diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
index 94be516fc..b18014183 100644
--- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
@@ -265,7 +265,7 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -308,7 +308,7 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -404,6 +404,7 @@
     "prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=energy_groups, by_nuclide=True)\n",
     "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
     "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
+    "#delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
     "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
     "\n",
     "chi_prompt.nuclides = ['U235', 'Pu239']\n",
@@ -580,8 +581,8 @@
       "      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:       ad9fe27d26940a7120ed920d37d9cb176bde6402\n",
-      "      Date/Time:      2016-08-06 15:47:51\n",
+      "      Git SHA1:       be7e6e035d22944a8c80ca32f99935b6822854c9\n",
+      "      Date/Time:      2016-08-10 15:46:45\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -668,20 +669,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.7100E-01 seconds\n",
-      "   Reading cross sections          =  2.6500E-01 seconds\n",
-      " Total time in simulation          =  8.5400E+01 seconds\n",
-      "   Time in transport only          =  8.5378E+01 seconds\n",
-      "   Time in inactive batches        =  4.8000E+00 seconds\n",
-      "   Time in active batches          =  8.0600E+01 seconds\n",
-      "   Time synchronizing fission bank =  1.0000E-02 seconds\n",
-      "     Sampling source sites         =  8.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
+      " Total time for initialization     =  7.7400E-01 seconds\n",
+      "   Reading cross sections          =  4.7500E-01 seconds\n",
+      " Total time in simulation          =  8.9596E+01 seconds\n",
+      "   Time in transport only          =  8.9573E+01 seconds\n",
+      "   Time in inactive batches        =  4.8730E+00 seconds\n",
+      "   Time in active batches          =  8.4723E+01 seconds\n",
+      "   Time synchronizing fission bank =  3.0000E-03 seconds\n",
+      "     Sampling source sites         =  1.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
       "   Time accumulating tallies       =  1.0000E-03 seconds\n",
       " Total time for finalization       =  7.2000E-02 seconds\n",
-      " Total time elapsed                =  8.5969E+01 seconds\n",
-      " Calculation Rate (inactive)       =  10416.7 neutrons/second\n",
-      " Calculation Rate (active)         =  2481.39 neutrons/second\n",
+      " Total time elapsed                =  9.0468E+01 seconds\n",
+      " Calculation Rate (inactive)       =  10260.6 neutrons/second\n",
+      " Calculation Rate (active)         =  2360.63 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -794,61 +795,28 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-Delayed-Group XS\n",
-      "\tReaction Type  =\tdelayed-nu-fission\n",
-      "\tDomain Type    =\tcell\n",
-      "\tDomain ID      =\t1\n",
-      "\tNuclide        =\tU235\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Delayed Group 1:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t5.14e-06 +/- 1.76e-01%\n",
-      "\n",
-      "            Delayed Group 2:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t2.65e-05 +/- 1.76e-01%\n",
-      "\n",
-      "            Delayed Group 3:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t2.53e-05 +/- 1.76e-01%\n",
-      "\n",
-      "            Delayed Group 4:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t5.68e-05 +/- 1.76e-01%\n",
-      "\n",
-      "            Delayed Group 5:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t2.33e-05 +/- 1.76e-01%\n",
-      "\n",
-      "            Delayed Group 6:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t9.76e-06 +/- 1.76e-01%\n",
-      "\n",
-      "\n",
-      "\tNuclide        =\tPu239\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Delayed Group 1:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t1.16e-06 +/- 1.90e-01%\n",
-      "\n",
-      "            Delayed Group 2:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t7.58e-06 +/- 1.90e-01%\n",
-      "\n",
-      "            Delayed Group 3:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t5.74e-06 +/- 1.90e-01%\n",
-      "\n",
-      "            Delayed Group 4:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t1.05e-05 +/- 1.90e-01%\n",
-      "\n",
-      "            Delayed Group 5:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t5.46e-06 +/- 1.90e-01%\n",
-      "\n",
-      "            Delayed Group 6:\t\n",
-      "            Group 1 [1e-09      - 19.9526231497MeV]:\t1.65e-06 +/- 1.90e-01%\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([[[[  5.14239169e-06,   1.16429778e-06]],\n",
+       "\n",
+       "        [[  2.65434434e-05,   7.58244504e-06]],\n",
+       "\n",
+       "        [[  2.53406770e-05,   5.73814391e-06]],\n",
+       "\n",
+       "        [[  5.68158884e-05,   1.04761254e-05]],\n",
+       "\n",
+       "        [[  2.32937121e-05,   5.45676114e-06]],\n",
+       "\n",
+       "        [[  9.75765501e-06,   1.65156185e-06]]]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "delayed_nu_fission.get_condensed_xs(one_group).print_xs()"
+    "delayed_nu_fission.get_condensed_xs(one_group).get_xs()"
    ]
   },
   {
@@ -865,14 +833,6 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
-      "  return c.reshape(shape_out)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -891,131 +851,111 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>198</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>0.000228</td>\n",
-       "      <td>4.753468e-07</td>\n",
+       "      <td>9.533842e-11</td>\n",
+       "      <td>4.789050e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>199</th>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>0.000081</td>\n",
-       "      <td>1.885620e-07</td>\n",
+       "      <td>1.606499e-11</td>\n",
+       "      <td>8.071081e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>398</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>0.001175</td>\n",
-       "      <td>2.453594e-06</td>\n",
+       "      <td>1.224131e-09</td>\n",
+       "      <td>6.149449e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>399</th>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>0.000531</td>\n",
-       "      <td>1.228003e-06</td>\n",
+       "      <td>2.602518e-10</td>\n",
+       "      <td>1.307590e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>598</th>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>0.001122</td>\n",
-       "      <td>2.342414e-06</td>\n",
+       "      <td>9.033000e-10</td>\n",
+       "      <td>4.537601e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>599</th>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>0.000402</td>\n",
-       "      <td>9.293122e-07</td>\n",
+       "      <td>1.522295e-10</td>\n",
+       "      <td>7.648264e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>798</th>\n",
        "      <td>1</td>\n",
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>0.002516</td>\n",
-       "      <td>5.251885e-06</td>\n",
+       "      <td>1.749138e-09</td>\n",
+       "      <td>8.786432e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>799</th>\n",
        "      <td>1</td>\n",
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>0.000733</td>\n",
-       "      <td>1.696645e-06</td>\n",
+       "      <td>2.400317e-10</td>\n",
+       "      <td>1.205943e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>998</th>\n",
        "      <td>1</td>\n",
        "      <td>5</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>0.001031</td>\n",
-       "      <td>2.153199e-06</td>\n",
+       "      <td>2.724017e-10</td>\n",
+       "      <td>1.368376e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>999</th>\n",
        "      <td>1</td>\n",
        "      <td>5</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>0.000382</td>\n",
-       "      <td>8.837413e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.000432</td>\n",
-       "      <td>9.019675e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>0.000116</td>\n",
-       "      <td>2.674761e-07</td>\n",
+       "      <td>4.749191e-11</td>\n",
+       "      <td>2.386080e-11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    cell  delayedgroup  group in nuclide      mean     std. dev.\n",
-       "0      1             1         1    U235  0.000228  4.753468e-07\n",
-       "1      1             1         1   Pu239  0.000081  1.885620e-07\n",
-       "2      1             2         1    U235  0.001175  2.453594e-06\n",
-       "3      1             2         1   Pu239  0.000531  1.228003e-06\n",
-       "4      1             3         1    U235  0.001122  2.342414e-06\n",
-       "5      1             3         1   Pu239  0.000402  9.293122e-07\n",
-       "6      1             4         1    U235  0.002516  5.251885e-06\n",
-       "7      1             4         1   Pu239  0.000733  1.696645e-06\n",
-       "8      1             5         1    U235  0.001031  2.153199e-06\n",
-       "9      1             5         1   Pu239  0.000382  8.837413e-07\n",
-       "10     1             6         1    U235  0.000432  9.019675e-07\n",
-       "11     1             6         1   Pu239  0.000116  2.674761e-07"
+       "     cell  delayedgroup  group in nuclide          mean     std. dev.\n",
+       "198     1             1         1    U235  9.533842e-11  4.789050e-11\n",
+       "199     1             1         1   Pu239  1.606499e-11  8.071081e-12\n",
+       "398     1             2         1    U235  1.224131e-09  6.149449e-10\n",
+       "399     1             2         1   Pu239  2.602518e-10  1.307590e-10\n",
+       "598     1             3         1    U235  9.033000e-10  4.537601e-10\n",
+       "599     1             3         1   Pu239  1.522295e-10  7.648264e-11\n",
+       "798     1             4         1    U235  1.749138e-09  8.786432e-10\n",
+       "799     1             4         1   Pu239  2.400317e-10  1.205943e-10\n",
+       "998     1             5         1    U235  2.724017e-10  1.368376e-10\n",
+       "999     1             5         1   Pu239  4.749191e-11  2.386080e-11"
       ]
      },
      "execution_count": 19,
@@ -1024,8 +964,8 @@
     }
    ],
    "source": [
-    "df = beta.get_condensed_xs(one_group).get_pandas_dataframe()\n",
-    "df.head(12)"
+    "df = delayed_nu_fission.get_pandas_dataframe()\n",
+    "df.head(10)"
    ]
   },
   {
@@ -1061,8 +1001,8 @@
    },
    "outputs": [],
    "source": [
-    "chi_prompt.build_hdf5_store(filename='mgxs', append=True)\n",
-    "chi_delayed.build_hdf5_store(filename='mgxs', append=True)"
+    "chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n",
+    "chi_delayed.build_hdf5_store(filename='mdgxs', append=True)"
    ]
   },
   {
@@ -1315,9 +1255,9 @@
     },
     {
      "data": {
-      "image/png": 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7A3OBycDYiJhess0pwM4RcaqkMcDhETFW0k7A9cAeZG08DwCjIiIk7QMsBa6J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\nhwIT0vNbgf3S80PIksyKiJg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OAnaPiLMljQSOiYhRknYDbgX2IWvjeRgYHhEh6UBgOXBTRHys5FijgWURUdoe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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fb26110>"
+       "<matplotlib.figure.Figure at 0x110daa510>"
       ]
      },
      "metadata": {},
@@ -1378,9 +1318,9 @@
     },
     {
      "data": {
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Mlaq6KxsjXsZzetV0v6a4TThuWrRoETJD6auvvgp+vvfee7n33nur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\nPaN0emYRw7RYwEN9odxfzQdRrf4r2P9v/hLwWyE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oXeI190z1eA2RhpoMtUO1Jp9TEdNjMMRDpr4n7mUoNbk9KT1YgJYk5vn46/Nw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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fb0e250>"
+       "<matplotlib.figure.Figure at 0x110ef5310>"
       ]
      },
      "metadata": {},
@@ -1388,21 +1328,15 @@
     }
    ],
    "source": [
-    "chi_d_u235  = chi_delayed.xs_tally.get_values(nuclides=['U235'])\n",
-    "chi_d_pu239 = chi_delayed.xs_tally.get_values(nuclides=['Pu239'])\n",
-    "chi_p_u235  = chi_prompt.xs_tally.get_values(nuclides=['U235'])\n",
-    "chi_p_pu239 = chi_prompt.xs_tally.get_values(nuclides=['Pu239'])\n",
+    "chi_d_u235  = np.squeeze(chi_delayed.get_xs(nuclides=['U235'], order_groups='decreasing'))\n",
+    "chi_d_pu239 = np.squeeze(chi_delayed.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n",
+    "chi_p_u235  = np.squeeze(chi_prompt.get_xs(nuclides=['U235'], order_groups='decreasing'))\n",
+    "chi_p_pu239 = np.squeeze(chi_prompt.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n",
     "\n",
