diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index c43fab6ff..e35c205c3 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",
+      "/home/wbinventor/miniconda3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
+      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n",
+      "/home/wbinventor/miniconda3/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning:  This call to matplotlib.use() has no effect\n",
       "because the backend has already been chosen;\n",
       "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
       "or matplotlib.backends is imported for the first time.\n",
@@ -453,8 +455,8 @@
       "         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 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n",
-      "         Date/Time | 2016-10-31 12:29:16\n",
+      "          Git SHA1 | 2c25ec97653482825b529215b8a61b54d468d34e\n",
+      "         Date/Time | 2016-11-28 09:10:24\n",
       "    OpenMP Threads | 4\n",
       "\n",
       " ===========================================================================\n",
@@ -465,11 +467,16 @@
       " Reading geometry XML file...\n",
       " Reading materials XML file...\n",
       " Reading cross sections XML file...\n",
-      " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
-      " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
+      " Reading U235 from\n",
+      " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/U235.h5\n",
+      " Reading U238 from\n",
+      " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/U238.h5\n",
+      " Reading O16 from\n",
+      " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/O16.h5\n",
+      " Reading H1 from\n",
+      " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/H1.h5\n",
+      " Reading Zr90 from\n",
+      " /home/wbinventor/Documents/NSE-CRPG-Codes/openmc/data/nndc_hdf5/Zr90.h5\n",
       " Maximum neutron transport energy: 2.00000E+07 eV for U235\n",
       " Reading tallies XML file...\n",
       " Building neighboring cells lists for each surface...\n",
@@ -531,7 +538,7 @@
       "       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 10050\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10057\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",
@@ -557,7 +564,7 @@
       "       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 10050\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10057\n",
       " The estimated number of batches is 74\n",
       "       74/1    1.22437    1.22487 +/- 0.00188\n",
       " Triggers satisfied for batch 74\n",
@@ -570,20 +577,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  5.0262E-01 seconds\n",
-      "   Reading cross sections          =  3.4207E-01 seconds\n",
