diff --git a/.gitignore b/.gitignore
index c8d4d7140..f328d7b9a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 # Compiled objects and modules
 *.a
 *.o
-*.mod
 *.log
 *.out
 
@@ -35,9 +34,6 @@ build
 # build from src/utils/setup.py
 src/utils/build
 
-# xml-fortran reader
-src/xml-fortran/xmlreader
-
 # Test results error file
 results_error.dat
 inputs_error.dat
@@ -79,17 +75,6 @@ scripts/G4EMLOW*/
 # IPython notebook checkpoints
 .ipynb_checkpoints
 
-# Jupyter notebooks
-examples/jupyter/*.xml
-examples/jupyter/*.png
-examples/jupyter/*.xls
-examples/jupyter/*.ace
-examples/jupyter/*.endf
-examples/jupyter/mgxs
-examples/jupyter/tracks
-examples/jupyter/fission-rates
-examples/jupyter/plots
-
 # Cython files
 *.c
 *.html
diff --git a/MANIFEST.in b/MANIFEST.in
index 795bfcf23..e660a1161 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -21,8 +21,8 @@ recursive-include docs *.tex
 recursive-include docs *.txt
 recursive-include docs Makefile
 recursive-include examples *.h5
-recursive-include examples *.ipynb
 recursive-include examples *.png
+recursive-include examples *.cpp
 recursive-include examples *.py
 recursive-include examples *.xml
 recursive-include include *.h
diff --git a/docs/requirements-rtd.txt b/docs/requirements-rtd.txt
index fd76dfffb..bf75537a8 100644
--- a/docs/requirements-rtd.txt
+++ b/docs/requirements-rtd.txt
@@ -2,7 +2,6 @@ sphinx-numfig
 jupyter
 sphinxcontrib-katex
 sphinxcontrib-svg2pdfconverter
-nbsphinx
 numpy
 scipy
 h5py
diff --git a/docs/source/conf.py b/docs/source/conf.py
index ed790a2e2..5847af13b 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -44,7 +44,6 @@ extensions = [
     'sphinx.ext.viewcode',
     'sphinxcontrib.katex',
     'sphinx_numfig',
-    'nbsphinx'
 ]
 if not on_rtd:
     extensions.append('sphinxcontrib.rsvgconverter')
diff --git a/docs/source/examples/cad-based-geometry.ipynb b/docs/source/examples/cad-based-geometry.ipynb
deleted file mode 120000
index ee3727e67..000000000
--- a/docs/source/examples/cad-based-geometry.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/cad-based-geometry.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/candu.ipynb b/docs/source/examples/candu.ipynb
deleted file mode 120000
index 480d99fb1..000000000
--- a/docs/source/examples/candu.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/candu.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/capi.ipynb b/docs/source/examples/capi.ipynb
deleted file mode 120000
index f69a37093..000000000
--- a/docs/source/examples/capi.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/capi.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/expansion-filters.ipynb b/docs/source/examples/expansion-filters.ipynb
deleted file mode 120000
index e75734135..000000000
--- a/docs/source/examples/expansion-filters.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/expansion-filters.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/hexagonal-lattice.ipynb b/docs/source/examples/hexagonal-lattice.ipynb
deleted file mode 120000
index e2b63d243..000000000
--- a/docs/source/examples/hexagonal-lattice.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/hexagonal-lattice.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
deleted file mode 100644
index 38bc51e26..000000000
--- a/docs/source/examples/index.rst
+++ /dev/null
@@ -1,73 +0,0 @@
-.. _examples:
-
-========
-Examples
-========
-
-The following series of `Jupyter <https://jupyter.org/>`_ Notebooks provide
-examples for how to use various features of OpenMC by leveraging the
-:ref:`pythonapi`.
-
--------------
-General Usage
--------------
-
-.. toctree::
-    :maxdepth: 1
-
-    pincell
-    post-processing
-    pandas-dataframes
-    tally-arithmetic
-    capi
-    expansion-filters
-    search
-    nuclear-data
-    nuclear-data-resonance-covariance
-    pincell_depletion
-
---------
-Geometry
---------
-
-.. toctree::
-    :maxdepth: 1
-
-    hexagonal-lattice
-    triso
-    candu
-    cad-based-geometry
-
------------------------------------
-Multigroup Cross Section Generation
------------------------------------
-
-.. toctree::
-    :maxdepth: 1
-
-    mgxs-part-i
-    mgxs-part-ii
-    mgxs-part-iii
-    mdgxs-part-i
-    mdgxs-part-ii
-
----------------
-Multigroup Mode
----------------
-
-.. toctree::
-    :maxdepth: 1
-
-    mg-mode-part-i
-    mg-mode-part-ii
-    mg-mode-part-iii
-
------------------
-Unstructured Mesh
------------------
-
-.. toctree::
-   :maxdepth: 1
-
-   unstructured-mesh-part-i
-   unstructured-mesh-part-ii
diff --git a/docs/source/examples/mdgxs-part-i.ipynb b/docs/source/examples/mdgxs-part-i.ipynb
deleted file mode 120000
index 01eb1172d..000000000
--- a/docs/source/examples/mdgxs-part-i.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mdgxs-part-i.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mdgxs-part-ii.ipynb b/docs/source/examples/mdgxs-part-ii.ipynb
deleted file mode 120000
index 2d9d33907..000000000
--- a/docs/source/examples/mdgxs-part-ii.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mdgxs-part-ii.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mg-mode-part-i.ipynb b/docs/source/examples/mg-mode-part-i.ipynb
deleted file mode 120000
index d1577f700..000000000
--- a/docs/source/examples/mg-mode-part-i.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mg-mode-part-i.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mg-mode-part-ii.ipynb b/docs/source/examples/mg-mode-part-ii.ipynb
deleted file mode 120000
index a09306376..000000000
--- a/docs/source/examples/mg-mode-part-ii.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mg-mode-part-ii.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mg-mode-part-iii.ipynb b/docs/source/examples/mg-mode-part-iii.ipynb
deleted file mode 120000
index 6fa0d180e..000000000
--- a/docs/source/examples/mg-mode-part-iii.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mg-mode-part-iii.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mgxs-part-i.ipynb b/docs/source/examples/mgxs-part-i.ipynb
deleted file mode 120000
index a04b1da89..000000000
--- a/docs/source/examples/mgxs-part-i.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mgxs-part-i.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mgxs-part-ii.ipynb b/docs/source/examples/mgxs-part-ii.ipynb
deleted file mode 120000
index dd8af3c43..000000000
--- a/docs/source/examples/mgxs-part-ii.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mgxs-part-ii.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/mgxs-part-iii.ipynb b/docs/source/examples/mgxs-part-iii.ipynb
deleted file mode 120000
index d6acc76ba..000000000
--- a/docs/source/examples/mgxs-part-iii.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/mgxs-part-iii.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/nuclear-data-resonance-covariance.ipynb b/docs/source/examples/nuclear-data-resonance-covariance.ipynb
deleted file mode 120000
index 0ab0dd62b..000000000
--- a/docs/source/examples/nuclear-data-resonance-covariance.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/nuclear-data-resonance-covariance.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/nuclear-data.ipynb b/docs/source/examples/nuclear-data.ipynb
deleted file mode 120000
index 59721abdc..000000000
--- a/docs/source/examples/nuclear-data.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/nuclear-data.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/pandas-dataframes.ipynb b/docs/source/examples/pandas-dataframes.ipynb
deleted file mode 120000
index f41f570be..000000000
--- a/docs/source/examples/pandas-dataframes.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/pandas-dataframes.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/pincell.ipynb b/docs/source/examples/pincell.ipynb
deleted file mode 120000
index edbbb8de2..000000000
--- a/docs/source/examples/pincell.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/pincell.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/pincell_depletion.ipynb b/docs/source/examples/pincell_depletion.ipynb
deleted file mode 120000
index 1f25930fc..000000000
--- a/docs/source/examples/pincell_depletion.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/pincell_depletion.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/post-processing.ipynb b/docs/source/examples/post-processing.ipynb
deleted file mode 120000
index 1f3f4cd71..000000000
--- a/docs/source/examples/post-processing.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/post-processing.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/search.ipynb b/docs/source/examples/search.ipynb
deleted file mode 120000
index fbfb67bc6..000000000
--- a/docs/source/examples/search.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/search.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/tally-arithmetic.ipynb b/docs/source/examples/tally-arithmetic.ipynb
deleted file mode 120000
index 7ffe19462..000000000
--- a/docs/source/examples/tally-arithmetic.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/tally-arithmetic.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/triso.ipynb b/docs/source/examples/triso.ipynb
deleted file mode 120000
index 0e51d8add..000000000
--- a/docs/source/examples/triso.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/triso.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/unstructured-mesh-part-i.ipynb b/docs/source/examples/unstructured-mesh-part-i.ipynb
deleted file mode 120000
index b1358788b..000000000
--- a/docs/source/examples/unstructured-mesh-part-i.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/unstructured-mesh-part-i.ipynb
\ No newline at end of file
diff --git a/docs/source/examples/unstructured-mesh-part-ii.ipynb b/docs/source/examples/unstructured-mesh-part-ii.ipynb
deleted file mode 120000
index 9074811b7..000000000
--- a/docs/source/examples/unstructured-mesh-part-ii.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-../../../examples/jupyter/unstructured-mesh-part-ii.ipynb
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3b2f5b053..a01055374 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -36,7 +36,7 @@ Forum <https://openmc.discourse.group/>`_.
     :maxdepth: 1
 
     quickinstall
-    examples/index
+    Examples <https://github.com/openmc-dev/openmc/wiki/Example-Jupyter-Notebooks>
     releasenotes/index
     methods/index
     usersguide/index
diff --git a/examples/jupyter/c5g7.h5 b/examples/jupyter/c5g7.h5
deleted file mode 100644
index b3dea952b..000000000
Binary files a/examples/jupyter/c5g7.h5 and /dev/null differ
diff --git a/examples/jupyter/cad-based-geometry.ipynb b/examples/jupyter/cad-based-geometry.ipynb
deleted file mode 100644
index 99be260c7..000000000
--- a/examples/jupyter/cad-based-geometry.ipynb
+++ /dev/null
@@ -1,780 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using CAD-Based Geometries\n",
-    "In this notebook we'll be exploring how to use CAD-based geometries in OpenMC via the [DagMC](https://svalinn.github.io/DAGMC/index.html) toolkit. The models we'll be using in this notebook have already been created using [Trelis](https://coreform.com/products/trelisnew/) and faceted into a surface mesh represented as `.h5m` files in the [Mesh Oriented DatABase](https://sigma.mcs.anl.gov/moab-library/) format. We'll be retrieving these files using the function below.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import urllib.request\n",
-    "\n",
-    "fuel_pin_url = 'https://tinyurl.com/y3ugwz6w' # 1.2 MB\n",
-    "teapot_url = 'https://tinyurl.com/y4mcmc3u' # 29 MB\n",
-    "\n",
-    "def download(url):\n",
-    "    \"\"\"\n",
-    "    Helper function for retrieving dagmc models\n",
-    "    \"\"\"\n",
-    "    u = urllib.request.urlopen(url)\n",
-    "    \n",
-    "    if u.status != 200:\n",
-    "        raise RuntimeError(\"Failed to download file.\")\n",
-    "    \n",
-    "    # save file as dagmc.h5m\n",
-    "    with open(\"dagmc.h5m\", 'wb') as f:\n",
-    "        f.write(u.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook is intended to demonstrate how DagMC problems are run in OpenMC. For more information on how DagMC models are created, please refer to the [DagMC User's Guide](https://svalinn.github.io/DAGMC/usersguide/index.html).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from IPython.display import Image\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To start, we'll be using a simple U235 fuel pin surrounded by a water moderator, so let's create those materials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    " # materials\n",
-    "u235 = openmc.Material(name=\"fuel\")\n",
-    "u235.add_nuclide('U235', 1.0, 'ao')\n",
-    "u235.set_density('g/cc', 11)\n",
-    "u235.id = 40\n",
-    "\n",
-    "water = openmc.Material(name=\"water\")\n",
-    "water.add_nuclide('H1', 2.0, 'ao')\n",
-    "water.add_nuclide('O16', 1.0, 'ao')\n",
-    "water.set_density('g/cc', 1.0)\n",
-    "water.add_s_alpha_beta('c_H_in_H2O')\n",
-    "water.id = 41\n",
-    "\n",
-    "mats = openmc.Materials([u235, water])\n",
-    "mats.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's get our DAGMC geometry. We'll be using prefabricated models in this notebook. For information on how to create your own DAGMC models, you can refer to the instructions [here](https://svalinn.github.io/DAGMC/usersguide/trelis_workflow.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's download the DAGMC model. These models come in the form of triangle surface meshes stored using the the Mesh Oriented datABase ([MOAB](https://sigma.mcs.anl.gov/moab-library/)) in an HDF5 file with the extension `.h5m`. An example of a coarse triangle mesh looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "image/png": {
-       "width": 350
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/cylinder_mesh.png\", width=350)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we'll need to grab some pre-made DagMC models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download(fuel_pin_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To create a geometry where DAGMC represents the entire model, we'll make a DAGMC universe and use it as the root universe of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dagmc_univ = openmc.DAGMCUniverse(filename=\"dagmc.h5m\")\n",
-    "geometry = openmc.Geometry(root=dagmc_univ)\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "settings.batches = 10\n",
-    "settings.inactive = 2\n",
-    "settings.particles = 5000\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unlike conventional geometries in OpenMC, we really have no way of knowing what our model looks like at this point. Thankfully DagMC geometries can be plotted just like any other OpenMC geometry to give us an idea of what we're now working with.\n",
-    "\n",
-    "Note that material assignments have already been applied to this model. Materials can be assigned either using ids or names of materials in the `materials.xml` file. It is recommended that material names are used for assignment for readability."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = openmc.Plot()\n",
-    "p.width = (25.0, 25.0)\n",
-    "p.pixels = (400, 400)\n",
-    "p.color_by = 'material'\n",
-    "p.colors = {u235: 'yellow', water: 'blue'}\n",
-    "openmc.plot_inline(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we've had a chance to examine the model a bit, we can finish applying our settings and add a source."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings.source = openmc.Source(space=openmc.stats.Box([-4., -4., -4.],\n",
-    "                                                       [ 4.,  4.,  4.]))\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Tallies work in the same way when using DAGMC geometries too. We'll add a tally on the fuel cell here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tally = openmc.Tally()\n",
-    "tally.scores = ['total']\n",
-    "tally.filters = [openmc.CellFilter(1)]\n",
-    "tallies = openmc.Tallies([tally])\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note:** Applying tally filters in DagMC models requires prior knowledge of the model. Here, we know that the fuel cell's volume ID in the CAD sofware is 1. To identify cells without use of CAD software, load them into the [OpenMC plotter](https://github.com/openmc-dev/plotter) where cell, material, and volume IDs can be identified for native both OpenMC and DagMC geometries."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we're ready to run the simulation just like any other OpenMC run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 0aafa81907ac79b9bdb4a86aede0b63fee3a9a9b\n",
-      "         Date/Time | 2021-06-30 09:01:58\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      "Set overlap thickness = 0\n",
-      "Set numerical precision = 0.001\n",
-      "Loading file dagmc.h5m\n",
-      "Initializing the GeomQueryTool...\n",
-      "Using faceting tolerance: 0.0001\n",
-      "Building acceleration data structures...\n",
-      "Implicit Complement assumed to be Vacuum\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.16613\n",
-      "        2/1    1.05158\n",
-      "        3/1    0.99824\n",
-      "        4/1    0.98119    0.98971 +/- 0.00853\n",
-      "        5/1    0.99111    0.99018 +/- 0.00494\n",
-      "        6/1    0.98946    0.99000 +/- 0.00350\n",
-      "        7/1    0.96858    0.98571 +/- 0.00507\n",
-      "        8/1    0.96709    0.98261 +/- 0.00518\n",
-      "        9/1    0.97415    0.98140 +/- 0.00454\n",
-      "       10/1    0.93691    0.97584 +/- 0.00681\n",
-      " Creating state point statepoint.10.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.0815e-01 seconds\n",
-      "   Reading cross sections          = 9.2095e-02 seconds\n",
-      " Total time in simulation          = 1.5220e+00 seconds\n",
-      "   Time in transport only          = 1.5148e+00 seconds\n",
-      "   Time in inactive batches        = 2.9113e-01 seconds\n",
-      "   Time in active batches          = 1.2309e+00 seconds\n",
-      "   Time synchronizing fission bank = 3.1554e-03 seconds\n",
-      "     Sampling source sites         = 2.7998e-03 seconds\n",
-      "     SEND/RECV source sites        = 3.4967e-04 seconds\n",
-      "   Time accumulating tallies       = 3.0953e-05 seconds\n",
-      "   Time writing statepoints        = 2.7201e-03 seconds\n",
-      " Total time for finalization       = 2.3371e-04 seconds\n",
-      " Total time elapsed                = 1.7509e+00 seconds\n",
-      " Calculation Rate (inactive)       = 34348.7 particles/second\n",
-      " Calculation Rate (active)         = 32497.7 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 0.97672 +/- 0.00532\n",
-      " k-effective (Track-length)  = 0.97584 +/- 0.00681\n",
-      " k-effective (Absorption)    = 0.96793 +/- 0.00613\n",
-      " Combined k-effective        = 0.97097 +/- 0.00582\n",
-      " Leakage Fraction            = 0.57298 +/- 0.00284\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More Complicated Geometry"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Neat! But this pincell is something we could've done with CSG. Let's take a look at something more complex. We'll download a pre-built model of the [Utah teapot](https://en.wikipedia.org/wiki/Utah_teapot) and use it here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "download(teapot_url)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {
-      "image/jpeg": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/teapot.jpg\", width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our teapot is made out of iron, so we'll want to create that material and make sure it is in our `materials.xml` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "iron = openmc.Material(name=\"iron\")\n",
-    "iron.add_nuclide(\"Fe54\", 0.0564555822608)\n",
-    "iron.add_nuclide(\"Fe56\", 0.919015287728)\n",
-    "iron.add_nuclide(\"Fe57\", 0.0216036861685)\n",
-    "iron.add_nuclide(\"Fe58\", 0.00292544384231)\n",
-    "iron.set_density(\"g/cm3\", 7.874)\n",
-    "mats = openmc.Materials([iron, water])\n",
-    "mats.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To make sure we've updated the file correctly, let's make a plot of the teapot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p = openmc.Plot()\n",
-    "p.basis = 'xz'\n",
-    "p.origin = (0.0, 0.0, 0.0)\n",
-    "p.width = (30.0, 20.0)\n",
-    "p.pixels = (450, 300)\n",
-    "p.color_by = 'material'\n",
-    "p.colors = {iron: 'gray', water: 'blue'}\n",
-    "openmc.plot_inline(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we start to see some of the advantages CAD geometries provide. This particular file was pulled from the [GrabCAD](https://grabcad.com/library) and pushed through the DAGMC workflow without modification (other than the addition of material assignments). It would take a considerable amount of time to create a model like this using CSG!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "p.width = (18.0, 6.0)\n",
-    "p.basis = 'xz'\n",
-    "p.origin = (10.0, 0.0, 5.0)\n",
-    "p.pixels = (600, 200)\n",
-    "p.color_by = 'material'\n",
-    "openmc.plot_inline(p)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's brew some tea! ... using a very hot neutron source. We'll use some well-placed point sources distributed throughout the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "settings.batches = 10\n",
-    "settings.particles = 5000\n",
-    "settings.run_mode = \"fixed source\"\n",
-    "\n",
-    "src_locations = ((-4.0, 0.0, -2.0),\n",
-    "                 ( 4.0, 0.0, -2.0),\n",
-    "                 ( 4.0, 0.0, -6.0),\n",
-    "                 (-4.0, 0.0, -6.0),\n",
-    "                 (10.0, 0.0, -4.0),\n",
-    "                 (-8.0, 0.0, -4.0))\n",
-    "\n",
-    "# we'll use the same energy for each source\n",
-    "src_e = openmc.stats.Discrete(x=[12.0,], p=[1.0,])\n",
-    "\n",
-    "# create source for each location\n",
-    "sources = []\n",
-    "for loc in src_locations:\n",
-    "    src_pnt = openmc.stats.Point(xyz=loc)\n",
-    "    src = openmc.Source(space=src_pnt, energy=src_e)\n",
-    "    sources.append(src)\n",
-    "\n",
-    "src_str = 1.0 / len(sources)\n",
-    "for source in sources:\n",
-    "    source.strength = src_str\n",
-    "\n",
-    "settings.source = sources\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "...and setup a couple of mesh tallies. One for the kettle, and one for the water inside."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mesh = openmc.RegularMesh()\n",
-    "mesh.dimension = (120, 1, 40)\n",
-    "mesh.lower_left = (-20.0, 0.0, -10.0)\n",
-    "mesh.upper_right = (20.0, 1.0, 4.0)\n",
-    "\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "pot_filter = openmc.CellFilter([1])\n",
-    "pot_tally = openmc.Tally()\n",
-    "pot_tally.filters = [mesh_filter, pot_filter]\n",
-    "pot_tally.scores = ['flux']\n",
-    "\n",
-    "water_filter = openmc.CellFilter([5])\n",
-    "water_tally = openmc.Tally()\n",
-    "water_tally.filters = [mesh_filter, water_filter]\n",
-    "water_tally.scores = ['flux']\n",
-    "\n",
-    "\n",
-    "tallies = openmc.Tallies([pot_tally, water_tally])\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 0aafa81907ac79b9bdb4a86aede0b63fee3a9a9b\n",
-      "         Date/Time | 2021-06-30 09:02:13\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      "Set overlap thickness = 0\n",
-      "Set numerical precision = 0.001\n",
-      "Loading file dagmc.h5m\n",
-      "Initializing the GeomQueryTool...\n",
-      "Using faceting tolerance: 0.001\n",
-      "Building acceleration data structures...\n",
-      "Implicit Complement assumed to be Vacuum\n",
-      " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n",
-      " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n",
-      " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n",
-      " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for Fe58\n",
-      "\n",
-      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
-      "\n",
-      " Simulating batch 1\n",
-      " Simulating batch 2\n",
-      " Simulating batch 3\n",
-      " Simulating batch 4\n",
-      " Simulating batch 5\n",
-      " Simulating batch 6\n",
-      " Simulating batch 7\n",
-      " Simulating batch 8\n",
-      " Simulating batch 9\n",
-      " Simulating batch 10\n",
-      " Creating state point statepoint.10.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 4.8117e+00 seconds\n",
-      "   Reading cross sections          = 4.6543e-01 seconds\n",
-      " Total time in simulation          = 1.4498e+01 seconds\n",
-      "   Time in transport only          = 1.4493e+01 seconds\n",
-      "   Time in active batches          = 1.4498e+01 seconds\n",
-      "   Time accumulating tallies       = 3.1628e-04 seconds\n",
-      "   Time writing statepoints        = 4.8840e-03 seconds\n",
-      " Total time for finalization       = 1.5489e-02 seconds\n",
-      " Total time elapsed                = 1.9328e+01 seconds\n",
-      " Calculation Rate (active)         = 3448.64 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " Leakage Fraction            = 0.63090 +/- 0.00246\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the performance is significantly lower than our pincell model due to the increased complexity of the model, but it allows us to examine tally results like these:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp = openmc.StatePoint(\"statepoint.10.h5\")\n",
-    "\n",
-    "water_tally = sp.get_tally(scores=['flux'], id=water_tally.id)\n",
-    "water_flux = water_tally.mean\n",
-    "water_flux.shape = (40, 120)\n",
-    "water_flux = water_flux[::-1, :]\n",
-    "\n",
-    "pot_tally = sp.get_tally(scores=['flux'], id=pot_tally.id)\n",
-    "pot_flux = pot_tally.mean\n",
-    "pot_flux.shape = (40, 120)\n",
-    "pot_flux = pot_flux[::-1, :]\n",
-    "\n",
-    "del sp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f05093e09e8>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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oNmvfPmFtYcUBAAAAAAAoxcQBAAAAAAAoxcQBAAAAAAAoRY6DY0hj5/1zj+2R3aFuQ/3x8djRLaG85551ofxjz7537vFrfuqGUHfGUDz3P+mpgzcWwPIpxC/6sy4MVRPv3BHK0zfHeMTz3nBrKHvKrQIAa0aO885x9ymGujIzNPd4eG88tDoVvws95wpI8epdeQyGKqXHVlM7WzmXQA7/rxTi21P8fz3lPPA+I/lKvZiXIOVayHHziaVYeas3C49j/oi+eQhyHoNmj+Pz55rzEOQY/Z7XjX2d80nkdnstHl/MGeHpPbRyu1J3VtPxxf7TULyOzaR7N/dnVw6JZmmdNXvnl8i5LLqOn+r83NhU/Bkamo7l6qHhUK4ciHmSbLpTtv0HQ11r72OxPJVyIGDNYcUBAAAAAAAoxcQBAAAAAAAoRajCMcqnp0O5eefdobxlX1wu1Bg9M5S/O/GEuccfuGAk1P3fJ/2feLFKXqrF0mZgOeUtFR/6xRiO8Kpf/8Lc4/HKdaHu0294fiif8/VvLHHrAGBleCMum85bAOYl2JX9naXQlUNpm8ORoVD2vJS81XtJe6W4jL9ri8R0rpG8tDyfu1NfH4vtbIzFc82sz0vvY3F0b6cPimELkuT1FLqQ+qu4LWTmtbRdYA4T6XpBj9CEvAy/zxaJfV9f+Kx8KNXlUISh8tAESWqMd9rSHM6hCvHUQ5M9tl+U1Brp/LMr35u5pyuT6d5OYTchBKNf6Eb+uegXopKvVZTCV2r74xaKmor//mg91tlSsZm2W1zKbTaxMlhxAAAAAAAASjFxAAAAAAAASjFxAAAAAAAASpHj4DhV3LpRkrb85YOhfNL5Z889vvvyU+OLnxSLj7zh4lDeetX1i28ggDnTL/2JUP7oX7wnlJ/7tfNC+fNv+6m5x0NfuDnUmb69xK0DgFWSt9I7OBnrU9y9jY8WXhoD1HN8v4+mAPaUh6ArqrwYcp5zA4zFc1lud9oez7vPPqc+Huv2nZWakXdJLMTCW97xL12mMp3i/VPeh+L2jDnng9Qnx0GOwy+Wc6x7iqNXtc92jLmct37sIeeyaI3E8vTGTvnAGXlbw3iusQfi+1i/M+W6KFyrNh0/qJzzIN8jeVvJnnkI8laZfXIa9Mo14LmuEc/deDRuqUies2MbKw4AAAAAAEApJg4AAAAAAEApJg4AAAAAAEApchxgVopJat5599zjJ/7evaHuZ258Wyhv+tWd8Vz/O+8pzD6twEBSvOaDr5sK5df/+MtC+ey931ruFgHAmtfcvz+UqynW3Q92Yr2tNRZfXIsB65bixD3HzaeYcxXyFPhQOtd0ivtOIec+Wh6T3xqKdfWJWL7pte8K5XU2EspPedebO9fZnU6efte0RuI/C2qTh+LhxVj5PLbrN9brFXc/nPJJ5PwJlZRfIuWu6PpsvJDXIbcjf27N+FnVx2N9fV3nDHe86c/z2YIfe8+vxlOPxKsPHei8j9ZwvG7tQLpHcn6OVA6fXcp34CkPQVfOiPxZ5RwRoR3xXK0DB2M9OQ2OK6w4AAAAAAAApZg4AAAAAAAApZg4AAAAAAAApchxgL5yrNT4p24I5b0Tzwzlxq+cFspbr7p+eRoGHKP2vO6SUD7jvTHOtLk37ZsMAOiK3W4djN+dlQ3rOoUcB55V49/WzFO+hBwrX4jht64Y/RRDXo3lSj3GibdGO/HvQ/tj3chYvO5P/FXMO2XpbW3c02lL7VBMrlA7FA+uHpiJ52rlZAw+/+P55Jj8rPj66XhdVWP8v/p8VDYU/znjVjh3jt9P76kyE9s5vC+WG4X+Pu+aN8Xrpre48aHYJ/mzq+3vvE9Ln7lNxTwFuZ1d/V3s3668DX1yGuT7s5nyeRRe7/3yJ+C4wooDAAAAAABQqu/EgZmNmtmNZvZtM7vdzP6g/fxZZnaDmd1jZn9rZsPL31wAAHA8YjwCAMDqWciKg2lJz3f3CyVdJOnFZnaJpD+S9B53P0fSo5Jev2ytBAAAxzvGIwAArJK+OQ7c3SUdaBeH2v+5pOdLek37+WskvV3S+5e+iVjrNv1VzGHwwFufFcqTP/uMucc5PwKAWf7MC0vr7F+/tXINAdYoxiMYlNdj7LwfmuoUUpy3jeQY8hQbX4tD5q74/2JcfjPFhacYfGuk16Y4/NqBzut9fVxAM7Y7xpjXbo35ACr1+D6qU51r1Q6luPqZ2I4cd5/zFFgx3r2Z3kPi9XrPeivG5ecY/JyXoOvkvvByPfaX2VAoV6Zj/dD++DfVicKpalM5l0K8bM6PUJtM/Vfo366+TnkwunIe5P4uvsd+OQ1yfeKpvpjHIP8M4fi2oBwHZlY1s29JekjSFyV9X9Jedz98Z+2QdFrJywEAABaN8QgAAKtjQRMH7t5094sknS7pYklPWugFzOwKM7vZzG6ua/rIWgkAAI57jEcAAFgdA+2q4O57JX1J0jMlbTKzw2t2Tpe0s+Q1V7n709396UMaWUxbAQAAGI8AALDC+uY4MLMTJdXdfa+ZjUl6kWYTEX1J0s9L+pikyyV9ejkbiqPH49779VB+4C2dnAcTT/vRUOe33L4ibQLWmtpZZ4by1Hjn63jz1dfnw4HjHuMRLFZrcnLusaW47kqOA6/G3AEaTvkAKvlvb4WY9Pzaajp2Jsavd+VLKORAqNbiayszMRa+Oh1fW5lO76uYTyHldahMpnakPrBGn9j5ovQeLOUp8F6vzf2Vjs35Err6Ph1vtcL50rk9521Ir805D4ZnOsfXDsZ/NjUmYrmYm0KSKjPlOQ667oGcwyCVfbpHrgFPr015Hbr6Puc0yMeT1wAl+k4cSDpF0jVmVtXsCoWPu/tnzOwOSR8zs3dIulXSh5axnQAA4PjGeAQAgFWykF0VbpP0lHme36bZ+EIAAIBlxXgEAIDVs5AVB8CiPO59ndCF/a+8JNTVToljvdHP3LgibQJWWuP5TwvlZloyWrvulpVsDgAc13w6Jshs5WX6Y2OxnJaDe9qeUVZYPp+X7eeLp+0Z8yL+YohALS8jH4vbM/pUXLZvM/H44taGXXV52X6vLf+kGOqQwyvy8vc+2zXKOucqbv+3EN4V2pHeR7E+dldX2Iil/q3kdhfCHiqTcQl/ZTJu7ZhDTmwyJWEtfhbTabvFQbdQnCpuLdqnr3tstzhfGSgzUHJEAAAAAABwfGHiAAAAAAAAlGLiAAAAAAAAlCLHAVbUuo9/I5Sr558Tyo+lHAjrPnFTPEGrd8wXsFpsJO4Lf+DlF4Xy+rsfC+XWbd9b7iYBABaoK+774GQoV0bjd3zeLs+KsfN5C8CubQ7j8NuGYqy8KoWsCH22KszbHtpUiqsvvj7HwvfKYTDP8aGP8rF5S8CufAnpXCnEv6ehlKigz1jQ651r5a0b++VxyNszhq0NU39U0paKyp9Fyp+Qt0kMzUg5N7rkPASFtvTLUZC3W2QsjSPFigMAAAAAAFCKiQMAAAAAAFCKiQMAAAAAAFCKHAdYVc277gnlTSk278HXXxzKWz8c97r3etxTF1gplvbwrpzzhFDe+PX7Qrmx64HlbhIAYKmkOPDWoUOhXEl5bbxZzHHQI/+BpBRF3zdvQc+6Su+/AfrkVOe1tZQvIb22K87e0rmLfZJzHOTr5rj7Zo+4+pwbIOd16Bf/nw11fj/71FSsq8Rz5z7xyfg55xwJoa6e6vKx9ZgDIfRJnxwQuX99Jo53vXjP5GMZG2OZsOIAAAAAAACUYuIAAAAAAACUYuIAAAAAAACUIscB1pTGfdtD+aR/iXsb+wVPjC/43rZO3aAxcMCAKuPjc4+bF54b63YfCGVyGgDAMSTlIWilMYcND3cet2K8esh/IEn1GP+fY/q9WM6x71nOB5AV8gf4VKPHgfPweLwX4/JbfdqVT9Ujx0HX+0993XWtXJ/7YCbmFgjXqqV8ADkXQyXmW/BKj/71dJ2cE6LHubuum/MhdPVBKhfuC++TbwJYKqw4AAAAAAAApZg4AAAAAAAApZg4AAAAAAAApchxgDWtefe2UK6deUYoz1x8wdxjr8W4tPq6eHvvOyOW1z2Q9mguhLE1R+K5xh6Jx+4/PZ1rV4xVq0x3Ys8qzRh7Vtsf99et7t4fyp72e7ZG59o+FK/r43Ef6a69kHc8GOtzTF1R2pO61z7S0jzxeZs3zT1sTYyGqsqBuC9ya9NEbNa6mMui6MCpw6GcP5tKasbw/hgLuedJnQ/2xG/FGMLmaOzrof3xZMO74/7P9Y2dPhq5J/ZtY8fO3HQAwLEqxaAX8yz1jTjPv19TbLwV4+xz3HxW6f27elFy7PxirpVyHIS4/B75D+aV+6S+8NwNXpnpWd9v7BPOlfMQZD3eV1degpzLot+5gVXAigMAAAAAAFCKiQMAAAAAAFCKiQMAAAAAAFCKHAc4qjTu2x7KlVQuyjvvjs57VOFco50jbP36UGfrxkN55OF1odwajj9Ktd0H5h43N8XXKuU8aK0fi9eaSnH4J3ReX917INTl6+ZcAcP1rfHcBybnHvtoyo+Q4ukam2MegspkjAu0ybiHdWt9oYdTjKCPxTwF+VzVajy++kgn78OmA7Gvczure2Kf+L6YM2LdP3byK7QmJzWIHGFYvKcG3A0bAIBZOX7dc/z/CrZlDVhL75fMAkA5VhwAAAAAAIBSTBwAAAAAAIBShCoAba2pwtZ7U3EbPj2cDr43FvPmPb02FsrL4AZZFte1PD61I4dnDLjBUU+LWUk46NK/QdpNyAAAAACwvFhxAAAAAAAASjFxAAAAAAAASjFxAAAAAAAASjFxAAAAAAAASjFxAAAAAAAASjFxAAAAAAAASjFxAAAAAAAASpn7oDusL+JiZg9Luk/SVkmPrNiFj37012Dor8HQX4OhvwZDfy3Mme5+4mo34njBeOSI0V+Dob8GQ38Nhv4aDP21MKXjkRWdOJi7qNnN7v70Fb/wUYr+Ggz9NRj6azD012DoL6xl3J+Dob8GQ38Nhv4aDP01GPpr8QhVAAAAAAAApZg4AAAAAAAApVZr4uCqVbru0Yr+Ggz9NRj6azD012DoL6xl3J+Dob8GQ38Nhv4aDP01GPprkVYlxwEAAAAAADg6EKoAAAAAAABKrejEgZm92MzuMrN7zOzKlbz20cDMzjCzL5nZHWZ2u5m9pf38ZjP7opnd3f7/Cavd1rXEzKpmdquZfaZdPsvMbmjfZ39rZsOr3ca1wsw2mdknzOx7ZnanmT2T+6ucmf1G+2fxu2b2UTMb5f6KzOxqM3vIzL5beG7ee8pm/Um7724zs6euXstxPGM80hvjkSPDeGThGI8MhvFIf4xHlt+KTRyYWVXSn0l6iaQLJL3azC5YqesfJRqSftPdL5B0iaRfa/fRlZKuc/dzJV3XLqPjLZLuLJT/SNJ73P0cSY9Kev2qtGptep+k/+fuT5J0oWb7jftrHmZ2mqRfl/R0d3+ypKqkV4n7K/uwpBen58ruqZdIOrf93xWS3r9CbQTmMB5ZEMYjR4bxyMIxHlkgxiML9mExHllWK7ni4GJJ97j7NnefkfQxSZeu4PXXPHff5e7fbD/er9kv0dM020/XtA+7RtJlq9LANcjMTpf0MkkfbJdN0vMlfaJ9CP3VZmYbJT1X0ockyd1n3H2vuL96qUkaM7OapHFJu8T9Fbj7VyTtSU+X3VOXSvqIz/qGpE1mdsqKNBToYDzSB+ORwTEeWTjGI0eE8UgfjEeW30pOHJwmaXuhvKP9HOZhZk+Q9BRJN0g62d13tasekHTyarVrDXqvpN+S1GqXt0ja6+6Ndpn7rOMsSQ9L+sv2UsoPmtmEuL/m5e47Jf2xpB9q9hf0Y5JuEffXQpTdU/wewFrAfTgAxiML9l4xHlkoxiMDYDyyKIxHlhDJEdcgM1sn6ZOS3uru+4p1PrsNBlthSDKzl0t6yN1vWe22HCVqkp4q6f3u/hRJB5WWAXJ/dbTj4C7V7ADnVEkT6l4Chz64p4CjF+ORhWE8MjDGIwNgPLI0uKcWbyUnDnZKOqNQPr39HArMbEizv6T/xt0/1X76wcPLZ9r/f2i12rfGPFvSK8zsB5pdavp8zcbMbWov5ZK4z4p2SNrh7je0y5/Q7C9u7q/5vVDSve7+sLvXJX1Ks/cc91d/ZfcUvwewFnAfLgDjkYEwHhkM45HBMB45coxHltBKThzcJOncdgbQYc0m9bh2Ba+/5rXj4T4k6U53f3eh6lpJl7cfXy7p0yvdtrXI3X/H3U939ydo9n76Z3f/BUlfkvTz7cPorzZ3f0DSdjM7v/3UCyTdIe6vMj+UdImZjbd/Ng/3F/dXf2X31LWSXtvOZnyJpMcKSwiBlcJ4pA/GI4NhPDIYxiMDYzxy5BiPLCGbXbWxQhcze6lmY8Cqkq529z9csYsfBczsOZK+Kuk76sTI/a5m4wo/Lunxku6T9Ep3z8k/jmtm9jxJ/9XdX25mZ2t2xn+zpFsl/aK7T69i89YMM7tIs4mbhiVtk/Q6zU4gcn/Nw8z+QNJ/1GyG8VslvUGzMXDcX21m9lFJz5O0VdKDkn5f0t9rnnuqPeD5U80usZyU9Dp3v3kVmo3jHOOR3hiPHDnGIwvDeGQwjEf6Yzyy/FZ04gAAAAAAABxdSI4IAAAAAABKMXEAAAAAAABKMXEAAAAAAABKMXEAAAAAAABKMXEAAAAAAABKMXEAAAAAAABKMXEAAAAAAABKMXEAAAAAAABK/X/7HKJKd3U92QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1296x1152 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import pyplot as plt\n",
-    "fig = plt.figure(figsize=(18, 16))\n",
-    "\n",
-    "sub_plot1 = plt.subplot(121, title=\"Kettle Flux\")\n",
-    "sub_plot1.imshow(pot_flux)\n",
-    "\n",
-    "sub_plot2 = plt.subplot(122, title=\"Water Flux\")\n",
-    "sub_plot2.imshow(water_flux)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/candu.ipynb b/examples/jupyter/candu.ipynb
deleted file mode 100644
index 3e5bc7283..000000000
--- a/examples/jupyter/candu.ipynb
+++ /dev/null
@@ -1,1110 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling a CANDU Bundle\n",
-    "In this example, we will create a typical CANDU bundle with rings of fuel pins. At present, OpenMC does not have a specialized lattice for this type of fuel arrangement, so we must resort to manual creation of the array of fuel pins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from math import pi, sin, cos\n",
-    "import numpy as np\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's begin by creating the materials that will be used in our model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel = openmc.Material(name='fuel')\n",
-    "fuel.add_element('U', 1.0)\n",
-    "fuel.add_element('O', 2.0)\n",
-    "fuel.set_density('g/cm3', 10.0)\n",
-    "\n",
-    "clad = openmc.Material(name='zircaloy')\n",
-    "clad.add_element('Zr', 1.0)\n",
-    "clad.set_density('g/cm3', 6.0)\n",
-    "\n",
-    "heavy_water = openmc.Material(name='heavy water')\n",
-    "heavy_water.add_nuclide('H2', 2.0)\n",
-    "heavy_water.add_nuclide('O16', 1.0)\n",
-    "heavy_water.add_s_alpha_beta('c_D_in_D2O')\n",
-    "heavy_water.set_density('g/cm3', 1.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our materials created, we'll now define key dimensions in our model. These dimensions are taken from the example in section 11.1.3 of the [Serpent manual](http://montecarlo.vtt.fi/download/Serpent_manual.pdf)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Outer radius of fuel and clad\n",
-    "r_fuel = 0.6122\n",
-    "r_clad = 0.6540\n",
-    "\n",
-    "# Pressure tube and calendria radii\n",
-    "pressure_tube_ir = 5.16890\n",
-    "pressure_tube_or = 5.60320\n",
-    "calendria_ir = 6.44780\n",
-    "calendria_or = 6.58750\n",
-    "\n",
-    "# Radius to center of each ring of fuel pins\n",
-    "ring_radii = np.array([0.0, 1.4885, 2.8755, 4.3305])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To begin creating the bundle, we'll first create annular regions completely filled with heavy water and add in the fuel pins later. The radii that we've specified above correspond to the center of each ring. We actually need to create cylindrical surfaces at radii that are half-way between the centers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# These are the surfaces that will divide each of the rings\n",
-    "radial_surf = [openmc.ZCylinder(r=r) for r in\n",
-    "               (ring_radii[:-1] + ring_radii[1:])/2]\n",
-    "\n",
-    "water_cells = []\n",
-    "for i in range(ring_radii.size):\n",
-    "    # Create annular region\n",
-    "    if i == 0:\n",
-    "        water_region = -radial_surf[i]\n",
-    "    elif i == ring_radii.size - 1:\n",
-    "        water_region = +radial_surf[i-1]\n",
-    "    else:\n",
-    "        water_region = +radial_surf[i-1] & -radial_surf[i]\n",
-    "        \n",
-    "    water_cells.append(openmc.Cell(fill=heavy_water, region=water_region))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what our geometry looks like so far. In order to plot the geometry, we create a universe that contains the annular water cells and then use the `Universe.plot()` method. While we're at it, we'll set some keyword arguments that can be reused for later plots."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f2b8ca58a20>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_args = {'width': (2*calendria_or, 2*calendria_or)}\n",
-    "bundle_universe = openmc.Universe(cells=water_cells)\n",
-    "bundle_universe.plot(**plot_args)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we need to create a universe that contains a fuel pin. Note that we don't actually need to put water outside of the cladding in this universe because it will be truncated by a higher universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surf_fuel = openmc.ZCylinder(r=r_fuel)\n",
-    "\n",
-    "fuel_cell = openmc.Cell(fill=fuel, region=-surf_fuel)\n",
-    "clad_cell = openmc.Cell(fill=clad, region=+surf_fuel)\n",
-    "\n",
-    "pin_universe = openmc.Universe(cells=(fuel_cell, clad_cell))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f2b8a9627f0>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pin_universe.plot(**plot_args)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The code below works through each ring to create a cell containing the fuel pin universe. As each fuel pin is created, we modify the region of the water cell to include everything outside the fuel pin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_pins = [1, 6, 12, 18]\n",
-    "angles = [0, 0, 15, 0]\n",
-    "\n",
-    "for i, (r, n, a) in enumerate(zip(ring_radii, num_pins, angles)):\n",
-    "    for j in range(n):\n",
-    "        # Determine location of center of pin\n",
-    "        theta = (a + j/n*360.) * pi/180.\n",
-    "        x = r*cos(theta)\n",
-    "        y = r*sin(theta)\n",
-    "        \n",
-    "        pin_boundary = openmc.ZCylinder(x0=x, y0=y, r=r_clad)\n",
-    "        water_cells[i].region &= +pin_boundary\n",
-    "        \n",
-    "        # Create each fuel pin -- note that we explicitly assign an ID so \n",
-    "        # that we can identify the pin later when looking at tallies\n",
-    "        pin = openmc.Cell(fill=pin_universe, region=-pin_boundary)\n",
-    "        pin.translation = (x, y, 0)\n",
-    "        pin.id = (i + 1)*100 + j\n",
-    "        bundle_universe.add_cell(pin)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f2b8a8e0ef0>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAAD4CAYAAAAn+OBPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdoElEQVR4nO2df4xdR3XHv6dOAiq4JHGcIv9o7bZAhZcAUbC6ikoLSVEMq0RE+SO2AiUoskAlAslaa71W/8xm5a2ASEWVViaRoiSmKDgQrRJCEKVSpU2CCYTsxoCihCYOoLiOK6yiksac/vHevL3v7tx7586ce++8N+cjrbT73tuZeXPnO2d+nDlDzAxFUdLhD7ougKIo7aKiV5TEUNErSmKo6BUlMVT0ipIYF3SR6SUbLuWtF27tImtFSYJX/u8VnD3/Gtne60T0Wy/cigf/5KEuslaUJLjppY8XvqfDe0VJDBW9oiSGil5REkNFryiJoaJXlMQQET0RXUxEDxLRT4noJBFNSqSrKIo8Ult2dwH4NjPfREQXAfhDoXQVRREmWPRE9DYAHwTwKQBg5tcBvB6arqIozSAxvN8J4DSAe4joR0R0lIjekv8QEe0nohNEdOLs+dcEslUUxQcJ0V8A4EoA/8LM7wfwPwBm8h9i5kVmvoqZr7pkw6UC2SqK4oOE6E8BOMXMT/b/fhC9TkBRlAgJFj0z/xrAy0T0rv5L1wB4LjRdRVGaQWr1/nYA9/dX7l8AcKtQuoqiCCMiemb+MYCrJNJSFKVZ1CNPURJDRa8oiaGiV5TEUNErSmKo6BUlMVT0ipIYKnpFSQwVvaIkhopeURJDRa8oiaGiV5TE6OSGG6U75nbdOPh9dvX4yOah+EPM3HqmE29+D+u1Vu0xt+tGrEyui2syYGJ5PlicbeShuHPTSx/Hyv8+a73LTkU/plSJ0MbE8jwAd+tsLLpPPtoBNEuZ6HVOryiJoXP6McTHygNrFnsON1ZaYt88TD4ueSjNoJZ+zAgRo2FlcmZoMa6LPJTmUNErVspEHSp4pVtU9GOEhAXOp+fymi9q7btBRa8oiaELeRFRZPVcF7ykh90rkzPAavt5FBFaP0oPFX0EVO1375t8Kum97bVpi71+5tCrv1Trpy4q+g6p49yyMjmDfZNPAWjXuWVu19rWWq+87SziDa9PlOeZ3WoEVPxV6Jy+I0yj9t1Pb2sBLCugNjsa33rRxcFqxERPRBv6t9YuSaU5rkjtc+cxbrSjhK3M6gPQLJKW/vMATgqmN5ZIbqs13bBtgmy6Y5H6Tir8YkRET0TbAHwMwFGJ9BRFaQ4pS/9lAAcB/L7oA0S0n4hOENGJs+dfE8o2bfIjhlFcwMqXWb39midY9EQ0BeBVZv5h2eeYeZGZr2Lmqy7ZcGlotiOLdKPOD2Glht9FOwSzq8dF88giPRzXDsSOhKW/GsD1RPQLAF8D8GEiuk8g3bGjjTmmhCirtgTbyEMKndevJ1j0zHyImbcx8w4ANwP4HjPfElwyxRtfUU4szzuL0eThm88oTkXGBXXOGVNmV48PnFVchrk+Qhw47cBtR6JuZB6lGURFz8zfB/B9yTQVfwbiWh0e5hqBDovQX4izq8eH8sinL5GHIoda+haZXT0+cKXtIu8BgwMudhFe8uKyU5pnd07a86hIv010VLEeFf2I49uoXYXtm0a+Q3Chy04xJdT3vmUkPdrqpHXJi8tDP03jm19X9ZMSaukDsM2TgfIFK7PAJrGHXGbl80Kbnloo/OzC0nRwWermUTQSkLL2ZQuTttON69cfxheNe+9BHf/5osa377awhm1Lt0zoVeWdWJ73Fv/01IJT+oaqDkDifMIDR3eve03iuY0KetmFIL6XSNgakNRlEbbhs4sQbekC7pbfdCo++djyyIpf+rIOyec2CuhlF4qiDFBLX4PQYadtyOmarstw3uBj5fM8evh86ft77tgQlH7ZdMJnuF9mlUOmUqNq7XV4L4DEPLOqAbkGfixbDZcQPFAuyjbyAOzit1FVp00/txhR0QsQuvBm8G1ArtteoRY4i02UUoI3VI0oDD77/pIBS4pGabGic/pAuj6p5Sr4si2zWHEtcxu+BWV03QYkUdG3TB3L05YjTRG2snZ5Rr1ufeh5ejsqege6aDxdWzZD1hLHMpLoom7GqQNR0XdA1VDRt1GPYsP0LXNVHY3TcFwadcONiFis+6hg6stnkS9lVPQOTCzPi1pR2+q9i698laecdDnzeS4sTWOPsL56XnTl36uqLi55cdl6zFfyxF6vnKO1bVeEij4CjOCH3VrXi9cILsRPflQY3hq010XWbdgmfMWOzulbJnvwJLsabRq56712bSyqdXHZBeDuC2Dqy9RFfnVfj9baUdErSmKoR54jUt5dxrMra5FCvNzyQ31Jj7kibzkprz/JsufTMkN9CU/KcXPDVUvviMRDN8PN7JA+VKT5/5Wa65cNjaWGzfmyhtaDqU9grY4lyjpqgq9CRV+DkAZkrIXPHL6K/Pxe4iKKss5jYWlaJI8sEmsUtjl+6MUc47guoKKvge8FD00PD23WPuSyC5fRgsnDNx9JK1+Fj/DrXPwxauic3hOXqDdlEW4kT8OZvHyPwtaNmGNLH6iui6aP6mbJrkfUicgzLhdylM3pg/fpiWg7gHsB/DEABrDIzHeFphs7RRdJDL0Hu+B7ImnPZXZhaRpYOl/i5NIT45m9B/3SP3ak94tDHm0xPbUw6GSye/jmYg6g6LmNtthdkHDOeQPAAWZ+mog2AvghET3OzM8JpN0aZZa7qvevsgqxuNfmLe2ZvQe9hZ5PxzADYJPpBCKiyGvPhZC2ESPBomfmXwH4Vf/3c0R0EsBWACMhepdhunlv3+RTIz/PkxB5nTxi7ABcWZsKVLcNc2/gKLQNUTdcItoB4P0AnrS8tx/AfgDYcsEWyWy98dl7X5mcwRxudH64sVj5NsRelm8M4q/jqlu3bWTFH7vwxURPRG8F8A0AX2Dm3+TfZ+ZFAItAbyFPKl9fQpxtXIXfteCN4ObPbQbObS4dns5sPB2U1/y5zQBKhsBTC4M8uuwAXITfRtvoEpHVeyK6EMASgMeY+YtVn49h9b4NT60y0Uuv3ue9587sPVgqRBt1O4CQ9PPCb7o+spSJXtrzsisa9cgjIgLwVQAnXQSvKEq3SDjnXA3gEwA+TEQ/7v98VCDdxpCKqlJmEaqG9k1d1GhW5Of7w/m689KVyZmBBS8jNP38zkGbF1eWPRspf4GYI/cEi56Z/4OZiZmvYOb39X8ekShcU0g6gvg+3BCPNltawPAcPtSPvUz4kumbMkucGajjUWhDUqgxhy5TN9wGcF3AW1iaDm7sptOQErzBZQszNP288EM7wDr12fUia5ckJ/oYh12+jd1YtewwWdLC2Ky9y9DflWxZz+w9GHSQJ8aDMTG2NSBB0UuTbbi+ceoXlqbx6OHzzg13Ynkejx4+v07wkoIE2ol7ny2zEb5vXdQl/6xiHpJLojHyApEMmFjmIz94H0Cor3zMnNl7EJuOHRnUBVB87Fbap7+JwKIxoqIXouwGWYNpUHkrlrdSLlbLJvgmGuz8uc2DfXvpkQTQL/PKgaHXjPANrlY83znY6jufVophtJMTvXRo5CLWjotWD5GnUW/FeRwtfJ688KsoO56bfd1E0W0jmnCsXnlJzuklF31sD7bu+fCVyRnsuWNDNNdGjRLTUwvYc8eG2vVtq2tJkca4sGhIUvRSD1c65l1VaOuqo7BNNLSsS26of76NsjJXfV/JupaMqQfEa+WBREUPyMSRyz5YqZh3Rf/fxZC+q7j3eYq+u0RdZ4NpAn6htfLEbOWBhEWvKKmSrOhDevSmA2n4zu2bGH43jW+Zm1z/iLltSJCs6AH/KKn5YJfSgR3zadUZ2ksNLSeW562CnNl4WjQPV/J1IFXf2bDZ2W1XibYRK0mLHug93AeO7q48/DKxPI8Hju5u7aGGWPvY55RAcafiQlu7HNm2UYRpN222jVCS26cvIhvd1k43D9RnAW9m42nMB3iXVQmy6fSLqLt3L0U2gu56RkPoWVT0Y4oRJuA2FB4S4ka39I0nnevJPqnQXEoYKvoAYj+eORDXyoHC0FZFQrx3dqk07U/OTQ3lU9TBDKXv0Jl0TQr33Cct+qKjj7HMzaqG9jZf+CIrmu0AhsgIMS/0Xx661ZrWljvvGfrsJ+emnNK3Uec7AP06aeAMgA+xt58ikhR9Vax7E98e6O4B5o/NAsMCKSr7LfAbRmdF/MtDt/bSL/Jlf/hnmFiex5Y77xn8b9byV1EVUDP7HYD132NhaRp7OjLGw23HXv7YY+And5edT7TToq2Ypu6mswXH8ImI4yr+e2eXhqx63ei2QM/6Vwm/bvTcbD7Z77Dp2BHxbdKiu+8Mku2mDRq9y25UcLnJpgiXWOZNnsX2DYFl/me+ZLXcCD4kfQDAoVtx753FFj8kjNfK5EzpdwhhreMqPnXnGxbbtBsgLqufxD69eWihwRzLwh9JBbrMW3mJmHdFgS6rFut8sKUp/R1CQ2sZTF2XHbMNjYNv2l1MobOSEL2iKGuMveilbizJY9uuC41uawvuIOluasN3aG9Lv2i1v6nvEGrti55VE1uxMVn7sRe9JK4Prk5gR4NN8NLhqbLpmWF4E2cGskP8Jr8D4Cd8E0yziqYMRteIiJ6IriOinxHR80QUVS119dBc5vjm/aJork2Xvcgyx5ZmFludZCPoutR3G6GybMTSgQSv3hPRBgBfAfB3AE4B+AERPczMnd9P37PK3VX0oHHlItzmo9q2hS0IZSt5tkS2vgFY6rwbsWeZ29X9jbYSW3a7ATzPzC8AABF9DcANADoXfROsTM6UHL4opq51aSLy7Lgwf24zXM7ZhVr0WCyzNBLD+60AXs78far/2hBEtJ+IThDRibPnXxPIVlEUH1pzzmHmRQCLQM8jr618pfG93MI+vC9mZuNp3FI7lzRwddKpW+d5xvXyCwnRvwJge+bvbf3XOqetGPdFmEaX99M2fuNm0amthaWJ5fnWT7q1KZzh+gbydd52fdvoej4PyAzvfwDgHUS0k4guAnAzgIcF0hWhqygyLtFxzftFMe+bLrs5MBN7mllsdZKNfe9S313dLxBLRCOJ++nfAPA5AI8BOAng68zssdQVP64HKOpevgDYY95L+5pn0zM+8pIN0aSVP2svST49n4M3pqOtQiIcdoyI7NMz8yPM/E5m/nNmvkMiTSmaenC2k1j5GOp1sQm/6SCUW+68RySP7FFb17x98sgSetKu6Fk1EUQjpqCZ6pGnKImRhOiNtQ+xOFU9teQNN9NTC4MAkBLRbYsCUdYJfOGKLU3p7yB1nt52w02e0JGiaXexWHkgofP0ptLn0EwwhCZXqH2jz7oE0fjk3BTuvXMJaDiIRt1Anfl8mgqmOSjLUrEv/uzq8cbaTRckFzkHcAuo4RIuK3txpbToHz183itcFiAYLquE/BxeMlyWSd+Q/x6bjh0RjVRk8jNbeWVzeqm20zRlkXOSFL0hNLBhk6J3sW51g0pWUScwZpaQaYLPd5AIypHHVfRZYg6MqeGyCojh4ZSx6diR0oi4dQTuEgI7K957Z5dK99zzQq8bYttQt5PadOwI0NE+e57Y208RSYs+lLM7J6OOfe8yjC6Ko1d3uF6Uh3ndN0pv24x7zHtART+21B0Cr0zODHz9XacWPk4xQHmgTqV5VPR9qhZoulqJrRri2wid81ZFn206/SK6uMcOKI+gE8OiXV2S2KcvY27Xjdh321NOftv7bnuqtThnvodCmljkaoKiCL0utHVgJts2ijDtps22EUrSoveJgZaPk3d256RIOOYs+bTqWDjJIJQ2UUp2KnXSydeBpGuvWbnPzucl2kasJCv6kKCHTT/cECs/asRo7WNuGxIkK3pFSZVkRS/ht53t0SVvuLHRxSKWrY66WC8o+u5N3HAjEfY69jWVJEUvNfwyD9fMBU0D8m2MVeGZNx07Uir+JhpbdvjdxPShrMxV31eyrs0zlKrDmIf4SYpeUhy2h1u3MZbFvlfKyca8d6Woc5UUaszWPrl9+rZi4S8sTa+Ld28aQr6B1hW7z979qFF3OrOwNI1pDNerrb7bjH8fQ4x7G8mJvimKXHKHBD04vlne6IrOd2fTsgm/iSCUWQeaJiL02oJ15gXvUh+2v13qOwW32zwq+kB8L7+wsXZazy7cbBTdhaXpsbT4RvDDJxeL60PymqqYh+SSJDmnlyQ7dDy7c9LLcmSjubqQjaCbtYrS/uy2ebJ0vMFsmU1EHN+6qEv+WY1jEEwbyYk+xjlWiCNIXviSDdfWiUh2LNmyhobAitFKx9jWgARF3wau1j40ei6w1tglY+oB5Z2HVPrZmHdAuHDr1GeKc3lDkqKXtIa+vblUIE2TFiAn/KqjtZLpZ+fwoYReZiFpmWOeKiQbLmvfbeHXXVUdty0LsCEd4+3Rw8OBHc/sPegURCNL3SAXIennV+ibro8sVTHwJDriB47uDk4jhLJwWUGWnogWiOinRPQTInqIiC4OSU9RlOYJHd4/DmCCma8A8HMAh8KL1A4SftshQ3tp8mluOnYEMxtPD4biRd/XvHffyoHB510xn79v5YBTHubzrvvwIYQM8Zs6PxELQfv0zPydzJ9PALgprDjt4RvLHHAXfNcx9Iy4FtAb7mPlwPoPCd1iO+gsCvLoKupNHpcFvDbaRpdIOud8GsC/Fr1JRPsB7AeALRdsEczWH/NwgXrz0joPtWvhG4zo2nbmiUXsQL0V+7rCH6WwWZWiJ6LvAni75a3DzPyt/mcOA3gDwP1F6TDzIoBFoLeQ51XaBsjefAPYxT/8QON/qGVkRdhUBxCT0EOYXT0OrJbHTxwlsRsqRc/M15a9T0SfAjAF4BruYitAiMFDs7rUlj/Q/OmsfAOIxdqvm+ee2zzkwurbCWRFPj21sC4ufQynB21Wvuq5rXvdo23ESNDwnoiuA3AQwN8w829lijQaDPf+wxZg32RvOzA7FcgKf2FpeuBH3wZlPv3Gfx0AFjwttOlMqvJoU/y2s/JAfktu/XMbRctdl6B9eiJ6HsCbAJzpv/QEM3+m6v9i2Kf3xeUuszxZ8Td1FVZ+X9on/YH4HcU5LPZ6+eTzkNynt11RVXf/fdTF39g+PTP/BTNvZ+b39X8qBT/KmIYTW5TU/BaRb4dSx6MtxKPQlkeT21y+kW1HIcilD8l65PkQ6sVnLH7W2gPhPud5Ky9hNcuG41KjFOly50cqZ3dOinjYde1d50Njlj4lJHp86Zh6gN3KS+By/10oktY+H+RSMubduFl7Fb0DUv7YJi1geHHJR/i2uHrS6wS2DkTSey4/zPeJdwcUB7mUDIA6TsJX0StKYqjoWyZribORdurEzW9r+6uruPeuI5/slVTA+shFMQbWiAGNkRcBZg9/IORcFF3DmtC7d3ZpGhNNGLBPKfJRbVMOilEXXb13QOLsfZYi//1Qrz3pM+nA8KhCes3AUHb23YUibzvpso7SKn7Z6r1a+ogwjTcGl91RQK27Hzqn74AqLy/fxhz7OW4bvmWuqqNR9aRrAxW9A12IKRYrll0wjOHgDNBN3Yxih1qEir5l6jQe3zj6UrQR974OdetjnIQqiYrega6Hiq4NPRZLXAfXMnc98um6DUiiC3mOSNwTVxV1p8jra3b1+FCjL1vok7rPrsgXwFwUKZVHnXvmyuqn6HXfsFdZeuUcH9Hrll0NQreBirZ8XNK1dRhF4pfYWqvaRpM4HFNk5W1i96kfQ8iW6yjEvLNRtmWnoq+Jj/CLGo7P2Xxbejbxj9p5eqAs2IV7uoDd8ks+t1FARS9MnQZU1HCkjulmyYs/68nmYil91wRcOpj1d8Sv4WPZq7CNqiSe26igR2sVRRmglj6A7MJS1oJUhVpq4+qkMqufR2rVv04eZavxTV85ZptWZUcio2zhDTq8jwhJn/A6Q9CuXXtdt9y6qp9xQ33vI0LyEMjK5ExBWOb15EXXdCfgu6/eVf2khIp+xJnbdaOXNSsTpWuHIO0w0xt26xn4plHRt0iXjdq2/lC09uAr5vxcOYZ5sm+nOM6o6MeYsgs51l5fu+TBVxxr8/DhPLJD9VQukhgFVPRjSt0FsZXJmcF9fq6irOtcZD43B7W+XSKyT09EB4iIiegyifSUMHxXwOtc8OB78YfJZ5yiy44awaInou0APgLgpfDijDdtWDeJLa8qUbaRhxQ6oliPhKX/EnqXWI7sjbVtIn3GO9+opba8ikQpuY+eT0daoHqe3k6Q6InoBgCvMPMzDp/dT0QniOjE2fOvhWSr9Mk36lEcMufLrEJtnsqFPCL6LoC3W946DGAWvaF9Jcy8CGAR6Hnk1SijoiiCVFp6Zr6WmSfyPwBeALATwDNE9AsA2wA8TUS2DkLpM7t6XMyaNT1f7eKyC6nvlLILbhXew3tmfpaZL2fmHcy8A8ApAFcy86/FSjemSAjf9v+jeKOLrcwSdaOCL0aP1naEEb5PA2+zUWfn3G2tGfh2iqY+VfDliDnn9K29UgPTOI1TTJmlHm7M7TXqrIBmV49j36TsbT+l+fYPy1TtGKinXz3UIy8C8uK3v59ugzYdQN3AmIodFX1EhDZeqUi42fTynU0beRSh4pZB5/SKkhgq+jFCcjvQpOfymi+66NYNKnrFSlnnoV5zo42KfsyQ8gEos8Bt5KE0hy7kjSG+1znV2foyeQDhl3Uo7aKWXlESQ0NgJ4CLc0uo5W0jD8UdjXuvDMg6uDQlwjbyUMrRuPfKgDZEqEKPG53TK0piqOgVJTFU9IqSGCp6RUkMFb2iJIaKXlESQ0WvKImholeUxFDRK0piqOgVJTFU9IqSGCp6RUkMFb2iJIbE/fS3E9FPiWiViI5IFEpRlOYIOlpLRB8CcAOA9zLz74jocpliKYrSFKGW/rMA5pn5dwDAzK+GF0lRlCYJFf07Afw1ET1JRP9ORB8o+iAR7SeiE0R04uz51wKzVRTFl8rhPRF9F4DtzvnD/f+/FMBfAfgAgK8T0Z+xJQYXMy8CWAR64bJCCq0oij+Vomfma4veI6LPAjjeF/lTRPR7AJcBOC1XREVRJAkd3n8TwIcAgIjeCeAiAP8VmKaiKA0SGhjzbgB3E9EKgNcB/L1taK8oSjwEiZ6ZXwdwi1BZFEVpAfXIU5TEUNErSmKo6BUlMVT0ipIYndxlR0SnAfynw0cvQ/dbgFoGLcMoluFPmXmz7Y1ORO8KEZ1g5qu0DFoGLYNcGXR4ryiJoaJXlMSIXfSLXRcAWgaDlqHHyJch6jm9oijyxG7pFUURRkWvKIkRvehjCbxJRAeIiInosg7yXujXwU+I6CEiurjFvK8jop8R0fNENNNWvpn8txPRvxHRc/028Pm2y5ApywYi+hERLXWU/8VE9GC/LZwkokmfdKIWfS7w5i4A/9RRObYD+AiAl7rIH8DjACaY+QoAPwdwqI1MiWgDgK8A2APg3QD2EtG728g7wxsADjDzu9GL0PQPHZTB8HkAJzvKGwDuAvBtZv5LAO/1LUvUokc8gTe/BOAggE5WPZn5O8z8Rv/PJwBsaynr3QCeZ+YX+seov4ZeJ9wazPwrZn66//s59Br61jbLAABEtA3AxwAcbTvvfv5vA/BBAF8Fesfamfm/fdKKXfTOgTebgohuAPAKMz/Tdt4FfBrAoy3ltRXAy5m/T6EDwRmIaAeA9wN4soPsv4xex//7DvIGgJ3ohaG7pz/FOEpEb/FJKDRyTjBSgTcbLMMsekP7RikrAzN/q/+Zw+gNd+9vujyxQURvBfANAF9g5t+0nPcUgFeZ+YdE9Ldt5p3hAgBXAridmZ8korsAzAD4R5+EOiWGwJtFZSCi96DXwz5DREBvWP00Ee1m5l+3UYZMWT4FYArANS2GJHsFwPbM39v6r7UKEV2InuDvZ+bjbecP4GoA1xPRRwG8GcAfEdF9zNxm1KhTAE4xsxnlPIie6GsT+/D+m+gw8CYzP8vMlzPzDmbegV7FXykt+CqI6Dr0hpbXM/NvW8z6BwDeQUQ7iegiADcDeLjF/EG93varAE4y8xfbzNvAzIeYeVu/DdwM4HstCx79NvcyEb2r/9I1AJ7zSatzS1+BBt7s8c8A3gTg8f6I4wlm/kzTmTLzG0T0OQCPAdgA4G5mXm063xxXA/gEgGeJ6Mf912aZ+ZGWyxEDtwO4v98BvwDgVp9E1A1XURIj9uG9oijCqOgVJTFU9IqSGCp6RUkMFb2iJIaKXlESQ0WvKInx/2GLfveGA8enAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "bundle_universe.plot(**plot_args)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looking pretty good! Finally, we create cells for the pressure tube and calendria and then put our bundle in the middle of the pressure tube."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pt_inner = openmc.ZCylinder(r=pressure_tube_ir)\n",
-    "pt_outer = openmc.ZCylinder(r=pressure_tube_or)\n",
-    "calendria_inner = openmc.ZCylinder(r=calendria_ir)\n",
-    "calendria_outer = openmc.ZCylinder(r=calendria_or, boundary_type='vacuum')\n",
-    "\n",
-    "bundle = openmc.Cell(fill=bundle_universe, region=-pt_inner)\n",
-    "pressure_tube = openmc.Cell(fill=clad, region=+pt_inner & -pt_outer)\n",
-    "v1 = openmc.Cell(region=+pt_outer & -calendria_inner)\n",
-    "calendria = openmc.Cell(fill=clad, region=+calendria_inner & -calendria_outer)\n",
-    "\n",
-    "root_universe = openmc.Universe(cells=[bundle, pressure_tube, v1, calendria])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the final product. We'll export our geometry and materials and then use `plot_inline()` to get a nice-looking plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = openmc.Geometry(root_universe)\n",
-    "geometry.export_to_xml()\n",
-    "\n",
-    "materials = openmc.Materials(geometry.get_all_materials().values())\n",
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot = openmc.Plot.from_geometry(geometry)\n",
-    "plot.color_by = 'material'\n",
-    "plot.colors = {\n",
-    "    fuel: 'black',\n",
-    "    clad: 'silver',\n",
-    "    heavy_water: 'blue'\n",
-    "}\n",
-    "plot.to_ipython_image()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interpreting Results\n",
-    "\n",
-    "One of the difficulties of a geometry like this is identifying tally results when there was no lattice involved. To address this, we specifically gave an ID to each fuel pin of the form 100\\*ring + azimuthal position. Consequently, we can use a distribcell tally and then look at our `DataFrame` which will show these cell IDs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "settings.particles = 1000\n",
-    "settings.batches = 20\n",
-    "settings.inactive = 10\n",
-    "settings.source = openmc.Source(space=openmc.stats.Point())\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel_tally = openmc.Tally()\n",
-    "fuel_tally.filters = [openmc.DistribcellFilter(fuel_cell)]\n",
-    "fuel_tally.scores = ['flux']\n",
-    "\n",
-    "tallies = openmc.Tallies([fuel_tally])\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openmc.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return code of `0` indicates that OpenMC ran successfully. Now let's load the statepoint into a `openmc.StatePoint` object and use the `Tally.get_pandas_dataframe(...)` method to see our results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">level 1</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">level 2</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">level 3</th>\n",
-       "      <th>distribcell</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>univ</th>\n",
-       "      <th>cell</th>\n",
-       "      <th>univ</th>\n",
-       "      <th>cell</th>\n",
-       "      <th>univ</th>\n",
-       "      <th>cell</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.211385</td>\n",
-       "      <td>0.010406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>200</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.182015</td>\n",
-       "      <td>0.006170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>201</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.184911</td>\n",
-       "      <td>0.005997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>202</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.193177</td>\n",
-       "      <td>0.009182</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>203</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.193422</td>\n",
-       "      <td>0.007610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>204</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.188808</td>\n",
-       "      <td>0.005277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>205</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.191424</td>\n",
-       "      <td>0.003950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>300</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.154932</td>\n",
-       "      <td>0.007590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>301</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.153688</td>\n",
-       "      <td>0.006581</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>302</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.161471</td>\n",
-       "      <td>0.004471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>303</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.151202</td>\n",
-       "      <td>0.008047</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>304</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.153905</td>\n",
-       "      <td>0.005394</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>305</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>12</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.153289</td>\n",
-       "      <td>0.010077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>306</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>13</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.171559</td>\n",
-       "      <td>0.007727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>307</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>14</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.163802</td>\n",
-       "      <td>0.006032</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>308</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.153771</td>\n",
-       "      <td>0.005325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>309</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>16</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.159070</td>\n",
-       "      <td>0.005367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>310</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>17</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.150078</td>\n",
-       "      <td>0.005973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>311</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>18</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.150065</td>\n",
-       "      <td>0.007176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>400</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>19</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.105439</td>\n",
-       "      <td>0.004338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>401</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>20</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.108502</td>\n",
-       "      <td>0.004957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>402</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>21</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.103088</td>\n",
-       "      <td>0.007733</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>403</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>22</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.102237</td>\n",
-       "      <td>0.003835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>404</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>23</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.107133</td>\n",
-       "      <td>0.006022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>405</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>24</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.104177</td>\n",
-       "      <td>0.004377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>406</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>25</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.110281</td>\n",
-       "      <td>0.005753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>407</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>26</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.103171</td>\n",
-       "      <td>0.003678</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>408</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>27</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.103534</td>\n",
-       "      <td>0.005595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>409</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>28</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.107240</td>\n",
-       "      <td>0.003574</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>410</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>29</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.108162</td>\n",
-       "      <td>0.006636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>411</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>30</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.118550</td>\n",
-       "      <td>0.005535</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>412</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>31</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.114435</td>\n",
-       "      <td>0.006232</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>413</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>32</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.109227</td>\n",
-       "      <td>0.003769</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>414</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>33</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.113148</td>\n",
-       "      <td>0.006165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>415</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>34</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.112006</td>\n",
-       "      <td>0.005454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>416</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>35</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.099973</td>\n",
-       "      <td>0.004764</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>3</td>\n",
-       "      <td>44</td>\n",
-       "      <td>1</td>\n",
-       "      <td>417</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>36</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.104952</td>\n",
-       "      <td>0.005613</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   level 1      level 2      level 3      distribcell nuclide score     mean  \\\n",
-       "      univ cell    univ cell    univ cell                                      \n",
-       "        id   id      id   id      id   id                                      \n",
-       "0        3   44       1  100       2    5           0   total  flux 2.11e-01   \n",
-       "1        3   44       1  200       2    5           1   total  flux 1.82e-01   \n",
-       "2        3   44       1  201       2    5           2   total  flux 1.85e-01   \n",
-       "3        3   44       1  202       2    5           3   total  flux 1.93e-01   \n",
-       "4        3   44       1  203       2    5           4   total  flux 1.93e-01   \n",
-       "5        3   44       1  204       2    5           5   total  flux 1.89e-01   \n",
-       "6        3   44       1  205       2    5           6   total  flux 1.91e-01   \n",
-       "7        3   44       1  300       2    5           7   total  flux 1.55e-01   \n",
-       "8        3   44       1  301       2    5           8   total  flux 1.54e-01   \n",
-       "9        3   44       1  302       2    5           9   total  flux 1.61e-01   \n",
-       "10       3   44       1  303       2    5          10   total  flux 1.51e-01   \n",
-       "11       3   44       1  304       2    5          11   total  flux 1.54e-01   \n",
-       "12       3   44       1  305       2    5          12   total  flux 1.53e-01   \n",
-       "13       3   44       1  306       2    5          13   total  flux 1.72e-01   \n",
-       "14       3   44       1  307       2    5          14   total  flux 1.64e-01   \n",
-       "15       3   44       1  308       2    5          15   total  flux 1.54e-01   \n",
-       "16       3   44       1  309       2    5          16   total  flux 1.59e-01   \n",
-       "17       3   44       1  310       2    5          17   total  flux 1.50e-01   \n",
-       "18       3   44       1  311       2    5          18   total  flux 1.50e-01   \n",
-       "19       3   44       1  400       2    5          19   total  flux 1.05e-01   \n",
-       "20       3   44       1  401       2    5          20   total  flux 1.09e-01   \n",
-       "21       3   44       1  402       2    5          21   total  flux 1.03e-01   \n",
-       "22       3   44       1  403       2    5          22   total  flux 1.02e-01   \n",
-       "23       3   44       1  404       2    5          23   total  flux 1.07e-01   \n",
-       "24       3   44       1  405       2    5          24   total  flux 1.04e-01   \n",
-       "25       3   44       1  406       2    5          25   total  flux 1.10e-01   \n",
-       "26       3   44       1  407       2    5          26   total  flux 1.03e-01   \n",
-       "27       3   44       1  408       2    5          27   total  flux 1.04e-01   \n",
-       "28       3   44       1  409       2    5          28   total  flux 1.07e-01   \n",
-       "29       3   44       1  410       2    5          29   total  flux 1.08e-01   \n",
-       "30       3   44       1  411       2    5          30   total  flux 1.19e-01   \n",
-       "31       3   44       1  412       2    5          31   total  flux 1.14e-01   \n",
-       "32       3   44       1  413       2    5          32   total  flux 1.09e-01   \n",
-       "33       3   44       1  414       2    5          33   total  flux 1.13e-01   \n",
-       "34       3   44       1  415       2    5          34   total  flux 1.12e-01   \n",
-       "35       3   44       1  416       2    5          35   total  flux 1.00e-01   \n",
-       "36       3   44       1  417       2    5          36   total  flux 1.05e-01   \n",
-       "\n",
-       "   std. dev.  \n",
-       "              \n",
-       "              \n",
-       "0   1.04e-02  \n",
-       "1   6.17e-03  \n",
-       "2   6.00e-03  \n",
-       "3   9.18e-03  \n",
-       "4   7.61e-03  \n",
-       "5   5.28e-03  \n",
-       "6   3.95e-03  \n",
-       "7   7.59e-03  \n",
-       "8   6.58e-03  \n",
-       "9   4.47e-03  \n",
-       "10  8.05e-03  \n",
-       "11  5.39e-03  \n",
-       "12  1.01e-02  \n",
-       "13  7.73e-03  \n",
-       "14  6.03e-03  \n",
-       "15  5.33e-03  \n",
-       "16  5.37e-03  \n",
-       "17  5.97e-03  \n",
-       "18  7.18e-03  \n",
-       "19  4.34e-03  \n",
-       "20  4.96e-03  \n",
-       "21  7.73e-03  \n",
-       "22  3.84e-03  \n",
-       "23  6.02e-03  \n",
-       "24  4.38e-03  \n",
-       "25  5.75e-03  \n",
-       "26  3.68e-03  \n",
-       "27  5.59e-03  \n",
-       "28  3.57e-03  \n",
-       "29  6.64e-03  \n",
-       "30  5.53e-03  \n",
-       "31  6.23e-03  \n",
-       "32  3.77e-03  \n",
-       "33  6.16e-03  \n",
-       "34  5.45e-03  \n",
-       "35  4.76e-03  \n",
-       "36  5.61e-03  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with openmc.StatePoint('statepoint.{}.h5'.format(settings.batches)) as sp:\n",
-    "    output_tally = sp.get_tally()\n",
-    "    df = output_tally.get_pandas_dataframe()\n",
-    "    \n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that in the 'level 2' column, the 'cell id' tells us how each row corresponds to a ring and azimuthal position."
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/capi.ipynb b/examples/jupyter/capi.ipynb
deleted file mode 100644
index e696d6b01..000000000
--- a/examples/jupyter/capi.ipynb
+++ /dev/null
@@ -1,475 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Using the C/C++ API\n",
-    "This notebook shows how to use the OpenMC C/C++ API through the openmc.lib module. This module is particularly useful for multiphysics coupling because it allows you to update the density of materials and the temperatures of cells in memory, without stopping the simulation.\n",
-    "\n",
-    "Warning: these bindings are still somewhat experimental and may be subject to change in future versions of OpenMC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import openmc\n",
-    "import openmc.lib"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>Generate Input Files</b>\n",
-    "\n",
-    "Let's start by creating a fuel rod geometry. We will make 10 zones in the z-direction which will allow us to make changes to each zone. Changes in temperature have to be made on the cell, so will make 10 cells in the axial direction. Changes in density have to be made on the material, so we will make 10  water materials. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Materials: we will make a fuel, helium, zircaloy, and 10 water materials. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "material_list = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uo2 = openmc.Material(material_id=1, name='UO2 fuel at 2.4% wt enrichment')\n",
-    "uo2.set_density('g/cm3', 10.29769)\n",
-    "uo2.add_element('U', 1., enrichment=2.4)\n",
-    "uo2.add_element('O', 2.)\n",
-    "material_list.append(uo2)\n",
-    "\n",
-    "helium = openmc.Material(material_id=2, name='Helium for gap')\n",
-    "helium.set_density('g/cm3', 0.001598)\n",
-    "helium.add_element('He', 2.4044e-4)\n",
-    "material_list.append(helium)\n",
-    "\n",
-    "zircaloy = openmc.Material(material_id=3, name='Zircaloy 4')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_element('Sn', 0.014, 'wo')\n",
-    "zircaloy.add_element('Fe', 0.00165, 'wo')\n",
-    "zircaloy.add_element('Cr', 0.001, 'wo')\n",
-    "zircaloy.add_element('Zr', 0.98335, 'wo')\n",
-    "material_list.append(zircaloy)\n",
-    "\n",
-    "for i in range(4, 14):\n",
-    "    water = openmc.Material(material_id=i)\n",
-    "    water.set_density('g/cm3', 0.7)\n",
-    "    water.add_element('H', 2.0)\n",
-    "    water.add_element('O', 1.0)\n",
-    "    water.add_s_alpha_beta('c_H_in_H2O')\n",
-    "    material_list.append(water)\n",
-    "    \n",
-    "materials_file = openmc.Materials(material_list)\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cells: we will make a fuel cylinder, a gap cylinder, a cladding cylinder, and a water exterior. Each one will be broken into 10 cells which are the 10 axial zones. The z_list is the list of axial positions that delimit those 10 zones. To keep track of all the cells, we will create lists: fuel_list, gap_list, clad_list, and water_list. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pitch = 1.25984\n",
-    "fuel_or = openmc.ZCylinder(r=0.39218)\n",
-    "clad_ir = openmc.ZCylinder(r=0.40005)\n",
-    "clad_or = openmc.ZCylinder(r=0.4572)\n",
-    "left = openmc.XPlane(x0=-pitch/2)\n",
-    "right = openmc.XPlane(x0=pitch/2)\n",
-    "back = openmc.YPlane(y0=-pitch/2)\n",
-    "front = openmc.YPlane(y0=pitch/2)\n",
-    "z = [0., 30., 60., 90., 120., 150., 180., 210., 240., 270., 300.]\n",
-    "z_list = [openmc.ZPlane(z0=z_i) for z_i in z]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "left.boundary_type = 'reflective'\n",
-    "right.boundary_type = 'reflective'\n",
-    "front.boundary_type = 'reflective'\n",
-    "back.boundary_type = 'reflective'\n",
-    "z_list[0].boundary_type = 'vacuum'\n",
-    "z_list[-1].boundary_type = 'vacuum'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel_list = []\n",
-    "gap_list = []\n",
-    "clad_list = []\n",
-    "water_list = []\n",
-    "for i in range(1, 11):\n",
-    "    fuel_list.append(openmc.Cell(cell_id=i))\n",
-    "    gap_list.append(openmc.Cell(cell_id=i+10))\n",
-    "    clad_list.append(openmc.Cell(cell_id=i+20))\n",
-    "    water_list.append(openmc.Cell(cell_id=i+30))\n",
-    "    \n",
-    "for j, fuels in enumerate(fuel_list):\n",
-    "    fuels.region = -fuel_or & +z_list[j] & -z_list[j+1]\n",
-    "    fuels.fill = uo2\n",
-    "    fuels.temperature = 800.\n",
-    "\n",
-    "for j, gaps in enumerate(gap_list):\n",
-    "    gaps.region = +fuel_or & -clad_ir & +z_list[j] & -z_list[j+1]\n",
-    "    gaps.fill = helium\n",
-    "    gaps.temperature = 700.\n",
-    "\n",
-    "for j, clads in enumerate(clad_list):\n",
-    "    clads.region = +clad_ir & -clad_or & +z_list[j] & -z_list[j+1]\n",
-    "    clads.fill = zircaloy\n",
-    "    clads.temperature = 600.\n",
-    "\n",
-    "for j, waters in enumerate(water_list):\n",
-    "    waters.region = +clad_or & +left & -right & +back & -front & +z_list[j] & -z_list[j+1]\n",
-    "    waters.fill = material_list[j+3]\n",
-    "    waters.temperature = 500."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root = openmc.Universe(name='root universe')\n",
-    "root.add_cells(fuel_list)\n",
-    "root.add_cells(gap_list)\n",
-    "root.add_cells(clad_list)\n",
-    "root.add_cells(water_list)\n",
-    "geometry_file = openmc.Geometry(root)\n",
-    "geometry_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you are coupling this externally to a heat transfer solver, you will want to know the heat deposited by each fuel cell. So let's create a cell filter for the recoverable fission heat. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cell_filter = openmc.CellFilter(fuel_list)\n",
-    "t = openmc.Tally(tally_id=1)\n",
-    "t.filters.append(cell_filter)\n",
-    "t.scores = ['fission-q-recoverable']\n",
-    "tallies = openmc.Tallies([t])\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot our geometry to make sure it looks like we expect. Since we made new water materials in each axial cell, and we have centered the plot at 150, we should see one color for the water material in the bottom half and a different color for the water material in the top half. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x126d642e0>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "root.plot(basis='yz', width=[2, 10], color_by='material', origin=[0., 0., 150.], pixels=[400, 400])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Settings: everything will be standard except for the temperature settings. Since we will be working with specified temperatures, you will need temperature dependent data. I typically use the endf data found here: https://openmc.org/official-data-libraries/\n",
-    "Make sure your cross sections environment variable is pointing to temperature-dependent data before using the following settings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lower_left = [-0.62992, -pitch/2, 0]\n",
-    "upper_right = [+0.62992, +pitch/2, +300]\n",
-    "uniform_dist = openmc.stats.Box(lower_left, upper_right, only_fissionable=True)\n",
-    "\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = 100\n",
-    "settings_file.inactive = 10\n",
-    "settings_file.particles = 10000\n",
-    "settings_file.temperature = {'multipole': True, 'method': 'interpolation', 'range': [290, 2500]}\n",
-    "settings_file.source = openmc.source.Source(space=uniform_dist)\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run a regular simulation, just use openmc.run(). \n",
-    "However, we want to run a simulation that we can stop in the middle and update the material and cell properties. So we will use openmc.lib."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openmc.lib.init()\n",
-    "openmc.lib.simulation_init()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are 10 inactive batches, so we need to run next_batch() at least 10 times before the tally is activated. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _ in range(14):\n",
-    "    openmc.lib.next_batch()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the tally. There are 10 entries, one for each cell in the fuel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 4178272.4202991 ]\n",
-      " [ 9595363.82759911]\n",
-      " [12307462.30060902]\n",
-      " [11772927.66594472]\n",
-      " [11892601.29001472]\n",
-      " [12203397.88895767]\n",
-      " [12851791.20965905]\n",
-      " [11760027.45873386]\n",
-      " [ 9293110.94735569]\n",
-      " [ 4511597.61592287]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "t = openmc.lib.tallies[1]\n",
-    "print(t.mean)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's make some changes to the temperatures. For this, we need to identify each cell by its id. We can use get_temperature() to compare the temperatures of the cells before and after the change. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fuel temperature is: \n",
-      "800.0\n",
-      "gap temperature is: \n",
-      "700.0\n",
-      "clad temperature is: \n",
-      "600.0\n",
-      "water temperature is: \n",
-      "500.00000000000006\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"fuel temperature is: \")\n",
-    "print(openmc.lib.cells[5].get_temperature())\n",
-    "print(\"gap temperature is: \")\n",
-    "print(openmc.lib.cells[15].get_temperature())\n",
-    "print(\"clad temperature is: \")\n",
-    "print(openmc.lib.cells[25].get_temperature())\n",
-    "print(\"water temperature is: \")\n",
-    "print(openmc.lib.cells[35].get_temperature())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(1, 11):\n",
-    "    temp = 900.0\n",
-    "    openmc.lib.cells[i].set_temperature(temp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fuel temperature is: \n",
-      "899.9999999999999\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"fuel temperature is: \")\n",
-    "print(openmc.lib.cells[5].get_temperature())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's make a similar change for the water density. Again, we need to identify each material by its id."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i in range(4, 14):\n",
-    "    density = 0.65\n",
-    "    openmc.lib.materials[i].set_density(density, units='g/cm3')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The new batches we run will use the new material and cell properties."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for _ in range(14):\n",
-    "    openmc.lib.next_batch()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When you're ready to end the simulation, use the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openmc.lib.simulation_finalize()\n",
-    "openmc.lib.finalize()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/jupyter/chain_simple.xml b/examples/jupyter/chain_simple.xml
deleted file mode 120000
index 96f6fa881..000000000
--- a/examples/jupyter/chain_simple.xml
+++ /dev/null
@@ -1 +0,0 @@
-../../tests/chain_simple.xml
\ No newline at end of file
diff --git a/examples/jupyter/expansion-filters.ipynb b/examples/jupyter/expansion-filters.ipynb
deleted file mode 100644
index 3b0413a05..000000000
--- a/examples/jupyter/expansion-filters.ipynb
+++ /dev/null
@@ -1,1548 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Functional Expansions\n",
-    "OpenMC's general tally system accommodates a wide range of tally *filters*. While most filters are meant to identify regions of phase space that contribute to a tally, there are a special set of functional expansion filters that will multiply the tally by a set of orthogonal functions, e.g. Legendre polynomials, so that continuous functions of space or angle can be reconstructed from the tallied moments.\n",
-    "\n",
-    "In this example, we will determine the spatial dependence of the flux along the $z$ axis by making a Legendre polynomial expansion. Let us represent the flux along the z axis, $\\phi(z)$, by the function\n",
-    "\n",
-    "$$ \\phi(z') = \\sum\\limits_{n=0}^N a_n P_n(z') $$\n",
-    "\n",
-    "where $z'$ is the position normalized to the range [-1, 1]. Since $P_n(z')$ are known functions, our only task is to determine the expansion coefficients, $a_n$. By the orthogonality properties of the Legendre polynomials, one can deduce that the coefficients, $a_n$, are given by\n",
-    "\n",
-    "$$ a_n = \\frac{2n + 1}{2} \\int_{-1}^1 dz' P_n(z') \\phi(z').$$\n",
-    "\n",
-    "Thus, the problem reduces to finding the integral of the flux times each Legendre polynomial -- a problem which can be solved by using a Monte Carlo tally. By using a Legendre polynomial filter, we obtain stochastic estimates of these integrals for each polynomial order."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import openmc\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To begin, let us first create a simple model. The model will be a slab of fuel material with reflective boundaries conditions in the x- and y-directions and vacuum boundaries in the z-direction. However, to make the distribution slightly more interesting, we'll put some B<sub>4</sub>C in the middle of the slab."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define fuel and B4C materials\n",
-    "fuel = openmc.Material()\n",
-    "fuel.add_element('U', 1.0, enrichment=4.5)\n",
-    "fuel.add_nuclide('O16', 2.0)\n",
-    "fuel.set_density('g/cm3', 10.0)\n",
-    "\n",
-    "b4c = openmc.Material()\n",
-    "b4c.add_element('B', 4.0)\n",
-    "b4c.add_element('C', 1.0)\n",
-    "b4c.set_density('g/cm3', 2.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define surfaces used to construct regions\n",
-    "zmin, zmax = -10., 10.\n",
-    "box = openmc.model.rectangular_prism(10., 10., boundary_type='reflective')\n",
-    "bottom = openmc.ZPlane(z0=zmin, boundary_type='vacuum')\n",
-    "boron_lower = openmc.ZPlane(z0=-0.5)\n",
-    "boron_upper = openmc.ZPlane(z0=0.5)\n",
-    "top = openmc.ZPlane(z0=zmax, boundary_type='vacuum')\n",
-    "\n",
-    "# Create three cells and add them to geometry\n",
-    "fuel1 = openmc.Cell(fill=fuel, region=box & +bottom & -boron_lower)\n",
-    "absorber = openmc.Cell(fill=b4c, region=box & +boron_lower & -boron_upper)\n",
-    "fuel2 = openmc.Cell(fill=fuel, region=box & +boron_upper & -top)\n",
-    "geom = openmc.Geometry([fuel1, absorber, fuel2])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the starting source, we'll use a uniform distribution over the entire box geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "spatial_dist = openmc.stats.Box(*geom.bounding_box)\n",
-    "settings.source = openmc.Source(space=spatial_dist)\n",
-    "settings.batches = 210\n",
-    "settings.inactive = 10\n",
-    "settings.particles = 1000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Defining the tally is relatively straightforward. One simply needs to list 'flux' as a score and then add an expansion filter. For this case, we will want to use the `SpatialLegendreFilter` class which multiplies tally scores by Legendre polynomials evaluated on normalized spatial positions along an axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a flux tally\n",
-    "flux_tally = openmc.Tally()\n",
-    "flux_tally.scores = ['flux']\n",
-    "\n",
-    "# Create a Legendre polynomial expansion filter and add to tally\n",
-    "order = 8\n",
-    "expand_filter = openmc.SpatialLegendreFilter(order, 'z', zmin, zmax)\n",
-    "flux_tally.filters.append(expand_filter)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The last thing we need to do is create a `Tallies` collection and export the entire model, which we'll do using the `Model` convenience class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tallies = openmc.Tallies([flux_tally])\n",
-    "model = openmc.model.Model(geometry=geom, settings=settings, tallies=tallies)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running a simulation is now as simple as calling the `run()` method of `Model`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp_file = model.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that the run is finished, we need to load the results from the statepoint file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint(sp_file) as sp:\n",
-    "    df = sp.tallies[flux_tally.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We've used the `get_pandas_dataframe()` method that returns tally data as a Pandas dataframe. Let's see what the raw data looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>spatiallegendre</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>P0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>36.523601</td>\n",
-       "      <td>0.081540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>P1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.002830</td>\n",
-       "      <td>0.041466</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>P2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-4.411923</td>\n",
-       "      <td>0.027161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>P3</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.004316</td>\n",
-       "      <td>0.020245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>P4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.277281</td>\n",
-       "      <td>0.014558</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>P5</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.010604</td>\n",
-       "      <td>0.011350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>P6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.109212</td>\n",
-       "      <td>0.010280</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>P7</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.002705</td>\n",
-       "      <td>0.009100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>P8</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.088469</td>\n",
-       "      <td>0.007889</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  spatiallegendre nuclide score      mean  std. dev.\n",
-       "0              P0   total  flux  3.65e+01   8.15e-02\n",
-       "1              P1   total  flux -2.83e-03   4.15e-02\n",
-       "2              P2   total  flux -4.41e+00   2.72e-02\n",
-       "3              P3   total  flux  4.32e-03   2.02e-02\n",
-       "4              P4   total  flux -2.77e-01   1.46e-02\n",
-       "5              P5   total  flux  1.06e-02   1.13e-02\n",
-       "6              P6   total  flux  1.09e-01   1.03e-02\n",
-       "7              P7   total  flux -2.71e-03   9.10e-03\n",
-       "8              P8   total  flux -8.85e-02   7.89e-03"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the expansion coefficients are given as\n",
-    "\n",
-    "$$ a_n = \\frac{2n + 1}{2} \\int_{-1}^1 dz' P_n(z') \\phi(z')$$\n",
-    "\n",
-    "we just need to multiply the Legendre moments by $(2n + 1)/2$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n = np.arange(order + 1)\n",
-    "a_n = (2*n + 1)/2 * df['mean']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To plot the flux distribution, we can use the `numpy.polynomial.Legendre` class which represents a truncated Legendre polynomial series. Since we really want to plot $\\phi(z)$ and not $\\phi(z')$ we first need to perform a change of variables. Since\n",
-    "\n",
-    "$$ \\lvert \\phi(z) dz \\rvert = \\lvert \\phi(z') dz' \\rvert $$\n",
-    "\n",
-    "and, for this case, $z = 10z'$, it follows that\n",
-    "\n",
-    "$$ \\phi(z) = \\frac{\\phi(z')}{10} = \\sum_{n=0}^N \\frac{a_n}{10} P_n(z'). $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phi = np.polynomial.Legendre(a_n/10, domain=(zmin, zmax))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot it and see how our flux looks!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux [n/src]')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = np.linspace(zmin, zmax, 1000)\n",
-    "plt.plot(z, phi(z))\n",
-    "plt.xlabel('Z position [cm]')\n",
-    "plt.ylabel('Flux [n/src]')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you might expect, we get a rough cosine shape but with a flux depression in the middle due to the boron slab that we introduced. To get a more accurate distribution, we'd likely need to use a higher order expansion.\n",
-    "\n",
-    "One more thing we can do is confirm that integrating the distribution gives us the same value as the first moment (since $P_0(z') = 1$). This can easily be done by numerically integrating using the trapezoidal rule:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "36.523562389125146"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.trapz(phi(z), z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to being able to tally Legendre moments, there are also functional expansion filters available for spherical harmonics (`SphericalHarmonicsFilter`) and Zernike polynomials over a unit disk (`ZernikeFilter`). A separate `LegendreFilter` class can also be used for determining Legendre scattering moments (i.e., an expansion of the scattering cosine, $\\mu$)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Zernike polynomials\n",
-    "\n",
-    "Now let's look at an example of functional expansion tallies using Zernike polynomials as the basis functions.\n",
-    "\n",
-    "In this example, we will determine the spatial dependence of the flux along the radial direction $r'$ and $/$ or azimuthal angle $\\theta$ by making a Zernike polynomial expansion. Let us represent the flux along the radial and azimuthal direction, $\\phi(r', \\theta)$, by the function\n",
-    "\n",
-    "$$ \\phi(r', \\theta) = \\sum\\limits_{n=0}^N \\sum\\limits_{m=-n}^n a_n^m Z_n^m(r', \n",
-    "\\theta) $$\n",
-    "\n",
-    "where $r'$ is the position normalized to the range [0, r] (r is the radius of cylindrical geometry), and the azimuthal lies within the range [0, $ 2\\pi$]. \n",
-    "\n",
-    "Since $Z_n^m(r', \\theta)$ are known functions, we need to determine the expansion coefficients, $a_n^m$. By the orthogonality properties of the Zernike polynomials, one can deduce that the coefficients, $a_n^m$, are given by\n",
-    "\n",
-    "$$ a_n^m = k_n^m \\int_{0}^r dr' \\int_{0}^{2\\pi} d\\theta Z_n^m(r',\\theta) \\phi(r', \\theta).$$\n",
-    "$$ k_n^m = \\frac{2n + 2}{\\pi}, m \\ne 0. $$\n",
-    "$$ k_n^m = \\frac{n+1}{\\pi}, m = 0.$$\n",
-    "\n",
-    "Similarly, the problem reduces to finding the integral of the flux times each Zernike polynomial."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To begin with, let us first create a simple model. The model will be a pin-cell fuel material with vacuum boundary condition in both radial direction and axial direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define fuel \n",
-    "fuel = openmc.Material()\n",
-    "fuel.add_element('U', 1.0, enrichment=5.0)\n",
-    "fuel.add_nuclide('O16', 2.0)\n",
-    "fuel.set_density('g/cm3', 10.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define surfaces used to construct regions\n",
-    "zmin, zmax, radius = -1., 1., 0.5 \n",
-    "pin = openmc.ZCylinder(x0=0.0, y0=0.0, r=radius, boundary_type='vacuum')\n",
-    "bottom = openmc.ZPlane(z0=zmin, boundary_type='vacuum')\n",
-    "top = openmc.ZPlane(z0=zmax, boundary_type='vacuum')\n",
-    "\n",
-    "# Create three cells and add them to geometry\n",
-    "fuel = openmc.Cell(fill=fuel, region= -pin & +bottom & -top)\n",
-    "geom = openmc.Geometry([fuel])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the starting source, we'll use a uniform distribution over the entire box geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "spatial_dist = openmc.stats.Box(*geom.bounding_box)\n",
-    "settings.source = openmc.Source(space=spatial_dist)\n",
-    "settings.batches = 100\n",
-    "settings.inactive = 20\n",
-    "settings.particles = 100000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Defining the tally is relatively straightforward. One simply needs to list 'flux' as a score and then add an expansion filter. For this case, we will want to use the `SpatialLegendreFilter`, `ZernikeFilter`, `ZernikeRadialFilter` classes which multiplies tally scores by Legendre, azimuthal Zernike and  radial-only Zernike polynomials evaluated on normalized spatial positions along radial and axial directions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a flux tally\n",
-    "flux_tally_legendre = openmc.Tally()\n",
-    "flux_tally_legendre.scores = ['flux']\n",
-    "\n",
-    "# Create a Legendre polynomial expansion filter and add to tally\n",
-    "order = 10\n",
-    "cell_filter = openmc.CellFilter(fuel)\n",
-    "legendre_filter = openmc.SpatialLegendreFilter(order, 'z', zmin, zmax)\n",
-    "flux_tally_legendre.filters = [cell_filter, legendre_filter]\n",
-    "\n",
-    "# Create a Zernike azimuthal polynomial expansion filter and add to tally \n",
-    "flux_tally_zernike = openmc.Tally()\n",
-    "flux_tally_zernike.scores = ['flux']\n",
-    "zernike_filter = openmc.ZernikeFilter(order=order, x=0.0, y=0.0, r=radius)\n",
-    "flux_tally_zernike.filters = [cell_filter, zernike_filter]\n",
-    "\n",
-    "# Create a Zernike radial polynomial expansion filter and add to tally \n",
-    "flux_tally_zernike1d = openmc.Tally()\n",
-    "flux_tally_zernike1d.scores = ['flux']\n",
-    "zernike1d_filter = openmc.ZernikeRadialFilter(order=order, x=0.0, y=0.0, r=radius)\n",
-    "flux_tally_zernike1d.filters = [cell_filter, zernike1d_filter]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The last thing we need to do is create a `Tallies` collection and export the entire model, which we'll do using the `Model` convenience class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tallies = openmc.Tallies([flux_tally_legendre, flux_tally_zernike, flux_tally_zernike1d])\n",
-    "model = openmc.model.Model(geometry=geom, settings=settings, tallies=tallies)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running a simulation is now as simple as calling the `run()` method of `Model`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sp_file = model.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that the run is finished, we need to load the results from the statepoint file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint(sp_file) as sp:\n",
-    "    df1 = sp.tallies[flux_tally_legendre.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We've used the `get_pandas_dataframe()` method that returns tally data as a Pandas dataframe. Let's see what the raw data looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>spatiallegendre</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.543425</td>\n",
-       "      <td>0.000599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000635</td>\n",
-       "      <td>0.000523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.055989</td>\n",
-       "      <td>0.000342</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P3</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000053</td>\n",
-       "      <td>0.000284</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000749</td>\n",
-       "      <td>0.000230</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P5</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000111</td>\n",
-       "      <td>0.000149</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000692</td>\n",
-       "      <td>0.000177</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P7</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000064</td>\n",
-       "      <td>0.000152</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P8</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000139</td>\n",
-       "      <td>0.000142</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>9</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P9</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000201</td>\n",
-       "      <td>0.000119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>10</td>\n",
-       "      <td>4</td>\n",
-       "      <td>P10</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000051</td>\n",
-       "      <td>0.000112</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    cell spatiallegendre nuclide score      mean  std. dev.\n",
-       "0      4              P0   total  flux  5.43e-01   5.99e-04\n",
-       "1      4              P1   total  flux  6.35e-04   5.23e-04\n",
-       "2      4              P2   total  flux -5.60e-02   3.42e-04\n",
-       "3      4              P3   total  flux -5.27e-05   2.84e-04\n",
-       "4      4              P4   total  flux -7.49e-04   2.30e-04\n",
-       "5      4              P5   total  flux  1.11e-04   1.49e-04\n",
-       "6      4              P6   total  flux -6.92e-04   1.77e-04\n",
-       "7      4              P7   total  flux -6.41e-05   1.52e-04\n",
-       "8      4              P8   total  flux -1.39e-04   1.42e-04\n",
-       "9      4              P9   total  flux  2.01e-04   1.19e-04\n",
-       "10     4             P10   total  flux -5.05e-05   1.12e-04"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the scaling factors for expansion coefficients will be provided by the Python API, thus, we do not need to multiply the moments by scaling factors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a_n = df1['mean']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Loading the coefficients is realized via calling the OpenMC Python API as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "phi = openmc.legendre_from_expcoef(a_n, domain=(zmin, zmax))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot it and see how our flux looks!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux [n/src]')"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z = np.linspace(zmin, zmax, 1000)\n",
-    "plt.plot(z, phi(z))\n",
-    "plt.xlabel('Z position [cm]')\n",
-    "plt.ylabel('Flux [n/src]')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A rough cosine shape is obtained. \n",
-    "One can also numerically integrate the function using the trapezoidal rule."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.543424143829605"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.trapz(phi(z), z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cases show how to reconstruct the flux distribution Zernike polynomials tallied results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint(sp_file) as sp:\n",
-    "    df2 = sp.tallies[flux_tally_zernike.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>zernike</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z0,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.543425</td>\n",
-       "      <td>0.000599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z1,-1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000236</td>\n",
-       "      <td>0.000398</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z1,1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.000126</td>\n",
-       "      <td>0.000325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z2,-2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000104</td>\n",
-       "      <td>0.000206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z2,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.064291</td>\n",
-       "      <td>0.000404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>61</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z10,2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000130</td>\n",
-       "      <td>0.000099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>62</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z10,4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000057</td>\n",
-       "      <td>0.000092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>63</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z10,6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000048</td>\n",
-       "      <td>0.000109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>64</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z10,8</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000056</td>\n",
-       "      <td>0.000092</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>65</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z10,10</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000046</td>\n",
-       "      <td>0.000098</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>66 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    cell zernike nuclide score      mean  std. dev.\n",
-       "0      4    Z0,0   total  flux  5.43e-01   5.99e-04\n",
-       "1      4   Z1,-1   total  flux -2.36e-04   3.98e-04\n",
-       "2      4    Z1,1   total  flux  1.26e-04   3.25e-04\n",
-       "3      4   Z2,-2   total  flux -1.04e-04   2.06e-04\n",
-       "4      4    Z2,0   total  flux -6.43e-02   4.04e-04\n",
-       "..   ...     ...     ...   ...       ...        ...\n",
-       "61     4   Z10,2   total  flux -1.30e-04   9.86e-05\n",
-       "62     4   Z10,4   total  flux -5.70e-05   9.21e-05\n",
-       "63     4   Z10,6   total  flux -4.79e-05   1.09e-04\n",
-       "64     4   Z10,8   total  flux -5.59e-05   9.23e-05\n",
-       "65     4  Z10,10   total  flux -4.58e-05   9.76e-05\n",
-       "\n",
-       "[66 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot the flux in radial direction with specific azimuthal angle ($\\theta = 0.0$)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux')"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = df2['mean'] \n",
-    "zz = openmc.Zernike(z_n, radius)\n",
-    "rr = np.linspace(0, radius, 100)\n",
-    "plt.plot(rr, zz(rr, 0.0)) \n",
-    "plt.xlabel('Radial position [cm]')\n",
-    "plt.ylabel('Flux')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A polar figure with all azimuthal can be plotted like this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = df2['mean']\n",
-    "zz = openmc.Zernike(z_n, radius=radius) \n",
-    "#\n",
-    "# Using linspace so that the endpoint of 360 is included...\n",
-    "azimuths = np.radians(np.linspace(0, 360, 50))\n",
-    "zeniths = np.linspace(0, radius, 100)\n",
-    "r, theta = np.meshgrid(zeniths, azimuths)\n",
-    "values = zz(zeniths, azimuths)\n",
-    "fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\n",
-    "ax.contourf(theta, r, values, cmap='jet')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sometimes, we just need the radial-only Zernike polynomial tallied flux distribution. \n",
-    "Let us extract the tallied coefficients first."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint(sp_file) as sp:\n",
-    "    df3 = sp.tallies[flux_tally_zernike1d.id].get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>zernikeradial</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z0,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>0.543425</td>\n",
-       "      <td>0.000599</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z2,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.064291</td>\n",
-       "      <td>0.000404</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z4,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000601</td>\n",
-       "      <td>0.000223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z6,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000454</td>\n",
-       "      <td>0.000227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z8,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>0.000166</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Z10,0</td>\n",
-       "      <td>total</td>\n",
-       "      <td>flux</td>\n",
-       "      <td>-0.000102</td>\n",
-       "      <td>0.000161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell zernikeradial nuclide score      mean  std. dev.\n",
-       "0     4          Z0,0   total  flux  5.43e-01   5.99e-04\n",
-       "1     4          Z2,0   total  flux -6.43e-02   4.04e-04\n",
-       "2     4          Z4,0   total  flux -6.01e-04   2.23e-04\n",
-       "3     4          Z6,0   total  flux -4.54e-04   2.27e-04\n",
-       "4     4          Z8,0   total  flux -1.15e-05   1.66e-04\n",
-       "5     4         Z10,0   total  flux -1.02e-04   1.61e-04"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A plot along with r-axis is also done."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Flux')"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = df3['mean']  \n",
-    "zz = openmc.ZernikeRadial(z_n, radius=radius)\n",
-    "rr = np.linspace(0, radius, 50)\n",
-    "plt.plot(rr, zz(rr)) \n",
-    "plt.xlabel('Radial position [cm]')\n",
-    "plt.ylabel('Flux')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can also re-construct the polar figure based on radial-only Zernike polinomial coefficients. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "z_n = df3['mean']  \n",
-    "zz = openmc.ZernikeRadial(z_n, radius=radius)\n",
-    "azimuths = np.radians(np.linspace(0, 360, 50))\n",
-    "zeniths = np.linspace(0, radius, 100)\n",
-    "r, theta = np.meshgrid(zeniths, azimuths)\n",
-    "values = [[i for i in zz(zeniths)] for j in range(len(azimuths))]\n",
-    "fig, ax = plt.subplots(subplot_kw=dict(projection='polar'), figsize=(6,6))\n",
-    "ax.contourf(theta, r, values, cmap='jet')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Based on Legendre polynomial coefficients and the azimuthal or radial-only Zernike coefficient, it's possible to reconstruct the flux both on radial and axial directions. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7ffe048a2f90>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 360x720 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Reconstruct 3-D flux based on radial only Zernike and Legendre polynomials\n",
-    "z_n = df3['mean']  \n",
-    "zz = openmc.ZernikeRadial(z_n, radius=radius)\n",
-    "azimuths = np.radians(np.linspace(0, 360, 100)) # azimuthal mesh \n",
-    "zeniths = np.linspace(0, radius, 100) # radial mesh \n",
-    "zmin, zmax = -1.0, 1.0 \n",
-    "z = np.linspace(zmin, zmax, 100) # axial mesh \n",
-    "# \n",
-    "# flux = np.matmul(np.matrix(phi(z)).transpose(), np.matrix(zz(zeniths))) \n",
-    "# flux = np.array(flux) # change np.matrix to np.array\n",
-    "# np.matrix is not recommended for use anymore\n",
-    "flux = np.array([phi(z)]).T @ np.array([zz(zeniths)])\n",
-    "#\n",
-    "plt.figure(figsize=(5,10))\n",
-    "plt.title('Flux distribution')\n",
-    "plt.xlabel('Radial Position [cm]')\n",
-    "plt.ylabel('Axial Height [cm]')\n",
-    "plt.pcolor(zeniths, z, flux, cmap='jet')\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One can also reconstruct the 3D flux distribution based on Legendre and Zernike polynomial tallied coefficients."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define needed function first \n",
-    "def cart2pol(x, y):\n",
-    "    rho = np.sqrt(x**2 + y**2)\n",
-    "    phi = np.arctan2(y, x)\n",
-    "    return(rho, phi)\n",
-    "\n",
-    "# Reconstruct 3-D flux based on azimuthal Zernike and Legendre polynomials\n",
-    "z_n = df2['mean']\n",
-    "zz = openmc.Zernike(z_n, radius=radius) \n",
-    "#\n",
-    "xstep = 2.0*radius/20\n",
-    "hstep = (zmax - zmin)/20\n",
-    "x = np.linspace(-radius, radius, 50)\n",
-    "x = np.array(x)\n",
-    "[X,Y] = np.meshgrid(x,x)\n",
-    "h = np.linspace(zmin, zmax, 50)\n",
-    "h = np.array(h)\n",
-    "[r, theta] = cart2pol(X,Y)\n",
-    "flux3d = np.zeros((len(x), len(x), len(h)))\n",
-    "flux3d.fill(np.nan)\n",
-    "#\n",
-    "for i in range(len(x)):\n",
-    "    for j in range(len(x)):\n",
-    "        if r[i][j]<=radius:\n",
-    "            for k in range(len(h)):\n",
-    "                flux3d[i][j][k] = phi(h[k]) * zz(r[i][j], theta[i][j])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us print out with VTK format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# You'll need to install pyevtk as a prerequisite\n",
-    "from pyevtk.hl import gridToVTK\n",
-    "import numpy as np\n",
-    "#\n",
-    "# Dimensions\n",
-    "nx, ny, nz = len(x), len(x), len(h)\n",
-    "lx, ly, lz = 2.0*radius, 2.0*radius, (zmax-zmin)\n",
-    "dx, dy, dz = lx/nx, ly/ny, lz/nz\n",
-    "#\n",
-    "ncells = nx * ny * nz\n",
-    "npoints = (nx + 1) * (ny + 1) * (nz + 1)\n",
-    "#\n",
-    "# Coordinates\n",
-    "x = np.arange(0, lx + 0.1*dx, dx, dtype='float64')\n",
-    "y = np.arange(0, ly + 0.1*dy, dy, dtype='float64')\n",
-    "z = np.arange(0, lz + 0.1*dz, dz, dtype='float64')\n",
-    "# Print out \n",
-    "path = gridToVTK(\"./rectilinear\", x, y, z, cellData = {\"flux3d\" : flux3d})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use VisIt or ParaView to plot it as you want. Then, the plot can be loaded and shown as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7ffe050592d0>"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f1 = plt.imread('./images/flux3d.png')\n",
-    "plt.imshow(f1, cmap='jet')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/hexagonal-lattice.ipynb b/examples/jupyter/hexagonal-lattice.ipynb
deleted file mode 100644
index 07c3ab834..000000000
--- a/examples/jupyter/hexagonal-lattice.ipynb
+++ /dev/null
@@ -1,411 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling Hexagonal Lattices\n",
-    "In this example, we will create a hexagonal lattice and show how the orientation can be changed via the cell rotation property. Let's first just set up some materials and universes that we will use to fill the lattice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel = openmc.Material(name='fuel')\n",
-    "fuel.add_nuclide('U235', 1.0)\n",
-    "fuel.set_density('g/cm3', 10.0)\n",
-    "\n",
-    "fuel2 = openmc.Material(name='fuel2')\n",
-    "fuel2.add_nuclide('U238', 1.0)\n",
-    "fuel2.set_density('g/cm3', 10.0)\n",
-    "\n",
-    "water = openmc.Material(name='water')\n",
-    "water.add_nuclide('H1', 2.0)\n",
-    "water.add_nuclide('O16', 1.0)\n",
-    "water.set_density('g/cm3', 1.0)\n",
-    "\n",
-    "materials = openmc.Materials((fuel, fuel2, water))\n",
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we will set up two universes that represent pin-cells: one with a small pin and one with a big pin. Since we will be using these universes in a lattice, it's always a good idea to have an \"outer\" universe as well that is applied outside the defined lattice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r_pin = openmc.ZCylinder(r=0.25)\n",
-    "fuel_cell = openmc.Cell(fill=fuel, region=-r_pin)\n",
-    "water_cell = openmc.Cell(fill=water, region=+r_pin)\n",
-    "pin_universe = openmc.Universe(cells=(fuel_cell, water_cell))\n",
-    "\n",
-    "r_big_pin = openmc.ZCylinder(r=0.5)\n",
-    "fuel2_cell = openmc.Cell(fill=fuel2, region=-r_big_pin)\n",
-    "water2_cell = openmc.Cell(fill=water, region=+r_big_pin)\n",
-    "big_pin_universe = openmc.Universe(cells=(fuel2_cell, water2_cell))\n",
-    "\n",
-    "all_water_cell = openmc.Cell(fill=water)\n",
-    "outer_universe = openmc.Universe(cells=(all_water_cell,))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's create a hexagonal lattice using the `HexLattice` class:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lattice = openmc.HexLattice()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need to set the `center` of the lattice, the `pitch`, an `outer` universe (which is applied to all lattice elements outside of those that are defined), and a list of `universes`. Let's start with the easy ones first. Note that for a 2D lattice, we only need to specify a single number for the pitch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lattice.center = (0., 0.)\n",
-    "lattice.pitch = (1.25,)\n",
-    "lattice.outer = outer_universe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we need to set the `universes` property on our lattice. It needs to be set to a list of lists of Universes, where each list of Universes corresponds to a ring of the lattice. The rings are ordered from outermost to innermost, and within each ring the indexing starts at the \"top\". To help visualize the proper indices, we can use the `show_indices()` helper method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                  (0, 0)\n",
-      "            (0,17)      (0, 1)\n",
-      "      (0,16)      (1, 0)      (0, 2)\n",
-      "(0,15)      (1,11)      (1, 1)      (0, 3)\n",
-      "      (1,10)      (2, 0)      (1, 2)\n",
-      "(0,14)      (2, 5)      (2, 1)      (0, 4)\n",
-      "      (1, 9)      (3, 0)      (1, 3)\n",
-      "(0,13)      (2, 4)      (2, 2)      (0, 5)\n",
-      "      (1, 8)      (2, 3)      (1, 4)\n",
-      "(0,12)      (1, 7)      (1, 5)      (0, 6)\n",
-      "      (0,11)      (1, 6)      (0, 7)\n",
-      "            (0,10)      (0, 8)\n",
-      "                  (0, 9)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(lattice.show_indices(num_rings=4))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's set up a lattice where the first element in each ring is the big pin universe and all other elements are regular pin universes. \n",
-    "\n",
-    "From the diagram above, we see that the outer ring has 18 elements, the first ring has 12, and the second ring has 6 elements. The innermost ring of any hexagonal lattice will have only a single element. \n",
-    "\n",
-    "We build these rings through 'list concatenation' as follows: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outer_ring = [big_pin_universe] + [pin_universe]*17 # Adds up to 18\n",
-    "\n",
-    "ring_1 = [big_pin_universe] + [pin_universe]*11 # Adds up to 12\n",
-    "\n",
-    "ring_2 = [big_pin_universe] + [pin_universe]*5 # Adds up to 6\n",
-    "\n",
-    "inner_ring = [big_pin_universe]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now assign the rings (and the universes they contain) to our lattice. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "HexLattice\n",
-      "\tID             =\t4\n",
-      "\tName           =\t\n",
-      "\tOrientation    =\ty\n",
-      "\t# Rings        =\t4\n",
-      "\t# Axial        =\tNone\n",
-      "\tCenter         =\t(0.0, 0.0)\n",
-      "\tPitch          =\t(1.25,)\n",
-      "\tOuter          =\t3\n",
-      "\tUniverses      \n",
-      "   2\n",
-      "  1 1\n",
-      " 1 2 1\n",
-      "1 1 1 1\n",
-      " 1 2 1\n",
-      "1 1 1 1\n",
-      " 1 2 1\n",
-      "1 1 1 1\n",
-      " 1 1 1\n",
-      "1 1 1 1\n",
-      " 1 1 1\n",
-      "  1 1\n",
-      "   1\n"
-     ]
-    }
-   ],
-   "source": [
-    "lattice.universes = [outer_ring, \n",
-    "                     ring_1, \n",
-    "                     ring_2,\n",
-    "                     inner_ring]\n",
-    "print(lattice)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's put our lattice inside a circular cell that will serve as the top-level cell for our geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outer_surface = openmc.ZCylinder(r=5.0, boundary_type='vacuum')\n",
-    "main_cell = openmc.Cell(fill=lattice, region=-outer_surface)\n",
-    "geometry = openmc.Geometry([main_cell])\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's create a plot to see what our geometry looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot = openmc.Plot.from_geometry(geometry)\n",
-    "plot.color_by = 'material'\n",
-    "plot.colors = colors = {\n",
-    "    water: 'blue',\n",
-    "    fuel: 'olive',\n",
-    "    fuel2: 'yellow'\n",
-    "}\n",
-    "plot.to_ipython_image()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this point, if we wanted to simulate the model, we would need to create an instance of `openmc.Settings`, export it to XML, and run."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lattice orientation\n",
-    "\n",
-    "Now let's say we want our hexagonal lattice orientated such that two sides of the lattice are parallel to the x-axis. This can be achieved by two means: either we can rotate the cell that contains the lattice, or we can can change the `HexLattice.orientation` attribute. By default, the `orientation` is set to \"y\", indicating that two sides of the lattice are parallel to the y-axis, but we can also change it to \"x\" to make them parallel to the x-axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Change the orientation of the lattice and re-export the geometry\n",
-    "lattice.orientation = 'x'\n",
-    "geometry.export_to_xml()\n",
-    "\n",
-    "# Run OpenMC in plotting mode\n",
-    "plot.to_ipython_image()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we change the orientation to 'x', you can see that the first universe in each ring starts to the right along the x-axis. As before, the universes are defined in a clockwise fashion around each ring. To see the proper indices for a hexagonal lattice in this orientation, we can again call `show_indices` but pass an extra orientation argument:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                  (0,12)      (0,13)      (0,14)      (0,15)\n",
-      "\n",
-      "            (0,11)      (1, 8)      (1, 9)      (1,10)      (0,16)\n",
-      "\n",
-      "      (0,10)      (1, 7)      (2, 4)      (2, 5)      (1,11)      (0,17)\n",
-      "\n",
-      "(0, 9)      (1, 6)      (2, 3)      (3, 0)      (2, 0)      (1, 0)      (0, 0)\n",
-      "\n",
-      "      (0, 8)      (1, 5)      (2, 2)      (2, 1)      (1, 1)      (0, 1)\n",
-      "\n",
-      "            (0, 7)      (1, 4)      (1, 3)      (1, 2)      (0, 2)\n",
-      "\n",
-      "                  (0, 6)      (0, 5)      (0, 4)      (0, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(lattice.show_indices(4, orientation='x'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hexagonal prisms\n",
-    "\n",
-    "OpenMC also contains a convenience function that can create a hexagonal prism representing the interior region of six surfaces defining a hexagon. This can be useful as a bounding surface of a hexagonal lattice. For example, if we wanted the outer boundary of our geometry to be hexagonal, we could change the `region` of the main cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "main_cell.region = openmc.model.hexagonal_prism(\n",
-    "    edge_length=4*lattice.pitch[0],\n",
-    "    orientation='x',\n",
-    "    boundary_type='vacuum'\n",
-    ")\n",
-    "geometry.export_to_xml()\n",
-    "\n",
-    "# Run OpenMC in plotting mode\n",
-    "plot.color_by = 'cell'\n",
-    "plot.to_ipython_image()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/images/cylinder_mesh.png b/examples/jupyter/images/cylinder_mesh.png
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diff --git a/examples/jupyter/images/teapot.jpg b/examples/jupyter/images/teapot.jpg
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diff --git a/examples/jupyter/images/umesh_heating.png b/examples/jupyter/images/umesh_heating.png
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diff --git a/examples/jupyter/images/umesh_w_assembly.png b/examples/jupyter/images/umesh_w_assembly.png
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diff --git a/examples/jupyter/mdgxs-part-i.ipynb b/examples/jupyter/mdgxs-part-i.ipynb
deleted file mode 100644
index c37547a33..000000000
--- a/examples/jupyter/mdgxs-part-i.ipynb
+++ /dev/null
@@ -1,1491 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup (Delayed) Cross Section Generation Part I: Introduction\n",
-    "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-energy-group and multi-delayed-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n",
-    "\n",
-    "* Creation of multi-delayed-group cross sections for an **infinite homogeneous medium**\n",
-    "* Calculation of delayed neutron precursor concentrations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction to Multi-Delayed-Group Cross Sections (MDGXS)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. Furthermore, kinetics calculations typically separate out parameters that involve delayed neutrons into prompt and delayed components and further subdivide delayed components by delayed groups. An example is the energy spectrum for prompt and delayed neutrons for U-235 and Pu-239 computed for a light water reactor spectrum."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 350
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename='images/mdgxs.png', width=350)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations and different delayed group models (e.g. 6, 7, or 8 delayed group models) for fine-mesh heterogeneous deterministic neutron transport applications.\n",
-    "\n",
-    "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-energy-group and multi-delayed-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Introductory Notation\n",
-    "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Spatial and Energy Discretization\n",
-    "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n",
-    "\n",
-    "The delayed neutrons created from fissions are created from > 30 delayed neutron precursors. Modeling each of the delayed neutron precursors is possible, but this approach has not recieved much attention due to large uncertainties in certain precursors. Therefore, the delayed neutrons are often combined into \"delayed groups\" that have a set time constant, $\\lambda_d$. Some cross section libraries use the same group time constants for all nuclides (e.g. JEFF 3.1) while other libraries use different time constants for all nuclides (e.g. ENDF/B-VII.1). Multi-delayed-group cross sections can either be created with the entire delayed group set, a subset of delayed groups, or integrated over all delayed groups.\n",
-    "\n",
-    "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### General Scalar-Flux Weighted MDGXS\n",
-    "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section. For instance, the delayed-nu-fission multi-energy-group and multi-delayed-group cross section, $\\nu_d \\sigma_{f,x,k,g}$, can be computed as follows:\n",
-    "\n",
-    "$$\\nu_d \\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\nu_d \\sigma_{f,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
-    "\n",
-    "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for only the delayed-nu-fission and delayed neutron fraction reaction type at the moment. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](../usersguide/tallies.rst#filters) on the energy range and spatial zone (material, cell, universe, or mesh) define the bounds of integration for both numerator and denominator."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-Group Prompt and Delayed Fission Spectrum\n",
-    "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n",
-    "\n",
-    "Computing the cumulative energy spectrum of emitted neutrons, $\\chi_{n}(\\mathbf{r},E)$, has been presented in the `mgxs-part-i.ipynb` notebook. Here, we will present the energy spectrum of prompt and delayed emission neutrons, $\\chi_{n,p}(\\mathbf{r},E)$ and $\\chi_{n,d}(\\mathbf{r},E)$, respectively. Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n,p}(\\mathbf{r},E)$ and $\\nu_{n,d}(\\mathbf{r},E)$ for prompt and delayed neutrons, respectively. The multi-group fission spectrum $\\chi_{n,k,g,d}$ is then the probability of fission neutrons emitted into energy group $g$ and delayed group $d$. There are not prompt groups, so inserting $p$ in place of $d$ just denotes all prompt neutrons. \n",
-    "\n",
-    "Similar to before, spatial homogenization and energy condensation are used to find the multi-energy-group and multi-delayed-group fission spectrum $\\chi_{n,k,g,d}$ as follows:\n",
-    "\n",
-    "$$\\chi_{n,k,g',d} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n,d}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n,d}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n",
-    "\n",
-    "The fission production-weighted multi-energy-group and multi-delayed-group fission spectrum for delayed neutrons is computed using OpenMC tallies with energy in, energy out, and delayed group filters. Alternatively, the delayed group filter can be omitted to compute the fission spectrum integrated over all delayed groups.\n",
-    "\n",
-    "This concludes our brief overview on the methodology to compute multi-energy-group and multi-delayed-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import openmc\n",
-    "import openmc.mgxs as mgxs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem. Let's create a material for the homogeneous medium."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Material and register the Nuclides\n",
-    "inf_medium = openmc.Material(name='moderator')\n",
-    "inf_medium.set_density('g/cc', 5.)\n",
-    "inf_medium.add_nuclide('H1',  0.03)\n",
-    "inf_medium.add_nuclide('O16', 0.015)\n",
-    "inf_medium.add_nuclide('U235', 0.0001)\n",
-    "inf_medium.add_nuclide('U238', 0.007)\n",
-    "inf_medium.add_nuclide('Pu239', 0.00003)\n",
-    "inf_medium.add_nuclide('Zr90', 0.002)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our material, we can now create a `Materials` object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection and export to XML\n",
-    "materials_file = openmc.Materials([inf_medium])\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate boundary Planes\n",
-    "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n",
-    "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n",
-    "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n",
-    "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Cell\n",
-    "cell = openmc.Cell(cell_id=1, name='cell')\n",
-    "\n",
-    "# Register bounding Surfaces with the Cell\n",
-    "cell.region = +min_x & -max_x & +min_y & -max_y\n",
-    "\n",
-    "# Fill the Cell with the Material\n",
-    "cell.fill = inf_medium"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "openmc_geometry = openmc.Geometry([cell])\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "openmc_geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 50\n",
-    "inactive = 10\n",
-    "particles = 5000\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we are ready to generate multi-group cross sections! First, let's define a 100-energy-group structure and 1-energy-group structure using the built-in `EnergyGroups` class. We will also create a 6-delayed-group list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a 100-group EnergyGroups object\n",
-    "energy_groups = mgxs.EnergyGroups()\n",
-    "energy_groups.group_edges = np.logspace(-3, 7.3, 101)\n",
-    "\n",
-    "# Instantiate a 1-group EnergyGroups object\n",
-    "one_group = mgxs.EnergyGroups()\n",
-    "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])\n",
-    "\n",
-    "delayed_groups = list(range(1,7))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now use the `EnergyGroups` object and delayed group list, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n",
-    "\n",
-    "* `TotalXS`\n",
-    "* `TransportXS`\n",
-    "* `AbsorptionXS`\n",
-    "* `CaptureXS`\n",
-    "* `FissionXS`\n",
-    "* `NuFissionMatrixXS`\n",
-    "* `KappaFissionXS`\n",
-    "* `ScatterXS`\n",
-    "* `ScatterMatrixXS`\n",
-    "* `Chi`\n",
-    "* `InverseVelocity`\n",
-    "\n",
-    "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n",
-    "\n",
-    "* `DelayedNuFissionXS`\n",
-    "* `ChiDelayed`\n",
-    "* `Beta`\n",
-    "* `DecayRate`\n",
-    "\n",
-    "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. \n",
-    "\n",
-    "In this case, let's create the multi-group chi-prompt, chi-delayed, and prompt-nu-fission cross sections with our 100-energy-group structure and multi-group delayed-nu-fission and beta cross sections with our 100-energy-group and 6-delayed-group structures. \n",
-    "\n",
-    "The prompt chi and nu-fission data can actually be gathered using the `Chi` and `FissionXS` classes, respectively, by passing in a value of `True` for the optional `prompt` parameter upon initialization."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a few different sections\n",
-    "chi_prompt = mgxs.Chi(domain=cell, groups=energy_groups, by_nuclide=True, prompt=True)\n",
-    "prompt_nu_fission = mgxs.FissionXS(domain=cell, groups=energy_groups, by_nuclide=True, nu=True, prompt=True)\n",
-    "chi_delayed = mgxs.ChiDelayed(domain=cell, energy_groups=energy_groups, by_nuclide=True)\n",
-    "delayed_nu_fission = mgxs.DelayedNuFissionXS(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
-    "beta = mgxs.Beta(domain=cell, energy_groups=energy_groups, delayed_groups=delayed_groups, by_nuclide=True)\n",
-    "decay_rate = mgxs.DecayRate(domain=cell, energy_groups=one_group, delayed_groups=delayed_groups, by_nuclide=True)\n",
-    "\n",
-    "chi_prompt.nuclides = ['U235', 'Pu239']\n",
-    "prompt_nu_fission.nuclides = ['U235', 'Pu239']\n",
-    "chi_delayed.nuclides = ['U235', 'Pu239']\n",
