diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index 43bec06fa..cd563521c 100644
--- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
@@ -1975,7 +1975,7 @@
     "\n",
     "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.plotter` module to plot continuous-energy cross sections from the openly available cross section library distributed by NNDC.\n",
     "\n",
-    "There is a simpler way to plot the MGXS data (using the same interface as is used for the continuous-energy data), however this example series has not yet introduced the pre-requisite information and so we will do this manually here."
+    "The MGXS data can also be plotted using the openmc.plot_xs command, however we will do this manually here to show how the openmc.Mgxs.get_xs method can be used to obtain data."
    ]
   },
   {
diff --git a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
index 5f26c7cdb..b070748ba 100644
--- a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
@@ -425,7 +425,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDIwOjEyOjM5LTA1OjAwwekMZQAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDE4OjEyOjAzLTA1OjAwPb6RcwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -727,8 +727,8 @@
       "         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 | f76ee0867ca2f46c4e84a3cda6511b0ab64eb6a5\n",
-      "         Date/Time | 2016-11-20 20:12:40\n",
+      "          Git SHA1 | 346deb258f969a2522bef0774ae1576043ac0de7\n",
+      "         Date/Time | 2016-12-02 18:12:03\n",
       "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
@@ -815,20 +815,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  3.3283E-01 seconds\n",
-      "   Reading cross sections          =  2.4897E-01 seconds\n",
-      " Total time in simulation          =  8.0427E+00 seconds\n",
-      "   Time in transport only          =  7.7850E+00 seconds\n",
-      "   Time in inactive batches        =  9.2530E-01 seconds\n",
-      "   Time in active batches          =  7.1174E+00 seconds\n",
-      "   Time synchronizing fission bank =  5.0076E-03 seconds\n",
-      "     Sampling source sites         =  3.4455E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.5240E-03 seconds\n",
-      "   Time accumulating tallies       =  1.1716E-04 seconds\n",
-      " Total time for finalization       =  3.6200E-06 seconds\n",
-      " Total time elapsed                =  8.3927E+00 seconds\n",
-      " Calculation Rate (inactive)       =  54036.4 neutrons/second\n",
-      " Calculation Rate (active)         =  28100.0 neutrons/second\n",
+      " Total time for initialization     =  4.7681E-01 seconds\n",
+      "   Reading cross sections          =  3.4878E-01 seconds\n",
+      " Total time in simulation          =  7.8339E+00 seconds\n",
+      "   Time in transport only          =  7.6987E+00 seconds\n",
+      "   Time in inactive batches        =  9.5272E-01 seconds\n",
+      "   Time in active batches          =  6.8812E+00 seconds\n",
+      "   Time synchronizing fission bank =  4.8350E-03 seconds\n",
+      "     Sampling source sites         =  3.3404E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.4577E-03 seconds\n",
+      "   Time accumulating tallies       =  1.0196E-04 seconds\n",
+      " Total time for finalization       =  2.6290E-06 seconds\n",
+      " Total time elapsed                =  8.3293E+00 seconds\n",
+      " Calculation Rate (inactive)       =  52481.1 neutrons/second\n",
+      " Calculation Rate (active)         =  29064.6 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -1086,7 +1086,7 @@
      "data": {
       "image/png": 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VaeeKdyuZjh3hhRfg3HNh2DC3ydzNN9smc8ZAad/Yw4grCSjUTz/BmBIMWM+Z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\nk+T3IhRjvYR0Tz9d93jECNcrMWOGe5OaOjV4qm4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64E9R6pu8FDhb0t80nUvSqNb9EbEM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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f74e03a0710>"
+       "<matplotlib.figure.Figure at 0x7fc8f4517550>"
       ]
      },
      "metadata": {},
@@ -1096,7 +1096,7 @@
      "data": {
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zOLrt7u5HuXvJ92rpCLp2hRNPDGt97LIL/PjHsNNO0Nxc6chERJIppMbjq6/y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\nZmaVceFhZmZmlfk/5oOPgFlJQ7YAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f74b9129588>"
+       "<matplotlib.figure.Figure at 0x7fc8f451b240>"
       ]
      },
      "metadata": {},
@@ -1106,7 +1106,7 @@
      "data": {
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FFqfHY9NoM+AXGV9vCmwIvAYcXuD4pBXr2ROefjr0gOywAzz1VLkjEhGRYsu7\nx8Pdtwcws78BJ6hehxTCCiuEQab77AO77QZ//zvsu2+5oxIRkWJJMsbjRHIkLGa2UlPFxURy6doV\nxo6FvfeG/fcPa7uIiEjrlCTxuBs4KEf7AdE+kdiWWSb0dpx0EowaBaefDosXlzsqEREptCQrZ2wB\nnJSj/WngwmZFI21aVRVcfjmsvnpIQKZPh5tvho4dyx2ZiIgUSpLEY5kGzusAdG5eOOWlyqWVIZWC\n1VaDQw+FL76A++6D5fUQT0SkopSyculTwBR3H5XVfg3Qz90Hx7pgBVDl0sr09NMwbBiss04osa4q\npyIilSdu5dIkPR5nA+PMrD/wRNQ2FNgc2DnB9URy2m67UOV0t91gyy3hwQehX79yRyUiIs0Re3Cp\nuz8PbAV8ShhQuifwHqG349nChidt3S9+Af/9L3TvHqqcPvhguSMSEZHmSDKrBXef5O7D3b2vu2/m\n7iPd/d1CBycCsMYaoedjxx1hr73giisg5hNCERGpEIkSDzP7uZn90czuMrNVorbdzKxvYcMTCbp2\nDYNMTzsNTjkFjjgC5s8vd1QiIhJX7MTDzLYFXidMq92XsDgcQH/gvMKFJlJfVRVcfDHceivcfntY\n5+Wbb8odlYiIxJGkx+MS4Gx33wnI/JvzScLYD5GiOuwwePJJmDIFfvlLeO21ckckIiL5SpJ4/AJ4\nIEf7l0D35oUjkp9Bg+CVV0J9j622gtrackckIiL5SJJ4zAJyVVTYFJjevHBE8rf22vD882FRueHD\nQ7XTBQvKHZWIiDQm6Votl5rZqoADVWa2DXA5cHshgxNpSpcuYbzHVVfBX/4SZr5MV/orIlKxkiQe\nZwJvAZ8QBpa+CYwHXgD+WLjQRPJjFhaWe/JJeP996N8fHnqo3FGJiEguSQqIzXf3I4DewB7ACGBD\ndz/E3RcVOkCRfA0eDJMmhSqne+wRpt1qyq2ISGVJVMcDwN0/cfeHgfuBzwsXkkhyK68cqptecUV4\n/DJoEEybVu6oREQkLe/Ew8z2NLPDs9rOAn4AZpnZY2a2YoHjK6lUKkV1dTW1miLRopmFgabPPx/q\nfPTvDzfcoGqnIiKFVFtbS3V1NalUKtZ5ea9OG61Ke6+7XxN9vTXwLPAHYCpwIfAfdz8pVgQVQKvT\ntl7ffQcnnww33RQKjt10UyjBLiIihRF3ddo4j1r6EgaQpu0HPO7uF7r7/cDJhAXjRCrG8svDjTfC\nww/D66+QYzldAAAgAElEQVTDxhvDbbep90NEpFziJB7LAZkFqgcBT2R8/QawWiGCEim03XYLlU6r\nq+Hww2GnneDtt8sdlYhI2xMn8ZgO9AEws2UJa7Nk9oB0B+YULjSRwlpxxVDz4+GH4YMPoF8/+MMf\nYO7cckcmItJ2xEk8/glcaWaHADcCXwD/zdi/GaC/IaXipXs/Tj0VLrkkPH659149fhERKYU4icf5\nwCvAVcAmwIisuh01wIMFjE2kaDp3hgsuCOM+NtgA9t8ftt46zIQREZHiyTvxcPe57n6ou6/o7n3c\n/dms/du7+6WFD1GkeDbYIDx6GTcOfvop1P3Ye2+YPLnckYmItE6JC4iJtCZDh8Krr8Idd4Tqp/37\nhwRkQpMTw0REJI725Q6gkkz9aqpqsLZxGw2Ff2wbekFuvgUG7gFbbAE1NbDNNlDVglL15Toux3rd\n1yt3GCIi9SjxyDDi/hH1h8tK2/ar8PIS8NJUQpm8Fuad495R8iEiFUWJR4Y797mTPv36lDsMqTDu\nYcxHbW1YAdcsLEi3556hF6R9Bf5XNPWrqYx4YATfz/++3KGIiNRTkP9lmtkK7j6rENcqpz49+jCg\nl0qmy9IGrga/3hW++gruugtuvRVOuissSlddDcOGhaJknTqVO1IRkcoW+4m1mZ1mZgdmfH0P8I2Z\nTTez/gWNTqTC9OgBJ5wAEyeGQai/+Q0891xIPlZeOQxIveYaeOst1QUREcklyVC53wGfAJjZTsBO\nwG7Af4DLCheaSGXr3z8UIHv7bXjzTTjzzLAabioFffqExegOPhiuugpefFEVUkVEINmjll5EiQew\nB3CPuz9mZh8SxuFVBDPbA7gcMGC0u99c5pCkFevTJ2xnngk//hh6QZ54Ap55JlRFnT8/jAXZeGMY\nMAA22ihsffrAz37WsmbLiIg0R5LE41tgTULysStwdtRuQLsCxdUsZtYOuALYFvgeqDOz+1rDOBSp\nfF27wi67hA1C0vH66/DKK2GbNAnuvhvmRCsbdekC664La68Na61Vf+vVKzzeWWaZsn07IiIFlSTx\nuB+4y8zeJSwM95+ofRPgvUIF1ky/BKa4+xcAZvYwsAvwj7JGJW1Sx44wcGDYjjoqtC1eDJ98Eh7R\nvPkmvP8+fPhh6CX56KPQa5JphRVglVXqbyuuCN26hW355Ze879YNPl8Qzps7L9xLPSoiUimSJB4p\n4ENCr8ep7v5D1N4LuLZAcTXXaoTVdNM+A1YvUywiS6mqWtKrsdtu9fe5w8yZIQH54gv48sv624wZ\nMG0azJoFs2eHbfHirBv0An4Hg7YBPg89Jp06hTVqOneu/75z57C/Q4ewtW8ftvT7XG3Z+9u1C99T\nejOr/1qo92b1t+y2JMdU0nVF2oLYiYe7LyCMnchuv7IQAZnZYOD3wEDC/z6HufvYrGOOBU4BVgVe\nA0a5+yuZh+QKvRDxiRSbGXTvHrZ8uIfHNukkZPZsePUzGPV6WAivF2Fga+Y2b97SX//0U+hpWbgQ\nFiwIr9nvG9u3eHGIZfHiJe81sye+5iY07dqFXraOHUNCmc9r586w7LJhW265+q/p98stF2ZuLbec\nkiRpntiJh5kdBnzt7g9FX48GjgTeBGrc/aNmxtQVmATcAtyX4/4HEsZvHAm8TOiBedTM1nf3r6PD\npgNrZJy2OhU08FWkkMzCuJKuXWG11UJbx8+B1+FXv4IBvcoXWzr5SCck2YlJnPeZyUyu6+b7dSmP\nKcd1Fy0K44rmzw/JZPbrTz+FRHP27CVtc+fCDz+E7fvvQ1tDOnQICUj37uF15ZXDo7811liyrbkm\nrL56SGhEsiV51HImcDSAmW0FHAecSJjhMgbYpzkBufsjwCPR9XPl1Sngene/PTrmKGB3YCQwOjrm\nZaCvmfUiDC7dFTi/OXGJSHyZjxDaVcTQc8nHggWh9+v775ckJLNnh+niX3+95DW9vfMOfPppeESY\naeWVw8DpDTaADTcM2wYbhLYOHcrzvUn5JUk81mTJINJhwL3ufoOZPQ88XajAcjGzDoRHMBel29zd\nzWwcsFVG2yIzOzmKx4BL3f3bYsYmItJadOgQBjSvsEK88+bMCQnIp5+GwdMffwzvvQdTp8K//hWS\nFwhjjPr1CwOuBwwIr337hsc+0volSTx+IMxm+RjYmdDLATAPKHbH2sqEKbszstpnABtkNrj7v4F/\nx7l4KpWiW7du9dpqamqoqamJH6mISBvTpQusv37YsrmHgdFvvQWvvQZ1dTB+PFx/fXhM1LEj/PKX\nsO22Ydtmm3A9qSy1tbXU1tbWa5udzijzlCTxeBy4ycwmAusDD0XtfQmzXcrBKMDg0TFjxjBggNZq\nEREpNDNYddWwbbfdkvYffwyJyCuvLElELrww9IrsvHNYhmCPPcJjGym/XH+MT5gwgYEDB+Z9jSSz\n+48FXgR6APu6+zdR+0CgtsGzCuNrYBHQM6t9FZbuBRERkQrXtStsvXVYA+m++8KU8SlTwoysb76B\nkSNDsrLHHmH//PnljliaK8l02lmEAaXZ7ecWJKLG773AzOqAocBY+N8A1KHAVc29fvpRix6viIiU\nh1kY79G3L5xySqhl88ADYUXo/fYLs2kOPhh+/WvYZJNyR9u2pR+7xH3UYp5gor2ZrQD8BuhDeMQx\nFbjZ3ePdPfe1uwLrEh6fTABOAp4CZrr7J2Z2AHAbYbG69HTa/YAN3f2rhPccANTV1dXpUYu0ChM+\nn8DAGwZSd2QdA3rpd1pahzffhL/9De64I4wX2WQTOPFEGD5cs2TKKeNRy0B3n9DU8bEftZjZZsD7\nhA/8lQgDPlPA+9EHeHNtBkwE6ghJzRWEBOQ8AHe/BziZMD12ItAP2CVp0iEiIi3DRhvBZZeFGTNj\nx4aaIYcfHqbn/uUvS9Y/ksqWZIzHGMJjjrXdfR933xtYhzCDpNnVS939GXevcvd2WdvIjGOudfe1\n3b2zu2/l7q82974iItIydOgAe+4JDz4YFmAcPDj0fKy9NlxxRSiIJpUrSeKxGaEuxsJ0Q/R+dLSv\nxUqlUlRXVy81VUhERCrTxhvDnXfCu+/CsGFw2mmw3nrhkcxSaxhJQdXW1lJdXU0qlYp1XpLE4zvg\nZzna1yRUCW2xxowZw9ixYzWwVESkhendG264IRQrGzw4zIbZemt4Vf3hRVNTU8PYsWMZM2ZM0wdn\nSJJ4/AO42cwONLM1zWwNMzsIuIniT6cVERFp0HrrQW1tqAkyZ04oSnbUUWFqbjE9/HBYNVqaliTx\nOAW4H7idUDDsI+BW4F7gtEIFJiIiktTgwTBhAlx5ZUhE1l8/FCdbtKjw93KH3XcP406kabETD3ef\n7+4nACsCmwCbAiu5e8rdG1nTUEREpHTat4fjjw+L2FVXh56PLbaAlwq8VvmsWeH1zTcLe93WKlYB\nMTNrT1iTZRN3nwK8XpSoykQFxKS1mfrV1HKHIFIRRl0E2x4El1wCW+4dan+MGlWYhemmTQN6hfcT\nPm/+9VqKRx54hEf/9SjffxdveGfsAmJmNg3Y291fi3ViBVMBMWlt3v3mXda/OsdKXSIihfYZcAOQ\nZwGxJIvEXQhcZGaHuPvMBOeLSJGt13093jnuHb6f36InmokUzdSpcMYZ8O23cNnl8MvNk1/r4Yfh\nnHPC+xdfLEwvSksydfJURtwwIu/jkyQexxFKmn9mZh8BP2budHd1GYhUgPW6r1fuEEQq1oBesPuj\ncMABcNw+cO65IRFpn+BT8YmZQPSIZfUqWLNXQUOtfDEfLyVJPP6V4BwREZGKssIKobfi/PPh//4P\nHn0U/v53WGuteNf54ov679dcs6BhtjpJVqc9rxiBVAINLhURaVvatw+Jx847w4gRMHAg3HMP7LBD\n/tf4/HPYYAN4++36SUhrV/TVac1sRWAEcJu7f5e1rxtwaK59LYEGl4qIyMyZcNBB8OSTcPnlcMIJ\nYNb0edtvDz16wH33wXXXwRFHFD/WSlLM1WmPA4bkSizcfTYwGBgV43oiIiIVY6WVwqOXVCpshx+e\n34Jzn30WVsrt0aNt9XgkFSfx2Be4rpH91wP7NS8cERGR8mnfHi67LCw8d889MGQIfPJJw8cvXgwf\nfRRWxl11VSUe+YiTePwceLeR/e9Gx4iIiLRoBx8Mzz8PM2bAZpvBs8/mPu6LL+Cnn2CddZR45CtO\n4rEIWK2R/asBWoRYRERahQEDwuq2ffqEwabXXRfWZcn0/vvhVT0e+YuTeEwEhjWyf+/omBYrlUpR\nXV1Nba0W2RUREVhlFXj8cTj66LCdcUb95OPFF6FLF9hww7aXeNTW1lJdXU0qlYp1XpzptFcDd5vZ\np8Bf3X0RgJm1A44BUsDwWHevMGPGjNGsFhERqadDB7jqKujdOww6nTQJLr0U+vWDsWNh0KBwTDrx\ncM9vNkxLly49kTGrJS95Jx7ufp+ZjQauAi6M1mxxwriOZYHL3P3emHGLiIi0CCeeGMZynHoqbLpp\nSETefz8kHxASjzlz4IcfYLnlyhtrJYvzqAV3PwvYEriVsCzMF8DfgK3c/fSCRyciIlJB9toLpkyB\nW2+FHXcMlU733DPsWy0aBfnxx2ULr0VIUrn0ZeDlIsQiIiJS8Tp0gEMPDVumfv3Ca10d9O1b+rha\nilg9HiIiIpLbCiuE0ukvvVTuSCqbEg8REZEC2WGHUP00n9VI3nwT+veH6dOLH1clUeIhIiJSIPvs\nAx9+2HDBsUwPPACTJ4e1YdoSJR4ZVMdDRESaY4cdwviOSy5p+tj0oq6ff97wMW+9BfPmFSa2QitF\nHQ8AzGwdoL27v5vVvh6wwN0/jHvNSqE6HiIi0hxVVXDmmaHk+hNPwNChDR+bnv3S2KOWPn3CINbb\nbitsnIWQtI5Hkh6PW4Gtc7RvEe0TERFpsw46KCwud+SR8OOPDR+XXnzus88av15dXeFiqwRJEo9N\ngedztP8X2KR54YiIiLRsVVVw442hiunvftfwQNN0j0dm4vHll/D998WPsZySJB4O5KrJ1g1o17xw\nREREWr7114ebbw4FxkaPXnr/woUh4VhppfqJx6abhnVfWrMkicd44IxojRbgf+u1nAE8V6jARERE\nWrKDDoKzz4bTT4dbbqm/7/PPYfFi2HLLkHgsjtZ2/+yzph+9tHSxB5cCpxGSj7fNLD1haDCwPLBD\noQITERFp6c4/H776Co44IvRuDIvWeJ82Lbxuu22o+/H557D66uWLs5Ri93i4+5tAP+AeYBXCY5fb\ngQ3dfUphwxMREWm5zOCaa2C//eDAA+Ff/wrtdXXQuTPstlv4Op2ItAVJejxw98+AMwsci4iISKvT\nrh3ccQeMGBEKjF1wAfy//wdbbw3rrhuOmTYNBg8ub5ylklfiYWb9gCnuvjh63yB3n1yQyMoglUrR\nrVu3/81NFhERKYSOHeHuu0ONj3POCT0h//536PVYe+1QwTSfMuuVpLa2ltraWmanK6HlyTyP79TM\nFgOruvuX0XsHLMeh7u4tbmaLmQ0A6urq6lRATEREiuqzz+Cnn2CddcLXBx0En34Kjz8OXbqEtvRH\ns1mohDqlggcyZBQQG+juE5o6Pt9HLesAX2W8FxERkQRWW63+11tuCWecUX82y7x50KlTaeMqlbwG\nl7r7Rx51jUTvG9yKG66IiEjrsssuIdG4664lbbNnL+n1mD8/1ANpaY9iGpJokTgz28DMrjazJ8xs\nXPR+g0IHJyIi0tr16QP9+8Olly5pmzVrSaLx7rthYOrzuWqGt0CxEw8z2xeYAgwEXgMmAwOAKdE+\nERERieGUU+qv65KZeKQ1tu5LS5JkOu1o4GJ3/0Nmo5mdF+27rxCBiYiItBXDh4eejQ4dwqyXDz+E\n7LkO+TxqmTs3rBXz3XfQo0dRQm22JI9aehEKhmW7M9onIiIiMVRVwXnnhRLrq64Kb7yxpIx6WlOJ\nR3pWTKdOsMoqxYu1uZIkHk8TSqRnGwQ8m6NdRERE8rT55jBu3NKJRmOJx7ffws47FzeuQknyqGUs\ncKmZDQT+G7VtCewPnGtm1ekD3X1s80MUERFpOw49FPbfH159tX77GWfAr36V+5z584sfV6EkSTyu\njV6PibZc+yAUGWtxxcRERETKaa+9YMMN4fjj67dPbrF1weuLnXi4e6IpuCIiItK0Dh3g6qthxx3L\nHUlxKIkQERGpMEOHwrBh5Y6iOJIWENvWzB40s/fM7F0zG2tmbWRdPRERkeKrrc3/WMu1elqFSlJA\nbAQwDpgDXAVcDcwFnjCz4YUNr7RSqRTV1dXUxvnXFhERKYJOneDee+u3LVhQnlhyqa2tpbq6mlQq\nFeu8vFanrXeC2VTgBncfk9V+EnCEu/eJdcEKoNVpRUSkUvXpA2+9Fd5/+CGstVb9/YccAnfeufR5\nixeXpick7uq0SR619AYezNE+Fq1cKyIiUlAvvQR33BHeP/ro0vtzJR0ACxcWL6bmSJJ4fAIMzdE+\nNNonIiIiBbL88mGRuL32gtGj808oKumxTKYkdTyuAK4ys02AFwj1OgYBhwMnFC40ERERSTvvPNhk\nE7j9dhg5sunjKzXxiN3j4e5/BQ4CfgFcCfwZ2Bg40N2vL2x4IiIiAtC/P+y3H1x8ccPl0y+4AA44\nILzPTDzcl177pVwSTad19wfcfZC7d4+2Qe7+/wodnIiIiCxx9NHw3ntLl1NP22mnUHIdYObM8PXn\nn8NNN0G7dmH12nJLMp12czPbIkf7Fma2WWHCEhERkWxDhkDnzvBsA0uy9uoFPXuG93ffHRabW201\neOCB0DZnTmnibEySHo9rgDVztK8e7RMREZEiaN8eNt4YpkwJPRnZi8Otuiqst154P336kvaYlTOK\nKknisRGQa57uxGifiIiIFMnPfgZ//zussQZslvWcoWNH6NYNVl8d3nxzSXs68aiECqdJEo+fgJ45\n2nsBFTprWEREpHVYY43Q09GhA3zzTe5jdt4ZnntuydctvcfjMeBiM+uWbjCzFYCLgMcLFZiIiIgs\nbc1osMOwYfDii0vaM3s/tsgaifnZZ8WPK19JEo9TCGM8PjKzp8zsKeADYFXg5EIGJyIiIvX17h1e\ne/YMj10OPjh8/cQTS45ZYYX650yZEl4roecjSR2P6UA/4FTgTaCOUDjsF+6uyqUiIiJFlB482ida\nGe3OO0NCsfzyS45Zdtnc5y5aFMZ5/OUvxY2xMUkql+LuPwI3FDgWERERacLGG8Mzz8CgQQ0fs9xy\nudvTRcVuvRVGjSp4aHlJUsfjMDPbPePr0WY2y8xeMLO1Gju3lMzsfjObaWb3lDsWERGRQhoyBKoa\n+QRfaaXc7T/9FF7LObslyRiPM4G5AGa2FXAc4bHL18CYwoXWbH8GDil3ECIiIqXWo0fu9nTiUU5J\nEo81gfei98OAe939BuAMYHChAmsud38G+KHccYiIiJRajx5w2GFLt28UVdtqaT0ePwDdo/c7A+Oi\n9/OAzoUISkRERJKrqgrjON59N/f+RYtKGk49SRKPx4GbzOwmYH3goai9L/BhkiDMbLCZjTWz6Wa2\n2MyqcxxzrJl9YGZzzey/ZrZ5knuJiIi0Feuum7t94sTSxpEpSeJxLPAi0APY193TddMGArUJ4+gK\nTIquvdQsYzM7ELgCOBfYFHgNeNTMVs445hgzm2hmE8xsmYRxiIiISBHFnk7r7rMIA0qz289NGoS7\nPwI8AmCW88lTCrje3W+PjjkK2B0YCYyOrnEtcG3WeRZtIiIibdJ998G++y7dPnMm3HtvKEK2666l\niydRHY+oRPpvgD6EHoqpwM3uPruAsaXv1YHQm3JRus3d3czGAVs1ct7jhEJnXc3sY2B/d3+p0PGJ\niIhUsn32gU8/DWu8ZOrefcn7UlY0jZ14mNlmwKOEKbUvE3oUUsCZZrazu+daubY5VgbaATOy2mcA\nGzR0krvvFPdGqVSKbt261WurqamhpqYm7qVEREQqxuqrF+Y6tbW11NbWH1Uxe3a8PgfzmGmOmT1L\nmE57hLsvjNraAzcBvd19SKwLLn39xcAwdx8bfd0LmA5sldljYWajgUHuvnVz7hddawBQV1dXx4AB\nA5p7ORERkYozbRpcdx1cdtnS+5rT4zFhwgQGDhwIMDCfzockg0s3Ay5NJx0A0fvR0b5C+xpYBPTM\nal+FpXtBREREJIfevWH06Ib3pcupF1uSxOM74Gc52tcEvm9eOEtz9wWEheiGptuiAahDgRcKfT8R\nEZG25oMP4Jtvmj6uEJIkHv8AbjazA81sTTNbw8wOIjxqSTSd1sy6mll/M9skauodfb1m9PWfgCPN\n7FAz2xC4DugC3Jrkfg1JpVJUV1cv9fxKRESktdhmm9ztvXrBlVfmf53a2lqqq6tJpVKx7p9kjEdH\n4DLgKJYMTl0A/BU43d1jV4I3s22Bp1i6hsdt7j4yOuYYwpowPQk1P0a5+6tx79XA/TXGQ0RE2ow+\nfeCtt3LvmzULunWDSZPCKrhHHQXLNFIdq+hjPNx9vrufAKwIbEIo6LWSu6eSJB3RNZ9x9yp3b5e1\njcw45lp3X9vdO7v7VoVKOkRERNqazIko995bf18qBXPmwHbbwYknQqdOhb13rMTDzNqb2UIz29jd\n57j76+4+2d3nFDYsERERKZbf/x66dAn1PbbKqoj1t79B1671k5NCipV4RLNXPibU1Wh1NMZDRETa\nglQKfvwx1PdYbrmGj0uXtho0CL7+uv6+Uo7x+A2wD3CIu8+MdXKF0hgPERFpqxYvhhVXhNtug+uv\nh0ceCY9X5s2DHXeEceOWHHvAAaHtiCOWtMUd45GkZPpxwLrAZ2b2EfBj5k531ye3iIhIC1FVteSx\nyq67wsKFcOCB8PDDUF0dSqv/4x9h/z33wEMP1U884kqSePwr+e1ERESkUqUHktbUhMRjm21g1Ci4\n++4wCPUPf4CpU0Pdj3XWSXaP2I9aWqP0o5YhQ4bQrVs3rc8iIiJt3nvvwbrr1m97/vkw3gNg++1r\n6dq1lu++m8348eMhz0ctScZ4bA5UZa/0amZbAIta4jRXjfEQERHJz8knw5/+FN63bw+XXTaBVKq4\na7VcQyiPnm31aJ+IiIi0UpddFmbFbLxxGA8Sc1JLosRjIyBXRjMx2iciIiKtVFVV6PF4/nm44IIE\n5ye4508svVIsQC9gYY52ERERaWWWXx5OPz3+eUkSj8eAi82sW7rBzFYALgIeT3A9ERERaYHM4p+T\nZDrtKcB44CMzmxi1bQLMAA5JcL2KkUqlNKtFREQkD7W1tVGl73i11RNNpzWzrsDBQH9gLjAZqHX3\nBbEvVgE0q0VERCQZswlAcSuX4u4/AjckOVdERETarkSJB4CZbQT8DOiY2e7uY5sblIiIiLQMZhDn\n4UnsxMPMegMPAL8AHEgPLUnftlWuXCsiIiJLi5t4JJnV8mfgA8KU2jlAX2AI8CqwXYLriYiISAtV\nFTOTSJJ4bAX8wd2/AhYDi939OeAM4KoE16sYqVSK6urqaJSuiIiINKS2tpbq6moWLYpXujTJWi3f\nEkauTjOz94HfuvtTZvZz4HV37xLrghVAs1pERESS6dhxAgsWFHdWyxSgHzANeAk41czmA0dGbSIi\nItJGxH3UkiTx+CPQNXr/B+DfwLPAN8CBCa4nIiIiLVTc6qWxEw93fzTj/XvAhma2EvCtJ6lGJiIi\nIi3WvHnxjk9cxyOTu88sxHVERESkdcs78TCzW/I5zt1HJg9HREREWrM4Q0IOB7YHVgBWbGRrsTSd\nVkREJD/p6bSbb16k6bRmdi1wEPAxcAtwZ2t5xKLptCIiIslMmDCBgQPzn06bd4+Hux8D9AIuBfYE\nPjGze8xsF7O4Y1pFRESkLYo1+9bdf3L3WnffCdgIeAO4FvjIzJYtRoAiIiLSeiQpmZ7mLFkkrjnX\nERERkTYiVsJgZsuYWY2ZPQ68TVih9jjgZ+7+QzECFBERkdYjznTazMGlfwMOcvdvihWYiIiItD5x\nCogdRUg6PgC2BbbNNabU3fcpTGgiIiLS2sRJPG4njOkQERERSSTvxMPdDy9iHCIiItIGFGStltYi\nlUrRrVs3ampqqKmpKXc4IiIiFau2tpba2lpmz54d67y8K5e2ZqpcKiIikkzRKpeKiIiINJcSDxER\nESkZJR4iIiJSMko8REREpGSUeIiIiEjJKPEQERGRklHiISIiIiWjxENERERKRomHiIiIlIwSDxER\nESkZJR4iIiJSMko8REREpGS0Om0GrU4rIiKSH61O2wxanVZERCQZrU4rIiIiFUuJh4iIiJSMEg8R\nEREpGSUeIiIiUjJKPERERKRklHiIiIhIySjxEBERkZJR4iEiIiIlo8RDRERESkaJh4iIiJSMEg8R\nEREpGSUeIiIiUjJKPERERKRkWmXiYWZrmNlTZvaGmU0ys/3KHZOIiIhA+3IHUCQLgRPcfbKZ9QTq\nzOwhd59b7sBERETaslbZ4+HuX7j75Oj9DOBrYKXyRhVfbW1tuUPISXHFo7jyV4kxgeKKS3HlrxJj\nguLG1SoTj0xmNhCocvfp5Y4lrrb4C9kciiueSoyrEmMCxRWX4spfJcYEbSDxMLPBZjbWzKab2WIz\nq85xzLFm9oGZzTWz/5rZ5nlcdyXgNuCIYsQtIiIi8VRE4gF0BSYBxwKevdPMDgSuAM4FNgVeAx41\ns5UzjjnGzCaa2QQzW8bMOgIPABe5+0ul+CYKbfr0yuykUVzxKK78VWJMoLjiUlz5q8SYoLhxVcTg\nUnd/BHgEwMwsxyEp4Hp3vz065ihgd2AkMDq6xrXAtekTzKwWeMLd7ypu9MXTFn8hm0NxxVOJcVVi\nTKC44lJc+avEmKANJB6NMbMOwEDgonSbu7uZjQO2auCcbYD9gclmtjehF+UQd3+jgdt0Apg6dWoh\nQ2+2BQsWMGHChHKHsRTFFY/iyl8lxgSKKy7Flb9KjAnixZXx2dkpn+PNfaknG2VlZouBYe4+Nvq6\nF8a6Xo0AAAuHSURBVDAd2CrzkYmZXQoMcfecyUfMew4H/t7c64iIiLRhB+fzlKHiezwaYeQYD5LQ\no8DBwIfAvAJdU0REpC3oBKxN+CxtUktIPL4GFgE9s9pXAWYU4gbu/g3QYseCiIiIlNkL+R5YKbNa\nGuTuC4A6YGi6LRqAOpQY36iIiIiUX0X0eJhZV2BdwuMTgN5m1h+Y6e6fAH8CbjOzOuBlwiyXLsCt\nZQhXREREEqqIwaVmti3wFEuP2bjN3UdGxxwDnEp45DIJGOXur5Y0UBEREWmWikg8REREpG2o+DEe\nlcLMPjSzSVF11CfKHU8mM+scxTe63LEAmFk3M3slqiI72ez/t3fvsXKUdRjHv49QKlLul3K/1IJG\nQJBSJFQEJARFoQQBJQZQQYNATLykKVUgUdREETXExJByKQFBBKyEVBsKlIsFobRSWqWArRTkViy0\npRQKPT//eGdPp8spPbtnduft6fNJNuecncs+Z3bm3V/eeWdW59adCUDS7pLulTSveC9PrTsTgKTb\nJS2RdEvdWRokfUHSk5LmSzqn7jwNuW2rjPepLI/BhtzaLMi3jZe0t6R7in3scUmb15xnv9JdwmdL\nerOvrzl533W4x6N/JC0A9o+IlXVnaSbpMtIYmUURMS6DPAKGRsRbxUEyDxgVEa/VnGtnYKeImCNp\nOGnQ8r51v6fFqcZhwNkRcXqdWYo8mwD/BI4ClpO20+ER8XqtwchyW+W6T2V5DDbk1mZBvm28pOnA\nhIiYIWkbYFlE9NQcC+gdn7kQ2KuV7eYej/4TGW4vSSOBjwBT6s7SEEnjfiiN6ryvW+F3VUS8FBFz\nit9fJl2qvV29qSAi7gPeqDtHyWHA3GJ7rSDtW8fXnAnIb1tlvE9leQxCnm1WIbs2XtLHgFURMQMg\nIl7PpegonET6apKWirWsNnLmeoDpkv5e3Ok0F5cDF5FJo9JQdPX+A1gE/CIiltSdqUzSKOADEZHn\nFyXUa1fS3YIbXgB2qynLBiO3fSrjYzDLNos82/h9gRWS/ixppqSL6g7U5HTgD60uNCgLD0lHSrpD\n0n8l9fR1/knSBZIWSlop6WFJo9ez2jERMRoYC0yQtH/duYrl50fEM42nWs3UiVwAEbE0Ig4G9gG+\nImnHHHIVy2wHTAK+kUumqlSUr6/9aEDnZHPcblVmGsg+1alcVRyDVeeqqs2qOldhwG18B3INAT4F\nfAs4AjhO0rHN6+lypsZ8WxaZWu65GpSFB7AF6ZLbC+ijwZT0JeCXwKXAJ4DHgamSdijNc77WDKAZ\nGhEvQepaJW3oUXXnIp2D/7LSucnLgXMl/bDuXJKGNp6PiMXAHODIHHJJ2gz4E/DT8nf/1JmpjQwd\nzUfq7di99PduwIsZ5KpaJZkq2Kc6kqthgMdg1bkOp5o2q+pcVNTGV53reeDRiHghIlYVuQ6uOVPD\nWGBqkas1ETGoH6Tus5OannsY+E3pb5He4HHrWMeHgGHF78OAmaSBWrXmalr2bODnmWyv4aXttTXw\nBGnQVu3bC7gJuCSXfas039HAH6vINdB8wCbAfGCXYn//F7Bt3bly3FZV71MVvoeVH4NVvofF9Era\nrIq2V+VtfEW5NiENWN6a1FFwB3BCDu9hkeXz7bz2YO3xWCdJQ0iVbO/lUpG24jRgXd90Oxx4UNJs\n0m3ar4uIxzLI1XFt5toTeKDYXveRduR5deeSNAY4DTi51OMwoO7UgWYqlruLdJ70c5IWSfpkVZna\nyRcRq4HvAdOBWcDl0cGrIVrZbrltq07vU+3mogvHYJu5uqqFXB1v49vJVRyLE4AHSD0VT0VERwbl\ntngcbgWMpp9fCtcsi1umd9kOpCqy+QvmXiaNtH6PiFjIwLq3OpKrLCImdSIU7W2vR0nddJ3UTq6/\n0dl9vq33MCKO62Cmsn7ni4g7gTszzJXVturCPtVurm4cgy3nKutgm1XW3+3VjTa+5VwAETGVNj/g\nO5hpGalHtC0bXY/H+xADHEDXIc7Vmhxz5ZipLNd8OebKMRM4V6ucq/8qz7QxFh6vAqtJXWtlO/He\nSq+bnKs1OebKMVNZrvlyzJVjJnCuVjlX/3Ut00ZXeETEO6TBOr2XJElS8fcM53KuwZSpLNd8OebK\nMZNzOddgyTQox3go3cZ1JGuuER8h6SBgSUQ8B1wBTJL0GPAI8B3SqObrnMu5NrRMG0K+HHPlmMm5\nnKuTubLJVMVlObk9SPe36CF1G5Uf15TmOR/4D7ASeAg41Lmca0PMtCHkyzFXjpmcy7k2hkz+kjgz\nMzPrmo1ujIeZmZnVx4WHmZmZdY0LDzMzM+saFx5mZmbWNS48zMzMrGtceJiZmVnXuPAwMzOzrnHh\nYWZmZl3jwsPMzMy6xoWHmZmZdY0LDzMblCRdKqlH0mpJ3+7A+hcW6++RtFXV6zcbrFx4mBmSri19\nSPeUfp9Sd7YBmgvsDFzVn5klfVfSEkmb9TFtc0lLJV1YPHUo8EXAX3hl1gIXHmbW8BfSh3TjsQtw\nRidfUNKQTq4feDciFkfEW/2c/3rS14Cf0se004AhwA0AEfE/YEklKc02Ii48zKzh7eJD+pXSY2lj\nYtELco6k2yWtkPSUpBPLK5B0gKQpkpZLeknS9ZK2L02/V9KVkn4laTHw1+L5j0p6UNJKSXMlHVu8\n3knF9LslXdn0WjtIelvS0a38k5K2ljRR0itFD8Y0SR8HiIhXgTuBr/ex6NeAyRHxeiuvZ2Zrc+Fh\nZq24BLgZOBCYAtwoaRtIH+jA3cBjwCHA8cBOwC1N6zgLeBs4AjhPkoDJwHJgNPBN4CesfQpjInBG\nUw/JmcDzETG9xf/hVmD7It8hwCxgWuP/AK4GPiNpj8YCkkYAny5ymNkAuPAws4YTi56KxmOZpPFN\n81wbEbdExAJgArAFcFgx7UJgVkRcHBFPR8TjwLnAMZJGltbxTESML+Z5mlQA7AOcFRFzI2IG8ANA\npWVuK36OLT13NnBtK/+gpDGksRmnR8TsiPh3RIwDlgKnFrNNBV4k9XA0fBV4NiLuaeX1zOy9Nq07\ngJll4x7gPNb+wG8ew/BE45eIeFPSclKvBsBBpJ6C5U3LBPBh4Jni75lN0/cDnouIxaXnHllrBRGr\nJN1AOgVyq6RDgAOAtU719MNBwJbAktTR0uuDRUYiokfSJFKx8aOiR+YsUk+ImQ2QCw8za1gREQvX\nM887TX8Ha3pOhwF3AONYu3iB1IPQ+zpN00T/rgyZCMyWtCupN+LuiHiuH8uVDQNeAI7qI2N57MY1\nwHhJx5Dayd2B61p8LTPrgwsPM6vKLNLVIM9GRE8Lyz0J7Clpx1Kvx2HNM0XEXEkzSWNAzgDObzPj\nzsDqiFi0rpkiYoGk+4FzSAXKtDaKHDPrg8d4mFnDUEnDmx7br3+xXr8FtgNulnSopBGSjpd0jZrO\nazS5C1gAXC/pwGIcxmWkXpDmnpCrgfGkYmByC9kAiIhpwEPAZEnHSdpL0hGSLitO3zS/1inAyfg0\ni1llXHiYWcNnSachyo8HStP7Oh3S+1xEvAiMIbUrU4E5wBXAaxERzfOXlushDRrdgjS24yrgx6Ti\novn+GzcB7wI3RsSq1v69XicA95NOp8wHfg/sCbzcNN9tpKtvVtBGkWNmfdOa9sDMLA9Fr8f9wMjy\nuBNJe5MGqY4qrpp5v3VcCoyNiOaejCpzHk26hHjbiFjWqdcxG0w8xsPMaifpZOAN4GlgX+DXwION\nokPSpsCuwM+Ah9ZXdJQcKGkZMC4ifldx5rnACHzLdLOWuMfDzGon6UzgYtLVI6+Sxn18PyJeK6Yf\nBdxLGoh6WkTM68c6tyGNOQFYHBHNl/kONPMepFuoU9zXxMz6wYWHmZmZdY0Hl5qZmVnXuPAwMzOz\nrnHhYWZmZl3jwsPMzMy6xoWHmZmZdY0LDzMzM+saFx5mZmbWNS48zMzMrGtceJiZmVnX/B8eGJ8j\nuCVeMgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f74b8046160>"
+       "<matplotlib.figure.Figure at 0x7fc8f40657f0>"
       ]
      },
      "metadata": {},
@@ -1117,7 +1117,8 @@
     "# First lets plot the fuel data\n",
     "# We will first add the continuous-energy data\n",
     "fig = openmc.plot_xs(fuel, ['total'])\n",
-    "# We will now add in the corresponding multi-group data\n",
+    "\n",
+    "# We will now add in the corresponding multi-group data and show the result\n",
     "openmc.plot_xs(fuel_mg, ['total'], plot_CE=False, mg_cross_sections='./mgxs.h5', axis=fig.axes[0])\n",
     "fig.axes[0].legend().set_visible(False)\n",
     "plt.show()\n",
@@ -1130,7 +1131,7 @@
     "plt.show()\n",
     "plt.close()\n",
     "\n",
-    "# Then finally repeat for the water data\n",
+    "# And finally repeat for the water data\n",
     "fig = openmc.plot_xs(water, ['total'])\n",
     "openmc.plot_xs(water_mg, ['total'], plot_CE=False, mg_cross_sections='./mgxs.h5', axis=fig.axes[0])\n",
     "fig.axes[0].legend().set_visible(False)\n",
@@ -1186,8 +1187,8 @@
       "         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 | f76ee0867ca2f46c4e84a3cda6511b0ab64eb6a5\n",
-      "         Date/Time | 2016-11-20 20:12:55\n",
+      "          Git SHA1 | 346deb258f969a2522bef0774ae1576043ac0de7\n",
+      "         Date/Time | 2016-12-02 18:12:18\n",
       "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
@@ -1253,15 +1254,15 @@
       "       39/1    0.98827    1.02090 +/- 0.00289\n",
       "       40/1    1.01740    1.02079 +/- 0.00279\n",
       "       41/1    1.02920    1.02106 +/- 0.00272\n",
-      "       42/1    1.02496    1.02118 +/- 0.00263\n",
-      "       43/1    1.04288    1.02184 +/- 0.00264\n",
-      "       44/1    1.03749    1.02230 +/- 0.00260\n",
-      "       45/1    1.04338    1.02290 +/- 0.00259\n",
-      "       46/1    1.03146    1.02314 +/- 0.00253\n",
-      "       47/1    1.04668    1.02377 +/- 0.00254\n",
-      "       48/1    1.02707    1.02386 +/- 0.00248\n",
-      "       49/1    1.02589    1.02391 +/- 0.00241\n",
-      "       50/1    1.02100    1.02384 +/- 0.00235\n",
+      "       42/1    1.02541    1.02119 +/- 0.00263\n",
+      "       43/1    1.01457    1.02099 +/- 0.00256\n",
+      "       44/1    1.00618    1.02056 +/- 0.00252\n",
+      "       45/1    1.03521    1.02098 +/- 0.00248\n",
+      "       46/1    1.01586    1.02083 +/- 0.00242\n",
+      "       47/1    1.03337    1.02117 +/- 0.00238\n",
+      "       48/1    1.01726    1.02107 +/- 0.00232\n",
+      "       49/1    1.03974    1.02155 +/- 0.00231\n",
+      "       50/1    1.04169    1.02205 +/- 0.00230\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -1271,27 +1272,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  5.1234E-02 seconds\n",
