diff --git a/docs/source/pythonapi/examples/mgxs-part-i.ipynb b/docs/source/pythonapi/examples/mgxs-part-i.ipynb
index 897af8e3ff..94deeec7b4 100644
--- a/docs/source/pythonapi/examples/mgxs-part-i.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-i.ipynb
@@ -146,6 +146,8 @@
     "\n",
     "import openmc\n",
     "import openmc.mgxs as mgxs\n",
+    "from openmc.source import Source\n",
+    "from openmc.stats import Box\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -341,7 +343,8 @@
     "settings_file.particles = particles\n",
     "settings_file.output = {'tallies': True, 'summary': True}\n",
     "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "settings_file.set_source_space('fission', bounds)\n",
+    "settings_file.source = Source(space=Box(\n",
+    "    bounds[:3], bounds[3:], only_fissionable=True))\n",
     "\n",
     "# Export to \"settings.xml\"\n",
     "settings_file.export_to_xml()"
@@ -420,24 +423,22 @@
      "data": {
       "text/plain": [
        "OrderedDict([('flux', Tally\n",
-       "\tID             =\t10000\n",
-       "\tName           =\t\n",
-       "\tFilters        =\t\n",
-       "                \t\tcell\t[1]\n",
-       "                \t\tenergy\t[  0.00000000e+00   6.25000000e-07   2.00000000e+01]\n",
-       "\tNuclides       =\ttotal \n",
-       "\tScores         =\t['flux']\n",
-       "\tEstimator      =\ttracklength\n",
-       "), ('absorption', Tally\n",
-       "\tID             =\t10001\n",
-       "\tName           =\t\n",
-       "\tFilters        =\t\n",
-       "                \t\tcell\t[1]\n",
-       "                \t\tenergy\t[  0.00000000e+00   6.25000000e-07   2.00000000e+01]\n",
-       "\tNuclides       =\ttotal \n",
-       "\tScores         =\t['absorption']\n",
-       "\tEstimator      =\ttracklength\n",
-       ")])"
+       "              \tID             =\t10000\n",
+       "              \tName           =\t\n",
+       "              \tFilters        =\t\n",
+       "                              \t\tcell\t[1]\n",
+       "                              \t\tenergy\t[  0.00000000e+00   6.25000000e-07   2.00000000e+01]\n",
+       "              \tNuclides       =\ttotal \n",
+       "              \tScores         =\t['flux']\n",
+       "              \tEstimator      =\ttracklength), ('absorption', Tally\n",
+       "              \tID             =\t10001\n",
+       "              \tName           =\t\n",
+       "              \tFilters        =\t\n",
+       "                              \t\tcell\t[1]\n",
+       "                              \t\tenergy\t[  0.00000000e+00   6.25000000e-07   2.00000000e+01]\n",
+       "              \tNuclides       =\ttotal \n",
+       "              \tScores         =\t['absorption']\n",
+       "              \tEstimator      =\ttracklength)])"
       ]
      },
      "execution_count": 13,
@@ -516,10 +517,9 @@
       "\n",
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
-      "      Version:        0.7.0\n",
-      "      Git SHA1:       c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
-      "      Date/Time:      2015-12-02 09:11:05\n",
-      "      MPI Processes:  1\n",
+      "      Version:        0.7.1\n",
+      "      Git SHA1:       ea9fb637f63f9374c7436456141afa850b84acf9\n",
+      "      Date/Time:      2016-01-14 07:16:05\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -604,20 +604,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.1700E-01 seconds\n",
-      "   Reading cross sections          =  8.9000E-02 seconds\n",
-      " Total time in simulation          =  1.4728E+01 seconds\n",
-      "   Time in transport only          =  1.4712E+01 seconds\n",
-      "   Time in inactive batches        =  1.7890E+00 seconds\n",
-      "   Time in active batches          =  1.2939E+01 seconds\n",
-      "   Time synchronizing fission bank =  5.0000E-03 seconds\n",
-      "     Sampling source sites         =  3.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  1.0000E-03 seconds\n",
+      " Total time for initialization     =  1.1720E+00 seconds\n",
+      "   Reading cross sections          =  9.0300E-01 seconds\n",
+      " Total time in simulation          =  1.7319E+01 seconds\n",
+      "   Time in transport only          =  1.7310E+01 seconds\n",
+      "   Time in inactive batches        =  1.9120E+00 seconds\n",
+      "   Time in active batches          =  1.5407E+01 seconds\n",
+      "   Time synchronizing fission bank =  2.0000E-03 seconds\n",
+      "     Sampling source sites         =  2.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  0.0000E+00 seconds\n",
+      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
       " Total time for finalization       =  1.0000E-03 seconds\n",
-      " Total time elapsed                =  1.5155E+01 seconds\n",
-      " Calculation Rate (inactive)       =  13974.3 neutrons/second\n",
-      " Calculation Rate (active)         =  7728.57 neutrons/second\n",
+      " Total time elapsed                =  1.8507E+01 seconds\n",
+      " Calculation Rate (inactive)       =  13075.3 neutrons/second\n",
+      " Calculation Rate (active)         =  6490.56 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -779,7 +779,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -890,7 +890,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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@@ -966,7 +966,7 @@
     {
      "data": {
       "text/html": [
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+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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@@ -1035,7 +1035,7 @@
     {
      "data": {
       "text/html": [
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+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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@@ -1111,7 +1111,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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@@ -1187,7 +1187,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index 6194b154ac..b41b322410 100644
--- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
@@ -34,14 +34,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning:  This call to matplotlib.use() has no effect\n",
+      "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:1318: 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",
       "\n",
       "  warnings.warn(_use_error_msg)\n",
-      "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.rxname is not yet QA compliant.\n",
-      "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:9: QAWarning: pyne.ace is not yet QA compliant.\n"
+      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:11: QAWarning: pyne.rxname is not yet QA compliant.\n",
+      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:11: QAWarning: pyne.ace is not yet QA compliant.\n"
      ]
     }
    ],
@@ -52,6 +52,8 @@
     "\n",
     "import openmc\n",
     "import openmc.mgxs as mgxs\n",
+    "from openmc.source import Source\n",
+    "from openmc.stats import Box\n",
     "import openmoc\n",
     "from openmoc.compatible import get_openmoc_geometry\n",
     "import pyne.ace\n",
@@ -286,7 +288,8 @@
     "settings_file.particles = particles\n",
     "settings_file.output = {'tallies': True, 'summary': True}\n",
     "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "settings_file.set_source_space('fission', bounds)\n",
+    "settings_file.source = Source(space=Box(\n",
+    "        bounds[:3], bounds[3:], only_fissionable=True))\n",
     "\n",
     "# Activate tally precision triggers\n",
     "settings_file.trigger_active = True\n",
@@ -446,10 +449,9 @@
       "\n",
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
-      "      Version:        0.7.0\n",
-      "      Git SHA1:       c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
-      "      Date/Time:      2015-12-02 09:13:42\n",
-      "      MPI Processes:  3\n",
+      "      Version:        0.7.1\n",
+      "      Git SHA1:       ea9fb637f63f9374c7436456141afa850b84acf9\n",
+      "      Date/Time:      2016-01-14 08:03:40\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -568,20 +570,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  7.5700E-01 seconds\n",
-      "   Reading cross sections          =  1.5800E-01 seconds\n",
-      " Total time in simulation          =  1.4921E+02 seconds\n",
-      "   Time in transport only          =  1.4336E+02 seconds\n",
-      "   Time in inactive batches        =  8.6210E+00 seconds\n",
-      "   Time in active batches          =  1.4059E+02 seconds\n",
-      "   Time synchronizing fission bank =  5.6060E+00 seconds\n",
-      "     Sampling source sites         =  1.4000E-02 seconds\n",
-      "     SEND/RECV source sites        =  4.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  6.0000E-03 seconds\n",
-      " Total time for finalization       =  1.3000E-02 seconds\n",
-      " Total time elapsed                =  1.5002E+02 seconds\n",
-      " Calculation Rate (inactive)       =  11599.6 neutrons/second\n",
-      " Calculation Rate (active)         =  2845.11 neutrons/second\n",
+      " Total time for initialization     =  3.8300E-01 seconds\n",
+      "   Reading cross sections          =  1.0700E-01 seconds\n",
+      " Total time in simulation          =  2.5629E+02 seconds\n",
+      "   Time in transport only          =  2.5623E+02 seconds\n",
+      "   Time in inactive batches        =  1.5743E+01 seconds\n",
+      "   Time in active batches          =  2.4055E+02 seconds\n",
+      "   Time synchronizing fission bank =  3.4000E-02 seconds\n",
+      "     Sampling source sites         =  2.1000E-02 seconds\n",
+      "     SEND/RECV source sites        =  1.3000E-02 seconds\n",
+      "   Time accumulating tallies       =  1.0000E-03 seconds\n",
+      " Total time for finalization       =  7.0000E-03 seconds\n",
+      " Total time elapsed                =  2.5674E+02 seconds\n",
+      " Calculation Rate (inactive)       =  6352.03 neutrons/second\n",
+      " Calculation Rate (active)         =  1662.87 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -607,7 +609,7 @@
    "source": [
     "# Run OpenMC\n",
     "executor = openmc.Executor()\n",
-    "executor.run_simulation(output=True, mpi_procs=3)"
+    "executor.run_simulation(output=True)"
    ]
   },
   {
@@ -801,10 +803,20 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  return c.reshape(shape_out)\n",
+      "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: 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": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1012,10 +1024,20 @@
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  return c.reshape(shape_out)\n",
+      "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: 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": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1189,13 +1211,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[  NORMAL ]  Importing ray tracing data from file...\n",
+      "[  NORMAL ]  Ray tracing for track segmentation...\n",
+      "[  NORMAL ]  Dumping tracks to file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.574633\tres = 1.959E-316\n",
+      "[  NORMAL ]  Iteration 0:\tk_eff = 0.574633\tres = 0.000E+00\n",
       "[  NORMAL ]  Iteration 1:\tk_eff = 0.679931\tres = 4.254E-01\n",
       "[  NORMAL ]  Iteration 2:\tk_eff = 0.660910\tres = 1.832E-01\n",
-      "[  NORMAL ]  Iteration 3:\tk_eff = 0.658975\tres = 2.797E-02\n",
-      "[  NORMAL ]  Iteration 4:\tk_eff = 0.642976\tres = 2.928E-03\n",
+      "[  NORMAL ]  Iteration 3:\tk_eff = 0.658975\tres = 2.798E-02\n",
+      "[  NORMAL ]  Iteration 4:\tk_eff = 0.642976\tres = 2.927E-03\n",
       "[  NORMAL ]  Iteration 5:\tk_eff = 0.625710\tres = 2.428E-02\n",
       "[  NORMAL ]  Iteration 6:\tk_eff = 0.606520\tres = 2.685E-02\n",
       "[  NORMAL ]  Iteration 7:\tk_eff = 0.587277\tres = 3.067E-02\n",
@@ -1206,20 +1229,20 @@
       "[  NORMAL ]  Iteration 12:\tk_eff = 0.510221\tres = 2.575E-02\n",
       "[  NORMAL ]  Iteration 13:\tk_eff = 0.500691\tres = 2.241E-02\n",
       "[  NORMAL ]  Iteration 14:\tk_eff = 0.493392\tres = 1.868E-02\n",
-      "[  NORMAL ]  Iteration 15:\tk_eff = 0.488317\tres = 1.458E-02\n",
+      "[  NORMAL ]  Iteration 15:\tk_eff = 0.488318\tres = 1.458E-02\n",
       "[  NORMAL ]  Iteration 16:\tk_eff = 0.485438\tres = 1.028E-02\n",
       "[  NORMAL ]  Iteration 17:\tk_eff = 0.484705\tres = 5.896E-03\n",
-      "[  NORMAL ]  Iteration 18:\tk_eff = 0.486045\tres = 1.510E-03\n",
+      "[  NORMAL ]  Iteration 18:\tk_eff = 0.486046\tres = 1.510E-03\n",
       "[  NORMAL ]  Iteration 19:\tk_eff = 0.489362\tres = 2.766E-03\n",
       "[  NORMAL ]  Iteration 20:\tk_eff = 0.494546\tres = 6.824E-03\n",
       "[  NORMAL ]  Iteration 21:\tk_eff = 0.501481\tres = 1.059E-02\n",
-      "[  NORMAL ]  Iteration 22:\tk_eff = 0.510041\tres = 1.402E-02\n",
-      "[  NORMAL ]  Iteration 23:\tk_eff = 0.520094\tres = 1.707E-02\n",
-      "[  NORMAL ]  Iteration 24:\tk_eff = 0.531507\tres = 1.971E-02\n",
+      "[  NORMAL ]  Iteration 22:\tk_eff = 0.510042\tres = 1.402E-02\n",
+      "[  NORMAL ]  Iteration 23:\tk_eff = 0.520095\tres = 1.707E-02\n",
+      "[  NORMAL ]  Iteration 24:\tk_eff = 0.531508\tres = 1.971E-02\n",
       "[  NORMAL ]  Iteration 25:\tk_eff = 0.544144\tres = 2.194E-02\n",
       "[  NORMAL ]  Iteration 26:\tk_eff = 0.557872\tres = 2.378E-02\n",
       "[  NORMAL ]  Iteration 27:\tk_eff = 0.572557\tres = 2.523E-02\n",
-      "[  NORMAL ]  Iteration 28:\tk_eff = 0.588072\tres = 2.632E-02\n",
+      "[  NORMAL ]  Iteration 28:\tk_eff = 0.588073\tres = 2.632E-02\n",
       "[  NORMAL ]  Iteration 29:\tk_eff = 0.604293\tres = 2.710E-02\n",
       "[  NORMAL ]  Iteration 30:\tk_eff = 0.621101\tres = 2.758E-02\n",
       "[  NORMAL ]  Iteration 31:\tk_eff = 0.638382\tres = 2.781E-02\n",
@@ -1231,128 +1254,129 @@
       "[  NORMAL ]  Iteration 37:\tk_eff = 0.746558\tres = 2.562E-02\n",
       "[  NORMAL ]  Iteration 38:\tk_eff = 0.764569\tres = 2.490E-02\n",
       "[  NORMAL ]  Iteration 39:\tk_eff = 0.782396\tres = 2.412E-02\n",
-      "[  NORMAL ]  Iteration 40:\tk_eff = 0.799989\tres = 2.332E-02\n",
+      "[  NORMAL ]  Iteration 40:\tk_eff = 0.799990\tres = 2.332E-02\n",
       "[  NORMAL ]  Iteration 41:\tk_eff = 0.817301\tres = 2.249E-02\n",
       "[  NORMAL ]  Iteration 42:\tk_eff = 0.834292\tres = 2.164E-02\n",
-      "[  NORMAL ]  Iteration 43:\tk_eff = 0.850927\tres = 2.079E-02\n",
-      "[  NORMAL ]  Iteration 44:\tk_eff = 0.867177\tres = 1.994E-02\n",
-      "[  NORMAL ]  Iteration 45:\tk_eff = 0.883017\tres = 1.910E-02\n",
+      "[  NORMAL ]  Iteration 43:\tk_eff = 0.850928\tres = 2.079E-02\n",
+      "[  NORMAL ]  Iteration 44:\tk_eff = 0.867178\tres = 1.994E-02\n",
+      "[  NORMAL ]  Iteration 45:\tk_eff = 0.883018\tres = 1.910E-02\n",
       "[  NORMAL ]  Iteration 46:\tk_eff = 0.898427\tres = 1.827E-02\n",
-      "[  NORMAL ]  Iteration 47:\tk_eff = 0.913389\tres = 1.745E-02\n",
-      "[  NORMAL ]  Iteration 48:\tk_eff = 0.927891\tres = 1.665E-02\n",
-      "[  NORMAL ]  Iteration 49:\tk_eff = 0.941925\tres = 1.588E-02\n",
-      "[  NORMAL ]  Iteration 50:\tk_eff = 0.955483\tres = 1.512E-02\n",
-      "[  NORMAL ]  Iteration 51:\tk_eff = 0.968562\tres = 1.439E-02\n",
-      "[  NORMAL ]  Iteration 52:\tk_eff = 0.981161\tres = 1.369E-02\n",
-      "[  NORMAL ]  Iteration 53:\tk_eff = 0.993282\tres = 1.301E-02\n",
-      "[  NORMAL ]  Iteration 54:\tk_eff = 1.004928\tres = 1.235E-02\n",
-      "[  NORMAL ]  Iteration 55:\tk_eff = 1.016104\tres = 1.172E-02\n",
-      "[  NORMAL ]  Iteration 56:\tk_eff = 1.026816\tres = 1.112E-02\n",
-      "[  NORMAL ]  Iteration 57:\tk_eff = 1.037073\tres = 1.054E-02\n",
-      "[  NORMAL ]  Iteration 58:\tk_eff = 1.046883\tres = 9.989E-03\n",
-      "[  NORMAL ]  Iteration 59:\tk_eff = 1.056257\tres = 9.460E-03\n",
-      "[  NORMAL ]  Iteration 60:\tk_eff = 1.065205\tres = 8.954E-03\n",
-      "[  NORMAL ]  Iteration 61:\tk_eff = 1.073739\tres = 8.472E-03\n",
-      "[  NORMAL ]  Iteration 62:\tk_eff = 1.081871\tres = 8.012E-03\n",
-      "[  NORMAL ]  Iteration 63:\tk_eff = 1.089613\tres = 7.573E-03\n",
-      "[  NORMAL ]  Iteration 64:\tk_eff = 1.096979\tres = 7.156E-03\n",
-      "[  NORMAL ]  Iteration 65:\tk_eff = 1.103980\tres = 6.760E-03\n",
+      "[  NORMAL ]  Iteration 47:\tk_eff = 0.913390\tres = 1.745E-02\n",
+      "[  NORMAL ]  Iteration 48:\tk_eff = 0.927892\tres = 1.665E-02\n",
+      "[  NORMAL ]  Iteration 49:\tk_eff = 0.941926\tres = 1.588E-02\n",
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      ]
     }
    ],
@@ -1386,8 +1410,8 @@
      "output_type": "stream",
      "text": [
       "openmc keff = 1.223729\n",
-      "openmoc keff = 1.219868\n",
-      "bias [pcm]: -386.1\n"
+      "openmoc keff = 1.219878\n",
+      "bias [pcm]: -385.1\n"
      ]
     }
    ],
@@ -1462,237 +1486,237 @@
      "text": [
       "[  NORMAL ]  Importing ray tracing data from file...\n",
       "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:\tk_eff = 0.495594\tres = 1.959E-316\n",
-      "[  NORMAL ]  Iteration 1:\tk_eff = 0.557312\tres = 5.044E-01\n",
+      "[  NORMAL ]  Iteration 0:\tk_eff = 0.495594\tres = 0.000E+00\n",
+      "[  NORMAL ]  Iteration 1:\tk_eff = 0.557313\tres = 5.044E-01\n",
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-      "[  NORMAL ]  Iteration 3:\tk_eff = 0.509016\tres = 7.033E-02\n",
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+      "[  NORMAL ]  Iteration 3:\tk_eff = 0.509017\tres = 7.033E-02\n",
+      "[  NORMAL ]  Iteration 4:\tk_eff = 0.496280\tres = 1.756E-02\n",
+      "[  NORMAL ]  Iteration 5:\tk_eff = 0.488358\tres = 2.502E-02\n",
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-      "[  NORMAL ]  Iteration 12:\tk_eff = 0.492575\tres = 9.091E-03\n",
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      ]
     }
    ],
@@ -1809,9 +1833,9 @@
     },
     {
      "data": {
-      "image/png": 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NiQo+A2zbVhpS7/HHs7juuuDB588+s3PyyQURB6bT7T6mu1ai9ZoTfG5s5bMg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\nnYz04Ol8fel6baJlPT25tggrTElUAlN2J1IrGZq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2tiSFhl1NDedRMiYdWmwpqOpiVR0EbA5soaoD3emzjWmTeC5dLWMdh867iXff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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7e2c03a610>"
+       "<matplotlib.figure.Figure at 0x7fb087647550>"
       ]
      },
      "metadata": {},
@@ -1857,7 +1881,18 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  return c.reshape(shape_out)\n",
+      "/usr/local/lib/python2.7/dist-packages/numpy/lib/shape_base.py:872: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
+      "  return c.reshape(shape_out)\n"
+     ]
+    }
+   ],
    "source": [
     "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n",
     "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n",
@@ -1892,9 +1927,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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b4CNioFHyGpI+PVjhEbEAcDqwOukqhf0k3TfPtTQrUKex7bi2qhioBX/0AO81Bniv1eeA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\nq6n/BzrS9bjl9QXqAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f7e2c0e0810>"
+       "<matplotlib.figure.Figure at 0x7fb08763fe90>"
       ]
      },
      "metadata": {},
@@ -1939,7 +1974,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
index 9302036600..119fd0b564 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": [
-      "/usr/lib/pymodules/python2.7/matplotlib/__init__.py:1173: UserWarning:  This call to matplotlib.use() has no effect\n",
+      "/usr/local/lib/python2.7/dist-packages/matplotlib/__init__.py:1318: 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",
@@ -52,6 +52,8 @@
     "import openmc.mgxs\n",
     "from openmc.statepoint import StatePoint\n",
     "from openmc.summary import Summary\n",
+    "from openmc.source import Source\n",
+    "from openmc.stats import Box\n",
     "\n",
     "import openmoc\n",
     "import openmoc.process\n",
@@ -392,7 +394,8 @@
     "settings_file.particles = particles\n",
     "settings_file.output = {'tallies': False, 'summary': True}\n",
     "source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
-    "settings_file.set_source_space('fission', source_bounds)\n",
+    "settings_file.source = Source(Box(\n",
+    "        source_bounds[:3], source_bounds[3:], only_fissionable=True))\n",
     "\n",
     "# Export to \"settings.xml\"\n",
     "settings_file.export_to_xml()"
@@ -467,7 +470,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -733,10 +736,9 @@
       "\n",
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
-      "      Version:        0.7.0\n",
-      "      Git SHA1:       c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
-      "      Date/Time:      2015-11-30 21:20:07\n",
-      "      MPI Processes:  1\n",
+      "      Version:        0.7.1\n",
+      "      Git SHA1:       ea9fb637f63f9374c7436456141afa850b84acf9\n",
+      "      Date/Time:      2016-01-14 08:12:09\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -822,20 +824,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  4.2800E-01 seconds\n",
-      "   Reading cross sections          =  9.1000E-02 seconds\n",
-      " Total time in simulation          =  4.1240E+01 seconds\n",
-      "   Time in transport only          =  4.1215E+01 seconds\n",
-      "   Time in inactive batches        =  4.0230E+00 seconds\n",
-      "   Time in active batches          =  3.7217E+01 seconds\n",
-      "   Time synchronizing fission bank =  8.0000E-03 seconds\n",
-      "     Sampling source sites         =  6.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  2.0000E-03 seconds\n",
+      " Total time for initialization     =  4.1800E-01 seconds\n",
+      "   Reading cross sections          =  1.4300E-01 seconds\n",
+      " Total time in simulation          =  4.1206E+01 seconds\n",
+      "   Time in transport only          =  4.1193E+01 seconds\n",
+      "   Time in inactive batches        =  4.1760E+00 seconds\n",
+      "   Time in active batches          =  3.7030E+01 seconds\n",
+      "   Time synchronizing fission bank =  3.0000E-03 seconds\n",
+      "     Sampling source sites         =  2.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
       " Total time for finalization       =  0.0000E+00 seconds\n",
-      " Total time elapsed                =  4.1683E+01 seconds\n",
-      " Calculation Rate (inactive)       =  6214.27 neutrons/second\n",
-      " Calculation Rate (active)         =  2686.94 neutrons/second\n",
+      " Total time elapsed                =  4.1648E+01 seconds\n",
+      " Calculation Rate (inactive)       =  5986.59 neutrons/second\n",
+      " Calculation Rate (active)         =  2700.51 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -921,17 +923,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1514: RuntimeWarning: invalid value encountered in true_divide\n",
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1515: RuntimeWarning: invalid value encountered in true_divide\n",
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/tallies.py:1516: RuntimeWarning: invalid value encountered in true_divide\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize MGXS Library with OpenMC statepoint data\n",
     "mgxs_lib.load_from_statepoint(sp)"
@@ -990,13 +982,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python2.7/dist-packages/openmc-0.7.0-py2.7.egg/openmc/mgxs/mgxs.py:1254: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n"
+      "/home/romano/openmc/openmc/tallies.py:1642: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n"
      ]
     },
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1014,7 +1007,7 @@
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>U-235</td>\n",
-       "      <td>8.063513e-03</td>\n",
+       "      <td>0.008064</td>\n",
        "      <td>4.062984e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1022,7 +1015,7 @@
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>U-238</td>\n",
-       "      <td>7.335515e-03</td>\n",
+       "      <td>0.007336</td>\n",
        "      <td>4.459335e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1030,7 +1023,7 @@
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>O-16</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000e+00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1038,7 +1031,7 @@
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>U-235</td>\n",
-       "      <td>3.613274e-01</td>\n",
+       "      <td>0.361327</td>\n",
        "      <td>1.902492e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1046,7 +1039,7 @@
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>U-238</td>\n",
-       "      <td>6.738424e-07</td>\n",
+       "      <td>0.000001</td>\n",
        "      <td>3.536787e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1054,7 +1047,7 @@
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>O-16</td>\n",
-       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.000000e+00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1062,13 +1055,13 @@
        "</div>"
       ],
       "text/plain": [
-       "    cell  group in nuclide          mean     std. dev.\n",
-       "3  10000         1   U-235  8.063513e-03  4.062984e-05\n",
-       "4  10000         1   U-238  7.335515e-03  4.459335e-05\n",
-       "5  10000         1    O-16  0.000000e+00  0.000000e+00\n",
-       "0  10000         2   U-235  3.613274e-01  1.902492e-03\n",
-       "1  10000         2   U-238  6.738424e-07  3.536787e-09\n",
-       "2  10000         2    O-16  0.000000e+00  0.000000e+00"
+       "    cell  group in nuclide      mean     std. dev.\n",
+       "3  10000         1   U-235  0.008064  4.062984e-05\n",
+       "4  10000         1   U-238  0.007336  4.459335e-05\n",
+       "5  10000         1    O-16  0.000000  0.000000e+00\n",
+       "0  10000         2   U-235  0.361327  1.902492e-03\n",
+       "1  10000         2   U-238  0.000001  3.536787e-09\n",
+       "2  10000         2    O-16  0.000000  0.000000e+00"
       ]
      },
      "execution_count": 31,
@@ -1209,7 +1202,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1342,21 +1335,21 @@
       "[  NORMAL ]  Iteration 2:\tk_eff = 0.761131\tres = 6.380E-02\n",
       "[  NORMAL ]  Iteration 3:\tk_eff = 0.731467\tres = 5.066E-02\n",
       "[  NORMAL ]  Iteration 4:\tk_eff = 0.709897\tres = 3.910E-02\n",
-      "[  NORMAL ]  Iteration 5:\tk_eff = 0.695110\tres = 2.954E-02\n",
-      "[  NORMAL ]  Iteration 6:\tk_eff = 0.685966\tres = 2.085E-02\n",
+      "[  NORMAL ]  Iteration 5:\tk_eff = 0.695111\tres = 2.954E-02\n",
+      "[  NORMAL ]  Iteration 6:\tk_eff = 0.685967\tres = 2.085E-02\n",
       "[  NORMAL ]  Iteration 7:\tk_eff = 0.681511\tres = 1.317E-02\n",
       "[  NORMAL ]  Iteration 8:\tk_eff = 0.680926\tres = 6.520E-03\n",
       "[  NORMAL ]  Iteration 9:\tk_eff = 0.683509\tres = 1.046E-03\n",
       "[  NORMAL ]  Iteration 10:\tk_eff = 0.688659\tres = 3.848E-03\n",
-      "[  NORMAL ]  Iteration 11:\tk_eff = 0.695860\tres = 7.565E-03\n",
+      "[  NORMAL ]  Iteration 11:\tk_eff = 0.695861\tres = 7.565E-03\n",
       "[  NORMAL ]  Iteration 12:\tk_eff = 0.704674\tres = 1.048E-02\n",
       "[  NORMAL ]  Iteration 13:\tk_eff = 0.714726\tres = 1.269E-02\n",
-      "[  NORMAL ]  Iteration 14:\tk_eff = 0.725700\tres = 1.428E-02\n",
+      "[  NORMAL ]  Iteration 14:\tk_eff = 0.725701\tres = 1.428E-02\n",
       "[  NORMAL ]  Iteration 15:\tk_eff = 0.737329\tres = 1.537E-02\n",
       "[  NORMAL ]  Iteration 16:\tk_eff = 0.749388\tres = 1.604E-02\n",
-      "[  NORMAL ]  Iteration 17:\tk_eff = 0.761690\tres = 1.637E-02\n",
+      "[  NORMAL ]  Iteration 17:\tk_eff = 0.761691\tres = 1.637E-02\n",
       "[  NORMAL ]  Iteration 18:\tk_eff = 0.774081\tres = 1.643E-02\n",
-      "[  NORMAL ]  Iteration 19:\tk_eff = 0.786432\tres = 1.628E-02\n",
+      "[  NORMAL ]  Iteration 19:\tk_eff = 0.786431\tres = 1.628E-02\n",
       "[  NORMAL ]  Iteration 20:\tk_eff = 0.798638\tres = 1.597E-02\n",
       "[  NORMAL ]  Iteration 21:\tk_eff = 0.810618\tres = 1.553E-02\n",
       "[  NORMAL ]  Iteration 22:\tk_eff = 0.822303\tres = 1.501E-02\n",
@@ -1369,91 +1362,91 @@
       "[  NORMAL ]  Iteration 29:\tk_eff = 0.892891\tres = 1.054E-02\n",
       "[  NORMAL ]  Iteration 30:\tk_eff = 0.901206\tres = 9.920E-03\n",
       "[  NORMAL ]  Iteration 31:\tk_eff = 0.909080\tres = 9.320E-03\n",
-      "[  NORMAL ]  Iteration 32:\tk_eff = 0.916523\tres = 8.745E-03\n",
-      "[  NORMAL ]  Iteration 33:\tk_eff = 0.923546\tres = 8.194E-03\n",
+      "[  NORMAL ]  Iteration 32:\tk_eff = 0.916522\tres = 8.745E-03\n",
+      "[  NORMAL ]  Iteration 33:\tk_eff = 0.923545\tres = 8.194E-03\n",
       "[  NORMAL ]  Iteration 34:\tk_eff = 0.930162\tres = 7.669E-03\n",
       "[  NORMAL ]  Iteration 35:\tk_eff = 0.936387\tres = 7.171E-03\n",
       "[  NORMAL ]  Iteration 36:\tk_eff = 0.942236\tres = 6.698E-03\n",
       "[  NORMAL ]  Iteration 37:\tk_eff = 0.947725\tres = 6.252E-03\n",
       "[  NORMAL ]  Iteration 38:\tk_eff = 0.952869\tres = 5.830E-03\n",
-      "[  NORMAL ]  Iteration 39:\tk_eff = 0.957687\tres = 5.433E-03\n",
-      "[  NORMAL ]  Iteration 40:\tk_eff = 0.962193\tres = 5.060E-03\n",
+      "[  NORMAL ]  Iteration 39:\tk_eff = 0.957686\tres = 5.433E-03\n",
+      "[  NORMAL ]  Iteration 40:\tk_eff = 0.962192\tres = 5.060E-03\n",
       "[  NORMAL ]  Iteration 41:\tk_eff = 0.966404\tres = 4.710E-03\n",
-      "[  NORMAL ]  Iteration 42:\tk_eff = 0.970337\tres = 4.381E-03\n",
-      "[  NORMAL ]  Iteration 43:\tk_eff = 0.974006\tres = 4.073E-03\n",
+      "[  NORMAL ]  Iteration 42:\tk_eff = 0.970336\tres = 4.381E-03\n",
+      "[  NORMAL ]  Iteration 43:\tk_eff = 0.974005\tres = 4.073E-03\n",
       "[  NORMAL ]  Iteration 44:\tk_eff = 0.977426\tres = 3.785E-03\n",
       "[  NORMAL ]  Iteration 45:\tk_eff = 0.980613\tres = 3.515E-03\n",
       "[  NORMAL ]  Iteration 46:\tk_eff = 0.983580\tres = 3.264E-03\n",
-      "[  NORMAL ]  Iteration 47:\tk_eff = 0.986341\tres = 3.029E-03\n",
-      "[  NORMAL ]  Iteration 48:\tk_eff = 0.988908\tres = 2.809E-03\n",
+      "[  NORMAL ]  Iteration 47:\tk_eff = 0.986340\tres = 3.029E-03\n",
+      "[  NORMAL ]  Iteration 48:\tk_eff = 0.988907\tres = 2.809E-03\n",
       "[  NORMAL ]  Iteration 49:\tk_eff = 0.991293\tres = 2.605E-03\n",
       "[  NORMAL ]  Iteration 50:\tk_eff = 0.993509\tres = 2.415E-03\n",
       "[  NORMAL ]  Iteration 51:\tk_eff = 0.995566\tres = 2.238E-03\n",
       "[  NORMAL ]  Iteration 52:\tk_eff = 0.997475\tres = 2.073E-03\n",
       "[  NORMAL ]  Iteration 53:\tk_eff = 0.999246\tres = 1.920E-03\n",
-      "[  NORMAL ]  Iteration 54:\tk_eff = 1.000888\tres = 1.777E-03\n",
-      "[  NORMAL ]  Iteration 55:\tk_eff = 1.002409\tres = 1.645E-03\n",
+      "[  NORMAL ]  Iteration 54:\tk_eff = 1.000887\tres = 1.777E-03\n",
+      "[  NORMAL ]  Iteration 55:\tk_eff = 1.002408\tres = 1.645E-03\n",
       "[  NORMAL ]  Iteration 56:\tk_eff = 1.003818\tres = 1.522E-03\n",
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       "[  NORMAL ]  Iteration 58:\tk_eff = 1.006331\tres = 1.302E-03\n",
-      "[  NORMAL ]  Iteration 59:\tk_eff = 1.007450\tres = 1.203E-03\n",
-      "[  NORMAL ]  Iteration 60:\tk_eff = 1.008484\tres = 1.112E-03\n",
-      "[  NORMAL ]  Iteration 61:\tk_eff = 1.009440\tres = 1.028E-03\n",
+      "[  NORMAL ]  Iteration 59:\tk_eff = 1.007449\tres = 1.203E-03\n",
+      "[  NORMAL ]  Iteration 60:\tk_eff = 1.008483\tres = 1.112E-03\n",
+      "[  NORMAL ]  Iteration 61:\tk_eff = 1.009439\tres = 1.028E-03\n",
       "[  NORMAL ]  Iteration 62:\tk_eff = 1.010324\tres = 9.496E-04\n",
       "[  NORMAL ]  Iteration 63:\tk_eff = 1.011141\tres = 8.771E-04\n",
-      "[  NORMAL ]  Iteration 64:\tk_eff = 1.011897\tres = 8.100E-04\n",
-      "[  NORMAL ]  Iteration 65:\tk_eff = 1.012594\tres = 7.478E-04\n",
+      "[  NORMAL ]  Iteration 64:\tk_eff = 1.011896\tres = 8.100E-04\n",
+      "[  NORMAL ]  Iteration 65:\tk_eff = 1.012594\tres = 7.479E-04\n",
       "[  NORMAL ]  Iteration 66:\tk_eff = 1.013238\tres = 6.903E-04\n",
-      "[  NORMAL ]  Iteration 67:\tk_eff = 1.013833\tres = 6.371E-04\n",
-      "[  NORMAL ]  Iteration 68:\tk_eff = 1.014382\tres = 5.879E-04\n",
-      "[  NORMAL ]  Iteration 69:\tk_eff = 1.014889\tres = 5.424E-04\n",
+      "[  NORMAL ]  Iteration 67:\tk_eff = 1.013833\tres = 6.372E-04\n",
+      "[  NORMAL ]  Iteration 68:\tk_eff = 1.014382\tres = 5.880E-04\n",
+      "[  NORMAL ]  Iteration 69:\tk_eff = 1.014889\tres = 5.425E-04\n",
       "[  NORMAL ]  Iteration 70:\tk_eff = 1.015357\tres = 5.004E-04\n",
-      "[  NORMAL ]  Iteration 71:\tk_eff = 1.015789\tres = 4.615E-04\n",
-      "[  NORMAL ]  Iteration 72:\tk_eff = 1.016187\tres = 4.255E-04\n",
-      "[  NORMAL ]  Iteration 73:\tk_eff = 1.016554\tres = 3.923E-04\n",
+      "[  NORMAL ]  Iteration 71:\tk_eff = 1.015788\tres = 4.615E-04\n",
+      "[  NORMAL ]  Iteration 72:\tk_eff = 1.016186\tres = 4.256E-04\n",
+      "[  NORMAL ]  Iteration 73:\tk_eff = 1.016553\tres = 3.924E-04\n",
       "[  NORMAL ]  Iteration 74:\tk_eff = 1.016892\tres = 3.617E-04\n",
-      "[  NORMAL ]  Iteration 75:\tk_eff = 1.017204\tres = 3.333E-04\n",
-      "[  NORMAL ]  Iteration 76:\tk_eff = 1.017492\tres = 3.072E-04\n",
-      "[  NORMAL ]  Iteration 77:\tk_eff = 1.017757\tres = 2.831E-04\n",
-      "[  NORMAL ]  Iteration 78:\tk_eff = 1.018001\tres = 2.608E-04\n",
-      "[  NORMAL ]  Iteration 79:\tk_eff = 1.018226\tres = 2.403E-04\n",
-      "[  NORMAL ]  Iteration 80:\tk_eff = 1.018433\tres = 2.213E-04\n",
-      "[  NORMAL ]  Iteration 81:\tk_eff = 1.018624\tres = 2.038E-04\n",
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-      "[  NORMAL ]  Iteration 83:\tk_eff = 1.018962\tres = 1.728E-04\n",
+      "[  NORMAL ]  Iteration 75:\tk_eff = 1.017204\tres = 3.334E-04\n",
+      "[  NORMAL ]  Iteration 76:\tk_eff = 1.017491\tres = 3.073E-04\n",
+      "[  NORMAL ]  Iteration 77:\tk_eff = 1.017756\tres = 2.831E-04\n",
+      "[  NORMAL ]  Iteration 78:\tk_eff = 1.018000\tres = 2.608E-04\n",
+      "[  NORMAL ]  Iteration 79:\tk_eff = 1.018225\tres = 2.403E-04\n",
+      "[  NORMAL ]  Iteration 80:\tk_eff = 1.018432\tres = 2.213E-04\n",
+      "[  NORMAL ]  Iteration 81:\tk_eff = 1.018623\tres = 2.038E-04\n",
+      "[  NORMAL ]  Iteration 82:\tk_eff = 1.018799\tres = 1.877E-04\n",
+      "[  NORMAL ]  Iteration 83:\tk_eff = 1.018961\tres = 1.729E-04\n",
       "[  NORMAL ]  Iteration 84:\tk_eff = 1.019110\tres = 1.591E-04\n",
-      "[  NORMAL ]  Iteration 85:\tk_eff = 1.019248\tres = 1.465E-04\n",
-      "[  NORMAL ]  Iteration 86:\tk_eff = 1.019374\tres = 1.348E-04\n",
-      "[  NORMAL ]  Iteration 87:\tk_eff = 1.019490\tres = 1.241E-04\n",
-      "[  NORMAL ]  Iteration 88:\tk_eff = 1.019597\tres = 1.142E-04\n",
+      "[  NORMAL ]  Iteration 85:\tk_eff = 1.019247\tres = 1.465E-04\n",
+      "[  NORMAL ]  Iteration 86:\tk_eff = 1.019373\tres = 1.349E-04\n",
+      "[  NORMAL ]  Iteration 87:\tk_eff = 1.019489\tres = 1.241E-04\n",
+      "[  NORMAL ]  Iteration 88:\tk_eff = 1.019596\tres = 1.142E-04\n",
       "[  NORMAL ]  Iteration 89:\tk_eff = 1.019695\tres = 1.051E-04\n",
-      "[  NORMAL ]  Iteration 90:\tk_eff = 1.019786\tres = 9.670E-05\n",
-      "[  NORMAL ]  Iteration 91:\tk_eff = 1.019869\tres = 8.895E-05\n",
-      "[  NORMAL ]  Iteration 92:\tk_eff = 1.019946\tres = 8.183E-05\n",
-      "[  NORMAL ]  Iteration 93:\tk_eff = 1.020016\tres = 7.528E-05\n",
-      "[  NORMAL ]  Iteration 94:\tk_eff = 1.020081\tres = 6.922E-05\n",
-      "[  NORMAL ]  Iteration 95:\tk_eff = 1.020141\tres = 6.368E-05\n",
-      "[  NORMAL ]  Iteration 96:\tk_eff = 1.020195\tres = 5.857E-05\n",
-      "[  NORMAL ]  Iteration 97:\tk_eff = 1.020246\tres = 5.385E-05\n",
-      "[  NORMAL ]  Iteration 98:\tk_eff = 1.020292\tres = 4.954E-05\n",
-      "[  NORMAL ]  Iteration 99:\tk_eff = 1.020335\tres = 4.553E-05\n",
-      "[  NORMAL ]  Iteration 100:\tk_eff = 1.020374\tres = 4.185E-05\n",
-      "[  NORMAL ]  Iteration 101:\tk_eff = 1.020410\tres = 3.848E-05\n",
-      "[  NORMAL ]  Iteration 102:\tk_eff = 1.020443\tres = 3.537E-05\n",
-      "[  NORMAL ]  Iteration 103:\tk_eff = 1.020474\tres = 3.253E-05\n",
-      "[  NORMAL ]  Iteration 104:\tk_eff = 1.020502\tres = 2.989E-05\n",
-      "[  NORMAL ]  Iteration 105:\tk_eff = 1.020527\tres = 2.746E-05\n",
+      "[  NORMAL ]  Iteration 90:\tk_eff = 1.019785\tres = 9.673E-05\n",
+      "[  NORMAL ]  Iteration 91:\tk_eff = 1.019869\tres = 8.899E-05\n",
+      "[  NORMAL ]  Iteration 92:\tk_eff = 1.019945\tres = 8.187E-05\n",
+      "[  NORMAL ]  Iteration 93:\tk_eff = 1.020016\tres = 7.532E-05\n",
+      "[  NORMAL ]  Iteration 94:\tk_eff = 1.020081\tres = 6.925E-05\n",
+      "[  NORMAL ]  Iteration 95:\tk_eff = 1.020141\tres = 6.372E-05\n",
+      "[  NORMAL ]  Iteration 96:\tk_eff = 1.020195\tres = 5.861E-05\n",
+      "[  NORMAL ]  Iteration 97:\tk_eff = 1.020246\tres = 5.388E-05\n",
+      "[  NORMAL ]  Iteration 98:\tk_eff = 1.020292\tres = 4.956E-05\n",
+      "[  NORMAL ]  Iteration 99:\tk_eff = 1.020335\tres = 4.555E-05\n",
+      "[  NORMAL ]  Iteration 100:\tk_eff = 1.020374\tres = 4.187E-05\n",
+      "[  NORMAL ]  Iteration 101:\tk_eff = 1.020410\tres = 3.850E-05\n",
+      "[  NORMAL ]  Iteration 102:\tk_eff = 1.020443\tres = 3.539E-05\n",
+      "[  NORMAL ]  Iteration 103:\tk_eff = 1.020473\tres = 3.254E-05\n",
+      "[  NORMAL ]  Iteration 104:\tk_eff = 1.020501\tres = 2.991E-05\n",
+      "[  NORMAL ]  Iteration 105:\tk_eff = 1.020527\tres = 2.747E-05\n",
       "[  NORMAL ]  Iteration 106:\tk_eff = 1.020551\tres = 2.526E-05\n",
-      "[  NORMAL ]  Iteration 107:\tk_eff = 1.020573\tres = 2.319E-05\n",
-      "[  NORMAL ]  Iteration 108:\tk_eff = 1.020593\tres = 2.134E-05\n",
-      "[  NORMAL ]  Iteration 109:\tk_eff = 1.020611\tres = 1.960E-05\n",
-      "[  NORMAL ]  Iteration 110:\tk_eff = 1.020628\tres = 1.800E-05\n",
-      "[  NORMAL ]  Iteration 111:\tk_eff = 1.020643\tres = 1.652E-05\n",
-      "[  NORMAL ]  Iteration 112:\tk_eff = 1.020657\tres = 1.518E-05\n",
-      "[  NORMAL ]  Iteration 113:\tk_eff = 1.020670\tres = 1.398E-05\n",
-      "[  NORMAL ]  Iteration 114:\tk_eff = 1.020682\tres = 1.283E-05\n",
-      "[  NORMAL ]  Iteration 115:\tk_eff = 1.020693\tres = 1.178E-05\n",
-      "[  NORMAL ]  Iteration 116:\tk_eff = 1.020704\tres = 1.083E-05\n"
+      "[  NORMAL ]  Iteration 107:\tk_eff = 1.020573\tres = 2.321E-05\n",
+      "[  NORMAL ]  Iteration 108:\tk_eff = 1.020592\tres = 2.134E-05\n",
+      "[  NORMAL ]  Iteration 109:\tk_eff = 1.020611\tres = 1.961E-05\n",
+      "[  NORMAL ]  Iteration 110:\tk_eff = 1.020628\tres = 1.802E-05\n",
+      "[  NORMAL ]  Iteration 111:\tk_eff = 1.020643\tres = 1.653E-05\n",
+      "[  NORMAL ]  Iteration 112:\tk_eff = 1.020657\tres = 1.519E-05\n",
+      "[  NORMAL ]  Iteration 113:\tk_eff = 1.020671\tres = 1.398E-05\n",
+      "[  NORMAL ]  Iteration 114:\tk_eff = 1.020683\tres = 1.283E-05\n",
+      "[  NORMAL ]  Iteration 115:\tk_eff = 1.020694\tres = 1.178E-05\n",
+      "[  NORMAL ]  Iteration 116:\tk_eff = 1.020704\tres = 1.082E-05\n"
      ]
     }
    ],
@@ -1598,7 +1591,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7fb9acb6fc10>"
+       "<matplotlib.text.Text at 0x7fa72eda50d0>"
       ]
      },
      "execution_count": 44,
@@ -1607,9 +1600,9 @@
     },
     {
      "data": {
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IKGGLiETCc059OSuA9dikDluBWduVmFG5gsWOQTHtjtkgHuoLzwaxLNwUpzjauscx84Rj\n/A0j+xzL5WhrT0dbrw8s17ajwu2svjvczq4vO5apK9zWGz0z7QRm0RmkFQRiuyVQwWpHI+fUYKak\nnKOdCxztXOGINc/gqc872rrc0ZZn5hrPcnlmmvKsQ897Nd/RVmiAUiiuBpOwc0A7EBhHJxIdxbY0\npMEeEhmqocEiWVNsS8MZTMLOAXcAS4CP16Y7Ig1BsS0NaTCHRI4CVgF7AIuwo4qLa9EpkSGm2JaG\nNJiEvSr5+xxwE/bDTL+g7lheuN++q91EqtG5zm51Eozt61P335jcRKrxp+QG0LKucpBXm7BHAc3A\nBmA0MBu4uLhQx+uqrF2kSPs4u+Vd3J1ZU67Y/lBmzctrTfoLv3XcOBb0lT/PrNqEPQHb8sjXcQNw\ne5V1iTQSxbY0rGoTdhdwcC07ItIgFNvSsLKdceaIQImN4UpWPxIuM9ExeqTLMQ2M5wT63R2zoXh4\n2vrdy+EyMx31hAYhHOAZfeOw0fF+jp7uqCg0zQrQtNL+OGrLQi60ye05gXuTo0xoIIVnRpX1jjKe\nGVU8PLPo1GJmFnCFCZ7JqDxbrZ5BQ6McZULreWRbG2/XjDMiIvFTwhYRiYQStohIJJSwRUQioYQt\nIhIJJWwRkUgoYYuIREIJW0QkEoO5+FPYUZVf3vqjcBWeQTGMDheZ5qjnBsfgmtMnh8ss6wqXmeLo\n87GOd2eDY7BK0IGOMo51M2JlbeoJzVQEgKetDG2uQR2eQS+hkUGefnja8fC8dZ7+eOrxfOy3Ocp4\n+tPqKFOrdTjYuNEWtohIJJSwRUQioYQtIhIJJWwRkUgoYYuIREIJW0QkEkrYIiKRUMIWEYlEtgNn\n7gs07ph9pHnJhcEy87kkWGa/cFN8mHBbrV3htk5yTN/Rsjbc1m2O5XKM4+HQwHK98nC4nU2O2W/G\nbAwvU/f6cFt7nRZui984ymQoNNjCMxvKWY54uzoQA55Zaz7vaOcyR6x5Bn1c4mjrQkdbnhleLnC0\ndbmjrWZHW2c72vLkoeCMM4HXtYUtIhIJJWwRkUgoYYuIREIJW0QkEkrYIiKRUMIWEYmEEraISCSU\nsEVEIhGa0GIwcrm9AyUcw3aWOmZvOcgxAKd3WbjMyJ3CZcY62vJMqfETR5lZjqamOgbp5AKvD5sd\nrmPZwnAZzyCOgzxTiewTLtK0xP44astC7pZAAc8gkz5HmRE1aMdTxvO2eGaKWe8oM8ZRxtOfXkeZ\nUY4ynhlntjjKjK1BW61tbczu7oYysa0tbBGRSChhi4hEQglbRCQSStgiIpFQwhYRiYQStohIJJSw\nRUQioYQtIhKJ0NCV+cDx2Dnzb0ieGw/8BGgDVgCnAOuqaXzNU+EynkEx6x31tDoGxfQ6ZlV56ZFw\nmRXhIsGZJwB2c/S5yTH4qCnU2MpwHdMd78Mix+Ak1+gCzxQggzeo2A4NgPAMVgkNioHwB9Qz6MMz\ne4uHZzCLpy1PPR4tjjKe9ZPttFv9hfoz2BlnrgHmFD33BWARsC9wZ/JYJDaKbYlOKGEvBl4oeu5E\nYEFyfwFwUq07JVIHim2JTjXHsCdQGMbfmzwW2REotqWhDfZHxxzhawuJxEixLQ2nmuPtvcBEYDUw\niQoX8epIXY6sfSdoDx1RFymjc73dMuaO7WtT9w9KbiLVWJrcAIavq3z+RjUJ+1bgDODryd+byxXs\n8JwRIOLQPsZueRc/k0kz7tj+SCbNy2tR+gt/5Lhx/Liv/IV3Q4dEbgTuAfbDTv46E/ga8E7gb8Ax\nyWOR2Ci2JTqhLezTyjx/bK07IlJnim2JTqbnjK/pqfz61m3hOpqXXRgss236JcEytzoGdZxMuK17\nCLflOVR/pKOtbWPDbXlmrvngs5Xb2rZnuJ1HHe28y7FMdy0Lt/VWz6w+Q+yVwOue2XfmOdbXZY54\nC7nA0c4VjnZCy+xt63JHW57E9Kk6rT/wLdf8GqzDUErU0HQRkUgoYYuIREIJW0QkEkrYIiKRUMIW\nEYmEEraISCSUsEVEIqGELSISiaYM687lTg+UuCdcyUbHgI0nN7r6E7Szo0zxBZRLmeqYMWW1Y9DQ\n3o4ZZ8a83dHWosqve4JggmOakI2O92H0rHCZNXeFy+xu6y/L+K0kd0sNKnnaUcYTbyGeQSie68h6\nZtHxDBga5SjjmSlmtaOM42Pm4pkhakoN2mlta2N2dzeUiW1tYYuIREIJW0QkEkrYIiKRUMIWEYmE\nEraISCSUsEVEIqGELSISCSVsEZFIZDrjDK8GXt8tXMWGrnCZgx2zQVzkmA3iHeGmaHGUaXVMOTPx\npXCZMZPDZXIPhstsCLz+ekc7zT3hdbxhtGN2jy3hIru9LlwGxwxCWQoN7PDMzuL58H05ENuX1mhG\nFU9fPINZPPV4PkOeejyjpnKOMhc68sfVjvVci+UKpQ5tYYuIREIJW0QkEkrYIiKRUMIWEYmEEraI\nSCSUsEVEIqGELSISCSVsEZFIZDvjzGmBEn921DIjXOTRn4XL7H9wuMzwh8Mn0G/bO3wC/fKV4bam\nO07Wf8Jxsv6UseG2Wvsqt7Vtcrid3OhwO02OvuCoh15HW4/bH0dtWcjdW4NKtjrKPBZ43TNA5xOO\nWJvviDXHmCfOrtEgFM/AmXmOtq6sUVszHWU8A2dCRrS1cahmnBERiZ8StohIJJSwRUQioYQtIhIJ\nJWwRkUgoYYuIREIJW0QkEkrYIiKRCA08mA8cDzwLvCF5rgP4GPBc8vgC4Fcl/jeXe3+g9mcdPZzu\nKPMLRxnHwJnc8nCZtY6ZTlocZ+J3vRwuc5Bj0BCO2VlCy/Xrx8N1zHGsP5c3Oco0h4s0XW1/BtGT\nQcX2o4HKPQNaNjvKrA287hl8s8ZRZpSjjMemBmvLMamVa8DLeEeZWszI09LWxr6DGDhzDTCn6Lkc\n8G3gkORWKqBFGp1iW6ITStiLgRdKPD9UQ4JFakWxLdGp9hj2ucBS4EfAuNp1R2TIKbalYVUza/pV\n8PcrqlwKfAuYV6pgR+pAX/se0L5nFa2JAJ09dsuYO7avTN0/HJiVbb9kB3Y/8EByf9i6dRXLVpOw\n0z8V/hD4ebmCHQdUUbtICe2T7ZZ38YOZNOOO7XMyaV5ei2ZR+MJvGTeO7/X1lS1bzSGRSan7J+O7\nSKpIDBTb0tBCW9g3AkcDuwMrgYuAduwkuRzQBZyVYf9EsqLYluiEEnapKQjmZ9ERkTpTbEt0qjmG\n7Rf6JeY2Rx1LHGUOc5RxzIbS5BhhsNuEcJkux49j0x0zr2xdFS7jOem/KdDnOaHRGeBaf4xxlPHo\nqlE9GQoNjKnVBytUj2fgzMQatAO+gT6eASaeejyDUDzh5lk/9XqvIBw3oXNKNTRdRCQSStgiIpFQ\nwhYRiYQStohIJOqasDufrGdrg9e5fqh7MHCdG4e6BwNXhxGMmXogXKTh/GmoO1CFpUPdgQG6P4M6\nlbAriDJhe6452WA6HWfDNDLPiUyNRgk7e1l8keuQiIhIJLI9D3vCof0fj+6BCakLQuzrqONFTzuO\nMjs7yhR/fW3ugf0m93/Ocf22EY4TX5s8J5o6LuTP7kWPH+uB/Yv6HLqKu+dk7mmOMo5zy0ueqPtE\nD+yT6vMIRz23/9FRKDuthxZie3hPD62T+69zz1u3zVEmF3jds6pKfch36ulhTKrPnv562topw3p2\n6ulhl1SfPevPM5GEp8+eSRdGFj0e3tPDyKK4CPV5+KRJYBMYlJTltX87saG/Iln4HTaUfCh0otiW\n7AxlbIuIiIiIiIiIvFbNAR4HlgHnD3FfvFZgZz89RDanVNbCfKCX/tdtHg8sAv4G3E5jTXNVqr8d\nwNPYen6I7SfGbXSK7dqLLa5hB4rtZuAJYCp2PsLDwMyh7JBTF76Ljw2lt2Gze6eD5BvAecn984Gv\n1btTFZTq70XAZ4amO4Om2M5GbHENdYrtepyHPQsL6hXY1Q4XAu+uQ7u10OgzaJea+ftEYEFyfwFw\nUl17VNmONlO5YjsbscU11Cm265Gwp2AzeuQ9nTzX6HLAHdhAto8PcV8GYgK2a0by13OW+lCLdaZy\nxXb9xBjXUOPYrkfCDp3736iOwnZxjsPmXH3b0HanKjkaf/1fhQ3LORhYhc1UHotGX7flxB7bMcQ1\nZBDb9UjYzwB7px7vjW2JNLr8FS6eA24iPH9Oo+ilMMnIJPrPBN6InqXwAfwh8axnUGzXU2xxDRnE\ndj0S9hJgOvbDzAjgVODWOrQ7GKOAXZL7o4HZxDOD9q3AGcn9M4Cbh7AvHjHPVK7Yrp/Y4hoiju3j\ngL9iP9BcMMR98ZiG/eL/MPAIjdvnG4EeYAt2LPVM7Nf/O2jM05+K+/tR4FrsFLOl2IcwlmOTeYrt\n2ostrmHHjG0RERERERERERERERERERERERERERERERGR+vh/6pWcKtkPGKMAAAAASUVORK5CYII=\n",
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C1AQ5bCGEqAly2EIIURPksIUQoibIYQshRE1o78SZ8tJsG0t5iQkdADyaFhl9aFpmVJNF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\ngg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fb9acc75890>"
+       "<matplotlib.figure.Figure at 0x7fa72efe62d0>"
       ]
      },
      "metadata": {},
@@ -1645,7 +1638,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/pandas-dataframes.ipynb b/docs/source/pythonapi/examples/pandas-dataframes.ipynb
index 1b05f82075..60c2c1c403 100644
--- a/docs/source/pythonapi/examples/pandas-dataframes.ipynb
+++ b/docs/source/pythonapi/examples/pandas-dataframes.ipynb
@@ -26,6 +26,8 @@
     "import openmc\n",
     "from openmc.statepoint import StatePoint\n",
     "from openmc.summary import Summary\n",
+    "from openmc.source import Source\n",
+    "from openmc.stats import Box\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -304,7 +306,8 @@
     "settings_file.trigger_active = True\n",
     "settings_file.trigger_max_batches = max_batches\n",
     "source_bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
-    "settings_file.set_source_space('box', source_bounds)\n",
+    "settings_file.source = Source(space=Box(\n",
+    "    source_bounds[:3], source_bounds[3:]))\n",
     "\n",
     "# Export to \"settings.xml\"\n",
     "settings_file.export_to_xml()"
@@ -379,7 +382,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAAAFzUkdC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\nMFKLvWUAAAAASUVORK5CYII=\n",
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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -567,10 +570,9 @@
       "\n",
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
-      "      Version:        0.7.0\n",
-      "      Git SHA1:       21738db07debeabde824c9b955bd3bf0c9a16366\n",
-      "      Date/Time:      2015-10-28 20:55:18\n",
-      "      MPI Processes:  1\n",
+      "      Version:        0.7.1\n",
+      "      Git SHA1:       ea9fb637f63f9374c7436456141afa850b84acf9\n",
+      "      Date/Time:      2016-01-14 07:08:19\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -634,20 +636,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  7.3800E-01 seconds\n",
-      "   Reading cross sections          =  1.5600E-01 seconds\n",
-      " Total time in simulation          =  1.5998E+01 seconds\n",
-      "   Time in transport only          =  1.5965E+01 seconds\n",
-      "   Time in inactive batches        =  2.3990E+00 seconds\n",
-      "   Time in active batches          =  1.3599E+01 seconds\n",
-      "   Time synchronizing fission bank =  3.0000E-03 seconds\n",
-      "     Sampling source sites         =  1.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
-      "   Time accumulating tallies       =  3.0000E-03 seconds\n",
+      " Total time for initialization     =  1.2110E+00 seconds\n",
+      "   Reading cross sections          =  9.4900E-01 seconds\n",
+      " Total time in simulation          =  1.0453E+01 seconds\n",
+      "   Time in transport only          =  1.0440E+01 seconds\n",
+      "   Time in inactive batches        =  1.5590E+00 seconds\n",
+      "   Time in active batches          =  8.8940E+00 seconds\n",
+      "   Time synchronizing fission bank =  4.0000E-03 seconds\n",
+      "     Sampling source sites         =  3.0000E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
+      "   Time accumulating tallies       =  0.0000E+00 seconds\n",
       " Total time for finalization       =  0.0000E+00 seconds\n",
-      " Total time elapsed                =  1.6754E+01 seconds\n",
-      " Calculation Rate (inactive)       =  5210.50 neutrons/second\n",
-      " Calculation Rate (active)         =  2757.56 neutrons/second\n",
+      " Total time elapsed                =  1.1681E+01 seconds\n",
+      " Calculation Rate (inactive)       =  8017.96 neutrons/second\n",
+      " Calculation Rate (active)         =  4216.33 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -772,13 +774,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.18257268]]\n",
+      "[[[ 0.21161313]]\n",
       "\n",
-      " [[ 0.07111957]]\n",
+      " [[ 0.07979747]]\n",
       "\n",
-      " [[ 0.40880276]]\n",
+      " [[ 0.40532194]]\n",
       "\n",
-      " [[ 0.16407535]]]\n"
+      " [[ 0.19458598]]]\n"
      ]
     }
    ],
@@ -801,7 +803,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -831,8 +833,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000202</td>\n",
-       "      <td>0.000037</td>\n",
+       "      <td>0.000165</td>\n",
+       "      <td>0.000035</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -841,8 +843,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000492</td>\n",
-       "      <td>0.000090</td>\n",
+       "      <td>0.000403</td>\n",
+       "      <td>0.000085</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -851,7 +853,7 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000076</td>\n",
+       "      <td>0.000077</td>\n",
        "      <td>0.000004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -861,8 +863,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000204</td>\n",
-       "      <td>0.000010</td>\n",
+       "      <td>0.000207</td>\n",
+       "      <td>0.000011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -871,8 +873,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000375</td>\n",
-       "      <td>0.000039</td>\n",
+       "      <td>0.000350</td>\n",
+       "      <td>0.000043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -881,8 +883,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000914</td>\n",
-       "      <td>0.000094</td>\n",
+       "      <td>0.000853</td>\n",
+       "      <td>0.000105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
@@ -891,8 +893,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000107</td>\n",
-       "      <td>0.000013</td>\n",
+       "      <td>0.000106</td>\n",
+       "      <td>0.000015</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
@@ -901,8 +903,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000278</td>\n",
-       "      <td>0.000032</td>\n",
+       "      <td>0.000274</td>\n",
+       "      <td>0.000039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
@@ -911,8 +913,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000564</td>\n",
-       "      <td>0.000056</td>\n",
+       "      <td>0.000552</td>\n",
+       "      <td>0.000060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -921,8 +923,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.001374</td>\n",
-       "      <td>0.000137</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.000146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
@@ -931,8 +933,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000149</td>\n",
-       "      <td>0.000007</td>\n",
+       "      <td>0.000148</td>\n",
+       "      <td>0.000008</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
@@ -941,8 +943,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000388</td>\n",
-       "      <td>0.000018</td>\n",
+       "      <td>0.000384</td>\n",
+       "      <td>0.000021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
@@ -951,8 +953,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000669</td>\n",
-       "      <td>0.000044</td>\n",
+       "      <td>0.000682</td>\n",
+       "      <td>0.000054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
@@ -961,8 +963,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.001631</td>\n",
-       "      <td>0.000108</td>\n",
+       "      <td>0.001662</td>\n",
+       "      <td>0.000132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
@@ -971,8 +973,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000165</td>\n",
-       "      <td>0.000011</td>\n",
+       "      <td>0.000162</td>\n",
+       "      <td>0.000012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
@@ -981,8 +983,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000433</td>\n",
-       "      <td>0.000029</td>\n",
+       "      <td>0.000424</td>\n",
+       "      <td>0.000031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
@@ -991,8 +993,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000932</td>\n",
-       "      <td>0.000069</td>\n",
+       "      <td>0.000911</td>\n",
+       "      <td>0.000076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
@@ -1001,8 +1003,8 @@
        "      <td>1</td>\n",
        "      <td>(0.0e+00 - 6.3e-07)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.002270</td>\n",
