diff --git a/docs/source/io_formats/mgxs_library.rst b/docs/source/io_formats/mgxs_library.rst
index 57a3770402..942c503054 100644
--- a/docs/source/io_formats/mgxs_library.rst
+++ b/docs/source/io_formats/mgxs_library.rst
@@ -90,7 +90,7 @@ Temperature-dependent data, provided for temperature <TTT>K.
 :Datasets: - **total** (*double[]* or *double[][][]*) -- Total cross section.
              This is a 1-D vector if `representation` is "isotropic", or a 3-D
              vector if `representation` is "angle" with dimensions of
-             [groups][azimuthal][polar].
+             [polar][azimuthal][groups].
            - **absorption** (*double[]* or *double[][][]*) -- Absorption
              cross section.
              This is a 1-D vector if `representation` is "isotropic", or a 3-D
@@ -100,19 +100,19 @@ Temperature-dependent data, provided for temperature <TTT>K.
              cross section.
              This is a 1-D vector if `representation` is "isotropic", or a 3-D
              vector if `representation` is "angle" with dimensions of
-             [groups][azimuthal][polar].  This is only required if the dataset
+             [polar][azimuthal][groups].  This is only required if the dataset
              is fissionable and fission-tallies are expected to be used.
            - **kappa-fission** (*double[]* or *double[][][]*) -- Kappa-Fission
              (energy-release from fission) cross section.
              This is a 1-D vector if `representation` is "isotropic", or a 3-D
              vector if `representation` is "angle" with dimensions of
-             [groups][azimuthal][polar].  This is only required if the dataset
+             [polar][azimuthal][groups].  This is only required if the dataset
              is fissionable and fission-tallies are expected to be used.
            - **chi** (*double[]* or *double[][][]*) -- Fission neutron energy
              spectra.
              This is a 1-D vector if `representation` is "isotropic", or a 3-D
              vector if `representation` is "angle" with dimensions of
-             [groups][azimuthal][polar].  This is only required if the dataset
+             [polar][azimuthal][groups].  This is only required if the dataset
              is fissionable and fission-tallies are expected to be used.
            - **nu-fission** (*double[]* to *double[][][][]*) -- Nu-Fission
              cross section.
@@ -122,8 +122,11 @@ Temperature-dependent data, provided for temperature <TTT>K.
              spectra as well and thus will have one additional dimension
              for the outgoing energy group.  In this case, `nu-fission` has the
              same dimensionality as `multiplicity matrix`.
-           - **inverse-velocity** (*double[]*) -- Average inverse velocity
-             for each of the groups in the library. This dataset is optional.
+           - **inverse-velocity** (*double[]* or *double[][][]*) --
+             Average inverse velocity for each of the groups in the library.
+             This dataset is optional. This is a 1-D vector if `representation`
+             is "isotropic", or a 3-D vector if `representation` is "angle"
+             with dimensions of [polar][azimuthal][groups].
 
