{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method of Characteristics: C5G7 Benchmark\n",
    "\n",
    "C5G7 is a well known multi-group benchmark in nuclear reactor physics. It is 1/4th of a 2D mini-core geometry that contains 4 assemblies. Two assemblies have UO2 fuel while the two others have MOX fuel. In this example we will simulate this benchmark using the Method of Characteristics solver in Scarabée. We can start by importing all the necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scarabee as scrb\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will define all of the multi-group cross sections for the UO2 fuel, the different MOX fuels, the guide tubes, the fission chambers, and the moderator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UO2 Fuel\n",
    "Et = np.array([1.77949E-01, 3.29805E-01, 4.80388E-01, 5.54367E-01, 3.11801E-01, 3.95168E-01, 5.64406E-01])\n",
    "Ea = np.array([8.02480E-03, 3.71740E-03, 2.67690E-02, 9.62360E-02, 3.00200E-02, 1.11260E-01, 2.82780E-01])\n",
    "Ef = np.array([7.21206E-03, 8.19301E-04, 6.45320E-03, 1.85648E-02, 1.78084E-02, 8.30348E-02, 2.16004E-01])\n",
    "nu = np.array([2.78145, 2.47443, 2.43383, 2.43380, 2.43380, 2.43380, 2.43380])\n",
    "chi = np.array([5.87910E-01, 4.11760E-01, 3.39060E-04, 1.17610E-07, 0., 0., 0.])\n",
    "Es = np.array([[1.27537E-01, 4.23780E-02, 9.43740E-06, 5.51630E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 3.24456E-01, 1.63140E-03, 3.14270E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 4.50940E-01, 2.67920E-03, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 4.52565E-01, 5.56640E-03, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 1.25250E-04, 2.71401E-01, 1.02550E-02, 1.00210E-08],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.29680E-03, 2.65802E-01, 1.68090E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 8.54580E-03, 2.73080E-01]])\n",
    "UO2xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"UO2\")\n",
    "\n",
    "# 4.3% MOX Fuel\n",
    "Et = np.array([1.78731E-01, 3.30849E-01, 4.83772E-01, 5.66922E-01, 4.26227E-01, 6.78997E-01, 6.82852E-01])\n",
    "Ea = np.array([8.43390E-03, 3.75770E-03, 2.79700E-02, 1.04210E-01, 1.39940E-01, 4.09180E-01, 4.09350E-01])\n",
    "Ef = np.array([7.62704E-03, 8.76898E-04, 5.69835E-03, 2.28872E-02, 1.07635E-02, 2.32757E-01, 2.48968E-01])\n",
    "nu = np.array([2.85209,     2.89099,     2.85486,     2.86073,     2.85447,     2.86415,     2.86780])\n",
    "chi = np.array([5.87910E-01, 4.11760E-01, 3.39060E-04, 1.17610E-07, 0.,          0.,          0.])\n",
    "Es = np.array([[1.28876E-01, 4.14130E-02, 8.22900E-06, 5.04050E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 3.25452E-01, 1.63950E-03, 1.59820E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 4.53188E-01, 2.61420E-03, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 4.57173E-01, 5.53940E-03, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 1.60460E-04, 2.76814E-01, 9.31270E-03, 9.16560E-09],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00510E-03, 2.52962E-01, 1.48500E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 8.49480E-03, 2.65007E-01]])\n",
    "M4xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"4.3% MOX\")\n",
    "\n",
    "# 7.0% MOX Fuel\n",
    "Et = np.array([1.81323E-01, 3.34368E-01, 4.93785E-01, 5.91216E-01, 4.74198E-01, 8.33601E-01, 8.53603E-01])\n",
    "Ea = np.array([9.06570E-03, 4.29670E-03, 3.28810E-02, 1.22030E-01, 1.82980E-01, 5.68460E-01, 5.85210E-01])\n",
    "Ef = np.array([8.25446E-03, 1.32565E-03, 8.42156E-03, 3.28730E-02, 1.59636E-02, 3.23794E-01, 3.62803E-01])\n",
    "nu = np.array([2.88498,     2.91079,     2.86574,     2.87063,     2.86714,     2.86658,     2.87539])\n",
    "chi = np.array([5.87910E-01, 4.11760E-01, 3.39060E-04, 1.17610E-07, 0.,          0.,          0.])\n",
    "Es = np.array([[1.30457E-01, 4.17920E-02, 8.51050E-06, 5.13290E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 3.28428E-01, 1.64360E-03, 2.20170E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 4.58371E-01, 2.53310E-03, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 4.63709E-01, 5.47660E-03, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 1.76190E-04, 2.82313E-01, 8.72890E-03, 9.00160E-09],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.27600E-03, 2.49751E-01, 1.31140E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 8.86450E-03, 2.59529E-01]])\n",