-    "# Reshape the betas\n",
-    "chi_d_u235.shape  = (chi_d_u235.shape[0])\n",
-    "chi_d_pu239.shape = (chi_d_pu239.shape[0])\n",
-    "chi_p_u235.shape  = (chi_p_u235.shape[0])\n",
-    "chi_p_pu239.shape = (chi_p_pu239.shape[0])\n",
-    "\n",
-    "chi_d_u235  = np.append(chi_d_u235[0] , chi_d_u235)\n",
-    "chi_d_pu239 = np.append(chi_d_pu239[0], chi_d_pu239)\n",
-    "chi_p_u235  = np.append(chi_p_u235[0] , chi_p_u235)\n",
-    "chi_p_pu239 = np.append(chi_p_pu239[0], chi_p_pu239)\n",
+    "chi_d_u235  = np.append(chi_d_u235 , chi_d_u235[0])\n",
+    "chi_d_pu239 = np.append(chi_d_pu239, chi_d_pu239[0])\n",
+    "chi_p_u235  = np.append(chi_p_u235 , chi_p_u235[0])\n",
+    "chi_p_pu239 = np.append(chi_p_pu239, chi_p_pu239[0])\n",
     "\n",
     "# Create a step plot for the MGXS\n",
     "plt.semilogx(energy_groups.group_edges, chi_d_u235 , drawstyle='steps', color='b', linestyle='--', linewidth=3)\n",
@@ -1443,7 +1377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb
index c6bf077f8..eb2471e22 100644
--- a/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mdgxs-part-ii.ipynb
@@ -30,7 +30,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1350: UserWarning:  This call to matplotlib.use() has no effect\n",
+      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1357: 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",
@@ -456,7 +456,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -607,8 +607,8 @@
       "      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:       ad9fe27d26940a7120ed920d37d9cb176bde6402\n",
-      "      Date/Time:      2016-08-06 15:52:56\n",
+      "      Git SHA1:       be7e6e035d22944a8c80ca32f99935b6822854c9\n",
+      "      Date/Time:      2016-08-10 15:48:40\n",
       "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
@@ -713,20 +713,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.2200E-01 seconds\n",
-      "   Reading cross sections          =  2.3800E-01 seconds\n",
-      " Total time in simulation          =  7.5197E+01 seconds\n",
-      "   Time in transport only          =  7.4942E+01 seconds\n",
-      "   Time in inactive batches        =  4.8400E+00 seconds\n",
-      "   Time in active batches          =  7.0357E+01 seconds\n",
-      "   Time synchronizing fission bank =  4.0000E-03 seconds\n",
-      "     Sampling source sites         =  4.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  0.0000E+00 seconds\n",
-      "   Time accumulating tallies       =  2.1900E-01 seconds\n",
-      " Total time for finalization       =  8.0000E-03 seconds\n",
-      " Total time elapsed                =  7.5653E+01 seconds\n",
-      " Calculation Rate (inactive)       =  5165.29 neutrons/second\n",
-      " Calculation Rate (active)         =  1421.32 neutrons/second\n",
+      " Total time for initialization     =  5.2300E-01 seconds\n",
+      "   Reading cross sections          =  3.3300E-01 seconds\n",
+      " Total time in simulation          =  7.3672E+01 seconds\n",
+      "   Time in transport only          =  7.3396E+01 seconds\n",
+      "   Time in inactive batches        =  5.0250E+00 seconds\n",
+      "   Time in active batches          =  6.8647E+01 seconds\n",
+      "   Time synchronizing fission bank =  1.3000E-02 seconds\n",
+      "     Sampling source sites         =  0.0000E+00 seconds\n",
+      "     SEND/RECV source sites        =  3.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  2.0800E-01 seconds\n",
+      " Total time for finalization       =  7.0000E-03 seconds\n",
+      " Total time elapsed                =  7.4227E+01 seconds\n",
+      " Calculation Rate (inactive)       =  4975.12 neutrons/second\n",
+      " Calculation Rate (active)         =  1456.73 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -1249,7 +1249,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x115a28890>"
+       "<matplotlib.text.Text at 0x1167039d0>"
       ]
      },
      "execution_count": 24,
@@ -1260,7 +1260,7 @@
      "data": {
       "image/png": 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FfAdYOSKObOJ9mpn1HV8TF+Y9HMysOG+QY2bWWk1uGpnvydB9//eHyTaBfETS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\nDmZmZmZmZmbW5TzgYGZmZmZmZmZdzgMOZmZmZmZmZtblPOBgZmZmZmZ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4LCLGphcquwJ4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vBL4PTUKFHpX1i3QRgASbsAY4Db8nywWtzgYGa5vcTwpupTd63zyVpZp0bEV6veHw78ENgXeAKY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\nGjgXDpIkSZIkaeBcOEiSJEmSpIH7Db/BFUInlBauAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x115438fd0>"
+       "<matplotlib.figure.Figure at 0x113900910>"
       ]
      },
      "metadata": {},
@@ -1332,7 +1332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index cb4df0fad..ca4832809 100644
--- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
@@ -34,7 +34,9 @@
      "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",
+      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:878: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
+      "  warnings.warn(self.msg_depr % (key, alt_key))\n",
+      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1357: 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",
@@ -440,9 +442,10 @@
       "\n",
       "      Copyright:      2011-2016 Massachusetts Institute of Technology\n",
       "      License:        http://openmc.readthedocs.io/en/latest/license.html\n",
-      "      Version:        0.7.1\n",
-      "      Git SHA1:       3d68c07625e33cd64188df03ee03e9c31b3d4b74\n",
-      "      Date/Time:      2016-07-22 21:32:41\n",
+      "      Version:        0.8.0\n",
+      "      Git SHA1:       be7e6e035d22944a8c80ca32f99935b6822854c9\n",
+      "      Date/Time:      2016-08-10 15:31:07\n",
+      "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -452,11 +455,11 @@
       " Reading geometry XML file...\n",
       " Reading cross sections XML file...\n",
       " Reading materials XML file...\n",
-      " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n",
-      " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n",
-      " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n",
-      " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n",
-      " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n",
+      " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n",
+      " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n",
+      " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n",
+      " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n",
+      " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n",
       " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n",
       " Reading tallies XML file...\n",
       " Building neighboring cells lists for each surface...\n",
@@ -518,7 +521,7 @@
       "       48/1    1.21610    1.22612 +/- 0.00251\n",
       "       49/1    1.22199    1.22602 +/- 0.00245\n",
       "       50/1    1.20860    1.22558 +/- 0.00243\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10054\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10052\n",
       " The estimated number of batches is 73\n",
       " Creating state point statepoint.050.h5...\n",
       "       51/1    1.21850    1.22541 +/- 0.00237\n",
@@ -544,7 +547,7 @@
       "       71/1    1.19720    1.22444 +/- 0.00195\n",
       "       72/1    1.23770    1.22465 +/- 0.00193\n",
       "       73/1    1.23894    1.22488 +/- 0.00191\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10054\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10052\n",
       " The estimated number of batches is 74\n",
       "       74/1    1.22437    1.22487 +/- 0.00188\n",
       " Triggers satisfied for batch 74\n",
@@ -557,20 +560,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.3000E-01 seconds\n",
-      "   Reading cross sections          =  2.6000E-01 seconds\n",
-      " Total time in simulation          =  3.4077E+02 seconds\n",
-      "   Time in transport only          =  3.4068E+02 seconds\n",
-      "   Time in inactive batches        =  2.3968E+01 seconds\n",
-      "   Time in active batches          =  3.1680E+02 seconds\n",
-      "   Time synchronizing fission bank =  3.0000E-02 seconds\n",
+      " Total time for initialization     =  4.0400E-01 seconds\n",
+      "   Reading cross sections          =  2.1100E-01 seconds\n",
+      " Total time in simulation          =  2.8243E+02 seconds\n",
+      "   Time in transport only          =  2.8236E+02 seconds\n",
+      "   Time in inactive batches        =  1.8781E+01 seconds\n",
+      "   Time in active batches          =  2.6365E+02 seconds\n",
+      "   Time synchronizing fission bank =  2.7000E-02 seconds\n",
       "     Sampling source sites         =  1.7000E-02 seconds\n",
-      "     SEND/RECV source sites        =  1.3000E-02 seconds\n",
-      "   Time accumulating tallies       =  3.0000E-03 seconds\n",
-      " Total time for finalization       =  1.6000E-02 seconds\n",
-      " Total time elapsed                =  3.4129E+02 seconds\n",
-      " Calculation Rate (inactive)       =  4172.23 neutrons/second\n",
-      " Calculation Rate (active)         =  1262.62 neutrons/second\n",
+      "     SEND/RECV source sites        =  8.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  2.0000E-03 seconds\n",
+      " Total time for finalization       =  2.4000E-02 seconds\n",
+      " Total time elapsed                =  2.8293E+02 seconds\n",
+      " Calculation Rate (inactive)       =  5324.53 neutrons/second\n",
+      " Calculation Rate (active)         =  1517.17 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -769,6 +772,14 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  return c.reshape(shape_out)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1159,169 +1170,239 @@
      "text": [
       "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.574672\tres = 0.000E+00\n",
-      "[  NORMAL ]  Iteration 1:\tk_eff = 0.679815\tres = 4.253E-01\n",
-      "[  NORMAL ]  Iteration 2:\tk_eff = 0.660826\tres = 1.830E-01\n",
-      "[  NORMAL ]  Iteration 3:\tk_eff = 0.658941\tres = 2.793E-02\n",
-      "[  NORMAL ]  Iteration 4:\tk_eff = 0.643012\tres = 2.852E-03\n",
-      "[  NORMAL ]  Iteration 5:\tk_eff = 0.625810\tres = 2.417E-02\n",
-      "[  NORMAL ]  Iteration 6:\tk_eff = 0.606678\tres = 2.675E-02\n",
-      "[  NORMAL ]  Iteration 7:\tk_eff = 0.587485\tres = 3.057E-02\n",
-      "[  NORMAL ]  Iteration 8:\tk_eff = 0.569029\tres = 3.164E-02\n",
-      "[  NORMAL ]  Iteration 9:\tk_eff = 0.551707\tres = 3.142E-02\n",
-      "[  NORMAL ]  Iteration 10:\tk_eff = 0.536035\tres = 3.044E-02\n",
-      "[  NORMAL ]  Iteration 11:\tk_eff = 0.522274\tres = 2.841E-02\n",
-      "[  NORMAL ]  Iteration 12:\tk_eff = 0.510609\tres = 2.567E-02\n",
-      "[  NORMAL ]  Iteration 13:\tk_eff = 0.501106\tres = 2.234E-02\n",
-      "[  NORMAL ]  Iteration 14:\tk_eff = 0.493831\tres = 1.861E-02\n",
-      "[  NORMAL ]  Iteration 15:\tk_eff = 0.488780\tres = 1.452E-02\n",
-      "[  NORMAL ]  Iteration 16:\tk_eff = 0.485923\tres = 1.023E-02\n",
-      "[  NORMAL ]  Iteration 17:\tk_eff = 0.485210\tres = 5.846E-03\n",
-      "[  NORMAL ]  Iteration 18:\tk_eff = 0.486569\tres = 1.467E-03\n",
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      ]
     }
    ],
@@ -1354,8 +1435,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223474\n",
-      "openmoc keff = 1.220892\n",
-      "bias [pcm]: -258.1\n"
+      "openmoc keff = 1.220814\n",
+      "bias [pcm]: -266.0\n"
      ]
     }
    ],
@@ -1430,237 +1511,346 @@
      "text": [
       "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.495816\tres = 0.000E+00\n",
-      "[  NORMAL ]  Iteration 1:\tk_eff = 0.557477\tres = 5.042E-01\n",
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+      "[  NORMAL ]  Iteration 294:\tk_eff = 1.222043\tres = 3.114E-05\n",
+      "[  NORMAL ]  Iteration 295:\tk_eff = 1.222079\tres = 3.038E-05\n",
+      "[  NORMAL ]  Iteration 296:\tk_eff = 1.222115\tres = 2.964E-05\n",
+      "[  NORMAL ]  Iteration 297:\tk_eff = 1.222149\tres = 2.891E-05\n",
+      "[  NORMAL ]  Iteration 298:\tk_eff = 1.222183\tres = 2.821E-05\n",
+      "[  NORMAL ]  Iteration 299:\tk_eff = 1.222216\tres = 2.752E-05\n",
+      "[  NORMAL ]  Iteration 300:\tk_eff = 1.222248\tres = 2.685E-05\n",
+      "[  NORMAL ]  Iteration 301:\tk_eff = 1.222279\tres = 2.619E-05\n",
+      "[  NORMAL ]  Iteration 302:\tk_eff = 1.222309\tres = 2.555E-05\n",
+      "[  NORMAL ]  Iteration 303:\tk_eff = 1.222339\tres = 2.492E-05\n",
+      "[  NORMAL ]  Iteration 304:\tk_eff = 1.222368\tres = 2.432E-05\n",
+      "[  NORMAL ]  Iteration 305:\tk_eff = 1.222396\tres = 2.372E-05\n",
+      "[  NORMAL ]  Iteration 306:\tk_eff = 1.222424\tres = 2.314E-05\n",
+      "[  NORMAL ]  Iteration 307:\tk_eff = 1.222451\tres = 2.258E-05\n",
+      "[  NORMAL ]  Iteration 308:\tk_eff = 1.222477\tres = 2.202E-05\n",