-       "      <td>0.000168</td>\n",
+       "      <td>0.002221</td>\n",
+       "      <td>0.000186</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
@@ -1011,8 +1013,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>fission</td>\n",
-       "      <td>0.000183</td>\n",
-       "      <td>0.000011</td>\n",
+       "      <td>0.000178</td>\n",
+       "      <td>0.000013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
@@ -1021,8 +1023,8 @@
        "      <td>1</td>\n",
        "      <td>(6.3e-07 - 2.0e+01)</td>\n",
        "      <td>nu-fission</td>\n",
-       "      <td>0.000477</td>\n",
-       "      <td>0.000028</td>\n",
+       "      <td>0.000464</td>\n",
+       "      <td>0.000032</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1031,26 +1033,26 @@
       "text/plain": [
        "   mesh 1               energy [MeV]       score      mean std. dev.\n",
        "        x  y  z                                                     \n",
-       "0       1  1  1  (0.0e+00 - 6.3e-07)     fission  0.000202  0.000037\n",
-       "1       1  1  1  (0.0e+00 - 6.3e-07)  nu-fission  0.000492  0.000090\n",
-       "2       1  1  1  (6.3e-07 - 2.0e+01)     fission  0.000076  0.000004\n",
-       "3       1  1  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000204  0.000010\n",
-       "4       1  2  1  (0.0e+00 - 6.3e-07)     fission  0.000375  0.000039\n",
-       "5       1  2  1  (0.0e+00 - 6.3e-07)  nu-fission  0.000914  0.000094\n",
-       "6       1  2  1  (6.3e-07 - 2.0e+01)     fission  0.000107  0.000013\n",
-       "7       1  2  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000278  0.000032\n",
-       "8       1  3  1  (0.0e+00 - 6.3e-07)     fission  0.000564  0.000056\n",
-       "9       1  3  1  (0.0e+00 - 6.3e-07)  nu-fission  0.001374  0.000137\n",
-       "10      1  3  1  (6.3e-07 - 2.0e+01)     fission  0.000149  0.000007\n",
-       "11      1  3  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000388  0.000018\n",
-       "12      1  4  1  (0.0e+00 - 6.3e-07)     fission  0.000669  0.000044\n",
-       "13      1  4  1  (0.0e+00 - 6.3e-07)  nu-fission  0.001631  0.000108\n",
-       "14      1  4  1  (6.3e-07 - 2.0e+01)     fission  0.000165  0.000011\n",
-       "15      1  4  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000433  0.000029\n",
-       "16      1  5  1  (0.0e+00 - 6.3e-07)     fission  0.000932  0.000069\n",
-       "17      1  5  1  (0.0e+00 - 6.3e-07)  nu-fission  0.002270  0.000168\n",
-       "18      1  5  1  (6.3e-07 - 2.0e+01)     fission  0.000183  0.000011\n",
-       "19      1  5  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000477  0.000028"
+       "0       1  1  1  (0.0e+00 - 6.3e-07)     fission  0.000165  0.000035\n",
+       "1       1  1  1  (0.0e+00 - 6.3e-07)  nu-fission  0.000403  0.000085\n",
+       "2       1  1  1  (6.3e-07 - 2.0e+01)     fission  0.000077  0.000004\n",
+       "3       1  1  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000207  0.000011\n",
+       "4       1  2  1  (0.0e+00 - 6.3e-07)     fission  0.000350  0.000043\n",
+       "5       1  2  1  (0.0e+00 - 6.3e-07)  nu-fission  0.000853  0.000105\n",
+       "6       1  2  1  (6.3e-07 - 2.0e+01)     fission  0.000106  0.000015\n",
+       "7       1  2  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000274  0.000039\n",
+       "8       1  3  1  (0.0e+00 - 6.3e-07)     fission  0.000552  0.000060\n",
+       "9       1  3  1  (0.0e+00 - 6.3e-07)  nu-fission  0.001346  0.000146\n",
+       "10      1  3  1  (6.3e-07 - 2.0e+01)     fission  0.000148  0.000008\n",
+       "11      1  3  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000384  0.000021\n",
+       "12      1  4  1  (0.0e+00 - 6.3e-07)     fission  0.000682  0.000054\n",
+       "13      1  4  1  (0.0e+00 - 6.3e-07)  nu-fission  0.001662  0.000132\n",
+       "14      1  4  1  (6.3e-07 - 2.0e+01)     fission  0.000162  0.000012\n",
+       "15      1  4  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000424  0.000031\n",
+       "16      1  5  1  (0.0e+00 - 6.3e-07)     fission  0.000911  0.000076\n",
+       "17      1  5  1  (0.0e+00 - 6.3e-07)  nu-fission  0.002221  0.000186\n",
+       "18      1  5  1  (6.3e-07 - 2.0e+01)     fission  0.000178  0.000013\n",
+       "19      1  5  1  (6.3e-07 - 2.0e+01)  nu-fission  0.000464  0.000032"
       ]
      },
      "execution_count": 25,
@@ -1075,9 +1077,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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raRQdlzCRUVgXAa8GrgLWTEEZNEnGNNRrPGfbL6+lsZv9g9SLiF//46VZmNLMKZAX4Grg\nxnHea3ejgo2OjjI0NATA4OAgw8PD1e6Vyonkfmv7Fd1SHvfdd7+9f/+lUolyucx48loIs4H7gZOB\nPcAWIqB9XybNcuCc9LyUaDUszcl7NDFCCuAPiO6p04kA+NVpfwFwCxFkr29teJ+GJE2jZvdp5LU0\n9hEVwiZiNNSVxEX/7PT6FUQrYTkRtH4KODMnL8CngNcBzwEPAx9Mx+8FrkvP+4APYfeUpCYuvtj5\np9rNO8JVVSqVqk1WqRcMDJQYGxvpdDFmJO8IlyRNmi0NST3Luaemjy0NSdKkWWmoKjv0TuoNpU4X\noO9YaUjqWc491X7GNCRJBzCmIUmaNCsNVRnTUK/xnG0/Kw1JUmHGNCRJBzCmIWnGcd6p9rPSUJX9\nw+o1q1eXOl2EvmOlIUkqzJiGpJ7l3FPTx5iGJGnSilQay4AdxEp75zdJsza9vh04rkDezxALMm0H\nrgdelo4PAU8D29Lj8gLl0xQxpqHeU+p0AfpOXqUxC1hHXPwXE8u1HlOXZjmxJOvRwFnA+gJ5bwJe\nDxwLPABcmHm/h4iK5zhi5T5Jasi5p9ovr9JYQlzEy8CzwLXAyro0K4CNaXszMAjMy8l7M/B8Js/C\nCZZfU8hV+9RrNmwY6XQR+k5epbEA2JnZ35WOFUkzv0BegA8Q64xXHE50TZWA43PKJ0lqo7xKo+i4\nhImOwroIeAa4Ou3vARYRXVPnpuOHTPC91SJjGuo1nrPtNzvn9d3ERbxiEdFiGC/NwpRmTk7eUSIe\ncnLm2DPpAbAVeJiIlWytL9jo6ChDQ0MADA4OMjw8XO1eqZxI7re2X9Et5XHffffb+/dfKpUol8uM\nJ6+FMBu4n7iw7wG2EAHt+zJplgPnpOelwJr0PF7eZcBngROBxzLvdSjwOPAccARwO/AG4Im6cnmf\nhiRNo4nep7GPqBA2AfcCXyIu+menB0Q84ntE0PsKaiOemuUFuAw4mAiIZ4fWnkgMw90GfDl9Rn2F\nIUmAc091gneEq6pUKlWbrFIvGBgoMTY20ulizEjeES5JmjRbGpJ6lnNPTR9bGpKkSbPSUFV26J3U\nG0qdLkDfsdKQ1LOce6r9jGlIkg5gTEOSNGlWGqoypqFe4znbflYakqTCjGlIkg5gTEPSjOPcU+1n\npaEq+4fVa1avLnW6CH3HSkOSVJgxDUk9y7mnpo8xDUnSpBWpNJYBO4AHgfObpFmbXt9OrO+dl/cz\nxIJM24HrgZdlXrswpd8BnFKgfJoixjTUe0qdLkDfyas0ZgHriIv/YmK51mPq0iwHjiLW8j4LWF8g\n703A64FjgQeIioKU7rT0vIxY0c/WkKSGnHuq/fIuyEuIZVzLwLPAtcDKujQrgI1pezMwCMzLyXsz\n8Hwmz8K0vRK4JqUvp/xLWvlCmjhX7VOv2bBhpNNF6Dt5lcYCYGdmf1c6ViTN/AJ5AT5ArDNOyrOr\nQB5JUgfkVRpFxyVMdBTWRcAzwNVTUAZNkjEN9RrP2fabnfP6bmBRZn8R+7cEGqVZmNLMyck7SsRD\nTs55r92NCjY6OsrQ0BAAg4ODDA8PV7tXKieS+63tV3RLedx33/32/v2XSiXK5TLjyWshzAbuJy7s\ne4AtRED7vkya5cA56XkpsCY9j5d3GfBZ4ETgscx7LSZaHUuIbqlbiCB7fWvD+zQkaRpN9D6NfUSF\nsAm4F/gScdE/Oz0g4hHfI4LWVwAfyskLcBlwMBEQ30aMkiKluy49fyO9l7WDpIace6r9vCNcVaVS\nqdpklXrBwECJsbGRThdjRvKOcEnSpNnSkNSznHtq+tjSkCRNmpWGqrJD76TeUOp0AfqOlYakrjB3\nbnQ3tfKA1vPMndvZ79nrjGlI6grtik8YBynGmIYkadKsNFS1Zk2p00WQWmIcrv2sNFR1112dLoGk\nbmdMQ1XLlsE3v9npUqhfGdPoLs1iGnmz3GqGK5XiAbBpU20un5GReEhSli0NVc2bV+KHPxzpdDHU\npybSApjIfGm2NIqxpaGG1qyBG26I7b17a62Ld70LPvKRjhVLUpeypaGq4WGD4eocYxrdxZaGqgYG\nmv1WuI2BgZOa5rOillRkyO0yYAfwIHB+kzRr0+vbgeMK5H0v8F3gOeDNmeNDwNPEwkzZxZk0hcbG\nxho+xnvNCkPdyPs02i+vpTELWAe8nVir+++Br3Hgcq9HAUcDbwHWE8u9jpf3HuA3iZX+6j3E/hWP\nJKlL5LU0lhAX8TLwLHAtsLIuzQpgY9reDAwC83Ly7gAemFTJNQ1GOl0AqSWuNNl+eZXGAmBnZn9X\nOlYkzfwCeRs5nOiaKgHHF0gvSWqTvEqjaEf2VI3C2gMsIrqnzgWuBg6ZovdWrlKnCyC1xJhG++XF\nNHYTF/GKRUSLYbw0C1OaOQXy1nsmPQC2Ag8TsZKt9QlHR0cZGhoCYHBwkOHh4WpTtXIiud/a/hln\n0FXlcb+/9ivdo9P9eVCiVOr89+22/cp2uVxmPHkthNnA/cDJRCtgC/B+DgyEn5OelwJr0nORvLcB\nHwXuTPuHAo8To6qOAG4H3gA8UVcu79OQZhjv0+guE71PYx9RIWwiRkNdSVz0z06vXwHcSFQYDwFP\nAWfm5IUYObWWqCS+TsQwTgVOBFYTgfPn0+fUVxiSpA7xjnBVlSYwj480VZx7qru4cp8kadJsaUjq\nCsY0uostDeWqrKUhSc1Yaahq9epSp4sgtSQ7XFTtYaUhSSrMmIaq7OtVJxnT6C7GNCRJk2aloYxS\npwsgtcSYRvtZaaiqMveUJDVjTENSVzCm0V2MaUiSJs1KQ1X2D6vXeM62n5WGJKkwYxqSuoIxje5i\nTEO5nHtKUp4ilcYyYAfwIHB+kzRr0+vbifW98/K+F/gusULfm+ve68KUfgdwSoHyaYo495R6jTGN\n9surNGYB64iL/2JiudZj6tIsB44i1vI+C1hfIO89xOp9t9e912LgtPS8DLi8QBklSW2Sd0FeQizj\nWiaWYL0WWFmXZgWwMW1vBgaBeTl5dwAPNPi8lcA1KX055V9S7Kto8kY6XQCpJa402X55lcYCYGdm\nf1c6ViTN/AJ5681P6VrJI0lqk7xKo+gYg+kcheU4h7YpdboAUkuMabTf7JzXdwOLMvuL2L8l0CjN\nwpRmToG8eZ+3MB07wOjoKENDQwAMDg4yPDxcbapWTiT3W9uvzD3VLeVxv7/2K92j0/15UKJU6vz3\n7bb9yna5XGY8eS2E2cD9wMnAHmALEdC+L5NmOXBOel4KrEnPRfLeBnwUuDPtLwauJuIYC4BbiCB7\nfWvD+zSkGcb7NLpLs/s08loa+4gKYRMxGupK4qJ/dnr9CuBGosJ4CHgKODMnL8TIqbXAocDXgW3A\nqcC9wHXpeR/wIeyekqSu4R3hqiqVSpkmvNReE2kBTOSctaVRjHeES5ImzZaGpK5gTKO72NJQLuee\nkpTHSkNVzj2lXpMdLqr2sNKQJBVmTENV9vWqk4xpdBdjGpKkSbPSUEap0wWQWmJMo/2sNGaouXOj\nGd7KA1rPM3duZ7+npPYypjFD2T+snjPQxsuRJ22uic49JUltMcBY+37oTP/HzFh2T6nK/mH1Gs/Z\n9rPSkCQVZkxjhjKmoV7jOdtdvE9DkjRpRSqNZcAO4EHg/CZp1qbXtwPHFcg7F7gZeAC4CRhMx4eA\np4lFmbYBlxcon6aI/cPqNZ6z7ZdXacwC1hEX/8XEcq3H1KVZTizJejRwFrC+QN4LiErjtcCtab/i\nIaLiOY5YuU+S1CXyKo0lxEW8DDwLXAusrEuzAtiYtjcTrYZ5OXmzeTYC75pg+TWFXLVPvcZztv3y\nKo0FwM7M/q50rEia+ePkfSWwN23vTfsVhxNdUyXg+JzySZLaKK/SKDrGoMgorIEm7zeWOb4HWER0\nTZ0LXA0cUrAMmiT7h9VrPGfbL++O8N3ERbxiEdFiGC/NwpRmToPju9P2XqIL64fAq4BH0/Fn0gNg\nK/AwESvZWl+w0dFRhoaGABgcHGR4eLjaVK2cSP2+D62mp6vK735/7bd6vk50H0qUSp3/vt22X9ku\nl8uMJ6+FMBu4HziZaAVsIQLa92XSLAfOSc9LgTXpeby8lwI/Bj5NBMEH0/OhwOPAc8ARwO3AG4An\n6srlfRo5HPOuXuM5210mOvfUPqJC2ESMhrqSuOifnV6/AriRqDAeAp4CzszJC3AJcB3w74hA+fvS\n8ROAPyEC58+nz6mvMCRJHeId4TPURH5NlUqlTBN++j5HasRztrt4R7gkadJsacxQ9g+r17RrOY2X\nvxx+8pP2fFYvcz0NSV1tIj8+/NHSfnZPqSo79E7qDaVOF6DvWGlIkgozpjFDGdNQP/D8mz7GNPrM\nGANt+UkwlvmvpJnP7qkZaoCx+AnWwqN0220t5xmwwlAHnXFGqdNF6DtWGpJ61uhop0vQf4xpzFDG\nNCRNhneES5ImzUpDVd6noV7jOdt+VhqSpMIccjuDtT6Xz0jLn/Hyl7ecRZoypdIILhPeXgbCVWVQ\nW73Gc3b6TCYQvgzYATwInN8kzdr0+nZife+8vHOBm4EHgJuIlfsqLkzpdwCnFCifpkyp0wWQWlTq\ndAH6Tl6lMQtYR1z8FxPLtR5Tl2Y5cBSxlvdZwPoCeS8gKo3XAremfVK609LzMuDyAmXUlLmr0wWQ\nWuQ52255F+QlxDKuZWIJ1muBlXVpVgAb0/ZmotUwLydvNs9G4F1peyVwTUpfTvmXtPKFNBmurKvu\nNDAw0PABf9j0tYF2LdDRZ/IqjQXAzsz+rnSsSJr54+R9JbA3be9N+6Q8u3I+T1KfGRsba/hYtWpV\n09eMe06PvNFTRf/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8WtJB4DlgSUTsO+qtNrMJzX1PNZ8iil62GDskxXis91hXKpXc0rBxRQIfCkaHJCLiiOaG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\n/j8k35yq9tqQdwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdb2da35110>"
+       "<matplotlib.figure.Figure at 0x7f38f16e1750>"
       ]
      },
      "metadata": {},
@@ -1100,18 +1102,26 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar instance at 0x7fdb2d6d5680>"
+       "<matplotlib.colorbar.Colorbar instance at 0x7f38eda14c20>"
       ]
      },
      "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python2.7/dist-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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      "metadata": {},
@@ -1181,7 +1191,7 @@
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      "data": {
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-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1199,144 +1209,144 @@
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y0,0</td>\n",
-       "      <td>0.037095</td>\n",
-       "      <td>0.001150</td>\n",
+       "      <td>0.038027</td>\n",
+       "      <td>0.001350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y1,-1</td>\n",
-       "      <td>0.000266</td>\n",
-       "      <td>0.000323</td>\n",
+       "      <td>0.000071</td>\n",
+       "      <td>0.000383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y1,0</td>\n",
-       "      <td>-0.000417</td>\n",
-       "      <td>0.000274</td>\n",
+       "      <td>-0.000579</td>\n",
+       "      <td>0.000250</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y1,1</td>\n",
-       "      <td>-0.000228</td>\n",
-       "      <td>0.000237</td>\n",
+       "      <td>-0.000176</td>\n",
+       "      <td>0.000282</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y2,-2</td>\n",
-       "      <td>0.000026</td>\n",
-       "      <td>0.000199</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.000224</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y2,-1</td>\n",
-       "      <td>-0.000115</td>\n",
-       "      <td>0.000185</td>\n",
+       "      <td>-0.000077</td>\n",
+       "      <td>0.000221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y2,0</td>\n",
-       "      <td>0.000151</td>\n",
-       "      <td>0.000159</td>\n",
+       "      <td>0.000134</td>\n",
+       "      <td>0.000181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y2,1</td>\n",
-       "      <td>-0.000122</td>\n",
-       "      <td>0.000280</td>\n",
+       "      <td>-0.000117</td>\n",
+       "      <td>0.000308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-235</td>\n",
        "      <td>scatter-Y2,2</td>\n",
-       "      <td>0.000008</td>\n",
-       "      <td>0.000181</td>\n",
+       "      <td>0.000039</td>\n",
+       "      <td>0.000211</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y0,0</td>\n",
-       "      <td>2.328632</td>\n",
-       "      <td>0.013107</td>\n",
+       "      <td>2.340987</td>\n",
+       "      <td>0.014310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y1,-1</td>\n",
-       "      <td>0.024530</td>\n",
-       "      <td>0.002272</td>\n",
+       "      <td>0.022817</td>\n",
+       "      <td>0.002458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y1,0</td>\n",
-       "      <td>-0.000059</td>\n",
-       "      <td>0.002804</td>\n",
+       "      <td>0.001589</td>\n",
+       "      <td>0.003051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y1,1</td>\n",
-       "      <td>-0.027990</td>\n",
-       "      <td>0.002536</td>\n",
+       "      <td>-0.027146</td>\n",
+       "      <td>0.002511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y2,-2</td>\n",
-       "      <td>-0.004861</td>\n",
-       "      <td>0.001575</td>\n",
+       "      <td>-0.004146</td>\n",
+       "      <td>0.001722</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y2,-1</td>\n",
-       "      <td>0.000557</td>\n",
-       "      <td>0.002018</td>\n",
+       "      <td>0.001765</td>\n",
+       "      <td>0.002474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y2,0</td>\n",
-       "      <td>0.006236</td>\n",
-       "      <td>0.001627</td>\n",
+       "      <td>0.006038</td>\n",
+       "      <td>0.001917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y2,1</td>\n",
-       "      <td>-0.000648</td>\n",
-       "      <td>0.001551</td>\n",
+       "      <td>0.000167</td>\n",
+       "      <td>0.001438</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>10000</td>\n",
        "      <td>U-238</td>\n",
        "      <td>scatter-Y2,2</td>\n",
-       "      <td>-0.001031</td>\n",
-       "      <td>0.001310</td>\n",
+       "      <td>-0.001684</td>\n",
+       "      <td>0.001535</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1344,24 +1354,24 @@
       ],
       "text/plain": [
        "     cell nuclide          score      mean  std. dev.\n",
-       "0   10000   U-235   scatter-Y0,0  0.037095   0.001150\n",
-       "1   10000   U-235  scatter-Y1,-1  0.000266   0.000323\n",
-       "2   10000   U-235   scatter-Y1,0 -0.000417   0.000274\n",
-       "3   10000   U-235   scatter-Y1,1 -0.000228   0.000237\n",
-       "4   10000   U-235  scatter-Y2,-2  0.000026   0.000199\n",
-       "5   10000   U-235  scatter-Y2,-1 -0.000115   0.000185\n",
-       "6   10000   U-235   scatter-Y2,0  0.000151   0.000159\n",
-       "7   10000   U-235   scatter-Y2,1 -0.000122   0.000280\n",
-       "8   10000   U-235   scatter-Y2,2  0.000008   0.000181\n",
-       "9   10000   U-238   scatter-Y0,0  2.328632   0.013107\n",
-       "10  10000   U-238  scatter-Y1,-1  0.024530   0.002272\n",
-       "11  10000   U-238   scatter-Y1,0 -0.000059   0.002804\n",
-       "12  10000   U-238   scatter-Y1,1 -0.027990   0.002536\n",
-       "13  10000   U-238  scatter-Y2,-2 -0.004861   0.001575\n",
-       "14  10000   U-238  scatter-Y2,-1  0.000557   0.002018\n",
-       "15  10000   U-238   scatter-Y2,0  0.006236   0.001627\n",
-       "16  10000   U-238   scatter-Y2,1 -0.000648   0.001551\n",
-       "17  10000   U-238   scatter-Y2,2 -0.001031   0.001310"
+       "0   10000   U-235   scatter-Y0,0  0.038027   0.001350\n",
+       "1   10000   U-235  scatter-Y1,-1  0.000071   0.000383\n",
+       "2   10000   U-235   scatter-Y1,0 -0.000579   0.000250\n",
+       "3   10000   U-235   scatter-Y1,1 -0.000176   0.000282\n",
+       "4   10000   U-235  scatter-Y2,-2  0.000105   0.000224\n",
+       "5   10000   U-235  scatter-Y2,-1 -0.000077   0.000221\n",
+       "6   10000   U-235   scatter-Y2,0  0.000134   0.000181\n",
+       "7   10000   U-235   scatter-Y2,1 -0.000117   0.000308\n",
+       "8   10000   U-235   scatter-Y2,2  0.000039   0.000211\n",
+       "9   10000   U-238   scatter-Y0,0  2.340987   0.014310\n",
+       "10  10000   U-238  scatter-Y1,-1  0.022817   0.002458\n",
+       "11  10000   U-238   scatter-Y1,0  0.001589   0.003051\n",
+       "12  10000   U-238   scatter-Y1,1 -0.027146   0.002511\n",
+       "13  10000   U-238  scatter-Y2,-2 -0.004146   0.001722\n",
+       "14  10000   U-238  scatter-Y2,-1  0.001765   0.002474\n",
+       "15  10000   U-238   scatter-Y2,0  0.006038   0.001917\n",
+       "16  10000   U-238   scatter-Y2,1  0.000167   0.001438\n",
+       "17  10000   U-238   scatter-Y2,2 -0.001684   0.001535"
       ]
      },
      "execution_count": 29,
@@ -1395,8 +1405,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.00131009  0.01310707]\n",
-      "  [ 0.00018089  0.00114976]]]\n"
+      "[[[ 0.00153535  0.0143096 ]\n",
+      "  [ 0.00021107  0.00135025]]]\n"
      ]
     }
    ],
@@ -1464,7 +1474,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.04537029]]]\n"
+      "[[[ 0.04318886]]]\n"
      ]
     }
    ],
@@ -1493,7 +1503,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1509,141 +1519,141 @@
        "      <th>558</th>\n",
        "      <td>279</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000093</td>\n",
-       "      <td>0.000013</td>\n",
+       "      <td>0.000102</td>\n",
+       "      <td>0.000016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>559</th>\n",
        "      <td>279</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.013504</td>\n",
-       "      <td>0.000805</td>\n",
+       "      <td>0.013889</td>\n",
+       "      <td>0.000964</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>560</th>\n",
        "      <td>280</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000084</td>\n",
-       "      <td>0.000010</td>\n",
+       "      <td>0.000087</td>\n",
+       "      <td>0.000012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>561</th>\n",
        "      <td>280</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.014215</td>\n",
-       "      <td>0.000612</td>\n",
+       "      <td>0.014347</td>\n",
+       "      <td>0.000652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>562</th>\n",
        "      <td>281</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000091</td>\n",
-       "      <td>0.000008</td>\n",
+       "      <td>0.000087</td>\n",
+       "      <td>0.000010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>563</th>\n",
        "      <td>281</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.014545</td>\n",
-       "      <td>0.000590</td>\n",
+       "      <td>0.014283</td>\n",
+       "      <td>0.000715</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>564</th>\n",
        "      <td>282</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000112</td>\n",
+       "      <td>0.000111</td>\n",
        "      <td>0.000012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>565</th>\n",
        "      <td>282</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.016321</td>\n",
-       "      <td>0.000729</td>\n",
+       "      <td>0.016374</td>\n",
+       "      <td>0.000865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>566</th>\n",
        "      <td>283</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000092</td>\n",
-       "      <td>0.000007</td>\n",
+       "      <td>0.000090</td>\n",
+       "      <td>0.000008</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>567</th>\n",
        "      <td>283</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.016163</td>\n",
-       "      <td>0.000661</td>\n",
+       "      <td>0.015839</td>\n",
+       "      <td>0.000795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>568</th>\n",
        "      <td>284</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000104</td>\n",
-       "      <td>0.000011</td>\n",
+       "      <td>0.000103</td>\n",
+       "      <td>0.000012</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>569</th>\n",
        "      <td>284</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.017384</td>\n",
-       "      <td>0.000599</td>\n",
+       "      <td>0.017182</td>\n",
+       "      <td>0.000660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>570</th>\n",
        "      <td>285</td>\n",
        "      <td>absorption</td>\n",
        "      <td>0.000111</td>\n",
-       "      <td>0.000011</td>\n",
+       "      <td>0.000014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>571</th>\n",
        "      <td>285</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.018015</td>\n",
-       "      <td>0.000774</td>\n",
+       "      <td>0.017565</td>\n",
+       "      <td>0.000862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>572</th>\n",
        "      <td>286</td>\n",
        "      <td>absorption</td>\n",
        "      <td>0.000125</td>\n",
-       "      <td>0.000012</td>\n",
+       "      <td>0.000014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>573</th>\n",
        "      <td>286</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.018294</td>\n",
-       "      <td>0.000828</td>\n",
+       "      <td>0.018128</td>\n",
+       "      <td>0.000931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>574</th>\n",
        "      <td>287</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000119</td>\n",
-       "      <td>0.000013</td>\n",
+       "      <td>0.000124</td>\n",
+       "      <td>0.000016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>575</th>\n",
        "      <td>287</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.017483</td>\n",
-       "      <td>0.000757</td>\n",
+       "      <td>0.017253</td>\n",
+       "      <td>0.000902</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>576</th>\n",
        "      <td>288</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000113</td>\n",
-       "      <td>0.000014</td>\n",
+       "      <td>0.000119</td>\n",
+       "      <td>0.000016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>577</th>\n",
        "      <td>288</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.018248</td>\n",
-       "      <td>0.000782</td>\n",
+       "      <td>0.018482</td>\n",
+       "      <td>0.000861</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1651,26 +1661,26 @@
       ],
       "text/plain": [
        "     distribcell       score      mean  std. dev.\n",
-       "558          279  absorption  0.000093   0.000013\n",
-       "559          279     scatter  0.013504   0.000805\n",
-       "560          280  absorption  0.000084   0.000010\n",
-       "561          280     scatter  0.014215   0.000612\n",
-       "562          281  absorption  0.000091   0.000008\n",
-       "563          281     scatter  0.014545   0.000590\n",
-       "564          282  absorption  0.000112   0.000012\n",
-       "565          282     scatter  0.016321   0.000729\n",
-       "566          283  absorption  0.000092   0.000007\n",
-       "567          283     scatter  0.016163   0.000661\n",
-       "568          284  absorption  0.000104   0.000011\n",
-       "569          284     scatter  0.017384   0.000599\n",
-       "570          285  absorption  0.000111   0.000011\n",
-       "571          285     scatter  0.018015   0.000774\n",
-       "572          286  absorption  0.000125   0.000012\n",
-       "573          286     scatter  0.018294   0.000828\n",
-       "574          287  absorption  0.000119   0.000013\n",
-       "575          287     scatter  0.017483   0.000757\n",
-       "576          288  absorption  0.000113   0.000014\n",
-       "577          288     scatter  0.018248   0.000782"
+       "558          279  absorption  0.000102   0.000016\n",
+       "559          279     scatter  0.013889   0.000964\n",
+       "560          280  absorption  0.000087   0.000012\n",
+       "561          280     scatter  0.014347   0.000652\n",
+       "562          281  absorption  0.000087   0.000010\n",
+       "563          281     scatter  0.014283   0.000715\n",
+       "564          282  absorption  0.000111   0.000012\n",
+       "565          282     scatter  0.016374   0.000865\n",
+       "566          283  absorption  0.000090   0.000008\n",
+       "567          283     scatter  0.015839   0.000795\n",
+       "568          284  absorption  0.000103   0.000012\n",
+       "569          284     scatter  0.017182   0.000660\n",
+       "570          285  absorption  0.000111   0.000014\n",
+       "571          285     scatter  0.017565   0.000862\n",
+       "572          286  absorption  0.000125   0.000014\n",
+       "573          286     scatter  0.018128   0.000931\n",
+       "574          287  absorption  0.000124   0.000016\n",
+       "575          287     scatter  0.017253   0.000902\n",
+       "576          288  absorption  0.000119   0.000016\n",
+       "577          288     scatter  0.018482   0.000861"
       ]
      },
      "execution_count": 33,
@@ -1703,7 +1713,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -1757,8 +1767,8 @@
        "      <td>10000</td>\n",
        "      <td>0</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000123</td>\n",
-       "      <td>0.000012</td>\n",
+       "      <td>0.000113</td>\n",
+       "      <td>0.000013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -1772,278 +1782,278 @@
        "      <td>10000</td>\n",
        "      <td>0</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.017805</td>\n",
-       "      <td>0.000808</td>\n",
+       "      <td>0.017337</td>\n",
+       "      <td>0.000749</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000217</td>\n",
-       "      <td>0.000020</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.000021</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>1</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.028867</td>\n",
-       "      <td>0.001263</td>\n",
+       "      <td>0.027631</td>\n",
+       "      <td>0.001348</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>2</td>\n",
        "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000318</td>\n",
-       "      <td>0.000020</td>\n",
+       "      <td>0.000319</td>\n",
+       "      <td>0.000025</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>2</td>\n",
        "      <td>0</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>2</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.040493</td>\n",
-       "      <td>0.001269</td>\n",
+       "      <td>0.040052</td>\n",
+       "      <td>0.001427</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>3</td>\n",
        "      <td>0</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>3</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000386</td>\n",
-       "      <td>0.000018</td>\n",
+       "      <td>0.000388</td>\n",
+       "      <td>0.000022</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>3</td>\n",
        "      <td>0</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>3</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.048576</td>\n",
-       "      <td>0.001337</td>\n",
+       "      <td>0.048578</td>\n",
+       "      <td>0.001561</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>4</td>\n",
        "      <td>0</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>4</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000501</td>\n",
-       "      <td>0.000026</td>\n",
+       "      <td>0.000511</td>\n",
+       "      <td>0.000030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>4</td>\n",
        "      <td>0</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>4</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.057063</td>\n",
-       "      <td>0.001715</td>\n",
+       "      <td>0.057903</td>\n",
+       "      <td>0.001988</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>5</td>\n",
        "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>5</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000484</td>\n",
-       "      <td>0.000026</td>\n",
+       "      <td>0.000481</td>\n",
+       "      <td>0.000033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>5</td>\n",
        "      <td>0</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>5</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.060822</td>\n",
-       "      <td>0.001581</td>\n",
+       "      <td>0.061211</td>\n",
+       "      <td>0.001989</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>6</td>\n",
        "      <td>0</td>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>6</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000532</td>\n",
-       "      <td>0.000039</td>\n",
+       "      <td>0.000542</td>\n",
+       "      <td>0.000045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>6</td>\n",
        "      <td>0</td>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>6</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.069101</td>\n",
-       "      <td>0.002249</td>\n",
+       "      <td>0.070888</td>\n",
+       "      <td>0.002497</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>7</td>\n",
        "      <td>0</td>\n",
+       "      <td>7</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>7</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000577</td>\n",
-       "      <td>0.000039</td>\n",
+       "      <td>0.000587</td>\n",
+       "      <td>0.000047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>7</td>\n",
        "      <td>0</td>\n",
+       "      <td>7</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>7</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.076722</td>\n",
-       "      <td>0.002335</td>\n",
+       "      <td>0.078107</td>\n",
+       "      <td>0.002794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>8</td>\n",
        "      <td>0</td>\n",
+       "      <td>8</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>8</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000649</td>\n",
-       "      <td>0.000039</td>\n",
+       "      <td>0.000627</td>\n",
+       "      <td>0.000033</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>8</td>\n",
        "      <td>0</td>\n",
+       "      <td>8</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>8</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.081564</td>\n",
-       "      <td>0.001610</td>\n",
+       "      <td>0.082031</td>\n",
+       "      <td>0.001740</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>9</td>\n",
        "      <td>0</td>\n",
+       "      <td>9</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>9</td>\n",
        "      <td>absorption</td>\n",
-       "      <td>0.000680</td>\n",
-       "      <td>0.000032</td>\n",
+       "      <td>0.000667</td>\n",
+       "      <td>0.000028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>10003</td>\n",
        "      <td>0</td>\n",
        "      <td>10001</td>\n",
-       "      <td>9</td>\n",
        "      <td>0</td>\n",
+       "      <td>9</td>\n",
        "      <td>0</td>\n",
        "      <td>10002</td>\n",
        "      <td>10000</td>\n",
        "      <td>9</td>\n",
        "      <td>scatter</td>\n",
-       "      <td>0.087715</td>\n",
-       "      <td>0.001959</td>\n",
+       "      <td>0.088516</td>\n",
+       "      <td>0.002037</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2055,48 +2065,48 @@
        "        id   id      id  x  y  z      id     id                           \n",
        "0    10003    0   10001  0  0  0   10002  10000           0  absorption   \n",
        "1    10003    0   10001  0  0  0   10002  10000           0     scatter   \n",
-       "2    10003    0   10001  1  0  0   10002  10000           1  absorption   \n",
-       "3    10003    0   10001  1  0  0   10002  10000           1     scatter   \n",
-       "4    10003    0   10001  2  0  0   10002  10000           2  absorption   \n",
-       "5    10003    0   10001  2  0  0   10002  10000           2     scatter   \n",
-       "6    10003    0   10001  3  0  0   10002  10000           3  absorption   \n",
-       "7    10003    0   10001  3  0  0   10002  10000           3     scatter   \n",
-       "8    10003    0   10001  4  0  0   10002  10000           4  absorption   \n",
-       "9    10003    0   10001  4  0  0   10002  10000           4     scatter   \n",
-       "10   10003    0   10001  5  0  0   10002  10000           5  absorption   \n",
-       "11   10003    0   10001  5  0  0   10002  10000           5     scatter   \n",
-       "12   10003    0   10001  6  0  0   10002  10000           6  absorption   \n",
-       "13   10003    0   10001  6  0  0   10002  10000           6     scatter   \n",
-       "14   10003    0   10001  7  0  0   10002  10000           7  absorption   \n",
-       "15   10003    0   10001  7  0  0   10002  10000           7     scatter   \n",
-       "16   10003    0   10001  8  0  0   10002  10000           8  absorption   \n",
-       "17   10003    0   10001  8  0  0   10002  10000           8     scatter   \n",
-       "18   10003    0   10001  9  0  0   10002  10000           9  absorption   \n",
-       "19   10003    0   10001  9  0  0   10002  10000           9     scatter   \n",
+       "2    10003    0   10001  0  1  0   10002  10000           1  absorption   \n",
+       "3    10003    0   10001  0  1  0   10002  10000           1     scatter   \n",
+       "4    10003    0   10001  0  2  0   10002  10000           2  absorption   \n",
+       "5    10003    0   10001  0  2  0   10002  10000           2     scatter   \n",
+       "6    10003    0   10001  0  3  0   10002  10000           3  absorption   \n",
+       "7    10003    0   10001  0  3  0   10002  10000           3     scatter   \n",
+       "8    10003    0   10001  0  4  0   10002  10000           4  absorption   \n",
+       "9    10003    0   10001  0  4  0   10002  10000           4     scatter   \n",
+       "10   10003    0   10001  0  5  0   10002  10000           5  absorption   \n",
+       "11   10003    0   10001  0  5  0   10002  10000           5     scatter   \n",
+       "12   10003    0   10001  0  6  0   10002  10000           6  absorption   \n",
+       "13   10003    0   10001  0  6  0   10002  10000           6     scatter   \n",
+       "14   10003    0   10001  0  7  0   10002  10000           7  absorption   \n",
+       "15   10003    0   10001  0  7  0   10002  10000           7     scatter   \n",
+       "16   10003    0   10001  0  8  0   10002  10000           8  absorption   \n",
+       "17   10003    0   10001  0  8  0   10002  10000           8     scatter   \n",
+       "18   10003    0   10001  0  9  0   10002  10000           9  absorption   \n",
+       "19   10003    0   10001  0  9  0   10002  10000           9     scatter   \n",
        "\n",
        "        mean std. dev.  \n",
        "                        \n",
        "                        \n",
-       "0   0.000123  0.000012  \n",
-       "1   0.017805  0.000808  \n",
-       "2   0.000217  0.000020  \n",
-       "3   0.028867  0.001263  \n",
-       "4   0.000318  0.000020  \n",
-       "5   0.040493  0.001269  \n",
-       "6   0.000386  0.000018  \n",
-       "7   0.048576  0.001337  \n",
-       "8   0.000501  0.000026  \n",
-       "9   0.057063  0.001715  \n",
-       "10  0.000484  0.000026  \n",
-       "11  0.060822  0.001581  \n",
-       "12  0.000532  0.000039  \n",
-       "13  0.069101  0.002249  \n",
-       "14  0.000577  0.000039  \n",
-       "15  0.076722  0.002335  \n",
-       "16  0.000649  0.000039  \n",
-       "17  0.081564  0.001610  \n",
-       "18  0.000680  0.000032  \n",
-       "19  0.087715  0.001959  "
+       "0   0.000113  0.000013  \n",
+       "1   0.017337  0.000749  \n",
+       "2   0.000204  0.000021  \n",
+       "3   0.027631  0.001348  \n",
+       "4   0.000319  0.000025  \n",
+       "5   0.040052  0.001427  \n",
+       "6   0.000388  0.000022  \n",
+       "7   0.048578  0.001561  \n",
+       "8   0.000511  0.000030  \n",
+       "9   0.057903  0.001988  \n",
+       "10  0.000481  0.000033  \n",
+       "11  0.061211  0.001989  \n",
+       "12  0.000542  0.000045  \n",
+       "13  0.070888  0.002497  \n",
+       "14  0.000587  0.000047  \n",
+       "15  0.078107  0.002794  \n",
+       "16  0.000627  0.000033  \n",
+       "17  0.082031  0.001740  \n",
+       "18  0.000667  0.000028  \n",
+       "19  0.088516  0.002037  "
       ]
      },
      "execution_count": 34,
@@ -2122,7 +2132,7 @@
     {
      "data": {
       "text/html": [
-       "<div>\n",
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
@@ -2149,38 +2159,38 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.000418</td>\n",
-       "      <td>0.000022</td>\n",
+       "      <td>0.000419</td>\n",
+       "      <td>0.000024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.000239</td>\n",
-       "      <td>0.000009</td>\n",
+       "      <td>0.000237</td>\n",
+       "      <td>0.000010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>0.000018</td>\n",
-       "      <td>0.000004</td>\n",
+       "      <td>0.000015</td>\n",
+       "      <td>0.000003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>0.000202</td>\n",
-       "      <td>0.000015</td>\n",
+       "      <td>0.000207</td>\n",
+       "      <td>0.000017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>0.000402</td>\n",
-       "      <td>0.000021</td>\n",
+       "      <td>0.000415</td>\n",
+       "      <td>0.000023</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>0.000615</td>\n",
-       "      <td>0.000027</td>\n",
+       "      <td>0.000030</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>0.000892</td>\n",
-       "      <td>0.000044</td>\n",
+       "      <td>0.000901</td>\n",
+       "      <td>0.000055</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2191,13 +2201,13 @@
        "                             \n",
        "                             \n",
        "count  289.000000  289.000000\n",
-       "mean     0.000418    0.000022\n",
-       "std      0.000239    0.000009\n",
-       "min      0.000018    0.000004\n",
-       "25%      0.000202    0.000015\n",
-       "50%      0.000402    0.000021\n",
-       "75%      0.000615    0.000027\n",
-       "max      0.000892    0.000044"
+       "mean     0.000419    0.000024\n",
+       "std      0.000237    0.000010\n",
+       "min      0.000015    0.000003\n",
+       "25%      0.000207    0.000017\n",
+       "50%      0.000415    0.000023\n",
+       "75%      0.000615    0.000030\n",
+       "max      0.000901    0.000055"
       ]
      },
      "execution_count": 35,
@@ -2232,7 +2242,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mann-Whitney Test p-value: 0.414863173548\n"
+      "Mann-Whitney Test p-value: 0.456115837774\n"
      ]
     }
    ],
@@ -2270,7 +2280,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mann-Whitney Test p-value: 3.28554363741e-42\n"
+      "Mann-Whitney Test p-value: 4.59783355073e-42\n"
      ]
     }
    ],
@@ -2306,7 +2316,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python2.7/dist-packages/IPython/kernel/__main__.py:4: SettingWithCopyWarning: \n",
+      "/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -2316,7 +2326,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes.AxesSubplot at 0x7fdb2d5c3e10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f38ed931d10>"
       ]
      },
      "execution_count": 38,
@@ -2325,9 +2335,9 @@
     },
     {
      "data": {
-      "image/png": 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rwqRJ3zB//i80adIo17P7fvzxx+zeHUlc3C8kJHxMfPwnPPzwxc7HeXmtW7eO\nl19+i8TEdZw+vZbY2Kl07XonHo/H11nTNO08fN1poUiKiYmRYsVKCwQKjBL4XJzO8vLRRxNl3bp1\n0q1bTwkLqyJW6zMCewXqCYQKOKVz59slLS1N7rnnQXG7y0hQUGMpVixCnM5ggX/MUedzJTg4PGNE\nek5SU1Nl7NhxUqfOtQI3CiSax++SYsXKXqZP49LMmDFDAgNvyxhtDyJOZ4gcPXrU11nTtByRD72t\nLtaaCye5LHz9HRRJQ4cOE6gtMNjrobdYKlSol5GmXr1WAr+ZD/WXBTwCxwQqyPfffy8ej0c2bdok\nf/31l8yaNUuCg9tneoD6+5eX7du3nzcfd9zRWwyjlcBYgXYC1wucFqezt9x++70F/THki7Vr14ph\nlBY4YN77bxIUVFJPt6IValyGKdlzcs2lXljznbVr1wF7UJMFpHMQHx8PQEpKCjExe4HxqN8JD6N6\nW5UA7mH16jVYLBZq1qxJs2bNqFWrFsnJ64GD5rnWk5Z2klKlSuWYhwMHDvDTT3OIj/8ZeBw1kcFO\nrNbiREWd4dNPx573Hk6fPs3Jkycv4u7zV/369Rk27BlcrroEBTUiMLAnP/wwI2MqF027Uul/4VeZ\nqVO/4PffV6LaKz4CJgI/AT1p3rw+AMuXLychwR84AiRxdlb9JGy2X6lcOfO4jerVqzN06ADc7msI\nDr4Bw2jLp59OwN/fP8d8JCQkYLUanJ0jy05QUBn++GM+8+Z9S1BQULbHpaSkcOed9xEaWpqSJcvS\nsWN3EhMTL/LTyB9Dhw5k27Z/+O23D9m7d+tVtZqlpmXnDBCbw99pH+bLm69Lf0VO+fJ1BaIFagi8\nKnCrQFvx8yst8+bNk5MnT8ro0aPF37+eQJrACAF/gWvEZisrN910q6SmpmZ77s2bN8vcuXNl9+7d\nF8xHamqq1Kp1rTgcTwmsFpvtFSlTpoqcOXPmvMeNHPm6GEZ7gTiBRHG7u8mAAUMu6rPQtKsVBVxt\nFYBaCja7v+x/Fp6rA7AF2AYMziHNGHP/P5xbHWZD1Zv8lMvraReQkHAGWAT0Rk0csB6LZRl16lTA\n4/FQqVJtRo78jvj4I0BDoD1+frdStWoqf/45k59//vac9UJEhBUrVrBz504aNmxI+fLlL5gPm81G\ndPQcOnU6RmRkb9q2Xc3Spb+ft7QCsHDhCuLjH0GtVOwkIaEfixatOO8xmqblv9z1pYTrgSqo2XXD\nUIFl1wVwWUL1AAAgAElEQVSOsaFWIGwPHABWAj+ilqJN19E8b1XgOuBD1NxZ6Z5CLWEbiHZJkpOT\nef311/nvv6PAClQB0gocRORZ1q5NoHPnexB5E5FHgCQslhaI3ERyspXY2HBzQsPMMxp4PB7uvPN+\n5s37C5utEh7PWn755XuaN29+wTyFhYXx3Xdf5Ok+qlaNZNGiaFJSugMW7PaFVK4cmadzaJp2eYxA\nrSH+r/k+AvgrF8c1Q7WCphvC2Zl5030EeK+bugUIN1+XBeajVizMqeTh69JfkZCcnCxNmrQRqzVC\nYILZK8gjUElgqlcvqWJeC0KJwEsC1wrsEZgmLlcxOXDgQKZzz5o1SwICGgokmMd8L+XK1cjX/Kem\npsrx48fF4/HIsWPHpGLF2hIY2FICA9tImTJVzslTdo4dOybjxo2Td95554K9wDTtSsdl6m11K9CV\n9BFhqhQRkIvjIoB9Xu/3m9tym+ZdYBCgR1tdotmzZ7NpUyIeTwiqgAdn56oq7ZUyEotlEurf1Qng\nSzNdInA3ycnX8ueff2Y6965du0hObgm4zC3tOXjwQoXS3Js581uCgkIJD48kMrIGhw8fZsOGFUyf\nPpRp055hy5bVlClT5rznOHToELVqNWbgwCUMGfIv9es3ZdWqVfmWR027GuUmeCSR+QF+/krps3Ib\n2bLO7GgBOqO6+qzJZr+WRydOnECkMqr28Q1UMIjB4TiN0zkANfflIlyuY5Qs+QU2W2lUwe9a1ATK\nbYBDwD4CAzPXIDZs2BC7/UcgBgCr9SNq1WqUq3z9999/PP30c9x667189NFEJMsMydu3b6d3737E\nx/9OSkos+/cP4sYbu+F2u+nYsSOdO3c+Jz/ZefPNdzh+/BYSEqaRnPwhcXFv8tRTw3KVR03Tspeb\nNo9vgAlACPAI8ADwcS6OOwCU83pfDlWyOF+asua221GlnY6on7RBwFRUK28mI0aMyHgdFRWlu0lm\no1WrVogMRnXL/RQIxGKx8NxzL+ByOZkwoTd2u52XXnqNHj3uIDAwBNWHIb3heyEQRfXqgbRv3z7T\nuaOionjhhccYMaIaDkcQoaHBfPfdnAvmKTY2loYNW3LwYBtSUtrx66/j2LRpG2PGvAWowDFu3Dgs\nlpqc7UfxEMeODeK///4jNDQ01/d/+PBxUlMbo/pkDAP2sXnzCVJSUnA4HBc4WtOKvujo6Ms+X5wF\niARuBEabfzfk8lg7sAOoAPgBa4GaWdJ0BOaar5sCy7I5T2t0m8clmz9/vpQrV1MMo5i0adNZDh06\nlG26lJQUsdud5mhy1fZhsbSUTp06SVJSUo7nP336tOzdu1eSk5Nl9Oj3pH372+SBBx6TmJiYbNNP\nnz5dAgJu8mpfiRGr1SHTp0+Xjz/+RNzuUDGMW8wFqPqYbTTrxOUKlOTk5Bzz4fF45JNPPpMOHe6U\ne+99RHbs2CHTp88Ql6uiQAmB8QKLxG6/Xh588PG8fYiadoXgMkxPYkFNxX6xbkat/7EdGGpu62v+\npRtr7v8H1Tc0q9aoXlrZ8fV3cEXq33+gGMa1Ap+J3d5PQkLCpXv33vLyy69KXFzceY/t2/cpMYzm\nAjPEbn9OwsMryokTJ85J9/nnn0tAQPqcUAnm1CTVJSCgs4BTYJW574xAOXG7W4vbHSZffDHtvNd/\n5ZU3xDBqC3whVutwCQkpLQcOHJCOHTubU7JcL/CAwEZxOgMv6XPStKKKy7SS4BRgHKp/Z2Fjfg5a\nfhIRPvpoIr/+uoRNmzaxb59BQkIvXK751K59hGXLfs92xty0tDRcLn9SU2MAtXa5w9EBP7+VBAQU\n48UXB/L44/0AOHLkCDVqXMOpU8/g8exBdbSbh+qXURKIJ/2fp9t9B336lKR///7UqFHjvHkvViyC\nkyfnk17I9fN7iNdeq8EHH3zK3r2NULWus4G5GMYR4uL+u+TPS9OKmvxYSTA3tqKWh9uJWgxqPbCu\noC+aSz6O31e2I0eOmGuYx5qlgDQJCKgrixcvzjZ9SkqK2Gx+Aqe8qqNuFnhTYJUYRiWZOfPbjPSb\nNm2Shg1biGGUMCdGbGKmr2BOligC68XtLikbN248b15jY2Nl/Pjx4nSGCOzIuL7D8ZgMHjxY3O4y\n5oj59G7KVeShh/rl6+elaUUFl6mr7k1AZaAt0MX863qpF9YKv6SkJGw2J2o0N4AVqzWYxMREfv/9\nd5544hleemkER44cAcBut3P33b1xu29HlSJGojrMPQA0Ij5+MF9/PRtQc1T16/cs//4bT2KiG9Wp\n712gG2rp2+GoxaYa8+ij91KrVq1MeTt+/Dh33HE/FSrUIyqqM3XqNObZZ38lObmWeY5fsFjG4nR+\nQ6dOnVC/f1LNowWbLYVu3ToXzAenaVeB3PS22l3QmdAKl23btjFixJscPXqSkiXDOHiwH8nJD2Oz\n/YphxLBjxy6eeeZl4uOfxG7fxYQJ17FhwwrCwsL49NPxVKjwBvPmjWbHjh2cODEQUD2jbLZdhIaq\n1Qe//PJL/v47gbi4laiOfLNQkxc0B/4EagB9cbsHUrVqlUz5ExFuuKEbGzbUITl5Cnv3zkPkL1TN\nagDQH4fjPtq2bcmoUfOpW7cuLVs25fffm+Dx1AZiSEuz8sADT7B1awtCQkIu6nMSEXbs2EFiYiLV\nq1fXPbc0rQjxdenvirNnzx4JCgoXq/VVgWliGNWlfv3mUqnSNXLjjbfJ7t27pXTpqgJ/ZVQN+fnd\nL6NHjz7nXH/99ZcYRqhYrQPF4XhEihUrI3v27BERkVdeeUWs1iHmOUoIbPeq6upm9opaJ253uKxd\nuzbTefft2ydud0mvaigxR8L/ar4+IIGBJTPSezwe6dKlh0B1gUfNHlz/E3//rjJ16tSL+pxSUlKk\nU6c7xO0uLQEBVaRq1QY59mDTtMKGfKi2yu3cVtpV4quvviIh4XY8nhcAiI+vw4EDXTh6dHdGmsTE\neM7OIgOpqeHExcWfc65mzZqxcuVCvv12Fk5nGL16rcwYDd6sWTNcroeIj38UNZHATcBzqE53v2Gx\nzMbl8mfixPHUr18/03ldLhdpaYmoxvVAVJXUMdTYlHo4nc9y000dMtIvXbqUP/5YjWqucwLPAzUQ\nuZH9+/fz9NODSE1No0+fnjRqlLsBjh98MI4FC06QkLAL8GP37sE88sgAfvhhWq6O1zTNt3wdwK84\nr776mthsT3r9ot8sxYuXy5Smb9+nxO2+QdSys9+L211CmjVrL4ZRXMqVqym//vprrq41atQ74nC4\nxWYzBNwCXQUGCXwpwcHhOY7nSE1NlRtu6CJOZxOBseJy3SqVK9eTkiUrimEUl9tu6yWxsbEZ6X/4\n4QcJCrrZ6548AsUkIKC42Vj/osCrYhihsmjRooxr7N+/XxISErLNQ8+eD5ulo/Rz/p1pJUZNK8zw\n4TK0hYWvv4MrzubNm8VqDRAYIzBRoLQ0atQ00xoeSUlJ0r//IImIqCE1a14ndeo0FYfjUYHDAvPE\nMEJl69atubpecnKyfPHFFxIU1MXrQSzicoVlO8AwJSVFoqI6ib9/XXE664vdHiJ9+z4q8fHxOV4j\nJiZGAgLCBH40e469Jg5HiNSv30TgFa/rfipRUV1k7dq1UrJkeXG7w8XlCpIpUz4/55xvvfW2uN0d\nBJIFPGK3vyCdOt2Zq3vWNF9DBw8dPPLbjz/+KG53bYEWAoEC3QWqSrt2XbJdBCo1NVWsVrtAUsZD\n2O2+W+6+++5cz2C7bt06cx3wGPMcSyQgoES2JY/PPvtM/P1bC6SYaWdK5cr1L3iNxYsXS0REdbFY\nHAIhomYQriZQXGCDea7Z0rhxOwkPryjwhbltg7jdYbJly5ZM50tKSpJ27bqIYZSXwMA6UqFCrVzN\n7qtphQE+XMNcu0IdP34cm60BarLjn1BTm21i2bKjzJo165z0VqsVlysANRMNwAESEuYwc6YwZMg2\nGjRoxrBhL1K3bksaNGid6Rxr167lyy+/JCEhgSFDnsLlqkdwcAv8/bvx9defZ9t7ad++fSQkNOds\nc11LDh3KOmXauVq2bMmHH76F01kWqGTe31bgf8BdwBIMYxA9enTk5MkTQE/zyNo4HC1Zty7z0CY/\nPz9+/fV7li+fzR9/fJYxu+/evXv57rvvWLp06TkTPWqaVnj4OoBfcbZv3y5udwkBh0B8RmnC6XxM\n3n///WyPmTDhYzGMCLHZBovdXkXAu82kj1itkWZPqB/Fai0hjz3WX0aNelcMo7QEBvYQwygnL7zw\nsuzYsUMWLlwohw8fzjF/v/zyixhGJYH9AuvFYmkoISEVZfLkqeLxeM57bxMmTBCHo6HAEK/8HRWL\nxS2VK18j7733gSQnJ4thhAisNPefEMMoL8uXL7/gZzdvnqqyCwrqIv7+VeSeex68YJ40zRfQ1VY6\neBSEX3/9VRyOEuZDNk1gsxhGaVmxYkWOxyxatEheffVVadSoldlWkv5wvk5gttf7iWK1RpjtKnvN\nbUfE7Q7L9SJNr702ypy80S3whsBXYhjV5d13x5z3uHXr1pkj5msLnDSv/Y7Ur98yU7qXXx4pat32\nFmKxhEmvXg9dME8ej0dCQkoJLDTPGycBAbXll19+ydU9FWVnzpyRPn0ek8jIOnLdde3P6VqtFT7o\n4KGDR0E5cOCA1KvXXGw2P3G5AuWTTz4TEZHDhw/LM88Mlh49HpAvv5x2zi/rr76aLoZRXWCzWTqI\nEJjmFTzeErhLwCWwWmCuQIwEBzfL6OmUlcfjkR9//FHGjh0ry5YtExGR559/QazWAV7nXSmlS1e7\n4H198cU0sdkCBQyxWiMlLKy8/Pvvvxn7zzauTxaYJTBYIiKqZtve4y0pKUksFpt4jz0xjPtl4sSJ\nF8xTYRAbGysvvviy3HlnHxkzZuwF79dbx47dxeXqIbBGYKIEBpaU/fv3F2ButUuFDh46eBS0+Ph4\nSUtLExGREydOSOnSlcXheFzgIzGMmjJy5OvnHPPmm29LQEBJs2TQWaCkwLsCr4kaEPiLgGEGlhsE\niovLFSxHjx4951wej0e6d+8tAQH1xeXqK4YRIe+/P06GDXtJrNZBXsFjtZQqVTVX9+TxeGTbtm2y\nfv16SUxMzLRvzpw5EhR0o9d5RQzj7ODG86lSpb5YLB8InBaYL253Kfn7778zpUlJSZEFCxbInDlz\nsp1t2BeSk5OlXr1m4nTeJTBRDKOl3Hvvw7k+1mp1yNlliEX8/XvI5MmTCzjX2qVABw8dPPLDokWL\npHPnu6RDhztk7ty5OaabNGmSGMZtXg/WHeJ2h2SbdvHixRIcfJ2ZbrHAI2bgGCZOZx2x2SqYD1kR\nmC+GUULmzJkjJ0+ezHSeJUuWiL9/Va+H007x8/OXtWvXir9/qMAHAj+IYdSR119/K+O4NWvWSP/+\n/SUysrZERNSU++9/VM6cOXPBz2LlypXi719B1FTwIrBHHA5D5s2bJ//99995j926dasUK1Za1JTy\nJcQwQmXVqlUZ+xMSEqRJkzYSEFBPgoLaSokS5WTr1q2ydetW+f333302Qv3333+XgIBrRI1/EYHT\n4nAEyPHjxy94bGpqqjgcbjnbU84jAQHtZMaMGZch59rFQgcPHTwu1ZIlS8QwwgQmCEwWwygjP/zw\nQ7Zpx44dKy7XA17B4z9xOIxsG4VPnTolxYtHCHwqcEys1nfE6Swh11wTJQ0bNhK7/R6v86QJWCUw\nMErCwsrLpk2bpG/fp6RUqaoSEVFTDKNZppKA01lcDh06JKtXr5abb75Dmje/WcaN+ygjH3PmzBGX\nq7hAkMAnAmvFbr9NypWrLu+9954kJibKwoULpXv3+6R79/tkyZIlGfn2eDzSq9fDEhBQR9zuR8Ru\nLyEOR3EJDm4mgYElc5xRWERk48aN5rQp/5p5/VrCwiIz8vXWW6PF5eoqkCpqka33pGzZWuJ2l5Tg\n4Fbi7x8qP//886V8nRdl7ty5EhQU5fUZp4rTWSzXwWzYsJfNNVTGiNPZW6pUqXfBdV8030IHDx08\nLtXtt/eWs9Ofqwde8+Ydsk27e/dusz1gosBScbs7SM+eOTcmr1u3TmrWvFbc7mBp0KClbNu2TR56\n6AlxuWqaVVm7zWtOENWILWK1vi4hIeXE4eggavzFd6IarycKJIvV+raUL1/rvL2YypevIzBAoKfX\nfcUJ2AUaSenSlcXtDhMYJ/CBGEZYpvYWj8cjc+fOlaefflpcrgoC/5nnmCOhoeVyvO706dMlMPD2\nTIHOz+9sddzDDz9hVt+l718naszJEfP9j+JyBcqSJUsuay+tU6dOSXh4RbHZXhNYKk5nH2natF2u\n8+DxeGTatGnSp8+j8tJLL59TetQKH4pA8OiAWuVnGzA4hzRjzP3/cHaxahewHLV07Sbg9RyO9fV3\nUOTddtu9Ah96PdC+laZNb8ox/erVq6VFiw5StWpjefrpweddmjarw4cPm72dTpnVTQGiBumFCWw0\nr79cVFvJgYw82WxPir9/iFitNqlVq8kFe2UVKxYh8L7AjV5VMXvNIJQmajXB+73ueay0adPpnF/L\nH3/8sfj73+eVziMWi12qVLlGAgJCpVWrjplGwa9YsUIMI9Ir2Pwl/v7FJTU1VdLS0mTYsGHidFYV\n2CWQJjZbP7HZqphpVwiECzQRw6gst9xyd0Zb0+Wwa9cuuemm26Vq1cbSu3dfOXXq1EWfa82aNTJp\n0iSZN29egQfBpKQkmTp1qowePfq8vQG1zCjkwcOGWl62AuDgwmuYX0fmNczTF5Gwm9tbZnMNX38H\nRd6CBQvMqpYpAtPFMMrKN9/MLJBr7dy5UwwjIlPdOtQwf31XFjXqO9QMKqsyHtouVw/54IMPcv0w\nveeeh8TpvEWglln6eFegqsD/zHM+I2rkvAj8IRAiNltJcbtD5NtvZ2WcZ+nSpWIY5bwC2VdisQQI\nfCVwUOz2oVKr1rWZHpADB74gbndpCQ5uJ4YRKrNnz5bk5GRp166LBARUFz+/6wQM8fMLlpo1G4vL\nFSZq8ap6AjPM6ySKy9VIevbsKTNmzLisQeRSffLJZ2IYpcQw7hd//9py5533F1gASU5OliZN2oi/\nf5T4+T0phlFKpk79okCudaWhkAePZqgVgdINMf+8fQT08Hq/Be/pWhUDWAnU4ly+/g6uCL/99pu0\nbXuLtGrVWWbNmnXhAy5SamqqVK/eUOz2wQJbBd4RNT36i2bpo4TA5wKvmr/C3xDoJVZrkFx7bZtz\npghJFx8fLzt37szoORUXFyd33nm/uN0hYrUaonp2tRVIFNggFktx8fMrJlDTLOXMNx/aq8QwSmQq\nTbz22ihxOoMlMLCGBASUFH//FplKIk5nMTly5Eim/GzYsEHmzZuX0V31o48+EsNoJ2oeLBGLZbzU\nrdtcPB6PjBs3Qfz8AgX8BI57nftpsVqbi79/E+nU6Y4iMdgwOTlZnM4AgS3mPcSLv3+1HLtgX6oZ\nM2aIv39LOds9eo0EBoYVyLWuNBTy4NEdmOT1vhfwQZY0P6FW/0k3H0ifE9uGKq3EAqNyuIavvwMt\nix9++EG6desl9977iGzYsOGc/QcPHpS2bbuImjervRlExCwl9PB6eM4WNcr9WoE/xWIZIyVKlD2n\nB9DMmd+K2x0i/v7lJCgoXBYuXCgiqoqsePEIsVpHCnxpBiOrWCxuGTFipAQGhptBq4rXNUWCg6+X\nP/74I9M1Dh8+LOvXr5d58+ZJQEAdOTuv1g6xWAxp3Lid9O7dN9uuxiIizz77nKhuymd7qXnPVHz3\n3X3EYilpBk2PwEGzJPaLQJIEBNTMuK/C7NixY2a15NnPMyjoVvn6668L5Hrjx48Xt/thr+slitVq\nL1IlNV+hkAeP28ld8Gjh9X4+0DBLmmBUtVVUNteQ4cOHZ/wtWLDA19/JVW3q1C/MOv9JYrG8LgEB\nobJ58+Zz0sXGxord7paza517zId4K68qrT1m8Ej1ehC1zdQbaf/+/eaU6n+baX6RoKCSEh8fLx9/\n/LEYxp1eD5ajYrO5JC0tTZYvXy5BQdeIGmUeLLDJTLNP3O5Q2bFjR7b3l5aWJm3adBZ//yiB58Vq\nLSE2260Cc8Vu7y+VKtWVhIQEiY2NlaVLl8rGjRvF4/HIV199Jf7+9c2ShUfs9iHSrt0tIqIGJTqd\nxUR1DqgjUNoshTzqdd9dCqREmJSUJNOnT5dx48ZdcI343PB4PFK+fC2xWN4zSwN/imGEys6dO/Mh\nt+fasGGD2VNwocApcTielJYtc26vu5otWLAg07OSQh48mpK52moo5zaaf4SalS5ddtVWAC8CA7PZ\n7uvvRPNStWpjryogEYvlBRkwYFC2aR944DExjOZmoLlLVO+rcqJWERwlbndVs8rphKR3Hw0IqJPp\nF/j8+fMlOLh1pl+6AQGVZcuWLfLJJ5+IYdzhte+wOBxu8Xg8snXrVnG7S4mann2qqHaWJuJyhcmo\nUe+KiMjJkydl1qxZ8sMPP2RaGyQlJUU+/fRTefrpAWK3h2RURamgUEc+++wzCQ2NlMDAhmK3l5Qy\nZWrIuHEfymOPDRA/v0Bxu0tJjRqNMqrGtm3bJv7+kWbQTBXVsF9TrNYHRbUJ/SQBAWH5PmI7MTFR\nGjduLQEBrcTtflgMI0x++umnSz7vtm3bpGrVa8RqtUtQUEmZPXt2ro9dv369dO/eW2644Xb5/PMv\nc3XMjz/+KGFh5cXhcEvr1h1zLP1pmVHIg4cdNdVqBcCPCzeYN+Vsg3koamFrADewCGiXzTV8/R1o\nXipWbCDwp9cD+1V5/PEB2aZNS0uTsWPHS/fu98nAgUPk7bfflqFDh8ojj/SVxx57Wr777jvp2/cp\nMYxGAqPF7e4sTZu2k5SUlIxzbNu2zexym96gvVFcrmA5deqUHD16VEJDy4nN9pLAt2IYzeTxx5/J\nOLZXr4fF37+hWCxDxe2uKR06dMlYg2Tfvn0SHl5RAgNvlMDANhIZWeOch1JMTIyohv1Er9JTdSlV\nKlIslvSuz3ECDcXPr4IMHPi8/Pfff7Jnz55M1SqpqalStWoDsduHCmwRq/VtKVEiUho0aCkOh1vK\nlq0uixYtkri4uDz1bLuQyZMni79/OznbXrBAwsMr5dv5ExMT89ROs3XrVgkICBOLZbSo5Y+rygcf\njM+3/GiZUciDB8DNqHmvt6NKHgB9zb90Y839/3C2yqousBoVcNah1inNjq+/A83L22+/bw4W+1lg\nqrjdobJy5cqLPp/H45EpU6ZIv35Pyttvv3POVCIiIq+99pa43eESHHyjuN2hMmXK57J27Vrp0aOP\ntG17i1x/fTtp3bqLjBr1TqaHtsfjka+//lpGjhwp3377baYH3R133Cc224sZQdDh6C99+z6V6bqb\nN28W1RDfRWCmqBH0EeJw+It3N2PVrvKMOBzuHOviY2Ji5MYbb5Pw8MrSvPlNsm3btox9sbGx0q5d\nV7HZnGK3O2XAgCH50ng+atQocTi85wY7Lk5n4CWf92INHTpMrNbnvPKzVMqVq3XB4w4fPiyTJk2S\niRMn6jXk84AiEDwKmq+/A82Lx+ORsWM/lIYN20jLlh0lOjr6slx38+bNMmfOHNm5c6ds3LjRnLbk\nLYHPxDDKZ7sS4Pk0atRWVGN1+oNshrRvf1vG/sTERClVqpLZTnOTqOVzHxIoIVZrgFgso8zjTgk0\nEPhM7HZnniYbTHffff3E6expVo8dFX//a2Ty5Ck5pj906JC89dZb8vLLI2XdunU5plu2bJlZdbdW\nIFEcjselXbuuec5ffhky5AWxWIZ6feYrpWzZmuc9ZteuXVK8eIQYxl1iGHdLsWJlcmyv0jJDBw8d\nPAqj06dPyy233COGUUzCwyvlatzInj17ZMqUKTJr1qxsSxjnM336DImMrC1hYRWlXr0m5q/99IfQ\nb1Kt2rV5Ot/Agc+L291F1HxasWIYbTLNm7Vx40YJCKgqanxGDVHjU/wEnhB4T6zWQFGTPoYI3C4u\nVwe5664+ecpDugoV6ovqLfaQwH0Cj0uHDrfKjBkzMpVQRNRMyKGh5cTP7wGxWgeJYYTKggULJCYm\nRm69tZfUqHGd9Or1SMaEjFOnfiGBgWFitdrl+utvlmPHjl1UHvPD2aA/XuB7MYzaMmrUO+c95q67\nHhCbbUTGd221virdu/e+TDku2tDBQwePwqhbt3vE6ewlak3zxWIY4eetvlq6dKn4+4dKQMBdEhDQ\nQurVaybx8fHi8Xjk6NGjmdo5soqOjjaXsF0gsMWccNG7W+wiqVy5YZ7yn5iYKF273iV2u0tsNqfc\nc8+DmUoNqodUiKgpU4qbJRC3wFNm24dVZs6cKddf30Fq124uzz77/EW3VzRo0MIMQqNEzQRQQmy2\nQAkKulXc7lCZOfPbjLSDBg0Vm817Ia4ZUq9eCylfvqbY7UMEloif38PSoEELSUtLk5SUFNm9e3eh\nmU5k1apV0qFDd2ne/Gb58MOJF6yea9Wqi8C3Xvf7ozRvfvNlyq36kXTfff2kYsUG0qbN2TazvEpI\nSJB///1XTp8+nc85zBk6eOjgURgZRjEzcKhGY6v1TunXr1+2a5KLiNSs2URgekbDs9vdVZ577jkJ\nD68oTmeIGEbmkd/e+vd/Rs6OHPceBf6pwE9iGDVkzJhxF3UfZ86cyXGCv8aNW5kljraiBjd+IhAp\n8JaUKFFW/vjjj0zrhKTL+kBMSko6b3C89da7s9zfdwItzdd/i59fgPz+++/i8XjkgQceE3gvU9VP\n6dKVJTCwode2NDGMCJk7d66Eh1cUw4gQP78Aef/9i/uM8iIuLu6895pXb731rhhGU1HjYg6LYbSQ\n//3vrQsfmE9at+5o/khaIVbr21K8eESuS28ej0fmzJkjTz/9tPnDqaK4XMG57mV2qdDBQwePwig8\nvJKoadhjzGqduuJ01pR69Zpl6vaarnjxcgI7vR5wI8QwQkWNNk8f+R0qu3btOufYF18cLnb7o17H\nziTGVtsAACAASURBVJayZatLmzZdpUmTG2TixI/zbXT24sWL5aWXhsugQYPE7Y4QOJaRP1U66G4u\nnlVMgoNbissVJi+++IqIiOzdu1caNGgpVqtNSpQoZw6mvEdsNj+x253Sv/9AWbFihQwe/LyMHPmK\nHDhwQETUVCtqnq70+/tF1Nxc6e/dYhgV5aGH+svcuXPNcTbLBXaIYURJz573S0BADTk7XiZBXK5Q\niYioKvCxuW2nGEaZTNPH56djx45J06btxGZzisPhljfeGJ0v501LS5Mnnxwkfn7+4ufnL489NuCi\n2pUuxsmTJ8VuN+RsV22RwMCbczUeJy0tTRo1aiEWS7ioThdzzXNsELe74MbFeEMHDx08CqNvvpkp\nhhEuFkt9gYEZv3idzl4yaNAL56Tv2vVu8fN72PwfcY+4XOXFzy/U6wEpEhTUSb7//vtzjj148KCE\nhUWKw/GwwEtiGHkbW5BbkydPFcMoIxbLC+Lnd51YrW0z5U+NFSkpaoXExZI+tsQwImT16tVSo0Yj\nsdlGiurau0Ds9mBxOruKWif+mDid1cThKCbwktjtj0rx4hGyb98++fnnn8XPr4RZMpsjqi3lZfP8\nEwWqCZwSf/8KsmLFCpk06f/tnXd4FNX6x7/bd2ZbSJY0AoTepERQigpBijQRQZSOCKICIoIFBaVX\nFRQv0otc4UooIlJ+CEhERDAgxAb3Kh2kSA2QUJL9/v44s8luCiQkIQmez/Psk93ZOWfemeycd855\n2zyGhpZnsWIlOGDAECYlJfGBB6JptXYksICK0pwtW3bQqh56UuW32Xpy7ty5eX7dPB4PW7ToQJNp\nAL1xLKpaLk9Tz3s8nmw9IHg8Hl65ciVPHiauXr1Ko9HKtJQyHtpsdbl27dpM9z969Ci3bNnCY8eO\nsU+fftpD1QKKzAe+GQ4ey5ffb3oglYdUHoWVuLg4hoRUJBDrc3MsYuvWnTLse+HCBTZs2JIGg5km\nk8KxYyfSYnFQRF2TwEWqaqksn4xPnTrFsWPH8c033+bOnTvz5XyKFQtnWiT7fylqhXgzAS/XniAH\nUSxl+afnWLhwofaUmjZY6/UlCWz12VaVouyt+N5gGMT+/QcyLKwMTaYK1OlKU68PYvv2HbVro1Ik\ne/wtS+X6zTffcOrUqVy2bBmHDRvBdu26cfz4ybx+/bpWtGqzdrwEGgylGRZWnk880YUnT57M9Bp4\nPB6uX7+e06ZNy9STLikpiR98MIX9+7/KmTNnsn79ZjQYzNTpbD6zHBIYwbffHn7L651dhZBdtm3b\nxqCgCOr1ZppMAaxV6yG/wmdHjhxh16592LDh45w48f1szWDat+9MUUpgBoGnqdc7M/VwmzNnPhUl\niC7Xw1SUIK3WzG/ag0MARTlmEjhJVQ3Nk2j/2wGpPKTyKMw891x/Wiw9tCfORCpKc44bNynL/a9d\nu5YaC7Fo0WdUlOJ0ONrTZotk//5Dbnu8U6dOcdWqVdyyZUue5zcSCf/OEzhFYBf1+sY0Gm1U1RI0\nGp0EBlIE3IUTWEVRA/1R6vWBXLx4Mc1mG9OKRN2gXh+o7WsgcD9FWpIffQbYybTbQwlEEKipKYuy\nfPDBR5mUlMTg4NIE5mjK54fUZb1jx47x8OHDfOedMbTZytJsHkBVrcKQkAp0OILpcpWkxWKjogTQ\nbHZRVetQ5BmrRmAnjcY3WLZs9Uw93vr2HUibrQqt1peoqmVSl+RIkRSxdu2GWrGrydTrK1Cna0AR\nKPk9hWPBLwRSqCgtOGPGjCyv9YQJ71FRXDQaLXzqqR5MSkrK1f/uwoULdDiCKfKleSjsRkG0WoO5\nYcMGnj17lsWLl6LBMJzASqrqQ3zxxVdu2afH42GjRm0I9KDwhBtJYDi7d++bus/333/PqKiG1OlU\npuVw20fhXOFV3Mu1B5HaVJRgjh49MVfnml0glYdUHoWZhIQE1qvXhFZrMC2WYmzbtlOWRvPM2L9/\nP5cuXcoffviBR48e5bRp0zh9+nSePn06tf+4uDgePnyYcXFxdDiC6XS2ot1ejY0bt86RcTYlJYUx\nMTGcNGkSN27cyOvXr3Po0HdZu/ajbNeuK5s0aUODoR5FLqxqBBROmzaNhw4d4tSp0zTD7XltpqVQ\npFpZQWAGVdXNrl270WRy0GRqQkWpSYPBrg1i1yiKcakEamtPpLE0mQKo1zdg2pr6xwQq0WQK4sWL\nF7lr1y6WLFmJRqOVdnsQv/jiCz722JO0Wt20WkOo0zkoXIk9FMklh1AELy6hWF7bSoMhmEZjGEVK\nmHAKY7uHDkfVDLXXRZXEMKblIztFi8WV+r/YsGED7fbaTItYP0OxhJeofe5Ci+V+2u11WadOoyzd\nsZcvX05VrajJfomK8kSGAM2c8sMPP9DlqkPfGaHII/YuW7V6Rou29y2vfJZGoyXLB5BZs+ZSVQO0\nWcP/+bRbwLZtu6ReL2G3G6Ep/7RjWyxVaDaXJPAZgVHU6RROmjQpy6zR+QGk8pDKo7Dj8Xh49OhR\nvzTnOeW3336jwxFMq/U5Wq1dGRQUwTVr1jAgIIxOZy1arUEsVqwURTwECdykqkZz3rx52ZbxySe7\n0mZ7gEbjYKpqWVar9gAV5TECG2gwjKHLFUK93kXgT3pdgO32ICYlJTElJYUvvTSIBoOZBoNZG2S/\n8xkwmtFojKBe/zrN5gdYrlxVGo0PpBvMilNkGS5Gt7sMW7RoQ38vqwOaQnLQaLTRaLTylVfeYEJC\nAj0eD4cPH6XFplynyPrbnqKa4t/aIOfx6au1prCCmGb0P0oxA6lOwMxy5Wr6JbWMjY2ly9XAT2aH\nowJ///13kuSKFSvocLT2+T6ZIn3LeQLJtNnqceDAgVy9evUtHyB69epHfweBn1i6dPU7/u2QIpjQ\nag1iWsXGv7RrMolNm7bjggULaLP5VoA8R8BApzOECxb4B2Ru3bpVq0nzO0VJgaoE9lDM/soyJmYZ\nSXL06DE0GIZo1z+QafVp3ifgoMlkp9tdjh06dL1jF9/cAKk8pPL4J9CixVPU6dLKtxoMb1NVg5nm\n3vs3gVCKJQDvADCc7747Ilv979ixgzZbeYqgQLH2LIL+0tKMWK31qCi+db5Jm62kn2fMjRs3mJSU\nxHLloijsGSRwRXsC9/Z1nYoSSWFgv+JzPJXACzQaB3PixIlcvHgxrdaaFJl/PQTeJlCWorjVTQJn\nabNFcdGiRSTJpk3bM62YlNcr6z5tILRqx6DWtjpFgSxfF15SLJ2NIXCROt10BgdHpi4ZnTt3jk5n\nCMVs6jqBuSxevHTqDOLMmTMMCAijMOLvo9H4PHW6AFos/Wm3P8IGDZr5zQTnzVvAoKBStNmC2L17\n39TjDB8+gkZjRwJPUBTIasSoqEdy/RsaNmwULZYIAk9q59meQADt9kDu2rWLQUERWsDhaoqZWmMC\nu6mq4X417seNG0eDwZtGxUNgOAEnIyKqcMaMWan7TZ48mWZzb22/LyhmrMGaQo0l8DfN5t5s3vzJ\nXJ/bnQCpPKTy+Cdw//2N6Z8u5FMCOvqma9fre1Cv95adPUmbrWK2vVbWrVtHl6upT/8e7Wb/JXWb\nqjai2RxIUUKWBL6nzRaY6Xr8lCkf0mgMItCMomKhnb5P/g5HCzocYQQqEehHIJJiKexdqmqZ1LiN\n559/WTM2uymWlZwE4n3k/JC9e/cnSc1l9VmmZed9iDpdMQJOmkx2mkyhBPoTqEudrgItlie0tfiN\nWpulmpxJPnJWYXx8fOp57dixgyVKVKReb2DZsjW4bt06v7iGn3/+mbVrRzMkRBjeN2/ezA8//JBL\nlizxm218/fXXWoXGXQRO0Gptw+efH0hSzBJEhP54CgeFZ1m5cu07Mp4fPXqUP/30U2qszvbt2xka\nWooiZUwXAt9Sr3+L/foN4sGDBxkSUp7Cm601gQcJ9KRO9yZHjx6d2qdI9d+MactzGxgRUTn1+zNn\nzrBPnz5s06YN7fZAGgyvEfiEilKazZo1p9ncz+f/d4kmk5Lj88oLIJWHVB7/BEaOHE9VfYTi6f0g\nbbaaDAgo4fOkfZaKUoalS1eixRJIk0nlO++Mvn3HGqdPn9YMqssITCIQSZ2uGE2mCgQ+p9E4hGFh\n5Thp0ge0WovR6axNm83t563jJSEhgaVKVabB8ByBT6jXl6PTGa491Z4lsIwORzBjY2NptwfRYqlI\noAR1OieNRiVDkNvJkyc5ffp0TpkyhdWrN/DJ2Ouh1dqR48ZNICliKSwWN4XrbgSFK+guijK7QbRY\n6tBodLFLly58//33OX36dK5atYoBAaE0Gq10u0vRYgmiSANPAudptQby2LFjGc5x2bJl1OttFMZ+\nG9u27ZgjB4WBA4cQmOAziP7G0NAKJIXtxOHwjWNJodXqTo178eXy5cvcuHEjv/322wxLYa++OpRW\nayCdzvvodpfkL7/8QpKsXLku02aFJPAJO3fuza1bt2pK2psl+ap23Zrwk0/Ssvteu3aNtWs3pN3+\nCFW1F1XVza+//pqkqC9jMgUSqE/geQIOPvRQQ3bu3JtffPGFZlvxPuCQQByLFQvPcF7bt2/n228P\n5+TJk3nu3LlsX9ecAKk8pPL4J5CcnMx+/V6lorhoswVy+PBRjIuL02weNWm1BvGllwbxs88+46ef\nfpqh2mB22LFjBwMDI7RZwPcEvqHJVII1atRjnz4DUt1XT5w4wR07dmR5U//73/+mzea79n+MJpPC\nunWbUFFcjIy8j0uXLuWVK1d47tw5rlmzhps3b+aJEyeYmJh4Sxl/++03FisWToejNR2OuqxevR6v\nXLlCUnj22O3VKGwtNejvIj2dwiNoNxXF5eeG6vF4ePnyZXo8Hvbq1Y82Ww0ajUNos1Xlyy9nrMVy\n/PhxGgwOprkZzyPg4scfZ4xQ37NnD0ePHsMpU6b4Xa8xY8bSbO7lI98XrFz5QZLkt99+S7u9hs+T\nfQLNZkeG633kyBGGhZWj0/kw7faarFXrIV65coUrV65ky5ZttZnfEa2PeSxfvhZ37tzJxo2b0mQq\nTeAHimJVpbl69Wq+8sor2kzQd/YZzrJlq2XIMnD9+nXGxMRw9uzZfvaK1q0fJ/CQj3L4nkajK/X7\npKQk3ndfXapqSxqNQ6iqoVy8eIlf3ytWrKCqhhJ4h2ZzT4aHl88XBQKpPKTy+Cdz+fJlxsXFcc2a\nNXQ4gmm3d6Dd3oDVq9fLMq3IrXjggaYEvvIZQBawTZvOOepjzpw5VNVuPn1cTs2ou3r1V1TVQNrt\nZamqgfzqq5wHg505c4YrVqzgunXr/PJlbd26VUtD4iFQj2n2IFIkihQ5r0wmZ5YpNH755Rf279+f\nzz77LL/88kt6PB6uXr2awcFlaLHY2aRJW37++eeaC66vrSSYTz7pf502bNhAVS1Ovf51WizdGBpa\nlqdOneL27du5cuVKhoeXp9X6NI3GQTSbizEoKIIuVxi7dOnNqKiHabV2IDCdqtqAPXq8kEHW1q2f\n9kmKmEKrtROjo5vTai1HkQesPYVNJ5EiRY6BihJMYc8IJuCi212Sc+YIp4p33nlX2z6Bwq32XQL2\nTLMaeElMTOR7773Pvn1f5meffcaoqLr0rQApPNPMGdrMnj2bEyZM4I4dOzL0WapUNW226PXM6sH3\n3sv7lCuQykMqDwkZFdWQIlrXu5zTnpMn5/yGi45uS5ETS9y4Ot0kdur0XI76OHbsmLYENovATirK\nE2zfvhvPnj2rlczdofX/A222oDuaJWXG+vXr6XCEUhjJbRTeRCMpPK6KE/iDwHwqijtT+0FMzDIq\nSnHabD2pKDVZp05DLemkt8zreZpM/Vi7diPq9cEUy1t76fXOstvD2KZNR44ZM56bNm2i2RxMoDRF\npuFEmky9WapUBdrtlel0PsygoAi+++677N+/Py2WQIrElkdotbZjp069OG7cBHbv3pczZszKdEms\nYsUHCGz3GahnU6ezUHiNeWcO0RRLm4toMBSj8GZ7hsKWtYhmcwAPHz5MkoyPj6fVWky7fk4C5Wkw\nNGdoaNlUd2Rfbty4wfvvf4RWazsCU6iqtdigQWMKW9lOCmeI51mixK3TyqdHpOr5k2m/weEcNuyd\nHPWRHSCVh1QeEmqGzn0+A8mkLCsY3orvvvtO880fTZ1uGG02t1/E8Lx5C1ihQm2WK3c/P/74kyyN\nuHv37uVDD7Vg2bJRfOmlV5mUlKTVTff3bnI6a+WqWJaXpUtjqKrhFDVMXqZwwV1Dke23nqZMggiE\ns3btRhnaX758mQaDjWnupDcIVKbJZNXSxnhlTiRgYJs2HSk8llyasj1P4bYaRpPpUer1dm3Q/oUi\nhuRZAuNpMJSn8PYi9foP+PDDLTly5Cjq9b51PI7Q5Qq77Tl3796XZvNzFM4BV6goj1CnMzLNZkEK\nj61Q2u3FtXTvZgpbhvjeZOrEOXPmpPa5efNm7TrO9tlnAAcPfjN1nwMHDrBevaa0293U66swbXnt\nLI1GhR06PEPhOWdgcHC5TG01t6J37wFUlNaast9ERQnJdIaSW1BElEcLiNrkfyBjDXMv07Tv4wFE\nadtKAtgC4DcAvwIYmEm7PL+okqLHU0/10AaSmwT+oqpW4fLlt68hkhm7d+/myy8P5quvvu4X5xAT\ns4yqGqk9IW+lqlbivHkLst3vyZMntSfbP7TB5n+0Wotlq/rdyZMnOXnyZI4ePYa//vorSX9FJp5W\nN/kMmi9ps44JFEkUTxPYT0V52K8uiReRfNFAX+81MeA3oU73INPW8H8i4GKJEhU4ZswYms21/JSh\nUFZvEvC1Z5wjYKPRWJz+Szq/MiysIqdOnUqrtZPP9i2ZDrpnz57lY4+1p9MZwnLlanH9+vWsW/dR\nWq3FaTa7+Mwzz/LRRx+n8G77naI2fSDNZhd37drFzp2fI2Bhmrech4rSlIsX+2exrVChDoU9xCvP\nDHbp0oekWHIKDy9Pvf49ipllY5/9kgkoqZmU4+PjOXnyZH7yySe8dOlStn8n165dY+/eA+h2l2bp\n0vdlms8tL0ARUB4GiBKzkQBMuH0d87pIq2MeCqCW9t4OUc42fdt8ubCSosXFixe13FiiVGtOPK2y\ny2OPPcW0LL8ksIr167fIUR8zZ86horjpckVTUdycPTvzIMb58xeyatX6rFatAadM+ZCBgSVoNveh\nwTCEqurm6NFjNEX2DYXhuhSB0T6yjSTwNIH/o8FQnHq9yNzbp8+ATHM2iZnb/Vq7FE1JBGt92wk0\noMjbFUpgMZ3O2ly0aJEWLOeNVTlHsVT2HoEWPrL8Rp1OZe/efaiqdSlmN40JBDEoqCz37dvHUqUq\n02J5hjrdUAJOKkppWq3F/FJ11KvXVEuueJzAStpsxXno0CGeOHGCZ86cSQ2UNBqFe7JOF0STycn3\n3vuQpEhkWLNmPQpPtPdpNHZm+fI1Uh0OvLz66lAtOPQMgf9RVSvxP//5nKSoN6KqVVJnGsJ9+l8U\nWQFeoE4XyWHD3tVckYvTZBpERenA0qWrpBbgKiygCCiP+gD+z+fzUO3ly0wAz/h83g8gJJO+VgFo\nkm5bQf8PJIWIvK4X4UuHDj0olmbS1tibNWt/+4bpOHToEDdu3JilIXbx4v9QVcsQ+JrABhqNgdTr\nB/sc9990uSIzKDKdLoJAHIFVNJkCGRxcmhERFTlt2r+YnJx8y0R/NWo8pA2C9xHQUyR3XEqdbghd\nrnDtiV3Rtg+nqpZjXFwcu3V7njZblKZYyhDoRb1+GPV6pxZz8h5VtQynTZvOdevWUVHcFEs6HxI4\nQINhGMPDK7B8+Sg6HG4aDAqFsZsUM8gI/vjjj0xKSqLBYKbvzMhu75QaIEkKxWyzVdcG8n20Wmtw\nxIiMDxErV67kiy8O5Jgx4zKdEVy/fp09erxAi8VBmy2IEya8x8TERP78889ctGiRpiC9S19x2jUJ\nJFCHwFj27fsyy5ePolg2FLKazd04aVLWOd0KAhQB5fEUgDk+n7sB+DjdPl8BaODzeROA2un2iQRw\nBGIG4ktB/w8k/xD27NmjrZu/S2AMVdXN77//Ps+P06jR4wRifBRDMwpXW+/n72m3l0inyOYwMrIG\nIyNrsHLlBxgcHEmHozEdjscYHBzJI0eO3PKY27dvp83mpqo+S6OxEgEzrdbiWoDiKIrlL29a+Gos\nU6YaU1JS6PF4uHz5cr7zzjt85JGmLFOmJps3b8/4+HiOGjWG/foN4po1a7h//37NlvQ+RQyErzus\nm8BMCiOzyec70m7vwoULFzI5OZlms0rgML3eVXZ7PX755Zep5/Doo+3SXbcvc1VVcNeuXVyyZAlj\nYmIYFFSSDkcVLR9ZBIEoikj8ugSeo1gyfJCqWoZfffUV3e5IpiXBJIHRfO21N29/0LsI8kB5GHPb\nwW3IroC6W7SzA1gO4BUAV9I3HDlyZOr76OhoREdH50hAiSQ71KpVCzt3xmL27AVISfGgd++vERUV\ndfuGOcRqtQC44LOlDAyGsUhJeRBAAFR1KJ555nF8/vl4XL16EYAJqvoRFi/+Eg0aNEC/fq9i7txk\n3LwpntESE0dh0KBhWLny31kes379+vj5553YuHEjbLYmaNy4MTp16o1t25oDGKztFQLgPwDGIixs\nFvR6PQCgQ4cO6NChQ4Y+a9Sokfp+1qxZANoCiAYwA8ANAGYACQCuAWgHIBiAG2IFuxWAk0hJ+RYV\nKvSHwWDAuHHjMGJEYyQmdoWq7kLlyha0bNky9RhBQS7odAdBbeTQ6Q4gKMiVQa6EhAScP38eERER\nMBozH/5GjBiH99+fCYOhPq5c2QDyIwDPAjgDoCYAPYC1APoD6ArgYxiNBzBx4mi0adMGrVuvxdKl\nb+LatRkAjkFVZ6NVq0VZXv+7QWxsLGJjYwtUhpxSD/7LVm8ho9F8JoBOPp99l61MADYAGJRF/wWt\nwCWSPGX79u3aU/pEAuNptQawQ4eOdLsjWaxYBF955Q3evHmTv/76KwcOHML+/V/lTz/9lNq+efOn\nCPzH56l3I6OionMkw8mTJ7Ugu4V+S2PAY9TpJrFdu6456i8mJoZ2+8MUDg1PEmhEYBzN5urU6+/z\nOcZM6nQ2LauuQoPBSaczWKtH8g6bNGnOdu2e5PDhw7l69Wq/ZJv79++n0xlMk6kfjcYBtNuLpzoX\nePngg49oNtupqiUYGlqWv//+O+Pj47lmzRoePXqUJHnw4EFarW6K1Psp2jLezVQZdboeNBoraDOx\ntjSZutHhCPY7VmJiIjt1eo6qGki3uxTnz1+Yo+t1N0ARWLYyAjgAsexkxu0N5vWQZjDXAVgEYOot\n+i/o/4FEkufExcWxT5/+rFmzPi2WELpczaiqbn7xxe09b0QKlUco4jCSqChtOXjwWzk6vkjKWI/C\nEL+BwsOsFA2GRnQ6Q3KcOvz69eusXbshbbamNBgG02x2sXnzVpw5cyZDQsrQZOpDYAJVtQTff3+K\nFg/zdaryE/aWSIqqe0HU6Zx0OqNps7m5YcOG1OMcPnyYEydO5IQJE3jgwAE/GXbs2EFVjaA36lyn\nm0mXK4KqWoIu12NUVTdXrfqS3333HV2uuppd423tmO0oAv4u0GaryEGDBnHYsGF84403OH369FTF\nU5RAEVAeANASwlPqT4iZBwC8oL28/Ev7Ph7A/dq2hwF4IBTOHu3VIl3fBf0/kEjyhd27d2uDnTdl\n+o9U1YAs05lv27aNjzzSiiVKVKRO59TsByZWqVInx8WUVq5cSbu9IUW9iXoU6U4MnDRpEo8ePco5\nc+bR6Qym0Whlq1YdmZCQcNs+r127xnnz5nH8+PGpWWoPHz7MWbNmsVu37hww4FVu2rSJP/74I53O\n9C7AoQQ6URjMkymix98g8C2dzuLZSpo4c+ZMqmpvnz63a/YWbxnZnVTVYjx9+rRm26pLoCNFFuFn\nqNe7abUGc8CA13J0LQsrKCLKIz8p6P+BRJIvLF++nE7nE36DqNUalGmJ2Pj4eG2p62OKmhx/UgT0\n/ZdWa1BqFHV2SUxMZKVK99Ni6UJgGlW1Rmrt+S1btmhK7WcCCbRYurFDh+45Pr9vvvmGNpubDkdH\n2u01+eijjzM5OZnHjx/X4mGOaed9nMKbabWfMRxoRcBDo1HJlvLasGEDbbYqBC5rfbxN4YyQdn0t\nlgD+/fffmldVoM9yVQoVpQJXrFiR4/MsrKAIGMwlknznr7/+QkxMDFJSUtChQwdERkYWtEi5pnr1\n6rh5czuECbAygBWw2RScPn0a27ZtQ6VKlVC9enUAwJIlS5GY+CKAhhCxteW0XirCYqmAY8eOoXTp\n0pke5+bNmzCZTH7bFEVBXFwspk79CIcP70OTJm+gS5cuAIBNm75BYuJzAMSxr18fh82b6+f4/Lp1\newFXr/4bYjHhMmJjq8FsDoDZrKBRo/r47rsHYTDUw40b23HjRjLILwC0gRjzlgOoCGAJdDoL5s5d\niEGDBkCnS+93k0azZs3QsWM0YmKqwWSqiBs3dsHjMeL69f8CqARgMXQ6I8qWrQaDwQSTCbh509uf\nDkajGRERETk+T0nhpaAVuKSAOXDgAAMCwmix9KbZ/CIdjuDU9NtFnQULPqXV6qSqRjAwsAQHDhxM\nVQ2l09mOqhrGiRM/IEkOH/4u9fohmp2juGYnEEszNpubZ86cydD3pk2bGBhYgjqdnpGR1fyi6bPi\n0KFDfPHFF2k2N2Na1PlalilT45btLl26xBYtOtBotNLlCuWCBZ9qrrfekrYvaDOJowS2EXBx9OjR\njImJYXx8PJcsWUKzOYg6XSkaDKUpAhcDKQIZp1NVa3Ls2MzjKP744w8OHTqMQ4a8yT179nD37t1c\nt24dT548yXnzFtJicVBVw2m1ivTrQoafqNeH0miMJrCWZvMLrFr1gRyVUC7sQC5bSeXxT6dbt+ep\n13uzq5I63Yds2bJjQYuVZ1y5coUHDx7k4cOHabEEMC3N+HFarYE8evQoDxw4QIcjmHr9KAKv3c3W\nQQAAD0pJREFUEVBpNhenzRbItWvXZujzxIkT2jLXJgqPoulUFPctB8dFiz6jorjpdDahTueiyVSN\nVuuLVFU3N27ceMtzaNu2My2Wnpqy2ENVDWe1anVpMAyjN/W5KLPrXUIazlq1HvTr48aNG4yLi+Ou\nXbv4yiuDKVKdeOu7/8SwsIrcsmULv/zyy1Rl+fvvv9NuL069/g2KQltuv6qApAgsPXLkCEuXrs60\n3F4k8CGrVq3DunWbs0+fAXmWwLKwgDxQHvo8GMAlkgLjzJkL8Hgqpn4mK+Ls2Qu3aFG0sNlsKFOm\nDM6ePQuLpTSAUto3JWA2l8GJEydQtmxZ7Nr1HXr2PIUnnzyNzz9fgD//3I1z5/5Cq1atMvS5Z88e\n3LxZBSJhgx5APyQleTBx4uRMZbh06RL69u2PpKRYJCRsAvkbgJN4/fXi2LVrK5o2bXrLc9iyZROu\nXx8PwAmgFq5d64nHHnsYVatuhtFoA3AZwEGfFn/AZDL49WEymVCnTh3Url0bDocder0K4ckPAFdw\n7tx5PP74y+jefQYqVKiBvXv3Yvz4qbh69RV4PJMAjEJi4nt4++0Jfv2qqopSpUohMDAQIr2ewGj8\nH0wmPQ4c+B82b96KrVu33vIcJUWPglbgkgJm9uy5VNXqFDUYDlJV63LChPcLWqw85+LFi3Q4ijPN\nhXUz7Xb3HT0Rx8XFUacL8TEeHyKg8qmnMjd879u3j3Z7eT/jssvViJs2bcrW8UqWrOIjt4eK0pbT\np4viUQkJCXzppX4UadBfI/AUdTq732zm22+/5RtvvMVJkybx/Pnz/PPPP+lwBFOnG0dgLo1GJw2G\nqppxnQQW8r776vOJJ7pSFKvyyv053e4ybNXqGU6Z8pFfqvetW7dSVd00GgfRau1GqzWIVmsT7Xe1\nkYoSnC/ZbQsKyGUrqTz+6Xg8Ho4aNZ4uVygdjmAOGfJWjkqiFiViY2PpdAbTag2iw1GcmzdvvqN+\nPB4Pw8IqEShHkV6jBI3G2hw5ckym+1+9elVTXF5byk9U1SAeP348W8dbv349FcVNi+Ul2mzNWaVK\nnQzFukaMGMmIiPIsX/4+v9rzixcvoaqGERhJs7k7IyIq8sKFC9y3bx87d+5FpzOCBsN9FMkYQwjs\nIXCYxYqV4IoVK7UEkt8SiKVO56LB0I/AZ1TVh1Lrv58/f54jRoxix45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lFF22YsUKr6ur9/Hjp3pd3SkFV0uljlFXV++jRx+RMb7mtnBsTktadVpV1YSs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\nTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdb2d810250>"
+       "<matplotlib.figure.Figure at 0x7f38edafbf90>"
       ]
      },
      "metadata": {},
@@ -2354,7 +2364,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fdb2d555390>"
+       "<matplotlib.legend.Legend at 0x7f38ed6f4210>"
       ]
      },
      "execution_count": 39,
@@ -2363,9 +2373,9 @@
     },
     {
      "data": {
-      "image/png": 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J5jcB84EOQD9gVLDZKODMsGIQEakp48ePp0OHDnTo0IHFixczduzYqEMKRUqGuTCzXOBw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\nReuGFnegfMcAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdb2d555bd0>"
+       "<matplotlib.figure.Figure at 0x7f38ed836f10>"
       ]
      },
      "metadata": {},
@@ -2398,7 +2408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/post-processing.ipynb b/docs/source/pythonapi/examples/post-processing.ipynb
index 6e2dd94299..7fbca68644 100644
--- a/docs/source/pythonapi/examples/post-processing.ipynb
+++ b/docs/source/pythonapi/examples/post-processing.ipynb
@@ -21,6 +21,8 @@
     "\n",
     "import openmc\n",
     "from openmc.statepoint import StatePoint\n",
+    "from openmc.source import Source\n",
+    "from openmc.stats import Box\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -272,7 +274,8 @@
     "settings_file.inactive = inactive\n",
     "settings_file.particles = particles\n",
     "source_bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "settings_file.set_source_space('box', source_bounds)\n",
+    "settings_file.source = Source(space=Box(\n",
+    "        source_bounds[:3], source_bounds[3:]))\n",
     "\n",
     "# Export to \"settings.xml\"\n",
     "settings_file.export_to_xml()"
@@ -347,7 +350,7 @@
    "outputs": [
     {
      "data": {
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+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nFMTl8QAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -457,10 +460,9 @@
       "\n",
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
-      "      Version:        0.7.0\n",
-      "      Git SHA1:       c4b14a5ef87f004528d35cbf33fef3ed15a386ca\n",
-      "      Date/Time:      2015-11-29 16:46:53\n",
-      "      MPI Processes:  1\n",
+      "      Version:        0.7.1\n",
+      "      Git SHA1:       ea9fb637f63f9374c7436456141afa850b84acf9\n",
+      "      Date/Time:      2016-01-14 07:02:06\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -596,20 +598,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  3.7900E-01 seconds\n",
-      "   Reading cross sections          =  8.7000E-02 seconds\n",
-      " Total time in simulation          =  2.2064E+02 seconds\n",
-      "   Time in transport only          =  2.2060E+02 seconds\n",
-      "   Time in inactive batches        =  8.7100E+00 seconds\n",
-      "   Time in active batches          =  2.1193E+02 seconds\n",
-      "   Time synchronizing fission bank =  1.4000E-02 seconds\n",
-      "     Sampling source sites         =  8.0000E-03 seconds\n",
-      "     SEND/RECV source sites        =  2.0000E-03 seconds\n",
+      " Total time for initialization     =  3.6000E-01 seconds\n",
+      "   Reading cross sections          =  1.0600E-01 seconds\n",
+      " Total time in simulation          =  2.5756E+02 seconds\n",
+      "   Time in transport only          =  2.5751E+02 seconds\n",
+      "   Time in inactive batches        =  9.7270E+00 seconds\n",
+      "   Time in active batches          =  2.4783E+02 seconds\n",
+      "   Time synchronizing fission bank =  2.1000E-02 seconds\n",
+      "     Sampling source sites         =  1.3000E-02 seconds\n",
+      "     SEND/RECV source sites        =  8.0000E-03 seconds\n",
       "   Time accumulating tallies       =  1.3000E-02 seconds\n",
-      " Total time for finalization       =  1.6600E-01 seconds\n",
-      " Total time elapsed                =  2.2120E+02 seconds\n",
-      " Calculation Rate (inactive)       =  5740.53 neutrons/second\n",
-      " Calculation Rate (active)         =  2123.37 neutrons/second\n",
+      " Total time for finalization       =  1.4600E-01 seconds\n",
+      " Total time elapsed                =  2.5809E+02 seconds\n",
+      " Calculation Rate (inactive)       =  5140.33 neutrons/second\n",
+      " Calculation Rate (active)         =  1815.75 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -866,7 +868,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fe38b3027d0>"
+       "<matplotlib.image.AxesImage at 0x7fc3808ca950>"
       ]
      },
      "execution_count": 24,
@@ -875,9 +877,9 @@
     },
     {
      "data": {
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PFI0uXxK+SsPl49ro4/zR5SHsnEyg3WQksIGs9WhIPrqoPON+mVOjt7jx7GPs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\nY5pK8xQxW5o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3CYgyeqSJ80yPit9HFT8AEYpYSBioHKmt0DB03g+epCiHqRAgQIVn3W8gNKjKPvxUiZFD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\nLjAFNhpx8reeoGFzET+yyjCzlBb9bL7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iMlLCwDArSILGbeUwvcIabqNCCTsKTUKkKeDF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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe367555950>"
+       "<matplotlib.figure.Figure at 0x7fc3811b25d0>"
       ]
      },
      "metadata": {},
@@ -907,9 +909,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe366cabc10>"
+       "<matplotlib.figure.Figure at 0x7fc380839b50>"
       ]
      },
      "metadata": {},
@@ -1023,7 +1025,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7fe3652fd810>"
+       "<matplotlib.text.Text at 0x7fc380511390>"
       ]
      },
      "execution_count": 28,
@@ -1032,9 +1034,9 @@
     },
     {
      "data": {
-      "image/png": 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GlUqYsKkZZ/mbetoFju0tliVNMWOz/EnQvh/v08Djdeszgd8lyzuJY2Y+3+OQuiCW9w/K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\nJEmSJEmSJEmSJEmSJEmSJHXm/wHv3X8uqw7iTQAAAABJRU5ErkJggg==\n",
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5mt6MuyeOOl2jbyNJ6lyTuaqOonfn0z/0ix6Q5LlVdfFKx1bVBcAFQ2WnD21vW+EcFwMr\nfpYkaTqa3FX1ZuApVXUlQJLD6N1a+8guA5Mkzacmd1Xtt5Q0AKrquzR8cFCStPY0SQBfS/Iu4O/o\nDYxvBb7aaVSSpLnVJHG8ENhGb00OgC8A7+gsIknSXBubOPq31F5WVQ8F3jSdkCRJ82zsGEdV3QJc\nmeSBU4pHkjTnmlyqWqD35PiX6T1rAb0HAI/pLixJ0rxqkjhe2f9z8ClDH8iTpH3UuLmq9qc3MP4Q\n4FvAu5s8MS5JWtvGjXGcCTyKXtLYBLxxKhFJkubauEtVG6rq4QBJzgC+Mp2QJM2bhYXeSn/LcXnY\nfc+4xHHL0pv+hIVTCEfSPHJ5WA0alzgekeQnA9v7D2xXVbkynyTtg8ZNq75umoFIklaHJpMcSpJ0\nOxOHJKkVE4ckqRUThySgd8ttsvzLW241yAWZJAHecqvm7HFIkloxcUiSWjFxSJJaMXFIkloxcUiS\nWjFxSNKUrF8/+pbnhYVZR9ect+NK0pTs2jV632qagNwehySpFROHJKkVE4ckqRUThySpFROHJKkV\nE4ckqRUThySpFROHJKkVE4ckqRUThySplc4TR5Kjk1yR5Kokp4yoc2p//2VJDu+XHZzkc0kuT/Kd\nJC/pOlZJ0so6TRxJ1gFvB44GNgInJNkwVGcT8JCqOhQ4CTitv+tm4OVV9TDgSODFw8dKkqav6x7H\nEcDVVXVNVd0MnA0cO1TnGOBMgKq6FDggyX2r6v9V1Tf75T8FFoH7dxyvJGkFXSeOA4FrB7av65et\nVOegwQpJDgEOBy6deISSpFa6nla9GtYbnlD49uOS3BP4CPDSfs/j12zevPn29xs2bGDjxo17EKYA\nduzYMesQ1pR5bM+TTtrMTTfdddl997jHL9i+/ZwpR9TMPLbl5G1h+/btnZx5586dLC4uTux8XSeO\n64GDB7YPptejGFfnoH4ZSe4MnAP8XVV9dLkPOOec+fyhr1ZbtmyZdQhryry159atUCP/O3dXYL7i\nHTRvbTlpW7dO7ztmLxf/6PpS1VeBQ5MckuQuwHHAeUN1zgOeA5DkSOCHVXVjet/sDGBnVb214zgl\nSQ112uOoqluSbAMuBNYBZ1TVYpKT+/tPr6rzk2xKcjVwE3Bi//DHAn8MfCvJN/plr6iqT3QZsyRp\nvM6Xjq2qC4ALhspOH9retsxxl+ADipL2EUvrkY/aN27Z2WlzzXFJmgOraT1y/0cvSWrFxCFJasXE\nIUlqxcQhSWrFxCGtMQsLvcHU5V7r1886Oq0F3lUlrTG7d497Olzae/Y4JEmtmDgkSa2YOCRJrZg4\npFVq1CC4A+DqmoPj0irlILhmxR6HJKkVE4ckqRUThySpFROHJKkVE4ckqRUThySpFROHJKkVE4ck\nzbml9ciHXwsLs4nHBwAlac6NWo98VmuR2+OQ5phra2ge2eOQ5pjTimge2eOQZsxehVYbE4c0BeOS\nA/R6Fcu9Rl3blmbJS1XSFHjJSWuJPQ5JUismDmlChi9Hbd26xbEKrUkmDmlCli5HLb0+8IHtjlVo\nTTJxSJJacXBcklappalIRunqhgx7HFILPnOhebJr1+hbubu8i88eh9SCt9VK9jgkSS2ZOKRljLok\n5eUoqePEkeToJFckuSrJKSPqnNrff1mSw9scK3Vl+NZab6uVfqWzxJFkHfB24GhgI3BCkg1DdTYB\nD6mqQ4GTgNOaHqvJ27lz56xDWFNsz8mxLedLlz2OI4Crq+qaqroZOBs4dqjOMcCZAFV1KXBAkvs1\nPFYTtri4OOsQ9si4O5329DWJS1KrtT3nkW05X7pMHAcC1w5sX9cva1Ln/g2OnZqLLrqo8+NWqjtq\nf5vy4bI9/V5NjPvHfNRylysdMyre4ctKn/vcRWNvURxXZ6l8+JLUrNtzlL35zKbH7ulvc9S+vSnr\n2jz/XR+1bxa/zS4TR9ObFme0+GFz8/xjalq+sACPf/xRv/aP8fD2ueee0+p/5ePWOx41RvCa17wW\naD+9ONwx3lG9gybtvtoS8Sgmjsma57/ro/bN4reZ6uim9CRHAq+tqqP7268Abquq1w/U+R/ARVV1\ndn/7CuBxwINWOrZf7h31krQHqmqP/9Pe5QOAXwUOTXIIcANwHHDCUJ3zgG3A2f1E88OqujHJDxoc\nu1dfXJK0ZzpLHFV1S5JtwIXAOuCMqlpMcnJ//+lVdX6STUmuBm4CThx3bFexSpKa6+xSlSRpbfLJ\ncUlSKyYOSVIrazJxJHloktOSfCjJv591PKtdkmOTvDPJ2UmePOt4VrMkD0ryriQfnnUsq1mSeyQ5\ns/+73DLreFa7tr/LNT3GkeROwNlV9exZx7IWJDkAeGNVPX/Wsax2ST5cVX806zhWqyR/Auyqqo8n\nObuqjp91TGtB09/lXPc4krw7yY1Jvj1U3mTyxKcDH6c3XYnYu/bseyW9OcT2eRNoSw1p2aaDs07c\nOtVAV4kuf6NznTiA99Cb6PB2oyZATPInSd6S5P4AVfWxqvoD4LnTDnqO7VF7puf1wAVV9c3phz2X\n9vi3qZEatym9aYgO7leb93/HZqVNe7Yy1w1eVV8Adg8VLzsBYlW9v6peXlU3JHlckrclOR343LTj\nnld72p7AnwFPBP5w6Tmcfd1e/DYX+jMm/I49kl/Xpk2Bc4HNSd5B70FiDWnTnm1/l6tx6djlJkZ8\nzGCFqroYuHiaQa1iTdrzVODUaQa1SjVpy13AC6cZ1Cq3bJtW1c+AfzebkFa1Ue3Z6nc51z2OEdbu\naP5s2J6TY1tOnm06WRNpz9WYOK7nV9c26b+/bkaxrAW25+TYlpNnm07WRNpzNSaO2ydPTHIXehMg\neo1zz9mek2NbTp5tOlkTac+5ThxJzgK+CByW5NokJ1bVLfRm1L0Q2Al80AkQm7E9J8e2nDzbdLK6\nbM81/QCgJGny5rrHIUmaPyYOSVIrJg5JUismDklSKyYOSVIrJg5JUismDklSKyYOrUlJbk3yjYHX\nX846piVJPp3kXv33tyV5/8C+/ZJ8P8nHxhx/9yT/tHSOgfKPJnl2kmOSvKq7b6B93WqcHVdq4mdV\ndfgkT5hkv/6Tt3tzjicAV1bVT/pFNwEPS3K3qvo58GR6cweNfDK3qn6W5ELgmcD7+uf9DeCxwPHA\nL4H/kuRv+lNnSxNlj0P7lCTXJHltkq8l+VaS3+6X36O/YtqlSb6e5Jh++fOSnJfkM8Cnkuyf3lr2\nlyc5N8n/TvKoJCcmecvA57wgyZuXCWEL8D+Hys4Hntp/fwJwFpBxcfXrDC6X+kzgE1X186q6DfgS\n8JS9aStpFBOH1qr9hy5VLa2jXMD3q+pRwGnAX/TL/wr4TFU9BngC8N+T3L2/73Bgc1U9Hngx8IOq\nehjwKuBR/XN+CHh6f4U1gOcBZywT12PpTTQ36IPA8UnuCjwcuHRg33Jx7Q98EnhkkvX9esfTSyZL\nvgz82/FNJO0ZL1VprfrnMZeqzu3/+XXgWf33T6H3D/9SIrkr8AB6SeFTVfXDfvljgbcCVNXlSb7V\nf39Tks/2z3EFcOequnyZz75/f9Gc21XVt5McQq+38fGh+svGVVVXJjkP+KMk5wK/Q2/iuiU3MLRs\nqDQpJg7ti37R//NWfv3vwLOq6qrBikkeQ28c4teKR5z3XfR6CIvAu1vGdB7wRuBxwH2G9t0hrr6z\n6PV6Any0qm4d2HcnXARJHfFSldRzIfCSpY0kS72V4SSxA3h2v85GepeWAKiqLwMH0RvHOIvl3ZDk\nN5cpfzfw2mV6KaPiArgIOIze5bPhz/uXwD+MiEHaKyYOrVXDYxx/vUyd4lf/K38dcOf+gPl3gP+8\nTB2AdwD3SXJ5/5jLgR8N7P8QcElVDZYNugR49FAMVNX1VfX2FnFRvTURPgwsVNXFQ59zBPD5ETFI\ne8X1OKQWktyJ3vjFL5I8GPgUcNjSbbr95y/eXFWfG3H8UcBxVfWijmP8OvDovb19WFqOPQ6pnXsA\nlyT5Jr1B9hdV1S1JDkhyJb3nR5ZNGgBVdRG9pTvvNarOBDwN+IhJQ12xxyFJasUehySpFROHJKkV\nE4ckqRUThySpFROHJKkVE4ckqZX/D2CKDT5bRBuyAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe3675b1f50>"
+       "<matplotlib.figure.Figure at 0x7fc3811963d0>"
       ]
      },
      "metadata": {},
@@ -1085,15 +1087,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if self._edgecolors == 'face':\n"
+      "/usr/local/lib/python2.7/dist-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  if self._edgecolors == str('face'):\n"
      ]
     },
     {
      "data": {
-      "image/png": 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HPnOh1RzoOBe2Z0FuAyRNhdh/glyD6LAiiykAw3ko2Yr24CmcAZXQUALmCLhoBXcXiO8A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\nD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O/JQ8OBPXYkplAEh0GlUdpJ7oZsNHUQwSu8+LIOE39Gj+TZAjRBqAaR0h/zqEnIQ8ai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\n10/XwS8YxvZBMhjwDPKCfrdic6TgyjqmfG+zgIe3sl9FNgDBtKWMQ7+9pgY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T0BgIG1rgkS7gNEPzYRg5BV5cDg+/DYkaqJwMWWEQIuDIJpRpy9C51Sy97h7qx7eFdu3Q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\nclHkFUihhRAohR0VsOwWkGqg0A3bZEiIYuCmh5lmjmRIv4cvW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UQ9gffLu/CticR/rBt/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CSgttCq5LrehW2AN556pPoN9IfJE6xO+Hwf52Xhv2IE83rMITl0DH1AsYI+7H2D4GtnyIt64Z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\nDQ2NFuT/AMH9R/ORrjlAAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe36516b5d0>"
+       "<matplotlib.figure.Figure at 0x7fc38036a3d0>"
       ]
      },
      "metadata": {},
@@ -1126,7 +1128,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/examples/tally-arithmetic.ipynb b/docs/source/pythonapi/examples/tally-arithmetic.ipynb
index 3ec974e057..87cdc9b663 100644
--- a/docs/source/pythonapi/examples/tally-arithmetic.ipynb
+++ b/docs/source/pythonapi/examples/tally-arithmetic.ipynb
@@ -36,6 +36,8 @@
     "import openmc\n",
     "from openmc.statepoint import StatePoint\n",
     "from openmc.summary import Summary\n",
+    "from openmc.source import Source\n",
+    "from openmc.stats import Box\n",
     "\n",
     "%matplotlib inline"
    ]
@@ -288,7 +290,8 @@
     "settings_file.particles = particles\n",
     "settings_file.output = {'tallies': True, 'summary': True}\n",
     "source_bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "settings_file.set_source_space('box', source_bounds)\n",
+    "settings_file.source = Source(space=Box(\n",
+    "        source_bounds[:3], source_bounds[3:]))\n",
     "\n",
     "# Export to \"settings.xml\"\n",
     "settings_file.export_to_xml()"
@@ -363,26 +366,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN\n",
-       "AAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX///9yEhLpgJFNv8Tq\n",
-       "QYT7AAAAAWJLR0QAiAUdSAAAAAd0SU1FB98LGQ4UM+6dthcAAALKSURBVGje7dpLcqQwDAbgHHE2\n",
-       "YeEj+D4cwQucBUfo+3CEXoSp8OhuhF70T4qpKXmdr21LogK2Pj7A8QmNP+HDhw8fPnz48Kf6VH9G\n",
-       "+66vy+je8k19jnf8C5dXIPv86ms56lPdjvaYbyodx3ze+XLE76cXFiD4zPji99z0/AJ4n1lfvJ6f\n",
-       "nl0A6x+578efMSg1wPr172/jPO5yFXM+Ef78gdblM+WPHyguP//t1/g6pA0wfln+ho/fwgYYn19C\n",
-       "/xwDvwHGc9OvC+hs37DTrwuwfWanXxdQTC9Mvyygs3wjTL8uwPJpn/tNDbSGz7T0SBEWw4vLXzbQ\n",
-       "6b6RoveIoO6TvPxlA63qs7z8ZQPF9F+SH22vbX8OQKf5Rtv+EgDNJ3X58wZaxWd1+fMGiuFvir8b\n",
-       "vjp8J/tGy/6jAmRvhW8fwL3vVT+o3grfPoB7r/IpALI3tz8FoJN84/NV873hB8UnM3xzANtf8nb4\n",
-       "dwmg3grfFEDJO8JPE0i9Ff4pAYL3pI8mkHor/HMCeO9JH00g9SafEsh7T/ppARBvp48UwJnelT5S\n",
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-       "vY/v9gEOHz58+PBX+6v/f/wPvnd54f3j6venE/yl769Xv7+j3x/o98/V32/o9+fl389Xnx+g5x/o\n",
-       "+Qt6/oOeP6HnX+j5G3z+h54/ouefV5/foufP6Pk3ev4On/+j9w/o/Qd6/4Le/6D3T/D9V67Y/ZsV\n",
-       "QBq+s+8f0ftP+P41axXguP9NWgDuu/Cdfv+N3r/D9/9TAID+A7T/Ae2/gPs/0P4TtP8F7r9J3AIO\n",
-       "9P+g/Udw/9Oygbf7r9D+L7j/DO1/Q/vv4P4/tP8Q7n9E+y/h/k+0/xTuf4X7b+H+X7T/+BPuf3aM\n",
-       "8OHDhw8fPnz4w/4vzcvgeY10sY0AAAAldEVYdGRhdGU6Y3JlYXRlADIwMTUtMTEtMjVUMTQ6MjA6\n",
-       "NTEtMDg6MDDVsKLDAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE1LTExLTI1VDE0OjIwOjUxLTA4OjAw\n",
-       "pO0afwAAAABJRU5ErkJggg==\n"
-      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nSwQkewAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -591,10 +575,9 @@
       "\n",
       "      Copyright:      2011-2015 Massachusetts Institute of Technology\n",
       "      License:        http://mit-crpg.github.io/openmc/license.html\n",
-      "      Version:        0.7.0\n",
-      "      Git SHA1:       74ffcb447521c968fb64fdaa63e40598783f2fba\n",
-      "      Date/Time:      2015-11-25 14:20:51\n",
-      "      MPI Processes:  1\n",
+      "      Version:        0.7.1\n",
+      "      Git SHA1:       ea9fb637f63f9374c7436456141afa850b84acf9\n",
+      "      Date/Time:      2016-01-14 07:00:14\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -634,13 +617,13 @@
       "       11/1    1.07867    1.05536 +/- 0.01277\n",
       "       12/1    1.04203    1.05345 +/- 0.01096\n",
       "       13/1    1.04482    1.05237 +/- 0.00955\n",
-      "       14/1    1.04116    1.05113 +/- 0.00852\n",
-      "       15/1    1.07569    1.05358 +/- 0.00800\n",
-      "       16/1    1.04188    1.05252 +/- 0.00732\n",
-      "       17/1    1.03775    1.05129 +/- 0.00679\n",
-      "       18/1    0.98462    1.04616 +/- 0.00808\n",
-      "       19/1    1.08613    1.04902 +/- 0.00801\n",
-      "       20/1    1.00571    1.04613 +/- 0.00800\n",
+      "       14/1    1.04117    1.05113 +/- 0.00852\n",
+      "       15/1    1.07581    1.05360 +/- 0.00801\n",
+      "       16/1    1.04235    1.05257 +/- 0.00731\n",
+      "       17/1    1.02710    1.05045 +/- 0.00701\n",
+      "       18/1    1.01970    1.04809 +/- 0.00687\n",
+      "       19/1    1.01022    1.04538 +/- 0.00691\n",
+      "       20/1    1.01449    1.04332 +/- 0.00675\n",
       " Creating state point statepoint.20.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -650,27 +633,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  7.9600E-01 seconds\n",
-      "   Reading cross sections          =  2.1200E-01 seconds\n",
-      " Total time in simulation          =  1.8740E+01 seconds\n",
-      "   Time in transport only          =  1.8727E+01 seconds\n",
-      "   Time in inactive batches        =  2.5970E+00 seconds\n",
-      "   Time in active batches          =  1.6143E+01 seconds\n",
-      "   Time synchronizing fission bank =  2.0000E-03 seconds\n",
-      "     Sampling source sites         =  1.0000E-03 seconds\n",
+      " Total time for initialization     =  1.2510E+00 seconds\n",
+      "   Reading cross sections          =  9.7600E-01 seconds\n",
+      " Total time in simulation          =  1.5844E+01 seconds\n",
+      "   Time in transport only          =  1.5834E+01 seconds\n",
+      "   Time in inactive batches        =  2.2840E+00 seconds\n",
+      "   Time in active batches          =  1.3560E+01 seconds\n",
+      "   Time synchronizing fission bank =  3.0000E-03 seconds\n",
+      "     Sampling source sites         =  2.0000E-03 seconds\n",
       "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
       "   Time accumulating tallies       =  0.0000E+00 seconds\n",
-      " Total time for finalization       =  2.0000E-03 seconds\n",
-      " Total time elapsed                =  1.9553E+01 seconds\n",
-      " Calculation Rate (inactive)       =  4813.25 neutrons/second\n",
-      " Calculation Rate (active)         =  2322.99 neutrons/second\n",
+      " Total time for finalization       =  1.0000E-03 seconds\n",
+      " Total time elapsed                =  1.7110E+01 seconds\n",
+      " Calculation Rate (inactive)       =  5472.85 neutrons/second\n",
+      " Calculation Rate (active)         =  2765.49 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     =  1.04597 +/-  0.00663\n",
-      " k-effective (Track-length)  =  1.04613 +/-  0.00800\n",
-      " k-effective (Absorption)    =  1.04087 +/-  0.00627\n",
-      " Combined k-effective        =  1.04322 +/-  0.00570\n",
+      " k-effective (Collision)     =  1.03935 +/-  0.00682\n",
+      " k-effective (Track-length)  =  1.04332 +/-  0.00675\n",
+      " k-effective (Absorption)    =  1.03845 +/-  0.00598\n",
+      " Combined k-effective        =  1.04024 +/-  0.00523\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -775,10 +758,10 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> total</td>\n",
-       "      <td> (nu-fission / absorption)</td>\n",
-       "      <td> 1.040687</td>\n",
-       "      <td> 0.010913</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(nu-fission / absorption)</td>\n",
+       "      <td>1.040166</td>\n",
+       "      <td>0.009069</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -786,7 +769,7 @@
       ],
       "text/plain": [
        "  nuclide                      score      mean  std. dev.\n",
-       "0   total  (nu-fission / absorption)  1.040687   0.010913"
+       "0   total  (nu-fission / absorption)  1.040166   0.009069"
       ]
      },
      "execution_count": 26,
@@ -836,19 +819,19 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> (0.0e+00 - 6.2e-01)</td>\n",
-       "      <td> total</td>\n",
-       "      <td> absorption</td>\n",
-       "      <td> 0.959302</td>\n",
-       "      <td> 0.010033</td>\n",
+       "      <td>(0.0e+00 - 6.2e-01)</td>\n",
+       "      <td>total</td>\n",
+       "      <td>absorption</td>\n",
+       "      <td>0.95938</td>\n",
+       "      <td>0.008187</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          energy [MeV] nuclide       score      mean  std. dev.\n",
-       "0  (0.0e+00 - 6.2e-01)   total  absorption  0.959302   0.010033"
+       "          energy [MeV] nuclide       score     mean  std. dev.\n",
+       "0  (0.0e+00 - 6.2e-01)   total  absorption  0.95938   0.008187"
       ]
      },
      "execution_count": 27,
@@ -896,19 +879,19 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> (0.0e+00 - 6.2e-01)</td>\n",
-       "      <td> total</td>\n",
-       "      <td> nu-fission</td>\n",
-       "      <td> 1.09103</td>\n",
-       "      <td> 0.012491</td>\n",
+       "      <td>(0.0e+00 - 6.2e-01)</td>\n",
+       "      <td>total</td>\n",
+       "      <td>nu-fission</td>\n",
+       "      <td>1.090899</td>\n",
+       "      <td>0.010602</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          energy [MeV] nuclide       score     mean  std. dev.\n",
-       "0  (0.0e+00 - 6.2e-01)   total  nu-fission  1.09103   0.012491"
+       "          energy [MeV] nuclide       score      mean  std. dev.\n",
+       "0  (0.0e+00 - 6.2e-01)   total  nu-fission  1.090899   0.010602"
       ]
      },
      "execution_count": 28,
@@ -958,12 +941,12 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> (0.0e+00 - 6.2e-01)</td>\n",
-       "      <td> 10000</td>\n",
-       "      <td> total</td>\n",
-       "      <td> absorption</td>\n",
-       "      <td> 0.803182</td>\n",
-       "      <td> 0.008664</td>\n",
+       "      <td>(0.0e+00 - 6.2e-01)</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>absorption</td>\n",
+       "      <td>0.803413</td>\n",
+       "      <td>0.007031</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -971,7 +954,7 @@
       ],
       "text/plain": [
        "          energy [MeV]   cell nuclide       score      mean  std. dev.\n",
-       "0  (0.0e+00 - 6.2e-01)  10000   total  absorption  0.803182   0.008664"
+       "0  (0.0e+00 - 6.2e-01)  10000   total  absorption  0.803413   0.007031"
       ]
      },
      "execution_count": 29,
@@ -1019,12 +1002,12 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> (0.0e+00 - 6.2e-01)</td>\n",
-       "      <td> 10000</td>\n",
-       "      <td> total</td>\n",
-       "      <td> (nu-fission / absorption)</td>\n",
-       "      <td> 1.237982</td>\n",
-       "      <td> 0.014179</td>\n",
+       "      <td>(0.0e+00 - 6.2e-01)</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(nu-fission / absorption)</td>\n",
+       "      <td>1.237053</td>\n",
+       "      <td>0.011765</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1032,10 +1015,10 @@
       ],
       "text/plain": [
        "          energy [MeV]   cell nuclide                      score      mean  \\\n",
-       "0  (0.0e+00 - 6.2e-01)  10000   total  (nu-fission / absorption)  1.237982   \n",
+       "0  (0.0e+00 - 6.2e-01)  10000   total  (nu-fission / absorption)  1.237053   \n",
        "\n",
        "   std. dev.  \n",
-       "0   0.014179  "
+       "0   0.011765  "
       ]
      },
      "execution_count": 30,
@@ -1082,12 +1065,12 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> (0.0e+00 - 6.2e-01)</td>\n",
-       "      <td> 10000</td>\n",
-       "      <td> total</td>\n",
-       "      <td> (((absorption * nu-fission) * absorption) * (n...</td>\n",
-       "      <td> 1.040687</td>\n",
-       "      <td> 0.022989</td>\n",
+       "      <td>(0.0e+00 - 6.2e-01)</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>total</td>\n",
+       "      <td>(((absorption * nu-fission) * absorption) * (n...</td>\n",
+       "      <td>1.040166</td>\n",
+       "      <td>0.019018</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1098,7 +1081,7 @@
        "0  (0.0e+00 - 6.2e-01)  10000   total   \n",
        "\n",
        "                                               score      mean  std. dev.  \n",
-       "0  (((absorption * nu-fission) * absorption) * (n...  1.040687   0.022989  "
+       "0  (((absorption * nu-fission) * absorption) * (n...  1.040166   0.019018  "
       ]
      },
      "execution_count": 31,
@@ -1162,75 +1145,75 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (0.0e+00 - 6.3e-07)</td>\n",
-       "      <td> (U-238 / total)</td>\n",
-       "      <td> (nu-fission / flux)</td>\n",
-       "      <td> 0.000001</td>\n",
-       "      <td> 8.078651e-09</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(0.0e+00 - 6.3e-07)</td>\n",
+       "      <td>(U-238 / total)</td>\n",
+       "      <td>(nu-fission / flux)</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>7.377419e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (0.0e+00 - 6.3e-07)</td>\n",
-       "      <td> (U-238 / total)</td>\n",
-       "      <td>    (scatter / flux)</td>\n",
-       "      <td> 0.209990</td>\n",
-       "      <td> 2.449396e-03</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(0.0e+00 - 6.3e-07)</td>\n",
+       "      <td>(U-238 / total)</td>\n",
+       "      <td>(scatter / flux)</td>\n",
+       "      <td>0.209989</td>\n",
+       "      <td>2.303838e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (0.0e+00 - 6.3e-07)</td>\n",
-       "      <td> (U-235 / total)</td>\n",
-       "      <td> (nu-fission / flux)</td>\n",
-       "      <td> 0.356117</td>\n",
-       "      <td> 4.364366e-03</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(0.0e+00 - 6.3e-07)</td>\n",
+       "      <td>(U-235 / total)</td>\n",
+       "      <td>(nu-fission / flux)</td>\n",
+       "      <td>0.356420</td>\n",
+       "      <td>3.951669e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (0.0e+00 - 6.3e-07)</td>\n",
-       "      <td> (U-235 / total)</td>\n",
-       "      <td>    (scatter / flux)</td>\n",
-       "      <td> 0.005555</td>\n",
-       "      <td> 6.495710e-05</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(0.0e+00 - 6.3e-07)</td>\n",
+       "      <td>(U-235 / total)</td>\n",
+       "      <td>(scatter / flux)</td>\n",
+       "      <td>0.005555</td>\n",
+       "      <td>6.101004e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (6.3e-07 - 2.0e+01)</td>\n",
-       "      <td> (U-238 / total)</td>\n",
-       "      <td> (nu-fission / flux)</td>\n",
-       "      <td> 0.007190</td>\n",
-       "      <td> 7.596666e-05</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(6.3e-07 - 2.0e+01)</td>\n",
+       "      <td>(U-238 / total)</td>\n",
+       "      <td>(nu-fission / flux)</td>\n",
+       "      <td>0.007155</td>\n",
+       "      <td>8.053460e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (6.3e-07 - 2.0e+01)</td>\n",
-       "      <td> (U-238 / total)</td>\n",
-       "      <td>    (scatter / flux)</td>\n",
-       "      <td> 0.227843</td>\n",
-       "      <td> 1.024510e-03</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(6.3e-07 - 2.0e+01)</td>\n",
+       "      <td>(U-238 / total)</td>\n",
+       "      <td>(scatter / flux)</td>\n",
+       "      <td>0.227770</td>\n",
+       "      <td>1.079289e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (6.3e-07 - 2.0e+01)</td>\n",
-       "      <td> (U-235 / total)</td>\n",
-       "      <td> (nu-fission / flux)</td>\n",
-       "      <td> 0.008086</td>\n",
-       "      <td> 6.251590e-05</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(6.3e-07 - 2.0e+01)</td>\n",
+       "      <td>(U-235 / total)</td>\n",
+       "      <td>(nu-fission / flux)</td>\n",
+       "      <td>0.008067</td>\n",
+       "      <td>5.254797e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (6.3e-07 - 2.0e+01)</td>\n",
-       "      <td> (U-235 / total)</td>\n",
-       "      <td>    (scatter / flux)</td>\n",
-       "      <td> 0.003365</td>\n",
-       "      <td> 1.646663e-05</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(6.3e-07 - 2.0e+01)</td>\n",
+       "      <td>(U-235 / total)</td>\n",
+       "      <td>(scatter / flux)</td>\n",
+       "      <td>0.003367</td>\n",
+       "      <td>1.647058e-05</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1239,23 +1222,23 @@
       "text/plain": [
        "    cell         energy [MeV]          nuclide                score      mean  \\\n",
        "0  10000  (0.0e+00 - 6.3e-07)  (U-238 / total)  (nu-fission / flux)  0.000001   \n",
-       "1  10000  (0.0e+00 - 6.3e-07)  (U-238 / total)     (scatter / flux)  0.209990   \n",
-       "2  10000  (0.0e+00 - 6.3e-07)  (U-235 / total)  (nu-fission / flux)  0.356117   \n",
+       "1  10000  (0.0e+00 - 6.3e-07)  (U-238 / total)     (scatter / flux)  0.209989   \n",
+       "2  10000  (0.0e+00 - 6.3e-07)  (U-235 / total)  (nu-fission / flux)  0.356420   \n",
        "3  10000  (0.0e+00 - 6.3e-07)  (U-235 / total)     (scatter / flux)  0.005555   \n",
-       "4  10000  (6.3e-07 - 2.0e+01)  (U-238 / total)  (nu-fission / flux)  0.007190   \n",
-       "5  10000  (6.3e-07 - 2.0e+01)  (U-238 / total)     (scatter / flux)  0.227843   \n",
-       "6  10000  (6.3e-07 - 2.0e+01)  (U-235 / total)  (nu-fission / flux)  0.008086   \n",
-       "7  10000  (6.3e-07 - 2.0e+01)  (U-235 / total)     (scatter / flux)  0.003365   \n",
+       "4  10000  (6.3e-07 - 2.0e+01)  (U-238 / total)  (nu-fission / flux)  0.007155   \n",
+       "5  10000  (6.3e-07 - 2.0e+01)  (U-238 / total)     (scatter / flux)  0.227770   \n",
+       "6  10000  (6.3e-07 - 2.0e+01)  (U-235 / total)  (nu-fission / flux)  0.008067   \n",
+       "7  10000  (6.3e-07 - 2.0e+01)  (U-235 / total)     (scatter / flux)  0.003367   \n",
        "\n",
        "      std. dev.  \n",
-       "0  8.078651e-09  \n",
-       "1  2.449396e-03  \n",
-       "2  4.364366e-03  \n",
-       "3  6.495710e-05  \n",
-       "4  7.596666e-05  \n",
-       "5  1.024510e-03  \n",
-       "6  6.251590e-05  \n",
-       "7  1.646663e-05  "
+       "0  7.377419e-09  \n",
+       "1  2.303838e-03  \n",
+       "2  3.951669e-03  \n",
+       "3  6.101004e-05  \n",
+       "4  8.053460e-05  \n",
+       "5  1.079289e-03  \n",
+       "6  5.254797e-05  \n",
+       "7  1.647058e-05  "
       ]
      },
      "execution_count": 33,
@@ -1286,11 +1269,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[  6.65302296e-07]\n",
-      "  [  3.56116716e-01]]\n",
+      "[[[  6.65702880e-07]\n",
+      "  [  3.56420449e-01]]\n",
       "\n",
-      " [[  7.19004460e-03]\n",
-      "  [  8.08598751e-03]]]\n"
+      " [[  7.15488656e-03]\n",
+      "  [  8.06673774e-03]]]\n"
      ]
     }
    ],
@@ -1318,9 +1301,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.00555516]]\n",
+      "[[[ 0.00555533]]\n",
       "\n",
-      " [[ 0.00336498]]]\n"
+      " [[ 0.0033668 ]]]\n"
      ]
     }
    ],
@@ -1342,8 +1325,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[ 0.22784316]\n",
-      "  [ 0.00336498]]]\n"
+      "[[[ 0.22777006]\n",
+      "  [ 0.0033668 ]]]\n"
      ]
     }
    ],
@@ -1388,39 +1371,39 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (0.0e+00 - 6.3e-07)</td>\n",
-       "      <td> U-238</td>\n",
-       "      <td> nu-fission</td>\n",
-       "      <td> 0.000002</td>\n",
-       "      <td> 1.450189e-08</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(0.0e+00 - 6.3e-07)</td>\n",
+       "      <td>U-238</td>\n",
+       "      <td>nu-fission</td>\n",
+       "      <td>0.000002</td>\n",
+       "      <td>1.283958e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (0.0e+00 - 6.3e-07)</td>\n",
-       "      <td> U-235</td>\n",
-       "      <td> nu-fission</td>\n",
-       "      <td> 0.870882</td>\n",
-       "      <td> 7.895515e-03</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(0.0e+00 - 6.3e-07)</td>\n",
+       "      <td>U-235</td>\n",
+       "      <td>nu-fission</td>\n",
+       "      <td>0.868553</td>\n",
+       "      <td>6.880390e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (6.3e-07 - 2.0e+01)</td>\n",
-       "      <td> U-238</td>\n",
-       "      <td> nu-fission</td>\n",
-       "      <td> 0.082484</td>\n",
-       "      <td> 8.253437e-04</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(6.3e-07 - 2.0e+01)</td>\n",
+       "      <td>U-238</td>\n",
+       "      <td>nu-fission</td>\n",
+       "      <td>0.082149</td>\n",
+       "      <td>8.837250e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td> 10000</td>\n",
-       "      <td> (6.3e-07 - 2.0e+01)</td>\n",
-       "      <td> U-235</td>\n",
-       "      <td> nu-fission</td>\n",
-       "      <td> 0.092762</td>\n",
-       "      <td> 6.444580e-04</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>(6.3e-07 - 2.0e+01)</td>\n",
+       "      <td>U-235</td>\n",
+       "      <td>nu-fission</td>\n",
+       "      <td>0.092618</td>\n",
+       "      <td>5.195308e-04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1428,10 +1411,10 @@
       ],
       "text/plain": [
        "    cell         energy [MeV] nuclide       score      mean     std. dev.\n",
-       "0  10000  (0.0e+00 - 6.3e-07)   U-238  nu-fission  0.000002  1.450189e-08\n",
-       "1  10000  (0.0e+00 - 6.3e-07)   U-235  nu-fission  0.870882  7.895515e-03\n",
-       "2  10000  (6.3e-07 - 2.0e+01)   U-238  nu-fission  0.082484  8.253437e-04\n",
-       "3  10000  (6.3e-07 - 2.0e+01)   U-235  nu-fission  0.092762  6.444580e-04"
+       "0  10000  (0.0e+00 - 6.3e-07)   U-238  nu-fission  0.000002  1.283958e-08\n",
+       "1  10000  (0.0e+00 - 6.3e-07)   U-235  nu-fission  0.868553  6.880390e-03\n",
+       "2  10000  (6.3e-07 - 2.0e+01)   U-238  nu-fission  0.082149  8.837250e-04\n",
+       "3  10000  (6.3e-07 - 2.0e+01)   U-235  nu-fission  0.092618  5.195308e-04"
       ]
      },
      "execution_count": 37,
@@ -1471,84 +1454,84 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.0e-08 - 1.1e-07)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 4.630154</td>\n",
-       "      <td> 0.044512</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.0e-08 - 1.1e-07)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>4.619398</td>\n",
+       "      <td>0.040124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.1e-07 - 1.2e-06)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 2.042984</td>\n",
-       "      <td> 0.011429</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.1e-07 - 1.2e-06)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>2.030757</td>\n",
+       "      <td>0.011239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.2e-06 - 1.3e-05)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 1.657517</td>\n",
-       "      <td> 0.008617</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.2e-06 - 1.3e-05)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>1.658488</td>\n",
+       "      <td>0.009777</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.3e-05 - 1.4e-04)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 1.863326</td>\n",
-       "      <td> 0.008848</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.3e-05 - 1.4e-04)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>1.853002</td>\n",
+       "      <td>0.007378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.4e-04 - 1.5e-03)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 2.043916</td>\n",
-       "      <td> 0.014195</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.4e-04 - 1.5e-03)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>2.050773</td>\n",
+       "      <td>0.012484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.5e-03 - 1.6e-02)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 2.134458</td>\n",
-       "      <td> 0.007561</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.5e-03 - 1.6e-02)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>2.131759</td>\n",
+       "      <td>0.007821</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.6e-02 - 1.7e-01)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 2.209947</td>\n",
-       "      <td> 0.013848</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.6e-02 - 1.7e-01)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>2.213710</td>\n",
+       "      <td>0.015159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.7e-01 - 1.9e+00)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 2.006967</td>\n",
-       "      <td> 0.009368</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.7e-01 - 1.9e+00)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>2.011925</td>\n",
+       "      <td>0.009406</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td> 10002</td>\n",
-       "      <td> (1.9e+00 - 2.0e+01)</td>\n",
-       "      <td> H-1</td>\n",
-       "      <td> scatter</td>\n",
-       "      <td> 0.373895</td>\n",
-       "      <td> 0.002964</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>(1.9e+00 - 2.0e+01)</td>\n",
+       "      <td>H-1</td>\n",
+       "      <td>scatter</td>\n",
+       "      <td>0.371280</td>\n",
+       "      <td>0.003949</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1556,15 +1539,15 @@
       ],
       "text/plain": [
        "    cell         energy [MeV] nuclide    score      mean  std. dev.\n",
-       "0  10002  (1.0e-08 - 1.1e-07)     H-1  scatter  4.630154   0.044512\n",
-       "1  10002  (1.1e-07 - 1.2e-06)     H-1  scatter  2.042984   0.011429\n",
-       "2  10002  (1.2e-06 - 1.3e-05)     H-1  scatter  1.657517   0.008617\n",
-       "3  10002  (1.3e-05 - 1.4e-04)     H-1  scatter  1.863326   0.008848\n",
-       "4  10002  (1.4e-04 - 1.5e-03)     H-1  scatter  2.043916   0.014195\n",
-       "5  10002  (1.5e-03 - 1.6e-02)     H-1  scatter  2.134458   0.007561\n",
-       "6  10002  (1.6e-02 - 1.7e-01)     H-1  scatter  2.209947   0.013848\n",
-       "7  10002  (1.7e-01 - 1.9e+00)     H-1  scatter  2.006967   0.009368\n",
-       "8  10002  (1.9e+00 - 2.0e+01)     H-1  scatter  0.373895   0.002964"
+       "0  10002  (1.0e-08 - 1.1e-07)     H-1  scatter  4.619398   0.040124\n",
+       "1  10002  (1.1e-07 - 1.2e-06)     H-1  scatter  2.030757   0.011239\n",
+       "2  10002  (1.2e-06 - 1.3e-05)     H-1  scatter  1.658488   0.009777\n",
+       "3  10002  (1.3e-05 - 1.4e-04)     H-1  scatter  1.853002   0.007378\n",
+       "4  10002  (1.4e-04 - 1.5e-03)     H-1  scatter  2.050773   0.012484\n",
+       "5  10002  (1.5e-03 - 1.6e-02)     H-1  scatter  2.131759   0.007821\n",
+       "6  10002  (1.6e-02 - 1.7e-01)     H-1  scatter  2.213710   0.015159\n",
+       "7  10002  (1.7e-01 - 1.9e+00)     H-1  scatter  2.011925   0.009406\n",
+       "8  10002  (1.9e+00 - 2.0e+01)     H-1  scatter  0.371280   0.003949"
       ]
      },
      "execution_count": 38,
diff --git a/docs/source/pythonapi/index.rst b/docs/source/pythonapi/index.rst
index 6d513d5d5b..6dd2ae10d0 100644
--- a/docs/source/pythonapi/index.rst
+++ b/docs/source/pythonapi/index.rst
@@ -35,6 +35,8 @@ on a given module or class.
     opencg_compatible
     plots
     settings
+    source
+    stats
     surface
     tallies
     trigger
diff --git a/docs/source/pythonapi/source.rst b/docs/source/pythonapi/source.rst
new file mode 100644
index 0000000000..4bc770363a
--- /dev/null
+++ b/docs/source/pythonapi/source.rst
@@ -0,0 +1,8 @@
+.. _pythonapi_source:
+
+======
+Source
+======
+
+.. automodule:: openmc.source
+    :members:
diff --git a/docs/source/pythonapi/stats.rst b/docs/source/pythonapi/stats.rst
new file mode 100644
index 0000000000..58060cacbc
--- /dev/null
+++ b/docs/source/pythonapi/stats.rst
@@ -0,0 +1,58 @@
+.. _pythonapi_stats:
+
+=====================
+Statistical Functions
+=====================
+
+----------------------------
+Summary of Available Classes
+----------------------------
+
+Univariate Probability Distributions
+------------------------------------
+
+.. currentmodule:: openmc.stats.univariate
+
+.. autosummary::
+
+    Univariate
+    Discrete
+    Uniform
+    Maxwell
+    Watt
+    Tabular
+
+Angular Distributions
+---------------------
+
+.. currentmodule:: openmc.stats.multivariate
+
+.. autosummary::
+
+    UnitSphere
+    PolarAzimuthal
+    Isotropic
+    Monodirectional
+
+Spatial Distributions
+---------------------
+
+.. autosummary::
+
+    Spatial
+    CartesianIndependent
+    Box
+    Point
+
+
+Univariate Probability Distributions
+------------------------------------
+
+.. automodule:: openmc.stats.univariate
+    :members:
+
+Multivariate Probability Distributions
+--------------------------------------
+
+.. automodule:: openmc.stats.multivariate
+    :members:
diff --git a/docs/source/usersguide/input.rst b/docs/source/usersguide/input.rst
index c4538b889f..30e9ed07b9 100644
--- a/docs/source/usersguide/input.rst
+++ b/docs/source/usersguide/input.rst
@@ -424,9 +424,17 @@ pseudo-random number generator.
 
