diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
index f7582fcbe..05366758a 100644
--- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
@@ -365,20 +365,15 @@
     "\n",
     "* `TotalXS`\n",
     "* `TransportXS`\n",
-    "* `NuTransportXS`\n",
     "* `AbsorptionXS`\n",
     "* `CaptureXS`\n",
     "* `FissionXS`\n",
-    "* `NuFissionXS`\n",
+    "* `NuFissionMatrixXS`\n",
     "* `KappaFissionXS`\n",
     "* `ScatterXS`\n",
-    "* `NuScatterXS`\n",
     "* `ScatterMatrixXS`\n",
-    "* `NuScatterMatrixXS`\n",
     "* `Chi`\n",
-    "* `ChiPrompt`\n",
     "* `InverseVelocity`\n",
-    "* `PromptNuFissionXS`\n",
     "\n",
     "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n",
     "\n",
@@ -387,7 +382,11 @@
     "* `Beta`\n",
     "* `DecayRate`\n",
     "\n",
-    "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. "
+    "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. \n",
+    "\n",
+    "In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. \n",
+    "\n",
+    "The prompt chi and nu-fission data can actually be gathered using the `Chi` and `FissionXS` classes, respectively, but passing in a value of `True` for the optional `prompt` parameter upon initialization."
    ]
   },
   {
@@ -399,8 +398,8 @@
    "outputs": [],
    "source": [
     "# Instantiate a few different sections\n",
-    "chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n",
-    "prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=energy_groups, by_nuclide=True)\n",
+    "chi_prompt = mgxs.Chi(domain=cell, groups=energy_groups, by_nuclide=True, prompt=True)\n",
+    "prompt_nu_fission = mgxs.FissionXS(domain=cell, groups=energy_groups, by_nuclide=True, nu=True, prompt=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",
     "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
@@ -542,8 +541,8 @@
       "         Copyright | 2011-2017 Massachusetts Institute of Technology\n",
       "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
       "           Version | 0.8.0\n",
-      "          Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n",
-      "         Date/Time | 2017-02-11 14:15:38\n",
+      "          Git SHA1 | 5e313cf5f1d601074ad95c17ae589bf564972adb\n",
+      "         Date/Time | 2017-02-26 06:05:10\n",
       "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
@@ -617,10 +616,10 @@
       "       44/1    1.24424    1.23133 +/- 0.00389\n",
       "       45/1    1.24767    1.23179 +/- 0.00381\n",
       "       46/1    1.22998    1.23174 +/- 0.00370\n",
-      "       47/1    1.26352    1.23260 +/- 0.00370\n",
-      "       48/1    1.23155    1.23257 +/- 0.00360\n",
-      "       49/1    1.22059    1.23227 +/- 0.00352\n",
-      "       50/1    1.24724    1.23264 +/- 0.00345\n",
+      "       47/1    1.26195    1.23256 +/- 0.00369\n",
+      "       48/1    1.23146    1.23253 +/- 0.00359\n",
+      "       49/1    1.22059    1.23222 +/- 0.00351\n",
+      "       50/1    1.24724    1.23260 +/- 0.00345\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -630,27 +629,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.1132E-01 seconds\n",
-      "   Reading cross sections          =  3.2075E-01 seconds\n",
-      " Total time in simulation          =  1.3772E+01 seconds\n",
-      "   Time in transport only          =  1.2971E+01 seconds\n",
-      "   Time in inactive batches        =  6.3146E-01 seconds\n",
-      "   Time in active batches          =  1.3140E+01 seconds\n",
-      "   Time synchronizing fission bank =  5.4819E-03 seconds\n",
-      "     Sampling source sites         =  3.8838E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.5557E-03 seconds\n",
-      "   Time accumulating tallies       =  5.3270E-04 seconds\n",
-      " Total time for finalization       =  4.9668E-02 seconds\n",
-      " Total time elapsed                =  1.4246E+01 seconds\n",
-      " Calculation Rate (inactive)       =  79181.7 neutrons/second\n",
-      " Calculation Rate (active)         =  15220.4 neutrons/second\n",
+      " Total time for initialization     =  3.8846E-01 seconds\n",
+      "   Reading cross sections          =  3.0221E-01 seconds\n",
+      " Total time in simulation          =  1.2666E+01 seconds\n",
+      "   Time in transport only          =  1.2196E+01 seconds\n",
+      "   Time in inactive batches        =  5.1652E-01 seconds\n",
+      "   Time in active batches          =  1.2150E+01 seconds\n",
+      "   Time synchronizing fission bank =  5.1914E-03 seconds\n",
+      "     Sampling source sites         =  3.6297E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.5222E-03 seconds\n",
+      "   Time accumulating tallies       =  5.2027E-04 seconds\n",
+      " Total time for finalization       =  4.8293E-02 seconds\n",
+      " Total time elapsed                =  1.3117E+01 seconds\n",
+      " Calculation Rate (inactive)       =  96801.2 neutrons/second\n",
+      " Calculation Rate (active)         =  16461.5 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     =  1.23260 +/-  0.00309\n",
-      " k-effective (Track-length)  =  1.23264 +/-  0.00345\n",
+      " k-effective (Collision)     =  1.23256 +/-  0.00308\n",
+      " k-effective (Track-length)  =  1.23260 +/-  0.00345\n",
       " k-effective (Absorption)    =  1.23111 +/-  0.00186\n",
-      " Combined k-effective        =  1.23135 +/-  0.00185\n",
+      " Combined k-effective        =  1.23135 +/-  0.00184\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -759,17 +758,17 @@
     {
      "data": {
       "text/plain": [
-       "array([[[  5.14239169e-06,   1.16429778e-06]],\n",
+       "array([[[  5.14223507e-06,   1.16426087e-06]],\n",
        "\n",
-       "       [[  2.65434434e-05,   7.58244504e-06]],\n",
+       "       [[  2.65426350e-05,   7.58220468e-06]],\n",
        "\n",
-       "       [[  2.53406770e-05,   5.73814391e-06]],\n",
+       "       [[  2.53399053e-05,   5.73796202e-06]],\n",
        "\n",
-       "       [[  5.68158884e-05,   1.04761254e-05]],\n",
+       "       [[  5.68141581e-05,   1.04757933e-05]],\n",
        "\n",
-       "       [[  2.32937121e-05,   5.45676114e-06]],\n",
+       "       [[  2.32930026e-05,   5.45658817e-06]],\n",
        "\n",
-       "       [[  9.75765501e-06,   1.65156185e-06]]])"
+       "       [[  9.75735783e-06,   1.65150949e-06]]])"
       ]
      },
      "execution_count": 18,
@@ -819,8 +818,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>9.533842e-11</td>\n",
-       "      <td>4.789050e-11</td>\n",
+       "      <td>9.534320e-11</td>\n",
+       "      <td>4.789291e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199</th>\n",
