unstructured_mesh_flux_and_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4659b074-dc20-4c51-9302-5d7bad3b685b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from IPython.display import Image\n",
    "import openmc\n",
    "import openmc.lib\n",
    "\n",
    "# ensure one of the two mesh libraries is enabled\n",
    "assert(openmc.lib._dagmc_enabled() or openmc.lib._libmesh_enabled())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3972b77f-3938-4a1d-8f4b-386fb33dbbf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (30,10)\n",
    "\n",
    "import urllib.request\n",
    "\n",
    "pin_mesh_moab_url = 'https://tinyurl.com/u9ce9d7' # MOAB file - 22 MB\n",
    "pin_mesh_libmesh_url = 'https://tinyurl.com/yysgs3tr' # Exodus file - 9.7 MB\n",
    "\n",
    "def download(url, filename='dagmc.h5m'):\n",
    "    \"\"\"\n",
    "    Helper function for retrieving dagmc models\n",
    "    \"\"\"\n",
    "    u = urllib.request.urlopen(url)\n",
    "    \n",
    "    if u.status != 200:\n",
    "        raise RuntimeError(\"Failed to download file.\")\n",
    "    \n",
    "    # save file as dagmc.h5m\n",
    "    with open(filename, 'wb') as f:\n",
    "        f.write(u.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1279845c-4072-4ccc-90eb-016ea40b1f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = openmc.examples.pwr_assembly()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d954e78b-82a5-413e-a3e9-5aeb4a1f9f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "min_z = openmc.ZPlane(z0=-10.0)\n",
    "max_z = openmc.ZPlane(z0=10.0)\n",
    "\n",
    "z_region = +min_z & -max_z\n",
    "\n",
    "cells = model.geometry.get_all_cells()\n",
    "for cell in cells.values():\n",
    "    cell.region &= z_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c4d3dc7-98fa-4a04-b171-dcdbd599a335",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "surfaces = model.geometry.get_all_surfaces()\n",
    "# modify the boundary condition of the\n",
    "# planar surfaces bounding the assembly\n",
    "for surface in surfaces.values():\n",
    "    if isinstance(surface, openmc.Plane):\n",
    "        surface.boundary_type = 'vacuum'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16acad89-16b0-4694-8aff-bd20f43f85b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x [cm]', ylabel='z [cm]'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 258.065x389.61 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "root_univ = model.geometry.root_universe\n",
    "\n",
    "# axial image\n",
    "root_univ.plot(width=(22.0, 22.0),\n",
    "               pixels=(200, 300),\n",
    "               basis='xz',\n",
    "               color_by='material',\n",
    "               seed=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3df09b6b-b96e-4489-8c44-8928ffa58bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='x [cm]', ylabel='y [cm]'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 516.129x519.481 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# radial image\n",
    "root_univ.plot(width=(22.0, 22.0),\n",
    "               pixels=(400, 400),\n",
    "               basis='xy',\n",
    "               color_by='material',\n",
    "               seed=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7eec6fda-e5f4-41ef-a15f-90bb92a3c2b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | eea52238dac0162a8b7924b81800af4a3c78798e\n",
      "       Date/Time | 2023-10-22 17:10:28\n",
      "  OpenMP Threads | 8\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      " Reading U234 from /home/dresden/endfb-viii.0-hdf5/neutron/U234.h5\n",
      " Reading U235 from /home/dresden/endfb-viii.0-hdf5/neutron/U235.h5\n",
      " Reading U238 from /home/dresden/endfb-viii.0-hdf5/neutron/U238.h5\n",
      " Reading O16 from /home/dresden/endfb-viii.0-hdf5/neutron/O16.h5\n",
      " Reading Zr90 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr90.h5\n",
      " Reading Zr91 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr91.h5\n",
      " Reading Zr92 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr92.h5\n",
      " Reading Zr94 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr94.h5\n",
      " Reading Zr96 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr96.h5\n",
