OpenMC_NEA_Training_nov25/course/live_notebooks/NuclearData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nuclear Data\n",
    "\n",
    "In this section, 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 ENDF and ACE files. Most importantly, the package provides a mean to generate HDF5 nuclear data libraries that are used by OpenMC's transport solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import openmc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235.043928117"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_mass('U235')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07589"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.NATURAL_ABUNDANCE['Li6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Li6', 0.07589), ('Li7', 0.92411)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.isotopes('Li')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.011115164865895"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_weight('C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.ATOMIC_NUMBER['Mo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mo'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.ATOMIC_SYMBOL[42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 109, 0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.zam('Ag109')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 242, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.zam('Am242_m1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.32843734145104"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.half_life('H3') / (365 * 24 * 3600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "energies, psv_cm2 = openmc.data.dose_coefficients('photon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.000e+04, 1.500e+04, 2.000e+04, 3.000e+04, 4.000e+04, 5.000e+04,\n",
       "       6.000e+04, 7.000e+04, 8.000e+04, 1.000e+05, 1.500e+05, 2.000e+05,\n",
       "       3.000e+05, 4.000e+05, 5.000e+05, 5.110e+05, 6.000e+05, 6.620e+05,\n",
       "       8.000e+05, 1.000e+06, 1.117e+06, 1.330e+06, 1.500e+06, 2.000e+06,\n",
       "       3.000e+06, 4.000e+06, 5.000e+06, 6.000e+06, 6.129e+06, 8.000e+06,\n",
       "       1.000e+07, 1.500e+07, 2.000e+07, 3.000e+07, 4.000e+07, 5.000e+07,\n",
       "       6.000e+07, 8.000e+07, 1.000e+08, 1.500e+08, 2.000e+08, 3.000e+08,\n",
       "       4.000e+08, 5.000e+08, 6.000e+08, 8.000e+08, 1.000e+09, 1.500e+09,\n",
       "       2.000e+09, 3.000e+09, 4.000e+09, 5.000e+09, 6.000e+09, 8.000e+09,\n",
       "       1.000e+10])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.85e-02, 1.56e-01, 2.25e-01, 3.12e-01, 3.50e-01, 3.69e-01,\n",
       "       3.89e-01, 4.11e-01, 4.43e-01, 5.18e-01, 7.47e-01, 1.00e+00,\n",
       "       1.51e+00, 2.00e+00, 2.47e+00, 2.52e+00, 2.91e+00, 3.17e+00,\n",
       "       3.73e+00, 4.49e+00, 4.90e+00, 5.60e+00, 6.12e+00, 7.48e+00,\n",
       "       9.75e+00, 1.17e+01, 1.34e+01, 1.50e+01, 1.51e+01, 1.78e+01,\n",
       "       2.05e+01, 2.61e+01, 3.08e+01, 3.79e+01, 4.32e+01, 4.71e+01,\n",
       "       5.01e+01, 5.45e+01, 5.78e+01, 6.32e+01, 6.72e+01, 7.23e+01,\n",
       "       7.54e+01, 7.74e+01, 7.87e+01, 8.04e+01, 8.16e+01, 8.37e+01,\n",
       "       8.50e+01, 8.66e+01, 8.78e+01, 8.86e+01, 8.91e+01, 8.99e+01,\n",
       "       9.04e+01])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psv_cm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff83f8a4dd0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(energies, psv_cm2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dose_tally = openmc.Tally()\n",
    "dose_tally.scores = ['flux']\n",
    "\n",
    "dose_filter = openmc.EnergyFunctionFilter(energies, psv_cm2)\n",
    "energy_filter = openmc.EnergyFilter(energies)\n",
    "dose_tally.filters = [dose_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resolve_paths': True, 'cross_sections': PosixPath('/home/ubuntu/data/endfb71_hdf5/cross_sections.xml'), 'chain_file': PosixPath('/home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml')}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "photon_dist = openmc.data.decay_photon_energy('Co58')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.199594e+02, 7.079362e+02, 7.260200e+02, 7.417410e+02,\n",
       "       6.349850e+03, 6.362710e+03, 7.015360e+03, 7.016950e+03,\n",
       "       7.070490e+03, 7.070660e+03, 5.109989e+05, 8.107592e+05,\n",
       "       8.639510e+05, 1.674725e+06])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photon_dist.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.27628864e-10, 1.37158094e-10, 3.75981860e-12, 1.17677572e-10,\n",
       "       8.77712399e-09, 1.71971670e-08, 1.05727163e-09, 2.07835104e-09,\n",
       "       5.20184412e-13, 7.59527050e-13, 3.37400092e-08, 1.12594029e-07,\n",
       "       7.76898797e-10, 5.85488948e-10])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "photon_dist.p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.stem(photon_dist.x, photon_dist.p)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `IncidentNeutron` Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of 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 start with a pregenerated HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "filename = 'Gd157.h5'\n",
    "gd157 = openmc.data.IncidentNeutron.from_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Cross sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "mt = 1\n",
    "total = gd157[mt]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Reaction: MT=1 (n,total)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7ff83d3675f0>}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_xs = total.xs['294K']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.0000e-05, 1.0325e-05, 1.0650e-05, ..., 1.9500e+07, 1.9900e+07,\n",
       "       2.0000e+07], shape=(12074,))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_xs.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.72315622e+06, 9.56895374e+06, 9.42186568e+06, ...,\n",
       "       5.11730931e+00, 5.09171761e+00, 5.08668897e+00], shape=(12074,))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_xs.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(total_xs.x, total_xs.y, 'k.')\n",
    "plt.xlim(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openmc.data.function.Tabulated1D at 0x7ff83d3675f0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1493.2739493045215)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_xs(2.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([142.64747021,   6.22687001, 759.86470045])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_xs([1.0, 10.0, 100.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "n2n = gd157.reactions[16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Reaction: MT=16 (n,2n)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6400881.,  6500000.,  6599146.,  7000000.,  7500000.,  7800000.,\n",
       "        8000000.,  8080608.,  8500000.,  9000000.,  9500000., 10000000.,\n",
       "       10500000., 11000000., 11500000., 12000000., 12500000., 13000000.,\n",
       "       13500000., 14000000., 14458300., 14500000., 14991700., 14991760.,\n",
       "       15000000., 15500000., 16000000., 16500000., 17000000., 17500000.,\n",
       "       18000000., 18500000., 19000000., 19100000., 19500000., 19900000.,\n",
       "       20000000.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.xs['294K'].x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff837b23860>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mPv89AxW1jSvNhnsp8fyYEAwLcuL1fDIbS+7tibSCChy7eAWzNx7D9nlDYK/g5U5zwUG3RAaqtl6D/yVexGf7LqC0Sg2gcRG32NHBGB3myqJCZqmoog73fnIAeeW1uCvUBaun9edihkaMg26JjJi6QYstf2Tj4z3pKKxonPHj72SLZ0cH457e7vzjTGbN2U6O/z7WDw+vSkT8uUIs33Ue82NCxI5FnYCFhchANGi02HYiBx/uTkNOWQ2AxhkSz0QH4YG+nrCQSUVOSGQY+nh1xVsP9sZzW/7EJ3vT0cPdHuP7uIsdizoYCwuRyLRaAT+fysOKXeeRUVwFAHCxk+NfowIxaYA35Bac8UP0d/f39cLZXBVW/56J+d/8CT8nW4R5cEiAKWNhIRLRqcvleOHbP3EuvwIA4GBjiadGBOIfg3w4NZnoFl4aG4pz+RX4Pa0Yc/53DD8+PRSOtlZix6IOwnPMRCLZm1qIyZ8n4lx+BezkFogdHYzfXxqF2Xf6s6wQtYKFTIqPp/ZFd0cbXL5Sg6e/Po4GjVbsWNRBWFiIRLD12CXM+uIYqtUaDAtywu8vjcS/7wpCF950kEgvXW2ssHpaf9hYyXDoQgne2JEidiTqICwsRJ1IEAR8uDsNL357EhqtgAfu8MTa6QPQ1YansYnaKsTNDssnRQAA1h/MwtZjl8QNRB2ChYWokzRotPjP96fwwe7zAIB5IwPw/sPhsLLgv4ZEt2tsLzc8c1cQAODV70/jRPYVkRNRe+NfSqJOUK1uwJz/JWHT0UuQSoDXJ/bCCzGhXPyNqB09c1cQxoS5Qq3R4on/JaFAVSt2JGpHLCxEHay4sg5TPz+MPecKIbeQYtU/+uGxQT5ixyIyOVKpBMsnRyDIpQsKK+rw5JdJqGvQiB2L2gkLC1EHyiquwoOfHcKfl8vhYGOJr2cPwpiebmLHIjJZXeQWWD2tP+wVFjiRXYaF28+IHYnaCQsLUQdJvlSGBz87hIsl1fB2tMa3cwejn4+D2LGITJ6vky0+eeQOSCXAlmOXcCi9WOxI1A5YWIg6QHxKAaZ+fhglVWr08rTHd3MHI8C5i9ixiMzGncHOukuvb+xIgVZr9Pf5NXssLETtbNPRbMzeeAw19RrcGeyMzXOi4GKnEDsWkdn5911BsJNb4EyuCtuTc8SOQ7eJhYWonQiCgOW7zmPBtlPQCsBD/bywdnp/LgZHJJJuXeR4amQgAOC931JRW88BuMaMhYWoHdRrtHjpu5P4KD4NAPDvUYF496E+sOQdlolENWOILzyUCuSW12LdwUyx49Bt4F9TottUVdeA2RuPYeuxy5BKgDfv743YMSFcY4XIACgsZXhhbAgA4NO9F1BSWSdyImorFhai21BUUYcpnx9GQmoRFJZSfP5YfzwS2V3sWET0F/eFe6KXpz0q6xp0Z0HJ+LCwELVRTlkNHvzsEE7llMPR1gqbZg9CdJir2LGI6G+kUgn+M64HAOCrI9m4UFQpciJqCxYWojbILavB1M8PI7u0Gt0dbfDd3MHo251rrBAZqsGBTrgr1AUNWgFv/3pO7DjUBiwsRHrKK6/BlKtlxaebDbY8MQh+TrZixyKiW3h5XCikEmDn2QIczSwVOw7piYWFSA/55bW6stLd0QabZg+Cu9Ja7FhE1ApBrnaYMrBxjNkbO1IgCFxMzpiwsBC1UmNZSdQttb9pziB4dGVZITImz0YHwdZKhj8vleHnk3lixyE9sLAQtUKBqhZTVx9GVkk1vByssWn2IHiyrBAZHRc7BZ4YHgAAeDvuHO/mbERYWIhuoVBVi6mfH0ZmcRU8uzaWFS8HG7FjEVEbzRrmB1d7OS5fqcHGQxfFjkOtpHdh2b9/PyZMmAAPDw9IJBJs3779ptsnJCRAIpFc98jPz2+23cqVK+Hr6wuFQoHIyEgcPXpU32hE7a5QVYspqw8j42pZ2TxnELwdWVaIjJmNlQWeH9O4mNzHe9JQVq0WORG1ht6FpaqqCuHh4Vi5cqVe+6WmpiIvL0/3cHFx0X1vy5YtiI2NxaJFi3D8+HGEh4cjJiYGhYWF+sYjajeFFY2XgTKKquChVLCsEJmQB+/wQqibHVS1Dfh4T7rYcagV9C4s48aNw9KlS3H//ffrtZ+Liwvc3Nx0D6n02o9evnw5Zs+ejRkzZiAsLAyrVq2CjY0N1q1bp288onZRVFGHR1YfwQVdWYliWSEyITKpBP+5u3ExuY2JWbhYUiVyIrqVThvDEhERAXd3d4wePRoHDx7UPa9Wq5GUlITo6OhroaRSREdHIzExscXXqqurg0qlavYgai+NZeUw0gsr4a5UYNOcQejejWWFyNTcGeyMO4OdUa8R8M5vqWLHoVvo8MLi7u6OVatW4bvvvsN3330Hb29vjBgxAsePHwcAFBcXQ6PRwNW1+ZLmrq6u141zabJs2TIolUrdw9vbu6PfBpmJ4so6PLrmMNIKK+Fmr8Cm2YPg042LwhGZqgXjQiGRAL+czEPSxStix6Gb6PDCEhISgieeeAL9+vXD4MGDsW7dOgwePBgffPBBm19zwYIFKC8v1z0uXbrUjonJXJVU1uHR1UdwvqASrvZybJozCL5cwZbIpPVwt8fD/bwAAG9yMTmDJsq05oEDByI9vXGQk5OTE2QyGQoKCpptU1BQADc3txb3l8vlsLe3b/Yguh0llXV4dM0RpBZUwMVOjs1zorjcPpGZiB0dAoWlFEkXr+C3My2f2SfxiVJYkpOT4e7uDgCwsrJCv379EB8fr/u+VqtFfHw8oqKixIhHZqa0So1H1xzBufzGsrJpDu8NRGRO3JQKzBnmDwB469dzUDdoRU5ELbHQd4fKykrd2REAyMzMRHJyMhwdHdG9e3csWLAAOTk52LhxIwBgxYoV8PPzQ8+ePVFbW4s1a9Zgz5492Llzp+41YmNjMX36dPTv3x8DBw7EihUrUFVVhRkzZrTDWyS6sSt/KSvOV8tKgHMXsWMRUSebMzwAXx+9hKySanx15CJmDPETOxL9jd6F5dixYxg5cqTu69jYWADA9OnTsWHDBuTl5SE7O1v3fbVajeeffx45OTmwsbFBnz59sHv37mavMXnyZBQVFWHhwoXIz89HREQE4uLirhuIS9SemspKSp4KTl3k2DSbZYXIXHWRW+C50UF45fvT+Cg+DQ/c4QWltaXYsegvJIIJjDBSqVRQKpUoLy/neBZqFUEQMGPDH0hILYJTFytsnjMIgS52YsciIhE1aLQY++HvSC+sxBPD/bFgXA+xI5k8fT6/eS8hMku/nSlAQmoRrGRSfDkrkmWFiGAhk+I/d4cCANYfzMLlK9UiJ6K/YmEhs1OtbsDrP58FAMy50x+hbjwrR0SNRoa4IMq/G9QNWrzHxeQMCgsLmZ2Ve9ORU1YDz67WmDcyUOw4RGRAJBIJXhnfeCloe3IuTl4uEzcQ6bCwkFnJKKrE5/szAAALJ4TB2komciIiMjS9PJV4oK8nAC4mZ0hYWMhsCIKART+eQb1GwIgQZ4wJ4yw0ImrZ8zEhsLKQ4nBGKXanFIodh8DCQmYk7nQ+fk8rhpVMisUTekIikYgdiYgMlGdXa8wc2rgWy//9fAY1ao3IiYiFhczCXwfaPjncn/cIIqJbmjcyEB5KBS6V1mBF/Hmx45g9FhYyC5/sSUdueS28HKwxdwQH2hLRrXWRW+D/7usFAFjzeybO5qpETmTeWFjI5F0oqsTq3xsH2i6a0JMDbYmo1aLDXHF3bzdotAIWbDsJjZYDcMXCwkImTRAELL460HZUqAuie7iIHYmIjMyiCT1hJ7fAn5fLsTExS+w4ZouFhUzar00DbS2kWDQhjANtiUhvrvYKvDSucQXc935LRW5ZjciJzBMLC5msqrq/DrQNgE83DrQlorZ5ZGB39PNxQJVag4U/nOHaLCJgYSGT9fGedORdHWj71IgAseMQkRGTSiVY9kBvWMok2J1SgN/O5IsdyeywsJBJSi+swJqrA20XT+gJhSUH2hLR7Ql2tcMTdzb+x8/CH85AVVsvciLzwsJCJqdpRdsGrYC7Ql0QzRVtiaidPD0qEH5OtiisqMO7cbw5YmdiYSGT88upPBxML7k60Lan2HGIyIQoLGV44/7GtVm+PHIRSReviJzIfLCwkEmp/MtA26dGBKB7NxuRExGRqRkc4ISH+nlBEID/bDsFdYNW7EhmgYWFTMrH8WkoUNWhu6MNnhzOgbZE1DFeubsHHG2tkFpQoVuYkjoWCwuZjPTCCqw9kAkAWHxvGAfaElGHcbC1wmv39AAAfBifhsziKpETmT4WFjIJgiBg4Q+NA22je7hiVCgH2hJRx5oY4YlhQU5QN2jxyvenuDZLB2NhIZPw88k8HLpQAvnVFW2JiDqaRCLB0om9ILeQ4tCFEnx3PEfsSCaNhYWMXmVdA5b+0jTQNhDejhxoS0Sdw6ebLZ6NDgYAvPHLWZRU1omcyHSxsJDR++gvA22fGO4vdhwiMjOzhvkh1M0OV6rr8cYvKWLHMVksLGTUzhdUYB0H2hKRiCxlUix7oDckEmDbiRwcSCsWO5JJYmEho9U40PY0GrQCRodxoC0RiadvdwdMG+QDAHhl+ynU1mtETmR6WFjIaP34Zy4OZ5RCbiHFwns40JaIxDU/JgRu9gpcLKnGR/FpYscxOSwsZJQq6xp014qfHsmBtkQkPjuFJZbc13g7kM/3Z+BcvkrkRKaFhYWM0qd701FYUQefbjaYfScH2hKRYYjp6YaYnq5o0Ap4+btT0Gi5Nkt7YWEho3P5SjXWXB1o+8rdPTjQlogMypJ7e6GL3ALJl8rw1ZGLYscxGSwsZHTejkuFukGLKP9uGB3GgbZEZFjclAq8ODYEAPBOXCryy2tFTmQaWFjIqCRdvIKf/syFRAK8Mr4HJBKJ2JGIiK7zaKQPIry7orKuAYt+PC12HJPAwkJGQxAE3Yq2D93hhV6eSpETERG1TCaVYNkDvWEhleC3MwXYf75I7EhGj4WFjMbPJ/NwIrsMNlYyzI8JETsOEdFN9XC3x2NRjWuzvL/rPG+OeJtYWMgo1NZr8Nav5wAATw4PgKu9QuRERES3NndEAKwtZfjzUhniUwrFjmPU9C4s+/fvx4QJE+Dh4QGJRILt27ffdPtt27Zh9OjRcHZ2hr29PaKiovDbb78122bx4sWQSCTNHqGhofpGIxO27mAmcspq4GavwOxhnMZMRMbBxU6B6YN9AQDLd52HltOc20zvwlJVVYXw8HCsXLmyVdvv378fo0ePxo4dO5CUlISRI0diwoQJOHHiRLPtevbsiby8PN3jwIED+kYjE1VUUYdP914AALw4NgTWVpzGTETG44k7/dFFboGzeSrEnckXO47RstB3h3HjxmHcuHGt3n7FihXNvn7zzTfxww8/4KeffkLfvn2vBbGwgJubm75xyAx8sPs8Kusa0MdLiYkRnmLHISLSi4OtFf451A8fxafhg13nEdPTDTIpZzjqq9PHsGi1WlRUVMDR0bHZ82lpafDw8IC/vz8effRRZGdn3/A16urqoFKpmj3INJ3LV2Hz0cbfhVfHh0HKf8mJyAjNHOoHpbUl0gor8dOfuWLHMUqdXljee+89VFZWYtKkSbrnIiMjsWHDBsTFxeGzzz5DZmYmhg0bhoqKihZfY9myZVAqlbqHt7d3Z8WnTiQIAt74JQVaARjXyw0D/RxvvRMRkQFSWltiztXbiKzYfR4NGq3IiYxPpxaWr7/+GkuWLMHWrVvh4uKie37cuHF4+OGH0adPH8TExGDHjh0oKyvD1q1bW3ydBQsWoLy8XPe4dOlSZ70F6kQJ54vwe1oxLGUSvDyOg7CJyLg9PtgXjrZWyCqpxrbjOWLHMTqdVlg2b96MWbNmYevWrYiOjr7ptl27dkVwcDDS09Nb/L5cLoe9vX2zB5mWBo1Wdzfmxwf7wqebrciJiIhuj63cAnOHBwAAPoxPQ12DRuRExqVTCsumTZswY8YMbNq0CePHj7/l9pWVlbhw4QLc3d07IR0Zok1Hs5FeWAkHG0s8PSpI7DhERO3iH4N84GInR05ZDbb+wasD+tC7sFRWViI5ORnJyckAgMzMTCQnJ+sGyS5YsADTpk3Tbf/1119j2rRpeP/99xEZGYn8/Hzk5+ejvLxct838+fOxb98+ZGVl4dChQ7j//vshk8kwderU23x7ZIzKa+rxwe40AMBzo4OhtLYUORERUfuwtpLh6VGBAIBP9qajtp5nWVpL78Jy7Ngx9O3bVzclOTY2Fn379sXChQsBAHl5ec1m+Hz++edoaGjAvHnz4O7urns888wzum0uX76MqVOnIiQkBJMmTUK3bt1w+PBhODs73+77IyP06d50lFapEeBsi6kDu4sdh4ioXU0e4A0PpQIFqjp8efii2HGMhkQwgZsbqFQqKJVKlJeXczyLkcsuqUb08n1Qa7RY93h/jAp1FTsSEVG723w0Gy9vOwWnLlbY98JI2Mr1XhbNJOjz+c17CZFBeSsuBWqNFsOCnDAyxOXWOxARGaEH+3nBp5sNiivV+CIxS+w4RoGFhQzGH1ml2HEqH1IJ8Mr4HpBIuEgcEZkmS5kUz9zVOKHgv/syoKqtFzmR4WNhIYOg1QpY+vNZAI3Xd0PdeGmPiEzbfRGeCHC2RXlNPdYdyBQ7jsFjYSGD8OOfufjzcjlsrWR4bnSw2HGIiDqcTCpB7OgQAMDa3zNRVq0WOZFhY2Eh0dWoNXg77hwA4KmRgXCxU4iciIioc4zr5YZQNztU1DXg8/0ZYscxaCwsJLo1v2cgr7wWnl2tMXOon9hxiIg6jVQqwfNjGs+yrD+YheLKOpETGS4WFhJVoaoWn+27AAB4cWwIFJYykRMREXWu6B4uCPdSoqZeg88SLogdx2CxsJCo3t95HtVqDSK8u+LecA+x4xARdTqJRILYq2dZvjx8EfnltSInMkwsLCSaM7nl2JrUeC+N1+4J4zRmIjJbdwY5YYCvA+oatFi5t+Ub/5o7FhYShSAIeOOXFAgCcE8fd/TzcRA7EhGRaCSSazOGNv+RjctXqkVOZHhYWEgU8SmFOHShBFYWUrw0NlTsOEREoosK6IYhgd1QrxHwcTzPsvwdCwt1ugaNFst+TQEA/HOIH7wdbURORERkGJrOsnx7/DKyiqtETmNYWFio0207kYMLRVXoamOJp0YGiB2HiMhg9PNxwMgQZ2i0Aj6MTxM7jkFhYaFOVdegwYe7G/8lfGpEAOwVliInIiIyLE1nWbYn5yCtoELkNIaDhYU61aYj2cgpq4GrvRzTonzFjkNEZHB6eykxtqcbBAFYsZtnWZqwsFCnqVY34JOr0/X+NSqIi8QREd3Ac6ODIZEAv5zKw5nccrHjGAQWFuo0jctOq9Hd0QaT+nuLHYeIyGCFuNlhQp/GxTQ/2HVe5DSGgYWFOkV5TT3+e3UJ/udGB8HKgr96REQ380x0EKQSYHdKIZIvlYkdR3T81KBOsXp/BlS1DQh27YJ7wz3FjkNEZPACnLvggTu8AADv70wVOY34WFiowxVV1GHdwUwAwPNjQiCTcgl+IqLWeOauIFhIJfg9rRhHM0vFjiMqFhbqcJ8mpKNarUG4lxJjwlzFjkNEZDS8HW0waUDjmL/3dqZCEASRE4mHhYU6VE5ZDb46nA0AeCEmlDc4JCLS079GBcLKQoqjmaU4mF4idhzRsLBQh/podxrUGi2i/BvvkUFERPpxV1rj0cjuAID3d5nvWRYWFuowF4oq8e3xywCA+TEhPLtCRNRGc0cEQGEpxYnsMuxNLRQ7jihYWKjDfLDrPDRaAdE9XNDPx0HsOERERsvFToHpg30BAO/vPA+t1vzOsrCwUIc4k1uOn0/mAWicGURERLfnyTsD0EVugTO5Kvx0MlfsOJ2OhYU6xPs7G1dmvDfcAz3c7UVOQ0Rk/BxsrfDkcH8AwDtxqait14icqHOxsFC7O5ZVij3nCiGTSvDc6GCx4xARmYyZQ/3hZq9ATlkNNhzKEjtOp2JhoXYlCALe+a1xRcZJ/b3g52QrciIiItNhbSXD/JjGy+wr96SjtEotcqLOw8JC7appNUYrCyn+NSpI7DhERCbngb6eCHO3R0VdAz6KTxM7TqdhYaF2IwgC3r16duWxQT7w6GotciIiItMjlUrw6vgeAIAvD19ERlGlyIk6BwsLtZvfzuTjVE45bK1keGpEgNhxiIhM1uBAJ4wKdUGDVsBbv54TO06nYGGhdqHRCnjv6sygmUP90K2LXORERESmbcG4UMikEuw8W4AjGaa/ZL/ehWX//v2YMGECPDw8IJFIsH379lvuk5CQgDvuuANyuRyBgYHYsGHDddusXLkSvr6+UCgUiIyMxNGjR/WNRiLafiIH6YWVUFpbYtad/mLHISIyeUGudphy9caIb+5IMfnF5PQuLFVVVQgPD8fKlStbtX1mZibGjx+PkSNHIjk5Gc8++yxmzZqF3377TbfNli1bEBsbi0WLFuH48eMIDw9HTEwMCgvNc/lhY6Nu0OKD3Y1nV+aOCIC9wlLkRERE5uHZ6GDYWsnw5+Vyk19MTiLcxl2UJBIJvv/+e0ycOPGG27z00kv45ZdfcPr0ad1zU6ZMQVlZGeLi4gAAkZGRGDBgAD755BMAgFarhbe3N/71r3/h5ZdfvmUOlUoFpVKJ8vJy2NtzkbLO9r/ELLz2wxk428mx/4WRsLaSiR2JiMhsfLInDe/tPA/PrtaIf344FJbG8zdYn8/vDh/DkpiYiOjo6GbPxcTEIDExEQCgVquRlJTUbBupVIro6GjdNn9XV1cHlUrV7EHiqFFr8NGedADAv0cFsqwQEXUyc1lMrsMLS35+PlxdXZs95+rqCpVKhZqaGhQXF0Oj0bS4TX5+fouvuWzZMiiVSt3D29u7w/LTzX2RmIWiijp4OVhj8oDuYschIjI75rKYnFHOElqwYAHKy8t1j0uXLokdySypauvxWcIFAMBz0cGwsjDKXyciIqNnDovJdfgnjJubGwoKCpo9V1BQAHt7e1hbW8PJyQkymazFbdzc3Fp8TblcDnt7+2YP6nxr9megvKYegS5dMLGvp9hxiIjMljksJtfhhSUqKgrx8fHNntu1axeioqIAAFZWVujXr1+zbbRaLeLj43XbkOEprqzDmgOZAID5Y4Ihk0pETkREZN5MfTE5vQtLZWUlkpOTkZycDKBx2nJycjKys7MBNF6umTZtmm77J598EhkZGXjxxRdx7tw5fPrpp9i6dSuee+453TaxsbFYvXo1vvjiC6SkpGDu3LmoqqrCjBkzbvPtUUf5LOECqtUa9PZUIqZny2fCiIioc5nyYnJ6F5Zjx46hb9++6Nu3L4DGstG3b18sXLgQAJCXl6crLwDg5+eHX375Bbt27UJ4eDjef/99rFmzBjExMbptJk+ejPfeew8LFy5EREQEkpOTERcXd91AXDIMuWU1+N/hiwCA+TEhkEh4doWIyBCY8mJyt7UOi6HgOiyd673fUvHJ3nQM9HXElicGsbAQERmQooo6jHh3L6rUGnw4JQL3RRjuGEODWoeFTIsgCPjxz8bVFP8R5cOyQkRkYJzt5Jh79Qa078SlorZeI3Ki9sHCQnpJvlSG7NJq2FjJEN3DRew4RETUAlNcTI6FhfTyQ3Lj2ZUxYa6wsbIQOQ0REbXE2kqGF0xsMTkWFmq1Bo0WP5/MAwDcG+EhchoiIrqZ+/+ymNyHV29Qa8xYWKjVEjNKUFxZBwcbSwwLchY7DhER3cRfF5P76kg2Lhj5YnIsLNRqP169HHR3b3dYyvirQ0Rk6P66mNzSn8+KHee28FOHWqW2XoO40403ozTkKXJERNTcK+N7wFImwd7UIuw5V3DrHQwUCwu1SkJqISrqGuCuVKC/j4PYcYiIqJUCnLvgn0P8AAD/99NZ1DUY5zRnFhZqlabZQfeGe0DK+wYRERmVp0cFwtlOjqySaqw7kCV2nDZhYaFbUtXWI/5cIQDODiIiMkZ2Cku8PDYUAPDJnjQUqGpFTqQ/Fha6pZ1nCqBu0CLQpQvC3HnrAyIiY3R/X0/07d4VVWoN3jbCuzmzsNAt/ZCcAwC4L9yDS/ETERkpqVSCxRN6QiIBtp3IQdLFUrEj6YWFhW6qqKIOB9OLAQATwnk5iIjImIV7d8XD/bwAAIt/PAuNEd3NmYWFbuqXk7nQCo2/5L5OtmLHISKi2/RCTCjs5BY4lVOOb45dEjtOq7Gw0E013Zn5Pp5dISIyCc52cjwTHQQAeOe3VJTX1IucqHVYWOiGskuqcTy7DFIJcE8fd7HjEBFRO5k+2BeBLl1QWqXGh7vTxI7TKiwsdEM/nWw8uxIV0A0u9gqR0xARUXuxlEmxaEIYAOCLxCykFVSInOjWWFjohq7NDuJS/EREpmZYkDPGhLlCoxWw+KczEATDHoDLwkItOpevwvmCSljJpIjp5SZ2HCIi6gCvjg+DlYUUB9NL8NsZw77PEAsLtahpKf6Roc5QWluKnIaIiDpC9242mDPMHwCw9JezqK033PsMsbDQdbRaAT9eLSy8MzMRkWl7amQA3OwVuHylBp/vzxA7zg2xsNB1jmdfQU5ZDbrILTAq1EXsOERE1IFsrCzwn/E9AACfJqQjt6xG5EQtY2Gh6zRdDhrT0xUKS5nIaYiIqKNN6OOOgb6OqK3X4s0dKWLHaRELCzVTr9Fix6k8ALwcRERkLiQSCRbdGwapBPj5ZB4OZ5SIHek6LCzUzMH0YpRUqdHN1gpDArqJHYeIiDpJTw8lHonsDgBY/OMZNGi0IidqjoWFmmkabDu+jzssZPz1ICIyJ8+PDoHS2hLn8iuw6Wi22HGa4ScS6dSoNfjtTD4A4L4I3juIiMjcONha4fkxwQCA93aex5UqtciJrmFhIZ095wpRpdbAy8Ead3R3EDsOERGJ4JGB3RHqZofymnq8YUADcFlYSKdpKf57wz0gkUhETkNERGKwkEnx+sRekEiAb5MuY/uJHLEjAWBhoavKq+uRkFoEgLODiIjM3QBfR/x7VBAA4JXvTyGjqFLkRCwsdFXcmTyoNVqEuNohxM1O7DhERCSyf98VhEH+jqhSa/D01ydEX7afhYUAXFss7l4OtiUiIgAyqQQfTukLR1srnM1TYZnI41lYWAiFqlokXl0k6N5wFhYiImrkaq/A8knhAID/Hb6I9ELxLg21qbCsXLkSvr6+UCgUiIyMxNGjR2+47YgRIyCRSK57jB8/XrfN448/ft33x44d25Zo1AY/ncyDIAD9fBzg7WgjdhwiIjIgI0Jc8NLYUHw5MxKBLl1Ey2Gh7w5btmxBbGwsVq1ahcjISKxYsQIxMTFITU2Fi8v1N8rbtm0b1Opr87hLSkoQHh6Ohx9+uNl2Y8eOxfr163Vfy+VyfaNRG/14dXYQ114hIqKWzB0RIHYE/c+wLF++HLNnz8aMGTMQFhaGVatWwcbGBuvWrWtxe0dHR7i5uekeu3btgo2NzXWFRS6XN9vOwYHrgHSGzOIq/Hm5HDKpBHf3dhc7DhERUYv0KixqtRpJSUmIjo6+9gJSKaKjo5GYmNiq11i7di2mTJkCW1vbZs8nJCTAxcUFISEhmDt3LkpKbnzjpbq6OqhUqmYPapuf/mwcbDsk0AlOXXhWi4iIDJNehaW4uBgajQaurq7Nnnd1dUV+fv4t9z969ChOnz6NWbNmNXt+7Nix2LhxI+Lj4/H2229j3759GDduHDSalqdQLVu2DEqlUvfw9vbW523QVYIgYHvT5SAOtiUiIgOm9xiW27F27Vr07t0bAwcObPb8lClTdP/cu3dv9OnTBwEBAUhISMBdd9113essWLAAsbGxuq9VKhVLSxucyVUho6gKcgspxvR0vfUOREREItHrDIuTkxNkMhkKCgqaPV9QUAA3N7eb7ltVVYXNmzdj5syZt/w5/v7+cHJyQnp6eovfl8vlsLe3b/Yg/f149XLQXT1cYKewFDkNERHRjelVWKysrNCvXz/Ex8frntNqtYiPj0dUVNRN9/3mm29QV1eHf/zjH7f8OZcvX0ZJSQnc3TkItKNotYJu/Mq94VyKn4iIDJves4RiY2OxevVqfPHFF0hJScHcuXNRVVWFGTNmAACmTZuGBQsWXLff2rVrMXHiRHTr1q3Z85WVlXjhhRdw+PBhZGVlIT4+Hvfddx8CAwMRExPTxrdFt/JHVinyymthp7DAiBBnseMQERHdlN5jWCZPnoyioiIsXLgQ+fn5iIiIQFxcnG4gbnZ2NqTS5j0oNTUVBw4cwM6dO697PZlMhpMnT+KLL75AWVkZPDw8MGbMGLz++utci6UD/XD17Mq4Xm5QWMpETkNERHRzEkEQBLFD3C6VSgWlUony8nKOZ2kFdYMWA9/cjbLqenw5MxJDg5zEjkRERGZIn89v3kvIDB1IL0JZdT2cusgRFdDt1jsQERGJjIXFDDXdmXlCuDtkUonIaYiIiG6NhcXM1NZrsOts47R03pmZiIiMBQuLmTmYXoxqtQYeSgUivLuKHYeIiKhVWFjMTNPZlegwV0gkvBxERETGgYXFjGi1AnanFAIARodxKX4iIjIeLCxmJPlyGYor62Ant0CkH2cHERGR8WBhMSNNl4OGhzjDyoL/1xMRkfHgp5YZ2X21sPByEBERGRsWFjORVVyFtMJKWEglGBHiInYcIiIivbCwmImmy0GR/o5QWluKnIaIiEg/LCxmYlfK1ctBPXg5iIiIjA8LixkorVLjWFYpgMb1V4iIiIwNC4sZ2HuuEFoB6OFuDy8HG7HjEBER6Y2FxQzs4uwgIiIyciwsJq62XoP9aUUAOH6FiIiMFwuLiUu8UIJqtQZu9gr08rQXOw4REVGbsLCYuJ26mx268GaHRERktFhYTFjjzQ6bxq+4iZyGiIio7VhYTNjJnHIUVdShi9wCg/wdxY5DRETUZiwsJmzX2XwAwPBgZ8gtZCKnISIiajsWFhO2+2whAE5nJiIi48fCYqKyS6qRWlABmVSCkbzZIRERGTkWFhO18+rloIG+jlDa8GaHRERk3FhYTFTT7CDeO4iIiEwBC4sJKqtW44+sKwCAMSwsRERkAlhYTNCec4XQaAWEutnB25E3OyQiIuPHwmKCri0Wx7MrRERkGlhYTExdgwb7UhtvdhjNmx0SEZGJYGExMYculKBKrYGrvRy9PZVixyEiImoXLCwmZnfTzQ57uEIq5c0OiYjINLCwmJC/3uyQ05mJiMiUsLCYkFM55ShQ1cHWSobBAd3EjkNERNRuWFhMSNPZlTt5s0MiIjIxbSosK1euhK+vLxQKBSIjI3H06NEbbrthwwZIJJJmD4VC0WwbQRCwcOFCuLu7w9raGtHR0UhLS2tLNLO26yynMxMRkWnSu7Bs2bIFsbGxWLRoEY4fP47w8HDExMSgsLDwhvvY29sjLy9P97h48WKz77/zzjv46KOPsGrVKhw5cgS2traIiYlBbW2t/u/ITF0qrca5/MabHY4K5c0OiYjItOhdWJYvX47Zs2djxowZCAsLw6pVq2BjY4N169bdcB+JRAI3Nzfdw9X12hkAQRCwYsUKvPrqq7jvvvvQp08fbNy4Ebm5udi+fXub3pQ5ajq70t/HAV1trEROQ0RE1L70KixqtRpJSUmIjo6+9gJSKaKjo5GYmHjD/SorK+Hj4wNvb2/cd999OHPmjO57mZmZyM/Pb/aaSqUSkZGRN3zNuro6qFSqZg9zx8tBRERkyvQqLMXFxdBoNM3OkACAq6sr8vPzW9wnJCQE69atww8//IAvv/wSWq0WgwcPxuXLlwFAt58+r7ls2TIolUrdw9vbW5+3YXLKqtU4mlUKgIWFiIhMU4fPEoqKisK0adMQERGB4cOHY9u2bXB2dsZ///vfNr/mggULUF5erntcunSpHRMbn4TUImi0AoJdu8Cnm63YcYiIiNqdXoXFyckJMpkMBQUFzZ4vKCiAm5tbq17D0tISffv2RXp6OgDo9tPnNeVyOezt7Zs9zBkvBxERkanTq7BYWVmhX79+iI+P1z2n1WoRHx+PqKioVr2GRqPBqVOn4O7uDgDw8/ODm5tbs9dUqVQ4cuRIq1/TnNU1aJCQ2jhDa3RY60ojERGRsbHQd4fY2FhMnz4d/fv3x8CBA7FixQpUVVVhxowZAIBp06bB09MTy5YtAwD83//9HwYNGoTAwECUlZXh3XffxcWLFzFr1iwAjTOInn32WSxduhRBQUHw8/PDa6+9Bg8PD0ycOLH93qmJOpxRiiq1Bi52cvThzQ6JiMhE6V1YJk+ejKKiIixcuBD5+fmIiIhAXFycbtBsdnY2pNJrJ26uXLmC2bNnIz8/Hw4ODujXrx8OHTqEsLAw3TYvvvgiqqqqMGfOHJSVlWHo0KGIi4u7boE5ut6us40Dk+/izQ6JiMiESQRBEMQOcbtUKhWUSiXKy8vNajyLIAiIWrYH+aparHu8P0aFcgwLEREZD30+v3kvISN2OkeFfFUtbKxkGBzgJHYcIiKiDsPCYsSaLgfdGeQMhSVvdkhERKaLhcWI7UppnB0UzenMRERk4lhYjNSl0mqk5KkglYA3OyQiIpPHwmKkdqdcvdmhryMcbXmzQyIiMm0sLEaqqbCM7sHLQUREZPpYWIxQeU09jmQ03uyQ41eIiMgcsLAYoYTUQjRoBQS6dIGfE292SEREpo+FxQjxZodERGRuWFiMjLpBi32pRQBYWIiIyHywsBiZwxklqKhrgFMXOSK8uoodh4iIqFOwsBiZ+Kuzg6J7uPBmh0REZDZYWIyIIAjYe/VyEBeLIyIic8LCYkQyi6uQXVoNS5kEgwN5s0MiIjIfLCxGJOHq2ZUBvo7oIrcQOQ0REVHnYWExIgnnGwvLiBBnkZMQERF1LhYWI1Gj1uBwRgkAYEQIx68QEZF5YWExEoczSqBu0MJDqUCQSxex4xAREXUqFhYjkZBaCAAYHuICiYTTmYmIyLywsBgJjl8hIiJzxsJiBDKLq3CxpHE68xBOZyYiIjPEwmIEmi4H9ffhdGYiIjJPLCxGoGn9FV4OIiIic8XCYuBq6zmdmYiIiIXFwCVmlKCuQQt3pQLBrpzOTERE5omFxcAlnGscvzKC05mJiMiMsbAYOE5nJiIiYmExaJzOTERE1IiFxYBxOjMREVEjFhYDxunMREREjVhYDBSnMxMREV3DwmKgOJ2ZiIjoGhYWA7XvL5eDOJ2ZiIjMXZsKy8qVK+Hr6wuFQoHIyEgcPXr0htuuXr0aw4YNg4ODAxwcHBAdHX3d9o8//jgkEkmzx9ixY9sSzWQ0DbgdHszLQURERHoXli1btiA2NhaLFi3C8ePHER4ejpiYGBQWFra4fUJCAqZOnYq9e/ciMTER3t7eGDNmDHJycpptN3bsWOTl5ekemzZtats7MgFZxVXIKqmGhVSCIYHdxI5DREQkOr0Ly/LlyzF79mzMmDEDYWFhWLVqFWxsbLBu3boWt//qq6/w1FNPISIiAqGhoVizZg20Wi3i4+ObbSeXy+Hm5qZ7ODg4tO0dmQDddGZfB9gpLEVOQ0REJD69CotarUZSUhKio6OvvYBUiujoaCQmJrbqNaqrq1FfXw9HR8dmzyckJMDFxQUhISGYO3cuSkpKbvgadXV1UKlUzR6m5NrqtrwcREREBOhZWIqLi6HRaODq6trseVdXV+Tn57fqNV566SV4eHg0Kz1jx47Fxo0bER8fj7fffhv79u3DuHHjoNFoWnyNZcuWQalU6h7e3t76vA2DVluvQeKFpunMXH+FiIgIADp1+dS33noLmzdvRkJCAhQKhe75KVOm6P65d+/e6NOnDwICApCQkIC77rrrutdZsGABYmNjdV+rVCqTKS2Hr05ndrNXIMTVTuw4REREBkGvMyxOTk6QyWQoKCho9nxBQQHc3Nxuuu97772Ht956Czt37kSfPn1uuq2/vz+cnJyQnp7e4vflcjns7e2bPUxF0+q2I0M5nZmIiKiJXoXFysoK/fr1azZgtmkAbVRU1A33e+edd/D6668jLi4O/fv3v+XPuXz5MkpKSuDu7q5PPJOw7+r4FU5nJiIiukbvWUKxsbFYvXo1vvjiC6SkpGDu3LmoqqrCjBkzAADTpk3DggULdNu//fbbeO2117Bu3Tr4+voiPz8f+fn5qKysBABUVlbihRdewOHDh5GVlYX4+Hjcd999CAwMRExMTDu9TeNwsaQKmcVVnM5MRET0N3qPYZk8eTKKioqwcOFC5OfnIyIiAnFxcbqBuNnZ2ZBKr/Wgzz77DGq1Gg899FCz11m0aBEWL14MmUyGkydP4osvvkBZWRk8PDwwZswYvP7665DL5bf59oxL0+UgTmcmIiJqTiIIgiB2iNulUqmgVCpRXl5u1ONZZqw/ir2pRXh5XCieHB4gdhwiIqIOpc/nN+8lZCBq6zVIzOB0ZiIiopawsBiII5mlqK3ndGYiIqKWsLAYiL3nGpfj592ZiYiIrsfCYiD26Zbj5+UgIiKiv2NhMQDNpzM7iR2HiIjI4LCwGICm6cz9fDidmYiIqCUsLAYgIbVp/ApXtyUiImoJC4vIOJ2ZiIjo1lhYRPbX6cyhbpzOTERE1BIWFpFduxzE6cxEREQ3wsIisn2pnM5MRER0KywsIsouqUYGpzMTERHdEguLiBLON14O4nRmIiKim2NhEVGC7nIQpzMTERHdDAuLSGrrNTh0oRgAx68QERHdCguLSI5yOjMREVGrsbCIpOly0PBgTmcmIiK6FRYWkTQNuOXlICIioltjYRHBpdJqZBRdnc4cxOnMREREt8LCIoKm1W3v8HGAPaczExER3RILiwgSuLotERGRXlhYOlnjdOard2cO5vorRERErcHC0smOZpaipl4DV3s5erhzOjMREVFrsLB0Mt3loGAXTmcmIiJqJRaWTsbpzERERPqzEDuAuait12D5rvOczkxERNQGLCydICVPhee2JONcfgUAYO6IAE5nJiIi0gMLSwfSaAWs+T0D7+88D7VGC6cuVnj7wT64q4er2NGIiIiMCgtLB7l8pRqxW//E0cxSAEB0D1e89WBvOHWRi5yMiIjI+LCwtDNBELDteA4W/3gGFXUNsLWSYdGEnni4vxdnBREREbURC0s7ulKlxn++P4VfT+cDAPr5OOCDSRHo3s1G5GRERETGjYWlnSSkFuLFb0+isKIOFlIJnhsdjCeHB0Am5VkVIiKi28XCcptq1Bq8uSMF/zt8EQAQ6NIFKyZHoJenUuRkREREpqNNC8etXLkSvr6+UCgUiIyMxNGjR2+6/TfffIPQ0FAoFAr07t0bO3bsaPZ9QRCwcOFCuLu7w9raGtHR0UhLS2tLtE7156UyjP/od11ZeXywL37+11CWFSIionamd2HZsmULYmNjsWjRIhw/fhzh4eGIiYlBYWFhi9sfOnQIU6dOxcyZM3HixAlMnDgREydOxOnTp3XbvPPOO/joo4+watUqHDlyBLa2toiJiUFtbW3b31kHatBo8eHuNDzw2SFkFFfB1V6O/80ciMX39oTCUiZ2PCIiIpMjEQRB0GeHyMhIDBgwAJ988gkAQKvVwtvbG//617/w8ssvX7f95MmTUVVVhZ9//ln33KBBgxAREYFVq1ZBEAR4eHjg+eefx/z58wEA5eXlcHV1xYYNGzBlypRbZlKpVFAqlSgvL4e9vb0+b0dvmcVVeG5LMpIvlQEA7unjjqUTe6GrjVWH/lwiIiJTo8/nt15jWNRqNZKSkrBgwQLdc1KpFNHR0UhMTGxxn8TERMTGxjZ7LiYmBtu3bwcAZGZmIj8/H9HR0brvK5VKREZGIjExscXCUldXh7q6Ot3XKpVKn7fRavUaLWas/0P3tQABxy+WoaZeAzuFBZZO7IV7wz04XZmIiKiD6VVYiouLodFo4OrafKVWV1dXnDt3rsV98vPzW9w+Pz9f9/2m5260zd8tW7YMS5Ys0Sd6mwgCcCC9+LrnBwd0w3sPh8Ojq3WHZyAiIiIjnSW0YMGCZmdtVCoVvL292/3nyKQSfDglotlzDjZWGBroBCmnKxMREXUavQqLk5MTZDIZCgoKmj1fUFAANze3Fvdxc3O76fZN/1tQUAB3d/dm20RERLT4mnK5HHJ5xy9xL5NKcF+EZ4f/HCIiIro5vWYJWVlZoV+/foiPj9c9p9VqER8fj6ioqBb3iYqKarY9AOzatUu3vZ+fH9zc3Jpto1KpcOTIkRu+JhEREZkXvS8JxcbGYvr06ejfvz8GDhyIFStWoKqqCjNmzAAATJs2DZ6enli2bBkA4JlnnsHw4cPx/vvvY/z48di8eTOOHTuGzz//HAAgkUjw7LPPYunSpQgKCoKfnx9ee+01eHh4YOLEie33TomIiMho6V1YJk+ejKKiIixcuBD5+fmIiIhAXFycbtBsdnY2pNJrJ24GDx6Mr7/+Gq+++ir+85//ICgoCNu3b0evXr1027z44ouoqqrCnDlzUFZWhqFDhyIuLg4KhaId3iIREREZO73XYTFEnbkOCxEREbUPfT6/27Q0PxEREVFnYmEhIiIig8fCQkRERAaPhYWIiIgMHgsLERERGTwWFiIiIjJ4LCxERERk8FhYiIiIyOCxsBAREZHB03tpfkPUtFivSqUSOQkRERG1VtPndmsW3TeJwlJRUQEA8Pb2FjkJERER6auiogJKpfKm25jEvYS0Wi1yc3NhZ2cHiURyw+1UKhW8vb1x6dIls77nEI9DIx6Ha3gsGvE4NOJxuIbHolFHHQdBEFBRUQEPD49mN05uiUmcYZFKpfDy8mr19vb29mb9i9eEx6ERj8M1PBaNeBwa8Thcw2PRqCOOw63OrDThoFsiIiIyeCwsREREZPDMqrDI5XIsWrQIcrlc7Cii4nFoxONwDY9FIx6HRjwO1/BYNDKE42ASg26JiIjItJnVGRYiIiIyTiwsREREZPBYWIiIiMjgsbAQERGRwTO5wrJy5Ur4+vpCoVAgMjISR48even2ZWVlmDdvHtzd3SGXyxEcHIwdO3Z0UtqOo+9xWLFiBUJCQmBtbQ1vb28899xzqK2t7aS0HWP//v2YMGECPDw8IJFIsH379lvuk5CQgDvuuANyuRyBgYHYsGFDh+fsaPoeh23btmH06NFwdnaGvb09oqKi8Ntvv3VO2A7Wlt+JJgcPHoSFhQUiIiI6LF9nactxqKurwyuvvAIfHx/I5XL4+vpi3bp1HR+2A7XlOHz11VcIDw+HjY0N3N3d8c9//hMlJSUdH7YDLVu2DAMGDICdnR1cXFwwceJEpKam3nK/b775BqGhoVAoFOjdu3eHf3aaVGHZsmULYmNjsWjRIhw/fhzh4eGIiYlBYWFhi9ur1WqMHj0aWVlZ+Pbbb5GamorVq1fD09Ozk5O3L32Pw9dff42XX34ZixYtQkpKCtauXYstW7bgP//5Tycnb19VVVUIDw/HypUrW7V9ZmYmxo8fj5EjRyI5ORnPPvssZs2aZfQf1voeh/3792P06NHYsWMHkpKSMHLkSEyYMAEnTpzo4KQdT99j0aSsrAzTpk3DXXfd1UHJOldbjsOkSZMQHx+PtWvXIjU1FZs2bUJISEgHpux4+h6HgwcPYtq0aZg5cybOnDmDb775BkePHsXs2bM7OGnH2rdvH+bNm4fDhw9j165dqK+vx5gxY1BVVXXDfQ4dOoSpU6di5syZOHHiBCZOnIiJEyfi9OnTHRdUMCEDBw4U5s2bp/tao9EIHh4ewrJly1rc/rPPPhP8/f0FtVrdWRE7hb7HYd68ecKoUaOaPRcbGysMGTKkQ3N2JgDC999/f9NtXnzxRaFnz57Nnps8ebIQExPTgck6V2uOQ0vCwsKEJUuWtH8gEelzLCZPniy8+uqrwqJFi4Tw8PAOzdXZWnMcfv31V0GpVAolJSWdE0oErTkO7777ruDv79/suY8++kjw9PTswGSdr7CwUAAg7Nu374bbTJo0SRg/fnyz5yIjI4Unnniiw3KZzBkWtVqNpKQkREdH656TSqWIjo5GYmJii/v8+OOPiIqKwrx58+Dq6opevXrhzTffhEaj6azY7a4tx2Hw4MFISkrSXTbKyMjAjh07cPfdd3dKZkORmJjY7LgBQExMzA2Pm7nQarWoqKiAo6Oj2FFEsX79emRkZGDRokViRxHNjz/+iP79++Odd96Bp6cngoODMX/+fNTU1IgdrVNFRUXh0qVL2LFjBwRBQEFBAb799luT+1tZXl4OADf9d16Mv5cmcfNDACguLoZGo4Grq2uz511dXXHu3LkW98nIyMCePXvw6KOPYseOHUhPT8dTTz2F+vp6o/3j1Jbj8Mgjj6C4uBhDhw6FIAhoaGjAk08+afSXhPSVn5/f4nFTqVSoqamBtbW1SMnE9d5776GyshKTJk0SO0qnS0tLw8svv4zff/8dFhYm8+dSbxkZGThw4AAUCgW+//57FBcX46mnnkJJSQnWr18vdrxOM2TIEHz11VeYPHkyamtr0dDQgAkTJuh9idGQabVaPPvssxgyZAh69ep1w+1u9PcyPz+/w7KZzBmWttBqtXBxccHnn3+Ofv36YfLkyXjllVewatUqsaN1qoSEBLz55pv49NNPcfz4cWzbtg2//PILXn/9dbGjkci+/vprLFmyBFu3boWLi4vYcTqVRqPBI488giVLliA4OFjsOKLSarWQSCT46quvMHDgQNx9991Yvnw5vvjiC7M6y3L27Fk888wzWLhwIZKSkhAXF4esrCw8+eSTYkdrN/PmzcPp06exefNmsaNcx2T+k8HJyQkymQwFBQXNni8oKICbm1uL+7i7u8PS0hIymUz3XI8ePZCfnw+1Wg0rK6sOzdwR2nIcXnvtNTz22GOYNWsWAKB3796oqqrCnDlz8Morr0AqNY9e6+bm1uJxs7e3N8uzK5s3b8asWbPwzTffXHfq1xxUVFTg2LFjOHHiBJ5++mkAjR/cgiDAwsICO3fuxKhRo0RO2Tnc3d3h6ekJpVKpe65Hjx4QBAGXL19GUFCQiOk6z7JlyzBkyBC88MILAIA+ffrA1tYWw4YNw9KlS+Hu7i5ywtvz9NNP4+eff8b+/fvh5eV1021v9PfyRp8z7cFkPomsrKzQr18/xMfH657TarWIj49HVFRUi/sMGTIE6enp0Gq1uufOnz8Pd3d3oywrQNuOQ3V19XWlpKnECWZ0q6moqKhmxw0Adu3adcPjZso2bdqEGTNmYNOmTRg/frzYcURhb2+PU6dOITk5Wfd48sknERISguTkZERGRoodsdMMGTIEubm5qKys1D13/vx5SKXSW36wmRJT/VspCAKefvppfP/999izZw/8/PxuuY8ofy87bDivCDZv3izI5XJhw4YNwtmzZ4U5c+YIXbt2FfLz8wVBEITHHntMePnll3XbZ2dnC3Z2dsLTTz8tpKamCj///LPg4uIiLF26VKy30C70PQ6LFi0S7OzshE2bNgkZGRnCzp07hYCAAGHSpElivYV2UVFRIZw4cUI4ceKEAEBYvny5cOLECeHixYuCIAjCyy+/LDz22GO67TMyMgQbGxvhhRdeEFJSUoSVK1cKMplMiIuLE+sttAt9j8NXX30lWFhYCCtXrhTy8vJ0j7KyMrHeQrvR91j8nanMEtL3OFRUVAheXl7CQw89JJw5c0bYt2+fEBQUJMyaNUust9Au9D0O69evFywsLIRPP/1UuHDhgnDgwAGhf//+wsCBA8V6C+1i7ty5glKpFBISEpr9O19dXa3b5u+fGwcPHhQsLCyE9957T0hJSREWLVokWFpaCqdOneqwnCZVWARBED7++GOhe/fugpWVlTBw4EDh8OHDuu8NHz5cmD59erPtDx06JERGRgpyuVzw9/cX3njjDaGhoaGTU7c/fY5DfX29sHjxYiEgIEBQKBSCt7e38NRTTwlXrlzp/ODtaO/evQKA6x5N73369OnC8OHDr9snIiJCsLKyEvz9/YX169d3eu72pu9xGD58+E23N2Zt+Z34K1MpLG05DikpKUJ0dLRgbW0teHl5CbGxsc0+0IxRW47DRx99JISFhQnW1taCu7u78OijjwqXL1/u/PDtqKVjAKDZ37+WPj+3bt0qBAcHC1ZWVkLPnj2FX375pUNzSq6GJSIiIjJYJjOGhYiIiEwXCwsREREZPBYWIiIiMngsLERERGTwWFiIiIjI4LGwEBERkcFjYSEiIiKDx8JCREREN7R//35MmDABHh4ekEgk2L59u177L168GBKJ5LqHra2tXq/DwkJEREQ3VFVVhfDwcKxcubJN+8+fPx95eXnNHmFhYXj44Yf1eh0WFiIiIrqhcePGYenSpbj//vtb/H5dXR3mz58PT09P2NraIjIyEgkJCbrvd+nSBW5ubrpHQUEBzp49i5kzZ+qVg4WFiIiI2uzpp59GYmIiNm/ejJMnT+Lhhx/G2LFjkZaW1uL2a9asQXBwMIYNG6bXz2FhISIiojbJzs7G+vXr8c0332DYsGEICAjA/PnzMXToUKxfv/667Wtra/HVV1/pfXYFACzaIzARERGZn1OnTkGj0SA4OLjZ83V1dejWrdt123///feoqKjA9OnT9f5ZLCxERETUJpWVlZDJZEhKSoJMJmv2vS5duly3/Zo1a3DPPffA1dVV75/FwkJERERt0rdvX2g0GhQWFt5yTEpmZib27t2LH3/8sU0/i4WFiIiIbqiyshLp6em6rzMzM5GcnAxHR0cEBwfj0UcfxbRp0/D++++jb9++KCoqQnx8PPr06YPx48fr9lu3bh3c3d0xbty4NuWQCIIg3Pa7ISIiIpOUkJCAkSNHXvf89OnTsWHDBtTX12Pp0qXYuHEjcnJy4OTkhEGDBmHJkiXo3bs3AECr1cLHxwfTpk3DG2+80aYcLCxERERk8DitmYiIiAweCwsREREZPBYWIiIiMngsLERERGTwWFiIiIjI4LGwEBERkcFjYSEiIiKDx8JCREREBo+FhYiIiAweCwsREREZPBYWIiIiMngsLERERGTw/h+Ix6jRavx3LwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = n2n.xs['294K']\n",
    "plt.plot(xs.x, xs.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "neutron = n2n.products[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Product: neutron, emission=prompt, yield=polynomial>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neutron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{2.0}$"
      ],
      "text/plain": [
       "Polynomial([2.], domain=[-1.,  1.], window=[-1.,  1.], symbol='x')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neutron.yield_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "openmc.data.function.Polynomial"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(neutron.yield_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = neutron.distribution[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6400880.,  6500000.,  7000000.,  7500000.,  8000000.,  8500000.,\n",
       "        9000000.,  9500000., 10000000., 10500000., 11000000., 11500000.,\n",
       "       12000000., 12500000., 13000000., 13500000., 14000000., 14500000.,\n",
       "       15000000., 15500000., 16000000., 16500000., 17000000., 17500000.,\n",
       "       18000000., 18500000., 19100000., 19500000., 19900000., 20000000.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openmc.stats.univariate.Tabular at 0x7ff837ba5ee0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_out = dist.energy_out[15]\n",
    "dist_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff8374584d0>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dist_out.x, dist_out.p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(1435801.2472724603)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_out.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting HDF5 Data\n",
    "\n",
    "If you have an instance of `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 dataset and modify cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd157.export_to_hdf5('Gd157_new.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with ENDF Files\n",
    "\n",
    "The `openmc.data` package can also read ENDF files in the same way as it does for ACE and HDF5 files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "filename = 'Gd157.endf'\n",
    "endf = openmc.data.IncidentNeutron.from_endf('Gd157.endf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: <Reaction: MT=1 (n,total)>,\n",
       " 2: <Reaction: MT=2 (n,elastic)>,\n",
       " 4: <Reaction: MT=4 (n,level)>,\n",
       " 16: <Reaction: MT=16 (n,2n)>,\n",
       " 17: <Reaction: MT=17 (n,3n)>,\n",
       " 22: <Reaction: MT=22 (n,na)>,\n",
       " 24: <Reaction: MT=24 (n,2na)>,\n",
       " 28: <Reaction: MT=28 (n,np)>,\n",
       " 41: <Reaction: MT=41 (n,2np)>,\n",
       " 51: <Reaction: MT=51 (n,n1)>,\n",
       " 52: <Reaction: MT=52 (n,n2)>,\n",
       " 53: <Reaction: MT=53 (n,n3)>,\n",
       " 54: <Reaction: MT=54 (n,n4)>,\n",
       " 55: <Reaction: MT=55 (n,n5)>,\n",
       " 56: <Reaction: MT=56 (n,n6)>,\n",
       " 57: <Reaction: MT=57 (n,n7)>,\n",
       " 58: <Reaction: MT=58 (n,n8)>,\n",
       " 59: <Reaction: MT=59 (n,n9)>,\n",
       " 60: <Reaction: MT=60 (n,n10)>,\n",
       " 61: <Reaction: MT=61 (n,n11)>,\n",
       " 62: <Reaction: MT=62 (n,n12)>,\n",
       " 63: <Reaction: MT=63 (n,n13)>,\n",
       " 64: <Reaction: MT=64 (n,n14)>,\n",
       " 65: <Reaction: MT=65 (n,n15)>,\n",
       " 66: <Reaction: MT=66 (n,n16)>,\n",
       " 67: <Reaction: MT=67 (n,n17)>,\n",
       " 68: <Reaction: MT=68 (n,n18)>,\n",
       " 69: <Reaction: MT=69 (n,n19)>,\n",
       " 70: <Reaction: MT=70 (n,n20)>,\n",
       " 71: <Reaction: MT=71 (n,n21)>,\n",
       " 72: <Reaction: MT=72 (n,n22)>,\n",
       " 73: <Reaction: MT=73 (n,n23)>,\n",
       " 74: <Reaction: MT=74 (n,n24)>,\n",
       " 75: <Reaction: MT=75 (n,n25)>,\n",
       " 76: <Reaction: MT=76 (n,n26)>,\n",
       " 77: <Reaction: MT=77 (n,n27)>,\n",
       " 78: <Reaction: MT=78 (n,n28)>,\n",
       " 79: <Reaction: MT=79 (n,n29)>,\n",
       " 80: <Reaction: MT=80 (n,n30)>,\n",
       " 81: <Reaction: MT=81 (n,n31)>,\n",
       " 82: <Reaction: MT=82 (n,n32)>,\n",
       " 83: <Reaction: MT=83 (n,n33)>,\n",
       " 84: <Reaction: MT=84 (n,n34)>,\n",
       " 85: <Reaction: MT=85 (n,n35)>,\n",
       " 86: <Reaction: MT=86 (n,n36)>,\n",
       " 87: <Reaction: MT=87 (n,n37)>,\n",
       " 88: <Reaction: MT=88 (n,n38)>,\n",
       " 89: <Reaction: MT=89 (n,n39)>,\n",
       " 91: <Reaction: MT=91 (n,nc)>,\n",
       " 102: <Reaction: MT=102 (n,gamma)>,\n",
       " 103: <Reaction: MT=103 (n,p)>,\n",
       " 107: <Reaction: MT=107 (n,a)>,\n",
       " 600: <Reaction: MT=600 (n,p0)>,\n",
       " 601: <Reaction: MT=601 (n,p1)>,\n",
       " 602: <Reaction: MT=602 (n,p2)>,\n",
       " 603: <Reaction: MT=603 (n,p3)>,\n",
       " 604: <Reaction: MT=604 (n,p4)>,\n",
       " 605: <Reaction: MT=605 (n,p5)>,\n",
       " 606: <Reaction: MT=606 (n,p6)>,\n",
       " 607: <Reaction: MT=607 (n,p7)>,\n",
       " 608: <Reaction: MT=608 (n,p8)>,\n",
       " 609: <Reaction: MT=609 (n,p9)>,\n",
       " 610: <Reaction: MT=610 (n,p10)>,\n",
       " 611: <Reaction: MT=611 (n,p11)>,\n",
       " 612: <Reaction: MT=612 (n,p12)>,\n",
       " 613: <Reaction: MT=613 (n,p13)>,\n",
       " 614: <Reaction: MT=614 (n,p14)>,\n",
       " 615: <Reaction: MT=615 (n,p15)>,\n",
       " 616: <Reaction: MT=616 (n,p16)>,\n",
       " 617: <Reaction: MT=617 (n,p17)>,\n",
       " 618: <Reaction: MT=618 (n,p18)>,\n",
       " 619: <Reaction: MT=619 (n,p19)>,\n",
       " 620: <Reaction: MT=620 (n,p20)>,\n",
       " 621: <Reaction: MT=621 (n,p21)>,\n",
       " 622: <Reaction: MT=622 (n,p22)>,\n",
       " 623: <Reaction: MT=623 (n,p23)>,\n",
       " 624: <Reaction: MT=624 (n,p24)>,\n",
       " 625: <Reaction: MT=625 (n,p25)>,\n",
       " 626: <Reaction: MT=626 (n,p26)>,\n",
       " 627: <Reaction: MT=627 (n,p27)>,\n",
       " 628: <Reaction: MT=628 (n,p28)>,\n",
       " 629: <Reaction: MT=629 (n,p29)>,\n",
       " 630: <Reaction: MT=630 (n,p30)>,\n",
       " 631: <Reaction: MT=631 (n,p31)>,\n",
       " 632: <Reaction: MT=632 (n,p32)>,\n",
       " 633: <Reaction: MT=633 (n,p33)>,\n",
       " 634: <Reaction: MT=634 (n,p34)>,\n",
       " 635: <Reaction: MT=635 (n,p35)>,\n",
       " 649: <Reaction: MT=649 (n,pc)>}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endf.reactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0K': <openmc.data.function.ResonancesWithBackground at 0x7ff836479be0>}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endf.reactions[2].xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'ResonancesWithBackground' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mendf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreactions\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m0K\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10.0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'ResonancesWithBackground' object is not callable"
     ]
    }
   ],
   "source": [
