nuclear-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nuclear Data\n",
    "In this notebook, we will go through the salient features of the `openmc.data` package in the Python API. This package enables inspection, analysis, and conversion of nuclear data from ACE files. Most importantly, the package provides a mean to generate HDF5 nuclear data libraries that are used by the transport solver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from pprint import pprint\n",
    "import shutil\n",
    "import subprocess\n",
    "import urllib.request\n",
    "\n",
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "import openmc.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical Data\n",
    "\n",
    "Some very helpful physical data is available as part of `openmc.data`: atomic masses, natural abundances, and atomic weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.939608306"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_mass('Fe54')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00015574"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.NATURAL_ABUNDANCE['H2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.011115164864455"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openmc.data.atomic_weight('C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The IncidentNeutron class\n",
    "\n",
    "The most useful class within the `openmc.data` API is `IncidentNeutron`, which stores to continuous-energy incident neutron data. This class has factory methods `from_ace`, `from_endf`, and `from_hdf5` which take a data file on disk and parse it into a hierarchy of classes in memory. To demonstrate this feature, we will download an ACE file (which can be produced with [NJOY 2016](https://github.com/njoy/NJOY2016)) and then load it in using the `IncidentNeutron.from_ace` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://anl.box.com/shared/static/kxm7s57z3xgfbeq29h54n7q6js8rd11c.ace'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'gd157.ace')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load ACE data into object\n",
    "gd157 = openmc.data.IncidentNeutron.from_ace('gd157.ace')\n",
    "gd157"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Python, it's easy to explore (and modify) the nuclear data. Let's start off by reading the total cross section. Reactions are indexed using their \"MT\" number -- a unique identifier for each reaction defined by the ENDF-6 format. The MT number for the total cross section is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Reaction: MT=1 (n,total)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = gd157[1]\n",
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross sections for each reaction can be stored at multiple temperatures. To see what temperatures are available, we can look at the reaction's `xs` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7fd70079e610>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the cross section at a particular energy, 1 eV for example, simply get the cross section at the appropriate temperature and then call it as a function. Note that our nuclear data uses eV as the unit of energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.64747"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs['294K'](1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `xs` attribute can also be called on an array of energies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([142.64747   ,  38.6541761 , 175.40019642])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.xs['294K']([1.0, 2.0, 3.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation for each temperature present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': array([1.0000e-05, 1.0325e-05, 1.0650e-05, ..., 1.9500e+07, 1.9900e+07,\n",
       "        2.0000e+07])}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross section (b)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energies = gd157.energy['294K']\n",
    "total_xs = total.xs['294K'](energies)\n",
    "plt.loglog(energies, total_xs)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reaction Data\n",
    "\n",
    "Most of the interesting data for an `IncidentNeutron` instance is contained within the `reactions` attribute, which is a dictionary mapping MT values to `Reaction` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<Reaction: MT=1 (n,total)>,\n",
      " <Reaction: MT=101 (n,disappear)>,\n",
      " <Reaction: MT=301 heating>,\n",
      " <Reaction: MT=2 (n,elastic)>,\n",
      " <Reaction: MT=16 (n,2n)>,\n",
      " <Reaction: MT=17 (n,3n)>,\n",
      " <Reaction: MT=22 (n,na)>,\n",
      " <Reaction: MT=24 (n,2na)>,\n",
      " <Reaction: MT=28 (n,np)>,\n",
      " <Reaction: MT=41 (n,2np)>]\n"
     ]
    }
   ],
   "source": [
    "pprint(list(gd157.reactions.values())[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's suppose we want to look more closely at the (n,2n) reaction. This reaction has an energy threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold = 6400881.0 eV\n"
     ]
    }
   ],
