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: Gd157.71c>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get filename for Gd-157\n",
    "filename ='/home/smharper/nuclear-data/nndc-hdf5/Gd157_71c.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": [
    "To find the cross section at a particular energy, 1 eV for example, simply call the reaction's `xs` attribute at that energy. Note that our nuclear data uses MeV as the unit of energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.6474702147809"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs(1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `xs` attribute can also be called on an array of energies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 142.64747021,   38.65417611,  175.40019668])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs([1e-6, 2e-6, 3e-6])"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e-11,   1.03250000e-11,   1.06500000e-11, ...,\n",
       "         1.95000000e+01,   1.99000000e+01,   2.00000000e+01])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f237f0f0810>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PtXKtkYiUIwUMqXhnnAG33AIrV66enhkw2reHZcsKz+ubb0IH+1df5X5enObP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239c356b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(gd157.energy, total.xs(gd157.energy))\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": 8,
   "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 6.400881 MeV\n"
     ]
    }
   ],
   "source": [
    "n2n = gd157[16]\n",
    "print('Threshold = {} MeV'.format(n2n.threshold))"
   ]
  },
  {
   "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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openmc.data.function.Tabulated1D at 0x7f237f748650>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.400881, 20.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KdSXV09NPwymnKCmISHFTYqhA3UgiIkoM35k/P2zKc+SRcUciIhIvJYaEUaOg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\nICIilSgxiIhIJf8f4IdhH/peRHEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f237ef64c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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]))"
   ]
  },
  {
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=2.0>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7f237f766710>]"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.stats.univariate.Tabular at 0x7f237f766790>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f766810>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f7668d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f766990>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f766b10>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f766d50>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6ed050>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6ed3d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6ed7d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6edc50>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6f6190>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6f6710>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6f6c90>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f700310>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f700990>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f70a0d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f70a810>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f70afd0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f713810>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f71d050>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f71d8d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f7251d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f725ad0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6af450>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6afdd0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6b87d0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6c2210>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6c2b50>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6ca590>,\n",
       " <openmc.stats.univariate.Tabular at 0x7f237f6cafd0>]"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rm8NpInzR+/mjLRe9n8UIg0FxeikIQM8qR5IKYDaAN5rToK+vb4tGFqmFqUgvTkdOaY78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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f237ef48610>"
      ]
     },
     "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": [
    "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": 16,
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openmc.data.function.Sum at 0x7f237f7a9f90>"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f237eccbf10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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i448/jjx1q+Wbb77BffexS0AMHDgQhw8fjnfKWUEsH4jRaMRFF12ERx99VHMT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\nxAnEzMwKcQIxM7NC/g/XBW/wr41qsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f237ecb7a10>"
      ]
     },
     "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(gd157.energy, total.xs(gd157.energy), '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": [
    "## 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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157.71c>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = '/home/smharper/nuclear-data/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": 20,
   "metadata": {
    "collapsed": true
   },
   "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": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
    "gd157_ace[16].xs.y - gd157_reconstructed[16].xs.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": 22,
   "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['Gd157.71c/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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<HDF5 dataset \"xs\": shape (37,), type \"<f8\">,\n",
      " <HDF5 group \"/Gd157.71c/reactions/reaction_016/product_0\" (2 members)>,\n",
      " <HDF5 group \"/Gd157.71c/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": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n_group['xs'].value"
   ]
  }
 ],
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