-      "   Reading cross sections          =  4.1626E-03 seconds\n",
-      " Total time in simulation          =  8.7422E+00 seconds\n",
-      "   Time in transport only          =  8.1461E+00 seconds\n",
-      "   Time in inactive batches        =  6.9527E-01 seconds\n",
-      "   Time in active batches          =  8.0470E+00 seconds\n",
-      "   Time synchronizing fission bank =  5.3647E-03 seconds\n",
-      "     Sampling source sites         =  3.6250E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.6626E-03 seconds\n",
-      "   Time accumulating tallies       =  1.2803E-04 seconds\n",
-      " Total time for finalization       =  3.1250E-06 seconds\n",
-      " Total time elapsed                =  8.8120E+00 seconds\n",
-      " Calculation Rate (inactive)       =  71914.1 neutrons/second\n",
-      " Calculation Rate (active)         =  24854.1 neutrons/second\n",
+      " Total time for initialization     =  4.3487E-02 seconds\n",
+      "   Reading cross sections          =  3.0800E-03 seconds\n",
+      " Total time in simulation          =  8.6325E+00 seconds\n",
+      "   Time in transport only          =  8.0901E+00 seconds\n",
+      "   Time in inactive batches        =  6.8907E-01 seconds\n",
+      "   Time in active batches          =  7.9435E+00 seconds\n",
+      "   Time synchronizing fission bank =  5.1527E-03 seconds\n",
+      "     Sampling source sites         =  3.5060E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.5677E-03 seconds\n",
+      "   Time accumulating tallies       =  1.1610E-04 seconds\n",
+      " Total time for finalization       =  3.1340E-06 seconds\n",
+      " Total time elapsed                =  8.6927E+00 seconds\n",
+      " Calculation Rate (inactive)       =  72561.5 neutrons/second\n",
+      " Calculation Rate (active)         =  25177.9 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     =  1.02316 +/-  0.00205\n",
-      " k-effective (Track-length)  =  1.02384 +/-  0.00235\n",
-      " k-effective (Absorption)    =  1.02372 +/-  0.00194\n",
-      " Combined k-effective        =  1.02369 +/-  0.00173\n",
+      " k-effective (Collision)     =  1.02178 +/-  0.00213\n",
+      " k-effective (Track-length)  =  1.02205 +/-  0.00230\n",
+      " k-effective (Absorption)    =  1.02440 +/-  0.00198\n",
+      " Combined k-effective        =  1.02354 +/-  0.00179\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -1373,8 +1374,8 @@
      "output_type": "stream",
      "text": [
       "Continuous-Energy keff = 1.024739\n",
-      "Multi-Group keff = 1.023689\n",
-      "bias [pcm]: 105.1\n"
+      "Multi-Group keff = 1.023545\n",
+      "bias [pcm]: 119.4\n"
      ]
     }
    ],
@@ -1471,7 +1472,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f74b8f95400>"
+       "<matplotlib.text.Text at 0x7fc8f339d7b8>"
       ]
      },
      "execution_count": 41,
@@ -1480,9 +1481,9 @@
     },
     {
      "data": {
-      "image/png": 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ckgEREZGcUzIgIiKSc0oGREREck7JgIiISM4pGRAREck5JQMiIiI5p2RAREQk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\nAeef8FtX/KD9/+IuA+f98O/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IiIjknJIBERGRnFMyICIiknNKBkRERHJOyYCIiEjOKRkQERHJOSUDIiIiOddi\nHlRE/z1ggx6ZQl+4NltcQatbfA84AQjX+x6SUbWjuwiet2+64t8L3d1lnDTY9wSlJWes7orfYd83\nXPEAL7G7K/6qM892l3Gk3eeKPzP4HyCz68k/cMUPo78rfsYzm7niO0ya6YqvuJ0BzzPEvu6b/fd/\nP9IVfzUv+QoAOnf+oSv+ZnwPnwI45PjRrvi3b/PvA9prvj5yu6//zxU/+uFDXPEAtpszfqK7CJY/\n5uvnJ57pf+jddDZ0xW+3je+7XTg+W5+tkQEREZGcUzIgIiKSc0oGREREck7JgIiISM4pGRAREck5\nJQMiIiI5p2RAREQk55QMiIiI5JySARERkZxTMiAiIpJzSgZERERyruU8m+CFVrBGttxlzy7jXbOu\nOt/c1XkW342xd10w1l3GXsH3mUnW113GNaf/zBV/Pv/nK2CcP9+8qOdAV/wf8MUDhNm+eo1cZx93\nGX3w3TP+Bp73FdDFF85cZ3yltQXWzB5uC3yzv5djXfGn2Oe+AoDFoZ0r/scMdZcx8u+9XfE/Cne5\ny6Dvclf44/YTV/wLhzpXHnCCDXfF2wMNeCbDYb747ZjkLuML294VfzC+Z2r0tlnAHfXGlXVkwMwG\nmtny1Mv31AURaVHU7kWav0qMDEwA9gcKu+PLKlAHESkvtXuRZqwSycCyEMKMCpQrIpWjdi/SjFXi\nBMLuZvaxmU0ys7vNbNMK1EFEykvtXqQZK3cy8CJwInAQcBqwJfCsmbUvcz1EpHzU7kWaubIeJggh\njCr6c4KZvQx8ABwD3F7nh6cPgNYdq0/r0C++RKS6h4fBI9XPtl40f05FqtLgdv+3AdA+1eb37ge9\n1OZFSvl82OPMGfZktWnz5i7J9NmKXloYQphrZu8A3eoN3nAQrNGj6Sslsio4rF98FWn3v/HMO3yX\nClVohczt/uRBsJXavEhW6/Q7kHX6HVhtWu/xs7ihZ/3XSFb0pkNmthawFfBpJeshIuWjdi/S/JT7\nPgPXmFkvM9vczL4N3E+8xGhYOeshIuWjdi/S/JX7MMEmwFBgPWAGMAbYPYQwq8z1EJHyUbsXaebK\nfQKhzvwRyRm1e5HmTw8qEhERybmW86CiKW8ASzOFhk6+hwi1/ofvIRwAXfv+zRU/Zq293GW0/ay1\nK77Thge4y3i51a6u+JFhX1f8/3qe7ooHuHGe7+FJx3T4hruMiesc6Yrf1ia6y+i7fIgr/tXfn+Ar\n4F5feItHJljiAAAThElEQVRL/Z8DHF976OSbffc9J7jiD2O+rwCgv/PBQ+G1a9xl7Lmt70Fr02dv\n4S5j7garu+KHtn7UFf/ic75+BWB6r7Nd8Tud2stdxlh8V990acAg2Ek7vun7gPMK4e2/m+3BfS2t\nexAREZFGpmRAREQk55QMiIiI5JySARERkZxTMiAiIpJzSgZERERyTsmAiIhIzikZEBERyTklAyIi\nIjmnZEBERCTnlAyIiIjkXIt5NsFRr7xO5x7Znnh6+7zurnkbwV2f3/ErV/zH1sVdRp8N/uqKv4Gz\n3GUspJ0r/mZOdcU/+Kz/Xt0H9nrQFb/E2rjLqAq+5z70eMn/bILhHX/sij/lV7e64g844SlXPG8u\ngYN9H6moscAajvj1fbOfucT3gXvbHu0rAPiUjV3xw+ad5C5jU/vIFT94Y//zQvZntCt+A6a74hf0\n9P8UzbGOrvhjw9/dZTz19iGu+Iu2udRdhj2+yBUfbmrvK2CDbM+u0MiAiIhIzikZEBERyTklAyIi\nIjmnZEBERCTnlAyIiIjknJIBERGRnFMyICIiknNKBkRERHJOyYCIiEjOKRkQERHJOSUDIiIiOadk\nQEREJOdazIOKnmu1N6u32j5T7Okd/uKa93X2S3+F/v2IKzwcl+1hEcXGf7iH7wPfGe8uY88hZ7ri\n7WtVrviw0J9vLp3r+65W7+irE8BVDHPF99v6XncZbTr56rXfzb6HJ3FVW1/86qv74ittcYBl2R8i\nduDbvgdcjbTDXfFvhr1c8QDbTZ7s+0Cv5e4y/orvYTrj2MVdRl8ecMXff6+v3Yd7/f3jxsNnu+J/\nSS93GdbfV6+rxl3iLmP6Rr7fq3WumOOK32R8J4ZmiNPIgIiISM4pGRAREck5JQMiIiI5p2RAREQk\n55QMiIiI5JySARERkZxTMiAiIpJzSgZERERyTsmAiIhIzikZEBERyTklAyIiIjnXYp5NsH14k3XC\nwkyxf3rO96yBd3qNcNfnuN7fc8VP+XBLdxmDedcV3+WJ0e4yTufPrvjDq9ZwxR/b55+ueIDzudYV\nv++vnff0B+47Mfs97wH+0c23vgGOXNdXr3tmfd9XwOm+e5rTYZovvtJmfQEsyhz++GrdXbO/pOpi\nV/xndpYrHmCLLae44tuHU91lnPOpr//aq8tz7jJeD9u44mcfvbsrft9uL7riAV5hR1f8zjbFXcaz\nY33PcejZwd8XtZl/vSt+Az5zxbezNpniNDIgIiKSc0oGREREck7JgIiISM4pGRAREck5JQMiIiI5\np2RAREQk55QMiIiI5JySARERkZxTMiAiIpJzSgZERERyTsmAiIhIzrWYZxM8/Z8+MLNHptgzD7zG\nNe/2lv3+5wU7hAmu+BNnD3GXsXj2uq74i7v/2l3GrLCeK77dwiWu+MM7PuiKBxgberri913mv6/5\nU932dMX3veAhdxmXzfXFn2zP+D7w6r2++LfHQ9/LfZ+poE6vzGG1HrMyx8