@@ -828,8 +827,8 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>1.606499e-11</td>\n",
-       "      <td>8.071081e-12</td>\n",
+       "      <td>1.606580e-11</td>\n",
+       "      <td>8.071486e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>398</th>\n",
@@ -837,8 +836,8 @@
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>1.224131e-09</td>\n",
-       "      <td>6.149449e-10</td>\n",
+       "      <td>1.224152e-09</td>\n",
+       "      <td>6.149552e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>399</th>\n",
@@ -846,8 +845,8 @@
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>2.602518e-10</td>\n",
-       "      <td>1.307590e-10</td>\n",
+       "      <td>2.602562e-10</td>\n",
+       "      <td>1.307612e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>598</th>\n",
@@ -855,8 +854,8 @@
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>9.033000e-10</td>\n",
-       "      <td>4.537601e-10</td>\n",
+       "      <td>9.032969e-10</td>\n",
+       "      <td>4.537585e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>599</th>\n",
@@ -864,8 +863,8 @@
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>1.522295e-10</td>\n",
-       "      <td>7.648264e-11</td>\n",
+       "      <td>1.522290e-10</td>\n",
+       "      <td>7.648238e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>798</th>\n",
@@ -873,8 +872,8 @@
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
-       "      <td>1.749138e-09</td>\n",
-       "      <td>8.786432e-10</td>\n",
+       "      <td>1.749268e-09</td>\n",
+       "      <td>8.787082e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>799</th>\n",
@@ -882,8 +881,8 @@
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
-       "      <td>2.400317e-10</td>\n",
-       "      <td>1.205943e-10</td>\n",
+       "      <td>2.400495e-10</td>\n",
+       "      <td>1.206032e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
@@ -909,14 +908,14 @@
       ],
       "text/plain": [
        "     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",
+       "198     1             1         1    U235  9.534320e-11  4.789291e-11\n",
+       "199     1             1         1   Pu239  1.606580e-11  8.071486e-12\n",
+       "398     1             2         1    U235  1.224152e-09  6.149552e-10\n",
+       "399     1             2         1   Pu239  2.602562e-10  1.307612e-10\n",
+       "598     1             3         1    U235  9.032969e-10  4.537585e-10\n",
+       "599     1             3         1   Pu239  1.522290e-10  7.648238e-11\n",
+       "798     1             4         1    U235  1.749268e-09  8.787082e-10\n",
+       "799     1             4         1   Pu239  2.400495e-10  1.206032e-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"
       ]
@@ -998,7 +997,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.120780</td>\n",
-       "      <td>0.000551</td>\n",
+       "      <td>0.000549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1007,7 +1006,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.113370</td>\n",
-       "      <td>0.000452</td>\n",
+       "      <td>0.000451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1016,7 +1015,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.302780</td>\n",
-       "      <td>0.001381</td>\n",
+       "      <td>0.001378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1025,7 +1024,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.292500</td>\n",
-       "      <td>0.001166</td>\n",
+       "      <td>0.001163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1034,7 +1033,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.849490</td>\n",
-       "      <td>0.003875</td>\n",
+       "      <td>0.003865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1043,7 +1042,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.857490</td>\n",
-       "      <td>0.003419</td>\n",
+       "      <td>0.003411</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -1052,7 +1051,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>2.853000</td>\n",
-       "      <td>0.013013</td>\n",
+       "      <td>0.012980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -1061,7 +1060,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>2.729700</td>\n",
-       "      <td>0.010884</td>\n",
+       "      <td>0.010858</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1073,14 +1072,14 @@
        "1      1             1         1   Pu239  0.013271   0.000053\n",
        "2      1             2         1    U235  0.032739   0.000149\n",
        "3      1             2         1   Pu239  0.030881   0.000123\n",
-       "4      1             3         1    U235  0.120780   0.000551\n",
-       "5      1             3         1   Pu239  0.113370   0.000452\n",
-       "6      1             4         1    U235  0.302780   0.001381\n",
-       "7      1             4         1   Pu239  0.292500   0.001166\n",
-       "8      1             5         1    U235  0.849490   0.003875\n",
-       "9      1             5         1   Pu239  0.857490   0.003419\n",
-       "10     1             6         1    U235  2.853000   0.013013\n",
-       "11     1             6         1   Pu239  2.729700   0.010884"
+       "4      1             3         1    U235  0.120780   0.000549\n",
+       "5      1             3         1   Pu239  0.113370   0.000451\n",
+       "6      1             4         1    U235  0.302780   0.001378\n",
+       "7      1             4         1   Pu239  0.292500   0.001163\n",
+       "8      1             5         1    U235  0.849490   0.003865\n",
+       "9      1             5         1   Pu239  0.857490   0.003411\n",
+       "10     1             6         1    U235  2.853000   0.012980\n",
+       "11     1             6         1   Pu239  2.729700   0.010858"
       ]
      },
      "execution_count": 20,
@@ -1169,7 +1168,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fcd0b533550>"
+       "<matplotlib.legend.Legend at 0x7fcb1e0dd588>"
       ]
      },
      "execution_count": 23,
@@ -1180,7 +1179,7 @@
      "data": {
       "image/png": 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trXiWGahyBYsB/AvArgObHrJDe3EqNB+AipNeuJY8TvBBMFZCSg8o94AyTwjIA7MrFPhB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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcd0b5b5630>"
+       "<matplotlib.figure.Figure at 0x7fcb1e15a518>"
       ]
      },
      "metadata": {},
@@ -1251,8 +1250,8 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>8.779406e-08</td>\n",
-       "      <td>4.667924e-10</td>\n",
+       "      <td>8.779139e-08</td>\n",