    "endf.reactions[2].xs['0K'](10.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "resolved = endf.resonances.ranges[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "<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>0.107200</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>0.097000</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>0.091000</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>0.080500</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>0.088000</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>0.114000</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>0.121000</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>0.085000</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>0.110000</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>0.096000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>48.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.3800</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.101000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>66.6500</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014667</td>\n",
       "      <td>0.067000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>81.4800</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>82.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.006160</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>87.4600</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.010160</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>96.5900</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.020267</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>100.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>105.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057333</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>107.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.005600</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>110.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.042400</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>115.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.019200</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>120.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.132000</td>\n",
       "      <td>0.091000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>135.1900</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000880</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>137.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.047200</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>138.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.049600</td>\n",
       "      <td>0.086000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>139.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.006000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>143.5400</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.088000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>148.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>156.3800</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.019760</td>\n",
       "      <td>0.091000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>164.8300</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.034267</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>167.8800</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>169.5182</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>171.1800</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.044000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>178.4800</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.145000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>183.7600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.029333</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>190.5800</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>194.3700</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.044800</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>202.6900</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>205.3500</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000976</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>206.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>208.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>0.114000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>216.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>220.6500</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>228.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.006560</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>239.3000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>244.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>246.3900</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.009280</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>250.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005733</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>255.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.002160</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>255.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>260.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.021867</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>265.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>268.0200</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.010480</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>281.0200</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>287.6000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.014240</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>290.8000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.065333</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>293.7000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.061333</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>300.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.053333</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>306.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.002800</td>\n",
       "      <td>0.084998</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      0.107200            0.0            0.0\n",
       "1     2.8250  0  2.0      0.000345      0.097000            0.0            0.0\n",
       "2    16.2400  0  1.0      0.000400      0.091000            0.0            0.0\n",
       "3    16.7700  0  2.0      0.012800      0.080500            0.0            0.0\n",
       "4    20.5600  0  2.0      0.011360      0.088000            0.0            0.0\n",
       "5    21.6500  0  2.0      0.000376      0.114000            0.0            0.0\n",
       "6    23.3300  0  1.0      0.000813      0.121000            0.0            0.0\n",
       "7    25.4000  0  2.0      0.001840      0.085000            0.0            0.0\n",
       "8    40.1700  0  1.0      0.001307      0.110000            0.0            0.0\n",
       "9    44.2200  0  2.0      0.008960      0.096000            0.0            0.0\n",
       "10   48.8000  0  2.0      0.024000      0.090000            0.0            0.0\n",
       "11   58.3800  0  2.0      0.028000      0.101000            0.0            0.0\n",
       "12   66.6500  0  1.0      0.014667      0.067000            0.0            0.0\n",
       "13   81.4800  0  1.0      0.020000      0.108000            0.0            0.0\n",
       "14   82.3000  0  2.0      0.006160      0.085000            0.0            0.0\n",
       "15   87.4600  0  2.0      0.010160      0.128000            0.0            0.0\n",
       "16   96.5900  0  1.0      0.020267      0.110000            0.0            0.0\n",
       "17  100.2000  0  1.0      0.046667      0.094000            0.0            0.0\n",
       "18  105.3000  0  1.0      0.057333      0.070000            0.0            0.0\n",
       "19  107.7000  0  2.0      0.005600      0.085000            0.0            0.0\n",
       "20  110.5000  0  2.0      0.042400      0.085000            0.0            0.0\n",
       "21  115.4000  0  2.0      0.019200      0.112000            0.0            0.0\n",
       "22  120.9000  0  2.0      0.132000      0.091000            0.0            0.0\n",
       "23  135.1900  0  2.0      0.000880      0.085000            0.0            0.0\n",
       "24  137.9000  0  2.0      0.047200      0.085000            0.0            0.0\n",
       "25  138.8000  0  2.0      0.049600      0.086000            0.0            0.0\n",
       "26  139.3000  0  2.0      0.006000      0.085000            0.0            0.0\n",
       "27  143.5400  0  2.0      0.060000      0.088000            0.0            0.0\n",
       "28  148.4000  0  1.0      0.024000      0.085000            0.0            0.0\n",
       "29  156.3800  0  2.0      0.019760      0.091000            0.0            0.0\n",
       "30  164.8300  0  1.0      0.034267      0.100000            0.0            0.0\n",
       "31  167.8800  0  2.0      0.002000      0.085000            0.0            0.0\n",
       "32  169.5182  0  2.0      0.003280      0.085000            0.0            0.0\n",
       "33  171.1800  0  1.0      0.044000      0.085000            0.0            0.0\n",
       "34  178.4800  0  2.0      0.016000      0.145000            0.0            0.0\n",
       "35  183.7600  0  1.0      0.029333      0.085000            0.0            0.0\n",
       "36  190.5800  0  1.0      0.028000      0.085000            0.0            0.0\n",
       "37  194.3700  0  2.0      0.044800      0.085000            0.0            0.0\n",
       "38  202.6900  0  1.0      0.009600      0.085000            0.0            0.0\n",
       "39  205.3500  0  2.0      0.000976      0.085000            0.0            0.0\n",
       "40  206.9000  0  2.0      0.001360      0.085000            0.0            0.0\n",
       "41  208.5000  0  2.0      0.108000      0.114000            0.0            0.0\n",
       "42  216.9000  0  1.0      0.008000      0.085000            0.0            0.0\n",
       "43  220.6500  0  1.0      0.004000      0.085000            0.0            0.0\n",
       "44  228.0500  0  2.0      0.006560      0.085000            0.0            0.0\n",
       "45  239.3000  0  1.0      0.253333      0.085000            0.0            0.0\n",
       "46  244.6000  0  1.0      0.004400      0.085000            0.0            0.0\n",
       "47  246.3900  0  2.0      0.009280      0.085000            0.0            0.0\n",
       "48  250.2000  0  1.0      0.005733      0.085000            0.0            0.0\n",
       "49  255.0000  0  2.0      0.002160      0.085000            0.0            0.0\n",
       "50  255.2000  0  2.0      0.002240      0.085000            0.0            0.0\n",
       "51  260.0500  0  1.0      0.021867      0.085000            0.0            0.0\n",
       "52  265.8000  0  2.0      0.006400      0.085000            0.0            0.0\n",
       "53  268.0200  0  2.0      0.010480      0.085000            0.0            0.0\n",
       "54  281.0200  0  1.0      0.064000      0.085000            0.0            0.0\n",
       "55  287.6000  0  2.0      0.014240      0.085000            0.0            0.0\n",
       "56  290.8000  0  1.0      0.065333      0.085000            0.0            0.0\n",
       "57  293.7000  0  1.0      0.061333      0.085000            0.0            0.0\n",
       "58  300.9000  0  1.0      0.053333      0.085000            0.0            0.0\n",
       "59  306.4000  0  2.0      0.002800      0.084998            0.0            0.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolved.parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "Cannot export incident neutron data that originated from an ENDF file.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[80], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mendf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport_to_hdf5\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGd157_from_endf.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/openmc/openmc/data/neutron.py:371\u001b[0m, in \u001b[0;36mIncidentNeutron.export_to_hdf5\u001b[0;34m(self, path, mode, libver)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;66;03m# If data come from ENDF, don't allow exporting to HDF5\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_evaluation\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 371\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCannot export incident neutron data that \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    372\u001b[0m                               \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moriginated from an ENDF file.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    374\u001b[0m \u001b[38;5;66;03m# Open file and write version\u001b[39;00m\n\u001b[1;32m    375\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m h5py\u001b[38;5;241m.\u001b[39mFile(\u001b[38;5;28mstr\u001b[39m(path), mode, libver\u001b[38;5;241m=\u001b[39mlibver) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: Cannot export incident neutron data that originated from an ENDF file."