   "source": [
    "n2n = gd157[16]\n",
    "print('Threshold = {} eV'.format(n2n.xs['294K'].x[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (n,2n) cross section, like all basic cross sections, is represented by the `Tabulated1D` class. The energy and cross section values in the table can be directly accessed with the `x` and `y` attributes. Using the `x` and `y` has the nice benefit of automatically acounting for reaction thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7fd6c4f45940>}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6400881.0, 20000000.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = n2n.xs['294K']\n",
    "plt.plot(xs.x, xs.y)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')\n",
    "plt.xlim((xs.x[0], xs.x[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
       " <Product: photon, emission=prompt, yield=tabulated>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n.products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7fd6c4f45af0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neutron = n2n.products[0]\n",
    "neutron.distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the neutrons emitted have a correlated angle-energy distribution. Let's look at the `energy_out` attribute to see what the outgoing energy distributions are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.stats.univariate.Tabular at 0x7fd6c4f45ac0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f45b80>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f45ca0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f45dc0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f4a040>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f4a3a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f4a7c0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd7007eec10>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f4f2e0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4f4f9a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4153160>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c41539a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4157220>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4157b80>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c415c580>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4161040>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4161b20>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4165700>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c416a340>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c416af40>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c416fc40>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c41739a0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4179760>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c417e580>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4182400>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c41882e0>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c418d220>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4ed2040>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4ed2f40>,\n",
       " <openmc.stats.univariate.Tabular at 0x7fd6c4ed6ee0>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist = neutron.distribution[0]\n",
    "dist.energy_out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see we have a tabulated outgoing energy distribution for each incoming energy. Note that the same probability distribution classes that we could use to create a source definition are also used within the `openmc.data` package. Let's plot every fifth distribution to get an idea of what they look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for e_in, e_out_dist in zip(dist.energy[::5], dist.energy_out[::5]):\n",
    "    plt.semilogy(e_out_dist.x, e_out_dist.p, label='E={:.2f} MeV'.format(e_in/1e6))\n",
    "plt.ylim(top=1e-6)\n",
    "plt.legend()\n",
    "plt.xlabel('Outgoing energy (eV)')\n",
    "plt.ylabel('Probability/eV')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unresolved resonance probability tables\n",
    "\n",
    "We can also look at unresolved resonance probability tables which are stored in a `ProbabilityTables` object. In the following example, we'll create a plot showing what the total cross section probability tables look like as a function of incoming energy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross section(b)')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cm = matplotlib.cm.Spectral_r\n",
    "\n",
    "# Determine size of probability tables\n",
    "urr = gd157.urr['294K']\n",
    "n_energy = urr.table.shape[0]\n",
    "n_band = urr.table.shape[2]\n",
    "\n",
    "for i in range(n_energy):\n",
    "    # Get bounds on energy\n",
    "    if i > 0:\n",
    "        e_left = urr.energy[i] - 0.5*(urr.energy[i] - urr.energy[i-1])\n",
    "    else:\n",
    "        e_left = urr.energy[i] - 0.5*(urr.energy[i+1] - urr.energy[i])\n",
    "\n",
    "    if i < n_energy - 1:\n",
    "        e_right = urr.energy[i] + 0.5*(urr.energy[i+1] - urr.energy[i])\n",