/83Xau+Y8z33Z2E2e4\n4gEO5V+u+AmXfMtdRtUVvvvh/7bK30+0a73YFT/bOf//7tTN+Qn41qa+Phj/Yx/o/MArvg9c0IAy\nwgxX/MV3XueK72/jM8U16siAme1tZg+Z2cdmttzMajzdxcwuN7NPzGyRmT1hZv6tQESaDbV7kZav\nsQ8TtAdeA84EajwWzswuBH4G/BTYFVgIjDKz1Ru5HiJSPmr3Ii1cox4mCCGMBEYCmJmVCDkHuCKE\n8HASczwwHTgCuKcx6yIi5aF2L9Lyle0EQjPbEtgIeKowLYQwD3gJ2KNc9RCR8lG7F2kZynk1wUbE\nIcTpqenTk/dEZNWjdi/SAjSHSwuNEscZRWSVpnYv0oyU89LCacQOYEOq7yVsALxa76dvHgDtO1Wf\ntk9f2Ldf49VQZFXx2DAYObzapEUL51SiJg1u9wvOu5xWHTtUm9a27/do2+/wxq6jyKrhxWHwYvV2\n/wzZ2n3ZkoEQwmQzmwbsD/wXwMw6ALsBN9U7g58Ogu7Z7jMgknt9+sVXkXZvj2de313KWo2Vafdr\nXXcJq/XYoekrKbKq2L1ffBXpbeMZekL97b5RkwEzaw90I+4JAHQ1s52A2SGED4E/Ab82s/eAKcAV\nwEeA/840ItIsqN2LtHyNPTKwC/A08VhgAK5Npg8BTgohXG1m7YCbgU7Ac0CfEILvtnYi0pyo3Yu0\ncI19n4FnqOekxBDCpcCljVmuiFSO2r1Iy9ccriYQERGRCmoxDypabdsF2E7zMsUe4TwUuX8Y46/Q\nJpe5wq/71PfQIQCrqnLF/+5Qf24Xjip1w7ja2YnLXfGnvOav08idevs+8AdfnQB2rWrr+0Br3/cE\nMNC5/t6yLV3xN+x0iit+etVCrnR9orK2tYl0tC8zx4/qtqlr/v8KR7rir7QXXPEAAxjk+8AV/m15\nnl3oin/O9naX0T1MdcUPbe1r95cM9LcvPvR9V8+08vdFvX/irNdvfG0e4FjnYzrWPn6+K77T+C4M\nzRCnkQEREZGcUzIgIiKSc0oGREREck7JgIiISM4pGRAREck5JQMiIiI5p2RAREQk55QMiIiI5JyS\nARERkZxTMiAiIpJzSgZERERyrsU8m+DUTrfQZb0NMsX+xU5zzXvQ8hHu+vxrN1/8GUcHdxnhr61d\n8TMfXstdRn/+7op//AxfnW4efLwrHmBTPnTFh9G+OgEs2a+dK956LXWXsfx6X71OPGe0K/7lVt9y\nxXfo8Bow3PWZSnpl7rew2Ttnjm978GzX/EdylCv+ZF5zxQNMoqsrPmzq35bHfuRbpz+xv7rLCJ19\n9Rp4g7OALxvQP87y1an3b91FYC/56rX8ZP/6O/C2d1zxf7OfuOIX2eqZ4jQyICIiknNKBkRERHJO\nyYCIiEjOKRkQERHJOSUDIiIiOadkQEREJOeUDIiIiOSckgEREZGcUzIgIiKSc0oGREREck7JgIiI\nSM4pGRAREcm5FvOgoolsyzS2yBR7/5D+rnkv28P/NYQRy30fuNSfd9176SGu+BPn3e4uY0THo13x\nkwdv6Iq/gx+74gFe+WRXV/yr++3kLmOHyye54sMlzvUNdFw4wxW/4KnOzhLec8bPdMZX1rLT14J2\nHTLHL13HN/8fPvQPV/zc9tkelFZso7HOdn+Cuwiu5heu+KXm7+8mffY1V/yU1h+74g/o7QqP9nI+\n3KiXuYsIv6pyxdsP/f386vgegvYRm7ji27B2pjiNDIiIiOSckgEREZGcUzIgIiKSc0oGREREck7J\ngIiISM4pGRAREck5JQMiIiI5p2RAREQk55QMiIiI5JySARERkZxTMiAiIpJzLebZBP9lB9rwjUyx\noZPvHtRndb/aXZ8b323tih808HR3Gedxkyv+6On+3G7iOr7vqmuV717dnexBVzzAZxuv74pfj4Xu\nMqZf0skVv85c3/oG2KnjKFf889vs7SyhmzN+njO+wj4zWM2xfTq/jvnv+Z410PoG573wgaP+OsQV\nf88uP3KXMZYfuOKfDb3cZdxiZ7nily3f0hU/OOzjigc43XzPYrGd/P3j8gG+dv/2PzZ3l/HJTN9z\nH06dfpcrvv+M8cBv6o3TyICIiEjOKRkQERHJOSUDIiIiOadkQEREJOeUDIiIiOSckgEREZGcUzIg\nIiKSc0oGREREck7JgIiISM4pGRAREck5JQMiIiI5p2RAREQk51rMg4pmDdsExnTNFryzb9732ffd\n9ZnffW1X/Hlc6y4j9PU9JGPW8PbuMrbr5XvIz+t83RXfz7sygOHWzxV/xmT/Q4Ru6HqxK37jjp+6\ny3j+zu+44s/80TWu+Jvo74qHmc74yvrZk1fTpUf2hwldNnega/4bdJzhip+62zaueIAxrfZ0xR+/\n/FZ3GXdf/k/fB55xF8FGT/nay0+cDw87fZDvgU4ALLjTFR6u8Bfxi+9d5op/wb7tLqPfekNd8b3W\ne84Vv9oX25KlhEYdGTCzvc3sITP72MyWm9n3Uu/fnkwvfj3amHUQkfJSuxdp+Rr7MEF74DXgTKC2\n530+BmwIbJS8fLuBItLcqN2LtHCNepgghDASGAlgZrU9iPzLEIJvfE5Emi21e5GWrxInEO5jZtPN\n7C0zG2xm61agDiJSXmr3Is1YuU8gfAwYAUwGtgJ+DzxqZnuEEGobXhSRlk3tXqSZK2syEEK4p+jP\nN83sDWASsA/wdJ0fvn8ArNmp+rQefaGnDj2K1PQA8GC1KYsWza9ITRra7h8Z8BxrdmpbbdqOfbdm\n535bN0U1RVq8l4ZN4eXhU6tNsznjM322opcWhhAmm9lMoBv1JQPfHwSb9ihLvURaviOS1wrt2r3J\nvHkHV6Y6RbK2+0MH7e26tFAk73brtwW79dui2rTVxm/LibtcUu9nK3rTITPbBFgP8F/ELSItktq9\nSPPTqCMDZtaemO0XzijuamY7AbOT10DiscNpSdxVwDvAqMash4iUj9q9SMvX2IcJdiEO+4XkVbjt\n3hDgDGBH4HigE/AJsTO4JISwtJHrISLlo3Yv0sI19n0GnqHuQw+VP2ApIo1K7V6k5bPmfmWPmfUA\nxrHrOOiQ8QTCg33LdOV557nrdfGFvmcN3HjVKe4yTp1zmyt+jUFV7jK6X/a6K76nZTsztaBrmOSK\nB7hy/ytd8Xs99YS7jDFvHuD7wJvuItj+mLG+Il7fxVfAN333Te/QYTbz5t0I0DOE4FuRZVRo87eO\n3ZZterTL/Lleb7/iK2iCL5xjfN83AO/Uf+JWNSNru2dTHc7+qy9+qL8vYpKvT7V9fPHLF/ifL8Ld\nzvje/iKWz/Wtj1PPu95dxi0vnOOKb9Ntniu+7zsTGNprD6in3euphSIiIjmnZEBERCTnlAyIiIjk\nnJIBERGRnFMyICIiknMtOxmYNqzSNaiIf4yodA0qZHo+1zeP5XS5S3hy2OxKV6FyxuZzOxhW943q\nV1nDnijvlX5KBlqg/CYDwytdg8oYmdPlLuHJ4TlOBsblczsY9u9K16Ayhj9Z3vJadjIgIiIiK03J\ngIiISM4pGRAREcm5xn5QUVNYA4CFE2u+s2wuzCtxd8WPfCdefDz+M3+tpvvu5vrh+JnuIl6dX3r6\n3Hnwaom7CIdP/XeY/WL8O6742faBK37NMM0VD8D8WpZj2ZyS7y0Y/66/jEnr+eIn+4tYPP4t3wfe\nqSU3XzAHJpb6TnxPAK6qmlv47xquD5bfGgAfvLW4xhsL51Tx9vhFpT81xbn9v++sVWjAE5ffdNbp\nwzpuf7t4DnxYYn7B1yaZ3IA7UX/i61PD27742lYpwNyFULKJz3IVAc6vCWD5At9yzBz/obuM8bV8\nV3MXlH4vLHrNNf/ZH3x1S/g6231LeDZBf+Dvla6HyCrk2BDC0EpXojZq8yJNos523xKSgfWAg4Ap\nwBeVrY1Ii7YGsAUwKoTg3a8qG7V5kUaVqd03+2RAREREmpZOIBQREck5JQMiIiI5p2RAREQk55QM\niIiI5JySARERkZxrkcmAmZ1pZpPNbLGZvWhm36p0nZqamQ00s+Wp1/8qXa/GZmZ7m9lDZvZxsozf\nKxFzuZl9YmaLzOwJM+tWibo2pvqW28xuL7H+H61UfSshb+1ebb5azCrX5qF5tfsWlwyY2Q+Ba4GB\nwDeB14FRZrZ+RStWHhOADYGNktdela1Ok2gPvAacCdS47tXMLgR+BvwU2BVYSFz/q5ezkk2gzuVO\nPEb19d+vPFWrvBy3e7X5VbfNQzNq9y3hdsRpA4CbQwh3ApjZacAhwEnA1ZWsWBksCyHMqHQlmlII\nYSQwEsDMSt2b9RzgihDCw0nM8cB04AjgnnLVs7FlWG6AL1f19V+HvLZ7tflVtM1D82r3LWpkwMza\nAD2BpwrTQrxr0pPAHpWqVxl1T4aTJpnZ3Wa2aaUrVE5mtiUxMy5e//OAl8jH+t/HzKab2VtmNtjM\n1q10hcoh5+1ebT7fbR7K1O5bVDIArA+0JmaFxaYTN5hV2YvAicTbtJ4GbAk8a2btK1mpMtuIOJSW\nx/X/GHA8sB/wC6A38GgdexOrkry2e7X5fLd5KGO7b4mHCUoxaj/eskoIIYwq+nOCmb1MfA7XMcDt\nlalVs5GH9V88HPqmmb0BTAL2AZ6uSKUqb5Ve72rzdVql131BOdt9SxsZmAlUEU+mKLYBNTPHVVoI\nYS7wDrBKnFWb0TRiJ6D1H8JkYnvIw/pXu0dtPjU9V+u+oCnbfYtKBkIIS4FxwP6Faclwyf7AC5Wq\nVyWY2VrAVngfat+CJQ1hGtXXfwdgN/K3/jcB1iMH61/tPlKbj/La5qFp231LPExwHTDEzMYBLxPP\nMm4H3FHJSjU1M7sGeJg4TNgFuAxYBgyrZL0aW3I8tBtxbwCgq5ntBMwOIXwI/An4tZm9R3zE7RXA\nR8CDFahuo6lruZPXQGAEsWPsBlxF3EscVXNuq6TctXu1+VW7zUMza/chhBb3As4gbhSLgf8Au1S6\nTmVY5mHEBrAYmAoMBbasdL2aYDl7A8uJw8LFr9uKYi4FPgEWJY2iW6Xr3ZTLTXwe+cikQ/gCeB/4\nM9C50vUu83eUq3avNr9qt/n6lr3c7d6SComIiEhOtahzBkRERKTxKRkQERHJOSUDIiIiOadkQERE\nJOeUDIiIiOSckgEREZGcUzIgIiKSc0oGREREck7JgIiISM4pGRAREck5JQMiIiI59//2qVXHDrM8\n7AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f74b90a6978>"
+       "<matplotlib.figure.Figure at 0x7fc8f4139908>"
       ]
      },
      "metadata": {},
diff --git a/openmc/plotter.py b/openmc/plotter.py
index b3febb525..f32f0cebc 100644
--- a/openmc/plotter.py
+++ b/openmc/plotter.py
@@ -67,7 +67,7 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
             sab_name=None, ce_cross_sections=None, mg_cross_sections=None,
             enrichment=None, plot_CE=True, orders=None, divisor_orders=None,
             **kwargs):
-    """Creates a figure of continuous-energy cross sections for this item
+    """Creates a figure of continuous-energy cross sections for this item.
 