 **/<library name>/<TTT>K/scatter_data/**
 
diff --git a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
index 8eeb0308d6..f7582fcbe6 100644
--- a/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mdgxs-part-i.ipynb
@@ -434,22 +434,16 @@
        "OrderedDict([('delayed-nu-fission', Tally\n",
        "              \tID             =\t10000\n",
        "              \tName           =\t\n",
-       "              \tFilters        =\t\n",
-       "                              \t\tCellFilter\t[1]\n",
-       "                              \t\tDelayedGroupFilter\t[1 2 3 4 5 6]\n",
-       "                              \t\tEnergyFilter\t[  1.00000000e-03   1.99526231e+07]\n",
+       "              \tFilters        =\tCellFilter, DelayedGroupFilter, EnergyFilter\n",
        "              \tNuclides       =\tU235 Pu239 \n",
        "              \tScores         =\t['delayed-nu-fission']\n",
-       "              \tEstimator      =\tanalog), ('decay-rate', Tally\n",
+       "              \tEstimator      =\ttracklength), ('decay-rate', Tally\n",
        "              \tID             =\t10001\n",
        "              \tName           =\t\n",
-       "              \tFilters        =\t\n",
-       "                              \t\tCellFilter\t[1]\n",
-       "                              \t\tDelayedGroupFilter\t[1 2 3 4 5 6]\n",
-       "                              \t\tEnergyFilter\t[  1.00000000e-03   1.99526231e+07]\n",
+       "              \tFilters        =\tCellFilter, DelayedGroupFilter, EnergyFilter\n",
        "              \tNuclides       =\tU235 Pu239 \n",
        "              \tScores         =\t['decay-rate']\n",
-       "              \tEstimator      =\tanalog)])"
+       "              \tEstimator      =\ttracklength)])"
       ]
      },
      "execution_count": 13,
@@ -545,12 +539,12 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         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 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n",
-      "         Date/Time | 2016-10-31 14:07:44\n",
-      "    OpenMP Threads | 4\n",
+      "          Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n",
+      "         Date/Time | 2017-02-11 14:15:38\n",
+      "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -560,12 +554,12 @@
       " Reading geometry XML file...\n",
       " Reading materials XML file...\n",
       " Reading cross sections XML file...\n",
-      " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\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 Pu239 from /home/romano/openmc/scripts/nndc_hdf5/Pu239.h5\n",
-      " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
+      " Reading H1 from /opt/xsdata/nndc/H1.h5\n",
+      " Reading O16 from /opt/xsdata/nndc/O16.h5\n",
+      " Reading U235 from /opt/xsdata/nndc/U235.h5\n",
+      " Reading U238 from /opt/xsdata/nndc/U238.h5\n",
+      " Reading Pu239 from /opt/xsdata/nndc/Pu239.h5\n",
+      " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n",
       " Maximum neutron transport energy: 2.00000E+07 eV for H1\n",
       " Reading tallies XML file...\n",
       " Building neighboring cells lists for each surface...\n",
@@ -636,20 +630,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  6.3901E-01 seconds\n",
-      "   Reading cross sections          =  4.7650E-01 seconds\n",
-      " Total time in simulation          =  3.8488E+01 seconds\n",
-      "   Time in transport only          =  3.8300E+01 seconds\n",
-      "   Time in inactive batches        =  2.6205E+00 seconds\n",
-      "   Time in active batches          =  3.5868E+01 seconds\n",
-      "   Time synchronizing fission bank =  1.0040E-02 seconds\n",
-      "     Sampling source sites         =  7.0596E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.8789E-03 seconds\n",
-      "   Time accumulating tallies       =  1.9011E-03 seconds\n",
-      " Total time for finalization       =  4.0220E-02 seconds\n",
-      " Total time elapsed                =  3.9193E+01 seconds\n",
-      " Calculation Rate (inactive)       =  19080.5 neutrons/second\n",
-      " Calculation Rate (active)         =  5576.04 neutrons/second\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",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -765,12 +759,17 @@
     {
      "data": {
       "text/plain": [
-       "array([[  5.14239169e-06,   1.16429778e-06],\n",
-       "       [  2.65434434e-05,   7.58244504e-06],\n",
-       "       [  2.53406770e-05,   5.73814391e-06],\n",
-       "       [  5.68158884e-05,   1.04761254e-05],\n",
-       "       [  2.32937121e-05,   5.45676114e-06],\n",
-       "       [  9.75765501e-06,   1.65156185e-06]])"
+       "array([[[  5.14239169e-06,   1.16429778e-06]],\n",
+       "\n",
+       "       [[  2.65434434e-05,   7.58244504e-06]],\n",
+       "\n",
+       "       [[  2.53406770e-05,   5.73814391e-06]],\n",
+       "\n",
+       "       [[  5.68158884e-05,   1.04761254e-05]],\n",
+       "\n",
+       "       [[  2.32937121e-05,   5.45676114e-06]],\n",
+       "\n",
+       "       [[  9.75765501e-06,   1.65156185e-06]]])"
       ]
      },
      "execution_count": 18,
@@ -793,7 +792,8 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -962,7 +962,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.013336</td>\n",
-       "      <td>0.003509</td>\n",
+       "      <td>0.000061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -971,7 +971,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.013271</td>\n",
-       "      <td>0.007154</td>\n",
+       "      <td>0.000053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -980,7 +980,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.032739</td>\n",
-       "      <td>0.003864</td>\n",
+       "      <td>0.000149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -989,7 +989,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.030881</td>\n",
-       "      <td>0.008109</td>\n",
+       "      <td>0.000123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -998,7 +998,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.120780</td>\n",
-       "      <td>0.017277</td>\n",
+       "      <td>0.000551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1007,7 +1007,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.113370</td>\n",
-       "      <td>0.031354</td>\n",
+       "      <td>0.000452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1016,7 +1016,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.302780</td>\n",
-       "      <td>0.027190</td>\n",
+       "      <td>0.001381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1025,7 +1025,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.292500</td>\n",
-       "      <td>0.057372</td>\n",
+       "      <td>0.001166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1034,7 +1034,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>0.849490</td>\n",
-       "      <td>0.120338</td>\n",
+       "      <td>0.003875</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1043,7 +1043,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>0.857490</td>\n",
-       "      <td>0.231893</td>\n",
+       "      <td>0.003419</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -1052,7 +1052,7 @@
        "      <td>1</td>\n",
        "      <td>U235</td>\n",
        "      <td>2.853000</td>\n",
-       "      <td>0.659168</td>\n",
+       "      <td>0.013013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -1061,7 +1061,7 @@
        "      <td>1</td>\n",
        "      <td>Pu239</td>\n",
        "      <td>2.729700</td>\n",
-       "      <td>1.342167</td>\n",
+       "      <td>0.010884</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1069,18 +1069,18 @@
       ],
       "text/plain": [
        "    cell  delayedgroup  group in nuclide      mean  std. dev.\n",
-       "0      1             1         1    U235  0.013336   0.003509\n",
-       "1      1             1         1   Pu239  0.013271   0.007154\n",
-       "2      1             2         1    U235  0.032739   0.003864\n",
-       "3      1             2         1   Pu239  0.030881   0.008109\n",
-       "4      1             3         1    U235  0.120780   0.017277\n",
-       "5      1             3         1   Pu239  0.113370   0.031354\n",
-       "6      1             4         1    U235  0.302780   0.027190\n",
-       "7      1             4         1   Pu239  0.292500   0.057372\n",
-       "8      1             5         1    U235  0.849490   0.120338\n",
-       "9      1             5         1   Pu239  0.857490   0.231893\n",
-       "10     1             6         1    U235  2.853000   0.659168\n",
-       "11     1             6         1   Pu239  2.729700   1.342167"
+       "0      1             1         1    U235  0.013336   0.000061\n",
+       "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"
       ]
      },
      "execution_count": 20,
@@ -1124,7 +1124,16 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
+     ]
+    }
+   ],
    "source": [
     "chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n",
     "chi_delayed.build_hdf5_store(filename='mdgxs', append=True)"
@@ -1160,7 +1169,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fc336841080>"
+       "<matplotlib.legend.Legend at 0x7fcd0b533550>"
       ]
      },
      "execution_count": 23,
@@ -1169,9 +1178,9 @@
     },
     {
      "data": {
-      "image/png": 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48WI8PDyw2Wx4eHiQmZnJgw8+yAsvvIDFYqFFixb2bCuO5uzsswT1DsJkMZGV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VEmVXRcCPGl4dSSSB4t3nnnHd555x2WLVuGj48PzZs354svvqgVxnfjmY3xmuVlUh0szczo7uzM\nf+PjmRYTQy+1mmfK5ZQ1CFZW8MMPUFwpbc8erQc8N1cb8FxLcLVzZfjTwxFC4OHgQciwENq4tDG4\n4T1gAAQHa5MO5uVJ41sikUiMQVVhJ8V3RvtKFolEUo6ePXty+vRpDh8+TJdaYsmUNuAK8wvJvmKE\nKpOVMMzVlRudO/Nz69b8xdBeb9AW1Rk0qGT7jTe0aQiLShvXNhRFYXLnyTzt+rRRSs63b681vAFO\nnza4OIlEIpFQhee7OB2gEOIj46kjkTzaVFQBrTaQcTyD6FHRZJ3PwsLBgq4pXU2ih6MpJh0NGgRL\nlmjX8/IgKemRyKuXlZ/F7pjd9G7WGzPFMGkrR4yAWbO067t2wZ07YC9dKxKJRGJQ9CmyY60oyvuK\novxHUZTvixdjKCeRPKrcvHmT//3vf7r836ZGk6Yh63wWFIAmVUP2ZdN5v4UQHMvIYPqVKyw0Ro67\nLl2gONtKaio8AqW7RwePpsGiBnz5+5ekZKcYTI63NzzzjHY9NxdKVZyWSCQSiYHQx52yGmgA9AT2\nAo2AO4ZUSiJ5lAkODqZ58+aEhoZiX0vciGp/Nepeat327W2mS9W/+dYt+p4+zd60NPydnQ0v0Nwc\nevbUrjdrBsnJ2qwnR44YXvYDkJ2fTYu6Lfiyx5fsfms39WzrVd/pIWjdumT98mWDipJIJBIJ+hnf\nTYUQM4G7QohVQB+06QclEkk5Dhw4wDfffMPdu3fx8vKie/fuplZJR91X6urWb4eYxvi+mZdH4Nmz\nJOXnc/TOHXyMFf4xcyZcugTffw9vvw2DB0NkpHFk3wenbpyi3uf1mLJrCp8c+AQhRPWdHpLJk8HM\nDF56CYYMMbg4iUQieeLRJwAzv+gzTVGUNsANwMVwKkkkjy45OTns3LkTgM2bN7N48WKjTJzThzpP\nl2QXSd2VSkF2AeY25kbVwcXSkmft7DiZmUm+EGxPSeE1FyP8nRRXkalbV5vxpFkzw8t8AFrVb4WF\nmfZv+WraVcIvh/Pn7T/p16IfXk5eBpHZvr32ZYBaXX1biUQikTw8+ni+VyiK4gzMBIKBc8BCg2ol\nkTyivPjiizgWZfGIjY1l7NixLFiwwMRaabH2ssaingWKlYJzT2fyb+VX38kA9C9V7XLqlSv0OnXK\neMKdnGqt4Q1gaW7JK01f0W0P+mUQUTeiyCvIM5hMRQE7O9i+HcaMgWHDDCZKIpFIahWjRo1iVvGs\ncyNSrfEthPg/IUSqEGKvEKKJEMJFCPFfYygnkTxqqFQq+vTpo9v+448/ylS/NCXWnta0O9COrqld\naRvWFmsPa5Po0b9uSfjLjdxcvjZFhpg7d2DzZli1yviyq6G42iXAU/Wf4vv+39O8bnODykxJgc8/\n11a8/Pe/DSpKInkgvLy8sLW1xcHBATc3N0aNGkVWVtZ9nycvL4/Ro0fj5eWFo6Mj7dq1Y/v27brj\nb7zxBm5ubjg5OdGyZUu+++67Mv2jo6Pp3r07Tk5ONG/evNZMqteXoKAgWrdujZ2dHc2aNePgwYMV\ntlu2bBkdOnTA2tqat99++76PS6pGn2wnToqiTFAU5UtFUb4uXoyhnETyKDJgwADdek5ODp07dzah\nNmWxbWGLuY05BTkFZF28/xtXTdDWzo7GVlYA5AhBbG6ucRU4d05bYGfZMigsNK5sPXil6SuYK9pw\noGMJx0i8k2hwmQ0awG+/wb/+BU8/bXBxEsl9oygKW7duJSMjgxMnTnD8+HHmzZt33+fRaDR4enqy\nf/9+0tPTmTt3LkOGDCG2KPPStGnTiImJIS0tjeDgYGbMmMHJkycBKCgooH///vTr14/U1FS+/fZb\nRowYwaVLl2r0Wg3Fzp07mTZtGqtWrSIzM5N9+/bRpEmTCts2bNiQmTNn8s477zzQcUnV6BN2EgZ4\nAaeByFKLRCKpgJ49e2JhYaHzjGg0GlOrpCPneg6n+57mkMshrn18zSQ6KIrCaDc3xrq7s/2ZZ3jB\nyYk8YxnBhYWQmQkTJ8I338CoUcaRex842zjTu1lvBrUaxP/1/T9OJp5k6q6p7Lu2z2g61MJnEolE\nNwHZzc2NV155hTNnzgBgZmbGlStXdO2qCiWwtbVl1qxZeHh4ANCnTx+8vb2JLJqA3bp1a6ytrXXy\nFEXhclEaoOjoaBITE/nggw9QFAU/Pz+6dOnC6tWrK9W5e/futeYeMGfOHGbNmkWHDh0A7Ti6ublV\n2HbAgAH069cPdSWTQao7XpoFCxbQqFEjHBwcaNWqFREREbpjiYmJDB48GBcXF3x8fFi6dKnu2PXr\n1wkMDMTFxYX69eszYcIE3bHo6Gj8/Pxwdnbm6aefJqRU4TRvb2+++OIL2rZti7OzM8OGDSMvTxu6\nd/LkSdq3b4+joyNDhw4lJydHb11rEn2Mb2shxCQhxA9CiFXFi0G0kUgeAxwcHDh58iRXr15lwIAB\njB49muHDh5taLQBUdVW4vO7CX67+hVarW5lMjxleXnzp40PknTu8fOoULY8eNUpmD8aMgY4dYe5c\n2LTJ8PIekC1Dt7BhyAaupl1l9t7ZqMxUNLBrYFCZt2/D6NHg6VmSFl0iqY3ExcURFhZGu3btHvpc\nSUlJXLx4kaeeekq37/3336dOnTq0atUKd3d3evfuXWl/IYTuIaA88fHxAFjUcHGxvn374uzsjFqt\nvuezX79+FfYpLCzk+PHj3Lx5k2bNmuHp6cn48ePJNfCbxwsXLrBs2TIiIyPJyMhgx44deHl5Adqx\n69u3L8899xyJiYn89ttvLFmyhJ07d1JYWEhAQADe3t7ExsYSHx/P0KFDAe3bi759+9KrVy+Sk5P5\n+uuvef3117l48aJO7vr16wkPDycmJoZTp06xcuVK8vPzGThwIG+99RYpKSm8+uqrbNiwQS9daxq9\n8nwrivKuoihuiqKoixeDaCORPCa0adOGzMxM1qxZg6+v7wO9HjUE5jbmuA51RaVWmVoVrMzMuFNQ\nwL89PTnToYNxssJ0LVXZc8MGWLwY/vEPw8u9T4rH4mO/jzn27jHmvjTX4HHfaWnw3XcQFwd378ID\nhNNKHmfmzNEuNbX9AAwYMAC1Ws1f//pX/Pz8mDZt2kOdT6PRMGLECEaOHEnz5iW/r2XLlpGZmcmB\nAwcYNGgQVkVhci1atMDFxYVFixah0WgIDw9n7969Fcae79y5k0mTJtGgQQN++umnanUpbQRWR0hI\nCKmpqaSkpNzzGRwcXGGfpKQk8vPz2bBhAwcPHiQqKoqTJ08a/N5kbm5OXl4eZ86c0YX8eHt7A3Ds\n2DFu3brF9OnTMTc3x8vLi9GjRxMUFMTRo0dJTExk4cKFWFtbY2lpqQvhPHz4MHfv3uXDDz/EwsIC\nPz8/AgIC+Pnnn3VyP/jgA1xdXXFycqJv375ERUVx+PBhNBoNEyZMwNzcnMDAQN1bgOp0rWn0Mb7z\ngM+B3ykJOTluEG0kkseIhg0bEhISwnvvvVdpXJ2pyE/J5/qy65zqcYqC7AKT6KAoCvObNKFX3brY\nmhsp5WFxsR2A48fh5Enw8zOO7AfAmGkqfXygVdHLkLw82Ge8KBeJRC+2bNlCSkoKMTExLF26VGcU\nV8batWuxt7fHwcGhzER40HpdR4wYgZWVVZlQh2IURaFz587ExcWxfPlyQOvB3rx5M6Ghobi5ufHV\nV1/x2muv0ahRo3v6v/zyy5ibmzNp0iRGjBgBQHp6Ohs3bmT+/Pll2mZkZGBnZ8elS5fYtGkTH3/8\nMSdOnLivsakOm6KaChMmTMDFxQW1Ws2kSZMICwurUTnl8fHxYfHixcyZMwdXV1eGDx9OYqJ2Hsu1\na9eIj49HrVbrvPfz588nKSmJuLg4GjdujJnZvWZqQkKCLmyomMaNG+veNAC4urrq1m1tbcnMzCQh\nIYGGDRve008fXWsafYzvyWgL7XgJIbyLltplSUgkjwC1Je5Pk67hkPshLo27ROrOVNL2pplaJYQQ\nxGQboeS9qyuUflXdrx/07294uQ9BUmYSK6NWMvTXoayMWmlQWaWfTWpxVI7kCaWy0DRbW9sy3ucb\nN24AMHz4cO7cuUNGRgZbt24t0+edd97h1q1bbNy4EfMqHv41Go0u5hu0bzX37NlDcnIy27Zt4/Ll\ny/j6+lbYNyoqivbt2+u2HR0dad++Pfn5ZdO87t69Gz8/P0JCQmjYsCETJ05k0aJFlerUu3dv3UNF\n+aX8Q0YxTk5O9zwkGOvhfujQoezfv59r17TzjKZOnQqAh4cHTZo0ISUlRee9T09PJzQ0FA8PD2Jj\nYymsYAKKu7s7cXFxZfbFxsbeY1iXx83NjevXr9/TTx9daxp9jO9LgHwBKZE8AJmZmXz22We8+OKL\nNRKfWBNYOFpQP7C+bjslLMVkuhQKwd///BOP33/H/9Qp40y8fKUkjzbbthle3kOy4/IOtl7Yin8T\nf3r69Ky+w0Pg71+y/v33UGCalyKS2kgtCDupjOeee461a9dSWFjI9u3b2bt3b5Xtx4wZQ3R0NMHB\nwVhaWur2Jycns27dOu7evUthYSE7duwgKCgI/1I/jNOnT5Obm0tWVhaLFi3ixo0bjBw58h4Z586d\no1XRq6SgoKAq9cnNzcXS0pKJEyfi6+vL9evXqwx3CAsL0z1UlF/KP2SUZtSoUSxdupTk5GRSU1P5\n6quv6Nu3b4VtCwoKyMnJoaCgAI1GQ25uLgWl/hCqO17MhQsXiIiIIC8vD0tLS2xsbHTebF9fX+zt\n7Vm4cKHuXGfPnuX48eP4+vri5ubG1KlTycrKIjc3l0OHDgHQsWNHbG1tWbhwIRqNhj179hAaGsqw\naooUdOrUCZVKxdKlS9FoNGzcuJGjR4/qpWtNo89Z7wJRiqJ8K1MNSiT3R1JSEr/99hvu7u78/vvv\nplZHR4NRJZP3UraZzvj+X2Iie9LSSMzL47sWLbA00B9dGfr0gRdegHnzwNdXG/PdsSPUkjcTpVl7\nei2hF0L5LeY3XvB8ATf7ijMT1BQvvQTFc8M0GkhKMqg4iURvqvLSLl68mODgYJydnfn5558ZOHBg\npW1jY2NZsWIFUVFRuLq66jzIP//8M4qisHz5cjw8PFCr1UyZMoUlS5aU8SavXr0aNzc3GjRoQERE\nBDt37kSluncOjVqtxtHRkaCgILp161apPunp6Tg7O5fZt3nzZqZPn17FaDwYM2fO5Pnnn6d58+Y8\n9dRTtG/fnn8XJfbv3bs3n332ma7tvHnzsLW1ZcGCBaxZswZbW1s++eQTvY8Xk5uby9SpU6lfvz7u\n7u4kJyfrwm7MzMwIDQ0lKioKb29vXFxcePfdd8nIyMDMzIyQkBAuXryIp6cnHh4e/PLLL4C2nkZI\nSAhhYWHUq1ePcePGsXr1apoVFVCr7LuiUqnYsGEDP/zwA3Xr1mX9+vUEBgbqpWtNo1SXYUBRlLcq\n2m/sjCeKogijZEN4DFi7dm2tya5RmzH0ON2+fZv69esjhMDCwoLbt2/j4OBgMHn3Q2FuIfvV+xFZ\n2t+U70VfbJvaVtjWkOM05s8/+bYopm6apyefGjM2XggYMEBrgPfuDc8+qy33+BDU9FgNCBrAlj+3\nAPB1r68Z33G8Lv2ZoViwABo2hJdf1kbpGAL5H6U/iqIghDB4fIC8xxqHa9eusXLlSmbPng3Apk2b\nCAgI0BnwISEhdOvWjRs3buiMScmjSVW/XX0qXK4CfgEOy1SDEon+1K1bl2effRbQxg3+9ttv98Sp\nmQrFQoFSER63Q2+bRI+epXLEht2+zfbbt42TchC0hvaWLTB9Ojz33EMb3oagdJjJ0qNL6fh/HZm+\nu+Y9YqX58EMYMQLUaij1RlYikTwkmZmZ/Prrr0RGRnL27FlAW4it2PDetGkTc+fOJTAwUOfllTye\nVJt8UlGUvsAiwBLwVhTlWeBjIUTFySQlEomOnj176qqjvfnmmzRs2JBz584ZLI5MXxRzhWZLm5Fx\nKAP1K2qcX3auvpMBeMnZGXOgADh19y5zr12jo4MDzhW8xjU4+flgCrlV0MOnhxVrFdcAACAASURB\nVG49Ji2Gr1/5Gj8vw2ZnEQJefx3CwqBpU9i7F+rUMahIieSJwM7OjsmTJzN58mQAUlNTqV+/ZP7N\nwIEDqwyZkTw+6GMBzAF8gTQAIUQUILOdSCR60LNU+ggHB4daYXgX4z7anZbft8TlVRdUTqYxOh0t\nLOjk6Kjbfq9hQ+Mb3itXajOe1KsHycnGlV0NPmoffJx9ANAUarC2sMbKour0ag+LosDbb8OFC9ps\njNLwlkgMw8GDB6uMB5c8vuhjBeQLIdLL7ZPFhyUSPejcuTN2dnaANjfppUuXTKxRWfJu5nFj9Q3O\nv3mewlzT/Kx7Fk02clGpyDRFeo2EBBg4EC5fhlJeqNpCsffb0tySi7cvIoQgPaf8X3LN4u8PLi4G\nFSGRPPEEBATUePVLyaOBPsb3WUVRhgPmiqI0UxRlKXDIwHpJJI8FlpaWvP3223zwwQe6mdm7d+82\ntVo6Tvc5za1Nt3B8wRFRYJrJViMbNOBE+/Ykdu6Mv7Mzy+LjuWsMI/z8eRg/XptT7/DhWltTfczz\nYwgbHsbv7/zOwbiDuH/pzozdM4wiOzYWPvqoViaCkUgkkkcWfR65xgPTgVxgLRAOzDWkUhLJ48SS\nJUsQQjBo0CCGDBlCu3bt6NixI3Vqwfv8dkfbGbWKYkU0sramkbU1fU+f5vidO/RSqxlUrx51DF31\nMjkZvvlGu56frw12TkjQer9L5f41Nc+4PsMzrs9wOeUyHRt2ZNaLs2jibNjIv8JCbcaTololDBlS\nUv1SIpFIJA9Htca3ECILrfGtm2KvKIonEFtpJ4lEUgZFUfjnP//JypUrcSwV42xqShveBVkFiAKB\nhb1pXoP+X4sWuKhUxnsY6NQJHBwgI0Pr4m3VSmuQ790LbdoYR4f7wEftw1j1WKPIMjMra3yfPCmN\nb4lEIqkpqgw7URSlk6IogxVFcSnafkZRlLXAQaNoJ5E8RnTp0qVWGd7FXF9ynSPNjrDfbj9xn5su\nFaKrpaVxvfAqVdmSji+/rK0qUwsN7/Jk5Gbw560/DSpjwICS9e3bDSpKIpFInigqNb4VRfkc+B4I\nBLYqijIPbcjJEUBmfpdIHpCYmBiWL19Obm6uqVUBIHljMtmXskFA+mHDTuSrjtzCQnalpPCvy5e5\nmp1teIGlS81HR5eUd6ylRN+K5sWVL9Lwy4b89/h/DSqrV6+S9Y0b4eZNg4qTSCSSJ4aqPN99gOeE\nEMOAHsA/gL8IIZYIIXKMop1E8pjRq1cv/vKXv3D48GEyMjJMrQ4ArVaXxBOk70unIMsEGUeKGHDm\nDH+7cIF0jQYrY6RkLG1hZmRoY7+PHKl1KQeLsVHZ8ILnCxwbfYyven1lUFnt2pWkPb97F6KiDCpO\nIpFInhiqurvlFBvZQohU4KIQ4qpRtJJIHkMWLlzI7du3uX37NvPnzy9TXMGUWHtaY/uUtrS8yBWk\n/pZqEj0+uXaN7SkpxOTkYGVmhpuVYfNZA9CoEaxfD/Hx2lzfrq4wejTUspSQADN3z8RniQ+f7P+E\n0IuhBpdnZqbNwGhrC336QN26BhcpkUgkTwRVGd9NFEUJLl7QVrcsvS2RSO6D7du3c/z4cQoKCggP\nDze1OmWo06ok80ri94km0aGjvb1ufUdKivEEDx4M7u4waBCcPQunT2snY9YyvJy8KBDatxLbL23n\nfPJ5dlzaYVCZX38NKSkQGgrt2xtUlEQikTwxVGV89we+KLWU35ZIJPdB6WqXP/74I1OmTCE+Pt6E\nGpVg1UjrZTazM8O6sbVJdOjq6IhNUajJxexsev/xB/vT0oynQMuW4OZmPHn3SelS87/F/EbPn3qy\n79o+g8p0dYXMTAgKglGj4MwZg4qTSCSSJ4JKZxcJIfYaUxGJ5HGnV69eTJ06FYB9+/bRtWtXzA2d\ny1pPmixoQr1B9XD4iwNmKiPEWleAtbk53Zyc2Fbk9XZRqWhtilzot27Bb7/B88+Dj4/x5VeCh6MH\nreq14vyt8wCsCFhBr2a9qun18EybBomJ0LOnrHopkUgeTUaNGoWHhwcff/yxqVUB9KtwKZFIaoBn\nnnmGBg0aAFBQUEBAQIBu29SYWZrh9IITZiozNHc0aDJNU9Kwp1qtW08vKKBu8Yw/YzFjhtbg/ukn\nbbxFLaOnT8nbk/ArxgldWrECQkJg3DhpfEtMh5mZGVeuXCmz76OPPuKNN96osH1eXh6jR4/Gy8sL\nR0dH2rVrx/ZSOTPfeOMN3NzccHJyomXLlnz33Xdl+nfr1g0bGxscHBywt7en1SOS6L66677fttHR\n0XTv3h0nJyeaN2/O5s2bjXEZjz3S+JZIjISiKPTooQ0daNCgAYmJpomtrozkjclEvRTFoQaHSN1l\nmkmXvdRqnqlThykeHkxq1Mi4wjUa6NgRPv9ca2126GBc+XrQw6cHDewa8GbbN+nq2ZVtF7ex9MhS\nU6slkRicymoAVLZfo9Hg6enJ/v37SU9PZ+7cuQwZMoTYWG19wGnTphETE0NaWhrBwcHMmDGDkydP\nljnvf/7zHzIyMrhz5w7nz5+v+YsyANVd9/20LSgooH///vTr14/U1FS+/fZbRowYwaVaOCH9UUMa\n3xKJEfnwww+Jiori+PHj3L59myFDhnDkyBFTqwWAqq4Kj3960DmpM/UHmCYTSwtbW0516MDrrq7s\nSEnh+ePHWVVcZtGQpKRAvXrQrx9MmABZWYaX+QD08OlBwqQEFvdczMjNI1lwcAF5BXkGl3voEAwb\npg2J/9//DC5OIrkHIcR9tbe1tWXWrFl4eHgA0KdPH7y9vYmMjASgdevWWFtb686tKAqXL19+YJnd\nu3dHozHNG8PSVHfd99M2OjqaxMREPvjgAxRFwc/Pjy5durB69eoKZS9YsIBGjRrh4OBAq1atiIiI\nACAxMZHBgwfj4uKCj48PS5eWdRhcv36dwMBAXFxcqF+/PhMmTADg/Pnz+Pn54ezszNNPP01ISEiZ\nft7e3nzxxRe0bdsWZ2dnhg0bRl6e9v/w5MmTtG/fHkdHR4YOHUpOTtkM2ZXpaiyqNb4VRWmuKMr/\nFEUJVxRld/FiDOUkkseN1q1b07ZtWxYvXsyOHTt45ZVXaNq0qanVAsDpRSfq9q6LhZ3pC838kZlJ\nIfBl06YMN0asg1pdMtkyNxd++AEWLIBjxwwv+z4wNzNHURScbZxJnJzInpF7mNx5ssHl/v3v2kmX\nN25o06FLJI8aSUlJXLx4kaeeekq37/3336dOnTq0atUKd3d3evfuXabPtGnTcHFx4YUXXmDv3sqn\nwRVPnLeo4SJdffv2xdnZGbVafc9nv3799DpHRdddVdsLFy5U2VYIwZkKZl5fuHCBZcuWERkZSUZG\nBjt27MDLywshBH379uW5554jMTGR3377jSVLlrBz504ACgsLCQgIwNvbm9jYWOLj4xk6dCgajYZ+\n/frRq1cvkpOT+frrr3n99de5ePFiGbnr168nPDycmJgYTp06xcqVK8nPz2fgwIG89dZbpKSk8Oqr\nr7Jhw4ZqdTUm+ni+1wMngBnAv0otEonkAfn8889Zt24do0aNom4tS6Cccz2H2M9jSVxpurCYEQ0a\n8GmTJvzVyQmVMYrtgLa8fDGzZsH162BjYxzZD0AdS+NNRn3vvZJ1IzuIJLWEOTExKHv23LPMiYnR\nu31lbQ2NRqNhxIgRjBw5kubNm+v2L1u2jMzMTA4cOMCgQYOwKlVbYOHChVy5coX4+Hjeffdd+vbt\nS0wF+u/cuZNJkybRoEEDfvrpp2p1KW0EVkdISAipqamkpKTc8xkcXH3G58quu6q2o0aN0rVt0aIF\nLi4uLFq0CI1GQ3h4OHv37iWrgjeD5ubm5OXlcebMGV04i7e3N8eOHePWrVtMnz4dc3NzvLy8GD16\nNEFBQQAcOXKExMREFi5ciLW1NZaWlnTu3JnDhw9z9+5dPvzwQywsLPDz8yMgIICff/65jNwPPvgA\nV1dXnJyc6Nu3L1FRURw+fBiNRsOECRMwNzcnMDCQDqXCCCvT1Zjoc1fTCCGWCyGOCiEiixeDayaR\nSIzOhfEXOOxxmCtTrhD3eZyp1QGgUAiyCoxQddPfv2TdywuWLoU2bQwv9yG4knqF/xz7D38L+ZtB\n5ZTKksnu3dqKlxKJMTE3Nyc/P7/Mvvz8fFQqFWvXrsXe3h4HBwf69OlTpo0QghEjRmBlZXVPuANo\nY7s7d+5MXFwcy5cv1+3v0KEDderUQaVS8eabb9KlSxfCwsLu6f/yyy9jbm7OpEmTGDFiBADp6els\n3LiR+fPnl2mbkZGBnZ0dly5dYtOmTXz88cecOHHigcekKqq7bn3aWlhYsHnzZkJDQ3Fzc+Orr77i\ntddeo1EF83F8fHxYvHgxc+bMwcXFheHDh5OYmMi1a9eIj49HrVbrPPfz58/n5s2bgDbkpHHjxpiV\nc7IkJCTowmGKady48T3peV1dXXXrtra2ZGZmkpCQQMOGDe/pW5Gurq6uOl2NiT7Gd4iiKO8piuKm\nKIq6eDG4ZhLJY86JEyeYOXMmnTp1YuPGjaZWBwCXISUhHtkXsk2W9QTgUHo6I86do8GhQ6xISDC8\nwBdfhOLUjydPalMO1mLyCvLosboHR+KP4OflR6EoNJisJk20xUABsrO1SWEkEmPi6enJ1atXy+yL\niYmhcePGDB8+nDt37pCRkcHWrVvLtHnnnXe4desWGzdurDK1q0ajuSfmuzSKolQaAx4VFUX7UlWo\nHB0dad++/T0PC7t378bPz4+QkBAaNmzIxIkTWbRoUaUye/furXuoKL+Uf8goj77XXV3bNm3asGfP\nHpKTk9m2bRuXL1/G19e3wvMMHTqU/fv36yZsTp06FQ8PD5o0aUJKSorOc5+enq6L3/bw8CA2NpbC\nwrL/X+7u7sTFlXUAxcbG3mNUV4SbmxvXr1+/p29Ful67dk2nqzHRx/h+C22YySEgsmg5bkilJJLH\nHSEE27Zt48iRI4wbN+6eWENT4fSCE3XaasMZhEaQvjfdJHpczc7m24QE9qen86KTE/8o5wExCI6O\n4OcHr7wCH38MW7fCmDHaz1pGXHocKyJX0LJeS551fZZhTw/DTDFseE7btiXrxooEktQe5nh7I7p1\nu2eZU8nr+oraV9ZWH1577TXmzZtHfHw8Qgh27dpFaGgogwcPrrTPmDFjiI6OJjg4GEtLS93+5ORk\n1q1bx927dyksLGTHjh0EBQXhX/T2Kz09nfDwcHJzcykoKGDNmjXs37+fXr3uzat/7tw5XRrC4lCK\nysjNzcXS0pKJEyfi6+vL9evXqwx3CAsL0z1UlF/KP2Toc90P0vb06dPk5uaSlZXFokWLuHHjBiNH\njryn3YULF4iIiCAvLw9LS0tsbGwwNzfH19cXe3t7Fi5cSE5ODgUFBZw9e5bjx7VmpK+vL25ubkyd\nOpWsrCxyc3M5dOgQHTt2xNbWloULF6LRaNizZw+hoaEMHTq0yusB6NSpEyqViqVLl6LRaNi4cSNH\njx6tUtfynndDU600IYR3BUsTYygnkTyujB07lhkzZrBz506Sk5N1s+5rA87+zrr1m+tvmkSHm/n5\n/JiURGxuLgfS0+8708EDEx4OYWFgZaWdYdi8OegxUcnY7L22l/HbxrP14lY2RW8yiszJk2H0aPj1\nV5g50ygiJRIds2bNonPnznTt2hW1Ws3UqVNZu3YtrVu3rrB9bGwsK1asICoqCldXV50H+eeff0ZR\nFJYvX46HhwdqtZopU6awZMkSnTc5Pz+fGTNm6LJvLFu2jC1btlQ4OV6tVuPo6EhQUBDdunWrVP/0\n9HScnZ3L7Nu8eTPTp09/8EG5z+sGrTf9s88+06stwOrVq3Fzc6NBgwZERESwc+dOVBXUX8jNzWXq\n1KnUr18fd3d3kpOT+fTTTzEzMyM0NJSoqCi8vb1xcXHh3XffJaNo5raZmRkhISFcvHgRT09PPDw8\n+OWXX1CpVISEhBAWFka9evUYN24cq1evLhO7XlmaSZVKxYYNG/jhhx+oW7cu69evJzAwsEpdy4cI\nGRqlupuaoigqYCzw16Jde4BvhRD5lXYyAIqiCKPdgB9x1q5dy/Dhw02tRq3HlOO0YsUK/v73vwPa\nsvPbtm1Do9FU+KdmbP4c+yeJ/9XGv1l7WXPlkytGH6cCIXA5eJCUotRdB557jqY2NrhW48UxNcb6\nTiVlJtHgC22BJjPFjHG+4ziReIKItyKwMDN8tpqcHO3i5PRg/eV/lP4UhTtUbGXUrBx5j61Brl27\nxsqVK5k9ezYAmzZtIiAgQPcfHxISQrdu3bhx4wbNmjUzpaoSA1HVb1cfP/tyoD3wn6KlfdE+iUTy\ngPQsNYNt165duLu7s27dOhNqVILnvzyxfcqWhuMb0nRJUzDB/dhcUXi5lJfopagofrlpGi98bcTV\nzpVnGzwLQKEo5GbmTeZ3n4+CYW208HDt5Mv69WHtWoOKkkgeWTIzM/n111+JjIzk7NmzAOTk5OgM\n702bNjF37lwCAwP55ZdfTKmqxETo4yLpIIQoFe3HbkVRThlKIYnkSaBx48Y0a9aMixcvUlBQwKJF\ni3j99ddNrRYANk1s8D1TakKNiYysnmo165KTAejk4MB4Y1e8vHQJNm+GnTvh1Ve1MRe1iJ4+PYm6\nEQWA2kZNV8+uBpdZv7427eC6dQ/u9ZZIHnfs7OyYPHkykydrc/CnpqZSv35J4bKBAwcycOBAU6kn\nqQXo4/kuUBTFp3hDUZQmgBHyfkkkjzfdu3fXrdfG0sXZV7NJ+L8EVBdNEwrTQ12SVOl6bi4Fxn4l\nfuwYXL6srTBTxaQuU9HDp4du/UDcAeD+qwDeL889B/37S8NbIrkfDh48WGU8uOTJQx/P97+ACEVR\nrgAK0BgYZVCtJJIngB49enDs2DH8/f3p168fd+7cobCwEEdHR1OrRuznscQtitNOvjRROGJDKytW\ntWxJRwcHmllbE52VhQK0qmPg4jIaDaxfD7t2aVMOLltWK9N7dPHowr+7/hv/Jv6cSz7HkPVDOBB7\ngEsTLmGrsjW4/AsXoKAAihI9SCSSSggICDC1CpJahj7ZTn5De/udAIwHWgghZI0zieQhGThwIMeP\nH6dDhw5MmjQJNzc3tm3bZmq1AGg4riGdb3Sm9ZrW5Dc36tzqMrzZoAF/ZmXR6PBh+pw+zWFj1DY3\nM4N//AO+/15rfEdpQzswRqGf+8DKwopPun+Cn7cfsemx9GnWh6PvHjW44f3hh1CnDrRoAZ9+alBR\nEolE8lhSqedbUZSXhBC7FUUZVO5Q06IZnLWjKohE8ojj4eHB7Nmz6dKlC7a2hvdY6oO5TdlCC0KI\nStM6GRpfe3sOPPccTYxV6t3MDLp3h+J0W+PHw+3b8NZbMG2acXS4Txa8vMBosqytobi6tKx0KZFI\nJPdPVWEnLwK7gb4VHBOANL4lkhqgsmphpib7ajZX517FZYsLJ/9zknYH2plEjwZWVsYX6u9fYnwn\nJ2tnGJauMlOLycjNwMHKwWDnf+01bQ0igIgI7QuBagroSSQSiaQUlRrfQojZRasfCyFiSh9TFOXB\ny1RJJJIKEULw559/UqdOHTyMUdGxGhL/l0jS90lYYEHmyUwKNYWYWZgu9vmORsO+9HQczM15wdAz\n/ooq3QFw/bo2sLkWxn2XZlbELLZd2saF2xe4PvE69lb2BpHTqhW4uUFiIqSlaWsR1ZJEPRKJRPJI\noM/dZEMF+36taUUkkieZVatW4eHhQc+ePXVld02N9zxvrDy0XufCrELuHLtjMl3W37yJ66FDzIiJ\nISkvz/ACPT211S1BW03m1CnIz4fUVMPLfkAcrRwZ7zuem/+8aTDDG0BRoEmpGsdhYQYTJZFIJI8l\nlRrfiqK0VBQlEHBUFGVQqWUkUHtqYUskjziRkZH8/vvvODs788knn9Sa/K+KopQpNZ+6yzSG57m7\ndxl38SLZhYVkFRQw2MXFOILnzIFffoHgYJg7F+rVg//9zziy74PjCcd59r/P8s+d/2T1H6uxsjB8\nmM6wYdpPR8eSZxSJRCKR6EdVnu8WQADghDbuu3hpB7xreNUkkieDHTt28O2333LmzBl27txpanXK\nYP98iQc1aXWSSXRoYm1NRlGmkQvZ2cTl5BhH8LBh2uI6Xl7w5pvanN9TphhH9n3QwK4Bp5K0dc8O\nxB4gKz+LszfPGlTmsGFw5AjcugWzZ1ffXiKRSCQlVGp8CyG2CCFGAQFCiFGllglCiENG1FEieawp\nXWwnPDycsLAwTp8+bUKNSlDMSzKcaNI1FOYXGl0Ha3NzupbKff7vK1fYUFT50ii0aQNDhmg937WQ\nRg6NaFG3BQA5mhwaftmQgesGkpaTZjCZajX4+sKNG7BqlTYlukQikUj0Q5+Y7zGKouhmNymK4qwo\nyvf6ClAUpZeiKNGKolxQFOXDKtp1UBQlv4LUhhLJY0379u11hXVu3LjBnDlzSEhIMLFWWtz/7k7a\n6DTan2hP58TOmKlMM+nQ37kk/OX3jAycLPSpD2YAEhK0HvBaRnfvkge44W2Gc2H8BZysDTspddUq\nePZZ2LoVCo3/TCaRSCSPLPrcSZ8RQuhcKEKIVOA5fU6uKIoZ8A3QE3gKGKYoSstK2n0G7NDnvBLJ\n44SFhQV+fn667TfffJOePXuaUKOyZPllYf+cPYqZYvDy5ZVR2vi+W1jIS8aub75rFzz1lNYLvnWr\ncWXrgX+TkuwsxxKOGUXm0KFw86Y2LL5Hj+rbSyQSSW1j1KhRzJo1y+hy9TG+zRRF0d35FEVRo19Z\negBf4KIQ4poQIh8IAvpX0G482gwqN/U8r0TyWFE69OTEiRMm1ORezBPMiZkVw4nOJ7j60VWT6PCs\nnR0uKhWdHBwY7eZGrrFdrV5e2qqXyckwYYJxZetBN69umClmeDl50c6tHWnZaYT8GUKhMNw4WVnV\n+uyLkscILy8vbG1tcXBwwM3NjVGjRpFVXO3pPsjLy2P06NF4eXnh6OhIu3bt2L59u+74G2+8gZub\nG05OTrRs2ZLvvvuuTP/o6Gi6d++Ok5MTzZs3Z/PmzQ99bcaguusuT3XjYG9vj4ODAw4ODtjb22Nh\nYcEHH3xg6Mt4bNDnr/ML4HdFUeYqijIPOAQs1PP8DYG4UtvXi/bpUBTFHRgghFgOmKaEnkRiYvr2\n7cuyZcuIjo7mww8/ZPny5ezZs8fUagFgnmpOYV4hXh974fmhp2l0UBRiO3ViZ9u2dHFwYEZMDEuv\nXze8YCFgwAB4+mn4298gPt7wMh8AZxtnrv3jGjEfxJBwJwGPxR4sPbqU1GzDZqjJz4cNG7RDNHeu\nQUVJnnAURWHr1q1kZGRw4sQJjh8/zrx58+77PBqNBk9PT/bv3096ejpz585lyJAhxMbGAjBt2jRi\nYmJIS0sjODiYGTNmcPLkSQAKCgro378//fr1IzU1lW+//ZYRI0Zw6dKlGr1WQ1DddZenqnEAuHPn\nDhkZGWRkZHDjxg1sbW0ZMmSIsS7nkada41sI8SMQCCQBN4BBQojVNajDYqB0LLg0wCVPHI0bN+a9\n995j+/btdO/enSNHjqBSqUytFgB5T+Xh85kPan/1PWXnjYmVmRn70tL4LDYWRwsL/mqM0BNF0dZS\nL86wEh4Ohw7B4cOGl32fNHJoBMBn/p+R/K9kwt8Ip65tXYPKfOstGDwYtmzRpkKXSAxJcdibm5sb\nr7zyCmfOnAHAzMyMK1eu6NpVFUpga2vLrFmzdIXM+vTpg7e3N5GRkQC0bt0aa2trnTxFUbhcNM8j\nOjqaxMREPvjgAxRFwc/Pjy5durB6deUmUffu3dFoNA955Q9PddddnqrGoTy//vorLi4udOnSpcLj\nCxYsoFGjRjg4ONCqVSsiIiJ0xxITExk8eDAuLi74+PiwdOlS3bHr168TGBiIi4sL9evXZ0Kpt47R\n0dH4+fnh7OzM008/TUhIiO6Yt7c3X3zxBW3btsXZ2Zlhw4aRV1Qf4uTJk7p5VkOHDiWnXPasqnSt\nSfQKHxFCnFUUJZmi/N6KongKISp+XCpLPFDaVdaoaF9pngeCFEVRgHrAK4qi5AshgsufLDAwULfe\nqlUrWrdurY/6TxwHDx40tQqPBLVxnJydnVmwYAGKonDt2jWuXbtmapXKjJN5kjnmN8zJa2uEQjeV\n8Leiz7NFi6FpVbeubpJL4ZgxpDdsyJ89e5a52RdTW75TUUQZRY63tyugDZk6dCidtWv1i4evLeNU\nGzl37hznz583tRq1mri4OMLCwhg8ePBDnyspKYmLFy/y1FNP6fa9//77rFy5kuzsbNq1a0fv3r0r\n7S+E0D0ElCe+6E2ZRQ1PEO/bty8HDhxAURSdYVz82bVrV4KD7zGf7qGi6y6PvuPw448/8uabb1Z4\n7MKFCyxbtozIyEhcXV2JjY2loCh1rBCCvn37MnDgQNatW0dcXBz+/v60bNmS7t27ExAQgL+/P2vW\nrMHMzExXgE6j0dC3b19Gjx7Nzp072b9/P/379ycyMpJmzZoBsH79esLDw7GysqJz586sXLmSUaNG\nMXDgQCZNmsT777/P5s2bGTZsGFOnTq1W1xpHCFHlAvQDLgJ3gRigEDhbXb+ivubAJaAxYAlEAa2q\naP8DWs96RceERD/WrFljahUeCeQ46ceaNWtEckiy2Gu3V0QQIfbZ7xOFhYWmVst4REYKoQ1AEaJe\nPSGquPba8p0qLCwUp5NOi8W/Lxa5mlyDycnOFsLaumR4YmP161dbxulRoOjeV+399mGX6u6xs69c\nEbOvXKmx7fvFy8tL2NvbC2dnZ+Hl5SXGjRsncnJyhBBCKIoiLl++rGs7cuRIMXPmzGrPmZ+fL/z9\n/cXYsWPvOVZYWCgOHjwoPvnkE6HRaHTtfXx8xOeffy7y8/PFjh07hKWlpejVq9c9/cPDw8WQIUPE\n8OHDxerVq6vV5ddff622TU1R1XWXp6JxKM3Vq1eFhYWFuHr1aoX9L126K/KYTwAAIABJREFUJFxd\nXcWuXbtEfn5+mWNHjhwRjRs3LrNv/vz54u233xa///67cHFxEQUFBfecc//+/cLNza3MvmHDhomP\nPvpICKH9rqxdu1Z3bMqUKWLs2LFi3759omHDhmX6de7cWfddqUrXB6Gq364+Md9zgb8AF4QQ3mjd\nHHq9cxVCFADjgHC0TqogIcR5RVH+rijK3yrqos95JZLHmaysLMLDw5kyZQrr1q0ztToAWLpbIjTa\nn2fBnQKyL2SbTJekvDyWx8cz+MwZPjRG2r9nn9UmtgZtVZlKvFy1ia4/dKV/UH/OJZ/jTu4dg8mx\nttbm+y7mn/80mCiJhC1btpCSkkJMTAxLly7Fyqrqaq5r167VTQzs06dPmWNCCEaMGIGVlVWZUIdi\nFEWhc+fOxMXFsXz5ckDrwd68eTOhoaG4ubnx1Vdf8dprr9GoUaN7+r/88suYm5szadIkRowYAUB6\nejobN25k/vz5ZdpmZGRgZ2fHpUuX2LRpEx9//LHBJt5Xd93lqWgcSrN69Wq6du1K48aNK+zv4+PD\n4sWLmTNnDq6urgwfPpzExEQArl27Rnx8PGq1GrVajbOzM/PnzycpKYm4uDgaN26MWQWzuhMSEnTh\nM8U0btxY96YBwNXVVbdua2tLZmYmCQkJNGzY8J5++uha0+hjfOcLIW6jzXpiJoSIQBsqohdCiO1C\niBZCiGZCiM+K9n0rhFhRQdu3hRAb9dZeInkMWbNmDVOnTiU/P7/KV4LGxKGdA+qeat22qUrN5xQU\n8HNSEt/Ex5OSn88/Krjp1ThmZtC9Ozg4aGcW3rwJa9dCLcxyUCgKOZl4kp5NevLe8+/xbd9vDR73\n3bRpyXqe6aKRJE8AopJUp7a2tmUyn9y4cQOA4cOH6yYGbi2XIvSdd97h1q1bbNy4EXPzyueyaDSa\nMrHObdq0Yc+ePSQnJ7Nt2zYuX76Mb+kn0FJERUXRvn173bajoyPt27cnPz+/TLvdu3fj5+dHSEgI\nDRs2ZOLEiSxatKhSnXr37l0m20jppfxDRnn0ve7ylB+HYlavXs3IkSOr7Dt06FD279+vC6MsDvPw\n8PCgSZMmpKSkkJKSQmpqKunp6YSGhuLh4UFsbCyFFWS2cnd3Jy4ursy+2NjYewzr8ri5uXG93ET9\n8hNOK9O1ptHH+E5TFMUO2AesURRlCdoQFIlEUsOsWrWKmTNncvLkSaysrGjTpo2pVdLh7F+Sazs5\n2IgVJkuRqtEw8fJlzmVlcTAjAwdjFdv5z3/g9m1txpPBg2H9+lpZWeZ4wnHarWjH7L2z+fLwl0bJ\ny/7ee9C6NYwfD++/b3BxEhMxx9ubOd7eNbZdkzz33HOsXbuWwsJCtm/fzt69e6tsP2bMGKKjowkO\nDsbS0lK3Pzk5mXXr1nH37l0KCwvZsWMHQUFB+PuX5NE/ffo0ubm5ZGVlsWjRIm7cuFGh8Xnu3Dla\ntWoFQFBQUJX65ObmYmlpycSJE/H19eX69et4VzFWYWFhZbKNlF7KP2Toc93l0WccAA4dOkRCQkKV\nsfcXLlwgIiKCvLw8LC0tsbGx0XmzfX19sbe3Z+HCheTk5FBQUMDZs2c5fvw4vr6+uLm5MXXqVLKy\nssjNzeXQIW1x9Y4dO2Jra8vChQvRaDTs2bOH0NBQhg0bVqkeAJ06dUKlUrF06VI0Gg0bN27k6NGj\neula0+hz1v5AFjAR2A5cBvoaRBuJ5AnHycmJpKQkAHbVsprdqrol2VfS96ZTqDG+8elmZUVrW1sA\n8oTgQHo6BcYo/FOvHlhYgL+/Ntf3pk0wqPYV423v1h5HK2211IQ7Cfw38r/8Y/s/yMjNMJzM9nD2\nLHz9tXZ4JBJDoM3JUDGLFy8mODgYZ2dnfv75ZwYOHFhp29jYWFasWEFUVBSurq46D/LPP/+Moigs\nX74cDw8P1Go1U6ZMYcmSJWW8yatXr8bNzY0GDRoQERHBzp07K8xMpVarcXR0JCgoiG7dulWqT3p6\nOs6liogBbN68menTp1cxGvdPVdcNWm/6Z599BqDXOIB2omVgYCB16tSpVG5ubi5Tp06lfv36uLu7\nk5ycrAu7MTMzIzQ0lKioKLy9vXFxceHdd98lIyMDMzMzQkJCuHjxIp6ennh4ePDLL78AoFKpCAkJ\nISwsjHr16jFu3DhWr16tm2xZ2XdFpVKxYcMGfvjhB+rWrcv69evLJPKoSteaRqnKM6IoijmwSwjh\nV2kjI6EoijCGF+dxYO3atQwfPtzUatR6auM4paWlUbduXd2rtn79+vHSSy+ZtHhB8ThlX83mRIcT\nqNxU1O1Zl8YzG2PhYPwy7x9cvMjXRbF9rioVPdVqVhV5mExNbfhODVw3kM3R2pCY5nWbM7LtSMY8\nPwZnG+dqej44V6/CTz9pC4E++ywsXlx1+9owTo8KRZksDJ6CV95jjcO1a9dYuXIls2fPBmDTpk0E\nBAToDPiQkBC6devGjRs3dMak5NGkqt9ulZ7vogmThYqiOBpEM4lEUgYnJyc6dOig2/b09OTVV181\noUYl2HjZ8Jdrf8H3D198PvcxieENZUvNO1hY8L8WLYyrgEYDR47Ap59CBROQTE1375Jqqa3rt2ba\nC9MManiD9mVAWhp8+CE8QN0TieSJIDMzk19//ZXIyEjOntUmSs3JydEZ3ps2bWLu3LkEBgbqvLyS\nxxN97p6ZwGlFUXZSKtZbCFH7aixLJI8B/v7+HDlyBNBOcnF3dzexRiWY25ojhCD7QjaFOYXYtbUz\nug4vOjlhDhQAl7KzySwoQG3MOucRETB5snYSZteuxpOrJ6WN74iYCAoKCzA3M2xxpA4dtItEIqkc\nOzs7Jk+ezOTJkwFITU2lfv36uuMDBw6sMmRG8vigj/G9sWiRSCRGoHv37nz22Wd07NhRN+FSFBVQ\nMDVpe9M4P+I8KOAx2cMkxreDhQXTGjfG08qK7s7OOFlYcDs/n7rGqAgaF6ctMf/MM/DRR9oMKLWM\nlvVaMqjVIJ51fZam6qYsO7aM3TG7Wd5nOW72bgaXLwRkZ0NRaL5EIqmEgwcP0qtXL1OrITEBlRrf\nxVUshRCrjKmQRPKk07VrV1JSUkhISGDVqlV06NBBZ5CbmjpP16Htb22xaWZj0oeBud7eRN+9y9Qr\nV4hIS2NAvXrGCT/p3x9OntSuDxkC/foZXuZ9oigKG4ZsAOCNTW9gZW7F0DZDsbeyN6jcrVth5kw4\nd04b931Yr2oQEsmTS0BAgKlVkJiIqjzfm4F2AIqibBBCBFbRViKR1BAqlQqVSsWZM2dQqVT/z96d\nx0VV748ff50Zhk12ZRAUZERR3Ei9Um4pqUUKGmK5oelVb6VlafeX+M3I0qxMS/Oi1r3e7JpKLrig\nmFtqLpmJgrmCirKICrIJyDIwvz9GBxBwZRb083w8eHBmOGfeb07JfM5nPuf95uuvv+bZZ581dloA\nKJwUKJwMMMP8AOzMzBjQsCFfe3nR1NLSMEH79q0YfC9ZAlu2aCuhzJljmPgPaUXwCoPFOneu4tTc\no4qZIAjCU+9eCyUrT2s113cigiBU1a1bNz799FN69ux5z5qshlauLidrexanR5wmrm+c0fJws7Dg\n9caNDTfwhqq19A4cgHbt4PXXDRffhFUuXnL4MBSIbhCCIAg1utfgW1PLtiAIBlZeXk5env5qNT+M\nrF+yOBFwguurr5OzO4eSDOO3NcwuLeWsIUZ7PXpUTOvm52sb7hi62spD2n1xN+9vfx/fpb6cyzyn\ntziNG2uvRQBKSyFK3CkkCIJQo3sNvn0lScqTJOkm0OH2dp4kSTclSTKNUYAgPOGOHz/O8OHDady4\nMZ999pmx0wG0nS7tulbcaJjza47RcjmZn49fbCwehw/zn/R0/Qe0tobu3Sse796t/W7C9ZH3XtqL\nrYUt3wV+h5eTl15jtW9fsf3tt3oNJQiCUG/VOvjWaDRyjUZjp9FobDUajdnt7TuPTe8Wf0F4wty8\neZM//viDGzdu8NFHH/Hll18aOyUA5JZynF5y0j3O3pVtlDyyS0uZkZTE2cJCrCSJr7z0O7DUGTlS\n20v9hx/gwgXo1w9GjTJM7IeQfjOdydsms/7MemISY3iu6XOYyfRbm/3llyu2bQxfCEcQBKFeME6X\nDEEQ7mvlypW89dZbAFhYWPDOO+8YOaMKDn0cYKZ2O2NDBt7fexu8+om9mRkHcnO5WVbGTeBkQQHt\nDTHiGzdO+/38ee0dhu+8A7166T/uQ7I0syTizwjKNeVISGTfysbWwhaZJEMm6acu+iuvwLx52hLo\nHTroJYQgCEK9Z8DOFIIgPIy+lW7u27t3LxkZGSQlJRkxowplhWW6W7LVN9QUXSwyeA4ySeKFSt0u\n/3f1Kr9mG3AWvkULWLhQW27Q3vSaADtaOdLZtTMAGjS8vPJlnL9y5sS1E3qLaWur7T/0zDPalvNH\nj+otlCAIQr0lBt+CYKK8vLxo1qwZoG1L3KxZM/73v/8ZOSstp75ONJvZjBYLW9DlVBcsmxuw4kgl\nfSoNvhdfucLvxrwptbDQeLFr0bd5xQWcrYUtZyad4ZnGz+g1ZmwsNG+uXRr/yy96DSUIglAvicG3\nIJgoSZLo16+f7vGUKVP4+OOPjZhRBUkmoQpX0XRyUxq0aWC0hjt9Kw2+JeD/ubsbNoHLl+HNN7Wz\n4Ca47rtyq/mU3BQa2zTWe0xvb9i8Ga5cgRkz9B5OEASh3hGDb0EwYZWXnuzZs8eImVRXVlRG1q4s\nLoRd4NKnl4ySQ3NLSzwtLVFIEl3s7LheYuCyhxYW4OkJ69Zpv0xMd4/uWJppP5XQoCG/JJ+MggyK\n1cV6i2lrqy05aMQGqIIgCFWMHTuW8PBwY6ehIwbfgmDC+vTpw9ChQ/nPf/7DqlWrOHnyJEeOHDF2\nWgAUnCzg0sxLyCxlOAU43f8APZAkiS3t25Pdowcb27UjNj+f5YYoOQiweDEMHQrh4dolJyY42rQ0\ns2TD0A2kTElhUpdJPP/D87RY1IK/rv+l99i3bsH//geLFuk9lPAUkMlkXLx4scpzn3zyCaNq+cSp\npKSE8ePH4+npib29PZ06deKXSuugRo0ahaurKw4ODrRu3Zply5ZVOb53795YWVlhZ2eHra0tPj4+\ndf9L6Ul2djbBwcHY2NigUqlYvXp1rftevnyZAQMG4OTkhJubG++88w7l5eXA/c+h8OjE4FsQTFij\nRo2IjIykdevWPPfcc7zyyiscOnTI2GkBYPc3Ozod6IRqpgo7P+NVH23boAEpRUU0/f13ItLSKDZU\nze3YWPjtN21HmV27oLiYBhkZhon9EAJaBNDUrimtGrZi0cuLyPx/mfzN7W96jfn999Cggbb553/+\no9dQwlOitqVttT2vVqvx8PBg//795ObmMmvWLF577TWSk5MBmD59OklJSeTk5LB582ZmzJjB8ePH\nq7zu4sWLycvL4+bNm5w5c6bufyk9mThxIpaWlmRkZPDTTz/x1ltv1Zr/xIkTUSqVXLt2jbi4OPbt\n28fixYuB+59D4dGJwbcg1APt27fn999/5/z587z33nvGTqcajUZDUYrhK57c4W1tTUa3buzw9eUN\nNzfDBK3cav6bb8DZmXYbNhgm9iN4qcVLdPfojkKu0Husbt0q+g5dugRqtd5DCk84zUNeVFtbWxMe\nHo777ftABgwYgEqlIjY2FoA2bdpgaWmpe21Jkrhw4cIjx+zTpw9qE/gfvbCwkKioKGbPno2VlRXd\nu3dn0KBBrFixosb9L126xNChQ1EoFCiVSgICAjh16hRw/3N4ty+//JKmTZtiZ2eHj4+Pbqlkeno6\nQ4YMQalU4uXlxaK7Pg5LTU0lJCQEpVKJs7MzkydPBuDMmTP4+/vj6OhI+/btiY6OrnKcSqVi/vz5\n+Pr64ujoyPDhwym5vfTw+PHjdO7cGXt7e4YNG0ZRUdX3p9pyNRQx+BaEesDOzg6VSmXsNKopvlpM\nbLdY9jfYz2HVYcqKyoySh0ySsJTLDRu0T8XNjOTlwenT/PGPfxg2h0dQVl7G0StHyb6lv7KMbdtC\n06ba7bw8+PNPvYUSDCRpZhJ7pb3VvpJm1lz+tKb9a9vXEK5du0ZiYiJt27bVPTdp0iQaNGiAj48P\nbm5u9O/fv8ox06dPR6lU0rNnT/bt21fra6elpQFgZla3rVOCgoJwdHTEycmp2veBAwfWeExCQgIK\nhQKvSk3HfH19dQPqu7333ntERkZy69Yt0tLS2LZtGy9X7pZVSU3nsHLciIgIYmNjycvLY/v27Xh6\neqLRaAgKCqJjx46kp6eze/duFi5cyM6dOwEoLy8nMDAQlUpFcnIyaWlpDBs2DLVazcCBAwkICCAj\nI4Nvv/2WkSNHkpiYWCXu2rVr2bFjB0lJScTHx7N8+XJKS0sJDg7m9ddfJysri1dffZX169ffN1dD\nEoNvQahH8vLyiI6OZuPGjcZOBYCy/DIKTxZSfqscyiDvkPFK/Wk0Gk4VFLAgJYWPDVEPXakEX1/t\ndnk5nNBf/ey6MmvfLFzmuTB6w2jOZ53XWxxJ0jb+1MWdpbdQgnBfarWa0NBQxowZg7e3t+75iIgI\n8vPzOXDgAIMHD8bCwkL3s7lz53Lx4kXS0tKYMGECQUFBNfZZ2LlzJ1OnTqVx48b89NNP982l8iDw\nfqKjo8nOziYrK6va982bN9d4TH5+PnZ2VZcB2tnZcfPmzRr379mzJydPnsTOzg4PDw+6dOlS48C+\ntnN4h1wup6SkhJMnT+qWq6hUKv78808yMzP58MMPkcvleHp6Mn78eCIjIwH4448/SE9PZ+7cuVha\nWmJubk63bt04fPgwBQUFTJs2DTMzM/z9/QkMDKy2fv3dd9/FxcUFBwcHgoKCiIuL4/Dhw6jVaiZP\nnoxcLickJIQuXbrcN1dDEoNvQagnDh48iJubG1999RU5OTnGTgcA6xbWuIxy0T02Vqt50Ha4fCEu\njv9du0YnW1vDBL2z9KRRI8jIwPrGDTh82DCxH5K6XI3KUUV4r3BOTzpNlyZd7n/QY2jdumL7rvvk\nBOGhyeVySktLqzxXWlqKQqFg1apV2NraYmdnx4ABA6rso9FoCA0NxcLCotpyB9Cu7e7WrRspKSks\nWbJE93yXLl1o0KABCoWC0aNH0717d2JiYqod369fP+RyOVOnTiU0NBSA3NxcoqKi+Pzzz6vsm5eX\nh42NDefPn2fDhg18+umnHDt27JHPSU1sbGzIu6vfQW5uLrY1/E3UaDQEBAQwZMgQCgsLyczMJCsr\ni2nTplXb717nELR9KRYsWMDMmTNRKpWMGDGC9PR0Ll++TFpaGk5OTrqZ+88//5zr168D2iUnzZo1\nQyarOhy9cuWKbrnLHc2aNdN9ynCHi0vF+4+1tTX5+flcuXKFJk2aVDu2plxdXFx0uRqSGHwLQj0Q\nHx/P/PnzUSgUuLm5MWbMGGOnpOPYt6LWdtbOLKPkoC4vp+fx41wvLeV4fj7tGzQwTOC33oJjx+DA\nAZgzh4APPzTJkoM3Cm/QaG4jRm0YRdiuMIrU+l+f//rr2hnwjh1hyJCKNeBC/aSaqaK3pne1L9XM\nmmcMa9q/tn0fhIeHB5cuXaryXFJSEs2aNWPEiBHcvHmTvLw8tm7dWmWfcePGkZmZSVRUFPJ7LE1T\nq9XV1nxXJklSrWvA4+Li6Ny5s+6xvb09nTt3rnax8Ouvv+Lv7090dDRNmjRhypQpzJs3r9aY/fv3\n111U3P1190XGHd7e3tV+l/j4+BqXimRlZZGSksKkSZNQKBQ4OjoyduxYtm3bVmW/Bz2Hw4YNY//+\n/bobMsPCwnB3d6d58+ZkZWXpZu5zc3N167fd3d1JTk7WVVi5w83NjZSUlCrPJScnVxtU18TV1ZXU\n1NRqx9aU6+XLl3W5GpIYfAtCPaDRaNiwYQM5OTns3r272h8qY7L2sdZt5x/NpzS79B5764eZTEb3\nSi3edxqqzbyXl3Z06ekJP/9M1OLFcI83U2NpaN0QFxvtDNEt9S22JW5j3el1JOfqr2qBiwvcuKG9\nNpk92yQrMQr1yNChQ5k9ezZpaWloNBp27drFli1bGDJkSK3HvPnmm5w9e5bNmzdjbm6uez4jI4Of\nf/6ZgoICysvL2b59O5GRkbq+Crm5uezYsYPi4mLKyspYuXIl+/fvJyAgoFqM06dP68oQ3llKUZvi\n4mLMzc2ZMmUKfn5+pKam3nO5Q0xMjO6i4u6vuy8y7rC2tmbw4MGEh4dTWFjIgQMHiI6OrrEkY8OG\nDVGpVCxdupSysjJycnL48ccf6dChw33P4d0SEhLYs2cPJSUlmJubY2VlhVwux8/PD1tbW+bOnUtR\nURFlZWWcOnWKo0ePAuDn54erqythYWEUFhZSXFzMoUOHePbZZ7G2tmbu3Lmo1Wr27t3Lli1bGDZs\n2D3PMUDXrl1RKBQsWrQItVpNVFRUlRK9NeV698y7vonBtyDUAx06dMDZ2RmAzMxMwsLCav34z9AU\nTgrM3cxBApsuNpSkG7jRzW39KnW7nJeczMjTpw0X3MICnnkGDPwH/GG82PxF3faw9cP4Ie4HMgsz\n9RrTxgb27dN2upwyRa+hhCdceHg43bp1o0ePHjg5OREWFsaqVato06ZNjfsnJyfz/fffExcXh4uL\ni24GefXq1UiSxJIlS3B3d8fJyYkPPviAhQsX6maTS0tLmTFjhq76RkREBJs2baJFixbV4jg5OWFv\nb09kZCS9e/euNf/c3FwcK/2NAti4cSMffvjho5+UWkRERFBYWIhSqSQ0NJSlS5fqLhD69+/PF198\nods3KiqKmJgYnJ2d8fb2xtzcnG+++Qa49zm8W3FxMWFhYTg7O+Pm5kZGRgZz5sxBJpOxZcsW4uLi\nUKlUKJVKJkyYoFsaI5PJiI6OJjExEQ8PD9zd3VmzZg0KhYLo6GhiYmJo1KgRb7/9NitWrKiy3ry2\nMpMKhYL169fzww8/0LBhQ9auXUtISMg9c717iZC+SQ9bvsdYJEnS1JdcjW3VqlWMGDHC2GmYvPp2\nnkaOHMmqVasA7XrEjz/+uNaPHuvSg5yngjMFmLuao3DQfxm72pwqKKDd7bIaljIZ+555Bj87w9Yf\nj/zf/ximUsG1a9q1FiYk+lw0AyO1N1J1cOlA/Jvxeo+ZnAwhIdqbL19+GXr21D5f3/7tGdPt5Q56\n/9xAvMfWrcuXL7N8+XI+/vhjADZs2EBgYCAKhfZvZHR0NL179+bq1au0bNnSmKkKenKvf7umO00j\nCEIV/SqVj7C3tzfIwPtBNfBpgMJBgaZcQ8k148x8t7G2xu32x6JF5eUYfBhx/jwhb7wBU6ea5B2G\nvT17YybTlkI7ce0E1/Kv6T2mh4e2zOCcORUDb0F40uXn57Nu3TpiY2N1Jf6Kiop0A+8NGzYwa9Ys\nQkJCWLNmjTFTFYykbotSCoKgN3cG3+bm5lhYWOgaQ5iC4rRiLvy/C2TvzsbOz4720e0NnoMkSQxX\nKrleWko/R0e8rawMd440GtBoOBESQufPPoNKd9abClsLW/w9/dGgoa+qL2cyzvC/+P/xUouX6ODS\n4f4vIAjCA7GxseH999/n/fffB7Tt3u8sGwQIDg4mODjYWOkJJkDMfAtCPdGkSRP27t3LlStXmDhx\nYpU/7sZm5mCGYx9HOv3RySgD7zvmtWjBUm9vctVqxpw9S5daOrHVuTfeAG9vOq9cCVu2GCbmI9ge\nup2do3aSnp/OG1vf4FLOJd1suL5kZUFYGLRrBzWUBxaEJ97BgwfvuR5cePqImW9BqEd69erFuXPn\n+Oqrr+jbt2+Nd98bg7yBHNdxrsZOAwC5JHGioIDhSiUv3HWDk97cabYDsHkz2NpqS32Y2F2Gdz4F\nmNtvLgsCFhgk5l9/wZdfarcbNhQlB4WnT2BgoLFTEEyMGHwLQj3TqlUr9uzZY+w0alRWWEbW9iyy\ntmXh/Z23UZbFWMhkfN+qlWGD3mm2A7Bzp3bwXUv7Z1NgLq+9ZFhd69lTO+i+cUP79ddfBgstCIJg\nksSyE0EQ6kRRahEHHA5wavAp0v+dTsHJAmOnBEC+Wq3/IN7e0LSpdlujgX/+E0aP1n/cx5BTlMPG\nsxuZtHUSuy/u1lscmQz69Kl4/MsvegslCIJQL4jBtyDUQzk5OSxevJjg4GAGDRpk7HQAMHcxx65r\nRWk/Y7aaLywr4/3z52n/5588c/RorZ3p6owkVZ393rVLv/HqwKI/FrHk6BJUjiq8nLz0GqtHj4rt\nWbP0GkoQBMHkicG3INRD169fZ926dTRu3JjvvvvO2OkAIFPIcBnpontszMH3T1evsi0ri4u3brH7\nmWcMs/zlxRfJatZMW2rQzQ1mzoRx4/Qf9yFpNBrWn15Pal4qqXmpvOP3Dp4OnnqNWfnWBLUaSkvF\nW48gCE8vseZbEOqZEydO0LFjR8rLy0lMTGTx4sXGTknHsW/FDY7Zu7MpLylHZm74gdbPGRmcKSwE\nYG9ODq83bqz/oMOH84tGw4j+/aFXL3jxRRg5Uv9xH5IkSYTtDuN81nkADqYc5AXVC3qN2bIlTJgA\n7dtrG+7ExpbrNZ4gCIIpE9MPglDPtGnTBhsbGwBSU1M5d+4cxcXFRs5KS2YlA7l2W1OsIe9InlHy\nqNxqfvuNG5wpMOD6cwcHiI+Hr76CF/Q7qH1UlVvNf/bbZwxYNYCVJ1bqNeb338M770CrVpCdbaXX\nWIIgCKZMDL4FoZ4xMzPD399f97hv3768YCKDPPPG5rj+3ZVGIY1ouaQl1i2tjZJHPycn3XZkRgaj\nz56lXNS403nRq2LwfeL6CcY+M5b+LfvrNWZBAYwZo70vdd68XqLkoCAITy0x+BaEeqhyq3lPT0/2\n7t1rvGQqkSSJVt+3ot26djR5swnmLoYraVdZRxsbGpppV9VpgGWtWiEzZNlDtRp+/lk72uzQAcpN\na5lFb8/eyCXtRxSZhZn0atYLRyv91kS3ttaWHdy7Fz777BdMpDkZzNN7AAAgAElEQVSrIAiCwYnB\ntyDUQ5UH3ydOnDBiJjUrKygjMzqT5K+SjRJfJkn0ub30xFomI+H2+m/DJSDTNtvx84MNG7SPTYi9\npT3PNX1O9/hI2hEAyjX6u0iQJO39py1bIgbegiA81UzrHUEQhAfSsmVLunbtyujRo1m8eDGlpaVc\nvnzZ2GkBoM5Tc8jtEKnfpCIpjDfK+qe7O7/6+pLVowe9HByIuXHDMIGPHYPwcLhwAZycwEu/Zfwe\n1QfdP2Dl4JXsCN3Bjgs7aBPRhgWHDdP18tYtM7ZsMUgoQRCEWo0dO5bw8HCDxxWDb0GohyRJ4tCh\nQ8yfP59Nmzbh7u7OhAkTjJ0WAGZ2ZnRL78Yzvz6D+3vuRsuji50dzzs48Pzx47T44w++u3IFtSGW\nf2zbBp99Bn/8oZ39BjD0zPsDGNhqICPaj6BcU46rrSsrglfw7rPv6jVmaio0bgzjx7/Ka6+Z3Goc\nwYR5enpibW2NnZ0drq6ujB07lsJH+HdVUlLC+PHj8fT0xN7enk6dOvFLpc5Po0aNwtXVFQcHB1q3\nbs2yZcuqHH/27Fn69OmDg4MD3t7ebNy48bF/N0PJzs4mODgYGxsbVCoVq1evrnXfy5cvM2DAAJyc\nnHBzc+Odd96hvNI/2IiICLp06YKlpSV///vfDZH+E0UMvgWhHnNwcGDgwIH89ddf7Nixw9jp6Mit\n5brtcnU5mnLj3F0nlyS+b9WKzO7d2dS+PWaGWP4xYEDF9saN2rKDTZpo7zg0QS+1eImwHmF0duuM\nXCa//wGPQamE3FwAiVu3IClJr+GEJ4gkSWzdupW8vDyOHTvG0aNHmT179kO/jlqtxsPDg/3795Ob\nm8usWbN47bXXSE7WLpGbPn06SUlJ5OTksHnzZmbMmMHx48cBKCsrY9CgQQwcOJDs7Gy+++47QkND\nOX/+fJ3+rvoyceJELC0tycjI4KeffuKtt97izJkzte6rVCq5du0acXFx7Nu3r0pZ2yZNmvDRRx8x\nzgR7GdQHYvAtCPWYmZkZo0aNws3NzdipVJO2JI2jnY+y33o/OQdyjJaHr40NCkOuufb11TbZAbh1\nC4KCIC0NGjQwXA6PqLSslLxi/ZWHNDevWn2xHjQCFUzInU61rq6uvPzyy5w8eRIAmUzGxYsXdfvd\naymBtbU14eHhuLtrP5UbMGAAKpWK2NhYQFvK1dLSUhdPkiQuXLgAaGe909PTeffdd5EkCX9/f7p3\n786KFStqzblPnz6o1erH/M0fX2FhIVFRUcyePRsrKyu6d+/OoEGDas390qVLDB06FIVCgVKpJCAg\ngFOnTul+/sorrzBw4ECcKlWWqs2XX35J06ZNsbOzw8fHhz179uh+lp6ezpAhQ1AqlXh5ebFo0SLd\nz1JTUwkJCUGpVOLs7MzkyZN1Pzt79iz+/v44OjrSvn17oqOjdT9TqVTMnz8fX19fHB0dGT58OCUl\nJQAcP36czp07Y29vz7BhwygqKnrgXOuSGHwLwhMiMzOzysenxqQp15A0I4n8Y/loSjVkxWQZNZ9y\njYbYmzf5NjXVMK3m+1cq25eVpS31YcL2XdrHwNUDcf7KmWXHlt3/gMdQ6V5hluk3lFCHkmYmkTQz\nqc4eP46UlBRiYmLo1KnTY7/WtWvXSExMpG3btrrnJk2aRIMGDfDx8cHNzY3+/Wsvw6nRaHQXAXdL\nS0sDtJMkdSkoKAhHR0ecnJyqfR84cGCNxyQkJKBQKPCqdA+Kr69vlQF1Ze+99x6RkZHcunWLtLQ0\ntm3bxssvv/zQuSYkJBAREUFsbCx5eXls374dT09PQHvugoKC6NixI+np6ezevZuFCxeyc+dOysvL\nCQwMRKVSkZycTFpaGsOGDQO0n14EBQUREBBARkYG3377LSNHjiQxMVEXd+3atezYsYOkpCTi4+NZ\nvnw5paWlBAcH8/rrr5OVlcWrr77K+vXrHyjXuiYG34JQz5WUlNC9e3e8vLz473//W2VdnrFIMolW\ny1rpHmf9YrzBt0ajofnhwwz86y8SCgspNsT5qbz05MQJ0Gjg7FmTXeRspbDimcbPcPKtk0zpOkWv\nsSrPfMfHg4n0hxLqgVdeeQUnJyeef/55/P39mT59+mO9nlqtJjQ0lDFjxuDt7a17PiIigvz8fA4c\nOMDgwYOxsLAAoFWrViiVSubNm4darWbHjh3s27evxrXnO3fuZOrUqTRu3JiffvrpvrlUHgTeT3R0\nNNnZ2WRlZVX7vvnOfSZ3yc/Px87OrspzdnZ23Lx5s8b9e/bsycmTJ7Gzs8PDw4MuXbrUOrC/F7lc\nTklJCSdPntQt+VGpVAD8+eefZGZm8uGHHyKXy/H09GT8+PFERkZy5MgR0tPTmTt3LpaWlpibm9Ot\nWzcADh8+TEFBAdOmTdP1vQgMDKyyhv3dd9/FxcUFBwcHgoKCiIuL4/Dhw6jVaiZPnoxcLickJIQu\nXbo8UK51TQy+BaEeKygoYOnSpQB4eXmxZs0aZCZS1s6xj6Ou2klBfAHFacYZZY07d47LxcVcKSmh\nh709lnL9rmsGoG9fbYfL06e1PdVVKm27+StX9B/7Ib215S1e+PEFZv02i/PZ+l+72r492NvfQiaD\n556DHOOtSBLqmU2bNpGVlUVSUhKLFi3SDYprs2rVKmxtbbGzs2NA5QtitBfloaGhWFhYVFnqcIck\nSXTr1o2UlBSWLFkCaGewN27cyJYtW3B1deWbb75h6NChNG3atNrx/fr1Qy6XM3XqVEJDQwHIzc0l\nKiqKzz//vMq+eXl52NjYcP78eTZs2MCnn37KsWPHHurc3I+NjQ15eVWXlOXm5mJra1ttX41GQ0BA\nAEOGDKGwsJDMzEyysrKYNm3aQ8f18vJiwYIFzJw5ExcXF0aMGEF6ejqgvakzLS0NJycn3ez9559/\nzrVr10hJSaFZs2Y1vp9duXJFt2zojmbNmuk+aQBwcXHRbVtbW5Ofn8+VK1do0qRJteMeJNe6Zhrv\n0oIgPBK5XM60adM4dOgQx48f59KlS8ZOScfM1gzr9hXLLa79fM0oeXhUeoPemZ1tmKA2NvDPf4KP\nD/ToAVu3wuXL2vaOJkaSJApKtTeD7riwg9S8VM5k1HwTVt3EgylTfiMjA/btg0rvkYIJU81UoZqp\nqrPHj6K2JWPW1tZVZp+vXr0KwIgRI7h58yZ5eXls3bq1yjHjxo0jMzOTqKgo5Pe4IFer1bo13wDt\n2rVj7969ZGRksG3bNi5cuICfn1+Nx8bFxdG5c2fdY3t7ezp37kxpaWmV/X799Vf8/f2Jjo6mSZMm\nTJkyhXnz5tWaU//+/XUXFXd/3X2RcYe3t3e13yU+Pr7Kcps7srKySElJYdKkSSgUChwdHRk7dizb\ntm2rNad7GTZsGPv379eVww0LCwPA3d2d5s2bk5WVpZu9z83NZcuWLbi7u5OcnFzjJ7lubm6kpKRU\neS45ObnawPpurq6upKamVjvuQXKta2LwLQj1mKWlZZVW84sXL+aLL76grKzMiFlVkFveflMzg7Jc\n4+RUudX8psxMxp45Q+btm28MYsAAaNvWZDvL9GtesQh7weEF+C71Zdv5R3uTfVAtW96gQQPYvh3e\nfRfuuudJEB5Kx44dWbVqFeXl5fzyyy/s27fvnvu/+eabnD17ls2bN2NuXtGFNyMjg59//pmCggLK\ny8vZvn07kZGR9O3bV7fPX3/9RXFxMYWFhcybN4+rV68yZsyYajFOnz6Nj48PAJGRkffMp7i4GHNz\nc6ZMmYKfnx+pqan3XO4QExOju6i4++vui4w7rK2tGTx4MOHh4RQWFnLgwAGio6MZNWpUtX0bNmyI\nSqVi6dKllJWVkZOTw48//oivr69un7KyMoqKiigrK0OtVlNcXFzj+05CQgJ79uyhpKQEc3NzrKys\ndLPZfn5+2NraMnfuXN1rnTp1iqNHj+Ln54erqythYWEUFhZSXFzMoUOHAHj22WextrZm7ty5qNVq\n9u7dy5YtWxg+fPg9z3PXrl1RKBQsWrQItVpNVFQUR44ceaBc65oYfAtCPVd5HV5ERATXr1+nwETK\n2rWJbEO7Te3okd0D1Sf6WTt3P362ttjdntm6oVbT2MLCsNVP7igs1NYAz9NfNZFH4a/y17Wav6W+\nxamJp5jadare4/boAbNmgasr3DURKAjVSPe4eF2wYAGbN2/G0dGR1atXExwcXOu+ycnJfP/998TF\nxeHi4qKbQV69ejWSJLFkyRLc3d1xcnLigw8+YOHChVVmk1esWIGrqyuNGzdmz5497Ny5E4VCUS2O\nk5MT9vb2REZG0rt371rzyc3NxfF2N947Nm7cyIcffniPs/FoIiIiKCwsRKlUEhoaytKlS3UXCP37\n9+eLL77Q7RsVFUVMTAzOzs54e3tjbm7O119/rfv57Nmzsba25ssvv2TlypVYW1vz2WefVYtZXFxM\nWFgYzs7OuLm5kZGRoVt2I5PJ2LJlC3FxcahUKpRKJRMmTCAvLw+ZTEZ0dDSJiYl4eHjg7u7OmjVr\nAFAoFERHRxMTE0OjRo14++23WbFiBS1btgRq/39FoVCwfv16fvjhBxo2bMjatWsJCQl5oFzrnEaj\nqRdf2lSFB7Fy5Upjp1AvPCnnKSUlRQNoAI1CodDk5ubW6evX1XkqKy7TlKvL6+S1HtbQkyc17Nmj\nYc8ezaykJL3FqfVcvfeeRmNjo9E8/7xGc/as3uI/qm7LummYiYaZaCL/itR7vJUrV2qKivQe5olw\n+71PvMc+IS5duqSZOXOm7nFUVJSmpKRE93jz5s2avLw8TUJCgjHSE+rQvf7tiplvQajnmjZtSseO\nHQHtR4u1lY4yloz1GZwMOclB5UHy4/KNksPARo1wMzfnDVdX/O+aZdI7jUbbaGfFCu0i51at7n+M\ngfVr3g8bcxuCvINwtHTkYPJBNpzZoNeY97lXThCeOPn5+axbt47Y2Fjd3+mioiLdzPmGDRuYNWsW\nISEhulle4ckkBt+C8ASYM2cOu3fv5uLFi1y9epVx48ZVuyHFWNQ5ahq90ohnE57FtnP1O+sN4TVn\nZ1K6duWf7u4czsvDPy6OPYa4+fLcOWjWDIKD4f33tQNxEzS161RufHCD+S/OZ+SGkUyMmcilnEt6\nj3vhArzzDnh6wu+/6z2cIBiVjY0N77//Pps3b6Zt27ZkZ2fj7Oys+3lwcDBHjhxhx44dell2IpiO\nuq38LgiCUQQEBADaP94FBQUEBgbSwEQ6KrqOczV2Crq28quuXyetuJj3mzblubtq3uqFSgV3BvkX\nL8Lu3ZCQAC+9BJWaXRibnYX2XKgcVcT+IxYPew+DxO3eHa7dLoJz4QJ07WqQsIJgEg4ePKj72y08\nXcTgWxCeIFFRUfe8McmY1Hlqrq+9jrnSnEZBjYySQ7ieupXVytxc29Jxw+0lHMHBMHgw3OMGLGMy\nk5kZbOANMGECzJ6t3d61C26XQxaEp0JgYKCxUxCMRCw7EYQniKkOvBMnJ3LA/gAJ4xO4PPuysdMB\ntDeblxqi42Tl1tRdusCPP0KbNvqP+xiybmWx8sRKpu96vA6C9xMUVLG9dSuo1XoNJwiCYBLE4FsQ\nnjAXLlzgm2++oU+fPmzfvt3Y6QBg38Net13wVwFlt4xXh/z4zZu8k5hI8z/+4DtDdJysPPjev9/k\nSg3erbC0EO9F3qw5vYYWTi1qbWxSF/72N2jYULudmQkff6y3UIIgCCZDDL4F4Qnzww8/sHHjRoYP\nH06PHj2MnQ4AyteUWLWyAqD8Vjk5+4zTU/xaSQmL09LYeuMGnW1smHSfjmh1ws0NOncGPz+YPh32\n7IEPPoC9e/Uf+yHlFuWy+dxmXlC9QIBXAOM6jdPrpykyGbRrV/FYzHwLgvA00PvgW5KkAEmSzkqS\nlCBJ0rQafj5CkqT4218HJElqr++cBOFJFRYWxpw5c/jtt9/IyckxmZsuARr2b6jbvrbSOK3mLxUV\n8Z+rV0kqKuK33FwMsOhE6+BB+OMP7facOWBlBYYY+D+kqDNRDF8/nLWn17Lq5CqDxJw2DV55BZYt\ng6n67+0jCIJgdHodfEuSJAP+BbwEtAWGS5LU+q7dLgLPazQaX2A28G995iQITzJvb2/dMoHo6Gjt\numYTaR8omVfMoObsNs7MdxdbW1xu19TNKC3lt5wcrhQX6z/wnaLWn3yiHYR/8gnc7sZmSgK9A5FJ\n2reFA8kHmL57Or2W96KwtFBvMV9+WXs/6t//Dk5OFcVhBEEQnlT6nvn2AxI1Gs1ljUZTCkQCgyrv\noNFoDms0mtzbDw8DpjcdJAj1xIABA3TLBH777TeaN2/Ov/71LyNnpeU63hWLphYoRyrxmueFptzw\nNa9lkkRQo4pKKwEnTvCf9HTDJWCiN8Te4dzAme7u3XWPj6QdYUbPGZjLzfUa99gxGDECXFxg8WK9\nhhIEQTA6fQ++mwCVO32kcu/B9Xhgm14zEoQnmIuLC88++6zu8fjx43nvvfeMmFEF6xbWPJf8HG1+\naoPLCBckmXEGogMbVix/aWZpafjyg9euwQ8/wNCh8P33ho39AAa1qpgfsVZY08+rH2Yy/Vallcu1\n1Rf/+gtEbxFBEJ50JnPDpSRJ/sBYoNq6cEEQHtzAgQN12/Hx8SZVflCSJEqzS7n+83WydmQZJYc+\njo5Y3W66k6NWk1lSYtgEdu+GX36BgACo9N/KVAxqXTH4Pph8kJKyEso15XqteuLrC//4h0kugxcE\nQahz+m6ykwZU7tjQ9PZzVUiS1AH4HgjQaDS1rvgLCQnRbfv4+NDGxGvlGsvBgweNnUK98KSeJ5lM\nhpOTE506daJFixYsX76cwsJCHBwcHun16vI8WR61xGGJAyWtSyjoU0BxpgHWW9dgtJUVrmo1XqWl\nrExNJVsmw7sO1sbf71wpT5+mybFjuMXHs6t3b4p//fWxY+pDkGMQXpZeyJAR8G0AxwuOM63JNDws\n6qYBz73OU1aWJdeu2eLjk1Enseqb06dPc+bMGWOnIQiCPmk0Gr19AXLgPNAMMAfiAJ+79vEAEoHn\n7vNaGuHBrFy50tgp1AtP6nkqLy/XlJeXa44dO6YZOHCgxtbWVvPxxx8/8uvV5XlSF6g16kJ1nb3e\n40goKNC0P3JE0+jAAc2HFy7UyWve91z17KnRgPZr+XLtc0VFdRJbH77Y/4Vm/qH5moTMhDp93ZrO\n04YNGk3DhtpT065dnYar126/9+n1vVoj3mOFJ9yYMWM0H330kUFj3uvfrl6XnWg0mjLgbWAHcAqI\n1Gg0ZyRJekOSpH/c3u0jwAlYLEnScUmSjugzJ0F40kmShCRJ2NnZMXToUJKSkpg5c6ax0wJAbi1H\nbiXXPdbocSnD/TS1sGCptzdXu3VjdvPmhgk6YEDF9rx50KGDtua3iZrWYxpTu06lZUP9V2ZxdIQb\nN7Tb6elQZrw+TIIJkslkXLx4scpzn3zyCaNGjapx/5KSEsaPH4+npyf29vZ06tSJX375RffzUaNG\n4erqioODA61bt2bZsmVVju/duzdWVlbY2dlha2uLj49P3f9SepKdnU1wcDA2NjaoVCpWr15d4373\nO0eVJSYmYmVlxejRo/WZ+lND38tO0Gg0vwCt7nruu0rbE4AJ+s5DEJ42Xl5eeHl5GTuNatSFalK+\nSCFjXQalWaV0S+9mlHXpVnI53ezt779jXRowAMLCtNvnzsH27dCrl2FzeER5xXnIJBk25jZ6ef3n\nn9f2I7pyRTsI//13MJEeUYIJqO1vRG3Pq9VqPDw82L9/P+7u7mzdupXXXnuNkydP4uHhwfTp0/n3\nv/+NpaUlCQkJ9OrVi06dOtGxY0fd6y5evJixY8fq7XfSl4kTJ2JpaUlGRgbHjh1jwIABPPPMM9Uu\nIO53jip7++238fPzM+Sv8UQzmRsuBUHQn5SUFA4fPmzsNAC4+edNLs++TOGZQkqvlVJ4Wn81pB+E\nuryc33JyWJpW7XaUute2Ldx5Uyst1bZ4lJn2n+FNZzfx4ooXafJ1E/Ze2qu3OJIEgYEVj7/8Um+h\nhHroYT8ls7a2Jjw8HHd3d0BbhlWlUhEbGwtAmzZtsLS01L22JElcuHDhkWP26dMHtQm0aC0sLCQq\nKorZs2djZWVF9+7dGTRoECtWrKi27/3O0R2RkZE4OjrSp0+fe8b+8ssvadq0KXZ2dvj4+LBnzx4A\n0tPTGTJkCEqlEi8vLxYtWlTluNTUVEJCQlAqlTg7OzN58mQAzpw5g7+/P46OjrRv357o6Ogqx6lU\nKubPn4+vry+Ojo4MHz6ckts30B8/fpzOnTtjb2/PsGHDKCoqeqBcDcW0/+oLgvBYzp07h6+vLx07\ndmTLli3GTgcAh+cdaDS4otb2jZgbRsvlplqN8tAhQs+c4aohqp5IEvTvX/H4yBHtIPyvv/Qf+xFp\n0NDRtSNpU9II9A68/wGPofKHAL//rtdQwkOaOXOmbklb5a/alrTVtL8xl79du3aNxMRE2rZtq3tu\n0qRJNGjQAB8fH9zc3Ohf+d8mMH36dJRKJT179mTfvn21vnba7Qt3M7O6XUwQFBSEo6MjTk5O1b4P\nrKVSUkJCAgqFosqnnr6+vpw6deq+8Wo6R3l5eXz88cd8/fXX97wYSUhIICIigtjYWPLy8ti+fTue\nnp5oNBqCgoLo2LEj6enp7N69m4ULF7Jz504AysvLCQwMRKVSkZycTFpaGsOGDUOtVjNw4EACAgLI\nyMjg22+/ZeTIkSQmJlaJu3btWnbs2EFSUhLx8fEsX76c0tJSgoODef3118nKyuLVV19l/fr1983V\nkMTgWxCeUDk5OURFRSGTyWjVqhWzZ882dkqA9uPchgMqam1fX3fdaLm8fvYs2Wo1KcXFdDXUEpSx\nY7WdZM6ehQsXtGstpkzR3oZpYkasH8HQdUOZe3AuF3Mu3v+AxxQcrG0GamEBfn5QaNwPRYQnhFqt\nJjQ0lDFjxuDt7a17PiIigvz8fA4cOMDgwYOxuNOJFpg7dy4XL14kLS2NCRMmEBQURFJSUrXX3rlz\nJ1OnTqVx48b89NNP982l8iDwfqKjo8nOziYrK6va982bN9d4TH5+PnZ2dlWes7Oz4+bNm/eMVds5\nCg8PZ8KECbi5ud3zeLlcTklJCSdPntQtZ1GpVPz5559kZmby4YcfIpfL8fT0ZPz48URGRgLwxx9/\nkJ6ezty5c7G0tMTc3Jxu3bpx+PBhCgoKmDZtGmZmZvj7+xMYGFht/fq7776Li4sLDg4OBAUFERcX\nx+HDh1Gr1UyePBm5XE5ISAhdunS5b66GJAbfgvCEKi8v56OPPiIuLo7ff/+dq1evGjslnYaBDXV/\nffKP5HPrwi2j5OF5+2NngM2ZmYYJ6ucHb70F3t7aGy6PHIFdu0yy+6UkSZSUaT8R2HR2Ewk3EriQ\ndeE+Rz06KyvYswcyMyEmBqyt9RZKqGfkcjmld5UDLS0tRaFQsGrVKmxtbbGzs2NA5Zua0S4dCQ0N\nxcLCotpyB9D+P96tWzdSUlJYsmSJ7vkuXbrQoEEDFAoFo0ePpnv37sTExFQ7vl+/fsjlcqZOnUpo\naCgAubm5REVF8fnnn1fZNy8vDxsbG86fP8+GDRv49NNPOXbs2COfk5rY2NiQl5dX5bnc3FxsbW1r\nPaa2cxQXF8euXbseqFGbl5cXCxYsYObMmSiVSkaMGEF6ejqXL18mLS0NJycn3cz9559/zvXr2kmX\n1NRUmjVrhuyu5XdXrlzRLYe5o1mzZrpPGe5wcXHRbVtbW5Ofn8+VK1doclfTgGbNmtWYq4uLiy5X\nQxKDb0F4Qjk5OdHj9h1rGo2GBQsW8N///tfIWWlJZhJmdtqPaOUOcgrPGWeKs3K3y9XXr/P6mTOU\nGWoGWpJg4kQw8IzLw3il1Su67c8PfI7/j/78nqrf9SBdu2q/R0XBe++Z5AcCT6WZM2fWWDLtXstO\nHnTfB+Hh4cGlS5eqPJeUlESzZs0YMWIEN2/eJC8vj61bt1bZZ9y4cWRmZhIVFYVcLqc2arW62prv\nyiRJqnXZRVxcHJ07d9Y9tre3p3PnztUuFn799Vf8/f2Jjo6mSZMmTJkyhXnz5tUas3///rqLiru/\n7r7IuMPb27va7xIfH19lKcndajtH+/bt4/Lly3h4eODq6sq8efNYt24df/vb32p8nWHDhrF//36S\nk5MBCAsLw93dnebNm5OVlaWbuc/NzdWt33Z3dyc5OZny8vIqr+Xm5kZKSkqV55KTk6sNqmvi6upK\nampqtWNryvXy5cu6XA1JDL4F4QlWeV3g0qVLuXLlihGzqaBwVNB+a3vaRbej+7XuNOzf8P4H6UF3\ne3scbr/ZZKvVqCwtURtjtJeVBf/5D+TnGz72PQS0CMBcbg5AcVkxB8YeILRDqF5jlpdDu3awdCl4\neWmXxAvC0KFDmT17NmlpaWg0Gnbt2sWWLVsYMmRIrce8+eabnD17ls2bN2Nubq57PiMjg59//pmC\nggLKy8vZvn07kZGR9O3bF9DOFO/YsYPi4mLKyspYuXIl+/fvJyAgoFqM06dP66qI3FlKUZvi4mLM\nzc2ZMmUKfn5+pKam3nO5Q0xMjO6i4u6vuy8y7rC2tmbw4MGEh4dTWFjIgQMHiI6OrrUkY23nCOCN\nN97gwoULxMXFER8fz5tvvklgYCA7duyo9joJCQns2bOHkpISzM3NsbKyQi6X4+fnh62tLXPnzqWo\nqIiysjJOnTrF0aNHAfDz88PV1ZWwsDAKCwspLi7m0KFDPPvss1hbWzN37lzUajV79+5ly5YtDBs2\n7J7nGKBr164oFAoWLVqEWq0mKiqKI0cqqljXlOvdM+/6JgbfgvAECwoK0m2XlJTw/vvvGzGbquy7\n2dMosBEycxnFV4uNUvNbIZMxoNLstyRJWBi6+si772pnv7dvh+xaG/waha2FLX1UFRUONp+reZ1p\nXZLJICEBduyAd96Bu8YDwlMqPDycbt260aNHD5ycnAgLC4iMRrsAACAASURBVGPVqlW1drpOTk7m\n+++/Jy4uDhcXF90M8urVq5EkiSVLluDu7o6TkxMffPABCxcu1M0ml5aWMmPGDF31jYiICDZt2kSL\nFi2qxXFycsLe3p7IyEh69+5da/65ubk4OjpWeW7jxo18+OGHj35SahEREUFhYSFKpZLQ0FCWLl2q\nu0Do378/X3zxBXDvcwRgaWmJUqnUfdnY2GBpaYmTk1O1mMXFxYSFheHs7IybmxsZGRnMmTMHmUzG\nli1biIuLQ6VSoVQqmTBhgm5pjEwmIzo6msTERDw8PHB3d2fNmjUoFAqio6OJiYmhUaNGvP3226xY\nsaLKevTaykwqFArWr1/PDz/8QMOGDVm7dm2VDuk15Xr3EiF9k4zZ5OJhSJKkqS+5GtuqVasYMWKE\nsdMweU/LefLx8eHs2bMolUp27txJhw4dHup4fZ6n6+uuk/7vdG4euUnno52x8rLSS5x7WXv9OtMv\nXmRQo0YMVyr52103Kz2Mhz5X5eWwfDlYWoKJ/r/4fez3TNs1jUDvQEZ3GI0GDWXlZbzc8uVHfs2n\n5d9eXbi93EHvNwSI99i6dfnyZZYvX87HH38MwIYNGwgMDEShUADamyl79+7N1atXadlS/02sBMO7\n179dMfMtCE+4b775hsOHD5OYmEh8fDwvvviiydx8WXqtFNe/u9I1ratRBt4AIc7OJD77LKMbNyYm\nKwu/2FgSDVFm4/RpaNYMxo2DDz/UDsRN0Gjf0Vz/53Xe7Pwmr659lU/2fUJGYYbe4x4/Dq+/Dk2a\nwF1LPwXBpOXn57Nu3TpiY2N1Jf6Kiop0A+8NGzYwa9YsQkJCWLNmjTFTFYxE7x0uBUEwrjvrFANv\ndzAZP348Dg4OxkxJp8mk+988o2+y2x9dLkpNxUImY3HLlrSwMsCFgJcXFBRoty9dgpUr4fx5+Nvf\noNJyIWOzNNNWhOno2pHEdxJxbuBskLg9e1acnlOn4K7CB4JgsmxsbHj//fd1y/yys7Nxdq74dxMc\nHExwcLCx0hNMgBh8C8JTYtOmTfe829+YSm6UkPp1KpoSDV5fed3/AD34T+vWhg1oYQGvvQbffad9\n/NZb8I9/aAflJshaYY21wnC1/8aMgYgI7fbq1VDDvW6CUC8cPHiwxps1haeXWHYiCE+JygNvjUZT\nrd2usVz98SqHGh0ieU4yqRGplBWVGTslbpWVkV5crP9AlSsQyOUwZw7UcgOZqci+lc3iPxczYNUA\nysr1999qzJiK7TVr4K5KYYJQbwQGBtZ590uhfhODb0F4ily9epUvvviCtm3bMnfuXGOnA4D98/aY\nu2lLWmhuaciKyTJaLkm3bvHmuXM0+f13lhmi6UK3bhV1vvPyoJbyYaZCo9HQdVlXfrv8G5P9Jtda\nbaAudO4MrVppt4uKwIQK9QiCIDwWMfgWhKfIvn37WL9+PaGhoXz00UfGTgcAK5UVrn931T2+tuqa\nUfLIV6v5d3o6GzIzcTM3Z4anp/6DSpJ29rt7d/jXv7QdZf7+d3j1Vf3HfkhZt7JYdnwZXk5eNLRq\nyEstXkIm6e8tRJLgxRcrHptYCXRBEIRHJgbfgvCUWLNmDcOHD+fo0aPExMToddbyYSlHKHXbmRsz\nUeeqDZ5DqUbDN6mpXC8t5VRhIbE3bxom8Mcfw4ED8MorMH++tuX8ggWGif0QEm4kMCF6AjGJMfx8\n6mdKykooKy+jWK2/5Tnh4RAYCOvWwaZNegsjCIJgUGLwLQhPiV69eunWfR88eJATJ07w559/Gjkr\nLYumFkhmty8GyiBzU6bBc3BUKHi1UkWCb1NT+dEQJRnvNPVp0gR+/13bU/0BWigbml8TP5raNQXg\nxq0bDFs3DM+Fnqw7vU5vMRs1guhoCAmBwkLttiAIQn0nBt+C8JRwcXHRlRsEeO6555gxY4ZROkve\nzczWDNVnKhr/vTG+u31xGelilDzGu1Ysf/nftWvszMqi2Fj1t02s26VMkjG+43jd4z+v/EnMiBhG\ndhip17hlZRAaCp6esGqV9rEgCEJ9JgbfgvAUGTdunG7b2tqaLVu2mMzyE48PPGi9rDWOLzgiyY2T\nU097e7wr1fju6+ho+HbzP/wAfftqSw7m5ho29n2M7zQeuaT99CQ1LxWFXKH3mHK5dkXOhQvakoMm\nWi1TEAThgYnBtyA8RQICAnC9Pbt748YNfvvtNyNnVFXpjVLSlqRxrMcxCs4WGDy+JEm62W8fa2sa\nGGOkd/mydrSZlgb29oaPfw9N7JowsNVAAFxtXEnKTiK/JJ8/Uv/Qa9whQ6BhQ72GEARBMBgx+BaE\np4iZmRnvvfce7777LidOnOCZZ54hIiKCS5cuGTs1AM5POU/Ovhw8wjyM1m5+bOPGHOjYkVNduuBj\nbc0/z59nZ5YByh/euAFvv62terJgAVha6j/mI5jeYzrrX1vPiTdPsPb0Wty/cWdp7FKDxI6PhwED\nICbGIOEEQRD0QlR9F4SnzAcffADAF198wRdffEH//v3p16+fkbPSav1ja6Mvg2lkbk4jc3O+TU1l\nbnIyrzduTEtDtJu3toYVK7T1vm/c0N58aWUFbm7gYpw18DXp0qQLXZp0oVxTTifXTszpM4fGNo31\nHrfyoLtdO+jfX+8hBUEQ9ELMfAvCU2ro0KFcvnyZVatW4e3tbex0AKoMvItSi8jaZbyGO39v3JjL\nXbvyWfPmeBpi8G1lVbW+d//+MHgwnD2r/9iPQCbJeNvvbYMMvKHqYHvTJm1JdEEQhMcxduxYwsPD\nDR5XDL4F4SmlUqmwN7E1xQC5v+fyu+p3Drsf5tSrp4xWjcXGzAx5pYuBckPkUbndvEajHXj36qX/\nuI8pNS+Vz377jCs3r+gtxpgxYGOj3T53Dg4d0lsowQR5enpibW2NnZ0drq6ujB07lsLCwod+nZKS\nEsaPH4+npyf29vZ06tSJX375RffzUaNG4erqioODA61bt2bZsmVVjj979ix9+vTBwcEBb29vNm7c\n+Ni/m6FkZ2cTHByMjY0NKpWK1atX17jf/c4RQO/evbGyssLOzg5bW1t8fHwM8Ss8McTgWxCecmfP\nnuX//u//TKbjZXlJOSVXSgAoyynj5hEDNbupQZlGw46sLIadOoV/XJz+A/bsCR4e2u28PNi2Tf8x\nH9P/7f4/OizpQEpeCmXl+qsD2KABDB1a8Xj4cL2FEkyQJEls3bqVvLw8jh07xtGjR5k9e/ZDv45a\nrcbDw4P9+/eTm5vLrFmzeO2110hOTgZg+vTpJCUlkZOTw+bNm5kxYwbHjx8HoKysjEGDBjFw4ECy\ns7P57rvvCA0N5fz583X6u+rLxIkTsbS0JCMjg59++om33nqLM2fOVNvvfucItP89Fi9eTF5eHjdv\n3qzxdYTaicG3IDzF4uLi6NmzJ/Hx8QytPLIxIsdejlXqfF9baZx28wCnCwoYf+4c27OzWdCihf4D\nymQwcqT2+0svaad6N22CNWv0H/sR5BTlYC43x8vRi7n95uJu767XeMHBFds3boiW80+bO5+Cubq6\n8vLLL3Py5EkAZDIZFy9e1O13r6UE1tbWhIeH4+6u/X91wIABqFQqYmNjAWjTpg2Wt2921mg0SJLE\nhQsXAO1ERXp6Ou+++y6SJOHv70/37t1ZsWJFrTn36dMHtdrwHXvvVlhYSFRUFLNnz8bKyoru3bsz\naNCgGnO/3zm640E/lfzyyy9p2rQpdnZ2+Pj4sGfPHt3P0tPTGTJkCEqlEi8vLxYtWqT7WWpqKiEh\nISiVSpydnZk8ebLuZ2fPnsXf3x9HR0fat29PdKUOXCqVivnz5+Pr64ujoyPDhw+npEQ7oXP8+HE6\nd+6Mvb09w4YNo6io6IFzrUti8C0IT6mcnBzeeOMNMjMz2bNnj+4PrSmoPPi++tNVytXGaXQTdvEi\nKcXF5KjVrM/IMEzQ996D1FSYPVtbY+/bb03qhsvKXlzxIp/s+4Sj6UdZeWKl3uP17w+dOmmvT6Kj\ntfeoCoYxc+ZMZs6cWWePH0dKSgoxMTF06tTpsV/r2rVrJCYm0rZtW91zkyZNokGDBvj4+ODm5kb/\ne9zdq9FodBcBd0tLSwO0VabqUlBQEI6Ojjg5OVX7PnDgwBqPSUhIQKFQ4OXlpXvO19eXU6dO3Tde\nTecItJ8SKJVKevbsyb59+2qNGxERQWxsLHl5eWzfvh1PT09Ae+6CgoLo2LEj6enp7N69m4ULF7Jz\n507Ky8sJDAxEpVKRnJxMWloaw4YNA7Qz80FBQQQEBJCRkcG3337LyJEjSUxM1MVdu3YtO3bsICkp\nifj4eJYvX05paSnBwcG8/vrrZGVl8eqrr7J+/foHyrWuicG3IDyl7O3tKS4uBuDWrVusXr1aNztg\nbHbP2SGz0v55KssuI3uXcbo9Vu54+cPVq5wrKKBE3x0vlUpwdYVnnoEdO2D3bpNd9z3ad7Rue/7v\n85m4dSL/3PFPvcWTJPjzT/jpJ3jhBbh6FUxgUlEwkFdeeQUnJyeef/55/P39mT59+mO9nlqtJjQ0\nlDFjxlS56TwiIoL8/HwOHDjA4MGDsbCwAOD/t3fn0VEVaePHv9VZCCEhIUAggbDIoiwCGhZBZ8YN\nF0AdxR9ugDqK8irKAIPyKq+gHgUZFQURB0dxRPZFTCLINkYRZFEMS1jCFrIQSEKWTsjeqd8ft9NJ\ngCxAbndDns85fbh1u7rroWi6n66uW3XttdcSHBzM+++/T0lJCevXr+enn3664NzzDRs2MH78eFq2\nbMk333xTYywVk8CaREZGkpmZSUZGxnl/RkREXPAxubm5NG7cuNK5xo0bk5NT/bS+qvpoxowZHDt2\njOTkZEaNGsV9993H8ePHz3u8h4cHRUVF7Nu3zzGdpX379gDs3LmT9PR0Xn/9dTw8PGjXrh3PPvss\nS5YsYceOHaSkpDBjxgx8fHzw9vZmwIABAGzbto2zZ8/y6quv4unpyW233caQIUMqzWEfO3YsLVq0\nIDAwkPvuu4+YmBi2bdtGSUkJL7/8Mh4eHgwdOpQ+ffrUKta6Jsm3EPWUUqrSjpeTJ08mNDSUrKws\nF0Zl8GjkQcCfAvBs6kmrl1rRsL1r1vwe0rQpwV7GLo4ni4rot2sXBy/hIq9L4ukJffuWl4uLjZsb\nGdFjBL5exvDz0cyjFNmKeKnvS6a2abEYSw4OGmQsOeimi8EIE3z33XdkZGRw/PhxZs+e7UiKq7Jo\n0SL8/f1p3LgxgwcPrnSf1prhw4fToEGDSlMdyiilGDBgAImJicydOxcwRrBXr15NVFQUISEhzJw5\nk0ceeYTWrVuf9/iBAwfi4eHB+PHjGT58OADZ2dmsWrWKadOmVaprtVrx8/PjyJEjfPvtt7z11lvs\n2rXrovqmJn5+flit1krnsrOz8ff3r/Ix1fVRnz59aNSoEV5eXowcOZKbb76ZNRdYgL9Dhw589NFH\nTJ06lRYtWvD444+TkpICwIkTJ0hOTiYoKMgxej9t2jROnz5NYmIibdu2xXKBHYZPnjx53i+1bdu2\ndfzSANCiwq+Fvr6+5ObmcvLkSVq1anXe42oTa12T5FuIeuyJJ55wfICdOXOG5cuXExgY6OKoDN2W\ndWPAqQF0mtUJ32tdM7/Ay2LhqZblS+n9KSCAHmVLbjhLfj58+il06gTff+/ctmsQ4BPA490fd5SL\nbEW0DWxbzSPqRlKSccFlYqKRgAvzucO0k6rmGPv6+lYafT516hQAjz/+ODk5OVitVr4/5//OM888\nQ3p6OqtWrcKjmp1sS0pKHHO+Abp37050dDRpaWmsXbuWo0eP0rfil+QKYmJiCA8Pd5QDAgIIDw+n\n+Jwv0f/973+57bbbiIyMpFWrVowbN47333+/ypgGDRrk+FJx7u3cLxllOnfufN7fZffu3edNJamo\ntn0ExpeVqv59Hn30UTZv3syJEycAmDRpEgBhYWFcc801ZGRkOEbvs7OziYqKIiwsjISEBEov8Etj\naGgoiYmJlc4lJCScl1ifKyQkhKSkpPMeV5tY65ok30LUY0FBQTxY4So2d1o2yzPAE4unhcKThcSN\niSNzk+unnuTYbJSYPe3kXF9+aUxwXrLE2HbezYzuPdpxHJsWi63URlaBub+ePPecsSpjo0amNiOu\nEDfccAOLFi2itLSUH374ocr5x2VGjx7NwYMHiYiIwNvb23E+LS2NpUuXcvbsWUpLS1m3bh1Llizh\nzjvvdNTZu3cvhYWF5OXl8f7773Pq1Cmeeuqp89rYv3+/Y/m9JUuWVBtPYWEh3t7ejBs3jr59+5KU\nlFTtdIc1a9Y4vlScezv3S0YZX19fHnroId544w3y8vL45ZdfiIyMZETF5U1r0UdgjJivX7+ewsJC\nbDYbCxcuZPPmzdxzzz3nPU9cXBw//vgjRUVFeHt707BhQ8dodt++ffH392fGjBkUFBRgs9mIjY3l\nt99+o2/fvoSEhDBp0iTy8vIoLCxkq3190X79+uHr68uMGTMoKSkhOjqaqKgoHqthCaT+/fvj5eXF\n7NmzKSkpYdWqVezYsaNWsdY1Sb6FqOeeeeYZ2rZty9SpUxk/fjyJiYnnjQa4StrKNHZevxOLj4VG\nPVyTaXXy9WV2x47s79OHH3v1YntODuucsd08GHMqoqNhyxa45hrntHmRwkPDee2W1/jpqZ9469a3\neHj5w7T/uD3peelOaX/9elho/rWewsWq2/n2o48+IiIigiZNmrB48eJKAwrnSkhIYN68ecTExNCi\nRQvHCPLixYtRSjF37lzCwsIICgrilVde4eOPP640mrxgwQJCQkJo2bIlP/74Ixs2bMDLPjWtoqCg\nIAICAliyZAm33nprlfFkZ2fTpEmTSudWr17N66+/Xk1vXJo5c+aQl5dHcHAww4cP57PPPnN8QRg0\naBDTp08Hqu8jgOLiYiZPnuxYhWTOnDl89913dLzAilCFhYVMmjSJ5s2bExoaSlpammPajcViISoq\nipiYGNq3b09wcDCjRo3CarVisViIjIzk8OHDtGnThrCwMJbZV33y8vIiMjKSNWvW0KxZM8aMGcOC\nBQvo1KkTUPVrxcvLi5UrVzJ//nyaNm3K8uXLGTp0aK1irXNa6yviZoQqamPhwoWuDuGKIP1ksNls\n2maz6c2bN+s77rhDt2nTRq9fv95xvyv7qfBUoS44WeCy9ivak5OjO23bpq/bvl3/JyXlgnXqtK9K\nS7Xu1UtrY7sdrceO1TovT+v//Edrm63u2qlDkzZM0vN+m6ezC7KrrVcX/bRihdaNGxtdc8MNl/10\nbsv+2SefsVeJ+Ph4PXXqVEd51apVuqioyFGOiIjQVqtVx8XFuSI8UYeq+78rI99C1HMWiwWLxUJg\nYCB33XUXBw8eZODAga4OCwDvFt40CDHmpGutSZmfwtkDZ10SS3sfH76+7jr29+nDyJZO2FJdKXjz\nzfLyJ59A27awciVkZ5vf/iWYduc0RoWPonGDxjVXvkxhYcY+RAD79oGzVoIU4lLl5uayYsUKfv/9\nd8cSfwUFBY6R82+//Za3336boUOHOkZ5xdWpbhefFEJcsbp37073ClevaRdt634h6VHpHHrmEMWp\nxTS5uwk9f+jp9Bj8PD25KSDAUS4sLcVTqUpb0Ne5++6Dm26CbdvAZoObb4ZvvzWvvTq08dhGzuSd\n4ZHu5mze1Lcv9O8Pv/5qLAIzcya8+64pTQlRJ/z8/JgwYQITJkwAjO3emzdv7rj/wQcfrHbKjLh6\nyMi3EOI827Zt49Zbb620a5grnYk6Q3GqsUJA5vpMcve6bmtDrTWr0tLoumMHP5g991upyhllRITb\nr62XejaV+xffz/NRz+Pnbe7KMC++WH48ffoV871ECAC2bNlS7XxwcfWS5FsIUclXX33FoEGDGDBg\nQLU7uznTtZ9dS9P7mxoFDfFvxrsslhkJCTx18CDtfXwY3LSp+Q3edhsMHAg+PjBhgvHnjBnGNo9u\nKKcwB1upjZf7vszgzhde9qyuPPaYMf0EjInxUVGmNidEnRoyZEid734prgySfAshHPbt28fMmTPJ\nzMy84IYJrtRuajvHcfrKdKw7rVVXNsmJggKmxMeTY7OxKSuLzc7akOjTT+HIEXjjDWOuRUwMvPqq\nc9q+CD+f+JnOn3RmzZE1vP/r+xSWFFKqS8nMN2eZSIsFvvgCvLxg4kRj6okQQrg7Sb6FEA4BAQEc\nOXIEgD179jiWm6ppC2Jn8L/Bn4Ydy3e6TJ6dXE1tc7T18WFIhdHuF+LimJWUxImCAnMb7tgRWrUC\nPz/YswcWLYIePcxt8xL0a9WPpg2N/kmyJjE6ajR9P+/Lu5vNm4w9cKCx2c6MGca6319+Cc5aCVII\nIS6FJN9CCIewsDBee+01R3ns2LEsWLDgvN3EXKXNa23waulFx1kd6Tyvs0ti+LBjR3ztGy/sy8tj\nTnIyxc7ceKfCBVpYrWDS9seXooFnA6b8ZYqj/NXurxjWbRgzBs4wtd0WLWDrVujdG+bPB2f9ICGE\nEJdCkm8hRCUTJkxw7K5WUlJC165d6dq1q4ujMoQ8HUL/hP60fqk1Hj4elFhLnB5DGx8fJrct30I9\npbAQf2fP2zxzBqZMgQ4d3O4qw9G9R3NT65sc5e8OfYfG/JVzrFb43/+Fn3922/2IhBACkORbCHEO\nHx8fZlaYPJuQkEBxcbHbLD1o8bJw9uBZYh+NJebWGJfENT4sjE4NG9JAKcaFhdHYw4PckhInpJh2\nx4/Dzp3w8cfwwgvOarVWPCwefH7f53hZvLAoC/1b96eopIil+5ayaO8i09q95x4YNsxYIAYgP9+0\npoQQ4rLIZbZCiPPcf//9DB8+nNtvvx1PT082bdrE5MmTWb58uWNU3FW0TXPgsQM0f6Q51/772mq3\nnTZLA4uFhV26EOTlRTsfH+anpPBGfDyjL7DNdJ07ehQmTYJNmyApCR591Ljy0I10D+7OnEFzCA8N\np7lvc+5ccCd5xXnMuneW6W2XlsLrr8MHH8Dy5fDAA6Y3eVXw8fE5rZRq4eo4hLha+Pj4nK7qPkm+\nhRDnUUqxYMECwFgO68iRI0ybNo127dq5NjBAeSjCd4W7JOmuqE9jYxfHGQkJRJ05Q0T37sTFx5vf\nsL+/sekOwN69MG+eMe/7ueeMizLdxKjwUQAUlBQwuvdoHuv+GB4WD9PbveMOiI42jr/4QpLv2srP\nz3fCtq1CCJBpJ0KIGgwaNIh9+/bx4IMPujzhLVMWR2lJKfFvxRNzZ4zLYvl769b81KsXvRubv6U6\nAMHBMG5cefnFF40RcDf5tzmXj6cPw3sMdyTeRbYirCXmLRP59tvlx5GRsG6daU0JIcQlkeRbCFGt\nwMBAx0YQpaWlrF271i3mfxeeLGRL0Bbip8STtSmLrM2uWeLC22JxfBkoVIp3T5wgrajI3EYnTIAm\nTYzj0lLo0wdCQ81t8zJprYk4FEG3T7uxybrJtHZuuQX+9rfy8siRkJpqWnNCCHHRJPkWQtTKhg0b\n6NmzJ6+//jrZ2dmuDgevYC8adWvkKB/5+xFseTaXxROZns7LwcEsPH2aYrO/nAQGGvO+y3z+ubHF\n48qVxlxwN3TozCHG/jCWAa0H8GDQg6a29c470NC+JHxqKvzrX6Y2J4QQF0WSbyFEjUpKSpg9ezbH\njh2jbdu2BAYGujokLJ4WuizsgvI0Rp1zd+USc3sMpSVOXHPbrsBmY1pCArkeHuzPyyPe7E13AMaM\ngW7dYNo0Y5LzG2/A+PFGYu5mimxFvLflPeKz4vl6z9ccyj9kanstW8ITTxjHrVpBly6mNieEEBdF\nLrgUQtRo9+7dREZGArB69Wo2bNhAkyZNCA937YWPDa9pSMePO3L4xcMA5GzPIWF6Au0mt3NqHD4e\nHoR4ezvKLx4+zMaePWlq5uonvr7GbpcWC6SlwcGDsGOHseOMm/GyeJGel+4of376c5ptb0agTyAj\neo4wpc1PPoHOnY2VGBs1gpwc41pVIYRwNRn5FkLUKDw8nJEjRzrKTz75JH/9619JS0tzYVSGVi+0\nwqe9DwCBtwXS+qXWLoljZseOeNt3uozJzaX377+TZzN5GkzZEoPNmxvr6pUl3kVFxioobjA3H4wL\nZD8d9Cl+3n4ApBSn8OG2D+nZsqdpbTZoABMngrc3zJ4N994LZ8+a1pwQQtSaJN9CiFqZPn06fn72\n5Cklha5du9LYWSt81KBPbB86f9aZHmt74BngSeaPmeTF5Tk1hjY+PjyQm+sopxcVkWr2hZcXcuoU\n3HorrF0Lzpj+UkthAWG8e/u7jnKyNRkvi/HLQLGt2LR2p06FDz+EpUuNEXAhhHA1Sb6FELUSEhLC\nm2++6SgfPXoUq9VKUVERy5Ytc2Fk4NHQg9DnQ7E0sJC6IpX9j+ynKNX5ie/g3Fyu8TFG4fs2bkxo\ngwZkl5QwMzHReSvEvPMOHDoETz5ZftWhm3ihzwv0a9UPgOaNmpN6NpU9p/fQfW53cgpzTGlz3DjY\nuLF8CfRTp+C110xpSgghakWSbyFErf39739n/PjxBAcHs27dOgICAnj44YdZtGgRNrOnWNRCUXoR\nx187To/1PQi8xfkXHnoBX3fpwk2NG7OyWzeKtWbwnj0cddZe5wcOwIoVkJFh7Hy5aRMcPmzMB3cD\nZVvPDwwYyP4X9hPqH8q9C+/l7dvexr+BOROymzWDDh2M4+xs6NoVpk+Hb74xpTkhhKiRJN9CiFqz\nWCx88MEH7N69m44dO/Lee+/h7e3N8uXL8fAwf/fCmng386ZPbB/8exmJnC3fxh9/+oPcvbk1PLLu\n3BwQwNYbbiDQy4uPk5Lo7OvLrE6dnHNhamBg+dyKwkIYPBj69YM//jC/7Vq6vsX1PBX8FAE+AeSX\n5DP9jukM6zbMcf/BdPO+KNx9N2RmGlPhR4wwLsoUQghnk+RbCHHRWrY0dqKeOHEiixcvxsu+qseh\nQ4f47LPPXBkaFi/jba0ovYht7beR/Us2e4fspei0dG+9zwAADstJREFU86ahlCXar4SF8fm112Kx\nl9dnZPD8IROX2QsJMeZYtLZfdFpYCMXFbrvWXo8WPSqtdjL9l+kMXTbUtDngCxcaI99lXnoJ+vd3\nm+tShRD1hCTfQohL1rBhQ0fiHRsbS+/evYmLi3NxVIZT809RnGYkcYUJhewZtIeSnBKnxuBpseBh\nT7y3ZmczZO9eduXmklNiYhzt2sGGDcYKKAC5uUY5MxOGDwc3mB50Id/s+Yap0VPp2KQjxaXmJN8d\nOsDmzXDTTeXntm2DBQtMaU4IIS5Ikm8hRJ147LHHyM3NZfbs2Xz99deUljp/s5uK2kxsQ7dV3Rzv\ncrm7ctnZdSeZP2Y6PRatNaPj4ijWmt9ycvhzTAzJBQUUmtVH110H69YZ01D++U9jzb133jHWBneD\n6UEXsvHYRgpthUTERTDgiwHsPb2X+X/Mr/MLVYOCjO8i7doZ5bvvNqbHa+1Wi8MIIa5iknwLIS5b\nTk4OVqsVMHbDfPLJJxk6dCibN292aVzNH2hO57mdHeXCpEJK842EtzjDvOXtLuSBZs0cxzG5uXTZ\nuZORBw6Y1+ANN0BcHPzjH3DmjDG8+9Zb5fdPmwbff29e+xehVJfS0LN8ZZbdp3dz47wbWbF/hSnt\n+fkZ16BOmQIrVxprgU+ZYkxDEUIIs0nyLYS4bP7+/mzdupXrr7/ecW716tU8++yzlJaWYrPZmDVr\nlktWRAl9LpSAWwLAAn7hfgTdG4TtrI0dXXZQkOicoU6lFG+3b8/nnTtTNu6cY7Pxc1YWWmsKS0v5\nZ0JC3S9HWDb1pGlTYyUU+1x9jhwxss0DB4x54S5mURbmDpnLvCHzHGt/l5SWsPbIWrYmbsVaaOWR\nFY9Qquvul4IGDYw1wBs1gg8+gGXLjB8HALKy4OmnZVMeIYQ5TE++lVL3KKUOKqXilFKvVlFnllLq\nsFIqRinVy+yYrnb79+93dQhXBOmn2qltP4WGhvLzzz9zyy23OM71798fi8XCmjVrWLhwoWNFFKet\neW3X6+de9I3ry3Xzr0MpRcq/Uwi4OQCfMGNN7vzj+Rwee5jSostL7mrqq2dDQ/m+Rw8a2OeBP9S8\nOUopVqensy4jw3GhZr7NRnFdT0kJCio/fvll40LMiROhbVt45RUYOBCcNFWoqn4aFT6Kn576iZaN\njC8JQzoPoX9Yf5bFLqPYVoxFGR9Zp3NPs+f0njqLp2wqSnCwUZ4yBb76yrh+9ZlnjPtcsV+SEOLq\nZGryrZSyAJ8AdwPdgMeUUtedU+deoIPWuhPwPODapRKuAgfM/Cn7KiL9VDsX00+BgYFs2LCBSZMm\nERwczBNPPAHA7NmzGTNmjKPexIkTGThwIOnp6QBYrVZTE3KlFL4dfPG73tihM/9oPm0mtXHcf+hv\nh0ielcz2Dts5PPYwR8YfIXVFKiXWi7swsjZ9dXdQENtuvJGejRrxiD3b+3dKCs+EhDjqPHPwID12\n7mR9RgYlpaXk22x12z9ZWeXHp08b88Kjo8vX3vvoI9ixo7xOHf/bVNdP/cP6s+v5XYzoMYIFDy7A\noix8FfMVT/d62lHn2YhnGbZ8GMtjl2MttJKel46t9NJ/VXn6aQgLKy8vWmT8mZMDX34Jd91lrBe+\nZIlxvti5M5aEEFcZs0e++wKHtdYntNbFwBLggXPqPAB8DaC13g4EKKVamBxXJdHR0aY8pro6Vd13\nofPnnqupbBZX9FNV91/qOWf0lVn9VFM9d3lN+fj4MG3aNJKTk7n99tsBePLJJxk2zFjLWWvNvHnz\n2LhxI82bN8ff35/g4GDCw8M5ceIEYGxln5SU5HjO5cuXExsby7Fjx1i6dCnbt28nJSXFcVFnbm5u\npeQ0IiKC/Px8CgoKKCgoIDs7m/z8fLTWREdH0+afbfDvY6wFXphSyMnokxRRRG5SLvGz4ombGUfM\n/4th8wJjzvq+5/aReyDX0TexU2M5+d1JNnywgfRf0jm+6Dhn/jgD9oHjvLS8ShecZhzL4OyZs+Rn\n5ZOfnU/brBK2dOzKzf5GDE/5NeEB+8i01pq1B05wODWbwb/+TpN1/6XZt+vo+eNm4vPyAPi/vQdJ\nqrBxz4p9R9mVksb+tEzi0rPYcOgEi6LWU2Kf5nM6K7fSlJ/j8xZwZuq7ZLW5hiwPH+K9Azlj88DW\nyliiMPvrxdhKShyvg6RrryfzoUewfjAL69JVbBv2FJmR67DZp6xYo3/hx/UbHM+ftH03a5etxnoy\nFWtKGkl/HGTN8u+w2Vd5sRRrxzFA0v5jZKakYU3LxJqWiS0xn49v+gA/T2PN8k/+9AkD290JGPPD\nt//+K8dOHGfEghG0ejOUbv/Xhb989GeSs5KIjo7m5aVjOJER73j+z6M+49c9v7D36B72H40l6qcI\nDhyOpcSeRS9dscRxDHBL/z106pBIA69TNPA6ReOG+yg4m4hFGXXCe50m5o/y+uHXb2fM/yQz68NU\nFi9MZc7Mg2z8IZn16zYBsGtnKkWFxY7XT/SGgxw9lEzi8VSWLY5kx5aDHI9LpriomOjoaJJOpFJc\nJBm+EFctrbVpN2AoMK9CeTgw65w6kcCACuWNwI0XeC5tlilTppjymOrqVHXfhc6fe66m8kMPPVRj\nbJfCFf1U1f2Xeq5i+Urrp5rqmfWaqut+2rp1qwYueDt16pTWWuuQkBCdkJDgeExV9a1Wq9Zaa39/\nf52Xl1dj/by8PD1lyhTt4+PjqJ+flF9l/TfGvaG11tobb515ONPRN1XVf3jQw476ORk5NcZTVqdi\n/d+ys6usf/xUhqP+4ZQzNT7/6bRsR/1TqVk11k8/nal1cbH2xlufiT9pvA5KS6usf+Z4suP5p4yd\nWOPzn0lKddQvO662fnJaeX378dGMo1XWjzt8SE+ZMkV7460PxO2v8fkT4084nv/E8eM11j8Wl+io\nH7s7qcb6415+rfzf60BSja+fY3FJjviPxSU5nlub+DktN7nJzfk3pbWpP/UOBe7WWj9nLw8H+mqt\nX65QJxKYprXeai9vBF7RWu8657lkGwQhhBD1jtbaCdujCiGcxdPk508G2lQot7afO7dOWA115M1H\nCCGEEEJc8cye870T6KiUaquU8gYeBSLOqRMBjARQSt0EZGmtT5sclxBCCCGEEE5n6si31tqmlBoD\nrMdI9L/QWh9QSj1v3K3naa3XKKUGKaWOAGeBp6t7TiGEEEIIIa5Ups75FkIIIYQQQpSTHS6FEEII\nIYRwkis6+VZKXaeUmquUWqaUGu3qeNyZUuoBpdQ8pdRipdRAV8fjrpRS7ZVS/1ZKLXN1LO5MKeWr\nlPpKKfUvpdTjro7HXcnrqfbkPap25HNPiCvfVTHtRBl7Mv9Haz3S1bG4O6VUIPBPrfUoV8fizpRS\ny7TWw1wdh7uyLxuaqbX+Xim1RGv9qKtjcmfyeqo9eY+qHfncE+LK5RYj30qpL5RSp5VSe845f49S\n6qBSKk4p9WoVj70PiALWOCNWV7ucvrKbDMwxN0rXq4N+qlcuob9aA4n240vf1/sKI6+r2ruMvqoX\n71FlLqWf6tvnnhBXG7dIvoH5wN0VTyilLMAn9vPdgMeUUtfZ7xuhlPpQKRWitY7UWg/G2D2zPrjU\nvgpVSk0H1mitY5wdtAtc8muqrLozg3UDF9VfGIl367KqzgrSDVxsPzmqOSc8t3LRfVXP3qPKXHQ/\n1cPPPSGuKm6RfGutfwEyzzndFzistT6htS4GlgAP2Osv0FqPBzorpT5WSn0GfO/UoF3kMvpqKHAH\n8LBS6jlnxuw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trXiWGahyBYsB/AvArgObHrJDe3EqNB+AipNeuJY8TvBBMFZCSg8o94AyTwjIA7MrFPhB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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc336841048>"
+       "<matplotlib.figure.Figure at 0x7fcd0b5b5630>"
       ]
      },
      "metadata": {},
@@ -1243,7 +1252,7 @@
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>8.779406e-08</td>\n",
-       "      <td>2.310240e-08</td>\n",
+       "      <td>4.667924e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1252,7 +1261,7 @@
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>7.150041e-09</td>\n",
-       "      <td>3.854534e-09</td>\n",
+       "      <td>3.565013e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -1261,7 +1270,7 @@
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>9.528171e-07</td>\n",
-       "      <td>1.124883e-07</td>\n",
+       "      <td>5.066035e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -1270,7 +1279,7 @@
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>1.303200e-07</td>\n",
-       "      <td>3.422243e-08</td>\n",
+       "      <td>6.497762e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -1279,7 +1288,7 @@
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>2.353975e-07</td>\n",
-       "      <td>3.367779e-08</td>\n",
+       "      <td>1.251585e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -1288,7 +1297,7 @@
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>2.032960e-08</td>\n",
-       "      <td>5.622830e-09</td>\n",
+       "      <td>1.013634e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -1297,7 +1306,7 @@
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>4.720335e-07</td>\n",
-       "      <td>4.240950e-08</td>\n",
+       "      <td>2.509756e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -1306,7 +1315,7 @@
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>2.626392e-08</td>\n",
-       "      <td>5.152078e-09</td>\n",
+       "      <td>1.309520e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -1315,7 +1324,7 @@
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>2.828001e-08</td>\n",
-       "      <td>4.006859e-09</td>\n",
+       "      <td>1.503620e-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -1324,7 +1333,7 @@
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>2.430664e-09</td>\n",
-       "      <td>6.573695e-10</td>\n",
+       "      <td>1.211930e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -1333,7 +1342,7 @@
        "      <td>U235</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>1.477575e-09</td>\n",
-       "      <td>3.414084e-10</td>\n",
+       "      <td>7.856122e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -1342,7 +1351,7 @@
        "      <td>Pu239</td>\n",
        "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
        "      <td>6.994534e-11</td>\n",
-       "      <td>3.439208e-11</td>\n",
+       "      <td>3.487477e-13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1364,18 +1373,18 @@
        "11     1             6   Pu239   \n",
        "\n",
        "                                                score     mean  std. dev.  \n",
-       "0   (((delayed-nu-fission / nu-fission) * (delayed... 8.78e-08   2.31e-08  \n",
-       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09   3.85e-09  \n",
-       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07   1.12e-07  \n",
-       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07   3.42e-08  \n",
-       "4   (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07   3.37e-08  \n",
-       "5   (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08   5.62e-09  \n",
-       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07   4.24e-08  \n",
-       "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08   5.15e-09  \n",
-       "8   (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08   4.01e-09  \n",
-       "9   (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09   6.57e-10  \n",
-       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   3.41e-10  \n",
-       "11  (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11   3.44e-11  "
+       "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",
+       "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",
+       "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  "
       ]
      },
      "execution_count": 24,
@@ -1427,9 +1436,9 @@
     },
     {
      "data": {
-      "image/png": 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dFaQusznp/QRJr5LNwjyhfNt77qnt0dPDhw+vvNEazPE3bcSI4o9R5L7bIn7/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\nQmCC\n",
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iMzOzQjjBmJlZIZxgzMysEE4wZmZWCCcYMzMrhBOMmZkVwgnGzGw90KtXL0aPHs3OO+/M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\nUXFWRrt2AGEAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc334382fd0>"
