diff --git a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb b/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb
index 4b0d2022b..32f39a6fc 100644
--- a/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb
+++ b/docs/source/pythonapi/examples/multi-group-cross-sections.ipynb
@@ -444,7 +444,7 @@
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
       "      Git SHA1:       21738db07debeabde824c9b955bd3bf0c9a16366\n",
-      "      Date/Time:      2015-10-28 12:11:50\n",
+      "      Date/Time:      2015-10-28 21:15:49\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -530,20 +530,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  1.7630E+00 seconds\n",
-      "   Reading cross sections          =  4.0800E-01 seconds\n",
-      " Total time in simulation          =  4.1757E+01 seconds\n",
-      "   Time in transport only          =  4.1713E+01 seconds\n",
-      "   Time in inactive batches        =  6.5950E+00 seconds\n",
-      "   Time in active batches          =  3.5162E+01 seconds\n",
-      "   Time synchronizing fission bank =  1.0000E-02 seconds\n",
-      "     Sampling source sites         =  6.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  1.0000E-03 seconds\n",
-      " Total time for finalization       =  5.0000E-03 seconds\n",
-      " Total time elapsed                =  4.3566E+01 seconds\n",
-      " Calculation Rate (inactive)       =  3790.75 neutrons/second\n",
-      " Calculation Rate (active)         =  2843.98 neutrons/second\n",
+      " Total time for initialization     =  7.4700E-01 seconds\n",
+      "   Reading cross sections          =  1.5400E-01 seconds\n",
+      " Total time in simulation          =  2.0496E+01 seconds\n",
+      "   Time in transport only          =  2.0451E+01 seconds\n",
+      "   Time in inactive batches        =  3.1940E+00 seconds\n",
+      "   Time in active batches          =  1.7302E+01 seconds\n",
+      "   Time synchronizing fission bank =  5.0000E-03 seconds\n",
+      "     Sampling source sites         =  4.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
+      " Total time for finalization       =  3.0000E-03 seconds\n",
+      " Total time elapsed                =  2.1260E+01 seconds\n",
+      " Calculation Rate (inactive)       =  7827.18 neutrons/second\n",
+      " Calculation Rate (active)         =  5779.68 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -909,7 +909,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code OpenMOC. First, we will use OpenCG to reconstruct our OpenMC geometry from the summary file into a equivalent OpenMOC geometry."
+    "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code OpenMOC. We will extract an OpenCG geometry from the summary file and convert it into an equivalent OpenMOC geometry."
    ]
   },
   {
@@ -920,9 +920,6 @@
    },
    "outputs": [],
    "source": [
-    "# Create an OpenCG Geometry from the OpenMC Geometry stored in the summary\n",
-    "su.make_opencg_geometry()\n",
-    "\n",
     "# Create an OpenMOC Geometry from the OpenCG Geometry\n",
     "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)"
    ]
@@ -979,29 +976,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  NORMAL ]  Ray tracing for track segmentation...\n",
-      "[  NORMAL ]  Dumping tracks to file...\n",
+      "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.685182\tres = 9.665E-317\n",
+      "[  NORMAL ]  Iteration 0:\tk_eff = 0.685183\tres = 0.000E+00\n",
       "[  NORMAL ]  Iteration 1:\tk_eff = 0.785638\tres = 3.148E-01\n",
-      "[  NORMAL ]  Iteration 2:\tk_eff = 0.750178\tres = 1.466E-01\n",
-      "[  NORMAL ]  Iteration 3:\tk_eff = 0.728835\tres = 4.513E-02\n",
+      "[  NORMAL ]  Iteration 2:\tk_eff = 0.750179\tres = 1.466E-01\n",
+      "[  NORMAL ]  Iteration 3:\tk_eff = 0.728836\tres = 4.513E-02\n",
       "[  NORMAL ]  Iteration 4:\tk_eff = 0.695616\tres = 2.845E-02\n",
-      "[  NORMAL ]  Iteration 5:\tk_eff = 0.663332\tres = 4.558E-02\n",
+      "[  NORMAL ]  Iteration 5:\tk_eff = 0.663333\tres = 4.558E-02\n",
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+      "[  NORMAL ]  Iteration 12:\tk_eff = 0.519138\tres = 2.416E-02\n",
+      "[  NORMAL ]  Iteration 13:\tk_eff = 0.513828\tres = 1.729E-02\n",
+      "[  NORMAL ]  Iteration 14:\tk_eff = 0.512170\tres = 1.023E-02\n",
+      "[  NORMAL ]  Iteration 15:\tk_eff = 0.513942\tres = 3.227E-03\n",
       "[  NORMAL ]  Iteration 16:\tk_eff = 0.518887\tres = 3.460E-03\n",
-      "[  NORMAL ]  Iteration 17:\tk_eff = 0.526728\tres = 9.623E-03\n",
+      "[  NORMAL ]  Iteration 17:\tk_eff = 0.526729\tres = 9.623E-03\n",
       "[  NORMAL ]  Iteration 18:\tk_eff = 0.537170\tres = 1.511E-02\n",
-      "[  NORMAL ]  Iteration 19:\tk_eff = 0.549909\tres = 1.982E-02\n",
