OpenMC/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n",
    "\n",
    "* Calculation of multi-energy-group and multi-delayed-group cross sections for a **fuel assembly**\n",
    "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n",
    "* Steady-state pin-by-pin **delayed neutron fractions (beta)** for each delayed group.\n",
    "* Generation of surface currents on the interfaces and surfaces of a Mesh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Input Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning:  This call to matplotlib.use() has no effect\n",
      "because the backend has already been chosen;\n",
      "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
      "or matplotlib.backends is imported for the first time.\n",
      "\n",
      "  warnings.warn(_use_error_msg)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import pickle\n",
    "\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import openmc\n",
    "import openmc.mgxs\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to define materials that will be used in the problem. Before defining a material, we must create nuclides that are used in the material."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate some Nuclides\n",
    "h1 = openmc.Nuclide('H1')\n",
    "b10 = openmc.Nuclide('B10')\n",
    "o16 = openmc.Nuclide('O16')\n",
    "u235 = openmc.Nuclide('U235')\n",
    "u238 = openmc.Nuclide('U238')\n",
    "zr90 = openmc.Nuclide('Zr90')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the nuclides we defined, we will now create three materials for the fuel, water, and cladding of the fuel pins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1.6 enriched fuel\n",
    "fuel = openmc.Material(name='1.6% Fuel')\n",
    "fuel.set_density('g/cm3', 10.31341)\n",
    "fuel.add_nuclide(u235, 3.7503e-4)\n",
    "fuel.add_nuclide(u238, 2.2625e-2)\n",
    "fuel.add_nuclide(o16, 4.6007e-2)\n",
    "\n",
    "# borated water\n",
    "water = openmc.Material(name='Borated Water')\n",
    "water.set_density('g/cm3', 0.740582)\n",
    "water.add_nuclide(h1, 4.9457e-2)\n",
    "water.add_nuclide(o16, 2.4732e-2)\n",
    "water.add_nuclide(b10, 8.0042e-6)\n",
    "\n",
    "# zircaloy\n",
    "zircaloy = openmc.Material(name='Zircaloy')\n",
    "zircaloy.set_density('g/cm3', 6.55)\n",
    "zircaloy.add_nuclide(zr90, 7.2758e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our three materials, we can now create a `Materials` object that can be exported to an actual XML file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate a Materials object\n",
    "materials_file = openmc.Materials((fuel, water, zircaloy))\n",
    "materials_file.default_xs = '71c'\n",
    "\n",
    "# Export to \"materials.xml\"\n",
    "materials_file.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create cylinders for the fuel and clad\n",
    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n",
    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n",
    "\n",
    "# Create boundary planes to surround the geometry\n",
    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
    "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n",
    "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n",
    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
    "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n",
    "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a Universe to encapsulate a fuel pin\n",
    "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
    "\n",
    "# Create fuel Cell\n",
    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
    "fuel_cell.fill = fuel\n",
    "fuel_cell.region = -fuel_outer_radius\n",
    "fuel_pin_universe.add_cell(fuel_cell)\n",
    "\n",
    "# Create a clad Cell\n",
    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
    "clad_cell.fill = zircaloy\n",
    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
    "fuel_pin_universe.add_cell(clad_cell)\n",
    "\n",
    "# Create a moderator Cell\n",
    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
    "moderator_cell.fill = water\n",
    "moderator_cell.region = +clad_outer_radius\n",
    "fuel_pin_universe.add_cell(moderator_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, we can construct a control rod guide tube with the same surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a Universe to encapsulate a control rod guide tube\n",
    "guide_tube_universe = openmc.Universe(name='Guide Tube')\n",
    "\n",
    "# Create guide tube Cell\n",
    "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n",
    "guide_tube_cell.fill = water\n",
    "guide_tube_cell.region = -fuel_outer_radius\n",
    "guide_tube_universe.add_cell(guide_tube_cell)\n",
    "\n",
    "# Create a clad Cell\n",
    "clad_cell = openmc.Cell(name='Guide Clad')\n",
    "clad_cell.fill = zircaloy\n",
    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
    "guide_tube_universe.add_cell(clad_cell)\n",
    "\n",
    "# Create a moderator Cell\n",
    "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n",
    "moderator_cell.fill = water\n",
    "moderator_cell.region = +clad_outer_radius\n",
    "guide_tube_universe.add_cell(moderator_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create fuel assembly Lattice\n",