 The ``source`` element gives information on an external source distribution to
 be used either as the source for a fixed source calculation or the initial
-source guess for criticality calculations. It takes the following
+source guess for criticality calculations. Multiple ``<source>`` elements may be
+specified to define different source distributions. Each one takes the following
 attributes/sub-elements:
 
+  :strength:
+    The strength of the source. If multiple sources are present, the source
+    strength indicates the relative probability of choosing one source over the
+    other.
+
+    *Default*: 1.0
+
   :file:
     If this attribute is given, it indicates that the source is to be read from
     a binary source file whose path is given by the value of this element. Note,
@@ -440,13 +448,13 @@ attributes/sub-elements:
     has the following attributes:
 
     :type:
-
-      The type of spatial distribution. Valid options are "box", "fission", and
-      "point". A "box" spatial distribution has coordinates sampled uniformly in
-      a parallelepiped. A "fission" spatial distribution samples locations from
-      a "box" distribution but only locations in fissionable materials are
-      accepted. A "point" spatial distribution has coordinates specified by a
-      triplet.
+      The type of spatial distribution. Valid options are "box", "fission",
+      "point", and "cartesian". A "box" spatial distribution has coordinates
+      sampled uniformly in a parallelepiped. A "fission" spatial distribution
+      samples locations from a "box" distribution but only locations in
+      fissionable materials are accepted. A "point" spatial distribution has
+      coordinates specified by a triplet. An "cartesian" spatial distribution
+      specifies independent distributions of x-, y-, and z-coordinates.
 