+       "<matplotlib.figure.Figure at 0x7fcd0b364438>"
       ]
      },
      "metadata": {},
@@ -1490,9 +1499,9 @@
     },
     {
      "data": {
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sFnh1BeK8V1ATsr3GlwrMWFOdE1YxiEgBcJWd9lPgOxFZo6p3hcxoMNSCI0eO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\n",
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OMwwjp+hRI4vuZsiQIWzZsiXq3ObNm9ljjz3YsGFDxDB9++23A+4iwtNOO41zzjmHb37z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\nkr1doNMAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc3368feb70>"
+       "<matplotlib.figure.Figure at 0x7fcd0b35ef60>"
       ]
      },
      "metadata": {},
@@ -1540,7 +1549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb
index 03195b198a..d076823bea 100644
--- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb
@@ -421,17 +421,13 @@
        "OrderedDict([('flux', Tally\n",
        "              \tID             =\t10000\n",
        "              \tName           =\t\n",
-       "              \tFilters        =\t\n",
-       "                              \t\tCellFilter\t[1]\n",
-       "                              \t\tEnergyFilter\t[  0.00000000e+00   6.25000000e-01   2.00000000e+07]\n",
+       "              \tFilters        =\tCellFilter, EnergyFilter\n",
        "              \tNuclides       =\ttotal \n",
        "              \tScores         =\t['flux']\n",
        "              \tEstimator      =\ttracklength), ('absorption', Tally\n",
        "              \tID             =\t10001\n",
        "              \tName           =\t\n",
-       "              \tFilters        =\t\n",
-       "                              \t\tCellFilter\t[1]\n",
-       "                              \t\tEnergyFilter\t[  0.00000000e+00   6.25000000e-01   2.00000000e+07]\n",
+       "              \tFilters        =\tCellFilter, EnergyFilter\n",
        "              \tNuclides       =\ttotal \n",
        "              \tScores         =\t['absorption']\n",
        "              \tEstimator      =\ttracklength)])"
@@ -521,12 +517,12 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         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 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n",
-      "         Date/Time | 2016-10-31 12:23:45\n",
-      "    OpenMP Threads | 4\n",
+      "          Git SHA1 | 54b65c8bda6af5788bd762b8cf9855d1a8008238\n",
+      "         Date/Time | 2017-02-12 13:36:24\n",
+      "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -536,11 +532,11 @@
       " Reading geometry XML file...\n",
       " Reading materials XML file...\n",
       " Reading cross sections XML file...\n",
-      " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\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 Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
+      " Reading H1 from /opt/xsdata/nndc/H1.h5\n",
+      " Reading O16 from /opt/xsdata/nndc/O16.h5\n",
+      " Reading U235 from /opt/xsdata/nndc/U235.h5\n",
+      " Reading U238 from /opt/xsdata/nndc/U238.h5\n",
+      " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n",
       " Maximum neutron transport energy: 2.00000E+07 eV for H1\n",
       " Reading tallies XML file...\n",
       " Building neighboring cells lists for each surface...\n",
@@ -611,20 +607,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  7.3858E-01 seconds\n",
-      "   Reading cross sections          =  5.3599E-01 seconds\n",
-      " Total time in simulation          =  2.1031E+01 seconds\n",
-      "   Time in transport only          =  1.9960E+01 seconds\n",
-      "   Time in inactive batches        =  2.6543E+00 seconds\n",
-      "   Time in active batches          =  1.8376E+01 seconds\n",
-      "   Time synchronizing fission bank =  5.8389E-03 seconds\n",
-      "     Sampling source sites         =  4.2676E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.4523E-03 seconds\n",
-      "   Time accumulating tallies       =  1.5633E-04 seconds\n",
-      " Total time for finalization       =  7.9179E-04 seconds\n",
-      " Total time elapsed                =  2.1784E+01 seconds\n",
-      " Calculation Rate (inactive)       =  9418.63 neutrons/second\n",
-      " Calculation Rate (active)         =  5441.81 neutrons/second\n",
+      " Total time for initialization     =  4.1327E-01 seconds\n",
+      "   Reading cross sections          =  3.2638E-01 seconds\n",
+      " Total time in simulation          =  2.2324E+00 seconds\n",
+      "   Time in transport only          =  2.1226E+00 seconds\n",
+      "   Time in inactive batches        =  3.0650E-01 seconds\n",
+      "   Time in active batches          =  1.9259E+00 seconds\n",
+      "   Time synchronizing fission bank =  2.7640E-03 seconds\n",
+      "     Sampling source sites         =  2.0198E-03 seconds\n",
+      "     SEND/RECV source sites        =  7.0929E-04 seconds\n",
+      "   Time accumulating tallies       =  4.5355E-05 seconds\n",
+      " Total time for finalization       =  4.1885E-04 seconds\n",
+      " Total time elapsed                =  2.6534E+00 seconds\n",
+      " Calculation Rate (inactive)       =  81567.1 neutrons/second\n",
+      " Calculation Rate (active)         =  51923.2 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -832,7 +828,7 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -915,7 +911,7 @@
        "      <td>2.000000e+07</td>\n",
        "      <td>total</td>\n",
        "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>-7.771561e-16</td>\n",
+       "      <td>-3.330669e-16</td>\n",
        "      <td>0.002570</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -929,7 +925,7 @@
        "\n",
        "                                               score      mean  std. dev.  \n",
        "0  (((total / flux) - (absorption / flux)) - (sca... -2.66e-15   1.13e-02  \n",
-       "1  (((total / flux) - (absorption / flux)) - (sca... -7.77e-16   2.57e-03  "
+       "1  (((total / flux) - (absorption / flux)) - (sca... -3.33e-16   2.57e-03  "
       ]
      },
      "execution_count": 22,
@@ -1192,7 +1188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index cd563521cc..bab3b6bfcc 100644
--- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
@@ -34,7 +34,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/lib/python3.5/site-packages/matplotlib/__init__.py:1357: UserWarning:  This call to matplotlib.use() has no effect\n",
+      "/usr/lib/python3.6/site-packages/matplotlib/__init__.py:1401: 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",
@@ -450,11 +450,11 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         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 | d2979851f07f4162f0f02d95a847019bf9f2068d\n",
-      "         Date/Time | 2016-11-30 19:38:02\n",
+      "          Git SHA1 | 54b65c8bda6af5788bd762b8cf9855d1a8008238\n",
+      "         Date/Time | 2017-02-12 13:37:37\n",
       "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
@@ -531,7 +531,7 @@
       "       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 10057\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.25496 for flux in tally 10050\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 +557,7 @@
       "       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 10057\n",
+      " Triggers unsatisfied, max unc./thresh. is 1.00243 for flux in tally 10050\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 +570,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  3.0758E-01 seconds\n",
-      "   Reading cross sections          =  2.2344E-01 seconds\n",
-      " Total time in simulation          =  2.7832E+01 seconds\n",
-      "   Time in transport only          =  2.7708E+01 seconds\n",
-      "   Time in inactive batches        =  1.7095E+00 seconds\n",
-      "   Time in active batches          =  2.6122E+01 seconds\n",
-      "   Time synchronizing fission bank =  1.4982E-02 seconds\n",
-      "     Sampling source sites         =  1.0718E-02 seconds\n",
-      "     SEND/RECV source sites        =  4.2104E-03 seconds\n",
-      "   Time accumulating tallies       =  4.1146E-04 seconds\n",
-      " Total time for finalization       =  1.5648E-02 seconds\n",
-      " Total time elapsed                =  2.8184E+01 seconds\n",
-      " Calculation Rate (inactive)       =  58495.1 neutrons/second\n",
-      " Calculation Rate (active)         =  15312.7 neutrons/second\n",
+      " Total time for initialization     =  3.4833E-01 seconds\n",
+      "   Reading cross sections          =  2.2326E-01 seconds\n",
+      " Total time in simulation          =  3.0446E+01 seconds\n",
+      "   Time in transport only          =  2.9495E+01 seconds\n",
+      "   Time in inactive batches        =  1.6469E+00 seconds\n",
+      "   Time in active batches          =  2.8800E+01 seconds\n",
+      "   Time synchronizing fission bank =  1.5962E-02 seconds\n",
+      "     Sampling source sites         =  1.1235E-02 seconds\n",
+      "     SEND/RECV source sites        =  4.6646E-03 seconds\n",
+      "   Time accumulating tallies       =  4.9838E-04 seconds\n",
+      " Total time for finalization       =  1.5461E-02 seconds\n",
+      " Total time elapsed                =  3.0842E+01 seconds\n",
+      " Calculation Rate (inactive)       =  60719.1 neutrons/second\n",
+      " Calculation Rate (active)         =  13889.1 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -724,7 +724,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
      ]
     }
@@ -794,10 +794,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
-      "/usr/lib/python3.5/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
-      "  return c.reshape(shape_out)\n"
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
      ]
     },
     {
@@ -1011,14 +1009,6 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/lib/python3.5/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
-      "  return c.reshape(shape_out)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1151,11 +1141,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1975: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
       "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1976: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
       "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
      ]
     }
@@ -1510,11 +1500,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1975: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
       "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1976: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
       "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
      ]
     }
@@ -1989,7 +1979,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
      ]
     },
@@ -2005,9 +1995,9 @@
     },
     {
      "data": {
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nc1xDpszbwvb9aSQkHmf81JVcd35bLu7TEuspykCFEMKvQkKwxbhfxsAWaoXo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QkY64ZlgVkZH47lnU8DIzyTlwINKpCIucSCcAON/1A8ATwb+vIj2Dg3dMoHjc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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5e344eed68>"
+       "<matplotlib.figure.Figure at 0x7f15d2ff0d68>"
       ]
      },
      "metadata": {},
@@ -2055,16 +2045,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/lib/python3.5/site-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
-      "  return c.reshape(shape_out)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n",
     "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n",
@@ -2099,9 +2080,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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7SQeRhhrTJD1SaSCTGTuQQXdKdU1dcvEW1TUGHMURlbX12IyVKmvruSlLVdbW8HvGD7VO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\ni7uZWYH+P/3NVBUejmSDAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5e346ec588>"
+       "<matplotlib.figure.Figure at 0x7f15986ac2b0>"
       ]
      },
      "metadata": {},
@@ -2146,7 +2127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
index b85e90cb7c..2927466daf 100644
--- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
@@ -32,7 +32,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/romano/miniconda3/envs/default/lib/python3.5/site-packages/matplotlib/__init__.py:1350: UserWarning:  This call to matplotlib.use() has no effect\n",
+      "/usr/lib/python3.6/site-packages/matplotlib/__init__.py:1401: 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",
@@ -457,7 +457,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -739,12 +739,12 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         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 | da5563eddb5f2c2d6b2c9839d518de40962b78f2\n",
-      "         Date/Time | 2016-10-31 12:34:36\n",
-      "    OpenMP Threads | 4\n",
+      "          Git SHA1 | 54b65c8bda6af5788bd762b8cf9855d1a8008238\n",
+      "         Date/Time | 2017-02-12 13:39:31\n",
+      "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -754,12 +754,12 @@
       " 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 B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
+      " Reading U235 from /opt/xsdata/nndc/U235.h5\n",
+      " Reading U238 from /opt/xsdata/nndc/U238.h5\n",
+      " Reading O16 from /opt/xsdata/nndc/O16.h5\n",
+      " Reading H1 from /opt/xsdata/nndc/H1.h5\n",
+      " Reading B10 from /opt/xsdata/nndc/B10.h5\n",
+      " Reading Zr90 from /opt/xsdata/nndc/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",
@@ -830,20 +830,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  5.5103E-01 seconds\n",
-      "   Reading cross sections          =  4.0231E-01 seconds\n",
-      " Total time in simulation          =  2.8316E+01 seconds\n",
-      "   Time in transport only          =  2.8154E+01 seconds\n",
-      "   Time in inactive batches        =  2.5391E+00 seconds\n",
-      "   Time in active batches          =  2.5777E+01 seconds\n",
-      "   Time synchronizing fission bank =  4.7698E-03 seconds\n",
-      "     Sampling source sites         =  3.4259E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.2484E-03 seconds\n",
-      "   Time accumulating tallies       =  7.7215E-04 seconds\n",
-      " Total time for finalization       =  1.8490E-05 seconds\n",
-      " Total time elapsed                =  2.8886E+01 seconds\n",
-      " Calculation Rate (inactive)       =  9845.87 neutrons/second\n",
-      " Calculation Rate (active)         =  3879.38 neutrons/second\n",
+      " Total time for initialization     =  4.2114E-01 seconds\n",
+      "   Reading cross sections          =  2.9270E-01 seconds\n",
+      " Total time in simulation          =  7.0359E+00 seconds\n",
+      "   Time in transport only          =  6.4162E+00 seconds\n",
+      "   Time in inactive batches        =  4.6555E-01 seconds\n",
+      "   Time in active batches          =  6.5703E+00 seconds\n",
+      "   Time synchronizing fission bank =  2.7486E-03 seconds\n",
+      "     Sampling source sites         =  1.8012E-03 seconds\n",
+      "     SEND/RECV source sites        =  9.0751E-04 seconds\n",
+      "   Time accumulating tallies       =  3.3323E-04 seconds\n",
+      " Total time for finalization       =  2.6483E-05 seconds\n",
+      " Total time elapsed                =  7.4667E+00 seconds\n",
+      " Calculation Rate (inactive)       =  53700.2 neutrons/second\n",
+      " Calculation Rate (active)         =  15219.9 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -966,6 +966,14 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1094,6 +1102,14 @@
       "\n",
       "\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1506: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  data = self.std_dev[indices] / self.mean[indices]\n"
+     ]
     }
    ],
    "source": [
@@ -1113,7 +1129,20 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
+     ]
+    }
+   ],
    "source": [
     "# Store the cross section data in an \"mgxs/mgxs.h5\" HDF5 binary file\n",
     "mgxs_lib.build_hdf5_store(filename='mgxs.h5', directory='mgxs')"
@@ -1179,6 +1208,14 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1282,7 +1319,20 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
+     ]
+    }
+   ],
    "source": [
     "# Load the library into the OpenMOC geometry\n",
     "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)"
@@ -1309,124 +1359,131 @@
      "text": [
       "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.854512\tres = 0.000E+00\n",
-      "[  NORMAL ]  Iteration 1:\tk_eff = 0.802081\tres = 1.520E-01\n",
-      "[  NORMAL ]  Iteration 2:\tk_eff = 0.761887\tres = 6.346E-02\n",
-      "[  NORMAL ]  Iteration 3:\tk_eff = 0.732476\tres = 5.030E-02\n",
-      "[  NORMAL ]  Iteration 4:\tk_eff = 0.711139\tres = 3.873E-02\n",
-      "[  NORMAL ]  Iteration 5:\tk_eff = 0.696569\tres = 2.918E-02\n",
-      "[  NORMAL ]  Iteration 6:\tk_eff = 0.687626\tres = 2.051E-02\n",
-      "[  NORMAL ]  Iteration 7:\tk_eff = 0.683359\tres = 1.286E-02\n",
-      "[  NORMAL ]  Iteration 8:\tk_eff = 0.682952\tres = 6.234E-03\n",
-      "[  NORMAL ]  Iteration 9:\tk_eff = 0.685703\tres = 8.636E-04\n",
-      "[  NORMAL ]  Iteration 10:\tk_eff = 0.691013\tres = 4.084E-03\n",
-      "[  NORMAL ]  Iteration 11:\tk_eff = 0.698367\tres = 7.776E-03\n",
-      "[  NORMAL ]  Iteration 12:\tk_eff = 0.707327\tres = 1.067E-02\n",
-      "[  NORMAL ]  Iteration 13:\tk_eff = 0.717520\tres = 1.285E-02\n",
-      "[  NORMAL ]  Iteration 14:\tk_eff = 0.728629\tres = 1.443E-02\n",
-      "[  NORMAL ]  Iteration 15:\tk_eff = 0.740388\tres = 1.550E-02\n",
-      "[  NORMAL ]  Iteration 16:\tk_eff = 0.752573\tres = 1.616E-02\n",
-      "[  NORMAL ]  Iteration 17:\tk_eff = 0.764996\tres = 1.647E-02\n",
-      "[  NORMAL ]  Iteration 18:\tk_eff = 0.777503\tres = 1.652E-02\n",
-      "[  NORMAL ]  Iteration 19:\tk_eff = 0.789966\tres = 1.636E-02\n",
-      "[  NORMAL ]  Iteration 20:\tk_eff = 0.802282\tres = 1.604E-02\n",
-      "[  NORMAL ]  Iteration 21:\tk_eff = 0.814365\tres = 1.560E-02\n",
-      "[  NORMAL ]  Iteration 22:\tk_eff = 0.826151\tres = 1.507E-02\n",
-      "[  NORMAL ]  Iteration 23:\tk_eff = 0.837587\tres = 1.448E-02\n",
-      "[  NORMAL ]  Iteration 24:\tk_eff = 0.848635\tres = 1.385E-02\n",
-      "[  NORMAL ]  Iteration 25:\tk_eff = 0.859266\tres = 1.320E-02\n",
-      "[  NORMAL ]  Iteration 26:\tk_eff = 0.869461\tres = 1.254E-02\n",
-      "[  NORMAL ]  Iteration 27:\tk_eff = 0.879207\tres = 1.187E-02\n",
-      "[  NORMAL ]  Iteration 28:\tk_eff = 0.888500\tres = 1.122E-02\n",
-      "[  NORMAL ]  Iteration 29:\tk_eff = 0.897337\tres = 1.058E-02\n",
-      "[  NORMAL ]  Iteration 30:\tk_eff = 0.905724\tres = 9.955E-03\n",
-      "[  NORMAL ]  Iteration 31:\tk_eff = 0.913666\tres = 9.354E-03\n",
-      "[  NORMAL ]  Iteration 32:\tk_eff = 0.921173\tres = 8.776E-03\n",
-      "[  NORMAL ]  Iteration 33:\tk_eff = 0.928257\tres = 8.224E-03\n",
-      "[  NORMAL ]  Iteration 34:\tk_eff = 0.934932\tres = 7.697E-03\n",
-      "[  NORMAL ]  Iteration 35:\tk_eff = 0.941213\tres = 7.197E-03\n",
-      "[  NORMAL ]  Iteration 36:\tk_eff = 0.947114\tres = 6.723E-03\n",
-      "[  NORMAL ]  Iteration 37:\tk_eff = 0.952653\tres = 6.275E-03\n",
-      "[  NORMAL ]  Iteration 38:\tk_eff = 0.957845\tres = 5.853E-03\n",
-      "[  NORMAL ]  Iteration 39:\tk_eff = 0.962707\tres = 5.455E-03\n",
-      "[  NORMAL ]  Iteration 40:\tk_eff = 0.967255\tres = 5.080E-03\n",
-      "[  NORMAL ]  Iteration 41:\tk_eff = 0.971506\tres = 4.729E-03\n",
-      "[  NORMAL ]  Iteration 42:\tk_eff = 0.975476\tres = 4.399E-03\n",
-      "[  NORMAL ]  Iteration 43:\tk_eff = 0.979181\tres = 4.090E-03\n",
-      "[  NORMAL ]  Iteration 44:\tk_eff = 0.982635\tres = 3.801E-03\n",
-      "[  NORMAL ]  Iteration 45:\tk_eff = 0.985853\tres = 3.531E-03\n",
-      "[  NORMAL ]  Iteration 46:\tk_eff = 0.988850\tres = 3.278E-03\n",
-      "[  NORMAL ]  Iteration 47:\tk_eff = 0.991638\tres = 3.043E-03\n",
-      "[  NORMAL ]  Iteration 48:\tk_eff = 0.994231\tres = 2.823E-03\n",
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      ]
     }
    ],
@@ -1459,8 +1516,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.025390\n",
-      "openmoc keff = 1.026401\n",
-      "bias [pcm]: 101.1\n"
+      "openmoc keff = 1.026386\n",
+      "bias [pcm]: 99.6\n"
      ]
     }
    ],
@@ -1568,7 +1625,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f38ed5ab8d0>"
+       "<matplotlib.text.Text at 0x7f4c441b32e8>"
       ]
      },
      "execution_count": 43,
@@ -1577,9 +1634,9 @@
     },
     {
      "data": {
-      "image/png": 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gb5MkYj5pq2qun4TT67guhNQr9G2SROpl5ZpfnJldyURXpvH8U/575fW37EEo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\n",
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l7ixJ16bavpJqR9HqA8yQNBt4Cvijmf2pUdYiCOpB+HaQV9x7/GZWanzG6+vQ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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f38ed60df60>"
+       "<matplotlib.figure.Figure at 0x7f4c44263c88>"
       ]
      },
      "metadata": {},
@@ -1619,7 +1676,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
index b070748ba3..125340af7c 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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\nVDE4OjEyOjAzLTA1OjAwPb6RcwAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDE0OjEyOjA0LTA1OjAwvoI2MQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -571,7 +571,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/mgxs/library.py:398: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n",
+      "/home/nelsonag/git/openmc/openmc/mgxs/library.py:426: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n",
       "  warn(msg, RuntimeWarning)\n"
      ]
     }
@@ -724,11 +724,11 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         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 | 346deb258f969a2522bef0774ae1576043ac0de7\n",
-      "         Date/Time | 2016-12-02 18:12:03\n",
+      "          Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n",
+      "         Date/Time | 2017-02-11 14:12:04\n",
       "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
@@ -815,20 +815,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\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",
+      " Total time for initialization     =  3.6381E-01 seconds\n",
+      "   Reading cross sections          =  2.7394E-01 seconds\n",
+      " Total time in simulation          =  8.2977E+00 seconds\n",
+      "   Time in transport only          =  8.2033E+00 seconds\n",
+      "   Time in inactive batches        =  1.0881E+00 seconds\n",
+      "   Time in active batches          =  7.2096E+00 seconds\n",
+      "   Time synchronizing fission bank =  5.2924E-03 seconds\n",
+      "     Sampling source sites         =  3.6536E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.6013E-03 seconds\n",
+      "   Time accumulating tallies       =  1.1932E-04 seconds\n",
+      " Total time for finalization       =  3.3830E-06 seconds\n",
+      " Total time elapsed                =  8.6802E+00 seconds\n",
+      " Calculation Rate (inactive)       =  45950.7 neutrons/second\n",
+      " Calculation Rate (active)         =  27740.7 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -971,11 +971,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1974: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1975: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
       "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1976: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
       "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
      ]
     }
@@ -1084,9 +1084,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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VaeeKdyuZjh3hhRfg3HNh2DC3ydzNN9smc8ZAad/Yw4grCSjUTz/BmBIMWM+Z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\n0snAZcBFwH7Ao8AMSduWtpmi9wbQjIqI5yE1rZIO9ISmc5H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JbT24jJc1xwtVNUNV89yfC1W1VTwK11g92+Vz5WG7M2HaMi4cP51tpdZ0ZeIjFT7Mv1wc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\n/BrIB/4P2Bc4F1uPwxhjmi0vvaqmuz9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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc8f4517550>"
+       "<matplotlib.figure.Figure at 0x7f23842eb048>"
       ]
      },
      "metadata": {},
@@ -1094,9 +1094,9 @@
     },
     {
      "data": {
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zOLrt7u5HuXvJ92rpCLp2hRNPDGt97LIL/PjHsNNO0Nxc6chERJIppMbjq6/y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\n0e0O1haPA5I0hFTJdt8uFWkPLgUO9Eu3I4EVkp4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C7t6pgj7dOtCve0f6duvI8L5dGda7S9JxC1KwRPOT3zySu59/n7uf/4Cn5i5r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\n2zkpAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc8f451b240>"
+       "<matplotlib.figure.Figure at 0x7f2383a7b0b8>"
       ]
      },
      "metadata": {},
@@ -1104,9 +1104,9 @@
     },
     {
      "data": {
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FFqfHY9NoM+AXGV9vCmwIvAYcXuD4pBXr2ROefjr0gOywAzz1VLkjEhGRYsu7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\nYWZmZl3jwsPMzMy6xoWHmZmZdY0LDzMblCRdKql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Xzd1nuvvk4uLiZIciIiluxMAu3H3haJ6+6kgOGdyVm5/7kBNunc1Tb6+muT+o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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc8f40657f0>"
+       "<matplotlib.figure.Figure at 0x7f2383a24ef0>"
       ]
      },
      "metadata": {},
@@ -1184,11 +1184,11 @@
       "                                     %%%%%%%%%%%\n",
       "\n",
       "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
+      "         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 | 346deb258f969a2522bef0774ae1576043ac0de7\n",
-      "         Date/Time | 2016-12-02 18:12:18\n",
+      "          Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n",
+      "         Date/Time | 2017-02-11 14:12:19\n",
       "    OpenMP Threads | 8\n",
       "\n",
       " ===========================================================================\n",
@@ -1213,56 +1213,56 @@
       "\n",
       "  Bat./Gen.      k            Average k         \n",
       "  =========   ========   ====================   \n",
-      "        1/1    0.98369                       \n",
-      "        2/1    1.01520                       \n",
-      "        3/1    1.03642                       \n",
-      "        4/1    1.02658                       \n",
-      "        5/1    1.03102                       \n",
-      "        6/1    1.05382                       \n",
-      "        7/1    1.01978                       \n",
-      "        8/1    1.01753                       \n",
-      "        9/1    1.02420                       \n",
-      "       10/1    0.99889                       \n",
-      "       11/1    1.04874                       \n",
-      "       12/1    1.01382    1.03128 +/- 0.01746\n",
-      "       13/1    1.03987    1.03414 +/- 0.01048\n",
-      "       14/1    1.02282    1.03131 +/- 0.00793\n",
-      "       15/1    1.03282    1.03162 +/- 0.00615\n",
-      "       16/1    0.99669    1.02579 +/- 0.00769\n",
-      "       17/1    1.00052    1.02218 +/- 0.00743\n",
-      "       18/1    1.01124    1.02082 +/- 0.00658\n",
-      "       19/1    1.00629    1.01920 +/- 0.00602\n",
-      "       20/1    1.05322    1.02260 +/- 0.00637\n",
-      "       21/1    1.00763    1.02124 +/- 0.00592\n",
-      "       22/1    1.01841    1.02101 +/- 0.00541\n",
-      "       23/1    1.03430    1.02203 +/- 0.00508\n",
-      "       24/1    1.03064    1.02264 +/- 0.00474\n",
-      "       25/1    1.03272    1.02331 +/- 0.00447\n",
-      "       26/1    1.01226    1.02262 +/- 0.00424\n",
-      "       27/1    1.00883    1.02181 +/- 0.00406\n",
-      "       28/1    1.02712    1.02211 +/- 0.00384\n",
-      "       29/1    1.03146    1.02260 +/- 0.00367\n",
-      "       30/1    1.02964    1.02295 +/- 0.00350\n",
-      "       31/1    0.99832    1.02178 +/- 0.00353\n",
-      "       32/1    1.03420    1.02234 +/- 0.00341\n",
-      "       33/1    1.01860    1.02218 +/- 0.00326\n",
-      "       34/1    1.03328    1.02264 +/- 0.00316\n",
-      "       35/1    1.01865    1.02248 +/- 0.00303\n",
-      "       36/1    1.02643    1.02264 +/- 0.00292\n",
-      "       37/1    1.01070    1.02219 +/- 0.00284\n",
-      "       38/1    1.01871    1.02207 +/- 0.00274\n",
-      "       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    1.02541    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",
+      "        1/1    0.98551                       \n",
+      "        2/1    1.05279                       \n",
+      "        3/1    1.03429                       \n",
+      "        4/1    1.01472                       \n",
+      "        5/1    1.01149                       \n",
+      "        6/1    1.07167                       \n",
+      "        7/1    1.00534                       \n",
+      "        8/1    1.02138                       \n",
+      "        9/1    1.03005                       \n",
+      "       10/1    0.99297                       \n",
+      "       11/1    1.00323                       \n",
+      "       12/1    1.00757    1.00540 +/- 0.00217\n",
+      "       13/1    1.02578    1.01219 +/- 0.00691\n",
+      "       14/1    1.01920    1.01395 +/- 0.00519\n",
+      "       15/1    1.02700    1.01656 +/- 0.00479\n",
+      "       16/1    1.02370    1.01775 +/- 0.00409\n",
+      "       17/1    1.00321    1.01567 +/- 0.00403\n",
+      "       18/1    1.01318    1.01536 +/- 0.00351\n",
+      "       19/1    1.02439    1.01636 +/- 0.00325\n",
+      "       20/1    1.03004    1.01773 +/- 0.00321\n",
+      "       21/1    1.01840    1.01779 +/- 0.00291\n",
+      "       22/1    1.00581    1.01679 +/- 0.00284\n",
+      "       23/1    1.01457    1.01662 +/- 0.00261\n",
+      "       24/1    1.03033    1.01760 +/- 0.00261\n",
+      "       25/1    1.00450    1.01673 +/- 0.00258\n",
+      "       26/1    1.03680    1.01798 +/- 0.00272\n",
+      "       27/1    1.02786    1.01856 +/- 0.00262\n",
+      "       28/1    1.01150    1.01817 +/- 0.00250\n",
+      "       29/1    0.99850    1.01714 +/- 0.00258\n",
+      "       30/1    1.02729    1.01764 +/- 0.00250\n",
+      "       31/1    1.02587    1.01803 +/- 0.00241\n",
+      "       32/1    1.01423    1.01786 +/- 0.00231\n",
+      "       33/1    1.05449    1.01945 +/- 0.00272\n",
+      "       34/1    0.99908    1.01861 +/- 0.00274\n",
+      "       35/1    1.02441    1.01884 +/- 0.00264\n",
+      "       36/1    1.01844    1.01882 +/- 0.00253\n",
+      "       37/1    1.03198    1.01931 +/- 0.00249\n",
+      "       38/1    1.05078    1.02043 +/- 0.00265\n",
+      "       39/1    1.01134    1.02012 +/- 0.00257\n",
+      "       40/1    1.01423    1.01992 +/- 0.00249\n",
+      "       41/1    1.03987    1.02057 +/- 0.00250\n",
+      "       42/1    1.05441    1.02162 +/- 0.00264\n",
+      "       43/1    1.02068    1.02160 +/- 0.00256\n",
+      "       44/1    1.06387    1.02284 +/- 0.00277\n",
+      "       45/1    1.04844    1.02357 +/- 0.00279\n",
+      "       46/1    1.01967    1.02346 +/- 0.00272\n",
+      "       47/1    1.01830    1.02332 +/- 0.00264\n",
+      "       48/1    1.00888    1.02294 +/- 0.00260\n",
+      "       49/1    1.03033    1.02313 +/- 0.00254\n",
+      "       50/1    1.02732    1.02324 +/- 0.00248\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -1272,27 +1272,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\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",
+      " Total time for initialization     =  3.6247E-02 seconds\n",
+      "   Reading cross sections          =  2.9810E-03 seconds\n",
+      " Total time in simulation          =  7.6874E+00 seconds\n",
+      "   Time in transport only          =  7.5290E+00 seconds\n",
+      "   Time in inactive batches        =  5.9831E-01 seconds\n",
+      "   Time in active batches          =  7.0890E+00 seconds\n",
+      "   Time synchronizing fission bank =  5.0012E-03 seconds\n",
+      "     Sampling source sites         =  3.4413E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.4835E-03 seconds\n",
+      "   Time accumulating tallies       =  1.1537E-04 seconds\n",
+      " Total time for finalization       =  3.0480E-06 seconds\n",
+      " Total time elapsed                =  7.7461E+00 seconds\n",
+      " Calculation Rate (inactive)       =  83568.1 neutrons/second\n",
+      " Calculation Rate (active)         =  28212.6 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\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",
+      " k-effective (Collision)     =  1.02169 +/-  0.00234\n",
+      " k-effective (Track-length)  =  1.02324 +/-  0.00248\n",
+      " k-effective (Absorption)    =  1.02565 +/-  0.00155\n",
+      " Combined k-effective        =  1.02525 +/-  0.00163\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -1374,8 +1374,8 @@
      "output_type": "stream",
      "text": [
       "Continuous-Energy keff = 1.024739\n",
-      "Multi-Group keff = 1.023545\n",
-      "bias [pcm]: 119.4\n"
+      "Multi-Group keff = 1.025255\n",
+      "bias [pcm]: -51.5\n"
      ]
     }
    ],
@@ -1472,7 +1472,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7fc8f339d7b8>"
+       "<matplotlib.text.Text at 0x7f238421fc18>"
       ]
      },
      "execution_count": 41,
@@ -1481,9 +1481,9 @@
     },
     {
      "data": {
-      "image/png": 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IiIjknJIBERGRnFMyICIiknNKBkRERHJOyYCIiEjOKRkQERHJOSUDIiIiOddi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\n7AAAAABJRU5ErkJggg==\n",
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eaMoLgPPj4z+V/ULNUjYINbv2jHvwvTI59rUXBsUTqGzPK6Hmzq/FZb3753gyo9GcFmpG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\nA2gShaIjcDJaAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc8f4139908>"
+       "<matplotlib.figure.Figure at 0x7f2383cc7898>"
       ]
      },
      "metadata": {},
@@ -1533,7 +1533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/examples/python/pincell_multigroup/build-xml.py b/examples/python/pincell_multigroup/build-xml.py
index 9cc23300db..337044508c 100644
--- a/examples/python/pincell_multigroup/build-xml.py
+++ b/examples/python/pincell_multigroup/build-xml.py
@@ -13,7 +13,7 @@ inactive = 10
 particles = 1000
 
