xref: /dports/science/openmc/openmc-0.12.2/examples/jupyter/nuclear-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nuclear Data\n",
    "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": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from pprint import pprint\n",
    "import shutil\n",
    "import subprocess\n",
    "import urllib.request\n",
    "\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "import openmc.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical Data\n",
    "\n",
    "Some very helpful physical data is available as part of `openmc.data`: atomic masses, natural abundances, and atomic weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.939608306"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_mass('Fe54')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00015574"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.NATURAL_ABUNDANCE['H2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.011115164864455"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_weight('C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The IncidentNeutron class\n",
    "\n",
    "The most useful class within the `openmc.data` API is `IncidentNeutron`, which stores to continuous-energy incident neutron data. This class has factory methods `from_ace`, `from_endf`, and `from_hdf5` which take a data file on disk and parse it into a hierarchy of classes in memory. To demonstrate this feature, we will download an ACE file (which can be produced with [NJOY 2016](https://github.com/njoy/NJOY2016)) and then load it in using the `IncidentNeutron.from_ace` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://anl.box.com/shared/static/kxm7s57z3xgfbeq29h54n7q6js8rd11c.ace'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'gd157.ace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load ACE data into object\n",
    "gd157 = openmc.data.IncidentNeutron.from_ace('gd157.ace')\n",
    "gd157"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Python, it's easy to explore (and modify) the nuclear data. Let's start off by reading the total cross section. Reactions are indexed using their \"MT\" number -- a unique identifier for each reaction defined by the ENDF-6 format. The MT number for the total cross section is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Reaction: MT=1 (n,total)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = gd157[1]\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross sections for each reaction can be stored at multiple temperatures. To see what temperatures are available, we can look at the reaction's `xs` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x1461b4d2f160>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the cross section at a particular energy, 1 eV for example, simply get the cross section at the appropriate temperature and then call it as a function. Note that our nuclear data uses eV as the unit of energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.64747"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs['294K'](1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `xs` attribute can also be called on an array of energies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([142.64747   ,  38.6541761 , 175.40019642])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs['294K']([1.0, 2.0, 3.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation for each temperature present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': array([1.0000e-05, 1.0325e-05, 1.0650e-05, ..., 1.9500e+07, 1.9900e+07,\n",
       "        2.0000e+07])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross section (b)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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txqwl2zhSWkn7FJsTYUxrEpXLfVsfROy4dER3qmqUt1ftdToUY0w9UZkgTOwY2q0dfTun8t7qfU6HYoypJyoThHVSxw4R4aJTurB080GOlFY6HY4xxkewi/UN8A55fV9E5tU9Ih1cQ6yJKbZMGtqF6lpl7roDTodijPER7GJ9/wGeAP4B2KB1E1bDczLolpHEe6v3cuXIHKfDMcZ4BZsgqlX18YhGYtqsumam5z/ZQXFFNWmJwf63NMZEUrB9EG+KyPdEpKuIdKx7RDSyRlgfROyZNLQLldW1LNhgzUzGtBbBJoib8OwJsQRY6X04tten9UHEnrzeHemc5uZdG81kTKsR7ES5PpEOxLRtrjhh4pAuvP7FbsqrakhKcDkdkjFtXrCjmBJE5Aci8pL3cbuIJEQ6ONO2XDQ0m9LKGpZutiXAjWkNgm1iehwYCTzmfYz0HjMmbEb37USK28UHtnifMa1CsMNFzlDVU32ezxOR/0YiINN2JSW4OK9/Jh+u249edortNGeMw4KtQdSISL+6JyLSFwfnQ9gopth1weAs9h+tYPXuo06HYkybF2yC+AkwX0QWiMhCYB5wV+TCapyNYopd4wdlIYLtEWFMKxDsKKYPRaQ/MNB7aIOqVkQuLNNWdUpL5PSeHZi7bj8/njjA6XCMadMCbTk63vv1cmAykOt9TPYeMybsJgzOZs2eo+wtKnM6FGPatEBNTOd7v07x87gkgnGZNmzC4CwAW7zPGIcF2nL0Pu+3v1bVrb6viUjYJ8+JSCqwELhfVd8K9/1NdMjNSqNXpxQ+XLefG0f3cjocY9qsYDupX/Zz7KVAF4nITBE5ICKr6x2fJCIbRCRfRO7xeemnwItBxmRilIhwwaBsluQfpKSi2ulwjGmzAvVBDBKRK4AMEbnc5/EtICmI+88CJtW7pwuYDlwMDAGuE5EhIjIRWAtYu4JhwpAsKmtqWbyp0OlQjGmzAo1iGoinr6E9nn6HOseAmwPdXFUXiUjveofPBPJVdQuAiLwAXAqkAal4kkaZiLyjqrVBlMHEoDN6dyQ9KZ656/Yz6ZQuTodjTJsUqA/ideB1ETlLVZeG6T27Azt9nu8CRqnq7QDe2klhQ8lBRG4BbgHo2bNnmEIyrU2CK45xA7OYv/4ANbWKK85mVRvT0oLtg7hVRNrXPRGRDiIyMxIBqeqsxjqoVXWGquapal5mZmYkQjCtxAWDszhYUskXOw87HYoxbVKwCWK4qh6pe6Kqh4HTQnzP3UAPn+c53mNBs6U22oaxA7OIjxM+WGvdUsY4IdgEESciHeqeeHeTC3VfyE+B/iLSR0TcwLXAGyHey8SwjOQEzujdkQ9t2Q1jHBFsgvgzsFREfiMiv8Gzs9wfA10kIrOBpcBAEdklItNUtRq4HZgDrANeVNU1TQna1mJqOy4YnMWmA8XsOFjqdChNdqS0ko9sFJaJYkElCFV9Brgc2O99XK6qzwZx3XWq2lVVE1Q1R1Wf8h5/R1UHqGo/Vf1dU4O2Jqa2Y+KQbCA6F+/79qxPueGpTyirdGzhY2OaJdgaBEBHoERVHwUKIjGTOlhWg2g7enVKJTcrLSoTxKb9xQBU1dpobROdgt1y9D48s5x/5j2UADwXqaCCiMdqEG3IBYOzWL71EEfLq5wOxZg2JdgaxNeBqUAJgKruAdIjFVQgVoNoWyYOzqa6Vlm4ocDpUJqkbuaGqqNhGBOyYBNEpaoqoHB8UT1jWsRpPTvQMdUdfc1M3gxRW2sZwkSnYBPEiyLyd6C9iNwMzAX+EbmwGmdNTG2LK04YOzCTBRsKqK6Jvvb8rQdLOP/B+Rw4Vu50KMY0SbCjmP6EZ/XWl/Gsz/RLVf1bJAMLEI81MbUxEwdnU1RWxYrt0Ter+qmPtrL9YClvf7nX6VCMaZJgO6lTgXmq+hM8NYdkEUmIaGTG+Dh3QCZuV1x0TprztjCFoy+itlZ568s91mxlWkSwTUyLgEQR6Q68B9yIZylvR1gTU9uTlhjP6H6donKXOfVmiHB8pL/w6U5u/9fnPL98RxjuZkzjgk0QoqqleCbLPa6qVwFDIxdW46yJqW2aMDiLrYUlbC4odjqU4NTLCBqGKkRdP0bBsYpm38uYQIJOECJyFnA98Lb3mCsyIRnj3/hBnr2qo7KZyZgoFGyC+CGeSXKvquoaEekLzI9cWMacLKdDCoO7tmNulK3uWldxqA1Qg3jo/Q2s3m3Npqb1CHYU0yJVnaqqf/A+36KqP4hsaA2zPoi2a8LgLFZsP8ThkkqnQwmaBtlJ/ci8fC7520eRD8iYIDVlLaZWw/og2q4LBmdTq7BgY/TUIgLVHODE/one97zN859sj2RIxgQlKhOEabuGd88gMz0xKpqZtN7XpvjHoi0Bbm7DXE3kWYIwUSUuTrhgUBYLNxZQWR0ds6qPNzEFcY4xrUmwE+X+KCLtRCRBRD4UkQIRuSHSwRnjzwWDsymuqGb51kNOhxKkIJqYmnyFMZEXbA3iQlU9ClwCbANygZ9EKqhArJO6bTsntzOJ8XFRs3hfbRhnUhvTkoJNEHX7T08G/qOqjn4yWyd125bsdnFObmfmrtsflslnkVIXWzAx1j9HGjjPmJYUbIJ4S0TWAyOBD0UkE7ClKY1jLhicza7DZWzcH95Z1b96cw1jfv9hWO/5VWd18MnM35kHjpXz8NxNANSosvtI2cnXqbbqpGmiS7DzIO4BxgB5qlqFZ+OgSyMZmDGNuWCwZ1Z1uJuZnv54G3uKwvu3TzCf18F8pL/jsxrs9PmbOfuBeezzxrp6dxHXzlhKn5+9w23PfRZipMacKNhO6quAKlWtEZGf49lutFtEIzOmEdntkhiekxEV/RDBzYMIfJ/731x70rHCYs+aTPe+uoplWzyd9u+t2de0AI1pQLBNTL9Q1WMicg4wAXgKeDxyYRkT2AWDsvli55GILFxXE8bltIOZST1vffMSnTUqmUgINkHUeL9OBmao6tuAO5yBiMhgEXlCRF4SkdvCeW8TmyYMyUIV5q8P/6S56trmz7EItu+hsLiCW+s1C9VPJsfKqxp/L8sQJgKCTRC7vVuOXgO8IyKJwVwrIjNF5ICIrK53fJKIbBCRfBG5B0BV16nqrcDVwNlNK4Zpi4Z0bUe3jKSINDNV14S/BtGQYCb8Pb5gc+PvYXUIEwHBJoirgTnARap6BOhIcPMgZgGTfA+IiAuYDlwMDAGuE5Eh3tem4llO/J0g4zJtmIgwfnAWizcVUl5VE/iCJmjJBOGP1BvnWhPgJlaDMJEQ7CimUmAzcJGI3A5kqer7QVy3CKg/3fVMIN+7Imwl8ALeEVGq+oaqXoxn3wljApowOJuyqhqWbj4Y1vtWhaGJqU5tE+ZD1GnqB77tQGoiIdhRTD8EngeyvI/nROSOEN+zO7DT5/kuoLuIjBWRR7xNWQ3WIETkFhFZISIrCgoKQgzBxIrRfTuR4naFvZkpHJ3U9T/kI/lXvs19MJEQH/gUAKYBo1S1BEBE/gAsBf4WrkBUdQGwIIjzZojIXmCK2+0eGa73N9EpKcHFuf078+G6A/z2MkXqt82EqKomfDWIQB/dYQrZmLALestRvhrJhPf7UP9b7wZ6+DzP8R4Lmi21YXxNGJzNvqPlrNlzNGz3DG8fRNP7D/YWlbF+X3DlOVJayfp9x0IJzZhGBZsgngY+EZH7ReR+YBmeuRCh+BToLyJ9RMQNXAu80ZQb2GJ9xtf4QVnECbwfxgli4RjmWieU1qqqGmXSw4s5WFzBoo2NN6V+tuNwiJEZ07hgO6kfAr6Np8P5EPBtVX040HUiMhtPU9RAEdklItNUtRq4Hc+oqHXAi6q6JtQCGNMpLZG83h2ZsyZ8/RDVYez1Pd5JHcK1Nzy1nG/OXE5NGGs0xgQrYB+Ed1jqGlUdBDRpkRdVva6B4+/QjKGsqvom8GZeXt7Nod7DxJaLhnbhN2+tZVthCb07pzb7fuFoYqo/NyGUfuTNBZ7FCAMNczUmEgLWIFS1BtggIj1bIB5jQnLhkGwA5oSpmSmsndTez/ZaVXrf8zbT5+ef8HpjndRx3tcONLCcSEV1DfuPhn+pEWMg+D6IDsAa725yb9Q9IhlYY6wPwtTXo2MKQ7u1C2OCCH8ndV2/xsNzNwZ9bZw3e7zts5Krr9ue+4yfvbKqmREa41+ww1x/EdEomsiamIw/Fw3twkMfbOTA0XKy2iU1617VYaxBNKc7Iy7AGNiGahbGhEOjNQgRyRWRs1V1oe8DzzDXXS0Tot+4rAZhTnLR0C4AvL+2+Z3VVWGcKNecvgibI2GcFKiJ6WHA32DsIu9rjrB5EMafAdlp9O6UEnKC8J09Hc4axFd9EE2/NlANwphICpQgslX1pAZO77HeEYnImBCJCBcN7cLSzYUcDbA8tj++HdPh7KSuSwxf1SiCFxdifpj50daAS4QbE0igBNG+kdeSwxlIU1gTk2nIhUO7UFWjIe0RcWKCCH8ndV1TU1PWTQq1BvHrt9Yy7P6A62ka06hACWKFiJzUESwi3wVWRiakwKyJyTTktB7tyUpPDGk0k29SCO9M6sarDo3li3CtLWVMKAKNYvoR8KqIXM9XCSEPz25yX49kYMaEIi5OmDgkm1c/3015VQ1JCa6gr60Ocw2i7g619fJD/Ts39k6hNjEZEw6N1iBUdb+qjgF+BWzzPn6lqmepqu2Mblqli4Z2obSyho82FTbpusoI9UFovf0gmjKKqbmd1KWV1c263rRtQc2DUNX5wPwIxxI0EZkCTMnNzXU6FNMKje7bifSkeOas2ccE7wzrYJzQxBSBHeUaSgyN9Uk0twZRXF5NijvY6U7GnCjYmdStivVBmMa44+MYPyiLuev2N2m4anXERjE1vlhfJPsgbAUn0xxRmSCMCeSioV04XFrFp9uCXwrbt4kpLKu51pv/YOvtmWhjCcLEpPMHZJIYH8d7q/2vYeRPRbVPDaI6nDvKnTjMtSlsEJNxkiUIE5NSE+MZNzCLd1fvC3p/6YoqnwQRzv0gvLdtuA+i4WstQRgnWYIwMWvy8K4cOFbBim2Hgjq/ovqrXXXDu9RGgC1HI9hTYM1apjmiMkHYTGoTjPGDskhKiOPtVcE1M/k2MYV3R7m6r/7v2WgNIuSt341pvqhMEDaKyQQjNTGe8YOyeGdVcM1MvgmiMgx9EHU1g7rd4J5Zur3Z9ww1BmNCEZUJwphgTR7WjcLiCpZvDdzMVFHl08QUzqU2AiSnxl61PgjjJEsQJqaNG5RJcoKLt1ftCXhuXQ1CJLwT5QI1VzVl8T5jWpIlCBPTUtzxjB+cxXur9wXseK5LEGmJ8WFdzTVQDWLj/mMNvmYVCOOkVpUgROQyEfmHiPxbRC50Oh4TGy4Z1pXC4sqAzUzl3iam9MT4EybNhaquYlDTSA1hX1E5tz73WbPfK1AMxoQi4glCRGaKyAERWV3v+CQR2SAi+SJyD4CqvqaqNwO3AtdEOjbTNowdmEWK28VbAUYz1dUg2iUnUFZZ0+i5TdFYB/mRsspGr7Xlvo2TWqIGMQuY5HtARFzAdOBiYAhwnYgM8Tnl597XjWm2ZLeLCwZnB2xmKqusJikhjhS363htIhyCnajnj6UH46SIJwhVXQTUr9ufCeSr6hZVrQReAC4Vjz8A76pq5Ordps25ZHhXDpVU8lF+w0uAF1dUk56UQIo7nrJwJojmtPNYhjAOcqoPojuw0+f5Lu+xO4AJwJUicqu/C0XkFhFZISIrCgoKIh+piQljB2bSPiWBVz7b3eA5x8qrSU+MJynBRWkYm5ic7AewLgjTHK1qoXhVfQR4JMA5M0RkLzDF7XaPbJnITLRLjHcxZXg3Xlyxk2PlVaQnJZx0TnFFNWlJ8SSHqYkpmA/nQDOlrQJhnORUDWI30MPneY73WFBsJrUJxeWnd6eiupZ3V/nfDLG4vJq0xHhSElxh7aQ2Jlo5lSA+BfqLSB8RcQPXAm8Ee7GtxWRCMaJHe/p2TuXlz3b5fb24wpMgkt2usPRBhGMCnI1iMk5qiWGus4GlwEAR2SUi01S1GrgdmAOsA15U1TXB3tNqECYUIsLlp3fnk62H2Hmo9KTXj5Z5mp6SwlSDCCY9RHqtJJulbZqjJUYxXaeqXVXxumiwAAAUpElEQVQ1QVVzVPUp7/F3VHWAqvZT1d815Z5WgzChuuy07gC8+vmJLZqqSmFJJZ3T3aS4XVTW1IZ1ye9QWf3BOKlVzaQOltUgTKhyOqRwTm5nXli+44QEcLS8msrqWjLTEklOcAFQ3owVXVW1Vcxifv2LPQGX+jCmIVGZIKwGYZrjxrN6saeonLnrDhw/VnCsAoDM9ESS3Z4EUVpRHfJ7NGdynK/mdkE8OGcDU6d/xMeNzP8wpiFRmSCsBmGa44JBWXTLSOLZZduOH9tXVA5AVnoS7ZI9Q2CPlleF/B7BbjgUeJhr8zLEhMHZHC6p4vonP+GGJz/hi51HmnU/07ZEZYKwGoRpjnhXHNeP7sXH+QdZt/coAJsLigHol5lKe2+COFIaeoJoLTWISad04cO7zufnkwezdu9RLpv+Mbc8s4IN+xpeQdaYOlGZIKwGYZrrhlG9SE+K56EPNgKQf6CY9MR4MtMTaZ/S/AQRzi1LmyspwcV3z+3LorvH8eMJA1iy+SCT/rqIO//9BTsOnjyay5g6UZkgjGmujJQEbjm3Lx+s3c+S/EKWbz3EsJwMRIT2yW4AjpQ5X4NoLt8KSFpiPD+c0J/Fd4/jlnP78vaqvYz/8wJ+/toq9h8tdyxG03pFZYKwJiYTDtPO7UPfzFRunLmcDfuPcfEpXQBP8gAoakaCCOeWpc3hL011SHXzs68NZtHd47j2zB68sHwn5/1xPve/seZ4X4wxEKUJwpqYTDikuOP5xzfzGNmrA5OHdeXqMzyrv6QnxiMCRaWN79XQmPD1QURuJkR2uyR+e9kw5t01limnduPZZds574/zuffVVew6bE1PppUt1mdMS+uXmcaL/3PWCcfi4oSM5AQON6cPIoxbljZHMOmlZ6cU/nTVqfzwgv48tmAzL67Yyb8/3cnlp3fne2Nz6d05NeJxmtYpKmsQxkRaVnpis9rlq4KchR2ogtCSM6l7dEzh95cPY9Hd47hhdC9e/2IP4/+8gB//+4tG9802sSsqE4T1QZhI65qRzN5mtMdXBDkLO1ACcGKtvq4Zydw/dSiLfzqOaef04b3V+7jwL4u4aeZyFm8qsPWd2pCoTBDWB2EirVv7JPYWlYV8fd1+Eu2SGm/Fbc2LtWalJ3Hv5CF8fM94/vfCAazde5Qbn1rOpIcX8+KnO8O6LatpnaIyQRgTaV0zkiksrgz5Q7C8ylODyGqX1Oh5gf4Ybw1/rHdMdXP7+P589NNx/OmqUxGBu1/+knP+MI+H5248vkyJiT2WIIzxo2uG54M91Gam8mpPYslMS2z0vECf/83ND+GsoSTGu7hyZA7v/vBc/vXdUQzPac/Dczdx9gPz+PG/v+CzHYet+SnG2CgmY/zol5UGeGZY9wlhFE+FtwaR3S5AgojCz1MRYUxuZ8bkdmZzQTHPLt3OSyt38ernuxmQncbVeT24/PQcOqa6nQ7VNJPVIIzxY0B2OgAb9h0N6foKbw2ifUrjH5KBNgxq7X+R98tM4/6pQ1n2/y7ggcuHkeKO57dvr2PU/83l+89/xqKNBbbceBSLyhqEiEwBpuTm5jodiolRaYnx5HRIZsP+4pCur+u7yPXWREb16cgnWw81+T6tPD8cl5YYz7Vn9uTaM3uyYd8x/v3pTl75fBdvr9pLt4wkLjm1G5OHdWW4dzkTEx2iMkGo6pvAm3l5eTc7HYuJXYO7tmPVrtCWx64b5nrR0C50THUzflAWg37x3knnBUoAtdGSIXwM7JLOL6cM4acXD+SDtft5eeUunv54KzMWbSGnQzKTh3dl8rCuDOtuyaK1i8oEYUxLGNWnIx+s3c/eojK6ZiQ36dq6Pa1T3C6+Nqwr4FnC41i9TYgCjmJq0ruezMnWncR4F5cM78Ylw7tRVFrF+2v38faqvTy1eCt/X7iFHh2TmTzMU7M4pXs7SxatkCUIYxpwVr9OACzJP8gVI3OadO3R8ipccUKKd3c6gBpvNvD9HAzUB9HcGkRr2FcbPAsgXpXXg6vyenCktJL31+zn7VV7eXLxFp5YuJlenVK4YFA25w/MZFSfjiQluALf1EScJQhjGjC4Szs6pyXywdr9TU4Qx8qrSU+KP+GvYnd8HKWVNSfUGiI9D6KqFXYQt09xc/UZPbj6jB4cLqnk/bX7eGfVPp77ZDszP95KUkIcZ/XtxNiBWZw/INPWgnJQq0kQItIXuBfIUNUrnY7HmLg4YcqpXXl+2Q6KyqrI8O40F4yjZVW0Szrx/DN6e5qsXHHBN6U0dxTTpKFdmnV9pHVIdXPNGT255oyelFXWsGzLQRZuLGDBhgPM37AGgF6dUhjTrzNj+nVidN9OZKY3PnTYhE9EE4SIzAQuAQ6o6ik+xycBfwVcwJOq+oCqbgGmichLkYzJmKa4/LQcnv54Gy+t3MW0c/oEfd3R8mraJZ/46/V/Xx/G5zuOnLCQX+BO6iaFe5LOadEzFyHZ7WLcoCzGDcoChrKtsISFGwtYtLGAt/67h9nLdwAwIDuNMf06c9HQLsebAU1kRHoexCxgku8BEXEB04GLgSHAdSIyJMJxGBOSYTkZnNmnI08u3kJlkAvwgf8aRGZ6Ilfl5VBa+VVHdaA+iOb2IURzx2/vzqncNKY3T33rDD7/5URe+/7Z3D1pINntknjh0x3cNHO50yHGvIgmCFVdBNQf/H0mkK+qW1S1EngBuDSScRjTHLePy2VvUTmzlmwN+ppDJZV08DNJLi0xnqoaPT5PItDGQqW2IB4A8a44RvRoz/fG5vLstFHccl4/KltJB3wsc2ImdXdgp8/zXUB3EekkIk8Ap4nIzxq6WERuEZEVIrKioKAg0rEaw3kDMpkwOJu/fLCJHQcD77SmquwtKj++npOvLt7F+/Yc8awUG2iUUmll4wnivAGZAeOJZbsOl7b62ebRrNUstaGqB1X1VlXtp6q/b+