     ]
    }
   ],
   "source": [
    "endf.export_to_hdf5('Gd157_from_endf.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "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 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 <sup>2</sup>H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2 = openmc.data.IncidentNeutron.from_njoy('H2.endf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7ff8362882f0>,\n",
       " '0K': <openmc.data.function.Tabulated1D at 0x7ff837b490a0>}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2.reactions[2].xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2.export_to_hdf5('Hydrogen2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " njoy 2016.66  18Nov21                                       11/26/25 10:55:56\n",
      " *****************************************************************************\n",
      "\n",
      " reconr...                                                                0.0s\n",
      "\n",
      " broadr...                                                                0.0s\n",
      "      300.0 deg                                                           0.0s\n",
      "      600.0 deg                                                           0.0s\n",
      "     1000.0 deg                                                           0.1s\n",
      "\n",
      " heatr...                                                                 0.1s\n",
      "\n",
      " heatr...                                                                 0.2s\n",
      "\n",
      " gaspr...                                                                 0.3s\n",
      "\n",
      " purr...                                                                  0.3s\n",
      "\n",
      " mat =  128                                                               0.3s\n",
      "\n",
      " ---message from purr---mat  128 has no resonance parameters\n",
      "                          copy as is to nout\n",
      "\n",
      " acer...                                                                  0.3s\n",
      "\n",
      " acer...                                                                  0.5s\n",
      "\n",
      " acer...                                                                  0.6s\n",
      "                                                                          0.7s\n",
      " *****************************************************************************\n"
     ]
    }
   ],
   "source": [
    "h2 = openmc.data.IncidentNeutron.from_njoy('H2.endf', stdout=True, temperatures=[300., 600., 1000.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2 = openmc.data.IncidentNeutron.from_ace('ace1')\n",
    "h2.add_temperature_from_ace('ace2')\n",
    "h2.add_temperature_from_ace('ace3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'300K': <openmc.data.function.Tabulated1D at 0x7ff83747fc20>,\n",
       " '600K': <openmc.data.function.Tabulated1D at 0x7ff83624a600>,\n",
       " '1000K': <openmc.data.function.Tabulated1D at 0x7ff83624a840>}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2.reactions[102].xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff83610a180>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for T, xs in h2.reactions[2].xs.items():\n",
    "    plt.loglog(xs.x, xs.y, label=T)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resolve_paths': True, 'cross_sections': PosixPath('/home/ubuntu/data/endfb71_hdf5/cross_sections.xml'), 'chain_file': PosixPath('/home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml')}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  <library materials=\"c_Al27\" path=\"c_Al27.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_Be\" path=\"c_Be.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_Be_in_BeO\" path=\"c_Be_in_BeO.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_C6H6\" path=\"c_C6H6.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_D_in_D2O\" path=\"c_D_in_D2O.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_Fe56\" path=\"c_Fe56.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_Graphite\" path=\"c_Graphite.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_H_in_CH2\" path=\"c_H_in_CH2.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_H_in_CH4_liquid\" path=\"c_H_in_CH4_liquid.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_H_in_CH4_solid\" path=\"c_H_in_CH4_solid.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_H_in_H2O\" path=\"c_H_in_H2O.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_H_in_ZrH\" path=\"c_H_in_ZrH.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_O_in_BeO\" path=\"c_O_in_BeO.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_O_in_UO2\" path=\"c_O_in_UO2.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_SiO2_alpha\" path=\"c_SiO2_alpha.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_U_in_UO2\" path=\"c_U_in_UO2.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_Zr_in_ZrH\" path=\"c_Zr_in_ZrH.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_ortho_D\" path=\"c_ortho_D.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_ortho_H\" path=\"c_ortho_H.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_para_D\" path=\"c_para_D.h5\" type=\"thermal\" />\n",
      "  <library materials=\"c_para_H\" path=\"c_para_H.h5\" type=\"thermal\" />\n"
     ]
    }
   ],
   "source": [
    "!cat /home/ubuntu/data/endfb71_hdf5/cross_sections.xml | grep thermal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "lw = openmc.data.ThermalScattering.from_hdf5('/home/ubuntu/data/endfb71_hdf5/c_H_in_H2O.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(3.75)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lw.energy_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff81b14b5f0>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = lw.inelastic.xs['294K']\n",
    "plt.loglog(xs.x, xs.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "openmc.data.ThermalScattering.from_njoy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "openmc.data.njoy.make_ace  # Create ACE file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "h2 = openmc.data.IncidentNeutron.from_njoy('H2.endf', acer='H2.ace', input_filename='njoy.in')\n",
    "#openmc.data.njoy.make_ace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a `cross_sections.xml` file\n",
    "\n",
    "The `DataLibrary` class enables you to easily build your own custom cross_sections.xml files (analog to MCNP's xsdir and Serpent's xsdata files)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resolve_paths': True, 'cross_sections': PosixPath('/home/ubuntu/data/endfb71_hdf5/cross_sections.xml'), 'chain_file': PosixPath('/home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml')}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf-8'?>\n",
      "<cross_sections>\n",
      "  <library materials=\"H1\" path=\"H1.h5\" type=\"neutron\" />\n",
      "  <library materials=\"H2\" path=\"H2.h5\" type=\"neutron\" />\n",
      "  <library materials=\"H3\" path=\"H3.h5\" type=\"neutron\" />\n",
      "  <library materials=\"He3\" path=\"He3.h5\" type=\"neutron\" />\n",
      "  <library materials=\"He4\" path=\"He4.h5\" type=\"neutron\" />\n",
      "  <library materials=\"Li6\" path=\"Li6.h5\" type=\"neutron\" />\n",
      "  <library materials=\"Li7\" path=\"Li7.h5\" type=\"neutron\" />\n",
      "  <library materials=\"Be7\" path=\"Be7.h5\" type=\"neutron\" />\n",
      "  <library materials=\"Be9\" path=\"Be9.h5\" type=\"neutron\" />\n",
      "  <library materials=\"B10\" path=\"B10.h5\" type=\"neutron\" />\n",
      "  <library materials=\"B11\" path=\"B11.h5\" type=\"neutron\" />\n",
      "  <library materials=\"C0\" path=\"C0.h5\" type=\"neutron\" />\n",
      "  <library materials=\"N14\" path=\"N14.h5\" type=\"neutron\" />\n",
      "  <library materials=\"N15\" path=\"N15.h5\" type=\"neutron\" />\n",
      "  <library materials=\"O16\" path=\"O16.h5\" type=\"neutron\" />\n",
      "  <library materials=\"O17\" path=\"O17.h5\" type=\"neutron\" />\n",
      "  <library materials=\"F19\" path=\"F19.h5\" type=\"neutron\" />\n",
      "  <library materials=\"Na22\" path=\"Na22.h5\" type=\"neutron\" />\n"
     ]
    }
   ],
   "source": [
    "!head -n20 /home/ubuntu/data/endfb71_hdf5/cross_sections.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib = openmc.data.DataLibrary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib.register_file('Gd157.h5')\n",
    "lib.register_file('Hydrogen2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib.export_to_xml('new_xs_library.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Decay` and `FissionProductYields`\n",
    "\n",
    "You can also find classes for reading decay and fission product yield data stored in ENDF files. Unlike the `IncidentNeutron` class, there are no factory `from_*` methods because these data only come from a single source: ENDF files. Thus, we instantiate them directly with the filename:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.pyenv/versions/3.12.4/lib/python3.12/site-packages/uncertainties/core.py:1024: UserWarning: Using UFloat objects with std_dev==0 may give unexpected results.\n",
      "  warn(\"Using UFloat objects with std_dev==0 may give unexpected results.\")\n"
     ]
    }
   ],
   "source": [
    "ga70 = openmc.data.Decay('dec-031_Ga_070.endf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1268.4+/-1.8"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga70.half_life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0005464736522863018+/-7.755066021092267e-07"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga70.decay_constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "651336.5240000001+/-1578.4858345548403"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga70.decay_energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<DecayMode: (beta-), Ga70 -> Ge70, 0.9959+/-0.0006>,\n",
       " <DecayMode: (ec/beta+), Ga70 -> Zn70, 0.0041+/-0.0006>]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga70.modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'energy': 893.318+/-83.89285,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 2.299821e-05+/-2.149379e-06},\n",
       " {'energy': 1009.506+/-115.3364,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 1.977473e-05+/-2.2519e-06},\n",
       " {'energy': 1041.89+/-144.2507,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 2.227722e-07+/-3.076247e-08},\n",
       " {'energy': 1043.483+/-114.2704,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 1.270781e-05+/-1.399605e-06},\n",
       " {'energy': 1050.559+/-88.45322,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 1.362449e-06+/-1.142428e-07},\n",
       " {'energy': 1184.554+/-122.1556,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 1.944496e-06+/-1.997225e-07},\n",
       " {'energy': 1223.789+/-123.6488,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 1.180502e-06+/-1.194963e-07},\n",
       " {'energy': 1263.857+/-126.8012,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 1.822705e-08+/-1.852793e-09},\n",
       " {'energy': 8571.9+/-85.71899,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 0.0005089932+/-9.021782e-05},\n",
       " {'energy': 8596.4+/-85.964,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 0.0009910901+/-0.0001756683},\n",
       " {'energy': 9528.66+/-95.2866,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 6.294193e-05+/-1.115631e-05},\n",
       " {'energy': 9531.81+/-95.3181,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 0.0001230566+/-2.181148e-05},\n",
       " {'energy': 9605.55+/-96.0555,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 8.509015e-08+/-1.508202e-08},\n",
       " {'energy': 9605.939+/-96.05939,\n",
       "  'from_mode': ['ec/beta+'],\n",
       "  'type': None,\n",
       "  'intensity': 1.230276e-07+/-2.180635e-08},\n",
       " {'energy': 9811.6+/-98.116,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 3.697443e-05+/-3.90528e-06},\n",
       " {'energy': 9844.2+/-98.442,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 7.176153e-05+/-7.579531e-06},\n",
       " {'energy': 10937.6+/-109.376,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 4.779354e-06+/-5.048007e-07},\n",
       " {'energy': 10942.0+/-109.42,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 9.337615e-06+/-9.862492e-07},\n",
       " {'energy': 11028.2+/-110.282,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 8.775789e-09+/-9.26908e-10},\n",
       " {'energy': 11028.8+/-110.288,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 1.265867e-08+/-1.337023e-09},\n",
       " {'energy': 11060.5+/-110.605,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 7.794889e-08+/-8.233047e-09},\n",
       " {'energy': 11060.7+/-110.607,\n",
       "  'from_mode': ['beta-'],\n",
       "  'type': None,\n",
       "  'intensity': 1.4947e-07+/-1.578719e-08}]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga70.spectra['xray']['discrete']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "photons = ga70.sources['photon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.stem(photons.x, photons.p)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.pyenv/versions/3.12.4/lib/python3.12/site-packages/uncertainties/core.py:1024: UserWarning: Using UFloat objects with std_dev==0 may give unexpected results.\n",
      "  warn(\"Using UFloat objects with std_dev==0 may give unexpected results.\")\n"
     ]
    }
   ],
   "source": [
    "u235 = openmc.data.FissionProductYields('nfy-092_U_235.endf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.53e-02, 5.00e+05, 1.40e+07])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u235.energies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'V66': 2.05032e-19+/-1.3122e-19,\n",
       " 'Cr66': 2.40981e-14+/-1.54228e-14,\n",
       " 'Cr67': 0.0+/-0,\n",
       " 'Cr68': 0.0+/-0,\n",
       " 'Cr69': 2.10819e-18+/-1.34924e-18,\n",
       " 'Cr70': 0.0+/-0,\n",
       " 'Mn66': 7.19949e-12+/-4.60767e-12,\n",
       " 'Mn67': 5.37962e-12+/-3.44296e-12,\n",
       " 'Mn68': 6.65953e-13+/-4.2621e-13,\n",
       " 'Mn69': 8.02922e-14+/-5.1387e-14,\n",
       " 'Mn70': 0.0+/-0,\n",
       " 'Mn71': 0.0+/-0,\n",
       " 'Mn72': 6.08289e-18+/-3.89305e-18,\n",
       " 'Mn73': 6.41071e-19+/-4.10286e-19,\n",
       " 'Fe66': 3.75973e-10+/-2.40623e-10,\n",
       " 'Fe67': 6.86951e-10+/-4.39649e-10,\n",
       " 'Fe68': 3.91972e-10+/-2.50862e-10,\n",
       " 'Fe69': 1.21991e-10+/-7.80745e-11,\n",
       " 'Fe70': 3.23977e-11+/-2.07345e-11,\n",
       " 'Fe71': 4.91965e-12+/-3.14858e-12,\n",
       " 'Fe72': 7.14943e-13+/-4.57564e-13,\n",
       " 'Fe73': 5.53954e-14+/-3.54531e-14,\n",
       " 'Fe74': 0.0+/-0,\n",
       " 'Fe75': 1.00027e-16+/-6.40174e-17,\n",
       " 'Fe76': 0.0+/-0,\n",
       " 'Co66': 2.8098e-10+/-1.79827e-10,\n",
       " 'Co67': 1.99986e-09+/-1.27991e-09,\n",
       " 'Co68': 2.46983e-09+/-1.58069e-09,\n",
       " 'Co69': 3.24977e-09+/-2.07985e-09,\n",
       " 'Co70': 2.08985e-09+/-1.33751e-09,\n",
       " 'Co71': 1.4799e-09+/-9.47133e-10,\n",
       " 'Co72': 5.79959e-10+/-3.71174e-10,\n",
       " 'Co73': 2.20984e-10+/-1.4143e-10,\n",
       " 'Co74': 2.99979e-11+/-1.91986e-11,\n",
       " 'Co75': 3.80963e-12+/-2.43816e-12,\n",
       " 'Co76': 2.52982e-13+/-1.61909e-13,\n",
       " 'Co77': 6.52643e-15+/-4.17691e-15,\n",
       " 'Co78': 4.27929e-16+/-2.73875e-16,\n",
       " 'Ni66': 5.77959e-11+/-3.69894e-11,\n",
       " 'Ni67': 9.03936e-10+/-5.78519e-10,\n",
       " 'Ni68': 4.1897e-09+/-2.68141e-09,\n",
       " 'Ni69': 1.11992e-08+/-7.16749e-09,\n",
       " 'Ni70': 2.7798e-08+/-1.77907e-08,\n",
       " 'Ni71': 4.34969e-08+/-2.7838e-08,\n",
       " 'Ni72': 7.59946e-08+/-4.86366e-08,\n",
       " 'Ni73': 7.1295e-08+/-4.56288e-08,\n",
       " 'Ni74': 4.57968e-08+/-2.93099e-08,\n",
       " 'Ni75': 1.52989e-08+/-9.79131e-09,\n",
       " 'Ni76': 5.01965e-09+/-3.21257e-09,\n",
       " 'Ni77': 5.63954e-10+/-3.6093e-10,\n",
       " 'Ni78': 5.0296e-11+/-3.21895e-11,\n",
       " 'Ni80': 1.55989e-14+/-9.98329e-15,\n",
       " 'Ni82': 0.0+/-0,\n",
       " 'Cu66': 1.4299e-13+/-9.15135e-14,\n",
       " 'Cu67': 9.67932e-12+/-6.19476e-12,\n",
       " 'Cu68': 3.14978e-11+/-2.01586e-11,\n",
       " 'Cu68_m1': 7.35948e-11+/-4.71007e-11,\n",
       " 'Cu69': 1.13992e-09+/-7.29548e-10,\n",
       " 'Cu70': 1.48989e-09+/-9.53533e-10,\n",
       " 'Cu70_m1': 4.45968e-09+/-2.8542e-09,\n",
       " 'Cu71': 3.47975e-08+/-2.22704e-08,\n",
       " 'Cu72': 1.26991e-07+/-8.12743e-08,\n",
       " 'Cu73': 4.70967e-07+/-3.01419e-07,\n",
       " 'Cu74': 6.81952e-07+/-4.36449e-07,\n",
       " 'Cu75': 1.00993e-06+/-6.46354e-07,\n",
       " 'Cu76': 8.27941e-07+/-5.29883e-07,\n",
       " 'Cu77': 4.44969e-07+/-2.8478e-07,\n",
       " 'Cu78': 1.08992e-07+/-6.97551e-08,\n",
       " 'Cu79': 0.0+/-0,\n",
       " 'Cu80': 5.24963e-10+/-3.35976e-10,\n",
       " 'Cu81': 0.0+/-0,\n",
       " 'Cu82': 1.4799e-12+/-9.47133e-13,\n",
       " 'Cu83': 7.3133e-15+/-4.68051e-15,\n",
       " 'Zn66': 0.0+/-0,\n",
       " 'Zn67': 0.0+/-0,\n",
       " 'Zn68': 4.73967e-13+/-3.03339e-13,\n",
       " 'Zn69': 2.47982e-12+/-1.58709e-12,\n",
       " 'Zn69_m1': 1.05993e-11+/-6.78352e-12,\n",
       " 'Zn70': 3.02979e-10+/-1.93906e-10,\n",
       " 'Zn71': 7.70946e-10+/-4.93405e-10,\n",
       " 'Zn71_m1': 3.28977e-09+/-2.10545e-09,\n",
       " 'Zn72': 6.09957e-08+/-3.90372e-08,\n",
       " 'Zn73': 4.62967e-07+/-2.96299e-07,\n",
       " 'Zn74': 2.50982e-06+/-1.60629e-06,\n",
       " 'Zn75': 7.66946e-06+/-4.90845e-06,\n",
       " 'Zn76': 1.83787e-05+/-1.17624e-05,\n",
       " 'Zn77': 3.14378e-05+/-2.01202e-05,\n",
       " 'Zn78': 3.56475e-05+/-2.28144e-05,\n",
       " 'Zn79': 1.63888e-05+/-7.37498e-06,\n",
       " 'Zn80': 2.41983e-06+/-1.54869e-06,\n",
       " 'Zn81': 0.0+/-0,\n",
       " 'Zn82': 1.07992e-07+/-6.91151e-08,\n",
       " 'Zn83': 6.31948e-10+/-4.04447e-10,\n",
       " 'Zn84': 1.21991e-08+/-7.80745e-09,\n",
       " 'Zn85': 1.54373e-13+/-9.87986e-14,\n",
       " 'Zn86': 2.27984e-12+/-1.4591e-12,\n",
       " 'Ga66': 0.0+/-0,\n",
       " 'Ga67': 0.0+/-0,\n",
       " 'Ga68': 0.0+/-0,\n",
       " 'Ga69': 0.0+/-0,\n",
       " 'Ga70': 1.4699e-13+/-9.40734e-14,\n",
       " 'Ga71': 9.38934e-12+/-6.00918e-12,\n",
       " 'Ga72_m1': 1.92986e-10+/-1.23511e-10,\n",
       " 'Ga72': 1.92986e-10+/-1.23511e-10,\n",
       " 'Ga73': 1.20991e-08+/-7.74345e-09,\n",
       " 'Ga74_m1': 7.29948e-08+/-4.67167e-08,\n",
       " 'Ga74': 7.29948e-08+/-4.67167e-08,\n",
       " 'Ga75': 1.92986e-06+/-1.23511e-06,\n",
       " 'Ga76': 1.04593e-05+/-6.69393e-06,\n",
       " 'Ga77': 4.08171e-05+/-2.6123e-05,\n",
       " 'Ga78': 0.000102813+/-6.58001e-05,\n",
       " 'Ga79': 0.000171078+/-1.88186e-05,\n",
       " 'Ga80': 0.000117062+/-1.87299e-05,\n",
       " 'Ga81': 8.17742e-05+/-2.61678e-05,\n",
       " 'Ga82': 6.26156e-05+/-4.0074e-05,\n",
       " 'Ga83': 1.91986e-06+/-1.22871e-06,\n",
       " 'Ga84': 0.000110672+/-7.08302e-05,\n",
       " 'Ga85': 5.87943e-09+/-3.76284e-09,\n",
       " 'Ga86': 3.27977e-07+/-2.09905e-07,\n",
       " 'Ga87': 2.54114e-10+/-1.62633e-10,\n",
       " 'Ga88': 4.42392e-12+/-2.83131e-12,\n",
       " 'Ge66': 0.0+/-0,\n",
       " 'Ge67': 0.0+/-0,\n",
       " 'Ge68': 0.0+/-0,\n",
       " 'Ge69': 0.0+/-0,\n",
       " 'Ge70': 0.0+/-0,\n",
       " 'Ge71': 0.0+/-0,\n",
       " 'Ge71_m1': 0.0+/-0,\n",
       " 'Ge72': 3.63974e-13+/-2.32944e-13,\n",
       " 'Ge73': 2.37983e-11+/-1.52309e-11,\n",
       " 'Ge73_m1': 5.5896e-12+/-3.57735e-12,\n",
       " 'Ge74': 1.66988e-09+/-1.06872e-09,\n",
       " 'Ge75': 6.80952e-09+/-4.35809e-09,\n",
       " 'Ge75_m1': 4.55968e-08+/-2.91819e-08,\n",
       " 'Ge76': 1.29991e-06+/-8.31941e-07,\n",
       " 'Ge77': 5.95958e-06+/-3.81413e-06,\n",
       " 'Ge77_m1': 8.90937e-07+/-5.702e-07,\n",
       " 'Ge78': 6.94151e-05+/-4.44257e-05,\n",
       " 'Ge79_m1': 0.000116247+/-7.43979e-05,\n",
       " 'Ge79': 0.000116247+/-7.43979e-05,\n",
       " 'Ge80': 0.00101818+/-4.07271e-05,\n",
       " 'Ge81': 0.00126259+/-5.05036e-05,\n",
       " 'Ge82': 0.00126469+/-5.05876e-05,\n",
       " 'Ge83': 0.000478856+/-0.000306468,\n",
       " 'Ge84': 0.000189947+/-4.36877e-05,\n",
       " 'Ge85': 2.12985e-05+/-1.3631e-05,\n",
       " 'Ge86': 0.00628666+/-0.00100586,\n",
       " 'Ge87': 2.19582e-05+/-1.40532e-05,\n",
       " 'Ge88': 5.19959e-07+/-3.32774e-07,\n",
       " 'Ge89': 4.0431e-11+/-2.58758e-11,\n",
       " 'Ge90': 1.17072e-12+/-7.4926e-13,\n",
       " 'Ge91': 6.13734e-14+/-3.9279e-14,\n",
       " 'As69': 0.0+/-0,\n",
       " 'As71': 0.0+/-0,\n",
       " 'As72': 0.0+/-0,\n",
       " 'As73': 0.0+/-0,\n",
       " 'As74': 4.98965e-14+/-3.19337e-14,\n",
       " 'As74_m1': 1.15992e-13+/-7.42348e-14,\n",
       " 'As75': 2.55982e-11+/-1.63828e-11,\n",
       " 'As76': 1.64988e-09+/-1.05593e-09,\n",
       " 'As77': 4.3097e-08+/-2.75821e-08,\n",
       " 'As78': 1.29991e-06+/-2.98979e-07,\n",
       " 'As79': 2.65681e-05+/-1.19557e-05,\n",
       " 'As80': 0.00013695+/-8.76482e-05,\n",
       " 'As81': 0.000609047+/-4.87238e-05,\n",
       " 'As82': 0.0012875+/-0.000823999,\n",
       " 'As82_m1': 0.000271051+/-0.000173473,\n",
       " 'As83': 0.00290689+/-0.000116276,\n",
       " 'As84_m1': 0.000986965+/-0.000631658,\n",
       " 'As84': 0.000986965+/-0.000631658,\n",
       " 'As85': 0.00121362+/-0.00077672,\n",
       " 'As86': 0.000199226+/-0.000127505,\n",
       " 'As87': 0.000505324+/-0.000323408,\n",
       " 'As88': 0.00124298+/-0.000795509,\n",
       " 'As89': 1.53985e-06+/-9.85504e-07,\n",
       " 'As90': 3.27811e-08+/-2.09799e-08,\n",
       " 'As91': 2.33746e-09+/-1.49598e-09,\n",
       " 'As92': 3.54764e-12+/-2.27049e-12,\n",
       " 'As93': 2.84347e-13+/-1.81982e-13,\n",
       " 'Se72': 0.0+/-0,\n",
       " 'Se73': 0.0+/-0,\n",
       " 'Se73_m1': 0.0+/-0,\n",
       " 'Se74': 0.0+/-0,\n",
       " 'Se75': 0.0+/-0,\n",
       " 'Se76': 3.03979e-13+/-1.94546e-13,\n",
       " 'Se77': 2.74981e-12+/-1.75988e-12,\n",
       " 'Se77_m1': 1.83987e-11+/-1.17752e-11,\n",
       " 'Se78': 2.46983e-09+/-1.58069e-09,\n",
       " 'Se79': 1.09992e-07+/-7.0395e-08,\n",
       " 'Se79_m1': 1.64988e-08+/-1.05593e-08,\n",
       " 'Se80': 4.88965e-06+/-3.12938e-06,\n",
       " 'Se81': 1.03793e-05+/-2.38723e-06,\n",
       " 'Se81_m1': 6.92651e-05+/-1.5931e-05,\n",
       " 'Se82': 0.000370984+/-2.96787e-05,\n",
       " 'Se83': 0.0014292+/-8.57519e-05,\n",
       " 'Se83_m1': 0.000335246+/-2.01148e-05,\n",
       " 'Se84': 0.00630945+/-0.000126189,\n",
       " 'Se85': 0.00446688+/-0.00285881,\n",
       " 'Se85_m1': 0.00446688+/-0.00285881,\n",
       " 'Se86': 0.00835835+/-0.000234034,\n",
       " 'Se87': 0.00731048+/-0.000292419,\n",
       " 'Se88': 0.00267874+/-0.000160724,\n",
       " 'Se89': 0.000485486+/-5.34034e-05,\n",
       " 'Se90': 0.000126988+/-8.12725e-05,\n",
       " 'Se91': 6.65719e-06+/-4.2606e-06,\n",
       " 'Se92': 4.16967e-07+/-2.66859e-07,\n",
       " 'Se93': 2.45707e-08+/-1.57252e-08,\n",
       " 'Se94': 1.70988e-10+/-1.09432e-10,\n",
       " 'Se95': 6.62575e-13+/-4.24048e-13,\n",
       " 'Se96': 1.27991e-13+/-8.19142e-14,\n",
       " 'Br75': 0.0+/-0,\n",
       " 'Br77': 0.0+/-0,\n",
       " 'Br77_m1': 0.0+/-0,\n",
       " 'Br78': 4.93965e-14+/-3.16138e-14,\n",
       " 'Br79': 3.10978e-12+/-1.99026e-12,\n",
       " 'Br79_m1': 1.03993e-11+/-6.65553e-12,\n",
       " 'Br80': 3.70974e-10+/-2.37423e-10,\n",
       " 'Br80_m1': 1.10992e-09+/-7.1035e-10,\n",
       " 'Br81': 8.85937e-08+/-5.67e-08,\n",
       " 'Br82': 3.82973e-07+/-2.45103e-07,\n",
       " 'Br82_m1': 1.62988e-07+/-1.04313e-07,\n",
       " 'Br83': 0.000195376+/-3.12602e-05,\n",
       " 'Br84': 0.000185797+/-2.04377e-05,\n",
       " 'Br84_m1': 0.000167248+/-1.33799e-05,\n",
       " 'Br85': 0.00235019+/-0.000141012,\n",
       " 'Br86': 0.00229736+/-6.4326e-05,\n",
       " 'Br86_m1': 0.00229736+/-0.00147031,\n",
       " 'Br87': 0.0127177+/-0.000356095,\n",
       " 'Br88': 0.0138742+/-0.000277485,\n",
       " 'Br89': 0.0103927+/-0.000415707,\n",
       " 'Br90': 0.00552553+/-0.000331532,\n",
       " 'Br91': 0.00223704+/-0.000246075,\n",
       " 'Br92': 0.000267711+/-0.000171335,\n",
       " 'Br93': 3.08432e-05+/-1.97397e-05,\n",
       " 'Br94': 1.65988e-06+/-1.06232e-06,\n",
       " 'Br95': 2.52348e-08+/-1.61503e-08,\n",
       " 'Br96': 1.90987e-08+/-1.22231e-08,\n",
       " 'Br97': 3.43679e-12+/-2.19955e-12,\n",
       " 'Br98': 1.38928e-10+/-8.8914e-11,\n",
       " 'Kr77': 0.0+/-0,\n",
       " 'Kr78': 0.0+/-0,\n",
       " 'Kr79_m1': 0.0+/-0,\n",
       " 'Kr79': 0.0+/-0,\n",
       " 'Kr80': 6.24956e-14+/-3.99972e-14,\n",
       " 'Kr81': 9.42933e-12+/-6.03477e-12,\n",
       " 'Kr81_m1': 1.4099e-12+/-9.02336e-13,\n",
       " 'Kr82': 3.34976e-10+/-2.14385e-10,\n",
       " 'Kr83': 6.62953e-08+/-4.2429e-08,\n",
       " 'Kr83_m1': 1.54989e-08+/-9.9193e-09,\n",
       " 'Kr84': 3.24977e-06+/-2.07985e-06,\n",
       " 'Kr85': 0.000255332+/-4.08531e-05,\n",
       " 'Kr85_m1': 5.89058e-05+/-9.42493e-06,\n",
       " 'Kr86': 0.000872228+/-6.97783e-05,\n",
       " 'Kr87': 0.00463375+/-0.000278025,\n",
       " 'Kr88': 0.0173221+/-0.000692885,\n",
       " 'Kr89': 0.0343845+/-0.000481383,\n",
       " 'Kr90': 0.0439695+/-0.000615573,\n",
       " 'Kr91': 0.0315598+/-0.000315598,\n",
       " 'Kr92': 0.016581+/-0.000464268,\n",
       " 'Kr93': 0.00486312+/-0.000194525,\n",
       " 'Kr94': 0.000868149+/-9.54964e-05,\n",
       " 'Kr95': 7.18697e-05+/-4.59966e-05,\n",
       " 'Kr96': 0.000378113+/-0.000241992,\n",
       " 'Kr97': 2.96976e-07+/-1.90064e-07,\n",
       " 'Kr98': 1.63287e-05+/-1.04504e-05,\n",
       " 'Kr99': 1.2276e-11+/-7.85662e-12,\n",
       " 'Kr100': 1.14992e-08+/-7.35948e-09,\n",
       " 'Kr101': 4.13579e-13+/-2.64691e-13,\n",
       " 'Rb79': 0.0+/-0,\n",
       " 'Rb81': 0.0+/-0,\n",
       " 'Rb83': 2.29984e-12+/-1.4719e-12,\n",
       " 'Rb84': 2.15985e-11+/-1.3823e-11,\n",
       " 'Rb85': 2.37483e-05+/-1.51989e-05,\n",
       " 'Rb86_m1': 3.22477e-08+/-2.06385e-08,\n",
       " 'Rb86': 3.22477e-08+/-2.06385e-08,\n",
       " 'Rb87': 2.50182e-05+/-1.60117e-05,\n",
       " 'Rb88': 0.000223014+/-1.78411e-05,\n",
       " 'Rb89': 0.00204542+/-8.18166e-05,\n",
       " 'Rb90': 0.00138623+/-8.31739e-05,\n",
       " 'Rb90_m1': 0.0070662+/-0.00452237,\n",
       " 'Rb91': 0.0222492+/-0.000311489,\n",
       " 'Rb92': 0.0313173+/-0.000626347,\n",
       " 'Rb93': 0.0306672+/-0.000429341,\n",
       " 'Rb94': 0.0156647+/-0.000438612,\n",
       " 'Rb95': 0.00763687+/-0.000305475,\n",
       " 'Rb96': 0.00168422+/-0.000134738,\n",
       " 'Rb97': 0.000379373+/-3.03499e-05,\n",
       " 'Rb98': 2.35883e-05+/-1.06147e-05,\n",
       " 'Rb99': 4.67542e-07+/-2.99227e-07,\n",
       " 'Rb100': 0.000347615+/-0.000222474,\n",
       " 'Rb101': 1.57516e-08+/-1.0081e-08,\n",
       " 'Rb102': 1.4718e-11+/-9.41955e-12,\n",
       " 'Rb103': 2.38107e-13+/-1.52389e-13,\n",
       " 'Sr83': 0.0+/-0,\n",
       " 'Sr84': 0.0+/-0,\n",
       " 'Sr85_m1': 3.92472e-13+/-2.51182e-13,\n",
       " 'Sr85': 3.92472e-13+/-2.51182e-13,\n",
       " 'Sr86': 1.18992e-10+/-7.61546e-11,\n",
       " 'Sr87': 1.06992e-08+/-6.84752e-09,\n",
       " 'Sr87_m1': 2.50982e-09+/-1.60629e-09,\n",
       " 'Sr88': 7.67946e-07+/-4.91485e-07,\n",
       " 'Sr89': 0.000175198+/-0.000112126,\n",
       " 'Sr90': 0.000737128+/-4.42277e-05,\n",
       " 'Sr91': 0.00250483+/-0.00015029,\n",
       " 'Sr92': 0.0107518+/-0.000645107,\n",
       " 'Sr93': 0.0256971+/-0.000513941,\n",
       " 'Sr94': 0.0451267+/-0.000631774,\n",
       " 'Sr95': 0.0453728+/-0.000907456,\n",
       " 'Sr96': 0.0356811+/-0.000713623,\n",
       " 'Sr97': 0.0172068+/-0.000481791,\n",
       " 'Sr98': 0.00808091+/-0.000484855,\n",
       " 'Sr99': 0.00133158+/-0.000146474,\n",
       " 'Sr100': 8.15242e-05+/-5.21755e-05,\n",
       " 'Sr101': 4.48611e-05+/-2.87111e-05,\n",
       " 'Sr102': 1.72986e-06+/-1.10711e-06,\n",
       " 'Sr103': 2.0575e-08+/-1.3168e-08,\n",
       " 'Sr104': 1.30991e-09+/-8.38341e-10,\n",
       " 'Sr105': 0.0+/-0,\n",
       " 'Sr106': 0.0+/-0,\n",
       " 'Sr107': 0.0+/-0,\n",
       " 'Sr108': 0.0+/-0,\n",
       " 'Y85': 0.0+/-0,\n",
       " 'Y87': 9.99929e-14+/-6.39955e-14,\n",
       " 'Y88': 1.69988e-11+/-1.08792e-11,\n",
       " 'Y89': 4.39969e-10+/-2.8158e-10,\n",
       " 'Y89_m1': 1.87987e-09+/-1.20311e-09,\n",
       " 'Y90_m1': 4.48468e-08+/-2.8702e-08,\n",
       " 'Y90': 4.48468e-08+/-2.8702e-08,\n",
       " 'Y91_m1': 1.64988e-06+/-1.05593e-06,\n",
       " 'Y91': 1.64988e-06+/-1.05593e-06,\n",
       " 'Y92': 0.000714829+/-0.000321673,\n",
       " 'Y93_m1': 0.000543342+/-0.000347739,\n",
       " 'Y93': 0.000543342+/-0.000347739,\n",
       " 'Y94': 0.00389833+/-0.000311867,\n",
       " 'Y95': 0.011054+/-0.00353729,\n",
       " 'Y96': 0.00224388+/-0.00143608,\n",
       " 'Y96_m1': 0.020195+/-0.00646239,\n",
       " 'Y97_m1': 0.0157025+/-0.0100496,\n",
       " 'Y97': 0.0157025+/-0.0100496,\n",
       " 'Y98': 0.0110632+/-0.00354023,\n",
       " 'Y98_m1': 0.0110632+/-0.00354023,\n",
       " 'Y99': 0.0194933+/-0.0011696,\n",
       " 'Y100': 0.00567391+/-0.0036313,\n",
       " 'Y101': 0.00278002+/-0.000305803,\n",
       " 'Y102': 0.00267979+/-0.00171507,\n",
       " 'Y103': 2.58276e-05+/-1.65297e-05,\n",
       " 'Y104': 5.6796e-06+/-3.63494e-06,\n",
       " 'Y105': 4.66997e-12+/-2.98878e-12,\n",
       " 'Y106': 4.97911e-17+/-3.18663e-17,\n",
       " 'Y107': 6.27991e-18+/-4.01914e-18,\n",
       " 'Y108': 9.18765e-21+/-5.88009e-21,\n",
       " 'Y109': 4.45818e-14+/-2.85324e-14,\n",
       " 'Y110': 7.9035e-16+/-5.05824e-16,\n",
       " 'Zr87': 0.0+/-0,\n",
       " 'Zr88': 0.0+/-0,\n",
       " 'Zr89': 2.06985e-14+/-1.32471e-14,\n",
       " 'Zr90_m1': 2.09485e-12+/-1.34071e-12,\n",
       " 'Zr90': 2.09485e-12+/-1.34071e-12,\n",
       " 'Zr91': 4.41969e-10+/-2.8286e-10,\n",
       " 'Zr92': 0.000118982+/-7.61482e-05,\n",
       " 'Zr93': 1.3699e-06+/-8.76738e-07,\n",
       " 'Zr94': 0.000194946+/-0.000124766,\n",
       " 'Zr95': 0.00127244+/-0.00020359,\n",
       " 'Zr96': 0.0033705+/-0.000370755,\n",
       " 'Zr97': 0.0109237+/-0.000655425,\n",
       " 'Zr98': 0.0257356+/-0.011581,\n",
       " 'Zr99': 0.0358394+/-0.00824306,\n",
       " 'Zr100': 0.0497641+/-0.0159245,\n",
       " 'Zr101': 0.0278782+/-0.00111513,\n",
       " 'Zr102': 0.0178133+/-0.000712534,\n",