+      "[  NORMAL ]  Iteration 19:\tk_eff = 0.549910\tres = 1.982E-02\n",
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@@ -1021,18 +1017,18 @@
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-      "[  NORMAL ]  Iteration 39:\tk_eff = 0.929867\tres = 1.832E-02\n",
+      "[  NORMAL ]  Iteration 39:\tk_eff = 0.929866\tres = 1.832E-02\n",
       "[  NORMAL ]  Iteration 40:\tk_eff = 0.944826\tres = 1.718E-02\n",
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-      "[  NORMAL ]  Iteration 44:\tk_eff = 0.997328\tres = 1.312E-02\n",
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-      "[  NORMAL ]  Iteration 48:\tk_eff = 1.038836\tres = 9.851E-03\n",
+      "[  NORMAL ]  Iteration 44:\tk_eff = 0.997327\tres = 1.312E-02\n",
+      "[  NORMAL ]  Iteration 45:\tk_eff = 1.008684\tres = 1.223E-02\n",
+      "[  NORMAL ]  Iteration 46:\tk_eff = 1.019372\tres = 1.139E-02\n",
+      "[  NORMAL ]  Iteration 47:\tk_eff = 1.029414\tres = 1.060E-02\n",
+      "[  NORMAL ]  Iteration 48:\tk_eff = 1.038835\tres = 9.851E-03\n",
       "[  NORMAL ]  Iteration 49:\tk_eff = 1.047661\tres = 9.152E-03\n",
-      "[  NORMAL ]  Iteration 50:\tk_eff = 1.055917\tres = 8.495E-03\n",
+      "[  NORMAL ]  Iteration 50:\tk_eff = 1.055917\tres = 8.496E-03\n",
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@@ -1040,75 +1036,75 @@
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+      "[  NORMAL ]  Iteration 59:\tk_eff = 1.108970\tres = 4.227E-03\n",
+      "[  NORMAL ]  Iteration 60:\tk_eff = 1.112960\tres = 3.901E-03\n",
+      "[  NORMAL ]  Iteration 61:\tk_eff = 1.116652\tres = 3.598E-03\n",
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+      "[  NORMAL ]  Iteration 124:\tk_eff = 1.158821\tres = 1.271E-05\n",
+      "[  NORMAL ]  Iteration 125:\tk_eff = 1.158833\tres = 1.159E-05\n",
+      "[  NORMAL ]  Iteration 126:\tk_eff = 1.158844\tres = 1.054E-05\n"
      ]
     }
    ],
@@ -1142,8 +1138,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.161200\n",
-      "openmoc keff = 1.158846\n",
-      "bias [pcm]: -235.5\n"
+      "openmoc keff = 1.158844\n",
+      "bias [pcm]: -235.6\n"
      ]
     }
    ],
@@ -1490,7 +1486,7 @@
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
       "      Git SHA1:       21738db07debeabde824c9b955bd3bf0c9a16366\n",
-      "      Date/Time:      2015-10-28 12:12:36\n",
+      "      Date/Time:      2015-10-28 21:16:11\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -1597,20 +1593,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  1.2980E+00 seconds\n",
-      "   Reading cross sections          =  3.1700E-01 seconds\n",
-      " Total time in simulation          =  6.3378E+02 seconds\n",
-      "   Time in transport only          =  6.3364E+02 seconds\n",
-      "   Time in inactive batches        =  4.2049E+01 seconds\n",
-      "   Time in active batches          =  5.9173E+02 seconds\n",
-      "   Time synchronizing fission bank =  5.4000E-02 seconds\n",
-      "     Sampling source sites         =  3.9000E-02 seconds\n",
-      "     SEND/RECV source sites        =  1.5000E-02 seconds\n",
-      "   Time accumulating tallies       =  4.0000E-03 seconds\n",
-      " Total time for finalization       =  2.5000E-02 seconds\n",
-      " Total time elapsed                =  6.3523E+02 seconds\n",
-      " Calculation Rate (inactive)       =  2378.18 neutrons/second\n",
-      " Calculation Rate (active)         =  675.978 neutrons/second\n",
+      " Total time for initialization     =  6.1100E-01 seconds\n",
+      "   Reading cross sections          =  1.2200E-01 seconds\n",
+      " Total time in simulation          =  2.4424E+02 seconds\n",
+      "   Time in transport only          =  2.4418E+02 seconds\n",
+      "   Time in inactive batches        =  1.8776E+01 seconds\n",
+      "   Time in active batches          =  2.2547E+02 seconds\n",
+      "   Time synchronizing fission bank =  2.1000E-02 seconds\n",
+      "     Sampling source sites         =  1.5000E-02 seconds\n",
+      "     SEND/RECV source sites        =  4.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  7.0000E-03 seconds\n",
+      " Total time for finalization       =  1.3000E-02 seconds\n",
+      " Total time elapsed                =  2.4492E+02 seconds\n",
+      " Calculation Rate (inactive)       =  5325.95 neutrons/second\n",
+      " Calculation Rate (active)         =  1774.08 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -1997,7 +1993,7 @@
      "data": {
       "image/png": 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nUHUn8JOrhWrC45RQIXoOf+eID2Wat1jmac/xQjMGsuS1eswiIoFRYRYRCYwKs4hIYFSY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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f84102a1190>"