-      " Total time in simulation          =  1.2843E+02 seconds\n",
-      "   Time in transport only          =  1.2831E+02 seconds\n",
-      "   Time in inactive batches        =  8.1328E+00 seconds\n",
-      "   Time in active batches          =  1.2030E+02 seconds\n",
-      "   Time synchronizing fission bank =  2.9797E-02 seconds\n",
-      "     Sampling source sites         =  2.1385E-02 seconds\n",
-      "     SEND/RECV source sites        =  8.2632E-03 seconds\n",
-      "   Time accumulating tallies       =  1.4577E-03 seconds\n",
-      " Total time for finalization       =  1.3462E-02 seconds\n",
-      " Total time elapsed                =  1.2901E+02 seconds\n",
-      " Calculation Rate (inactive)       =  12295.9 neutrons/second\n",
-      " Calculation Rate (active)         =  3325.12 neutrons/second\n",
+      " Total time for initialization     =  3.1270E-01 seconds\n",
+      "   Reading cross sections          =  1.8695E-01 seconds\n",
+      " Total time in simulation          =  1.1922E+02 seconds\n",
+      "   Time in transport only          =  1.1903E+02 seconds\n",
+      "   Time in inactive batches        =  8.5633E+00 seconds\n",
+      "   Time in active batches          =  1.1066E+02 seconds\n",
+      "   Time synchronizing fission bank =  2.4702E-02 seconds\n",
+      "     Sampling source sites         =  1.8297E-02 seconds\n",
+      "     SEND/RECV source sites        =  6.2794E-03 seconds\n",
+      "   Time accumulating tallies       =  2.6114E-03 seconds\n",
+      " Total time for finalization       =  1.6967E-02 seconds\n",
+      " Total time elapsed                =  1.1961E+02 seconds\n",
+      " Calculation Rate (inactive)       =  11677.8 neutrons/second\n",
+      " Calculation Rate (active)         =  3614.65 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -1172,169 +1179,239 @@
      "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",
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      ]
     }
    ],
@@ -1367,8 +1444,8 @@
      "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"
      ]
     }
    ],
@@ -1443,237 +1520,346 @@
      "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 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"
      ]
     }
    ],
@@ -1699,8 +1885,8 @@
      "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"
      ]
     }
    ],
@@ -1753,7 +1939,7 @@
    "outputs": [],
    "source": [
     "# Parse ACE data into memory\n",
-    "u235 = openmc.data.IncidentNeutron.from_ace('../../../../scripts/nndc/293.6K/U_235_293.6K.ace')\n",
+    "u235 = openmc.data.IncidentNeutron.from_hdf5('../../../../data/nndc_hdf5/U235.h5')\n",
     "\n",
     "# Extract the continuous-energy U-235 fission cross section data\n",
     "fission = u235[18]"
@@ -1785,9 +1971,9 @@