     Parameters
     ----------
@@ -233,7 +233,7 @@ def plot_xs(this, types, divisor_types=None, temperature=294., axis=None,
 
 def calculate_cexs(this, types, temperature=294., sab_name=None,
                    cross_sections=None, enrichment=None):
-    """Calculates continuous-energy cross sections of a requested type
+    """Calculates continuous-energy cross sections of a requested type.
 
     Parameters
     ----------
@@ -295,7 +295,7 @@ def calculate_cexs(this, types, temperature=294., sab_name=None,
 
 def _calculate_cexs_nuclide(this, types, temperature=294., sab_name=None,
                             cross_sections=None):
-    """Calculates continuous-energy cross sections of a requested type
+    """Calculates continuous-energy cross sections of a requested type.
 
     Parameters
     ----------
@@ -489,7 +489,7 @@ def _calculate_cexs_nuclide(this, types, temperature=294., sab_name=None,
 def _calculate_cexs_elem_mat(this, types, temperature=294.,
                              cross_sections=None, sab_name=None,
                              enrichment=None):
-    """Calculates continuous-energy cross sections of a requested type
+    """Calculates continuous-energy cross sections of a requested type.
 
     Parameters
     ----------
@@ -607,11 +607,11 @@ def _calculate_cexs_elem_mat(this, types, temperature=294.,
 def calculate_mgxs(this, types, orders=None, temperature=294.,
                    cross_sections=None, ce_cross_sections=None,
                    enrichment=None):
-    """Calculates continuous-energy cross sections of a requested type
+    """Calculates continuous-energy cross sections of a requested type.
 