+       "<matplotlib.figure.Figure at 0x7f0b9108e210>"
       ]
      },
      "metadata": {},
@@ -2204,9 +2200,6 @@
    },
    "outputs": [],
    "source": [
-    "# Create an OpenCG Geometry from the OpenMC Geometry stored in the summary\n",
-    "su.make_opencg_geometry()\n",
-    "\n",
     "# Create an OpenMOC Geometry from the OpenCG Geometry\n",
     "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)"
    ]
@@ -2306,8 +2299,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223863\n",
-      "openmoc keff = 1.220753\n",
-      "bias [pcm]: -311.0\n"
+      "openmoc keff = 1.220767\n",
+      "bias [pcm]: -309.6\n"
      ]
     }
    ],
@@ -2337,7 +2330,6 @@
    },
    "outputs": [],
    "source": [
-    "su.make_opencg_geometry()\n",
     "openmoc_geometry = get_openmoc_geometry(su.opencg_geometry)\n",
     "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n",
     "\n",
@@ -2400,8 +2392,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223863\n",
-      "openmoc keff = 1.223860\n",
-      "bias [pcm]: -0.3\n"
+      "openmoc keff = 1.223848\n",
+      "bias [pcm]: -1.5\n"
      ]
     }
    ],
diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb
index 294c35ba0..1e4c3c9cd 100644
--- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb
+++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb
@@ -379,7 +379,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -568,8 +568,8 @@
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
-      "      Git SHA1:       170155e8d7935b57fad57bfad6aff1034a80206e\n",
-      "      Date/Time:      2015-10-12 23:49:04\n",
+      "      Git SHA1:       21738db07debeabde824c9b955bd3bf0c9a16366\n",
+      "      Date/Time:      2015-10-28 20:55:18\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -588,6 +588,7 @@
       " Loading ACE cross section table: 1001.71c\n",
       " Loading ACE cross section table: 5010.71c\n",
       " Loading ACE cross section table: 40090.71c\n",
+      " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n",
       " Initializing source particles...\n",
       "\n",
       " ===========================================================================\n",
@@ -633,20 +634,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.0100E-01 seconds\n",
-      "   Reading cross sections          =  8.9000E-02 seconds\n",
-      " Total time in simulation          =  9.1400E+00 seconds\n",
-      "   Time in transport only          =  9.1280E+00 seconds\n",
-      "   Time in inactive batches        =  1.3230E+00 seconds\n",
-      "   Time in active batches          =  7.8170E+00 seconds\n",
-      "   Time synchronizing fission bank =  1.0000E-03 seconds\n",
+      " Total time for initialization     =  7.3800E-01 seconds\n",
+      "   Reading cross sections          =  1.5600E-01 seconds\n",
+      " Total time in simulation          =  1.5998E+01 seconds\n",
+      "   Time in transport only          =  1.5965E+01 seconds\n",
+      "   Time in inactive batches        =  2.3990E+00 seconds\n",
+      "   Time in active batches          =  1.3599E+01 seconds\n",
+      "   Time synchronizing fission bank =  3.0000E-03 seconds\n",
       "     Sampling source sites         =  1.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  0.0000E+00 seconds\n",
+      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
       "   Time accumulating tallies       =  3.0000E-03 seconds\n",
       " Total time for finalization       =  0.0000E+00 seconds\n",
-      " Total time elapsed                =  9.5500E+00 seconds\n",
-      " Calculation Rate (inactive)       =  9448.22 neutrons/second\n",
-      " Calculation Rate (active)         =  4797.24 neutrons/second\n",
+      " Total time elapsed                =  1.6754E+01 seconds\n",
+      " Calculation Rate (inactive)       =  5210.50 neutrons/second\n",
+      " Calculation Rate (active)         =  2757.56 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -800,7 +801,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -1076,7 +1077,7 @@