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n",
      " Reading H1 from /home/dresden/endfb-viii.0-hdf5/neutron/H1.h5\n",
      " Reading B10 from /home/dresden/endfb-viii.0-hdf5/neutron/B10.h5\n",
      " Reading B11 from /home/dresden/endfb-viii.0-hdf5/neutron/B11.h5\n",
      " Reading c_H_in_H2O from /home/dresden/endfb-viii.0-hdf5/neutron/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for Zr90\n",
      " Initializing source particles...\n",
      "\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      "\n",
      "  Bat./Gen.      k            Average k\n",
      "  =========   ========   ====================\n",
      "        1/1    0.26116\n",
      "        2/1    0.30426\n",
      "        3/1    0.30141\n",
      "        4/1    0.17102\n",
      "        5/1    0.28199\n",
      "        6/1    0.21919\n",
      "        7/1    0.23269    0.22594 +/- 0.00675\n",
      "        8/1    0.24033    0.23074 +/- 0.00618\n",
      "        9/1    0.25545    0.23692 +/- 0.00757\n",
      "       10/1    0.23164    0.23586 +/- 0.00596\n",
      " Creating state point statepoint.10.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 3.6731e+00 seconds\n",
      "   Reading cross sections          = 3.6548e+00 seconds\n",
      " Total time in simulation          = 1.9240e-02 seconds\n",
      "   Time in transport only          = 1.1008e-02 seconds\n",
      "   Time in inactive batches        = 6.2928e-03 seconds\n",
      "   Time in active batches          = 1.2947e-02 seconds\n",
      "   Time synchronizing fission bank = 7.3156e-05 seconds\n",
      "     Sampling source sites         = 6.4650e-05 seconds\n",
      "     SEND/RECV source sites        = 5.1690e-06 seconds\n",
      "   Time accumulating tallies       = 2.3840e-06 seconds\n",
      "   Time writing statepoints        = 7.7669e-03 seconds\n",
      " Total time for finalization       = 1.0910e-06 seconds\n",
      " Total time elapsed                = 3.6995e+00 seconds\n",
      " Calculation Rate (inactive)       = 79456.4 particles/second\n",
      " Calculation Rate (active)         = 38618.8 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     = 0.19806 +/- 0.01380\n",
      " k-effective (Track-length)  = 0.23586 +/- 0.00596\n",
      " k-effective (Absorption)    = 0.16169 +/- 0.02607\n",
      " Combined k-effective        = 0.22872 +/- 0.01200\n",
      " Leakage Fraction            = 0.81800 +/- 0.01068\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/dresden/tutorial_trial/statepoint.10.h5')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d555932d-7dbe-4d34-ac1b-500206ab0f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 774.194x779.221 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "pitch = 1.26 # cm\n",
    "\n",
    "img = root_univ.plot(width=(22.0, 22.0),\n",
    "                           pixels=(600, 600),\n",
    "                           basis='xy',\n",
    "                           color_by='material',\n",
    "                           seed=1)\n",
    "\n",
    "# highlight channels\n",
    "for i in range(0, 4):\n",
    "    corner = (i * pitch - pitch / 2.0, -i * pitch - pitch / 2.0)\n",
    "    rect = Rectangle(corner,\n",
    "                     pitch,\n",
    "                     pitch,\n",
    "                     edgecolor='blue',\n",
    "                     fill=False)\n",
    "    img.axes.add_artist(rect)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81e16692-2e78-4483-a3b3-ed948c45a547",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_library = 'moab' # change to 'libmesh' to use libMesh instead\n",
    "\n",
    "if mesh_library == 'moab':\n",
    "    assert(openmc.lib._dagmc_enabled())\n",
    "    mesh_file = 'pins1-4.h5m'\n",
    "    mesh_url = pin_mesh_moab_url\n",
    "elif mesh_library == 'libmesh':\n",
    "    assert(openmc.lib._libmesh_enabled())\n",
    "    mesh_file = 'pins1-4.e'\n",
    "    mesh_url = pin_mesh_libmesh_url\n",
    "    \n",
    "# download the file and create the UnstructuredMesh object\n",
    "download(mesh_url, mesh_file)\n",