+       "      <td>4.658590e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1260,8 +1259,8 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>7.150041e-09</td>\n",
-       "      <td>3.565013e-11</td>\n",
+       "      <td>7.149814e-09</td>\n",
+       "      <td>3.559010e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1269,8 +1268,8 @@
        "      <td>2</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>9.528171e-07</td>\n",
-       "      <td>5.066035e-09</td>\n",
+       "      <td>9.527880e-07</td>\n",
+       "      <td>5.055905e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1278,8 +1277,8 @@
        "      <td>2</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>1.303200e-07</td>\n",
-       "      <td>6.497762e-10</td>\n",
+       "      <td>1.303159e-07</td>\n",
+       "      <td>6.486820e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1287,8 +1286,8 @@
        "      <td>3</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.353975e-07</td>\n",
-       "      <td>1.251585e-09</td>\n",
+       "      <td>2.353903e-07</td>\n",
+       "      <td>1.249083e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1296,8 +1295,8 @@
        "      <td>3</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.032960e-08</td>\n",
-       "      <td>1.013634e-10</td>\n",
+       "      <td>2.032895e-08</td>\n",
+       "      <td>1.011928e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1305,8 +1304,8 @@
        "      <td>4</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>4.720335e-07</td>\n",
-       "      <td>2.509756e-09</td>\n",
+       "      <td>4.720191e-07</td>\n",
+       "      <td>2.504737e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1314,8 +1313,8 @@
        "      <td>4</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.626392e-08</td>\n",
-       "      <td>1.309520e-10</td>\n",
+       "      <td>2.626309e-08</td>\n",
+       "      <td>1.307315e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1323,8 +1322,8 @@
        "      <td>5</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.828001e-08</td>\n",
-       "      <td>1.503620e-10</td>\n",
+       "      <td>2.827915e-08</td>\n",
+       "      <td>1.500614e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1332,8 +1331,8 @@
        "      <td>5</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.430664e-09</td>\n",
-       "      <td>1.211930e-11</td>\n",
+       "      <td>2.430587e-09</td>\n",
+       "      <td>1.209889e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -1341,8 +1340,8 @@
        "      <td>6</td>\n",
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>1.477575e-09</td>\n",
-       "      <td>7.856122e-12</td>\n",
+       "      <td>1.477530e-09</td>\n",
+       "      <td>7.840413e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -1350,8 +1349,8 @@
        "      <td>6</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>6.994534e-11</td>\n",
-       "      <td>3.487477e-13</td>\n",
+       "      <td>6.994312e-11</td>\n",
+       "      <td>3.481605e-13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1373,18 +1372,18 @@
        "11     1             6   Pu239   \n",
        "\n",
        "                                                score     mean  std. dev.  \n",
-       "0   (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08   4.67e-10  \n",
-       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09   3.57e-11  \n",
-       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07   5.07e-09  \n",
-       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07   6.50e-10  \n",
+       "0   (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08   4.66e-10  \n",
+       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09   3.56e-11  \n",
+       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07   5.06e-09  \n",
+       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07   6.49e-10  \n",
        "4   (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07   1.25e-09  \n",
        "5   (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08   1.01e-10  \n",
-       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07   2.51e-09  \n",
+       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07   2.50e-09  \n",
        "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08   1.31e-10  \n",
        "8   (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08   1.50e-10  \n",
        "9   (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09   1.21e-11  \n",
-       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   7.86e-12  \n",
-       "11  (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11   3.49e-13  "
+       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   7.84e-12  \n",
+       "11  (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11   3.48e-13  "
       ]
      },
      "execution_count": 24,
@@ -1438,7 +1437,7 @@
      "data": {
       "image/png": 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iMzOzQjjBmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMmZkVwgnGzGw90KtXL0aPHs3OO+/M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\nUXFWRrt2AGEAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcd0b364438>"
+       "<matplotlib.figure.Figure at 0x7fcb1df13860>"
       ]
      },
      "metadata": {},
@@ -1499,9 +1498,9 @@
     },
     {
      "data": {
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OMwwjp+hRI4vuZsiQIWzZsiXq3ObNm9ljjz3YsGFDxDB9++23A+4iwtNOO41zzjmHb37z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\nLMiGYDlO3H4DbgZ+LSLHAE9lQ7AcJlGfAZwP3NO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q3njjDfr06RNZUBeE9oQtv/766ykuLua1117j/vvvjyiX9mBhy41086v9lka2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\nhqrqf1S1vXaKo4B1qvqu195iXGO5X1m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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fcd0b35ef60>"
+       "<matplotlib.figure.Figure at 0x7fcb1e2aa320>"
       ]
      },
      "metadata": {},
diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py
index 284be6ca3..3ff17b35f 100644
--- a/openmc/mgxs/mgxs.py
+++ b/openmc/mgxs/mgxs.py
@@ -151,6 +151,8 @@ class MGXS(object):
         Reaction type (e.g., 'total', 'nu-fission', etc.)
     nu : bool
         If True, the cross section data will include neutron multiplication
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     domain : Material or Cell or Universe or Mesh
@@ -233,6 +235,7 @@ class MGXS(object):
         self._hdf5_key = None
         self._valid_estimators = ESTIMATOR_TYPES
         self._nu = False
+        self._prompt = False
 
