{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03228f54-7693-4b5e-be0e-66490007558d",
   "metadata": {},
   "source": [
    "# PWR Assembly Depletion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6a26f26-f908-4da1-a082-3bfb37e8044a",
   "metadata": {},
   "source": [
    "In this example, we will demonstrate how to perform a PWR assembly depletion simulation based on the 3.1% enriched assembly (without any burnable poison rods) from the BEAVRS benchmark. We can begin by importing all the tools we will need from Scarabée."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff60fb2e-6d6c-4569-beb3-4fa01d82c309",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scarabee import (\n",
    "    NDLibrary,\n",
    "    MaterialComposition,\n",
    "    Material,\n",
    "    Fraction,\n",
    "    DensityUnits,\n",
    ")\n",
    "from scarabee.reseau import FuelPin, GuideTube, PWRAssembly, Symmetry\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a3b77f5-ad0a-4801-a4b5-f2504eea7062",
   "metadata": {},
   "source": [
    "The `scarabee.reseau` submodule contains all of the elements needed for lattice physics calculations (reseau is French for lattice). In this case, we have directly imported only the classes needed for this example.\n",
    "\n",
    "We can now create our Nuclear Data Library (NDL) object, and define the materials we will need for the fuel pins and guide tubes. In this example, we will not provide a path to the `NDLibrary` constructor, which means it will automatically look at our `SCARABEE_ND_LIBRARY` environment variable to find the path to the NDL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "441fa30d-1f25-46d2-8faa-1cc28c74cc35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Loading Nuclear Data Library from /home/hunter/Documents/nuclear_data/scarabee/endf8_scarabee125.h5\n",
      "[info] Nuclear Data Library based on ENDF/B-VIII.0 using SCARABEE-125 group structure\n"
     ]
    }
   ],
   "source": [
    "ndl = NDLibrary()\n",
    "\n",
    "# Define Fuel at 3.1 w/o enrichment\n",
    "Fuel31Comp = MaterialComposition(Fraction.Atoms, name=\"Fuel 3.1%\")\n",
    "Fuel31Comp.add_leu(3.1, 1.0) # The add_leu method assumes weight enrichment !\n",
    "Fuel31Comp.add_element(\"O\", 2.0)\n",
    "Fuel31 = Material(Fuel31Comp, 900.0, 10.30166, DensityUnits.g_cm3, ndl)\n",
    "\n",
    "# Define the cladding\n",
    "CladComp = MaterialComposition(Fraction.Weight, name=\"Zircaloy 4\")\n",
    "CladComp.add_element(\"O\", 0.00125)\n",
    "CladComp.add_element(\"Cr\", 0.0010)\n",
    "CladComp.add_element(\"Fe\", 0.0021)\n",
    "CladComp.add_element(\"Zr\", 0.98115)\n",
    "CladComp.add_element(\"Sn\", 0.0145)\n",
    "Clad = Material(CladComp, 575.0, 6.55, DensityUnits.g_cm3, ndl)\n",
    "\n",
    "# Define the helium gas\n",
    "HeComp = MaterialComposition(Fraction.Atoms, name=\"He Gas\")\n",
    "HeComp.add_element(\"He\", 1.0)\n",
    "He = Material(HeComp, 575.0, 0.0015981, DensityUnits.g_cm3, ndl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1038264-e34c-422b-8dbd-592cec50998d",
   "metadata": {},
   "source": [
    "In this simulation, we have assumed a fuel temperature of 900 K, and a cladding and gas temperature of 575 K. You might have noticed that we have not defined any moderator material. This will be taken care of automatically by the `PWRAssembly` class later on.\n",
    "\n",
    "The next step is to define the fuel pin and guide tubes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8311aee6-9c4d-49ca-9836-ae8ee0d8da70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a fuel pin\n",
    "fp = FuelPin(\n",
    "    fuel=Fuel31,\n",
    "    fuel_radius=0.39218,\n",
    "    gap=He,\n",
    "    gap_radius=0.40005,\n",
    "    clad=Clad,\n",
    "    clad_radius=0.45720,\n",
    ")\n",
    "\n",
    "# Create a guide tube\n",
    "gt = GuideTube(inner_radius=0.56134, outer_radius=0.60198, clad=Clad)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e891334e-699d-440c-b7d4-c2ee284040d0",
   "metadata": {},
   "source": [
    "Now we need to define the arrangement of the fuel pins and guide tubes in the assembly. The full 2D assembly would normally be 17x17, but since the assembly is symmetric, we can use quarter symmetry to reduce the size of the problem (and get results faster). When running with quarter symmetry, the the left hand side (-x) and the bottom (-y) pins will be half pin cells (because our full assembly had an odd number of pins on each side)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79d010d2-f63d-4a4d-a63a-de09358ccd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#         |--- Half pin cells\n",
    "#         v\n",
    "cells = [[fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [gt, fp, fp, gt, fp, fp, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, gt, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [gt, fp, fp, gt, fp, fp, gt, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [fp, fp, fp, fp, fp, fp, fp, fp, fp],\n",
    "         [gt, fp, fp, gt, fp, fp, gt, fp, fp]] # <- Half pin cells\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae56e835-a0a6-4a4f-8220-ba4db20b5ead",
   "metadata": {},
   "source": [
    "It is now time to define the `PWRAssembly` object, which will be responsible for orchestrating the entire depletion calculation. It requires all the assembly information (such a pin pitch, assembly pitch, moderator parameters, linear power density, and more). We will assume the moderator is at 575 K and a pressure of 15.5132 MPa with a soluble boron concentration of 975 parts per million. By default, Scarabée assumes a linear power density of 42 kW/cm, which is reasonable for most PWRs, and we have kept this choice. Keep in mind that **the linear power density that is provided is for the linear power of the entire assembly** and not just for a single fule pin !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce6790a9-5f32-49bc-b6ae-7a34353c0c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "asmbly = PWRAssembly(\n",