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raRQdlzCRUVgXAa8GrgLWTEEZNEnGNNRrPGfbL6+lsZv9g9SLiF//46VZmNLMKZAX4Grg\nxnHea3ejgo2OjjI0NATA4OAgw8PD1e6Vyonkfmv7Fd1SHvfdd7+9f/+lUolyucx48loIs4H7gZOB\nPcAWIqB9XybNcuCc9LyUaDUszcl7NDFCCuAPiO6p04kA+NVpfwFwCxFkr29teJ+GJE2jZvdp5LU0\n9hEVwiZiNNSVxEX/7PT6FUQrYTkRtH4KODMnL8CngNcBzwEPAx9Mx+8FrkvP+4APYfeUpCYuvtj5\np9rNO8JVVSqVqk1WqRcMDJQYGxvpdDFmJO8IlyRNmi0NST3Luaemjy0NSdKkWWmoKjv0TuoNpU4X\noO9YaUjqWc491X7GNCRJBzCmIUmaNCsNVRnTUK/xnG0/Kw1JUmHGNCRJBzCmIWnGcd6p9rPSUJX9\nw+o1q1eXOl2EvmOlIUkqzJiGpJ7l3FPTx5iGJGnSilQay4AdxEp75zdJsza9vh04rkDezxALMm0H\nrgdelo4PAU8D29Lj8gLl0xQxpqHeU+p0AfpOXqUxC1hHXPwXE8u1HlOXZjmxJOvRwFnA+gJ5bwJe\nDxwLPABcmHm/h4iK5zhi5T5Jasi5p9ovr9JYQlzEy8CzwLXAyro0K4CNaXszMAjMy8l7M/B8Js/C\nCZZfU8hV+9RrNmwY6XQR+k5epbEA2JnZ35WOFUkzv0BegA8Q64xXHE50TZWA43PKJ0lqo7xKo+i4\nhImOwroIeAa4Ou3vARYRXVPnpuOHTPC91SJjGuo1nrPtNzvn9d3ERbxiEdFiGC/NwpRmTk7eUSIe\ncnLm2DPpAbAVeJiIlWytL9jo6ChDQ0MADA4OMjw8XO1eqZxI7re2X9Et5XHffffb+/dfKpUol8uM\nJ6+FMBu4n7iw7wG2EAHt+zJplgPnpOelwJr0PF7eZcBngROBxzLvdSjwOPAccARwO/AG4Im6cnmf\nhiRNo4nep7GPqBA2AfcCXyIu+menB0Q84ntE0PsKaiOemuUFuAw4mAiIZ4fWnkgMw90GfDl9Rn2F\nIUmAc091gneEq6pUKlWbrFIvGBgoMTY20ulizEjeES5JmjRbGpJ6lnNPTR9bGpKkSbPSUFV26J3U\nG0qdLkDfsdKQ1LOce6r9jGlIkg5gTEOSNGlWGqoypqFe4znbflYakqTCjGlIkg5gTEPSjOPcU+1n\npaEq+4fVa1avLnW6CH3HSkOSVJgxDUk9y7mnpo8xDUnSpBWpNJYBO4AHgfObpFmbXt9OrO+dl/cz\nxIJM24HrgZdlXrswpd8BnFKgfJoixjTUe0qdLkDfyas0ZgHriIv/YmK51mPq0iwHjiLW8j4LWF8g\n703A64FjgQeIioKU7rT0vIxY0c/WkKSGnHuq/fIuyEuIZVzLwLPAtcDKujQrgI1pezMwCMzLyXsz\n8Hwmz8K0vRK4JqUvp/xLWvlCmjhX7VOv2bBhpNNF6Dt5lcYCYGdmf1c6ViTN/AJ5AT5ArDNOyrOr\nQB5JUgfkVRpFxyVMdBTWRcAzwNVTUAZNkjEN9RrP2fabnfP6bmBRZn8R+7cEGqVZmNLMyck7SsRD\nTs55r92NCjY6OsrQ0BAAg4ODDA8PV7tXKieS+63tV3RLedx33/32/v2XSiXK5TLjyWshzAbuJy7s\ne4AtRED7vkya5cA56XkpsCY9j5d3GfBZ4ETgscx7LSZaHUuIbqlbiCB7fWvD+zQkaRpN9D6NfUSF\nsAm4F/gScdE/Oz0g4hHfI4LWVwAfyskLcBlwMBEQ30aMkiKluy49fyO9l7WDpIace6r9vCNcVaVS\nqdpklXrBwECJsbGRThdjRvKOcEnSpNnSkNSznHtq+tjSkCRNmpWGqrJD76TeUOp0AfqOlYakrjB3\nbnQ3tfKA1vPMndvZ79nrjGlI6grtik8YBynGmIYkadKsNFS1Zk2p00WQWmIcrv2sNFR1112dLoGk\nbmdMQ1XLlsE3v9npUqhfGdPoLs1iGnmz3GqGK5XiAbBpU20un5GReEhSli0NVc2bV+KHPxzpdDHU\npybSApjIfGm2NIqxpaGG1qyBG26I7b17a62Ld70LPvKRjhVLUpeypaGq4WGD4eocYxrdxZaGqgYG\nmv1WuI2BgZOa5rOillRkyO0yYAfwIHB+kzRr0+vbgeMK5H0v8F3gOeDNmeNDwNPEwkzZxZk0hcbG\nxho+xnvNCkPdyPs02i+vpTELWAe8nVir+++Br3Hgcq9HAUcDbwHWE8u9jpf3HuA3iZX+6j3E/hWP\nJKlL5LU0lhAX8TLwLHAtsLIuzQpgY9reDAwC83Ly7gAemFTJNQ1GOl0AqSWuNNl+eZXGAmBnZn9X\nOlYkzfwCeRs5nOiaKgHHF0gvSWqTvEqjaEf2VI3C2gMsIrqnzgWuBg6ZovdWrlKnCyC1xJhG++XF\nNHYTF/GKRUSLYbw0C1OaOQXy1nsmPQC2Ag8TsZKt9QlHR0cZGhoCYHBwkOHh4WpTtXIiud/a/hln\n0FXlcb+/9ivdo9P9eVCiVOr89+22/cp2uVxmPHkthNnA/cDJRCtgC/B+DgyEn5OelwJr0nORvLcB\nHwXuTPuHAo8To6qOAG4H3gA8UVcu79OQZhjv0+guE71PYx9RIWwiRkNdSVz0z06vXwHcSFQYDwFP\nAWfm5IUYObWWqCS+TsQwTgVOBFYTgfPn0+fUVxiSpA7xjnBVlSYwj480VZx7qru4cp8kadJsaUjq\nCsY0uostDeWqrKUhSc1Yaahq9epSp4sgtSQ7XFTtYaUhSSrMmIaq7OtVJxnT6C7GNCRJk2aloYxS\npwsgtcSYRvtZaaiqMveUJDVjTENSVzCm0V2MaUiSJs1KQ1X2D6vXeM62n5WGJKkwYxqSuoIxje5i\nTEO5nHtKUp4ilcYyYAfwIHB+kzRr0+vbifW98/K+F/gusULfm+ve68KUfgdwSoHyaYo495R6jTGN\n9surNGYB64iL/2JiudZj6tIsB44i1vI+C1hfIO89xOp9t9e912LgtPS8DLi8QBklSW2Sd0FeQizj\nWiaWYL0WWFmXZgWwMW1vBgaBeTl5dwAPNPi8lcA1KX055V9S7Kto8kY6XQCpJa402X55lcYCYGdm\nf1c6ViTN/AJ5681P6VrJI0lqk7xKo+gYg+kcheU4h7YpdboAUkuMabTf7JzXdwOLMvuL2L8l0CjN\nwpRmToG8eZ+3MB07wOjoKENDQwAMDg4yPDxcbapWTiT3W9uvzD3VLeVxv7/2K92j0/15UKJU6vz3\n7bb9yna5XGY8eS2E2cD9wMnAHmALEdC+L5NmOXBOel4KrEnPRfLeBnwUuDPtLwauJuIYC4BbiCB7\nfWvD+zSkGcb7NLpLs/s08loa+4gKYRMxGupK4qJ/dnr9CuBGosJ4CHgKODMnL8TIqbXAocDXgW3A\nqcC9wHXpeR/wIeyekqSu4R3hqiqVSpkmvNReE2kBTOSctaVRjHeES5ImzZaGpK5gTKO72NJQLuee\nkpTHSkNVzj2lXpMdLqr2sNKQJBVmTENV9vWqk4xpdBdjGpKkSbPSUEap0wWQWmJMo/2sNGaouXOj\nGd7KA1rPM3duZ7+npPYypjFD2T+snjPQxsuRJ22uic49JUltMcBY+37oTP/HzFh2T6nK/mH1Gs/Z\n9rPSkCQVZkxjhjKmoV7jOdtdvE9DkjRpRSqNZcAO4EHg/CZp1qbXtwPHFcg7F7gZeAC4CRhMx4eA\np4lFmbYBlxcon6aI/cPqNZ6z7ZdXacwC1hEX/8XEcq3H1KVZTizJejRwFrC+QN4LiErjtcCtab/i\nIaLiOY5YuU+S1CXyKo0lxEW8DDwLXAusrEuzAtiYtjcTrYZ5OXmzeTYC75pg+TWFXLVPvcZztv3y\nKo0FwM7M/q50rEia+ePkfSWwN23vTfsVhxNdUyXg+JzySZLaKK/SKDrGoMgorIEm7zeWOb4HWER0\nTZ0LXA0cUrAMmiT7h9VrPGfbL++O8N3ERbxiEdFiGC/NwpRmToPju9P2XqIL64fAq4BH0/Fn0gNg\nK/AwESvZWl+w0dFRhoaGABgcHGR4eLjaVK2cSP2+D62mp6vK735/7bd6vk50H0qUSp3/vt22X9ku\nl8uMJ6+FMBu4HziZaAVsIQLa92XSLAfOSc9LgTXpeby8lwI/Bj5NBMEH0/OhwOPAc8ARwO3AG4An\n6srlfRo5HPOuXuM5210mOvfUPqJC2ESMhrqSuOifnV6/AriRqDAeAp4CzszJC3AJcB3w74hA+fvS\n8ROAPyEC58+nz6mvMCRJHeId4TPURH5NlUqlTBN++j5HasRztrt4R7gkadJsacxQ9g+r17RrOY2X\nvxx+8pP2fFYvcz0NSV1tIj8+/NHSfnZPqSo79E7qDaVOF6DvWGlIkgozpjFDGdNQP/D8mz7GNPrM\nGANt+UkwlvmvpJnP7qkZaoCx+AnWwqN0220t5xmwwlAHnXFGqdNF6DtWGpJ61uhop0vQf4xpzFDG\nNCRNhneES5ImzUpDVd6noV7jOdt+VhqSpMIccjuDtT6Xz0jLn/Hyl7ecRZoypdIILhPeXgbCVWVQ\nW73Gc3b6TCYQvgzYATwInN8kzdr0+nZife+8vHOBm4EHgJuIlfsqLkzpdwCnFCifpkyp0wWQWlTq\ndAH6Tl6lMQtYR1z8FxPLtR5Tl2Y5cBSxlvdZwPoCeS8gKo3XAremfVK609LzMuDyAmXUlLmr0wWQ\nWuQ52255F+QlxDKuZWIJ1muBlXVpVgAb0/ZmotUwLydvNs9G4F1peyVwTUpfTvmXtPKFNBmurKvu\nNDAw0PABf9j0tYF2LdDRZ/IqjQXAzsz+rnSsSJr54+R9JbA3be9N+6Q8u3I+T1KfGRsba/hYtWpV\n09eMe06PvNFTRf/Vi1TpA03ebyznc/w/P8XG+wU2MLC66Wv+EarblMvlTheh7+RVGruBRZn9Rezf\nEmiUZmFKM6fB8d1pey/RhfVD4FXAo+O8124OtH1gYODYnLJritncVzfauHFjfiJNxPaJZJoNPAwM\nAS8gok6NAuE3pu2lwHcK5L2U2miqC4BL0vbilO4FwOEpv1cqSeohpwL3E0HpC9Oxs9OjYl16fTvw\n5py8EENub6HxkNuPp/Q7gHdM1ZeQJEmSJE3Sh4F7gZ8AH5tA/r+d2uJIk/LLRLf1ncARTOz8XA2c\nPJWFkmaS+4jhy9JMcAFwUacLIc1Unwd+AdwNfAS4LB1/L3AP8YvtW+nY64kbMrcR8agj0/GfpucB\n4DMp393A+9LxEWL+hi8TFdRfTccX0YwxRJwnXwD+EdgEvIg4h34lpTkUeKRB3uXA/yNGZN6ajlXO\nz1cBtxPn7z3A24h7zzZQO2f/U0q7AXhP2j4Z2Jpev5IYeANxQ/HFRIvmbuB1rX5RqVc9Qgw2OIOY\nFwzij+BVaful6Xkt8DtpezbxhwzwZHp+DzFQYQD4JeD7xFDpEeL28fnptW8Tf7BSI0PELA9vSvtf\nAn4XuI3awJlmlQbAKuDczH7l/DyPGDgDcR4eTFRCN2XSVs71q4B3E+f4D4ipjyBmpKhULI8Av5+2\nPwj8Rd4X60fO6zRzDWQeEP3AG4F/T+3+nL8j/ug+Rvxh/7zuPY4HriZusHyUaKH8q7S/BdiTtu9K\n+aVmHiF+uED8kh9qMX+jofdbgDOJSuVNRAvkYSLusZYYfflkJv0A0Xp4hBihCfE3cUImzfXpeesE\nytgXrDRmtuwt3B8E/oi4efJOoiVyDfAbwNPEvTYnNchf/8daec9fZI49h2uzaHyNzpd9xMSmUGvl\nQrQKtgH/J+c97wB+lbgBeANwOtECPpbo+vqPwBfr8tRPa1A/U0WlnJ7TTVhpzGzZC/6RxC+zVcCP\niLvtDyf6cS8Dvgq8sS7/HcSswwcBhxG/yLbgDZeaGmVqMY3fyhw/k1hi4ddz8r+aOJe/mB5vBl5B\nVETXA3/M/ks1jBH3jQ1Ri9+dTi3GpwKsSWemsboHxF34RxMX/FuIroLziT+aZ4lg4ycz+QG+AryV\nCJKPAf+Z6KY6hgN/sTkxlcbT6Hz5M+A6YkmFrzdI0yx/Zfsk4KPE+fsk8HvEBKdXUftBfAH7+wVR\nKX2ZuP5tIQaPNPoMz2lJkiRJkiRJkiRJkiRJkiRJkiRJmqG8wVaSZriXEHcw30VMwf0+YiLHb6dj\nm1OaFxFiTxIpAAABF0lEQVR3J99NTIA3kvKPAl8jpvq+DfgXwF+mfFuBFW35FpKktngPsTZExUuJ\n2VUr8ygdTMx/dB61CfNeR0wt/0Ki0tgJDKbX/pSYKpx07H6iIpEkzQBHE9NrX0JMH/9G4P82SHc9\ntdYFxIJBbyTWOfnLzPF/IFos29KjjAsAqUvZnyq17kFi9tR3Av+V6GJqptmMwE/V7b87va/U1Zwa\nXWrdq4gFq/4nMVPrEmJFw3+ZXj+E6J66g1q302uJqbx3cGBFsgn4cGb/OKQuZUtDat0bibXTnwee\nIRa4OohYl+TFwM+AtwOXA+uJQPg+olvqWQ6cdvsTwJqU7iDgexgMlyRJkiRJkiRJkiRJkiRJkiRJ\nkiRJEsA/A3sng0v+IomuAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0e3f22ffd0>"
+       "<matplotlib.figure.Figure at 0x7fdb2da35110>"
       ]
      },
      "metadata": {},
@@ -1099,7 +1100,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x7f0e3ecbe6c8>"
+       "<matplotlib.colorbar.Colorbar instance at 0x7fdb2d6d5680>"
       ]
      },
      "execution_count": 27,
@@ -1110,7 +1111,7 @@
      "data": {
       "image/png": 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@@ -1492,7 +1493,7 @@
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@@ -2324,9 +2325,9 @@
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xZop5HUgHLsPhSOTAgd3s37+f/fv3c/bZZ/PLL7/Qs+dDHDyYPXu+D6+3BitW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rwqRJ3zB//i80adIo17P7fvzxx+zeHUlc3C8kJHxMfPwnPPzwxc7HeXmtW7eO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\nIXCYxYqV4IoVK7UEkt8SiKVO56LB0I/AZ1TVh1Lrv58/f54jRoxix45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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0e3ec72f90>"
+       "<matplotlib.figure.Figure at 0x7fdb2d810250>"
       ]
      },
      "metadata": {},
@@ -2353,7 +2354,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f0e3e9f3750>"
+       "<matplotlib.legend.Legend at 0x7fdb2d555390>"
       ]
      },
      "execution_count": 39,
@@ -2362,9 +2363,9 @@
     },
     {
      "data": {
-      "image/png": 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KYk+LbZCzAtYfbzuSxiecCm8/DafdZfp1RCLUpyBWhEIhc+ex/uPMWbh+r9N7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\ne2mjEk/73Jg3WRKNcUPkr98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SEBERHyUFERHxUVIQEREfJQUREfFRUhARER8lBRER8VFSEBERHyUFERHxUVIQEREfJQUR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\nIiIiIiIiIiIiIiIiIiIiInXH/wcEsNz5+JgaMgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f0e3ece5250>"
+       "<matplotlib.figure.Figure at 0x7fdb2d555bd0>"
       ]
      },
      "metadata": {},
diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb
index ecf027fda..87ae42b41 100644
--- a/docs/source/pythonapi/examples/post-processing.ipynb
+++ b/docs/source/pythonapi/examples/post-processing.ipynb
@@ -347,7 +347,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDIzOjUxOjAwLTA0OjAwvsbegAAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDIxOjA0OjQyLTA0OjAw/RjytgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -458,8 +458,8 @@
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
-      "      Git SHA1:       170155e8d7935b57fad57bfad6aff1034a80206e\n",
-      "      Date/Time:      2015-10-12 23:51:01\n",
+      "      Git SHA1:       21738db07debeabde824c9b955bd3bf0c9a16366\n",
+      "      Date/Time:      2015-10-28 21:04:43\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -478,6 +478,7 @@
       " Loading ACE cross section table: 1001.71c\n",
       " Loading ACE cross section table: 5010.71c\n",
       " Loading ACE cross section table: 40090.71c\n",
+      " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n",
       " Initializing source particles...\n",
       "\n",
       " ===========================================================================\n",
@@ -507,7 +508,19 @@
       "       19/1    1.00071    1.04214 +/- 0.00800\n",
       "       20/1    1.05587    1.04351 +/- 0.00729\n",
       "       21/1    1.03886    1.04309 +/- 0.00660\n",
-      "       22/1    1.04335    1.04311 +/- 0.00603\n"
+      "       22/1    1.04335    1.04311 +/- 0.00603\n",