    "    else:\n",
    "        e_right = urr.energy[i] + 0.5*(urr.energy[i] - urr.energy[i-1])\n",
    "        \n",
    "    for j in range(n_band):\n",
    "        # Determine maximum probability for a single band\n",
    "        max_prob = np.diff(urr.table[i,0,:]).max()\n",
    "        \n",
    "        # Determine bottom of band\n",
    "        if j > 0:\n",
    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
    "            value = (urr.table[i,0,j] - urr.table[i,0,j-1])/max_prob\n",
    "        else:\n",
    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
    "            value = urr.table[i,0,j]/max_prob\n",
    "\n",
    "        # Determine top of band\n",
    "        if j < n_band - 1:\n",
    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
    "        else:\n",
    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
    "    \n",
    "        # Draw rectangle with appropriate color\n",
    "        ax.add_patch(Rectangle((e_left, xs_bottom), e_right - e_left, xs_top - xs_bottom,\n",
    "                     color=cm(value)))\n",
    "\n",
    "# Overlay total cross section\n",
    "ax.plot(gd157.energy['294K'], total.xs['294K'](gd157.energy['294K']), 'k')\n",
    "\n",
    "# Make plot pretty and labeled\n",
    "ax.set_xlim(1.0, 1.0e5)\n",
    "ax.set_ylim(1e-1, 1e4)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Energy (eV)')\n",
    "ax.set_ylabel('Cross section(b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting HDF5 data\n",
    "\n",
    "If you have an instance `IncidentNeutron` that was created from ACE or HDF5 data, you can easily write it to disk using the `export_to_hdf5()` method. This can be used to convert ACE to HDF5 or to take an existing data set and actually modify cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd157.export_to_hdf5('gd157.h5', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With few exceptions, the HDF5 file encodes the same data as the ACE file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
    "np.all(gd157[16].xs['294K'].y == gd157_reconstructed[16].xs['294K'].y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And one of the best parts of using HDF5 is that it is a widely used format with lots of third-party support. You can use `h5py`, for example, to inspect the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reaction_002, (n,elastic)\n",
      "reaction_016, (n,2n)\n",
      "reaction_017, (n,3n)\n",
      "reaction_022, (n,na)\n",
      "reaction_024, (n,2na)\n",
      "reaction_028, (n,np)\n",
      "reaction_041, (n,2np)\n",
      "reaction_051, (n,n1)\n",
      "reaction_052, (n,n2)\n",
      "reaction_053, (n,n3)\n"
     ]
    }
   ],
   "source": [
    "h5file = h5py.File('gd157.h5', 'r')\n",
    "main_group = h5file['Gd157/reactions']\n",
    "for name, obj in sorted(list(main_group.items()))[:10]:\n",
    "    if 'reaction_' in name:\n",
    "        print('{}, {}'.format(name, obj.attrs['label'].decode()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<HDF5 group \"/Gd157/reactions/reaction_016/294K\" (1 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/product_0\" (2 members)>,\n",
      " <HDF5 group \"/Gd157/reactions/reaction_016/product_1\" (2 members)>]\n"
     ]
    }
   ],
   "source": [
    "n2n_group = main_group['reaction_016']\n",
    "pprint(list(n2n_group.values()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we see that the hierarchy of data within the HDF5 mirrors the hierarchy of Python objects that we manipulated before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.000000e+00, 3.026796e-13, 1.291101e-02, 6.511110e-02,\n",
       "       3.926270e-01, 5.752268e-01, 6.969600e-01, 7.399378e-01,\n",
       "       9.635450e-01, 1.142130e+00, 1.308020e+00, 1.463500e+00,\n",
       "       1.557600e+00, 1.640550e+00, 1.688960e+00, 1.711400e+00,\n",
       "       1.739450e+00, 1.782070e+00, 1.816650e+00, 1.845280e+00,\n",
       "       1.865409e+00, 1.867240e+00, 1.881558e+00, 1.881560e+00,\n",
       "       1.881800e+00, 1.894470e+00, 1.869570e+00, 1.821200e+00,\n",
       "       1.716000e+00, 1.600540e+00, 1.431620e+00, 1.283460e+00,\n",
       "       1.101660e+00, 1.065300e+00, 9.307300e-01, 8.029800e-01,\n",
       "       7.777400e-01])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2n_group['294K/xs'][()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with ENDF files\n",
    "\n",
    "In addition to being able to load ACE and HDF5 data, we can also load ENDF data directly into an `IncidentNeutron` instance using the `from_endf()` factory method. Let's download the ENDF/B-VII.1 evaluation for $^{157}$Gd and load it in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IncidentNeutron: Gd157>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download ENDF file\n",
    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/Gd/157'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'gd157.endf')\n",
    "\n",
    "# Load into memory\n",
    "gd157_endf = openmc.data.IncidentNeutron.from_endf(filename)\n",
