shielded_room_weight_window.ipynb

{
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   "source": [
    "# Variance Reduction - Weight Windows\n",
    "\n",
    "## Creating and utilizing a wight window to accelerate the deep shielding simulations\n",
    "\n",
    "This example simulates a shield room / bunker with corridor entrance a neutron source in the center of the room with This example implements a single step of the Magic method of weight window generation. \n",
    "\n",
    "In this tutorial we shall focus on generating a weight window to accelerate the simulation of particles through a shield.\n",
    "\n",
    "Weight Windows are found using the MAGIC method and used to accelerate the simulation.\n",
    "\n",
    "The variance reduction method used for this simulation is well documented in the OpenMC documentation\n",
    "https://docs.openmc.org/en/stable/methods/neutron_physics.html\n",
    "\n",
    "The MAGIC method is well described in the original publication\n",
    "https://scientific-publications.ukaea.uk/wp-content/uploads/Published/INTERN1.pdf\n",
    "\n",
    "This example has been copied with permission from the neutronics-workshop\n",
    "https://github.com/fusion-energy/neutronics-workshop\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f00eddb1-1e3a-4973-ba0c-d8feeb2a6704",
   "metadata": {},
   "source": [
    "First we import ```openmc``` including ```openmc.lib``` and other packages needed for the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3448d1db-4328-42e3-8960-50d53896f541",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time  # used to time the simulation\n",
    "\n",
    "import openmc\n",
    "import openmc.lib  # this example makes use of openmc lib to run the simulations\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import LogNorm  # used for plotting log scale graphs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8179e89a-304b-4684-91a9-8a908b75e8cd",
   "metadata": {},
   "source": [
    "We create a couple of materials for the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "47cb21c7-0afa-446c-9a6b-76c8dfb2f93e",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_air = openmc.Material(name=\"Air\")\n",
    "mat_air.add_element(\"N\", 0.784431)\n",
    "mat_air.add_element(\"O\", 0.210748)\n",
    "mat_air.add_element(\"Ar\", 0.0046)\n",
    "mat_air.set_density(\"g/cc\", 0.001205)\n",
    "\n",
    "mat_concrete = openmc.Material()\n",
    "mat_concrete.add_element(\"H\",0.168759)\n",
    "mat_concrete.add_element(\"C\",0.001416)\n",
    "mat_concrete.add_element(\"O\",0.562524)\n",
    "mat_concrete.add_element(\"Na\",0.011838)\n",
    "mat_concrete.add_element(\"Mg\",0.0014)\n",
    "mat_concrete.add_element(\"Al\",0.021354)\n",
    "mat_concrete.add_element(\"Si\",0.204115)\n",
    "mat_concrete.add_element(\"K\",0.005656)\n",
    "mat_concrete.add_element(\"Ca\",0.018674)\n",
    "mat_concrete.add_element(\"Fe\",0.00426)\n",
    "mat_concrete.set_density(\"g/cm3\", 2.3)\n",
    "\n",
    "materials = openmc.Materials([mat_air, mat_concrete])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "424b234c-48a2-4d1b-b374-f69430e464db",
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   "source": [
    "Now we define and plot the geometry. This geometry is define by parameters for every width and height. The parameters input into the geometry in a stacked manner so they can easily be adjusted to change the geometry without creating overlapping cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a72dc5e3-cb8e-41c2-9cfb-f8f7489c7f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "width_a = 100\n",
    "width_b = 200\n",
    "width_c = 500\n",
    "width_d = 250\n",
    "width_e = 200\n",
    "width_f = 200\n",
    "width_g = 100\n",
    "\n",
    "depth_a = 100\n",
    "depth_b = 200\n",
    "depth_c = 700\n",
    "depth_d = 600\n",
    "depth_e = 200\n",
    "depth_f = 100\n",
    "\n",
    "height_j = 100\n",
    "height_k = 500\n",
    "height_l = 100\n",
    "\n",
    "xplane_0 = openmc.XPlane(x0=0, boundary_type=\"vacuum\")\n",
    "xplane_1 = openmc.XPlane(x0=xplane_0.x0 + width_a)\n",
    "xplane_2 = openmc.XPlane(x0=xplane_1.x0 + width_b)\n",
    "xplane_3 = openmc.XPlane(x0=xplane_2.x0 + width_c)\n",
    "xplane_4 = openmc.XPlane(x0=xplane_3.x0 + width_d)\n",
    "xplane_5 = openmc.XPlane(x0=xplane_4.x0 + width_e)\n",
    "xplane_6 = openmc.XPlane(x0=xplane_5.x0 + width_f)\n",
    "xplane_7 = openmc.XPlane(x0=xplane_6.x0 + width_g, boundary_type=\"vacuum\")\n",
    "\n",
    "yplane_0 = openmc.YPlane(y0=0, boundary_type=\"vacuum\")\n",