+      "       23/1    1.04057    1.04292 +/- 0.00555\n",
+      "       24/1    1.01976    1.04126 +/- 0.00540\n",
+      "       25/1    1.05811    1.04238 +/- 0.00515\n",
+      "       26/1    1.02351    1.04120 +/- 0.00496\n",
+      "       27/1    1.05261    1.04188 +/- 0.00471\n",
+      "       28/1    1.03355    1.04141 +/- 0.00446\n",
+      "       29/1    1.02797    1.04071 +/- 0.00428\n",
+      "       30/1    1.03758    1.04055 +/- 0.00406\n",
+      "       31/1    1.04883    1.04094 +/- 0.00388\n",
+      "       32/1    1.03557    1.04070 +/- 0.00371\n",
+      "       33/1    1.02947    1.04021 +/- 0.00358\n",
+      "       34/1    1.03651    1.04006 +/- 0.00343\n"
      ]
     }
    ],
diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb
index 0c9869767..fce805f18 100644
--- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb
+++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb
@@ -363,7 +363,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDIzOjUyOjA4LTA0OjAwZh4r5AAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nVDIxOjE1OjA2LTA0OjAwo1FsoQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -573,8 +573,8 @@
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
       "      Version:        0.7.0\n",
-      "      Git SHA1:       170155e8d7935b57fad57bfad6aff1034a80206e\n",
-      "      Date/Time:      2015-10-12 23:52:08\n",
+      "      Git SHA1:       21738db07debeabde824c9b955bd3bf0c9a16366\n",
+      "      Date/Time:      2015-10-28 21:15:07\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -593,6 +593,7 @@
       " Loading ACE cross section table: 1001.71c\n",
       " Loading ACE cross section table: 5010.71c\n",
       " Loading ACE cross section table: 40090.71c\n",
+      " Maximum neutron transport energy: 20.0000 MeV for 92235.71c\n",
       " Initializing source particles...\n",
       "\n",
       " ===========================================================================\n",
@@ -630,20 +631,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.1300E-01 seconds\n",
-      "   Reading cross sections          =  9.6000E-02 seconds\n",
-      " Total time in simulation          =  1.6248E+01 seconds\n",
-      "   Time in transport only          =  1.6236E+01 seconds\n",
-      "   Time in inactive batches        =  2.3150E+00 seconds\n",
-      "   Time in active batches          =  1.3933E+01 seconds\n",
-      "   Time synchronizing fission bank =  0.0000E+00 seconds\n",
-      "     Sampling source sites         =  0.0000E+00 seconds\n",
-      "     SEND/RECV source sites        =  0.0000E+00 seconds\n",
-      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
-      " Total time for finalization       =  2.0000E-03 seconds\n",
-      " Total time elapsed                =  1.6672E+01 seconds\n",
-      " Calculation Rate (inactive)       =  5399.57 neutrons/second\n",
-      " Calculation Rate (active)         =  2691.45 neutrons/second\n",
+      " Total time for initialization     =  6.3800E-01 seconds\n",
+      "   Reading cross sections          =  1.3500E-01 seconds\n",
+      " Total time in simulation          =  2.3556E+01 seconds\n",
+      "   Time in transport only          =  2.3532E+01 seconds\n",
+      "   Time in inactive batches        =  3.1100E+00 seconds\n",
+      "   Time in active batches          =  2.0446E+01 seconds\n",
+      "   Time synchronizing fission bank =  2.0000E-03 seconds\n",
+      "     Sampling source sites         =  1.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  1.0000E-03 seconds\n",
+      " Total time for finalization       =  3.0000E-03 seconds\n",
+      " Total time elapsed                =  2.4210E+01 seconds\n",
+      " Calculation Rate (inactive)       =  4019.29 neutrons/second\n",
+      " Calculation Rate (active)         =  1834.10 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -741,7 +742,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -801,7 +802,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -861,7 +862,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -920,7 +921,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -981,7 +982,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1039,7 +1040,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1117,7 +1118,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1343,7 +1344,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1426,7 +1427,7 @@
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
diff --git a/openmc/filter.py b/openmc/filter.py
index 5c0343df7..92677709d 100644
--- a/openmc/filter.py
+++ b/openmc/filter.py
@@ -601,8 +601,7 @@ class Filter(object):
                           'to use a Summary for distribcell dataframes'
                     raise ImportError(msg)
 
