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": [
    "## Importing from HDF5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `openmc.data` module can read OpenMC's HDF5-formatted data into Python objects. The easiest way to do this is with the `openmc.data.IncidentNeutron.from_hdf5(...)` factory method. Replace the `filename` variable below with a valid path to an HDF5 data file on your computer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get filename for Gd-157\n",
    "filename ='/home/romano/openmc/scripts/nndc_hdf5/Gd157.h5'\n",
    "\n",
    "# Load HDF5 data into object\n",
    "gd157 = openmc.data.IncidentNeutron.from_hdf5(filename)\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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Reaction: MT=1 (n,total)>"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Sum at 0x7fc3263ca1d0>}"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.6474702147809"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 142.64747021,   38.65417611,  175.40019668])"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc323c68748>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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9wixISUmJxcnQOLl0LbkWJ5euJRfjpCrwpCEij4vIBhFZGrf9PBFZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3263c25f8>"
      ]
     },
     "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": 9,
   "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": 10,
   "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7fc3263ca4e0>}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6400881.0, 20000000.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+ARoXu7194r5fuPvqYtefMLMRZtbA3VeWfLFJkyZFFmhprroqrH048cTjyj22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\nShAiIpKUEoSIiCSlBCEiIkn9P3NUzNvU9HnaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc32631dc18>"
      ]
     },
     "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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7fc326323eb8>]"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.stats.univariate.Tabular at 0x7fc3263d9b00>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263dcb38>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263dccc0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263dce10>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326362048>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263622b0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263625c0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326362940>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326362d30>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263681d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263686a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326368b70>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32636f128>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32636f6d8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32636fcf8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326376390>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326376a58>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32637c198>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32637c898>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32637cfd0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263837b8>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326383f98>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc3263897f0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