       *Default*: None
 
@@ -459,67 +467,123 @@ attributes/sub-elements:
 
       For a "point" spatial distribution, ``parameters`` should be given as
       three real numbers which specify the (x,y,z) location of an isotropic
-      point source
+      point source.
+
+      For an "cartesian" distribution, no parameters are specified. Instead,
+      the ``x``, ``y``, and ``z`` elements must be specified.
 
       *Default*: None
 
+    :x:
+      For an "cartesian" distribution, this element specifies the distribution
+      of x-coordinates. The necessary sub-elements/attributes are those of a
+      univariate probability distribution (see the description in
+      :ref:`univariate`).
+
+    :y:
+      For an "cartesian" distribution, this element specifies the distribution
+      of y-coordinates. The necessary sub-elements/attributes are those of a
+      univariate probability distribution (see the description in
+      :ref:`univariate`).
+
+    :z:
+      For an "cartesian" distribution, this element specifies the distribution
+      of z-coordinates. The necessary sub-elements/attributes are those of a
+      univariate probability distribution (see the description in
+      :ref:`univariate`).
+
   :angle:
     An element specifying the angular distribution of source sites. This element
     has the following attributes:
 
     :type:
-      The type of angular distribution. Valid options are "isotropic" and
-      "monodirectional". The angle of the particle emitted from a source site is
-      isotropic if the "isotropic" option is given. The angle of the particle
-      emitted from a source site is the direction specified in the <parameters>
-      attribute if "monodirectional" option is given.
+      The type of angular distribution. Valid options are "isotropic",
+      "monodirectional", and "mu-phi". The angle of the particle emitted from a
+      source site is isotropic if the "isotropic" option is given. The angle of
+      the particle emitted from a source site is the direction specified in the
+      ``reference_uvw`` element/attribute if "monodirectional" option is
+      given. The "mu-phi" option produces directions with the cosine of the
+      polar angle and the azimuthal angle explicitly specified.
 
       *Default*: isotropic
 
-    :parameters:
-      For an "isotropic" angular distribution, ``parameters`` should not be
-      specified.
+    :reference_uvw:
+      The direction from which the polar angle is measured. Represented by the
+      x-, y-, and z-components of a unit vector. For a monodirectional
+      distribution, this defines the direction of all sampled particles.
 
-      For a "monodirectional" angular distribution, ``parameters`` should be
-      given as three real numbers which specify the angular cosines with respect
-      to each axis.
+    :mu:
+      An element specifying the distribution of the cosine of the polar
+      angle. Only relevant when the type is "mu-phi". The necessary
+      sub-elements/attributes are those of a univariate probability distribution
+      (see the description in :ref:`univariate`).
 
-      *Default*: None
+    :phi:
+      An element specifying the distribution of the azimuthal angle. Only
+      relevant when the type is "mu-phi". The necessary sub-elements/attributes
+      are those of a univariate probability distribution (see the description in
+      :ref:`univariate`).
 
   :energy:
-    An element specifying the energy distribution of source sites. This element
-    has the following attributes:
-
-    :type:
-
-      The type of energy distribution. Valid options are "monoenergetic",
-      "watt", and "maxwell". The "monoenergetic" option produces source sites at
-      a single energy. The "watt" option produces source sites whose energy is
-      sampled from a Watt fission spectrum. The "maxwell" option produce source
-      sites whose energy is sampled from a Maxwell fission spectrum.
-
-      *Default*: watt
-
-    :parameters:
-      For a "monoenergetic" energy distribution, ``parameters`` should be
-      given as the energy in MeV of the source sites.
-
-      For a "watt" energy distribution, ``parameters`` should be given as two
-      real numbers :math:`a` and :math:`b` that parameterize the distribution
-      :math:`p(E) dE = c e^{-E/a} \sinh \sqrt{b \, E} dE`.
-
-      For a "maxwell" energy distribution, ``parameters`` should be given as one
-      real number :math:`a` that parameterizes the distribution :math:`p(E) dE =
-      c E e^{-E/a} dE`.
-
-      *Default*: 0.988 2.249
+    An element specifying the energy distribution of source sites. The necessary
+    sub-elements/attributes are those of a univariate probability distribution
+    (see the description in :ref:`univariate`).
 