-                # Create and extract the OpenCG geometry the Summary
-                summary.make_opencg_geometry()
+                # Extract the OpenCG geometry from the Summary
                 opencg_geometry = summary.opencg_geometry
                 openmc_geometry = summary.openmc_geometry
 
diff --git a/openmc/summary.py b/openmc/summary.py
index b38e6d759..981cec794 100644
--- a/openmc/summary.py
+++ b/openmc/summary.py
@@ -8,6 +8,15 @@ class Summary(object):
     """Information summarizing the geometry, materials, and tallies used in a
     simulation.
 
+    Attributes
+    ----------
+    openmc_geometry : openmc.Geometry
+        An OpenMC geometry object reconstructed from the summary file
+    opencg_geometry : opencg.Geometry
+        An OpenCG geometry object equivalent to the OpenMC geometry
+        encapsulated by the summary file. Use of this attribute requires
+        installation of the OpenCG Python module.
+
     """
 
     def __init__(self, filename):
@@ -23,13 +32,30 @@ class Summary(object):
             raise ValueError(msg)
 
         self._f = h5py.File(filename, 'r')
-        self.openmc_geometry = None
-        self.opencg_geometry = None
+        self._openmc_geometry = None
+        self._opencg_geometry = None
 
         self._read_metadata()
         self._read_geometry()
         self._read_tallies()
 