+      "[  NORMAL ]  Iteration 309:\tk_eff = 1.222503\tres = 2.149E-05\n",
+      "[  NORMAL ]  Iteration 310:\tk_eff = 1.222528\tres = 2.096E-05\n",
+      "[  NORMAL ]  Iteration 311:\tk_eff = 1.222552\tres = 2.045E-05\n",
+      "[  NORMAL ]  Iteration 312:\tk_eff = 1.222576\tres = 1.995E-05\n",
+      "[  NORMAL ]  Iteration 313:\tk_eff = 1.222599\tres = 1.946E-05\n",
+      "[  NORMAL ]  Iteration 314:\tk_eff = 1.222622\tres = 1.899E-05\n",
+      "[  NORMAL ]  Iteration 315:\tk_eff = 1.222644\tres = 1.852E-05\n",
+      "[  NORMAL ]  Iteration 316:\tk_eff = 1.222665\tres = 1.807E-05\n",
+      "[  NORMAL ]  Iteration 317:\tk_eff = 1.222686\tres = 1.763E-05\n",
+      "[  NORMAL ]  Iteration 318:\tk_eff = 1.222707\tres = 1.720E-05\n",
+      "[  NORMAL ]  Iteration 319:\tk_eff = 1.222727\tres = 1.678E-05\n",
+      "[  NORMAL ]  Iteration 320:\tk_eff = 1.222746\tres = 1.637E-05\n",
+      "[  NORMAL ]  Iteration 321:\tk_eff = 1.222766\tres = 1.597E-05\n",
+      "[  NORMAL ]  Iteration 322:\tk_eff = 1.222784\tres = 1.558E-05\n",
+      "[  NORMAL ]  Iteration 323:\tk_eff = 1.222802\tres = 1.520E-05\n",
+      "[  NORMAL ]  Iteration 324:\tk_eff = 1.222820\tres = 1.483E-05\n",
+      "[  NORMAL ]  Iteration 325:\tk_eff = 1.222837\tres = 1.446E-05\n",
+      "[  NORMAL ]  Iteration 326:\tk_eff = 1.222854\tres = 1.411E-05\n",
+      "[  NORMAL ]  Iteration 327:\tk_eff = 1.222870\tres = 1.377E-05\n",
+      "[  NORMAL ]  Iteration 328:\tk_eff = 1.222886\tres = 1.343E-05\n",
+      "[  NORMAL ]  Iteration 329:\tk_eff = 1.222902\tres = 1.310E-05\n",
+      "[  NORMAL ]  Iteration 330:\tk_eff = 1.222917\tres = 1.278E-05\n",
+      "[  NORMAL ]  Iteration 331:\tk_eff = 1.222932\tres = 1.247E-05\n",
+      "[  NORMAL ]  Iteration 332:\tk_eff = 1.222947\tres = 1.216E-05\n",
+      "[  NORMAL ]  Iteration 333:\tk_eff = 1.222961\tres = 1.187E-05\n",
+      "[  NORMAL ]  Iteration 334:\tk_eff = 1.222975\tres = 1.158E-05\n",
+      "[  NORMAL ]  Iteration 335:\tk_eff = 1.222988\tres = 1.129E-05\n",
+      "[  NORMAL ]  Iteration 336:\tk_eff = 1.223001\tres = 1.102E-05\n",
+      "[  NORMAL ]  Iteration 337:\tk_eff = 1.223014\tres = 1.075E-05\n",
+      "[  NORMAL ]  Iteration 338:\tk_eff = 1.223027\tres = 1.049E-05\n",
+      "[  NORMAL ]  Iteration 339:\tk_eff = 1.223039\tres = 1.023E-05\n"
      ]
     }
    ],
@@ -1686,8 +1876,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223474\n",
-      "openmoc keff = 1.223227\n",
-      "bias [pcm]: -24.7\n"
+      "openmoc keff = 1.223039\n",
+      "bias [pcm]: -43.5\n"
      ]
     }
    ],
@@ -1772,9 +1962,9 @@
     },
     {
      "data": {
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G8+Z9GpmxtKVYd8uGmkyG98UX8xg7djRnn306c+fOoXfv3vzyy88ArLNOHyZNup+JE+9m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\nRK5CYII=\n",
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9zMSJ97Nq1ara12y9tRM0nGyp/QDo3LmYgoJ8/vhjXb1yg8vq3bsPGRkZZGdn07Pn9rWJ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\nYwHYAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f57b2a729e8>"
+       "<matplotlib.figure.Figure at 0x11c1d0d90>"
       ]
      },
      "metadata": {},
@@ -1790,6 +1980,7 @@
     "energy_groups = nufission.energy_groups\n",
     "x = energy_groups.group_edges\n",
     "y = nufission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n",
+    "y = np.squeeze(y)\n",
     "\n",
     "# Fix low energy bound to the value defined by the ACE library\n",
     "x[0] = fission.xs.x[0]\n",
@@ -1855,9 +2046,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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PGk5EmdWAc9tqbcACL2nQp8YjYgHgHCCAWcA4SU8MNp5ZkQab285rq4qmBT4ftja9vEvS\n1i3E3wnolrRZRIwBvg/sMs+9NCtQAbntvLZK6G8EP6Hd4JKujoiemxGvDDzXbkyzAkxo583Oa6uK\npgVe0pQiGpA0JyIuII1wdisiplk7isht57VVQUeWKpC0D7A6cG5ELNqJNs3K5ry2oa7se7LuGRHj\n88M3gNn5n1llOa+tKlq6o1NELA9sRrpiYKqkv7cY/2fAxIiYktv6iqS3BtVTsxIMMred11YJraxF\nsydwKnA7MAI4KyIOkPTzgd4raSawR9u9NCvBYHPbeW1V0coI/lvABpKeBoiIUcC1wIAF3myIc25b\nrbUyB/8q8GzPA0lPAj4ctTpwbluttTKCvwf4eURMJM1T7g48GxF7A0iaVGL/zMrk3LZaa6XAL0oa\n5XwmP56Z/21F+jagNwKrKue21Vora9GM60RHzDrNuW1118pVNE/Qx7odknzXG6s057bVXStTNFs2\n/LwQsCswspTeDAdfLS7ULl2bFBcMmPPi5oXFOmGZrxUW68ZRRd57dtvGB1s2/OzcbtPmnFRYrO7t\nPllYrK4ZBd4S908TiovVAa1M0TzZ66lTImI68J1yumTWGc5tq7tWpmi2aHjYBaxJOjllVmnObau7\nVqZoGm+K0A28AIwtpztmHeXctlprZYpmK4CIWBIYIenl0ntl1gHObau7VqZoVgUuBVYDuiLiSWAP\nSTNaaSAv5jQd2KbV95h1gnPb6q6VpQp+DJwsaVlJywAnAj9pJXhELAicTfryiNlQ49y2WmulwC8n\n6cqeB5IuB5ZpMf6pwFnAM4Pom1nZnNtWa60U+DcjYv2eBxGxAS2MWiJiH+B5STeRrlAwG2qc21Zr\nrVxF8xXgpxHxEimZl6G1tbDHAXMiYltgXWBSROws6flB99asWM5tq7VWCvxypPtOrk4a8auVu9dI\nGtPzc0TcChzkDcCGGOe21VorBf5kSdcDD7XRToHfFTYrjHPbaq2VAv/HiDgfmAa83vPkvKyVLWnr\nQfTNrGzObau1Vgr8i6T5ycbVf7xWttWBc9tqzevB27Dl3La667fAR8QhwHOSJkfENOADwGxgB0mP\ndaKDZmVwbttw0PQ6+Ig4Evg8c09ALUq6ldkPgSPL75pZOZzbNlz090WnvYFdGtbYmJ3Xzz6Ld98o\nwaxqnNs2LPRX4GdLeq3h8XcAJM0GXi21V2blcm7bsNDfHPwCEbGkpFcBJP0UICKWBuZ0onM2gOkT\nCg23wLJLFRar+5zibtm3xv4PFxYr37LPuV2K1wd+SYu6ftnSmm8t6f5ucatJdD1R8Ncezp5QbLxe\n+hvBX0L6CvY7W31ELAGcD1xcaq/MyuXctmGhvxH8SeTV8iLiYdL1wWsAF0n6Xic6Z1YS57YNC00L\nfJ6PPDAijgM2zk9Pl/RUR3pmVhLntg0XrXzR6Wlgcgf6YtZRzm2ru1aWKmhLRNwH9Nzr8glJ+5Xd\nplnZnNdWBaUW+IgYCXR7QSarE+e1VUXZI/h1gMUj4kZgBHCUpGklt2lWNue1VUIrt+xrx0zgFEnb\nA4cAl0RE2W2alc15bZVQdlLOIF1zjKQ/kJZnXaHkNs3K5ry2Sii7wO8LnAYQESsCSwLPltymWdmc\n11YJZc/BnwdMjIippK+A7yvJXwW3qnNeWyWUWuAlvQ3sWWYbZp3mvLaq8IkhM7OacoE3M6spF3gz\ns5pygTczqykXeDOzmnKBNzOrqdJXk7QSvTbwS+bFIi/vU1isrh/8Z2Gxuj9e3C3XeKS4UFampwuL\n1PWNKwuL1X1EgbkIdG1T8C0Ae/EI3sysplzgzcxqygXezKymXODNzGqqE7fsGw/sDCwEnClpYtlt\nmpXNeW1VUOoIPiLGAJtI2hTYElipzPbMOsF5bVVR9gh+e+D3EXEVac3sw0tuz6wTnNdWCWUX+OWA\nDwOfA1YFrgE+VnKbZmVzXlsllH2S9UXgRkmzJM0A3oiI5Upu06xszmurhLIL/O3AZ+CdW5stRto4\nzKrMeW2VUGqBl3Q9cH9E3A1cDRwqqdzv5pqVzHltVVH6ZZKSxpfdhlmnOa+tCvxFJzOzmnKBNzOr\nKRd4M7OacoE3M6spF3gzs5pygTczq6mu7u6hc/lu1xSGTmeGo0WKC3Xz6E0Li3Vb152FxZrQ3V3s\nPdda0NU1wXldG0cVGu3nLFxYrB36yG2P4M3MasoF3sysplzgzcxqygXezKymSl2LJiLGAvsA3cCi\nwDrAByW9Uma7ZmVyXltVlFrgJV0IXAgQEacD53ojsKpzXltVdGSKJiI2BNaQdF4n2jPrBOe1DXWd\nmoM/EjiuQ22ZdYrz2oa00gt8RCwNhKQpZbdl1inOa6uCTozgtwB+1YF2zDrJeW1DXicKfACPd6Ad\ns05yXtuQ14lb9p1adhtmnea8tirwF53MzGrKBd7MrKZc4M3MasoF3sysplzgzcxqygXezKymhtQt\n+8zMrDgewZuZ1ZQLvJlZTbnAm5nVlAu8mVlNucCbmdWUC7yZWU2VvppkUSKiCziTdIPjN4D9JbW1\nXGtEjAZOkrRVGzEWBM4HVgYWBk6QdO0gYy0AnENainYWME7SE4PtW465PDAd2EbSjDbi3Ae8nB8+\nIWm/NmKNB3YGFgLOlDRxkHFqcfPronN7qOV1jldobheV1zlWbXO7SiP4XYCRkjYl3Srte+0Ei4jD\nSQk3ss1+7Qm8IGkLYEfg9DZi7QR0S9oMOBb4fjsdyxvp2cDMNuOMzP3aOv9rZwMYA2yS/z9uCaw0\n2FiSLpS0laStgXuBL1WtuGeF5fYQzWsoMLeLyuscq9a5XaUCvxlwA4CkacCGbcZ7DNi13U4BlwNH\n55+7gLcHG0jS1cCB+eHKwHNt9QxOBc4CnmkzzjrA4hFxY0T8Ko8QB2t74PcRcRVwDXBdm32rw82v\ni8ztIZfXUHhuF5XXUPPcrlKBXwr4R8PjWfmwb1AkTSYdKrZF0kxJ/xsRSwJXAEe1GW9ORFwA/BC4\ncrBxImIf4HlJN5E20HbMBE6RtD1wCHBJG3/75YANgN1yrP9us29Q/ZtfF5bbQzWvc8y2c7vgvIaa\n53aVCvwrwJINjxeQNGd+daZRRKwE3AJcKOmyduNJ2gdYHTg3IhYdZJhxwLYRcSuwLjApz1sOxgzg\nkty3PwAvAisMMtaLwI2SZuW50zciYrlBxqrLza+HZG4XnddQSG4XmddQ89yuUoG/gzQXSER8Evhd\nQXHbGgVExD8BNwJHSLqwzVh75pM0kE62zc7/5pmkMXkObyvgAWBvSc8Psmv7AqflPq5IKkbPDjLW\n7cBnGmItRtowBqsON78uI7eHTF7neIXkdsF5DTXP7cpcRQNMJu2578iPxxUUt93V1o4E3gccHRHH\n5Hg7SHpzELF+BkyMiCmk/zdfkfRWm/2D9j/jeaR+TQXmAPsOdoQp6fqI2Dwi7iYVoUMltdO/Otz8\nuozcHkp5DeXkdhErJdY6t72apJlZTVVpisbMzOaBC7yZWU25wJuZ1ZQLvJlZTbnAm5nVlAu8mVlN\nVek6+EqJiBHAeOCLpOtrRwCTJJ3Y4X58FDgFWIP0BRMBh0v60wDvmwDcJOmO/l5nw49zuzo8gi/P\nWaRFo0ZLWgvYCPh0RBzSqQ7kr3DfAlwqaXVJawNXAXdExLIDvH0MacM16825XRH+olMJIuJDpNHE\nio1LfEbE6sCakiZHxERgWWA14AjgBdIiTCPzzwdJejyvuXGspNsiYhTwa0mr5Pe/Ttq4lgS+I+ni\nXv04Fhglad9ez18GPCjphIiYI2mB/PxY0jKnt5DWJ38W2FXSQ4X+gayynNvV4hF8OTYGHu69frOk\nGXm1vx4vSFoT+CVwKemrzesBP86P+9K4R14VGA18Gji1j0WXNgLu7iPGbfl3veNBWhv7ItLNFPar\n+wZg88y5XSEu8OV5J7ki4vMRcX9EPBgR0xpe0/Pz6sBLku4DkHQlsFpeqrU/EyXNkfQ0aaGjzfro\nQ1/nWRZu6F9/i1IVsRyr1Y9zuyJc4MsxHVgjIpYAkPTTPHrZCfhAw+tez/9dgPcmXBdpnrC74XcL\n9XpN47rfI3jvOuDTgE376N8mwD19PN87vllvzu0KcYEvgaSngIuAC/Oazj1XHuxE30ukClgmIjbI\nr90deFLSy6Q5yzXz63rfqWf3/PpRpEPnqb1+fybwqYj4Qs8TEbE3acM4Oz/1t4hYI98XdOeG987C\nV1lZL87tanGBL4mkQ0nrfN8aEQ+QbiywHrBDfkl3w2vfAvYAzoiIB4FD82OAk4HDImI6773P5mL5\n+WuBAyT9vVcfXgI2B3aNiEci4lFSom+Wfwfpcrfrc18fbXj7DcDZeX1ys3c4t6vDV9FUVL7S4FZJ\nk+Z3X8yK5Nwujkfw1eU9s9WVc7sgHsGbmdWUR/BmZjXlAm9mVlMu8GZmNeUCb2ZWUy7wZmY15QJv\nZlZT/x9xJfW8VvykSgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f57b2b5d748>"
+       "<matplotlib.figure.Figure at 0x11c1d02d0>"
       ]
      },
      "metadata": {},
@@ -1897,21 +2088,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
index a1eaa7ad8..9d2b89c51 100644
--- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
@@ -32,7 +32,7 @@
      "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",
+      "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/__init__.py:1357: 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",
@@ -458,7 +458,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDE2OjQyOjMyLTA1OjAwv06KfAAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDE4OjMzOjI4LTA0OjAwavq5fAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -727,9 +727,10 @@
       "\n",
       "      Copyright:      2011-2016 Massachusetts Institute of Technology\n",
       "      License:        http://openmc.readthedocs.io/en/latest/license.html\n",
-      "      Version:        0.7.1\n",
-      "      Git SHA1:       3d68c07625e33cd64188df03ee03e9c31b3d4b74\n",
-      "      Date/Time:      2016-07-23 16:42:32\n",
+      "      Version:        0.8.0\n",
+      "      Git SHA1:       be7e6e035d22944a8c80ca32f99935b6822854c9\n",
+      "      Date/Time:      2016-08-10 18:33:28\n",
+      "      MPI Processes:  1\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -739,12 +740,12 @@
       " Reading geometry XML file...\n",
       " Reading cross sections XML file...\n",
       " Reading materials XML file...\n",
-      " Reading U235.71c from /home/romano/openmc/data/nndc_hdf5/U235_71c.h5\n",
-      " Reading U238.71c from /home/romano/openmc/data/nndc_hdf5/U238_71c.h5\n",
-      " Reading O16.71c from /home/romano/openmc/data/nndc_hdf5/O16_71c.h5\n",
-      " Reading H1.71c from /home/romano/openmc/data/nndc_hdf5/H1_71c.h5\n",
-      " Reading B10.71c from /home/romano/openmc/data/nndc_hdf5/B10_71c.h5\n",
-      " Reading Zr90.71c from /home/romano/openmc/data/nndc_hdf5/Zr90_71c.h5\n",
+      " Reading U235.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U235_71c.h5\n",
+      " Reading U238.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/U238_71c.h5\n",
+      " Reading O16.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/O16_71c.h5\n",
+      " Reading H1.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/H1_71c.h5\n",
+      " Reading B10.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/B10_71c.h5\n",
+      " Reading Zr90.71c from /Users/sam/git/openmc-sam/data/nndc_hdf5/Zr90_71c.h5\n",
       " Maximum neutron transport energy: 20.0000 MeV for U235.71c\n",
       " Reading tallies XML file...\n",
       " Building neighboring cells lists for each surface...\n",
@@ -780,32 +781,32 @@
       "       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    0.97445    1.02444 +/- 0.00665\n",
-      "       26/1    1.04737    1.02588 +/- 0.00638\n",
-      "       27/1    1.04656    1.02709 +/- 0.00612\n",
-      "       28/1    1.03464    1.02751 +/- 0.00578\n",
-      "       29/1    1.02528    1.02739 +/- 0.00547\n",
-      "       30/1    1.02799    1.02742 +/- 0.00519\n",
-      "       31/1    1.05846    1.02890 +/- 0.00516\n",
-      "       32/1    1.03811    1.02932 +/- 0.00493\n",
-      "       33/1    1.00894    1.02843 +/- 0.00480\n",
-      "       34/1    1.02049    1.02810 +/- 0.00460\n",
-      "       35/1    1.00690    1.02726 +/- 0.00450\n",
-      "       36/1    1.03129    1.02741 +/- 0.00432\n",
-      "       37/1    0.98864    1.02597 +/- 0.00440\n",
-      "       38/1    1.00017    1.02505 +/- 0.00434\n",
-      "       39/1    1.03635    1.02544 +/- 0.00421\n",
-      "       40/1    1.07090    1.02696 +/- 0.00434\n",
-      "       41/1    1.03141    1.02710 +/- 0.00420\n",
-      "       42/1    1.02624    1.02707 +/- 0.00406\n",
-      "       43/1    1.02668    1.02706 +/- 0.00394\n",
-      "       44/1    1.05940    1.02801 +/- 0.00394\n",
-      "       45/1    1.01149    1.02754 +/- 0.00385\n",
-      "       46/1    1.06958    1.02871 +/- 0.00392\n",
-      "       47/1    1.02674    1.02866 +/- 0.00381\n",
-      "       48/1    1.02542    1.02857 +/- 0.00371\n",
-      "       49/1    1.03516    1.02874 +/- 0.00362\n",
-      "       50/1    1.06818    1.02973 +/- 0.00366\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    1.01043    1.02739 +/- 0.00406\n",
+      "       50/1    1.01457    1.02707 +/- 0.00397\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -815,27 +816,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.3400E-01 seconds\n",
-      "   Reading cross sections          =  2.7900E-01 seconds\n",
-      " Total time in simulation          =  6.1121E+01 seconds\n",