 ###############################################################################
-#                 Exporting to OpenMC mgxs.xml file
+#                 Exporting to OpenMC mgxs.h5 file
 ###############################################################################
 
 # Instantiate the energy group data
diff --git a/openmc/filter.py b/openmc/filter.py
index dd3e21e5c8..3f070ba8eb 100644
--- a/openmc/filter.py
+++ b/openmc/filter.py
@@ -1008,7 +1008,7 @@ class EnergyFilter(RealFilter):
         # 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)
+        tile_factor = int(data_size / len(lo_bins))
         lo_bins = np.tile(lo_bins, tile_factor)
         hi_bins = np.tile(hi_bins, tile_factor)
 
@@ -1453,7 +1453,7 @@ class PolarFilter(RealFilter):
                 msg = 'Unable to add bin edge "{0}" to a "{1}" ' \
                       'since it is less than 0'.format(edge, type(self))
                 raise ValueError(msg)
-            elif edge > np.pi:
+            elif not np.isclose(edge, np.pi) and edge > np.pi:
                 msg = 'Unable to add bin edge "{0}" to a "{1}" ' \
                       'since it is greater than pi'.format(edge, type(self))
                 raise ValueError(msg)
@@ -1552,11 +1552,11 @@ class AzimuthalFilter(RealFilter):
                       'since it is a non-integer or floating point ' \
                       'value'.format(edge, type(self))
                 raise ValueError(msg)
-            elif edge < -np.pi:
+            elif not np.isclose(edge, -np.pi) and edge < -np.pi:
                 msg = 'Unable to add bin edge "{0}" to a "{1}" ' \
                       'since it is less than -pi'.format(edge, type(self))
                 raise ValueError(msg)
-            elif edge > np.pi:
+            elif not np.isclose(edge, np.pi) and edge > np.pi:
                 msg = 'Unable to add bin edge "{0}" to a "{1}" ' \
                       'since it is greater than pi'.format(edge, type(self))
                 raise ValueError(msg)
diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py
index c3be755908..f5342e844c 100644
--- a/openmc/mgxs/library.py
+++ b/openmc/mgxs/library.py
@@ -73,6 +73,10 @@ class Library(object):
         Energy group structure for energy condensation
     num_delayed_groups : int
         Number of delayed groups
+    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
     estimator : str or None
         The tally estimator used to compute multi-group cross sections.
         If None, the default for each MGXS type is used.
@@ -107,6 +111,8 @@ class Library(object):
         self._domain_type = None
         self._domains = 'all'
         self._energy_groups = None
+        self._num_polar = 1
+        self._num_azimuthal = 1
         self._num_delayed_groups = 0
         self._correction = 'P0'
         self._scatter_format = 'legendre'
@@ -144,6 +150,8 @@ class Library(object):
             clone._legendre_order = self.legendre_order
             clone._histogram_bins = self.histogram_bins
             clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
+            clone._num_polar = self.num_polar
+            clone._num_azimuthal = self.num_azimuthal
             clone._num_delayed_groups = self.num_delayed_groups
             clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
             clone._all_mgxs = copy.deepcopy(self.all_mgxs)
@@ -217,6 +225,14 @@ class Library(object):
     def num_delayed_groups(self):
         return self._num_delayed_groups
 
+    @property
+    def num_polar(self):
+        return self._num_polar
+
+    @property
+    def num_azimuthal(self):
+        return self._num_azimuthal
+
     @property
     def correction(self):
         return self._correction
@@ -353,6 +369,18 @@ class Library(object):
                               equality=True)
         self._num_delayed_groups = num_delayed_groups
 