    "yplane_1 = openmc.YPlane(y0=yplane_0.y0 + depth_a)\n",
    "yplane_2 = openmc.YPlane(y0=yplane_1.y0 + depth_b)\n",
    "yplane_3 = openmc.YPlane(y0=yplane_2.y0 + depth_c)\n",
    "yplane_4 = openmc.YPlane(y0=yplane_3.y0 + depth_d)\n",
    "yplane_5 = openmc.YPlane(y0=yplane_4.y0 + depth_e)\n",
    "yplane_6 = openmc.YPlane(y0=yplane_5.y0 + depth_f, boundary_type=\"vacuum\")\n",
    "\n",
    "zplane_1 = openmc.ZPlane(z0=0, boundary_type=\"vacuum\")\n",
    "zplane_2 = openmc.ZPlane(z0=zplane_1.z0 + height_j)\n",
    "zplane_3 = openmc.ZPlane(z0=zplane_2.z0 + height_k)\n",
    "zplane_4 = openmc.ZPlane(z0=zplane_3.z0 + height_l, boundary_type=\"vacuum\")\n",
    "\n",
    "outside_left_region = +xplane_0 & -xplane_1 & +yplane_1 & -yplane_5 & +zplane_1 & -zplane_4\n",
    "wall_left_region = +xplane_1 & -xplane_2 & +yplane_2 & -yplane_4 & +zplane_2 & -zplane_3\n",
    "wall_right_region = +xplane_5 & -xplane_6 & +yplane_2 & -yplane_5 & +zplane_2 & -zplane_3\n",
    "wall_top_region = +xplane_1 & -xplane_4 & +yplane_4 & -yplane_5 & +zplane_2 & -zplane_3\n",
    "outside_top_region = +xplane_0 & -xplane_7 & +yplane_5 & -yplane_6 & +zplane_1 & -zplane_4\n",
    "wall_bottom_region = +xplane_1 & -xplane_6 & +yplane_1 & -yplane_2 & +zplane_2 & -zplane_3\n",
    "outside_bottom_region = +xplane_0 & -xplane_7 & +yplane_0 & -yplane_1 & +zplane_1 & -zplane_4\n",
    "wall_middle_region = +xplane_3 & -xplane_4 & +yplane_3 & -yplane_4 & +zplane_2 & -zplane_3\n",
    "outside_right_region = +xplane_6 & -xplane_7 & +yplane_1 & -yplane_5 & +zplane_1 & -zplane_4\n",
    "\n",
    "room_region = +xplane_2 & -xplane_3 & +yplane_2 & -yplane_4 & +zplane_2 & -zplane_3\n",
    "gap_region = +xplane_3 & -xplane_4 & +yplane_2 & -yplane_3 & +zplane_2 & -zplane_3\n",
    "corridor_region = +xplane_4 & -xplane_5 & +yplane_2 & -yplane_5 & +zplane_2 & -zplane_3\n",
    "\n",
    "roof_region = +xplane_1 & -xplane_6 & +yplane_1 & -yplane_5 & +zplane_1 & -zplane_2\n",
    "floor_region = +xplane_1 & -xplane_6 & +yplane_1 & -yplane_5 & +zplane_3 & -zplane_4\n",
    "\n",
    "outside_left_cell = openmc.Cell(region=outside_left_region, fill=mat_air)\n",
    "outside_right_cell = openmc.Cell(region=outside_right_region, fill=mat_air)\n",
    "outside_top_cell = openmc.Cell(region=outside_top_region, fill=mat_air)\n",
    "outside_bottom_cell = openmc.Cell(region=outside_bottom_region, fill=mat_air)\n",
    "wall_left_cell = openmc.Cell(region=wall_left_region, fill=mat_concrete)\n",
    "wall_right_cell = openmc.Cell(region=wall_right_region, fill=mat_concrete)\n",
    "wall_top_cell = openmc.Cell(region=wall_top_region, fill=mat_concrete)\n",
    "wall_bottom_cell = openmc.Cell(region=wall_bottom_region, fill=mat_concrete)\n",
    "wall_middle_cell = openmc.Cell(region=wall_middle_region, fill=mat_concrete)\n",
    "room_cell = openmc.Cell(region=room_region, fill=mat_air)\n",
    "gap_cell = openmc.Cell(region=gap_region, fill=mat_air)\n",
    "corridor_cell = openmc.Cell(region=corridor_region, fill=mat_air)\n",
    "\n",
    "roof_cell = openmc.Cell(region=roof_region, fill=mat_concrete)\n",
    "floor_cell = openmc.Cell(region=floor_region, fill=mat_concrete)\n",
    "\n",
    "geometry = openmc.Geometry(\n",
    "    [\n",
    "        outside_bottom_cell,\n",
    "        outside_top_cell,\n",
    "        outside_left_cell,\n",
    "        outside_right_cell,\n",
    "        wall_left_cell,\n",
    "        wall_right_cell,\n",
    "        wall_top_cell,\n",
    "        wall_bottom_cell,\n",
    "        wall_middle_cell,\n",
    "        room_cell,\n",
    "        gap_cell,\n",
    "        corridor_cell,\n",
    "        roof_cell,\n",
    "        floor_cell,\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "51713fe1",
   "metadata": {},
   "source": [
    "Now we plot the geometry and color by materials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "94c41f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 232.258x292.715 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "geometry.root_universe.plot(basis='xy', color_by='material') \n",
    "plt.savefig('geometry_top_down_view.png', bbox_inches=\"tight\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d6bd790",
   "metadata": {},
   "source": [
    "Next we create a point source, this also uses the same geometry parameters to place in the center of the room regardless of the values of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fd4986b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# location of the point source\n",