-      "   Time in transport only          =  6.1101E+01 seconds\n",
-      "   Time in inactive batches        =  5.0660E+00 seconds\n",
-      "   Time in active batches          =  5.6055E+01 seconds\n",
-      "   Time synchronizing fission bank =  5.0000E-03 seconds\n",
-      "     Sampling source sites         =  3.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
+      " Total time for initialization     =  4.3000E-01 seconds\n",
+      "   Reading cross sections          =  2.2800E-01 seconds\n",
+      " Total time in simulation          =  6.1235E+01 seconds\n",
+      "   Time in transport only          =  6.1207E+01 seconds\n",
+      "   Time in inactive batches        =  5.0280E+00 seconds\n",
+      "   Time in active batches          =  5.6207E+01 seconds\n",
+      "   Time synchronizing fission bank =  7.0000E-03 seconds\n",
+      "     Sampling source sites         =  4.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  2.0000E-03 seconds\n",
       " Total time for finalization       =  0.0000E+00 seconds\n",
-      " Total time elapsed                =  6.1576E+01 seconds\n",
-      " Calculation Rate (inactive)       =  4934.86 neutrons/second\n",
-      " Calculation Rate (active)         =  1783.96 neutrons/second\n",
+      " Total time elapsed                =  6.1689E+01 seconds\n",
+      " Calculation Rate (inactive)       =  4972.16 neutrons/second\n",
+      " Calculation Rate (active)         =  1779.14 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     =  1.02763 +/-  0.00343\n",
-      " k-effective (Track-length)  =  1.02973 +/-  0.00366\n",
-      " k-effective (Absorption)    =  1.02732 +/-  0.00319\n",
-      " Combined k-effective        =  1.02826 +/-  0.00259\n",
+      " k-effective (Collision)     =  1.02489 +/-  0.00308\n",
+      " k-effective (Track-length)  =  1.02707 +/-  0.00397\n",
+      " k-effective (Absorption)    =  1.02637 +/-  0.00325\n",
+      " Combined k-effective        =  1.02581 +/-  0.00264\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -955,8 +956,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/romano/openmc/openmc/tallies.py:1941: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
+      "/Users/sam/.local/lib/python2.7/site-packages/openmc-0.8.0-py2.7.egg/openmc/tallies.py:1944: RuntimeWarning: invalid value encountered in true_divide\n"
      ]
     },
     {
@@ -980,16 +980,16 @@
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>8.055246e-03</td>\n",
-       "      <td>2.857567e-05</td>\n",
+       "      <td>8.046809e-03</td>\n",
+       "      <td>2.697198e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>U238</td>\n",
-       "      <td>7.339215e-03</td>\n",
-       "      <td>4.349466e-05</td>\n",
+       "      <td>7.366624e-03</td>\n",
+       "      <td>4.255197e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1004,16 +1004,16 @@
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>U235</td>\n",
-       "      <td>3.615565e-01</td>\n",
-       "      <td>2.050486e-03</td>\n",
+       "      <td>3.614917e-01</td>\n",
+       "      <td>2.135233e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>U238</td>\n",
-       "      <td>6.742638e-07</td>\n",
-       "      <td>3.795256e-09</td>\n",
+       "      <td>6.741607e-07</td>\n",
+       "      <td>3.924924e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1029,11 +1029,11 @@
       ],
       "text/plain": [
        "    cell  group in nuclide          mean     std. dev.\n",
-       "3  10000         1    U235  8.055246e-03  2.857567e-05\n",
-       "4  10000         1    U238  7.339215e-03  4.349466e-05\n",
+       "3  10000         1    U235  8.046809e-03  2.697198e-05\n",
+       "4  10000         1    U238  7.366624e-03  4.255197e-05\n",
        "5  10000         1     O16  0.000000e+00  0.000000e+00\n",
-       "0  10000         2    U235  3.615565e-01  2.050486e-03\n",
-       "1  10000         2    U238  6.742638e-07  3.795256e-09\n",
+       "0  10000         2    U235  3.614917e-01  2.135233e-03\n",
+       "1  10000         2    U238  6.741607e-07  3.924924e-09\n",
        "2  10000         2     O16  0.000000e+00  0.000000e+00"
       ]
      },
@@ -1071,18 +1071,18 @@
       "\tDomain ID      =\t10000\n",
       "\tNuclide        =\tU235\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [6.25e-07   - 20.0      MeV]:\t8.06e-03 +/- 3.55e-01%\n",
-      "            Group 2 [0.0        - 6.25e-07  MeV]:\t3.62e-01 +/- 5.67e-01%\n",
+      "            Group 1 [6.25e-07   - 20.0      MeV]:\t8.05e-03 +/- 3.35e-01%\n",
+      "            Group 2 [0.0        - 6.25e-07  MeV]:\t3.61e-01 +/- 5.91e-01%\n",
       "\n",
       "\tNuclide        =\tU238\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [6.25e-07   - 20.0      MeV]:\t7.34e-03 +/- 5.93e-01%\n",
-      "            Group 2 [0.0        - 6.25e-07  MeV]:\t6.74e-07 +/- 5.63e-01%\n",
+      "            Group 1 [6.25e-07   - 20.0      MeV]:\t7.37e-03 +/- 5.78e-01%\n",
+      "            Group 2 [0.0        - 6.25e-07  MeV]:\t6.74e-07 +/- 5.82e-01%\n",
       "\n",
       "\tNuclide        =\tO16\n",
       "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [6.25e-07   - 20.0      MeV]:\t0.00e+00 +/- nan%\n",
-      "            Group 2 [0.0        - 6.25e-07  MeV]:\t0.00e+00 +/- nan%\n",
+      "            Group 1 [6.25e-07   - 20.0      MeV]:\t0.00e+00 +/- 0.00e+00%\n",
+      "            Group 2 [0.0        - 6.25e-07  MeV]:\t0.00e+00 +/- 0.00e+00%\n",
       "\n",
       "\n",
       "\n"
@@ -1193,16 +1193,16 @@
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>0.074860</td>\n",
-       "      <td>0.000303</td>\n",
+       "      <td>0.074734</td>\n",
+       "      <td>0.000325</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>U238</td>\n",
-       "      <td>0.005952</td>\n",
-       "      <td>0.000035</td>\n",
+       "      <td>0.005977</td>\n",
+       "      <td>0.000034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1218,8 +1218,8 @@
       ],
       "text/plain": [
        "    cell  group in nuclide      mean  std. dev.\n",
-       "0  10000         1    U235  0.074860   0.000303\n",
-       "1  10000         1    U238  0.005952   0.000035\n",
+       "0  10000         1    U235  0.074734   0.000325\n",
+       "1  10000         1    U238  0.005977   0.000034\n",
        "2  10000         1     O16  0.000000   0.000000"
       ]
      },
@@ -1300,127 +1300,133 @@
      "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 = 0.854370\tres = 0.000E+00\n",
-      "[  NORMAL ]  Iteration 1:\tk_eff = 0.801922\tres = 1.521E-01\n",
-      "[  NORMAL ]  Iteration 2:\tk_eff = 0.761745\tres = 6.349E-02\n",
-      "[  NORMAL ]  Iteration 3:\tk_eff = 0.732366\tres = 5.029E-02\n",
-      "[  NORMAL ]  Iteration 4:\tk_eff = 0.711073\tres = 3.869E-02\n",
-      "[  NORMAL ]  Iteration 5:\tk_eff = 0.696554\tres = 2.912E-02\n",
-      "[  NORMAL ]  Iteration 6:\tk_eff = 0.687670\tres = 2.044E-02\n",
-      "[  NORMAL ]  Iteration 7:\tk_eff = 0.683465\tres = 1.277E-02\n",
-      "[  NORMAL ]  Iteration 8:\tk_eff = 0.683124\tres = 6.142E-03\n",
-      "[  NORMAL ]  Iteration 9:\tk_eff = 0.685943\tres = 7.897E-04\n",
-      "[  NORMAL ]  Iteration 10:\tk_eff = 0.691322\tres = 4.180E-03\n",
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+      "[  NORMAL ]  Iteration 0:\tk_eff = 0.823582\tres = 0.000E+00\n",
+      "[  NORMAL ]  Iteration 1:\tk_eff = 0.780361\tres = 1.940E-01\n",
+      "[  NORMAL ]  Iteration 2:\tk_eff = 0.739500\tres = 6.545E-02\n",
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+      "[  NORMAL ]  Iteration 73:\tk_eff = 1.020138\tres = 5.437E-04\n",
+      "[  NORMAL ]  Iteration 74:\tk_eff = 1.020613\tres = 5.042E-04\n",
+      "[  NORMAL ]  Iteration 75:\tk_eff = 1.021054\tres = 4.676E-04\n",
+      "[  NORMAL ]  Iteration 76:\tk_eff = 1.021462\tres = 4.335E-04\n",
+      "[  NORMAL ]  Iteration 77:\tk_eff = 1.021841\tres = 4.019E-04\n",
+      "[  NORMAL ]  Iteration 78:\tk_eff = 1.022193\tres = 3.725E-04\n",
+      "[  NORMAL ]  Iteration 79:\tk_eff = 1.022518\tres = 3.453E-04\n",
+      "[  NORMAL ]  Iteration 80:\tk_eff = 1.022820\tres = 3.200E-04\n",
+      "[  NORMAL ]  Iteration 81:\tk_eff = 1.023100\tres = 2.965E-04\n",
+      "[  NORMAL ]  Iteration 82:\tk_eff = 1.023359\tres = 2.748E-04\n",
+      "[  NORMAL ]  Iteration 83:\tk_eff = 1.023600\tres = 2.546E-04\n",
+      "[  NORMAL ]  Iteration 84:\tk_eff = 1.023822\tres = 2.358E-04\n",
+      "[  NORMAL ]  Iteration 85:\tk_eff = 1.024028\tres = 2.185E-04\n",
+      "[  NORMAL ]  Iteration 86:\tk_eff = 1.024219\tres = 2.023E-04\n",
+      "[  NORMAL ]  Iteration 87:\tk_eff = 1.024396\tres = 1.874E-04\n",
+      "[  NORMAL ]  Iteration 88:\tk_eff = 1.024560\tres = 1.735E-04\n",
+      "[  NORMAL ]  Iteration 89:\tk_eff = 1.024712\tres = 1.607E-04\n",
+      "[  NORMAL ]  Iteration 90:\tk_eff = 1.024852\tres = 1.488E-04\n",
+      "[  NORMAL ]  Iteration 91:\tk_eff = 1.024983\tres = 1.378E-04\n",
+      "[  NORMAL ]  Iteration 92:\tk_eff = 1.025103\tres = 1.275E-04\n",
+      "[  NORMAL ]  Iteration 93:\tk_eff = 1.025215\tres = 1.181E-04\n",
+      "[  NORMAL ]  Iteration 94:\tk_eff = 1.025318\tres = 1.093E-04\n",
+      "[  NORMAL ]  Iteration 95:\tk_eff = 1.025413\tres = 1.012E-04\n",
+      "[  NORMAL ]  Iteration 96:\tk_eff = 1.025502\tres = 9.364E-05\n",
+      "[  NORMAL ]  Iteration 97:\tk_eff = 1.025584\tres = 8.666E-05\n",
+      "[  NORMAL ]  Iteration 98:\tk_eff = 1.025659\tres = 8.020E-05\n",
+      "[  NORMAL ]  Iteration 99:\tk_eff = 1.025729\tres = 7.422E-05\n",
+      "[  NORMAL ]  Iteration 100:\tk_eff = 1.025794\tres = 6.868E-05\n",
+      "[  NORMAL ]  Iteration 101:\tk_eff = 1.025854\tres = 6.355E-05\n",
+      "[  NORMAL ]  Iteration 102:\tk_eff = 1.025910\tres = 5.880E-05\n",
+      "[  NORMAL ]  Iteration 103:\tk_eff = 1.025961\tres = 5.440E-05\n",
+      "[  NORMAL ]  Iteration 104:\tk_eff = 1.026009\tres = 5.033E-05\n",
+      "[  NORMAL ]  Iteration 105:\tk_eff = 1.026053\tres = 4.656E-05\n",
+      "[  NORMAL ]  Iteration 106:\tk_eff = 1.026093\tres = 4.307E-05\n",
+      "[  NORMAL ]  Iteration 107:\tk_eff = 1.026131\tres = 3.984E-05\n",
+      "[  NORMAL ]  Iteration 108:\tk_eff = 1.026166\tres = 3.685E-05\n",
+      "[  NORMAL ]  Iteration 109:\tk_eff = 1.026198\tres = 3.409E-05\n",
+      "[  NORMAL ]  Iteration 110:\tk_eff = 1.026228\tres = 3.153E-05\n",
+      "[  NORMAL ]  Iteration 111:\tk_eff = 1.026255\tres = 2.916E-05\n",
+      "[  NORMAL ]  Iteration 112:\tk_eff = 1.026281\tres = 2.697E-05\n",
+      "[  NORMAL ]  Iteration 113:\tk_eff = 1.026304\tres = 2.494E-05\n",
+      "[  NORMAL ]  Iteration 114:\tk_eff = 1.026326\tres = 2.307E-05\n",
+      "[  NORMAL ]  Iteration 115:\tk_eff = 1.026346\tres = 2.133E-05\n",
+      "[  NORMAL ]  Iteration 116:\tk_eff = 1.026365\tres = 1.973E-05\n",
+      "[  NORMAL ]  Iteration 117:\tk_eff = 1.026382\tres = 1.824E-05\n",
+      "[  NORMAL ]  Iteration 118:\tk_eff = 1.026398\tres = 1.687E-05\n",
+      "[  NORMAL ]  Iteration 119:\tk_eff = 1.026413\tres = 1.560E-05\n",
+      "[  NORMAL ]  Iteration 120:\tk_eff = 1.026426\tres = 1.442E-05\n",
+      "[  NORMAL ]  Iteration 121:\tk_eff = 1.026439\tres = 1.333E-05\n",
+      "[  NORMAL ]  Iteration 122:\tk_eff = 1.026451\tres = 1.233E-05\n",
+      "[  NORMAL ]  Iteration 123:\tk_eff = 1.026461\tres = 1.140E-05\n",
+      "[  NORMAL ]  Iteration 124:\tk_eff = 1.026471\tres = 1.054E-05\n"
      ]
     }
    ],
@@ -1452,9 +1458,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "openmc keff = 1.028263\n",
-      "openmoc keff = 1.028491\n",
-      "bias [pcm]: 22.8\n"
+      "openmc keff = 1.025806\n",
+      "openmoc keff = 1.026471\n",
+      "bias [pcm]: 66.5\n"
      ]
     }
    ],
@@ -1562,7 +1568,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f4fa48d84e0>"
+       "<matplotlib.text.Text at 0x11a263e90>"
       ]
      },
      "execution_count": 43,
@@ -1571,9 +1577,9 @@
     },
     {
      "data": {
-      "image/png": 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VNdtPwnmHuC2E1Cu0bRJH6iVyzU5JR96okOa+NACcdbbdB/qxnmvqYIn9CqQF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\nSUVORK5CYII=\n",
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AePrTp0xN7/+M3l8XyRewfd6R1gKqmpQheiKiwM4AxZ/tSrqNsDUP29e3df8l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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4fa6b0deb8>"
+       "<matplotlib.figure.Figure at 0x11a12b050>"
       ]
      },
      "metadata": {},
@@ -1613,7 +1619,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.11"
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,
diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py
index 6d2665566..42cfdd06c 100644
--- a/openmc/mgxs/library.py
+++ b/openmc/mgxs/library.py
@@ -328,16 +328,19 @@ class Library(object):
     @delayed_groups.setter
     def delayed_groups(self, delayed_groups):
 