    "M7xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"7.0% MOX\")\n",
    "\n",
    "# 8.7% MOX Fuel\n",
    "Et = np.array([1.83045E-01, 3.36705E-01, 5.00507E-01, 6.06174E-01, 5.02754E-01, 9.21028E-01, 9.55231E-01])\n",
    "Ea = np.array([9.48620E-03, 4.65560E-03, 3.62400E-02, 1.32720E-01, 2.08400E-01, 6.58700E-01, 6.90170E-01])\n",
    "Ef = np.array([8.67209E-03, 1.62426E-03, 1.02716E-02, 3.90447E-02, 1.92576E-02, 3.74888E-01, 4.30599E-01])\n",
    "nu = np.array([2.90426,     2.91795,     2.86986,     2.87491,     2.87175,     2.86752,     2.87808])\n",
    "chi = np.array([5.87910E-01, 4.11760E-01, 3.39060E-04, 1.17610E-07, 0.,          0.,          0.])\n",
    "Es = np.array([[1.31504E-01, 4.20460E-02, 8.69720E-06, 5.19380E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 3.30403E-01, 1.64630E-03, 2.60060E-09, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 4.61792E-01, 2.47490E-03, 0.00000E+00, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 4.68021E-01, 5.43300E-03, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 1.85970E-04, 2.85771E-01, 8.39730E-03, 8.92800E-09],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.39160E-03, 2.47614E-01, 1.23220E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 8.96810E-03, 2.56093E-01]])\n",
    "M8xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"8.7 % MOX\")\n",
    "\n",
    "# Water Moderator\n",
    "Et = np.array([1.59206E-01, 4.12970E-01, 5.90310E-01, 5.84350E-01, 7.18000E-01, 1.25445E+00, 2.65038E+00])\n",
    "Ea = np.array([6.01050E-04, 1.57930E-05, 3.37160E-04, 1.94060E-03, 5.74160E-03, 1.50010E-02, 3.72390E-02])\n",
    "Es = np.array([[4.44777E-02, 1.13400E-01, 7.23470E-04, 3.74990E-06, 5.31840E-08, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 2.82334E-01, 1.29940E-01, 6.23400E-04, 4.80020E-05, 7.44860E-06, 1.04550E-06],\n",
    "               [0.00000E+00, 0.00000E+00, 3.45256E-01, 2.24570E-01, 1.69990E-02, 2.64430E-03, 5.03440E-04],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 9.10284E-02, 4.15510E-01, 6.37320E-02, 1.21390E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 7.14370E-05, 1.39138E-01, 5.11820E-01, 6.12290E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.21570E-03, 6.99913E-01, 5.37320E-01],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.32440E-01, 2.48070E+00]])\n",
    "H2Oxs = scrb.CrossSection(Et, Ea, Es, name=\"Water\")\n",
    "\n",
    "# Fission Chamber\n",
    "Et = np.array([1.26032E-01, 2.93160E-01, 2.84250E-01, 2.81020E-01, 3.34460E-01, 5.65640E-01, 1.17214E+00])\n",
    "Ea = np.array([5.11320E-04, 7.58130E-05, 3.16430E-04, 1.16750E-03, 3.39770E-03, 9.18860E-03, 2.32440E-02])\n",
    "Ef = np.array([4.79002E-09, 5.82564E-09, 4.63719E-07, 5.24406E-06, 1.45390E-07, 7.14972E-07, 2.08041E-06])\n",
    "nu = np.array([2.76283,     2.46239,     2.43380,     2.43380,     2.43380,     2.43380,     2.43380])\n",
    "chi = np.array([5.87910E-01, 4.11760E-01, 3.39060E-04, 1.17610E-07, 0.,          0.,          0.])\n",
    "Es = np.array([[6.61659E-02, 5.90700E-02, 2.83340E-04, 1.46220E-06, 2.06420E-08, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 2.40377E-01, 5.24350E-02, 2.49900E-04, 1.92390E-05, 2.98750E-06, 4.21400E-07],\n",
    "               [0.00000E+00, 0.00000E+00, 1.83425E-01, 9.22880E-02, 6.93650E-03, 1.07900E-03, 2.05430E-04],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 7.90769E-02, 1.69990E-01, 2.58600E-02, 4.92560E-03],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 3.73400E-05, 9.97570E-02, 2.06790E-01, 2.44780E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 9.17420E-04, 3.16774E-01, 2.38760E-01],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 4.97930E-02, 1.09910E+00]])\n",
    "FCxs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"Fisson Chamber\")\n",
    "\n",
    "# Guide Tube\n",
    "Et = np.array([1.26032E-01, 2.93160E-01, 2.84240E-01, 2.80960E-01, 3.34440E-01, 5.65640E-01, 1.17215E+00])\n",