    "gd157_endf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just as before, we can get a reaction by indexing the object directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "elastic = gd157_endf[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, if we look at the cross section now, we see that it isn't represented as tabulated data anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0K': <openmc.data.function.ResonancesWithBackground at 0x7fd6c15174f0>}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you had [Cython](https://cython.org/) installed when you built/installed OpenMC, you should be able to evaluate resonant cross sections from ENDF data directly, i.e., OpenMC will reconstruct resonances behind the scenes for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998.7871174521487"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elastic.xs['0K'](0.0253)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When data is loaded from an ENDF file, there is also a special `resonances` attribute that contains resolved and unresolved resonance region data (from MF=2 in an ENDF file)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<openmc.data.resonance.ReichMoore at 0x7fd6c20ad6a0>,\n",
       " <openmc.data.resonance.Unresolved at 0x7fd6c20add60>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157_endf.resonances.ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that $^{157}$Gd has a resolved resonance region represented in the Reich-Moore format as well as an unresolved resonance region. We can look at the min/max energy of each region by doing the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1e-05, 306.6), (306.6, 54881.1)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(r.energy_min, r.energy_max) for r in gd157_endf.resonances.ranges]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With knowledge of the energy bounds, let's create an array of energies over the entire resolved resonance range and plot the elastic scattering cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross section (b)')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create log-spaced array of energies\n",
    "resolved = gd157_endf.resonances.resolved\n",
    "energies = np.logspace(np.log10(resolved.energy_min),\n",
    "                       np.log10(resolved.energy_max), 1000)\n",
    "\n",
    "# Evaluate elastic scattering xs at energies\n",
    "xs = elastic.xs['0K'](energies)\n",
    "\n",
    "# Plot cross section vs energies\n",
    "plt.loglog(energies, xs)\n",
    "plt.xlabel('Energy (eV)')\n",
    "plt.ylabel('Cross section (b)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resonance ranges also have a useful `parameters` attribute that shows the energies and widths for resonances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>energy</th>\n",
       "      <th>L</th>\n",
       "      <th>J</th>\n",
       "      <th>neutronWidth</th>\n",
       "      <th>captureWidth</th>\n",
       "      <th>fissionWidthA</th>\n",
       "      <th>fissionWidthB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0314</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>0.1072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.8250</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.0970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.2400</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.0910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.7700</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.012800</td>\n",
       "      <td>0.0805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.5600</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.011360</td>\n",
       "      <td>0.0880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.6500</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3300</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.4000</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001840</td>\n",
       "      <td>0.0850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.1700</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44.2200</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.008960</td>\n",
       "      <td>0.0960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
       "0   0.0314  0  2.0      0.000474        0.1072            0.0            0.0\n",
       "1   2.8250  0  2.0      0.000345        0.0970            0.0            0.0\n",
       "2  16.2400  0  1.0      0.000400        0.0910            0.0            0.0\n",
       "3  16.7700  0  2.0      0.012800        0.0805            0.0            0.0\n",
       "4  20.5600  0  2.0      0.011360        0.0880            0.0            0.0\n",
       "5  21.6500  0  2.0      0.000376        0.1140            0.0            0.0\n",
       "6  23.3300  0  1.0      0.000813        0.1210            0.0            0.0\n",
       "7  25.4000  0  2.0      0.001840        0.0850            0.0            0.0\n",
       "8  40.1700  0  1.0      0.001307        0.1100            0.0            0.0\n",