-        cv.check_type('delayed groups', delayed_groups, list, int)
-        cv.check_greater_than('num delayed groups', len(delayed_groups), 0)
+        if delayed_groups != None:
 
-        # Check that the groups are within [1, MAX_DELAYED_GROUPS]
-        for group in delayed_groups:
-            cv.check_greater_than('delayed group', group, 0)
-            cv.check_less_than('delayed group', group,
-                               openmc.mgxs.MAX_DELAYED_GROUPS, equality=True)
+            cv.check_type('delayed groups', delayed_groups, list, int)
+            cv.check_greater_than('num delayed groups', len(delayed_groups), 0)
 
-        self._delayed_groups = delayed_groups
+            # Check that the groups are within [1, MAX_DELAYED_GROUPS]
+            for group in delayed_groups:
+                cv.check_greater_than('delayed group', group, 0)
+                cv.check_less_than('delayed group', group,
+                                   openmc.mgxs.MAX_DELAYED_GROUPS,
+                                   equality=True)
+
+            self._delayed_groups = delayed_groups
 
     @correction.setter
     def correction(self, correction):
@@ -508,7 +511,7 @@ class Library(object):
         ----------
         domain : Material or Cell or Universe or Integral
             The material, cell, or universe object of interest (or its ID)
-        mgxs_type : {'total', 'transport', 'nu-transport', 'absorption', 'capture', 'fission', 'nu-fission', 'kappa-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'multiplicity matrix', 'nu-fission matrix', chi', 'chi-prompt', 'inverse-velocity', 'prompt-nu-fission'}
+        mgxs_type : {'total', 'transport', 'nu-transport', 'absorption', 'capture', 'fission', 'nu-fission', 'kappa-fission', 'scatter', 'nu-scatter', 'scatter matrix', 'nu-scatter matrix', 'multiplicity matrix', 'nu-fission matrix', chi', 'chi-prompt', 'inverse-velocity', 'prompt-nu-fission', 'delayed-nu-fission', 'chi-delayed', 'beta'}
             The type of multi-group cross section object to return
 
         Returns
diff --git a/openmc/mgxs/mdgxs.py b/openmc/mgxs/mdgxs.py
index c01b0b575..d853ebed3 100644
--- a/openmc/mgxs/mdgxs.py
+++ b/openmc/mgxs/mdgxs.py
@@ -14,6 +14,9 @@ import openmc
 from openmc.mgxs import MGXS
 import openmc.checkvalue as cv
 
+if sys.version_info[0] >= 3:
+    basestring = str
+
 # Supported cross section types
 MDGXS_TYPES = ['delayed-nu-fission',
                'chi-delayed',
@@ -102,12 +105,12 @@ class MDGXS(MGXS):
         are not specified by the user, all nuclides in the spatial domain
         are included. This attribute is 'sum' if by_nuclide is false.
     sparse : bool
-        Whether or not the MDGXS' tallies use SciPy's LIL sparse matrix format
+        Whether or not the MGXS' tallies use SciPy's LIL sparse matrix format
         for compressed data storage
     loaded_sp : bool
         Whether or not a statepoint file has been loaded with tally data
     derived : bool
-        Whether or not the MDGXS is merged from one or more other MDGXS
+        Whether or not the MGXS is merged from one or more other MGXS
     hdf5_key : str
         The key used to index multi-group cross sections in an HDF5 data store
 
@@ -165,23 +168,25 @@ class MDGXS(MGXS):
     @property
     def num_delayed_groups(self):
         if self.delayed_groups == None:
-            return 0
+            return 1
         else:
             return len(self.delayed_groups)
 
     @delayed_groups.setter
     def delayed_groups(self, delayed_groups):
 
-        cv.check_type('delayed groups', delayed_groups, list, int)
-        cv.check_greater_than('num delayed groups', len(delayed_groups), 0)
+        if delayed_groups != None:
 
-        # Check that the groups are within [1, MAX_DELAYED_GROUPS]
-        for group in delayed_groups:
-            cv.check_greater_than('delayed group', group, 0)
-            cv.check_less_than('delayed group', group, MAX_DELAYED_GROUPS,
-                               equality=True)
+            cv.check_type('delayed groups', delayed_groups, list, int)
+            cv.check_greater_than('num delayed groups', len(delayed_groups), 0)
 
-        self._delayed_groups = delayed_groups
+            # Check that the groups are within [1, MAX_DELAYED_GROUPS]
+            for group in delayed_groups:
+                cv.check_greater_than('delayed group', group, 0)
+                cv.check_less_than('delayed group', group, MAX_DELAYED_GROUPS,
+                                   equality=True)
+
+            self._delayed_groups = delayed_groups
 
     @property
     def filters(self):
@@ -251,12 +256,13 @@ class MDGXS(MGXS):
 
     def get_xs(self, groups='all', subdomains='all', nuclides='all',
                xs_type='macro', order_groups='increasing',
-               value='mean', delayed_groups='all', **kwargs):
+               value='mean', delayed_groups='all', squeeze=True, **kwargs):
         """Returns an array of multi-delayed-group cross sections.
 
-        This method constructs a 2D NumPy array for the requested
-        multi-delayed-group cross section data data for one or more energy
-        groups, delayed groups, and subdomains.
+        This method constructs a 4D NumPy array for the requested
+        multi-delayed-group cross section data for one or more
+        subdomains (1st dimension), delayed groups (2nd demension),
+        energy groups (3rd dimension), and nuclides (4th dimension).
 
         Parameters
         ----------
@@ -280,6 +286,10 @@ class MDGXS(MGXS):
             A string for the type of value to return. Defaults to 'mean'.
         delayed_groups : list of int or 'all'
             Delayed groups of interest. Defaults to 'all'.
+        squeeze : bool
+            A boolean representing whether to eliminate the extra dimensions
+            of the multi-dimensional array this is to be retured. Defaults to
+            True.
 
         Returns
         -------
@@ -359,25 +369,36 @@ class MDGXS(MGXS):
             if value == 'mean' or value == 'std_dev':
                 xs /= densities[np.newaxis, :, np.newaxis]
 
+        # Eliminate the trivial score dimension
+        xs = np.squeeze(xs, axis=len(xs.shape) - 1)
+        xs = np.nan_to_num(xs)
+
+        if groups == 'all':
+            num_groups = self.num_groups
+        else:
+            num_groups = len(groups)
+
+        if delayed_groups == 'all':
+            num_delayed_groups = self.num_delayed_groups
+        else:
+            num_delayed_groups = len(delayed_groups)
+
+        # Reshape tally data array with separate axes for domain, energy groups,
+        # delayed groups, and nuclides
+        num_subdomains = int(xs.shape[0] / (num_groups * num_delayed_groups))
+        new_shape = (num_subdomains, num_delayed_groups, num_groups)
+        new_shape += xs.shape[1:]
+        xs = np.reshape(xs, new_shape)
+
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            if groups == 'all':
-                num_groups = self.num_groups
-            else:
-                num_groups = len(groups)
+            xs = xs[:, :, ::-1, :]
 
-            # Reshape tally data array with separate axes for domain and energy
-            num_subdomains = int(xs.shape[0] / num_groups)
-            new_shape = (num_subdomains, num_groups) + xs.shape[1:]
-            xs = np.reshape(xs, new_shape)
+        if squeeze:
+            xs = np.squeeze(xs)
+            xs = np.atleast_1d(xs)
 
-            # Reverse energies to align with increasing energy groups
-            xs = xs[:, ::-1, :]
-
-        # Eliminate trivial dimensions
-        xs = np.squeeze(xs)
-        xs = np.atleast_1d(xs)
         return xs
 
     def get_slice(self, nuclides=[], groups=[], delayed_groups=[]):
@@ -467,11 +488,11 @@ class MDGXS(MGXS):
         return slice_xs
 
     def merge(self, other):
-        """Merge another MDGXS with this one
+        """Merge another MGXS with this one
 
-        MDGXS are only mergeable if their energy groups and nuclides are either
+        MGXS are only mergeable if their energy groups and nuclides are either
         identical or mutually exclusive. If results have been loaded from a
-        statepoint, then MDGXS are only mergeable along one and only one of
+        statepoint, then MGXS are only mergeable along one and only one of
         energy groups or nuclides.
 