   :write_initial:
     An element specifying whether to write out the initial source bank used at
     the beginning of the first batch. The output file is named
-    "initial_source.binary(h5)"
+    "initial_source.h5"
 
-      *Default*: false
+    *Default*: false
+
+.. _univariate:
+
+Univariate Probability Distributions
+++++++++++++++++++++++++++++++++++++
+
+Various components of a source distribution involve probability distributions of
+a single random variable, e.g. the distribution of the energy, the distribution
+of the polar angle, and the distribution of x-coordinates. Each of these
+components supports the same syntax with an element whose tag signifies the
+variable and whose sub-elements/attributes are as follows:
+
+:type:
+  The type of the distribution. Valid options are "uniform", "discrete",
+  "tabular", "maxwell", and "watt". The "uniform" option produces variates
+  sampled from a uniform distribution over a finite interval. The "discrete"
+  option produces random variates that can assume a finite number of values
+  (i.e., a distribution characterized by a probability mass function). The
+  "tabular" option produces random variates sampled from a tabulated
+  distribution where the density function is either a histogram or
+  linearly-interpolated between tabulated points. The "watt" option produces
+  random variates is sampled from a Watt fission spectrum (only used for
+  energies). The "maxwell" option produce variates sampled from a Maxwell
+  fission spectrum (only used for energies).
+
+  *Default*: None
+
+:parameters:
+  For a "uniform" distribution, ``parameters`` should be given as two real
+  numbers :math:`a` and :math:`b` that define the interval :math:`[a,b]` over
+  which random variates are sampled.
+
+  For a "discrete" or "tabular" distribution, ``parameters`` provides the
+  :math:`(x,p)` pairs defining the discrete/tabular distribution. All :math:`x`
+  points are given first followed by corresponding :math:`p` points.
+
+  For a "watt" distribution, ``parameters`` should be given as two real numbers
+  :math:`a` and :math:`b` that parameterize the distribution :math:`p(x) dx = c
+  e^{-x/a} \sinh \sqrt{b \, x} dx`.
+
+  For a "maxwell" distribution, ``parameters`` should be given as one real
+  number :math:`a` that parameterizes the distribution :math:`p(x) dx = c x
+  e^{-x/a} dx`.
+
+:interpolation:
+  For a "tabular" distribution, ``interpolation`` can be set to "histogram" or
+  "linear-linear" thereby specifying how tabular points are to be interpolated.
+
+  *Default*: histogram
 
 ``<state_point>`` Element
 -------------------------
diff --git a/examples/python/basic/build-xml.py b/examples/python/basic/build-xml.py
index 488603d33f..97591c9920 100644
--- a/examples/python/basic/build-xml.py
+++ b/examples/python/basic/build-xml.py
@@ -1,4 +1,6 @@
 import openmc
+from openmc.source import Source
+from openmc.stats import Box
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -92,7 +94,7 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', [-4, -4, -4, 4, 4, 4])
+settings_file.source = Source(space=Box([-4, -4, -4], [4, 4, 4]))
 settings_file.export_to_xml()
 
 
diff --git a/examples/python/boxes/build-xml.py b/examples/python/boxes/build-xml.py
index 9c28d37bb3..2ae3ee6129 100644
--- a/examples/python/boxes/build-xml.py
+++ b/examples/python/boxes/build-xml.py
@@ -1,6 +1,8 @@
 import numpy as np
 
 import openmc
+from openmc.source import Source
+from openmc.stats import Box
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -117,7 +119,7 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', np.concatenate(outer_cube.bounding_box))
+settings_file.source = Source(space=Box(*outer_cube.bounding_box))
 settings_file.export_to_xml()
 
 ###############################################################################
diff --git a/examples/python/lattice/hexagonal/build-xml.py b/examples/python/lattice/hexagonal/build-xml.py
index 5fa0f9b1b9..1125e8ce04 100644
--- a/examples/python/lattice/hexagonal/build-xml.py
+++ b/examples/python/lattice/hexagonal/build-xml.py
@@ -1,5 +1,6 @@
 import openmc
-
+from openmc.source import Source
+from openmc.stats import Box
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -125,7 +126,8 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', [-1, -1, -1, 1, 1, 1])
+settings_file.source = Source(space=Box(
+    [-1, -1, -1], [1, 1, 1]))
 settings_file.keff_trigger = {'type' : 'std_dev', 'threshold' : 5E-4}
 settings_file.trigger_active = True
 settings_file.trigger_max_batches = 100
diff --git a/examples/python/lattice/nested/build-xml.py b/examples/python/lattice/nested/build-xml.py
index 501b3ee4b4..389af8e9b7 100644
--- a/examples/python/lattice/nested/build-xml.py
+++ b/examples/python/lattice/nested/build-xml.py
@@ -1,5 +1,6 @@
 import openmc
-
+from openmc.source import Source
+from openmc.stats import Box
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -136,7 +137,8 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', [-1, -1, -1, 1, 1, 1])
+settings_file.source = Source(space=Box(
+    [-1, -1, -1], [1, 1, 1]))
 settings_file.export_to_xml()
 
 
diff --git a/examples/python/lattice/simple/build-xml.py b/examples/python/lattice/simple/build-xml.py
index 00fbea22a4..e648c3d5b3 100644
--- a/examples/python/lattice/simple/build-xml.py
+++ b/examples/python/lattice/simple/build-xml.py
@@ -1,4 +1,6 @@
 import openmc
+from openmc.source import Source
+from openmc.stats import Box
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -125,7 +127,8 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', [-1, -1, -1, 1, 1, 1])
+settings_file.source = Source(space=Box(
+    [-1, -1, -1], [1, 1, 1]))
 settings_file.trigger_active = True
 settings_file.trigger_max_batches = 100
 settings_file.export_to_xml()
diff --git a/examples/python/pincell/build-xml.py b/examples/python/pincell/build-xml.py
index b3bb932dc8..ca71b04e5c 100644
--- a/examples/python/pincell/build-xml.py
+++ b/examples/python/pincell/build-xml.py
@@ -1,4 +1,6 @@
 import openmc
+from openmc.source import Source
+from openmc.stats import Box
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -168,8 +170,8 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', [-0.62992, -0.62992, -1, \
-                                      0.62992, 0.62992, 1])
+settings_file.source = Source(space=Box(
+    [-0.62992, -0.62992, -1], [0.62992, 0.62992, 1]))
 settings_file.entropy_lower_left = [-0.39218, -0.39218, -1.e50]
 settings_file.entropy_upper_right = [0.39218, 0.39218, 1.e50]
 settings_file.entropy_dimension = [10, 10, 1]
diff --git a/examples/python/reflective/build-xml.py b/examples/python/reflective/build-xml.py
index 44b544d20d..7e4fd30be5 100644
--- a/examples/python/reflective/build-xml.py
+++ b/examples/python/reflective/build-xml.py
@@ -1,6 +1,8 @@
 import numpy as np
 
 import openmc
+from openmc.stats import Box
+from openmc.source import Source
 
 ###############################################################################
 #                      Simulation Input File Parameters
@@ -84,5 +86,5 @@ settings_file = openmc.SettingsFile()
 settings_file.batches = batches
 settings_file.inactive = inactive
 settings_file.particles = particles
-settings_file.set_source_space('box', np.concatenate(cell.region.bounding_box))
+settings_file.source = Source(space=Box(*cell.region.bounding_box))
 settings_file.export_to_xml()
diff --git a/openmc/settings.py b/openmc/settings.py
index e8ec6463af..4ad207c7a3 100644
--- a/openmc/settings.py
+++ b/openmc/settings.py
@@ -9,6 +9,7 @@ import numpy as np
 from openmc.clean_xml import *
 from openmc.checkvalue import (check_type, check_length, check_value,
                                check_greater_than, check_less_than)
+from openmc.source import Source
 
 if sys.version_info[0] >= 3:
     basestring = str
@@ -36,8 +37,8 @@ class SettingsFile(object):
         type are 'variance', 'std_dev', and 'rel_err'. The threshold value
         should be a float indicating the variance, standard deviation, or
         relative error used.
-    source_file : str
-        Path to a source file
+    source : Iterable of openmc.source.Source
+        Distribution of source sites in space, angle, and energy
     output : dict
         Dictionary indicating what files to output. Valid keys are 'summary',
         'cross_sections', 'tallies', and 'distribmats'. Values corresponding to
@@ -134,14 +135,7 @@ class SettingsFile(object):
         self._keff_trigger = None
 
         # Source subelement
-        self._source_subelement = None
-        self._source_file = None
-        self._source_space_type = None
-        self._source_space_params = None
-        self._source_angle_type = None
-        self._source_angle_params = None
-        self._source_energy_type = None
-        self._source_energy_params = None
+        self._source = None
 
         self._confidence_intervals = None
         self._cross_sections = None
@@ -228,32 +222,8 @@ class SettingsFile(object):
         return self._keff_trigger
 
     @property
-    def source_file(self):
-        return self._source_file
-
-    @property
-    def source_space_type(self):
-        return self._source_space_type
-
-    @property
-    def source_space_params(self):
-        return self._source_space_params
-
-    @property
-    def source_angle_type(self):
-        return self._source_angle_type
-
-    @property
-    def source_angle_params(self):
-        return self._source_angle_params
-
-    @property
-    def source_energy_type(self):
-        return self._source_energy_type
-
-    @property
-    def source_energy_params(self):
-        return self._source_energy_params
+    def source(self):
+        return self._source
 
     @property
     def confidence_intervals(self):
@@ -468,124 +438,13 @@ class SettingsFile(object):
 
         self._keff_trigger = keff_trigger
 
-    @source_file.setter
-    def source_file(self, source_file):
-        check_type('source file', source_file, basestring)
-        self._source_file = source_file
-
-    def set_source_space(self, stype, params):
-        """Defined the spatial bounds of the external/starting source.
-
-        Parameters
-        ----------
-        stype : str
-            The type of spatial distribution. Valid options are "box",
-            "fission", and "point". A "box" spatial distribution has coordinates
-            sampled uniformly in a parallelepiped. A "fission" spatial
-            distribution samples locations from a "box" distribution but only
-            locations in fissionable materials are accepted. A "point" spatial
-            distribution has coordinates specified by a triplet.
-        params : Iterable of float
-            For a "box" or "fission" spatial distribution, ``params`` should be
-            given as six real numbers, the first three of which specify the
-            lower-left corner of a parallelepiped and the last three of which
-            specify the upper-right corner. Source sites are sampled uniformly
-            through that parallelepiped.
-
-            For a "point" spatial distribution, ``params`` should be given as
-            three real numbers which specify the (x,y,z) location of an
-            isotropic point source
-
-        """
-
-        check_type('source space type', stype, basestring)
-        check_value('source space type', stype, ['box', 'fission', 'point'])
-        check_type('source space parameters', params, Iterable, Real)
-        if stype in ['box', 'fission']:
-            check_length('source space parameters for a '
-                         'box/fission distribution', params, 6)
-        elif stype == 'point':
-            check_length('source space parameters for a point source',
-                         params, 3)
-
-        self._source_space_type = stype
-        self._source_space_params = params
-
-    def set_source_angle(self, stype, params=[]):
-        """Defined the angular distribution of the external/starting source.
-
-        Parameters
-        ----------
-        stype : str
-            The type of angular distribution. Valid options are "isotropic" and
-            "monodirectional". The angle of the particle emitted from a source
-            site is isotropic if the "isotropic" option is given. The angle of
-            the particle emitted from a source site is the direction specified
-            in ``params`` if the "monodirectional" option is given.
-        params : Iterable of float
-            For an "isotropic" angular distribution, ``params`` should not
-            be specified.
-
-            For a "monodirectional" angular distribution, ``params`` should
-            be given as three floats which specify the angular cosines
-            with respect to each axis.
-
-        """
-
-        check_type('source angle type', stype, basestring)
-        check_value('source angle type', stype,
-                    ['isotropic', 'monodirectional'])
-        check_type('source angle parameters', params, Iterable, Real)
-        if stype == 'isotropic' and params is not None:
-            msg = 'Unable to set source angle parameters since they are not ' \
-                  'it is not supported for isotropic type sources'
-            raise ValueError(msg)
-        elif stype == 'monodirectional':
-            check_length('source angle parameters for a monodirectional '
-                         'source', params, 3)
-
-        self._source_angle_type = stype
-        self._source_angle_params = params
-
-    def set_source_energy(self, stype, params=[]):
-        """Defined the energy distribution of the external/starting source.
-
-        Parameters
-        ----------
-        stype : str
-            The type of energy distribution. Valid options are "monoenergetic",
-            "watt", and "maxwell". The "monoenergetic" option produces source
-            sites at a single energy. The "watt" option produces source sites
-            whose energy is sampled from a Watt fission spectrum. The "maxwell"
-            option produce source sites whose energy is sampled from a Maxwell
-            fission spectrum.
-        params : Iterable of float
-            For a "monoenergetic" energy distribution, ``params`` should be
-            given as the energy in MeV of the source sites.
-
-            For a "watt" energy distribution, ``params`` should be given as two
-            real numbers :math:`a` and :math:`b` that parameterize the
-            distribution :math:`p(E) dE = c e^{-E/a} \sinh \sqrt{b \, E} dE`.
-
-            For a "maxwell" energy distribution, ``params`` should be given as
-            one real number :math:`a` that parameterizes the distribution
-            :math:`p(E) dE = c E e^{-E/a} dE`.
-
-        """
-
-        check_type('source energy type', stype, basestring)
-        check_value('source energy type', stype,
-                    ['monoenergetic', 'watt', 'maxwell'])
-        check_type('source energy parameters', params, Iterable, Real)
-        if stype in ['monoenergetic', 'maxwell']:
-            check_length('source energy parameters for a monoenergetic '
-                         'or Maxwell source', params, 1)
-        elif stype == 'watt':
-            check_length('source energy parameters for a Watt source',
-                         params, 2)
-
-        self._source_energy_type = stype
-        self._source_energy_params = params
+    @source.setter
+    def source(self, source):
+        if isinstance(source, Source):
+            self._source = [source,]
+        else:
+            check_type('source distribution', source, Iterable, Source)
+            self._source = source
 
     @output.setter
     def output(self, output):
@@ -924,45 +783,9 @@ class SettingsFile(object):
                 subelement.text = str(self._keff_trigger[key]).lower()
 
     def _create_source_subelement(self):
-        self._create_source_space_subelement()
-        self._create_source_energy_subelement()
-        self._create_source_angle_subelement()
-
-    def _create_source_space_subelement(self):
-        if self._source_space_params is not None:
-            if self._source_subelement is None:
-                self._source_subelement = ET.SubElement(self._settings_file,
-                                                        "source")
-
-            element = ET.SubElement(self._source_subelement, "space")
-            element.set("type", self._source_space_type)
-
-            subelement = ET.SubElement(element, "parameters")
-            subelement.text = ' '.join(map(str, self._source_space_params))
-
-    def _create_source_angle_subelement(self):
-        if self._source_angle_params is not None:
-            if self._source_subelement is None:
-                self._source_subelement = ET.SubElement(self._settings_file,
-                                                        "source")
-
-            element = ET.SubElement(self._source_subelement, "angle")
-            element.set("type", self._source_angle_type)
-
-            subelement = ET.SubElement(element, "parameters")
-            subelement.text = ' '.join(map(str, self._source_angle_params))
-
-    def _create_source_energy_subelement(self):
-        if self._source_energy_params is not None:
-            if self._source_subelement is None:
-                self._source_subelement = ET.SubElement(self._settings_file,
-                                                        "source")
-
-            element = ET.SubElement(self._source_subelement, "energy")
-            element.set("type", self._source_energy_type)
-
-            subelement = ET.SubElement(element, "parameters")
-            subelement.text = ' '.join(map(str, self._source_energy_params))
+        if self.source is not None:
+            for source in self.source:
+                self._settings_file.append(source.to_xml())
 
     def _create_output_subelement(self):
         if self._output is not None:
diff --git a/openmc/source.py b/openmc/source.py
new file mode 100644
index 0000000000..36a36e5948
--- /dev/null
+++ b/openmc/source.py
@@ -0,0 +1,117 @@
+from numbers import Real
+import sys
+from xml.etree import ElementTree as ET
+
+from openmc.stats.univariate import Univariate
+from openmc.stats.multivariate import UnitSphere, Spatial
+import openmc.checkvalue as cv
+
+if sys.version_info[0] >= 3:
+    basestring = str
+
+
+class Source(object):
+    """Distribution of phase space coordinates for source sites.
+
+    Parameters
+    ----------
+    space : openmc.stats.Spatial, optional
+        Spatial distribution of source sites
+    angle : openmc.stats.UnitSphere, optional
+        Angular distribution of source sites
+    energy : openmc.stats.Univariate, optional
+        Energy distribution of source sites
+    filename : str, optional
+        Source file from which sites should be sampled
+    strength : Real
+        Strength of the source
+
+    Attributes
+    ----------
+    space : openmc.stats.Spatial or None
+        Spatial distribution of source sites
+    angle : openmc.stats.UnitSphere or None
+        Angular distribution of source sites
+    energy : openmc.stats.Univariate or None
+        Energy distribution of source sites
+    file : str or None
+        Source file from which sites should be sampled
+    strength : Real
+        Strength of the source
+
+    """
+
+    def __init__(self, space=None, angle=None, energy=None, filename=None, strength=1.0):
+        self._space = None
+        self._angle = None
+        self._energy = None
+        self._file = None
+
+        if space is not None:
+            self.space = space
+        if angle is not None:
+            self.angle = angle
+        if energy is not None:
+            self.energy = energy
+        if filename is not None:
+            self.file = filename
+        self.strength = strength
+
+    @property
+    def file(self):
+        return self._file
+
+    @property
+    def space(self):
+        return self._space
+
+    @property
+    def angle(self):
+        return self._angle
+
+    @property
+    def energy(self):
+        return self._energy
+
+    @property
+    def strength(self):
+        return self._strength
+
+    @file.setter
+    def file(self, filename):
+        cv.check_type('source file', filename, basestring)
+        self._file = filename
+
+    @space.setter
+    def space(self, space):
+        cv.check_type('spatial distribution', space, Spatial)
+        self._space = space
+
+    @angle.setter
+    def angle(self, angle):
+        cv.check_type('angular distribution', angle, UnitSphere)
+        self._angle = angle
+
+    @energy.setter
+    def energy(self, energy):
+        cv.check_type('energy distribution', energy, Univariate)
+        self._energy = energy
+
+    @strength.setter
+    def strength(self, strength):
+        cv.check_type('source strength', strength, Real)
+        cv.check_greater_than('source strength', strength, 0.0, True)
+        self._strength = strength
+
+    def to_xml(self):
+        element = ET.Element("source")
+        element.set("strength", str(self.strength))
+        if self.file is not None:
+            element.set("file", self.file)
+        if self.space is not None:
+            element.append(self.space.to_xml())
+        if self.angle is not None:
+            element.append(self.angle.to_xml())
+        if self.energy is not None:
+            element.append(self.energy.to_xml('energy'))
+        return element
diff --git a/openmc/stats/__init__.py b/openmc/stats/__init__.py
new file mode 100644
index 0000000000..3d7b80b281
--- /dev/null
+++ b/openmc/stats/__init__.py
@@ -0,0 +1,2 @@
+from openmc.stats.univariate import *
+from openmc.stats.multivariate import *
diff --git a/openmc/stats/multivariate.py b/openmc/stats/multivariate.py
new file mode 100644
index 0000000000..29258ee8dd
--- /dev/null
+++ b/openmc/stats/multivariate.py
@@ -0,0 +1,360 @@
+from abc import ABCMeta, abstractmethod
+from collections import Iterable
+from math import pi
+from numbers import Real
+import sys
+from xml.etree import ElementTree as ET
+
+import numpy as np
+
+import openmc.checkvalue as cv
+from openmc.stats.univariate import Univariate, Uniform
+
+if sys.version_info[0] >= 3:
+    basestring = str
+
+
+class UnitSphere(object):
+    """Distribution of points on the unit sphere.
+
+    This abstract class is used for angular distributions, since a direction is
+    represented as a unit vector (i.e., vector on the unit sphere).
+
+    Parameters
+    ----------
+    reference_uvw : Iterable of Real
+        Direction from which polar angle is measured
+
+    Attributes
+    ----------
+    reference_uvw : Iterable of Real
+        Direction from which polar angle is measured
+
+    """
+
+    __metaclass__ = ABCMeta
+
+    def __init__(self, reference_uvw=None):
+        self._reference_uvw = None
+        if reference_uvw is not None:
+            self.reference_uvw = reference_uvw
+
+    @property
+    def reference_uvw(self):
+        return self._reference_uvw
+
+    @reference_uvw.setter
+    def reference_uvw(self, uvw):
+        cv.check_type('reference direction', uvw, Iterable, Real)
+        uvw = np.asarray(uvw)
+        self._reference_uvw = uvw/np.linalg.norm(uvw)
+
+    @abstractmethod
+    def to_xml(self):
+        return ''
+
+
+class PolarAzimuthal(UnitSphere):
+    """Angular distribution represented by polar and azimuthal angles
+
+    This distribution allows one to specify the distribution of the cosine of
+    the polar angle and the azimuthal angle independently of once another.
+
+    Parameters
+    ----------
+    mu : Univariate
+        Distribution of the cosine of the polar angle
+    phi : Univariate
+        Distribution of the azimuthal angle in radians
+    reference_uvw : Iterable of Real
+        Direction from which polar angle is measured. Defaults to the positive
+        z-direction.
+
+    Attributes
+    ----------
+    mu : Univariate
+        Distribution of the cosine of the polar angle
+    phi : Univariate
+        Distribution of the azimuthal angle in radians
+
+    """
+
+    def __init__(self, mu=None, phi=None, reference_uvw=[0., 0., 1.]):
+        super(PolarAzimuthal, self).__init__(reference_uvw)
+        if mu is not None:
+            self.mu = mu
+        else:
+            self.mu = Uniform(-1., 1.)
+
+        if phi is not None:
+            self.phi = phi
+        else:
+            self.phi = Uniform(0., 2*pi)
+
+    @property
+    def mu(self):
+        return self._mu
+
+    @property
+    def phi(self):
+        return self._phi
+
+    @mu.setter
+    def mu(self, mu):
+        cv.check_type('cosine of polar angle', mu, Univariate)
+        self._mu = mu
+
+    @phi.setter
+    def phi(self, phi):
+        cv.check_type('azimuthal angle', phi, Univariate)
+        self._phi = phi
+
+    def to_xml(self):
+        element = ET.Element('angle')
+        element.set("type", "mu-phi")
+        if self.reference_uvw is not None:
+            element.set("reference_uvw", ' '.join(map(str, self.reference_uvw)))
+        element.append(self.mu.to_xml('mu'))
+        element.append(self.phi.to_xml('phi'))
+        return element
+
+
+class Isotropic(UnitSphere):
+    """Isotropic angular distribution.
+
+    """
+
+    def __init__(self):
+        super(Isotropic, self).__init__()
+
+    def to_xml(self):
+        element = ET.Element('angle')
+        element.set("type", "isotropic")
+        return element
+
+
+class Monodirectional(UnitSphere):
+    """Monodirectional angular distribution.
+
+    A monodirectional angular distribution is one for which the polar and
+    azimuthal angles are always the same. It is completely specified by the
+    reference direction vector.
+
+    Parameters
+    ----------
+    reference_uvw : Iterable of Real
+        Direction from which polar angle is measured. Defaults to the positive
+        x-direction.
+
+    """
+
+
+    def __init__(self, reference_uvw=[1., 0., 0.]):
+        super(Monodirectional, self).__init__(reference_uvw)
+
+    def to_xml(self):
+        element = ET.Element('angle')
+        element.set("type", "monodirectional")
+        if self.reference_uvw is not None:
+            element.set("reference_uvw", ' '.join(map(str, self.reference_uvw)))
+        return element
+
+
+class Spatial(object):
+    """Distribution of locations in three-dimensional Euclidean space.
+
+    Classes derived from this abstract class can be used for spatial
+    distributions of source sites.
+
+    """
+
+    __metaclass__ = ABCMeta
+
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def to_xml(self):
+        return ''
+
+
+class CartesianIndependent(Spatial):
+    """Spatial distribution with independent x, y, and z distributions.
+
+    This distribution allows one to specify a coordinates whose x-, y-, and z-
+    components are sampled independently from one another.
+
+    Parameters
+    ----------
+    x : Univariate
+        Distribution of x-coordinates
+    y : Univariate
+        Distribution of y-coordinates
+    z : Univariate
+        Distribution of z-coordinates
+
+    Attributes
+    ----------
+    x : Univariate
+        Distribution of x-coordinates
+    y : Univariate
+        Distribution of y-coordinates
+    z : Univariate
+        Distribution of z-coordinates
+
+    """
+
+
+    def __init__(self, x, y, z):
+        super(CartesianIndependent, self).__init__()
+        self.x = x
+        self.y = y
+        self.z = z
+
+    @property
+    def x(self):
+        return self._x
+
+    @property
+    def y(self):
+        return self._y
+
+    @property
+    def z(self):
+        return self._z
+
+    @x.setter
+    def x(self, x):
+        cv.check_type('x coordinate', x, Univariate)
+        self._x = x
+
+    @y.setter
+    def y(self, y):
+        cv.check_type('y coordinate', y, Univariate)
+        self._y = y
+
+    @z.setter
+    def z(self, z):
+        cv.check_type('z coordinate', z, Univariate)
+        self._z = z
+
+    def to_xml(self):
+        element = ET.Element('space')
+        element.set('type', 'cartesian')
+        element.append(self.x.to_xml('x'))
+        element.append(self.y.to_xml('y'))
+        element.append(self.z.to_xml('z'))
+        return element
+
+
+class Box(Spatial):
+    """Uniform distribution of coordinates in a rectangular cuboid.
+
+    Parameters
+    ----------
+    lower_left : Iterable of Real
+        Lower-left coordinates of cuboid
+    upper_right : Iterable of Real
+        Upper-right coordinates of cuboid
+    only_fissionable : bool, optional
+        Whether spatial sites should only be accepted if they occur in
+        fissionable materials
+
+    Attributes
+    ----------
+    lower_left : Iterable of Real
+        Lower-left coordinates of cuboid
+    upper_right : Iterable of Real
+        Upper-right coordinates of cuboid
+    only_fissionable : bool, optional
+        Whether spatial sites should only be accepted if they occur in
+        fissionable materials
+
+    """
+
+
+    def __init__(self, lower_left, upper_right, only_fissionable=False):
+        super(Box, self).__init__()
+        self.lower_left = lower_left
+        self.upper_right = upper_right
+        self.only_fissionable = only_fissionable
+
+    @property
+    def lower_left(self):
+        return self._lower_left
+
+    @property
+    def upper_right(self):
+        return self._upper_right
+
+    @property
+    def only_fissionable(self):
+        return self._only_fissionable
+
+    @lower_left.setter
+    def lower_left(self, lower_left):
+        cv.check_type('lower left coordinate', lower_left, Iterable, Real)
+        cv.check_length('lower left coordinate', lower_left, 3)
+        self._lower_left = lower_left
+
+    @upper_right.setter
+    def upper_right(self, upper_right):
+        cv.check_type('upper right coordinate', upper_right, Iterable, Real)
+        cv.check_length('upper right coordinate', upper_right, 3)
+        self._upper_right = upper_right
+
+    @only_fissionable.setter
+    def only_fissionable(self, only_fissionable):
+        cv.check_type('only fissionable', only_fissionable, bool)
+        self._only_fissionable = only_fissionable
+
+    def to_xml(self):
+        element = ET.Element('space')
+        if self.only_fissionable:
+            element.set("type", "fission")
+        else:
+            element.set("type", "box")
+        params = ET.SubElement(element, "parameters")
+        params.text = ' '.join(map(str, self.lower_left)) + ' ' + \
+                      ' '.join(map(str, self.upper_right))
+        return element
+
+
+class Point(Spatial):
+    """Delta function in three dimensions.
+
+    This spatial distribution can be used for a point source where sites are
+    emitted at a specific location given by its Cartesian coordinates.
+
+    Parameters
+    ----------
+    xyz : Iterable of Real
+        Cartesian coordinates of location
+
+    Attributes
+    ----------
+    xyz : Iterable of Real
+        Cartesian coordinates of location
+
+    """
+
+    def __init__(self, xyz):
+        super(Point, self).__init__()
+        self.xyz = xyz
+
+    @property
+    def xyz(self):
+        return self._xyz
+
+    @xyz.setter
+    def xyz(self, xyz):
+        cv.check_type('coordinate', xyz, Iterable, Real)
+        cv.check_length('coordinate', xyz, 3)
+        self._xyz = xyz
+
+    def to_xml(self):
+        element = ET.Element('space')
+        element.set("type", "point")
+        params = ET.SubElement(element, "parameters")
+        params.text = ' '.join(map(str, self.xyz))
+        return element
diff --git a/openmc/stats/univariate.py b/openmc/stats/univariate.py
new file mode 100644
index 0000000000..04e70bd004
--- /dev/null
+++ b/openmc/stats/univariate.py
@@ -0,0 +1,310 @@
+from abc import ABCMeta, abstractmethod
+from collections import Iterable
+from numbers import Real
+import sys
+from xml.etree import ElementTree as ET
+
+import openmc.checkvalue as cv
+
+if sys.version_info[0] >= 3:
+    basestring = str
+
+
+class Univariate(object):
+    """Probability distribution of a single random variable.
+
+    The Univariate class is an abstract class that can be derived to implement a
+    specific probability distribution.
+
+    """
+
+    __metaclass__ = ABCMeta
+
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def to_xml(self):
+        return ''
+
+
+class Discrete(Univariate):
+    """Distribution characterized by a probability mass function.
+
+    The Discrete distribution assigns probability values to discrete values of a
+    random variable, rather than expressing the distribution as a continuous
+    random variable.
+
+    Parameters
+    ----------
+    x : Iterable of Real
+        Values of the random variable
+    p : Iterable of Real
+        Discrete probability for each value
+
+    Attributes
+    ----------
+    x : Iterable of Real
+        Values of the random variable
+    p : Iterable of Real
+        Discrete probability for each value
+
+    """
+
+    def __init__(self, x, p):
+        super(Discrete, self).__init__()
+        self.x = x
+        self.p = p
+
+    @property
+    def x(self):
+        return self._x
+
+    @property
+    def p(self):
+        return self._p
+
+    @x.setter
+    def x(self, x):
+        if cv._isinstance(x, Real):
+            x = [x]
+        cv.check_type('discrete values', x, Iterable, Real)
+        self._x = x
+
+    @p.setter
+    def p(self, p):
+        if cv._isinstance(p, Real):
+            p = [p]
+        cv.check_type('discrete probabilities', p, Iterable, Real)
+        for pk in p:
+            cv.check_greater_than('discrete probability', pk, 0.0, True)
+        self._p = p
+
+    def to_xml(self, element_name):
+        element = ET.Element(element_name)
+        element.set("type", "discrete")
+
+        params = ET.SubElement(element, "parameters")
+        params.text = ' '.join(map(str, self.x)) + ' ' + ' '.join(map(str, self.p))
+
+        return element
+
+
+class Uniform(Univariate):
+    """Distribution with constant probability over a finite interval [a,b]
+
+    Parameters
+    ----------
+    a : float, optional
+        Lower bound of the sampling interval. Defaults to zero.
+    b : float, optional
+        Upper bound of the sampling interval. Defaults to unity.
+
+    Attributes
+    ----------
+    a : float
+        Lower bound of the sampling interval
+    b : float
+        Upper bound of the sampling interval
+
+    """
+
+    def __init__(self, a=0.0, b=1.0):
+        super(Uniform, self).__init__()
+        self.a = a
+        self.b = b
+
+    @property
+    def a(self):
+        return self._a
+
+    @property
+    def b(self):
+        return self._b
+
+    @a.setter
+    def a(self, a):
+        cv.check_type('Uniform a', a, Real)
+        self._a = a
+
+    @b.setter
+    def b(self, b):
+        cv.check_type('Uniform b', b, Real)
+        self._b = b
+
+    def to_xml(self, element_name):
+        element = ET.Element(element_name)
+        element.set("type", "uniform")
+        element.set("parameters", '{} {}'.format(self.a, self.b))
+        return element
+
+
+class Maxwell(Univariate):
+    """Maxwellian distribution in energy.
+
+    The Maxwellian distribution in energy is characterized by a single parameter
+    :math:`\theta` and has a density function :math:`p(E) dE = c E e^{-E/\theta}
+    dE`.
+
+    Parameters
+    ----------
+    theta : float
+        Effective temperature for distribution
+
+    Attributes
+    ----------
+    theta : float
+        Effective temperature for distribution
+
+    """
+
+    def __init__(self, theta):
+        super(Maxwell, self).__init__()
+        self.theta = theta
+
+    @property
+    def theta(self):
+        return self._theta
+
+    @theta.setter
+    def theta(self, theta):
+        cv.check_type('Maxwell temperature', theta, Real)
+        cv.check_greater_than('Maxwell temperature', theta, 0.0)
+        self._theta = theta
+
+    def to_xml(self, element_name):
+        element = ET.Element(element_name)
+        element.set("type", "maxwell")
+        element.set("parameters", str(self.theta))
+        return element
+
+
+class Watt(Univariate):
+    """Watt fission energy spectrum.
+
+    The Watt fission energy spectrum is characterized by two parameters
+    :math:`a` and :math:`b` and has density function :math:`p(E) dE = c e^{-E/a}
+    \sinh \sqrt{b \, E} dE`.
+
+    Parameters
+    ----------
+    a : float
+        First parameter of distribution
+    b : float
+        Second parameter of distribution
+
+    Attributes
+    ----------
+    a : float
+        First parameter of distribution
+    b : float
+        Second parameter of distribution
+
+    """
+
+    def __init__(self, a=0.988, b=2.249):
+        super(Watt, self).__init__()
+        self.a = a
+        self.b = b
+
+    @property
+    def a(self):
+        return self._a
+
+    @property
+    def b(self):
+        return self._b
+
+    @a.setter
+    def a(self, a):
+        cv.check_type('Watt a', a, Real)
+        cv.check_greater_than('Watt a', a, 0.0)
+        self._a = a
+
+    @b.setter
+    def b(self, b):
+        cv.check_type('Watt b', b, Real)
+        cv.check_greater_than('Watt b', b, 0.0)
+        self._b = b
+
+    def to_xml(self, element_name):
+        element = ET.Element(element_name)
+        element.set("type", "watt")
+        element.set("parameters", '{} {}'.format(self.a, self.b))
+        return element
+
+
+class Tabular(Univariate):
+    """Piecewise continuous probability distribution.
+
+    This class is used to represent a probability distribution whose density
+    function is tabulated at specific values and is either histogram or linearly
+    interpolated between points.
+
+    Parameters
+    ----------
+    x : Iterable of Real
+        Tabulated values of the random variable
+    p : Iterable of Real
+        Tabulated probabilities
+    interpolation : {'histogram', 'linear-linear'}, optional
+        Indicate whether the density function is constant between tabulated
+        points or linearly-interpolated.
+
+    Attributes
+    ----------
+    x : Iterable of Real
+        Tabulated values of the random variable
+    p : Iterable of Real
+        Tabulated probabilities
+    interpolation : {'histogram', 'linear-linear'}, optional
+        Indicate whether the density function is constant between tabulated
+        points or linearly-interpolated.
+
+    """
+
+    def __init__(self, x, p, interpolation='linear-linear'):
+        super(Tabular, self).__init__()
+        self.x = x
+        self.p = p
+        self.interpolation = interpolation
+
+    @property
+    def x(self):
+        return self._x
+
+    @property
+    def p(self):
+        return self._p
+
+    @property
+    def interpolation(self):
+        return self._interpolation
+
+    @x.setter
+    def x(self, x):
+        cv.check_type('tabulated values', x, Iterable, Real)
+        self._x = x
+
+    @p.setter
+    def p(self, p):
+        cv.check_type('tabulated probabilities', p, Iterable, Real)
+        for pk in p:
+            cv.check_greater_than('tabulated probability', pk, 0.0, True)
+        self._p = p
+
+    @interpolation.setter
+    def interpolation(self, interpolation):
+        cv.check_value('interpolation', interpolation,
+                       ['linear-linear', 'histogram'])
+        self._interpolation = interpolation
+
+    def to_xml(self, element_name):
+        element = ET.Element(element_name)
+        element.set("type", "tabular")
+        element.set("interpolation", self.interpolation)
+
+        params = ET.SubElement(element, "parameters")
+        params.text = ' '.join(map(str, self.x)) + ' ' + ' '.join(map(str, self.p))
+
+        return element
diff --git a/setup.py b/setup.py
index d401f5fbaf..0bf4549c03 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,7 @@ except ImportError:
 
 kwargs = {'name': 'openmc',
           'version': '0.7.1',
-          'packages': ['openmc', 'openmc.mgxs'],
+          'packages': ['openmc', 'openmc.mgxs', 'openmc.stats'],
           'scripts': glob.glob('scripts/openmc-*'),
 
           # Metadata
diff --git a/src/constants.F90 b/src/constants.F90
index e5585fcb92..1238677195 100644
--- a/src/constants.F90
+++ b/src/constants.F90
@@ -360,28 +360,6 @@ module constants
   integer, parameter :: STREAM_TALLIES  = 2
   integer, parameter :: STREAM_SOURCE   = 3
 
-  ! ============================================================================
-  ! EXTERNAL SOURCE PARAMETERS
-
-  ! Source spatial distribution types
-  integer, parameter :: &
-       SRC_SPACE_BOX     = 1, & ! Source in a rectangular prism
-       SRC_SPACE_POINT   = 2, & ! Source at a single point
-       SRC_SPACE_FISSION = 3    ! Source in prism filtered by fissionable mats
-
-  ! Source angular distribution types
-  integer, parameter :: &
-       SRC_ANGLE_ISOTROPIC = 1, & ! Isotropic angular
-       SRC_ANGLE_MONO      = 2, & ! Monodirectional source
-       SRC_ANGLE_TABULAR   = 3    ! Tabular distribution
-
-  ! Source energy distribution types
-  integer, parameter :: &
-       SRC_ENERGY_MONO    = 1, & ! Monoenergetic source
-       SRC_ENERGY_MAXWELL = 2, & ! Maxwell fission spectrum
-       SRC_ENERGY_WATT    = 3, & ! Watt fission spectrum
-       SRC_ENERGY_TABULAR = 4    ! Tabular distribution
-
   ! ============================================================================
   ! MISCELLANEOUS CONSTANTS
 
diff --git a/src/distribution_multivariate.F90 b/src/distribution_multivariate.F90
new file mode 100644
index 0000000000..c288c40c19
--- /dev/null
+++ b/src/distribution_multivariate.F90
@@ -0,0 +1,162 @@
+module distribution_multivariate
+
+  use constants, only: ONE, TWO, PI
+  use distribution_univariate, only: Distribution
+  use math, only: rotate_angle
+  use random_lcg, only: prn
+
+  implicit none
+
+!===============================================================================
+! UNITSPHEREDISTRIBUTION type defines a probability density function for points
+! on the unit sphere. Extensions of this type are used to sample angular
+! distributions for starting sources
+!===============================================================================
+
+  type, abstract :: UnitSphereDistribution
+    real(8) :: reference_uvw(3)
+  contains
+    procedure(iSample), deferred :: sample
+  end type UnitSphereDistribution
+
+  abstract interface
+    function iSample(this) result(uvw)
+      import UnitSphereDistribution
+      class(UnitSphereDistribution), intent(in) :: this
+      real(8) :: uvw(3)
+    end function iSample
+  end interface
+
+!===============================================================================
+! Derived classes of UnitSphereDistribution
+!===============================================================================
+
+  ! Explicit distribution of polar and azimuthal angles
+  type, extends(UnitSphereDistribution) :: PolarAzimuthal
+    class(Distribution), allocatable :: mu
+    class(Distribution), allocatable :: phi
+  contains
+    procedure :: sample => polar_azimuthal_sample
+  end type PolarAzimuthal
+
+  ! Uniform distribution on the unit sphere
+  type, extends(UnitSphereDistribution) :: Isotropic
+  contains
+    procedure :: sample => isotropic_sample
+  end type Isotropic
+
+  ! Monodirectional distribution
+  type, extends(UnitSphereDistribution) :: Monodirectional
+  contains
+    procedure :: sample => monodirectional_sample
+  end type Monodirectional
+
+!===============================================================================
+! SPATIALDISTRIBUTION type defines a probability density function for arbitrary
+! points in Euclidean space.
+!===============================================================================
+
+  type, abstract :: SpatialDistribution
+  contains
+    procedure(iSampleSpatial), deferred :: sample
+  end type SpatialDistribution
+
+  abstract interface
+    function iSampleSpatial(this) result(xyz)
+      import SpatialDistribution
+      class(SpatialDistribution), intent(in) :: this
+      real(8) :: xyz(3)
+    end function iSampleSpatial
+  end interface
+
+  type, extends(SpatialDistribution) :: CartesianIndependent
+    class(Distribution), allocatable :: x
+    class(Distribution), allocatable :: y
+    class(Distribution), allocatable :: z
+  contains
+    procedure :: sample => cartesian_independent_sample
+  end type CartesianIndependent
+
+  type, extends(SpatialDistribution) :: SpatialBox
+    real(8) :: lower_left(3)
+    real(8) :: upper_right(3)
+    logical :: only_fissionable = .false.
+  contains
+    procedure :: sample => spatial_box_sample
+  end type SpatialBox
+
+  type, extends(SpatialDistribution) :: SpatialPoint
+    real(8) :: xyz(3)
+  contains
+    procedure :: sample => spatial_point_sample
+  end type SpatialPoint
+
+contains
+
+  function polar_azimuthal_sample(this) result(uvw)
+    class(PolarAzimuthal), intent(in) :: this
+    real(8) :: uvw(3)
+
+    real(8) :: mu     ! cosine of polar angle
+    real(8) :: phi    ! azimuthal angle
+
+    ! Sample cosine of polar angle
+    mu = this % mu % sample()
+    if (mu == ONE) then
+      uvw(:) = this % reference_uvw
+    else
+      ! Sample azimuthal angle
+      phi = this % phi % sample()
+      uvw(:) = rotate_angle(this % reference_uvw, mu, phi)
+    end if
+  end function polar_azimuthal_sample
+
+  function isotropic_sample(this) result(uvw)
+    class(Isotropic), intent(in) :: this
+    real(8) :: uvw(3)
+
+    real(8) :: phi
+    real(8) :: mu
+
+    phi = TWO*PI*prn()
+    mu = TWO*prn() - ONE
+    uvw(1) = mu
+    uvw(2) = sqrt(ONE - mu*mu) * cos(phi)
+    uvw(3) = sqrt(ONE - mu*mu) * sin(phi)
+  end function isotropic_sample
+
+  function monodirectional_sample(this) result(uvw)
+    class(Monodirectional), intent(in) :: this
+    real(8) :: uvw(3)
+
+    uvw(:) = this % reference_uvw
+  end function monodirectional_sample
+
+  function cartesian_independent_sample(this) result(xyz)
+    class(CartesianIndependent), intent(in) :: this
+    real(8) :: xyz(3)
+
+    xyz(1) = this % x % sample()
+    xyz(2) = this % y % sample()
+    xyz(3) = this % z % sample()
+  end function cartesian_independent_sample
+
+  function spatial_box_sample(this) result(xyz)
+    class(SpatialBox), intent(in) :: this
+    real(8) :: xyz(3)
+
+    integer :: i
+    real(8) :: r(3)
+
+    r = [ (prn(), i = 1,3) ]
+    xyz(:) = this % lower_left + r*(this % upper_right - this % lower_left)
+  end function spatial_box_sample
+
+  function spatial_point_sample(this) result(xyz)
+    class(SpatialPoint), intent(in) :: this
+    real(8) :: xyz(3)
+
+    xyz(:) = this % xyz
+  end function spatial_point_sample
+
+end module distribution_multivariate
diff --git a/src/distribution_univariate.F90 b/src/distribution_univariate.F90
new file mode 100644
index 0000000000..b3d77e2f82
--- /dev/null
+++ b/src/distribution_univariate.F90
@@ -0,0 +1,347 @@
+module distribution_univariate
+
+  use constants, only: ZERO, HALF, HISTOGRAM, LINEAR_LINEAR, MAX_LINE_LEN, &
+       MAX_WORD_LEN
+  use error, only: fatal_error
+  use math, only: maxwell_spectrum, watt_spectrum
+  use random_lcg, only: prn
+  use string, only: to_lower
+  use xml_interface
+
+  implicit none
+
+!===============================================================================
+! DISTRIBUTION type defines a probability density function
+!===============================================================================
+
+  type, abstract :: Distribution
+  contains
+    procedure(iSample), deferred :: sample
+  end type Distribution
+
+  type DistributionContainer
+    class(Distribution), allocatable :: obj
+  end type DistributionContainer
+
+  abstract interface
+    function iSample(this) result(x)
+      import Distribution
+      class(Distribution), intent(in) :: this
+      real(8) :: x
+    end function iSample
+  end interface
+
+!===============================================================================
+! Derived classes of Distribution
+!===============================================================================
+
+  ! Discrete distribution
+  type, extends(Distribution) :: Discrete
+    real(8), allocatable :: x(:)
+    real(8), allocatable :: p(:)
+  contains
+    procedure :: sample => discrete_sample
+    procedure :: initialize => discrete_initialize
+  end type Discrete
+
+  ! Uniform distribution over the interval [a,b]
+  type, extends(Distribution) :: Uniform
+    real(8) :: a
+    real(8) :: b
+  contains
+    procedure :: sample => uniform_sample
+  end type Uniform
+
+  ! Maxwellian distribution of form c*E*exp(-E/a)
+  type, extends(Distribution) :: Maxwell
+    real(8) :: theta
+  contains
+    procedure :: sample => maxwell_sample
+  end type Maxwell
+
+  ! Watt fission spectrum with form c*exp(-E/a)*sinh(sqrt(b*E))
+  type, extends(Distribution) :: Watt
+    real(8) :: a
+    real(8) :: b
+  contains
+    procedure :: sample => watt_sample
+  end type Watt
+
+  ! Histogram or linear-linear interpolated tabular distribution
+  type, extends(Distribution) :: Tabular
+    integer :: interpolation
+    real(8), allocatable :: x(:) ! tabulated independent variable
+    real(8), allocatable :: p(:) ! tabulated probability density
+    real(8), allocatable :: c(:) ! cumulative distribution at tabulated values
+  contains
+    procedure :: sample => tabular_sample
+    procedure :: initialize => tabular_initialize
+  end type Tabular
+
+contains
+
+  function discrete_sample(this) result(x)
+    class(Discrete), intent(in) :: this
+    real(8) :: x
+
+    integer :: i  ! loop counter
+    integer :: n  ! size of distribution
+    real(8) :: c  ! cumulative frequency
+    real(8) :: xi ! sampled CDF value
+
+    n = size(this%x)
+    if (n > 1) then
+      xi = prn()
+      c = ZERO
+      do i = 1, size(this%x)
+        c = c + this%p(i)
+        if (xi < c) exit
+      end do
+      x = this%x(i)
+    else
+      x = this%x(1)
+    end if
+  end function discrete_sample
+
+  subroutine discrete_initialize(this, x, p)
+    class(Discrete), intent(inout) :: this
+    real(8), intent(in) :: x(:)
+    real(8), intent(in) :: p(:)
+
+    integer :: n
+
+    ! Check length of x, p arrays
+    if (size(x) /= size(p)) then
+      call fatal_error('Tabulated probabilities not of same length as &
+           &independent variable.')
+    end if
+
+    ! Copy probability density function
+    n = size(x)
+    allocate(this%x(n), this%p(n))
+    this%x(:) = x(:)
+    this%p(:) = p(:)
+
+    ! Normalize density function
+    this%p(:) = this%p(:)/sum(this%p)
+  end subroutine
+
+  function uniform_sample(this) result(x)
+    class(Uniform), intent(in) :: this
+    real(8) :: x
+
+    x = this%a + prn()*(this%b - this%a)
+  end function uniform_sample
+
+  function maxwell_sample(this) result(x)
+    class(Maxwell), intent(in) :: this
+    real(8) :: x
+
+    x = maxwell_spectrum(this%theta)
+  end function maxwell_sample
+
+  function watt_sample(this) result(x)
+    class(Watt), intent(in) :: this
+    real(8) :: x
+
+    x = watt_spectrum(this%a, this%b)
+  end function watt_sample
+
+  function tabular_sample(this) result(x)
+    class(Tabular), intent(in) :: this
+    real(8) :: x
+
+    integer :: i
+    real(8) :: c         ! sampled cumulative frequency
+    real(8) :: m         ! slope of PDF
+    real(8) :: x_i, x_i1 ! i-th and (i+1)th x values
+    real(8) :: c_i, c_i1 ! i-th and (i+1)th cumulative distribution values
+    real(8) :: p_i, p_i1 ! i-th and (i+1)th probability density values
+
+    ! Sample value of CDF
+    c = prn()
+
+    ! Find first CDF bin which is above the sampled value
+    c_i = this%c(1)
+    do i = 1, size(this%c) - 1
+      c_i1 = this%c(i + 1)
+      if (c <= c_i1) exit
+      c_i = c_i1
+    end do
+
+    ! Determine bounding PDF values
+    x_i = this%x(i)
+    p_i = this%p(i)
+
+    if (this%interpolation == HISTOGRAM) then
+      ! Histogram interpolation
+      if (p_i > ZERO) then
+        x = x_i + (c - c_i)/p_i
+      else
+        x = x_i
+      end if
+    else
+      ! Linear-linear interpolation
+      x_i1 = this%x(i + 1)
+      p_i1 = this%p(i + 1)
+
+      m = (p_i1 - p_i)/(x_i1 - x_i)
+      if (m == ZERO) then
+        x = x_i + (c - c_i)/p_i
+      else
+        x = x_i + (sqrt(max(ZERO, p_i*p_i + 2*m*(c - c_i))) - p_i)/m
+      end if
+    end if
+  end function tabular_sample
+
+  subroutine tabular_initialize(this, x, p, interp)
+    class(Tabular), intent(inout) :: this
+    real(8), intent(in) :: x(:)
+    real(8), intent(in) :: p(:)
+    integer, intent(in) :: interp
+
+    integer :: i
+    integer :: n
+
+    ! Check interpolation parameter
+    if (interp /= HISTOGRAM .and. interp /= LINEAR_LINEAR) then
+      call fatal_error('Only histogram and linear-linear interpolation for tabular &
+           &distribution is supported.')
+    end if
+
+    ! Check length of x, p arrays
+    if (size(x) /= size(p)) then
+      call fatal_error('Tabulated probabilities not of same length as &
+           &independent variable.')
+    end if
+
+    ! Copy probability density function and interpolation parameter
+    n = size(x)
+    allocate(this%x(n), this%p(n), this%c(n))
+    this%interpolation = interp
+    this%x(:) = x(:)
+    this%p(:) = p(:)
+
+    ! Calculate cumulative distribution function
+    this%c(1) = ZERO
+    do i = 2, n
+      if (this%interpolation == HISTOGRAM) then
+        this%c(i) = this%c(i-1) + this%p(i-1)*(this%x(i) - this%x(i-1))
+      elseif (this%interpolation == LINEAR_LINEAR) then
+        this%c(i) = this%c(i-1) + HALF*(this%p(i-1) + this%p(i)) * &
+             (this%x(i) - this%x(i-1))
+      end if
+    end do
+
+    ! Normalize density and distribution functions
+    this%p(:) = this%p(:)/this%c(n)
+    this%c(:) = this%c(:)/this%c(n)
+  end subroutine tabular_initialize
+
+  subroutine distribution_from_xml(dist, node_dist)
+    class(Distribution), allocatable, intent(inout) :: dist
+    type(Node), pointer :: node_dist
+
+    character(MAX_WORD_LEN) :: type
+    character(MAX_LINE_LEN) :: temp_str
+    integer :: n
+    integer :: temp_int
+    real(8), allocatable :: temp_real(:)
+
+    if (check_for_node(node_dist, "type")) then
+      ! Determine type of distribution
+      call get_node_value(node_dist, "type", type)
+
+      ! Determine number of parameters specified
+      if (check_for_node(node_dist, "parameters")) then
+        n = get_arraysize_double(node_dist, "parameters")
+      else
+        n = 0
+      end if
+
+      ! Allocate extension of Distribution
+      select case (to_lower(type))
+      case ('uniform')
+        allocate(Uniform :: dist)
+        if (n /= 2) then
+          call fatal_error('Uniform distribution must have two &
+               &parameters specified.')
+        end if
+
+      case ('maxwell')
+        allocate(Maxwell :: dist)
+        if (n /= 1) then
+          call fatal_error('Maxwell energy distribution must have one &
+               &parameter specified.')
+        end if
+
+      case ('watt')
+        allocate(Watt :: dist)
+        if (n /= 2) then
+          call fatal_error('Watt energy distribution must have two &
+               &parameters specified.')
+        end if
+
+      case ('discrete')
+        allocate(Discrete :: dist)
+
+      case ('tabular')
+        allocate(Tabular :: dist)
+
+      case default
+        call fatal_error('Invalid distribution type: ' // trim(type) // '.')
+
+      end select
+
+      ! Read parameters and interpolation for distribution
+      select type (dist)
+      type is (Uniform)
+        allocate(temp_real(2))
+        call get_node_array(node_dist, "parameters", temp_real)
+        dist%a = temp_real(1)
+        dist%b = temp_real(2)
+        deallocate(temp_real)
+
+      type is (Maxwell)
+        call get_node_value(node_dist, "parameters", dist%theta)
+
+      type is (Watt)
+        allocate(temp_real(2))
+        call get_node_array(node_dist, "parameters", temp_real)
+        dist%a = temp_real(1)
+        dist%b = temp_real(2)
+        deallocate(temp_real)
+
+      type is (Discrete)
+        allocate(temp_real(n))
+        call get_node_array(node_dist, "parameters", temp_real)
+        call dist%initialize(temp_real(1:n/2), temp_real(n/2+1:n))
+        deallocate(temp_real)
+
+      type is (Tabular)
+        ! Read interpolation
+        if (check_for_node(node_dist, "interpolation")) then
+          call get_node_value(node_dist, "interpolation", temp_str)
+          select case(to_lower(temp_str))
+          case ('histogram')
+            temp_int = HISTOGRAM
+          case ('linear-linear')
+            temp_int = LINEAR_LINEAR
+          case default
+            call fatal_error("Unknown interpolation type for distribution: " &
+                 // trim(temp_str))
+          end select
+        else
+          temp_int = HISTOGRAM
+        end if
+
+        ! Read and initialize tabular distribution
+        allocate(temp_real(n))
+        call get_node_array(node_dist, "parameters", temp_real)
+        call dist%initialize(temp_real(1:n/2), temp_real(n/2+1:n), temp_int)
+        deallocate(temp_real)
+      end select
+    end if
+  end subroutine distribution_from_xml
+
+end module distribution_univariate
diff --git a/src/error.F90 b/src/error.F90
index ecd7eb4530..9e5ca84792 100644
--- a/src/error.F90
+++ b/src/error.F90
@@ -3,8 +3,6 @@ module error
   use, intrinsic :: ISO_FORTRAN_ENV
   use constants
 