    "source_x = width_a + width_b + width_c * 0.5\n",
    "source_y = depth_a + depth_b + depth_c * 0.75\n",
    "source_z = height_j + height_k * 0.5\n",
    "space = openmc.stats.Point((source_x, source_y, source_z))\n",
    "\n",
    "angle = openmc.stats.Isotropic()\n",
    "\n",
    "# all (100%) of source particles are 2.5MeV energy\n",
    "energy = openmc.stats.Discrete([2.5e6], [1.0])\n",
    "\n",
    "source = openmc.IndependentSource(space=space, angle=angle, energy=energy)\n",
    "source.particle = \"neutron\"\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1456ac2f",
   "metadata": {},
   "source": [
    "Next we create a mesh that encompasses the entire geometry and scores neutron flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9d7bbee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jshimwell/openmc/openmc/mixin.py:70: IDWarning: Another Tally instance already exists with id=42.\n",
      "  warn(msg, IDWarning)\n"
     ]
    }
   ],
   "source": [
    "mesh = openmc.RegularMesh().from_domain(geometry)\n",
    "mesh.dimension = (500, 500, 1)\n",
    "\n",
    "mesh_filter = openmc.MeshFilter(mesh)\n",
    "particle_filter = openmc.ParticleFilter('neutron')\n",
    "\n",
    "flux_tally = openmc.Tally(name=\"flux tally\")\n",
    "flux_tally.filters = [mesh_filter, particle_filter]\n",
    "flux_tally.scores = [\"flux\"]\n",
    "flux_tally.id = 42  # we set the ID because we need to access this later\n",
    "\n",
    "tallies = openmc.Tallies([flux_tally])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2bc34eac",
   "metadata": {},
   "source": [
    "Creates the simulation settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ea32fb73",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings = openmc.Settings()\n",
    "\n",
    "settings.run_mode = \"fixed source\"\n",
    "settings.source = source\n",
    "settings.particles = 40000\n",
    "settings.batches = 5\n",
    "# no need to write the tallies.out file which saves space and time when large meshes are used\n",
    "settings.output = {'tallies': False}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9bc4e52b",
   "metadata": {},
   "source": [
    "Creates and export the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6d8042a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = openmc.Model(geometry, materials, settings, tallies)\n",
    "model.export_to_xml()  # this is necessary as openmc.lib loads up the model.xml file"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "488391db",
   "metadata": {},
   "source": [
    "Now we make use of openmc.lib to control the simulation. Documentation on openmc.lib is here\n",
    "https://docs.openmc.org/en/stable/pythonapi/capi.html\n",
    "\n",
    "We also time the simulation so that we can perform the same simulation again with weight windows and try to fine tune the particle so that both simulations take the same time and we are making a fair comparision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9159a336",
   "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 | cd13d3471da30213bf68b91739994b73bb07cb90\n",
      "       Date/Time | 2023-08-07 10:11:14\n",
      "  OpenMP Threads | 28\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading N14 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/N14.h5\n",
      " Reading N15 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/N15.h5\n",
      " Reading O16 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/O16.h5\n",
      " Reading O17 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/O17.h5\n",
      " Reading O18 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/O18.h5\n",
      " Reading Ar36 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ar36.h5\n",
      " Reading Ar38 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ar38.h5\n",
      " Reading Ar40 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ar40.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading H1 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/H1.h5\n",
      " Reading H2 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/H2.h5\n",
      " Reading C12 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/C12.h5\n",
      " Reading C13 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/C13.h5\n",
      " Reading Na23 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Na23.h5\n",
      " Reading Mg24 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Mg24.h5\n",
      " Reading Mg25 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Mg25.h5\n",