    "umesh = openmc.UnstructuredMesh(mesh_file, library=mesh_library)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b7966c0c-2337-42b5-af60-41cfabbc27b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_filter = openmc.MeshFilter(umesh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c436cdff-4c65-46f0-852d-b69f7120a628",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "tally = openmc.Tally()\n",
    "tally.filters = [mesh_filter]\n",
    "tally.scores = ['heating', 'flux']\n",
    "# Only collision estimators are supported for \n",
    "if umesh.library == 'libmesh':\n",
    "    tally.estimator = 'collision'\n",
    "model.tallies = [tally]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9548d4fd-0847-4f5a-9529-51a27207a6db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2023 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.4-dev\n",
      "        Git SHA1 | eea52238dac0162a8b7924b81800af4a3c78798e\n",
      "       Date/Time | 2023-10-22 17:17:28\n",
      "  OpenMP Threads | 8\n",
      "\n",
      " Reading model XML file './model.xml' ...\n",
      " WARNING: Other XML file input(s) are present. These files may be ignored in\n",
      "          favor of the ./model.xml file.\n",
      " Reading cross sections XML file...\n",
      " Reading U234 from /home/dresden/endfb-viii.0-hdf5/neutron/U234.h5\n",
      " Reading U235 from /home/dresden/endfb-viii.0-hdf5/neutron/U235.h5\n",
      " Reading U238 from /home/dresden/endfb-viii.0-hdf5/neutron/U238.h5\n",
      " Reading O16 from /home/dresden/endfb-viii.0-hdf5/neutron/O16.h5\n",
      " Reading Zr90 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr90.h5\n",
      " Reading Zr91 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr91.h5\n",
      " Reading Zr92 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr92.h5\n",
      " Reading Zr94 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr94.h5\n",
      " Reading Zr96 from /home/dresden/endfb-viii.0-hdf5/neutron/Zr96.h5\n",
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n",
      " Reading H1 from /home/dresden/endfb-viii.0-hdf5/neutron/H1.h5\n",
      " Reading B10 from /home/dresden/endfb-viii.0-hdf5/neutron/B10.h5\n",
      " Reading B11 from /home/dresden/endfb-viii.0-hdf5/neutron/B11.h5\n",
      " Reading c_H_in_H2O from /home/dresden/endfb-viii.0-hdf5/neutron/c_H_in_H2O.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Preparing distributed cell instances...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for Zr90\n",
      " Initializing source particles...\n",
      "\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      "\n",
      "  Bat./Gen.      k            Average k\n",
      "  =========   ========   ====================\n",
      "        1/1    0.22044\n",
      "        2/1    0.24692\n",
      "        3/1    0.23062\n",
      "        4/1    0.23878\n",
      "        5/1    0.23020\n",
      "        6/1    0.22981\n",
      "        7/1    0.24291\n",
      "        8/1    0.22419\n",
      "        9/1    0.23067\n",
      "       10/1    0.23179\n",
      "       11/1    0.23870\n",
      "       12/1    0.22173\n",
      "       13/1    0.22872\n",
      "       14/1    0.22279\n",
      "       15/1    0.23158\n",
      "       16/1    0.22825\n",
      "       17/1    0.23753\n",
      "       18/1    0.22892\n",
      "       19/1    0.22355\n",
      "       20/1    0.23478\n",
      "       21/1    0.23998\n",
      "       22/1    0.24226    0.24112 +/- 0.00114\n",
      "       23/1    0.22703    0.23642 +/- 0.00474\n",
      "       24/1    0.23599    0.23631 +/- 0.00335\n",
      "       25/1    0.22303    0.23366 +/- 0.00372\n",
      "       26/1    0.23364    0.23365 +/- 0.00303\n",
      "       27/1    0.23423    0.23374 +/- 0.00257\n",
      "       28/1    0.22060    0.23209 +/- 0.00276\n",
      "       29/1    0.22930    0.23178 +/- 0.00246\n",
      "       30/1    0.22621    0.23123 +/- 0.00227\n",
      "       31/1    0.22879    0.23100 +/- 0.00206\n",
      "       32/1    0.23357    0.23122 +/- 0.00189\n",
      "       33/1    0.23390    0.23142 +/- 0.00175\n",
      "       34/1    0.23341    0.23157 +/- 0.00163\n",
      "       35/1    0.23720    0.23194 +/- 0.00156\n",
      "       36/1    0.23038    0.23184 +/- 0.00147\n",
      "       37/1    0.22815    0.23163 +/- 0.00139\n",