S8Gaqap6p5mZlt+5fDtJxfXTqUeJdw2/MrA67werSsmrKqGrr4SRA9OqYAsP2QJ9EE+iPYX7PWtd6tUQGe+c6ZgUKPSemJnv6dc/4wn1N/9T5XPbGEe17+kn8s2sK89fvZc6TMEkcYODGKaTfQw+d5jvdY0GypDdPSurdP5q/XjuA7s1Zwx+zPeez600lw+f/7aqd3P+du7U+eXDeoazpxAp9vP8y4gVknNDF986xePLN0e1CxACS4PP0LaYnxFNebgAdww+iegQsWpb59dm+G5WSwuaCYNXuOkr+/mA/W7ueFkq8aJzqluhnaPYMRORmM6NmefplpdGuf3OC/mzmZEwniU6C/iPTBkxiuBb7hQBzGNMn4Qdn8aupQ7ntjDbc9t5KHrz2NtMSTf4XW7fUs8DewS/pJr7VLSmB4Tns+WHeAH08ccEIT048mDDieIM7J7cxH+YV+40jxvuf1o3oBcHZuJ+as2Q/AH68YzvjBWTy/bAe3je3XjNK2bvGuOEb39Qx79XWktJL8A56ksXp3Eat2F/Ho/ILjo8HixJO4e3ZMoWfHFHp0TCGnQzLpSfEkJbhITnCR4o4nOcFFQrzgEiEuzufr8e/B7YqL6kEAwYj0MNfZwFigs4jsAu5T1adE5HZgDp5hrjNVdU1T7mtrMRmn3DSmN3EC97+5lsumf8zD14zglO4n7my4cvth0hLj6d3J/wSvK0fm8PPXVrNsy6ETEoTvrOsnbhzJKffN8Xt9WqLnvBJvreHha05jc0HxCXH8cEL/0AoY5dqnuMnr3ZG83h2PHyupqGbt3qNsKyxh56FSth8qZcehUuau209hcWXI7+V2xdEpzU2nNDfdMpLpn53GwC7tGNWnI9kBNoqKFhFNEKp6XQPH3wHeCfW+1sRknHTjWb3pl5nGD174gqmPfsQNo3vxP+f3o3v7ZI6VV/Hemn2MG5TV4IS4K07P4bH5+fzqzTXcOXHA8eNuVxx/uupUCosrSEuMJys9kQP1dmvrl5l6fPhsUVkV4Jk/UD9Jma+kJsZzRu+OnOGTNOqUVFSz50gZJZU1lFXWUF5VQ1lVDaWVNVTV1FJTq9SqUlOrPt97BhccK6/mYHEFhcUVbCksYd76A1R7qyp9M1MZ068TY/p1ZnTfTlG7N4ZEc0dOXl6erlixwukwTBtVVFbFg3PWM3u5p937lO4ZHC2rYvvBEl753tmM6NG+wWs/WLufm59ZQee0RAqLPUlg2wOTTzjn+//6jLe/3Hv8eWJ8HMvvncCeI2Vc/NfF/GB8LndeODACJTOhqKyuZeP+YyzdfJAlmwtZvvUQJZU1iMDoPp24fnRPLhrapVX0gYjISlXNC3heNCYInxrEzZs2bXI6HNPG7T5SxgvLd7B86yFE4Lvn9GXCkOyA1/36zbXM/Ngzt+L3lw/jujNP7FR+dtl2fvHa6uPPe3RMZvHd4wFYvbuIQV3SiW8FHzbGv6qaWlbtLmLhhgJe/mwXuw6X0b19Mt8fl8uVI3Nwxzv3bxfTCaKO1SBMNKuqqeXul77kaFkVT96Ud1KHZ2FxBXm/nYs7Po7K6loS4+PY8NuLHYrWNEdNrTJv/QEenZ/Pf3ceoXv7ZG48qxfjB2XRLzOtwebIyupa9hwpY4e33+RYeTVdMhLp2TGVU3MyQv4DwRKEMTHg4/xCstITmfiXRVxxeg5/vvpUp0MyzaCqLNxYwGMLNrPcu8NgitvFkK7t6JTmJjHeRWllNYdKKtl/tIK9RWUNrsd1/5QhfOvs4NcH8xXTCcKamExbs2RzIUO6tgu4x7WJHtsKS/hsx2G+3FXE2j1HKSqroqK6hhR3PB1T3WSmJ9LDOxy37pGeFM++o+Ws33uMvN4dQh4tFdMJoo7VIIwxpumCTRDWw2WMMcavqEwQIjJFRGYUFRU5HYoxxsSsqEwQqvqmqt6SkWGTg4wxJlKiMkEYY4yJPEsQxhhj/IrKBGF9EMYYE3lRmSCsD8IYYyIvKhOEMcaYyIvqiXIiUgAE3oLLOZ0B/7u+RI9YKAPERjlioQwQG+WI9jL0UtWAezZHdYJo7URkRTCzFVuzWCgDxEY5YqEMEBvliIUyBMOamIwxxvhlCcIYY4xfliAia4bTAYRBLJQBYqMcsVAGiI1yxEIZArI+CGOMMX5ZDcIYY4xfliCMMcb4ZQnCGGOMX5YgHCQiqSKyQkQucTqWUIjIZSLyDxH5t4hc6HQ8TeH92f/TG//1TscTimj++dcXA78LcSLyOxH5m4jc5HQ84WIJIgQiMlNEDojI6nrHJ4nIBhHJF5F7grjVT4EXIxNl48JRBlV9TVVvBm4FrolkvMFoYpkuB17yxj+1xYNtQFPK0Np+/r5C+P/l2O9CQ5pYhkuBHKAK2NXSsUaKJYjQzAIm+R4QERcwHbgYGAJcJyJDRGSYiLxV75ElIhOBtcCBlg7eaxbNLIPPpT/3Xue0WQRZJjy/zDu9p9W0YIyBzCL4MtRpLT9/X7MI/v+X078LDZlF8P8WA4ElqnoncFsLxxkx8U4HEI1UdZGI9K53+EwgX1W3AIjIC8Clqvp74KRqs4iMBVLx/CcrE5F3VLU2knH7ClMZBHgAeFdVP4tsxIE1pUx4/srLAb6gFf2h1JQyiMg6WtHP31cT/y3ScPB3oSFNLMNOoNJ7Tmv6g6NZLEGET3e++osUPB9Aoxo6WVXvBRCRbwGFreEXgiaWAbgDmABkiEiuqj4RyeBC1FCZHgEeFZHJwJtOBNYEDZUhGn7+vvyWQ1Vvh1b3u9CQhv4t/gr8TUTOBRY5EVgkWIJwmKrOcjqGUKnqI3g+aKOOqpYA33Y6juaI5p+/P1H+u1AKTHM6jnBrNVXrGLAb6OHzPMd7LJrEQhnqi4UyxUIZIDbKEQtlCJoliPD5FOgvIn1ExA1cC7zhcExNFQtlqC8WyhQLZYDYKEcslCFoliBCICKzgaXAQBHZJSLTVLUauB2YA6wDXlTVNU7G2ZhYKEN9sVCmWCgDxEY5YqEMzWWL9RljjPHLahDGGGP8sgRhjDHGL0sQxhhj/LIEYYwxxi9LEMYYY/yyBGGMMcYvSxCmTRCRGhH5wucRzHLsLUJEXhKRvo28fp+I/L7esRHexfoQkbki0iHScZq2xxKEaSvKVHWEz+OB5t5QRJq9lpmIDAVcdauDNmA2J+/3cK33OMCzwPeaG4sx9VmCMG2aiGwTkV+JyGciskpEBnmPp3o3jFkuIp+LyKXe498SkTdEZB7woXh2EntMRNaLyAci8o6IXCki40XkNZ/3mSgir/oJ4XrgdZ/zLhSRpd54/iMiaaq6ETgsIr4r617NVwniDeC68P5kjLEEYdqO5HpNTL5/kReq6unA48D/eo/dC8xT1TOBccCDIpLqfe104EpVPR/PznS98exlcCNwlvec+cAgEcn0Pv82MNNPXGcDKwFEpDOezX8meONZAdzpPW82nloDIjIaOKSqmwBU9TCQKCKdQvi5GNMgW+7btBVlqjqigdde8X5diecDH+BCYKqI1CWMJKCn9/sPVPWQ9/tzgP949zDYJyLzAVRVReRZ4AYReRpP4vimn/fuChR4vx+NJ9F87NmLCTeetYAA/g0sEZG7OLF5qc4BoBtwsIEyGtNkliCMgQrv1xq++p0Q4ApV3eB7oreZpyTI+z6NZzOicjxJpNrPOWV4kk/de36gqic1F6nqThHZCpwPXMFXNZU6Sd57GRM21sRkjH9zgDu826oiIqc1cN7HwBXevohsYGzdC6q6B9iDp9no6QauXwfker9fBpwtIrne90wVkQE+584G/gJsUdVddQe9MXYBtjWlgMYEYgnCtBX1+yACjWL6DZAAfCkia7zP/XkZz7aTa4HngM+AIp/Xnwd2quq6Bq5/G29SUdUC4FvAbBH5Ek/z0iCfc/8DDOXk5qWRwLIGaijGhMyW+zammbwjjYq9ncTLgbNVdZ/3tUeBz1X1qQauTcbToX22qoa02b2I/BV4Q1U/DK0ExvhnfRDGNN9bItIeT6fyb3ySw0o8/RV3NXShqpaJyH1Ad2BHiO+/2pKDiQSrQRhjjPHL+iCMMcb4ZQnCGGOMX5YgjDHG+GUJwhhjjF+WIIwxxvhlCcIYY4xf/x9XQMerjNRhWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energies = gd157.energy['294K']\n",
    "total_xs = total.xs['294K'](energies)\n",
    "plt.loglog(energies, total_xs)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reaction Data\n",
    "\n",
    "Most of the interesting data for an `IncidentNeutron` instance is contained within the `reactions` attribute, which is a dictionary mapping MT values to `Reaction` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<Reaction: MT=1 (n,total)>,\n",
      " <Reaction: MT=101 (n,disappear)>,\n",
      " <Reaction: MT=301 heating>,\n",
      " <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"
     ]
    }
   ],
   "source": [
    "pprint(list(gd157.reactions.values())[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's suppose we want to look more closely at the (n,2n) reaction. This reaction has an energy threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 6400881.0 eV\n"
     ]
    }
   ],
   "source": [
    "n2n = gd157[16]\n",
    "print('Threshold = {} eV'.format(n2n.xs['294K'].x[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (n,2n) cross section, like all basic cross sections, is represented by the `Tabulated1D` class. The energy and cross section values in the table can be directly accessed with the `x` and `y` attributes. Using the `x` and `y` has the nice benefit of automatically acounting for reaction thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x1461b4d594e0>}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6400881.0, 20000000.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = n2n.xs['294K']\n",
    "plt.plot(xs.x, xs.y)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')\n",
    "plt.xlim((xs.x[0], xs.x[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x1461b4a66908>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neutron = n2n.products[0]\n",