-    "delayed_nu_fission.nuclides = ['U235', 'Pu239']\n",
-    "beta.nuclides = ['U235', 'Pu239']\n",
-    "decay_rate.nuclides = ['U235', 'Pu239']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Decay Rate` object as follows. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('delayed-nu-fission',\n",
-       "              Tally\n",
-       "              \tID             =\t1\n",
-       "              \tName           =\t\n",
-       "              \tFilters        =\tCellFilter, DelayedGroupFilter\n",
-       "              \tNuclides       =\tU235 Pu239\n",
-       "              \tScores         =\t['delayed-nu-fission']\n",
-       "              \tEstimator      =\ttracklength),\n",
-       "             ('decay-rate',\n",
-       "              Tally\n",
-       "              \tID             =\t2\n",
-       "              \tName           =\t\n",
-       "              \tFilters        =\tCellFilter, DelayedGroupFilter\n",
-       "              \tNuclides       =\tU235 Pu239\n",
-       "              \tScores         =\t['decay-rate']\n",
-       "              \tEstimator      =\ttracklength)])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "decay_rate.tallies"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `Beta` object includes tracklength tallies for the 'nu-fission' and 'delayed-nu-fission' scores in the 100-energy-group and 6-delayed-group structure in cell 1. Now that each `MGXS` and `MDGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=7.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=13.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()\n",
-    "\n",
-    "# Add chi-prompt tallies to the tallies file\n",
-    "tallies_file += chi_prompt.tallies.values()\n",
-    "\n",
-    "# Add prompt-nu-fission tallies to the tallies file\n",
-    "tallies_file += prompt_nu_fission.tallies.values()\n",
-    "\n",
-    "# Add chi-delayed tallies to the tallies file\n",
-    "tallies_file += chi_delayed.tallies.values()\n",
-    "\n",
-    "# Add delayed-nu-fission tallies to the tallies file\n",
-    "tallies_file += delayed_nu_fission.tallies.values()\n",
-    "\n",
-    "# Add beta tallies to the tallies file\n",
-    "tallies_file += beta.tallies.values()\n",
-    "\n",
-    "# Add decay rate tallies to the tallies file\n",
-    "tallies_file += decay_rate.tallies.values()\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-28 08:47:21\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n",
-      " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n",
-      " Reading Pu239 from /opt/data/xs/nndc_hdf5/Pu239.h5\n",
-      " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for H1\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.26527\n",
-      "        2/1    1.23398\n",
-      "        3/1    1.25383\n",
-      "        4/1    1.24224\n",
-      "        5/1    1.22254\n",
-      "        6/1    1.20745\n",
-      "        7/1    1.22674\n",
-      "        8/1    1.22724\n",
-      "        9/1    1.23185\n",
-      "       10/1    1.22019\n",
-      "       11/1    1.24342\n",
-      "       12/1    1.23258    1.23800 +/- 0.00542\n",
-      "       13/1    1.20626    1.22742 +/- 0.01103\n",
-      "       14/1    1.19410    1.21909 +/- 0.01141\n",
-      "       15/1    1.24556    1.22439 +/- 0.01030\n",
-      "       16/1    1.27632    1.23304 +/- 0.01207\n",
-      "       17/1    1.25083    1.23558 +/- 0.01051\n",
-      "       18/1    1.23155    1.23508 +/- 0.00912\n",
-      "       19/1    1.27501    1.23952 +/- 0.00918\n",
-      "       20/1    1.24863    1.24043 +/- 0.00827\n",
-      "       21/1    1.18837    1.23569 +/- 0.00885\n",
-      "       22/1    1.21978    1.23437 +/- 0.00819\n",
-      "       23/1    1.22815    1.23389 +/- 0.00754\n",
-      "       24/1    1.24244    1.23450 +/- 0.00701\n",
-      "       25/1    1.21128    1.23295 +/- 0.00671\n",
-      "       26/1    1.22836    1.23267 +/- 0.00628\n",
-      "       27/1    1.21573    1.23167 +/- 0.00598\n",
-      "       28/1    1.19115    1.22942 +/- 0.00607\n",
-      "       29/1    1.24854    1.23042 +/- 0.00583\n",
-      "       30/1    1.24486    1.23115 +/- 0.00558\n",
-      "       31/1    1.23967    1.23155 +/- 0.00532\n",
-      "       32/1    1.24406    1.23212 +/- 0.00511\n",
-      "       33/1    1.24808    1.23282 +/- 0.00493\n",
-      "       34/1    1.23056    1.23272 +/- 0.00472\n",
-      "       35/1    1.24209    1.23310 +/- 0.00454\n",
-      "       36/1    1.23203    1.23305 +/- 0.00437\n",
-      "       37/1    1.21629    1.23243 +/- 0.00425\n",
-      "       38/1    1.22928    1.23232 +/- 0.00409\n",
-      "       39/1    1.23665    1.23247 +/- 0.00395\n",
-      "       40/1    1.24100    1.23276 +/- 0.00383\n",
-      "       41/1    1.26373    1.23375 +/- 0.00384\n",
-      "       42/1    1.25002    1.23426 +/- 0.00375\n",
-      "       43/1    1.24100    1.23447 +/- 0.00364\n",
-      "       44/1    1.25701    1.23513 +/- 0.00359\n",
-      "       45/1    1.23027    1.23499 +/- 0.00349\n",
-      "       46/1    1.25747    1.23562 +/- 0.00345\n",
-      "       47/1    1.24960    1.23599 +/- 0.00338\n",
-      "       48/1    1.23535    1.23598 +/- 0.00329\n",
-      "       49/1    1.21318    1.23539 +/- 0.00325\n",
-      "       50/1    1.27184    1.23630 +/- 0.00330\n",
-      " Creating state point statepoint.50.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.6337e-01 seconds\n",
-      "   Reading cross sections          = 2.5312e-01 seconds\n",
-      " Total time in simulation          = 2.6057e+01 seconds\n",
-      "   Time in transport only          = 2.6028e+01 seconds\n",
-      "   Time in inactive batches        = 9.8414e-01 seconds\n",
-      "   Time in active batches          = 2.5072e+01 seconds\n",
-      "   Time synchronizing fission bank = 1.1907e-02 seconds\n",
-      "     Sampling source sites         = 9.8847e-03 seconds\n",
-      "     SEND/RECV source sites        = 1.9926e-03 seconds\n",
-      "   Time accumulating tallies       = 1.1168e-03 seconds\n",
-      "   Time writing statepoints        = 7.9423e-03 seconds\n",
-      " Total time for finalization       = 6.1998e-03 seconds\n",
-      " Total time elapsed                = 2.6333e+01 seconds\n",
-      " Calculation Rate (inactive)       = 50805.8 particles/second\n",
-      " Calculation Rate (active)         = 7976.89 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.23688 +/- 0.00324\n",
-      " k-effective (Track-length)  = 1.23630 +/- 0.00330\n",
-      " k-effective (Absorption)    = 1.23291 +/- 0.00216\n",
-      " Combined k-effective        = 1.23399 +/- 0.00202\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the tallies from the statepoint into each MGXS object\n",
-    "chi_prompt.load_from_statepoint(sp)\n",
-    "prompt_nu_fission.load_from_statepoint(sp)\n",
-    "chi_delayed.load_from_statepoint(sp)\n",
-    "delayed_nu_fission.load_from_statepoint(sp)\n",
-    "beta.load_from_statepoint(sp)\n",
-    "decay_rate.load_from_statepoint(sp)\n",
-    "# Close statepoint file now that we have the info we need\n",
-    "sp.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Voila! Our multi-group cross sections are now ready to rock 'n roll!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting and Storing MGXS Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's first inspect our delayed-nu-fission section by printing it to the screen after condensing the cross section down to one group."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[5.15913423e-06, 1.16842422e-06]],\n",
-       "\n",
-       "       [[2.66298632e-05, 7.60931827e-06]],\n",
-       "\n",
-       "       [[2.54231809e-05, 5.75848069e-06]],\n",
-       "\n",
-       "       [[5.70008690e-05, 1.05132542e-05]],\n",
-       "\n",
-       "       [[2.33695515e-05, 5.47610066e-06]],\n",
-       "\n",
-       "       [[9.78942387e-06, 1.65741521e-06]]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "delayed_nu_fission.get_condensed_xs(one_group).get_xs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the `openmc.mgxs` module uses [tally arithmetic](tally-arithmetic.ipynb) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](https://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>delayedgroup</th>\n",
-       "      <th>group in</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>9.493755e-08</td>\n",
-       "      <td>9.395351e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>199</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>1.602797e-08</td>\n",
-       "      <td>1.586127e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>398</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>4.900384e-07</td>\n",
-       "      <td>4.849591e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>399</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>1.043816e-07</td>\n",
-       "      <td>1.032959e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>598</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>4.678332e-07</td>\n",
-       "      <td>4.629841e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>599</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>7.899251e-08</td>\n",
-       "      <td>7.817094e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>798</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>1.048921e-06</td>\n",
-       "      <td>1.038048e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>799</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>1.442166e-07</td>\n",
-       "      <td>1.427166e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>4.300427e-07</td>\n",
-       "      <td>4.255852e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>7.511894e-08</td>\n",
-       "      <td>7.433765e-08</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     cell  delayedgroup  group in nuclide          mean     std. dev.\n",
-       "198     1             1         1    U235  9.493755e-08  9.395351e-08\n",
-       "199     1             1         1   Pu239  1.602797e-08  1.586127e-08\n",
-       "398     1             2         1    U235  4.900384e-07  4.849591e-07\n",
-       "399     1             2         1   Pu239  1.043816e-07  1.032959e-07\n",
-       "598     1             3         1    U235  4.678332e-07  4.629841e-07\n",
-       "599     1             3         1   Pu239  7.899251e-08  7.817094e-08\n",
-       "798     1             4         1    U235  1.048921e-06  1.038048e-06\n",
-       "799     1             4         1   Pu239  1.442166e-07  1.427166e-07\n",
-       "998     1             5         1    U235  4.300427e-07  4.255852e-07\n",
-       "999     1             5         1   Pu239  7.511894e-08  7.433765e-08"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = delayed_nu_fission.get_pandas_dataframe()\n",
-    "df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>delayedgroup</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.013336</td>\n",
-       "      <td>0.000056</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>0.013271</td>\n",
-       "      <td>0.000050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.032739</td>\n",
-       "      <td>0.000137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>0.030881</td>\n",
-       "      <td>0.000116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.120780</td>\n",
-       "      <td>0.000504</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>0.113370</td>\n",
-       "      <td>0.000427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.302780</td>\n",
-       "      <td>0.001264</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>0.292500</td>\n",
-       "      <td>0.001101</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.849490</td>\n",
-       "      <td>0.003545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>0.857490</td>\n",
-       "      <td>0.003228</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>2.853000</td>\n",
-       "      <td>0.011907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>2.729700</td>\n",
-       "      <td>0.010276</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    cell  delayedgroup nuclide      mean  std. dev.\n",
-       "0      1             1    U235  0.013336   0.000056\n",
-       "1      1             1   Pu239  0.013271   0.000050\n",
-       "2      1             2    U235  0.032739   0.000137\n",
-       "3      1             2   Pu239  0.030881   0.000116\n",
-       "4      1             3    U235  0.120780   0.000504\n",
-       "5      1             3   Pu239  0.113370   0.000427\n",
-       "6      1             4    U235  0.302780   0.001264\n",
-       "7      1             4   Pu239  0.292500   0.001101\n",
-       "8      1             5    U235  0.849490   0.003545\n",
-       "9      1             5   Pu239  0.857490   0.003228\n",
-       "10     1             6    U235  2.853000   0.011907\n",
-       "11     1             6   Pu239  2.729700   0.010276"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = decay_rate.get_pandas_dataframe()\n",
-    "df.head(12)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta.export_xs_data(filename='beta', format='excel')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following code snippet shows how to export the chi-prompt and chi-delayed `MGXS` to the same HDF5 binary data store."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chi_prompt.build_hdf5_store(filename='mdgxs', append=True)\n",
-    "chi_delayed.build_hdf5_store(filename='mdgxs', append=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta`, `DelayedNuFissionXS`, and `DecayRate` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
-    "\n",
-    "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
-    "\n",
-    "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$\n",
-    "\n",
-    "First, let's investigate the decay rates for U235 and Pu235. The fraction of the delayed neutron precursors remaining as a function of time after fission for each delayed group and fissioning isotope have been plotted below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f361f5a1198>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Get the decay rate data\n",
-    "dr_tally = decay_rate.xs_tally\n",
-    "dr_u235 = dr_tally.get_values(nuclides=['U235']).flatten()\n",
-    "dr_pu239 = dr_tally.get_values(nuclides=['Pu239']).flatten()\n",
-    "\n",
-    "# Compute the exponential decay of the precursors\n",
-    "time = np.logspace(-3,3)\n",
-    "dr_u235_points = np.exp(-np.outer(dr_u235, time))\n",
-    "dr_pu239_points = np.exp(-np.outer(dr_pu239, time))\n",
-    "\n",
-    "# Create a plot of the fraction of the precursors remaining as a f(time)\n",
-    "colors = ['b', 'g', 'r', 'c', 'm', 'k']\n",
-    "legend = []\n",
-    "fig = plt.figure(figsize=(8,6))\n",
-    "for g,c in enumerate(colors):\n",
-    "    plt.semilogx(time, dr_u235_points [g,:], color=c, linestyle='--', linewidth=3)\n",
-    "    plt.semilogx(time, dr_pu239_points[g,:], color=c, linestyle=':' , linewidth=3)\n",
-    "    legend.append('U-235 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_u235[g]))\n",
-    "    legend.append('Pu-239 $t_{1/2}$ = ' + '{0:1.2f} seconds'.format(np.log(2) / dr_pu239[g]))\n",
-    "\n",
-    "plt.title('Delayed Neutron Precursor Decay Rates')\n",
-    "plt.xlabel('Time (s)')\n",
-    "plt.ylabel('Fraction Remaining')\n",
-    "plt.legend(legend, loc=1, bbox_to_anchor=(1.55, 0.95))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's compute the initial concentration of the delayed neutron precursors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>delayedgroup</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>8.808003e-08</td>\n",
-       "      <td>4.352878e-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>7.175417e-09</td>\n",
-       "      <td>3.446159e-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>9.559207e-07</td>\n",
-       "      <td>4.724119e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>1.307826e-07</td>\n",
-       "      <td>6.281133e-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.361643e-07</td>\n",
-       "      <td>1.167114e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.040175e-08</td>\n",
-       "      <td>9.798408e-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>4.735711e-07</td>\n",
-       "      <td>2.340368e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.635713e-08</td>\n",
-       "      <td>1.265862e-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.837213e-08</td>\n",
-       "      <td>1.402138e-10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>2.439290e-09</td>\n",
-       "      <td>1.171525e-11</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>1.482388e-09</td>\n",
-       "      <td>7.325898e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Pu239</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>7.019358e-11</td>\n",
-       "      <td>3.371208e-13</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    cell  delayedgroup nuclide  \\\n",
-       "0      1             1    U235   \n",
-       "1      1             1   Pu239   \n",
-       "2      1             2    U235   \n",
-       "3      1             2   Pu239   \n",
-       "4      1             3    U235   \n",
-       "5      1             3   Pu239   \n",
-       "6      1             4    U235   \n",
-       "7      1             4   Pu239   \n",
-       "8      1             5    U235   \n",
-       "9      1             5   Pu239   \n",
-       "10     1             6    U235   \n",
-       "11     1             6   Pu239   \n",
-       "\n",
-       "                                                score     mean  std. dev.  \n",
-       "0   (((delayed-nu-fission / nu-fission) * (delayed... 8.81e-08   4.35e-10  \n",
-       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.18e-09   3.45e-11  \n",
-       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.56e-07   4.72e-09  \n",
-       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.31e-07   6.28e-10  \n",
-       "4   (((delayed-nu-fission / nu-fission) * (delayed... 2.36e-07   1.17e-09  \n",
-       "5   (((delayed-nu-fission / nu-fission) * (delayed... 2.04e-08   9.80e-11  \n",
-       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.74e-07   2.34e-09  \n",
-       "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.64e-08   1.27e-10  \n",
-       "8   (((delayed-nu-fission / nu-fission) * (delayed... 2.84e-08   1.40e-10  \n",
-       "9   (((delayed-nu-fission / nu-fission) * (delayed... 2.44e-09   1.17e-11  \n",
-       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   7.33e-12  \n",
-       "11  (((delayed-nu-fission / nu-fission) * (delayed... 7.02e-11   3.37e-13  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use tally arithmetic to compute the precursor concentrations\n",
-    "precursor_conc = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n",
-    "    delayed_nu_fission.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / \\\n",
-    "    decay_rate.xs_tally.summation()\n",
-    "\n",
-    "# Get the Pandas DataFrames for inspection\n",
-    "precursor_conc.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can plot the delayed neutron fractions for each nuclide."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Beta (U-235) : 0.006504 +/- 0.000006\n",
-      "Beta (Pu-239): 0.002245 +/- 0.000002\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 7.0)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "energy_filter = [f for f in beta.xs_tally.filters if type(f) is openmc.EnergyFilter]\n",
-    "beta_integrated = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True)\n",
-    "beta_u235  = beta_integrated.get_values(nuclides=['U235'])\n",
-    "beta_pu239 = beta_integrated.get_values(nuclides=['Pu239'])\n",
-    "\n",
-    "# Reshape the betas\n",
-    "beta_u235.shape = (beta_u235.shape[0])\n",
-    "beta_pu239.shape = (beta_pu239.shape[0])\n",
-    "\n",
-    "df = beta_integrated.summation(filter_type=openmc.DelayedGroupFilter, remove_filter=True).get_pandas_dataframe()\n",
-    "print('Beta (U-235) : {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'U235']['mean'][0], df[df['nuclide'] == 'U235']['std. dev.'][0]))\n",
-    "print('Beta (Pu-239): {:.6f} +/- {:.6f}'.format(df[df['nuclide'] == 'Pu239']['mean'][1], df[df['nuclide'] == 'Pu239']['std. dev.'][1]))\n",
-    "\n",
-    "beta_u235 = np.append(beta_u235[0], beta_u235)\n",
-    "beta_pu239 = np.append(beta_pu239[0], beta_pu239)\n",
-    "\n",
-    "# Create a step plot for the MGXS\n",
-    "plt.plot(np.arange(0.5, 7.5, 1), beta_u235, drawstyle='steps', color='b', linewidth=3)\n",
-    "plt.plot(np.arange(0.5, 7.5, 1), beta_pu239, drawstyle='steps', color='g', linewidth=3)\n",
-    "\n",
-    "plt.title('Delayed Neutron Fraction (beta)')\n",
-    "plt.xlabel('Delayed Group')\n",
-    "plt.ylabel('Beta(fraction total neutrons)')\n",
-    "plt.legend(['U-235', 'Pu-239'])\n",
-    "plt.xlim([0,7])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also plot the energy spectrum for fission emission of prompt and delayed neutrons."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1000.0, 20000000.0)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "chi_d_u235  = np.squeeze(chi_delayed.get_xs(nuclides=['U235'], order_groups='decreasing'))\n",
-    "chi_d_pu239 = np.squeeze(chi_delayed.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n",
-    "chi_p_u235  = np.squeeze(chi_prompt.get_xs(nuclides=['U235'], order_groups='decreasing'))\n",
-    "chi_p_pu239 = np.squeeze(chi_prompt.get_xs(nuclides=['Pu239'], order_groups='decreasing'))\n",
-    "\n",
-    "chi_d_u235  = np.append(chi_d_u235 , chi_d_u235[0])\n",
-    "chi_d_pu239 = np.append(chi_d_pu239, chi_d_pu239[0])\n",
-    "chi_p_u235  = np.append(chi_p_u235 , chi_p_u235[0])\n",
-    "chi_p_pu239 = np.append(chi_p_pu239, chi_p_pu239[0])\n",
-    "\n",
-    "# Create a step plot for the MGXS\n",
-    "plt.semilogx(energy_groups.group_edges, chi_d_u235 , drawstyle='steps', color='b', linestyle='--', linewidth=3)\n",
-    "plt.semilogx(energy_groups.group_edges, chi_d_pu239, drawstyle='steps', color='g', linestyle='--', linewidth=3)\n",
-    "plt.semilogx(energy_groups.group_edges, chi_p_u235 , drawstyle='steps', color='b', linestyle=':', linewidth=3)\n",
-    "plt.semilogx(energy_groups.group_edges, chi_p_pu239, drawstyle='steps', color='g', linestyle=':', linewidth=3)\n",
-    "\n",
-    "plt.title('Energy Spectrum for Fission Neutrons')\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Fraction on emitted neutrons')\n",
-    "plt.legend(['U-235 delayed', 'Pu-239 delayed', 'U-235 prompt', 'Pu-239 prompt'],loc=2)\n",
-    "plt.xlim(1.0e3, 20.0e6)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/mdgxs-part-ii.ipynb b/examples/jupyter/mdgxs-part-ii.ipynb
deleted file mode 100644
index a9102dfb8..000000000
--- a/examples/jupyter/mdgxs-part-ii.ipynb
+++ /dev/null
@@ -1,1186 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup (Delayed) Cross Section Generation Part II: Advanced Features\n",
-    "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n",
-    "\n",
-    "* Calculation of multi-energy-group and multi-delayed-group cross sections for a **fuel assembly**\n",
-    "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n",
-    "* Steady-state pin-by-pin **delayed neutron fractions (beta)** for each delayed group.\n",
-    "* Generation of surface currents on the interfaces and surfaces of a Mesh."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import math\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "import openmc\n",
-    "import openmc.mgxs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem: fuel, water, and cladding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6 enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_nuclide('U235', 3.7503e-4)\n",
-    "fuel.add_nuclide('U238', 2.2625e-2)\n",
-    "fuel.add_nuclide('O16', 4.6007e-2)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_nuclide('H1', 4.9457e-2)\n",
-    "water.add_nuclide('O16', 2.4732e-2)\n",
-    "water.add_nuclide('B10', 8.0042e-6)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we can now create a `Materials` object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a materials collection and export to XML\n",
-    "materials = openmc.Materials((fuel, water, zircaloy))\n",
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(r=0.45720)\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n",
-    "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n",
-    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n",
-    "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
-    "\n",
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "fuel_cell.fill = fuel\n",
-    "fuel_cell.region = -fuel_outer_radius\n",
-    "fuel_pin_universe.add_cell(fuel_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "fuel_pin_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "fuel_pin_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Likewise, we can construct a control rod guide tube with the same surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a control rod guide tube\n",
-    "guide_tube_universe = openmc.Universe(name='Guide Tube')\n",
-    "\n",
-    "# Create guide tube Cell\n",
-    "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n",
-    "guide_tube_cell.fill = water\n",
-    "guide_tube_cell.region = -fuel_outer_radius\n",
-    "guide_tube_universe.add_cell(guide_tube_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='Guide Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "guide_tube_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "guide_tube_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create fuel assembly Lattice\n",
-    "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n",
-    "assembly.pitch = (1.26, 1.26)\n",
-    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create array indices for guide tube locations in lattice\n",
-    "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n",
-    "                       11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n",
-    "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n",
-    "                       8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n",
-    "\n",
-    "# Create universes array with the fuel pin and guide tube universes\n",
-    "universes = np.tile(fuel_pin_universe, (17,17))\n",
-    "universes[template_x, template_y] = guide_tube_universe\n",
-    "\n",
-    "# Store the array of universes in the lattice\n",
-    "assembly.universes = universes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell', fill=assembly)\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry that is assigned a root universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and export to XML\n",
-    "geometry = openmc.Geometry(root_universe)\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 50\n",
-    "inactive = 10\n",
-    "particles = 2500\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings = openmc.Settings()\n",
-    "settings.batches = batches\n",
-    "settings.inactive = inactive\n",
-    "settings.particles = particles\n",
-    "settings.output = {'tallies': False}\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us also create a plot to verify that our fuel assembly geometry was created successfully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot our geometry\n",
-    "plot = openmc.Plot.from_geometry(geometry)\n",
-    "plot.pixels = (250, 250)\n",
-    "plot.color_by = 'material'\n",
-    "openmc.plot_inline(plot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an MGXS Library"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we are ready to generate multi-group cross sections! First, let's define a 20-energy-group and 1-energy-group."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a 20-group EnergyGroups object\n",
-    "energy_groups = openmc.mgxs.EnergyGroups()\n",
-    "energy_groups.group_edges = np.logspace(-3, 7.3, 21)\n",
-    "\n",
-    "# Instantiate a 1-group EnergyGroups object\n",
-    "one_group = openmc.mgxs.EnergyGroups()\n",
-    "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will instantiate an `openmc.mgxs.Library` for the energy and delayed groups with our the fuel assembly geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=5.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=17.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=23.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate a tally mesh                                                                                                                                                                  \n",
-    "mesh = openmc.RegularMesh(mesh_id=1)\n",
-    "mesh.dimension = [17, 17, 1]\n",
-    "mesh.lower_left = [-10.71, -10.71, -10000.]\n",
-    "mesh.width = [1.26, 1.26, 20000.]\n",
-    "\n",
-    "# Initialize an 20-energy-group and 6-delayed-group MGXS Library\n",
-    "mgxs_lib = openmc.mgxs.Library(geometry)\n",
-    "mgxs_lib.energy_groups  = energy_groups\n",
-    "mgxs_lib.num_delayed_groups = 6\n",
-    "\n",
-    "# Specify multi-group cross section types to compute\n",
-    "mgxs_lib.mgxs_types = ['total', 'transport', 'nu-scatter matrix', 'kappa-fission', 'inverse-velocity', 'chi-prompt',\n",
-    "                      'prompt-nu-fission', 'chi-delayed', 'delayed-nu-fission', 'beta']\n",
-    "\n",
-    "# Specify a \"mesh\" domain type for the cross section tally filters\n",
-    "mgxs_lib.domain_type = 'mesh'\n",
-    "\n",
-    "# Specify the mesh domain over which to compute multi-group cross sections\n",
-    "mgxs_lib.domains = [mesh]\n",
-    "\n",
-    "# Construct all tallies needed for the multi-group cross section library\n",
-    "mgxs_lib.build_library()\n",
-    "\n",
-    "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n",
-    "\n",
-    "# Instantiate a current tally\n",
-    "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n",
-    "current_tally = openmc.Tally(name='current tally')\n",
-    "current_tally.scores = ['current']\n",
-    "current_tally.filters = [mesh_filter]\n",
-    "\n",
-    "# Add current tally to the tallies file\n",
-    "tallies_file.append(current_tally)\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can run OpenMC to generate the cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 13:44:53\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.03409\n",
-      "        2/1    1.02768\n",
-      "        3/1    1.01526\n",
-      "        4/1    1.03910\n",
-      "        5/1    1.00144\n",
-      "        6/1    1.00485\n",
-      "        7/1    1.03532\n",
-      "        8/1    1.01159\n",
-      "        9/1    0.97707\n",
-      "       10/1    1.06530\n",
-      "       11/1    1.08615\n",
-      "       12/1    1.00438    1.04527 +/- 0.04089\n",
-      "       13/1    1.03145    1.04066 +/- 0.02405\n",
-      "       14/1    1.04785    1.04246 +/- 0.01710\n",
-      "       15/1    1.04634    1.04323 +/- 0.01327\n",
-      "       16/1    1.02264    1.03980 +/- 0.01137\n",
-      "       17/1    1.00157    1.03434 +/- 0.01105\n",
-      "       18/1    1.09443    1.04185 +/- 0.01216\n",
-      "       19/1    1.02810    1.04032 +/- 0.01084\n",
-      "       20/1    1.01800    1.03809 +/- 0.00995\n",
-      "       21/1    1.02815    1.03719 +/- 0.00904\n",
-      "       22/1    1.00064    1.03414 +/- 0.00880\n",
-      "       23/1    0.99013    1.03076 +/- 0.00877\n",
-      "       24/1    1.01182    1.02940 +/- 0.00823\n",
-      "       25/1    1.07537    1.03247 +/- 0.00826\n",
-      "       26/1    1.02924    1.03227 +/- 0.00772\n",
-      "       27/1    1.01499    1.03125 +/- 0.00733\n",
-      "       28/1    1.01749    1.03049 +/- 0.00695\n",
-      "       29/1    1.04072    1.03102 +/- 0.00660\n",
-      "       30/1    0.99990    1.02947 +/- 0.00645\n",
-      "       31/1    1.03239    1.02961 +/- 0.00613\n",
-      "       32/1    1.02613    1.02945 +/- 0.00585\n",
-      "       33/1    1.04340    1.03006 +/- 0.00562\n",
-      "       34/1    1.05081    1.03092 +/- 0.00545\n",
-      "       35/1    1.02511    1.03069 +/- 0.00524\n",
-      "       36/1    0.99923    1.02948 +/- 0.00517\n",
-      "       37/1    0.97758    1.02756 +/- 0.00534\n",
-      "       38/1    0.99628    1.02644 +/- 0.00526\n",
-      "       39/1    1.06004    1.02760 +/- 0.00521\n",
-      "       40/1    1.08287    1.02944 +/- 0.00536\n",
-      "       41/1    1.02731    1.02937 +/- 0.00518\n",
-      "       42/1    1.02634    1.02928 +/- 0.00502\n",
-      "       43/1    1.04269    1.02968 +/- 0.00488\n",
-      "       44/1    1.05044    1.03029 +/- 0.00478\n",
-      "       45/1    1.05183    1.03091 +/- 0.00468\n",
-      "       46/1    1.02770    1.03082 +/- 0.00455\n",
-      "       47/1    1.07099    1.03191 +/- 0.00455\n",
-      "       48/1    1.04467    1.03224 +/- 0.00444\n",
-      "       49/1    1.02996    1.03218 +/- 0.00433\n",
-      "       50/1    1.03418    1.03223 +/- 0.00422\n",
-      " Creating state point statepoint.50.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.5756e-01 seconds\n",
-      "   Reading cross sections          = 2.4233e-01 seconds\n",
-      " Total time in simulation          = 1.2099e+01 seconds\n",
-      "   Time in transport only          = 1.2046e+01 seconds\n",
-      "   Time in inactive batches        = 8.7582e-01 seconds\n",
-      "   Time in active batches          = 1.1223e+01 seconds\n",
-      "   Time synchronizing fission bank = 4.8051e-03 seconds\n",
-      "     Sampling source sites         = 4.1028e-03 seconds\n",
-      "     SEND/RECV source sites        = 6.7903e-04 seconds\n",
-      "   Time accumulating tallies       = 3.0247e-02 seconds\n",
-      "   Time writing statepoints        = 1.4253e-02 seconds\n",
-      " Total time for finalization       = 5.3700e-07 seconds\n",
-      " Total time elapsed                = 1.2369e+01 seconds\n",
-      " Calculation Rate (inactive)       = 28544.8 particles/second\n",
-      " Calculation Rate (active)         = 8910.20 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.02658 +/- 0.00374\n",
-      " k-effective (Track-length)  = 1.03223 +/- 0.00422\n",
-      " k-effective (Absorption)    = 1.02640 +/- 0.00361\n",
-      " Combined k-effective        = 1.02849 +/- 0.00321\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize MGXS Library with OpenMC statepoint data\n",
-    "mgxs_lib.load_from_statepoint(sp)\n",
-    "\n",
-    "# Extrack the current tally separately\n",
-    "current_tally = sp.get_tally(name='current tally')\n",
-    "\n",
-    "# Close statepoint file now that we have the info we need\n",
-    "sp.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
-    "\n",
-    "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
-    "\n",
-    "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
-       "      <th>delayedgroup</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>z</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000079</td>\n",
-       "      <td>1.583613e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.001001</td>\n",
-       "      <td>1.983614e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000636</td>\n",
-       "      <td>1.248704e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000503</td>\n",
-       "      <td>9.721681e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000018</td>\n",
-       "      <td>3.397815e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>2.710107e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000092</td>\n",
-       "      <td>2.407283e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.001165</td>\n",
-       "      <td>3.017676e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000739</td>\n",
-       "      <td>1.899840e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
-       "      <td>0.000582</td>\n",
-       "      <td>1.478539e-04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  mesh 1       delayedgroup nuclide  \\\n",
-       "       x  y  z                        \n",
-       "0      1  1  1            1   total   \n",
-       "1      1  1  1            2   total   \n",
-       "2      1  1  1            3   total   \n",
-       "3      1  1  1            4   total   \n",
-       "4      1  1  1            5   total   \n",
-       "5      1  1  1            6   total   \n",
-       "6      2  1  1            1   total   \n",
-       "7      2  1  1            2   total   \n",
-       "8      2  1  1            3   total   \n",
-       "9      2  1  1            4   total   \n",
-       "\n",
-       "                                               score      mean     std. dev.  \n",
-       "                                                                              \n",
-       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.000079  1.583613e-05  \n",
-       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.001001  1.983614e-04  \n",
-       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.000636  1.248704e-04  \n",
-       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.000503  9.721681e-05  \n",
-       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.000018  3.397815e-06  \n",
-       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.000001  2.710107e-07  \n",
-       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.000092  2.407283e-05  \n",
-       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.001165  3.017676e-04  \n",
-       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.000739  1.899840e-04  \n",
-       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.000582  1.478539e-04  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Set the time constants for the delayed precursors (in seconds^-1)\n",
-    "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n",
-    "precursor_lambda = math.log(2.0) / precursor_halflife\n",
-    "\n",
-    "beta = mgxs_lib.get_mgxs(mesh, 'beta')\n",
-    "\n",
-    "# Create a tally object with only the delayed group filter for the time constants\n",
-    "beta_filters = [f for f in beta.xs_tally.filters if type(f) is not openmc.DelayedGroupFilter]\n",
-    "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n",
-    "for f in beta_filters:\n",
-    "    lambda_tally = lambda_tally.summation(filter_type=type(f), remove_filter=True) * 0. + 1.\n",
-    "\n",
-    "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n",
-    "lambda_tally._mean = precursor_lambda\n",
-    "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n",
-    "\n",
-    "# Set a total nuclide and lambda score\n",
-    "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n",
-    "lambda_tally.scores = ['lambda']\n",
-    "\n",
-    "delayed_nu_fission = mgxs_lib.get_mgxs(mesh, 'delayed-nu-fission')\n",
-    "\n",
-    "# Use tally arithmetic to compute the precursor concentrations\n",
-    "precursor_conc = beta.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n",
-    "    delayed_nu_fission.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / lambda_tally\n",
-    "    \n",
-    "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
-    "precursor_conc.get_pandas_dataframe().head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another useful feature of the Python API is the ability to extract the surface currents for the interfaces and surfaces of a mesh. We can inspect the currents for the mesh by getting the pandas dataframe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
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-       "      <td>y-min in</td>\n",
-       "      <td>total</td>\n",
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-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>y-max out</td>\n",
-       "      <td>total</td>\n",
-       "      <td>current</td>\n",
-       "      <td>0.02981</td>\n",
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-       "      <th>7</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>z-min in</td>\n",
-       "      <td>total</td>\n",
-       "      <td>current</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  mesh 1                  nuclide    score     mean std. dev.\n",
-       "       x  y  z       surf                                    \n",
-       "0      1  1  1  x-min out   total  current  0.00000  0.000000\n",
-       "1      1  1  1   x-min in   total  current  0.00000  0.000000\n",
-       "2      1  1  1  x-max out   total  current  0.03154  0.000643\n",
-       "3      1  1  1   x-max in   total  current  0.03030  0.000662\n",
-       "4      1  1  1  y-min out   total  current  0.00000  0.000000\n",
-       "5      1  1  1   y-min in   total  current  0.00000  0.000000\n",
-       "6      1  1  1  y-max out   total  current  0.02981  0.000595\n",
-       "7      1  1  1   y-max in   total  current  0.03083  0.000732\n",
-       "8      1  1  1  z-min out   total  current  0.00000  0.000000\n",
-       "9      1  1  1   z-min in   total  current  0.00000  0.000000"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "current_tally.get_pandas_dataframe().head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cross Section Visualizations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to inspecting the data in the tallies by getting the pandas dataframe, we can also plot the tally data on the domain mesh. Below is the delayed neutron fraction tallied in each mesh cell for each delayed group."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Beta - delayed group 6')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1296x648 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Extract the energy-condensed delayed neutron fraction tally\n",
-    "beta_by_group = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n",
-    "beta_by_group.mean.shape = (17, 17, 6)\n",
-    "beta_by_group.mean[beta_by_group.mean == 0] = np.nan\n",
-    "\n",
-    "# Plot the betas\n",
-    "plt.figure(figsize=(18,9))\n",
-    "fig = plt.subplot(231)\n",
-    "plt.imshow(beta_by_group.mean[:,:,0], interpolation='none', cmap='jet')\n",
-    "plt.colorbar()\n",
-    "plt.title('Beta - delayed group 1')\n",
-    "\n",
-    "fig = plt.subplot(232)\n",
-    "plt.imshow(beta_by_group.mean[:,:,1], interpolation='none', cmap='jet')\n",
-    "plt.colorbar()\n",
-    "plt.title('Beta - delayed group 2')\n",
-    "\n",
-    "fig = plt.subplot(233)\n",
-    "plt.imshow(beta_by_group.mean[:,:,2], interpolation='none', cmap='jet')\n",
-    "plt.colorbar()\n",
-    "plt.title('Beta - delayed group 3')\n",
-    "\n",
-    "fig = plt.subplot(234)\n",
-    "plt.imshow(beta_by_group.mean[:,:,3], interpolation='none', cmap='jet')\n",
-    "plt.colorbar()\n",
-    "plt.title('Beta - delayed group 4')\n",
-    "\n",
-    "fig = plt.subplot(235)\n",
-    "plt.imshow(beta_by_group.mean[:,:,4], interpolation='none', cmap='jet')\n",
-    "plt.colorbar()\n",
-    "plt.title('Beta - delayed group 5')\n",
-    "\n",
-    "fig = plt.subplot(236)\n",
-    "plt.imshow(beta_by_group.mean[:,:,5], interpolation='none', cmap='jet')\n",
-    "plt.colorbar()\n",
-    "plt.title('Beta - delayed group 6')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/mg-mode-part-i.ipynb b/examples/jupyter/mg-mode-part-i.ipynb
deleted file mode 100644
index 97a73d1e0..000000000
--- a/examples/jupyter/mg-mode-part-i.ipynb
+++ /dev/null
@@ -1,696 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup Mode Part I: Introduction\n",
-    "This Notebook illustrates the usage of OpenMC's multi-group calculational mode with the Python API. This example notebook creates and executes the 2-D [C5G7](https://www.oecd-nea.org/jcms/pl_17882) benchmark model using the `openmc.MGXSLibrary` class to create the supporting data library on the fly."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate MGXS Library"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.colors as colors\n",
-    "import numpy as np\n",
-    "\n",
-    "import openmc\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now create the multi-group library using data directly from Appendix A of the [C5G7](https://www.oecd-nea.org/jcms/pl_17882) benchmark documentation. All of the data below will be created at 294K, consistent with the benchmark.\n",
-    "\n",
-    "This notebook will first begin by setting the group structure and building the groupwise data for UO2.  As you can see, the cross sections are input in the order of increasing groups (or decreasing energy).\n",
-    "\n",
-    "*Note*: The C5G7 benchmark uses transport-corrected cross sections. So the total cross section we input here will technically be the transport cross section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a 7-group structure with arbitrary boundaries (the specific boundaries are unimportant)\n",
-    "groups = openmc.mgxs.EnergyGroups(np.logspace(-5, 7, 8))\n",
-    "\n",
-    "uo2_xsdata = openmc.XSdata('uo2', groups)\n",
-    "uo2_xsdata.order = 0\n",
-    "\n",
-    "# When setting the data let the object know you are setting the data for a temperature of 294K.\n",
-    "uo2_xsdata.set_total([1.77949E-1, 3.29805E-1, 4.80388E-1, 5.54367E-1,\n",
-    "                      3.11801E-1, 3.95168E-1, 5.64406E-1], temperature=294.)\n",
-    "\n",
-    "uo2_xsdata.set_absorption([8.0248E-03, 3.7174E-3, 2.6769E-2, 9.6236E-2,\n",
-    "                           3.0020E-02, 1.1126E-1, 2.8278E-1], temperature=294.)\n",
-    "uo2_xsdata.set_fission([7.21206E-3, 8.19301E-4, 6.45320E-3, 1.85648E-2,\n",
-    "                        1.78084E-2, 8.30348E-2, 2.16004E-1], temperature=294.)\n",
-    "\n",
-    "uo2_xsdata.set_nu_fission([2.005998E-2, 2.027303E-3, 1.570599E-2, 4.518301E-2,\n",
-    "                           4.334208E-2, 2.020901E-1, 5.257105E-1], temperature=294.)\n",
-    "\n",
-    "uo2_xsdata.set_chi([5.87910E-1, 4.11760E-1, 3.39060E-4, 1.17610E-7,\n",
-    "                    0.00000E-0, 0.00000E-0, 0.00000E-0], temperature=294.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now add the scattering matrix data. \n",
-    "\n",
-    "*Note*: Most users familiar with deterministic transport libraries are already familiar with the idea of entering one scattering matrix for every order (i.e. scattering order as the outer dimension). However, the shape of OpenMC's scattering matrix entry is instead [Incoming groups, Outgoing Groups, Scattering Order] to best enable other scattering representations. We will follow the more familiar approach in this notebook, and then use numpy's `numpy.rollaxis` function to change the ordering to what we need (scattering order on the inner dimension)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The scattering matrix is ordered with incoming groups as rows and outgoing groups as columns\n",
-    "# (i.e., below the diagonal is up-scattering).\n",
-    "scatter_matrix = \\\n",
-    "    [[[1.27537E-1, 4.23780E-2, 9.43740E-6, 5.51630E-9, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n",
-    "      [0.00000E-0, 3.24456E-1, 1.63140E-3, 3.14270E-9, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n",
-    "      [0.00000E-0, 0.00000E-0, 4.50940E-1, 2.67920E-3, 0.00000E-0, 0.00000E-0, 0.00000E-0],\n",
-    "      [0.00000E-0, 0.00000E-0, 0.00000E-0, 4.52565E-1, 5.56640E-3, 0.00000E-0, 0.00000E-0],\n",
-    "      [0.00000E-0, 0.00000E-0, 0.00000E-0, 1.25250E-4, 2.71401E-1, 1.02550E-2, 1.00210E-8],\n",
-    "      [0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 1.29680E-3, 2.65802E-1, 1.68090E-2],\n",
-    "      [0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 0.00000E-0, 8.54580E-3, 2.73080E-1]]]\n",
-    "scatter_matrix = np.array(scatter_matrix)\n",
-    "scatter_matrix = np.rollaxis(scatter_matrix, 0, 3)\n",
-    "uo2_xsdata.set_scatter_matrix(scatter_matrix, temperature=294.)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that the UO2 data has been created, we can move on to the remaining materials using the same process.\n",
-    "\n",
-    "However, we will actually skip repeating the above for now.  Our simulation will instead use the `c5g7.h5` file that has already been created using exactly the same logic as above, but for the remaining materials in the benchmark problem.\n",
-    "\n",
-    "For now we will show how you would use the `uo2_xsdata` information to create an `openmc.MGXSLibrary` object and write to disk."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize the library\n",
-    "mg_cross_sections_file = openmc.MGXSLibrary(groups)\n",
-    "\n",
-    "# Add the UO2 data to it\n",
-    "mg_cross_sections_file.add_xsdata(uo2_xsdata)\n",
-    "\n",
-    "# And write to disk\n",
-    "mg_cross_sections_file.export_to_hdf5('mgxs.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate 2-D C5G7 Problem Input Files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To build the actual 2-D model, we will first begin by creating the `materials.xml` file.\n",
-    "\n",
-    "First we need to define materials that will be used in the problem. In other notebooks, either nuclides or elements were added to materials at the equivalent stage. We can do that in multi-group mode as well.  However, multi-group cross-sections are sometimes provided as macroscopic cross-sections; the C5G7 benchmark data are macroscopic. In this case, we can instead use the `Material.add_macroscopic` method to specify a macroscopic object. Unlike for nuclides and elements, we do not need provide information on atom/weight percents as no number densities are needed.\n",
-    "\n",
-    "When assigning macroscopic objects to a material, the density can still be scaled by setting the density to a value that is not 1.0. This would be useful, for example, when slightly perturbing the density of water due to a small change in temperature (while of course ignoring any resultant spectral shift). The density of a macroscopic dataset is set to 1.0 in the `openmc.Material` object by default when a macroscopic dataset is used; so we will show its use the first time and then afterwards it will not be required.\n",
-    "\n",
-    "Aside from these differences, the following code is very similar to similar code in other OpenMC example Notebooks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# For every cross section data set in the library, assign an openmc.Macroscopic object to a material\n",
-    "materials = {}\n",
-    "for xs in ['uo2', 'mox43', 'mox7', 'mox87', 'fiss_chamber', 'guide_tube', 'water']:\n",
-    "    materials[xs] = openmc.Material(name=xs)\n",
-    "    materials[xs].set_density('macro', 1.)\n",
-    "    materials[xs].add_macroscopic(xs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can go ahead and produce a `materials.xml` file for use by OpenMC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection, register all Materials, and export to XML\n",
-    "materials_file = openmc.Materials(materials.values())\n",
-    "\n",
-    "# Set the location of the cross sections file to our pre-written set\n",
-    "materials_file.cross_sections = 'c5g7.h5'\n",
-    "\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our next step will be to create the geometry information needed for our assembly and to write that to the `geometry.xml` file.\n",
-    "\n",
-    "We will begin by defining the surfaces, cells, and universes needed for each of the individual fuel pins, guide tubes, and fission chambers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create the surface used for each pin\n",
-    "pin_surf = openmc.ZCylinder(x0=0, y0=0, r=0.54, name='pin_surf')\n",
-    "\n",
-    "# Create the cells which will be used to represent each pin type.\n",
-    "cells = {}\n",
-    "universes = {}\n",
-    "for material in materials.values():\n",
-    "    # Create the cell for the material inside the cladding\n",
-    "    cells[material.name] = openmc.Cell(name=material.name)\n",
-    "    # Assign the half-spaces to the cell\n",
-    "    cells[material.name].region = -pin_surf\n",
-    "    # Register the material with this cell\n",
-    "    cells[material.name].fill = material\n",
-    "    \n",
-    "    # Repeat the above for the material outside the cladding (i.e., the moderator)\n",
-    "    cell_name = material.name + '_moderator'\n",
-    "    cells[cell_name] = openmc.Cell(name=cell_name)\n",
-    "    cells[cell_name].region = +pin_surf\n",
-    "    cells[cell_name].fill = materials['water']\n",
-    "    \n",
-    "    # Finally add the two cells we just made to a Universe object\n",
-    "    universes[material.name] = openmc.Universe(name=material.name)\n",
-    "    universes[material.name].add_cells([cells[material.name], cells[cell_name]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "The next step is to take our universes (representing the different pin types) and lay them out in a lattice to represent the assembly types"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lattices = {}\n",
-    "\n",
-    "# Instantiate the UO2 Lattice\n",
-    "lattices['UO2 Assembly'] = openmc.RectLattice(name='UO2 Assembly')\n",
-    "lattices['UO2 Assembly'].dimension = [17, 17]\n",
-    "lattices['UO2 Assembly'].lower_left = [-10.71, -10.71]\n",
-    "lattices['UO2 Assembly'].pitch = [1.26, 1.26]\n",
-    "u = universes['uo2']\n",
-    "g = universes['guide_tube']\n",
-    "f = universes['fiss_chamber']\n",
-    "lattices['UO2 Assembly'].universes = \\\n",
-    "    [[u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, u, u, u, g, u, u, g, u, u, g, u, u, u, u, u],\n",
-    "     [u, u, u, g, u, u, u, u, u, u, u, u, u, g, u, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, g, u, u, g, u, u, g, u, u, g, u, u, g, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, g, u, u, g, u, u, f, u, u, g, u, u, g, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, g, u, u, g, u, u, g, u, u, g, u, u, g, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, u, g, u, u, u, u, u, u, u, u, u, g, u, u, u],\n",
-    "     [u, u, u, u, u, g, u, u, g, u, u, g, u, u, u, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u],\n",
-    "     [u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u, u]]\n",
-    "    \n",
-    "# Create a containing cell and universe\n",
-    "cells['UO2 Assembly'] = openmc.Cell(name='UO2 Assembly')\n",
-    "cells['UO2 Assembly'].fill = lattices['UO2 Assembly']\n",
-    "universes['UO2 Assembly'] = openmc.Universe(name='UO2 Assembly')\n",
-    "universes['UO2 Assembly'].add_cell(cells['UO2 Assembly'])\n",
-    "\n",
-    "# Instantiate the MOX Lattice\n",
-    "lattices['MOX Assembly'] = openmc.RectLattice(name='MOX Assembly')\n",
-    "lattices['MOX Assembly'].dimension = [17, 17]\n",
-    "lattices['MOX Assembly'].lower_left = [-10.71, -10.71]\n",
-    "lattices['MOX Assembly'].pitch = [1.26, 1.26]\n",
-    "m = universes['mox43']\n",
-    "n = universes['mox7']\n",
-    "o = universes['mox87']\n",
-    "g = universes['guide_tube']\n",
-    "f = universes['fiss_chamber']\n",
-    "lattices['MOX Assembly'].universes = \\\n",
-    "    [[m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m],\n",
-    "     [m, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, m],\n",
-    "     [m, n, n, n, n, g, n, n, g, n, n, g, n, n, n, n, m],\n",
-    "     [m, n, n, g, n, o, o, o, o, o, o, o, n, g, n, n, m],\n",
-    "     [m, n, n, n, o, o, o, o, o, o, o, o, o, n, n, n, m],\n",
-    "     [m, n, g, o, o, g, o, o, g, o, o, g, o, o, g, n, m],\n",
-    "     [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n",
-    "     [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n",
-    "     [m, n, g, o, o, g, o, o, f, o, o, g, o, o, g, n, m],\n",
-    "     [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n",
-    "     [m, n, n, o, o, o, o, o, o, o, o, o, o, o, n, n, m],\n",
-    "     [m, n, g, o, o, g, o, o, g, o, o, g, o, o, g, n, m],\n",
-    "     [m, n, n, n, o, o, o, o, o, o, o, o, o, n, n, n, m],\n",
-    "     [m, n, n, g, n, o, o, o, o, o, o, o, n, g, n, n, m],\n",
-    "     [m, n, n, n, n, g, n, n, g, n, n, g, n, n, n, n, m],\n",
-    "     [m, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, m],\n",
-    "     [m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m, m]]\n",
-    "    \n",
-    "# Create a containing cell and universe\n",
-    "cells['MOX Assembly'] = openmc.Cell(name='MOX Assembly')\n",
-    "cells['MOX Assembly'].fill = lattices['MOX Assembly']\n",
-    "universes['MOX Assembly'] = openmc.Universe(name='MOX Assembly')\n",
-    "universes['MOX Assembly'].add_cell(cells['MOX Assembly'])\n",
-    "    \n",
-    "# Instantiate the reflector Lattice\n",
-    "lattices['Reflector Assembly'] = openmc.RectLattice(name='Reflector Assembly')\n",
-    "lattices['Reflector Assembly'].dimension = [1,1]\n",
-    "lattices['Reflector Assembly'].lower_left = [-10.71, -10.71]\n",
-    "lattices['Reflector Assembly'].pitch = [21.42, 21.42]\n",
-    "lattices['Reflector Assembly'].universes = [[universes['water']]]\n",
-    "\n",
-    "# Create a containing cell and universe\n",
-    "cells['Reflector Assembly'] = openmc.Cell(name='Reflector Assembly')\n",
-    "cells['Reflector Assembly'].fill = lattices['Reflector Assembly']\n",
-    "universes['Reflector Assembly'] = openmc.Universe(name='Reflector Assembly')\n",
-    "universes['Reflector Assembly'].add_cell(cells['Reflector Assembly'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Let's now create the core layout in a 3x3 lattice where each lattice position is one of the assemblies we just defined.\n",
-    "\n",
-    "After that we can create the final cell to contain the entire core."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lattices['Core'] = openmc.RectLattice(name='3x3 core lattice')\n",
-    "lattices['Core'].dimension= [3, 3]\n",
-    "lattices['Core'].lower_left = [-32.13, -32.13]\n",
-    "lattices['Core'].pitch = [21.42, 21.42]\n",
-    "r = universes['Reflector Assembly']\n",
-    "u = universes['UO2 Assembly']\n",
-    "m = universes['MOX Assembly']\n",
-    "lattices['Core'].universes = [[u, m, r],\n",
-    "                              [m, u, r],\n",
-    "                              [r, r, r]]\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "min_x = openmc.XPlane(x0=-32.13, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+32.13, boundary_type='vacuum')\n",
-    "min_y = openmc.YPlane(y0=-32.13, boundary_type='vacuum')\n",
-    "max_y = openmc.YPlane(y0=+32.13, boundary_type='reflective')\n",
-    "\n",
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell')\n",
-    "root_cell.fill = lattices['Core']\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(name='root universe', universe_id=0)\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we commit to the geometry, we should view it using the Python API's plotting capability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fee111ddba8>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "root_universe.plot(origin=(0., 0., 0.), width=(3 * 21.42, 3 * 21.42), pixels=(500, 500),\n",
-    "                   color_by='material')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OK, it looks pretty good, let's go ahead and write the file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "geometry = openmc.Geometry(root_universe)\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now create the tally file information. The tallies will be set up to  give us the pin powers in this notebook. We will do this with a mesh filter, with one mesh cell per pin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tallies_file = openmc.Tallies()\n",
-    "\n",
-    "# Instantiate a tally Mesh\n",
-    "mesh = openmc.RegularMesh()\n",
-    "mesh.dimension = [17 * 2, 17 * 2]\n",
-    "mesh.lower_left = [-32.13, -10.71]\n",
-    "mesh.upper_right = [+10.71, +32.13]\n",
-    "\n",
-    "# Instantiate tally Filter\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "# Instantiate the Tally\n",
-    "tally = openmc.Tally(name='mesh tally')\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['fission']\n",
-    "\n",
-    "# Add tally to collection\n",
-    "tallies_file.append(tally)\n",
-    "\n",
-    "# Export all tallies to a \"tallies.xml\" file\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters for the `settings.xml` file. Note the use of the `energy_mode` attribute of our `settings_file` object. This is used to tell OpenMC that we intend to run in multi-group mode instead of the default continuous-energy mode. If we didn't specify this but our cross sections file was not a continuous-energy data set, then OpenMC would complain.\n",
-    "\n",
-    "This will be a relatively coarse calculation with only 500,000 active histories. A benchmark-fidelity run would of course require many more!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 150\n",
-    "inactive = 50\n",
-    "particles = 5000\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "\n",
-    "# Tell OpenMC this is a multi-group problem\n",
-    "settings_file.energy_mode = 'multi-group'\n",
-    "\n",
-    "# Set the verbosity to 6 so we dont see output for every batch\n",
-    "settings_file.verbosity = 6\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-32.13, -10.71, -1e50, 10.71, 32.13, 1e50]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Tell OpenMC we want to run in eigenvalue mode\n",
-    "settings_file.run_mode = 'eigenvalue'\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's go ahead and execute the simulation! You'll notice that the output for multi-group mode is exactly the same as for continuous-energy. The differences are all under the hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-28 08:49:29\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections HDF5 file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Loading cross section data...\n",
-      " Loading uo2 data...\n",
-      " Loading mox43 data...\n",
-      " Loading mox7 data...\n",
-      " Loading mox87 data...\n",
-      " Loading fiss_chamber data...\n",
-      " Loading guide_tube data...\n",
-      " Loading water data...\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      " Creating state point statepoint.150.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.1582e-02 seconds\n",
-      "   Reading cross sections          = 1.0391e-02 seconds\n",
-      " Total time in simulation          = 1.7437e+01 seconds\n",
-      "   Time in transport only          = 1.7387e+01 seconds\n",
-      "   Time in inactive batches        = 4.2441e+00 seconds\n",
-      "   Time in active batches          = 1.3193e+01 seconds\n",
-      "   Time synchronizing fission bank = 3.2421e-02 seconds\n",
-      "     Sampling source sites         = 2.6798e-02 seconds\n",
-      "     SEND/RECV source sites        = 5.5395e-03 seconds\n",
-      "   Time accumulating tallies       = 5.3440e-04 seconds\n",
-      "   Time writing statepoints        = 2.9755e-03 seconds\n",
-      " Total time for finalization       = 1.7629e-03 seconds\n",
-      " Total time elapsed                = 1.7465e+01 seconds\n",
-      " Calculation Rate (inactive)       = 58905.6 particles/second\n",
-      " Calculation Rate (active)         = 37899.4 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.18598 +/- 0.00158\n",
-      " k-effective (Track-length)  = 1.18629 +/- 0.00194\n",
-      " k-effective (Absorption)    = 1.18569 +/- 0.00111\n",
-      " Combined k-effective        = 1.18574 +/- 0.00111\n",
-      " Leakage Fraction            = 0.00184 +/- 0.00006\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Results Visualization\n",
-    "\n",
-    "Now that we have run the simulation, let's look at the fission rate and flux tallies that we tallied."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Load the last statepoint file and keff value\n",
-    "with openmc.StatePoint('statepoint.' + str(batches) + '.h5') as sp:\n",
-    "    # Get the OpenMC pin power tally data\n",
-    "    mesh_tally = sp.get_tally(name='mesh tally')\n",
-    "    fission_rates = mesh_tally.get_values(scores=['fission'])\n",
-    "\n",
-    "# Reshape array to 2D for plotting\n",
-    "fission_rates.shape = mesh.dimension\n",
-    "\n",
-    "# Normalize to the average pin power\n",
-    "fission_rates /= np.mean(fission_rates[fission_rates > 0.])\n",
-    "\n",
-    "# Force zeros to be NaNs so their values are not included when matplotlib calculates\n",
-    "# the color scale\n",
-    "fission_rates[fission_rates == 0.] = np.nan\n",
-    "\n",
-    "# Plot the pin powers and the fluxes\n",
-    "plt.figure()\n",
-    "plt.imshow(fission_rates, interpolation='none', cmap='jet', origin='lower')\n",
-    "plt.colorbar()\n",
-    "plt.title('Pin Powers')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There we have it! We have just successfully run the C5G7 benchmark model!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/mg-mode-part-ii.ipynb b/examples/jupyter/mg-mode-part-ii.ipynb
deleted file mode 100644
index 01ae6bf37..000000000
--- a/examples/jupyter/mg-mode-part-ii.ipynb
+++ /dev/null
@@ -1,1570 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup Mode Part II: MGXS Library Generation with OpenMC\n",
-    "The previous Notebook in this series used multi-group mode to perform a calculation with previously defined cross sections.  However, in many circumstances the multi-group data is not given and one must instead generate the cross sections for the specific application (or at least verify the use of cross sections from another application).  \n",
-    "\n",
-    "This Notebook illustrates the use of the openmc.mgxs.Library class specifically for the calculation of MGXS to be used in OpenMC's multi-group mode. This example notebook is therefore very similar to the MGXS Part III notebook, except OpenMC is used as the multi-group solver instead of OpenMOC.\n",
-    "\n",
-    "During this process, this notebook will illustrate the following features:\n",
-    "\n",
-    "   - Calculation of multi-group cross sections for a fuel assembly\n",
-    "   - Automated creation and storage of MGXS with openmc.mgxs.Library\n",
-    "   - Steady-state pin-by-pin fission rates comparison between continuous-energy and multi-group OpenMC.\n",
-    "   - Modification of the scattering data in the library to show the flexibility of the multi-group solver\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import os\n",
-    "\n",
-    "import openmc\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will begin by creating three materials for the fuel, water, and cladding of the fuel pins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6% enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_element('U', 1., enrichment=1.6)\n",
-    "fuel.add_element('O', 2.)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_element('Zr', 1.)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_element('H', 4.9457e-2)\n",
-    "water.add_element('O', 2.4732e-2)\n",
-    "water.add_element('B', 8.0042e-6)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we can now create a Materials object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials object\n",
-    "materials_file = openmc.Materials((fuel, zircaloy, water))\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/surface.py:1511: FutureWarning: \"ZCylinder(...) accepts an argument named 'r', not 'R'. Future versions of OpenMC will not accept the capitalized version.\n",
-      "  FutureWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "# The x0 and y0 parameters (0. and 0.) are the default values for an\n",
-    "# openmc.ZCylinder object. We could therefore leave them out to no effect\n",
-    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n",
-    "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n",
-    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n",
-    "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
-    "\n",
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "fuel_cell.fill = fuel\n",
-    "fuel_cell.region = -fuel_outer_radius\n",
-    "fuel_pin_universe.add_cell(fuel_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "fuel_pin_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "fuel_pin_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Likewise, we can construct a control rod guide tube with the same surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a control rod guide tube\n",
-    "guide_tube_universe = openmc.Universe(name='Guide Tube')\n",
-    "\n",
-    "# Create guide tube Cell\n",
-    "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n",
-    "guide_tube_cell.fill = water\n",
-    "guide_tube_cell.region = -fuel_outer_radius\n",
-    "guide_tube_universe.add_cell(guide_tube_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='Guide Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "guide_tube_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "guide_tube_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create fuel assembly Lattice\n",
-    "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n",
-    "assembly.pitch = (1.26, 1.26)\n",
-    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create array indices for guide tube locations in lattice\n",
-    "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n",
-    "                       11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n",
-    "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n",
-    "                       8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n",
-    "\n",
-    "# Initialize an empty 17x17 array of the lattice universes\n",
-    "universes = np.empty((17, 17), dtype=openmc.Universe)\n",
-    "\n",
-    "# Fill the array with the fuel pin and guide tube universes\n",
-    "universes[:, :] = fuel_pin_universe\n",
-    "universes[template_x, template_y] = guide_tube_universe\n",
-    "\n",
-    "# Store the array of universes in the lattice\n",
-    "assembly.universes = universes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell')\n",
-    "root_cell.fill = assembly\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(name='root universe', universe_id=0)\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before proceeding lets check the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f106f80f668>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "root_universe.plot(origin=(0., 0., 0.), width=(21.42, 21.42), pixels=(500, 500), color_by='material')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks good!\n",
-    "\n",
-    "We now must create a geometry that is assigned a root universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root universe\n",
-    "geometry = openmc.Geometry(root_universe)\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 600\n",
-    "inactive = 50\n",
-    "particles = 3000\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': False}\n",
-    "settings_file.run_mode = 'eigenvalue'\n",
-    "settings_file.verbosity = 4\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an MGXS Library\n",
-    "\n",
-    "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in EnergyGroups class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a 2-group EnergyGroups object\n",
-    "groups = openmc.mgxs.EnergyGroups([0., 0.625, 20.0e6])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will instantiate an openmc.mgxs.Library for the energy groups with our the fuel assembly geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize a 2-group MGXS Library for OpenMC\n",
-    "mgxs_lib = openmc.mgxs.Library(geometry)\n",
-    "mgxs_lib.energy_groups = groups"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we must specify to the Library which types of cross sections to compute. OpenMC's multi-group mode can accept isotropic flux-weighted cross sections or angle-dependent cross sections, as well as supporting anisotropic scattering represented by either Legendre polynomials, histogram, or tabular angular distributions.  We will create the following multi-group cross sections needed to run an OpenMC simulation to verify the accuracy of our cross sections: \"total\", \"absorption\", \"nu-fission\", '\"fission\", \"nu-scatter matrix\", \"multiplicity matrix\", and \"chi\".\n",
-    "\n",
-    "The \"multiplicity matrix\" type is a relatively rare cross section type.  This data is needed to provide OpenMC's multi-group mode with additional information needed to accurately treat scattering multiplication (i.e., (n,xn) reactions)), including how this multiplication varies depending on both incoming and outgoing neutron energies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify multi-group cross section types to compute\n",
-    "mgxs_lib.mgxs_types = ['total', 'absorption', 'nu-fission', 'fission',\n",
-    "                       'nu-scatter matrix', 'multiplicity matrix', 'chi']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports \"material\", \"cell\", \"universe\", and \"mesh\" domain types. In this simple example, we wish to compute multi-group cross sections only for each material and therefore will use a \"material\" domain type.\n",
-    "\n",
-    "**NOTE:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell, universe, or mesh) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify a \"cell\" domain type for the cross section tally filters\n",
-    "mgxs_lib.domain_type = \"material\"\n",
-    "\n",
-    "# Specify the cell domains over which to compute multi-group cross sections\n",
-    "mgxs_lib.domains = geometry.get_all_materials().values()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will instruct the library to not compute cross sections on a nuclide-by-nuclide basis, and instead to focus on generating material-specific macroscopic cross sections.\n",
-    "\n",
-    "**NOTE:** The default value of the `by_nuclide` parameter is `False`, so the following step is not necessary but is included for illustrative purposes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Do not compute cross sections on a nuclide-by-nuclide basis\n",
-    "mgxs_lib.by_nuclide = False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we will set the scattering order that we wish to use.  For this problem we will use P3 scattering.  A warning is expected telling us that the default behavior (a P0 correction on the scattering data) is over-ridden by our choice of using a Legendre expansion to treat anisotropic scattering."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mgxs/library.py:418: RuntimeWarning: The P0 correction will be ignored since the scattering order 3 is greater than zero\n",
-      "  warn(msg, RuntimeWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set the Legendre order to 3 for P3 scattering\n",
-    "mgxs_lib.legendre_order = 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that the `Library` has been setup let's verify that it contains the types of cross sections which meet the needs of OpenMC's multi-group solver.  Note that this step is done automatically when writing the Multi-Group Library file later in the process (as part of `mgxs_lib.write_mg_library()`), but it is a good practice to also run this before spending all the time running OpenMC to generate the cross sections.\n",
-    "\n",
-    "If no error is raised, then we have a good set of data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check the library - if no errors are raised, then the library is satisfactory.\n",
-    "mgxs_lib.check_library_for_openmc_mgxs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great, now we can use the `Library` to construct the tallies needed to compute all of the requested multi-group cross sections in each domain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Construct all tallies needed for the multi-group cross section library\n",
-    "mgxs_lib.build_library()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The tallies can now be exported to a \"tallies.xml\" input file for OpenMC.\n",
-    "\n",
-    "**NOTE:** At this point the `Library` has constructed nearly 100 distinct Tally objects. The overhead to tally in OpenMC scales as O(N) for N tallies, which can become a bottleneck for large tally datasets. To compensate for this, the Python API's `Tally`, `Filter` and `Tallies` classes allow for the smart merging of tallies when possible. The `Library` class supports this runtime optimization with the use of the optional `merge` parameter (`False` by default) for the `Library.add_to_tallies_file(...)` method, as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition, we instantiate a fission rate mesh tally that we will eventually use to compare with the corresponding multi-group results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=66.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=11.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate a tally Mesh\n",
-    "mesh = openmc.RegularMesh()\n",
-    "mesh.dimension = [17, 17]\n",
-    "mesh.lower_left = [-10.71, -10.71]\n",
-    "mesh.upper_right = [+10.71, +10.71]\n",
-    "\n",
-    "# Instantiate tally Filter\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "# Instantiate the Tally\n",
-    "tally = openmc.Tally(name='mesh tally')\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['fission']\n",
-    "\n",
-    "# Add tally to collection\n",
-    "tallies_file.append(tally, merge=True)\n",
-    "\n",
-    "# Export all tallies to a \"tallies.xml\" file\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Time to run the calculation and get our results!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 14:38:16\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.16510 +/- 0.00094\n",
-      " k-effective (Track-length)  = 1.16422 +/- 0.00106\n",
-      " k-effective (Absorption)    = 1.16519 +/- 0.00081\n",
-      " Combined k-effective        = 1.16502 +/- 0.00071\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To make sure the results we need are available after running the multi-group calculation, we will now rename the statepoint and summary files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Move the statepoint File\n",
-    "ce_spfile = './statepoint_ce.h5'\n",
-    "os.rename('statepoint.{}.h5'.format(batches), ce_spfile)\n",
-    "# Move the Summary file\n",
-    "ce_sumfile = './summary_ce.h5'\n",
-    "os.rename('summary.h5', ce_sumfile)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing\n",
-    "\n",
-    "Our simulation ran successfully and created statepoint and summary output files.  Let's begin by loading the StatePoint file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the statepoint file\n",
-    "sp = openmc.StatePoint(ce_spfile, autolink=False)\n",
-    "\n",
-    "# Load the summary file in its new location\n",
-    "su = openmc.Summary(ce_sumfile)\n",
-    "sp.link_with_summary(su)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize MGXS Library with OpenMC statepoint data\n",
-    "mgxs_lib.load_from_statepoint(sp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next step will be to prepare the input for OpenMC to use our newly created multi-group data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Multi-Group OpenMC Calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now use the `Library` to produce a multi-group cross section data set for use by the OpenMC multi-group solver.  \n",
-    "Note that since this simulation included so few histories, it is reasonable to expect some data has not had any scores, and thus we could see division by zero errors.  This will show up as a runtime warning in the following step. The `Library` class is designed to gracefully handle these scenarios."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a MGXS File which can then be written to disk\n",
-    "mgxs_file = mgxs_lib.create_mg_library(xs_type='macro', xsdata_names=['fuel', 'zircaloy', 'water'])\n",
-    "\n",
-    "# Write the file to disk using the default filename of \"mgxs.h5\"\n",
-    "mgxs_file.export_to_hdf5()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC's multi-group mode uses the same input files as does the continuous-energy mode (materials, geometry, settings, plots, and tallies file).  Differences would include the use of a flag to tell the code to use multi-group transport, a location of the multi-group library file, and any changes needed in the materials.xml and geometry.xml files to re-define materials as necessary. The materials and geometry file changes could be necessary if materials or their nuclide/element/macroscopic constituents need to be renamed.\n",
-    "\n",
-    "In this example we have created macroscopic cross sections (by material), and thus we will need to change the material definitions accordingly.\n",
-    "\n",
-    "First we will create the new materials.xml file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=1.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Material instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Re-define our materials to use the multi-group macroscopic data\n",
-    "# instead of the continuous-energy data.\n",
-    "\n",
-    "# 1.6% enriched fuel UO2\n",
-    "fuel_mg = openmc.Material(name='UO2', material_id=1)\n",
-    "fuel_mg.add_macroscopic('fuel')\n",
-    "\n",
-    "# cladding\n",
-    "zircaloy_mg = openmc.Material(name='Clad', material_id=2)\n",
-    "zircaloy_mg.add_macroscopic('zircaloy')\n",
-    "\n",
-    "# moderator\n",
-    "water_mg = openmc.Material(name='Water', material_id=3)\n",
-    "water_mg.add_macroscopic('water')\n",
-    "\n",
-    "# Finally, instantiate our Materials object\n",
-    "materials_file = openmc.Materials((fuel_mg, zircaloy_mg, water_mg))\n",
-    "\n",
-    "# Set the location of the cross sections file\n",
-    "materials_file.cross_sections = 'mgxs.h5'\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "No geometry file neeeds to be written as the continuous-energy file is correctly defined for the multi-group case as well."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can make the changes we need to the simulation parameters.\n",
-    "These changes are limited to telling OpenMC to run a multi-group vice contrinuous-energy calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the energy mode\n",
-    "settings_file.energy_mode = 'multi-group'\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets clear the tallies file so it doesn't include tallies for re-generating a multi-group library, but then put back in a tally for the fission mesh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "\n",
-    "# Add fission and flux mesh to tally for plotting using the same mesh we've already defined\n",
-    "mesh_tally = openmc.Tally(name='mesh tally')\n",
-    "mesh_tally.filters = [openmc.MeshFilter(mesh)]\n",
-    "mesh_tally.scores = ['fission']\n",
-    "tallies_file.append(mesh_tally)\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Before running the calculation let's visually compare a subset of the newly-generated multi-group cross section data to the continuous-energy data. We will do this using the cross section plotting functionality built-in to the OpenMC Python API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# First lets plot the fuel data\n",
-    "# We will first add the continuous-energy data\n",
-    "fig = openmc.plot_xs(fuel, ['total'])\n",
-    "\n",
-    "# We will now add in the corresponding multi-group data and show the result\n",
-    "openmc.plot_xs(fuel_mg, ['total'], plot_CE=False, mg_cross_sections='mgxs.h5', axis=fig.axes[0])\n",
-    "fig.axes[0].legend().set_visible(False)\n",
-    "plt.show()\n",
-    "plt.close()\n",
-    "\n",
-    "# Then repeat for the zircaloy data\n",
-    "fig = openmc.plot_xs(zircaloy, ['total'])\n",
-    "openmc.plot_xs(zircaloy_mg, ['total'], plot_CE=False, mg_cross_sections='mgxs.h5', axis=fig.axes[0])\n",
-    "fig.axes[0].legend().set_visible(False)\n",
-    "plt.show()\n",
-    "plt.close()\n",
-    "\n",
-    "# And finally repeat for the water data\n",
-    "fig = openmc.plot_xs(water, ['total'])\n",
-    "openmc.plot_xs(water_mg, ['total'], plot_CE=False, mg_cross_sections='mgxs.h5', axis=fig.axes[0])\n",
-    "fig.axes[0].legend().set_visible(False)\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this point, the problem is set up and we can run the multi-group calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 14:41:06\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.16517 +/- 0.00094\n",
-      " k-effective (Track-length)  = 1.16588 +/- 0.00106\n",
-      " k-effective (Absorption)    = 1.16485 +/- 0.00050\n",
-      " Combined k-effective        = 1.16499 +/- 0.00048\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run the Multi-Group OpenMC Simulation\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Results Comparison\n",
-    "Now we can compare the multi-group and continuous-energy results.\n",
-    "\n",
-    "We will begin by loading the multi-group statepoint file we just finished writing and extracting the calculated keff."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Move the StatePoint File\n",
-    "mg_spfile = './statepoint_mg.h5'\n",
-    "os.rename('statepoint.' + str(batches) + '.h5', mg_spfile)\n",
-    "# Move the Summary file\n",
-    "mg_sumfile = './summary_mg.h5'\n",
-    "os.rename('summary.h5', mg_sumfile)\n",
-    "\n",
-    "# Rename and then load the last statepoint file and keff value\n",
-    "mgsp = openmc.StatePoint(mg_spfile, autolink=False)\n",
-    "\n",
-    "# Load the summary file in its new location\n",
-    "mgsu = openmc.Summary(mg_sumfile)\n",
-    "mgsp.link_with_summary(mgsu)\n",
-    "\n",
-    "# Get keff\n",
-    "mg_keff = mgsp.k_combined"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can load the continuous-energy eigenvalue for comparison."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ce_keff = sp.k_combined"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets compare the two eigenvalues, including their bias"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Continuous-Energy keff = 1.165017+/-0.000706\n",
-      "Multi-Group keff = 1.164985+/-0.000485\n",
-      "bias [pcm]: 3.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "bias = 1.0E5 * (ce_keff - mg_keff)\n",
-    "\n",
-    "print('Continuous-Energy keff = {0:1.6f}'.format(ce_keff))\n",
-    "print('Multi-Group keff = {0:1.6f}'.format(mg_keff))\n",
-    "print('bias [pcm]: {0:1.1f}'.format(bias.nominal_value))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This shows a small but nontrivial pcm bias between the two methods.  Some degree of mismatch is expected simply to the very few histories being used in these example problems.  An additional mismatch is always inherent in the practical application of multi-group theory due to the high degree of approximations inherent in that method."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pin Power Visualizations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we will visualize the pin power results obtained from both the Continuous-Energy and Multi-Group OpenMC calculations.\n",
-    "\n",
-    "First, we extract volume-integrated fission rates from the Multi-Group calculation's mesh fission rate tally for each pin cell in the fuel assembly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the OpenMC fission rate mesh tally data\n",
-    "mg_mesh_tally = mgsp.get_tally(name='mesh tally')\n",
-    "mg_fission_rates = mg_mesh_tally.get_values(scores=['fission'])\n",
-    "\n",
-    "# Reshape array to 2D for plotting\n",
-    "mg_fission_rates.shape = (17,17)\n",
-    "\n",
-    "# Normalize to the average pin power\n",
-    "mg_fission_rates /= np.mean(mg_fission_rates[mg_fission_rates > 0.])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now do the same for the Continuous-Energy results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the OpenMC fission rate mesh tally data\n",
-    "ce_mesh_tally = sp.get_tally(name='mesh tally')\n",
-    "ce_fission_rates = ce_mesh_tally.get_values(scores=['fission'])\n",
-    "\n",
-    "# Reshape array to 2D for plotting\n",
-    "ce_fission_rates.shape = (17,17)\n",
-    "\n",
-    "# Normalize to the average pin power\n",
-    "ce_fission_rates /= np.mean(ce_fission_rates[ce_fission_rates > 0.])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can easily use Matplotlib to visualize the two fission rates side-by-side."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Multi-Group Fission Rates')"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Force zeros to be NaNs so their values are not included when matplotlib calculates\n",
-    "# the color scale\n",
-    "ce_fission_rates[ce_fission_rates == 0.] = np.nan\n",
-    "mg_fission_rates[mg_fission_rates == 0.] = np.nan\n",
-    "\n",
-    "# Plot the CE fission rates in the left subplot\n",
-    "fig = plt.subplot(121)\n",
-    "plt.imshow(ce_fission_rates, interpolation='none', cmap='jet')\n",
-    "plt.title('Continuous-Energy Fission Rates')\n",
-    "\n",
-    "# Plot the MG fission rates in the right subplot\n",
-    "fig2 = plt.subplot(122)\n",
-    "plt.imshow(mg_fission_rates, interpolation='none', cmap='jet')\n",
-    "plt.title('Multi-Group Fission Rates')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "These figures really indicate that more histories are probably necessary when trying to achieve a fully converged solution, but hey, this is good enough for our example!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scattering Anisotropy Treatments\n",
-    "\n",
-    "We will next show how we can work with the scattering angular distributions. OpenMC's MG solver has the capability to use group-to-group angular distributions which are represented as any of the following: a truncated Legendre series of up to the 10th order, a histogram distribution, and a tabular distribution. Any combination of these representations can be used by OpenMC during the transport process, so long as all constituents of a given material use the same representation. This means it is possible to have water represented by a tabular distribution and fuel represented by a Legendre if so desired.\n",
-    "\n",
-    "*Note*: To have the highest runtime performance OpenMC natively converts Legendre series to a tabular distribution before the transport begins. This default functionality can be turned off with the `tabular_legendre` element of the `settings.xml` file (or for the Python API, the `openmc.Settings.tabular_legendre` attribute).\n",
-    "\n",
-    "This section will examine the following:\n",
-    "- Re-run the MG-mode calculation with P0 scattering everywhere using the `openmc.Settings.max_order` attribute\n",
-    "- Re-run the problem with only the water represented with P3 scattering and P0 scattering for the remaining materials using the Python API's ability to convert between formats."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### Global P0 Scattering\n",
-    "First we begin by re-running with P0 scattering (i.e., isotropic) everywhere. If a global maximum order is requested, the most effective way to do this is to use the  `max_order` attribute of our `openmc.Settings` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the maximum scattering order to 0 (i.e., isotropic scattering)\n",
-    "settings_file.max_order = 0\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can re-run OpenMC to obtain our results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 14:42:24\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.16293 +/- 0.00093\n",
-      " k-effective (Track-length)  = 1.16281 +/- 0.00107\n",
-      " k-effective (Absorption)    = 1.16309 +/- 0.00049\n",
-      " Combined k-effective        = 1.16306 +/- 0.00048\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run the Multi-Group OpenMC Simulation\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then get the eigenvalue differences from the Continuous-Energy and P3 MG solution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "P3 bias [pcm]: 3.2\n",
-      "P0 bias [pcm]: 195.7\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Move the statepoint File\n",
-    "mgp0_spfile = './statepoint_mg_p0.h5'\n",
-    "os.rename('statepoint.' + str(batches) + '.h5', mgp0_spfile)\n",
-    "# Move the Summary file\n",
-    "mgp0_sumfile = './summary_mg_p0.h5'\n",
-    "os.rename('summary.h5', mgp0_sumfile)\n",
-    "\n",
-    "# Load the last statepoint file and keff value\n",
-    "mgsp_p0 = openmc.StatePoint(mgp0_spfile, autolink=False)\n",
-    "\n",
-    "# Get keff\n",
-    "mg_p0_keff = mgsp_p0.k_combined\n",
-    "\n",
-    "bias_p0 = 1.0E5 * (ce_keff - mg_p0_keff)\n",
-    "\n",
-    "print('P3 bias [pcm]: {0:1.1f}'.format(bias.nominal_value))\n",
-    "print('P0 bias [pcm]: {0:1.1f}'.format(bias_p0.nominal_value))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixed Scattering Representations\n",
-    "OpenMC's Multi-Group mode also includes a feature where not every data in the library is required to have the same scattering treatment. For example,  we could represent the water with P3 scattering, and the fuel and cladding with P0 scattering. This series will show how this can be done.\n",
-    "\n",
-    "First we will convert the data to P0 scattering, unless its water, then we will leave that as P3 data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert the zircaloy and fuel data to P0 scattering\n",
-    "for i, xsdata in enumerate(mgxs_file.xsdatas):\n",
-    "    if xsdata.name != 'water':\n",
-    "        mgxs_file.xsdatas[i] = xsdata.convert_scatter_format('legendre', 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use whatever scattering format that we want for the materials in the library. As an example, we will take this P0 data and convert zircaloy to a histogram anisotropic scattering format and the fuel to a tabular anisotropic scattering format"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert the formats as discussed\n",
-    "for i, xsdata in enumerate(mgxs_file.xsdatas):\n",
-    "    if xsdata.name == 'zircaloy':\n",
-    "        mgxs_file.xsdatas[i] = xsdata.convert_scatter_format('histogram', 2)\n",
-    "    elif xsdata.name == 'fuel':\n",
-    "        mgxs_file.xsdatas[i] = xsdata.convert_scatter_format('tabular', 2)\n",
-    "        \n",
-    "mgxs_file.export_to_hdf5('mgxs.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally we will re-set our `max_order` parameter of our `openmc.Settings` object to our maximum order so that OpenMC will use whatever scattering data is available in the library.\n",
-    "\n",
-    "After we do this we can re-run the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 14:43:41\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.16377 +/- 0.00093\n",
-      " k-effective (Track-length)  = 1.16368 +/- 0.00111\n",
-      " k-effective (Absorption)    = 1.16357 +/- 0.00047\n",
-      " Combined k-effective        = 1.16357 +/- 0.00047\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "settings_file.max_order = None\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()\n",
-    "\n",
-    "# Run the Multi-Group OpenMC Simulation\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For a final step we can again obtain the eigenvalue differences from this case and compare with the same from the P3 MG solution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "P3 bias [pcm]: 3.2\n",
-      "Mixed Scattering bias [pcm]: 144.4\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load the last statepoint file and keff value\n",
-    "mgsp_mixed = openmc.StatePoint('./statepoint.' + str(batches) + '.h5')\n",
-    "\n",
-    "mg_mixed_keff = mgsp_mixed.k_combined\n",
-    "bias_mixed = 1.0E5 * (ce_keff - mg_mixed_keff)\n",
-    "\n",
-    "print('P3 bias [pcm]: {0:1.1f}'.format(bias.nominal_value))\n",
-    "print('Mixed Scattering bias [pcm]: {0:1.1f}'.format(bias_mixed.nominal_value))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Our tests in this section showed the flexibility of data formatting within OpenMC's multi-group mode: every material can be represented with its own format with the approximations that make the most sense. Now, as you'll see above, the runtimes from our P3, P0, and mixed cases are not significantly different and therefore this might not be a useful strategy for multi-group Monte Carlo.  However, this capability provides a useful benchmark for the accuracy hit one may expect due to these scattering approximations before implementing this generality in a deterministic solver where the runtime savings are more significant.\n",
-    "\n",
-    "**NOTE**: The biases obtained above with P3, P0, and mixed representations do not necessarily reflect the inherent accuracies of the options. These cases were *not* run with a sufficient number of histories to truly differentiate methods improvement from statistical noise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Close all StatePoint files as a matter of best practice\n",
-    "sp.close()\n",
-    "mgsp_p0.close()\n",
-    "mgsp.close()\n",
-    "mgsp_mixed.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/mg-mode-part-iii.ipynb b/examples/jupyter/mg-mode-part-iii.ipynb
deleted file mode 100644
index 52a6ff239..000000000
--- a/examples/jupyter/mg-mode-part-iii.ipynb
+++ /dev/null
@@ -1,1437 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup Mode Part III: Advanced Feature Showcase\n",
-    "This Notebook illustrates the use of the the more advanced features of OpenMC's multi-group mode and the openmc.mgxs.Library class. During this process, this notebook will illustrate the following features:\n",
-    "\n",
-    "   - Calculation of multi-group cross sections for a simplified BWR 8x8 assembly with isotropic and angle-dependent MGXS.\n",
-    "   - Automated creation and storage of MGXS with openmc.mgxs.Library\n",
-    "   - Fission rate comparison between continuous-energy and the two multi-group OpenMC cases.\n",
-    "\n",
-    "To avoid focusing on unimportant details, the BWR assembly in this notebook is greatly simplified. The descriptions which follow will point out some areas of simplification."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import openmc\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will be running a rodded 8x8 assembly with Gadolinia fuel pins. Let's start by creating the materials that we will use later.\n",
-    "\n",
-    "Material Definition Simplifications:\n",
-    "\n",
-    "- This model will be run at room temperature so the NNDC ENDF-B/VII.1 data set can be used but the water density will be representative of a module with around 20% voiding. This water density will be non-physically used in all regions of the problem.\n",
-    "- Steel is composed of more than just iron, but we will only treat it as such here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "materials = {}\n",
-    "\n",
-    "# Fuel\n",
-    "materials['Fuel'] = openmc.Material(name='Fuel')\n",
-    "materials['Fuel'].set_density('g/cm3', 10.32)\n",
-    "materials['Fuel'].add_element('O', 2)\n",
-    "materials['Fuel'].add_element('U', 1, enrichment=3.)\n",
-    "\n",
-    "# Gadolinia bearing fuel\n",
-    "materials['Gad'] = openmc.Material(name='Gad')\n",
-    "materials['Gad'].set_density('g/cm3', 10.23)\n",
-    "materials['Gad'].add_element('O', 2)\n",
-    "materials['Gad'].add_element('U', 1, enrichment=3.)\n",
-    "materials['Gad'].add_element('Gd', .02)\n",
-    "\n",
-    "# Zircaloy\n",
-    "materials['Zirc2'] = openmc.Material(name='Zirc2')\n",
-    "materials['Zirc2'].set_density('g/cm3', 6.55)\n",
-    "materials['Zirc2'].add_element('Zr', 1)\n",
-    "\n",
-    "# Boiling Water\n",
-    "materials['Water'] = openmc.Material(name='Water')\n",
-    "materials['Water'].set_density('g/cm3', 0.6)\n",
-    "materials['Water'].add_element('H', 2)\n",
-    "materials['Water'].add_element('O', 1)\n",
-    "\n",
-    "# Boron Carbide for the Control Rods\n",
-    "materials['B4C'] = openmc.Material(name='B4C')\n",
-    "materials['B4C'].set_density('g/cm3', 0.7 * 2.52)\n",
-    "materials['B4C'].add_element('B', 4)\n",
-    "materials['B4C'].add_element('C', 1)\n",
-    "\n",
-    "# Steel \n",
-    "materials['Steel'] = openmc.Material(name='Steel')\n",
-    "materials['Steel'].set_density('g/cm3', 7.75)\n",
-    "materials['Steel'].add_element('Fe', 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now create a Materials object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials object\n",
-    "materials_file = openmc.Materials(materials.values())\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. The first step is to define some constants which will be used to set our dimensions and then we can start creating the surfaces and regions for the problem, the 8x8 lattice, the rods and the control blade.\n",
-    "\n",
-    "Before proceeding let's discuss some simplifications made to the problem geometry:\n",
-    "- To enable the use of an equal-width mesh for running the multi-group calculations, the intra-assembly gap was increased to the same size as the pitch of the 8x8 fuel lattice\n",
-    "- The can is neglected\n",
-    "- The pin-in-water geometry for the control blade is ignored and instead the blade is a solid block of B4C\n",
-    "- Rounded corners are ignored\n",
-    "- There is no cladding for the water rod"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set constants for the problem and assembly dimensions\n",
-    "fuel_rad = 0.53213\n",
-    "clad_rad = 0.61341\n",
-    "Np = 8\n",
-    "pin_pitch = 1.6256\n",
-    "length = float(Np + 2) * pin_pitch\n",
-    "assembly_width = length - 2. * pin_pitch\n",
-    "rod_thick = 0.47752 / 2. + 0.14224\n",
-    "rod_span = 7. * pin_pitch\n",
-    "\n",
-    "surfaces = {}\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "surfaces['Global x-'] = openmc.XPlane(0., boundary_type='reflective')\n",
-    "surfaces['Global x+'] = openmc.XPlane(length, boundary_type='reflective')\n",
-    "surfaces['Global y-'] = openmc.YPlane(0., boundary_type='reflective')\n",
-    "surfaces['Global y+'] = openmc.YPlane(length, boundary_type='reflective')\n",
-    "\n",
-    "# Create cylinders for the fuel and clad\n",
-    "surfaces['Fuel Radius'] = openmc.ZCylinder(r=fuel_rad)\n",
-    "surfaces['Clad Radius'] = openmc.ZCylinder(r=clad_rad)\n",
-    "\n",
-    "surfaces['Assembly x-'] = openmc.XPlane(pin_pitch)\n",
-    "surfaces['Assembly x+'] = openmc.XPlane(length - pin_pitch)\n",
-    "surfaces['Assembly y-'] = openmc.YPlane(pin_pitch)\n",
-    "surfaces['Assembly y+'] = openmc.YPlane(length - pin_pitch)\n",
-    "\n",
-    "# Set surfaces for the control blades\n",
-    "surfaces['Top Blade y-'] = openmc.YPlane(length - rod_thick)\n",
-    "surfaces['Top Blade x-'] = openmc.XPlane(pin_pitch)\n",
-    "surfaces['Top Blade x+'] = openmc.XPlane(rod_span)\n",
-    "surfaces['Left Blade x+'] = openmc.XPlane(rod_thick)\n",
-    "surfaces['Left Blade y-'] = openmc.YPlane(length - rod_span)\n",
-    "surfaces['Left Blade y+'] = openmc.YPlane(9. * pin_pitch)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now construct regions with these surfaces before we use those to create cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set regions for geometry building\n",
-    "regions = {}\n",
-    "regions['Global'] = \\\n",
-    "    (+surfaces['Global x-'] & -surfaces['Global x+'] &\n",
-    "     +surfaces['Global y-'] & -surfaces['Global y+'])\n",
-    "regions['Assembly'] = \\\n",
-    "    (+surfaces['Assembly x-'] & -surfaces['Assembly x+'] &\n",
-    "     +surfaces['Assembly y-'] & -surfaces['Assembly y+'])\n",
-    "regions['Fuel'] = -surfaces['Fuel Radius']\n",
-    "regions['Clad'] = +surfaces['Fuel Radius'] & -surfaces['Clad Radius']\n",
-    "regions['Water'] = +surfaces['Clad Radius']\n",
-    "regions['Top Blade'] = \\\n",
-    "    (+surfaces['Top Blade y-'] & -surfaces['Global y+']) & \\\n",
-    "    (+surfaces['Top Blade x-'] & -surfaces['Top Blade x+'])\n",
-    "regions['Top Steel'] = \\\n",
-    "    (+surfaces['Global x-'] & -surfaces['Top Blade x-']) & \\\n",
-    "    (+surfaces['Top Blade y-'] & -surfaces['Global y+'])\n",
-    "regions['Left Blade'] = \\\n",
-    "    (+surfaces['Left Blade y-'] & -surfaces['Left Blade y+']) & \\\n",
-    "    (+surfaces['Global x-'] & -surfaces['Left Blade x+'])\n",
-    "regions['Left Steel'] = \\\n",
-    "    (+surfaces['Left Blade y+'] & -surfaces['Top Blade y-']) & \\\n",
-    "    (+surfaces['Global x-'] & -surfaces['Left Blade x+'])\n",
-    "regions['Corner Blade'] = \\\n",
-    "    regions['Left Steel'] | regions['Top Steel']\n",
-    "regions['Water Fill'] = \\\n",
-    "    regions['Global'] & ~regions['Assembly'] & \\\n",
-    "    ~regions['Top Blade'] & ~regions['Left Blade'] &\\\n",
-    "    ~regions['Corner Blade']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will begin building the 8x8 assembly. To do that we will have to build the cells and universe for each pin type (fuel, gadolinia-fuel, and water)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "universes = {}\n",
-    "cells = {}\n",
-    "\n",
-    "for name, mat, in zip(['Fuel Pin', 'Gd Pin'],\n",
-    "                      [materials['Fuel'], materials['Gad']]):\n",
-    "    universes[name] = openmc.Universe(name=name)\n",
-    "    cells[name] = openmc.Cell(name=name)\n",
-    "    cells[name].fill = mat\n",
-    "    cells[name].region = regions['Fuel']\n",
-    "    universes[name].add_cell(cells[name])\n",
-    "    \n",
-    "    cells[name + ' Clad'] = openmc.Cell(name=name + ' Clad')\n",
-    "    cells[name + ' Clad'].fill = materials['Zirc2']\n",
-    "    cells[name + ' Clad'].region = regions['Clad']\n",
-    "    universes[name].add_cell(cells[name + ' Clad'])\n",
-    "    \n",
-    "    cells[name + ' Water'] = openmc.Cell(name=name + ' Water')\n",
-    "    cells[name + ' Water'].fill = materials['Water']\n",
-    "    cells[name + ' Water'].region = regions['Water']\n",
-    "    universes[name].add_cell(cells[name + ' Water'])\n",
-    "\n",
-    "universes['Hole'] = openmc.Universe(name='Hole')\n",
-    "cells['Hole'] = openmc.Cell(name='Hole')\n",
-    "cells['Hole'].fill = materials['Water']\n",
-    "universes['Hole'].add_cell(cells['Hole'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use this pin information to create our 8x8 assembly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create fuel assembly Lattice\n",
-    "universes['Assembly'] = openmc.RectLattice(name='Assembly')\n",
-    "universes['Assembly'].pitch = (pin_pitch, pin_pitch)\n",
-    "universes['Assembly'].lower_left = [pin_pitch, pin_pitch]\n",
-    "\n",
-    "f = universes['Fuel Pin']\n",
-    "g = universes['Gd Pin']\n",
-    "h = universes['Hole']\n",
-    "\n",
-    "lattices = [[f, f, f, f, f, f, f, f],\n",
-    "            [f, f, f, f, f, f, f, f],\n",
-    "            [f, f, f, g, f, g, f, f],\n",
-    "            [f, f, g, h, h, f, g, f],\n",
-    "            [f, f, f, h, h, f, f, f],\n",
-    "            [f, f, g, f, f, f, g, f],\n",
-    "            [f, f, f, g, f, g, f, f],\n",
-    "            [f, f, f, f, f, f, f, f]]\n",
-    "\n",
-    "# Store the array of lattice universes\n",
-    "universes['Assembly'].universes = lattices\n",
-    "\n",
-    "cells['Assembly'] = openmc.Cell(name='Assembly')\n",
-    "cells['Assembly'].fill = universes['Assembly']\n",
-    "cells['Assembly'].region = regions['Assembly']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So far we have the rods and water within the assembly , but we still need the control blade and the water which fills the rest of the space. We will create those cells now"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The top portion of the blade, poisoned with B4C\n",
-    "cells['Top Blade'] = openmc.Cell(name='Top Blade')\n",
-    "cells['Top Blade'].fill = materials['B4C']\n",
-    "cells['Top Blade'].region = regions['Top Blade']\n",
-    "\n",
-    "# The left portion of the blade, poisoned with B4C\n",
-    "cells['Left Blade'] = openmc.Cell(name='Left Blade')\n",
-    "cells['Left Blade'].fill = materials['B4C']\n",
-    "cells['Left Blade'].region = regions['Left Blade']\n",
-    "\n",
-    "# The top-left corner portion of the blade, with no poison\n",
-    "cells['Corner Blade'] = openmc.Cell(name='Corner Blade')\n",
-    "cells['Corner Blade'].fill = materials['Steel']\n",
-    "cells['Corner Blade'].region = regions['Corner Blade']\n",
-    "\n",
-    "# Water surrounding all other cells and our assembly\n",
-    "cells['Water Fill'] = openmc.Cell(name='Water Fill')\n",
-    "cells['Water Fill'].fill = materials['Water']\n",
-    "cells['Water Fill'].region = regions['Water Fill']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create our root universe and fill it with the cells just defined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Universe\n",
-    "universes['Root'] = openmc.Universe(name='root universe', universe_id=0)\n",
-    "universes['Root'].add_cells([cells['Assembly'], cells['Top Blade'],\n",
-    "                             cells['Corner Blade'], cells['Left Blade'],\n",
-    "                             cells['Water Fill']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What do you do after you create your model? Check it! We will use the plotting capabilities of the Python API to do this for us.\n",
-    "\n",
-    "When doing so, we will coloring by material with fuel being red, gadolinia-fuel as yellow, zirc cladding as a light grey, water as blue, B4C as black and steel as a darker gray."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f3970319ef0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "universes['Root'].plot(origin=(length / 2., length / 2., 0.),\n",
-    "                       pixels=(500, 500), width=(length, length),\n",
-    "                       color_by='material',\n",
-    "                       colors={materials['Fuel']: (1., 0., 0.),\n",
-    "                               materials['Gad']: (1., 1., 0.),\n",
-    "                               materials['Zirc2']: (0.5, 0.5, 0.5),\n",
-    "                               materials['Water']: (0.0, 0.0, 1.0),\n",
-    "                               materials['B4C']: (0.0, 0.0, 0.0),\n",
-    "                               materials['Steel']: (0.4, 0.4, 0.4)})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks pretty good to us!\n",
-    "\n",
-    "We now must create a geometry that is assigned a root universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root universe\n",
-    "geometry = openmc.Geometry(universes['Root'])\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters, including how to run the model and what we want to learn from the model (i.e., define the tallies). We will start with our simulation parameters in the next block.\n",
-    "\n",
-    "This will include setting the run strategy, telling OpenMC not to bother creating a `tallies.out` file, and limiting the verbosity of our output to just the header and results to not clog up our notebook with results from each batch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 1000\n",
-    "inactive = 20\n",
-    "particles = 1000\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': False}\n",
-    "settings_file.verbosity = 4\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [pin_pitch, pin_pitch, 10, length - pin_pitch, length - pin_pitch, 10]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an MGXS Library\n",
-    "\n",
-    "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in EnergyGroups class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a 2-group EnergyGroups object\n",
-    "groups = openmc.mgxs.EnergyGroups()\n",
-    "groups.group_edges = np.array([0., 0.625, 20.0e6])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will instantiate an openmc.mgxs.Library for the energy groups with our the problem geometry. This library will use the default setting of isotropically-weighting the multi-group cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize a 2-group Isotropic MGXS Library for OpenMC\n",
-    "iso_mgxs_lib = openmc.mgxs.Library(geometry)\n",
-    "iso_mgxs_lib.energy_groups = groups"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we must specify to the Library which types of cross sections to compute. OpenMC's multi-group mode can accept isotropic flux-weighted cross sections or angle-dependent cross sections, as well as supporting anisotropic scattering represented by either Legendre polynomials, histogram, or tabular angular distributions.  \n",
-    "\n",
-    "Just like before, we will create the following multi-group cross sections needed to run an OpenMC simulation to verify the accuracy of our cross sections: \"total\", \"absorption\", \"nu-fission\", '\"fission\", \"nu-scatter matrix\", \"multiplicity matrix\", and \"chi\".\n",
-    "\"multiplicity matrix\" is needed to provide OpenMC's multi-group mode with additional information needed to accurately treat scattering multiplication (i.e., (n,xn) reactions)) explicitly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify multi-group cross section types to compute\n",
-    "iso_mgxs_lib.mgxs_types = ['total', 'absorption', 'nu-fission', 'fission',\n",
-    "                           'nu-scatter matrix', 'multiplicity matrix', 'chi']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports \"material\" \"cell\", \"universe\", and \"mesh\" domain types. \n",
-    "\n",
-    "For the sake of example we will use a mesh to gather our cross sections. This mesh will be set up so there is one mesh bin for every pin cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a tally Mesh\n",
-    "mesh = openmc.RegularMesh()\n",
-    "mesh.dimension = [10, 10]\n",
-    "mesh.lower_left = [0., 0.]\n",
-    "mesh.upper_right = [length, length]\n",
-    "\n",
-    "# Specify a \"mesh\" domain type for the cross section tally filters\n",
-    "iso_mgxs_lib.domain_type = \"mesh\"\n",
-    "\n",
-    "# Specify the mesh over which to compute multi-group cross sections\n",
-    "iso_mgxs_lib.domains = [mesh]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we will set the scattering treatment that we wish to use.\n",
-    "\n",
-    "In the [mg-mode-part-ii](mg-mode-part-ii.html) notebook, the cross sections were generated with a typical P3 scattering expansion in mind. Now, however, we will use a more advanced technique: OpenMC will directly provide us a histogram of the change-in-angle (i.e., $\\mu$) distribution.\n",
-    "\n",
-    "Where as in the [mg-mode-part-ii](mg-mode-part-ii.html) notebook, all that was required was to set the `legendre_order` attribute of `mgxs_lib`, here we have only slightly more work: we have to tell the Library that we want to use a histogram distribution (as it is not the default), and then tell it the number of bins.\n",
-    "\n",
-    "For this problem we will use 11 bins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the scattering format to histogram and then define the number of bins\n",
-    "\n",
-    "# Avoid a warning that corrections don't make sense with histogram data\n",
-    "iso_mgxs_lib.correction = None\n",
-    "# Set the histogram data\n",
-    "iso_mgxs_lib.scatter_format = 'histogram'\n",
-    "iso_mgxs_lib.histogram_bins = 11"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ok, we made our isotropic library with histogram-scattering!\n",
-    "\n",
-    "Now why don't we go ahead and create a library to do the same, but with angle-dependent MGXS. That is, we will avoid making the isotropic flux weighting approximation and instead just store a cross section for every polar and azimuthal angle pair.\n",
-    "\n",
-    "To do this with the Python API and OpenMC, all we have to do is set the number of polar and azimuthal bins. Here we only need to set the number of bins, the API will convert all of angular space into equal-width bins for us.\n",
-    "\n",
-    "Since this problem is symmetric in the z-direction, we only need to concern ourselves with the azimuthal variation here.  We will use eight angles.\n",
-    "\n",
-    "Ok, we will repeat all the above steps for a new library object, but will also set the number of azimuthal bins at the end."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Let's repeat all of the above for an angular MGXS library so we can gather\n",
-    "# that in the same continuous-energy calculation\n",
-    "angle_mgxs_lib = openmc.mgxs.Library(geometry)\n",
-    "angle_mgxs_lib.energy_groups = groups\n",
-    "angle_mgxs_lib.mgxs_types = ['total', 'absorption', 'nu-fission', 'fission',\n",
-    "                             'nu-scatter matrix', 'multiplicity matrix', 'chi']\n",
-    "\n",
-    "angle_mgxs_lib.domain_type = \"mesh\"\n",
-    "angle_mgxs_lib.domains = [mesh]\n",
-    "angle_mgxs_lib.correction = None\n",
-    "angle_mgxs_lib.scatter_format = 'histogram'\n",
-    "angle_mgxs_lib.histogram_bins = 11\n",
-    "\n",
-    "# Set the angular bins to 8\n",
-    "angle_mgxs_lib.num_azimuthal = 8"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that our libraries have been setup, let's make sure they contain the types of cross sections which meet the needs of OpenMC's multi-group solver.  Note that this step is done automatically when writing the Multi-Group Library file later in the process (as part of the `mgxs_lib.write_mg_library()`), but it is a good practice to also run this before spending all the time running OpenMC to generate the cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check the libraries - if no errors are raised, then the library is satisfactory.\n",
-    "iso_mgxs_lib.check_library_for_openmc_mgxs()\n",
-    "angle_mgxs_lib.check_library_for_openmc_mgxs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lastly, we use our two `Library` objects to construct the tallies needed to compute all of the requested multi-group cross sections in each domain.\n",
-    "\n",
-    "We expect a warning here telling us that the default Legendre order is not meaningful since we are using histogram scattering."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mgxs/mgxs.py:4872: UserWarning: The legendre order will be ignored since the scatter format is set to histogram\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Construct all tallies needed for the multi-group cross section library\n",
-    "iso_mgxs_lib.build_library()\n",
-    "angle_mgxs_lib.build_library()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The tallies within the libraries can now be exported to a \"tallies.xml\" input file for OpenMC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "iso_mgxs_lib.add_to_tallies_file(tallies_file, merge=True)\n",
-    "angle_mgxs_lib.add_to_tallies_file(tallies_file, merge=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition, we instantiate a fission rate mesh tally for eventual comparison of results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=1.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=11.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=22.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=12.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=18.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate tally Filter\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "# Instantiate the Tally\n",
-    "tally = openmc.Tally(name='mesh tally')\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['fission']\n",
-    "\n",
-    "# Add tally to collection\n",
-    "tallies_file.append(tally, merge=True)\n",
-    "\n",
-    "# Export all tallies to a \"tallies.xml\" file\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Time to run the calculation and get our results!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 15:15:04\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 0.83621 +/- 0.00102\n",
-      " k-effective (Track-length)  = 0.83692 +/- 0.00114\n",
-      " k-effective (Absorption)    = 0.83714 +/- 0.00103\n",
-      " Combined k-effective        = 0.83686 +/- 0.00084\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To make the files available and not be over-written when running the multi-group calculation, we will now rename the statepoint and summary files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Move the StatePoint File\n",
-    "ce_spfile = './statepoint_ce.h5'\n",
-    "os.rename('statepoint.' + str(batches) + '.h5', ce_spfile)\n",
-    "# Move the Summary file\n",
-    "ce_sumfile = './summary_ce.h5'\n",
-    "os.rename('summary.h5', ce_sumfile)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing\n",
-    "\n",
-    "Our simulation ran successfully and created statepoint and summary output files.  Let's begin by loading the StatePoint file, but not automatically linking the summary file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the statepoint file, but not the summary file, as it is a different filename than expected.\n",
-    "sp = openmc.StatePoint(ce_spfile, autolink=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. This is necessary for the `openmc.Library` to properly process the tally data. We first create a `Summary` object and link it with the statepoint.  Normally this would not need to be performed, but since we have renamed our summary file to avoid conflicts with the Multi-Group calculation's summary file, we will load this in explicitly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "su = openmc.Summary(ce_sumfile)\n",
-    "sp.link_with_summary(su)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed. To create our libraries we simply have to load the tallies from the statepoint into each `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize MGXS Library with OpenMC statepoint data\n",
-    "iso_mgxs_lib.load_from_statepoint(sp)\n",
-    "angle_mgxs_lib.load_from_statepoint(sp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next step will be to prepare the input for OpenMC to use our newly created multi-group data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Isotropic Multi-Group OpenMC Calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now use the `Library` to produce the isotropic multi-group cross section data set for use by the OpenMC multi-group solver.  \n",
-    "\n",
-    "If the model to be run in multi-group mode is the same as the continuous-energy mode, the `openmc.mgxs.Library` class has the ability to directly create the multi-group geometry, materials, and multi-group library for us. \n",
-    "Note that this feature is only useful if the MG model is intended to replicate the CE geometry - it is not useful if the CE library is not the same geometry (like it would be for generating MGXS from a generic spectral region).\n",
-    "\n",
-    "This method creates and assigns the materials automatically, including creating a geometry which is equivalent to our mesh cells for which the cross sections were derived."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Universe instance already exists with id=0.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Allow the API to create our Library, materials, and geometry file\n",
-    "iso_mgxs_file, materials_file, geometry_file = iso_mgxs_lib.create_mg_mode()\n",
-    "\n",
-    "# Tell the materials file what we want to call the multi-group library\n",
-    "materials_file.cross_sections = 'mgxs.h5'\n",
-    "\n",
-    "# Write our newly-created files to disk\n",
-    "iso_mgxs_file.export_to_hdf5('mgxs.h5')\n",
-    "materials_file.export_to_xml()\n",
-    "geometry_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can make the changes we need to the settings file.\n",
-    "These changes are limited to telling OpenMC to run a multi-group calculation and provide the location of our multi-group cross section file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set the energy mode\n",
-    "settings_file.energy_mode = 'multi-group'\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's clear up the tallies file so it doesn't include all the extra tallies for re-generating a multi-group library"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "\n",
-    "# Add our fission rate mesh tally\n",
-    "tallies_file.append(tally)\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before running the calculation let's look at our meshed model. It might not be interesting, but let's take a look anyways."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f39700d15c0>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "geometry_file.root_universe.plot(origin=(length / 2., length / 2., 0.),\n",
-    "                       pixels=(300, 300), width=(length, length),\n",
-    "                       color_by='material')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, we see a 10x10 grid with a different color for every material, sounds good!\n",
-    "\n",
-    "At this point, the problem is set up and we can run the multi-group calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 15:16:33\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 0.82516 +/- 0.00104\n",
-      " k-effective (Track-length)  = 0.82465 +/- 0.00106\n",
-      " k-effective (Absorption)    = 0.82332 +/- 0.00074\n",
-      " Combined k-effective        = 0.82369 +/- 0.00066\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Execute the Isotropic MG OpenMC Run\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we go the angle-dependent case, let's save the StatePoint and Summary files so they don't get over-written"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Move the StatePoint File\n",
-    "iso_mg_spfile = './statepoint_mg_iso.h5'\n",