      "       38/1    0.22607    0.23132 +/- 0.00135\n",
      "       39/1    0.23190    0.23135 +/- 0.00128\n",
      "       40/1    0.24004    0.23178 +/- 0.00129\n",
      "       41/1    0.23358    0.23187 +/- 0.00123\n",
      "       42/1    0.24023    0.23225 +/- 0.00123\n",
      "       43/1    0.23366    0.23231 +/- 0.00118\n",
      "       44/1    0.23974    0.23262 +/- 0.00117\n",
      "       45/1    0.22878    0.23247 +/- 0.00113\n",
      "       46/1    0.23163    0.23243 +/- 0.00109\n",
      "       47/1    0.24604    0.23294 +/- 0.00116\n",
      "       48/1    0.23944    0.23317 +/- 0.00114\n",
      "       49/1    0.25283    0.23385 +/- 0.00129\n",
      "       50/1    0.23483    0.23388 +/- 0.00125\n",
      "       51/1    0.22612    0.23363 +/- 0.00124\n",
      "       52/1    0.23199    0.23358 +/- 0.00120\n",
      "       53/1    0.22819    0.23342 +/- 0.00117\n",
      "       54/1    0.22928    0.23329 +/- 0.00114\n",
      "       55/1    0.22206    0.23297 +/- 0.00116\n",
      "       56/1    0.22536    0.23276 +/- 0.00114\n",
      "       57/1    0.23789    0.23290 +/- 0.00112\n",
      "       58/1    0.23641    0.23299 +/- 0.00109\n",
      "       59/1    0.22754    0.23285 +/- 0.00108\n",
      "       60/1    0.22341    0.23262 +/- 0.00107\n",
      "       61/1    0.23573    0.23269 +/- 0.00105\n",
      "       62/1    0.22753    0.23257 +/- 0.00103\n",
      "       63/1    0.23481    0.23262 +/- 0.00101\n",
      "       64/1    0.23748    0.23273 +/- 0.00099\n",
      "       65/1    0.23250    0.23273 +/- 0.00097\n",
      "       66/1    0.23825    0.23285 +/- 0.00096\n",
      "       67/1    0.21815    0.23253 +/- 0.00099\n",
      "       68/1    0.22557    0.23239 +/- 0.00098\n",
      "       69/1    0.23442    0.23243 +/- 0.00096\n",
      "       70/1    0.23020    0.23239 +/- 0.00094\n",
      "       71/1    0.22620    0.23226 +/- 0.00093\n",
      "       72/1    0.23509    0.23232 +/- 0.00091\n",
      "       73/1    0.22370    0.23216 +/- 0.00091\n",
      "       74/1    0.22459    0.23202 +/- 0.00090\n",
      "       75/1    0.22670    0.23192 +/- 0.00089\n",
      "       76/1    0.23318    0.23194 +/- 0.00088\n",
      "       77/1    0.23464    0.23199 +/- 0.00086\n",
      "       78/1    0.22956    0.23195 +/- 0.00085\n",
      "       79/1    0.23086    0.23193 +/- 0.00083\n",
      "       80/1    0.22316    0.23178 +/- 0.00083\n",
      "       81/1    0.22932    0.23174 +/- 0.00082\n",
      "       82/1    0.22944    0.23171 +/- 0.00081\n",
      "       83/1    0.22554    0.23161 +/- 0.00080\n",
      "       84/1    0.23114    0.23160 +/- 0.00079\n",
      "       85/1    0.22733    0.23153 +/- 0.00078\n",
      "       86/1    0.22440    0.23143 +/- 0.00077\n",
      "       87/1    0.24168    0.23158 +/- 0.00078\n",
      "       88/1    0.23157    0.23158 +/- 0.00077\n",
      "       89/1    0.23436    0.23162 +/- 0.00076\n",
      "       90/1    0.22904    0.23158 +/- 0.00075\n",
      "       91/1    0.22476    0.23149 +/- 0.00074\n",
      "       92/1    0.23210    0.23150 +/- 0.00073\n",
      "       93/1    0.22100    0.23135 +/- 0.00074\n",
      "       94/1    0.23795    0.23144 +/- 0.00073\n",
      "       95/1    0.22719    0.23138 +/- 0.00072\n",
      "       96/1    0.23115    0.23138 +/- 0.00071\n",
      "       97/1    0.23149    0.23138 +/- 0.00070\n",
      "       98/1    0.23102    0.23138 +/- 0.00070\n",
      "       99/1    0.23825    0.23146 +/- 0.00069\n",
      "      100/1    0.23317    0.23149 +/- 0.00068\n",
      " Creating state point statepoint.100.h5...\n",
      " WARNING: Output for a MOAB mesh (mesh 1) was requested but will not be written.\n",
      "          Please use the Python API to generated the desired VTK tetrahedral\n",
      "          mesh.\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.5710e+04 seconds\n",
      "   Reading cross sections          = 1.6415e+00 seconds\n",
      " Total time in simulation          = 3.7489e+02 seconds\n",
      "   Time in transport only          = 3.7381e+02 seconds\n",
      "   Time in inactive batches        = 2.7341e+00 seconds\n",
      "   Time in active batches          = 3.7216e+02 seconds\n",