    "neutron.distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the neutrons emitted have a correlated angle-energy distribution. Let's look at the `energy_out` attribute to see what the outgoing energy distributions are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.stats.univariate.Tabular at 0x1461b4d59550>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d59630>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d596d8>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d59828>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d59a20>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d59c88>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d59f98>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d63358>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d63748>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d63ba8>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d6b0b8>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d6b588>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4d6bb00>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4aaa0f0>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4aaa710>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4aaad68>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ab1470>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ab1b70>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ab92b0>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ab9a20>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ac1208>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ac19e8>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ac9240>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ac9a90>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4acf358>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4acfc18>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ad7470>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ad7d30>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ae0668>,\n",
       " <openmc.stats.univariate.Tabular at 0x1461b4ae0f98>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = neutron.distribution[0]\n",
    "dist.energy_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see we have a tabulated outgoing energy distribution for each incoming energy. Note that the same probability distribution classes that we could use to create a source definition are also used within the `openmc.data` package. Let's plot every fifth distribution to get an idea of what they look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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(top=1e-6)\n",
    "plt.legend()\n",
    "plt.xlabel('Outgoing energy (eV)')\n",
    "plt.ylabel('Probability/eV')\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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross section(b)')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cm = matplotlib.cm.Spectral_r\n",
    "\n",
    "# Determine size of probability tables\n",
    "urr = gd157.urr['294K']\n",
    "n_energy = urr.table.shape[0]\n",
    "n_band = urr.table.shape[2]\n",
    "\n",
    "for i in range(n_energy):\n",
    "    # Get bounds on energy\n",
    "    if i > 0:\n",
    "        e_left = urr.energy[i] - 0.5*(urr.energy[i] - urr.energy[i-1])\n",
    "    else:\n",
    "        e_left = urr.energy[i] - 0.5*(urr.energy[i+1] - urr.energy[i])\n",
    "\n",
    "    if i < n_energy - 1:\n",
    "        e_right = urr.energy[i] + 0.5*(urr.energy[i+1] - urr.energy[i])\n",
    "    else:\n",
    "        e_right = urr.energy[i] + 0.5*(urr.energy[i] - urr.energy[i-1])\n",
    "        \n",
    "    for j in range(n_band):\n",
    "        # Determine maximum probability for a single band\n",
    "        max_prob = np.diff(urr.table[i,0,:]).max()\n",
    "        \n",
    "        # Determine bottom of band\n",
    "        if j > 0:\n",
    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
    "            value = (urr.table[i,0,j] - urr.table[i,0,j-1])/max_prob\n",
    "        else:\n",
    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
    "            value = urr.table[i,0,j]/max_prob\n",
    "\n",
    "        # Determine top of band\n",
    "        if j < n_band - 1:\n",
    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
    "        else:\n",
    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
    "    \n",
    "        # Draw rectangle with appropriate color\n",
    "        ax.add_patch(Rectangle((e_left, xs_bottom), e_right - e_left, xs_top - xs_bottom,\n",
    "                     color=cm(value)))\n",
    "\n",
    "# Overlay total cross section\n",
    "ax.plot(gd157.energy['294K'], total.xs['294K'](gd157.energy['294K']), 'k')\n",
    "\n",
    "# Make plot pretty and labeled\n",
    "ax.set_xlim(1.0, 1.0e5)\n",
    "ax.set_ylim(1e-1, 1e4)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Energy (eV)')\n",
    "ax.set_ylabel('Cross section(b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting HDF5 data\n",
    "\n",
    "If you have an instance `IncidentNeutron` that was created from ACE or HDF5 data, you can easily write it to disk using the `export_to_hdf5()` method. This can be used to convert ACE to HDF5 or to take an existing data set and actually modify cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd157.export_to_hdf5('gd157.h5', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With few exceptions, the HDF5 file encodes the same data as the ACE file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
    "np.all(gd157[16].xs['294K'].y == gd157_reconstructed[16].xs['294K'].y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And one of the best parts of using HDF5 is that it is a widely used format with lots of third-party support. You can use `h5py`, for example, to inspect the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<HDF5 group \"/Gd157/reactions/reaction_016/294K\" (1 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/product_0\" (2 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/product_1\" (2 members)>]\n"
     ]
    }
   ],
   "source": [
    "n2n_group = main_group['reaction_016']\n",
    "pprint(list(n2n_group.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see that the hierarchy of data within the HDF5 mirrors the hierarchy of Python objects that we manipulated before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.000000e+00, 3.026796e-13, 1.291101e-02, 6.511110e-02,\n",
       "       3.926270e-01, 5.752268e-01, 6.969600e-01, 7.399378e-01,\n",
       "       9.635450e-01, 1.142130e+00, 1.308020e+00, 1.463500e+00,\n",
       "       1.557600e+00, 1.640550e+00, 1.688960e+00, 1.711400e+00,\n",
       "       1.739450e+00, 1.782070e+00, 1.816650e+00, 1.845280e+00,\n",
       "       1.865409e+00, 1.867240e+00, 1.881558e+00, 1.881560e+00,\n",