         self.name = name
         self.by_nuclide = by_nuclide
@@ -415,6 +418,10 @@ class MGXS(object):
     def nu(self):
         return self._nu
 
+    @property
+    def prompt(self):
+        return self._prompt
+
     @property
     def by_nuclide(self):
         return self._by_nuclide
@@ -585,6 +592,11 @@ class MGXS(object):
         cv.check_type('nu', nu, bool)
         self._nu = nu
 
+    @prompt.setter
+    def prompt(self, prompt):
+        cv.check_type('prompt', prompt, bool)
+        self._prompt = prompt
+
     @by_nuclide.setter
     def by_nuclide(self, by_nuclide):
         cv.check_type('by_nuclide', by_nuclide, bool)
@@ -742,13 +754,14 @@ class MGXS(object):
         elif mgxs_type == 'chi':
             mgxs = Chi(domain, domain_type, energy_groups)
         elif mgxs_type == 'chi-prompt':
-            mgxs = ChiPrompt(domain, domain_type, energy_groups)
+            mgxs = Chi(domain, domain_type, energy_groups, prompt=True)
         elif mgxs_type == 'inverse-velocity':
             mgxs = InverseVelocity(domain, domain_type, energy_groups)
         elif mgxs_type == 'prompt-nu-fission':
-            mgxs = PromptNuFissionXS(domain, domain_type, energy_groups)
+            mgxs = FissionXS(domain, domain_type, energy_groups, prompt=True)
         elif mgxs_type == 'prompt-nu-fission matrix':
-            mgxs = PromptNuFissionMatrixXS(domain, domain_type, energy_groups)
+            mgxs = NuFissionMatrixXS(domain, domain_type, energy_groups,
+                                     prompt=True)
 
         mgxs.by_nuclide = by_nuclide
         mgxs.name = name
@@ -2612,6 +2625,9 @@ class TransportXS(MGXS):
        \sigma_{tr} &= \frac{\langle \sigma_t \phi \rangle - \langle \sigma_{s1}
        \phi \rangle}{\langle \phi \rangle}
 
+    To incorporate the effect of scattering multiplication in the above
+    relation, the `nu` parameter can be set to `True`.
+
     Parameters
     ----------
     domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
@@ -3046,6 +3062,16 @@ class FissionXS(MGXS):
        \sigma_f (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi}
        d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}.
 
+    To incorporate the effect of neutron multiplication in the above
+    relation, the `nu` parameter can be set to `True`.
+
+    This class can also be used to gather a prompt-nu-fission cross section
+    (which only includes the contributions from prompt neutrons). This is
+    accomplished by setting the :attr:`FissionXS.prompt` attribute to `True`.
+    Since the prompt-nu-fission cross section requires neutron multiplication,
+    the `nu` parameter will automatically be set to `True` if `prompt` is also
+    `True`.
+
     Parameters
     ----------
     domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
@@ -3057,6 +3083,10 @@ class FissionXS(MGXS):
     nu : bool
         If True, the cross section data will include neutron multiplication;
         defaults to False
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons;
+        defaults to False which includes prompt and delayed in total. Setting
+        this to True will also set nu to True
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     name : str, optional
@@ -3077,6 +3107,8 @@ class FissionXS(MGXS):
         Reaction type (e.g., 'total', 'nu-fission', etc.)
     nu : bool
         If True, the cross section data will include neutron multiplication
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     domain : Material or Cell or Universe or Mesh
@@ -3138,15 +3170,20 @@ class FissionXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, groups=None, nu=False,
-                 by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
+                 prompt=False, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(FissionXS, self).__init__(domain, domain_type,
                                         groups, by_nuclide, name, num_polar,
                                         num_azimuthal)
-        if not nu:
-            self._rxn_type = 'fission'
+        if not prompt:
+            if not nu:
+                self._rxn_type = 'fission'
+            else:
+                self._rxn_type = 'nu-fission'
+            self.nu = nu
         else:
-            self._rxn_type = 'nu-fission'
-
+            self._rxn_type = 'prompt-nu-fission'
+            self.nu = True
 
