mdgxs-part-ii.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multigroup (Delayed) Cross Section Generation Part II: Advanced Features\n",
    "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, and/or nuclides. In particular, this Notebook illustrates the following features:\n",
    "\n",
    "* Calculation of multi-energy-group and multi-delayed-group cross sections for a **fuel assembly**\n",
    "* Automated creation, manipulation and storage of `MGXS` with **`openmc.mgxs.Library`**\n",
    "* Steady-state pin-by-pin **delayed neutron fractions (beta)** for each delayed group.\n",
    "* Generation of surface currents on the interfaces and surfaces of a Mesh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Input Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import openmc\n",
    "import openmc.mgxs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to define materials that will be used in the problem: fuel, water, and cladding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.6 enriched fuel\n",
    "fuel = openmc.Material(name='1.6% Fuel')\n",
    "fuel.set_density('g/cm3', 10.31341)\n",
    "fuel.add_nuclide('U235', 3.7503e-4)\n",
    "fuel.add_nuclide('U238', 2.2625e-2)\n",
    "fuel.add_nuclide('O16', 4.6007e-2)\n",
    "\n",
    "# borated water\n",
    "water = openmc.Material(name='Borated Water')\n",
    "water.set_density('g/cm3', 0.740582)\n",
    "water.add_nuclide('H1', 4.9457e-2)\n",
    "water.add_nuclide('O16', 2.4732e-2)\n",
    "water.add_nuclide('B10', 8.0042e-6)\n",
    "\n",
    "# zircaloy\n",
    "zircaloy = openmc.Material(name='Zircaloy')\n",
    "zircaloy.set_density('g/cm3', 6.55)\n",
    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our three materials, we can now create a `Materials` object that can be exported to an actual XML file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a materials collection and export to XML\n",
    "materials = openmc.Materials((fuel, water, zircaloy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's move on to the geometry. This problem will be a square array of fuel pins and control rod guide tubes for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create cylinders for the fuel and clad\n",
    "fuel_outer_radius = openmc.ZCylinder(r=0.39218)\n",
    "clad_outer_radius = openmc.ZCylinder(r=0.45720)\n",
    "\n",
    "# Create boundary planes to surround the geometry\n",
    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
    "max_x = openmc.XPlane(x0=+10.71, boundary_type='reflective')\n",
    "min_y = openmc.YPlane(y0=-10.71, boundary_type='reflective')\n",
    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
    "min_z = openmc.ZPlane(z0=-10., boundary_type='reflective')\n",
    "max_z = openmc.ZPlane(z0=+10., boundary_type='reflective')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Universe to encapsulate a fuel pin\n",
    "fuel_pin_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
    "\n",
    "# Create fuel Cell\n",
    "fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
    "fuel_cell.fill = fuel\n",
    "fuel_cell.region = -fuel_outer_radius\n",
    "fuel_pin_universe.add_cell(fuel_cell)\n",
    "\n",
    "# Create a clad Cell\n",
    "clad_cell = openmc.Cell(name='1.6% Clad')\n",
    "clad_cell.fill = zircaloy\n",
    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
    "fuel_pin_universe.add_cell(clad_cell)\n",
    "\n",
    "# Create a moderator Cell\n",
    "moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
    "moderator_cell.fill = water\n",
    "moderator_cell.region = +clad_outer_radius\n",
    "fuel_pin_universe.add_cell(moderator_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Likewise, we can construct a control rod guide tube with the same surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Universe to encapsulate a control rod guide tube\n",
    "guide_tube_universe = openmc.Universe(name='Guide Tube')\n",
    "\n",
    "# Create guide tube Cell\n",
    "guide_tube_cell = openmc.Cell(name='Guide Tube Water')\n",
    "guide_tube_cell.fill = water\n",
    "guide_tube_cell.region = -fuel_outer_radius\n",
    "guide_tube_universe.add_cell(guide_tube_cell)\n",
    "\n",
    "# Create a clad Cell\n",