      " Reading Mg26 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Mg26.h5\n",
      " Reading Al27 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Al27.h5\n",
      " Reading Si28 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Si28.h5\n",
      " Reading Si29 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Si29.h5\n",
      " Reading Si30 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Si30.h5\n",
      " Reading K39 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/K39.h5\n",
      " Reading K40 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/K40.h5\n",
      " Reading K41 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/K41.h5\n",
      " Reading Ca40 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca40.h5\n",
      " Reading Ca42 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca42.h5\n",
      " Reading Ca43 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca43.h5\n",
      " Reading Ca44 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca44.h5\n",
      " Reading Ca46 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca46.h5\n",
      " Reading Ca48 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca48.h5\n",
      " Reading Fe54 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe54.h5\n",
      " Reading Fe56 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe56.h5\n",
      " Reading Fe57 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe57.h5\n",
      " Reading Fe58 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe58.h5\n",
      " Minimum neutron data temperature: 0 K\n",
      " Maximum neutron data temperature: 1.7976931348623157e+308 K\n",
      " Reading tallies XML file...\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for N15\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Creating state point statepoint.5.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.6774e+00 seconds\n",
      "   Reading cross sections          = 1.6547e+00 seconds\n",
      " Total time in simulation          = 3.6922e+00 seconds\n",
      "   Time in transport only          = 3.6782e+00 seconds\n",
      "   Time in active batches          = 3.6922e+00 seconds\n",
      "   Time accumulating tallies       = 2.0352e-03 seconds\n",
      "   Time writing statepoints        = 1.1032e-02 seconds\n",
      " Total time for finalization       = 9.9600e-07 seconds\n",
      " Total time elapsed                = 5.3843e+00 seconds\n",
      " Calculation Rate (active)         = 54167.8 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.00387 +/- 0.00013\n",
      "\n",
      "total time without weight windows 5.441159009933472s\n"
     ]
    }
   ],
   "source": [
    "# this helps time the simulation\n",
    "t0 = time.time()\n",
    "\n",
    "# first we initialize openmc lib, this reads in the model.xml and material cross sections \n",
    "openmc.lib.init()\n",
    "\n",
    "# This runs openmc with the settings provided earlier\n",
    "openmc.lib.run()\n",
    "\n",
    "# End the connection to openmc lib and write out the statepoint file\n",
    "openmc.lib.finalize()\n",
    "\n",
    "# End the connection to openmc lib and write out the statepoint file\n",
    "openmc.lib.finalize()\n",
    "\n",
    "t1 = time.time()\n",
    "\n",
    "total = t1-t0\n",
    "\n",
    "print(f'total time without weight windows {total}s')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "79ec2340",
   "metadata": {},
   "source": [
    "Now we want to plot the results of the simulation. We want to do this twice to compare the results so I've written this up as a function that we can call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e6f60d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mesh_tally(statepoint_filename, image_filename):\n",
    "\n",
    "    with openmc.StatePoint(statepoint_filename) as sp:\n",
    "        flux_tally = sp.get_tally(name=\"flux tally\")\n",
    "\n",
    "    mesh_extent = mesh.bounding_box.extent['xy']\n",
    "\n",
    "    # get a slice of mean values on the xy basis mid z axis\n",
    "    flux_mean = flux_tally.get_reshaped_data(value='mean', expand_dims=True).squeeze()\n",
    "    plt.subplot(1, 2, 1)\n",
    "    # create a plot of the mean flux values\n",
    "    plt.imshow(\n",
    "        flux_mean.T,\n",
    "        origin=\"lower\",\n",
    "        extent=mesh_extent,\n",
    "        norm=LogNorm(),\n",
    "    )\n",
    "    plt.title(\"Flux Mean\")\n",
    "\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    # get a slice of std dev values on the xy basis mid z axis\n",