 class KappaFissionXS(MGXS):
     r"""A recoverable fission energy production rate multi-group cross section.
@@ -3305,6 +3342,9 @@ class ScatterXS(MGXS):
        \Omega) \right ]}{\int_{r \in V} dr \int_{4\pi} d\Omega
        \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}.
 
+    To incorporate the effect of scattering multiplication from (n,xn)
+    reactions in the above relation, the `nu` parameter can be set to `True`.
+
     Parameters
     ----------
     domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
@@ -3455,6 +3495,8 @@ class ScatterMatrixXS(MatrixMGXS):
        \phi \rangle - \delta_{gg'} \sum_{g''} \langle \sigma_{s,1,g''\rightarrow
        g} \phi \rangle}{\langle \phi \rangle}
 
+    To incorporate the effect of neutron multiplication from (n,xn) reactions
+    in the above relation, the `nu` parameter can be set to `True`.
 
     Parameters
     ----------
@@ -4481,6 +4523,11 @@ class NuFissionMatrixXS(MatrixMGXS):
        \nu\sigma_{f,g'\rightarrow g} &= \frac{\langle \nu\sigma_{f,g'\rightarrow
        g} \phi \rangle}{\langle \phi \rangle}
 
+    This class can also be used to gather a prompt-nu-fission cross section
+    (which only includes the contributions from prompt neutrons). This is
+    accomplished by setting the :attr:`NuFissionMatrixXS.prompt` attribute to
+    `True`.
+
     Parameters
     ----------
     domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
@@ -4489,6 +4536,9 @@ class NuFissionMatrixXS(MatrixMGXS):
         The domain type for spatial homogenization
     groups : openmc.mgxs.EnergyGroups
         The energy group structure for energy condensation
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons;
+        defaults to False which includes prompt and delayed in total
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     name : str, optional
@@ -4507,6 +4557,8 @@ class NuFissionMatrixXS(MatrixMGXS):
         Name of the multi-group cross section
     rxn_type : str
         Reaction type (e.g., 'total', 'nu-fission', etc.)
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     domain : Material or Cell or Universe or Mesh
@@ -4568,12 +4620,17 @@ class NuFissionMatrixXS(MatrixMGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, groups=None,
-                 by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
+                 prompt=False, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(NuFissionMatrixXS, self).__init__(domain, domain_type,
                                                 groups, by_nuclide, name,
                                                 num_polar, num_azimuthal)
-        self._rxn_type = 'nu-fission'
-        self._hdf5_key = 'nu-fission matrix'
+        if not prompt:
+            self._rxn_type = 'nu-fission'
+            self._hdf5_key = 'nu-fission matrix'
+        else:
+            self._rxn_type = 'prompt-nu-fission'
+            self._hdf5_key = 'prompt-nu-fission matrix'
         self._estimator = 'analog'
         self._valid_estimators = ['analog']
         self.nu = True
@@ -4610,6 +4667,10 @@ class Chi(MGXS):
        \chi_g &= \frac{\langle \nu\sigma_{f,g' \rightarrow g} \phi \rangle}
        {\langle \nu\sigma_f \phi \rangle}
 
+    This class can also be used to gather a prompt-chi (which only includes the
+    outgoing energy spectrum of prompt neutrons). This is accomplished by
+    setting the :attr:`Chi.prompt` attribute to `True`.
+
     Parameters
     ----------
     domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
@@ -4618,6 +4679,9 @@ class Chi(MGXS):
         The domain type for spatial homogenization
     groups : openmc.mgxs.EnergyGroups
         The energy group structure for energy condensation
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons;
+        defaults to False which includes prompt and delayed in total
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     name : str, optional
@@ -4636,6 +4700,8 @@ class Chi(MGXS):
         Name of the multi-group cross section
     rxn_type : str
         Reaction type (e.g., 'total', 'nu-fission', etc.)
+    prompt : bool
+        If true, computes cross sections which only includes prompt neutrons
     by_nuclide : bool
         If true, computes cross sections for each nuclide in domain
     domain : Material or Cell or Universe or Mesh
@@ -4697,13 +4763,18 @@ class Chi(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, groups=None,
-                 by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
+                 prompt=False, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(Chi, self).__init__(domain, domain_type, groups, by_nuclide,
                                   name, num_polar, num_azimuthal)
-        self._rxn_type = 'chi'
+        if not prompt:
+            self._rxn_type = 'chi'
+        else:
+            self._rxn_type = 'chi-prompt'
         self._estimator = 'analog'
         self._valid_estimators = ['analog']
         self.nu = True
+        self.prompt = prompt
 
     @property
     def _dont_squeeze(self):
@@ -4717,7 +4788,10 @@ class Chi(MGXS):
 
     @property
     def scores(self):
-        return ['nu-fission', 'nu-fission']
+        if not self.prompt:
+            return ['nu-fission', 'nu-fission']
+        else:
+            return ['prompt-nu-fission', 'prompt-nu-fission']
 
     @property
     def filters(self):
@@ -5132,135 +5206,6 @@ class Chi(MGXS):
         return '%'
 
 
-class ChiPrompt(Chi):
-    r"""The prompt fission spectrum.
-
-    This class can be used for both OpenMC input generation and tally data
-    post-processing to compute spatially-homogenized and energy-integrated
-    multi-group cross sections for multi-group neutronics calculations. At a
-    minimum, one needs to set the :attr:`ChiPrompt.energy_groups` and
-    :attr:`ChiPrompt.domain` properties. Tallies for the flux and appropriate
-    reaction rates over the specified domain are generated automatically via the
-    :attr:`ChiPrompt.tallies` property, which can then be appended to a
-    :class:`openmc.Tallies` instance.
-
-    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:`ChiPrompt.xs_tally` property.
-
-    For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the
-    fission spectrum is calculated as:
-
-    .. math::
-
-       \langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle &= \int_{r \in V}
-       dr \int_{4\pi} d\Omega' \int_0^\infty dE' \int_{E_g}^{E_{g-1}} dE \;
-       \chi(E)^p \nu^p \sigma_f (r, E') \psi(r, E', \Omega')\\
-       \langle \nu^p \sigma_f \phi \rangle &= \int_{r \in V} dr \int_{4\pi}
-       d\Omega' \int_0^\infty dE' \int_0^\infty dE \; \chi(E) \nu^p \sigma_f (r,
-       E') \psi(r, E', \Omega') \\
-       \chi_g^p &= \frac{\langle \nu^p \sigma_{f,g' \rightarrow g} \phi \rangle}
-       {\langle \nu^p \sigma_f \phi \rangle}
-
-    Parameters
-    ----------
-    domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
-        The domain for spatial homogenization
-    domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
-        The domain type for spatial homogenization
-    groups : openmc.mgxs.EnergyGroups
-        The energy group structure for energy condensation
-    by_nuclide : bool
-        If true, computes cross sections for each nuclide in domain
-    name : str, optional
-        Name of the multi-group cross section. Used as a label to identify
-        tallies in OpenMC 'tallies.xml' file.
-    num_polar : Integral, optional
-        Number of equi-width polar angle bins for angle discretization;
-        defaults to one bin
-    num_azimuthal : Integral, optional
-        Number of equi-width azimuthal angle bins for angle discretization;
-        defaults to one bin
-
-    Attributes
-    ----------
-    name : str, optional
-        Name of the multi-group cross section
-    rxn_type : str
-        Reaction type (e.g., 'total', 'nu-fission', etc.)
-    by_nuclide : bool
-        If true, computes cross sections for each nuclide in domain
-    domain : Material or Cell or Universe or Mesh
-        Domain for spatial homogenization
-    domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
-        Domain type for spatial homogenization
-    energy_groups : openmc.mgxs.EnergyGroups
-        Energy group structure for energy condensation
-    num_polar : Integral
-        Number of equi-width polar angle bins for angle discretization
-    num_azimuthal : Integral
-        Number of equi-width azimuthal angle bins for angle discretization
-    tally_trigger : openmc.Trigger
-        An (optional) tally precision trigger given to each tally used to
-        compute the cross section
-    scores : list of str
-        The scores in each tally used to compute the multi-group cross section
-    filters : list of openmc.Filter
-        The filters in each tally used to compute the multi-group cross section
-    tally_keys : list of str
-        The keys into the tallies dictionary for each tally used to compute
-        the multi-group cross section
-    estimator : 'analog'
-        The tally estimator used to compute the multi-group cross section
-    tallies : collections.OrderedDict
-        OpenMC tallies needed to compute the multi-group cross section. The keys
-        are strings listed in the :attr:`ChiPrompt.tally_keys` property and
-        values are instances of :class:`openmc.Tally`.
-    rxn_rate_tally : openmc.Tally
-        Derived tally for the reaction rate tally used in the numerator to
-        compute the multi-group cross section. This attribute is None
-        unless the multi-group cross section has been computed.
-    xs_tally : openmc.Tally
-        Derived tally for the multi-group cross section. This attribute
-        is None unless the multi-group cross section has been computed.
-    num_subdomains : int
-        The number of subdomains is unity for 'material', 'cell' and 'universe'
-        domain types. This is equal to the number of cell instances
-        for 'distribcell' domain types (it is equal to unity prior to loading
-        tally data from a statepoint file).
-    num_nuclides : int
-        The number of nuclides for which the multi-group cross section is
-        being tracked. This is unity if the by_nuclide attribute is False.
-    nuclides : Iterable of str or 'sum'
-        The optional user-specified nuclides for which to compute cross
-        sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides
-        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 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 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
-
-    """
-
-    def __init__(self, domain=None, domain_type=None, groups=None,
-                 by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
-        super(ChiPrompt, self).__init__(domain, domain_type, groups,
-                                        by_nuclide, name, num_polar,
-                                        num_azimuthal)
-        self._rxn_type = 'chi-prompt'
-
-    @property
-    def scores(self):
-        return ['prompt-nu-fission', 'prompt-nu-fission']
-
-
 class InverseVelocity(MGXS):
     r"""An inverse velocity multi-group cross section.
 
