPny5ycnLKyLVGi\n9Zb0Wv2bp+AeAAAgAElEQVTeCllLFzYhRHCJ4egspTSppnijI4SQZWP49Vf4978ZumIFr+y2xD2q\nmA4f+3LsWCDu7vNwdZ15ZzsLvLDjBYq1xbwzsoo1x7fegk8+gZ07+SI0lJN9+7Lp6lU+8u1An28L\niFsch9MUJ7wXeWNkb1RhFVIWk519nIyM3WRk7CYz8yCmpt7Y2g7Fzm4oNjZDMDKybaRR1pwNGzZU\n6aHSkmmOYxNC1NttVdGyqOw7L0mveSwkauhWK4S4SwjxHyFEPLAE+Bhwq01DTUK5Q3tWKVqM3Y1J\nTv4AQ0MrXFweudO9A+DZwGdZE7GmahnXBQvghRdg8GCcEhL4T/v2bO7cmWdjL/BScBadInoh8yVH\nOh0hYXlChQENhTDA2jqAtm3/QffuPzNgQCodO36MsbEzyckfEhraloiIEVy69BkFBamNOGKFQtFa\nqG7T+3UhxAXgf0AyMEBKGSyl/EhK2fyEqxMTkSVhQUxTijHxMOHKlVX4+Lxeq6CCjYm7tTsT/Sfy\n/pFqQpLMnQtvv83QN96AgwcZZGtLREAAlgYG9IqLIPY1R3ru70lWWBZHOh3h8urLyOLKnxw1GiNs\nbALx8nqRHj12EBR0GVfX2aSn7yAszJeIiGFcuvQJBQXXGnjECoWitVDdDCMPGCmlvEtK+baUMqkp\nOlVnEhPJ8vJCCIH2UgHG7sbcvHkRC4vGPS5fW/4x4B/8L/x/5BTkVF3w4YcJnTsXxo2Dn3/G0tCQ\nDzt2ZHWnTjx+9ixzZAJtNnak8+bOXPn8CuE9wkndllqjJQcDAwucnB6iS5ctBAVdwtX1CdLTfycs\nrD0nTtzLpUsfU1BwtYFGrFAoWgNVGgwp5b+klOeEEOZCiFeEEJ8CCCE6CCFq7sfVVCQmcsnDAzdj\nY/KT8zFwzUKjMa1Ui/tO0dGhI0O8h7A2Ym21ZS937w7btsHMmVDiq32vvT2Rd92Fq7ExXcPDWd82\nh257e9DuzXZcfPEiJ4acqFVQQ53xeJAuXTYTFHQZd/cnycjYQ1hYR06cuJvk5JUUFKTUebwKhaJ1\nUNPQIKuBfKB/yftk4NVG6VF9SEgg2ckJdxMTCpILkA6XMDX1udO9qpAJfhPYeXFnzQoHBsKuXbBw\nIbyvW8qyNDRkua8vu3v0YOPVqwQdP078EBPuirgLl5kuRD0cRcTICDIP1S4aroGBOW3aTKBz540l\nxuMpMjMPcOSIHydODCU5+UPy86/UdrgKhaIVUFOD4SulXA4UAkgpc4FG3RQQQvgIIT4TQmyp0Q1S\n6mYYtra0LTJCm6el0DgBM7PmaTAGew1mf/x+tDWNF9WlC+zfrzMYixbpxgt0tbRkb8+ePOnmxqiT\nJ3n64nnMpjrS71w/2kxoQ9SUKE7ce4KMvRm17qOBgRlt2oync+f19O9/GQ+P+WRmHiY83J/jx4eQ\nlPSBmnkoFH8hamowCoQQZoAEKDn5nd9ovQKklLFSytk1viEtDUxMuCQEPhkGGLsbk5cX12xnGO7W\n7tia2hJ1rfr49WV4e8OBA/Dzz/DEE1CkC4muEYJHXF2J7NuXfK2WzuHhrE5LwfkxV/qd64dziDNn\nZp3h+JDjpP+eXie3SgMDUxwdx9K58zr697+Mp+fzZGeHERbWiZMn7yclZQPFxTdqXa9CoWg51NRg\nLAZ+ATyFEOuB34H/q8mNQohVQogUIcTJW9JHCiHOCCFihBALatXriih1qS0owD1Ng4m7CXl5sc3W\nYAAM8RrC3ri9tbvJyQl274a4OHjoIbh5syzLwciITzp14oeuXVlz5Qo9jx5lR1Y6Lo+40PdMX1xn\nuxLzZAzHBx7n+i/X6+yPrzMeY/D3/5KgoGScnaeSkrKOQ4fciY6eTlraDqRU2uUKRWujRgZDSrkT\nmAA8AmwEAqSUe2rYxmpgRPkEIYQG+KAkvQswWQjhV5I3reTMR2m835otfZVT2nNKBRMPE27ejMXM\nrF0Nu9n0DPYazN74WhoMACsr+PFHMDODESMgQ3+56S5ra/b27MmrPj48c/48I06e5FReLi7TXOgb\n1Rf3p9y5+H8XOdrjKFfWXkFbUPcw6gYGFjg7T6F795/p1+8sVlZ9iI19mcOHPTl//nmys4+rg2IK\nRSuhunMYvUsvwAu4DFwC2pakVYuU8gBwa2S8vsA5KWW8lLIQ2ASMLSn/pZTyOSBfCLES6FmjGUi5\nQ3t2V2XLmGF4D2Ff/L66/aAaG8O6ddC7NwwaBMnJetlCCMY6OnL6rrsY6+jI8IgIHj1zhktFBThP\nciYgIoB2y9txZe0VwnzDSPh3AkVZ9VP9MzZ2xsPjGfr0CadHj10YGJgTGTmB8PBuJCS8RV5e8/bK\nVtx5mkKiFeC3336jT58+WFpa0rZtW77++utK69uwYQPe3t5YWVkxYcIEMso9oLVEidb6UF1486PA\naaD0KHD5p30JVD/CinEHEsu9T0JnRP6sXMo0oEaSeBMnTiTk9GnyDA2JHjeOlNAU0pzzsL6ZwPff\nHy4ZRvNDSklRfhFvr3kbN+OKD85XG4HzrrvwT0mhQ8+e7FmwgCy32+uxA14Tgq1pafhdusSQ3FxG\n5+RgpdXCo2AUa0T6t+mcX3qe3CG55IzMQWvfEOJN/sAyjI1juHbtV8zMXqWw0Ivc3AHk5d2FlOZ1\njjDaEmjNY2ssSiVa6xPevFSi9aWXXiIoKOi2/KioKEJCQvjyyy+59957yczM1DMC5YmMjGTOnDls\n376dXr168dhjjzF37twyxbzyEq2XLl1i6NChdOnSheHDh1dYX3mJVjs7O6D2Eq2vvPIK8fHxeHl5\nlaVXJ9G6YcMGoqKiiI6up6pnVYGmgPnAAeAnYBpgWdtgVSX1eAEny72fCHxS7v1U4L061q2LpDVl\niixes0Ya7dkjI8adlInfhMuDB90rCcfVfJj67VT5ydFPKs2vcQC7zz+X0sVFyuPHqyyWePOmnHP2\nrLTfv1/+8+JFmV5QUJaXG5srY56Jkfvt9suo6VEy63hWzdquIUVFN2VKylfy5Mkxct8+axkZOUl+\n/fU/ZHFxYYO201xQwQdrT0NFq5VSyqKiIimEKIs2W8qUKVPkokWLalTHSy+9JENC/oxyfeHCBWls\nbFwWgNDNzU3+9ttvZfmLFi2SkydPrrCuPXv2SA8PDzl37lz54YcfSimlLC4ulu7u7nLZsmV6wQej\no6PlsGHDpL29vfTz85Nbtmwpyxs+fLhctmyZXt19+/aV77//foXtVvadU4fgg9Ud3HtXSjkQeArw\nBH4XQmwRQvSsn5kiGWhb7r1HSVqdWLJkCRmnT3OtbVtsDA0pvFQAzpebrUtteYZ4DanbPsatzJyp\nc7kdMQJCQyst5mFqysqOHTnapw/J+fl0OHKEV+PiyC4qwszbjA7vdqDfhX5YdLbg1AOnOHH3CVJ/\nTEVq678PYWBgipPTg3Tr9gP9+l3AxmYgVlbfcviwB+fPP6v2OxRV0lASraGhoUgp6d69O+7u7kyf\nPr1SJb/IyEh69OhR9r5du3aYmJgQExPTYiVa66OHUdNN74vAD8AOdEtHHWvZjkB/OSscaC+E8BJC\nGAOTgK21rLOMJUuWYJuVRbKzM+7GxuQn5aO1bb6H9spTuvHdID+UDz4Iq1fDmDE6T6oq8DEz43M/\nPw726kV0bi6+YWGsSEjgRnExRnZGtF3QlsDYQFxnuxK3JI4j/kdIXplM8Y2G8X4yNnbE3X0eqan/\nolevfRgYWBEZOYGjR7uTkLCc/Pw6Pz8o6skesafeV30YN26cniFYtWoVQJlEa6moUPnXaWlpFS4/\nVURSUhLr1q3ju+++49y5c+Tm5vLUU09VWDYnJ+c2+dZSGdaGlGgtT3mJViGEnkQrwPjx40lJSSG0\n5MGwthKtwcHBdTYYVe5hCCHaofsxH4tuz2ET8LqU8mZV991SxwYgGHAQQiQAi6WUq4UQT6EzQBpg\nlZSyzotrSxcv5pXkZC7Z2eF+s4DCa7kUmSRiatB8PaRK6WDfgSJtEXEZcfjYNYCBu/9+2LwZ/vY3\nWLNG974KOpqbs75zZ07n5LAkLo5/JybyvKcnT7q5YWlkiPMUZ5wmO5F5IJOk/yQRtygO19muuP/d\n/Ta99Lpibt4RH59/4e29hMzMg6SkrCU8vBtWVn1wdp6Oo+N4DA0tG6QtRfUEy+A72n5jS7SamZnx\n6KOP4uurExJ96aWXGDZsWIVlLS0tycrK0ksrlWFtqRKte/bs+VOhtJZUN8M4D/wN3RmMw+iWkeYK\nIZ4TQjxXkwaklFOklG5SShMpZVsp5eqS9O1Syk5Syg5Syjfr1PsSFs+Zg8bOjmStlnbZhhg5GJFX\nENcilqSEEHV3r62MoUP/jD9VhfdHebpaWvJ116783qMHf2Rn4xsWxpvx8WQXFSGEwHaQLV2/60rv\n0N4U3ygmvFs40dOiyT5W9dNUbRBCg63tIDp1+pT+/ZNxdX2Mq1c3c/iwB9HR00lP/x1Z05PxihZL\nZbPthpJoLb+EVB1dunTRk2C9cOEChYWFdOzYscVKtNZnhlGdwVgKfAdoAUvA6pareVDuDIZ3mgEm\nHibk5V1sEUtSoNvH2Be/r2ErDQzUCUo9/TR88UWNb+tqacnmLl3Y1bMnETdu4BsWxmvx8WSVnCo3\n8zWjw3sd6HexHxbdLTg99jQnhp7QRcltgH2OUgwMzHBy+hvdu/9Iv35nsbTszYULLxAa6s3Fiy+T\nmxvTYG0pWgYDBw4kOzubrKwsvas0bcCAAWVl8/PzycvLAyAvL4/8/D8DU8ycOZPVq1cTGxtLbm4u\nb731FqNHj66wzZCQELZt28bBgwe5ceMGixYtYuLEiVhYWAC6mcKrr75KRkYG0dHRfPrpp8ycWb1Q\nm7e3N/v27ePVV28PyffAAw8QExPDunXrKCoqorCwkKNHj5btYQAMGjQIGxsbHn/8cSZNmoShYXUO\nrw1EVTviwGTAobY76U15AXLTQw/JqwMHytlnzsh1n56RJ8eelAcPusibNyuRQm1mnE45Ldv9t12F\nefX2tImOlrJtWyn//e863R6VkyOnREZKxwMH5LLYWJlRqO/RVFxQLK9suCKPBhyVoR1CZdL/kmRR\nTlGN66/t+LKzI+S5c8/JAwec5R9/BMqkpJWyoCCtVnU0FcpLqvY0tkRrKUuWLJFt2rSRTk5OcsaM\nGTIjI6Msr7xEq5RSbty4UbZt21ZaWlrK8ePHl+mBS9kyJVp3794tFy9e3PASrSUH5kYARujCgWwH\njsiqbmpihBBSvvMOXLzI/bNn88xPJnhfyufKg3cxeHAuQhjc6S5Wi1ZqcVrhxIk5J/Cw9tDLaxCZ\nz8REGD4cxo6FN96AOohJnc3N5dX4eH5JS+Npd3ee9vDAptxTjZSSzIO6fY7M/Zm4Pl6yz+Fa9T5H\nXcen1RaRnv4rV66sIS3tV+ztR+DiMgM7uxFoNE30tFUNSqJV0RxoMolWKeVbUsq7gfuBCOBR4JgQ\nYoMQYroQwrk2jTUaCQllS1JW17Ro2l3D1LRtizAWABqhYbDX4IZflirF01MX6Xb3bnjssbKghbWh\nk7k5X/r7c7BXL87fvEn7sDD+FRdHRmEhoPvHZzvQlq7fluxzZBUT3iWcMzPPkHO6GqGoOqDRGOLg\nMIouXbYQGBiHre3dxMe/SmioJ+fPv0BOzqkGb1Oh+KtTU7fabCnld1LKJ6SUvdBpYbQBqlcAagIi\nf/2VyOxskgsKML1SjHC/gqlp8/eQKk+jGgwAR0f4/Xedcf3b36Bkfbe2dDQ3Z42/P4d69eJiieFY\nGhdHZjkjZOZrRof3O9DvfD/MOphxcvhJIkZGkLYzrVGebo2M7HB3n0Pv3ofp2XMPGo0xp07dz9Gj\nfUhKek9plisU5Wj0cxhCiG+FEPeXBA1EShkldZKtI6q7tynoYmVF+xEjyCwqQlwuRDq2jDMY5Wmw\nA3xVYWmp854yNNS5297iLlgbOpib84W/P6G9exNbYjhej48np5zhMLI3wuslLwJjA3F62IkLz13g\naM/6BzysCnPzTrRr9zqBgXG0a/cWWVlHCAtrz+nT47l27Xu02oJGaVehaCk0ppdUKf8DQoBzQog3\nhRA1C3zSVCQmctnVFRdjYwqSCyiyTGoRLrXl6e7cnSs5V0jJaWRBIhMT2LgROnXSud9erZ9ud/sS\nw3GgVy8ib9ygfVgY/05IILf4zwN+GhMNrjNdCTgZgO9yX1LWpRDqE0r86/GI7MbR4RLCAHv7e0v0\nOxJwcBhNUtJ/OHzYnXPnniIrK1yt5SsUtaSmS1K/SSlDgN5AHPCbEOKQEGKmEMKoMTtYE4qvXOGn\n2FjcjIzIT86nyDihxc0wDDQGDPAcwP6E/U3QmAH873+6WcagQRAfX+8qO5UcAPy9Z0/CsrNpHxbG\nf5OSyCtnOIQQ2I+wp8eOHnT/pTs3z9/E+TlnYp6MITcmt959qAxDQ2tcXR+lV6999O59BCOjNkRF\nTSY8vAvx8W+Sl5dYfSUKRSuh0ZekAIQQDuj0MGYDx4H/ojMgNRSmbjwM3Nxw6tYNn3xjhKEgv6j5\nKu1VRZ0EleqKELBsGcydqzMa9Y1iWUIXCwu+6tKFn7t14/f0dDocOcJnly5RpNVfgrLsZonf535c\nXXEVIwcjjg88zqkxp0jfUzdFwJpiZuaDt/ci+vU7R6dOn5GXF8fRoz05ceJerlxZS1FRw2/QKxTN\niUZfkhJCfAfsB8yB0VLKMVLKzVLKp9Ad6LuzeHpyqaCAdhmGmLjrhJNaosHo5tyNmLQmPpA2fz68\n9ppueerIkQartqeVFVu7dePrLl1Yf/Uq3Y4e5dtr124zBlpbLT7LfAiMC8RhlAMxc2L4o88fXP7i\nMsV5jafap4sBFESnTh/Rv38ybm5PcO3aVxw+7EFUVAjXr/+MVlvYaO0rFC2Rms4wPpVSdpZSviGl\nvAwghDABkFIGNFrvakqJS637dYGRbz6gxcioZoG4mhPOFs6Nv4dREdOmwaefwgMP6DypGpB+1tbs\n6tGDd3x9WRYfT+CxY+yuIDKogbkBbk+40TeqLz7LfLi66SqhXqFcfPkieUl18+iqKbooug/Rrds2\n+vU7h41NEPHxyzh82KNkvyNM7XcoFNTcYNx+fl0XW6p5UKK055wqMOx4FVNTH0QdDqfdaZwsnLh6\no36b0HVm9Ghd3KnJk+Hbbxu0aiEEIx0c+KNPH+Z7eDD77FlGRkRwvIKonkIjcBjlQI9fetBrXy+K\ns4o52v0okQ9HknEgo9F/uI2N2+DuPo/evQ/Tu/chjIzaEB09nbCwDsTGLlYhSRR/aaqTaHURQvQB\nzIQQvcpJtgajW55qFmw/fZqoq1exvSYRXiktcjkKoI1FG67lXkN7pwLsDR4MO3bAvHlQEkq5IdEI\nwWRnZ6L79mWMoyP3nzrFx7a2XCoX56c85p3M6fB+BwLjArEZYMPZmWd1y1WrL1Oc23jLVaWYmfni\n7b2Ivn3P0LnzRoqKMjl+fDBHjwaQkPBvtVneCDSFRGt6ejoPP/wwjo6OODk5MW3aNHJyKt672rt3\nLwYGBnr9Ka9F0RIlWuuz6V1dnKYZwG4gu+Rv6bUVmFDbOCSNcQFSfvut7BgaKkNnnpanNv1Tnjs3\nv8rYKs0Zuzft5LUb18re35F4RCdOSOnsLOVXXzVqM5mFhXL01q3SYf9+uSw2VuYWVR2DSluslak/\np8qIURFyv/1+GfP3GJl9MrtR+3grxcWF8vr1nTI6epbcv99eHjs2UCYlfSDz81NuK6tiSdUeb29v\nuWvXrnrVkZKSIleuXClDQ0OlRqO5TXFv7ty5csSIETInJ0dmZWXJe++9Vz7//PMV1lVV/CcppVy4\ncKEcPHiwzMzMlNHR0dLFxUX++uuvldbl5OQkXV1dZVran/HPnnvuOenn56cXS6oykpKSpJGRkYyL\ni9NLf//992VAQECF91T2ndMIintrpJRDgUeklEPLXWOklA27blEfSja9jVKK0Nolt9gZBoCzpfOd\nW5YqpUcP+OUX+Pvf4ZtvGq0Za0NDJmVnE96nDydv3MDvyBE2pKRUuuwkNAKH+xzo/mN3Ao4HYGhv\nyMn7TnKs/zHdJnkTzDo0GkPs7e/Fz+8zgoIu4en5f2RmHiIsrCMREcO5fPlzCgsr1odW1IzKvv+a\n4uTkxJw5cwgICKiwrri4OMaNG4eFhQVWVlaMHz++SpW8qli7di2LFi3C2toaPz8/Hn/8cb6oIjq0\nsbEx48aNK9ME12q1bN68mZCQEL1yZ86cYfjw4Tg4OODv718mnuTu7s7QoUP1ZjmgE1GaMWNGncZQ\nG6pbkppa8tK7VAOj/NXovashWW5uSCkpTi6gyCyxRRsMJwunO7PxfSs9e8L27brlqe++a9SmfMzM\n2NKlC+v9/XknKYn+x44RmplZ5T2mbU3xWarzrmr7YluufX2Nw56Hifl7DDkRTeMaq9GY4Og4ms6d\n1xMUlIyr62yuX/+R0NC2nDz5AGZm+5TxaEAaSqJ13rx5bNu2jYyMDNLT0/nmm2+4vwqhsatXr+Lq\n6oqvry/PPfccubm6M0MtVaK1PlQX1tOi5O+dd52tgkvW1riZmFCQXIChQcs7tFceZ4tmMMMopVcv\n+PlnuO8+3fvx4xu1uYG2toT17s36lBQmRkZyv4MDb7Zrh4NR5WdDNYYaHMc44jjGkbyEPC6vusyp\nMacwtDPEZboLTlOcMHFpGGXAqjAwsMDJ6W84Of2NoqIsrl/fRnLyO4SGtsXGZhBt2jyEo+NYjIzs\nGr0v9WXPnvo7jAQH132WMG7cOAwNDXXhtIVgxYoVzJo1q0yitb707t2bgoICHBwcEEJwzz33MHfu\n3ArL+vv7c+LECfz8/IiPj2f69Ok8//zzrFy5skElWkuNEOhLtAJ6Eq2vvPIK48eP58knnyQ0NJTA\nwMBaS7TWhyoNhpTy45K/Sxu9J/Xg9c8+w6pzTwqzJcVFCS0uLEh5nCycSLnRDGYYpfTurTMa99+v\nO+w3blyjNqcRgmkuLoxxdOSV2Fi6HDnCm+3aMcPFpVrPt9JZh/dibzL2ZpCyNoVw/3Cs+1vjPN0Z\nx7GOGJg1fgRjQ0NrnJ1DSE8XDBv2ANev/8i1a19x/vzT2NgMLGc87Bu9L3WhPj/2DUFjS7Q+9NBD\n9OzZk23btqHVann++ecJCQlh8+bNt5V1cnLCyckJAC8vL5YvX87o0aNZuXLlX1KitTpN7/eqypdS\n1s59oZEYNmkS5ievYdwpFmlgjYGBRfU3NVOa1QyjlD594KefYMwYOHoUFi+GKp76GwIbQ0Pe69CB\n6c7OzImJYfWVK6zs2JHOFtV/t0IjsBtqh91QO4o/KCb1+1SurL7CuSfP4TjBEecQZ2wH2yIMGt/1\nWmc8puDsPIWiouxyxuMZrK374eg4HkfHcZiYuDV6X1oKle1hHDhwgPvuu++2B4fSmcj27dv1VPcq\nIyIigpUrV5ZJo86ZM4dBgwbVuH/akqgF5SVa77nnnrK6ayrR2r59ex555JFKJVp//fXXSu+fMWMG\n48ePZ/z48XWSaA0ODmbp0trPA6o7h/FHNVez4FJBAW3TDDDsnNqil6OgGe1h3EpAABw/rjMYQ4ZA\nXFzTNGttTVifPvzNyYkhJ07w4sWLeoENq8PAwgDnEGd6/NqDu07dhXkncy68cIFD7oeImRdDxt4M\nZHHTPFEbGlrh7DyZrl2/JSjoMm5uc8nKOkx4eFeOHetPQsJycnPPNUlfWiINJdHat29fPvvsM/Ly\n8rh58yYff/xxpTrfe/bsKXPLTUxMZOHChYwrN8v+q0m01sRLqtKrSXpYA5Lz83G7Dga+KS16OQpK\nvKRym9kMoxRnZ93y1MSJ0LcvVDCFbwwMhGCeuzsnAwKIy8uja3h4hafFq8PE3YS2/2hLwB8B9Nrf\nCxN3E849c47DHoc599Q5MvZnNKgueVUYGFjQps0E/P2/JCjoCt7eS8nLi+XEicGEh3cjNnYRWVlH\nkXfqTM4dZPTo0VhbW5ddt55bqAlmZmZYW1sjhMDPzw9z8z+PjX3++efExsbi4eGBp6cncXFxrFnz\n58+ZlZUVBw8eBOD48eMEBQVhaWnJwIED6dmzJ//973/Lyi5dupR27drh5eXF3XffzcKFCxk2bFiN\n+hgUFISLi8tt6ZaWluzYsYNNmzbh5uaGm5sbCxcupKBAPzT/9OnTSUhIKNvraAqqk2h9V0o5Xwix\nDbitoJRyTGN2riYIIeTEU6eY8Y0B7k4fYTvCgnbtXr/T3aozhxMP8+yvzxI6OxRonjKfgG6mMXmy\nbrbx3/9CDZaKKqIu4/sxNZU5MTGMdXTkrXbtsKzn01Xu2VyufnWVa1uuUXi9EMfxjjiOc8R2iC0a\noxrH57yNuoxNSi1ZWaGkpn7H9es/UliYjoPD/Tg4jMLObhiGhtZ17g8oida/Ig0p0Vrd/7RS361/\n16bSpia5oACbqybILpcxNa2ZdW+uNLtN78oICIBjx3RnNfr2hZ07wa1p1uEfcHTklI0N88+fp/vR\no6zq1ImhdnX3PjLvZI73P73x/qc3N87cIPW7VGJfjuXmuZs4jHLAcZwjdiPsMLRs/Gm/EBpsbIKw\nsQnC13cFN29e4Pr1n7h06RPOnHkEK6t+ODiMwsFhFObmHRu9PwpFearzkvqj5O9eIYQx4IdupnFW\nStlspMsu5edjesWQYuskzMxaljTrrTSLg3s1xcoK1qyBN9/URbvdvbvJjIadkRFr/P35MTWVadHR\nDTbbsPCzwOJFC7xe9CI/OZ/Uralc+uQSZ2aewTbYFsdxjjg84ICxk3EDjaRqzMx88fB4Gg+Ppykq\nyiE9/TfS0n4iMfHfGBiYY2d3L7a2d2NrG4yxcZsm6ZPir0uN/ncJIUYBHwEXAAH4CCGekFJub8zO\n1ZTLBQVoLpuQb9yyD+0BWBhZIKUkpyAHS+NmffzlTxYuBCnh7rt1RqPcIaTGpnS28ez583Q7epTP\n66JlaWkAACAASURBVDnbKI+Juwnuc91xn+tOYUYhaT+nkfp9KuefO49FFwscxzjiMNYB807mTRLs\n0tDQkjZtxtGmzTiklNy4cZL09N+5cmUNZ8/OxtTUBzu7u0sMyOB6L18pFLdS08ext4GhUsrzAEII\nX+AnoFkYDMOCAnLi0xBcxcTE8053p14IIcpmGS3GYAC8+KLOaJTONJrQaNgZGfGFvz8/Xb/O1Oho\nQpydedXHB2NN3fcfbsXI1gjnKc44T3FGm68lY08GqVtTOTnsJBozDQ5jHHAc44h1kDUaw4ZrtzKE\nEFha9sDSsgeens+h1RaSnf0HGRm7SEp6h+joyVhYdMXGZgjW1v2wtu6nXHcVQCOewyhHdqmxKOEi\nuoCEzYIO1jZgdB5jY1c0mjuuGFtvSl1r29m1sOW1l176c6axa1eTGg2AUQ4OnAgIYNbZswQdO8b6\nzp3pZN7wQZU1JhrsR9hjP8Ie+YEk53gOqVtTOT//PPmJ+TiMdsBxvCM04aKtRmOEjU0gNjaBeHm9\nRHFxHllZh8nM3Mfly59y9uxsDAyaTYBpxR2kPucwqju4N6Hk5VEhxM/AFnR7GA8B4bVurZFon2uM\nge9VzMxb9nJUKc3y8F5Nefll/eWpCtwGG5M2xsb80LUrH126xMDjx3ndx4fZrq6NtmQkhMCqtxVW\nva3wWeJDXnweqd+nkvh2Ii7hLkT+HInjBEcc7nfA0LppfOVBJwplZzcUOzvdiWkpJTdvXgA6NFkf\nFK2P6v4Fjy73OgUoDcR+DTBrlB7VgXZpBhiUCCe1BlqMp1Rl/POf+stTTWw0hBDMdXdniK0tU6Ki\n2J6WxqedOlUZk6qhMPUyxeMZDzye8WDTR5vwN/In5csUYh6PwWaQDW0mtMFxgiNGdk07ExZCYG7e\nHi8vrxYpLqaoO15eXg1WV3VeUtUfWWwGeKRr0HilYGrawpZwKqFFzzBKeeUV3d/SmYazc5N3obOF\nBWF9+vDSxYv0PHqUL/z8uKeBNsRrgtZai+v/t3fn8VHV5+LHP89kTyYkZCUJBNmSEDAECBB3rK1a\n17qjYlvxttdr22v11163qtjrrdYutldb/dkqKq241xVba1utIpBAgAAhAWQLaxKQJRtkee4f5wSH\nNJDJMnNm+b5fr7xIzsyc85yE5Jnv9nyvyyLrpizaD7azd+Fe6l+tZ+PtG0k+K5mMazNIuySNiATf\n17fqsqWXFfqqypEjO2lsXE1T02qamtbQ1LSa5uYaIiOHEB9fQFxcPvHx+ZSV7eKCC24mNnYkIv67\nB38J2DVQDvJ2llQscBMwATha+ERV5/gorj7JrAeydxMX1/uOVcEgIyGDjfs29v7EQHfvvce2NBxI\nGjEuF78YO5bzUlL4ZnU1l9oVcAc6/bavIodEkjkrk8xZmbQfbKfhjQb2PL+H9f+xntSvppJxbQYp\n56fgivb9gPmJiAgxMTnExOSQmnr+0eOqnRw+vIPm5hqam6tpaanB7f6QlStfpK2tgbi4MR7JpID4\neCupmJlaocXb35r5QDVwHvBj4Hpgna+C6quh9dA5Ibg3TvKU6c7k0+3e1fYPePfdd+xAuANJA+Dc\nlBQqS0q4beNGJi1bxryCAs5MTnYklsghVun1YV8fxpH6I9S/Wk/tz2upvrGa9CvTyZqTReL0xIDq\nOhJxERs7gtjYEaSkfBmA8vIXOO+86+joaKK5ef3RZLJv37ts3/4LmpvXExmZdDSBJCRMxO0uJiFh\nEpGRQTQD0DjK24QxVlWvEpFLVfU5EXkB+NiXgfVFQl0nHQnbQydhJGQGZgHC/rr//oBIGl3Tb99p\naODaqiquTE/nJ6NHkxDhXHdKdHr00bUerbWt7PnDHtbNXodEC1k3ZZE5O9NviwT7KyIigcTEySQm\nTj7muNUq2X40kTQ2VrJ793M0Na0lJmY4bncxbrf1Ore7mOhoZ/5fGN7zNmG02f/uF5GJwG4gwzch\nfUFELgUuBBKBZ1T1rz09L2rvQVpdTURH+3dw1VeCftC7J3PnHjt7KsPn/32Oq2ux360bN1K8bBnz\n8vM53aHWhqfYEbGMvGskuXfmcuDjA+x6Zhdb8rYw9EtDybopi6HnDfXLGo/BYrVKcomNzSUl5YuS\nPZ2d7XYCWUlj4wq2bfspjY0rcbliSUo6jaSks0hOnklCQiEiwXO/4cDbhPGUiAwF7gXewtqB716f\nRWVT1TeBN0UkGfgZ0GPC6GzbSkxkbkA14QciqMqD9MXcuda/X/oSLFoEHjuV+VtKVBTzx4/njfp6\nrq6qYlZGBg+OGkW8g62NLiJC8pnJJJ+ZTPvBdupeqmPrg1up+XYNWTdlkX1zNjHZvt9F0Fdcrkjc\n7om43RMBaxdoVaW1dQsHDnzC/v0fsn37r2hv309y8pkkJ59FUtJZuN1FJoE4zKvvvqr+XlU/V9WP\nVHW0qmZ07cbnDRF5WkT2iEhlt+Pni0i1iKwXkTtOcIofAb853oNtspW4+DHehhPwUuJSOHj4IG0d\nbb0/OZiIWEnjlFPgttucjgaAr6WnU1lSwp4jR5i0bBkf7w+sPbgjh0SS/a1spiyewqT3J9G2r43y\nieVUXVvFgcUHQqbyrIgQFzeKYcNuoKDgaUpLN1JSspL09CtoalpLVdU1LFqUxpo1l7Fr1zyOHKl3\nOuSw5FXCEJFUEXlMRCpEZLmI/EpE+rKB7DysAXPPc7qAx+3jE4BrRaTAfuwGEfmliGSLyMPAQlVd\nebyTa9ou4hJDY/wCwCUu0uLTqG8OwV8KEXj0UfjoI3jrLaejASAtOpo/Fhbys9Gjuaaqils3bKCp\nD5s0+UvChATyHs+jdHMpiTMSWTd7HRXTK9g9fzedh0Nv34zY2OFkZl5Pfv5TzJhRw7Rpa0lLu5x9\n+xaydOlYVqw4i9raR2lp2eR0qGHD2/bdi0AdcAVwJdAAeL17jqp+AnTf8WY6sEFVt6pqm32NS+3n\nz1fV2+3rnQNcKSLfPu5NjKkL+iq13QXsznuDwe2GZ5+Fm28m5uBBp6M56mvp6ayZNo197e0UlZfz\nUYC1NrpEJkUy4vsjmLF+BiPvH8me+XtYPHIxm+/bzJE9AVNEetDFxGQxbNgNTJjwCqeeuofc3P+i\nubmKiopTKC8vYvPm+zh0qCJkWl2ByNuEkaWq/62qm+2PB4GBTmnIAWo9vt5uHztKVR9T1Wmqeouq\nPnW8E8nw3SEzQ6pLSCzeO5EzzoDZs5n+9NPWYHiA6Brb+NXYsVxfVcV316+nsb3d6bB6JBFC2kVp\nTHp/EsX/KKatvo2y8WVs+N4GWre1Oh2eT0VExJKaeiH5+b/j1FN3kpf3BJ2dLVRVXUNZWR61tb+k\nrW2f02GGHG8Hvd8XkVlYtaTAamUcf4dyP2uM38iPfvQbGhr+yPjx4yksLHQ6pAFrrm/mjQ/ewLU6\ndAf5XIWFnPH003x6yy1sOeMMp8P5F/eLMH//fvK2beP2ffvI7mPi6Nrm02/OANdEF/vf209tYS0t\nJS00XtJIxzDfdK/5/f56NRkoJipqA3v3vs6GDffS2jqNpqav0NbW9zeUgXd/A1NVVcW6dQNbPtdb\n8cFDWMUGBfg+8Af7IRfQCPxgANfeAeR6fD3cPtZncdmf8/jjrxEV5fzUyMGy/C/LyUrMIntIdkiX\nJ3hv506++qtfcerdd8OIwCtN/y3gmV27uHPTJp7Jz+eitLQ+vd6Rn913oG1vG9v/dzs7frKDlHNT\nyL07F/fEwV8sF7j/N+dy5Egdu3Y9w86dTxIdnUlOzi2kp19NRIT3ZfAC9/4Grj+zSk/49lVVE1V1\niP2vS1Uj7Q+XqvZ1zb/YH13KgbEiMtLezW8W1pTdPmtvg0WLjjsmHpRCegzDw+cnnQS33gpz5kBn\nYA7czsnK4q2JE7l5/Xoe3LIlKPrIo1KjGPXAKEo3leKe5GbVl1ex+muraVzd6HRofhMdncHIkXdS\nWvoZI0feS13dSyxZkstnn/3Qrtwbnj788EPmdk1x7yOv+ztE5BIR+bn9cVFfLmKvDP8UyBORbSJy\no6p2AN8D3gfWAi+qar/aSwlRY5k5c2Z/XhqwMt2Zobd473juuAMOHYInnnA6kuMqTUqibOpU3t23\nj6vWrg3YcY3uIodEkntHLqWbSkk+K5lV56yi+t+qObzzsNOh+Y1IBGlpF1FUtJApU5YAQkVFKZWV\nX6Wh4W2sP0XhY+bMmb5NGPbU1luBKvvjVhF5yNuLqOp1qpqtqjGqmquq8+zj76lqvqqOU9WH+3MD\nALt2aL93kApUIT/o7SkyEp5/3lqjUVvb69Odkh0Tw4fFxSRFRnLKihV81tLidEhei4iPYMRtI5i+\nfjpRqVGUn1zO5vs2034oOBLfYImLG8OYMY9QWrqNjIxZbN36IEuWjGHr1oc4ciQ8ft/80cK4APiK\nqj6jqs8A52OV7AgIhZPOCbkWRkiWBzmRvDy46iorcQSwGJeL3+fnc3N2NqdWVPByXR0dQdBF1SUq\nOYoxPx1DSUUJrZtbKcsrY8eTO+hsD8zuQF+JiIhj2LBvMHXqUiZMeJWWlo2UleVTVTWbAwc+DYpu\nx/7yeQvD5jmi7FxNhx78c+n60GthhGp5kBOZPRv+8IeAmmbbExHhOzk5vDphAo9u3864pUt5tLaW\nA0HSTQXWRk/j54/n5HdPpv6VepadvIyGtxpC+g/l8QwZUkJBwdPMmPEZiYlTqa7+BsuXTyEubhGd\nncHzM/WWP1oYDwErRORZEXkOWA78T7+u6AOXX/ndkGthpMenU99UT6eG0Tu/U06BI0dg+XKnI/HK\nGcnJLJ4yhRfGj6fs0CFGLVnCrRs2sLG52enQvJY4JZFJH0xizC/GsOmuTaw6ZxWHVhxyOixHREWl\nMGLEbUyfXsOoUf9DfPzfKCvLZ8eOJ+noCJ11LT5tYYg19+oToBR4HXgNOEVVvV7p7WuhtsobICYy\nhoToBJo7g+ePz4CJfNHKCCKlSUksKCyksqSEhIgITlmxgktWr6YqOjoo3rGLCKkXpFKyqoT0q9Op\n/Gol1XPCa2Dck4iL1NQL2Lv3PsaPf569e99h6dLRbNv2CO3tgVOZwAm9Jgy1/scvVNVdqvqW/bHb\nD7F57ec/nx9yXVJgjWMc6DjgdBj+NXs2LFgAQdS902V4bCw/GT2araWlXJSayjPJyZRWVPB6fX1Q\njHO4Il3k3JzDjJoZRGVEUV5UzpYfb6GjKbxmEXlKSjqNoqJ3KCr6M42NK1myZDSbNv0oqIsf+qNL\nqkJEpvXrCn5w330PhlyXFFgzpQ6G2zuaceNg9Gj4a4+V7INCfEQE387O5pG6Ou7MzeWRbdsoLCvj\n9zt3cjhA15p4ikyKZMzDY5i6bCrN65opKyhj9/O70c7AT3q+4nYXUVj4AlOnLqWtrYGysnw2bvx/\ntLV1L5EX+Pwx6D0DWCIin4lIpYis7l6q3Bh8YdnCAKuVMX++01EMmAu4LD2dxVOm8FR+Pq83NDBq\nyRJ+um0bh4KgBRV3UhyFCwopfLmQnU/spGJGBYdWhuf4Rpe4uDHk5z/JtGlr6OhooqysgB07ngjJ\nwfGeeJswzgNGA18CLgYusv81fCgzIZODHWHWwgC45hpYuNBazBcCRISzkpNZWFTEn4uKWNnYyNTl\ny6luanI6NK8knZLE5E8nk31LNpXnVrLprk10tIRvNxVATEw2+flPMmnS+9TXv8zy5ZPZt+8Dp8Py\nuRMmDBGJFZHvAz/EWnuxwy5HvlVVt/olQi/MnTvXjGGEkrQ0OPNMeP11pyMZdEVuNwsKC7krN5cz\nV67k3b17nQ7JKyJC1o1ZlFSW0LKphWVFy4heG9h7jfuD2z2JSZP+zkkn/Zj16/+d1asvpbl5o9Nh\nnZAvxzCeA0qA1cBXgV/06yo+Nnfu3NAcw3BncqA9DBMGwA03hES31PHcmJXFmxMn8u2aGh7eujUo\nZlMBxAyLYcJLExjzyzEk//9kar5VQ9vnIbYzZB+JCOnplzFt2lqSkk6loqKUzz77Ie0B+rvryzGM\nQlWdbW/HeiUQeDWoQ1jYdkkBXHQRVFTAjn4VMA4Kp9j1qV5vaOD6detoDsBd/o4n7eI06h+uR2KE\n8gnl1L1aFzRJz1ciImLJzb2DadPW0Na2j7KyAjZvvo+DB8vREFlP1VvCOPrWQVXDY1QngIRtlxRA\nXBxcfjm88ILTkfhUTkwMHxUXEyHCGStWUNsaPAvENF7JezyPCa9MYMt9W1h59kp2PbuL9oPh/aci\nJmYYBQVPU1T0Zzo7W6mu/jqLF+dQXf1v1Ne/QUdHcIxd9aS3hDFJRA7aH4eAoq7PRSRg3vqG6hhG\npjuMWxgQlIv4+iMuIoLnCwq4NiODGRUVLA+ywf6k05IoWVFCzndzaHijgcUjFrP2mrU0vN1A55HQ\neGfdH273JMaMeYTp09dRXPwxbvfJ7NjxOJ9+OozKyq+yY8dvaG31/1DwQMYwTriBkqpG9Ousftbf\nmw90GQkZ4TuGAdbA9+efQ2UlFBU5HY1PiQg/yM1leEwMs6qqWFVSQnxEUPz6AeCKcZFxZQYZV2bQ\ntreNulfqqH2klpo5NaRflU7m7EyGnDKkX5v2hIL4+LHEx9/K8OG30t5+gH37/srevW+zZcsDgIvE\nxMm43cVHP+LixiHim902Z86cycyZM3nggQf6/Fpvt2g1HJAYnUgnnTQdaSIhOsHpcPzP5YLrr7da\nGY884nQ0fjErM5O39+7lR5s388uxY50Op1+iUqPIuTmHnJtzaNncQt0LddTcVENbQxtJpyeRdEYS\nSacn4Z7sxhUVulsQH09kZBIZGVeSkXElqsrhw7U0Nq6ksXEldXUvsWnTXbS11ZOQcDJudzFJSWeQ\nnn4VLpfzf66dj8A4LhFhSMQQ6prqGBXd9z2JQ8INN8BXvgIPPQRB9I57IP533DhOLi/n8rQ0Tk8O\n7m2H40bFMfKekYy8ZyStta0c+OQABz4+wO5nd9O6uZXE6YlHk0jyWclhl0BEhNjYXGJjc0lLu+To\n8ba2/TQ1raKxcSU7dz7B1q0PMnr0Q6SmXuxoKy28fjpBKCkiKfzKnHsqLIRhw+Djj52OxG9So6L4\nzbhxzKmpCaqZU72JHRFL5rWZ5P02j2mV0yjdVsqI20egR5TNd2+mrKCMXfN2hd3eHD2JikomOfks\nhg+/leLijxg9+qds2nQ3K1eeyYEDix2LyySMADckYkh4baTUk4UL4ayznI7Cry5LT2dqYiL3bt7s\ndCg+EzU0itQLUxn90Gimlk2l4JkC9jy/52jtKpM4LCJCWtpFTJu2imHD5lBVdTVr1lxBc3ON32MJ\niYQRqrOkAJIik9jTGOYJIzPTKn0eZh4bO5YX6ur49EB4THxIPiuZ4n8Uk/+7fHb9fhflheXs/sNu\ntCO813d0EYkgK+tGpk9fz5AhM1ix4nRqam7m8OFdfTqPP6rVBrRQXekNpksqnKVFR/P4uHHcWF1N\nSwh1TfVm6NlDKf6omLwn8tj55E7KJpSxZ8GesF8Y2CUiIo7c3P9i+vQaIiMTKS+fSH39n7x+vb+2\naDUcYLqkwtsV6elMdru5b8sWp0PxKxFh6DlDmfzxZMY9Po6tP97K9l9vdzqsgBIVlcKYMT+joOBZ\namt/5pdrmoQR4JIiTQsj3D02bhx/2LOHxWHSNeVJREj5cgonv3cytT+t5fO/Bd/+E76WknI+zc3r\naW3d5vNrmYQR4EwLw0iPjuaxsWPDrmvKU9xJcYxfMJ6q66to2dTidDgBxeWKIj39MurqXvb9tXx+\nBWNAzBiGAXBlRgb58fHM2x1QuyP71dCZQxl5z0jWfG0N7Y3hXa+qu/T0a6ivf8nn1zEJI8AlRyZT\nlBnaZTEM71ycmsrSg2FcWwzI+W4OiSWJ1NxYYwbBPSQnz6S1dRstLZ/59DomYQS4xIhEFlyxwOkw\njAAwJTGRisZGp8NwlIiQ90QerbWtbPuJ7/vsg4XLFUl6+hU+75YKiYQRyuswDKPLhIQEPmtpCanV\n3/3hinEx8fWJ7HhiBw3vNDgdTsDIyLiGurreu6XMOowQXodhGF1iXC7Gx8dTGeatDICY7BgmvDqB\nmjk1NFUH7/4Sgykp6XTa2up6XQFu1mEYRpgw3VJfSCpNYvTDo1lz6Rra9of3NrFgrQRPT7/Kq1ZG\nf5mEYRhBZKrbHXQbLPlS1pwsUs5NYd3160wJEbq6pV702YQAkzAMI4iYFsa/GvPLMaRdkuZ0GAFh\nyJBSOjoaaWpa45Pzm4RhGEGkKCGBmuZmWsN84NuTK8pF9r9nIxHhV6CyOxEX6elX+6xbyiQMwwgi\nsRERjIuLY02TGeg1epaRYS3i80W3lEkYhhFkTLeUcSKJiSWodtLYuGLQzx2wCUNECkTkCRF5WURu\ndjoewwgUZuDbOBERISPDN91SAZswVLVaVf8DuAY41el4DCNQmBaG0RurttTLg94t5fOEISJPi8ge\nEansdvx8EakWkfUicsdxXnsx8A6w0NdxGkawmOR2s7apiSOdZgtTo2du9yREojl0qGxQz+uPFsY8\n4DzPAyLiAh63j08ArhWRAvuxG0TklyKSpapvq+qFwGw/xGkYQSEhIoJRsbFUmYFv4zisbinvSoX0\nhc8Thqp+AnTf9WQ6sEFVt6pqG/AicKn9/PmqejuQJyK/FpEngXd9HadhBBPTLWX0xkoYL6M6eC3R\nyEE7U9/kALUeX2/HSiJHqepHwEfenOyKK644+vn48eMpLCwchBADw6JFi5wOwadC+f58em8JCSyI\njCTWwV34QvlnB6Fxf+npwmuvPcCRI/lUVVWxbt26AZ3PqYQxqF577TWnQ/Cp6667zukQfCqU789X\n9zZ8/37u2LSJ6y680Cfn91Yo/+wg+O9vy5ZNtLXtYdy4f70Pkb4vdHRqltQOINfj6+H2sX4x5c2N\ncDPZ7aaysZF2M/BtnIC1iO9VVL+oDBAM5c3F/uhSDowVkZEiEg3MAt7q78lNeXMj3CRGRjI8Jobq\n5manQzECWHz8OKKjs9i//59HjwV0eXMReQH4FGsQe5uI3KhWuvse8D6wFnhRVfvduWZaGEY4mpKY\nyHIz8G30ovtsqYBuYajqdaqaraoxqpqrqvPs4++par6qjlPVhwdyDdPCMMLRVLebCrPi2+hFevrV\nNDS8RmentWdIQLcwDMPwDTO11vBGXNwo3O7JNDdXD/hcITFLqquFYVoZRjiZ4nazsrGRDlUi+jHj\nxQgfRUV/OTor6sMPP+x3F35ItDBMl5QRjpKjosiIimKDGfg2euE5hdZ0SRlGmDID34Y/hUTCMLOk\njHBlBr6NvgroWVL+YLqkjHA1JTHR7I1h9InpkjKMMDXF7WZFYyOdPtiO0zC6MwnDMIJYWnQ0yZGR\nbGppcToUIwyERMIwYxhGOJtqBr6NPjBjGGYMwwhjU8zAt9EHZgzDMMKYGfg2/MUkDMMIclPtEiFq\nBr4NHwuJhGHGMIxwlhkdTZzLxdbWVqdDMYKAGcMwYxhGmDMD34a3BjKGERLFBw0j3D1w0klkREc7\nHYYR4kzCMIwQMDkx0ekQjDAQEl1ShmEYhu+ZhGEYhmF4JSQShpklZRiG4Z2BzJIKiTGM/t68YRhG\nuOnanfSBBx7o82tDooVhGIZh+J5JGIZhGIZXTMIwDMMwvGIShmEYhuEVkzAMwzAMr4REwjDTag3D\nMLxjptWaabWGYRheMdNqDcMwDJ8zCcMwDMPwikkYhmEYhldMwjAMwzC8YhKGYRiG4RWTMAzDMAyv\nmIRhGIZheCWgE4aIxItIuYhc4HQshmEY4S6gEwZwB/CS00E4qaqqyukQfCqU7y+U7w3M/YUjnycM\nEXlaRPaISGW34+eLSLWIrBeRO3p43ZeBKqAeEF/HGajWrVvndAg+Fcr3F8r3Bub+wpE/SoPMAx4D\nnu86ICIu4HHgHGAnUC4ib6pqtYjcAEwBhgAHgAlAM/CuH2I1DMMwjsPnCUNVPxGRkd0OTwc2qOpW\nABF5EbgUqFbV+cD8rieKyNeBBl/HaRiGYZyYU8UHc4Baj6+3YyWRf6Gqz/d03JNIaPdYmfsLXqF8\nb2DuL9wEfbVaVTU/UcMwDD9wapbUDiDX4+vh9jHDMAwjQPkrYQjHznQqB8aKyEgRiQZmAW/5KRbD\nMAyjH/wxrfYF4FMgT0S2iciNqtoBfA94H1gLvKiqZg6bYRhGAPN5wlDV61Q1W1VjVDVXVefZx99T\n1XxVHaeqD/f1vL2t4whmIjJcRP4uImtFZLWI/KfTMfmCiLhEpEJEQq51KSJJIvKKiKyzf44znI5p\nMInIbSKyRkQqReSPdk9B0OppvZiIDBWR90WkRkT+IiJJTsY4EMe5v0fs/58rReQ1ERnS23kCfaV3\njzzWcZyHtU7jWhEpcDaqQdUO3K6qE4BTgO+E2P11uRVrcWYo+jWwUFXHA5OAkGlBi0g2Vg/BFFUt\nwpo8M8vZqAZsHtbfE093Ah+oaj7wd+Auv0c1eHq6v/eBCapaDGzAi/sLyoSBxzoOVW0DutZxhARV\n3a2qK+3PG7H+2OQ4G9XgEpHhwAXA752OZbDZ79TO8GhNt6vqQYfDGmwRQIKIRALxWAtwg5aqfgJ8\n3u3wpcBz9ufPAV/za1CDqKf7U9UPVLXT/nIJ1uSjEwrWhNHTOo6Q+oPaRUROAoqBpc5GMugeBX4I\nqNOB+MAooEFE5tldbk+JSJzTQQ0WVd0J/ALYhjW7cb+qfuBsVD6Roap7wHoTB2Q4HI8vzQHe6+1J\nwZowwoKIuIFXgVvtlkZIEJELgT12K6r7DLpQEIlV3uY3qjoFq7TNnc6GNHhEJBnr3fdIIBtwi8h1\nzkblF6H45gYRuQdoU9UXentusCaMkF/HYTf1XwXmq+qbTsczyE4DLhGRTcAC4GwR6XVFfxDZDtSq\n6jL761exEkio+DKwSVX32TMeXwdOdTgmX9gjIpkAIjIMqHM4nkEnIt/E6hr2KuEHa8IIh3Ucm7eK\negAABj9JREFUzwBVqvprpwMZbKp6tz1jbjTWz+7vqvp1p+MaLHY3Rq2I5NmHziG0Bve3AaUiEitW\n7YxzCI1B/e6t3beAb9qffwMI9jdux9yfiJyP1S18iaoe9uYEQVkaRFU7ROS7WKP8LuDpUFrHISKn\nAdcDq0VkBVZT+G5V/bOzkRl98J/AH0UkCtgE3OhwPINGVctE5FVgBdBm//uUs1ENjL1ebCaQKiLb\ngPuBh4FXRGQOsBW42rkIB+Y493c3EA381a6ZtURVbznheVRDslvOMAzDGGTB2iVlGIZh+JlJGIZh\nGIZXTMIwDMMwvGIShmEYhuEVkzAMwzAMr5iEYRiGYXjFJAzDESKSIyJv2OXpN4jIo/bq9t5eN6CK\noSLygIh8aSDnCAZ2afWT7M+3iMhH3R5f6Vnq+jjn+ExExnU79qiI/FBEJorIvMGO2whsJmEYTnkd\neF1V84A8IBH4iRevu3sgF1XV+1X17wM5hy+JSMQgnKMQcKnqFvuQAokikmM/XoB3dZEW4FG23F7V\nfSWwQFXXADl21WEjTJiEYfid/Q6/RVWfB1Br9ehtwBy73MQ3ROQxj+e/LSJnishDQJxdAXa+/di9\n9kZa/xSRF0Tkdvt4sYgs9tgcJsk+Pk9ELrc/3ywic0VkuYis6irlISJp9sY5q0Xkd/Y79JQe7uMr\nIvKpiCwTkZdEJL6X88bbG9kssR+72D7+DRF5U0T+Bnwglt+KSJUdx7sicrmInC0if/K4/pdF5PUe\nvsXX869lLF7miz/+1wJHC82JtZHVIyKy1P5+fct+6EWO3efiTGCLqm63v36H4N8Hw+gDkzAMJ0wA\nlnseUNVDWOUXxnYd6v4iVb0LaFbVKap6g4iUAJcBJ2MVUCvxePpzwA/tzWHWYJVC6Emdqk4FngR+\nYB+7H/ibqp6MVThwRPcXiUgq8CPgHFUtse/n9l7Oe4993lLgS8DP5Yuy55OBy1X1bOByIFdVC4Eb\nsDbRQlX/AeTb1war3MjTPdzTaRz7/VXgNazvFcDFwNsej9+EVaJ8BtZeM98WkZF2K6JDRE62nzcL\nq9XRZRlwRg/XN0KUSRhGIPGmzLnnc04D3lTVNrv8+9twdAOjJHvTGLCSx5nHOV/XO/blwEn256dj\nvbtGVf/Cv26sA1AKFAKL7HpfX+fYCso9nfdc4E77+R9i1fHpes1fVfWAx/Vfsa+/B/iHx3nnA7Pt\nFlMpPe9hkAXUdzu2F/hcRK7BKoTY4vHYucDX7biWAilA19jFi8Asu6vsa11x2eqwypsbYSIoiw8a\nQa8Kqy/8KPuP/AhgI9aWpp5vZmL7cQ1v99joqtLZwfF/H3o6lwDvq+r1fTivAFeo6oZjTiRSCjR5\nGe+zWInxMPCKx45pnprp+Xv2MvAbrOR2TAjA91T1rz285kWsIp//BFapqmciiuXYxGOEONPCMPxO\nVf+GNRYxG44O9P4cmKeqrcAWoNjuyx+B1U3S5YjHwPAi4GIRiRFrs6mL7PMfBPaJVfUXrG6dY2YJ\n9WIRcI0d27lAcg/PWQKcJiJj7OfFd59R1IO/YFWxxX5N8Qmuf4V9/5lYVUYBUNVdWNuh3oO1T3NP\n1vFF1x58kfD+BPwUKwF0j+sWsWepici4rq4yVd0ENGBVbl3Q7XV5WN19RpgwCcNwymXA1SKyHqjG\neqd6D4CqLsJKGmuBX3Fsf/xTWGXf59sbFL0FrALeBSqBrm6db2KNEazEarH82D7uOTZyvJlCDwBf\nsaedXgHsBg55PkFVG+xrLBCRVcCnQH4v5/1vIEpEKkVkjUdM3b2GtQnTWuB5rPs/4PH4H7E2aKo5\nzusXAmd7hmvH3KiqP1PV9m7P/z1Wq69CRFZjjbt4trYW2PfWfYD9bKzvuxEmTHlzI6iJSIKqNtnv\niP8JfMve+nUg54wGOux9V0qB39pbrfqNx32lYI0rnKaqdfZjjwEVqtpjC0NEYoG/26/xyS+4/T36\nEDj9ON1iRggyYxhGsHtKrHUHMcCzA00WtlzgZRFxYY0VfKuX5/vCO2LtnR0F/NgjWSwDGjl2RtYx\nVLVVRO4HcrBaKr6QC9xpkkV4MS0MwzAMwytmDMMwDMPwikkYhmEYhldMwjAMwzC8YhKGYRiG4RWT\nMAzDMAyv/B9ZBCMoWOfF0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1681d4d0b8>"