    "Ea = np.array([5.11320E-04, 7.58010E-05, 3.15720E-04, 1.15820E-03, 3.39750E-03, 9.18780E-03, 2.32420E-02])\n",
    "Es = np.array([[6.61659E-02, 5.90700E-02, 2.83340E-04, 1.46220E-06, 2.06420E-08, 0.00000E+00, 0.00000E+00],\n",
    "               [0.00000E+00, 2.40377E-01, 5.24350E-02, 2.49900E-04, 1.92390E-05, 2.98750E-06, 4.21400E-07],\n",
    "               [0.00000E+00, 0.00000E+00, 1.83297E-01, 9.23970E-02, 6.94460E-03, 1.08030E-03, 2.05670E-04],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 7.88511E-02, 1.70140E-01, 2.58810E-02, 4.92970E-03],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 3.73330E-05, 9.97372E-02, 2.06790E-01, 2.44780E-02],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 9.17260E-04, 3.16765E-01, 2.38770E-01],\n",
    "               [0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 4.97920E-02, 1.09912E+00]])\n",
    "GTxs = scrb.CrossSection(Et, Ea, Es, name=\"Guide Tube\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The provided names to each `CrossSection` are not required, but that are useful when plotting the problem geometry later on, as we will be able to see the name of the material at a given location. This can be useful when debugging problem inputs.\n",
    "\n",
    "With material properties defined, we can now work on the geometry. We will start by constructing the different types of pin cells required for the simulation. For this, we will use the `PinCell` class, which takes a list of radii and cross section objects as arguments, in addition to the pitch. The `PinCell` class will automatically break the cell into 8 octants, but it is up to the user to define the desired radial divisions. In the C5G7 benchmark, fuel pins are homogenous with a radius of 0.54 cm, and pin cells have a pitch of 1.26 cm. To get better results in the simulation, we will break each fuel pin into two annular regions of equal area:\n",
    "\n",
    "$$\n",
    "    \\frac{A}{2} = \\pi (R_{f}^2 - R_{in}^2)\n",
    "$$\n",
    "\n",
    "$$\n",
    "    R_{in} = \\sqrt{R_f^2 - \\frac{A}{2\\pi}} = \\sqrt{R_f^2 - \\frac{R_f^2}{2}} = \\frac{R_f}{\\sqrt{2}} = \\frac{0.54\\text{ cm}}{\\sqrt{2}} \\approx 0.382\\text{ cm}.\n",
    "$$\n",
    "\n",
    "Additionally, we will add a ring of moderator around each fuel pin which extends to the outer surface of a pin cell, and therefore has a radius of half the pitch, 0.63 cm. Each list of materials has one more `CrossSection` than there are radii. This is to be used as the material outside the outer most radius and inside the pin cell boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants for geometry\n",
    "pitch = 1.26\n",
    "Rin = 0.382\n",
    "Rf  = 0.54\n",
    "\n",
    "# UO2 Pin Cell\n",
    "radii = [Rin,   Rf,    0.5*pitch]\n",
    "mats =  [UO2xs, UO2xs, H2Oxs,     H2Oxs]\n",
    "U2 = scrb.PinCell(radii, mats, pitch, pitch)\n",
    "\n",
    "# 4.3% MOX Pin Cell\n",
    "mats =  [M4xs, M4xs, H2Oxs, H2Oxs]\n",
    "M4 = scrb.PinCell(radii, mats, pitch, pitch)\n",
    "\n",
    "# 7.0% MOX Pin Cell\n",
    "mats =  [M7xs, M7xs, H2Oxs, H2Oxs]\n",
    "M7 = scrb.PinCell(radii, mats, pitch, pitch)\n",
    "\n",
    "# 8.7% MOX Pin Cell\n",
    "mats =  [M8xs, M8xs, H2Oxs, H2Oxs]\n",
    "M8 = scrb.PinCell(radii, mats, pitch, pitch)\n",
    "\n",
    "# Fission Chamber Pin Cell\n",
    "mats =  [FCxs, FCxs, H2Oxs, H2Oxs]\n",
    "FC = scrb.PinCell(radii, mats, pitch, pitch)\n",
    "\n",
    "# Guide Tube Pin Cell\n",
    "mats =  [GTxs, GTxs, H2Oxs, H2Oxs]\n",
    "GT = scrb.PinCell(radii, mats, pitch, pitch)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to define the UO2 assembly and the MOX assembly. Each one is 17x17, and can be created with a `Cartesian2D` lattice object. The *tiles* of the lattice can in this case be our `PinCell` objects (though in general, it could also be a nested lattice)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dx = [pitch]*17\n",
    "dy = dx\n",
    "UO2 = scrb.Cartesian2D(dx, dy)\n",
    "UO2.set_tiles([U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,GT,U2,U2,U2,U2,U2,U2,U2,U2,U2,GT,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,GT,U2,U2,GT,U2,U2,FC,U2,U2,GT,U2,U2,GT,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,GT,U2,U2,U2,U2,U2,U2,U2,U2,U2,GT,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,GT,U2,U2,GT,U2,U2,GT,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,\n",
