{
 "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",
    "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",
    "Et = Ea + np.sum(Es, 1)\n",
    "UO2xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"UO2\")\n",
    "\n",
    "# 4.3% MOX Fuel\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",
    "Et = Ea + np.sum(Es, 1)\n",
    "M4xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"4.3% MOX\")\n",
    "\n",
    "# 7.0% MOX Fuel\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",
    "Et = Ea + np.sum(Es, 1)\n",
    "M7xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"7.0% MOX\")\n",
    "\n",
    "# 8.7% MOX Fuel\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",
    "Et = Ea + np.sum(Es, 1)\n",
    "M8xs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"8.7 % MOX\")\n",
    "\n",
    "# Water Moderator\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",
    "Et = Ea + np.sum(Es, 1)\n",
    "H2Oxs = scrb.CrossSection(Et, Ea, Es, name=\"Water\")\n",
    "\n",
    "# Fission Chamber\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",
    "Et = Ea + np.sum(Es, 1)\n",
    "FCxs = scrb.CrossSection(Et, Ea, Es, Ef, nu*Ef, chi, name=\"Fisson Chamber\")\n",
    "\n",
    "# Guide Tube\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",
    "Et = Ea + np.sum(Es, 1)\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: 13.414 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: 4.71655E+04\n",
      "[info]      iteration time: 1.57091E+00 s\n",
      "[info] CMFD solve time: 1.00651E+00 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    2          keff: 1.25079\n",
      "[info]      keff difference:     1.71795E-02\n",
      "[info]      max flux difference: 1.87948E+00\n",
      "[info]      iteration time: 1.36152E+00 s\n",
      "[info] CMFD solve time: 7.70765E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    3          keff: 1.21645\n",
      "[info]      keff difference:     2.82345E-02\n",
      "[info]      max flux difference: 3.65248E+00\n",
      "[info]      iteration time: 1.25834E+00 s\n",
      "[info] CMFD solve time: 6.74979E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    4          keff: 1.19848\n",
      "[info]      keff difference:     1.49948E-02\n",
      "[info]      max flux difference: 6.60670E-01\n",
      "[info]      iteration time: 1.05424E+00 s\n",
      "[info] CMFD solve time: 5.18417E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    5          keff: 1.19075\n",
      "[info]      keff difference:     6.48528E-03\n",
      "[info]      max flux difference: 2.89648E-01\n",
      "[info]      iteration time: 1.16326E+00 s\n",
      "[info] CMFD solve time: 5.83274E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    6          keff: 1.18793\n",
      "[info]      keff difference:     2.37880E-03\n",
      "[info]      max flux difference: 1.50786E-01\n",
      "[info]      iteration time: 1.09737E+00 s\n",
      "[info] CMFD solve time: 5.05474E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    7          keff: 1.18702\n",
      "[info]      keff difference:     7.65127E-04\n",
      "[info]      max flux difference: 8.60915E-02\n",
      "[info]      iteration time: 9.61895E-01 s\n",
      "[info] CMFD solve time: 3.84462E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    8          keff: 1.18677\n",
      "[info]      keff difference:     2.10570E-04\n",
      "[info]      max flux difference: 4.99709E-02\n",
      "[info]      iteration time: 9.13827E-01 s\n",
      "[info] CMFD solve time: 3.41598E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration    9          keff: 1.18672\n",
      "[info]      keff difference:     3.87723E-05\n",
      "[info]      max flux difference: 2.78978E-02\n",
      "[info]      iteration time: 9.48890E-01 s\n",
      "[info] CMFD solve time: 3.80996E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   10          keff: 1.18673\n",
      "[info]      keff difference:     2.25634E-06\n",
      "[info]      max flux difference: 1.53309E-02\n",
      "[info]      iteration time: 9.34643E-01 s\n",
      "[info] CMFD solve time: 3.65553E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   11          keff: 1.18673\n",
      "[info]      keff difference:     5.86712E-06\n",
      "[info]      max flux difference: 8.58599E-03\n",
      "[info]      iteration time: 9.33870E-01 s\n",
      "[info] CMFD solve time: 3.46371E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   12          keff: 1.18674\n",
      "[info]      keff difference:     2.18559E-06\n",
      "[info]      max flux difference: 4.80348E-03\n",
      "[info]      iteration time: 8.41543E-01 s\n",
      "[info] CMFD solve time: 3.05485E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   13          keff: 1.18674\n",
      "[info]      keff difference:     7.69800E-08\n",
      "[info]      max flux difference: 2.63867E-03\n",
      "[info]      iteration time: 8.81257E-01 s\n",
      "[info] CMFD solve time: 2.80277E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   14          keff: 1.18673\n",
      "[info]      keff difference:     1.40724E-06\n",