     },
     {
      "data": {
-      "image/png": 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9XXjr7b+y8zNjUla2RCW78zkTk6HJfXETg4g8qKpnisg8nOsWwtsBUNVONCOO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\nSwzGGGOiWGIwxhgT5f8B4ykN0fh7FfYAAAAASUVORK5CYII=\n",
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Lz0uD3U1J7t+RuIJ5zpwMNmzQPgbVPIJJDGOAfwJnY3U+f2xvUyqi/K3hcA6w\ny5HHzqumkTY1/oa0Op1WDSDYGoNOfaHiXTCjkoqAy2JQFtUCOXPzghqRlOfaReq9t7MrLhOD9bu6\nOrhxqsEkBp2FVTUnv4nBGPO8iIw2xvyKVVNwcwAuEdk76qVTSS+UYa3Z1bv4scgRd01K7ppCMMNV\nlUoEgWoME+3fR8eiIKplKhs3odErnj2bmJ55Jp0rrqiMdrFC4k4IwTYlKRXvAq3gtsX+0wF0EpFf\ngL8BNwLJMc5QJZxnnkmPuzPzYEYlab+CSiTBDHuYB1QaYw4FLgJeBAqjWiql/NhtNxfvvtv8S4ge\nemhu7QVt9S9w85UEPLf98YeDP/4ILd5VV2Xy5JPpQT32ww9T2bJF+yhU+IJJDC4R+RQ4FbhPRN7A\nY8EepWLp7LOr+Pe/gztARtOGDSmU23MO1x+u6ovnfZdfns3gwaFdyPb00xnMm9fwdbtcsHq199d4\nxIgcrrsus/b2n39Cly7Bz12lVDDDVfOMMX2BUcBfjTGZwO7hBDPG9AReAWaJyFx72yygP+AEJonI\nSmNMf6zaSSpQKCKfhxNPJZ+p12QzFaBdw/tiNUur++y/ft9CTY11vuRrEr36tYjGrlFYvz64c6+v\nvkrhb3/LpajI/6VFW7akUFqq53IqeMHUGGZizZH0kH0F9HRgfqiBjDE5WE1Qb3tsGwB0F5EjsRLB\nHPuuXcA44F7gmFBjqeQS7EytsZql1Z0I6pqS3FNaWGfpjTUlBeOXXxp+Nb/9tmETWlWV7+d7Jift\n31ChajQxiMjzwKEiMtuuLcwVkZlhxCoHhgGbPLYNwapBICJrgNbGmDwR+RrIBMYCT4URSyWR0inT\nQkoO9c2cCWPHRm4d6vo1BPeB9+2307xuex6QfR2cy8v9H9g9eTZDPftsWlB9LE5n8LUOpeoLZnbV\nacDl9hn/58B/jTH/DDWQiDhFpP4cSx2w1pJ2+x3oYIxpBdwFTBOR7aHGUsml/kytRVt2YPat5rVX\nS2q3BTJ7Nrz4YuT6Jep3MtfvW/C13VdiOPjgPMaPb5iwnnsuzavpZ9Wquq/ppEnZ/P3vDQcFtmuX\nXzsZH8Drr6fTp4/2K6jwBNPHcDJwFHAusEBErjHGLI1Sedyf7GuAfOAGY8z7IvJyY08sKIjdgiYa\nq/ljjRsHTz+dw/Dhje9727bwYn7xBXTvDrn1+okz7X7dNWvy6NwZsrO972/Vyjpwt22bT5r9DSsr\na7j/7dtN2J3mAAAgAElEQVQdiDRMWP/+t/cOW7du2FHtfi277153X2VlHgUFDR9XVGT9XV6ez157\nNSxHsBLp8xHP8RIhVjCJoUpEXPYaDLPtbZEaL7gRq9bg1hHYJCLXh7qj4uLYzOtXUJCvseIg1imn\nwG235bJ8eRkHHuj0WirUc9+VlVBSkk9mpovi4tAWOzjkkHwuvbSSW27xruhu3w6Qz6hRcOWVFaSm\ngtXyadm2rRTI4b33SthnHyc5OTBkSA6+vjY1NTUNtldXe2/7448SwDs5FBfvpKAg3+u+7dtLKS6u\nwTqnqnvctm0pQC57703ATupAEu3zEa/x4ilWoKQRTOfzdmPMQmB/EfnIGHMS1giipnDXDN7EGu2E\nMaY3sEFESpq4b9UC5ObC5ZdXctddgaemXrs2hX33tdryw7kyucTHp9FzTqSqKv9NSUOG5FJYmMHz\nz6fx1VfBn0utWuX9WHfndjg+/LD5r/lQiSeYxHAm1qikofbtChos7d44Y0xvY8wy+7kT7eao74BV\nxpgPsEYgjQ91v6rlOu+8Kr76KpUVK/wf/L79NoVDD4W8PNgRxoJxvoaeeiYYp9PRoP9g3bq6r9V3\n36UwYUK9tiYPwYwYeuaZhs1Nvl7LGWc0jDNiRA7bt3u/iAUL0nSkkgoo0CR6w0RkETDa3nSyMcZ9\n917A46EEEpFVwCAfd00LZT9KueXkwC23VHDttZmc6ucxX3+dSq9e8NFHLrZvd7D77qEdEVN95BzP\nGoLT2bDGcP31dR3KO3c2fWRQUVHDfQwenMspp8AJJ9Rt83etwvDh3p3VY8Zk8/PPO8nRiW2UH4Fq\nDAfbv4/x8aMT66m4cMIJ1XTp4v9gv2pVCv36QV6ei127Qj9Ip/j4hniu7exODDk5LkaObDj2dMWK\nYLrxAvNVa1m/PoU5cxpuX7kyuMV9fO1TKbdAn9pFACJyAYAxZg8R2RqTUikVJIcDZs0qh54N76uo\nsGoMfftCq1ausM7efSWG+n0VNTUOOnd2htU809QmnZ9/9i7gsGG6ZrRqukCnF/fWu/2faBZEqXD5\nW59h2bJUevWqIT8f8vNhZxiDQXwlhvpNR06nNYS1OkpLWP/5p/+ENnas//6LYLlcVie9Um6BPg31\nP41a+VQJ4aOPUqmogNmzMznzTKt5Jz8/cjWGykrv/TidkJHhCuoq5nB89lnkRhbVT147dsB992Vw\n9NFa01B1AjUl1T8N03EMKiGMHZvFjh0Ohg6tZtQo60iYlxd8YlizJoX337cOxr7a4j0TgMtl/Vg1\nhtATT6yX8PziCyvTuV/XzTdn8vTTgYf8qpan6T1jSsWZzz4rYft2B23b1p3LWE1JwR2E7703g5de\nsoaI+qoxlJR476emBrKygpv3qD7Poa2x4Nmn4XTCkiV1h4CJE7Po1MnJ1KnxtUKeir1AieFIY8x6\nj9vt7Nu65rOKa2lpeCUFsJqSdgV54bNn53JqasOKsmdfhctlPT4z00VZWWK1tj78cDpFRXWJ6bnn\nrGQ4dWolpaXocNYWLFBiMAHuUyqh5Oe7KC4O7uzcsx3e13UM5eUNawzR7HyOpBtusK6xuPbaTObP\n99+E1KVLPh9/vItu3bQFuSXymxjsNZ6VSgqhdD571hh8NSW5V24Dd43BQVaWi7VrU1m1qokFjbKV\nK61MFygpuC1enMa4cVHqUVdxTceoqRYh3OGqvq4zqKjwTjDV1VYfw7ZtKRx2WJgFjEPTp0duDQuV\nWLTzWSWdgnatGmw73/7xtSRofYs8b8wC50PeS4Y2rDHUTcWdDNq1q5t188svUzj4YCevvQavvJLJ\nnXfWX1JFJaOgagzGmGOMMVcaY64wxhwR7UIpFapgV3gLR/0lQ+t3MlujkpKzLX7o0Fyqq+H++2He\nPB3W2lIEs4LbP4G7gT2BvwCF9qpuSsWNUJb/DIfnkqEVHifN7hpDVhK3uui8Si1PME1Jg4AjRcQJ\nYIxJA94Dor/qulJBKhs3obapp76Cgny+/noXgwfn8M03jS/3MXJkNu+/b6/f7OOCf8+mJLD6GAoK\nkrPGoFqmYJqSUtxJAUBEqmn6Qj1KxVQos6v6m77azXO4qstlrcnQtm3yfiU8L9zbbz+dOqMlCKbG\nsNIY8xrwtn37WODT6BVJqcjLybGW+ayqgnSPdW9+/tlBTQ3ss0/dGX9paeB9+aox+LreIVnsvXc+\nxx1n/W0tE6qSXTD/5cnAfKAr0AV4GrgyimVSKuIcDmvIav2rn0eNyuGII7z7JrZtC1xj8Byu6u5j\nSObE4I+IJolkFUyNYaqI3AE8F+3CKBVN+fku/vzTexW3+ste1tTA1q2NJQbv2y0hMSxZUvd3eTks\nWpTGpZdm89FHu7xqWyo5BJMYehpjuovID1EvjVJRtM8+Tr77LpUuXermrqg/4mbbNge77ebymRzc\n10e87LnxCfv3ihY0/fDecAnWD2EOXnfmel8bouJLMHXBg4HvjDGbjTHrjTG/1ptcT6mEcPjhNfzv\nf4FP7YuKHF4L/5SlRW8IbEtW/9oQFV+CSQwnA92BftSt93xMNAulVDT4Sgz1p7woKnJ4DT19fv8b\nonp9REvmeW2Iii/BJIZc4DIR+cWeWG86oN8UlXB6967hm29SGowq8lRUZK3jkJPjwuFw8Uq3K9j6\n00Zm3FaGAxfFRTvoc1g1aalOHLg479wKDulVzVtv7sKBq8X9DDimqvbv4qIdQf2o+BdMYrgfeMPj\n9uPA3OgUR6noycuD7t2dfPFFXa2hfo1h506rj+Htt0u4886K2plW3bOsbtrkoKjIQZ7HqVGyD1cN\nxH0hoEouwSSGNBF5333D82+lEk3//jW8+67/xLBrl4O8PBfdu7to29ZVmxjcndTnn5/Nr7+m0KaN\nq/b5TmdwiWHgwARYsEEpgksMfxpjxhpj9jfGHGiMuQoIYwJjpZrfeedVMW9eOhs2WEf6homB2tpA\naqp1VTPUJYb1660/PFeIC7bG4DnR3pFHJl+SePTR9MYfpBJCMInhAuAw4AXgWaCHvU2phNOjh5Px\n4ys588xsNmxw1PY3uBOEu8YA1rKenov2gNUUNWNGuddBvqbGQVqai9tvD9B5gXfyaNUq+Qa3Xndd\nEs8k2MI02kAoIsXARTEoi1IxMX58FS6Xg2OOyWXPPV1s3mxNl5GZWT8x0KCPYccOB/3717BkSd1X\np7raun/YsGqm2fMOd+zo5IEHyjnllLqFkz2vmfC1MlwyqK621txWic3vv9AY87yIjDbG/IqPa3dE\nZO+olkypKHE4YMKESgYMqCY7G04/PZuNGx107eryakpKSaFBH8POnQ5atXLVnv07HFYfQ1oatG5d\n9zWZPr2Cbt38T6yXrInh9dfTGDEi+ZrJWppAuX2i/fvoSAUzxvQEXgFmichce9ssoD/WjK2TReQz\nY0wHYDawREQej1R8pTz16mUduA89tIaPP06la9fqBjUGp31sdyeGHTusUUvu2y5XXR9DTg707Alf\nfw0jRlTz++/eV0+3hBpDlS4RnRQCfTyNMWYA0NnPT0iMMTlAIXWztGLvv7uIHInVXFVo3+UEHgo1\nhlLhOOOMKh5/PAOXC0pK6hJDWpp10PfkrlG4+yScTu8ZW999Fz74wFrzITXVu6LtOatrSx3eqhJD\noMSwHHgQuBBrudwLPH7ODyNWOTAM2OSxbQhWDQIRWQO0NsbkiUgRUNNwF0pF3rHH1lBRAcuWpfpo\nSvIelZST431QdzodVFY6yMy0kkCbNlYHt/v5niZPrqRrV6fX/pJN/VFeKjEFakoagJUEjgYWAv8W\nkVXhBrIX+6kwxnhu7gB85nH7d3ube8K+JP36qHiSkmIdtAsLMxqMSqrflLTbbi6v2zU1Ddd4cKtf\nK8jPd9G3bw0//ZRCnz41vPSSDu9U8clvYhCRFcAKY0w2MBK4y277nw88Y0+PEWkOAGPMYGAs0MoY\n87uIvNrYEwsK8qNQHI2VDLGCiXf++TBlCuzcCV265NGmDbRtayWAgoL82lrEbrulUFCQX5sIMjLS\nqayEv/wlvzYRuGPl5HjHKCjIIzPT+nvatCyOPx56947QC4wTrVplU1AQ/OP9/V/i7fPR0mIFM1y1\nDPi3MeZZYAwwA2uhnrZhRfS2EauG4NYR2GRP8b00lB0VF8fmmruCgnyNlUCxQonXt282S5emUVGx\nk+Ji2LEjhYqKLIqLS9m+PR3IIiurhuLiUiors4E0du6sAtLYtm1Xg1jWNRJ1X8xt23ZRXp4JpFNc\nvJPt21OwpiJLHn/+WUZxceBRSZ55w9f/JV4/H8kWK1DSaHRshH3F87+AH7H6CC7FOoA3hbuJ6E1g\nlB2nN7BBRBpfrV2pKDjgAKtbyz0O33O4qvt3To53I3p5uYOMDN/7q9/HkJrq3QafjCOT6o/EUokp\n0HUMl2D1MbiwlvM8VES2hRvIPvDPxBrRVGWMGQmcBqwyxnyA1dk8Ptz9K9VURxxRw3331d32HK7q\n/l2/eaiszHf/AkBGhpVsvv3WamNKS/NOKsmYGL7/PglfVAsUqCnpQeB7rOae04G/e3Yci8jgUALZ\nHdeDfNw1LZT9KBUtxx5bw88/11W9Pa98dg9bzc31PrhXVFA7IsmXI47wTAze9/lKDJ06OfntN+uO\nvDwXt91WzqRJ2SG+kuYzf34G995b0fgDVVwLlBi6xqwUSsUJzxqBNVeS1TRSvynJXYMoL3f4rTGA\nd9NRw6akuhsHHFDDq6+WAtCjRz5dujj53/+sVtVJk8J8MUqFKdCopGiMOlIqYXj2MVRVWQki3+6v\nc9cgKir8NyWBdyJorMaw2251fzv9z6ahVNRpg6BSfqSl1SWGykrrd36+y+t2WZmDjIzgrupKS/NO\nFJ4XuemFYSqeaGJQyg/PPgZ3Ith9d+sI7q5BlJcHrjF4SknxrgkE6nxO5CujCwv9DNNSCUMTg1J+\neCaGigpr8rzhw602JHezUEWF/+GqUFcTePZZq//Ac5K5ZByVBDBvXrrWgBKczpyulB+eZ/gVFXDt\ntRW0a2cd8QYOrObTT1MpLydgYnBz1wCqq+uqAp6Jof6B1HOFuESTlgYPPpjOgAE1rFiRypYtDn77\nLYVbbqmgffvEfV0tiSYGpfywagzWgXz7dkdtMxLA1VdX0q9fDaNG5QTsY3B3UvfsaWUYd5OUe/++\nfPnlLq8V4hJNYWE599yTwV13ZVJSUpcIX3klnaIiXRU4ESRpZVappvNc2vP33x20aVN3sHY4YI89\nrNuB+hiGDq2hX7/q2pqG5zTe/jqfO3Rw0bp1k4vfbI44ooZnnilj4MBqrr7a+5qG8sCrn6o4oYlB\nKT/cfQxVVfDFF6m1Z/1u7uaeQE1JJ5xQzYIFZT7vS9Y+BrCS5bx55UydWum1/bHHdEbZRJDEH02l\nmsbdx/D55yl07uxs0O7vvh3uNQeB+hiSSVHRTk44wep1nzdPRywlAk0MSvmRmmo1/bz7bhoDBjRc\nN8rdR1BZ2eAuv7p0qcsinlc+J7vHHivnt992smFDAo/DbUE0MSjlR0aGlRhWrEjlmGP8TyUdytn+\nbbdVIGJ1wHrWGBL5uoVgpKZa7+eHH3pPnrx8eSqdOuU1U6mUP5oYlPIjJcUaevnxx6kcdlhkVprN\nyoLdd6/bv1uyJwa3rl29s+jpp+dQWelg+3br9urVekiKBzpcVakAqqocZGUFHiUUbieyZzJoKYnB\nn6OOyqW42HojV66EvfZq5gK1cJqelWqEe51nf8JNDOE+L1CzVqL54IMSbrutnK1b6zLjaadBu3b5\nLFiQxujR2SxapOevsabvuFKNiKfEcNRR1fz1rzW8/374X93MTBcVFfFRRenRw0mPHk5eeimdlSut\n3vxf7Hmdb745k/XrUxBJoaYGTjopeRJivNMag1KNaNUq8P2RSAzBNiVFoslpn33ib07vk0+uYo89\nnMyeXca338Jnn+1i/XrrDdq4MYULL7QWK9q0yUFRkYOSEu+LBVVkaWJQqhGtWkWnxhBOH4PDEd/9\nEW3bhpd0xo2r4rvvSvi//6tm//1h771djB5dxWGH1bBsmTWS6Y47MujVK4/hw3O4/PIs/vtfbfCI\nFk0MSgWQkeGiTx//I5L69avm5JOr/N4fiOfCPYGunp49u+7K6WCSwllnBb6wIthpwsNxwAGRq43M\nnl3OwoWlHHCAk5Ejq5g1K5MDD6zhxx9TWLgwnQUL0tm8OY6zZALTxKBUAP/7XwkTJvg/0C5YUMbp\np4fXpuF5kH/8cd/TZoA1gZ+v57jNmOE9AVHfvoGH1kazKSmS03ykpFg/Dgc88EA5b71Vwvz5ZbVD\nh996K42DD85jzZqUpL5yPBJcLmso8B13ZPDxx35mb/SgiUGpADp2dJGZGf04f/mL/yNbjcdx3mpK\nsh7rvh7iootCq7F07Rq9xBDN96pXLyd77unittvKayfna9/eyTnnZHPggbmMH5/Fe++l6rKoPtx3\nXwYXXpjNrl0OLr88ixNPzAn4eG2kUyrOea7h0KlT3VHv7bfhsMMaPt5XrWL33V388Yd1x9VXVzJz\nZgyyXZT07u2kd+9Kxoypqp3hdv16B0uWpDF9eiZ//ung9NOrOOOMKjp31qpEUZGD++9P5403SunW\nzcWNN1bw/vupgP/koDUGpZrRmDGNT7TkbiZZs2Ynt99eN421u8YQjAceqGuq8rcOhKeHH/bftBWI\nuzYTC+6kAFZn9cUXV7F0aSlPPFHGn386OP74HPr0yWXMmCzmzMnggw9SvWpfyaamBmbPzuCaazJ5\n9NF0li61pjmfPj2TM86oplu3utmAhwwJ/EZoYlCqGe23X+PtHqecYjUVtWljNdWEMirJ3Z8Qahv8\niBGJOxb0oIOczJhRwTfflPD886UMG1ZNUZGDm27K5PDDc7n//vTaKTiSybhxWbz7bir77ONEJIV/\n/AO6d89j1apUpkypaHwHHrQpSalmFMwBu107F2lpDR/Y2HOLinYyaVIW69bF7/lfQTvfF4kURGj/\n7YH+9TfebP9EIZ4/ztw8SqdMg5uui8r+P/44lc8+S+XDD0tq+3kKCjL46qsSsrNd5OaGtr/4/cQo\n1QIEkxjy8mDjxl21tzt2DL25JlCcSZOss8n09Ng0AzlzW95sqiklu8i5+/ao7f/hh9O5/PLKBp3/\nHTq42G230PeniUGpZhTOMMsRI6r54Qf/ayd7XzjXeID8fOv3WWcFN7rprruatj5n6ZRpLTY5RENJ\nibVmiLvJMRK0KUmpZhROYnA4rGk6dnrkhuHDq3jtNf9XrjUWZ926neTnwxNPNL7C2umnVzF1alaw\nxW2gbNwEysZN8Ht/QUE+xcX+E1+k1Y+3ZYuD5ctTWbo0jRUrUmnTxsXgwTUcd1w1RxxRE/KV5/6a\nyzyVl8O6dSns2uWgUycn7du7vC6ADGTp0jQOPbSGNm1CK1cgMUsMxpiewCvALBGZa2+bhdUE6AQm\nichKY0xf4FLAAUwXkV9jVUalYi1SF2Y9+mg5M2c6qap30hjsQcxdawhGoH0edVTiD/tp397F6NHV\njB5dTVUVfPttCk89lc7kyVlkZro4+ugaUlOhdWsXBx1Uw0EHWddXBPVeOxx++zPCnWn8QvuHdg3v\nC9h3EuDDF5PEYIzJAQqBtz22DQC6i8iRxpj9gMeBI4HL7J9OwMXAjbEoo1LN4eSTq/njj9BGjLjV\n/15fdZU19PW559IaPMb9u6io4Zm4v+NDRoaLysrAR7tFi0oYNiyXbt2cfPRRSVzP4xSO9HTrwrqZ\nMytwuSr45JNUvvgiBacTtm518PjjGXz1VQrt27u49NJKDj7YSefOTq/OXmduXtSakaIlVjWGcmAY\ncK3HtiFYNQhEZI0xprUxJg9IF5EqY8wmfOZApZJH+/YupkwJYdHoIAS6itqtTRsn27YF7mLs0sXJ\n2rW+L3r417/KufrqrNoLyA45JPQmlkTjcED//jX07+9dK3K54J13Unn66XTuuy+FX35JITXVmgur\nshImVt3EjSk3k+tMnOQQk8QgIk6gwhjjubkD8JnH7WJ7W4kxJhOrxrA+FuVTKhHtuSccemjDpptj\njqnh11+tmoH7YF2/VnDggU7ef993YrjjjnKv5/py7rlVXH11FhkZemWxwwFDh9YwdKj1v3C5YNcu\n64KzjAxwOC6lJOtSSh2R6T/ZsQPmzcvgwQetPqVbbqlg1KiG1500FitQM1M8dT67P6UPAXOBVCDo\nQb8FBSE0kjaRxkqsWLGOF8tYq1alAv7jZdl9xLvtZk1/4C6b5wyrubmZFBTUjXPMy8uioCCLRx6x\nFs055xxr++23w7Rp1j6ys+GPP6B163w7TjoFBZGbtjXRPx/tArR1NDVWQQHceitMnQo//wwHHZTt\nN4mHG6s5E8NGrBqCW0dgk4iUAGNC3VmsRjHEcsSExkq8ePEWq7w8E8hg+/ZSIKf28VVV2bi//rt2\nVVBc7G7OymfnznKKi6vYbz/Ybz9rG8B55+1kzpxc/vyzhF12q0hxsXV/VVUVxcVNG8YayuuKpHj7\nn4Vizz3h99/DixUoaTRHYnDntjeB6cAjxpjewAY7KSilIqz+0Ed/Hc6HHVbDkUf6HlmUlgarVzf8\nii5eXEKXLjqlaTKJ1aik3sBMoDNQZYwZCZwGrDLGfADUAONjURalWqJBg2pYuND3eZdnkli0qLTB\n/ccdV82SJf4PFb17a1JINrHqfF4FDPJx17RYxFeqpXK3PaemQt++4R3A584to7g4tm3+qnnplBhK\ntWApKS569Qp8UVp+PvTrF6MCqbgQT6OSlFIRNnp04OmzN29OnLH1KnY0MSiVxPr1q6Ffv8SfpkLF\nljYlKdUCRWqOJpWcNDEopZTyoolBKaWUF00MSrVAPXs6ycrS9iTlmyYGpVqgm2+u4McfdUSS8k1H\nJSnVAqWkWD9K+aIfDaWUUl40MSillPKiiUEppZQXTQxKKaW8aGJQSinlRRODUkopL5oYlFJKedHE\noJRSyosmBqWUUl40MSillPKiiUEppZQXTQxKKaW8aGJQSinlRRODUkopL5oYlFJKedHEoJRSyosm\nBqWUUl40MSillPKiiUEppZQXTQxKKaW8pEU7gDGmJ/AKMEtE5trbZgH9AScwWUQ+83h8B2A2sERE\nHo92+ZRSSnmLao3BGJMDFAJve2wbAHQXkSOBi+z7PTmBh6JZLqWUUv5FuympHBgGbPLYNgSrBoGI\nrAFaG2Py3HeKSBFQE+VyKaWU8iOqiUFEnCJSUW9zB6DY43Yx0MEYc5ExxrP24Ihm2ZRSSvkW9T6G\nIKQAiMijAMaYwcBYoJUx5ncReTWIfTgKCvKjWERvGiuxYsU6nsZKrFixjpcIsZojMWzEqjW4dcSj\nqUlElgJLY10opZRSllgOV3U3Db0JjAIwxvQGNohISQzLoZRSKgCHy+WK2s7tA/9MoDNQBWwATgOu\nAQZgdTKPF5GvolYIpZRSIYlqYlBKKZV49MpnpZRSXjQxKKWU8qKJQSmllBdNDEoppbzEwwVujQpj\nIr6bgE7AduBpEfkyWrHs+zsAq4BOIuKM4us6ErgMSAfuFpFVwcYKM15/rPmsUoFCEfk8irGaNHli\nEPEmichKY0xf4FKs4dPTReTXKMSaLCKfRWpCyBBeW9j/rzBiNemzGGSs2s9IuN+xMF5X2MeOMGK1\nB67DOg4/ICJfhxorxHhnAIcBBcB3InKnv33GfY0hzIn4XEAp1hu+McqxAK4Algcbpwmx/rS3zwIG\nxiDeLmAccC9wTJRjhT15YpDx5th3XYZ1Zf2twMVRiuV+bU2eEDLE1xbW/yvMWGF/FkOI5fkZCfk7\nFmKsOR5PCfnYEWasMcAvdrzNocYKNZ6IPCciU7Be132B9hv3iYEwJuIDHgamAPdgfaCiFssYcxbw\nElB/TqiIxxKRb+zH3A68HIN4XwOZWAfSp6IcqymTJ4YSL11EquzHtotmrAhNCBlKvHD/X+HEaspn\nMaRYTfiOhRwLK5GHc+wIJ9bewH+wjleTw4gVajyMMT2AosYuKo77xBDmRHz7A9VYZzUZUYw1BzgC\nOB44BDgjirEKjTF9RWQRMBq4MthYTYjXCrgLmCYi26MZy2N7yJMnhhIPKDHGZGI1F6yPUqzf8Z72\nJewJIUOJF+7/K8RY7v/b4eF+FkONBfQjjO9YmLEOIIxjR5ixNmMdg3cB2aHGCiGe5+fxTOCFxvab\nEH0MQag/Ed+JwBNAJXBHNGO5GWM6A89FM5Yx5jhjzENADvDvCMfyFe82IB+4wRjzvoiEc2YYbKxw\nJk8MOR7WGeFcrHb46yIcw80BMXlNXvGwZhSI1v/Lzf0+7h7lz2JtLBGZCFH7jnnFwjpAP0F0jh31\nYz0O/NO+fXuUYoH3iUlXEWm0iSxRE0NjE/EtBBbGIpZHzAujHUtElgBLIhAn2HjXxzBWpCdP9BnP\nrkKPiWCcQLF+IDoTQvqLF8n/V2OxfiCyn0W/sdw3IvQdCxjLfl2ROnY0FqsEOD/CsfzGAxCRoOLF\nfVNSPbGciC9ZY8U6nr62xIynsRIrVkTjxf1cSbGciC9ZY8U6nr42fW0aK7E/H3GfGJRSSsVWojUl\nKaWUijJNDEoppbxoYlBKKeVFE4NSSikvmhiUUkp50cSglFLKiyYGpZRSXhJ1SgylQmbPtSPAh/Ym\nB9YU7QtFZGYzluuvWHMALRORM/085klgpYgU1tsuwDzgFKBMRAZHu7wq+WliUC1NUZwePBc1MhfQ\nY1hTQdcmBmPMEUC1iNxhjHkWK0Eo1WSaGJSyGWO2Yy3gMwxrErLTReQbY8xBWNMOpGGtWHa5iHxh\njFkGrMaaDnowcAEwCSgCVgBDgZuB60VkkB3jcGCOiPQLUI7Lgb/b8dYA40TkPXttggPttRAAzsVK\nGEpFlPYxKFWnFfCliAwBnsda/QrgGeBSu6YxHu+D8U77oJ+HtRbCEBE5FtgXcInI20BHuxkL4HTg\nEWa+JIoAAAIKSURBVH8FMNbSo6eKyF9F5CjqVkoDq0Zwvv24DOBUwluQR6mAtMagWpp2xpil1M1E\n6QKmSt3608vt378A+xhjCgADPGaMcT8nz+Nvd3/FvsDPIvK7fftF6lblegw4D2vu/WHA9ADlG2jH\ndZcxB2ttAIAngY+NMVOx+hRWeMRTKmI0MaiWJlAfgwtr9S43B9ZykuW+nmOMgbqDdor9fDfPJT3n\nAe8aY94EPhaRXQHKVwG85l6cxpOIbDLGrAaOA84GHgywH6XCpk1JqqUJtMxmg/tEZAfwszFmGIAx\nZl9jzA0+nrsO6GaM2c2+farHPoqBL4G7abxP4ANgmDEm14431hjj2R/xGNZCQwcCixvZl1Jh0RqD\namnaejTTuOzfP4rIGLzP+D2dBxQaY67F+s641ziufbyIbDPGzAA+MMb8AqzEWuzd7UngXyLyIQGI\nyEpjzP3AcmNMGdZqXJ6jjRZi1RQeFRGdM19FhSYG1WKIyC8EWHRdRFI9/n4S62COiKzGWvSk/uPr\nNy9tAY4Wke3GmCuwrplwOxFruKk/tbUVEbkXuNdPGWuwlmr09fxAtSGlgqZNSUpFTh6wzBizHKuT\n+VZjzJ7GmI+BXBF5NMBzjzPGzA8nqDHmOOBZwBnO85WqT1dwU0op5UVrDEoppbxoYlBKKeVFE4NS\nSikvmhiUUkp50cSglFLKy/8DFQOc8G/UVmwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2ae5a247f0>"
+       "<matplotlib.figure.Figure at 0x7fbd166e3a90>"
       ]
      },
      "metadata": {},