32638f080>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32638f908>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326396208>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326396a20>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32639c320>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc32639cc18>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fc326323588>]"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x7b/B3FyXFrxhA3z4YZOmPzk5sS80lLcuXGDeiRNUoMX9YXcikiKoTKskZlgM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\nIjHGGBOS/wft2ckz6ND5XgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3236f62e8>"
      ]
     },
     "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<Reaction: MT=101 (n,disappear)>,\n",
      " <Reaction: MT=27 (n,absorption)>,\n",
      " <Reaction: MT=4 (n,level)>,\n",
      " <Reaction: MT=3>,\n",
      " <Reaction: MT=1 (n,total)>]\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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Sum at 0x7fc3263b16a0>}"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc3234bba90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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biz59+she6wmaBxAQHuEOaanmIfhDkql5JMOElMwkwXRcPxZqj7qZ4wNHJrco\nYnfgZ2fLf2MfOIqCf9sQuldat/gBwU86+4tIoCpyShZhTJbmofT55R4AXxJC1hBCvgLwBYDk1z+I\nE0F4sLjnnntgtVrxhz/8Ie7zV1VVoby8nCl8CgsLUV/Pru+T7YT7NaRmK6nDPBXCo6tSIbGi9Bxd\nzT+55UmyM/M82zGpk3/f2vOMaM2X/4tET88TmTZtGtNaEytKo61WdxZHHNI59AOl1NXVMdmCSqXC\n66+/jnHjxmHq1Kk455xzYj7Hzp07MWLECOZrpaWl2Lo1YuWUrCZc8zAajUyH+caNGzFlypSU+DwE\nE5gSU1I0n4dSlORyxEKkCgPpFh1qDeDLQCyDo84OYy95WK7jhB3GOAo13jCMgnX3Fu2RP+fq9X64\nXIFxjdoPry/wt5JeIpzEidbPYzql9IuwdrQCAztjhN9L4dwUE8lsJVBUVISlS5di/vz52LBhg6gb\noBK2bNmCsWPHMl8rLy/Hxx9ntJ1J3IRrF5Ec5uPHj0evXr1SonlEKraYyk6CgvBIdU2qjzzVqPHb\nMYIWYBQKUEBSmwc0bLT4SXrvjvQ8PS/v96LifeNNRIzEuT8N+Ro7wp4J/vuDOFfE1sAu/t1VW9xw\nc1ZPM2Oly2E+FQET1VzGaxRA1giPaEydOhV33HEH5s2bh6+//hpGo3K78ZYtW3D99ezAsvLy8ohF\n87KdrsxWTqczeI80Gk1SNQ/hWEFoSLWBWIRHrD6MroRHsgXKZE1vbPTV4z38iByqw0jkYzQtwGDk\nQkeU9cBINio1MhbZ5W6wQ1cYVua/xQGVTdnv0KQGOhTOW6P3w+sKaSqRNBFpW1wBaXvcnmbGSovD\nnFL6x84//0wpFWWZE0KSmTiYFu69915s374dN9xwA5YsWaJokaKUYtOmTXjuueeYr5eVleHYsWPJ\nnmpacDgcQQEhFR4OhyOYNa/ValOqeUQyJQkZ6MmkK+GR7Gv9RNMb49zF8FOKQ2jFLjTiA/8hHEU7\nBiEXI0k+RpECUGpMW0/1fgPYGlD1ISe8ybXoydg65zXRdnGF/DPIeWs+89ibhltlY0/tYPfSGfpT\ncZWA9d+yw3z7blMW6OJXAZdfJs57sdkMWPiC1CBzaqE02updAOMkY+8AGJ/c6aQWQggWL16Ms846\nC48//jjuvffeqMfs2rULJpMJfftKiwEHKCwsRFtbm2gh7i50JTycTmfKhYfg65AKD+HcHo8nWF8r\nWXkeyfasjDydAAAgAElEQVR5KEFFCAbAhgGw4QJVf9ipB7vRiB20Ef/xH8Yz/yU4q6AYZxUWYXJ+\nEXLijApMhLETxMUSN3/fDqkrSqWm8PuyJwTWpCHoSKDvOjNrHQxzFkOw9zRtJB6i+TyGItBjwybx\ne+Qg0I42K4jm8wjHaDTigw8+wOTJk1FeXo4rrmDXwBH4z3/+g5kzZ0Z8XaVSBbUPVh5INiMVHuEO\nc6nwkJqtEsmXEJ6yI2ke4UIlmvAQyEazVSTMRIvT0Qunk16glKLXOD/+e/IE3jp6GPft3Ioh1hyc\nVVAMK7WiElaoMpD0Vlohr8HVf5h8HlUHkntdf7NDcX/2W4eHkg6f3NaMdi/7O2nU+eBgtMo9MqyQ\nuf/gTcfFLvuwMigC2SNCYyddPo8hCHT+y4XY79EG4IaEr54kYg07Ky8vx8qVKzFjxgzk5ORg7lyW\nSyfA0qVL8be//S3q+Y4ePdrthEe4gDCZTLDb7czXNBpNUPOIt48FyywTSXiEax7RiFXzEMqsZFJ4\nhEMIwUCLFQMtVlzTdwCcPh82Np3E1yfr8Tb2oBVuDKd5GIkCjEA+8oh8Ue9JOG75gDluervrZMR7\nTwuZpu77vhbtYW6L689hh9L//YMSRXNS+QFWBFh3bYubLp+H0EN8MqX024SulGWMGDEC//73vzF3\n7lw89dRTzEqb69evR2NjI6ZPn97luSorK3Ho0CFFmk82Ea55SCvKhjvMw81WyWyC5PP5ZE2ows8d\n3l8kWWTCbBULBrUaUwqLMaWwGGcfrkQjdWInGrEdJ/Em9iOX6nHu7mKcVVSE0/PzYVBnxvGezfy/\n08TL2odVsX3mPi2B2hP7g8SpZspS6vO4mRCyh9JAnWZCSB6AJyil16Zuaqln4sSJWL16Nc4991zs\n3bsXDz74YDB01ePx4M4778Tvf//7qP23Bw8ezOwPke2ECw+bzYaWlpagkzqS2UpYfBMpUxIebWUw