    "clad_cell = openmc.Cell(name='Guide Clad')\n",
    "clad_cell.fill = zircaloy\n",
    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
    "guide_tube_universe.add_cell(clad_cell)\n",
    "\n",
    "# Create a moderator Cell\n",
    "moderator_cell = openmc.Cell(name='Guide Tube Moderator')\n",
    "moderator_cell.fill = water\n",
    "moderator_cell.region = +clad_outer_radius\n",
    "guide_tube_universe.add_cell(moderator_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create fuel assembly Lattice\n",
    "assembly = openmc.RectLattice(name='1.6% Fuel Assembly')\n",
    "assembly.pitch = (1.26, 1.26)\n",
    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a NumPy array of fuel pin and guide tube universes for the lattice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create array indices for guide tube locations in lattice\n",
    "template_x = np.array([5, 8, 11, 3, 13, 2, 5, 8, 11, 14, 2, 5, 8,\n",
    "                       11, 14, 2, 5, 8, 11, 14, 3, 13, 5, 8, 11])\n",
    "template_y = np.array([2, 2, 2, 3, 3, 5, 5, 5, 5, 5, 8, 8, 8, 8,\n",
    "                       8, 11, 11, 11, 11, 11, 13, 13, 14, 14, 14])\n",
    "\n",
    "# Create universes array with the fuel pin and guide tube universes\n",
    "universes = np.tile(fuel_pin_universe, (17,17))\n",
    "universes[template_x, template_y] = guide_tube_universe\n",
    "\n",
    "# Store the array of universes in the lattice\n",
    "assembly.universes = universes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create root Cell\n",
    "root_cell = openmc.Cell(name='root cell', fill=assembly)\n",
    "\n",
    "# Add boundary planes\n",
    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
    "\n",
    "# Create root Universe\n",
    "root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
    "root_universe.add_cell(root_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now must create a geometry that is assigned a root universe and export it to XML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Geometry and export to XML\n",
    "geometry = openmc.Geometry(root_universe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 40 active batches each with 2500 particles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenMC simulation parameters\n",
    "batches = 50\n",
    "inactive = 10\n",
    "particles = 2500\n",
    "\n",
    "# Instantiate a Settings object\n",
    "settings = openmc.Settings()\n",
    "settings.batches = batches\n",
    "settings.inactive = inactive\n",
    "settings.particles = particles\n",
    "settings.output = {'tallies': False}\n",
    "\n",
    "# Create an initial uniform spatial source distribution over fissionable zones\n",
    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
    "settings.source = openmc.Source(space=uniform_dist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll tie the materials, geometry, and settings into a single model object and export the necessary XML files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = openmc.Model(geometry, materials, settings)\n",
    "model.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also create a plot to verify that our fuel assembly geometry was created successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6CAIAAAAHjs1qAAAUJklEQVR4nO2dzZEkuQ2FuR1zU4wXa4ccUMxtDuvIhGSCIsaRPeytQw7IjvViHdChQlXZJAg+EACJbOKdZjJfIb8EWT/JepX9yz//9mdJpc7Q226AVGqdvlT///n7r9f//viNe+2/mnmnyHxrBpE5PoPIHJ/hl+uHmZ+///r+9ft197e//iCrP4riZtC5mKF0ercMOAIDb47QNEOGN8ZaSnn/+r16ujy3g2ZRWZG5NMMmZSBrOgE/nJX5sSWbJmUgMZCyb4z1+YC2LukUqT09KUPPrGEgj9UzmzCIlE271plggC5V8fldHdJwQkwzkFv0RxkyTJfKpvkxzKzMLH5Jm5DVABiKaVrAfgZsYKW5ps1M98e1ArJreMXtpF3HZYQ3LYKCDJx508bTnbm+RoRj8UfBGdojihgYs4hhumk8A44kYlA2bXrv3BHJoyAPfxO5ebO0v625x9BuZ4DbslbAOANJtbhpSoYeld/AtTJvGrfuzixwkmbGWZr1ZtAsZdgCnAytOebA/dJmZp6XKcgT6GHGnXvNERhKNk1hVpYlpnsq9VmVEbHUQcrpnjpIOd1TB4kOAL9//c5fLJfLBcFTQ/OzrMiMMOBmc4Y54Kc2Mmxs2gTDVdMMr0tVMqqBxzJNzMp4qp+5NyTZNMYcsGmDDzN4LHPC3DviSgZl8HUlgwgjm0ZqJgAsEvNwUZZ1TWhJxMAgZdN6DOubtjQADD7QUJosq6HwLGs27amIAeBU6qaCpvt0jipIEHdLwnY6y7qlaUFCyHjT+Af29EpE9hzKRpiEb5XRRVwihi1NaxlMmiZiaJG2Dxz/cGJlRhQNdTKTIh/uxyBKqK5vGg62eOBAtvUDd91YB4DL/68P8C8gPMzXHGkEBsYcoWlt+DZC03YxMOZMRKYOUq7MpA5STvfUQcrpnjpIxHT/+fuv+Bd7ruZkmDAnA2PurswUux/MltmFjgkzz3DdW/3XkIE334ihhBk4q5+HDwLAvep+WVY/BrKCBwNjvhFDr/L6gTNkeH2YEcVxnDKcfgxkK3/89ud0+o1hIGXFoGwaziDqcE940/STB2F4BYAZtyajZxJqU7YY/x6bn8EaBpHWN61tkf6l57kXrKNpGshgfEvUCFnWjcdlGJinXLUrIPyu4+IzDZTxLVGDKEgS876K38C5SegeAF7/3GiPqHlPnxb+mo2/DygZGLV4WwZuXQCYyXCChxSJzNOKwrcmYG2bTBicsqxKBpMnUpCBI481ZHi9upMcTrHMHhnO0KvTYyBfXMmN6xlWNi0CA1nHb6ZxiUjRGytudip7O4bKHIGBN38yhgwApw5SRsRSBymne+og5XRPHaQv1f/xC6k15ggMQ3MytOYIDK2Z+9tMZbSeVcUyeyh42UItivEpWZyhBQbLWpldmzZtNmza9MAxlW2b9sZYCxszqszvir9UfzWLVqxJhl6wrjWDZa3MhWoaafNj8GuaZuCYyiRDawPLjm+JCtZ97gKLkKfXezHAw80iifK0rdmkadKwJ86gTCzjDMVt4JhXjbmmjS9VleFbkyzrcO8aBlya1LTJpHyWAp2fYOAQs3EAuIWYqK9XhAxtJfMsq6viD1zEAPCuHGnA/CrTtICB6vgD5xUA5n82O4Swem5EYMAlalrLYPUEiNC0ZQyIeRwAxktLnxjkdQxpJq+N9HOCZHDKsjo1TRoAdmqa08CZN21y3b01R1i+jbDuzjCQZsa5HfjzDRyRiAz1xdgtGETm+zKIzDEZMgCcOkgZEUsdpJzuqYOU0z11kLgAcJH8glBkvvtPHq0YROYIwBEYROZuALhd8SkWN6okzSbBVyWDFBhcJnNlKNk0OUO5NO3DhxkwlikK9/Vime3DGXMvGqph6FUQMZBFsmk8A1lkTdPGAWCnAJM0y9qaTTDaOiYBYGXTnBicmiYdOL8ZNWSALlWnc2p+54YzkFt6MkwCVqXwRKQfAyNN06zUPjfMB843ALxLAQPA91L8BoYIAFe7guRItyRscYYITYvA0B7XPDX9SkQydTUnb5LhVIaQcfF5vTUMoofnwA0ZCrky4xTLNMlwRmDA87ROd/fsmXPgeF0rDP7y3nA9HzdfnYWNyFZlReYbMdwOOD7D0EwnIpmkskaisn5mXMngzbAYOAPAqYOUEbHUQcrpnjpIOd1TB+lDAHh6ZQa5Xn7ufTzQ/AIfYXgaeIaiW5H4ZAwF6/DEwF2nhOFMg1ZmXKOh7fbeRvLL4d7NHpwYCvUd9UoGUeWes3Ru0biMgTF7MCDm14cZk2goaSbJfnRuDwvKlQHPYzgxiMK3OJgVg3LgnBiQpr0CwD0+5emJlmDxHCkjTYLNKoTsFABuD2rVNBFDi2TCoFmA57O3sgBwpS1ZVo22BJ6qc2cYql1bmrYrE1ZpOjUNyveWqKmUk1SJSF7TOaoIWdYieb00BMZfs/H3ASUDoxYveGqaf2BP41uiKk+bGeYKUZQj9XsiiRicmhaBgZHTwCk/xSEMb9UOxvqUNMNJnga5kazcW3UyYQDPTjR1nBj4Iy5jIDeKBs6EYW7g6ogY/sbaQuNm/rXZyRwBOBkmzIbAmYhMHaTMzKQOUk731EHK6Z46SF/aTc8P+8PFvutlAW5G1hCdzKEYROYIwBEYRObWyf1tpmGGE/yVcZsx5M3V98O4mQGWMkwDR2ialKFnjjBwpHm6aYMAcK+6XzR0pTkZ4jCsAR7fErWVSTSUNDPBOpCBlBVwBAa8aREYojVtfKmqnBOic5vea8iAh295humvxKUTwoThcw/cU763RN0VAA4SPL7KPMvqqvgDFzEAvCtFHSS9fRXTtICB6vgDFzEATG6Z0xoGq2Hmfxw9ZDi2ad4MMwFgk/FQZlmdGMhj9cwiBmY8RJPyqKaZM7xe3UUZzrb6t7/+EMUyybJO5oezBW5rugKTDPo8bQ4cXpYLADt91zU0T+dIIwAnw4R5GXAGgFMHKSNiqYOU0z11kHK6pw5SHQB2utqI8JPHEg84GSbMmrJcALjY3RK1LdvjFjGU5svkxQxSYGXTTBjK7qYpGaTAV+cbYy39mJHyRpW9wIOIgawjYiAxRAy4WRTuc2IonbO+18D1MBCGcQCYzHCSTpHa05MymCSrRFlWDbBITgxOA0ceq2feO3DQpep0Ts3q3DQM5Bb9UaSl8ESkHwOjCE1rnxvmDL4B4F0KGAC+l+I3MEQAuNoVP0e6jCF+0yIwTDcN1Hi6a7KsxSjDKWJQ5kgZs3cae+Lh2TSE4alxABgvLe0vnuFUMlgBg2VJZdP4sqTMm1avuxfJH0+7fn7iV4XLxw9buNmKQQMsZTABzqZ5MBCJyOdlCvIEepjBpxpuToYJcwTg4AwZAE4dpIyIpQ5STvfUQcrpnjpIHwLAz8/471+/8xfL5eNVMGh+GIbXHM/KySCtjAMfwlCZB7dEtYplttt7G0vz/fBiBlHlZGDMvam5kqEyDz7MmGRZSbLeE9QpT9tjwCuDtK4MIjA/BtDco13JUGkmACzS8H3n+l88R8pIAyxiYJCcAsDtQa2aJmJokUyapgzqhAgAgw901ZbAU3XuDEO1a0vTIuTSiiQ1zT+wp1yZSR0k31uixnzNWCP8NRt/H/BTzPdhk2ToVTO3RBUJ/6zGM2zPkbYMDJJfANipaSKGFsmkacqnHMIweHXXR0PJK+7SP7fhXS1tGfDKIK0rgwjMjwE092hXMlQiAsAlzNcrySCtHOornggMlTkTkamDlCszqYOU0z11kHK6pw7Sl3YT89unnhlc8MLNyTBhjgAcnCF/mm3PYAKcTfNgGASAe9WV8VQ/sz4tHAE4AsPtgJGy40RkK1EssyfS7MRgBQyWJZVN48uSMm/a+FJ1ZY7UhAEPXpMMePhWxCBSNk0q8FlkfEvUIAHgCIEnPMsapGkRGKabBsr4lqhBFCSJeV/Fb+DcJHQPAFs9NzSJSPMc6UQphsEvNX2vprUXoCECwCanR0ZDRQwmT6T2XERZVqfUtBOD08CRx+qZ9w7c69W9l+Eke+QUDRUxkHVEDCSGiAE3m+RplQylc9b3GrgeBsJQJyJFv6y5mnFnEHMyxGEQmTVlMwCcOkgZEUsdpJzuqYOU0z11kOoAMH5N4Gd2uojxMydD6xSZlwHXAeDqu1kmK1eaeGoPBS/rZ44AnAzbgd8Ya2FjRpX5/et3PFjXK+tkfjhb4LamKzDJQGL4MZSzB27mlqiiDGdP7elFYCCP1TNLs6wTuzQMn6Bp5gy+t0RlIKRalmUVMLEM06VMJsSzFOgM0jRvBuMA8ByEuSJkaCuZZ1ldFX/gIgaAd+VIA+ZX8URkBMUfOK8AMP/T3SGE1XNjGQNjFjFMzxhRZpA5Sg5cq3EAGC8t7a8owwkykLICjsCANy0CQ7SmcevuzAInaWacRXIPienlW35FVsQwDRyhaVKGnjnCwJHm6aYRicjn5YLTd13I89LJHIpBZI4AHIFBZG6dGQBOHaSMiKUOUk731EHK6Z46SFwAuLCXBe0XYLh5y08eIwAnw4TZEPjDLVFL890sE8tsv8VlzO12ciNZOQhDobq8kqFXWTpwHgykOQhDuQzchw8zolhme7CemSTDz43J0+oZ8OBr+/CenBj4Iy5j0CeWTRjmBm4mACwS/r7DM7RmDRUjkyyrsmkRGBg5DZwyqLM/ALwlWNeeNt5HQ2D8s2m1y4+BUYu3JadZAd8sAJxKOSlEALjaFSSIu+VNBn/NFi1oOCnILwSqczdPTb8SkUxdzQCI3k+ZYJ13EnqCgW/aNAP/8JbBpGkihhbJhEHzlOODlsTKjPKGnYyZPI3eKgEoVwZ8pJ0YmLPTgFkxKAfOiQFp2uAv7w3XVR5mPsBZnczjgWCGcxgNFTE8DTxD+bjagDMUrGn3YihYhycG7jolDGcaw5CJyNRBysxM6iDldE8dpJzuqYNET3enVVhRWT9zMsRhWAxsszKDmKcXGUTmGzHcDjg+w9D8IQBMfjHbC51qYpmisgcy3A74LgyvDzOiOI4yltkz41rP0FbwYxCZc+B4XSu8AsCMW3N6+sGQMmgSbAxDUSRDpTJJAubAXfc+/mGciIwQAG6PGzzLGqFpERja4+JNA+V7S9RdCpLEvK/iN1CViOQ1nUbkc2pOao8oCvdZYUxnWf0YGGmaZqX2AtR84Ma3RHU6bTJPaxK+FamtI2JwapoTg1PTpAPnN6OGDG/VDsb6lD6W2SPrmfGY7nQ0dI6BLJJN4xnIImuaVici8V/WtFczuJl/mXEyB2QQmSMAR2AQmStnBoBTBykjYqmDlNM9dZByuqcO0pd20/PD/nDd6npZ4GS+BYPIfF8GkTkmA/e3mQq7ltRmWXsoVdpzWLbaxfxu14/Bow+kGYzIbgH+fAPnHgCWlu2ds6ay/tT8zBEYSHOEgeuZtQFgPlAG1pXG3/D1VPzcRBLlaVtzhKbxaUSQQaSVA2fetPGlqjLDaZJlHe5dw4BLE741mZTPUqDzEwwcYva9JWqQHGkEmWdZXRV/4CIGgHflSAPmV/FEZATFHzivADD/s9khhNVzIwIDLlHTWgarJ0CEpi1jQMzjALCotOjcyOsY0kxeG+nnBMnglGUVhft60geAnZrmNHBMoHKuaa9Xd2WG89tff+ijoT86f6CnZyYZetHQ1gyWtTIXqmmkzY/Br2magWMqkwytDSzLBYBjfjG2nmFoTobWHIGhNWcAOHWQMiKWOkg53VMHKad76iDVAeBzfvK4haHMXn3uMn8yhskAMJ5T65XtcYsYSvNlMmMW5fhiMhSLpkVgKJKmKWfa1fnGWIvb3T17gQecoVenx9BbAI7AsLJpERjIOn4z7eocB4CdAkzSLKsmfMvIKQCsZHNiMAl+BRk48lhDBuhSdTqn5nduOEORRBsMk4D4J85qlx8DoxZvy8BVwObZW98A8C4FDADfS/EbGCIAXO2KnyNdxsBMINHKwxoFGTjz1PQrEcnU1Zy8SY5UGUIWvaczwBoGkdY3rW2RVfh2TdNAhteru1OWVWT2YyBnfG+1hJQyT2vFoGwaziDqcE940/STB2EY/OW9YnEPico8LCtiqMw8w3UvM8+UDLz5RgwlzMCBM23IQCQiH48Bnz2u5mSYMCcDY84AcOogZUQsdZByuqcOUk731EH6EAC+XtjyV9beZvyafQ0DY47QNOlChx9wBAbGPLglqlMss2fuOQt1hpGBD2Qo1Bf70YBfH2aUSUsTMyny4X4Moljb+qbhYIsHDmRbP3DXja8AcI/PKcvaSpQjNTmikmFL0zThWyuGFmn7wPEPXxcABh/oqi0RTjzLGqFpQVKuEQPAqdRNBU336UjQlhwpeee09RjTWdZs2gQD2LTxLVH9sqwVIs+wZhKIGPjwrQYjmzYhhGHw6q6PZTJmENqVATSLXguyaVJaV+Cr6Fuivn/9Dq7nV8fjzc+yIjPCgJvNGeaAn9rIsLFpEwxXTTNkIjJ1kHJlJnWQcrqnDlJO99RB+tJuel4ZIMtY+E+qJsqamyMwlGyawqwsy90S1eqn2W1cTvPrWp5hC3AytOaYAzcIAPeqK+Oprub//uPf7cawwGEZSil//8+/ljHogZGy41uittJnentmJlgHMhRqmdYKGGcgqRY3TcnQo/IbuFbmTRtfqq7MkU7v5Y9okmWVMkw3TTohTBiUTZveO3dE8ijIw41viRohy7rxuIzwpkVQkIEzb5rxLVGDaEuokBeeiIyggA2sNNc09wCw1VhOM5Bb9EcZMkyXyqb5McwEgE1Oj4yGihj0c4KclKIs6+IX5mzatc4Ew+vVvZfhJCf3t+Yv1ffMorIic9EFX3vdcQJ+OCvzY0s2TcpAYiBl6QDwtURblzTjX86Jyt6OQWSOzyAyx2fIAHDqIGVELHWQ/gcg1yPxJKXdEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot our geometry\n",
    "plot = openmc.Plot.from_geometry(geometry)\n",
    "plot.pixels = (250, 250)\n",
    "plot.color_by = 'material'\n",
    "openmc.plot_inline(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, we have a nice array of fuel and guide tube pin cells with fuel, cladding, and water!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create an MGXS Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to generate multi-group cross sections! First, let's define a 20-energy-group and 1-energy-group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a 20-group EnergyGroups object\n",
    "energy_groups = openmc.mgxs.EnergyGroups()\n",
    "energy_groups.group_edges = np.logspace(-3, 7.3, 21)\n",
    "\n",
    "# Instantiate a 1-group EnergyGroups object\n",
    "one_group = openmc.mgxs.EnergyGroups()\n",
    "one_group.group_edges = np.array([energy_groups.group_edges[0], energy_groups.group_edges[-1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will instantiate an `openmc.mgxs.Library` for the energy and delayed groups with our the fuel assembly geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a tally mesh                                                                                                                                                                  \n",
    "mesh = openmc.RegularMesh(mesh_id=1)\n",
    "mesh.dimension = [17, 17, 1]\n",
    "mesh.lower_left = [-10.71, -10.71, -10000.]\n",
    "mesh.width = [1.26, 1.26, 20000.]\n",
    "\n",
    "# Initialize an 20-energy-group and 6-delayed-group MGXS Library\n",
    "mgxs_lib = openmc.mgxs.Library(geometry)\n",
    "mgxs_lib.energy_groups  = energy_groups\n",
    "mgxs_lib.num_delayed_groups = 6\n",
    "\n",
    "# Specify multi-group cross section types to compute\n",
    "mgxs_lib.mgxs_types = ['total', 'transport', 'nu-scatter matrix', 'kappa-fission', 'inverse-velocity', 'chi-prompt',\n",
    "                      'prompt-nu-fission', 'chi-delayed', 'delayed-nu-fission', 'beta']\n",
    "\n",
    "# Specify a \"mesh\" domain type for the cross section tally filters\n",
    "mgxs_lib.domain_type = 'mesh'\n",
    "\n",
    "# Specify the mesh domain over which to compute multi-group cross sections\n",
    "mgxs_lib.domains = [mesh]\n",
    "\n",
    "# Construct all tallies needed for the multi-group cross section library\n",
    "mgxs_lib.build_library()\n",
    "\n",
    "# Create a \"tallies.xml\" file for the MGXS Library\n",
    "tallies = openmc.Tallies()\n",
    "mgxs_lib.add_to_tallies_file(tallies, merge=True)\n",
    "\n",
    "# Instantiate a current tally\n",
    "mesh_filter = openmc.MeshSurfaceFilter(mesh)\n",
    "current_tally = openmc.Tally(name='current tally')\n",
    "current_tally.scores = ['current']\n",
    "current_tally.filters = [mesh_filter]\n",
    "\n",
    "# Add current tally to the tallies file\n",
    "tallies.append(current_tally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can run OpenMC to generate the cross sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tie geometry, materials, settings, and tallies together into a model object\n",
    "model = openmc.Model(geometry=geometry,\n",
    "                     materials=materials,\n",
    "                     settings=settings,\n",
    "                     tallies=tallies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=1.\n",
      "  warn(msg, IDWarning)\n",
      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=2.\n",
      "  warn(msg, IDWarning)\n",
      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=5.\n",
      "  warn(msg, IDWarning)\n",
      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=6.\n",
      "  warn(msg, IDWarning)\n",
      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=17.\n",
      "  warn(msg, IDWarning)\n",
      "/home/pshriwise/.pyenv/versions/3.9.1/lib/python3.9/site-packages/openmc/mixin.py:70: IDWarning: Another Filter instance already exists with id=23.\n",
      "  warn(msg, IDWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.1\n",
      "        Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n",
      "       Date/Time | 2022-10-03 23:30:12\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading U235 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U235.h5\n",
      " Reading U238 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/U238.h5\n",
      " Reading O16 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/O16.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/H1.h5\n",
      " Reading B10 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/B10.h5\n",
      " Reading Zr90 from /home/pshriwise/data/xs/openmc/endfb71_hdf5/Zr90.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Reading tallies XML file...\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for U235\n",
      " Initializing source particles...\n",
      "\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      "\n",
      "  Bat./Gen.      k            Average k\n",
      "  =========   ========   ====================\n",
      "        1/1    0.99225\n",
      "        2/1    0.99354\n",
      "        3/1    1.02644\n",
      "        4/1    1.06300\n",
      "        5/1    1.03396\n",
      "        6/1    1.00753\n",
      "        7/1    1.04194\n",
      "        8/1    1.04023\n",
      "        9/1    1.03320\n",
      "       10/1    1.04267\n",
      "       11/1    1.02172\n",
      "       12/1    1.07125    1.04648 +/- 0.02477\n",
      "       13/1    1.04987    1.04761 +/- 0.01434\n",
      "       14/1    1.01403    1.03922 +/- 0.01317\n",
      "       15/1    1.04432    1.04024 +/- 0.01025\n",
      "       16/1    1.06785    1.04484 +/- 0.00955\n",
      "       17/1    1.04639    1.04506 +/- 0.00807\n",
      "       18/1    1.06538    1.04760 +/- 0.00744\n",
      "       19/1    1.00283    1.04263 +/- 0.00823\n",
      "       20/1    1.00930    1.03929 +/- 0.00808\n",
      "       21/1    1.02698    1.03817 +/- 0.00740\n",
      "       22/1    1.04975    1.03914 +/- 0.00682\n",
      "       23/1    1.03265    1.03864 +/- 0.00629\n",
      "       24/1    0.98957    1.03513 +/- 0.00680\n",
      "       25/1    1.01457    1.03376 +/- 0.00648\n",
      "       26/1    1.00560    1.03200 +/- 0.00631\n",
      "       27/1    1.00933    1.03067 +/- 0.00608\n",
      "       28/1    1.01275    1.02967 +/- 0.00581\n",
      "       29/1    1.04347    1.03040 +/- 0.00555\n",
      "       30/1    1.03126    1.03044 +/- 0.00526\n",
      "       31/1    1.05165    1.03145 +/- 0.00511\n",
      "       32/1    1.02594    1.03120 +/- 0.00488\n",
      "       33/1    1.00047    1.02987 +/- 0.00485\n",
      "       34/1    1.04045    1.03031 +/- 0.00466\n",
      "       35/1    1.01414    1.02966 +/- 0.00452\n",
      "       36/1    1.03056    1.02970 +/- 0.00434\n",
      "       37/1    1.05870    1.03077 +/- 0.00431\n",
      "       38/1    0.97655    1.02883 +/- 0.00458\n",
      "       39/1    1.05223    1.02964 +/- 0.00450\n",
      "       40/1    1.08089    1.03135 +/- 0.00467\n",
      "       41/1    1.05155    1.03200 +/- 0.00456\n",
      "       42/1    1.01221    1.03138 +/- 0.00446\n",
      "       43/1    1.03906    1.03161 +/- 0.00433\n",
      "       44/1    1.00455    1.03082 +/- 0.00427\n",
      "       45/1    1.00183    1.02999 +/- 0.00423\n",
      "       46/1    1.05348    1.03064 +/- 0.00416\n",
      "       47/1    1.06321    1.03152 +/- 0.00414\n",
      "       48/1    1.05862    1.03224 +/- 0.00410\n",
      "       49/1    1.04610    1.03259 +/- 0.00401\n",
      "       50/1    1.02808    1.03248 +/- 0.00391\n",
      " Creating state point statepoint.50.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 7.2704e-01 seconds\n",
      "   Reading cross sections          = 7.1697e-01 seconds\n",
      " Total time in simulation          = 6.0982e+01 seconds\n",
      "   Time in transport only          = 6.0103e+01 seconds\n",
      "   Time in inactive batches        = 4.4142e+00 seconds\n",
      "   Time in active batches          = 5.6568e+01 seconds\n",
      "   Time synchronizing fission bank = 1.0706e-02 seconds\n",
      "     Sampling source sites         = 9.8511e-03 seconds\n",
      "     SEND/RECV source sites        = 8.2068e-04 seconds\n",
      "   Time accumulating tallies       = 8.5453e-01 seconds\n",
      "   Time writing statepoints        = 8.0261e-03 seconds\n",
      " Total time for finalization       = 2.0600e-06 seconds\n",
      " Total time elapsed                = 6.1735e+01 seconds\n",
      " Calculation Rate (inactive)       = 5663.53 particles/second\n",
      " Calculation Rate (active)         = 1767.79 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     = 1.03096 +/- 0.00285\n",
      " k-effective (Track-length)  = 1.03248 +/- 0.00391\n",
      " k-effective (Absorption)    = 1.02246 +/- 0.00327\n",
      " Combined k-effective        = 1.02749 +/- 0.00267\n",
      " Leakage Fraction            = 0.00000 +/- 0.00000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Run OpenMC\n",
    "statepoint_filename = model.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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the last statepoint file\n",
    "sp = openmc.StatePoint('statepoint.50.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statepoint is now ready to be analyzed by the `Library`. We simply have to load the tallies from the statepoint into the `Library` and our `MGXS` objects will compute the cross sections for us under-the-hood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize MGXS Library with OpenMC statepoint data\n",
    "mgxs_lib.load_from_statepoint(sp)\n",
    "\n",
    "# Extrack the current tally separately\n",
    "current_tally = sp.get_tally(name='current tally')\n",
    "\n",
    "# Close statepoint file now that we have the info we need\n",
    "sp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Tally Arithmetic to Compute the Delayed Neutron Precursor Concentrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we illustrate how one can leverage OpenMC's [tally arithmetic](tally-arithmetic.ipynb) data processing feature with `MGXS` objects. The `openmc.mgxs` module uses tally arithmetic to compute multi-group cross sections with automated uncertainty propagation. Each `MGXS` object includes an `xs_tally` attribute which is a \"derived\" `Tally` based on the tallies needed to compute the cross section type of interest. These derived tallies can be used in subsequent tally arithmetic operations. For example, we can use tally artithmetic to compute the delayed neutron precursor concentrations using the `Beta` and `DelayedNuFissionXS` objects. The delayed neutron precursor concentrations are modeled using the following equations:\n",
    "\n",
    "$$\\frac{\\partial}{\\partial t} C_{k,d} (t) = \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t)\\Phi(\\mathbf{r},E',t) - \\lambda_{d} C_{k,d} (t) $$\n",
    "\n",
    "$$C_{k,d} (t=0) = \\frac{1}{\\lambda_{d}} \\int_{0}^{\\infty}\\mathrm{d}E'\\int_{\\mathbf{r} \\in V_{k}}\\mathrm{d}\\mathbf{r} \\beta_{k,d} (t=0) \\nu_d \\sigma_{f,x}(\\mathbf{r},E',t=0)\\Phi(\\mathbf{r},E',t=0) $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>delayedgroup</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>2.537729e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.001282</td>\n",
       "      <td>3.179682e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>2.001150e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>1.556332e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>5.409817e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>4.317338e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>2.667910e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>3.344146e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>2.105053e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>total</td>\n",
       "      <td>(((delayed-nu-fission / nu-fission) * (delayed...</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>1.637544e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mesh 1       delayedgroup nuclide  \\\n",
       "       x  y  z                        \n",
       "0      1  1  1            1   total   \n",
       "1      1  1  1            2   total   \n",
       "2      1  1  1            3   total   \n",
       "3      1  1  1            4   total   \n",
       "4      1  1  1            5   total   \n",
       "5      1  1  1            6   total   \n",
       "6      2  1  1            1   total   \n",
       "7      2  1  1            2   total   \n",
       "8      2  1  1            3   total   \n",
       "9      2  1  1            4   total   \n",
       "\n",
       "                                               score      mean     std. dev.  \n",
       "                                                                              \n",
       "0  (((delayed-nu-fission / nu-fission) * (delayed...  0.000101  2.537729e-05  \n",
       "1  (((delayed-nu-fission / nu-fission) * (delayed...  0.001282  3.179682e-04  \n",
       "2  (((delayed-nu-fission / nu-fission) * (delayed...  0.000813  2.001150e-04  \n",
       "3  (((delayed-nu-fission / nu-fission) * (delayed...  0.000641  1.556332e-04  \n",
       "4  (((delayed-nu-fission / nu-fission) * (delayed...  0.000023  5.409817e-06  \n",
       "5  (((delayed-nu-fission / nu-fission) * (delayed...  0.000002  4.317338e-07  \n",
       "6  (((delayed-nu-fission / nu-fission) * (delayed...  0.000105  2.667910e-05  \n",
       "7  (((delayed-nu-fission / nu-fission) * (delayed...  0.001332  3.344146e-04  \n",
       "8  (((delayed-nu-fission / nu-fission) * (delayed...  0.000845  2.105053e-04  \n",
       "9  (((delayed-nu-fission / nu-fission) * (delayed...  0.000665  1.637544e-04  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the time constants for the delayed precursors (in seconds^-1)\n",
    "precursor_halflife = np.array([55.6, 24.5, 16.3, 2.37, 0.424, 0.195])\n",
    "precursor_lambda = math.log(2.0) / precursor_halflife\n",
    "\n",
    "beta = mgxs_lib.get_mgxs(mesh, 'beta')\n",
    "\n",
    "# Create a tally object with only the delayed group filter for the time constants\n",
    "beta_filters = [f for f in beta.xs_tally.filters if type(f) is not openmc.DelayedGroupFilter]\n",
    "lambda_tally = beta.xs_tally.summation(nuclides=beta.xs_tally.nuclides)\n",
    "for f in beta_filters:\n",
    "    lambda_tally = lambda_tally.summation(filter_type=type(f), remove_filter=True) * 0. + 1.\n",
    "\n",
    "# Set the mean of the lambda tally and reshape to account for nuclides and scores\n",
    "lambda_tally._mean = precursor_lambda\n",
    "lambda_tally._mean.shape = lambda_tally.std_dev.shape\n",
    "\n",
    "# Set a total nuclide and lambda score\n",
    "lambda_tally.nuclides = [openmc.Nuclide(name='total')]\n",
    "lambda_tally.scores = ['lambda']\n",
    "\n",
    "delayed_nu_fission = mgxs_lib.get_mgxs(mesh, 'delayed-nu-fission')\n",
    "\n",
    "# Use tally arithmetic to compute the precursor concentrations\n",
    "precursor_conc = beta.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) * \\\n",
    "    delayed_nu_fission.xs_tally.summation(filter_type=openmc.EnergyFilter, remove_filter=True) / lambda_tally\n",
    "    \n",
    "# The difference is a derived tally which can generate Pandas DataFrames for inspection\n",
    "precursor_conc.get_pandas_dataframe().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another useful feature of the Python API is the ability to extract the surface currents for the interfaces and surfaces of a mesh. We can inspect the currents for the mesh by getting the pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mesh 1</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>surf</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03118</td>\n",
       "      <td>0.000831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>x-max in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03188</td>\n",
       "      <td>0.000783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-max out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03172</td>\n",
       "      <td>0.000610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>y-max in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.03070</td>\n",
       "      <td>0.000660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>z-min out</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>z-min in</td>\n",
       "      <td>total</td>\n",
       "      <td>current</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  mesh 1                  nuclide    score     mean std. dev.\n",
       "       x  y  z       surf                                    \n",
       "0      1  1  1  x-min out   total  current  0.00000  0.000000\n",
       "1      1  1  1   x-min in   total  current  0.00000  0.000000\n",
       "2      1  1  1  x-max out   total  current  0.03118  0.000831\n",
       "3      1  1  1   x-max in   total  current  0.03188  0.000783\n",
       "4      1  1  1  y-min out   total  current  0.00000  0.000000\n",
       "5      1  1  1   y-min in   total  current  0.00000  0.000000\n",
       "6      1  1  1  y-max out   total  current  0.03172  0.000610\n",
       "7      1  1  1   y-max in   total  current  0.03070  0.000660\n",
       "8      1  1  1  z-min out   total  current  0.00000  0.000000\n",
       "9      1  1  1   z-min in   total  current  0.00000  0.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_tally.get_pandas_dataframe().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Section Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to inspecting the data in the tallies by getting the pandas dataframe, we can also plot the tally data on the domain mesh. Below is the delayed neutron fraction tallied in each mesh cell for each delayed group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Beta - delayed group 6')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x648 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the energy-condensed delayed neutron fraction tally\n",
    "beta_by_group = beta.get_condensed_xs(one_group).xs_tally.summation(filter_type='energy', remove_filter=True)\n",
    "beta_by_group.mean.shape = (17, 17, 6)\n",
    "beta_by_group.mean[beta_by_group.mean == 0] = np.nan\n",
    "\n",
    "# Plot the betas\n",
    "plt.figure(figsize=(18,9))\n",
    "fig = plt.subplot(231)\n",
    "plt.imshow(beta_by_group.mean[:,:,0], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 1')\n",
    "\n",
    "fig = plt.subplot(232)\n",
    "plt.imshow(beta_by_group.mean[:,:,1], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 2')\n",
    "\n",
    "fig = plt.subplot(233)\n",
    "plt.imshow(beta_by_group.mean[:,:,2], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 3')\n",
    "\n",
    "fig = plt.subplot(234)\n",
    "plt.imshow(beta_by_group.mean[:,:,3], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 4')\n",
    "\n",
    "fig = plt.subplot(235)\n",
    "plt.imshow(beta_by_group.mean[:,:,4], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 5')\n",
    "\n",
    "fig = plt.subplot(236)\n",
    "plt.imshow(beta_by_group.mean[:,:,5], interpolation='none', cmap='jet')\n",
    "plt.colorbar()\n",
    "plt.title('Beta - delayed group 6')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}