         Parameters
@@ -718,9 +739,112 @@ class MDGXS(MGXS):
 
         """
 
+        if not isinstance(groups, basestring):
+            cv.check_iterable_type('groups', groups, Integral)
+        if nuclides != 'all' and nuclides != 'sum':
+            cv.check_iterable_type('nuclides', nuclides, basestring)
         if not isinstance(delayed_groups, basestring):
             cv.check_type('delayed groups', delayed_groups, list, int)
 
+        cv.check_value('xs_type', xs_type, ['macro', 'micro'])
+
+        # Get a Pandas DataFrame from the derived xs tally
+        if self.by_nuclide and nuclides == 'sum':
+
+            # Use tally summation to sum across all nuclides
+            query_nuclides = self.get_all_nuclides()
+            xs_tally = self.xs_tally.summation(nuclides=query_nuclides)
+            df = xs_tally.get_pandas_dataframe(
+                distribcell_paths=distribcell_paths)
+
+            # Remove nuclide column since it is homogeneous and redundant
+            if self.domain_type == 'mesh':
+                df.drop('nuclide', axis=1, level=0, inplace=True)
+            else:
+                df.drop('nuclide', axis=1, inplace=True)
+
+        # If the user requested a specific set of nuclides
+        elif self.by_nuclide and nuclides != 'all':
+            xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
+            df = xs_tally.get_pandas_dataframe(
+                distribcell_paths=distribcell_paths)
+
+        # If the user requested all nuclides, keep nuclide column in dataframe
+        else:
+            df = self.xs_tally.get_pandas_dataframe(
+                distribcell_paths=distribcell_paths)
+
+        # Remove the score column since it is homogeneous and redundant
+        if self.domain_type == 'mesh':
+            df = df.drop('score', axis=1, level=0)
+        else:
+            df = df.drop('score', axis=1)
+
+        # Override energy groups bounds with indices
+        all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
+        all_groups = np.repeat(all_groups, self.num_nuclides)
+        if 'energy low [MeV]' in df and 'energyout low [MeV]' in df:
+            df.rename(columns={'energy low [MeV]': 'group in'},
+                      inplace=True)
+            in_groups = np.tile(all_groups, int(self.num_subdomains *
+                                                self.num_delayed_groups))
+            in_groups = np.repeat(in_groups, int(df.shape[0] / in_groups.size))
+            df['group in'] = in_groups
+            del df['energy high [MeV]']
+
+            df.rename(columns={'energyout low [MeV]': 'group out'},
+                      inplace=True)
+            out_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
+            df['group out'] = out_groups
+            del df['energyout high [MeV]']
+            columns = ['group in', 'group out']
+
+        elif 'energyout low [MeV]' in df:
+            df.rename(columns={'energyout low [MeV]': 'group out'},
+                      inplace=True)
+            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
+            df['group out'] = in_groups
+            del df['energyout high [MeV]']
+            columns = ['group out']
+
+        elif 'energy low [MeV]' in df:
+            df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True)
+            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
+            df['group in'] = in_groups
+            del df['energy high [MeV]']
+            columns = ['group in']
+
+        # Select out those groups the user requested
+        if not isinstance(groups, basestring):
+            if 'group in' in df:
+                df = df[df['group in'].isin(groups)]
+            if 'group out' in df:
+                df = df[df['group out'].isin(groups)]
+
+        # If user requested micro cross sections, divide out the atom densities
+        if xs_type == 'micro':
+            if self.by_nuclide:
+                densities = self.get_nuclide_densities(nuclides)
+            else:
+                densities = self.get_nuclide_densities('sum')
+            densities = np.repeat(densities, len(self.rxn_rate_tally.scores))
+            tile_factor = df.shape[0] / len(densities)
+            df['mean'] /= np.tile(densities, tile_factor)
+            df['std. dev.'] /= np.tile(densities, tile_factor)
+
+        # Sort the dataframe by domain type id (e.g., distribcell id) and
+        # energy groups such that data is from fast to thermal
+        if self.domain_type == 'mesh':
+            mesh_str = 'mesh {0}'.format(self.domain.id)
+            df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'), \
+                               (mesh_str, 'z')] + columns, inplace=True)
+        else:
+            df.sort_values(by=[self.domain_type] + columns, inplace=True)
+
+        return df
+
+
+
         df = super(MDGXS, self).get_pandas_dataframe(groups, nuclides, xs_type,
                                                      distribcell_paths)
 
@@ -744,7 +868,7 @@ class ChiDelayed(MDGXS):
     domain are generated automatically via the :attr:`ChiDelayed.tallies`
     property, which can then be appended to a :class:`openmc.Tallies` instance.
 
-    For post-processing, the :meth:`MDGXS.load_from_statepoint` will pull in the
+    For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the
     necessary data to compute multi-group cross sections from a
     :class:`openmc.StatePoint` instance. The derived multi-group cross
     section can then be obtained from the :attr:`ChiDelayed.xs_tally` property.
@@ -961,7 +1085,7 @@ class ChiDelayed(MDGXS):
 
             # Slice nu-fission-out tally along energyout filter
             delayed_nu_fission_out = slice_xs.tallies['delayed-nu-fission-out']
-            tally_slice = delayed_nu_fission_out.get_slice\
+            tally_slice = delayed_nu_fission_out.get_slice \
                           (filters=filters, filter_bins=filter_bins)
             slice_xs._tallies['delayed-nu-fission-out'] = tally_slice
 
@@ -980,8 +1104,8 @@ class ChiDelayed(MDGXS):
 
         Parameters
         ----------
-        other : openmc.mdgxs.MDGXS
-            MDGXS to merge with this one
+        other : openmc.mdgxs.MGXS
+            MGXS to merge with this one
 
         Returns
         -------
@@ -1030,12 +1154,13 @@ class ChiDelayed(MDGXS):
 
     def get_xs(self, groups='all', subdomains='all', nuclides='all',
                xs_type='macro', order_groups='increasing',
-               value='mean', delayed_groups='all', **kwargs):
+               value='mean', delayed_groups='all', squeeze=True, **kwargs):
         """Returns an array of the delayed fission spectrum.
 
-        This method constructs a 2D NumPy array for the requested multi-group
-        and multi-delayed group cross section data data for one or more energy
-        groups and subdomains.
+        This method constructs a 4D NumPy array for the requested
+        multi-delayed-group cross section data for one or more
+        subdomains (1st dimension), delayed groups (2nd demension),
+        energy groups (3rd dimension), and nuclides (4th dimension).
 
         Parameters
         ----------
@@ -1052,13 +1177,17 @@ class ChiDelayed(MDGXS):
             cross section summed over all nuclides. Defaults to 'all'.
         xs_type: {'macro', 'micro'}
             This parameter is not relevant for chi but is included here to
-            mirror the parent MDGXS.get_xs(...) class method
+            mirror the parent MGXS.get_xs(...) class method
         order_groups: {'increasing', 'decreasing'}
             Return the cross section indexed according to increasing or
             decreasing energy groups (decreasing or increasing energies).
             Defaults to 'increasing'.
         value : {'mean', 'std_dev', 'rel_err'}
             A string for the type of value to return. Defaults to 'mean'.
+        squeeze : bool
+            A boolean representing whether to eliminate the extra dimensions
+            of the multi-dimensional array this is to be retured. Defaults to
+            True.
 
         Returns
         -------
@@ -1162,27 +1291,37 @@ class ChiDelayed(MDGXS):
             xs = self.xs_tally.get_values(filters=filters,
                                           filter_bins=filter_bins, value=value)
 
+        # Eliminate the trivial score dimension
+        xs = np.squeeze(xs, axis=len(xs.shape) - 1)
+        xs = np.nan_to_num(xs)
+
+        # Reshape tally data array with separate axes for domain and energy
+        if groups == 'all':
+            num_groups = self.num_groups
+        else:
+            num_groups = len(groups)
+
+        if delayed_groups == 'all':
+            num_delayed_groups = self.num_delayed_groups
+        else:
+            num_delayed_groups = len(delayed_groups)
+
+        # Reshape tally data array with separate axes for domain, energy groups,
+        # delayed groups, and nuclides
+        num_subdomains = int(xs.shape[0] / (num_groups * num_delayed_groups))
+        new_shape = (num_subdomains, num_delayed_groups, num_groups)
+        new_shape += xs.shape[1:]
+        xs = np.reshape(xs, new_shape)
+
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
+            xs = xs[:, :, ::-1, :]
 
-            # Reshape tally data array with separate axes for domain and energy
-            if groups == 'all':
-                num_groups = self.num_groups
-            else:
-                num_groups = len(groups)
-            num_subdomains = int(xs.shape[0] / num_groups)
-            new_shape = (num_subdomains, num_groups) + xs.shape[1:]
-            xs = np.reshape(xs, new_shape)
-
-            # Reverse energies to align with increasing energy groups
-            xs = xs[:, ::-1, :]
-
-            # Eliminate trivial dimensions
+        if squeeze:
             xs = np.squeeze(xs)
             xs = np.atleast_1d(xs)
 
-        xs = np.nan_to_num(xs)
         return xs
 
 
@@ -1199,7 +1338,7 @@ class DelayedNuFissionXS(MDGXS):
     :attr:`DelayedNuFissionXS.tallies` property, which can then be appended to a
     :class:`openmc.Tallies` instance.
 
-    For post-processing, the :meth:`MDGXS.load_from_statepoint` will pull in the
+    For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the
     necessary data to compute multi-group cross sections from a
     :class:`openmc.StatePoint` instance. The derived multi-group cross section
     can then be obtained from the :attr:`DelayedNuFissionXS.xs_tally` property.
@@ -1315,7 +1454,7 @@ class Beta(MDGXS):
     generated automatically via the :attr:`Beta.tallies` property, which can
     then be appended to a :class:`openmc.Tallies` instance.
 
-    For post-processing, the :meth:`MDGXS.load_from_statepoint` will pull in the
+    For post-processing, the :meth:`MGXS.load_from_statepoint` will pull in the
     necessary data to compute multi-group cross sections from a
     :class:`openmc.StatePoint` instance. The derived multi-group cross section
     can then be obtained from the :attr:`Beta.xs_tally` property.
diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py
index 4fc4edb38..5a4c5866b 100644
--- a/openmc/mgxs/mgxs.py
+++ b/openmc/mgxs/mgxs.py
@@ -724,11 +724,13 @@ class MGXS(object):
 
     def get_xs(self, groups='all', subdomains='all', nuclides='all',
                xs_type='macro', order_groups='increasing',
-               value='mean', **kwargs):
+               value='mean', squeeze=True, **kwargs):
         r"""Returns an array of multi-group cross sections.
 
-        This method constructs a 2D NumPy array for the requested multi-group
-        cross section data data for one or more energy groups and subdomains.
+        This method constructs a 3D NumPy array for the requested
+        multi-group cross section data for one or more subdomains
+        (1st dimension), energy groups (2nd dimension), and nuclides
+        (3rd dimension).
 
         Parameters
         ----------
@@ -750,6 +752,10 @@ class MGXS(object):
             Defaults to 'increasing'.
         value : {'mean', 'std_dev', 'rel_err'}
             A string for the type of value to return. Defaults to 'mean'.
+        squeeze : bool
+            A boolean representing whether to eliminate the extra dimensions
+            of the multi-dimensional array this is to be retured. Defaults to
+            True.
 
         Returns
         -------
@@ -819,25 +825,29 @@ class MGXS(object):
             if value == 'mean' or value == 'std_dev':
                 xs /= densities[np.newaxis, :, np.newaxis]
 
+        # Eliminate the trivial score dimension
+        xs = np.squeeze(xs, axis=len(xs.shape) - 1)
+        xs = np.nan_to_num(xs)
+
+        if groups == 'all':
+            num_groups = self.num_groups
+        else:
+            num_groups = len(groups)
+
+        # Reshape tally data array with separate axes for domain and energy
+        num_subdomains = int(xs.shape[0] / num_groups)
+        new_shape = (num_subdomains, num_groups) + xs.shape[1:]
+        xs = np.reshape(xs, new_shape)
+
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            if groups == 'all':
-                num_groups = self.num_groups
-            else:
-                num_groups = len(groups)
-
-            # Reshape tally data array with separate axes for domain and energy
-            num_subdomains = int(xs.shape[0] / num_groups)
-            new_shape = (num_subdomains, num_groups) + xs.shape[1:]
-            xs = np.reshape(xs, new_shape)
-
-            # Reverse energies to align with increasing energy groups
             xs = xs[:, ::-1, :]
 
-        # Eliminate trivial dimensions
-        xs = np.squeeze(xs)
-        xs = np.atleast_1d(xs)
+        if squeeze:
+            xs = np.squeeze(xs)
+            xs = np.atleast_1d(xs)
+
         return xs
 
     def get_condensed_xs(self, coarse_groups):
@@ -1350,8 +1360,6 @@ class MGXS(object):
                 std_dev = self.get_xs(subdomains=[subdomain], nuclides=[nuclide],
                                       xs_type=xs_type, value='std_dev',
                                       row_column=row_column)
-                average = average.squeeze()
-                std_dev = std_dev.squeeze()
 
                 # Add MGXS results data to the HDF5 group
                 nuclide_group.require_dataset('average', dtype=np.float64,
@@ -1517,14 +1525,14 @@ class MGXS(object):
         if 'energy low [MeV]' in df and 'energyout low [MeV]' in df:
             df.rename(columns={'energy low [MeV]': 'group in'},
                       inplace=True)
-            in_groups = np.tile(all_groups, df.shape[0] / all_groups.size)
-            in_groups = np.repeat(in_groups, df.shape[0] / in_groups.size)
+            in_groups = np.tile(all_groups, int(self.num_subdomains))
+            in_groups = np.repeat(in_groups, int(df.shape[0] / in_groups.size))
             df['group in'] = in_groups
             del df['energy high [MeV]']
 
             df.rename(columns={'energyout low [MeV]': 'group out'},
                       inplace=True)
-            out_groups = np.tile(all_groups, df.shape[0] / all_groups.size)
+            out_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
             df['group out'] = out_groups
             del df['energyout high [MeV]']
             columns = ['group in', 'group out']
@@ -1532,14 +1540,14 @@ class MGXS(object):
         elif 'energyout low [MeV]' in df:
             df.rename(columns={'energyout low [MeV]': 'group out'},
                       inplace=True)
-            in_groups = np.tile(all_groups, df.shape[0] / all_groups.size)
+            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
             df['group out'] = in_groups
             del df['energyout high [MeV]']
             columns = ['group out']
 
         elif 'energy low [MeV]' in df:
             df.rename(columns={'energy low [MeV]': 'group in'}, inplace=True)
-            in_groups = np.tile(all_groups, df.shape[0] / all_groups.size)
+            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
             df['group in'] = in_groups
             del df['energy high [MeV]']
             columns = ['group in']
@@ -1570,6 +1578,7 @@ class MGXS(object):
                                (mesh_str, 'z')] + columns, inplace=True)
         else:
             df.sort_values(by=[self.domain_type] + columns, inplace=True)
+
         return df
 
     def get_units(self, xs_type='macro'):
@@ -1700,11 +1709,13 @@ class MatrixMGXS(MGXS):
     def get_xs(self, in_groups='all', out_groups='all',
                subdomains='all', nuclides='all',
                xs_type='macro', order_groups='increasing',
-               row_column='inout', value='mean', **kwargs):
+               row_column='inout', value='mean', squeeze=True, **kwargs):
         """Returns an array of multi-group cross sections.
 