-    "os.rename('statepoint.' + str(batches) + '.h5', iso_mg_spfile)\n",
-    "# Move the Summary file\n",
-    "iso_mg_sumfile = './summary_mg_iso.h5'\n",
-    "os.rename('summary.h5', iso_mg_sumfile)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Angle-Dependent Multi-Group OpenMC Calculation\n",
-    "\n",
-    "Let's now run the calculation with the angle-dependent multi-group cross sections. This process will be the exact same as above, except this time we will use the angle-dependent Library as our starting point.\n",
-    "\n",
-    "We do not need to re-write the materials, geometry, or tallies file to disk since they are the same as for the isotropic case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Universe instance already exists with id=0.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Let's repeat for the angle-dependent case\n",
-    "angle_mgxs_lib.load_from_statepoint(sp)\n",
-    "angle_mgxs_file, materials_file, geometry_file = angle_mgxs_lib.create_mg_mode()\n",
-    "angle_mgxs_file.export_to_hdf5()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this point, the problem is set up and we can run the multi-group calculation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 15:16:54\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 0.83683 +/- 0.00105\n",
-      " k-effective (Track-length)  = 0.83685 +/- 0.00108\n",
-      " k-effective (Absorption)    = 0.83600 +/- 0.00077\n",
-      " Combined k-effective        = 0.83624 +/- 0.00070\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Execute the angle-dependent OpenMC Run\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Results Comparison\n",
-    "In this section we will compare the eigenvalues and fission rate distributions of the continuous-energy, isotropic multi-group and angle-dependent multi-group cases.\n",
-    "\n",
-    "We will begin by loading the multi-group statepoint files, first the isotropic, then angle-dependent. The angle-dependent was not renamed, so we can autolink its summary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the isotropic statepoint file\n",
-    "iso_mgsp = openmc.StatePoint(iso_mg_spfile, autolink=False)\n",
-    "iso_mgsum = openmc.Summary(iso_mg_sumfile)\n",
-    "iso_mgsp.link_with_summary(iso_mgsum)\n",
-    "\n",
-    "# Load the angle-dependent statepoint file\n",
-    "angle_mgsp = openmc.StatePoint('statepoint.' + str(batches) + '.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Eigenvalue Comparison\n",
-    "Next, we can load the eigenvalues for comparison and do that comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ce_keff = sp.k_combined\n",
-    "iso_mg_keff = iso_mgsp.k_combined\n",
-    "angle_mg_keff = angle_mgsp.k_combined\n",
-    "\n",
-    "# Find eigenvalue bias\n",
-    "iso_bias = 1.0e5 * (ce_keff - iso_mg_keff)\n",
-    "angle_bias = 1.0e5 * (ce_keff - angle_mg_keff)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's compare the eigenvalues in units of pcm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Isotropic to CE Bias [pcm]: 1317.7\n",
-      "Angle to CE Bias [pcm]: 62.5\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('Isotropic to CE Bias [pcm]: {0:1.1f}'.format(iso_bias.nominal_value))\n",
-    "print('Angle to CE Bias [pcm]: {0:1.1f}'.format(angle_bias.nominal_value))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see a large reduction in error by switching to the usage of angle-dependent multi-group cross sections! \n",
-    "\n",
-    "Of course, this rodded and partially voided BWR problem was chosen specifically to exacerbate the angular variation of the reaction rates (and thus cross sections). Such improvements should not be expected in every case, especially if localized absorbers are not present.\n",
-    "\n",
-    "It is important to note that both eigenvalues can be improved by the application of finer geometric or energetic discretizations, but this shows that the angle discretization may be a factor for consideration.\n",
-    "\n",
-    "### Fission Rate Distribution Comparison\n",
-    "Next we will visualize the mesh tally results obtained from our three cases.\n",
-    "\n",
-    "This will be performed by first obtaining the one-group fission rate tally information from our state point files. After we have this information we will re-shape the data to match the original mesh laydown. We will then normalize, and finally create side-by-side plots of all."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sp_files = [sp, iso_mgsp, angle_mgsp]\n",
-    "titles = ['Continuous-Energy', 'Isotropic Multi-Group',\n",
-    "         'Angle-Dependent Multi-Group']\n",
-    "fiss_rates = []\n",
-    "fig = plt.figure(figsize=(12, 6))\n",
-    "for i, (case, title) in enumerate(zip(sp_files, titles)):\n",
-    "    # Get our mesh tally information\n",
-    "    mesh_tally = case.get_tally(name='mesh tally')\n",
-    "    fiss_rates.append(mesh_tally.get_values(scores=['fission']))\n",
-    "    \n",
-    "    # Reshape the array\n",
-    "    fiss_rates[-1].shape = mesh.dimension\n",
-    "    \n",
-    "    # Normalize the fission rates\n",
-    "    fiss_rates[-1] /= np.mean(fiss_rates[-1][fiss_rates[-1] > 0.])\n",
-    "    \n",
-    "    # Set 0s to NaNs so they show as white\n",
-    "    fiss_rates[-1][fiss_rates[-1] == 0.] = np.nan\n",
-    "\n",
-    "    fig = plt.subplot(1, len(titles), i + 1)\n",
-    "    # Plot only the fueled regions\n",
-    "    plt.imshow(fiss_rates[-1][1:-1, 1:-1], cmap='jet', origin='lower',\n",
-    "               vmin=0.4, vmax=4.)\n",
-    "    plt.title(title + '\\nFission Rates')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "With this colormap, dark blue is the lowest power and dark red is the highest power.\n",
-    "\n",
-    "We see general agreement between the fission rate distributions, but it looks like there may be less of a gradient near the rods in the continuous-energy and angle-dependent MGXS cases than in the isotropic MGXS case.  \n",
-    "\n",
-    "To better see the differences, let's plot ratios of the fission powers for our two multi-group cases compared to the continuous-energy case t"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f396fdc32b0>"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Calculate and plot the ratios of MG to CE for each of the 2 MG cases\n",
-    "ratios = []\n",
-    "fig, axes = plt.subplots(figsize=(12, 6), nrows=1, ncols=2)\n",
-    "for i, (case, title, axis) in enumerate(zip(sp_files[1:], titles[1:], axes.flat)):\n",
-    "    # Get our ratio relative to the CE (in fiss_ratios[0])\n",
-    "    ratios.append(np.divide(fiss_rates[i + 1], fiss_rates[0]))\n",
-    "    \n",
-    "    # Plot only the fueled regions\n",
-    "    im = axis.imshow(ratios[-1][1:-1, 1:-1], cmap='bwr', origin='lower',\n",
-    "                     vmin = 0.9, vmax = 1.1)\n",
-    "    axis.set_title(title + '\\nFission Rates Relative\\nto Continuous-Energy')\n",
-    "    \n",
-    "# Add a color bar\n",
-    "fig.subplots_adjust(right=0.8)\n",
-    "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
-    "fig.colorbar(im, cax=cbar_ax)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With this ratio its clear that the errors are significantly worse in the isotropic case. These errors are conveniently located right where the most anisotropy is espected: by the control blades and by the Gd-bearing pins!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Close all StatePoint files as a matter of best practice\n",
-    "for sp_file in sp_files:\n",
-    "    sp_file.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/mgxs-part-i.ipynb b/examples/jupyter/mgxs-part-i.ipynb
deleted file mode 100644
index 10e02e96c..000000000
--- a/examples/jupyter/mgxs-part-i.ipynb
+++ /dev/null
@@ -1,1191 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup Cross Section Generation Part I: Introduction\n",
-    "This IPython Notebook introduces the use of the `openmc.mgxs` module to calculate multi-group cross sections for an infinite homogeneous medium. In particular, this Notebook introduces the the following features:\n",
-    "\n",
-    "* **General equations** for scalar-flux averaged multi-group cross sections\n",
-    "* Creation of multi-group cross sections for an **infinite homogeneous medium**\n",
-    "* Use of **tally arithmetic** to manipulate multi-group cross sections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction to Multi-Group Cross Sections (MGXS)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Many Monte Carlo particle transport codes, including OpenMC, use continuous-energy nuclear cross section data. However, most deterministic neutron transport codes use *multi-group cross sections* defined over discretized energy bins or *energy groups*. An example of U-235's continuous-energy fission cross section along with a 16-group cross section computed for a light water reactor spectrum is displayed below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 350
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename='images/mgxs.png', width=350)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A variety of tools employing different methodologies have been developed over the years to compute multi-group cross sections for certain applications, including NJOY (LANL), MC$^2$-3 (ANL), and Serpent (VTT). The `openmc.mgxs` Python module is designed to leverage OpenMC's tally system to calculate multi-group cross sections with arbitrary energy discretizations for fine-mesh heterogeneous deterministic neutron transport applications.\n",
-    "\n",
-    "Before proceeding to illustrate how one may use the `openmc.mgxs` module, it is worthwhile to define the general equations used to calculate multi-group cross sections. This is only intended as a brief overview of the methodology used by `openmc.mgxs` - we refer the interested reader to the large body of literature on the subject for a more comprehensive understanding of this complex topic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Introductory Notation\n",
-    "The continuous real-valued microscopic cross section may be denoted $\\sigma_{n,x}(\\mathbf{r}, E)$ for position vector $\\mathbf{r}$, energy $E$, nuclide $n$ and interaction type $x$. Similarly, the scalar neutron flux may be denoted by $\\Phi(\\mathbf{r},E)$ for position $\\mathbf{r}$ and energy $E$. **Note**: Although nuclear cross sections are dependent on the temperature $T$ of the interacting medium, the temperature variable is neglected here for brevity."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Spatial and Energy Discretization\n",
-    "The energy domain for critical systems such as thermal reactors spans more than 10 orders of magnitude of neutron energies from 10$^{-5}$ - 10$^7$ eV. The multi-group approximation discretization divides this energy range into one or more energy groups. In particular, for $G$ total groups, we denote an energy group index $g$ such that $g \\in \\{1, 2, ..., G\\}$. The energy group indices are defined such that the smaller group the higher the energy, and vice versa. The integration over neutron energies across a discrete energy group is commonly referred to as **energy condensation**.\n",
-    "\n",
-    "Multi-group cross sections are computed for discretized spatial zones in the geometry of interest. The spatial zones may be defined on a structured and regular fuel assembly or pin cell mesh, an arbitrary unstructured mesh or the constructive solid geometry used by OpenMC. For a geometry with $K$ distinct spatial zones, we designate each spatial zone an index $k$ such that $k \\in \\{1, 2, ..., K\\}$. The volume of each spatial zone is denoted by $V_{k}$. The integration over discrete spatial zones is commonly referred to as **spatial homogenization**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### General Scalar-Flux Weighted MGXS\n",
-    "The multi-group cross sections computed by `openmc.mgxs` are defined as a *scalar flux-weighted average* of the microscopic cross sections across each discrete energy group. This formulation is employed in order to preserve the reaction rates within each energy group and spatial zone. In particular, spatial homogenization and energy condensation are used to compute the general multi-group cross section $\\sigma_{n,x,k,g}$ as follows:\n",
-    "\n",
-    "$$\\sigma_{n,x,k,g} = \\frac{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,x}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
-    "\n",
-    "This scalar flux-weighted average microscopic cross section is computed by `openmc.mgxs` for most multi-group cross sections, including total, absorption, and fission reaction types. These double integrals are stochastically computed with OpenMC's tally system - in particular, [filters](../usersguide/tallies.rst#filters) on the energy range and spatial zone (material, cell or universe) define the bounds of integration for both numerator and denominator."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-Group Scattering Matrices\n",
-    "The general multi-group cross section $\\sigma_{n,x,k,g}$ is a vector of $G$ values for each energy group $g$. The equation presented above only discretizes the energy of the incoming neutron and neglects the outgoing energy of the neutron (if any). Hence, this formulation must be extended to account for the outgoing energy of neutrons in the discretized scattering matrix cross section used by deterministic neutron transport codes. \n",
-    "\n",
-    "We denote the incoming and outgoing neutron energy groups as $g$ and $g'$ for the microscopic scattering matrix cross section $\\sigma_{n,s}(\\mathbf{r},E)$. As before, spatial homogenization and energy condensation are used to find the multi-group scattering matrix cross section $\\sigma_{n,s,k,g \\to g'}$ as follows:\n",
-    "\n",
-    "$$\\sigma_{n,s,k,g\\rightarrow g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\sigma_{n,s}(\\mathbf{r},E'\\rightarrow E'')\\Phi(\\mathbf{r},E')}{\\int_{E_{g}}^{E_{g-1}}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\Phi(\\mathbf{r},E')}$$\n",
-    "\n",
-    "This scalar flux-weighted multi-group microscopic scattering matrix is computed using OpenMC tallies with both energy in and energy out filters."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Multi-Group Fission Spectrum\n",
-    "The energy spectrum of neutrons emitted from fission is denoted by $\\chi_{n}(\\mathbf{r},E' \\rightarrow E'')$ for incoming and outgoing energies $E'$ and $E''$, respectively. Unlike the multi-group cross sections $\\sigma_{n,x,k,g}$ considered up to this point, the fission spectrum is a probability distribution and must sum to unity. The outgoing energy is typically much less dependent on the incoming energy for fission than for scattering interactions. As a result, it is common practice to integrate over the incoming neutron energy when computing the multi-group fission spectrum. The fission spectrum may be simplified as $\\chi_{n}(\\mathbf{r},E)$ with outgoing energy $E$.\n",
-    "\n",
-    "Unlike the multi-group cross sections defined up to this point, the multi-group fission spectrum is weighted by the fission production rate rather than the scalar flux. This formulation is intended to preserve the total fission production rate in the multi-group deterministic calculation. In order to mathematically define the multi-group fission spectrum, we denote the microscopic fission cross section as $\\sigma_{n,f}(\\mathbf{r},E)$ and the average number of neutrons emitted from fission interactions with nuclide $n$ as $\\nu_{n}(\\mathbf{r},E)$. The multi-group fission spectrum $\\chi_{n,k,g}$ is then the probability of fission neutrons emitted into energy group $g$. \n",
-    "\n",
-    "Similar to before, spatial homogenization and energy condensation are used to find the multi-group fission spectrum $\\chi_{n,k,g}$ as follows:\n",
-    "\n",
-    "$$\\chi_{n,k,g'} = \\frac{\\int_{E_{g'}}^{E_{g'-1}}\\mathrm{d}E''\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\chi_{n}(\\mathbf{r},E'\\rightarrow E'')\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}{\\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r}\\nu_{n}(\\mathbf{r},E')\\sigma_{n,f}(\\mathbf{r},E')\\Phi(\\mathbf{r},E')}$$\n",
-    "\n",
-    "The fission production-weighted multi-group fission spectrum is computed using OpenMC tallies with both energy in and energy out filters.\n",
-    "\n",
-    "This concludes our brief overview on the methodology to compute multi-group cross sections. The following sections detail more concretely how users may employ the `openmc.mgxs` module to power simulation workflows requiring multi-group cross sections for downstream deterministic calculations."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import openmc\n",
-    "import openmc.mgxs as mgxs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We being by creating a material for the homogeneous medium."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Material and register the Nuclides\n",
-    "inf_medium = openmc.Material(name='moderator')\n",
-    "inf_medium.set_density('g/cc', 5.)\n",
-    "inf_medium.add_nuclide('H1',  0.028999667)\n",
-    "inf_medium.add_nuclide('O16', 0.01450188)\n",
-    "inf_medium.add_nuclide('U235', 0.000114142)\n",
-    "inf_medium.add_nuclide('U238', 0.006886019)\n",
-    "inf_medium.add_nuclide('Zr90', 0.002116053)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our material, we can now create a `Materials` object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection and export to XML\n",
-    "materials_file = openmc.Materials([inf_medium])\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. This problem will be a simple square cell with reflective boundary conditions to simulate an infinite homogeneous medium. The first step is to create the outer bounding surfaces of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate boundary Planes\n",
-    "min_x = openmc.XPlane(boundary_type='reflective', x0=-0.63)\n",
-    "max_x = openmc.XPlane(boundary_type='reflective', x0=0.63)\n",
-    "min_y = openmc.YPlane(boundary_type='reflective', y0=-0.63)\n",
-    "max_y = openmc.YPlane(boundary_type='reflective', y0=0.63)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now create a cell that is defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Cell\n",
-    "cell = openmc.Cell(cell_id=1, name='cell')\n",
-    "\n",
-    "# Register bounding Surfaces with the Cell\n",
-    "cell.region = +min_x & -max_x & +min_y & -max_y\n",
-    "\n",
-    "# Fill the Cell with the Material\n",
-    "cell.fill = inf_medium"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root universe and add our square cell to it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root universe\n",
-    "root_universe = openmc.Universe(name='root universe', cells=[cell])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry that is assigned a root universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "openmc_geometry = openmc.Geometry(root_universe)\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "openmc_geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 50\n",
-    "inactive = 10\n",
-    "particles = 2500\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a 2-group EnergyGroups object\n",
-    "groups = mgxs.EnergyGroups()\n",
-    "groups.group_edges = np.array([0., 0.625, 20.0e6])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now use the `EnergyGroups` object, along with our previously created materials and geometry, to instantiate some `MGXS` objects from the `openmc.mgxs` module. In particular, the following are subclasses of the generic and abstract `MGXS` class:\n",
-    "\n",
-    "* `TotalXS`\n",
-    "* `TransportXS`\n",
-    "* `AbsorptionXS`\n",
-    "* `CaptureXS`\n",
-    "* `FissionXS`\n",
-    "* `KappaFissionXS`\n",
-    "* `ScatterXS`\n",
-    "* `ScatterMatrixXS`\n",
-    "* `Chi`\n",
-    "* `ChiPrompt`\n",
-    "* `InverseVelocity`\n",
-    "* `PromptNuFissionXS`\n",
-    "\n",
-    "Of course, we are aware that the fission cross section (`FissionXS`) can sometimes be paired with the fission neutron multiplication to become $\\nu\\sigma_f$. This can be accomodated in to the `FissionXS` class by setting the `nu` parameter to `True` as shown below.\n",
-    "\n",
-    "Additionally, scattering reactions (like (n,2n)) can also be defined to take in to account the neutron multiplication to become $\\nu\\sigma_s$. This can be accomodated in the the transport (`TransportXS`), scattering (`ScatterXS`), and scattering-matrix (`ScatterMatrixXS`) cross sections types by setting the `nu` parameter to `True` as shown below.\n",
-    "\n",
-    "These classes provide us with an interface to generate the tally inputs as well as perform post-processing of OpenMC's tally data to compute the respective multi-group cross sections. In this case, let's create the multi-group total, absorption and scattering cross sections with our 2-group structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a few different sections\n",
-    "total = mgxs.TotalXS(domain=cell, groups=groups)\n",
-    "absorption = mgxs.AbsorptionXS(domain=cell, groups=groups)\n",
-    "scattering = mgxs.ScatterXS(domain=cell, groups=groups)\n",
-    "\n",
-    "# Note that if we wanted to incorporate neutron multiplication in the\n",
-    "# scattering cross section we would write the previous line as:\n",
-    "# scattering = mgxs.ScatterXS(domain=cell, groups=groups, nu=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each multi-group cross section object stores its tallies in a Python dictionary called `tallies`. We can inspect the tallies in the dictionary for our `Absorption` object as follows. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('flux',\n",
-       "              Tally\n",
-       "              \tID             =\t1\n",
-       "              \tName           =\t\n",
-       "              \tFilters        =\tCellFilter, EnergyFilter\n",
-       "              \tNuclides       =\ttotal\n",
-       "              \tScores         =\t['flux']\n",
-       "              \tEstimator      =\ttracklength),\n",
-       "             ('absorption',\n",
-       "              Tally\n",
-       "              \tID             =\t2\n",
-       "              \tName           =\t\n",
-       "              \tFilters        =\tCellFilter, EnergyFilter\n",
-       "              \tNuclides       =\ttotal\n",
-       "              \tScores         =\t['absorption']\n",
-       "              \tEstimator      =\ttracklength)])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "absorption.tallies"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `Absorption` object includes tracklength tallies for the 'absorption' and 'flux' scores in the 2-group structure in cell 1. Now that each `MGXS` object contains the tallies that it needs, we must add these tallies to a `Tallies` object to generate the \"tallies.xml\" input file for OpenMC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()\n",
-    "\n",
-    "# Add total tallies to the tallies file\n",
-    "tallies_file += total.tallies.values()\n",
-    "\n",
-    "# Add absorption tallies to the tallies file\n",
-    "tallies_file += absorption.tallies.values()\n",
-    "\n",
-    "# Add scattering tallies to the tallies file\n",
-    "tallies_file += scattering.tallies.values()\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-28 08:44:52\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n",
-      " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n",
-      " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n",
-      " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for H1\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.18505\n",
-      "        2/1    1.17297\n",
-      "        3/1    1.16184\n",
-      "        4/1    1.14929\n",
-      "        5/1    1.09928\n",
-      "        6/1    1.18675\n",
-      "        7/1    1.19772\n",
-      "        8/1    1.17470\n",
-      "        9/1    1.17208\n",
-      "       10/1    1.09993\n",
-      "       11/1    1.14342\n",
-      "       12/1    1.10127    1.12234 +/- 0.02107\n",
-      "       13/1    1.19914    1.14794 +/- 0.02834\n",
-      "       14/1    1.18411    1.15698 +/- 0.02199\n",
-      "       15/1    1.14556    1.15470 +/- 0.01718\n",
-      "       16/1    1.20337    1.16281 +/- 0.01621\n",
-      "       17/1    1.13853    1.15934 +/- 0.01413\n",
-      "       18/1    1.18208    1.16218 +/- 0.01256\n",
-      "       19/1    1.11842    1.15732 +/- 0.01210\n",
-      "       20/1    1.15248    1.15684 +/- 0.01083\n",
-      "       21/1    1.14903    1.15613 +/- 0.00982\n",
-      "       22/1    1.23456    1.16266 +/- 0.01110\n",
-      "       23/1    1.18876    1.16467 +/- 0.01040\n",
-      "       24/1    1.13591    1.16262 +/- 0.00985\n",
-      "       25/1    1.19559    1.16481 +/- 0.00943\n",
-      "       26/1    1.16947    1.16511 +/- 0.00882\n",
-      "       27/1    1.13198    1.16316 +/- 0.00851\n",
-      "       28/1    1.15329    1.16261 +/- 0.00805\n",
-      "       29/1    1.16538    1.16275 +/- 0.00761\n",
-      "       30/1    1.18229    1.16373 +/- 0.00729\n",
-      "       31/1    1.15060    1.16311 +/- 0.00696\n",
-      "       32/1    1.15460    1.16272 +/- 0.00665\n",
-      "       33/1    1.13875    1.16168 +/- 0.00644\n",
-      "       34/1    1.13479    1.16056 +/- 0.00626\n",
-      "       35/1    1.21125    1.16258 +/- 0.00634\n",
-      "       36/1    1.15914    1.16245 +/- 0.00609\n",
-      "       37/1    1.10457    1.16031 +/- 0.00624\n",
-      "       38/1    1.17215    1.16073 +/- 0.00603\n",
-      "       39/1    1.18462    1.16155 +/- 0.00588\n",
-      "       40/1    1.15361    1.16129 +/- 0.00568\n",
-      "       41/1    1.14983    1.16092 +/- 0.00551\n",
-      "       42/1    1.14087    1.16029 +/- 0.00537\n",
-      "       43/1    1.18725    1.16111 +/- 0.00527\n",
-      "       44/1    1.19094    1.16199 +/- 0.00519\n",
-      "       45/1    1.17371    1.16232 +/- 0.00505\n",
-      "       46/1    1.18552    1.16297 +/- 0.00495\n",
-      "       47/1    1.14194    1.16240 +/- 0.00485\n",
-      "       48/1    1.12045    1.16130 +/- 0.00484\n",
-      "       49/1    1.18476    1.16190 +/- 0.00476\n",
-      "       50/1    1.17063    1.16212 +/- 0.00464\n",
-      " Creating state point statepoint.50.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.9038e-01 seconds\n",
-      "   Reading cross sections          = 2.7861e-01 seconds\n",
-      " Total time in simulation          = 3.9987e+00 seconds\n",
-      "   Time in transport only          = 3.9829e+00 seconds\n",
-      "   Time in inactive batches        = 4.9461e-01 seconds\n",
-      "   Time in active batches          = 3.5041e+00 seconds\n",
-      "   Time synchronizing fission bank = 5.2042e-03 seconds\n",
-      "     Sampling source sites         = 4.4431e-03 seconds\n",
-      "     SEND/RECV source sites        = 7.3804e-04 seconds\n",
-      "   Time accumulating tallies       = 3.6977e-04 seconds\n",
-      "   Time writing statepoints        = 6.6301e-03 seconds\n",
-      " Total time for finalization       = 1.9945e-04 seconds\n",
-      " Total time elapsed                = 4.2948e+00 seconds\n",
-      " Calculation Rate (inactive)       = 50544.8 particles/second\n",
-      " Calculation Rate (active)         = 28537.8 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.16239 +/- 0.00461\n",
-      " k-effective (Track-length)  = 1.16212 +/- 0.00464\n",
-      " k-effective (Absorption)    = 1.15435 +/- 0.00325\n",
-      " Combined k-effective        = 1.15666 +/- 0.00304\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition to the statepoint file, our simulation also created a summary file which encapsulates information about the materials and geometry. By default, a `Summary` object is automatically linked when a `StatePoint` is loaded. This is necessary for the `openmc.mgxs` module to properly process the tally data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the tallies from the statepoint into each MGXS object\n",
-    "total.load_from_statepoint(sp)\n",
-    "absorption.load_from_statepoint(sp)\n",
-    "scattering.load_from_statepoint(sp)\n",
-    "# Close the statepoint file now that we're done getting info\n",
-    "sp.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Voila! Our multi-group cross sections are now ready to rock 'n roll!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting and Storing MGXS Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's first inspect our total cross section by printing it to the screen."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-Group XS\n",
-      "\tReaction Type  =\ttotal\n",
-      "\tDomain Type    =\tcell\n",
-      "\tDomain ID      =\t1\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t6.81e-01 +/- 2.69e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t1.40e+00 +/- 6.00e-01%\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "total.print_xs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the `openmc.mgxs` module uses [tally arithmetic](../examples/tally-arithmetic.rst) under-the-hood, the cross section is stored as a \"derived\" `Tally` object. This means that it can be queried and manipulated using all of the same methods supported for the `Tally` class in the OpenMC Python API. For example, we can construct a [Pandas](https://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>group in</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>0.668083</td>\n",
-       "      <td>0.001798</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>total</td>\n",
-       "      <td>1.292060</td>\n",
-       "      <td>0.007737</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  group in nuclide      mean  std. dev.\n",
-       "1     1         1   total  0.668083   0.001798\n",
-       "0     1         2   total  1.292060   0.007737"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = scattering.get_pandas_dataframe()\n",
-    "df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each multi-group cross section object can be easily exported to a variety of file formats, including CSV, Excel, and LaTeX for storage or data processing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "absorption.export_xs_data(filename='absorption-xs', format='excel')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following code snippet shows how to export all three `MGXS` to the same HDF5 binary data store."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total.build_hdf5_store(filename='mgxs', append=True)\n",
-    "absorption.build_hdf5_store(filename='mgxs', append=True)\n",
-    "scattering.build_hdf5_store(filename='mgxs', append=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Comparing MGXS with Tally Arithmetic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](../examples/tally-arithmetic.rst) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to confirm that the `TotalXS` is equal to the sum of the `AbsorptionXS` and `ScatterXS` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>4.440892e-16</td>\n",
-       "      <td>0.011435</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((total / flux) - (absorption / flux)) - (sca...</td>\n",
-       "      <td>-2.553513e-15</td>\n",
-       "      <td>0.002567</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0     1         0.00e+00          6.25e-01   total   \n",
-       "1     1         6.25e-01          2.00e+07   total   \n",
-       "\n",
-       "                                               score      mean  std. dev.  \n",
-       "0  (((total / flux) - (absorption / flux)) - (sca...  4.44e-16   1.14e-02  \n",
-       "1  (((total / flux) - (absorption / flux)) - (sca... -2.55e-15   2.57e-03  "
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use tally arithmetic to compute the difference between the total, absorption and scattering\n",
-    "difference = total.xs_tally - absorption.xs_tally - scattering.xs_tally\n",
-    "\n",
-    "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
-    "difference.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can use tally arithmetic to compute the ratio of `AbsorptionXS` and `ScatterXS` to the `TotalXS`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>total</td>\n",
-       "      <td>((absorption / flux) / (total / flux))</td>\n",
-       "      <td>0.076103</td>\n",
-       "      <td>0.000658</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>total</td>\n",
-       "      <td>((absorption / flux) / (total / flux))</td>\n",
-       "      <td>0.019441</td>\n",
-       "      <td>0.000093</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0     1         0.00e+00          6.25e-01   total   \n",
-       "1     1         6.25e-01          2.00e+07   total   \n",
-       "\n",
-       "                                    score     mean  std. dev.  \n",
-       "0  ((absorption / flux) / (total / flux)) 7.61e-02   6.58e-04  \n",
-       "1  ((absorption / flux) / (total / flux)) 1.94e-02   9.26e-05  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use tally arithmetic to compute the absorption-to-total MGXS ratio\n",
-    "absorption_to_total = absorption.xs_tally / total.xs_tally\n",
-    "\n",
-    "# The absorption-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n",
-    "absorption_to_total.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>total</td>\n",
-       "      <td>((scatter / flux) / (total / flux))</td>\n",
-       "      <td>0.923897</td>\n",
-       "      <td>0.007833</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>total</td>\n",
-       "      <td>((scatter / flux) / (total / flux))</td>\n",
-       "      <td>0.980559</td>\n",
-       "      <td>0.003729</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0     1         0.00e+00          6.25e-01   total   \n",
-       "1     1         6.25e-01          2.00e+07   total   \n",
-       "\n",
-       "                                 score     mean  std. dev.  \n",
-       "0  ((scatter / flux) / (total / flux)) 9.24e-01   7.83e-03  \n",
-       "1  ((scatter / flux) / (total / flux)) 9.81e-01   3.73e-03  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use tally arithmetic to compute the scattering-to-total MGXS ratio\n",
-    "scattering_to_total = scattering.xs_tally / total.xs_tally\n",
-    "\n",
-    "# The scattering-to-total ratio is a derived tally which can generate Pandas DataFrames for inspection\n",
-    "scattering_to_total.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lastly, we sum the derived scatter-to-total and absorption-to-total ratios to confirm that they sum to unity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.007861</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((absorption / flux) / (total / flux)) + ((sc...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.003731</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0     1         0.00e+00          6.25e-01   total   \n",
-       "1     1         6.25e-01          2.00e+07   total   \n",
-       "\n",
-       "                                               score     mean  std. dev.  \n",
-       "0  (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00   7.86e-03  \n",
-       "1  (((absorption / flux) / (total / flux)) + ((sc... 1.00e+00   3.73e-03  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Use tally arithmetic to ensure that the absorption- and scattering-to-total MGXS ratios sum to unity\n",
-    "sum_ratio = absorption_to_total + scattering_to_total\n",
-    "\n",
-    "# The sum ratio is a derived tally which can generate Pandas DataFrames for inspection\n",
-    "sum_ratio.get_pandas_dataframe()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/mgxs-part-ii.ipynb b/examples/jupyter/mgxs-part-ii.ipynb
deleted file mode 100644
index 495f01211..000000000
--- a/examples/jupyter/mgxs-part-ii.ipynb
+++ /dev/null
@@ -1,2745 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup Cross Section Generation Part II: Advanced Features\n",
-    "This IPython Notebook illustrates the use of the `openmc.mgxs` module to calculate multi-group cross sections for a heterogeneous fuel pin cell geometry. In particular, this Notebook illustrates the following features:\n",
-    "\n",
-    "* Creation of multi-group cross sections on a **heterogeneous geometry**\n",
-    "* Calculation of cross sections on a **nuclide-by-nuclide basis**\n",
-    "* The use of **[tally precision triggers](../io_formats/settings.rst#trigger-element)** with multi-group cross sections\n",
-    "* Built-in features for **energy condensation** in downstream data processing\n",
-    "* The use of the **`openmc.data`** module to plot continuous-energy vs. multi-group cross sections\n",
-    "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n",
-    "\n",
-    "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system in order to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn-dark')\n",
-    "\n",
-    "import openmoc\n",
-    "\n",
-    "import openmc\n",
-    "import openmc.mgxs as mgxs\n",
-    "import openmc.data\n",
-    "from openmc.openmoc_compatible import get_openmoc_geometry\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem. We'll create three distinct materials for water, clad and fuel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6% enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_nuclide('U235', 3.7503e-4)\n",
-    "fuel.add_nuclide('U238', 2.2625e-2)\n",
-    "fuel.add_nuclide('O16', 4.6007e-2)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_nuclide('H1', 4.9457e-2)\n",
-    "water.add_nuclide('O16', 2.4732e-2)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our materials, we can now create a `Materials` object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection\n",
-    "materials_file = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
-    "\n",
-    "# Create box to surround the geometry\n",
-    "box = openmc.model.rectangular_prism(1.26, 1.26, boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
-    "\n",
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "fuel_cell.fill = fuel\n",
-    "fuel_cell.region = -fuel_outer_radius\n",
-    "pin_cell_universe.add_cell(fuel_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "pin_cell_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius & box\n",
-    "pin_cell_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry with the pin cell universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "openmc_geometry = openmc.Geometry(pin_cell_universe)\n",
-    "\n",
-    "# Export to \"geometry.xml\"\n",
-    "openmc_geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we must define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 10,000 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 50\n",
-    "inactive = 10\n",
-    "particles = 10000\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Activate tally precision triggers\n",
-    "settings_file.trigger_active = True\n",
-    "settings_file.trigger_max_batches = settings_file.batches * 4\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we are finally ready to make use of the `openmc.mgxs` module to generate multi-group cross sections! First, let's define \"coarse\" 2-group and \"fine\" 8-group structures using the built-in `EnergyGroups` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a \"coarse\" 2-group EnergyGroups object\n",
-    "coarse_groups = mgxs.EnergyGroups([0., 0.625, 20.0e6])\n",
-    "\n",
-    "# Instantiate a \"fine\" 8-group EnergyGroups object\n",
-    "fine_groups = mgxs.EnergyGroups([0., 0.058, 0.14, 0.28,\n",
-    "                                 0.625, 4.0, 5.53e3, 821.0e3, 20.0e6])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we will instantiate a variety of `MGXS` objects needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we define transport, fission, nu-fission, nu-scatter and chi cross sections for each of the three cells in the fuel pin with the 8-group structure as our energy groups."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extract all Cells filled by Materials\n",
-    "openmc_cells = openmc_geometry.get_all_material_cells().values()\n",
-    "\n",
-    "# Create dictionary to store multi-group cross sections for all cells\n",
-    "xs_library = {}\n",
-    "\n",
-    "# Instantiate 8-group cross sections for each cell\n",
-    "for cell in openmc_cells:\n",
-    "    xs_library[cell.id] = {}\n",
-    "    xs_library[cell.id]['transport']  = mgxs.TransportXS(groups=fine_groups)\n",
-    "    xs_library[cell.id]['fission'] = mgxs.FissionXS(groups=fine_groups)\n",
-    "    xs_library[cell.id]['nu-fission'] = mgxs.FissionXS(groups=fine_groups, nu=True)\n",
-    "    xs_library[cell.id]['nu-scatter'] = mgxs.ScatterMatrixXS(groups=fine_groups, nu=True)\n",
-    "    xs_library[cell.id]['chi'] = mgxs.Chi(groups=fine_groups)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we showcase the use of OpenMC's [tally precision trigger](../io_formats/settings.rst#trigger-element) feature in conjunction with the `openmc.mgxs` module. In particular, we will assign a tally trigger of 1E-2 on the standard deviation for each of the tallies used to compute multi-group cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a tally trigger for +/- 0.01 on each tally used to compute the multi-group cross sections\n",
-    "tally_trigger = openmc.Trigger('std_dev', 1e-2)\n",
-    "\n",
-    "# Add the tally trigger to each of the multi-group cross section tallies\n",
-    "for cell in openmc_cells:\n",
-    "    for mgxs_type in xs_library[cell.id]:\n",
-    "        xs_library[cell.id][mgxs_type].tally_trigger = tally_trigger"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we must loop over all cells to set the cross section domains to the various cells - fuel, clad and moderator - included in the geometry. In addition, we will set each cross section to tally cross sections on a per-nuclide basis through the use of the `MGXS` class' boolean `by_nuclide` instance attribute.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=53.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=21.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=41.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/romano/openmc/openmc/mixin.py:71: IDWarning: Another Filter instance already exists with id=15.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()\n",
-    "\n",
-    "# Iterate over all cells and cross section types\n",
-    "for cell in openmc_cells:\n",
-    "    for rxn_type in xs_library[cell.id]:\n",
-    "\n",
-    "        # Set the cross sections domain to the cell\n",
-    "        xs_library[cell.id][rxn_type].domain = cell\n",
-    "        \n",
-    "        # Tally cross sections by nuclide\n",
-    "        xs_library[cell.id][rxn_type].by_nuclide = True\n",
-    "                \n",
-    "        # Add OpenMC tallies to the tallies file for XML generation\n",
-    "        for tally in xs_library[cell.id][rxn_type].tallies.values():\n",
-    "            tallies_file.append(tally, merge=True)\n",
-    "\n",
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2019 MIT and OpenMC contributors\n",
-      "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
-      "           Version | 0.11.0-dev\n",
-      "          Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n",
-      "         Date/Time | 2019-07-19 07:08:16\n",
-      "    OpenMP Threads | 4\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n",
-      " Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n",
-      " Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n",
-      " Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n",
-      " Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n",
-      " Maximum neutron transport energy: 20000000.000000 eV for U235\n",
-      " Reading tallies XML file...\n",
-      " Writing summary.h5 file...\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.20332\n",
-      "        2/1    1.22209\n",
-      "        3/1    1.24322\n",
-      "        4/1    1.21622\n",
-      "        5/1    1.25850\n",
-      "        6/1    1.22581\n",
-      "        7/1    1.21118\n",
-      "        8/1    1.23377\n",
-      "        9/1    1.24254\n",
-      "       10/1    1.21241\n",
-      "       11/1    1.21042\n",
-      "       12/1    1.23539    1.22290 +/- 0.01249\n",
-      "       13/1    1.22436    1.22339 +/- 0.00723\n",
-      "       14/1    1.22888    1.22476 +/- 0.00529\n",
-      "       15/1    1.22553    1.22491 +/- 0.00410\n",
-      "       16/1    1.24194    1.22775 +/- 0.00439\n",
-      "       17/1    1.24755    1.23058 +/- 0.00466\n",
-      "       18/1    1.21117    1.22815 +/- 0.00471\n",
-      "       19/1    1.22530    1.22784 +/- 0.00417\n",
-      "       20/1    1.20762    1.22582 +/- 0.00424\n",
-      "       21/1    1.20377    1.22381 +/- 0.00433\n",
-      "       22/1    1.24305    1.22541 +/- 0.00426\n",
-      "       23/1    1.22434    1.22533 +/- 0.00392\n",
-      "       24/1    1.22937    1.22562 +/- 0.00364\n",
-      "       25/1    1.22458    1.22555 +/- 0.00339\n",
-      "       26/1    1.18978    1.22332 +/- 0.00388\n",
-      "       27/1    1.20582    1.22229 +/- 0.00379\n",
-      "       28/1    1.22719    1.22256 +/- 0.00358\n",
-      "       29/1    1.21307    1.22206 +/- 0.00343\n",
-      "       30/1    1.20915    1.22141 +/- 0.00331\n",
-      "       31/1    1.22799    1.22173 +/- 0.00317\n",
-      "       32/1    1.21251    1.22131 +/- 0.00305\n",
-      "       33/1    1.20540    1.22062 +/- 0.00299\n",
-      "       34/1    1.20052    1.21978 +/- 0.00299\n",
-      "       35/1    1.24552    1.22081 +/- 0.00304\n",
-      "       36/1    1.21685    1.22066 +/- 0.00293\n",
-      "       37/1    1.22395    1.22078 +/- 0.00282\n",
-      "       38/1    1.22379    1.22089 +/- 0.00272\n",
-      "       39/1    1.20951    1.22049 +/- 0.00265\n",
-      "       40/1    1.25199    1.22154 +/- 0.00277\n",
-      "       41/1    1.23243    1.22190 +/- 0.00270\n",
-      "       42/1    1.20973    1.22152 +/- 0.00264\n",
-      "       43/1    1.24682    1.22228 +/- 0.00268\n",
-      "       44/1    1.20694    1.22183 +/- 0.00263\n",
-      "       45/1    1.22196    1.22183 +/- 0.00256\n",
-      "       46/1    1.20687    1.22142 +/- 0.00252\n",
-      "       47/1    1.22023    1.22139 +/- 0.00245\n",
-      "       48/1    1.22204    1.22140 +/- 0.00239\n",
-      "       49/1    1.22077    1.22139 +/- 0.00232\n",
-      "       50/1    1.23166    1.22164 +/- 0.00228\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.17623 for flux in tally 53\n",
-      " The estimated number of batches is 66\n",
-      " Creating state point statepoint.050.h5...\n",
-      "       51/1    1.20071    1.22113 +/- 0.00228\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.26577 for flux in tally 53\n",
-      " The estimated number of batches is 76\n",
-      "       52/1    1.21423    1.22097 +/- 0.00223\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.24 for flux in tally 53\n",
-      " The estimated number of batches is 75\n",
-      "       53/1    1.25595    1.22178 +/- 0.00233\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.2112 for flux in tally 53\n",
-      " The estimated number of batches is 74\n",
-      "       54/1    1.21806    1.22170 +/- 0.00227\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.18484 for flux in tally 53\n",
-      " The estimated number of batches is 72\n",
-      "       55/1    1.22911    1.22186 +/- 0.00223\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.1596 for flux in tally 53\n",
-      " The estimated number of batches is 71\n",
-      "       56/1    1.23054    1.22205 +/- 0.00219\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.13453 for flux in tally 53\n",
-      " The estimated number of batches is 70\n",
-      "       57/1    1.19384    1.22145 +/- 0.00222\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11914 for flux in tally 53\n",
-      " The estimated number of batches is 69\n",
-      "       58/1    1.20625    1.22114 +/- 0.00220\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11471 for flux in tally 53\n",
-      " The estimated number of batches is 70\n",
-      "       59/1    1.21977    1.22111 +/- 0.00216\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.10334 for flux in tally 53\n",
-      " The estimated number of batches is 70\n",
-      "       60/1    1.20813    1.22085 +/- 0.00213\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09813 for flux in tally 53\n",
-      " The estimated number of batches is 71\n",
-      "       61/1    1.22077    1.22085 +/- 0.00209\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.10221 for flux in tally 53\n",
-      " The estimated number of batches is 72\n",
-      "       62/1    1.21956    1.22082 +/- 0.00205\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11395 for flux in tally 53\n",
-      " The estimated number of batches is 75\n",
-      "       63/1    1.22360    1.22087 +/- 0.00201\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09283 for flux in tally 53\n",
-      " The estimated number of batches is 74\n",
-      "       64/1    1.23955    1.22122 +/- 0.00200\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.07416 for flux in tally 53\n",
-      " The estimated number of batches is 73\n",
-      "       65/1    1.21143    1.22104 +/- 0.00197\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.06461 for flux in tally 53\n",
-      " The estimated number of batches is 73\n",
-      "       66/1    1.21791    1.22099 +/- 0.00194\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.13207 for flux in tally 53\n",
-      " The estimated number of batches is 82\n",
-      "       67/1    1.24897    1.22148 +/- 0.00196\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11277 for flux in tally 53\n",
-      " The estimated number of batches is 81\n",
-      "       68/1    1.22221    1.22149 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09514 for flux in tally 53\n",
-      " The estimated number of batches is 80\n",
-      "       69/1    1.25627    1.22208 +/- 0.00199\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.07653 for flux in tally 53\n",
-      " The estimated number of batches is 79\n",
-      "       70/1    1.21493    1.22196 +/- 0.00196\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.12831 for flux in tally 53\n",
-      " The estimated number of batches is 87\n",
-      "       71/1    1.23406    1.22216 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.11005 for flux in tally 53\n",
-      " The estimated number of batches is 86\n",
-      "       72/1    1.23842    1.22242 +/- 0.00192\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.09352 for flux in tally 53\n",
-      " The estimated number of batches is 85\n",
-      "       73/1    1.24542    1.22279 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.08766 for flux in tally 53\n",
-      " The estimated number of batches is 85\n",
-      "       74/1    1.21314    1.22263 +/- 0.00190\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Triggers unsatisfied, max unc./thresh. is 1.07419 for flux in tally 53\n",
-      " The estimated number of batches is 84\n",
-      "       75/1    1.26484    1.22328 +/- 0.00198\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.06788 for flux in tally 53\n",
-      " The estimated number of batches is 85\n",
-      "       76/1    1.22243    1.22327 +/- 0.00195\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.05164 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       77/1    1.21865    1.22320 +/- 0.00192\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.04022 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       78/1    1.23500    1.22338 +/- 0.00190\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.0275 for flux in tally 53\n",
-      " The estimated number of batches is 82\n",
-      "       79/1    1.22125    1.22334 +/- 0.00187\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.0283 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       80/1    1.23793    1.22355 +/- 0.00186\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.01363 for flux in tally 53\n",
-      " The estimated number of batches is 82\n",
-      "       81/1    1.24238    1.22382 +/- 0.00185\n",
-      " Triggers unsatisfied, max unc./thresh. is 1.01172 for flux in tally 53\n",
-      " The estimated number of batches is 83\n",
-      "       82/1    1.23493    1.22397 +/- 0.00183\n",
-      " Triggers satisfied for batch 82\n",
-      " Creating state point statepoint.082.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 9.5644e-01 seconds\n",
-      "   Reading cross sections          = 9.0579e-01 seconds\n",
-      " Total time in simulation          = 9.9887e+01 seconds\n",
-      "   Time in transport only          = 9.9333e+01 seconds\n",
-      "   Time in inactive batches        = 5.4841e+00 seconds\n",
-      "   Time in active batches          = 9.4403e+01 seconds\n",
-      "   Time synchronizing fission bank = 7.3998e-02 seconds\n",
-      "     Sampling source sites         = 5.9021e-02 seconds\n",
-      "     SEND/RECV source sites        = 1.4787e-02 seconds\n",
-      "   Time accumulating tallies       = 1.2234e-03 seconds\n",
-      " Total time for finalization       = 2.8416e-02 seconds\n",
-      " Total time elapsed                = 1.0094e+02 seconds\n",
-      " Calculation Rate (inactive)       = 18234.5 particles/second\n",
-      " Calculation Rate (active)         = 7626.89 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.22348 +/- 0.00169\n",
-      " k-effective (Track-length)  = 1.22397 +/- 0.00183\n",
-      " k-effective (Absorption)    = 1.22467 +/- 0.00117\n",
-      " Combined k-effective        = 1.22448 +/- 0.00108\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.082.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed by our multi-group cross sections. We simply have to load the tallies from the `StatePoint` into each object as follows and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Iterate over all cells and cross section types\n",
-    "for cell in openmc_cells:\n",
-    "    for rxn_type in xs_library[cell.id]:\n",
-    "        xs_library[cell.id][rxn_type].load_from_statepoint(sp)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's it! Our multi-group cross sections are now ready for the big spotlight. This time we have cross sections in three distinct spatial zones - fuel, clad and moderator - on a per-nuclide basis."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting and Storing MGXS Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's first inspect one of our cross sections by printing it to the screen as a microscopic cross section in units of barns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-Group XS\n",
-      "\tReaction Type  =\tnu-fission\n",
-      "\tDomain Type    =\tcell\n",
-      "\tDomain ID      =\t1\n",
-      "\tNuclide        =\tU235\n",
-      "\tCross Sections [barns]:\n",
-      "            Group 1 [821000.0   - 20000000.0eV]:\t3.30e+00 +/- 2.14e-01%\n",
-      "            Group 2 [5530.0     - 821000.0  eV]:\t3.96e+00 +/- 1.33e-01%\n",
-      "            Group 3 [4.0        - 5530.0    eV]:\t5.50e+01 +/- 2.29e-01%\n",
-      "            Group 4 [0.625      - 4.0       eV]:\t8.85e+01 +/- 3.10e-01%\n",
-      "            Group 5 [0.28       - 0.625     eV]:\t2.90e+02 +/- 3.94e-01%\n",
-      "            Group 6 [0.14       - 0.28      eV]:\t4.49e+02 +/- 4.12e-01%\n",
-      "            Group 7 [0.058      - 0.14      eV]:\t6.87e+02 +/- 3.01e-01%\n",
-      "            Group 8 [0.0        - 0.058     eV]:\t1.44e+03 +/- 2.79e-01%\n",
-      "\n",
-      "\tNuclide        =\tU238\n",
-      "\tCross Sections [barns]:\n",
-      "            Group 1 [821000.0   - 20000000.0eV]:\t1.06e+00 +/- 2.53e-01%\n",
-      "            Group 2 [5530.0     - 821000.0  eV]:\t1.21e-03 +/- 2.60e-01%\n",
-      "            Group 3 [4.0        - 5530.0    eV]:\t5.73e-04 +/- 2.93e+00%\n",
-      "            Group 4 [0.625      - 4.0       eV]:\t6.54e-06 +/- 2.72e-01%\n",
-      "            Group 5 [0.28       - 0.625     eV]:\t1.07e-05 +/- 3.83e-01%\n",
-      "            Group 6 [0.14       - 0.28      eV]:\t1.55e-05 +/- 4.13e-01%\n",
-      "            Group 7 [0.058      - 0.14      eV]:\t2.30e-05 +/- 3.01e-01%\n",
-      "            Group 8 [0.0        - 0.058     eV]:\t4.24e-05 +/- 2.79e-01%\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "nufission = xs_library[fuel_cell.id]['nu-fission']\n",
-    "nufission.print_xs(xs_type='micro', nuclides=['U235', 'U238'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our multi-group cross sections are capable of summing across all nuclides to provide us with macroscopic cross sections as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-Group XS\n",
-      "\tReaction Type  =\tnu-fission\n",
-      "\tDomain Type    =\tcell\n",
-      "\tDomain ID      =\t1\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [821000.0   - 20000000.0eV]:\t2.52e-02 +/- 2.41e-01%\n",
-      "            Group 2 [5530.0     - 821000.0  eV]:\t1.51e-03 +/- 1.31e-01%\n",
-      "            Group 3 [4.0        - 5530.0    eV]:\t2.07e-02 +/- 2.29e-01%\n",
-      "            Group 4 [0.625      - 4.0       eV]:\t3.32e-02 +/- 3.10e-01%\n",
-      "            Group 5 [0.28       - 0.625     eV]:\t1.09e-01 +/- 3.94e-01%\n",
-      "            Group 6 [0.14       - 0.28      eV]:\t1.69e-01 +/- 4.12e-01%\n",
-      "            Group 7 [0.058      - 0.14      eV]:\t2.58e-01 +/- 3.01e-01%\n",
-      "            Group 8 [0.0        - 0.058     eV]:\t5.40e-01 +/- 2.79e-01%\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "nufission = xs_library[fuel_cell.id]['nu-fission']\n",
-    "nufission.print_xs(xs_type='macro', nuclides='sum')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although a printed report is nice, it is not scalable or flexible. Let's extract the microscopic cross section data for the moderator as a [Pandas](https://pandas.pydata.org/) `DataFrame` ."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>group in</th>\n",
-       "      <th>group out</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>126</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>0.233991</td>\n",
-       "      <td>0.003752</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>127</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>1.569288</td>\n",
-       "      <td>0.006360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>1.587279</td>\n",
-       "      <td>0.003098</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>125</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.285599</td>\n",
-       "      <td>0.001422</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>0.010482</td>\n",
-       "      <td>0.000220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>123</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>120</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>0.000009</td>\n",
-       "      <td>0.000006</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>121</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>118</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>0.000005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>119</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     cell  group in  group out nuclide      mean  std. dev.\n",
-       "126     3         1          1      H1  0.233991   0.003752\n",
-       "127     3         1          1     O16  1.569288   0.006360\n",
-       "124     3         1          2      H1  1.587279   0.003098\n",
-       "125     3         1          2     O16  0.285599   0.001422\n",
-       "122     3         1          3      H1  0.010482   0.000220\n",
-       "123     3         1          3     O16  0.000000   0.000000\n",
-       "120     3         1          4      H1  0.000009   0.000006\n",
-       "121     3         1          4     O16  0.000000   0.000000\n",
-       "118     3         1          5      H1  0.000005   0.000005\n",
-       "119     3         1          5     O16  0.000000   0.000000"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n",
-    "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n",
-    "df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we illustate how one can easily take multi-group cross sections and condense them down to a coarser energy group structure. The `MGXS` class includes a `get_condensed_xs(...)` method which takes an `EnergyGroups` parameter with a coarse(r) group structure and returns a new `MGXS` condensed to the coarse groups. We illustrate this process below using the 2-group structure created earlier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extract the 8-group transport cross section for the fuel\n",
-    "fine_xs = xs_library[fuel_cell.id]['transport']\n",
-    "\n",
-    "# Condense to the 2-group structure\n",
-    "condensed_xs = fine_xs.get_condensed_xs(coarse_groups)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Group condensation is as simple as that! We now have a new coarse 2-group `TransportXS` in addition to our original 8-group `TransportXS`. Let's inspect the 2-group `TransportXS` by printing it to the screen and extracting a Pandas `DataFrame` as we have already learned how to do."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-Group XS\n",
-      "\tReaction Type  =\ttransport\n",
-      "\tDomain Type    =\tcell\n",
-      "\tDomain ID      =\t1\n",
-      "\tNuclide        =\tU235\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t7.79e-03 +/- 2.12e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t1.82e-01 +/- 1.92e-01%\n",
-      "\n",
-      "\tNuclide        =\tU238\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t2.17e-01 +/- 1.12e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t2.53e-01 +/- 1.89e-01%\n",
-      "\n",
-      "\tNuclide        =\tO16\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t1.45e-01 +/- 1.12e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t1.74e-01 +/- 2.03e-01%\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "condensed_xs.print_xs()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>group in</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>20.763062</td>\n",
-       "      <td>0.044093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>9.579086</td>\n",
-       "      <td>0.010757</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>3.157274</td>\n",
-       "      <td>0.003531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>485.349036</td>\n",
-       "      <td>0.930937</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>11.199167</td>\n",
-       "      <td>0.021167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>3.788383</td>\n",
-       "      <td>0.007676</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  group in nuclide        mean  std. dev.\n",
-       "3     1         1    U235   20.763062   0.044093\n",
-       "4     1         1    U238    9.579086   0.010757\n",
-       "5     1         1     O16    3.157274   0.003531\n",
-       "0     1         2    U235  485.349036   0.930937\n",
-       "1     1         2    U238   11.199167   0.021167\n",
-       "2     1         2     O16    3.788383   0.007676"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = condensed_xs.get_pandas_dataframe(xs_type='micro')\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Verification with OpenMOC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's verify our cross sections using OpenMOC. First, we construct an equivalent OpenMOC geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create an OpenMOC Geometry from the OpenMC Geometry\n",
-    "openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we we can inject the multi-group cross sections into the equivalent fuel pin cell OpenMOC geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get all OpenMOC cells in the gometry\n",
-    "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n",
-    "\n",
-    "# Inject multi-group cross sections into OpenMOC Materials\n",
-    "for cell_id, cell in openmoc_cells.items():\n",
-    "    \n",
-    "    # Ignore the root cell\n",
-    "    if cell.getName() == 'root cell':\n",
-    "        continue\n",
-    "    \n",
-    "    # Get a reference to the Material filling this Cell\n",
-    "    openmoc_material = cell.getFillMaterial()\n",
-    "    \n",
-    "    # Set the number of energy groups for the Material\n",
-    "    openmoc_material.setNumEnergyGroups(fine_groups.num_groups)\n",
-    "    \n",
-    "    # Extract the appropriate cross section objects for this cell\n",
-    "    transport = xs_library[cell_id]['transport']\n",
-    "    nufission = xs_library[cell_id]['nu-fission']\n",
-    "    nuscatter = xs_library[cell_id]['nu-scatter']\n",
-    "    chi = xs_library[cell_id]['chi']\n",
-    "    \n",
-    "    # Inject NumPy arrays of cross section data into the Material\n",
-    "    # NOTE: Sum across nuclides to get macro cross sections needed by OpenMOC\n",
-    "    openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n",
-    "    openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n",
-    "    openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n",
-    "    openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are now ready to run OpenMOC to verify our cross-sections from OpenMC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Initializing a default angular quadrature...\n",
-      "[  NORMAL ]  Initializing 2D tracks...\n",
-      "[  NORMAL ]  Initializing 2D tracks reflections...\n",
-      "[  NORMAL ]  Initializing 2D tracks array...\n",
-      "[  NORMAL ]  Ray tracing for 2D track segmentation...\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 0.09 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 10.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 19.94 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 29.87 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 39.80 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 49.72 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 59.65 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 69.58 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 79.50 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 89.43 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 100.00 %\n",
-      "[  NORMAL ]  Initializing FSR lookup vectors\n",
-      "[  NORMAL ]  Total number of FSRs 3\n",
-      "[  RESULT ]  Total Track Generation & Segmentation Time...........2.5566E-02 sec\n",
-      "[  NORMAL ]  Initializing MOC eigenvalue solver...\n",
-      "[  NORMAL ]  Initializing solver arrays...\n",
-      "[  NORMAL ]  Centering segments around FSR centroid...\n",
-      "[  NORMAL ]  Max boundary angular flux storage per domain =   0.42 MB\n",
-      "[  NORMAL ]  Max scalar flux storage per domain =   0.00 MB\n",
-      "[  NORMAL ]  Max source storage per domain =   0.00 MB\n",
-      "[  NORMAL ]  Number of azimuthal angles = 128\n",
-      "[  NORMAL ]  Azimuthal ray spacing = 0.100000\n",
-      "[  NORMAL ]  Number of polar angles = 6\n",
-      "[  NORMAL ]  Source type = Flat\n",
-      "[  NORMAL ]  MOC transport undamped\n",
-      "[  NORMAL ]  CMFD acceleration: OFF\n",
-      "[  NORMAL ]  Using 1 threads\n",
-      "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:  k_eff = 0.423133   res = 5.671E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -57686 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 1:  k_eff = 0.475953   res = 2.442E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  5282 D.R. = 4.31\n",
-      "[  NORMAL ]  Iteration 2:  k_eff = 0.491468   res = 4.764E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1551 D.R. = 1.95\n",
-      "[  NORMAL ]  Iteration 3:  k_eff = 0.487446   res = 2.253E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -402 D.R. = 0.47\n",
-      "[  NORMAL ]  Iteration 4:  k_eff = 0.483930   res = 6.957E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -351 D.R. = 0.31\n",
-      "[  NORMAL ]  Iteration 5:  k_eff = 0.477280   res = 3.902E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -665 D.R. = 5.61\n",
-      "[  NORMAL ]  Iteration 6:  k_eff = 0.468938   res = 3.161E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -834 D.R. = 0.81\n",
-      "[  NORMAL ]  Iteration 7:  k_eff = 0.460319   res = 2.480E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -861 D.R. = 0.78\n",
-      "[  NORMAL ]  Iteration 8:  k_eff = 0.450591   res = 9.377E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -972 D.R. = 0.38\n",
-      "[  NORMAL ]  Iteration 9:  k_eff = 0.441377   res = 3.085E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -921 D.R. = 3.29\n",
-      "[  NORMAL ]  Iteration 10:  k_eff = 0.431990   res = 1.028E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -938 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 11:  k_eff = 0.422932   res = 1.180E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -905 D.R. = 1.15\n",
-      "[  NORMAL ]  Iteration 12:  k_eff = 0.414487   res = 1.633E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -844 D.R. = 1.38\n",
-      "[  NORMAL ]  Iteration 13:  k_eff = 0.406708   res = 1.754E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -777 D.R. = 1.07\n",
-      "[  NORMAL ]  Iteration 14:  k_eff = 0.399378   res = 5.021E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -732 D.R. = 2.86\n",
-      "[  NORMAL ]  Iteration 15:  k_eff = 0.393067   res = 9.074E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -631 D.R. = 0.18\n",
-      "[  NORMAL ]  Iteration 16:  k_eff = 0.387427   res = 4.840E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -564 D.R. = 0.53\n",
-      "[  NORMAL ]  Iteration 17:  k_eff = 0.382668   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -475 D.R. = 4.75\n",
-      "[  NORMAL ]  Iteration 18:  k_eff = 0.378741   res = 1.573E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -392 D.R. = 0.68\n",
-      "[  NORMAL ]  Iteration 19:  k_eff = 0.375642   res = 7.017E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -309 D.R. = 4.46\n",
-      "[  NORMAL ]  Iteration 20:  k_eff = 0.373489   res = 4.053E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -215 D.R. = 0.58\n",
-      "[  NORMAL ]  Iteration 21:  k_eff = 0.372357   res = 4.235E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -113 D.R. = 1.04\n",
-      "[  NORMAL ]  Iteration 22:  k_eff = 0.371974   res = 6.352E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -38 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 23:  k_eff = 0.372581   res = 3.267E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  60 D.R. = 0.51\n",
-      "[  NORMAL ]  Iteration 24:  k_eff = 0.374056   res = 1.573E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  147 D.R. = 0.48\n",
-      "[  NORMAL ]  Iteration 25:  k_eff = 0.376384   res = 3.630E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  232 D.R. = 2.31\n",
-      "[  NORMAL ]  Iteration 26:  k_eff = 0.379563   res = 2.420E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  317 D.R. = 0.07\n",
-      "[  NORMAL ]  Iteration 27:  k_eff = 0.383583   res = 3.146E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  401 D.R. = 13.00\n",
-      "[  NORMAL ]  Iteration 28:  k_eff = 0.388380   res = 1.089E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  479 D.R. = 0.35\n",
-      "[  NORMAL ]  Iteration 29:  k_eff = 0.393938   res = 6.049E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 5.56\n",
-      "[  NORMAL ]  Iteration 30:  k_eff = 0.400234   res = 3.267E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  629 D.R. = 0.54\n",
-      "[  NORMAL ]  Iteration 31:  k_eff = 0.407235   res = 2.420E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  700 D.R. = 0.74\n",
-      "[  NORMAL ]  Iteration 32:  k_eff = 0.414884   res = 1.815E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  764 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 33:  k_eff = 0.423172   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  828 D.R. = 0.40\n",
-      "[  NORMAL ]  Iteration 34:  k_eff = 0.432051   res = 6.049E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  887 D.R. = 8.33\n",
-      "[  NORMAL ]  Iteration 35:  k_eff = 0.441471   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  942 D.R. = 0.96\n",
-      "[  NORMAL ]  Iteration 36:  k_eff = 0.451430   res = 2.662E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  995 D.R. = 0.46\n",
-      "[  NORMAL ]  Iteration 37:  k_eff = 0.461853   res = 8.227E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1042 D.R. = 3.09\n",
-      "[  NORMAL ]  Iteration 38:  k_eff = 0.472730   res = 5.928E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1087 D.R. = 0.72\n",
-      "[  NORMAL ]  Iteration 39:  k_eff = 0.484006   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1127 D.R. = 0.39\n",
-      "[  NORMAL ]  Iteration 40:  k_eff = 0.495653   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1164 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 41:  k_eff = 0.507634   res = 7.017E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1198 D.R. = 3.05\n",
-      "[  NORMAL ]  Iteration 42:  k_eff = 0.519914   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1227 D.R. = 0.28\n",
-      "[  NORMAL ]  Iteration 43:  k_eff = 0.532458   res = 1.089E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1254 D.R. = 0.56\n",
-      "[  NORMAL ]  Iteration 44:  k_eff = 0.545234   res = 6.291E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1277 D.R. = 5.78\n",
-      "[  NORMAL ]  Iteration 45:  k_eff = 0.558210   res = 3.509E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1297 D.R. = 0.56\n",
-      "[  NORMAL ]  Iteration 46:  k_eff = 0.571353   res = 3.025E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1314 D.R. = 0.86\n",
-      "[  NORMAL ]  Iteration 47:  k_eff = 0.584635   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1328 D.R. = 0.24\n",
-      "[  NORMAL ]  Iteration 48:  k_eff = 0.598027   res = 4.961E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1339 D.R. = 6.83\n",
-      "[  NORMAL ]  Iteration 49:  k_eff = 0.611500   res = 9.014E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1347 D.R. = 1.82\n",
-      "[  NORMAL ]  Iteration 50:  k_eff = 0.625029   res = 5.203E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1352 D.R. = 0.58\n",
-      "[  NORMAL ]  Iteration 51:  k_eff = 0.638590   res = 1.512E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1356 D.R. = 0.29\n",
-      "[  NORMAL ]  Iteration 52:  k_eff = 0.652158   res = 2.359E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1356 D.R. = 1.56\n",
-      "[  NORMAL ]  Iteration 53:  k_eff = 0.665710   res = 4.598E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1355 D.R. = 1.95\n",
-      "[  NORMAL ]  Iteration 54:  k_eff = 0.679228   res = 2.783E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1351 D.R. = 0.61\n",
-      "[  NORMAL ]  Iteration 55:  k_eff = 0.692689   res = 2.117E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1346 D.R. = 0.76\n",
-      "[  NORMAL ]  Iteration 56:  k_eff = 0.706075   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1338 D.R. = 0.91\n",
-      "[  NORMAL ]  Iteration 57:  k_eff = 0.719370   res = 5.505E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1329 D.R. = 2.84\n",
-      "[  NORMAL ]  Iteration 58:  k_eff = 0.732556   res = 2.238E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1318 D.R. = 0.41\n",
-      "[  NORMAL ]  Iteration 59:  k_eff = 0.745619   res = 7.864E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1306 D.R. = 0.35\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Iteration 60:  k_eff = 0.758546   res = 4.477E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1292 D.R. = 5.69\n",
-      "[  NORMAL ]  Iteration 61:  k_eff = 0.771323   res = 4.658E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1277 D.R. = 1.04\n",
-      "[  NORMAL ]  Iteration 62:  k_eff = 0.783939   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1261 D.R. = 0.21\n",
-      "[  NORMAL ]  Iteration 63:  k_eff = 0.796383   res = 4.235E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1244 D.R. = 0.44\n",
-      "[  NORMAL ]  Iteration 64:  k_eff = 0.808646   res = 9.074E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1226 D.R. = 2.14\n",
-      "[  NORMAL ]  Iteration 65:  k_eff = 0.820719   res = 6.412E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1207 D.R. = 7.07\n",
-      "[  NORMAL ]  Iteration 66:  k_eff = 0.832594   res = 2.420E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1187 D.R. = 0.04\n",
-      "[  NORMAL ]  Iteration 67:  k_eff = 0.844264   res = 2.299E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1167 D.R. = 9.50\n",
-      "[  NORMAL ]  Iteration 68:  k_eff = 0.855724   res = 5.928E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1146 D.R. = 2.58\n",
-      "[  NORMAL ]  Iteration 69:  k_eff = 0.866968   res = 6.049E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1124 D.R. = 1.02\n",
-      "[  NORMAL ]  Iteration 70:  k_eff = 0.877992   res = 2.722E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1102 D.R. = 0.45\n",
-      "[  NORMAL ]  Iteration 71:  k_eff = 0.888792   res = 1.633E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1079 D.R. = 0.60\n",
-      "[  NORMAL ]  Iteration 72:  k_eff = 0.899364   res = 2.117E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1057 D.R. = 1.30\n",
-      "[  NORMAL ]  Iteration 73:  k_eff = 0.909708   res = 3.327E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1034 D.R. = 1.57\n",
-      "[  NORMAL ]  Iteration 74:  k_eff = 0.919819   res = 4.477E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1011 D.R. = 1.35\n",
-      "[  NORMAL ]  Iteration 75:  k_eff = 0.929699   res = 3.569E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  987 D.R. = 0.80\n",
-      "[  NORMAL ]  Iteration 76:  k_eff = 0.939346   res = 4.840E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  964 D.R. = 0.14\n",
-      "[  NORMAL ]  Iteration 77:  k_eff = 0.948758   res = 4.840E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  941 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 78:  k_eff = 0.957938   res = 6.049E-10  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  917 D.R. = 0.12\n",
-      "[  NORMAL ]  Iteration 79:  k_eff = 0.966885   res = 2.057E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  894 D.R. = 34.00\n",
-      "[  NORMAL ]  Iteration 80:  k_eff = 0.975601   res = 4.235E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  871 D.R. = 2.06\n",
-      "[  NORMAL ]  Iteration 81:  k_eff = 0.984087   res = 5.868E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  848 D.R. = 1.39\n",
-      "[  NORMAL ]  Iteration 82:  k_eff = 0.992344   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  825 D.R. = 0.12\n",
-      "[  NORMAL ]  Iteration 83:  k_eff = 1.000375   res = 1.512E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  803 D.R. = 2.08\n",
-      "[  NORMAL ]  Iteration 84:  k_eff = 1.008182   res = 7.864E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  780 D.R. = 0.52\n",
-      "[  NORMAL ]  Iteration 85:  k_eff = 1.015768   res = 7.259E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  758 D.R. = 0.92\n",
-      "[  NORMAL ]  Iteration 86:  k_eff = 1.023136   res = 3.025E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  736 D.R. = 0.42\n",
-      "[  NORMAL ]  Iteration 87:  k_eff = 1.030288   res = 1.210E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  715 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 88:  k_eff = 1.037228   res = 3.690E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  693 D.R. = 3.05\n",
-      "[  NORMAL ]  Iteration 89:  k_eff = 1.043960   res = 5.203E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  673 D.R. = 1.41\n",
-      "[  NORMAL ]  Iteration 90:  k_eff = 1.050486   res = 6.231E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  652 D.R. = 1.20\n",
-      "[  NORMAL ]  Iteration 91:  k_eff = 1.056812   res = 6.775E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  632 D.R. = 1.09\n",
-      "[  NORMAL ]  Iteration 92:  k_eff = 1.062939   res = 2.964E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  612 D.R. = 0.44\n",
-      "[  NORMAL ]  Iteration 93:  k_eff = 1.068872   res = 5.505E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  593 D.R. = 1.86\n",
-      "[  NORMAL ]  Iteration 94:  k_eff = 1.074616   res = 4.235E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  574 D.R. = 0.08\n",
-      "[  NORMAL ]  Iteration 95:  k_eff = 1.080173   res = 2.541E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 6.00\n",
-      "[  NORMAL ]  Iteration 96:  k_eff = 1.085550   res = 1.996E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  537 D.R. = 0.79\n",
-      "[  NORMAL ]  Iteration 97:  k_eff = 1.090748   res = 3.388E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  519 D.R. = 1.70\n",
-      "[  NORMAL ]  Iteration 98:  k_eff = 1.095774   res = 3.085E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  502 D.R. = 0.91\n",
-      "[  NORMAL ]  Iteration 99:  k_eff = 1.100629   res = 3.267E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  485 D.R. = 1.06\n",
-      "[  NORMAL ]  Iteration 100:  k_eff = 1.105320   res = 3.025E-09  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 101:  k_eff = 1.109851   res = 6.654E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 453 D.R. = 2.20\n",
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-      "[  NORMAL ]  ...  = 421 D.R. = 1.46\n",
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-      "[  NORMAL ]  ...  = 291 D.R. = 1.78\n",
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-      "[  NORMAL ]  Iteration 162:  k_eff = 1.213522   res = 1.996E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 35 D.R. = 33.00\n",
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-      "[  NORMAL ]  Iteration 218:  k_eff = 1.220441   res = 6.049E-10  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.03\n",
-      "[  NORMAL ]  Iteration 219:  k_eff = 1.220464   res = 1.270E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 21.00\n",
-      "[  NORMAL ]  Iteration 220:  k_eff = 1.220486   res = 1.452E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 1.14\n",
-      "[  NORMAL ]  Iteration 221:  k_eff = 1.220507   res = 2.299E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 1.58\n",
-      "[  NORMAL ]  Iteration 222:  k_eff = 1.220527   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.42\n",
-      "[  NORMAL ]  Iteration 223:  k_eff = 1.220545   res = 7.078E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 7.31\n",
-      "[  NORMAL ]  Iteration 224:  k_eff = 1.220563   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.14\n",
-      "[  NORMAL ]  Iteration 225:  k_eff = 1.220580   res = 3.146E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 3.25\n",
-      "[  NORMAL ]  Iteration 226:  k_eff = 1.220596   res = 1.633E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.52\n",
-      "[  NORMAL ]  Iteration 227:  k_eff = 1.220612   res = 3.569E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.19\n",
-      "[  NORMAL ]  Iteration 228:  k_eff = 1.220627   res = 2.722E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.76\n",
-      "[  NORMAL ]  Iteration 229:  k_eff = 1.220641   res = 1.875E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.69\n",
-      "[  NORMAL ]  Iteration 230:  k_eff = 1.220655   res = 3.448E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.84\n",
-      "[  NORMAL ]  Iteration 231:  k_eff = 1.220667   res = 8.046E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.33\n",
-      "[  NORMAL ]  Iteration 232:  k_eff = 1.220679   res = 3.751E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.47\n",
-      "[  NORMAL ]  Iteration 233:  k_eff = 1.220690   res = 5.686E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.52\n",
-      "[  NORMAL ]  Iteration 234:  k_eff = 1.220701   res = 1.270E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.22\n",
-      "[  NORMAL ]  Iteration 235:  k_eff = 1.220711   res = 6.715E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 0 D.R. = 5.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Generate tracks for OpenMOC\n",
-    "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, azim_spacing=0.1)\n",
-    "track_generator.generateTracks()\n",
-    "\n",
-    "# Run OpenMOC\n",
-    "solver = openmoc.CPUSolver(track_generator)\n",
-    "solver.computeEigenvalue()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "openmc keff = 1.224484\n",
-      "openmoc keff = 1.220711\n",
-      "bias [pcm]: -377.3\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print report of keff and bias with OpenMC\n",
-    "openmoc_keff = solver.getKeff()\n",
-    "openmc_keff = sp.k_combined.n\n",
-    "bias = (openmoc_keff - openmc_keff) * 1e5\n",
-    "\n",
-    "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
-    "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n",
-    "print('bias [pcm]: {0:1.1f}'.format(bias))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a sanity check, let's run a simulation with the coarse 2-group cross sections to ensure that they also produce a reasonable result."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openmoc_geometry = get_openmoc_geometry(sp.summary.geometry)\n",
-    "openmoc_cells = openmoc_geometry.getRootUniverse().getAllCells()\n",
-    "\n",
-    "# Inject multi-group cross sections into OpenMOC Materials\n",
-    "for cell_id, cell in openmoc_cells.items():\n",
-    "    \n",
-    "    # Ignore the root cell\n",
-    "    if cell.getName() == 'root cell':\n",
-    "        continue\n",
-    "    \n",
-    "    openmoc_material = cell.getFillMaterial()\n",
-    "    openmoc_material.setNumEnergyGroups(coarse_groups.num_groups)\n",
-    "    \n",
-    "    # Extract the appropriate cross section objects for this cell\n",
-    "    transport = xs_library[cell_id]['transport']\n",
-    "    nufission = xs_library[cell_id]['nu-fission']\n",
-    "    nuscatter = xs_library[cell_id]['nu-scatter']\n",
-    "    chi = xs_library[cell_id]['chi']\n",
-    "    \n",
-    "    # Perform group condensation\n",
-    "    transport = transport.get_condensed_xs(coarse_groups)\n",
-    "    nufission = nufission.get_condensed_xs(coarse_groups)\n",
-    "    nuscatter = nuscatter.get_condensed_xs(coarse_groups)\n",
-    "    chi = chi.get_condensed_xs(coarse_groups)\n",
-    "    \n",
-    "    # Inject NumPy arrays of cross section data into the Material\n",
-    "    openmoc_material.setSigmaT(transport.get_xs(nuclides='sum').flatten())\n",
-    "    openmoc_material.setNuSigmaF(nufission.get_xs(nuclides='sum').flatten())\n",
-    "    openmoc_material.setSigmaS(nuscatter.get_xs(nuclides='sum').flatten())\n",
-    "    openmoc_material.setChi(chi.get_xs(nuclides='sum').flatten())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Initializing a default angular quadrature...\n",
-      "[  NORMAL ]  Initializing 2D tracks...\n",
-      "[  NORMAL ]  Initializing 2D tracks reflections...\n",
-      "[  NORMAL ]  Initializing 2D tracks array...\n",
-      "[  NORMAL ]  Ray tracing for 2D track segmentation...\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 0.09 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 10.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 19.94 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 29.87 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 39.80 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 49.72 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 59.65 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 69.58 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 79.50 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 89.43 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 100.00 %\n",
-      "[  NORMAL ]  Initializing FSR lookup vectors\n",
-      "[  NORMAL ]  Total number of FSRs 3\n",
-      "[  RESULT ]  Total Track Generation & Segmentation Time...........3.9517E-02 sec\n",
-      "[  NORMAL ]  Initializing MOC eigenvalue solver...\n",
-      "[  NORMAL ]  Initializing solver arrays...\n",
-      "[  NORMAL ]  Centering segments around FSR centroid...\n",
-      "[  NORMAL ]  Max boundary angular flux storage per domain =   0.10 MB\n",
-      "[  NORMAL ]  Max scalar flux storage per domain =   0.00 MB\n",
-      "[  NORMAL ]  Max source storage per domain =   0.00 MB\n",
-      "[  NORMAL ]  Number of azimuthal angles = 128\n",
-      "[  NORMAL ]  Azimuthal ray spacing = 0.100000\n",
-      "[  NORMAL ]  Number of polar angles = 6\n",
-      "[  NORMAL ]  Source type = Flat\n",
-      "[  NORMAL ]  MOC transport undamped\n",
-      "[  NORMAL ]  CMFD acceleration: OFF\n",
-      "[  NORMAL ]  Using 1 threads\n",
-      "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:  k_eff = 0.366880   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -63312 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 1:  k_eff = 0.391184   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  2430 D.R. = 0.20\n",
-      "[  NORMAL ]  Iteration 2:  k_eff = 0.392990   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  180 D.R. = 6.00\n",
-      "[  NORMAL ]  Iteration 3:  k_eff = 0.381099   res = 9.195E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -1189 D.R. = 1.58\n",
-      "[  NORMAL ]  Iteration 4:  k_eff = 0.375018   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -608 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 5:  k_eff = 0.369593   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -542 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 6:  k_eff = 0.365543   res = 1.065E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -405 D.R. = 5.50\n",
-      "[  NORMAL ]  Iteration 7:  k_eff = 0.363054   res = 1.065E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -248 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 8:  k_eff = 0.361473   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -158 D.R. = 0.18\n",
-      "[  NORMAL ]  Iteration 9:  k_eff = 0.361280   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -19 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 10:  k_eff = 0.362003   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  72 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 11:  k_eff = 0.363718   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  171 D.R. = 2.50\n",
-      "[  NORMAL ]  Iteration 12:  k_eff = 0.366338   res = 1.258E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  262 D.R. = 2.60\n",
-      "[  NORMAL ]  Iteration 13:  k_eff = 0.369804   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  346 D.R. = 0.77\n",
-      "[  NORMAL ]  Iteration 14:  k_eff = 0.373989   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  418 D.R. = 0.40\n",
-      "[  NORMAL ]  Iteration 15:  k_eff = 0.378923   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  493 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 16:  k_eff = 0.384479   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 17:  k_eff = 0.390637   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  615 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 18:  k_eff = 0.397338   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  670 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 19:  k_eff = 0.404533   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  719 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 20:  k_eff = 0.412184   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  765 D.R. = 1.67\n",
-      "[  NORMAL ]  Iteration 21:  k_eff = 0.420253   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  806 D.R. = 0.80\n",
-      "[  NORMAL ]  Iteration 22:  k_eff = 0.428686   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  843 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 23:  k_eff = 0.437462   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  877 D.R. = 1.67\n",
-      "[  NORMAL ]  Iteration 24:  k_eff = 0.446538   res = 1.161E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  907 D.R. = 1.20\n",
-      "[  NORMAL ]  Iteration 25:  k_eff = 0.455883   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  934 D.R. = 0.17\n",
-      "[  NORMAL ]  Iteration 26:  k_eff = 0.465469   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  958 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 27:  k_eff = 0.475265   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  979 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 28:  k_eff = 0.485246   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  998 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 29:  k_eff = 0.495385   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1013 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 30:  k_eff = 0.505661   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1027 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 31:  k_eff = 0.516051   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1038 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 32:  k_eff = 0.526534   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1048 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 33:  k_eff = 0.537092   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1055 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 34:  k_eff = 0.547706   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1061 D.R. = 0.60\n",
-      "[  NORMAL ]  Iteration 35:  k_eff = 0.558361   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1065 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 36:  k_eff = 0.569040   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1067 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 37:  k_eff = 0.579730   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1068 D.R. = 1.67\n",
-      "[  NORMAL ]  Iteration 38:  k_eff = 0.590416   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1068 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 39:  k_eff = 0.601087   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1067 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 40:  k_eff = 0.611731   res = 1.549E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1064 D.R. = 1.60\n",
-      "[  NORMAL ]  Iteration 41:  k_eff = 0.622338   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1060 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 42:  k_eff = 0.632897   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1055 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 43:  k_eff = 0.643400   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1050 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 44:  k_eff = 0.653837   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1043 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 45:  k_eff = 0.664203   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1036 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 46:  k_eff = 0.674488   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1028 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 47:  k_eff = 0.684688   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1020 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 48:  k_eff = 0.694796   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1010 D.R. = 0.40\n",
-      "[  NORMAL ]  Iteration 49:  k_eff = 0.704807   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1001 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 50:  k_eff = 0.714715   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  990 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 51:  k_eff = 0.724517   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  980 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 52:  k_eff = 0.734209   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  969 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 53:  k_eff = 0.743787   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  957 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 54:  k_eff = 0.753247   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  946 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 55:  k_eff = 0.762588   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  934 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 56:  k_eff = 0.771806   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  921 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 57:  k_eff = 0.780901   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  909 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 58:  k_eff = 0.789868   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  896 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 59:  k_eff = 0.798708   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  884 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 60:  k_eff = 0.807419   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  871 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 61:  k_eff = 0.816000   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  858 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 62:  k_eff = 0.824450   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  844 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 63:  k_eff = 0.832768   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  831 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 64:  k_eff = 0.840954   res = 1.742E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  818 D.R. = 2.25\n",
-      "[  NORMAL ]  Iteration 65:  k_eff = 0.849008   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  805 D.R. = 0.44\n",
-      "[  NORMAL ]  Iteration 66:  k_eff = 0.856930   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  792 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 67:  k_eff = 0.864720   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  778 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 68:  k_eff = 0.872378   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  765 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 69:  k_eff = 0.879905   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  752 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 70:  k_eff = 0.887301   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  739 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 71:  k_eff = 0.894566   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  726 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 72:  k_eff = 0.901702   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  713 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 73:  k_eff = 0.908710   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  700 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 74:  k_eff = 0.915590   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  687 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 75:  k_eff = 0.922342   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  675 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 76:  k_eff = 0.928971   res = 1.161E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  662 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 77:  k_eff = 0.935474   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  650 D.R. = 0.17\n",
-      "[  NORMAL ]  Iteration 78:  k_eff = 0.941853   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  637 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 79:  k_eff = 0.948112   res = 9.679E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  625 D.R. = 5.00\n",
-      "[  NORMAL ]  Iteration 80:  k_eff = 0.954249   res = 1.065E-07  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  613 D.R. = 1.10\n",
-      "[  NORMAL ]  Iteration 81:  k_eff = 0.960267   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  601 D.R. = 0.09\n",
-      "[  NORMAL ]  Iteration 82:  k_eff = 0.966168   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  590 D.R. = 8.00\n",
-      "[  NORMAL ]  Iteration 83:  k_eff = 0.971953   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  578 D.R. = 0.75\n",
-      "[  NORMAL ]  Iteration 84:  k_eff = 0.977623   res = 1.936E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  566 D.R. = 0.33\n",
-      "[  NORMAL ]  Iteration 85:  k_eff = 0.983179   res = 2.904E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 86:  k_eff = 0.988624   res = 0.000E+00  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  544 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 87:  k_eff = 0.993958   res = 6.775E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  533 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 88:  k_eff = 0.999184   res = 9.679E-09  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  522 D.R. = 0.14\n",
-      "[  NORMAL ]  Iteration 89:  k_eff = 1.004304   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  511 D.R. = 5.00\n",
-      "[  NORMAL ]  Iteration 90:  k_eff = 1.009318   res = 3.872E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  501 D.R. = 0.80\n",
-      "[  NORMAL ]  Iteration 91:  k_eff = 1.014228   res = 4.840E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  491 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 92:  k_eff = 1.019037   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  480 D.R. = 1.20\n",
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-      "[  NORMAL ]  ...  460 D.R. = 2.33\n",
-      "[  NORMAL ]  Iteration 95:  k_eff = 1.032865   res = 7.743E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  451 D.R. = 1.14\n",
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-      "[  NORMAL ]  ...  441 D.R. = 1.75\n",
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-      "[  NORMAL ]  ...  432 D.R. = 0.43\n",
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-    {
-     "name": "stdout",
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-      "[  NORMAL ]  Iteration 99:  k_eff = 1.049974   res = 5.807E-08  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  413 D.R. = 1.20\n",
-      "[  NORMAL ]  Iteration 100:  k_eff = 1.054024   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 405 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 101:  k_eff = 1.057987   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 396 D.R. = 0.83\n",
-      "[  NORMAL ]  Iteration 102:  k_eff = 1.061864   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 387 D.R. = 0.20\n",
-      "[  NORMAL ]  Iteration 103:  k_eff = 1.065657   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 379 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 104:  k_eff = 1.069367   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 370 D.R. = 1.33\n",
-      "[  NORMAL ]  Iteration 105:  k_eff = 1.072996   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 362 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 106:  k_eff = 1.076545   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 354 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 107:  k_eff = 1.080016   res = 6.775E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 347 D.R. = 3.50\n",
-      "[  NORMAL ]  Iteration 108:  k_eff = 1.083411   res = 1.161E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 339 D.R. = 1.71\n",
-      "[  NORMAL ]  Iteration 109:  k_eff = 1.086731   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 331 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 110:  k_eff = 1.089976   res = 1.936E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 111:  k_eff = 1.093150   res = 7.743E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 112:  k_eff = 1.096253   res = 7.743E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 310 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 113:  k_eff = 1.099286   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 303 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 114:  k_eff = 1.102251   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 296 D.R. = 1.25\n",
-      "[  NORMAL ]  Iteration 115:  k_eff = 1.105150   res = 9.679E-09  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 116:  k_eff = 1.107983   res = 1.452E-07  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 117:  k_eff = 1.110753   res = 4.840E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 118:  k_eff = 1.113459   res = 8.711E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 270 D.R. = 1.80\n",
-      "[  NORMAL ]  Iteration 119:  k_eff = 1.116105   res = 1.065E-07  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 120:  k_eff = 1.118690   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 258 D.R. = 0.36\n",
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-      "[  NORMAL ]  ...  = 252 D.R. = 0.25\n",
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-      "[  NORMAL ]  Iteration 125:  k_eff = 1.130758   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 230 D.R. = inf\n",
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-      "[  NORMAL ]  Iteration 212:  k_eff = 1.214337   res = 2.904E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  Iteration 219:  k_eff = 1.216136   res = 9.679E-08  delta-k (pcm)\n",
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-      "[  NORMAL ]  ...  = 14 D.R. = -nan\n",
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-      "[  NORMAL ]  Iteration 313:  k_eff = 1.224764   res = 1.161E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 314:  k_eff = 1.224786   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.17\n",
-      "[  NORMAL ]  Iteration 315:  k_eff = 1.224809   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 1.50\n",
-      "[  NORMAL ]  Iteration 316:  k_eff = 1.224831   res = 1.065E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 3.67\n",
-      "[  NORMAL ]  Iteration 317:  k_eff = 1.224852   res = 1.161E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 1.09\n",
-      "[  NORMAL ]  Iteration 318:  k_eff = 1.224873   res = 0.000E+00  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 319:  k_eff = 1.224893   res = 7.743E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 320:  k_eff = 1.224913   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.63\n",
-      "[  NORMAL ]  Iteration 321:  k_eff = 1.224932   res = 6.775E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.40\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Iteration 322:  k_eff = 1.224951   res = 8.711E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.29\n",
-      "[  NORMAL ]  Iteration 323:  k_eff = 1.224969   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.11\n",
-      "[  NORMAL ]  Iteration 324:  k_eff = 1.224987   res = 7.743E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 8.00\n",
-      "[  NORMAL ]  Iteration 325:  k_eff = 1.225005   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.25\n",
-      "[  NORMAL ]  Iteration 326:  k_eff = 1.225022   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 327:  k_eff = 1.225039   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 328:  k_eff = 1.225055   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 3.00\n",
-      "[  NORMAL ]  Iteration 329:  k_eff = 1.225071   res = 1.065E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.83\n",
-      "[  NORMAL ]  Iteration 330:  k_eff = 1.225086   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.27\n",
-      "[  NORMAL ]  Iteration 331:  k_eff = 1.225102   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 332:  k_eff = 1.225116   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.00\n",
-      "[  NORMAL ]  Iteration 333:  k_eff = 1.225131   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.00\n",
-      "[  NORMAL ]  Iteration 334:  k_eff = 1.225145   res = 4.840E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.83\n",
-      "[  NORMAL ]  Iteration 335:  k_eff = 1.225159   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.20\n",
-      "[  NORMAL ]  Iteration 336:  k_eff = 1.225172   res = 5.807E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 6.00\n",
-      "[  NORMAL ]  Iteration 337:  k_eff = 1.225185   res = 1.355E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 2.33\n",
-      "[  NORMAL ]  Iteration 338:  k_eff = 1.225198   res = 1.258E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.93\n",
-      "[  NORMAL ]  Iteration 339:  k_eff = 1.225210   res = 0.000E+00  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 340:  k_eff = 1.225222   res = 9.679E-09  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 341:  k_eff = 1.225233   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 4.00\n",
-      "[  NORMAL ]  Iteration 342:  k_eff = 1.225245   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.50\n",
-      "[  NORMAL ]  Iteration 343:  k_eff = 1.225256   res = 0.000E+00  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.00\n",
-      "[  NORMAL ]  Iteration 344:  k_eff = 1.225267   res = 7.743E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = inf\n",
-      "[  NORMAL ]  Iteration 345:  k_eff = 1.225278   res = 2.904E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.38\n",
-      "[  NORMAL ]  Iteration 346:  k_eff = 1.225288   res = 3.872E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 1.33\n",
-      "[  NORMAL ]  Iteration 347:  k_eff = 1.225298   res = 1.936E-08  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 0 D.R. = 0.50\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Generate tracks for OpenMOC\n",
-    "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=128, azim_spacing=0.1)\n",
-    "track_generator.generateTracks()\n",
-    "\n",
-    "# Run OpenMOC\n",
-    "solver = openmoc.CPUSolver(track_generator)\n",
-    "solver.computeEigenvalue()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "openmc keff = 1.224484\n",
-      "openmoc keff = 1.225298\n",
-      "bias [pcm]: 81.4\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print report of keff and bias with OpenMC\n",
-    "openmoc_keff = solver.getKeff()\n",
-    "openmc_keff = sp.k_combined.n\n",
-    "bias = (openmoc_keff - openmc_keff) * 1e5\n",
-    "\n",
-    "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
-    "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n",
-    "print('bias [pcm]: {0:1.1f}'.format(bias))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is a non-trivial bias in both the 2-group and 8-group cases. In the case of a pin cell, one can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n",
-    "\n",
-    "* Appropriate transport-corrected cross sections\n",
-    "* Spatial discretization of OpenMOC's mesh\n",
-    "* Constant-in-angle multi-group cross sections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "## Visualizing MGXS Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is often insightful to generate visual depictions of multi-group cross sections. There are many different types of plots which may be useful for multi-group cross section visualization, only a few of which will be shown here for enrichment and inspiration.\n",
-    "\n",
-    "One particularly useful visualization is a comparison of the continuous-energy and multi-group cross sections for a particular nuclide and reaction type. We illustrate one option for generating such plots with the use of the `openmc.plotter` module to plot continuous-energy cross sections from the openly available cross section library distributed by NNDC.\n",
-    "\n",
-    "The MGXS data can also be plotted using the openmc.plot_xs command, however we will do this manually here to show how the openmc.Mgxs.get_xs method can be used to obtain data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1e-05, 20000000.0)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create a figure of the U-235 continuous-energy fission cross section \n",
-    "fig = openmc.plot_xs('U235', ['fission'])\n",
-    "\n",
-    "# Get the axis to use for plotting the MGXS\n",
-    "ax = fig.gca()\n",
-    "\n",
-    "# Extract energy group bounds and MGXS values to plot\n",
-    "fission = xs_library[fuel_cell.id]['fission']\n",
-    "energy_groups = fission.energy_groups\n",
-    "x = energy_groups.group_edges\n",
-    "y = fission.get_xs(nuclides=['U235'], order_groups='decreasing', xs_type='micro')\n",
-    "y = np.squeeze(y)\n",
-    "\n",
-    "# Fix low energy bound\n",
-    "x[0] = 1.e-5\n",
-    "\n",
-    "# Extend the mgxs values array for matplotlib's step plot\n",
-    "y = np.insert(y, 0, y[0])\n",
-    "\n",
-    "# Create a step plot for the MGXS\n",
-    "ax.plot(x, y, drawstyle='steps', color='r', linewidth=3)\n",
-    "\n",
-    "ax.set_title('U-235 Fission Cross Section')\n",
-    "ax.legend(['Continuous', 'Multi-Group'])\n",
-    "ax.set_xlim((x.min(), x.max()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another useful type of illustration is scattering matrix sparsity structures. First, we extract Pandas `DataFrames` for the H-1 and O-16 scattering matrices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Construct a Pandas DataFrame for the microscopic nu-scattering matrix\n",
-    "nuscatter = xs_library[moderator_cell.id]['nu-scatter']\n",
-    "df = nuscatter.get_pandas_dataframe(xs_type='micro')\n",
-    "\n",
-    "# Slice DataFrame in two for each nuclide's mean values\n",
-    "h1 = df[df['nuclide'] == 'H1']['mean']\n",
-    "o16 = df[df['nuclide'] == 'O16']['mean']\n",
-    "\n",
-    "# Cast DataFrames as NumPy arrays\n",
-    "h1 = h1.values\n",
-    "o16 = o16.values\n",
-    "\n",
-    "# Reshape arrays to 2D matrix for plotting\n",
-    "h1.shape = (fine_groups.num_groups, fine_groups.num_groups)\n",
-    "o16.shape = (fine_groups.num_groups, fine_groups.num_groups)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Matplotlib's `imshow` routine can be used to plot the matrices to illustrate their sparsity structures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create plot of the H-1 scattering matrix\n",
-    "fig = plt.subplot(121)\n",
-    "fig.imshow(h1, interpolation='nearest', cmap='jet')\n",
-    "plt.title('H-1 Scattering Matrix')\n",
-    "plt.xlabel('Group Out')\n",
-    "plt.ylabel('Group In')\n",
-    "\n",
-    "# Create plot of the O-16 scattering matrix\n",
-    "fig2 = plt.subplot(122)\n",
-    "fig2.imshow(o16, interpolation='nearest', cmap='jet')\n",
-    "plt.title('O-16 Scattering Matrix')\n",
-    "plt.xlabel('Group Out')\n",
-    "plt.ylabel('Group In')\n",
-    "\n",
-    "# Show the plot on screen\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/mgxs-part-iii.ipynb b/examples/jupyter/mgxs-part-iii.ipynb
deleted file mode 100644
index 60f44ac61..000000000
--- a/examples/jupyter/mgxs-part-iii.ipynb
+++ /dev/null
@@ -1,1684 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multigroup Cross Section Generation Part III: Libraries\n",
-    "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n",
-    "\n",
-    "* Calculation of multi-group cross sections for a **fuel assembly**\n",
-    "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n",
-    "* **Validation** of multi-group cross sections with **[OpenMOC](https://mit-crpg.github.io/OpenMOC/)**\n",
-    "* Steady-state pin-by-pin **fission rates comparison** between OpenMC and [OpenMOC](https://mit-crpg.github.io/OpenMOC/)\n",
-    "\n",
-    "**Note:** This Notebook was created using [OpenMOC](https://mit-crpg.github.io/OpenMOC/) to verify the multi-group cross-sections generated by OpenMC. You must install [OpenMOC](https://mit-crpg.github.io/OpenMOC/) on your system to run this Notebook in its entirety. In addition, this Notebook illustrates the use of [Pandas](https://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "import pickle\n",
-    "\n",
-    "from IPython.display import Image\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "import openmc\n",
-    "import openmc.mgxs\n",
-    "from openmc.openmoc_compatible import get_openmoc_geometry\n",
-    "import openmoc\n",
-    "import openmoc.process\n",
-    "from openmoc.materialize import load_openmc_mgxs_lib\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6 enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_nuclide('U235', 3.7503e-4)\n",
-    "fuel.add_nuclide('U238', 2.2625e-2)\n",
-    "fuel.add_nuclide('O16', 4.6007e-2)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_nuclide('H1', 4.9457e-2)\n",
-    "water.add_nuclide('O16', 2.4732e-2)\n",
-    "water.add_nuclide('B10', 8.0042e-6)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we can now create a `Materials` object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials object\n",
-    "materials_file = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n",
-    "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n",
-    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n",
-    "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
-    "\n",
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "fuel_cell.fill = fuel\n",
-    "fuel_cell.region = -fuel_outer_radius\n",
-    "fuel_pin_universe.add_cell(fuel_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "fuel_pin_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "fuel_pin_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Likewise, we can construct a control rod guide tube with the same surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a control rod guide tube\n",
-    "guide_tube_universe = openmc.Universe(name='Guide Tube')\n",
-    "\n",
-    "# Create guide tube Cell\n",
-    "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n",
-    "guide_tube_cell.fill = water\n",
-    "guide_tube_cell.region = -fuel_outer_radius\n",
-    "guide_tube_universe.add_cell(guide_tube_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='Guide Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "guide_tube_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "guide_tube_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create fuel assembly Lattice\n",
-    "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n",
-    "assembly.pitch = (1.26, 1.26)\n",
-    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create array indices for guide tube locations in lattice\n",
-    "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n",
-    "                       11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n",
-    "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n",
-    "                       8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n",
-    "\n",
-    "# Initialize an empty 17x17 array of the lattice universes\n",
-    "universes = np.empty((17, 17), dtype=openmc.Universe)\n",
-    "\n",
-    "# Fill the array with the fuel pin and guide tube universes\n",
-    "universes[:,:] = fuel_pin_universe\n",
-    "universes[template_x, template_y] = guide_tube_universe\n",
-    "\n",
-    "# Store the array of universes in the lattice\n",
-    "assembly.universes = universes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the assembly and then assign it to the root universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell')\n",
-    "root_cell.fill = assembly\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry that is assigned a root universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "geometry = openmc.Geometry(root_universe)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 50\n",
-    "inactive = 10\n",
-    "particles = 10000\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': False}\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us also create a plot to verify that our fuel assembly geometry was created successfully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Instantiate a Plot\n",
-    "plot = openmc.Plot.from_geometry(geometry)\n",
-    "plot.pixels = (250, 250)\n",
-    "plot.color_by = 'material'\n",
-    "plot.to_ipython_image()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create an MGXS Library"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we are ready to generate multi-group cross sections! First, let's define a 2-group structure using the built-in `EnergyGroups` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a 2-group EnergyGroups object\n",
-    "groups = openmc.mgxs.EnergyGroups()\n",
-    "groups.group_edges = np.array([0., 0.625, 20.0e6])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we will instantiate an `openmc.mgxs.Library` for the energy groups with the fuel assembly geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize a 2-group MGXS Library for OpenMOC\n",
-    "mgxs_lib = openmc.mgxs.Library(geometry)\n",
-    "mgxs_lib.energy_groups = groups"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we must specify to the `Library` which types of cross sections to compute. In particular, the following are the multi-group cross section `MGXS` subclasses that are mapped to string codes accepted by the `Library` class:\n",
-    "\n",
-    "* `TotalXS` (`\"total\"`)\n",
-    "* `TransportXS` (`\"transport\"` or `\"nu-transport` with `nu` set to `True`)\n",
-    "* `AbsorptionXS` (`\"absorption\"`)\n",
-    "* `CaptureXS` (`\"capture\"`)\n",
-    "* `FissionXS` (`\"fission\"` or `\"nu-fission\"` with `nu` set to `True`)\n",
-    "* `KappaFissionXS` (`\"kappa-fission\"`)\n",
-    "* `ScatterXS` (`\"scatter\"` or `\"nu-scatter\"` with `nu` set to `True`)\n",
-    "* `ScatterMatrixXS` (`\"scatter matrix\"` or `\"nu-scatter matrix\"` with `nu` set to `True`)\n",
-    "* `Chi` (`\"chi\"`)\n",
-    "* `ChiPrompt` (`\"chi prompt\"`)\n",
-    "* `InverseVelocity` (`\"inverse-velocity\"`)\n",
-    "* `PromptNuFissionXS` (`\"prompt-nu-fission\"`)\n",
-    "* `DelayedNuFissionXS` (`\"delayed-nu-fission\"`)\n",
-    "* `ChiDelayed` (`\"chi-delayed\"`)\n",
-    "* `Beta` (`\"beta\"`)\n",
-    "\n",
-    "In this case, let's create the multi-group cross sections needed to run an OpenMOC simulation to verify the accuracy of our cross sections. In particular, we will define `\"nu-transport\"`, `\"nu-fission\"`, `'\"fission\"`, `\"nu-scatter matrix\"` and `\"chi\"` cross sections for our `Library`.\n",
-    "\n",
-    "**Note**: A variety of different approximate transport-corrected total multi-group cross sections (and corresponding scattering matrices) can be found in the literature. At the present time, the `openmc.mgxs` module only supports the `\"P0\"` transport correction. This correction can be turned on and off through the boolean `Library.correction` property which may take values of `\"P0\"` (default) or `None`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify multi-group cross section types to compute\n",
-    "mgxs_lib.mgxs_types = ['nu-transport', 'nu-fission', 'fission', 'nu-scatter matrix', 'chi']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we must specify the type of domain over which we would like the `Library` to compute multi-group cross sections. The domain type corresponds to the type of tally filter to be used in the tallies created to compute multi-group cross sections. At the present time, the `Library` supports `\"material\"`, `\"cell\"`, `\"universe\"`, and `\"mesh\"` domain types. We will use a `\"cell\"` domain type here to compute cross sections in each of the cells in the fuel assembly geometry.\n",
-    "\n",
-    "**Note:** By default, the `Library` class will instantiate `MGXS` objects for each and every domain (material, cell or universe) in the geometry of interest. However, one may specify a subset of these domains to the `Library.domains` property. In our case, we wish to compute multi-group cross sections in each and every cell since they will be needed in our downstream OpenMOC calculation on the identical combinatorial geometry mesh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify a \"cell\" domain type for the cross section tally filters\n",
-    "mgxs_lib.domain_type = 'cell'\n",
-    "\n",
-    "# Specify the cell domains over which to compute multi-group cross sections\n",
-    "mgxs_lib.domains = geometry.get_all_material_cells().values()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can easily instruct the `Library` to compute multi-group cross sections on a nuclide-by-nuclide basis with the boolean `Library.by_nuclide` property. By default, `by_nuclide` is set to `False`, but we will set it to `True` here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compute cross sections on a nuclide-by-nuclide basis\n",
-    "mgxs_lib.by_nuclide = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lastly, we use the `Library` to construct the tallies needed to compute all of the requested multi-group cross sections in each domain and nuclide."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Construct all tallies needed for the multi-group cross section library\n",
-    "mgxs_lib.build_library()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The tallies can now be export to a \"tallies.xml\" input file for OpenMC. \n",
-    "\n",