\nGBRpHsn2eaSrtlWi5BMDzkYpzkZp0PFu17ThH/v2Y29rK8bl5+GswiJMsvTCILM17e9BqwM8DBmv\nUkHmM0kEZ50dBkY+SSIYdD44Geas2jPFlbjLv2yQZahH62x4KqBUeIwWBAcAUEqbCCGnpWhOMROL\nz0PKyJEj8f333+OGG27A+PHjcfvtt6OkpATPPPMM8vLycN1110U9x+DBg/Hmm29G3S/bCBceQln2\n9vZ2WK1WmdlKEB6CNhCr8IhkthL6oYQTyZHOoqcLj3AEx/vZA3rj14MHodXjwXcnT+Lr+nq8fmg9\nXH4fJucV4Yy8QhT6rChSpz6A4/Qp7K6Ehw8mV8N7v1yeTzL/xK3Q5EcXKHoV4GIIsnnTT8gHAXz4\njdgX4rDKa3AZ7R5mIqg0KgvIvsisdBdGVBFC8iilTQBACMmP4diUk2iqfWlpKT7++GN8/PHHeOON\nN3Dy5En89Kc/xa9//WuoFZgFBg8ejP379yc0h0wQHo4LALm5uWhubpYJj3CzVbzCg4UgPKS1waKF\n8CZCuh3mhAT8rZG2hevGIrjam4TvpBpnaMpwRkkZHuwPVHe04/vmBvy38QTWNu+CiWgwWleAUdp8\njNTmIVeVWX+J10uh0YTep98XyDuJl7rrXgn+3WvRTRHb657Thx299uJe9vdLq/HDE97kSgdAol3F\nksWebeasdBdGfALAt4SQtzu3LwHw14SunGUQQjB37twuneeRGDhwIA4cOAC/3x/VxJVNhPs1gIDp\nqrm5GeXl5XC73dDrA4uNVqsN+kOSYbYS8Pv90Gq18Pv9onsn9BhR4vPI5lBdjYagqJd44fIwbOla\nPbskR2wJfRR9TRb0NVlwaWkldm/rwBG/Hds9jfjaVYvn23ejQGXA6d5CjDMVYIwpH1Z1ekOCD+0X\nVzTSaOWaUVFZfN+r5aWh9rpzj/xCUel4oxpwMC535mlNou19ZfLA0qObc2RjFT+cZBZgBHpmnojS\n2lavE0I2AhA8xxdSSnenblrdC4vFgvz8fBw+fBiVlZWZno5iOjo6RMJD0Dw6Ojqg1+uDT8PJ0DzC\nn6zDQ3XVajW0Wi08Hk9QWHm9XhiNRkU+j1jn4PF44PP5sHSpvLx4tlHeR7mmIBVA9naKfJgwDSZM\nQzl8Gj+qaTtqNK34oLkaD9duRR+9BeNMBRhnKsAUWyHMmvTnl8jpqmiIMrZMeUu0PemHq5n7XT2E\nbfL63y0dzPFoOCzsz8vcJi8DaD9hx1UXLAlu5+Qa8Myrl8R13UwRi+kpH4C9s/NfESGknzTr/FRm\n7Nix2LJlS7cSHq2trbDZbMHt3NxctLS0oK2tTTTO8nkkEm0V7jBnCY9IjvSuzqVEuAgCKVI3xEyE\n6qYLNVGhP8nBjOJi/AID4fb7sMfRgs0dDVjaeBB/rNmMISYbxlsLMdaSjz7ECgMVLw8uJ4XekFq/\nkNbA1sISwd/kgCpPuf/HrAHsYV+RHB1kTasMej+cLmVWBqZzXbLdyuhqePvVb8vGs0nIKG1D+0cA\nExDI+3gFga6CSwH8JHVT616MGzcOmzdvxrx58zI9FcW0traipCQU6y5oHq2trcjJCanlWq02mH0u\naCDJ8nmECw+BWDSPWISHUoF0KqBTqTHGnI8x5nxcUwRQnR/b7U3Y0NaAxbX78IOjBf3MFkzIz8P4\nvHxMyMvHf1fKHxjOnM72M2QTbbe8xRwveIudqvarkWINwqCWayivDWH0F3nKCBXDD8IStzKBwpBD\nLIHCGssUSjWPeQBOA7AZACilNYQQebGZDJFItFWyGD9+PBYtWpSx68eDVEjk5eXh5MmTaG1thdUa\n+njTLTx8Ph8sFktMPg8lGI3GoAbFEWNUazAppwiTcgINlsyFXuxsbcGmpib8+9gx/HHnDqipGoNg\nwyDkYiBsKEVyQ2e7QqMBpApjopqQu6EDusL42+pKqe1nY44P3FbXmWgYdu0IRRnTQbqjrdyUUkoI\noQBACEnft0YByWhskijjx4/Hpk2bUlLML1W0tLSIzFO9e/fG8ePHU6J5KPF5CMTj81AiRKxWKzo6\nOnq0eSpZ6NVqjM/Lx/i8fKD/AFBK8eaKBuxHC/ajGStxGHZ4MGFrPsblFmC8LR+jcnKh74xOVGsp\nfB7x70CW+xGDe2PYaPmSs25VqCKCRotgYUefjzLb90rZ9BO232vS3ujh+QYVhdMvvoZO54ObkTfS\nUiifu61B7FfxE+DSK5aLrwHG7UnC0pLuaKu3CCGLAOQSQm4AcC0A5YX8TwHKysqgVqvx448/YsCA\nAZmejiKkQqK0tBRfffWVbFyj0aCjowOEEDgcDqhUqoQ0D41GA6/XC7/fHxQe4VpGPD4PJXDhET+E\nEJQQM0pgxtkoBQC0UBfcNhe2tTXikdpdOORsw0BjDsZa8jFzqA0TCnNRYAjlSOz8VuyQ12oZuT/e\nQGOrWJl4Vsh89t0au+i1oafFFqJMWzpAbCGNxNXQAb1EQ7m8UG7CqzqLXaV303ITqFv8XpWE+vo1\nDFuWn+IXFy6FLdeAZ1++uMvjU43SaKv/I4TMAtCKgN/jD5TSVSmdWTeDEILp06dj9erV3Up4hGse\nJSUlqKmpYWoeDocDZrMZTqcTer0+IYe5kDvj8/mgUqlgMBhE5iSv1xuzf0KJQMjJyYHdbu/2wiNb\nqtvaiB6j8vIxPS/gN3P4vNjV0Yyt7Y14dd9h/GrddvQ26nF6UR4mFuUiz5OPUo2pS828rjoFEV9q\nADE86/gfeF20/c2/5drEz3bKyxkZ1BROxudimya/+I9bC0Tb/XY3yBOAGAhnb8kC34dSh7kZwBeU\n0lWEkCEAhhBCtJRS7n0MY+bMmVixYgVuvPHGTE9FESzNo7a2FidOnEBxcXFwPC8vUL1UaFWr1+sj\nRixFgrVguFwuqNVqmM1mUVFGwecRXuU30oKTTM2juwiVPiPlnfEaqlNf5DpayRGjWoMJ1kJMsBai\nYqgHPj/FnuY2rK9vwpraBqytOQAP/Bipz8NIQx7OMhdhqNkGbYpzowpHsL87+7Yl9/Oe05ftT3vs\nuFzzUWv88IUlIrLKw/tUBGq/eCxo6cv8s4Nis9XXAM7qrGm1EsBGAJcB6Lqe+SnGjBkzcM8993Sb\nZMGWlhaZ8Dh69Chqa2vRu3fv4LgQkWU0GuF0OmE2m9HWxm7EE4nwxV8QPHa7HTqdDoQQUS8Rn88H\nm80mEijR/EhKfR52uz0pRR2TidtFodPL35/fT6FSKVsl3G4KnS60b6SFPpJPTj4ud0gU95aX6egK\ntYpgZH4ORubn4NohfbHzW4I6rwM7nU3Y5WzCnw5uw1GnHUMtNow052K4JRdDjbko18u1E0IoKBWP\nsZzoMc0vgb7vznoHDEXKwn9tOqBFEvvRd2S7aPugWl4e3tIsF0a9q1tkY5lCqfAglNIOQsh1ABZS\nSh8nhGxN5cS6IxUVFSguLsZ3332HM888M