    "               U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2,U2])\n",
    "\n",
    "MOX = scrb.Cartesian2D(dx, dy)\n",
    "MOX.set_tiles([M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,\n",
    "               M4,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M4,\n",
    "               M4,M7,M7,M7,M7,GT,M7,M7,GT,M7,M7,GT,M7,M7,M7,M7,M4,\n",
    "               M4,M7,M7,GT,M7,M8,M8,M8,M8,M8,M8,M8,M7,GT,M7,M7,M4,\n",
    "               M4,M7,M7,M7,M8,M8,M8,M8,M8,M8,M8,M8,M8,M7,M7,M7,M4,\n",
    "               M4,M7,GT,M8,M8,GT,M8,M8,GT,M8,M8,GT,M8,M8,GT,M7,M4,\n",
    "               M4,M7,M7,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M7,M7,M4,\n",
    "               M4,M7,M7,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M7,M7,M4,\n",
    "               M4,M7,GT,M8,M8,GT,M8,M8,FC,M8,M8,GT,M8,M8,GT,M7,M4,\n",
    "               M4,M7,M7,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M7,M7,M4,\n",
    "               M4,M7,M7,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M8,M7,M7,M4,\n",
    "               M4,M7,GT,M8,M8,GT,M8,M8,GT,M8,M8,GT,M8,M8,GT,M7,M4,\n",
    "               M4,M7,M7,M7,M8,M8,M8,M8,M8,M8,M8,M8,M8,M7,M7,M7,M4,\n",
    "               M4,M7,M7,GT,M7,M8,M8,M8,M8,M8,M8,M8,M7,GT,M7,M7,M4,\n",
    "               M4,M7,M7,M7,M7,GT,M7,M7,GT,M7,M7,GT,M7,M7,M7,M7,M4,\n",
    "               M4,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M7,M4,\n",
    "               M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4,M4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before defining the core geometry, we need to create empty \"water assemblies\", which will act as the reflector for the reactor. These will have the same dimensions as the fuel assemblies above. The only other type of cell in Scarabée is an `EmptyCell`, which is a single rectangular shaped flat source region. To ensure that the reflector geometry is adequately refined with flat source regions, we can define a reflector assembly as a nested set of lattices. The first lattice with have a total width and length equal to the pin pitch, and we will use it to divide the area of a single pin cell into uniform regions. Here is an example where each empty pin cell is divided into 4x4 flat source regions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NW = 4 # Number of empty water cells in a pin cell\n",
    "dx = [pitch / NW] * NW\n",
    "WC = scrb.EmptyCell(H2Oxs, pitch / NW, pitch / NW) # Water Cell\n",
    "WT = scrb.Cartesian2D(dx, dx)                      # Water Tile\n",
    "WT.set_tiles([WC]*NW*NW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create a water assembly, where each tile is one of the previously defined water tiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Water assembly\n",
    "dx = [pitch]*17\n",
    "WAS = scrb.Cartesian2D(dx, dx)\n",
    "WAS.set_tiles([WT]*17*17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now in a position where we can define the entire 2D core geometry, by nesting the previously defined assemblies into another lattice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Core assembly\n",
    "dx = [pitch*17, pitch*17, pitch*17]\n",
    "core = scrb.Cartesian2D(dx, dx)\n",
    "core.set_tiles([UO2, MOX, WAS,\n",
    "                MOX, UO2, WAS,\n",
    "                WAS, WAS, WAS])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, the geometry has been completely defined. All we need to do is set up the MOC solver, which is done with the `MOCDriver` class. When setting up the boundary conditions for the problem, it is important to remember that if you have added the line breaks in the correct spots in your code, \"what you see is what you get\". By this, it is meant that the outer corner UO2 assembly will be in the II quadrant of the geometry (touching the +y and -x sides of the problem). For this problem, reflective boundary conditions should be used on the sides where there are fuel assemblies, and vacuum boundary conditions should be used on the sides with water assemblies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "moc = scrb.MOCDriver(core)\n",