+    @num_polar.setter
+    def num_polar(self, num_polar):
+        cv.check_type('num_polar', num_polar, Integral)
+        cv.check_greater_than('num_polar', num_polar, 0)
+        self._num_polar = num_polar
+
+    @num_azimuthal.setter
+    def num_azimuthal(self, num_azimuthal):
+        cv.check_type('num_azimuthal', num_azimuthal, Integral)
+        cv.check_greater_than('num_azimuthal', num_azimuthal, 0)
+        self._num_azimuthal = num_azimuthal
+
     @correction.setter
     def correction(self, correction):
         cv.check_value('correction', correction, ('P0', None))
@@ -470,9 +498,13 @@ class Library(object):
             self.all_mgxs[domain.id] = OrderedDict()
             for mgxs_type in self.mgxs_types:
                 if mgxs_type in openmc.mgxs.MDGXS_TYPES:
-                    mgxs = openmc.mgxs.MDGXS.get_mgxs(mgxs_type, name=self.name)
+                    mgxs = openmc.mgxs.MDGXS.get_mgxs(
+                        mgxs_type, name=self.name, num_polar=self.num_polar,
+                        num_azimuthal=self.num_azimuthal)
                 else:
-                    mgxs = openmc.mgxs.MGXS.get_mgxs(mgxs_type, name=self.name)
+                    mgxs = openmc.mgxs.MGXS.get_mgxs(
+                        mgxs_type, name=self.name, num_polar=self.num_polar,
+                        num_azimuthal=self.num_azimuthal)
 
                 mgxs.domain = domain
                 mgxs.domain_type = self.domain_type
@@ -530,7 +562,7 @@ class Library(object):
                         mgxs.delayed_groups = None
                     else:
                         mgxs.delayed_groups \
-                            = list(range(1, self.num_delayed_groups+1))
+                            = list(range(1, self.num_delayed_groups + 1))
 
                 for tally in mgxs.tallies.values():
                     tallies_file.append(tally, merge=merge)
@@ -953,11 +985,16 @@ class Library(object):
         name = xsdata_name
         if nuclide != 'total':
             name += '_' + nuclide
-        xsdata = openmc.XSdata(name, self.energy_groups)
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            representation = 'angle'
+        else:
+            representation = 'isotropic'
+        xsdata = openmc.XSdata(name, self.energy_groups,
+                               representation=representation)
         xsdata.num_delayed_groups = self.num_delayed_groups
-
-        # Right now only isotropic weighting is supported
-        self.representation = 'isotropic'
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            xsdata.num_polar = self.num_polar
+            xsdata.num_azimuthal = self.num_azimuthal
 
         if nuclide != 'total':
             xsdata.atomic_weight_ratio = self._nuclides[nuclide][1]
@@ -1098,15 +1135,28 @@ class Library(object):
                 if 'total' in self.mgxs_types or 'transport' in self.mgxs_types:
                     if xsdata.scatter_format == 'legendre':
                         for i in range(len(xsdata.temperatures)):
-                            xsdata._absorption[i] = \
-                                np.subtract(xsdata._total[i], np.sum(
-                                    xsdata._scatter_matrix[i][0, :, :], axis=1))
+                            if representation == 'isotropic':
+                                xsdata._absorption[i] = \
+                                    np.subtract(xsdata._total[i], np.sum(
+                                        xsdata._scatter_matrix[i][:, :, 0],
+                                        axis=1))
+                            elif representation == 'angle':
+                                xsdata._absorption[i] = \
+                                    np.subtract(xsdata._total[i], np.sum(
+                                        xsdata._scatter_matrix[i][:, :, :, :, 0],
+                                        axis=3))
                     elif xsdata.scatter_format == 'histogram':
                         for i in range(len(xsdata.temperatures)):
-                            xsdata._absorption[i] = \
-                                np.subtract(xsdata._total[i], np.sum(np.sum(
-                                    xsdata._scatter_matrix[i][:, :, :], axis=0),
-                                    axis=1))
+                            if representation == 'isotropic':
+                                xsdata._absorption[i] = \
+                                    np.subtract(xsdata._total[i], np.sum(np.sum(
+                                        xsdata._scatter_matrix[i][:, :, :],
+                                        axis=2), axis=1))
+                            elif representation == 'angle':
+                                xsdata._absorption[i] = \
+                                    np.subtract(xsdata._total[i], np.sum(np.sum(
+                                        xsdata._scatter_matrix[i][:, :, :, :, :],
+                                        axis=4), axis=3))
 
         return xsdata
 
diff --git a/openmc/mgxs/mdgxs.py b/openmc/mgxs/mdgxs.py
index d37957c233..fecc4ce6df 100644
--- a/openmc/mgxs/mdgxs.py
+++ b/openmc/mgxs/mdgxs.py
@@ -1,6 +1,7 @@
 from __future__ import division
 
 from collections import Iterable, OrderedDict
+import itertools
 from numbers import Integral
 import warnings
 import os
@@ -55,6 +56,12 @@ class MDGXS(MGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -72,6 +79,10 @@ class MDGXS(MGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -118,10 +129,12 @@ class MDGXS(MGXS):
         The key used to index multi-group cross sections in an HDF5 data store
 
     """
+
     def __init__(self, domain=None, domain_type=None, energy_groups=None,
-                 delayed_groups=None, by_nuclide=False, name=''):
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         super(MDGXS, self).__init__(domain, domain_type, energy_groups,
-                                    by_nuclide, name)
+                                    by_nuclide, name, num_polar, num_azimuthal)
 
         self._delayed_groups = None
 
@@ -142,6 +155,8 @@ class MDGXS(MGXS):
             clone._domain_type = self.domain_type
             clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
             clone._delayed_groups = copy.deepcopy(self.delayed_groups, memo)
+            clone._num_polar = self.num_polar
+            clone._num_azimuthal = self.num_azimuthal
             clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
             clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo)
             clone._xs_tally = copy.deepcopy(self._xs_tally, memo)
@@ -160,13 +175,23 @@ class MDGXS(MGXS):
         else:
             return existing
 
+    @property
+    def _dont_squeeze(self):
+        """Create a tuple of axes which should not be removed during the get_xs
+        process
+        """
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            return (0, 1, 3, 4)
+        else:
+            return (1, 2)
+
     @property
     def delayed_groups(self):
         return self._delayed_groups
 
     @property
     def num_delayed_groups(self):
-        if self.delayed_groups == None:
+        if self.delayed_groups is None:
             return 1
         else:
             return len(self.delayed_groups)
@@ -174,7 +199,7 @@ class MDGXS(MGXS):
     @delayed_groups.setter
     def delayed_groups(self, delayed_groups):
 
-        if delayed_groups != None:
+        if delayed_groups is not None:
 
             cv.check_type('delayed groups', delayed_groups, list, int)
             cv.check_greater_than('num delayed groups', len(delayed_groups), 0)
@@ -196,14 +221,15 @@ class MDGXS(MGXS):
 
         if self.delayed_groups != None:
             delayed_filter = openmc.DelayedGroupFilter(self.delayed_groups)
-            return [[energy_filter], [delayed_filter, energy_filter]]
+            filters = [[energy_filter], [delayed_filter, energy_filter]]
         else:
-            return [[energy_filter], [energy_filter]]
+            filters = [[energy_filter], [energy_filter]]
+        return self._add_angle_filters(filters)
 
     @staticmethod
-    def get_mgxs(mdgxs_type, domain=None, domain_type=None,
-                 energy_groups=None, delayed_groups=None,
-                 by_nuclide=False, name=''):
+    def get_mgxs(mdgxs_type, domain=None, domain_type=None, energy_groups=None,
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         """Return a MDGXS subclass object for some energy group structure within
         some spatial domain for some reaction type.
 
@@ -229,7 +255,12 @@ class MDGXS(MGXS):
             tallies in OpenMC 'tallies.xml' file. Defaults to the empty string.
         delayed_groups : list of int
             Delayed groups to filter out the xs
-
+        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
         Returns
         -------
         openmc.mgxs.MDGXS
@@ -256,6 +287,8 @@ class MDGXS(MGXS):
 
         mdgxs.by_nuclide = by_nuclide
         mdgxs.name = name
+        mdgxs.num_polar = num_polar
+        mdgxs.num_azimuthal = num_azimuthal
         return mdgxs
 
     def get_xs(self, groups='all', subdomains='all', nuclides='all',
@@ -386,21 +419,29 @@ class MDGXS(MGXS):
         else:
             num_delayed_groups = len(delayed_groups)
 
-        # Reshape tally data array with separate axes for domain, energy groups,
-        # delayed groups, and nuclides
-        num_subdomains = int(xs.shape[0] / (num_groups * num_delayed_groups))
-        new_shape = (num_subdomains, num_delayed_groups, num_groups)
+        # Reshape tally data array with separate axes for domain,
+        # energy groups, delayed groups, and nuclides
+        # Accommodate the polar and azimuthal bins if needed
+        num_subdomains = \
+            int(xs.shape[0] / (num_groups * num_delayed_groups *
+                               self.num_polar * self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            new_shape = (self.num_polar, self.num_azimuthal, num_subdomains,
+                         num_delayed_groups, num_groups)
+        else:
+            new_shape = (num_subdomains, num_delayed_groups, num_groups)
         new_shape += xs.shape[1:]
         xs = np.reshape(xs, new_shape)
 
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            xs = xs[:, :, ::-1, :]
+            xs = xs[..., ::-1, :]
 
         if squeeze:
-            xs = np.squeeze(xs)
-            xs = np.atleast_1d(xs)
+            # We want to squeeze out everything but the polar, azimuthal,
+            # delayed group, and energy group data.
+            xs = self._squeeze_xs(xs)
 
         return xs
 
@@ -539,7 +580,7 @@ class MDGXS(MGXS):
 
         """
 
-        if self.delayed_groups == None:
+        if self.delayed_groups is None:
             super(MDGXS, self).print_xs(subdomains, nuclides, xs_type)
             return
 
@@ -549,7 +590,7 @@ class MDGXS(MGXS):
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
         elif self.domain_type == 'mesh':
-            xyz = [range(1, x+1) for x in self.domain.dimension]
+            xyz = [range(1, x + 1) for x in self.domain.dimension]
             subdomains = list(itertools.product(*xyz))
         else:
             subdomains = [self.domain.id]
@@ -581,6 +622,14 @@ class MDGXS(MGXS):
             print(string)
             return
 
+        # Set polar/azimuthal bins
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            polar_bins = np.linspace(0., np.pi, num=self.num_polar + 1,
+                                     endpoint=True)
+            azimuthal_bins = np.linspace(-np.pi, np.pi,
+                                         num=self.num_azimuthal + 1,
+                                         endpoint=True)
+
         # Loop over all subdomains
         for subdomain in subdomains:
 
@@ -605,20 +654,45 @@ class MDGXS(MGXS):
 
                     template = '{0: <12}Group {1} [{2: <10} - {3: <10}eV]:\t'
 
-                    # Loop over energy groups ranges
-                    for group in range(1, self.num_groups+1):
-                        bounds = self.energy_groups.get_group_bounds(group)
-                        string += template.format('', group, bounds[0], bounds[1])
-                        average = self.get_xs([group], [subdomain], [nuclide],
-                                              xs_type=xs_type, value='mean',
-                                              delayed_groups=[delayed_group])
-                        rel_err = self.get_xs([group], [subdomain], [nuclide],
-                                              xs_type=xs_type, value='rel_err',
-                                              delayed_groups=[delayed_group])
-                        average = average.flatten()[0]
-                        rel_err = rel_err.flatten()[0] * 100.
-                        string += '{:.2e} +/- {:1.2e}%'.format(average, rel_err)
-                        string += '\n'
+                    average_xs = self.get_xs(nuclides=[nuclide],
+                                             subdomains=[subdomain],
+                                             xs_type=xs_type, value='mean',
+                                             delayed_groups=[delayed_group])
+                    rel_err_xs = self.get_xs(nuclides=[nuclide],
+                                             subdomains=[subdomain],
+                                             xs_type=xs_type, value='rel_err',
+                                             delayed_groups=[delayed_group])
+                    rel_err_xs = rel_err_xs * 100.
+
+                    if self.num_polar > 1 or self.num_azimuthal > 1:
+                        # Loop over polar, azimuthal, and energy group ranges
+                        for pol in range(len(polar_bins) - 1):
+                            pol_low, pol_high = polar_bins[pol: pol + 2]
+                            for azi in range(len(azimuthal_bins) - 1):
+                                azi_low, azi_high = azimuthal_bins[azi: azi + 2]
+                                string += '\t\tPolar Angle: [{0:5f} - {1:5f}]'.format(
+                                    pol_low, pol_high) + \
+                                    '\tAzimuthal Angle: [{0:5f} - {1:5f}]'.format(
+                                    azi_low, azi_high) + '\n'
+                                for group in range(1, self.num_groups + 1):
+                                    bounds = \
+                                        self.energy_groups.get_group_bounds(group)
+                                    string += '\t' + template.format('', group,
+                                                                     bounds[0],
+                                                                     bounds[1])
+                                    string += '{0:.2e} +/- {1:.2e}%'.format(
+                                        average_xs[pol, azi, group - 1],
+                                        rel_err_xs[pol, azi, group - 1])
+                                    string += '\n'
+                                string += '\n'
+                    else:
+                        # Loop over energy groups ranges
+                        for group in range(1, self.num_groups+1):
+                            bounds = self.energy_groups.get_group_bounds(group)
+                            string += template.format('', group, bounds[0], bounds[1])
+                            string += '{0:.2e} +/- {1:.2e}%'.format(
+                                average_xs[group - 1], rel_err_xs[group - 1])
+                            string += '\n'
                     string += '\n'
                 string += '\n'
 
@@ -755,7 +829,6 @@ class MDGXS(MGXS):
         if self.by_nuclide and nuclides == 'sum':
 
             # Use tally summation to sum across all nuclides
-            query_nuclides = [nuclides]
             xs_tally = self.xs_tally.summation(nuclides=self.get_nuclides())
             df = xs_tally.get_pandas_dataframe(
                 distribcell_paths=distribcell_paths)
@@ -768,14 +841,12 @@ class MDGXS(MGXS):
 
         # If the user requested a specific set of nuclides
         elif self.by_nuclide and nuclides != 'all':
-            query_nuclides = nuclides
             xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
             df = xs_tally.get_pandas_dataframe(
                 distribcell_paths=distribcell_paths)
 
         # If the user requested all nuclides, keep nuclide column in dataframe
         else:
-            query_nuclides = self.nuclides
             df = self.xs_tally.get_pandas_dataframe(
                 distribcell_paths=distribcell_paths)
 
@@ -785,40 +856,9 @@ class MDGXS(MGXS):
         else:
             df = df.drop('score', axis=1)
 
-        # Override energy groups bounds with indices
-        all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
-        all_groups = np.repeat(all_groups, len(query_nuclides))
-        if 'energy low [eV]' in df and 'energyout low [eV]' in df:
-            df.rename(columns={'energy low [eV]': 'group in'},
-                      inplace=True)
-            in_groups = np.tile(all_groups, int(self.num_subdomains *
-                                                self.num_delayed_groups))
-            in_groups = np.repeat(in_groups, int(df.shape[0] / in_groups.size))
-            df['group in'] = in_groups
-            del df['energy high [eV]']
-
-            df.rename(columns={'energyout low [eV]': 'group out'},
-                      inplace=True)
-            out_groups = np.repeat(all_groups, self.xs_tally.num_scores)
-            out_groups = np.tile(out_groups, int(df.shape[0] / out_groups.size))
-            df['group out'] = out_groups
-            del df['energyout high [eV]']
-            columns = ['group in', 'group out']
-
-        elif 'energyout low [eV]' in df:
-            df.rename(columns={'energyout low [eV]': 'group out'},
-                      inplace=True)
-            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
-            df['group out'] = in_groups
-            del df['energyout high [eV]']
-            columns = ['group out']
-
-        elif 'energy low [eV]' in df:
-            df.rename(columns={'energy low [eV]': 'group in'}, inplace=True)
-            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
-            df['group in'] = in_groups
-            del df['energy high [eV]']
-            columns = ['group in']
+        # Convert azimuthal, polar, energy in and energy out bin values in to
+        # bin indices
+        columns = self._df_convert_columns_to_bins(df)
 
         # Select out those groups the user requested
         if not isinstance(groups, string_types):
@@ -834,7 +874,7 @@ class MDGXS(MGXS):
             else:
                 densities = self.get_nuclide_densities('sum')
             densities = np.repeat(densities, len(self.rxn_rate_tally.scores))
-            tile_factor = df.shape[0] / len(densities)
+            tile_factor = int(df.shape[0] / len(densities))
             df['mean'] /= np.tile(densities, tile_factor)
             df['std. dev.'] /= np.tile(densities, tile_factor)
 
@@ -842,7 +882,7 @@ class MDGXS(MGXS):
         # energy groups such that data is from fast to thermal
         if self.domain_type == 'mesh':
             mesh_str = 'mesh {0}'.format(self.domain.id)
-            df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'), \
+            df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'),
                                (mesh_str, 'z')] + columns, inplace=True)
         else:
             df.sort_values(by=[self.domain_type] + columns, inplace=True)
@@ -896,6 +936,12 @@ class ChiDelayed(MDGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -913,6 +959,10 @@ class ChiDelayed(MDGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -962,9 +1012,11 @@ class ChiDelayed(MDGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, energy_groups=None,
-                 delayed_groups=None, by_nuclide=False, name=''):
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         super(ChiDelayed, self).__init__(domain, domain_type, energy_groups,
-                                         delayed_groups, by_nuclide, name)
+                                         delayed_groups, by_nuclide, name,
+                                         num_polar, num_azimuthal)
         self._rxn_type = 'chi-delayed'
         self._estimator = 'analog'
 
@@ -978,11 +1030,13 @@ class ChiDelayed(MDGXS):
         group_edges = self.energy_groups.group_edges
         energyout = openmc.EnergyoutFilter(group_edges)
         energyin = openmc.EnergyFilter([group_edges[0], group_edges[-1]])
-        if self.delayed_groups != None:
+        if self.delayed_groups is not None:
             delayed_filter = openmc.DelayedGroupFilter(self.delayed_groups)
-            return [[delayed_filter, energyin], [delayed_filter, energyout]]
+            filters = [[delayed_filter, energyin], [delayed_filter, energyout]]
         else:
-            return [[energyin], [energyout]]
+            filters = [[energyin], [energyout]]
+
+        return self._add_angle_filters(filters)
 
     @property
     def tally_keys(self):
@@ -1326,21 +1380,28 @@ class ChiDelayed(MDGXS):
         else:
             num_delayed_groups = len(delayed_groups)
 
-        # Reshape tally data array with separate axes for domain, energy groups,
-        # delayed groups, and nuclides
-        num_subdomains = int(xs.shape[0] / (num_groups * num_delayed_groups))
-        new_shape = (num_subdomains, num_delayed_groups, num_groups)
+        # Reshape tally data array with separate axes for domain, energy
+        # groups, and accomodate the polar and azimuthal bins if needed
+        num_subdomains = int(xs.shape[0] / (num_delayed_groups *
+                                            num_groups * self.num_polar *
+                                            self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            new_shape = (self.num_polar, self.num_azimuthal, num_subdomains,
+                         num_delayed_groups, num_groups)
+        else:
+            new_shape = (num_subdomains, num_delayed_groups, num_groups)
         new_shape += xs.shape[1:]
         xs = np.reshape(xs, new_shape)
 
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            xs = xs[:, :, ::-1, :]
+            xs = xs[..., ::-1, :]
 
         if squeeze:
-            xs = np.squeeze(xs)
-            xs = np.atleast_1d(xs)
+            # We want to squeeze out everything but the polar, azimuthal,
+            # and energy group data.
+            xs = self._squeeze_xs(xs)
 
         return xs
 
@@ -1389,6 +1450,12 @@ class DelayedNuFissionXS(MDGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -1406,6 +1473,10 @@ class DelayedNuFissionXS(MDGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -1455,10 +1526,12 @@ class DelayedNuFissionXS(MDGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, energy_groups=None,
-                 delayed_groups=None, by_nuclide=False, name=''):
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         super(DelayedNuFissionXS, self).__init__(domain, domain_type,
                                                  energy_groups, delayed_groups,
-                                                 by_nuclide, name)
+                                                 by_nuclide, name, num_polar,
+                                                 num_azimuthal)
         self._rxn_type = 'delayed-nu-fission'
 
 
@@ -1513,6 +1586,12 @@ class Beta(MDGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -1530,6 +1609,10 @@ class Beta(MDGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -1579,9 +1662,11 @@ class Beta(MDGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, energy_groups=None,
-                 delayed_groups=None, by_nuclide=False, name=''):
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         super(Beta, self).__init__(domain, domain_type, energy_groups,
-                                   delayed_groups, by_nuclide, name)
+                                   delayed_groups, by_nuclide, name, num_polar,
+                                   num_azimuthal)
         self._rxn_type = 'beta'
 
     @property
@@ -1685,6 +1770,12 @@ class DecayRate(MDGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -1702,6 +1793,10 @@ class DecayRate(MDGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -1751,9 +1846,11 @@ class DecayRate(MDGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, energy_groups=None,
-                 delayed_groups=None, by_nuclide=False, name=''):
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         super(DecayRate, self).__init__(domain, domain_type, energy_groups,
-                                   delayed_groups, by_nuclide, name)
+                                        delayed_groups, by_nuclide, name,
+                                        num_polar, num_azimuthal)
         self._rxn_type = 'decay-rate'
 
     @property
@@ -1771,11 +1868,14 @@ class DecayRate(MDGXS):
         group_edges = self.energy_groups.group_edges
         energy_filter = openmc.EnergyFilter(group_edges)
 
-        if self.delayed_groups != None:
+        if self.delayed_groups is not None:
             delayed_filter = openmc.DelayedGroupFilter(self.delayed_groups)
-            return [[delayed_filter, energy_filter], [delayed_filter, energy_filter]]
+            filters = [[delayed_filter, energy_filter], [delayed_filter,
+                                                         energy_filter]]
         else:
-            return [[energy_filter], [energy_filter]]
+            filters = [[energy_filter], [energy_filter]]
+
+        return self._add_angle_filters(filters)
 
     @property
     def xs_tally(self):
@@ -1847,6 +1947,12 @@ class MatrixMDGXS(MDGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -1864,6 +1970,10 @@ class MatrixMDGXS(MDGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -1911,6 +2021,16 @@ class MatrixMDGXS(MDGXS):
 
     """
 
+    @property
+    def _dont_squeeze(self):
+        """Create a tuple of axes which should not be removed during the get_xs
+        process
+        """
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            return (0, 1, 3, 4, 5)
+        else:
+            return (1, 2, 3)
+
     @property
     def filters(self):
         # Create the non-domain specific Filters for the Tallies
@@ -1920,9 +2040,11 @@ class MatrixMDGXS(MDGXS):
 
         if self.delayed_groups is not None:
             delayed = openmc.DelayedGroupFilter(self.delayed_groups)
-            return [[energy], [delayed, energy, energyout]]
+            filters = [[energy], [delayed, energy, energyout]]
         else:
-            return [[energy], [energy, energyout]]
+            filters = [[energy], [energy, energyout]]
+
+        return self._add_angle_filters(filters)
 
     def get_xs(self, in_groups='all', out_groups='all',
                subdomains='all', nuclides='all',
@@ -2076,25 +2198,39 @@ class MatrixMDGXS(MDGXS):
             num_delayed_groups = len(delayed_groups)
 
         # Reshape tally data array with separate axes for domain and energy
-        num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups *
-                                            num_delayed_groups))
-        new_shape = (num_subdomains, num_delayed_groups, num_in_groups,
-                     num_out_groups)
-        new_shape += xs.shape[1:]
-        xs = np.reshape(xs, new_shape)
+        # Accomodate the polar and azimuthal bins if needed
+        num_subdomains = int(xs.shape[0] / (num_delayed_groups *
+                                            num_in_groups * num_out_groups *
+                                            self.num_polar *
+                                            self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            new_shape = (self.num_polar, self.num_azimuthal, num_subdomains,
+                         num_delayed_groups, num_in_groups, num_out_groups)
+            new_shape += xs.shape[1:]
+            xs = np.reshape(xs, new_shape)
 
-        # Transpose the matrix if requested by user
-        if row_column == 'outin':
-            xs = np.swapaxes(xs, 2, 3)
+            # Transpose the matrix if requested by user
+            if row_column == 'outin':
+                xs = np.swapaxes(xs, 4, 5)
+        else:
+            new_shape = (num_subdomains, num_delayed_groups, num_in_groups,
+                         num_out_groups)
+            new_shape += xs.shape[1:]
+            xs = np.reshape(xs, new_shape)
+
+            # Transpose the matrix if requested by user
+            if row_column == 'outin':
+                xs = np.swapaxes(xs, 2, 3)
 
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            xs = xs[:, :, ::-1, ::-1, :]
+            xs = xs[..., ::-1, ::-1, :]
 
         if squeeze:
-            xs = np.squeeze(xs)
-            xs = np.atleast_2d(xs)
+            # We want to squeeze out everything but the polar, azimuthal,
+            # and in/out energy group data.
+            xs = self._squeeze_xs(xs)
 
         return xs
 
@@ -2184,7 +2320,7 @@ class MatrixMDGXS(MDGXS):
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
         elif self.domain_type == 'mesh':
-            xyz = [range(1, x+1) for x in self.domain.dimension]
+            xyz = [range(1, x + 1) for x in self.domain.dimension]
             subdomains = list(itertools.product(*xyz))
         else:
             subdomains = [self.domain.id]
@@ -2217,13 +2353,20 @@ class MatrixMDGXS(MDGXS):
             return
 
         string += '{0: <16}\n'.format('\tEnergy Groups:')
-        template = '{0: <12}Group {1} [{2: <10} - {3: <10}MeV]\n'
+        template = '{0: <12}Group {1} [{2: <10} - {3: <10}eV]\n'
 
         # Loop over energy groups ranges
         for group in range(1, self.num_groups + 1):
             bounds = self.energy_groups.get_group_bounds(group)
             string += template.format('', group, bounds[0], bounds[1])
 
+        # Set polar and azimuthal bins if necessary
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            pol_bins = np.linspace(0., np.pi, num=self.num_polar + 1,
+                                   endpoint=True)
+            azi_bins = np.linspace(-np.pi, np.pi, num=self.num_azimuthal + 1,
+                                   endpoint=True)
+
         # Loop over all subdomains
         for subdomain in subdomains:
 
@@ -2250,47 +2393,97 @@ class MatrixMDGXS(MDGXS):
 
                         template = '{0: <12}Group {1} -> Group {2}:\t\t'
 
-                        # Loop over incoming/outgoing energy groups ranges
-                        for in_group in range(1, self.num_groups + 1):
-                            for out_group in range(1, self.num_groups + 1):
-                                string += template.format('', in_group, out_group)
-                                average = self.get_xs([in_group], [out_group],
-                                                      [subdomain], [nuclide],
-                                                      xs_type=xs_type,
-                                                      value='mean',
-                                                      delayed_groups=[delayed_group])
-                                rel_err = self.get_xs([in_group], [out_group],
-                                                      [subdomain], [nuclide],
-                                                      xs_type=xs_type,
-                                                      value='rel_err',
-                                                      delayed_groups=[delayed_group])
-                                average = average.flatten()[0]
-                                rel_err = rel_err.flatten()[0] * 100.
-                                string += '{:.2e} +/- {:.2e}%'.format(average,
-                                                                      rel_err)
+                        average_xs = self.get_xs(nuclides=[nuclide],
+                                                 subdomains=[subdomain],
+                                                 xs_type=xs_type, value='mean',
+                                                 delayed_groups=[delayed_group])
+                        rel_err_xs = self.get_xs(nuclides=[nuclide],
+                                                 subdomains=[subdomain],
+                                                 xs_type=xs_type,
+                                                 value='rel_err',
+                                                 delayed_groups=[delayed_group])
+                        rel_err_xs = rel_err_xs * 100.
+
+                        if self.num_polar > 1 or self.num_azimuthal > 1:
+                            # Loop over polar, azi, and in/out group ranges
+                            for pol in range(len(pol_bins) - 1):
+                                pol_low, pol_high = pol_bins[pol: pol + 2]
+                                for azi in range(len(azi_bins) - 1):
+                                    azi_low, azi_high = azi_bins[azi: azi + 2]
+                                    string += '\t\tPolar Angle: [{0:5f} - {1:5f}]'.format(
+                                        pol_low, pol_high) + \
+                                        '\tAzimuthal Angle: [{0:5f} - {1:5f}]'.format(
+                                        azi_low, azi_high) + '\n'
+                                    for in_group in range(1, self.num_groups + 1):
+                                        for out_group in range(1, self.num_groups + 1):
+                                            string += '\t' + template.format(
+                                                '', in_group, out_group)
+                                            string += '{0:.2e} +/- {1:.2e}%'.format(
+                                                average_xs[pol, azi, in_group - 1,
+                                                           out_group - 1],
+                                                rel_err_xs[pol, azi, in_group - 1,
+                                                           out_group - 1])
+                                            string += '\n'
+                                        string += '\n'
+                                    string += '\n'
+                        else:
+                            # Loop over incoming/outgoing energy groups ranges
+                            for in_group in range(1, self.num_groups + 1):
+                                for out_group in range(1, self.num_groups + 1):
+                                    string += template.format(
+                                        '', in_group, out_group)
+                                    string += '{:.2e} +/- {:.2e}%'.format(
+                                        average_xs[in_group-1, out_group-1],
+                                        rel_err_xs[in_group-1, out_group-1])
+                                    string += '\n'
                                 string += '\n'
-                            string += '\n'
                         string += '\n'
                 else:
 
                     template = '{0: <12}Group {1} -> Group {2}:\t\t'
 
-                    # Loop over incoming/outgoing energy groups ranges
-                    for in_group in range(1, self.num_groups + 1):
-                        for out_group in range(1, self.num_groups + 1):
-                            string += template.format('', in_group, out_group)
-                            average = self.get_xs([in_group], [out_group],
-                                                  [subdomain], [nuclide],
-                                                  xs_type=xs_type, value='mean')
-                            rel_err = self.get_xs([in_group], [out_group],
-                                                  [subdomain], [nuclide],
-                                                  xs_type=xs_type, value='rel_err')
-                            average = average.flatten()[0]
-                            rel_err = rel_err.flatten()[0] * 100.
-                            string += '{:.2e} +/- {:.2e}%'.format(average,
-                                                                  rel_err)
+                    average_xs = self.get_xs(nuclides=[nuclide],
+                                             subdomains=[subdomain],
+                                             xs_type=xs_type, value='mean')
+                    rel_err_xs = self.get_xs(nuclides=[nuclide],
+                                             subdomains=[subdomain],
+                                             xs_type=xs_type, value='rel_err')
+                    rel_err_xs = rel_err_xs * 100.
+
+                    if self.num_polar > 1 or self.num_azimuthal > 1:
+                        # Loop over polar, azi, and in/out energy group ranges
+                        for pol in range(len(pol_bins) - 1):
+                            pol_low, pol_high = pol_bins[pol: pol + 2]
+                            for azi in range(len(azi_bins) - 1):
+                                azi_low, azi_high = azi_bins[azi: azi + 2]
+                                string += '\t\tPolar Angle: [{0:5f} - {1:5f}]'.format(
+                                    pol_low, pol_high) + \
+                                    '\tAzimuthal Angle: [{0:5f} - {1:5f}]'.format(
+                                    azi_low, azi_high) + '\n'
+                                for in_group in range(1, self.num_groups + 1):
+                                    for out_group in range(1, self.num_groups + 1):
+                                        string += '\t' + template.format(
+                                            '', in_group, out_group)
+                                        string += '{0:.2e} +/- {1:.2e}%'.format(
+                                            average_xs[pol, azi, in_group - 1,
+                                                       out_group - 1],
+                                            rel_err_xs[pol, azi, in_group - 1,
+                                                       out_group - 1])
+                                        string += '\n'
+                                    string += '\n'
+                                string += '\n'
+                    else:
+                        # Loop over incoming/outgoing energy groups ranges
+                        for in_group in range(1, self.num_groups + 1):
+                            for out_group in range(1, self.num_groups + 1):
+                                string += template.format('', in_group,
+                                                          out_group)
+                                string += '{0:.2e} +/- {1:.2e}%'.format(
+                                    average_xs[in_group - 1, out_group - 1],
+                                    rel_err_xs[in_group - 1, out_group - 1])
+                                string += '\n'
                             string += '\n'
-                        string += '\n'
+                    string += '\n'
                 string += '\n'
             string += '\n'
 
@@ -2346,6 +2539,12 @@ class DelayedNuFissionMatrixXS(MatrixMDGXS):
         tallies in OpenMC 'tallies.xml' file.
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
     ----------
@@ -2363,6 +2562,10 @@ class DelayedNuFissionMatrixXS(MatrixMDGXS):
         Energy group structure for energy condensation
     delayed_groups : list of int
         Delayed groups to filter out the xs
+    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
@@ -2412,11 +2615,14 @@ class DelayedNuFissionMatrixXS(MatrixMDGXS):
     """
 
     def __init__(self, domain=None, domain_type=None, energy_groups=None,
-                 delayed_groups=None, by_nuclide=False, name=''):
+                 delayed_groups=None, by_nuclide=False, name='',
+                 num_polar=1, num_azimuthal=1):
         super(DelayedNuFissionMatrixXS, self).__init__(domain, domain_type,
                                                        energy_groups,
                                                        delayed_groups,
-                                                       by_nuclide, name)
+                                                       by_nuclide, name,
+                                                       num_polar,
+                                                       num_azimuthal)
         self._rxn_type = 'delayed-nu-fission'
         self._hdf5_key = 'delayed-nu-fission matrix'
         self._estimator = 'analog'
diff --git a/openmc/mgxs/mgxs.py b/openmc/mgxs/mgxs.py
index fab4127eb6..9eb642e322 100644
--- a/openmc/mgxs/mgxs.py
+++ b/openmc/mgxs/mgxs.py
@@ -4,7 +4,6 @@ from collections import OrderedDict
 from numbers import Integral
 import warnings
 import os
-import sys
 import copy
 from abc import ABCMeta
 import itertools
@@ -66,6 +65,53 @@ MU_TREATMENTS = ('legendre', 'histogram')
 _MAX_LEGENDRE = 10
 
 
+def _df_column_convert_to_bin(df, current_name, new_name, values_to_bin,
+                              reverse_order=False):
+    """Convert a Pandas DataFrame column from the bin edges to an index for
+    each bin. This method operates on the DataFrame, df, in-place.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame
+        A Pandas DataFrame containing the cross section data.
+    current_name : str
+        Name of the column to replace with bins
+    new_name : str
+        New name for column after the data is replaced with bins
+    values_to_bin : Iterable of Real
+        Values of the bin edges to be used for identifying the bins
+    reverse_order : bool
+        Whether the bin indices should be reversed
+
+    """
+
+    # Get the current values
+    df_bins = np.asarray(df[current_name])
+    new_vals = np.zeros_like(df_bins, dtype=int)
+    # Replace the values with the index of the closest entry in values_to_bin
+    # The closest is used because it is expected that the values in df could
+    # have lost precision along the way
+    for i, df_val in enumerate(df_bins):
+        idx = np.searchsorted(values_to_bin, df_val)
+        # Check to make sure if the value is just above the search result
+        if idx > 0 and np.isclose(values_to_bin[idx - 1], df_val):
+            idx -= 1
+        # If it is just below the search result then we are done
+        new_vals[i] = idx
+    # Switch to a one-based indexing
+    new_vals += 1
+
+    # Reverse the ordering if requested (this is for energy group ordering)
+    if reverse_order:
+        new_vals = (len(values_to_bin) - 1) - new_vals + 1
+
+    # Assign the values
+    df[current_name] = new_vals[:]
+
+    # And rename the column
+    df.rename(columns={current_name: new_name}, inplace=True)
+
+
 @add_metaclass(ABCMeta)
 class MGXS(object):
     """An abstract multi-group cross section for some energy group structure
@@ -90,6 +136,12 @@ class MGXS(object):
     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
     ----------
@@ -105,6 +157,10 @@ class MGXS(object):
         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
@@ -151,8 +207,10 @@ class MGXS(object):
         The key used to index multi-group cross sections in an HDF5 data store
 
     """
+
     def __init__(self, domain=None, domain_type=None,
-                 energy_groups=None, by_nuclide=False, name=''):
+                 energy_groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         self._name = ''
         self._rxn_type = None
         self._by_nuclide = None
@@ -161,6 +219,8 @@ class MGXS(object):
         self._domain = None
         self._domain_type = None
         self._energy_groups = None
+        self._num_polar = 1
+        self._num_azimuthal = 1
         self._tally_trigger = None
         self._tallies = None
         self._rxn_rate_tally = None
@@ -180,6 +240,8 @@ class MGXS(object):
             self.domain = domain
         if energy_groups is not None:
             self.energy_groups = energy_groups
+        self.num_polar = num_polar
+        self.num_azimuthal = num_azimuthal
 
     def __deepcopy__(self, memo):
         existing = memo.get(id(self))
@@ -194,6 +256,8 @@ class MGXS(object):
             clone._domain = self.domain
             clone._domain_type = self.domain_type
             clone._energy_groups = copy.deepcopy(self.energy_groups, memo)
+            clone._num_polar = self._num_polar
+            clone._num_azimuthal = self._num_azimuthal
             clone._tally_trigger = copy.deepcopy(self.tally_trigger, memo)
             clone._rxn_rate_tally = copy.deepcopy(self._rxn_rate_tally, memo)
             clone._xs_tally = copy.deepcopy(self._xs_tally, memo)
@@ -214,6 +278,128 @@ class MGXS(object):
         else:
             return existing
 
+    def _add_angle_filters(self, filters):
+        """Add the azimuthal and polar bins to the MGXS filters if needed.
+        Filters will be provided as a ragged 2D list of openmc.Filter objects.
+
+        Parameters
+        ----------
+        filters : Iterable of Iterable of openmc.Filter
+            Ragged 2D list of openmc.Filter objects for the energy and spatial
+            domains. The angle filters will be added to the list.
+
+        Returns
+        -------
+        Iterable of Iterable of openmc.Filter
+            Ragged 2D list of openmc.Filter objects for the energy and spatial
+            domains with the angle filters added to the list.
+
+        """
+
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            # Then the user has requested angular data, so create the bins
+            pol_bins = np.linspace(0., np.pi, num=self.num_polar + 1,
+                                   endpoint=True)
+            azi_bins = np.linspace(-np.pi, np.pi, num=self.num_azimuthal + 1,
+                                   endpoint=True)
+
+            for filt in filters:
+                filt.insert(0, openmc.PolarFilter(pol_bins))
+                filt.insert(1, openmc.AzimuthalFilter(azi_bins))
+
+        return filters
+
+    def _squeeze_xs(self, xs):
+        """Remove dimensions which are not needed from a cross section array
+        due to user options. This is used by the openmc.Mgxs.get_xs(...) method
+
+        Parameters
+        ----------
+        xs : np.ndarray
+            Cross sections array with dimensions to be squeezed
+
+        Returns
+        -------
+        np.ndarray
+            Squeezed array of cross sections
+
+        """
+
+        # numpy.squeeze will return a ValueError if the axis has a size
+        # greater than 1, to avoid this we will try each axis one at a
+        # time to preclude the ValueError.
+        initial_shape = len(xs.shape)
+        for axis in range(initial_shape - 1, -1, -1):
+            if axis not in self._dont_squeeze and xs.shape[axis] == 1:
+                xs = np.squeeze(xs, axis=axis)
+        return xs
+
+    def _df_convert_columns_to_bins(self, df):
+        """This method converts all relevant and present DataFrame columns from
+        their bin boundaries to the index for each bin. This method operates on
+        the DataFrame, df, in place. The method returns a list of the columns
+        in which it has operated on.
+
+        Parameters
+        ----------
+        df : pandas.DataFrame
+            A Pandas DataFrame containing the cross section data.
+
+        Returns
+        -------
+        columns : Iterable of str
+            Names of the re-named and re-valued columns
+
+        """
+        # Override polar and azimuthal bounds with indices
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            # First for polar
+            bins = np.linspace(0., np.pi, self.num_polar + 1, True)
+            _df_column_convert_to_bin(df, 'polar low', 'polar bin', bins)
+            del df['polar high']
+
+            # Second for azimuthal
+            bins = np.linspace(-np.pi, np.pi, self.num_azimuthal + 1, True)
+            _df_column_convert_to_bin(df, 'azimuthal low', 'azimuthal bin',
+                                      bins)
+            del df['azimuthal high']
+            columns = ['polar bin', 'azimuthal bin']
+        else:
+            columns = []
+
+        # Override energy groups bounds with indices
+        if 'energy low [eV]' in df:
+            _df_column_convert_to_bin(df, 'energy low [eV]', 'group in',
+                                      self.energy_groups.group_edges,
+                                      reverse_order=True)
+            del df['energy high [eV]']
+            columns += ['group in']
+        if 'energyout low [eV]' in df:
+            _df_column_convert_to_bin(df, 'energyout low [eV]', 'group out',
+                                      self.energy_groups.group_edges,
+                                      reverse_order=True)
+            del df['energyout high [eV]']
+            columns += ['group out']
+
+        if 'mu low' in df and hasattr(self, 'histogram_bins'):
+            # Only the ScatterMatrix class has the histogram_bins attribute
+            bins = np.linspace(-1., 1., self.histogram_bins + 1, True)
+            _df_column_convert_to_bin(df, 'mu low', 'mu bin', bins)
+            del df['mu high']
+            columns += ['mu bin']
+
+        return columns
+
+    @property
+    def _dont_squeeze(self):
+        """Create a tuple of axes which should not be removed during the get_xs
+        process
+        """
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            return (0, 1, 3)
+        else:
+            return (1, )
+
     @property
     def name(self):
         return self._name
@@ -238,6 +424,14 @@ class MGXS(object):
     def energy_groups(self):
         return self._energy_groups
 
+    @property
+    def num_polar(self):
+        return self._num_polar
+
+    @property
+    def num_azimuthal(self):
+        return self._num_azimuthal
+
     @property
     def tally_trigger(self):
         return self._tally_trigger
@@ -254,7 +448,11 @@ class MGXS(object):
     def filters(self):
         group_edges = self.energy_groups.group_edges
         energy_filter = openmc.EnergyFilter(group_edges)
-        return [[energy_filter]] * len(self.scores)
+        filters = []
+        for i in range(len(self.scores)):
+            filters.append([energy_filter])
+
+        return self._add_angle_filters(filters)
 
     @property
     def tally_keys(self):
@@ -416,6 +614,18 @@ class MGXS(object):
         cv.check_type('energy groups', energy_groups, openmc.mgxs.EnergyGroups)
         self._energy_groups = energy_groups
 
+    @num_polar.setter
+    def num_polar(self, num_polar):
+        cv.check_type('num_polar', num_polar, Integral)
+        cv.check_greater_than('num_polar', num_polar, 0)
+        self._num_polar = num_polar
+
+    @num_azimuthal.setter
+    def num_azimuthal(self, num_azimuthal):
+        cv.check_type('num_azimuthal', num_azimuthal, Integral)
+        cv.check_greater_than('num_azimuthal', num_azimuthal, 0)
+        self._num_azimuthal = num_azimuthal
+
     @tally_trigger.setter
     def tally_trigger(self, tally_trigger):
         cv.check_type('tally trigger', tally_trigger, openmc.Trigger)
@@ -448,7 +658,8 @@ class MGXS(object):
 
     @staticmethod
     def get_mgxs(mgxs_type, domain=None, domain_type=None,
-                 energy_groups=None, by_nuclide=False, name=''):
+                 energy_groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         """Return a MGXS subclass object for some energy group structure within
         some spatial domain for some reaction type.
 
@@ -471,6 +682,12 @@ class MGXS(object):
         name : str, optional
             Name of the multi-group cross section. Used as a label to identify
             tallies in OpenMC 'tallies.xml' file. Defaults to the empty string.
+        num_polar : Integral, optional
+            Number of equi-width polar angles for angle discretization;
+            defaults to no discretization
+        num_azimuthal : Integral, optional
+            Number of equi-width azimuthal angles for angle discretization;
+            defaults to no discretization
 
         Returns
         -------
@@ -523,6 +740,8 @@ class MGXS(object):
 
         mgxs.by_nuclide = by_nuclide
         mgxs.name = name
+        mgxs.num_polar = num_polar
+        mgxs.num_azimuthal = num_azimuthal
         return mgxs
 
     def get_nuclides(self):
@@ -702,7 +921,7 @@ class MGXS(object):
         # NOTE: This is important if tally merging was used
         if self.domain_type == 'mesh':
             filters = [_DOMAIN_TO_FILTER[self.domain_type]]
-            xyz = [range(1, x+1) for x in self.domain.dimension]
+            xyz = [range(1, x + 1) for x in self.domain.dimension]
             filter_bins = [tuple(itertools.product(*xyz))]
         elif self.domain_type != 'distribcell':
             filters = [_DOMAIN_TO_FILTER[self.domain_type]]
@@ -829,7 +1048,8 @@ class MGXS(object):
             xs = xs_tally.get_values(filters=filters,
                                      filter_bins=filter_bins, value=value)
         else:
-            xs = self.xs_tally.get_values(filters=filters, filter_bins=filter_bins,
+            xs = self.xs_tally.get_values(filters=filters,
+                                          filter_bins=filter_bins,
                                           nuclides=query_nuclides, value=value)
 
         # Divide by atom number densities for microscopic cross sections
@@ -851,18 +1071,26 @@ class MGXS(object):
             num_groups = len(groups)
 
         # Reshape tally data array with separate axes for domain and energy
-        num_subdomains = int(xs.shape[0] / num_groups)
-        new_shape = (num_subdomains, num_groups) + xs.shape[1:]
+        # Accomodate the polar and azimuthal bins if needed
+        num_subdomains = int(xs.shape[0] / (num_groups * self.num_polar *
+                                            self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            new_shape = (self.num_polar, self.num_azimuthal, num_subdomains,
+                         num_groups)
+        else:
+            new_shape = (num_subdomains, num_groups)
+        new_shape += xs.shape[1:]
         xs = np.reshape(xs, new_shape)
 
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            xs = xs[:, ::-1, :]
+            xs = xs[..., ::-1, :]
 
         if squeeze:
-            xs = np.squeeze(xs)
-            xs = np.atleast_1d(xs)
+            # We want to squeeze out everything but the polar, azimuthal,
+            # and energy group data.
+            xs = self._squeeze_xs(xs)
 
         return xs
 
@@ -929,7 +1157,8 @@ class MGXS(object):
                 else:
                     tally_filter.bins = coarse_groups.group_edges
                     mean = np.add.reduceat(mean, energy_indices, axis=i)
-                    std_dev = np.add.reduceat(std_dev**2, energy_indices, axis=i)
+                    std_dev = np.add.reduceat(std_dev**2, energy_indices,
+                                              axis=i)
                     std_dev = np.sqrt(std_dev)
 
             # Reshape condensed data arrays with one dimension for all filters
@@ -1286,7 +1515,7 @@ class MGXS(object):
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
         elif self.domain_type == 'mesh':
-            xyz = [range(1, x+1) for x in self.domain.dimension]
+            xyz = [range(1, x + 1) for x in self.domain.dimension]
             subdomains = list(itertools.product(*xyz))
         else:
             subdomains = [self.domain.id]
@@ -1318,6 +1547,13 @@ class MGXS(object):
             print(string)
             return
 
+        # Set polar/azimuthal bins
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            pol_bins = np.linspace(0., np.pi, num=self.num_polar + 1,
+                                   endpoint=True)
+            azi_bins = np.linspace(-np.pi, np.pi, num=self.num_azimuthal + 1,
+                                   endpoint=True)
+
         # Loop over all subdomains
         for subdomain in subdomains:
 
@@ -1335,18 +1571,45 @@ class MGXS(object):
                 string += '{0: <16}\n'.format(xs_header)
                 template = '{0: <12}Group {1} [{2: <10} - {3: <10}eV]:\t'
 
-                # Loop over energy groups ranges
-                for group in range(1, self.num_groups+1):
-                    bounds = self.energy_groups.get_group_bounds(group)
-                    string += template.format('', group, bounds[0], bounds[1])
-                    average = self.get_xs([group], [subdomain], [nuclide],
-                                          xs_type=xs_type, value='mean')
-                    rel_err = self.get_xs([group], [subdomain], [nuclide],
-                                          xs_type=xs_type, value='rel_err')
-                    average = average.flatten()[0]
-                    rel_err = rel_err.flatten()[0] * 100.
-                    string += '{:.2e} +/- {:1.2e}%'.format(average, rel_err)
-                    string += '\n'
+                average_xs = self.get_xs(nuclides=[nuclide],
+                                         subdomains=[subdomain],
+                                         xs_type=xs_type, value='mean')
+                rel_err_xs = self.get_xs(nuclides=[nuclide],
+                                         subdomains=[subdomain],
+                                         xs_type=xs_type, value='rel_err')
+                rel_err_xs = rel_err_xs * 100.
+
+                if self.num_polar > 1 or self.num_azimuthal > 1:
+                    # Loop over polar, azimuthal, and energy group ranges
+                    for pol in range(len(pol_bins) - 1):
+                        pol_low, pol_high = pol_bins[pol: pol + 2]
+                        for azi in range(len(azi_bins) - 1):
+                            azi_low, azi_high = azi_bins[azi: azi + 2]
+                            string += '\t\tPolar Angle: [{0:5f} - {1:5f}]'.format(
+                                pol_low, pol_high) + \
+                                '\tAzimuthal Angle: [{0:5f} - {1:5f}]'.format(
+                                azi_low, azi_high) + '\n'
+                            for group in range(1, self.num_groups + 1):
+                                bounds = \
+                                    self.energy_groups.get_group_bounds(group)
+                                string += '\t' + template.format('', group,
+                                                                 bounds[0],
+                                                                 bounds[1])
+
+                                string += '{0:.2e} +/- {1:.2e}%'.format(
+                                    average_xs[pol, azi, group - 1],
+                                    rel_err_xs[pol, azi, group - 1])
+                                string += '\n'
+                            string += '\n'
+                else:
+                    # Loop over energy groups
+                    for group in range(1, self.num_groups + 1):
+                        bounds = self.energy_groups.get_group_bounds(group)
+                        string += template.format('', group, bounds[0],
+                                                  bounds[1])
+                        string += '{0:.2e} +/- {1:.2e}%'.format(
+                            average_xs[group - 1], rel_err_xs[group - 1])
+                        string += '\n'
                 string += '\n'
             string += '\n'
 
@@ -1612,7 +1875,6 @@ class MGXS(object):
         if self.by_nuclide and nuclides == 'sum':
 
             # Use tally summation to sum across all nuclides
-            query_nuclides = [nuclides]
             xs_tally = self.xs_tally.summation(nuclides=self.get_nuclides())
             df = xs_tally.get_pandas_dataframe(
                 distribcell_paths=distribcell_paths)
@@ -1625,14 +1887,12 @@ class MGXS(object):
 
         # If the user requested a specific set of nuclides
         elif self.by_nuclide and nuclides != 'all':
-            query_nuclides = nuclides
             xs_tally = self.xs_tally.get_slice(nuclides=nuclides)
             df = xs_tally.get_pandas_dataframe(
                 distribcell_paths=distribcell_paths)
 
         # If the user requested all nuclides, keep nuclide column in dataframe
         else:
-            query_nuclides = self.nuclides
             df = self.xs_tally.get_pandas_dataframe(
                 distribcell_paths=distribcell_paths)
 
@@ -1642,49 +1902,9 @@ class MGXS(object):
         else:
             df = df.drop('score', axis=1)
 
-        # Determine if change-in-angle bins are included in the MGXS to
-        # properly tile the group boundaries
-        if 'mu low' in df:
-            # Find the length of the mu filters indirectly from the number
-            # of times the mu bins repeats.
-            num_mu = int(df.shape[0] /
-                         df[df['mu low'] == df['mu low'][0]].shape[0])
-        else:
-            num_mu = 1
-
-        # Override energy groups bounds with indices
-        all_groups = np.arange(self.num_groups, 0, -1, dtype=np.int)
-        all_groups = np.repeat(all_groups, len(query_nuclides) * num_mu)
-        if 'energy low [eV]' in df and 'energyout low [eV]' in df:
-            df.rename(columns={'energy low [eV]': 'group in'},
-                      inplace=True)
-            in_groups = np.tile(all_groups, int(self.num_subdomains))
-            in_groups = np.repeat(in_groups, int(df.shape[0] / in_groups.size))
-            df['group in'] = in_groups
-            del df['energy high [eV]']
-
-            df.rename(columns={'energyout low [eV]': 'group out'},
-                      inplace=True)
-            out_groups = np.repeat(all_groups, self.xs_tally.num_scores)
-            out_groups = np.tile(out_groups, int(df.shape[0] / out_groups.size))
-            df['group out'] = out_groups
-            del df['energyout high [eV]']
-            columns = ['group in', 'group out']
-
-        elif 'energyout low [eV]' in df:
-            df.rename(columns={'energyout low [eV]': 'group out'},
-                      inplace=True)
-            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
-            df['group out'] = in_groups
-            del df['energyout high [eV]']
-            columns = ['group out']
-
-        elif 'energy low [eV]' in df:
-            df.rename(columns={'energy low [eV]': 'group in'}, inplace=True)
-            in_groups = np.tile(all_groups, int(df.shape[0] / all_groups.size))
-            df['group in'] = in_groups
-            del df['energy high [eV]']
-            columns = ['group in']
+        # Convert azimuthal, polar, energy in and energy out bin values in to
+        # bin indices
+        columns = self._df_convert_columns_to_bins(df)
 
         # Select out those groups the user requested
         if not isinstance(groups, string_types):
@@ -1700,7 +1920,7 @@ class MGXS(object):
             else:
                 densities = self.get_nuclide_densities('sum')
             densities = np.repeat(densities, len(self.rxn_rate_tally.scores))
-            tile_factor = df.shape[0] / len(densities)
+            tile_factor = int(df.shape[0] / len(densities))
             df['mean'] /= np.tile(densities, tile_factor)
             df['std. dev.'] /= np.tile(densities, tile_factor)
 
@@ -1712,7 +1932,7 @@ class MGXS(object):
         # energy groups such that data is from fast to thermal
         if self.domain_type == 'mesh':
             mesh_str = 'mesh {0}'.format(self.domain.id)
-            df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'), \
+            df.sort_values(by=[(mesh_str, 'x'), (mesh_str, 'y'),
                                (mesh_str, 'z')] + columns, inplace=True)
         else:
             df.sort_values(by=[self.domain_type] + columns, inplace=True)
@@ -1767,6 +1987,12 @@ class MatrixMGXS(MGXS):
     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
     ----------
@@ -1782,6 +2008,10 @@ class MatrixMGXS(MGXS):
         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
@@ -1828,14 +2058,25 @@ class MatrixMGXS(MGXS):
         The key used to index multi-group cross sections in an HDF5 data store
 
     """
+    @property
+    def _dont_squeeze(self):
+        """Create a tuple of axes which should not be removed during the get_xs
+        process
+        """
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            return (0, 1, 3, 4)
+        else:
+            return (1, 2)
+
     @property
     def filters(self):
         # Create the non-domain specific Filters for the Tallies
         group_edges = self.energy_groups.group_edges
         energy = openmc.EnergyFilter(group_edges)
         energyout = openmc.EnergyoutFilter(group_edges)
+        filters = [[energy], [energy, energyout]]
 
-        return [[energy], [energy, energyout]]
+        return self._add_angle_filters(filters)
 
     def get_xs(self, in_groups='all', out_groups='all',
                subdomains='all', nuclides='all',
@@ -1975,23 +2216,37 @@ class MatrixMGXS(MGXS):
             num_out_groups = len(out_groups)
 
         # Reshape tally data array with separate axes for domain and energy
-        num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups))
-        new_shape = (num_subdomains, num_in_groups, num_out_groups)
-        new_shape += xs.shape[1:]
-        xs = np.reshape(xs, new_shape)
+        # Accomodate the polar and azimuthal bins if needed
+        num_subdomains = int(xs.shape[0] / (num_in_groups * num_out_groups *
+                                            self.num_polar *
+                                            self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            new_shape = (self.num_polar, self.num_azimuthal, num_subdomains,
+                         num_in_groups, num_out_groups)
+            new_shape += xs.shape[1:]
+            xs = np.reshape(xs, new_shape)
 
-        # Transpose the matrix if requested by user
-        if row_column == 'outin':
-            xs = np.swapaxes(xs, 1, 2)
+            # Transpose the matrix if requested by user
+            if row_column == 'outin':
+                xs = np.swapaxes(xs, 3, 4)
+        else:
+            new_shape = (num_subdomains, num_in_groups, num_out_groups)
+            new_shape += xs.shape[1:]
+            xs = np.reshape(xs, new_shape)
+
+            # Transpose the matrix if requested by user
+            if row_column == 'outin':
+                xs = np.swapaxes(xs, 1, 2)
 
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            xs = xs[:, ::-1, ::-1, :]
+            xs = xs[..., ::-1, ::-1, :]
 
         if squeeze:
-            xs = np.squeeze(xs)
-            xs = np.atleast_2d(xs)
+            # We want to squeeze out everything but the polar, azimuthal,
+            # and in/out energy group data.
+            xs = self._squeeze_xs(xs)
 
         return xs
 
@@ -2076,7 +2331,7 @@ class MatrixMGXS(MGXS):
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
         elif self.domain_type == 'mesh':
-            xyz = [range(1, x+1) for x in self.domain.dimension]
+            xyz = [range(1, x + 1) for x in self.domain.dimension]
             subdomains = list(itertools.product(*xyz))
         else:
             subdomains = [self.domain.id]
@@ -2116,12 +2371,18 @@ class MatrixMGXS(MGXS):
             bounds = self.energy_groups.get_group_bounds(group)
             string += template.format('', group, bounds[0], bounds[1])
 
+        # Set polar and azimuthal bins if necessary
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            pol_bins = np.linspace(0., np.pi, num=self.num_polar + 1,
+                                   endpoint=True)
+            azi_bins = np.linspace(-np.pi, np.pi, num=self.num_azimuthal + 1,
+                                   endpoint=True)
+
         # Loop over all subdomains
         for subdomain in subdomains:
 
-            if self.domain_type == 'distribcell':
-                string += \
-                    '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain)
+            if self.domain_type == 'distribcell' or self.domain_type == 'mesh':
+                string += '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain)
 
             # Loop over all Nuclides
             for nuclide in nuclides:
@@ -2134,24 +2395,47 @@ class MatrixMGXS(MGXS):
                 string += '{0: <16}\n'.format(xs_header)
                 template = '{0: <12}Group {1} -> Group {2}:\t\t'
 
-                # Loop over incoming/outgoing energy groups ranges
-                for in_group in range(1, self.num_groups + 1):
-                    for out_group in range(1, self.num_groups + 1):
-                        string += template.format('', in_group, out_group)
-                        average = \
-                            self.get_xs([in_group], [out_group],
-                                        [subdomain], [nuclide],
-                                        xs_type=xs_type, value='mean')
-                        rel_err = \
-                            self.get_xs([in_group], [out_group],
-                                        [subdomain], [nuclide],
-                                        xs_type=xs_type, value='rel_err')
-                        average = average.flatten()[0]
-                        rel_err = rel_err.flatten()[0] * 100.
-                        string += '{:1.2e} +/- {:1.2e}%'.format(average,
-                                                                rel_err)
+                average_xs = self.get_xs(nuclides=[nuclide],
+                                         subdomains=[subdomain],
+                                         xs_type=xs_type, value='mean')
+                rel_err_xs = self.get_xs(nuclides=[nuclide],
+                                         subdomains=[subdomain],
+                                         xs_type=xs_type, value='rel_err')
+                rel_err_xs = rel_err_xs * 100.
+
+                if self.num_polar > 1 or self.num_azimuthal > 1:
+                    # Loop over polar, azi, and in/out energy group ranges
+                    for pol in range(len(pol_bins) - 1):
+                        pol_low, pol_high = pol_bins[pol: pol + 2]
+                        for azi in range(len(azi_bins) - 1):
+                            azi_low, azi_high = azi_bins[azi: azi + 2]
+                            string += '\t\tPolar Angle: [{0:5f} - {1:5f}]'.format(
+                                pol_low, pol_high) + \
+                                '\tAzimuthal Angle: [{0:5f} - {1:5f}]'.format(
+                                azi_low, azi_high) + '\n'
+                            for in_group in range(1, self.num_groups + 1):
+                                for out_group in range(1, self.num_groups + 1):
+                                    string += '\t' + template.format('',
+                                                                     in_group,
+                                                                     out_group)
+                                    string += '{0:.2e} +/- {1:.2e}%'.format(
+                                        average_xs[pol, azi, in_group - 1,
+                                                   out_group - 1],
+                                        rel_err_xs[pol, azi, in_group - 1,
+                                                   out_group - 1])
+                                    string += '\n'
+                                string += '\n'
+                            string += '\n'
+                else:
+                    # Loop over incoming/outgoing energy groups ranges
+                    for in_group in range(1, self.num_groups + 1):
+                        for out_group in range(1, self.num_groups + 1):
+                            string += template.format('', in_group, out_group)
+                            string += '{0:.2e} +/- {1:.2e}%'.format(
+                                average_xs[in_group - 1, out_group - 1],
+                                rel_err_xs[in_group - 1, out_group - 1])
+                            string += '\n'
                         string += '\n'
-                    string += '\n'
                 string += '\n'
             string += '\n'
 
@@ -2197,6 +2481,12 @@ class TotalXS(MGXS):
     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
     ----------
@@ -2212,6 +2502,10 @@ class TotalXS(MGXS):
         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
@@ -2261,9 +2555,11 @@ class TotalXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(TotalXS, self).__init__(domain, domain_type,
-                                      groups, by_nuclide, name)
+                                      groups, by_nuclide, name, num_polar,
+                                      num_azimuthal)
         self._rxn_type = 'total'
 
 
@@ -2315,6 +2611,12 @@ class TransportXS(MGXS):
     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
     ----------
@@ -2330,6 +2632,10 @@ class TransportXS(MGXS):
         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
@@ -2379,9 +2685,11 @@ class TransportXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(TransportXS, self).__init__(domain, domain_type,
-                                          groups, by_nuclide, name)
+                                          groups, by_nuclide, name, num_polar,
+                                          num_azimuthal)
         self._rxn_type = 'transport'
         self._estimator = 'analog'
         self._valid_estimators = ['analog']
@@ -2395,7 +2703,9 @@ class TransportXS(MGXS):
         group_edges = self.energy_groups.group_edges
         energy_filter = openmc.EnergyFilter(group_edges)
         energyout_filter = openmc.EnergyoutFilter(group_edges)
-        return [[energy_filter], [energy_filter], [energyout_filter]]
+        filters = [[energy_filter], [energy_filter], [energyout_filter]]
+
+        return self._add_angle_filters(filters)
 
     @property
     def rxn_rate_tally(self):
@@ -2448,6 +2758,12 @@ class NuTransportXS(TransportXS):
     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
     ----------
@@ -2463,6 +2779,10 @@ class NuTransportXS(TransportXS):
         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
@@ -2512,9 +2832,11 @@ class NuTransportXS(TransportXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(NuTransportXS, self).__init__(domain, domain_type,
-                                            groups, by_nuclide, name)
+                                            groups, by_nuclide, name,
+                                            num_polar, num_azimuthal)
         self._rxn_type = 'nu-transport'
 
     @property
@@ -2569,6 +2891,12 @@ class AbsorptionXS(MGXS):
     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
     ----------
@@ -2584,6 +2912,10 @@ class AbsorptionXS(MGXS):
         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
@@ -2634,9 +2966,11 @@ class AbsorptionXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(AbsorptionXS, self).__init__(domain, domain_type,
-                                           groups, by_nuclide, name)
+                                           groups, by_nuclide, name, num_polar,
+                                           num_azimuthal)
         self._rxn_type = 'absorption'
 
 
@@ -2686,6 +3020,12 @@ class CaptureXS(MGXS):
     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
     ----------
@@ -2701,6 +3041,10 @@ class CaptureXS(MGXS):
         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
@@ -2750,9 +3094,11 @@ class CaptureXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(CaptureXS, self).__init__(domain, domain_type,
-                                        groups, by_nuclide, name)
+                                        groups, by_nuclide, name, num_polar,
+                                        num_azimuthal)
         self._rxn_type = 'capture'
 
     @property
@@ -2808,6 +3154,12 @@ class FissionXS(MGXS):
     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
     ----------
@@ -2823,6 +3175,10 @@ class FissionXS(MGXS):
         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
@@ -2872,9 +3228,11 @@ class FissionXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(FissionXS, self).__init__(domain, domain_type,
-                                        groups, by_nuclide, name)
+                                        groups, by_nuclide, name, num_polar,
+                                        num_azimuthal)
         self._rxn_type = 'fission'
 
 
@@ -2919,6 +3277,12 @@ class NuFissionXS(MGXS):
     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
     ----------
@@ -2934,6 +3298,10 @@ class NuFissionXS(MGXS):
         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
@@ -2983,11 +3351,14 @@ class NuFissionXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(NuFissionXS, self).__init__(domain, domain_type,
-                                          groups, by_nuclide, name)
+                                          groups, by_nuclide, name, num_polar,
+                                          num_azimuthal)
         self._rxn_type = 'nu-fission'
 
+
 class KappaFissionXS(MGXS):
     r"""A recoverable fission energy production rate multi-group cross section.
 
@@ -3034,6 +3405,12 @@ class KappaFissionXS(MGXS):
     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
     ----------
@@ -3049,6 +3426,10 @@ class KappaFissionXS(MGXS):
         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
@@ -3098,9 +3479,11 @@ class KappaFissionXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(KappaFissionXS, self).__init__(domain, domain_type,
-                                             groups, by_nuclide, name)
+                                             groups, by_nuclide, name,
+                                             num_polar, num_azimuthal)
         self._rxn_type = 'kappa-fission'
 
 
@@ -3147,6 +3530,12 @@ class ScatterXS(MGXS):
     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
     ----------
@@ -3162,6 +3551,10 @@ class ScatterXS(MGXS):
         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
@@ -3211,9 +3604,11 @@ class ScatterXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(ScatterXS, self).__init__(domain, domain_type,
-                                        groups, by_nuclide, name)
+                                        groups, by_nuclide, name, num_polar,
+                                        num_azimuthal)
         self._rxn_type = 'scatter'
 
 
@@ -3262,6 +3657,12 @@ class NuScatterXS(MGXS):
     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
     ----------
@@ -3277,6 +3678,10 @@ class NuScatterXS(MGXS):
         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
@@ -3326,9 +3731,11 @@ class NuScatterXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(NuScatterXS, self).__init__(domain, domain_type,
-                                          groups, by_nuclide, name)
+                                          groups, by_nuclide, name, num_polar,
+                                          num_azimuthal)
         self._rxn_type = 'nu-scatter'
         self._estimator = 'analog'
         self._valid_estimators = ['analog']
@@ -3390,6 +3797,12 @@ class ScatterMatrixXS(MatrixMGXS):
     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
     ----------
@@ -3418,6 +3831,10 @@ class ScatterMatrixXS(MatrixMGXS):
         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
@@ -3467,9 +3884,11 @@ class ScatterMatrixXS(MatrixMGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(ScatterMatrixXS, self).__init__(domain, domain_type,
-                                              groups, by_nuclide, name)
+                                              groups, by_nuclide, name,
+                                              num_azimuthal)
         self._rxn_type = 'scatter'
         self._correction = 'P0'
         self._scatter_format = 'legendre'
@@ -3487,6 +3906,22 @@ class ScatterMatrixXS(MatrixMGXS):
         clone._histogram_bins = self.histogram_bins
         return clone
 
+    @property
+    def _dont_squeeze(self):
+        """Create a tuple of axes which should not be removed during the get_xs
+        process
+        """
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            if self.scatter_format == 'histogram':
+                return (0, 1, 3, 4, 5)
+            else:
+                return (0, 1, 3, 4)
+        else:
+            if self.scatter_format == 'histogram':
+                return (1, 2, 3)
+            else:
+                return (1, 2)
+
     @property
     def correction(self):
         return self._correction
@@ -3534,7 +3969,7 @@ class ScatterMatrixXS(MatrixMGXS):
                                endpoint=True)
             filters = [[energy], [energy, energyout, openmc.MuFilter(bins)]]
 
-        return filters
+        return self._add_angle_filters(filters)
 
     @property
     def rxn_rate_tally(self):
@@ -3891,40 +4326,53 @@ class ScatterMatrixXS(MatrixMGXS):
             num_mu_bins = 1
 
         # Reshape tally data array with separate axes for domain and energy
-        num_subdomains = int(xs.shape[0] /
-                             (num_mu_bins * num_in_groups * num_out_groups))
-        if self.scatter_format == 'histogram':
-            new_shape = (num_subdomains, num_in_groups, num_out_groups,
-                         num_mu_bins)
+        # Accomodate the polar and azimuthal bins if needed
+        num_subdomains = int(xs.shape[0] / (num_mu_bins * num_in_groups *
+                                            num_out_groups * self.num_polar *
+                                            self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            if self.scatter_format == 'histogram':
+                new_shape = (self.num_polar, self.num_azimuthal,
+                             num_subdomains, num_in_groups, num_out_groups,
+                             num_mu_bins)
+            else:
+                new_shape = (self.num_polar, self.num_azimuthal,
+                             num_subdomains, num_in_groups, num_out_groups)
+            new_shape += xs.shape[1:]
+            xs = np.reshape(xs, new_shape)
+
+            # Transpose the scattering matrix if requested by user
+            if row_column == 'outin':
+                xs = np.swapaxes(xs, 3, 4)
+
+            # Reverse data if user requested increasing energy groups since
+            # tally data is stored in order of increasing energies
+            if order_groups == 'increasing':
+                xs = xs[:, :, :, ::-1, ::-1, ...]
         else:
-            new_shape = (num_subdomains, num_in_groups, num_out_groups)
-        new_shape += xs.shape[1:]
-        xs = np.reshape(xs, new_shape)
+            if self.scatter_format == 'histogram':
+                new_shape = (num_subdomains, num_in_groups, num_out_groups,
+                             num_mu_bins)
+            else:
+                new_shape = (num_subdomains, num_in_groups, num_out_groups)
+            new_shape += xs.shape[1:]
+            xs = np.reshape(xs, new_shape)
 
-        # Transpose the scattering matrix if requested by user
-        if row_column == 'outin':
-            xs = np.swapaxes(xs, 1, 2)
+            # Transpose the scattering matrix if requested by user
+            if row_column == 'outin':
+                xs = np.swapaxes(xs, 1, 2)
 
-        # Reverse data if user requested increasing energy groups since
-        # tally data is stored in order of increasing energies
-        if order_groups == 'increasing':
-            xs = xs[:, ::-1, ::-1, ...]
+            # Reverse data if user requested increasing energy groups since
+            # tally data is stored in order of increasing energies
+            if order_groups == 'increasing':
+                xs = xs[:, ::-1, ::-1, ...]
 