    "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n",
    "assembly.pitch = (1.26, 1.26)\n",
    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create array indices for guide tube locations in lattice\n",
    "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n",
    "                       11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n",
    "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n",
    "                       8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n",
    "\n",
    "# Initialize an empty 17x17 array of the lattice universes\n",
    "universes = np.empty((17, 17), dtype=openmc.Universe)\n",
    "\n",
    "# Fill the array with the fuel pin and guide tube universes\n",
    "universes[:,:] = fuel_pin_universe\n",
    "universes[template_x, template_y] = guide_tube_universe\n",
    "\n",
    "# Store the array of universes in the lattice\n",
    "assembly.universes = universes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create root Cell\n",
    "root_cell = openmc.Cell(name='root cell')\n",
    "root_cell.fill = assembly\n",
    "\n",
    "# Add boundary planes\n",
    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
    "\n",
    "# Create root Universe\n",
    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
    "root_universe.add_cell(root_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now must create a geometry that is assigned a root universe and export it to XML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create Geometry and set root Universe\n",
    "geometry = openmc.Geometry()\n",
    "geometry.root_universe = root_universe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Export to \"geometry.xml\"\n",
    "geometry.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# OpenMC simulation parameters\n",
    "batches = 50\n",
    "inactive = 10\n",
    "particles = 2500\n",
    "\n",
    "# Instantiate a Settings object\n",
    "settings_file = openmc.Settings()\n",
    "settings_file.batches = batches\n",
    "settings_file.inactive = inactive\n",
    "settings_file.particles = particles\n",
    "settings_file.output = {'tallies': False}\n",
    "\n",
    "# Create an initial uniform spatial source distribution over fissionable zones\n",
    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
    "settings_file.source = openmc.source.Source(space=uniform_dist)\n",
    "\n",
    "# Export to \"settings.xml\"\n",
    "settings_file.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also create a `Plots` file that we can use to verify that our fuel assembly geometry was created successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate a Plot\n",
    "plot = openmc.Plot(plot_id=1)\n",
    "plot.filename = 'materials-xy'\n",
    "plot.origin = [0, 0, 0]\n",
    "plot.pixels = [250, 250]\n",
    "plot.width = [-10.71*2, -10.71*2]\n",
    "plot.color = 'mat'\n",
    "\n",
    "# Instantiate a Plots object, add Plot, and export to \"plots.xml\"\n",
    "plot_file = openmc.Plots([plot])\n",
    "plot_file.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run openmc in plotting mode\n",
    "openmc.plot_geometry(output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDE0OjA5OjQyLTA1OjAwsrHgdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert OpenMC's funky ppm to png\n",
    "!convert materials-xy.ppm materials-xy.png\n",
    "\n",
    "# Display the materials plot inline\n",
    "Image(filename='materials-xy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an MGXS Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to generate multi-group cross sections! First, let's define 20-energy-group, 1-energy-group, and 6-delayed-group structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate a 20-group EnergyGroups object\n",
    "energy_groups = openmc.mgxs.EnergyGroups()\n",
    "energy_groups.group_edges = np.logspace(-3, 7.3, 21)\n",
    "\n",
    "# Instantiate a 1-group EnergyGroups object\n",
    "one_group = openmc.mgxs.EnergyGroups()\n",
    "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n",
    "\n",
    "# Instantiate a 6-delayed-group list\n",
    "delayed_groups = list(range(1,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will instantiate an `openmc.mgxs.Library` for the energy and delayed groups with our the fuel assembly geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate a tally mesh                                                                                                                                                                  \n",
    "mesh = openmc.Mesh(mesh_id=1)\n",
    "mesh.type = 'regular'\n",
    "mesh.dimension = [17, 17, 1]\n",
    "mesh.lower_left = [-10.71, -10.71, -10000.]\n",
    "mesh.width = [1.26, 1.26, 20000.]\n",
    "\n",
    "# Initialize an 20-energy-group and 6-delayed-group MGXS Library\n",
    "mgxs_lib = openmc.mgxs.Library(geometry)\n",
    "mgxs_lib.energy_groups  = energy_groups\n",
    "mgxs_lib.delayed_groups = delayed_groups\n",
    "\n",
    "# Specify multi-group cross section types to compute\n",
    "mgxs_lib.mgxs_types = ['total', 'transport', 'nu-scatter matrix', 'kappa-fission', 'inverse-velocity', 'chi-prompt',\n",
    "                      'prompt-nu-fission', 'chi-delayed', 'delayed-nu-fission', 'beta']\n",