@@ -5408,253 +5353,3 @@ class InverseVelocity(MGXS):
         else:
             raise ValueError('Unable to return the units of InverseVelocity'
                              ' for xs_type other than "macro"')
-
-
-class PromptNuFissionXS(MGXS):
-    r"""A prompt fission neutron production multi-group cross section.
-
-    This class can be used for both OpenMC input generation and tally data
-    post-processing to compute spatially-homogenized and energy-integrated
-    multi-group cross sections for multi-group neutronics calculations. At a
-    minimum, one needs to set the :attr:`PromptNuFissionXS.energy_groups` and
-    :attr:`PromptNuFissionXS.domain` properties. Tallies for the flux and
-    appropriate reaction rates over the specified domain are generated
-    automatically via the :attr:`PromptNuFissionXS.tallies` property, which can
-    then be appended to a :class:`openmc.Tallies` instance.
-
-    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:`PromptNuFissionXS.xs_tally` property.
-
-    For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the
-    fission spectrum is calculated as:
-
-    .. math::
-
-       \frac{\int_{r \in V} dr \int_{4\pi} d\Omega \int_{E_g}^{E_{g-1}} dE \;
-       \nu\sigma_f^p (r, E) \psi (r, E, \Omega)}{\int_{r \in V} dr \int_{4\pi}
-       d\Omega \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega)}.
-
-    Parameters
-    ----------
-    domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
-        The domain for spatial homogenization
-    domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
-        The domain type for spatial homogenization
-    groups : openmc.mgxs.EnergyGroups
-        The energy group structure for energy condensation
-    by_nuclide : bool
-        If true, computes cross sections for each nuclide in domain
-    name : str, optional
-        Name of the multi-group cross section. Used as a label to identify
-        tallies in OpenMC 'tallies.xml' file.
-    num_polar : Integral, optional
-        Number of equi-width polar angle bins for angle discretization;
-        defaults to one bin
-    num_azimuthal : Integral, optional
-        Number of equi-width azimuthal angle bins for angle discretization;
-        defaults to one bin
-
-    Attributes
-    ----------
-    name : str, optional
-        Name of the multi-group cross section
-    rxn_type : str
-        Reaction type (e.g., 'total', 'nu-fission', etc.)
-    by_nuclide : bool
-        If true, computes cross sections for each nuclide in domain
-    domain : Material or Cell or Universe or Mesh
-        Domain for spatial homogenization
-    domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
-        Domain type for spatial homogenization
-    energy_groups : openmc.mgxs.EnergyGroups
-        Energy group structure for energy condensation
-    num_polar : Integral
-        Number of equi-width polar angle bins for angle discretization
-    num_azimuthal : Integral
-        Number of equi-width azimuthal angle bins for angle discretization
-    tally_trigger : openmc.Trigger
-        An (optional) tally precision trigger given to each tally used to
-        compute the cross section
-    scores : list of str
-        The scores in each tally used to compute the multi-group cross section
-    filters : list of openmc.Filter
-        The filters in each tally used to compute the multi-group cross section
-    tally_keys : list of str
-        The keys into the tallies dictionary for each tally used to compute
-        the multi-group cross section
-    estimator : {'tracklength', 'collision', 'analog'}
-        The tally estimator used to compute the multi-group cross section
-    tallies : collections.OrderedDict
-        OpenMC tallies needed to compute the multi-group cross section. The keys
-        are strings listed in the :attr:`PromptNuFissionXS.tally_keys` property
-        and values are instances of :class:`openmc.Tally`.
-    rxn_rate_tally : openmc.Tally
-        Derived tally for the reaction rate tally used in the numerator to
-        compute the multi-group cross section. This attribute is None
-        unless the multi-group cross section has been computed.
-    xs_tally : openmc.Tally
-        Derived tally for the multi-group cross section. This attribute
-        is None unless the multi-group cross section has been computed.
-    num_subdomains : int
-        The number of subdomains is unity for 'material', 'cell' and 'universe'
-        domain types. This is equal to the number of cell instances
-        for 'distribcell' domain types (it is equal to unity prior to loading
-        tally data from a statepoint file).
-    num_nuclides : int
-        The number of nuclides for which the multi-group cross section is
-        being tracked. This is unity if the by_nuclide attribute is False.
-    nuclides : Iterable of str or 'sum'
-        The optional user-specified nuclides for which to compute cross
-        sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides
-        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 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 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
-
-    """
-
-    def __init__(self, domain=None, domain_type=None, groups=None,
-                 by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
-        super(PromptNuFissionXS, self).__init__(domain, domain_type, groups,
-                                                by_nuclide, name, num_polar,
-                                                num_azimuthal)
-        self._rxn_type = 'prompt-nu-fission'
-        self.nu = True
-
-
-class PromptNuFissionMatrixXS(MatrixMGXS):
-    r"""A prompt fission neutron production matrix multi-group cross section.
-