9PTiYpU8ygoKIBWq8XmzZtx7bWhmpeCIMnLy4PD4UBO\nTg5OnGDXBVKC1+uFyWRCe3s7dDoddDqdSFB4vV7FwiOWUN2cnBw0NjZmVPNgCYR1n7NNENYc5bU7\nvvtSrI1UDmRrIoHFVv4+DUbxw47enJiA9bgotAyB2EtjRC+LETMspSgs0qCderG7vRm72pux6mQt\nnm7fA7vPi2FmG4abczG88/8hvQ2ywoOjx4f8HH4fharTSU5UAFUw/QERNBJp2RTWZ7biTHkH7oEb\nz4M/Ry5Qnp8uj+i66YsOUe5IJGe7FEEboQoCAlKNYuFBCJmMgKYhhCLwWtAMLr30Urz11ltZLzzs\ndjtUKpUow5wQgtGjR2P16tV45JFHguOlpQEHaUFBAZxOJ0wmE7xebzBaKhYopfD5fDAajbDb7dBq\ntaJoLkop/H5/0D8RPjfpecL/l/7NQtA80iU8Sit0cLnE5zxxvIdZegkFqHwh2/xfQCqkrDlEVG6l\nsdEHgGAg8jBQn4fz9UDOABUavW7ssTdjt70ZHzUcxWPVO0H3UIzKsWGULTfwL8cGE0KLclNDSFrk\n5ouXNa8T0MRg1WuqEe/ccEKecW7Lky+d5Q//m33C538jG3pqhvh3839lJ2X7rP3ACo9TLNQbyrIm\nQ0Kx8LgDwP0A3qeU7iKE9AfwZeqmFRvZkOchcNlll2HGjBl44oknYl5Y04nUryFw5plnYvXq1Rg1\nalRwbMSIEQCAfv36Yfv27dBqtUH/h8USW5KY1+uFRqOBXq8Pah7hjagEJ7rFYlEkPAQTFKVUkfCQ\n+jyGDRuGgwcPwu12dxufRzaRk8t+Aq45Kh/rN0y8EB7aI7/fHhdghQ4TDcWYaCgGCjofOAqc2NnS\ngu0tzVhSXYWdrc3QEXVQO6lUWTHImIMihpT48Sv27zCvlxckTutyLCZFR50dxijl5M1qCrvE2X7G\n+XLT8Np3rKAeAlUCcQVpzfOglH6NgN9D2P4RwK8TvnqSyIY8D4GhQ4eirKwMK1euxJw5czI9nYjU\n19ejqKhINn7PPfdgzpw5IqFgNBpRV1eHt956C+vWrUOvXr2C/g+lwkNYmN1uNzQaDbRabdDnEd4/\n3efzQaPRwGw2i6rtKtE8ovkyWJqHSqWKu7VuaWkpampqYjqGEzuEEPTWG1HSy4hZvQImVEopfqhz\nYJe9Bbvbm/FOcxX2OVpBQVGptqJSY0U/TQ76aawo9WuZTnlPpPIiJHpvjrYWeQSV162BhuEWWt5n\noWzsvMPXwtg7JFDu6isvy/K3ajXsEie6bXwCTp5O0p3nwYmBW2+9Ff/85z+zWnhE0jysVismTZok\nGy8uLoZGo0FraysGDBgQ1DyUEi48tFotdDpdUPMIj7byer3MCCwlmke0xT83Nxetra0i4eHz+YL+\nl1hLl2g0mfn5eNyBJkyphPoheypPdgIsqzx9V/uKtwnKDRaUGyyYXVAGvTEwv3q3C+uqGnHQ1YZt\nrga82/Yj6jc60M9oxWBTDgaacjDAaMVAUw7MVMt8P9IhrRZQ8tX4cXukD0W+4P+7z8ui7SsPzodG\nUn/rd2N6Q8q1Nc1ocQG2LOhGzIVHCrjssstw3333Yfv27Rg9enSmp8MkkubRFRqNBi0tLTCbzTAY\nDDEJD2Fhd7lc0Gg0QSd5V5pHV8JDSFKMRfMoKipCU1OT7Bir1Yr29vaYzVaZMkvu2yBfOQhxiBZi\nn5dCrVG+0EvNMKyncrfbh0SKFkqjynJy5ffP7Yz//IQQFOsNmGQuxiRz6MFIl0NxwNGGfR0tONjR\nhjWNx3HQ0QY/KAZbrRhksWCQxYrBFisGWa0oMulAw7LHJ/xErl2v/287lObJGnJUcLZ2/d088iu5\nv6T/J3fJxp49N/OOcgEuPFKA0WjEfffdhwcffBAffvhhpqfDJJLm0RUajQZ2ux1mszlotlJKeF9y\nwUkernkI/UIEzcNisaC9vV12vIAgPLrSPFQqlWgsNzcXTqczmM0+adIk3H333XjggQdQW1ur+L0I\nJEt4qNVgLkSxPOmbzOK5HKli5xwMGsYOL206KX46LihR9t5YIbQAYDTKhc/WdeLH94HDYslNYdUy\nYdc3kYYpG9UajLLkYZQlT7Rfq9+Ng442HOhoxZ7GNnx0tBYHOtpg0BEMzTNjWL4Fw/LMKGovwCBz\nDnK0IUfDwKHy+yhtdiVw6XMlsrElvzwmigjz+wPzDsfTYIdWWtqk3QFYjIDdAbBLaaUNpUmCjwN4\nGIADgTyP0QDupJRmf1OEDHHzzTfjiSeewLp167Iy8qquri4YRaUUIazXYrGItAUlsDSP9vZ29O7d\nG3l5eTh8+DCAgHDR6XTIy8tDU1OoKY/UpCTVPFglU/785z/DYDDgt7/9LYCA8BOKPwLAnXfeiUsu\nuQSPPfaY4vcRjlBCviukGgBLULAWIgA4fMiFZJcnTzYFFezildUHkquVGazyJ/f25tBiHm5iK6kQ\nX9vnYfs2tB49CnR6TLSFciwopVBXNmFfix27m9qx6UQrttUcx8GONpjVGvQ1WtDPaEExNaKv3oI+\negtKdEZoiAr797A18fHuHKh04jmZTOJtj0N+v7b8VF5oddS5YV+evy5gXi9dKNU8fkop/R0hZB6A\nKgAXIuBA58IjAgaDAY8//jhuuukmbNq0CTpdio3UMVJdXY0zzjgjpmP69OkDAOjVqxdyc3NFi3s0\nwjUPqdmqoKAg6Bx3uVzQ6/UoLCxEfX198HhphV3BvCWcV6plAAENMLy/ilqtRn5+flB4CE/1hYXy\nBC0lhFceZtHW6kPNEfG8h4xIfV/xbEWqEcQSsZSS+TBzJQhMbTaMVdkwtgBAAdCap4ZKA9S5najq\naEOVox3ba1vwbXM9jnk70OhzoZfaiEJqRG+Y0Bsm9IIRvWFGDrRw7WiQXUurC/iuBFj3gql5alSA\n1w/oMm80UjoDYb85AN6mlLako3IsIaQfgN8DyKGUXpryCyaZ+fPnY+nSpXjkkUeyKiIMCAiPvn37\nxnRM//79AQSKQBYWFsp6j3eF1GFuMBjQ3NwMrVaLgoKC4IIuCI+ioiLR+SMJD+G8hBCm2Sq8R7v0\nWgKxamACd911Fy69lP21PN8S272NhUjmkUSOlS5USh3mLHMLwM5wL+4tji9ta5FrE2ZLpAgoeT5J\n+HshKhr0U4QnDHbOHCzzVqTqu5SKneY/7AppFDmwYDQsmGwLfWfc1IcabweOejpwzGfHYW8rvvUd\nR43XDj8o/rnEhIG5Jgy0mdAvx4R+OUb0m5SD/M7qCgBw7KD8Xng9FH5JeRL95D7s+5MBlAqPjwkh\nexEwW91CCCkCkPLKXJ2dCq8nhLC7uWQ5hBC8+OKLmDBhAqZMmYKZM2dmekpB4hEe+fn5ePXVV3He\needhy5YtskW4K6Rmq5ycHBw5cgR6vV60oDudThgMBuTn56O5uTmY98HyeWi12mBBRZbZSq1Wi4SH\nXq9Hfn6+TOiVlZUpeg9XXXUVXnvtteB2QUFBxH0vyEmd8JD2AgcAi1UlModFimQ6tJ/9sw0s7KED\nPC65GcXllIeTOtvYGnVhsXxpaWmKP8w0r1zuFFq7MnS+6ReFVvu968XvpaCIvczVHmWb3AaN1Iju\nHetemiyh+2OCGrnQoZ/DGiwLL9Dqd6NwUhsOtnXgYEsHPqk6gapWB35sccBPgT5mI/qaTchzm1Gm\nN6NMZ0KZzoTeOiN6Vcg/A1+HD2qTGj6Hn9U/Kq0ozfO4r9Pv0UIp9RFC7ADOj/VihJDFAH4OoI5S\nOjps/BwATyPQT2sxpTQ+I3QWUlpaiqVLl+KKK67AN998kxUVd+12O9rb22N2mAOBBRRAzJqHVHhY\nrVbU1tbCarUyNQ+1Wo3c3FycPHlSJCQEBM1D8LuwNA+W8CgoKAiWVhGe+pRqHtKclq7qlwUXGwLR\nekv9FCQFpprBw8XmsIP72EJC6hjPOiT3S0BaMgQQ+4+8bgRzLKTlSeSaSIBImodUO2NFhbEYORGM\nGmV6NNdZ0E9DMLPTDAYAx4+60eT0otbbgRpPB+rgwK62ZnzuqUGNp9MUtt+APiYTKkwmVBjN6GM0\nof8/GjHYZoFepUGfDDctVeowvwTAyk7B8QCAcQg40I/HeL1XADwDIFjzmBCiAvAsgBkAagBsIIR8\nSCndGz6FGK+TVUyfPh1/+MMfMHv2bHzzzTeiooOZoKqqCn369EmoeGNJSQnWrl2reH9hYXc4HDAY\nDLBarWhtbYXFYgkKD0ppUHgAAY3g6NGjwWPDy6H4fD7o9XpR1JUS4VFSUhKMrBKER3gr3gceeABr\n167Fl1/KCyhI8zq6irbyqf3QaAjMkigotweQro75BeyfYWR/QBpqcEVYcNlzUfbzlAYPsGp3WXLY\n57I3yPftOyBkBvtxe+izLygS73uihi0w62rYyRsajTgQguWPMJmJKJwXANavZp+vcqBG1rtk7HQf\n4AcAEwATju7RQ6sL/R7dfj/+800jTrQ5caLVgd3UjjW0AbVaOybmFeLPg05jXiudKDVbPUgpfZsQ\nMgXATAB/A7AQgDybrAsopd8QQqT6/EQA+yml1QBACFmOgFazlxCSD+CvAMYSQu7tzhrJLbfcgpMn\nT2LatGn47LPPgs7nTLBr165gyZF4GTRoEF599VXF+wsLe3t7O0wmkyxyS6/Xo6mpSSQ8+vfvjwMH\nDsDj8Ygq+gIhs5WAUDMrHLVaLcpl0el0KC0txZ49e0T7VVRUBP+uq6uLqJERQnDnnXfiqaeeAhAQ\nJgsXLsQtt9wi2m+sIR/FsRRTihF2aK94EY9ktopWVl0gvFZU6Fj5wm6ysZMdWk7I62dU/SjWHkeN\nY5XsUC6Mwn0wag2Fzxv42+Om0OqinyO8+2A40nvH8s0MGilfOo/XsM+3Z4c8CmvwBVaow4TF1teb\n4ZYoi710RuR7jBiKUIjxD9YGrG+rR+1RD0Yy3lM6USo8hG/IHAAvUEo/IYQ8nKQ5lAE4ErZ9FAGB\nAkppI4BbWAd1Rx544AGYzWacddZZ+PTTTxNewONl586dCV978ODB+OGHHxTnIgiObbvdDqPRGIxU\nEoRBZWUlqqqqZMJj79690Ol0QU1F2F/IBzEYDHA6nfD5fEzNo6KiAt9++y0mT54Mk8mE0tJSHDt2\nDEBI8zjttNPw6aef4p133sHZZ58dUaNSqVQioW80GjF+/HjZfv9XGtMzFSItmJGqww4dJa/SaskX\n7yjN+xCIVKk32VXqvR4/NFqxZisVeh6PH1rJPqyQXGF+sjwId8gHM3hCSLv4z3LxOQZFyCc5nZH8\nBwTqa4XT1OgVFXQEAAoKIvnMRozXMc1g333pkAn7lu0O0e9mxjyt7P0529UyU93eToVY6kjPBEqF\nx7HONrSzADxGCNEDGffXBLnooouCfw8bNgzDhw/P4Gy6plevXpgzZw4mT56Mq666CpMnT07auZWa\nkVasWIEzzjgDy5Yti/tawpP+008/jV69ekXd/+DBgwACRdlOnjyJH3/8EQCwadMmtLe3Q6vVYsmS\nJaCUoqGhAcuWLUNjYyPWrl0LtVoNnU6H1157Lbh419bWBlviAoGM+ffeE5fJXr9+PfR6PdxuNyor\nK/Huu+9i3759wbl88803Il/KjBkzAISaX0k5cOCAyFS1atUqlJWV4YUXXsDy5cvxxRdfAAiE6ApI\nbeCsaCJTDsAyRUUyZyWCUs2DJbhY/TNYUVkAsHOr/Gm7/2CxX2Yv44n8f8rYocztJ1mVANlvRDr3\nyA84bKEtFWqVA3Wyzo1anR+EiD+zY3vZNbHK+ujlQoX6RNqNo40RYFCvks27vc2LDo8fDdQV8+93\n9+7dMq07EZR+Oy8FcA6A/6OUNhNCSgDck6Q5HAMQbsMp7xxTzLvvvpukqaSHyy+/HDfccAMuuugi\neL1ePP7441FzBmI5d1dQSnHXXXfh7bffFuVAxMOnn34Ki8US9ZpA6DMaPnw4nE4nrrjiCrz44ou4\n+uqr0a9fP6xfvx7l5eXIy8tDc3MzLr/8cgwYMAAXXHABrFYrBg0ahNNPPz24wO/ZswcvvvgiVCoV\nOjo6oFarcd555+E3vwmVv54yZUpwbldffTWAgL9HKAg3bdo0nH8+O+7j4YcfxtixY7Fjx47g2Esv\nvYSOjg787W9/AwBcfPHF6NevH4BAGLAgPMJR1luCvYhFWvS8HgqNxIYudfJG8lmUVbJ/8g3HxZPs\nVS4/tqCXfGH3edmZ7PHiclLoDcoWep0+tMCHO9TzJOXSA/kUcuGs1bPHt60RJ7/OvlSeDOqyyyWm\n0+Fnno8VZTbwNLVI9rE+0w67X2YqPO1MNd5Z34SlhTtQ8Omn6N27N/r06YOKigpUVFSgvLw82Jcn\nGommWyiNtuoghBwEMJsQMhvAfymln8V5TQLxt2ADgIGdvpBaAPMBxJQ6mU0l2ZVy2mmnYfPmzbj7\n7rsxcuRIPP/88zj33HNTft2DBw9Co9HEHKbL4rzzzsMLL7yAG264Ieq+Qp5GW1sbTCYTxo4di8sv\nv+61YWUAABvJSURBVDw4j6FDh2LDhg0YOXJksK/6mDFjcPz4cfTt2xfFxcWoq6sLnk9wngtO7Pb2\ndqbPQ0q4c1wwgbFQqVQoKSnBjh07gqVSzGazKKHQZAqZj8K13fBFu7iX+EfM6j+u1gCsRSfSoldz\nVP7EXlquE+17PIIzuO9AZc+LLOHDzhFhCz6dHnBL5IrUlyBdLAFg3Sp2lNj/zDVCfi9CC3hzbWiB\n9/vdokU3Um5LJN+ISg2RmYqlXbEWe42WwuthF1qU+p+sA7RQh11703MuqGQ5NHJH/dzTC9HLqkWb\nxwvf7Nmora3Fvn37sHr1ahw+fBjHjh1DY2MjrFYrioqKZP+EKEmhokMiKI22ugPADQAEu8BSQsgL\nlNJnYrkYIWQZgGkACgghhwH8