       " 'Zr103': 0.00498868+/-0.00319275,\n",
       " 'Zr104': 0.000827871+/-0.000132459,\n",
       " 'Zr105': 0.00115837+/-0.000741356,\n",
       " 'Zr106': 1.67988e-08+/-1.07512e-08,\n",
       " 'Zr107': 1.55771e-09+/-9.96933e-10,\n",
       " 'Zr108': 3.09978e-12+/-1.98386e-12,\n",
       " 'Zr109': 3.85235e-09+/-2.4655e-09,\n",
       " 'Zr110': 9.28926e-11+/-5.94513e-11,\n",
       " 'Zr111': 2.10107e-14+/-1.34469e-14,\n",
       " 'Zr112': 8.89352e-16+/-5.69185e-16,\n",
       " 'Nb89': 0.0+/-0,\n",
       " 'Nb90': 0.0+/-0,\n",
       " 'Nb91': 0.0+/-0,\n",
       " 'Nb92': 2.26984e-13+/-1.4527e-13,\n",
       " 'Nb93': 3.61974e-11+/-2.31664e-11,\n",
       " 'Nb93_m1': 8.4894e-12+/-5.43322e-12,\n",
       " 'Nb94': 1.4599e-09+/-9.34334e-10,\n",
       " 'Nb94_m1': 1.00993e-09+/-6.46354e-10,\n",
       " 'Nb95': 1.05993e-06+/-6.78352e-07,\n",
       " 'Nb95_m1': 2.47982e-07+/-1.58709e-07,\n",
       " 'Nb96': 5.43962e-06+/-3.48135e-06,\n",
       " 'Nb97': 0.000107552+/-1.72084e-05,\n",
       " 'Nb97_m1': 2.44683e-05+/-5.6277e-06,\n",
       " 'Nb98': 0.00115633+/-0.00074005,\n",
       " 'Nb98_m1': 0.000385493+/-0.000246715,\n",
       " 'Nb99': 0.000299649+/-3.29614e-05,\n",
       " 'Nb99_m1': 0.00406579+/-0.000325263,\n",
       " 'Nb100': 0.00318902+/-0.00204098,\n",
       " 'Nb100_m1': 0.00318902+/-0.00204098,\n",
       " 'Nb101': 0.0191624+/-0.000766497,\n",
       " 'Nb102_m1': 0.0078944+/-0.00505242,\n",
       " 'Nb102': 0.0078944+/-0.00505242,\n",
       " 'Nb103': 0.0141029+/-0.00324366,\n",
       " 'Nb104_m1': 0.00285216+/-0.00182538,\n",
       " 'Nb104': 0.00285216+/-0.00182538,\n",
       " 'Nb105': 0.00138613+/-0.000110891,\n",
       " 'Nb106': 0.000157369+/-0.000100716,\n",
       " 'Nb107': 2.31368e-05+/-1.48076e-05,\n",
       " 'Nb108': 8.99936e-07+/-5.75959e-07,\n",
       " 'Nb109': 4.83581e-06+/-3.09492e-06,\n",
       " 'Nb110': 1.52989e-07+/-9.79131e-08,\n",
       " 'Nb111': 8.00212e-10+/-5.12136e-10,\n",
       " 'Nb112': 2.49026e-11+/-1.59376e-11,\n",
       " 'Nb113': 1.28956e-14+/-8.25316e-15,\n",
       " 'Nb114': 3.74332e-16+/-2.39572e-16,\n",
       " 'Mo90': 0.0+/-0,\n",
       " 'Mo91': 0.0+/-0,\n",
       " 'Mo92': 0.0+/-0,\n",
       " 'Mo93_m1': 0.0+/-0,\n",
       " 'Mo93': 0.0+/-0,\n",
       " 'Mo94': 2.99979e-14+/-1.91986e-14,\n",
       " 'Mo95': 4.93965e-12+/-3.16138e-12,\n",
       " 'Mo96': 5.32962e-10+/-3.41096e-10,\n",
       " 'Mo97': 2.48982e-08+/-1.59349e-08,\n",
       " 'Mo98': 9.57932e-07+/-6.13077e-07,\n",
       " 'Mo99': 0.00042814+/-6.85024e-05,\n",
       " 'Mo100': 0.000729498+/-0.00023344,\n",
       " 'Mo101': 0.00185841+/-0.000297345,\n",
       " 'Mo102': 0.00650731+/-0.000520585,\n",
       " 'Mo103': 0.0103685+/-0.000622109,\n",
       " 'Mo104': 0.0112745+/-0.000676472,\n",
       " 'Mo105': 0.00668015+/-0.000267206,\n",
       " 'Mo106': 0.0035919+/-0.000143676,\n",
       " 'Mo107': 0.00121442+/-0.000777231,\n",
       " 'Mo108': 0.000302039+/-0.000193305,\n",
       " 'Mo109': 0.000155269+/-9.93722e-05,\n",
       " 'Mo110': 3.88673e-05+/-2.4875e-05,\n",
       " 'Mo111': 2.27904e-06+/-1.45858e-06,\n",
       " 'Mo112': 9.64683e-08+/-6.17397e-08,\n",
       " 'Mo113': 1.11432e-09+/-7.13162e-10,\n",
       " 'Mo114': 4.39965e-11+/-2.81578e-11,\n",
       " 'Mo115': 2.36831e-14+/-1.51572e-14,\n",
       " 'Mo116': 0.0+/-0,\n",
       " 'Mo117': 2.32224e-16+/-1.48623e-16,\n",
       " 'Tc93': 0.0+/-0,\n",
       " 'Tc95': 0.0+/-0,\n",
       " 'Tc95_m1': 0.0+/-0,\n",
       " 'Tc97_m1': 8.79938e-14+/-5.6316e-14,\n",
       " 'Tc97': 8.79938e-14+/-5.6316e-14,\n",
       " 'Tc98': 8.88937e-09+/-5.6892e-09,\n",
       " 'Tc99': 1.22991e-09+/-7.87144e-10,\n",
       " 'Tc99_m1': 2.8898e-10+/-1.84947e-10,\n",
       " 'Tc100': 5.5896e-08+/-3.57735e-08,\n",
       " 'Tc101': 1.60989e-06+/-1.03033e-06,\n",
       " 'Tc102': 9.55432e-05+/-6.11477e-05,\n",
       " 'Tc102_m1': 9.55432e-05+/-6.11477e-05,\n",
       " 'Tc103': 0.000821682+/-0.000525876,\n",
       " 'Tc104': 0.000926325+/-0.000148212,\n",
       " 'Tc105': 0.000484586+/-0.000310135,\n",
       " 'Tc106': 0.000267671+/-0.000171309,\n",
       " 'Tc107': 0.000224314+/-0.000143561,\n",
       " 'Tc108': 0.000237663+/-0.000152104,\n",
       " 'Tc109': 0.00013444+/-8.60419e-05,\n",
       " 'Tc110': 0.000116392+/-7.44907e-05,\n",
       " 'Tc111': 4.49068e-05+/-2.87404e-05,\n",
       " 'Tc112': 6.85952e-06+/-4.39009e-06,\n",
       " 'Tc113': 1.39879e-06+/-8.95224e-07,\n",
       " 'Tc114': 7.0095e-08+/-4.48608e-08,\n",
       " 'Tc115': 9.01994e-10+/-5.77276e-10,\n",
       " 'Tc116': 6.30955e-11+/-4.03811e-11,\n",
       " 'Tc117': 8.84445e-12+/-5.66045e-12,\n",
       " 'Tc118': 5.88722e-15+/-3.76782e-15,\n",
       " 'Tc119': 7.57945e-17+/-4.85085e-17,\n",
       " 'Ru95': 0.0+/-0,\n",
       " 'Ru96': 0.0+/-0,\n",
       " 'Ru97': 0.0+/-0,\n",
       " 'Ru98': 0.0+/-0,\n",
       " 'Ru99': 1.09992e-14+/-7.0395e-15,\n",
       " 'Ru100': 2.11985e-12+/-1.3567e-12,\n",
       " 'Ru101': 1.61989e-10+/-1.03673e-10,\n",
       " 'Ru102': 9.75931e-09+/-6.24596e-09,\n",
       " 'Ru103': 2.35983e-07+/-1.51029e-07,\n",
       " 'Ru104': 3.26977e-06+/-2.09265e-06,\n",
       " 'Ru105': 1.10992e-09+/-7.1035e-10,\n",
       " 'Ru106': 9.06936e-09+/-5.80439e-09,\n",
       " 'Ru107': 4.93965e-08+/-3.16138e-08,\n",
       " 'Ru108': 1.66988e-07+/-1.06872e-07,\n",
       " 'Ru109_m1': 8.56439e-06+/-5.48121e-06,\n",
       " 'Ru109': 8.56439e-06+/-5.48121e-06,\n",
       " 'Ru110': 9.8853e-05+/-6.32659e-05,\n",
       " 'Ru111': 0.000118252+/-7.56811e-05,\n",
       " 'Ru112': 9.9213e-05+/-6.34963e-05,\n",
       " 'Ru113': 6.05457e-05+/-3.87493e-05,\n",
       " 'Ru114': 1.72588e-05+/-1.10456e-05,\n",
       " 'Ru115': 2.56892e-06+/-1.64411e-06,\n",
       " 'Ru116': 2.45983e-07+/-1.57429e-07,\n",
       " 'Ru117': 2.51894e-08+/-1.61212e-08,\n",
       " 'Ru118': 6.91945e-10+/-4.42845e-10,\n",
       " 'Ru119': 6.54946e-12+/-4.19166e-12,\n",
       " 'Ru120': 3.02979e-12+/-1.93906e-12,\n",
       " 'Ru121': 3.78056e-15+/-2.41956e-15,\n",
       " 'Ru122': 0.0+/-0,\n",
       " 'Ru124': 0.0+/-0,\n",
       " 'Rh99': 0.0+/-0,\n",
       " 'Rh101': 3.25977e-13+/-2.08625e-13,\n",
       " 'Rh101_m1': 0.0+/-0,\n",
       " 'Rh102': 5.90958e-12+/-3.78213e-12,\n",
       " 'Rh102_m1': 4.72967e-12+/-3.02699e-12,\n",
       " 'Rh103': 6.37955e-13+/-4.08291e-13,\n",
       " 'Rh103_m1': 4.2697e-12+/-2.73261e-12,\n",
       " 'Rh104': 4.88965e-11+/-3.12938e-11,\n",
       " 'Rh104_m1': 1.4699e-10+/-9.40734e-11,\n",
       " 'Rh105': 0.0+/-0,\n",
       " 'Rh105_m1': 0.0+/-0,\n",
       " 'Rh106': 0.0+/-0,\n",
       " 'Rh106_m1': 0.0+/-0,\n",
       " 'Rh107': 0.0+/-0,\n",
       " 'Rh108': 0.0+/-0,\n",
       " 'Rh108_m1': 0.0+/-0,\n",
       " 'Rh109': 2.05985e-08+/-1.31831e-08,\n",
       " 'Rh109_m1': 2.05985e-08+/-1.31831e-08,\n",
       " 'Rh110': 5.6496e-07+/-3.61574e-07,\n",
       " 'Rh110_m1': 5.6496e-07+/-3.61574e-07,\n",
       " 'Rh111': 8.79938e-06+/-5.6316e-06,\n",
       " 'Rh112': 2.29584e-05+/-1.46934e-05,\n",
       " 'Rh113': 6.84252e-05+/-4.37921e-05,\n",
       " 'Rh114': 5.02664e-05+/-3.21705e-05,\n",
       " 'Rh115': 3.62874e-05+/-2.3224e-05,\n",
       " 'Rh116': 8.68939e-06+/-5.56121e-06,\n",
       " 'Rh117': 4.64967e-06+/-2.97579e-06,\n",
       " 'Rh118': 3.62974e-07+/-2.32304e-07,\n",
       " 'Rh119': 1.85987e-08+/-1.19032e-08,\n",
       " 'Rh120': 2.38983e-08+/-1.52949e-08,\n",
       " 'Rh121': 1.43986e-10+/-9.21511e-11,\n",
       " 'Rh122': 2.8498e-12+/-1.82387e-12,\n",
       " 'Rh123': 8.5194e-14+/-5.45241e-14,\n",
       " 'Rh124': 1.03993e-14+/-6.65553e-15,\n",
       " 'Pd99': 0.0+/-0,\n",
       " 'Pd101': 0.0+/-0,\n",
       " 'Pd102': 0.0+/-0,\n",
       " 'Pd103': 0.0+/-0,\n",
       " 'Pd104': 0.0+/-0,\n",
       " 'Pd105': 0.0+/-0,\n",
       " 'Pd106': 0.0+/-0,\n",
       " 'Pd107': 0.0+/-0,\n",
       " 'Pd107_m1': 0.0+/-0,\n",
       " 'Pd108': 0.0+/-0,\n",
       " 'Pd109': 2.93979e-12+/-1.88147e-12,\n",
       " 'Pd109_m1': 5.45961e-12+/-3.49415e-12,\n",
       " 'Pd110': 2.14985e-09+/-1.3759e-09,\n",
       " 'Pd111': 2.53982e-08+/-1.62549e-08,\n",
       " 'Pd111_m1': 4.71967e-08+/-3.02059e-08,\n",
       " 'Pd112': 1.27991e-06+/-8.19142e-07,\n",
       " 'Pd113': 1.16292e-05+/-7.44267e-06,\n",
       " 'Pd114': 4.15571e-05+/-2.65965e-05,\n",
       " 'Pd115': 7.1285e-05+/-4.56224e-05,\n",
       " 'Pd116': 6.81552e-05+/-4.36193e-05,\n",
       " 'Pd117': 8.77138e-05+/-5.61368e-05,\n",
       " 'Pd118': 3.17778e-05+/-2.03378e-05,\n",
       " 'Pd119': 4.34969e-06+/-2.7838e-06,\n",
       " 'Pd120': 2.70981e-05+/-1.73428e-05,\n",
       " 'Pd121': 4.72967e-07+/-3.02699e-07,\n",
       " 'Pd122': 4.76966e-08+/-3.05258e-08,\n",
       " 'Pd123': 3.98972e-09+/-2.55342e-09,\n",
       " 'Pd124': 2.72981e-09+/-1.74708e-09,\n",
       " 'Pd125': 0.0+/-0,\n",
       " 'Pd126': 0.0+/-0,\n",
       " 'Pd127': 0.0+/-0,\n",
       " 'Pd128': 1.33991e-14+/-8.57539e-15,\n",
       " 'Pd129': 0.0+/-0,\n",
       " 'Pd130': 6.78952e-13+/-4.34529e-13,\n",
       " 'Ag103': 0.0+/-0,\n",
       " 'Ag105': 0.0+/-0,\n",
       " 'Ag105_m1': 0.0+/-0,\n",
       " 'Ag106': 0.0+/-0,\n",
       " 'Ag106_m1': 0.0+/-0,\n",
       " 'Ag107': 0.0+/-0,\n",
       " 'Ag107_m1': 0.0+/-0,\n",
       " 'Ag108_m1': 0.0+/-0,\n",
       " 'Ag108': 0.0+/-0,\n",
       " 'Ag109_m1': 0.0+/-0,\n",
       " 'Ag109': 0.0+/-0,\n",
       " 'Ag110': 0.0+/-0,\n",
       " 'Ag110_m1': 2.27984e-14+/-1.4591e-14,\n",
       " 'Ag111': 1.23991e-12+/-7.93544e-13,\n",
       " 'Ag111_m1': 8.27941e-12+/-5.29883e-12,\n",
       " 'Ag112': 8.28941e-09+/-5.30523e-09,\n",
       " 'Ag113': 5.41962e-09+/-3.46855e-09,\n",
       " 'Ag113_m1': 3.61974e-08+/-2.31664e-08,\n",
       " 'Ag114': 9.16935e-06+/-5.86839e-06,\n",
       " 'Ag115': 1.92986e-06+/-1.23511e-06,\n",
       " 'Ag115_m1': 1.3699e-05+/-8.76738e-06,\n",
       " 'Ag116': 4.74366e-05+/-3.03595e-05,\n",
       " 'Ag116_m1': 7.64946e-06+/-4.89565e-06,\n",
       " 'Ag117': 1.51889e-05+/-3.49345e-06,\n",
       " 'Ag117_m1': 1.51889e-05+/-3.49345e-06,\n",
       " 'Ag118': 3.41576e-05+/-7.85624e-06,\n",
       " 'Ag118_m1': 2.99279e-05+/-1.91538e-05,\n",
       " 'Ag119': 7.25349e-05+/-2.32112e-05,\n",
       " 'Ag120_m1': 4.37469e-06+/-2.7998e-06,\n",
       " 'Ag120': 4.37469e-06+/-2.7998e-06,\n",
       " 'Ag121': 2.57782e-05+/-1.6498e-05,\n",
       " 'Ag122_m1': 3.33976e-06+/-2.13745e-06,\n",
       " 'Ag122': 3.33976e-06+/-2.13745e-06,\n",
       " 'Ag123': 2.74981e-06+/-1.75988e-06,\n",
       " 'Ag124': 5.51961e-06+/-3.53255e-06,\n",
       " 'Ag125': 1.65988e-11+/-1.06232e-11,\n",
       " 'Ag126': 4.74966e-12+/-3.03979e-12,\n",
       " 'Ag127': 1.57989e-12+/-1.01113e-12,\n",
       " 'Ag128': 1.11992e-13+/-7.16749e-14,\n",
       " 'Ag129': 2.9186e-17+/-1.86791e-17,\n",
       " 'Ag130': 6.65953e-08+/-4.2621e-08,\n",
       " 'Ag131': 1.59446e-09+/-1.02046e-09,\n",
       " 'Cd105': 0.0+/-0,\n",
       " 'Cd106': 0.0+/-0,\n",
       " 'Cd107': 0.0+/-0,\n",
       " 'Cd108': 0.0+/-0,\n",
       " 'Cd109': 0.0+/-0,\n",
       " 'Cd110': 0.0+/-0,\n",
       " 'Cd111': 0.0+/-0,\n",
       " 'Cd111_m1': 0.0+/-0,\n",
       " 'Cd112': 1.05993e-11+/-3.39176e-12,\n",
       " 'Cd113': 2.58982e-12+/-1.65748e-12,\n",
       " 'Cd113_m1': 8.67939e-12+/-5.55481e-12,\n",
       " 'Cd114': 7.64946e-10+/-4.89565e-10,\n",
       " 'Cd115': 8.99936e-09+/-2.8798e-09,\n",
       " 'Cd115_m1': 5.52961e-08+/-1.76947e-08,\n",
       " 'Cd116': 2.99979e-07+/-1.91986e-07,\n",
       " 'Cd117': 1.11992e-06+/-7.16749e-07,\n",
       " 'Cd117_m1': 3.72974e-06+/-2.38703e-06,\n",
       " 'Cd118': 1.71488e-05+/-1.09752e-05,\n",
       " 'Cd119': 2.44683e-05+/-1.56597e-05,\n",
       " 'Cd119_m1': 2.24884e-05+/-1.43926e-05,\n",
       " 'Cd120': 8.38841e-05+/-5.36858e-05,\n",
       " 'Cd121_m1': 3.61924e-05+/-2.31632e-05,\n",
       " 'Cd121': 3.61924e-05+/-2.31632e-05,\n",
       " 'Cd122': 0.000119302+/-7.6353e-05,\n",
       " 'Cd123': 0.000101013+/-6.46482e-05,\n",
       " 'Cd124': 0.000121421+/-5.46396e-05,\n",
       " 'Cd125': 5.54561e-05+/-3.54919e-05,\n",
       " 'Cd126': 8.07843e-05+/-5.17019e-05,\n",
       " 'Cd127': 8.15442e-05+/-5.21883e-05,\n",
       " 'Cd128': 3.55275e-05+/-2.27376e-05,\n",
       " 'Cd129': 7.23949e-09+/-4.63327e-09,\n",
       " 'Cd130': 0.000877978+/-0.000561906,\n",
       " 'Cd131': 0.000137779+/-8.81783e-05,\n",
       " 'Cd132': 0.0+/-0,\n",
       " 'Cd133': 4.48941e-11+/-2.87322e-11,\n",
       " 'Cd134': 9.32934e-13+/-5.97078e-13,\n",
       " 'Cd136': 0.0+/-0,\n",
       " 'In107': 0.0+/-0,\n",
       " 'In109': 0.0+/-0,\n",
       " 'In111': 0.0+/-0,\n",
       " 'In112': 0.0+/-0,\n",
       " 'In112_m1': 0.0+/-0,\n",
       " 'In113': 0.0+/-0,\n",
       " 'In113_m1': 0.0+/-0,\n",
       " 'In114': 0.0+/-0,\n",
       " 'In114_m1': 0.0+/-0,\n",
       " 'In115': 1.62988e-12+/-1.04313e-12,\n",
       " 'In115_m1': 3.81973e-13+/-2.44463e-13,\n",
       " 'In116_m2': 2.56649e-11+/-1.64255e-11,\n",
       " 'In116': 2.56649e-11+/-1.64255e-11,\n",
       " 'In116_m1': 2.56649e-11+/-1.64255e-11,\n",
       " 'In117': 3.65974e-09+/-2.34223e-09,\n",
       " 'In117_m1': 8.58939e-10+/-5.49721e-10,\n",
       " 'In118_m2': 8.4494e-09+/-5.40762e-09,\n",
       " 'In118': 5.05964e-08+/-3.23817e-08,\n",
       " 'In118_m1': 8.4494e-09+/-5.40762e-09,\n",
       " 'In119': 4.71967e-06+/-1.51029e-06,\n",
       " 'In119_m1': 1.58989e-07+/-1.01753e-07,\n",
       " 'In120_m2': 2.09985e-06+/-1.34391e-06,\n",
       " 'In120': 2.09985e-06+/-1.34391e-06,\n",
       " 'In120_m1': 2.09985e-06+/-1.34391e-06,\n",
       " 'In121': 2.47183e-05+/-7.90984e-06,\n",
       " 'In121_m1': 3.23977e-06+/-2.07345e-06,\n",
       " 'In122_m2': 8.37941e-06+/-5.36282e-06,\n",
       " 'In122': 8.37941e-06+/-5.36282e-06,\n",
       " 'In122_m1': 8.37941e-06+/-5.36282e-06,\n",
       " 'In123': 3.91072e-05+/-2.50286e-05,\n",
       " 'In123_m1': 2.50982e-06+/-1.60629e-06,\n",
       " 'In124_m1': 1.69538e-05+/-1.08504e-05,\n",
       " 'In124': 1.69538e-05+/-1.08504e-05,\n",
       " 'In125': 4.72267e-05+/-1.51125e-05,\n",
       " 'In125_m1': 4.72267e-05+/-1.51125e-05,\n",
       " 'In126_m1': 1.64788e-05+/-1.05465e-05,\n",
       " 'In126': 1.64788e-05+/-1.05465e-05,\n",
       " 'In127': 0.000411691+/-0.000131741,\n",
       " 'In127_m1': 6.25456e-05+/-2.81455e-05,\n",
       " 'In128_m1': 0.000131471+/-8.41413e-05,\n",
       " 'In128': 0.000131471+/-8.41413e-05,\n",
       " 'In129': 0.000275381+/-0.000123921,\n",
       " 'In129_m1': 0.000253412+/-0.000162184,\n",
       " 'In130_m2': 3.14011e-05+/-2.00967e-05,\n",
       " 'In130_m1': 3.14011e-05+/-2.00967e-05,\n",
       " 'In130': 3.14011e-05+/-2.00967e-05,\n",
       " 'In131_m1': 5.55961e-05+/-3.55815e-05,\n",
       " 'In131': 5.55961e-05+/-3.55815e-05,\n",
       " 'In132': 6.22656e-05+/-1.9925e-05,\n",
       " 'In133': 1.70983e-06+/-1.09429e-06,\n",
       " 'In134': 3.46975e-08+/-2.22064e-08,\n",
       " 'In135': 7.25545e-11+/-4.64349e-11,\n",
       " 'In136': 1.67988e-12+/-1.07512e-12,\n",
       " 'In137': 2.15233e-12+/-1.37749e-12,\n",
       " 'Sn111': 0.0+/-0,\n",
       " 'Sn112': 0.0+/-0,\n",
       " 'Sn113_m1': 0.0+/-0,\n",
       " 'Sn113': 0.0+/-0,\n",
       " 'Sn114': 0.0+/-0,\n",