    "    pitch=1.25984,\n",
    "    assembly_pitch=21.50364,\n",
    "    shape=(17, 17),\n",
    "    symmetry=Symmetry.Quarter,\n",
    "    moderator={'temperature': 575., 'pressure': 15.5132, 'boron-ppm': 975.},\n",
    "    cells=cells,\n",
    "    ndl=ndl,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68db3ddc-88eb-4163-b311-42b54812c0ec",
   "metadata": {},
   "source": [
    "At this point, if we were to call `asmbly.solve()`, it would run a single k-eigenvalue problem, and not actually perform any depletion. For that, we need to tell it what the series of time steps should be. We can either set `asmbly.depletion_time_steps` to be an array containing the durration of all time steps in days, or we can set `asmbly.depletion_exposure_steps` to be an array containing the durration of all time steps in MWd/kg of initial heavy mental. We will use the later of these two option here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b213724-66b0-45f4-be7f-9c37f71881b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "asmbly.depletion_exposure_steps = np.array(5*[0.01] + [0.15] + [0.8] + [1.] + 15*[2.])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6c676a-94a9-422c-a8da-1d82eff33219",
   "metadata": {},
   "source": [
    "The first few time steps are rather small (on the order of about 0.5 days). This is to accurately predict the build up of strong fission product poisons, particularly Xe-135.\n",
    "\n",
    "Scarabée uses an LE/QI predictor-corrector method for depletion. That means for each time step a transport calculation is performed for the predictor *and* the corrector. This is the most accurate option, but it might not be necessary for your problem. In our case, we don't have any burnable poisons so we likely don't need to perform a second transport calcualtion on the corrector step. We will instead just use the flux from the predictor step with the nuclide concentrations obtained with the predictor step. This will help the simulation run almost twice as fast, but should likely only be used if you know what you are doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3a7b2a5-81b2-4113-83b2-63a3bd26178f",
   "metadata": {},
   "outputs": [],
   "source": [
    "asmbly.corrector_transport = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8935843d-584f-43b7-a4dc-dd2fa6884e4f",
   "metadata": {},
   "source": [
    "We can now finally launch the simulation, which will take approximately 10-30 minutes depending on your computer and the group strucutre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db9d88fa-7581-4c32-bab3-0a918721f71b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[info] Running Time Step 0\n",
      "[info] Exposure: 0.000E+00 MWd/kg\n",
      "[info] Time    : 0.000E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Initializing Dancoff correction calculation components.\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] Creating quadrature\n",
      "[info] Number of azimuthal angles: 64\n",
      "[info] Maximum track spacing: 0.03 cm\n",
      "[info] Tracing tracks\n",
      "[info] Renormalizing segment lengths\n",
      "[info] Determining track connections\n",
      "[info] Time spent drawing tracks: 0.13736 s.\n",
      "[info] \n",
      "[info] Kinf: 1.21004\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.21004\n",
      "[info] Buckling: 0.00355\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.19074\n",
      "[info] Buckling: 0.00325\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 1\n",
      "[info] Exposure: 1.000E-02 MWd/kg\n",
      "[info] Time    : 2.758E-01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.19596\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.19596\n",
      "[info] Buckling: 0.00333\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.19262\n",
      "[info] Buckling: 0.00328\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 2\n",
      "[info] Exposure: 2.000E-02 MWd/kg\n",
      "[info] Time    : 5.516E-01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.18645\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.18645\n",
      "[info] Buckling: 0.00318\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17929\n",
      "[info] Buckling: 0.00307\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 3\n",
      "[info] Exposure: 3.000E-02 MWd/kg\n",
      "[info] Time    : 8.274E-01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.17906\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17906\n",
      "[info] Buckling: 0.00306\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17555\n",
      "[info] Buckling: 0.00301\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 4\n",
      "[info] Exposure: 4.000E-02 MWd/kg\n",
      "[info] Time    : 1.103E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.17545\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17545\n",
      "[info] Buckling: 0.00300\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17357\n",
      "[info] Buckling: 0.00297\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 5\n",
      "[info] Exposure: 5.000E-02 MWd/kg\n",
      "[info] Time    : 1.379E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.17352\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.17352\n",