-    "**NOTE**: At this point the `Library` has constructed nearly 100 distinct `Tally` objects. The overhead to tally in OpenMC scales as $O(N)$ for $N$ tallies, which can become a bottleneck for large tally datasets. To compensate for this, the Python API's `Tally`, `Filter` and `Tallies` classes allow for the smart *merging* of tallies when possible. The `Library` class supports this runtime optimization with the use of the optional `merge` paramter (`False` by default) for the `Library.add_to_tallies_file(...)` method, as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a \"tallies.xml\" file for the MGXS Library\n",
-    "tallies_file = openmc.Tallies()\n",
-    "mgxs_lib.add_to_tallies_file(tallies_file, merge=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In addition, we instantiate a fission rate mesh tally to compare with OpenMOC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a tally Mesh\n",
-    "mesh = openmc.RegularMesh(mesh_id=1)\n",
-    "mesh.dimension = [17, 17]\n",
-    "mesh.lower_left = [-10.71, -10.71]\n",
-    "mesh.upper_right = [+10.71, +10.71]\n",
-    "\n",
-    "# Instantiate tally Filter\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "# Instantiate the Tally\n",
-    "tally = openmc.Tally(name='mesh tally')\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['fission', 'nu-fission']\n",
-    "\n",
-    "# Add tally to collection\n",
-    "tallies_file.append(tally)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=126.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=21.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=4.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=96.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=15.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=114.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Export all tallies to a \"tallies.xml\" file\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 16:01:58\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /opt/data/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /opt/data/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /opt/data/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /opt/data/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /opt/data/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /opt/data/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.04638\n",
-      "        2/1    1.00498\n",
-      "        3/1    1.00535\n",
-      "        4/1    1.02695\n",
-      "        5/1    1.00781\n",
-      "        6/1    1.02035\n",
-      "        7/1    1.03808\n",
-      "        8/1    1.02532\n",
-      "        9/1    1.02783\n",
-      "       10/1    1.01371\n",
-      "       11/1    1.03205\n",
-      "       12/1    1.01947    1.02576 +/- 0.00629\n",
-      "       13/1    1.01843    1.02332 +/- 0.00438\n",
-      "       14/1    1.03269    1.02566 +/- 0.00388\n",
-      "       15/1    1.03879    1.02829 +/- 0.00399\n",
-      "       16/1    1.02251    1.02732 +/- 0.00340\n",
-      "       17/1    1.01274    1.02524 +/- 0.00355\n",
-      "       18/1    1.02454    1.02515 +/- 0.00307\n",
-      "       19/1    1.01993    1.02457 +/- 0.00277\n",
-      "       20/1    1.00665    1.02278 +/- 0.00306\n",
-      "       21/1    1.02200    1.02271 +/- 0.00277\n",
-      "       22/1    1.03748    1.02394 +/- 0.00281\n",
-      "       23/1    1.02803    1.02425 +/- 0.00260\n",
-      "       24/1    1.03052    1.02470 +/- 0.00245\n",
-      "       25/1    1.02027    1.02441 +/- 0.00230\n",
-      "       26/1    1.02597    1.02450 +/- 0.00216\n",
-      "       27/1    1.01776    1.02411 +/- 0.00206\n",
-      "       28/1    1.02652    1.02424 +/- 0.00195\n",
-      "       29/1    1.01063    1.02353 +/- 0.00198\n",
-      "       30/1    1.03300    1.02400 +/- 0.00194\n",
-      "       31/1    1.02020    1.02382 +/- 0.00185\n",
-      "       32/1    1.03720    1.02443 +/- 0.00187\n",
-      "       33/1    1.02797    1.02458 +/- 0.00179\n",
-      "       34/1    1.01994    1.02439 +/- 0.00172\n",
-      "       35/1    1.02626    1.02446 +/- 0.00166\n",
-      "       36/1    1.03183    1.02475 +/- 0.00162\n",
-      "       37/1    1.03410    1.02509 +/- 0.00159\n",
-      "       38/1    1.00919    1.02452 +/- 0.00164\n",
-      "       39/1    1.04388    1.02519 +/- 0.00171\n",
-      "       40/1    1.01254    1.02477 +/- 0.00171\n",
-      "       41/1    1.01584    1.02448 +/- 0.00168\n",
-      "       42/1    1.01002    1.02403 +/- 0.00169\n",
-      "       43/1    1.00277    1.02339 +/- 0.00176\n",
-      "       44/1    1.00672    1.02289 +/- 0.00177\n",
-      "       45/1    1.03974    1.02338 +/- 0.00179\n",
-      "       46/1    1.00460    1.02285 +/- 0.00181\n",
-      "       47/1    1.03954    1.02331 +/- 0.00182\n",
-      "       48/1    1.01414    1.02306 +/- 0.00179\n",
-      "       49/1    1.02016    1.02299 +/- 0.00174\n",
-      "       50/1    0.99791    1.02236 +/- 0.00181\n",
-      " Creating state point statepoint.50.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.4372e-01 seconds\n",
-      "   Reading cross sections          = 2.2745e-01 seconds\n",
-      " Total time in simulation          = 4.7933e+01 seconds\n",
-      "   Time in transport only          = 4.7887e+01 seconds\n",
-      "   Time in inactive batches        = 3.4521e+00 seconds\n",
-      "   Time in active batches          = 4.4481e+01 seconds\n",
-      "   Time synchronizing fission bank = 2.0174e-02 seconds\n",
-      "     Sampling source sites         = 1.7952e-02 seconds\n",
-      "     SEND/RECV source sites        = 2.1985e-03 seconds\n",
-      "   Time accumulating tallies       = 1.1888e-03 seconds\n",
-      "   Time writing statepoints        = 1.2929e-02 seconds\n",
-      " Total time for finalization       = 3.9800e-07 seconds\n",
-      " Total time elapsed                = 4.8193e+01 seconds\n",
-      " Calculation Rate (inactive)       = 28967.6 particles/second\n",
-      " Calculation Rate (active)         = 8992.65 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.02393 +/- 0.00174\n",
-      " k-effective (Track-length)  = 1.02236 +/- 0.00181\n",
-      " k-effective (Absorption)    = 1.02412 +/- 0.00167\n",
-      " Combined k-effective        = 1.02362 +/- 0.00128\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created statepoint and summary output files. We begin our analysis by instantiating a `StatePoint` object. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.50.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize MGXS Library with OpenMC statepoint data\n",
-    "mgxs_lib.load_from_statepoint(sp)\n",
-    "# Retrieve OpenMC's k-effective value\n",
-    "openmc_keff = sp.k_combined.nominal_value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Voila! Our multi-group cross sections are now ready to rock 'n roll!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extracting and Storing MGXS Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `Library` supports a rich API to automate a variety of tasks, including multi-group cross section data retrieval and storage. We will highlight a few of these features here. First, the `Library.get_mgxs(...)` method allows one to extract an `MGXS` object from the `Library` for a particular domain and cross section type. The following cell illustrates how one may extract the `NuFissionXS` object for the fuel cell.\n",
-    "\n",
-    "**Note:** The `MGXS.get_mgxs(...)` method will accept either the domain *or* the integer domain ID of interest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Retrieve the NuFissionXS object for the fuel cell from the library\n",
-    "fuel_mgxs = mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `NuFissionXS` object supports all of the methods described previously in the `openmc.mgxs` tutorials, such as [Pandas](https://pandas.pydata.org/) `DataFrames`:\n",
-    "Note that since so few histories were simulated, we should expect a few division-by-error errors as some tallies have not yet scored any results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>group in</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>8.099261e-03</td>\n",
-       "      <td>1.626934e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>7.326723e-03</td>\n",
-       "      <td>2.168273e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>3.613773e-01</td>\n",
-       "      <td>1.025247e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>6.739270e-07</td>\n",
-       "      <td>1.911057e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  group in nuclide          mean     std. dev.\n",
-       "3     1         1    U235  8.099261e-03  1.626934e-05\n",
-       "4     1         1    U238  7.326723e-03  2.168273e-05\n",
-       "5     1         1     O16  0.000000e+00  0.000000e+00\n",
-       "0     1         2    U235  3.613773e-01  1.025247e-03\n",
-       "1     1         2    U238  6.739270e-07  1.911057e-09\n",
-       "2     1         2     O16  0.000000e+00  0.000000e+00"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = fuel_mgxs.get_pandas_dataframe()\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can use the `MGXS.print_xs(...)` method to view a string representation of the multi-group cross section data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Multi-Group XS\n",
-      "\tReaction Type  =\tnu-fission\n",
-      "\tDomain Type    =\tcell\n",
-      "\tDomain ID      =\t1\n",
-      "\tNuclide        =\tU235\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t8.10e-03 +/- 2.01e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t3.61e-01 +/- 2.84e-01%\n",
-      "\n",
-      "\tNuclide        =\tU238\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t7.33e-03 +/- 2.96e-01%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t6.74e-07 +/- 2.84e-01%\n",
-      "\n",
-      "\tNuclide        =\tO16\n",
-      "\tCross Sections [cm^-1]:\n",
-      "            Group 1 [0.625      - 20000000.0eV]:\t0.00e+00 +/- 0.00e+00%\n",
-      "            Group 2 [0.0        - 0.625     eV]:\t0.00e+00 +/- 0.00e+00%\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/tallies.py:1216: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  data = self.std_dev[indices] / self.mean[indices]\n"
-     ]
-    }
-   ],
-   "source": [
-    "fuel_mgxs.print_xs()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One can export the entire `Library` to HDF5 with the `Library.build_hdf5_store(...)` method as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Store the cross section data in an \"mgxs/mgxs.h5\" HDF5 binary file\n",
-    "mgxs_lib.build_hdf5_store(filename='mgxs.h5', directory='mgxs')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The HDF5 store will contain the numerical multi-group cross section data indexed by domain, nuclide and cross section type. Some data workflows may be optimized by storing and retrieving binary representations of the `MGXS` objects in the `Library`. This feature is supported through the `Library.dump_to_file(...)` and `Library.load_from_file(...)` routines which use Python's [`pickle`](https://docs.python.org/3/library/pickle.html) module. This is illustrated as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Store a Library and its MGXS objects in a pickled binary file \"mgxs/mgxs.pkl\"\n",
-    "mgxs_lib.dump_to_file(filename='mgxs', directory='mgxs')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a new MGXS Library from the pickled binary file \"mgxs/mgxs.pkl\"\n",
-    "mgxs_lib = openmc.mgxs.Library.load_from_file(filename='mgxs', directory='mgxs')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `Library` class may be used to leverage the energy condensation features supported by the `MGXS` class. In particular, one can use the `Library.get_condensed_library(...)` with a coarse group structure which is a subset of the original \"fine\" group structure as shown below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a 1-group structure\n",
-    "coarse_groups = openmc.mgxs.EnergyGroups(group_edges=[0., 20.0e6])\n",
-    "\n",
-    "# Create a new MGXS Library on the coarse 1-group structure\n",
-    "coarse_mgxs_lib = mgxs_lib.get_condensed_library(coarse_groups)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>group in</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>0.074479</td>\n",
-       "      <td>0.000151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>0.005950</td>\n",
-       "      <td>0.000017</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>O16</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  group in nuclide      mean  std. dev.\n",
-       "0     1         1    U235  0.074479   0.000151\n",
-       "1     1         1    U238  0.005950   0.000017\n",
-       "2     1         1     O16  0.000000   0.000000"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Retrieve the NuFissionXS object for the fuel cell from the 1-group library\n",
-    "coarse_fuel_mgxs = coarse_mgxs_lib.get_mgxs(fuel_cell, 'nu-fission')\n",
-    "\n",
-    "# Show the Pandas DataFrame for the 1-group MGXS\n",
-    "coarse_fuel_mgxs.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Verification with OpenMOC"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Of course it is always a good idea to verify that one's cross sections are accurate. We can easily do so here with the deterministic transport code [OpenMOC](https://mit-crpg.github.io/OpenMOC/). We first construct an equivalent OpenMOC geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create an OpenMOC Geometry from the OpenMC Geometry\n",
-    "openmoc_geometry = get_openmoc_geometry(mgxs_lib.geometry)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can inject the multi-group cross sections into the equivalent fuel assembly OpenMOC geometry. The `openmoc.materialize` module supports the loading of `Library` objects from OpenMC as illustrated below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ WARNING ]  Group cross sections by nuclides are not currently supported.\n",
-      "[ WARNING ]  ...  Contributions from all nuclides will be summed.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load the library into the OpenMOC geometry\n",
-    "materials = load_openmc_mgxs_lib(mgxs_lib, openmoc_geometry)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We are now ready to run OpenMOC to verify our cross-sections from OpenMC."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Initializing a default angular quadrature...\n",
-      "[  NORMAL ]  Initializing 2D tracks...\n",
-      "[  NORMAL ]  Initializing 2D tracks reflections...\n",
-      "[  NORMAL ]  Initializing 2D tracks array...\n",
-      "[  NORMAL ]  Ray tracing for 2D track segmentation...\n",
-      "[ WARNING ]  The Geometry was set with non-infinite z-boundaries and supplied\n",
-      "[ WARNING ]  ...  to a 2D TrackGenerator. The min-z boundary was set to -10.00 \n",
-      "[ WARNING ]  ... and the max-z boundary was set to 10.00. Z-boundaries are \n",
-      "[ WARNING ]  ... assumed to be infinite in 2D TrackGenerators.\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 0.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 10.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 20.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 30.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 40.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 50.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 60.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 70.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 80.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 90.02 %\n",
-      "[  NORMAL ]  Progress Segmenting 2D tracks: 100.00 %\n",
-      "[  NORMAL ]  Initializing FSR lookup vectors\n",
-      "[  NORMAL ]  Total number of FSRs 867\n",
-      "[  NORMAL ]  Initializing MOC eigenvalue solver...\n",
-      "[  NORMAL ]  Initializing solver arrays...\n",
-      "[  NORMAL ]  Centering segments around FSR centroid...\n",
-      "[  NORMAL ]  Max boundary angular flux storage per domain =   0.42 MB\n",
-      "[  NORMAL ]  Max scalar flux storage per domain =   0.01 MB\n",
-      "[  NORMAL ]  Max source storage per domain =   0.01 MB\n",
-      "[  NORMAL ]  Number of azimuthal angles = 32\n",
-      "[  NORMAL ]  Azimuthal ray spacing = 0.100000\n",
-      "[  NORMAL ]  Number of polar angles = 6\n",
-      "[  NORMAL ]  Source type = Flat\n",
-      "[  NORMAL ]  MOC transport undamped\n",
-      "[  NORMAL ]  CMFD acceleration: OFF\n",
-      "[  NORMAL ]  Using 1 threads\n",
-      "[  NORMAL ]  Computing the eigenvalue...\n",
-      "[  NORMAL ]  Iteration 0:  k_eff = 0.823342   res = 9.831E-02  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -17665 D.R. = 0.0983\n",
-      "[  NORMAL ]  Iteration 1:  k_eff = 0.780160   res = 4.646E-02  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -4318 D.R. = 0.4725\n",
-      "[  NORMAL ]  Iteration 2:  k_eff = 0.739336   res = 9.631E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -4082 D.R. = 0.2073\n",
-      "[  NORMAL ]  Iteration 3:  k_eff = 0.710750   res = 8.566E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -2858 D.R. = 0.8894\n",
-      "[  NORMAL ]  Iteration 4:  k_eff = 0.689598   res = 5.205E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -2115 D.R. = 0.6076\n",
-      "[  NORMAL ]  Iteration 5:  k_eff = 0.675031   res = 3.605E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -1456 D.R. = 0.6926\n",
-      "[  NORMAL ]  Iteration 6:  k_eff = 0.665895   res = 2.538E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -913 D.R. = 0.7040\n",
-      "[  NORMAL ]  Iteration 7:  k_eff = 0.661318   res = 1.889E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -457 D.R. = 0.7443\n",
-      "[  NORMAL ]  Iteration 8:  k_eff = 0.660529   res = 1.493E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  -78 D.R. = 0.7904\n",
-      "[  NORMAL ]  Iteration 9:  k_eff = 0.662872   res = 1.268E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  234 D.R. = 0.8490\n",
-      "[  NORMAL ]  Iteration 10:  k_eff = 0.667780   res = 1.140E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  490 D.R. = 0.8995\n",
-      "[  NORMAL ]  Iteration 11:  k_eff = 0.674766   res = 1.065E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  698 D.R. = 0.9337\n",
-      "[  NORMAL ]  Iteration 12:  k_eff = 0.683410   res = 1.014E-03  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  864 D.R. = 0.9527\n",
-      "[  NORMAL ]  Iteration 13:  k_eff = 0.693354   res = 9.759E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  994 D.R. = 0.9623\n",
-      "[  NORMAL ]  Iteration 14:  k_eff = 0.704290   res = 9.438E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1093 D.R. = 0.9671\n",
-      "[  NORMAL ]  Iteration 15:  k_eff = 0.715957   res = 9.154E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1166 D.R. = 0.9698\n",
-      "[  NORMAL ]  Iteration 16:  k_eff = 0.728134   res = 8.892E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1217 D.R. = 0.9714\n",
-      "[  NORMAL ]  Iteration 17:  k_eff = 0.740633   res = 8.644E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1249 D.R. = 0.9721\n",
-      "[  NORMAL ]  Iteration 18:  k_eff = 0.753297   res = 8.404E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1266 D.R. = 0.9722\n",
-      "[  NORMAL ]  Iteration 19:  k_eff = 0.765995   res = 8.167E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1269 D.R. = 0.9719\n",
-      "[  NORMAL ]  Iteration 20:  k_eff = 0.778619   res = 7.930E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1262 D.R. = 0.9710\n",
-      "[  NORMAL ]  Iteration 21:  k_eff = 0.791079   res = 7.691E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1246 D.R. = 0.9698\n",
-      "[  NORMAL ]  Iteration 22:  k_eff = 0.803304   res = 7.447E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1222 D.R. = 0.9683\n",
-      "[  NORMAL ]  Iteration 23:  k_eff = 0.815235   res = 7.199E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1193 D.R. = 0.9667\n",
-      "[  NORMAL ]  Iteration 24:  k_eff = 0.826828   res = 6.947E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1159 D.R. = 0.9650\n",
-      "[  NORMAL ]  Iteration 25:  k_eff = 0.838047   res = 6.693E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1121 D.R. = 0.9634\n",
-      "[  NORMAL ]  Iteration 26:  k_eff = 0.848868   res = 6.436E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1082 D.R. = 0.9617\n",
-      "[  NORMAL ]  Iteration 27:  k_eff = 0.859271   res = 6.179E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  1040 D.R. = 0.9600\n",
-      "[  NORMAL ]  Iteration 28:  k_eff = 0.869247   res = 5.922E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  997 D.R. = 0.9585\n",
-      "[  NORMAL ]  Iteration 29:  k_eff = 0.878788   res = 5.667E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  954 D.R. = 0.9570\n",
-      "[  NORMAL ]  Iteration 30:  k_eff = 0.887894   res = 5.415E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  910 D.R. = 0.9556\n",
-      "[  NORMAL ]  Iteration 31:  k_eff = 0.896566   res = 5.168E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  867 D.R. = 0.9543\n",
-      "[  NORMAL ]  Iteration 32:  k_eff = 0.904810   res = 4.925E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  824 D.R. = 0.9530\n",
-      "[  NORMAL ]  Iteration 33:  k_eff = 0.912635   res = 4.688E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  782 D.R. = 0.9519\n",
-      "[  NORMAL ]  Iteration 34:  k_eff = 0.920049   res = 4.457E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  741 D.R. = 0.9508\n",
-      "[  NORMAL ]  Iteration 35:  k_eff = 0.927066   res = 4.233E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  701 D.R. = 0.9498\n",
-      "[  NORMAL ]  Iteration 36:  k_eff = 0.933696   res = 4.016E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  663 D.R. = 0.9488\n",
-      "[  NORMAL ]  Iteration 37:  k_eff = 0.939954   res = 3.807E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  625 D.R. = 0.9479\n",
-      "[  NORMAL ]  Iteration 38:  k_eff = 0.945855   res = 3.606E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  590 D.R. = 0.9471\n",
-      "[  NORMAL ]  Iteration 39:  k_eff = 0.951412   res = 3.413E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  555 D.R. = 0.9464\n",
-      "[  NORMAL ]  Iteration 40:  k_eff = 0.956641   res = 3.227E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  522 D.R. = 0.9456\n",
-      "[  NORMAL ]  Iteration 41:  k_eff = 0.961556   res = 3.049E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  491 D.R. = 0.9448\n",
-      "[  NORMAL ]  Iteration 42:  k_eff = 0.966174   res = 2.879E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  461 D.R. = 0.9443\n",
-      "[  NORMAL ]  Iteration 43:  k_eff = 0.970507   res = 2.716E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  433 D.R. = 0.9435\n",
-      "[  NORMAL ]  Iteration 44:  k_eff = 0.974571   res = 2.561E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  406 D.R. = 0.9430\n",
-      "[  NORMAL ]  Iteration 45:  k_eff = 0.978380   res = 2.414E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  380 D.R. = 0.9423\n",
-      "[  NORMAL ]  Iteration 46:  k_eff = 0.981948   res = 2.273E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  356 D.R. = 0.9417\n",
-      "[  NORMAL ]  Iteration 47:  k_eff = 0.985287   res = 2.140E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  333 D.R. = 0.9413\n",
-      "[  NORMAL ]  Iteration 48:  k_eff = 0.988410   res = 2.013E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  312 D.R. = 0.9409\n",
-      "[  NORMAL ]  Iteration 49:  k_eff = 0.991331   res = 1.893E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  292 D.R. = 0.9402\n",
-      "[  NORMAL ]  Iteration 50:  k_eff = 0.994060   res = 1.779E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  272 D.R. = 0.9399\n",
-      "[  NORMAL ]  Iteration 51:  k_eff = 0.996609   res = 1.671E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  254 D.R. = 0.9393\n",
-      "[  NORMAL ]  Iteration 52:  k_eff = 0.998988   res = 1.569E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  237 D.R. = 0.9390\n",
-      "[  NORMAL ]  Iteration 53:  k_eff = 1.001209   res = 1.473E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  222 D.R. = 0.9386\n",
-      "[  NORMAL ]  Iteration 54:  k_eff = 1.003280   res = 1.382E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  207 D.R. = 0.9380\n",
-      "[  NORMAL ]  Iteration 55:  k_eff = 1.005211   res = 1.296E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  193 D.R. = 0.9378\n",
-      "[  NORMAL ]  Iteration 56:  k_eff = 1.007012   res = 1.215E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  180 D.R. = 0.9374\n",
-      "[  NORMAL ]  Iteration 57:  k_eff = 1.008689   res = 1.138E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  167 D.R. = 0.9369\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[  NORMAL ]  Iteration 58:  k_eff = 1.010251   res = 1.066E-04  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  156 D.R. = 0.9369\n",
-      "[  NORMAL ]  Iteration 59:  k_eff = 1.011706   res = 9.981E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  145 D.R. = 0.9362\n",
-      "[  NORMAL ]  Iteration 60:  k_eff = 1.013060   res = 9.344E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  135 D.R. = 0.9361\n",
-      "[  NORMAL ]  Iteration 61:  k_eff = 1.014320   res = 8.743E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  125 D.R. = 0.9357\n",
-      "[  NORMAL ]  Iteration 62:  k_eff = 1.015492   res = 8.177E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  117 D.R. = 0.9353\n",
-      "[  NORMAL ]  Iteration 63:  k_eff = 1.016582   res = 7.648E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  109 D.R. = 0.9353\n",
-      "[  NORMAL ]  Iteration 64:  k_eff = 1.017596   res = 7.147E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  101 D.R. = 0.9345\n",
-      "[  NORMAL ]  Iteration 65:  k_eff = 1.018538   res = 6.680E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  94 D.R. = 0.9346\n",
-      "[  NORMAL ]  Iteration 66:  k_eff = 1.019414   res = 6.237E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  87 D.R. = 0.9338\n",
-      "[  NORMAL ]  Iteration 67:  k_eff = 1.020227   res = 5.828E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  81 D.R. = 0.9343\n",
-      "[  NORMAL ]  Iteration 68:  k_eff = 1.020983   res = 5.442E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  75 D.R. = 0.9338\n",
-      "[  NORMAL ]  Iteration 69:  k_eff = 1.021685   res = 5.080E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  70 D.R. = 0.9336\n",
-      "[  NORMAL ]  Iteration 70:  k_eff = 1.022337   res = 4.739E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  65 D.R. = 0.9328\n",
-      "[  NORMAL ]  Iteration 71:  k_eff = 1.022942   res = 4.422E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  60 D.R. = 0.9331\n",
-      "[  NORMAL ]  Iteration 72:  k_eff = 1.023504   res = 4.124E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  56 D.R. = 0.9327\n",
-      "[  NORMAL ]  Iteration 73:  k_eff = 1.024026   res = 3.843E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  52 D.R. = 0.9317\n",
-      "[  NORMAL ]  Iteration 74:  k_eff = 1.024510   res = 3.586E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  48 D.R. = 0.9331\n",
-      "[  NORMAL ]  Iteration 75:  k_eff = 1.024959   res = 3.341E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  44 D.R. = 0.9317\n",
-      "[  NORMAL ]  Iteration 76:  k_eff = 1.025375   res = 3.115E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  41 D.R. = 0.9325\n",
-      "[  NORMAL ]  Iteration 77:  k_eff = 1.025762   res = 2.898E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  38 D.R. = 0.9303\n",
-      "[  NORMAL ]  Iteration 78:  k_eff = 1.026120   res = 2.703E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  35 D.R. = 0.9327\n",
-      "[  NORMAL ]  Iteration 79:  k_eff = 1.026452   res = 2.519E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  33 D.R. = 0.9318\n",
-      "[  NORMAL ]  Iteration 80:  k_eff = 1.026760   res = 2.341E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  30 D.R. = 0.9295\n",
-      "[  NORMAL ]  Iteration 81:  k_eff = 1.027046   res = 2.180E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  28 D.R. = 0.9312\n",
-      "[  NORMAL ]  Iteration 82:  k_eff = 1.027311   res = 2.028E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  26 D.R. = 0.9301\n",
-      "[  NORMAL ]  Iteration 83:  k_eff = 1.027556   res = 1.889E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  24 D.R. = 0.9315\n",
-      "[  NORMAL ]  Iteration 84:  k_eff = 1.027783   res = 1.757E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  22 D.R. = 0.9305\n",
-      "[  NORMAL ]  Iteration 85:  k_eff = 1.027994   res = 1.632E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  21 D.R. = 0.9284\n",
-      "[  NORMAL ]  Iteration 86:  k_eff = 1.028189   res = 1.521E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  19 D.R. = 0.9322\n",
-      "[  NORMAL ]  Iteration 87:  k_eff = 1.028370   res = 1.412E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  18 D.R. = 0.9285\n",
-      "[  NORMAL ]  Iteration 88:  k_eff = 1.028538   res = 1.311E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  16 D.R. = 0.9287\n",
-      "[  NORMAL ]  Iteration 89:  k_eff = 1.028693   res = 1.222E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  15 D.R. = 0.9316\n",
-      "[  NORMAL ]  Iteration 90:  k_eff = 1.028837   res = 1.134E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  14 D.R. = 0.9279\n",
-      "[  NORMAL ]  Iteration 91:  k_eff = 1.028970   res = 1.056E-05  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  13 D.R. = 0.9314\n",
-      "[  NORMAL ]  Iteration 92:  k_eff = 1.029093   res = 9.786E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  12 D.R. = 0.9268\n",
-      "[  NORMAL ]  Iteration 93:  k_eff = 1.029207   res = 9.093E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  11 D.R. = 0.9292\n",
-      "[  NORMAL ]  Iteration 94:  k_eff = 1.029313   res = 8.445E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  10 D.R. = 0.9287\n",
-      "[  NORMAL ]  Iteration 95:  k_eff = 1.029411   res = 7.848E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  9 D.R. = 0.9294\n",
-      "[  NORMAL ]  Iteration 96:  k_eff = 1.029502   res = 7.272E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  9 D.R. = 0.9265\n",
-      "[  NORMAL ]  Iteration 97:  k_eff = 1.029586   res = 6.769E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  8 D.R. = 0.9309\n",
-      "[  NORMAL ]  Iteration 98:  k_eff = 1.029663   res = 6.294E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  7 D.R. = 0.9298\n",
-      "[  NORMAL ]  Iteration 99:  k_eff = 1.029735   res = 5.827E-06  delta-k (pcm) =\n",
-      "[  NORMAL ]  ...  7 D.R. = 0.9259\n",
-      "[  NORMAL ]  Iteration 100:  k_eff = 1.029802   res = 5.413E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 6 D.R. = 0.9290\n",
-      "[  NORMAL ]  Iteration 101:  k_eff = 1.029863   res = 5.032E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 6 D.R. = 0.9295\n",
-      "[  NORMAL ]  Iteration 102:  k_eff = 1.029920   res = 4.648E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 5 D.R. = 0.9237\n",
-      "[  NORMAL ]  Iteration 103:  k_eff = 1.029973   res = 4.328E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 5 D.R. = 0.9313\n",
-      "[  NORMAL ]  Iteration 104:  k_eff = 1.030022   res = 4.004E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 4 D.R. = 0.9249\n",
-      "[  NORMAL ]  Iteration 105:  k_eff = 1.030067   res = 3.734E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 4 D.R. = 0.9326\n",
-      "[  NORMAL ]  Iteration 106:  k_eff = 1.030109   res = 3.452E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 4 D.R. = 0.9246\n",
-      "[  NORMAL ]  Iteration 107:  k_eff = 1.030148   res = 3.195E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 3 D.R. = 0.9253\n",
-      "[  NORMAL ]  Iteration 108:  k_eff = 1.030184   res = 2.979E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 3 D.R. = 0.9325\n",
-      "[  NORMAL ]  Iteration 109:  k_eff = 1.030217   res = 2.750E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 3 D.R. = 0.9230\n",
-      "[  NORMAL ]  Iteration 110:  k_eff = 1.030247   res = 2.541E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 3 D.R. = 0.9240\n",
-      "[  NORMAL ]  Iteration 111:  k_eff = 1.030276   res = 2.364E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.9302\n",
-      "[  NORMAL ]  Iteration 112:  k_eff = 1.030302   res = 2.178E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.9215\n",
-      "[  NORMAL ]  Iteration 113:  k_eff = 1.030326   res = 2.022E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.9285\n",
-      "[  NORMAL ]  Iteration 114:  k_eff = 1.030349   res = 1.896E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.9374\n",
-      "[  NORMAL ]  Iteration 115:  k_eff = 1.030369   res = 1.753E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 2 D.R. = 0.9246\n",
-      "[  NORMAL ]  Iteration 116:  k_eff = 1.030389   res = 1.619E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9236\n",
-      "[  NORMAL ]  Iteration 117:  k_eff = 1.030406   res = 1.500E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9266\n",
-      "[  NORMAL ]  Iteration 118:  k_eff = 1.030423   res = 1.406E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9375\n",
-      "[  NORMAL ]  Iteration 119:  k_eff = 1.030438   res = 1.287E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9154\n",
-      "[  NORMAL ]  Iteration 120:  k_eff = 1.030452   res = 1.193E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9269\n",
-      "[  NORMAL ]  Iteration 121:  k_eff = 1.030465   res = 1.095E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9174\n",
-      "[  NORMAL ]  Iteration 122:  k_eff = 1.030477   res = 1.033E-06  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9437\n",
-      "[  NORMAL ]  Iteration 123:  k_eff = 1.030488   res = 9.328E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9029\n",
-      "[  NORMAL ]  Iteration 124:  k_eff = 1.030498   res = 8.716E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 1 D.R. = 0.9344\n",
-      "[  NORMAL ]  Iteration 125:  k_eff = 1.030508   res = 8.527E-07  delta-k (pcm)\n",
-      "[  NORMAL ]  ...  = 0 D.R. = 0.9783\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Generate tracks for OpenMOC\n",
-    "track_generator = openmoc.TrackGenerator(openmoc_geometry, num_azim=32, azim_spacing=0.1)\n",
-    "track_generator.generateTracks()\n",
-    "\n",
-    "# Run OpenMOC\n",
-    "solver = openmoc.CPUSolver(track_generator)\n",
-    "solver.computeEigenvalue()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We report the eigenvalues computed by OpenMC and OpenMOC here together to summarize our results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "openmc keff = 1.023625\n",
-      "openmoc keff = 1.030508\n",
-      "bias [pcm]: 688.3\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print report of keff and bias with OpenMC\n",
-    "openmoc_keff = solver.getKeff()\n",
-    "bias = (openmoc_keff - openmc_keff) * 1e5\n",
-    "\n",
-    "print('openmc keff = {0:1.6f}'.format(openmc_keff))\n",
-    "print('openmoc keff = {0:1.6f}'.format(openmoc_keff))\n",
-    "print('bias [pcm]: {0:1.1f}'.format(bias))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is a non-trivial bias between the eigenvalues computed by OpenMC and OpenMOC. One can show that these biases do not converge to <100 pcm with more particle histories. For heterogeneous geometries, additional measures must be taken to address the following three sources of bias:\n",
-    "\n",
-    "* Appropriate transport-corrected cross sections\n",
-    "* Spatial discretization of OpenMOC's mesh\n",
-    "* Constant-in-angle multi-group cross sections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Flux and Pin Power Visualizations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will conclude this tutorial by illustrating how to visualize the fission rates computed by OpenMOC and OpenMC. First, we extract volume-integrated fission rates from OpenMC's mesh fission rate tally for each pin cell in the fuel assembly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get the OpenMC fission rate mesh tally data\n",
-    "mesh_tally = sp.get_tally(name='mesh tally')\n",
-    "openmc_fission_rates = mesh_tally.get_values(scores=['nu-fission'])\n",
-    "\n",
-    "# Close the statepoint file now that we're done getting information from it\n",
-    "sp.close()\n",
-    "\n",
-    "# Reshape array to 2D for plotting\n",
-    "openmc_fission_rates.shape = (17,17)\n",
-    "\n",
-    "# Normalize to the average pin power\n",
-    "openmc_fission_rates /= np.mean(openmc_fission_rates[openmc_fission_rates > 0.])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we extract OpenMOC's volume-averaged fission rates into a 2D 17x17 NumPy array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create OpenMOC Mesh on which to tally fission rates\n",
-    "openmoc_mesh = openmoc.process.Mesh()\n",
-    "openmoc_mesh.dimension = np.array(mesh.dimension)\n",
-    "openmoc_mesh.lower_left = np.array(mesh.lower_left)\n",
-    "openmoc_mesh.upper_right = np.array(mesh.upper_right)\n",
-    "openmoc_mesh.width = openmoc_mesh.upper_right - openmoc_mesh.lower_left\n",
-    "openmoc_mesh.width /= openmoc_mesh.dimension\n",
-    "\n",
-    "# Tally OpenMOC fission rates on the Mesh\n",
-    "openmoc_fission_rates = openmoc_mesh.tally_fission_rates(solver)\n",
-    "openmoc_fission_rates = np.squeeze(openmoc_fission_rates)\n",
-    "openmoc_fission_rates = np.fliplr(openmoc_fission_rates)\n",
-    "\n",
-    "# Normalize to the average pin fission rate\n",
-    "openmoc_fission_rates /= np.mean(openmoc_fission_rates[openmoc_fission_rates > 0.])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can easily use Matplotlib to visualize the fission rates from OpenMC and OpenMOC side-by-side."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'OpenMOC Fission Rates')"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Ignore zero fission rates in guide tubes with Matplotlib color scheme\n",
-    "openmc_fission_rates[openmc_fission_rates == 0] = np.nan\n",
-    "openmoc_fission_rates[openmoc_fission_rates == 0] = np.nan\n",
-    "\n",
-    "# Plot OpenMC's fission rates in the left subplot\n",
-    "fig = plt.subplot(121)\n",
-    "plt.imshow(openmc_fission_rates, interpolation='none', cmap='jet')\n",
-    "plt.title('OpenMC Fission Rates')\n",
-    "\n",
-    "# Plot OpenMOC's fission rates in the right subplot\n",
-    "fig2 = plt.subplot(122)\n",
-    "plt.imshow(openmoc_fission_rates, interpolation='none', cmap='jet')\n",
-    "plt.title('OpenMOC Fission Rates')"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/nuclear-data-resonance-covariance.ipynb b/examples/jupyter/nuclear-data-resonance-covariance.ipynb
deleted file mode 100644
index 8fe85161d..000000000
--- a/examples/jupyter/nuclear-data-resonance-covariance.ipynb
+++ /dev/null
@@ -1,963 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nuclear Data: Resonance Covariance\n",
-    "In this notebook we will explore features of the Python API that allow us to import and manipulate resonance covariance data. A full description of the ENDF-VI and ENDF-VII formats can be found in the [ENDF102 manual](https://www.oecd-nea.org/dbdata/data/manual-endf/endf102.pdf)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import os\n",
-    "from pprint import pprint\n",
-    "import shutil\n",
-    "import subprocess\n",
-    "import urllib.request\n",
-    "\n",
-    "import h5py\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import openmc.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ENDF: Resonance Covariance Data\n",
-    "\n",
-    "Let's download the ENDF/B-VII.1 evaluation for $^{157}$Gd and load it in:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<IncidentNeutron: Gd157>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Download ENDF file\n",
-    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/Gd/157'\n",
-    "filename, headers = urllib.request.urlretrieve(url, 'gd157.endf')\n",
-    "\n",
-    "# Load into memory\n",
-    "gd157_endf = openmc.data.IncidentNeutron.from_endf(filename, covariance=True)\n",
-    "gd157_endf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can access the parameters contained within File 32 in a similar manner to the File 2 parameters from before. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy</th>\n",
-       "      <th>J</th>\n",
-       "      <th>neutronWidth</th>\n",
-       "      <th>captureWidth</th>\n",
-       "      <th>fissionWidthA</th>\n",
-       "      <th>fissionWidthB</th>\n",
-       "      <th>L</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0314</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000474</td>\n",
-       "      <td>0.1072</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.8250</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000345</td>\n",
-       "      <td>0.0970</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>16.2400</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>0.0910</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.7700</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.012800</td>\n",
-       "      <td>0.0805</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.5600</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.011360</td>\n",
-       "      <td>0.0880</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    energy    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB  L\n",
-       "0   0.0314  2.0      0.000474        0.1072            0.0            0.0  0\n",
-       "1   2.8250  2.0      0.000345        0.0970            0.0            0.0  0\n",
-       "2  16.2400  1.0      0.000400        0.0910            0.0            0.0  0\n",
-       "3  16.7700  2.0      0.012800        0.0805            0.0            0.0  0\n",
-       "4  20.5600  2.0      0.011360        0.0880            0.0            0.0  0"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd157_endf.resonance_covariance.ranges[0].parameters[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The newly created object will contain multiple resonance regions within `gd157_endf.resonance_covariance.ranges`. We can access the full covariance matrix from File 32 for a given range by:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "covariance = gd157_endf.resonance_covariance.ranges[0].covariance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This covariance matrix currently only stores the upper triangular portion as covariance matrices are symmetric. Plotting the covariance matrix:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x14cf90c73550>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(covariance, cmap='seismic',vmin=-0.008, vmax=0.008)\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The correlation matrix can be constructed using the covariance matrix and also give some insight into the relations among the parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x14cf90b682e8>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "corr = np.zeros([len(covariance),len(covariance)])\n",
-    "for i in range(len(covariance)):\n",
-    "    for j in range(len(covariance)):\n",
-    "        corr[i, j]=covariance[i, j]/covariance[i, i]**(0.5)/covariance[j, j]**(0.5)\n",
-    "plt.imshow(corr, cmap='seismic',vmin=-1.0, vmax=1.0)\n",
-    "plt.colorbar()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Sampling and Reconstruction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The covariance module also has the ability to sample a new set of parameters using the covariance matrix. Currently the sampling uses numpy.multivariate_normal(). Because parameters are assumed to have a multivariate normal distribution this method doesn't not currently guarantee that sampled parameters will be positive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/romano/openmc/openmc/data/resonance_covariance.py:233: UserWarning: Sampling routine does not guarantee positive values for parameters. This can lead to undefined behavior in the reconstruction routine.\n",
-      "  warnings.warn(warn_str)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "openmc.data.resonance.ReichMoore"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rm_resonance = gd157_endf.resonances.ranges[0]\n",
-    "n_samples = 5\n",
-    "samples = gd157_endf.resonance_covariance.ranges[0].sample(n_samples)\n",
-    "type(samples[0])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The sampling routine requires the incorporation of the `openmc.data.ResonanceRange` for the same resonance range object. This allows each sample itself to be its own `openmc.data.ResonanceRange` with a new set of parameters. Looking at some of the sampled parameters below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sample 1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy</th>\n",
-       "      <th>L</th>\n",
-       "      <th>J</th>\n",
-       "      <th>neutronWidth</th>\n",
-       "      <th>captureWidth</th>\n",
-       "      <th>fissionWidthA</th>\n",
-       "      <th>fissionWidthB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.030679</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000473</td>\n",
-       "      <td>0.108576</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.823843</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000351</td>\n",
-       "      <td>0.086418</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>16.281147</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000458</td>\n",
-       "      <td>0.106825</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.771760</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.013598</td>\n",
-       "      <td>0.072837</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.561545</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.011164</td>\n",
-       "      <td>0.086616</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
-       "0   0.030679  0  2.0      0.000473      0.108576            0.0            0.0\n",
-       "1   2.823843  0  2.0      0.000351      0.086418            0.0            0.0\n",
-       "2  16.281147  0  1.0      0.000458      0.106825            0.0            0.0\n",
-       "3  16.771760  0  2.0      0.013598      0.072837            0.0            0.0\n",
-       "4  20.561545  0  2.0      0.011164      0.086616            0.0            0.0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print('Sample 1')\n",
-    "samples[0].parameters[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sample 2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy</th>\n",
-       "      <th>L</th>\n",
-       "      <th>J</th>\n",
-       "      <th>neutronWidth</th>\n",
-       "      <th>captureWidth</th>\n",
-       "      <th>fissionWidthA</th>\n",
-       "      <th>fissionWidthB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.032858</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000479</td>\n",
-       "      <td>0.105208</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.823859</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000361</td>\n",
-       "      <td>0.093748</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>16.203069</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000264</td>\n",
-       "      <td>0.015233</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.765055</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.013648</td>\n",
-       "      <td>0.076119</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.557679</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.011140</td>\n",
-       "      <td>0.097548</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
-       "0   0.032858  0  2.0      0.000479      0.105208            0.0            0.0\n",
-       "1   2.823859  0  2.0      0.000361      0.093748            0.0            0.0\n",
-       "2  16.203069  0  1.0      0.000264      0.015233            0.0            0.0\n",
-       "3  16.765055  0  2.0      0.013648      0.076119            0.0            0.0\n",
-       "4  20.557679  0  2.0      0.011140      0.097548            0.0            0.0"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print('Sample 2')\n",
-    "samples[1].parameters[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can reconstruct the cross section from the sampled parameters using the reconstruct method of  `openmc.data.ResonanceRange`. For more on reconstruction see the Nuclear Data example notebook. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<openmc.data.resonance.ReichMoore at 0x14cf93855d68>,\n",
-       " <openmc.data.resonance.Unresolved at 0x14cf938c02e8>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd157_endf.resonances.ranges"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cross section (b)')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "energy_range = [rm_resonance.energy_min, rm_resonance.energy_max]\n",
-    "energies = np.logspace(np.log10(energy_range[0]),\n",
-    "                       np.log10(energy_range[1]), 10000)\n",
-    "for sample in samples:\n",
-    "    xs = sample.reconstruct(energies)\n",
-    "    elastic_xs = xs[2]\n",
-    "    plt.loglog(energies, elastic_xs)\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Cross section (b)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Subset Selection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another capability of the covariance module is selecting a subset of the resonance parameters and the corresponding subset of the covariance matrix. We can do this by specifying the value we want to discriminate and the bounds within one energy region. Selecting only resonances with J=2:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy</th>\n",
-       "      <th>L</th>\n",
-       "      <th>J</th>\n",
-       "      <th>neutronWidth</th>\n",
-       "      <th>captureWidth</th>\n",
-       "      <th>fissionWidthA</th>\n",
-       "      <th>fissionWidthB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0314</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000474</td>\n",
-       "      <td>0.1072</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.8250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000345</td>\n",
-       "      <td>0.0970</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.7700</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.012800</td>\n",
-       "      <td>0.0805</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.5600</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.011360</td>\n",
-       "      <td>0.0880</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>21.6500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000376</td>\n",
-       "      <td>0.1140</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
-       "0   0.0314  0  2.0      0.000474        0.1072            0.0            0.0\n",
-       "1   2.8250  0  2.0      0.000345        0.0970            0.0            0.0\n",
-       "3  16.7700  0  2.0      0.012800        0.0805            0.0            0.0\n",
-       "4  20.5600  0  2.0      0.011360        0.0880            0.0            0.0\n",
-       "5  21.6500  0  2.0      0.000376        0.1140            0.0            0.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lower_bound = 2;  # inclusive\n",
-    "upper_bound = 2;  # inclusive\n",
-    "rm_res_cov_sub = gd157_endf.resonance_covariance.ranges[0].subset('J',[lower_bound,upper_bound])\n",
-    "rm_res_cov_sub.file2res.parameters[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The subset method will also store the corresponding subset of the covariance matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(180, 180)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rm_res_cov_sub.covariance\n",
-    "gd157_endf.resonance_covariance.ranges[0].covariance.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Checking the size of the new covariance matrix to be sure it was sampled properly: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of parameters\n",
-      "Original: 60\n",
-      "Subet: 36\n",
-      "Covariance Size\n",
-      "Original: (180, 180)\n",
-      "Subset: (108, 108)\n"
-     ]
-    }
-   ],
-   "source": [
-    "old_n_parameters = gd157_endf.resonance_covariance.ranges[0].parameters.shape[0]\n",
-    "old_shape = gd157_endf.resonance_covariance.ranges[0].covariance.shape\n",
-    "new_n_parameters = rm_res_cov_sub.file2res.parameters.shape[0]\n",
-    "new_shape = rm_res_cov_sub.covariance.shape\n",
-    "print('Number of parameters\\nOriginal: '+str(old_n_parameters)+'\\nSubet: '+str(new_n_parameters)+'\\nCovariance Size\\nOriginal: '+str(old_shape)+'\\nSubset: '+str(new_shape))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And finally, we can sample from the subset as well"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy</th>\n",
-       "      <th>L</th>\n",
-       "      <th>J</th>\n",
-       "      <th>neutronWidth</th>\n",
-       "      <th>captureWidth</th>\n",
-       "      <th>fissionWidthA</th>\n",
-       "      <th>fissionWidthB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.030488</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000473</td>\n",
-       "      <td>0.108946</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.825944</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000328</td>\n",
-       "      <td>0.098328</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>16.773886</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.012984</td>\n",
-       "      <td>0.076779</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>20.565737</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.011628</td>\n",
-       "      <td>0.088958</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>21.646469</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000389</td>\n",
-       "      <td>0.127833</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
-       "0   0.030488  0  2.0      0.000473      0.108946            0.0            0.0\n",
-       "1   2.825944  0  2.0      0.000328      0.098328            0.0            0.0\n",
-       "2  16.773886  0  2.0      0.012984      0.076779            0.0            0.0\n",
-       "3  20.565737  0  2.0      0.011628      0.088958            0.0            0.0\n",
-       "4  21.646469  0  2.0      0.000389      0.127833            0.0            0.0"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "samples_sub = rm_res_cov_sub.sample(n_samples)\n",
-    "samples_sub[0].parameters[:5]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/nuclear-data.ipynb b/examples/jupyter/nuclear-data.ipynb
deleted file mode 100644
index 6a8aeb90c..000000000
--- a/examples/jupyter/nuclear-data.ipynb
+++ /dev/null
@@ -1,1504 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nuclear Data\n",
-    "In this notebook, we will go through the salient features of the `openmc.data` package in the Python API. This package enables inspection, analysis, and conversion of nuclear data from ACE files. Most importantly, the package provides a mean to generate HDF5 nuclear data libraries that are used by the transport solver."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import os\n",
-    "from pprint import pprint\n",
-    "import shutil\n",
-    "import subprocess\n",
-    "import urllib.request\n",
-    "\n",
-    "import h5py\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.cm\n",
-    "from matplotlib.patches import Rectangle\n",
-    "\n",
-    "import openmc.data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Physical Data\n",
-    "\n",
-    "Some very helpful physical data is available as part of `openmc.data`: atomic masses, natural abundances, and atomic weights."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "53.939608306"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "openmc.data.atomic_mass('Fe54')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.00015574"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "openmc.data.NATURAL_ABUNDANCE['H2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12.011115164864455"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "openmc.data.atomic_weight('C')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The IncidentNeutron class\n",
-    "\n",
-    "The most useful class within the `openmc.data` API is `IncidentNeutron`, which stores to continuous-energy incident neutron data. This class has factory methods `from_ace`, `from_endf`, and `from_hdf5` which take a data file on disk and parse it into a hierarchy of classes in memory. To demonstrate this feature, we will download an ACE file (which can be produced with [NJOY 2016](https://github.com/njoy/NJOY2016)) and then load it in using the `IncidentNeutron.from_ace` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = 'https://anl.box.com/shared/static/kxm7s57z3xgfbeq29h54n7q6js8rd11c.ace'\n",
-    "filename, headers = urllib.request.urlretrieve(url, 'gd157.ace')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<IncidentNeutron: Gd157>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load ACE data into object\n",
-    "gd157 = openmc.data.IncidentNeutron.from_ace('gd157.ace')\n",
-    "gd157"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cross sections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From Python, it's easy to explore (and modify) the nuclear data. Let's start off by reading the total cross section. Reactions are indexed using their \"MT\" number -- a unique identifier for each reaction defined by the ENDF-6 format. The MT number for the total cross section is 1."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Reaction: MT=1 (n,total)>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total = gd157[1]\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cross sections for each reaction can be stored at multiple temperatures. To see what temperatures are available, we can look at the reaction's `xs` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'294K': <openmc.data.function.Tabulated1D at 0x1461b4d2f160>}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total.xs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To find the cross section at a particular energy, 1 eV for example, simply get the cross section at the appropriate temperature and then call it as a function. Note that our nuclear data uses eV as the unit of energy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "142.64747"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total.xs['294K'](1.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `xs` attribute can also be called on an array of energies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([142.64747   ,  38.6541761 , 175.40019642])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "total.xs['294K']([1.0, 2.0, 3.0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation for each temperature present."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'294K': array([1.0000e-05, 1.0325e-05, 1.0650e-05, ..., 1.9500e+07, 1.9900e+07,\n",
-       "        2.0000e+07])}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd157.energy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cross section (b)')"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "energies = gd157.energy['294K']\n",
-    "total_xs = total.xs['294K'](energies)\n",
-    "plt.loglog(energies, total_xs)\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Cross section (b)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reaction Data\n",
-    "\n",
-    "Most of the interesting data for an `IncidentNeutron` instance is contained within the `reactions` attribute, which is a dictionary mapping MT values to `Reaction` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[<Reaction: MT=1 (n,total)>,\n",
-      " <Reaction: MT=101 (n,disappear)>,\n",
-      " <Reaction: MT=301 heating>,\n",
-      " <Reaction: MT=2 (n,elastic)>,\n",
-      " <Reaction: MT=16 (n,2n)>,\n",
-      " <Reaction: MT=17 (n,3n)>,\n",
-      " <Reaction: MT=22 (n,na)>,\n",
-      " <Reaction: MT=24 (n,2na)>,\n",
-      " <Reaction: MT=28 (n,np)>,\n",
-      " <Reaction: MT=41 (n,2np)>]\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint(list(gd157.reactions.values())[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's suppose we want to look more closely at the (n,2n) reaction. This reaction has an energy threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Threshold = 6400881.0 eV\n"
-     ]
-    }
-   ],
-   "source": [
-    "n2n = gd157[16]\n",
-    "print('Threshold = {} eV'.format(n2n.xs['294K'].x[0]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The (n,2n) cross section, like all basic cross sections, is represented by the `Tabulated1D` class. The energy and cross section values in the table can be directly accessed with the `x` and `y` attributes. Using the `x` and `y` has the nice benefit of automatically acounting for reaction thresholds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'294K': <openmc.data.function.Tabulated1D at 0x1461b4d594e0>}"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n2n.xs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(6400881.0, 20000000.0)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xs = n2n.xs['294K']\n",
-    "plt.plot(xs.x, xs.y)\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Cross section (b)')\n",
-    "plt.xlim((xs.x[0], xs.x[-1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
-       " <Product: photon, emission=prompt, yield=tabulated>]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n2n.products"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x1461b4a66908>]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "neutron = n2n.products[0]\n",
-    "neutron.distribution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the neutrons emitted have a correlated angle-energy distribution. Let's look at the `energy_out` attribute to see what the outgoing energy distributions are."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<openmc.stats.univariate.Tabular at 0x1461b4d59550>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d59630>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d596d8>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d59828>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d59a20>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d59c88>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d59f98>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d63358>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d63748>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d63ba8>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d6b0b8>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d6b588>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4d6bb00>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4aaa0f0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4aaa710>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4aaad68>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ab1470>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ab1b70>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ab92b0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ab9a20>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ac1208>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ac19e8>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ac9240>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ac9a90>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4acf358>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4acfc18>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ad7470>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ad7d30>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ae0668>,\n",
-       " <openmc.stats.univariate.Tabular at 0x1461b4ae0f98>]"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dist = neutron.distribution[0]\n",
-    "dist.energy_out"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we see we have a tabulated outgoing energy distribution for each incoming energy. Note that the same probability distribution classes that we could use to create a source definition are also used within the `openmc.data` package. Let's plot every fifth distribution to get an idea of what they look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/35wtk/eQk5VbXr8WCYlKR3Fl5J88eUKHDh2oVasWnp6erFq16pXtmjZtSvXq1Qv6z1cJlmTkJXBR3gJEsK1NQmaC6tjOMLSqVsUkKEgVjdxJoVlv9+eikdCtG7l+4ih13+9MnbYdMDR/tWJoPnINBV7NWuHZpAW3zoUSum0jh1Yu5uSmddRp0wEf9y4YXwqh4RBTTs7bxEOxK5smHqLjuMboGWmV5q9AQuKdoLgy8osWLcLDw4MdO3YQFxdH9erV6dWrF5qami+1/eOPP/D1fX7phSQjL4FrTrjqP08T7QCaj1PQqV0bZLKCaKR6feuCazJSkrmwbxfuDRrT9KMBRTqRZxFkMtzq1Sd42myCJs3EukpVjm9Yw9IflnMk0RvL60ew+e59jB8sISlByYbJx4iPSVXrPUtISPyLIAikpKQgiiKpqamYmpqiofHmz+GSjHwlRl1aW67ZN8HAFgysiH9yA0EUkaWkITc1eW00cm7XNnKyMvHv0r2Qnou0H3sPL+w9vIi7E8mZ7Zs5f/wIFx5kUiP9IgmDvTH7dQ5pFkPZPOM0bYfVxsFD0uSSqATsGaMqqVcn1jWhbeFTVcWVkR8+fDgdO3bE1taWlJQU1q9fj+w1qtz9+/dHLpfzwQcf8O233yIIgiQjX5lR134krjkR4PQ00Z4Rj24mCLl5yE1M/41GAv6NRjJTU7mwdzvV/N/D3MGpRPeQj4WTC+0+G817gY05+/MQroSeQnkij9vuRjjemEtu9gB2LIDGParj1cReLWNKSLxrFFdGft++ffj4+HDo0CFu3bpFq1ataNSoEYaGhs+1++OPP7CzsyMlJYUPPviA//3vf6/ct6QwJBn5dxDtvHRscqPBVvVhiM+MxzBdde5BjrkqGvnI/TlBxnO7t5GdkUH9D9QfWhpV86NFPXMCnjzkgv1nXNi3kzhdAzSz/kDvcW2OrM3jcUwajYKqvlYkUkKi3CkicihriopIVq1axZgxYxAEATc3N1xcXLh+/Tp+fn7PtbezswPAwMCAnj17EhoaSp8+fSQZ+f86Ljm3kD1NtIMqIrHM0kIkncuROhiYaT2XG8lMS+XCnu1U9WuAhaNz6Rjl0xPdHSN5770a+HXqxrH9mzi6dQ3ZqVeQJ1zn4j4/Ht9txPvD/dHRfzkZKCEh8TxFRSSOjo4cPHiQRo0a8fDhQ27cuPGSwq5SqSQpKQlzc3NycnLYuXMnLVu2BCQZ+f88rjk3Vf+xUb1Z8Znx2Cj1UGroEB8v4tnI9rkn/wt7dpCVnlYq0UgBnl1AQxvC1qHQ0qZ5h940mzyWw7UfoksGyswT3Lkwm5WjphJ+5lrp2SEhUckoroz8hAkTOHHiBDVr1qRFixb88MMPBfmN/D/kWVlZBAYG4u3tjY+PD3Z2dgwapJpVl2Tk3wFKIiP/98zOuGdfwXyiaoOagfsG4v73PZrtzOSU/yRa9vegur8qIslKT2fZ8P7Y16hJ56++LazbkrOxP9w+DF/eBA1V1LH88nJ+CZ3LzMPOxMXn8cBQAeRiZOVIrVYtcX+vMQamLyf3JCTKAklGvmIjyciXIq45N7mtqFrwOj4zHvMsTbIV+gDoGCgKzl3Yu4OstDQCSjMaycenJ2QkQvi+gkMDvAbQwq0t3zS/g4erJY1uPsLQoAHJj7M4umYlS4f1Z8PUcVw+tJ/MNKlcWEJCQj1IjqQwMpOxzY15zpEkZCZgkilHqa9KlOXnILIz0jm3ayuudeph5aqmrX0Lw7UZ6FvBxX9rxwVBYOp7U6lqUYNhDa5h1DaAgBMhuFm2RNOwP+aOzUl+HMf+JfNZPLg322ZP58bJv8nOSC99eyUkJN5ZpGR7YcSHk4uswJEo85QkZiZimG6C0ki1wDA/Ignbv5vM1BQCPiijagm5BtT8EE4vgbR40Hvq2DR0mN98PsG7gvm8zlWWGPfAbeVUjFoP52qqD3omfrTtrsvDW2e5ceIoEWdOItfQwMGrFm6+/lSp64++6aurSSQkJCRexTsdkZR4PxK7uvS1/j+uaNUCICkrCRER3TQlSgNVrkFHX5PszAzO7tiCi09drN2qqcv8oqkVDHk5cGXzc4et9ayZ12wejzLimFwrAvNRI7Hcv5AGeQfJyxX5KyQJC+e2DFq0iqBJM/EJbE/S/VgOLP+FJUP78se4UZzaHELc3ShJsl5CQqJI3mlHoo79SHIELXIFVdSRL4+inZJFjo4xmtpy5AoZF//cQ0ZKMvXLKhrJx9pLtZr34rqXTnlbeDO5wWTOPDjDYu9HWE2ciOZfWwiIWoadqz5/rbvJwd9uYOnqTtM+A/l43lL6zl5Ewx59EAQZxzes4fevhrNi5CD+WrOS2JvXEV8QhZOQkJAAaWrrrYjPVDkSjZQMcjQN0DHQJCcrk7M7tuDkXRvbau5lb1StYNg3DuJugEX15051qNKBiKQIVl5ZSVX/cbw/fx6xX31NjcfjsOr/A+f+fkjc3RTaDPbCzE4fcwcnzB2c8O/SndTEBG6fDyUi9CTnd2/n7I4t6JuaUdWvAVX9ArCr4VmgXiwhIfHf5p2OSNRNfkQiS0olW66HjoGCSwf2kv4kqexyIy9S80MQ5K+MSgBG1B5BU/um/BD6A1c89XD6/TfEjAyM5w2ldaA2WRlKNs08y9W/Y5+bxtI3McW7RRu6jp3C0GVraDv8S6yrVOXywX1smDqOxZ/0Yf/SBdy9clGa/pKoNBRXRv769esEBASgpaXF7Nmznzv38ccfY2lpWagkiSiKjBgxAjc3N7y9vTl//nzBud9++42qVatStWpVfvvtt1de37RpUxwdHZ/7rnXu3Bl9ff1C7W7WrBn79u177tjcuXMZOnRoode9LZIjeQsSMhPQyhYhK4ssQQtNHYEz2zfj6OWNnbtH+RilbwluLeHSBsh7eW8SuUzOzMYzcTFyYfSR0dx31Md5/Xo0LCzImTCEdr4JWLkacXjNdXb/epn05OyX+tDW08ejUTM6jf6Wocv/oP3nY3D09Ob68aNsnDae1V8OI2zfLqn6S6LCk6+1lf/zpgsSTU1NmT9/PqNHj37pXL9+/di7d2+h1+/Zs4fw8HDCw8NZunRpwR/yhIQEpkyZwunTpwkNDWXKlCkkJia+sg9jY+MCtd+kpCTu379fpN0vqgKDShlY3RIqkiN5C+Iz4jHLVOVLsnMViMpY0pISqft+l/I1rFYPSI6BqGOvPK2n0GNRi0VoyjUZdnAYqeY6OK9bi27duiRO/IYGGid4r5sb964msG7qaW5dePTaoTS1dage0JD2n3/D0GVraDNsFAotbQ6u/JUlQ/tyaNUSEmKjS+tOJSTKBUtLS+rVq4dCoXjpXOPGjV+SMnmRbdu20adPHwRBoH79+gWOYN++fbRq1QpTU1NMTExo1arVa51SviowwJYtW+jatetz53/88Ufq1auHt7c3kyZNAlSS9bt27SI7W/WAGBUVRWxsLI0aNXrr30FhSDmStyA+Mx67PENEMsnMkSGSBoCJrV35Gla9HWgZQdg6cG36yia2+rYsaL6A/vv6M/LQSJYHLsdx6RLuT5pM/KKFWLS7TbdRYzi0Poq9S65Qvb41jYKqoaXz+o+IQlMLzyYt8GzSgvvhN7iwbycX/9zDhb07cPKuTe027XGp7SvlUiRe4ofQH7iecF2tfbqbuvON3zeFtimujHxJeVZGHsDe3p6YmJjXHn8VLVq0YNCgQeTm5hISEsLSpUuZNm0aAPv37yc8PJzQ0FBEUaRjx44cPXqUxo0b4+fnx549e+jUqRMhISF07969UJ2v4iA5krcgPjMe22yVzpYogpirWh2ub1zOe4AotMGri2p6K+sn0Hr1vGlNi5pMbzid0X+NZsLxCfzQ6Adsvp+OpqsLcXPmohV+kw5z5nHpmhnn9twh5kYiLfrWwN696PuzqVodm6rVadL7Yy4f3MfFA3vYOmsaRpZW1Gr9Pl7NWqGjb6DuO5eQeCuKKyNfEZDL5TRs2JCQkBAyMjJwdnYuOLd//372799P7doqcdnU1FTCw8Np3LhxwfRWviNZsWKF2m2THMlbkJCRgEeONjkK1R/EXGUKmjq6KF4h6VzmeHSGc6vh3ilVzuQ1BDoHci/lHvPOz8PZ0JlhPsMwHzQIHU9PYr4czd2g7nj8MBPnr/w4sPoq2+aGUbOpPfU7uaJZSHSSj56xCfU/6EG9Tt2IOHOKsH07ObpmJSfWr8G9YVNqt2mPpXPZCspJVDyKihzKmtKOSPJl5POJjo7Gzs4OOzs7jhw58tzxpk2bvrafHj160KVLFyZPnvzccVEUGTt2LJ988slL13Tq1IlRo0Zx/vx50tPTqVu3bklv5yUkR/IWqHIkFgU6W8qsZPRNKsiOhLZPw/X7lwp1JKDS5Ip8EsmvF3/F0dCR9q7t0WvQAJfNm4ge+TnRwz/DbNAgPvzmU07vuMOlI9HcDoujcY9quPpYvJE5cg0Nqgc0pHpAQ+LuRHJh306uHTvClcP7sXP3wCewPVX9GiB/i+1KJSRKi9KOSDp27MjChQvp0aMHp0+fxsjICBsbGwIDAxk3blxBgn3//v3MmDHjtf00atSIsWPHvpQsDwwMZMKECfTq1Qt9fX1iYmJQKBRYWlqir69Ps2bN+Pjjj0ttnxLpW/yG5Il5JGQmYJxpRY6OMQBZaU/QLyLJVmbomICxIzy4VGRTQRCYHDCZ2NRYJh6fiI2eDXWt6qKwtcXpjzU8nP498cuWkXHlMgE//UQ1P2sO/3GdPYsv41LLnEZB1TAwffMozMLJhdaDP6Nxz/5cOfInYft3sWveLPRMTPFp1Y5age9L014SZcKLOZI2bdq8UQnwgwcP8PX1JTk5GZlMxty5c7l69SqGhoYEBwdz5MgRHj9+jL29PVOmTGHAgAEFEu9DhgyhXbt27N69Gzc3N3R1dVm1ahWgqgabMGEC9erVA2DixImFJu4FQXhl5Vjr1q25du0aAQEBAOjr67NmzRosLVVSTsHBwXTp0uWlCi51IcnIF0HQkpMALOlbg0brG7Eo1JPcezZcte2AQrYGew9P2g3/Up3mFp+QXvDoGow4X3Rb4EnWE3rv7k1iViJr2q7B2ci54FzS5i08mDIFuYkJtj/MRLueHxcP3uPMjkgEmYB/R1dqNrNHJnv7pF1eXi5RYee5sHcHURfPo6GlRc3mranbrjNGllZv3Z9E5UCSka/Y/Odk5AVBaCQIwmJBEJYLgnCiLMZMyEwAQDdNSY6BGaIokv4koeJMbQHY1IKEW5CV8kbNjbSM+KXlL8gFOcMODiu4RwDjD7riHLIOma4ud/v15/Hs2fg0tSF4kj82bsb8vTGcTTPP8uhO8lubKZPJca1Tjw/GTaXPjwup5v8eF/fvZsXIQeya/yMPI2+9dZ8SEhLlR5k7EkEQVgqC8EgQhCsvHG8jCMINQRAiBEEoNLsliuIxURSHADuBVy8FVTP58ihaKVnk6Jig0MohV6msWI7E2lv174Mrhbd7BgcDB+Y3n8+j9EeMODSCTGVmwTltDw9ctmzGOLgHCatWEdU9CK3EaNoP96b1QE9Sk7LYOPMsh9dcJyPl5YWMb4KFozNtP/2CAfOXU6ddJ26dC2XNmJFsmj6BqEsXpFXzEhKVgPKISFYDbZ49IAiCHFgEtAU8gGBBEDwEQagpCMLOF34sn7m0J7C2LIzOl0fRSE5HqWWIpnYWAHomFUhy3SbfkRSdJ3mWWha1mNFoBpfiLjH+7/Hkif+KM8p0dLCZNAn7X39B+egRkd0+JHHNH7jVtaTXZH9qNXPg+on7rJl4iosH75GbWzxhR0NzC5p+NIDBv6yiYXBfHt+NYvP0CawZ8znXj/9FXu7Lq/YlJCQqBmXuSERRPAokvHDYD4gQRfG2KIrZQAjQSRTFy6Iotn/h5xGAIAiOwBNRFF85jyMIwmBBEM4KgnA2Li6uxHbnRyRCUgpZGnooFBkAFSsiMbABXXNV5dZb0sqpFV/6fsn+O/uZe37uy103a4br9m3o1vfn4fTp3Bv8CbKUBBp2r0rQBD+sXAz5e2M46787w72rL769b462nj7+nT9k4MKVtP5kBDnZWeya/yMrPx/MhX07ycnKLLoTCQmJMqWi5EjsgHvPvI4zRUBaAAAgAElEQVR+eqwwBgCrXndSFMWloij6iqLoa2HxZiWrhRGfEY9mngwxJYVsQRuZ/KkjqShVWwCCoIpKHlws1uV9PPoQVD2IVVdWsenmppfOa5ib47B4MVYTJ5B+5gy323cgafNmTKx16fBZLdoOqUluTi7b54ex+9dLPInLKPataCgU1Gzemv4//ULH0ePRNTLm0MrFLPv0Y05uWkdGytvnZiQkJEqHiuJI3hpRFCeJolgmiXZQJdvt81T7mmTnKeCpPIpeea9qfxFrb3h0HZRvn7MQBIExfmN4z+49pp+aztkHL1e6CYKAac+euG7bira7O/fHf8u9AQNRxsbi6mNB8CR/6nd25d71RNZOOcWJLRFkZyiLfTuCTEbVegEET5tN0KSZ2FStzomNf7D00/4cXr2U5Mev1wWTkJAoGyqKI4kBHJ55bf/0WIko8Q6JzxCfEY+90hARVDpbealo6xugoalZ4r7Vio23atfEuGvFulxDpsGsxrOwN7DniyNfEJP66rdB08kJx99WYz1pIhlhYdzu0JGEtWuRywXqtnGm1+T6VPO14sL+u6yZeJIrR2PIK2b+BFQOzN7Diy7fTFJVevk1IGz/LlaMGMSehT/x+G5UsfuW+O+gbhn5zMxM/Pz8qFWrFp6engViiS+yevVqLCwsCsZdvnx5wTlJRl59nAGqCoLgIgiCJtAD2F7STtWxQ2I+8ZnxWOfoPtXZEsjNSalY+ZF8rFXbAhcnT5KPoaYhC1ssRCkq+ezQZ6TlpL2ynSCTYRIcjOuO7ejUrs3DqdO426cv2VFR6Jto0aKfBx+O9cXYSpe/1t5g/fQz3L0aX2y78rFwdKbt8C8ZMH8ZPq3f52boCX77ajj/98MUYq5fLXH/Eu8u6paR19LS4tChQ1y8eJGwsDD27t3LqVOnXtlHUFBQwbgDBw4EJBn5YiMIwjrgJFBdEIRoQRAGiKKoBIYD+4BrwAZRFP9Rw1hqjUgss//V2crJTEavIjoSU1fQ1H/ryq0XcTJ0YnaT2dxOus3YY2Ofq+R6EYWdHQ7Ll2EzfTqZN25wu2MnHi9egpiTg6WTIV2+rEObwV4os3PZMf8iOxdeJPHBq53T22BobkmzfoMZvGgVAd16Eht+g5BJX7Nu4tfcOhcqbQ0soTZeJyMvCEJBVJCTk0NOTs5bKetKMvLFRBTFV7pCURR3A7vVPNYOYIevr++gEvWDSHxmPGZZ9mRrqhxJVvoT9E2qqMNM9SKTgZVXiSKSfBrYNuCrel8xM3QmCy8sZESdEa9tKwgCxh90Ra9hQx5+/z1xc+eSvGsXNtOmouPjQ5U6ljjXNOfi4Xuc2x3FuqmheDW2w6+9C9r6L+/x8DboGBjS4MOe1OvQlcuH93N2x/+xddZUzOwd8ev8Ie4NGiOTS1L2FYkH339P1jX1yshr1XDHety4QtuUhox8bm4udevWJSIigk8//RR/f/9Xttu8eTNHjx6lWrVqzJkzBwcHB0lG/r9EHllk5WZhlA45Cn1EUSQrtQLpbL2IjTeErYW8PJVjKQE93XsSnhjOssvLqGpSlbYubQttr7CyxH7eXFIOHeLB1GlEBffEpGdPLEZ9jlxfnzqtnXCvb0Pozkiu/BXNzdAH1HvfBa8mdsg1SmarQlubOm07UqtVO26cOErotk3sWfgTx9evoV6Hrng2a4lCU6tEY0hUbkpDRl4ulxMWFkZSUhJdunThypUrL22726FDB4KDg9HS0mLJkiX07duXQ4cOvfU4kox8OSAIQgegg5ubW4n6yRVUpab6GSLphhYgZiDm5VbMqS1QVW5lL4WE22BesnsXBIHx/uOJfBLJhOMTsNe3p6ZFzSKvM2jeHF0/f+LmzSNxzRpSDhzAeuIEDFq0QNdQk6Y9q1OziR3HN4Xz98ZwLv8VzXvdquJc06zET0tyDQ08GjenRsOm3Dp/htCtGzi48ldObl5HnXad8GndDi1dvRKNIVEyioocyhp1yMgbGxvTrFkz9u7d+5IjMTP7d+HywIED+frrrwHeGRn5ipJsLxXUlWxXonIkOqk55BqYI+Y93dCqojqSghXuxVtP8iIKuYI5zeZgoWPBZ4c+IzY19o2uk+vrYT1+HM7rQ5AbGxP96XCiR4wk56GqZNfMTp8OI3x4/1NvBEFg9y+X2D4vjMfRqWqxW5DJcPP1J3jabLpP/B4LJxf+Xvcbyz79mL9Dfif9SZJaxpGo/MyZM+e5JPybJuPj4uJISlJ9jjIyMvjzzz9xd3d/qd2zifHt27cXiCMGBgayf/9+EhMTSUxMZP/+/QQGBr52vMJk5FeuXElqquq7ExMTw6NHqu+ZJCNfQVA+jUi0kjPJ0TVFXrCqvQLJozyLRQ2QKVR5Eq8P1NKlqbYpi1osovfu3nx68FN+b/s7BppvJv2u4+2Ny6aNxK9azeNFi0g7eRLLL7/EuPuHCDIZzjXNcfAw5Z+jMYTujGTD9FBqNLSlfkdXdAxKXl4tCAIOnt44eHrz8HYEoVs3cnrrRs7t3IpX89bU69AVQwvLojuSqPSoW0b+/v379O3bl9zcXPLy8ujevTvt27cHVJLwvr6+dOzYkfnz57N9+3Y0NDQwNTVl9erVgCQjX6koqYx8gvwIDxRr2bzRgasOQTxSpJHycDeDFq3C0Lzkq+ZLhcUNQc8CPvo/tXZ76v4phv45FH8bfxa2WIiG7O2eRbLv3OH+pMmknzqFTt262EydglaVf4sWMtNyOLMrkitHYtDQklPvfWdqNrUvcf7kRRJiozmzfTNXjx4GRGo0bEa9Th9gZudQ5LUSxUOSka/Y/Odk5N8UdZX/5kckJKWQraGHXJYOqLaVrbBY11JFJGp+UKhvU5/x9cdzPPY4M0NnvrU6r6aTE46rVmLz/fdkR0Rwu3MX4hYsJO9peaK2noJG3asRNMEPa1dDjm+KIGRaKFGXHqtVCdjU1p7AISML1qLcOHmM1V8OY/vP3/PwdoTaxpGQ+C/wTjsSdeVIcknBWNOI3KQksgUdIB1dI+OKvU2sjTekP4aUohctvS3dqnWjv2d/1t9Yzx/X/njr6wVBwLhrF1x378IwMJDHixYR2bUrGRf/zemY2ujR4TNV/gRg1y+X2LngIgmxJV9/8iyG5hY06zeYQYtW4t+5O3cvX2TN2M/Z/P1Eoq9ekWTsJSTegHfakagLpZCMnWgCSiVZoiZiXmrFrdjKJ39vEjWsJ3kVn9f9nJaOLZl1ZhZH7h0pVh8aZmbYzf4RhyWLyUtNIyq4Jw9n/kBexr9ij841zekxwY/3urnxIDKZkO9CObr+JpmpOWq6ExW6hkY07PERgxatpGFwXx5G3mL9lDGETPyaW+dOS4sbJSQKQXIkb4BSSMYu1wARyFLKUFZUeZRnsfYChBKvcH8dMkHG942+p4ZZDb4++jXXE4q/uEy/SRNcd+7AuPuHJKxeze1OnUk7HVpwXq4hw6elI72n1sejoS1XjkSzZuLJEu1/8jq0dPXw7/whgxauoHn/T0hNjGfrrGn8/vVnXD12mFxl8QUoJSTeVd5pR6KuHEkuKdg8o7OVk5lc8R2JloFKLuW+ekqAX4WOhg4Lmi/AUNOQ4QeHE5de/H1f5Pr62EyejONT0bq7fftyf9JkclP/LQXWMVCtPwn61g9LJwP+3hhOyFT1508AFFra1G7TgY/nLqXt8C8RRZE9C39i5eefSPuiSEi8wDvtSNS2jkRIxiJLi2yFAaKYR3ZGBdXZehEb71KLSPKx1LVkYYuFJGcn89mhz8hQFn8PEgA9fz9ct23FtH9/kjZu5HbHjqS9IIL37PoTUOVPdswPIz5GPetPnkWuoYFHo2b0/XEhnb+egJ6JiWpflOEDOLVlPZmp6h9TQqKy8U47EnWQRzZ5QiYmmXJyNA1ATAdRrPgRCYCZGzyJVkmllCLupu7MajyLq/FXGXdsXKECj2+CTEcHq2++xnntH8g0tbjbrz8Pvpv+XO5EEARV/mSiHw27V+XRnRTWfxfK4T+uk/Ykq6S39BKCTEaVuv4ET/2RoMkzsa5SlePr/8fSYf04/NsykuOkfVEqA+qWkQeVEm+3bt1wd3enRo0anDx58qXrRVFkxIgRuLm54e3tzfnz5wvOvQsy8hW47KhioES1k69RhqDS2Xq6qr1C7dX+OnRMQMyDrGTQMS7VoZo6NGW072h+PPsj88/P5/O6n5e4Tx0fH1z+bwuPfp5D4v/+R9qxY9jMnIHuUz0hALlcRq3mDlT3t+bMzkiu/BXDzdMP8GnlSO1Wjmhqq/cjLggC9jW8sK/hRdydSM7u2ELYvp1c2LuD6gGN8O3QFSuXCijmKQG8XmurKPJl5Ldu3frSuZEjR9KmTRs2bdpEdnY26enpL7XZs2cP4eHhhIeHc/r0aYYOHcrp06cLZOTPnj2LIAjUrVuXjh07YmLy8tKCfBn5hg0bvrWM/LOr5UNCQpg1a9Zb/gYKR4pIiqBAZystj2w9U8Q8VflppYhItJ86j8yykQL5yOMjPqz2ISuurOD/wtWzEFKmo4P1+HE4rl5NXk42d3r15tFPPxesO8lHW09Bo6BqBE/2x7mmOWd3RbFmwkmu/BWt9oR8PhZOLk/3RVlOnXaduH0+lDVjRrJx2ngiw85JpcPvEK+TkX/y5AlHjx5lwIABAGhqamJs/PJD27Zt2+jTpw+CIFC/fv0CRyDJyFcC1CHamL8YUTs1m1wDK0SxgutsPYvO06eajEQwcS714QRBYKz/WKJTopl6airORs7Utqxd9IVvgF59f1y3b+fhjBnEL1tG6tGj2P00G60X3ltjS10CB3lRq+UTTmyO4K91N7l4KJqAzlVw8TFXu3w2qNaiNP1oAAEf9ODin3u4sGc7W2ZMwtzRmbrtOuHesCkaipJJ5b9rHNtwk8f31JtfMnfQp1H3aoW2UbeMfGRkJBYWFvTv35+LFy9St25d5s2bh57e86Kgr5OLf1dk5N/piEQdyfZ8R6KZkolS1xSZLB1BkKFrVLpTRWrhWUdSRihkCmY3nY2Nng2jj4zmccZjtfUt19fHdvp07H/5BWVcHJEfdCMxZP0rn/ytXYzo8mUd2g3zRhBgz5LLbJ51jntXE0otUtDS1cOvUzcGLlxBm2GjQBTZt3geyz7tz4mNayWRyArAizskBgUFAcUXbVQqlZw/f56hQ4dy4cIF9PT03jjv8ra8qYx8nTp1uH79OuHh4cDzuySWxu6I8I5HJOogP0cif5JKtpkRMjEOXWPjyrFRUn5eJKNs/4AZahoyp+kceu/uzVd/fcWy1sveWpOrMAyaN0Nn21ZivxnDg8mTSTt+HJtpU5G/MKUgCAIu3uY4eZpy7cR9zu6OYvv8MGyrGuPXwQW7aqUjcSPXUODZpAUejZtz9/JFzu3eyslNawndugH3hk2p+35nLBydS2XsykJRkUNZU9yIxN7eHnt7+4LNrLp16/ZKR2JnZ8e9e/cKXkdHR2NnZyfJyP9XyBWSkYna5CUmkaPQRyC9ckxrQblEJPlUN63OxICJnH14lnnn56m9fw0LCxyWL8Pyq69IOXKE2527kH7mzCvbyuQyPBvZ0XtqAI2CqpH0KJ2tP19g29wLPLhd8m2YX4cgCDh5+9B1zGT6/fwrXs1acePEMX7/ajgbp42XtgOuQBQ3IrG2tsbBwYEbN24AcPDgQTw8PF5q17FjR37//XdEUeTUqVMYGRlhY2Pz35CRFwShniiKr/52/kdQCsloiIYoExPJlumQl5uKnkklUYjNT7aXgyMB6FClAxfjLrL6n9V4W3jTyqmVWvsXZDLMBnyMrp8fMaO/5E7ffpgP+QTzYcMQXqGDJlfI8G5mj8d7Nlw5GsP5fXfYPOscjp5m+HVwwcrZUK32PYuZnQMtB37Kez36cOnAXsL27WTrrKkYW9vg07o9nk1boK1XeCmnRMlRt4y8oaEhCxYsoFevXmRnZ+Pq6sqqVasAWLx4MQBDhgyhXbt27N69Gzc3N3R1dQva/Cdk5AVBuADoAyHAOlEUr5aKFaVMSWTk/Vd+gEKpZNnPNznRch6pT1bg0bgRrQYNV7OVpcR31uA3EFp/Vy7DZ+dm039vfyKSIljXfh2uRq6lMk5uahoPv/uOJ1u3ouvvj93PP6FhVniJdk5WLpePRHN+/x2y0pTYu5tQp40T9tVNSiUp/5y9SiU3Tx8nbO9OYm9eQ6GljUfjZvgEtsfcwalUxy4vJBn5ik2pyciLolgbaA8ogU2CIFwUBGGMIAjOxTe3cqEUUjBO10FEIFMJyuy0iruh1avQMSm3iARAU67JT01/QkuuxajDo0jPebnGXh3I9fWwnTkDmxkzyAgLI/KDbmRcKnxVv0JLTp1AJ/p814CArlVIiE1j+9wwNs08y63zj8jLK73yXbmGBjXea0LwtB/pPWMu1QIacuXIAX4b/Skbpo4jPPQEebm5pTa+hIQ6KTJHIoriDVEUp4ii6AH0AYyAg4IgHC9160qIOrS2coVkjNO0VDpbeU/3IaksORJ46kjKt1rIWs+aWU1mEZUcxddHvyYrV/0rz/Mx7tIZ53VrEeRy7vTqTeKGDUVeo6mjQZ3WTnw0PYCmvaqTla5k79IrrJtymqvHY8nNKd08hpWrG22Gfs7gX1bTqGc/kh7eZ/tP37N8xEBObVlPWlL5PQhISLwJb5xsFwRBBlgCVoAeUOE1IUpa/puTl0OukIZRugbZz6xq1y9kDrPCoWNc7o4EVBtijfMbx1/Rf/HpgU9Jy1HvviLPou3hgfOmjej6+fFg4iRiv/2WvKyinZeGQo5nIzt6TqlP4CAvFFpyDv/vOr9/e4KzuyNJT84uso+SoGtopCofnr+cjqPHY2JtWyDDsuPnGdy5HCYl5yUqJEXWZAqC0AgIBjoDl1HlS0aJolh65S4VhMRM1ZOgYYaMHE2DZ1a1V7KprYTI8rYCgCD3IHQVukw4PoGB+wbyS8tfMNEunRJcDRMTHJYuIW7+AuKXLCHr+g3s589DYWtb5LUymYBbXUuq1LEg+loiFw7c5fT2SM7sjqKqrxXezeyxdCq9xLxMLqdqvQCq1gsg8X4Mlw7u48qRA9w8fRxjaxu8W7bFs0kLdA1LJkYqIaEuiqraugfcQeU8JouiWOGjEHUSnxEPgGE6z0cklWpqy7hccyQv0qFKBww0DRj912j67e3HklZLsNazLpWxBLkcy1Gfo+Ndk9ivvyHyw+44LFqIzjNVO4VeLwg4eJji4GFK4oM0Lh+O5vqpB9w49QBrVyO8m9vjWtsCubz0quhNbOxo0vtj3uvem/DTx7l4YA9H16zkeMjvVPV/D69mrXD09EaQSZX8EuVHUVVbTqIo3nnmta4oiqWTLS1Filu1lZ6TTtCqLbx/LBS32Eyu2NsjZp/j8z/+r/J8cfeNh7MrYbz6t9wtCWcenOGzQ59hqGnI0lZLcTZyLtXxsm7d4t6QoSgfPsR25gwM27UrXj8ZSq6fuM+lI9Ekx2WgZ6RJjYa2eLxni4GptpqtfjWP793h0oG9XD16iKz0NAzMLfBs0gLPxi0wtrYpExuKg1S1VbEpzaqtO087CxAE4Spw/enrWoIg/FJ8kysHugpddMUq6GdkoTS0eLrFrknlcSKgmtrKSYecirURUz3reqwMXElWbhZ99/Yl8knpTr9pVamC8/oQtL28iPniSx4vXlwsqRQtHQ1qtXCg95T6vP+pN2b2+pzdHcX/xp9g56KLRF56TF4piUTmY+7gRPP+n/DJkt95f8RXmNk5cGrLelaMHETIpG+4fHg/2RmV7nmvTCgNGfk5c+bg6emJl5cXwcHBZGa+/F1bvXo1FhYWBeMuX7684Nx/SUZ+LhAIbAcQRfGiIAiN1WpJBUY3IwWlSTUEIbJy5Ufg39XtmUmgKJ0ppOLiYebB6jar6bunL6MOj2Lt+2vRVeiW2ngapqY4rlrJ/fHfEjd3HtmRUVhPm4pMU/Ot+xJkqv1QnGuak/w4g6vHY7l2/D67L19Cz1iLGu/ZlHqUotDUwv29Jri/14SU+MdcPXaYf44cYP/i+RxatYSqfg2oHtAIJ+/akmjkU9QtIx8TE8P8+fO5evUqOjo6dO/enZCQEPr16/dSH0FBQSxcuPC5Y/85GXlRFO+9cOg/U+Sul5FCjrYxiGmVq/QXyk1v601xMXJhVpNZRCZHMunEpFKXXpdpaWH74yzMPxvOk23buPvxxygTS5ZDMjTXoX6nKvSZ0YC2n9TEzE7v3yhl4UVuX4grNSn7fAzMzPHv/CH95ywmeNqPeDRsxu1zoWydNZVfB/Vi98KfiDh7GmV26Vaevau8TkYeVMKNGRkZKJVK0tPTsX2Dgo58/msy8vcEQWgAiIIgKICRwDW1WlKB0c1IJUlTHzE9tXKV/kK56m29KfVt6vNZ7c+Yd34etSxq0dujd6mOJwgCFp9+iqaTM/fHjSOqRw8cly1D09GxRP3K5TJca1vgWtuiIEq5fuI+e5ZcRsdAgXuAKkoxtiq9qEsQBGyr1cC2Wg2af/wJdy6HcfPUcW6dOcW1Y4fR1NHBtY4f1QIa4lyrDgpNrVKzpTAOr17Kozu31dqnpZMrzfoNLrSNumXk7ezsGD16NI6Ojujo6NC6dWtat279yrabN2/m6NGjVKtWjTlz5uDg4PDOyMi/qSMZAswD7IAYYD/wqVotKQXUsR8JqKa2sgRN8nIzKu/UVgV2JAADvAZwOe4yP539iRpmNahrpX6F0hcxav8+CltboocOJapnLxyXLUVbTcng/CjFr70Ld68mcPXvWMIO3OPC/rvYVjWmxns2uNWxREOz9FSk5RoKXGvXw7V2PXIHKbl35SI3Tx8n/Mwprh//C4WWNg6eNXHyroNzrdqY2NiVujRMefO6qa05c+YUq7/ExES2bdtGZGQkxsbGfPjhh6xZs4bevZ9/GOrQoQPBwcFoaWmxZMkS+vbty6FDh95qrDeVkQdITU0lPDycxo0bF0xv5TuSFStWFOteC6Oo8t9gYL8oio+BXmofvZQRRXEHsMPX13dQcfuQ5eWim5lGZl4OUMlWtUOZ75JYXARB4LuG3xG8K5jRf41mQ/sNWOhalPq4unVq47T2D+4OGMidj/pgv2gRev5+autfJpcV5FLSnmRx/eR9rh6/z8HV1/h7QzjV61vj2cgOUxu9ojsrAXINDZx96uLsU5cWA4YRffUKEWdPEnXxPLfPq3RZDS0scfKujXOtOjh61kK7iERuSSgqcihrihuRHDhwABcXFywsVJ/Vrl27cuLEiZccidkzum8DBw7k66+/BnhnZOSLikgcgY1Pp7MOAnuAUPE/tIeoTkYqIgJZT6ueKtUaEqg0EQmAgaYBc5rOodfuXoz+azTLA5ejkJV+klirShWc163l7sBB3Bs0CNvZP2L4mumJkqBnpEXdNs7UCXQi5mYS/xyL4cpfMVw6FI1dNWM8G9vh6mOBXKN0qwLlGho4efvg5K2a4kl6+IA7l84TdfECN04c4/LBfQiCDOsqVbGt7o5ttRrYVHPHwNS8VO0qT4obkTg6OnLq1CnS09PR0dHh4MGD+Pq+XC17//59bGxUpdnbt28vKLMNDAxk3LhxJD7N0+3fv58ZM2a8drzCZOQnTJhAr1690NfXJyYmBoVCgaWlZfnLyIui+APwgyAIBkBL4GNgsSAI14C9wD5RFB+WimUVBN2M1Od0tiqdI9EyBEFWKRwJQFWTqkwOmMw3x77h57M/843fN2UyrsLGBqc1/yN6yFBiPh9F7uRJmHTvXipjCYKAfXUT7KubkJ6czbUTsfxzLJb9y/9Bx0BBjQa2eDayxdBcp1TGfxFjK2uMW7WjVqt25CqVPIi4SdSl89y9comw/bs5t2sbAAZmFthUc8e2qju21d2xdHZFrlG5qsHULSPv7+9Pt27dqFOnDhoaGtSuXZvBg1XR1sSJE/H19aVjx47Mnz+f7du3o6GhgampKatXrwb+IzLyr71IEDyAtkBrURRfvwtLBaEkMvLffLuKD3et5nitDigzjjBsxTp09A3UbGEp84MzeHWD92cX2bSi8EPoD6y5toZZjWfR1qVtmY2bl55O9Oefk3b0GBYjR2A2ZEiZ5A3EPJG71xL452gMUZdU2xO71rbAp6Uj1q7lJ4WSq8zhUdRt7t+8TszN69y/eZ2U+DhAFdmYO7pg5VIFK1c3LF2qYO7ghMZryqmlBYkVm5IsSHyjZLsgCFuA5cBeURTznu5LchX4qRj2Vip0M1Ke6mylIpMrKufmQ+UsJV8cvvD9gn/i/2HSiUlUNa6Km0nJCibeFJmuLg6LFhE7fjxx8+aT8/Ah1t9++8qNstSJIBNw8jTDydOMlIRMLh+J5urfsdw6H4eViyG1WjhQpbYFslKUY3kVcg0FNm7VsXGrTp12nQBISXjM/ZvXuR9xk0eREdw4dYxLB1UlqzK5HDMHJ6xcqmDpUgUrFzcsnJxRaJXNqn+J8uFNvx2/AP2BBYIgbARWiaJ4o/TMqjjoZqSqdLbENHSNjCtnVUsldCQKmYLZTWbTfUd3Rh1RLVY00CybSFBQKLCdOROFpSXxy1eQExOL3ZyfkZdi8vlZDEy1adDVDd92zlw/+YBLh+6xf/k/6Jtq4d3MAY+GtmjplK5jK9w+cwzqN6Ra/YaAKtGbHPeQh7cjeBh5i0eRt7h19jRXDv8JgCDIMLN3oGb3PqQ9SUKhqYWGlhayyqQQIVEob/RpFEXxAHBAEAQjVErAB54KOi4D1oiimFOKNpYrehkp5CgMIC+18lVs5aNtXOGrtl6Fpa4ls5vMZuD+gXz797fMbTa3zBy5IJNhOXo0CkdHHkyZyp2evXBY/OsbqQerC01tDbyb2ePVxI6oS4+5ePAeJzZHcHZXJN7NHajV3AFt/fLPUQiCgJGlNUaW1s85l5T4xzyKvMXDyAgeRd5CmZVFctyjgvdQQ1MTTW0dFDo6aK1uPFYAACAASURBVGprV7p8y7tESeun3vixRhAEM6A38BFwAfgDaAj0BZqWyIoKjG5GCuk6poh59zE0qyR7tb+IjgkkVgwp+bfF19qXL+p+wY9nf2TllZUMqDmgTMc36d4dhZ0dMSM/JzIoCIdffkWnpleZ2iCTCbj6WODq8//t3Xd4VVXW+PHvyk2nk4CUFEJvgYggOGAdcFRAXwUVB1EcR2esozN2x9c283Mc9R3HPtgrRRQHlRFsiAWUojSRIhAJXQi9pK3fH+cEYyYhN9xyblmf57kPNyfnnrM2Se66Z+991m7BlsJdLHivkHnT1rLww3Xkn5RFweBs0hrVv8xLKIkIjTNb0DizBR37DQBgzZo1+NLTadygAWUlByk9eID9e3azb5ezIoUvKYnk1DSS09JISk3Dl5gYnT0AUUZV2bZtG6mpR9796O8YyRSgC/AyMFxVK4u8TBSRIxvFjhIN9u1mf1pbVFfSsI41wCNWhJWSr68x3cew6MdFPPL1I/TM7En/1v3Dev6GAwfSbvxrrPvd7ykcM4a2Dz5Ao8GDwxpDpZa5jTntd/lsW7+H+f9Zy4IZhSz6eB09T2hLwZAcGjTx5k51f2RlZVFUVMS27dsPbVNVKsrLKC8tpay0lPLS0kOLd4nPR1JyMokpKfgSkyyphFBqaipZWVlH/Hp/r0ieVtVpVTeISIqqHvRnRD+ape/fzdYGDaCiJPruaq+U1gwO7ISKCojCfmkR4Z5f3MPK4pXc+MmNTBo+KWRrmNQmpVMn2k2cwLorr6LommtpedNNNB97sWdvbhltG3Lqb3vSb9he5v3HuTpZ/Ml6ehzfhr6nt4u4KxSApKQk8vLyDruPVlSwbf06ir5dwpqF8ylc9DXlpaWkNmpMh2OOpdOxx5GTX+BZaRdTM7+m/4rIAlXtU9e2SBXI9N+PBw1hefuz2XtwGqdf9Ue6n3BKkKMLg9mPw/Tb4ObCn4o4RqHVO1dz/tvnc0rOKdx/wv2exFCxfz8bbr6F3TNm0PS882h1x5+RCKisu2PLPua/V8jyOZtISk6gz2m59Dolm6QQlmAJh5ID+1n7zXxWfjWbNV/P4+C+vSSlpNKuoA9djjueDn0HWGXjEArK9F8RaYVTXytNRI4GKj9+NQZCV3muDiKSAzwCbAdWqKp/iwocgQb7d1OW4CTbqB5sB6d7K4oTSfsm7RndbTTPLnmWsT3G0i0j/PckJKSl0fbhf7D14X+ybdw4Stb9QNbDD+Nr4u2yt01bpvPLi7px9JAcZk/5njlvrWbxzPX0PzOPLgNak5AQnd1CyalpdHZniJWXlbJu6WJWzZ3NqrlzWPnlF6Q1akyPkwaTf8qvaN6mrdfhxq26Vki8GBgL9AWqfqTfDbygqm/W+4QizwHDgC2q2rPK9tNwCkP6gGcOlxxEZCjQTFVfEZGJqnr+4c55pFckqsrSHvl8dOxllO5/n7EPPUlGVhQOuH83DSZcAJfPhDZHex1NQHaV7OL0N04nPzOfp4Y85WksO96cwsY77yQ5K4vsp54kOTfX03iq2rCymM/f+J4ta3fRvE0DfnFOR3J6NI+ZcQatqKBw0dcs+nA638//korycrK755M/+DQ69Tuu1psiTf34e0Xib9fWCFV9I0iBnQDsAV6qTCQi4gNWAEOAImAuzjRjH1C98MxvcNZCmQwo8LKqPn+4cx5pIinfvZvl/fozo/9Yyg/M4urnJ5KSHtrieiFROBuePw3GTIEOUdg1V82LS1/kwXkP8sypz4R94L26fXPnUnT1NQBkPfYo6W6pi0igqqyav4U5b33Prh8PkNW1Gcef15nmbaLwd/gw9hRvZ+nMD1j88Qx2bt5EaqPG9DjhZHoPOYNmre0qJRBBSSQicqH7qf9POG/aP6Oq/3eEwbUD3qmSSI4D7qostyIit7rHr7F6mYjcgFM8cpaITFbVkTXsczlwOUBOTs4xhYWF1XepU0lhId8NPYeP+gxDyxdz3StvROcnui3L4IkBMPI56DnC62gCdrD8IMOmDCMjNYPxQ8d7/jMpKSxk3e+voKSoiNb33EPTs//H03iqKy+rYMms9cx9Zw2lB8opGJJD36Hton78pDqtqOCHJYtY9OF7rJo7G1Wlx4mDOW7kBTTODH0l6VgUlDXbgcqPLg2BRjU8gqUtUHUFxiJ3W23eA64VkaeAtTXtoKrjVLWvqvatLPFcXxUHDrK1eS5asYe0hlF6VztUqQAcfTcl1iTFl8JVBVexdNtSZhTO8DocknNzaTdhPOnHHMPGW29l+yuveh3Sz/gSE+h9Sja/vmsAnY89igXTCxl/15escWt6xQpJSCC3VwHDr7+Fy594gYJfDWXZpx/x3HWXM/OlZw7dr2KC74iKNgZ80v++IhkJnKaqv3W/HgP0V9Wrg3G+QGZtXfbQZ3Rb8DKZbRty0d+jtLRY6QH461Fwyh1wwn9XDo1G5RXljHx7JKUVpUw5a0pYys3XRUtLKbruevZ8+CGt7rk7ZNWDA7VhZTEzX1tB8ca95PXO5PjzO4d0bXkv7dyymdmTX+PbWR+TlJpC32HncMzQs0hO82yuUFQJyhWJiDxyuEfwwmU9UHUUO8vdFhARGS4i43buPPJPIkllQEUUrtVeVVIqJKZF9U2J1fkSfPyhzx8o3FXIlJVTvA4HcGp0tf3H/9HghOPZdOdd7Jjyltch1ahNp2acf3s/jju7A+uWbee1u+awYHohFSFeV94LTVoexWlXXs9FDzxKTs/efPH6qzxz7WUsmPZvykpjtrJT2NXVtTW/jkewzAU6iUieiCQDo4CpgR5UVd9W1cubBDA1M7m0Aq3YQ+PMKF/UJ61ZVNbbOpwTs06kT8s+PLnwSfaV7vM6HAASkpPJeuQRGhw3gI23387Od9/1OqQa+RIT6POrXC64sz9ZXZsze8r3vPngAnZsiYz/x2DLzM7lrBv+zAX3Pkhmdi4fv/g0z113OUtmfkBFRbnX4UW9wyYSVX3xcI8jOaGIjAdmA11EpEhELlXVMuBqYDqwDJikqkuP5PjBllxyECijScsYSCQxMkZSSUS4/pjr+XH/j7yy7BWvwzkkITWVrMcfJ71PHzbcdDO7Zng/jlObxhlpDL2yF6f+tgc7Nu9j4l/n8u3nGwIu4hep2nTuyrl3/JURt99LeuMmTH/yYV668RpWfvVFzLY5HOqatfWwql4nIm9T86ytM0MZXKBEZDgwvGPHjpetXLnyiI5xx01v0rTwOc649ka6DTwxuAGG0/NnOP9eMu3w+0Whaz66hvmb5vPJ+Z+Q5PN+rKRS+Z69rPvtb9m/dClZj/yTRief7HVIh7Wn+AAfvLCM9cuLyeudycljupLWMHbvx1BVVn75OZ9NfIXiDUW06tiZ4y+4mJyevb0OLWIEa9bWy+6/D+IsYlX9EdGC0bWVdHAPEIVL7FYXg1cklc7ueDa7S3fzzdZvvA7lZ3wNG5D99DhSu3Rh/bV/YMcbbx4qSBiJGjZL5aw/FDBwZEcKl25jwj1fUbh0m9dhhYyI0HnAIMY++Din/u5a9hRv5/V7b2fyX+9g0/dH9sEzXtXVtTXf/fcTnO6oYpyyJLPdbTEvqWQ3EAuJJLorAB9O/9b9SUxI5NP1n3odyn/xNWpEzjNPk9qzJxtvv521I89l75dfeR1WrSRBKBicw7m39CO1YRLvPLqQWRNWUFYSu+MICT4f+aecyqUPj+PEMZeyec33vHrb9bzzz7+ze3tsTZEOFb9KwbolSb7HqW/1GLBKRMK3kPYRCsasLV+pc0US1bO2wKm3FaOJpEFSA/q07MPn6z/3OpQa+Zo2JffVV2jzwN8pKy7mh4svZt2VV3FwdeSuEZOZ1ZBzb+1L719ms3hmEZPum8ePRXu8DiukEpOT6TvsbH77yDMMOOd8Vs2dzfPXX8G8d6ZQXlbmdXgRzd+a4g8BJ6vqSap6InAy8I/QhRUcweja8pXtQROcldyiWlozKNvv3FMSgwa2HciK4hVs3rvZ61BqJAkJNBk+nA7/mUaLP/6RfV9+yeozz2TTvX+hrDgyE3xiko9B53bizGsLOLi3lMl/m8fCj9bF/KB0Sno6A88fw9gHnyCrWw8+eflZXrnlDxR9u8Tr0CKWv4lkt6quqvL1apzCjTFNK5SEsj2UJ4Znre6Qqry7PcamAFca1NZZ4vWLDV94HMnhJaSmknn5ZXSYMZ2m546keMIEvv/VaWx/5VU0Qj/1Zndvzqg7jiW7WzM+m7SSdx9fxL5dJV6HFXJNW7Xm7Jvv5Kwb/kzJgf1MvPsW/vPYQ+zdEZmJ30t13ZB4joicA8wTkWkiMtatCPw2zr0fES3Qrq2D+8vQir2UJQWzGoxH0qqUko9BnZp2omV6y4gcJ6lJYkYGre+8k/ZT/01az55s/stfWDNiJPuOsAJDqKU1SuaMK3txwqjOFH1XzIS/xPZAfCURoWO/AYx96An6n30e333xKc9d9zsW/OdtKspjd9yovuq6IhnuPlKBzcCJOOuzbwUivq8n0K6t/btLQPdQlhxDVyQxOnNLRBjUdhBzNsyhrCIyP9nXJKVDB7KffYa2jz5C+e5dFF44hvU33Ejp5i1eh/ZfRIT8k7I499a+pLkD8Z9NWkl5aeTORAuWpJRUBo26iIsffJzWnbrw8Qv/YsKdN7FzS2R2pYZbXbO2LjncI1xBeiXBJ1ToXg6kxsIVSWUiic0rEoCBbQayu3Q3i7Yu8jqUehERGg8ZQod33yXzyivZPWMGq08/nW3PPotG4KfejLYNOfeWvuSflMXCj9bx5oPz2VN80OuwwqJ5m7aMuO0ezrjmBrYVrePlm69l+ezPvA7Lc/7O2koVkatE5AkRea7yEergvJacVo5oOXsbxkAiSY3tri2AAW0G4BMfn62Pzj/shLQ0Wlx7De3ffYf0AQPY8sCDbLrrroi89yQx2ccJozpz+u/yKd60j9f/NpdNa+Kjuq6I0G3QSYy5/xGatWnLOw//jfeffozSkvhIpjXxd7D9ZaAV8CvgE5yiihE/2B7oGMne7U4fcGksdW3F6GA7QOPkxvRu0TtqE0ml5Oxssp94nMwrr2DH65PZ/P/ui9iZUu2PbsGIm44hMSmBtx76mu/mbPQ6pLBpelQrRt39d/qdOYJFH7zHq7dez4/r6r/uUSzwN5F0VNU7gL1uja2hgLdL0/kh0DGS9KbNWN11KHsax8AqaymNQRJi+ooEnNlby7Yv48f90X8jWeY119D8kksofuUVtj70UMQmE6erqx+tOjTmwxeW8fnklVRURGasweZLTOSE0Zcw4ta72b97F6/eej2LPngvYn9WoeJvIqmst7xDRHoCTYCWoQkpcqQ3bsKPbQooSTvy+1AiRkICpDaJ+UQysO1AIPKnAftDRGh50400vWAU2555lh+feMLrkGqV2jCJ4dcWkH9SFt98sI53H1/IwX3xU6a9XcExXPT3R2nbrQfvP/0Y7zx8f1wtpOVvIhknIs2AO3DKu38L3B+yqExoxHC9rUpdm3clIzUj6ru3KokIre64gyZnn82Pjz7Gtmef9TqkWvl8CZwwqjMnje5C0XfFTL5/Pts2xPbd8FU1aNqMEbfezfG/HsuqubMZd8XFvP2Pv7Hmm/kxX6o+0Z+dVPUZ9+knQPvQhWNCKq1ZzF+RJEgCA9sOZFbRLMoryvElRP+65JKQQOu/3IsePMCWBx5EUlNpPnq012HVqsfxbWnWugHv/WsxE+79ivYFLTh6SA6t2sfAlX0dJCGBY88aSYdjjmXRB+/x7WczWTHnMxo2z6DHib+kx0mDadaqjddhBp1fiUREMoC7gIE45eQ/Be5V1Yi+I6lKGXmvQ4kMMVxvq6pBbQcx9fupLN22lF4tenkdTlCIz0eb+++n4mAJm+/9C76mTWkydKjXYdWqTcemjLqjP4s+XseST9az+uuttO7YhKNPzaVdzwwkQbwOMaQysnI4eezlHD/6ElbP/5IlMz/gq7cm8+WUSWR160mPkwbToW9/0mJhRih+rtkuIu8Ds4DK1YNGAyep6uAQxhY0gazZfv6/ZgMw8XfHBTMkb0y+FDYsgGu/9jqSkNpxYAcnTjqR3/f6PVcUXOF1OEFVUVJC4ZgxlG3YSIcZ00lIi/j7gik5UMayzzey8MN17N5+gGat0ikYkkOXY1vhS/K3dz367d7+I9/O+pilM9+neOMGwFm5sW23nmR17U7bbj1o1DyyFtDzdz0SfxPJElXtWW3bYlXNDyDGsLFE4nr3BlgyGW5e63UkITd62mhQeHXoq16HEnT75s2j8MIxtLzxRjIu/Y3X4fitoryCVQu28PWMH/hx3R5S0hPJ6ZFBXq9Mcno0JyU9chYlCyVVZePK5axbuoiiZUtYv3wZpQf2A9DkqFZkde1JVrcedOo/kJT0dE9j9TeR+NW1BcwQkVHAJPfrkTjL4ppoktbUGWyvqHBmccWwQW0G8eTCJ9lxYAdNK2/GjBHpffvSYNAgto0bR9Pzz8PXMDruc0rwJdC5Xys69T2KouXFrJizibVLtrFy7mYSEoTWnZqS1yuTdr0yaNLC2zfQUBIR2nTuSpvOXel/9nlUlJeztXANRcuWULRsKd8v+Iqln3zA7DfGc9qV15PdPfI/r9e11O5unDERARoAlbfYJgB7VLVxyCMMArsicc1+HKbfBjcX/lTEMUYt3rqYX0/7Nfcffz9ntD/D63CCbv/iJaw991wyr76aFldf5XU4R6yiQtm8ZhdrF21lzaJtFG/cC0BmdkOOP68zbTrF9u9pTVSVomVLmPGvR9ixeRPHDP0fBp0/hsTk8C97HJSldlW1kao2dv9NUNVE95EQLUnEVBEH9bYqdc/oTqIksnJHbC6Zmpbfk0ZDBrP9+ecjdj0TfyQkCK07NOG4szvy6zv7c+G9xzHo3E4c3FfGlIcWMPO15ZTsj54inMEgImR3z+ei+x+l9+DTmP/OFF697Xq2rF3tdWi18rt/Q0TOFJEH3cewUAZlQiQO6m1V8iX4EJGYvsO4xbXXUrFvH9sj+N6S+mrSIo3ev8xm1B3H0vuUbJZ+up7X7v6SNYuiv1JBfSWlpjL4t1dxzi13OXfN3/ZHvpwyKSLvSfG3aOPfgD/g3Ij4LfAHEbkvlIEFQzCW2o0pcVBvK56kdOpE4+HD2P7Kq5Ruibyy84FITk1k0HmdGHHTMaSkJzLtiUVMf2ZJXCyoVV3e0X25+MHH6dhvAJ9NeImJd97Cjk2RVdPM3yuSM4Ahqvqcqj4HnIZTbyuiBWOp3ZgSR11b8aLF1VejZWVse+pfXocSEq3ymnDebf04dngeq7/Zymt3z2F5HBWGrJTWqDHDrruZM67+E9uKfuClm65h48rlXod1SH2m7lQd9bJ35mgU46skxqPknByannMOxa+/TknReq/DCQlfYgL9huZx/u3H0rxVAz54YRkr58XfglIiQrfjT+aiBx5DEoQlM9/3OqRD/E0k9wFfi8gLIvIiMB/4a+jCMiFxaIzEurZiSeaVVyAiEV3UMRiat27A//ypDy1zG/HpxBUc2Bs/RSGrapzZgqzu+fyweKHXoRxSZyIREQE+AwYAbwJvAMep6sQQx2aCLSkVktLtiiTGJLVqRbMLLmDnW29xcHXkzuwJhoQE4aQLu3Jgbxmz31zldTieyc0vYMfmjezcssnrUAA/Eok6016mqepGVZ3qPiIjelN/qU3tiiQGZVx+GZKaytZHH/U6lJBrkd2IgsHZfPv5RtaviM8PRbn5RwNQGCFXJf52bS0QkX4hjcSER1ozm7UVgxIzMmg6YgS7P/gQLYv9+y76DcujcWYqM19dTllp5E2HDbXmbbNo2Kw5Pyz+xutQAP8TSX9gjoh8LyKLRGSxiCwKZWAmRJLToWSv11GYEEjt3h1KSyktKvI6lJBLSvZx4q+7sGPzPub/J/6WtxURcvIL+GHJQrSiou4XhJi/ieRXOOuQnAIMB4a5/5qoE9vlu+NZSl47AA6uXuNpHOGS0z2Dzv2PYsH0wrhaQKtSbn4B+3fvYkuh9z/vwyYSEUkVkeuAG3HuHVmvqoWVj7BEGAC7IdHEk+S8PABK1nj/xhIug0Z2IinVx8xXlqNxsk58pZyevQEionurriuSF4G+wGLgdOChkEcURHZDooknviZN8GVkcHBNbM/cqiqtUTKDRnZi0+qdLP1sg9fhhFXD5hlkZOVQGAWJpLuqXqiq/8IpHX98GGIyxhyhlLw8Stas9TqMsOoyoBVZXZsx+81V7Ck+6HU4YZWbX8D6776lrMTb0jF1JZJDd/yoauxPBTEmyiXn5VES4/eSVCcinPjrLpSXK59OWuF1OGGVk19AWclBNqz4ztM46kokvUVkl/vYDfSqfC4iu8IRoDHGf8nt21NeXBzVpeWPRNOW6fQb2o7VX29lw8r4md6e3b0nkpBA4WJvl8+uaz0Sn7seSeWaJIlVntt6JMZEmOS8dgBx170F0L6gBQB7ig94HEn4JKel07pTV88H3GN7vVVj4kxK+/ZAfM3cquRUc4o/ufkFbFq9igN7vJsCbYnEmBiS1LYtkpRESRzN3Ip3ufkFoMq6pd7dI26JxJgYIj4fye1yORiHXVvxqlXHziSlpnk6DdgSiTExJrld/M3cime+xESyu/fkhyWWSIwxQZLcvj0l69ahpfG5Xkc8ys0voHjjBnZt9WbJZUskxsSY5Lx2UFZGybrYL95oHLm9KsvKe3NVEpWJRES6i8gkEXlSREZ6HY8xkeTQzK218TdzK141b5tNg2bN4yeRiMhzIrJFRJZU236aiCwXkVUicksdhzkdeFRVrwAuClmwxkShQ8UbbZwkbogIuT17e1ZW3osrkhdwKgkfIiI+4HGcBNEduMC96sgXkXeqPVoCLwOjROQBICPM8RsT0XyNGuFrkcnBOLyXJJ7l5Bewf9dOtv6wNuznTgz3CVV1loi0q7b5WGCVqq4GEJEJwFmqeh/O2ic1ucpNQG+GKlZjolVKuzxK4mRdEuPIyXfKyhcu/oaW7dqH9dyRMkbSFlhX5esid1uNRKSdiIwDXgIeqGWfy0VknojM27p1a1CDNSbSJbdvH5d3t8ezRs0zycjK8aRcSqQkknpR1bWqermqjlbVz2rZZ5yq9lXVvi1atAh3iMZ4KjmvHeU7dsRd8cZ4l5Pfm6JlSykL89TvSEkk64HsKl9nudsCYiskmngVzzW34lmuW1Z+44plYT1vpCSSuUAnEckTkWRgFDA10IPaCokmXtnMrfiU1S3fLSu/MKzn9WL673hgNtBFRIpE5FJ30ayrgenAMmCSqi4Nd2zGxIqkNm2Q5GSbuRVnUtLTad2xS9jHSbyYtXVBLdunAdOCeS4RGQ4M79ixYzAPa0zEE5+P5Nxcm7kVh3J7FTDnjYkc2LOH1IYNw3LOSOnaCgnr2jLxzGZuxaecnr1RraDou/B16sR0IjEmniXntXOKN5aUeB2KCaOGzTMBOLg3fAtdxXQisVlbJp6ltG8P5eWUFFnxRhNaMZ1IrGvLxDObuWXCJaYTiTHxrDKR2MwtE2oxnUisa8vEM1/DhiS2aGEzt0zIxXQisa4tE+9s5pYJh5hOJMbEu+S8dhxcswZV9ToUE8MskRgTw1Lat6di507KrXijCaGYTiQ2RmLinc3cMuEQ04nExkhMvEvOc6oA28wtE0oxnUiMUeJ7bCCpTWskJcVmbpmQskRiYpYgXofgOUlIILldO5u5ZULKEokxMS45L4+Da2yMxIROTCcSG2w3BlLa51FatJ4KK95oQiSmE4kNthvjztwqL6f0hx+8DsXEqJhOJMYYSM7OBqB0/XqPIzGxyhKJMbEuMQkALa/wOBATqyyRGGNiilWDCT9LJMYYYwJiicQYY0xAYjqR2PRfY4wJvZhOJDb91xhjQi+mE4kxxpjQs0RijDEmIJZIjDHGBMQSiTHGmIBYIjHGGBMQSyTGGGMCYonEGGNMQGI6kdgNicYYE3oxnUjshkRjjAm9mE4kxhhjQs8SiTHGmIBYIjHGGBMQSyTGGGMCYonEGGNMQCyRGGOMCYglEmOMMQGxRGKMMSYglkiMMcYExBKJMcaYgER8IhGR9iLyrIhMrrKtgYi8KCJPi8hoL+Mzxph4F9JEIiLPicgWEVlSbftpIrJcRFaJyC2HO4aqrlbVS6ttPgeYrKqXAWcGOWxjjDH1kBji478APAa8VLlBRHzA48AQoAiYKyJTAR9wX7XX/0ZVt9Rw3Cxgsfu8PMgxG2OMqYeQJhJVnSUi7aptPhZYpaqrAURkAnCWqt4HDPPz0EU4yeQboqB7zhhjYpkXb8JtgXVVvi5yt9VIRDJE5CngaBG51d38JjBCRJ4E3q7ldZeLyDwRmbd169YghW6MMaa6UHdtBUxVtwG/r7ZtL3BJHa8bB4wD6Nu3r4YsQGOMiXNeXJGsB7KrfJ3lbgs6WyHRGGNCz4tEMhfoJCJ5IpIMjAKmhuJEtkKiMcaEXqin/44HZgNdRKRIRC5V1TLgamA6sAyYpKpLQxmHMcaY0An1rK0Latk+DZgWynOD07UFDO/YsWOoT2WMMXErpqfOWteWMcaEXkwnEmOMMaEX04nEZm0ZxWZ+GxNqohr7f2gishUoDOAQmcCPQQrHa9aWyBMr7QBrS6Q60rbkqmqLunaKi0QSKBGZp6p9vY4jGKwtkSdW2gHWlkgV6rbEdNeWMcaY0LNEYowxJiCWSPwzzusAgsjaEnlipR1gbYlUIW2LjZEYY4wJiF2RGGOMCYglEmOMMQGxROKqax15EUkRkYnu97+sYeXHiOFHW/4oIt+KyCIR+VBEcr2I0x91taXKfiNEREUkYqdr+tMWETnP/dksFZHXwh2jv/z4HcsRkY9F5Gv39+wML+L0h4g8JyJbRGRJLd8XEXnEbesiEekT7hj94Uc7RrvxLxaRL0Skd9BOrqpx/8BZL/57oD2QDCwEkmUySgAACH5JREFUulfb50rgKff5KGCi13EH0JaTgXT3+RXR3BZ3v0bALGAO0NfruAP4uXQCvgaauV+39DruANoyDrjCfd4dWOt13IdpzwlAH2BJLd8/A/gPIMAA4EuvYz7Cdvyiyu/W6cFsh12ROA6tI6+qJcAE4Kxq+5wFvOg+nwz8UkQkjDH6q862qOrHqrrP/XIOzuJikcifnwvAvcD9wIFwBldP/rTlMuBxVS0GUNUtYY7RX/60RYHG7vMmwIYwxlcvqjoL2H6YXc4CXlLHHKCpiLQOT3T+q6sdqvpF5e8WQf67t0Ti8Gcd+UP7qLOmyk4gIyzR1Y8/banqUpxPW5Gozra43QzZqvpuOAM7Av78XDoDnUXkcxGZIyKnhS26+vGnLXcBF4pIEc6SEdeEJ7SQqO/fVDQI6t99xK/ZbkJHRC4E+gIneh3LkRCRBOD/gLEehxIsiTjdWyfhfFqcJSL5qrrD06iOzAXAC6r6kIgcB7wsIj1VtcLrwOKdiJyMk0gGBeuYdkXi8Gcd+UP7iEgizuX6trBEVz/+tAURGQzcDpypqgfDFFt91dWWRkBPYKaIrMXpv54aoQPu/vxcioCpqlqqqmuAFTiJJdL405ZLgUkAqjobSMUpHBiN/PqbigYi0gt4BjhLVYP2/mWJxOHPOvJTgYvd5yOBj9QdtYowdbZFRI4G/oWTRCK1Hx7qaIuq7lTVTFVtp6rtcPp9z1TVed6Ee1j+/I69hXM1gohk4nR1rQ5nkH7ypy0/AL8EEJFuOIlka1ijDJ6pwEXu7K0BwE5V3eh1UPUlIjnAm8AYVV0RzGNb1xbOmIeIVK4j7wOeU9WlInIPME9VpwLP4lyer8IZ0BrlXcS187MtDwANgdfd+QI/qOqZngVdCz/bEhX8bMt04FQR+RYoB24M5qfGYPGzLX8CnhaR63EG3sdG6AcvRGQ8TgLPdMd07gSSAFT1KZwxnjOAVcA+4BJvIj08P9rxvzjjuk+4f/dlGqSKwFYixRhjTECsa8sYY0xALJEYY4wJiCUSY4wxAbFEYowxJiCWSIwxJsbUVcCx2r7/EJFv3McKEan3DbCWSExUEZEsEfm3iKwUke9F5J/uvQx1ve62AM97j3sTZ0wTkcki0v4w379TRO6rtq1ARJa5zz8QkWahjtPU6QXArxI7qnq9qhaoagHwKM69JvViicREDbdI5pvAW6raCeeGvYbAX/14eUCJRFX/V1U/COQYoeRWWwj0GD0An6oe7ibI8cD51baNcrcDvIxTKdt4qKYCjiLSQUTeE5H5IvKpiHSt4aUX8NPP0m+WSEw0OQU4oKrPA6hqOXA98BsRSReRsSLyWOXOIvKOiJwkIn8D0txL91fd790hznoan4nIeBG5wd1e4BZMXCQiUyo/XYvICyIy0n2+VkTuFpEF7toOXd3tLUTkfXHWEnlGRArdO9R/RkROFZHZ7utfF5GGdRy3gdtV8ZU463uc5W4fKyJTReQj4EMRSRCRJ0TkOzeOaSIyUkROEZG3qpx/iIhMqeH/dzTw78PF6d4RXSwi/au87jx+evOZivNmZCLPOOAaVT0GuAF4ouo3xVmXKA/4qL4HtkRiokkPYH7VDaq6C6ccR8faXqSqtwD73cv30SLSDxgB9MZZl6Hq3b0vATerai9gMc7dwTX5UVX7AE/i/FHi7vuRqvbAWWogp/qL3MTyZ2Cw+/p5wB/rOO7t7nGPxVlL5gERaeB+rw8wUlVPBM4B2uGs/zEGOM7d52Ogq4i0cL++BHiuhjYNxP3/rSPO8biVHcQpGbJdVVcCuGXKU0QkEitjxy33w8ovcKpZfINTIql6KfxRwGT3A1q9WIkUE48GAv9W1QPAARF5G0BEmgBNVfUTd78XgddrOUZlP/J8nDdwcKqpng2gqu+JSHENrxuA80b/uVumIhmYXcdxTwXOrLxqwqlbVZmk3lfVyi6MQcDrboXdTSLysRuLisjLOGXdn8dJMBfVEFtrfqqHdbg4JwJfiMif+Hm3VqUtQBsis6hpvEoAdrjjILUZBVx1JAe3RGKiybc4BTMPEZHGOG+qq4Be/PwqOzWEsVRWTC6nfn9HgvPmX1v3T03HFWCEqi7/2YGc7qW9fp73eeBtnMW/XnfX1KluPz/9n9Uap6quE5E1OMsPjOCnK59Kqe6xTIRQ1V0iskZEzlXV193xxl6quhDA7UZtxs8/1PjNurZMNPkQSBeRiwBExAc8hLPuxT5gLVDgjhVk46zkV6lURJLc558Dw0Uk1b3kHwZONWGc/v/j3f3GAJ/gv89xxgsQkVNx/jCrmwMMFJGO7n4NRKRzHcedDlzj/vFXVm+u7fwj3PYfhVtJGEBVN+CsUvhnnKRSk2X81EVYV5zjgX8Aq1W1qHKjG2MrnJ+F8Yg4BRxnA11EpEhELsUZA7tURBYCS/n5qpajgAlHWljTrkhM1HC7aM7GqV56B84HoWn8NCPrc2ANzpXLMmBBlZePAxaJyAJ3nGQqsAjYjDMWstPd72LgKRFJxynhXp9Kr3cD40VkDM4f8SZgd7U2bBWRse5+Ke7mP+OsPVKbe4GH3fgT3DYOq2G/N3BKt3+Ls6LfgirtAngVaKGqy2o5z7s4yecDP+J8HXiE/1758BhgTi1XPCZMDnPFW+OUYFW9K5DzWfVfE5fcGUh73IQxC7hcVRfU9bo6jpkClLtl1o8DnqyjTzroqrQrA/gKGKiqm9zvPQZ8rarP1vLaNJyB+YFHMuDqHuOfOItzfXhkLTDRyK5ITLwaJyLdcfrzXww0ibhygEnuVUMJcFkQjllf74hIU5zB8XurJJH5OOMpf6rthaq6X0TuxFmP/IcjPP8SSyLxx65IjDHGBMQG240xxgTEEokxxpiAWCIxxhgTEEskxhhjAmKJxBhjTED+P683bg8UIm5WAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for e_in, e_out_dist in zip(dist.energy[::5], dist.energy_out[::5]):\n",
-    "    plt.semilogy(e_out_dist.x, e_out_dist.p, label='E={:.2f} MeV'.format(e_in/1e6))\n",
-    "plt.ylim(top=1e-6)\n",
-    "plt.legend()\n",
-    "plt.xlabel('Outgoing energy (eV)')\n",
-    "plt.ylabel('Probability/eV')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Unresolved resonance probability tables\n",
-    "\n",
-    "We can also look at unresolved resonance probability tables which are stored in a `ProbabilityTables` object. In the following example, we'll create a plot showing what the total cross section probability tables look like as a function of incoming energy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cross section(b)')"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure()\n",
-    "ax = fig.add_subplot(111)\n",
-    "cm = matplotlib.cm.Spectral_r\n",
-    "\n",
-    "# Determine size of probability tables\n",
-    "urr = gd157.urr['294K']\n",
-    "n_energy = urr.table.shape[0]\n",
-    "n_band = urr.table.shape[2]\n",
-    "\n",
-    "for i in range(n_energy):\n",
-    "    # Get bounds on energy\n",
-    "    if i > 0:\n",
-    "        e_left = urr.energy[i] - 0.5*(urr.energy[i] - urr.energy[i-1])\n",
-    "    else:\n",
-    "        e_left = urr.energy[i] - 0.5*(urr.energy[i+1] - urr.energy[i])\n",
-    "\n",
-    "    if i < n_energy - 1:\n",
-    "        e_right = urr.energy[i] + 0.5*(urr.energy[i+1] - urr.energy[i])\n",
-    "    else:\n",
-    "        e_right = urr.energy[i] + 0.5*(urr.energy[i] - urr.energy[i-1])\n",
-    "        \n",
-    "    for j in range(n_band):\n",
-    "        # Determine maximum probability for a single band\n",
-    "        max_prob = np.diff(urr.table[i,0,:]).max()\n",
-    "        \n",
-    "        # Determine bottom of band\n",
-    "        if j > 0:\n",
-    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
-    "            value = (urr.table[i,0,j] - urr.table[i,0,j-1])/max_prob\n",
-    "        else:\n",
-    "            xs_bottom = urr.table[i,1,j] - 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
-    "            value = urr.table[i,0,j]/max_prob\n",
-    "\n",
-    "        # Determine top of band\n",
-    "        if j < n_band - 1:\n",
-    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j+1] - urr.table[i,1,j])\n",
-    "        else:\n",
-    "            xs_top = urr.table[i,1,j] + 0.5*(urr.table[i,1,j] - urr.table[i,1,j-1])\n",
-    "    \n",
-    "        # Draw rectangle with appropriate color\n",
-    "        ax.add_patch(Rectangle((e_left, xs_bottom), e_right - e_left, xs_top - xs_bottom,\n",
-    "                     color=cm(value)))\n",
-    "\n",
-    "# Overlay total cross section\n",
-    "ax.plot(gd157.energy['294K'], total.xs['294K'](gd157.energy['294K']), 'k')\n",
-    "\n",
-    "# Make plot pretty and labeled\n",
-    "ax.set_xlim(1.0, 1.0e5)\n",
-    "ax.set_ylim(1e-1, 1e4)\n",
-    "ax.set_xscale('log')\n",
-    "ax.set_yscale('log')\n",
-    "ax.set_xlabel('Energy (eV)')\n",
-    "ax.set_ylabel('Cross section(b)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exporting HDF5 data\n",
-    "\n",
-    "If you have an instance `IncidentNeutron` that was created from ACE or HDF5 data, you can easily write it to disk using the `export_to_hdf5()` method. This can be used to convert ACE to HDF5 or to take an existing data set and actually modify cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gd157.export_to_hdf5('gd157.h5', 'w')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With few exceptions, the HDF5 file encodes the same data as the ACE file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
-    "np.all(gd157[16].xs['294K'].y == gd157_reconstructed[16].xs['294K'].y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And one of the best parts of using HDF5 is that it is a widely used format with lots of third-party support. You can use `h5py`, for example, to inspect the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "reaction_002, (n,elastic)\n",
-      "reaction_016, (n,2n)\n",
-      "reaction_017, (n,3n)\n",
-      "reaction_022, (n,na)\n",
-      "reaction_024, (n,2na)\n",
-      "reaction_028, (n,np)\n",
-      "reaction_041, (n,2np)\n",
-      "reaction_051, (n,n1)\n",
-      "reaction_052, (n,n2)\n",
-      "reaction_053, (n,n3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "h5file = h5py.File('gd157.h5', 'r')\n",
-    "main_group = h5file['Gd157/reactions']\n",
-    "for name, obj in sorted(list(main_group.items()))[:10]:\n",
-    "    if 'reaction_' in name:\n",
-    "        print('{}, {}'.format(name, obj.attrs['label'].decode()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[<HDF5 group \"/Gd157/reactions/reaction_016/294K\" (1 members)>,\n",
-      " <HDF5 group \"/Gd157/reactions/reaction_016/product_0\" (2 members)>,\n",
-      " <HDF5 group \"/Gd157/reactions/reaction_016/product_1\" (2 members)>]\n"
-     ]
-    }
-   ],
-   "source": [
-    "n2n_group = main_group['reaction_016']\n",
-    "pprint(list(n2n_group.values()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So we see that the hierarchy of data within the HDF5 mirrors the hierarchy of Python objects that we manipulated before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.000000e+00, 3.026796e-13, 1.291101e-02, 6.511110e-02,\n",
-       "       3.926270e-01, 5.752268e-01, 6.969600e-01, 7.399378e-01,\n",
-       "       9.635450e-01, 1.142130e+00, 1.308020e+00, 1.463500e+00,\n",
-       "       1.557600e+00, 1.640550e+00, 1.688960e+00, 1.711400e+00,\n",
-       "       1.739450e+00, 1.782070e+00, 1.816650e+00, 1.845280e+00,\n",
-       "       1.865409e+00, 1.867240e+00, 1.881558e+00, 1.881560e+00,\n",
-       "       1.881800e+00, 1.894470e+00, 1.869570e+00, 1.821200e+00,\n",
-       "       1.716000e+00, 1.600540e+00, 1.431620e+00, 1.283460e+00,\n",
-       "       1.101660e+00, 1.065300e+00, 9.307300e-01, 8.029800e-01,\n",
-       "       7.777400e-01])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n2n_group['294K/xs'][()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Working with ENDF files\n",
-    "\n",
-    "In addition to being able to load ACE and HDF5 data, we can also load ENDF data directly into an `IncidentNeutron` instance using the `from_endf()` factory method. Let's download the ENDF/B-VII.1 evaluation for $^{157}$Gd and load it in:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<IncidentNeutron: Gd157>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Download ENDF file\n",
-    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/Gd/157'\n",
-    "filename, headers = urllib.request.urlretrieve(url, 'gd157.endf')\n",
-    "\n",
-    "# Load into memory\n",
-    "gd157_endf = openmc.data.IncidentNeutron.from_endf(filename)\n",
-    "gd157_endf"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just as before, we can get a reaction by indexing the object directly:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "elastic = gd157_endf[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, if we look at the cross section now, we see that it isn't represented as tabulated data anymore."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'0K': <openmc.data.function.ResonancesWithBackground at 0x1461b11824a8>}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "elastic.xs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you had [Cython](https://cython.org/) installed when you built/installed OpenMC, you should be able to evaluate resonant cross sections from ENDF data directly, i.e., OpenMC will reconstruct resonances behind the scenes for you."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "998.7871174521487"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "elastic.xs['0K'](0.0253)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When data is loaded from an ENDF file, there is also a special `resonances` attribute that contains resolved and unresolved resonance region data (from MF=2 in an ENDF file)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<openmc.data.resonance.ReichMoore at 0x1461b1fcb898>,\n",
-       " <openmc.data.resonance.Unresolved at 0x1461b2692ba8>]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd157_endf.resonances.ranges"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that $^{157}$Gd has a resolved resonance region represented in the Reich-Moore format as well as an unresolved resonance region. We can look at the min/max energy of each region by doing the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(1e-05, 306.6), (306.6, 54881.1)]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(r.energy_min, r.energy_max) for r in gd157_endf.resonances.ranges]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With knowledge of the energy bounds, let's create an array of energies over the entire resolved resonance range and plot the elastic scattering cross section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Cross section (b)')"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create log-spaced array of energies\n",
-    "resolved = gd157_endf.resonances.resolved\n",
-    "energies = np.logspace(np.log10(resolved.energy_min),\n",
-    "                       np.log10(resolved.energy_max), 1000)\n",
-    "\n",
-    "# Evaluate elastic scattering xs at energies\n",
-    "xs = elastic.xs['0K'](energies)\n",
-    "\n",
-    "# Plot cross section vs energies\n",
-    "plt.loglog(energies, xs)\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Cross section (b)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Resonance ranges also have a useful `parameters` attribute that shows the energies and widths for resonances."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy</th>\n",
-       "      <th>L</th>\n",
-       "      <th>J</th>\n",
-       "      <th>neutronWidth</th>\n",
-       "      <th>captureWidth</th>\n",
-       "      <th>fissionWidthA</th>\n",
-       "      <th>fissionWidthB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0314</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000474</td>\n",
-       "      <td>0.1072</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.8250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000345</td>\n",
-       "      <td>0.0970</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>16.2400</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000400</td>\n",
-       "      <td>0.0910</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.7700</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.012800</td>\n",
-       "      <td>0.0805</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>20.5600</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.011360</td>\n",
-       "      <td>0.0880</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>21.6500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.000376</td>\n",
-       "      <td>0.1140</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>23.3300</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.000813</td>\n",
-       "      <td>0.1210</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>25.4000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.001840</td>\n",
-       "      <td>0.0850</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>40.1700</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.001307</td>\n",
-       "      <td>0.1100</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>44.2200</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.008960</td>\n",
-       "      <td>0.0960</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    energy  L    J  neutronWidth  captureWidth  fissionWidthA  fissionWidthB\n",
-       "0   0.0314  0  2.0      0.000474        0.1072            0.0            0.0\n",
-       "1   2.8250  0  2.0      0.000345        0.0970            0.0            0.0\n",
-       "2  16.2400  0  1.0      0.000400        0.0910            0.0            0.0\n",
-       "3  16.7700  0  2.0      0.012800        0.0805            0.0            0.0\n",
-       "4  20.5600  0  2.0      0.011360        0.0880            0.0            0.0\n",
-       "5  21.6500  0  2.0      0.000376        0.1140            0.0            0.0\n",
-       "6  23.3300  0  1.0      0.000813        0.1210            0.0            0.0\n",
-       "7  25.4000  0  2.0      0.001840        0.0850            0.0            0.0\n",
-       "8  40.1700  0  1.0      0.001307        0.1100            0.0            0.0\n",
-       "9  44.2200  0  2.0      0.008960        0.0960            0.0            0.0"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resolved.parameters.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Heavy-nuclide resonance scattering\n",
-    "\n",
-    "OpenMC has two methods for accounting for resonance upscattering in heavy nuclides, DBRC and RVS. These methods rely on 0 K elastic scattering data being present. If you have an existing ACE/HDF5 dataset and you need to add 0 K elastic scattering data to it, this can be done using the `IncidentNeutron.add_elastic_0K_from_endf()` method. Let's do this with our original `gd157` object that we instantiated from an ACE file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gd157.add_elastic_0K_from_endf('gd157.endf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's check to make sure that we have both the room temperature elastic scattering cross section as well as a 0K cross section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'294K': <openmc.data.function.Tabulated1D at 0x1461b4d2ff60>,\n",
-       " '0K': <openmc.data.function.Tabulated1D at 0x1461b0e59128>}"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gd157[2].xs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generating data from NJOY\n",
-    "\n",
-    "To run OpenMC in continuous-energy mode, you generally need to have ACE files already available that can be converted to OpenMC's native HDF5 format. If you don't already have suitable ACE files or need to generate new data, both the `IncidentNeutron` and `ThermalScattering` classes include `from_njoy()` methods that will run [NJOY](https://www.njoy21.io/) to generate ACE files and then read those files to create OpenMC class instances. The `from_njoy()` methods take as input the name of an ENDF file on disk. By default, it is assumed that you have an executable named `njoy` available on your path. This can be configured with the optional `njoy_exec` argument. Additionally, if you want to show the progress of NJOY as it is running, you can pass `stdout=True`.\n",
-    "\n",
-    "Let's use `IncidentNeutron.from_njoy()` to run NJOY to create data for $^2$H using an ENDF file. We'll specify that we want data specifically at 300, 400, and 500 K."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " njoy 2016.49  25Jan19                                       07/19/19 06:12:49\n",
-      " *****************************************************************************\n",
-      "\n",
-      " reconr...                                                                0.0s\n",
-      "\n",
-      " broadr...                                                                0.1s\n",
-      "      300.0 deg                                                           0.1s\n",
-      "      400.0 deg                                                           0.2s\n",
-      "      500.0 deg                                                           0.3s\n",
-      "\n",
-      " heatr...                                                                 0.3s\n",
-      "\n",
-      " gaspr...                                                                 0.6s\n",
-      "\n",
-      " purr...                                                                  0.7s\n",
-      "\n",
-      " mat =  128                                                               0.7s\n",
-      "\n",
-      " ---message from purr---mat  128 has no resonance parameters\n",
-      "                          copy as is to nout\n",
-      "\n",
-      " acer...                                                                  0.7s\n",
-      "\n",
-      " acer...                                                                  1.0s\n",
-      "\n",
-      " acer...                                                                  1.1s\n",
-      "                                                                          1.2s\n",
-      " *****************************************************************************\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Download ENDF file\n",
-    "url = 'https://t2.lanl.gov/nis/data/data/ENDFB-VII.1-neutron/H/2'\n",
-    "filename, headers = urllib.request.urlretrieve(url, 'h2.endf')\n",
-    "\n",
-    "# Run NJOY to create deuterium data\n",
-    "h2 = openmc.data.IncidentNeutron.from_njoy('h2.endf', temperatures=[300., 400., 500.], stdout=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use our `h2` object just as we did before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'300K': <openmc.data.function.Tabulated1D at 0x1461b066ebe0>,\n",
-       " '400K': <openmc.data.function.Tabulated1D at 0x1461b23a0978>,\n",
-       " '500K': <openmc.data.function.Tabulated1D at 0x1461b1009400>,\n",
-       " '0K': <openmc.data.function.Tabulated1D at 0x1461b101b358>}"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "h2[2].xs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that 0 K elastic scattering data is automatically added when using `from_njoy()` so that resonance elastic scattering treatments can be used."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Windowed multipole\n",
-    "\n",
-    "OpenMC can also be used with an experimental format called windowed multipole.  Windowed multipole allows for analytic on-the-fly Doppler broadening of the resolved resonance range.  Windowed multipole data can be downloaded with the `openmc-get-multipole-data` script.  This data can be used in the transport solver, but it can also be used directly in the Python API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "url = 'https://github.com/mit-crpg/WMP_Library/releases/download/v1.1/092238.h5'\n",
-    "filename, headers = urllib.request.urlretrieve(url, '092238.h5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "u238_multipole = openmc.data.WindowedMultipole.from_hdf5('092238.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `WindowedMultipole` object can be called with energy and temperature values.  Calling the object gives a tuple of 3 cross sections: elastic scattering, radiative capture, and fission."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array(9.13284265), array(0.50530278), array(2.9316765e-06))"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "u238_multipole(1.0, 294)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An array can be passed for the energy argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1461b0ed9978>]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "E = np.linspace(5, 25, 1000)\n",
-    "plt.semilogy(E, u238_multipole(E, 293.606)[1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The real advantage to multipole is that it can be used to generate cross sections at any temperature.  For example, this plot shows the Doppler broadening of the 6.67 eV resonance between 0 K and 900 K."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1461b09755c0>]"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "E = np.linspace(6.1, 7.1, 1000)\n",
-    "plt.semilogy(E, u238_multipole(E, 0)[1])\n",
-    "plt.semilogy(E, u238_multipole(E, 900)[1])"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/pandas-dataframes.ipynb b/examples/jupyter/pandas-dataframes.ipynb
deleted file mode 100644
index a8b042e48..000000000
--- a/examples/jupyter/pandas-dataframes.ipynb
+++ /dev/null
@@ -1,2721 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pandas Dataframes\n",
-    "This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "from IPython.display import Image\n",
-    "import matplotlib.pyplot as plt\n",
-    "import scipy.stats\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import openmc\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem. We will create three materials for the fuel, water, and cladding of the fuel pin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6 enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_nuclide('U235', 3.7503e-4)\n",
-    "fuel.add_nuclide('U238', 2.2625e-2)\n",
-    "fuel.add_nuclide('O16', 4.6007e-2)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_nuclide('H1', 4.9457e-2)\n",
-    "water.add_nuclide('O16', 2.4732e-2)\n",
-    "water.add_nuclide('B10', 8.0042e-6)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we can now create a materials file object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection\n",
-    "materials = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. This problem will be a square array of fuel pins for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "# Use both reflective and vacuum boundaries to make life interesting\n",
-    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+10.71, boundary_type='vacuum')\n",
-    "min_y = openmc.YPlane(y0=-10.71, boundary_type='vacuum')\n",
-    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-10.71, boundary_type='reflective')\n",
-    "max_z = openmc.ZPlane(z0=+10.71, boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel', fill=fuel,\n",
-    "                        region=-fuel_outer_radius)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad', fill=zircaloy)\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator', fill=water,\n",
-    "                             region=+clad_outer_radius)\n",
-    "\n",
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin', cells=[\n",
-    "    fuel_cell, clad_cell, moderator_cell\n",
-    "])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create fuel assembly Lattice\n",
-    "assembly = openmc.RectLattice(name='1.6% Fuel - 0BA')\n",
-    "assembly.pitch = (1.26, 1.26)\n",
-    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2\n",
-    "assembly.universes = [[pin_cell_universe] * 17] * 17"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell', fill=assembly)\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(name='root universe')\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry that is assigned a root universe and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and export to \"geometry.xml\"\n",
-    "geometry = openmc.Geometry(root_universe)\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 minimum active batches each with 2500 particles. We also tell OpenMC to turn tally triggers on, which means it will keep running until some criterion on the uncertainty of tallies is reached."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "min_batches = 20\n",
-    "max_batches = 200\n",
-    "inactive = 5\n",
-    "particles = 2500\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings = openmc.Settings()\n",
-    "settings.batches = min_batches\n",
-    "settings.inactive = inactive\n",
-    "settings.particles = particles\n",
-    "settings.output = {'tallies': False}\n",
-    "settings.trigger_active = True\n",
-    "settings.trigger_max_batches = max_batches\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Instantiate a Plot\n",
-    "plot = openmc.Plot(plot_id=1)\n",
-    "plot.filename = 'materials-xy'\n",
-    "plot.origin = [0, 0, 0]\n",
-    "plot.width = [21.5, 21.5]\n",
-    "plot.pixels = [250, 250]\n",
-    "plot.color_by = 'material'\n",
-    "\n",
-    "# Show plot\n",
-    "openmc.plot_inline(plot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate an empty Tallies object\n",
-    "tallies = openmc.Tallies()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instantiate a fission rate mesh Tally"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a tally Mesh\n",
-    "mesh = openmc.RegularMesh(mesh_id=1)\n",
-    "mesh.dimension = [17, 17]\n",
-    "mesh.lower_left = [-10.71, -10.71]\n",
-    "mesh.width = [1.26, 1.26]\n",
-    "\n",
-    "# Instantiate tally Filter\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "# Instantiate energy Filter\n",
-    "energy_filter = openmc.EnergyFilter([0, 0.625, 20.0e6])\n",
-    "\n",
-    "# Instantiate the Tally\n",
-    "tally = openmc.Tally(name='mesh tally')\n",
-    "tally.filters = [mesh_filter, energy_filter]\n",
-    "tally.scores = ['fission', 'nu-fission']\n",
-    "\n",
-    "# Add mesh and Tally to Tallies\n",
-    "tallies.append(tally)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instantiate a cell Tally with nuclides"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate tally Filter\n",
-    "cell_filter = openmc.CellFilter(fuel_cell)\n",
-    "\n",
-    "# Instantiate the tally\n",
-    "tally = openmc.Tally(name='cell tally')\n",
-    "tally.filters = [cell_filter]\n",
-    "tally.scores = ['scatter']\n",
-    "tally.nuclides = ['U235', 'U238']\n",
-    "\n",
-    "# Add mesh and tally to Tallies\n",
-    "tallies.append(tally)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a \"distribcell\" Tally. The distribcell filter allows us to tally multiple repeated instances of the same cell throughout the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate tally Filter\n",
-    "distribcell_filter = openmc.DistribcellFilter(moderator_cell)\n",
-    "\n",
-    "# Instantiate tally Trigger for kicks\n",
-    "trigger = openmc.Trigger(trigger_type='std_dev', threshold=5e-5)\n",
-    "trigger.scores = ['absorption']\n",
-    "\n",
-    "# Instantiate the Tally\n",
-    "tally = openmc.Tally(name='distribcell tally')\n",
-    "tally.filters = [distribcell_filter]\n",
-    "tally.scores = ['absorption', 'scatter']\n",
-    "tally.triggers = [trigger]\n",
-    "\n",
-    "# Add mesh and tally to Tallies\n",
-    "tallies.append(tally)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Export to \"tallies.xml\"\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zsh:1: no matches found: statepoint.*\n",
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 15:27:04\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    0.56788\n",
-      "        2/1    0.66344\n",
-      "        3/1    0.67801\n",
-      "        4/1    0.64662\n",
-      "        5/1    0.69912\n",
-      "        6/1    0.72720\n",
-      "        7/1    0.71337    0.72029 +/- 0.00691\n",
-      "        8/1    0.70564    0.71540 +/- 0.00630\n",
-      "        9/1    0.69707    0.71082 +/- 0.00639\n",
-      "       10/1    0.69745    0.70815 +/- 0.00563\n",
-      "       11/1    0.68982    0.70509 +/- 0.00552\n",
-      "       12/1    0.69541    0.70371 +/- 0.00486\n",
-      "       13/1    0.69724    0.70290 +/- 0.00429\n",
-      "       14/1    0.71009    0.70370 +/- 0.00387\n",
-      "       15/1    0.69614    0.70294 +/- 0.00354\n",
-      "       16/1    0.68883    0.70166 +/- 0.00345\n",
-      "       17/1    0.68635    0.70038 +/- 0.00340\n",
-      "       18/1    0.70665    0.70087 +/- 0.00316\n",
-      "       19/1    0.65747    0.69777 +/- 0.00426\n",
-      "       20/1    0.69228    0.69740 +/- 0.00399\n",
-      " Triggers unsatisfied, max unc./thresh. is 93.89673349356296 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 132254 --- greater than max batches\n",
-      " Creating state point statepoint.020.h5...\n",
-      "       21/1    0.70753    0.69803 +/- 0.00378\n",
-      " Triggers unsatisfied, max unc./thresh. is 88.01427279112123 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 123950 --- greater than max batches\n",
-      "       22/1    0.71245    0.69888 +/- 0.00365\n",
-      " Triggers unsatisfied, max unc./thresh. is 84.70931054978398 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 121992 --- greater than max batches\n",
-      "       23/1    0.65293    0.69633 +/- 0.00429\n",
-      " Triggers unsatisfied, max unc./thresh. is 86.59494174559383 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 134982 --- greater than max batches\n",
-      "       24/1    0.66415    0.69464 +/- 0.00439\n",
-      " Triggers unsatisfied, max unc./thresh. is 83.04784122808066 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 131047 --- greater than max batches\n",
-      "       25/1    0.71052    0.69543 +/- 0.00424\n",
-      " Triggers unsatisfied, max unc./thresh. is 81.86075666631334 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 134029 --- greater than max batches\n",
-      "       26/1    0.64576    0.69307 +/- 0.00468\n",
-      " Triggers unsatisfied, max unc./thresh. is 77.87299751571092 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 127354 --- greater than max batches\n",
-      "       27/1    0.66691    0.69188 +/- 0.00462\n",
-      " Triggers unsatisfied, max unc./thresh. is 74.42216649772053 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 121856 --- greater than max batches\n",
-      "       28/1    0.65694    0.69036 +/- 0.00467\n",
-      " Triggers unsatisfied, max unc./thresh. is 71.15138613523133 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 116443 --- greater than max batches\n",
-      "       29/1    0.69674    0.69062 +/- 0.00447\n",
-      " Triggers unsatisfied, max unc./thresh. is 69.56665853404124 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 116154 --- greater than max batches\n",
-      "       30/1    0.63556    0.68842 +/- 0.00482\n",
-      " Triggers unsatisfied, max unc./thresh. is 66.8810661090428 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 111832 --- greater than max batches\n",
-      "       31/1    0.69809    0.68879 +/- 0.00465\n",
-      " Triggers unsatisfied, max unc./thresh. is 64.26124103679346 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 107373 --- greater than max batches\n",
-      "       32/1    0.64298    0.68710 +/- 0.00479\n",
-      " Triggers unsatisfied, max unc./thresh. is 62.020922539187175 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 103864 --- greater than max batches\n",
-      "       33/1    0.68559    0.68704 +/- 0.00461\n",
-      " Triggers unsatisfied, max unc./thresh. is 60.42853939278924 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 102251 --- greater than max batches\n",
-      "       34/1    0.65886    0.68607 +/- 0.00455\n",