       "       1.881800e+00, 1.894470e+00, 1.869570e+00, 1.821200e+00,\n",
       "       1.716000e+00, 1.600540e+00, 1.431620e+00, 1.283460e+00,\n",
       "       1.101660e+00, 1.065300e+00, 9.307300e-01, 8.029800e-01,\n",
       "       7.777400e-01])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n_group['294K/xs'][()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with ENDF files\n",
    "\n",
    "In addition to being able to load ACE and HDF5 data, we can also load ENDF data directly into an `IncidentNeutron` instance using the `from_endf()` factory method. Let's download the ENDF/B-VII.1 evaluation for $^{157}$Gd and load it in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download ENDF file\n",
    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/Gd/157'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'gd157.endf')\n",
    "\n",
    "# Load into memory\n",
    "gd157_endf = openmc.data.IncidentNeutron.from_endf(filename)\n",
    "gd157_endf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as before, we can get a reaction by indexing the object directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic = gd157_endf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we look at the cross section now, we see that it isn't represented as tabulated data anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0K': <openmc.data.function.ResonancesWithBackground at 0x1461b11824a8>}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you had [Cython](https://cython.org/) installed when you built/installed OpenMC, you should be able to evaluate resonant cross sections from ENDF data directly, i.e., OpenMC will reconstruct resonances behind the scenes for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998.7871174521487"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.xs['0K'](0.0253)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When data is loaded from an ENDF file, there is also a special `resonances` attribute that contains resolved and unresolved resonance region data (from MF=2 in an ENDF file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.resonance.ReichMoore at 0x1461b1fcb898>,\n",
       " <openmc.data.resonance.Unresolved at 0x1461b2692ba8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_endf.resonances.ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that $^{157}$Gd has a resolved resonance region represented in the Reich-Moore format as well as an unresolved resonance region. We can look at the min/max energy of each region by doing the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1e-05, 306.6), (306.6, 54881.1)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(r.energy_min, r.energy_max) for r in gd157_endf.resonances.ranges]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With knowledge of the energy bounds, let's create an array of energies over the entire resolved resonance range and plot the elastic scattering cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross section (b)')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zJ9a4HU7W8OREOe/nB8uL9T0F4D4AfwEQcC4cIopHb2Z6dNlnONLSjjKOZrIk0gfh0H4QFi+76fOjMZ9bv+cI3tu6H2fUZnyLnZisNjH5lVL3KqU+VEqt1G92BiIiX9Ym5j0hIufbeW2irmTGuOpQM9MGNjNZ5VQTU2SP6xT6IEyOPb1qd1rx2M1qgnhRRP5DRKpFpLd+S/QiEXlIROpFZF3U8QtFZLOIbBWRWwFAGz57LYDrAXwl6d+EqJuYOLAn+mvNTGSNU8t9R18/udd4v43JaoL4N4T6IN4DsFK7rbDwukcQ2ss6TERyEJqh/SWE9re+XERGG075ifY8EZnQm5ne/mQ/Gps5mskKh3ccTXjZLMgFpqxOlDvB5DbUwuveAnAw6vApALYqpbYppdoA/BPARRLyGwAvK6VWJfuLEHUnM8azmSkVjo1iSuG6H9c12h+IzaxuOZonIjeJyNPa7QYRSbV3bACAXYbHu7VjNwI4D8B8Ebk+TizXicgKEVnR0NCQYghE2W3iwJ4Y0LMIL3EJcEv05hy9ielYqx/H29LfgCk8kzpBHcLs+Xc+2Z/2+zvNahPTvQAmA7hHu03WjtlGKXWXUmqyUup6pdR9cc57QCk1RSk1pW/fvnaGQJQ1RAQzxlXh7U8a2MyUBP2b/pjbXsHY216x/bpOe2tLA97cnLmlVqwmiJOVUv+mlFqs3a4GcHKK71kHYKDhcY12jIiSMGNcNdoDCq+xmSkhfbSRcS0mO5qbrK7FZFcC+fpDH+Kqh5fbczELrCaIgIicqD8QkaFIfT7EcgDDROQEEckHcBmABclcQERmi8gDjY3eb8MjcspJWjPTwrV73A4la7i1mmu2spogvg9giYi8KSJLASwG8L1ELxKRxwEsAzBCRHaLyDVKKT+AGwC8AmAjgCeVUuuTCVop9aJS6rry8vJkXkbUpYgIZk/oj7c/2Y8DTa1uh5MdumY57hhLM6mVUm+IyDAAI7RDm5VSCf9FKqUuj3F8EYBFlqMkIlNzJw7AfUs/xcK1e/FvZwxxOxzPc2pHuZTmQdgfhu0SbTk6Tfs5D8BMALXabaZ2zBVsYiIKGVFVilHVZXhuNbvxrHBqR7lMv2+mJGpiOkf7OdvkNsvBuOJiExNRxNyJ/bFm12Fs33/M7VA8TwFo8wftv65NCcBqwsmURFuO3qbd/aVSarvxORE5wbGoiMiyi04agF+/vAnPr67DzdOHux2OpwWVQqvfvvVG05kHkQ2sdlI/Y3LsaTsDIaLUVJYVYuqJffD8mrqsWN/HTUo50/afyjBXs7qC2bLgbkrUBzFSRC4GUC4i8wy3qwAUZiRC87jYB0Fk8OWJA/DZgeNYtfOw26F4nLK1PyD1tVy7QCc1QqOWZgHoiY79D5MAXOtsaLGxD4KoowvGVKIwz4fn2VkdV1DBkZI5Xs2tPRBEc3t2bqOTqA/iBQAviMjpSqllGYqJiJJUWpiH6aOrsHDtHvx01mjk53I3YTO2j2LS2oTiXXbePe9lxcJ8Zqz+K7peRHrqD0Skl4g85FBMRJSCuRP749Dxdry1hYtYxqK0/2VStiYHwHqCGK+UCjduKqUOAZjoTEhElIqzhvVF75J8zokwofcVBJUzcxK66tgAqwnCJyK99AfabnJW97O2HTupiTrLy/HhopP647UN+3DoWJvb4XiKXn4r5dieco5cFQCa2wL4tKHJsevHYzVB3AlgmYj8SkR+hdDOcr91Lqz42ElNZO4rJw9EWyCI59ewFmFGKXu3+gyPYnKwBnHj46vwxTuXdpjgl6kvAFZ3lPsbgHkA9mm3eUqpR50MjIiSN7KqDONryvHE8l2cE2EQGY7qTA3CyU/6na2hjYUChvXJb312rYPvGJHMUIfeAI4ppf4MoIEzqYm86ZIpA7Hp86NYV3fE7VA8x46cuWXfUTz0jrawhMX9IOxgTG1HmtPfDc8Kq1uO3gbghwB+pB3KA/B3p4IiotTNmdAfBbk+PLliV+KTuxk7Oqln3fUOfrlwQ4djTtbW9PWZjG+RqZFYVmsQcwHMAXAMAJRSewCUOhVUIuykJoqtvCgPXxpbhefX1KElSydoOSXUSZ1e4doWCIavlc5Maqv05TecWqo8HqsJok2FUqQCABEpcS6kxNhJTRTfpVMG4miLH6+s/9ztUDxFhf/Phmsp8/t2Mw7RzcT7GVlNEE+KyP0AeorItQBeB/AX58IionScNrQCA3sX4YnlbGYCYOgrsK9xJqiUYSa1PVc1rtV3x8ubsHhTZL9xYzNWpuoSVneU+72ITAdwBKH1mX6mlHrN0ciIKGU+n+CSyQPxh9e2YOeB4xhUUex2SO7SSlRl40S5oMOl9H1LP8V9S4EeBbkZeT8zVjupSwAsVkp9H6GaQ5GI5DkaGRGlZf7kGvgE+OfynW6H4hnplLGbPz+KC//4VvhxsGOvsWP0WkUgmJn3M7LaxPQWgAIRGQDgXwCuBPCIU0ERUfr69yzCtJGVeGL5Lls3yclKho7eVJuD7nx1MzZ9fjT8ON38YLkmY2gei7yft0YxiVLqOEKT5e5VSl0CYIxzYSUIhqOYiCy58vTBOHCsDf9a1707q31aX0EwmPp+ENGb+QSNo5gy3Em9fMch597QwHKCEJHTAVwB4CXtWI4zISXGUUxE1pxV2wdDKorx6LLP3A7FVT6tlA0EU//uHb1fdDDNb/TJvsbLw1y/g9AkueeUUutFZCiAJc6FRUR28PkEXzttMFZ8dggb9nTfmdV6DcIfVLZNagsGDXtSp3DJoMVeZ32kVCDq/EwspWJ1Laa3lFJzlFK/0R5vU0rd5GxoRGSH+ZNrUJDrw98/6L61CL2QDSr7thwNpjnsNBBMfA4QOwllYlQTt50i6uJ6FudjzoT+eH51HY60tLsdjiv0JiZ/GqWqeR+EvgxG8te12mQU6YPoeH4mmpyYIIi6gStPH4zjbQE8t6p7LgNu7KQ2SqeZJt1Rp8kW8NHnRzc5OYEJgqgbGF/TExNqyvHX93ZYbvvuSnJ8xj6IyPFkyujoGkS6fQBWC3hj85iRZ2oQIvJbESkTkTwReUNEGkTka04HR0T2+caZJ2Db/mNYsrne7VBcExrFFClY0ylkO5TvqXRSW3yN2TBXwFs1iPOVUkcAzAKwA0AtgO87FRQR2W/GuGpUlxfiL29vczuUjNO/7QeiahDJlLFmw1zDHcipDHO12gcRYzXXoMVO7nRYTRD6mk0zATyllHJ1hhonyhElLy/Hh6unDsH72w5iXV33+m9HTwTR37qTqkGYdFLrUqmIJFsDiE4IAa80MQFYKCKbAEwG8