      ]
     },
     "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))\n",
    "plt.ylim(ymax=10)\n",
    "plt.legend()\n",
    "plt.xlabel('Outgoing energy (MeV)')\n",
    "plt.ylabel('Probability/MeV')\n",
    "plt.show()"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1681e7b780>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEWCAYAAABIVsEJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1681cd88d0>"
      ]
     },
     "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\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(total.xs.x, total.xs.y, 'k')\n",
    "\n",
    "# Make plot pretty and labeled\n",
    "ax.set_xlim(1e-6, 1e-1)\n",
    "ax.set_ylim(1e-1, 1e4)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Energy (MeV)')\n",
    "ax.set_ylabel('Cross section(b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting to HDF5\n",
    "\n",
    "To create an HDF5 nuclear data file for a nuclide, we can use the `export_to_hdf5()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gd157.export_to_hdf5('gd157.h5', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see what's in the HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gd157.71c']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5file = h5py.File('gd157.h5', 'r')\n",
    "list(h5file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the groups containing reaction data are only labeled sequentially, we can look at the group attributes to figure out what they actually are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reaction_0, MT=2 (n,elastic)\n",
      "reaction_1, MT=16 (n,2n)\n",
      "reaction_10, MT=54 (n,n4)\n",
      "reaction_11, MT=55 (n,n5)\n",
      "reaction_12, MT=56 (n,n6)\n",
      "reaction_13, MT=57 (n,n7)\n",
      "reaction_14, MT=58 (n,n8)\n",
      "reaction_15, MT=59 (n,n9)\n",
      "reaction_16, MT=60 (n,n10)\n"
     ]
    }
   ],
   "source": [
    "main_group = h5file['Gd157.71c']\n",
    "for name, obj in list(main_group.items())[:10]:\n",
    "    if 'mt' in obj.attrs:\n",
    "        print('{}, MT={} {}'.format(name, obj.attrs['mt'],\n",
    "                                    obj.attrs['label'].decode()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<HDF5 group \"/Gd157.71c/reaction_1/product_0\" (2 members)>,\n",
      " <HDF5 group \"/Gd157.71c/reaction_1/product_1\" (2 members)>,\n",
      " <HDF5 dataset \"xs\": shape (37,), type \"<f8\">]\n"
     ]
    }
   ],
   "source": [
    "n2n_group = main_group['reaction_1']\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": 15,
   "metadata": {
    "collapsed": false
   },
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n_group['xs'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we can go the other direction, converting data from an HDF5 file into a `NeutronTable` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5(main_group)\n",
    "gd157.reactions[16].xs.y - gd157_reconstructed.reactions[16].xs.y"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}