From d00695032616a8d1a78b0679d391bfefcb451353 Mon Sep 17 00:00:00 2001
From: Paul Romano <paul.k.romano@gmail.com>
Date: Mon, 3 Apr 2017 21:18:46 -0500
Subject: [PATCH] Fix MDGXS part II notebook

---
 docs/source/examples/mdgxs-part-ii.ipynb | 517 +++++++++--------------
 1 file changed, 205 insertions(+), 312 deletions(-)

diff --git a/docs/source/examples/mdgxs-part-ii.ipynb b/docs/source/examples/mdgxs-part-ii.ipynb
index cbeb3ea8da..14736f7be5 100644
--- a/docs/source/examples/mdgxs-part-ii.ipynb
+++ b/docs/source/examples/mdgxs-part-ii.ipynb
@@ -25,68 +25,28 @@
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "%matplotlib inline\n",
     "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"
+    "import openmc.mgxs"
    ]
   },
   {
    "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."
+    "First we need to define materials that will be used in the problem: fuel, water, and cladding."
    ]
   },
   {
    "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
    },
@@ -95,21 +55,21 @@
     "# 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",
+    "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",
+    "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)"
+    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
    ]
   },
   {
@@ -121,18 +81,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "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()"
+    "# Create a materials collection and export to XML\n",
+    "materials = openmc.Materials((fuel, water, zircaloy))\n",
+    "materials.export_to_xml()"
    ]
   },
   {
@@ -144,15 +101,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "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",
+    "fuel_outer_radius = openmc.ZCylinder(R=0.39218)\n",
+    "clad_outer_radius = openmc.ZCylinder(R=0.45720)\n",
     "\n",
     "# Create boundary planes to surround the geometry\n",
     "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
@@ -172,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "collapsed": true
    },
@@ -209,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -246,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -267,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -279,11 +236,8 @@
     "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",
+    "# Create universes array with the fuel pin and guide tube universes\n",
+    "universes = np.tile(fuel_pin_universe, (17,17))\n",
     "universes[template_x, template_y] = guide_tube_universe\n",
     "\n",
     "# Store the array of universes in the lattice\n",
@@ -299,15 +253,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell')\n",
-    "root_cell.fill = assembly\n",
+    "root_cell = openmc.Cell(name='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",
@@ -326,26 +279,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "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",
+    "# Create Geometry and export to XML\n",
+    "geometry = openmc.Geometry(root_universe)\n",
     "geometry.export_to_xml()"
    ]
   },
@@ -358,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -370,104 +311,52 @@
     "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",
+    "settings = openmc.Settings()\n",
+    "settings.batches = batches\n",
+    "settings.inactive = inactive\n",
+    "settings.particles = particles\n",
+    "settings.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",
+    "settings.source = openmc.source.Source(space=uniform_dist)\n",
     "\n",
     "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
+    "settings.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."
+    "Let us also create a plot 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,
+   "execution_count": 12,
    "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nMDB/tUH+AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 16,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "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')"
+    "# Plot our geometry\n",
+    "plot = openmc.Plot.from_geometry(geometry)\n",
+    "plot.pixels = (250, 250)\n",
+    "plot.color_by = 'material'\n",
+    "openmc.plot_inline(plot)"
    ]
   },
   {
@@ -488,12 +377,12 @@
    "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."