-  use global
-
 #ifdef MPI
   use message_passing
 #endif
@@ -87,6 +85,9 @@ contains
     integer :: line_wrap ! length of line
     integer :: length    ! length of message
     integer :: indent    ! length of indentation
+#ifdef MPI
+    integer :: mpi_err
+#endif
 
 
     ! set default error code
@@ -136,16 +137,6 @@ contains
       end if
     end do
 
-    ! Write information on current batch, generation, and particle
-    if (current_batch > 0) then
-      write(ERROR_UNIT,'(1X,A,I12) ') 'Batch:     ', current_batch
-      write(ERROR_UNIT,'(1X,A,I12) ') 'Generation:', current_gen
-      write(ERROR_UNIT,*)
-    end if
-
-    ! Release memory from all allocatable arrays
-    call free_memory()
-
 #ifdef MPI
     ! Abort MPI
     call MPI_ABORT(MPI_COMM_WORLD, code, mpi_err)
diff --git a/src/global.F90 b/src/global.F90
index 88ace73b6d..a5943b6b74 100644
--- a/src/global.F90
+++ b/src/global.F90
@@ -12,7 +12,7 @@ module global
   use plot_header,      only: ObjectPlot
   use set_header,       only: SetInt
   use surface_header,   only: SurfaceContainer
-  use source_header,    only: ExtSource
+  use source_header,    only: SourceDistribution
   use tally_header,     only: TallyObject, TallyMap, TallyResult
   use trigger_header,   only: KTrigger
   use timer_header,     only: Timer
@@ -182,7 +182,7 @@ module global
   logical :: satisfy_triggers = .false.       ! whether triggers are satisfied
 
   ! External source
-  type(ExtSource), target :: external_source
+  type(SourceDistribution), allocatable :: external_source(:)
 
   ! Source and fission bank
   type(Bank), allocatable, target :: source_bank(:)
@@ -289,9 +289,6 @@ module global
   character(MAX_FILE_LEN) :: path_particle_restart ! Path to particle restart
   character(MAX_FILE_LEN) :: path_output = ''      ! Path to output directory
 
-  ! Random number seed
-  integer(8) :: seed = 1_8
-
   ! The verbosity controls how much information will be printed to the
   ! screen and in logs
   integer :: verbosity = 7
@@ -453,12 +450,7 @@ contains
     if (allocated(micro_xs)) deallocate(micro_xs)
 
     ! Deallocate external source
-    if (allocated(external_source % params_space)) &
-         deallocate(external_source % params_space)
-    if (allocated(external_source % params_angle)) &
-         deallocate(external_source % params_angle)
-    if (allocated(external_source % params_energy)) &
-         deallocate(external_source % params_energy)
+    if (allocated(external_source)) deallocate(external_source)
 
     ! Deallocate k and entropy
     if (allocated(k_generation)) deallocate(k_generation)
diff --git a/src/input_xml.F90 b/src/input_xml.F90
index 1ca8cc8799..1427458901 100644
--- a/src/input_xml.F90
+++ b/src/input_xml.F90
@@ -3,6 +3,8 @@ module input_xml
   use cmfd_input,       only: configure_cmfd
   use constants
   use dict_header,      only: DictIntInt, ElemKeyValueCI
+  use distribution_multivariate
+  use distribution_univariate
   use energy_grid,      only: grid_method, n_log_bins
   use error,            only: fatal_error, warning
   use geometry_header,  only: Cell, Lattice, RectLattice, HexLattice
@@ -11,7 +13,7 @@ module input_xml
   use mesh_header,      only: RegularMesh
   use output,           only: write_message
   use plot_header
-  use random_lcg,       only: prn
+  use random_lcg,       only: prn, seed
   use surface_header
   use stl_vector,       only: VectorInt
   use string,           only: to_lower, to_str, str_to_int, str_to_real, &
@@ -54,11 +56,11 @@ contains
     character(MAX_LINE_LEN) :: temp_str
     integer :: i
     integer :: n
-    integer :: coeffs_reqd
     integer :: temp_int
     integer :: temp_int_array3(3)
     integer, allocatable :: temp_int_array(:)
     integer(8) :: temp_long
+    real(8), allocatable :: temp_real(:)
     integer :: n_tracks
     logical :: file_exists
     character(MAX_FILE_LEN) :: env_variable
@@ -67,6 +69,8 @@ contains
     type(Node), pointer :: doc            => null()
     type(Node), pointer :: node_mode      => null()
     type(Node), pointer :: node_source    => null()
+    type(Node), pointer :: node_space     => null()
+    type(Node), pointer :: node_angle     => null()
     type(Node), pointer :: node_dist      => null()
     type(Node), pointer :: node_cutoff    => null()
     type(Node), pointer :: node_entropy   => null()
@@ -79,6 +83,7 @@ contains
     type(Node), pointer :: node_trigger   => null()
     type(Node), pointer :: node_keff_trigger => null()
     type(NodeList), pointer :: node_scat_list => null()
+    type(NodeList), pointer :: node_source_list => null()
 
     ! Display output message
     call write_message("Reading settings XML file...", 5)
@@ -339,185 +344,249 @@ contains
     ! ==========================================================================
     ! EXTERNAL SOURCE
 
-    ! Get pointer to source
-    if (check_for_node(doc, "source")) then
-      call get_node_ptr(doc, "source", node_source)
-    else
-      call fatal_error("No source specified in settings XML file.")
-    end if
+    ! Get point to list of <source> elements and make sure there is at least one
+    call get_node_list(doc, "source", node_source_list)
+    n = get_list_size(node_source_list)
+    if (n == 0) call fatal_error("No source specified in settings XML file.")
 
-    ! Check if we want to write out source
-    if (check_for_node(node_source, "write_initial")) then
-      call get_node_value(node_source, "write_initial", temp_str)
-      temp_str = to_lower(temp_str)
-      if (trim(temp_str) == 'true' .or. trim(temp_str) == '1') &
-           write_initial_source = .true.
-    end if
+    ! Allocate array for sources
+    allocate(external_source(n))
 
-    ! Check for external source file
-    if (check_for_node(node_source, "file")) then
-      ! Copy path of source file
-      call get_node_value(node_source, "file", path_source)
+    ! Read each source
+    do i = 1, n
+      ! Get pointer to source
+      call get_list_item(node_source_list, i, node_source)
 
-      ! Check if source file exists
-      inquire(FILE=path_source, EXIST=file_exists)
-      if (.not. file_exists) then
-        call fatal_error("Binary source file '" // trim(path_source) &
-             &// "' does not exist!")
+      ! Check if we want to write out source
+      if (check_for_node(node_source, "write_initial")) then
+        call get_node_value(node_source, "write_initial", temp_str)
+        temp_str = to_lower(temp_str)
+        if (trim(temp_str) == 'true' .or. trim(temp_str) == '1') &
+             write_initial_source = .true.
       end if
 
-    else
-
-      ! Spatial distribution for external source
-      if (check_for_node(node_source, "space")) then
-
-        ! Get pointer to spatial distribution
-        call get_node_ptr(node_source, "space", node_dist)
-
-        ! Check for type of spatial distribution
-        type = ''
-        if (check_for_node(node_dist, "type")) &
-             call get_node_value(node_dist, "type", type)
-        select case (to_lower(type))
-        case ('box')
-          external_source % type_space = SRC_SPACE_BOX
-          coeffs_reqd = 6
-        case ('fission')
-          external_source % type_space = SRC_SPACE_FISSION
-          coeffs_reqd = 6
-        case ('point')
-          external_source % type_space = SRC_SPACE_POINT
-          coeffs_reqd = 3
-        case default
-          call fatal_error("Invalid spatial distribution for external source: "&
-               &// trim(type))
-        end select
-
-        ! Determine number of parameters specified
-        if (check_for_node(node_dist, "parameters")) then
-          n = get_arraysize_double(node_dist, "parameters")
-        else
-          n = 0
-        end if
-
-        ! Read parameters for spatial distribution
-        if (n < coeffs_reqd) then
-          call fatal_error("Not enough parameters specified for spatial &
-               &distribution of external source.")
-        elseif (n > coeffs_reqd) then
-          call fatal_error("Too many parameters specified for spatial &
-               &distribution of external source.")
-        elseif (n > 0) then
-          allocate(external_source % params_space(n))
-          call get_node_array(node_dist, "parameters", &
-               external_source % params_space)
-        end if
+      ! Check for source strength
+      if (check_for_node(node_source, "strength")) then
+        call get_node_value(node_source, "strength", external_source(i)%strength)
       else
-        call fatal_error("No spatial distribution specified for external &
-             &source.")
+        external_source(i)%strength = ONE
       end if
 
-      ! Determine external source angular distribution
-      if (check_for_node(node_source, "angle")) then
+      ! Check for external source file
+      if (check_for_node(node_source, "file")) then
+        ! Copy path of source file
+        call get_node_value(node_source, "file", path_source)
 
-        ! Get pointer to angular distribution
-        call get_node_ptr(node_source, "angle", node_dist)
-
-        ! Check for type of angular distribution
-        type = ''
-        if (check_for_node(node_dist, "type")) &
-             call get_node_value(node_dist, "type", type)
-        select case (to_lower(type))
-        case ('isotropic')
-          external_source % type_angle = SRC_ANGLE_ISOTROPIC
-          coeffs_reqd = 0
-        case ('monodirectional')
-          external_source % type_angle = SRC_ANGLE_MONO
-          coeffs_reqd = 3
-        case ('tabular')
-          external_source % type_angle = SRC_ANGLE_TABULAR
-        case default
-          call fatal_error("Invalid angular distribution for external source: "&
-               &// trim(type))
-        end select
-
-        ! Determine number of parameters specified
-        if (check_for_node(node_dist, "parameters")) then
-          n = get_arraysize_double(node_dist, "parameters")
-        else
-          n = 0
+        ! Check if source file exists
+        inquire(FILE=path_source, EXIST=file_exists)
+        if (.not. file_exists) then
+          call fatal_error("Binary source file '" // trim(path_source) &
+               &// "' does not exist!")
         end if
 
-        ! Read parameters for angle distribution
-        if (n < coeffs_reqd) then
-          call fatal_error("Not enough parameters specified for angle &
-               &distribution of external source.")
-        elseif (n > coeffs_reqd) then
-          call fatal_error("Too many parameters specified for angle &
-               &distribution of external source.")
-        elseif (n > 0) then
-          allocate(external_source % params_angle(n))
-          call get_node_array(node_dist, "parameters", &
-               external_source % params_angle)
-        end if
       else
-        ! Set default angular distribution isotropic
-        external_source % type_angle  = SRC_ANGLE_ISOTROPIC
-      end if
 
-      ! Determine external source energy distribution
-      if (check_for_node(node_source, "energy")) then
+        ! Spatial distribution for external source
+        if (check_for_node(node_source, "space")) then
 
-        ! Get pointer to energy distribution
-        call get_node_ptr(node_source, "energy", node_dist)
+          ! Get pointer to spatial distribution
+          call get_node_ptr(node_source, "space", node_space)
 
-        ! Check for type of energy distribution
-        type = ''
-        if (check_for_node(node_dist, "type")) &
-             call get_node_value(node_dist, "type", type)
-        select case (to_lower(type))
-        case ('monoenergetic')
-          external_source % type_energy = SRC_ENERGY_MONO
-          coeffs_reqd = 1
-        case ('maxwell')
-          external_source % type_energy = SRC_ENERGY_MAXWELL
-          coeffs_reqd = 1
-        case ('watt')
-          external_source % type_energy = SRC_ENERGY_WATT
-          coeffs_reqd = 2
-        case ('tabular')
-          external_source % type_energy = SRC_ENERGY_TABULAR
-        case default
-          call fatal_error("Invalid energy distribution for external source: " &
-               &// trim(type))
-        end select
+          ! Check for type of spatial distribution
+          type = ''
+          if (check_for_node(node_space, "type")) &
+               call get_node_value(node_space, "type", type)
+          select case (to_lower(type))
+          case ('cartesian')
+            allocate(CartesianIndependent :: external_source(i)%space)
+
+          case ('box')
+            allocate(SpatialBox :: external_source(i)%space)
+
+          case ('fission')
+            allocate(SpatialBox :: external_source(i)%space)
+            select type(space => external_source(i)%space)
+            type is (SpatialBox)
+              space%only_fissionable = .true.
+            end select
+
+          case ('point')
+            allocate(SpatialPoint :: external_source(i)%space)
+
+          case default
+            call fatal_error("Invalid spatial distribution for external source: "&
+                 // trim(type))
+          end select
+
+          select type (space => external_source(i)%space)
+          type is (CartesianIndependent)
+            ! Read distribution for x coordinate
+            if (check_for_node(node_space, "x")) then
+              call get_node_ptr(node_space, "x", node_dist)
+              call distribution_from_xml(space%x, node_dist)
+            else
+              allocate(Discrete :: space%x)
+              select type (dist => space%x)
+              type is (Discrete)
+                allocate(dist%x(1), dist%p(1))
+                dist%x(1) = ZERO
+                dist%p(1) = ONE
+              end select
+            end if
+
+            ! Read distribution for y coordinate
+            if (check_for_node(node_space, "y")) then
+              call get_node_ptr(node_space, "y", node_dist)
+              call distribution_from_xml(space%y, node_dist)
+            else
+              allocate(Discrete :: space%y)
+              select type (dist => space%y)
+              type is (Discrete)
+                allocate(dist%x(1), dist%p(1))
+                dist%x(1) = ZERO
+                dist%p(1) = ONE
+              end select
+            end if
+
+            if (check_for_node(node_space, "z")) then
+              call get_node_ptr(node_space, "z", node_dist)
+              call distribution_from_xml(space%z, node_dist)
+            else
+              allocate(Discrete :: space%z)
+              select type (dist => space%z)
+              type is (Discrete)
+                allocate(dist%x(1), dist%p(1))
+                dist%x(1) = ZERO
+                dist%p(1) = ONE
+              end select
+            end if
+
+          type is (SpatialBox)
+            ! Make sure correct number of parameters are given
+            if (get_arraysize_double(node_space, "parameters") /= 6) then
+              call fatal_error('Box/fission spatial source must have &
+                   &six parameters specified.')
+            end if
+
+            ! Read lower-right/upper-left coordinates
+            allocate(temp_real(6))
+            call get_node_array(node_space, "parameters", temp_real)
+            space%lower_left(:) = temp_real(1:3)
+            space%upper_right(:) = temp_real(4:6)
+            deallocate(temp_real)
+
+          type is (SpatialPoint)
+            ! Make sure correct number of parameters are given
+            if (get_arraysize_double(node_space, "parameters") /= 3) then
+              call fatal_error('Point spatial source must have &
+                   &three parameters specified.')
+            end if
+
+            ! Read location of point source
+            allocate(temp_real(3))
+            call get_node_array(node_space, "parameters", temp_real)
+            space%xyz(:) = temp_real
+            deallocate(temp_real)
+
+          end select
 
-        ! Determine number of parameters specified
-        if (check_for_node(node_dist, "parameters")) then
-          n = get_arraysize_double(node_dist, "parameters")
         else
-          n = 0
+          call fatal_error("No spatial distribution specified for external &
+               &source.")
         end if
 
-        ! Read parameters for energy distribution
-        if (n < coeffs_reqd) then
-          call fatal_error("Not enough parameters specified for energy &
-               &distribution of external source.")
-        elseif (n > coeffs_reqd) then
-          call fatal_error("Too many parameters specified for energy &
-               &distribution of external source.")
-        elseif (n > 0) then
-          allocate(external_source % params_energy(n))
-          call get_node_array(node_dist, "parameters", &
-               external_source % params_energy)
+        ! Determine external source angular distribution
+        if (check_for_node(node_source, "angle")) then
+
+          ! Get pointer to angular distribution
+          call get_node_ptr(node_source, "angle", node_angle)
+
+          ! Check for type of angular distribution
+          type = ''
+          if (check_for_node(node_angle, "type")) &
+               call get_node_value(node_angle, "type", type)
+          select case (to_lower(type))
+          case ('isotropic')
+            allocate(Isotropic :: external_source(i)%angle)
+
+          case ('monodirectional')
+            allocate(Monodirectional :: external_source(i)%angle)
+
+          case ('mu-phi')
+            allocate(PolarAzimuthal :: external_source(i)%angle)
+
+          case default
+            call fatal_error("Invalid angular distribution for external source: "&
+                 // trim(type))
+          end select
+
+          ! Read reference directional unit vector
+          if (check_for_node(node_angle, "reference_uvw")) then
+            n = get_arraysize_double(node_angle, "reference_uvw")
+            if (n /= 3) then
+              call fatal_error('Angular distribution reference direction must have &
+                   &three parameters specified.')
+            end if
+            call get_node_array(node_angle, "reference_uvw", &
+                 external_source(i)%angle%reference_uvw)
+          else
+            ! By default, set reference unit vector to be positive z-direction
+            external_source(i)%angle%reference_uvw(:) = [ZERO, ZERO, ONE]
+          end if
+
+          ! Read parameters for angle distribution
+          select type (angle => external_source(i)%angle)
+          type is (Monodirectional)
+            call get_node_array(node_angle, "reference_uvw", &
+                 external_source(i)%angle%reference_uvw)
+
+          type is (PolarAzimuthal)
+            if (check_for_node(node_angle, "mu")) then
+              call get_node_ptr(node_angle, "mu", node_dist)
+              call distribution_from_xml(angle%mu, node_dist)
+            else
+              allocate(Uniform :: angle%mu)
+              select type (mu => angle%mu)
+              type is (Uniform)
+                mu%a = -ONE
+                mu%b = ONE
+              end select
+            end if
+
+            if (check_for_node(node_angle, "phi")) then
+              call get_node_ptr(node_angle, "phi", node_dist)
+              call distribution_from_xml(angle%phi, node_dist)
+            else
+              allocate(Uniform :: angle%phi)
+              select type (phi => angle%phi)
+              type is (Uniform)
+                phi%a = ZERO
+                phi%b = TWO*PI
+              end select
+            end if
+          end select
+
+        else
+          ! Set default angular distribution isotropic
+          allocate(Isotropic :: external_source(i)%angle)
+          external_source(i)%angle%reference_uvw(:) = [ZERO, ZERO, ONE]
+        end if
+
+        ! Determine external source energy distribution
+        if (check_for_node(node_source, "energy")) then
+          call get_node_ptr(node_source, "energy", node_dist)
+          call distribution_from_xml(external_source(i)%energy, node_dist)
+        else
+          ! Default to a Watt spectrum with parameters 0.988 MeV and 2.249 MeV^-1
+          allocate(Watt :: external_source(i)%energy)
+          select type(energy => external_source(i)%energy)
+          type is (Watt)
+            energy%a = 0.988_8
+            energy%b = 2.249_8
+          end select
         end if
-      else
-        ! Set default energy distribution to Watt fission spectrum
-        external_source % type_energy = SRC_ENERGY_WATT
-        allocate(external_source % params_energy(2))
-        external_source % params_energy = (/ 0.988_8, 2.249_8 /)
       end if
-    end if
+    end do
 
     ! Survival biasing
     if (check_for_node(doc, "survival_biasing")) then
diff --git a/src/math.F90 b/src/math.F90
index 15aa672e15..9a64c88d7d 100644
--- a/src/math.F90
+++ b/src/math.F90
@@ -557,6 +557,60 @@ contains
 
   end function calc_rn
 
+!===============================================================================
+! ROTATE_ANGLE rotates direction cosines through a polar angle whose cosine is
+! mu and through an azimuthal angle sampled uniformly. Note that this is done
+! with direct sampling rather than rejection as is done in MCNP and SERPENT.
+!===============================================================================
+
+  function rotate_angle(uvw0, mu, phi) result(uvw)
+    real(8), intent(in) :: uvw0(3) ! directional cosine
+    real(8), intent(in) :: mu      ! cosine of angle in lab or CM
+    real(8), optional   :: phi     ! azimuthal angle
+    real(8)             :: uvw(3)  ! rotated directional cosine
+
+    real(8) :: phi_   ! azimuthal angle
+    real(8) :: sinphi ! sine of azimuthal angle
+    real(8) :: cosphi ! cosine of azimuthal angle
+    real(8) :: a      ! sqrt(1 - mu^2)
+    real(8) :: b      ! sqrt(1 - w^2)
+    real(8) :: u0     ! original cosine in x direction
+    real(8) :: v0     ! original cosine in y direction
+    real(8) :: w0     ! original cosine in z direction
+
+    ! Copy original directional cosines
+    u0 = uvw0(1)
+    v0 = uvw0(2)
+    w0 = uvw0(3)
+
+    ! Sample azimuthal angle in [0,2pi) if none provided
+    if (present(phi)) then
+      phi_ = phi
+    else
+      phi_ = TWO * PI * prn()
+    end if
+
+    ! Precompute factors to save flops
+    sinphi = sin(phi_)
+    cosphi = cos(phi_)
+    a = sqrt(max(ZERO, ONE - mu*mu))
+    b = sqrt(max(ZERO, ONE - w0*w0))
+
+    ! Need to treat special case where sqrt(1 - w**2) is close to zero by
+    ! expanding about the v component rather than the w component
+    if (b > 1e-10) then
+      uvw(1) = mu*u0 + a*(u0*w0*cosphi - v0*sinphi)/b
+      uvw(2) = mu*v0 + a*(v0*w0*cosphi + u0*sinphi)/b
+      uvw(3) = mu*w0 - a*b*cosphi
+    else
+      b = sqrt(ONE - v0*v0)
+      uvw(1) = mu*u0 + a*(u0*v0*cosphi + w0*sinphi)/b
+      uvw(2) = mu*v0 - a*b*cosphi
+      uvw(3) = mu*w0 + a*(v0*w0*cosphi - u0*sinphi)/b
+    end if
+
+  end function rotate_angle
+
 !===============================================================================
 ! MAXWELL_SPECTRUM samples an energy from the Maxwell fission distribution based
 ! on a direct sampling scheme. The probability distribution function for a
diff --git a/src/physics.F90 b/src/physics.F90
index a01a3a30cb..217d232182 100644
--- a/src/physics.F90
+++ b/src/physics.F90
@@ -9,7 +9,7 @@ module physics
   use global
   use interpolation,          only: interpolate_tab1
   use material_header,        only: Material
-  use math,                   only: maxwell_spectrum, watt_spectrum
+  use math,                   only: rotate_angle, maxwell_spectrum, watt_spectrum
   use mesh,                   only: get_mesh_indices
   use output,                 only: write_message
   use particle_header,        only: Particle
@@ -1517,55 +1517,6 @@ contains
 
   end function sample_angle
 
-!===============================================================================
-! ROTATE_ANGLE rotates direction cosines through a polar angle whose cosine is
-! mu and through an azimuthal angle sampled uniformly. Note that this is done
-! with direct sampling rather than rejection as is done in MCNP and SERPENT.
-!===============================================================================
-
-  function rotate_angle(uvw0, mu) result(uvw)
-    real(8), intent(in) :: uvw0(3) ! directional cosine
-    real(8), intent(in) :: mu      ! cosine of angle in lab or CM
-    real(8)             :: uvw(3)  ! rotated directional cosine
-
-    real(8) :: phi    ! azimuthal angle
-    real(8) :: sinphi ! sine of azimuthal angle
-    real(8) :: cosphi ! cosine of azimuthal angle
-    real(8) :: a      ! sqrt(1 - mu^2)
-    real(8) :: b      ! sqrt(1 - w^2)
-    real(8) :: u0     ! original cosine in x direction
-    real(8) :: v0     ! original cosine in y direction
-    real(8) :: w0     ! original cosine in z direction
-
-    ! Copy original directional cosines
-    u0 = uvw0(1)
-    v0 = uvw0(2)
-    w0 = uvw0(3)
-
-    ! Sample azimuthal angle in [0,2pi)
-    phi = TWO * PI * prn()
-
-    ! Precompute factors to save flops
-    sinphi = sin(phi)
-    cosphi = cos(phi)
-    a = sqrt(max(ZERO, ONE - mu*mu))
-    b = sqrt(max(ZERO, ONE - w0*w0))
-
-    ! Need to treat special case where sqrt(1 - w**2) is close to zero by
-    ! expanding about the v component rather than the w component
-    if (b > 1e-10) then
-      uvw(1) = mu*u0 + a*(u0*w0*cosphi - v0*sinphi)/b
-      uvw(2) = mu*v0 + a*(v0*w0*cosphi + u0*sinphi)/b
-      uvw(3) = mu*w0 - a*b*cosphi
-    else
-      b = sqrt(ONE - v0*v0)
-      uvw(1) = mu*u0 + a*(u0*v0*cosphi + w0*sinphi)/b
-      uvw(2) = mu*v0 - a*b*cosphi
-      uvw(3) = mu*w0 + a*(v0*w0*cosphi - u0*sinphi)/b
-    end if
-
-  end function rotate_angle
-
 !===============================================================================
 ! SAMPLE_ENERGY samples an outgoing energy distribution, either for a secondary
 ! neutron from a collision or for a prompt/delayed fission neutron
diff --git a/src/random_lcg.F90 b/src/random_lcg.F90
index 1ebe651d3c..8f50477c5b 100644
--- a/src/random_lcg.F90
+++ b/src/random_lcg.F90
@@ -7,6 +7,9 @@ module random_lcg
   private
   save
 
+  ! Random number seed
+  integer(8), public :: seed = 1_8
+
   integer(8) :: prn_seed0  ! original seed
   integer(8) :: prn_seed(N_STREAMS) ! current seed
   integer(8) :: prn_mult   ! multiplication factor, g
@@ -56,8 +59,6 @@ contains
 
   subroutine initialize_prng()
 
-    use global, only: seed
-
     integer :: i
 
     prn_seed0  = seed
diff --git a/src/relaxng/settings.rnc b/src/relaxng/settings.rnc
index a95b1f297e..68f221f677 100644
--- a/src/relaxng/settings.rnc
+++ b/src/relaxng/settings.rnc
@@ -65,36 +65,36 @@ element settings {
   element seed { xsd:positiveInteger }? &
 
   element source {
-    element file { xsd:string { maxLength = "255" } }? &
-    element space {
-      (element type { xsd:string { maxLength = "16" } } |
-        attribute type { xsd:string { maxLength = "16" } }) &
-      (element length { xsd:int } | attribute length { xsd:int })? &
-      (element interpolation { xsd:string { maxLength = "10" } } |
-        attribute interplation { xsd:string { maxLength = "10" } })? &
-      (element parameters { list { xsd:double+ } } |
-        attribute parameters { list { xsd:double+ } })?
-    }? &
-    element angle {
-      (element type { xsd:string { maxLength = "16" } } |
-        attribute type { xsd:string { maxLength = "16" } }) &
-      (element length { xsd:int } | attribute length { xsd:int })? &
-      (element interpolation { xsd:string { maxLength = "10" } } |
-        attribute interplation { xsd:string { maxLength = "10" } })? &
-      (element parameters { list { xsd:double+ } } |
-        attribute parameters { list { xsd:double+ } })?
-    }? &
-    element energy {
-      (element type { xsd:string { maxLength = "16" } } |
-        attribute type { xsd:string { maxLength = "16" } }) &
-      (element length { xsd:int } | attribute length { xsd:int })? &
-      (element interpolation { xsd:string { maxLength = "10" } } |
-        attribute interplation { xsd:string { maxLength = "10" } })? &
-      (element parameters { list { xsd:double+ } } |
-        attribute parameters { list { xsd:double+ } })?
-    }? &
-    (element write_initial { xsd:boolean } | attribute write_initial { xsd:boolean })?
-  }? &
+    grammar {
+      start =
+        (element strength { xsd:double } | attribute strength { xsd:double })? &
+        (element file { xsd:string } | attribute file { xsd:string })? &
+        element space {
+          (element type { xsd:string } | attribute type { xsd:string }) &
+          (element parameters { list { xsd:double+ } } |
+            attribute parameters { list { xsd:double+ } })? &
+          element x { distribution }? &
+          element y { distribution }? &
+          element z { distribution }?
+        }? &
+        element angle {
+          (element type { xsd:string } | attribute type { xsd:string }) &
+          (element reference_uvw { list { xsd:double, xsd:double, xsd:double } } |
+            attribute reference_uvw { list { xsd:double, xsd:double, xsd:double } })? &
+          element mu { distribution }? &
+          element phi { distribution }?
+        }? &
+        element energy { distribution }? &
+        (element write_initial { xsd:boolean } | attribute write_initial { xsd:boolean })?
+      distribution =
+        (element type { xsd:string { maxLength = "16" } } |
+          attribute type { xsd:string { maxLength = "16" } }) &
+        (element interpolation { xsd:string } |
+          attribute interpolation { xsd:string })? &
+        (element parameters { list { xsd:double+ } } |
+          attribute parameters { list { xsd:double+ } })?
+    }
+  }* &
 
   element state_point {
     (
diff --git a/src/relaxng/settings.rng b/src/relaxng/settings.rng
index 8dd1a6febc..ea3efaff9f 100644
--- a/src/relaxng/settings.rng
+++ b/src/relaxng/settings.rng
@@ -264,209 +264,184 @@
         <data type="positiveInteger"/>
       </element>
     </optional>
-    <optional>
+    <zeroOrMore>
       <element name="source">
-        <interleave>
-          <optional>
-            <element name="file">
-              <data type="string">
-                <param name="maxLength">255</param>
-              </data>
-            </element>
-          </optional>
-          <optional>
-            <element name="space">
-              <interleave>
+        <grammar>
+          <start>
+            <interleave>
+              <optional>
                 <choice>
-                  <element name="type">
-                    <data type="string">
-                      <param name="maxLength">16</param>
-                    </data>
+                  <element name="strength">
+                    <data type="double"/>
                   </element>
-                  <attribute name="type">
-                    <data type="string">
-                      <param name="maxLength">16</param>
-                    </data>
+                  <attribute name="strength">
+                    <data type="double"/>
                   </attribute>
                 </choice>
-                <optional>
-                  <choice>
-                    <element name="length">
-                      <data type="int"/>
-                    </element>
-                    <attribute name="length">
-                      <data type="int"/>
-                    </attribute>
-                  </choice>
-                </optional>
-                <optional>
-                  <choice>
-                    <element name="interpolation">
-                      <data type="string">
-                        <param name="maxLength">10</param>
-                      </data>
-                    </element>
-                    <attribute name="interplation">
-                      <data type="string">
-                        <param name="maxLength">10</param>
-                      </data>
-                    </attribute>
-                  </choice>
-                </optional>
-                <optional>
-                  <choice>
-                    <element name="parameters">
-                      <list>
-                        <oneOrMore>
-                          <data type="double"/>
-                        </oneOrMore>
-                      </list>
-                    </element>
-                    <attribute name="parameters">
-                      <list>
-                        <oneOrMore>
-                          <data type="double"/>
-                        </oneOrMore>
-                      </list>
-                    </attribute>
-                  </choice>
-                </optional>
-              </interleave>
-            </element>
-          </optional>
-          <optional>
-            <element name="angle">
-              <interleave>
+              </optional>
+              <optional>
                 <choice>
-                  <element name="type">
-                    <data type="string">
-                      <param name="maxLength">16</param>
-                    </data>
+                  <element name="file">
+                    <data type="string"/>
                   </element>
-                  <attribute name="type">
-                    <data type="string">
-                      <param name="maxLength">16</param>
-                    </data>
+                  <attribute name="file">
+                    <data type="string"/>
                   </attribute>
                 </choice>
-                <optional>
-                  <choice>
-                    <element name="length">
-                      <data type="int"/>
-                    </element>
-                    <attribute name="length">
-                      <data type="int"/>
-                    </attribute>
-                  </choice>
-                </optional>
-                <optional>
-                  <choice>
-                    <element name="interpolation">
-                      <data type="string">
-                        <param name="maxLength">10</param>
-                      </data>
-                    </element>
-                    <attribute name="interplation">
-                      <data type="string">
-                        <param name="maxLength">10</param>
-                      </data>
-                    </attribute>
-                  </choice>
-                </optional>
-                <optional>
-                  <choice>
-                    <element name="parameters">
-                      <list>
-                        <oneOrMore>
-                          <data type="double"/>
-                        </oneOrMore>
-                      </list>
-                    </element>
-                    <attribute name="parameters">
-                      <list>
-                        <oneOrMore>
-                          <data type="double"/>
-                        </oneOrMore>
-                      </list>
-                    </attribute>
-                  </choice>
-                </optional>
-              </interleave>
-            </element>
-          </optional>
-          <optional>
-            <element name="energy">
-              <interleave>
+              </optional>
+              <optional>
+                <element name="space">
+                  <interleave>
+                    <choice>
+                      <element name="type">
+                        <data type="string"/>
+                      </element>
+                      <attribute name="type">
+                        <data type="string"/>
+                      </attribute>
+                    </choice>
+                    <optional>
+                      <choice>
+                        <element name="parameters">
+                          <list>
+                            <oneOrMore>
+                              <data type="double"/>
+                            </oneOrMore>
+                          </list>
+                        </element>
+                        <attribute name="parameters">
+                          <list>
+                            <oneOrMore>
+                              <data type="double"/>
+                            </oneOrMore>
+                          </list>
+                        </attribute>
+                      </choice>
+                    </optional>
+                    <optional>
+                      <element name="x">
+                        <ref name="distribution"/>
+                      </element>
+                    </optional>
+                    <optional>
+                      <element name="y">
+                        <ref name="distribution"/>
+                      </element>
+                    </optional>
+                    <optional>
+                      <element name="z">
+                        <ref name="distribution"/>
+                      </element>
+                    </optional>
+                  </interleave>
+                </element>
+              </optional>
+              <optional>
+                <element name="angle">
+                  <interleave>
+                    <choice>
+                      <element name="type">
+                        <data type="string"/>
+                      </element>
+                      <attribute name="type">
+                        <data type="string"/>
+                      </attribute>
+                    </choice>
+                    <optional>
+                      <choice>
+                        <element name="reference_uvw">
+                          <list>
+                            <data type="double"/>
+                            <data type="double"/>
+                            <data type="double"/>
+                          </list>
+                        </element>
+                        <attribute name="reference_uvw">
+                          <list>
+                            <data type="double"/>
+                            <data type="double"/>
+                            <data type="double"/>
+                          </list>
+                        </attribute>
+                      </choice>
+                    </optional>
+                    <optional>
+                      <element name="mu">
+                        <ref name="distribution"/>
+                      </element>
+                    </optional>
+                    <optional>
+                      <element name="phi">
+                        <ref name="distribution"/>
+                      </element>
+                    </optional>
+                  </interleave>
+                </element>
+              </optional>
+              <optional>
+                <element name="energy">
+                  <ref name="distribution"/>
+                </element>
+              </optional>
+              <optional>
                 <choice>
-                  <element name="type">
-                    <data type="string">
-                      <param name="maxLength">16</param>
-                    </data>
+                  <element name="write_initial">
+                    <data type="boolean"/>
                   </element>
-                  <attribute name="type">
-                    <data type="string">
-                      <param name="maxLength">16</param>
-                    </data>
+                  <attribute name="write_initial">
+                    <data type="boolean"/>
                   </attribute>
                 </choice>
-                <optional>
-                  <choice>
-                    <element name="length">
-                      <data type="int"/>
-                    </element>
-                    <attribute name="length">
-                      <data type="int"/>
-                    </attribute>
-                  </choice>
-                </optional>
-                <optional>
-                  <choice>
-                    <element name="interpolation">
-                      <data type="string">
-                        <param name="maxLength">10</param>
-                      </data>
-                    </element>
-                    <attribute name="interplation">
-                      <data type="string">
-                        <param name="maxLength">10</param>
-                      </data>
-                    </attribute>
-                  </choice>
-                </optional>
-                <optional>
-                  <choice>
-                    <element name="parameters">
-                      <list>
-                        <oneOrMore>
-                          <data type="double"/>
-                        </oneOrMore>
-                      </list>
-                    </element>
-                    <attribute name="parameters">
-                      <list>
-                        <oneOrMore>
-                          <data type="double"/>
-                        </oneOrMore>
-                      </list>
-                    </attribute>
-                  </choice>
-                </optional>
-              </interleave>
-            </element>
-          </optional>
-          <optional>
-            <choice>
-              <element name="write_initial">
-                <data type="boolean"/>
-              </element>
-              <attribute name="write_initial">
-                <data type="boolean"/>
-              </attribute>
-            </choice>
-          </optional>
-        </interleave>
+              </optional>
+            </interleave>
+          </start>
+          <define name="distribution">
+            <interleave>
+              <choice>
+                <element name="type">
+                  <data type="string">
+                    <param name="maxLength">16</param>
+                  </data>
+                </element>
+                <attribute name="type">
+                  <data type="string">
+                    <param name="maxLength">16</param>
+                  </data>
+                </attribute>
+              </choice>
+              <optional>
+                <choice>
+                  <element name="interpolation">
+                    <data type="string"/>
+                  </element>
+                  <attribute name="interpolation">
+                    <data type="string"/>
+                  </attribute>
+                </choice>
+              </optional>
+              <optional>
+                <choice>
+                  <element name="parameters">
+                    <list>
+                      <oneOrMore>
+                        <data type="double"/>
+                      </oneOrMore>
+                    </list>
+                  </element>
+                  <attribute name="parameters">
+                    <list>
+                      <oneOrMore>
+                        <data type="double"/>
+                      </oneOrMore>
+                    </list>
+                  </attribute>
+                </choice>
+              </optional>
+            </interleave>
+          </define>
+        </grammar>
       </element>
-    </optional>
+    </zeroOrMore>
     <optional>
       <element name="state_point">
         <choice>
diff --git a/src/relaxng/tallies.rnc b/src/relaxng/tallies.rnc
index 75afb0f231..0f3672c6f2 100644
--- a/src/relaxng/tallies.rnc
+++ b/src/relaxng/tallies.rnc
@@ -41,7 +41,7 @@ element tallies {
       (element type { xsd:string } | attribute type { xsd:string }) &
       (element threshold { xsd:double} | attribute threshold { xsd:double }) &
       (element scores { list { xsd:string { maxLength = "20" }+ } } | attribute scores { list { xsd:string { maxLength = "20"}+ } } )?
-    }?
+    }*
   }* &
 
   element assume_separate { xsd:boolean }?
diff --git a/src/relaxng/tallies.rng b/src/relaxng/tallies.rng
index d35c170042..36bd5cb85f 100644
--- a/src/relaxng/tallies.rng
+++ b/src/relaxng/tallies.rng
@@ -145,10 +145,10 @@
                       <value>mesh</value>
                       <value>energy</value>
                       <value>energyout</value>
-                      <value>delayedgroup</value>
                       <value>mu</value>
                       <value>polar</value>
                       <value>azimuthal</value>
+                      <value>delayedgroup</value>
                     </choice>
                   </element>
                   <attribute name="type">
@@ -162,10 +162,10 @@
                       <value>mesh</value>
                       <value>energy</value>
                       <value>energyout</value>
-                      <value>delayedgroup</value>
                       <value>mu</value>
                       <value>polar</value>
                       <value>azimuthal</value>
+                      <value>delayedgroup</value>
                     </choice>
                   </attribute>
                 </choice>
@@ -208,7 +208,7 @@
               </oneOrMore>
             </list>
           </element>
-          <optional>
+          <zeroOrMore>
             <element name="trigger">
               <interleave>
                 <choice>
@@ -251,7 +251,7 @@
                 </optional>
               </interleave>
             </element>
-          </optional>
+          </zeroOrMore>
         </interleave>
       </element>
     </zeroOrMore>
diff --git a/src/source.F90 b/src/source.F90
index a16eb245d0..d1bead3804 100644
--- a/src/source.F90
+++ b/src/source.F90
@@ -2,6 +2,8 @@ module source
 
   use bank_header,      only: Bank
   use constants
+  use distribution_univariate, only: Discrete
+  use distribution_multivariate, only: SpatialBox
   use error,            only: fatal_error
   use geometry,         only: find_cell
   use geometry_header,  only: BASE_UNIVERSE
@@ -99,13 +101,9 @@ contains
     type(Bank), intent(inout) :: site ! source site
 
     integer :: i          ! dummy loop index
+    integer :: n_source   ! number of source distributions
+    real(8) :: c          ! cumulative frequency
     real(8) :: r(3)       ! sampled coordinates
-    real(8) :: phi        ! azimuthal angle
-    real(8) :: mu         ! cosine of polar angle
-    real(8) :: p_min(3)   ! minimum coordinates of source
-    real(8) :: p_max(3)   ! maximum coordinates of source
-    real(8) :: a          ! Arbitrary parameter 'a'
-    real(8) :: b          ! Arbitrary parameter 'b'
     logical :: found      ! Does the source particle exist within geometry?
     type(Particle) :: p   ! Temporary particle for using find_cell
     integer, save :: num_resamples = 0 ! Number of resamples encountered
@@ -116,130 +114,80 @@ contains
     ! Set the random number generator to the source stream.
     call prn_set_stream(STREAM_SOURCE)
 