kVL6CiHkdgCfIRSqG5NulW0JeErJzc3F4sWL8fnnn+Omm27C8OHD\n8b//+78YOTJ1rrCVK1dixowZSamOOm/ePNxxxx3YunUrxo4d2+W+QnmRhoYGGI1G2Gw2/Otf/wq+\nPm7cOCxatAgVFRVB4WEwGNCvXz84HA5UVlYGTV1AqC+IYLZitZdlRZOFlxQRtJhICI7ziy++GK++\n+ioIISCEoKqqCpWVlSJBIgiP5b3EuTzScNDSAXI1xN6sYtrKI7VJ1erldnmlFWojaTPSharxhHyf\n+uPyvIipQ9kO87N+Jn/ybTsp6edxQH4cS+gAERIURQIuJMSUNtw6Ws22vJeWizUN6icy7fHATvnn\nOGk2W8Xcska+zDbuEN+35kZlVRYNOjXOLssHNCrYfvEL5j5+vx9NTU2or68X/ROERn19fUKdQAWU\nmq2uAzCJUmoHAELIYwC+RSDsVjGUUqZ9g1K6AsCKWM7Vk5g5cyZ2796NhQsXYsaMGTj33HNx3333\nYejQoUm/1ttvvx2s9ZQoOp0Of/rTn/CrX/0KX331VZehq4LmcfToUaZwHDVqFH744QccO3ZM9PrK\nlSvh8XiwZcsWfPzxx8FxIZlQwOfzweFwYMiQIdi4cWOXZkChnHw0ATps2DAAwMsvv4zq6uqg4Onb\nty88Ho/o/VosFhw6dAi7zrxZdI6TDeLVv3ywSuYErT8GsDSMgSPYP08zwyVzbL9435P17PBUjZZ9\nrZYm8eJV1FtZtyFpNnaX45IscZagmHUx+7puByCdd31d6D3m99LKXo8Xba4eHkb/8HB0BUa4T4o1\nQJ9ZD7Vdfpw6Xwdfo0TwWnVAW2hMX2SEq158Pn2hEa4G8Zh6RCF0bjc8hsh11VQqFQoKClBQUNDl\nGpIWsxUCIj382+VDFuVedEezlRS9Xo/f/OY3uOaaa/DUU09h6tSpmDhxIu666y5MmzYtKZrCnj17\nsHfvXsyaNSsJMw5w/fXX480338Tvf/97PProoxH3E8qSVFdXY+rUqbLXjUYjxo0bh8WLF+P9998P\njg8ePBhA4Iv+4IMPBp9AnU4njEZjMHHPYrGgsbERBoMhOBapxHpJSYlIi4nEXXfdhfnz54MQIvNn\nsHp5VFZWYq9WBZ8n9AQqTQq0N8kXR5/Py9Q8IpX+YPkF5KaZCCU5IpRql2o5LJMJS7uRCkIBj1P+\ngtEifjKfPk9+D5X0HmdBcgygrQGTl77IAFd9yPwl3RbQFRrhbpCbAKd+LvaD1fzuI9AW8fEztt4u\nO25/K0OVAjCUIQ9VRHx/ztPJc7+k+wDAdydC1Z+nMK8WnbR2EkQgue97Qojwq74AwOKEr54kuqvZ\nioXNZsNDDz2Ee++9F0uXLsVtt90Gr9eLX/7yl7jyyisTcnL/6U9/wu233y56Yk8UtVqNt956C5Mn\nT4bNZsP999/P3M/pdCI/Px/V1dURy3osWLAAa9euxZgxY2SvDRs2DD6fD3v37sWwYcOCyYZ//etf\nsXv3bjz44IM4fvy4qACl1Aci0KdPH0XCw2AwBOt5KaXfIPFKseU7sfOVldV87DC7TIYtj+2TWb9C\nXs3YaGJpGnLhefyIvMQIAAwYJi4zUrVPfj5pb/AACWSsWwxAu2RRtxqANobfQ/KkDgDaAgM8JwP7\nFr8ecpPOoeL9NIRdQkWjivD03lwv2rQsvIS9n4QOL4FJI7+37W7Aout6zOnzw6COHsDq8BAYtRQO\nhm9FKWntJEgpfZIQsgYhYXcNpXRLQldOIj1B85BiNBpxww034Prrr8f69evx+uuvY8KECRgyZAjm\nzp2Ln//85xgxYoRijeS1117D5s2b8fLLL0ffOUYKCwvx1VdfYdasWTh27BiefPJJWRXh1tZWVFRU\nYOPGjREz7G+++WZMnjyZKSAJIbjooouwZMkSPPLII0Gz1fjx4zF+/Hg899xzItMSgIjNrjKZoNlQ\nq7CDUHfBbALscmFGcvSgrRITTo4eCBszPH2F/LhIGQDeFtnQZE1yIhSltHkBa9jK2OGlMEm0tVa3\nDzmSMuuL9sjzbwDAwdDCpMzsK39/P+uTD6skD+alzaH3PKtCeoQy0qZ5EELUAHZRSocC2JzwFVNA\nT9I8pBBCMGnSJEyaNAlPPvkk1qxZg48++ijY4nbKlCk488wzMXnyZFnlWQA4duwYnn76aSxbtgyr\nVq0SRQglk9LSUnzzzTe46qqrMHXqVCxfvlwU0dXU1ISZM2di48aNGDJkCPMcarUa48aNi3iNW265\nBVOmTMGDDz4Y1DzCr19VVRUUWq+88kpE81yk6ycDdZ4JvqbQYqovMsFVH9rW5BvhbZTatk1wNcgX\nYLXNCF+L3KyiKzLALTHDSE0wukID3A3yJ3hNnhHeJvk5pYu9Nt8Ij2Se6jw9fE1igWB85NeycwFA\nrldert9PUyc42z0Uls7oJ7uHwhwWCRX+WjitLoocWS0q4L794gefMrNcW6tqFWsnAAAfgVaXvOS9\nR7fK7yF1G0B0ANjKqiLSpnl01rP6gRDSh1KaeHwXJ270ej1mz56N2bNn45lnnsEPP/yAdevW4dtv\nv8XChQuxb98+3H///SgpKYFarUZ9fT2am5uxYMECbNq0KeU1tfLy8vDBBx/giSeewIQJE/Dwww/j\nxhtvhNvtRltbG6688kocOnQobtPb4MGDcdZZZ+Ef//gHLBaLKJmvtLQU+/fvD2oeXfVPufXWW1MW\n0TZw2S9F20MkZUp8DDOPCuwnU2kGs0CJW54GZdKI28pFWqhNhG0Ka/GJKyT3Ucuf6p2+Nuax8cJ6\noo+00Ns9gFniOwjf97FtIaFmloQ+n3Sxnd9NTeyFPkKOqAivm0AjERS7v8pn7tt/UotsX58LUIdZ\nzTocBCZjdMHT8UXYvVFmTUsZSn0eeQB2EULWAwg2lqaUnpeSWcVITzRbRYMQgqFDh2Lo0KG49tpr\nAQBLly7FtGnTUFdXB5/Ph4KCAlRWVqa117ZKpcI999yDOXPm4Oqrr8Ybb7yBOXPmYNCgQTjzzDOx\nYkViQXWPPPIIpkyZgvnz54vMUpWVlXjvvfcwceLEqOfIzc3Feeel5qvb7qWwxNlrozvS5vHBqpV/\nv6RP/4BcMDy3Wy5I69nN+KAiLD9d6HhddHdB3LicBHqDeGHfu04uKFR+P8Co/7Vvk1waWVrEAq2a\ncdzYc1uglRoKOsPYEnm76XaYP5jwlVJITzZbxYJKpUJ5eTnKy8szPRUMHz4c69atw2uvvYZ//etf\n+Mtf/pKU8w4ZMgQLFizAM888gyVLlgTHx4wZg5qaGuTns5/+0sXfdjeLtu8ZVQxL2OLa4fXDpBH/\n9Ns9PtE+oX19MGnk4w4vYIzyy2U91Qeu5YdFK1965KYe+X4sgfCHjeyeLgbG80qbpKJwURKbKrrd\nBLoYTUY+D6BmREJ5nUC4wvj1h4xm4Yy5m+zs/JLWPLngIz4/aBQH+YE35Mfp3YmX1E+L2YoQMhBA\nL0rpV5LxKQhkg3M4EdFoNLjuuutw3XXXJfW8jz/+OAYNGoR580INDYQILWnPjUzz3J460Tarynmz\ni62p9DaxF0O7V77/dUP8MIct9osi9NN2etl9591+IHxh91O5iSpw+swX5BPwuAi0+sB8vvs+VF9t\nxuQ66MI0BakwEKjZwjbhGe0Sk2cKOkjnnxB/Do29zfAriLbKJqJpHk8DYMVetnS+NjfpM+JwomAw\nGHD77eI4+6KiIvz85z9n5pCcCjy/xx59pyzD5SbQS7QFj4dAq2WVapE7o3d9GWYOCnPRfLsiV7Sf\n3hnhaT1ydZqoqLx++DXKFnsl+xYflQvrdhtD4vkpoErMbJUsogmPXpTSHdJBSukOQkhlSmYUB6ei\nz4Mj56OPPsr0FOB2q6DThTLdnC4Cgz57ntbTBcuMJBUAX68vlB4Gr4e9LPoZ678BysqQREKpAGCZ\nmMp+bJbt11xoZJqi+u2Vm/a8asJOzY+CsSNxs1W6fB65XbyWRItlYnCfBydb+OJzcQKkTy82/s+b\ndhwGifPV7SLQMQSM00lk+wLKBJLLRaBn7BPJNyCdg1IfQiRNYd0auWBQSXpQkK5WlzRQWiXPrQCA\nkyVi02dRTbui80lNUV2hc4tT6f2KujYmh3SF6m4khNxAKX0xfJAQcj2ATQldmcM5Bfn0E3l2vT9C\nu1mtix1u67TIvbznnysudLfqP+wsfprDftImrZLFjDGns6Y2iHwJALBxQ4SuixF6baSKcC1C5fOL\n/AdKnNMZh1EMLNvnHU14/AbA+4SQKxASFhMQcCFluP06h8MRcDkI9AryBBJh/UqGqpDk4LZYfAnh\ni2vFvsbguFToajxsQeZlRJ0BcuHDJFJFyDjRuuVzzHXJgx58Cu9NOuhSeFBK6wCcSQj5HyDYMvcT\nSqm8600G4T4PTrag8fnhzcDT4tf/loSTZo1ROTZK9sl9CQBwZLBcSpVUtwb/9kUQBPFQfETsvHYx\n4qL1TrlW6NGnIZ+qU2jZWM50haQ1z4NS+iWALxO+WorgPg9OtlC2X1xS4scxbLNOthOLBpCOc6o9\nPvgYuTAJkWTtIZbzsWseR0fto3jtgyvjODJEWgsjcjic+JAueqwFNFbbtpJFONI5VR4//IyndOL3\ng4bVfy+tljuTT/ZSnovAMv1IHdS1o3PhV5gaPmBng2zMG+keSBbxSAu1xsuuMCyjMzw2Glq3H0ve\nky/sV1y2TH4VNcHSN0PtjW6/+m20NrO7KGYrXHhwOCmk7252BnY4kcwdjUUmpgDo80OjbOxERY5o\nu6iGnfeh8rPt/x599KWAlYtw2FrAFEaRIpnCKd8sfx8sE1GsKBYKCjE65OGxkYIcmPOJEPgQzjOv\nygtVXT1viax/SjIVpUThwoPDyVIK65Qn/ily8qaAPjvZwjGZPghFKNQOuhOsXmZKWg2nCy48OJwk\nQpCZAh5SrcBhTkFNjSwmGclzMcHwb9hyk9dkrTvQI4QHj7biZAvSMFF3OiJwTlWS7fCOARUFXns/\nMcd1NHJyDTI/SE4SBFS6q+pmNTzaisPpBihZ7GMwP4WHy9JkmqwyKJTCYflBkgGPtuJwTlWSvAgr\nvkaCi6rWEz2L3dzOrlcVa/mOpe+EWtzedt27aGmRRzIRwvYhqCiyy7mQpXDhweGkEukinoSnWo1P\nHk0kXYjNdnaf0v/f3r3HylGWcRz//iCUUi9F+IMgDdVYudREuWiDQqTchEgQaRWhgAgxKjHwB14w\nkUiDl6gxaNIiQqwtVE9LaynlGorIJRQxUJQqpxUk3DFc5KICVm0f/5g53emye87OXmZmd3+fpDm7\nM7Ozzz7ZnufM+77zvm/UL8fXRKM7npO5qbZ/36bDZSvk0kVztz0eGRlh3rx54xwNZ35y6bj7LeHi\nYdZD9cM8G/213fTO5AEcQTQoutH30O9cPMy6aOrUyQ2bSNoxpUkTTkfDYJsUpKpPwle2K689o+wQ\nKsfFw6yLsk0k0FoTiLZG5x2+LTaHvfUfjZuz6nU8T1O3r5oq0oltNQNRPDxU1/rZ217Z3HB7nruY\n6/soCr9Jr06jItVJQWrUB7O1SV9SJ5MG9kKvhty2y0N1MzxU12y4KWDpqtMmPrAEvRpy265uDdV1\nI6eZtW9rB0NaPRy2rw3ElYdZ3+hG232F2v8bTRrYqkZrYlRpsSMbn4uHWYEaTWuR976CHf8XLL1m\n+yaaM+b8suPYzPJwmTezYnS7mSpzvmGblLAKfOVh1kP1I20KG2XTadNWg9c3m86jVTv9Z+t290t0\neid3EZMTWnMuHmY9VNZIm2zTVnZKjlabt5qtilfPU3kMr0o3W0maImmJpMsljT8hjZlZF9RfHXoq\nksaqfuUxB1gZETdKWg6MlB2QmQ22qt2XUVWFXnlIWiTpOUkb6rYfJ2mTpIclXZDZNQ14Kn088ULA\nZoOkSQdDo87h+m3uQLZeK/rKYzGwALhqbIOkHYCFwFHAs8B9ktZExCaSwjEN2ECywqfZ0MjTIbzw\nF5/qcTQFqdA9LDa+QotHRNwtaXrd5lnAIxHxBEDaPHUisAlYDSyUdDxwfZGxmg2iqbtO5tVXujPr\nby/suCW48to3F0x3zFdPFfo89qLWNAXwNElBISJeB84uIyizQdTsCsU3GVpeVSgeHZs7tzYN9v77\n78/MmTNLjKY869atKzuEyhiUXIyMdD5GpN1cdOO92zlnnvfNG+OgfC/aMTo6ysaNG7t2vioUj2eA\nvTPPp6XbWrZq1aquBtTPJlpic5j0Sy5uWdG8SaZbn2Gi89z86zdfebTy3uPFPlEczV7b6H3zHJsn\nhmGmDvuWyrjPQ2zf+X0fMEPSdEmTgFOA6/KccP78+V2Zn95sWHm01vC44447urKMRaFXHpJGgNnA\n7pKeBC6KiMWSzgXWkhSzRRGR69rK63mYdWZgRmvZhLq1nkfRo60aXi9GxM3Aze2e1ysJmpm1xisJ\nZvjKw8ysNV5J0MyGRqP5pTznVLkG5srDzVbWr+qnbc9ut4Tnm+oeN1tluNnK+pl/MVqR3GxlZmal\nGYji4fs8zKrPzXDV0Jf3efSKm63Mqie75KxVh5utzMysNANRPNxsZWbWmm41Ww1M8fAwXbPiNevH\ncP9Gdc2ePdt9HmZWLg8zHl4DceVhZmbFcvEwM7PcBqJ4uMPczKw1vs8jw/d5mJm1xvd5mJlZaVw8\nzMwsNxcPMzPLbSCKhzvMzcxa4w7zDHeYm5m1xh3mZmZWGhcPMzPLzcXDzMxyc/EwM7PcXDzMzCw3\nFw8zM8ttIIqH7/MwM2uN7/PI8H0eZmat8X0eZmZWGhcPMzPLzcXDzMxyc/EwM7PcXDzMzCw3Fw8z\nM8utssVD0rsl/VzSirJjMTOz7VW2eETEYxHx+bLj6Cejo6Nlh1AZzkWNc1HjXHRPz4uHpEWSnpO0\noW77cZI2SXpY0gW9