    "moc.x_max_bc = scrb.BoundaryCondition.Vacuum\n",
    "moc.y_min_bc = scrb.BoundaryCondition.Vacuum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like, you could now bring up the graphical geometry plotter using `moc.plot()`. This would bring up a new windows where you can interactively explore the geometry to ensure it was correctly defined. Here is an example of what the geometry plot looks like for this system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "moc.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![MOC Plotter](../_images/moc_plotter.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before generating the tracks across the problem, we should initialize the CMFD acceleration. CMFD stands for Coarse Mesh Finite-Difference diffusion. This is a technique which solves a diffusion problem using standard cell-centered finite differences. The algorithm is slightly modified, however, to make use of the neutron currents tabulated from the MOC simulation. Since we are able to incorporate the knowledge of the true transport solution, we can make sure that the diffusion and transport solutions are equivalent. This diffusion problem can be solved quite quickly at each MOC iteration, and its results are used to correct the MOC fluxes, leading to much faster convergence.\n",
    "\n",
    "CMFD is applied over a Cartesian mesh with cells generally on the order of being the size of a pin-cell. The boundaries/division of the CMFD mesh must conform to the boundaries of flat source regions in the MOC geometry. If this is not the case, the simulation might be unstable. While you can perform CMFD in the same group structure as the MOC simulation, you can also perform CMFD with energy condensation, which can often lead to faster CMFD solutions and faster MOC run-times. Let's create a CMFD instance at the pin cell level to accelerate our MOC simulation, using only 3 energy groups instead of the 7 used in the MOC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "moc.cmfd = scrb.CMFD(3*17*[pitch], 3*17*[pitch], [[0,1], [2,4], [5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will set the simulation parameters and run the solver. We have chosen to use 64 azimuthal angles with a track spacing of 0.05 cm. For the polar quadrature, we are using the Yamamoto-Tabuchi 6 point polar quadrature, which will typically always be the best option. The flux and eigenvalue convergence criteria have been set to their default values explicitly, so you can see how you could change them if needed. Typically, however, these default values are very reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Creating quadrature\n",
      "[info] Number of azimuthal angles: 64\n",
      "[info] Maximum track spacing: 0.05 cm\n",
      "[info] Tracing tracks\n",
      "[info] Renormalizing segment lengths\n",
      "[info] Determining track connections\n",
      "[info] Time spent drawing tracks: 9.6152 s.\n",
      "[info] Solving for keff.\n",
      "[info] keff tolerance: 1.00000E-05\n",
      "[info] Flux tolerance: 1.00000E-05\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    1          keff: 1.27228\n",
      "[info]      keff difference:     3.59033E-01\n",
      "[info]      max flux difference: 2.34399E+00\n",
      "[info]      iteration time: 8.39932E-01 s\n",
      "[info] CMFD solve time: 3.17632E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    2          keff: 1.25079\n",
      "[info]      keff difference:     1.71795E-02\n",
      "[info]      max flux difference: 2.32439E+00\n",
      "[info]      iteration time: 7.74950E-01 s\n",
      "[info] CMFD solve time: 2.27344E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    3          keff: 1.21645\n",
      "[info]      keff difference:     2.82345E-02\n",
      "[info]      max flux difference: 1.81440E+00\n",
      "[info]      iteration time: 7.69582E-01 s\n",
      "[info] CMFD solve time: 2.07215E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    4          keff: 1.19848\n",
      "[info]      keff difference:     1.49948E-02\n",
      "[info]      max flux difference: 2.45452E-01\n",