       "9  44.2200  0  2.0      0.008960        0.0960            0.0            0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resolved.parameters.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heavy-nuclide resonance scattering\n",
    "\n",
    "OpenMC has two methods for accounting for resonance upscattering in heavy nuclides, DBRC and RVS. These methods rely on 0 K elastic scattering data being present. If you have an existing ACE/HDF5 dataset and you need to add 0 K elastic scattering data to it, this can be done using the `IncidentNeutron.add_elastic_0K_from_endf()` method. Let's do this with our original `gd157` object that we instantiated from an ACE file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd157.add_elastic_0K_from_endf('gd157.endf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check to make sure that we have both the room temperature elastic scattering cross section as well as a 0K cross section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'294K': <openmc.data.function.Tabulated1D at 0x7fd6c4fb35e0>,\n",
       " '0K': <openmc.data.function.Tabulated1D at 0x7fd6c12713a0>}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd157[2].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating data from NJOY\n",
    "\n",
    "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://www.njoy21.io/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n",
    "\n",
    "Let's use `IncidentNeutron.from_njoy()` to run NJOY to create data for $^2$H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " njoy 2016.67  xxXxx22                                       10/04/22 13:31:26\n",
      " *****************************************************************************\n",
      "\n",
      " reconr...                                                                0.0s\n",
      "\n",
      " broadr...                                                                0.0s\n",
      "      300.0 deg                                                           0.0s\n",
      "      400.0 deg                                                           0.1s\n",
      "      500.0 deg                                                           0.2s\n",
      "\n",
      " heatr...                                                                 0.3s\n",
      "\n",
      " heatr...                                                                 0.4s\n",
      "\n",
      " gaspr...                                                                 0.5s\n",
      "\n",
      " purr...                                                                  0.6s\n",
      "\n",
      " mat =  128                                                               0.6s\n",
      "\n",
      " ---message from purr---mat  128 has no resonance parameters\n",
      "                          copy as is to nout\n",
      "\n",
      " acer...                                                                  0.6s\n",
      "\n",
      " acer...                                                                  0.7s\n",
      "\n",
      " acer...                                                                  0.9s\n",
      "                                                                          1.0s\n",
      " *****************************************************************************\n"
     ]
    }
   ],
   "source": [
    "# Download ENDF file\n",
    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/H/2'\n",
    "filename, headers = urllib.request.urlretrieve(url, 'h2.endf')\n",
    "\n",
    "# Run NJOY to create deuterium data\n",
    "h2 = openmc.data.IncidentNeutron.from_njoy('h2.endf', temperatures=[300., 400., 500.], stdout=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use our `h2` object just as we did before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'300K': <openmc.data.function.Tabulated1D at 0x7fd6c11d88b0>,\n",
       " '400K': <openmc.data.function.Tabulated1D at 0x7fd6c1227cd0>,\n",
       " '500K': <openmc.data.function.Tabulated1D at 0x7fd6c091ef10>,\n",
       " '0K': <openmc.data.function.Tabulated1D at 0x7fd6c1190280>}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h2[2].xs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that 0 K elastic scattering data is automatically added when using `from_njoy()` so that resonance elastic scattering treatments can be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Windowed multipole\n",
    "\n",
    "OpenMC can also be used with an experimental format called windowed multipole.  Windowed multipole allows for analytic on-the-fly Doppler broadening of the resolved resonance range.  Windowed multipole data can be downloaded with the `openmc-get-multipole-data` script.  This data can be used in the transport solver, but it can also be used directly in the Python API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://github.com/mit-crpg/WMP_Library/releases/download/v1.1/092238.h5'\n",