IaI9AXQ4lxY8XGiHFFqLjtlEEryc/B/3awWoX/Dj54ol0Z+6FBgp5QgTF4U3c9hfGfP9kEopW4FcAaAKUqpdoQ2DrrIycCIyH5lhXn4ysmDsHDtXuxtbHY7nIzRy9LoiXLp9UGkNw/C6lvHbmLySIIQkUsAtCulAiLyE4S2G+3vaGRE5Iirpw5BUCk88u4Ot0PJGL0oDUbtB5FMM42IWR9E6vMgLI9iCr9f1Ou9UoMA8FOl1FERORPAeQAeBHCvc2ERkVMG9i7GjHHV+McHO3H4eJvb4WSEXoBHD3MNBNybB2G1gI9Vg/DSKCZ9reCZAB5QSr0EIN+ZkIjIad/+Qi2aWv145L0dboeSEXrZGkoIkYK1PYmhQNHdAx32pE6hrE621hF9vpdGMdVpW45+BcAiESlI4rVE5DGjqsswfXQlHnpnO452g+U39KI1+lu736YaRCp1COtNTPpifVGv91AT06UAXgFwgVLqMIDe4DwIoqx207RhONLix9+6wVLgseZBpNNMk+4oomQX6/PyKKbjAD4FcIGI3ACgn1LqVUcjIyJHjaspx7kj+uLBd7bjeJvf7XAcZZwHYSxW262W0jBfrE+X2jyI2O9tbE6KuVifV/ogROQ7AP4BoJ92+7uI3OhkYETkvBun1eLgsTY89kHX3rc6Vg0imdVdzeZBRGZSJy/ee5slnOhjXmpiugbAqUqpnymlfgbgNADXOhcWEWXC5MG9ccaJFbhv6ac41tp1axHhPoiotZiSq0HE2VHO5pnUxmdibRjkpVFMgshIJmj3Tef8EVF2+d75I7C/qQ0Pv7vd7VAc03GiXOR4un0Q6azFFL8G0fk5L6/F9DCAD0Tk5yLycwDvIzQXwhVci4nIPpMH98L00ZW4f+k2HDrWNedFRJqYOpaq7UmMYurUxKQiI4xSyTPWaxDaMa82MSml/gDgagAHtdvVSqk/OhlYgni4FhORjb5/wQg0tflx79JP3Q7FEXpR2hYIduyDSKKJqdM1lQrvMxGvwzkWq30Qbo5iyk10gojkAFivlBoJYJXjERFRxg2vLMW8iTV45L0duHrqEFSXF9n+Hk+u2IWivBzMnpD5VXr0srTNH+zQHJTUFqSdRjEBuVqCSKYmoos7iskQY2QehAdHMSmlAgA2i8ggx6MhItfcPH0YoIA7X93iyPV/8PRa3Pj4akeunYj+bbvVH0x5FJPZNXNz9ASRQg0iRlLZ39SKET/5V/hxpAbR8TwvdVL3ArBe201ugX5zMjAiyqyaXsW4euoQPL1yNz7addjtcGwVbmLydyzI/YEglFK4b+mn2Hck/g4GZhsG5eX4tOskX1hHx6K/x4Y9RzocO6j1C3UaxeSFJibNTx2Ngog84YZptXhmVR1+/uJ6PPutMzoN7cxaWlna6u/cSb21vgl3vLwJr23Yh2e+dYblSwaDxiam5GsQLe0B0+PRH/nRltDw41Z/x/NdH8UkIrUiMlUptdR4Q2iY627nwyOiTCotzMMPLhyB1TsP44U1e9wOxzZ6m35bVBNTIKjCTTdHmuOvSWU2kzpXq0Gk0gfRYlKDADrXVHTRyc0Lo5j+COCIyfFG7Tki6mLmT6rB+Jpy/PrljV1m8lzQUIMwdgDvOdwMX4xRQtE6D3NVyNP6IKJHQ/kDwU5NRdHMahAisXaVA1qjzvfCWkyVSqmPow9qx4Y4EhERucrnE9w2ewz2HWnFH1+3v8M6E6NvoqlwJ3WgQw1i1c5D8PlSm8sQVAo+rTRvDyos33EQR7SVcX//6hbMuOttfLLvaMzXR9cIdLEa9aLPP3d43+QCTkGiBNEzznP2j4MjIk+YPLgXvnrqIDz4znZ8vNveCanNMdrenWTspDbmgcbmduTEWMoiWvQ3e2PH9NGWdlxy3zJ8868rAACrdx4CADQ0tca8Xsz3i1WDMCSIX100JiP9Q4kSxAoR6bTmkoh8E8BKZ0IiIi/44YUjUdGjALc+uzatCWXR3Gi2UsYmJkMVoqnVH3MiWiJths9EL7zX1XVMprH6E+KJ9Rpjk9TpJ1Ykfd1UJEoQ3wVwtYi8KSJ3arelCC3e9x3nwyMit5QX5eEXc8Zg/Z4jeMjGdZqaMpwg9ISQl9NxUltpYS6aWvzhpqVETV/RBbexNqIPWbWjWyBmH0SHJqbMjC6LO8xVKbUPwBki8gUAY7XDLymlFjseGRG57ktjq3DeqEr84bUtOG9UJYb27ZH2NY+1ZraJSS+0C3Jz0B7wh4eLlhbk4mirP9zUk2wfRLtJDUIfWZROnojdBxH53DI1+tjqWkxLlFJ/0m5MDkTdhIjgv748FgW5Obj5yY9saWrKeA1C+1mYFyruWtpDv0NpYR6aWvyRhfwSjWKKKpTb/MHwxdu0wjt6FdZkC/L3tx3A8zGGFze3RT77TM1O4b7SRBRXVXkhbp87Fh/tOoy7l6S/mF+m+yD0voWC3BwAkW/ipYW5aG4PhPsSzJbYNoou7I1NPnrSCV8ixSrEjgPH8fiH5ps3HT4eWWk3UxMYmSCIKKFZ4/tj7sQBuGvxJ2kvw3HweGaXFNcLbb0G0RquQYRa2I80hxJW4rWNOhbK7YHI5kP6lq3RtRA7i/H9cUZEOcUzCUJEhorIgyLytNuxEFFnP58zBpWlBfjOP1eHx/unouFoZgs6vQZRlB+qQbSEaxB5AIBXN3wOIPnF74xrKelJxsm5axv2xp945wRHE4SIPCQi9SKyLur4hSKyWUS2isitAKCU2qaUusbJeIgodeVFefjfyydi16Fm/PDptQmbZKLpM5YTLYpnt3ANQm9iiqpBPPzuDgDJHMaQAAAWHklEQVSJO6k79UEEIpPuGmMs02HlE9JHVyViXM6jq/RBPALgQuMBbX+JuwF8CcBoAJeLyGiH4yAiG5w8pDd+eOEIvLzu83DBaoVSkTWPMp0g9BpEYZ7eBxHppDY7zypjgR2dIPSmJyvX1BOXFzmaIJRSbyG0A53RKQC2ajWGNgD/BHCRk3EQkX2uPWsopo+uxH8v2oiVnx2y9Bpj8029S01MkQQRamIqK8o1Pc8q48J/sWaHW1lxtawoL/FJLnGjD2IAgF2Gx7sBDBCRChG5D8BEEflRrBeLyHUiskJEVjQ0NDgdKxFFERH8/pIJqO5ZiBseW2Wp89S4MU/9kUwniNDP6GGuvYrzO56XoDCPbtaJXn7bSE8cVlZcvf/KyQnP6RSLl+ZBZIJS6oBS6nql1IlKqV/HOe8BpdQUpdSUvn2dX6yKiDorL8rDvVdMxsFjbbjhsVUJ50dEvsX7sLexOTzqJxP0vpIirQahL1nRq7jjN/f2JDdYONYa6LAyrBkrCxMO6FmEIRXFcc/R953INDcSRB2AgYbHNdoxIsoiYweU4/a54/D+toP4zb82xT1Xr0GMr+mJoAI27o29yqnd9DK6pCDUpNTcpjUxRfVBJPqy33kjn9gjufRLWRkZJQI8+e+nxz0nna1R0+FGglgOYJiInCAi+QAuA5DU9qUiMltEHmhstHeVSSJKzvzJNfj66YPxl7e348WPYm8wFNA6dE8aGFogev2ezP23q9deSgpCNYhjWu0l2clm0WsxNbX6EyYVKwW7QNCvrND0uRnjqkLnCPDTWaM7vCYTnB7m+jiAZQBGiMhuEblGKeUHcAOAVwBsBPCkUmp9MtdVSr2olLquvLzc/qCJKCk/mTkakwf3wg+eXovNn5vXDPS2+JpeRagqK8SyTw9kLD69EC/O71iDEAFumz066tzYBXqsrUDjidXxbXVoq77ndY/8XMwcV23pNXZyehTT5UqpaqVUnlKqRin1oHZ8kVJquNbfcLuTMRCRs/JzfbjniknoUZiLf390hemcAL2pJccnOG90P7y5uSHmnsx20wv9HloTU7gGgdCwXaNkJvE1tfoTznOI1cSkj6gKBxKDniCKC3JQVW5ey3CSZzqpiSh7VZYV4p4rJmH3oWbc8sSaTp2zekGZ6xPMGt8fze0BPLMqM9va66Hk5/qQlyM4Hq5BCPr0KOhw7vo4s5Wjy/F4iw7qSSlWDcKYIOK1dOkrxpZotZ+BvTO7T1tWJgj2QRB5z8lDeuOns0bjjU31+NPirR2ei9QgfDj1hN6YUFOO+5du67BchVP0QtonoZFM+mKBIkDvko5DXRPtI20UWgk2UjMx851/rjE9rg+5TWRgr2KMHVCG/543znJcdsrKBME+CCJv+vrpgzFv4gD88Y0tWLxpX/i4P5wgQt/cvzt9OHYePI4/L9ka61K20ROEiKCkIDdSg0CoVmG0fEf0vN6I6E5tf1Ch1R9Anx75nc5N1PRUZKxBxDkvP9eHhTeehdOGhnaQ02s8uRb7MNKVlQmCiLxJRHD73HEYVVWG//fUWjQeD/VHGGsQAPCFEf0wd+IA3L1kK5Zsrnc0Jr2VxyeCovyccIKIdsqQ3nhv64G4w1ejHWnxhzu/dc0xrm/UsYkpVNiv/Ml5OCNqK9HoFqr7r5yM3148Hv17ZqapiQmCiGxVlJ+D310yHoePt+F/Xt8CwJAgDN/C/+vLYzGyqhTf/scqfLDNuVFNxiam4vyc8CS96Lb/WROq0RYIYuHavQmvqdc8mlraO11nb2NzwtcXmtQgKnoUoCCqRhM9Ea9faSEuPXkgMiUrEwT7IIi8bUz/clw6ZSAe+3AnGo62dhjFpCspyMVDV52MqvJCfP2hD/GvdZ87EkvQUIMozss1LLLXsWSfPLgXRlWX4a/v7TAd7mpMBPokO7OhrjsOHIs5P0JPAB1GMRlce9bQOL9J5mVlgmAfBJH3XXf2ULQHgnh02Y4Oo5iMKssK8fT1Z2BkdRmu//tK/HrRxg57Pdsh0gcR2RNCf2yMKccnuHrqEGz6/Khps5c+5BQAemrLdDQ2h2oQxt9rW8OxmLHo1yg01BSMieeM2j4dzndyfwkrsjJBEJH3De3bA2cN64vn1+yBX1vnKMdkTaHeJfl44rrTcMWpg3D/W9tw6f3LsLXevqU49EJWRFCc37lpR+/w9Ylg7sQBGNS7GL9/ZUunobrGgry3ttCfvnR4uWFF1h0HjsUcuqr//nnGBBGnm9rl/MAEQUTOmTG2CjsPHse6ulBzsFmCAEJNLrfPHYe7Lp+I7fuPYcb/voM/L/7EltqEMg5zze/cOax/q/dJ6P4t04djw94jWLQuqi/CUFr3KokkBIGE13kSAbbWN3WobRjpM6jzc8xrEF6TlQmCfRBE2eGcEaEVl5dpndCxEoRuzoT+eO3mczB9TCV+/+oWzP7TO1iT5h7YHfog8ju3/euFuZ4wZk/ojxGVpfjDq1s6rFJr/DYfvVS4ft2RVWX4ZF9TzDrB/qbQftzb9sduhurA5TamrEwQ7IMgyg7V5aG1l1bsCG0slChBAEDf0gLc/dVJuP/KyTh0vA1z73kXP1+wPu7M5Xg6jmKKDEnVI8kzNDHpMd48fTi27T+GBYYFCI0d14V5OeHJbiKRlWIH9y7GgWNtCZfs+ChO0htfEynX2MRERF3aSQN7hneRS2ZfgwvGVOG1W87BlacNxl+X7cD0PyzFaxv2JXxdNONEuSKTJS5yfZEmJt35oysxqroMf1q8NVyLMH6ZFwF6FkVqEXoNoqZXaH6C5RoCOjcxLbjhTNwyfXin93QDEwQROWp4ZY/wfV+SG9+UFebhlxeNxTPfOgNlhXm49m8rcP2jK5Pa11rFaGLSO4f1GoRxYT2fT3DTtFps338Mb2wKjWgyltUCCY9kEgBTBocW/Tt1aMeJbqnySrcEEwQROWpIn5Lw/VR3Rps0qBcW3nQmfnDhCCzZXI/z7lyKxz7YGXd5bl30RDmd/s1d74OIzI8ImT66Ev1KC/Dk8tAOyca38knHkUs3TqvF89+eivNG9QsnDqvij2JiHwQRdWHGZSF8aQzZycvx4T/OrcWrN5+N8QPL8Z/PfYzvPflRwqUtjJ3URfmdF9aLJIiOI6Zyc3yYP7kGSzbXd9p32+eTSIIQgc8nOGlgT4gIBveOv31oNLOPRD/GJqYUcBQTUfboWxpZUtuOReYGV5Tg0W+cilumD8dza+pwyf3vdSrAjcJLbkfVIHQ3a+39g032hb5gTBWCCvjVwg1YZ9gFTxB7FdeaZBOE2TGPjH3NygTBUUxE2aNDgkixiSmazye46YvD8H9fn4Kt9U24/IH3UX/UvF8iMg9CTGdSTx9diR13zERpYeemoTH9y1BamIsX1uzBh9sPGl5rmPsQ9ZrqGNuHpoKjmIioSys1fNNOp4nJzBdHVeLhq05B3eFmXPPICtNd6iKd1EBxXudO6nhyc3zhfbSNfBLa5c1Mb5Plv6NNGhS5ZrzaApuYiKhLMxaA+pBSO51+YgXuumwi1u1pxH8++3Gn5ztOlDPMg7CYq4ZUlHQ6JhLaJxpAeBkRXZ+Sgk7nR/vrN06JXMvkeY+0MDFBEFHmOJAfAADnja7ETdOG4dnVdXhlfcdVYRMt1pdIdc/OTUY+QxNT9P4S0bvUGZ02NDQc1ticFS8OjmIioi5v/uQaFOb50LM4cfNLqm6YVouRVaX45Ysb0OqPFNpBQx+EWSd1IoW5nV8T6oMIHT/e2jFBlMTZgvR38ydgxx0zE75nuPmLTUzJ4ygmouzyu/njsfa2C+Lu35yuvBwf/nPGKNQdbsbjH+wMH080US6RApP9o43blbZFDY+Nl4TMh7R2PsgmpjRwFBNRdhGRTvs/O+GsYX0weXAvPPLejvBy3cGYq7lau6bZyCufCPJzQtdq81tPEMniKCYiIpuICL522iDsOHAc730aWkFW76QWkY7LbCdxzWg+iV2DKIpbg7D2rvpZVmaKO4kJgoi6lC+NrUZZYS6eW10HoGMntbGAtlqDMBuaK8YEEVWDKIqxnWgyOJOaiMgBhXk5mDayHxZv2gd/INhholxHyX2b73AsqjZiVGyynEdy72i9f8RpTBBE1OVMG1WJQ8fbsW7PkQ4T5VJhNjQ3GFQx+1QKTTq1kzVrQjX6lhbgitMGp32tdDBBEFGXc9oJofkGy7cf7DBRziidJqb2oEJBjAQhInjs2lNNn7PaYlRdXoTlPz4PJ/TpPEkvk5ggiKjL6VdWiMEVxfhwx8EOfRBGVisUZUWd12jyB4JxR2WdcWKf8D4TRsGg2+OSksMEQURd0oSantiw50h4I6Do7U6tjig6d3hf/G7++A7H/EEVsw8ifH2P9COkIysTBCfKEVEiI6tLUXe4ObwUeHSBXmJxvoKI4JIpAzsca09Qgwi9LvRzomFhvqDbw5KSlJUJghPliCiRUdVlAIC1u0NfJAuihp+aLe9tlT8Qu5NapyeI2788Lnwsy/JDdiYIIqJEhleWAgDW1YUSRHQNIp3RRv6ghRqE1sTUuyQfQ7TNiLIsPzBBEFHXVFVWiPwcHz5taAKATgV6Oru2tQcs9EFol/cZJuixiYmIyANyfIKa3kVoD4QK5VjDUlPhDwQtdFJrP0VMZ0b/9uLxppsReYlzSysSEblscO9ibGs4BiDSxPTU9aej/kjsPaytaA8q+BLMvNNrDT4xX1vp0pMH4tKTB5q80juYIIioyxpcUQKgAXk5Ei7QTx7SO+3rtketv2RGTwo+kfBku+xqYGITExF1YQN7hzqH9WYmuwSsTHjTMoRIZESVHQv5ZRJrEETUZQ3WEoTd2i0kiHCtQQG/uXg8vnrqoHDCyhasQRBRlzW4wqEEYaWJybBraFF+Dk4bWuFILE5igiCiLsvOb+wvf+cs/M9XJgAALjslcedyb23/7WxecCMrm5hEZDaA2bW1tW6HQkQeVpiXg6m1FfjCiH5pX2tUdRlGVZdh7sQaS+f/7ZpTsHhTPXqV5Kf93m4Rt7e0S8eUKVPUihUr3A6DiLqpIbe+BADYccdMlyNJjoisVEpNSXReVtYgiIi84JozT0CxxUX/shETBBFRin46a7TbITiKndRERGSKCYKIiEwxQRARkSkmCCIiMsUEQUREppggiIjIFBMEERGZYoIgIiJTWb3Uhog0APhMe1gOoDHO/eiffQDsT+LtjNe0+nz0MTdjtCO+6FjzkowvEzF69e8c7xj/zt76O8d6riv9nXsqpfomjEAp1SVuAB6Id9/k54pUr2/1+ehjbsZoR3zRMSYbXyZi9OrfOUGs/Dt76O8c67mu+HdOdOtKTUwvJrgf/TOd61t9PvqYmzHaEZ/xvldj9OrfOZs+Q+N9r8aY6fjMjneFv3NcWd3ElA4RWaEsrGboJq/H6PX4AMZoB6/HBzBGp3SlGkSyHnA7AAu8HqPX4wMYox28Hh/AGB3RbWsQREQUX3euQRARURxMEEREZIoJgoiITDFBmBCRc0XkbRG5T0TOdTseMyJSIiIrRGSW27GYEZFR2uf3tIh8y+14zIjIl0XkLyLyhIic73Y8ZkRkqIg8KCJPux2LTvu391fts7vC7XjMePFzM8qGf3tAF0wQIvKQiNSLyLqo4xeKyGYR2Soitya4jALQBKAQwG4PxgcAPwTwpJ2x2RmjUmqjUup6AJcCmOrRGJ9XSl0L4HoAX/FojNuUUtfYHVu0JGOdB+Bp7bOb43RsqcSYqc8tjfgc/bdnm2RnSHr9BuBsAJMArDMcywHwKYChAPIBfARgNIBxABZG3foB8GmvqwTwDw/GNx3AZQCuAjDLi5+h9po5AF4G8FWvxqi97k4Akzwe49Me+u/mRwBO0s55zMm4Uo0xU5+bDfE58m/Prlsuuhil1FsiMiTq8CkAtiqltgGAiPwTwEVKqV8DiNdEcwhAgdfi05q9ShD6j7VZRBYppYJeilG7zgIAC0TkJQCP2RWfXTGKiAC4A8DLSqlVdsZnV4yZkkysCNWqawCsQQZbIZKMcUOm4tIlE5+IbISD//bs0uWamGIYAGCX4fFu7ZgpEZknIvcDeBTAnx2ODUgyPqXUj5VS30Wo0P2LnckhjmQ/w3NF5C7tc1zkdHCapGIEcCOA8wDMF5HrnQzMINnPsUJE7gMwUUR+5HRwUWLF+iyAi0XkXqS+jIRdTGN0+XMzivUZuvFvL2ldrgZhB6XUswj9R+BpSqlH3I4hFqXUmwDedDmMuJRSdwG4y+044lFKHUCondozlFLHAFztdhzxePFzM8qGf3tA96lB1AEYaHhcox3zCq/HBzBGu2RDjLpsiNXrMXo9vri6S4JYDmCYiJwgIvkIdfAucDkmI6/HBzBGu2RDjLpsiNXrMXo9vvjc7iW3+wbgcQB7AbQj1N53jXZ8BoAtCI0o+DHjY4yMMbti9XqMXo8vlRsX6yMiIlPdpYmJiIiSxARBRESmmCCIiMgUEwQREZligiAiIlNMEEREZIoJgroFEQmIyBrDzcqS6hkhoT0zhsZ5/jYR+XXUsZO0Bd8gIq+LSC+n46TuhwmCuotmpdRJhtsd6V5QRNJey0xExgDIUdpqnzE8js57BlymHQdCi0r+R7qxEEVjgqBuTUR2iMgvRGSViHwsIiO14yXaBjAfishqEblIO36ViCwQkcUA3hARn4jcIyKbROQ1EVkkIvNFZJqIPG94n+ki8pxJCFcAeMFw3vkiskyL5ykR6aGU2gLgkIicanjdpYgkiAUALrf3kyFigqDuoyiqicn4jXy/UmoSgHsB/D/t2I8BLFZKnQLgCwB+JyIl2nOTAMxXSp2D0O5qQxDam+NKAKdr5ywBMFJE+mqPrwbwkElcUwGsBAAR6QPgJwDO0+JZAeAW7bzHEao1QEROA3BQKfUJACilDgEoEJGKFD4Xopi43Dd1F81KqZNiPKcv7b4SoQIfAM4HMEdE9IRRCGCQdv81pdRB7f6ZAJ5SoT05PheRJQCglFIi8iiAr4nIwwgljq+bvHc1gAbt/mkIJZp3Q3sZIR/AMu25JwC8JyLfQ8fmJV09gP4ADsT4HYmSxgRBBLRqPwOI/DchAC5WSm02nqg18xyzeN2HEdpQpwWhJOI3OacZoeSjv+drSqlOzUVKqV0ish3AOQAuRqSmoivUrkVkGzYxEZl7BcCN2rakEJGJMc57F6Hd1XwiUgngXP0JpdQeAHsQajZ6OMbrNwKo1e6/D2CqiNRq71kiIsMN5z4O4H8AbFNK7dYPajFWAdiRzC9IlAgTBHUX0X0QiUYx/QpAHoC1IrJee2zmGYSWdt4A4O8AVgFoNDz/DwC7lFIbY7z+JWhJRSnVAOAqAI+LyFqEmpdGGs59CsAYdG5emgzg/Rg1FKKUcblvojRpI42atE7iDwFMVUp9rj33ZwCrlVIPxnhtEUId2lOVUoEU3/9/ASxQSr2R2m9AZI59EETpWygiPRHqVP6VITmsRKi/4nuxXqiUahaR2xDayH5niu+/jsmBnMAaBBERmWIfBBERmWKCICIiU0wQRERkigmCiIhMMUEQEZEpJggiIjL1/wHR9W468kvsQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-spaced array of energies\n",
    "resolved = gd157_endf.resonances.resolved\n",
    "energies = np.logspace(np.log10(resolved.energy_min),\n",
    "                       np.log10(resolved.energy_max), 1000)\n",
    "\n",
    "# Evaluate elastic scattering xs at energies\n",
    "xs = elastic.xs['0K'](energies)\n",
    "\n",
    "# Plot cross section vs energies\n",
    "plt.loglog(energies, xs)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resonance ranges also have a useful `parameters` attribute that shows the energies and widths for resonances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>energy</th>\n",
       "      <th>L</th>\n",
       "      <th>J</th>\n",
       "      <th>neutronWidth</th>\n",
       "      <th>captureWidth</th>\n",
       "      <th>fissionWidthA</th>\n",
       "      <th>fissionWidthB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0314</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.1072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.8250</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.0970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.2400</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.0910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.7700</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.012800</td>\n",
       "      <td>0.0805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.5600</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.011360</td>\n",
       "      <td>0.0880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.6500</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3300</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.1700</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44.2200</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.0960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
       "0   0.0314  0  2.0      0.000474        0.1072            0.0            0.0\n",
       "1   2.8250  0  2.0      0.000345        0.0970            0.0            0.0\n",
       "2  16.2400  0  1.0      0.000400        0.0910            0.0            0.0\n",
       "3  16.7700  0  2.0      0.012800        0.0805            0.0            0.0\n",
       "4  20.5600  0  2.0      0.011360        0.0880            0.0            0.0\n",
       "5  21.6500  0  2.0      0.000376        0.1140            0.0            0.0\n",