-        This method constructs a 2D NumPy array for the requested multi-group
-        matrix data for one or more energy groups and subdomains.
+        This method constructs a 4D NumPy array for the requested
+        multi-group cross section data for one or more subdomains
+        (1st dimension), energy groups in (2nd dimension), energy groups out
+        (3rd dimension), and nuclides (4th dimension).
 
         Parameters
         ----------
@@ -1733,6 +1744,10 @@ class MatrixMGXS(MGXS):
             Defaults to 'inout'.
         value : {'mean', 'std_dev', 'rel_err'}
             A string for the type of value to return. Defaults to 'mean'.
+        squeeze : bool
+            A boolean representing whether to eliminate the extra dimensions
+            of the multi-dimensional array this is to be retured. Defaults to
+            True.
 
         Returns
         -------
@@ -1804,8 +1819,6 @@ class MatrixMGXS(MGXS):
                                           filter_bins=filter_bins,
                                           nuclides=query_nuclides, value=value)
 
-        xs = np.nan_to_num(xs)
-
         # Divide by atom number densities for microscopic cross sections
         if xs_type == 'micro':
             if self.by_nuclide:
@@ -1815,33 +1828,36 @@ class MatrixMGXS(MGXS):
             if value == 'mean' or value == 'std_dev':
                 xs /= densities[np.newaxis, :, np.newaxis]
 
+        # Eliminate the trivial score dimension
+        xs = np.squeeze(xs, axis=len(xs.shape) - 1)
+        xs = np.nan_to_num(xs)
+
+        if in_groups == 'all':
+            num_in_groups = self.num_groups
+        else:
+            num_in_groups = len(in_groups)
+
+        if out_groups == 'all':
+            num_out_groups = self.num_groups
+        else:
+            num_out_groups = len(out_groups)
+
+        # Reshape tally data array with separate axes for domain and energy
+        num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups))
+        new_shape = (num_subdomains, num_in_groups, num_out_groups)
+        new_shape += xs.shape[1:]
+        xs = np.reshape(xs, new_shape)
+
+        # Transpose the matrix if requested by user
+        if row_column == 'outin':
+            xs = np.swapaxes(xs, 1, 2)
+
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            if in_groups == 'all':
-                num_in_groups = self.num_groups
-            else:
-                num_in_groups = len(in_groups)
-            if out_groups == 'all':
-                num_out_groups = self.num_groups
-            else:
-                num_out_groups = len(out_groups)
-
-            # Reshape tally data array with separate axes for domain and energy
-            num_subdomains = int(xs.shape[0] /
-                                 (num_in_groups * num_out_groups))
-            new_shape = (num_subdomains, num_in_groups, num_out_groups)
-            new_shape += xs.shape[1:]
-            xs = np.reshape(xs, new_shape)
-
-            # Transpose the matrix if requested by user
-            if row_column == 'outin':
-                xs = np.swapaxes(xs, 1, 2)
-
-            # Reverse energies to align with increasing energy groups
             xs = xs[:, ::-1, ::-1, :]
 
-            # Eliminate trivial dimensions
+        if squeeze:
             xs = np.squeeze(xs)
             xs = np.atleast_2d(xs)
 
@@ -3518,11 +3534,13 @@ class ScatterMatrixXS(MatrixMGXS):
     def get_xs(self, in_groups='all', out_groups='all',
                subdomains='all', nuclides='all', moment='all',
                xs_type='macro', order_groups='increasing',
-               row_column='inout', value='mean'):
+               row_column='inout', value='mean', squeeze=True):
         r"""Returns an array of multi-group cross sections.
 
-        This method constructs a 2D NumPy array for the requested scattering
-        matrix data data for one or more energy groups and subdomains.
+        This method constructs a 5D NumPy array for the requested
+        multi-group cross section data for one or more subdomains
+        (1st dimension), energy groups in (2nd dimension), energy groups out
+        (3rd dimension), nuclides (4th dimension), and moments (5th dimension).
 
         NOTE: The scattering moments are not multiplied by the :math:`(2l+1)/2`
         prefactor in the expansion of the scattering source into Legendre
@@ -3558,6 +3576,10 @@ class ScatterMatrixXS(MatrixMGXS):
             Defaults to 'inout'.
         value : {'mean', 'std_dev', 'rel_err'}
             A string for the type of value to return. Defaults to 'mean'.
+        squeeze : bool
+            A boolean representing whether to eliminate the extra dimensions
+            of the multi-dimensional array this is to be retured. Defaults to
+            False.
 
         Returns
         -------
@@ -3636,8 +3658,6 @@ class ScatterMatrixXS(MatrixMGXS):
                                           filter_bins=filter_bins,
                                           nuclides=query_nuclides, value=value)
 
-        xs = np.nan_to_num(xs)
-
         # Divide by atom number densities for microscopic cross sections
         if xs_type == 'micro':
             if self.by_nuclide:
@@ -3647,32 +3667,35 @@ class ScatterMatrixXS(MatrixMGXS):
             if value == 'mean' or value == 'std_dev':
                 xs /= densities[np.newaxis, :, np.newaxis]
 
+        # Convert and nans to zero
+        xs = np.nan_to_num(xs)
+
+        if in_groups == 'all':
+            num_in_groups = self.num_groups
+        else:
+            num_in_groups = len(in_groups)
+
+        if out_groups == 'all':
+            num_out_groups = self.num_groups
+        else:
+            num_out_groups = len(out_groups)
+
+        # Reshape tally data array with separate axes for domain and energy
+        num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups))
+        new_shape = (num_subdomains, num_in_groups, num_out_groups)
+        new_shape += xs.shape[1:]
+        xs = np.reshape(xs, new_shape)
+
+        # Transpose the scattering matrix if requested by user
+        if row_column == 'outin':
+            xs = np.swapaxes(xs, 1, 2)
+
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            if in_groups == 'all':
-                num_in_groups = self.num_groups
-            else:
-                num_in_groups = len(in_groups)
-            if out_groups == 'all':
-                num_out_groups = self.num_groups
-            else:
-                num_out_groups = len(out_groups)
-
-            # Reshape tally data array with separate axes for domain and energy
-            num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups))
-            new_shape = (num_subdomains, num_in_groups, num_out_groups)
-            new_shape += xs.shape[1:]
-            xs = np.reshape(xs, new_shape)
-
-            # Transpose the scattering matrix if requested by user
-            if row_column == 'outin':
-                xs = np.swapaxes(xs, 1, 2)
-
-            # Reverse energies to align with increasing energy groups
             xs = xs[:, ::-1, ::-1, :]
 
-            # Eliminate trivial dimensions
+        if squeeze:
             xs = np.squeeze(xs)
             xs = np.atleast_2d(xs)
 
@@ -3729,7 +3752,7 @@ class ScatterMatrixXS(MatrixMGXS):
         if self.legendre_order > 0:
             # Insert a column corresponding to the Legendre moments
             moments = ['P{}'.format(i) for i in range(self.legendre_order+1)]
-            moments = np.tile(moments, df.shape[0] / len(moments))
+            moments = np.tile(moments, int(df.shape[0] / len(moments)))
             df['moment'] = moments
 
             # Place the moment column before the mean column
@@ -4513,11 +4536,13 @@ class Chi(MGXS):
 
     def get_xs(self, groups='all', subdomains='all', nuclides='all',
                xs_type='macro', order_groups='increasing',
-               value='mean', **kwargs):
+               value='mean', squeeze=True, **kwargs):
         """Returns an array of the fission spectrum.
 
-        This method constructs a 2D NumPy array for the requested multi-group
-        cross section data data for one or more energy groups and subdomains.
+        This method constructs a 3D NumPy array for the requested
+        multi-group cross section data for one or more subdomains
+        (1st dimension), energy groups (2nd dimension), and nuclides
+        (3rd dimension).
 
         Parameters
         ----------
@@ -4539,6 +4564,10 @@ class Chi(MGXS):
             Defaults to 'increasing'.
         value : {'mean', 'std_dev', 'rel_err'}
             A string for the type of value to return. Defaults to 'mean'.
+        squeeze : bool
+            A boolean representing whether to eliminate the extra dimensions
+            of the multi-dimensional array this is to be retured. Defaults to
+            True.
 
         Returns
         -------
@@ -4630,27 +4659,29 @@ class Chi(MGXS):
             xs = self.xs_tally.get_values(filters=filters,
                                           filter_bins=filter_bins, value=value)
 
+        # Eliminate the trivial score dimension
+        xs = np.squeeze(xs, axis=len(xs.shape) - 1)
+        xs = np.nan_to_num(xs)
+
+        # Reshape tally data array with separate axes for domain and energy
+        if groups == 'all':
+            num_groups = self.num_groups
+        else:
+            num_groups = len(groups)
+
+        num_subdomains = int(xs.shape[0] / num_groups)
+        new_shape = (num_subdomains, num_groups) + xs.shape[1:]
+        xs = np.reshape(xs, new_shape)
+
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-
-            # Reshape tally data array with separate axes for domain and energy
-            if groups == 'all':
-                num_groups = self.num_groups
-            else:
-                num_groups = len(groups)
-            num_subdomains = int(xs.shape[0] / num_groups)
-            new_shape = (num_subdomains, num_groups) + xs.shape[1:]
-            xs = np.reshape(xs, new_shape)
-
-            # Reverse energies to align with increasing energy groups
             xs = xs[:, ::-1, :]
 
-            # Eliminate trivial dimensions
+        if squeeze:
             xs = np.squeeze(xs)
             xs = np.atleast_1d(xs)
 
-        xs = np.nan_to_num(xs)
         return xs
 
     def get_pandas_dataframe(self, groups='all', nuclides='all',
diff --git a/tests/test_mgxs_library_condense/test_mgxs_library_condense.py b/tests/test_mgxs_library_condense/test_mgxs_library_condense.py
index a3e849d6c..551229e6b 100644
--- a/tests/test_mgxs_library_condense/test_mgxs_library_condense.py
+++ b/tests/test_mgxs_library_condense/test_mgxs_library_condense.py
@@ -9,6 +9,7 @@ from testing_harness import PyAPITestHarness
 from input_set import PinCellInputSet
 import openmc
 import openmc.mgxs
+import numpy as np
 
 
 class MGXSTestHarness(PyAPITestHarness):
@@ -24,7 +25,7 @@ class MGXSTestHarness(PyAPITestHarness):
                                                               20.])
 
         # Initialize a six-delayed-group structure
-        delayed_groups = range(1,7)
+        delayed_groups = list(range(1,7))
 
         # Initialize MGXS Library for a few cross section types
         self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry)
diff --git a/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py b/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py
index 3103e0738..fe1ca9c0f 100644
--- a/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py
+++ b/tests/test_mgxs_library_distribcell/test_mgxs_library_distribcell.py
@@ -9,6 +9,7 @@ from testing_harness import PyAPITestHarness
 from input_set import AssemblyInputSet
 import openmc
 import openmc.mgxs
+import numpy as np
 
 
 class MGXSTestHarness(PyAPITestHarness):
@@ -23,7 +24,7 @@ class MGXSTestHarness(PyAPITestHarness):
         energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 20.])
 
         # Initialize a six-delayed-group structure
-        delayed_groups = range(1,7)
+        delayed_groups = list(range(1,7))
 
         # Initialize MGXS Library for a few cross section types
         # for one material-filled cell in the geometry
diff --git a/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py b/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py
index 4359b2793..b4d7e5dbb 100644
--- a/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py
+++ b/tests/test_mgxs_library_hdf5/test_mgxs_library_hdf5.py
@@ -25,7 +25,7 @@ class MGXSTestHarness(PyAPITestHarness):
                                                               20.])
 
         # Initialize a six-delayed-group structure
-        delayed_groups = range(1,7)
+        delayed_groups = list(range(1,7))
 
         # Initialize MGXS Library for a few cross section types
         self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry)
diff --git a/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py b/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py
index bcf240010..750274b1f 100644
--- a/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py
+++ b/tests/test_mgxs_library_mesh/test_mgxs_library_mesh.py
@@ -8,6 +8,7 @@ sys.path.insert(0, os.pardir)
 from testing_harness import PyAPITestHarness
 import openmc
 import openmc.mgxs
+import numpy as np
 
 
 class MGXSTestHarness(PyAPITestHarness):
@@ -19,7 +20,7 @@ class MGXSTestHarness(PyAPITestHarness):
         energy_groups = openmc.mgxs.EnergyGroups(group_edges=[0, 20.])
 