-    ! Sample position
-    select case (external_source%type_space)
-    case (SRC_SPACE_BOX)
+    ! Sample from among multiple source distributions
+    n_source = size(external_source)
+    if (n_source > 1) then
+      r(1) = prn()*sum(external_source(:)%strength)
+      c = ZERO
+      do i = 1, n_source
+        c = c + external_source(i)%strength
+        if (r(1) < c) exit
+      end do
+    else
+      i = 1
+    end if
+
+    ! Repeat sampling source location until a good site has been found
+    found = .false.
+    do while (.not.found)
       ! Set particle defaults
       call p%initialize()
-      ! Repeat sampling source location until a good site has been found
-      found = .false.
-      do while (.not.found)
-        ! Coordinates sampled uniformly over a box
-        p_min = external_source%params_space(1:3)
-        p_max = external_source%params_space(4:6)
-        r = (/ (prn(), i = 1,3) /)
-        site%xyz = p_min + r*(p_max - p_min)
 
-        ! Fill p with needed data
-        p%coord(1)%xyz = site%xyz
-        p%coord(1)%uvw = [ ONE, ZERO, ZERO ]
+      ! Sample spatial distribution
+      site%xyz(:) = external_source(i)%space%sample()
 
-        ! Now search to see if location exists in geometry
-        call find_cell(p, found)
-        if (.not. found) then
-          num_resamples = num_resamples + 1
-          if (num_resamples == MAX_EXTSRC_RESAMPLES) then
-            call fatal_error("Maximum number of external source spatial &
-                 &resamples reached!")
+      ! Fill p with needed data
+      p%coord(1)%xyz(:) = site%xyz
+      p%coord(1)%uvw(:) = [ ONE, ZERO, ZERO ]
+
+      ! Now search to see if location exists in geometry
+      call find_cell(p, found)
+      if (.not. found) then
+        num_resamples = num_resamples + 1
+        if (num_resamples == MAX_EXTSRC_RESAMPLES) then
+          call fatal_error("Maximum number of external source spatial &
+               &resamples reached!")
+        end if
+      end if
+
+      ! Check if spatial site is in fissionable material
+      select type (space => external_source(i)%space)
+      type is (SpatialBox)
+        if (space%only_fissionable) then
+          if (p%material == MATERIAL_VOID) then
+            found = .false.
+          elseif (.not. materials(p%material)%fissionable) then
+            found = .false.
           end if
         end if
-      end do
-      call p%clear()
+      end select
+    end do
 
-    case (SRC_SPACE_FISSION)
-      ! Repeat sampling source location until a good site has been found
-      found = .false.
-      do while (.not.found)
-        ! Set particle defaults
-        call p%initialize()
-
-        ! Coordinates sampled uniformly over a box
-        p_min = external_source%params_space(1:3)
-        p_max = external_source%params_space(4:6)
-        r = (/ (prn(), i = 1,3) /)
-        site%xyz = p_min + r*(p_max - p_min)
-
-        ! Fill p with needed data
-        p%coord(1)%xyz = site%xyz
-        p%coord(1)%uvw = [ ONE, ZERO, ZERO ]
-
-        ! Now search to see if location exists in geometry
-        call find_cell(p, found)
-        if (.not. found) then
-          num_resamples = num_resamples + 1
-          if (num_resamples == MAX_EXTSRC_RESAMPLES) then
-            call fatal_error("Maximum number of external source spatial &
-                 &resamples reached!")
-          end if
-          cycle
-        end if
-        if (p%material == MATERIAL_VOID) then
-          found = .false.
-          cycle
-        end if
-        if (.not. materials(p%material)%fissionable) found = .false.
-      end do
-      call p%clear()
-
-    case (SRC_SPACE_POINT)
-      ! Point source
-      site%xyz = external_source%params_space
-
-    end select
+    call p%clear()
 
     ! Sample angle
-    select case (external_source%type_angle)
-    case (SRC_ANGLE_ISOTROPIC)
-      ! Sample isotropic distribution
-      phi = TWO*PI*prn()
-      mu = TWO*prn() - ONE
-      site%uvw(1) = mu
-      site%uvw(2) = sqrt(ONE - mu*mu) * cos(phi)
-      site%uvw(3) = sqrt(ONE - mu*mu) * sin(phi)
+    site%uvw(:) = external_source(i)%angle%sample()
 
-    case (SRC_ANGLE_MONO)
-      ! Monodirectional source
-      site%uvw = external_source%params_angle
-
-    case default
-      call fatal_error("No angle distribution specified for external source!")
-    end select
-
-    ! Sample energy distribution
-    select case (external_source%type_energy)
-    case (SRC_ENERGY_MONO)
-      ! Monoenergtic source
-      site%E = external_source%params_energy(1)
-      if (site%E >= energy_max_neutron) then
+    ! Check for monoenergetic source above maximum neutron energy
+    select type (energy => external_source(i)%energy)
+    type is (Discrete)
+      if (any(energy%x >= energy_max_neutron)) then
         call fatal_error("Source energy above range of energies of at least &
              &one cross section table")
       end if
-
-    case (SRC_ENERGY_MAXWELL)
-      a = external_source%params_energy(1)
-      do
-        ! Sample Maxwellian fission spectrum
-        site%E = maxwell_spectrum(a)
-
-        ! resample if energy is greater than maximum neutron energy
-        if (site%E < energy_max_neutron) exit
-      end do
-
-    case (SRC_ENERGY_WATT)
-      a = external_source%params_energy(1)
-      b = external_source%params_energy(2)
-      do
-        ! Sample Watt fission spectrum
-        site%E = watt_spectrum(a, b)
-
-        ! resample if energy is greater than maximum neutron energy
-        if (site%E < energy_max_neutron) exit
-      end do
-
-    case default
-      call fatal_error("No energy distribution specified for external source!")
     end select
 
+    do
+      ! Sample energy spectrum
+      site%E = external_source(i)%energy%sample()
+
+      ! resample if energy is greater than maximum neutron energy
+      if (site%E < energy_max_neutron) exit
+    end do
+
+    ! Set delayed group
+    site%delayed_group = 0
+
     ! Set the random number generator back to the tracking stream.
     call prn_set_stream(STREAM_TRACKING)
 
diff --git a/src/source_header.F90 b/src/source_header.F90
index 11c74d110c..eefc007a47 100644
--- a/src/source_header.F90
+++ b/src/source_header.F90
@@ -1,19 +1,20 @@
 module source_header
 
+  use distribution_univariate, only: Distribution
+  use distribution_multivariate, only: UnitSphereDistribution, SpatialDistribution
+
   implicit none
 
 !===============================================================================
-! EXTSOURCE describes an external source of neutrons for a fixed-source problem
-! or for the starting source in a k eigenvalue problem
+! SOURCEDISTRIBUTION describes an external source of particles for a
+! fixed-source problem or for the starting source in a k eigenvalue problem
 !===============================================================================
 
-  type ExtSource
-    integer :: type_space              ! spacial distribution, e.g. 'box' or 'point'
-    integer :: type_angle              ! angle distribution, e.g. 'isotropic'
-    integer :: type_energy             ! energy distribution, e.g. 'Watt'
-    real(8), allocatable :: params_space(:)  ! parameters for spatial distribution
-    real(8), allocatable :: params_angle(:)  ! parameters for angle distribution
-    real(8), allocatable :: params_energy(:) ! parameters for energy distribution
-  end type ExtSource
+  type SourceDistribution
+    real(8) :: strength ! source strength
+    class(SpatialDistribution),    allocatable :: space  ! spatial distribution
+    class(UnitSphereDistribution), allocatable :: angle  ! angle distribution
+    class(Distribution),           allocatable :: energy ! energy distribution
+  end type SourceDistribution
 
 end module source_header
diff --git a/src/state_point.F90 b/src/state_point.F90
index 046c857fd2..e89cdf12a8 100644
--- a/src/state_point.F90
+++ b/src/state_point.F90
@@ -22,6 +22,7 @@ module state_point
   use tally_header,       only: TallyObject
   use mesh_header,        only: RegularMesh
   use dict_header,        only: ElemKeyValueII, ElemKeyValueCI
+  use random_lcg,         only: seed
 
 #ifdef MPI
   use message_passing
diff --git a/src/string.F90 b/src/string.F90
index ce130a212d..b51763a611 100644
--- a/src/string.F90
+++ b/src/string.F90
@@ -3,7 +3,6 @@ module string
   use constants, only: MAX_WORDS, MAX_LINE_LEN, ERROR_INT, ERROR_REAL, &
        OP_LEFT_PAREN, OP_RIGHT_PAREN, OP_COMPLEMENT, OP_INTERSECTION, OP_UNION
   use error,     only: fatal_error, warning
-  use global,    only: master
   use stl_vector, only: VectorInt
 
   implicit none
@@ -50,8 +49,8 @@ contains
         if (i_end > 0) then
           n = n + 1
           if (i_end - i_start + 1 > len(words(n))) then
-            if (master) call warning("The word '" // string(i_start:i_end) &
-                 &// "' is longer than the space allocated for it.")
+            call warning("The word '" // string(i_start:i_end) &
+                 // "' is longer than the space allocated for it.")
           end if
           words(n) = string(i_start:i_end)
           ! reset indices
diff --git a/tests/input_set.py b/tests/input_set.py
index 87b857f4a9..c13a4f9cc7 100644
--- a/tests/input_set.py
+++ b/tests/input_set.py
@@ -1,4 +1,6 @@
 import openmc
+from openmc.source import Source
+from openmc.stats import Box
 
 
 class InputSet(object):
@@ -558,7 +560,8 @@ class InputSet(object):
         self.settings.batches = 10
         self.settings.inactive = 5
         self.settings.particles = 100
-        self.settings.set_source_space('box', (-160, -160, -183, 160, 160, 183))
+        self.settings.source = Source(space=Box(
+            [-160, -160, -183], [160, 160, 183]))
 
     def build_defualt_plots(self):
         plot = openmc.Plot()
diff --git a/tests/test_distribmat/inputs_true.dat b/tests/test_distribmat/inputs_true.dat
index 9d0a70c471..fddab0a60d 100644
--- a/tests/test_distribmat/inputs_true.dat
+++ b/tests/test_distribmat/inputs_true.dat
@@ -1 +1 @@
-83a9f1412b1ddfdd8b9fa8c7e8b44be8137dc6aa785d44c3eea9f0928242475aea4ba73ba4eef01afdf63bd1bff18116e40b798eeafab50c856186de4c68d5a6
\ No newline at end of file
+401b8be1b296db7f21ccae089c7ac480044d953b7264ca0ae8e34bb79e24cbb57195bcb568deda6f2f7e07366bbfac408a92306351b9169edd04499723707e1b
\ No newline at end of file
diff --git a/tests/test_distribmat/test_distribmat.py b/tests/test_distribmat/test_distribmat.py
index b1252a8e7b..a0608c108a 100644
--- a/tests/test_distribmat/test_distribmat.py
+++ b/tests/test_distribmat/test_distribmat.py
@@ -5,6 +5,8 @@ import sys
 sys.path.insert(0, os.pardir)
 from testing_harness import TestHarness, PyAPITestHarness
 import openmc
+from openmc.stats import Box
+from openmc.source import Source
 
 
 class DistribmatTestHarness(PyAPITestHarness):
@@ -85,7 +87,7 @@ class DistribmatTestHarness(PyAPITestHarness):
         sets_file.batches = 5
         sets_file.inactive = 0
         sets_file.particles = 1000
-        sets_file.set_source_space('box', [-1, -1, -1, 1, 1, 1])
+        sets_file.source = Source(space=Box([-1, -1, -1], [1, 1, 1]))
         sets_file.output = {'summary': True}
         sets_file.export_to_xml()
 
diff --git a/tests/test_filter_azimuthal/inputs_true.dat b/tests/test_filter_azimuthal/inputs_true.dat
index 42b6e820be..e4a964700f 100644
--- a/tests/test_filter_azimuthal/inputs_true.dat
+++ b/tests/test_filter_azimuthal/inputs_true.dat
@@ -1 +1 @@
-1d5f81d12f607f4a8436dfb65167e2a2be55dbf86fbc2cc465cec274671be5eaff517d781a4d40e264bb695e3c66c7ff61a650217c99de2ca8c15ca747fe6b80
\ No newline at end of file
+57d6fd9cb5180c38efd2729a5dea0708cbd5fd0bf7dcf0c9d5c9cef5d818aeab5a926d03e70dedcf1b60d5740938fb3ba80e6ccdb09c661d159c0893da3bd593
\ No newline at end of file
diff --git a/tests/test_filter_cell/inputs_true.dat b/tests/test_filter_cell/inputs_true.dat
index 2320ff6ab4..d7f0a9e7f8 100644
--- a/tests/test_filter_cell/inputs_true.dat
+++ b/tests/test_filter_cell/inputs_true.dat
@@ -1 +1 @@
-caae173f01f7073d634a68a5c4ce97177423e13596a863976e1c40303dc8c05afed457d5a1aa0ae73627ec953143e4f9c1f45bdbd3b0cca76433062467d59777
\ No newline at end of file
+f8359184c02fbab5dca5368689a84924066ab1fb09cae575588ceddd696d5461db577498df9959365d89fe933e9b338390e44e362c603c6f2aa5bcf4acc14b20
\ No newline at end of file
diff --git a/tests/test_filter_cellborn/inputs_true.dat b/tests/test_filter_cellborn/inputs_true.dat
index 5aef4cbb06..a4f1a74b8f 100644
--- a/tests/test_filter_cellborn/inputs_true.dat
+++ b/tests/test_filter_cellborn/inputs_true.dat
@@ -1 +1 @@
-2f24eb86cda981982a8db5bb110c72e9cef542c06e15b748c1c7e459f96b0d8ba0978b51dffc006e813cd2e2ae1fa0357336f8322ae263189841afde01f0327b
\ No newline at end of file
+8ae662f8881ce8cdec550069c6233c2c91e9a10f7200af6892cf6f2d77712ccfa17895dbd2eee02e6daf3d665c6ed84b29e17d89ff519e70c37b36d75a431d53
\ No newline at end of file
diff --git a/tests/test_filter_delayedgroup/inputs_true.dat b/tests/test_filter_delayedgroup/inputs_true.dat
index 21bce00fd2..6b06d41735 100644
--- a/tests/test_filter_delayedgroup/inputs_true.dat
+++ b/tests/test_filter_delayedgroup/inputs_true.dat
@@ -1 +1 @@
-e771470681d3b4af57a70d148f5eb728df57f1fd7bdb5d6f76ac556b69f10bf0cdd5645fe488f1e17f07cbb72e9fb34af2fd7ede95c8e39c5ffa6ea9b6c5810e
\ No newline at end of file
+a7c8ce7ffbc3a7b965d8a3077a4d9132130561afef19047b279b2d23198e248b09664856a092a32394894e19fef7708cebad99b3839d735c4e98ae0c9af58cb7
\ No newline at end of file
diff --git a/tests/test_filter_energy/inputs_true.dat b/tests/test_filter_energy/inputs_true.dat
index b120e9fd62..4098aaece9 100644
--- a/tests/test_filter_energy/inputs_true.dat
+++ b/tests/test_filter_energy/inputs_true.dat
@@ -1 +1 @@
-49835200052ee1a4c9583a7bb4e9430de7d01a6d7a8d4f63ae37be9bbac4fd8c7ed9135ecbc4ab4cb075fd4d77b322265783463dd07c127decceee8c9fe25bfd
\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_filter_energyout/inputs_true.dat b/tests/test_filter_energyout/inputs_true.dat
index 7096648e6c..be1ade923f 100644
--- a/tests/test_filter_energyout/inputs_true.dat
+++ b/tests/test_filter_energyout/inputs_true.dat
@@ -1 +1 @@
-183b4a06cbd0930cfa4f28d0c385cf3ae93ab97c171580c2ba9eec0e4b716102ad83f510d62258ef6769c446e72e1d5ba9f630b171ee239ec04c9bbd1e56742e
\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_filter_group_transfer/inputs_true.dat b/tests/test_filter_group_transfer/inputs_true.dat
index 813bb43c18..9e3bbdd1ce 100644
--- a/tests/test_filter_group_transfer/inputs_true.dat
+++ b/tests/test_filter_group_transfer/inputs_true.dat
@@ -1 +1 @@
-461a6a4ec3b0b6dc7199c09fa8f527e51cc7a1ea4281a708f8b7bcdbb12f7162027928dfcef68a24eaf6c06037a68e151df9aac9eac6ce56617466f2f5270b71
\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_filter_material/inputs_true.dat b/tests/test_filter_material/inputs_true.dat
index aaa4a4939f..59afbce647 100644
--- a/tests/test_filter_material/inputs_true.dat
+++ b/tests/test_filter_material/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_filter_mu/inputs_true.dat b/tests/test_filter_mu/inputs_true.dat
index b0a607ad34..19d08cb457 100644
--- a/tests/test_filter_mu/inputs_true.dat
+++ b/tests/test_filter_mu/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_filter_polar/inputs_true.dat b/tests/test_filter_polar/inputs_true.dat
index db67a890f1..b3773a9d6b 100644
--- a/tests/test_filter_polar/inputs_true.dat
+++ b/tests/test_filter_polar/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_filter_universe/inputs_true.dat b/tests/test_filter_universe/inputs_true.dat
index 7cda1ab848..a55ba45f68 100644
--- a/tests/test_filter_universe/inputs_true.dat
+++ b/tests/test_filter_universe/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_mgxs_library_condense/inputs_true.dat b/tests/test_mgxs_library_condense/inputs_true.dat
index 37397c5947..b94f64122d 100644
--- a/tests/test_mgxs_library_condense/inputs_true.dat
+++ b/tests/test_mgxs_library_condense/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_mgxs_library_hdf5/inputs_true.dat b/tests/test_mgxs_library_hdf5/inputs_true.dat
index 37397c5947..b94f64122d 100644
--- a/tests/test_mgxs_library_hdf5/inputs_true.dat
+++ b/tests/test_mgxs_library_hdf5/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_mgxs_library_no_nuclides/inputs_true.dat b/tests/test_mgxs_library_no_nuclides/inputs_true.dat
index 37397c5947..b94f64122d 100644
--- a/tests/test_mgxs_library_no_nuclides/inputs_true.dat
+++ b/tests/test_mgxs_library_no_nuclides/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_mgxs_library_nuclides/inputs_true.dat b/tests/test_mgxs_library_nuclides/inputs_true.dat
index 2e299773a3..f87bc242dc 100644
--- a/tests/test_mgxs_library_nuclides/inputs_true.dat
+++ b/tests/test_mgxs_library_nuclides/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_MT/inputs_true.dat b/tests/test_score_MT/inputs_true.dat
index b361c28cf2..33e732bcba 100644
--- a/tests/test_score_MT/inputs_true.dat
+++ b/tests/test_score_MT/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_absorption/inputs_true.dat b/tests/test_score_absorption/inputs_true.dat
index c4c133600a..7932cb78ec 100644
--- a/tests/test_score_absorption/inputs_true.dat
+++ b/tests/test_score_absorption/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_delayed_nufission/inputs_true.dat b/tests/test_score_delayed_nufission/inputs_true.dat
index 5a0ec8a211..11a92dea2c 100644
--- a/tests/test_score_delayed_nufission/inputs_true.dat
+++ b/tests/test_score_delayed_nufission/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_events/inputs_true.dat b/tests/test_score_events/inputs_true.dat
index bb0fef8e5d..4862547c16 100644
--- a/tests/test_score_events/inputs_true.dat
+++ b/tests/test_score_events/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_fission/inputs_true.dat b/tests/test_score_fission/inputs_true.dat
index d90ca274ee..fb5e6f8915 100644
--- a/tests/test_score_fission/inputs_true.dat
+++ b/tests/test_score_fission/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_flux/inputs_true.dat b/tests/test_score_flux/inputs_true.dat
index 2e7358c49e..934ab9d120 100644
--- a/tests/test_score_flux/inputs_true.dat
+++ b/tests/test_score_flux/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_flux_yn/inputs_true.dat b/tests/test_score_flux_yn/inputs_true.dat
index c7a1a1b52e..5ece3ef1ba 100644
--- a/tests/test_score_flux_yn/inputs_true.dat
+++ b/tests/test_score_flux_yn/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_inverse_velocity/inputs_true.dat b/tests/test_score_inverse_velocity/inputs_true.dat
index 0546cf828f..96cbc7378a 100644
--- a/tests/test_score_inverse_velocity/inputs_true.dat
+++ b/tests/test_score_inverse_velocity/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_kappafission/inputs_true.dat b/tests/test_score_kappafission/inputs_true.dat
index ecb42ddb46..b89696c82b 100644
--- a/tests/test_score_kappafission/inputs_true.dat
+++ b/tests/test_score_kappafission/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_nufission/inputs_true.dat b/tests/test_score_nufission/inputs_true.dat
index 52f7765fa7..0e6d19262d 100644
--- a/tests/test_score_nufission/inputs_true.dat
+++ b/tests/test_score_nufission/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_nuscatter/inputs_true.dat b/tests/test_score_nuscatter/inputs_true.dat
index 80840f900d..aaf16deca5 100644
--- a/tests/test_score_nuscatter/inputs_true.dat
+++ b/tests/test_score_nuscatter/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_nuscatter_n/inputs_true.dat b/tests/test_score_nuscatter_n/inputs_true.dat
index c63f891d26..cd16f80122 100644
--- a/tests/test_score_nuscatter_n/inputs_true.dat
+++ b/tests/test_score_nuscatter_n/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_nuscatter_pn/inputs_true.dat b/tests/test_score_nuscatter_pn/inputs_true.dat
index ef53ee8f65..ae7c108b7a 100644
--- a/tests/test_score_nuscatter_pn/inputs_true.dat
+++ b/tests/test_score_nuscatter_pn/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_nuscatter_yn/inputs_true.dat b/tests/test_score_nuscatter_yn/inputs_true.dat
index 632a144031..da8fc3f2fe 100644
--- a/tests/test_score_nuscatter_yn/inputs_true.dat
+++ b/tests/test_score_nuscatter_yn/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_scatter/inputs_true.dat b/tests/test_score_scatter/inputs_true.dat
index 35557cd3c5..28e2bebeee 100644
--- a/tests/test_score_scatter/inputs_true.dat
+++ b/tests/test_score_scatter/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_scatter_n/inputs_true.dat b/tests/test_score_scatter_n/inputs_true.dat
index e6e3a395bd..ea71722993 100644
--- a/tests/test_score_scatter_n/inputs_true.dat
+++ b/tests/test_score_scatter_n/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_scatter_pn/inputs_true.dat b/tests/test_score_scatter_pn/inputs_true.dat
index 3878939c50..9c74a13f93 100644
--- a/tests/test_score_scatter_pn/inputs_true.dat
+++ b/tests/test_score_scatter_pn/inputs_true.dat
@@ -1 +1 @@
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\ No newline at end of file
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\ No newline at end of file
diff --git a/tests/test_score_scatter_yn/inputs_true.dat b/tests/test_score_scatter_yn/inputs_true.dat
index 1ae9f6047d..0bfb564ff8 100644
--- a/tests/test_score_scatter_yn/inputs_true.dat
+++ b/tests/test_score_scatter_yn/inputs_true.dat
@@ -1 +1 @@
-d80a9e8befab978bc84a231437a2b96c8f6dba81984c9e82a79360feb26bc9875b661131dbaa2deeb66b0aa50a39b2e738303bc40c5c65ee1995cf52f687ae46
\ No newline at end of file
+8dd146ebd2008801a4c83470e8d9fcc8d39fe5e687c65f980eca209dd5f7fb8d17bfadf3bf3d452eaa564ee5f35a8fc97adc3dda44160d6ad574b784f2dd0030
\ No newline at end of file
diff --git a/tests/test_score_total/inputs_true.dat b/tests/test_score_total/inputs_true.dat
index c8b979c725..45bebe24c2 100644
--- a/tests/test_score_total/inputs_true.dat
+++ b/tests/test_score_total/inputs_true.dat
@@ -1 +1 @@
-8813917cab656135c4eebfdbe5f0d95e6a9409a2b36df1dfb483bdd193a3c0978727918a58399b259b82a7d51ae3f1801148bd978603fec11f27acdbf89520e2
\ No newline at end of file
+7bf8aa36c31ca8b34f7c410901cb96e1ba7d389c33c850591519b15753c1322b75e5051c960dbc2e254cf46cc9720dd5e348aac0acbb70d10c71165e2f5ab9e4
\ No newline at end of file
diff --git a/tests/test_score_total_yn/inputs_true.dat b/tests/test_score_total_yn/inputs_true.dat
index b2818f8568..939252597c 100644
--- a/tests/test_score_total_yn/inputs_true.dat
+++ b/tests/test_score_total_yn/inputs_true.dat
@@ -1 +1 @@
-4dbbd9cec921d2420e7567533c833afbe94335073aa760d1fd937adb695191b3ab7c070ec0d32721dddb4a46054e68322cf6a058420e20e439eb1d44f09f4be4
\ No newline at end of file
+3c05a49f78866eda267c9f8eab2669a3a1aeb4eabfade9697d551b13f482d9076190c63a52a8aade59c20d917cebdd3e9bedabda6bc5bfd549db43cc61d7466f
\ No newline at end of file
diff --git a/tests/test_source/inputs_true.dat b/tests/test_source/inputs_true.dat
new file mode 100644
index 0000000000..01130ed2e5
--- /dev/null
+++ b/tests/test_source/inputs_true.dat
@@ -0,0 +1 @@
+5c2fdde85affcd44c1b02c07c300acb8e5c189c1adbf7aa079e37a68e8b8313678fc292bd7f6e0d0957f723e05b8146bd165cf3315dde5f6b2f88ebc954cd65e
\ No newline at end of file
diff --git a/tests/test_source/results_true.dat b/tests/test_source/results_true.dat
new file mode 100644
index 0000000000..18fb895f77
--- /dev/null
+++ b/tests/test_source/results_true.dat
@@ -0,0 +1,2 @@
+k-combined:
+3.014392E-01 7.185055E-03
diff --git a/tests/test_source/test_source.py b/tests/test_source/test_source.py
new file mode 100644
index 0000000000..9d303b06bc
--- /dev/null
+++ b/tests/test_source/test_source.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+
+from math import pi
+import os
+import sys
+
+import numpy as np
+
+sys.path.insert(0, os.pardir)
+from testing_harness import PyAPITestHarness
+import openmc
+import openmc.stats
+from openmc.source import Source
+
+
+class SourceTestHarness(PyAPITestHarness):
+    def _build_inputs(self):
+        mat1 = openmc.Material(material_id=1)
+        mat1.set_density('g/cm3', 4.5)
+        mat1.add_nuclide(openmc.Nuclide('U-235', '71c'), 1.0)
+        materials = openmc.MaterialsFile()
+        materials.add_material(mat1)
+        materials.export_to_xml()
+
+        sphere = openmc.Sphere(surface_id=1, R=10.0, boundary_type='vacuum')
+        inside_sphere = openmc.Cell(cell_id=1)
+        inside_sphere.region = -sphere
+        inside_sphere.fill = mat1
+
+        root = openmc.Universe(universe_id=0)
+        root.add_cell(inside_sphere)
+        geometry = openmc.Geometry()
+        geometry.root_universe = root
+        geometry_xml = openmc.GeometryFile()
+        geometry_xml.geometry = geometry
+        geometry_xml.export_to_xml()
+
+        # Create an array of different sources
+        x_dist = openmc.stats.Uniform(-3., 3.)
+        y_dist = openmc.stats.Discrete([-4., -1., 3.], [0.2, 0.3, 0.5])
+        z_dist = openmc.stats.Tabular([-2., 0., 2.], [0.2, 0.3, 0.2])
+        spatial1 = openmc.stats.CartesianIndependent(x_dist, y_dist, z_dist)
+        spatial2 = openmc.stats.Box([-4., -4., -4.], [4., 4., 4.])
+        spatial3 = openmc.stats.Point([1.2, -2.3, 0.781])
+
+        mu_dist = openmc.stats.Discrete([-1., 0., 1.], [0.5, 0.25, 0.25])
+        phi_dist = openmc.stats.Uniform(0., 6.28318530718)
+        angle1 = openmc.stats.PolarAzimuthal(mu_dist, phi_dist)
+        angle2 = openmc.stats.Monodirectional(reference_uvw=[0., 1., 0.])
+        angle3 = openmc.stats.Isotropic()
+
+        E = np.logspace(-6, 1)
+        p = np.sin(np.linspace(0., pi))
+        p /= sum(np.diff(E)*p[:-1])
+        energy1 = openmc.stats.Maxwell(1.2895)
+        energy2 = openmc.stats.Watt(0.988, 2.249)
+        energy3 = openmc.stats.Tabular(E, p, interpolation='histogram')
+
+        source1 = Source(spatial1, angle1, energy1, strength=0.5)
+        source2 = Source(spatial2, angle2, energy2, strength=0.3)
+        source3 = Source(spatial3, angle3, energy3, strength=0.2)
+
+        settings = openmc.SettingsFile()
+        settings.batches = 10
+        settings.inactive = 5
+        settings.particles = 1000
+        settings.source = [source1, source2, source3]
+        settings.export_to_xml()
+
+
+if __name__ == '__main__':
+    harness = SourceTestHarness('statepoint.10.h5')
+    harness.main()
diff --git a/tests/test_source_angle_mono/geometry.xml b/tests/test_source_angle_mono/geometry.xml
deleted file mode 100644
index bc56030e18..0000000000
--- a/tests/test_source_angle_mono/geometry.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0"?>
-<geometry>
-
-  <!-- Sphere with radius 10 -->
-  <surface id="1" type="sphere" coeffs="0 0 0 10" boundary="vacuum"/>
-  <cell id="1" material="1" region="-1" />
-    
-</geometry>
diff --git a/tests/test_source_angle_mono/materials.xml b/tests/test_source_angle_mono/materials.xml
deleted file mode 100644
index 315c0fa848..0000000000
--- a/tests/test_source_angle_mono/materials.xml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0"?>
-<materials>
-
-  <material id="1">
-    <density value="4.5" units="g/cc" />
-    <nuclide name="U-235" xs="71c" ao="1.0" />
-  </material>
-
-</materials>
diff --git a/tests/test_source_angle_mono/results_true.dat b/tests/test_source_angle_mono/results_true.dat
deleted file mode 100644
index 42e948758a..0000000000
--- a/tests/test_source_angle_mono/results_true.dat
+++ /dev/null
@@ -1,2 +0,0 @@
-k-combined:
-2.964943E-01 1.201478E-02
diff --git a/tests/test_source_angle_mono/settings.xml b/tests/test_source_angle_mono/settings.xml
deleted file mode 100644
index 5029aa816c..0000000000
--- a/tests/test_source_angle_mono/settings.xml
+++ /dev/null
@@ -1,15 +0,0 @@
-<?xml version="1.0"?>
-<settings>
-
-  <eigenvalue>
-    <batches>10</batches>
-    <inactive>5</inactive>
-    <particles>1000</particles>
-  </eigenvalue>
-
-  <source>
-    <space type="box" parameters="-4 -4 -4  4  4  4" />
-    <angle type="monodirectional" parameters="1 0 0" />
-  </source>
-
-</settings>
diff --git a/tests/test_source_angle_mono/test_source_angle_mono.py b/tests/test_source_angle_mono/test_source_angle_mono.py
deleted file mode 100644
index 2a595f3e66..0000000000
--- a/tests/test_source_angle_mono/test_source_angle_mono.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import sys
-sys.path.insert(0, os.pardir)
-from testing_harness import TestHarness
-
-
-if __name__ == '__main__':
-    harness = TestHarness('statepoint.10.*')
-    harness.main()
diff --git a/tests/test_source_energy_maxwell/geometry.xml b/tests/test_source_energy_maxwell/geometry.xml
deleted file mode 100644
index bc56030e18..0000000000
--- a/tests/test_source_energy_maxwell/geometry.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0"?>
-<geometry>
-
-  <!-- Sphere with radius 10 -->
-  <surface id="1" type="sphere" coeffs="0 0 0 10" boundary="vacuum"/>
-  <cell id="1" material="1" region="-1" />
-    
-</geometry>
diff --git a/tests/test_source_energy_maxwell/materials.xml b/tests/test_source_energy_maxwell/materials.xml
deleted file mode 100644
index 315c0fa848..0000000000
--- a/tests/test_source_energy_maxwell/materials.xml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0"?>
-<materials>
-
-  <material id="1">
-    <density value="4.5" units="g/cc" />
-    <nuclide name="U-235" xs="71c" ao="1.0" />
-  </material>
-
-</materials>
diff --git a/tests/test_source_energy_maxwell/results_true.dat b/tests/test_source_energy_maxwell/results_true.dat
deleted file mode 100644
index 37b8b36b99..0000000000
--- a/tests/test_source_energy_maxwell/results_true.dat
+++ /dev/null
@@ -1,2 +0,0 @@
-k-combined:
-2.886671E-01 7.534631E-03
diff --git a/tests/test_source_energy_maxwell/settings.xml b/tests/test_source_energy_maxwell/settings.xml
deleted file mode 100644
index d0543785b5..0000000000
--- a/tests/test_source_energy_maxwell/settings.xml
+++ /dev/null
@@ -1,15 +0,0 @@
-<?xml version="1.0"?>
-<settings>
-
-  <eigenvalue>
-    <batches>10</batches>
-    <inactive>5</inactive>
-    <particles>1000</particles>
-  </eigenvalue>
-
-  <source>
-    <space type="box" parameters="-4 -4 -4  4  4  4" />
-    <energy type="maxwell" parameters="1.2895" />
-  </source>
-
-</settings>
diff --git a/tests/test_source_energy_maxwell/test_source_energy_maxwell.py b/tests/test_source_energy_maxwell/test_source_energy_maxwell.py
deleted file mode 100644
index 2a595f3e66..0000000000
--- a/tests/test_source_energy_maxwell/test_source_energy_maxwell.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import sys
-sys.path.insert(0, os.pardir)
-from testing_harness import TestHarness
-
-
-if __name__ == '__main__':
-    harness = TestHarness('statepoint.10.*')
-    harness.main()
diff --git a/tests/test_source_energy_mono/geometry.xml b/tests/test_source_energy_mono/geometry.xml
deleted file mode 100644
index bc56030e18..0000000000
--- a/tests/test_source_energy_mono/geometry.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0"?>
-<geometry>
-
-  <!-- Sphere with radius 10 -->
-  <surface id="1" type="sphere" coeffs="0 0 0 10" boundary="vacuum"/>
-  <cell id="1" material="1" region="-1" />
-    
-</geometry>
diff --git a/tests/test_source_energy_mono/materials.xml b/tests/test_source_energy_mono/materials.xml
deleted file mode 100644
index 315c0fa848..0000000000
--- a/tests/test_source_energy_mono/materials.xml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0"?>
-<materials>
-
-  <material id="1">
-    <density value="4.5" units="g/cc" />
-    <nuclide name="U-235" xs="71c" ao="1.0" />
-  </material>
-
-</materials>
diff --git a/tests/test_source_energy_mono/results_true.dat b/tests/test_source_energy_mono/results_true.dat
deleted file mode 100644
index 029376bf37..0000000000
--- a/tests/test_source_energy_mono/results_true.dat
+++ /dev/null
@@ -1,2 +0,0 @@
-k-combined:
-3.002731E-01 7.561170E-03
diff --git a/tests/test_source_energy_mono/settings.xml b/tests/test_source_energy_mono/settings.xml
deleted file mode 100644
index 9093efec97..0000000000
--- a/tests/test_source_energy_mono/settings.xml
+++ /dev/null
@@ -1,15 +0,0 @@
-<?xml version="1.0"?>
-<settings>
-
-  <eigenvalue>
-    <batches>10</batches>
-    <inactive>5</inactive>
-    <particles>1000</particles>
-  </eigenvalue>
-
-  <source>
-    <space type="box" parameters="-4 -4 -4  4  4  4" />
-    <energy type="monoenergetic" parameters="0.0253e-6" />
-  </source>
-
-</settings>
diff --git a/tests/test_source_energy_mono/test_source_energy_mono.py b/tests/test_source_energy_mono/test_source_energy_mono.py
deleted file mode 100644
index 2a595f3e66..0000000000
--- a/tests/test_source_energy_mono/test_source_energy_mono.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import sys
-sys.path.insert(0, os.pardir)
-from testing_harness import TestHarness
-
-
-if __name__ == '__main__':
-    harness = TestHarness('statepoint.10.*')
-    harness.main()
diff --git a/tests/test_source_point/geometry.xml b/tests/test_source_point/geometry.xml
deleted file mode 100644
index bc56030e18..0000000000
--- a/tests/test_source_point/geometry.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0"?>
-<geometry>
-
-  <!-- Sphere with radius 10 -->
-  <surface id="1" type="sphere" coeffs="0 0 0 10" boundary="vacuum"/>
-  <cell id="1" material="1" region="-1" />
-    
-</geometry>
diff --git a/tests/test_source_point/materials.xml b/tests/test_source_point/materials.xml
deleted file mode 100644
index 315c0fa848..0000000000
--- a/tests/test_source_point/materials.xml
+++ /dev/null
@@ -1,9 +0,0 @@
-<?xml version="1.0"?>
-<materials>
-
-  <material id="1">
-    <density value="4.5" units="g/cc" />
-    <nuclide name="U-235" xs="71c" ao="1.0" />
-  </material>
-
-</materials>
diff --git a/tests/test_source_point/results_true.dat b/tests/test_source_point/results_true.dat
deleted file mode 100644
index fe9a0d78d4..0000000000
--- a/tests/test_source_point/results_true.dat
+++ /dev/null
@@ -1,2 +0,0 @@
-k-combined:
-3.041148E-01 4.558319E-03
diff --git a/tests/test_source_point/settings.xml b/tests/test_source_point/settings.xml
deleted file mode 100644
index 3b3c47150e..0000000000
--- a/tests/test_source_point/settings.xml
+++ /dev/null
@@ -1,14 +0,0 @@
-<?xml version="1.0"?>
-<settings>
-
-  <eigenvalue>
-    <batches>10</batches>
-    <inactive>5</inactive>
-    <particles>1000</particles>
-  </eigenvalue>
-
-  <source>
-    <space type="point" parameters="1.2 -2.3 0.781" />
-  </source>
-
-</settings>
diff --git a/tests/test_source_point/test_source_point.py b/tests/test_source_point/test_source_point.py
deleted file mode 100644
index 2a595f3e66..0000000000
--- a/tests/test_source_point/test_source_point.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import sys
-sys.path.insert(0, os.pardir)
-from testing_harness import TestHarness
-
-
-if __name__ == '__main__':
-    harness = TestHarness('statepoint.10.*')
-    harness.main()
diff --git a/tests/test_tally_arithmetic/inputs_true.dat b/tests/test_tally_arithmetic/inputs_true.dat
index 8e8838131a..1b6046f1ae 100644
--- a/tests/test_tally_arithmetic/inputs_true.dat
+++ b/tests/test_tally_arithmetic/inputs_true.dat
@@ -1 +1 @@
-df6318b76cd37a29ef9dd09da48a70c191ed07c1a2ceb75eb502ab35086c31250872406f22c2587edfcee16f67dd95c81db012ccd728710ed2509084438aea56
\ No newline at end of file
+57384883e37964076aa82c19fa542434331cdb09735d710485b5aa0ca3445d543729e40cb9c7b6a70e7101ef186923eb1ff6315c73b01ff257052838add68fc7
\ No newline at end of file