      "[info]      max flux difference: 1.53563E-03\n",
      "[info]      iteration time: 8.30591E-01 s\n",
      "[info] CMFD solve time: 2.40764E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   15          keff: 1.18673\n",
      "[info]      keff difference:     1.38091E-06\n",
      "[info]      max flux difference: 1.10771E-03\n",
      "[info]      iteration time: 7.98670E-01 s\n",
      "[info] CMFD solve time: 2.23454E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   16          keff: 1.18673\n",
      "[info]      keff difference:     1.22722E-06\n",
      "[info]      max flux difference: 8.02205E-04\n",
      "[info]      iteration time: 7.29691E-01 s\n",
      "[info] CMFD solve time: 2.01597E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   17          keff: 1.18673\n",
      "[info]      keff difference:     9.81541E-07\n",
      "[info]      max flux difference: 5.82762E-04\n",
      "[info]      iteration time: 7.26310E-01 s\n",
      "[info] CMFD solve time: 1.87472E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   18          keff: 1.18673\n",
      "[info]      keff difference:     5.30342E-07\n",
      "[info]      max flux difference: 4.23144E-04\n",
      "[info]      iteration time: 7.84300E-01 s\n",
      "[info] CMFD solve time: 1.82535E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   19          keff: 1.18673\n",
      "[info]      keff difference:     5.10832E-07\n",
      "[info]      max flux difference: 3.09456E-04\n",
      "[info]      iteration time: 7.41696E-01 s\n",
      "[info] CMFD solve time: 1.61858E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   20          keff: 1.18673\n",
      "[info]      keff difference:     4.22133E-07\n",
      "[info]      max flux difference: 2.26790E-04\n",
      "[info]      iteration time: 6.95492E-01 s\n",
      "[info] CMFD solve time: 1.40892E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   21          keff: 1.18673\n",
      "[info]      keff difference:     1.50195E-07\n",
      "[info]      max flux difference: 1.65141E-04\n",
      "[info]      iteration time: 7.26819E-01 s\n",
      "[info] CMFD solve time: 1.45240E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   22          keff: 1.18673\n",
      "[info]      keff difference:     5.88778E-08\n",
      "[info]      max flux difference: 1.20878E-04\n",
      "[info]      iteration time: 7.09888E-01 s\n",
      "[info] CMFD solve time: 1.42580E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   23          keff: 1.18673\n",
      "[info]      keff difference:     1.55472E-07\n",
      "[info]      max flux difference: 8.97403E-05\n",
      "[info]      iteration time: 7.04260E-01 s\n",
      "[info] CMFD solve time: 1.19236E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   24          keff: 1.18673\n",
      "[info]      keff difference:     2.39317E-08\n",
      "[info]      max flux difference: 6.54081E-05\n",
      "[info]      iteration time: 6.67643E-01 s\n",
      "[info] CMFD solve time: 1.27304E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   25          keff: 1.18673\n",
      "[info]      keff difference:     1.26680E-08\n",
      "[info]      max flux difference: 4.79790E-05\n",
      "[info]      iteration time: 6.91578E-01 s\n",
      "[info] CMFD solve time: 1.22305E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   26          keff: 1.18673\n",
      "[info]      keff difference:     2.54890E-08\n",
      "[info]      max flux difference: 3.53072E-05\n",
      "[info]      iteration time: 6.76282E-01 s\n",
      "[info] CMFD solve time: 1.21425E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   27          keff: 1.18673\n",
      "[info]      keff difference:     2.79617E-08\n",
      "[info]      max flux difference: 2.60287E-05\n",
      "[info]      iteration time: 6.95128E-01 s\n",
      "[info] CMFD solve time: 1.23611E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   28          keff: 1.18673\n",
      "[info]      keff difference:     4.36046E-08\n",
      "[info]      max flux difference: 1.97460E-05\n",
      "[info]      iteration time: 6.71203E-01 s\n",
      "[info] CMFD solve time: 9.96425E-02 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   29          keff: 1.18673\n",
      "[info]      keff difference:     3.42624E-09\n",
      "[info]      max flux difference: 1.43451E-05\n",
      "[info]      iteration time: 6.63062E-01 s\n",
      "[info] CMFD solve time: 1.05476E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   30          keff: 1.18673\n",
      "[info]      keff difference:     1.49671E-08\n",
      "[info]      max flux difference: 1.05024E-05\n",
      "[info]      iteration time: 6.72758E-01 s\n",
      "[info] CMFD solve time: 1.03482E-01 s\n",
      "[info] -------------------------------------\n",
      "[info] Iteration   31          keff: 1.18673\n",
      "[info]      keff difference:     1.88816E-08\n",
      "[info]      max flux difference: 7.72313E-06\n",
      "[info]      iteration time: 7.07397E-01 s\n",
      "[info] CMFD solve time: 1.06156E-01 s\n",
      "[info] \n",
      "[info] Simulation Time: 2.68848E+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
}