         # Initialize a six-delayed-group structure
-        delayed_groups = range(1,7)
+        delayed_groups = list(range(1,7))
 
         # Initialize MGXS Library for a few cross section types
         # for one material-filled cell in the geometry
diff --git a/tests/test_mgxs_library_no_nuclides/results_true.dat b/tests/test_mgxs_library_no_nuclides/results_true.dat
index 54650970f..3563f141b 100644
--- a/tests/test_mgxs_library_no_nuclides/results_true.dat
+++ b/tests/test_mgxs_library_no_nuclides/results_true.dat
@@ -29,49 +29,49 @@
 1     10000         1   total  0.385188   0.026946
 0     10000         2   total  0.412389   0.015425
     material  group in  group out nuclide moment      mean  std. dev.
-1      10000         1          1   total     P0  0.016482   0.004502
-3      10000         1          1   total     P1 -0.010499   0.010438
-5      10000         1          1   total     P2 -0.000768   0.000768
-7      10000         1          1   total     P3 -0.000171   0.000172
 9      10000         1          1   total     P0 -0.000207   0.000149
 11     10000         1          1   total     P1  0.000234   0.000128
 13     10000         1          1   total     P2  0.051870   0.006983
 15     10000         1          1   total     P3  0.009478   0.002234
+8      10000         1          2   total     P0  0.000989   0.000482
+10     10000         1          2   total     P1 -0.000103   0.000184
+12     10000         1          2   total     P2  0.384199   0.027001
+14     10000         1          2   total     P3  0.020069   0.002846
+1      10000         2          1   total     P0  0.016482   0.004502
+3      10000         2          1   total     P1 -0.010499   0.010438
+5      10000         2          1   total     P2 -0.000768   0.000768
+7      10000         2          1   total     P3 -0.000171   0.000172
 0      10000         2          2   total     P0  0.411465   0.015245
 2      10000         2          2   total     P1  0.006371   0.010551
 4      10000         2          2   total     P2  0.000925   0.000925
 6      10000         2          2   total     P3  0.000494   0.000494
-8      10000         2          2   total     P0  0.000989   0.000482
-10     10000         2          2   total     P1 -0.000103   0.000184
-12     10000         2          2   total     P2  0.384199   0.027001
-14     10000         2          2   total     P3  0.020069   0.002846
     material  group in  group out nuclide moment      mean  std. dev.
-1      10000         1          1   total     P0  0.016482   0.004502
-3      10000         1          1   total     P1 -0.010499   0.010438
-5      10000         1          1   total     P2 -0.000768   0.000768
-7      10000         1          1   total     P3 -0.000171   0.000172
 9      10000         1          1   total     P0 -0.000207   0.000149
 11     10000         1          1   total     P1  0.000234   0.000128
 13     10000         1          1   total     P2  0.051870   0.006983
 15     10000         1          1   total     P3  0.009478   0.002234
+8      10000         1          2   total     P0  0.000989   0.000482
+10     10000         1          2   total     P1 -0.000103   0.000184
+12     10000         1          2   total     P2  0.384199   0.027001
+14     10000         1          2   total     P3  0.020069   0.002846
+1      10000         2          1   total     P0  0.016482   0.004502
+3      10000         2          1   total     P1 -0.010499   0.010438
+5      10000         2          1   total     P2 -0.000768   0.000768
+7      10000         2          1   total     P3 -0.000171   0.000172
 0      10000         2          2   total     P0  0.411465   0.015245
 2      10000         2          2   total     P1  0.006371   0.010551
 4      10000         2          2   total     P2  0.000925   0.000925
 6      10000         2          2   total     P3  0.000494   0.000494
-8      10000         2          2   total     P0  0.000989   0.000482
-10     10000         2          2   total     P1 -0.000103   0.000184
-12     10000         2          2   total     P2  0.384199   0.027001
-14     10000         2          2   total     P3  0.020069   0.002846
    material  group in  group out nuclide  mean  std. dev.
-1     10000         1          1   total   1.0   1.414214
 3     10000         1          1   total   1.0   0.078516
+2     10000         1          2   total   1.0   0.687184
+1     10000         2          1   total   1.0   1.414214
 0     10000         2          2   total   1.0   0.041130
-2     10000         2          2   total   1.0   0.687184
    material  group in  group out nuclide      mean  std. dev.
-1     10000         1          1   total  0.454366   0.027426
 3     10000         1          1   total  0.020142   0.003149
+2     10000         1          2   total  0.000000   0.000000
+1     10000         2          1   total  0.454366   0.027426
 0     10000         2          2   total  0.000000   0.000000
-2     10000         2          2   total  0.000000   0.000000
    material  group out nuclide  mean  std. dev.
 1     10000          1   total   1.0   0.046071
 0     10000          2   total   0.0   0.000000
@@ -154,49 +154,49 @@
 1     10001         1   total  0.310121   0.033788
 0     10001         2   total  0.296264   0.043792
     material  group in  group out nuclide moment      mean  std. dev.
-1      10001         1          1   total     P0 -0.011214   0.016180
-3      10001         1          1   total     P1 -0.003270   0.007329
-5      10001         1          1   total     P2  0.000000   0.000000
-7      10001         1          1   total     P3  0.000000   0.000000
 9      10001         1          1   total     P0  0.000000   0.000000
 11     10001         1          1   total     P1  0.000000   0.000000
 13     10001         1          1   total     P2  0.038230   0.008484
 15     10001         1          1   total     P3  0.007964   0.003732
+8      10001         1          2   total     P0  0.000000   0.000000
+10     10001         1          2   total     P1  0.000000   0.000000
+12     10001         1          2   total     P2  0.310121   0.033788
+14     10001         1          2   total     P3  0.020745   0.004696
+1      10001         2          1   total     P0 -0.011214   0.016180
+3      10001         2          1   total     P1 -0.003270   0.007329
+5      10001         2          1   total     P2  0.000000   0.000000
+7      10001         2          1   total     P3  0.000000   0.000000
 0      10001         2          2   total     P0  0.296264   0.043792
 2      10001         2          2   total     P1  0.008837   0.011504
 4      10001         2          2   total     P2  0.000000   0.000000
 6      10001         2          2   total     P3  0.000000   0.000000
-8      10001         2          2   total     P0  0.000000   0.000000
-10     10001         2          2   total     P1  0.000000   0.000000
-12     10001         2          2   total     P2  0.310121   0.033788
-14     10001         2          2   total     P3  0.020745   0.004696
     material  group in  group out nuclide moment      mean  std. dev.
-1      10001         1          1   total     P0 -0.011214   0.016180
-3      10001         1          1   total     P1 -0.003270   0.007329
-5      10001         1          1   total     P2  0.000000   0.000000
-7      10001         1          1   total     P3  0.000000   0.000000
 9      10001         1          1   total     P0  0.000000   0.000000
 11     10001         1          1   total     P1  0.000000   0.000000
 13     10001         1          1   total     P2  0.038230   0.008484
 15     10001         1          1   total     P3  0.007964   0.003732
+8      10001         1          2   total     P0  0.000000   0.000000
+10     10001         1          2   total     P1  0.000000   0.000000
+12     10001         1          2   total     P2  0.310121   0.033788
+14     10001         1          2   total     P3  0.020745   0.004696
+1      10001         2          1   total     P0 -0.011214   0.016180
+3      10001         2          1   total     P1 -0.003270   0.007329
+5      10001         2          1   total     P2  0.000000   0.000000
+7      10001         2          1   total     P3  0.000000   0.000000
 0      10001         2          2   total     P0  0.296264   0.043792
 2      10001         2          2   total     P1  0.008837   0.011504
 4      10001         2          2   total     P2  0.000000   0.000000
 6      10001         2          2   total     P3  0.000000   0.000000
-8      10001         2          2   total     P0  0.000000   0.000000
-10     10001         2          2   total     P1  0.000000   0.000000
-12     10001         2          2   total     P2  0.310121   0.033788
-14     10001         2          2   total     P3  0.020745   0.004696
    material  group in  group out nuclide  mean  std. dev.
-1     10001         1          1   total   0.0   0.000000
 3     10001         1          1   total   1.0   0.108779
+2     10001         1          2   total   0.0   0.000000
+1     10001         2          1   total   0.0   0.000000
 0     10001         2          2   total   1.0   0.142427
-2     10001         2          2   total   0.0   0.000000
    material  group in  group out nuclide  mean  std. dev.
-1     10001         1          1   total   0.0        0.0
 3     10001         1          1   total   0.0        0.0
+2     10001         1          2   total   0.0        0.0
+1     10001         2          1   total   0.0        0.0
 0     10001         2          2   total   0.0        0.0
-2     10001         2          2   total   0.0        0.0
    material  group out nuclide  mean  std. dev.
 1     10001          1   total   0.0        0.0
 0     10001          2   total   0.0        0.0
@@ -279,49 +279,49 @@
 1     10002         1   total  0.671269   0.026186
 0     10002         2   total  2.035388   0.258060
     material  group in  group out nuclide moment      mean  std. dev.
-1      10002         1          1   total     P0  0.509941   0.051236
-3      10002         1          1   total     P1  0.024988   0.008312
-5      10002         1          1   total     P2  0.000400   0.000401
-7      10002         1          1   total     P3  0.000214   0.000215
 9      10002         1          1   total     P0  0.008758   0.000926
 11     10002         1          1   total     P1 -0.003785   0.000817
 13     10002         1          1   total     P2  0.381167   0.016243
 15     10002         1          1   total     P3  0.009148   0.003889
+8      10002         1          2   total     P0  0.031368   0.001728
+10     10002         1          2   total     P1 -0.002568   0.001014
+12     10002         1          2   total     P2  0.639901   0.024709
+14     10002         1          2   total     P3  0.152392   0.008156
+1      10002         2          1   total     P0  0.509941   0.051236
+3      10002         2          1   total     P1  0.024988   0.008312
+5      10002         2          1   total     P2  0.000400   0.000401
+7      10002         2          1   total     P3  0.000214   0.000215
 0      10002         2          2   total     P0  2.034945   0.257800
 2      10002         2          2   total     P1  0.111175   0.013020
 4      10002         2          2   total     P2  0.000443   0.000445
 6      10002         2          2   total     P3  0.000320   0.000321
-8      10002         2          2   total     P0  0.031368   0.001728
-10     10002         2          2   total     P1 -0.002568   0.001014
-12     10002         2          2   total     P2  0.639901   0.024709
-14     10002         2          2   total     P3  0.152392   0.008156
     material  group in  group out nuclide moment      mean  std. dev.
-1      10002         1          1   total     P0  0.509941   0.051236
-3      10002         1          1   total     P1  0.024988   0.008312
-5      10002         1          1   total     P2  0.000400   0.000401
-7      10002         1          1   total     P3  0.000214   0.000215
 9      10002         1          1   total     P0  0.008758   0.000926
 11     10002         1          1   total     P1 -0.003785   0.000817
 13     10002         1          1   total     P2  0.381167   0.016243
 15     10002         1          1   total     P3  0.009148   0.003889
+8      10002         1          2   total     P0  0.031368   0.001728
+10     10002         1          2   total     P1 -0.002568   0.001014
+12     10002         1          2   total     P2  0.639901   0.024709
+14     10002         1          2   total     P3  0.152392   0.008156
+1      10002         2          1   total     P0  0.509941   0.051236
+3      10002         2          1   total     P1  0.024988   0.008312
+5      10002         2          1   total     P2  0.000400   0.000401
+7      10002         2          1   total     P3  0.000214   0.000215
 0      10002         2          2   total     P0  2.034945   0.257800
 2      10002         2          2   total     P1  0.111175   0.013020
 4      10002         2          2   total     P2  0.000443   0.000445
 6      10002         2          2   total     P3  0.000320   0.000321
-8      10002         2          2   total     P0  0.031368   0.001728
-10     10002         2          2   total     P1 -0.002568   0.001014
-12     10002         2          2   total     P2  0.639901   0.024709
-14     10002         2          2   total     P3  0.152392   0.008156
    material  group in  group out nuclide  mean  std. dev.
-1     10002         1          1   total   1.0   1.414214
 3     10002         1          1   total   1.0   0.038609
+2     10002         1          2   total   1.0   0.067667
+1     10002         2          1   total   1.0   1.414214
 0     10002         2          2   total   1.0   0.135929
-2     10002         2          2   total   1.0   0.067667
    material  group in  group out nuclide  mean  std. dev.
-1     10002         1          1   total   0.0        0.0
 3     10002         1          1   total   0.0        0.0
+2     10002         1          2   total   0.0        0.0
+1     10002         2          1   total   0.0        0.0
 0     10002         2          2   total   0.0        0.0
-2     10002         2          2   total   0.0        0.0
    material  group out nuclide  mean  std. dev.
 1     10002          1   total   0.0        0.0
 0     10002          2   total   0.0        0.0
diff --git a/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py b/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py
index 5ca90875d..5dee9c407 100644
--- a/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py
+++ b/tests/test_mgxs_library_no_nuclides/test_mgxs_library_no_nuclides.py
@@ -9,6 +9,7 @@ from testing_harness import PyAPITestHarness
 from input_set import PinCellInputSet
 import openmc
 import openmc.mgxs
+import numpy as np
 
 
 class MGXSTestHarness(PyAPITestHarness):
@@ -24,7 +25,7 @@ class MGXSTestHarness(PyAPITestHarness):
                                                               20.])
 
         # Initialize a six-delayed-group structure
-        delayed_groups = range(1,7)
+        delayed_groups = list(range(1,7))
 
         # Initialize MGXS Library for a few cross section types
         self.mgxs_lib = openmc.mgxs.Library(self._input_set.geometry)
diff --git a/tests/test_mgxs_library_nuclides/results_true.dat b/tests/test_mgxs_library_nuclides/results_true.dat
index 9671ad785..d3e3235cc 100644
--- a/tests/test_mgxs_library_nuclides/results_true.dat
+++ b/tests/test_mgxs_library_nuclides/results_true.dat
@@ -1 +1 @@
-cb61db73f66b40ed1a59a59e6f4fd52678e9dc41c7bb8ad327989233c3b8d78a71d84c3cb8ad9bc8b1585b319e1f1d66a8667e7cad2ead4cc574f415f8f7a35d
\ No newline at end of file
+8142ae4e107002a835999e4ace85c17376f262a7059fc224f3756a2de19aba6ca4c4fa14ca2085c87d7729aa8d6d6f78fdae21ac6dfe33ca303449c769076074
\ No newline at end of file
diff --git a/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py b/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py
index da613d78a..ac24334e4 100644
--- a/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py
+++ b/tests/test_mgxs_library_nuclides/test_mgxs_library_nuclides.py
@@ -9,6 +9,7 @@ from testing_harness import PyAPITestHarness
 from input_set import PinCellInputSet
 import openmc
 import openmc.mgxs
+import numpy as np
 
 
 class MGXSTestHarness(PyAPITestHarness):