-      " Triggers unsatisfied, max unc./thresh. is 58.33270366370514 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 98684 --- greater than max batches\n",
-      "       35/1    0.68743    0.68611 +/- 0.00440\n",
-      " Triggers unsatisfied, max unc./thresh. is 61.22701811574928 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 112468 --- greater than max batches\n",
-      "       36/1    0.69354    0.68635 +/- 0.00426\n",
-      " Triggers unsatisfied, max unc./thresh. is 59.98985062145117 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 111568 --- greater than max batches\n",
-      "       37/1    0.65095    0.68525 +/- 0.00427\n",
-      " Triggers unsatisfied, max unc./thresh. is 58.08611480075532 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 107973 --- greater than max batches\n",
-      "       38/1    0.74212    0.68697 +/- 0.00449\n",
-      " Triggers unsatisfied, max unc./thresh. is 56.42863990001401 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 105084 --- greater than max batches\n",
-      "       39/1    0.70258    0.68743 +/- 0.00438\n",
-      " Triggers unsatisfied, max unc./thresh. is 56.00920365845771 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 106665 --- greater than max batches\n",
-      "       40/1    0.73458    0.68878 +/- 0.00446\n",
-      " Triggers unsatisfied, max unc./thresh. is 54.62032757641821 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 104424 --- greater than max batches\n",
-      "       41/1    0.66256    0.68805 +/- 0.00439\n",
-      " Triggers unsatisfied, max unc./thresh. is 53.09826256808107 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 101505 --- greater than max batches\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       42/1    0.68262    0.68790 +/- 0.00427\n",
-      " Triggers unsatisfied, max unc./thresh. is 51.64336419771116 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 98686 --- greater than max batches\n",
-      "       43/1    0.70218    0.68828 +/- 0.00418\n",
-      " Triggers unsatisfied, max unc./thresh. is 50.670570340468814 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 97571 --- greater than max batches\n",
-      "       44/1    0.67643    0.68797 +/- 0.00408\n",
-      " Triggers unsatisfied, max unc./thresh. is 49.69199356343392 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 96308 --- greater than max batches\n",
-      "       45/1    0.67103    0.68755 +/- 0.00400\n",
-      " Triggers unsatisfied, max unc./thresh. is 48.44632012698348 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 93887 --- greater than max batches\n",
-      "       46/1    0.65599    0.68678 +/- 0.00398\n",
-      " Triggers unsatisfied, max unc./thresh. is 47.34469866924748 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 91908 --- greater than max batches\n",
-      "       47/1    0.64796    0.68586 +/- 0.00399\n",
-      " Triggers unsatisfied, max unc./thresh. is 46.49104551849076 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 90785 --- greater than max batches\n",
-      "       48/1    0.69638    0.68610 +/- 0.00390\n",
-      " Triggers unsatisfied, max unc./thresh. is 45.709058495601944 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 89846 --- greater than max batches\n",
-      "       49/1    0.69484    0.68630 +/- 0.00382\n",
-      " Triggers unsatisfied, max unc./thresh. is 44.85300785365741 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 88524 --- greater than max batches\n",
-      "       50/1    0.69678    0.68653 +/- 0.00374\n",
-      " Triggers unsatisfied, max unc./thresh. is 43.84946296160474 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 86530 --- greater than max batches\n",
-      "       51/1    0.72852    0.68745 +/- 0.00377\n",
-      " Triggers unsatisfied, max unc./thresh. is 43.787094662843266 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 88202 --- greater than max batches\n",
-      "       52/1    0.69916    0.68769 +/- 0.00370\n",
-      " Triggers unsatisfied, max unc./thresh. is 43.14074745408116 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 87478 --- greater than max batches\n",
-      "       53/1    0.70809    0.68812 +/- 0.00364\n",
-      " Triggers unsatisfied, max unc./thresh. is 42.78319111442477 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 87865 --- greater than max batches\n",
-      "       54/1    0.67049    0.68776 +/- 0.00359\n",
-      " Triggers unsatisfied, max unc./thresh. is 41.90152321392576 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 86037 --- greater than max batches\n",
-      "       55/1    0.67612    0.68753 +/- 0.00352\n",
-      " Triggers unsatisfied, max unc./thresh. is 41.247941809938425 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 85075 --- greater than max batches\n",
-      "       56/1    0.68035    0.68739 +/- 0.00346\n",
-      " Triggers unsatisfied, max unc./thresh. is 40.46515891712033 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 83514 --- greater than max batches\n",
-      "       57/1    0.70520    0.68773 +/- 0.00341\n",
-      " Triggers unsatisfied, max unc./thresh. is 40.05474461357334 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 83433 --- greater than max batches\n",
-      "       58/1    0.70938    0.68814 +/- 0.00337\n",
-      " Triggers unsatisfied, max unc./thresh. is 39.36707588204034 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 82143 --- greater than max batches\n",
-      "       59/1    0.71202    0.68858 +/- 0.00333\n",
-      " Triggers unsatisfied, max unc./thresh. is 38.66137944699882 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 80719 --- greater than max batches\n",
-      "       60/1    0.66559    0.68816 +/- 0.00330\n",
-      " Triggers unsatisfied, max unc./thresh. is 37.98682899231639 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 79370 --- greater than max batches\n",
-      "       61/1    0.65921    0.68765 +/- 0.00328\n",
-      " Triggers unsatisfied, max unc./thresh. is 38.029460967594694 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 80995 --- greater than max batches\n",
-      "       62/1    0.66584    0.68726 +/- 0.00324\n",
-      " Triggers unsatisfied, max unc./thresh. is 37.381474566315354 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 79656 --- greater than max batches\n",
-      "       63/1    0.69512    0.68740 +/- 0.00319\n",
-      " Triggers unsatisfied, max unc./thresh. is 36.76408883031811 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 78398 --- greater than max batches\n",
-      "       64/1    0.72240    0.68799 +/- 0.00319\n",
-      " Triggers unsatisfied, max unc./thresh. is 36.18011705137993 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 77237 --- greater than max batches\n",
-      "       65/1    0.68739    0.68798 +/- 0.00314\n",
-      " Triggers unsatisfied, max unc./thresh. is 35.60153732818344 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 76054 --- greater than max batches\n",
-      "       66/1    0.63734    0.68715 +/- 0.00320\n",
-      " Triggers unsatisfied, max unc./thresh. is 35.03171980405627 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74866 --- greater than max batches\n",
-      "       67/1    0.67068    0.68689 +/- 0.00315\n",
-      " Triggers unsatisfied, max unc./thresh. is 34.51557177789461 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73868 --- greater than max batches\n",
-      "       68/1    0.67713    0.68673 +/- 0.00311\n",
-      " Triggers unsatisfied, max unc./thresh. is 33.96995049011678 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72705 --- greater than max batches\n",
-      "       69/1    0.71729    0.68721 +/- 0.00310\n",
-      " Triggers unsatisfied, max unc./thresh. is 33.74180433617202 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72870 --- greater than max batches\n",
-      "       70/1    0.69537    0.68733 +/- 0.00305\n",
-      " Triggers unsatisfied, max unc./thresh. is 33.2256923807649 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71762 --- greater than max batches\n",
-      "       71/1    0.67200    0.68710 +/- 0.00301\n",
-      " Triggers unsatisfied, max unc./thresh. is 33.22256499834962 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72852 --- greater than max batches\n",
-      "       72/1    0.71865    0.68757 +/- 0.00300\n",
-      " Triggers unsatisfied, max unc./thresh. is 32.735500285071055 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71804 --- greater than max batches\n",
-      "       73/1    0.69590    0.68769 +/- 0.00296\n",
-      " Triggers unsatisfied, max unc./thresh. is 32.26730455835508 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70806 --- greater than max batches\n",
-      "       74/1    0.68222    0.68762 +/- 0.00292\n",
-      " Triggers unsatisfied, max unc./thresh. is 32.10780833972545 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71138 --- greater than max batches\n",
-      "       75/1    0.70643    0.68788 +/- 0.00289\n",
-      " Triggers unsatisfied, max unc./thresh. is 31.818936053589276 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70877 --- greater than max batches\n",
-      "       76/1    0.67658    0.68772 +/- 0.00285\n",
-      " Triggers unsatisfied, max unc./thresh. is 31.799932820980022 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71803 --- greater than max batches\n",
-      "       77/1    0.67857    0.68760 +/- 0.00282\n",
-      " Triggers unsatisfied, max unc./thresh. is 32.26711337228593 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74969 --- greater than max batches\n",
-      "       78/1    0.69108    0.68765 +/- 0.00278\n",
-      " Triggers unsatisfied, max unc./thresh. is 31.82734318749477 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73953 --- greater than max batches\n",
-      "       79/1    0.68226    0.68757 +/- 0.00274\n",
-      " Triggers unsatisfied, max unc./thresh. is 31.40561277542678 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72993 --- greater than max batches\n",
-      "       80/1    0.67648    0.68742 +/- 0.00271\n",
-      " Triggers unsatisfied, max unc./thresh. is 31.182065681219466 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72930 --- greater than max batches\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       81/1    0.65907    0.68705 +/- 0.00270\n",
-      " Triggers unsatisfied, max unc./thresh. is 30.770354748329673 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71963 --- greater than max batches\n",
-      "       82/1    0.63285    0.68635 +/- 0.00276\n",
-      " Triggers unsatisfied, max unc./thresh. is 30.494055493974837 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71607 --- greater than max batches\n",
-      "       83/1    0.65128    0.68590 +/- 0.00276\n",
-      " Triggers unsatisfied, max unc./thresh. is 30.31368198832468 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71681 --- greater than max batches\n",
-      "       84/1    0.65957    0.68556 +/- 0.00274\n",
-      " Triggers unsatisfied, max unc./thresh. is 29.927509740512043 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70762 --- greater than max batches\n",
-      "       85/1    0.69936    0.68574 +/- 0.00271\n",
-      " Triggers unsatisfied, max unc./thresh. is 29.684480624889716 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70499 --- greater than max batches\n",
-      "       86/1    0.71538    0.68610 +/- 0.00270\n",
-      " Triggers unsatisfied, max unc./thresh. is 30.42821373418053 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75001 --- greater than max batches\n",
-      "       87/1    0.67737    0.68600 +/- 0.00267\n",
-      " Triggers unsatisfied, max unc./thresh. is 30.08396896848712 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74219 --- greater than max batches\n",
-      "       88/1    0.67516    0.68587 +/- 0.00264\n",
-      " Triggers unsatisfied, max unc./thresh. is 29.820034394835567 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73812 --- greater than max batches\n",
-      "       89/1    0.71831    0.68625 +/- 0.00264\n",
-      " Triggers unsatisfied, max unc./thresh. is 29.52222916170289 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73217 --- greater than max batches\n",
-      "       90/1    0.69057    0.68630 +/- 0.00261\n",
-      " Triggers unsatisfied, max unc./thresh. is 29.192694352726093 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72444 --- greater than max batches\n",
-      "       91/1    0.72527    0.68676 +/- 0.00262\n",
-      " Triggers unsatisfied, max unc./thresh. is 28.882854654006614 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71748 --- greater than max batches\n",
-      "       92/1    0.68240    0.68671 +/- 0.00259\n",
-      " Triggers unsatisfied, max unc./thresh. is 28.746406095976795 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71898 --- greater than max batches\n",
-      "       93/1    0.68284    0.68666 +/- 0.00256\n",
-      " Triggers unsatisfied, max unc./thresh. is 28.55969476551871 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71783 --- greater than max batches\n",
-      "       94/1    0.67323    0.68651 +/- 0.00254\n",
-      " Triggers unsatisfied, max unc./thresh. is 28.562644465757202 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72614 --- greater than max batches\n",
-      "       95/1    0.67226    0.68635 +/- 0.00251\n",
-      " Triggers unsatisfied, max unc./thresh. is 28.266619522119196 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71916 --- greater than max batches\n",
-      "       96/1    0.69225    0.68642 +/- 0.00249\n",
-      " Triggers unsatisfied, max unc./thresh. is 28.090316080385687 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71810 --- greater than max batches\n",
-      "       97/1    0.67708    0.68632 +/- 0.00246\n",
-      " Triggers unsatisfied, max unc./thresh. is 27.821561823906112 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71217 --- greater than max batches\n",
-      "       98/1    0.68583    0.68631 +/- 0.00243\n",
-      " Triggers unsatisfied, max unc./thresh. is 27.550146728325792 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70593 --- greater than max batches\n",
-      "       99/1    0.64118    0.68583 +/- 0.00246\n",
-      " Triggers unsatisfied, max unc./thresh. is 27.31334482933601 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70131 --- greater than max batches\n",
-      "      100/1    0.67711    0.68574 +/- 0.00243\n",
-      " Triggers unsatisfied, max unc./thresh. is 27.045284650239612 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69493 --- greater than max batches\n",
-      "      101/1    0.68084    0.68569 +/- 0.00241\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.783343195485262 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 68871 --- greater than max batches\n",
-      "      102/1    0.69024    0.68573 +/- 0.00238\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.507109203671433 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 68160 --- greater than max batches\n",
-      "      103/1    0.66758    0.68555 +/- 0.00236\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.75706619598529 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70168 --- greater than max batches\n",
-      "      104/1    0.70685    0.68576 +/- 0.00235\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.50422283567677 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69550 --- greater than max batches\n",
-      "      105/1    0.65411    0.68545 +/- 0.00235\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.700673042186363 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71298 --- greater than max batches\n",
-      "      106/1    0.66632    0.68526 +/- 0.00233\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.660322235538125 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71794 --- greater than max batches\n",
-      "      107/1    0.72095    0.68561 +/- 0.00234\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.493921637614655 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71602 --- greater than max batches\n",
-      "      108/1    0.68486    0.68560 +/- 0.00231\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.261958180540656 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71044 --- greater than max batches\n",
-      "      109/1    0.70300    0.68577 +/- 0.00230\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.026316770700436 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70452 --- greater than max batches\n",
-      "      110/1    0.63861    0.68532 +/- 0.00232\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.926028971288112 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70582 --- greater than max batches\n",
-      "      111/1    0.68795    0.68534 +/- 0.00230\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.717124436169865 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70111 --- greater than max batches\n",
-      "      112/1    0.64471    0.68496 +/- 0.00231\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.505784119998573 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69614 --- greater than max batches\n",
-      "      113/1    0.68637    0.68498 +/- 0.00229\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.844123747859776 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72141 --- greater than max batches\n",
-      "      114/1    0.70597    0.68517 +/- 0.00227\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.41373710688748 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 76053 --- greater than max batches\n",
-      "      115/1    0.67258    0.68506 +/- 0.00225\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.35672818667791 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 76420 --- greater than max batches\n",
-      "      116/1    0.68671    0.68507 +/- 0.00223\n",
-      " Triggers unsatisfied, max unc./thresh. is 26.123859680914105 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75758 --- greater than max batches\n",
-      "      117/1    0.65467    0.68480 +/- 0.00223\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.891227809553317 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75085 --- greater than max batches\n",
-      "      118/1    0.68299    0.68478 +/- 0.00221\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.663579354019788 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74429 --- greater than max batches\n",
-      "      119/1    0.71640    0.68506 +/- 0.00221\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.798931576936234 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75882 --- greater than max batches\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      120/1    0.67682    0.68499 +/- 0.00219\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.600502408827925 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75375 --- greater than max batches\n",
-      "      121/1    0.64612    0.68465 +/- 0.00220\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.442400998883397 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75094 --- greater than max batches\n",
-      "      122/1    0.65743    0.68442 +/- 0.00219\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.314147051671682 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74980 --- greater than max batches\n",
-      "      123/1    0.67460    0.68434 +/- 0.00217\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.120655024301723 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74469 --- greater than max batches\n",
-      "      124/1    0.63622    0.68393 +/- 0.00219\n",
-      " Triggers unsatisfied, max unc./thresh. is 25.173748425745888 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 75418 --- greater than max batches\n",
-      "      125/1    0.74014    0.68440 +/- 0.00222\n",
-      " Triggers unsatisfied, max unc./thresh. is 24.969908358640748 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74825 --- greater than max batches\n",
-      "      126/1    0.66532    0.68424 +/- 0.00221\n",
-      " Triggers unsatisfied, max unc./thresh. is 24.763458789173267 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74206 --- greater than max batches\n",
-      "      127/1    0.66151    0.68406 +/- 0.00220\n",
-      " Triggers unsatisfied, max unc./thresh. is 24.715390259532615 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74529 --- greater than max batches\n",
-      "      128/1    0.66861    0.68393 +/- 0.00219\n",
-      " Triggers unsatisfied, max unc./thresh. is 24.532144885232228 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 74030 --- greater than max batches\n",
-      "      129/1    0.69363    0.68401 +/- 0.00217\n",
-      " Triggers unsatisfied, max unc./thresh. is 24.350853769848076 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73533 --- greater than max batches\n",
-      "      130/1    0.70857    0.68421 +/- 0.00216\n",
-      " Triggers unsatisfied, max unc./thresh. is 24.17444245442967 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73056 --- greater than max batches\n",
-      "      131/1    0.67714    0.68415 +/- 0.00215\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.98323256055421 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72480 --- greater than max batches\n",
-      "      132/1    0.67410    0.68407 +/- 0.00213\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.867449107003456 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72352 --- greater than max batches\n",
-      "      133/1    0.69079    0.68412 +/- 0.00211\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.685770754896556 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71816 --- greater than max batches\n",
-      "      134/1    0.67606    0.68406 +/- 0.00210\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.73618218941846 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72685 --- greater than max batches\n",
-      "      135/1    0.69637    0.68416 +/- 0.00208\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.69643071032316 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73003 --- greater than max batches\n",
-      "      136/1    0.67044    0.68405 +/- 0.00207\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.57110141899803 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72789 --- greater than max batches\n",
-      "      137/1    0.69621    0.68414 +/- 0.00206\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.48120874987551 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72786 --- greater than max batches\n",
-      "      138/1    0.71568    0.68438 +/- 0.00206\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.304158910242545 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72236 --- greater than max batches\n",
-      "      139/1    0.69041    0.68443 +/- 0.00204\n",
-      " Triggers unsatisfied, max unc./thresh. is 23.13692986052565 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71738 --- greater than max batches\n",
-      "      140/1    0.68872    0.68446 +/- 0.00203\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.97436361147689 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71261 --- greater than max batches\n",
-      "      141/1    0.68717    0.68448 +/- 0.00201\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.918186999320493 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71439 --- greater than max batches\n",
-      "      142/1    0.70148    0.68460 +/- 0.00200\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.90664792171847 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71891 --- greater than max batches\n",
-      "      143/1    0.70002    0.68471 +/- 0.00199\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.800726503137792 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71748 --- greater than max batches\n",
-      "      144/1    0.68586    0.68472 +/- 0.00197\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.666610393936825 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71420 --- greater than max batches\n",
-      "      145/1    0.64403    0.68443 +/- 0.00198\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.850786290626733 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73108 --- greater than max batches\n",
-      "      146/1    0.64825    0.68417 +/- 0.00198\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.780126501480474 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73175 --- greater than max batches\n",
-      "      147/1    0.66962    0.68407 +/- 0.00197\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.713317279463887 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73263 --- greater than max batches\n",
-      "      148/1    0.67953    0.68404 +/- 0.00196\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.555984073152782 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72760 --- greater than max batches\n",
-      "      149/1    0.70028    0.68415 +/- 0.00195\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.467338789571908 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72694 --- greater than max batches\n",
-      "      150/1    0.66858    0.68405 +/- 0.00194\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.317894720265652 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72228 --- greater than max batches\n",
-      "      151/1    0.64729    0.68379 +/- 0.00194\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.256511579898202 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72327 --- greater than max batches\n",
-      "      152/1    0.68669    0.68381 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 22.10461716023024 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71832 --- greater than max batches\n",
-      "      153/1    0.65299    0.68361 +/- 0.00193\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.973197597061482 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71463 --- greater than max batches\n",
-      "      154/1    0.68069    0.68359 +/- 0.00191\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.8366209055588 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71054 --- greater than max batches\n",
-      "      155/1    0.69270    0.68365 +/- 0.00190\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.693707386898733 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70598 --- greater than max batches\n",
-      "      156/1    0.67507    0.68359 +/- 0.00189\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.593867638588357 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70416 --- greater than max batches\n",
-      "      157/1    0.68589    0.68360 +/- 0.00188\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.661355782353464 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71326 --- greater than max batches\n",
-      "      158/1    0.69068    0.68365 +/- 0.00187\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.5257902006611 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70900 --- greater than max batches\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      159/1    0.67616    0.68360 +/- 0.00185\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.48299566167353 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71079 --- greater than max batches\n",
-      "      160/1    0.67492    0.68355 +/- 0.00184\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.352926864543797 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70677 --- greater than max batches\n",
-      "      161/1    0.69218    0.68360 +/- 0.00183\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.3145021538723 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70878 --- greater than max batches\n",
-      "      162/1    0.66037    0.68345 +/- 0.00183\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.1820405677372 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70448 --- greater than max batches\n",
-      "      163/1    0.65640    0.68328 +/- 0.00182\n",
-      " Triggers unsatisfied, max unc./thresh. is 21.086966040066464 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70262 --- greater than max batches\n",
-      "      164/1    0.65113    0.68308 +/- 0.00182\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.95392716842 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69817 --- greater than max batches\n",
-      "      165/1    0.70691    0.68323 +/- 0.00182\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.837836560668773 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69480 --- greater than max batches\n",
-      "      166/1    0.67295    0.68317 +/- 0.00181\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.769653148158348 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69457 --- greater than max batches\n",
-      "      167/1    0.66620    0.68306 +/- 0.00180\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.676559522637568 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69264 --- greater than max batches\n",
-      "      168/1    0.66855    0.68297 +/- 0.00179\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.72956424681471 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70049 --- greater than max batches\n",
-      "      169/1    0.69800    0.68306 +/- 0.00178\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.880997251947857 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71512 --- greater than max batches\n",
-      "      170/1    0.69435    0.68313 +/- 0.00177\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.817259102451693 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71510 --- greater than max batches\n",
-      "      171/1    0.65628    0.68297 +/- 0.00177\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.7516484421445 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71490 --- greater than max batches\n",
-      "      172/1    0.68216    0.68297 +/- 0.00176\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.633920344795456 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71107 --- greater than max batches\n",
-      "      173/1    0.67774    0.68293 +/- 0.00175\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.517288947571853 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70727 --- greater than max batches\n",
-      "      174/1    0.71232    0.68311 +/- 0.00175\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.39737483131609 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70318 --- greater than max batches\n",
-      "      175/1    0.65712    0.68295 +/- 0.00174\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.340808883881543 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70343 --- greater than max batches\n",
-      "      176/1    0.67973    0.68294 +/- 0.00173\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.225760579774068 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 69958 --- greater than max batches\n",
-      "      177/1    0.70262    0.68305 +/- 0.00173\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.188734303225726 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70110 --- greater than max batches\n",
-      "      178/1    0.66234    0.68293 +/- 0.00172\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.36584641647614 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71760 --- greater than max batches\n",
-      "      179/1    0.67778    0.68290 +/- 0.00171\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.266332203155603 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71472 --- greater than max batches\n",
-      "      180/1    0.63949    0.68265 +/- 0.00172\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.153693315273593 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71085 --- greater than max batches\n",
-      "      181/1    0.66641    0.68256 +/- 0.00171\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.06530998234231 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70866 --- greater than max batches\n",
-      "      182/1    0.70248    0.68267 +/- 0.00171\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.994849139740012 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70769 --- greater than max batches\n",
-      "      183/1    0.68906    0.68271 +/- 0.00170\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.092026018527292 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71862 --- greater than max batches\n",
-      "      184/1    0.64815    0.68252 +/- 0.00170\n",
-      " Triggers unsatisfied, max unc./thresh. is 20.010212645636404 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71679 --- greater than max batches\n",
-      "      185/1    0.66689    0.68243 +/- 0.00169\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.898829466229213 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 71279 --- greater than max batches\n",
-      "      186/1    0.68573    0.68245 +/- 0.00168\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.796150792368255 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70937 --- greater than max batches\n",
-      "      187/1    0.66673    0.68236 +/- 0.00167\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.701525271465893 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 70649 --- greater than max batches\n",
-      "      188/1    0.65564    0.68222 +/- 0.00167\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.864636825432147 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72218 --- greater than max batches\n",
-      "      189/1    0.69960    0.68231 +/- 0.00167\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.855911612557982 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72549 --- greater than max batches\n",
-      "      190/1    0.68178    0.68231 +/- 0.00166\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.76297244962571 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72262 --- greater than max batches\n",
-      "      191/1    0.69760    0.68239 +/- 0.00165\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.760934073441348 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 72637 --- greater than max batches\n",
-      "      192/1    0.66122    0.68228 +/- 0.00164\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.804180280770698 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73348 --- greater than max batches\n",
-      "      193/1    0.69346    0.68234 +/- 0.00164\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.801359777905503 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73719 --- greater than max batches\n",
-      "      194/1    0.67831    0.68231 +/- 0.00163\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.738915362738556 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73645 --- greater than max batches\n",
-      "      195/1    0.65285    0.68216 +/- 0.00163\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.653034572834297 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73391 --- greater than max batches\n",
-      "      196/1    0.66672    0.68208 +/- 0.00162\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.625879744409815 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73574 --- greater than max batches\n",
-      "      197/1    0.67536    0.68204 +/- 0.00161\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.52848856663241 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73227 --- greater than max batches\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "      198/1    0.66675    0.68196 +/- 0.00161\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.51191007087699 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73483 --- greater than max batches\n",
-      "      199/1    0.64492    0.68177 +/- 0.00161\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.511121460032857 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73858 --- greater than max batches\n",
-      "      200/1    0.69441    0.68184 +/- 0.00160\n",
-      " Triggers unsatisfied, max unc./thresh. is 19.41826259948151 for absorption in\n",
-      " tally 3\n",
-      " WARNING: The estimated number of batches is 73534 --- greater than max batches\n",
-      " Creating state point statepoint.200.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 3.2763e-01 seconds\n",
-      "   Reading cross sections          = 3.1447e-01 seconds\n",
-      " Total time in simulation          = 2.3755e+01 seconds\n",
-      "   Time in transport only          = 2.3701e+01 seconds\n",
-      "   Time in inactive batches        = 4.2962e-01 seconds\n",
-      "   Time in active batches          = 2.3325e+01 seconds\n",
-      "   Time synchronizing fission bank = 2.0652e-02 seconds\n",
-      "     Sampling source sites         = 1.7317e-02 seconds\n",
-      "     SEND/RECV source sites        = 3.2308e-03 seconds\n",
-      "   Time accumulating tallies       = 1.7916e-03 seconds\n",
-      "   Time writing statepoints        = 8.5764e-03 seconds\n",
-      " Total time for finalization       = 7.5200e-07 seconds\n",
-      " Total time elapsed                = 2.4088e+01 seconds\n",
-      " Calculation Rate (inactive)       = 29095.3 particles/second\n",
-      " Calculation Rate (active)         = 20900.3 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 0.68290 +/- 0.00158\n",
-      " k-effective (Track-length)  = 0.68184 +/- 0.00160\n",
-      " k-effective (Absorption)    = 0.68167 +/- 0.00151\n",
-      " Combined k-effective        = 0.68200 +/- 0.00134\n",
-      " Leakage Fraction            = 0.33996 +/- 0.00079\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Remove old HDF5 (summary, statepoint) files\n",
-    "!rm statepoint.*\n",
-    "\n",
-    "# Run OpenMC!\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We do not know how many batches were needed to satisfy the \n",
-    "# tally trigger(s), so find the statepoint file(s)\n",
-    "statepoints = glob.glob('statepoint.*.h5')\n",
-    "\n",
-    "# Load the last statepoint file\n",
-    "sp = openmc.StatePoint(statepoints[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Analyze the mesh fission rate tally**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tally\n",
-      "\tID             =\t1\n",
-      "\tName           =\tmesh tally\n",
-      "\tFilters        =\tMeshFilter, EnergyFilter\n",
-      "\tNuclides       =\ttotal\n",
-      "\tScores         =\t['fission', 'nu-fission']\n",
-      "\tEstimator      =\ttracklength\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Find the mesh tally with the StatePoint API\n",
-    "tally = sp.get_tally(name='mesh tally')\n",
-    "\n",
-    "# Print a little info about the mesh tally to the screen\n",
-    "print(tally)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the new Tally data retrieval API with pure NumPy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[0.04969994]]\n",
-      "\n",
-      " [[0.02200773]]\n",
-      "\n",
-      " [[0.12289506]]\n",
-      "\n",
-      " [[0.05303359]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the relative error for the thermal fission reaction \n",
-    "# rates in the four corner pins \n",
-    "data = tally.get_values(scores=['fission'],\n",
-    "                        filters=[openmc.MeshFilter, openmc.EnergyFilter], \\\n",
-    "                        filter_bins=[((1,1),(1,17), (17,1), (17,17)), \\\n",
-    "                                    ((0., 0.625),)], value='rel_err')\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>z</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>2.10e-04</td>\n",
-       "      <td>1.04e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>5.11e-04</td>\n",
-       "      <td>2.54e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>7.19e-05</td>\n",
-       "      <td>1.83e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>1.89e-04</td>\n",
-       "      <td>4.75e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>2.19e-04</td>\n",
-       "      <td>9.36e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>5.33e-04</td>\n",
-       "      <td>2.28e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>6.98e-05</td>\n",
-       "      <td>1.63e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>1.85e-04</td>\n",
-       "      <td>4.24e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>2.23e-04</td>\n",
-       "      <td>1.02e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>5.44e-04</td>\n",
-       "      <td>2.48e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>6.69e-05</td>\n",
-       "      <td>1.65e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>1.76e-04</td>\n",
-       "      <td>4.27e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>2.09e-04</td>\n",
-       "      <td>9.24e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>5.09e-04</td>\n",
-       "      <td>2.25e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>6.72e-05</td>\n",
-       "      <td>1.65e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>1.77e-04</td>\n",
-       "      <td>4.32e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>2.11e-04</td>\n",
-       "      <td>8.34e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.00e+00</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>5.15e-04</td>\n",
-       "      <td>2.03e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>fission</td>\n",
-       "      <td>6.47e-05</td>\n",
-       "      <td>1.40e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>5</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6.25e-01</td>\n",
-       "      <td>2.00e+07</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>1.70e-04</td>\n",
-       "      <td>3.66e-06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   mesh 1       energy low [eV] energy high [eV]       score     mean  \\\n",
-       "        x  y  z                                                         \n",
-       "0       1  1  1        0.00e+00         6.25e-01     fission 2.10e-04   \n",
-       "1       1  1  1        0.00e+00         6.25e-01  nu-fission 5.11e-04   \n",
-       "2       1  1  1        6.25e-01         2.00e+07     fission 7.19e-05   \n",
-       "3       1  1  1        6.25e-01         2.00e+07  nu-fission 1.89e-04   \n",
-       "4       2  1  1        0.00e+00         6.25e-01     fission 2.19e-04   \n",
-       "5       2  1  1        0.00e+00         6.25e-01  nu-fission 5.33e-04   \n",
-       "6       2  1  1        6.25e-01         2.00e+07     fission 6.98e-05   \n",
-       "7       2  1  1        6.25e-01         2.00e+07  nu-fission 1.85e-04   \n",
-       "8       3  1  1        0.00e+00         6.25e-01     fission 2.23e-04   \n",
-       "9       3  1  1        0.00e+00         6.25e-01  nu-fission 5.44e-04   \n",
-       "10      3  1  1        6.25e-01         2.00e+07     fission 6.69e-05   \n",
-       "11      3  1  1        6.25e-01         2.00e+07  nu-fission 1.76e-04   \n",
-       "12      4  1  1        0.00e+00         6.25e-01     fission 2.09e-04   \n",
-       "13      4  1  1        0.00e+00         6.25e-01  nu-fission 5.09e-04   \n",
-       "14      4  1  1        6.25e-01         2.00e+07     fission 6.72e-05   \n",
-       "15      4  1  1        6.25e-01         2.00e+07  nu-fission 1.77e-04   \n",
-       "16      5  1  1        0.00e+00         6.25e-01     fission 2.11e-04   \n",
-       "17      5  1  1        0.00e+00         6.25e-01  nu-fission 5.15e-04   \n",
-       "18      5  1  1        6.25e-01         2.00e+07     fission 6.47e-05   \n",
-       "19      5  1  1        6.25e-01         2.00e+07  nu-fission 1.70e-04   \n",
-       "\n",
-       "   std. dev.  \n",
-       "              \n",
-       "0   1.04e-05  \n",
-       "1   2.54e-05  \n",
-       "2   1.83e-06  \n",
-       "3   4.75e-06  \n",
-       "4   9.36e-06  \n",
-       "5   2.28e-05  \n",
-       "6   1.63e-06  \n",
-       "7   4.24e-06  \n",
-       "8   1.02e-05  \n",
-       "9   2.48e-05  \n",
-       "10  1.65e-06  \n",
-       "11  4.27e-06  \n",
-       "12  9.24e-06  \n",
-       "13  2.25e-05  \n",
-       "14  1.65e-06  \n",
-       "15  4.32e-06  \n",
-       "16  8.34e-06  \n",
-       "17  2.03e-05  \n",
-       "18  1.40e-06  \n",
-       "19  3.66e-06  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get a pandas dataframe for the mesh tally data\n",
-    "df = tally.get_pandas_dataframe(nuclides=False)\n",
-    "\n",
-    "# Set the Pandas float display settings\n",
-    "pd.options.display.float_format = '{:.2e}'.format\n",
-    "\n",
-    "# Print the first twenty rows in the dataframe\n",
-    "df.head(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create a boxplot to view the distribution of\n",
-    "# fission and nu-fission rates in the pins\n",
-    "bp = df.boxplot(column='mean', by='score')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f286e8b0128>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Extract thermal nu-fission rates from pandas\n",
-    "fiss = df[df['score'] == 'nu-fission']\n",
-    "fiss = fiss[fiss['energy low [eV]'] == 0.0]\n",
-    "\n",
-    "# Extract mean and reshape as 2D NumPy arrays\n",
-    "mean = fiss['mean'].values.reshape((17,17))\n",
-    "\n",
-    "plt.imshow(mean, interpolation='nearest')\n",
-    "plt.title('fission rate')\n",
-    "plt.xlabel('x')\n",
-    "plt.ylabel('y')\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Analyze the cell+nuclides scatter-y2 rate tally**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tally\n",
-      "\tID             =\t2\n",
-      "\tName           =\tcell tally\n",
-      "\tFilters        =\tCellFilter\n",
-      "\tNuclides       =\tU235 U238\n",
-      "\tScores         =\t['scatter']\n",
-      "\tEstimator      =\ttracklength\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Find the cell Tally with the StatePoint API\n",
-    "tally = sp.get_tally(name='cell tally')\n",
-    "\n",
-    "# Print a little info about the cell tally to the screen\n",
-    "print(tally)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>3.82e-02</td>\n",
-       "      <td>4.48e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>2.34e+00</td>\n",
-       "      <td>2.52e-03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell nuclide    score     mean  std. dev.\n",
-       "0     1    U235  scatter 3.82e-02   4.48e-05\n",
-       "1     1    U238  scatter 2.34e+00   2.52e-03"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get a pandas dataframe for the cell tally data\n",
-    "df = tally.get_pandas_dataframe()\n",
-    "\n",
-    "# Print the first twenty rows in the dataframe\n",
-    "df.head(20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the new Tally data retrieval API with pure NumPy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[2.52057084e-03]\n",
-      "  [4.48210552e-05]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the standard deviations the total scattering rate\n",
-    "data = tally.get_values(scores=['scatter'], \n",
-    "                        nuclides=['U238', 'U235'], value='std_dev')\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Analyze the distribcell tally**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tally\n",
-      "\tID             =\t3\n",
-      "\tName           =\tdistribcell tally\n",
-      "\tFilters        =\tDistribcellFilter\n",
-      "\tNuclides       =\ttotal\n",
-      "\tScores         =\t['absorption', 'scatter']\n",
-      "\tEstimator      =\ttracklength\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Find the distribcell Tally with the StatePoint API\n",
-    "tally = sp.get_tally(name='distribcell tally')\n",
-    "\n",
-    "# Print a little info about the distribcell tally to the screen\n",
-    "print(tally)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the new Tally data retrieval API with pure NumPy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[0.01301475]]\n",
-      "\n",
-      " [[0.0136507 ]]\n",
-      "\n",
-      " [[0.01260538]]\n",
-      "\n",
-      " [[0.01293374]]\n",
-      "\n",
-      " [[0.01219012]]\n",
-      "\n",
-      " [[0.01191931]]\n",
-      "\n",
-      " [[0.0127884 ]]\n",
-      "\n",
-      " [[0.01339325]]\n",
-      "\n",
-      " [[0.01309524]]\n",
-      "\n",
-      " [[0.0133674 ]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get the relative error for the scattering reaction rates in\n",
-    "# the first 10 distribcell instances \n",
-    "data = tally.get_values(scores=['scatter'], filters=[openmc.DistribcellFilter],\n",
-    "                        filter_bins=[tuple(range(10))], value='rel_err')\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we're done getting data from the statepoint file, we'll close it to free the file handle for subsequent OpenMC runs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Close the statepoint file now that we're done gathering info from it\n",
-    "sp.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Print the distribcell tally dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">level 1</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">level 2</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">level 3</th>\n",
-       "      <th>distribcell</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>univ</th>\n",
-       "      <th>cell</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">lat</th>\n",
-       "      <th>univ</th>\n",
-       "      <th>cell</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th>x</th>\n",
-       "      <th>y</th>\n",
-       "      <th>id</th>\n",
-       "      <th>id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>558</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>279</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>7.23e-04</td>\n",
-       "      <td>1.05e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>559</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>279</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>9.08e-02</td>\n",
-       "      <td>6.39e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>560</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>280</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>6.75e-04</td>\n",
-       "      <td>1.09e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>561</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>280</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>8.49e-02</td>\n",
-       "      <td>6.75e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>562</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>9</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>281</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>6.22e-04</td>\n",
-       "      <td>1.06e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>563</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>9</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>281</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>7.85e-02</td>\n",
-       "      <td>6.36e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>564</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>282</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>5.66e-04</td>\n",
-       "      <td>1.01e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>565</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>10</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>282</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>7.17e-02</td>\n",
-       "      <td>5.80e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>566</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>11</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>283</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>4.94e-04</td>\n",
-       "      <td>9.18e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>567</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>11</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>283</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>6.35e-02</td>\n",
-       "      <td>5.45e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>568</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>284</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>4.34e-04</td>\n",
-       "      <td>8.41e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>569</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>284</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>5.54e-02</td>\n",
-       "      <td>5.15e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>570</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>13</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>285</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>3.58e-04</td>\n",
-       "      <td>7.60e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>571</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>13</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>285</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>4.70e-02</td>\n",
-       "      <td>4.81e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>572</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>286</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>2.83e-04</td>\n",
-       "      <td>7.08e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>573</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>14</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>286</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>3.84e-02</td>\n",
-       "      <td>4.47e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>574</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>15</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>287</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>2.19e-04</td>\n",
-       "      <td>6.36e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>575</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>15</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>287</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>2.99e-02</td>\n",
-       "      <td>3.89e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>576</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>288</td>\n",
-       "      <td>absorption</td>\n",
-       "      <td>1.18e-04</td>\n",
-       "      <td>4.29e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>577</th>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>16</td>\n",
-       "      <td>16</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>288</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>1.85e-02</td>\n",
-       "      <td>2.75e-04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    level 1      level 2         level 3      distribcell       score  \\\n",
-       "       univ cell     lat            univ cell                           \n",
-       "         id   id      id   x   y      id   id                           \n",
-       "558       3    4       2   7  16       1    3         279  absorption   \n",
-       "559       3    4       2   7  16       1    3         279     scatter   \n",
-       "560       3    4       2   8  16       1    3         280  absorption   \n",
-       "561       3    4       2   8  16       1    3         280     scatter   \n",
-       "562       3    4       2   9  16       1    3         281  absorption   \n",
-       "563       3    4       2   9  16       1    3         281     scatter   \n",
-       "564       3    4       2  10  16       1    3         282  absorption   \n",
-       "565       3    4       2  10  16       1    3         282     scatter   \n",
-       "566       3    4       2  11  16       1    3         283  absorption   \n",
-       "567       3    4       2  11  16       1    3         283     scatter   \n",
-       "568       3    4       2  12  16       1    3         284  absorption   \n",
-       "569       3    4       2  12  16       1    3         284     scatter   \n",
-       "570       3    4       2  13  16       1    3         285  absorption   \n",
-       "571       3    4       2  13  16       1    3         285     scatter   \n",
-       "572       3    4       2  14  16       1    3         286  absorption   \n",
-       "573       3    4       2  14  16       1    3         286     scatter   \n",
-       "574       3    4       2  15  16       1    3         287  absorption   \n",
-       "575       3    4       2  15  16       1    3         287     scatter   \n",
-       "576       3    4       2  16  16       1    3         288  absorption   \n",
-       "577       3    4       2  16  16       1    3         288     scatter   \n",
-       "\n",
-       "        mean std. dev.  \n",
-       "                        \n",
-       "                        \n",
-       "558 7.23e-04  1.05e-05  \n",
-       "559 9.08e-02  6.39e-04  \n",
-       "560 6.75e-04  1.09e-05  \n",
-       "561 8.49e-02  6.75e-04  \n",
-       "562 6.22e-04  1.06e-05  \n",
-       "563 7.85e-02  6.36e-04  \n",
-       "564 5.66e-04  1.01e-05  \n",
-       "565 7.17e-02  5.80e-04  \n",
-       "566 4.94e-04  9.18e-06  \n",
-       "567 6.35e-02  5.45e-04  \n",
-       "568 4.34e-04  8.41e-06  \n",
-       "569 5.54e-02  5.15e-04  \n",
-       "570 3.58e-04  7.60e-06  \n",
-       "571 4.70e-02  4.81e-04  \n",
-       "572 2.83e-04  7.08e-06  \n",
-       "573 3.84e-02  4.47e-04  \n",
-       "574 2.19e-04  6.36e-06  \n",
-       "575 2.99e-02  3.89e-04  \n",
-       "576 1.18e-04  4.29e-06  \n",
-       "577 1.85e-02  2.75e-04  "
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get a pandas dataframe for the distribcell tally data\n",
-    "df = tally.get_pandas_dataframe(nuclides=False)\n",
-    "\n",
-    "# Print the last twenty rows in the dataframe\n",
-    "df.tail(20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>2.89e+02</td>\n",
-       "      <td>2.89e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>4.19e-04</td>\n",
-       "      <td>6.85e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>2.43e-04</td>\n",
-       "      <td>2.55e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.65e-05</td>\n",
-       "      <td>1.25e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.08e-04</td>\n",
-       "      <td>4.81e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.95e-04</td>\n",
-       "      <td>6.87e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>6.19e-04</td>\n",
-       "      <td>8.77e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>9.04e-04</td>\n",
-       "      <td>1.51e-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          mean std. dev.\n",
-       "                        \n",
-       "                        \n",
-       "count 2.89e+02  2.89e+02\n",
-       "mean  4.19e-04  6.85e-06\n",
-       "std   2.43e-04  2.55e-06\n",
-       "min   1.65e-05  1.25e-06\n",
-       "25%   2.08e-04  4.81e-06\n",
-       "50%   3.95e-04  6.87e-06\n",
-       "75%   6.19e-04  8.77e-06\n",
-       "max   9.04e-04  1.51e-05"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Show summary statistics for absorption distribcell tally data\n",
-    "absorption = df[df['score'] == 'absorption']\n",
-    "absorption[['mean', 'std. dev.']].dropna().describe()\n",
-    "\n",
-    "# Note that the maximum standard deviation does indeed\n",
-    "# meet the 5e-5 threshold set by the tally trigger"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Perform a statistical test comparing the tally sample distributions for two categories of fuel pins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mann-Whitney Test p-value: 0.4886239022425303\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Extract tally data from pins in the pins divided along y=-x diagonal \n",
-    "multi_index = ('level 2', 'lat',)\n",
-    "lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n",
-    "upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n",
-    "lower = lower[lower['score'] == 'absorption']\n",
-    "upper = upper[upper['score'] == 'absorption']\n",
-    "\n",
-    "# Perform non-parametric Mann-Whitney U Test to see if the \n",
-    "# absorption rates (may) come from same sampling distribution\n",
-    "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
-    "print('Mann-Whitney Test p-value: {0}'.format(p))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the symmetry implied by the y=-x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **not reject** the null hypothesis that the two sampling distributions are identical.\n",
-    "\n",
-    "Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mann-Whitney Test p-value: 3.2855436374070945e-42\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Extract tally data from pins in the pins divided along y=x diagonal\n",
-    "multi_index = ('level 2', 'lat',)\n",
-    "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n",
-    "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n",
-    "lower = lower[lower['score'] == 'absorption']\n",
-    "upper = upper[upper['score'] == 'absorption']\n",
-    "\n",
-    "# Perform non-parametric Mann-Whitney U Test to see if the \n",
-    "# absorption rates (may) come from same sampling distribution\n",
-    "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
-    "print('Mann-Whitney Test p-value: {0}'.format(p))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that the asymmetry implied by the y=x diagonal ensures that the two sampling distributions are *not* identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **reject** the null hypothesis that the two sampling distributions are identical."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/.pyenv/versions/3.7.3/lib/python3.7/site-packages/ipykernel_launcher.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",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  after removing the cwd from sys.path.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:title={'center':'Scattering Rates'}, xlabel='mean', ylabel='rel. err.'>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Extract the scatter tally data from pandas\n",
-    "scatter = df[df['score'] == 'scatter']\n",
-    "\n",
-    "scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n",
-    "\n",
-    "# Show a scatter plot of the mean vs. the std. dev.\n",
-    "scatter.plot(kind='scatter', x='mean', y='rel. err.', title='Scattering Rates')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f286e7bba90>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot a histogram and kernel density estimate for the scattering rates\n",
-    "scatter['mean'].plot(kind='hist', bins=25)\n",
-    "scatter['mean'].plot(kind='kde')\n",
-    "plt.title('Scattering Rates')\n",
-    "plt.xlabel('Mean')\n",
-    "plt.legend(['KDE', 'Histogram'])"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/pincell.ipynb b/examples/jupyter/pincell.ipynb
deleted file mode 100644
index dd0cf1313..000000000
--- a/examples/jupyter/pincell.ipynb
+++ /dev/null
@@ -1,1583 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling a Pin-Cell\n",
-    "This notebook is intended to demonstrate the basic features of the Python API for constructing input files and running OpenMC. In it, we will show how to create a basic reflective pin-cell model that is equivalent to modeling an infinite array of fuel pins. If you have never used OpenMC, this can serve as a good starting point to learn the Python API. We highly recommend having a copy of the [Python API reference documentation](https://docs.openmc.org/en/stable/pythonapi/index.html) open in another browser tab that you can refer to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defining Materials\n",
-    "\n",
-    "Materials in OpenMC are defined as a set of nuclides with specified atom/weight fractions. To begin, we will create a material by making an instance of the `Material` class. In OpenMC, many objects, including materials, are identified by a \"unique ID\" that is simply just a positive integer. These IDs are used when exporting XML files that the solver reads in. They also appear in the output and can be used for identification. Since an integer ID is not very useful by itself, you can also give a material a `name` as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Material\n",
-      "\tID             =\t1\n",
-      "\tName           =\tuo2\n",
-      "\tTemperature    =\tNone\n",
-      "\tDensity        =\tNone [sum]\n",
-      "\tS(a,b) Tables  \n",
-      "\tNuclides       \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "uo2 = openmc.Material(1, \"uo2\")\n",
-    "print(uo2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "On the XML side, you have no choice but to supply an ID. However, in the Python API, if you don't give an ID, one will be automatically generated for you:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Material\n",
-      "\tID             =\t2\n",
-      "\tName           =\t\n",
-      "\tTemperature    =\tNone\n",
-      "\tDensity        =\tNone [sum]\n",
-      "\tS(a,b) Tables  \n",
-      "\tNuclides       \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "mat = openmc.Material()\n",
-    "print(mat)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that an ID of 2 was automatically assigned. Let's now move on to adding nuclides to our `uo2` material. The `Material` object has a method `add_nuclide()` whose first argument is the name of the nuclide and second argument is the atom or weight fraction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on method add_nuclide in module openmc.material:\n",
-      "\n",
-      "add_nuclide(nuclide, percent, percent_type='ao') method of openmc.material.Material instance\n",
-      "    Add a nuclide to the material\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    nuclide : str\n",
-      "        Nuclide to add, e.g., 'Mo95'\n",
-      "    percent : float\n",
-      "        Atom or weight percent\n",
-      "    percent_type : {'ao', 'wo'}\n",
-      "        'ao' for atom percent and 'wo' for weight percent\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(uo2.add_nuclide)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that by default it assumes we want an atom fraction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Add nuclides to uo2\n",
-    "uo2.add_nuclide('U235', 0.03)\n",
-    "uo2.add_nuclide('U238', 0.97)\n",
-    "uo2.add_nuclide('O16', 2.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we need to assign a total density to the material. We'll use the `set_density` for this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uo2.set_density('g/cm3', 10.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You may sometimes be given a material specification where all the nuclide densities are in units of atom/b-cm. In this case, you just want the density to be the sum of the constituents. In that case, you can simply run `mat.set_density('sum')`.\n",
-    "\n",
-    "With UO2 finished, let's now create materials for the clad and coolant. Note the use of `add_element()` for zirconium."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "zirconium = openmc.Material(name=\"zirconium\")\n",
-    "zirconium.add_element('Zr', 1.0)\n",
-    "zirconium.set_density('g/cm3', 6.6)\n",
-    "\n",
-    "water = openmc.Material(name=\"h2o\")\n",
-    "water.add_nuclide('H1', 2.0)\n",
-    "water.add_nuclide('O16', 1.0)\n",
-    "water.set_density('g/cm3', 1.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An astute observer might now point out that this water material we just created will only use free-atom cross sections. We need to tell it to use an $S(\\alpha,\\beta)$ table so that the bound atom cross section is used at thermal energies. To do this, there's an `add_s_alpha_beta()` method. Note the use of the GND-style name \"c_H_in_H2O\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "water.add_s_alpha_beta('c_H_in_H2O')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we go to run the transport solver in OpenMC, it is going to look for a `materials.xml` file. Thus far, we have only created objects in memory. To actually create a `materials.xml` file, we need to instantiate a `Materials` collection and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "materials = openmc.Materials([uo2, zirconium, water])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that `Materials` is actually a subclass of Python's built-in `list`, so we can use methods like `append()`, `insert()`, `pop()`, etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "materials = openmc.Materials()\n",
-    "materials.append(uo2)\n",
-    "materials += [zirconium, water]\n",
-    "isinstance(materials, list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we can create the XML file with the `export_to_xml()` method. In a Jupyter notebook, we can run a shell command by putting `!` before it, so in this case we are going to display the `materials.xml` file that we created."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<materials>\r\n",
-      "  <material depletable=\"true\" id=\"1\" name=\"uo2\">\r\n",
-      "    <density units=\"g/cm3\" value=\"10.0\" />\r\n",
-      "    <nuclide ao=\"0.03\" name=\"U235\" />\r\n",
-      "    <nuclide ao=\"0.97\" name=\"U238\" />\r\n",
-      "    <nuclide ao=\"2.0\" name=\"O16\" />\r\n",
-      "  </material>\r\n",
-      "  <material id=\"3\" name=\"zirconium\">\r\n",
-      "    <density units=\"g/cm3\" value=\"6.6\" />\r\n",
-      "    <nuclide ao=\"0.5145\" name=\"Zr90\" />\r\n",
-      "    <nuclide ao=\"0.1122\" name=\"Zr91\" />\r\n",
-      "    <nuclide ao=\"0.1715\" name=\"Zr92\" />\r\n",
-      "    <nuclide ao=\"0.1738\" name=\"Zr94\" />\r\n",
-      "    <nuclide ao=\"0.028\" name=\"Zr96\" />\r\n",
-      "  </material>\r\n",
-      "  <material id=\"4\" name=\"h2o\">\r\n",
-      "    <density units=\"g/cm3\" value=\"1.0\" />\r\n",
-      "    <nuclide ao=\"2.0\" name=\"H1\" />\r\n",
-      "    <nuclide ao=\"1.0\" name=\"O16\" />\r\n",
-      "    <sab name=\"c_H_in_H2O\" />\r\n",
-      "  </material>\r\n",
-      "</materials>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "materials.export_to_xml()\n",
-    "!cat materials.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Element Expansion\n",
-    "\n",
-    "Did you notice something really cool that happened to our Zr element? OpenMC automatically turned it into a list of nuclides when it exported it! The way this feature works is as follows:\n",
-    "\n",
-    "- First, it checks whether `Materials.cross_sections` has been set, indicating the path to a `cross_sections.xml` file.\n",
-    "- If `Materials.cross_sections` isn't set, it looks for the `OPENMC_CROSS_SECTIONS` environment variable.\n",
-    "- If either of these are found, it scans the file to see what nuclides are actually available and will expand elements accordingly.\n",
-    "\n",
-    "Let's see what happens if we change O16 in water to elemental O."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<materials>\r\n",
-      "  <material depletable=\"true\" id=\"1\" name=\"uo2\">\r\n",
-      "    <density units=\"g/cm3\" value=\"10.0\" />\r\n",
-      "    <nuclide ao=\"0.03\" name=\"U235\" />\r\n",
-      "    <nuclide ao=\"0.97\" name=\"U238\" />\r\n",
-      "    <nuclide ao=\"2.0\" name=\"O16\" />\r\n",
-      "  </material>\r\n",
-      "  <material id=\"3\" name=\"zirconium\">\r\n",
-      "    <density units=\"g/cm3\" value=\"6.6\" />\r\n",
-      "    <nuclide ao=\"0.5145\" name=\"Zr90\" />\r\n",
-      "    <nuclide ao=\"0.1122\" name=\"Zr91\" />\r\n",
-      "    <nuclide ao=\"0.1715\" name=\"Zr92\" />\r\n",
-      "    <nuclide ao=\"0.1738\" name=\"Zr94\" />\r\n",
-      "    <nuclide ao=\"0.028\" name=\"Zr96\" />\r\n",
-      "  </material>\r\n",
-      "  <material id=\"4\" name=\"h2o\">\r\n",
-      "    <density units=\"g/cm3\" value=\"1.0\" />\r\n",
-      "    <nuclide ao=\"2.0\" name=\"H1\" />\r\n",
-      "    <nuclide ao=\"0.999621\" name=\"O16\" />\r\n",
-      "    <nuclide ao=\"0.000379\" name=\"O17\" />\r\n",
-      "    <sab name=\"c_H_in_H2O\" />\r\n",
-      "  </material>\r\n",
-      "</materials>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "water.remove_nuclide('O16')\n",
-    "water.add_element('O', 1.0)\n",
-    "\n",
-    "materials.export_to_xml()\n",
-    "!cat materials.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that now O16 and O17 were automatically added. O18 is missing because our cross sections file (which is based on ENDF/B-VII.1) doesn't have O18. If OpenMC didn't know about the cross sections file, it would have assumed that all isotopes exist."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The `cross_sections.xml` file\n",
-    "\n",
-    "The `cross_sections.xml` tells OpenMC where it can find nuclide cross sections and $S(\\alpha,\\beta)$ tables. It serves the same purpose as MCNP's `xsdir` file and Serpent's `xsdata` file. As we mentioned, this can be set either by the `OPENMC_CROSS_SECTIONS` environment variable or the `Materials.cross_sections` attribute.\n",
-    "\n",
-    "Let's have a look at what's inside this file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\n",
-      "<cross_sections>\n",
-      "  <library materials=\"H1\" path=\"H1.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"H2\" path=\"H2.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"H3\" path=\"H3.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"He3\" path=\"He3.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"He4\" path=\"He4.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"Li6\" path=\"Li6.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"Li7\" path=\"Li7.h5\" type=\"neutron\" />\n",
-      "  <library materials=\"Be7\" path=\"Be7.h5\" type=\"neutron\" />\n",
-      "    ...\n",
-      "  <library materials=\"Cf253\" path=\"wmp/098253.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Cf254\" path=\"wmp/098254.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Es251\" path=\"wmp/099251.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Es252\" path=\"wmp/099252.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Es253\" path=\"wmp/099253.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Es254\" path=\"wmp/099254.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Es254_m1\" path=\"wmp/099254m1.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Es255\" path=\"wmp/099255.h5\" type=\"wmp\" />\n",
-      "  <library materials=\"Fm255\" path=\"wmp/100255.h5\" type=\"wmp\" />\n",
-      "</cross_sections>\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat $OPENMC_CROSS_SECTIONS | head -n 10\n",
-    "print('    ...')\n",
-    "!cat $OPENMC_CROSS_SECTIONS | tail -n 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Enrichment\n",
-    "\n",
-    "Note that the `add_element()` method has a special argument `enrichment` that can be used for Uranium. For example, if we know that we want to create 3% enriched UO2, the following would work:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uo2_three = openmc.Material()\n",
-    "uo2_three.add_element('U', 1.0, enrichment=3.0)\n",
-    "uo2_three.add_element('O', 2.0)\n",
-    "uo2_three.set_density('g/cc', 10.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixtures\n",
-    "\n",
-    "In OpenMC it is also possible to define materials by mixing existing materials. For example, if we wanted to create MOX fuel out of a mixture of UO2 (97 wt%) and PuO2 (3 wt%) we could do the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create PuO2 material\n",
-    "puo2 = openmc.Material()\n",
-    "puo2.add_nuclide('Pu239', 0.94)\n",
-    "puo2.add_nuclide('Pu240', 0.06)\n",
-    "puo2.add_nuclide('O16', 2.0)\n",
-    "puo2.set_density('g/cm3', 11.5)\n",
-    "\n",
-    "# Create the mixture\n",
-    "mox = openmc.Material.mix_materials([uo2, puo2], [0.97, 0.03], 'wo')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The 'wo' argument in the `mix_materials()` method specifies that the fractions are weight fractions. Materials can also be mixed by atomic and volume fractions with 'ao' and 'vo', respectively. For 'ao' and 'wo' the fractions must sum to one. For 'vo', if fractions do not sum to one, the remaining fraction is set as void."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Defining Geometry\n",
-    "\n",
-    "At this point, we have three materials defined, exported to XML, and ready to be used in our model. To finish our model, we need to define the geometric arrangement of materials. OpenMC represents physical volumes using constructive solid geometry (CSG), also known as combinatorial geometry. The object that allows us to assign a material to a region of space is called a `Cell` (same concept in MCNP, for those familiar). In order to define a region that we can assign to a cell, we must first define surfaces which bound the region. A *surface* is a locus of zeros of a function of Cartesian coordinates $x$, $y$, and $z$, e.g.\n",
-    "\n",
-    "- A plane perpendicular to the x axis: $x - x_0 = 0$\n",
-    "- A cylinder parallel to the z axis: $(x - x_0)^2 + (y - y_0)^2 - R^2 = 0$\n",
-    "- A sphere: $(x - x_0)^2 + (y - y_0)^2 + (z - z_0)^2 - R^2 = 0$\n",
-    "\n",
-    "Between those three classes of surfaces (planes, cylinders, spheres), one can construct a wide variety of models. It is also possible to define cones and general second-order surfaces (tori are not currently supported).\n",
-    "\n",
-    "Note that defining a surface is not sufficient to specify a volume -- in order to define an actual volume, one must reference the half-space of a surface. A surface *half-space* is the region whose points satisfy a positive or negative inequality of the surface equation. For example, for a sphere of radius one centered at the origin, the surface equation is $f(x,y,z) = x^2 + y^2 + z^2 - 1 = 0$. Thus, we say that the negative half-space of the sphere, is defined as the collection of points satisfying $f(x,y,z) < 0$, which one can reason is the inside of the sphere. Conversely, the positive half-space of the sphere would correspond to all points outside of the sphere.\n",
-    "\n",
-    "Let's go ahead and create a sphere and confirm that what we've told you is true."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere = openmc.Sphere(r=1.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that by default the sphere is centered at the origin so we didn't have to supply `x0`, `y0`, or `z0` arguments. Strictly speaking, we could have omitted `R` as well since it defaults to one. To get the negative or positive half-space, we simply need to apply the `-` or `+` unary operators, respectively.\n",
-    "\n",
-    "(NOTE: Those unary operators are defined by special methods: `__pos__` and `__neg__` in this case)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inside_sphere = -sphere\n",
-    "outside_sphere = +sphere"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's see if `inside_sphere` actually contains points inside the sphere:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True False\n",
-      "False True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print((0,0,0) in inside_sphere, (0,0,2) in inside_sphere)\n",
-    "print((0,0,0) in outside_sphere, (0,0,2) in outside_sphere)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Everything works as expected! Now that we understand how to create half-spaces, we can create more complex volumes by combining half-spaces using Boolean operators: `&` (intersection), `|` (union), and `~` (complement). For example, let's say we want to define a region that is the top part of the sphere (all points inside the sphere that have $z > 0$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_plane = openmc.ZPlane(z0=0)\n",
-    "northern_hemisphere = -sphere & +z_plane"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For many regions, OpenMC can automatically determine a bounding box. To get the bounding box, we use the `bounding_box` property of a region, which returns a tuple of the lower-left and upper-right Cartesian coordinates for the bounding box:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([-1., -1.,  0.]), array([1., 1., 1.]))"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "northern_hemisphere.bounding_box"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we see how to create volumes, we can use them to create a cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cell = openmc.Cell()\n",
-    "cell.region = northern_hemisphere\n",
-    "\n",
-    "# or...\n",
-    "cell = openmc.Cell(region=northern_hemisphere)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default, the cell is not filled by any material (void). In order to assign a material, we set the `fill` property of a `Cell`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cell.fill = water"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Universes and in-line plotting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A collection of cells is known as a universe (again, this will be familiar to MCNP/Serpent users) and can be used as a repeatable unit when creating a model. Although we don't need it yet, the benefit of creating a universe is that we can visualize our geometry while we're creating it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "universe = openmc.Universe()\n",
-    "universe.add_cell(cell)\n",
-    "\n",
-    "# this also works\n",
-    "universe = openmc.Universe(cells=[cell])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `Universe` object has a `plot` method that will display our the universe as current constructed:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f7df827b250>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "universe.plot(width=(2.0, 2.0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By default, the plot will appear in the $x$-$y$ plane. We can change that with the `basis` argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f7df8112100>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "universe.plot(width=(2.0, 2.0), basis='xz')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we have particular fondness for, say, fuchsia, we can tell the `plot()` method to make our cell that color."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f7df80edd30>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "universe.plot(width=(2.0, 2.0), basis='xz',\n",
-    "              colors={cell: 'fuchsia'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pin cell geometry\n",
-    "\n",
-    "We now have enough knowledge to create our pin-cell. We need three surfaces to define the fuel and clad:\n",
-    "\n",
-    "1. The outer surface of the fuel -- a cylinder parallel to the z axis\n",
-    "2. The inner surface of the clad -- same as above\n",
-    "3. The outer surface of the clad -- same as above\n",
-    "\n",
-    "These three surfaces will all be instances of `openmc.ZCylinder`, each with a different radius according to the specification."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel_outer_radius = openmc.ZCylinder(r=0.39)\n",
-    "clad_inner_radius = openmc.ZCylinder(r=0.40)\n",
-    "clad_outer_radius = openmc.ZCylinder(r=0.46)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces created, we can now take advantage of the built-in operators on surfaces to create regions for the fuel, the gap, and the clad:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel_region = -fuel_outer_radius\n",
-    "gap_region = +fuel_outer_radius & -clad_inner_radius\n",
-    "clad_region = +clad_inner_radius & -clad_outer_radius"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can create corresponding cells that assign materials to these regions. As with materials, cells have unique IDs that are assigned either manually or automatically. Note that the gap cell doesn't have any material assigned (it is void by default)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel = openmc.Cell(name='fuel')\n",
-    "fuel.fill = uo2\n",
-    "fuel.region = fuel_region\n",
-    "\n",
-    "gap = openmc.Cell(name='air gap')\n",
-    "gap.region = gap_region\n",
-    "\n",
-    "clad = openmc.Cell(name='clad')\n",
-    "clad.fill = zirconium\n",
-    "clad.region = clad_region"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we need to handle the coolant outside of our fuel pin. To do this, we create x- and y-planes that bound the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pitch = 1.26\n",
-    "left = openmc.XPlane(x0=-pitch/2, boundary_type='reflective')\n",
-    "right = openmc.XPlane(x0=pitch/2, boundary_type='reflective')\n",
-    "bottom = openmc.YPlane(y0=-pitch/2, boundary_type='reflective')\n",
-    "top = openmc.YPlane(y0=pitch/2, boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The water region is going to be everything outside of the clad outer radius and within the box formed as the intersection of four half-spaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "water_region = +left & -right & +bottom & -top & +clad_outer_radius\n",
-    "\n",
-    "moderator = openmc.Cell(name='moderator')\n",
-    "moderator.fill = water\n",
-    "moderator.region = water_region"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC also includes a factory function that generates a rectangular prism that could have made our lives easier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "openmc.region.Intersection"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "box = openmc.rectangular_prism(width=pitch, height=pitch,\n",
-    "                               boundary_type='reflective')\n",
-    "type(box)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pay attention here -- the object that was returned is NOT a surface. It is actually the intersection of four surface half-spaces, just like we created manually before. Thus, we don't need to apply the unary operator (`-box`). Instead, we can directly combine it with `+clad_or`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "water_region = box & +clad_outer_radius"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final step is to assign the cells we created to a universe and tell OpenMC that this universe is the \"root\" universe in our geometry. The `Geometry` is the final object that is actually exported to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<geometry>\r\n",
-      "  <cell id=\"3\" material=\"1\" name=\"fuel\" region=\"-3\" universe=\"3\" />\r\n",
-      "  <cell id=\"4\" material=\"void\" name=\"air gap\" region=\"3 -4\" universe=\"3\" />\r\n",
-      "  <cell id=\"5\" material=\"3\" name=\"clad\" region=\"4 -5\" universe=\"3\" />\r\n",
-      "  <cell id=\"6\" material=\"4\" name=\"moderator\" region=\"6 -7 8 -9 5\" universe=\"3\" />\r\n",
-      "  <surface coeffs=\"0.0 0.0 0.39\" id=\"3\" type=\"z-cylinder\" />\r\n",
-      "  <surface coeffs=\"0.0 0.0 0.4\" id=\"4\" type=\"z-cylinder\" />\r\n",
-      "  <surface coeffs=\"0.0 0.0 0.46\" id=\"5\" type=\"z-cylinder\" />\r\n",
-      "  <surface boundary=\"reflective\" coeffs=\"-0.63\" id=\"6\" type=\"x-plane\" />\r\n",
-      "  <surface boundary=\"reflective\" coeffs=\"0.63\" id=\"7\" type=\"x-plane\" />\r\n",
-      "  <surface boundary=\"reflective\" coeffs=\"-0.63\" id=\"8\" type=\"y-plane\" />\r\n",
-      "  <surface boundary=\"reflective\" coeffs=\"0.63\" id=\"9\" type=\"y-plane\" />\r\n",
-      "</geometry>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "root_universe = openmc.Universe(cells=(fuel, gap, clad, moderator))\n",
-    "\n",
-    "geometry = openmc.Geometry()\n",
-    "geometry.root_universe = root_universe\n",
-    "\n",
-    "# or...\n",
-    "geometry = openmc.Geometry(root_universe)\n",
-    "geometry.export_to_xml()\n",
-    "!cat geometry.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Starting source and settings\n",
-    "\n",
-    "The Python API has a module ``openmc.stats`` with various univariate and multivariate probability distributions. We can use these distributions to create a starting source using the ``openmc.Source`` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a point source\n",
-    "point = openmc.stats.Point((0, 0, 0))\n",
-    "source = openmc.Source(space=point)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's create a `Settings` object and give it the source we created along with specifying how many batches and particles we want to run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()\n",
-    "settings.source = source\n",
-    "settings.batches = 100\n",
-    "settings.inactive = 10\n",
-    "settings.particles = 1000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<settings>\r\n",
-      "  <run_mode>eigenvalue</run_mode>\r\n",
-      "  <particles>1000</particles>\r\n",
-      "  <batches>100</batches>\r\n",
-      "  <inactive>10</inactive>\r\n",
-      "  <source strength=\"1.0\">\r\n",
-      "    <space type=\"point\">\r\n",
-      "      <parameters>0 0 0</parameters>\r\n",
-      "    </space>\r\n",
-      "  </source>\r\n",
-      "</settings>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "settings.export_to_xml()\n",
-    "!cat settings.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## User-defined tallies\n",
-    "\n",
-    "We actually have all the *required* files needed to run a simulation. Before we do that though, let's give a quick example of how to create tallies. We will show how one would tally the total, fission, absorption, and (n,$\\gamma$) reaction rates for $^{235}$U in the cell containing fuel. Recall that filters allow us to specify *where* in phase-space we want events to be tallied and scores tell us *what* we want to tally:\n",
-    "\n",
-    "$$X = \\underbrace{\\int d\\mathbf{r} \\int d\\mathbf{\\Omega} \\int dE}_{\\text{filters}} \\; \\underbrace{f(\\mathbf{r},\\mathbf{\\Omega},E)}_{\\text{scores}} \\psi (\\mathbf{r},\\mathbf{\\Omega},E)$$\n",
-    "\n",
-    "In this case, the *where* is \"the fuel cell\". So, we will create a cell filter specifying the fuel cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cell_filter = openmc.CellFilter(fuel)\n",
-    "\n",
-    "tally = openmc.Tally(1)\n",
-    "tally.filters = [cell_filter]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The *what* is the total, fission, absorption, and (n,$\\gamma$) reaction rates in $^{235}$U. By default, if we only specify what reactions, it will gives us tallies over all nuclides. We can use the `nuclides` attribute to name specific nuclides we're interested in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tally.nuclides = ['U235']\n",
-    "tally.scores = ['total', 'fission', 'absorption', '(n,gamma)']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similar to the other files, we need to create a `Tallies` collection and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<tallies>\r\n",
-      "  <filter id=\"1\" type=\"cell\">\r\n",
-      "    <bins>3</bins>\r\n",
-      "  </filter>\r\n",
-      "  <tally id=\"1\">\r\n",
-      "    <filters>1</filters>\r\n",
-      "    <nuclides>U235</nuclides>\r\n",
-      "    <scores>total fission absorption (n,gamma)</scores>\r\n",
-      "  </tally>\r\n",
-      "</tallies>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "tallies = openmc.Tallies([tally])\n",
-    "tallies.export_to_xml()\n",
-    "!cat tallies.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running OpenMC\n",
-    "\n",
-    "Running OpenMC from Python can be done using the `openmc.run()` function. This function allows you to set the number of MPI processes and OpenMP threads, if need be."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.12.0\n",
-      "          Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n",
-      "         Date/Time | 2020-08-25 14:58:51\n",
-      "    OpenMP Threads | 4\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /home/master/data/nuclear/endfb71_hdf5/U235.h5\n",
-      " Reading U238 from /home/master/data/nuclear/endfb71_hdf5/U238.h5\n",
-      " Reading O16 from /home/master/data/nuclear/endfb71_hdf5/O16.h5\n",
-      " Reading Zr90 from /home/master/data/nuclear/endfb71_hdf5/Zr90.h5\n",
-      " Reading Zr91 from /home/master/data/nuclear/endfb71_hdf5/Zr91.h5\n",
-      " Reading Zr92 from /home/master/data/nuclear/endfb71_hdf5/Zr92.h5\n",
-      " Reading Zr94 from /home/master/data/nuclear/endfb71_hdf5/Zr94.h5\n",
-      " Reading Zr96 from /home/master/data/nuclear/endfb71_hdf5/Zr96.h5\n",
-      " Reading H1 from /home/master/data/nuclear/endfb71_hdf5/H1.h5\n",
-      " Reading O17 from /home/master/data/nuclear/endfb71_hdf5/O17.h5\n",
-      " Reading c_H_in_H2O from /home/master/data/nuclear/endfb71_hdf5/c_H_in_H2O.h5\n",
-      " Minimum neutron data temperature: 294.000000 K\n",
-      " Maximum neutron data temperature: 294.000000 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.000000 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.42066\n",
-      "        2/1    1.39831\n",
-      "        3/1    1.46207\n",
-      "        4/1    1.44888\n",
-      "        5/1    1.42595\n",
-      "        6/1    1.35549\n",
-      "        7/1    1.36717\n",
-      "        8/1    1.45095\n",
-      "        9/1    1.36061\n",
-      "       10/1    1.36554\n",
-      "       11/1    1.36973\n",
-      "       12/1    1.44276    1.40625 +/- 0.03652\n",
-      "       13/1    1.35512    1.38920 +/- 0.02711\n",
-      "       14/1    1.54216    1.42744 +/- 0.04277\n",
-      "       15/1    1.39353    1.42066 +/- 0.03382\n",
-      "       16/1    1.38650    1.41497 +/- 0.02820\n",
-      "       17/1    1.38760    1.41106 +/- 0.02415\n",
-      "       18/1    1.38413    1.40769 +/- 0.02118\n",
-      "       19/1    1.39088    1.40582 +/- 0.01877\n",
-      "       20/1    1.47468    1.41271 +/- 0.01815\n",
-      "       21/1    1.45695    1.41673 +/- 0.01690\n",
-      "       22/1    1.40308    1.41559 +/- 0.01547\n",
-      "       23/1    1.40821    1.41503 +/- 0.01424\n",
-      "       24/1    1.32301    1.40845 +/- 0.01473\n",
-      "       25/1    1.36702    1.40569 +/- 0.01399\n",
-      "       26/1    1.30968    1.39969 +/- 0.01440\n",
-      "       27/1    1.38099    1.39859 +/- 0.01357\n",
-      "       28/1    1.42103    1.39984 +/- 0.01285\n",
-      "       29/1    1.39741    1.39971 +/- 0.01216\n",
-      "       30/1    1.36548    1.39800 +/- 0.01166\n",
-      "       31/1    1.41573    1.39884 +/- 0.01112\n",
-      "       32/1    1.39788    1.39880 +/- 0.01061\n",
-      "       33/1    1.35942    1.39709 +/- 0.01028\n",
-      "       34/1    1.40483    1.39741 +/- 0.00985\n",
-      "       35/1    1.39418    1.39728 +/- 0.00944\n",
-      "       36/1    1.41492    1.39796 +/- 0.00910\n",
-      "       37/1    1.49392    1.40151 +/- 0.00945\n",
-      "       38/1    1.45114    1.40329 +/- 0.00928\n",
-      "       39/1    1.42619    1.40408 +/- 0.00899\n",
-      "       40/1    1.35249    1.40236 +/- 0.00885\n",
-      "       41/1    1.35401    1.40080 +/- 0.00870\n",
-      "       42/1    1.40220    1.40084 +/- 0.00842\n",
-      "       43/1    1.36437    1.39974 +/- 0.00824\n",
-      "       44/1    1.33642    1.39787 +/- 0.00821\n",
-      "       45/1    1.36953    1.39706 +/- 0.00801\n",
-      "       46/1    1.30034    1.39438 +/- 0.00824\n",
-      "       47/1    1.44097    1.39564 +/- 0.00811\n",
-      "       48/1    1.37981    1.39522 +/- 0.00790\n",
-      "       49/1    1.34870    1.39403 +/- 0.00779\n",
-      "       50/1    1.41247    1.39449 +/- 0.00761\n",
-      "       51/1    1.33382    1.39301 +/- 0.00756\n",
-      "       52/1    1.37043    1.39247 +/- 0.00740\n",
-      "       53/1    1.38754    1.39236 +/- 0.00723\n",
-      "       54/1    1.40160    1.39257 +/- 0.00707\n",
-      "       55/1    1.37511    1.39218 +/- 0.00692\n",
-      "       56/1    1.38589    1.39204 +/- 0.00677\n",
-      "       57/1    1.40630    1.39234 +/- 0.00663\n",
-      "       58/1    1.29944    1.39041 +/- 0.00677\n",
-      "       59/1    1.40019    1.39061 +/- 0.00663\n",
-      "       60/1    1.42384    1.39127 +/- 0.00653\n",
-      "       61/1    1.36502    1.39076 +/- 0.00643\n",
-      "       62/1    1.37042    1.39037 +/- 0.00631\n",
-      "       63/1    1.42295    1.39098 +/- 0.00622\n",
-      "       64/1    1.40042    1.39116 +/- 0.00611\n",
-      "       65/1    1.36382    1.39066 +/- 0.00602\n",
-      "       66/1    1.31659    1.38934 +/- 0.00606\n",
-      "       67/1    1.36101    1.38884 +/- 0.00597\n",
-      "       68/1    1.46359    1.39013 +/- 0.00601\n",
-      "       69/1    1.41012    1.39047 +/- 0.00591\n",
-      "       70/1    1.27411    1.38853 +/- 0.00613\n",
-      "       71/1    1.45399    1.38960 +/- 0.00612\n",
-      "       72/1    1.40455    1.38984 +/- 0.00603\n",
-      "       73/1    1.33020    1.38890 +/- 0.00601\n",
-      "       74/1    1.44599    1.38979 +/- 0.00598\n",
-      "       75/1    1.34985    1.38917 +/- 0.00592\n",
-      "       76/1    1.36183    1.38876 +/- 0.00584\n",
-      "       77/1    1.41080    1.38909 +/- 0.00576\n",
-      "       78/1    1.43991    1.38984 +/- 0.00573\n",
-      "       79/1    1.35613    1.38935 +/- 0.00566\n",
-      "       80/1    1.31659    1.38831 +/- 0.00568\n",
-      "       81/1    1.51344    1.39007 +/- 0.00587\n",
-      "       82/1    1.38404    1.38999 +/- 0.00579\n",
-      "       83/1    1.39613    1.39007 +/- 0.00571\n",
-      "       84/1    1.43037    1.39061 +/- 0.00566\n",
-      "       85/1    1.47316    1.39172 +/- 0.00569\n",
-      "       86/1    1.39220    1.39172 +/- 0.00561\n",
-      "       87/1    1.44400    1.39240 +/- 0.00558\n",
-      "       88/1    1.42419    1.39281 +/- 0.00552\n",
-      "       89/1    1.30930    1.39175 +/- 0.00556\n",
-      "       90/1    1.46976    1.39273 +/- 0.00557\n",
-      "       91/1    1.38334    1.39261 +/- 0.00550\n",
-      "       92/1    1.35260    1.39212 +/- 0.00546\n",
-      "       93/1    1.38505    1.39204 +/- 0.00539\n",
-      "       94/1    1.38290    1.39193 +/- 0.00533\n",
-      "       95/1    1.42597    1.39233 +/- 0.00528\n",
-      "       96/1    1.41624    1.39261 +/- 0.00523\n",
-      "       97/1    1.42053    1.39293 +/- 0.00518\n",
-      "       98/1    1.36268    1.39258 +/- 0.00513\n",
-      "       99/1    1.39175    1.39258 +/- 0.00507\n",
-      "      100/1    1.38148    1.39245 +/- 0.00502\n",
-      " Creating state point statepoint.100.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 6.9022e-01 seconds\n",
-      "   Reading cross sections          = 6.7913e-01 seconds\n",
-      " Total time in simulation          = 1.7892e+00 seconds\n",
-      "   Time in transport only          = 1.7650e+00 seconds\n",
-      "   Time in inactive batches        = 1.5005e-01 seconds\n",
-      "   Time in active batches          = 1.6391e+00 seconds\n",
-      "   Time synchronizing fission bank = 4.2308e-03 seconds\n",
-      "     Sampling source sites         = 3.4593e-03 seconds\n",
-      "     SEND/RECV source sites        = 6.2601e-04 seconds\n",
-      "   Time accumulating tallies       = 9.5555e-05 seconds\n",
-      " Total time for finalization       = 7.4948e-05 seconds\n",
-      " Total time elapsed                = 2.4836e+00 seconds\n",
-      " Calculation Rate (inactive)       = 66645.8 particles/second\n",
-      " Calculation Rate (active)         = 54907.5 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.39516 +/- 0.00457\n",
-      " k-effective (Track-length)  = 1.39245 +/- 0.00502\n",
-      " k-effective (Absorption)    = 1.40443 +/- 0.00333\n",
-      " Combined k-effective        = 1.40145 +/- 0.00319\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Great! OpenMC already told us our k-effective. It also spit out a file called `tallies.out` that shows our tallies. This is a very basic method to look at tally data; for more sophisticated methods, see other example notebooks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " ============================>     TALLY 1     <============================\r\n",
-      "\r\n",
-      " Cell 3\r\n",
-      "   U235\r\n",
-      "     Total Reaction Rate                  0.726151 +/- 0.00251702\r\n",
-      "     Fission Rate                         0.543836 +/- 0.00205084\r\n",
-      "     Absorption Rate                      0.652874 +/- 0.002424\r\n",
-      "     (n,gamma)                            0.10904 +/- 0.000385793\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat tallies.out"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Geometry plotting\n",
-    "\n",
-    "We saw before that we could call the `Universe.plot()` method to show a universe while we were creating our geometry. There is also a built-in plotter in the codebase that is much faster than the Python plotter and has more options. The interface looks somewhat similar to the `Universe.plot()` method. Instead though, we create `Plot` instances, assign them to a `Plots` collection, export it to XML, and then run OpenMC in geometry plotting mode. As an example, let's specify that we want the plot to be colored by material (rather than by cell) and we assign yellow to fuel and blue to water."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot = openmc.Plot()\n",
-    "plot.filename = 'pinplot'\n",
-    "plot.width = (pitch, pitch)\n",
-    "plot.pixels = (200, 200)\n",
-    "plot.color_by = 'material'\n",
-    "plot.colors = {uo2: 'yellow', water: 'blue'}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our plot created, we need to add it to a `Plots` collection which can be exported to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<plots>\r\n",
-      "  <plot basis=\"xy\" color_by=\"material\" filename=\"pinplot\" id=\"1\" type=\"slice\">\r\n",
-      "    <origin>0.0 0.0 0.0</origin>\r\n",
-      "    <width>1.26 1.26</width>\r\n",
-      "    <pixels>200 200</pixels>\r\n",
-      "    <color id=\"1\" rgb=\"255 255 0\" />\r\n",
-      "    <color id=\"4\" rgb=\"0 0 255\" />\r\n",
-      "  </plot>\r\n",
-      "</plots>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "plots = openmc.Plots([plot])\n",
-    "plots.export_to_xml()\n",
-    "!cat plots.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run OpenMC in plotting mode by calling the `plot_geometry()` function. Under the hood this is calling `openmc --plot`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.12.0\n",
-      "          Git SHA1 | 3d90a9f857ec72eae897e054d4225180f1fa4d93\n",
-      "         Date/Time | 2020-08-25 14:58:54\n",
-      "    OpenMP Threads | 4\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Reading plot XML file...\n",
-      "\n",
-      " =======================>     PLOTTING SUMMARY     <========================\n",
-      "\n",
-      "Plot ID: 1\n",
-      "Plot file: pinplot.ppm\n",
-      "Universe depth: -1\n",
-      "Plot Type: Slice\n",
-      "Origin: 0.0 0.0 0.0\n",
-      "Width: 1.26 1.26\n",
-      "Coloring: Materials\n",
-      "Basis: XY\n",
-      "Pixels: 200 200\n",
-      "\n",
-      " Processing plot 1: pinplot.ppm...\n"
-     ]
-    }
-   ],
-   "source": [
-    "openmc.plot_geometry()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC writes out a peculiar image with a `.ppm` extension. If you have ImageMagick installed, this can be converted into a more normal `.png` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!convert pinplot.ppm pinplot.png"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use functionality from IPython to display the image inline in our notebook:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(\"pinplot.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That was a little bit cumbersome. Thankfully, OpenMC provides us with a method on the `Plot` class that does all that \"boilerplate\" work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot.to_ipython_image()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/pincell_depletion.ipynb b/examples/jupyter/pincell_depletion.ipynb
deleted file mode 100644
index 3fa46c00b..000000000
--- a/examples/jupyter/pincell_depletion.ipynb
+++ /dev/null
@@ -1,997 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pincell Depletion\n",
-    "This notebook is intended to introduce the reader to the depletion interface contained in OpenMC. It is recommended that you are moderately familiar with building models using the OpenMC Python API. The earlier examples are excellent starting points, as this notebook will not focus heavily on model building.\n",
-    "\n",
-    "If you have a real power reactor, the fuel composition is constantly changing as fission events produce energy, remove some fissile isotopes, and produce fission products. Other reactions, like $(n, \\alpha)$ and $(n, \\gamma)$ will alter the composition as well. Furthermore, some nuclides undergo spontaneous decay with widely ranging frequencies. Depletion is the process of modeling this behavior.\n",
-    "\n",
-    "In this notebook, we will model a simple fuel pin in an infinite lattice using the Python API. We will then build and examine some of the necessary components for performing depletion analysis. Then, we will use the depletion interface in OpenMC to simulate the fuel pin producing power over several months. Lastly, we will wrap up with some helpful tips to improve the fidelity of depletion simulations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import math\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build the Geometry\n",
-    "\n",
-    "Much of this section is borrowed from the \"Modeling a Pin-Cell\" example. If you find yourself not understanding some aspects of this section, feel free to refer to that example, as some details may be glossed over for brevity.\n",
-    "\n",
-    "First, we will create our fuel, cladding, and water materials to represent a typical PWR."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel = openmc.Material(name=\"uo2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel.add_element(\"U\", 1, percent_type=\"ao\", enrichment=4.25)\n",
-    "fuel.add_element(\"O\", 2)\n",
-    "fuel.set_density(\"g/cc\", 10.4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clad = openmc.Material(name=\"clad\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clad.add_element(\"Zr\", 1)\n",
-    "clad.set_density(\"g/cc\", 6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "water = openmc.Material(name=\"water\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "water.add_element(\"O\", 1)\n",
-    "water.add_element(\"H\", 2)\n",
-    "water.set_density(\"g/cc\", 1.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "water.add_s_alpha_beta(\"c_H_in_H2O\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "materials = openmc.Materials([fuel, clad, water])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we are going to use the `openmc.model.pin` function to build our pin cell. The `pin` function anticipates concentric cylinders and materials to fill the inner regions. One additional material is needed than the number of cylinders to cover the domain outside the final ring. \n",
-    "\n",
-    "To do this, we define two radii for the outer radius of our fuel pin, and the outer radius of the cladding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radii = [0.42, 0.45]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using these radii, we define concentric `ZCylinder` objects. So long as the cylinders are concentric and increasing in radius, any orientation can be used. We also take advantage of the fact that the `openmc.Materials` object is a subclass of the `list` object to assign materials to the regions defined by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pin_surfaces = [openmc.ZCylinder(r=r) for r in radii]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pin_univ = openmc.model.pin(pin_surfaces, materials)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first material, in our case `fuel`, is placed inside the first cylinder in the inner-most region. The second material, `clad`, fills the space between our cylinders, while `water` is placed outside the last ring. The `pin` function returns an `openmc.Universe` object, and has some additional features we will mention later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f9aea1a50d0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pin_univ.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bound_box = openmc.rectangular_prism(0.62, 0.62, boundary_type=\"reflective\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_cell = openmc.Cell(fill=pin_univ, region=bound_box)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "root_univ = openmc.Universe(cells=[root_cell])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry = openmc.Geometry(root_univ)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lastly we construct our settings. For the sake of time, a relatively low number of particles will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings = openmc.Settings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings.particles = 100\n",
-    "settings.inactive = 10\n",
-    "settings.batches = 50"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The depletion interface relies on `OpenMC` to perform the transport simulation and obtain reaction rates and other important information. We then have to create the `xml` input files that `openmc` expects, specifically `geometry.xml`, `settings.xml`, and `materials.xml`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we write the material file, we must add one bit of information: the volume of our fuel. In order to translate the reaction rates obtained by `openmc` to meaningful units for depletion, we have to normalize them to a correct power. This requires us to know, or be able to calculate, how much fuel is in our problem. Correctly setting the volumes is a critical step, and can lead to incorrect answers, as the fuel is over- or under-depleted due to poor normalization.\n",
-    "\n",
-    "For our problem, we can assign the \"volume\" to be the cross-sectional area of our fuel. This is identical to modeling our fuel pin inside a box with height of 1 cm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel.volume = math.pi * radii[0] ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setting up for depletion\n",
-    "\n",
-    "The OpenMC depletion interface can be accessed from the `openmc.deplete` module, and has a variety of classes that will help us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import openmc.deplete"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to run the depletion calculation we need the following information:\n",
-    "\n",
-    "1. Nuclide decay, fission yield, and reaction data\n",
-    "2. Operational power or power density\n",
-    "3. Desired depletion schedule\n",
-    "4. Desired time integration scheme\n",
-    "\n",
-    "The first item is necessary to determine the paths by which nuclides transmute over the depletion simulation. This includes spontaneous decay, fission product yield distributions, and nuclides produced through neutron-reactions. For example,\n",
-    "* Te129 decays to I129 with a half life of ~70 minutes\n",
-    "* A fission event for U-235 produces fission products like Xe135 according to a distribution\n",
-    "* For thermal problems, Am241 will produce metastable Am242 about 8% of the time during an $(n,\\gamma)$ reaction. The other 92% of capture reactions will produce ground state Am242\n",
-    "\n",
-    "These data are often distributed with other nuclear data, like incident neutron cross sections with ENDF/B-VII.\n",
-    "OpenMC uses the [`openmc.deplete.Chain`](https://docs.openmc.org/en/latest/pythonapi/generated/openmc.deplete.Chain.html#openmc.deplete.Chain) to collect represent the various decay and transmutation pathways in a single object.\n",
-    "While a complete `Chain` can be created using nuclear data files, users may prefer to download pre-generated XML-representations instead.\n",
-    "Such files can be found at https://openmc.org/depletion-chains/ and include full and compressed chains, with capture branching ratios derived using PWR- or SFR-spectra.\n",
-    "\n",
-    "For this problem, we will be using a much smaller depletion chain that contains very few nuclides. In a realistic problem, over 1000 isotopes may be included in the depletion chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = openmc.deplete.Chain.from_xml(\"./chain_simple.xml\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('I135', 0),\n",
-       "             ('Xe135', 1),\n",
-       "             ('Xe136', 2),\n",
-       "             ('Cs135', 3),\n",
-       "             ('Gd157', 4),\n",
-       "             ('Gd156', 5),\n",
-       "             ('U234', 6),\n",
-       "             ('U235', 7),\n",
-       "             ('U238', 8)])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.nuclide_dict"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The primary entry point for depletion is the `openmc.deplete.Operator`. It relies on the `openmc.deplete.Chain` and helper classes to run `openmc`, retrieve and normalize reaction rates, and other perform other tasks. For a thorough description, please see the full API documentation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will create our Operator using the geometry and settings from above, and our simple chain file. The materials are read in automatically using the `materials.xml` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "operator = openmc.deplete.Operator(geometry, settings, \"./chain_simple.xml\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will then simulate our fuel pin operating at linear power of 174 W/cm, or 174 W given a unit height for our problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "power = 174"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this problem, we will take depletion step sizes of 30 days, and instruct OpenMC to re-run a transport simulation every 30 days until we have modeled the problem over a six month cycle. The depletion interface expects the time to be given in seconds, so we will have to convert. Note that these values are not cumulative."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time_steps = [30 * 24 * 60 * 60] * 6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And lastly, we will use the basic predictor, or forward Euler, time integration scheme. Other, more advanced methods are provided to the user through `openmc.deplete`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "integrator = openmc.deplete.PredictorIntegrator(operator, time_steps, power)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To perform the simulation, we use the `integrate` method, and let `openmc` take care of the rest."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "integrator.integrate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Processing the outputs\n",
-    "\n",
-    "The depletion simulation produces a few output files. First, the statepoint files from each individual transport simulation are written to `openmc_simulation_n<N>.h5`, where `<N>` indicates the current depletion step. Any tallies that we defined in `tallies.xml` will be included in these files across our simulations. We have 7 such files, one for each our of 6 depletion steps and the initial state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "c5g7.h5\t\t\t openmc_simulation_n2.h5  openmc_simulation_n6.h5\r\n",
-      "depletion_results.h5\t openmc_simulation_n3.h5  statepoint.50.h5\r\n",
-      "openmc_simulation_n0.h5  openmc_simulation_n4.h5  summary.h5\r\n",
-      "openmc_simulation_n1.h5  openmc_simulation_n5.h5\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls *.h5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `depletion_results.h5` file contains information that is aggregated over all time steps through depletion. This includes the multiplication factor, as well as concentrations. We can process this file using the `openmc.deplete.ResultsList` object"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = openmc.deplete.ResultsList.from_hdf5(\"./depletion_results.h5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time, k = results.get_eigenvalue()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "time /= (24 * 60 * 60)  # convert back to days from seconds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.76882937, 0.00982155],\n",
-       "       [0.75724033, 0.00827689],\n",
-       "       [0.75532242, 0.01031746],\n",
-       "       [0.74796855, 0.00919769],\n",
-       "       [0.74066561, 0.01157708],\n",
-       "       [0.73184492, 0.00971504],\n",
-       "       [0.7207293 , 0.00703074]])"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "k"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first column of `k` is the value of `k-combined` at each point in our simulation, while the second column contains the associated uncertainty. We can plot this using `matplotlib`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.errorbar(time, k[:, 0], yerr=k[:, 1])\n",
-    "pyplot.xlabel(\"Time [d]\")\n",
-    "pyplot.ylabel(\"$k_{eff}\\pm \\sigma$\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Due to the low number of particles selected, we have not only a very high uncertainty, but likely a horrendously poor fission source. This pin cell should have $k>1$, but we can still see the decline over time due to fuel consumption."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can then examine concentrations of atoms in each of our materials. This requires knowing the material ID, which can be obtained from the `materials.xml` file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_time, u5 = results.get_atoms(\"1\", \"U235\")\n",
-    "_time, xe135 = results.get_atoms(\"1\", \"Xe135\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.plot(time, u5, label=\"U235\")\n",
-    "pyplot.xlabel(\"Time [d]\")\n",
-    "pyplot.ylabel(\"Number of atoms - U235\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.plot(time, xe135, label=\"Xe135\")\n",
-    "pyplot.xlabel(\"Time [d]\")\n",
-    "pyplot.ylabel(\"Number of atoms - Xe135\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also examine reaction rates over time using the `ResultsList`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_time, u5_fission = results.get_reaction_rate(\"1\", \"U235\", \"fission\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pyplot.plot(time, u5_fission)\n",
-    "pyplot.xlabel(\"Time [d]\")\n",
-    "pyplot.ylabel(\"Fission reactions / s\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Helpful tips\n",
-    "\n",
-    "Depletion is a tricky task to get correct. Use too short of time steps and you may never get your results due to running many transport simulations. Use long of time steps and you may get incorrect answers. Consider the xenon plot from above. Xenon-135 is a fission product with a thermal absorption cross section on the order of millions of barns, but has a half life of ~9 hours. Taking smaller time steps at the beginning of your simulation to build up some equilibrium in your fission products is highly recommended.\n",
-    "\n",
-    "When possible, differentiate materials that reappear in multiple places. If we had built an entire core with the single `fuel` material, every pin would be depleted using the same averaged spectrum and reaction rates which is incorrect. The `Operator` can differentiate these materials using the `diff_burnable_mats` argument, but note that the volumes will be copied from the original material.\n",
-    "\n",
-    "Using higher-order integrators, like the `CECMIntegrator`, `EPCRK4Integrator` with a fourth order Runge-Kutta, or the `LEQIIntegrator`, can improve the accuracy of a simulation, or at least allow you to take longer depletion steps between transport simulations with similar accuracy.\n",
-    "\n",
-    "Fuel pins with integrated burnable absorbers, like gadolinia, experience strong flux gradients until the absorbers are mostly burned away. This means that the spectrum and magnitude of the flux at the edge of the fuel pin can be vastly different than that in the interior. The helper `pin` function can be used to subdivide regions into equal volume segments, as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "div_surfs_1 = [openmc.ZCylinder(r=1)]\n",
-    "div_1 = openmc.model.pin(div_surfs_1, [fuel, water], subdivisions={0: 10})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f9ae8572a90>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "div_1.plot(width=(2.0, 2.0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The innermost region has been divided into 10 equal volume regions. We can pass additional arguments to divide multiple regions, except for the region outside the last cylinder."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Register depletion chain\n",
-    "\n",
-    "The depletion chain we created can be registered into the OpenMC `cross_sections.xml` file, so we don't have to always pass the `chain_file` argument to the `Operator`. To do this, we create a `DataLibrary` using `openmc.data`. Without any arguments, the `from_xml` method will look for the file located at `OPENMC_CROSS_SECTIONS`. For this example, we will just create a bare library."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_lib = openmc.data.DataLibrary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_lib.register_file(\"./chain_simple.xml\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_lib.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<cross_sections>\r\n",
-      "  <depletion_chain path=\"chain_simple.xml\" type=\"depletion_chain\" />\r\n",
-      "</cross_sections>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat cross_sections.xml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This allows us to make an `Operator` simply with the geometry and settings arguments, provided we exported our library to `OPENMC_CROSS_SECTIONS`. For a problem where we built and registered a `Chain` using all the available nuclear data, we might see something like the following."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_op = openmc.deplete.Operator(geometry, settings)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3820"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(new_op.chain.nuclide_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'H7', 'He3', 'He4', 'He5']"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[nuc.name for nuc in new_op.chain.nuclides[:10]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Ds268',\n",
-       " 'Ds269',\n",
-       " 'Ds270',\n",
-       " 'Ds270_m1',\n",
-       " 'Ds271',\n",
-       " 'Ds271_m1',\n",
-       " 'Ds272',\n",
-       " 'Ds273',\n",
-       " 'Ds279_m1',\n",
-       " 'Rg272']"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[nuc.name for nuc in new_op.chain.nuclides[-10:]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Choice of depletion step size\n",
-    "\n",
-    "A general rule of thumb is to use depletion step sizes around 2 MWd/kgHM, where kgHM is really the initial heavy metal mass in kg. If your problem includes integral burnable absorbers, these typically require shorter time steps at or below 1 MWd/kgHM. These are typically valid for the predictor scheme, as the point of recent schemes is to extend this step size. A good convergence study, where the step size is decreased until some convergence metric is satisfied, is a beneficial exercise.\n",
-    "\n",
-    "We can use the `Operator` to determine our maximum step size using this recommendation. The `heavy_metal` attribute returns the mass of initial heavy metal in g, which, using our power, can be used to compute this step size. $$\\frac{2\\,MWd}{kgHM} = \\frac{P\\times\\Delta}{hm_{op}}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5.080339195584719"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "operator.heavy_metal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_step = 2 * operator.heavy_metal / power * 1E3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\"Maximum\" depletion step:  58.4 [d]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\\"Maximum\\\" depletion step: {:5.3} [d]\".format(max_step))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Alternatively, if we were provided the power density of our problem, we can provide this directly with `openmc.deplete.PredictorIntegrator(operator, time_steps, power_density=pdens)`. The values of `power` and `power_density` do not have to be scalars. For problems with variable power, we can provide an iterable with the same number of elements as `time_steps`."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/post-processing.ipynb b/examples/jupyter/post-processing.ipynb
deleted file mode 100644
index 8684ccaae..000000000
--- a/examples/jupyter/post-processing.ipynb
+++ /dev/null
@@ -1,992 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Post Processing\n",
-    "This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigenvalue calculation. The problem we will use is a simple reflected pin-cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from IPython.display import Image\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6 enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_nuclide('U235', 3.7503e-4)\n",
-    "fuel.add_nuclide('U238', 2.2625e-2)\n",
-    "fuel.add_nuclide('O16', 4.6007e-2)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_nuclide('H1', 4.9457e-2)\n",
-    "water.add_nuclide('O16', 2.4732e-2)\n",