    "    flux_std_dev = flux_tally.get_reshaped_data(value='std_dev', expand_dims=True).squeeze()\n",
    "    # create a plot of the flux relative error\n",
    "    plt.imshow(\n",
    "        flux_std_dev.T,\n",
    "        origin=\"lower\",\n",
    "        extent=mesh_extent,\n",
    "        norm=LogNorm(),\n",
    "    )\n",
    "    plt.title(\"Flux Std. Dev.\")\n",
    "\n",
    "    plt.savefig(image_filename)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b15e186f",
   "metadata": {},
   "source": [
    "This next section calls the plotting function and saves an image of the mesh tally as no_ww.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8fb8f31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mesh_tally(f'statepoint.{settings.batches}.h5', 'no_ww.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2abd8995",
   "metadata": {},
   "source": [
    "Now we want to run the simulation twice. Once to generate weight windows and once to make use of the weight windows. We are going to adjust the number of particles to try and spend the same total amount of computer time as the last simulation and make this a fair test. This might require some adaption of the particles as the simulation is set up for my laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d519ac33",
   "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 | cd13d3471da30213bf68b91739994b73bb07cb90\n",
      "       Date/Time | 2023-08-07 10:11:20\n",
      "  OpenMP Threads | 28\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading N14 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/N14.h5\n",
      " Reading N15 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/N15.h5\n",
      " Reading O16 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/O16.h5\n",
      " Reading O17 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/O17.h5\n",
      " Reading O18 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/O18.h5\n",
      " Reading Ar36 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ar36.h5\n",
      " Reading Ar38 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ar38.h5\n",
      " Reading Ar40 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ar40.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading H1 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/H1.h5\n",
      " Reading H2 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/H2.h5\n",
      " Reading C12 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/C12.h5\n",
      " Reading C13 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/C13.h5\n",
      " Reading Na23 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Na23.h5\n",
      " Reading Mg24 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Mg24.h5\n",
      " Reading Mg25 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Mg25.h5\n",
      " Reading Mg26 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Mg26.h5\n",
      " Reading Al27 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Al27.h5\n",
      " Reading Si28 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Si28.h5\n",
      " Reading Si29 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Si29.h5\n",
      " Reading Si30 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Si30.h5\n",
      " Reading K39 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/K39.h5\n",
      " Reading K40 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/K40.h5\n",
      " Reading K41 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/K41.h5\n",
      " Reading Ca40 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca40.h5\n",
      " Reading Ca42 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca42.h5\n",
      " Reading Ca43 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca43.h5\n",
      " Reading Ca44 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca44.h5\n",
      " Reading Ca46 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca46.h5\n",
      " Reading Ca48 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Ca48.h5\n",
      " Reading Fe54 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe54.h5\n",
      " Reading Fe56 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe56.h5\n",
      " Reading Fe57 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe57.h5\n",
      " Reading Fe58 from /home/jshimwell/ENDF-B-VIII.0-NNDC/h5_files/neutron/Fe58.h5\n",
      " Minimum neutron data temperature: 0 K\n",
      " Maximum neutron data temperature: 1.7976931348623157e+308 K\n",