       " 'Sn115': 0.0+/-0,\n",
       " 'Sn116': 0.0+/-0,\n",
       " 'Sn117_m1': 2.49482e-13+/-1.59669e-13,\n",
       " 'Sn117': 2.49482e-13+/-1.59669e-13,\n",
       " 'Sn118': 4.09971e-11+/-2.62381e-11,\n",
       " 'Sn119': 3.39976e-10+/-2.17585e-10,\n",
       " 'Sn119_m1': 1.13992e-09+/-7.29548e-10,\n",
       " 'Sn120': 2.99979e-08+/-1.91986e-08,\n",
       " 'Sn121': 3.57975e-06+/-2.29104e-06,\n",
       " 'Sn121_m1': 2.7898e-07+/-1.78547e-07,\n",
       " 'Sn122': 3.64974e-06+/-2.33583e-06,\n",
       " 'Sn123': 8.03943e-06+/-3.61774e-06,\n",
       " 'Sn123_m1': 3.09978e-06+/-9.919301e-07,\n",
       " 'Sn124': 0.000106912+/-6.8424e-05,\n",
       " 'Sn125': 8.5424e-05+/-6.83392e-06,\n",
       " 'Sn125_m1': 0.000104573+/-1.67316e-05,\n",
       " 'Sn126': 0.000444699+/-0.000284607,\n",
       " 'Sn127': 0.000866709+/-5.20025e-05,\n",
       " 'Sn127_m1': 7.91744e-05+/-5.06716e-05,\n",
       " 'Sn128_m1': 0.0015049+/-0.000963138,\n",
       " 'Sn128': 0.0015049+/-0.000963138,\n",
       " 'Sn129': 0.00230314+/-0.000253345,\n",
       " 'Sn129_m1': 0.00195858+/-0.000117515,\n",
       " 'Sn130_m1': 0.00542239+/-0.00347033,\n",
       " 'Sn130': 0.00542239+/-0.00347033,\n",
       " 'Sn131_m1': 0.004405+/-0.0028192,\n",
       " 'Sn131': 0.004405+/-0.0028192,\n",
       " 'Sn132': 0.00591824+/-0.00023673,\n",
       " 'Sn133': 0.0013772+/-0.00088141,\n",
       " 'Sn134': 0.000176927+/-0.000113234,\n",
       " 'Sn135': 6.26948e-06+/-4.01247e-06,\n",
       " 'Sn136': 1.56989e-07+/-1.00473e-07,\n",
       " 'Sn137': 1.85985e-07+/-1.1903e-07,\n",
       " 'Sn138': 3.28977e-11+/-2.10545e-11,\n",
       " 'Sn139': 3.67554e-13+/-2.35235e-13,\n",
       " 'Sb113': 0.0+/-0,\n",
       " 'Sb115': 0.0+/-0,\n",
       " 'Sb117': 0.0+/-0,\n",
       " 'Sb118': 0.0+/-0,\n",
       " 'Sb118_m1': 0.0+/-0,\n",
       " 'Sb119': 3.88973e-14+/-2.48942e-14,\n",
       " 'Sb120': 9.02936e-13+/-5.77879e-13,\n",
       " 'Sb120_m1': 1.29991e-12+/-8.31941e-13,\n",
       " 'Sb121': 3.52975e-11+/-1.12952e-11,\n",
       " 'Sb122': 1.83987e-09+/-1.17752e-09,\n",
       " 'Sb122_m1': 2.07985e-09+/-1.33111e-09,\n",
       " 'Sb123': 5.16963e-08+/-3.30857e-08,\n",
       " 'Sb124_m2': 1.20991e-08+/-7.74345e-09,\n",
       " 'Sb124': 7.60946e-08+/-4.87006e-08,\n",
       " 'Sb124_m1': 1.20991e-08+/-7.74345e-09,\n",
       " 'Sb125': 2.73981e-07+/-1.75348e-07,\n",
       " 'Sb126_m2': 8.59439e-06+/-5.50041e-06,\n",
       " 'Sb126': 6.50954e-06+/-4.16611e-06,\n",
       " 'Sb126_m1': 8.59439e-06+/-5.50041e-06,\n",
       " 'Sb127': 7.0895e-05+/-1.63058e-05,\n",
       " 'Sb128': 0.000106672+/-6.82704e-05,\n",
       " 'Sb128_m1': 6.36555e-05+/-4.07395e-05,\n",
       " 'Sb129': 0.000640685+/-0.000288308,\n",
       " 'Sb130': 0.0021451+/-0.00137286,\n",
       " 'Sb130_m1': 0.00356752+/-0.00228321,\n",
       " 'Sb131': 0.0165069+/-0.000660274,\n",
       " 'Sb132': 0.0130348+/-0.000782087,\n",
       " 'Sb132_m1': 0.00862766+/-0.00051766,\n",
       " 'Sb133': 0.0225655+/-0.00135393,\n",
       " 'Sb134_m1': 0.00358248+/-0.00229279,\n",
       " 'Sb134': 0.00358248+/-0.00229279,\n",
       " 'Sb135': 0.00145137+/-0.000116109,\n",
       " 'Sb136': 0.000114892+/-7.35308e-05,\n",
       " 'Sb137': 0.000743307+/-0.000475717,\n",
       " 'Sb138': 3.91972e-07+/-2.50862e-07,\n",
       " 'Sb139': 1.39986e-08+/-8.95913e-09,\n",
       " 'Sb140': 1.43888e-09+/-9.20883e-10,\n",
       " 'Sb141': 3.7504e-12+/-2.40026e-12,\n",
       " 'Sb142': 1.76106e-13+/-1.12708e-13,\n",
       " 'Te115': 0.0+/-0,\n",
       " 'Te117': 0.0+/-0,\n",
       " 'Te118': 0.0+/-0,\n",
       " 'Te119': 0.0+/-0,\n",
       " 'Te120': 0.0+/-0,\n",
       " 'Te121_m1': 0.0+/-0,\n",
       " 'Te121': 0.0+/-0,\n",
       " 'Te122': 6.09957e-13+/-3.90372e-13,\n",
       " 'Te123': 5.13964e-12+/-3.28937e-12,\n",
       " 'Te123_m1': 1.71988e-11+/-1.10072e-11,\n",
       " 'Te124': 3.41976e-09+/-2.18865e-09,\n",
       " 'Te125': 6.87951e-14+/-4.40289e-14,\n",
       " 'Te125_m1': 2.29984e-13+/-1.4719e-13,\n",
       " 'Te126': 8.24942e-12+/-5.27963e-12,\n",
       " 'Te127': 9.9193e-11+/-6.34835e-11,\n",
       " 'Te127_m1': 2.42983e-10+/-1.55509e-10,\n",
       " 'Te128': 1.71988e-06+/-5.50361e-07,\n",
       " 'Te129': 5.7296e-08+/-3.66694e-08,\n",
       " 'Te129_m1': 1.3999e-07+/-8.95937e-08,\n",
       " 'Te130': 0.000578719+/-9.25951e-05,\n",
       " 'Te131': 0.000969901+/-0.000106689,\n",
       " 'Te131_m1': 0.00233331+/-0.000186664,\n",
       " 'Te132': 0.0153084+/-0.000918503,\n",
       " 'Te133': 0.0114813+/-0.000688881,\n",
       " 'Te133_m1': 0.0298605+/-0.00179163,\n",
       " 'Te134': 0.062155+/-0.0024862,\n",
       " 'Te135': 0.0321618+/-0.00192971,\n",
       " 'Te136': 0.0131733+/-0.00105387,\n",
       " 'Te137': 0.00392386+/-0.000313909,\n",
       " 'Te138': 0.000661823+/-0.000423567,\n",
       " 'Te139': 6.66353e-05+/-4.26466e-05,\n",
       " 'Te140': 0.000169117+/-0.000108235,\n",
       " 'Te141': 3.24075e-07+/-2.07408e-07,\n",
       " 'Te142': 2.06984e-08+/-1.3247e-08,\n",
       " 'Te143': 4.88094e-12+/-3.1238e-12,\n",
       " 'Te144': 5.57665e-13+/-3.56906e-13,\n",
       " 'I121': 0.0+/-0,\n",
       " 'I123': 0.0+/-0,\n",
       " 'I125': 0.0+/-0,\n",
       " 'I126': 1.69988e-12+/-1.08792e-12,\n",
       " 'I127': 0.0+/-0,\n",
       " 'I128': 1.03993e-08+/-6.65553e-09,\n",
       " 'I129': 0.0+/-0,\n",
       " 'I130': 1.54989e-06+/-9.919301e-07,\n",
       " 'I130_m1': 6.65953e-07+/-4.2621e-07,\n",
       " 'I131': 3.91572e-05+/-4.3073e-06,\n",
       " 'I132_m1': 9.13085e-05+/-5.84375e-05,\n",
       " 'I132': 9.13085e-05+/-5.84375e-05,\n",
       " 'I133_m1': 0.000825122+/-0.000528078,\n",
       " 'I133': 0.000825122+/-0.000528078,\n",
       " 'I134': 0.00500237+/-0.000300142,\n",
       " 'I134_m1': 0.00364183+/-0.00233077,\n",
       " 'I135': 0.0292737+/-0.000819663,\n",
       " 'I136': 0.0131936+/-0.00105549,\n",
       " 'I136_m1': 0.0125125+/-0.000750752,\n",
       " 'I137': 0.0262236+/-0.00104895,\n",
       " 'I138': 0.0142412+/-0.000398753,\n",
       " 'I139': 0.00771429+/-0.000617144,\n",
       " 'I140': 0.00136749+/-0.000314523,\n",
       " 'I141': 0.000406807+/-9.35656e-05,\n",
       " 'I142': 5.85659e-05+/-3.74822e-05,\n",
       " 'I143': 1.85895e-07+/-1.18973e-07,\n",
       " 'I144': 1.56151e-08+/-9.99365e-09,\n",
       " 'I145': 8.28533e-12+/-5.30261e-12,\n",
       " 'I146': 9.01799e-13+/-5.77152e-13,\n",
       " 'I147': 2.06341e-13+/-1.32058e-13,\n",
       " 'Xe124': 0.0+/-0,\n",
       " 'Xe125_m1': 0.0+/-0,\n",
       " 'Xe125': 0.0+/-0,\n",
       " 'Xe126': 0.0+/-0,\n",
       " 'Xe127_m1': 0.0+/-0,\n",
       " 'Xe127': 0.0+/-0,\n",
       " 'Xe128': 8.32941e-13+/-2.66541e-13,\n",
       " 'Xe129_m1': 0.0+/-0,\n",
       " 'Xe129': 0.0+/-0,\n",
       " 'Xe130': 1.96986e-10+/-6.30355e-11,\n",
       " 'Xe131': 1.4199e-09+/-9.08736e-10,\n",
       " 'Xe131_m1': 3.47975e-09+/-2.22704e-09,\n",
       " 'Xe132': 4.2197e-07+/-2.70061e-07,\n",
       " 'Xe133': 6.65953e-06+/-3.99572e-07,\n",
       " 'Xe133_m1': 1.88587e-05+/-1.20695e-05,\n",
       " 'Xe134': 0.000105483+/-6.75088e-05,\n",
       " 'Xe134_m1': 0.000246123+/-0.000157518,\n",
       " 'Xe135': 0.000785125+/-4.71075e-05,\n",
       " 'Xe135_m1': 0.00178122+/-0.000106873,\n",
       " 'Xe136': 0.0219242+/-0.0098659,\n",
       " 'Xe137': 0.031942+/-0.000894376,\n",
       " 'Xe138': 0.0481413+/-0.00134796,\n",
       " 'Xe139': 0.0432328+/-0.000864656,\n",
       " 'Xe140': 0.0350576+/-0.000981612,\n",
       " 'Xe141': 0.0121914+/-0.00034136,\n",
       " 'Xe142': 0.00434173+/-0.000260504,\n",
       " 'Xe143_m1': 0.000264718+/-0.00016942,\n",
       " 'Xe143': 0.000264718+/-0.00016942,\n",
       " 'Xe144': 6.04901e-05+/-3.87137e-05,\n",
       " 'Xe145': 7.15941e-07+/-4.58202e-07,\n",
       " 'Xe146': 1.05992e-07+/-6.78346e-08,\n",
       " 'Xe147': 1.78301e-08+/-1.14113e-08,\n",
       " 'Xe148': 1.09992e-11+/-7.0395e-12,\n",
       " 'Xe149': 9.51933e-14+/-6.09237e-14,\n",
       " 'Xe150': 0.0+/-0,\n",
       " 'Cs127': 0.0+/-0,\n",
       " 'Cs129': 0.0+/-0,\n",
       " 'Cs131': 2.53982e-14+/-1.62549e-14,\n",
       " 'Cs132': 7.37948e-10+/-4.72287e-10,\n",
       " 'Cs133': 7.91944e-09+/-2.53422e-09,\n",
       " 'Cs134_m1': 3.85473e-08+/-2.46703e-08,\n",
       " 'Cs134': 3.85473e-08+/-2.46703e-08,\n",
       " 'Cs135_m1': 2.45483e-06+/-1.57109e-06,\n",
       " 'Cs135': 2.45483e-06+/-1.57109e-06,\n",
       " 'Cs136_m1': 2.7688e-05+/-1.77203e-05,\n",
       " 'Cs136': 2.7688e-05+/-1.77203e-05,\n",
       " 'Cs137': 0.000599988+/-6.59986e-05,\n",
       " 'Cs138': 0.00243195+/-0.000267514,\n",
       " 'Cs138_m1': 0.00223374+/-0.000245712,\n",
       " 'Cs139': 0.0130586+/-0.000522345,\n",
       " 'Cs140': 0.0206988+/-0.000579568,\n",
       " 'Cs141': 0.0291513+/-0.000583025,\n",
       " 'Cs142': 0.0227813+/-0.000637875,\n",
       " 'Cs143': 0.0140295+/-0.000561179,\n",
       " 'Cs144': 0.00417681+/-0.000250609,\n",
       " 'Cs145': 0.000756187+/-6.04949e-05,\n",
       " 'Cs146': 7.64046e-05+/-8.40451e-06,\n",
       " 'Cs147': 2.23819e-05+/-1.43244e-05,\n",
       " 'Cs148': 1.30991e-07+/-8.38341e-08,\n",
       " 'Cs149': 3.62552e-09+/-2.32034e-09,\n",
       " 'Cs150': 1.98986e-11+/-1.27351e-11,\n",
       " 'Cs151': 9.46903e-14+/-6.06018e-14,\n",
       " 'Cs152': 1.26762e-15+/-8.11279e-16,\n",
       " 'Ba129': 0.0+/-0,\n",
       " 'Ba131': 0.0+/-0,\n",
       " 'Ba132': 0.0+/-0,\n",
       " 'Ba133': 1.03993e-14+/-6.65553e-15,\n",
       " 'Ba134': 5.25963e-12+/-3.36616e-12,\n",
       " 'Ba135_m1': 3.81973e-10+/-2.44463e-10,\n",
       " 'Ba135': 3.81973e-10+/-2.44463e-10,\n",
       " 'Ba136_m1': 2.36483e-08+/-1.51349e-08,\n",
       " 'Ba136': 2.36483e-08+/-1.51349e-08,\n",
       " 'Ba137': 5.42962e-07+/-3.47495e-07,\n",
       " 'Ba137_m1': 1.32991e-06+/-8.5114e-07,\n",
       " 'Ba138': 4.11571e-05+/-2.63405e-05,\n",
       " 'Ba139': 0.000687681+/-0.000309457,\n",
       " 'Ba140': 0.0048836+/-0.00112323,\n",
       " 'Ba141': 0.0165949+/-0.00182544,\n",
       " 'Ba142': 0.0301423+/-0.00180854,\n",
       " 'Ba143': 0.041004+/-0.00164016,\n",
       " 'Ba144': 0.0397478+/-0.00111294,\n",
       " 'Ba145': 0.0186621+/-0.00111973,\n",
       " 'Ba146': 0.00913827+/-0.000365531,\n",
       " 'Ba147': 0.00243638+/-0.00038982,\n",
       " 'Ba148': 0.000221844+/-0.00014198,\n",
       " 'Ba149': 1.03256e-05+/-6.60841e-06,\n",
       " 'Ba150': 5.01965e-07+/-3.21257e-07,\n",
       " 'Ba151': 8.18226e-09+/-5.23665e-09,\n",
       " 'Ba152': 1.48988e-10+/-9.53524e-11,\n",
       " 'Ba153': 3.72805e-13+/-2.38595e-13,\n",
       " 'Ba154': 0.0+/-0,\n",
       " 'La133': 0.0+/-0,\n",
       " 'La135': 0.0+/-0,\n",
       " 'La137': 6.92951e-11+/-4.43489e-11,\n",
       " 'La138': 3.17978e-07+/-2.03506e-07,\n",
       " 'La139': 2.26984e-07+/-1.4527e-07,\n",
       " 'La140': 5.21563e-05+/-3.338e-05,\n",
       " 'La141': 0.000184907+/-2.95851e-05,\n",
       " 'La142': 0.000965172+/-0.000308855,\n",
       " 'La143': 0.00379478+/-0.00242866,\n",
       " 'La144': 0.0106974+/-0.000641847,\n",
       " 'La145': 0.0191497+/-0.00153197,\n",
       " 'La146_m1': 0.00744941+/-0.00476762,\n",
       " 'La146': 0.00744941+/-0.00476762,\n",
       " 'La147': 0.00643265+/-0.000707591,\n",
       " 'La148': 0.00336285+/-0.00215223,\n",
       " 'La149': 0.000798754+/-0.000511202,\n",
       " 'La150': 0.000104223+/-6.67025e-05,\n",
       " 'La151': 1.02711e-05+/-6.5735e-06,\n",
       " 'La152': 4.52968e-07+/-2.899e-07,\n",
       " 'La153': 1.41986e-08+/-9.08712e-09,\n",
       " 'La154': 1.4599e-10+/-9.34334e-11,\n",
       " 'La155': 2.95078e-13+/-1.8885e-13,\n",
       " 'La156': 4.87482e-15+/-3.11989e-15,\n",
       " 'La157': 5.3114e-17+/-3.3993e-17,\n",
       " 'Ce135': 0.0+/-0,\n",
       " 'Ce137': 0.0+/-0,\n",
       " 'Ce138': 4.74966e-12+/-3.03979e-12,\n",
       " 'Ce139': 2.8698e-12+/-1.83667e-12,\n",
       " 'Ce139_m1': 7.0295e-12+/-4.49888e-12,\n",
       " 'Ce140': 1.14992e-09+/-7.35948e-10,\n",
       " 'Ce141': 4.98965e-08+/-3.19337e-08,\n",
       " 'Ce142': 1.75988e-06+/-1.12632e-06,\n",
       " 'Ce143': 0.000313348+/-3.44683e-05,\n",
       " 'Ce144': 0.000345456+/-0.000221092,\n",
       " 'Ce145': 0.00085438+/-0.000546803,\n",
       " 'Ce146': 0.00582932+/-0.000641225,\n",
       " 'Ce147': 0.00997971+/-0.00109777,\n",
       " 'Ce148': 0.0123548+/-0.00284161,\n",
       " 'Ce149': 0.00697909+/-0.00111665,\n",
       " 'Ce150': 0.00391764+/-0.00250729,\n",
       " 'Ce151': 0.00099025+/-0.00063376,\n",
       " 'Ce152': 0.000205365+/-0.000131434,\n",
       " 'Ce153': 1.70488e-05+/-1.09112e-05,\n",
       " 'Ce154': 9.11936e-07+/-5.83639e-07,\n",
       " 'Ce155': 2.54979e-08+/-1.63187e-08,\n",
       " 'Ce156': 5.72955e-10+/-3.66691e-10,\n",
       " 'Ce157': 4.58962e-12+/-2.93736e-12,\n",
       " 'Ce158': 3.86973e-14+/-2.47662e-14,\n",
       " 'Ce159': 1.28119e-16+/-8.19961e-17,\n",
       " 'Ce160': 0.0+/-0,\n",
       " 'Pr139': 0.0+/-0,\n",
       " 'Pr140': 0.0+/-0,\n",
       " 'Pr141': 4.68967e-13+/-3.00139e-13,\n",
       " 'Pr142_m1': 2.31484e-11+/-1.4815e-11,\n",
       " 'Pr142': 2.31484e-11+/-1.4815e-11,\n",
       " 'Pr143': 4.49968e-09+/-2.8798e-09,\n",
       " 'Pr144': 1.4299e-08+/-9.15135e-09,\n",
       " 'Pr144_m1': 1.27991e-07+/-8.19142e-08,\n",
       " 'Pr145': 3.32976e-06+/-2.13105e-06,\n",
       " 'Pr146': 3.62374e-05+/-2.3192e-05,\n",
       " ...}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thermal_yields = u235.independent[0]\n",
    "thermal_yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1247"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(thermal_yields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0292737+/-0.000819663"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thermal_yields['I135']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11664/416765326.py:1: FutureWarning: AffineScalarFunc.__gt__() is deprecated. It will be removed in a future release.\n",
      "  max(thermal_yields.items(), key=lambda x: x[1])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('Te134', 0.062155+/-0.0024862)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(thermal_yields.items(), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resolve_paths': True, 'cross_sections': PosixPath('/home/ubuntu/data/endfb71_hdf5/cross_sections.xml'), 'chain_file': PosixPath('/home/ubuntu/data/depletion_chains/chain_endfb71_pwr.xml')}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "carbon = openmc.data.IncidentNeutron.from_hdf5('/home/ubuntu/data/endfb71_hdf5/C0.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "carbon = openmc.Material()\n",
    "carbon.add_element('C', 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Material\n",
       "\tID             =\t2\n",
       "\tName           =\t\n",
       "\tTemperature    =\tNone\n",
       "\tDensity        =\tNone [sum]\n",
       "\tVolume         =\tNone [cm^3]\n",
       "\tDepletable     =\tFalse\n",
       "\tS(a,b) Tables  \n",
       "\tNuclides       \n",
       "\tC0             =\t1.0          [ao]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carbon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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