@@ -1869,9 +2055,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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14iDr9sA3gO0lzSl7fWad4Ly2Kih7Rqd3ACcB4yTNKnNdZp3ivLaqKLsHvyvwTuDCfBCq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\nNzOrKRd4M7OacoE3M6up/w91pX7x6KMxWwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2ae5b62128>"
+       "<matplotlib.figure.Figure at 0x7fbd1678fa58>"
       ]
      },
      "metadata": {},
@@ -1898,6 +2084,15 @@
     "# Show the plot on screen\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/openmc/filter.py b/openmc/filter.py
index 88a88f99d..0320cdeca 100644
--- a/openmc/filter.py
+++ b/openmc/filter.py
@@ -1,8 +1,7 @@
-from abc import ABCMeta, abstractproperty
+from abc import ABCMeta
 from collections import Iterable, OrderedDict
 import copy
 from numbers import Real, Integral
-import sys
 from xml.etree import ElementTree as ET
 
 from six import add_metaclass
@@ -829,7 +828,7 @@ class EnergyFilter(Filter):
             if bins[index] < bins[index-1]:
                 msg = 'Unable to add bin edges "{0}" to a "{1}" Filter ' \
                       'since they are not monotonically ' \
-                      'increasing'.format(bins, self.type)
+                      'increasing'.format(bins, type(self))
                 raise ValueError(msg)
 
     def can_merge(self, other):
@@ -848,7 +847,7 @@ class EnergyFilter(Filter):
     def merge(self, other):
         if not self.can_merge(other):
             msg = 'Unable to merge "{0}" with "{1}" ' \
-                  'filters'.format(self.type, other.type)
+                  'filters'.format(type(self), other.type)
             raise ValueError(msg)
 
         # Merge unique filter bins
@@ -1324,13 +1323,11 @@ class PolarFilter(Filter):
         # Extract the lower and upper angle bounds, then repeat and tile
         # them as necessary to account for other filters.
         lo_bins = np.repeat(self.bins[:-1], self.stride)
-        hi_bins = np.repeat(self.bins[1:], self.stride)
         tile_factor = data_size / len(lo_bins)
         lo_bins = np.tile(lo_bins, tile_factor)
-        hi_bins = np.tile(hi_bins, tile_factor)
 
         # Add the new angle columns to the DataFrame.
-        df.loc[:, self.type + ' low'] = lo_bins
+        df.loc[:, 'polar low'] = lo_bins
 
         return df
 
@@ -1402,13 +1399,11 @@ class AzimuthalFilter(Filter):
         # Extract the lower and upper angle bounds, then repeat and tile
         # them as necessary to account for other filters.
         lo_bins = np.repeat(self.bins[:-1], self.stride)
-        hi_bins = np.repeat(self.bins[1:], self.stride)
         tile_factor = data_size / len(lo_bins)
         lo_bins = np.tile(lo_bins, tile_factor)
-        hi_bins = np.tile(hi_bins, tile_factor)
 
         # Add the new angle columns to the DataFrame.
-        df.loc[:, self.type + ' low'] = lo_bins
+        df.loc[:, 'azimuthal low'] = lo_bins
 
         return df