+    "Now we are ready to generate multi-group cross sections! First, let's define a 20-energy-group and 1-energy-group."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -505,10 +394,7 @@
     "\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))"
+    "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])"
    ]
   },
   {
@@ -520,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -536,7 +422,7 @@
     "# 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",
+    "mgxs_lib.num_delayed_groups = 6\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",
@@ -577,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -612,16 +498,11 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         Copyright | 2011-2017 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",
+      "          Git SHA1 | f7edad68f0654d775ed363bfdcbe4aa5d3cfba23\n",
+      "         Date/Time | 2017-04-03 21:15:56\n",
       "\n",
       " Reading settings XML file...\n",
       " Reading geometry XML file...\n",
@@ -638,92 +519,85 @@
       " 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",
+      "        4/1    1.04397                       \n",
+      "        5/1    1.06262                       \n",
+      "        6/1    1.06657                       \n",
+      "        7/1    0.98574                       \n",
+      "        8/1    1.04364                       \n",
+      "        9/1    1.01253                       \n",
+      "       10/1    1.02094                       \n",
+      "       11/1    0.99586                       \n",
+      "       12/1    1.00508    1.00047 +/- 0.00461\n",
+      "       13/1    1.05292    1.01795 +/- 0.01769\n",
+      "       14/1    1.04732    1.02530 +/- 0.01450\n",
+      "       15/1    1.04886    1.03001 +/- 0.01218\n",
+      "       16/1    1.00948    1.02659 +/- 0.01052\n",
+      "       17/1    1.02684    1.02662 +/- 0.00889\n",
+      "       18/1    0.97234    1.01984 +/- 0.01026\n",
+      "       19/1    0.99754    1.01736 +/- 0.00938\n",
+      "       20/1    0.98964    1.01459 +/- 0.00884\n",
+      "       21/1    1.04140    1.01703 +/- 0.00836\n",
+      "       22/1    1.03854    1.01882 +/- 0.00784\n",
+      "       23/1    1.05917    1.02192 +/- 0.00785\n",
+      "       24/1    1.02413    1.02208 +/- 0.00727\n",
+      "       25/1    1.03113    1.02268 +/- 0.00679\n",
+      "       26/1    1.05113    1.02446 +/- 0.00660\n",
+      "       27/1    1.03252    1.02494 +/- 0.00622\n",
+      "       28/1    1.05196    1.02644 +/- 0.00605\n",
+      "       29/1    0.99663    1.02487 +/- 0.00593\n",
+      "       30/1    1.01820    1.02454 +/- 0.00564\n",
+      "       31/1    1.02753    1.02468 +/- 0.00537\n",
+      "       32/1    1.02162    1.02454 +/- 0.00512\n",
+      "       33/1    1.04083    1.02525 +/- 0.00494\n",
+      "       34/1    1.03335    1.02558 +/- 0.00474\n",
+      "       35/1    1.01304    1.02508 +/- 0.00458\n",
+      "       36/1    0.99299    1.02385 +/- 0.00457\n",
+      "       37/1    1.04936    1.02479 +/- 0.00450\n",
+      "       38/1    1.02856    1.02493 +/- 0.00433\n",
+      "       39/1    1.03706    1.02535 +/- 0.00420\n",
+      "       40/1    1.08118    1.02721 +/- 0.00447\n",
+      "       41/1    1.00149    1.02638 +/- 0.00440\n",
+      "       42/1    1.00233    1.02563 +/- 0.00433\n",
+      "       43/1    1.03023    1.02577 +/- 0.00419\n",
+      "       44/1    1.03230    1.02596 +/- 0.00407\n",
+      "       45/1    0.98123    1.02468 +/- 0.00416\n",
+      "       46/1    1.02126    1.02458 +/- 0.00404\n",
+      "       47/1    0.99772    1.02386 +/- 0.00400\n",
+      "       48/1    1.02773    1.02396 +/- 0.00389\n",
+      "       49/1    1.01690    1.02378 +/- 0.00379\n",
+      "       50/1    1.02890    1.02391 +/- 0.00370\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",
+      " Total time for initialization     =  3.7616E-01 seconds\n",
+      "   Reading cross sections          =  3.2363E-01 seconds\n",
+      " Total time in simulation          =  5.8159E+01 seconds\n",
+      "   Time in transport only          =  5.7959E+01 seconds\n",
+      "   Time in inactive batches        =  3.9349E+00 seconds\n",
+      "   Time in active batches          =  5.4224E+01 seconds\n",
+      "   Time synchronizing fission bank =  3.6903E-03 seconds\n",
+      "     Sampling source sites         =  2.4990E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.1328E-03 seconds\n",
+      "   Time accumulating tallies       =  1.7598E-01 seconds\n",
+      " Total time for finalization       =  3.5126E-03 seconds\n",
+      " Total time elapsed                =  5.8551E+01 seconds\n",
+      " Calculation Rate (inactive)       =  6353.42 neutrons/second\n",
+      " Calculation Rate (active)         =  1844.20 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",
+      " k-effective (Collision)     =  1.02621 +/-  0.00393\n",
+      " k-effective (Track-length)  =  1.02391 +/-  0.00370\n",
+      " k-effective (Absorption)    =  1.02077 +/-  0.00423\n",
+      " Combined k-effective        =  1.02331 +/-  0.00353\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -734,7 +608,7 @@
        "0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -760,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -779,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -812,11 +686,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/romano/openmc/openmc/tallies.py:1875: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
+      "/home/romano/openmc/openmc/tallies.py:1876: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
+      "/home/romano/openmc/openmc/tallies.py:1877: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n",
+      "/home/romano/openmc/openmc/tallies.py:1869: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
+      "/home/romano/openmc/openmc/tallies.py:1870: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -853,8 +743,8 @@
        "      <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",
+       "      <td>0.011301</td>\n",