    "filename, headers = urllib.request.urlretrieve(url, '092238.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "u238_multipole = openmc.data.WindowedMultipole.from_hdf5('092238.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `WindowedMultipole` object can be called with energy and temperature values.  Calling the object gives a tuple of 3 cross sections: elastic scattering, radiative capture, and fission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(9.13284265), array(0.50530278), array(2.9316765e-06))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u238_multipole(1.0, 294)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An array can be passed for the energy argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd6c11a0e20>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.linspace(5, 25, 1000)\n",
    "plt.semilogy(E, u238_multipole(E, 293.606)[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real advantage to multipole is that it can be used to generate cross sections at any temperature.  For example, this plot shows the Doppler broadening of the 6.67 eV resonance between 0 K and 900 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd6c0956100>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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34f42vA9FmTD2xUbfiXoyrTU/7jrGkI7hBPt5m5Bgw1zczfi2sDTFpNF73xuND74lfzM2BhduwfTirpTqqpSarpSaq5S6x+z4QjRI+QlY/boxMyRuuCkh9+WUcPB4GWMc3JKpl9g6iLgwfxbtOGpOQA9PGPO88QG44QNzYgqns6q4K6U+UkrlKKV2nvb4WKXUHqVUmlLqSQCt9W6t9R+Ba4GG73oghJlWv25sn3fRX00LuWD7UZSCS7o6p7grpRjfux1r9ueRW1xpTtD4kZBwMaz6l6w74yasHbl/Aow9+QGllCfwDnAZ0A2YopTqVvfcFcBCYJFpmQrRUIVZsP596HVto5fzPZ3WmvlbsxjaMYzIYD9TYjbGuF7tsGhYvNOk0TvAxX+HikJY/YZ5MYXTWFXctdYrgdPveR4IpGmtD2itq4DZwJV1x8/XWl8GmHP1SojGWPESWGptXj/mZNsyCzl4vIwre0eZFrMxEtsE0bl1IN9vM7G4t+lpfBCun258MAqXZkvPPQrIOOnnTCBKKXWhUupNpdT7nGPkrpS6SymVrJRKzs2V3WGEyXL3wpYvYMDt0DLOtLDfbsnCx8uDsT3bmBazscb1asfGQ/kcLTTxIuiop0Bb4JcXzYspnML0C6pa61+01g9ore/WWr9zjuNmaK2TtNZJERGOuX1bNCM/Pwve/jDiMdNC1tRaWLD9KKMTI50yS+Z043q1NXYJ3G7i6L1lLAy409gsPCfVvLjC4Wwp7llA9Ek/t697zGqyh6qwiyNbYfd8GDINAs0bOKzZf5y8kkqu6tvOtJi26BARSK/2IczdlGn7WjMnG/Eo+ATCMlmWwJXZUtw3Ap2UUvFKKR9gMtCg7dVlD1VhFyteMW7OGXKvqWHnbsok2M+LCxMdf+PS2VyTFE3qsWJ2HWnk9ntnEhBm7Nq0ZyFkbTIvrnAoa6dCzgLWAolKqUyl1O1a6xpgGvAjsBuYo7XeZb9UhbDC0e1GURp8r1HgTXKitIrFO49xdd8oh64lcz5X9GqHj5cHXyVnnP/ghhh0N7RoCcul9+6qrJ0tM0Vr3VZr7a21bq+1/k/d44u01p211h211s839M2lLSNMt/JV8A02ipOJvtmSRVWthckDY0yNa6sQf2/Gdm/Dt1uP2LZD0+n8go1Nw9OWGFsSCpfj1OUHpC0jTJWdYvTa60edJtFaM3vjYXpHh9K1re2LjpntmqT2FJZXs3S3yeuyD7gT/MNheYPHbaIJkLVlhPtY+apxIXCwub32LRkF7M0uYfKA6PMf7ARDO4YTFdqC2RtMbs34BsLwh+HAL3DwV3NjC7uT4i7cQ+4e2PUNDLwT/FuZGvq/6w/j7+PJ+N5NY5bM6Tw9FFMGRrM6LY+0nBJzgyfdBoGtYfkLsiSwi3FqcZeeuzDNyn+Cdwtj+qOJ8koqmb/1CBP7tSfQ18vU2GaaPDAGH08PPl970NzAPv7G1MhDqyF9pbmxhV1Jz124vrw02DnXuBs1INzU0DPXHaaq1sKtw+JMjWu28EBfxvVqy9xNmRRXVJsbvN8tEBxl9N5l9O4ypC0jXN+qf4GnjzE320SVNbV8vu4QoxIj6BgRaGpse7hlaBylVbV8vdnkdWG8/YzRe8Z62L/M3NjCbqS4C9eWfwC2f1nXGzb35qIF246SV1LJ1GG2b6btCL2jQ+kdHcqnaw9isZg8wu57E4REG/PeZfTuEqTnLlzbqtfAw8v0UbvWmo/XpJMQGciITua2euxp6tA4DuSWsnyPSbs01fPyMUbvWcmQJqN3VyA9d+G6ThyCbbOg/y0Q3NbU0L+mHWdnVhG3DYtHKWVqbHu6vFdbokJbMH3FfvOD97kBQmLgF5k54wqkLSNc1+rXQXnAsIdMD/328n20DvZlYn/nrtveUN6eHtw5Ip6NB0+w8eDpWzDYyMsHRj5qrDeTttTc2MJ0UtyFayrMNNZr73sjhJhbgJMP5rPuQD53jeyIr1fTWUfGWtcNiKFVgA/Tf7HD6L339RAaI/PeXYAUd+GaVr8BaOMOSpO9vTyNVgE+TBnYNO9IPZ8WPp7cOjSOZak5pB4zcbVIqOu9PwZHNsO+JebGFqaSC6rC9RQdgc2fQp+6UaSJdmYV8sueXG4fHo+/T9O9ael8bh4Si7+PJ2//nGZ+8Pr/79J7b9LkgqpwPb/+29gbdfgjpod+fclegv28uGlIrOmxHSnU34epw+JYsP0ou4+aPHr39IaRf4IjW2DfT+bGFqaRtoxwLcXHYNMn0HsytDJ3/vnGg/ksS83hngsTmsQ2era6a0RHgvy8eG3JXvOD954CobHGXqsyem+SpLgL17LmLaitMuZcm0hrzcs/pBIZ5MutQ+NMje0sIf7e3DWiA0tSstmaUWBu8JNH73t/NDe2MIUUd+E6SnJh43+g57UQ1tHU0D+n5pB86AQPXtyJFj6uN0PmbKYOj6dVgA//+mmP+cF7T4aWcTJ6b6LkgqpwHWvfgpoKGPmYqWFrLZpXFu8hLsyfa5Ncc4bM2QT6enHPBR1ZtS+PtfuPmxu8fvR+dCvsXWxubGEzuaAqXEPpcdjwIfSYCOGdTA09b1Mme7KLefTSRLw93e/L7E1DYokKbcFzC1OoNXvNmV6ToWW8jN6bIPf7TRbuad07UF1m+qi9qKKaV35MpV9MKON6mbuEQVPh5+3JE5d1YdeRIuZtyjQ3uKdX3eh9G+z5wdzYwiZS3EXTV5YP62dAtyshsqupod9cuo/jpVU8c0UPl1pDpqHG92pLv5hQXvlxDyWVNeYG73WdjN6bICnuoulb9x5UFRsjRBOl5RTzyZqDXJcUTc/27t0aVErx9Lhu5JVU8u5yk29s8vSCCx6HY9thzyJzY4tGk+IumrbyE7B+OnQdD216mBZWa80z36fQwseTx8Ykmha3Kesb05Kr+0bx4ep00vNKzQ3e81po1UFG702IFHfRtK17DyqL4IInTA07f9sRVu3L4+GLOxMe6Gtq7Kbsycu64OvpwdPf7kSbWYQ9vWDk43BsB6QuNC+uaDQp7qLpKi+AddOhyzho09O0sCdKq/jH9yn0bh/CLW5yw5K1Wgf78fjYRFan5fHd1iPmBu95DbTqCL+8BBaLubFFg8k8d9F0rZ8OlYWmj9qfW7ibwvJqXprYC08P972IejbXD4qld3Qozy5IoaCsyrzA9b337B2wR0bvzibz3EXTVF4Aa981Ru1te5kWdvW+POZtzuTuCzrQtW2waXFdiaeH4oWre1BQXs3Li1PNDd5jEoQlyOi9CZC2jGia1r9fN2p/3LSQpZU1/PmbHXQID+D+0ebeCOVqurcL4fbh8czakMGatDzzAtf33rN3QuoC8+KKBpPiLpqeikLjpqXEy6Ftb9PCPrcwhYwTZbw0sRd+3u6zfkxjPXxxZzqEB/Cnudspqqg2L3BPGb03BVLcRdOz/n2jwF9oXq99SUo2szZkcPfIjgyMb2VaXFfWwseTf17bm6OF5Ty3IMW8wB6exnWSnF2Q+r15cUWDSHEXTUv5CVj7NiT+wbRRe25xJU/O2063tsE8cklnU2K6i34xLbnnwo7MSc5kaUq2eYF7TISwTjJ6dyIp7qJp+fVNqCiCUU+ZEk5rzZPztlNcWcMbk/vg4yW/8qd74KJOdGkTxJNf7+B4SaU5QX8bvafA7vnmxBQNIr/poukoPmZMf+w5ybS7Uf+zOp1lqTk8ObYLnVsHmRLT3fh6efL6dX0oqqjmkTnbsJi1cmSPCRDeGVa8LKN3J5DiLpqOla8auyyN+rMp4TYdOsFLP6RyabfWTB0WZ0pMd9W1bTB/HdeNFXtzmbHqgDlBTxm9f2dOTGE1Ke6iachPN/ZG7XezsUaJreFKq5j23820DfXj1Wt6u/WKj2a5YVAMl/dsy6s/7mHToXxzgna/GsIT4