    "\n",
    "# Specify a \"mesh\" domain type for the cross section tally filters\n",
    "mgxs_lib.domain_type = 'mesh'\n",
    "\n",
    "# Specify the mesh domain over which to compute multi-group cross sections\n",
    "mgxs_lib.domains = [mesh]\n",
    "\n",
    "# Construct all tallies needed for the multi-group cross section library\n",
    "mgxs_lib.build_library()\n",
    "\n",
    "# Create a \"tallies.xml\" file for the MGXS Library\n",
    "tallies_file = openmc.Tallies()\n",
    "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n",
    "\n",
    "# Instantiate a current tally\n",
    "mesh_filter = openmc.MeshFilter(mesh)\n",
    "current_tally = openmc.Tally(name='current tally')\n",
    "current_tally.scores = ['current']\n",
    "current_tally.filters = [mesh_filter]\n",
    "\n",
    "# Add current tally to the tallies file\n",
    "tallies_file.append(current_tally)\n",
    "\n",
    "# Export to \"tallies.xml\"\n",
    "tallies_file.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can run OpenMC to generate the cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "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",
      "\n",
      "                   | The OpenMC Monte Carlo Code\n",
      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
      "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
      "           Version | 0.8.0\n",
      "          Git SHA1 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n",
      "         Date/Time | 2016-10-31 14:09:42\n",
      "    OpenMP Threads | 4\n",
      "\n",
      " ===========================================================================\n",
      " ========================>     INITIALIZATION     <=========================\n",
      " ===========================================================================\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading geometry XML file...\n",
      " Reading materials XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n",
      " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n",
      " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n",
      " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
      " Reading B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n",
      " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
      " Maximum neutron transport energy: 2.00000E+07 eV for U235\n",
      " Reading tallies XML file...\n",
      " Building neighboring cells lists for each surface...\n",
      " Initializing source particles...\n",
      "\n",
      " ===========================================================================\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      " ===========================================================================\n",
      "\n",
      "  Bat./Gen.      k            Average k         \n",
      "  =========   ========   ====================   \n",
      "        1/1    1.03852                       \n",
      "        2/1    0.99743                       \n",
      "        3/1    1.02987                       \n",
      "        4/1    1.04472                       \n",
      "        5/1    1.02183                       \n",
      "        6/1    1.05263                       \n",
      "        7/1    0.99048                       \n",
      "        8/1    1.02753                       \n",
      "        9/1    1.03159                       \n",
      "       10/1    1.04005                       \n",
      "       11/1    1.05278                       \n",
      "       12/1    1.02555    1.03917 +/- 0.01362\n",
      "       13/1    0.99400    1.02411 +/- 0.01699\n",
      "       14/1    1.03508    1.02685 +/- 0.01232\n",
      "       15/1    1.00055    1.02159 +/- 0.01090\n",
      "       16/1    1.01334    1.02022 +/- 0.00900\n",
      "       17/1    0.99822    1.01707 +/- 0.00823\n",
      "       18/1    1.01767    1.01715 +/- 0.00713\n",
      "       19/1    1.05052    1.02086 +/- 0.00730\n",
      "       20/1    1.03133    1.02190 +/- 0.00661\n",
      "       21/1    1.04112    1.02365 +/- 0.00623\n",
      "       22/1    1.04175    1.02516 +/- 0.00588\n",
      "       23/1    1.01909    1.02469 +/- 0.00543\n",
      "       24/1    1.07119    1.02801 +/- 0.00603\n",
      "       25/1    0.97414    1.02442 +/- 0.00666\n",
      "       26/1    1.04709    1.02584 +/- 0.00639\n",
      "       27/1    1.05872    1.02777 +/- 0.00631\n",
      "       28/1    1.03930    1.02841 +/- 0.00598\n",
      "       29/1    1.01488    1.02770 +/- 0.00570\n",
      "       30/1    1.04513    1.02857 +/- 0.00548\n",
      "       31/1    0.99538    1.02699 +/- 0.00545\n",
      "       32/1    1.00106    1.02581 +/- 0.00532\n",
      "       33/1    0.99389    1.02442 +/- 0.00527\n",
      "       34/1    0.99938    1.02338 +/- 0.00516\n",
      "       35/1    1.02161    1.02331 +/- 0.00495\n",
      "       36/1    1.04084    1.02398 +/- 0.00480\n",
      "       37/1    0.98801    1.02265 +/- 0.00481\n",
      "       38/1    1.01348    1.02232 +/- 0.00464\n",
      "       39/1    1.06693    1.02386 +/- 0.00474\n",
      "       40/1    1.07729    1.02564 +/- 0.00491\n",
      "       41/1    1.03191    1.02585 +/- 0.00475\n",