-    "water.add_nuclide('B10', 8.0042e-6)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we can now create a materials file object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection\n",
-    "materials = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n",
-    "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n",
-    "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n",
-    "max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
-    "\n",
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "fuel_cell.fill = fuel\n",
-    "fuel_cell.region = -fuel_outer_radius\n",
-    "pin_cell_universe.add_cell(fuel_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "pin_cell_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "pin_cell_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell')\n",
-    "root_cell.fill = pin_cell_universe\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "geometry = openmc.Geometry(root_universe)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 90 active batches each with 5000 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "settings = openmc.Settings()\n",
-    "settings.batches = 100\n",
-    "settings.inactive = 10\n",
-    "settings.particles = 5000\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot = openmc.Plot.from_geometry(geometry)\n",
-    "plot.pixels = (250, 250)\n",
-    "plot.to_ipython_image()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a 2D mesh tally."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate an empty Tallies object\n",
-    "tallies = openmc.Tallies()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create mesh which will be used for tally\n",
-    "mesh = openmc.RegularMesh()\n",
-    "mesh.dimension = [100, 100]\n",
-    "mesh.lower_left = [-0.63, -0.63]\n",
-    "mesh.upper_right = [0.63, 0.63]\n",
-    "\n",
-    "# Create mesh filter for tally\n",
-    "mesh_filter = openmc.MeshFilter(mesh)\n",
-    "\n",
-    "# Create mesh tally to score flux and fission rate\n",
-    "tally = openmc.Tally(name='flux')\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['flux', 'fission']\n",
-    "tallies.append(tally)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Export to \"tallies.xml\"\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 15:28:06\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    1.05252\n",
-      "        2/1    1.03787\n",
-      "        3/1    1.01943\n",
-      "        4/1    1.03989\n",
-      "        5/1    1.06679\n",
-      "        6/1    1.03713\n",
-      "        7/1    1.02400\n",
-      "        8/1    1.04289\n",
-      "        9/1    1.05130\n",
-      "       10/1    1.00878\n",
-      "       11/1    1.06773\n",
-      "       12/1    1.03922    1.05347 +/- 0.01426\n",
-      "       13/1    1.05156    1.05283 +/- 0.00826\n",
-      "       14/1    1.06049    1.05475 +/- 0.00614\n",
-      "       15/1    1.01018    1.04583 +/- 0.01010\n",
-      "       16/1    1.04020    1.04490 +/- 0.00830\n",
-      "       17/1    1.05579    1.04645 +/- 0.00719\n",
-      "       18/1    1.01592    1.04264 +/- 0.00730\n",
-      "       19/1    1.06881    1.04554 +/- 0.00707\n",
-      "       20/1    1.02985    1.04397 +/- 0.00651\n",
-      "       21/1    1.01496    1.04134 +/- 0.00645\n",
-      "       22/1    1.05330    1.04233 +/- 0.00598\n",
-      "       23/1    1.05170    1.04305 +/- 0.00554\n",
-      "       24/1    1.02888    1.04204 +/- 0.00523\n",
-      "       25/1    1.04083    1.04196 +/- 0.00487\n",
-      "       26/1    1.01235    1.04011 +/- 0.00492\n",
-      "       27/1    1.02785    1.03939 +/- 0.00468\n",
-      "       28/1    1.04556    1.03973 +/- 0.00442\n",
-      "       29/1    1.05400    1.04048 +/- 0.00425\n",
-      "       30/1    1.06213    1.04157 +/- 0.00417\n",
-      "       31/1    0.99934    1.03955 +/- 0.00445\n",
-      "       32/1    1.04433    1.03977 +/- 0.00425\n",
-      "       33/1    1.05184    1.04030 +/- 0.00409\n",
-      "       34/1    1.03971    1.04027 +/- 0.00392\n",
-      "       35/1    1.05272    1.04077 +/- 0.00379\n",
-      "       36/1    1.06881    1.04185 +/- 0.00380\n",
-      "       37/1    1.03344    1.04154 +/- 0.00367\n",
-      "       38/1    1.04726    1.04174 +/- 0.00354\n",
-      "       39/1    1.01440    1.04080 +/- 0.00354\n",
-      "       40/1    1.03534    1.04062 +/- 0.00343\n",
-      "       41/1    1.04429    1.04073 +/- 0.00332\n",
-      "       42/1    1.02142    1.04013 +/- 0.00327\n",
-      "       43/1    1.03895    1.04010 +/- 0.00317\n",
-      "       44/1    1.05985    1.04068 +/- 0.00313\n",
-      "       45/1    1.04737    1.04087 +/- 0.00304\n",
-      "       46/1    1.04796    1.04106 +/- 0.00297\n",
-      "       47/1    1.06708    1.04177 +/- 0.00297\n",
-      "       48/1    1.06523    1.04238 +/- 0.00295\n",
-      "       49/1    0.99626    1.04120 +/- 0.00311\n",
-      "       50/1    1.04077    1.04119 +/- 0.00303\n",
-      "       51/1    1.06327    1.04173 +/- 0.00301\n",
-      "       52/1    1.06508    1.04229 +/- 0.00299\n",
-      "       53/1    1.03689    1.04216 +/- 0.00292\n",
-      "       54/1    1.02899    1.04186 +/- 0.00287\n",
-      "       55/1    1.03267    1.04166 +/- 0.00281\n",
-      "       56/1    1.05790    1.04201 +/- 0.00277\n",
-      "       57/1    1.04353    1.04204 +/- 0.00271\n",
-      "       58/1    1.04657    1.04214 +/- 0.00266\n",
-      "       59/1    1.02914    1.04187 +/- 0.00261\n",
-      "       60/1    1.04882    1.04201 +/- 0.00257\n",
-      "       61/1    1.01905    1.04156 +/- 0.00255\n",
-      "       62/1    1.03995    1.04153 +/- 0.00251\n",
-      "       63/1    1.05377    1.04176 +/- 0.00247\n",
-      "       64/1    1.02909    1.04153 +/- 0.00243\n",
-      "       65/1    1.06892    1.04202 +/- 0.00244\n",
-      "       66/1    1.04216    1.04203 +/- 0.00240\n",
-      "       67/1    1.03473    1.04190 +/- 0.00236\n",
-      "       68/1    1.04114    1.04188 +/- 0.00232\n",
-      "       69/1    1.04955    1.04201 +/- 0.00228\n",
-      "       70/1    1.05464    1.04222 +/- 0.00225\n",
-      "       71/1    1.02859    1.04200 +/- 0.00223\n",
-      "       72/1    1.05387    1.04219 +/- 0.00220\n",
-      "       73/1    1.05039    1.04232 +/- 0.00217\n",
-      "       74/1    1.04338    1.04234 +/- 0.00213\n",
-      "       75/1    1.05838    1.04259 +/- 0.00211\n",
-      "       76/1    1.03831    1.04252 +/- 0.00208\n",
-      "       77/1    1.03555    1.04242 +/- 0.00205\n",
-      "       78/1    1.05684    1.04263 +/- 0.00204\n",
-      "       79/1    1.04267    1.04263 +/- 0.00201\n",
-      "       80/1    1.05813    1.04285 +/- 0.00199\n",
-      "       81/1    1.03512    1.04274 +/- 0.00196\n",
-      "       82/1    1.07081    1.04313 +/- 0.00198\n",
-      "       83/1    1.04476    1.04315 +/- 0.00195\n",
-      "       84/1    1.05153    1.04327 +/- 0.00192\n",
-      "       85/1    1.03939    1.04322 +/- 0.00190\n",
-      "       86/1    1.04218    1.04320 +/- 0.00187\n",
-      "       87/1    1.03688    1.04312 +/- 0.00185\n",
-      "       88/1    1.03480    1.04301 +/- 0.00183\n",
-      "       89/1    1.05089    1.04311 +/- 0.00181\n",
-      "       90/1    1.06251    1.04336 +/- 0.00180\n",
-      "       91/1    1.04054    1.04332 +/- 0.00178\n",
-      "       92/1    1.05340    1.04344 +/- 0.00176\n",
-      "       93/1    1.05938    1.04364 +/- 0.00175\n",
-      "       94/1    1.02741    1.04344 +/- 0.00174\n",
-      "       95/1    1.08249    1.04390 +/- 0.00178\n",
-      "       96/1    1.02858    1.04372 +/- 0.00177\n",
-      "       97/1    1.03983    1.04368 +/- 0.00175\n",
-      "       98/1    1.04715    1.04372 +/- 0.00173\n",
-      "       99/1    1.07443    1.04406 +/- 0.00175\n",
-      "      100/1    1.04461    1.04407 +/- 0.00173\n",
-      " Creating state point statepoint.100.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.4568e-01 seconds\n",
-      "   Reading cross sections          = 2.3233e-01 seconds\n",
-      " Total time in simulation          = 1.1761e+02 seconds\n",
-      "   Time in transport only          = 1.1757e+02 seconds\n",
-      "   Time in inactive batches        = 2.0641e+00 seconds\n",
-      "   Time in active batches          = 1.1554e+02 seconds\n",
-      "   Time synchronizing fission bank = 2.1808e-02 seconds\n",
-      "     Sampling source sites         = 1.8421e-02 seconds\n",
-      "     SEND/RECV source sites        = 3.3183e-03 seconds\n",
-      "   Time accumulating tallies       = 2.6283e-03 seconds\n",
-      "   Time writing statepoints        = 4.2804e-03 seconds\n",
-      " Total time for finalization       = 2.2731e-02 seconds\n",
-      " Total time elapsed                = 1.1789e+02 seconds\n",
-      " Calculation Rate (inactive)       = 24223.6 particles/second\n",
-      " Calculation Rate (active)         = 3894.66 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.04491 +/- 0.00149\n",
-      " k-effective (Track-length)  = 1.04407 +/- 0.00173\n",
-      " k-effective (Absorption)    = 1.04203 +/- 0.00169\n",
-      " Combined k-effective        = 1.04355 +/- 0.00131\n",
-      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC!\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load the statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.100.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we need to get the tally, which can be done with the ``StatePoint.get_tally(...)`` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tally\n",
-      "\tID             =\t1\n",
-      "\tName           =\tflux\n",
-      "\tFilters        =\tMeshFilter\n",
-      "\tNuclides       =\ttotal\n",
-      "\tScores         =\t['flux', 'fission']\n",
-      "\tEstimator      =\ttracklength\n"
-     ]
-    }
-   ],
-   "source": [
-    "tally = sp.get_tally(scores=['flux'])\n",
-    "print(tally)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](../methods/tallies.rst#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0.41279586, 0.        ]],\n",
-       "\n",
-       "       [[0.41176924, 0.        ]],\n",
-       "\n",
-       "       [[0.41096843, 0.        ]],\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "       [[0.4095409 , 0.        ]],\n",
-       "\n",
-       "       [[0.40836217, 0.        ]],\n",
-       "\n",
-       "       [[0.40852022, 0.        ]]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tally.sum"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, the mean and standard deviation of the mean are usually what you are more interested in. The Tally class also has properties ``mean`` and ``std_dev`` which automatically calculate these statistics on-the-fly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(10000, 1, 2)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(array([[[0.00458662, 0.        ]],\n",
-       " \n",
-       "        [[0.00457521, 0.        ]],\n",
-       " \n",
-       "        [[0.00456632, 0.        ]],\n",
-       " \n",
-       "        ...,\n",
-       " \n",
-       "        [[0.00455045, 0.        ]],\n",
-       " \n",
-       "        [[0.00453736, 0.        ]],\n",
-       " \n",
-       "        [[0.00453911, 0.        ]]]),\n",
-       " array([[[1.74741992e-05, 0.00000000e+00]],\n",
-       " \n",
-       "        [[1.68457472e-05, 0.00000000e+00]],\n",
-       " \n",
-       "        [[1.75888801e-05, 0.00000000e+00]],\n",
-       " \n",
-       "        ...,\n",
-       " \n",
-       "        [[1.79971274e-05, 0.00000000e+00]],\n",
-       " \n",
-       "        [[1.89308740e-05, 0.00000000e+00]],\n",
-       " \n",
-       "        [[1.75231302e-05, 0.00000000e+00]]]))"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "print(tally.mean.shape)\n",
-    "(tally.mean, tally.std_dev)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The tally data has three dimensions: one for filter combinations, one for nuclides, and one for scores. We see that there are 10000 filter combinations (corresponding to the 100 x 100 mesh bins), a single nuclide (since none was specified), and two scores. If we only want to look at a single score, we can use the ``get_slice(...)`` method as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tally\n",
-      "\tID             =\t2\n",
-      "\tName           =\tflux\n",
-      "\tFilters        =\tMeshFilter\n",
-      "\tNuclides       =\ttotal\n",
-      "\tScores         =\t['flux']\n",
-      "\tEstimator      =\ttracklength\n"
-     ]
-    }
-   ],
-   "source": [
-    "flux = tally.get_slice(scores=['flux'])\n",
-    "fission = tally.get_slice(scores=['fission'])\n",
-    "print(flux)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get the bins into a form that we can plot, we can simply change the shape of the array since it is a numpy array."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "flux.std_dev.shape = (100, 100)\n",
-    "flux.mean.shape = (100, 100)\n",
-    "fission.std_dev.shape = (100, 100)\n",
-    "fission.mean.shape = (100, 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f3f6e467e10>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.subplot(121)\n",
-    "fig.imshow(flux.mean)\n",
-    "fig2 = plt.subplot(122)\n",
-    "fig2.imshow(fission.mean)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's say we want to look at the distribution of relative errors of our tally bins for flux. First we create a new variable called ``relative_error`` and set it to the ratio of the standard deviation and the mean, being careful not to divide by zero in case some bins were never scored to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPFklEQVR4nO3df6zddX3H8ed7LRQVR6E0TdeWtUwyo2YD1iELy0LolkFLKH+gI1m0M02aTNxQtkiZyXRLlhT3AzBZMB1Vy2QDhiQ0arYwwGT7w2oLyK+OcS3FtilQCaDOKOt474/zKZxe7u09t/f8uPft85Gc3O/38/18v+fz4du8+JzP93u+JzITSVItPzfqBkiS+s9wl6SCDHdJKshwl6SCDHdJKmj+qBsAcOaZZ+bKlStH3QxJmlN27979/cxcPNG2WRHuK1euZNeuXaNuhiTNKRHx3GTbnJaRpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kqyHCXpIIMd0kqyHCXpIJmxTdUVdfKzV+bsHzflnVDbon0s8WRuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQV5H3uGgnvf5cGy5G7JBVkuEtSQYa7JBVkuEtSQYa7JBVkuEtSQYa7JBXkfe7qi8nuW5c0Go7cJakgw12SCjLcJakgw12SCvKCqmYVHygm9Ycjd0kqyHCXpIIMd0kqyHCXpIJ6CveI+EREPBkRT0TEP0fEKRGxKiJ2RsRYRNwVESe3ugva+ljbvnKgPZAkvcWU4R4Ry4A/BlZn5vuAecDVwI3ATZn5LuBlYGPbZSPwciu/qdWTJA1Rr9My84G3RcR84O3AIeAS4J62fTtwZVte39Zp29dERPSltZKknkwZ7pl5EPgb4Ht0Qv1VYDfwSmYeadUOAMva8jJgf9v3SKu/aPxxI2JTROyKiF2HDx+eaT8kSV16mZY5nc5ofBXwC8A7gEtn+saZuTUzV2fm6sWLF8/0cJKkLr1My/w28GxmHs7M/wXuBS4CFrZpGoDlwMG2fBBYAdC2nwa81NdWS5KOq5dw/x5wYUS8vc2drwGeAh4Crmp1NgD3teUdbZ22/cHMzP41WZI0lV7m3HfSuTD6MPB422crcD1wXUSM0ZlT39Z22QYsauXXAZsH0G5J0nH09OCwzPw08OlxxXuBCyao+xPgAzNvmmYjf3FJmhv8hqokFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFWS4S1JBhrskFdTTs2WkUZvsmTb7tqwbckukucGRuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQV5FMhNaHJnsIoaW5w5C5JBRnuklSQ4S5JBRnuklSQ4S5JBRnuklSQ4S5JBXmfu+a0ye7H37dl3ZBbIs0ujtwlqaCewj0iFkbEPRHxXxGxJyJ+IyLOiIj7I+KZ9vf0Vjci4nMRMRYRj0XE+YPtgiRpvF5H7rcA/5qZ7wZ+FdgDbAYeyMxzgAfaOsBlwDnttQm4ta8tliRNacpwj4jTgN8CtgFk5muZ+QqwHtjeqm0HrmzL64Hbs+ObwMKIWNrndkuSjqOXkfsq4DDwxYh4JCJui4h3AEsy81Cr8zywpC0vA/Z37X+glR0jIjZFxK6I2HX48OET74Ek6S16Cff5wPnArZl5HvA/vDkFA0BmJpDTeePM3JqZqzNz9eLFi6ezqyRpCr2E+wHgQGbubOv30An7F45Ot7S/L7btB4EVXfsvb2WSpCGZMtwz83lgf0T8citaAzwF7AA2tLINwH1teQfw4XbXzIXAq13TN5KkIej1S0x/BNwREScDe4GP0Pkfw90RsRF4Dvhgq/t1YC0wBvy41ZUkDVFP4Z6ZjwKrJ9i0ZoK6CVwzs2ZJkmbCb6hKUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkGGuyQVZLhLUkG9/liHClq5+WujboKkAXHkLkkFOXJXSZN9Ktm3Zd2QWyKNhiN3SSrIcJekggx3SSrIcJekggx3SSrIcJekggx3SSrIcJekggx3SSrIcJekggx3SSrIcJekggx3SSrIcJekggx3SSrIcJekggx3SSrIcJekgnr+mb2ImAfsAg5m5uURsQq4E1gE7AY+lJmvRcQC4Hbg14CXgN/LzH19b7l65g9hSz97pjNyvxbY07V+I3BTZr4LeBnY2Mo3Ai+38ptaPUnSEPUU7hGxHFgH3NbWA7gEuKdV2Q5c2ZbXt3Xa9jWtviRpSHodud8MfBJ4va0vAl7JzCNt/QCwrC0vA/YDtO2vtvrHiIhNEbErInYdPnz4xFovSZrQlHPuEXE58GJm7o6Ii/v1xpm5FdgKsHr16uzXcaXjmez6w74t64bcEmmwermgehFwRUSsBU4Bfh64BVgYEfPb6Hw5cLDVPwisAA5ExHzgNDoXViVJQzLltExm3pCZyzNzJXA18GBm/j7wEHBVq7YBuK8t72jrtO0PZqYjc0kaopnc5349cF1EjNGZU9/WyrcBi1r5dcDmmTVRkjRdPd/nDpCZ3wC+0Zb3AhdMUOcnwAf60DZJ0gnyG6qSVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVJDhLkkFGe6SVNC0vqGq2c1fXJJ0lCN3SSrIcJekggx3SSrIcJekggx3SSrIu2Uk/G1V1ePIXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKMtwlqSDDXZIKmjLcI2JFRDwUEU9FxJMRcW0rPyMi7o+IZ9rf01t5RMTnImIsIh6LiPMH3QlJ0rF6+YHsI8CfZObDEfFOYHdE3A/8AfBAZm6JiM3AZuB64DLgnPZ6P3Br+yvNOZP9cDb449ma3aYcuWfmocx8uC3/ENgDLAPWA9tbte3AlW15PXB7dnwTWBgRS/vdcEnS5HoZub8hIlYC5wE7gSWZeahteh5Y0paXAfu7djvQyg51lRERm4BNAGedddZ02/0z7XijSUmCaVxQjYhTga8AH8/MH3Rvy8wEcjpvnJlbM3N1Zq5evHjxdHaVJE2hp3CPiJPoBPsdmXlvK37h6HRL+/tiKz8IrOjafXkrkyQNSS93ywSwDdiTmX/XtWkHsKEtbwDu6yr/cLtr5kLg1a7pG0nSEPQy534R8CHg8Yh4tJX9GbAFuDsiNgLPAR9s274OrAXGgB8DH+lngyVJU5sy3DPzP4GYZPOaCeoncM0M2yVJmgG/oSpJBRnuklSQ4S5JBRnuklSQ4S5JBU3r8QOS3jTZYyB8oJhmA0fuklSQ4S5JBRnuklSQ4S5JBRnuklSQd8tIfeZdNJoNHLlLUkGGuyQV5LTMLOZvpUo6UY7cJakgw12SCjLcJakgw12SCvKC6izghVNJ/Wa4S0Pil5s0TE7LSFJBhrskFWS4S1JBhrskFWS4S1JBhrskFeStkNKIeYukBsGRuyQV5Mh9iPwmqqRhceQuSQU5cpdmKefiNROGuzTHGPrqheE+AM6tSxo1w10qwhG9uhnuM+AIXdJsNZBwj4hLgVuAecBtmbllEO8j6cQ50q+t7+EeEfOAvwd+BzgAfDsidmTmU/1+L0lTm+4nzH5+IvV/FKMziJH7BcBYZu4FiIg7gfXAnA13p1+k/vJTw+ANItyXAfu71g8A7x9fKSI2AZva6o8i4um2fCbw/QG0azao3Dewf3Nd3/sXNw62/jRVPH+/ONmGkV1QzcytwNbx5RGxKzNXj6BJA1e5b2D/5jr7V8sgHj9wEFjRtb68lUmShmQQ4f5t4JyIWBURJwNXAzsG8D6SpEn0fVomM49ExMeAf6NzK+QXMvPJaRziLVM1hVTuG9i/uc7+FRKZOeo2SJL6zEf+SlJBhrskFdT3cI+ISyPi6YgYi4jNE2xfEBF3te07I2Jl17YbWvnTEfG7rWxFRDwUEU9FxJMRcW1X/TMi4v6IeKb9Pb3f/Rlx/z4TEQcj4tH2WjsH+3dKRHwrIr7T+vcXXfVXtWOMtWOeXKhvX4qIZ7vO3bmD7Nsg+te1bV5EPBIRX+0qG+q5a+85zP4N/fz1XWb27UXnAup3gbOBk4HvAO8ZV+ejwOfb8tXAXW35Pa3+AmBVO848YClwfqvzTuC/jx4T+CywuS1vBm7sZ39mQf8+A/zpIPs0hP4FcGqrcxKwE7iwrd8NXN2WPw/8YaG+fQm4ai6fu679rgP+CfhqV9nQzt2I+jfU8zeIV79H7m88eiAzXwOOPnqg23pge1u+B1gTEdHK78zMn2bms8AYcEFmHsrMhwEy84fAHjrfgh1/rO3AlX3uz3jD7t+wDaJ/mZk/avVPaq9s+1zSjgGDP39D69sA+3A8fe8fQEQsB9YBtx09yAjOHQyxf1X0O9wnevTA+KB6o05mHgFeBRb1sm/7mHUenRESwJLMPNSWnweWzLgHxzfs/gF8LCIei4gvxOCnnQbSv/ax91HgReD+zNzZ9nmlHWOy9+qnYfbtqL9q5+6miFjQx75MZFD/Nm8GPgm83rV92OeOKdr4ljoz7N9Rwzx/fTdnLqhGxKnAV4CPZ+YPxm/PzmepOXtf5yT9uxX4JeBc4BDwt6Np3cxk5v9l5rl0vq18QUS8b8RN6pvj9O0G4N3ArwNnANePpoUnLiIuB17MzN2jbssgTNG/OX/++h3uvTx64I06ETEfOA146Xj7RsRJdILvjsy8t6vOCxGxtNVZSmf0NEhD7V9mvtDC43XgH2gfJQdoIP07KjNfAR4CLm37LGzHmOy9+mmYfaNNt2Vm/hT4InPz3F0EXBER++hMg1wSEV9m+OfumLYf5z371b9RnL/+6+cEPp1vvO6lc9Hi6EWP946rcw3HXvS4uy2/l2MveuzlzYtWtwM3T/B+f82xF1Q/28/+zIL+Le1a/gSdecO51r/FwMJW523AfwCXt/V/4diLch8t1Lel7W/Q+ei/Za6du3H7XsyxFxyHdu5G1L+hnr+B/DcbwElYS+eOj+8Cn2plfwlc0ZZPaf8wxoBvAWd37fuptt/TwGWt7DfpTLc8BjzaXmvbtkXAA8AzwL8DZwz8P9hw+/ePwONt2w66wn4O9e9XgEdaH54A/ryr/tntGGPtmAsK9e3Bdu6eAL5Mu6tmLvVv3LEv5tjwG+q5G0H/hn7++v3y8QOSVNCcuaAqSeqd4S5JBRnuklSQ4S5JBRnuklSQ4S5JBRnuklTQ/wMXWF17qjWDXQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Determine relative error\n",
-    "relative_error = np.zeros_like(flux.std_dev)\n",
-    "nonzero = flux.mean > 0\n",
-    "relative_error[nonzero] = flux.std_dev[nonzero] / flux.mean[nonzero]\n",
-    "\n",
-    "# distribution of relative errors\n",
-    "ret = plt.hist(relative_error[nonzero], bins=50)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Source Sites"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Source sites can be accessed from the ``source`` property. As shown below, the source sites are represented as a numpy array with a structured datatype."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([((0.20665803,  0.15081559, -0.57355059), ( 0.49473673,  0.67921184, -0.54213177), 2077978.15846043, 1., 0, 0, 0),\n",
-       "       ((0.02302023, -0.02944101, -0.45025678), ( 0.53648981,  0.51827967,  0.66600666),  206149.19886773, 1., 0, 0, 0),\n",
-       "       ((0.19282602,  0.25572118, -0.11262284), ( 0.75853515,  0.55187444,  0.34649535), 1153689.72115824, 1., 0, 0, 0),\n",
-       "       ...,\n",
-       "       ((0.14718062, -0.23794414, -0.17253588), (-0.27354594,  0.15713747,  0.94893648),  350211.6847914 , 1., 0, 0, 0),\n",
-       "       ((0.14718062, -0.23794414, -0.17253588), ( 0.16444666, -0.98360966,  0.0739549 ), 3259134.69914602, 1., 0, 0, 0),\n",
-       "       ((0.14718062, -0.23794414, -0.17253588), ( 0.16444666, -0.98360966,  0.0739549 ), 3259134.69914602, 1., 0, 0, 0)],\n",
-       "      dtype={'names':['r','u','E','wgt','delayed_group','surf_id','particle'], 'formats':[[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],[('x', '<f8'), ('y', '<f8'), ('z', '<f8')],'<f8','<f8','<i4','<i4','<i4'], 'offsets':[0,24,48,56,64,68,72], 'itemsize':96})"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sp.source"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want, say, only the energies from the source sites, we can simply index the source array with the name of the field:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2077978.15846043,  206149.19886773, 1153689.72115824, ...,\n",
-       "        350211.6847914 , 3259134.69914602, 3259134.69914602])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sp.source['E']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can look at things like the energy distribution of source sites. Note that we don't directly use the ``matplotlib.pyplot.hist`` method since our binning is logarithmic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'Probability/eV')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create log-spaced energy bins from 1 keV to 10 MeV\n",
-    "energy_bins = np.logspace(3,7)\n",
-    "\n",
-    "# Calculate pdf for source energies\n",
-    "probability, bin_edges = np.histogram(sp.source['E'], energy_bins, density=True)\n",
-    "\n",
-    "# Make sure integrating the PDF gives us unity\n",
-    "print(sum(probability*np.diff(energy_bins)))\n",
-    "\n",
-    "# Plot source energy PDF\n",
-    "plt.semilogx(energy_bins[:-1], probability*np.diff(energy_bins), drawstyle='steps')\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Probability/eV')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also look at the spatial distribution of the sites. To make the plot a little more interesting, we can also include the direction of the particle emitted from the source and color each source by the logarithm of its energy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-0.5, 0.5)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.quiver(sp.source['r']['x'], sp.source['r']['y'],\n",
-    "           sp.source['u']['x'], sp.source['u']['y'],\n",
-    "           np.log(sp.source['E']), cmap='jet', scale=20.0)\n",
-    "plt.colorbar()\n",
-    "plt.xlim((-0.5,0.5))\n",
-    "plt.ylim((-0.5,0.5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Close the statepoint file as a matter of best practice\n",
-    "sp.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/search.ipynb b/examples/jupyter/search.ipynb
deleted file mode 100644
index d2163f8e2..000000000
--- a/examples/jupyter/search.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Criticality Search\n",
-    "This notebook illustrates the usage of the OpenMC Python API's generic eigenvalue search capability.  In this Notebook, we will do a critical boron concentration search of a typical PWR pin cell.\n",
-    "\n",
-    "To use the search functionality, we must create a function which creates our model according to the input parameter we wish to search for (in this case, the boron concentration).  \n",
-    "\n",
-    "This notebook will first create that function, and then, run the search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize third-party libraries and the OpenMC Python API\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "import openmc\n",
-    "import openmc.model\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Parametrized Model\n",
-    "\n",
-    "To perform the search we will use the `openmc.search_for_keff` function.  This function requires a different function be defined which creates an parametrized model to analyze. This model is required to be stored in an `openmc.model.Model` object. The first parameter of this function will be modified during the search process for our critical eigenvalue.\n",
-    "\n",
-    "Our model will be a pin-cell from the [Multi-Group Mode Part II](mg-mode-part-ii.ipynb) assembly, except this time the entire model building process will be contained within a function, and the Boron concentration will be parametrized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create the model. `ppm_Boron` will be the parametric variable.\n",
-    "\n",
-    "def build_model(ppm_Boron):\n",
-    "    \n",
-    "    # Create the pin materials\n",
-    "    fuel = openmc.Material(name='1.6% Fuel')\n",
-    "    fuel.set_density('g/cm3', 10.31341)\n",
-    "    fuel.add_element('U', 1., enrichment=1.6)\n",
-    "    fuel.add_element('O', 2.)\n",
-    "\n",
-    "    zircaloy = openmc.Material(name='Zircaloy')\n",
-    "    zircaloy.set_density('g/cm3', 6.55)\n",
-    "    zircaloy.add_element('Zr', 1.)\n",
-    "\n",
-    "    water = openmc.Material(name='Borated Water')\n",
-    "    water.set_density('g/cm3', 0.741)\n",
-    "    water.add_element('H', 2.)\n",
-    "    water.add_element('O', 1.)\n",
-    "\n",
-    "    # Include the amount of boron in the water based on the ppm,\n",
-    "    # neglecting the other constituents of boric acid\n",
-    "    water.add_element('B', ppm_Boron * 1e-6)\n",
-    "    \n",
-    "    # Instantiate a Materials object\n",
-    "    materials = openmc.Materials([fuel, zircaloy, water])\n",
-    "    \n",
-    "    # Create cylinders for the fuel and clad\n",
-    "    fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n",
-    "    clad_outer_radius = openmc.ZCylinder(r=0.45720)\n",
-    "\n",
-    "    # Create boundary planes to surround the geometry\n",
-    "    min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n",
-    "    max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n",
-    "    min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n",
-    "    max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n",
-    "\n",
-    "    # Create fuel Cell\n",
-    "    fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "    fuel_cell.fill = fuel\n",
-    "    fuel_cell.region = -fuel_outer_radius\n",
-    "\n",
-    "    # Create a clad Cell\n",
-    "    clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "    clad_cell.fill = zircaloy\n",
-    "    clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "\n",
-    "    # Create a moderator Cell\n",
-    "    moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "    moderator_cell.fill = water\n",
-    "    moderator_cell.region = +clad_outer_radius & (+min_x & -max_x & +min_y & -max_y)\n",
-    "\n",
-    "    # Create root Universe\n",
-    "    root_universe = openmc.Universe(name='root universe')\n",
-    "    root_universe.add_cells([fuel_cell, clad_cell, moderator_cell])\n",
-    "\n",
-    "    # Create Geometry and set root universe\n",
-    "    geometry = openmc.Geometry(root_universe)\n",
-    "    \n",
-    "    # Instantiate a Settings object\n",
-    "    settings = openmc.Settings()\n",
-    "    \n",
-    "    # Set simulation parameters\n",
-    "    settings.batches = 300\n",
-    "    settings.inactive = 20\n",
-    "    settings.particles = 1000\n",
-    "    \n",
-    "    # Create an initial uniform spatial source distribution over fissionable zones\n",
-    "    bounds = [-0.63, -0.63, -10, 0.63, 0.63, 10.]\n",
-    "    uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "    settings.source = openmc.source.Source(space=uniform_dist)\n",
-    "    \n",
-    "    # We dont need a tallies file so dont waste the disk input/output time\n",
-    "    settings.output = {'tallies': False}\n",
-    "    \n",
-    "    model = openmc.model.Model(geometry, materials, settings)\n",
-    "    \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Search for the Critical Boron Concentration\n",
-    "\n",
-    "To perform the search we imply call the `openmc.search_for_keff` function and pass in the relvant arguments.  For our purposes we will be passing in the model building function (`build_model` defined above), a bracketed range for the expected critical Boron concentration (1,000 to 2,500 ppm), the tolerance, and the method we wish to use. \n",
-    "\n",
-    "Instead of the bracketed range we could have used a single initial guess, but have elected not to in this example. Finally, due to the high noise inherent in using as few histories as are used in this example, our tolerance on the final keff value will be rather large (1.e-2) and the default 'bisection' method will be used for the search."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Iteration: 1; Guess of 1.00e+03 produced a keff of 1.08504 +/- 0.00169\n",
-      "Iteration: 2; Guess of 2.50e+03 produced a keff of 0.95243 +/- 0.00158\n",
-      "Iteration: 3; Guess of 1.75e+03 produced a keff of 1.01269 +/- 0.00163\n",
-      "Iteration: 4; Guess of 2.12e+03 produced a keff of 0.98165 +/- 0.00155\n",
-      "Iteration: 5; Guess of 1.94e+03 produced a keff of 0.99773 +/- 0.00158\n",
-      "Iteration: 6; Guess of 1.84e+03 produced a keff of 1.00872 +/- 0.00170\n",
-      "Iteration: 7; Guess of 1.89e+03 produced a keff of 1.00462 +/- 0.00154\n",
-      "Iteration: 8; Guess of 1.91e+03 produced a keff of 1.00202 +/- 0.00154\n",
-      "Iteration: 9; Guess of 1.93e+03 produced a keff of 0.99816 +/- 0.00155\n",
-      "Critical Boron Concentration: 1926 ppm\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Perform the search\n",
-    "crit_ppm, guesses, keffs = openmc.search_for_keff(build_model, bracket=[1000., 2500.],\n",
-    "                                                  tol=1e-2, print_iterations=True)\n",
-    "\n",
-    "print('Critical Boron Concentration: {:4.0f} ppm'.format(crit_ppm))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, the `openmc.search_for_keff` function also provided us with `List`s of the guesses and corresponding keff values generated during the search process with OpenMC. Let's use that information to make a quick plot of the value of keff versus the boron concentration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x324 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(8, 4.5))\n",
-    "plt.title('Eigenvalue versus Boron Concentration')\n",
-    "# Create a scatter plot using the mean value of keff\n",
-    "plt.scatter(guesses, [keffs[i].nominal_value for i in range(len(keffs))])\n",
-    "plt.xlabel('Boron Concentration [ppm]')\n",
-    "plt.ylabel('Eigenvalue')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "We see a nearly linear reactivity coefficient for the boron concentration, exactly as one would expect for a pure 1/v absorber at small concentrations."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/jupyter/tally-arithmetic.ipynb b/examples/jupyter/tally-arithmetic.ipynb
deleted file mode 100644
index 5f04c6884..000000000
--- a/examples/jupyter/tally-arithmetic.ipynb
+++ /dev/null
@@ -1,1768 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tally Arithmetic\n",
-    "This notebook shows the how tallies can be combined (added, subtracted, multiplied, etc.) using the Python API in order to create derived tallies. Since no covariance information is obtained, it is assumed that tallies are completely independent of one another when propagating uncertainties. The target problem is a simple pin cell."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "\n",
-    "from IPython.display import Image\n",
-    "import numpy as np\n",
-    "import openmc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate Input Files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1.6 enriched fuel\n",
-    "fuel = openmc.Material(name='1.6% Fuel')\n",
-    "fuel.set_density('g/cm3', 10.31341)\n",
-    "fuel.add_nuclide('U235', 3.7503e-4)\n",
-    "fuel.add_nuclide('U238', 2.2625e-2)\n",
-    "fuel.add_nuclide('O16', 4.6007e-2)\n",
-    "\n",
-    "# borated water\n",
-    "water = openmc.Material(name='Borated Water')\n",
-    "water.set_density('g/cm3', 0.740582)\n",
-    "water.add_nuclide('H1', 4.9457e-2)\n",
-    "water.add_nuclide('O16', 2.4732e-2)\n",
-    "water.add_nuclide('B10', 8.0042e-6)\n",
-    "\n",
-    "# zircaloy\n",
-    "zircaloy = openmc.Material(name='Zircaloy')\n",
-    "zircaloy.set_density('g/cm3', 6.55)\n",
-    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our three materials, we can now create a materials file object that can be exported to an actual XML file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate a Materials collection\n",
-    "materials_file = openmc.Materials([fuel, water, zircaloy])\n",
-    "\n",
-    "# Export to \"materials.xml\"\n",
-    "materials_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six planes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create cylinders for the fuel and clad\n",
-    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
-    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
-    "\n",
-    "# Create boundary planes to surround the geometry\n",
-    "# Use both reflective and vacuum boundaries to make life interesting\n",
-    "min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n",
-    "min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n",
-    "max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-100., boundary_type='vacuum')\n",
-    "max_z = openmc.ZPlane(z0=+100., boundary_type='vacuum')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a Universe to encapsulate a fuel pin\n",
-    "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
-    "\n",
-    "# Create fuel Cell\n",
-    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
-    "fuel_cell.fill = fuel\n",
-    "fuel_cell.region = -fuel_outer_radius\n",
-    "pin_cell_universe.add_cell(fuel_cell)\n",
-    "\n",
-    "# Create a clad Cell\n",
-    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
-    "clad_cell.fill = zircaloy\n",
-    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
-    "pin_cell_universe.add_cell(clad_cell)\n",
-    "\n",
-    "# Create a moderator Cell\n",
-    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
-    "moderator_cell.fill = water\n",
-    "moderator_cell.region = +clad_outer_radius\n",
-    "pin_cell_universe.add_cell(moderator_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create root Cell\n",
-    "root_cell = openmc.Cell(name='root cell')\n",
-    "root_cell.fill = pin_cell_universe\n",
-    "\n",
-    "# Add boundary planes\n",
-    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
-    "\n",
-    "# Create root Universe\n",
-    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
-    "root_universe.add_cell(root_cell)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Geometry and set root Universe\n",
-    "geometry = openmc.Geometry(root_universe)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Export to \"geometry.xml\"\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 active batches each with 2500 particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# OpenMC simulation parameters\n",
-    "batches = 20\n",
-    "inactive = 5\n",
-    "particles = 2500\n",
-    "\n",
-    "# Instantiate a Settings object\n",
-    "settings_file = openmc.Settings()\n",
-    "settings_file.batches = batches\n",
-    "settings_file.inactive = inactive\n",
-    "settings_file.particles = particles\n",
-    "settings_file.output = {'tallies': True}\n",
-    "\n",
-    "# Create an initial uniform spatial source distribution over fissionable zones\n",
-    "bounds = [-0.63, -0.63, -100., 0.63, 0.63, 100.]\n",
-    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
-    "settings_file.source = openmc.Source(space=uniform_dist)\n",
-    "\n",
-    "# Export to \"settings.xml\"\n",
-    "settings_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Instantiate a Plot\n",
-    "plot = openmc.Plot(plot_id=1)\n",
-    "plot.filename = 'materials-xy'\n",
-    "plot.origin = [0, 0, 0]\n",
-    "plot.width = [1.26, 1.26]\n",
-    "plot.pixels = [250, 250]\n",
-    "plot.color_by = 'material'\n",
-    "\n",
-    "# Show plot\n",
-    "openmc.plot_inline(plot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate an empty Tallies object\n",
-    "tallies_file = openmc.Tallies()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create Tallies to compute microscopic multi-group cross-sections\n",
-    "\n",
-    "# Instantiate energy filter for multi-group cross-section Tallies\n",
-    "energy_filter = openmc.EnergyFilter([0., 0.625, 20.0e6])\n",
-    "\n",
-    "# Instantiate flux Tally in moderator and fuel\n",
-    "tally = openmc.Tally(name='flux')\n",
-    "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n",
-    "tally.filters.append(energy_filter)\n",
-    "tally.scores = ['flux']\n",
-    "tallies_file.append(tally)\n",
-    "\n",
-    "# Instantiate reaction rate Tally in fuel\n",
-    "tally = openmc.Tally(name='fuel rxn rates')\n",
-    "tally.filters = [openmc.CellFilter(fuel_cell)]\n",
-    "tally.filters.append(energy_filter)\n",
-    "tally.scores = ['nu-fission', 'scatter']\n",
-    "tally.nuclides = ['U238', 'U235']\n",
-    "tallies_file.append(tally)\n",
-    "\n",
-    "# Instantiate reaction rate Tally in moderator\n",
-    "tally = openmc.Tally(name='moderator rxn rates')\n",
-    "tally.filters = [openmc.CellFilter(moderator_cell)]\n",
-    "tally.filters.append(energy_filter)\n",
-    "tally.scores = ['absorption', 'total']\n",
-    "tally.nuclides = ['O16', 'H1']\n",
-    "tallies_file.append(tally)\n",
-    "\n",
-    "# Instantiate a tally mesh\n",
-    "mesh = openmc.RegularMesh(mesh_id=1)\n",
-    "mesh.dimension = [1, 1, 1]\n",
-    "mesh.lower_left = [-0.63, -0.63, -100.]\n",
-    "mesh.width = [1.26, 1.26, 200.]\n",
-    "meshsurface_filter = openmc.MeshSurfaceFilter(mesh)\n",
-    "\n",
-    "# Instantiate thermal, fast, and total leakage tallies\n",
-    "leak = openmc.Tally(name='leakage')\n",
-    "leak.filters = [meshsurface_filter]\n",
-    "leak.scores = ['current']\n",
-    "tallies_file.append(leak)\n",
-    "\n",
-    "thermal_leak = openmc.Tally(name='thermal leakage')\n",
-    "thermal_leak.filters = [meshsurface_filter, openmc.EnergyFilter([0., 0.625])]\n",
-    "thermal_leak.scores = ['current']\n",
-    "tallies_file.append(thermal_leak)\n",
-    "\n",
-    "fast_leak = openmc.Tally(name='fast leakage')\n",
-    "fast_leak.filters = [meshsurface_filter, openmc.EnergyFilter([0.625, 20.0e6])]\n",
-    "fast_leak.scores = ['current']\n",
-    "tallies_file.append(fast_leak)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# K-Eigenvalue (infinity) tallies\n",
-    "fiss_rate = openmc.Tally(name='fiss. rate')\n",
-    "abs_rate = openmc.Tally(name='abs. rate')\n",
-    "fiss_rate.scores = ['nu-fission']\n",
-    "abs_rate.scores = ['absorption']\n",
-    "tallies_file += (fiss_rate, abs_rate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Resonance Escape Probability tallies\n",
-    "therm_abs_rate = openmc.Tally(name='therm. abs. rate')\n",
-    "therm_abs_rate.scores = ['absorption']\n",
-    "therm_abs_rate.filters = [openmc.EnergyFilter([0., 0.625])]\n",
-    "tallies_file.append(therm_abs_rate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Thermal Flux Utilization tallies\n",
-    "fuel_therm_abs_rate = openmc.Tally(name='fuel therm. abs. rate')\n",
-    "fuel_therm_abs_rate.scores = ['absorption']\n",
-    "fuel_therm_abs_rate.filters = [openmc.EnergyFilter([0., 0.625]),\n",
-    "                               openmc.CellFilter([fuel_cell])]\n",
-    "tallies_file.append(fuel_therm_abs_rate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Fast Fission Factor tallies\n",
-    "therm_fiss_rate = openmc.Tally(name='therm. fiss. rate')\n",
-    "therm_fiss_rate.scores = ['nu-fission']\n",
-    "therm_fiss_rate.filters = [openmc.EnergyFilter([0., 0.625])]\n",
-    "tallies_file.append(therm_fiss_rate)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Instantiate energy filter to illustrate Tally slicing\n",
-    "fine_energy_filter = openmc.EnergyFilter(np.logspace(np.log10(1e-2), np.log10(20.0e6), 10))\n",
-    "\n",
-    "# Instantiate flux Tally in moderator and fuel\n",
-    "tally = openmc.Tally(name='need-to-slice')\n",
-    "tally.filters = [openmc.CellFilter([fuel_cell, moderator_cell])]\n",
-    "tally.filters.append(fine_energy_filter)\n",
-    "tally.scores = ['nu-fission', 'scatter']\n",
-    "tally.nuclides = ['H1', 'U238']\n",
-    "tallies_file.append(tally)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=6.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=3.\n",
-      "  warn(msg, IDWarning)\n",
-      "/home/shriwise/opt/openmc/openmc/openmc/mixin.py:68: IDWarning: Another Filter instance already exists with id=2.\n",
-      "  warn(msg, IDWarning)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Export to \"tallies.xml\"\n",
-    "tallies_file.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2021 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.13.0-dev\n",
-      "          Git SHA1 | 3dd81a1316ac3b5a0633e4b7a290f3bc97a066d9\n",
-      "         Date/Time | 2021-06-26 15:46:22\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
-      " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Reading tallies XML file...\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    0.99327\n",
-      "        2/1    1.05535\n",
-      "        3/1    1.02165\n",
-      "        4/1    1.02898\n",
-      "        5/1    1.02521\n",
-      "        6/1    1.04782\n",
-      "        7/1    1.07014    1.05898 +/- 0.01116\n",
-      "        8/1    1.07724    1.06507 +/- 0.00886\n",
-      "        9/1    1.06268    1.06447 +/- 0.00630\n",
-      "       10/1    0.99628    1.05083 +/- 0.01448\n",
-      "       11/1    1.07257    1.05446 +/- 0.01237\n",
-      "       12/1    1.01603    1.04897 +/- 0.01181\n",
-      "       13/1    1.01047    1.04415 +/- 0.01130\n",
-      "       14/1    1.03639    1.04329 +/- 0.01000\n",
-      "       15/1    1.00699    1.03966 +/- 0.00966\n",
-      "       16/1    0.99098    1.03524 +/- 0.00979\n",
-      "       17/1    1.03990    1.03562 +/- 0.00895\n",
-      "       18/1    1.02076    1.03448 +/- 0.00831\n",
-      "       19/1    0.99685    1.03179 +/- 0.00815\n",
-      "       20/1    1.04833    1.03290 +/- 0.00767\n",
-      " Creating state point statepoint.20.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.5195e-01 seconds\n",
-      "   Reading cross sections          = 2.3830e-01 seconds\n",
-      " Total time in simulation          = 3.6172e+00 seconds\n",
-      "   Time in transport only          = 3.6041e+00 seconds\n",
-      "   Time in inactive batches        = 4.5282e-01 seconds\n",
-      "   Time in active batches          = 3.1644e+00 seconds\n",
-      "   Time synchronizing fission bank = 2.4821e-03 seconds\n",
-      "     Sampling source sites         = 2.0826e-03 seconds\n",
-      "     SEND/RECV source sites        = 3.8781e-04 seconds\n",
-      "   Time accumulating tallies       = 2.8497e-04 seconds\n",
-      "   Time writing statepoints        = 8.5742e-03 seconds\n",
-      " Total time for finalization       = 3.1545e-04 seconds\n",
-      " Total time elapsed                = 3.8763e+00 seconds\n",
-      " Calculation Rate (inactive)       = 27604.9 particles/second\n",
-      " Calculation Rate (active)         = 11850.8 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 1.03497 +/- 0.00681\n",
-      " k-effective (Track-length)  = 1.03290 +/- 0.00767\n",
-      " k-effective (Absorption)    = 1.02663 +/- 0.00590\n",
-      " Combined k-effective        = 1.03085 +/- 0.00535\n",
-      " Leakage Fraction            = 0.01728 +/- 0.00077\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Run OpenMC!\n",
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tally Data Processing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, the tally results are not read into memory because they might be large, even large enough to exceed the available memory on a computer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Load the statepoint file\n",
-    "sp = openmc.StatePoint('statepoint.20.h5')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a tally of the total fission rate and the total absorption rate, so we can calculate k-eff as:\n",
-    "$$k_{eff} = \\frac{\\langle \\nu \\Sigma_f \\phi \\rangle}{\\langle \\Sigma_a \\phi \\rangle + \\langle L \\rangle}$$\n",
-    "In this notation, $\\langle \\cdot \\rangle^a_b$ represents an OpenMC that is integrated over region $a$ and energy range $b$. If $a$ or $b$ is not reported, it means the value represents an integral over all space or all energy, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>total</td>\n",
-       "      <td>(nu-fission / (absorption + current))</td>\n",
-       "      <td>1.025847</td>\n",
-       "      <td>0.009998</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  nuclide                                  score     mean  std. dev.\n",
-       "0   total  (nu-fission / (absorption + current)) 1.03e+00   1.00e-02"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the fission and absorption rate tallies\n",
-    "fiss_rate = sp.get_tally(name='fiss. rate')\n",
-    "abs_rate = sp.get_tally(name='abs. rate')\n",
-    "\n",
-    "# Get the leakage tally\n",
-    "leak = sp.get_tally(name='leakage')\n",
-    "leak = leak.summation(filter_type=openmc.MeshSurfaceFilter, remove_filter=True)\n",
-    "\n",
-    "# Compute k-infinity using tally arithmetic\n",
-    "keff = fiss_rate / (abs_rate + leak)\n",
-    "keff.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Notice that even though the neutron production rate, absorption rate, and current are separate tallies, we still get a first-order estimate of the uncertainty on the quotient of them automatically!\n",
-    "\n",
-    "Often in textbooks you'll see k-eff represented using the six-factor formula $$k_{eff} = p \\epsilon f \\eta P_{FNL} P_{TNL}.$$ Let's analyze each of these factors, starting with the resonance escape probability which is defined as $$p=\\frac{\\langle\\Sigma_a\\phi\\rangle_T + \\langle L \\rangle_T}{\\langle\\Sigma_a\\phi\\rangle + \\langle L \\rangle_T}$$ where the subscript $T$ means thermal energies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>total</td>\n",
-       "      <td>((absorption + current) / (absorption + current))</td>\n",
-       "      <td>0.695924</td>\n",
-       "      <td>0.007275</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0         0.00e+00          6.25e-01   total   \n",
-       "\n",
-       "                                               score     mean  std. dev.  \n",
-       "0  ((absorption + current) / (absorption + current)) 6.96e-01   7.27e-03  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute resonance escape probability using tally arithmetic\n",
-    "therm_abs_rate = sp.get_tally(name='therm. abs. rate')\n",
-    "thermal_leak = sp.get_tally(name='thermal leakage')\n",
-    "thermal_leak = thermal_leak.summation(filter_type=openmc.MeshSurfaceFilter, remove_filter=True)\n",
-    "res_esc = (therm_abs_rate + thermal_leak) / (abs_rate + thermal_leak)\n",
-    "res_esc.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The fast fission factor can be calculated as\n",
-    "$$\\epsilon=\\frac{\\langle\\nu\\Sigma_f\\phi\\rangle}{\\langle\\nu\\Sigma_f\\phi\\rangle_T}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(nu-fission / nu-fission)</td>\n",
-       "      <td>1.203449</td>\n",
-       "      <td>0.01381</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV] nuclide                      score  \\\n",
-       "0         0.00e+00          6.25e-01   total  (nu-fission / nu-fission)   \n",
-       "\n",
-       "      mean  std. dev.  \n",
-       "0 1.20e+00   1.38e-02  "
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute fast fission factor factor using tally arithmetic\n",
-    "therm_fiss_rate = sp.get_tally(name='therm. fiss. rate')\n",
-    "fast_fiss = fiss_rate / therm_fiss_rate\n",
-    "fast_fiss.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The thermal flux utilization is calculated as\n",
-    "$$f=\\frac{\\langle\\Sigma_a\\phi\\rangle^F_T}{\\langle\\Sigma_a\\phi\\rangle_T}$$\n",
-    "where the superscript $F$ denotes fuel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>cell</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(absorption / absorption)</td>\n",
-       "      <td>0.74975</td>\n",
-       "      <td>0.009032</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV]  cell nuclide                      score  \\\n",
-       "0         0.00e+00          6.25e-01     1   total  (absorption / absorption)   \n",
-       "\n",
-       "      mean  std. dev.  \n",
-       "0 7.50e-01   9.03e-03  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute thermal flux utilization factor using tally arithmetic\n",
-    "fuel_therm_abs_rate = sp.get_tally(name='fuel therm. abs. rate')\n",
-    "therm_util = fuel_therm_abs_rate / therm_abs_rate\n",
-    "therm_util.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next factor is the number of fission neutrons produced per absorption in fuel, calculated as $$\\eta = \\frac{\\langle \\nu\\Sigma_f\\phi \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle^F_T}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>cell</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(nu-fission / absorption)</td>\n",
-       "      <td>1.663575</td>\n",
-       "      <td>0.020557</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV]  cell nuclide                      score  \\\n",
-       "0         0.00e+00          6.25e-01     1   total  (nu-fission / absorption)   \n",
-       "\n",
-       "      mean  std. dev.  \n",
-       "0 1.66e+00   2.06e-02  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Compute neutrons produced per absorption (eta) using tally arithmetic\n",
-    "eta = therm_fiss_rate / fuel_therm_abs_rate\n",
-    "eta.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are two leakage factors to account for fast and thermal leakage. The fast non-leakage probability is computed as $$P_{FNL} = \\frac{\\langle \\Sigma_a\\phi \\rangle + \\langle L \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle + \\langle L \\rangle}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>total</td>\n",
-       "      <td>((absorption + current) / (absorption + current))</td>\n",
-       "      <td>0.984639</td>\n",
-       "      <td>0.00883</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0         0.00e+00          6.25e-01   total   \n",
-       "\n",
-       "                                               score     mean  std. dev.  \n",
-       "0  ((absorption + current) / (absorption + current)) 9.85e-01   8.83e-03  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p_fnl = (abs_rate + thermal_leak) / (abs_rate + leak)\n",
-    "p_fnl.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final factor is the thermal non-leakage probability and is computed as $$P_{TNL} = \\frac{\\langle \\Sigma_a\\phi \\rangle_T}{\\langle \\Sigma_a \\phi \\rangle_T + \\langle L \\rangle_T}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(absorption / (absorption + current))</td>\n",
-       "      <td>0.997372</td>\n",
-       "      <td>0.011707</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV] nuclide  \\\n",
-       "0         0.00e+00          6.25e-01   total   \n",
-       "\n",
-       "                                   score     mean  std. dev.  \n",
-       "0  (absorption / (absorption + current)) 9.97e-01   1.17e-02  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p_tnl = therm_abs_rate / (therm_abs_rate + thermal_leak)\n",
-    "p_tnl.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can calculate $k_{eff}$ using the product of the factors form the four-factor formula."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>cell</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>1</td>\n",
-       "      <td>total</td>\n",
-       "      <td>(((((((absorption + current) / (absorption + c...</td>\n",
-       "      <td>1.025847</td>\n",
-       "      <td>0.028224</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   energy low [eV]  energy high [eV]  cell nuclide  \\\n",
-       "0         0.00e+00          6.25e-01     1   total   \n",
-       "\n",
-       "                                               score     mean  std. dev.  \n",
-       "0  (((((((absorption + current) / (absorption + c... 1.03e+00   2.82e-02  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "keff = res_esc * fast_fiss * therm_util * eta * p_fnl * p_tnl\n",
-    "keff.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that the value we've obtained here has exactly the same mean as before. However, because of the way it was calculated, the standard deviation appears to be larger.\n",
-    "\n",
-    "Let's move on to a more complicated example now. Before we set up tallies to get reaction rates in the fuel and moderator in two energy groups for two different nuclides. We can use tally arithmetic to divide each of these reaction rates by the flux to get microscopic multi-group cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Compute microscopic multi-group cross-sections\n",
-    "flux = sp.get_tally(name='flux')\n",
-    "flux = flux.get_slice(filters=[openmc.CellFilter], filter_bins=[(fuel_cell.id,)])\n",
-    "fuel_rxn_rates = sp.get_tally(name='fuel rxn rates')\n",
-    "mod_rxn_rates = sp.get_tally(name='moderator rxn rates')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>(U238 / total)</td>\n",
-       "      <td>(nu-fission / flux)</td>\n",
-       "      <td>6.670761e-07</td>\n",
-       "      <td>7.788171e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>(U238 / total)</td>\n",
-       "      <td>(scatter / flux)</td>\n",
-       "      <td>2.099931e-01</td>\n",
-       "      <td>2.336727e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>(U235 / total)</td>\n",
-       "      <td>(nu-fission / flux)</td>\n",
-       "      <td>3.571972e-01</td>\n",
-       "      <td>4.203420e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>(U235 / total)</td>\n",
-       "      <td>(scatter / flux)</td>\n",
-       "      <td>5.555637e-03</td>\n",
-       "      <td>6.200519e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>(U238 / total)</td>\n",
-       "      <td>(nu-fission / flux)</td>\n",
-       "      <td>7.269233e-03</td>\n",
-       "      <td>6.814579e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>(U238 / total)</td>\n",
-       "      <td>(scatter / flux)</td>\n",
-       "      <td>2.276519e-01</td>\n",
-       "      <td>7.674381e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>(U235 / total)</td>\n",
-       "      <td>(nu-fission / flux)</td>\n",
-       "      <td>8.069479e-03</td>\n",
-       "      <td>5.324215e-05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>(U235 / total)</td>\n",
-       "      <td>(scatter / flux)</td>\n",
-       "      <td>3.363165e-03</td>\n",
-       "      <td>1.147434e-05</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV]         nuclide  \\\n",
-       "0     1         0.00e+00          6.25e-01  (U238 / total)   \n",
-       "1     1         0.00e+00          6.25e-01  (U238 / total)   \n",
-       "2     1         0.00e+00          6.25e-01  (U235 / total)   \n",
-       "3     1         0.00e+00          6.25e-01  (U235 / total)   \n",
-       "4     1         6.25e-01          2.00e+07  (U238 / total)   \n",
-       "5     1         6.25e-01          2.00e+07  (U238 / total)   \n",
-       "6     1         6.25e-01          2.00e+07  (U235 / total)   \n",
-       "7     1         6.25e-01          2.00e+07  (U235 / total)   \n",
-       "\n",
-       "                 score     mean  std. dev.  \n",
-       "0  (nu-fission / flux) 6.67e-07   7.79e-09  \n",
-       "1     (scatter / flux) 2.10e-01   2.34e-03  \n",
-       "2  (nu-fission / flux) 3.57e-01   4.20e-03  \n",
-       "3     (scatter / flux) 5.56e-03   6.20e-05  \n",
-       "4  (nu-fission / flux) 7.27e-03   6.81e-05  \n",
-       "5     (scatter / flux) 2.28e-01   7.67e-04  \n",
-       "6  (nu-fission / flux) 8.07e-03   5.32e-05  \n",
-       "7     (scatter / flux) 3.36e-03   1.15e-05  "
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fuel_xs = fuel_rxn_rates / flux\n",
-    "fuel_xs.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see that when the two tallies with multiple bins were divided, the derived tally contains the outer product of the combinations. If the filters/scores are the same, no outer product is needed. The `get_values(...)` method allows us to obtain a subset of tally scores. In the following example, we obtain just the neutron production microscopic cross sections."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[6.67076123e-07]\n",
-      "  [3.57197248e-01]]\n",
-      "\n",
-      " [[7.26923324e-03]\n",
-      "  [8.06947893e-03]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Show how to use Tally.get_values(...) with a CrossScore\n",
-    "nu_fiss_xs = fuel_xs.get_values(scores=['(nu-fission / flux)'])\n",
-    "print(nu_fiss_xs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The same idea can be used not only for scores but also for filters and nuclides."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[0.00555564]]\n",
-      "\n",
-      " [[0.00336316]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Show how to use Tally.get_values(...) with a CrossScore and CrossNuclide\n",
-    "u235_scatter_xs = fuel_xs.get_values(nuclides=['(U235 / total)'], \n",
-    "                                scores=['(scatter / flux)'])\n",
-    "print(u235_scatter_xs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[[0.22765194]\n",
-      "  [0.00336316]]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Show how to use Tally.get_values(...) with a CrossFilter and CrossScore\n",
-    "fast_scatter_xs = fuel_xs.get_values(filters=[openmc.EnergyFilter], \n",
-    "                                     filter_bins=[((0.625, 20.0e6),)], \n",
-    "                                     scores=['(scatter / flux)'])\n",
-    "print(fast_scatter_xs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A more advanced method is to use `get_slice(...)` to create a new derived tally that is a subset of an existing tally. This has the benefit that we can use `get_pandas_dataframe()` to see the tallies in a more human-readable format."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>1.382881e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>6.250000e-01</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>0.858278</td>\n",
-       "      <td>7.512185e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>U238</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>0.082753</td>\n",
-       "      <td>7.469156e-04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0.625</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>U235</td>\n",
-       "      <td>nu-fission</td>\n",
-       "      <td>0.091863</td>\n",
-       "      <td>5.596607e-04</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV] nuclide       score     mean  \\\n",
-       "0     1         0.00e+00          6.25e-01    U238  nu-fission 1.60e-06   \n",
-       "1     1         0.00e+00          6.25e-01    U235  nu-fission 8.58e-01   \n",
-       "2     1         6.25e-01          2.00e+07    U238  nu-fission 8.28e-02   \n",
-       "3     1         6.25e-01          2.00e+07    U235  nu-fission 9.19e-02   \n",
-       "\n",
-       "   std. dev.  \n",
-       "0   1.38e-08  \n",
-       "1   7.51e-03  \n",
-       "2   7.47e-04  \n",
-       "3   5.60e-04  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# \"Slice\" the nu-fission data into a new derived Tally\n",
-    "nu_fission_rates = fuel_rxn_rates.get_slice(scores=['nu-fission'])\n",
-    "nu_fission_rates.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cell</th>\n",
-       "      <th>energy low [eV]</th>\n",
-       "      <th>energy high [eV]</th>\n",
-       "      <th>nuclide</th>\n",
-       "      <th>score</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std. dev.</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.000000e-02</td>\n",
-       "      <td>1.080060e-01</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>4.542677</td>\n",
-       "      <td>0.037555</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.080060e-01</td>\n",
-       "      <td>1.166529e+00</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>2.019002</td>\n",
-       "      <td>0.011992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.166529e+00</td>\n",
-       "      <td>1.259921e+01</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>1.622688</td>\n",
-       "      <td>0.011889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.259921e+01</td>\n",
-       "      <td>1.360790e+02</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>1.834317</td>\n",
-       "      <td>0.012322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.360790e+02</td>\n",
-       "      <td>1.469734e+03</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>2.040064</td>\n",
-       "      <td>0.012967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.469734e+03</td>\n",
-       "      <td>1.587401e+04</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>2.107758</td>\n",
-       "      <td>0.012781</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.587401e+04</td>\n",
-       "      <td>1.714488e+05</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>2.175480</td>\n",
-       "      <td>0.014165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.714488e+05</td>\n",
-       "      <td>1.851749e+06</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>1.983382</td>\n",
-       "      <td>0.012931</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1.851749e+06</td>\n",
-       "      <td>2.000000e+07</td>\n",
-       "      <td>H1</td>\n",
-       "      <td>scatter</td>\n",
-       "      <td>0.372359</td>\n",
-       "      <td>0.003689</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cell  energy low [eV]  energy high [eV] nuclide    score     mean  \\\n",
-       "0     3         1.00e-02          1.08e-01      H1  scatter 4.54e+00   \n",
-       "1     3         1.08e-01          1.17e+00      H1  scatter 2.02e+00   \n",
-       "2     3         1.17e+00          1.26e+01      H1  scatter 1.62e+00   \n",
-       "3     3         1.26e+01          1.36e+02      H1  scatter 1.83e+00   \n",
-       "4     3         1.36e+02          1.47e+03      H1  scatter 2.04e+00   \n",
-       "5     3         1.47e+03          1.59e+04      H1  scatter 2.11e+00   \n",
-       "6     3         1.59e+04          1.71e+05      H1  scatter 2.18e+00   \n",
-       "7     3         1.71e+05          1.85e+06      H1  scatter 1.98e+00   \n",
-       "8     3         1.85e+06          2.00e+07      H1  scatter 3.72e-01   \n",
-       "\n",
-       "   std. dev.  \n",
-       "0   3.76e-02  \n",
-       "1   1.20e-02  \n",
-       "2   1.19e-02  \n",
-       "3   1.23e-02  \n",
-       "4   1.30e-02  \n",
-       "5   1.28e-02  \n",
-       "6   1.42e-02  \n",
-       "7   1.29e-02  \n",
-       "8   3.69e-03  "
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# \"Slice\" the H-1 scatter data in the moderator Cell into a new derived Tally\n",
-    "need_to_slice = sp.get_tally(name='need-to-slice')\n",
-    "slice_test = need_to_slice.get_slice(scores=['scatter'], nuclides=['H1'],\n",
-    "                                     filters=[openmc.CellFilter], filter_bins=[(moderator_cell.id,)])\n",
-    "slice_test.get_pandas_dataframe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Close the statepoint file as a matter of best practice\n",
-    "sp.close()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/triso.ipynb b/examples/jupyter/triso.ipynb
deleted file mode 100644
index ae17ee344..000000000
--- a/examples/jupyter/triso.ipynb
+++ /dev/null
@@ -1,366 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling TRISO Particles\n",
-    "OpenMC includes a few convenience functions for generationing TRISO particle locations and placing them in a lattice. To be clear, this capability is not a stochastic geometry capability like that included in MCNP. It's also important to note that OpenMC does not use delta tracking, which would normally speed up calculations in geometries with tons of surfaces and cells. However, the computational burden can be eased by placing TRISO particles in a lattice."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from math import pi\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import openmc\n",
-    "import openmc.model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's first start by creating materials that will be used in our TRISO particles and the background material."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fuel = openmc.Material(name='Fuel')\n",
-    "fuel.set_density('g/cm3', 10.5)\n",
-    "fuel.add_nuclide('U235', 4.6716e-02)\n",
-    "fuel.add_nuclide('U238', 2.8697e-01)\n",
-    "fuel.add_nuclide('O16',  5.0000e-01)\n",
-    "fuel.add_element('C', 1.6667e-01)\n",
-    "\n",
-    "buff = openmc.Material(name='Buffer')\n",
-    "buff.set_density('g/cm3', 1.0)\n",
-    "buff.add_element('C', 1.0)\n",
-    "buff.add_s_alpha_beta('c_Graphite')\n",
-    "\n",
-    "PyC1 = openmc.Material(name='PyC1')\n",
-    "PyC1.set_density('g/cm3', 1.9)\n",
-    "PyC1.add_element('C', 1.0)\n",
-    "PyC1.add_s_alpha_beta('c_Graphite')\n",
-    "\n",
-    "PyC2 = openmc.Material(name='PyC2')\n",
-    "PyC2.set_density('g/cm3', 1.87)\n",
-    "PyC2.add_element('C', 1.0)\n",
-    "PyC2.add_s_alpha_beta('c_Graphite')\n",
-    "\n",
-    "SiC = openmc.Material(name='SiC')\n",
-    "SiC.set_density('g/cm3', 3.2)\n",
-    "SiC.add_element('C', 0.5)\n",
-    "SiC.add_element('Si', 0.5)\n",
-    "\n",
-    "graphite = openmc.Material()\n",
-    "graphite.set_density('g/cm3', 1.1995)\n",
-    "graphite.add_element('C', 1.0)\n",
-    "graphite.add_s_alpha_beta('c_Graphite')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To actually create individual TRISO particles, we first need to create a universe that will be used within each particle. The reason we use the same universe for each TRISO particle is to reduce the total number of cells/surfaces needed which can substantially improve performance over using unique cells/surfaces in each."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create TRISO universe\n",
-    "spheres = [openmc.Sphere(r=1e-4*r)\n",
-    "           for r in [215., 315., 350., 385.]]\n",
-    "cells = [openmc.Cell(fill=fuel, region=-spheres[0]),\n",
-    "         openmc.Cell(fill=buff, region=+spheres[0] & -spheres[1]),\n",
-    "         openmc.Cell(fill=PyC1, region=+spheres[1] & -spheres[2]),\n",
-    "         openmc.Cell(fill=SiC, region=+spheres[2] & -spheres[3]),\n",
-    "         openmc.Cell(fill=PyC2, region=+spheres[3])]\n",
-    "triso_univ = openmc.Universe(cells=cells)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we need a region to pack the TRISO particles in. We will use a 1 cm x 1 cm x 1 cm box centered at the origin."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min_x = openmc.XPlane(x0=-0.5, boundary_type='reflective')\n",
-    "max_x = openmc.XPlane(x0=0.5, boundary_type='reflective')\n",
-    "min_y = openmc.YPlane(y0=-0.5, boundary_type='reflective')\n",
-    "max_y = openmc.YPlane(y0=0.5, boundary_type='reflective')\n",
-    "min_z = openmc.ZPlane(z0=-0.5, boundary_type='reflective')\n",
-    "max_z = openmc.ZPlane(z0=0.5, boundary_type='reflective')\n",
-    "region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we need to randomly select locations for the TRISO particles. In this example, we will select locations at random within the box with a packing fraction of 30%. Note that `pack_spheres` can handle up to the theoretical maximum of 60% (it will just be slow)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outer_radius = 425.*1e-4\n",
-    "centers = openmc.model.pack_spheres(radius=outer_radius, region=region, pf=0.3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have the locations of the TRISO particles determined and a universe that can be used for each particle, we can create the TRISO particles."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "trisos = [openmc.model.TRISO(outer_radius, triso_univ, center) for center in centers]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each TRISO object actually **is** a Cell, in fact; we can look at the properties of the TRISO just as we would a cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cell\n",
-      "\tID             =\t6\n",
-      "\tName           =\t\n",
-      "\tFill           =\t1\n",
-      "\tRegion         =\t-11\n",
-      "\tRotation       =\tNone\n",
-      "\tTranslation    =\t[-0.33455672  0.31790187  0.24135378]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(trisos[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's confirm that all our TRISO particles are within the box."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.45718713 -0.45730405 -0.45725048]\n",
-      "[0.45705454 0.45743843 0.45741142]\n"
-     ]
-    }
-   ],
-   "source": [
-    "centers = np.vstack([triso.center for triso in trisos])\n",
-    "print(centers.min(axis=0))\n",
-    "print(centers.max(axis=0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also look at what the actual packing fraction turned out to be:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.2996893513959326"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(trisos)*4/3*pi*outer_radius**3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have our TRISO particles created, we need to place them in a lattice to provide optimal tracking performance in OpenMC. We can use the box we created above to place the lattice in. Actually creating a lattice containing TRISO particles can be done with the `model.create_triso_lattice()` function. This function requires that we give it a list of TRISO particles, the lower-left coordinates of the lattice, the pitch of each lattice cell, the overall shape of the lattice (number of cells in each direction), and a background material."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "box = openmc.Cell(region=region)\n",
-    "lower_left, upper_right = box.region.bounding_box\n",
-    "shape = (3, 3, 3)\n",
-    "pitch = (upper_right - lower_left)/shape\n",
-    "lattice = openmc.model.create_triso_lattice(\n",
-    "    trisos, lower_left, pitch, shape, graphite)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can set the fill of our box cell to be the lattice:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "box.fill = lattice"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, let's take a look at our geometry by putting the box in a universe and plotting it. We're going to use the Fortran-side plotter since it's much faster."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "universe = openmc.Universe(cells=[box])\n",
-    "\n",
-    "geometry = openmc.Geometry(universe)\n",
-    "geometry.export_to_xml()\n",
-    "\n",
-    "materials = list(geometry.get_all_materials().values())\n",
-    "openmc.Materials(materials).export_to_xml()\n",
-    "\n",
-    "settings = openmc.Settings()\n",
-    "settings.run_mode = 'plot'\n",
-    "settings.export_to_xml()\n",
-    "\n",
-    "plot = openmc.Plot.from_geometry(geometry)\n",
-    "plot.to_ipython_image()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we plot the universe by material rather than by cell, we can see that the entire background is just graphite."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot.color_by = 'material'\n",
-    "plot.colors = {graphite: 'gray'}\n",
-    "plot.to_ipython_image()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/jupyter/unstructured-mesh-part-i.ipynb b/examples/jupyter/unstructured-mesh-part-i.ipynb
deleted file mode 100644
index 1b035fa84..000000000
--- a/examples/jupyter/unstructured-mesh-part-i.ipynb
+++ /dev/null
@@ -1,737 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Unstructured Mesh: Introduction\n",
-    "\n",
-    "In this example we'll look at how to setup and use unstructured mesh tallies in OpenMC. Unstructured meshes are able to provide results over spatial regions of a problem while conforming to a specific geometric features -- something that is often difficult to do using the regular and rectilinear meshes in OpenMC.\n",
-    "\n",
-    "Here, we'll apply an unstructured mesh tally to the PWR assembly model from the OpenMC examples.\n",
-    "\n",
-    "**_NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC or libMesh support enabled._**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image\n",
-    "import openmc\n",
-    "import openmc.lib\n",
-    "\n",
-    "# ensure one of the two mesh librares is enabled\n",
-    "assert(openmc.lib._dagmc_enabled() or openmc.lib._libmesh_enabled())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll need to download the unstructured mesh file used in this notebook. We'll be retrieving those using the function and URLs below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from matplotlib import pyplot as plt\n",
-    "plt.rcParams[\"figure.figsize\"] = (30,10)\n",
-    "\n",
-    "import urllib.request\n",
-    "\n",
-    "pin_mesh_moab_url = 'https://tinyurl.com/u9ce9d7' # MOAB file - 22 MB\n",
-    "pin_mesh_libmesh_url = 'https://tinyurl.com/yysgs3tr' # Exodus file - 9.7 MB\n",
-    "\n",
-    "def download(url, filename='dagmc.h5m'):\n",
-    "    \"\"\"\n",
-    "    Helper function for retrieving dagmc models\n",
-    "    \"\"\"\n",
-    "    u = urllib.request.urlopen(url)\n",
-    "    \n",
-    "    if u.status != 200:\n",
-    "        raise RuntimeError(\"Failed to download file.\")\n",
-    "    \n",
-    "    # save file as dagmc.h5m\n",
-    "    with open(filename, 'wb') as f:\n",
-    "        f.write(u.read())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we'll import that model from the set of OpenMC examples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = openmc.examples.pwr_assembly()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll make a couple of adjustments to this 2D model as it won't play very well with the 3D mesh we'll be looking at. First, we'll bound the pincell between +/- 10 cm in the Z dimension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "min_z = openmc.ZPlane(z0=-10.0)\n",
-    "max_z = openmc.ZPlane(z0=10.0)\n",
-    "\n",
-    "z_region = +min_z & -max_z\n",
-    "\n",
-    "cells = model.geometry.get_all_cells()\n",
-    "for cell in cells.values():\n",
-    "    cell.region &= z_region"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The other adjustment we'll make is to remove the reflective boundary conditions on the X and Y boundaries. (This is purely to generate a more interesting flux profile.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surfaces = model.geometry.get_all_surfaces()\n",
-    "# modify the boundary condition of the\n",
-    "# planar surfaces bounding the assembly\n",
-    "for surface in surfaces.values():\n",
-    "    if isinstance(surface, openmc.Plane):\n",
-    "        surface.boundary_type = 'vacuum'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a quick look at the model to ensure our changs have been added properly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5f451a0860>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2160x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "root_univ = model.geometry.root_universe\n",
-    "\n",
-    "# axial image\n",
-    "root_univ.plot(width=(22.0, 22.0),\n",
-    "               pixels=(200, 300),\n",
-    "               basis='xz',\n",
-    "               color_by='material',\n",
-    "               seed=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5f452f0cf8>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2160x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# radial image\n",
-    "root_univ.plot(width=(22.0, 22.0),\n",
-    "               pixels=(400, 400),\n",
-    "               basis='xy',\n",
-    "               color_by='material',\n",
-    "               seed=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks good! Let's run some particles through the problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.12.1-dev\n",
-      "          Git SHA1 | e62681221a625ce7eb635df966826379fb4e9453\n",
-      "         Date/Time | 2021-01-10 00:51:10\n",
-      "     MPI Processes | 1\n",
-      "    OpenMP Threads | 2\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      " Reading U234 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U234.h5\n",
-      " Reading U235 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U235.h5\n",
-      " Reading U238 from /home/shriwise/opt/openmc/xs/nndc_hdf5/U238.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading Zr90 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr90.h5\n",
-      " Reading Zr91 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr91.h5\n",
-      " Reading Zr92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr92.h5\n",
-      " Reading Zr94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr94.h5\n",
-      " Reading Zr96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Zr96.h5\n",
-      " Reading H1 from /home/shriwise/opt/openmc/xs/nndc_hdf5/H1.h5\n",
-      " Reading B10 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B10.h5\n",
-      " Reading B11 from /home/shriwise/opt/openmc/xs/nndc_hdf5/B11.h5\n",
-      " Reading c_H_in_H2O from /home/shriwise/opt/openmc/xs/nndc_hdf5/c_H_in_H2O.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for U235\n",
-      " Initializing source particles...\n",
-      "\n",
-      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
-      "\n",
-      "  Bat./Gen.      k            Average k\n",
-      "  =========   ========   ====================\n",
-      "        1/1    0.20444\n",
-      "        2/1    0.15502\n",
-      "        3/1    0.19804\n",
-      "        4/1    0.22159\n",
-      "        5/1    0.19776\n",
-      "        6/1    0.20086\n",
-      "        7/1    0.21896    0.20991 +/- 0.00905\n",
-      "        8/1    0.23134    0.21706 +/- 0.00885\n",
-      "        9/1    0.29029    0.23536 +/- 0.01935\n",
-      "       10/1    0.20094    0.22848 +/- 0.01649\n",
-      " Creating state point statepoint.10.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 2.1129e+00 seconds\n",
-      "   Reading cross sections          = 2.0943e+00 seconds\n",
-      " Total time in simulation          = 1.1595e-01 seconds\n",
-      "   Time in transport only          = 1.1219e-01 seconds\n",
-      "   Time in inactive batches        = 5.8272e-02 seconds\n",
-      "   Time in active batches          = 5.7678e-02 seconds\n",
-      "   Time synchronizing fission bank = 2.3802e-04 seconds\n",
-      "     Sampling source sites         = 1.3992e-04 seconds\n",
-      "     SEND/RECV source sites        = 2.9613e-05 seconds\n",
-      "   Time accumulating tallies       = 1.4423e-05 seconds\n",
-      "   Time writing statepoints        = 3.0105e-03 seconds\n",
-      " Total time for finalization       = 4.0100e-06 seconds\n",
-      " Total time elapsed                = 2.2442e+00 seconds\n",
-      " Calculation Rate (inactive)       = 8580.51 particles/second\n",
-      " Calculation Rate (active)         = 8668.78 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " k-effective (Collision)     = 0.25094 +/- 0.02010\n",
-      " k-effective (Track-length)  = 0.22848 +/- 0.01649\n",
-      " k-effective (Absorption)    = 0.21556 +/- 0.04156\n",
-      " Combined k-effective        = 0.20707 +/- 0.01965\n",
-      " Leakage Fraction            = 0.79200 +/- 0.03382\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('/home/shriwise/opt/openmc/openmc/examples/jupyter/statepoint.10.h5')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now it's time to apply our mesh tally to the problem. We'll be using the tetrahedral mesh \"pins1-4.h5m\" shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/pin_mesh.png\", width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This mesh was generated using Trelis with radii that match the fuel/coolant channels of the PWR model. These four channels correspond to the highlighted channels of the assembly below.\n",
-    "\n",
-    "Two of the channels are coolant and the other two are fuel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 2160x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib.patches import Rectangle\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "pitch = 1.26 # cm\n",
-    "\n",
-    "img = root_univ.plot(width=(22.0, 22.0),\n",
-    "                           pixels=(600, 600),\n",
-    "                           basis='xy',\n",
-    "                           color_by='material',\n",
-    "                           seed=0)\n",
-    "\n",
-    "# highlight channels\n",
-    "for i in range(0, 4):\n",
-    "    corner = (i * pitch - pitch / 2.0, -i * pitch - pitch / 2.0)\n",
-    "    rect = Rectangle(corner,\n",
-    "                     pitch,\n",
-    "                     pitch,\n",
-    "                     edgecolor='blue',\n",
-    "                     fill=False)\n",
-    "    img.axes.add_artist(rect)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Applying an unstructured mesh tally"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To use this mesh, we'll create an unstructured mesh instance and apply it to a mesh filter. We do this by specifying a mesh file and mesh library on an `UnstructuredMesh` object. The specified mesh library will be used to load the mesh file during simulation initialization. OpenMC must be built with support for the specified mesh library enabled."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mesh_library = 'moab' # change to 'libmesh' to use libMesh instead\n",
-    "\n",
-    "if mesh_library == 'moab':\n",
-    "    assert(openmc.lib._dagmc_enabled())\n",
-    "    mesh_file = 'pins1-4.h5m'\n",
-    "    mesh_url = pin_mesh_moab_url\n",
-    "elif mesh_library == 'libmesh':\n",
-    "    assert(openmc.lib._libmesh_enabled())\n",
-    "    mesh_file = 'pins1-4.e'\n",
-    "    mesh_url = pin_mesh_libmesh_url\n",
-    "    \n",
-    "# download the file and create the UnstructuredMesh object\n",
-    "download(mesh_url, mesh_file)\n",
-    "umesh = openmc.UnstructuredMesh(mesh_file, library=mesh_library)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Regardless of the library used to represent the mesh, we can apply this mesh object in a `MeshFilter`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mesh_filter = openmc.MeshFilter(umesh)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now apply this filter like any other. For this demonstration we'll score both the flux and heating in these pins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tally = openmc.Tally()\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['heating', 'flux']\n",
-    "# Only collision estimators are supported for \n",
-    "if umesh.library == 'libmesh':\n",
-    "    tally.estimator = 'collision'\n",
-    "model.tallies = [tally]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we'll run this model with the unstructured mesh tally applied. Notice that the simulation takes some time to start due to some additional data structures used by the unstructured mesh tally. Additionally, the particle rate drops dramatically during the active cycles of this simulation.\n",
-    "\n",
-    "Unstructured meshes are useful, but they can be computationally expensive!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "PosixPath('/home/shriwise/opt/openmc/openmc/examples/jupyter/statepoint.100.h5')"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.settings.particles = 100_000\n",
-    "model.settings.inactive = 20\n",
-    "model.settings.batches = 100\n",
-    "model.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At the end of the simulation, we see the statepoint file along with a file named \"tally_1.100.vtk\". This file contains the results of the unstructured mesh tally with convenient labels for the scores applied. In our case the following scores will be present in the VTK:\n",
-    "\n",
-    "  - flux_total_value\n",
-    "  - flux_total_std_dev\n",
-    "  - heating_total_value\n",
-    "  - heating_total_std_dev\n",
-    "  \n",
-    "Where \"total\" represents the nuclide entry in the tally. If a set of nuclides are specified in the tally, a different score will be added to the VTK for each one in addition to \"total\".\n",
-    "  \n",
-    "\n",
-    "Currently, an unstructured VTK file will only be generated for tallies if the unstructured mesh is is the only filter applied to that tally. All results for the unstructured mesh tally are present in the statepoint file regardless of the number of filters applied, however.\n",
-    "\n",
-    "These files can be viewed using free tools like [Paraview](https://www.paraview.org/) and [VisIt](https://wci.llnl.gov/simulation/computer-codes/visit/) to examine the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tally_1.100.vtk\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls *.vtk"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Flux"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/umesh_flux.png\", width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Heating\n",
-    "Here is an image of the heating score as viewed in VisIt. Note that no heating is scored in the water-filled channels as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/umesh_heating.png\", width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Statepoint Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint(\"statepoint.100.h5\") as sp:\n",
-    "    tally = sp.tallies[1]\n",
-    "    umesh = sp.meshes[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Enough information for visualization of results on the unstructured mesh is also provided in the statepoint file. Namely, the mesh element volumes and centroids are available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.43381086e-04 1.48043747e-04 1.60408339e-04 ... 7.04197023e-05\n",
-      " 7.04197023e-05 7.04197023e-05]\n",
-      "[[ 2.88485691 -2.55429784  9.97768184]\n",
-      " [ 2.87565092 -2.60469781  9.8884092 ]\n",
-      " [ 2.85832254 -2.65291228  9.97768184]\n",
-      " ...\n",
-      " [ 1.46082175 -1.15569203 -3.62914358]\n",
-      " [ 1.4443143  -1.1321793  -3.65475081]\n",
-      " [ 1.46884412 -1.15657736 -3.68206543]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(umesh.volumes)\n",
-    "print(umesh.centroids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The combination of these values can provide for an appoxmiate visualization of the unstructured mesh without its explicit representation or use of an additional mesh library.\n",
-    "\n",
-    "We hope you've found this example notebook useful!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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JuwK7hqQLSp9vmYVb6Ug6XVpJxLQmKfbU6HtD6ysEIoCoKSGKo1rtgqLPMMcOGwCGqZ1M9m90Ocyjzv6tZRXYdbjf7wvHor4c0tN6GUNcYmHRbYoscul+YRPpuUCibBrtBKeFZgbiMDLzYo6jnZaal2zaBRWeftYNFW44l2JLoRcqg579yxj6t0eVDzvDHAtsABiGAXTpIP1CmEZdDaen/nm0yatp9p+OnJeynJrV9lZCFxcQZQUyVZL6mYYqJjFlpsVOjTlG6jhkGc15NaHV0QaUIQwGXj6xyz4yqNAQwxwLbAAYpl7m9ehP7fSVds64jPX1dcUSIGtZ1LYitmyGUmCaQ2eYV/YvkcFaZTtZEJRB67OFYwFBX76lPYfz5E2LoOKKlzyZM6q6IHP01HTU0rI1NcEwRw8bAIapkcLsv0tbbpuGkjmp0+IytMp2stApV5n+rxVF7muao2eoT1mBhTLdsYB8Agv6hJtYJGr9zOvSalKUoROXbxlpSoifF1PZPPmGqmsyDHNGYAPAMMeArCzLicx0LmJdVpbB2rQsk9nY/RJwGZS0qWv1wETU7xzqUNZ6Ie3xKkNdYyFmooWILqlHw6LbFHehzc6rYCSeCdOeG0biEq2sQIor7k/tV7vzBIRzxk42jWyiuhTDMNawAWCYutAu/klf/+Z1LRQ6iml7i4a63a50LOKVwvzDNPMw9QDE1L9r9TNP2puVraHYIdPeCupYXiVRD4g2TVRA6bbFaFDKC3YJutplSUzFZZGBcmKnxdVNmJoWvPzJ0sZTepun8KsjP+thKitRnJDWmgxzumEDwDCnkzoue1JzXuJpncwlPONhpAmhQPcARmmHDNPKwsQOGclKiD030pRQ8l07ZYE6R6+irKCKstYDmGb/kj75SVmm4jJeK649i9J0U3MB2g3pPbfwLaDpS6Ml/klUFhT6lvyZYKTJMAsCGwCGqQXt9D+TIX8RrXJZVUxnEnOXQvJbZZIPuzxDsE64LcRoeZhwTZQkKQ3FWoh36ZoSbbIL23oIBWvlmrJ/CtXFFSm+afYv6erKd4Wps5Z0VUTrW2AiTlcWKPTzL0pxijLDLAhsABhm/hhl//JKyRePo6H6OKcVqqsJKDqUmDyZrcocSz5SjZG7oJgKQZdcvZEQTZeFchqFeBVlSrHFmlrF66OvW81FPOvyaJUFdvrEkgvDLAhsABjmSJHpPnIXmH5tN/DZqTHHQk0HS8rOV1+odV9er5UPMKJQszAbo/sKgWmmTv88miqnofgWRUAZxMHRZsNVsBgNSU296utWc8kwXUgBRHGGWQTYADDMnCmc/idOKaXr1DC5BKr1paz4J10WylI+McMgks5y5qXJHBl1HLK05rz0pbtAyafG7mOCl+MVn3SLkosM036BSIifza5uJU8Z2i8oa98CQuGiivgRYDGeDHP0sAFgmNrpGd59SF+uaqRsOjulNS3irbS7IF72KMrWeZhAnR/YaWZgx3LSSZuBeZE3GIUBpuS3KvMthcEKMl3VfuFQwjIx9O8omKykt6u3EL8D7cQFxP4zzPHCBoBh5klm+l+b6arRXoxNlWW8Qtmuz33a4lqQu52J0coKTDtPlJWU1UOq1FjSKHyLtSZzXMz9kKUF5ysu1crOQDuzJLdKyxZ+PE0/O+kw4uedPknRtV3QRXQXDLMIOF/5ylcK3zD9YSCOV8Pxak5H/Hyzf/XliphG5ymUTe/vfJUlVUZDe+W2Ftcqw0qcIisocxSF0GUlWn0LzTS91FIQLokwR0Ph93PhqW5dGBEQP/vqD9GdO3cA3L59W75id32hw/FqOF7AFQCGqYuK00jqfNdUk0gV5b7hEiMj6sj+BXPP/kEeClNxepkFZPF0AEVWkEm28q30qy3iUlRCJBayhci25iXIHAvy8M3xOKbrHuraCMOcONgAMMx8KLz318IDaLN/+S5dU0BRhu3i1/qm/9VUV1bk01XsEHTKsOo2xQNYdFvGa08PkDufCaAogyyehqiMXA6XaUX8s4pvEWjdi7Uyc4xYH7W3335bFAEYZqFgA8Aw9ZK5bOQzG7sitYwkpkrE7N8OrbiFERIQlcveVbNOvt3QFMr0v+lQSNTlBevRwGGX5mstQDMtAiN9o9oF3VdU8S0CSisgK6fJFF7SaD8pWtIHKL07VTQZhllY2AAwzBzQ/vKXvIjKhLX6ZVUoaDNgo4YymupUpnrOocBoItbaWtSHOo2GVZ9R85irqWgt1B7AKPXPoy2JQHcy59H2WWKqL5VBO5QUffGuNIcUWYnUL2ziaAoj1rIZMq3MS5ZhTh9sABimKl/4whfov/uLGq5JcxdEygkgt0iakhLlydsVRTHESL97WF6ALkkyLbbIGHpuR0m/tJ6tDIo4THqbRitu3e1FwG5M5FYK3wIr8b6yjCOx0K+7MCJ6rlUWGOlrz8BCFN8neHmcTZUlZae9tSDDLAJsABiG0ZC5zlW87MnNK+qkIUoRw9JkNik0LRZJRjqMkh7R9S1Mi0Qtbl24ICZ2FsoUrLtdN0QPYNdtijg9O88gNtEeUFN9I+sCsn4mgKIsUTchX6d/PBlmcWADwDCVMJ3+Z2qlpgtw2rRk6iGo0KjYsHBmvV9h3QXdXRjpi5ie4b0cxOy/W/NdIqayAq14rVSvt2iLdXbDIlCLE7PzPH3ayiuLYyrjtQfUtPPy06rtNsMsDmwAGMYebfZfVp4+3sSCqUIdRy2vWb0V6S7kK5mE0q6JzFaFJZGyYDV03yKgf4hkDEVWQtG39i0g3IVSRVygTdBrwjSBzqAtX1TpPLF8YdF5usFgmEWADQDDzB/t9U+81TdfOgKduIVgHsX1tYosc7zM8dhJqe5hWQBz0qeIUGIyaH1LGkrqL7FI0+n6FuKC43UXgirix0gVdyGY48eBYeqDDQDDWJKf/tfm/Xlk7VgXSBXPBBAvQpkrFmXikKicRl0PEVjISqr0janC3Ad87oKCQt+Cys2JzdOaisKIaVsyU4fu42/6OaIrZ1jX3QTcPVzVZuEBKF+GUr8OahVnmMWBDQDDzAdigm5Hz2rBK3FNqtF8VSZz0kIZlvy7xM6gRL8w/aJrplHvqZ0msyDUcfjSZmBeyA+dOjm2aDSvrLAu6U20ZMKIX19GtRHxB1FZQvwqsLAuqGDzGOboYQPAMDZkpv8paa4aRaZukf0LtB5Apv5JkhQGlCEnyRTXOetuUygrJpSRDqNcm+XeaYPtZpEzvqKOkghbl1NGfYcsrZxx+NUblY4of0Lm659GzXVNKhh0a4GXw7TKEqMmGObYYQPAMMbkF/8UXt6IEGekLKBckKybVu+y9WhoTQtsrUVfd2ehhFI2kciEibgVpfPVSyLQJS5yNOjKksLscI4pI3Ps1HEQ85rVW5EKGd9SFmZEZqvCDxRP+TMnFzYADHOcaFNS60xaoE6jy97Sop7+71Wuh9TUba0HEEm8xbWc6AAp2X8GiiOC1ZgTlSXyoBfGZ16ky0q0uzCXZEt9mKooM8dOHYcv4zGg+/ZjmJMCGwCGMUPx6E+jYjRol5B0jFZZQqkqEHPWDJRN7JTp0Mchg9ZuVUSR9Vqk/nS0qXMZRA9gkfF0UzOy2g2J/c8EaGUl6bNRbTCq1DHKtq2iySwgfByZ0wEbAIaZD8SrAjEsQ5f8zD6jdR0yUydmxkb5HKW3GSi+BVbKIIyMNg9WQBwZO7SFiypoxasMi9YKElP/DCJe9JxiXYj6MoxiiiTa/D79uja4EPVeWK+hz8C1EYY5U7ABYBgDtL/8hdqulEI2PbGaecsOse3Tp08LX8fLKQuxIdlJYqauTc3zEJUFRH2RrWrD8hCzf6M+p9Em6CDnuHko4tZQNq+p56gwMlpTJOiZLxiTHw3KmUbsf8a3gHAqSohNpKGLS9KnQWZzxVummO47w5xx2AAwDBVK9n80zP0ipxC0bkvhWPJhdNLuAroVHZmttHRTM9aUTYyCLaBYly65OpRHq9/VTeEvOBZjIhAbKkam7mGxSM2NlkjJ/hObEGEWtRG1R0q/ZZfBZwYqszsWXwKFzPEbjGEWBDYADMPUztyvkVKwMEOt0pw0GGWXfFOTIJB2qEe7GUCbmqex8wDEJuT+aiMzaLcSAabdNqJWcWu0tsoi+8+jyNSt9eUm2pOB7hMkYliIGxINTPrdPmHNWBriKKXrRXRxhjl22AAwDInFmf5nMtRx0VVrVmmx2+3KzRUeo/B1BdIDiH+WZS3EpL8QbUJvGqnNg8ug51va7K0M7RIg685TqFVcYD0yAnWabt15ogcguug8RgaG3oRR7YVhFgQ2AAyj5wtf+ILiXcVc1LwK0JhrssgsCPM9drWaFvGHSM7UwTBpq1tzYUT8QVGW0N0FdHlwGcStqpQvKPq1Yt0BrfmhnIEK+oQbPIhnY54+7bFaDLMIsAFgGBL56X9KgfgICtACumwaeSlNkiTd0Nx9S0URxpSaBrwmWQDdbjdtM8piCl9XkNlEXRiBYRNd2nS1RO4XJb5b87IukL9bCtE2QR+WDBXz++pU7AB7AOakwAaAYTQULv6xniKiXBiMUv90mFZZkC9ZJEmSDpCv213J8v0vdBemshkU1+mKyszxMvfDJwULZ5erNKc1LWlMG0rHU74NiNm/QIZRlCXauXOBqTXKoN6q2+3mn1o2RwpPkup86Utfun37ti6KYY4ONgAMY4ZRdp5BW32uIk7B1LeYugtK/+W74g96VYQiDquHIabJXPvTXki+aKcsqdI9piI1DXtNsjhUFv+fr+nNbDL38ggMT3V6sIjRfhXkIRoYVFt/BdpeMMzx4nzlK18pfCMzI6iF49VwvJqFjc9M/xNz0DK0U3SmCXoatbi65+rxoWQA1j3XjonAQp+ojKKSiBq6skTbBGWQKZTliBVlmbPAfMsjErVvke/S25LfV/QvZIvPl8V3DspnNO7cuSMrANbXIyIcr4bjBVwBYJhS8ot/qleH68ihQUtJrcUFamthDaUqUqXn6nUI9AQiTf9wmS90Yw5yE6bFFonWWmQwEpekj3K6ocyxsxOXqA8WUzc1Db5atkqjYlupcLwVEoFFQwxzLLABYBhjLKrDxIUuFsoSbZprB7Fobt1t9chU6bnWWsgwXUgBfcIt3cTUP0O/36eIw1A/E6YVB81aZMyARQafaaVWd1H4ekVZZnGY+6FMCxbO/sy9RYY5MtgAMEwxZQ/+J04LCSgT8xWhNNG1eqKIhCKuCKiIXZ8pVKwtQDljXau4xKKJPu0pJUbuQiCDtd0GTT/vLkATlxBbsVgfIsmc/LKttHuxkGUWED6OzGmCDQDD2CCuBL3UTy8pwogQrUWVxEJsS8nqKL6iCnR9a99CmUS3ht7/OqDktQq0tZEq+mKrmtwFxbqAVrtIYyQuUe9F+nU54HRxgXpHqnwV5OEKCcOcKdgAMEwBxN/9rePSqNXUBhQit+oV/V5sPh00aqXb7co6gDblMppt7drWLijZvwywEAeh/xbdFhD1rVEPDvE4qtFm0nb6lPKFdf/pFQyLJig9lxANTP5dirigrHCRx9q9CNQVEjtNhmGqwwaAYbJ8/vOfp2T/dVPTpTEtK/82fapAIfkOy4lVRYwW6QGgTFOMfIU1R5a1qJuw9kUg3NTRrbamS6vfq7w4SktFfUWaTkzNCyF6AAt3ARP3AvMm0pEUfQGlQlLRD8gm5j6pkUfxoaiozDDHAhsAhmHmSaHBqIhWRxtQhtgwk/KmcwjrBCUdScm0TPXtILYiAijdzkDUt7MuR4a2/9ad79e5+AqHG9ZhMGDiMSyaoIsLMk3k2yprnagvKPxaUGAkzjDHCxsAhnkJ+vR/z/yWRKYi9Y12mW+p2KI0GMilLJlE0LQhusEQ2TYxNU+jrbpkIDbRtS1f0Is8FuICbfniRKAtMtgNjqSmJVgg2wC7Jvq0CozAqIlMGEWfYY4dNgAMQ6Ww+p/+m5gAaSmsNVeXZY6XtBmYF1KzbH2CdRwLWWgAACAASURBVKPStGRy4kyuQ0/K05h6AKNWurQ1Y3kon1/TnmfQGgzFoaRDGaVasRscQa1FEqIH6NmuUiPqM8wiwAaAYV5QOP1Pv96ImD7tIe6SQl+hgK6cRp1ViHsA7JSZRaCmY5eXla9UrIAVWpf5lkeImbqRu4CtB6C3IgKMxAVadyEx7bwpdetXQWswKsIegDkpsAFgmBkVs/80xEI2TPStq8yUJtI3AdOVBXkDU3h9NZXNw4WRhWJeI5/WmZemoNBjZALsErX0JupPFj3vTyODKd8MAkr5QmAhLiDui2LAKWiz8263+/TpU3XMImB3ajHMkcEGgGGKoeTNatTf/tX11VhUsY3mrhT9z79Il81gVB6x0IeyNmInyCwaiuNY5RBLg4Ea3GnhtuJTMBeDTfcwMHEXAjuPQTQYAusig9ZgCKz1GeakwAaAYYDc9H+VGSyJIuW1yM4l2jS9Sue14gJT95KOpFzd6frpsoO225LCZSd50qsFiMoZKMfCTjlDvqG5yDJEahrtjOx8W5Fqcs4+/VlIJ8oW7dINhsVyL7p4BqKT6VYoYhCbYJhjhw0Aw+Dzn/985hVxATC6tKQxugfAFMoFpkrPoRSnp+Z5KO7CWp8iLjBqwtS6wLBqISGKS7St9A3vRVEw9+ltZtEQR3Pux1TKluXTVVos3Law/Cj/pjdn4TEqThYwzBHDBoBhACCz+r/i9I8aa3GJOoEmXq4KSc9510Qd2b9A23kxOHbFfbGJ9tJuMf7SuoCgD5NR6vf7ckwoygKttchDF0+j/SDYyTILSH2HMqMs/0n/QJVRtm2/ZCFWlbYY5uhhA8CcdQrv/a11+qdr9RQREKbn5bumygKtPj31tECbDmohWpcq/VdUGKr0X2tdBBbuAqn91Z6ZMDnE+RiKPkwMhoV7keRbKRxhC+U8XCFZQOY4/mUeg2FONGwAGKYUmamLf2byFbvKsiCtrE2DLHIgU3dBbMLauoDQRLfasiuBdhes0fYfhEOpQOsBqhgM0CoYFcdf4Y7SMUZNGLkXQZmHUbRLF5dobUwV9yJRHPQqsgzDMGwAmDON9nd/01fZ+V5x6Wr0SEE6Xp1syYyTMv0sMLIuEmIm1LUtX6wTFrtXTKBB6L+dNaJQvfPqIaJP/BfSJ9xvYJr6p6G4F1g1kY7X6sNwFZmpuETrLvCyYzQST0M8r6z101Rfk8MwzBxhA8CcXbTZ/9FQ0xVRK5sOSP8OgBaxocIGZOyEtidpTD0A0VqgQvmC3oQ12uxZYNrzI4ZSAaiCWp+YyxbSp91BLg2G0YEwsgGU1F+SidGKS4xaWTe5QSVN+ojIhsQfVb4iyig8AeaizDCnEjYADHOaqe/6J5Xn3kS325UeQ+0uTJtOxxNTH5BbqWIwtE1Yiwu0FZ5uheceSrR7UTd2gyPo178ES3sUrOswRAMjMK2TWNgM7Y4UvkVRzqB1Mplht2hCoDj6dM0vfelLt2/f1kUxzBHBBoA5oyzI9D+jQDqB+aLV1AaUkdlQkQBZLN4wdS8CIw9jurJLQDEwIn+qz8BU1AehiaPBuv9Eg2GtD1oTIGT/eYyKJAL6vsgwukcSFFYw8vTnsRaLYY4eNgAMU4x2wo+/60809R0+qSzrGIoYU9QeI5+f0RuSvaXbALq7qFJhoLQi9e08AMXDVNmFI8MowbVD3YRF9i8h1jF6vV6SJBZNGNkMbW0hTT5Gq88wxw4bAOYsopj+N/reh9UXfbqJshk1C9kyZHNz1GTo1DHsGY8xR/KC6Q9CldUU6Xjth8uiIesEnZL9o1qRgTJxLrDTl1B2xBriXlTpv7bIYHeI02ibQAUbIzYptDFvv/32nTt3SrZjmGOADQBz5ijM/k3zfpDnqySFTRQ2t3541x3Mr+j5DTMK1sppeoc3Bc5FjVlAavIYQk2drFu3mBcv+zjbGQwYVkgElAqGoFvhTg9tUiuwEzfiCJo4AqrsAsVjMMyxwwaAYQyWk2agewCjJuQ0ki5wRjoL1/ZEBsgkXhn+gnxSlXcyc1kLq53kqyLOLAi1HsSMe8mfUdatZ2wA5roKC4Z1Ehh+4qobDG0rVZoAoRqjto4LBeW6wDDHCBsA5myRn/6vfjlRf8sbpf5p5FZafW1MRei7kAmj90o7ZStZt30oIXLVj8JDbyGrgCski8DcBz8tOHdxHGrKZDf/oTDK+9Pk4ymfOJg0ZO0BtNl/Ggt9AXFixVqfYU4KbACYM8TnP//5zCtl11cjFDM9Fd2F9lpVZZJJXKe1CvTsP41RbURuoo4UyFIDRVxS6BkKN6+Ssiv2Ze4VksxUaBVBZmGRTkAXaIYU7KVKGWUxFqQNjPpzbbcWK/27JcTvDQHRzFh7GAGxFYY5XtgAMGeL/Op/u694AWXKqoq+moruQqDo/1zckRptflCGkcHodrvaMImIpFgjCX2gqldIcCiSkerP6XnnIJxXVcSZhaLuQyk+eoozqkoHMtuW1UnSHw16c3QPI7FohWGOETYAzFmh8N5f+U1N/IqXaOfmJVWmkWq9kGjzvIr0dXfCVeyAVh8VKiRdcnkE5iePRKsvwyhN5GMo4gJ5LP5g6P3tj30YABw4YZJ4DoB/9nv3P/eZm+LvZLw3lyKJAgtZBVV6y8yFWkdeipe1UrF1os3IRzLMgsMGgGGA1FyRLtCsvNs1LyXTLyfEJLUQ4lb0budRG5iKqTMIHoy4j9YQ8/JCKO4FFUaJXiEB0Ov1vJXlbrf75bsfAHDc5J/+3nuf+5Gbv/yND37iB1/13OTv/MitGHDi5Nf/zfuf+/TN9ZurznDX6Nyz2xGieBqZ7ufb6qdWYVkol8EG49STPrJ8lJlTAxsA5kygePC//ELvlS+HzUQaIbfSZj/01L8ilFlYHHZD2+1CtNmthTXKU/dA1YrWA9glzUaIDD5prV680I3HyVvff8ONEkzxd374tTjG8soUQBQ7X7770EmSH//BG41+6IZJEgBLK7+zsfO3Lo8oHsDUKaWDteICRd6foX+4gMqiwpbO9XuHd2JkRNIfLlP9NIoPaRVZhmEYARsA5vTz1ltv6UKAmi+rIlGY+0VdbEWcgyxLWRSYegCL6VULD0BphWhyytBuLgIsOi/ReqSKEA0GmitX1lcAJK7jAF/+k4eN/RBAGLif/8wtAIjRbEXhvuMA8TXv//qD9z73mZtwnS/+4Ku9974LZaVFO4xqxNhqTyc7pyTjtfoC+fER/yzbKv16ZhMi2t2RR9ZUOY/6AFXXT2M3GgzD1AQbAOZMUDb9f8TUdPErm4PMvG7XutyqLCOxLlxIR0RMo40a6tqujyK6KbWd06JN4KqIm+E4iQNHPFUlwY//wA0AjgvxnJVf/aMHses0W/CXk99877uA757DP/v2e3HsAPh7f/1TAF5//XUAX/3qVwvlKUdWjfogatNlBcSFUtaZa/fQn1O2lUecsi+ZGIq+IH1eURoS0PXTZD5KUiTdBzvlMupTZpjTBxsA5pTz1ltvLUj2f2TM/cqXvn4T82MiWpH0NLY2OEO6w9ptLfara7uKSZv9o36DIRHpaQI4h684LgA4Dn7ljx7AQWMcJmNMmr7nJe3OFMDeBwGQYFVuAQCf+cxnkOPevXv379/Pv07k1q1b4g/hMQq5d+/e7/zO75S9q4VYJ6GMZBniUFI8humJJO9qIHayV2EtFgj6AtmZsvjM68TOF6LwM3JwxD/t9CWZ7weLrwuGWTTYADAMY0BNFzxirmBB5oKtDjMls5U6u6In5YJaDQYO9cPh3j/91vBzn74JAFEC1xFz/7/xW++sAABGy8G05btRMtrzwtA5d36cLLvRo2cH/af46x8rlz86pE+w49atW2qDAaUDKSt9mGJ6lCVaDyPQfgQUEEslsMrmLVJqerUkH0DRT5PxZnmbkR58U3GGOUbYADCnGeL0v+LSyF/op4Naj6MUV5gNuw4QDYb1NGe32330tP/Dl/yNZ/2vbuwD+LGP3BIOYHStIWImY8+NEgBL56YAnu9fQRMNd++gXPZMUVj6yHDv3j21x+h2u3alkvRW2gRdmy5rUTdhkf1LuoclL+1emNYxBOlNKD08So/BMMcCGwDm1KLI/i0K7hbf6ZlEMNOcxUNImBPB3A9rmcGYS0Pdbtdrd7zA/7cj13G9ja3n3evXo/Hgs5+6+atffyhiVt78IQBBfwPAJNqJnRCAG7QUsszRIKofxLVSdh5DkG+isPRR5ZxU+GdJlTqGQHwPaz1GOpiOqc1gmGOEDQBzhrD4Wk+vsiV+oRNb6ff7/WpP81Csf0WFWeE88804mYrM+ygkSNxoEne7Xcf1APSePIbr/b+//dvn/6O3ATjb+yEQDnbD5Wb4vA+g1d/du7iKyqkYc6LJlz7UhQ4i2moJKpsZCvRrRB7ioiyGOV7YADCnk7Lpf7uvdaMlsEZNSGWQEztiE1UK02UeRv4zc3mjKxfCS7COkwRIItf34boJEsd1u90uXC8IgmQ6cA9iAP5g5HQaAJxBx5mEAMZbT1qNPY0yw9RGlRs/tHd9aFdkUeyH0bc6wxw9bACYM4HFmp882qqxnT59usiuCbo+TMoX8m+iNcpgekRM9UFbLWAhe2qJp/CCRisBMB3GfiO5ciN69F5jeYjzb24+erAqA0frq8HWCPtPyrUYxgYxwb/4aO1H4aKsO3fulIQzzDHABoA5hWSm/ymJoBqRQFukuUQoFQa77F9A0YdtE0RxAdFgSOQSLF3gS5i6C1g5gcx5VbYKy0L5aEm8AK4HAFGC8TBptmfP93z0XkMGvfGJ4cO/DJtXgsE7e2gBwGR3Wbw1l4Ufhdy/f1+baTFHSU0H+rTCw8UsMmwAmNNGfvFPt9u1S23T1JE6S7SZYkV9gVq/7C06Wg9gkZcL+oT79gSmAyUNhrbzaYg7kl7fBVr/8ygs03yeP3j4NP/+Ri+MmwBcz/vd3/1dwHnlRgxg788m7ZUAQBx7rhsBWGn1Vq7h2VZnMp02gqBUGQBw69YtynoJhmEY5ihhA8CcFSyepw5Cao7DFM1OH+TpbWt9kJuw1heZrtZgWOuD5gGsbYzsmDaNtqtgwNBjpHdE0VD+LaK+pNfrhUmy8Z3tdsdPvCCMm54XAx6AJEHvQdK94QB49PuN8OJ45bz75H6I88vJdLnh7ycrS+KmYYZhGObEwQaAOVWU3fvbtfpNJeKTOrsVfrSVYjAERj1Po23CuvNHidbDmM79F6JdhQXbA9En34lhtyN0DyOQ+xLGSdBuAIhj1/Ni8e5SezrcC1b2nOHeNPlQB8BKMH1p+3A02njsOC/9GLCCf723v78XAOh0wt2dhusCQBQ5npe4B9G//yY/UZSZwctmGOZoYAPAnB7eeustxbsiMSpL1tMztel4I8S2lOzNtBUZRhFPo83+xbtPnz5VBKihNAHznufRNlEF7V5Uyf4l9RkMibbOUNjKNPRcH0kYOb539+7dSxebXidybsySfj/em4zdzUej5pJ3bmnDd8KtBzi37v73t//n/+Ef/GRBG4e8/vrrf/8f/8a04QHN5bUpgChygkYMYDzylnYnAKKu+1tPRyL+R6+wE7DneFPnk3L/LsMwAjYAzKmC8ru/+fRIFAeq5JdyWyFFiTRFFjHEP9VpojadzWBdIRF/aBsSAUbiEsq+VFyFRaSiOL0IUDeZHUkSIELkOp6bABAJOgBRGeief/bdf3MRgNt0gbF4q7XS3IzXltcvopwv/U//fNr1nTWn7UVxjOnUDaeetx8BSbTs+X7iXHdGWy52AcCLEgC/+XyEc86/d7WpkGWYKvBt5QwjYAPAnBIUv/urRZu/0pmjVB6iODEsQ60GQ2CRoBMbKivsUKDbGIv+S4g7Uh1thSGLAwCJCweIYqfhAsBgFCZJa/dZuHrJB/DhT25+5xttucWzp16nCQDD3oMCQQDAf/1//CtcbCBEGDqdpdB1EUWO58fB5QRAgDCOnTh2GueS5v2Zqdi53AkmEbYTXC1TZc40XGRgmDnCBoBhTiR1pJJETWJYBrrBoGfkadL69DTdNC+v4gGIrVg3oS0vdEsehxVHjuPC9xPxzyhMkLjNtrNy0fvoa3viMrH/+hhA7/1wMu0AQwCXX3F2v/vCFWQ1JwDQGkwbwACNVjt0nCTaccZw3VU0GpHnJQBGQ3/6SjsaO4kDAJMm31VcC5w6pznetVIMsyCwAWBOA1Wm/5lCiNmqEVpNbYCa/OaFmbS1wUBqiRcxRzcyGNZ1DKM9yhgM33HgJQngukkcO3fv3nXdpNGK4zC+eW1rNEKr1djdG168gM2tFytzpsMBACdYLmgAAJA4cKNk0vRiz3XjZHLgeeFsZVG7He5uNgC0VqKgEYeh47eTcOi296cABiua54oyCwsn1gxzgmADwJx41Pf+pinMrig5EzN3ah32MvHqjWZsgBbTFruGN2MYpf4o1w+CGPAcJ3HdWR2g2Yo/vS4uEPEfD30AD9/xAf9cp4e15vQ5Nh+9/Fygl5HpvhvFYeC12iEAbCbtvQmeYw3hcKURj+BPogYw6Qax6xysNrwwjnY2gSsKZYZhGKY6bACY04Bi+l8m/WWTwTKAmEKpKZvBnYs4Y0pNw16TrECIU6oBdt3IbNXv95PEiULH8QAgDF0A3uEvfL3zfJbH9568+FVgAH67Axw4Tgcl7Pa+t+QtL7XFXcJTbMO96eIaDoYtANGB0xqEAMKGN+oEGKM1mIaXLk62e4PtJ2wAmNMHfaKKYY4GNgDMyaZs8Q/xiYr5ANOkSm0wBNJmmIpL5OZcxDg71HpYpXi323269dxv+q4f/PZv/3azEQPwXdd38QdPvIutGKns32/EAKZRywfaa83dEnFBw2tNLp4D4Db2AYQbTtx0AcSxE8TRcHm21KfZipZe/cjB4+dIQgDLa3xVYo6NudwswQ8aYk4E/FXLnDYoGXkh4imN2seoC4gGQ5AOo4gL0rm+UChrTj5fki6eJ98czFeYMCeRq1fX3SBwvAbghJHji9tzp87uVoILwWavMRo6AD75qccA7v9lG8Dms2Ufu56rsgCTcyuNJ4/i195Yud4FEHqXxOuJ0wIQjwZruyGA0aVhFM3WHfnNZnxQIscwJ5zbt2/rQhjm6GADwJxg8tP/Rnm5HRWbIJYCTFux8xg4bKhstUlhB4z0JZkm8h5DYCfOVCGKnCR2/A6SBACmoQMXHS8BsNmbzf2/cnMv2QcAz0MU4Y3XBsFO/OxbpZoNfwhgcm6l2WpsP9p95WNLQG/7oOu5U2AaJisAdlb9ZmMPwOTpQeA2oskegKR1uVSUYYD79+/rQhiG0cMGgDmp1JT9UxJQ6ybEVL26iSp7IbfS7oVpK2X5uhZtQ9VXYaHEwAjlfIct9PNkWpyL5nHhe0kcA0AYul4QJ7PpeAwGjUvdKYDl6ex+39D1gUn32jjeQgzHbah+sSvZee6uXwXgtRoHw2UAnjttdBoAVttD8QMEw50GgEboTPwEgJMMFYJMFThvZhgmDRsA5pSgvWNyLvSKHqNORySj2lVG1ZugYNdKfR5DQNcHbblX2VsU/Qz0PbIQz3CUd5NHMbwAv/7rvyr+6bqYTD1/Onskf9CMoylGA9dzX1wvdobeBWD9SuljQAFcvjVqroeP72+cvzlb/DPeixodLC/tAwjjztaTKeC5HzwDMPXCS9ew8S4Oek/wcb4JmGEYpl7YADAnkvz0P+WpKWqIc/PVUbRS0WAgdT9AGfQstgxtE5jHjmhtkvWO9A9v9gAtnza1GUbiGbQ7RSkiEZH75TUDrxEAaDRmq/Hj0IlD58GXf+Hqf/dFETMcNHc2HACXuk8AXF2Nt55i5JQ+s39pDb2NCzfW4QbNadTBYGsSXQaw1xsu3wKA8WDiBm0AfqPVuhUdvDsFkLjtoN38J7/y5f/iiz9RpswwdTOXagn/KgKz4LABYE4b9GeoS6rfQUuBmLdZ9F+i3ZG5eBj1jlRvgpLjahNlNZQmBBZORi490noYidHu9Pv9tAcjNiHJ+xmvGfjNwImmrgsHCYDpFEEzghs9e9oEEE3jRjMB8OYb+xsbaHkA8O77yejpZmETkqcPl8Qf06jTWG0CaDXHF646AA6+CwD+o2ci4LWPjb77DRfApWve/hwexMIwDMOocHUBDLNwfO5znyt89Ge32xXJ0Pr6unaKWiAi5YYUiMoZ7LaaO2I3iYlmFao3oTUSdTfR6/Ussn+J2FC7FzDM/iWm8WJ3vvWtb/UPkW+FMb76h18HEAQBgDBymq3QdWa3AgwOmgAmY+fNN/blJlsTuJ4/nqjuAfAa3pvr7yJoAogjTIezGwkevuM+/K43GK4kjt94o9V4o4VoJLdqLDWXVl76zQGGYRhm7nAFgDlhlGX/kkwqLxOd6jeDivhe6ldgtUmYdko+Q9fwh2AFppPBpvoSyu4cwVqs6hD9mN0oSfq6tVJ2qb+kT35wLaWhaeT4PsLIka84wGC/AQcAPn5r8Jf3WwDW/D0A3Ub0fomOIAxdHLqDKHIdxADGB+GNWwCwvxMNhnjl2hMAz7faAAYHnQvr29Pdnc37G2Waed7+hX8JoL0UDg98AEEjnmw7ANB2w3C2I3/7Nb7MMQzDZOFvRubUIrIibW5kCl2QHplHJI7qjI2SjueRHgM6/TR2bVlASc1FOmttYyTahUwVm6DsS0W0HgOEVUyOA8dJkCCJX7yYJLME+uor+41gIv6+dXU82UZ/EDeAtc62e1PT9J/dP9c8DwAJ3Dh2ANz/89b6KwMAly5tA9h6fm50bxvorN2Y9bC1HEyWVPcWC/7e//6bAPxxAuBg6APwlpLBgY8A586P9/u+D0S+mzjOP383BLC0O/l3P91RazIMw5wd2AAwJwnt9P8RU0dCTNGkxCiQpQxdoHFtQSCDjbJnuseoUmQg7pFowqj/GbS7Y70LaSjZvzogR+I4ABzHSQBcfXX2u1xbY+fKpaF4dieACG6YeEDKMbzM3k7jtTcOnjxc8YJ4sr114dXl1nv7m1eXP/Th5wAOxmvjveng4BwA/3oQPpqtDlq9EAw2V+DvlMkK/u7/9pvNVgRgnMweVdQaTDGB03adBLubjcYkHHd8AJ39CYDGMATwO9/c/1vfr7cWDMMwZwE2AMyJ4XOf+5wu5LRByYat6Xa7MpNO5LPfU+8WbUQlv3lhMp3OX41a7FZYK0VsqGt7v4SRx0CFOgNldyhm6e7du34zcL3ZOZAkcADxWH7Pj6PQvdBMAOyMMZgm0zBeacDZHn/8w5f++Jul3R737gEXr766tzNeX3k+wqvLAC4+2d++ev7c6nMR4yCcDsezv4NGHA7ho73afPSHj8pkAfw3/+g3nKY3GbrL26MGsHe+7cbJpOW396dLO2MAYeBNVnwHaG9Pp00XQGc3DhuuH8a/9XT0o1daCnGGYZgzAhsA5iSxUNP/pwORPuYNwFzQptraAAXdwyVe6QQ3nUnLxJqSKJdhtFbKtCFKdl4GsS2Kx4gnEzQDAA0/GU/hu0ASt32In/+KYydK4DkAcL6BPvD+o5Y3cT96Lm63wjJNwZMHy5ecCQAxYd/4a8k4xvbu+Xg6ufKhCMD3vtUGEPmzCsAgugJ8r7Gkurc4uJwECPf3gsFKs7M3Fsp+PzpYbQAIJhEAbxRjyZlc9B03afSmo44f+258mR96wTAMM4MNAHMyMFr8U5hUaVMlplbqG/+0svxbJr5V2i3cNuMx1MFa0lvVYTModQZh/poekgRuHEaOL9J932tMJ7M1NtMYcJznU7z/iDSD7jjJxjtP0Ppw62Px6DvuZr+NC7jqNBvewWg0+/WAp+9jeXW6895wtkmwNOw/2rrXcwLVQp3phjNp+wDcOBktNbALN07ChtschcE42l9rAnDdBBFau9PBcmNyzhMLgdyN+Nn9u/ixH1GIM6ebW7du6UIY5qzABoA5DciMn5hCEZOnPL3DH3jKewxrTaYO5ns4Cj3GHJErmsQ/yzwGrFoXm5Tp37171wHiyTS9yXjqDiYOgE9dnQB4sjd7vfcsuLy+DaC5Fe9OEOim1C9cHk4n573X8W99eudr31gDcLDnfeQTAwB/+o0Ll7sHAC583wBAsre/gxYAf/mCEx62V0TzYNo8mPV2/3wrdme3JZzvHQA4PwqHh08RnTS9zv5ksNwYLDeCIJ7sbg2eqxYXMcfLvXun9gcg3nrrLV0Iwxw1bACYE0DZ9D/l4YYSxfNA1aTdhZhGLWyxX+G3mdIo1oRUkWUWnCPwGOKPsmdADcbTZsePgSh0AYiH9vzpowaAlaURgEE4Wye2v+83gUkEx3nxzNAMfpA0GjEA13PiKAFwqTv+0CvRt7+zDOBPv3FBhD170nj1VYyGfhO4enXUmzZ2dUui+gfvB+211eZFAO39KYDhcgBgsDZbOOREs36GgRcGHoDGOPS8tQm2nEa7WJRhGOaMwQaAWXQK7/01Sv3zUB6djqLCgrrFzLuUJiSytqDYKu0NjMQz5D1G5h6AKuLMgiMPrvjj7t27yeEqIJHOu24CwPMTAB+/OgHwB99eAnD+3HDl3GRtkFxqTg+eFijniSMHgN/E/cOFQx//2P7mbufmG/sA3n/npbX+B+Pm2uruY2dp6dJaXipN02vFK8sAotYQAKZY7Q8BDFYak6YPYHl7BGBpN/G/7xMAJu8/bq88PPiT990OPwXIhtO0cub111/XhVSibKxu375d+DrDHBdsAJgTQOH0v3X2T6SixyBCyfsl6UlcuaFyixeoV0lJAyDmhk3F81Duba2iz8wRsZBHZP9J4rQaL7nBG8vJ0xsHALZ38UoH6Jz77sOdFcB3ncG0dA1QHDl7e8GFi47jAsBwIJblxAcTtBqD0aSzu+2fuzQF8O47qwAuX2gCGE4DYLT9/mN88vvLlAG4QXP6vOe/fnPpwjUAjuteDHwAz2/MCguYTAA4288nQPhow/WD0XS59bGL++9MylWZVyovJgAAIABJREFU46fu7JxhGAkbAGahyS/+qZ6X9+fx20lqiE2IPyzy4MKbEMowGrH0QilinURidCeG0IfV7gsoI2AtnqdibxeTX/u1X5N/TyOn4wCAe/h4/6aXvLfn3FhOAHxkDb/f9++PsNJ58eSf/b0GSnDcFy4iiZ2/9sldAONkeSnAwRS727NLz4c/tP/sYfDDP7jX60XP9z0AV2+On2wHhZozNdHJoAlguPW8c+mieH37oy0Hg2TYWeptAxg4YwCNzXD9UzGAR98u02OYGaf4JgSGycMGgDlJUHI+LfTU3Bo5j67OF6tkk8IDaJuwdjJyK2InjWwGDiNNbYbFIijrxL3wNCh80UK8jIz+HJUVzNb/xBOgFbiznwPu+IlYS/+vn7ivLUUiUmT/vpMErhO4+FB3PA2ngR/cu3cvM3ebJGi1Is+PX708AvB4uHStnQDYDx04uPLKAMB0FKU3WQ43t+BBh+NMDpJBa+WyHzYARHHbc4firTDqLB1MouUOgM5OCGBjZXoOePq9Pa/RdNuNyfZDhTKzyHB2zjDzhQ0As7jkp/+NZr4LIT5CkZ7IKlCv5tf2gYI2+1e8S0GMg7YV6+ESpRLiaNil8kSnlMbUzAjo+mkKyybSo1oYHi1lZ8XhLb5IEoibez0HS8Hs1b0p1teD7d2w6WJjo73axH6IDzZLKwAAgiD2vGRz4F3sRAAeD53psLG8MgVwqQkAT0Z4PnQ+/Mk9AOfa7sY2xEKkK+urClkAjVGIy63x7rOP/43Og8eI4vbOG1EYdQDg+hSPgmE8wkpjeW8CYHP78miYTHb6K8totC6plcvIOxw6nLmeQazPFoY5MtgAMAuK+sH/dj+eSnkqvEiP7PQFlFaOALssNoN6X6obDNS/XMoIOzOT3oTYQ/XRyb9eZcVUxkVIhV/6pV8KggCAO0v4cfhETUQxwnAaBIGYkH8yeKEWJ7h1bbh/KFn6GCAAQBQ5AB70Wp/sYnMMAPt7wceuTB4Pk0ttB8CnL+MbH6A3ct6/t/Tp17F1bzcaDjd6O/hYqWbQiONOxwXQaj14cOnGjfcePL4JIMB2oxMAwPVpx20A2PwganWSye5Wc82f7GBvKy4VZRiGOWOwAWBOGCJ96fV69BydOOsvqFJk0CayAmt9iXUuWAfEo1DGQi2XUryrheJkUDLlryW9Ygrk0aAHy+d5ug7iBAAGIQIHbR8AVn0A2J4gcGYxAJZ9vHZ58r98+Z/8V//Zf1mgCHgOLi6H08MbhS828XSMj12Z3YY7inG5k6QdxLcftD3sFgi9zNoq9kIA8MIEwIMHl5Yv+eJa9sobEwDf/qMWgLXLL25CaC/74Zo/3IC3QvoVM4ZhmFMPGwBmEdH+7m839cNJhYlUlUfypzchZmkWHkObjxZyZHPhlD2qYpbS1JqXg6ZgN/efhuJkKpZlpMfQnjyU1F9M/wviBAHgOgiCID58JJQLPDlwAdxYircnAPA3r4Xv7PjbA+DK5eTZhuKHwFp+AmAQIQCarai35wH46CvjOIbrOsvNWd7/+CAZDoLOUhgEMYBWM1pa8vcamjsB3DjxnAABAEzGHpIRnNZ4lHzvz4I3PjX7jbAkHJ+74gINL/bj/Y2ltSAaNlKVDIYp5v79+7oQhjkNsAFgFo7CB//nkclNTamwkJU5buGqjHSkEbKOkf6nFkpWJ5EeyTTdpO9Xr9pyKcp8+RzR7k5FRHa+OK1QYtKI9TGug2kMYDbZD2C9E7sOLjSxNcY7O7NLxoUmllem704RvZhnzyLeWVmaxnAajXAymW277ON6O/nOrrMxFIl+NBr6Fy7vbW20vPil24IL2eg1b/xA+Ozdh8H6q7JysbOVtNfaADYehs3VBoCd5/Ha+Uk0eO43vAngNhoAPAwVyoX8/K//KYBJy2/vfxPA3vWOH0QA/Cfh7f/2P9FsTIDvEGAY5lhgA8AsIurp/yNGOgFdoDHSBqSnqNMNZRyCaR+6ujpJHsrEvyRjY4ygNzSXOgNlvtzayQi0fqb6XlAgtiIfABoEwXQ6BeAAYYxpjDiB62A6nTYbweX2i3Xz0hIs+QDwrc2g48/+zjNJECVoeQDgIgHguPFK8OLHg693cKsd//HGiyKC5007nvtwb3IwevGkUUH+HtxLr4Xty4Od0YVGK9553gQwHYZB2wewurzXfxwA2HneWFtpAHAbjb2nm81zF/ZA5e//498ILibTTacFTFp+4jmJ5wBYfjrcX2smjjNdbvzknX8hgn/67f9YKXb6OTIbw9PzDDMv2AAwi4V28c/pQ5GbUvJjNWoFuaRE/BCYXXNyK0r2TC8vZDCqfgjkJtqtpMew9gDEWXnQRkmB1maIfSF2RtJwE98DUkvyZeKfJBgn7mQaA7jSiQH8xQb6IzR9NJtR2S8C/5//6H/9u7d/Svy9P/CXnGQ6U0vaPnbDWTsHe43l5YPBINjZ0j8AVNBejZ5vNS9eHu3tNNFEEMRLS5PdnQaA6fbo8psxgMHABbC/NQIwfL4HZ8nvDLbe3ZhES2pxAP/gf/x/3DjpAPtLbSxhsOR5+9HKh6aA86zXFjHil4YBjJYCfxoLJ3DqbQA/2YZhThNsABjmTFCYEcoX5S8B2yELFIrpZ/muaW6K1LaU1LasnKKmSp2BmP3Lgoy1B6CXTUyJEscHogQJ0PAQxgAQJvjB9eibzzwAcYJPXoj3dl3PjfsjANiZYA2qJUC+H+8MvNZhQDuIgNldxa+vTAF8+U864q3pxO1enADwno0BXHzteoHcIXH6WT6jDQQXAayuTQbbM1Px9IHbbI03H7/oWexQ7/39yTv/ogHEruPGyfKT4XClMWl6UdPbfYDYdXxEjXEUu07Y8BrDcLjScBJEvhtMoul5/+1f+Jd3/vP/QNcCwzDMQsAGgFkgLKb/TZfRM7WiPgpVjpHcVp2mU+b7y0hvqM3R7UoZRk2kMW1O7ZTSPwAscADhAcX6H99F0086fgLg+y9FAPZGs3z65srk+uvu7z2Jn/p4tK+atm8EiVjv02mH+wMfQOAijOG7+L+/0wFw5dIIwAcPmyL+lU7y7Ylz87XL777zuFT0kO2tpnim/8XLw71Bp9OZ4NkSgK2n40tXEwBra0MAm4/C2Gm5k6fNThAstafNjlIVwThMXCSuGwOJ40w7ngOs9oZulAB4fnVJPMNoZWuUeM7S7njncsf34zBxnf24McyuXGIYhllY2AAwiwLl3l+Z/MmnIubfAjlJIsIeY6Go+yhQ9CkxCoxsgMWsvwgm1lvEPQCuM8vvmx6mMVxn9h8A8dbldghg3Rmlt50mL346oJDVdtT0sDP0bq6+SI63xi8CogRx4gA4mDroJGvBi2n7Mlw3WVmdDA6Cwa7fWQ33dgNgt73SwY3n4k5fAJP9EeA47gu1y9fdZ+9huvesUFPeYzBpB7HrhIELYHl75Ex8ALsX2+JSubw8Cd6ZAnh+dQnA0vPR0vNR2PCmDS9xnTCgrmI6lfACoTLeeustXQjDHANsAJgFomz6v1fyHJ6yB4BSFoqUkfEYEvkIdoG1fiH1KTN2HMFRkCuCFAFlb1FI2wAQ1BwHbhSKmwDETwGMImdz5AG43A6/t9MAcD4Z7028+09GDnC1E49beOKUpuyrAa52MADW2hEAB/jeLtbbANBZno5H3tUOPjgAgMsXhs/7+Mtt14/gAksN9c+LAUBnaTpIktHAk4v69/c7r39y5k82ny4BeHZvDATnr447nvv84dPzV9d2/rJE7pDEgZMkjXHkxMm06QeTqDWYiucZbV/u7O02cKVx7tkAceKF8WgpcOMEgBsniJNYbYYYhmEWCTYAzEJQuPinLO9Xk47XJj0SbVvpt9JmgN6EpMxjZLBQVlOlz0wdHMGBkDZAvXQqShwvAQDfD+LpBMB+6Kw28cv/8Ff+03/4xX/1IDjvR9eXsDeZTXK/v+9cjZy2F+9MSrPevSkAjCNsbrW6V2dPF+0PAWB8qPPKEj710cHXNxwAnkv6pd5mK2o2o+3nTXRcJPFrN8YA7n1wQby72ZstKEpSTxT94OGqPx3Gu++3Vpt5wTSD3e+e92+KvyftIBhHYl5/3PaDSQRg2vD2zrXcKGmMIwCx40zavj+NEsfZffDnAN8DcKa5detW4eu3b98ufJ1hjhE2AMxphlIK6Jn/AlR+ARIFrccQ5AO0u5Ank+1lNGX/LZS1sM1YQNLHIn8DgETcBhCKOX0HYYLASe5uzFL8tof/b8MBMNx3gNnSHQUdH3GC4ZPexatdoB24uHC4Av/NSyGAvcO1QG3P+WDfB3CFsIhmMk4FOe4wTAC0lxMAB3uznzYT2f/F1/x4dLD1eM/x/L1++9xKu7Wsv+QN9zbaV25Fk4EXxrHniJX9g/MNbxQDcJKkefiU0s7uZH+tCWAlbu0NtxSaDMMwi4b+25Bh6uazn/1sZvqfmCsrIP5ekkX2L5EbUloRfxi1ZWEzjBrq9/v9w8eAGrWSRtGiFBdoR8kI4gJ3hkiczJ7nI27bDWM0PfgOJiF+9NVwf+qu+dP+IACw3sR+M7nWwcNdTGJH8aO9jdTvBG+O0fKwFAFA08OzEQCIZ2k+Gczivv/65MkDvPfcXyGspVk7P771+i6AR/3rl9YPAIR/Hm0CF/9G3Hvc6nb3AAwH/vbOSz9W7Lf0jwGNV1cPBs+WOpcal64B2O/fb+9NVnvD4crsBgO51Kd5880mMBpsAyMA7ZivpwtN2fQ8w5xN+AuLOWY++9nPZl6pnv3jMKNVVwCqZP8CosdAhd0RG2pbgW1DIt70dwCIbaU9DKUaU0aZzSj0SNat5Dlr1YwoQeAmLS/Zjx0ACRC4yf50lkNfX55caSFOcK0DAE+H6ADiSUGFbI6xHuF8E6ELAKMIAIYRhhFaTXgOAByEzvbzAED3/OTxAO8/z16S7t+/n8nbOq0oTuA4cA9/rODxo5VgaZaU9x7Pnvg5HPjb/RGAT3x0+O3vrSxfjIfbrZ0Hj/BXvg/lTEaud67jHQySVmu8v9VcvnCl9WpzdRlA/1qniSaA1rPtvQ+tTbZ3d4C1jQMA49Fewwleuj+aYRhmsWEDwBw/men/boUnsqdRJ21zaQK6vHYuHgOEVkBIx7Voc3SjCoNEpumm0/bE/crYAO1eFJI+HwqrGXjZaVg0ocB0ZKwpW//TcBPPBSDud8VykLhu0nCTaYQnAwfApXOzRP9qBx/sAMCFJp5Nnah8IdAbq9GjA+f6UtLykABR4myNZ78DAGB7gtUAByEAXDo3HW5iEDqel3zycvTNzTLJF4jVSptjOB4gfmPso77vx9df2QOwsznr1Q99Ytjr4fqN4ZPvYTLxvKbmHgBMx+FgB0tr07XlYGc//37r2bb8uxn7+/4Uq0vjYDR8+l7UnuTjmYWCH1XEMBI2AMxxkl/8g8qpOf2xidrMUg29oSoQ1zJVhN6K9aBlVgSpsTBOFiumBESbgZebsLAZ+RNbW9AwbSJDxlcoPlnD4XS52fBdTCInjB3HSdzDXwX+d64NAdx92gTQbQ9F/MfPT3//Ec4140e6Z9+PY+wO3dFwaf3KAMAwxEfWcH8mgwT4K9emAO6nkv6loEBHsj/wvQ8ejTqvf/v9zuUrAwDNVuz6L24gfr7ZdjFyveiHPjGUL15dH36vj+baaoFijsBrRps9XOyuRI3JamOCZHj1IoAx4FxYBdB4vOu0fACTlVZjbwTAC5qRbiiYRebevXu6ECpsM5gTARsAZuHo2v5gqumPJVk0IaFk/yLfqt6KGnr+WoWKrkxA9AAW2b9Ebkg5B+yGzq6gQW9LxqzbPtBWbCUP2be+9S11vCBOEMYQT/VMEgdADCTAH2+0/+rlIYCVID5IAOBiE71BcBBCPZ0+ihyxQGitFY9G6D/teM7+D9xYBnCpBQDv7c7m6Z8MAKDjJ8FKvDHS3ACw8957a594pdGIAGw87QSrPoBzKzMD8HyzLf74yPXpt7/XAODFPoDzTafViaJdpbc4xFlqJgdjP3Z2nYPVZKk5DJv3e9u3uucv7gEYftABEIzCncbIDVpRuNO80BiPVzoNkjjDMMwiwAaAOTYKp//TiGRRmzOZ5v2CijaD0pZowlQ/A6Wh6tQ9/S+gzJ1XHzGBthXxh3Vb9JoJPfXPIJowshk4bK4w6b97926n00m/Mp1Og2CWtk7H01YbAMaTsNPyojhpesl0CgDf3WndWh0DeAos+dgGwhg3lpOD5vWb5Y8BFQym+FALP/DGwZ9sAkDTBYBXWgmAa60YwGBvZgDeWE2+s4GOhxeLbErYOfC7r0yvvTIA8MG247oJgKtXhgDefb8J4Ps/LFYFwfXx4C9WAJy/vrm7MfX3NEchWPZaKw0AzlJzeYj9djZg8GSluX8AJwDgphL+oN0ArwBiGObkwAaAOR7U2X960YJ2zpieG2Uw8gB2NgNkG5NBu9cZiHtRyPr6urgJ+AgwHT0LiENnPVxptDP01tm/QNZMiA3Bti3HgeckbjzxHN9zEB/+xG+zEbQOn6PztV7jenMfwPYEyz4OprrUP3TfXI0f7LvDCAC+7yL+ZBN3nzkA4HkAPrMeb4ycJeAT5/DeAbYnTofwGNBGI15ZCgF88KT1ytURgOsf2l9Ziw6XLGH1XOgmCQD38Pp25frgIMSrr077+wUN5O8zjg96F7dXceX88hCDbjPwmgCc4XDwZAXAzqXG2rPJTmPsh4i2NlZf8/a+OcAKWufP58UZInNcgcMwDAU2AMxCU3e+2NX9GqsMUweUITakr5+xsBndCvdMy2pG3QZAO8KCeU3/w2QA68P6uKShlxqqDJ08/O7sl4CdOME0nqXUk9BxZs/AxDR2AeyHmMT4vovx8BHp17veffj82rXzHzuHp06yN3UA/Ic3JztDXGkne1P8+TawdK61vx0DuxNQzsWGl0TNuLfVeOXWDuACSOJkMPFWz81W4j8aeB+/OgIw3RsBePeDBmX5/3TsNcZBiHivu7ry9HF8/ZXg2fbOpXOu4wGYRMO45S/vI2o1/HMOgCRYAQYAWm5v494zjTrDMMzCwAaAOQa0i3+OEkpqVZFut0tJ0617IjckpoD0VUySdDCxFQm9ORFjqp+G0lb1ezNAa0hQpRUK2vOKRIIocZIECeA4CGP4LsTU/+YEAK528ENXJr93bzaDLp/xvzl66UH7eW4sx44L4MVTO3/s5vhbuzOde7vuw2cA8OY5PD186E5HeV2KE2c4dlcThBPHbyQAkiQeTDwA19YcYAjg0eFM/0HoXFidAtgAek8jJ5yWCwOAk3LCwwtL4VpraWe0tDtB0ESrGV6MgBj77m4ragDTvW0Ae9/cdG62p+8sA2wATjz379/XhTDMKYENAHPU5B/8f0agJItVINoMVOiJkdOwqGbAsIkMxJlyOUoVPcDRQCyeaLl7967i3RizHwJruMkAAOAAw8gBsBoAwN2+KAEMXQcXWwmAeyG+2lNl/+MIW2P3QjN+sO/cWE62pwDwN1+brZT/9nN/2Z1VD948BwAfOTf9zgaWfHxt33nzUrGmZGurtXIV4cTZ3W4B+OjFCADg/94frgP4qz/wFMDB4f0J/WfezWvjx4PIWXqlTDBNZxT7bSGIg3OtxqAgxt3zW0v/P3tvFiNZlt73/c65W+wZuVXWXl3VtfQyM5wecUY0KVI0LMAwbD9wxmrDIGBLFuUH23r3m2DJC5q2YcIGKJoWTdnywxACBBsQLMvgMiJFztLkLL1MTS+1dFVlVe4Z+93P8cONjIrJzIi4EZFVXcv5YTDIjDj3fHEjsiv+/3O+7zs20Dyz6tEBLFE4ZpwhN2Oa55gEIYPhxDEGwPA58Ows/7945FHAc5InRJ4xo5hqt2Fmm5HHKR1L/rX/jHlsRp5Ya9PUmg9X/Q6TZf7EB039HQlQcRTwWQdgwQZoKbKjf0u2Ar64FH//4chKgIcf37tw7exOIL94TgGZoP5gzwXOL2ig4bNSwF0rhr7vSfaC7PtI36iPa6gphQaCnv1zF4JWjKoHmQcIFD/+uFZfChp7fSH+YNcBkq4AGp1w9JSPsT3tdWNtiXTJtvYShm5OLUelqgv0tqJCbIfKp+0VqiEQfLwV12u0N0ZNazAYDM8axgAYnirPVPKPYWbyy9+ZWctRnjHPy5jKZjCT05hnq2HacDOEGEaALVBJHKsiYAmdapEqAfzy2ehbD12gaAFEKUrTieUXlyak0xRtnerHEvqNxRhwKwlws2UDcijb/1GPUixSBYx0FBnO0K5DzaEBlpXWPWvD7z946ky3l4qSpQGvmCQdgsB2hVw+t9q69/C4KYcQGhCpBhJ3UDEs4nNli17QjtRPv8fNRx1LutHpVb37qRbH7RQYXm7efvvtSUMMhs8HYwAMzxaH1mUPaZo8C6KGF4an8EHnDJFz2FGyCwdOJo9Gn+GPfKKZGZ//A6AFB1lAArLzfRONLQC+spLaOgXubfNKFaqLd9cbZ239qGcnYyt2N28/XLtyFghSUbA0sBPYS4UE2I/5q6f6K/3f2gfYCvDAs/VmMq4ZUJhypqQ6xeRBRwDXV/UnbYDTRW6D6/VTd3qpqFXjMBGN/YP65dz4C26powErVaklI1fwsJGeraskWtsNgPVLVv2RAnac/uT1NXtjf8yUBoPB8GxhDIDh6TFm+T9nE8Ps2UFvxKlE0jBjbMa0K6+GF4An/VkfsgETR87GRBswHim0hFSjNAVLR6mI41jZbpj2l9zbsfy5tQhox48F+lIhPX46iJVwBEFCxVFBav3eT0r//ms9P2W9+/h75/fXbeBMIb5Wj/5wr/+gI/Xv/qN/+Df/xq8dO60lEFAoJnFkOW4KXFuLUu0C115tAZ89KO9uFoDlmn+mlBbO+cDHN61SVfkjX2yfOJDRkgXEruVEqRWlFml6usbBl+XmtcLaJ0E2uH0q9LDbDzdd7e1v1wqViQcYPBEO9TA1PGu88847k4YYDJ8DxgAYnhLH1v7m1P1H2ZrjjKQ8HmOY/PMfZcwNzulhDM8dT+ezzpM6NeBQPUCWXp+t6Q+v7HuWbvgS8J1q0usBP7Mc/vAjG9jyJ7Tub8UCWCmky/Xk06brVfuPJ4o/3bCLFn7KtXq/MvjS5eVbt3fRqEmtQKulBIgj62FPAF9bBdjz6SVi9XRvwUoBEfSLjB/u20A3EVpMyC/KsMLUjlMtEEuO3osBqxWnZQccoLkQVxpJWnTLtRAI1z2giBuMn9TwomOcmOH5whgAw9Pj2OX/aaX/gME+QB7yV0kOyMZnNmMq6ZbHZhw7YKoowwxsxtEZxjxleFGZ7bOWQgACXMeR9M/VkkLfadnAopMA5yuxtKPbLXfLtwGNWPZUnCSOfcxXiSP1UiGJXf3tLRu44nGqCPDF5cBxnX9y28uGvbHIv7jvAjdqHUDpx7XIx6LAlYSpKEntuOpsqe8V3tsT0nl8oZ+y17IBHWugUBxXWDygUE4XL4Q7m0VZlqqrALHkWK0YkN2eKpeKzb7OT6R2IPDt4mnd/ckeYAfmILAni+nRaTCcIMYAGJ4GR5N/cqrk8eTxADM3e8kYbDXkEVUz3NTAZkwaeAyDDZAxr22QfDK/Exh+J4+tzWCOyQ1Pjm9+85vjBziOE8dxorFgsERetJRnacfSXz0VAEH8eE0+SMWZcpIWE2D74MFbt24dauM4OEdsxdM7oQBOl5Lzy30B/devhP/iM+dcuT/4l86k3/5Yfmk5KTm6I/WZ6xcZgRxyB3EkAa2pugosFSfSsT1LD1J93lpLv9/h0lKcpPpeV9TdSRXGzuNzzWRZum4CuMsCSO8qdzVOmgCJ1EDg979AvXOy+aP7fs+cA2AwGJ4bjAEwfA7MKcoH5Oz7PpUin42Zc5kyBlfluZ08uv8Qg5EDJ5D/2jyu5knsZmQcmzo18+SGMQyksWtpDRLR8tOS8IDdwLrbtIBGxLJOgIK0gM/a7n4oR2lqKdgP7UsFLIszJX26CLATOMAf3fOAZUetd+HgHIAvLSfA3bYoDR00dhRLkJmRc7U0+zUrRP6F0+l7uzKxHhuVlXJ6v8MMrJ3rqkIMhKHjeTGQ3u0bA/uq9erFJvD+h0txJ3RcnSQukC7Wq4vt0VMaDAbDs4UxAIYnztHl/7VpOpfPw0k5jZzMc0fZIvr4rYb5V/Gzdz7P5Xl0/1GG86aY5nXmd1BbW1tbc1eBZ4wJ+qLuaWTnABx7GoAthNJCHzTuVGhb9v+3VkruNq1GBPBKNfreVqHdlb2Fc7S3Fzz1e//0t3/17f/0cCSIFcBOwBeLessXwFbP/sJyAvzbFy3gO48ey/xTxfgnqchS/++2x50vpjRlW9fLOkzxLABb8t5u/5IwFcDlMsCjgDsPi5dKDeBhT9iCx8v7I+h2nGJglSrKsnSaCsB28LyEG/Q+klEIcPOTpaRfs4DtihhKFXVQwPwsYk7RepqMOc7MYHimMAbA8GQZU/s7T+fy/Ppv5hAZeZJzTsTMZKI2zx3lGTOK7J3P6QFmvqn895Ixwxs4PD5/IHIbm0NOhimjDPP5pk5NzP8ZkGqV1fNGqSjYAhhk/XhS/9K5oBHwx48K39t6fN7tfoRWIlHH7wFkj35pOdZwrqSydfq90AZqJe537ET1BfkXlh4X0J4vUaulm4PUoiMoTaJElrDUTdjZcheq0VoJIEj7dcw/2bGAvUfF7BLHAoiV8MY5i8f0Ok65jP/Isi88frDyutp7YAFWTUACFCsA1690HnwGEMVT9xs1PDVMha7BcAhjAAxPnKO1v/PsAORR5Bnz24yMPOJszih5bupEdjPy7ADklMjjyS6f+NbNGWvrKVZo5IwyIM+exrFPTRVlmInZU6PI9gQUwpM6S+gJUwBpkSjxUaPwpSUfuNXyzpW1Eingh6oXS2CpkI45W8sSuurUS8V4N7A7CRUboJfINLSASwvylXIA/MmdAtBtBxcrk/p0ghTYUkcppz22A4BHI7BKAAAgAElEQVRm211bxrPwLPZadrGYAK6nfvm6D3jdBPjxCJdyiIXFME2FV0i9QtrBJtsTWEAeOAerJoBSOQTCLhfqDx5slaXVtj3Clj96YoPBYHi2MAbA8AQZ0/h/oHLyyxSmXDFdOziHdTbybzKcFHli5RkzD/O8Y8PkEc0zKPLZGLyM2WINMo5yeoAZ7msw+IlmT43HEjpKs84/aEi1GDT4fG+v2I3FZlddqOiirQAfIsXZsmrv4VrHZ9YUbQ3sh40Sp5YLyV5CkLCSWFmL0Yqjk1Q4UsdK/JsXfOCf7tKNrev19A9uyyAdma2TpQm9Uk0VrBaorEZAM3G9g5e7v1/4yvnepy0BdFM8+Gcflc9X9h90RDDJXyglJISBBSxfDa1KAFgHPY4y9V8sdBcWE8CX4e37VddTQBLLhaVJvUufT16YtfMxyTkmS8rwEmIMgOFzI5M4R2X6qDQJplFFw5zKfQhrxlQ2Y36mcho5Neix5FGW82zOHGXiSz2pQBOZ86a2cvSbypgzUMbE923ADGaDI+3/h/GDxLEeHwJgCV13k/Vuf/xaMV7yXOCDOzZwqWJ/AGp0Zv1a0b7fceqJbIb2167E39vK5hE36sGHu/38nN3Arjk2sOyFwO+vW4Kxk4IU+lHPKscA512y4oS9kCWPIOh/qZ0r6p/sAfzZrYNOQ9PQbrnA+XNRp+kCaSIBy0oA19WA3/R934UgCmV7qwzmGACDwfA8YQyA4UkxZvn/EIc8wAnq78EMmz99NNJRwTSPzRgen1+KzXabUw0+Ss7L58loGlybM9bTYebbGZDHA8y/f5I/qSnPwv+7777rOM4YuT+M0pRsDbiuE0ShJbQt+01Bz5Xj1+vVj/b7J93GKqh76kot3rpvJ6N79vcSseGnNzwFLHgJcKHSz9EH3lz2i5b+0XY/b/5fPnRuNll2eWNRffBowlL6biDDkIGur7t0YMEFOLUcAH7cf2qtyPL5EPjOD21gYpK+SoVSQkpdrvanqC+HSnqddv/UgsWVOPbxu5bv9ydzPVUqJ2XR6OzXjp/0GcB00DcYDIcwBsDwRMiv/jMmyp05yeYfaKZ55P4ojs7zJGxGnlX8QwwuyXnV0T2ZaclTAbx5EhUaExU5JxSIHLHmfNMG5HEaGXPe0bEUbG1LpOh38bckv/Ff/N7/8lu/BlyuloCd/R3gSi0G1rs+B413jvK//sY/+PXf/m+AiqOA7OywdtQfXLQ0sNGTgJQCWHYB/ETy0+cQH2WloLL19kTzSQtL4FZpRiy4FC2AzQBg9aBcOWs2emM12e4eN90RlBKlcgxsPCidf6ULVKoh4JX7pQCdtmeLEMjyfwwGg+G5wxgAw0vERFU6P8Pq+ZCMO5Hoh5zMmDmn1f2HmDZviin3NE7EZpAvFlPeyFHyx5ozEE/GaWRlvuPH2EJIIQGlNeBIbQlhCTyLUHU8WYn7qptmhCVsoJvIdiQcRmbWFyxtSb3ecc9VIsAS2rUAPmoUgC8v+5drKRB2cC3EQaHtckG1xciNBSHY9OVZm4tVdbslgVQDtGLRitnerrz2Sv9E4ez/t3wcyY3VXCcBR5HUu3fdlVcae159KQQe3C2fvcL6/SqwshYDg92A/Z1i0W4DC/Vuq0etOrsZeK5X6E0CvcHwPGIMgOHk+cY3vjHV8v8LRh6ZOCeDEMdqwezZeV7GoWvHiNo59zQGqVlT6eb8cpyhKFOFOMrEcPNbmgHjY63lq9N49913xw8Yg2dZfpJqtEYrpRMVJWpPawE0D1rgN6IUKNvqfjqyuaZnqWVP+rDecZcqAjhVioG2tnYD8QfrpShRgDxI3P/Z1fCjZtoSOhmtpbWmZFNxAK7U1D0NYEuWPL0XCqBis+oC7Df7bYL2e9bepnNjLX7QkW/k2FzxvBRo7HkRFmC76aVXG5/dqq+sBUCvIxnqMVqt9TZvlxGytTGmH5LhOeDEbdjbb789aYjB8LlhDIDhhPnGN74xaYjhxJioSudkohId82weBhsaEwX6nE5j8PNE3TzMVE4jY+KNjCdP9tRUHHvm17H0/Mgr2BYyUcKCom2B8hMZJ+LTlne1FgIp6aJnAdsIoOKou6GlGLlav+XbO4GSCQquF7I1eK8TS2U9vuRsSW0EABfKCvjuI/cCxAxaEB1DI+JVi3/5aXFhMXz1IhykDF2q6OVy/+zfdsx6D+DVCvu9ERMdh2Vrx02zU8BOX+xurPdrDZZXez/5YOm1L+wB5Wrc2ddAtTTN1E8Sc/6UwWCYCmMADCdP/uX/USumJ6uBDHPypD+OwZbFmA2N+cm5FTBVItMhnprTGIycKkpOXIs0oZco2xLewYPf3SqFsfr5071u0k8lOlWobNg7q8V0zxqZsW8JeomsgOz/qq8tFH+wEwKLHnUveW+7v3twupQ8arLguH/t4u6337OzFqLHUnU0B6eMNfc9LuIn+KG4WOlf0knwDqp9/9q55M593jqVfns3vt+l4EzI0lGphBSoVOJOxwGkraLAiqK+IWk2XK/QT3laqIe2ArCEj4U9aXLDS8g777wzaYjB8PlgDIDhJJmY/DOs8EZpl5xdUPIzJjHjBKMY5udJfxzDyVFH/yqGH5/nleRPbRro/hlyh3L6mYkMtggkJJoEZWmptSpaxGAJ7dh6N+jLX6WpOkUglhrYCcat02e0Y/Hzy0GkRNEuZ+cMvLXiKat3u/24Jc/FSgxcXxAcJPSP4X/6H37rnd/+b4Hzq2HB5tZ2/exiBNzrCCBObWB5JSo7qIPq5KqbAmkqG02XocN9R7FQULv7HiAOeha5bhpF1pnz/e2FJJaXr+/tbRUA35dAt2MPGhy9kIzfYTD7DwbDc4cxAIanweY03cqzYYNeKDNIsYk2Y7DKO7/aG8ZsaDxHjPpQTuTDGkwyUdwPe5JpGeM0ZigA0JCGCWWkEL0o9lxH6tSziBVXatH1hbXN7v1s5F9sa6fbrXvpciHtyn4Pn2NZKiTnygngSg24lt70Ox/vF37hAleqEdAO+jbAkTpUIVB3C0VbN8f2AVKaZkQBggQcHu67i6f7T31hLQEuFFPgA/j/7jlvLNWbe42zJe63x0zZRwgdhVY3EQvLYXPfA9ZO96CSnQ3sFq3sfIBiKQaWTgXdbVWuqMpq8HGnMmFqwwvNC3NcmuHlwRgAw4lx7PL/QP3kVP/D5O+JnpE/1vCAU9OfvTrMKG8znLM+z/xHMTbj+eKJfi75nUZO/DDxXBtItQKksKRQ1xce30In8aFQ91Jg2avfY2PUVEAv6Wf47AT2QmTFqagvcn0x+NP7hS+dToBrCyHw/rpcLqTAg46TJR05cpwBiBWA2NnUK2u/dCm61+H6igu8uyMApfm4aQFvLPULlgMF8NZq8rCd67iuxr63tszCYghlQCnh9+xiKQHOLYc3G6Wo6a0steNAAu2GU4OvXu/88P0XeQfAYDC8YBgDYDgZjtb+5pfjE5noAeaJlV0ysffiISbuaYx6ajY5ePQGh3M/shc/GDNbiGM5pClPcGbDiZPz0xlTFpxqDUhhAdkhYGGaIuV+uA6U7RqwT3BjMfhn7xWAc9E6MLpjJ47UC651/26jeHoFcCwNXK6evVzlX+00X62FJTt7JeluYFW1OFdO7u/4gBz0BD2OgqUbEY2IKw7AxQrAn+8ihG5F/QZBWz5frabA3ba13uVsIW+Cvm0rwC2kZVvvH+h+oN1yL9f6P197pfODv6gBqhtmJwaMx2TIjGd8I1HTZtRgeBIYA2A4MY7N/p9BkQ+TbQLkVDZzxppoMwbDmCnWtBsajM1lGv712A0NcivCYY5dRT72ZmeYfAzzvGbDIb75zW9OGnIMmUB2JRolDkS9I4kVD3vZMbo/dYzWV1bDzXsAavRhwMWD+uC1kmikAqg5ajd4sFw4D3ywV7hc9oG1UgL0fGKFK61FL22O7QLUS8S5AjsBp4saqHsJUQPq2bONkJu3y+fPdYG77f4899rStZhoAqLIGtT4dhOBYKUWU462dwpAKxbAtVf6lQCv32h++H16XafZLNyHNBnthAwGg+EZwxgAwwlwNPnnRLIRGFrbHqMLN3P0RM/JxEDZDzOH28p9yCuzOo3ZaifGOI1hDrmOGQINOPYvZG1EeS6zRjnKVAbshSHPQWASpECA1hpQWkshlNaWoBHadS8BWpG1KAF++WwK9h83XVYvMDoLqJPIjZ4GvmAvx/bj88LC1F/ykvXEAQq2bqWCfsWtaMcTVTpAoqi7LHpJdDD8eo1E0zr49cF6+V97vX21lnzasi+UuQNAPOLQ4gHxzqfULnpeuv5ZBbjwev81r64E2zuF8yXNQbWx4/YjVRai/SbBriUnV0QbXnDMVo/hOcIYAMMTYS3fWUV5eJpybWKsE7mjPBp0nndvcOHEKMPMEG6GPQ1yLPYffXx+Pzk8w7CzOurHprqXF48ojosU0DpzApa0UpUCV6vlH+30dwDCFMASzpeXpUAsvnFx1GxVR50qplu+9ePG3pnSQiO0Ey3q7lL2bNHWe6F91o6B0yXWm/zJI6+z75Q1nWTk6cKAZ1GwCFJut5zzVXZ8u5diVwCu1QDK1x5vVtzr0OtwpqyBB8fP9xgptOOoS8vxhog7LQfYbrqrZZotx3XT7ZBVj147S1tSn96pRtJN4vVe15GTdxcM4zDS2WB4yhgDYJiXMbW/c/YozJP8M49QHpBnVf5EAuVhfrGbkb3anG9gtug7A1N5gInSfxSDCtdpL5y4aXMoe4p89mwMY3KoDv2ZzRPlSaC1EEiNzvJ/pLCUTgEpxFsr/W+Ky7VI9eplmy49QIw+AmyYbiyv1gqVYtIIbaAR7W35dlMVsmfvd5wlx2/H/MGDArDkEY6bDKDqpEDBYrWoegdOoRUBXPNSwBEa+O49u5sAnC5Cj0SJmptLpn/8sFA7R6UWL9UiwJXuaj3ebjiAgpV6BGw9sNtNF5AS4Nz13vadGf8jMhgMhqePMQCGuRh17u/afD1J8kj/AXPajIw84eYPlD9nZs5A5HgP5/mAhsmZ1zSbgh+Q/UXlV+czJFANZPpsL3VixMFTw0Xb00YZ5ugnuLa29o//8T/OefRvxqHBMjuzSwiBsITNgTPspU1L9L8vNnotYCcIGlFQtukQMQKBKNoL0HvU616jdqMeKF3eCWyg4ijAP7g0VuJXLpeA7/jJwo21mz8e11xoL7SWLCypH3TlmYX+g1dqAGdK8aOeA9yohzfq/J/v8VfORN+6iUiENa6uuI/WQmnCrp3uFM6/0t5rLWQeAChVkmWvn+XTDmRv17ZQ197a3zAVqk+F27dvTxpiMBimwBgAw7yMOvlrWNnk1GEDHTmtKppZmudX5POTXyU/TWZ734bJs3A+v9glt6ucKMTHM2wD8rzgGcIdGpwnyjB5NlLyJP0fRaP0QQN+obUWAhCIMO2Xvd5ru81WD+zayDmO4Y2l0mrBiZS43fK+WmelkOwEdiOwgC8v+8DNh3Z2Vtd7u/3UncvXzx6a5NatW4MskeHDwu60WfYA0i7nygCRkrFSH+wVvrAU/JUzEeBZRNBN6MUTTMDwTtiDu9UrP8NOww33K2fOdYGHPQHcu107c64DXHtrH5CWcj21uVlKQlMEYDAYnhuMATDMzsRzfwcMtO8h6TN/BnY2Po8uHGYG3b82x8Gr+Y3N2knUTuRxGidOnlsbP2Ai2aecU5c/F+TxToeYanB+BoZBaT1I7AlU6saR4zjbgQ14trLQTXilGm1047JDxZGAO7r6NVJ6N2jtBjarxWtwpRa+e9f7d14/v1bke/vtIBZAJ5aQtiOrmDSuLfDh9uREmgVX3esA4q2zbMBu2PcAP95nLy1cqGjgyys+8MOd7KCxXnZhN4cnEoLV0720EAGrC2Jz3wUerZeBhaX+a3u0Xrnx1maWTrRSj7bBK6TYufKLDE+f8ad0mTajhpcTYwAMMzIq+ecQh8TK4NdhyT6/oDk6w9Hc7uE87HkiTusBpo2YjZwqxIDh15Y/4jxMe3fzMz7W/PYpI09e0wxr/4cYRMkj6/O43DwNQMecA3CAcB0PrYAoRUuWvATYSGSKuFyNNJw+vfD+nS7wqCdcWRo7W59HDxtXLiy8uRg0w40F7zTQS4lSkR0SXHXTpEtKArRj6YhxYroVyWWbbsLZSkxn368uJormQTbR/Y447fWV+ukiwLd9Wh2AdJJEtyxdtFgtK3mq9/BRCVhbjJppvyqhULCA66/6QLuFhPX7latLjeVVv9N2x0xrMBgMzxrGABhmJ+fy/7FMlDvTMnHCL37xi+MHTGRUiGMl4DzKeHDVDOIy/23mzKgZQ857fMor99M6tGOZqP5PijxOY0CeN3CSuO8zygYoSHS/rrYopGPhWKxZp4ANdi5VClDY2d9Z8s6X7Jt7Kh3/JRIrUbSVFDrL+F/vOufKMWAplrx4L3CBku14VhSl4oNd+83lZMypAoewJVpzphxnueGvLQA0DvYP3t8t/KtNa1n1zpYo27Sg4uDnS9LpJlTh/Nnefq8EOGXNQZv/bsdx7b7VWL9fAbK6gEIxEd64zkWGz5en32Xo7bffnjTEYPg8MQbAMAv5k38+L/JIpZk5lHd0SMCdSOjxexrD5M8vOsqxGzJ5yCn9M+Z3GjlLCOaMMiDP3Z1UrIynGWsMAjHQ3xEpgHKy/pY9GV+t9U/aWvLOA6ESQN1NN/XIIuCPP3zwM19YA2IlHKlXi3GkWHJOZ89eXYiKB5W5C14KbHSdXiIvVQpNrzxqTuB0KREWoeLTlnO1Fl+rK2CpkADnChr4v+88/morO+mpAnEBwD9+vsdIqYs2rqTu0oo5W02BbcXGgzIgHae+FAJxKgs2r17uaM1PH49mMBgMzwfGABgMMzJRj87PsHSev17iWLJJtNYTV82HX8C0odem7OEzIE/B64DB2zX/JsDEcGsnUa0xFRNf0jxkNQCJ1p7QlhAJ2EICcRwXXOdmbw+4WKpljUETldrSO12UP1G5VuvPldOipYCiZftp0tNdxePFcq3TtaKGHD16fpqqA9BNrBUJYEudHLyenzlI1i/GKWBLLr2y+tnd7WwjYiK9BKDmAGz2ZKgef1E299041IsrAQcVwx/cL1iCspfu++b71PBTvPPOO5OGGAyfG+YfLMPUPPvL/y8ST1T2DVhbWxu/Tj/Y9Jj59Qw8ADluairdP8w8HiDPwv8hZogyTM7kn5xbKHkKAMajtbCFADQqKwV2D1bobaE3e3vAanHBll72YMFi05e9JEyS2LaPSSgScP/jzsXrte3AWS3EwGcd9+JaCrxeXwW+s7EDnBXsR5wqJVmg9a7fSzpjCgtiJV6rxY+6zo2FqBXblyrRZx0X2PHt8wUEGrhSi4D1He60LRS2BNgNrXHVoKC02GhZ58+yFQigfaty6kwPWFgKm3ueEH1fsb9TiLoauHC2V6nGfgjguCYF6DnGtBk1vGwYA2CYjpy1vwOOCpepBJbhaTLxo5k4YDyDy/PI2ZljDXsA8tVR5BTih1ibozEUU/qNLFbOwYc4Nt3/2AIAW2ohRKq0JWylk+xBS4hzZXez1y+E7Sb7SeIAftof4MoJ+wACESse9pywKYEo6QFbQQgseQC7bSGFBpQGOFUUPbs4ej7KQ19cNScBLlWih1Ele2QnsN9cCgA/EXfa/az/c+X4o5TKpG88z9JaAbhSR0oAW49KOH3D02m5wNrpNpA1LH24VThdbny6LkdmQRleAsZ3GTIYnk0m/XNoMBxh/PL/QNtNVEWzqZkxGLPxvPCkP5fh+YeXzwd/kydVtjF84cQ/+AEzlG1sTllIPds5AAKhtVaJSnVqS6F06tD3CWslrxv7QqdApHygZDlvXLvBJx/1RF9kD7fqz9AQKf9+d/Frr8TrXedsKQYsWQbOOKeAhr+x5dtlUFqcPVNz2okqHWjwA27fvn1UYKVanColndgC/FQULQ2cKiWxemxsNnrupWoXQMXAkseenNxjFGh1nBoISGKrVg9fv9YE/uLDhYuvtoDbn9SBpfp2NthPAZYqqePlmtxgMBieBYwBMEzBmOSfzdwtEY+OyalpjnI06CFhlzPhZFoOOY0Tn99wsgw+oCf0SWXTHus0MmZQ/Ic4unlyaKqJ+T85LYFCp1rZ0oK+CpfCrtjLm/4DQGlsYS24S81oq2gvAAWrImn83j/57V/9D/6zUXPuBgIoWLrmSGC9m54rW8CjeON8uXy+zEcb/YPGmnEbaMWpnwZjqoC3A+ui0IkWNTdtRRbw3m7hldP9F1xz1Z2Wy8FpBgVLN1KZKCxBMqloQQrqldR202bbKZUToNXwIP3j764A3r5VWwyBr7y1s7WVAG+dTj66x1LFJP8YDIbnDGMADPOSJ53jWIYb80/FGLMx6sGp0i2OMv4eZ05Yn0j+FV/Ds8CTdhoM2Ywn9FeXJeFgWaRKCstP4wIpsFY834geJQdr3EWrGoho03+U6iTVI32FI3WQqrNl1Y0lcLttXammgCuLO+lGNiZV0flyFYhT/4HUzWiyS8mwhd4NbOCThkf/QDGKVibxNeiyowJLA4kCSDVhPqG+WFQ+9Lo2cOpMdy8qDJ6ypL5yrZH9/NbpfhJUrNhvOmGYqyTaYDAYngWMATDk5djl//wL/2PIv04/c7jMA8ygp6eKmEWZNsRRhv3GGO8xfyDD88i0n3uOk79+CqWxJVGSWtIBFaYKaESPgKXChW6810m7ErnpPwIs4cBgtf0w2cM1JwUKlnpzsQz8uG1drgI4klgFElko02752SVKi2XP2xLjmgKV7PRRz7lcTWMtHKF7KUDW4UccJPmUHQVIoRMltgPrfDkZPd9jupGoKaTgzGKsNXKp2enZwBfe2gH8oF+Z3OjYV5f9dkzVoRX2X6qVL7/I8KLy9I8aMBjmwRgAQy6O1v7mz/UfT07RfFLhcnqAqXT/MINL8kQZJv8NzrBnMoYsrtbjtMu092J4fpEgIWsBlKq44HgFy/61/+Q3/u7/+NerjuzGe9mwVrxfsu2dQFnCbkXjems6Ui96C0IA3Ou0LlZqwB8+jP6NSx4Qq0Shju4htBO9cGSqQ9xpW+dX+LQlr9QBlCarRq446cVSBNxqecM5PxcqcVyasAUgxcEeCGSvuVJKag6tGOC10xGw3lYAmp2AnYBzFfXjdekVUj/Od8yYwWAwPAMYA2DIy7HZ/xPV6kTyy9k5Yz2bToPpzcahkTmjDHP0HkcZgOzTyb9FM4bxaVRzTv6SM7EAICeBHzmeDWRluEKIVMeZOr9UPg8kzW1gI24OLnnkN1aL1S3bPX7GA2yhEy0AP2l9cakIZI07LxTtrKQY2A7jRbFUtHZ7iV93x02YKHG2HPcSuewln7Wdq7X405YDFCz1g33ra6dSoBVZq4Uk1RHQOdD9WSbSGITUKwX8lBqsFvj2pwvXrjaBzANkJwPciiSwUGYneHzh9UX15+u5DhkwGAyGZwFjAAyT+frXv35I/c+c9z8bmydx4tJAy+bRmvOEG5Qc5Ik1z63NkNf0+ZqNMR0zB29axgyBhhnOjz/6tzrn5C8egxwhDbaQg28FrTWIOBVxym64vuydC0tFYCFeAvZ1/wjcrO/+KHqJ9JP2WbcKnCuVItVbKy4CyvYBneLKohDyfrgHNHUDcC2rm4SVMZMO8ZWVeK3IWjH+drPy5eUI+N6WW7PsLyyFwP91pwi8JnpAN5aupX7nd//h3/qbvzZqNg2upWyLM0WygodPPl342lfTboIl+GBffGFRA2erCsWrNX68z60HheuLRvobDIbnDGMADLNwrK6agamW5E+E8eHmkeNHmRiLabT4seT3AHPubOS3NMMcusfxobeGisKnDTRg+K9la2vr2IgnazaGeWHMhhSWJR1AoFyLrHtQpIJUxZZ0XFnMhl0uLgZesw1ixDm+kRLLXhEI0rBgeYArS2HaBRaLp4B2uieEBPxErhSSPVTR9o6d6ij7gbUYWcDNhv16PQF+uOt+4VQC3GvKD/Y8W/J6vXezIbIDvLKqgAs3Lo6dFWC5AGALXrvWBMK0You+H1gp6r+0pgG/w4/3AUqV5NauSBNRdkwNgMFgeG4wBsAwgaPL/ydC/syfE+Eph5vIybqanMwm/TOm2tZgDis1bAPyM629mS3KIQ6ZjWPHDN63PG/atGQv4I/+6I8mDTzMqLJgAUEQF8sloUnC2HJlVgxQsGXVWc7GFKxKKLpATdYBpVNAj94E2Az8y2IZ2Asj0esBV2p9iX+ntVm142zOlUICSOTZi7U7d7a1HtdR59MfP+gm8uLVs4NH/nTT+fkrAbAdl6B/7m/GjQU++MyuuYjIitSEFCBHsOXLxZSKq/75B5XXfubxU6eLXF9Rg2F/tmky/g0Gw3OMMQCGcXz9618f9dTawTGo5FZdA6aSRCe12zAxXBZlTJpKTvLf3ZyBBjyFrQamLKKYM1x2+cRYGXOajQH5w2U/5Aw6PCxniKOMMhv5PUzOcwCAOI5d1wYkUqOkkCmpEJbTacjQZ2kRII1pbzbLjp8mSqea43NgPItlz9ZoS2gQ4kDVP+g6pUL/IK3vbJZALWv7TCkZ5NkverVjJxwmUv2cfhUKeFy8G6Si5HKpHADvdwVQcwEKlgI8OS5dJ9UAjQjg3/pC5w7ew81iJXFfPxMB7UgAW74+VwI4VUmBzW7iOBpHr6emDajBYHhuMAbAMIExy/+ZmslsQB4xlF8ZH+V5cRp5JPIJkl//zU/+HYBpP6ZRTIw1/+c1YOJNZcxsbwaX5AyUcegGj8bd2tqatsvnGDIJrbXS6FQoSWqlVqpTobTT6Te/13HYijaBZtkB6u7CFu1RE/73v/5bv/N//P0gTYCqo9aKDtBL+svwKwXZjcTPrvp/vl08U+q36bzXsSrOwkba+d//0e/8R3/jbx07bdmWy156pRafqUb32u6ZkgLqrgJOV2PgL3bcZmgBtlN4zaMAACAASURBVEyvLgQVPwI+ayDFhCydVInz5TSGb90uAKeu9DX9zUcuUC72zcN6j1qxX1i8UuAR7LbtC+V8pwwYpuHWrVuThjyLvP3225OGGAyfM8YAGEaSM/knj6DJtHWekccyrTQfyOJpI04baJgZtHhO4zSKqezNiTAx1szv3iHyv5nzvIEZW1PWUcwTcaqdjWHmCToViVaAOugK1UvTqpYxKW4ZwO0vzNcCes0u1VN7nc1Eh914pPaNFXV3QSBqsu4nQdEuAEteuuhd7Mb7bqG4HzT/6hnarRLQCHuA0mmo/VETDghSAVysRpcqEbAXekteCjzyxdly/FnDXi0mVxf6nXr2QjvVYj+UYTLOA9Q9FSnRjoRX1WkigMWVINbFa2sRcG0BYM+RwNY+QUrNQdoUTTbQHDynEt9geN4xBsBwPGOSf45loqaZOGA82eWjBPqhlO45Yw3mya+6ptXigxuZ2QM85a2GqZj5pg7x1Jb/8zC/+s/I7wHyRHz33Xez5f/hfYDs5/w5P8NYQmb/LxD9c3Ol7Xg2lgOwtw7U5CLsH1whgaI9TlVLYQMt1Vih3o6D8+5irEKg7Cx24r2y3S8pVjpJtfjS0uK3HmxHSl1989yoCVuxKtpWkEqgFVmu9CIVAr+/XrpQt04V+3d9vhLFHQ/YC33AT8TlSrIzalIA2rEAfuF09Ocxlq3jSAK/eD0CNnoAmz27EfT3AbJspWw7wyukzcbLmAL07Mv3K1eujHn2yb3+d955Z9IQg+HzxBgAw0jyLP8/fY5VTnnkVE4GU42pcDi0Pj1b9GEPwCSdN2AGk7N2UK2RM8RRpjUbMwfKmMpNzXNfwzyDZoO538kZEFolicrOAfAybas1EMgYsMuLAD0VVxaongJUZVGwn4zOqy9aLvRb6OyHVtVNAUd6nYMzxVyreLfdqEtHpcmXlhaBqqMiqzxyxgOyuuGamwIf7heWRf/rbMt3/ES8tdrJfv20rZYLyc1990wp+WEkPGucVynaPOpJt+Fcvhh/uuleqj/e2ai63GnZJRtAQqzYDXmjzu7IBKgXgfHq2WAwPL8YA2A4hpzJPy82A0WYU4nOQDbzeBtwUmYj+2EqQTmVEM84EbNBvqBZrJkDDcifbnQi5A83/63lZLB1oLVACKEUIBFW/0gADcISDpC4EoiD/gkAqARw4jRr8XksiY6i1F8qvLoXbADtyPpwH+Bnz2hAIO62+9UFlnQgbcabZcfZHTUdAAVLW8LpJfJMaQW4302z77Lz5dRP+l9qrcgKlej2fJA3913gk6YLxHpCIyDAswCurkVQvlpTf3DH/cqFKHvqbpua3T9y+I06QMnWfgp9o2SYhVdffXX8gNu3b48fMJ6J8xsMLyHGABgMnzPDYveQ8B2sBOcRxBMZnmS8uJxhn+EQM3iA2YLOEGiYnJsbJ2U2MiZGXMtXi/Luu+9OGjI1GqGEltKGsP+QkIBrFaI0ALaCzUroAJFrgdVNHoye7KdwLftCubgV9Jqqnyqz6fvAagEg7CZAM94EEhWF2i+kI7cV/FS04mAVPm3tXa0tASuFZNFLgdM1H/j+TjE8iLJWjK2V8LubXpCyG05Q/7GiYtOJWZXshrR74moN4Pv3XeDK2f5LSjSJ5r19vrTIRq8fKBrbuvQF5gWQ1/MYDLNJYnhOMQbAcBiz/P/sMFEpTsuoCfXQ6uWcQQfbGjlLKYZXxGcLPbMHyKn+B8wcKCP/2n8e9X+I4UZAE5sCjWscpFOBA2ghUAcr20IA3WTXY2G1cMpv74euBWz6LSHkjTdfvfnpyBdcSK1Ux/2p4FSh1Ez5yjI1d7Xm8knz3lLhAtDq7uvtW4vuWeA+u5417iDgkq05WHH/YK9xplgHCnamzq2CZf/8WvxnD13gktsBvrvpARWbLbCtcTI9TIR14BGWPXbgTzasYikRUgNCWEB16J2LFBcqOvJY8vTHx8xnMBgMzyjGABh+ivy1v6M0ylSKyvB5cehj0iedvjCYf9gJjB85A4eunajO5zEbg+wmcgQ6Sv7UpgFz+o2JHLIBWdGwFhKBQAilsOysNWiq49utzxY9G0h0hFvywI1SoCKqo+Y/mNfLjgmrWIt+2gF+8fTV7kEBwFXfT6L7LF7Q2/1azFa0VdVlwm2cwqgptWbHty4okSoBlPvSn5v7Ba9nAz+/Ft9YDIBWU54tLV6p3Qc+3qLiiPEZ+7alN1rWKxrgTktUirhSK9BKCKmjFMARAMtef+/iZkO4EKZUzNepwWB4fjD/YhkOM2b5f7g2cZQSGjyVMZXiGcOgS+PJZsUYnjRP52OaNsq04zPWZurdNENqU55Ag/yfEzwHAJBCpkoBjlfIWut0Ir/oOsB+mCw7gRASUFICF8prd9LbbdF77bWLI2cUliO9SIdB0pKqnj3WiKJToDc+yn5N0iBZuwz4DzcA/IZGJumY1qIC2AutNXCkzrp/fn+7UDz4QvvBrv3GQtiJ5dnSYvZI2VGxpmSnaqzVlWBZqmCx4fcfiZSw4c1lBYSSRgj0awC2A9oxrVBWzQEABoPhecMYAMNjxif/HOpMMkYAZU+dyn1u1BiGgx6KOBwlY55Ao8hewJOY2XDiPIWP6dgQ40u3Z3tVhzKp8vuNOdFa2dJBpVEaO5YdqwRINJcr54FWuK+1oteQQGFcls5jYt/W1p3bG5evnAYcyXbwCAQgTt8Ik+5+sLkAwLc32xeyS4RsizjVI3uY2lKfryQXK7FCvFKR97vOhXIM/MLppK3Tex03G7bt241oHyg7CjhTSrYdZU+uAWa7K89A0el7hb/zxR7whw8LwI0691tcKAO0D15goln2dOCc8Daa4TniBSiEMLxsGANgmMDMHQkHAn0GDZ2nA3rGIbPBlIEGDGc0jY872/x5mNMsGZ4mw5/Uk/vUhrcCmPSXeYjhcwDy7xJotNIKcIXU4CapLbAFWYGrJe2sQyhuia1HwIfd+M3aQipaY+ZUAomldLLkrraTJuBa+oc7D16v95fnbavgJw+/vMzGRgC0ixbgTqqpTTXA3Y46VaVoVf+9V5YayRZwsRI96Lp3Wn0bsFYs7tDc9B2tiZWI0gnT1gr6K2vxmZX0Tttarug7nf54S7DWP7GA+11swaLLfkTR6ScgdeMXtgjYqFuD4cXDGABDn2OX//ML8VFkGUH5FdJsvVaO7gbkJKe9OfbZ/Dd1iDElnoeemjmE4YVhlNOYvwXQIUswbBiUQAmEBrCQFlIcpM4IIXfdsN8jyKvANmCJkWfhKtuRwlY6yX6t2gunrXpPRjWHm4194M36pewpgagFOq0u2XK/6lRhY9ScjhQ7gf1WYVlaCni12l/Vr9unbjaa2c9LhTRIRU1kxcoOsB3YQNUdfWYBZPsDD7rWGbhcTVtwuaL/wc3iG3UNNCI4OPkLuLPjAFFglzU7gSibHQCDwfD8YAyAAUbU/s6v/gfkWds+qXB5YpFb+h9leGcjT6Bh8u8zDMZMZZ/GMMZ1cFAEfCKBDE+Bb37zm5OGzIhEyCE1rx0v2z0ItA8ESQdYWljYazZXr1y6df/Wm4sLQHqg70chhVVI7SDt+mlyhnpJuT0Z+akE3ttdB+ooCyutLgGuLA6dNHwMWitXKmCloHcCAaQ62o3WgVdr/S+1Pf/xevzZcgRUFuWPHk1YpNe6v7Hwo10LuFzi6kLyqMWPGwI4VQW4ebcEXFvpVwl4bgrcqCfv3n9hdwAMBsOLhzEAhj6Hlv9PSo7n5ETC5ZfLJxKO3GYjY7agg/H5Aw2TJ+hg52Sq2xnPsX7jpCY3zMbwGv9EJDLWkSNshMyOAvio0Xyttgx04t26uwbY0gOfNBmz9q20SnRoC5c0Brdo2UCDJoog9QqWqrspQEBV1Fo0AUu4QgrpjjxcrJfIqqOCpAVLp0vurbYqWuqLy4tAFz9IJOBJBdiynwhkH4jzMYcWc5BWtNGyXjlNtufxadNGcLakgd5Bse/ymk/K5ZW4m9DY1jV31HwGg8HwjGIMgOGY5J/xq8VTkT8tZ1plPIqJKna2LKND5DcbzB0xizXxvobJo/sHbG1tHWoDmj/QMIOgYzpEMVNXnPEcfWemeq9eBvLXACi01qnUZFo31glapzp51NvLZG4n3gWksIK068qi1t08696JjoCFxEuLlR21DdjSvd0SYL1yXgFu2K8HyDqEtqP2mCJgR2ohUDqtuqf8pAlkmwndpHU/KAOrheQnDQ+wwi4QRxYQq6BoifFdgIq21pqlcgpIQckGuFbRQDcGqHv87b/cAL51i24C8GqN7Rbv79mJyQAyGAzPD8YAGI5h7emee3pSfiO/2TgpJgrNk7q1jInhmCO1aZg8gYY5FDRPQcWgOpwcfyGjGMzw/vvvH3rqCZmNo0z7Xs3Pk8v/GRDEiYXOMvuzGgDgUuWc6jQXKDYtP0i7gB34AHGgk4Njg49gS9cWLkKGrgQsv6PKLBfOA//xa8X/7SebwJJ7doMNoBVvAyiVaiEeJ9sfxpG6l4h2rIGivYAdA82oPejwIwRrxWTTt9eKCfBgH0vqRiiFEN7Yg8BiJYSgZHOxzNly9OctgBsVAKUZ5Dndb7sV16+4FC3aI2/d8JLy9ttvTxpiMHz+GAPwsjOq9WemaeYRkTOcfHQiTFTk89zUbMwfcaoNh3nCDdbv8+va2d7SQwJ9KvLvbxwdk/OmjmXYzg1mHnYyGfOEmMjJWsqjSA0QK+UW3CAMAMeSwHKhAMjKguq2FlRJ+I2OJ0Jvim+QMO1CXRdrrgzb0U7VXQH+w+t1Hu6qUwCtuAfc3CssqI2qU7DEuMlLtq46POptrnecf/fM5dvtHSBRouamBau/FP+vn7U/2dTAope2Ylm0dWPMjAB4lgbqngYedl08/tKKDgV7Byp/J+BPN9yLZYCiBVC2dcfWjhD+hDqIz4dbt25NGmIwGF5Gpvjn2/DiMebc382D5oN5ZNYhnsKy61Hy6+PZbuoQeezNCWq1PEI5vyweT36zMX/EabObZt7fGN52yBkr49CHOCqv6dAjJ/WfwAn+CU2L1BSEBLTor6t3kt0atbRQEUGn4/XX0TeD/dPFZd8enVkf+4DWiZSlhuUT+SvUARF0E7c/s4zCCuWKU/7OXggUrYXxRcBZwk9WrHyuEgMXK7Xbbd5cpJsQpCJV4o3FfgmBIzXwanX5/w3277RFdizxGFzJXiCuF5Pvbdm1Yv/BJQ/gy4sp8IMtca/La9UE2OjZVxbCzQcAY7cWDC8X77zzzqQhBsPnjDEALzujTv7KhMvm5mZ+uTwsUqfSPVnG0QydzsknxE+cnPo4Y9o7GsVTMxsZE4Xy/Oo/I6cHmFn6H2VirKNMG3SGjZRDnNTbOy1ayETrstZohcqy8G0tZMmu24P62aDf9b+Xtl67utRYp2gtJEli28d8obx38+HrX7oOVKhsbrQunFtrNcLztToQJG2gsHqZOAQIOz+3thCm3bt3ewBjlborixr2Q/t8KQnTriUd4MN9riypRmgBZbv+3t6jK9Wz97vrq4VV4FJFfzJmRgAEeBa7IcDXTiU/gff3hCzz1ooG/p+7HvCLZyLo90EtWvrPHhR2/LYtcUe2QjUYDIZnDmMAXl7Gn/ubMa12mXb8gLWDA4+mIs+6+MnyzEZcO7myjZwROSF5mj/c/AyvzU9kTgk+257DxKDvvvtuqVQ69GD+Gt+J+H5Y9UoAIjv6ywUl0gRIbNmN98rNEKgoD1hw1hIdNqJHYwp2s4PDhN8mrlUOvnFi1wWCdBcoyDKOF8oexULY2AEKVhXGve2O1BKx6esz8KBnvwKpilcKAmj1qJcA3tt7lA1uRdYnWy2gpJOLleTjkbMCKE3BUl0Adnx7bYmHPST8YEe8taL/zpfawLcfFYAksKXQwJaPhEWPnjVhb8FgMBieHYwBeEkZk/xzlIF8eaK5zodmG6WBZt5nyBi+ZCptN0NSx2yu5ij5I+bfrhlP/ognRZ6IJ3JrOZkoxKcijwc4wS2OGRhYCAGOkEgLaXOQ/KMt+Z//7f/5nd/8VeCurS9VzvHgM7xyokOAyNeov//3/u5/+ff+61u3bh06NVaR0t7GdkPP9sLEkq4j3E+au8D15RqA3+mP1IkUEugl+1boJ2PPFkh0vyXn+VKc6Gg3TJaKJSBSomxroC018OF+/zSxa/VgvQkwvgtQpESsxJUqP2nYKx7A2RLb8JdPKeDD/eKbiz5wqpg+7KK0kIJXazQ64+Y0PLOYAgnDy4wxAC8vE5f/jzJRxMzPqBAnGzpn0tGh1eIZXkN2yQySLv9C9YDBTc0QLiN/0HlKRIbJGfGkNjfItwlwIrYtYyt3QQUz/Z1MxfA5AOM2DbQCSBMAlQg8kabAQqsHlOoX+sO8cpQ+ksLCsh1ZuPrmuWMns7S8+Vn79UvVRIWJwxnhAivFZMe3s96gVqEGeM1t35OAl45s/jPAlXI3jFcttej1TcKyZ2uwpbalXolKO25QsDXwWn0F2Ntu7IXWetcuWlVbjHuTLUGqRaTwYCfkkgXw1VWlDg4A/uf3S0KpZmSVQR4k/UuhN3siKzZ4Brl9+/akIYYZuXLlyqQhBsMzijEALyO/8iu/MoP6f8rk1EwzM5h//Dr9/C8j587GgBm2GjLmka3TBj3BjKOcEec3G+T2G0+Z/B/cu+++y2jtPvz4PElBfpiW3Ajb09ISSgFYjopjKqeAIO0AlBY5KMMlDrRKPev4b5NUKN1tUDhXcZY78W6Y9jyrdEqunipT8OqN8BFgCxcohsq1StqiJKtXv3T9/VsjP+7/7td/8zd/978Kst7/qajay4BX1MB+2AZW9OK9qFlz017SzC45V/Liinjvfmd8aUGiKVjKkdRdyo4qDt1TpOR253Gdb7FSDLs9YLmQZiURn3VMFfAzhxHoBsMojAF4SfmN3/iNSUOm5tk3FaPIqUFnJpt/sGqeZ/AMDKJM3NkYMHgxWutp4w7G5wl0lKmqt+ff3BgmZ9ATiTXx456ZPMf65hlzBN2XsUIkQWgXPBAgsFwKZYKuIzwgxr/dfnhzP722kOJV0L1R01lxAmjHAcr2op+2/bS9kp7BcoGiVZNhCv6mzBLvi7EKgDDtSjFuK6DqFJe9pObYD7r60/YW8ObCKvCj3eKr5/u9e2yhXVkAerEEhLCARB0c53scWR3vbsASdGNZBeCztjxXBlj2AFoBdRdgOxDAbsNqh8K1OGg7ZDgxTiRF51BO2lHMDonh5cQYAMOJ8SRMBc+zrzhETt05J4dswESy8YdOAs7JzLp8KvXPNGvko5hqi2P8ptC0TIx4gtsp86MRQCykI6WyLKE16P6qeWsLUIUScNvfBq4tpABJCIjkeGGtHFdUlxMVK50KhC2dJGsu5BSCpJ2d9iW8cs1dAJoqdPF6qg3ceO34nKIDdFalfL4sAKX50W64H1rAzbAFhGnfyHzWaQEPulGiQiF0rCas02/58gzEiouV5HQpboRWUbfWuzXgTK0/phFxtty/30gBBCklx+wAvKRM9BgGwzOIMQCGZx3jK2ZgWHS+YKXbTL82P63f4GDwtIGGmTboDD7qSSAAdKLSrF9+KjRx3H84jbEcW3jAFW+R5qPPrNJPXXccQkgg1RGg0TVVAWhtUfj/2buzH8uO/MDv34iz3n3JPWsvkkUWl+6m2ItGLY9moJH9IBiw2gDhV8MvHvjND/M/lAwDhi14YBseQxBsDOwR1YYlDzyyoaXVi9RrcSeLtWflvtz9ni0i/HBOZhXJvHmT1cXuysr4gCBunoyMukUCeeMX8Yvfrw742olc8lylklPvsqWFUSarDSNtJvcWAMAR7sZY1zz1XMlZGymgFagXG0nXFFvxayNvuzMCt+TmPYPrsH7UjOA7eeMwgHsDdxGagerEzmwIMNq/lpyv/s9W1G7sNANMSQO3D5/SsizraWQDAOuUOj1xxfHXoI9t0h/xJf3RU3Ocfvnb27nHW5R/odOG3HHOUvILAEd4NOn/sS8A5NU/XeEA0gjHSMj3+CVguhsyT5PpFkU2g862KTd7ao3R8PD5tEGloVMbZz0gdstBJoFuujkfnMlbDRiVlFJB2icZEpRrw+jQqR6VmTSPEPqpAyyVnQsLSzvRXaDqKfZbGm+D2t/yH6u+0khzVAoQoPYPwy7W0jt9b6GkAseUfdVNHKCbME7YjpyvzKjd2AEGKRXYHEs9JWCxLMt6itgA4DT6sz/7sz/4gz+YNsp6HKcnrjjU8Ze8j+fR+Y9O1Pnl38nBojz/8jiRwGMs/Q8cJwZ41HFL+hzPoz9ulEIIR2nASFncA1YprTMMdgG8Up7BHygBxHpYdsK0VDlsYkxeK6e/VdbtkYyBu2nXWWwU33a9zf7qcrgE7IoOIOMRtUXSsWeSQyfMecIHRmtbweKcNip0qsBMeGEnurs59oCAFHipGb+3GwJXTPbOEdPtS7S4WNG9lBfLRZywMXYWSgrQhg86YrlsgJdbOtOi5plbPfGg5145asov10m/5HqcLH+bo29ZXwYbAFjWCXDK44pJHmOd/YUczH/MOxW/5Pv5ojHAlyFf7afDAWETnYnMAKSp5wfAzly96bVJxkCn6jDQJktdEWQ6dsTkTxM3UNUWUpYpBbIM/UCWgXS/fdhqtOZ6QZlmwo4KStCdONU+oVXFbfbT3TzdJ69N9P7eCjgXm+rOwAH2EndR8Eo7eq/Hx8IdZwZIzVF3i5UmUcIp0vol8EIjAVbios1vw+frM+lO5AC3esXZwuZYjjJ2jwpYLMuyni42ALCs0+sgrni8S8CTnPS44vMOXdkfp73XFzVpwr/4i7849PljO+LQQDguroN0jOtpR0iV3/RNt6O7+QDtOvilZsLayrtUSq70YT/n5nO0UegM6Oq9hmwBFyoLGg1Eqg80/BowjBNgcXn+9uZmV3WQ8Lm6oo92GYspgoeFUDf8xe34wXZ0Z77E5tgFrjR8nzaw28mX+8OapzrQ8MpTLwEbRMPnw4+322cX3BI/3vK/MZfMhGovdq42i7/jTKiAuRJbY1rlLK+B1PaPmNWayN6gtaxfCxsAWJb1hJ2S84onvvo/8Pk73E824efApKm0EIDQWgjPuBIpgVlmgfT+25x5CTA/+wluKEp1dvYMZHryBng8dDJV9+cNMO4B5eYSEBiA2JGA6tygNg94MgAaTmMj2504IXgyvNXvgft6sADMBmc0e0DZ1Wtj90IV4O/WdxdlB3i+3t4Yb7WDevul+vd/flTOifvI8cAr7fgjADIj8vpCreCzQc7VlvqL+3LOIXRYsXcALHjzzTenDbGsp4INACzLOhlOSVzxGb9MmPFFw4aD8dKAkAipjZLCwQ8Auqv5MPHgQ7xZ8Y1/JO6vAiQTmwAAeWqQ9jxx5wNz8WreZlhLCeRXAhwTOIb6hRd6d29QnVUmbYjqERPmpGG5LFdHepDuwkysBsNEuvub+7XuIAuKtXzJcUcQaxmrAdAIpxx2dWKWoO7rn20HS8s810iBFxox0NUl4N7AA4Yps2EM/NZSduMmFc8MvmjHBcuyrF8fGwBYlnWqnc644mhKGMV+tZz9NgBq/hKg83r/m9v0NlApgPQCtxRM6ANw7dp/97/+8R9qkwEiHlO9CEiVaccF9pLOvL9sBEIrqrNA4FRgq6+7icMRnbW0MKlO5kICp5L3DgMygzbiNYpqnb/TDH+05exhesOdyTN9iitNOyw6DFxppn242fWea6Q3ugHwXFsB2zjDFOCdXef1WeWIJ5k+Zz0Drl27Nm2IZf362QDglHrrrbe+853vTBtlWdZjeoJxxeLi4rQhT5IBKaSD43keKjFOUcnH6e8CcVgGKDXobQA405ORjOO6MuTCBZKxMXq/M0Ba9Zp7SQcwRuX9h3tiSGb6DI6eEBikUeAsxmo8THehLYW7OnQWyykwbraBj/Y6+ciXq7PvdvaAEVKj5bRPPf+Rywwzoe7G8m7P+/rF6L29EBgr6UDdo5fy+qwClsrZhwCcr9ocIMuyToyj6iFYlmVZz5LPZAEdmhRk0EKpvCEAIFQWJ5E2ilKDUkPiFK0AgrK5c9fc/ERVG1JjwoOmYJ+V6qg4K/BLsRpEWY+9lfxbZyoXjTH5J1FPFJ0Eym7DlyXvkROFSYUg92K3n8pMx2PVOy/76yNvfeRtx3vb8d6Fqn+h6lfdYlGedxqeShlSLVLNbiwbngIaQTHDmUq6lxTBQ6wJHJYryerQyTOCTqfjFPG0LOvpZAMAy7KsZ9/BZYAj5PGAKNoBGwCtEMIRriPcSGaRzNzuptvdVPsVPMXX/hEwc6k+aU7Alb6Rggc3eOfHoS6Wy97OA4Y7rL6bfymFW4+oR9we702e6aHAKW4XLJTqgCcC4FvzlW/NV85Wzp2tnNuM4s0oBu5FmxW3onSW6DTTU7qAAakW7UAD25ELrI3kSNFJijKgkZKeUxwR/GSzlL/Qhkxzb2A/Ty3LOjFsCpBlWZb1kECAKBL/AZWRxQgdbt4FMrdC+zydjmosiBdfZtx1xJQtcJnG46xP4NBuAsYYPXdRowId01iSaYxfAggqQKz21gYDgGhA7ahpgTOVEiCE3In7wOr24I3ZKrAbPbhQmQO6agisdNZXRmOgkbmvXZ49akYAYi0qnrnZc19sF8cgs2EGzNUE8INVqp5xhJmtpXf7XqKKdb9NALIs6wSxOxaWZVlPtfX19WlDvhzG4If4IbJY4uvFl3RzUesEcKIhkOf2zHS7r1w5M3kiyqnIzlwaLy4Wa32QOCCLz6BkzGBn6CTAQjkDLnpeWjmqFpDvlB3hrY9H23EPmAlqwFdmvNTE66P7+Rh3NEj0KNGjVEcLoWhk0ze8EiWk4P5AALECaAfGFZQcSg7v7qp3d5UjzMHF3wu1VAijYZiJhdIREz+zTnMV/5Pehtk65WwAcHq99dZb04ZYlvVUmFrB80kyGozOTwCSiCRK0xQ3ZOnlN93W4wAAIABJREFU/PvKKGfzNr1NpIMXmo3DE/QPGL+UV//0nBJQ8hri9nVx+zqjDqOOi3pYSDSoSORFB3TqxUd11hX7H17G8O7t+1I4c2Gz5BSZSK1g2dkPWvKRngy3pMrCcuQ7h054oOwq4P5ANH2UEb1EAH/5oAR82HE+7Dj3h/L+8OFH5zgTwChlvjQ9yepLcppX4Y/NXmCwTjkbAFiWZZ14x0nx/0LiONXC4Lg4bt4ILCd3V729jdJzL6Vz52fOLzLYEfOX4tk5k0RHzAakOkp1RDKmtylKJVEqAUlrPgn8vJRQxW0BjcS5dGEONxTl5hGzJXqMMWdK9Zmw0g4qflIk95ed5kLpbP66H+hIDceqH7pVoOVPyyiC0DGO4NuLcqlMxTdAxQNwBX+1Wro3KDKCzlU08PaOtxu5Lzbjhm8rgVqWdcLYAMCyLMt6yAiBkBIhVVYEAACGtfeLAb1d9+4H3vaKF0dApKdU7RRIeh158HET1szOntnZQ0q/uw1FHhFpbFbeQ2uqs6ijtv8BX/iJiYBMx8WTVOeb/akep3qsdBERzQRnAUe6Y9VTJs2vDh9hkD78WFSay/X4tZm4ud+SoBNTctiOJHC2YoCPOgFwoaZWTnE5oNPMHr9YJ5QNACzLsk6YJ77ffygvyLPaBVmxHJfrH+Lur4Vrs0DWXg6HI1cE+3HCYfZ/vOQ2AP29vxGzbTHbBsz6A++j67rS0pWW2fgEoD4PIKQZTO/e1Uk7EkfrLC9aKrWWWgcEAQFQcurasBWtAAdHBEeTAkfw16v63oAP9gRwo1P8fRfL6j99cZy/PlNRgWNqvn5tZsq5x4l20nPcj/P+J1WYtaxnng0ALMuyTqTPXwx4NDA4NEg43l0CgZQIgVYc1M3MEtPpd2dmANFe0hdf7TpR14ncvWNcUE6GBP7K936afyX/vd8BmHuONBIzM4BMxjIZD+bnxewFs3nTbN4ESJN/9T//j5OmTE0ySCMgX/oPnHjkZG93t97ubn042PhwsLGbrO8mxXtbHfXvZKPQqUrhKJNNmhO488m6hjNlgLMVgJdakSNM6BT/Hf7jy9FsWNT7uVhLgERTdVU3cc5Vj5rZsizrqWIDAMuyrNPoMxHCwZfjcYzKAJQCyCK0zjf46zfvuJWGqs8AVW+m6s0wcx5j3Gh4UN7n88zBHzTqmJ/9yPzsR1Qe1uJUV9/A8YGaLmYQrbOms0W1edCMbBJtcITnSD/vTfZKay5/fqnarHmtmtdqBwt1rwUsl2uAQOweY8velVyocqmeOvufkK+1ea3NWMmxkkCsRMnVP1gvAb39ZKWtsf08ferY/BzLmsT+wjrVbCEgy3q2HW/L//MESiElcr9mTpqIhWVx6VVgY7w2znpOEhcjAUjU+NCJAOozlJvML+OF4upVcfUqwKhrxqNs/mI+ZORkiecknmPmLuVPzLD76quXJsyI2++3FTPhjB+ngBQOkOn4xUb9pXIRXZScGuBK/8KZqsR9MJz8Dvdrwnz3u9+dCUykAO72POBGN/QlgVMNnOpcYOYC0wxUMygOBEaZ+L2zycbY1YZETQlXLMuynh42ALAsy7I+TTqAdl0AN8AL0BpgtMtot+5V8lHO9j20Mo0FU276ycRrCcpkJggB+luAqc+TjSnVAOfu++7GbaJePtLP19VeKGaW8UuSKdV1zHAHiLK+K33fKQW7G/nzINHaqGHWATajDcCVPqBMtmSmXC/ORYp+xldn1CutCPjrB0XwcGfg9BKnlzjA12YjYDsSQMUzs+GxrhlYz7A333xz2hDLelrYAMCyLOtE+vzu/qNPHnfvHwFRFCOljCK0ZjwECEr/+b/4P/Yqzl7FMeiqP8tgG6C7drmpWbtlShOLbErhit4WcZ/mMgfFPfubycwlERZpP2VZ8bVEyHwvH69oHTCJqLYQjik38tykleFu/jwYDIr8Jch0bIyZC+Yrjg8slf1Jsz1qfSRLrlmuZBdrGnClB1R99ZOtQckt+gzUfQWMM/n+bnij61U92wX4ybN1+i3rS2UDAMuyrBPvSdYFEiJP7FFhABCUMBqjcbyWnAFq/nwxsnWO2jylumgvCqWyI95DlgFs3AJEXhSoMgOYQd8M+pQbxbDeeqZjehvGKFRKOjFh/+3rnxy8rlOdC8NYjaL2QvFIyL24n78cZR2gn25pkwGy2v7sXJ8mH0nkWVnZvNkTdwf+q+3iLOLrc2cu1JJWoIDbPR/YjQFaQZaaZ/Pz1KbRfyHXrl2bNsSyngrP5i8sy7KsZ8xj7+h/YVoBaRw5RqRpRpYgXaSrVWbe/fvm+5+w8TEbH9NYBsgigDRisHvkpFBqiqUXzEe/YLgD5P8Wc2fE3BmiAdGAcRdwBx1ADHYBJyxPnE0679zaFEhUhsoqVPPHUbWK4+WdxfppUql7oVMFOscrAwpUPXWx5jR8/0z5U8lCN7rOX63u9xyQZqGUXaglF2rHSiiyLMt62tgA4LSz94At68R5kvv9hxAGSFOyFCgK/EdDklicuwBQnaU6y3CHcQchzd0bZmcb+Dd/8t9yWOaGjAb9Won9i8Jm5f49j3se95KdcaUyrlTM3Y/M3Y8Ayk2zsQqY+/eElGQxUzlFXJTpWCIlsk+/T3+u1J4rtYF+tpuaZLHU7KVZJ4lSPWXJXnaFKzxgO3KBnUjuRPLPbhddDv787sbsflhS91XdV66g6umtsTfO7CVgy7JODBsAWJZlWZ8igDw7P0swWsVjghJg1h/guZSblJsPCwQB9TLjWHqH9wL7xcfrQJ+hI13x/Cvi5W9WXFNxDVDaXittr4mls2LpLN5+izEQFy8Bpj88dEIojinuDXoYjXS6egfwnYrvVGrbG7XtDVcErgiULu4D5A2AMyM4bJX+mW5QW9FwL06EYCcu/kZfncm+OpP9/vm53z8/B9T9Sj+VgQNwvlpkByXaBgBPnm3UZVlfEhsAWJZlnUafOUZ4mGIkBBiMMX4A+90AjMYP2Hwkz0crVEZ/S8y0AeaWmEBmWU3Wapmb5bvvyRCoeO35Uou5ywDCRbhm+z5ai+XzOC5BlcHk1T/E+xcVBk4EOMJ1hDvOOuOsE89fiOcvALEaAqFTafqLg3R3IWydKTVjNeVUYazMxsgFhqkEnm8knYRmkDWD7Gfbqz/bXn0wHB9UFA0dtBGdxBHC1OxVYMuyTg4bAFiWZT2zHuPmgDbCQJrEgPFCXA8hSYvMGfXKN0jGAGa/Rmd1hsFIySNT9iHvFPbu6h3ioViPw1FxwVctXDYff2A+/kC0zxQj8/ln5k175vCpIDAGaIcZELlamUyZzBEP/7KDdCfVERA6NaDsNoBIDyvyyDe5b2Pkzpeyiqt/suUD21Excy9150Lyf/IN/6VKCoSO8eSUoqXWs+Ty5cvThljWU80GAJZlWafCoTcHDn0ojC7q9sDB0h/pildeLF5v3Ejr7bTeplQHRK3mPJoR9Ble+ME7N9HaNx5AUAFQSdjvFWt9SN9dpdKi0qIyQ+vsg7gfiaPuOWhHCiFc4VWHCVB2i+qimUlv9Tv561jHg6zbT7e1UZrjXgI2hnPV9Fw1LTkALzYUMEjlnX6wNirCgFGmR5n2ZAnwHR3Z7P/HYgt9WtavkQ0ALMuynnbr6+vThjxJRjgmL5sjwHUxGiHywwRXBkUZH3BXPwHIYtBie90M+xNnhLyuf8uZwQupztAs9vvlJ7/gIAhJY2CUdeqeDp2aNFP21A0GCAeDm/3O2hhlinkG6Z4x2hceUHFbgCuKRmD3x1NSgKqP3GT4+Y7fSZyFEt24WOK/2opGWZHqk+oxcLMbAItlLW0UYFnWyWEDAMsWArIsq7CfMmREyRdZIgzGLfpnGTSL+ycAjUX33Z+y2y3adZXblIKDrl6flYzN2gYbDxhsM9hmXPT9pb5gVj7KX3rfvmJu/cLc/4Ctm0BtmAB6v6XX50nhOkr5stwryaTWPMi/34nGz9UeVvovO9VUR3ku0LGZndhVRvxg0wcc8TAImSsV76fqeVXPe3un9PbOhL+ydTwnvcnASX//1mlmAwDLsqwT4DGy+T8jz/Y5zjwCig1tlYgkQuv8QnCm40zHurlYjPM909vN/vrHZm+dUvMgVPis/HmUkJ9jlJsIic4AMbMgZhb4rX+WDxSzy0C5PyCsKZMaZ3Ja0SOirAe8VF+I9Sh/UvNmIp25MgAM2qClIdbjQJbPVabcAUj3CwedKWvgK21enzGNoAgDbvU9d/9j82prfLU1BlwBxqT2DvDJYbOPLMsGAJZlWafXpHhACifv/5W3xhVKCaXczLiZAeJ/9Wfi/AWxuKx+dhMQM2cBRvtb+59hNBcu0m5w5hyOZz76KWBW3zer7x807eLKG+riy1m1iRsAhHVADweHTwjKZLhhZhLPKXlO6Yys5c+bgZuZNDMpMMiGQsjy1gbAcL9+kZ6yTh+ksh1kUpj5Uvb6THKzr4FvzjvfnHdCR4eOBiKVKpPmkcDBh6g2NgfIsqwT4/CyzZZlWdbJkqbpcXb3j+Z5XpqmCJEX+fE8D53gBXlTsNQBSP/of8sHmw8+dH7jOeG55uN7jAeiVD180oM1tzEIARghRFgFzN2bAPUNgOpZKVxKDbRitCoVwp+eYJOoke+UAYVq/eJ9QP/mt4CaVzsYU4lZC2XgVADMlNvAjsCTpJqSw1gB/HxH/O4MQKQkPNqugG7iVH1dbyQGeqndULMs68SwAYBlWdbJ80SW+0cwAoREyHyPO5Wu53n8yb8Bgj/4BhDdWQGE5wKi0WZ9oNXEtbVIRjjNIhfo+a8DBGUoaomK5ZcB58YvzOKVfLzZWIPw8LkAcIQbqUEw8KVoZjrWQQnof+1VoAKO9PKgQxst6guA75Shi8r0ezf45v5Nhsk8ydrYAbzEAXpJ3pSgdKGaKGWAT7qBn+QFiHQE2jAXHrfQkPWUsF3GrNPM7lhYlmVZn6YVj7bMVRkIEO7rF9zXL5j1LbO+BYgrD29Amkor7857CMcza5sA3S1AxGMRjxnsMtgVL39TvPzNke6PdB+QWZr6XhqG4uJLDHfV5A8oZbLQqQJ1fz5/IkAIRwjHkUVcpI0GNuRwxx1qnV2qx0Q9eXFu0py5XipTTaaZCTPgjVkHGGVilIlAmvWRB3zSDQBtmC8xE2ajTETK5v9YlnWS2BMAC+Ctt976zne+M22UZVlPlyJj50uiFSoBjfRQKRKxMA+IUhsQ/YjRLuR7+VPK7JhEEVQwms6GcYcAy7MYY97+IVB+8atA1xSZQp6evjPlGKGNojoL1HRZZhlQiwxAiViP1H7eUZ6pH3Z3AUoNU53YXOxR+Z3fxVKqUa/Psh577SAFMiOiTJ6tpCtDb7EMcKMTABWXvWj627Z+ZX71jbrefPPNaUMs6ylif2FZlmU9O55MPCAd8WgJTp0SR8SR6XRMpwOgEoBym9lL1BbM1gbjgc4e+ZFH6UwsNhl0WXoBQAiEYO455p/noOi+GwJm66bX3aK/QW+TSsuZfK3WSCmFjMw4UWOgR69HDxiWfCk8wHFlzZ2tubMz/pmmvzBqNEeN5qTZHpUoYiVngrYnDKB08d9zc+y91GwUY7SYL2Xv7LqbY+ze/1PrV1+j89q1a9OGWNbTwgYAlmVZz6YnEAwIieODSKVDEAK9Sxe2nd62H9HfMvduAGbtxrRZEH5Acx6g1halUJTCg3whsbygg4p2HMAsvQRQagAvvXxJi4lra2FAZWGigdRz6nvd+l73oMNAyWnUUzcxY0BRhCXaKPpbTGsutlAu3lgjWAK2ItZHad0rJnm1Vf/7zeJywsJ+W4CFsL1tt/8tyzpR7O8sy7KsZ9ln7gof6+qw1ggRp/s5/Y6npdBSqCtfq3gtoObNAqLVTE2SLV4Q7fZLl2dffeHw9Ppr//X/ALByR6MAc1AUSDriUnEfVzx4X9QX5HCP1jnCOvV54CsXHlby+Syd5SVEvTT1Vu+ms+fyx5VUuHfe86JhMQo9SjujtKMPiv9kUzoBhw6tQIxVl2jQMLXn60XTg3aQXd/tXd/tAT/fDjbHbt5S4J8sFQcLS2XbCMCyrBPDBgCWZVkn1bFW85NNPyJwPFSKzqTWUmvA3b43c3vd//hdIJ09C7h3bwDm3oqYvGGP4+tLLwC62sQYo1Qi95fLXkm880N2uwDlFmA++AnCmVKwX7oAjgswtwzouUu0z+ffNOt3dKnhigCojVVtrBzhVb0ZHG/qCcAwFfn14nj/P60QuNK40rjCuMKcqagzlSKcCB0d6dFY9xfKTqSmzGxZlvX0sAGAVXjrrbemDbEs69fp6OX+8Rv9TvKpeMAYtCpmyxfNjuemRdKLaLXqtdD94ffcrfuUQ5NMuwS8tyPjSMax7O/p6/u1F7dvE/eL15WSqtSVUObmdQA1LTLJB0iHoEo08HbXiudeyVz6qrn0VUCOe7K3ZXbWgZJbz8fLYHpvAaDk1DOdDJ3kZn8d2I687chrBaoVqFHmjDJnJsy2ogGgTQa4wneP1bbYsizrqWADAMuyrGfTYwcDo+hg/W0wGqMR+x8WjWUxt1j0661XFqUSQd5gC7r7q/nPi5Li3rBflhfn6Pb9zbv739NiaSl/5WzeBmg3TGfV9NaKXgGHUY6A/ciksQjIwV5Pd3q6o1H5P6T72T5Rj3jQSzbiwDOjvUlz5mp+8TftJilQcVXFVbNhOhs+jEnOVWPg/tCfC6vaZHf65pNePDxljcBu3rw5bYhlWU+v0/ULy7Is69kwPXvnlyCMMdIBgda4QbHc73aTP/nTfIBZXzXbO0hJb5j9+F3Wto+aDsTZc5SbrN3B8cRMq3iaJabXMb0OIOpNRwtmLy89vwwgj1FaRyuiHtt32L6TP6j3o3o/AtzxwB0PaC7RXBKXXwd6Ygj4vV1Vbkye8SGDBhIdV92g6gZzYRFK9RLnYi3Oo4/fmPGBtbECQkePMvt5elr86muMWtYTZ39hWZZlWZ92sPwuOoKJg5yc5E/+1PV9Qh+g0wfcf/pPaFbF5UtMPnAwm2vsPABYuQHQqFFqUGqI594Qz72hZs/j+nV65tZ10+ujNHGa/eimZGJWvYNL1CesM3upeBQPaJ2lddaNE6QPEA+Jh8B4dqnqzdQTKVrnJ014wBP+nf5gN+7OhXWg7BZ3fO8NgoOoZKEULJeDSA1Hqu/ZT1HLsk4g2wjMsizL+qx4HFfKFYwmHgP4LuXA+8cvidkiXQejdT/S3ZgLiPaU9rpUS/SHLFbZ7cIZgOYSYK5/D+Dq11VzUQD+A5IEc7xyOmFRI6jfqKmZc04SsXMHYPasKNUA0hQYDzcAOdwDiAfmmJMDUHWba+NN4Kx3AXaAZqDaXh3IdKwppvrdr5z9y+src6UJbRCeXV9eoX2bX2RZXza7d2FZlmV9xv5etxcCuC5eCeEAZnvN3LzBOAJ0NwbMBz9HpYQ1nMkfKKmiUgJoNwDhumzdYuuWOHtenD3PI0cObO6xuoVS4j/4LeNNvLB77Q//yAgY7tJbz58oPwRo7a/+gTQmjUtRFr7/LsZMrf+T24qii7Xq5tiL9TDWw/F+Ys/rM1nTDzpxcdW3n44FCFgqVSZP9mtml9Ffqi8v/rGsXwEbAFgP2UJAlvXUWl9fnzbkcI91FdgAxJHJX+Qtfl3/n1/7G7oDgEoZId03LrtvXObY5W+S792k08ctGaUIG4QNgPY5J1MyU7K7xcoGIC6eycfrIyIKEMIR9QWgNs4cg2N4mA4EZrhjVj4AzK0Pi0dhHSHyXgRHCJ0iTtiJig3+u32/m2zkry/W3Lv93l7cyb+sum0h5Np4WPV0yTlWfSHLsqyngQ0ALMuyTqmjbxKnaSqyJHF8lEZn9LuAuHBOnJlnFAEkCUkiLr2ITs3NDyiFE+dyJIEPJO9sAiKoggJF3GftfYDhLsDZBZo1s9cTZ15wpS/Vkek6WWKGu1TnuHOXNCLN39LIdFbNcGd/TCwuPi8uPj8sFfmu/mAwYbpCyXWHWedcNVkslz0ZXqy1gMApB075h+vih+sC6CUSqHvzwOqoD1Q9NVbjIye2vrBbt25hWdaXwwYAlmVZJ8Nj7eV/ytEr/ofzG4xSuA7SE9pQKgNUKqkQRSfdcqhf/jq1CpD8n39V/JTrHzLpp/m/cwXHJaya+7fM/VsAwkUlhFXCKoMRIObmAKIB0eS6okAyoNIGePXrgPLD9zv73QDSmFJTLL+Qf2VqMwBpxGj3sIk+pZ9meSOwg+bB35yvKZMqk15tR1fbEVD3i8hkpLoSMxd6jqCfHPck5FfJrqE/z2ZGWRY2ALAsyzrlPh8VmIc9fffz5lWmhAHUzz5WP/tYX3w1fyyWz3u/9fzFmbI4fynxJ66AheezsXMn278mm0aAuPIb1BapzrqDTfPjH5gf/0Df3hBzsyaNzM33zIODRgEPPbp0M4ZMR2RxEZMA8H48Smtt6gv5lzqsZpWG0lklkQD1JeMG//D9P2ea0Kl2kmg3HvaT7X6y/f01vr8GUHXFxti70Q1udINBtgPMhD4wzGTFm5JcZFmW9fSwAYBlWZb1KcYI4flpkiAdpCSNVZo5mXIyJV+7JF+75GzfczrrojGfjwbM7oY/HmfZtEo4tbrZ2zR7m2L5Qv5gGIjhzNzOn68BzjdfAXiwAejLV4iPajAsyi1XBAdfutJzpZkvZUBq0tSk2i+S8jOTmJXbZGMjBDoz3sOf+jxPGincVEd5/tG9oQecryXna8lHnSLH6WorutqKHOF/1HE+3DOro/R4F4ytZ9abb745bYhlPV1sAGBZlmUdxttP6fECHJc0I80QEiEPsn2SP/0bsXhJnH2JOAGYVLnfGFp153yLB6tIUfT52lshGebfn/0vvy2uXDbx2KxvMNvSl68cPs+jkiEYspj3rxNU82e7RZUeA0YIIYRw1j8BxOIiIMY9kmTihI8InCrQ9MOXmtWy11odFX9fg5oJs82xC7y/p4B6kAFnys26V588n/XUeeLJUdeuXZs2xLKeIjYAsD7FFgKyrJPi86k7U9sD5wOOvkuQf9cUdTMNGLQCcCXAvTXurSE9pIdfBvI+wWJ2nr3e5FkLve+tiyAQQUBaXJmt9Afl9RV0BhzM4KSp3LyvmvOT5gGUHxSXBK68BBhj5kvOfMnZHW54oyEgkkgkERA6NepLWbWN42XNtjxyu77k+EonQMMXwN1+8Zbu9f2Z8OH5RtmtLlfS5Uq6MPnms3UEm4hvWb9eNgCwLMs61Q6NBwSgMrQGkA7SwQu8ep1GlUYVk2EywP8Pv2HuvMdgGyGQ8rt//N9w6NrO9fF9KuX6f/Z73Wa92yw2y1VryQyLkppmb49On05fzCxdOtf+zAST9mtvro1MuUF3FXCl7+Y9gIOqpwSuh+uJ5VeATMfAwYHDMWU6diQrw97z9SIZ6e7Af2fXv1gLgaYfNv0QOFMOBtn068Uny+XLl6cNsSzrBLMBgGVZ1gn2y5cGOlzeMTdNiCNUikrJEoBGjcZ+p63eBiBmFwCEOKoR2IFb7wONTo/WWTMeAvrsVVGqm24f2P7L3e2/3DV3PkFnYu6CjI5arzs4JGNAZCmQxwChU6t6M2meBzTqMuoy7mC0q4WrBWHd3d5EHXVO4uJqdKbjwKkGTrXlF1d7X2pFi+V0sZwC/9dd/fZO+vNtA7iyuFEQZVMKjFqf8eU10rLRi2VN5U4bYFmWZZ0kaZo+maggU/gBo1HxpRcCIiybaGR+cR3QnbE4M5/96IbTCvGqKC2q+7HBZ+iM8RjPZelMY1wsqcXyi87Og+J1qxXPnZn9j3aBjcZi8VPj423YqySpLfhxGjoP//REqlEgANR+an5/E68EGHXkTWVZhDGxGgZOBXCEKXsamA3mgQvV7sFYX4aDbMMY7g281NgqQE+RLy+6sKxngw0ALMuyrIc8z3t4lyBLkbK42ptE+CEg6nPiZR8Qd/fr7gPVWbg5aXP97bfvXF3OK4cagKCCMebv/r8Iwq9/zcSDeO58MXS+zVgTD8zWOt5Rn1Bmd4OZBdwO1IEk8IDdeA04Nw6Tehuodjq9QQQMUdTKdBLCo0oAHegm8aLJsqwr0hnARQKBLAPPNzYBKTB+iaIGEpdr/D8D+3lqWdaJcYwTW+uUsfeALeuZMfVa8CTGGBV4xvM9z0NplMbzMRq/UoyozuadgN03rojL5zMJ6eSdddcTi5cQgto8SXGkIH77d8Vv/66JB4B/9+Ng87548RWAeADQG4rB/uHDYUS9mb9IPMd3Kr5TcbvbB9/13Up1bxdQ7bOqfRYou02iPtVZjrwErI3KdAIMUwmEjg4dnaEzdD/b7We7UBQxer8z2Iw6Na9UcpxEpy827XVgy7JODBsAWJZlnRifz+15Mtk+RwvLhGVE0efL/PQn+u0P9A++J5aXxPISEDVn3N4moL3yUfMAw12AZMTOHXP7HXP7HT68xYe3RKmo2U+a4ZVonRNnzpFlf/y//E8Tp/JKzCyNA0cbRWeNzhowH3lzqzu0z+VDzOamo4yjTCVSAH6Jhz3OjlJ2iZXcjdyW32j5jUFafFbe6sVyf4LZMAPyaCFSYmN8VNeCE8om0nyevWBgPRtsAGBZlvVMeTJBglJOlIg8XT4aEY3SNEU4B0k+Yr5tRgMzGuAG4WBAuc387MTZ8mX3TgcvNHdXzco6IOo1gBfO88J56gvUF6jOAsW/XZ/KUeFEP+3nL0K3DqCVasyZ3XXAvPN37K0QFFcCRowA0VlHK7JIhkdNm+lECtlNxNlqCUh0Ua7UE94PN4os/42xuzF2F8KW0kKR7cS2DZhlWSeMDQAsy7JOpMfoA5D7gvGAQRu0OajcL167Kn/z2+LyVSjSfpIiG0cU2TuHzKHf++GPAQZbqzMXURqI5s7Gz10VQRlAJagEEOdfSWRm1j7BDRCYvB/vYcpuyHC3NBhiNE6Rf68vvUYUi7MX8y/F86+ZlfdLK3fIKwWpFGfKE+gcAAAgAElEQVS/u9mRWoEAzlZLmU5GWTdRxbb/xtiVOEslA6yOOgfjm36obRRwmtizEeuks5eWLMuynllfcK3/kHBdAq8oBuo5pAogGZOMcRzAXP+J2RsCnH/JTzLigdFaTV6vA1y4zMo6rKtYba78ZO6bu/HVr4zac4B37zZALQC8B3cAc+9j4CvnW5OnM1TaDHfZXaFxDi9w1j8xex2A+iLAuIhGRKlEaqi0WbuFEEVzgwmEkI7wIhWtj0ZNX7lyIU/y2Rjrb82bu4PinsMrWbJeqgDX16e3P7Msy3ra2BMAy7Ks0+voQ4M4zhAC4eD7eXMwQP3ouums5q/lKy8BZueBSSJTrgvv8IuwZhQhJRsPaNWhuEUrzpwPe53SD38IiKUXxNIL5qffW0s3xNJzYuk50axRa4n6/p3jz3FwcAMaS7geWuVdgUWrCTDYZrDN3gp7K/geQKUNEHUB5xj9CkInBDqJAzSD5YO8/wtVN3Sq7X4KrAxHK8PRQiloBirR46pvy4BalnViTP89aJ1CthCQZZ16Bq2REumiFXmooPXDS7TaiKVZs7udd+MqfmZC5X6RZ/PXKgBaO994wfnWlez//WH+3dKdT/KuvetfuVLMs/oJ1XkA6Rw2HwBpzGALGJZ9AL9slq4ghGi3THfDdDdYeJGFFwHqCwlJQoL0xGjX5B3NJkh0uh0lkYqEQAh6+wGSFHSSpJMkwKjZes/1u4kDxLooVdRLJr9Vy7Ksp4wNACzLsk6G9fX1aUN+WYceCBg/MK57sBZ3vvmacIrXol4HEJIoFlkmqo3P/zhAlgq/RJyw2wPQBnCenzd3booXnz8YtZT6S6nPqCOas6azxu7W4bM9arBVSYTZeIB09u7dNYsvAGL2opi9yHCX4a5onckH+llxvVj400oVAbBcKjqI3R2sz4XMhdzuB7f7AXB7UMQ5F6vNUFZLTj1wKosl+3lqWdaJYX9hWZZlWZ81GiVog5CeI0WyX+DS89VP3mPxRYCl2WL175cAvbIzaSqATAHkdf3DgChiNNZre/k3xcyCeftHgFm7b9buA5QaANWyGUy4VQwmGlKbR8g49AGzdg8Q4z6NRZL9g4jhDtJFuv7OGt01tEK6ZPGkOXMbt1bf3fUQYrncSLUAru9613c9YDZMN6N+xdUVV3+1XQOEkMDaKJLCngCcUm+++ea0IZb11LEBgGVZ1onx2Jd6H5/rAUIphERlgPrzt8x+Ly2jFRAvnJGN8MVyYnp7h09Srpp0TLtBuw5kl67mj9VHG2Zh/wRg4waeg+eYvS0AA92Jq/+HqrPAqqmKcy/gBgd9ysYexAPcoLgPAOzv6Jvh4XlKOUcgkcDNXp/9RmC/OVf6zblSO0jzUj91T9Q94TmB75Q8GfiydLZSlcIW1TgBbt68OW3I47h27dq0IZb1dLEBgGVZ1jPuMcIGISUC4ogswd0vnen68tKivLwEUC7V62Vu3jN7awC+D7z21QuTJgSyn94GqJTc7/8tq1vCLT6A0n/3A/XJp7ObOg8YjmhUOaK4ptb0NjA6IISH63tKrbhadADA6HGjOW40i+8OtkmPugBw4MVmCvzDZpHff2dQlPrZjb2qpzUKEMiDf8dqmKlj1WC1pvqS1uiWZT3KBgDW4ew9YMs61fb3+BESwGiSKC/VjzGkmXA8QPzmPwWCzQeAOHvp8KnACIQXui8uUC6xvccLRZxgjMn+9f9eDGqeEZWyqJRFaw6gFORNBqbo58cF2lz/gfn4XcbFSr0kK7q9XLxOAZJyhaBCqaYbzcOnAuDDd9eEkBXXO1MBmA33u5sJebU5C3zYq4Sy6svSe3vr7+2tK5MqY5f+lmWdMDYAsCzLOkWO2SwMIcZxRhBgTBEDAErjOLz8LcBEY6Rj1j9eeu0NEVaOWP0XjKFeBTi/zE6HdkPtjAH3+QX3+QX3H39tf5wAjNai2qTSwJuYVyPKVfoD3IDRHiCuvJo/z3QUiFL+etCsuzJMgzDvCmyGHeGXxTFadqU6Ab4yo1IVN7zapVoRM3x7oeEKsz5+WPgI2I72yk7dPbK9gPVUuXXr1rQhlvWMswGAZVnW6TU9HsiL/7g+no+UAB/9xN3ZE+UqIJqLRD0cBwyOR+/wrP0/vPZHRqXESfx/vxP/23do1AD3hXn3hXm6fbp9gKhnen3T65l8Fz+sA8TT3h77I40WL7yclauAefCeefBerItlutfbBfxUi0rTGHNUaVHQRoPxndATQSArvlOUDBJws7/995tF999BNhTCCGG2ownXHqxfjl2jW9aXygYAlmVZJ9Xnl+/TF/RHeuS2gBFSpJkyUhLvb3g77sF63cQjE4/Sf/u3gNnaPHS2RwkvKFrwGgCxtCTOLIozi+L1r4nXv8Z+BzGxeF7MPydK9ZiYg8Zeh7n+4RqtGZIRnQdEfaQHuMO+O+yLaltU24AjPE9BpY3OoAgVVHf6kj3VEaBNBgghvr8xSvQYmC1lv7nQrnoaWC6zXGaUyZrnPxh38jdgWZZ1ItiqBZZlWaddmqaTLgoLrRLH96MRYUiW/vP//sf/8l/8zqMDzPaKmF9iW5mde0W5z8OY9U0uvBD8/lfo9kVYguJsYe+/+ndA5bfbAGcXTWeDsQv4qysgHl5FOIIXkkaAufMx3/w2QKUClKIxkJLt6C2CqkHT/cAMd0UQHDGZKz0vTgQyUgNgnHVcGQA/3jK/d3F+f4xZHXozAmWShVLxMboWfyov6Ffg8uXL04ZYlmUdzgYAlmVZJ5vnecff+M/X+sf4EWGMIRobNKNR0cTX9chSMTcHcP395J0t9zfOAro6w/aUQwA9SosTgEYNMK2z5sF7QON354BMFQt90Zg3Kx8Cotli7R7uxMW6MOaDTzavzrrcvQeYvFLRzh1gHF4ABnoPEG6D/cI/6oP7BOWDLmZHK7mNcdYFQrf+am0X6MYbQCNYWB0WwdJubFwZ3BuMZgKvvV8qyfr1+lLjoi91csv6VbIpQNZEthCQZZ1a2oj8hXAdQHcGjEYIoV3f9Hum30veKdr0ilJZZqlZu0mSUp3YZFfO12ifZa/LXteMhwCuxJVifkbMz4h6VdSrrGwUc9brZjwCTH96uo7aHgJi+ZyYabJ4lcWrJROUTNDyFlreAtD0F2M1SNTI+drLCIf4qEZgymSJ73kykMiK26r6c8B8cG4+OAfM0HLj5GLVuVh1tqIYuDcYAaEJAqd6xLTPGFup07JOOhsAWJZlPWum7e4fj3RS6SCE8FxZ9hSQt9DSGq3lf/Gf+L91UVy6AtBdAxjHeTeAw/k+929wsWgBxsr7NOs063gunms2O2azA5jdNXY75s69ZGaO0Ti/any4JCIZ4bnOt7/Biy8T9wFDBnRNp2s6ZClZOlbdfLi/8WDiVI9whJs39x2r3sHDvWR9L1mfGQvGHUAKVwq36Wug4SshjHJErI7Rtsz6lXjuueemDbGs086mAFmWZZ0kx8je+cI+cwHg4R9hNF4AIwx4LqWQNMV3STL35z/KB5v+IG7N4W3guUSlz0/+0JlLxAOWzokA5p7X7TLA7bfFxZeF0gB3908A3vhtPpm2x+x6Rf2fYYdK06QZILpbQFpXniwBGA1Eqh+khvayivZEWJHuUTtf2ihgmEUV7xJg1j4E5PNn8+bHu2Wgk6cydRIJ9FIJZDrpPeH/J0+707zIPs1/d+uZYU8ALMuyTrbHaPR7LFphDFmMoKjH77iAmL0olp9ntsVsy6zfAwIRXn7j62hDOvkibJwQD/WPrxdfbn3ixGMnHosLVzAZwPo2wNo2Spt/+Fv3H94mUzoeTZxQZQC+z6DPxn1RnxX12bwnANAY65FMRk6W79YDJqxMnOoR2mhPeJtj90a3A7B4hcUrgBQyrhcliRKdJDp5MPQyLS7X2rPhxKvPlmVZTycbAFiWZT3LfqnjAiGKa7iZIlMYg3TM/Y+hSAQSZ1/AaHPjOsmYve7Uoj3yjVfRCjfADczdD83dD4EkCMXMrHjlJZbnWZ4Hen+/Byw8EEfOJUmGqEyv7gAPY4+gOnN7PX9ZTkxJVEqiYrrrbNx0xgOMLroZHOlyzQdudDti8xOx+UnTW8ifV9zmTHgulCGwUHrYqFibbC44VoDxxJ3E3Wh7hcCyfu2m/x60LMuynk6T+gA8kTMBIQTGkMYYCAKCgDTxPE/MLxMPRLUuqnVz/yNAXLgCiDNnUUk2Id4QpYC9HW7eBOLv/ij+7o8Acf4K/W1/ewUgrIrlK2L5itocVi758vxs498/q5KHi+zPyiMTbQCzvku5hReaze3iu/WFitcSlTZAFgNi7jKA44n9ngOHcoWbmGQ3Hjd9dbEaqLmiw3Fjr6eNDt1a/mXJKV+uzVyuzYyyzuTJrIlOYtxiWc8SGwBYR7GFgCzr2fCFQoJ8sNGPNM0VAiFwJDozow5g7t039+4zjsX8GdPbMG//DL98dJPdnHn/Q//3XvZ/72X2y3GanR2zuWnu3QLoPACc54qK+6995cKEaVAmxQupz8rnzwL4+9cP0qF4/mv5y268bm7+3Nx9f9huD1QHxwfMePpt3apngE4yGqTb3WZNU5xsjNJuJ17Pbwknepx3B4syUXIbCpsIdBq9+eab04ZY1tPIBgCWZVnPskOX/l8gHvA8PI8kJolRGhCVFkKIV78uXv26uHAZ4M4DwHQ3GQz/9F//y0OnMb0BM/OcXQDY7rDdAeisoYsTA9Fu1JN1tHJeXMJ1iBKAKDp0NiDP7Ge4C4jLZ8z1H5kProulxeLbWWKSIinIPP86EDiVzHOvNDNROipXZ6wyV3i+LC8QVD3P2e/vq+efO1jx78VRrEdl5ZaVO0iLMwp90G7AOmWuXbs2bYhlPXVsAGBZlnVirK+vTxvy5CQKrdDK8zwciSNxPYRDbR4wKx+ZlY/M/8/encVIdmYHfv9/d4s9IiOXyKUya8laWWSRbKoXqbvVkjySvMiQhGmbbwYMDwyPYRjwGPCz7TcPDcx4GT8YM4Oxxzb8Ys60BEuQ2rKmrVZLrW42ySaLVawta889Y1/vdvxwI6OyMmPLrGKzKuv7gSAy7/3iZGRWRNxz7reVt0jm1fmz6tIltnZIp8UfNuXgztUH6uyi8c6vGu/8qjr31eigKsyr13+Z+dcA/68+m9muqvwEgGl2Jx/3o3yXMMTzUYpqHadX0pi0KrQqQFbl1OS0UXyYurUCmFuPAdTobgoQIOVFl0gVzSTOOjPRf0k7jBq1TD/rxHJOwg1bneDQPQB6KLymaV8WXQBomqZpg7ke7XZ3CJDanZVrWHLzkdx8pBYu9Bqqi1d6Xx9MbaXlycodDINWJ/yLPw3/4k8BWd2Uxu7k3bXrgPmNC+Y3Lsjj9e7ew4OJaQPMLEF3d2G1dIJEHggz02FmWm78jMY24P/oY8Bu1lQsrpJZaQ8bApSwbMNzbSMRJlJhImWpp3Y2qLjrFXfdD1XcTN9zK4CiOxwokKFTljVN014keh8ATdO0l8lz3wfA87y+I4JU3AJIpqmUQAFep2Xbtlz9cbT/lzq7IJ99QCpObBJwVV3WNkevsbNT5s0zAKUH0QEpb9OqPdVGKcoVggB/4G11JYSWxfo9zAnaHc7NA9F2YEYVIAB5dF8sx/zqeT5/BERzAMZkdDphLAbEzGTd3waulwDjjckwbXeLk0CCQLyYmUqa2Yw9etNi7culu1w0rWfUJ7X2ytPzgDXtVRdP4Pv4vlR379a7LePt8yqTIrW7ok5jxy5tE60d1I+aSAcbTTJpZibk+n25fh9Qy8s4ljqx241g2mzuptEjR9T4HYBEHCAeA4KpJVJ5UvliPCjGA7V4Si2eAlThPPms1IqEHqapnGGrAAGepfBarm34oWco01Bmx290/AZwZRKFkbLyKSs/HY8DMTMFCNL0B05X0DRNe9HoHgBN0zRtsGjbWycG0HYJBbN750gebZBJA1RqWI6KpQgl2ixsmFINhbqw6P7Lv7C/foaZnGytmBBYcYBCHlBLi+Enq+ab5ymvDAojLVeu3cGcY+YkoKJKIJEDJv7VHwPByTlAxZJRe+XEoDEo2l5Wo/Gg4y4hoJx6DZB0zFDWt+YKQNXdvFNdBRzpdIKg4VcUqtiphiOiapqmvUBGfVJrmqZpr5J9I4I6nm8EHhICKrO71GYy6//ZjwH1jZMAlU18V7Y2ALEGDLNpta3XFymVvWpbvdndV8v7yV3nbF5dOgXI6jquSzsAZLPaP8geKmYDzBcoVclnAXPrrjQeAer1ZYBaU00VqNapPFa5WQDZIfCkOawMCMX3kslCwgBiZjKahWzszhtu+3XHSC4k7dVmfe+s304woONDe/GsrAysKjXt1aGHAGmaph1/h1j38wkD18NzAToeHc/zPGxHLSxFp61vX0ECJCAMMSxcT/yQ9oi77PbZKQBD2d86rywDkM/vA7guoE7MR/8ZZ2bCmyvEd0uOgyzTWJqk0eTiGxROSq2OMpXjKMcBVHaGuCONcvbUJXxvLuWTyA4MtUcoYUigUE5oAuzu72sZjuJJkn82uxAzzZg5zoJCmqZpLxxdAGiapr3Enu+E4L1a7e7C9nYsIX4ofijFbnIv925Y33k72vZL6mUg6iIY4tPNDq0OmRSpROkHxeig9Y2z6rUzUQ8Apolpyu27gFRH9wDgBxgKoLoRHZBqRao1qdZotmX9IaAW3wAonCNboL7dTCelXLr81vLAmLtzGKKtvsRrEwbY8ZQ1EZ1NWJmEnWsFVWCzxWYLYMKamYypwdOVtWNieXnYK0fTXi66ANA0TdP6UQrHko4PqKSjkg5AucrEiei8+C6paTV7Tra3ZaeozlwwTkwNWbenK+4APN6k1qBcA1CGSk48aWCYavE8zbY6OUswtLxJ5YP7WxgG9S2VSatMWp18rXsqnVT5uWgZUNpVYJxdiuku66n8sFOTGsr4tFqJjseM5P36Q1T3otnwSmez02ez03mrMDiYdmh6oR5N+8XQBYA2ml4ISNNeIiP7BEY26BIBVMLGNBFBRM3kuqf8IPybT+VnNwCq64CanABwkgNiQaddPlHANNksTf5HX+seNA1AJbKAWlwgZkvLBSg/HhinxzSUiLncnU4g5aqUq4CaKqjlN6ODKjunTEu2H4bxTCOmAvHrc1PR7zVINNw/ZqaBWlgBfBMn4FFzB7hfux+Ebsx4skeBi9sOm46RsoxhYV8u+lb3EGfPnh3VRNNeAroA0DRNe+kdaYh/17B6QCAICXwsG8sGSMfZuBmdNH7lKxTv43fU1CTKQEKcJOHAPHj1kwdANJBenT0dZf+dB+vEM8QzsrUVNZN6lZmz5LMYiuqwTbtAMTED4KTU8htq+Q3ojkRSM8ty/Wr4N38GyOJlIFUqAemNIoOmKQMQig9iYmasyYSVvWJ1f53l2NRian4xNQ/EzNS1sldxN4Aw9AHTsDP2WHMMNE3TXgS6ANA0TdP6a7sBhgGKMCAMuhOCQc1MqVMFWX0MEMsA4bUV2tVhMwFMQymDap3C1OMHm/LwcXQQkBufyo1P3R/ewbKwDIDGzvJr5xDpLvPfl+/TruA2mToFUHxA8QFAbo7tu1ET9cY70iiqR9fUtZ8ydSq9URwY7YmoQFGAhQVYtTJOCicV7QkAfLRTjpq6QTNuJN2wrTDawfBaRTuEIy/Uo/suNG1MugDQNE17yTzL/X7GHwIUhhKGKAO386QHQBnMXiA1Cag3v5U9/7ZMzIrnqvOL0QgcwlFzAHoNDINchskcc9OA8xsXu8frTZpl70//mvWdcLM8IAp//3/4Z9J2/Z/dAshMk18iv1Qxmt3TO/eZmpC1FYBqA5D1O2r2pJo/MyhgxAglTswXtxXUWkGN9BSJXLH9oNh+AFTdrbpX/HpcfT2uEmYGqPpFIBC3c9SNAI6c7Gp96SE6mjYOXQBomqYdQyOz/H1VRK/9/uNuB6DTptN+sjtvehrbkut/AyhMgM2iyqXO1zYYxLQk9FEKP8B13Y/XAGoNQKXy6ux5lcuoXEZNTQDeD68D6vW3jen942r2ThJVccf6yhmqGxQfUXoYHfQMITfLwuXoW1m5JlslNTcH484DBqyAdri77bFphYQh4XrzYXe730SeRN4xk4lGK2YmlVJ1r5K2h40sehnpTHqkd999d1QTTXtB6QJAG8v7778/qommaV+aZ+wT6M8wlGEQhOLvubntujSK8vO/tHwfkDsfyuc/mS2VSCeDG4+AK7/yVv9oUSEBZLKAc2mq+HCbfA7Lol0H5OY9uXkvuLOuCjP27/y6/Ztfkxuf9A+1VzwLRL0TcvPnucZu2dPYUblJWm3AuPIawMJ5wgCUWIP3FgAQMSwg7zmJToAyUU/Khnl7LqcmlB1Tdsys7gBu2AJCIZRRXR/acfTee++NaqJpLyJdAGiapr3ERt7pf1aWCbgtD8fBNKOdudS5y+qdXw9vPAZkdRtQk1PG8rz5ztvUB4yzF2SrwuK58MbdcHWHmTwzeU6+DjBxgniOICQIrV+5AtBpAOrimySfrLdzcKhMuFqUW7eIpahWemm6Xdlq2t0du9Tcgrr0NXIL5BbU7gSGEUw7EK+l2jUnBFAqmg8wHT/tGMZOsAm0g3o7qMv6Y8kVEmYuaU1k7KwT6L2ANU17aegCQNM07Vg5WBKMUyQc7EMIRQGIqGRcxaxuhu25f/c/+Sdkdxe/f7ShJrPGr/5r+B01McsQlqlsEzBOFgCaLYCbH9LuYBgA89PMT7vf/5BkHkK5dVM27g2fUaDSMXXpMsDMAkA2Ja0q2TmgaSupFKMtC+TqT+TqT2iVUAb1bepbQ2KGEihlOEYCIJ4pd9bKnbW9DbaDNSMa9XTxK72DlhjhsRsCpGnaMaYLAE3TtGPoOQ0KEq/dATAtwgDDQCQE+fAHcvcj83d+B1Cvf7XbdOuRbD0iPdU/kmGoySzlVSYKxptvqIVZNZGNVvlUsZRKZNREVk1knd//W4Dcug3RbODdgfj9qHyedpXSBqbjTS1EB2XjZjOoNoNq5dSs3P+UnXvq9DKwdyTPEAaGH3bMQKItgRN2LmHngKZfMpQhdAuSh/VS0680/UrTHzhN+culd9TSNG0IXQBomqa9TNbX10c1eQ6i+kGiPbN8HwkxzCAE08Q0yXd3BAtLLRpF/HZ3Lc4hRKTZJpYKP/oECD+5BaAMas3wwx8A4nnieXhNuXdTah3/YVXOvvZk2nFfgUutCrD9EPBPnFOZWZWZnTZmpo0ZQKUzUUP19rfdVBrfBUgNKFEACKOdBCwHqEu16paqbskyunf3TWVPxpaiZUBT1mTKmgTuVEu3G3VjzCFGmqZpLwBdAGiapr0SjtIn4IcgAEGIhNFoeEDlppv/29/I/asAsd3df22bepPqdt9IEI2nN418mnsrKhWTOw/U7qrt4ad/ycojVh7J40e95karisg//Sf/84BwYNrMnfYKi2Sn7AB7+yGBS+ACbTNM21PE0sSzwU8+BBxj8C7FewihbSSEMB0m0sZEsLutmRu2YkYy58xF3xY7VjuotYPa+rAuii+ZXmBU07RBdAGgjUsvBKRpL4jDpvKHbf+EZSKCEwN2N/kS7HjjH/1RdN783d/HbeG22LlPqTIwDvz9f/CPVTLJ2iNsC8CyALl2jXabZHdZnrDUkq2SbJVwTLOQlOIWQXDl4tzAoIEXfvQR4MUTAPkluX1Dbt9AmfFwd9eCKLIEgXjEkmQL4e5uwUOoVh3A7wDTsXk/dCfMaaDtV/1wdzkjCCW4oIKT6eRCEtccuAWy9iLQY6I0bS9rVANN0zTtGLJte8jk4O5Zke7+X04M3wOw83he8vdeR8T3A0BKG4CsbamFE9Ko0Bw81N60mJ4C4cx5VAVo/fmtxLeXgODmmpgox1QnCgDFFbU0G/7w84GhABA/MC4t2J0OW4+JtYg9WTIIrxEHP5YE1G/9m4DRnQOgjGaZwZ0BIuKHHew4oGKphOlHx7e91ZlYd5rBcrYDmO1cJ2i2UxnAMRLdmcGapmkvA10AaJqmaf0pw0CEwCPwMA3CkHYD10V273YbBttlgIkMlRG3wKVeZUP4lbcAlZ+UUhFgcsL9s2vmfBpQV5Zp+YCyDNa2VMzEs5/8rANUMhsN1mfmBIR0GmpxHpCrPwbUxSty7zpgmLHa5CRAswyQnBwUsKdOHWal04BY1duaNvOAE+7vM/9gy1sI2+lKzWpUG8GTngFtiC/3TrweFqVpEV0AaJqmaf2JhNG9cAA/iA7Ztq0ufx0JGkE9DcxOASrInrZX79mzxvq9QdFUOksmCdAsks2pTDr5H/yGbG8D1nfe8R6vEpUcAHh3yiplgke9PShg5M5PPzl7ZpqpZZI5/wd/CThvLqqlJZrlaCOw2sl5oOVXAZTCbQ7pAbAMx5cgbqbFifuhm3Nm8dqlTguYjuV9ywByMgvUWi0gXakBJPMpqzowqPa0l3GP4eXd+SqadjzoAkDTNE0byopBE8vEDzBNnDi7u97KzQ/U0hVAPvrroSEApFbl0hKPHzIzKZ2OisUAdf5tJxWLGnjf/1S+cQ5QZ087Z+FHnwb3Syo7bLxO94vZUwDNCmD93m8r5QN4LfXWN4FYpdIKqrYRx7SBp0YKHRCKD1idDtBd/MeOZ2wfwHctHz8et1X0hFtnsp2AReP2Vax5P9SrAGma9tLQk4A1TdNedYMmAyhlEIaAnUgTBChw4gCtity9mqabScv9T6g1lWXhJKUzeFkc2wpjCYCtoppaJD3TXZcT5OdX5cFm9LXKzwLyaFWdPiXheDNr6zv+xDTJnPX7/xbKAphYYOZsqABaQRWw2Z0WLMMmAXuhmMpQiZxy28ptV9wN/E7CTCfMNICEVqvZCmutsLbW2gKM21eBsrtlG7tdJcfdlzuG58v1MvZdaFpfugDQDkEvBKRpL6BxNvrlSGsBiQgIfod2U7wAoNMCJesPowaqME/xocrkVT7bfUwovk0q4NAAACAASURBVN//+chGxajtEApnLspWN4J8+mOV7t7jt7+yiGNLoyjVKtm0PHpo5uPSHji2XhoVymXmlqJvfccGZPuu5PbsSVxazdgzOWdWDHVmeYFWafjuwo5h2kbMC9u9joJKWAbiLXdblbaNCuCHHT/sTMfSWdtqnD7ZWT6ftrKrzVeoB0DnwZr2stMFgKZpmjZYGG2MZapUEieGYQDq5CV18lJ3RE20mieQyKLUsK27LMXWDsCdzzFNKa5KrR6dCduB8c5r3WbVBqDmT6kLV9TSXLAzenJtZ2LSqletepV2dyC+SND0yureVcBQe650uQVKm32D9IS7XQRtM8hZUzlrartTfmQ0gaQ10XRUzEwBbtgGLCMGtILqUuJV6QHQNO0Y0AWApmnacTNmn8AgTx5umEQLAZlOYOye8try+Ufy+UeAVHaIpdRXvkZitwfAGdjPoGyTIMT3idlqsrukZvi4KDvlboOFAqUKga/yU4CUV6nXBkUDaLbJpNlejbVdvBZeC1DpKaDl7z7Q77Bzj517rN4aEqlHEF86pU6z5j5VJyza09NhpvftZrsjhLkwZSjDUIYb0pL6gWDacfbuu++OaqJpLy5dAGiaph0HRxjhM6ZO0422xML3ugvknzmnzpyT6z+JGsj2I9p1fI9WBZF/+X/+T/0DpZPRkkE0WvLxz3iw5v+kO5rcOFWgcL7bLJ8DQOF6Um9ZixP9Yj3Na0ujKo0qIM0SkLAySTdQ82cB4jniOUA27uN1gqnC0FhYyvFCBXhhm06DTmMheSI6lXQDS9nrzR1gYrceuF+vJUxzzJ2GtePkvffeG9VE015QugDQNE17+Txjuj9mF4FC8DwcGz80g925s3Zcrl+T69eAzuKZmhPWFxawYoA0yriDI9sWW0Ucm/OXgs1GdCwsd2R9myAEpFiRzZLcuCfVEhDeXlcz+YHRQOVz+F74cBsnpaaX1PQSoJL5zid/rZrdXgWpVWkUaRTpjDVGX5BW4OZjRs2TmJki2S0/QjtWsVw31h3nk7LChuU1LM9pDZ70rP1i6ZU6NW18ugDQDkfPA9a0V0ertZs0Rwtu2jFCH7el3v6Gevsb6kR39m2mFUijIq2ays5gW4Hh0G+tGGXHmc4D3L8D4NjWO6eMyTggW0X58P/rNjs5B4Qf/8wopGV1h3E8uAMgQqsiG48AKmty9SN5dDM6L6vrQLh0QXZWzc6IjQUydiZupoHtdh1Ypy6EhrKAVlB1Oq5jdtcmSoVx4EQyZirHl9FzFbQvmp6drGlj0gWApmnacfAsfQLDOgRicZRpx5xodq/neVi2rN2WtdtA3Mik7bxkp6PJwVixIDs1KJLUKmwWiTlMTZhvn44OGqdn1dK8WpoHaHeMX/62Wjgnq1uAtFwgLA2cBiCeR37BODmDYxMG0fqeKptRS+fl4TqAHxCPoZQ6MR+evjQozl5mEHaCRtMvT8czaYtPdzai44G4udh8zo8DhfiJOT+WMHOhHWvGzKHxtEN4lRcY1bRfMF0AaJqmHU9jjvMZancZfssEcNsEHmHYPnmuffIc2fnu2ZVPMAyVzsvGPcNzjcbAlL39eenOjTXqTYBsSl3+JqEQhoSheuubanEBkOsfGZcvqlPzKm6rCxeVM8Z1auk8IDevoRS5OUB95dvqK99md4FRDMP48MfG5j1aHYIRY4EMZZrKBuJWBkhZjhd2TDEAUnlSeaO+3WsciHe35rWDlsnRCzBN07RfsDE+WDVN07QX2BES/XEfIhAESEgYYu7e6pYgfuta/NY1AAl5dB3HVskcoJJJKqVBwcLNWvxCzl+tUqz2VgtVb3yLtS3WtgCmTsv6HfLZaDqvOnkKUKnBG/caBu0qhVNc+1iKq92D1Q2qG1QeU3msJhaUEyeRA1ic6zbw/f7RAAhM5YdeKL5l2I6ZPJPt/tZtaQFe0PaCNulCPWH5YQd4UAfIOTNmd3tg7VmtrKyMaqJp2rPSBYCmadorat+ooT5VgVJt14fdngClMCxMW01OqslJNm7I7Q8BRBCR4qaUq6TTDEnZAZAoXDLh/8H7SMD5U5w/Jes3sGxAZXIqP49hkMixdr87/WC4E7MA0xOkZ0hNk5omt0BugWYxOi9um3ZHxVJqeopkblgocMx4ICEQigcoZcTNTNzMlNqP6t523duuB92wofhrTXM5ExMJ/HDE1AJN07QXhy4ANE3TXjLr6+vDGxxM5ce95Q/sLQy6ybdgGij15GBqSmpVQGVSFCuUqlLaAnAsQ2HGEgdjAsZMWrzAem1BvJBkt43/h9/j1n1u3Qfk0VV2KnLvQffn3P0ckCE37D0fr83WA1xXzZ8n2vArep52DDvmTS9500v4bUDlJgHCgPawvQVMZfuhW3HNTtD0QzdtTaV9xw/bUX7vhp4beqayTWU3gwrwrVkL8KQtjFGoaJqmvRh0AaAdml4ISNNeCs8yLbgrmtorgmEB4nqYVpRhq7kzcvMemQKg5mZlfRul1MS0VKtSHrF0j3GyAHD3kfn2WfPNM+rNK+rNK1TqhGH27IJ6/R0prRGGamGJwgnljDG0xrYBNXeW9DRWDDuO18Hr2Bsr9sYK4BaWpLgdXrshnocxdNqu1wbmkgkgEB8gngnEC8RzjETaygI1r1TzSoChLAWGMvzQtfUQIE3TXh7WqAaapmnaC8e27UPd1D8aCQIJDK/TiVkKZapUGsAwaZVkYwOQn/1EfeXXAZW/D8jOBqDy032jhZu10DTMCY/s1JPtAgxDyrtzamtNDCX3fg4mxYo8LF60uNo3FgCfruxc/urrZBOsPnq8+mcnfvltCgXiGdq17m3+yZMAqw8AWd8BVCzuJZL9eygidtxQpqViCTML0GmWKK1Xs8CFqexqswHmmfRJoNypAkoZgKGsVjh002LtZaZ3GNCOH90DoGma9qoY3icw8Kxl48QBJKBRI/AANTurvvWb5LOAEADyaBMRJqaim+gHfVbrHg+vXgc4Oa9OXFLZSXXysjp5GUNhKEC9/e35r/yKWlggl5FqOxhnK4CFAiDFzZ2f/oBWCfG7w4G8Dl4HcO7eVKcXjItnsOOW4QyJJBICTb8cSLdEyZMHLkzM+OIVEg7Q8IoNr2go0w/bMU/csAViqWFhtS+XXmBU0/bRBYCmadrxd7ThQKJMZRigCAPP87pL97Tbcv9hlGGry19Pfv+PlB9ES+uoTAaA3dkC+wShkXVotIxvfItytTpfAMgU5ME1eXDNSsTURFZWt+Taj6Pj0YPe+s6F/tEgtGyUolkKbzwIa201d0rNnaqYbYDGDs0SpoVpSakIqFgMCOMp1Sj3jRbliIH4oXRXKAoJQsfxHevSxJxBd+DQfDJpKBNwgwZQM9vARsu3RW8I8KJ7xvWF9C5j2nGiCwBN0zStjyc1Q+h3ZwOLYFvEE4Dcu285jv+9PwBc5QNhw5NKDSTcKA6KGQn+1Q+A7PXb8vCa3Pq42wMA4bXd27TlVVn5jFZbpWxVqg6OxLW//BAwMnFAUnkgZiTbtkHoASSyJLLq3DvqxHIzN4HpGO0G/rChU6ayLMM2lBll+YCBAbhhK2nlklZ3BaFoq2DHTMXMVMxMTcYCTwWDYmqapr1odAGgaZr2ihqnW0CCwPNclEGwWwYE3n/8jz9R73wHsH7nt/nbv+tgz1SagJrIXTyVV1Pp/rHiMXVqgfmC+KIuvIFlAcQdILDt8Oxb6uJp49f+dRwHBvYi7GWIoBTJPAsF4+JJ1m5IdTPeagEULjB9puGXG36ZVhlIRruP2YnuzOan7b037Aad6Au1+ySawZNOA8dIJGpVwDFTu0eSKWvClxH7i2mapr049CRg7Sjef//97373u6NaacdKoVAY1aS/zc3NUU20F5hhiGkBmDamYNoIYbuF32Hnrj91BvD/8P8GzAsLFFsiQmJAAYC6/rD52mLCeusUoJYvqSTy2TUWW6bbClJZNXcxaie3b4YPttXMHEqxeW1ANAA1NQ2omWXZWlHZglQ3ZXs1Biy/gWkDKSO97QROeRO/gxN3DR9v2EZgKJVQMVNZLb+aNLM1bweIM+lLB5KyOzpoo7W2XnWB0K87zu5+w9or49133x3VRNNeaLoA0DRttCNn/zzbY3t0FfFlUYahwhBlIKGYTuB7JJMEATt3AfP6h7iueWEeoO6KCHD59ET/WBLKzjrLl9kuy61rBAHvXFGvX45OmfWy99EHnJzHdQFjLsfIMTVR18TdG6KKQCfuEF+ksoZpAGKaqXIHOk75yZAke3uT5KD6ZJfpRGt6NoNqo5UGChhJKwsoZdqdzgMnBM7ZSeAm1bK7ZoRiG3oZ0FfLe++9N6qJpr24dAGgadrR+b4PWJbl7+7WZFlfyKeKriKG8zxvnPE8h/JkpdFo7q9hqcADCAMAK8b0Mo9+AEQJtzpxXhU/EdeXh+v8Ut+Qu8N6/GB1vbIwl5PHD2v/4vOZv/dbALEMJ+e7DdJJlT8hf/UhhkE4cIMtcWIIzBaYKbC14aw9cOdPqtlTADt3mX8NkId3WHgdoJMEKAi3f0J+UEgQQcKAQKHaYetCZuFerQo0/arCy4fRLGfmk4trrcdAOLpM0TRNe+F8IZdqTdOOk4PJdy/dj77w++3VeqhKwPf9qP0XWkjoKuIIxPdxYgQugYfrE3ew43gu08tA68pbydIOH1wFZK0s7tDRNWGAbbGz5W3XAeZyQOZvX9r6h//P9N/5GpRVMgtg7ABS3BgWCgDVaGCYNOpQwDJVMh2rFMmcjtYhVRu3Sc+q82+xGYUyqe+oyohNlAFP3GhAf9xIABcm5lNiF4OtuBUrGbWUlZ+3F6OWCSs74VdITpSCFT/8wrdl0DRNe16e/yVW07TjbUjSv+/4OEl8L9q+gL2SgPHi7HPYpzG+l7qKGLJ92KA+BGX2pswK8RiA1wLYecD0qWRpB1Cvvwbw00+VY0mtoc6dOxgHwOrGlyBUpuF98ph/+206XubffZ12h3hMrl4D1OWvUlmltB48Klu/dEk+W+0fDYg7BB6pXPjJdWNpBiuG3wGIp2V9Rc3MADgpcvOAfPh9QHILOGs//as/+to3f6dvyJs3H7/15llbxQxlbrWbJ3ePT5oz2+2NmfQCICIGhmPEg9Al2RvvNLCn4uXyhW56pdfj17QXxPO8LmqvFD0P+NV0MFMfZOS9/OGFRO9UNL5oZBI/MhpHrQRG/iJH8OxVRKFQePz48b5sfm9+/7wGBYkygdC0CEKieFYcTKZPAeK11cJl+fivAKk1xR+6do+EKjPlr2w4v3YBsMUtfroyeWWZjudfegdgfRugsgrIxhj7fxkWoY/pAO7iGQwTJ4mTiLYqk50dKZfV8jvyyY+i5mpujruPhgUEwBev7FZPMjkTTwIKGqrV8quAY8QBPwyjlnW/WHaraWsK046LngMwFr2avqa9CJ7b9UzTtGNpb6o6fvbfc+Tsf2/L4TXAyFCH6g3Y23jQzfKRQfp67oVEoVDoPUPbtqPsf9+Rg49qNpvRF8lkErh3797BNnspiPb8MlMpAjc6ZNu23PpApZPKMAE1OQuocx53NtTsaW4NuGfv7i6U2eqQiJHLACqRIJGwHl33F18jmwak1tj8pzfCs4E5m5JHD5mf6h8N8F3ptGiU3V9+xwDaNeIZAK/VW+tTPvsrdfEtgBvXxGur7BzNB6gRm3alLCdmpjZaWxm3VEikgISVbflVGiWAegloIUDamgKQsK2O1TKgOk3XtOPt+VyHNE079sZJ1g8alLgfLdpBI7P/fYZ3Jvi+Pyjp5+l6YGQSP6SQ6N2bHxmkr8P+ysP1Cry+BUOz2WzUy0hgeJ4Rht077oEPotLdtS/l7sfgAGyN2P9L/FA2HpvzGeYKAIYLYJiEgcrN2rWie38d2PzDbcCcTbNVZzJHozIwomGoWJxcAQjFx0kSBhQfRMNyVGFeOWlZuQYQSwMqlafaGBgNgO9973v//H//b0NCYDYxA+5m69GF/AUgaWUDxzQbVW9yHqC4mrBydaq2Ea9LLS7O8MjaF+oLHbmkacfPUS4/mqa9gvYu9TO+LzT755ChniX73ytqNjwUgzsQOGQtwZ5fc+8In4ht270IQ4b4H9RqtWzbHv43eUoQYNvReBsAZUmlwvwyrXty5250bPnSqZW7j1m9V/i1AgNGOinHplIhl2MyB0ilAgihys8SAhT+nYXg3s5mx2dmEsBJHAwSEdPGSYKK1+utdArT7g7+Wb+rCt0FhdTSWRptvLaKpwDZfhjMzPHo1qCYgEiY8LsdCAkzFreyqlUDJJEpdR5jMWHkgLQ95YYt24gDKXsS9WSzMO1LoXstNG18433ua5r2SnqWoeqWZY2bWR7VobL/4cbP/nuGd26ME22cYfojo0WnfN/vG+rgj+gFjI73Iiul9rZ86oGhdGe4hgGGCWCYUeIOeKcusl4CiI9YX/9/ef9P/uF/859y8ozsrLFTRs0B4nbUyTlMQ6pb2AoI7u0AxsVTy7DycIdGtXC++zqMXpC9l6Vy26Ci55aoN8SvAUycBaRaBMjt/grRAqaB707PmveuSX1YP4Dl+77z5E/R9qskzgAKHjfsEymvFdSiUwkjXaFc87aU2wqMMfYu1jRNezF8sZdnTdOOE+vplTqHN3te2f/wQuJQ/RLDZxEc1jNm/z3PpZBgVP8Gu0XOwYB7j+ztTzhAor11vVrVzk2owgKJie4WAaAWT3H/IYmsSrTIbg+IAOD/6GP1+78kdx9hmoC6cApQlil+oBIxwDw/y8yk3HxENs3jLVLZQaFCJ4bXZmeN/Cx2jHKViSzAgzUunu42klB+/hEgxYa6eDFmpknlcdYOFreFQiE66FlmJ2h+cuPa+XNL89bJ29Xtgl8D4lZmKS1gJawc4FEDat4WQLNoGc9hyrWmadovxvO5QmuvJr0Q0PHW9/b/3uxwX948ft7/pRQSkecbra9xUvae4Z0AQ0Ktr69PTQ2eHXvAyC6O6Jn0aSMhdqLjhYZhEvoYFkDgy866WpwAnOJGe/4Ega8K52hXowcN/McNvOBXr5CZVW/Odv74h0BN7mf+/a+SmVOglhVAtQVEE4IB7jzgfP9ggNQrnalFAK8j5SblptSrgIrFAKltqfQUwJkFirdwEoRVdjawhk0CVig3FMdQCYkD57LTQCdsdtxmwcwUzTqbtwHauOlcxpx06TCxFKyt7H3LfFkrvWrPnZ5doB1LX/i1UNO0Y6aXQ/fuvh8hqx5ZSBz8+iBrd3WgkbUEEI/HB50aM0LPyHE743uOoTgw87gXvG/2P6j2iI73xhS12l48mQAwbUKXwCW2u96laVHZTXOTE7ierN2n7VJvDuyUsBOxjkttg8xs7LcuAonbG/5fXJPftlX+hP+jzwAWp+rnlpWalesfqNeuqOJPBnVuGL6nJmZirk98At9VhpJQyGcBFi4D3P1I6jvq3ClAXXkboLEjjbayRi/Z6YbSUm2j9LiZzSXJ2kbcC9tA3MxUcgrwnn5KFa+599teMVCr1QYNpavVakC9Xu97llesitBbBGjaL9KhL9uapmk9R0j9e55XIdHLDvsm8eN3I4w5g3bkAj5jxmGMUMON84OiVH5Is0FB9j6rUHyMGKHfm2WL66pTb+ye9snNAXL/I3bHwZdKxXx+kgM1gJSKtFKoHPVtDB+w3j7pf/wgXHwNsL71OlC3k4Bc/wCQB7fI5g7GeaJd9eMnACwHML71q8rpAPLTPwfUwjxAqyEPV2Wrpr7yLeykmpumOWzCri9u3odkPvo2Wa0wmTUwYkaSVB6vDBjKDMRNtP1QYRELxE2MWFn0KIZPwtk3I2KfWq0WlRavVBXxxdEzjLVj5ogXHk3Tjrdnmf57BEdOgnm6kGCMofB97XtI35x4nHw9KkjGTM0ZFe2wLMty3T6r0du7WwQcucMhVBIzTc9tA16nYzsOILc/5sQ56tsAxQeAmlnEr5Ncjx7V5yeaBrE4EqBMdfEdQGVM++xl46d/EX7tO2rhdSCz/pjadl1ELS4Gzkzwf3zfO9ntlOAgK1Z1G8Tm2H5ANgHgpAD15jcAiqsA2Vn1+qyqBIBsr+KNu2B/qdMuzuYsw84YcSAIXWCnU563Z2tBOdF+8nz80I0zsIuJQw4Me74O+17uW1f0qogv+j79ysrKqCaH9kU/Z017GT3Pa4+madqX7llS6n2dEnuHBo0fdm/LIxcSexuMzB2HB7QOM8DJ87xog7AnRIxQDFG9rbUAkmn83TR64gQP7wCtE4uAj49p/V//63//H/5n/zUH1Vty96F0coCkDGDnjVNTkgVMP/TXPwdo+IBaXAQsPzBfP8HgAUvSaU3GJgGmT1K6J5/9TE7PAY3cBSAm3S175dZVafiqsIDj4PmqMnDLAs/z4mYGIAzABvyw+/dvBfXq7nAdpYx1swW0Aw+Imalw79/n6YCtVmvQKUbNAzmo93o42sOPZu+IpiPfC/9y+yKeY2nx7rvvjmqiaS+6cS9pmtaXngf8CjraLfaXzvhjh/ra+/AhkxyGG9mfMGYtMb5+AaXp+slkiBnHtICnZtDWt5k4AbQnC6xtxc2MVb+3N9q+56bmZ5XteQ+q9sksEgJTlTa0mZ8BRASQShllEJgkcjgKK86gyRKGiedT3xJnTq3d5sQMa1vRfmSp+Trgp6cBtrbUyTNqpwEox2JfhbPHvr/zdDwzaRYAu7JVSprAXGLWbjf90iq59M1KDDiBB7T8iuF1+gYcUn35e/Z0GyeJ782siL7tPXzfCLpxQkUOvq7Gf+wRFAqFZ5kRwZddQuz13nvvjWqiaS+053bZ0DTt2Oh7hd6brOzNafaNwNEOepa/zJDHJhKJKGMeaW+QQeWEbdvRqQM/USnXJwxBuoNnrAQiuLtL6QceIvGdjaQ1CYSZ6aazpeIJ+j553+XsgmP7AMUKIL6rlk8BdOpKKQBlACRygDy+yeoai0TRVlZWlpeX79y58+QmtGMRBoDMn4MiUxOyUwT8/BxAp7tKKYFLYgK3IX6AaZvZDAP4vu+GLeyCMm3HC7G6CXG+GTywmWt3Z/qma81vzk4Adx9VDNVn+P++ZH24cWqA4bUEu0EsyxqnT2DQ0/N9v9VqRUEY9ZT62vvqOsLDh4s+mvq+7HuruB60r7R4caoITfsSHf2ypGnaK+Jg0n/wbGScTLcX7eDQlGe86X6MPfvfxLKsg3/wXn7W+8v3Sa0UyrbwAwDLxLB6Y+jVmTdoNwl8QKpV0pNu2DSinQGUkUgknsrUe8oV0haJGIk4QC4tOztqrrukqQSBymalXpPqJuUarkcu/T/+o//uP/97/8X+OPDP/vm/+Af/5d8hmVfNCs0yb32V+1fV+ZOA+cM/A/zpSQBlAbgAKj1pMOweM2Crp9YIqvs7uZnFx411YNuo5pw5FZuI5jzcr28BSWsiYTbEKe2LM2b2HxleAxzM/getTRm9s4YPGxunlug1GCeJjwIefIH5e3oLx4lzUO8XOdrD+xpnUoQuErRj71kvKpqmHTP7ro57U4HhejcghzfoRet797H39Tgp7972fR87TpBXSpTi97L8fd/SN80SUIbne069jgmhD6AMPztldVxAquvEMyqeYbvsAK1q3Exj9N8LTCo1ZhIsn6TRCO5vW1+7SKtOzAGorQPKNKX+JJNWU1PUYPA/pYrFaVVgguQEoJZeU7EQUGeuAHz+QfRgYikaLpZDpy7xjFgD5wAACoXfbhmuxGYFSVmTwInUnBd2kk4iauPmpjrl8lwiVW0khoR6XsZ8D0b8oYP0Rmb/+wzvT9gbrW/YvQfHTOL79k70fqkxgxx02I8XTTve+k9a0jRN4zDZ/0j7sv9nbOn7frvd9vfYd7bv8eGi9q2nHSrCl2vkXOGjmS7MIQGGoSxztx9gN3lyG3hNlcgCxFJAb6KwJ30GxAOkk2RSve/8n96Isn9ZeRQdCT+7jWOrTFZNn8a2ECEM3rq81D8aIAGmxaP7tKusfAKQzJHM0anTqZPLkMsweepJ+0QOUMHAf1bLsgK6Z29Xi0DN2wpCzws7gL31wN564Abd4U9e2K57O02/3AyqandT5OduyL/skPm4z+X1EL3+n1eokXE8z4sqir7vu+j4YQuY6FH7Pi4G/QhNe3XoIljTtP6OdoH0B999HD/gyCBjhvJHdUr02gxKTaKM5FBzbXvP7eDohTEj/CINmWH8hDKwLGwHr4MfYFkEnnn758yeBBqZNGYMoHEdIJZU5RWr0z+msmxMk4kF1Jq1PEv+FJ/9mNlJoPMHHxhfP60Kkyr25J66+C6xNGz1jRaFpFKhkGK7RGpG7n2GdRYIC4uAqvYeaGwaLcKWZxkhIfSfOxH9S5ntZtOeQsITSavmdSMYGEB7egGot3ccM5FUKczUjmoCgfhiPjUTIAo15guVX+BrY/ynNNLoV84BQx4yJPXfq9dg5F+s91lxMOa+IyNDadrxo3sAtGf1/vvvj2qivTT2jv85OKJ3HH0vpeNc18f3fENFKf7wZn3HJBwU/Zrert7x3pHD/h2i9lFHxD6jHvp8PBluIQLiYSCIaUXJrqzdJ5FL+SZApw7IvcdRc2U5fQMSS3Xn+ObmAUr3WV4klWz/6EH3gYVp2dkRry3b90glqn98F5Dm7oTjg0wbERZPcf5NlU7i+0gYrS+kfvznnzuJz50ExftR24I1ZQcQzVgYLIynlDKAZNidDOCL63ih44XxzfvxzfuO2S1RAkNCCTph01Bm35mpY2aWIwe3jBknMiSa12eS9zM51EtxSOOR78F9hr8Lxnyn9Boc6rfQtONBFwCapvXnH3K5T8uyDtV+iOcVJzLo6u6PMSahZ3jLKJMYWUuMXwbsC+gdMCi/6Zv29bKcvg6230ck7AQ+0RI9u2uAqvTuYppOinYNEdncIRnHa2M70co8fQQennvngw8oPiA1DajZM90wv/Gamp/HtMnujhGq1LK/ucjO3J5zXwAAIABJREFUev9QkcAlPwnQqgBqIhsN/jHKa+rSa8CpTBagU8vZmY5yMW3D90k8Nc13H0FqXlD3BcPcbIVZuwA0LR9g9hKzl4LQC0KvGhTZHexkKefgWkDRwkrWrn1nrT2ilgywN86gNj3Da4mjDaA/2qO+XOO8sCPjvxE07TgZ/WmiadqryRq7ByDKOcbJTsbXN9qYz2cv3/fj8T5btB4h1HDj1xIj+WNUJt7uKo1DVgL1PC9aWLNXRbBbS+wNEv0phv3zKdOQqBMA7HggfiA+oLIpq7bqG4ns9GK1WAJIxInWybQHbIvrdcicorbd7QFITctHfwMk/r1vRuelVafZAeTz+4DKTpJOq/jAdXuk42E61HbInxCvrhxbol23snmpFy9uFAEvlZZyjXQiVi1hWIDEnsxDOMjAnI5ltju1Jk1F+nZ1fdJKTMct12CSBCC7I4iaftXASFhZy/Abfv9ehV5yH/3f2zOndu/xkfY26/vqHZ769/QajHwLjBNw/E8Jhr7GRj7t8Y3/fPbxD3nLQ9NearoHQNO0roOr4+29Sdn3Ib1Twy+cw4PsM/zH9T0+yPg/9FkcNuEYntyPk/1Hel0BgxpEp/Zm//TLtEbGUUTZvwKi0TVWqP7uf/X/Mn0OaOSyjbCK11ZnL6mF0yTzmDZeu38sJ4EdZ3KRwJcHt6S4yolZTswC4nne93/q//C6OveOOvcOYHz718O7D/vH6TEU5S2A0mNAXA/bwrYIXJVIA2puBlATmViziRXDaxO4uP235o2E+FWv6hjSDjoxM7QNAbbbPtD0K02/MuE6E65jmwkgYWWjR6WsgXsL7PWMmW5UTiQSiUQiYT1teDfCXiO7FHqnhge0DzM3ZpyAz8WYz0fTXnH6faJp2jDDr6bjX2sty/L7rRS+r804AYcH2duMwzzD4YYnLmOm7CONn/2P5A/dQrivVquVSDy1qGUUQQIP4kiICM2GAmLx0A+xHeYvW27VDz38aNkf1S/wHm4DCVFG8MMf841l6g01O8meVYCs8zOUVwHj7TeobxtnloI/+UhkcNjsVDAzT7XCzDRhoAqnVT4N4FXAUK9/DZCHPyEZA7uZcKABBEP3T2sHnQlnsuwWLaUAQ8l8Mh2dKrvtCSfmJhMAFS9l5RveYwzLdF367QT8hRo/4+9rb//D3tfJYWuJ3teD3pVjpv4jG+yz9619cFeEMT8l9npenxWa9lLQL3dN08by7FfHXkZ+8MI8fvBey+GFxL4v+rY5VH4w/jMcKbr5OqrVWA6m+HuPeGPsCDsehVLdYf0xm46HYaE83BZOIuYbMWLV3PxcTtbvPaBRnH1t9uq1/nfupeNFnQPmTBrDoFLrnfLvFc3JhOzUye8ejKWibX1pDehPACAaj8TWNtlZ2bwnVQuQhAOQmuk2anaIZZM+2AmxUzSH9QCkrAyVtQnIOjNpO0jZ+Yf1BrCUTs3EMoDrt4CYEa18agG+bfvmqOLnBbb3dXKE18w4HQVjit4aw9+bfe8U7F0RdZzbDXsNeT8O2nBN0152z+cipL3i3n///e9+97ujWmkvtHF2x3xenj39HZ7ZDzq1T9Ry5D3ykfn6mHEiI5Ohw96273nGdH/Iw5Vh4nmYGZRLKNgWgQfgu/huEA33b7sAzu7M4AE+vVe6/HqFRI7lM9Ail5Eo1PyMNT/jfXZPnSxIuQa4mTlAnTxnlj0GlBOACkOnVOTcPKCmFlGKINpHLFSnr7B+CyCbUhMLbJSJZ6IeAGNwru55Xs2rkT5Pfafm7aSZaHglzGTTN2+U25dnQlvF4mYaaAYt35C77cpkTCresGWFXhHP8vKLjNnzMM57fJw2jN1POGS/BU17SY31DtE0TXvRjLxmjyO6lThOZ8JIB4dS9G0zfsCRdnZ2pqamRgYcktkfnJA6QIjtEK1yE4ZAYFmB50kyq5pVMxDADZq2Gae+LenJfLvx2lfO94/keTTbNNtMwlQK3+fhOsBiobq4kJo9C3D3etTW2VmTm5+DKaVm32C+79NpkMg/OSTSXFgC6maTYH0CmF7OTVNZ+cz7kw8A/8rryjC8TP9CJZoLkTCdjvLIZJt+6BgJX9zlzCRwtVRJWnnA813ANwSYjAmQtdOP2RkUs+8Ui32jrZ6vO3fujGryQhteBgyZ+L7P3vx+X9/CmKm/ph1X+qWvadov9Pb/C+jZk4BBEbzdtXqGN+vZ22BkLQFYljWk2Tg3U8choUijTcYlCEilADPEA1Y+FmDpTcA24wCmBaM2xHU9HJto2yzLwvXDr39HmUYOfDp4bbn5AHAufYOZs0xvsh2q3OBcOZYiNUUsRmpS7t5QS6cV3Vm5U5tlf+F1QHkuxQrA7/4t7q4DcatPwL1pesxIdcLGbHIWsJQDNPzymQymsqIRR02/IsQafskLJWklQqkbav+iGt7uIkt9K8xWq3XYrp5ee9/3W63W3iMH/61XVlZ4wXxZZcm+XH/k23C4d999d1QTTXsJPNPbQNO0462XuOjbZmPa+5fxff9od3mjTom+XQr7aokh44Wizg2l1Mgss1dLDPpnVYYimcSJ256H7wKYaTMUNX9O1m7TKgOqvCMTswSu8jrEUmYwoAwIQxaWeHiPVlst5ElNq8VTJoDPzn1SM5i2WpylVgfk7qfdwTr2wPpKmhXKq8yeoVEE5OE9FmcUatooMFcA1PZ9gFTc/L1/AzC319ha252y/FSo3teW4QAKtdHcmDcv3KjsnJg0HXOqFOwASimnsuPAg+BJv4QgfujuizZyAPreZZqGV2uDAu47Mn7JN+RV8YxjyYb70sfSPJfPrvfee29UE0170T2Hd4KmacfPoNuW+w6OeTV9LmNsXjrP8qv1fexhA3qel0wmo3HVBxM+e5c3YHfYPYmgUiKhBFg2hhLA69h2jEZRZScxbUAmZoFSfNTYjDC8/smt1/K2LF2EKo1tOCWrV8XZfQKdRm55ubKyQnVDTRVU+QFuVTmDd3IQIT1153H17LklNZeT7XVBonX6q6qe3NgBMO21whQVDN8DLr51/vraxwMDAmHQCiqmsr0Q4GJuCqBdzxMLe/0byfxERwEbRskL26F4vY3Axsz+e/xRy8/3HUG0z94GR6slenplyaHKgEEVxaGCfBG+rG4HTXvB/f/svVmPXEmW5/e37S6+x8YILskkmXtVZS3T1dKgMSP1aARIwmAeRg0l9AmEgaAPIUiAhMkvMBI0mhdBAwjoetHLPEiAgOnpZbqk7lqyKlcyuQQZEYzFI3y7m10zPZj75Q13v4sHyUwyaL8H0uPec4/fzdz+x+yYmV0HwPJi+MUvflFlYnlFmcv/kVKGYVguOLLwoI4uqUO5k6W8ECevPjxHle1yzITxvu+LHGaj1rqOZ02IJgSAIoCZ6VN4AEGjk9mQg28ANHlPnzwBoYVzYvp+4nnY6JHjh3oSgDAc3yduCwChRD/4Qu8/0vuPSLcLAI2eOUiNlywExjkXQhC3MdFjAJj0AZDNHQBN3kMqO9IDACWRBNfE1vZOT3FB2mv68S6Ys+hwjlQnN0SbaiQqDEkSzhYP3pvsJd0tAEorpRWArrPdUN5EVuU+XYg66j9D5roUlpKsuBB1uRlmIxwyt4uYvVVu5kkWqDqiglcwIcpi+X65YI1isVguJXXEQYasarmUNYKE/N463lDlsFLOzrHobVUPrwuc8yRJRPFA5KSg0ZcQSgACAmgopQhBOAYA0US3GekIAFpbAJzwWRrMUvRwPP3ktoBEj0fY6KX/7m/x8W0NiI1ucnQKQP32S3z4hwD0OAAIms8ijTycczDROD4DznD9Q312SPxm48kjjUe48R4aXX1wDwBhLOUMANn/ZqmfZ94MWvtBPPH4Gc6u46pLZv0PXOxN9szHARmPkgmArmoiHKWCl68tcDEuIHwrS2V9TNEoacJPqjoTKvfOkWn9xaPy17XqBZqftVWPslguN7Y8WCyWKStV1ZXUUf/1qROZGIOa1bysaiut6SdPdoYXOPZ7ZGmC0DypBEBAgZSmEpoDwNE9AG5rG0AEEE2U3062ror9u3BbS90QwYRUcHy0txA/Tn91P/w3XwHwP74NSnUQxR98DICu39wB9mskb+holF75IXv6GMEZfF8HY3gt4jcRj9HoAkAidSLJL/9v4grS2YCAHkY6eLb+gGFR5jZCCeCzs+Nbbb5+GAFA7xqAJmexCgAIavoEQkBrrdqcZa7MCN3np9ajqU2ySmdCRlFYWEf9Zxj9XXItd+/erXRYv7EgQ0oZhiGW/YBwO5zJ8mZjX32L5Y1mMf+nyLKIpU1rdfR6nvL2uZW8VYYBldI/v6uOPlh0mJwf1lnHyavMZBI6a804CKkQ0ApAwpgQAsIHYP5N/FQA9KRwwn6DVmr6QadPnOvXfgr82dfuP/kDsxIwefctt38EIGJN/cXf6r1TOM77a+q3qrB1nTTXAESbm0hT0tkEFBHajFRWSrK3Pz6ZPOk+OSCuAKBPT4FekSvTNyKlTKkGdwF06VofuD+UN5sAcIzTJp8mAnUCjUgBOHYcAJy6Dns24JvXXoIKs1ymcoP63vAKpN0vpfwSVgpOKst4ZvNiwwmL5TJhX3qLxXJBOOffQcW5kvpHaSyRaYI684ibhs/KyATFscTc9jr3qkiUZNvrOHl5aK0JAMpAGAgbdlsA/HDa2q2gz9re273Wk/4hhgdLPRDfg9OAjMnxQwDoON5/9U8A4Nvd+B/+QwDst38DQJ9OAKDXxsGS7P88etxXehsAXKO/KZCitTVpNkziTi9xsPUWWXtXf/5L/btH6e5d9tFb5MEsE+k8i9L5g25XgA+7aZwGAA4C3Gn3wmS6MvHYBQCpIqniWD+bFCjzU/7qmqdZrtdX7QQof0NWjSXKWdVVkX12gfUz9ctjgEr1P2eJqvtmsVw+7CBgi+XNZa75f6UqcCXjSl6gtxfoqpzKnoQMk9lcLkdMnMNz5PdmGy8QDhmCIAiCwHwmhJQ4WXJFBJRQTSmgQQiARMWJipt8rcnX4HXgdcws+C5bnvnzDDrNk8HmbQBk6wYAhMPkxx97g1MA5L2fkPd+AkeQm7dxcoZJSG6+D1U4vjbxPACUcAhf7z7Qn32GtetmF6WcUn4kxgD0578EQLYLm/8zhBBSJaEAvE5f9QEkkMkZc5i/wa7caU89PHWjgUhSlfBZO5rS504yP9h68ZmaP40NarD4ShSZlTgUs/leiwyWUuJwVVer2j8PKxUTrG5vsbzufHel0XLp+cUvfvEnf/InVVaWVxpTQ1fWhbxG+iyv3dZYx9uLouYpGYqyn/MGJXvrU96cmSeLAZYaZyecXSYhZNGmMtnakF07mfWXOJQkSgPglHPKqVIA1LgPAGBUJt5kcuqm2HgLT9UylwBhmPRBriEcmXHD+v6XAMjONT05dSenyW+/BkDe/wEAXN/G2S6AH/3w5nJvgKCuCCfwu+ACCdBrA4DbaKjpyW96t+ABjJ7cvhp1U0Y3QSjYt0UOM8J00HPeN58TyHV0ALixIo0um8RjAgAdujbBmctbid7z2fKRypnKT2bTrdYU/Rl5+6VPLXsZKj2L2ayvlU8fNcKJF/X+v1jqXNocpkAVFSuL5VJiewAsFsszMi2eMbe3aNccmZ8Sm1Ut67PU2wVkQQmy3iSJ9XmBd0DOuibKxznUDAMyNLRmXEEDiOJQ6XSkBiM1CHwR+EJplTCcuvWmwjzpm+XDQk/sbd5AmurHj8jOBwDIjW1yY1vv74Iy0uii6en7n+sHDwpdnR0BQHAGpcjVnfQHP0vdWS5+NI7kUH/51wBObl8F4MUFYcl5XKkc2iCghLBIjSM1BoCTh311RMzAYkAQdhLJ+8HhV8PjMl85xIwqw0JMl0K+V8H8uZJnY1lUhLPyyKtGJuTt61DiUKzeNVFivJIfi+WNxQYAFssbylz+TwbPkf/T87z89krKjed8LrXJW5YY5Cn/RqwoDkTxjJkvkFXbHY1xkXDP1P/SvXMsNZtrbzYOw0mE6UygAGWMCEZ4i3ZatONHU9GvtOw5Oz5rk9PHGB4tegbwv/yLfwXhYWMD3EUSuLv33d37ZGsTgH7wW0QxOANn5NrbABA9y6ovgvSugDC4LezeB8D7ewDGNML4JGIKgHrvp6mKu+LKhuoovxWRRFMqda2wZ5ycAEjScJQc93sNAP3ocT963KfTSYRuiQ6AcdL3WdesPvbdsJLiX4o5fC5DySxcnUUUVT7OxRIl77DZW+5wpRigjlkdmzkucIjF8vpiX3eLxVLI89eI5XK8Jplxkerl9cQ9r5f5gHpyRKySAvHyYom506h/ShmLaUJ5kiQBIZqAgp5XuARnewDAG4EciLQB8DA9yXw9efLk2rVr+QOmoU6a4OQEP/4YoyN8u4emg9u3yY0WEUSfHkFpAHp0RNau63sPQFyyfZUcLo8EpJSIJkAPcYB1D4QjmbCDu9jcBuAOTuE2kl/9hfrD/4BHIYCz5OlSP/M4TQAKyuft/kHylp/2m4wR0RknrtcDMBYJg7jVCAFca0xHNZDxaYnLV5Y5Xf7BBx8UWRaReSgqEXViCeRypVDaMVWnsFf+YszxksqmxfIqY196i8XycnkhlWvN+r6SzLJIKGcSpL7PSioF0KqdACg4vZpyJ0/JOIe5NCcNTVQKQGsFUEAjGgLQp0fY3gAA4QFwx8MD1w+aHs63iGfXqE/6+NnfQzSG8NF0AMDxEQdJbxu9bXzxtwDI2nUA5N3buPsEy8g6OpTjQSsID711ABANAM3TU3CgvQkAN3bo3ldDt90O0rWzoH90AODj968v+jRpUfl7eBicbPnr+uiwd4SO1wZwpJ4CaNFp71lfPm2KtWF8NEwOVbO76PM15Z133qkyWU7lq15JpQchRJ1ZvAx1ilVJRHHnzh08x92wWF5lbAqQ5UXyi1/8osrE8kpQlP/zisMLqDruHOYQMcNszP6s6TPvpMQsMyhxWPldeUxOTskhc+dT2SEgCrom8jq42WxraCkVGKeEEjKtOOIrt80iAAopZITgzIumX+cw77//7/7bpd7ItbcxOkYSANA//3vDn/4A0Qg65Z/9EgAEh+D66JG+9wUAwh04TQgXOfJpTtNTSUL4XQgXMtKfz5YPGx7FrkfW3wLQDlIAeus2AHXlLSxgpP/Us44ZFS5t+Ayj+IS8/QGAfgP9BijhlHBGRZiOQNmac3UYHwFo054+PwtQkqPOdkseUUzVoc/gq1DlzGK5bNiX3mKxvIlkVf7z1P2ccyllJkqS51j/q04ngKyaLKjSg0HMUjWK1D8W0qUoYRoKAKIAAFzXpLzHazthq4v0DAAmJwDCZnsbZ0fAj3/09hK/ANIY3jYog9ugqQYQdta8QR+9Ft/9XO4dAiC3PtSDb/SkcBGATD3/5ldff/TBP4hJkugY8OB3gMfwOoj78ZXpCZDODoYB0gQA2XyLKeD8SsBJwSStLWd9FJ8ACO68c929CoA7FMAo7E8tRkcd2p0gQPCUTRcGnn8N5jTr3NtSk7yxOVWz2HBSNVGVhS9r4K9T3JbyySefVJlYLK8HFykAFovltWZp839l9sjF6stLT/62XPgWZQdmT8FskbPZCefMMvb39zc2NrI/kyRpNBqNRmMymVTqyxL1n7eZRgsqJVpDq9l6W4qARmri8w6AdefGidOHs40k9IBAQFDPCO6MZ99FeX6Xy1oebaDXwL0DALg1S85ZaxO6g2gPo0Ozsu9yUgnA0UJILZsd9uWvcesagMhZc58+ABD/5kv67//RVP2HYwAYn+QdLKp/kWodDqnToJp2xOaESQCPRk8AvNe5BoBRAeCb0d4diAkCAGhtKnUfC+q/BFF7DElRfILzPUJ1vjSj5le/Fty5c2fVLJ3KN7+ETz/9tMrEYnkNuHgZsFgsl4OkxnSQfPVJspc6XMnDG8ji/Vn1tmOW+WA+LxWFQggz30s5Wf+GeUNikx4zHvOGr/g09SaQg4b2zeSaYu8e+DXI2Gcd4FwT+7n3JxxCtiGjWIWIhvA9DJ4CINdvAdB/+VcA8O/9xxgc6Ie/B9ZQyd4DpCp5dxtA+sFPMHg0cCSArSxq4EKPT8iVd9PxSYgIzWZ2aIm2BtBPnq6JK0rL3Yn8o8YGMF3IbBhNewDOXGXGRqc6JnFYX/0bzL0tb8UvP0ND3qD82ysLexAEnPOiU7p79y6KWQwq6t8Ki8XyHbNavWKxWC4ZlYLAkDco16OZ5VKfmQpcSdS+sbHE81zj0hgg2xjHxW3qOTItGKmYqOkywNkATE6c07jvE78fP3bYJm78SD7ZAwWCUy0jaAXg7t27Weusefo6laHnAHBGZ1hvOt98jh//FACCPgBye9oDQK79gPR/hcfH+ugItHi4GncAgFFKGD96qOMk/sEPkY4BnG50OXHFf3QbUUCuvAtgkBwCcGnjnIOFuaFCmkaCaDVmWBskR8PJOgDZbAOQagiAEQJgx28mKjR3gyVSCQerU6nXK8tmRmWsWL+wG5ul5zbX1p6J/pLyfoEwwMYSFsvL5uK1i8VieR1ZzP+pFAT1Ma7KHdaPJVDq8GKxRMacz4s5eRUozyQxvQH1M8UXLX3f11oHYaopT3WaCi4Io1KatJyes0aIv4m3z6Kx/s2fY/MOH53CbQEncOZ1NsxtPx16QYjOB5AR4gCAPn0CAN11AKRDAGBwgM4VdLZvd7bv/dn83J1GU2ZX/fsB/8FHb2noh1HzpjsG4LKmG8tkNlhZOu54cthM2Bq/ckr2oTW622ZX+d0TzEvS8EaDt0j3NNoD4LkuADobezxI5H4QOEwNyVjVW1ugPiup/0pqqv+MkhjAUC79Fy3rv4HlDmv6yWNCmte3jFssLwM7C5DlBWMnAnpdMLkHlZV3nkpln7UdPj8yx/PYLBqbP/l5ajpZykqn8ZpCCCUaFAyAadoHF752fe0CGCUnqY7Vx3/IiUB7C1qDOQpLlt3lnJONNbQ2AYC7AMjmFs5GOBuBUBAK4U7n/Dn4Wn/zGYI+ufl+4j9L2sn8mPEJ2nQOaA2AjU7BhBslbpQA8Hi7dfSUfvk3AJoJA2AWLogR5V2JhbEQLms41HdYw2OttrPZIl0A6+4NABvsyga78sUZ/+KMN0gTgMMUAI+1znLDwVfiYkcVURLPXOD9LPKWhROVPjObkhMzlP8iZUVspV+tL7/8MjuBMMelL7AWSyU2ALBYLC+AVWtTWdpXsFL1XGmWuTJaf6lN1j5d6c2QnWEQBMkMrHjmi1z4wCLqqK5ygykamhBAU1BoDcag1T/9b/7nIYYTddLgXaeph8kxgETHIGU1iw5DRGPIGNEY7SsA0PTR9CF8CB+gAEU0AkCuzwYELyN7mgRAcMZTDQDN2ZBowvn+NGGdT4ahQyF8rL01TPtROlH63FUvxgBTH3EEIECgGaOEb3q3MD7C+OiKL6/4EsC2t77hNK97W6lONtxzPR41qXv/X3NkVQxQv4NClv505M0y7t27N2cvz1PkxGK5xNgeMYvlDSKf/yOEMDMJ1qdEQF+Mpd4uViXL0i7+OqedxQDlZrJYyixurPO92TcmSSKEWHoCdfy8VFKtCLJFfnUqlSZIddqm3aE6o4S1xPqIBiFJBXEBPUnPaHyuoT2D9DZhFs1lAgDWbhAnAqD3vwUAdzqZpn6wi/XrIBTJUESFIxZIHCJNVLMHAGcnE3Z1MhreWN+GTHDtBxgekY3bmJx52gUQygGAxt98Q935LgUxWwICALROVKi0xDAlgHft7wAYyaMW3/yCaABrQgE4DvfbfMOjDQCBjKSKkMspqtmuX2JmdlW+kBnm5Esc8torYddhVVcl9vXVv0HOZscqKheyXpCAGq4slsuK7QGwWN5QkiRZqc5bybiSF+sNpbHEqt9VohtK1P8cNTVN5tDYJ8uo4yePXIAQYj5UHVoIIzTVUkNraFAGSrOhwG3aHSf9cdL3eRcAVJpSAEgEW+pKAXA8hAPFWUQiAKHvh75PrrxFrryltdZaw22RD39Y3pPwDMppFOLpfQCN8WS2VWF4ZD5F/+pfg7IAEw9ea/cpvdKZxh7nETOebdp4GwAJBiQYJGnQjx7dbvUAdMSVjrgCQEMhnuyFhy3RkjrO+8k/vjmKti9yLiwppVL91wxI5igPJ4p2fQdUvsyVBhmydrRgsVwmvs8CbLFYvkvmhv9mVXudmq+mCqkJf47Buy+b8lbSmrqtDivFEqZnoPKOGZU/py+zz1n3QqWfPNMQAmBKK5P9rxQACkpAMThAZ7sRawBnUBtXmse7R3E6aWg312OwwHAIgI76QBPxxP3VlwDkH/0xgNRrA8CTJwiH+nifNFtIgtQrzq4RLmQI7gAhbrwPIRox0N9FewvNNUSTlABAoIYAMDoCgDDSpXMgjeSEEqahoRTW3tJn+wDWr/1BP9kL0/FVX0RqDKDF1wDsqTEAAiqIl3diytdiGDAfY1RhYomS1zJ7mpVuVy3yJXtF6eDppaz01j0Pda5uDmn7ASxvHvZdt1jeaPKiYWnFWVP683r5M5llHZ81eYGuMJMCS7cvbiwnqZp7p76EqowBsnCixGcy62RoNBr5jUUnaWIJY2Ma/k2rvyLmfwJA9q4AGvEYgEQDwP146LM28JSr5QEAjSYgDQgfo0NXcUCS9z4CwE+fAgARAPToCABptpZ6OIdSkAkGT/H2hwBAGVQKAOEoyyZy/4s/9n77WfLxz/S9b81qA7h3VODuPEqC8mT7JoCUqI6zrUkoddJgPQCKRYfho01vLZCDUEpOl0wDuqRXYXXM4XOa27wJvu9XvmZ5KsMJzIp8pU+xSoYSL+2gMNtfVEEuvzqLxWKo18FqsayCnQjo9ULM0gzm8DzP87z6tbI5qtzAOKw0W+lLUSod6rtCbTWzEkU+66v/Sup3JhiS4nlUktxQ5rlwIoUCQDmjmlBNkCZI5bSZnwkwMUgOtVa3CEc0/uDEAU51AAAgAElEQVTmWkqfBQAyh9ly96v7aG0BQCqhElBinIxcMnIJANJcR3N91Gmivc2Cc8uKnYMQcAGvCQAyAWFgzqDbAoDhPvHb9OHvASQf/wwA+fHPb79zQwlX6bTQIdAWba6oQ/2QxKGeBHIQyAGbzWkkVUxBKehh+AhAIAcAYk7SFz0N6CJiAdRo+J/DHOj7fr6g5Qt+5rkSUSNDKfP58OHDkvV6a35jRmW5rjSwWN5wbAmxWN4IFqf/X+T5q8wiDxfwzKu6FCqlP3JtgXVOoNySr96sKArmlsFLiDQypV6/PTjfnyDOty4vxhJEK0aYAAWYaTgyif6MOgBOPe6yJotGAKAVkggASaciez4+8droduE24TYkp9xslnGyvgWgNZlgeHi88w6AcLhvVh2T/pIUoGkHRZroKIDbksLhCiM6aakGgHHL91q3jOWw5Tn3dwdrvTHGzUkkdUwnZ4sOF4nTicMaXWfnLN4fqlModEQvTMcABslTj/kAOpQfp8ecOIzUuu2vDkvjh5ovT4aYdSlkW+b6qVZyWLOUVZZ9vmI+T6VDi+XyYV93i8XyInkhlehSJ3mBXv9b+GyO//JDasYJonbqc6Xuqe/qxZKdWNFNLlRg2mT+aIAorRjhzONBcgbAZdNJdfaDE4cKtDdP4yPTAzCv/vP87V/i2n+K5hrWOnE69ozYV9OwQSQSQEs3DoOHXJ87VXOSU59MEEaRSgAYH7eGXWDS2f5orAb96HEvdh9trK8Bg7UegOYkAuCkJDkfUWSZTuZDnE4iRK4UndgDIqnjplgHYBZBdlnj4XgXMGsi4Dg9BiB1dBQVd1Ncap4zhMhY2vOQfxuzN7aynKJ2LIHin5Q7d+4sM7dYLgnVpchisVi+L/K1cp1afyk815kw11Q5Z1OOkRR1hLuoymmur04MlQ5XwnjTOpvIpxpFiNQqgVJkmjlqMl40NIBBcuDRltHHoctP4yMA73941Rw7d7s0FI6e6rfu4OM/4IcPsP0+AIc1xcHXAGRzS23exNkwEdzjHcQTADh9thiwPJ+bpAmF1wVlnHkA9NoN0t8F0Lj3jbdzBb4LQOnUKPhJhyMOUp5QMr2TWcoTcsHPZ7998OFHNwAMnLQTMw4uIdt8HUCqp3Obvt2+uRs9oIR2xUaYjhyCnng2BmCuOyX788Li+HJz9+7d7HPRLcpvr/nqcs49zysqaDXDiZK0JYvltcaOAbBYLj918n8uPZzzuZqe5yg6ag5jKWZk27PPZntNnzXlYKXDpU2nz8NihBMGCQAV61RPc+EZ4Yzwr89GX5+NPDodrbvu+sfRcBjTFm+7mi3VXr/99b3pp2gcb16P07He/Y1++OvpQmB8OnLXjC5IhdAnTwH8s3/2PyJL+8lBdKopAWUAZGeDHD+ASs2gZAB/MY5vNnYAMJkyOe1bYEli0piSJCnq8RDMH+pRpHDIU4BwiHE6zRoS1B0k9LcnuzQ3S+lInvBcN4IsYG7ZuPoky6g66DXj3r17WGCxrK0Kzw09WkqVg2d88sknVSYWy2uDDQAsL4U//dM/rTKxvBIU6ZWq495c8tLBnwFALAysLMdYluubzOfSvRl8FpZkW0oEkyjtTCj8LgJKSIpUa6RKTjeB3Om4dzpui3U0VC92WpG+7m9fbTQBBCTmnC/XqZ0WGfXRXHPO+tMtlAy6DblxDQA9OxDU5dSRKtJakevvq7UrgJYL6h+AJjROA0U0jr4FgK1p2gbpdsGn83IK6mkhtBBSJSMygdNQ0HUEdIMLAPfH+wASFZ4mBw3RNbtutxs+61LCOREtsQ5CQFlSHFFkVBrkMcXTLNi3WEgvHE4YLnc4MQdfoOqI5Xz66adVJhbL68EFy4DFYnldWNr8n+QyH+bgs4SZVevIRW+renh9ufCVZnd7TrJfzGFlQ2mm/ssF3xI/s5wLh3EzGCCJI+G4nDgAhvLU7B06apKOGHW0VnFKFq8LAEz8kCYYHWsKEQ7JD/8Q4RhIJvK0IbNmKQKAB6MwHQL40Y9vmq337t3LJ2cTrROlaBIDjBI6So7Q61A67gEA/qjtnsgTTl2ic3OSpglNpzk/WAYh9NefffnuB9upki4lHaFO4ie3xDuj5BjAw1H/3U4bwEiNs0Mc6hOnUV/Zy9nQiJJHli+kRZ5lvbEreYzbxbAzmfVgVL5FRTzPsRaL5TtmhV8Ni8VyCSiR/ob8rprCokij5KOImq4WuYDEee14/qvLPBBCFkVYeav/HJxzrTUh52bxJ4QQQihhlBElFWaLfPnEB8CFD2CI3XZMx5NIaxWpsekNkUtHTTR6SjjwOoTNNLRwO6mAiiUUWldw+BQAP3kCwAsiiShMFfBs5PTdu3enydlat6QiTgPdDQBNvj6WJwDgdcatRjMBAE4dSQDgJD5ec9aghlmiUTmM8lRJjzXDdMyJ6Dk7kZye8Ej2GRrmg9kiVenqYguURwuV5TSPMasU35n0X7rXbF/ysEqZM148tvKsLBbL90Kt+sBisVwOkuIJ4BfJLEuEo0lFKNqLBQVTR4Nmh+SPzcKAOh6KuJSxxP7+/sbGRvanuUVa62ygZJ2bNif7FkXbzJsmhIIQANMmdcaRxLEOHdrAbHhApMYO9RnhyH37M/+EAqBJjMP7459+BKCZjl24AEAdxMdwmgCMN/3tI7jTrJssnECeKEy9hpZRRBMo4M/+rybQ+0f/GOgDSLxm5/A0XmsP4oNnh4xPTK8CLxiKzVLlUr8l1oym3x2HAPvy4dE/urkJYKfhQ6HF1w7k1Ccl7LOTBGmw6OpirKT+DZUxQP3m+exlKLdf6SRrfrWhKAJZyYnFYinHjgGwWC4z+fyflRr2DLJ06dlK9T9HpQyV5ykxWNy1lMw+DMMwDOWsp2JVP68RdaRbfYx8932fANEkmi4BrDX0NId+nEzbvw/DR/A68DqIRu/cvp6okE0GeVdiBuLo8y92idNAo90cTprDCcyqApSi/wCA/v1fIzxDNERrk3z8M1CeqNRjDCWvEHePwzEA3LpmNsj2WnM8VeROLAXzBfN7TldpqXtXHdqo0yvSdrYAvN8w+USYyLMgnc716RHf9IpwIijo+90kSpevfPxdUlTG66t/Q6VxUmO0g6GOjSFJkiRJgiCQBSQz8kdVTtZZ5K38KIvl0nOpWsIsFks5q1Z75fJoJW+yqvW9Zq2ctyn3hmVnaLbMTSNYGZnkKTnJlfx8v6ya6aEBTYgGqKahlgA8Nb3YgERMCwAQz/JqBPUSv5n9ee7OuL6kRKoY/hbWrsdq4mnJTQ/Axm392S+nZms3Mdyd8BTkXEMV5zxLVgGQ+h4jQqrwGt+BBOm28cMPcHgP23cAiCg6aXsdBf/YTCTqASBKUcJQAuUaWmtFQbvONsLh+5S1b7QTFQLoOFtRHCnApWYxgVgDZwlpi9WefsnbsurTeUkUhQ2rnludcl2nc7Ko7BfN1FlT7r9GxdZieYHYHgDLy8JOBHQ5qKyVn586lfQiF1D/i9SXMuYM8/OuzFHlYAlygaojllPZXvv8kNmU/0hjTjg38+insVkJGMAV3TuJdk+iXbiNB6NJkA4LV8ZVkk0GlFAMDgA4tAGAHe9CKURjANEPf4zGOqJR6noua8JtuXR+hHSWWfQv/8X/AYCfHmnHBaDXp2OF+ckTZGGe18bmbWzeVlq5p0cgFDLCrFMic7WEOARw5k5Tm6SKvjybziV6Eu+O5HAkh95k7E/GLtUAWVVKlj+4Vb294pSUkTrqP0+d8lJp8/zlzmJ5rblUvy8WiyXP80//X57/s3RXES+wM8EgC9KTZG31bzC6pPL0KiV+ZlBHt5nTW/SZnE/gqeNqkewOXOzwpWxubQ9H/SSJPbcFGSUqZTNRnuqEg0vXWw93APwbPQbAiYjVZKkrzTgAAhqvbwfyzE9ZKFPTlq5P98h7H3uxjgEztX+qEwyfrQI2x/QC4zG8q4kKwUFOHgLA1h2cPgEAryUnfQBDefzsMBkp9uzO5O+2cSghUy1pHCrHARSjfORjHcJkFO2O73dYB4DHfABjDH6d8J7TOsYgc1X++plvKVf/q3YCVPpMVswCKqd++TKU2K90mRlSSs/zSvZKKfPri5UgqzonLZZLie0BsFjeFExjZ5XVlLJm0ZlByd6lXOCQIsrV7arqpASjJGpqlKTGsMjMYXmDaB1XeYzbIAjyfRTmz5UuYREpJSHk2ZmkCQBBGQDN+GQ2Aehpsj9IT/ry6fuupzQ4cTTO5VllpGp6JiajJmApAD0ZJs5UngYOOaGDE/QJKCcuACEVT9VSb1NOjh3iAQCbpiHtN9mRPz3nMB1qaA3taWfS7SGVlMzXfSIHAE4cuM1QDgHEaWhsPNb+qDft8VAaDd5p8M5vJacEjyeTBDLzs7h6A8+RfUs5NQtsVlRLfNb5ukVKjqpzYnlWtX+BLF1fbA5TRqqsLJZLxfdWJi0Wy/cCL5j5JG+Aqgq70skcJeHESn4M3430R3E7fQnl7awrOazZZFup740fuXRK/gKWnmdqRk4QopWKlfYptFYApI60pk2+NtYHAL4IQwCEULNEwCIa0M1eoiKXbT3dH7+9s7UfhPvtjR8BZOuOYtwFttevjgdxiDE92YXTAAbgy70BgFLptZugzEEzbvsAcPoETQZAQq6LbQAdugZgghMAoLQgNplCCI3UJNUSQCiH62wDQD85aPIegOuNmyeTPQAPRofZIYJqTs+dYabys6XiLsDcI8te76xAVUr/PDXfKBM9locTSZKs+iNQRJ1TWokLn5V8oZ1mFssrjn3XLZbLydL8n6K6Vq4+YX9m9pzhhJERq4qJmif5ClJT/RuSqvQkWdWZgJmeyyRd+a2TUg4Gg7n3xBzOCJTWKSVI4VBi2tS/OmMAfthFIAeM8JGMtxuyzTce9qXU8ZPHj69dv46Cl4QfPJTbN4lwgPBOpyuYB99zguGIJARwmC/1Cda20rMjyTni5QlFALTbSFSAwQE623q2QsEV77pwZ838zJngEEBE4ob2wd3JbDKfIjzWZEQ0eHcizzgRUicA/urp8Pa6bgsAGMTsiucB+FqPAKy7zrF6YdOA5sk/i7nnUrMnwWAs86/K4rHZu1TpdqUYoE6UsmqJrvxJKdprsVgMqxU5i8VyCcjXxEYXrlr7GkpigErdn2elCrukMwErujKUX/5Ker2c8nb6pbyozgTMFNtSh8msiyD7vGgDgIDESRJKTALZ9hkBJSAAwpQOkz6ABu+N5NM23wBAwvFEEr2sg4IRRoMRoLF2lceJUum1Ng3TYRfrUCnchjc6BRCDAHCIxwbHAJTzrBFdzgY5TE9Mpd44GHoE8dNwvAZg563ptDCn0ZMWvYI0dKg5fAAAMha0uD8BoGCQySA5Aa46rKGhGeGcCkABOIufKj0dCqx1CuBWO/3NXuyyVonPF06lRl9KdtRi3ChmLDtuCTVjgDrqv6arPDV/WywWSxF2DIDlJWInAvq+qD/8t359XwTn3PM8z/OyD+bzSjU0z1FpU2SQt6wyAWaKp9x41ftT6fAFUl/9Gy4cTpgDNbQGPA4AZLYS8Ec9+rMNAOiINQAew5dnA05E7FCP6T/9xb9cFJr/0z//34y3B8kEAInGAHrONex9gYOvn9kNDuI0GMl+0u5xMA2F8yMoMjTjEB4lHMA7re13WtsA6NF9rqdzfQrmjeTJSJ64UQKQmGNuneOMZ2cbjwfxtH706HQ+051GsjYLHByqGRWceZxid8x6LrRcbSXg7xexQNURSzAHmgEPS/F9v6bzrDBWFp8XZZOHr9JgYbFcDuzrbrFYXgzPWX1mh/NlDYH1nectixoURY1kmLxBTaldKXTEilO7lLOqt6ReCngRWkNpwghvCg6YVbAIgEQF684OAKlPG7z3w54PgIAKKrQuGLkrPOes//aGiziEf6vHG1AyWnvH5RoqNfP/BE3/etM/2DtJVACALfQkZJBogrTZVB4AUAoAue8N0kGQDjriCoAQ9wEI4s4ShZ49WSmlGTANgOhU+92dhg8gTiehGgNoj5M9AgBKP3up/t1BDOC9bvNRvxPq3GLD551fep7nvcpj/JgXO19480VVCDG3jsdSTAxQp0thqfo364sVrTBgsVwC6tapFovF8p1RKc0r4bOhBUt3LW4swgiISqldJ6KoKUcy6vhciUylXSwSSEE8qtOp/NVRqoRSgnqCeqfRPgDAezIZNIiPyWmYjhzWSAoCgJgqcIEkgnCH8dPhId5urwOYtN0GaYxoBMChXpxOPLipTkE5hFf0CJTjIYlBGVSKxDTDe2rzVoKpfevw6bB1C4D0mhoqUpNUxUnpPEsEWun0IDhsC8elzUiNAbzv9iDERJ4BuBmpAfX/eNv/X3dP7w/DxmiokObDCcye3bNehRcnlC89NfsNyplT9kXNCi+wiFksrxH2vbdYLhv1838uNy+kXi9ykpwfK1nzu+p3KTyP+i/R90sdJkniOE7lKTGilUacIpCyI9wUSmm4rAGgxRWAEeIbjbWTw30AE0nidFzUTuuwBtpb2LkBYLqmGHcho1iNY4ylVj3pHaQTs91lTSBI0pIEG5L2rpj5SQMEAE7TuEOvANj0bx+NH51trp+dPQEAMABeEBFCUaz+U6JjFZlZjBTS/XAPwJWNOzxOAHx5Rn++cRWj+wB+PzwEsONrFqQPYjrnMBurMLd9JWlb9FwuFsW9aZh+gOwzzs+kVHjYMj755JNPP/20yspieW2wYwAsljeFZEaQI988aSmBzzB/mrkd5zbWwRgvlW4iR6XbEj9LEVWDE+q4orPM+VRLqpXS+K//6T9XWjHG/vwgBHCWHD+lwb89UwCUTlNQAIvrMUXpOFYBTh4BSCFd1lTCVX6nN5IAetID4POun3LIiBHevdlAMQSEKYxYCMBPqZ9SlzaidHQWm34JaJ02ea/Je4/G/adhH9xhRJT3xjDKx/JsmzYBtMV0wakhC2M1XRNg1GmdpkfXGuQ/e6v7s82eoGybVden5WMt5jBlUxaA2n7mSBaoOuJScYEya7FcSqp/sCyW58GOA34VmFMS+V1zkqI+C4LkIk5eR55fPRgPJoQwcn8unKjp31hmMcNSm8x/ZTiROSlShMRM4EPgUC0VAFACrbVUEQCZkjMeKo0Ob/58i7cEZZRLvXysrcuaBBSnp+PkuBUoAPR0D8BfSfHFKUk8P/H8e8MnACTFYTgAQBRb6goASZMQgUubAU9Tx1OO5wdTmf5g+KDHN9fE9u7kdHcyXbMMXrvIlUFrneq0KXoAXNpMVdJgLJlJ/20/ya4qSiWAw/CpQ5tS1HpqdYpJvsAW2Zi9Kyn4ud+BjLn146rcFPKch79S2H5Uy6Wn1g+WxWJ5XZirt5LzY+mKyNuUK0V5fgbGpQb8Qpm1iz5X9fA68vzXmIUTfDbGIN+NsPQQkRtqmfdjxlbOabhkIdWEgYJopeExnqgk1YnH2+/2+gB+deR/QEb/76HsAQBaonD5XqljAM1HT7C+Ro8f4doNOj79uxubAIJZts/vJsM20q4jTqLSd1jGLmsCMEk7GjgRMWS87l0ZyxMAEurt1hYBY4001XJPD82UQSVorfrRWch7DMyhfqwCAAdB0KOxGUDc4J3fjk8BvnF2sA3sycGTcXVBy1i8q/ldKC1iefJmJX04dXzW/MY82auSPzb7BajTp2SxWL4vnrfusVgsryw11X+e51T/mVl9XZt5WzykZJdlKVnoZUKCCzTHLpVuJpyQaZDqlOQW0VU6jZX0eBtA10k36Ta2Huztj99fw8nYASBowYNT0mcdtCNwB8kEAJIAQEIUgFTHhLA77eu/6+85tGHUv0JhLAFokioBoSgYKLTyeSeQAwBvN2/I030OEDxLIno0HuZn8lmEEa6hfEYA7I5HT449gDd7p6bPfM3lB8H9TfcagI96V54eHB95xGHOzdYFl/tdpLKIrUQd9W+4c+eOlLJo6pu7d+9mu8p9Zj8Uq0YClW9sfVcWi6UcW61aLJeHfPN/ZVW6iKm2i9R2TfVvqKPdy78uv6vSMmOuJTK/Kzu8jp83GRM/ZH9qrbMtSoNRohwepwqAhgYgVbw77qcp2/TQjxiAJndPMAaQarm0t0Fl0/DLGEC4tpn2rgAYJ4cAXNIxO9/rdPb2j3f8NkuSH/zohiiYiyl1fcgIADUxAqH+0Z4P6BZCNeEAwhENR6OG82QyVhoU1ZNICuo2xbpZ7mDNTc11HYVivTmNQyjh73W6UkVBq9UElO7341GZx3pcoNhixZC7kqwfqcigfkSR2dy9e/fDDz8ssczShyrdrhoDLHX4Am+XxfKaYsuAxXJpqaxKV2JVb1JKz/NK9tavg3mNCTQzg/JWSV48PehSMm+ZMsv0R30nL5v9/f2NjY0Sg5KEk/ooDQKY7H+X0ShVlDBBVSDPAFz1WxOE73W3T7z0N/cmiQqOQ630uU6J7BGkOonUGM1t+J2TZtQA2OgEQG/jKoAgGScqDNIEgJtSliQAOHGLbjgLhtq9BUJA2VG8txk7cKYSP0wHreYmmpuj/pPM/mqj+cXk6VvFL5WCAkicToB210nvdNoAKCKzN1WpR6cvdpAOAByGkybrrZHDpd5WoijIeU4qi88iRe9MTZmep9zYjBG/d+9epSVypbjczFCzzaKmN4vlkmHfe4vFMmWu6TdPZSW6SInUXkmC16FmTZ+3qTwBY7moxrItmUKqdDXHa9EkuVT/ccpcyhKlYxUxitmaAHi3c+No8tih/kn0BMBH3fXfHO4DIOcXbMpf4yiJACAY+GxTa73PIwBr8hiAUgAQpWMAT9XZiARm1s7Mg+/7yLcE+z0SRwCgUgBHTry+8wEApSYO8aGRIKGEHYWjW+0trfXdw0dXGyx/dXNPhIB8O5C3VZjoCAAj3FzpT9cbVOg4DQCM5WmqY4d6guJaw/tWP4EoDHfn4JyXx2N1It48cw7v3r1r9PRLYqVzyygf9lDfZ50YoPIHYXHXq1YALZaXjZ0FyPLSsRMBfTcsTluxUpW2kvH3QnnTfn0BYSgXB7Le/Cp1bDKMW8wCrTmyvasic5gt5pSev8l/jjhMTM4PtOaEckIpoVwwsx6w1PEkPRvGNFZ4HD59MBKcalYw1lZQd4O3QEniOmbLzijZIevtr++2v77bSngrpj1np+fsaI2W2GzydSws/prdOn16iDS++7svxulpk7VkbrBAAz4oFdQsJjAlUhMxu0VZCJdHaeVx7bEWgFQjlIMknTAynYaIURbr6YxAjycTAIfhsdRJqqvDWuO//NGsGlXWt/wekVKWqP+l20soLywX+0GwWN40bABgsVie5WlUGa7GC3T4Al2hhuaoNMhTKTWMXjHir8jG7K10lZGJ/nyPhJnPcTEkeCEoDaVVGMeaQEPHSqdKA4jS+CB4EEoSSuJyBWAQcQBXb+8k6fLbKNUsnypJPN72eBu96wDID/7uw5vXQ4cAGMRPB/HTLW8LQCBnM3gWID0fgMka6orWUB4P5XGoxifqCIBUMYAdf30iTwFIlULrTz/9H8yxYgFKSFuog2AcphJAS2waS0addpCaz80YD0YTqcBBr3pbZ3FEQMRsAYcMY5z9mYUc5RizOu98fZ+rUuKzzol9N9y5c2fpkOWV3vyXUVgsllcfGwBYLJeTTEOU19aLYqXEstwgo9zhBSraTEAv3YUL+Sw5vZXUP6oSGGSNjIUMXq8rQM46KMypzp1wkqPE1ZzCE0JkSii/VBwhxDjJBu6axB5BAehUKY95ALqO33X8JCU+Yx1XrrlpmFJSUMUQQiKSQCk4zYMgy85XD0aPph8bvU7MOjEL05FUkaD+UA6XugKgextcUXDPZ47POqlOhkkwTAIAbbEZpkOpI3MmPusC6JJW4jg/+fh2oUNon7OuowC0hB+mQ07dNfcts3csk67YNp+3/BRApMOO4JEaIxdOGIP8ag8XkOlF5ahmmTXU+R3Iw0v7KMSKHRSGEvsL3JY6LC4/V0llubNYLhOrlWGLxfJqUrRsjRAiSZKi2tfsFULohfyKOfgsob+yjuRVnQmZxq2pIcq/sc4pLVKkOS7gqpKal5lReUUv4ySNzyyQyKtVszEIAhMAxKnyOYtTAOCJFEL8rq8B94YfAOBUA0gVOY0YgJ/++P2lERrFbFWv0dFhY/MwOPwP199BGt/sn5KbP5HBBMABCwCY+TpTXSskE9QD0OIb62bJYu6awz3SDDH59cnQjY6uoKm4IFk0swyttdbp/oRvKwKAU8f0ISQq/ItAvd0GgL8eR2+3WwBOaeygEaskUed8Pr+uzTyYD/nMLlNsi+brXGTuZJa+QlmQUHnm2a9KzVexPKJ4qbzUsRAWy2vNajWTxWJ57SiveutXzItCNl/9Z3vr6N1VYwBUua2vRVAjOCnaW4QoztZY6RrrMKfUK1lUjXMGs9b9MkE8ZRYkEk6QQgNKQSmsuSmA3bEDoIOAgTe43vbl7/siGAf/YHlAqBnhcBqQ0Udr3c/7ZwAiQeOda2aR3j/bP9gh8JmzmwwAMCJ87uF8h4+cda2kOgHjkHEoB5EOPNbUJw8BkNa7UsUObSmgHz8BnCtoAkhUSAlDQXoSAA2kWl9rAsDuOL6WBgA+6+/eaTcBKI3Hk0NAABjEZ4B/Fh+cRMyhNe7hc5B/dh988EGJZRHZa7D0ncxHfZXUjwHqqH9TgipdZWSxSolByV6LxQKbAmT5brDjgF8q3+Wq9fmql8/wPC/7XH54hrE0YcDi3kzhyarU+VXhVR0UWCUoehmUyCBZb2jyIkU+Vw0nJkFiIgVBNQAFKODxWDwei0HMdvykzV0AHhO/7wsAjCzvWdLQfP/J3b/867Ev2MOvfjQ8QDo7w2AISgD4zAHgMb1+Q5jgIwiCZIEgCDK3HWcbQJiOyfb7ZPv9VKeEMAD7wWMAf9jrBhwTrpSW0DAL+i6FAIkiWqdrbgvA5zPFYzQAACAASURBVKfT+/N/Pkh+su6az3c60WEwiFJyf3jYjygAUuzwlUIUUHXcPOYovoDZm/1Z33Odkj73LUU2qCrjc1zgEIvldce+7haLZWVeSE2ZOSmSpzW/Zc6s3FtNn/Wp1DcvvBPgApSkeNVX/4Y4JWZNXZdBaUIJrvgJgBad3nZKOUB+vqXuDkjqFQYzx+vdjZOzpljH1TWzZZSc7Pi3kAYA/v6Vrf3TgwbvtkWSKjkgQ54WXgIjAiqF4wXpgJH2MD46SiIAgXQB3OL0Gt88FmECaCgALmtqHIM7yfkp7c2YB845QDrCb/AegPea3SOqvx7Ef//OW5+dDABsuL2EJWYFgImkADymrjWcvfz0Q28M+fff3E8zSeuqzJWjxVJsCpExq5OyiNqFfdH4zp07AOpnWFksryO1iofFYrG8PGrW0+Xw3DpTS/cublyK8SPqrcck6k3VUp+SSzDUPLGMkvikfsaFQQGMgBGMJTFOTU6RGSz7Td85i+n77gkQPRgphzY+6uEvHs5y/c9DQHtOK91pIYlImqD/KOq9P93HBPa/AtBovg0gVRLAWXKy1M8cSqcAOs52kzMAaw4DgLHpIiBj2ZcqbvAu1SOpolQX9j4xwonWn5+OtpXnEPF4zK43UwD/5TtXYpwyMABdse00EgC7EwLgs/FplNKlj2al5/VaUxkM18E4EQvDHlZlTtYvfdbZ3spS/Mknn3z66aflNhbL68WLrLosFst3z3eZ//OKU1mL12Spn7yME7UnQuG5WX0q7SvN+Cp50otcTEhldFqdNA0dpuVszACnOkxxEPBtX2548ix2dvxrAD7HNwD2g1HHSZe6+tVvvvrBD2+nKkqoAoC1t9zRyIXTj55ssCsAwEScBg5xGeWJCvcDLllhgg3VhDChtGymDiduqqUZDdw4egRAch/Ab/oSoNu8CyAlymVNhcIG+1QnkY5+2msB6KfH/8mtDwGcxvub3i0A3wyG11ztUB/AcfIE4BN5+lZTDN106R1+Ti27GD9cwMlryvNfaXmhqyySFsslxr79FsvlJK8b5pTl81erbwJzbYf1Rf8c5pBsPMOiQabpazqv3wlQfs5ZToVJpp/L5Zh7SYyZ0jqMk3aLRzEAEIZNj/6o5x9GQwB3OvH9kZloH96Wpx+M7g2Wv2lSQaowSidC6oE+64QEvasAgMGjeH/S6gDYSl0AiQoBvN3i3yx1dJ5YUAAUlMt0SCbN7lUAEacA0H/60421x3tPH42GcToBCC0eAqcBh/kaKpCSU5DZ+OfT+IliGmAweUfB0QaaX8p9AN8OE06cIocouM8lzKUnLd1rqU/NwmWxvDnYImGxXDaKpMPilppaBLkQoig39xLzQi6wJBVhJf9zxkVCsH6q9KpdCqkiBHAYU0pmUwcFKX2v2wbwN4MQQEsoAIdh4XW5s/GyT+TwA7gDT7cYAGikPoPvMABP+k/ilJ5E8fXGltIH/PQE28u9/X+/+WrnvW5bNFMVI5rA8QC0dWPiAMDx5BjAD9caiYoejYaAWdFAM1V4c6QiWquJHHSxueZsfzM4AvBuczr8d8NLB7HuOABwJmIAnDq32vxuwbLHGVlXQHm5K5f+yA2dX6lXYbFFICMLBeePsVgslxc7C5DlO8JOBPQyWMz/SZIk0wdFzAmIcjJjKWUYhnIBs7HKzXLyfqpsLwl8gaojlpAdKArQWpd7XsxoKse4NZ/NhPeUcgCpln99FJvt94enh4F4t9Ne99ItD10n3fQK0mwojdKJyxrXvA0KOpZJoqNERwBaYrMheg3Rk4oAuN7YAuCy5nI/AABOOIBhMt4PEgCUsq+Ck5BNv7rF3Wu0d8X3XdZoO2rLg8faCIcxKUwBchkFsDtmHqMAfD611Fq3Rct8HsuTkaMZEVKrMA011EkUFjmco6TcVar/PDXNkPtZWFrWzEbTQbH08CKSYqoOtVgs3zMXqXssFssrSFK67OsissbsNPWleR1vBjkLJ0psarp6w8nfJc657/sXEF5ZIEEIySR+PiQQs84ErXWqSRgna9SbTCIw7jvUzH05iOn//igA3H+8rQEwLB/7m6FmawqM1aCLtTZ37w4CAEwI4KwfEAAbwLbf3D09kDoZxGdqbbPIm4Z2KeHU3fY5Zpn9D0en76yvT0+PBl13R0Wnm+7WKDmG1onjcOoWOVQ6TVT0djMBEMjBO43bABqchOkYwHWxM+ZRnE4AtMQGsLch3a/ik3Z1GAXUCLdqlriMpEaXQh2fsnajQJLreSj66sxP5fUusngOF3BisVjKsT0AFsvrSr75v061vRJFjYVLybUtVtiXmOV3VfqZIzs2nFHnZCwZSZLMdUcIIXzf933fbJ8qMAJKIDUB4HEWSBJIsuZIl2oAH2/IvclZlI6DVP3FgTgI2JPJkipGSkkIvfflkVRxM+VDGbVYT2kojR9vbP14Y2vDkxuedJkeJENZYw1gmkoAUkUN5QLQxw/ed1o7vgCgoTt8vevuAGCHDwAMpQrSMwDHk4oG+6OQT6QGMJbTOYg4df7t3nRYc6qlzzupTjbca2hubHmdmusAlBTVC5RiWaXaa6p/gyk15adRR/0j12VU/6JM10EQBPI82a76riwWSyW2mc1isSynvm7IKGm5r6/IpZScc1mvS6HIrZwFEsZJHVcZJee5kp/Xkex2Lb1SrSGoJkCqSZSmLicuUwD2xuI/vxMDOOgrAH975AK43kwfnu/wyR7raZQCeDjSN2nUhTNWw5022fKuxnIMYH/iAHibox+xh0Px8fr6w9Ewks8W/FqkkVD4XTjeiEXoNNeBTupO0lGYyi2+rdM4RoLN28PdL1ucHOs0TqMssWcRSqgLMR1AQUiQRkG6146bbdEAcJTupalmRAAYJyeAiNXk2+E4SJ93HQAhRH5ds+dnJfWfp7xXoX57vJgNfS43MzZFp7q4vX4xzI6dc3KBnwWL5ZJh336L5bUnWSVvOE/9irwmRap91XOrVP95WVlOZlOnss+MyyVLHVeL5M/2Yh5WRRTPF3SxR08J8iNnCUGY0rsDAeBfP+IA/k4L3w51pLDmYCTph71kTmmZDz7XjPCdhlRamlx/AIfh3pH0AXQcDYADW34a+NOb1uTuXIbSszEJlHz+7elHtwFyXVAfQJ9OGrwDxB7jmpNExgBAKYBvzqaHCTKfp3Tv3r3Z2k8EQNdJfaYBuExEaQIgSMc/25peyP4k7akxp+4oeWq2JKq6B6BcWCdJwlcflv1dUinlF6mMAVb6+arfOlD+E/HdF0aL5VXDpgBZLK8l+fwfIylWqsaKmngNRc3qlZT7LNpVRPkhqzqstJezcZAvSqxgdiellEEQJDlkjioftdjf368yWcKcGH0mqQvvgNYanGqlwYgG4HPlMrMqMJTG//PY/12fr83mw6Rgj588XvTiUKqgvjgVqSYAfvlUtUV7x7vxQdf9oOt2hLzVjgDECgA0lM/VcThNUloEgNRJ6JCzaI8RzggHMJGDNe9GS2wC+PP9oaP4abx/FJKeKx3qN5RQUHNODJxzgIAyl0FDx+mkwXtr7tZJNA0YHo9FMHtox9EIwFGUEuhNr2ykcvl7ZbhYVIbnOPACXOC7ymMerFKgUKXsM5s6heuFF0OL5TXCBgCW7w47EdBrBF8lnECV9P//2XuzGEuy9L7vf5bY7pp7VmbtS2/T3TM9w2k2ZzjkmCblTbBsQ80WDL/YBgwYJCDYMGBTNkCYMCgBDelBDwa8CLZsSbalaUEwvQkShxI5FDnD6Z6eXqa7q2vNqsqs3DPvGtuJc/wQ90ZF3SUi7q2sqqys83tI3LzxnX8s95w433fWKerXjCRTqGUTX2GufxaTePDZZonmSM9vOBgYKTKStNfi9plUZGriVfVNpvyIdkIaScIABiw7eGMhALBo4/V572ItMKnc9UY3iiuoUHrnKqLES2fKK99dnQPQiRpe1KrQGoB9jxHAougKeru1v1LKujVOeFcEHSEAhJHHhZyVpdmWD8Bgdhh1f2VptndaYNnpZVRGeNoxjcOAjY0NwzACGQFoBL0pzu1gL7aRiqx3eklKXLlRq8RVqIK6GZ2tWPGWBcOkM0AR77l40YuvOXusTnG1ghQsJo+V7Kw+aVkQBSIKjebkoQMAjeYkMFEnQOw0ZPgNBXUSeN6A2ikEB9plB5i0ts61n9StybaPTzepZjaJ058OHpKjA7HE8KkzLiZxgAYYTqtAvIgICSiYTEJKLyKvzHkVM54JYP7SivfyrPfhjn3t0FQggSR/6+/9D8NnVEoS0FnLBEBE0BWHO247dqB3w/VA0lMlBmBOzQA4XS4TkD1/bG0lIWtKVQzL4fXdLdclAQCUZ3+yc68b7APooHMQbQPksl2tKEci8piIVG8ya5pYkBMVqmDZKVPKGTV+smv+0Ubrtfqp+OiOx280eysI3W6ZAK43rG3X2/cVHg7tkJoym13iEmKz3PyPfpnK1Zw0BigoOxFHWxCyyS3pI5kulUbzTKMDAI3m2WN4+X+kYoBx3kP8peM4BWv3gn5DrlmuwUQceVU9nWDGMxzngo8jLDAKItHMkDX6g60T973IDy2ECMNwYIRSTNyx8JB1fwaAF5EgogAowe/8p987Wwku1vyvzLoA7rUNAFVT3W1Ti0YYgxcpm1V+tB19evdaGSUASkWhJGttE8BMVzUDejtsSuBup1Mx5sscv/vXfnekFAVTZtmiZQUZSq8RtpRTvR62AHzR6K51OvEaozVjaensSrI/gCfGVn8cDIBQAQMHcL7We+aUKE57j2DTNQKJ1XJAQF6oB+2QxroD4URBv3+AJBUfQ1yKCyonEUWeYb73Hx8qnrdzKXgLT4bpXgUazTNK/htBo9E8KyS1aVxDJ1X+FLUs78+0G1cpJgZFHAv07fOsgLz+hNwLGyZXcLqlV8T4mYiTekhhGBqGMU5wioiiyC8u+jMTMmzCfnASC1KioNAVDwb2MKKaPllrmQBsIurMvVIPrt3FgU8sBtuUJlMDgUR8j1XDvt5ofOecA9gt1SwZHGAAVkshVzgA0EHdxF3gbMm5vnVvyRbnXzqDUXhRT5yCSUTL9gqAZcc+lFE8MfeVmTkAe539RtiSSjhwTFoy2NhFe7pScmId+MF86AOoKZyrhk2xGx89Uwm320RIADApAAiJ02XxUzd/EvBEFPkRCzIsNfyLjDQbJh0DFLEvYlnwHZIQ21+6dKk/afuhQ0KIGzduALh58+aIxBqNBoDuAdBoTiTGw+SZjyZ27m3b5qMAEB/KkwFSLni2/YB+hlnG0UkpHkgMcISXYUwygqsgGfcVdxHkRhRhahBLrEYJsZgqccWpIkQRohzeq0RWSuG+zxq0Fo+KsRnmbbne5YlUghDi7/wv/6j3vfQA3G4pAPe75FarN33YjeS8bXFqLdiRK9tlPhtIVEw1cs6DxagvOx2x/8mB+7OD4Gpze72z3gjcGcoMqk45pBFuAWiELQCNoBfssTx3nRFUDA5A9vs9HFZuBr07ihTCiHRCYrNqxahSwi5Ux3Z3HBPSb4OkoCWdCRO9KJIwICMXxUdzlQ3DuHnzZvHMn7xMcm0mhffjCgDDoYVGc8LQAYDmiaLnAT86I8f/PFb4GPLSPURiP5A2UUsijTECgxS0zL1anjmNciRG3rjqSQUzEP1B/3mGD5FhP2l/AoYGKUWKGIYR+887rhISP9ymAL69bL484yza4tU5/3LdJ8Dpkhgeux/HAIeBVzWjunmqxGcWnXDWXH6hPg9gyVktG3ML9gIATjgAi5a23L0ydxKFmzdvxs9koG/hpToAXKzWADgMQeTG2xRwYnbEgU1pK2CxT08J5cQKh3hwkdKrGgxAxeCBJF5ENrq9o2dKzutz7I15BsBiCkAjaLVDaY/vUjhWGA+TZz6WRGH4McYU0Y+b6mPL3OKfGOSa5doMkG38zjvvvPvuuxkGGs2zyAQlRKPRaB6FdC07UfU8zEDy4dbuxKDIiWKbsJhPnOv9o7BUmgxNY/xy/o/IgHLGicIw5Jx7EYgkFlUNSThVUpKqqfZDvDGvbrVMP2wDuN/lJlDiat4Wd31WYgBw48aNgSbVMkeF415n+0x5ab1jvgCstXeWHONWa3PVnvFEq8xnAVi0BKArqFSiG414RGEYShW1QmpQ22aVr83BE821Zh3A67M+gKsN+mrd9qPOjkcAKNVz04mMhrNN7LkCOAyUVFFXhCVunC0rAD7ACP/KrA8YAP7wPl6xFQACzJjVSO2rYsNdTh659zv8648kVyexUSq9HcUIeGqvgNwuvikCBo3mBKBzvEbzLPHkm/+PM7w/E2Cg8p6uLjfyJjgahQfqTOSyZ/s9fJLZDgnjopQwDAnJG/syCsMwgiBQQWCUOIASU4ECoyqSpMQxb0d7HvuyYQHYbMvgwKqbSkTUYmpkq3gYhpYyKOF+RG80d7/xwqIXtZcd51CKVacCCItXrqvDswEH0ArdEpddSuPm9gw+PWi9Nlst8Znz4vD04hmgWoVah7veaQI4Xbb9qLPdkG3VaouWooMbgeHBSCcEknBqmVQJGdTMZQC81Bs7REF/tA2bYTEqA1hDe99vuYLEsUoyNqlIPtEMM1AcwmITWoYZ6E94xDYCjebkofO9RnOiGPY7p6s+nxWOqvIuolPEBg+bZYcBjy+iGNZMHCleeNJzknOSD0pBKOL5IlCEUyUkkYAEPtnnAE7bUd2Us1J8dAAAjYCFCuGYcTENGVLClhxsuxBRcHtrd3nB6QjnWth9oVa63jwEsIPDPd+bMblQZKsrHt6G+EG7cijJrGm0Q+9KJWiGe/GaPSaxAuWvyeZKCQcuAcCjNgCDWgBef+Xitc82hi4K6HuKNUN6kQdUAHTCPQB1lA6DZpX1woa6KTxuAGiFLYOibMg98dB4pORzMh4GhRn4rcXDs8MnknrWefSbHVm+ihQ6jeZko8uARvPMk7gL45qKp65En8O2zKO62bjlPnnyIweHFDxXIpUbAxSJKApKpXlw2Q93HgjVO9oKAKBekwAaAbtwuv6DLxtfd8L7ivAx/Q02I5vdsNtgV2p+J1IAdl2+4LiSmTt+a9kRhz7b8XreNieqFZLs9v85e74ZsJoZtYSqctKWhwDiKOVC1Qawtd8GQMEjKb0xm3bFUAKHM1eQTdcA4DcVgG/N20AAwOa1ry/cAXBtK4jtWyHd87hJR19ger5ybkkMH55ukZB8wzmfIqJIGPm7Tyf1bFGwrGk0zw+6SGg0zwzD43/GuQtpijsfMRnOR4yuSguSflA8NRx5igeY26tgGIZhGLljo5GSKhgDJEFFEARQ8CP4EXEM6UYE/XUk7h0YAGYMud7BFSO83sTFCnZ2eCMg9phxOwpy3o5C4HrT+uVVQghZ99m+z1brpMTloc8AcIL1toFKeL9jmBTNcPSqFQZVvvQ3u/uvmwsAWXJmADSDTs0sW1RZVDWDTmL8wY6wJAFAkDUUyotE2ZB7Hpu3o5rZW97HpOonu86vnQaAq4fmV+qzAH5vrfvSrDdrRZsqSzBuxc923MNiO0wnxtmWCRkNBHFmmDqc0Gg0zy4T10MazSPy3nvvvf3223lWmnyKuAsxRZyPxHLgwzgm8mLHqU0kcgJ4xPsdlzz+vuejZ5JkgIJXMtClEPvyQsGLCCeSEyhASLyxEh4GWO8AwPUmAKyUoj1DAohX4h+WDSJCCX95Bg6vzvikYaslRwAgxHAj+seb/NVZ0QzY6UoI4GItaBqyboxdZsdm9nqHv0HNSAZC+lKJOaOmlFzvGKfL4aw1A0B0A4tVfn65+WWbelHWkp1KgRGY1Lk46wOoGREAT7Tio55oVoy5n1vE5/fvp1Nx+kir6hV36NEvUEVKdPZbIreYpxmYyJu+4HQHRfwh98I0Gs3TpVAdoNFojhvFvf+YIjFALJgrK/r7cBVxIgtqFpEayXPYO1HwHnO9w1jHeHjqc5LEGDONWAYhrwDMCEVgUBWf5TAAgJUSdjz83JI48DltqQOfLV5YPbh5N508iSgshrttXARc0QorZ0vAcn0RQEtt7Hj81VkB4GtzlVvu3id7ZuAzCYgxTeyUMALy1TljoxsB7IwSACIlGOGnyyGA93e6AC5xDuDAb8Tevxtl9ZYY1Nhyg0u84ot2R1AAS4QwsG8suIAJ4HbrcMaUAF6bk7daxp7H5egJzw/ILTK5xWSA2H7cr1ykezAh9/2QXsknQ3ngy0nDgHFR0KQ6Go0ml0dqsdBoNE+M9PifiRoL04yrR0WKkQYDFHHri2gmRwueNyZO5Xme53kDpyh+CyeSKfwkzjnn3OnDU4y0ZwRSwaRQQCB74/LfWgpdge+uuAAOfA5g1x1MHg9SSpSFUiZTjSBsh+Gmuw/AFc3PDjZ2PA7A4fLVWSdU/q618J15e8GOpBy9epFhGASkHXoHgXu+UgJACG2L4HoDVw9FrPbVOXx1DgCCqDtr1Uvc9KKsus+LSEcIi6k9r2OyUs2Qi7ZNSe+OulHjTzY7Gx3DIJZBrPhLh8tx053TZDvrT5e47GRfSTz9oEgpE/0+imyzNInyMPGW1ROppUlL5dlqNM8LOgDQaJ4xJmrYGyCjBp1UMNteTOLWF6+bhRCJ0z98KPm+iNQAYjx5SU8IE3lX6cZzP8K+33OOHYbrTewH2HZhULXoSC8ihmEkocWwVKTgcNUI92612gBsJoOIXKqWAfx0TwBok25safOHXOw4ogAAQkymKhyREgu2uNGMADRD1gxZicuOoF821EEQ3ncFgK5o7PsCgDViFdAHzJiV+x0OYN93AfhR12IVh88cBr1kZyshISyCoIS/NjfPiRo52CkhI6bCVJFbwhEW6phxgpO+eUThGCCJKzJs4sI4URgwXIpF6gXyXJVujWYYHQBoNM8A6eb/qX2FBw7TUZA7EGWKyjUjSfEKW0wSA8SaSfviMIlNntIgYhR5iZ4o2Y7UyKOnlpYBMAIhQQkU4AUPzLZd481FMWcDwEanV7mcf+nMyKzSDNiCLVxBAby/bQO4WK38eMdsBgzAfbe97zcv1fyXZ/xuFDYCWjUjAmqkQMqrFhJCKQA2KwEwKEpclrhcduaWnblFp/fkdzz58Z5lMzluxZ5e3wdVN5puzZQdQU2mGGEmtTa7B7GNL8lKKRQSQvlJQl+SmlFoaNaRc4SF+sgRBWKAcJK4omDpTorb8Ifk8/EslRrNE0MHAJqnwHvvvZdnoskh1/8eIMM+t+1tUo68Wi1Y6ycUrNpFMe8kpoggUpc6MpxwXbegzjgK3trjIHE0kyfGCChgUNxsEgCtkLZCWuU4XYosQ661aMOnB8HodvFDnygFgymhyKtzvivIercJ4ExZ1syIEQXApADgiURB8YeJv/3b//M/CCQJJRjhlLCz5RDAC7XghVrwJ1udP9nqRIpUDLns0EWbztuRVCSQxFf9NUZTJKHFoh21QgrgwGcAIiUqhmyHe1cqq6ftMwA4BQP/gw0zlJ4fdQmw74+dWMxT0y3GMZ0fn51q0rcEhmb6DjBFxstOMpH3H1PwbVCwmDzFAqXRPF10AKDRnHxizybbIOPoE+NJXoaYZDhB0t6cbRa7EXF/wjib6TyemPQGXuJhAGxubg6nLe5WFnwUElAKlMAVAKAABex4cCP8/prx+2vG+9sUQLn/qOpjzh8qfLxnVrgEsNXtGb215H9++CDB9YbZCCgApfDyS8uD2xCkqHAaN+oTEJNaNrWuNc1rTXOlFK6UwroZAZAqkiq6XLUCSUpMAoinPcSPKN0/du/GpslwuSYOPF4z5M0W+/6G0Q5pO6QAdoO7Ox6/3+We9AFYrAJgtSwtOhgADMcV2eRmsDScc8dx8qwm4NKlS/GHjEud6AofH9nFZ1KHXhSLKDSaE8axKMwajSaD4eX/0zV0dr1VsOkxDEPOecEqkPcXncwznIAMtSOvnkWBhv8BwjA0DEOMX8VlIs3ELPcZJnedJBl5iiLO5TCEkAFXKf43+6qUAiGQCgC8CGUGRjBnAcBL1QjAj27jXodVDXQpQol2OHqwTd3A+WpEiDpTni3XAgCzptrxCICOoCsWA7DotAA0+sPuu+OzQCtUoST3Ot1lh31x6Lw8Q0yqFmxhcg7gbttYtMUfb1oA6l5r3+P7QNV40P41/ABtVtnzaBlYa5kXZnGxKmkcfVC6YJ8HNubsCAFemw1Yh99oNj/a43NmJf3oEs3iv85E5TpXNjEoXnayf3pjwv3jYnIz+XEgN9trNCcPneM1mmeVcY57UpPlughTUND7Lx5OPCuEeatqTuEbZbsdxYOKIjYJw7Lp6CL5PHyzcX5jBJTAZhAMgYRUkAoOB4BmCABXZqTs+/yhgj1+rm0rZAAawe6q+TKAq40AwNmZ0IvIpisAOFwe+KxuRtQmOx7LmmMLdAWdB7bc6KsrUaSwYAsAZa5KfEZF+wB+8ZQL4Pc+5gBmzKzM+fU3LwVR97RVWSwHbkRtJktcctp7IB/vrzNCGj7r+IwS3Gg2AbwxH/3f170pStzNmzfT/6bDhnRn0UBoUfxEsWWRwliksQBTFe0MTcMw0v1aTxcdBmieK/QQII3mGSZ2BZyHMfrkpX4Af5hcm5EGaUsUCBISsu2L66Qpcp0TMdHzLEK2117c+4+JfXeSWiozTpu+7DAMRYGBT7HBQOfAg6MSza4AIEIhFYSCUGgFaAW43e6dvWIAwLnziy1f2UyN1LG5PFsJOOE3GnYj2Pr7NxozZgSgGdAgIo2ANwIej7/fcXu3EIzfvKtuckJwGDCHy40uAXAYsNsts8RnANzvGn+65dxqmZTwN5e8RSc0mHrpldVxav/t3/y7biTXOwYAh0kAXUGXnZVlZ6UV7l2s2gBWyyEAV9BIkWsN3gjUUqnAOqCTkC7dRoq8dIMY/dVXxxWK+Psi4rFNwcJV3CzPZJCCylMQj4NKT4R455133n333fEpNJpniITxFAAAIABJREFUFR0AaDTHmuHxP4+PpE7lQ9i2PUWlm2ufPkWuZbZBQq5gcakBMhIW99QTcp2tSTVzBYs03CYi6ebnAShVDY8CKPWfh8MQb6v1Qj3yI+x5mLfUrTYAdEKE42c/f3l986UZAeBfO+txqhZssd01ABAoArXeNnddQyjcaRNGFKdK9BnQESrgRDV88sWBSYi6329TdngVwKwVAbhckwDmrVUA0Zg9xRIihTlLLDkVm6trDe5FNJR+KH0AftQGsNExYpGbTQZAAfG+YMeTdPDAHyaJLvI0HpDojDNIF8AiyhMVySLGnPOBrhWNRjNAfkHSaB4H77333ttvv51npXnSFKlcizCskzhtyaHi5+L9jYeLuLC5TCGS7cQIIYyphkePQ+TtxzSSIl0KxclQMykoU12fMAUFcAIAKyVcKCsArRAAKgDiGcP9VOHD8yiUIhsdw6DKl9GXDasbktUlDqAb4XbLcAMJYMn2OiEtcRkYEsCLL68k3RrDt1Pi8tUF7ye79mqJbnQlgJdnvS8ON85XqgBOl6MbTXquEhwEe66g6x3mdtT58eOdCBBvAFwzqpAwqDwM9gGsNcqAXTMDAC0Pl6p0+fzsQXD/i+uqEz4DDWpF3PEixDoZ2b74iQbCj+yMWuSlsba2lmcyCJ+w01KjOQHo7K7RHF+eZPP/Y2IKd38k6eQjXYTiJ0obFPGzi7R3Tupex2RoHnlEkWcygnDUtAdO4UewqUK8IUC8k1ff6u/+We3Nc7uewPoh2bhbOets+2P8wFtf3Dl9ZRXAZteoV9AWmLMEAAscwBceAQAGPyK3W2yBqs/2DbufNhnFJPrj43dc/hJhBrXfWkIgwwUbi2UZTz/uioMZS267/FwlAFAzFoHDWGfgsaRvtsQRKfLZgX+mEnz7NNvscpMQAIEkAG63TABfL4cADoL7APyItkM68omdbI7qfpMMnxSK5McdtlRKXbp0KWPF0qQLokjO196/5vlE53iNRvPMkK6qB6r2SevvIl57ruufZiKXPdttKnJtwxyVKzaSxLUlAKeQihACpWDz3mr9AFohvvNKp9EAgE9vVADcuG+j89C0hOQihSRBRGpmVDUiiymA/GjbAvDaEgBUOGqm6rSpxdRby/7/9b6x59GVoXE7iWCksOuRUleslHjdmAcABBQ4U7bjETur5bDdJHs+bzQOrjXYd05FP7r/YMRO8rTTv+CyIwDca5ungc0uvyAA4I15F8D/c9sGgBKut2SFGDebEH2xgTzwWH+UE8bAs0oXqCke4xE2GWg0JxKd7zWaZ5JhX3OKOvKZ5tGr7biZMMPVLn6KWKdIDGAYRhHZ2CZXLU18O0EQ5Bk+EqECUaBEAZCAF4FGWLQBYEtg34OSuN8lC8suZdJyvJvtQYV4FJDDFYBdl79iyLtdyilenI0qPPqyXQIQr9wPYMYS8LHn0a8uiJ3WoFTCvC1qZuRL+kVDiXoDwOu1WqQiAD/aLs0ZAYCuzwGwMXsAo78KavzMFdSux7w2+cqsuNs2AXQFAbC25wB4a8kDUMH8G/P4J5sNAApktfSoQWnMow+qOTE8+i0nTQbjivl0v5FGcwLQWV+jOaaMHP+Tdg4GqjSRWsPuUSrORPY5qRqP6jaTGCD+N/1LpX+OgqebqAcgN6gYuLaCjLQ3KAiBzdAlKpIomb2FPj/YRYWCEtxpU0oVZRLAdtdgfOw6oJxCAdsuvzgnbrU4gAvV8tmZDoA7TdYO6ZdtcqdtoGl9Y/EAgMlG++5CCIuyQJJ4WywhCYDvr1Og9MaKB5gAzlbCH2zzX1l1Pmns/epp58Bv+tHoC4vLEQHdcWl1Fp8d8LeW8OaiqNoUQKMbz3OgABYNNIMdTnG3zUZqiUmWckrMxv304pEXqXyeeyce8dFpNCcPXSQ0mmeG9LjnkYwbNZtN2vMY2W8+qeBzS/pBDTzJSZ/hgP1IJzJx4IqIJzYD49TH9VrEQcXIQ2FEANgMAmj5Upr4soGqAd73zynFuaq6tmspieWhzWrjKwkisu9j1kLFiGYtMWuJy3OLkRKhJDseKxtRO6S9cTUSHFiwo71BpQdIRErBZvJ2i7/phJxipeT/bN8C8K3lgEoTMH9lNZ5YPLuG7fFKPfZ9ctGRi1W579GGT1GVN1sWAMrjoxzAYWMXIB/ssiUbp8v0/XDEKkBJr0K2t51btGOSAp4rmGZAPJ0T4g/FpR4Herkejeap8AysWqA5qbz33nt5JpoeYRiG45dmH0AUWOs9JjFLlMUQKOCXjGSk1PNJEQd9JJzzOK0xCt4nT6ZH7DVme3tp8ZEGYQSTK6UQRAhEb1D+99/9e/s+zpZxtgxKISW5tmsBaHdKkWnt7e0l4oksJWr9+gaAP9s2Q0lCSQBsu627nd7l3W2zl2YiAJ6AF9Etl99ojO1McAXZcjmAl2cEACFxr2PWLdUMKID1rrvede91Dzvi0BWNzw+7Cqia47cVSDFnSwDf3+A2kxTq28uL315erBlRiUsADo8ArJSiD3dx4GfVp8Pl8caNG+lDxQtIccuR743hglnkXTFAOIa8dA+RPAGNRvPkKVpzaDSaJ8nw+J/itT76Tc5hZjNhQbcjbVDE1xT9RsqRxolaEalxJKfIMzwhHNWdxj+KUsowjAG3L84n8V+lxo6ST2OahooUAKUgImUz8uN9JqMIwDcW5KedoCWIYURbbZP0N5lK34jFYDEwor51KuDUaQVsz98HCIAzZQQi+tUz3f/tYwaAUETzS8bB1pgLAeecU3W5JlZKZjt0I0Xuts2l+QjAS3ULYPN2AKATAMBnB3zx9CL27x/64/coBiwmLaYOfTpjPdjY2GTqT7Z2ADCAE8WJCiP6i8shgLqpqtWxAYDI6wLKLYbD5HrbBQt4YpP9ukjIlhVHMRBx+NamltJoNOM4mnpFo9E8PuI2vDyrEWR7/9NpZhDX/dmOTnw0bncs7teKoY4InprVV1xHk5D+pRKPP/4mdxqxkHAFAWAx+BE4hSdoVwDAoo33ry/9f+vtmUoTgO1E7TYwKiBs+CTeK+BGwzx9GlUz+ucbDoC24ldqAYCf7pY6kfvGvAQJ73eMrZDyIQc7ncNDqQBUDAeAQdW+xwH847sEwIzhALhiBF82uEFhcyk5mbdH9wDE18n6Cw4d+vRfnvMA7Polk6nZsgDw5T6vGMoC+mOUQAn2g2kKVK4fP5Lc4lPc+49JYoAMm4KaSXxeMKKISU49ssUhPlpcTaPR5KKHAGk0x4508/90/kHMuLRTeP+59rkGaZIwINssjhM8z4s/DB9KG4wTGYcYT17SY81DPnHmzIEiDBurICAACJQiHZ8KQYOQAvB93moY739aB2CXhM2USXF5XgD4a//93x7UBShBV+Bum3xl1v9o1/5o164aqBoA8Le+MD1BXp5x35iXAO53Rlxw0qUQ56X7Xb7WMrZdf8fzS0wBoFRRql6dC16dC+LWegX5Qj2weW+kvjOqA4D3B1xFCp8fsDLH2Yr44qC3A0EQkaohf7JrAmiHpGpGdTP6o/vGH9839jxS5iPmADxWsl8Ok+bkbPtJX0RxUQrD8IsvvshYsB8Pj1MS44t5YjlGZixiDHnpNJoTjm4502iONUaBlSXHMZGrVwQxpt1RTD4mh+ctdS+GWv2zGXdtw2bxh3FP1UjtVjs1yVkeUef4kOQlqRAPiREKptFzeQlB4BEAZy62z1XCD7/stZ9/dLuMcCPcvzcsSAgAvDwbfbJvzS0DwJ9tcAB/5VshgO/fdQBcKR8IRSoz/rVDy6CqavQuY7g/ISHuzFiwxU/bBMBbqwJAjYUAgg7udzkAm8lPGvx2i509NUIhPsWBz+LZy/see+102BGsUpYlHgHsuyvevRZjBAiw3jEA9xeWonUv2paD2xQkcM7HFcZHKeCPg+xm++LlMSbXvmCvAgoX8IREM1d8IlmN5sSg873mafLee++9/fbbeVbPNdM5BxkOBwrUiMPEdWRGTfk4KtHi11nQ4U6Cioxh7ukHXvymBi4gnXCK6OiYIyUJPcENdANqchk/S0IpgHOVEIDjRNubsf/sVSuGU7KGRSiw7Kg7bXZ2AZ83yLeXo0UmAHyyzwBYFAAIgUHUtcNeckbHPkaTYqUsdlzjtbneWqF//nwXQIkvApDRoRfJzw8tAO2QtkPqPNxan8gmpabM1emKutPGuQoWbbrjSQDbrnHfN1+a9QCUeMQjebnmN+dUV1Avop4Y0aMexyrZoXh8tHhuTzjyCD+bKa6wCMVlJ7Us0sQQc5KKp0ZTEJ3pNZrjxcD037iBMLe9PE12ZTapWi6TtswV4QgvL6GIT5AQt4MWubVc/z5+2rlmA8Snzrja4lJHSxI5eT4FkYGghgXGzDs3ywACTwJo7PhgM+7BoWlF8EArlWGdQKIdklMmDAoAH+7SF1fwC8tuQC0A3aB3d3+4Yf/8fOt6k60LEo0fYpNsEfDpfvTLy6gZkgK7HmvIFoCWx06XJYCz5WAXACAVFpxo2O9PoER1BambOAzIRlcCcMOef3/1wI4iWTUiAB/sOBTerRY554hdMhhV8v6AIhRgolKZW8Yzjh4fprvO3FI5UUlHgSKs0ZxI9BwAjea4kwx7yDN80NyY4XMUdEeGybiA4nVtQkaSSevv4kykWcQ7EQXmPSM1ZKXgBcRmruuGY8AYqc3NzeEv0yShSPzBdd300SK3TAmUJEoR05QyIgaXjMVrAVGl6PxsZ36245QiQADoNqO2Kwm3h3V+73//hzOW3OigbMivzaoXawDwwy3nwGcHPqubom6K31uzGyE+P+ShJJxCju22QZnLCkewtS4V9oPe6P57HYtTxFOH/9mGUzWiw4DNWtFKKVQgvqBGn2HBdkilwvmqANAIWCNgAChRnClPAkBXMC+ir875oSRnyvj8wIj3NuYpsktimtgyNyMhlZcylO/evYtib4xhMmSnE3wqFCxoCUm5AJCesfDOO++8++67YxJpNM82z0x51mieZ5J+AAzVbelauaC3gcLNjTxv5A9SLdzH1j94TBHFpIJFHlSsmeuITzROyTCMxNE3TTMJIdDXcV23oPcJQEpCIh+qFI/8EYIGAQOwvNImlO7vx76+D6BaC1XokLYZJ7xx48bAZND1Dn29BAAbGwcHpdlv1mEQLDkCwFqDGVQBqHJAoGLIdohuNHaQvZDkepMD+AuL5bWOsdEx1g9KL9axbBMAN/Z7IcGiIw4BADaTQo1VA1A2JIBrDV7lCCMK4INd+uZiBMCgQIQDjy1TtAIGwGRy1sKOYI4ztO3ZhCS/wsjclbj+uSXdmLDnMIlYxhkY/WaIgoIxBTPVETLR5Q1w6dKlPBON5uTwpAunRqPJYHj5/4TsKj/XIUiTGGdUlsVrbjFhB3qu/aROxmNlnL/+OAKegt5/QlhsnFJadpx4HBUQQtIZKRw5H5QopQihinGDR75SNAwDwzABKCm9wASwdQ8A5mt+bY602pChNyjSZ9fDXEAB/NppcQiECv/rVeetZWkT0RX0373sAvif/owArBmikbk86YwpDwMK4FLNB7DerMxZ4h/dtlcdADLepXfH5ReqlQO/QQm2Hur8GERIUjGUAloCS04I4EYbBz771qL3010HAKNqvWMAOF0JAGxSFY6fBFyQ9NM2hiYHFyzjye5aafsiJT1Xf4qgApmykwoW4VFKZW5R0mhOEjqvazTPGLn1dEGK6BSpDnmqXyLXvqDZpPDMnopH8TCO/FKPluEYIO04xj49CgcVoz3+YRSUIkqSuO2/ZCrG5NZGeW4psKwugNqcBFAp06s/bHDrFNitkTI2w6wFr78c/5cNXKgAwI+26KxBAVxZxueHFuBfquIzhm577G8RKXQFNSnW2o2zM/XrDXOu1Dv0ypz35T4/X8EFkwK41dgH0OoP6B9HIEmkCAWWbOVGdM9l31qOdlwGoGyosLcBACJJAPxs33QFqRiFthYuTqHfIo9xIgN5ZqTNMMb4rsgBcr3/mFyDYY55kdRonhX0HADNU+a9997LM9E8Low+yTf8YTLSDpDtE4g+KFx/FzRDnvePPE9lHEbhUTFHghAiPTinIEbm/JDcGx9wv+ILyEhlGMbq8jJlijFFiGJMAoj/AjAM4gWOFzjdjjFbvnfv3jxjtWD//ji1ZkBCiRlTAfgn9ziAux2cq8iXZiWAw5D8sw1ns0urJhoBKIFTepCLhjH7lwHgSj0A0AzpmXLvm02XChX8ZJd+uGv96abFCF6eyXo4Rn/KwbZHAMw7EYB5O1pr9UY0eYLWzWjOFiUu31zyLKaamVsLP12MIRzHGS7+RUiSDL8iBl4dBZWN1H4OueQW9vjonTt3MgzGUfAaNJqTgc7uGs1xIWP8z+PmSGq+tMiwlzbRKbKlBsxylTnnIm9RnQGK+C7HYcBA0Tb7PiPtjaHRJhm3JoSQkgRB6Bg8EhRAGLJSSV28snv34yqAkPuE225Qi+2Jac+fro+UqpmqxCEkqXAwCwceBTBbwkpJdTyUuZoHAMTr9lS4IqTn4g//js2A7nr0m4ul9TY/2DO7Av/KS969jglg1+MXa8HFGj6+ZwPwBAD44kHHyMgHqBSqhsT+/a+9PLfjssv1wDKiw4AB+Ke3jTeXFYCzZQlgo4V7bYNTFS809JyQPLT4Qzr/TJQhY5IkuYU9Ns5YxjcZATVReX/qBVmjefLoTK/RaI6eI6lQE8c92ybjaEJaKtstSFo3M2ymI/dehh3xXHKdrUkFkRlUJI8u3sbLtKLAZ74bGoa1sbHgrEoATui3WgBQMltK8YjN3f3ZpyPv/f6e+RWiVivhRtswGKAw68ifbZR+toFXlru/uOJv7ot2SM9XJCPqRwFM+uB2xj1MoQCgxNEVbM6KHLs3b+BW09zz/VaAQEIoEIm9LrucygkDt9wK0RG0zCUnBMCNhrnbNgH8+cviP3hVfLprCIn7LgGw2TV+usfcFoX7/Pao5+bDIsR9Cxk5tvhZCrYgJJaPo7xrNMccnek1mmPBU2z+P84MV+RTV9WJ9885H3YyEt+ioD4vsKRPQpErjwWniAHGUbz5c4BSqZTRwgqACCUCSBsAGFMgSglZLbcBKMJgGqWwUa4z9xCBDEh4r35qdA/A7Ezwsx1+5gLb8eB1ydfPSgAbh/hXX/D2XPx011rfky/NSEYUgJIB/+FVgNI3aFC4Ap8fdF+cL52fDa83jCAiABxgs2tc3ecADl04rBchAGB9sZEPasGWfgQ/omttLmdwtUFmZnqH/vm6db4sALy/Yd455N+qtgDMWdjsTwHI6FgoTpINHlHnmaPI/V66dGlgRamRDLQgpH/odGEsUoQ1mpPH89tiodE8K8Qjs90+YYq8pCcKzvmjV9WxSHoAdExyNDt5mlhK9BlpkxwqqDzRb5rdWVHwjANku1/JUQYGQIQEAOMSDAd7/K3XwubNnlt9f401mnUA3DbpgYlRfnbVwJl6BOBCVdpMdZrtuqV+7YXerGBfAsChTz/cZbdbrBWgzB+EJeHDmyHcudob832jyQBcqYdnKuGZStgOWcWQUvUG9C+X8PKMnLPgigeTj8dRNRSAeDXSl+pKKAiFD3asqgkAHUEBVEvidovNWegIHPYXKTKGRsUUZGTRfj4L+1HBU9i2bdt28hpJvs/T0GhOJjrrazTHlAEXJyH9Tdy+VaTNbIBhf+J5qwiP/H4zWtwLnisxy/X2sl3/hDhv5KqNIxwaC9RzmAjxA4+FFuOIhAoDbtukVDd///9U1LQ6hx2yYaMWnHlRbRyYXrPS6H4Wdyl88cUXsU7Sw+AKfLxPSxwXzgLAh7tEKvHaEi9bdLchWyFaIdyIzEJVTdzy6f/4D//Bf/QX38GoJ1AxQAlenw/jTbtut0wAV9sMwFtLAYB7FABu9wfqLNgPDdkf6BryIvrxNn9rNfhwjxkKrZb5tRd6lnUDEsTh6lxFHeyTOLSYtxQpPwgpJo0BEsvsfpspSjpGXcakOsN7ODy75JYajeY5QZcEzdPnvffee/vtt/OsTjID438KegMJw47aOMYFFXh4ULWuI4sz/KwKjgsaSdI2KYTI8Num1s8m8arHea5xh4ZiFIwSEADcQChIFCEMeLz/l1kyVWuv8iI2b3SJ685epL/z7/+VkWpbLba34bx4Cl2BOUsBuB8AwN02XplVl6q4VHX++LYH4ONbpVmCSNDOzu6VK1cARFEE4LPPPoulot7uvLjTMq7MQSh0BV2wxbkyAPxomwM4TUNK1JIjP9qjFsOmR7/y8PWMLBf/xlnvn3ZQrQbLtgnAsQBASVxvkBWCKzVca2Cvw9Ah9tDOYgWjr4xSmSYxKFjYAYRhKEZNfx/olSouOExyg48iotFonjyPpRbRaDSPTq5DEFPQLSgSVEw0WCVhoEci/pAWmVTwWedI7jduax94nlOEFrF9boN04vpneP+JwUKttNcNFaIoVKDc4CGU8vdda6XsNgILALC1tViZude6i//ku78yTs20ouqs/0INWy5+cNf4pbPhkoOrW8YvnJYAZuzoTputlJQM1flKt7lJr0nTssdmcqkgFDa6uNwfKLTr8XopAvDNxQjAj+8AIDygF6u4tYcgQtKqnSwdk3Dg45dXg0bA7nbMl+pohAgVADiAlDjwyJyFP7nNAbgdBqAT0I43YiOw3BggzFx3dYDilumgYlyqJDsVb0HAwx5/+tZ0JKDRPFtMVpdoNJrHSsG2wAEKOoUFZUc68RmW4/oT4g+JF5urNsyw8hQizzQD9zvd7SexROyZxX5b2ktzHKfZbE4kPl8ySjbfCELISAjmlMjMadY69KnlsI82987MY3+3A/u3/r2fzxApc9RtWTOjmoktS9xukdABgHsdcq+DtxYIgCt1+WUDAeihj1ot6JT6+3s9jCcIAE5Q5rC5BHCvS+ombu8xAA2PvDGvgJBTADAphETLfzAF7tKlS/EHIcSnn36aVvYjYgIv1uViNQTgEwpgr7+LMKHq1YUokJgzvT/YwqRM5P2nyXbZi8sWNIsZdvFHXkNsNkUY8OhDlTQaTXEmeONrNJrHwaOv/5PdMFzcG5iI7MbFATNeYEHPtD0KKBdR0yQkj4tzHg/BTz/AIg8zcTp5f5zSDFec84ZShEStwwczdP/zb1jA6bFCfTjBxq51p83OVaJQwqBwGM7Nh/c6vYtxKL5/3apJAijTYN2g6jjmODWpcL/FvnsZG3HyEhoBQDBjouGhK8i5igKw3waABQvtUtay/fO2utlkHQHqkvkIANZaBoCFKgFwt6MA/OsXfQCf3AKAA5/XLfLJJ5+MVPv444/T/371q1/FJDMEBoiLxrjkU8vmBhUo5pGnu5uK2A9c8HCPYpI5i6hlULxpQ6N5HtDFQKM5XuQ6viN5xKpxgFxnXQgx0XWK/kDkXNmBD+MMJq3CB5RjD/hxD6l/WuQOO0HmLRdJHhOLlKOoFEV/+ZXYNY/wlcXsVAndCMvz/p5P1rvcrDEAZ8rgBPMWqRjqB5vsjVl1eSHc2Qan8CIszIUbd0dLLZRk5+76K1dWOwKHXSw6WDCx4wMEhwF+8VS048Yzg0kN8CIYTDUCZGCzXjyzWlI+wImyKG62UCsRAN9eFmttBuDA4zVDfX5IzgAEWWunpknHAxcuXBg2GLcM69ra2sjvB5iobMZkBxUxE71k4lyUm5GK9HkmYcBEQ5ViRipP/RrRaE4SOvdrjgXf+973fv3Xfz3P6gTy6M3/eGzV2Dh/fVLvP6GI919QuXgVniGbeCcDbdsTMaw8hcjjY6TPNJEXlet1cc6nvmWHA4DN0GxbABZsAGj3j0pg38fapnN51d05sGxKPH+0Zxz0twgwKEJgx8XCLFZKinHUrCiKICTKVL5Qxw+2ACDskllrpFKPrqA1ipqJqhFZ/bu/VMWNJgC8Wsf5SrS9zwHcbBGLAcCeP2IOwNFy/vz5+IPv+wA++uij+N+BgOHcuXMDCe/cuYNHINePH0luDBCG4c2bNyct8sUp8krJyLrvvPPOu+++O+6oRvOsM+VbW6PRPCb4JDvYI8/dnG78TzLGI89wMjIiCuTV08M8en/Co1CkxfTIH+AJw/XprU37tVXsd+ncqjdjqE+3Zl9bDgAwYNVRAEIJAJ/frizO7X/xUUlRe6RUR6AZkHpIVrkKKc5VUDJkIAmArqDrLQKgDNx3GYCvzUVXD3DXH6nUY7NNagCAH2waXytjrc0uVSKhSLsLAB/uMothoW98pow/umpaj93/n56BkGBkD4PrugA+/PDD5Juvf/3ryeeJgsaEjFTxxOtJy2ZuqU/MUEA83Y4QzwM5MaudajS55BckjUZzbInrwtwG2tyK8AlQsOYuTkG1gveeuPK5smLUAp1p0kdz1dKkLzURSf+4E6kdfyjtuaFzJRkBhyEpVYObLbyyCBkBwL+4ZcggAgIAhx2zbN/vjpEq9x/Mnk94CXfamJnFtkvOz6p4nf4X6+LTDQbApr3Nwkimvz5fUnOWutMmF0oqbvW/dAqcqK/NqU/3CQA/wuuLLoCPb2Oji7OzYvMwS/BZJAkGlFI/+clPso3TfOMb38gzmbJXAQV6IAt6/zFi1DKpGs3zwImqTjSaZ4vh8T9xp3nBCokX2AKs+HjuhOzm/yIXNszj6FLIjigmcgLS5MoWf54TBRUY4xINf5mttrm5OT8/P/z9cH9I7lXF5OYxAL/xG7+RZzIC32fn5j1GsLZtVYi5OBfUTdIIFADKyN1mb5Ues1o95ew0tsXcPG/c6K+/8zChRFtgzpImwbmqKhtSKHKqpADMWtEnO/SHHmcipH2n3yA4U84asm8xxQkuVdVqWa4bALDRZV+ZDfYEXptTNunt+fX9dQfwF2xsUkRFpwCcfD744IOMf9OcPn0aqR6JjY2NcZYTMVHBT94VBUuERnMy0NldozleDPtbA5VZUksV8cxiCkYUKDCeu7jUU2S0aqMpAAAgAElEQVRq7x+ZbvEUgrkD6BOKxBWxWnFPRQhBCCGExKM7YsL+8iyTRgLDTBFeplEKkcKXn++++MrCHrDfNEKTAvjoPnfsEECnxeO5uFs7zAYaRnmclC9RAXY8Kjs4B3RCesqIAISEt0IGqD93xv+DG7jXxaKJOUvd3sbddlYXwK5HziswAj8i3zkbArij8H98Vv7GOUUJVmxsdtlax6kaWIuw3oEQmBs9OkkzAaurq3kmANBqtd5///3k329+85vJ5ykKqUbzfDLle1+j0TxW0kvppf2zgt5kmqRXAZm140SN9BOFAUWUi6sl5MYq05HtXj+KvzuOSXsVisQAyfNM3P2BUxQ/Y0KS95L4SgiRDi0mpbm2tn/q3Ny81wxRX/TbbZNzBSAUcD0DsjdIn7Gw4zMZht39pi8rI6UMApOiEcAU5PufNn71tfpPdjmA15ex3iELjvpwz7rTCIDelsMir7WeAGtxhOARSvDpvnHQf95S4cNdBmAO8CIAmLOwMBf+vzeO8SSAE006GIhZWVlJ/ztuVaXNzc30v1O8hTSaZ5ejrz41mul43hYCKrL+zxTu/khydSbypHl/Qc8i9WVBN72gWkK27KRqaR6H9z/OXxfjR/5kkNurQAhByvXPsEzM4ivheSstxk5/nCQMw0mvfABCYRiy1TTDHef8IqlUwo27M6fOeCJEKAzH8ucX3cOt3u+4vbNMXff0xeo4NYfBIJix1KFP2iFpAX/psvvD/Z79J/cNIGBMtkJy6MMTCGWOv/76nPhkn5+vRgBemwt/0MRfeNG751k1E9sdvDSjGg2YFMsOADQCxmleVKE5Zpw6dSr53Gw2Afz4xz9GP2DQSwBpTjYPtkLUaDQnHmMMeekG4UOMPGrbdoY/PWyfZ9gj13I67z/7OUynGZNxwVP40EWuM5Etrl/wqRYXLAI3FIDGLt/Z7O3wtXnPBuB2+d3bNcPC/Iw/P+NLEc4sgZdK69e2x0pRzNlYtAHgagMA/v4Np8xR5rjfxUI9jAQJfLbRRTfCoqPmM0fsEII7bW5SAPizbaMRsD93pnfjFlNLDg58AiCeYbzv41aTiryIQqPRaI4POgDQaJ4CRZr/jz9pr33A6Z/IoU/ITcVTZJuhgEebxiiwD8AUkdIUSXKZwgUvchnZEc7j8P4JkbMVAZDFVffcqudQ2W7wFy521+/0xvncvFrr+E7Hd2YXVXdn23UrYM5IKaFwGODGPgPwQl2dLWOvYdxdL11r4loTp0u9HQZ+6VwIYN7CgU+sYg32rYABuHpIAfzgPrvaIH+8SQFUOLwIgcSMiSUHhICRQoIajUZzHNABgEajeST4w+SZjyVJzseQhBZ5SsCEMUAR7z8+WsSTTigiO5HgkTDgxBup2SbZxuNsfvu3f3vk97mEQa8C2t9z9tu9zx/+tLZyLqjUonOX3XOX3e2tkt/cv3dngVoz0tsj1tghQAAoVQ5XDu8N8S9VQgC7DQOARbEyI643iUnRCrHRYvdaWQ32oYSQ4ASEqFMONtosiMh3V3ox0oWqWHDEgY8974F95Un/khqNRjM9hWpHjUajeTIUdNmLMCA13MKdON9HeNIBcpWnaFPPiBkevZ1+5ByAiaYpF4cxdecOoQavLYRSmLuH7OJLXYIIQNmSAD77vAaOjlcH8PIL4XXJ1r7cGy1FECmULNkICYCPPq3/8i+EbgQBvlAPr7UZU5EFtEIQwCBo7JsUhRrsrzXI1xfwjcXoftcB8HMLvVR32xwIr29aZ0wYDI02F0f/hDQajeZx8biqPY1mCp6TecAZ438G3KxkaiaeRlPxySB+esPTcHl/KvOYdCNIG2c7xEXa/tMGk7rX45T5tLOfc4OKkRiPtgwoWG8YfmsfkpeWV7rrt0yz7ABg1FfEqpRb7Y6FWyZoG0CjWaeGlaFHgW0XSw5efbEBVE6XsBdhyyUAKhzgzs4Ndd4BgEtnujc+y+oAX3ZUicOPsOjg4wNSM1SrywD84nmstUFLvMxxEACAwQBARmQ269I0Go3meKGHAGk0x4J4TRXxMPGhZOmVR1l3JRxFXqKTAx8CBRz0kSTx2Egcx3EcJzlFLsWju0Q/W5kX2B5ugFg5I6gY+f2jQ4PI7xKjUqnOSM9lazerAByr2+lYzVatuS/3d+tM9jb/vXqdEfcus0cvAyokkREhBC/UVN1QgcSKA4sCwLKjzpRUvHFXvPewVNjv0roT/Zd//a+OVIsF4w/dEACaIVmuRsvVyOHq5RkF4DAAgPk5XyqEEd44/RyVJo1GcwJ4XG92jUZTkNjvz7MqulBjmsTFH9AfaHguqKZJGPaJJ+1MSJNOOByVJb/ORPpG4bZ5o99ZkW2fCBZXzsd5kPGWlhvbW/UwICuL7ZXFzidXz8Tf+82yV2kYrqmA2nI18rojlQhRJlddQT7eo1+dl02XAeAMrS4AzJnY2LMA2E77p1crAKJ2Q0YjlXpYTEUKC7YCA2HqYhW7/RFDUpF//LkN4PysCyBoA8BaG4d+oTFFGo1GcxzQPQAazRNlYPzPpL5UkVABQ/0JA0eHOxkmIp3c6zO12slgIu98JHGnwXB/QnI0O3madB9FhlliMCw+kDCjm2LqGcAA/uZ//VtoNVvbzeYWDtfKS8uNatW7fdv47MenSnZTorSwdBhbKt9Vh1Yoy92tjXFqXY/5HlsuqS2XzJaij+611zt4tSraAb3TpjLCL5zz4x6Aly625+Z8IbKqv44gAA4DEk/tlQp1E1UDzYC0Q3ztgvu1Cy6AAx+bXbrjQ4//0Wg0zxY6ANBonhphag+micgIG5KxPQVlhRDFuyCGGTj6iJFAhv5zAn+Y5Ju8dIMkqYzxYFrxmElj13FQwzaICcALq8ooKaMUlM1WZ6ZW74SqXJprU8sBwBfc5vrO3/iv/ouRIn/9t/8bJQHAjQAgkOgIsunig128NCsBfPuiD2CpElXqwU6HVTgYVe2t0VOKAex1aCuAH2HGkt9clPGXlKAj0BHohOiEOFvGV+ewUpFLDtohhBwnptFoNMcOHQBoNE+OdPP/RG56GiFEdssuppLNJhE8cmU8HDwgFQYkn/MENKMZiCWGvx+XMCHx8mP7JHI4Kv7qX/5L1LTtU5xV251O1Ttst+8Q1mgCaOz1qqdSyKLZCoDf/Q//7QwpxlW3Y2wfGozi1q0agNUSALw2G/7FS/6dDj49JI2gZ3wYsTCywMa223NDvjonggg3GrThU5Ni0cKMiTtt3GnDjdARAHC3AwBbXbLX4ocHmVuLaTQazXEivwLQaJ4kz8lCQI9Crt82qccsio1fLyibNsuVTbz8gW+GP6OA2gADUknySXWeRYyhkfrJXSulMGa5z1ziMEApFUehUygM8zv/zs8B+Bs/vgeAmbZ1zgTg3W5Rbi4v79z+0xrd3f2tf+s8sJqtww1ZqQUAbu4Y33ylCaDC8WINH+waAJqhAmBzUKoME1t7Vq0sNjMFPzvgr86JxVnRlgBQAQyCNxfxZQNtH5T09hs+Y2PZVv9iU5QrR9MlotFoNE+Ak18RajTHE8MwXNfNs5qMgoN5RjIuDBj20YuQG1RMJJuYFfHdE2X+8JqYyefk2oqojWSiIOepU8TXHw4YsonDgCO89//szTMAAOe/u9EU22b85b/JVvEdAJU4aMmm2+HufWd1htZmgqsb9kur3vUmyhw3tkoA/qUX2n903bZ5b+Muu0R8r1Sar49TiwRhBNca3FmhQuJmi6wDl6twgBfr+IObnJvSBOYsQOB2G1IS08ycVqzRaDTHiSN7fWs0mmyGp//yyVdt55Mv8phLrjc86UWimFs8kWwRQTwcV2Top53diZTTjPSYC6ppxvEfX67hcp7RKBhTZy+0Q8x1OvygU97cKH/3l30AV866AP7oug3g8oy844rPrtbnq3ub15zmxk6OKPDpPn25hktVtQUIhR9uQyoC4FvL8v4BAGx0YVI0Dy2etbOwRqPRHC/0HACN5umQzMLMM3xAEeMiNk+diVz/mFyfvqBNmuJt3rFm+DDxoYF/p0CMJy/p9BxJGPmbv/mbeSZPjtlyxAgW5wMAb359H8APP5w/DOEK5Qo1M++/sOp1+r9S4DEQym1znBrjigDtEIs2bIZZC+V+waJESUn+cM0AcL8LABUDM3OeDgA0Gs0zxDPgK2g0GvQ9+yPx24YZFzY8Dh90Ujc9QRQbVjQpBWVzXfwpehVQQLy4VMLm5ub8/Dz64lPkmXEjgoqMJnpahAqdkKoO40xVDPzKz+9/scMB+JICWLR6eePefadWC9x9bjuCn57LUgQAbLk4DbgCYAglzpRxp00AHBxY1wEAYYCDAEoRmT9MSaPRaI4LugdAo3kSDIz/iYmnUcYTK4ePxsRHHcc5qjmXaWLxDNkpvE8UGFN05EzdXp7t1seyEzXwh4UXdyoiHvY3cxhnMI50KreP6JOddpjit/8UERIyIs2GBeDLrd7yPocHltelXpfe3Td2XPXjz+oAQIjlSKZaGWoAOMXlmnpjXt5soRnCpHAFAJwpKcblwqK7d7AYN/pbFGWufKm7ADQazTODDgA0x47vfe97eSYnivSK7MNgkhbcIhFFTGxTJKgoojZMRpIp1GKmu5Jscm9/Ct83N4kQwnXdgnFF8YgiJh1XpE8RN+onasUFBxAp8myfKJSg0zaEoN2uUa4G/397d/YjyXHnB/yXVZF19D3Tc3CGQ3IkWbS1Wq28lrHwAQML/wGLBQxR90VJgAHShgyYMAwDQy9g2C8GDPvJgETdJ/nid8PAAsYCflhZWqxWFA/zHJLNOXr6rKyqzKr0Q3QFo/OI+EVUZXdV1vcDgZipjvxldlZp8vvLjMx6pxdsX4mIKElEkohOd0hE/+j39we0LVpjIqJme/edA3NNIvo/74VEdK9PRPTOQfP2Mb0bERFd3xj9wUeOHlmlg8PWzm77oN9YE7gEAAALY8ZHUwDIKzz9r7PGUA8qK6uglknPzJXKxQXvfmVR2bl/c03m5p0N84712E4V4g07ITk9lcjwVB/ZBoRhmNjmPukSbeIWp285e4Nh4+bl/p2E+pFIJyfj1zfjvQdtItrfXSeil++FRLR7pznutYho5eJKWbXD19/a/MT193rB9e1Rq0lEdKcfhK0xUTOKxD99tE9Ev7zXWJl8+ddoFNw/YP0fCgBgHnD/9QeARWEOoMzcr8iMmOSeqlk4jGxJXXGK7Na+gl8qj7nBM+QRoF3fNV1cNHGf3wPoJ/v1Urdu3Spf6BwcHYZxSpev9hrN9M67Fz724eP3j8JHLg1feneViLYuHifDtLsyIqLBPqWDg2Q4HsSrZdUGSUBElzqkZva//JsLRLSyERARPdL/5b0GEa2HRESXLwxeedv/DQIAOHtnfeQDgDM2TXZUVARXWTCTHa05Upe/OmEeaS3u1E4oMz9VX53CEK/4nZhXPUCapoXFZdkgWIyp7Z3uqJ8Ea0TjUUBEL762un0juH2//fjv7xHR7369QkQba72D3UanMxLX1nZ+3Y/37xkKvh9Rp0nrRDdWaZxS+sldInrl9e1/+In9//VGi4j+6OHkf78kLm4MiSgISIjJ5QAAgLmHewAAqmWd/7NYxESn0xEa23LFDIur15nFnbaEf7OEn7Ky59hUlPUP5j3g2lSco37U7Efhzoud3nG4ffloNGrGg5SItjrDrc6QiIJGcxSPVtdjQXud1ZCIRHetrFoSN4ioP6IkJSIap3T3Qesgan7q4/vDMRHRHz2cENHKSnKxTUf95uXtflkpAIA5hAYA5tGy3Qe8nPT8LU7TuwtzkTzrIoLXV8gBTtdPVF9RNsC60jJOmzElw23HZ7kZrhqTo9nBW82g0bh6/XA4DI8PxTv3Vt+5t3rj5tHDj+4TUUqtBwdX77+5+8gftpthx1DwYBi0mtQf0d88oJf2iYgG/ebrd9qv32lf20zePqI/f7Wz3abX7oVJ3BiOg9UOrgAAwMLwPBoBwPTkHIz8SdZ5jlkz552J8zKlyu5+5q9RCJEkieGGWl3I/mY3ZkEnTgUzE4pq8nmLxgd3wo/+497Rfnj7jUs3bh4/uCcuXEoajYSI9o9XiSjsJMM+rW42VonuvLprKCbCcbOR3ouCq0THx6142GhvBFsXBmmDiGhAjds7LSJ677hBRMNhc/du5/h+6deKAQDMG/uxCgC8lc3/UXEtiqLMj4QQ8qc1iWXnofAcP+eG17zMIoU5mx/9ybGpIHZxfkHF/AETxtsq5u0OYCJqXzg5Ab+2GdMwuXtve/tqcrgv2quN998OiGhtZe/qI6P7bx8Nj8X7b2+PqD+KLfN2rq6kYYOGRGFrTER7D9p//yMj+e2/RPTQpf6v/3KzQxERbV4YPHgDV9QBYGGwjlgAMCuG+RVSMnnspl8bUJYCXevUjzVDG8hlZXA3DHBijezM6E/uTYWZujl4JtXOTBKn3c3eay9eWdsKwrVArNDx/pj2W7v7dOWjMRG1gpMH+hw/iKndvnhpdf/ldlm1dByMxsG9fvrhJn3s4eHf7AV7h+Kjjx0TiWsrFCUUXewT0ebWYLBHFy713/2d2NgclFUDAJg39kMLAMyENfrrVBtgG3iCX3yaTiBTn7959TCr3zffTqionXl3+Gt06gEK71XILCt/Gsex+QFEcyWKWh/5eHT79e6FNSKi4d1GskaNRvr+2+KRR++3V8V7r48ubDeIRpvr77//imkKUHPySJ/X77ZuPkIf30r/4pAO4+Duu62/dWVIRI2ARiltX+1Fok9EnW5y/y73zQIAOHf4BwugKvn5P5yArpPjzfFLhTZzcf3CAj/PZabRZ6aFuHYpoNP3W2bH+u1S86QdOv1BYq6C/1E5f0etcHO48xbd+FAUjWk8DoioORima+tE1BBNOWpv/2KzfbC7e2Hc6CdJ6Tn74bB5+NY7F25ev3+3++IepRQc7LcP9tuPfrj/6p1WryfiQXrjxvG7b61fWN3v90Q/ErSyGM9LBQAgPAUI5lbNHgTEOS+bZ01p6sQ/s7VgDiOtphAiE1Uz+DV1SRHbQnWm71Lb2FJCiG63G2pocr4/nEwo4q9CjlyUHuBw763msUj74b0H14/3UyIK+0PaWA0a4ysPHUeD9Zf/eiUQnYM7R0S0silWt8L/+u+fsVWlazeOiCiglIg+9Qe7YWss7we4/vDxOKVuN3nzN+vvv9YdvZ4mCY6nALAw8A8WQCX00//8+TkZnEU4YzKs3Uhh9Dfgb4Oe9fMNQL/f1wf40QvaxtaTfOO63a76r1Puz5A9gN5FzKf/9h/+XbyxRkQba/e6a8GwN0we3mi9c/fi5t5ocHKm/+itWHRa41403NsZ7O2byqU0jBvxqNFuj956fbPdJCHGf/XbLSIaj2l9YziMG5c6JzOFws10fKPZ6S7p5w0AFhEaAID5ZU5scRx7ZFy5iKEHSJLEKSyKyYx228CTMeZ0rn7EKaiUhf6y15ecfPc5tyBL+ochDMM5fASQlLx/OxnFD3Y3kvHJEzmT1e5Bb/Ogtxn9crC6dTILaG3QWLvX/bMv/2l5JWo2UyI63G+JgLbX33jl1c1P/t7eJ39v7/JacnUjIaLr62Mi6h2FH/nEQas1urDdb0xuMgYAmH/cYzwAeAt5t2YWmvk5Vxnmysr6BWUhhAzZhrbBKdabS2VwKovJVCV+WUltSaa+a53z5XTjR6GF+H3/4794goj+05+/STvHyZXLo8Nh2F0dHaWrdw4GF9eD4dFoY23wYHeF6F8++Q9sxSgMx2lKvUGTiFqd0c6DkIgeuZrePRKNBv3qpfVR0hinx3JwPAwHvdJnCgEAzJsF+DcdYOFkbv/1Tv9mzDDtyjvqlS3ICeh5zLzOLM6slqGX1ZdVlxRcC56ZnZ2d7e1t26h6+rd//Fiapv/5VwMiiofHrbWwd6PTnDy8/9/8k+Kv5siIh43Dg9bVa3GjkY7HwfblaNDvjpLgt8dtIrp8JRolDSK6dCXq7TYff6z/V/9XjBu4og4AC2NOj14AdSKvAAiX+2WF7eZL14ILynApgBn9M5jXFsz5PtMMGEaWKdxs1yKzVXadavqrB+fiX/9h5nz8mGileGiRoJFubQ4P9tqbN4IkaQwHTRLUFOna+oCIhsNGPGp02kmjkT60mRJRf7/dXsM3AQPAwsAZC5hfdXoQkHoGi20g99Zbj0wmKxtu5XQN02fAvCtcN5jZMzhl+sIJQmXkpYMoiuIiURQlE7ZKJslpZcM8PkLLQ4TjaHByfLzzZjcIUiJqinGjQfJ/nXZCRO3W6LV3Or/8zdZjf/uo1cY9AACwMFhHOADgyz/+X1LXAag8LwrjBP08/kUATpzlV8swLMXM3K68Cya2KwDWAYWsi6gNNs8Hiydf/+y6Gea3IAiCIJjNU+qfeuop25A6iIcndwy/+9oaUbrz4upDH9sb0dbBfQo7TSJ67OYhEfWPTsbvPmgP9qOSYgAAc8fhAAMAU5LJXrUB+R854c8scuornKJnYjtZztm8s2S+wOL0uyvyd7QuyL8VRI201pTkHs7XV6+Eky8E4G+DYr6MUFtp2jsOP/R475hGRHTt8WMiGiUBEW1tf/D1YXt77eFwFIbjw/u4AxgAFgnr6AIATGWn/3XMIM6RCXaZoOaU+71xQqpHG2BO6lTlRQA/hrLem2qVJAkn06sx/N89DMMoOjmlzVykToLJ9FgRjpO4ETQpHdGFy4ON9uhXf3mFiFa24k5nKJonlwBaB8mgMbnRGABg7i3dP+sAtaQ6AdtAC5GboVQY/tSAKqKhrGmt7NFUSNbKs8XM6IXMeZ1fOY5jvUu07gHrhZ3aS8dERLv3Ow99dEhE93YuEdG16yMi2rqeEFFjPCSinVdWkvTkLeiuLe/uAoCFg5uAYa7V6T7gBaKffU+KiAlzHUkFes54fvo3DyhkXsqvnaBzist+W8tpGAorcxasE3kFYHDQPNhvE9HaxpCIfvvS1m9f2jrcE4d7QrRItGj9av/CQ+2VbpRcPNN3HwBgSmgAAGaGM/9ngYhytkWz1FKFi6tX1BhicNoSzsbzq+WZl/W7MjPzzK1uQclH/Py6ptkbNSDCtCnG4XDUOxY77642GunG1qDRSKNIjMcnx83eUdDrhb1eSETx0bA3uSEYAGD+LfU/8QBwlgyZstPplP3ITDAmAjn1FZwZMjrO2q1jCvm1DVZhGJb9gupSRtmAJXkEEBGlY0rHQbAdBIM0bQdx3AjDMRGlaWNtIyGig72mEGMiSobj6F5IRGGnkvcLAKAKDsc5APCWP8NaUbxbCE4J20Al+6ToYaNqLfzVyVL8HoAz+cc7/RvKJiWP/eGLF/Prvc7MaBSsbQxX1+KjUdAc0sFeh4iaUUpr1AqHw7hFRL1eOx1FRMEw7nUvbUU7u7aqAADzgnWQAwCrwvk/+s2XdDoL6tkRUWxKTuf4zVQPoP6aH2P+aYbHFQDr58GjpmSurMrm2wPrJtXNiKLjcLTfbqzR5s11okGUXBmtEqVp9EpIlB6vtsJ2SkTNULRXxZp442i3bysKADAv7EcvAJhG4ZRrRf+RR8YynAP2qAaSjPWFVxUyY5j0wdbT9iHvS6PlAGs1JnxactLxOEiiYL2RHO612ivjsNcnooZoDQW1EgrHDYqo1Wn29+5Tup4GHSI8BhQAFgZuAoZ5txAPAsqc/o/jOI7jKIr0c8kzlKmfocaYi3Bkai4VYWRbOkstEoZhPm2rp7gy078khCisVoZZfDnf7qyUkn5A4uTrk5P9tHGp1bjUal2Jt/9OOthOiUh09gLRiaMOEUXD9ZVrrHcNAGAe4B8sgPOXsK8DFM4mykgmU9hd53mrmkluanv+FXBl3nvmSf+F+Eld9gnW1iUMQ0PN5bkDmIio3WmM0+5Kkm42Gs0xxbS+fnh4uE5E793uNhpEXQqaHSJKW2FycD/ZPYwHI1tRAIB54Xa8AQAr9XVLtoGnCMbNAObZRDqV15k9QCb65/+sc82pUr6aX516mMnvni+iX/YJw3BlZUW+Yo3+ihAiSZIoijgfm3rrrl063h91W0mrQ2knOD4IP/T43ksvPrR+54CIji42+hRc/N3dFaIHjzb2Bx0a2CoCAMwN1iEBAAz0+T9+6Z8YT5/kp39F1rT2APxt1sdwAmWmZv6v/GAKBmof6jtTvvUeu1dMboFwbSPrpEGN0b2d9sN/j0bNNImp0br+6CFRGPSO4/VVInr4Q4dEtP9meHz5IOqvt4eH407bVhUAYF44HxsAoDpVJC1DQX70z+DEysR4E60aozofTs0MQ3GPavUjs7v8c+ajZf2kyd5MvkFBEARBYBhcV+Eb78U3rxFt0nhIRC//def6e/G71yhti+b/HN290KC11c1XD/vhu92k2YvxFCAAWBi4CRgWwELcByx5x3dhvKHT4/S/MpO7gXXq3LB5DNnSv+I0mCYbYEC8jeSYVZ2zt7OzYxti/2wIjXlkzfyXP3um0VkhojQIaSRoJP7fq9fkj9b34sdeOXrzkVZvTbTf2Yk319bGnV6C7wEGgEWCBgBgKvnn/5SNnE/e6da6oHWAjjmYme+Z1fJUC5F5gpN63bx4zXh3s/Vw6199Jmh3Lh80W+G9oBOOo71xsHb/QqNNorfR3ugFG73g7uMPDY92qbPSClrPfvFPbCUBAOYFGgCAWWI+aTGDM54zJkMYbyye5qoClW/PNFnZvFTCiP76YNctkSPlI1bVH3TEXnshtazrhs2D5XoE0MQzX//j97tR+3XafDDefDDevjcgosPOeG+Nojb1wnEnabYbncHh3W99/lO2YgAAc8Q5UgCAUvjtv2QLsjpzRtfxa+qs87z9ylYhsd0GTe47Qda0VlbR3zAmP8C6tYrabNVIqL/6dYwVCbVvrQbp1p/8XSL67//jDbG21aPBStymQX9wYX3lvftE1B8cPPnPbhLdNBcBAJg3c3HUAaiTTOYuy7uwhwUAABgWSURBVKz86E+TYU7xt7pMWV1lA6ffXTer9O/HWlxdbfDuBAr3jPp0mX8v5scP/vmf3tT+FqZpSnSRiCb/BQBYMM4HGwCwkrkqnjyC3TyMSQ22RmGn1sKV+X5QNcWlbICBoXJFZYkoSRKP6B/bHqFDjOifIWta2xVFv6qQD/qqWqagtSUoFEWRbQgAACwS3AMAi2EOHwRUNv9HyT/VJzytbEEzQ0BUYZdT3OOUs3WwdUAZ7wU5/DoHK0NZ1/TvRMZ6/c6EwhXlW4L8mDLJabbhAACwYCo86AIA8bI4kypVdh7XdV2yjuDdCSAzujWpywGcgnzMLSxj3eY5MfMLC6pD4O8BVV9vMG7dumVbDgAAFgn3qAAAc8WaFJn0OvmQrYKja4Lk4/QVrjXnATOj5yW2WUCulZk9QL618JsvBAAA8497UAcAnXX+z2JRKVxFbfVna3DMyIw3ZHdO9Fcjk8nzfGxjP2Ct71RNZ549lXjdVyCZLwJ4bzBNli3L9E4XFgAAYNHZD70AsDz0UMuJ5mXURKDCfsDjwoLQnuZpzcHM+q4dhc5c2fvceRiGhr6i8HUOzuQivw0GAIBFxD36AgC4UllWv7DAD/0ZmaaiLBAz039mDD/+WpO0N05l/nbqDH0Faaf/ORsAAAA1gKcAwcKYnwcBGeb/xEZlS9We0NjGcokinU7HdUVyJDP7ch6d5FTwHMkPJCb/AAAsIe4xEgCs9ESVIUOhHDD/0XCeZcK3340KGUKIJEk4z1ZirshcsJC1tfCoORN4BBAAQP2wDmYAoORP/xtyv6LPgTEMK6QHPn0ti3KmuVIe+7OQrCNK7grwWEtZqTz1DlrXwq+pM8//UX1F5vUYM4IAAOrL+agGADpO+tepkZx0ZS4uX1dz4jkF8zKnk60ZtPZmuAfkTCT19mV2NT/3T2NlZeWMrxgAAMD8q/DAA7Ak+Olfyj8bpxC/tZAFnU7ZysEqGqq1ZGbUcLYTzPL7UJy+m9mJvogh2ctPQrfbjeM4TdOyYdJ5TS4CAIDz4nz4AThHL7zwwqc//WnbqArp83/4Ad1VdZUlWT+KorIBqg2YZoZ92fb7VauBzC/uvR9U/2Du9zzqh2G4srLS6/VsAwEAYLE5HyEAYA4xLwJU2lpk7nPIB1A1Z6nwp+BE7cD8bSHKzs7O9va2+qvh46GaCpp8iYEcjDuAAQBqCcdggHnkkdGZU0r4lfXrANbKmfRfSHjdwypZF7RuYV15/+L5fkAIkaapep0/owwAABaL55EDYAnln/9jTaWFhBCGaBXHsXdZ8wC/Gd789G8dpo/hlFX0Swd500yph8I2IPMHAACoGfz7DuDP45T2eYWq6voKfvrP4ER2c/TXxyjWmjp9Wb1Bsj6VHwAAYHHhm4ABWAq//dcvUlvNVe7khO+KNpiT/nVO45MkSZJEhn455V3/qXwliiI5rKSGXaKxja0EpvEAAEAeGgBYMC+88IJtyBmR36/Ez75ycBiG1kzmERY5lfmbegasG+OXm5nj5TCV/g0jPW6bVn2IWotqJ1RH4VSwUtZPIwAA1M8cBQKAhSOfl6LmoJcNU2GXE7bkGKeAaA3THilWslauiMemMunRn0nOkk8YE5ZoUt/wiFW5almQptjDhbuIU838vH/9p0899VTZMAAAWGj2owUAFM7/kWReV22AYQAn/SvWpiIz0lxcNSqcgnllv9qUEXaBMHsAfnehtwH8HWiur5oKKnlTXD+EAABQV9wDDwAY6LlK5TP9RafgpXqGsgF6vGNW9ugB5FoM8VRWc4qwimGpxGUqfyHzJnlX5vyanPSvOCVya3fhVC1P7Rbv/QMAAIvCfjwDACfThDCdnNNvON1b+LqBee5HhjX9T8McMZ26lLNkbVr4u1eRi8z2woLk9N7pq+CsCAAAFhpuAobFc8b3ARvm/5yBsIRtuWLh5MblwnSoftTpdMrG5BexDcliThyyDihk+O1IuzG3Cn65mflWehQv+031NcodEkVRHMceqwAAgAXlc4gFgMUV2m5acE3eQjthb1iWM0YRXhcBqqtcHfO8Hf65/wx104JtYGnxW7duFb4OAAA1YD9eAiyz8z39X51M6EzTtGwkh0rVmcSpEjnzrH+GU1gXVc5ZkldOrGM8knp1rLcEJEkSBIFhAAAA1FUlB0sAWDaZrK//mTmbKEOmf7mg9Z4B4kV/NcYpqVtn6XifpydG8UplmgRrzwAAAPVgP2QCQKF48iTHfPJb5hTFCeJMKv1ba1oHKDKsq3lQtuEnI831na5UnBnzVYs53GAAADgz3KMmwBIqm/+jgmP+plIZuVRvkF8WXBVeWyBbKC/jFNY56V/yuLbAKe60tWXyPaq6ZIGPKADAcrIf2ADm0PPPP//EE0/YRs2eDFKGTJb5kV/AMoRIv4L1wAniHKqOOV67rk5WY94JwEn/NMV5evPnpOwXV0vhDmAAgHpzO8IBAD+TqZGc1K6nxrJVCCGmP2s7fYU60SO4353KOmsPoO95zoqsBQsxuwt8DAAAlpb9CASwnDLzf+I45kd/V9YLC5I+wDW96QlSrU4/F1682DKZyU4QkzuSZ3VtwWlyUT767+zsbG9v50eq1iL/IwAAqD2H4xDA0uJkLz8elWVw55/IN3QXM5lVL+mlpqlTG7PdCXq1ws8M86w//2MDAAA1NstDFEBtzOrx/wnjCTbEOPfvjX9tQWiP3WQqayoydZxqQhnrbrQOyBBCpGnaarXkX1VfgQ4BAKD23A4YAEuImaG9eVTmJHXXOUuuPYAsXrYKfT79lHPr86vwLlUPs/311Wwl5H4AgOXRsA0AmFPPP/+8bcj5E0IYcpVrRs8wTB/ymFlEk5P31k1SYzgjzQPKJBr9lfyfwcqa7NM01ZuKZ555xjAYAABqYJZnkgDqoXD+j/m2zjnkt7XWs8t6CjePlBLH0//m1kK9rt4OfuWMwlV4V6uBZf7dAQCWDf7FB7CQD2Fk5l2l0oZBZrWyM7seW+vEozgzrPNP7SfsqUo6vX/QF890NR6VAQAAFgiOcwAsHoG+2+0afuo3S6dq5mDtugcUTqR2La6nefNIyRDu1Yvq+gOzZobhV/ArWJHMFwtY5wgBAEDNzNExCWAeFM7/kYGJ3wNw0p5rTUlV9vuqVzNZnLPxrqyRmn/uP49TnHi/l94JcMYrchWGB3Tyt6FSeAwoAAAQbgKGhXaW9wGr56xnZo8o6kfdbjcMQ07M4owp5L3gNNTZcdtAZ9PUtC7rl7xlB2UtTpPWJY7jsks6sYZTMC85LYoi/a+2pd08/fTTtiEAALDw3A6KAMtMxm6Z88oCpUc01886W4dx6mcmeDhxDcrnTm5wYjxh7/FLJby2ISk/8V/GvKkZ+kdCX0tmjfyCUv4T4rRVAACw6PAvPsAHON//xYngfHoUE6fn7YjTD7pxXa9KxraBH8RHcwQUXtOKiDGzyLuyMvPwqq4AGCp7pH85A4eZtpn19QGcsjr9GgJzqwAAoAbwzz3AOTNcWBDGrxEwUH2FNVtb03mGtaCHKqaySNWFWmY6z+PMwnctLkday+r03rKiXQQAAHML/+4DnOCc/q+OU3rj0HsAOn0pIBP4wjBM05QYxOSrgvl5XQhhzZf8ixWFrPUrwg/oedbOxK+4tSzlZjdZxwMAQP3gJmCA2gonaBLEVRwPNbYyp/DPGeur4+CPlJj1/foKv6WYzOHee9WyrHnxTPoHAIDlhAYAFttZPghocYU5tiVMxGmFr3c6HU46VwS7r5CY48UU1xas2++9G8MwtG65n7LWQt/UzKr1RfAIIACAJVHJQQhg4Zzv/J9FpHKkyN27XLLEzDDTP/me8GY2DH6zdLzbBo7qWgsAAKgTHCoA7AqjXqVJbrHMJHSqWC/zd1lT4bQuwXiYTwa/ZwineNxqGdfbf50wGxsAAKg9+0EOYGnpOSwfnvSkOH0zwHk4zJLIn+Dnh/I8fRaQoYJ6f5lrka2Faw8QTu7HKBvgV9ZsZ2dne3vbugcAAGB54GAAUDD/h3M/pSTPLns8h5FyJ3r1v7qWqrfpY6veBpBWcJrJS05h3Rr9p2f+zKhLK2g1AQCgwqMRwILi5LlpqPqcBmPKrIamQpfP31Mmcn1x6w24zHU59RUSfxWycuGPcAcwAMDywFOAYOFN+SCgwtt/5cTx/OuF+CN15qXUBvBToC6esL4I01PTwBT9r3KANZrr5HjXhs26CusAAABYEjgeAJwSx7FHoFcTgQyhjT+tSOGU1XFW4T1hKSOzliUPl5lff1Z7Q3+P8p0b/8R/ePqboTNlp/wkAADAwpnNUQqgHubw7HjCfoINJ/2rAU59hWS9ZCH/wNxasBKn71sofLM89raYPBmJtMXn8JMPAADVcT54ANSJPv9n+gxkzdPWdO7N78IFX5J7NGeGRxIto69lhmUXVKV7oNLPDAAAzC3cAwBwQsb3aSKRoYWYMqCbK3sU599goM4WW68AcIYVSib6/X5mcfWjsmXBQ+aeBOxhAIBlgwYA6mDK+4B1fidc5S2b1isAFfFLb5weQGV65ipc87o+WF9XBrlsg0G+7BJSd4AIzTPPPGNbDgAA6sMn6wDUQ+b5P8w59HnWnoFzor2MmDxhxjbQGbOyxw5JTs8vNw/j1E8mN0Ik7DsiFFU/v1XM7QQAAKgZHPkATsgnpYjJ9yU5scboinhM/lGsYdq7MidS89O/pMK6dbOVwlXIv6ZpKv8ahuE0bYDemeiv+1UDAAA4GzhKwZIqfPy/nuM52ZR5Ej10+VKnPHN9v47FyjWge6iuOH/j1XwYfl8h6RcW1Jur3in1U6eaZ2nKzyQAACy0OT04AZy7wjO7mZ9ao7/icYex6yqcVBpMzWGan86n4VRfRWHOblGV8wFaf0W945yahQp/Be9qAAAACo4lAFkyuqkvTjIPc8I8W69W6rEKvkqLV8Sap5Mk4ezhPObekMU55871Ma6pvazHUBOWyL2mwdNPP20bAgAAtYKnAEFNOD0IqHD+T0ZoYyuQJRcRQhiim/5TzipUTdvAU5jj/ZK0mLAN9Oe3YWacTC/xR0ryPg3ONsthURTFE5kB+uucgmWS02zDAQCgbio8SANARji5tjDDCwvh5N5lYoRCp+6Ceb3Cg3dZuf3VdRdJ+RUG/rn/DKcmjVnf4/KC/JzI36LVahU2GAAAsCRYRw4AmCFO+HYSTm7oNLQBTtGfJunfrwewRlK/spK1eHX8EnMcxyHjJmOPHcKsTNq1BcLtvwAAgAYAlhBn/s/C0VPdrC4vCO2Zm9Z4yj83753+rcX9ykpO5+lnS222Ry6XPYBt1CkeiwAAQM2cw9EOAKpQRapTYd2c2vnpX1IjXSM7fxUerMW9z51zMrdfZY4kSXZ2djY2NmwDAQBgWeAmYKgPzn3AtTz9XykVi0WJTqdDjPRciLOUWot1sHDsQ/hcG5V5Y+gunn322bIfAQBAXc3+SAmw0PSTtfHkW6KMS9SfNVJbB+SJyq4tON26IE/qW99iWdDvJP05Ti6iktaF8ysDAECNnc8xCWAOqXgn75WUhBDqdWSm2RKTuwsKw/GUiZnTA8g3tNvtmocp3lOApvxdvHk/uQgAAOrtfA5LAOeibP6PSkj5yKhekZ3AND2Aup6AjkI383Bsvbbg91bKaq49AHMtTjV15vr6HnDdcgAAqLEZH3oBFkvM/k4lfQwz1Sl6g5HpKOSfXQuCmbWpCMPQOiZP9QDEiOzqPbWuSA2w1sw438lFAACwuHDkgOXFT/+KnK/CPH+s5znDtQWJU7CM3kgwt21JVBeOC9uAzJ53XbvTSXp++jeUxR3AAADLCU8BgloxPAgoM//HI/0rnISt6utn/cvIG0z54U9RS8m1RFEk/xtP2AqAG6EhovC0zBhjpVPUYM5Hi1zSv9pO20AAAFgi9uMHQP1Mk/6J/RAVj/rMytbtn9XlBX3CUsL4xtmlMtu9ISa3RJdNMVKv89O/JLSZSwAAAIQGAJbEDB//b83BcRx7RH9rWcWa/nX6MKcIqC+oLmIkk5trySWAAp95r7peWMhQc4FkF+H0eQAAgDrxP5YALCinAJ03TQIzs/YAzOsD09CDvnXMNHk0vwrvUrU0272hTzHCxDAAAJjlMQZgIcx5BjKnfL/LCxXRJwiZR2aoBTPXGQiXFyqjujXMCAIAABxlof5mOP9HqiigC8b9mn7FrdcW1DByXEUy+dpdp/qi/Fu6vJsKHS4vmMlm4Fvf+pZtIAAA1BOeAgR1Y3gQkM4jEcrYdF6nTqe/amGokGjKxhhwdqbeXVjX4r0xaqnktH6/710TAACgZuyHbQBwYji9PaUpy1bXuiS2KwAJL/frEpdrC8RYRTKZYsSvWSizimlKAQAAnAscuqDmCuf/yNsAxOTBi/kBOpXwqgvQRMS5tlBdazENa572Pu/O7AGs0V+nD3PK7vqC+rUU+WfXR3Oeizm/+wUAAM7MXB+uAM6AOVW7pn/VWjDzqFR1cDTXr/SxSN5lFU796ddiIIubo7P+eE3vdzP/W3iXAgAAMMDRBZaU/nVLhpjFzP06px7AtcHwwLm2MKXzzamc/Zwhk7r12gLx0r9OjbRWzlC/xdlcXnj66adtQwAAoLZwEzDUkLoP2Pr8n1BjftGDEMIQ2tRPmSvyS4Gc8Zy1l+Gcnp+SdVq/H2um95655HRFRa4liiIiiuO4cKvk61EUMWsWun37dqKxDQcAgDqzhwOAJTFNDs4IGZcXwjA0P/I/L3S5tkCTVTNXwS/LJ2vOvKzOu7jcLYntIoC1T8hj7nA6fXmBuSK5CKev06m9xFwLAADUm9tRBGCBWE//V00FQZW6MtGQnxQzhO32Zafo7818iUMf5hfTmfWnUUV95p0Aevpncu0YM6twWhcAANTY7A9+APNAzQJ64oknzCOp+lbBKbSZMa8tFL5uICYP22Emdblqa8al6dK/bUjlpknM5msLHulf4vcA3qsAAIDaO/9DLEClZCeQpqltIBHRZz7zGduQyrsFjvy1BfLK/TrVAxDj2gKxA7qasuLXBhhUV5mmmFkk8WO6H+vMpcL0H04eA/rss88WLwYAAMuBdQgHWBK/+MUv5B84DcNnP/tZ25DKu4XZpkyV/g3hkpn7FRXQmUn9DK4tkO8d1bMy5bl5ZnfhXR8AAGrvfI5/ADXw85//XP3Z2jB87nOfMw+g6rsFpsKLANNkZX4P4LoW15lLEr/H8MNJ55VKkgTpHwAADKo6BAKA7mc/+5n+V3PD8PnPf97wU6nqbmG2+ZhzbUEfyefaAzDTvywYTvHVudZVeDPfYczcDwAAsMyqOkQBgLef/vSnmVcMDcMXvvCFsh8pVXcLTCq26iF1+qCsegCyxV9m+pf8egDO5CJ+uwIAAFAF1oEQAObWT37yk/yLZQ3DF7/4xcLX9fFn0C0wI7iHssp6k+CK3wM4Tf7hl9VZV4HuAgAArHwOhwCwoH784x8Xvl7YMJR1C7oz6BY4Cq8t0Ol+wCP9qzCtP321EOfEf55fD8BZi6EyHgEEAAD2AwkALKeyboGKGoYvfelLhSN1Z9AtcMKxE31mkfnsu9OqXc/TW0/8AwAA8DkcsQAAyvzoRz8y/DTTMHz5y18uG6mcQbfAV3aDgVPoz1D3GJDtkZ2ulxf87l4AAIDlwT2iAADMyg9/+EPzAL1h+MpXvmIYKZ1Nt8CP4Eyqr7Ce4PdbNXoAAAAo5HNQAQA4Mz/4wQ9sQz5oGL761a8aBxKdVbfAV8XDkfTWAj0AAABkTHWMAQCYK9///vdtQ4gmDcPXvvY128Az7RamDP15+iwjIgrDEHcAAwAAoQEAgKX1ve99zzbkRJqmTz75pG3UmXYLTGXPRwIAgGWGBgAAwO673/2ubcgH5rBbEELM/AoDAAAsKBwPAABm7LnnnrMN+cDXv/512xCiWTQMOzs7tiEAALAU0AAAAJyn5557ruybmwt94xvfsA2ZQbcAAAA1hgYAAGCRfOc73+E3DN/85jdtQwAAYOmgAQAAqK1vf/vbVPTNzQAAsMwatgEAAAAAAFAfaAAAAAAAAJYIGgAAAAAAgCWCBgAAAAAAYImgAQAAAAAAWCJoAAAAAAAAlggaAAAAAACAJYIGAAAAAABgiaABAAAAAABYImgAAAAAAACWCBoAAAAAAIAlggYAAAAAAGCJoAEAAAAAAFgiaAAAAAAAAJYIGgAAAAAAgCWCBgAAAAAAYImgAQAAAAAAWCL/H/dI3rITXnYtAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/umesh_w_assembly.png\", width=600)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/jupyter/unstructured-mesh-part-ii.ipynb b/examples/jupyter/unstructured-mesh-part-ii.ipynb
deleted file mode 100644
index 3dcc9d418..000000000
--- a/examples/jupyter/unstructured-mesh-part-ii.ipynb
+++ /dev/null
@@ -1,647 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Unstructured Mesh: Tallies with CAD and Point Cloud Visualization\n",
-    "\n",
-    "In the first notebook on this topic, we looked at how to set up a tally using an unstructured mesh in OpenMC.\n",
-    "In this notebook, we will explore using unstructured mesh in conjunction with CAD-based geometry to perform detailed geometry analysis on complex geomerty.\n",
-    "\n",
-    "_**NOTE: This notebook will not run successfully if OpenMC has not been built with DAGMC support enabled.**_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from IPython.display import Image\n",
-    "import openmc\n",
-    "import openmc.lib\n",
-    "\n",
-    "assert(openmc.lib._dagmc_enabled())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll need to download our DAGMC geometry and unstructured mesh files. We'll be retrieving those using the function and URLs below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import display, clear_output\n",
-    "import urllib.request\n",
-    "\n",
-    "manifold_geom_url = 'https://tinyurl.com/rp7grox' # 99 MB\n",
-    "manifold_mesh_url = 'https://tinyurl.com/wojemuh' # 5.4 MB\n",
-    "\n",
-    "    \n",
-    "def download(url, filename):\n",
-    "    \"\"\"\n",
-    "    Helper function for retrieving dagmc models\n",
-    "    \"\"\"\n",
-    "    def progress_hook(count, block_size, total_size):\n",
-    "        prog_percent = 100 * count * block_size / total_size\n",
-    "        prog_percent = min(100., prog_percent)\n",
-    "        clear_output(wait=True)\n",
-    "        display('Downloading {}: {:.1f}%'.format(filename, prog_percent))\n",
-    "                    \n",