+       "      <td>0.003184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -864,8 +754,8 @@
        "      <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",
+       "      <td>0.000854</td>\n",
+       "      <td>0.000078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -875,8 +765,8 @@
        "      <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",
+       "      <td>0.007267</td>\n",
+       "      <td>0.000713</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -886,8 +776,8 @@
        "      <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",
+       "      <td>0.088903</td>\n",
+       "      <td>0.005501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -897,8 +787,8 @@
        "      <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",
+       "      <td>0.044257</td>\n",
+       "      <td>0.002890</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -908,8 +798,8 @@
        "      <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",
+       "      <td>0.001897</td>\n",
+       "      <td>0.000282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -919,8 +809,8 @@
        "      <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",
+       "      <td>0.007362</td>\n",
+       "      <td>0.001968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -930,8 +820,8 @@
        "      <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",
+       "      <td>0.000872</td>\n",
+       "      <td>0.000064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -941,8 +831,8 @@
        "      <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",
+       "      <td>0.007015</td>\n",
+       "      <td>0.000702</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -952,8 +842,8 @@
        "      <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",
+       "      <td>0.101115</td>\n",
+       "      <td>0.007008</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -975,19 +865,19 @@
        "\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  "
+       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.011301  0.003184  \n",
+       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.000854  0.000078  \n",
+       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.007267  0.000713  \n",
+       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.088903  0.005501  \n",
+       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.044257  0.002890  \n",
+       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.001897  0.000282  \n",
+       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.007362  0.001968  \n",
+       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.000872  0.000064  \n",
+       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.007015  0.000702  \n",
+       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.101115  0.007008  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -995,7 +885,7 @@
    "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",
+    "precursor_lambda = math.log(2.0) / precursor_halflife\n",
     "\n",
     "beta = mgxs_lib.get_mgxs(mesh, 'beta')\n",
     "\n",
@@ -1032,7 +922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -1095,8 +985,8 @@
        "      <td>y-min out</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.02985</td>\n",
-       "      <td>0.000678</td>\n",
+       "      <td>0.03153</td>\n",
+       "      <td>0.000566</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1106,8 +996,8 @@
        "      <td>y-max out</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03023</td>\n",
-       "      <td>0.000637</td>\n",
+       "      <td>0.03153</td>\n",
+       "      <td>0.000593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1139,8 +1029,8 @@
        "      <td>x-min in</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03085</td>\n",
-       "      <td>0.000628</td>\n",
+       "      <td>0.03265</td>\n",
+       "      <td>0.000753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1150,8 +1040,8 @@
        "      <td>x-max in</td>\n",
        "      <td>total</td>\n",
        "      <td>current</td>\n",
-       "      <td>0.03058</td>\n",
-       "      <td>0.000609</td>\n",
+       "      <td>0.03216</td>\n",
+       "      <td>0.000698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1184,17 +1074,17 @@
        "       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",
+       "2      1  1  1  y-min out   total  current  0.03153  0.000566\n",
+       "3      1  1  1  y-max out   total  current  0.03153  0.000593\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",
+       "6      1  1  1   x-min in   total  current  0.03265  0.000753\n",
+       "7      1  1  1   x-max in   total  current  0.03216  0.000698\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,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1219,26 +1109,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/romano/openmc/openmc/tallies.py:1875: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
+      "/home/romano/openmc/openmc/tallies.py:1876: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
+      "/home/romano/openmc/openmc/tallies.py:1877: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7fdb1d74f208>"
+       "<matplotlib.text.Text at 0x7fecd32c1da0>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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0s1FmNtHM5jd3SjTWIiLZVZgxzKwH8DdgJ6ARONvdU4afRUSkhK7cRESqr4Jc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\nzXMWm1mFc7hVnMNm9g5nca08q0KkTVP9o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0m4y0Cddhsw7na+K6WtHv8imydeU3bMFrmVk7ce/s2sS12KxTuRavLVyHzTqV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lAAAg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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdb1fe68518>"
+       "<matplotlib.figure.Figure at 0x7fecd5b15e80>"
       ]
      },
      "metadata": {},
@@ -1283,20 +1185,11 @@
     "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",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },