OpenMC/docs/source/pythonapi/examples/mdgxs-part-i.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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\n",
    "\n",
    "**Note:** This Notebook illustrates the use of [Pandas](http://pandas.pydata.org/) `DataFrames` to containerize multi-group cross section data. We recommend using [Pandas](http://pandas.pydata.org/) >v0.15.0 or later since OpenMC's Python API leverages the multi-indexing feature included in the most recent releases of [Pandas](http://pandas.pydata.org/)."
   ]
  },
  {
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yGm+vatvquLi4MHNzc2Ztbc1cXFxYREQEKygo4NMqlsBCQ0NrdNywsDA2ePBg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\nfmI+3IqsCwAAAABJRU5ErkJggg==\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](https://mit-crpg.github.io/openmc/pythonapi/filter.html) 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": {
    "collapsed": false
   },
   "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. Before defining a material, we must create nuclides that are used in the material."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate some Nuclides\n",
    "h1 = openmc.Nuclide('H1')\n",
    "o16 = openmc.Nuclide('O16')\n",
    "u235 = openmc.Nuclide('U235')\n",
    "u238 = openmc.Nuclide('U238')\n",
    "pu239 = openmc.Nuclide('Pu239')\n",
    "zr90 = openmc.Nuclide('Zr90')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the nuclides we defined, we will now create a material for the homogeneous medium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "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": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate a Materials collection and export to XML\n",
    "materials_file = openmc.Materials([inf_medium])\n",
    "materials_file.default_xs = '71c'\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": 6,
   "metadata": {
    "collapsed": true
   },
   "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": 7,
   "metadata": {
    "collapsed": false
   },
   "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate Universe\n",
    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
    "root_universe.add_cell(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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create Geometry and set root Universe\n",
    "openmc_geometry = openmc.Geometry()\n",
    "openmc_geometry.root_universe = 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": 10,
   "metadata": {
    "collapsed": false
   },
   "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.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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate a 100-group EnergyGroups object\n",
    "energy_groups = mgxs.EnergyGroups()\n",
    "energy_groups.group_edges = np.logspace(-9,1.3,101)\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",
    "* `NuTransportXS`\n",
    "* `AbsorptionXS`\n",
    "* `CaptureXS`\n",
    "* `FissionXS`\n",
    "* `NuFissionXS`\n",
    "* `KappaFissionXS`\n",
    "* `ScatterXS`\n",
    "* `NuScatterXS`\n",
    "* `ScatterMatrixXS`\n",
    "* `NuScatterMatrixXS`\n",
    "* `Chi`\n",
    "* `ChiPrompt`\n",
    "* `InverseVelocity`\n",
    "* `PromptNuFissionXS`\n",
    "\n",
    "A separate abstract `MDGXS` class is used for cross-sections and parameters that involve delayed neutrons. The subclasses of `MDGXS` include:\n",
    "\n",
    "* `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. 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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate a few different sections\n",
    "chi_prompt = mgxs.ChiPrompt(domain=cell, groups=energy_groups, by_nuclide=True)\n",
    "prompt_nu_fission = mgxs.PromptNuFissionXS(domain=cell, groups=energy_groups, by_nuclide=True)\n",
    "chi_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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('delayed-nu-fission', Tally\n",
       "              \tID             =\t10000\n",
       "              \tName           =\t\n",
       "              \tFilters        =\t\n",
       "                              \t\tCellFilter\t[1]\n",
       "                              \t\tDelayedGroupFilter\t[1 2 3 4 5 6]\n",
       "                              \t\tEnergyFilter\t[  1.00000000e-09   1.99526231e+01]\n",
       "              \tNuclides       =\tU235 Pu239 \n",
       "              \tScores         =\t['delayed-nu-fission']\n",
       "              \tEstimator      =\tanalog), ('decay-rate', Tally\n",
       "              \tID             =\t10001\n",
       "              \tName           =\t\n",
       "              \tFilters        =\t\n",
       "                              \t\tCellFilter\t[1]\n",
       "                              \t\tDelayedGroupFilter\t[1 2 3 4 5 6]\n",
       "                              \t\tEnergyFilter\t[  1.00000000e-09   1.99526231e+01]\n",
       "              \tNuclides       =\tU235 Pu239 \n",
       "              \tScores         =\t['decay-rate']\n",
       "              \tEstimator      =\tanalog)])"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 15,
   "metadata": {
    "collapsed": false
   },
   "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",
      "\n",
      "                   | The OpenMC Monte Carlo Code\n",
      "         Copyright | 2011-2016 Massachusetts Institute of Technology\n",
      "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
      "           Version | 0.8.0\n",
      "          Git SHA1 | 57371d217a270b4013af4e33ca20b0053654b190\n",
      "         Date/Time | 2016-09-20 16:22:31\n",
      "     MPI Processes | 1\n",
      "\n",
      " ===========================================================================\n",
      " ========================>     INITIALIZATION     <=========================\n",
      " ===========================================================================\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading geometry XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading H1 from /home/smharper/openmc/data/nndc_hdf5/H1.h5\n",
      " Reading O16 from /home/smharper/openmc/data/nndc_hdf5/O16.h5\n",
      " Reading U235 from /home/smharper/openmc/data/nndc_hdf5/U235.h5\n",
      " Reading U238 from /home/smharper/openmc/data/nndc_hdf5/U238.h5\n",
      " Reading Pu239 from /home/smharper/openmc/data/nndc_hdf5/Pu239.h5\n",
      " Reading Zr90 from /home/smharper/openmc/data/nndc_hdf5/Zr90.h5\n",
      " Maximum neutron transport energy: 20.0000 MeV for H1\n",
      " Reading tallies XML file...\n",
      " Building neighboring cells lists for each surface...\n",
      " Initializing source particles...\n",
      "\n",
      " ===========================================================================\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      " ===========================================================================\n",
      "\n",
      "  Bat./Gen.      k            Average k         \n",
      "  =========   ========   ====================   \n",
      "        1/1    1.21670                       \n",
      "        2/1    1.24155                       \n",
      "        3/1    1.21924                       \n",
      "        4/1    1.22486                       \n",
      "        5/1    1.21719                       \n",
      "        6/1    1.24330                       \n",
      "        7/1    1.22322                       \n",
      "        8/1    1.24133                       \n",
      "        9/1    1.21840                       \n",
      "       10/1    1.25141                       \n",
      "       11/1    1.21217                       \n",
      "       12/1    1.25625    1.23421 +/- 0.02204\n",
      "       13/1    1.22056    1.22966 +/- 0.01351\n",
      "       14/1    1.21757    1.22664 +/- 0.01002\n",
      "       15/1    1.24571    1.23045 +/- 0.00865\n",
      "       16/1    1.26489    1.23619 +/- 0.00910\n",
      "       17/1    1.22323    1.23434 +/- 0.00791\n",
      "       18/1    1.26108    1.23768 +/- 0.00762\n",
      "       19/1    1.23145    1.23699 +/- 0.00676\n",
      "       20/1    1.23548    1.23684 +/- 0.00605\n",
      "       21/1    1.20446    1.23390 +/- 0.00621\n",
      "       22/1    1.20533    1.23152 +/- 0.00615\n",
      "       23/1    1.22520    1.23103 +/- 0.00568\n",
      "       24/1    1.18367    1.22765 +/- 0.00625\n",
      "       25/1    1.23614    1.22821 +/- 0.00585\n",
      "       26/1    1.23746    1.22879 +/- 0.00550\n",
      "       27/1    1.23626    1.22923 +/- 0.00518\n",
      "       28/1    1.21334    1.22835 +/- 0.00497\n",
      "       29/1    1.25169    1.22958 +/- 0.00486\n",
      "       30/1    1.25579    1.23089 +/- 0.00479\n",
      "       31/1    1.23828    1.23124 +/- 0.00457\n",
      "       32/1    1.26911    1.23296 +/- 0.00468\n",
      "       33/1    1.20090    1.23157 +/- 0.00469\n",
      "       34/1    1.28606    1.23384 +/- 0.00503\n",
      "       35/1    1.23129    1.23374 +/- 0.00483\n",
      "       36/1    1.22535    1.23341 +/- 0.00465\n",
      "       37/1    1.20367    1.23231 +/- 0.00461\n",
      "       38/1    1.22886    1.23219 +/- 0.00444\n",
      "       39/1    1.24056    1.23248 +/- 0.00429\n",
      "       40/1    1.25038    1.23307 +/- 0.00419\n",
      "       41/1    1.21504    1.23249 +/- 0.00410\n",
      "       42/1    1.20762    1.23171 +/- 0.00404\n",
      "       43/1    1.20597    1.23093 +/- 0.00399\n",
      "       44/1    1.24424    1.23133 +/- 0.00389\n",
      "       45/1    1.24767    1.23179 +/- 0.00381\n",
      "       46/1    1.22998    1.23174 +/- 0.00370\n",
      "       47/1    1.26352    1.23260 +/- 0.00370\n",
      "       48/1    1.23155    1.23257 +/- 0.00360\n",
      "       49/1    1.22059    1.23227 +/- 0.00352\n",
      "       50/1    1.24724    1.23264 +/- 0.00345\n",
      " Creating state point statepoint.50.h5...\n",
      "\n",
      " ===========================================================================\n",
      " ======================>     SIMULATION FINISHED     <======================\n",
      " ===========================================================================\n",
      "\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     =  3.1400E-01 seconds\n",
      "   Reading cross sections          =  1.7900E-01 seconds\n",
      " Total time in simulation          =  7.6739E+01 seconds\n",
      "   Time in transport only          =  7.6714E+01 seconds\n",
      "   Time in inactive batches        =  5.1020E+00 seconds\n",
      "   Time in active batches          =  7.1637E+01 seconds\n",
      "   Time synchronizing fission bank =  5.0000E-03 seconds\n",
      "     Sampling source sites         =  3.0000E-03 seconds\n",
      "     SEND/RECV source sites        =  1.0000E-03 seconds\n",
      "   Time accumulating tallies       =  4.0000E-03 seconds\n",
      " Total time for finalization       =  4.2000E-02 seconds\n",
      " Total time elapsed                =  7.7119E+01 seconds\n",
      " Calculation Rate (inactive)       =  9800.08 neutrons/second\n",
      " Calculation Rate (active)         =  2791.85 neutrons/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     =  1.23260 +/-  0.00309\n",
      " k-effective (Track-length)  =  1.23264 +/-  0.00345\n",
      " k-effective (Absorption)    =  1.23111 +/-  0.00186\n",
      " Combined k-effective        =  1.23135 +/-  0.00185\n",
      " Leakage Fraction            =  0.00000 +/-  0.00000\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {
    "collapsed": false
   },
   "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": 17,
   "metadata": {
    "collapsed": false
   },
   "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)"
   ]
  },
  {
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.14239169e-06,   1.16429778e-06],\n",
       "       [  2.65434434e-05,   7.58244504e-06],\n",
       "       [  2.53406770e-05,   5.73814391e-06],\n",
       "       [  5.68158884e-05,   1.04761254e-05],\n",
       "       [  2.32937121e-05,   5.45676114e-06],\n",
       "       [  9.75765501e-06,   1.65156185e-06]])"
      ]
     },
     "execution_count": 18,
     "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](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) 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](http://pandas.pydata.org/) `DataFrame` of the multi-group cross section data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return c.reshape(shape_out)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>9.533842e-11</td>\n",
       "      <td>4.789050e-11</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>1.606499e-11</td>\n",
       "      <td>8.071081e-12</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>1.224131e-09</td>\n",
       "      <td>6.149449e-10</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>2.602518e-10</td>\n",
       "      <td>1.307590e-10</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>9.033000e-10</td>\n",
       "      <td>4.537601e-10</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>1.522295e-10</td>\n",
       "      <td>7.648264e-11</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>1.749138e-09</td>\n",
       "      <td>8.786432e-10</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>2.400317e-10</td>\n",
       "      <td>1.205943e-10</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>2.724017e-10</td>\n",
       "      <td>1.368376e-10</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>4.749191e-11</td>\n",
       "      <td>2.386080e-11</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  9.533842e-11  4.789050e-11\n",
       "199     1             1         1   Pu239  1.606499e-11  8.071081e-12\n",
       "398     1             2         1    U235  1.224131e-09  6.149449e-10\n",
       "399     1             2         1   Pu239  2.602518e-10  1.307590e-10\n",
       "598     1             3         1    U235  9.033000e-10  4.537601e-10\n",
       "599     1             3         1   Pu239  1.522295e-10  7.648264e-11\n",
       "798     1             4         1    U235  1.749138e-09  8.786432e-10\n",
       "799     1             4         1   Pu239  2.400317e-10  1.205943e-10\n",
       "998     1             5         1    U235  2.724017e-10  1.368376e-10\n",
       "999     1             5         1   Pu239  4.749191e-11  2.386080e-11"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = delayed_nu_fission.get_pandas_dataframe()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return c.reshape(shape_out)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>0.013336</td>\n",
       "      <td>0.003509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pu239</td>\n",
       "      <td>0.013271</td>\n",
       "      <td>0.007154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>0.032739</td>\n",
       "      <td>0.003864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Pu239</td>\n",
       "      <td>0.030881</td>\n",
       "      <td>0.008109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>0.120780</td>\n",
       "      <td>0.017277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Pu239</td>\n",
       "      <td>0.113370</td>\n",
       "      <td>0.031354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>0.302780</td>\n",
       "      <td>0.027190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Pu239</td>\n",
       "      <td>0.292500</td>\n",
       "      <td>0.057372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>0.849490</td>\n",
       "      <td>0.120338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Pu239</td>\n",
       "      <td>0.857490</td>\n",
       "      <td>0.231893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>2.853000</td>\n",
       "      <td>0.659168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Pu239</td>\n",
       "      <td>2.729700</td>\n",
       "      <td>1.342167</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cell  delayedgroup  group in nuclide      mean  std. dev.\n",
       "0      1             1         1    U235  0.013336   0.003509\n",
       "1      1             1         1   Pu239  0.013271   0.007154\n",
       "2      1             2         1    U235  0.032739   0.003864\n",
       "3      1             2         1   Pu239  0.030881   0.008109\n",
       "4      1             3         1    U235  0.120780   0.017277\n",
       "5      1             3         1   Pu239  0.113370   0.031354\n",
       "6      1             4         1    U235  0.302780   0.027190\n",
       "7      1             4         1   Pu239  0.292500   0.057372\n",
       "8      1             5         1    U235  0.849490   0.120338\n",
       "9      1             5         1   Pu239  0.857490   0.231893\n",
       "10     1             6         1    U235  2.853000   0.659168\n",
       "11     1             6         1   Pu239  2.729700   1.342167"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/lib/shape_base.py:873: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return c.reshape(shape_out)\n"
     ]
    }
   ],
   "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": 22,
   "metadata": {
    "collapsed": false
   },
   "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](https://mit-crpg.github.io/openmc/pythonapi/examples/tally-arithmetic.html) 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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f34eb15fba8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h+NT3ochmI7O0lMMlJeTbbPjYZ+Qb+/vzQosWNA1wzQhprUYFgMZO8geKi4nz\ndz2Po2VllDnFjSjy8WFT/fpQ33g6PDBoEMFJSZT7e1z02GMkBgbytlMfpddcgy0oiICAAMjKgiNH\nzLZhAxQVQUBAhY94ubeJ2+AF2dX5CgDNmlV83rMH4uMdcQkA0BqmTgWlsLRrR0iHDoR06ECsjw9t\nVq6Ed95xnBPAwMSBlQ5x8zk3c/M5Nzv2bdrmGDjIKswipzgHH4t55pqFN2P28NkkhLu+HxrPXjjl\nNAmtMPrTi1JpEub6fhwtOWoMfjul6ihH/NeC/fLtbXcPRU19gDsAiLptJDsiWgGfONr0OPcovbfv\n4ZzE1mQd8SUjAzIyYNMm4xwRFmZuCUBqqvnr9pqSmVntaeA0lsTatbBtW+WVLhER5pIPGWLiC3Tq\nBG3bmltXTSgMQRAEQRAE4Szj7/TTbyFQBxxiK/D19aVBgwY0aNCADh06uNQ1adKEhx56qFKb7du3\nk5aWRmpqKqmpqRw4cIC//vqLZcuWERQU5DIAkZ2dTaPyKUU7K1asOKZeLZwsinXr1tGzZ0+X+tmz\nZzNx4kSmTJnCgAED6NSpE2FhYeTn5xMcHOwyI+/XwI/oK1y9FQA6/9DZZX/i+xPp3qM72Yuy8Y3w\nJah5xRSlf4w/ob1CXeRzl+Ue8zwCE81Ag9aa/M35RF3uOit8+P3DHHjtAAdeOwBAQNMAgtsHo/wV\nEV1CaDsuFh2mUUrRJDCQ+5s2rXSMPb16kWe1crikhNTiYlJLSthWUMCSnBxCLBZ6h1bonWu1Euc2\naPB7jqdpaVda2Kdrtdasz8+nv93Vv5zXGzTgnvPOY3yjRvQLD6dLSAitg4I4arUS5uvrEoySiy6C\n9HSIjHQ9SFwcHD1qpofdGAOVjX63wSh27apYDL9hg9mc+fZb+O9/4eqrzf7IkTBoEEycWOV5W5SF\n0IBQQgNCiQ9zDRwZExLDDV0rOQCx866dZBZmkl6QTlp+Gqm5qWw/sp2VB1ailKJn44pn2Uf50CbK\ndfnMkr3u6wsqkxRpPHq01uzI2sFV7a5yqT/c6B2WJd9Fj553MzJxEF1iuxAfFs/m9M3EhMRQ36fi\nOWzXDl55pfIYjIfb4EL5KzpmzBi+/NJ1EADMmE9xsdk+/thszvTrB1OmVKxsyc+H4GCEWkZmMOsu\ncm8EQRCEvxN/J6P/e+BZbytxsgQEBNC0aVOaejBA3ZkzZ45jVr+cI0eO4O/vT0mJ53XXUGH0p6en\nc/jwYTqwLtcNAAAgAElEQVR2dF1Y/cMPP2C1WnnsscccZYmJiWRlZdGqVStmzJhB9+7dsViqierm\nxthrxwIQ3Lqy5dHsqWaVyuol1SNmXAz+sf6UppdStKuIot1mKyewmTH6i/cXY821EtzBte+81Xku\n+8X7iineZ1z1j3x1hD1P7eHc7HPxDfWlLK+M4v3F1Gtdz2VQQylFqK8vob6+JNWrV+05zmnXDpvb\nev0irYnw9aXQaqWoilgRLe1G/+GSEjJKS+nkZp39nJWFBt48eJA3D5oYDwFKYVGKWH9/HklI4NqY\nGPwtFhOmPirK/RCwzh5RsaDAxDnYtQu2boXVqxmTnw/OAz8BAWa62Jnvv6daCguhfLlLWRl8+SW4\nBcrk999h7lx46ik4xrWsCh+LD9HB0UQHR9Muul21sq9f8rrH8qh6UeQW5VJi8/yOtIw08SkyCjLI\nK8mjU0wnl/qfdv4EwMsrXublFS8DEBEYQYm1hBYRLfhnn39ydfurCfANoEl8KeNvs+Hr6zoYlJtb\n4dafmmrGWZYuhV9+MTP47U3sSMaMGcOLL4LbK8o331R76ixeXDFGo7XJMvDgg/DPf1bfTjg+xLCs\nu8i9EQRBEP5O/J2M/r8lvm5+vFdccQXFxcXk5OSwY8cOduzYQUpKCosXLyY1NZXs7Gya2Wd1N27c\nCFDJC6G83Jnd9uhlK1euZMiQIWRlZWGxWFi6dClPPfUUH374oSNeQW1Qr1U92r7btlJ5SUYJhSmF\n2AptBDQ2hlT58oB67V0NycLt1U+nBrcPxjfUXL+s/2Wx6apN9N7f29EvQNGBIgIaBbjOpleDxU1u\neFQUmeeei9aagyUlpBQWsqOwkJ+zsthXXEy+zUa83Ttgfb5ZY98pJMSlj032cmeKtQat2VVUxD93\n7OB6u8H9a3Y2k3bu5MsOHYh2W2oAGGO7TRuzXeieFdPOHA9pk48cqVzmTrmlumOHyYnn5kHCc88Z\na3XbNujbF/r0ge7d4YMPzGe35/BUMKzlMNL/Lx2rzcr+3P1sz9xOSmYKf+z/gx3ZO8gszHQsKdif\nux9fi28lo39jeuX3I6soC4D1aeu547s7GNPRGBw/7fqJEZ+MYPud212WHihlZv9jYioMenv2yUpM\nngzur9aiRcc+1052tQ8dMssPWrZ0rf+//4P33jN/Bw6ELl1kWYAgCIIgCMKZiPyE+5tSniKnPKjh\npEmTKsn06tWLZcuW0dLNGtjvOVKZg969ezsGGxYtWsTy5cuJcIuc/9xzzzFw4EB69ep1MqdRCf8o\nf/yjXI3Z0B6h9DnQp5Ksb5gv1kgrZTllHhfnh/aucM3PWZZDQNMAF4MfYEXiCpSvIvy8cCKHRhJ5\nUST1ko5/llopRVxAAHEBAfQPD+cGt2UZAOeFh7O5e3eaBbrGR0itxmsD4NywMEdsgl+zs9laUEAD\nPz8XmftSUhjeoAED3e5TjXn0UeOqv2kTbNxYsa1aZbwHGjWqsEw3bzZWrftM/5o1Ztp5wQKzgfFM\nsNnMUoDbb4crrzTlhYUmTkE1KRtPBh+LDwnhCSSEJzCk+RAmdJtQSaZro64UPFxQKRBgWn5atX33\ni+/naLN4z2JCA0JpXN81kt+Vn1zJFW2vYGynscfU9ZJLKpe98AIMHQp5eSbg359/mq08dmhsrMkK\nABUBAT15C6SlGaMfTGbLXr2Mo8a4cTB69KkPFCgIgiAIgiCcPGL0C1USFBTk0Sh/5JFH+OOPP9ix\nYwebN2+utISgv1MusaVLl9K7d28XV/+jR48yadIkGjduzKhRo7j88svp06cPVqsVPz+/Gs+anyzd\n13cHwFZso+CvAvI35ZP1vywyvsoA5Wr05y7LddkHyN+Ujy7V6FJN5oJMMhdkwj0mNkBAYgDxD8YT\nOSQSS0DNlzlUh6/FQlsPC6//06oVK/PySCks5M/8fPaXZxOwM8Apx9zinBz6hoW5eBxkl5Yyff9+\nZqamMigigkHh4VwWFUVjf3/8LRazLKAmREaaxeL9+lWUaQ2HD1dErAPo2hVmzwaneAdoXRFk0Jly\nH/RffjGDB+VG/8svm5D1O3eagQEv4efjV6ls1qWzWL5/OX8e/pN1h9aRXeQaJNE5iOHivYvpF9/P\n5Zk/UnCEeVvn8cvuX/h+x/cMTBzIoMRBWJSFnVk76Z/Q3xHQsCoaNIB//MO1TGszBrN1q2sMxvXr\nzXr+Zm6raHa6ZuXk6FH40Z4UddEiEyegfFVHXp7pw4u3QhAEQRAEQagCMfqF48Y5wGBJSQlbtmxh\nzZo1LFy4kA0bNjiMfpvNxrJly7j//vtd2n/66acApKam8uKLL/Liiy8SHR1Ns2bNOHDgAFu3biX4\nNEYVswRYCOkYQkjHEGJGm4hqWmu01ayx1zZNaVop0SNdAxIennPYY3/lsQE2Lt5I+8/bE32laWcr\ns2HxrX2raGxsLGOd0kMeKS1lXV4eS3Jy2FxQwGD77H2ZzcaynBweS0x0af9hmpmZLtaahZmZLMzM\n5MGdO2no50eBzcZPnTvTvX79ExuMUcpMKzvpR2IiXH+9q9yBAya0fXVcemnF54ULjX+6s5VZXAxP\nPAHjx5tjeInRHUYzusNowJ4hI2cPaw+uZe3Btaw7vI6hzU36xVJrKasOrOLZwa6hRj5Y/wFglgS8\nv/593l//PgDR9aLJK85j+c3L6RzrGgizJihlVkt07+5afs89cNVVrpdy61Y4hgMJfZycZx591MQc\nWLXquNUSBEEQBEEQTjFi9Asnhb+/P507d6Zz587ccINrNHWr1cqsWbPo1Ml1zfP8+fMr9ZOenk56\nejpg0hX+/vvvdOzYkdLSUj7++GOGDx9OmNOM9alGKYXyNUausih6bu/pGAQoJ2dx9ZH3lZ8iYqgx\nuEszS1nefDntP2lP5AWR1bY7WRr4+TE4MpLB7hH6gXkdOpDk5pP9ZUZGJTmANHt6yJ5r1rA8OZme\noaGU2mz85+BBro6OJspTTIATJS4Odu+GZctMQL/ffzfBBZ0HAsr92PPyjIX58suufcyda9IGBgeb\nBfDlAQu1dp3aPo0opUgMTyQxPJERbV3TePr5+JF6X2qlwZSvtn3lsa/0AvN+dHmzC0tvXEqfpn0o\nLC1k7LyxTBk4pVJsgZri51d5lj8iAu6806QOXLXKhFlwp9yhQ2sTl/Hii13rd++GmTPhoYcqJ4sQ\nBEEQBEEQTh9i9AunDD8/P0aOHFmpPPMYScj9/Pxo08akUlu6dCnjxo1j5cqVdOvW7ZToWVOUj6tx\n1un7TuT8nkNpWilZP2aR+W0mJYcqpkfD+oU5AgFmLszEmlM5g8Ch9w8R0DiA8EHhp3xZg6/Fwvke\nrC9dRdaAcmL9/elevz5gYgLcvn07vUNDa9foV8qkAUxIMIvFweSRW7nSDABs2lQReW7ZMrOw/IIL\nXPuYPdv8ffRReOwx6N3bLAdYvdr0+2zdS94REVQ5hoJ7jAB3GgY3dKQe/GHnD8zfOp+pg6e6yOQV\n51E/oP4J6xUTY1IJlpOWBkuWwKuvQkqKyfpYvvLnzz9NdoHLL3ft47nn4M03jeF/8cUwYoSJDXka\nx+4EQRAEQRAExOgXvMDPP//MypUr+fHHH5k/fz7ryyOJ2bnkkkvwswea++qrr2jUqJEj4GA5r776\nKi1btmTYsGGnTW93fIJ8iBxsjOiYMTFomybj6wz2PL0HpRQNLq0IqX5kwRFCuoZUCgS4feJ2rEet\nBHcOptGNjYgZG4Nfg8rrxE8lP3TpwuGSEhZnZ/NTdjZfZmRw0Mm3e0RUlCMGwJdHjhAfEEAXtwwC\n92zfTofgYG6Oi6s9xYKDTdj4gQNdy88/H/btgyZNXMtXrKj4rHWFxwCYKHUDB5rodkoZD4JTFATw\nZFl4zUK2ZGxh0e5Fjq18lh/gijZXONb0f7n1S9pEtaF1VGuXPvrO7kubqDZ8OvLTWtGpYUO44gqz\ngYmjWO4w8ttvxpAfMMC1zddfm7/5+fDpp2bz9TUOGDfeaFZiSDYAQRAEQRCEU4861izf2YJSKhlY\nvXr16koGpOBddu7cybx58/joo49Yu3Ytc+fOZcSIEWitSUpKYvDgwbz55psO+by8PCIiIujatSvv\nvPNOpZSCdQWtNUoptE3ze8zvxE2Io9lTFX7UGV9lsPEy1/Ruyt8sCYi+KprYa2MreRecDmxa80du\nLh8ePsy3mZm80aoVQyMj0VqTsHw5l0VFMSMpySGfWVpK1NKlhPn68lB8PDfFxtauF0BNyM01Punl\ngf880a2b8RwAmDYNvvjCLBOo49HnbNrG7/t+5/PNn/P55s959/J3Gdx8MFabldh/x3Jz15uZOqRi\npj8lM4WkGUlEBUUxqd8kbux6I+GB4ZTZyo7pRXCipKdDtFPIi8zMymkE3enZE5YvPyXqCIJwBrNm\nzRrOMdldztFar/G2PoIgCGcDYvQLdYrDhw8TFhZGYGAg+fn5XHPNNdx2222cf/75DplJkybx3HPP\nOfb79+/PxIkT8ff3x2q1ctVVV3lD9WopSS8BDf4NK4zh1d1Xk7cqr8o2fnF+dPqmE/W7nrib9slS\n/v2glOJoWRkTt2/nlkaN6Bce7pC57a+/eOPAAcd+gFKMbtiQ1vXqEe7ry22NG1fqt9bJz4ePPjJ5\n5n74wUT6d+eNN8xaf62hQwezXOCjjyrqrVYTva4O56GzaTOoYVEWSq2lfLrpU5IbJdM2uq1D5rZv\nbuON1W849oP9grmu83VYLBY2HN7AL9f9csqXkixdauIuZmVVLTN5MkyZYj6/8w6Eh5slAIIg/L0R\no18QBKH2EaNfOOOIioriyJEjlcp9fX1p3rw5P/74I02bNvWCZsfH2v5rObruKNY8z1Hrfer70Odg\nH3yCfSjLK6MwpdCrAwCe0FoTumQJR6uIvB/h68tLLVsypmFD/E7XjHpRkUnx9+GH8NlnxpCvVw8O\nHjRpAlevNrP+330HzstDFi6EUaNMuXNo+jOMxJcS2ZOzx2Nd4/qNmXXpLIa1HIZFnfr7ceiQcfP/\n4guT7q981YiPj4kD0LixKWvaFK6+GmbMOOUqCYJQxxGjXxAEofap236tguBGbm4uubm5HuvKysr4\n66+/SE5OptQeeX79+vXMmTPHsV+X6PpbV87NOpdO/+tEw9ENUQGus68NRzfEJ9is3U77OI3V3VZT\nfKjYG6pWSZHVSuNqXPmzysp4eOdOys9sdV4esw4coLQ6N/yTJTDQRIz74ANjdc6YYULIh4aa+nnz\nTArBIUNc2z3yiBkw2L/feAOcgWitia4XXWV9al4qN3x5A6VW8z58ve1rBv53IHnFVXucnAyxsXDL\nLbBgAWRkwN13m/AKl11mDH6A+fNNoMAJE1zbvvUWTJ9+StQSBEEQBEH4WyFGv3BGERoaSkpKCo88\n8ggNGzb0KDNu3DhHIMDp06czfvx4sqrzM/YiykcROTSSdh+1o8/BPkSPisYnzBj6sTdW5LY/NPsQ\nkRdEEhDrGgjQVnIKjecaEOTry5YePVjWtSvXxMTg78FtfEJcHL72Wf4X9+3jX/v24XO6UuhFRMAd\nd5ho/uU89ZQJRe8cRS4nB9auNdPOo0YZT4AFC0zEujvuMNPSZwBKKf645Q+W3LCEq9tfjY+qHKzw\n1nNuJcDXPEczV82koLTgpCL915T69eGll2D9enj//YryN96A/v2hfXtX+QcegPvugx49YM6c6sM1\nCIIgCIIgCFUjRr9wxhEfH8/TTz/Nvn37+Oijj+hXnjDczgT7lGF2djaffPIJd999t8sAgdb6mGnq\nvIFfhB/tP25Pv+x+dN/YndCeZmY6f3M+uctzaXRTIxf5nN9zWByymJR7UyjLLfOGyoAxNHuFhfF+\n27bs692bZ5o1I8o+6OKnFLfYI/pnlJTweXo6tzZq5MgGACZwYH4VywNOCRYLtGjhWjZ7tuvs/po1\ncMkl0K4dvP56hV/6GYBSir7xffnkqk/YdfcuHur7EBGBJjWgr8WX27rdBsDOrJ18n/K9Y7+cwtJC\nZq6cSVFZ0SnTsV4981drM8P/+OOu9d99VxEPYOVKGDsWkpNNyIY66LQjCIIgCIJQpxGjXzhj8ff3\nZ/To0fz2229s2LCBiRMnctVVV5Fkjyw/Z84c/P39+ec//+nSbu7cuXTr1o3s7GxvqF0jgtsHO4Kt\nFe8rJuScEJcUgADbbt6GLtXsf2k/yxOXs+eZPV41/gEa+vvzcEICB3v3Zl779jzVrBkxdvf//x46\nBMD1sbEubd44cICEZctI86ZhvWuX5/Ldu80U8003wbZtFeWbNp0WtU6WpmFNmTpkKqn3pfKfS//D\npHMn0ai+GTx6d927hAWGMarDKJc2zy15jru+u4t9OftOuX5KGceKwYNdyx95pLLsn3+a4IDh4bB4\n8SlXTRAEQRAE4axBAvkJZxXlafIAfvjhBw4dOsS1117rqLfZbMTExFBcXMzXX3/NAPfk4mcIOUtz\nWHvu2krlPuE+NLyqIc1faI5fmJ8XNKuaX7OzWZOXx71OQRZLbTYaLF1KntXKjbGxPNmsGY0DAqrp\n5RRRUmKC/j35JPz1V+X6evVg506IiYEVK6BXL/j5Zxg06PTrWksUlxWzOX0zXRt1dZRlF2UT80IM\npdZS7u55N48PeJyIIOMl4PxunWquu84EAKxuVc7WrdC69WlRRxCE04gE8hMEQah9ZKZfOKtwNkqG\nDh3qYvADvPbaa2RkZJCXl8fAgQO55JJL2LBhA6mpqawsz+F+BrDnac9rzK3ZVg6+dZDl8ctJ+yzt\nNGtVPQPCw10MfoApu3eTZ3ftn33oEEkrVvDIzp1sOHqUbzIyTt8yDH9/40O+aRP897/QvLlr/R13\nGIMf4JlnoE0bOEMHjMoJ8A1wMfgB7v/+fkqsJWg0L614iZYzWvLKilf4bfdv9HunH2n5p+eZevdd\nE9xv7tzKa/0BLr+8wuDftMmEYxAEQRAEQRA8IzP9wt+KJk2akJqa6lKmlKJly5ZkZmayf/9+AgMD\nvaRdzbGV2sj4IoO0j9PImJfhUSZ5ZTKh3UJPs2Y1R2tN3LJlHPLg1h+gFAEWC3t79SLMzwseC6Wl\nxvh/6ikz3bxrF0RFGR/zLl2MVTpuXIW81Qovvgi33lqRJeAMQ2tN9/90Z/XB1ZXqgnyDiAmJIeXO\nFHwslYMDnkqsVhP4b8IEKC42IRk2boS2bU39kCFw+LAJEHi64kMKgnDqkJl+QRCE2kdm+oW/DQUF\nBQQHB1cq11qzfft2srOzefzxxyk5A4K2WfwsNBzZkA5zO9BtXTeirohyqW9wSQOHwV92tIzdT+zG\nWnAag+XVAKvWTIiLo54HS61Ya3KtVrqvWUNemRfiFPj5mVxz27fDTz8Zgx9MOr++fWHMGFf5V181\n4eY3bDj9utYSZbYyBjcbjL9P5RSMhWWF7M7ezQUfXEB+ST5glgLszt59yvXy8YHrr4fMTJg61UT0\nLzf4f/jB3J6nnxaDXxAEQRAEoSrE6Bf+NtSrV4+NGzcyc+ZMYspdtZ2wWq0sW7bMke5vw4YNfPbZ\nZ6dbzeMmpHMIHeZ2oPnzzQlsHggKEiYnOOp3TdrF3uf3UnKwbg1m+FosTE5MZEevXtzaqBGe5o+7\nhIRQ355a780DB7h7+3aKTmek/4AAkzOunIsvNun+nL0PUlONwV+//hkdWt7Px49pQ6ex9fatjGw3\n0qNMoG8gwf5m4Gzakmm0frU12zK2eZStberVg4cegn/9y+zbbGa/Tx8YPtxV9oorTFwASfMnCIIg\nCIIgRr/wN8PPz4/bbruNlJQUpkyZUmnm//nnn0cpRUlJCePGjeOJJ56g9Awx5OL/L55eO3rRc0dP\nxyx/9uJsUl9NpfmzzQlqEeRlDT0TGxDAG61bs6F7d4ZHRjrKfZXi2WbNAMi3WpmyezfLcnPxqWtT\nujffbAIB5uWZwH533glHj5qAgC+/bPzTzyCaRTTj05GfsuSGJXSP6+4o91E+/GuosbgP5B3g5RUv\nc03Ha2gd5Z1oelYrXHklTJvmOsu/ZQvMnw/vvQc33gi5uV5RTxAEQRAEoc4gRr/wtyQkJITJkyez\nY8cObr/9dnx9fRkxYgS9e/cG4JlnnmHjxo289957jpn/M4WgZsa411qz/fbthPYNpfGdjV1kdj+9\nm00jN2EtrDsGadvgYL7s1InfunShZ/363BYXR0t7QvcZ+/eTXVbGe23a4GepQ19bJSUmrZ8zr74K\nHTvC6NHG6C8u9opqJ0vf+L4sv3k5H4z4gCahTZjQbQJto41f/ZO/PklsSCwvDXvJpU1ecR42fXqm\n1/384OGH4dxzXcv/8Y+Kz+++C507G+cMQRAEQRCEvyu+3lZAELxJTEwMr776Kvfccw8+PsbBPCUl\nhWeeeYZHH320UtDHBQsW0LdvX8LDw72h7nGhlKL9Z+3BB5SlYiq0aF8Ru6fsBivkrc2jzew2hPev\nO+fTLzycZcnJFNt9s8tsNl5JTeX55s1p4+aZ8fbBg5wbFkZr++DAaScrq2K9vzO7d5tt+PAz2sfc\noiyM7TSWEW1HUGo1Hi9aa0IDQnn38nepH1DfIau1ZszcMYQFhvHhFR96Rd/Nm2HdOtey3btNooUL\nLoC334ZGjbyimiAIgiAIgteQ6P2C4IbWmm+++YZhw4a5zPKvX7+ec845h8mTJ/Poo496UcOT44+2\nf1CwtcClLG5iHM2fa45v/bo5DrizsJDEwEAsTn7cv2VnM3DdOv7ZtCnPt2jhPeVsNpgxAyZNgsLC\nyvWXXGKSzoOZeo6KMrEBzjJeX/U6ExdM5KMrP2J0h9Fe0UFr4+7/7LNmtYU78fGwcGFFIEBBEOoe\ndTV6v1IqHvAwyisIguBVMrTWe48lVDd/4QuCF1FKcemll7qUlZSUcMkll1BWVsbBgwcpLCwkKKhu\nrpGvjoxvMyoZ/AAHZh4g7eM0kt5IImZk5SCH3qa527XOt1q5bONGNPCfAwfoGRrKldHR3lHOYoG7\n74aLLjKLyJ19yX18YPJk83nvXrj9drjhhrPO6E/JTOHu7+4G4KEfHyIhLIHeTc1SGa016jTFYVDK\nBPcbPRquvbayW//evfDdd2L0C4JwfCil4i0WyzabzVb3c/oKgvC3wmKxFCmlWh/L8BejXxBqwP/9\n3/+xb98+AGbOnMmvv/7KnDlzaNeuHatXr6Znz55e1rBmNBjWgOYvNCf9s3TyVrhOhZZllrHl6i3k\n3ZNHy+ktvaRhzZiwbRvZ9lR+2VYrV23axE2xsTzfvDlLc3O51JPL/akmKQl+/dV11v/BB6FbNzMF\nfccdEBYGzzxz+nU7xXyf8j2lNuP+vydnD/3e6cfT5z3NtZ2u5eI5FzP7stkkNzp9HlaJibBokYn0\n//DD5vKDibN4zz3mc14ebNtmbo8gCMIxiLLZbIEffPABbWXUUBCEOsKWLVu45pprAjFeSGL0C8LJ\nUFpayqJFi1zKNm3aRPfu3RkyZAj/+9//2LFjB/Hx8d5R8DhQFkX8/fE0vacp+2fsZ9cju7AVuK45\nD0kO8ZJ2NUNrzZaCyt4Kbx86xLyMDPKsVv7q0YNm3vDEcJ71f+45ePxxU/7LL8bFf+5cCA11bbNv\nHzRtevp1rSVs2sabq990KbNqK5N+msQLv7+Aj8WH+LDT/274+JhZ/+RkGD8esrPN6oryOJD33APz\n5pnZ//r1q+9LEAQBoG3btrJEVBCEM5I6FAZbEOomvr6+3HLLLQQGunr1lZSU8O2339K4cWMsdSmi\nfA1QPoqm9zSl+4buBCQEOMojL4ok5poK935bcd0LQqeU4ofOnbnagzt/VlkZNq35JC0NqzfjlSQl\nmahxAfZrO2gQ/O9/MGKEq9zChdCixRkdXt6iLHw79lv6J/SvVHek8AjFZcWsOei9Zbnnn2/S+P3w\nQ8XYyhdfwOzZ8MILYvALgiAIgnD2c2ZZKoLgBZRS3HHHHaxatYouXbpUqt+zZw8XXHABtjMwSntQ\n8yC6LulKo9saEdA0gFZvtHKsvz7wnwOsSl5FWU6Zl7WsTISfHx+3a8d/27QhxJ51oRwb8El6OmV2\no9+rxn85SsHQoa4J5bdtg5EjoV8/6NPHe7rVAk1Cm/DzuJ+ZPGAyFuX630pOcQ53fXcXZTbzHH26\n6VPeWvPWadUvKAi6dzef09LgllvgsstMCAZn6sKjIgiCIAiCUNuI0S8INaR9+/YsX76cBx54oFLd\nv//9bywWC1pr7r//fhYsWOAFDU+MwCaBtJ7Zmp4pPQlsarwZMn/MZPvE7YQPCMcn1OcYPXgHpRTX\nxcayrls3ejpN1wZZLHzUti0BFgs2rfnH5s1M2rnTi5p6IC/PLAEoLIT162HlSm9rdNL4WHyYMnAK\nP437ibj6cY5yfx9/PrryI3wtvuzP3c+t39zK9OXTKS4r9oqeERFw//0wa5brGExRESQkmDpBEARB\nEISzCTH6BeE4CAgIYNq0afz88880adIEgDvvvJNhw4YBMG3aNF588UUOHTrkTTVPCIu/+TrI35zP\npqs2ETEkgpavtDxtkddPlBZBQSzu2pXHEhKwANNbtqRNcLAZgNmxg8/S0+le13y4i4ogJwesVsjI\ngIEDzQJzMIH+Vqzwqnonw8DEgfw54U8uTjIZCl4Y+gJdG3XFpm1c/8X1BPsFs3DsQgJ8A47R06nB\nz8+s9W/YsKLMaoUuXUx4hVdegc8+84pqgiAIgiAIpwQJ5CcIJ8CgQYNYv349U6dO5YknngBgzpw5\nTJo0iccee4ybbrrJyxqeOD7BPkQOi6T1rNZYfCvGBa1FVjZevpHWb7cmsHHdylrkZ7HwZLNmjIyO\npkNwMAAv7t/PS/v381pSEld4K51fVTzyCBw5UrFfVARXXQWjRsHHH5so/2dIRghPRNWL4usxX/Pl\nti+5rPVlACzes5jVB1ez5IYlNA1zDVx4OtP6eWLQILPaAqCszNyGtDSTYVEQBEEQBOFMR2b6BeEE\niWXf1ToAACAASURBVIiI4PnnnyfIHiX+l19+Ydy4cY5BgHIKCgooLvaOK/OJEJgQSPuP2+MbWjEm\nqLVmTc81ZH2fxaquq8hbm1dND96jY0iIw3iM8fPj8YQEJjZu7CKTVVrKAW/fjxdeMBHmnNHaGPxt\n2sCtt3pHr1pEKcXlbS533I8BiQPYcvsW2jds7yK3dO9Shn04jJyiHG+oidXqOv4CFRkW77sPPCSK\nEARBEARBOKMQo18QaolZs2bx1ltvucxYlpWVMWrUKEaOHOlFzU6ezVdvJn99PgBl6WWsPXctGV9m\neFmr6rkmNpYnmjVzKSuyWrlg/Xou3bAB7c2obaGh8M03cPPNleu2boXLLzez/2DW/WdlnV79ThGx\nIbEu+5vSNnHxnIspsZZ4zd3fx8eEVbjttsp106dDx45m9l8QBEEQBOFYTJkypU5m9ap7GgnCGYpS\nCj8/P8e+1prx48ezYMECRo0a5UXNTo7CnYWkz013KbMV2Ng4YiN7X9jrXeP5OLBqzcUbNrAyL4/E\nwEC8rrWfn4kmN3Vq5brISJPuT2szMDBwoJmSPovYl7OPIe8PIb80n/MSzyPQ13tLRnx84LXX4Mkn\nK9ft3AkTJpx+nQRBEE4n5YZKZmamx/oOHTpw3nnnHbOfVatWcccdd9ChQwdCQkJISEhg1KhRbN++\n3UVu8+bNXH311bRo0YLg4GCio6MZMGAA33zzTaU+f/31VywWS6XNx8eHP/7448RO+AyjptcgPz+f\nyZMnc+GFF9KgQQMsFgvvvffecR1r9erVDBs2jLCwMEJDQ7ngggv4888/PcqmpKQwevRomjZtSnBw\nMG3btuWpp56isLDwpM73TEYpVSfjYcmafkE4RUybNo133nkHgEcffZQePXqQlJTkZa2On6DmQXT4\nogN7/7WX3CW5FRUadv7fTnJ+zaH93PaOQIB1lftSUvg5OxuAeRkZjN2yhXfbtMHfm6OxSpmocs2a\nwXXXQXExDBgAb71l6p5/HubMMW7/PnUzi8KJsjl9M5mFmZTZynh80eNYtZXJAyajlCI9P53o4NMb\nh0EpeOwxiIlxXV0RHCxGvyAIZz/HMlRqasRMmzaN33//nZEjR9KpUycOHTrEjBkzSE5OZsWKFbRr\n1w4w6Y6PHj3K9ddfT1xcHAUFBcydO5fhw4cza9YsbvbgCXfPPffQrVs3l7KWLVvW+Bznz5/PwIED\niYiIqHGbusaxrkFGRgZPPfUUCQkJdOnShUWLFh1X/2vWrKFfv37Ex8fzxBNPYLVamTlzJgMHDuSP\nP/5w+R27f/9+unfvTkREBHfeeSeRkZEsW7aMyZMns2bNGubPn39S5yrULmL0C8IpoLS01CVt3+7d\nu+nbty/fffcdTZs2Zf369QwZMsSLGh4fUcOjiBwWyV+3/sWh/7pmJjjyzRG237Gd1rNae0m7Y1Nk\ntfJFhutyhI/T0sgqLeX/2bvv8KiK/fHj77PpIZVUEkroLbQgHeldRJQmRQ2gglzggnyvqD/QUJQm\ncK+ICMilXYgUqSogIFGS0EMxEkroIRASII303fn9sWTJkp5gzgbm9TznITun7OfsWXZ3zsx8ZnGt\nWhxPSuIdT8989i4DQ4ZA5cr6GwDbt+tb+ffu1T/+9FP9+udIckYy//jlH2RoMwxlM36fQVJ6Eq0q\nt2LUzlEcGX2ERh6Nyjy299/Xj+P/17/0NwK2b4fs31fnz+tHWrRrV+ZhSZIklQtTpkwhMDAQc/Mn\nVYzBgwfTqFEj5s6da2h17t27N7179zbad/z48fj5+bFo0aI8K/3t27fnjTfeKFFcqampvPnmm4SF\nhZXrSn9hr4GXlxd3797F3d2dU6dO0aJFi2Idf/r06dja2nL06FGcnJwAGD58OHXq1OHTTz9lS47p\nbdatW0diYiJHjhyhXr16ALz77rtotVrWr19PQkICjo6OJThL6e9g2k1zklRO3b9/n5iYGKOy2NhY\nOnbsSIcOHRg5ciRp2WO2ywmNpYa6/61L9TnG4+TNXcypOrWqSlEVjbWZGV/VrInFUy0V+x4+pPmp\nUwRcv84jtbvPt2sHf/yhn0gewNcXPvoIZs0y3k6n00/zV47ZWdrxwUu5B9EvOrqIoT8OpV/dfrkS\n/pWlSZPg4EFYvx66d9eXRUVBz5765H7lZESLJElSmWvdurVRhR/0LdENGzYkIiKiwH0VRaFKlSrE\nP+6Vl5fk5GS0Jfi+Pn78OHZ2djRsqN53y7NS0GtgYWGBe845aYspODiYbt26GSr8AJ6enoahFyk5\nstsmJemTOj/9fJ6enmg0GiwtLQt8ruTkZCZNmkT16tWxtrbGw8ODHj16cObMGcM20dHRjBo1Ck9P\nT6ytrfH19TX0on1adHQ0o0ePxtvbG2tra2rUqMG4cePIypGY5/Tp0/Tu3RtHR0fs7e3p1q0bx56a\nJjl7qMuVK1fw9/fH2dkZJycnRo0aleu3e3BwMC1atMDGxobatWuzYsWKEp/r30229EvS38DT05Pg\n4GD69OnDqVOnDOWPHj3i4sWLzJs3D2tr05r2rigURaHqR1WJ2xZH0ukkFEXBd7svNjVt1A6tUIPc\n3XEyN+f18HAe6XSG8lSdDgtFISEriwpqd6HPeVOicmWYO9d4vVarb4oODtZnn7NSJ/ndszCl7RR9\n5f/nDxA5MizohA6d0KHVadGYqXdfukOHJ38/fAi9eoFGo2/5N8GhepIkmZjY2ILXOzgU/BGeng6J\niXmvM7VZaIsiJiYGX1/fXOUpKSmkpqaSkJDAzp072bNnD0OHDs3zGCNHjiQpKQkzMzNefvllFixY\nQPPmzYv0/CEhIbRp06ZU51BcWVlZJCQUbWaaihUrFmkIRWleg6JIT083zEqVk62tLRkZGYSHh9Oy\nZUsAOnXqxLx58xg1ahQzZszAxcWFkJAQvvvuO/75z3/meZycxowZw7Zt25gwYQL169fn/v37BAcH\nExERQdOmTbl37x6tWrXCzMyMiRMn4urqyp49exg9ejRJSUlMnDjRcKw7d+7QokULEhMTGTNmDHXr\n1uX27dts3bqVlJQUHBwcOH/+PB06dMDR0ZGPP/4Yc3Nzli9fTqdOnfjjjz8MvSKyr8PgwYOpUaMG\nc+fOJSwsjO+//x4PDw/mPM7FFB4eTs+ePXF3d2fmzJlkZmYSEBCQ502Xws61TAghXogF8APEqVOn\nhCSVlcTERNG1a1cBGC2KoogtW7aoHV6J6bJ0IuF4gojZFGMo02ZoRcSoCPHg4AMVIyvc0YQEUfHw\nYcGhQ0bLZ1evqh1awTIyhBgyRAiNRoj169WO5plZf3a9MJthJgjAaFkQssCwzfaI7UKr06oW47Rp\nQri4CBERoVoIkvTCOHXqVPZ3pZ8wgd+PooS/IfV9gvJfNm8ueP/Nm/Pf9+8QEBAgNBqNuH//fp7r\nfX19RefOnUt07PXr1wtFUcSaNWtyrRs7dqxQFEUoiiLMzMzE4MGDRXx8vNE2oaGhYtCgQWL16tVi\n9+7dYt68ecLNzU3Y2tqKM2fOFPjca9asESNGjBBubm6iTZs24q233hKHDh0q0XkUV1BQkOHcClo0\nGo24ceNGgccqyWtw8uRJoSiKWLt2bZFjbty4sahXr57Q6XSGsoyMDFGtWjWh0WjEtm3bjLafPXu2\nsLW1NTqX6dOnF+m5nJycxIQJE/JdP3r0aOHt7S0ePnxoVD506FDh7Ows0tLSDGVvv/22MDc3F2Fh\nYfker3///sLa2lpcv37dUHbnzh3h4OAgOnXqZCgLCAgQiqKI9957z2j/N954Q7i5uRkdz9bWVkRF\nRRnKLly4IMzNzYVGoynWuZZUcT4vVf8gLatFVvoltaSlpYlBgwYZVfrr1asnYmNjhRBCJCUlidDQ\nUJWjLJ2s5CxxttdZEWQRJGK2xBS+g8rCk5OFV0iIocI/ODxcZD3+gotJTxcLbtww+sIzCZMnC2Fm\nJoSNjRDP2efYtvPbhOUsS0OFv9f/eom0TP2X+cLQhYIAxIqTK1SLLzNTiIsXc5f/9ZcQM2aUfTyS\n9DyTlX6956XSHxERIRwdHUX79u3z/F69ePGiOHjwoFi/fr149dVXxYABA0RMTOG/IyIjI4Wtra3o\n3bt3keKoWLGiCAkJKXb8f/31l0hNTS32fkIIER8fLw4ePFikJT09vdjHL+w1KEml/7vvvhMajUb4\n+/uL8+fPiz///FMMGTJEWFlZCY1GIzZs2GC0/f/+9z/Ru3dvsWrVKrF9+3bx7rvvCo1GI5YuXVro\nc/n4+IiWLVuK6OjoPNc7OzuLsWPHiri4OKNl9erVQqPRGH4763Q64ejoKN544418n0ur1YoKFSqI\noUOH5lo3duxYYW5uLpKSkoQQT/4vnDx50mi7xYsXC41GI5KSkoRWqxW2trZi+PDhuY73yiuv5Kr0\nF3auJSUr/bLSL5mYrKws8cEHHwhAVK5c2XBH9+7du6J58+bC09NTpKSkqBxlyWTGZ4qTrU6KP+z+\nEPf35/1jwRRdS0kRtY8eFT3OnBHpWn0r8oVHj0SNI0eER3CwuJ3jDrLqdDohJk588ovP3V2IyEj9\nurQ0IZKT1Y3vGdgXuU/YzLYRHVZ3EI8yHgkhhNhwboMgAOG/w1+kZxX/B9Hf6eefhTA3F0JRhDhx\nQu1oJOn5ISv9eqZY6e/SpYsQQt/ye/fuXaNFq83dG+vu3buiRo0awsfHR9y5c6dIcfTo0UO0atWq\nSNsOHTpUWFtbF3qT/s8//xRWVlZ5Vqx//vlnsb6A3nOzZ88WQuh7DKxevVoMGzYsV2u3mgp6DUpS\n6RdCiGnTpgkrKytDy33Lli3F9OnThUajETt37jRsFxgYKGxtbXNVZEeOHCns7OzEgwcF9/zcvHmz\nsLW1FWZmZqJly5YiICBAXH3c6/LevXuG58+vd8SOHTuEEELExMQIRVEK7GFw9+5doSiK+Pzzz3Ot\n+89//iM0Go04f/68EOLJ/4V79+4ZbbdmzRqh0WjEzZs3Czzehx9+mKvSX9C5lkZxPi9lIj9JKgNm\nZmYsXbqUOXPmsG/fPqpWrcqlS5do06YNt2/fZs+ePYWOfTJVZnZm2DW1o8mhJlTsVtFonS5Dl89e\n6vOxsSG4WTO2+fpiqdFwOD6etmFhWGk0HPXzw8uUxsuvXAlff/3k8b17+kHm167Bq6/CsGHqxfaM\n9KjZg9/9f2f30