RcZvTua3KEqmoZfXgQPb2OOtI0sFs3DX27leEkV717fn5AWrr/ZtSMopXhxYk/ahfrxwKyt5ty96uFp3KuQuxtSvrU9nrCa3KEqnC97F2yfA4PugmDbN8z497J9rNiby1/Hd3P7pXzNFuznzdtT+pFTXMGDs7eas3NT96shoosxc6bW5LXkxVlJW0Y435K/gW8wDHvI5lALth/h38v2MbFfe24YFGN7bs1Q7+hQ/n5Fd1bszeWfZmys7eEJo/8CeXtgy2e2xxNWkeIunCttKaQtgQv+BP623Vy0I7OQx77aRv/Ylrwwwb13VrK3GwbFMmVgDO/9sp8F201YPbLLOIgdBj8/b0x1FXYnxV04T20N/PiUsUXbwLtsCpVTVMGdnyUTFuDL9Bv74+slywvY6pkrupMU25I/fbWdlCM2FmSlYMzzUJYHq18zJ0FxTlLchfNs+hhyU+HSZ8Gr8RtmlFfVcufnmygsr+aDm5OICGo+m2/Yk4+XB+/e2I+QFt7c+VkyOcUVtgVs1xd6TzFW+zxxyJwkxVlJcRfOUV4Ay1+AuBHGV/ZGqqm1cP+szWzPLOCNyX3o1q55LuNrL5FBfnxwcxL5pVXc9slGSm3dXHv006A8YNkz5iQozkqKu3COFa8Y68iMecH4yt4IWmv+8u1Olu7O4R9XdGdM9zYmJykAerYP4Z0b+pJypIj7Z22hptaG+eohUTDsAdg5Dw6vMy9J8TtS3IXjHd1uLDPQ/xabNuJ4fek+Zm/MYNqoBG4aEmdefuJ3RndpzbNX9eDn1Bz+Nn+XbfuvDnsQgtvDwkdlaqQdSXEXjmWxwIKHoUVLuPjvjQ7z+bpDvLlsH9cmtefRS2WlR0e4YVAsf7ygIzPXH+ad5WmND+QTAJe9ZGzosWGGeQmKU0hxF461+RPISjbaMS1aNirEV8kZPP3tTi7qEskLV/eUKY8O9PiYRK7uG8U/f9rLp2sONj5Ql3HQ6VJY/jwUmbxRtwCkuAtHKsmBpX83LqL2urZRIb7bmsXj87YzolM479zQDy9P+RV2JA8PxauTenFpt9b8bf4u5m7KbFwgpeCyV8BSAz+as1CcOJX8ZQjHWfwkVJXB5a816iLqoh1HeWTONgbHhzHjpiTZKs9JvDw9eOv6vozoFM7jc7fxw46jjQvUKh5GPAa7voF9S8xNUkhxFw6y61tjhsQFj0NEw3vkP+46xgOzttAvJpQPb0mihY8Udmfy9fLk/Zv60zemJQ/M3sLPqY3cxWnYA8a6M98/aGytKEwjxV3YX0kuLHzEuIll+MMNfvl3W7O4d+ZmerYP4aNbBxDg62WHJEVD+ft48dGtA+jSJpi7P9/ET7uONTyIly9c9a6xUYu0Z0xll+KulLpKKfWBUupLpdSl9ngP4SK0hgUPQWUJXDUdPBu2/O7sDYd56MutJMW25PPbBxHkJ8v3NiUhLbz54o5BdG8Xwr0zN7OoMS2aqP7G9MgtX0h7xkRWF3el1EdKqRyl1M7THh+rlNqjlEpTSj0JoLX+Vmt9J/BH4DpzUxYuZdssSF1grAoY2aVBL/1w1QGe/HoHF3SO4JOpAwmUEXuTFNLCm89vH0jv6FDun7WF77ZmNTzIhU8a7Zn5Dxh3LwubNWTk/gkw9uQHlFKewDvAZUA3YIpSqttJh/yl7nnRHOXuMW5UiRsBQ+6z+mVaa/69dB/PLdzNZT3aMOMm6bE3dUF+3nx220D6x7bk4S+3NnwWTX17piTbuA/ClpukBNCA4q61XgmcvvfWQCBNa31Aa10FzAauVIaXgR+01pvPFE8pdZdSKlkplZybm9vY/EVTVV0OX90K3v4w4QNjTW8r1NRa+PM3O3l96V4m9mvPW1P64uMll4ZcQYCvF59MHcDQjuE89tU2Zqzc37AAUf1h9FOw62vY8rl9kmxGbP2riQIyTvo5s+6x+4GLgUlKqT+e6YVa6xla6yStdVJERISNaYgmZ/GTxsbIE963enel0soa7vwsmVkbDnPfqI68OqmXzGN3Mf4+Xvzn1iQu79mWFxal8vzCFCwN2c1p2MMQfwEsehxyUu2XaDNglyam1vpN4E17xBYuYPNnxmbXwx+BhIuteklOcQW3fbKR3UeLeeHqnlwvuyi5LF8vT96c0pewQB8+WJVOXkkVr0zqhbc1H9QeHjBhBrw3DOZOhTuWGssViAazdViUBUSf9HP7usesIhtku6FDa2HBI9BxNIx6yqqX7Msu5up31nAgt5QPb06Swu4GPD0Uz1zRnccu7cw3W7K47ZONFFVUW/fioDbGN76c3fDdNOm/N5KtxX0j0EkpFa+U8gEmA/OtfbFskO1mCg7DlzdCy1iY9BF4nv+L4dKUbK5+dw1VtRa+vGsIo7pEOiBR4QhKKaaN7sTLE3uyZv9xJr23hoz8MutenHAxXPRXo//+67/tm6ibashUyFnAWiBRKZWplLpda10DTAN+BHYDc7TWu+yTqmjSygtg1hSorYYps8+7KJjWmreW7ePOz5PpEBHA/GnD6NlePuTd0XUDYvjstoEcK6zgynd+Jfng6fMyzmL4w9D9amM9on1L7ZqjO1I2rcts65srNR4Yn5CQcOe+ffucloewUXU5fDERMjbA9V9CwkXnPLy0sobHvtrGDzuPcXXfKF6c0FPWiWkG9ueWcMenyWSdKOeliT2Z0K/9+V9UVQr/GQMFh2DqD9Cmh/0TdSFKqU1a66QzPefUqQjSlnEDtTUw93Y4tAaunn7ewn7oeCkT31vDj7uO8ZfLu/Latb2lsDcTHSMC+ebeofSPbckjc7bx4qLd59/VyScArp8NPoEwcxIUZJz7ePEbmWcmGs9iMRZ82rMQLnsZek465+GLdhxl3JurOVJQzidTB3LHiA6yFnszE+rvw6e3DeSGQTG8v/IAN3+0gbySynO/KKQ93DjXWFH0i4lQZmVbp5lzanGX2TIuzGKB7x+ArV/ABU/AoLvPemhlTS1/+24n987cTIfIQBY+MIKRneXehubKx8uD56/uyauTerHp0AnGv7WaLYdPnPtFrbvD5JlwIt24tlNZ4phkXZi0ZUTDWWph/jTjLsKRj8OF/3fWQw8dL2XSe2v5dO0h7hgez1d3DyG6lb8DkxVN1TVJ0cy7Zyhenorr3l/HzPWHzr03a/wI427nzI3w3+uMfrw4K2nLiIaprYZv74GtM42iPvqps268sWD7Eca9uZpDx0uZcVN//jKumywlIE7RIyqE76cNZ2hCGE99s5OHvtxK8bnmw3e/yrjJ6fCaugJv5dTKZkj+0oT1Kktg1mTY/qWxyuOFT57xsMLyah7+civT/ruFjnVtmEu7t3FwssJVhPr78NEtA3j0ks4s2H6Uy99czdaMgrO/oOckuPp9OPQrzLpOWjRnIVMhhXVKcmDmNXBsB4x7HfrfcsbD1uzP47E528guruSB0Z24b1RHWR9GWC35YD4Pzt5KdlEFj41J5K4RHfDwOMtF921fGt8i2/aGG+ZCQJhjk20CzjUV0qnFvV5SUpJOTk52dhribHL3wH+vNQr8NZ9A5zG/O6SiupZ//riHD1en0yE8gNev60Pv6FCHpypcX2FZNf/3zXYW7TjG8IRwXru2N5HBfmc+eM8PxuqjIdFw0zcQGn3m49yUFHfReKmL4Ou7wNsPpnwJ7fv/7pCtGQU8Pncbe7NLuGlwLH/+Q1dZf13YRGvN7I0ZPPP9Lvy8PXn2yh6M793uzAcfWmv0330C4IavmtWNTk32JibRhFkssOIVmD0FwjrCXb/8rrCXVdXw7IIUJrz7K0XlNXw8dQDPXtVDCruwmVKKKQNjWHD/COLCArh/1hbum7mZ/NKq3x8cOwSmLgI0/OdS2L3A4fk2RTJyF79XfsJYjS91AfSaDOPfAO8Wpxyyel8e//fNdjLyy7lhUAxPXNaFYNnfVNhBTa2F91ce4I2lewlp4c0LV/c88wX6oqMw+3o4shlGPw0jHj3rTC530WTbMnJBtQk6vA7m3QHFR+GSZ2HwPaf8gRSWVfPcwhS+2pRJfHgAL07oyeAOze9ClnC83UeLeHTONlKOFjGhbxRPj+tGywCfUw+qLof598OOr6DHRBj/JvgGOidhB2iyxb2ejNybAEstrH4dlr9gXJSa+NEpbRiLRfP1lixe+mE3J8qquWtkBx68qJOsCyMcqqrGwtvL03h3eRrBLbz567huXNmn3anLWGgNq1+Dn5+DsAS45lNo3e3sQV2YFHdxbgWH4dt74eAqY7Qz7nXw+99dwylHivjrdztJPnSCvjGhPHtlD3pEyV3FwnlSjxXx5LwdbM0oYESncJ6/qicxYafd+XxghfEttLIYLv8n9L3ROcnakRR3cWZaw6aP4aenjZ/HvmT8AdSNggrLq3l9yV4+W3uQUH8fnrysC5P6tT/7vGMhHKjWopm5/hCvLN5DjcXCgxd15o4R8adu51ecDV/fAekrjetHf3jllIGLq5PiLn6v4LDRmzzwi7Eh8ZVvQ6ixvZ3Fopm3OZOXF6eSX1rFjYNjefSSREL85YKpaHqOFpbz9/m7+HFXNl3aBPH3K7qfeh3IUgsrX4UVL0NwFFz1LsSPdF7CJpLiLv7HUgvJH8HSZ0Bb4NJnIem230bra/bn8fzC3ew6UkS/mFD+IS0Y4SJ+3HWMf3yfQlZBOeN7t+PPf+hC25CTZnllbIRv7ob8/TD4Prjo6d/NAnM1Tba4y2wZB8vaDAsfgSNbjNH6FW9CyzgA0nJKeOmH3SzdnUNUaAseH5vI+F7tpAUjXEp5VS3TV+xn+or9eCjFtNEJ3DEiHl+vugv/VaWw5G+w8QMITzS+sUYPdG7SNmiyxb2ejNztrLzAmDmw8UMIjIQxLxgXTpXieEkl/162j5nrD+Pv7cm9oxKYOixOZsEIl5aRX8bzC3ezeNcxYsP8efryblzUNfJ/s2rSlsH8B6AoCwbcbmzG7YK9eCnuzZXFAjvmGBdMy/JgwB3Gao5+IZRU1vDx6nRmrDxAWXUt1w+M4aGLOxEW6OvsrIUwzap9uTzzfQppOSUM6RDGn//Q9X8bsVeWGIOe9dMhqA384Z/QdZxzE24gKe7N0aE18OOfjRZMu34w7jVo15eK6lq+WHeId3/ZT35pFRd3bc2TlyWSEBnk7IyFsIvqWguzNhzmjaX7yC+t4so+7Xjs0sT/bRqTucnYVSx7J3QZZ3yzbRnr3KStJMW9OclPhyV/hd3zIagdXPw36Hkt1RrmJGfw1rI0jhVVMDwhnEcv7UzfmJbOzlgIhyiuqGb6iv18uCodreGWobFMG9XJmAVWWw1r3zbWU9IWGPYQDHsQfJr2rmFS3JuD8gJY9U9Y/z54eBm/nEOnUePZgvnbjvDG0n0czi+jX0woj41JZGjHcGdnLIRTHC0s57Wf9jJ3cybBft7ce2FHbh4SZyx4V5hpDI52zjOWEb70Weh2VZNdo0aKuzurLDF6hmvehIoi6HM9jH6aKv/WfLMlk3d/2c+h42V0axvMY2M6Myox8tRbtYVopnYfLeKlH1JZsTeXiCBf7r2wI1MGxhiTCQ7+Cj88Adk7IG4EjH0R2vR0dsq/02SLu0yFtEF1hTFfffVrUJoLncfCqKeoCO/OnOQMpv+ynyOFFfSMCmHa6AQu6dpapjUKcQYbD+bzr5/2sO5APm1D/Jg2OoFr+kfj46Fh0yfGRdfyE9DrWhj1VJPqxzfZ4l5PRu4NUFttbE694hVjGlf8SBj9V8pa9+W/6w/z/soD5BZX0j+2JfePTuCCzhEyUhfCCmvS8vjXkr1sOnSC9i1b8MBFnZjQNwqvqiL49Q1Y957Rjx9wJ4x8DPxbOTtlKe5uoaYSts0yVm48cRDaD4DRT3M8cjCfrzvEp2sOcqKsmqEdw5g2OoEhHcKkqAvRQFprVuzN5bUle9meWUhsmD9/vKAjE/pF4Vt6DH550Rhc+QTC8Idg0D1Ovegqxd2VVZXB5k/h1zeh+IgxrfGCJ9jfchj/+fUg8zZlUlljYXSXSO4b1ZH+sc4fTQjh6rTWLN2dw9s/72NbZiFtgv24c2QHpgyMxr9gHyz7B+xZBAGRRpHvP9UpRV6KuyuqKDJukV77rnEDUuxw9IhH2ejRmxmr0lmWmo23pwcT+0Vx+/B4macuhB1orVmdlsfbP6exPj2fVgE+3DYsjpuGxBGSu8nY/yB9BQS2NmaoJU116Ho1UtxdSXE2bJhhFPaKQki4hJphD7O4OJ4PVqWzLaOAlv7e3DQ4lpuGxBERJHeUCuEIyQfzeWd5Gsv35BLk68WNQ2KZOjSOyPxNsOIlY1nhwNYw/GHof6tDirwUd1dwbCese9fYHqy2GrqOI7///XxxqBUz1x8iu6iSuDB/bh/RgUn92ssm1EI4yc6sQt79JY0fdh7Dy0NxRe8o7hgRT9fKHUZP/uAqCGwDQ+4zirxfsN1ykeLeVGltLGC09i1jXXVvf3SfG9gVcwMf7IJFO45SXasZ2TmCW4bEcmFiJJ4ynVGIJuFgXikf/5rOnORMyqtrGZ4Qzh0j4rnAZw9q5atGu8Y3BAbeAYP+aCzaZzIp7k1NVZkxQl/3LuSmQlBbqpPuZKHXGD7cfIKdWUUE+XlxTf9obhwcQ4cI993gVwhXV1BWxcz1h/l0zUFyiivpFBnIHSPiuSoyB9/1b0LKfPD0gb43wND7oVUH095bintTkZdm3Hi09Qujn96mJ0e738F/8vsyd1s2BWXVJLYO4uahsVzVJ4oAXy9nZyyEsFJVjYXvtx3hg1UHSD1WTFiAD9cNiOaWxBpa75hhTGW21BjLGQy9H6L62fyeUtydqbYG9i421lI/sBw8vKhJHM+qkCt4c38kWzIK8fZUXNq9DTcNjmVQfCuZny6EC9Nas2b/cT7+9SA/p2YDcHHX1tzex5+Bx2ajkj+CqmKIGQpD7oXEP4BH466hNdni7tbLDxRnw+bPjA2oi7LQwe051mkyH5WNYFZKJSWVNSREBjJ5QDQT+rWnVYCPszMWQpgs80QZM9cf5suNGeSXVtEhIoDbksKYpJbjt+kDKDxsbEw/+J5GxW+yxb2e24zca2sgbSls+dwYrVtqqI67kFWhV/Gvg/HsOlaKn7cH43q1Y/KAaPrHtpRRuhDNQEV1LYt2HOWztYfYmlGAv48nE/q05u7IVKL7jWn0UgbnKu7S1DVDXprRR986C0qOoQMiOJhwMx+WX8CcfT5U12p6RHny3FU9uKJPO4L9vJ2dsRDCgfy8PZnQrz0T+rVnR2Yhn609yFebj/BFTTDPeRVz42Dz7yyXkXtjVZVCynew+XM4vAatPClsP4oFnhfx+sE4jldoIoJ8uapPO67u255u7ew311UI4XoKyqqYtzmLsT3aEBXauBueZORuFosFDq2G7V/Cru+gqpjq0I6sj5vGa9n92LzPDz9vD8Z0b8OEfu0Z1jEML08PZ2cthGiCQv19uH14vN3iS3G3xrGdRkHfOQ+KsrB4B5AWfhGflg9n5rEoVLZiSIcwXr0oist6tiVQpjAKIZxMqtDZFGbCjrmwfQ7k7EJ7eJEVNpS5oTfzfnZnyot96do2mD+NactVfaMa/bVKCCHsQYr7yUqPQ+r3RlE/uBrQ5IX2ZkHoNN7K7sHxjGASIgO5+6K2jOvVjoRIuXNUCNE0SXGvL+i7vjVWddO1FAfEsjTkJt7K7ceBY5HEhfkz5cJ2jOvdlsTWQTJ9UQjR5DXP4n6Ggl7kH8PPAZP48EQfdh6PISrUn3HDjRF6j6hgKehCCJfSfIp7aR6kLjiloBe0iGaJ30Q+LuhDSkUsnSKDGDOyDS92byMFXQjh0ty7uB/fb2yFlboInbEOpS3k+0Xzg/cEZhb3JaUilt7tQxk3qA1vdW9DR1l9UQjhJtyruFsscGQL7FkIqYsgdzcAR/wSWKQm8nVFX/ZUxTEwLoxrL2jNpd3b0E5muQgh3JDrF/eaSkhfZbRc9vwAJcewKE9SfXryde3NLK7pT4Fuy8jO4UxNjOSirq1lkS4hhNtz7eK+/n30smdRVcVUebRgvUdf5lVNYLmlD60CWjN6UCSvdIkkKa4VPl5yp6gQovkwvbgrpToATwEhWutJZsc/2bKcAAqqBrKgqh/r6UGf+DaM7hLJA10iZfciIUSzZlVxV0p9BIwDcrTWPU56fCzwb8AT+FBr/ZLW+gBwu1Jqrj0SPpml4yWsKe/JpC6R/LtzuKy2KIQQdawduX8CvA18Vv+AUsoTeAe4BMgENiql5mutU8xO8mwu6daaS7q1dtTbCSGEy7CqEa21Xgnkn/bwQCBNa31Aa10FzAautPaNlVJ3KaWSlVLJubm5VicshBDi/Gy5yhgFZJz0cyYQpZQKU0pNB/oqpf7vbC/WWs/QWidprZMiIiJsSEMIIcTpTL+gqrU+DvzRmmNP2kPV7DSEEKJZs2XkngVEn/Rz+7rHrKa1/l5rfVdISIgNaQghhDidLcV9I9BJKRWvlPIBJgPzzUlLCCGELawq7kqpWcBaIFEplamUul1rXQNMA34EdgNztNa77JeqEEIIa1nVc9daTznL44uARY19c+m5CyGEfTj1nnzpuQshhH0orbWzc0AplQsccnYejRAO5Dk7CQdrbufc3M4X5JxdSazW+oxzyZtEcXdVSqlkrXWSs/NwpOZ2zs3tfEHO2V3IUolCCOGGpLgLIYQbkuJumxnOTsAJmts5N7fzBTlntyA9dyGEcEMychdCCDckxV0IIdyQFPfzUEqFKqXmKqVSlVK7lVJDTnv+BqXUdqXUDqXUGqVUb2flapbznfNJxw1QStUopey6naIjWHPOSqkLlVJblVK7lFIrnJGnmaz43Q5RSn2vlNpWd85TnZWrGZRSiXX/fvX/FSmlHjrtGKWUelMplVb3d93PSenazLU3yHaMfwOLtdaT6hZI8z/t+XTgAq31CaXUZRgXZgY5OkmTne+c63fiehn4ydHJ2ck5z1kpFQq8C4zVWh9WSkU6IUezne/f+T4gRWs9XikVAexRSs2s25zH5Wit9wB94Lff3yzgm9MOuwzoVPffIOA9XPTvWYr7OSilQoCRwK0Adb/Up/xia63XnPTjOoylj12WNedc535gHjDAYcnZiZXnfD3wtdb6cN0xOY7M0WxWnrMGgpRSCgjE2I2txoFp2tNFwH6t9el3xl8JfKaNmSbr6r7dtNVaH3V8iraRtsy5xQO5wMdKqS1KqQ+VUgHnOP524AfHpGY35z1npVQUcDXGqMYdWPPv3BloqZT6RSm1SSl1s+PTNJU15/w20BU4AuwAHtRaWxycp71MBmad4fEz7jDnkIxMJsX93LyAfsB7Wuu+QCnw5JkOVEqNwijuTzguPbuw5pzfAJ5woz90a87ZC+gPXA6MAZ5WSnV2aJbmsuacxwBbgXYY7Yy3lVLBDszRLupaUFcAXzk7F3uS4n5umUCm1np93c9zMf4gTqGU6gV8CFxZt82gK7PmnJOA2Uqpg8Ak4F2l1FUOy9B81pxzJvCj1rpUa50HrARc+eK5Nec8FaMVpbXWaRjXl7o4MEd7uQzYrLXOPsNzNu8w11RIcT8HrfUxIEMplVj30EVAysnHKKVigK+Bm7TWex2coumsOWetdbzWOk5rHYdRFO7VWn/r0ERNZM05A98Bw5VSXkopf4yLbLsdmKaprDznw3WPo5RqDSQCBxyWpP1M4cwtGTB2k7u5btbMYKDQFfvtIHeonpdSqg/GqNwH4xd7KnAdgNZ6ulLqQ2Ai/1uyuMbVV5c73zmfduwnwAKt9VzHZmkua85ZKfWnusctwIda6zeckatZrPjdbgd8ArQFFPCS1voLpyRrkrrrCoeBDlrrwrrH/gi/nbPCuNYwFigDpmqtk52Vry2kuAshhBuStowQQrghKe5CCOGGpLgLIYQbkuIuhBBuSIq7EEK4ISnuQgjhhqS4CyGEG/p/FbM5CZJ4D84AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "E = np.linspace(6.1, 7.1, 1000)\n",
    "plt.semilogy(E, u238_multipole(E, 0)[1])\n",
    "plt.semilogy(E, u238_multipole(E, 900)[1])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}