-    "    urllib.request.urlretrieve(url, filename, progress_hook)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model we'll be looking at in this example is a steel piping manifold:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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cpRL71Go+xAtQh6eAv/jrnYCyW8IdXx+Kz2OLTx4FJM3sOtFEgQ9PPX89Zs08DL19qxFG8F1pefl+EPfVQZ4BAoFAINSCudctRBw1xc4+Xc11AuL+fR4A5Rw24dfi/V2z8oCYEV/+0Dewx44/AAAsX/VNow5QyVsQKAAqCQGoZMW+zhUCwl5W1yEELBEguuQg97505T6sdI30+9NNyzoKyBorqh9gIwglRIH8T07ToTP17QqCFUjfPPx2m3aKsDcAIGYNgEkZwPu/6vGLvN6B3/z2UTzw6I8APK7VKScGXGn8/EK4Q4WuwezZV+G4444PvMfRDxIDBAKBQKiM+fNuRBQ1wFiM7q51JemP+LafUTarz0WASezthb6c8KoeAGuBrifeXyfiACfE9/782wAYdtv+O7j3F9+xxYM4Im3HKwCglU+b1dsF75ElHHh/oF1LIp4nBFS7/KyI/Ku2zbYU+2LsYKeL9sx0O89F+tVUrV21qoUiEs/MLlWwUQYFxFzzAhSReJ8YqEr+ffV4O2858uocm+Ox9adOAH/JmW055L7MOP66RYGJOu9/dIDEAIFAIBCCMW/uIkRRExFrgEURuprr5If9ZGsAZLw/c4f85HkALNIuybY6O84JvmvWn9dd8fD3MHm7bwIA7n/k3xy2VfGh2y3aAcj0GIi6rjR5FwoRN+1B1HG2ldUtQ/7t9iDOi4i9M11UdZB+5ikv+gEHSggBb5VOkL3Ef8nUxKK2q4qBsmKBt/N/Slrd43IAACCK8qnko09dkbt2YNbMzdHb97Qjx0f48/LMNBdoEbEJWjNAIBAIBC+um7MAUdRMw3+U2X+/AOAEPzDkxxHvb3sCAJOsh5L/rKYiHICVj/4QDAw7feareOCxc22yX0EAqO3YXgdYffITc26DnyvtZOXbJv91iwExNmY6k01pNXUhoSOfkzikgKNq3bwmARLDptVEiBAABk8MDGTH/5cdfX1rZ6xSUh0xVQw4+pnTxOTPfgu9fd8CsNZTohNi4GcAjgLxXwnyDBAIBAJBYM6181PyH6ez/91d6yOKOOk3Y/5tEeDe8UfxAGRiwB2Co5LrKuQfSnlJ/n0ehAdX/xg7bn0WAOChNT8x7EPrg9u+SwBAKe8QAN6Qn6yek/xXJfvqmPnakSXlwecBMKm42S+9PDNsWumWqWJylisGVGPt8rxEPcm3lyAp6JdKTF1iII+4hogAV9uXA9gDwP948nm9g1F98fAR2H7LM8BDhJAk3t6uWTvH6R1gLKvrna03LZb1FsyGe93APFx11ZU4/njaWQggMUAgEAhjGtdeMzcj/12IogbGdb8Lety/SfodC4A5yQ1Z9GuRZMfRJMU1kX/tPCu3as0FABh22PIL+PnjF2n9UNvzCwAYYsAm/rK80TdRF1qfcq9LCoLSYkBr00gX42Hm8Tb18syw6Uy34Cfe+jjkwSc2PHAxWPGmsPyKqVTwlBELitV8Uwz46HOopyHPRh+AmQCudeS1q5amp1aYTuKZ0h99BN3tvfrGvQC+gXAxoKYVeQl89ZHT3tgEiQECgUAYY5gzpw9x1IU4amL8uA2Kw34UQeAk/B7yHywCAIPAq6Q1b9ZdEvMQ8p+3Q8/PH78En/30P+MXT1waFg6k3YfZr9B7QhjZDyH/HsKvehX8QkO1r7Zp58kxcfRJ65dJkmW9QjHg6pLTZh5KkF1R1LkQwAE+A270JjEuDHGUomUWzG0nrD8cD0GfBf9LdvwT1M8q3F4eUjKtiQEGrcuer1CuPR15gqiI/BeJgXbvf3SB1gwQCATCKMc118wV5D+KmhjfvYE77MfpBWDwhvwY4T+2CGBw7gAE1ET+oZV3CwFuA1Bn/E2SzcDw8JOXYbstTsEjT17uteuc+fd6A6Ccm/eo3pvsh5fsu0i2Jk64bV0AmQJAF0QFpF8klUjX+qWXqSQIjGS3eAmBq7xJFEMEAIDE03uWT96nTb4Ei+89NacNAHjA236ebTfuAHASUjFg1mMAvonqIUKHYPJ252Djj83EU88vTPvn4u5OQWRj1syPo7fvZSUl1DNSdd3AjLR7xIEBkGeAQCAQRh2uvvo6xNmi3yhuYnz3u5W4/6LQHx73n7fot0AEQNZ3hscMBvk3SbBCzvOuH3nqp/jM5ifj0aevzO2Xi/jrfVXJuH2eJ0yKBUF1b4DzvsVlPrkPTldYu5UuskNIWH4Zpp1UIXU5dRI91EXrsTgJERMJN6XkL8np0z45eVXQg9RbcCr4d1j+q4ojAQAsyrYTFd4BYzw0UQg888KNznUDUyedi96+swC8qtgI7V/iODeP18NeN8DHhQCQGCAQCIQRj9mzr03Jf5zO/E8Y9+7gsB/+D5YHoHzIj1k+n/yrhDif/FuE2zzPylUh/zopTtMee/oqbPOpE7D6mau1vukCwOxb2My/a81DMdk3Cb7v2jMWZniUGGPDdoAgCCP3zKhmEjumnwWT+eIydgnmvtQWCJtnsIiso4S7P9YiWv45h4YG5QmFEPjExK+gCwHXotpQ6CFCjEX2RD6zPmUvXnj1dgBnKX0LgU80hIYJAcASXHnlFTjhhBMD2xy9oDAhAoFAGIG4evZ1GvnnL/4qDvvRQ390cq++Dbi8CHCTdh/5h1LeLwTqJf8mofUT69XPXI2tNj0Wa9bOCRADvG6IIHAJHxg2Ydg1rkWyHI/QF5Vp9+8j+JYAKUjnZxapN0m/Ow/OVJZXrNBOUUm5U6iPsjpXFTuyDQ8CU0enSmhPJ9AD4BZHWlWk9xJlOwFFiLSdV90CL+T+Qxb1hi3sdh/dIB5MngECgUAYEeCz/3zP/wnjNwwg/WYeJ6NmHH+eCJD5OhlWy3ae/Oct/pW2INvL7EFeGXY914rtNWvnYMtNjsYTz85TCDnzn2v3YJw7RUCJfgp7vjEwPBAuAVCHIIBarTzxt/NMO3k5nnLe6iF2PUTRfK+AUdYUE8xpiyk7Cg012iH/Ko4FMA177PhDABFeev0ufPLjB+K5lxSx4fh8ecovX74t5wVkITsKhRB3V+gQIQ8kBggEAmEY4qqrrpbkP+7ChHEbprP/rgW+OYJAkH9O1LUZ/TJx/9XIfzEJlgTX3tITnmsPiS66LiD/9jXw+LNz8elPHoknn+vTyXjuzL9iS9xzwf2bXgbR3YJxKC0AXESfKckOEq/1Qy9TSPyV9vQWDFJncbxQgZBfthge0s+0K6usRf4TNT8xktrp33ADDw2KtVSxbsDxXTBHzYW9d7kIH3r/jujt+3eUW9jNkScYSBAUgcQAgUAgDDHiv55kpZ10mJ22cOl0D+k3BQEniu3E/XOhAMCyYaSFkn+jfjH5l2kW6U0L5V+XJv8KeVHSnnhuPrbY+HA89cvrKwgCN/H3eUDM2X+7f+ECQCPwwd4Hk/j7xsXoo2IzNE9vJYfwMe9FqAVHCZsgukh/epV4qijkn9n1mSUWgAOmzMbP7j4O7a8N8GEJ2lsPUATHlqJQxIHyGQd/vkD6vBHlypL/YvvAYnR2XEY2aM0AgUAgDAGiv8hFa3NunoqINRBFDcRR05rd57P40/deaNm5dfnJBqlvL+7fmvF3kn+VrEtyas2CB5P/AIKbVsq/dhHcEDGg2LLbVOvJ2U+1D6p9vwgwiL/vXNiU7ZYRAGHjI/uoj4fapplujJc0pNdRr5mjjlbQ5h9lCKQNVqKKUjAL5SmUGUwUhn6mkH99FbIsOWqo1p7Ya+cLYIb0yL+N9D9lP0fzb7IYytiWKq9iNoB9HN/zsQfyDBAIBMIggouAOTdPTck/a6C7az1tVt+11SdjEW5dfhLklp9p+n67XQ4AWHLf6TkiQIoEpwhQ66jE00f+0wQPsc3zAkDaGFLyrxNZF7nVSS3DU88vxGYbTcczL9xojAe3pxJ35dwaH78IKPSGlCL8BsE3xkW1rX6mWh31zBv+wYzivjxTDngImIOYuUvWSOCcwiTxXLEc8cDA3zVg7ijkszuy8HkACvEHgAR47dcr8YkP74kXX7tT+ZvnMEeJ4eXXl1vrBqTNkM91uCzMHj0gMUAgEAgdxjVXz0MUNRBFTUTRLYijJsZxASDe6uvb+lOd0ZdeAmTpy+7/FzAWYZ9dLgQA3LHyLCkCwOCP+3eQf4XISoJbNMM9tOQ/n9T62pO23OTVnfb0CzfgU584BGtf7Pfck9rfaiKgzC5AZcdK2s+51sZGuX9rPMLyTAkg+wU9rRDMexlOB4tJuVzvy7RyWhvi/jxegiTAyzAiwT9N5b0Cymcdsdg1UIYF3/0z9PZthfRFaSaqrCHIq69iCX7608tx0kknl7Q5ukBigEAgEDqAa66enwmABrq71s12Aepyhv8IQaBce9/0a3gG+Iz/3Q99DQwMUyedK/qw/KFvOMi/SjirkH+3EHDNhueS2kAxoJPJYvLvFgMQfS9D/q00MDzz4k3Y9OMH4dmXfma1VywI1PsxxEQJEVAsAPQ+yz6Ej5k9bp6xs8YqL88kccx1sPNLIbSOWxgwMws66RelEz1Pr+ITCiMdu2Ha5MuQhgjZbwjWQ4Vg54PBfAsDxzMvLAJwMlzj3T75H8kCbHBAawYIBAKhBsy+6jowFhkCoIE47jYIvB3+Y878c3JdJADkAmEpHu77xXdE/m47/Cvu/fm3DfIP5dwvBArJrCoEXOQ1kPyHz2QrNQvJv8ouDUHhShN9MdLEeKnpDGtfvBmbfPxAPPfyrVYfisbKbMcvApg8dYybdu0dO2lLtmXYc9qHUc4cK7119zga6VqfzDwTJonMq1IHhzEIqLVemFlXeqcM4p8nFEYs/hmA/R1SYa4bcMHvGdBLlUP75H+sc2HyDBAIBEJFzJ49FxGLwFiMZnNCthVoA41MABSF/0hSz8m5JP0QXgJmiQXTY+CyBQArH/0hJn/2HADAioe/X4H8u8ltGPkPIKy55L+EGBDtG2niUJb8u9o00x3jaIxTLvEvDKMqGj8ElLHbyh8/H/k3UwLJP/PUUVsIEgcmfESzHOy4/kQzZEsSXSW4xEIODxZ1DtpzHm5adgRG1o5C7l2EVOh5zB6HgHcuzJr5HvT2/b6gLzTzXzdIDBAIBEIJXD17LvibfrsaE4QnoBGPQ3H4j0rsFQKPMgKACwZePzIILCeWab0HHzsPYAw7b3s2Vj76I4UMSlLfHvl3zYzDsOe5Rh75VwljCfLqCe+R3CRAEMiGvX157uVbsfFH98Pzryyxxit89t9B/INDgYw+m98B17iZoVzKffrH0B4zY0SMfpnp6jg47PlbchSpl/Tp1hLrRWN2cJCuEpiZZ24mVNjmSAL/DCPn25d//dtH8aG/n4TXf/OgVl4tV/QZHrD7bPT2HQfgbruuoy/lkAC4E7ZISncUGusgMUAgEAgFuHr2PDAWIWIxmkIANNFojBNE3hX+o5J7QewFWawmANIypgBQiSQz2kjTVq25AJO2+RIA4KHV58Mi/1BJq0ouPWS/NGkNFAMWWZZ1g8l/GUFgEWn9LhQGa6X/8pXF+MePTMMLr95u3J/r3lyz//xM9rvUeGptKKULxYdx7R0ztd3QdLUf7jwttTTJzy8baslJ9I3KzCzivxQVqgiE4Y8vAtgRB0yZDX6TagCU9q1weg50YZAAeP3fH3K8idj16VUl/nXYGTugNQMEAoHgwOyr5iJiMVgUodkYn8X/d6ERd8Mf/sMAa+ZeIecqwc+J+9ftKGsKAIM86eTPJQLU488fvxiMMeyw1Rexas2FfvKfSxaHgvwzpYpjNj835EcfL9l1F4n2pCv3a6Y//8pSbPSRvfHia3eIvLY8A+a4BYsCfm63Y46pNlbW2Kkk3zEe1piafTDz9N7bY2zXCWMlzHkaCqbNbtsGLLFgcCVxlcj6+soB+Z0tsj78ob5d2PxLM4KtNDGQmE4EaN8FA+Z3Ogx1EP8luPzyXsya9fmS9UYPyDNAIBAIGbgHgLFYEQDdYg2AO/zHntHXSbjqDQgVALGsD+QIAH6dLwLM/jzy5OXYfsvT8YsnLsXQk/8col+J/Jcg+dZkmEpUdBZnERXmKu8SAfy6pGfAup/y4+sVd5KpRAAAIABJREFUAUGiQG3TTFfqO8m9UcMxzq5a+tiGooISENXcQgDwi4U8kSAkQWKKA/OqYp+HDMZ3zfliNde1KgIDBJDxXbZBM/6dAokBAoEwZnH55T9FV3Mi+BqARjwuDf+Ju+QuQCL8h/lDfxTCLWf+mWOGn0F6C6QAiJTZf/nDC8gfOoU8aSKBn5cTAwwMjz51Jbbb4hQ88uTlUBrUiKl5rVM4m6zqBNu+FqkaKVZs1BXe4x07T3lx6crzkX+Z98Jry/CJD+2Jl16/23OPLH988zwDjnFOq3dWBBR7AMoLAGvsnDzONcKhKC59/eKDSlkMwaH7LMrO9HAjKQ7C+jZ8wT9bdbcg99ap6XOsgLQn7g1G/T4Dh41gjDQvzNCAxACBQBhTSLcATcn/+O4NkG4H2kSj0Z0KACv8RyX0qhdAJfYqGc9ZByBsxJDhPxx55NZFsMuKALvOY0/Pxmc2PxmPPnWFTQqNWWrZjXDyb5JQ879O8t+2IFDbVvvjyDMIqenl8JF/O89N3ENFgZv426IgLV6fCFCtuPqvt2veq1G76HPQjbjzjbwyyRzX31ZA9nsCd9hZvjw9vvkmsP76uUUXvflmmE0Fh+6zEIuWTkfndhSqE/p3RqQ53sQsQuKUnYMsOs7yPnelvVLwkf6RLMIGD7RmgEAgjHpcdWX2DgAWp4t+WYyINdBojMtCgPiMflHoj+ttvnyG3yMAlNAiGf7DESIAXCSyhAiw+qsTzdXPXINtNjsRjz0zu5h4umbOC8m/i0CrDNxNqiVPN/tgllXb0v5j9Eu2Z+dJUcAcdvW+m/ZTmy+9fhc+9sHd8fKv7tEIuqiZKwps4l+8y1I46fePeb4I8I2nllJE8E0CqVljZpYDMuP62wKIfFmyX4QCIRAER58WLenPzpSdbFidwqDO7UW3w0FT58L+3vP/qJusZp4BJmUCC5ydz3uHgUQnSH+6o9BY5sPkGSAQCKMSV105F1GUkvBUAESIoyYacbcSAsTgCtuRJDtvAXCeAJD/IjH7b5BmFprG5LloM0AMOMpaJDMjoGvWXoutNz0Oa9Zeq/W17DafOgl1pGX1rTRBRh3p1rgYeVqfHHmif2aO2W9f3x1tOwUM8NLrd+NjH5yCV964T2+rDW9A0JoL89q6hxxhoLWvXjvKKfdvDIg+QiECgNn5C4rIft1EPwQhs/6h/Sqqk/TXLAjqwFcBAK7wH/0Faxm4ZwCKCBBbtno8BRnS589RMAWGjiqEnUKFikBigEAgjBrMvmoujj34PgDAcYccJ9IXLjk0EwDq7L4r9EeZ+Q/wBqi2IjHzHzlIFyfXeppNrnSCbc8a54gAq4+mCOBtG+QTKel//NnrsOUmn8Pjz14n0zUiXTwDrdyp/FBKkn9tOFx5egF7LK38vNn/KuQ/716MNh0iQF4HEP8SIViFpL/QG+AWpHpSAcnXPwR7PFlBGM9wJPtViH5V9PQMQ0Ggfhd0km59JZB9ZxOtBMD4GgFDJAS1WwbtCIgluOyyS/H5z59Sod2RDxIDBAJhRCL503FW2rEHH4cFiw8SbwFOXwTGMF0s8JO4dflJEGRfIdb2zL8qFrgA0Gf/3QIAsAlXlga4CavTC8DPAWtxMopEAAxBkEcuGZ54bh4+/cmj8MRz85U7KUP+mSxuEkdnutqXnDzHeOmldSJaOPtfK/nXS77yq3vxkQ9Mxqu/vl/vSylvgJLHbSjjY4sL133lCAPre+kRBkobWl313gMEAKCIgJ4e4LXX0i05V6/WTff3oxSKQng6NavfSQw7QZB9ezSh6n4T83/+4Tm87z1b4Lf/9RS0bUXBwLQXLqQ2/uM/H3e8awCYNXNd9Pb91dOfPCERQvpXoP63M4980JoBAoEwotD647EAgLk/2xuMxVkoUAzGYjTi7nRRcKTs/gOGxfeeKklyNpu/324/FTaX3ne6QaTt3YKYMvMvPAIWiYI/DXALAI0M2sRQkDnuyRBp7YoAm2gyAE8+14ctNj4cT/5ygUUqodSS/TNIuTUOdro+FK4xMcfKyLPyQ2b/cwh+G+RfjreeH+4N4HVcpMsjBEKEgXZfrs+rQAjkia9gYZbaWnBbDzB1arYnP4B3vzs9TpkizSSJ+1+r5c8bTrP6dWJYCQLj78iRZeUwBiSZAFALMzVMSBEVCg7ecz56+w4HcH9Bf8qi7LqFsQXyDBAIhBEBLgLm3TItI/7jwCK+DmAcoqghCLA77EffHvSOlWeJcnvvciEA4I6VZ8l6Ssy/CP8BUJrsu4iWKFskCDhBdIUqqeRfqeMTAQVeAbWfT/3yemz+jzPw1PMLLarnDQdyEs28sXBYqWX2X2Uontn/DpB/tcSrb6zAh/9hZ7z2mwesfuqfq0H8c4SA+j2x79UQBto9hpL+gu+rMf7OsWV23oLbeoAZM1IBwMVAohCzkDRf3gEHpMdbbknTRir5d4ELAmCIRYH53UTh25j154nuIUCS2N8bR3vAThX7W7eIGBsgMUAgEIYtLr/8SnR3rYcoaiBiP1NEQHqM4ybS0BnAms3nxNh6R4AhFhjD3Q99HYxFmDrpXADAPavOkeE/AHSCGEierPhumabP5qpEz3Vub1/q9ACUEgE5ZDM7PPX8Qmy20XQ8/fwNBg/0EWcHkffkucm/kWfll539L0H+taTy5N/WDgyv/vp+fPjvd8Lrv3lQ/7wsctW+EHALA7WvDiEQIgy0dl33q46NPcYLbjsQOOWU9oSAK82VN5qEAEdPT/nQKQ3t7ij0TQCfxqF7L4L8EuS8jZlfM6buLOr4+9B2HjXq8rNnAvtoGqkqItIdhcYqSAwQCIRhh6tnL0AcNTFh/HsQZSFAEYsRx11oxN2ACNMB9Jj/ciLArLPi4e8iYg3suv23AQD3Pfxdi1SlbTrSZEZ2reQ7SJ3fC6ASNmWtQtZHYcki/yrBM0VAKLHU7+fp5xfhUxsdimdeuFG7F31MjDtmZp5RumSIiXcMRbIhCLI2qpL/4Hwt2yyjighDCDj6XGmhcIgwcKaFCoHqIgBAKgS+8hVg3XURjCpC4cc/Hp1CgGNIQ4aU76cnysb9lmXzb8gm/54/G19iYR8lQkTEpiXbGP2gNQMEAmFYIN0KtIk4aqLZmIAoSkVAHDWyhcByoW46OVVFBDBhxyTUUZS+e4CxCA88+iOARdhl268DAFY8/P1yoUEuUpvnBQDEvajnst/cjksEwCkMRMsVRIB6n0+/cAM+9YlDsPbFm6z712/fNQ7m+Bh5XsLJlK4ZZN81+18H+RdZ1ci/oyRe+/VKfOjvJ+FX/74KLiHgJOG1CQEmi/LvhmZf/3642zTvyicCVPGhXE+cqI2HRuLrwDe/ObqFAMeQryFg4PH+5nefaSxfPnfs0CFZNdH/M4ywBJdeeglOPfW0oe7IoIM8AwQCYchwee+VaDYnIo6a6Gqum8bnR3wx8LhUECjhP+n/I7CorAjI9vsXM+zSswDGELEYUdSw6M2Dq38MBoadtz0b9z/yA5HuFgBquscLAMAXwqORfiEcFPKvEX61DrNs+bwC5n+96cq96CMyfGb/zRegQbvS24Cdq2TZxF4j9SXIv1bJVBBtCIFbl5/saNUcL1eaHMPjD1kJALhy4T95u+yHk/YXlP9vYMKE4qJVBcKZZ44NITCkcD8btE+MJbC+FUKYJkCiCoO0bM6fVF4GoYMgMUAgEAYdV105F3HUhfHjNszWA6SLdOO4C3HUhHwTcBazz0VACU8AFwIRi5V0LiqYpEssMoSASp7Tf6vWXICdPvMVAMDKR3/ES2XFTQLH08p4AbL6BSJAEso8EcBbyhEBSr9D0p954UZs+omD8exLP1M+xaGc/W+f/N/14NlBJNoWIky5XfdY56Zl92L0zJHGRLcmjn+v3p5LbFlrDqTNGdP6Mf/W/QAAE8ZvqPWpCP7P2I83//w60NsLjB8fXIdQgCHxDijfVy3EJ7HKuCWd/A5zISC1n0NEoNTXrCJIbLhAYUIEAmFQcMVPrxVhQN1d6yFdB5AS8TjuhvkGYADVRAAYGCf3qggwvAGcMqXeB0m2JRKNZK5acyEYizBpmy/hgcfOE6VMQunzAoAxbP/pf/aOz8NP9urkX401d60PyNKttKoiQPvhtgn12hdvwiYfPwjPvXSLZkcdhQcyoeQk0Q4yH0aibdERSqJh9EH9veME20mite9CYLrWF/2e1H7ZOa7fYPX7H1hWNq+lHTT1Otxw+wx0NSd66nvsW2372vfkjxtXULYNHHPM2PQKDKog+DaAzTBjWj8S8WUwQoU8AkHxC0AW5H+DShNOBdEJTlrO5ljkxeQZIBAIHUPvZVei0RgnwoDiuAm5GLgbTPEAqLPauggwdtOxRICsG0UNbXZdFQopac6MZyk7bXt2pfvacesziws58Jf//o03b9vNZznTZ9+4S3sk2lWO22JmWn766meuVcbQ6BNj2ayz2k/dXjlirfbA8eOsxb7bZ0Zh89ZyyhvWHATbW9auZJW12nd22VPW3VghaW/EJjF3qhJnfT8tYs7Lf//taqCvD+ju9tbU640R4qXuCjSoQqbqjkL5f1+JuFQEgjjl38tELQj1LQP+777dlhtF4WVVvldjd0chEgMEAqF2cC/AuO53IY6b6cLcKEYcNbPdgWwiD0AQd+ebgBURoC4IjgwvAPcATP7sOYN+39f9bK9gYg0gh1jL8/HdGyi5YQQ6ON3RTz23DLkuKKt3w1O+DLmW6cUEu4A0sxJljX4VlhPJoWV9MdU5Za1svWzcGBdO6j1lcj8G005Xl6dIReI/kgUDFwEf/CDwzjvpv9/9Ti9TRhwMmneAGUcV9rsCEmdR47mSJHr53HZD+0eoAyQGCARCLbj88qsRK2FAUUb8o6iRzdjzEB9jPQDgFgFGaJApAvbY8Ydt9Xf+rfui1Wo5FuNCOTfCccSPW3o+fZ9FWLT0MGFzXNf6or6TZ+YRYIvDBhDgXGJtlNWS/P1wElan+QJyazQaRq4Dy/Jynn45yzqzQgm2e3xdPfURV2dZz9h6yxqnvk/MTGvE3UpOGPnPK+ZvN0PdYkCtO22anzxz4j0cQoh4X7beOl0kveGG8s3Jm2+uv0n5mWw7zOHQbxOMQVB3weDVzzGxUhjM3YT4CwdUCZE4FUFnKX6I9SW4+OKLcNpp/pDO0QhaM0AgECrjssuuyARAF7q71hViIN0SVFkIrAoBjdQosf5m/HtWdq+df1KqT9cvPshL3tV2u5rrIM1MkCRJ+p5Mfsxq6AuNGaRoAPgPS6Phi40uQ64DZqPLkmuR5KN/biIbXNZJsAOIcFmCnTMlHUqwC0mz1b1igi2bCSjLU3J/b31S0Kkqve2oObvt8F3c8cCX0Ii7cssW2ZMfWb6NF1+9A1i2zC8GvM3WwEP6+4F99gFaLeD224eOWHMRsNNOKdEfP14n/uq/Vis9brVVelyzJqzfg+IdYNZ/E/ExqeIg5Ilh7iiUlXD+6dTNSavZG2vcmDwDBAKhNC7vnY0oaqK7uV4aBhQ1EUcNRFHT6QHQQoEApwiYNvmSoLZvXHa4YosfW0iSBECCKGrqxL6VIEFLlE9foGMv0uVbj0pBIgWBKgDUNIDPunp+OEqRa95eibJWkeFAsJX0IJLtSM8l2p46PMX7I+6TT+Fk2yLozst8gh5STs8OI1s+xFGzsHaQzcDvJZqu9lyN1Ui2+vuB004DPvrRlFSfckqaNtiCoL8f2HPPtA/jxvlFgO/f9tsDq1YNMw8Bgz37z+w1A9oGQ8pnm71PwKprVaobY4vMtwsSAwQCIQipFyDd+rOra11xLkKA1PAf1Rug/gxkJHq/3S4vbG/R7TMkoU8yQp9dc+KPJJGE3rEVp+5xQNoX72y/QwCkheR9KLsQ7bfb5Vh63+n24swckuMP0XDPkuWWd56WIM+FhLv92W2Z4iPcvvJGXg7Z9hJ0K7k9Um3VKuLKwfZ8n0sVW3a5yCEG8kbda7PwfrOUhkErypD+orKTJtlEub8f+M53gPe+V5JqADjnnMEVBP39wAEHyIXTal+4ByDk3y67APfdV9zvL38ZSH6IznkHmOc8QBhYZphS1V5v4G+3LEgAVAWJAQKB4MWll16eEf4udHetJ8KAoqgLURRB7PTj2OFHxf5TrvC20XfbgYAg+K2M36sEXSf5jDFEnMRrAgB6mioAvGSfZTzHJvvWW4odW37GsRoO4WTzNRPuAvLsrBZInqsSbmanhxPskUOq3ZqiTVtWYo32MsSR+TNffiztruXYMMWAqFKSqIWU33779Pjud8s0TsDrfttxHvr7gcMOS4WA2r557ro2Q4aSBJg6FbjjjnxB8MILJTpYdUchF/jnklgp8mnnmf13CAOf9fL9qQtjc0chWjNAIBAsXHrpT9OFwM11EcVNsS5AegEYTCHA1wPkEf95t0yDRvIFKU/fKqyTbh9Jz9IUsh86m2+vS5BHa/cir6CQwiL2xGI7yXMQ2bavZFIJgi6yfKUKCLpxOVxJtTOlI6SaFRWwLOQXDVJA1e0pcHkGqrUTOAY+MZDbfEUectZZwJw5wLrryrTBFAEquBAoEgB5eWb6cABjnv6on1keqQ8QBuU71fF6Y40bk2eAQCAIXHbpFYiiJrqaEzPy30Qcy0XB9gx5hAN2v8ppa94t+yKKYqgkuhGPyyH7GWUvDN0pFgrOl5Q5+p73Fl+W214mBqJuP9/3/vDoFaqSdDepLrLhLzMqSLW3Qvuk2iVeikcncPxYzfaUcpHlGdDzCz+rwuz05Im11wGPPgrEcWAfefWKpOuQQ9LjOuukx6Ei0P39wIknykXTZQWBr9z06cVhTt/6FpB8C50JFVK+kywj9N4xZjAWDWjX/BNOtO9Uni31WAfK2lqCCy+8AKeffkaNfRjeIDFAIIxxXHLxZYL0N5sTUy9A3CXeFszfC8BDgA6Y4iH/t+6bbiXKYoAxNJvjHQS6ZOiONsMPxYaD5Ptm95XFyu627FAjpyBR6kz6zFdw/yP/ZoQJAfzO7AQi1e6s4U6q07J+4VXFJisa2pL23BY/s/nJWLXmQkSsIUrZ4jOoI8Wt8pMoQmVyL2wF1p8wQT9yqIR16dLOrhvo78dHP7grpk46D70bXF1dBPjqHHlkfv+ffLIz94XvA9gYh+93q0hhABKvl0CUyLlWRYF61Sl02v7oAokBAmGM4pJLehGxhhAAwgsQdyNiDURRnHoDWIQDplxp1e+77UBtgW4jHgfGIkR8MTGU3XkAjXRbaYKouLYYDSH5ZnkP4VfEiHurUNkviCvOoKRYANQZ14oE2Vt4+BPusJJtkMq67OVWb49su02Fjkw7RCWI0QPweQUCeuL4/gShjFegrGjYfHOdFO+/P3D33fm7F3XSW5AJAYGurvBQoDLXxxwzNDsjOSAFAeCe2Tc/U53+56d6TJQCCYCqoDUDBMIYwyUX9yKKGmg2JqQ7AmVbgzbi7mxNQIyePa6x6i1Y3KMQfIg3/8qdhIx/BglXFwBLMh5p5N0fwqOSfE/oj0nqlRl5KQ70cykWRI4uVviPC1NpU3b/rGGRwOE7w63Yq4Vwe2hkGAsvY9FhqkYS72+kNK8I/cS87QUllbMfMQc5LyFcwscvKxdF+cVE8TZ5xxlZ+IYpPgYrVMgQAnesPBN4/Z50DQPvR5X1Ar7rPHTknQP+zycVBPBH+YhCuQm5rcyauRq9feOLOllgpX2MJX5MngECYQzgoosuSd8EzFIRkC4K7kIcd4mFwQdNnWPVu37JIeKcZcRCvjsgtgRApIkB32y+m+ibs/r++oCgRo5wHv9Mv1JXkHv1XNo12wGvrZBRXyz28J7lDiXc4TbLE+7BJtsF9QqzKt5fQXZHbBslWGSLgdIEP6dp6x5CxUAofGSMx+fntdcpYbDRRv48vni6TiGQJMCsWTV4B6rsKOQn8fkhQ66qRQpCLVdHmXYw9nYUIjFAIIxiXHzRpZkXYLxcAxA10WiMQxw1cfCe87TyGvmHygttop+exxA7CmXigK8xyAvjCSL+om23ADBFAuAi+GqdzIZF9JnCOVQ7/NJMyxMD0MvmoWOz3KOdbAca9xRlZkJN7civUIfsmlcBzTg9A76KlUWK/rdaCWXr8dCgnXeu1l47+MEP9PCgDFt96lisfvpq4BvfKCcE8vJCvQPnnw8kX0TnFhKHrhMoyuafcxWh1mkBMLZBYoBAGIW4+OLLELEGGo3xiKKGCAdqxONw6N7Xa2UXagJAoUre8J+UuEcsQsQaYJHuJRBiwEv8GSC8A1mrGjHW4/bVfF94kN9joJ8L6wYRN4m+KUasNJhhQsUgst2ZdquR8OI2qpPwNgh+YXKZPkN682RCUdeC2rFL6H8/tSDPFp+BX7u22M4mm9TTHwDYbDNv1p/+/Gp60mhUWywckufDAw8AX/xi+ftpAwwB4UL5tb25vX1bAXiuUr/qwxJccMFPcMYZXxjifgwOaM0AgTBKcMEFF2XEv4FmnIqAKE7XAsyY1i/KSfLP9CMDfOE/nJxHLBbrClTir3sGIksM+GfjcwQAlBl+bV2AcdTIvWOtgLgMJfq8XbV/anqWE0Vjgmxbpdv+zegUCW/DdpAJv207J3CMmHVSn20g29o3vG7V8Xnw0fOA556rVwz48K//Cmy6KfDss4PTnopvf9vpFdDAxYA5q191RyF+/qUvDZuFxKXg5f3uz264MdKxwpHJM0AgjHBceOHF2VqATABEDcRRE0fsv1grt3DJodmZToZd4T/q7LsqACT5tz0B6j83uedNm0TdFACGEPAKAMVO8Iy+YPl2uqjiSpdpn/7kUViz9lpPCIZeLxSDSbbzS7KiApXa7BQJD7btrVSGhJewr5Z3VKnNtidJ/EWZnoHQdpj3oqyl+sHXCQw2Sdtmm8IiYpvRze70k31XWqgwGKnwCgIbCxb3ZAuIJxQXJtQGEgMEwgjFhRemi4IbjfFiK9CjD7xDKyMFAAQR53H8rp1/OOFWtxZNyX426x+ZxD+W9jQiz0l71rBoW+mMQvbVdQI+4u8k/eb6AmFX3LA8Mthl+Rmz000xo1nMFQKuFjwppfhMOyS/oL4zqwwJLy6vF+mAbbN8Kc3RJhFnztT67JupVra7fKFg7egYdQhDNVN79tn4yAcmY+pOP053D8pDaKhQUb55/s1v+r0Dvb1AMgudWTdgw7u7qA8BgmC7LU7Fy78CCYEhAIkBAmGE4aIL1UXBMT7Xc7eWv3Cp4gFQyLM+o6+Q9WwBMBcA6ToAfxgQJ/6aHXObUEsAmIRcCSGCsibAGfrjIf9qWwD8s/pquipHVDLE9P8yR5pyiJhrF5MCkpKb3Q7R7+RML5FwLTWQhJdtw2vF+k6H2d/4o/vimRdukqI1d5zquYdRjR12AADEcTfuevCrmDrpPK8gWLP2WszC59C7zYpyIsCVVmbdQJBIqrKjUF67I9hbUYixtaMQrRkgEEYAfvKTC7MQoBiNxjgce/C9VpmFS6dnZ5wEu14AppDxyBYAZhiQiP9XBUAmMtR1Ad6FvbxFIQy4QDHS0g4r/XNfSyHB75MLCA4Xqfel+8i+Iz2rI65CPAOBBNyfW+bZPMgkXEnqmH1YH1/tbdRNwoPtm7mVfobzxyxiUcfGzZnayTCWT37Sng0fzLCZM8/Exh/bH0ncVdju737/dHoSunbAlZaX/73vub0Dd94JnHxyxRv0wX2vidq3MmBek4OIjVFGDI0VjkyeAQJhGOOCCy5CxGKxHuCYg5Zr+VwAiPl0b9x+RqMz4q8LgIZC/BXPgSIABOn3CgBelsneaAKA9xKKIMgh/oJ855F/tb1AUi/6pKRp7Rvpom09nWmegU4Scea89FuokegRAXcWDO9hQMlSt1vu2yS+o4Myrhg8cs7babUGp73JkwHwrYQTJAlw3y/+Ndc7AEC+EK2qIMhLGwIk+n8618awwhKcf/6P8cUv/stQd6TjIDFAIAxDXHDBxemuQI3xOPbg+7S8RcID4BIAWapCxH0CQIv/F/v8Z6E+atiP842/6nsEVJJuEnQP2dd2F1KJPxQbvnN1LYIdhlQH2Rclrbz0qO/S0ulZcCLgxdVKqbHczOrzgG3OIAaKr9BWzBChKp0oVbUdklqmLi87MFC9vTI47TRstvEMJFETQErOkwLa+tQvF2AWZqB3p0fShLJEv8hTcN55bu/AVVcByfGoc91AGS/A+ut+AH/88ysFxiQ2WP8jxesvCIMCChMiEIYRLvjJxWI9gBoKtHDpdIVIK3H7MGbZOUGPGmIXoIjFtgCAPfsfIgCcLw4TO5b4ib92HUT+ORlXyL9TCJg2Ia/Vc5HnIPz6fxx5St/4mRUmRAQ8rKL7EyrdRuWq1cl1yYbat1nlI1QtmOtaav1cHKVD4trLwGeLvzfgnXfqa8uHqVMB8HeKJAoxTvDAY+d6vQO/+d1j2OwfZ6Tegbq9AnljXPMboJNSn2dO2WEz5V+tI2OBJ5NngEAYBkg9AU2ceNgqLX3R0sMAQNu7X85mS5LNWNQBAaCH/shjJHcpCSD6Rddewq8uJs6udfEDadNL+JXe5RJ+NU/2ziUE0s/Dt2aAaYdijGACztwXw5Jcs7LjHGS0Fit5dj1DHGbFucjdb7T83Rg1eNhOHYIgz8aKFembh99+u/12inDSSdjqU8cCUTPzBiQZQS7yDQC987cCrnminAcgNM03PkNGWosWNxcXqR/DRoGMCJAYIBCGEBdecAmiqIkTp0sRwAWAXMgL2CIgez+AIQBS4i8FACfb2hag5s4/jJ+7Q3/UBcVp60VEn/cRwgaUtBDyz991oFpykXzbnlnOsBGw2Ni8H10I8DbjDsy0BpRsm8jVQRban7X22q0dOWPUVnOdFQKlrPv0oU8MyALl28pruNUqLwSqCIcLL0zFwFtvFZedNKn6S7r23RdA6hVIGMAyMZD+Pz3/+eMXe70DO2/3daz4xXeBOXNtrGULAAAgAElEQVQ6IwhcuO024JhjCm4sfEeh9df9QGGZelGVwHeK+I+dHYVIDBAIQ4ALL0i3Bz1h+kMAgOuXHIwoampEWJ89T4/pjL/LA5C9EyCKIWbTmSIErN2A8gRAJOyWJ/7GNeAh/Mj6p65XgLStQc62u8poi6VFG3o5rZ4smC8ERJaRBmRjE4oKdIv5R6NjbZa0O9bIdYVCFcBqs5wvBkyRXKkF/VJd0FtE8tvNB4D//V+lKzV/HgccABx1FLbd4vNI1L/1JAGYFAN5HFSMPw/dqVsQXHZZB99InLbxp7+85rnHxHm1wXofxgbrfaQD/fG3TagHtGaAQBhEXHDBpYijBk6Y/iCA1AvAGEMcdQGQs9wpUoKuvQFYfRFYpL8QzI7xl0Ig4tuDinTpNQCYZrcq8Zf9N6+5d0Eucs4l/h7Sr9lV/mOJAd2AksegVjbTnKTfISwA9T0D7T4/PXc6poh1aaulOlCb5fo+oIJm6rFdJAbq0TxpgT13Ph/LVnwRePTRNLkdsu/Ke/hhnfguWwbsuSdw661ZN2r+PDICn3pUAUFAMyHAeBJL8OjTVzq9A6//5oH0hK8bKCsEQvJcmDsXSI5ELYuIE+vEzjLy/vCnF3Pz06Q0bcMNNgrtQEXUIRyW4Mc/Pg9nnnlWDbaGL8gzQCAMAi684FIcf+gDOOHQGQCAG26fmf6oCFIOSAId6R4Ax+x/ngBwCQH9XQFMExTCDrI+BBL/LTc5uu1xWfviTZpNCZWQ22nqtVMMiINLJLjKujwTzOAYap/C3kCcj5E2a53aHqvk2mfddVqf1epGi8KE2h4X1+dXFCpUdT2BWe9//kc/AvUKgkwMaLuGJak/QIiDrLnC1QNRVEzoqwqCnL7XAxelT1zZ4iKxM2R+Am/u1EnnobdvK8yauRq9feOrdZe8BpVBYoBA6CAuvOAyHH/oShwvRMDhSJIBhbDLmXMf6dev1bAahxAQZD/K6mYz8jykSElTif9Wmx5X+t6uWLg9AODTnzwSrdYAkqSFVjKAJBlA0hpA3OhGIx6HOOrCxz44xWljk48fVG1gFTz/6lKEE3wz3cjLEQAAw0c/MBmvvHFvcTx2JYwcot05gt1Jct1JUVDCfolu+L+nAXUDvqO1j4tvu88iEVCWyM+bl3oJ/vpXva56fuyx1UNoZs7ErMNX4wkh+hM5l5DoAoAVENBZt22E3v1fbG/2v4wguOkm4PDDC28xBJbQcZB/Vy3t3CkA7LqpZ+WeEkKgLPEnoZAHEgMEQgfAPQHHHzoDC5dORxw10Gq9Ay2shTFv3L8lAizir4f5uBb8RlGMHbb8QmFfe/u2xuPPzlVm2OUMOqAQP2PnoHHd7wIA/PLlxZklLi5MLwPDcy/fomxTl80dJS0kSNJj0sIBU66qNNYbfXhvZ/qLr92pXOWT/Nw8Iykde6NYh4jmSCLdnSXcnSTag+Q5qKG6bc1vv6xgreXzM9cNhJJ8/mwoKwrefFOe87rteggOO0yc8nBAbVacAUwJn0kAPPHcfGeo0O47/gC9D3wFuPHlrHhJAVBUttMoIP/uUKBwAaBim0+diJd/FdSRABDxLwtaM0Ag1IifnH8xTpj+II4/dAZuuH2m+EFutQaghulEURNR1EAcNZ1rAGRoj3vm3zzu9Jmznf35zz88ixuXHaGljR/3biV8KMa6E9/vifVPr1VxIAhD4LsEoAgA4T5O3Ne33/9FyF8Rmc/FgviHFpIkcaYdsud8cZ8f/9Ae1ni89Prd4lzel7xvLU8kG3nBJGukEe5A24M0s121lXZNlCHctTXaAYtCtNbQhWDh9E//FGYQAB54wE4zSW4eP+npSdcRfP3relnGgO98p7pXQAmzke9QMYJfmCS8eSPiXEQcegwpM3euvYi4vz/L3wf+dQNFOwolxrFoDQDw7nd9DL//4/OBAiDBezbYWBNPCZCFCI3z1PEhlPi3JxBGO1cmzwCBUAPOPfdczJr5GE6YfhhuXHYkgASt5B2wRA0HihBFMeKoiShqZseGvRBY22LTLQB23vZrou3evq3xzAs3ievurvURRXI70HUnvk+xHUPuIgTRDgDI8BlVECiiwPQWmHvwmw/LxBFD6kpDkiXrs0t6mvrCH/16+j6LXB8J5t2yLxKkMcxJkiBBC0cfeIez7Mu/uidfAKh0qO0QoXoJt158pM5yD+N+mxYc34/OoXo7YeSlXiGFJSUWre5T05aNv/1t+uzh/9qNmT/4YOy6w3eAj0yz/tY1UcD4iZ9kvvjaMgBf8r98zDyWEQvqUUVPD3DccbWMr3c9hOe+DcnkzPVvwhRC1tsl/p9CyJaqOsbG9qIkBgiENvHXXx+GWTMPQ/8dR6OVtDDQSve/VmP7IxYjirsQRw1NCPBdgvIWAu+y3TdEW719W6OrOREvvX6XIPgTx79XIfp81x4m29deMiZ3wWEeMs89ELY3QMnLyrtnqpP0R8T4wdBFgSvVFAzKA13xFADAQVOvE1k3LjsC8kVASVa2hUZjnDhPkgRIWph/635CGIB7F7I6R/eoYUXAq2+skBeqEOrIegGtodqqj6RZbstCx0h3B/patsQgaIn6v6c1d7qMcADc5JZ7B/baS4qBJUuqewWOOgpA+lLBF169HZt+4mCsffFmmORSrBNghXoAADBr0QfQO/2N9KIq4c8TDZ1CYp0YV2b7iX1ukf8yfa5LILQ7Tktw7rk/wllnfalNO8MXJAYIhIr466/T2NKb7zoWAwNvo9V6KyWdyuJgxjJPQNxUPAKpIOCz9DIUJz1O/uw5WjuXL/iMCO1ZZ+L7wJQ3BkOQfAb9DcPKW4XNXYeAjPi7iD6/NrwEijhwXQMwCHt25pjJh1HNKRgSOz9BItYV3HL3iYLEx3G3IP3pMRFH9VykJS1ArFfIrpFg3i3TZDllPcPneu4CAPz0+m1x0mEPi/FSQbPchS3VVNtjs7Zb6CxLD7deRz9y/EWD6t3oMHp6gNtv16+rQmwpKoVUxKLcGe90KBM8+9LNznUDe+3yE/Te9wVg8W9kYp3egY7AMSGTN+NvioaOCoB28wku0JoBAqEk/vLGdADAz+46DgOttzAw8LbcIUiJ84/jLsRRl1gbwNcJRDxUh0XYdfvvONu47uY9M68BEzH92gvDVBHAIsAi/6ZgUIg+YFy7PAOB19m0mPjRMGf1tesk+7/nByaRP0GmWNh3114AwJL7TgMSII67UqvcI+A4akKAewdyyugiQZafd8s0JEmC8eM2xHU37ynyW623MxH4NvaZfIn1GQ4O6e7ULLcjtZbud0Ig1Ge/LhOdJNqhltvxDJTrfVp6/ylX4Ja7Tyw/4x+KJUv8L9mq68Vb++2HPSb9CFC2Dk6fpymqEFyxdiM0VKjs8cYb7XGZPbumMCzjHhUB4hoLmZ0jHLy5eV6GwP6Vzi+H0cyXyTNAIARCFwFvY2DgLbSSAYOgM0RRF2LNE5CtDciI+pQdvmfZntO/h1g7EEUNNJsTjJl+F/mPdJEgSL9yFMRd/Mczy28Qfae3AEY7sFiDtWMQ5ExXkWAQLnflAb7PLhcBAJbdfyaABFHUVAg+lHN1jUGASNDSWu4yikhIhYIScoQWkqQbSSNNu2fVN0XewMBbaLXext5Z3x3DpGCkke6SdksVH3oC3U6NtpDbXLm+vO89m+M3v3usBHGp615HOFE6/ngAtohiyvai6h0GCwPGMOvw1eg9KvB9A1WOnUJiPpGrkG/FRo5YeM8GG6O3bysAT5ewHZpP3oIikBggEAogRcDxmSfgrXRxMP9fRshTD0BXKgDizBPAUhGwx44/0Gxe2z8F6vahzeZERJkY0Mm/58ViCuGXYUncxa0LgfSNmf4wH68A8O5gJO1Au2IA02P7kSRImHKtkXb7WvUa7LXT+bjrwbORioCGLKOtD4By7hMJalsekaCRfzVP9yioaxJEiJEacoQWGnE3kqSF5au+ARly1MLAwNsYaL2NaZkXIZ86DU/SPSgz82VQI4mupckO1SzfUhvrBUY4p68MR4jQK2/ch40+sjdeeHWZUjB9npjD5KObwh5f0wAMjhhYsgTVdxSyn8dmnnj8AnjvuzfGf/5hrVnCS/7/ZsNNtXCqrTY9Pjt72lne2X7pfBIEeSAxQCDkgAuBm+44GgOtt9AaeDsjycri4EwEcAHAPQFTJ50r7Fxz066QbwSO0WxM0N8yHKV/iuYaAn2WHwbxV48wytpzWE5BoAqAHPKv9oeDKf9VT2XbKlmX/dDXFtjXe+z4QwDAPavOkSIg4VYSrTwn6qqosLwRijAQdnKFQ4hI8HkUPCIhaaERj0OStHD3Q1+TIiFbcD7QeluEQhmj6kCNbM1pqn42OBIIdDjs73yNFuuxkOMVqHdE3QE0IxL8WecIr/KHXMn7ZgBefO0Oa93Asy/244N/t4P+JmK1zWydWfARGDzPgHhmGmmukkF9KSLqZn5VUVD3uIz+HYVozQCB4MCff3UoAKA/EwEDA2+nGQoZZyzOwoGkENh75wsAAFffuAuuuWk3Qf4b8fh05j9qIGIN8TZhgGXeAEm6D9zjmo7e2+33fzGI9NvCxJYC8tSVw8SFFhLE1BRdMEzZ4bu47xffQQLosbosc9Fn9fOFAZAvErI62kw/FHuGcPCIA/OY71FwiYQ0rYFUJNz14FdlOFLSwjut/0Or9Q722+2n2ucX/sQeTs/2+gl0PaaG0xipqNAvbYhZjolO3PNwHcdAzJoF7L479p58EVBKDHAEks8qfMsUBIMIN8FP9DOriEcsSKN5LZZML1NnS5TfVlTFEvzoRz/El7/8lTZsDF+QZ4BAUCBEwJ1Hp2EdA28BSIQ3QIYEdQkhsO+ulwGQAiBiMRqN8cobhBvawmGA4eA95w3J/S259zTsM/lib/7yVd+AKgC4BEhDjdJzDcwjAvRCMkW8ulMScp45+bPnYMXD30s9KFlZTuRFwBNL66jXUiSYpD4r4xAJmvegdNhR1oawDbh2MgrxKOSJhBjpLkl3rvyysN9KBsSahP13r/bG5npo23AkfxX7lFOtvrsc3PHq7Pa3oxCOECGVS0Ysls8WL/x5b/zHw8CUk4Hly/UZ/jwMAfmXMJ/TZrqrRqJe5JZ1IV0v8GROvbpFBEEFiQECAaoI+BwGBt7CQLZNqFgcnMXupzsENRHFXdhv117MvmEnXHvTFLBICgA5+5+uFzhkrz5vu323HQA1BEgL4dF+L3LCc8Rlzuw9ACDBoXsvdPZj8b2nIo6a2Gvnn1h5Kx7+bvab1HL2R4Yp2elG14x8Sax33vZrWPnoD6wfXcZU8p3KEojfyAKRwH9IuUgQgiGb9Wdh3gV/2BEKRAJvt6XXCxALuZ6E7P0Jd9x/prCTioT/s7wI4WhvJrpNS+UbGTYYBLlQpYnAGeg6RzcB0LPndehfdtTQ7CjUDoTnUxEDmQczzU69lFUFgSYy6ozG4ILBNS6VdxTqF2e2Z8AxAkI35HsR/CmakdxS9aUTTJAYIIx5/PlXh2Yi4O1MBAyAh8nwtQFx1EQcd+HA3a8GAFx942TMuXkquprriNn/OPMAHLLXAmc7fbcdaJF8+bp7ZrJmLxJkpFgtmyCbdVdz9DIJgIVLDrUJLYAZ0+QbjDluX/EFxHG3tfh5xcPf5b1XUnm8rSddZmp5jAGTtvkSHnjsXGUsEu0nR3vrZ45ISMSvdSI9C4AUCdk1s0KN3Oeuhccq6feLhGI7vF9pPbdYKBIJ0sOQnjca43DHyrOQaOIh9STsP+UKa+yHFzrsr6hsfriNkwl3/9gQ9Hu4j1Qudt0V++52uRIixP9mGd74j1/gw/+wM1779cpMEOQTzFffWGGtG5C7r/G4ScWG+VwM8Qh01GvQA+AMADspkx7iPxb+9j2b4T/+68kCm4l2MHHjsiMAPO6u47MVlE5iIBS0ZoAwZvHm64cAAG5YdjhaA2+h1XoH6gu71p34fvz3//svxHETPXtci6sW7YhrbtoNcdyF7q51EUVNJ4kGXDP+cnZJ/GxmxNk50++FoJuVBAFEbd5yam/B4oNkP7Lih+1zo9byspVnohGPw5Qdvqul3//Iv4HfkE76zWv1foF/2vpMPLT6fDy0+vxMCKQkPT0ogkCZ9TdFgn4tx8cWCUoZVRQA2XqERIyfqK+JhBDvAZTz8BAjv0hIw4iKPQoFIiEej9vv/xelXioS3hl4S7zELRRjh2y3369a78wylm+dftdL4PTTAfDZeykCTIjd2nxygP9tO8feQfiroKwIaGtHIWRtyfa88/yFfZL573vvpzWhtOUmx+DlX7mtlyP4gyMGRuvfFnkGCGMOXATcuOxwsd2j2M8/27s/YjH+9//+hEP2mp+GAvXvju7u9XHk/vZDNSX+gJwFV3b54eka8edpZR8qKbFniZ2mX7oEARRCq1ZR7SmzNwy4fvHBWuuHTZPiYNnKM9FsTEAcdWGX7b4u0h987DyogsI3R7nDVl/EqjUXChEgJ/UT4Z53ueVNkSAIvZFmCgIYtlQ7IaFGYgFzJZFQzXtg2zFs+DwK/DwTA1IAtABFNKQi4QtCdCRJglbrHQy03hIesM5hZJPtoUdR/4Z7/4cRIn1LZgHjucgsr4FxycyJF4lcz4AsVKLTnQZ//rT8eVpKIEHP5eadIP48bVu0t3gYGO07CpEYIIwpvPn6Ibhx2RFiXQCgvMwre5FXHHfhoKnX4eobd8Gcm6fiuEPu12zMv3V/bb99c9tN9xaegFyQWxUZHTWNmM9AhyDQi5izVKKSLOh4rl6/+CCRpXpE7lh5FprNiYijLuy87dkifdWaCxxtJth+yzPw88cv9vy46ue2W94lEow0TSQkonlLJIhQI2nDWrRseBHcIiERY54nDFTPgBQJtjdADWHK8x6oIoHn64LD89Zll0jQrsdj6YozpKBAKhL2r7weAaOQbHeydvstDcnsZf2TsIMD13oBJMrjUHp20785Y0JGXOi+SaMRZ5vDHdIPLRKsEunB9wwv87UoS/CrpLWLJfjBD/4NX/nKVztge2hBYoAwJsC9AQuXHiq2CWVKSBBYhOnZ4tprbtoVAHDswfcBAObfuh/UkBd9y03IPO3aOM/q1hHn6eJV0moC8fxWBIH8WZOknGP6PjdU6seCxelMy4xp6YKzJfeehrse/CqajQloNMZhx63PAgD8/HG+e1GCz376dDz8xGXGbidZ3xJki3r1dP1c/jxXFQm6IIA2u5+mqeE8/GPMFwl8MTLjxJwLLO2eSnoPhMDI0oTNRNoU526RYHojct+RUCASlq44Q5xzm63WO471CCEY2WQ7NHtwwTznQ9D8SIII4+TPSkBO5CjFwJCI3wFAPmfkMzX9k3WEGKm/AUDx70CZdQQdWT/An1GmZ0Bv5/1/szV+87vVzjy7mlsmydQ6if9IVaZDB1ozQBjV+NNraajLwqXTxTah+g5BNhk+5qB7MgEAcM+BOM8j/A7yb/kCGKenanriOAuEnPhW2jAtMkzfe5HXxM13HavY0omxSZTV2W0uAjh8W5Z+9tOnifNHnrzciM1F4blXJAjBk2/DFAlSHqk//bYg4HzcLxISsWiZcbKdF2qERIgPt2DQSX+lnY2EHWkzxLuQ+46EIpFw3+mGSGih1XoncD1CwO/PsPmJqt6Rwb4F87mz8PbDAADT977eXaEG7sRNHLL39bhh6WEjY0ehM88EdtgBB069Nv1NAKD84etQ89OE7MifC6pQ0PHEc3MBHKPYKvhG8L/ZoeZnmRhwfz0SUSb/6yNz//5vP6OtFwCA/juOwqyZq9HbVyfx76wYGI28mTwDhFGLP712MBYunY7WwNtIkgFFAET4019ew8kzHtHKcwFgxfw7PQEOwq+cW/GnKq20Zo6YcRbyINN/LBIl+VDHD/4td5+g90KtwOmyduAXLJtNlyImJcUs2wHC1V/5HoWlK84QL2L7xROXYLstTgUAPPrUFfqMkNBHBqn3pXvOU66dKENaJBLU+w4QCdpVfmiREAVOLwK/zgSDuFelvYT3hZP+rHe5YUcekWCUryYSdI+CFAnubVCX3PfP4O9S0ETC7rPhR7Uf2aH7ae5AyxVMLshC+CS20q560IN+9GPh0sOq9wsQ3lMXRiQ9Ul+2KBMdzxyVAPLZFv4XxrdWDnhuV/UMJEk1YVB5e9GLAOyiPQvlM8jomu++rfL+e+7te8dpuTitahmCCRIDhFEH7g1YsLgHrdZA9qKw9IVff/rL65oImHfLtOwhHym7RTApAFQhAASRf3X7UPnMlxS7+FfTLJDoZ4m0B8B6j8Gty09ykH6V4rNsup33ViH7mcs5VBBYfcvOblx2OFRRcMfKs9DdtR4efOw8NBsTsO0WnwcAPPr0lVptQdANiy6RwJj5Q+Q6V+z6RIKyaFmrr4iEREsrv7ORKhpEMIJ2LT0CaqgRFwn6AmaXMIDhPYBy7hIG6rlLJLjDjkybLo9CoUi49zTF05DWGxh4Gz3eN28PD8I9GA24avUtLp4B7wlYHNmDHizH8gq9knhz6ZtB5fx+yGEGbfGwRf/Fd/63//UU3v83W+Pff7daPn7F0z+xyvsw64p10XviX8I9A6IrbXxhK+8odC9uXX4S9t211+ycdpv6uwhsIp43JFtsfGSJnYSqlCEhEAoKEyKMKvzx1YMyEfAO+NuCGYusLUDn3TItJbnKW4E5iZdv380IPj/3CQEhHtIricR4hivkOcl/TDHjKlHTWaK9wfjW5SdrbcqWVNKvpVp5JvkPEgRCRMh29UOCG24/HIxJUXDnA19Gd9d6eGj1+Wg0xmPbzWcBAB57Zrb4IXUJgjIiwe9JsNN0u2r70ESCSf7TurpICNrZyAg/4jOKlijICTWSbSmhRlB/lCuGGGXtp/9XSb5u0yUSdA+GSyQoQsGxDWojbmHxvadq4UhIEuy/OxeLLpT/7Rpev3Zpb/oWH5hbKoToA2ib6Kt4E/mkv6hPCeuHuvMKW9yhkKF2IR7Qaeii9f0wZ2/MTSASNZ//PTL85nerrXcN9Ox5HXqXHQXcf79N9r39KoF77qn5hWz8+eHaUQj4wN9tjzf+4+fyiajdkn5/Pu9BgsQTIlSnEJiE9ncS4kh3FBqNvJk8A4RRgT++mrrJ59+6L/gsfyoC9Lh2vwgwxIDiFVCFgaij7ibkfTAw7aGv0k/FeRAEXvagqdcBAG67JyXR9hsi9TphgsBIswQBr2oIAu0acD+w0z6mogA4eM/5AIC7HvwqurvWxao1F6DRGI/PbHYSAGD1M9dkvcx+iETfw0WCi+DbIkFRLYWLll3tq2m6XS3NJPJZB/07G4XtamSFGkEKDU0kJJDrCAAMZoiR6sEQdjSxUCwShBfBEAkH7F7TeoQOo4joA4NP9ouIPhDep9D6ybT+4SkIlMXD0J92AMSfkywOYwc0pmYzOW3jeBTq80I5381OCIU24BMDaZ7+PNSFgX4fH3z/P1nrBaznsXVexzUhBCQGCCMef3z1oEwEZCQfDDP2vVnkz7tlWpYHXQRYYgBO4u8WA5B1PWQ4yeypJBXiTBL0REl1oWfqHHF+2z2f19phjBkPZP0HrQ5BoNkUpkxB4Pop1ZEkwA23Hw4AOGSvVBTc/dDX0N1cF6sevwjNeBy22exEAMCatXNkTwxBAEdaiCBwigTtqNQXJNosa59XEgkwPAvgXxUltEjxGpgiwRRLQaFGSFLBgKwdy6sQGmIk7fmEQbHNrO2KIkF4EbSFzQkO3H12RQ1QrpKr9HzxvhEbReR6OM3qdwI96EGSTcwMG1Fw9tnANtvg4L36ID9R+/mpfdjMLqe/9yXL1xP1vCIMm8XF+gSBuyvy7x2ytFI9n5j/3Xu3RG/fVgAe0to0+1DfdR1Ygu9//3s4++yvdcD20IHEAGHEQnoD9gP3BszURMC+IlTIntUv9gjIo6u+C8x95YytZ+IHwysC9rgWABcApmV1uooZD+sQQeDKcwgCGGsKsrK2tuAzbOoMsh833D4TgFzvsHzVN9HVXAc/f/xiNBvjsfWnjgcAPP7sdeIHWSWdbpGQKEM9yCIhgUIAioQDxGdRZWcjVTSw1Ij4wdbHIS/USBEMWhuJ9KAogiFdb6GQfLHlKeR5gEhIm+yUSGhh8b2nOERCK2c9QjtIR3f+bfsDSAnvr/Hr7G70/63G6jxDhVgf64vz4Uj2Q8D7VYuXoI4dhTSvgAeJdoC10Fj8vejl3E91LhTa8Aq46lddXBwI6Rmwfz/SzQGyPJFmFnLf02YbzcjOHpT2DevVrzshAkY3aM0AYUTiD6/0ZGTfDgmad+u+4DsC2eE86VFfAxA5BIApFlQPgA6VejtLMI0G6h4CB3E+MCMutghwt5oS40BBoPXUpQyMklmaJQiMo1xQDKfo8EEVBbev+AK6u9dHV3MifvHEpWg2xmGrTY8DADzx3DyL3Ksz5Wqa02uQkWN55/WKBNd52qRKFopEQiKGtFAkGOReH4syoUZ+LwLUayvUKKtjhh0p70VQRUr5dyS4hAGM/HCRcNs9n7dEQqv1Ng6aOhftYP5t+2Mf7CM+mQ2xofIpyeNUTBXnZf/XQksTAMOV7IeCewmG3EOg7CSkP66UZyPTDg4i7vAk5DcHTJokE1eu9BTywEWsi+pU3lGI/723oN4jEuAjH5iMV399v15WK6I/3z7yD5OtECFZJv9dBsNVFIw27kyeAcKIwh9eSX8I592yL6IoBsCEN4C/HCxicVY6I7YsToWBQe5Zobcgyn24q7DLubwEPE2ZReHT3khw4O5XAwAW33OK1v/8VsMFgZ3k8ARogoApBNXedci3w5BsIvxBfMPtM4WXYNnKM9HdtR66mhPx8JO9aMTjsNWmxwAAnnyuDyrNkndhiAQlxSbW7XkNygkH9VxpxycSnOm6SEi0ND38qHqokduL4BIJRaFG6rmLxPMrKwTJ944E1TvBawQsYE5nLaUwmJ9h6NQAACAASURBVL5P+T1u/vzXN7x5827dD0fiSLwb79Y+e1MMuNLK5N2Em0a8ADAxbAQBAGtRcPoFNFPSouaT3vED4X/qZYWfflomqcKgLEwhkYfKOwoBSTJgJPD0liORHwIIuepxsMrUdT0F9S0eHt0gMUAYMfjDKz346fXbYeKEv0EUxZi5788AAPNv3R9i+9CsbJK0wKIIEWtkpD4s/Md62APIn+9Ripg83FtQigIGiJczLb73FM2WPZsf0IUcQSDs5a0fEA0bs/9lBYHoRjlBAMjQIb7zUFdzHTz69JVoxuOwxSePxJO/XCCIq3oXmkgQt6kIAJMoZxWt8CMXea8oCEqLBGd9KCLBXqAMh0gwdzZyioS8UCPzXQlISoUaiX3Xc9cm6MJAPddEArgw4HYShIUgJcLLBgCLlk4XIkFseQruQeDp2TsRjLUImvcBLbz9zv/D6ThdEwJlBYErzcy7EleOOiHAMeSCwOsZgPX8Zr4M8Yz0VlWa424GpcQzz4T0NGvK6GU7QiKswazZlp7C/1YdIkF96hTh79+3LXr7/uxs031dRhCE/+6UR7qj0GgDiQHCiMAfXunB7Bt3wToT/haH73crAKDvtgMgPAEMaLUGkACIWIQo7kKUeQSchN8KFfI8woNcA/whbz6A/D8hPHX/KVdi8b2nKqUS7bRYEOh57vJFgsBX1yT/KCEIYC9lCMQNt89AK2lh+t4LsXTFGWi13kYreQdJYwBr1s7Blpscjad+eT3Uced35xIJGrnPCK1KuZhDEDjDj4LJezsioUg4GPUFF6mwsxHjVRPxiXYu1EgRDIA2468S/TBhoJ67REKC/Xb7KQCg/46jRZ047jLKOto3di1KDBHAhQLeASZiItpBnjgAgB/ih6NWCHAMqSDYcktMn3ZD9gzjiTa5156kWmiIp6z3mRf0Y+KHGZbiEhKbbtpeGxp6AeyOlhomlACf+PCeeOn1u0QpVSDYcKdv8vGDlfx2SH4ZsUDIA60ZIAxr/P7ldGu+ubfsg+MOvg8A0HfbgdlMf4xW653sRzq9jlgMFsVCCLjDfxgYonJEPzelOMcst/+UKwAAi+89zWGjTUHgJOF5giA/zxYERp+cggCKIAh9KEsqxBBhwW09mLFvP267ZxbGt95Bq+sddCUDWP3M1dhq02MBAE89v1D0UQ6B7GTWG2FfL6GQZm3mXKYNjUhQ++pOU+vUYVP1LNQdalQoCsxrptQrJRIS7LPLxQCAW5afCCSZADDtiDrZ/avnyn26hEaCBL//0wv4Hr5XWgzon0s+vo6vj3ohMKQ455zsRHmgiWsFynfchue3wbmTkPocGlkQHgDltoRAcN6qnviJD+/l2FK0le0itNxZpx4BsBc6FyK0BN/73nfx9a9/o0P2Bx/kGSAMW/z+5QOxZu21eOaFG3Dk/kuwYHEP+Oz+wMDbSIlKlK0JSAVAFDXkGgHnegC1hTD3bj5ctZLc/P2nXGGIANdMfmZHcTqUEwTmlqN5pd0pti6R5F/GqucIAl49I5d+6FSVl43jLiEIgDRsqPX/2fvuADmKK/2vemZXQsDJhDsf+MAEk8OBfNhE5ZxAAgwITDBgE43BZIMJlgHbOBIEGAGWjTBBK0BahAIKIATYRgYOkRFBBMPvfE6Hwdbu1O+PSq9ST3fPzO5qNU+a7e7q6qrq7uqq76v3XlWlA5x34unnf45SqS/22uXLeP6VewBFYHQ64sEx7j5Xrs2BGSmr2qSTBAVcfZJAnZZJTtY91psQZCMJ1YgDNPGzfCv046xCEixTI2OqE9Ii2CSBkAKSj2VqpOq/yjNCGEYfdK2+q/alZwoSkLSaPD2TInWlnTdIuTyS4NSBfuiHopKHFKwPUlg7UMuMQtpEyPy13wpp+Kw2vFp7H4tj8ly3ZAEeWXEJhu57JQCOnbabgFfefMi55Xh9jp0R2vCFTmg9CEDz2yoqTTLQlB4pf1w9CStX3Y4Bu52AvXc9Hr+eexg6O/8JQIABxhJbC5CUBRlQpkGBWYDyN8VFG+/wdUob0L7kzMBYdSwd0ynlJQTVphwliVrnbZAP1LIomU4ieLdhEkDPJ6UW3DVnMjg4pkyYjXnLzkal0oHO1g60tlTwzIszsNcuxwIAnn/1XgNE6TOwoXxAiwADZBEjCY7WQG45Pc5DEnSxHEIgwW+9SUIh7YKuAgQIuySBcVjOxepZknuLHWddGyFNqzDqwGvwsBp1JCTA0ySIC8L7HmFQz8QnCe99+DRuxs0WGag3uD8FpzS1Al0mjOy5bSX9MuT36zbtuk12JVQnGkwG0shG4RmFhL8A553ma6nYvgJptX/n7SZ4WoGdtzsYq9eoK+tNAJpkoKg0yUBTepQosyAAGLDbCbjzwYNR4R26Y2csQZKUkbBEEoEykkRpBspCCxB1BKZSoGH2LglrFnzgy3DwsJvR7pkE0WtiID8vIXCurLND8aQR06vmW03unSfml04jAfR8kpRQqXRi5pxJmDJhNuYsPg39KmuFlqDSiZWrbke51Io9dz4aq169z7teonSr27e1CCIszdQIoP4INoBwSYKlWZDbtDgmtwgo1xE4qS9uHtnCipIEV2sQJgk5TI3IsUUKqpkaEWA+4oCpWLj8QjFJAABqnmFG/e37iJsM0X11DSEkJL0+6JOhfWlKHuly3wHtzEvCuPtOnTbDuwCGqJpLvCvdFOoqebQNVWcUCokgAxXiLFxxpxrNKaZNCqWTFeRXi3cwmrMI5ZOmz0BTeoz8z+uH4MlnrsO+e52JX94/DrTDNj4Atl+AMgtKklKgg85Rt4NR/a4ge1Lir3JkDBGB8JW1EgI7vBghEHLI8J97qdMVkINL0VtmGGIr/iuHTI7Dx9xlpXnPQ0fqa8xfc624aeHkpwgBACxYfiEqlQ704R3gvB+eefGX2GuXLwMAVr16X1UNgQK01nPJaWqkSQQAOaTtaQ3M481AEizH2jBJEOFcPxpNlGlcbfrTAJJAyhh4qiZ1JvOrRhJcB2Z5DT2mDstD970cAPDIikvBkrI3hmvMirKFu8SBEgZz3sTvgz5wpQg5CF1zBI5Y57QCbWizjouWv0sJQWgmIdfW3yIHaeCXebthPwN1MmddcQdLGobZQtOL3gpgLDivYLcdDsMLr7WFLw3Irp+bHPAVAOYt+wZOnbIS02bSqUWRYz9PvFolPN2qmlGoN+HnpmagKT1C/uf1QwAA++51JmbMHgMNLixnYEUASmBSI5BIf4Gqg+UZR/XzSfpVE4fdlJEEVC9+zYTAi2uOD5aExRU91akFZEkKjl8CUSBAdbOKaDDO5Egv0LbgeD3rC8DxpbG/9tK+u/1L5EiYhXFUwCAIAcAxZcL9mPfoOWK2odYOtLRsiGdenIFS0oI9djoKL7w2y6RgkLN5a8x+DoYk1G5qZGCGVC2QEcRUkuCAZVPCAChPIwmx6+tBEggByUomrHt3piTNY2o05IuXYMmTVwAQGiOD4cmzYPbzU8CeE6JmhauHRJ+cQ3QB4LW3F2IWZgXJQBbpbdoERQJ2wA4AgAoq6EAH3sJbVrweR26uvNI6dId8guRAEwNfexCWwLsu+vq7HXA+iEd/exV222GlP51oQZk28z1ylJcMdAURUKRnBIRvQw+rww2QJhloSreKIgEA8IvZYpVOs3pwSZsDUQdhpRkQjsJyZiCFcOoitSeUhwjQXDP7BOQlBEQ5MHHYTVZM5XQJuKBfpsEgwLw+R8uhElalZ8RO3tZBULJAyzNr/pe9WVvcBaJ+3X64JB/m2plzDsGUCfdj7pLTUeEd0rm4gnKpL5596U78585HAwBeeK2N9KcOTA2SBB/e+iRBIcsAAbBIAtd8QGsaAiTBAvAM/vSnVk4hkkBLGyIJVa7PSRJCYblIQtRpOa5FGPSFi7DsN99FkpR17opUmBfjhEMfmLLoaszVKzBxCXGAFw60olU+u+JtRJFr6eh7d4NrVZZ9sA8AoD/6g5N/e2EvANDHz+E5ANnK3SXaAcaA3XbDURNmw27r6Vcq9lxi4FOB0LusJyjNKA0lDGIEX0znXFteO2wzVvoLBBYsq+t+LdIG4HSZ3g4AToWvIRDfwHe+cyUuvfTbdcq3e6VJBprSbUKJwB1tI4xTsJ4hKJGj/2VjFsRKsGYLkqsEcxB02UhhtKvwZcLQaQCymgVFM0FmQgCA81h8E67KBQDtS7+eEjWSlj3sr+OFdBDBLeOAXJ1YvSqmR3/1Wsf6psQCZMZk5Mhx9+pc7pp7qEcIAGDh4xehUulAa+vGaOEb4NkXf4VSqQW773gEXnx9NsGBBPxHSYJDEIAwSeDukkXMAZ+EAKSQBMvUSD8GhxBY4Bjk+WcjCVW1BplJAvTovk2FCLCn5ctJElytwUGfPw8A8NjvvidWHeeqVtkkIBous9fPmGgBVP2i92Wd5Xa4IgNpUpQojMTIIGBuQxsOw2GoyH/34/5uIQSKBAzGYHBwbIANABjQH/u3D/bBb/HbxhSqyIxC1F/AaurSiYF1rYwTnkU0NhlpnQF7YVCe15FYaAMqvAOq0fJn5rNlz52mBE2EzAJmRU2EsuwfgeIj+TvI7eYkXQ7gUtiEYDKAcwAcWDCfnidNn4GmdIv8v9cOBgDcMWu4APpqxN/SCJSJWVBJh6t1AtSUoVB/mde6a+H2HyJ2o1bLeN+EoTfWSALskfRMhADqtm2oBwATht6o9x9aeha52o3r5uqD/nCJZOyguZAiZ7a5kO5IZD4aZEqfAC5DTe4cnDHc89CRULbbR4035j8z50wSi89xjikTH8C8ZWejU2kJWirg6IvnXr4Le+4knJZffP1+775M0V2SYN9xTaZGQZJAwYQCGyFTIxCS4BKwLjAtyhyGjCRBvVekkgQGjv0HnIPHn/4hAOE3pB6F7bTJw+GUIBACoM5p1wfmhOsgQypeeH02FmMxWtCCNKm3KVAb2nAOzsHW2FqD62/gG2hDW5cSgja0YSzGahJQjQDQfwBwEA7CY3gsU5nPw3ng43/QcN8BocUEdL0IEoNYGyzjeK+b02pTf6kFr+kZhajPlpvekfDlTgCHoiJnEWJMfmVc9b2+viQmC5afD1g+JvUmA2nvK4t8D8B1ADbLkG4CAL3Gb6CpGWhKl4siAr9oGykcgBMJ/pU2wJohyNYUGG0A8z9C7gJZIzGAly7ZP/J6EAGaa/USOrEYtCmPIgEPLftGSqKBXMyAdRD00zz84hjwr98CScijFIyB8QScVQx4ZGqonMvkzKi6phVM+BMoYqCciWfOOQQzHzwYUyY+gLlLzgDvKwhBK+9EudQXz710J0qlVuy2w+F48fUHvKJ7wp3yWvFcLYIJM1EMmLBoUl1NjdRDBvwhSn/6U5NaI0mCybsoSRCPiWO/vc/Cit//BCxJrCKa52cCOOgzMed84mBy9AgCJ0+K+c+jHOku8xCAPHF3g1hNdnM5SknBdVeKIh7KV4KWI40AuGHDMRyLsKgqIXgdrzf2hohYX1OQGLjvq1rrHB8xLwwZGwo23XuZiZjTbIV3iLvnZBBOEQNyd3vvelxQK7DdVsMx6qBaHIcbTQRGye3GgbQ4gNtgP5veQQKUNMlAU7pMFAm4fdZwlJKytUBYkrgLhyWWloASAMYSBD9EZhr3YMPAa28urMzk3/FDb8BcsnZAvVKvPoWoPXo1fugNAIB5y84OgAYzMm/S93M0m4j2wEX5wZKqo8BWmwtx6VDMSHIOCWCc7AuAqAiBTAa/nnsoOLg2FVKEYPaCr2CjDT+NCl+L1paNwbkYzXz+lbux+45H4KXVD5pyWf0/IT7e/dDoBPxXIQkWGO0KUyOZpgVuzIkMgL4aSch2fVGSAHDsu9cZePKZn4lvn9yyeK7+N2E/Y/OEbY7E9bvS+XH7XIxUAHEyYJcjXyug4u+LfT2Q/B18B9MwDZtgE3IHXU8GAGEeRUF+bFuNJIzDOLSjvXv9HgLgmn7rFhkkj/vPf30Lm33qc/jjn19zrm7QO2kECWhvB/hRAJsZOJl2H3fhN8/egH32OEWXzSIGrHrd32Gb0XLVYfX88oD7rPvHoriJ0NcA3Ad4K4vHnkuTDDSlKblFmwW1jUC51BrwCxAzBAnNgDyXJFDmQIoAMEkKvO+Qpx4KqUYW5IUKvuqoKTJ+yA3OasL1lnRCMH7I9Xp/3rKzyVWhEXyXEKQB/njeBrA7hKCAuZAC9lDR4JIAbsxzRO+j0xWEIAF4RS5MVsGUiWLUf9LI28QzefQcVCrGuZiXbbOhl1bP0R2/Bf7JfWQhCXFTIyceAM8fgQXiyDLY1S9EEgKmRuCRqU9NGjZ1yAfyRXgdtQbBMIhZwizh1qNyKJUfT8YNEQhznt6FQx6ctShKsMtTr+lEQzIe4wEAG2NjXTYqMzCjS0yF2tCG43Cc9pXIQgjcrftvMiZXLfu38W3w8Vc22FTIHkyxQ+OkwO96ctYDT6PN4+eKpllV/EGCdJF+A5UOKA2ursvk+L/2+GpQK2BEzRyXB+jnJQVF5Fi5pSuLV0uzl5GB3mLv1JSeKx++OhGAmC1IEAHiG5C4TsHKZIhqA4x/gCIDHhBwqrFZRVaKRxYiINsCey5QtmX8kOvlsuqN+YbSaYDIf96j53gjmOb6CCGwQF29/AdUFAP+9dUksp8CMxg3SghgaQVEx6NMh0QEDoBxhplzDgHnFRw98UEAwJiBP0L7kjNR6duJCu9AH15BqdQHz700E6VSC3bb4XC8vHqOLr6Hq+G8XQ8bKCJkPQQotBp6+kHwT8iXAao2WAmTBFVhq5kaAcYfwYI7sE2N6I2kE4JQZ10PkrDPHl/Db5+7CQkrBeqvykitYUBTTSEG5FHG4tqOx4ZEqCBFBmr1C8hy/VfwFczGbGzojFJ2pWagDW04bcpKtM7MphXIuuXgmIIpqYTgeTzfiFsSkhHzWF8jZYnOui01t/5dRgBcyQOm2wAcIxcABcRdM+2vJkLi5fnslgfJPTo1aV6Qn4co5JUN5LYFIboXFtU/9Q4M3dQMNKWh8uGrE3H7rKEoJa0olVq1D0DCyjb4T4hfQFQboMLI55ry/VunPLKg/qYRhrgYIqBi6gkj6yo+IDfagHmPnkMiheF5kBB4nVkoDqCwkT9uxiWA96cbdamGAfyclNEcG22BTNYjBDIdBgnMmJWMRTKkloCxBHc+OBGcV3DMwXMxbsh1mL3wRGzU79PCj6BlI3DeCS47gJ22m4CX35ir0wH856khbGSKQZ8kmGLbzwIEkBK4wfx46STBgbRVTY1EWPZVltUzQApJ8IF/bD8rSfiv3U/C7/77Fv87t+LS2+S6nqJaXFVv6S165TVx1Xt5etVt+D1+72kGrGLU8cs/AScAgJ6tpzukDW3YfquheH3mYpye0UQo7/Z4HJ9KCKZiKvj4S6prB4rMKGSJX7fdMxwMn9p4a/zpL6vhfNj0Q7Xkf//8GkYddC0ZKa9DHakb8MwytacvnZUO0ndSIMyw395nRbUCO29/MKbNXJWSVz32T0ZxE6G+cqsgcRZiEW8P1kVpkoGmNEw+fHUi7pg1HOVSXyRJC1k5mDoE29oAiwA42gBhp+gA0JS2Mb2DVqDIxOHM7QjCZGHckOsIEegqYRg/5DoAhATYp3MSAjcqDbCZQG7/ActciAB+ieB9cyF5Rq0joHMzsw0ZEuCaCcHaQqah6s2vHhgPziv48iEPBZ/qy6vnIEnK2Gnb8XjlDQE6nGoB6zAzSbAJF4W/MZLQGFMjRVNJxFRTIxOWamoEwLmIpFiMJAzY7QQ8vWo6mJw+1L0mSgz0bWeIqyPkIRFAgqSugB+It0+KBLgzF3WVVkARAQDYfquhGHXQtWidOcMqQxH/gdB+mqzEyrrdU0j6b7xV7ms26b+dIQRQn4NDliO3tUn/bXPnp6VeJEDPKGSTcHv/DoSdiKfjuZd+hd13PFI2N6p9YTjg8+dGicB//Pt+cq8zkl+W/Sxxi8p5APYDsAxqhqBs6TfJQFOakiraLKhtJErlPiglrfasQEniEwIkcQJA9gExIl20EUgjElU7ewaMHfSTCBEQHYLTLdRFGAQBCZIAN2LmZ2PAuL7UT4yciBANhvA6BwFVelVzIQ36zTSj1gxDBNZSMyHqiMy046xaoIwBSPDL+8eCc45jJ83DkievQL8NNkNry0ZobdkQZfTFq28+jB23FfNvv/JGmDjkJgnMvkt9RYQkBE2NkEYSnDy5EycQz5CEWk2NSBxzwyINRr8kGjtOEvbe9Tj8/oU7UkyD/Nqpwq33kpdE6Kjue7LjJ2oawbp/3b4Mx3AsxVKUEDeTahQxoESAinIgrgb6Q2FpcU/BKV0+TaqSv/7fu+TIf56iWpjw/htvhT//9U1s0n+7QvkJEjEg+wUNn0UoL8AW2oRKZS1EHw1k6e0e/e138ehvp2fMryhBOB3FtQJq7ZBBGeMrotTLphbtLTfSlJ4hH7wyAbfdNwSlUitKpT4olVrIyL/vF6BWEdYEwCEDTK8rQOopc/v87B1jLZ35mEE/7nKNwLg0bUBIIoSgJodivedrAew9N2YI6HOYUW1zTLUF/gxDarTfnm3I5itGYyDCEoBVwHkCoCI1ThXMmD0ax056GEueuhKmI2MoM4ZX33oYCStjx23H4tU358EzeopUsyhJCMSvG0ngbgo0XhaSYKNnlRbjzvvPRBI4+SYjpkYpJGGvXY7BMy/OsE2DmCh4DApTsYhBkLOGiUEeIpF4I4bVJWtbsyt21WD4AlwAQCzsRWUZluXOP6/siB2D4U/8/qfYb8pZ6D/z3iDwV9s8pMANi8m1uBZ8/Ln1dSTOgHlcIkDlT399040ZPNyk/7beaPmpR6/EtEtSzL+6Ao+1twP8OIDdLgOyEAEAmAfgZOFEDEDNKDT4i9+u4jQM2FqBehOBWomxGhNflRKHvpdd5TZ/m9CTpakZaErd5INXJuD2WcNQLvVFqdQiTIOSUgD8p2gD5IJicBYWE/MZm1FIinuKEoNiosBt/FxajDySSRsQlToSAsecx8tDjcw7RMFWDtjEwNq66UvNgMHH4SlHLd5DCke1BQpZCp9mUacUIVj61JWkbEC53BcsYVj99iPYYZsxePXNh72EQzjTgF8HagZG7+tGEhh3cguRBLkXIAnmUwmRBEoQRByfJNgkQvzNTxL23HkKnn3pV5oIuLfETEGzE4MGEIk0YF9PbYEyDVrlAJNBkVHLeo6qX4NrglqBv/7fGgD2bEq1kAI3LE1WYEWhe4nK1VcDO+2Eow+eQwKz9xk85ahQ19Otg7EusM52A52VtVAz+g3b7zupRGD1msU4dcpKTJvZkZJv3n037BwU1woAxtwnL7jvXQPpTTLQlLrIB69MwB1tw4VGIBFEoJS0RPwCQtoA4zRM/QOMVoB0uQ4B6ApiILQCp5PEG9cQ5NYGuBIENepUkRmGQLBdVv8BddoGs3Y0mppPEhSwS5tyVEFZQwDo1jgUAxVALnIGMPxi9igcN2k+lj71HZ071IrWCbB6zSPYYZvRePWt+SJzQjSyEAQT164rHAhWnXSS4MTTGfug3SYJZMjbIQmMnvZIAgH3OopLEmScmkkCfCLgVi8ZyCJRgiGmKDAQNP0a80zo81Ln8n3vRQmCWsvAvf4FvBCMv6sepaxN9sAeVeNMxVRchsv0sW30VZwUnItzu81UKFqTqnYdNRCBHmONwREH27cg7DcwGa+++RC233okRh54TQaNADBt5uNO+nn3s8StRVT7k/e99JT3WB9pmgk1pWb5w8vjcUfbCOEbkJT1VmkFmEMC7BF/RxvgkgEwyyZPA0MVkJkYFJcxg35EiEA1EeCvCF2omQRQiYFzxMC+DdptgkABv3sufAUFnXFzIVKegLmQJgweIZDpq2JRYsAgzYy45VDMdHxBPMGAX8weieMmLcDS30wldQ5g6AuAYfWaxdjhs6Pw2lsLKLL06lUmggDY9dM/lUIS7Cdr4WrvsqwkgevE6k4SuBMnQhL23PEI/PfLvxbfvnfTVHxCFf+2aNngvCP3uUSus4ssD7umn6zX9KV55QpcEdQKKJn/2Lk4bcq12GJmOUgCQvt5tAdpcj2uBx9/RgPWHEjLO8e5emHSLpfQjELZbiYLETBagSyOw0XJwYWoTSsAFDf3UQNTvQNDNzUDTSksf3hZLIwjiIDRBiRkdWHbVyCdAJiZhJi1pUK/u3zEoFiLPWZgjAgUgftxqc0kKCK5CQEs8OSPm7qw0ScLtZsLkZIxYZ7CrdI6MwzBK4I5p8kCwOFqCSQpAPDXv60xZEEmVpYHq9csxuc+O1IQApKVixatZxUD/XUiCWawPA9JMBHoW7Ruu14kgQXiOCRh9x2/hOdfuSdsHuQVOg8RcB5yjGBFiQF3I3apdKWjspLP4/OZ4yqykpUQxLZu2CW4JKodyOSvkXt60RRKGPEVSLuueLxukOiMQjEQ7spkTJvZhu0yTMY0beaSSLq1koOe8Hx7BwlQ0rs8IJrSZfKHl8fjtvuG4I62kYYIlFqQlORWaQaSslxDQPysWYWsY0UMmNlKQqB/UOTAPqfNOwSlkMQiEl7XDzjWIKWNQPrSECJgSdZ7tpBh4FmZ836K9EQIgXnj1yQs9Facc9SJnOyrGS3MbFP+e7fqClT9EnVuxuzRmDjsZvzlb2vw8Sd/wj/XfoR/rv0IHZ3/QIV3oMI7NSGgdZHmYeqXIrUkTrAeOjXSjUuuif3T90l+5jtJ/LiSeFvxNCl34iESj/nx6P3SeGBOHNhxxLtLIj83TbEAIUJxrXulfkhpP5Nu4p0reb+ulK7WCADAt/AtbL/VMIw66NrUePMfOxcX42KUUEIZZZTkv2r75Yz/YrIIi+p9y0GpTgRiF2UK7IHCIbQD3PmpsHpIB4TzcIezHwrLcp6GXYLatQJU3OcQ+u1c5zx7jjTJQFNyiyICpaQFpaSMcrmv9hUoJS1IgZ79lAAAIABJREFUWJn87FmEwlvHoZh26JQAUDDmArEYMUgjDFXuM64VqJ80nAik3GT4CeQhBIFzcHE/M38J+DdhTl4a3LtlYR4hMKTQTpXRuJbPCdFI6fqU4BezR2HisJvw1/97B5988if8c+3/Ye3aj9DZ8Q9UKh3gvBNvrFmC7bceYfK0QHBegpBE4vZGkmADfDCG3XY4HC+81pZCAuxw0PbB+oXjVycZgbRTfvUW7vzrTtkf+wMAyqW+WLTikqqEAEBmEpCXHEzFVLShrdG3HJRCGoF1mQi0twP8a/AJACUHaTIZq9csjp4V9egqpIP7IoSAhtVL1L1WMvx6rzR9BpqSS95/aZwmAknSgnK5DxJWkjMHCfOghJUkCDOjcAK0pc8WZICdAXLiCMRsQQittrQNpwDThHMCNKVNumNi5HbK2YlAzHBBhKeZNTReIyCFIZ+5EOy4wTg6SpZ0RRw31SLmQiaRyDSjnv+AvQU3dUw5FDMk2qF4zuLTTOViQCuT9COBJATDsfrtR4JdpUtuYIpqxwkccG+HRnFqEQs9cRQzNwq8Q23WE1pIjXyIRcyNdvvcZLz4+v2yTQjUOyttUk67wO6ZKkLroR3qJemKaku6GORlyW8X7FKz0+25OBe7bj8JJTnX+tKnrnRWzfVl9ZSh2GHmsmBZs5oMpe2H5BbcAj7+q3X3G+D2n1iMjMHp97BJ/+0w7dJ+WYvWRdLpHNN7+BnCTsTpMuqgazFt5gAAcwPpZjUJqhZ2Ve5yxUWl6z4LJentTG/B0E2fgaZkFkEEhgoiUGpBudRHjP4rEqBWGU4jAGmzBTnEQIgPH90jJlChDU5gPtIgINOdPDwQNXrgDzF38ekkcjVJg/xh6TIioCQvIZDP1EnA3lcgO3Iu++xCKixUEkMS9EJjjHkzDFk5OdnSoqisPN8BJhbJO27yAkEIZI7qvZYBIGF4450l2G7rYVi95hFY75yHnmw2gmBFCRGEaPwYSXDC00hC4EQMIKeShMyrLavnaqegr2PRM8Fr4hK4xiEaoWce+5brRQaqpaPOd0aBSf1kCIYAAJKkBdWArJLttxqK12cuxs+wXIflIQPV9n+AHwR9B6rrcfNJYRIQPZX+/L6w52lZitUNoka7Q+C7mnwLq9csxnZBx/PLnLTqRQLqTcqpJopqG7LUt64dIGi0NMlAUzLJ+y+Nw+2zhmp/gHKpr9QIlKXjcBksKYGuHZDVQVhvLXIgxIwnxRoE7mh3c5xzgwB0dv5Tx7RAUaG+yAcWdZ0xqJDkIQQmqn8VIV2Rczbul2Gwwb8Jo3mlzy4k0iaEQJMEWOsPkPXNrHKacjFCChKAcdzRNhLHT16AuUtOdwCIcSp+452l2G6rYbaanAWqSF0IAvcStwB/jFTQtHVgjCTY4RSUe2nHSAKz64O+gnyAO29/MF5ePUeb3rhJsWBopDBBoQ88AOiDRINca5116Ui846+n1qArycCZOBN77nQUEjXPuryN5b/7QVXtQGjNgdBxUa2BK0UWfUuVqEmQH55GyrMCwp4LG2k9ywu6X/RChFbgvgzpZTnXFWRgMoCzIFYfFisrZ5ee+1aLSJMMNKWqKCKgQH+p1BdJUkLCxLGZPShBcL2AIBmADf7JlmoFFAY0YJV0IjzeSHALGEQ6K4LAOjv/CQ6O8UOux5zFp0JATAIHwwOGTv5BKKbT6XJtgCthHkBOxxccq3Y+bG7khhPw7xUqsE0zF6J/KeC31h9QxAK2uRDE24UkFEbNKyDHHW0jcPzkhcJMjJm6aWAik4RAjIi9sWaJDdDp43EfSI0EwYuWgSTk0QoYghA8VZgk7LzdRLz8RjugbfDdRdOqlZYmGjxA+IGn8XmnLqeUpZLTXrgIQaD28h11tYn2ZRRGAQASpiBAyosPCCUD4ursQL+IqVA72nEiTkwvVJYZhQqRgHhfEs0mEDbtzgHASy9luj6X5HV6BsiMQq6zsLv/fWQ1FVq9ZjGmzaQEsggByHLuukzlyS7q216b87peRgZ6i71TUxoj7704VhOBcqmPWEgsKUsSIFcZdpyAPQKQwSSImgapv9anRkCpv7qo3VjEtAE2CRA7nZW1OsAQAROJ0zLFUUUVEQCtW4mAEncYPssFhQiBzIPkN2HojfW5ByJt848TT5doCnTWTBBGV1MgtozEEdoBZcfOkOCOWcNx/KGLMHfJGdAvnAkfAjCGEhcaAsYSbLvVELzxzhITjzwenoUgINyfhwiCiBsmnnUhCb6yQSdSK0mw+xqvgFVEV8BwGn5MJ1aUglUREScrGahFSzAZk/F1fB2DMRhrcwOTfPI1fA0Ddj3eaAUcefKZn0W1A4P2+RZ22+FQ7D3zaQD1JQJd65tRhQCQzaf+5bP4019WF0ixzpIG/HfZJcfUqkrcNQDgHFe7o8mOqdA/U64tQgbceDeh/rP5KO3IJ3KbtYPfDd/5ztQc8Xu2NDUDTYmKIALDkCQtaCn3k2ZBxlnYrDBMZgWyCEDIQdjZapIAuB+VBeJA7MIZZKPokADQoPg5zjvBeSVDx2NAlwW/wlgsemL8kOsxb1kPIAJKUghBrQ7F44dcH812zuJTiTZHvRPzHpU2R2uAOJmPXMfjOg3OOQ4fc6eXz30PH2NKF7pNiyxAOxQLjJ1IEM5x+6xhOOHQR9C+5EzyVhlaZRJJIurvW+8+hm3/YwjeeGepSV/ekdYnpBEEqDI4wkNvSIFqhw7rCh4iCSS8G0jCTtuOx6tvPgxvcTErw2rfYjbgL2JmAP5p2QWyipGBvOC1WnyVzz/wD1Kc+oKNCZgAgGoF8okidSWUagb/oetuxI1Bv4Ff4Bfg44+rkxOxB/vNXuQVOUMg0XPhBArQhCIj/nmkvR3g5wPsaqSD8amoph0QJpOnVkkHNZ67LbUMxUWRgY8Rb80YgHEk/8saUI7ulSYZaEpQFBEoJS1oKW8AlpQsZ2FBBAwJ8GcQYhAmQ4AN+kOaAilk9iAb2MsDgoHErvib1SSowjuCcQBhwmO0AlQqMjdnZqCMhEAQgbPlqciwcLdJDkKQ4lA8bvB1OrR9yZkAaGfPLXMuMYLPTS6M6ZetfAXEwmWwKoLJjel0GQPunTfF5CfJwxFj77aKfs+8owJmQs6W+g4AAONIAEMIlp6J/qS+6nqbiMk0BSEYjDffWUbu39YI6HJnJAhBLQIPxw2RCUO2wpUuRBJsYBQiCf59ZSEJ1TXQ2cE+Pcvsw9S04tnxUKAlaZqBrE7BWc4pX4FP5ChljAiMxMjCMwnphc2SsFagGvlY8/4K7Pq5yTqdevgIZDEXqicpsup5rAKnX6VLFYqzaf/tq67Q6ydfA/iviTgoMJwVyLsyGYIs/IOEZQX4WeP9Co2b3/9mAKMA/B3pZIBK4316ulqaZKApnlhEoKWfBPtl4izcYpEAa9GwKtoA11zImj7U2jPQT4A4A10MUKQjOXESwHm2RVTisMmctWBD+gXhNHoKIQjzAHI6Pvo/dvBPreCHlp5l4spRa7MSMXN4BM9FCBxfYygSYE8pavwHGDjubv8SlHaBc46jxs/SZb37oSMccyFOrKDItKN6JViO22cNxQmHLkb70q8DGxsy0KoKkDAkjOGt95Zjm/8YhDffWRYELl1OEOAFmSxSTY3SSIJ7kE4SdtxmjFy9uVilT7/KfzZxSfv+3Qv9hIqSgbyaA2Ue9DE+JqWpX4MxGZNxDI7BF/c8HYnb/TvZPL1qeqoj8fE4HjMwA0BxQpC270pdnYiD4NkPy9Zr1Cv/LrzeklrJAGAIwTE50ipKGBolH8lt6HtbD8hA02egKVTefWGMQwSMRkD5CXhEICkh02xBURJgjSHCgEKQdoDr03p0V4JCAzbJrEPuMSIAF2Jk+8FHTgkPr+rEaGHImGoQU4lASytAz9VICMJjUQUTSvEfoM9r7KCf6PB5y872nqMCLBqUMvN8xLuSZEoD+jghUC/ZJQQuqdC3oIGrebZMAmQG4K65k6H8TKZMmK3L/Ov2w0Qe8hHY046q+xL53HbfEHzlsCV4aOlZYBtvpc+30nqcwBCCdx91nqa4H1fiBMHiCvH4MmKYIIRFOU8HLslGEphzTfDARPMX7uqqzp1KkQ/OXBMiA1lJQB4wfytuxViMxd/x90Bpau+rFaBOIlqBrM/ppdcfwGlTgP1n2u+23loCKm1owzEabNZD0sA/9wM9AB7+btOzLFD3G20ulGlGoW+huiOxIgSHZ0gvz/4DVfKth9wE4BQAP5bHzNme5ZRBPLPehJ+bmoGmaHn3hTG4Y9ZwlEqtwjRITh0qZhDqAzVtaMJKYEnIV0CMqKZOGRr0D2AWxhCLghnTEFc7YOFyQhLAqcKgkQ2oBLaI9wHjh1yPh5Z9QxYtAN8LEoLYmEUjCMGYQT/W+9r5mesxdHFIgSVXY/NACNQDNqBXDr6GEIiy0OlEY4TATofMIMQYGfn3CeXMOYdo/4OjJz6o7++uuZPtdJWWS16csBJuu28wvnLYUjy07Cz032hrqJmGWltEXU9kPm+9txzbfGYg3nr3UQsShhB7nCColwPrujBBCAgL1NAUgiAuqS9J2OGzo7D67UcC9T97xc9er2v/3lnwHQmhZKCon0AeMP83/M27xjNRKyDaRIiVZFrmbyjZZ1/6VVg7EPEbAIoTArp/B+4I+g3MxEzw8VPifgPVZhQiPkvmr73nH7pxab0PX15Y8gL/WoiCnlEoCxnImo8iBBMzplMtv3loPBEAgAUQZOBP8tglA670Qs1AdxegKT1DFBFoadlQriKcGLOgUqsARkmJzBxUMr4CCJgARRcXU8Ic/ED3TPdpIV1rVhs14sysqSSFhBZSoWJijxv8M6EVAMToMcva1aoOQoBPetH4ITdoIpB6fc0mQ/YzM6Uqmpa4WpGAhx/9JjnLPOBvhQMwJkKqRAYkMlk4bqIZkC/PUV6SlxDAuk6WS80YBMh1C+w7/tWDE8Q75xxfPkSAi5lzJgkths4zkQXlYChh+n2DceJhS/HQsm+gP9tKZ82wEQDjVPz2e4/js58ZiLfefcx70hbYrwtBkM+5JoIQp7bFSQKC1+WR7FfXd4TObQWqzfufpdXIqi2YjMloQxu+h+8FycB5OK+wv8CROBKnTVmJ5GWqGUh5fxFRZQn5DbjH9TQVqpfJlJdHAPz7R6G9jM8udEtdCfxTpZ5kABDg/UEAozOmF9t/BF1DBJR8E8APIRyhGYSpKIM/lenXARyAqVO/24Vla7w0yUBTBBFoG47W1o01wNeOwqVWxzfAdhoOmQaFSYD8RQiAdyyH+DXohwTe8tjscOcascMVIXDjFJY4WKISIgIcPNKJZUszfEX42YUNoaqIfG6aBKhRQA38aQ7wSEHIRCgcbjsHy2giFgvtZycElEh4+TMIgG1dY7QYjAG/vH8sOOc4dtI8AMCdDx4sSUuimQyDcCqefu8gnHj4Msxbdjb6b7yVrt+tcssSUe8FITjIIwQhTZEFLjITBPW3kQTBSdi6NE4SPrf1CLzxzpL6qNEbhYGUsOiBlgNxYOHkl5PVepVk+Ur/JEcp66ERAIApmKL3GSs5qeVL+813l2Gn7SZYdvxpgL5ehOAoHJWrnFHh3o5/yjsXaq/Trg+EZgX09Y4Xk/Z2gF8DsPNUgjRxZ/8cZF1zQMR5GMB+JCylv/fClmfMp57yhtyugcYreDBQjsZO+9td0vQZWM/lnVWjBRFo2ViO+lMi0EeCfl8boM2DYn4CZB/MHt+yhRIECd4oEFTB1vCxHWxrByj6iXdIgKMVMJFyDthLqG/1E36nESUEZm7LOoltqJNFxgz8EQBg/mPn6RTsJImmhtt3pjGqC/6DpkMgJECO3zPzmrx9kbXMhxACqPceJgTWysP6WJZd50Eqki4Yx4zZo8HBcdyk+QCAOx+cCDVKRE2Gpt87ECce/igefvRc/MtGso6rlbdZgoQzMFbC2++tEITgPQkIgyAknR7ECYK6Dxqq/tZKEAKShySknM8tXdBFBb9NWYfHDvoJHlr2DbSj2JSW4zAu9zVn4Axcj+txHI6DMFgTmtbpmF7zLEIvvNaGPXY8Aqtevc+JwfxdzrHq1fuijsSH43DMgnHQzwrui2gFALH4WJHn6YvbRgbydQaStL9TKDmm/3iyzx6n4O72LwEvvJBSnAytdUN9B+gidyESVFT+LLfViIA6fl5uu5oIKJkMoBXQCwCGyiHIQG/Dzk3NwHosggiMkERAAv2kBeVSq15ROEwCShr0BKcKTfUPkMcOAVDdsQB5XDoGE1twqG0V7YBGf+LI71y407a5wD00+lPtnID6E4bcgIeWnqWLkVkaQAjE3/Su1ZCA863r/MafQCUmSZruGM3ou7rU7Jr7YhL80xmG1KuwzXJkPALuQfYp6YpqCOATAyYXFtOXKwLJJDFhIL4NHHe0jQDAcfzkRQCAXz0w3oDchINVSrj1noNw0pcew6IVl0jTOrUSt/g+OJi2zXZeDbwK4tTj+GxE5Pp6EgSSvRffyjIQ7pCE7bcegTffXbaOdJZ+Ges5ew+ATCTCtY9/D+8BAN7G25oMLMKiwkQAEOZHQ754KVjIb8B9pXrwxSXNRl558yGcNgUYMTOsHXCP8xKCu3G391ymY3ptZECtUeLek9ePmKPNPvU5/M+fXvbChZhvR7UPm22yo0WcOIBTj16Jad/qa5Uha1kLn88lsWm33ePTkE878KITtqXc0gr3rnNNT5C0cozFd797VZeVpKukSQbWU1FEoE/LxtoZOEnKaCn19UhA2EwoAxmwJEwAAIUXCYClMwTZTAAGGZoUBIg0M9UEhdPOBhg3+Kd4YNHJ0MCUljeX74CQCUMJEdC5uKAsZi4UjF5FslwQnj9JkYAFyy8AwI3dvkq2ChlSj4s+bk0ACFrXo/oWKbCz0bxN1g1XS6DzovsqHcYAXjHnQ8SAEgIoQiACLGdhTUzkRfLJ3T5rGACOEw5dDACYcf8YgItnliQct95zIE760nIs/c1Uvf6GmWVLfBtvv78Cn93yQLz93vI4OQsiMvKAzZN3otSJIJAoIYJg8grUDWat020nZh2tI+TA50DdIpMxGUuwxDouKsfjeADQ7TUQm1FIDsDo75ibaXwdUaQiZipU7TjPubqL0x8gchQOdweP0q80LDvlnmoF/jUTgw5UJwJF8nDr7HsZ4vRkObK7C9AwaZKB9VDeWTUav7x/LPpIHwHGSiglZZQDRMAiAYkA/zYZoOCfmAYBCBMAIAjAQTQDMM2O7I68c5bpCQC3odIwmFfrYCIQPQchmDD0RrQv/bqdikGsGVKQF2S0T8rHG8yTGm2RAHPe/HUTJk/MA5TyqTECErkTQ4FLHg9Tt5xmKqS4hAbx6joOgCWycJwQA24TAlBiwPQ1av0J/YQsRsAAnoBJ8nDbfUMAcHzlsKWYMXu0pBUlCZqAj/7+AcqlvoIMJGWtWUu4dCh+fwW23vJAvP3e46GXFK6bPYgg6PdtZ+8RhO22Goq33l2O6GrDVHoQN4h/6d1byFoIABUzi5AgAy+/MRc7bzcRL78x10Tisi1U7ZZ17MvqNYuwwzaju5wMtKMdfPy44jMKpabPnfott9yJg1CbmbGuNAL477ln6v1GRc8o5NrB88g+AJyA7NoBV9Yl4B+ST7q7AA2Tps/AeiZrnh+FmXMOQbncV2gDtGkQJQL+OgIJK0HPEqTIQGAa0SIEgJrIUHtv7eipRv0pS6BD0i724WQ8XKZhi3NMh52dcJ8Q2OBHEQFxxqEVAdSeqh3IQQjyiCEBF8qQ8MiWLyzQz9n+A9YpAt4peAz6FTByLbX/4VwzBeU7ok16AM9sCNarU8SAQcwoJQB/iBiIOpqIWA4p0CPjTITS+UmV8/CM2aPBwJEkJR02/7HzPTKgzYVQkoTggCAhSKsT3I6IQKUCfY/1JQiUlru7YYKw7vUpzNqY3XXtPuIyHuMxbL/vSJJm3r+eLQuqrSNEgBy/9PoDUb8BOqOQ31oWIwOh47qI6gtq0gw4Ifq7iVwbyNM7F5NatAm5JOYzENv/MooTgnVZjsVVV12N3tQ2KKnjkn5NWVdEjfQnrIRSqY9HBBJ5TjsTOw7DyjxIjDLRLZlNSP6EI3FikQXlXMzkPO06DORYmZVYTshqCwvPmPxCn6gDfDnHuMHX4f5FJ1vhau55T3ioS4o3wlni1qOT4+Rvmowe+EMsWH4hFiy/UDwlBvK0AJB3Ff65Yt6VeIew4mrH8di7ZeZd6vfmapkY1UA5DuokfXcfbh11TN1UXnDyTZISkkTFgznn3Ku6x+n3DsSxkx7G8ZMX6u9l+r0DMeqg7+PjT/4X/1z7EdZ2/B0dnf9ApdIhyUYFDAxr3n8CW295QI43zSL/rCjWswXz312VFMyz0b8s19nPkpL6YJy8P/ee6vzT2k3Sbpl7TOxyrMNyMkRbJ76VBKaNBvSADh3gQfjYlVEHXQtAOEgngX/i6vhxln8P4AG0aWfO+gkn/xD7cU4GklLieefdvNQO939WxJTzsXOhdPJKezvA74DQDqyFIAYdVfZ754w66TKwuwvQUGmSgfVI1jw/CnfNPVSD/ZbyBnrqUEoQWCK1AQldVCwEtEg46cSjYN8KS+zOl8aNEQHShdmggd5lpOPmQv3LSePtj0PlIwRCK3CmF552WF14gWvCMnrgD7Hw8Yu8d2GDochPp1KNJJD36tYBSLLgkAL6vt2wNFJASSXT751cS+sZAgSWkXUySDnFPTjkQd0jM+fEoQi/9Z6D8PN7DsDxkxfp695+73EM3e8KfPKPv2Dt2r9jbcfH6OxciwrvALeARZbnWk0aTRBCADr9um3/YzDefn+FA7jpYZU659XBxv6zAL+uc+p92+WYOPSmOs1i0/WiTYRIfVcvUxMBMBhiQJ4/OXZl/mPn4t0PfqfTzkME8hw3RmzAH/yB41833Rkf/nFVPA6HjhsjA5tvsiOmXdhCsg4A+zzAPxReF3HBvrvvkoJDgQYQtZ4r35Bagd4pSe4GuvlbJ3+XX3aVfOElsKSMlpZ+YIntIJwoEsAi2gBrtWHTYQoxeYE5YD8U5oANBgr2oY8NYFMDdCQ+M/HlGUdMiBoFGj/kety/6CQSSkSD/qyEwKRuH7kxC2gHMrTvsSijB/5QEwFfYiDUeXrRuqRhhPejcTxioOuATQxAwmmYBv5pZJSCF+0YaaeRUIAf0RgwdZ0OJz8NRpi1roaqgz+/e38cf6ggBItWfAsA8NHHH+Afa/+GtWs/EoSg0iE0BNL/QGgH9o88+ozvJygs8M9NunEEIXR9vPxp91ng55Ur/DN11AH8ug2yCQJt6xop7WhvyAg4QB18ma4DVrtNvl31Lan7pschYWBiEbMagL97Tp0P1GAAYkahwqJmEyKA34D4it7ncMl7MDHzk+n966a7WqZUn9/9JJNvFtCfBfinaRdqkmqagRBBWL+ku3FcI39NB+L1RE48fBnubv8SkqSEcrmfBYzoOgKJBkLUSdgFZkyDIgP8lMjmmwXCVGdkhZlwpuxUOVL9BsR5NYIuI4bgtTolc7MWItOZO5auajoaO5I+p3wIJgydhvYlZ5p8nPhOFsjtTFwwrk8C0iiSm1K1PNULiKXBYNYPMG9DOwTrP2Z6UlId9PszlzJyIN+J9imwO2LQ2U/ARV2iSbAEZu0K5SNga4pEOhVA1nfORZ2rcAC8IuoPZ0iSEiq8Isx/GMMtd++Hrx7xBADjU7Bg+QUoJX2k/0AJSUsCVkn0gmRr3n8CW2+xvxhJ188l5bFnj+RICE5xJ0m3ntvppafgVoaUMrK0WI0F2764JAhOmxWPuy7J6TgdwzAMIw+42nmPDK+9tQCf++xIvP72Qvk9qkkBePg4JDI45jeQ5dhOrtCyidll993tnHVWaUM9aeUR56JtKnd30iLVGF6zhKYXje2r47EAHkLv9x1ow9VXX9PdhWioNM2E1gOZeqVYWTZJymgp9xNOw+SnRkETVkZ4FDVgFqRHl+gImhldssYI6cgc+QcnXG8NdZBSvTNWNv+cV0zjndoA2wec7le5bsLQaZi75IxIWpHAkLKhWieTs803RMAfjaVvpPqoqf2e7DeS/rNGXiFGWmkYVBitF1bpSJl0iByhDNZDV2Plxwv7ElDtADEf8tIva7M69VPlSpIylEPmLb/eFzff9QWcePgy3HrvQRh54Pfw8Sd/lOZC0n+Ad5D6aQiB+5y92p72yPNF0nH9f25STv1w0nGv3uYzA/HOH55KH30K3mm8jPX8Wf8CZaP10L7G1YCuW+ISAPt5MxKLPAt9TAaBwPDqmw9rPwE7Tfq0/BF+VyMQqn2hfypuSNSMQlFRMwq58uijAGR/of3EHI2A1gyowaPICD75+WaAShqgCXB/AwYUm0lIyXSlafkEwMdyq37/IL9/Oj+lIRiO3m0u1PuJANCcWrTXy9Qrf4RjJz2MWfOPRUt5gyAYEqZDct8CT6ozVGpyoy0QIptq88eEgZE+njbpJpzB7ZTIEDKZwYUEWsd6DImqfrXKtqL3VSM/fsgNuH/hiSa/qIaATpjodkYWbXA6K1c7kH4+HCefcABjBv4QALDw8Yut9K3yB7NIyZe5zz4Sm7sHdLVhBjpnvdLwmHhcTvXJTVJVtAVisiWVRpqmwMRhsM0BGBg4E50302dFORhPwFExKx7LBfBE3aqAVzoBzsB5JzivIEkAxpk0A+K46a59cMpRvwUA/O2j91Eq9SHrDwjykBBytOb9J7DVFvthzftPwH934QdtPfK0qpOqUXCBS6gW5tMgZCtUNvHISR3STMkh0GaF461rYpkIAfKbtEW051zUa9BvVH5K5HJXVGru9KJ2S+pf0+UaAUe4HmyJ5Wm381kk/Iicgal6aAEaphn4OEdctwwHAliO3qUhEARnfSACQHNq0V4tV1z+fZRLfQAApaQlTATc2YLA4JoDWbajeuTzLQm9AAAgAElEQVQIsNXqaSSAAlKXGChgrkBjekNHYxiYr44qZpRGkQJUrFEbA1dDhIAc+jsAgInDbrK0Arrji8S3JOVU9IIqU42OGfgjSQKEpBMAP6F40u46DtFoUmL0iUklh0MEiAmCfqtkilHVWWuaqG4pMylQqYTOi/KKlY8TqFFARSCFiZAyjaDkIQFPEiQog/MKOitrwXknKpUKkoSB8QSdlbWYNvPzOHXK05g47CbMW3Y2yqU+SJIWMGZW9eaJ/I48QlD1QdeJKDSCIIQAtjpoRD9TLM34NxIhCOuonIWzMAiDcNqUlfgcRkbjbbfV0GD4628v0t+sMhvyxBvYqV0aSgw0KBej/n4u3A/MCL5DsTgHpt05APj976unlxX4N4IMtLcD/DGA7RM4mTW//wLwO6w7hCBdm3HNNd+Te+tuG5BHmpqBXiqXX3Y1yuUNcMzBc3H/wpPgmv8obYBNBMK+AUpDINTFqEoCvBH/IAmwQ4SvAPfBTGo7ZMCfcvgS/yQpkGGCIFB7SEa2dJdSBS4HmJ24kVLEeEDt2oE4IaBEIM/IZhDkuNF42h07EUkCwddFgTt1BlHPQgJvRv0q9JAkoX5yxN8QMFsDQImAH+YQAVU3GNUWJKTeVMxPOK6Ia5ghMqyUiPNJBZVKByq8A4yVwJNO3HTXf4Fz4NQpv8PCxy9CqdRq/AeSEhi3tW21SZ2IQkGCsPWWB+DdD35b8D6K33smapuafOi5scBpsTNpxG2YvfAraEdksaseJupNvfXecuetMd1UbLXF/njnD7+hZ3T7vv3Ww6vm8dxLd2KLf90bgzEYy7CsUBm7UiOAn/4UGDTIMenh5K+RLf51L7z//54pnNXeu56AWQ8fA6xcaZ/oiWRASx7tAOA/td0ArELPIARZwX5TgCYZ6JXy7UunorVlI7SU+gKAZyudJMo/IDY7iztjkLK9pl2mAvrMDYENSpnX0RpfA5AzqkuoygCEcDKC4+1Tm09BDiYOuxltC07Qo7zMyofFCYG+A46Jw27G3CWnW/ciMzVwngOus7AF9gPnvTgZRBEBloEARKFiyqho9aLYJMC6LPj65D0zDb2hiYA2XZCdsmtOZGkRZDz9HM11xuDHaIZStQUhAsHEdYwlkkgKx2FBDCRJYBVwzki8ivA7QAt4pROVSgeSpIxKpRM/v3t/nHzECix96jtmQTJWBisn2jyDsQRr/vCk1A486TzfWiUHUShMEOI5ZJHaQX32lP0zzA0InS1agFyiZhRqxKrDYTLA8M4fnsJ//PsX9RSh8iwA4L0PnvbS3PLTn4/mV0/tQLU0p2N6TVO9WpNJOGesYF5B1ne/xb/tbc0kFPVdq4UMNJQIAMDzMED+OZVpzjS2B/C63G8UKajuo9AE+/mkSQZ6obS2bCjslMs2GfC1AWTWoKhWQJkOCQBvMH46CfD9AQwJUCOIHHAaNwn4nLbHpwdEI6C1CUYrYPkKcI7Oylp9nQL3LlyPEgILrLplSBvxT9EmpCsawhcQ7cCYgT/CIo8IhAhAaJTTj2uHxArmjxDHzlS7N/GIxVu1iIBns8yq+Bn4x0qlId6bmlVImASZheXStQWMEVLBGBhKRFOgTM8qgiioY0kQeCLINuetqPBOVCprMf3egahUOlHhndhnj6+iJDUEjCVISi0QX4UiBPtKQlCtgtQDFFR76zzCBUKBaWnlJZohyXdRVRqQF/yz/GXobjE620SDf3UGAKgmJ6tW570Pn46uROzmW6S8IS3BQiysK0nC0qW4f8GJOHjErQh+R5zu8nCcLPcY8xOoRQPghu27b23Ow45cc833cOGFRZyBNwegBvkSAFvI/fdInDzlbI7qd7U0fQZ6mVxx+Q9QLvVBa8uGOHz0nXjwkVMcbYA7W5AZ+fe1Amr0MisJ8LUAphNicDUCDNLsw/IXYFAj8VyHGQnBOEUElL8A9RWoVDr8tlyaoLiT3KURgonDb8Gcxaenmj77/gOBc25+qXG8DDBmkCAC9Jmbv2QvBfynQsAo6HHLbjbpLYitQdCvwqovkghYGhsG2wxIHivCqHINmhMBYLL2UEIAmxjobTVtgaqXMTMi95hXwFgFjAsCzkuiHlYqHVi56jZNDEYccBXAGFjCoNY0WPM+JQRpkvbU66dVCJFgygW23mJ/vPfB0w4xzZ5+LTFnLzw5ECpk0ohbU5LPD/7XxV5SkwHSZoeO/f188of/eQ6nTQGOmpmzPatR1IxCbG7EbEvNKBQBy3q2IBOi9z7z6X0sbUng6lCLGE6vCBnoai3B9OnAuHFgjOF73/t+7ssvuOB/MsTKTjKql2Fd/CJ7tjQ1A71ILr/sGrSU+6GlZUM5BSKE+YLSBiQlhFZ0VWFQ0/850zXC6ugzOAVb4YYEMMYkVqMA0ThswvIb8IkAAD3ib2kHFBEA0RFwDs47A2nILioHIaDAkHNGCEEKoJeAFFbqtZgLcYwZ9GNBBNzOXf3NO9qpgzLGdc9Zz8Hdo3GNtieWquUQbGkNzDvhTHXA8j3n0BpkJgaUIATiqzrnmxFRJ2THrIhXwErCWR+8Dzora1GpdGDxE5ehUlmLCu/E+CE36G8uOyGo8n7CT7pgmiZtL/Vgdtk6az+WCJm98CTvjCsuxqMzSWa5Pk0OHXW7VZ51UfbH/pg4dBpiJEB8+uRcwaphUlVfJ7eOVVgjiUEuIU7E6SBekoXICBC99jOf3sfTlgTNhIpqALrcXCifFCEQTelZ0iQDvUS+fel30dqyEcrlDYQGIBH2ogk1SSDzp4echAGfJARJgBcmwwFJKOQ5yyxIgjYGMWWjQwg8IuAtKkbE4gLcPs2pr0AMgeYjBAcPv1VqWBSQp88gBOjhpxeSlFOuZCMC+Uc83ZTsE3FqYL8RQ3DCcxAx/Sxj1zOQ980UdHCJgAH+hbUGgA/0GaoTAsbBeMUnCEzMIqSmKhXO64QIOEQB4EiSsqVV6Oj8B+Y9ejYqlQ5MGHIDAWgp+KwwFqgfUdhqi/3w3ocro2nGgD5QHaxntXx45BGz379/tmti8pe/mP1Z80+IxDoe4wC0s7m1ZdZgOR/nyz2WSgIolC+O1d32xCYBblhPEUXuuQkInEdh4D17/gnAU085idZBA9AoItDejgvOPx/f+34T2K+P0iQDvUAuveRK9Gn9F7SU+8r5zIU/AACUEsdR2PINIL4CMCsRg6wlwAKdRZbZgqivgbEFVfEl1OOANsFAChFg8DQC5h/0sdEOKPtwIYcMvxVt84/zRnjM4Lw7buUSAhnKuQZoLJUQKLIBr4MtYi6kiYDz3ENEwAP2UfDvh9vvOBYnJdTLywX/JFWvQ6OEgZmrLA1K/bQGlBiIq2S9kQ7EekvqG6Q2wCYIXNYfEt/aUvMhemzIQilpAS/3A+cVLHriUnR0fIJKZS1OPmIF3vnDU+ZZOI8rFRIUwgtZAFuQBlohRUb1Y0LBfi1CgX5M8phet/Hxer8nEgPjPGzaDJcE2C1G+Ls1Ur1uZCEBbjht67pMe8BVG0HNhHxvBZ7JeTj8oe258zFYTfKCux86zhKnh2kEmtJ7pOkzsI7LRRddgr59PoVyqS+SpFU6B5eQJC0A4JgEZZkxSJEElwSkmQKZcGtxMgouA9oAMSJrgBkFaToOCuKa0FV6NFmdZ9ZWwUlGzh8y/FY8+MjXTCgHIQQgfYVPCAyhcHUFrrlQ/C7GDPqxM2tQRiIQHNUPZMRY9Tjkffvx/OesOZ4V2wWzPvighIHpkX5zvR3LPra2+pnHtAaSVFhOxyIeAzywH3REpltZ3qrkAPB8C0JmRUlrGeAcM2aP1tqDtWs/AucVjB38U+8NBJ5+gIRGJPfHxTBn8empMboa6APVwX4dfSy99Nr4+LoRgnrNKETb7ygJoOTdxQF56oVuQ+iQTjoJUGG1gP+aZxTyfAZEmT675YF4+70VZHDCi5Ql8fDWPZ8WlnbMOTBoUP0rNiiBbMr6JE3NwDoufVo2Rkt5A5RKYtpCtZDYwcNuxvzHzstABBz/ALWWQBUSYDfirklQYO50taJrgBCIWWQAowVwiQBDDPqIqyJaAW7abY25HUJgzIXoKDIlBHBS4GQvPPpPy2WeUxrqD2sHxg76SXD60KxEwIX5JoFqBCAN/LvxfFDPgtHD5EEQB1Ueco7Z8en4IQLbVLLAQDrS0JYSCCeNICGA0R7AJQfyvXuEQdZROZ2o0opVc0RWZEH4G3A8suJScFTQ0fkPcF7BmIE/ijznaqHZSMOcR9KBvysUm/TUUf16y+TJ9SUE9RBirOm0E267zqz4ug3MgQednsA6FyMBobAuMyUKagacKKhkJkSf3fLAgL9ABacevRLTTuf5R/ub2oGmdIM0ycA6LJdeMhV9+/RHKWlFIs2DhI+AeK3W1KFkrYAYEVAzB8VJgB/m+gVoMiBjWeBfEwJxxqTLvYZXEwHGCjV+Jg8D8GxAyay4NiEAbBjvdFTaFh3OmRCoR5Aw+NoBO1KYCIRH9bIRAZNOmCSo92kfB+NYp5l33tyUfcgDccJpEVjvEgVL45GuLaDHhsap47hmgcYVM17JIzrSr8ySqElRgBwwlxyQ6w05ANS0p/7UpWEtQilpQUWRAxleqXSA8wpGD/whqkt10vDgI6dh8mTggw9ElXd/8vatn7u+Uj2kO4F+VumJhAAAaQ8CpICcdx1U8sFyO01qcJOFBLhheaSWGYXSyADtc3wDouqy5b99Xqw8vHx5NN3cx40mAXJGoaasn9IkA+uoXHzxt9Gv72aECJTAJBHQ9v7ejEEMtlag5JyTgF7nkpcEOM7CXKTmEQJAgzzbl4AB3PEfsBpie2w4KNzsKABHT/qj+eTACT9kxG14YNHJUCNlls6AA4ypEaa0GYbkcQDwx8yFDBGg0ZnZ5iECqVqAagSAXJWBJMTDOOUigThcbyhhYOCB+yedNFcmEGmj/mabiywwBdDt8zS/sCmQ0UzpdQ2iWoQQOQBJg25jpkXkl4jtohXfssyORh2U3yHwwUdOw8EHi/3NNlPP25yPWUGMHRsnCvRXEYuEY9kyEXddAPzVRBECoHv9CC7BJRiAAThk+K3e4EH4WJEF0QhZ7WsmACpS2Q/7RWOswIpMJKBWcpBJbrgBGDYM7ug/bRtcopCHEOy+4xFi57HHSAJ1JAFNzUBTGiBNn4F1VPq0/gtKpVZjHuSsKgwghQi4GgN1DrCHjwPEQAJ+6hzsbjXAZwC47SBmp8+hTYII7uc0y4ztngZiViKkCSfmQQKXm2MDy2V5gp+EKrvZmhlv4FwXAf0BQuDmMXbQTwkRIAQAodE989cQNpJ2KCwjAQgTkUAckldcHGLgdcDqHriTU4gsmDD/HqhpWXUCYKXNuFRQ+QSA5leVIKT6DFBwD+j66sZLS0NuXdMiWGZGMkzOZLT4icusayC1DyMPDC/c8+Ajp+GEE4BNN5V36gD+UFiRc3ff3TtIABV1Pyd2bzEAhNqLlLYCNJ4Su82Mye9fuA2jDroWq7BKxvVj74/9c5dfyXIsrx4pryxahIeXfROjDvqB1R5sv/VwrF6zWEZiTgvk39d2Ww31TYRUJa84mod1gRC0t+P8887DD669tjHpN6XHSlMzsA7KpZd81zEPUkSA+WRAgndIMyEzY5A7m5AB6a4/gNqKfiGJkwET03QIBHAzTrsZ7saUedFfqMFzOzAPWVphk0ZMx30PH40gsclACPRYGWc+TyCmRQLnpxGC8JGnHfDuM2YaFCJtaURAvb9q4D8E6p3Us8ZzRAN/Zh1519sqehJukYU4UWAW4aIPlRKAGFkgYKYIQWDynWrip85nIQjOVj4LN759rbkfzw8hQADorEZLnrzCi/vxJ3/GOecAm2xCnloKqK92PhY2bVrvIwJUxnWjyVC0bQi1FYzhwz8+j3/bbHd8+L8vOG2o/7UBwId/XGWtQjxgtxNx48wBmgyERvXVuSzikokDcSAA1HcVYlkZK7wzcMqAeLtvygbAOSpWHjWB/FjckSN79wfUlC6XJhlYx+Tiiy7DBn03tYlAQlYVhvQNAGDMgaiPQAlaE6BBPHNIQBhUmrUHfDKghXOdtw5SHQszcTQNYIDxViONndX2ErLinoJ74IcrzG6wP4HgFiEwF04acTvuX3iSDXw5t6e0hAvr0wiB3Lf7WH0lA8PYwT91zIMKEIGIqVCYGNAw+9iczgP6XbYUuoocaSBrhOs8s5KFakRBmIUp8xtxNl1bYBNUhwCECIJLCBA4bxGE6pqBMFGAHaafBdWImX1tpuQSBRBtgkUSKgD+jA03RFXJMzDp4pmrrur9OKYWH4JaZxSyjX8ibUBAO2A0CXb7ZhMEX1aumg7g6EJlDYlLJhSR2A27eXHrOaPQDtuMxmtvzdelKCpb/fu+mHbinwFr5tKChKBpItSULpImGViH5MILLsYGfTfzzIM8Z2DXTIhRsiAdhS1gr4Ts0anotD+AvXaAqxEQ4N5Fuq6LLUHmKn13LKjqIExaQ+2P/iowZAiBgmuMdHlumAmnf+FuNUHQd+d1ZvTeY+ZCgghcBJf4xIlANtAP5pbH1RCQsxHg7wHy4O3FCEIslFv3xa2YDmNySQNjJL59pYivoL1LFAxJMFWsACHQKZtrvGMGCczj5ylB0OZBMaIALv/bcVKJgr6e7seJwod/fBFXXw3064e6CsUvF1/c+4mAku5yKibDN9EBg9AAgt2+mRDjkxNeUnCdFNVGOJqBCq+EBzkCssM2oz0ToV22n2TSbwQBaJKBpjRImj4D65C0tm6McqmP7TQMnwj4DsSxqUVpJyD3giQgQgasRjI0NZwAyuaQaARAaIDsg7SWgYu0uc7DbwBDodzZhs/aKYQcii3LdQu4iyHmuLmQOuTkOdqda9qR7rStDlwH5CYCQdDPYsdeiggD/xDoZ8HdUIB7T4A3DimO6OvyHK+rkIaApid0bNch5YNgwLECQtYxuHRzoWmoOlxFi+Ac65mKuNq38wcAy8RIBAT260MUgMYSgaY0Xi7H5dgTe2LyiNuDoD+tjdD73P4uwl9tz5FCMwppMlCxWgfOO8lxbIAj/hT0t1lxTIXc/aLnOAfGj284o27iwvVPmpqBdUQuvuhy9O3zKZRKLUIrwBzToCgZKIE6ChutAAXzttmIRwIIAaDTj1L3Kg2gdbvF4caw4JelHZDEwJli1KYabkhICPCB36EJzM7hzv5jrzUATB55O2Yv/IoqlSQAoTUDQrBU5RUjBHKfBI0b/LMqWgESWpQIZCEGHvhPAf4sLY4Tt4qYxxQiFRmIgOYQLrnyrzeEIR7HG9UPkgQTZqY9VaZipB6ywLEF3kWd9I4hy2A9B7scIkoRshAjCGEyUC9A/9Wvrj9aASXdNuVoNSKQMrmAaQZMW2W3pV0nMS1rzXLzzcCoUdpnYJftD8FLrz8gM7XzrE4OjDy05OvA/PnGRKheBCB2Tb2lOb3oeitNMrAOyIUXfEv4CZRaUCq16ulD3VF/5RycsBIAiG1kRiEApEMAFBCtNlOQIQlUqKkJ4JMDboF93zDIHqMNSXoMt1FVf/S4pwZsgggosyEHZHr+AyEg6pID1WHa5kJphICaC40boogAudHYE7BweJ2JgEcMaB72cZAceIdeKtXFe/b21fQ9+sQh8q5SCIO952TN3NWvjemPuSJiDuRqE8g6BboGEFLgH0t4zuj9BsgCYAF7O6waWbAJwlvvrsCMGUCfPghKc6CwuHQlIbBMhIAM7YN9Tojz7QXIQaOlYSTAEeozIIgBM2MFgbYMELe/y/aHeCZCO207AauB9FmEejoZAID2dpz7zW/i2h9mWa+kKb1FmmRgHZDWlo1QSlqkeVBr0CzIaAFKYIkgA/asQXRUP2CCEvMHsDoToy0glzqdg+gwzGAvh5o+1FzE9a5elZgByjzI+tH8MrSDOorTgNpAnQoB9PQ6SQw0rRFDvFZZqpkLiaAYIfBBNXP3WSS8IBHQZlhufow5ISyQN02X7sa67ZydOaN3aV9vA2mXoKgYESJg4Rr/qdvvXsWNxYOuLnTpPLN2RjVtQpgUmGMTR6UHEk/XHEUgAJ0PSP6hMHoPIX8BIE4EYtLbCUJbmx/W0zUb1teY03SQfoF2K+mTg6p55xTXW6tLZM4cLHtqKg76ghiM8WYREh+3CHDb+oBYswjlBfZZicGkSfWphKpy9/QK3ZQuk6bPQA+Xiy+6An1a/wVJqVVoBVL8BBhLkCQlrRkIawWUKBKgQL7rD4DIPpP/Q8BQmOAYcX0EHF4gkRWzoZWdLo3L/Cg0L7Hhzj45rRbV4WoGGJDOMdAdETzJAn/hbSN7FiEwiY8bcj0WLr/QAuj2zTv3TsMzEYFwp2+RChY6doiB3qSQA1c8gBGNmC1GsJ1yQArUayZg3nuhJJ7ay0oYvHi0SsqRfALUNSkIaA5UtpavgE5GEQ8D5F1TIy9MFsbSNtAwmHJYZEETAqC1FVWlaHdxyCHrDu5QOGmXXWxcxznwyit23Kz3lFc7UOuMQtXNggIaApeQc1PhGz0eXQsRKDyjkBzB332Hw/Hfr9ztlMfZI83E7jt+ydMKAMDDy76JU49eiWlHVWojA43UCqjKPXGiuP+5c6OVuIkN1y9pagZ6sFx4wSXCT0BrBYh5kJrvnxIBVpK+BCUs+81VGPSFi7Fi5Y91PKuJCzgHA6RRJkQg7CcAKz0FbVT7wWnDpRGTAc7qSjOraPWGTlEHPxRweytuBRkopuG8JAL2YL4xb9JAjWoECKGAPGeN2MJNkBICCsIYxg+5HguWX2g3uG7nbD3tFA2B07EHNQBFiIGM53WM+jCtC8/akbgmZnmEXGCB6xCY9+Pr95NKGCxGaJ3x92jJCL0lmgMRxk22HlGQMbidjlmzgIbp1AgxAIx2gSAYEFMkcv8cwKtvzkN7ezYyYN1jHbACHX3vbrKgyrLvvuI5b7yxOFaEgHNgwAD7+Nlnu7/cVOyvN/vAgdnSeuR++dyt5g2XhmoLfvELqGW2eYXMKsTobUZbv6BMO+IdoNPtjHoQGQCAs84Ctt1WpHfmmb5zddNnYL2UJhnowdLashGSRDgMlxLHadhxHE6SElhShnEYNkCemvbEnIOBdCJgjxI4pII7M/BAjbzTzkVBeek7IIGzrTkgMwg5/ZJOI0O4gWFmTzttMqOH8NIAcOioX6BtwfFWmQ0kVASALhTmg8+YJsAwBzf/AMUKImTmB4U69oJEwNcGxEBEqGyuZAH5RTt6u1QWuPbS5HYYl5TPI16BuAAIKnfiOcfcfTdcH+payNQmRBT0nYjrtF8AJxiMrrrsEAZSr+KEgZSdmiMBaGlBVGoF/bHr29qAL39ZDFBy7mOSrpS2NmDYMFGODTawAX/a7wtfAH7zm2zlPucc4Edd5kycnQjYX33MREjGaCA2N7nkz0TPKJSGlyMV7JkXf2kHWJ+8uWcO4D93PiaoFdh+65HGX6AnawWmTgU23zw9zccfBw44oPb8mrJOSdNMqIfKxRddidaWDYlWoKSBPfURUKsPi5+9ngAAD/R74D9KBKS2IAASbUAJ0uGAaAQMkFZ9j8IiOq5GSlzHN5oFRWDoL8IFvDP+yIwmAZoQKNBO8bwD6lWIQFe2ZsH66zgTR/dEvPFDbhBaAa/8oX04fbDrwxHaJ/EC79clAXb3GyYGoWlo6bv1JUu7Eun2c5KHcCqKGTI7hMVsk20ioAF1qnmSrHUGacfjOGEqnCIPU6dAvgsER/TNrfmkgVllUTMe0TIa0qD2G0kGBg/2MVhbG3DBBcAWW0CX5dxzu54QtLWJmRrHjwf69s1GAFR51W/gQODRR6uX+803G347dt3O7DNgrgZc3Wto6MQPOW3KSpwxs299yl2L3HVX/NxRR/lh996LJ1b+GPvu/XXvlL5LXr1s1pSi9QD9obApU4p/HG1twI03Aptu6qd9/fX2h6cHEpvYcH2SpmagB8o3v3kuNur3aZSSVjJ7UMxXQDgMM0oWwAAyvWgILPpEwIBAXxugGgezT8ONcNKdiNFxrkdN6YxC7rHshgie14Qh9IAYIw101hET4hOgwZtrLkQaYmHsDRs6SsBIrlfB6c7EhBBYA2204xZ/fNCvTjIrnrtvkQIr7RzagEwkgGoHMoJzFj1IkVrhAbM2ajcEyo0YwK1JabW4VdPlVjHS4tH71UCf01swB8axnaSaiTQAxnfAhJVTeoI8mCBL3P/8T7HdfHMfYHeH9Onjl6EaAXB/I0YACxd2r8nQVEzFrtgVh436ZU4iEGhbANIWuy/Gfsn77HFqofI21ASISlrF6hTmQRXHTIiKKueA3b4S1AoAwKLlFwH33VcfIkD36/FRKLOfjTayw2NpX3tt2Hu+Kb1ammSgB0q/DTYXi4qVWsTW8xUgzsKJ0g4obYCZQUhIXiJQsoAlEBohYN6eblb0qDs5zwLH+iLbVMhtie30mXfeL5ccHuWmEBwcTNr+cx3LNxdynZ9VTLoOgaQQ3l94WwTNhSYMvQELll9A7sPtEimrYPZhBrJAgT99ty4JsI7lNanHERJgNA52uCupHX+Va3Onl3pdGsAHzDB6lvg2IUA0Lomnq2h6mjo9+jERsc2AFD8l8ZSPgYobIQ3PvfRrPPFEOhnw8q4Rw112GXDLLUD//iZMFf3mm7tOO9DWJgaLW1vDZCC2jf0mTADmzEkveyOnGWVVv91Qm0NfpqG19ilSyewdP4msZWyUhABuGqC+5x7gyCNhrUTsRM8Mx7NoBWrVDhSRDTcUW0UGuot1N6VHS5MM9DA5/7yL0W+DzaR5UAsxD/I1AtphOClBkACm/QUYiEMwAfcxsGhIhjtyREkF3WNWsL1eGHd8CRQoVrMNuQSApqzAtE8KKF1Qfr1c3Z/XvnF/XzsDiyDXXIjGdlcl1gwmjzNxZM8Xn2yFgD4Fzb55kDxOMwHT75qmWY0EwNcQpRAA20whdG+RuKli1y034e8AACAASURBVLVaJZqvfqd+7lkIQZ646eTBxGXWUZxoqEctML+fT5gcCCmVIkVQlxZ87qHrJk4U24039jFJV2KUtjbg+OPjWoGsW1d7cNhh9SMzRWcU8oiAdUwjut+1+OsMkbgJh9NCnu85u+ROs2ClqlAykFNGHXQtpgFay+DlW4QUuGEnnFC8Uh15JDBvnnCGMXa46dc89RTOOfts/OjHPy6WZ1PWOWn6DPQw6dtXzh5UEhqBhPkLjCWsLLQFSRmMlSFG9JnRHEjQt2Llj7D/gHPwm+emWaNGgA0WE+l0TIFnyEZcdxlWlTFAjQGwcIbC6bQ/YeSYcbPOQFCcMsGFU6G6a2KZgSwyT7sG7VxheEFMOHD4mF9h1vxj5bVGG6DS8jQCAWdis56ByVulMWHojZi//Hy/g5NIzsbO/nN234TZY4HzFLgzOw+LFJBUPLOwbCTAtVOOldq64ZRmpxioqEM7pipw5FSwngYIRH7yEMuX60iEw6bG1a+Gh8vgahSAxpEBQMy+QzGMGqTccMP8g7j1EmUBoWZPqoUMhLbV5IorgMsa6kjstN2hdiTlpdIz7sQQXSFFvn89vWgayJ4xI8zUZszA0//9c+y92wlWKfRfBnzxP8+ImggBAO68M30kvzs0AgBwqjTholOFhdJctMh+NokyM27iw/VFmpqBHiQXnH8JNui7qTQNavGdglkiCEJCtALUoVgDfAYK9tOJQNnWBkRGi0IEwB1x4jSqqxVwjrnbINlI380KGuBqx2JzDVP361+ky6VcFVwnYtcsxJj22OZB9jGImVE+c6H0ptWNw+zb1yCdPA8LnxNS4MRJ0wYEQT+LH6eVzTnjlzH1CbDgrh3U6M4pHSRZANvTHrmpBOB4hDxEYntpNyJuNTKQJnmxwuGHA/Pn2wucuWksWNA4U6G2NmD7rYcAAF5/ewnOOKP+ZOCEE9LL/+yzdbiRjJLuMEzCQE8ENEtwiYGdRq1SD61CO9rBv3wM2Ixf5gPU2m+ggxZIXgvsP+DsKBHYaov9TRr19hfIUvZqoj40Ce49iaUfi9+UXitNMtBD5NxzL0C/vptLp2ExjWiSlAHLT6Ckf5b5EAH/HiFQwsixNCNKEoUCfFCYiQR4o8AwmgF5qdECOMeaGtCr1Sg7AzcenHYezNE0kCj63j1iYaZWtGcVggPpYaVGr7cAvgZ0zr66f/3YTPiEodMw/7HzPFLgd4HMCfUJgn/GCfOIB9kLjv6T9x4EDxGgHwt36k2QqGUmCFnO1y9ata7XaIHchGIahRgUD43ai/A8BCJyhfUxuvfrxs/T79cyUHjGGWKrZi6K4ZBGaQcoEVCS1V8gth87f/LJXT8zkvudxb8tt52PnQdixCAWO480zH+AvhB1nCYeGTDxq1XFXT83GdOGPwt0Ip5nrVqBU08tXpGURmDIEDHlVdaPrpYRgqask9IkAz1E+rb2l6ZBLcYMiEwlmkgCoLQC3sxBep+AYgv8m311vQxI8RMAaFNvEwR3NJ6QAG6OjcOkCNFaAQbo6YMYk9eoNQhgjoPgmBm8r2cWYiRvE/++h4/BUePbcNfcyQ4hALT5kAxTpRbKAJGD0AaYJH1dgED/ximTXAdX3HtxnrJ1m3TknQUiGsBt3pkLzhmJxkg6jKRPSQE5Ju/ZpOeXJzM5UNnSY28/Fier1A4uMqcQjFiFEHgEInwNqYluQPA69yu0olnX8kD82gYB85CDtEHK7vBp3H7rIRh10LXY6DOf12WoBxGg52Py/e8D5zd6zQFmH/hte0p87gbW7wU13IlYriwMIBspeOgh4KST0Fn5p3OCY9AXLolqBbb89D5ip6dqBQCxgt6qVWJ/4MDs1zU1A+udNH0GeoCcd+7F2KDvJtJhWPgCWDMIJc56AsphmDoNa4BHACBjePKZ67DvXmfid/99iyQCyiyIdg5hAOjtuSTAxXFyNh69/pKjJVCkwBqLl0SAS1JgzIBsSEhJgdAOOMBakwsFxx2RTMAQAotSmA03hCWLuZDdWSrmo1gJI+HWk4qiThY9Iu/MejrOu7JIYIQUgNn7LugPkQCvXmQkASwSbl0aeD4B0aE9ts2Kl0t+GeRvSOKj/wC3pt7Nel2cUHA89ew0rFrlP85GPV6lERg8OB5n2bLG5B3SCigpl30MVg30h8Lc86HFXZU8+WQNN1NV0rQC4fjRoDqRgoYTACppo+zTp4dfyk034YVXZ2Gn7SbooKH7XZHqJ7DHjkdg2sDfxrUC9dAOnHVWca3A5ZeLrfqgX3ghPf6uu5r9ps/AeidNzUAPkNbWjeTCYi1m5B/KWViQAJaU5VoCgggwSgI0wHO3RpS/gQHUBlZ6INLEIgfUdCjctKv5zCkpEEHSX4CJfYHmZVwwQgRUI0ghb4QUaBJh3YV1D1bZVLmIuYUgBnRUX4ZzrjUaPgGwAb6Z7911Jjb3P3HYTZj/2HlO2RwyE5WQbT5zXk3Ku/NTscF/JiKQTgJyawGYe41T6mgHVKxjynZVwU7PvSwNK6nvI1P0iMZApgGe7dpqhKKrBgAvvRTYe28zSBmTQYPqnzfFOK6sWPlj7I+nsdl2n0/FZY3QDmSRojMK2S8+rKUMhXm1ygsKfyc3zhyAF/FiaupdJmlmQrGXsmABcMopWjsw4oCrU4nAv28uF8uIaQVqNQ1KK2tWUaY+RQB9kwSsd9IkA90s55/3LeE0TGYPUloB5TfgOwwzy2HYgASmgSGzttBEwAWPFAB64C1KAsINhQbssgPR/QgDGKfr9IpGzjgSi1BDbATo1rCJzP9v903qHiXHCJAII2qNAamX0CP7kMDdAfegkN+/X5NG+toDcac796lFQi1w7sZwCECKVsAEUlLg1oN6kYBAuM4veJfp50zCOaR4Z1ZTN1jlYn991ywSAAUMktcW0DakZNooDKDWMaiWvhq8TAPweWXq1LhW4G8fvQvANpGuNymIyXXXAWc22lSISiaOHSEE8IPdCN1KAEBmFMprJkSks3MtRg+8Nn3mIAgSueK664COAuA+K1E4//zanE4U0y/yUTfNhNY7aZKBbpTzzr0Iffr0F+ZBcuYgbR6UEGdh12FYgbgU8yDRNAtHYSEx3wAXAMKOY5EFHyR6okgAA8DpisT2SQHgJTyXdjvGpIGJODJ/rstJyi9H9DXbUPevHIglWPKaf0kClC+AGM2HiajOa2WFMqOJaAcEK/D3ZZoHD78ZDz96bhV1q0NegrvkXVlYO0AKPMDJnDPuNQZoZ/EVyGUKlJkcVAP/7nl7z5PU/q/7QEu+nF1NWbV08xEDoHHgHwB23tlgmVoGKWuRvffOFi9kKpS2n9WU6KKL4qZCPQVvbdp/e/zpr292dzHqIu1oBz/5JLBbfp5/lH3yZLyxZjFw9MrUaP+66a5YDQAdHX7aRcB/vf0ElDRH95uSQ5o+A90oLeV+KGvzoLKnBVAOw2YWIdcEyIB+eyuFAaVyH6xcdTsG7HYCnnnxlyI46jAcAog2CQiBTSESiMhhegZGNAT/n703DbekqNKF38h9qhi0PycEUUGZEVAUVLpVlBmhGKsKmVQoJrUcUMCnp0/vtb3a3qe5tx+7FVC7AAUKkKKY6gAyFKDd4tCIaKOozKCA2nZ/djtRdTK+HxErYq0VEZm599nn1CnJtU+enRmxYsjI3JnvG2tFhJ9/3xKEDrYBGBMXIiOCI92FyJog6xzSw0Q+EMYMpPUj1584IJjGD0g3Iev9L5zVAr5+1GZ6NWIb2iNYBJLBxMNIWd/wi6qusyZ2EYAb8UIQlgDD8uLXWV9zVniOBAxDAOS9Js+XE5IkjahLmrbtqLMkyWb62Vh48YfglvIL0ZwYNJQyq0KXfbZfNx/5CLD0+LsAlHt6v3nPZ7EH7sIWu+RdhUr7w1gNcnLrrcB739vtPNrkb/G3Yf+P8Y2+I3YczlVqGtaBNqvAN7/7GeDss8t5jwL+ddhHPjK7U1FpoWd+jw+fMTJH+iaeeXLWmX+JwWADVIP5jAhMwAQSMCEGDDtwxWcNkr37JvNdVfMcrGM9t3HwcMyHg0seLvbV+IRUGCEJ4JHVDywZD+OQVmA+CRoT+MusIeJ8eD2EWG+JsJDzrbg3dwixjBQEXdqQfNuQxrIQt3/4fp/HjV89M9NWTVJqY7qu8YhfO4j/EeSHsBZSwNtWWIBM2rbyfuB1y90nMVzeaybcj01xUFvpE87Rz6wV6pjdcit604J9fJvpT67MQt0atyHOMSxcOLuP/nWBKfbYw33f+NUzceCeZxf1/vPXDwFw1ouJCffdZZ9/N20f+1hc6EzLuecCC+whYznfHbADjnrr8rKClQd5WNwOlp/3nK1aAfM6F2JkdZ3uf/az5QuycCEefHR1MdvnP3c7t7N2rdumptL9XFhbvA6bibZo20g++Ulgxx3x6X/4h/HWo5c5Lb2b0DqSefM2xmAwHwMiAsElyK8qbAZiwLADN4AEwiWQ7b4H1TwQwLr7B1/Ea3Y6Affcdwk4iNb/Re+/tgSEY16oE8v+GaRWgeABxF9CIdyAzAZU9zgY1/fZ23huNny7tNSLH9pF1e+KG47D8Yddi0uuPTzUNo5jjjn5GHcO1LFPHkDBOqC9gTIuQmG/gzSBpMQ9CvFYEK7YbuHSgoWx/zJM5c3bL7EGGFYOq7gIYyVlLADxviqEs69snIgq3Ym5BjX5LIqyDpBro7TdS5nfYjF2emc3Ti+GmZY//3Ng+63eisGgW6W7jhvost91EPFMuAqlNsnSVMc61ejRc05Kg4ineQP/6/c+B3ziE2leo1oAcmH/63+NxypA+XIrSS+9FKS3DKwD+fBZf4WJwQZsrABzD2JrCYgBwyAwDPAeVPntxQATgw3dLvX8cnAXeoM5iOTlVNA9vLkeX765nlyqGxWh6pWpOwXFHt6YToDWEJQhRUErngfpSiHrAB2yHv1oGojhyYs9WgUsi8j+5/lNVwpAls43BeZGnLvh7Wl02rg/FBEwSO8Ruk+E21kMDz384WrHa1bq+U8sAzmdsFWpbmOaSm6GtlxPe2lr66Efx9ZuHQDbGq0ePA26dxqWBsQOows4XNJlm67QlOqDaj4mBvOx+s7/0Wgd+MrXzsID37kr9P7ntmGtBnz71KfyndFdLCY0o1CTlFeZnqZwo+j6JFNT7kaampL7XW6wj3600TrQagGYbti4hH6E1AZtWy/PaOnHDKwDmTfvWc5FKKwpMBCWAD1gWABfYQ1g4Ip9V9U8CQC5FHv7+TGDm6IXtulesV6fxgAAfGwADNyMQgZhnn/yzY+Dh/350JgDBTStqlccRGyCdcAwfV1n8vEXVgHfix+xu3ULn1m4c/E5yHHDJuRGsxLxvGSZza3WRdpaP16/CPgDnGdkIRDDAimQubUQAX4tYuaiNpx8JPmajK5Q1aH67Pk5pK2R6OrqFHW7xJU08mlmA0u11jbfvJ07SqdrEZjtTsoPfhDYZfvFrrffuwHe8a1P4MA9m2eKabMOTHdfy403AqedVo4fXtqeODy+MNvZ0Nd67rCFMKMQB7f6AlgL/P3fl0d2/9u/ZfM+cM+zce5HP5rm29U01CXs7/5ufGMFaEquNWtkeA7z7bZbLNfXpceGzyzp3YRmWf78w//Dryswj7kHVWLQcHAToh5aAnSCEDDQp9yD3DSiDPz5+Hvuuxi77vh2fP9HlwVEwHuDm0iAPCZh7jVsEDAnBAFJI01PkDoAfg/siUZQmTa4CEEQAhgDY4kosDDRHry6hOhj/W3IOZ5RBPhWY3v2Ki21hav7kfsvww13fIgzCDRKJjoJasGuEp6rtlYWhCwp6AL89X3hMi8ei/8JMWB5NZEDkWcTQWCpTRpW0lXFN+iPJvn7Zd0Jr8m4wXmTRQBo7/gcB/7Ybz/3XVUT9IN2dWtJ9517LwBwF3Z5Y1yRmGSmyMCMSaljovQoaq3j3AH8bTKJSdgPng7zf/5vDMwRgiHkwD3PxrlbXgE83ZBPUxmluJm8Ue64wy3cockASenHtj75AvYyNundhGZZBoMN/AJjHPynVoEAkBOAFEF//PZinHuQdJ0AS08HBjRAkrteEHHgVgV5rIXr8VJMeM4ou0MAXYYdJK4lzJ0k5BXcSHhrmKgvgCvPz8nl1x+Ntx++Kg4izrgL+YPwgdDl32C6CA9Pvp9K9wdsKx4SVh+1ldyDQt76WnJC2EwEJGJuJwLh7hzSPYjfAzk3n9w5a9eaUvsYpLp6AHOiL9KNMsB3Jl2Ihtna3YS6uvGUNp4Pl513dt/aQ0Jva9YAu+46vc7RpUuBV7/i7dH90i/YWJkB/uWus4vuQr/4lVsRrclVqLRVVfv+pz+ddxU6//zxDSJ2knMaKoFOrWkL2+yKVZ+RJOcmxI+bJDeQ+Omn3Q369NP5rSmuS5q///vxziBE1pEu9dDt1sszTnrLwCzKn3/4f2CD+f+Pd+Oh3n9tFXB26rw1AAxNc9DrvqtqXsBq2ioQwQ5kfo1gXkD4LEjlj+l0mlBwnxx/7L/4vrAOkA+Rcgvy8da3R3BFIuuA/7binB0Ik/i8hrWVq5JwF7K4dNVCHHvISly6ahFg4dyFfOnSXSgz3SizDNAuH7bXxV2oOT4F9rkE4UoJUsDaMOB+dm95JU4Z2ohBFyLACYS4gww/4uejQ/QJGq7WqBP2TE5H5ZcEDaGfK7vtQq9TkedQmoFxWGlLe/vtwF57pbgj1GpMbXbwwe7bdahEgu4mMrCNuJZIwmAwXI//dK0DM7beQGIKYE+kjJV0VLl0cjHuw33TqOYMkQ0Cw6UL8clPll2FmBy459k49/kXAHTvdr3Iw8R9/vPjn0qUfty//737Lv3IdHhPBp6R0o8ZmEWZmNgQgwFZBSq1hgBttKhYiRAQMAN47yVg3ExEGdDIIdr3f3QZXrnDMbj3JyswNAnI3Cv0Ygkz7jNCQP8J/POxApTW+kDjnPQZmZDA1j07TQTmxpMFS6SDkRrLB0BLcesNsBek9vl3gdy7wM1sVETzvr6+HokXrkiXz6QbCSjH5vKgtjM8vSAFKQjnejNCBHLEwKfNE4Du4F9i+Fx76Xu5o56oe04SzRZZR8/bQhsOQwamQxZ4J+VMyimnAK995cmoxG/R+n33fefdn86OHfjK187CgXueLcD5OMB/Lh2XmXkFW+THMTURAtLoLq971VIs3fM7+MDyjYeq2axIadxA1xvZWwfOfde78kSgKd9h9C68cGbWFDjnHOcz97vfxbDczVYgAz02fGZJbxmYJfnwWR/Bhhs8VxGAKs4g5K0DEuDnQX/4ZnhtYrBBxHIEurRVILhDMKIBsLy6kAC+b0O8sbmFxIgcuFpzQkCr/9JKw3H6UVVnD/g5+HeEgBEksgwY43v9a0TXjErU2draHzsd3nfPuACin7GJYwjIkqC+QQTFB8jXgH8Rh/dxi41AoNV2EpAe5BVNJj8eloB9llgQA2B8RMCoY6abJwe8TjyqQUfUr0FH1LNNLx/ShTCM/nod44uZZaU7AadDCJriqBzCJV07KYeRI46gPAbuUlrvYuLJvBuHpFwEldz/yE3ArXfh9fvvHsLGAf6b2uaaa4ATTmg6szijUPvCWxng30YIoAnA+MD6dIH/tNJPlwwADqSvXAksWdKcz6hEYKblyiuBRYuASy+NYfpHdswxkYz89V8Dr341/vEzn5n5uvUyp6QfMzBLMjFwVgE3LqAKYwTCVnGrgAe54RsR/ZTcgxhJyIEP7nd9709WYKdtF4X02sfagIox6sEhS5C9ziVyQflrqkH5RaBI2eQBAQOeysUJAfAbdp7KXxsGl00uxjuPvBEWtesjzI0fQHwBkYsB91oN38kLP+osPOACTN7+gSS/mL7tJZADpSGmMVlbXGoVUGBfomzWO2SS4/ESAboX07uM7pO8j38V42HA/fPB9OQno2Pign5CL9yjqS6MrFP6SRcv0+eV5luaHtRMc0unGQWkvz+feTG3tcXr8Qdczj3Xff/ud2777W/jRmG/+x1w4IGjd5JSj34VLKyDsH6Lqaq4lks1KOZB9zf5+5PP/yj7uXEDX/hCftzAxRePZ9xA+nzhUyfrcIsXPG97/Or/e8DHWbQ/m/IlxhxH9/WfbnqSZVjmdkpjBvj+Rz9aXoCMy9NPA3/4Q/Sx77LfRW/58pldafg3v3Hf//3fbvvNb+Q+xZP0U4w+Y6V3E5oFOevMv3ZWAf+CCoTAuwpJqwC9kDQhMAojRkBR+Z4wl9bF6TwimIqDB+W11+AOaVwm3gQXIevys/H1IC0Eskcd1HNnkHETUsDJn4u1MZyPHeBA0doIrgLwIaDn62LrKZjKgK8sJi3mNtOhH6dFTUC3pXaxDe9SneFwMu5faUIKwv2lCV0sfTgiIG5Ika8sB1liEJPqM5ea6f2q4uWPRtU3TScjGvRyYlKd9us2ypU1+ep2TcuEeuxH7fEXObdU5vLLgaOPBi66qJv+MLJ4MXDcccCfveZ9nlDSGAF6OjmgS/vf/t55hWlGXaWIWIzSyTuMi1AodVxtQZbXjIUA1odnf1cyj8IBnv/cbbNTs44C/LvKTtipg0UkI1NT4+mVJ+vA4sXN+Qy7f+21M0sEAODqq4F3vhP4z/90x3Sj0feZZ8o6eDLQ48JnnvSWgVmQicGGGFSpVaASVoHYuwhwSBMBcPgmgGWAwWC+iEuBiOxxJGD9wweuwSu2OSKkDZYAIVQuHRoRTvsCfqlee24h4OBenh9CvQQBED3AJoB6DjapdzcuqpTrJaaeUINLVy3CCQtvQm2nYG3t3Ya8sAe16Egjd4NkViH/zXrU5Hu0G8KSWuoadHomZ9pOh4U4nTIHXCOoDyGNREBeK04ExB0kev59WsPvXQqj8uT9xklfcm/wj+htz8Qn9xjreQ+fSv6muPWgtLF0opzsVslyh+nZN13yj+WkFga3/9Y3/x322CPf488nXtE9/qVNWwjuvFN2uv73f7vvX/8a+K//it//9V+xs3JUIfAex11FS4DeN/4ZnJP7H7kx5Kd7/MdlJWiq/6gibJeWZjzjPf02aCVxFBhMOqmVoGgz6Iiph+nxH4d1YBKTsB//m/EttrVwIbBiRezR/8Mf2vebLAOzQQRIPv5x4MMfBv7jP9JNS28ZeMZKP2ZghuXMM/4aG23wHDFWILUKRDeXaAUg0IUQHomA+w4DhgXZjzoQ4EaCJU4oUsQp4D0kiIxQzsKAusqN7+63QT0zbsDQwmC+p8qAWRZ8aqq7TYGNpdKNHzNgY5glYMctAzbjZkHWAVujBlB5U4UzavgzC4OKXcnJ4mJgccza4dpSvrzkjEJ6dqHQjVcUo3fC9eJbRgURr8twBt5ZSkHaMoA97POMQ14sjt2MQx2HOuRqjLS+/KxF+xl5CNUIyb2tjjL3eVYvJyaXd1axVUPrJk0zUt4mm88b3uC+//mf86k4p+3SYVjiwJOTwMknu05K3UEJAH/5l9N3EXr1K96Je354cbQEGATSHscvtYPMY491lozSOU2nQ7hU3njFP39NeCqDnmauPkbopnvIxneJGwbAz/hg4i7jBs46q9OsQli4ELjuOmD//fPxXW+E226bPSIAAHff7b6feiq8G2FMfgajN70Jn/nsZ2evbr3MGenJwAzLxMQG2bEC3Jc19t4BKSGgBzpJBIHGr0cgewhJi+WR6cEEDH700CrsuPXhuO/Ba6FLiAdlEBZJQUykwX3c92DY0IsozjYEzykaBxGz8yTXoOgq5GMZ4DeoYGkgcXAVqgBTh1pbW6Ou12JQzRPXTHjyMGIQzi/UN75eiRgsfutFWHXb+2TPtvBBErkXJa/B8mT3xKj40LC89L0T9oYlAkVioEG/zldXVJcnayZ/FCpcZFUgB+KwRYfVo9zWXQF+HpCPJb9iFjmNmG5y0n0vWNCxyIKUyAQX8rg4y3ua5EjBKPK2twFv3P0M7LojYKqKTWhAswi5ffolG1h8594Lsq5CDz1+G5Yefxf2OXL3sY4Rpf1LLkmxJ7XL9EUBf8uesRlSEFNMD5R3BfVd9MZKELqQgWFdhm6+GXjd6/I3rQ7Tx9/4xuwSARK6wQ44IJIBLe9+9+zXq5c5I/2YgRmUMz70l9hwg+e4RW+UVYAWwaGVhgnoRsiRgmDtHtRmFSAi4LzBaIskw8Dgxw9fjx23Pgw/evA6VfsGsMVvGfL3p1euMZEQGMSXcgD6gOulM2zsAJIpRh0Cj21Ar3A6x2gZcG4P1lrIQcO1n2WIuwq5z6WrFuLEhbfgwpX7Adairtf6a2RwybWH4/jDrsEl1x4erAWcGIhpRvV3m2T1fGBjHkZEGh2VBDZk0xgoe+eNvv5DE4EM8G885mWlJyfL4WUx3SZwn43T5QPRatGgk4lvB/ndLlKi1R7QNacM6YpCpGBUGYZMPP64u50q7/l38cWjY6S3v91977rj8QCAV+1w3GgZeaHB1VXVHfQ3xXXFnZOTbhDxpFmVj+8wo1B4pnYmBUBrz39DdBNwHydBGFn0kteli/GBD3SzDgBO59vfdvvbbx/DS0TgPrYGw7ogArzsm24q18XP+9tjwmem9JaBGZSJwQYYDDZgJICtMkxEgAB8xj0ounNEkE/uQU4KVgHhv6z8khHLkT22oVAlmgjoB57/tqy/SZGAOK2fcaA9IQKI7jvaOkCgMpCCeOwsA1XME2QVIBJQeVehAYyZQhhzEFyF4CtQe0IwT54eWQXIFQim1V0opnUEh1qv1V0o+583cBcIGO8X3W76W6ZS90BuvzMRMEFf1r8DMUjKKoUPTwLK58jrWohP6pGJbr06bSC/y9Vt0WlhI63ppyk5MpEjCNRJ+ad/CkxMOIvCdDASuQi5Dg2DHbY+BD9++Hqkv6D0aLuXvzXN0N9nuUHE4yYH4xI5ZqDBGkBxAOJCKjlXn1HLn57euIjBMizDAiyQZKDrxeoidMP++McxbLPNpM5TT0nduSBNn0x8kQAAIABJREFUdVmzZvbq0cuck54MzJCc8aG/wIbzn+P8+sOUdtRrrRcYkz32HKRL0OMBXTVggAwBVBlDBICBfeUaJEmAy+AnD9+IHbY+FD96SPdISdBV6jEIYwd8z75w/QnWAk4IwOLB0nhm4PXofIVVwO87YF4JXYMKMAOYysLUNhCCqhrA2gFsNeEGDZspLL/uSCxZdAsuvHI/X2SN2q5lRItDdviKWrabdxdiyuX9kFemPZPgNrMDB/8MWrckiaC9lGfcN3q/SARi4SMRgVYSkNYnnkYG7DeRgEYCwM+zpJPGlbF9MwhvBOn5igyXB2un7mlmVhYudB4TtD8dOeIIYL83/I24p9yzwO01neX9j9yUuAr96MFr8fKXvDlYBvjt14Yhx4k3RxNmrqROfeOfqUCMg/4af+Wm6xI0FmJAZKDLhTn11O7WARKuS+A/F7c+yJFH4rPnnLOua9HLOpKeDMyQDLxVgFYb5vNeV8FFiMYLSNeKAHyFVcA906N/e84qIMG+iE8sAww+ZoCLBlxNr9QA0AUhoDpqQhDTWABh+lED0AJk8BaEmDORAtY2qABjYVHB0QVy39HuQhUMBjDVBIytIzHwYwesT2cNgHoq9prRKzJYBVypwZhQchcCXN3JKtDVhahIIJpaXVylslonMQL06Glnm/fl/Ts+IsDvzTwJSMJDdBcSoOmAzDeVEuBv+m0U4jqA/G5g3YivbnmsOxLAZRxY6aST3LdeXJCmTxbSOu5DymGHufGiXIYhA/p4NsgAAXtxj1sWamLXyqYv2AW//I8fNebEDzd5/o6CNL12l9Nw+fVH417cOzeAv5JJTMJ+7jyYk05mBc3gBVrfwD+Xww5b1zXoZR1LNf3FbPpNbx/64J9jYrCBHCsQFhWLVgEO0ANIITwiiID7rsxEAPoBA1DvlwL7yaJFjBQIIuGB0/2P3IQdtjokhZjGA28GxEtbrBfbNyYtO3zzMiDSS0BIZAaqreXUoTFMLehGKz2H4wkYU2H5dUdgyaJbQdOGWrjxA0DuvWARphb15AOIr8z8a4R0uM2g48vHFg+EDIH1OwQ2A1d+X0QczK5TF+Avrq8/NiyekwPDQ2M6fo8l4caw2rG7Ofw+VDj77fB7j5fA7znwjZ9H7sMWJ9Nb+6ecNpxLdkpUlQd0Ouk2eOg+n5n2wOEmmZwc18DYvMQpReOzTh/zZ2B4FoktlUd/9i9YevxdYlpRY+Q0o9PZrr56htrFxmcZPa3AN4qnZ4+VYXHjUfnZl+Kzz6rw/KcpvkvcyLJ2rdvWrJH7+njtWjcAZSZv2LkqJ56Ic849d51hpn5b91u/zsAMyESwCkw0WAVMABrw++FbAC8gAq0qHBv2MjMsHYwBXyEVLDzoGZ5zPLj/0Zux/VYLZL7GpO/M4nuU5y/rxOEZQPiPx/F0RAxinY0olAMxcouS86jTlK2mimDAXY8Jfw0m2LnzVydZBGroFYpt+Ifwwk2+gyaZ6dlLN4iV/20Sov4j2e8mjKSJNuQaTXQiE2uSnbhvpGK4xsmxz9cYGR+SSnIQMg+Yn90zIdyk4T4vY9Jw+u3xOglwb2K+CXlQn/BbU1vTBw1gX68lkH7YZACZ8g3Pw+h0+pmwfr8CDj4YOOBNfwuw3z6Aji/AquH83fVuAvTGDHesNy3Llo3eDnG+pPjsyREDSQ4k+Ebrlpaq8+hCDIaJ2xW7jrbgGJcS8C+RgqOPfmYRgje+cV3XoJc5IOv3m2AOygdP/zAmJjb0VgFPAhKrAAf3EeBzEC6/vXuQAvMCyJQABCcBGhQy0BX/U5SfilOBBwmONBBBzF8BohzhSevCj2WbJCCMvdQ5EAiLPrEZm8RWse9qApdceziWLFqNCPotLly5P95xxCSEFUBZBRwxkHEc8NuEHKS7ncQmOy0Z8fYaRXS6DOhvPOTEIEcEKErmG2IEEWA583tLlTMKCZD3F7u7MgRAw/gEwLMaK814TglYbyIKpd9XAdQm6fOgP00nXWvWN6GZEKPl053jg4+txtZb7IukHWhCBbDnROH8jZFkYFjg33SsbhshNKPQ6JIB/rKbP4Hduc1CWwrKZICXPCzwb7IijMUqALRbA3TcM20g7Zln4pxzz13XtehlHUs/ZmDMMvCrDVdmIowLkFYBtq4A4ss84p2UEBjjVsvk0IQTBwEI1AtfAAKWfyiP/vu30wOP3oLtX34w7n/kKyOcvaHxvwiz4gSHfF+Ujd/GIIwN0NOTJtOMQo4Z4NOORgBkYU0cQ7Dvn368sbZfvuFYVNbNQx0GClL9AEzVazwJA/QUoy6MTovP/oMwu5E/Ao0wtsjPKBQKtYAcWMz3O4gRcLygw2C2EYFJPqUMTG4/C+79viACGglxIlAOK5EAdSLxPlfhRuXfKVwEp22SBZKZtivTMn0d5G+yMU0muDmtSY8ar/PcF6p+fM7J68nDm+Shx29PBhH/4P4rcd3q9+Hmm9mzwcuox4Sn+f6w0jq9KM+YPaO4GBYY1ZsqlI8LnkYK9JdzKeQzZHhXCTMKaXDfZZqnww6b3VWCe+llHUu/zsAY5QPvPwMbbvA8545S6bECjhSA9dDxXvL4rcWgMgMB4gGwnvAI9uF7yfmaAtQ7SiAswicJoEKoshY46f5QJpBBLjUGCINtGQ8I365MCxtYhImrGau3pug1VRaKN+x2ZlKXm/75L1DbtahruU3VT2Nq6mm87eDLgu5Ji1fjgiv3CXj8wpX748SFN+NLVx+ECgPEVZRTYhDOT1w86pXzhAjWTZ9KYJ8B/9L/mE+OKLi4lKQoYN9Rikk8EO6WZZuWjucEVZdjMmWHm79bGKVOwD6Vq+tklKoG0LnzS9u7qKerrMNL+qKY4dq4DPrX/+d+dLVhCzYitn90AbIFVJzspCpGNt8wREAD/umQgDbZGTu7MpUVMjkz/yjZ/IWvxs///V4I5WSPDvKVvuLG43AP7snGzTbwb5RcT3+XgcQHHQTccMMfNyFYuRLnnnfeet8x0Mv0pbcMjFEmJjbEYDDf+6ZHEhAHr0ZrgHtlsQF+PJx9V5W7RPE1J0FwdIdw+QnrAOknLjgS8Iv/ZoCHH78D277sANz/yE1CbxghEkAzBIlpOAk4WzAi4AiOe28bhEQF8A9rsMeu7wvl/ctdZ6O2U6jrKdR2LWw9haoaALV1BRoLVM4EXWEerLW4/Pq3YWrqaUxNrcE7jpiEtW7FYg6566k1qCZoLQPZyxeIAZtm1ImH9HxWoWCCN8haBbhw/J9oKQKQE1Ie6rJ1UM6CpzRd4hLE/2vgnxCBCK67WgNyBLbdEqDCZOXzccPoDAX+hwX+mTaX/4r6Ym89fv+///3A3nsDB73l7GhdBcS1EfcKAfTYBdFaxmH7notrb30PVq+WvfpA+birjBt7LcRCrMRKVjFfL63ICEIcz5TVBIvEZpu8SlhOdtv5JOAxlU+Ssnv4TJGBSUzCTq6C2Xc/X9AMko71TYgI9NILejIwVpkYbOitAhPMKuBJQMUWGAtTX5r4niq4B3EwTy977fcbev0ZCQgAmrkhUR4S/EfUWFF5AB7+6VcVIRhWIgnIvpF8qWH266x1gOpsAjHYfZdTQy7fuucc1HYK1k4B3j3IGIsKA9TGwvge/aryELq2qAIxmIBFHfxhv3T1QThp8e04f8XeogfPwmJqag0Gg/kJEVAMgJ2dBZpchZiFQPYsllyE2H7CHRqsBY2koA2NaLikAat0EcrBTQk4ZbgEu12IQFdrgGHt2UwOSiQgBYopcG4kAGMF/yXg340o6HbN6R25/xdw1c2nTnsF4pLQjELj7GANLkJiPQF5z/DBwcOQgFiGwdLj78JRp+yeJQE5XJkLn0mLAJev4qu4+tbTcPi+nxNmSnHGhj2OQ6Us+8+lYFEB2KQK8lumnh4ZeC1eO/3BwyS/+91w+tQ2b3gD8PWvu/313UKgBkX3RKAXLj0ZGJOc/oEPY+ONXqDGClRsOkuauSIO9I0vsLx7UJgZg44ZEQgoT/f6c2LAwggMxTCAu4iR25I/AAA88tOvOULw6M2Z2uVFvmgMDGzGKmA6Wge8ggVes9OJAIC7f3Ah6poIAJ1j5clDBUMuOaZCBQtUA9S19dOyOuuAe/VYDGABW/uZg9y6Ayctvg3nr9gHxgAXXLkfliy6FRdcuS9MvRZVNSHdADzsJg+e8P4N+LzFVQjs2xrPFzjw50yhRBQ6SEE9e881ajTFcwTMoS37r8c0SOycKUWDfnW/6rAAejPAvhjeQAKGsQAkJCajww67gv/hgb8ooEHPlKLXC4mXP7UKGBg8+rN/wZYvfiMefcIBOSN+nJ1LAYCw+Ng4hBODG29MSdKyZflVm7tI7OmX/kER6htx+nGAcT63+L+9zOkC/xl1EwKA3/++HNd2cXfbzX1/5ztznxC0zILUE4BeStKPGRiTzJvYyE9b6a0CfI77agCaNzwOGGZAvuAelLMKgMAvdwUS82lLy0AEE5EEIIQhhBlmFdAyzB0SgZ4aN5BotlkHgFdtf1zQvueHF8Oi9np+BWK4t7T14B/UJ25NPLbWW2l8mTVgDVkIathqHgaeDFx0zQK84/BJN35gxT7sZCxquxbGVsGNKJIY/4IHlJuQowoQC5BZxHEC1tcz346t0F8EatehrmQhg8Z5hfw9J3F0jkLEvTwgzZCG3LfJhJv0rkyJQJs1YAhyoID0uAiAMWlYVi8qN+rFEA38C3nmrvX6yAK8hPUFvGVAX79IH91zIH8vRgj66BNfTwYRy86S4es4WxYBLWSZTO8DK0/E1kUIz79KZSw97js4ZTkrs1SXEcPGKr///fAXQ+u/4hXAD3+47ghBh+lOz/vc55oVerzXS0F6y8AY5H3vPR3P2nhT5iLk5rcPhICIAAH+MAUmIihhPfw0e1CIRkybzLtv2KZJgCIERuTI/vO5tlnvGgA88tN/xjZb7o8HgnWg7WESH6CCBBgk1oHYMW4ZKbDYZfu3AQC+/+PLXY899XRZytWEl7w1bB8GxlYefBNJcPvw4wHIWmNN7UmChbVTqDwh+NLVB+GdR9wAixoGFS64cl8sWeQGF0/Vzl0ojAFW4whkCzgiEqwbYCRAWAhieAQ0igrQCs0ipsPYgU7SJQ+loVyEyrrsLutsFZAHRsV0IQI5wN/JJajNVSj5aqi9KenIsK7gvwzoh9HVrVmq39yWM84A3vQmYMHen473g3i+cRAvjwPs9M8S+i3lhe658dVd5F7Il6YXnTSr8vGFGYVCL33wZbLiXnDnbfHSF+2BJ35+t8rVZvbKIZu9YBecvFxC+FGB/0ySgzCj0L1ssPTChRJYb7VV9wy32gp46KGZIQQtYL8V6PfSyzSkJwNjkImJjfzUoW6RscQqwHrwea8+gXxAgqQwS5AYP6BnDorzZIePsjxw0iDBv3xZxrwzYoBHfkaE4JYOraGACYFlpK8UEx75DjnttM2RAIB7f7LCAW3SCj44njL4dpHWAb9v3BBhE8C4hUEdphx116V2MzRVFrauUVXzPBmYgrWOiPHxAwC9YGvU3l0osQwI/O4PONj3pgNnIQD4zEBxKlIJ/FPJ9PgLl6RRUEuOVHRydklihFFBo36xn/k2LfG6gEI5jURgWHIg6tYQn9SnRUeVWdTpAP6zoD7T9kXwn63v3BY9XqBkFXAhRj13qHPEirBsOey+1gOGR6nz7FkJYs++dPYxTKNuTU6Q/CWbvVZYTHZ9xTtZLJL96YbNhJWAA+l3a9D90EPdM9pkE+CFLwR+8QsZ3kYOxtGr30svMyg9GRiD0CJjlXEuQnHVWxoAnFlXAAX3IBMvSQQNRALUwmJGblGPCAFY/pSpBFjCPUhZBfjxoz/7OrbZcj888OitLHXOs1TGCPH4WFsHDCx23OYI/OD+q0I69/IkCuFJgI+gni9qx7i2ALyeBSyNH6i9e48znRtT+8HcFqauvZXAYlBNhLEDF119MN5xxPV+/MDeuODKfXHS4tvc1KN2CpbchbRlQLkJBRLgLRQAKXNXIU0ExEgEll8OhTQRgC7koEmnGawquKric4AzPW4sIcOJdFkCBCaKwxGBJhKgzzSpTRcC0JUk5E58BsH/+kUDnERjQLz+sX2NjBPPBpELCC0XoWeDm5C2Bs4lofFP6T3CwLsaPDxkAT7l9MnAbBCBSUziXe9+Fz53ngPb0wHd7/7lL/MRfa9+L+u59GMGpinvf98Z2GjDF4S1Bcgq4KYTrQBGAiJIjzNgJO5BfExB01Sh3jqQrinAXpACWJj0v3APyp9ftEb44yzOSxNbPo1QAP361WOx49aHAgB++MDVrPeMgWBjMusOUFvSAGuLsOgYDSYOU3pW0VXIWBgfH12FLCpTw5oJDCpnHajtFL501VvxziNvdCRgxT7+nBzIr+u1MNW8omUgDsxjJMAfwzS7CoGIgCAGsTnStQbKcVlNZklIZh3KKzJpO07jSv/FtymE8+/EPSgt3pTyGooINJEAo9Q6gPsGC4DYM3mdbD0y4D9fX6j2KNOARQdeiCu/cuJ6MaMQtwZpt7HsMXsWaSuB+8sDUG4ZIMlZCOYKMUgH88bzogHUW774jXj8yW815jJ8OaO5/jQdvxFvHN9MQl7GgXU+9/nPj1r4tMvupZeZlKpdpZcmcdOJDmAyVgFBBODdhJh7DyDf69q8TS48CIA8EoqsaxBiPCcZ6YuS8ufuQQoYCb9wF/7YE3di6y32jfUJ6fSmXti8wRhe22GrQ/CjB6/DfQ9em09rdCuxc0Zsl7C+AjjxImsMH1wtt8po8jaBQTUfg2oeTDXAF1ceAABYsng1APeyIutBXa8N5IC+o3neA3+wzfr0zIQP9tIWPXWWSgPzLSi9erXfLopHUX8avW8ctyY4OQtHpXLhnZi9R0rx4HdVhiQkIcMRARPC9T1OP0GTpmPnzhcVlPFG7gVSX9aJeiHzsh5v41C+yeYcz9c/M9YjsBIGD/O2UK6XghRQx4mypMr1WTKvQrpH1G0jLq0KG3bLybJl02ufMDsaPYdsfG4BzkXI2tJm2RbTqBL8f/epUYf90kfrtB330ksvsys9GZiGLF36fuciZNjAYb7AWHhBKTehjHtQYhXIvOgSApC4BkHuixeWBE2mGrDI0hnGlysdP/7kN7D1Fvv4YzluQWeY6zk1MNju5Qdh+5cfjB89tEqlyFXGAzRmVTF8NqWw8Zc9I1yNpGAAU7n1IKpqAOMHgA+q+Zio3OJxF1y5b6jJyYtvB/XwW9RucTMiBAzQW/Vas1a94GwkDi4dkJIGQJAFftz0rsw4JvNadZNhXscZApBlC7lUpdC27zQDI//Fey+HuBqJAAfLrCDDwSePkyRBlqdIQkcCEAEs1VXHGpFCgH/DY5UelJ7Rpc99+Yu/APbYwy0IljxP6ZkaOlDic0N0Foi2KyNzfn90IQE6vLTfRgZGlX/EP7odRQCsrR0BoCdBOM5sihhssfmfifECALD5C1+Dc5bvViQCwwL/3HEvvfQyu9KTgWnIxCASAb62QJzi01sCGMgXL/vwApM99AnoZwuLgVsK1MtQEALosigciOsXqHAGGAKcYMDOeADz+JPfxNZb7BPBT8BLKbiKh+5g25cdiJ88fCN+/PD1sk0EQdJtxtpRESX+QifQD2rTkKZsHXAWgjgdrBsIPg/VYD4GfuNCwD70nNFL1gP8S649HAsPuFD0yvmUCK5CYFYCbSFg+xSnLQTyW+9riSQjG6UDGkc52mJWUXIoXYey+86ksUmOSZaZ+1bkkxaqyUGZCPA6MqBfigMUScgAd/U7SdKL+xjsXPSvmYN6qpiE8wn4B9MzTXpjRqYzJBFY62dofDaGY6epEHjBQtBEGgtgvyk8pzcMGaAZhYrxfkahnKRgPz6vvELZMqDIgZZX7nAsAOBO3DljRGDcloFlmKappZdengFS5R6K/dZtmzexke9VnnA9zNwqkEwnGkEtoHABA8URvGbAbnjRVUqHgQX/woeIg9TLjBXgYCnu078UtD32xJ3Y6qV7xUyCWu4N50LdisZf8XrkMiXbKb7IM4OuS1YB5Nsm7ItwN5YjEgH/LawDAwyqed5laD6+dNVbw5mcfNQd/uXqX571FMAIgjahyx457SrUbCHIugpZqPQIcWJ/6Pdpzqc6je8eTqJBdLt6q1aLglF78Z40Kj4WaHL1NBroq9wTRJeWLAsrAPeEAJjwP2pT1HAEwJgmvfh8aQKmc0liPfmzVZEA3z5P/uIebP7CV8N0/Dz5i+/iwD3PDmV99wcXAnCL0FLZOSDfFta03XFHp4lmOsutuBU3fO1M5fLjnlVbvXQvPPLTrwUXorKrkFyIkQtZP4clArX/dD2eCVnXeKHf+m0ub71lYER579LTXQ+yn0WIA8yECIAB1BBGF0HN5mPoZa0WFoN+E7E8AugwDEZQlhKsmCpvFUjExLykPkAAMCEE/ovrGhhss+V+2GbL/XH/IzeFOoobsUAM3PmpVZtZexiWF2+bsNqzcA8y4doYYcUZsO84doAsBIOB27541YHs9GtId6GpAOyD7z+3DFhJFGQPfMZCwF2FkjED0jIQ02o60OBSZLmW5dpFvazY7G6QrvgyAef828hjfd/ye1/Em8x9a/ROCxFIziCSCfm7UnHht8rjTKIVg02antWD/+a7EAD3G1FliXaLBMAYVd4cF2p2I9rEpMd0DSiR2Cq1ZdB8plwdnUumiyqFdyhyWmIzYN+F+zFPHYjAy1/y5sRFKO3s6Ab0hyENe2GvsQ8ensQkTj3t1LHm2Usvf0zSTy06okxMbBSnE60YEVC+6wLIQr/Awkg4cH9WTgJCGg2CVbwgBIaX5cJcMRnwAAmgIplggF69sTjwI0Lw0OO3x7wNAVSDrbfcx69P4KbQtGzaHQP4RcN4D1QFCz/7D2qXjwHcdJ7u2HqCxStk+AFcvJtBiFYmdgA8kDMMUBnXb2/MwOVmrM82jgMA5gfs/sWVB+CEhTfh5KO+imVXvMWfp6unpTINA+lhq32bUq+/XzvB15VmPwJ8+4SZkOhcXIuIRcx8HlqvDdRZWKZpeEQhaYxIVdrLc2JyXwwIi9BcyoaALomQ3uv+9yJVDYsqg32J4EyqIZJqEgDdAGm8qnNOU+Svs803gKpaqnPUQZfgihuOn7EZhcYhhrWdbG/2zFLXOr2WSa7FmF/86gdYevxdOOeS3Vvr9vWvu29usOO3ynTXKugi0QqZ97u3YdXh0XrfrbU4Z/lu+Bq+lnQhDHPcpttLL73MrvRTi44g73rXe/CsjTaNbiVhkbEBCIyHXm7eo22MwgEK3FMvVQD5hXUFBAlA0E97Sfn/SD6yL0dOBPgLN3d7qOf2Y098gxGCkBJbbbE3HnxsNT/hkB+RAlddv3CYsTC2ZriWiAHNglG5fVuBALZoL0SS4NLlphu1vjYWMAOXygNzUw1gaouqmoCFoyXxhN124cr9ceLCm12+bD5vi9rVhcYEeEuAYWkDQTBswlAG8MXiY4I0SCJg/bSjBiakt+BTlZpQb0ETLLsArULnblhaHZ8jE03kgUvuHiyA88J30SpQPG6qx7iJQAHom2yorF3yG87UvitR0FUu6ORixi3jmF50t92AI/f/grj2KSnwxyTTese5tHwBWxI9vIbcicYhy5YBCxaMnl6Tge1e/tawRsyoRAAArl39btyBO2I56xkh6PFOL73kpbcMjCBhrICZ8NOKcrcT38vP3V2CCwxbD4BZBTig1aA/uhzE9QTIxC8IAbM4uEMJWsg9KIISDphSksJfrmWJcZIQGGy1xV546LHbYl0sh6YZUsAIQEoM3MrCsL4HntfRxraL9fZkzJXg1W3cB5EBB9araoC6dmGVGaAGUBnfW29cWlsBA5/fhVfu560Dbwb1/DuXHsPaXREAbR0gc4OhxcdMICXwIN/1/EMRBtIrE4FIKlhVSpcxB/I9aSgn62oRUPqdk2ng7A+7pM3pJADdFED/uiACeRKQjVfllCmOkdVpaBRZ5NwGSh/5iN9hLlP8eZY/zl/ncYi+H3OEoUmITOyyy3jqAzRbBqyd6pzPti87IHER2mnbRcBj0UVIlznKcW8h6KWXuSH9mIERZGKwUWEWIe6rytyEEMG9ezWxGW80AQgzCxEJSC0DnDjkCEGTe1D2tSiIBdfl+RiVOs2JCEEgBRxkCKtGzJOfk3SxMuBuV0IfaZvIMQj8U5hWFHIAMa07INYg8FONVmHK0XmYGGyIwcQGuMATguibG310AYgBfHrsQNjopeqYhG8XivNhPi6+fpke+MvTslCetiBWH6QldEg4jVd3e8pmCKfu8dbj9rxSssGB+jiIgP4d5olA/tdKz4NSfExpTJpTokOnZEp6c0/yLkLquaWtp6TXedNlmk4uQqOeTxPBnd6MQlNihiAX1j5OwG150qAHD/OxATX7tB03xR2IA8c+XgDoZxTqpZc26cnAkPKed78Pg8G8YBkgq0BFU36y2Ws02AUYXg9g1s8KJNyDFKjVJICNEQjWgwL8iWmZBj9uHSfQASRoDiKiFAHIkgKENgjAHZEEGAI4rB01UYp6JranYeU1TS8KTgr8jFDe4uNmGZqHQTXhB4zPw7zBRpg3byNcuHJ/N7uQIgRfuvogHHXQcnCQTcPj5FgCTxIYkC/OKBR0fRgnEGy6Uk4sLDjI5zQhH96Oz234yqta8dWSy4jC7u4u+DUB6fF3k+oVAH8BsQ1PBDJxrgAVatL4hATouppwy+u6CB1R3PpBAkhMaE72nAzHCMfiGYH4DGzfsqXO5CmNXbhlwBGCKeyw1SH48cPXo3mxsSm25ccbrLr9fbgFt2SB/HQIAP/00ksv60b6MQNDysTExqHXmAYOV2YAPRWmBO281zq6+sS3slyQTKwrwHqtBIhmhACqzAgZWHkk/IUq8osKZUChzLgiqcFLX7RHWOpeWge8G0tIbxB93pXATU35AAAgAElEQVR3ujGwQcWnMca7A8V6u0QGNpxHJTrYJf4i/xQ/qNjSQF/vOuSHIBi41U3r2rPkyqKuB2HFUy4TAMyEYflLdyGATPXGlSF0TEzD68F9/r2bkDtj50YEQ5YEAyP0TJI+7yoUXYA6C6WLrdqkqPbiEf+fSTIeGSmvBJq3ZKlAeyZhZyJgkpC0RKPKy9XONKTnMQ2/6/UB9Mb+CfYs8MeyfegZFo/zomnp3G+DNjkP5+EAHIDaA/rwhLJ1xhqYlx22PjRxEdpx68OHdhHqGjdbrkGTmMQpp56CZf/UWwl66UVLbxkYUiYmNoCpJmBo+km+2nAA49xliBbC4v7+aa8/7/mXPeJqTQHVqx4JARTIM6LjT/fECxFmdtLJvRhtBHAhe/efE4GXvuj1ePzJb6XrEMhCG9tZ1ye0rbIOcPJUdhdii5El7kHcNSiN44PDnavQRJhSdjCYj+XXHZF1FwrtBTcNqf4Iy0BYnMz31HM3IW0hUFOUatchHkp5cRegrBSjuNWioGqzoR3yzigoE8WoECG1kUkynL8VjQ7wu4Xw5DAD2hXYH44I0G+9gQj4n4MuIVenCI5zOrGcoxdcOq2Bq7MhscboQASQtQCQpVFvaVkzQxC48XLccgNuwO3f+niYQhQAbD2V9P7zaUb5lhN6fgzTyz/s5xAcMiMuQr300ku79GRgCDnt1Hf5OegzA4e5W4sgAuwFxMGpnjmIDzrmwJe7BwEQhICRBhEPStrFKsDJRYeXnyIBABIi8NOnvg0DKEKgKpE9lHHJIOpQQxOsMBERcfCv2i+4C2kXoejixdcaCC5Cwl2oioRgMM+tTuzdhhwhiO5CfEYhThIi8GcLAvGPWI8gugAFKK9WM9auQsHppwTQLVkW1LcmKIFEtHUmWhGYlmg77A8r7WnLxEcT4jzM1tr58JQklMB+kmMbEWBuQfmadIinX0f4mebORtWBE+0ZEppRaFj52MeAV70KWHTgBYjtqIhAcPXJXWd9vUqbkpj1tMH7sHksG7EDm9xtrJ3Cztstwg/vvyoL+mmTJCE/XuD6Oz6IG3DD2AlA7yLUSy9zQ3oyMITMm/essFJtxXuNGbgOIFVYCiRolT3/shc7iQd7wfGXH+37f03uQaklgRGBiARYz1j5bcVJAOCIwE+f+laawoMVTQiGeZdanpEG+aHt4iBs13Y0a5MfJJwMHo7pqgwxkFYCf53DzFETLGwCVTU/rFQca+xelReu3A9vO/gyAe7lh40fYJYBTQwCJFeLj4lFzDIDjsF0onWgtbGT6RLLyhkS0MUNIcdRkv8UUa5MNmZkjpECe7dbCBdIPKOT5bsl8pAvsxsRyAFYlj5bx5hO8ISZ6KIeo0gXJ/kMC88CfsyebSpUfFrJQCYs08/QaZstocGyZKEsgf6clWCnbRcmLkLbv3wBlh73nRkhAOMmAyv9p5deehlO+jEDQ8i8iY0EKHS9xn6lW8OAKrmuaNCauAcNMXOQcBMC+OtNiilgBP7yi6oyf2QSdhFX5ks3ex1++tS3RZix1hOCvfHQ47cVAJtBGmFZrKuTGzvgjw1gLb3t/bmxY/GCF9OP+nUIjCvDoHJ5+uMKfu6NALgsKgB1BVS1S15bE/YdjjK4/Pqj42JkoLECcKDfGhaWWXsgjBuoYaxbG4GPJRDjBWDUmgPWnz/XM4AeM0BtGtXkd3I91KUw/JqYmA+0MJ2QtOF/kkkoLA0TUcUKtOTVTUqpTCEyxd0m+e928+A9TZMrSJL3Ui2M/FfWydZnjkurWxB1kjS1OYsSR9OxWI0mbc1PMwpNmlX5eD+jUM695pU7HIN/+/GXCznnLHr586fZiDhg7+L/P8z+YiyetovQSqzE0TgaFhYVKqzACpHnMizDAizIPBd76aWX3jLQUU45+ZQwxWS2pxkG3C2I917HF3DFuokI3HOSwGcWYrMRhfS8Zx/5Mowvh6URYsDyoDqwyCHEWQUc+H/JZq/DT5/611TJ15UIwVZb7J2qdCnM1zu1DPDzkG2bm0VIki49xSi5B1UwFR8voCwFhi02560Gg2oevnz9MdJdCGC9bg7sSwtBLa0ABbchMc4gO6MQJxcURjrdrQMhVZJGpi1BBgErWr2ChncbKpc7Del68zXsJ4A/E6IDshqmeID23+m4iMDcBEq8l7+RCBimDP6d25JSOoZNT2bDWnAVrsI5y3eDtVOosxu5CU258QT1Wrxy+2MSqwAAbL3Fvjhw+WOhB38KU3POKrASK3EWzsJL8VJsgS2wJbbM6vTSSy956Rcd6ygT8zZiYJFcRzzA1EQAfF+apJPBrgzgg+szEhCAu3/xxbzAwC+Y9UBV3sQyYlre/9jlrSRBV5kImEQXxgQLwUtf9HpsvcU+fmXijG5DmYLgmJxlgKYFcudoMysVG8F/qeeYrlkUZw2wqGqEV5WbdcjG5cwqALWBrQxowbErbjgOJx91B5at2AsXXLkPlixajcsn3wYY4+vrQDOfXcjVzZsa+JgBYR1w9bUUxmcUgkVclZhbCNwZy/7xButAUMzcD7EoFe3r5pFN2hfPQ3I9/Dov4zv8uQUhl1e+enkZ3UIwlGSL6EASgNB+eQ2T/Vln8/0jJAJSWogAP1YpSnk1PYHG2SLTAf8LMNyo7ik43/+6tNCYbaf3ALDNlge4fGbQKjBdWYmV+BQ+hU2wCesOsfgEPiGsJguxsCcEvfRSkJ4MdJT5E3G8gGE9xmDAnYN/0WtPvVUGobc6gNAm9yDV6y0IQSjLSbAeCEsET8fJBWKqEdyDNBH42VN3tcJ6TggAMEKAdk7AlAyQuAvB0hSj/pxsbINAlvRKxcH64iTCRRvCCVuQ61AFRwDITagGUFXGH/s6VL5caxEW+0Ed6+jbXM4oFHJXBIATAyCQCEt6LixdldgTgYD0WVTA4k2g2kZdkUZ9W3ctBEfQrjwE6MNqyRrocwKQq5DNoKgcuB+RKMwST0jL6EgSkrgMUaD/YyACBsAxCy7HZZNHY3IyW6F1IrLKJeCvjk02sRMbd2by8o+r9/9SXFqMOxbHJmEE3sPMZlbC8LDnTXi77bwkaxUg3XGQgVL8cThuZBehN+PNAIDn4XkJuciRja/j6zjp5JNw/rLzRyqvl17+WKXqviDLM3wLi4xVYQBxnAFIziQUwH0A5hHsO2H5cvLA9eltZviLLk8IKFr2ehfQFSMYwxMB44nAv4b65JMX8jOxBR5/8lvYeot9Wssr5Wd8fqFtk7bUKxdnViCmKUfVwGIxiJjPLGTkLEMVcxuqqhh25VdOCO5C56/YG8csWBHdhcDchtSUooEAJOFuhqLElUjMPoTwwuczCmkXn9gZWJhZSM9mxKWpI9HSP34uOuEo+yrMNms1SynFeHooRxZTPFBBbUSgnHkxi5zODMuoMwo5KRCBBoIjnlVQQfTcNRkdn+/S4+8aroY8y2kKzSiUA7bRfTCVy3AZAKCup9zmBwvXdq0PW4u6XhvCSrLtyw7EOct3y7oIDbtfip+OfBAfxCW4BM/Gs/En/vNs//kT/EmizzvN+q3f+i1u/ZiBDnLqqe8Rqw3HbxPAp2HgnMLiS1a+lNx/ufhY+IR0xmXBCQHPn0lIw16SseSYf0zLicIwROD1YlzASzZ7LX7287tCvKpVIZuo5wjBvth6i319SJzSMuRg0v0Y4tuLtZloy0C6UksM39xHEgRKFwhBVWUJAR9bQIPLq2oCK2860REC6hXzZADi26YbUrBP4QlhYGFy5iHflv7QVwCSHCiSwIQuQXnsAPu2aV45zpCU04S/syQiF2KHzBihbYaTdUUWOoL0LmQh7ORz6/TbXYdimvot1HNBkwPeJQOxNeUzfP3Co2jIdF1Fjjaqw/7FuDjr/rIcy/GNe/5REABbe0JgIyF47S6nZq0CW73UddZcjstbAX3X/VzYqHIMjgEAPEt9no1nh/2rcJVoG/2W6qWXXpz0ZKCDzJ+3sXIR4mMFJPgX5ICDVWO8utwPQFSQAP764nkilqOIAX/IGVWvQATYc1Dq57cmcUTgOzzDTKoSIYi7jz/5TQDANlvuV1Bqz4Paw7BzFUCfM+DsdKO5AeFxsTinHwlBOpiYphqNA4sr4z3wgpmeWwWYlSDpTZdgv+sgY5E+EAEF5sP6B77ZCBi3rjvApNU6EMuN9eHhpNblPyXraDVguzbuNGinZeXDCmUNETV+0UShC8hpRdJzXuRZsw4TOgYaEbZ8xnV/4mX6EEYmADy/LjI5CRxnj88SgTo8Q1IJ4wbqtZ4ArA0EwIU1WwX4LEJde/mHJQLTIQMbY2MAwEaZz8bsw6XqIU8vvWSl/2V0kKqaF1Ya5oOGE8uABu2ip5736MuebE4CArgPecfUWQhv4EAr1+OgmKsLsgGVXyo87qUvej1+9tS/wkATgbY+xXYA8viT38TjT34T22y5P6tbRtNmQ1mYJAWOeNE6BOnsT9J6EK0I2U3kIceO8DUnwuDyaoCrbzklTDd67KFXpUQgZyFgr3nr3YPaXIkEWVBAntkLWCPme/qFBOuAO+huHZDlxSOr6kBfmX3ofSWF3v2CnaOcT6tKIUJZLmaUA0wTq49EEeY0P2gjAnndEuCXMTNLlqZDHgBpGdCfnBDQjm5BDvxbZhXYY9f3FcYKALfe+VG8YfkPMNXwIWDfZT9HBJZgycjjBQ7BIbgVt2Ki8Bn4D5feMtBLL3np1xlokZNOOgXP3ngzGDalqAN+Eczz3uicW44mATImAlGnwvQ1IRBWASQvw0QM0vqYGJntzAcEujEAXvKi1+NnT90FwODFm+2OJ35+NwwyIMgAsLkYHZZ/eT325DewzZb7AwDuf+QmkS73wsvWAQZuLEcNml3I0knbeJ3gwb2oDmuLGGRDnkIsUFWAtQa1BVAbPwTY+Fewsy1cc8upOPmoO1wSW8P68q2vi2X1cUCfesoaBhQbCz77kBF6iCTBoDyI2DrCGU7PAtZYse4ARSXty9IkCiKOLBF031E9GlcegFx/oKRDxVl/jrlK8BDbAgQyJ9MtqDWmS3S7FBIX8xy1sLn3PvjkJ4EddwSOOuhiH9J0JTMdHeJBwVPKp8dMnPk4X68a+JdIAMk1uAYn4ATU9dPZvLqwWO7TXyo7tz+M7ihyBs4AAAH2u+RH+j3u6aUXKf1sQi0yn1YdZuMFRC+03ue9+wzUwxj/Rd1DSHqxsgQDnBCAAVlWhgHbzxMSrwBRcOmBSDPVWE8Efn5Xy5tSwvIySOe9wPlcHnviG9hi8z/Fti87APc/clPocXbt4kCiUY99ThligIlkyFaAoUW//DG8GZyPN2AzDkVXrQbjmS+u8lnW1pVbWQO3sieBfq9OMwyxmYXczEj8GnkSwME/bWFaUUcEbJYsIIQZa/xUpBy7SzCksXskELHZJYFQaXRaEU6lKWplrecUxk9nSgWxfSgAL2YVamIhrN6UXqiHkxRnIXMsh5XLT+uSy2V9kaXH34VzLtl9Ts0oJCQ8y9y3O2xo5ywHKBODnEbnqs3Q5e4yo09Opuo18YC53O35ur8oWgUeeGw1zkeccWdYcN9l/z14z8hWgflwq77vhb2SuDtwRzFd7ybUSy956X8ZLTIYzGcuQmzgsCICGsSLsQIAIrgHIiDnOiZoxbygwL/q7RIvwxwRYCosD5dt0xvL6QUi4OXFm+6OJ35xN8uvkLQUJ3SyBwCAx564E489cSe2fdkB2O5lb23KCQC9YPL0g1+X6Oqj3IlyYwhaxxakLkSVoW3ABpwPcN3qpQCA4w+7Bon/P3cd6jSYOPoLBwIQXIbqECYGHYcxAQh5IfnWLWqz34GghO/sBfHf8brwBdKsUOQ9iZZlqfbVf9gkhOVTqlBDiM3rIWkbnYHNHRakLa/2crqnbQaIZc3u6aYjo88o1MXRo8PzJ1HIBnavlXzcj1X4jEKlMQPn4/zsIOLP4/P4/o8vx9TU026r12CqXtNIBEhybkFrsXZs+9OReZgHALg383mL+nDRbkO99NKLk54MNMiSJUvCarNiFVsG5rnrTkIOgABB4Y84LZD9/z6eSIAgBEjKigSAZa0lqRMFD//GckTgu+WyksAupvxmefSJrwMAtnv5QeUUObCmggKpYtONOvcsNo6AfzoNOOZ6jECEODn70KrV7wXg/HfluAEC7rXckrEEccBxlxWLA/AuTB0aiUBsR60X9WObBvXwnUkTyIMiARzY8sHEycBidgEzA5BDufyCJ3wmRwB07SGOc6C4PSyNb5MchWnfH01GymF2+MDokrUKtCbIB08DvM8kAciJ+rUnpCAn5OYzVT8dtr32+EgjEXjgsdU4D+dlQfx0icBa/3k/3j+yVQBg7j6Zzw/Uh0sXKtlLL89E6ccMNMhGGzzPzRLDpxQFA48KnMOw/SIJkPCfWwiyhEDlIx5mhoeV3IMYYRjiUfiSzV7nBwkbvHjT3SIRgEFEC6nLjlQpxKtcmuTRn30dgMV2Lz8IAPDjh69nwLSr1ym1pg1kwKKCc7eRlhnAgNyI3E/D6/BFy4xBkUdbKs6lq1AFeLvqtvfi7Ydfh0uuPQzW5xnchayJZcHChHEBlC9zH8ouTFYDpopEwFsKjKncoUF0z/HNEBcri9V23/Iusb6W0l2In2tTOwDCXci6hrWAcheyEeRZuPEL4aqxfWtBY2XEPrQ+lUiuQlTpWD9XRksYkKaNyr7OqiHcyfm6xP+N7ZXm0ho+vFa3nNZPyfaGqP3C06Ljw2hdvionJ4El9iQsM/8EQJLhpqfgzbgZ78F7MDXlXIUOeNOnWi0CQCQRuXKGcQcaxp1pGOFkYBghN6Ee9/TSi5TeMtAkxiC3tgAHkFnLAIyPpq4jMLchqB4pI8vzOSRuQmGAMUIZCQHIDhTmBAQjv9GSVLn6J0RlOGl6VTzys3/GIz/9GrZ/+cHYfqsFGdeWfDVKIEFYAMJH9voL8B8sC2UXIopHyb3I/9yOP+xavwhQbiGyuBaBsA7wT4PFQM8+pC0DelYgaSUgllWwEoSNXS9b+Oaa3F0o/NfuQpTWhkxsDBSpdZrsfihXhqWAROnYNE6myIW1xeTK03VtSdZUWjGtqlFWr3yOc0+m26/bkLoY5SnmHMGOTdaBkizEQtz/6E2diMCBe56Nf8A/ZHvzh+n5b4o/E2dOyyrwCXwCO2Nn3If7YDp8uPSWgV56yUtPBhrETRPpVx3OTCmaWAZon3o4AfVAUj3QhkC7JASULjy6MuWkBIBJyJORCHTvDYlWAeDFm+6GJ39xD+RjVD9gC1JmECO/XR/+6VcBADtufSh23PqwbP5lLpAjK9zVh71Csq5CdH0j6AciCZCuZBRO19mFT972fgAIU/zJMQO5sQMyTg4qLk1LakM+ZCUQLkI5cpDRzboJefRZmmoUrJxIIhDzhw15gOUf0+cIA6tHyAssHdu3OpxpCALJSQYLU3mqE1fCM7BJVHo+yVembB3bUEYS2gHEl85jDkv6qJhdQHfOJbvPanlNwgmAdhM6D+dlxw0MK+MG/zp+ujIdQN+TgV56yUu1rpY+nuvbqacuZS5CcaGxomVAk4IsCUjoAGIvPjcXUG8UIwRMsji60TogSUGzyHrqOEEIslkmtZ3m4zf2FJM8/PgdePjxOwAAr9jm8EK5pbCcMKsJbTCAIgHJWgXassBAvx6ErAnBOw6fDAv/yPUHeG8/W5jM9/hr0hCJQdRx7k8R5AvwnyMHNq8r0uSAf2YwcdIXXxpEzEF7Mn7Aiv14J/C8CvuaYGhyYCVRkMe88hmaoCwNKXDPh6VSSN+QrI0GlAlDuZal3J7/3G3xn79+GMcdeiUWLCjXae7L9EnOpasWY+nxd7UrzpJwAjCsdeCc5bs15n3gnmdjq+W3tgJ6De6Hif8r/NW0rALjknWNL/qt3+ba1lsGCjKo5kOSgAHrrTfSMgAOIBn8NdE1iBo8RwekI4+0LFC+oixWpvjmBERYB7qcsdN9yWavxRM/vxuAwebBKtCetqOtYDgRvajpi+6hx2/HQ4/fjldscwResc0RI+WtC4rX1hM/40mBsBwUBhWjSq0KjBTE+8G1z9sPX+WtA1PKXYi7DTlwr8lB4i5kIzTgcQTWA5AvuQ1pFyEOLgrWAg38c0TCJY+EpXkQcYYEBLBu5b7+nwB4DpQZOeCkIRYgj5P8WmC/zYSpSBFTxGwlkmGTMrrUI59vopzXmQUZfUahXC271Hw4tvX6V723a4VmXEozCnUZRDyMjNrbnyMEubBxil4NufTppZde2qUnAwWpwiqzqYtQBHQZUK4An+EA3QUgexD0eThP2ZUAMN0Q0wTO8zaAzTd9DZ785fcEocnVqRX3c2LTJqKj06IEWHTYQ4/dhgcfW42dtj0SO21LvU7NZQo3kqRcyiGCeneuhVWMC+A/sSIwojF5+wcA+PED9ZSyENgwfqDRdYhWKabNuxEJlyFBBNjUo1lCUAvSkNPNWROEPt84IQj/eRgjCYAiBAUSYFleoqeeIqieEGXlyIEA+10JgXY1ohonQJwD/wzQFoQqV7ckhSRSMkYXlZHuhGC9lNaqFxRyjxklH/jI3HET0kBXk4Mm+Rg+hgceW52NO3DPs3HO8t2GBvfDkISP4WNjsQrQefZkoJdexic9GSiIqfhc8dJFiH8cKEf8ZtaBCJuVNYCAv3YP4oRA8AcF9jnODWGMAHBTQKNZQMB6ZxX4xd1l959GQtDVOtBGDHT/q83G5F57Dz62Gg8+dit22nYRdt5uEXbebnFLUd3BkGHt68iRHDAcxw9oV6OYJpIF91nlxw8cf9g1iYUAyfSiJauBtgzE8AiuJWiP4D6OQZC99zldTh6Q6EniAHXVKA5SL1wCRgJ4mhBaJgGdxw9YpcPrE/IdHyFI4X+GlAgtK4LTO1FrtRGCHMpdPwmBJOuZ+EyIforkM55GpcYkO+4ILOyIjycngaX2vZjCVAC61GtP+5/Gp4vjBu5Bs5X3Y/jY2CwAM2kVoKubWwch9+mll17apZ9aNCMnnXQaNtrg+coFhAFttglCoHvqC73uLiq68GhrAACZB3f54d+JdUAB9uK1TWH7i4kIANj8hd4qkE1nM/uszEL/VE5bigJJI/X0ubAHHr0FFhbbbrk/dtnuKADA9398OctXg88OooCTJEMM9NMUpJoM2Cr0ZwVy4HNYtmIvnLz4dly6aiEsptzKwTCAnl7Ul2esC0/r71s5TD1au0NUrgoWMMaDeZ+9Na5NjKFTdNN1WgqzlukirBgc4/2phni36rHRxyAdf94+X7drEKY4tY54WVhfFp+eE7B0j3k9l6ZhutGQh9qHCXWmXCkgTi/qS/btBhFm2e/LsvLoN2hDXZEL8+VnpyAN9eBhUOGx3CgslxDVoEOHdAMorbkn+lxKYU6e/9xt8R+/fihVL+a9foi2ApT2u8oDj63GR5Z/BGsz6aUVr3uYjjsbZ49trMBO2AkAsAZrkjjdJaWF6tXjnl56kTKxriswF2VQzfcuQgPvIjQIPbsEvKVlgIUbMLKA1EUEEdY7MQzDc+AvH1gM46flsueaUbmnovM0DXFSLEXYqGNDpWxQKBGCrpKkbclM0AgCN9biJ4/cCPhe9VdufzQA4Hv3LR/pvS/OSHAVq779tefrEuhGC+oGq1a/Fycvvh3nr9gLJy2+HZetWoTa1qiSK0AgsvYANobT+Wri4EhA7K0ncC/BuxW4VJADAsGMCFiDlDCIGtoImYuEwOsG4C4JARIA7+F6IBAEhUdYf0AQAlXWHw0hcO0VMX6DDsWotp3T4FiyHRbOSY1N02QzivK852zdaQ7+dS1NawC0yUIsxAOPrcY2W+wjwp/G09m8SuRgmLjpvQ1SWYiFWImVWTLQJB/Hx/EKvAIXfemisdanl17+GKQnAxlx04ly9yBmGSCwxwE5HysQXrDcQgD23jLswIgwk4QxfSIB+gVoWPpG9yCTOXL58tWFN3/hq/HUL7+fliPAbg74R7ADq2Ahw8LpW9km/5sl95IXdIC5rbgXkQXwowevg4XFq3Y8Lmje/YMvtpQUXXDysX7P1iJc+jXQzD5BOWzWTsGixjW3vgsnLb4d56/YGyctvg2XTR4VgBo1nkksAdSodTwEu65srIBBDesXHzOmhrVVvIymBlAx4G4puQf8bjGzxDLAahAAe4YkDE8IoEA7JywmEBaXD8szIQQMvGvw6CsYLQTqOEsIXP2QkIRYZhMhAIWB2m8MhMAXS1aTFPAbhvEbdBCqGs51LktKA+KVS2KT0xnm/MbbFtnHyAjCXV9yoPt/439jJVZ26ol/4LHVOANnjAXkl+I+i8/OyAxCnMCU5C14Syh73KSkl17+mKQnAxmpzMBvjAgwwB8GjXJCUCIBXBiWN2qMAYF69xXTx10N5hk5gRF6qQnUqD2js8uUkMYK4C8AiBV6rAs5E+9FvxkbX9q2oKdeRmwWGu7vzn3ef/jA1SDC8JqdThDp7/q3f5L1E4NiU8LifPxtvv4B7PO6Ug5WDBiGrXH1zafgpMW34YIV+2DJ4tW4/PqjQZYAWJ69IgEh1CBSn1phvspdO+JmmhDASnJgq0ZXIaCGsZWyFiC4IE2PEEC5CREJsPGcRD6KEIT/RC48cfGVtdRWQxEC1u4lqwH8CQtCgDSMgf9AEigMVIeOhIDOn4pJYDLdby7/SAoKOjzGZ3/sIStw6arFmJzEjAjNKNTVbz6coyZ44EH5cyzlNlMyLvBPsmwZsGABEj/4YXvhyToAAO/D+xrzmK5l4Av4wowQgZtxM/bH/rgRNxbftzq8H0zcSy9l6ccMZMRUckrRrG9+gQj4HJo3YVkAoB5aKXTnIZReJyNSYFRYZo+pvHjT3fHkL74LA4MXvXBXbxUotkwkBIT4VDVCiLHe993GMotvx+HemvkXniIBYCQgIQ3u+977rwyAf+ftjsLuu5wSdL71vXNUORyRW9T1WpGvrlJnQKsAACAASURBVFFtp4Q+AXVra9T12jg4mOC7dQPslixejQuu3BdLFt2KL19/jEsbxgAwEhDypmtSetERYq9hUCXA3V0abS2whEs9RnYEwfLLKMYFECHQ4w1yhMBXnaUbhhDEDndNCDxYtvDjEiJYxjCEAArsWw+UjSIJIBchDrh5GLdqaEIQ6yXCRJ00IVDnpUiB9Q1TthJwUhCYQV4nHM/VdwO3BMQw998wQhDDU8380cg1mklWoWRcYwZOw2nZNNMlBrPRA09Wgd/hdyGsbbwAkYEe8/TSSyq9ZUDJySe9Gxtu8Ny8ixBZBtg4AAqXA4sRN8hDFuwjGSFgL2jp8hN1c5LlBhlKkT4rTaLbRQIEEjsUJ18FES4RbAXG+boIr3zei18iAYXEFsD3fnQpaIVfC4vXv2ppUtadd386gPmYXhEGWEzVa0DgH7DRM6iewlS9JtT1sH3PE/mvvGkJrJ3CkkW34sIr98OJi27BFTcci2Dh4GTA3yvCfYgRhIQ8mEoRghrGVAHEyIHF3K2IgX9woG8loLcAvMWhlRD4unIiwfMG0nTthICBZbafkIA2QhDSU0syAtDFRcimJMHwEdtgQN9mwoqEwDWKNfRr1mCdE5fpk4J0fy4IP2frLD/JOdhICFTK0lGTLD3+rmRq0ekC/+mkn5wE/sr+NT5u/qYRjP9P/M9GVyHyuz8BJ8i6jZEIXIpLZ2yBsd/gNwCA3+K32fgcMehnFuqll7L0ZEBJVc33U4pmXISgLANEEABIQMw+RurnKUGeEIhykodbWhc+ZkHXSDEQAMCL2aJiL3rhrnjq3/+tSDjiG0zCe+GLbAli+b5Oj/T4MfUMG2MKvvjIvquz9IHAuCXQ3UYCbHaz9VTQovTf/eGXAmi3tsZuO5+EP3vN6fn6jig33PEhWNSo6ymsWfvb6DYEwKLGhSv3x4kLb8YVNxyHMLOQrVjd1TiCAEKZlcDGS28DITBxPxACtx8xqiYEfmxBwKrMWkDg2UZSUSYEGVchD2KltQLKzQcJIUA2v+EIAQisG35Xl0jCaOMIxJgBmjUJVM9MWChfhbkCA1HwsD7G0QUfmRREvVyqdS0JOeLA36ThlKYR/NtkJ4maDnifKYvBFKY6A/eSECE4Fsc25jEKKbgSV87oSsOX4TK8DW/Dr/Fr9vaU7+DTcJqoQ08GeumlLD0ZUFJVA5gqWgUSFyFuFVBAvzheQGF/Z0lADPT78sVuCri8YUyCKrJEArTiizbxRKAxQwNOCOT4Aa0TgZEcP8D1S8dNL7VcryW533iAzLKxQs2KZBRcW+52w/K3BPYcGfjX738eboVgRz7I5Ye2N+z2IeTktm98DLSycF2v9S5Czq3HwsLWNdZO/d7X3+ldNrkYSxatxoVX7ocvXnUgTjjyKwCAFTe+HcJlyBLwz5AA+DuKZhOClQA/DCqWhAA+3iW1EZcafz0sv8y5sQMFVyGPN4Uu4r5PGviMRUokwPb12ODE4hD2mwkBXXUT2UwkAEW3Id8+nCRAWQRCrzUjCZDjCEoDiwPI93WyhoWFesHXDSzOx9NZWZZKdJXL3xyRAqkn6cackHBtM8Df8rrz500GGOvfekZe96r34PnP2Rrv/3+HW3BsGPA/HaLQhQz8Ff6qdSAxEQKuMywp0PursGpGiQDJp/FpnI7T8Sl8KoTliAEAnIWz8Hq8HhdfdPGM16uXXtZH6ccMKKmMm1KUuwkFywAfI2CMePCkzWjSzcg4/l8QAj6AOBAGIiAsC/bNi28jAgbA5pvuhid/eQ/XbpQI/OODX6exTCW+qh3oMmH2E8uAR7QORK4hy9D4X794+KDhcs2tOAp5hbdxmt6F0Eq/mkgwQO63f7nrbAf4+erAPq21zvpg7VTI3doatp7C2voP8D5EsNYPLK5rXHzNIThx0S24cOX++NJVb4UxFd5xxPUAgJVfOcHVIUzFmVmLAJlxBKbqRAjCvh5MjIL7EIE0i8IAZA7iPbhVwF6OR5DkIIwdTvLyYejmPlQmBG0DiRl54QSgi0Wgk9tQbhwBA70ltyHGTGL9OCCON62NCkUdoReKmQZiHZNE0BwJTg74U92NsBJQdOk3nj8arl4zo98kbdOLDuOISYRgARa05tVmGbgJN80KEQCAO3AHTsfp+BV+lZCAT+FToh606FmPd3rpJS/9CsRMlpx4qiICAwf0UcFDfvdtImg3NEbAEwTtqiPFyIdR8P/VWvIgZwkQ1grocpuJANd70Savws/brAKIpyhD5KbzTsNk3QF6OPNNq+dfak0kwIb/GRKQAPl8aiii4W0B4Y0eVwaO7kmCJJAFwdawdsrPHgRPGBwxmKrXRh1I3bcfvgrLrngLTlx4c3jhXnTNAlx8zaFYeOAXfRlxBeFQw0As0hWL4wxLtdT1ZQOkV4fzJ1IDps/bJpAly9unZvHqO+hQflamV/vWQp4bhfHyVB5kwRD5Wd5GKk8ep/KMRDDeB+E+4WWw+godulcZ8aTSIkhVYdayknj5VmhD1IXqnL//+X0Zq5P7HaR5HrPgy1iwADMmNKNQd8mdQ9y3FtjkeTvgV/95P/t96F9+23NAlWjTbbr6o5CDZcvc97hX312IhZjEJH7rP79nnz/gD3gaT4dv2ufhf8AfZpUIcPmZ/zzhP0/iyURnnCsg99LLH6P0bkJMBgNabKyCQZUMGI5jBwAx8FeDdeIFSLcU+LIejQRxp3mbXHkqRS6E/998U7fCsKYIzWJjfYtvMd8Dy309qOfOQFgHyE+cv4hd1ryvlV7Vqoe74PKTB0HxDG2oodcT5xLTxLUFGBBjYJCD//AJwJ++HbAX4wAYOK9rBr5DemcVoPOt7RTeePAPAXzFWQcwAIzFJdceiuMPuw4AcNXNJ4GAm3NLUeMIgosWtaFB2UIAhGlHUXvLTeUbj7kQMXeiMJ6ArUXgwq3s2Wc9/Mwjx10Tny4/01BMD7bP0wEAX/mYTjuOT3GZpjMW+XDYblYCugc4Z6X7ODvbELsMPr20EvgiocuOJ5BzG4IJIWkvv415kztS+ju3TNX4P/mbkbmOgFpnQCw763gOuv7ymZLb07nCAi943vZywTHL26ilXh2bZ9yWgbZeegD4ID7Yec2BhViI23AbdsNuIUx3ROWOv41vh/SzLWTVeDPejAoVbsft2brsg31wycWXzHr9eullfZGeDDAx5CKEyo0bAF9sTC48loJyMWwYkREoOuDHC6S96PxBq8YFGCILquc8i+ENC08VNt/0NXjql9+DgcFmm7wSP//3e7u2DsQ4AZ01e9MFCOKRXIQk7OXFxhdIfmF5JoxEZN6kERsx8mAC4NRTX3K/dohaGVagPKW0d9gDa5vZEHvS9Vb7BcYsYm8+XwvBWQSc3tsPuw7LrngLLrzwCwDgCcGN+NLVB8GggrUGl1x7GIypcNyhVwMArr7lVEQCxno8jSZSDujT7EJoJARs36CBQOQIQXQ3ioTAwzlNEoDoPoSUHPChKJwcBDCtSYVvgibXo0AOENPTvRSOKX9AugllXYn4mgR0uyiSIMo0iPA2TqEKflcm4F+vScChfgb0B2IQGyVLDOi2Eb9rRirmivh7JAH+Np4jJzXZZ4bPJ+oMXYWx6FkLvPKVw6yvEGVyEjjb/h+cbj4Q82sgBsPIQizEd/AdAMDW2LqRDNyP+0OadSkLsRBfxVeLdVmCJbNdpV56We+kHzPApDITfsxAhYrWFzB5C0EA9gz+F0XgekrLCIFhSgzwR8KhH8iUkycnJonM6jcqdRReVS5WDB5GJA5i9CeERUAPQ+bAL4XmshJkZeg6WFUTAWMYiOCMgpdsEHpucy/XCLlLLidAchaCYHgigGhJIP3aTgmXsjctuA/ADbjo6gXBJ9pai+XXHQljDI49xPlZXHvru2LNfN6x2dwJOaDipxlF7RutKhMC1LCYLiGwrBe3fK1SYO/SDTOwODc4uUgOWHrwY8S6UF7NJIHqpwhAZyuBD4s3HcKvSQ92DhnJe7VMDALa70QM3C5rhHUsAljb+FwpkoJiYv5rbAbLvBmGqt80dIYVcn1pIwLvxrs7WweACKgfxIMh7AV4AQD3DPklfin05oI01YXWJOixTi+9lKW3DHh55zuXYOMNX+CJgFpfIFgFqgj+w7gBtokDtSXjCUzyX/a7ANEVicem5AChnjpM5c8UhrMK8DwFHGExlhGCWFsJ9jlR8KCJ9erl3s9ZUA/wnZRI5AgFA/thCsukUFZLYzyQk4CLq0volV6pBHhwl6TgauRchsil6PjDrsX5K/bGF7/IVkP24gjBJC66eoErNSBkg0tXLYIxBscsWIFrb323Cw+DaImgxEV3XG+/nw0IQBgsTMAfjBAQCRiZEJhIBLKA3g8w57yMX3M6hbBvfZ6+tRWpSPLwF0u4DfF9dmm46xBNi0tWgHjchRTE+rr8jbxmosz4u3JtESspLAf+JLKgf8zEIJKiGUCxIwsD/4wU8CeL+NVlXADLeXYLHwfwHxcxWIu1jURgVOsACQfY/45/z4avD0JkoJdeeilLTwa8uLUF1ODhklUg9DBowI/GjjQ+/5D7YkdtnRbaRahQQlHJAJu/8DVqheG2QrnwXrimKkh0FQGaQmlEKozSRQR3JYIA8ernqJ+RCl4EAT/eCytAvgl1ctUz6Rs71Du9yMVqerGICnzMAWh8gR87cNyhV+PClfvh/AvOLeb1pgX34bxLf45nbfRCX3DFQL/B5ZNH4egFVwAArlu9FECFMB2ptXBjByoG9FNC4L6N0mshBCD9HCFQ10GQNn4fEDkgIsBnoUrT89uHXJBGcRuKvcxydiu6bNKFBw3TkHYhBT5ekQL+DIhWAhUG6ToUw+XeaMSATloC6LlFBeIPOzwBxBgJgH1B1t6KXQ2hi2VOA9jPhDWAyxqskeU1EIETcMJQ1gEt6xsB4HIsjsXyS5av62r00sucln42IS8TExsEFyGaRYjge2IVKKD+kILIg0pBqEAQgnCUIxgAuSIlZeW4gVIzuUCQVeAHuconxeczzykqd6mEMLH/BuKc0hmRYtvzb8qXlxFdpTKtatS3qJc8ppJSnyteL90eElrl2hqAIAJh3EFmXAHAezPLcvHFF+G/f/tz1FNro2tRsDQAl0++DV++/hgcus85OGTvz7gyyR2JPsE9yVsMLN8nnZrp0axBal/MHKT2WV7hGzQ4m0/balm6WNd4Tjx9LMORKZVe7efzyOzzY1YPkadwAbOsXaNTmAvjYKycP++5tnTtLdMJYWBhvGyk+mov6Ii6xpwkSKbyZb4A8LaDl6/7GYXY+SbnCYT6b/qCXfDL/7gvtru1Piq9DtBt4OXyyWOw9C/jGgOsCcXWFN817aiy1n/W+A8/zsUdj+OxEkNN27Tey77Yd11XoZde1gvpxwx4MYYtNlbReAHjejizVoGQskwQFFIP02gaSQ4ClCRgGoB+viwBxI1Rmib5n8WoDVG5+ge3GiPTOJzCexSjL6/oYTdOL+8ilHTVhY5L/h0GEhvjk9gYz8/aFyuiRGayEP4/qYAObmqkRKzcDyCbg223HXvoSnzxqgNx/gXnNBUWZPnyS3Dcccdh4402gYV1411gQIODLSy+fP0xMKbCUQe5XrHrbz8dAS76Zg9jCAwAv8qwG0/AVimGgQUfeAwECwOg1hZwi6C58QhAtDYAsUveAEW3IX9/hG78cOu4QcZgA85JJes25MekJHn46ut9ukTsmDA4/USzx0AHVyETAoKlgu4Kdh4umTs59lhg5ZpQGL9fxXpiom5MR0RYpqd1qEx2ArDpY28WJfATqFmVwKroJbRjFm3LpwEdbvqCncRMQrvvchqA24qAfVRrwLgsBcuWAQsWpG5CM+kytL7Ke/FeXLr80sy7tJdeeuHSWwa8xMXGBm7MAORiY9rfP/b+t0jA7TlgTwr82x+pHur2kgrxPji6CMVSh3k8hmZQqTUJgYpHoqn1aM+TmkBu+DeUng6htLmKl86ziTRN/8URXr2qN1n3nHOrwNTUH4YqY/ny5fjt736Jul7jNrs29s6zcq+44XisuPHtOHivT8ce4dCZSvXR9bIQ6xGINQiUxUBZD4TVgVkBEiuBleVCpOFWAlYXRqYQ6iN7fLNrGvByc/sNx1nLQCdLgc4LaVm58mkvsRSwcnU43+NWA33/iboxHWY1kHcwb9N1Le3nIa0tllkG2Fobqv1y5YQ9m27TiRuXZWByElhuL221BmirwZE48hljHdgVu67rKvTSy3ojPRnwYsIsQjRewPVuUi+ndPXhXVGaLOQ3ch+SwJMGIZdRrOFoNs8bkjAN0/n+Zpvsgv+/vTOPt6Mo8/6vzr0BccZxQQEX3JBFRAGDyA4BA2GHkBAgIjKK+4L7uDI687oNbqiggsqWhBBIMJCFBAgJZBEIm2xhFVRUFFDfdz4q5J56/6jtqerqPn2We5N7z+8LJ6e7urq6zrmnu59fPc9T/aenkhChUso+T7w9fPycCDBL8nOG7zGIgEr724UDJd+7FwbCiE9FRfvEX6T464W+tmw3GCl+/FIabzo2ok884nJcdMVh+Pn5P267t0YQPImh5rP2tT4jCozBfNnik3HY/t/Foft9G7kHg/k+aghDSoYChX7LZyRAGFpFARH6kRMCBRGRihMXwiQM4dBOIgQi8SEMXNGvyFgXfdiQoqBQJzL0c6IA8bG1+M3lDGYpDsrqCKP6D3+6Ay950Y5RPzYGIiFUJXJQ/M7SV7xP2fHi5VbGfd1tWgO77trZtKIpZYZ/LnTIrfcLZ+AMzJo5a0N3g5BRAROILY3GQCQEoqRhh5KmYMYoVHGd2HR0ZiXCflIQJA3lwo5Usi2Mosv90vZlA1VqohvcUXWcLOyOqbWto+2bCBdCCFTw73aXXB5vGiLkpxhFKIvv77IvybZC3eKnym8ulur0X9dHYawWRyidYQ088+z/K+9IC2bOnImTTjoJmz3nhTAxI01opdFoDFq13wCgoVQTly8+GVANHHfIBQCAxTd80v8awoSY8d/EfBYbQqQAF07kE4f9Q8wUwhSldlkBLqHYNKjhE7GV8rMIZcOFfD3Th5bJxcr+Bu1UpO7vIJOLQ9iQtnPWu89nGjbJwAjtRtvFb0L70ze/bvsJoDqp2NWRZQi/e5nILI/h0MlCENq6WEe2kZwXZb/zDe0QiPMIdMlnEUUqU7PND+HOyXS3smZ6Vd4JVTPlpELHrU/CJCzG4lGdFNyKuZhLIUBIGzBnAMCp73w/nrPpC8IMQuIBY85oMeSN6UKqcMZTEJoIATBxI2EMOqX1iHSmXIyWv/Qlu5gQoZ78rQu332hbEARy3Vh4WdPfiwdE9kl6xMjg9y2L44tj5vtW1q48aLU6iI5Xcgw/EpsIgXg0OYQHnXD4HFw4bxIuuLA4lWg7zJo1CyeeeCKes+kLoKHRcDMIKY1GY8DWcjMPNTH36lOgVAPHHvxz38aSGz8TiTA/Bafyf8loylBYgz/KJfBTlDoBoWCeNRCmGHV/8uLDxawA8LM7FYVh+D2F30xox8qYSIvq6JhZcYDUUB9ZUZArayUKHHJWomhTXXEAZHMO4prV58VwMnkycPrpwH77GSNdXNYA3yudFgiLu9jv1p8kLwbKytot76UY+Af+UWy/xiecgAlYhmVjUhDMxVxcMuuS7L2UEJKHngGYfAH/sLFG/LCxKDwoeU/mzykiBEBkpPpwGeUtB3mTC0cUQqTquqbKVuJjbrn5TvjTU/dWNNSKXCe82eLX5ah/bEiUiAOfTJwxO7LaQQkrCQXvQNajUOejle4ThyDI8vhf7Q8cmV6FcBUTH3/8YZdg5vyjsX6oN/NgB0HwfJj56DW0fW80NBr+dHfTjTYxb8mpVqA2cMzEIEiWrvxsEALu6brCeIc30t3DyUSisZuaVCYb+2lIg1Geegkqk4qjev4t8RKI/ZUVDgUvgRMVQhy4ZYt8loWyHzR6mFiJKAAQHctWCW37YwXjtf5zCkL7kvR3XtheWBBncWQ/ixPAvYlzDACmTLoAly0+BQsWYFhwMwqVh884M1dHfzD5eV62xXj84c93RvvIt5StXrJzlDwMAHMWvR23394bA38kxEAd47/QB2jsgT2wBmtGvSBIcyAumXXJBuoJIaMXigHAG/4+iRiJ8Q43sm+NDaUys02q/EsF2YCo3fQ96lDxri42mXfTlrwlpjf53pK5+VoKxgSUMODSuftTC0yOPoqjSaMNEAJDHrHMO9AbyscW0zrxSGIIDwpx9SEsSMbdmycNn3/Bj3rW51mzZuEdJ78LuqHRQBPhGQNNU+ZDhwDvKbAG9RVL3+1/V0e/7Se+1jWrvlAiBKSxDmuIV4QOCaN/w3gJgMIsQ372ImF4u+ZSo7xMJCA8TCx9uFj6ZOHoWMp3rW1RgKQcue0Ix4nqJAuFy1hUU/lGe3hqtUWIwQ9XGtnPgkAo8QrU6f+uO/473ExC3Rr4wykE3IxCf8ffK+u1Egq7YBfcjts3WkFQJ9mZxj8h3cMwIQBKDRjjxXoHorAgYWHH5rcz9t2rtHV3ECEFlL+j5XarmjEnFhRA4cCJInCr3XsFskfLbNWZ1fJwIflvromoMWudBWMurRhi3lt9ClVzPK28TrIlSVaMnn6qNQA5e5DG1ENnYtZVk7F+6J/o9Tl40cU/AwC84x2nec+AVtpO9qkRQocU3Kh99BAxBfzimvfATa171IHhIWjXrv6Sr+NH3bXwxvg/U9nUo4nQ82rPGXNCAETiQAoPWc+/1fQSKOElcG0JYQH3YK+8KEjXUyPdh/Ok6+6Ydr9oPTmWr5+KkOSYUXm6LVQp/FRLjWmpTlA8jzb8nUKIEj+wEAhDA82q3esdwwuQTI0uy9761t4kDy9YANymb8fr1Q4dewcAYHtsj3VYB2DkHy7Wytiffcnslm0wHIiQ7qFnALB5AkqEBclpKouGee62qBDrgvASScjRbDq5NouGfssLXXZYLxRu9ZJd8Mcn78KWm+9Us5HcTSXpd6o/UqPEVqoKF/KWkIjVSEOFhJYI1XNqQSYVy/aTo7ZN6Y46/tflBbgy+4+cLUY+8MgZK83mevzzn3/ttHctufDCc3Hy20+FEwB+BqCG6UPDJhk7K1JpLUbMnYHdxPzr3u9/x0dM+KFvf9maLxf+Xu7jGyPcPmMAQBw65FSDMfpbeQlCgULeS+BG+8P2IARiceA1pxQCiXGurQXunvbbUiS446FCFNh/sqIAKHgG3PG0+2O4NlJhIv/gufOwQ4GQq9jxedQlwTA3OSge9+Rt+6+X37WH34v1/Hk8DGJguJKx70W9QZ5X4VUttz2KR3smCOqM6tcx9gkhww/FAGBnETKvQCZfIPEA5KectEZLZNiruA27r7sDq6h5s3/YXtg1Y/KXeRiK9VqTms/iG2h7AEZadM7QMlacNOzDv0kXIhHgtsUWmDXX2v9cUojktpeSCoHUXJL5BRo+cVi7p/yGUcuhoWcwc9aMGsfsnIsu/jkA4JR3nAaNcSGx2AoEl2xscmUApRuxsWtFAqzxfdWyDwJWGBx+wPcBAMt++ZVi+BASOeh/AyZcqNxL4ArDuZSfcSith8iYN8ImIw6gvfiAbMv1CXE7KKwLkWAbjfMKYlEQaRAhFKRoNEImfIHu3C8TCkq0paUMKxUIOi6tKRA2Fs/AWWcBBxzg+uPmvII498y/W790T/zuj7f4/aJrd0TFea41PjB9Lf79Y+Pdaq5K5XrdOr0iZ1BPO2FaoexRPFqrvZwRnxMIvRjVJ4RsHFAMAIAXAtKQ9xtDvgCc+Vl/xD6O7Y/N82wbmZCR1mJDNlu8BW6x+U7401P3Vfe30FBv7l7S3I/bTay3gtVf3arZTReai6upHt+FC9YSvGEtV2Fi6COvgHvwFjSmTpqBmVcei6EeJQ7X4YILz8Xbp78Tg4PPCX1qNE3okPUaNNSgTSNwTw9GNMKfGvALrv8IFBQOO+B7/jjLb/rveMRe7OO+Hm8sZ0KD3JOEg1EvRIKoF7xBUlAIAeBCiHw7zlj3SsX2w8X3S1GQxvonIsF+N3AfAaG9YrJxq7yCuO30WAAicWY2iS80dKUHAiEpk+eOECcbDJ+7UBys8PpAN+33mOQTZXj5lm8pJA9v+eI34tTTx5fmDPRKHAw33RriqZjIGf409gkZO/R9zsA7T3kfNt3k34x5n3gGwn1RGvMieTgyyjMvJwSsR0FF7bb+3ouJzIUKZSsATIjQE0/ehS0yIUKydv4+lRgb2WPpsKrrbTfGmTO0glAIciGZGcjt6vVCMdq/kEhcSf18gTKMsZYeUcOHC7n8AWtRyKfvuhCGoaFncMFFPyk2PozMmHkBTjjhJIwb3AyDA5siDhsKeQVuel3Yl/mb5QSByTdYeP1H4bxZh+7/HX+8FTd/LQgBHRv3xnC2sxQhFhlwxq4IDXJ5InEIUeodUGHXVBzY864YOiStbxWECuSm8MuMQ4bceiwKonX4Qxe9BeKckCLAlQXPROI9QBAGZpdYHIhutBAIoiwaZVdin9COAnDsxPMwb+m7R3xGoWXLgKuWfQSHH/BdxNdmHb3BJapn8olasfMOJ5umXJMdGPkbQggMxz380tmXtj5uje+UEDI66HvPgJk9yIUJVeQLqPTSJ26sJTa7G6GSFYOoyNUWYUclMUHJKnKXZJWpWaob5GhnSa+Q+z78us4sxXXi3IHM/rHlIzYFqyky+KUwiKysYUAnC1oeT/tyLeqZKiE/wM0gBK0xZdLFuOSq4zDUHDmvgOSSS2YCAKZPPwWDA5tioOE+gwaUeUpvQw1CKR1esOKgQhA4I33R8o95YTBpv2/5495wyzdiQ75CEABIxAEQeRHcK+cdyCUYA8E7UBE6JEfM49mwVGSspz/XrEiwjeenJo29BdopJQjvga2XOzbk8f3XE4/qe+NfXsJcWWo/A0k914iOCuoL7t7jDXT3W/VdDM6ejgAAIABJREFUU0m9cE66Gm5Pt1pmxMp8gU4M/zrr++7bm+RhIMwoRAgh3UIx4EKEXJiQKXUmeTLi5pKBY1M8JB1Ha35rcURO/BOFEaXHC6a8b6dFiFB6m9ti8zfgz0/fVz4wJuxxIHOrLxUCdRACwC/GI/PO3CrWK2knt0/ro3eH1khsEETjpi4vQHoF7LIW29we65vPjGiIUI4ZMy7A9JPegSH1LMYNblYIGzKzDVnvgLbnh3LPJ7DLkSBQPt/A2e2LV3zCh98dsu83/bFvXPs/8QO/EkFgxITOiwOgMnSokGAsvQOtQoe8dyCXXCxEQmSox9OF2p+4/Vwl64g/V3bd/uO65cpSb4FDHgeiVOmkDsSX6uoIgWBKNB57fBVe+bK9TAy+2NbJVaCXuJwb99ndOfXqV+yPxx5fmdROLm5ASMrONw4AaGYmI+qFEBgOFiwAjj9+Gi69lCE7hJDO6XsxYJ4tEAz51GJXYUUY4pFciJE3nop8ASSlxiaJRUbW6M8cp1hHYauX7IwnnrwbW2z+hvz+ud2lQRO1WmUC1DG5M96BTDy/21raYmwNtThmsmN+yiNB2ca0XCfLsSgIOQIhkVjbocbjDrkQlyyYCt0cwkUX/7SN/g8PM2ZeiGnTpgHQaDTGmXwCETZkzg3nHRAvbcrM+WDEtLGVmwVBAN2EUg1cfcOn/flw8D5f931YufZbheRgM1CeCgIVchCAKKm2V6FDgIoM+CAKMiIB7vPBrgfvgAlrqvYWaC9kknW06S2QJALBfD+xxwDiu/N13Fmn4tL4Pbc2cgRnnJM0Ov1YkMn5AITCsqtiGOKVL9u7kC/w0i12xdkzxmPVqpJj92idEEI2Nvo+Z8CM9rkYaYQhuWi7fQ8l2XYiIaHEvkoIAlXeQiw6ULjZJbVK2inrW47kLpUOpBW+i9aktfP3wVRyiOHaqFYxVVBnVzqhTgOpIaQLS27oNswgZAs14DwD0iug9RCaemhY4nw74dJLTWzw8ccfj6Gm8RIMYlOYv0kTqjEApcNsW/5lQ4ecMDAegPjpw7EgUNA2xGjJjf/hxffb9v5qoU+rb/uu/wqDEQ9ALGt3HmldEALVsw4p8XOzuQM+L0G2IcSraBNA5B0w53hnCcfRui1TEMZjug7fffGP9CoKw95h68VehNQ7YOuksUcA0mk6N/SvNnqGQOIOicRAMtjQKlPojdufCABYmTgXujH8R1IUbCzXE0LI6KTvPQPKj24quFhnu0XcMIMx75KBodx2IQLcfpBehmIbpX2BQta89xohdzy3WhYitK70eFmD2BV1cG/JagctGkynGc3827J/bdD+numov1zLeAh06hWAGLkMDxmDMFKazSGsX/+Ptns23DhRcMK0EzE0uBnGDW6GgYFNbELxAHKegYLXQGvrZXPCWgoCIM05gAKWrvxc7EFTCgft+V9R39bcfpb9bp3hjTCVqV02OQimYZFqEv3svDjwokD5n5gTB15geOGeCI2CtyAYeV6kZBKO63gLWuUWQBxb+f65OhA/2cRDkBUJiZBAuUgoXxs5QlKvHP0338U2r3wbHv7Nsvj6UxA2KrsY2ndiPT5eevxO1ukZIIRs7PS1GFh2xTZwsf5AmElIRcnCeXEgN3t9IF5eOHiLwpZFTSbCIc1PyLQq2yr0xdJWiFCZIChpu7KZWkcSx6tl69cVBEVDvrhnxfhgNNRfdTxn9Ofqh+ThNF9g8sHn49KF06D1EC6e8bMan2fDcMnsWQCAE088GeOsKGg0xqGhBoxVrEzCcD6fwJXb70RrIwggQ4ea1qhvREnCwZAGrl31xSDMFXDgHl8u9POXd/zQ2eORIIBYtrZyURzY5Zw48OFstpL/xZR6C4regVbrsk3fXyARHxbfFWfMp2FE9kMm15bI+C8VCe5ciEVC5EUoPOgLOOqgH2L+tR8c8RmFQn9sR+R5qpvxY0MKF6RwHX7NyycUQoScYE/zBboZ+U/XDzqod8nDhBDSS/paDAAAVJhJKNkgbq7K35TlTUYmChc8BSrUVuJGJI17aXPHQUj1jPBirTaM98J+uVH57jEGV04AyGMmx3erdXUAQr2iqV8idrpyQmjxr7vpOwsuCAAnCNzfs6mH0Gyur3uQDcqsWRfh+OOnYWjoGYwb91yMG9wMxdCg8nyCsK1p92lEwiANHXLGbWzcmxH061afEcQBjFg/4K1fivp7053nZL0FcMtAyEdALELgl8NUod5oL3gL3HoYSY+8Bc7wz64L0QOzOYgP4T2IjiNG/qWoEeuuMfPzk+dtByJBxXXE0f0+G2Kgu9wzYMvRTDqWEQMV57iGxtkzxmP58t56AYbbK8AZhQghvaDvcwZCWEMyEm/voLHp7m7oFQa7v6e6kKKwT1Il7QiC6x+F95C3kOwt+inrtw4RynW8F3eu9NPpaFvZTELOSGqZ55uhKhY4f+z2PqvMFQhvOnr5f51HQGuYUKGwd7M5hPVD/xg18b1z5pjQoWnTTjSiwIYOxTkDVfkEriwNJ2rAJR/70CFAGOXityANemEML/vlV/w5qRSw/+5fiPp+869+HCcZo8QzAEQeBkiBINa16IRZL/EWSGFQCCNyycvOSC8PI0qFQXUYkSQc1623JRK0rAO4pHhxYdsgnHMOMHFiKgaA7V5zOB589OrMHnFHtfXklDH/2g9g2TJRv4cCYCTChEbLNYUQsnHS954BY7i4JGFvuyNnlSuVGuMq/0pj+zOCoNgPlay70rStXN8CW724nRChYi8gb/xt7Ve+LfeYsKKnQLZTZ+JQV1XWKtmjVzonPZxf13bBGU7NSEAcO/GnmLPoRGi9HkPNZ3vTkRFkdhI6NDjwHDQaAyaXQOYTpF4BLwCaRdGAhhAG8NZ48Aw4w1mKgxDCE8SBMb6vv+m/I3Gw31s+V/gca+86z9nZpeIAKBEFgInzV0LMFrwFXYQRCaHg5YbILwgz/iTroqP+LKwtElRyXri+mbWHf3MtXrv1QXj08RuRPqNgQ9F0TxgWFGYR8ohrphVDr3vVIYUQoR1fNxnAMmh3Gvt20+OUr2/InIEFC4ApU6bissvmDO+BCCFjlr4VA9fNey0uuuIwbDLuX+GfMRBNBQpECqGgAZKyaLO7oQojPjuyJgVE2Dfer+wY5SXGK3B/5f7ttNw1/macCgBppZctl9DmDTa0WNJ2RfhAsaIs9fMEeSvSrcuZWJrNIQw112PWrIva6/hGhOv79JNOtbkEg1BqQAiDKo9BLpSoGe8D96wPVS4OIISANXrj8B8jDlbc9NX4fFYK++72mejz3Hr3TwFtzVwVjLZKURCtO2Pd1penuBMCdcOIEISAnw60kF8ghIBbFx2LBUAdkRA+cCQS0uRbLX7jG3AEeskS4LrV/4kJe8QhYi3FgKtXcm1z56nLF+jUyC/bprUJ5WG+ACFkY6VvxcCBxz4MYCEuXTgNhRH7TAJxnOSr/Bb5jAK/xRkhStRUsj2XXOw0hWwboW5+NaylN/vCvc4VdDs0lRll7KKtyoeORcesPk7OJB8+dHLzF8OIYlYh4yDQoev2fTTlC7Rixsyf44QTppuwocYmaOoBNJR5qcaATTauSDKOtmlj3duX1somyLozSgj1Ei+BHzSH218Y7tbQVto8BVmKg33Gf6rw2W675/xhEAWuf3Z72r5rECooHUQD2uGz+w2ZM8THwSh/7G5EgrtGRedWkmA8kgRjfQiAGdG/7+Er2xg2yde8atlHsGRJWG/HyO+kHiGEbGwwZ0A1AG/MyxH5OEsAfntxq68i6qV7h225TuS3FASISo6Za6i0vNM7Ui9+HyL+OTf631H3dMlyL9HJoo5Ki0ctipNjJp6HOYunQ+sh6I3o+QLdMnv2TADAiSecjMHBzaDVAJpqAA1t370wEGFBufyCzDZjvJvfu3nQmPIiAZFAQMFLEP2sROJuMKC1jx+/8Zb/CeeebXvvN388+3lvv/fCalEAiLAetx4OXSUK8kKhJIwI5gPHicahQ8F8Tw5uV0pFQiokEEbMU2tWK+CwA76Dhdd/bERnFApiIHgCtG46GVWB2brDa48qhAht/9qjACyrnEWoWwEwUmJgrFxbCCEjT996BjyqgShx1xSG9zAAL0bqSy66CmJ0P3mv7kS0XBAaFbXLSvJ7tXtX6qG4KN0lbOhYstTeyVmPddoR46HJ3bysn7FUECXNJrTWeHb93+t2dNQw65IQOtRQg95LkAoDH0qEXH5BEi6kG9ZgDyE10lvgDGEF6UFQ8M81QBAQgHUqOFHhrHhrFZuHjsEb0itv/VYs5O35u+eup1d+D3fcNwPKGu4y6dkcBeHaIX9+yogTF6rk+goIUWD/0b4wJwz8UcJsSK5FLa9WFSJBZ+r4kmR60Q3kHTjvPOCww4piAIjPOpUu6Kg0QteYUrRbMXDMMcMbIsQZhQgh3dL3YkC5B44lN/9gxCvvOYi9Ayr7MvkCsgyokA+iTZURJbJWsqaSdmvdmxNDoVbd4cAZee3t5YztuuFB3Q3IyVFT1VZjbm6hsN6E1kN+NH0sMmPmzwEYUdC0xr8TBlosRzkGUX5BmnjsDH3xr31asEq9BVpBq4bPK5B5N7JeNOqurZgoeAxUNILv9ll123fdFcK2Ia8DwB67fLj0u7lz3SwhFIJBj+RYEMdzQiFOOrZ14PoMQCXGuxcMviB4TMLetUXCA79ehG1ffSge/s11odX2ToeeMn8+sOLmb+AD09fi7gcuQ+46lYoxANjxdccVny0AYNHyT3jvRruj/COZNDx3blhm7gEhpNf0tRi4cN4kvOPYxZh/7ftMgZj1R974paHv10sH74MRomRd35xCSGwUwiNpy08lGgmLKgNdYasXvwkA8Oen70f1KHurMfjhEAJxroDsR9SbdtwDte64NRvL6CRrsol+tfpeYhFw1EE/xtyrT4HWTTRtnPNYx4mCk0461QgBNRB7C6QwaDiBUJZ4HMKEghAw3p1g4LtzregtMMuNuEx4DNxvTYYOGePcJeqaYylrkPuZjFAM8Vlz+/d9QZg8wPyGdt/5A5Xf2V33zw5ngUqEAlJvgRMGxdmA5MBAEtjjf7sdi4TIUxbqyxHp4QoZksgR/NLEYauwWp2u277qUHxg+pk49ITxtj3Zdu+XO2XuXOCd7zRtNZvF8CknFI47bgouv/yy7g9ICOk7+jtnwCfxxvkAdmMwyENRZLg74yL6TxgC8Qid8D6IovTYclQz6U68b6ww8h/PvufvR2VWd/EIEh1t7SRUqGQ/mfXZU1ToaRqj4V52dLmVCKr+xCJISFgA5tkDTTSbYydfoA6zZp0PADhh2skYaIxD0xr+aRiRTr0FhRwC8xwCLQWBfZBZZPxnvQUqeAt8DoIT2MWZi2BHxX3oEMRPxp32YjrRSCTY0CQgzBTknhT8yzt+aNryAwF+DVAKb3nje0u/x7sfmJPMPGTK4zAi63VwO2n/YcS1JqljKibGvy1VSR0A7mnaqffykkvC8gknlH6MWtQRE04M3LluVmFb4crV4lJiwp9Mm93mAuSWtQamTetuJH/uXOCznwW22iq0+elPx4Jg8mTg4x8H9tkHfXWNIYT0jr72DCgozFk8HVMnzcDC6z8aDHEla8CM1kWzCCXiwduVwSvgy3MiIOlFOFJVDbGWCoMWN4ByI7Z6S709qiVDZa+kpVVnHx3e68welG9VlW6Jt7s1Z0Cp6t20NZhEHwEzeqm1htZjYyahdrlktskpmDr1eAwObJYVBmZ5MBNGZJ9eHAUMOYNeR94CrdyzDIS3QJuwnKK3IPYkRMIQjYLHAEh+qnLdlikIAz1rsAehqOxv34UA3Xznj6Idpcdwt51Oq/x+73lwrhDR4jpTEAiZOrJfEi3PAPdZmqGPQi1II3hW0T4vJXfJqiMmLr7YGL+6aT1top2ygYqdd3h7NkRom1dOxMQp4wtiYGPxCsydC3zrW8Dmm8ft5dqlBiCEdEPfioGpU6fhuZu9GArAL655D45+209w9Q2fKprtUQxyPNYfGxFWJBTyBarHm6PRwjBoaA+dGipV7VRTTxBUiZZ2jurK0yPGoULlxnqru2hNsQFru2ggPD1WF6u5m2xm//bR/j8APkRo/fp/dNje2MA9zfi4447DJuP+BUo1zLSkVhg0muvt8mAmv6BRMPS1tknHafhQZPDb31saUuREgtg3bA/1C+eeNYRbPRlY+5H54B1A5C2A/7EpCNtax+tKA7f86idwaiM6O20Y4pvf8K7af4N7H5rnl3WyUO1BkCE54fs4ZN+v4+ob/gMzk1SYOoZpzqDNiYk0JMbhw+5aXAp23fGUrBB47dYHmXZaPFugbMS/bt1uef7z816Ln/wk/m4oBggh3dC3YUIDjXGRkW9Q/iabu7oWhQAy67F0SA38sKL88eIWVLFfqSdANtrGn6/MRK8rXNonY9h7a6daEnR6PNdaMUE5k7OQHLr8+2mNSxouJA9rkzw81HyWLnwAc0Um5AnTToZqDqDRsF4BPYCGWo9GcwBNW9ZsyhAilQiDRBCgaYz1SCQ0bd3Ua9C0ScepIDBtynAjP5OR+WG5YBnE53HmScE5YSBG6Fs9hbj4sDGLCp6JtXedF65ZycUmDVncdcdT2vpb3ffQL8z7w7/ADq89Gvf/eqFQLuUDFL0yhMvaueQSYPVtZ2GPXT4Y10/P3Ip+yAeNdRMGVLX95JM7DxE6+mjz/q//mv8e0rJGw7zzGkMI6YS+9QwEQ9wY1Fct+xCOmPADXLPq84C42TvDPL7IFkwBRF4BnyCcHE+0Y1IPwn91Rv+l8V9lvnfuJRgOuj1aYYwyaV3ZeeatMRAdLpj3hV5EeQLx9x/qhl9B+ScIAsCECRkr7cgDz8bcJaearbrJxL4MLoRo2vHT0WiMg2o2hDBYb0OIYk+BSyz2uQV2GWgUDPrYa5ALI2ra7Y1YEORyEhCLACcOtDvX5XJklEshoBGuA+VPIfa/wEph4ERHEAY+DMgLkbjstnvOF9++uw7lrmum3Z13eHv099ru1Ydl/oqfa9v4r7JX64yuD1mnQO4hflIQvOWN7816BQBgu9cchrNnjMfs2Z2P+LfarxtOOQW48krgX/6lnhigBiCEdEP/igGX+CtGzxYt/xgO3f87uG71GYDbJvMFlHvZm6h7eSNd1vUHQlEYhD5AVhPvpV6CXDPiXzeTUMuPj1wQT4uCNKQgv7mNI8dmd+v9Qw0nAqLAbPnkVgjvgI8VyvUh178a36B2AsAuu3URIgTdNJ4BaEw+djLmzptb0WD/MvvSGX75+OOnY6AxCKUGMDCwScFToBoDBSGg7LNClH26sVtWiaFv8gpUPPIP41GQxr/xBjTtPtLzYAxst58bz5fiwHsOInGA0HZBKMALlF4Ig1plMOcGIAWKrefDmsyD1lxfd3n9yTZp1/22w99Pnlqdhgil5VVG9eWXA9OnA029vnAa18kletXL9wOAKLxpuLwDnfDud5v3TTetvw/FACGkG/pWDITbtDTUFZbc+BkcvM83AAArbv5ayb4qLYCM/VflNYvtJFdxbzAkeQRFT4AUHMCWL35j7gCGkhtTapbH+5UogQrKD9cLX0Ta07AezChrCiTegTg8KBpKrXlkXbEeREBxPxODoHUTmAhMXkpB0IpLI2FwEhpqEIMDm6IxMM7mFgz6JxujlShQym5PhYENBYJCHE4UREJpOBFgDPceiAO/7IUJEItR56mA8GIFYRCtWwM+JxYA95t1XgMI8QFxXNjPE9Ydt91zAXbd8RTccZ/7+5h99x7/Maxc+x1cfDH8/p3QiSGdega02HnPXT9a6hVw+Q9DQ7Ks98udstlm5n1Q3J1btevChAghpBP6NmcgGNyxaxwArln1BQw0BjFhj//EyrVnIozQp+a9il5K1Gs9uhx7D1p7AEL9umIj7NeG8ZsVAu21kzf9XWlqSKfokm1CBFgvgHkTfVLwsdUFgVDRs3LivrrpFc3/0htga3gPQVjX0Djl2KtxzhNmLvP+Pd/aZ86ckFE6dcoJaDTGYdy456JhZx5qqAFr9LcpClQD0KK+EASwoqEynAgAKsSBXwbClKbRciIUUBQHEG2XCgU471i4NrmHmkVeBI8TBqGFmFbbgyEtSmx5xR9SHkE0WWek/fzz80nEP/sZcOvdF2CX10+P+lzn/F625r9wwQX/Naxi4L3v7TxfYMIE4Prryw18d4xly8J3MzBgynh9IYR0Qt96BgBp4ue3Lr/p/2D/3T+PNbef5W/wblthrD4JJYpsarcSxRTL5fTI8Xuub/HGsprSi5A35Ft7BDqjF76AfKMZEeANKoSjBjWAjLugglSwiOLkGL6mFkIBwBETfoArrnm3FQfCeJoIHLv0WMy7IszqQuox57Iwof2UKSdg3OBzMG7wuf6hZV4U5Ax/tNqWhhVlwol86FDD2t7Oq2B/fUIcREIBbt8wwq8K4iARCmkbUhxI0eDbNv0JOQ1u3S21a/SH7Uqsw5Xqoh+sbqhQr2bkcYb8UPPZwrZ9d/tMqVfgFVvtAWAZ1gunwsbkFfjsZ837AQe0rrt8eVimZ4AQ0g19KwZMzH8j3LlU7qYI3HDLN7Hvbp8GIKb5g9/F3yzNSCSA7I01uaU6zYBwbNMnsTESD2HH2l6BwsYWI/u1hIAVITXveokJ0TW5kc6iMEh7oFsWhR6mpr4b9xfeisgQcuvOQ5BaBs1i+BAFQddcZoXB5MnHodEYxCbj/hWDA88JXoFahn8mAVmKArtv0VPQLIzwhyRilRcKkaHu2mkhFLzAaLGMJFRJ23JxsYnOGPn05TJRYL0W6bZbfvUT7PbG99hE5HS2rkCry0MvDOtFi4DTTgOGhp4JPVTA/rt/oVQIAMDym76Gc89tfdxuhMCHP9y5V2DcOPN+992t6+6/f1imGCCEdEPfioF4VF6O9BcN7lW3fhuNxjjsscuHsfbun1o7XRjrPtwo3jc3riaPH3IMFOI+5OqWf476VAmCNtrJVe3Y2q/YsWJTURi4Dcp/xFCjTAFo8ao4pAYKeQI6FgtalAEwIULeY0CGg7lzL/fLkydPRkONw6ab/lv8jAJp8CchREXDPyMipCgQ05rmjXhVnifgpgbNhROlxrzdH4U2kmUIj0HqPfDtS+RDyNzgRWab+xxmMdkGHyoUyeKaP/Nej7Z7MaCAg/b8r0oh4GjHK9BpMnGnhHCf1nXvuQfYcUezTDFACOkG5gyUbcus33TH2dh95w8AEDNtuJHAQhhRtXmtvBgplQoV/c5v+fPT91ccUe6fCIK2fwOZ+s56KL0hdnuntAa9cQWIZSsM/MdqNSzpXrpYlPQ1Z+AXvAdavBCOr71XINMf6x244hdX1P3wpAXz5gVPyzHHHIsB6zFQ9qFm/t09rwBFLwKEF6G1V0EMAijlvQVu5D4SCkg9Bi1CgbQbZOjCe2AFgREgcLUtdtTfG/wtREPYBYB44Jdl953fh5vu+BHOP983F9HtaPu55+bzBiZPBtY9shDbvPIgHLLvN1sKgYceW4Yf/KD6WN3OJPTJT3buFQDaEwMS7+Du2/s5IaQb+tYzEIx3efEUNz5/g4wvrjff+SMoNYDd3ngaAOBX62bJK3HYL9YFrtHwEuFJblfXL1kv9VLEy9Vyo3JbGD7vEbGBHn3uwpCZLu6XbCoz6WVtPwpaVrt0kzXYdaZrmW5Ko167HRHyBKLpRN2utvHpR833ycMRE4Fjlh5DQTAMXHGFFAbH2Cceh4eXNZR50JlbdiGD7SUiN/y5GqYoFYJAXF9yDy/Lj/bX9R4IoVDVnvsS3IxC4pyMhEGpaDDr5jdv1lbf9j3suetHcfOvfoSyi8dwhuDkqCMEHC7XoFfGf9n2TpG3kk72I4SQTuhfMeBv2GIdiK+qZQP3Clh790+hVANv3vFUAMC9D80rrwyFWC+o5JYrBYAocgvZ5bLPVRcrCHqnBkJbVU0W7f7CclqqIj2RyIFc2JMPFdJJfdNmyXh9dEzpBYg7mLblPANm7fADzsKV170f2XwBMuJccUUQW8dNnhp5CPwDzhKhUMg9SJOQC/kGQhBUCYNUIEihgOHxHhhcW2bZ2v/xNrE9CIOwkJ7SzWYzrVIwhnstCnJMngzMnTse27xyQmW9Q/Y9E9/+9viujf+qfT7/+e68AkDnRj3FACGkG/pWDEAa5MkofWHkXaXLYfH2ey9CozGIN21/IgBg3SNXia3pKBuibd5oyGyt6Hhhe/YZA7XYsHeQijH9pEadmq1ChVIJINZTQz8KIQqj/24I1YcC2dH/nFdAa40TDp+DGfOPAvYo6TS9AyPK5XPnROtSHDTUYDakqNEQU5iWeA18TkHO0G9XGLjzW9Rzxn173oMw4AEkxr90rUElv1575ZPiW8Vnzo1rz8Q+4z+JNbd/H7LxXo6yp8vdsPkLtgOQzxXopSDoJa5dGvmEkJGgb3MGChG0KjWzVbG2KpY77npgDhpqADu+zgwNPfDrRdXHlo6ASJNIoZDrba5/o4fu7p1WFCjA5w20eVRd6IUWr5I6XjMUBUW6l1lqYsqki3HpwmkozsueYAXBL+b/osbnIL1k7rzLCmXHHTfNCoFBLwSCYBiA8t6EknyDVoZ+nZAiBKO+lTfB5SuUTkNqkgiia0msfcWwh7i+RWeWeLK37ZwpFrkDu+54Mm675yKcd57YrcJo7qUo+O//NvkAZd6B3d74Hmy903gMDXUf/lNW9pWvdO8VkNQJPdpxx+Ix+/V+Tgjpjr72DMDH5yN6Ty+nBQ2Qa86+3/vQFVBQ2GGbo/22Bx9dImrJG7w8ouiL9L9nl0cXvRAA0vQvyoAqYVAlBDLVMptjr0A4pgnJyO2gcfnV7zB7tRIDZKPi8stnR+vHTZ4WQoicEMh5E1xisvUY1PIAiPyguAw1RUOZuEBcT8chQ4XLDOK8AC8QVLwuRcTym76K/Xf/HFbe+q3o+2pHIsqxAAAgAElEQVQ22w8XqrPt+9/PJxEDwK23FsscL/i3VwNAJAS68QAMV65A2ubQUH47bX1CyHDQt2JAIdw4c1vzy61qhKX7Hp5vRwwVtn/N4QCAhx+7tkQEtOppulzc589Pr0PmLt8Bcude+8DbH8n3RIkDwfjPyQCF4rSjlUIgS/wlhv21P4ILEXJPGnYc/bZzcfnik832OmJgInD00qPpHdgIuXzu7ELZ1CkniRCiwSicKPYcKLhZiCJDP2fgdx1mhApxIa4b/oTJXEuULFGxkavS2lYgNGOrtc5Tfau2VdXrhLfu/CFsse14rF8/fCLgG9/onVegaS8XZWKgjOEKVyKE9Ad9KwbyQyxFn4Aq3a6S7eUW+P2PLIRSCtu++lBf9tjjq7ItK9muSrfmSD0bnVL2fWjx3m5b7d6hkvq1DptWyu8U/Auwd870VbW7M/xNBQWNIT1UEByH7f9dLFx+Op559n8R5hiq6RmgIBg1zLlsZrQ+ZcpJdoYil4Q86JeL+QVBIHSWfIwOxYIw5qNrnxQoaVn4V16Q3L/XrT4DB+75Zay4+WsAgJ22m4K77r+s5fSdrbZ1IggmTy6GCh2y75k4e8Z4fP7z7R93Q+QMzJ0blp8tPljZk7t1UQwQQrqhz3MGpAkubop2SdauaqmuIf7Ao4vRUIMYaIzDq1+xvy//7R9ugg9ZKhMASuWP0pM/Xx2x0Wkbvb5LZax1WRTpl/hvEwuCmofTLjZIeCKUQrPp4g7M69D9vo2rb/g0ms31onGNZrO9MKF+PR9HM5dfPqtQNnXK25Nk5CAKkOQb+KlNc4KhhYFfV0DIfYN9nxEF0SiEOHcyAsJhfvNyvXsRkNvnf/6nPFQox9kzxuPTn26vD+326Xvf651XYPJk4CMfAQ44oFoM5AgJx7x+EELap789A2WD4XIwrGU79evIm++jv7vB36Bf+bK9ffnjT6yNdiw238YBa9GJsa8zZcWacRxyNwQzPgprTpKIXXiQjoP7If+oWjeDP8BXU7ATtBeOLJ8hEPfCvCbubUZFr1n5eVFu9jRhQusLbZYyEThq6VGYf+X8+vuQjZI5l10crU+dMh0NNWjO+XRqUiEK3LZQL+9R6CrUCEhG+hNDv0wsZMqvvuHTOGTfb+K61Wf4snQu/3S5als3HgLpHXjosWU4/fT2j99Of84+u7dJw0D47v75z/z2MlufngFCSDf0rRiQvgBfJkbl0+S5dN+8od4Zj/1+lb9Zb71VmIfy93+6HbG3IO1IvKH9/pTvX4zOrz5SrWPXvmGFqP/88TPlStVQH7EhXxZeFGpl6ljRMGEPY/wsW/Nl6DRkSIfWm802g38pCMYkcy6bUSg76qijsOkmzw8GvhcFOYEgPAoVAkHuW+kpKLnG5cRCVA5E1x0FhUXLP4ZD9/8Orln1eWz3mkm4/5HFOPNMs72XgqCuwfvQY8vwwQ/2PkRpJKYUdY7Ev/+93PBXCpg0KQiRM84AdtoJuJLXDEJIh/StGMgnxBVDcxBtz7XRzsi6uymX1/rtH35p6qkGXr7lbtG2P/75V4X6W26+U83j5/qTLuW21mmhFc6HjdSCTwtKjuJ94Nm7sDNUih4BJytqtZ7pdnjKMKCw15vDUOOKm7+GppaGvkwi1j6ESOs2xQDpG+bPzxtvU6e83QsDAFlvQhACuXCjXL1EMCRGf/ZqoErKS4SEDBeqk0jciRH+la9UhwqZh5AVR9Z7bfxfeGHvvQIAcM45wMSJRgyklImDNiMRCSGkQF/mDBx5xJF4nn4ZothY6RVwZeKtQAudUEoy4lbF40/cGt3s04eL/fHJuwAkMwlVHxip6dvuX79u/eH3WqehPfGIv3xYmHkaa5nk0cmSI6ztsctHAAC/vOOHJszIzhDkwoa03CUyILQ1vNrEegeuvOrK9vclo57LLi96ESRTjpsehRRlPQk+OTn2OBQTmOG9Bn7ZXw/ttTDa5gjXRwWF+de+F0cd9GMsXvEJvHbrA/Dwb67HV78aanfjBWh3RN4Jgne8o7226h5/9uzhEQKS//3feL3qNu2EVz/eywkhvaGvPANHHnGkWfgUEMeatyBxi8sgo3pUewMytbNEngGlvFfgxS/cHgDw5NP3t9V6Oz6N9mu2Lweye2SH7p0Zbr7Xgkcg05AXeFpY7AqJmAiV37zjv/vVm+/8kRUAmfAihciDEIcXdSgGAGAicOTSIykISIEqsXDc5BO90d9ojMuEFGVCj3wIEYB02XsBhCBQxWVAYd6SU3HswT/HguuNeO7GO1BlqH/hC60TiZ0gmDatu2Oly/PmDb8QOO884N3vhp+VyX3F7v0DH4j70O40pIQQktIXYsCLgI8AuNcsan+Vr/AKRGVRUaZ+3mRW/qUKIzelRna6QcOMgicb/vjkXdhy8528CNj8hdsVmnryLw9kj9S7MSRVXBR30LwkyFj4OlfqjH7xr1L2bxeMeLlfGrvfqufaLuy07bRo2+33Xohmcz2azfWFhOOCjNTy2Do69LumrsA5T4yv7gghPeLyucWZjSROLAwMbJLkHzjRKq9T0jNQIha8B8GUXLroRBx/6CwA4ytnFRpu7wAQBMGxx/bmeAsWDL8QAICFC40Y+MtfQlkqCCQUA4SQbhnzYuDII44E3gvg4bhcoyniwcuoM/5fNzynvL4qbOnAVFfO8I8LN3/BttnqT/3lwfaPIQ+WWay7Z/4br3OHD4IAgBcF7qFf6bz/vmVr8buZhACFHV93bFTnngfnQeshNPVQ4UFKkUdA64KlUEg0tnV8zHcXHHkEvQOkd1SJhcmTT4g8CQONTcyGTAiRXwZi8aAUZl55DDB9Lc6eMR5f/GJof6TFgMPlD3TjkVi6dGSEgOTJJ81XL4XAmWcW+7HXXsBVvEYQQrpgzOYMHHH4EWZhYkkFEdYBSLNfFUogS+TDf+LqRZRc6DQop+gRqLef6eVTf3lI3LxtPQW86AWvK23JCIWc2a6itxFDKSgdT/Bpip2XALEtLvIFXrv1gdkm1z1ylTfkQwhQTJm4gO9HqOmFgl1zcdxdMRHAUsYCk5Fh3rzi05ZTJh87TeQjiFmL/GxeAJTCBfMOwdnoXahQuvyJT9R75sDkyWakfb/94vL0lErX5fGWL68+xnDgvBrHHx8EQe4y8MEPmndeIwgh3TAmPQNHHH5EuQiwyLhuZ1CWX1DLLP1WQULpBlW2oaKsS0qafPpvjyDv+VCVQiHlqb8+VLIl53Up9wsUcXXtuwtN0NonA6ezLeX49W+X25wCjaHmMzYBuKwPVcOPOnm5Up1U1zYm24ywnjNjPN4/fS1DhciYYG4NweBYsAD46lePyD74qypOv926rZg8GVixAnjTm/IGdZkwuO22sP+GwAmCN78ZuPVWs572pd2HkxFCSI4xJwbqCAEA8EmffjTXLjcgYmKBypAYlSuUG51bPc0VyLkTqoVAXf9AdVv1Qpr+8rdHW1e1N+QXPX+b2r3qNY8/cauY3Uf7Zfe31InRHo/0lwmT4DEy05Iq/y6lgPK13G9Ii70UABOPTSFA+p3163tn/MvlD32o/hOJJ08G7rwzrL/qVeY9JwQeeSTss6GZPDkIgRyTJgELFlw1sp0ihIw5xlSY0OGHHV5PCECM6AqDTpsCQ2Lox7MIpSPq+TyA6KXkWq5mb2nVv3r5EK35y98eTUrc9yjDsIp38/CvtkXOQxNyOcIIfniqcPh7Je0micMyVKhg9CuYWUkjQRD+/jqpGx8mtKd1XN8licsZW7pmInDE0iOwYOGC7tsiZIRZuHABvv3tw/HhD4eyXgiB3HorpEH9aHLZesELQsLuxiACJGX9Oekk8z6W7uGEkA3DmPEMtCMEAIQRZMAnoAaj01p4VbZc4aFl6baika9Uu2Z/mT8gHc6q2NZi145pJ+KnS/KH0a3r6HRjrtO5cKa6HZLeBpdP0oB/Uixv0oRg4cIF+P73D8f73997IfCud9X3DkjS+n/5y8YnAlqRPliNEEI6ZUyIgXaFgEGHUKHIakxEQVWCcNWofkfJwzX6nA9g6gGtRMdwWf7dqgodvbU+UuwN8NpPo+VQY9gziIDgDVBQ2j38KYgCQoghjW/vhUegXe9AGaNNCADA9OlGaBFCSLeMemulMyEAnHrcdZDx4UYYIAr7iENcRIhQpRVelmPg8gfq9a89Q78sKKjCe9F2D3rQSls37iTXooubfjFUSIltuQNUH8zLACcC0oc52fKpk2bgnBld5gssBbBJd00QsqFZuHABzjvP5A88+6x5rV8f1uVyu9tOOsl4B/qJCRM2dA8IIWOJUZ0zcNihh3UkBBxxvkDiEYgMSFtHaWFei5j7bIJwPnm4lVHd3V+jKnyoalPd/ZJR/NqD+u2O/lcl99ZvKpsvIFrqRAiYfANlpzW1o/+qCWgXJmQEwTETf4orr/tAvY62osG4YDI2kN6BXngD3PLUqZ2FC41WPvxhYNGihV3PXkwIIcAY8Ax0hYYIE5LJpmGmoTjRGGGbC9mRjgCxmk8ezlAahpTuU88CrndvGO47iIijb5t6+8T5wxXfTSFvIN0YC4FsNSUTyO3Iv/MMyKRh6yE4YsIPMXfJO/Hb3/+yK7GKpQC2xPD/uQgZARYtWogZM6pH/HOj/1Xb5DIhhJDOGLVioBuvwPu3WItzZoyPDX1hF0b2o88rQCQO5H7SW1CdPFwWQpRUlBt1brHdUfZe0kWc0wY0anUk3nIeg9x3KuP/Q1iQaachBEFYnrTftzD/2vfhj3/6VXdCIHSBYoCMGRYtWojZs4NhXyckqK5AOPzw/ggXmjvXfI+EENIrRq0Y6IZznjAPgip6AMKofwgbcoTpLWPvQJga0+MNuKIwKJvQszq6X2dt1S023wlPPn1/yT69i/nv1hZte38V71e+f11RlFVUhe1hkzD8lQj/QaO4TQiFiXt/DQuXn47f/eHm7oXAUgDbw3z4vjxLyVhl0aKFmDcP+Mc/yl///Gd4PfNM/JJCIhUVhxwytgUBhQAhZDgY1TkD3RIMfyAKEwrTywBCJCgV6rkBZq0AZQVBeCyVJU0grpt4nFntCaUzIqWrRWkSR/G3ygHoboag7N6+sAfTh/g/kwj3QkgQdy/tBYCGVqGORgNKN21YUBMTdv8SAODqGz5lH4DWJUsBvAVBCCjmDJCxxeLFi7Bo0aGYPBl46qn29m01g9D++wPLl4+9/IG5c833xksBIaTXjImpRTsnyREQpYDTA8L4d0JAA1CuXOYcGOJn1Saz2NQxyFv2uVX9VsKjMzoz8d1e6Xv3dNWKglFy0v8QhXg17N9RiAKt4Z9IrIwQ2G+3z+La1V9Cs/kstG7isd+t7D5PYG9EQoCeATIWWbx4EebOPRQAsMce+TqdTh26++7ATTeNTkFQ5tlYvHjRyHaEENI39K0YOGeGCRW6dOEJ4Y6jS8KEnPFf6R1wFaPHUFmExyEx1Id7kGc4Mga0XKqy7du0+4vVVaawSyFhPR/KCQHVANA0y1qKAWX/wsFjoFXwFOy1y+kAgGVrvgytm3jw10tM+90KgQOjLgRBQMgYxBm4a9Ycip13bn//KrHwpjcBd9658QmCVmFMNPoJISNN34oBAJh55bE46ch5mLvknSiECSWj+1rbgWM3xaj0DvhpR2XqsZydyLwpFaLTlVUQbuy51OLzdXpHaVs9EinGfi9RArXFQ1lFBWO8t+yAHcEHoG2mhnIGP3zYjzmEEwYuDKgYNqStgHjLm94PALjhlm8abwCaeOCRRcC7ATxa3a1Sltr3SYiFgHwRMoZZvHgR7rjjUL++/fbx9nY9BK7+ttsCDzwwcoKgTr4CjX1CyMZG/+YMTAT++rfHMPuF0zDtsNmYf+37/CbnDXCJwXE4UKhlPAVypiHY9TThVydhRSJkxr/JZxjEu4YisVLr5pj723b/9y54B9rapxe1U7lhHgSnrWJT1sDXGVHg6kYhQl4YuJCg1DvQwPg3nAoAWHP7WRhqPgvoJu55wN75u/UGTAHwLIrGvwgT6tvzlPQNV1+92C+fdRZwyCGTWu6z5ZZwOf5oNMKyZOutgd/8xix3KwpaGfvyM5TBU5kQsrExej0DzwPwf9H1rC1PPf0gLtv8ZEyZdBEWXv9RER4kZgryYUKwXgFj3DuinAJb7EVBzqi1RquLQLe7WhtXuBAyN41QXGEsd3WzGc47VR1JEIcfZTMO/IoKHhe7rpwOc6JA2b+Odga1sn8jYTVoKwBEzoATAbvsMB0AcPOvfoxmcz201rjz3plmv17MGHQSwlee8wgwTIj0KXUM6z/+sbVgkOSMeScQ6ozq1+kTIYSMNkavGOgF1pj705P34Iot3o1jJp6HxSs+kQ8TggvnsYE+2hj8wTuQhggZlCgLLykEYqs/HAdRC21RO66ocyuzVUSPL4qs97BVJd9R8Iv4JwL4soIiyBQp+a8yiRxxiJCG9nkCpgcuZMgIM7duXm/cbhoA4LZ7zkezuR7QGrfe9TNT/0sAVrf/nXlcWNA7/eFij0BOEBBCCnRrnB9yyCQvAmjoE0L6ldErBrrMI42YCPz+idtw1cs+iCMm/BAAcO2qL/r4fpcX4IadQ35AyAdwU4wWRUHoqI8oUlYIRB4CoCgK5AcdueHhukdKg3VkaXE5FQhiQSmTdwFAJFZYsSUNfmPgK++hKfMSWIM/CheSeQO234lnYIdtjvF9vXPdTDSbQ9Ba46Y7zjGFXwdwbc0vJ4cTAe8DMIS8JyDnFaBngJBhgQKAEELGQs7AUvTmSa8Tgd88vgaLtv44BhrjcPA+3wAALL/pqz5MCHAhQYl3wIoFH30ijWOXgACTZIzEI2D2VdbgKw7px6Kg3b9VG/t0+DPwgsDH50SlYptbs2P/XgAUxYM36EORzwkItVWyLdSB+IaLOQRWdtlk4m1fHcIM7nlwLprNITT1eujmEFbf+l2z4TswxnunQsCJgNMBrEdrEZARBKP+PCWEEELIRsno9Qw4tgewDj0TBL/+zXJs86qJuHbVF9EYGIcJbzUPlFp567fD1KLSO+CGrgG7IQkpErkFARfGkggD7UJcgChvwHkeatv2dSt2IjCKtCsIkrF+YbRrb+i3ChfyX5MvEvsql0NghFYqCl77ygN939c9chW0blovwBBuuPnr4YOdDWO8d/rATycCPoNqTwBKyvmcAUIIIYQMM6NfDPSaicBDjxorbodtjsLym76KgYFx2Gf8pwAAv7zjByJsSBqi8dSiwTQ169HLxrNLZ4BMJo7zBtox1qs8C+Ul9XD75OOzWgsCt+oMfVlXDvHbY0Sj+Gm4EES4kCuUYUKIhMCrXr531NcHH10CrZv2pbFs9ZfDxp/BGO6XdvAVOZwI+BJiEYCS5TqhQoQQQgghw8DYEAO99A7AtvMi4L6H5gMA3rDdFKxceyYajXHYc9eP+mpr7/6p8A7oxIgHCjkDpkjkF0jzFkgN9U5EQW/G+lNUybIu1CoXBKrwXcQegcSTgCAComPKqKKkyLxrvGKr3aPtv/7tcmP4wwgAQOOalZ8PFWbBGO0XVH8LlSwVy/8HRU8ASpbrvgghhBBChoHRmzOQDlD3WhA8Fdq6+/7LfLF6g0KjMQ6NxiB22+k0X37nfTNh8gJM51JRYMKHjGjwQkAmEPuHkInpRsVHbdtTkFMF3gvRK/uyaJnnBYHY2ma4kOu4zwuQMkoBL31x8bGljz2+0hr/2nsAAI2rV3w6VJoHY7D/uIuPLwXAt2CehWYfZlww4stEQW6bexdhQoccfwiWzFnSRWcJIYQQQoqMWs/AkiuW4GAcPDIHcwLjDcBtd5vh493eeBrW3nUeGo1BNBqD2HmHtxd2u++hX8CHBhWsb5FALI3qlskBnYQPheP1njqCIBn1t4IgCABE9UKIUBABgMIWm+9YOPrv/nCTNfo1ABP2o6Gx8PrTi11dCGOsf6+LjysFwFm2vao/SbsegLIwIeYNEEIIIWQYGLViIEuvvQMpd9u2dwNu+dW5vniPXT+MO+69CKoxiIYaQKMxAKUG8HoxVSUAPPjo1YjzBtIpRkP4UEhWVt5Gbi+JuFfUOVgmdqdmndzsQpu/YNtsC3/40x1+tB9eABiuuu6DceVlCCP17vWdGh8lhxQA56AoAOTfpFWoTyehQswbIIQQQsgwMXrDhMoYbkEAALeI9g8A1tz2/UKV/Xb/LO59aB6UGkBDGXEgp7FMefTxG0umGC0OO9fTA7mchBrVOyY29svChVz5C5//6tKW/vz0fWaEPxntDx0FfrH0PfFOKxGM9CaAr3bzWRALgHMR8r/Dhyh+vW65LN6/SgjkyuRzBp4E1DVj7FwlhBBCyAZnbHkGNgTX29fBAAbCa8VNX4uqHbjXl/HArxdBqQEo1bAvBYUGoBRe/fL9Kg/z+ydutUsbs0Fo+vb8523dsuZTf30o5FHAJQo7ARDeL198Sr6BmxEb///Zfe8jAXC+aDs19lNPgNyWCgBkyjsJEyKEEEIIGQZGtxh4GYDHYbwBkpHwDqSkuZ2/ADAVRhw0gOtWnRFtPnjfr0NrBWVDhR757XIrDpQJmUHDPmhKQSmFV2z11uxhN3/hdsPwYbrjL3/7dRjJd0a+Xw/LzuCfs/Ck8sbuQBiVl6/P9aCjS5P1+QCOAnChKJMOj9QjUFbmyluN/pfVywkCQgghhJBhYHSLgSo2hCBImSOWZwJ4J4xhNwAsueE/Wu5+2ITv2Zh6hd/8fjWUMsPESiko1cDLthiPPz99X7JXHJeiAMCGgilnXSq/xW5za1VD0Lp8ScfrOtpmTP9LF0wrb/pe0UZq9APApyq61Q4547+MdMRfLleV1ckXqMofQEV9QgghhJAeM/ZyBjZmzhfL5wJ4P8pjxBWwcNlH0xYKvH/6WpwzYzymHHqxL1P+Hz8wb+xVl32s3cz/8AnKYULTdIjbcMlVU9v4oBkeQGzwy3cAaP1RO0MKgCrjv4wyLwBQFAKdhAql23JegVfZajxXCSGEENJjxq5nANg4vANVnNNi+3cAfBKVxuI5M8YD9wCXLSpObdpTHkT9EepUS2gAH+htd0ppZ/S/LnW8BJ2GCtV5LQWuufaaHnwQQgghhJCYsS0GgI1fELTizBbbD7Dv9yblOcO93bIy4/99Lfo0kvTa+J8P4B0ITyPOGf9AXgi48k5Chcr2f36Xn4cQQgghpILRLQZaTW3fD4xWkdMN3Yb+dEKrECFX5urmwoU68RDQK0AIIYSQYaQ/cgZGu3eg3xmO0J9OaCUEypJ9uwkVAnMFCCGEEDJ8jG7PwO/bqEtBMLrYEKP/dWnlJWgnVKgqqXgpcO111w7nJyGEEEJInzO6xQAZO2wso/9VtJNI3E6oUFquACygECCEEELI8NNfYoDegY2H0WL85/JS6iQS5/apKwTmUQgQQgghZGToj5wBCQXBhmNjDv1pl6r8gVZhQlV1LgGuW3ZduagghBBCCOkh/eUZICPLaBj975aqROJ2Eod/ajZft+y6kek3IYQQQghGuRi4btl1OHDCge3vSO/A8NAPxr+kTAhU1c+Jg3MoAgghhBCyYRjVYqArRlIQpEZyXUaDWBlLoT+d0OmMQgDwbfNGIUAIIYSQDcXYyBl4JYDHNnQnLDnD/z4ADfuSxmPTvrvX6wEsAfAMgH8mbW0swqDfRv8daTJx2XqrcCAA+Jp5W3b9slCXEEIIIWQDMOo9A8uuX4YJSyeYkf526ZV3QBrIVwPYDMCmADYBMA7AQBttScPxIgB/A/BXcYyRFgX9avx3QqsZhc4wb14EEEIIIYRsYEa9GACsIFi3gQTBUgBnAXg+gOcBeC7i0f7c1JRVSDHQEMsfh3nI2tIu+lqXfg/9AcqnFW1VP+cN+KypQhFACCGEkI2NMSEGPCMZLuQM5i8CeDGKIT/tioAUKQjkazyAteitIODof29Ipw79uFmkCCCEEELIxsrYyBkAcP3y63HA0gNG1jvw7wBeiiAEpCAAWouD3OhzWay5EwO9EgIc/R8+PmTerl9+vVkYG6cYIYQQQsYgY8ozcP3y63HAugNGThA0kfcIlBn/raaeBMpFQAMmh6CbcCYJBUBveW9Y9CKAEEIIIWQjZ0yJAU+n4ULtCIKJMAb2Z5CfGahMFKSCoMw74N5PS45ZFxr/w4f7m4m/DQUAIYQQQkYjY04MdBUu5KibpOsEgeMSxIa9Tt4lZQmq6TPUZsLMKHR5jf4w9Gd4kH+rd4ViCgBCCCGEjHbU8uXLu0113SjZf7/9OxME6wBMAzAb7Y3EHwbgYx0cL+UWmGcM/N2+zqqoy9H/4eMoABfAiIB3huLlK5ZvmP4QQgghhAwDY84zENFpuFATJga8nYd+LbSv0wA8B+YZA5vAjPTfh/IHULm8gyEAz8JPQ5mFxv/Icop5owAghBBCyFhlzHoGgA69A+sAHAfgRQD+zb6eBz9NpKeu16CT5wLknmIMADfCCIf9QCEwHBwVFikACCGEENIPjJmpRXOsuGEF9lu3X/uCIJcEfA7Mk4WfA/N04TKDPUc7dQHgDhhPwXrE05aS3nJUvLrihhVhZeyeFoQQQgghnrEdJuRoN1xIThlaNkvQApgwoHH2NWhfA+LlniAsnyQske01xctBg7T3CAEQGf+EEEIIIX3ImBcDK25Ygf2WtukdSIVAKggcaVlum0rK6hj4OeHgZrShQGiPqtF/QgghhJA+Z8yLAaCDcKFWzw0oe35Abtmt133YWK5szGZ1DAM0/gkhhBBCajOmcwYK1A0XaiavVqKg1ZOHgfa8AqQ9hAC44cYb4m38PgkhhBBCSukLzwBgjMR9l+5bzzuQCoBWTxfOCQC5rcogLXsCcToFKQkko/8FAUAIIYQQQmrRN2IAsIJgXQ1BIBN6W3kA5HtuW11vQFXdfg8VovFPCCGEEDIs9JUY8NQJF2o1mxAy6+1S5hVw7/3sGagK/SGEEEIIIT2hv9i9UDIAAAP+SURBVHIGANy48kbss3Sfau9AJ/kBrbbVSSDWYjndltYZaySj/zeuvDGs9NdPlBBCCCFkxOhLz8CNK2/EPutaCIJ2k4hzdDO9KDD2PQNCAETGPyGEEEIIGRH6Ugx4cuFC2wNYB+A41BMAdZOK251etOw5AwCwAsaQnt+ivY2NqtF/QgghhBAy4vStGGgZLlT3WQNlDxwra7OT6UVHq3eAxj8hhBBCyEZN3+UMSFauWom91+2dFwQygbgqVAgoegmQlLei1fSiwOgRBEIArFy1Mt42Wj4DIYQQQkif0LeegYhcuFAdAdAqmVgu100gztXbmI3oZPS/IAAIIYQQQshGS9+LgZWrVmLvpSXegVbPGwDKRUBd6kwvKpc39IxCNP4JIYQQQsYMfS8GgJJwoTRnIBUFQHH0H5lyWdbp9KIb2jNQFfpDCCGEEEJGLX2dM1AgDRdq53kDrfIIuple1L2P1J8qGf1ftXpVsT+EEEIIIWTUQ8+AZdXqVdhr6V7BO1AnebjdhOFOpxcdCS+BEACR8U8IIYQQQsYsFAOCVatXYa91e4WCTj0CVQ8j63R60bKyTqka/SeEEEIIIX0BxUAZaZ5Azjsg61YZ/3Woml4U6D5ciMY/IYQQQghJYM5Awuo1q7HnHnuaFZk8XMcrgJLydqcXzZXL97oIAbB6zep8m4QQQgghpG+hZyDD6jWrseeCPYHjUF8IVHkHWpGKgKq8gSrPQDL6XxAAhBBCCCGECCgGSli9ZjX2vNx6CD6KeqIgN9VoL6YXLfMM0PgnhBBCCCFdQDFQgTOu9/zenqHwe2j94LF0uZtEYocCsHu+f4QQQgghhHSCWrNmzYZ8nu2oY4+37lEsXAhgnHgN2teAfTXES9l3oPjsAffaLn/sNb9c03X/CSGEEEIIcVAM9ICsQOgCGv2EEEIIIWQkYJhQD6DxTgghhBBCRiOcWpQQQgghhJA+pdG6CiGEEEIIIWQsQjFACCGEEEJIn0IxQAghhBBCSJ/CnAFCCCGEEEL6FHoGCCGEEEII6VMoBgghhBBCCOlTKAYIIYQQQgjpU5gzQAghhBBCSJ9CzwAhhBBCCCF9CsUAIYQQQgghfQrFACGEEEIIIX0KcwYIIYQQQgjpU+gZIIQQQgghpE+hGCCEEEIIIaRPoRgghBBCCCGkT2HOACGEEEIIIX0KPQOEEEIIIYT0KRQDhBBCCCGE9CkUA4QQQgghhPQpzBkghBBCCCGkT6FngBBCCCGEkD6FYoAQQgghhJA+hWFChBBCCCGE9Cn0DBBCCCGEENKnUAwQQgghhBDSp1AMEEIIIYQQ0qcwZ4AQQgghhJA+hZ4BQgghhBBC+hSKAUIIIYQQQvqU/w/6n5RdpPpFiwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/manifold-cad.png\", width=800)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is a nice example of a model which would be extremely difficult to model using CSG. To get started, we'll need two files: \n",
-    "  1. the DAGMC gometry file on which we'll track particles and \n",
-    "  2. a tetrahedral mesh of the piping structure on which we'll score tallies\n",
-    "  \n",
-    "To start, let's create the materials we'll need for this problem. The pipes are steel and we'll model the surrounding area as air."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "air = openmc.Material(name='air')\n",
-    "air.set_density('g/cc', 0.001205)\n",
-    "air.add_element('N', 0.784431)\n",
-    "air.add_element('O', 0.210748)\n",
-    "air.add_element('Ar',0.0046)\n",
-    "\n",
-    "steel = openmc.Material(name='steel')\n",
-    "steel.set_density('g/cc', 8.0)\n",
-    "steel.add_element('Si', 0.010048)\n",
-    "steel.add_element('S', 0.00023)\n",
-    "steel.add_element('Fe', 0.669)\n",
-    "steel.add_element('Ni', 0.12)\n",
-    "steel.add_element('Mo', 0.025)\n",
-    "steel.add_nuclide('P31',0.00023)\n",
-    "steel.add_nuclide('Mn55',0.011014)\n",
-    "\n",
-    "materials = openmc.Materials([air, steel])\n",
-    "materials.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's download the geometry and mesh files.\n",
-    "(This may take some time.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Downloading manifold.h5m: 100.0%'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# get the manifold DAGMC geometry file\n",
-    "download(manifold_geom_url, 'dagmc.h5m') \n",
-    "# get the manifold tet mesh\n",
-    "download(manifold_mesh_url, 'manifold.h5m')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll create a 5 MeV neutron point source at the entrance the single pipe on the low side of the model with "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "src_pnt = openmc.stats.Point(xyz=(0.0, 0.0, 0.0))\n",
-    "src_energy = openmc.stats.Discrete(x=[5.e+06], p=[1.0])\n",
-    "\n",
-    "source = openmc.Source(space=src_pnt, energy=src_energy)\n",
-    "\n",
-    "settings = openmc.Settings()\n",
-    "settings.source = source\n",
-    "\n",
-    "settings.run_mode = \"fixed source\"\n",
-    "settings.batches = 10\n",
-    "settings.particles = 100\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll apply the DAGMC model as the root universe of the geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dagmc_univ = openmc.DAGMCUniverse(filename='dagmc.h5m')\n",
-    "geometry = openmc.Geometry(root=dagmc_univ)\n",
-    "geometry.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll run a few particles through this geometry to make sure everything is working properly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                %%%%%%%%%%%%%%%\n",
-      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
-      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
-      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
-      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
-      "                #######################     %%%%%%%%%%%%%%%%%%\n",
-      "                 #######################     %%%%%%%%%%%%%%%%%\n",
-      "                 ######################     %%%%%%%%%%%%%%%%%\n",
-      "                  ####################     %%%%%%%%%%%%%%%%%\n",
-      "                    #################     %%%%%%%%%%%%%%%%%\n",
-      "                     ###############     %%%%%%%%%%%%%%%%\n",
-      "                       ############     %%%%%%%%%%%%%%%\n",
-      "                          ########     %%%%%%%%%%%%%%\n",
-      "                                      %%%%%%%%%%%\n",
-      "\n",
-      "                   | The OpenMC Monte Carlo Code\n",
-      "         Copyright | 2011-2020 MIT and OpenMC contributors\n",
-      "           License | https://docs.openmc.org/en/latest/license.html\n",
-      "           Version | 0.12.1-dev\n",
-      "          Git SHA1 | 9fb298b039bcd447c1a745cd9fea47f0d04eebdf\n",
-      "         Date/Time | 2021-03-04 13:34:25\n",
-      "     MPI Processes | 1\n",
-      "    OpenMP Threads | 4\n",
-      "\n",
-      " Reading settings XML file...\n",
-      " Reading cross sections XML file...\n",
-      " Reading materials XML file...\n",
-      " Reading geometry XML file...\n",
-      "Set overlap thickness = 0\n",
-      "Set numerical precision = 0.001\n",
-      "Loading file dagmc.h5m\n",
-      "Initializing the GeomQueryTool...\n",
-      "Using faceting tolerance: 0.001\n",
-      "Building acceleration data structures...\n",
-      "Implicit Complement assumed to be Vacuum\n",
-      " Reading N14 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N14.h5\n",
-      " Reading N15 from /home/shriwise/opt/openmc/xs/nndc_hdf5/N15.h5\n",
-      " Reading O16 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O16.h5\n",
-      " Reading O17 from /home/shriwise/opt/openmc/xs/nndc_hdf5/O17.h5\n",
-      " Reading Ar36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar36.h5\n",
-      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n",
-      " Reading Ar38 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar38.h5\n",
-      " Reading Ar40 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ar40.h5\n",
-      " Reading Si28 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si28.h5\n",
-      " Reading Si29 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si29.h5\n",
-      " Reading Si30 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Si30.h5\n",
-      " Reading S32 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S32.h5\n",
-      " Reading S33 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S33.h5\n",
-      " Reading S34 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S34.h5\n",
-      " Reading S36 from /home/shriwise/opt/openmc/xs/nndc_hdf5/S36.h5\n",
-      " Reading Fe54 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe54.h5\n",
-      " Reading Fe56 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe56.h5\n",
-      " Reading Fe57 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe57.h5\n",
-      " Reading Fe58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Fe58.h5\n",
-      " Reading Ni58 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni58.h5\n",
-      " Reading Ni60 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni60.h5\n",
-      " Reading Ni61 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni61.h5\n",
-      " Reading Ni62 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni62.h5\n",
-      " Reading Ni64 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Ni64.h5\n",
-      " Reading Mo100 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo100.h5\n",
-      " Reading Mo92 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo92.h5\n",
-      " Reading Mo94 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo94.h5\n",
-      " Reading Mo95 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo95.h5\n",
-      " Reading Mo96 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo96.h5\n",
-      " Reading Mo97 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo97.h5\n",
-      " Reading Mo98 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mo98.h5\n",
-      " Reading P31 from /home/shriwise/opt/openmc/xs/nndc_hdf5/P31.h5\n",
-      " Reading Mn55 from /home/shriwise/opt/openmc/xs/nndc_hdf5/Mn55.h5\n",
-      " Minimum neutron data temperature: 294.0 K\n",
-      " Maximum neutron data temperature: 294.0 K\n",
-      " Preparing distributed cell instances...\n",
-      " Writing summary.h5 file...\n",
-      " Maximum neutron transport energy: 20000000.0 eV for N15\n",
-      "\n",
-      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
-      "\n",
-      " Simulating batch 1\n",
-      " Simulating batch 2\n",
-      " Simulating batch 3\n",
-      " Simulating batch 4\n",
-      " Simulating batch 5\n",
-      " Simulating batch 6\n",
-      " Simulating batch 7\n",
-      " Simulating batch 8\n",
-      " Simulating batch 9\n",
-      " Simulating batch 10\n",
-      " Creating state point statepoint.10.h5...\n",
-      "\n",
-      " =======================>     TIMING STATISTICS     <=======================\n",
-      "\n",
-      " Total time for initialization     = 1.7970e+02 seconds\n",
-      "   Reading cross sections          = 1.8083e+00 seconds\n",
-      " Total time in simulation          = 7.2061e-01 seconds\n",
-      "   Time in transport only          = 7.1887e-01 seconds\n",
-      "   Time in active batches          = 7.2061e-01 seconds\n",
-      "   Time accumulating tallies       = 2.7050e-06 seconds\n",
-      "   Time writing statepoints        = 1.5381e-03 seconds\n",
-      " Total time for finalization       = 1.1780e-06 seconds\n",
-      " Total time elapsed                = 1.8043e+02 seconds\n",
-      " Calculation Rate (active)         = 1387.71 particles/second\n",
-      "\n",
-      " ============================>     RESULTS     <============================\n",
-      "\n",
-      " Leakage Fraction            = 0.96800 +/- 0.00573\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "openmc.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's setup the unstructured mesh tally. We'll do this the same way we did in the [previous notebook](./unstructured-mesh-part-i.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unstructured_mesh = openmc.UnstructuredMesh(\"manifold.h5m\")\n",
-    "\n",
-    "mesh_filter = openmc.MeshFilter(unstructured_mesh)\n",
-    "\n",
-    "tally = openmc.Tally()\n",
-    "tally.filters = [mesh_filter]\n",
-    "tally.scores = ['flux']\n",
-    "tally.estimator = 'tracklength'\n",
-    "\n",
-    "tallies = openmc.Tallies([tally])\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "settings.batches = 200\n",
-    "settings.particles = 5000\n",
-    "settings.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openmc.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Again we should see that `tally_1.200.vtk` file which we can use to visualize our results in VisIt, ParaView, or another tool of your choice that supports VTK files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tally_1.200.vtk\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls *.vtk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {
-      "image/png": {
-       "width": "800"
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/manifold_flux.png\", width=\"800\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the purpose of this example, we haven't run enough particles to score in all of the tet elements, but we indeed see larger flux values near the source location at the bottom of the model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualization with statepoint data\n",
-    "\n",
-    "It was mentioned in the previous unstructured mesh example that the centroids and volumes of elements are written to the state point file. Here, we'll explore how to use that information to produce point cloud information for visualization of this data.\n",
-    "\n",
-    "This is particularly important when combining an unstructured mesh tally with other filters as a `.vtk` file will not automatically be written with the statepoint file in that scenario. To demonstrate this, let's setup a tally similar to the one above, but add an energy filter and re-run the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tally\n",
-      "\tID             =\t1\n",
-      "\tName           =\t\n",
-      "\tFilters        =\tMeshFilter, EnergyFilter\n",
-      "\tNuclides       =\t\n",
-      "\tScores         =\t['flux']\n",
-      "\tEstimator      =\ttracklength\n",
-      "EnergyFilter\n",
-      "\tValues         =\t[      0. 1000000. 5000000.]\n",
-      "\tID             =\t2\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# energy filter with bins from 0 to 1 MeV and 1 MeV to 5 MeV\n",
-    "energy_filter = openmc.EnergyFilter((0.0, 1.e+06, 5.e+06))\n",
-    "\n",
-    "tally.filters = [mesh_filter, energy_filter]\n",
-    "print(tally)\n",
-    "print(energy_filter)\n",
-    "tallies.export_to_xml()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<?xml version='1.0' encoding='utf-8'?>\r\n",
-      "<tallies>\r\n",
-      "  <mesh id=\"1\" type=\"unstructured\">\r\n",
-      "    <filename>manifold.h5m</filename>\r\n",
-      "  </mesh>\r\n",
-      "  <filter id=\"1\" type=\"mesh\">\r\n",
-      "    <bins>1</bins>\r\n",
-      "  </filter>\r\n",
-      "  <filter id=\"2\" type=\"energy\">\r\n",
-      "    <bins>0.0 1000000.0 5000000.0</bins>\r\n",
-      "  </filter>\r\n",
-      "  <tally id=\"1\">\r\n",
-      "    <filters>1 2</filters>\r\n",
-      "    <scores>flux</scores>\r\n",
-      "    <estimator>tracklength</estimator>\r\n",
-      "  </tally>\r\n",
-      "</tallies>\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!cat tallies.xml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openmc.run(output=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Noice the warning at the end of the output above indicating that the .vtk file we used before isn't written in this case.\n",
-    "\n",
-    "Let's open up this statepoint file and get the information we need to create the point cloud data instead.\n",
-    "\n",
-    "_**NOTE: You will need the Python vtk module installed to run this part of the notebook.**_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with openmc.StatePoint(\"statepoint.200.h5\") as sp:\n",
-    "    tally = sp.tallies[1]\n",
-    "    \n",
-    "    umesh = sp.meshes[1]\n",
-    "    centroids = umesh.centroids\n",
-    "    mesh_vols = umesh.volumes\n",
-    "    \n",
-    "    thermal_flux = tally.get_values(scores=['flux'], \n",
-    "                                    filters=[openmc.EnergyFilter],\n",
-    "                                    filter_bins=[((0.0, 1.e+06),)])\n",
-    "    fast_flux = tally.get_values(scores=['flux'],\n",
-    "                                 filters=[openmc.EnergyFilter],\n",
-    "                                 filter_bins=[((1.e+06, 5.e+06),)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_dict = {'Flux 0 - 1 MeV' : thermal_flux,\n",
-    "             'Flux 1 - 5 MeV' : fast_flux,\n",
-    "             'Total Flux' : thermal_flux + fast_flux}\n",
-    "\n",
-    "umesh.write_data_to_vtk(\"manifold\", data_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We should now see our new flux file in the directory. It can be used to visualize the results in the same way as our other `.vtk` files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "manifold.vtk  tally_1.200.vtk\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!ls *.vtk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(\"./images/manifold_pnt_cld.png\", width=800)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/setup.py b/setup.py
index 02f3c0842..6afac4df1 100755
--- a/setup.py
+++ b/setup.py
@@ -72,8 +72,7 @@ kwargs = {
     'extras_require': {
         'depletion-mpi': ['mpi4py'],
         'docs': ['sphinx', 'sphinxcontrib-katex', 'sphinx-numfig', 'jupyter',
-                 'sphinxcontrib-svg2pdfconverter', 'sphinx-rtd-theme',
-                 'nbsphinx'],
+                 'sphinxcontrib-svg2pdfconverter', 'sphinx-rtd-theme'],
         'test': ['pytest', 'pytest-cov', 'colorama'],
         'vtk': ['vtk'],
     },