      "       42/1    1.05209    1.02667 +/- 0.00468\n",
      "       43/1    1.02997    1.02677 +/- 0.00453\n",
      "       44/1    1.07288    1.02812 +/- 0.00460\n",
      "       45/1    1.01268    1.02768 +/- 0.00449\n",
      "       46/1    1.03759    1.02796 +/- 0.00437\n",
      "       47/1    1.02620    1.02791 +/- 0.00425\n",
      "       48/1    1.02509    1.02783 +/- 0.00414\n",
      "       49/1    1.01075    1.02740 +/- 0.00406\n",
      "       50/1    0.99403    1.02656 +/- 0.00404\n",
      " Creating state point statepoint.50.h5...\n",
      "\n",
      " ===========================================================================\n",
      " ======================>     SIMULATION FINISHED     <======================\n",
      " ===========================================================================\n",
      "\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     =  5.5777E-01 seconds\n",
      "   Reading cross sections          =  4.0851E-01 seconds\n",
      " Total time in simulation          =  3.0735E+01 seconds\n",
      "   Time in transport only          =  3.0468E+01 seconds\n",
      "   Time in inactive batches        =  2.5750E+00 seconds\n",
      "   Time in active batches          =  2.8160E+01 seconds\n",
      "   Time synchronizing fission bank =  7.0866E-03 seconds\n",
      "     Sampling source sites         =  5.6963E-03 seconds\n",
      "     SEND/RECV source sites        =  1.2941E-03 seconds\n",
      "   Time accumulating tallies       =  1.2199E-01 seconds\n",
      " Total time for finalization       =  3.0383E-03 seconds\n",
      " Total time elapsed                =  3.1316E+01 seconds\n",
      " Calculation Rate (inactive)       =  9708.82 neutrons/second\n",
      " Calculation Rate (active)         =  3551.09 neutrons/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     =  1.02436 +/-  0.00318\n",
      " k-effective (Track-length)  =  1.02656 +/-  0.00404\n",
      " k-effective (Absorption)    =  1.02601 +/-  0.00333\n",
      " Combined k-effective        =  1.02539 +/-  0.00276\n",
      " Leakage Fraction            =  0.00000 +/-  0.00000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run OpenMC\n",
    "openmc.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tally Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the last statepoint file\n",
    "sp = openmc.StatePoint('statepoint.50.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Initialize MGXS Library with OpenMC statepoint data\n",
    "mgxs_lib.load_from_statepoint(sp)\n",
    "\n",
    "# Extrack the current tally separately\n",
    "current_tally = sp.get_tally(name='current tally')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
    "\n",
    "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
    "\n",
    "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>delayedgroup</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>0.000557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.000073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.007412</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.072924</td>\n",
       "      <td>0.005015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.034008</td>\n",
       "      <td>0.002213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.002561</td>\n",
       "      <td>0.000366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.011966</td>\n",
       "      <td>0.004607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.007980</td>\n",
       "      <td>0.000879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.084940</td>\n",
       "      <td>0.005351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mesh 1       delayedgroup nuclide  \\\n",
       "       x  y  z                        \n",
       "0      1  1  1            1   total   \n",
       "1      1  1  1            2   total   \n",
       "2      1  1  1            3   total   \n",
       "3      1  1  1            4   total   \n",
       "4      1  1  1            5   total   \n",
       "5      1  1  1            6   total   \n",
       "6      1  2  1            1   total   \n",
       "7      1  2  1            2   total   \n",
       "8      1  2  1            3   total   \n",
       "9      1  2  1            4   total   \n",
       "\n",
       "                                               score      mean std. dev.  \n",
       "                                                                          \n",
       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.002406  0.000557  \n",
       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.000937  0.000073  \n",
       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.007412  0.000841  \n",
       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.072924  0.005015  \n",
       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.034008  0.002213  \n",
       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.002561  0.000366  \n",
       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.011966  0.004607  \n",
       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.000917  0.000075  \n",
       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.007980  0.000879  \n",