     If the data for the nuclide or macroscopic object in the library is
     represented as angle-dependent data then this method will return the
-    average cross section over all angles.
+    geometric average cross section over all angles.
 
     Parameters
     ----------
@@ -669,20 +669,16 @@ def calculate_mgxs(this, types, orders=None, temperature=294.,
     # Convert the data to the format needed
     data = np.zeros((len(types), 2 * library.energy_groups.num_groups))
     energy_grid = np.zeros(2 * library.energy_groups.num_groups)
-    i = 0
     for g in range(library.energy_groups.num_groups):
-        energy_grid[i: i + 2] = library.energy_groups.group_edges[g: g + 2]
-        i += 2
+        energy_grid[g * 2: g * 2 + 2] = \
+            library.energy_groups.group_edges[g: g + 2]
     # Ensure the energy will show on a log-axis by replacing 0s with a
     # sufficiently small number
-    if energy_grid[0] <= 0.:
-        energy_grid[0] = _MIN_E
+    energy_grid[0] = max(energy_grid[0], _MIN_E)
 
     for line in range(len(types)):
-        i = 0
         for g in range(library.energy_groups.num_groups):
-            data[line, i: i + 2] = mgxs[line, g]
-            i += 2
+            data[g * 2: g * 2 + 2] = mgxs[line, g]
 
     return np.flipud(energy_grid), data
 
@@ -690,11 +686,11 @@ def calculate_mgxs(this, types, orders=None, temperature=294.,
 def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
                               temperature=294.):
     """Determines the multi-group cross sections of a nuclide or macroscopic
-    object
+    object.
 
     If the data for the nuclide or macroscopic object in the library is
     represented as angle-dependent data then this method will return the
-    average cross section over all angles.
+    geometric average cross section over all angles.
 
     Parameters
     ----------
@@ -748,19 +744,34 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
             elif line == 'unity':
                 data[i, :] = 1.
             else:
+                # Now we have to get the cross section data and properly
+                # treat it depending on the requested type.
+                # First get the data in a generic fashion
                 temp_data = getattr(xsdata, _PLOT_MGXS_ATTR[line])[t]
                 shape = temp_data.shape[:]
-                # If we have angular data, then we will plot the average
-                # over all provided angles.  Since the angles are equi-distant,
-                # un-weighted averaging will suffice
+                # If we have angular data, then want the geometric
+                # average over all provided angles.  Since the angles are
+                # equi-distant, un-weighted averaging will suffice
                 if xsdata.representation == 'angle':
                     temp_data = np.mean(temp_data, axis=(0, 1))
+
+                # Now we can look at the shape of the data to identify how
+                # it should be modified to produce an array of values
+                # with groups.
                 if shape in (xsdata.xs_shapes["[G']"],
                              xsdata.xs_shapes["[G]"]):
+                    # Then the data is already an array vs groups so copy
+                    # and move along
                     data[i, :] = temp_data
                 elif shape == xsdata.xs_shapes["[G][G']"]:
+                    # Sum the data over outgoing groups to create our array vs
+                    # groups
                     data[i, :] = np.sum(temp_data, axis=1)
                 elif shape == xsdata.xs_shapes["[DG]"]:
+                    # Then we have a constant vs groups with a value for each
+                    # delayed group. The user-provided value of orders tells us
+                    # which delayed group we want. If none are provided, then
+                    # we sum all the delayed groups together.
                     if orders[i]:
                         if orders[i] < len(shape[0]):
                             data[i, :] = temp_data[orders[i]]
@@ -768,24 +779,39 @@ def _calculate_mgxs_nuc_macro(this, types, library, orders=None,
                         data[i, :] = np.sum(temp_data[:])
                 elif shape in (xsdata.xs_shapes["[G'][DG]"],
                                xsdata.xs_shapes["[G][DG]"]):
+                    # Then we have an array vs groups with values for each
+                    # delayed group. The user-provided value of orders tells us
+                    # which delayed group we want. If none are provided, then
+                    # we sum all the delayed groups together.
                     if orders[i]:
                         if orders[i] < len(shape[1]):
                             data[i, :] = temp_data[:, orders[i]]
                     else:
                         data[i, :] = np.sum(temp_data[:, :], axis=1)
                 elif shape == xsdata.xs_shapes["[G][G'][DG]"]:
+                    # Then we have a delayed group matrix. We will first
+                    # remove the outgoing group dependency
                     temp_data = np.sum(temp_data, axis=1)
+                    # And then proceed in exactly the same manner as the
+                    # "[G'][DG]" of "[G][DG]" shapes in the previous block.
                     if orders[i]:
                         if orders[i] < len(shape[1]):
                             data[i, :] = temp_data[:, orders[i]]
                     else:
                         data[i, :] = np.sum(temp_data[:, :], axis=1)
                 elif shape == xsdata.xs_shapes["[G][G'][Order]"]:
+                    # This is a scattering matrix with angular data
+                    # First remove the outgoing group dependence
                     temp_data = np.sum(temp_data, axis=1)
+                    # The user either provided a specific order or we resort
+                    # to the default 0th order
                     if orders[i]:
                         order = orders[i]
                     else:
                         order = 0
+                    # If the order is available, store the data for that order
+                    # if it is not available, then the expansion coefficient
+                    # is zero and thus we already have the correct value.
                     if order < shape[1]:
                         data[i, :] = temp_data[:, order]
     else:
@@ -799,11 +825,11 @@ def _calculate_mgxs_elem_mat(this, types, library, orders=None,
                              temperature=294., ce_cross_sections=None,
                              enrichment=None):
     """Determines the multi-group cross sections of an element or material
-    object
+    object.
 
     If the data for the nuclide or macroscopic object in the library is
     represented as angle-dependent data then this method will return the
-    average cross section over all angles.
+    geometric average cross section over all angles.
 
     Parameters
     ----------