N3YWthyP+U+7+9+n1mdZ/Hffv/F0qzghEBl6d//hr59IStL/xN84EB4Km+nJElS\nuZKdZyi/+dVTUlIM24SGhlKpUiW8vLwM/0ZFRRltn5iYSK9evUhMTGTv3r14FnGGnIEDB3LixAku\nX75c6LZVqlQhIyODR48eFbhdSEgIzZo1y5VYbtmyZSxatAidLu/fKikpKVSoUIFjx47h5eWFv78/\nixcvZsSIEdy/f7/Q+DIzM4mJiSnSkl8MhSnqa1Acs2bNIiYmhuDgYM6dO8exY8cMiQPr1Klj2G7Z\nsmX4+flRqVIlo/379etHSkoKp0+fLvB5Bg0axNWrV/nmm2/w9vbmq6++omHDhuzbt8/weowYMYID\nBw7kWvbv30+7v3kqHbN88jwJUfxsvgWda1mRifwkqYwoisLHH39seLxkyRKsrKw4dOgQ1apVUzGy\n0lHMFOp+l3u6vqTTSZzpfIY6y+rgMdRDhcgK557jB8C9zEya2NnxY8OGOFtYqBhVHt55B374AQ4d\nelIWGQmNG+trnbt3qxfbM9TC+8nUQi62Lpz74Bw1nGvk2i45Ixk7S7uyDM3I2rXGGfxv3IB+/fSX\nx9ZWtbAkSTIh9+4VvN7BoeD1/foVfoxnKft3yMWLF/H29jZal5qayq1bt+jZsycATZo04cCBA0bb\n5KzUp6en07dvXyIjIzl48CB16xZ9St/smw5FSYB35coVrK2tsbMr+Pvg8OHDtG3bNlf5Bx98wL0C\nXuQdO3bQv39/Dh8+zJYtW+jevTvu7u7Y2toSFRWFi4tLgc8bGhpK586dCz0PRVG4du0aVasWfyak\nor4GxeXo6Gj0mu3fv5/KlSsbpuYDfXLGihUr5to3MzMTwChrfn48PDwYO3YsY8eOJS4ujmbNmvHF\nF18QFBSEvb09Wq2WLl26FHgMNzc3HBwcCA8PL3AbW1tbLl68mGtdREQEGo2GKlWqFBpvzuPZ2Njk\neXPqwoULee6T37lm/7/6u8lKvySpZOHChTx69CjXfLExMTHY29tjW45rDw8OPuBcr3OQBRHvRGDl\nbYVTB6fCd1TRADc33nB1zZU9NzIlBR1QR83rYWWlTxvfsSOcPfukPDkZWrZ8bieOf7rCL4Rgfsh8\nlp5YStiYMFxtXVWJKyQEOneG48eflB0/Dq+9Bps2QR6/gSRJesGUNsO+lVXZZunv2rUrFhYWLFu2\njM6dOxt9Fy5fvhytVkufPn0AcHJyyrciptPpGDx4MMeOHWPXrl2GTO9Pi42Nxe2pE8zKymLt2rXY\n2NjQoEEDQ3lcXByursaf92fPnmX37t288sorhZ5bSEgICxcuBCAwMJCXX36ZypUrF7rf1atXGTZs\nGD4+PoZzP3/+PHZ2dnnORPC0pk2b5ro5kp/CekKU9jXIS2pqKjdv3sTV1bXAGxibNm3i5MmTLFq0\nyKi8Tp067N+/n8jISGrVqmUo37hxIxqNhsaNG+d7TJ1OR3JyMg457n65urri5eVFeno6Go2GAQMG\nEBgYyCeffJJrqsWcr4eiKPTv358NGzYQFhaGn59frufTaDT06NGDnTt3cvPmTcMNlpiYGMN7ojg3\nTjQaDT179mTHjh1ERUUZ3k8RERH8+uuvxTrXsiIr/ZKkEktLy1xdzS5evEjv3r3p2rUrK1euVCmy\n0nl46CHnup/DMAtbJvz56p80/b0p9k3tVY2tME9X+EMTEuj755+0dnDglwK+vMqEoyPs2QNt2uib\nlrMdPw6jR8O6dfq55JKSwM7uuZtXLlObyT9++Qcrw1YyvcN0XGwKbmH5O9na6jtXtGoF168/KT9w\nALp2hUJ6NEqSJJkcNzc3PvvsM6ZPn06HDh3o168ftra2hISE8MMPP9CrVy/69u1b6HE+/PBDdu/e\nTb9+/YiLi2PDhg1G64cPHw7opzBLTEykQ4cOeHt7c/fuXTZs2MDFixdZtGiRUcPHkCFDsLGxoW3b\ntri7u/PXX3+xcuVK7OzsDNOnFSQuLo769euTnJxMZGRkvlMC5nTnzh2jHg8uLi4IIfjss8/YtGlT\nvl2/c3J0dCy0lbqoivMaLF26lPj4eG7fvg3Arl27uHXrFgATJ07E3l7/W+z48eN07tyZgIAAPvvs\nM0DfK2LmzJn06NEDFxcXjhw5wpo1a+jTp4/RFHkA//rXv9i7dy/t27dn/PjxuLi4sHv3bvbt28d7\n771X4I2MpKQkKleuzMCBA2nSpAl2dnbs37/f6ObC3LlzCQoKolWrVrz33ns0aNCABw8ecOrUKX77\n7Tfi4uIMx/vyyy/Zv38/HTp04P3336d+/fpER0ezdetWQkJCcHBwYPbs2Rw4cIB27doxbtw4zMzM\nWLFiBRkZGcyfP7/Y12TGjBmG8x83bhyZmZl88803+Pr6cu7cuWKda5kobND/87IgE/lJJi40NFQ4\nOjoKFxcXo+lEyptHkY9EsGewOMQhoyXYI1ikRJafZIWbY2KEeVCQ4NAh8b8iJiAqExcu6OePy87o\npNEIsXy5ft3du0I0aiREEafLKU+m7p8qzGaYCetZ1uLoraNqhyOEEOL8eSEcHXMn2Vq3Tu3IJKn8\nel4S+ZVXGzduFG3bthX29vbCxsZGNGjQQMyePVtkZGQUaf9OnToJjUaT75Jt06ZNokePHqJSpUrC\n0tJSuLi4iB49eoiffvop1zGXLFkiWrduLVxdXYWlpaXw9vYW77zzjrhy5UqRYvriiy/EmDFjxIwZ\nM0TyU4lvAwIC8kx09/XXX4uEhASjsjlz5qj2HijOa+Dj45Pv659zasCgoCCh0WjEzJkzDWVXrlwR\nvXr1Eu7u7obrP3/+fJGZmZlnXCdOnBCvvPKK8PLyElZWVqJevXpi7ty5eSZ2zCkjI0NMnTpVNGvW\nTDg6Ogp7e3vRrFkzsTz798xjsbGxYsKECaJatWrCyspKeHl5ie7du4tVq1blOuatW7eEv7+/8PDw\nEDY2NqJWrVpi4sSJRrGfOXNG9O7dWzg4OAg7OzvRrVs3cezYMaPj5JfUMjuRX87X8PDhw6JFixbC\n2tpa1KpVS6xYscKwf3HPtSSK83mpiBIkIyiPFEXxA06dOnUqz24fkqSmtLQ0vL29iY+PR6fTMXfu\nXKZOnap2WCWmy9JxftB54nbEGZWbu5jT4s8WWFUyoSR5eUjRaql65Aj3H49Hq6DRENysGU3tTaSn\nwrFj0KUL6HQQGAj9+8OtW/pm5kePYP9+yNE1srzL0mXxj5//wYqwFQBUsqvEifdO4O3gbUio83Qv\njbKya5e+W3+2tm1hx46y7ZYrSc+TsLAwmjdvDtBcCBGmdjwgf0M+z2bMmEH16tV5++23jcpnz57N\ntGnTDI+3bNlC/fr18fX15fTp09jY2BiNb5ckNRTn81Jm75ckE7BixQoePHhgyFb68ccfs3XrVkCf\nPba80ZhrqB9YH8eOjkblWfezONv9LJnxmSpFVjRbY2MNFX6ARzodr4aHcyc9nZtpaSpG9lirVvDj\nj/rKff/+kJ6uH2SemQmHDz9XFX6AFadWGCr8AHeS7/D6ptdJSk9i/C/jWXx0sWqx9esHix8//ZAh\ncPDgkwr/gweqhSVJkiQVYtWqVRw4cIAtW7YYfnMBnDlzhmbNmhke//7774wePZouXbrg5uZGt27d\nqF27thohS1KJyUq/JJmAKlWq5GqpfOutt1i5ciU1atQgKChIncBKwczajEY7G2FT23gqQktPSxQz\n0x5v/paHB297GM84EJWeTofTp6l3/DibyjKlcn569YL27fV/W1nBwoX6Cn+N3Nnuy7v3m79Ptxrd\njMpORJ+g3tJ6LD+1HHtLdXtgTJoEv/4KGzfC4xmtWL8efHwgzCTaKSVJkqSnjR49msOHD7N7924G\nDhxoKN+7dy+9evUyPO7YsSOJiYncu3eP2NhY7t+/X6Qx/ZJkSmSlX5JMwOuvv54riUhaWhrvv/8+\nPj4+NG3aVKXISsfc0RyvsV5orPUfNW4D3Wj8c2PM7U07h6iiKKyoW5f2jsY9FSLT0nCxsODVQqbp\nUcVrr8HT2YhjYsDfv9w3OZtrzNk0cBM1nWsalUcnRfNOk3d4r/l7KkX2RPfuoNHoR/UHBMDbb8Og\nQdCokdqRSZIkSUWl1WoRQshKvfTckZV+STIRU6ZM4b33cldekpKSVBuv/CxU+bAKL517iaofV6XB\nDw3QWOk/drISs0i7ZQJd5fNhpdGwvWFDamQ33T4WlZ7OnJs3VYqqGA4ehCZNYO9euHZN7WhKraJN\nRXYN3ZWrVX/1mdX8dOknlaLKbcMGmDEDvvgCvv8eLCzUjkiSJEkqqsOHD5fZvOmSVJZkpV+STISi\nKCxdupRu3Yy7MZ8/f55ly5apFNWzYVvblhpzahi69SccSeBk05NcGHlB5cgK5mppyU+NGuGY446/\nhaJQM8eNgIzHeRhMyrp1+qZnR0f9lH76JC/lXgO3BmwcsBEF45tgl+9fBuBRxiMm753Mw9SHaoQH\nwNCh8Ntv8OmnuWdNfEHy5kqSJJVbnTp1kskapeeSrPRLkgmxsLAwZIjNNnXqVD766CNAn6Xzww8/\npLzOuqHL0nF95nVOv3waS09L6q6oq3ZIhapfoQKbGzbEDKhobs6BJk3wr1QJgG+iovA7eZKEHEn/\nTEL16uDtDZcuwTffqB3NM9W3Tl/mdNXPSWxnaceON3cwuc1k7ibfpdPaTqwMW8lfsX+pFp+ZmT6n\nYk5CwMCB0LKlfsIFSZIkSZKksiQr/ZJkYpycnPj555/x8vJi5cqVzJ07FyEEX375Ja1atSIoKIj4\n+Hi1wywRkS64t+ke1f5fNZr+0RSbGjaF72QCelSsyPr69Tnm50cHJyd0QvBhZCQTIiPpWbEi9qY0\n9m/bNn2Sv6go/eOvvoJDh/R/37wJCQnqxfaMfNTuIz5q+xFHRh+hX91+XIi7QOvvW3M78TaHRx6m\nfdX2aodokJEBfn76yRZOnYJfflE7IkmSJEmSXjSmnU1Lkl5Q1atX5/Lly9ja2gIwaNAgdu7cyccf\nf8znn3+OpaWlyhGWjFkFM14Ke8kwrj+nrOQszO1M9yNpaI5s/p9cvcp/oqJYUqsW459Onqe2WrUg\nZ88DIfRZ5ebNg/HjYdiwct/6rygK87rPMzy2tbClnms9Vr66kiqOVVSMzJhOp78ct27pHwsBI0ZA\neHjunIuSJEmSJEl/F9nSL0kmKrvCDzB27Fj++OMPvvjii3Jb4c/2dIVf6AQXP7hIqFsoKVdTVIqq\neMZ7e7OrUSPTq/ADNG4Mc+YYl0VFwfDh0KEDzJypTlx/o6qOVdk7Ym+uCr9WpyUxPVGlqPTZ/Dt2\nNC5LSIDRo+X4fkmSJEmSyo6s9EtSOdCjRw/atWuXqzw2NlaFaJ6d1JupHKt5jDvf3UGXpiNiaAS6\nLNMf9FzF2ppX8pi279DDh2hNoTY3aRJ07Zq7/I03oGLFso9HBZEPIum4piNvb39b1TjWrYMhQ4zL\nfv0Vpk9XJx5JkiRJkl48stIvSeVQVlYWM2fOpFq1avz5559qh1NiZzqcIe36k2n7ko4ncWPmDRUj\nKpmErCzeiYigy9mzbDOFGzEaDaxZA87OxuXjx8P162pEVGZ0Qse3J76lyXdNiE6K5v/a/p+q8SgK\nLFsGXl7G5V98Abt2qROTJEmSJEkvFlnpl6Ry5vLly7Rr146AgADGjRtHvXr11A6pxGotqoWlt/Fw\nhRtf3CD+j/KTqPCP+HjqHz/OhpgYmlaowABXV7VD0qtcGVasMC6rUgUePdL/vWEDvP/+c9fPcsc9\n1gAAIABJREFUPCEtgYCgADztPGldubVJJPVzdoZVq3KXL1wos/lLkiRJkvT3k5V+SSpnlixZwp9/\n/okQgujoaCwsLNQOqcTc3nDDd5svinmOCc11cP7N82Q+zFQvsCLSCsEPMTHczchAC5x59IhNptDS\nn23gQPD31/89YQKcPKmfzu/dd/UZ5VJTIdP0X+eiEkKw/cJ20rXpXH14lcDwQPZG7lU7LEA/oULO\nEToWFvqy5+yeiyRJkiRJJkhW+iWpHNmzZw+rVq0iNTUVgMDAQH788UeVoyodh5YO+MzyMSrLuJPB\nX4P+Qph4jSg+K4utcXHkjHL85cvEZGSoFlMuX3+tH0T+9ddgbQ09e8LGjfqm53XroJwnhswpNiWW\nD/d9aJS8b8xPY0hKTyLyQSQ7LuxQMTr4+Wd9Z4tmzfTT933yCZjSbI+SJEmSJD2fZKVfksqRJk2a\nYGVlZVT2wQcfEB0dzdy5c3n48KFKkZVO1Y+qYt/K3qgs/rd4ks8kqxRR0bhYWPBt7dpGZQ+yshh9\n4QIjL1zgRKJ6meMN7O2he3f934oC06bBiRMwapT+8XPEvYI7C7ovMCq7mXCTVwNfpcl3Tfg86HO0\nOq1K0YGjIxw8CEePQqNG+rIHD+CDD8CUOohIkiRJkvR8kZV+SSpHvLy8WLJkiVFZbGwsvr6+TJs2\njaCgIHUCKyVFo1Cxe0XDJ5JVNSuaHW6GfTP7gnc0AQPd3Rnk5mZU9vODB2y6d4/b6ekqRVWAnj2h\nYUPjMiHgt9/UiecZe9fvXTr7dDYq+/3G7/Ss2ZOQUSGYadRtWq9d+0nnip9/1l+KH36A8+dVDUuS\nJEmSpOeYrPRLUjkzbNgw+vfvb1T28OFDZs6cyeuvv65SVKXnM8OHBoENcH/TnZfOvIRjO0e1Qyqy\nb2rXxvWp3ApWikIbx3JwDomJ8Oab+in+jh5VO5pSUxSFFa+uwMbcxqg8Ii4Cc425SlHl9t130Lev\nvqv/X39Bx45qRyRJkiRJUlkICAhAoynbaris9EtSOaMoCsuWLaPiU/OtL1q0iJiYGJWiKj1Fo+A+\n2J0GgQ2wcNJXoLWpWiInR5JyOUXl6ArmbmmZq5t/vFbL7BsmPv1gWBj4+cHevbB5M7RurXZEz0St\nirWY1XmWUdmFuAvMC