      "   Time synchronizing fission bank = 1.1629e-01 seconds\n",
      "     Sampling source sites         = 1.0740e-01 seconds\n",
      "     SEND/RECV source sites        = 8.7722e-03 seconds\n",
      "   Time accumulating tallies       = 1.5406e-01 seconds\n",
      "   Time writing statepoints        = 7.5075e-01 seconds\n",
      " Total time for finalization       = 8.5738e-01 seconds\n",
      " Total time elapsed                = 1.6086e+04 seconds\n",
      " Calculation Rate (inactive)       = 73149.2 particles/second\n",
      " Calculation Rate (active)         = 2149.62 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     = 0.23225 +/- 0.00064\n",
      " k-effective (Track-length)  = 0.23149 +/- 0.00068\n",
      " k-effective (Absorption)    = 0.23325 +/- 0.00075\n",
      " Combined k-effective        = 0.23225 +/- 0.00064\n",
      " Leakage Fraction            = 0.79303 +/- 0.00051\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "model.settings.particles = 10_000\n",
    "model.settings.inactive = 20\n",
    "model.settings.batches = 100\n",
    "sp_file = model.run(output=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ecbadfb-1234-48a5-b581-7e7ed7094e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import openmc\n",
    "with openmc.StatePoint(\"statepoint.100.h5\") as sp:\n",
    "    sp_tally = sp.get_tally(id=1)\n",
    "    sp_umesh = sp_tally.find_filter(openmc.MeshFilter).mesh\n",
    "    \n",
    "# data coming from the tally object will have three dimensions.\n",
    "# make sure to shape the results to match the mesh elements\n",
    "flux_mean = sp_tally.get_values(scores=['flux'], value='mean').reshape(sp_umesh.dimension)\n",
    "flux_std_dev = sp_tally.get_values(scores=['flux'], value='std_dev').reshape(sp_umesh.dimension)\n",
    "heating_mean = sp_tally.get_values(scores=['heating'], value='mean').reshape(sp_umesh.dimension)\n",
    "heating_std_dev = sp_tally.get_values(scores=['heating'], value='std_dev').reshape(sp_umesh.dimension)\n",
    "\n",
    "vtk_data_dict = {'flux_mean': flux_mean,\n",
    "                 'flux_std_dev': flux_std_dev,\n",
    "                 'heating_mean': heating_mean,\n",
    "                 'heating_std_dev': heating_std_dev}\n",
    "sp_umesh.write_data_to_vtk('pin_data.vtk', datasets=vtk_data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b65c827c-e3df-4046-8d2b-8d2e5c4f67f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "energies = np.logspace(np.log10(1e-5), np.log10(20.0e6), 680289)\n",
    "e_filter = openmc.EnergyFilter(energies)\n",
    "tallies = openmc.Tallies()\n",
    "# Create tally with energy filter\n",
    "tally = openmc.Tally()\n",
    "tally.filters = [e_filter]\n",
    "tally.scores = ['flux']\n",
    "# Create energy filter using SHEM-361 group structure\n",
    "energies_shem = openmc.mgxs.GROUP_STRUCTURES['SHEM-361']\n",
    "shem_filter = openmc.EnergyFilter(openmc.mgxs.GROUP_STRUCTURES['SHEM-361'])\n",
    "\n",
    "tally_shem = openmc.Tally()\n",
    "tally_shem.filters = [shem_filter]\n",
    "tally_shem.scores = ['flux']\n",
    "tallies.append(tally)\n",
    "tallies.export_to_xml()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6fb7818c-2fe8-4b2e-b074-41162e729ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f37245b9de0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.step(energies[:-1], flux500_mean/np.diff(energies), where='post', label='500 group')\n",
    "#ax.step(energies_shem[:-1], flux_shem_mean/np.diff(energies_shem), where='post', label='SHEM-361')\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Energy [eV]')\n",
    "ax.set_ylabel('Flux [n-cm/eV-src]')\n",
    "ax.grid()\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c453579b-498e-4f07-b695-9254b1f97f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "with openmc.StatePoint(\"statepoint.100.h5\") as sp:\n",
    "    t = sp.tallies[1]\n",
    "    flux500_mean = t.mean.ravel()\n",
    "    flux500_unc = t.std_dev.ravel()\n",
    "    \n",
    "    t_shem = sp.tallies[1]\n",
    "    flux_shem_mean = t_shem.mean.ravel()\n",
    "    flux_shem_unc = t_shem.std_dev.ravel()\n",
    "\n",
    "print(flux_shem_e"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}