-    This class can be used for both OpenMC input generation and tally data
-    post-processing to compute spatially-homogenized and energy-integrated
-    multi-group cross sections for multi-group neutronics calculations. At a
-    minimum, one needs to set the :attr:`PromptNuFissionMatrixXS.energy_groups`
-    and :attr:`PromptNuFissionMatrixXS.domain` properties. Tallies for the flux
-    and appropriate reaction rates over the specified domain are generated
-    automatically via the :attr:`PromptNuFissionMatrixXS.tallies` property,
-    which can then be appended to a :class:`openmc.Tallies` instance.
-
-    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:`PromptNuFissionMatrixXS.xs_tally`
-    property.
-
-    For a spatial domain :math:`V` and energy group :math:`[E_g,E_{g-1}]`, the
-    fission spectrum is calculated as:
-
-    .. math::
-
-       \langle \nu\sigma_{f,g'\rightarrow g} \phi \rangle &= \int_{r \in V} dr
-       \int_{4\pi} d\Omega' \int_{E_{g'}}^{E_{g'-1}} dE' \int_{E_g}^{E_{g-1}} dE
-       \; \chi(E) \nu\sigma_f^p (r, E') \psi(r, E', \Omega')\\
-       \langle \phi \rangle &= \int_{r \in V} dr \int_{4\pi} d\Omega
-       \int_{E_g}^{E_{g-1}} dE \; \psi (r, E, \Omega) \\
-       \nu\sigma_{f,g'\rightarrow g} &= \frac{\langle \nu\sigma_{f,g'\rightarrow
-       g}^p \phi \rangle}{\langle \phi \rangle}
-
-    Parameters
-    ----------
-    domain : openmc.Material or openmc.Cell or openmc.Universe or openmc.Mesh
-        The domain for spatial homogenization
-    domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
-        The domain type for spatial homogenization
-    groups : openmc.mgxs.EnergyGroups
-        The energy group structure for energy condensation
-    by_nuclide : bool
-        If true, computes cross sections for each nuclide in domain
-    name : str, optional
-        Name of the multi-group cross section. Used as a label to identify
-        tallies in OpenMC 'tallies.xml' file.
-    num_polar : Integral, optional
-        Number of equi-width polar angle bins for angle discretization;
-        defaults to one bin
-    num_azimuthal : Integral, optional
-        Number of equi-width azimuthal angle bins for angle discretization;
-        defaults to one bin
-
-    Attributes
-    ----------
-    name : str, optional
-        Name of the multi-group cross section
-    rxn_type : str
-        Reaction type (e.g., 'total', 'nu-fission', etc.)
-    by_nuclide : bool
-        If true, computes cross sections for each nuclide in domain
-    domain : Material or Cell or Universe or Mesh
-        Domain for spatial homogenization
-    domain_type : {'material', 'cell', 'distribcell', 'universe', 'mesh'}
-        Domain type for spatial homogenization
-    energy_groups : openmc.mgxs.EnergyGroups
-        Energy group structure for energy condensation
-    num_polar : Integral
-        Number of equi-width polar angle bins for angle discretization
-    num_azimuthal : Integral
-        Number of equi-width azimuthal angle bins for angle discretization
-    tally_trigger : openmc.Trigger
-        An (optional) tally precision trigger given to each tally used to
-        compute the cross section
-    scores : list of str
-        The scores in each tally used to compute the multi-group cross section
-    filters : list of openmc.Filter
-        The filters in each tally used to compute the multi-group cross section
-    tally_keys : list of str
-        The keys into the tallies dictionary for each tally used to compute
-        the multi-group cross section
-    estimator : 'analog'
-        The tally estimator used to compute the multi-group cross section
-    tallies : collections.OrderedDict
-        OpenMC tallies needed to compute the multi-group cross section. The keys
-        are strings listed in the :attr:`PromptNuFissionXS.tally_keys` property
-        and values are instances of :class:`openmc.Tally`.
-    rxn_rate_tally : openmc.Tally
-        Derived tally for the reaction rate tally used in the numerator to
-        compute the multi-group cross section. This attribute is None
-        unless the multi-group cross section has been computed.
-    xs_tally : openmc.Tally
-        Derived tally for the multi-group cross section. This attribute
-        is None unless the multi-group cross section has been computed.
-    num_subdomains : int
-        The number of subdomains is unity for 'material', 'cell' and 'universe'
-        domain types. This is equal to the number of cell instances
-        for 'distribcell' domain types (it is equal to unity prior to loading
-        tally data from a statepoint file).
-    num_nuclides : int
-        The number of nuclides for which the multi-group cross section is
-        being tracked. This is unity if the by_nuclide attribute is False.
-    nuclides : Iterable of str or 'sum'
-        The optional user-specified nuclides for which to compute cross
-        sections (e.g., 'U-238', 'O-16'). If by_nuclide is True but nuclides
-        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 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 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
-
-    """
-
-    def __init__(self, domain=None, domain_type=None, groups=None,
-                 by_nuclide=False, name='', num_polar=1, num_azimuthal=1):
-        super(PromptNuFissionMatrixXS, self).__init__(domain, domain_type,
-                                                      groups, by_nuclide, name,
-                                                      num_polar, num_azimuthal)
-        self._rxn_type = 'prompt-nu-fission'
-        self._hdf5_key = 'prompt-nu-fission matrix'
-        self._estimator = 'analog'
-        self._valid_estimators = ['analog']
-        self.nu = True