jmMYbNy4sewQKsO5qHEuapyL7iniymMxcGx2g6QdgIXp9vcBp0raL913hqRL\nJO05dngBMW6Td5qTiY4fb3+jffXb8j7vJuei/XM7F+0fP9HrBjkXrX7mZtuLzEXPi0dE3A28XLd5\nFvBIRDwREf8FlgMnpscvjYjzgc2SLgMOKPLKxL8k2j+3c9H68c5F+68b5Fz0U/FQRHTtZE3fRJoO\nXB8R70+fzwWOjYgvpM9PB2ZFxHltnLv3H8DMbABFRNstO30/MWInH97MzNpT1mirZ4C9M8+npdvM\nzKwPFFU8xPYd3/cBMyRNlzQJOAW4rqBYzMysQ0UM1R0B7gH2kfSkpLMiYgtwLrAWeAhYHhEeQ2dm\n1icK6TA3M7PBUtk7zNslaYqkJZIulzSv7HjK5CleaiSdKOkKScskHVN2PGWStJ+kyyStkPSlsuMp\nW/o74z5JHy87ljJJOlzSXel346MTHT9wxQOYA6yMiC8Cnyg7mDJ5ipeaiFiTDg0/Bzi57HjKFBGb\nIuIc4DPAR8qOpwIuAK4uO4gKCOCfwM7A0xMdXPni0cb0JtOAp9LHWwoLtACe6qWmg1xcCFxaTJTF\naCcXkk4AbgBuKjLWXsubC0lHA6PACxQ8m0Wv5c1FRNwVEccD3wAunvANIqLS/4DDgAOADZltOwB/\nBaYDOwF/BPZL950GfDx9PFJ2/GXmInPMyrJjr0IugO8DR5YdexVykTnuhrLjLzMXwHeAS4BbgNVl\nx1+F7wUwCVgx0fkrf5NgRNyd3qGetW16EwBJY9ObbAJWAwslHQ9cX2iwPZY3F5J2A75LOsVLRPyg\n2Ih7p41cnAscBbxd0oyIuKLYiHunjVwcTtK8uzNwY6HB9ljeXETEhem2zwIvFhpsj7XxvTiJZL7B\nqSRzD46r8sWjib2oNU1B0j43CyAiXgfOLiOokoyXi5dI2viHxXi5WAAsKCOokoyXizuBO8sIqiRN\nczEmIq4qNKLyjPe9WE3yx3dLKt/nYWZm1dOvxcPTm9Q4FzXORY1zUeNc1HQtF/1SPDy9SY1zUeNc\n1DgXNc5FTc9yUfni4elNapyLGueixrmocS5qep0LT09iZma5Vf7Kw8zMqsfFw8zMcnPxMDOz3Fw8\nzMwsNxcPMzPLzcXDzMxyc/EwM7PcXDxsaEjaIukBSX9If3697JjGSFop6V3j7P+WpO/VbfuApNH0\n8a2SpvY2SrMaFw8bJq9FxEERcWD684ednlDSjl04x0xgh4h4fJzDlpGs/Jd1CjCSPr4K+HKnsZi1\nysXDhknDleIkPSZpvqT1kh6UtE+6fUq6Gtu96b4T0u1nSloj6TbgN0r8VNKopLWSbpQ0R9IRklZn\n3udoSdc0COE0YE3muGMk3SPpfklXS5oSEY8AL0n6UOZ1J5MUFUjWrjm1k+SY5eHiYcNkl7pmq09n\n9j0fEQcDPwO+mm77JnBbRBwCHAn8SNIu6b4DgTkRcQTJwkp7R8RM4AzgwwARcTuwr6Td09ecBSxq\nENehwHqA9NgLgaMi4oPp9q+kxy0nLRCSDgH+HhGPpu/1CjBJ0jvaTY5ZHv26GJRZO16PiIOa7Bu7\nQlgPnJQ+/hhwgqSvpc8nUZvO+taIeDV9fBiwEiAinpN0e+a8S4HTJS0BDiEpLvX2JFlDm/SYmcA6\nSSJZKvR36b6rgXXA+SRNWMvqzvMC8E7g5Saf0axrXDzMEpvTn1uo/b8QMDdtMtom/av/tRbPu4Sk\nSWkzyVryWxsc8zowOfOeayPitPqDIuLptIltNjCXpNBkTQbeaDEus4642cqGScM+j3HcApy37cXS\nAU2OWwfMTfs+9gBmj+2IiL8Bz5I0gS1u8vqNwIz08b3AoZLek77nFEnvzRy7HPgx8GhEPFt3nj2A\nxyf+WGadc/GwYTK5rs9jbOhrs3UJvg3sJGmDpD8DFzc5bhXJWtAPkYx6Wg+8mtn/K+CpiPhLk9ff\nBBwBEBEvAp8Dlkl6kGQ9hn0zx64kadYayZ5A0sHAvU2ubMy6zut5mHWBpLdExGuSdgN+DxwaEc+n\n+xYAD0REwysPSZOB36avaes/pKSfAGvSTnqznnOfh1l33CBpV5IO7oszheN+4F8kndwNRcS/JV0E\n7EVyBdOOP7lwWJF85WFmZrm5z8PMzHJz8TAzs9xcPMzMLDcXDzMzy83Fw8zMcnPxMDOz3P4Pk3dA\nAdQe9WAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc32374eac8>"
      ]
     },
     "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": [
    "## Converting ACE to HDF5\n",
    "\n",
    "The `openmc.data` package can also read ACE files and output HDF5 files. ACE files can be read with the `openmc.data.IncidentNeutron.from_ace(...)` factory method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = '/opt/data/ace/nndc/293.6K/Gd_157_293.6K.ace'\n",
    "gd157_ace = openmc.data.IncidentNeutron.from_ace(filename)\n",
    "gd157_ace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can store this formerly ACE data as HDF5 with the `export_to_hdf5()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gd157_ace.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": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
    "gd157_ace[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": 23,
   "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<HDF5 group \"/Gd157/reactions/reaction_016/product_0\" (2 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/294K\" (1 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": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n_group['294K/xs'].value"
   ]
  }
 ],
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