      "[info]      iteration time: 7.38605E-01 s\n",
      "[info] CMFD solve time: 1.76761E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    5          keff: 1.19075\n",
      "[info]      keff difference:     6.48527E-03\n",
      "[info]      max flux difference: 1.46429E-01\n",
      "[info]      iteration time: 7.35451E-01 s\n",
      "[info] CMFD solve time: 1.88176E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    6          keff: 1.18793\n",
      "[info]      keff difference:     2.37880E-03\n",
      "[info]      max flux difference: 9.39027E-02\n",
      "[info]      iteration time: 7.28087E-01 s\n",
      "[info] CMFD solve time: 1.69735E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    7          keff: 1.18702\n",
      "[info]      keff difference:     7.65127E-04\n",
      "[info]      max flux difference: 5.88262E-02\n",
      "[info]      iteration time: 6.88102E-01 s\n",
      "[info] CMFD solve time: 1.44799E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    8          keff: 1.18677\n",
      "[info]      keff difference:     2.10570E-04\n",
      "[info]      max flux difference: 3.63441E-02\n",
      "[info]      iteration time: 6.57545E-01 s\n",
      "[info] CMFD solve time: 1.35307E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    9          keff: 1.18672\n",
      "[info]      keff difference:     3.87722E-05\n",
      "[info]      max flux difference: 2.22983E-02\n",
      "[info]      iteration time: 6.42215E-01 s\n",
      "[info] CMFD solve time: 1.39045E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   10          keff: 1.18673\n",
      "[info]      keff difference:     2.25636E-06\n",
      "[info]      max flux difference: 1.36298E-02\n",
      "[info]      iteration time: 6.82332E-01 s\n",
      "[info] CMFD solve time: 1.41256E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   11          keff: 1.18673\n",
      "[info]      keff difference:     5.86714E-06\n",
      "[info]      max flux difference: 8.32356E-03\n",
      "[info]      iteration time: 6.87136E-01 s\n",
      "[info] CMFD solve time: 1.33708E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   12          keff: 1.18674\n",
      "[info]      keff difference:     2.18560E-06\n",
      "[info]      max flux difference: 5.09091E-03\n",
      "[info]      iteration time: 6.69850E-01 s\n",
      "[info] CMFD solve time: 1.24510E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   13          keff: 1.18674\n",
      "[info]      keff difference:     7.69781E-08\n",
      "[info]      max flux difference: 3.12145E-03\n",
      "[info]      iteration time: 6.29140E-01 s\n",
      "[info] CMFD solve time: 1.21062E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   14          keff: 1.18673\n",
      "[info]      keff difference:     1.40724E-06\n",
      "[info]      max flux difference: 1.91929E-03\n",
      "[info]      iteration time: 6.72514E-01 s\n",
      "[info] CMFD solve time: 1.18993E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   15          keff: 1.18673\n",
      "[info]      keff difference:     1.38091E-06\n",
      "[info]      max flux difference: 1.18421E-03\n",
      "[info]      iteration time: 6.58042E-01 s\n",
      "[info] CMFD solve time: 1.09388E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   16          keff: 1.18673\n",
      "[info]      keff difference:     1.22722E-06\n",
      "[info]      max flux difference: 7.33520E-04\n",
      "[info]      iteration time: 6.54614E-01 s\n",
      "[info] CMFD solve time: 1.03397E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   17          keff: 1.18673\n",
      "[info]      keff difference:     9.81543E-07\n",
      "[info]      max flux difference: 4.56108E-04\n",
      "[info]      iteration time: 6.42012E-01 s\n",
      "[info] CMFD solve time: 1.01349E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   18          keff: 1.18673\n",
      "[info]      keff difference:     5.30342E-07\n",
      "[info]      max flux difference: 2.84668E-04\n",
      "[info]      iteration time: 5.99661E-01 s\n",
      "[info] CMFD solve time: 9.79701E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   19          keff: 1.18673\n",
      "[info]      keff difference:     5.10833E-07\n",
      "[info]      max flux difference: 2.01665E-04\n",
      "[info]      iteration time: 5.89163E-01 s\n",
      "[info] CMFD solve time: 9.57783E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   20          keff: 1.18673\n",
      "[info]      keff difference:     4.22134E-07\n",
      "[info]      max flux difference: 1.47548E-04\n",
      "[info]      iteration time: 6.05976E-01 s\n",
      "[info] CMFD solve time: 9.87052E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   21          keff: 1.18673\n",
      "[info]      keff difference:     1.50195E-07\n",
      "[info]      max flux difference: 1.08083E-04\n",
      "[info]      iteration time: 5.66916E-01 s\n",
      "[info] CMFD solve time: 8.93837E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   22          keff: 1.18673\n",
      "[info]      keff difference:     5.88781E-08\n",
      "[info]      max flux difference: 7.92377E-05\n",
      "[info]      iteration time: 5.93222E-01 s\n",
      "[info] CMFD solve time: 9.00190E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   23          keff: 1.18673\n",
      "[info]      keff difference:     1.55473E-07\n",
      "[info]      max flux difference: 5.81301E-05\n",
      "[info]      iteration time: 6.29991E-01 s\n",
      "[info] CMFD solve time: 8.61458E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   24          keff: 1.18673\n",
      "[info]      keff difference:     2.39316E-08\n",
      "[info]      max flux difference: 4.26704E-05\n",
      "[info]      iteration time: 6.34037E-01 s\n",
      "[info] CMFD solve time: 8.91341E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   25          keff: 1.18673\n",
      "[info]      keff difference:     1.26681E-08\n",
      "[info]      max flux difference: 3.13204E-05\n",
      "[info]      iteration time: 6.21978E-01 s\n",
      "[info] CMFD solve time: 9.75675E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   26          keff: 1.18673\n",
      "[info]      keff difference:     2.54887E-08\n",
      "[info]      max flux difference: 2.29847E-05\n",
      "[info]      iteration time: 5.99739E-01 s\n",
      "[info] CMFD solve time: 9.36424E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   27          keff: 1.18673\n",
      "[info]      keff difference:     2.79619E-08\n",
      "[info]      max flux difference: 1.68576E-05\n",
      "[info]      iteration time: 5.78222E-01 s\n",
      "[info] CMFD solve time: 8.77104E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   28          keff: 1.18673\n",
      "[info]      keff difference:     4.36048E-08\n",
      "[info]      max flux difference: 1.23497E-05\n",
      "[info]      iteration time: 5.62797E-01 s\n",
      "[info] CMFD solve time: 8.23464E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   29          keff: 1.18673\n",
      "[info]      keff difference:     3.42566E-09\n",
      "[info]      max flux difference: 9.03447E-06\n",
      "[info]      iteration time: 6.53875E-01 s\n",
      "[info] CMFD solve time: 8.25375E-02 s\n",
      "[info] \n",
      "[info] Simulation Time: 1.91854E+01 s\n"
     ]
    }
   ],
   "source": [
    "moc.generate_tracks(64, 0.05, scrb.YamamotoTabuchi6())\n",
    "moc.keff_tolerance = 1.E-5\n",
    "moc.flux_tolerance = 1.E-5\n",
    "moc.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we solved the problem, we would like to look at plots of the flux ! The `MOCDriver` has a helper method which allows us to do this very easily. We can provide the desired resolution, and it will rasterize the and provide it to us as a 3D Numpy array, where the first index is the group, the second is x, and the third is y. Here is an example of using this method and then plotting the thermal flux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flux, x, y = moc.rasterize_flux(1000, 1000)\n",
    "\n",
    "plt.title(\"Flux in Group 7\")\n",
    "plt.pcolormesh(x, y, flux[6, :, :], cmap='jet')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example should give you all the basics you need to perform other 2D MOC simulations using Scarabée !"
   ]
  }
 ],
 "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}