         if squeeze:
-            # We want to squeeze out everything but the in_groups, out_groups,
-            # and, if needed, num_mu_bins dimension. These must not be squeezed
-            # so 1-group problems have the correct shape.
-            if self.scatter_format == 'histogram':
-                axes = (5, 4, 0)
-            else:
-                axes = (4, 3, 0)
-            # Squeeze will return a ValueError if the axis has a size greater
-            # than 1, so try each axis in axes one at a time, catching the
-            # ValueError as needed.
-            for axis in axes:
-                if xs.shape[axis] == 1:
-                    xs = np.squeeze(xs, axis=axis)
-
+            # We want to squeeze out everything but the angles, in_groups,
+            # out_groups, and, if needed, num_mu_bins dimension. These must
+            # not be squeezed so 1-group, 1-angle problems have the correct
+            # shape.
+            xs = self._squeeze_xs(xs)
         return xs
 
     def get_pandas_dataframe(self, groups='all', nuclides='all', moment='all',
@@ -4002,14 +4450,6 @@ class ScatterMatrixXS(MatrixMGXS):
                     'moment', moment, self.legendre_order, equality=True)
                 df = df[df['moment'] == 'P{}'.format(moment)]
 
-        elif self.scatter_format == 'histogram':
-            # Replace the mu low and mu high columns with a single mu bin
-            del df['mu high']
-            df.rename(columns={'mu low': 'mu bins'}, inplace=True)
-            bins = [i + 1 for i in range(self.histogram_bins)]
-            bins = np.tile(bins, int(df.shape[0] / len(bins)))
-            df['mu bins'] = bins
-
         return df
 
     def print_xs(self, subdomains='all', nuclides='all',
@@ -4041,7 +4481,7 @@ class ScatterMatrixXS(MatrixMGXS):
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
         elif self.domain_type == 'mesh':
-            xyz = [range(1, x+1) for x in self.domain.dimension]
+            xyz = [range(1, x + 1) for x in self.domain.dimension]
             subdomains = list(itertools.product(*xyz))
         else:
             subdomains = [self.domain.id]
@@ -4086,12 +4526,18 @@ class ScatterMatrixXS(MatrixMGXS):
             bounds = self.energy_groups.get_group_bounds(group)
             string += template.format('', group, bounds[0], bounds[1])
 
+        # Set polar and azimuthal bins if necessary
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            pol_bins = np.linspace(0., np.pi, num=self.num_polar + 1,
+                                   endpoint=True)
+            azi_bins = np.linspace(-np.pi, np.pi, num=self.num_azimuthal + 1,
+                                   endpoint=True)
+
         # Loop over all subdomains
         for subdomain in subdomains:
 
-            if self.domain_type == 'distribcell':
-                string += \
-                    '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain)
+            if self.domain_type == 'distribcell' or self.domain_type == 'mesh':
+                string += '{0: <16}=\t{1}\n'.format('\tSubdomain', subdomain)
 
             # Loop over all Nuclides
             for nuclide in nuclides:
@@ -4104,22 +4550,48 @@ class ScatterMatrixXS(MatrixMGXS):
                 string += '{0: <16}\n'.format(xs_header)
                 template = '{0: <12}Group {1} -> Group {2}:\t\t'
 
-                # Loop over incoming/outgoing energy groups ranges
-                for in_group in range(1, self.num_groups + 1):
-                    for out_group in range(1, self.num_groups + 1):
-                        string += template.format('', in_group, out_group)
-                        average = \
-                            self.get_xs([in_group], [out_group],
-                                        [subdomain], [nuclide], moment=moment,
-                                        xs_type=xs_type, value='mean')
-                        rel_err = \
-                            self.get_xs([in_group], [out_group],
-                                        [subdomain], [nuclide], moment=moment,
-                                        xs_type=xs_type, value='rel_err')
-                        average = average.flatten()[0]
-                        rel_err = rel_err.flatten()[0] * 100.
-                        string += '{:1.2e} +/- {:1.2e}%'.format(average,
-                                                                rel_err)
+                average_xs = self.get_xs(nuclides=[nuclide],
+                                         subdomains=[subdomain],
+                                         xs_type=xs_type, value='mean',
+                                         moment=moment)
+                rel_err_xs = self.get_xs(nuclides=[nuclide],
+                                         subdomains=[subdomain],
+                                         xs_type=xs_type, value='rel_err',
+                                         moment=moment)
+                rel_err_xs = rel_err_xs * 100.
+
+                if self.num_polar > 1 or self.num_azimuthal > 1:
+                    # Loop over polar, azi, and in/out energy group ranges
+                    for pol in range(len(pol_bins) - 1):
+                        pol_low, pol_high = pol_bins[pol: pol + 2]
+                        for azi in range(len(azi_bins) - 1):
+                            azi_low, azi_high = azi_bins[azi: azi + 2]
+                            string += '\t\tPolar Angle: [{0:5f} - {1:5f}]'.format(
+                                pol_low, pol_high) + \
+                                '\tAzimuthal Angle: [{0:5f} - {1:5f}]'.format(
+                                azi_low, azi_high) + '\n'
+                            for in_group in range(1, self.num_groups + 1):
+                                for out_group in range(1, self.num_groups + 1):
+                                    string += '\t' + template.format('',
+                                                                     in_group,
+                                                                     out_group)
+                                    string += '{0:.2e} +/- {1:.2e}%'.format(
+                                        average_xs[pol, azi, in_group - 1,
+                                                   out_group - 1],
+                                        rel_err_xs[pol, azi, in_group - 1,
+                                                   out_group - 1])
+                                    string += '\n'
+                                string += '\n'
+                            string += '\n'
+                else:
+                    # Loop over incoming/outgoing energy groups ranges
+                    for in_group in range(1, self.num_groups + 1):
+                        for out_group in range(1, self.num_groups + 1):
+                            string += template.format('', in_group, out_group)
+                            string += '{0:.2e} +/- {1:.2e}%'.format(
+                                average_xs[in_group - 1, out_group - 1],
+                                rel_err_xs[in_group - 1, out_group - 1])
+                            string += '\n'
                         string += '\n'
                     string += '\n'
                 string += '\n'
@@ -4163,6 +4635,12 @@ class NuScatterMatrixXS(ScatterMatrixXS):
     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
     ----------
@@ -4182,6 +4660,10 @@ class NuScatterMatrixXS(ScatterMatrixXS):
         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
@@ -4231,9 +4713,11 @@ class NuScatterMatrixXS(ScatterMatrixXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(NuScatterMatrixXS, self).__init__(domain, domain_type,
-                                                groups, by_nuclide, name)
+                                                groups, by_nuclide, name,
+                                                num_polar, num_azimuthal)
         self._rxn_type = 'nu-scatter'
         self._hdf5_key = 'nu-scatter matrix'
 
@@ -4289,6 +4773,12 @@ class MultiplicityMatrixXS(MatrixMGXS):
     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
     ----------
@@ -4304,6 +4794,10 @@ class MultiplicityMatrixXS(MatrixMGXS):
         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
@@ -4353,9 +4847,11 @@ class MultiplicityMatrixXS(MatrixMGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(MultiplicityMatrixXS, self).__init__(domain, domain_type, groups,
-                                                   by_nuclide, name)
+                                                   by_nuclide, name, num_polar,
+                                                   num_azimuthal)
         self._rxn_type = 'multiplicity matrix'
         self._estimator = 'analog'
         self._valid_estimators = ['analog']
@@ -4371,8 +4867,9 @@ class MultiplicityMatrixXS(MatrixMGXS):
         group_edges = self.energy_groups.group_edges
         energy = openmc.EnergyFilter(group_edges)
         energyout = openmc.EnergyoutFilter(group_edges)
+        filters = [[energy, energyout], [energy, energyout]]
 
-        return [[energy, energyout], [energy, energyout]]
+        return self._add_angle_filters(filters)
 
     @property
     def rxn_rate_tally(self):
@@ -4438,6 +4935,12 @@ class NuFissionMatrixXS(MatrixMGXS):
     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
     ----------
@@ -4453,6 +4956,10 @@ class NuFissionMatrixXS(MatrixMGXS):
         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
@@ -4502,9 +5009,11 @@ class NuFissionMatrixXS(MatrixMGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(NuFissionMatrixXS, self).__init__(domain, domain_type,
-                                                groups, by_nuclide, name)
+                                                groups, by_nuclide, name,
+                                                num_polar, num_azimuthal)
         self._rxn_type = 'nu-fission'
         self._hdf5_key = 'nu-fission matrix'
         self._estimator = 'analog'
@@ -4555,6 +5064,12 @@ class Chi(MGXS):
     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
     ----------
@@ -4570,6 +5085,10 @@ class Chi(MGXS):
         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
@@ -4619,12 +5138,24 @@ class Chi(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
-        super(Chi, self).__init__(domain, domain_type, groups, by_nuclide, name)
+                 groups=None, 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'
         self._estimator = 'analog'
         self._valid_estimators = ['analog']
 
+    @property
+    def _dont_squeeze(self):
+        """Create a tuple of axes which should not be removed during the get_xs
+        process
+        """
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            return (0, 1, 3)
+        else:
+            return (1,)
+
     @property
     def scores(self):
         return ['nu-fission', 'nu-fission']
@@ -4635,7 +5166,9 @@ class Chi(MGXS):
         group_edges = self.energy_groups.group_edges
         energyout = openmc.EnergyoutFilter(group_edges)
         energyin = openmc.EnergyFilter([group_edges[0], group_edges[-1]])
-        return [[energyin], [energyout]]
+        filters = [[energyin], [energyout]]
+
+        return self._add_angle_filters(filters)
 
     @property
     def tally_keys(self):
@@ -4859,7 +5392,8 @@ class Chi(MGXS):
 
         # Construct a collection of the domain filter bins
         if not isinstance(subdomains, string_types):
-            cv.check_iterable_type('subdomains', subdomains, Integral, max_depth=3)
+            cv.check_iterable_type('subdomains', subdomains, Integral,
+                                   max_depth=3)
             filters.append(_DOMAIN_TO_FILTER[self.domain_type])
             subdomain_bins = []
             for subdomain in subdomains:
@@ -4929,24 +5463,31 @@ class Chi(MGXS):
         xs = np.squeeze(xs, axis=len(xs.shape) - 1)
         xs = np.nan_to_num(xs)
 
-        # Reshape tally data array with separate axes for domain and energy
         if groups == 'all':
             num_groups = self.num_groups
         else:
             num_groups = len(groups)
 
-        num_subdomains = int(xs.shape[0] / num_groups)
-        new_shape = (num_subdomains, num_groups) + xs.shape[1:]
+        # Reshape tally data array with separate axes for domain and energy
+        # Accomodate the polar and azimuthal bins if needed
+        num_subdomains = int(xs.shape[0] / (num_groups * self.num_polar *
+                                            self.num_azimuthal))
+        if self.num_polar > 1 or self.num_azimuthal > 1:
+            new_shape = (self.num_polar, self.num_azimuthal, num_subdomains,
+                         num_groups) + xs.shape[1:]
+        else:
+            new_shape = (num_subdomains, num_groups) + xs.shape[1:]
         xs = np.reshape(xs, new_shape)
 
         # Reverse data if user requested increasing energy groups since
         # tally data is stored in order of increasing energies
         if order_groups == 'increasing':
-            xs = xs[:, ::-1, :]
+            xs = xs[..., ::-1, :]
 
         if squeeze:
-            xs = np.squeeze(xs)
-            xs = np.atleast_1d(xs)
+            # We want to squeeze out everything but the polar, azimuthal,
+            # and energy group data.
+            xs = self._squeeze_xs(xs)
 
         return xs
 
@@ -5001,7 +5542,7 @@ class Chi(MGXS):
                 densities = self.get_nuclide_densities(nuclides)
             else:
                 densities = self.get_nuclide_densities('sum')
-            tile_factor = df.shape[0] / len(densities)
+            tile_factor = int(df.shape[0] / len(densities))
             df['mean'] *= np.tile(densities, tile_factor)
             df['std. dev.'] *= np.tile(densities, tile_factor)
 
@@ -5076,6 +5617,12 @@ class ChiPrompt(Chi):
     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
     ----------
@@ -5091,6 +5638,10 @@ class ChiPrompt(Chi):
         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
@@ -5140,8 +5691,11 @@ class ChiPrompt(Chi):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
-        super(ChiPrompt, self).__init__(domain, domain_type, groups, by_nuclide, name)
+                 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
@@ -5191,6 +5745,12 @@ class InverseVelocity(MGXS):
     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
     ----------
@@ -5206,6 +5766,10 @@ class InverseVelocity(MGXS):
         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
@@ -5256,9 +5820,11 @@ class InverseVelocity(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(InverseVelocity, self).__init__(domain, domain_type,
-                                              groups, by_nuclide, name)
+                                              groups, by_nuclide, name,
+                                              num_polar, num_azimuthal)
         self._rxn_type = 'inverse-velocity'
 
     def get_units(self, xs_type='macro'):
@@ -5326,6 +5892,12 @@ class PromptNuFissionXS(MGXS):
     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
     ----------
@@ -5341,6 +5913,10 @@ class PromptNuFissionXS(MGXS):
         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
@@ -5390,9 +5966,11 @@ class PromptNuFissionXS(MGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(PromptNuFissionXS, self).__init__(domain, domain_type, groups,
-                                                by_nuclide, name)
+                                                by_nuclide, name, num_polar,
+                                                num_azimuthal)
         self._rxn_type = 'prompt-nu-fission'
 
 
@@ -5440,6 +6018,12 @@ class PromptNuFissionMatrixXS(MatrixMGXS):
     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
     ----------
@@ -5455,6 +6039,10 @@ class PromptNuFissionMatrixXS(MatrixMGXS):
         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
@@ -5504,9 +6092,11 @@ class PromptNuFissionMatrixXS(MatrixMGXS):
     """
 
     def __init__(self, domain=None, domain_type=None,
-                 groups=None, by_nuclide=False, name=''):
+                 groups=None, by_nuclide=False, name='', num_polar=1,
+                 num_azimuthal=1):
         super(PromptNuFissionMatrixXS, self).__init__(domain, domain_type,
-                                                      groups, by_nuclide, name)
+                                                      groups, by_nuclide, name,
+                                                      num_polar, num_azimuthal)
         self._rxn_type = 'prompt-nu-fission'
         self._hdf5_key = 'prompt-nu-fission matrix'
         self._estimator = 'analog'
diff --git a/openmc/mgxs_library.py b/openmc/mgxs_library.py
index 61a102c147..4fb347b73c 100644
--- a/openmc/mgxs_library.py
+++ b/openmc/mgxs_library.py
@@ -937,12 +937,8 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._total[i] = total.get_xs(nuclides=nuclide, xs_type=xs_type,
-                                          subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._total[i] = total.get_xs(nuclides=nuclide, xs_type=xs_type,
+                                      subdomains=subdomain)
 
     def set_absorption_mgxs(self, absorption, temperature=294.,
                             nuclide='total', xs_type='macro', subdomain=None):
@@ -983,13 +979,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._absorption[i] = absorption.get_xs(nuclides=nuclide,
-                                                    xs_type=xs_type,
-                                                    subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._absorption[i] = absorption.get_xs(nuclides=nuclide,
+                                                xs_type=xs_type,
+                                                subdomains=subdomain)
 
     def set_fission_mgxs(self, fission, temperature=294., nuclide='total',
                          xs_type='macro', subdomain=None):
@@ -1030,13 +1022,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._fission[i] = fission.get_xs(nuclides=nuclide,
-                                              xs_type=xs_type,
-                                              subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._fission[i] = fission.get_xs(nuclides=nuclide,
+                                          xs_type=xs_type,
+                                          subdomains=subdomain)
 
     def set_nu_fission_mgxs(self, nu_fission, temperature=294.,
                             nuclide='total', xs_type='macro', subdomain=None):
@@ -1078,13 +1066,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._nu_fission[i] = nu_fission.get_xs(nuclides=nuclide,
-                                                    xs_type=xs_type,
-                                                    subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._nu_fission[i] = nu_fission.get_xs(nuclides=nuclide,
+                                                xs_type=xs_type,
+                                                subdomains=subdomain)
 
         if np.sum(self._nu_fission) > 0.0:
             self._fissionable = True
@@ -1134,14 +1118,8 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation is 'isotropic':
-            self._prompt_nu_fission[i] = prompt_nu_fission.get_xs\
-                                         (nuclides=nuclide,
-                                          xs_type=xs_type,
-                                          subdomains=subdomain)
-        elif self.representation is 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._prompt_nu_fission[i] = prompt_nu_fission.get_xs(
+            nuclides=nuclide, xs_type=xs_type, subdomains=subdomain)
 
         if np.sum(self._prompt_nu_fission) > 0.0:
             self._fissionable = True
@@ -1191,14 +1169,8 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation is 'isotropic':
-            self._delayed_nu_fission[i] = delayed_nu_fission.get_xs\
-                                         (nuclides=nuclide,
-                                          xs_type=xs_type,
-                                          subdomains=subdomain)
-        elif self.representation is 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._delayed_nu_fission[i] = delayed_nu_fission.get_xs(
+            nuclides=nuclide, xs_type=xs_type, subdomains=subdomain)
 
         if np.sum(self._delayed_nu_fission) > 0.0:
             self._fissionable = True
@@ -1244,13 +1216,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._kappa_fission[i] = k_fission.get_xs(nuclides=nuclide,
-                                                      xs_type=xs_type,
-                                                      subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._kappa_fission[i] = k_fission.get_xs(nuclides=nuclide,
+                                                  xs_type=xs_type,
+                                                  subdomains=subdomain)
 
     def set_chi_mgxs(self, chi, temperature=294., nuclide='total',
                      xs_type='macro', subdomain=None):
@@ -1288,15 +1256,11 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._chi[i] = chi.get_xs(nuclides=nuclide,
-                                      xs_type=xs_type, subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._chi[i] = chi.get_xs(nuclides=nuclide, xs_type=xs_type,
+                                  subdomains=subdomain)
 
-    def set_chi_prompt_mgxs(self, chi_prompt, temperature=294., nuclide='total',
-                            xs_type='macro', subdomain=None):
+    def set_chi_prompt_mgxs(self, chi_prompt, temperature=294.,
+                            nuclide='total', xs_type='macro', subdomain=None):
         """This method allows for an openmc.mgxs.ChiPrompt
         to be used to set chi-prompt for this XSdata object.
 
@@ -1333,13 +1297,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation is 'isotropic':
-            self._chi_prompt[i] = chi_prompt.get_xs(nuclides=nuclide,
-                                                    xs_type=xs_type,
-                                                    subdomains=subdomain)
-        elif self.representation is 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._chi_prompt[i] = chi_prompt.get_xs(nuclides=nuclide,
+                                                xs_type=xs_type,
+                                                subdomains=subdomain)
 
     def set_chi_delayed_mgxs(self, chi_delayed, temperature=294.,
                              nuclide='total', xs_type='macro', subdomain=None):
@@ -1381,13 +1341,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation is 'isotropic':
-            self._chi_delayed[i] = chi_delayed.get_xs(nuclides=nuclide,
-                                                      xs_type=xs_type,
-                                                      subdomains=subdomain)
-        elif self.representation is 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._chi_delayed[i] = chi_delayed.get_xs(nuclides=nuclide,
+                                                  xs_type=xs_type,
+                                                  subdomains=subdomain)
 
     def set_beta_mgxs(self, beta, temperature=294.,
                       nuclide='total', xs_type='macro', subdomain=None):
@@ -1426,13 +1382,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation is 'isotropic':
-            self._beta[i] = beta.get_xs(nuclides=nuclide,
-                                        xs_type=xs_type,
-                                        subdomains=subdomain)
-        elif self.representation is 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._beta[i] = beta.get_xs(nuclides=nuclide,
+                                    xs_type=xs_type,
+                                    subdomains=subdomain)
 
     def set_decay_rate_mgxs(self, decay_rate, temperature=294.,
                             nuclide='total', xs_type='macro', subdomain=None):
@@ -1472,13 +1424,9 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation is 'isotropic':
-            self._decay_rate[i] = decay_rate.get_xs(nuclides=nuclide,
-                                                    xs_type=xs_type,
-                                                    subdomains=subdomain)
-        elif self.representation is 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._decay_rate[i] = decay_rate.get_xs(nuclides=nuclide,
+                                                xs_type=xs_type,
+                                                subdomains=subdomain)
 
     def set_scatter_matrix_mgxs(self, scatter, temperature=294.,
                                 nuclide='total', xs_type='macro',
@@ -1543,22 +1491,24 @@ class XSdata(object):
                             [self.order])
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            if self.scatter_format == 'legendre':
-                # Get the scattering orders in the outermost dimension
-                self._scatter_matrix[i] = \
-                    np.zeros(self.xs_shapes["[G][G'][Order]"])
+        if self.scatter_format == 'legendre':
+            self._scatter_matrix[i] = \
+                np.zeros(self.xs_shapes["[G][G'][Order]"])
+            # Get the scattering orders in the outermost dimension
+            if self.representation == 'isotropic':
                 for moment in range(self.num_orders):
                     self._scatter_matrix[i][:, :, moment] = \
                         scatter.get_xs(nuclides=nuclide, xs_type=xs_type,
                                        moment=moment, subdomains=subdomain)
-            else:
-                self._scatter_matrix[i] = \
-                    scatter.get_xs(nuclides=nuclide, xs_type=xs_type,
-                                   subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+            elif self.representation == 'angle':
+                for moment in range(self.num_orders):
+                    self._scatter_matrix[i][:, :, :, :, moment] = \
+                        scatter.get_xs(nuclides=nuclide, xs_type=xs_type,
+                                       moment=moment, subdomains=subdomain)
+        else:
+            self._scatter_matrix[i] = \
+                scatter.get_xs(nuclides=nuclide, xs_type=xs_type,
+                               subdomains=subdomain)
 
     def set_multiplicity_matrix_mgxs(self, nuscatter, scatter=None,
                                      temperature=294., nuclide='total',
@@ -1623,24 +1573,21 @@ class XSdata(object):
             check_value('domain_type', scatter.domain_type,
                         openmc.mgxs.DOMAIN_TYPES)
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            nuscatt = nuscatter.get_xs(nuclides=nuclide,
-                                       xs_type=xs_type, moment=0,
-                                       subdomains=subdomain)
-            if isinstance(nuscatter, openmc.mgxs.MultiplicityMatrixXS):
-                self._multiplicity_matrix[i] = nuscatt
-            else:
-                scatt = scatter.get_xs(nuclides=nuclide,
-                                       xs_type=xs_type, moment=0,
-                                       subdomains=subdomain)
-                if scatter.scatter_format == 'histogram':
-                    scatt = np.sum(scatt, axis=0)
-                if nuscatter.scatter_format == 'histogram':
-                    nuscatt = np.sum(nuscatt, axis=0)
-                self._multiplicity_matrix[i] = np.divide(nuscatt, scatt)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        nuscatt = nuscatter.get_xs(nuclides=nuclide,
+                                   xs_type=xs_type, moment=0,
+                                   subdomains=subdomain)
+        if isinstance(nuscatter, openmc.mgxs.MultiplicityMatrixXS):
+            self._multiplicity_matrix[i] = nuscatt
+        else:
+            scatt = scatter.get_xs(nuclides=nuclide,
+                                   xs_type=xs_type, moment=0,
+                                   subdomains=subdomain)
+            if scatter.scatter_format == 'histogram':
+                scatt = np.sum(scatt, axis=0)
+            if nuscatter.scatter_format == 'histogram':
+                nuscatt = np.sum(nuscatt, axis=0)
+            self._multiplicity_matrix[i] = np.divide(nuscatt, scatt)
+
         self._multiplicity_matrix[i] = \
             np.nan_to_num(self._multiplicity_matrix[i])
 
@@ -1684,13 +1631,8 @@ class XSdata(object):
         check_value('temperature', temperature, self.temperatures)
 
         i = np.where(self.temperatures == temperature)[0][0]
-        if self.representation == 'isotropic':
-            self._inverse_velocity[i] = inverse_velocity.get_xs\
-                                        (nuclides=nuclide, xs_type=xs_type,
-                                         subdomains=subdomain)
-        elif self.representation == 'angle':
-            msg = 'Angular-Dependent MGXS have not yet been implemented'
-            raise ValueError(msg)
+        self._inverse_velocity[i] = inverse_velocity.get_xs(
+            nuclides=nuclide, xs_type=xs_type, subdomains=subdomain)
 
     def to_hdf5(self, file):
         """Write XSdata to an HDF5 file
diff --git a/src/input_xml.F90 b/src/input_xml.F90
index 8f25d6301f..a9fd3ad1e1 100644
--- a/src/input_xml.F90
+++ b/src/input_xml.F90
@@ -3228,7 +3228,7 @@ contains
                 end do
                 filt % bins(Nangle + 1) = PI
               else
-                call fatal_error("Number of bins for mu filter must be&
+                call fatal_error("Number of bins for polar filter must be&
                      & greater than 1 on tally " &
                      // trim(to_str(t % id)) // ".")
               end if
@@ -3262,7 +3262,7 @@ contains
                 end do
                 filt % bins(Nangle + 1) = PI
               else
-                call fatal_error("Number of bins for mu filter must be&
+                call fatal_error("Number of bins for azimuthal filter must be&
                      & greater than 1 on tally " &
                      // trim(to_str(t % id)) // ".")
               end if
diff --git a/src/mgxs_data.F90 b/src/mgxs_data.F90
index f823304c0f..3a0d7c4b49 100644
--- a/src/mgxs_data.F90
+++ b/src/mgxs_data.F90
@@ -183,7 +183,7 @@ contains
       end select
 
       ! Do not read materials which we do not actually use in the problem to
-      ! save space
+      ! reduce storage
       if (allocated(kTs(i_mat) % data)) then
         call macro_xs(i_mat) % obj % combine(kTs(i_mat), mat, nuclides_MG, &
              num_energy_groups, num_delayed_groups, max_order, &
diff --git a/src/mgxs_header.F90 b/src/mgxs_header.F90
index 0fe48f7c94..04443e6d5e 100644
--- a/src/mgxs_header.F90
+++ b/src/mgxs_header.F90
@@ -438,14 +438,14 @@ module mgxs_header
       real(8)                     :: dmu, mu, norm, chi_sum
       integer                     :: order, order_dim, gin, gout, l, imu, length
       type(VectorInt)             :: temps_to_read
-      integer                     :: t, dg
+      integer                     :: t, dg, order_data
       type(Jagged2D), allocatable :: input_scatt(:), scatt_coeffs(:)
       type(Jagged1D), allocatable :: temp_mult(:)
       integer, allocatable        :: gmin(:), gmax(:)
 
       ! Call generic data gathering routine (will populate the metadata)
       call mgxs_from_hdf5(this, xs_id, temperature, method, tolerance, &
-                          temps_to_read, order_dim)
+                          temps_to_read, order_data)
 
       ! Set the number of delayed groups
       this % num_delayed_groups = delayed_groups
@@ -1009,7 +1009,7 @@ module mgxs_header
           length = 0
 
           do gin = 1, energy_groups
-            length = length + order_dim * (gmax(gin) - gmin(gin) + 1)
+            length = length + order_data * (gmax(gin) - gmin(gin) + 1)
           end do
 
           ! Allocate flattened array
@@ -1022,8 +1022,10 @@ module mgxs_header
           ! Compare the number of orders given with the maximum order of the
           ! problem.  Strip off the supefluous orders if needed.
           if (this % scatter_format == ANGLE_LEGENDRE) then
-            order = min(order_dim - 1, max_order)
+            order = min(order_data - 1, max_order)
             order_dim = order + 1
+          else
+            order_dim = order_data
           end if
 
           ! Convert temp_arr to a jagged array ((gin) % data(l, gout)) for
@@ -1038,6 +1040,8 @@ module mgxs_header
                 input_scatt(gin) % data(l, gout) = temp_arr(index)
                 index = index + 1
               end do
+              ! Adjust index for the orders we didnt take
+              index = index + (order_data - order_dim)
             end do
           end do
 
@@ -1108,7 +1112,7 @@ module mgxs_header
           deallocate(input_scatt)
 
           ! Now get the multiplication matrix
-          if (object_exists(scatt_grp, 'multiplicity matrix')) then
+          if (object_exists(scatt_grp, 'multiplicity_matrix')) then
 
             ! Now use this information to find the length of a container array
             ! to hold the flattened data
@@ -1120,7 +1124,7 @@ module mgxs_header
 
             ! Allocate flattened array
             allocate(temp_arr(length))
-            call read_dataset(temp_arr, scatt_grp, "multiplicity matrix")
+            call read_dataset(temp_arr, scatt_grp, "multiplicity_matrix")
 
             ! Convert temp_arr to a jagged array ((gin) % data(gout)) for
             ! passing to ScattData
@@ -1215,14 +1219,14 @@ module mgxs_header
       real(8)                     :: dmu, mu, norm, chi_sum
       integer                     :: order, order_dim, gin, gout, l, imu, dg
       type(VectorInt)             :: temps_to_read
-      integer                     :: t, length, ipol, iazi
+      integer                     :: t, length, ipol, iazi, order_data
       type(Jagged2D), allocatable :: input_scatt(:, :, :), scatt_coeffs(:, :, :)
       type(Jagged1D), allocatable :: temp_mult(:, :, :)
       integer, allocatable        :: gmin(:, :, :), gmax(:, :, :)
 
       ! Call generic data gathering routine (will populate the metadata)
       call mgxs_from_hdf5(this, xs_id, temperature, method, tolerance, &
-                          temps_to_read, order_dim)
+                          temps_to_read, order_data)
 
       ! Set the number of delayed groups
       this % num_delayed_groups = delayed_groups
@@ -1975,7 +1979,7 @@ module mgxs_header
           do ipol = 1, this % n_pol
             do iazi = 1, this % n_azi
               do gin = 1, energy_groups
-                length = length + order_dim * (gmax(gin, iazi, ipol) - &
+                length = length + order_data * (gmax(gin, iazi, ipol) - &
                      gmin(gin, iazi, ipol) + 1)
               end do
             end do
@@ -1991,8 +1995,10 @@ module mgxs_header
           ! Compare the number of orders given with the maximum order of the
           ! problem.  Strip off the superfluous orders if needed.
           if (this % scatter_format == ANGLE_LEGENDRE) then
-            order = min(order_dim - 1, max_order)
+            order = min(order_data - 1, max_order)
             order_dim = order + 1
+          else
+            order_dim = order_data
           end if
 
           ! Convert temp_1d to a jagged array ((gin) % data(l, gout)) for
@@ -2011,6 +2017,8 @@ module mgxs_header
                          temp_1d(index)
                     index = index + 1
                   end do ! gout
+                  ! Adjust index for the orders we didnt take
+                  index = index + (order_data - order_dim)
                 end do ! order
               end do ! gin
             end do ! iazi
@@ -2092,7 +2100,7 @@ module mgxs_header
           deallocate(input_scatt)
 
           ! Now get the multiplication matrix
-          if (object_exists(scatt_grp, 'multiplicity matrix')) then
+          if (object_exists(scatt_grp, 'multiplicity_matrix')) then
 
             ! Now use this information to find the length of a container array
             ! to hold the flattened data
@@ -2108,7 +2116,7 @@ module mgxs_header
 
             ! Allocate flattened array
             allocate(temp_1d(length))
-            call read_dataset(temp_1d, scatt_grp, "multiplicity matrix")
+            call read_dataset(temp_1d, scatt_grp, "multiplicity_matrix")
 
             ! Convert temp_1d to a jagged array ((gin) % data(gout)) for passing
             ! to ScattData
@@ -2959,9 +2967,9 @@ module mgxs_header
 
                 ! Now create our jagged data from the dense data
                 call jagged_from_dense_2D(scatt_coeffs(:, :, :, iazi, ipol), &
-                                          jagged_scatt)
+                                          jagged_scatt, gmin, gmax)
                 call jagged_from_dense_1D(temp_mult(:, :, iazi, ipol), &
-                                          jagged_mult, gmin, gmax)
+                                          jagged_mult)
 
                 ! Initialize the ScattData Object
                 call this % xs(t) % scatter(iazi, ipol) % obj % init(gmin, &
diff --git a/src/scattdata_header.F90 b/src/scattdata_header.F90
index 684be88b83..3d9df0bbf0 100644
--- a/src/scattdata_header.F90
+++ b/src/scattdata_header.F90
@@ -266,12 +266,12 @@ contains
       allocate(matrix(groups))
       do gin = 1, groups
         allocate(matrix(gin) % data(order, gmin(gin):gmax(gin)))
-        matrix(gin) % data = coeffs(gin) % data
+        matrix(gin) % data(:, :) = coeffs(gin) % data(:, :)
       end do
 
       ! Get scattxs value
       allocate(this % scattxs(groups))
-      ! Get this by summing the un-normalized P0 coefficient in matrix
+      ! Get this by summing the un-normalized angular distribution in matrix
       ! over all outgoing groups
       do gin = 1, groups
         this % scattxs(gin) = sum(matrix(gin) % data(:, :))
@@ -317,7 +317,7 @@ contains
                  this % dist(gin) % data(imu - 1, gout)
           end do
 
-          ! Now make sure integral norms to zero
+          ! Normalize the integral to unity
           norm = this % dist(gin) % data(order, gout)
           if (norm > ZERO) then
             this % fmu(gin) % data(:, gout) = &
@@ -578,7 +578,7 @@ contains
       imu = 1
     else
       imu = binary_search(this % dist(gin) % data(:, gout), &
-                          size(this % dist(gin) % data(:, gout)), xi)
+                          size(this % dist(gin) % data(:, gout)), xi) + 1
     end if
 
     ! Randomly select a mu in this bin.
diff --git a/tests/test_mg_basic/results_true.dat b/tests/test_mg_basic/results_true.dat
index 2192c3746d..ddb57d00b6 100644
--- a/tests/test_mg_basic/results_true.dat
+++ b/tests/test_mg_basic/results_true.dat
@@ -1,2 +1,2 @@
 k-combined:
-1.086852E+00 2.677280E-02
+1.073147E+00 1.602384E-02
diff --git a/tests/test_mg_max_order/results_true.dat b/tests/test_mg_max_order/results_true.dat
index 2189587d4f..adfcd44a85 100644
--- a/tests/test_mg_max_order/results_true.dat
+++ b/tests/test_mg_max_order/results_true.dat
@@ -1,2 +1,2 @@
 k-combined:
-1.077247E+00 2.412834E-02
+1.074551E+00 1.871525E-02
diff --git a/tests/test_mg_nuclide/results_true.dat b/tests/test_mg_nuclide/results_true.dat
index 2192c3746d..ddb57d00b6 100644
--- a/tests/test_mg_nuclide/results_true.dat
+++ b/tests/test_mg_nuclide/results_true.dat
@@ -1,2 +1,2 @@
 k-combined:
-1.086852E+00 2.677280E-02
+1.073147E+00 1.602384E-02
diff --git a/tests/test_mg_tallies/results_true.dat b/tests/test_mg_tallies/results_true.dat
index 8ab726bbb8..af328ff8aa 100644
--- a/tests/test_mg_tallies/results_true.dat
+++ b/tests/test_mg_tallies/results_true.dat
@@ -1 +1 @@
-864328b2c4f3c4bfa9c80c756acbedac6fbdf3d502c03c2f2a3e7461b774729c47a55341c9515eff246e1bd445485f0d0a05a5712663ee499046afdbe0c77aef
\ No newline at end of file
+1817642a0d20d8ef437c7970cae2e77c265a566ef9843ac1b7cd33f76af09365dc8bd5c045f4297360b38d509864bd629b06dfc4d043b4b1ae7d8a5e2e93fae3
\ No newline at end of file