55neKx2vojXX4f//lff2v/0dH6SJEllbe3atWg0GsNiY2ND3bp1mTBhAvfu\n3Xtmz3Py5EnGjx+Pr68vdnZ2VKtWjSFDhnD58uVc254/f57BgwdTs2ZNKlSogJubGx07duSnn37K\nte2pU6fo1asXjo6OODg40LNnT86ePfvM4jZ1v//+u9H1y17MzMw4fvy40bbFuQZ5Ker+xbl+LxpF\nUVDKeIil6TR7SJJUZJ6enixdupShQ4cayipUqMDt27fx8PAgIyMDc3PzMr+L+Cwln03m/NDzpF1L\nw7G9I7a1bdUOqUCD3N0ZGBvL1thYFGCitzdf1KgBQHxmJmaKgr25iX3kXroEaWnQooU+0/9z5J+t\n/8mmvzZxIvoEAIMbDmbsS2PRCR3LTixjy/kt7H9rPxZm6sx+4eEBI0fmLo+P14/9f87SLUiSVA4o\nisKsWbPw8fEhLS2N4OBgli1bxp49ewgPD8fa2rrUzzFv3jxCQ0MZNGgQjRs35u7duyxZsgQ/Pz+O\nHTtGgwYNDNveuHGD5ORk/P398fLyIiUlhR9//JF+/fqxYsUK3n33XQDCwsJ4+eWXqVq1KjNmzECr\n1fLtt9/SqVMnjh8/Tu2nbsrnZ/v27XTq1AlnZ+dSn6daJk2axEsvvWRUVqtWLaPHxbkGeSnq/kW9\nflIZEUK8EAvgB4hTp04JSXoe6HQ6MWDAAAGI999/XyQkJAghhAgPDxdNmzYVixYtUjnCkru//74I\nsgwSxxsfF8nhyWqHU2Qx6emizalT4o+HDw1lv96/LyqHhop/XLyoYmR5uHFDiI4dhdCP6BdiyRK1\nI3rmzt49K6otria2R2wXQghxI/6G6Lq2qyAAMe6ncSIlI0XlCI39+99CmJsLERCgdiSSpJ5Tp04J\nQAB+wgR+P4oX5DfkmjVrhEajyXWOU6ZMERqNRvzwww/P5HmOHDkiMjMzjcouX74srK2txVtvvVXo\n/jqdTjRt2lTUr1/fUNanTx/h4uIiHub47r1z546wt7cXAwcOLFJcKSkpwtLSUoSHhxfxTExLUFCQ\nUBRF/Pjjj4VuW9prUJr987p+L6KAgACh0WhKfZzifF6W32ZASXrBKYrCt99+y759+1i+fDl2dnb8\n+9//pnnz5qSnp9OpUye1QywxxzaO+HzuQ/PjzanQsILa4RSZu6UlIc2a8bKTE4+0Wv5x6RI9zp2j\nro0NH1WtqnZ4T+h00Ls3/P77k7J//UufTS4lBebP1/9bzjX2aMzlCZfpX68/QggGbxnMxfsX+XXE\nryx9ZSk2FjaFH6QM3L+vH9s/aRJkZcGmTWBKsz5KkvTi6tKlC0IIrl27BoC/vz/Vq1fPtV1Rxyi3\nbt0a86d6vdWqVYuGDRsSERFR6P6KolClShXi4+MNZcHBwXTr1g0nJydDmaenp6EreUoRvs+OHz+O\nnZ0dDZ9OdFsOJScno9XmP1NNaa9BafbP6/rlJzk5mUmTJlG9enWsra3x8PCgR48enDlzxmi76Oho\nRo0ahaenJ9bW1vj6+rJ69epcx4uOjmb06NF4e3tjbW1NjRo1GDduHFlZWYZtTp8+Te/evXF0dMTe\n3p5u3bpx7Ngxo+Nkv9evXLmCv78/zs7OODk5MWrUKNLS0nI9b3BwMC1atMDGxobatWuzYsWKEp9r\naZhYX1NJkorD3d2dHj16APrubVOmTGHChAnMmTMHGxvTqNCUhFkFM6p9Wi1XecbDDDJuZ2Dna6dC\nVEWTPUYrNiODH2NjWVKrFuO8vdGYUn9tjQYWL9Zn8s+WlgZ9+uhvCMTEQPPm+uR+5Vx2931FUVjb\nfy0edh44WTsVslfZmjYNcn6vR0TArFn6RZKk8ik2FuzsIOdXcUqKfnF1Nd72/n2wtoYKOe5xp6fr\n86y6uOg/srM9fAhl2fs8MjISANfHQec3Frm0Y5RjYmLw9fXNc11KSgqpqakkJCSwc+dO9uzZYzS8\nMT09Pc/fPLa2tmRkZBAeHk7Lli0LfP6QkBDatGlT4vhLIisri4SEhCJtW7FixSK9viNHjiQpKQkz\nMzNefvllFixYQPPmzYv0HAVdg9LsX9j1y8+YMWPYtm0bEyZMoH79+ty/f5/g4GAiIiJo2rQpAPfu\n3aNVq1aYmZkxceJEXF1d2bNnD6NHjyYpKYmJEycCcOfOHVq0aEFiYiJjxoyhbt263L59m61bt5KS\nkoKDgwPnz5+nQ4cOODo68vHHH2Nubs7y5cvp1KkTf/zxBy1atACe/M4bPHgwNWrUYO7cuYSFhfH9\n99/j4eHBnDlzDOcQHh5Oz549cXd3Z+bMmWRmZhIQEIC7u3uxz7XUCusK8LwsvABdsyTp8uXLaofw\nt9BmasXV6VdFkEWQCPYIFtpMrdohFUlKVpbaIRTsn/980r0/e6lTR4jn9H2UF61OK0JvhqoaQ1qa\nEPXrG18GMzMhjh9XNSxJUsXz0r0fhFi50rhs4UIh7O1zb+vtLcTnnxuXbd6sP8bjkXsGrVsXOYRi\nye7e/9tvv4m4uDgRFRUlfvjhB+Hq6ioqVKggoqOjhRBC+Pv7i+rVq+favzTdldevXy8URRFr1qzJ\nc/3YsWOFoihCURRhZmYmBg8eLOLj4w3rGzduLOrVqyd0Op2hLCMjQ1SrVk1oNBqxbdu2As97xIgR\nws3NTbRp00a89dZb4tChQyU6j+LK7pJf2KLRaMSNGzcKPFZoaKgYNGiQWL16tdi9e7eYN2+ecHNz\nE7a2tuLMmTOFxlLYNSjN/oVdv/w4OTmJCRMmFLjN6NGjhbe3t9HQDiGEGDp0qHB2dhZpaWlCCCHe\nfvttYW5uLsLCwvI9Vv/+/YW1tbW4fv26oezOnTvCwcFBdOrUyVAWEBAgFEUR7733ntH+b7zxhnBz\nc8t1TFtbWxEVFWUou3DhgjA3Nzf6/1KUc81LcT4vZUu/JD1Hnk7WAvDgwQMSEhLy7I5XHiSfTeZM\nlzNkPdB3v8qMyeTOyjt4f+CtcmSFszHLPSf8/gcPSNHpeO3pph41zJ0LBw7o54zLdvu2vnnqBXAz\n4Sajdo4i6HoQkRMj8XHyUSUOKyvYuFGfTzG7l6FWC2+8AXv2QCkaXiRJkopMCEHXHD28FEXBx8eH\nwMBAKlWq9Lc854ULFxg/fjzt2rXj7bffznObyZMnM2jQIKKjo9m8eTNarZb09HTD+nHjxjFu3DhG\njRrFRx99hFarZfbs2dy9exeA1NTUfJ//nXfe4Z133sHFxYWvvvqKtm3bFiv+8+fPU6NGjRIlOWza\ntCkHDhwo0raenp4Frm/Tpo1RT4W+ffsyYMAAGjduzCeffMIvv/yS775FuQYFKWz/wq5ffpycnDh2\n7Bh37tzJ9/23bds2hgwZglar5f79+4byHj168MMPPxAWFkbr1q3ZuXMn/fr1o1mzZnkeR6fTsX//\nfl5//XWqVXvS09TT05Nhw4bx/fffk5ycjN3j30eKojBmzBijY7z88svs2LHDsJ1Op+PXX3/l9ddf\nx9v7yW/WunXr0rNnT/bs2VOscy0tOaZfkp5T6enpLF68mFq1ajFhwgS1wykxbaqWrIQso7Jr06+R\n+TBTpYhK5pFWy7jHY/w3msrUitbWsG6dcar4R4/gs8/Ui6mMrDu7Dt9vfbl4/yJ7R+xVrcKfrWlT\nmD7duCwqCmRyY0mSykr2lMAHDhwgKCiI8+fPc+XKFbp161as42RmZhITE2O06HS6XNvFxMTwyiuv\n4OzszJYtW/Ltvl6nTh26dOnCiBEj2LVrF0lJSfTr18+wfsyYMXz66acEBgbSsGFDmjRpwrVr1/jo\no48ADBW1/ISHh/Po0aNcWe8BfvnlF/73v//lu+/27dsNFf61a9eyZs0ahg8fzvbt2wt8TgBHR0e6\ndOlSpMXS0rLQ4z2tZs2avPbaaxw6dCi7x0ouRb0G+SnK/oVdv/zMnz+f8PBwqlSpQqtWrZgxY4Yh\ntwRAbGws8fHxrFixAjc3N6Nl1KhRKIrCvXv3iI2NJTExscB8DbGxsaSkpFCnTp1c6+rXr49Op+PW\nrVtG5VWfytWUPevDw4cPDcdMTU3Ns0Gubt26xTrXZ0FW+iXpORQREUGDBg2YMmUKfn5+rFq1Su2Q\nSsyxtSONfmpkVJZ1P4sbs26oFFHxPcjMpOHx4yyPjsZOo2FJHl8AqvHzA3//J489PSG7pePwYRg8\n+LnMKnfp/iVcbV3xdfOla3XTyF3wySfw9ExJx45BaKg68UiS9OJp0aIFXbp0oUOHDrkqJkC+lcKc\nieNCQ0OpVKkSXl5ehn+joqKMtk9MTKRXr14kJiayd+/eQluycxo4cCAnTpwwmhd+1qxZxMTEEBwc\nzLlz5zh27JghprwqcjmFhITQrFmzXBXrZcuWsWjRojxvWIB+rHqFx4kYjh07hpeXF/7+/ixevJgR\nI0YYtTznJa+bI/kt+cVQmCpVqpCRkcGjR49yrSvNNSjN/nldv7wMGjSIq1ev8s033+Dt7c1XX31F\nw4YN2bdvH4DhNRkxYgQHDhzItezfv5927doV65yKwyyP3pxAvjdYClLYuT4Lsnu/JD2HzM3NSU9P\nRwjBiRMnSvxlYSpcerngOsCVuB/jDGVRX0dRaUwlKtQ17ez+Qgg2xMQQnZGBDkjW6Zh85QobCpkH\nt0zNng0//QQffKDP4p+QAMOHP+lzHhsL3qY/nKIohBAEhgey7MQyHqQ94Fr8NTb9tYk3fd9UOzQs\nLGDNGmjdWp9PUaOBkSPBlCZ+kCSpaO7dyz1SauxYyKv39Nmz+o5XOfXrl/cxCuilXSacnZ3zzLx+\n/fp1w99NmjTJ1W09Z4UwPT2dvn37EhkZycGDB/O8uVCQ7O76TyfBc3R0NOqev3//fipXrky9evUK\nPN7hw4fz7Nb/wQcfcO/evXz327FjB/379wfg0qVLbNmyhe7du+Pu7o6trS1RUVG4uLjku39oaCid\nO3cuMDbQ32i5du1arpblorhy5QrW1ta5ejuU9hqUZv/8rl9ePDw8GDt2LGPHjiUuLo5mzZrxxRdf\n0LNnT9zc3LC3t0er1dKlS5d8jyGEwMHBgfDw8Hy3cXNzw9bWlosXL+ZaFxERgUajoUqVKkU4O+Nj\n2tjY5Hlz48KFC7nKCjrXZ0FW+iXpORMdHc1LL71EYmIioL8TO3PmTJYtW6ZyZKVTc35N7u++j8h4\nfAdVCxfevkDzY0XLSqumAw8fkpnjzu/Ge/cY6u5OX1MY1w/g5QU3bjxJM92vH4SHw6pV+l4ARZiG\nqbxIykjiw30f8iDtgaFs4p6JdKvRDQuNBfuu7GNww8GqxdeiBXz5JRw5ov/XlO4NSZJUdG5uucts\nbfXL0/KqF1pZ5X2Msszcn5eaNWuSkJBAeHi4IVP7nTt32LFjh2EbJyenfCthOp2OwYMHc+zYMXbt\n2lVgVv3Y2FjcnnoRsrKyWLt2LTY2NjQo4ANy06ZNnDx5kkWLFhV6TiEhISxcuBCAwMBAXn75ZSpX\nrlzoflevXmXYsGEAvPXWW/Tp0wfQj/O3s7MrNBP+sxzTHxcXZ5hhIdvZs2fZvXs3r7zyilF5Ua9B\namoqN2/exNXV1ejmRVH3L8310+l0JCcn4+DgYChzdXXFy8vLkA9Ao9EwYMAAAgMD+eSTT3J1389+\nTRRFoX///mzYsIGwsDD8/PxyPZ9Go6FHjx7s3LmTmzdvGm6wxMTEGN4ThQ0TyeuYPXv2ZMeOHURF\nRRneUxEREfz666/FOtdnQVb6Jek54+XlRb9+/YzGoK1cuZLJkydz4cIFOnXqZPTBUl7Y1LDB5VUX\no9b+pONJJIQm4NjWUcXICqYoCt/VqcMfJ04Qn2Mu2H9ducLF1FResreno5MJTCGXc7qjFSv0c0qZ\nQlzPmIOVA9/0+YZBWwYZymJTYum9oTfXHl4jNSuVTj6dcK/gXsBR/l4ffWScZiEtDb79FgYMgGq5\nZ7KUJEl6JorSLfnNN99k6tSp9O/fn4kTJ/Lo0SO+++476tatS1hYWKH7f/jhh+zevZt+/foRFxfH\nhg0bjNYPHz7c8PeYMWNITEykQ4cOeHt7c/fuXTZs2MDFixdZtGgRto/voBw+fJiZM2fSo0cPXFxc\nOHLkCGvWrKFPnz6GKdsKEhcXR/369UlOTiYyMrJI08nduXPHKDkbgIuLC0IIPvvsMzZt2pRv9+9s\n2WP6n4UhQ4ZgY2ND27ZtcXd356+//mLlypXY2dkZTSEHRb8Gx48fp3PnzgQEBPBZjlw/Rd2/qNcv\nL0lJSVSuXJmBAwfSpEkT7Ozs2L9/f64bOXPnziUoKIhWrVrx3nvv0aBBAx48eMCpU6f47bffiIvT\n/2b88ssv2b9/Px06dOD999+nfv36REdHs3XrVkJCQnBwcGD27NkcOHCAdu3aMW7cOMzMzFixYgUZ\nGRnMnz+/BFcFZsyYwd69e2nfvj3jxo0jMzOTb775Bl9fX86dO1escy21wtL7Py8Lcso+6QUSFRUl\nrK2ts6fxEIBwc3MTgPj+++/VDq/Ebi+/LYKsgsQhDonQaqEiZlOM0RQ9pmx1dLTg0CGjRXPokJhf\nyDQ8JiUzU+0IngmdTide/+F1QQBGS8/1PUVUQlThBygjWVlCrFsnRNWq+in8SjiTkiSVK8/LlH3l\nTfaUfUU5xwMHDojGjRsLa2trUb9+fbFx48YiT9nXqVMnodFo8l1y2rRpk+jRo4eoVKmSsLS0FC4u\nLqJHjx7ip59+MtruypUrolevXsLd3V3Y2NiIBg0aiPnz54vMIn5nffHFF2LMmDFixowZIjk52Whd\nQECAWLt2ba59vv76a5Hw9HyKQog5c+ao8j5ZsmSJaN26tXB1dRWWlpbC29tbvPPOO+LKlSu5ti3q\nNQgKChIajUbMnDmzRPsX9frlJSMjQ0ydOlU0a9ZMODo6Cnt7e9GsWTOxfPnyXNvGxsaKCRMmiGrV\nqgkrKyvh5eUlunfvLlatWmW03a1bt4S/v7/w8PAQNjY2olatWmLixIlG75MzZ86I3r17CwcHB2Fn\nZye6desmjh07ZnSc7Pf6/fv3jcqz/w89Pb3i4cOHRYsWLYS1tbWoVauWWLFihdH/l+Kc69OK83mp\niBIkGyiPFEXxA06dOnUqz24dkvS8mTp1aq47k19//TXjx48vdnZWUyGEIGZDDOk30qn8YWXMbAq+\ni25KdELgd/IkZ3Mk06lkYcG1Nm2wMvXu8wkJMGOGPqNcSAgU0npRHtxJukP9pfVJSH8yprCuS13C\nx4VjrjGNTnBz5sCnn+pb+L/4Aoo55FKSyqWwsDCaN28O0FwIUXjTcRmQvyFfXDNmzKB69eq5pqKb\nPXs206ZNMyrbsmUL9evXx9fXl9OnT2NjY1NoPgFJKo3ifF6a+C9NSZJK6uOPP8bpqe7Zu3btKrcV\nftB3lfcc4Um1/1fNUOEXOkHiyUSVIyucRlGYU6OGUdmdzEy+i45WKaIi0Olg7Vp9bXP5cnjtNX3Z\nc6CSfSUW9TTuNnfx/kX+dy7/qZnK2rvv6sf2b90qK/ySJEllbdWqVRw4cIAtW7awdetWQ/mZM2dy\nzff++++/M3r0aLp06YKbmxvdunWjdu3aZR2yJOXLZCr9iqL8Q1GUa4qipCqKclRRlBaFbD9JUZQL\niqKkKIpyU1GURYqiWJVVvJJk6pydnfnkk0+MykJCQp75vJ9qSghNIKxNGKfbnCb99rNLdvJ36VWx\nIh0cn+QfeNPdnT4VK6oYUSG0WliwAOrUgQMH9HPKWVioHdUzM7LpSPwqPWm1a1+1PXVd6iKEYG/k\nXvZG7lUxOn0Sr9atVQ1BkiTphTV69GgOHz7M7t27GThwoKF879699OrVy2jbjh07kpiYaJgX/v79\n+4WO6ZeksmQSlX5FUYYAC4HPgWbAWWCfoih5prZWFGUYMOfx9vWAUcAQ4IsyCViSyokJEybg7e2N\nmZkZY8aMITIykurVq3P79m0WLFhQorlETYEQggj/CE63O43IEjQ52AQrb9O/56coCnNr1KBXxYqE\nNW9OYIMG1La15XhiIv3//JP4zEy1QzT255/6ZH6HD8N//qN2NM+coijM7jyb5pWas3f4Xv7w/wML\nMwu6rutK7w292fjnRrVDzGXXLujWDcrpf11JkqRyTavVIoSQFXqp3DGNgYswGVguhFgHoCjKWOAV\n9JX5vNIltgGChRCbHj++qShKIJD/vB+S9AKysbFh7dq1VKlShTp16pCUlMT06dNZuHAhFSpUYOjQ\noUWalsbUKIqCXSM7nFY74fm2J4qm/AxZaOPoyJ7GjQGITEnh02vX2BIbi2+FCkRnZOBkKi3pGzfC\niBFPapebNsE//wlt2kBGBsTF6af6K+d61epFr1q9UBSFE7dP0PL7ljR0a8juobt5pfYrhR+gjBw/\nDm+9BZcu6R8fOQJ5TCstSZIk/Y0OHz78zOZNl6SypHqlX1EUC6A58GV2mRBCKIpyAH3lPi+hwHBF\nUVoIIU4oilID6AOs/dsDlqRypmvXroC+dbxLly6Eh4czefJkpk6diqOj6U51V5gqU6qoHUKpfXrt\nGqEJCayuW5e3PD0xM6V8C7166Vv5Hz58Uvbhh/DZZzBpEnh7w2+/qRffM5Izx8VLXi+xY8gO+tbp\ni5nGdFpxUlOhXTvIMeMj//mPrPRLkiSVtU6dOqkdgiSViCl073cFzICYp8pjAM+8dhBCBKLv2h+s\nKEoGcBk4JISY93cGKknlmaIoLFq0iIsXL/Lll1+W6wp/XoQQ3Pr3LU61OIUuq3wkm1tSuzaXW7XC\nv1Il06rwA1SsqK/g53T0KPTpo2/h//prdeL6GymKwmv1XjOpCj+AjQ34+xuXbd4MJ06oEo4kSZIk\nSeWMKVT6i01RlE7Ap8BY9DkA3gD6KooyraD9JOlF9/LLL1O1alW1w3jmHh58yBHvI1yZfIWkk0lE\nLzfhjPg5eFhaYpPHuECTybUwbhzUqmVc5uoKv/wCvr7qxFTG7iTdYfwv44lOUvc9NX++vuNFTlOn\nyrH9kiRJkiQVTvXu/UAcoAU8nir3AO7ms89MYJ0QYvXjx38pimIHLAdmF/RkkydPztXCOXToUIYO\nHVrcuCWp3NNqtaxfv57ExEQmTpyodjglkh6Tzrle5xBZT2o/1z+/jscwDyycTWR8fBElZmUx/+ZN\nLqWmsrlhQ7XDAUtLfW3zjTeelMXF6Vv5p05VL64ykJSexILQBSw8shArMyv61e2Hl716OQycneHT\nT+Gjj56UHTqkn9Zv1SrVwpKkUgkMDCQwMNCoLCEhQaVoJEmSnl+qV/qFEJmKopwCugK7ABT9IMuu\nQH79R22Bp/vv6rL3FQU0ky1evBg/P7/8VkvSC+PAgQP83//9H2fPnmXkyJFqh1NiVh5WeH3gxe0l\ntw1lWfezuDHzBrUW1ypgT9ORodOxPDqagOvXSdJq+b8qVdAKYRpd/vv3hw4d4I8/npR9+61+fL+F\nBcTEgKKAu7t6Mf4NPjv0GctOLmNA/QF80+cbnG2c1Q6J8eP1Y/lvP3mr88svoNOBplz225NedHk1\nuoSFhdG8eXOVIpIkSXo+mcrPhEXAe4qivK0oSj3gO/QV+zUAiqKsUxTlyxzb7wY+UBRliKIoPoqi\ndEff+r+roAq/JEl6169fp2fPnkRHRzNs2DD++9//qh1SqdT+ujauA4xn+Lz9zW1SLqaoFFHxxGZm\n8v+uXSMxK4tMIXjd1dU0Kvygr9AvWqT/29wcJk6EsDCIjtbXQn18YM4cVUN81jK0GVSyq4STtRM7\nL+4kU2caUyna2MDYscZld+9CUJAq4UiSJEmSVE6YRKVfCLEZ+D/0FffTQGOgpxAi9vEEUj/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Aju7u6GOl5eXgQEBLB79+4KHeujIBv9kiQZef/99wkPD+fOnTsAfPXVV4SGhiob1EOq278utX1q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G7dunyfb1GSO/1CCFGE7r33XmrVqsW5c+fSy5555hmioqK44447WLJkiYnpbp1jeUcq3V+J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f34eb1cd780>"
      ]
     },
     "metadata": {},
     "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>8.779406e-08</td>\n",
       "      <td>2.310240e-08</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>7.150041e-09</td>\n",
       "      <td>3.854534e-09</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>9.528171e-07</td>\n",
       "      <td>1.124883e-07</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>1.303200e-07</td>\n",
       "      <td>3.422243e-08</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>2.353975e-07</td>\n",
       "      <td>3.367779e-08</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>2.032960e-08</td>\n",
       "      <td>5.622830e-09</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>4.720335e-07</td>\n",
       "      <td>4.240950e-08</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>2.626392e-08</td>\n",
       "      <td>5.152078e-09</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>2.828001e-08</td>\n",
       "      <td>4.006859e-09</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>2.430664e-09</td>\n",
       "      <td>6.573695e-10</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>1.477575e-09</td>\n",
       "      <td>3.414084e-10</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>6.994534e-11</td>\n",
       "      <td>3.439208e-11</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... 8.78e-08   2.31e-08  \n",
       "1   (((delayed-nu-fission / nu-fission) * (delayed... 7.15e-09   3.85e-09  \n",
       "2   (((delayed-nu-fission / nu-fission) * (delayed... 9.53e-07   1.12e-07  \n",
       "3   (((delayed-nu-fission / nu-fission) * (delayed... 1.30e-07   3.42e-08  \n",
       "4   (((delayed-nu-fission / nu-fission) * (delayed... 2.35e-07   3.37e-08  \n",
       "5   (((delayed-nu-fission / nu-fission) * (delayed... 2.03e-08   5.62e-09  \n",
       "6   (((delayed-nu-fission / nu-fission) * (delayed... 4.72e-07   4.24e-08  \n",
       "7   (((delayed-nu-fission / nu-fission) * (delayed... 2.63e-08   5.15e-09  \n",
       "8   (((delayed-nu-fission / nu-fission) * (delayed... 2.83e-08   4.01e-09  \n",
       "9   (((delayed-nu-fission / nu-fission) * (delayed... 2.43e-09   6.57e-10  \n",
       "10  (((delayed-nu-fission / nu-fission) * (delayed... 1.48e-09   3.41e-10  \n",
       "11  (((delayed-nu-fission / nu-fission) * (delayed... 6.99e-11   3.44e-11  "
      ]
     },
     "execution_count": 24,
     "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(filter_type=openmc.EnergyFilter, remove_filter=True)\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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beta (U-235) : 0.006504 +/- 0.000007\n",
      "Beta (Pu-239): 0.002245 +/- 0.000002\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 7)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OebezfPlyKisr1z3fdNNNMyYUv//977nrrrsYMWIE/fv3z9hW165d6dq1KwAn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\n0JiZmVnVc0JjZmZmVc8JjZmZmVW9/wd1AlmsLbVzVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f34e73bfba8>"
      ]
     },
     "metadata": {},
     "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": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.001, 20)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8++yz+Pn58e6771K7dm1+/PFHm56/vz8REREMGzaMli1bMnDgQIKDgzl79izb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UiiQrJF6GKNPNpd2vGHZEPI9Q1N5BcHqKqJ7pqU7q+isGR/1LbppRm8sOGFqG0NZ8/mgmeSHlJR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\ndTyMMcYYkzLW8TDGGGNMyvw/AwzlKmcUfoUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f34e6d699e8>"
      ]
     },
     "metadata": {},
     "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 (MeV)')\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(0.001,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}