      " Reading tallies XML file...\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for N15\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Creating state point statepoint.5.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.7459e+00 seconds\n",
      "   Reading cross sections          = 1.7213e+00 seconds\n",
      " Total time in simulation          = 6.6299e-01 seconds\n",
      "   Time in transport only          = 6.4699e-01 seconds\n",
      "   Time in active batches          = 6.6299e-01 seconds\n",
      "   Time accumulating tallies       = 4.1188e-03 seconds\n",
      "   Time writing statepoints        = 1.0360e-02 seconds\n",
      " Total time for finalization       = 1.1060e-06 seconds\n",
      " Total time elapsed                = 2.4252e+00 seconds\n",
      " Calculation Rate (active)         = 45249.9 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.00397 +/- 0.00034\n",
      "\n",
      " Maximum neutron transport energy: 20000000 eV for N15\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Creating state point statepoint.5.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.7459e+00 seconds\n",
      "   Reading cross sections          = 1.7213e+00 seconds\n",
      " Total time in simulation          = 4.6058e+00 seconds\n",
      "   Time in transport only          = 4.5387e+00 seconds\n",
      "   Time in active batches          = 4.6058e+00 seconds\n",
      "   Time accumulating tallies       = 4.4872e-02 seconds\n",
      "   Time writing statepoints        = 2.0340e-02 seconds\n",
      " Total time for finalization       = 1.9870e-06 seconds\n",
      " Total time elapsed                = 6.3840e+00 seconds\n",
      " Calculation Rate (active)         = 325.674 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.00402 +/- 0.00056\n",
      "\n",
      "total time without weight windows 6.514809846878052s\n"
     ]
    }
   ],
   "source": [
    "# this helps time the simulation\n",
    "t0 = time.time()\n",
    "\n",
    "# first we initialize openmc lib, this reads in the model.xml and material cross sections \n",
    "openmc.lib.init()\n",
    "\n",
    "# Now we find the mesh tally that will be used for creating the weight windows \n",
    "# This ID of 42 matches the tally ID we set earlier\n",
    "# At this point the tally is empty.\n",
    "tally = openmc.lib.tallies[42]\n",
    "\n",
    "# We create a weight window object from the empty tally. \n",
    "wws = openmc.lib.WeightWindows.from_tally(tally)\n",
    "\n",
    "# We are running a fraction of particles of the original simulation as we want\n",
    "# to take less simulation time for the first run and leave some simulation time\n",
    "# for the run with weight windows.\n",
    "openmc.lib.settings.particles = 6000\n",
    "\n",
    "# This runs openmc with the settings provided earlier\n",
    "openmc.lib.run()\n",
    "\n",
    "# The tally now contains meaningful information.\n",
    "# So we can update the weight windows from the tally.\n",
    "# This uses the MAGIC method\n",
    "wws.update_magic(tally)\n",
    "\n",
    "# Now we enable weight window usage, previously this was set to False by default\n",
    "openmc.lib.settings.weight_windows_on = True\n",
    "\n",
    "# As the particles are now splitting the number of particles run per second\n",
    "# will be significantly lower. Therefore we will reduce the number of particles\n",
    "# so that the simulation runs in  reasonable amount of time\n",
    "openmc.lib.settings.particles = 300\n",
    "\n",
    "# Now we run the simulation again, this time with the weight windows in use\n",
    "openmc.lib.run()\n",
    "\n",
    "# End the connection to openmc lib and write out the statepoint file\n",
    "openmc.lib.finalize()\n",
    "\n",
    "t1 = time.time()\n",
    "\n",
    "total = t1-t0\n",
    "\n",
    "print(f'total time without weight windows {total}s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fbf35ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mesh_tally(f'statepoint.{settings.batches}.h5', 'ww.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b086b6ec",
   "metadata": {},
   "source": [
    "On my laptop both simulations took 20 seconds to complete but the resulting flux map from the simulation with weight windows shows neutrons got further through the geometry.\n",
    "\n",
    "The weight windows allow the computer to spend a larger proportion of time simulating valuable neutrons that have reached regions of low neutron flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c56069a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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