       "6  23.3300  0  1.0      0.000813        0.1210            0.0            0.0\n",
       "7  25.4000  0  2.0      0.001840        0.0850            0.0            0.0\n",
       "8  40.1700  0  1.0      0.001307        0.1100            0.0            0.0\n",
       "9  44.2200  0  2.0      0.008960        0.0960            0.0            0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolved.parameters.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heavy-nuclide resonance scattering\n",
    "\n",
    "OpenMC has two methods for accounting for resonance upscattering in heavy nuclides, DBRC and RVS. These methods rely on 0 K elastic scattering data being present. If you have an existing ACE/HDF5 dataset and you need to add 0 K elastic scattering data to it, this can be done using the `IncidentNeutron.add_elastic_0K_from_endf()` method. Let's do this with our original `gd157` object that we instantiated from an ACE file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd157.add_elastic_0K_from_endf('gd157.endf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check to make sure that we have both the room temperature elastic scattering cross section as well as a 0K cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x1461b4d2ff60>,\n",
       " '0K': <openmc.data.function.Tabulated1D at 0x1461b0e59128>}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157[2].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating data from NJOY\n",
    "\n",
    "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://www.njoy21.io/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n",
    "\n",
    "Let's use `IncidentNeutron.from_njoy()` to run NJOY to create data for $^2$H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " njoy 2016.49  25Jan19                                       07/19/19 06:12:49\n",
      " *****************************************************************************\n",
      "\n",
      " reconr...                                                                0.0s\n",
      "\n",
      " broadr...                                                                0.1s\n",
      "      300.0 deg                                                           0.1s\n",
      "      400.0 deg                                                           0.2s\n",
      "      500.0 deg                                                           0.3s\n",
      "\n",
      " heatr...                                                                 0.3s\n",
      "\n",
      " gaspr...                                                                 0.6s\n",
      "\n",
      " purr...                                                                  0.7s\n",
      "\n",
      " mat =  128                                                               0.7s\n",
      "\n",
      " ---message from purr---mat  128 has no resonance parameters\n",
      "                          copy as is to nout\n",
      "\n",
      " acer...                                                                  0.7s\n",
      "\n",
      " acer...                                                                  1.0s\n",
      "\n",
      " acer...                                                                  1.1s\n",
      "                                                                          1.2s\n",
      " *****************************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Download ENDF file\n",
    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/H/2'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'h2.endf')\n",
    "\n",
    "# Run NJOY to create deuterium data\n",
    "h2 = openmc.data.IncidentNeutron.from_njoy('h2.endf', temperatures=[300., 400., 500.], stdout=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use our `h2` object just as we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'300K': <openmc.data.function.Tabulated1D at 0x1461b066ebe0>,\n",
       " '400K': <openmc.data.function.Tabulated1D at 0x1461b23a0978>,\n",
       " '500K': <openmc.data.function.Tabulated1D at 0x1461b1009400>,\n",
       " '0K': <openmc.data.function.Tabulated1D at 0x1461b101b358>}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2[2].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that 0 K elastic scattering data is automatically added when using `from_njoy()` so that resonance elastic scattering treatments can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Windowed multipole\n",
    "\n",
    "OpenMC can also be used with an experimental format called windowed multipole.  Windowed multipole allows for analytic on-the-fly Doppler broadening of the resolved resonance range.  Windowed multipole data can be downloaded with the `openmc-get-multipole-data` script.  This data can be used in the transport solver, but it can also be used directly in the Python API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://github.com/mit-crpg/WMP_Library/releases/download/v1.1/092238.h5'\n",
    "filename, headers = urllib.request.urlretrieve(url, '092238.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "u238_multipole = openmc.data.WindowedMultipole.from_hdf5('092238.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `WindowedMultipole` object can be called with energy and temperature values.  Calling the object gives a tuple of 3 cross sections: elastic scattering, radiative capture, and fission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(9.13284265), array(0.50530278), array(2.9316765e-06))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u238_multipole(1.0, 294)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array can be passed for the energy argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1461b0ed9978>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.linspace(5, 25, 1000)\n",
    "plt.semilogy(E, u238_multipole(E, 293.606)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real advantage to multipole is that it can be used to generate cross sections at any temperature.  For example, this plot shows the Doppler broadening of the 6.67 eV resonance between 0 K and 900 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1461b09755c0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.linspace(6.1, 7.1, 1000)\n",
    "plt.semilogy(E, u238_multipole(E, 0)[1])\n",
    "plt.semilogy(E, u238_multipole(E, 900)[1])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}