+    @property
+    def openmc_geometry(self):
+        return self._openmc_geometry
+
+    @property
+    def opencg_geometry(self):
+        try:
+            from openmc.opencg_compatible import get_opencg_geometry
+        except ImportError:
+            msg = 'Unable to import OpenCG.'
+            raise ImportError(msg)
+
+        if self._opencg_geometry is None:
+            self._opencg_geometry = get_opencg_geometry(self._openmc_geometry)
+        
+        return self._opencg_geometry
+
     def _read_metadata(self):
         # Read OpenMC version
         self.version = [self._f['version_major'].value,
@@ -444,7 +470,7 @@ class Summary(object):
 
     def _finalize_geometry(self):
         # Initialize Geometry object
-        self.openmc_geometry = openmc.Geometry()
+        self._openmc_geometry = openmc.Geometry()
 
         # Iterate over all Cells and add fill Materials, Universes and Lattices
         for cell_key in self._cell_fills.keys():
@@ -468,7 +494,7 @@ class Summary(object):
 
         # Set the root universe for the Geometry
         root_universe = self.get_universe_by_id(0)
-        self.openmc_geometry.root_universe = root_universe
+        self._openmc_geometry.root_universe = root_universe
 
     def _read_tallies(self):
         # Initialize dictionaries for the Tallies
@@ -531,22 +557,6 @@ class Summary(object):
             # Add Tally to the global dictionary of all Tallies
             self.tallies[tally_id] = tally
 
-    def make_opencg_geometry(self):
-        """Create OpenCG geometry based on the information contained in the summary
-        file. The geometry is stored as the 'opencg_geometry' attribute.
-
-        """
-
-        try:
-            from openmc.opencg_compatible import get_opencg_geometry
-        except ImportError:
-            msg = 'Unable to import opencg which is needed ' \
-                  'by Summary.make_opencg_geometry()'
-            raise ImportError(msg)
-
-        if self.opencg_geometry is None:
-            self.opencg_geometry = get_opencg_geometry(self.openmc_geometry)
-
     def get_material_by_id(self, material_id):
         """Return a Material object given the material id
 
diff --git a/openmc/tallies.py b/openmc/tallies.py
index c9e4854e2..575518734 100644
--- a/openmc/tallies.py
+++ b/openmc/tallies.py
@@ -2107,6 +2107,9 @@ class Tally(object):
 
         return new_tally
 
+    def __div__(self, other):
+        return self.__truediv__(other)
+
     def __pow__(self, power):
         """Raises this tally to another tally or scalar value power.