       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.084940  0.005351  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the time constants for the delayed precursors (in seconds^-1)\n",
    "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n",
    "precursor_lambda = -np.log(0.5) / precursor_halflife\n",
    "\n",
    "beta = mgxs_lib.get_mgxs(mesh, 'beta')\n",
    "\n",
    "# Create a tally object with only the delayed group filter for the time constants\n",
    "beta_filters = [f for f in beta.xs_tally.filters if type(f) is not openmc.DelayedGroupFilter]\n",
    "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n",
    "for f in beta_filters:\n",
    "    lambda_tally = lambda_tally.summation(filter_type=type(f), remove_filter=True) * 0. + 1.\n",
    "\n",
    "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n",
    "lambda_tally._mean = precursor_lambda\n",
    "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n",
    "\n",
    "# Set a total nuclide and lambda score\n",
    "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n",
    "lambda_tally.scores = ['lambda']\n",
    "\n",
    "delayed_nu_fission = mgxs_lib.get_mgxs(mesh, 'delayed-nu-fission')\n",
    "\n",
    "# Use tally arithmetic to compute the precursor concentrations\n",
    "precursor_conc = beta.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n",
    "    delayed_nu_fission.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / lambda_tally\n",
    "    \n",
    "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
    "precursor_conc.get_pandas_dataframe().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful feature of the Python API is the ability to extract the surface currents for the interfaces and surfaces of a mesh. We can inspect the currents for the mesh by getting the pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>surface</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.000678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03023</td>\n",
       "      <td>0.000637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>z-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>z-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03085</td>\n",
       "      <td>0.000628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-max in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03058</td>\n",
       "      <td>0.000609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-max in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mesh 1          surface nuclide    score     mean std. dev.\n",
       "       x  y  z                                               \n",
       "0      1  1  1  x-min out   total  current  0.00000  0.000000\n",
       "1      1  1  1  x-max out   total  current  0.00000  0.000000\n",
       "2      1  1  1  y-min out   total  current  0.02985  0.000678\n",
       "3      1  1  1  y-max out   total  current  0.03023  0.000637\n",
       "4      1  1  1  z-min out   total  current  0.00000  0.000000\n",
       "5      1  1  1  z-max out   total  current  0.00000  0.000000\n",
       "6      1  1  1   x-min in   total  current  0.03085  0.000628\n",
       "7      1  1  1   x-max in   total  current  0.03058  0.000609\n",
       "8      1  1  1   y-min in   total  current  0.00000  0.000000\n",
       "9      1  1  1   y-max in   total  current  0.00000  0.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_tally.get_pandas_dataframe().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Section Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to inspecting the data in the tallies by getting the pandas dataframe, we can also plot the tally data on the domain mesh. Below is the delayed neutron fraction tallied in each mesh cell for each delayed group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdb1d74f208>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBYAAAIhCAYAAAD6jTmFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzMzM8vmjgUzMzMzMzMzy/Z/miKBZjyK6DcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb1fe68518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the energy-condensed delayed neutron fraction tally\n",
    "beta_by_group = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n",
    "beta_by_group.mean.shape = (17, 17, 6)\n",
    "beta_by_group.mean[beta_by_group.mean == 0] = np.nan\n",
    "\n",
    "# Plot the betas\n",
    "plt.figure(figsize=(18,9))\n",
    "fig = plt.subplot(231)\n",
    "plt.imshow(beta_by_group.mean[:,:,0], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 1')\n",
    "\n",
    "fig = plt.subplot(232)\n",
    "plt.imshow(beta_by_group.mean[:,:,1], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 2')\n",
    "\n",
    "fig = plt.subplot(233)\n",
    "plt.imshow(beta_by_group.mean[:,:,2], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 3')\n",
    "\n",
    "fig = plt.subplot(234)\n",
    "plt.imshow(beta_by_group.mean[:,:,3], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 4')\n",
    "\n",
    "fig = plt.subplot(235)\n",
    "plt.imshow(beta_by_group.mean[:,:,4], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 5')\n",
    "\n",
    "fig = plt.subplot(236)\n",
    "plt.imshow(beta_by_group.mean[:,:,5], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 6')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}