      "[info] Buckling: 0.00297\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.16731\n",
      "[info] Buckling: 0.00287\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 6\n",
      "[info] Exposure: 2.000E-01 MWd/kg\n",
      "[info] Time    : 5.516E+00 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.16731\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.16731\n",
      "[info] Buckling: 0.00287\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.15683\n",
      "[info] Buckling: 0.00270\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 7\n",
      "[info] Exposure: 1.000E+00 MWd/kg\n",
      "[info] Time    : 2.758E+01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.15661\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.15661\n",
      "[info] Buckling: 0.00270\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.14779\n",
      "[info] Buckling: 0.00255\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 8\n",
      "[info] Exposure: 2.000E+00 MWd/kg\n",
      "[info] Time    : 5.516E+01 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.14753\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.14753\n",
      "[info] Buckling: 0.00255\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.12659\n",
      "[info] Buckling: 0.00220\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 9\n",
      "[info] Exposure: 4.000E+00 MWd/kg\n",
      "[info] Time    : 1.103E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.12633\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.12633\n",
      "[info] Buckling: 0.00220\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.10369\n",
      "[info] Buckling: 0.00181\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 10\n",
      "[info] Exposure: 6.000E+00 MWd/kg\n",
      "[info] Time    : 1.655E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.10408\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.10408\n",
      "[info] Buckling: 0.00182\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.08129\n",
      "[info] Buckling: 0.00143\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 11\n",
      "[info] Exposure: 8.000E+00 MWd/kg\n",
      "[info] Time    : 2.206E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.08226\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.08226\n",
      "[info] Buckling: 0.00145\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.05987\n",
      "[info] Buckling: 0.00106\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 12\n",
      "[info] Exposure: 1.000E+01 MWd/kg\n",
      "[info] Time    : 2.758E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.06124\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.06124\n",
      "[info] Buckling: 0.00108\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.03942\n",
      "[info] Buckling: 0.00070\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 13\n",
      "[info] Exposure: 1.200E+01 MWd/kg\n",
      "[info] Time    : 3.310E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.04102\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.04102\n",
      "[info] Buckling: 0.00073\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.01978\n",
      "[info] Buckling: 0.00035\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 14\n",
      "[info] Exposure: 1.400E+01 MWd/kg\n",
      "[info] Time    : 3.861E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.02150\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.02150\n",
      "[info] Buckling: 0.00038\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.00080\n",
      "[info] Buckling: 0.00001\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 15\n",
      "[info] Exposure: 1.600E+01 MWd/kg\n",
      "[info] Time    : 4.413E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 1.00258\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 1.00258\n",
      "[info] Buckling: 0.00005\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.98237\n",
      "[info] Buckling: -0.00032\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 16\n",
      "[info] Exposure: 1.800E+01 MWd/kg\n",
      "[info] Time    : 4.964E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.98413\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.98413\n",
      "[info] Buckling: -0.00029\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.96439\n",
      "[info] Buckling: -0.00065\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 17\n",
      "[info] Exposure: 2.000E+01 MWd/kg\n",
      "[info] Time    : 5.516E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.96612\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.96612\n",
      "[info] Buckling: -0.00061\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.94684\n",
      "[info] Buckling: -0.00097\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 18\n",
      "[info] Exposure: 2.200E+01 MWd/kg\n",
      "[info] Time    : 6.068E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.94851\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.94851\n",
      "[info] Buckling: -0.00094\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.92970\n",
      "[info] Buckling: -0.00129\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 19\n",
      "[info] Exposure: 2.400E+01 MWd/kg\n",
      "[info] Time    : 6.619E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.93129\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.93129\n",
      "[info] Buckling: -0.00126\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.91297\n",
      "[info] Buckling: -0.00160\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 20\n",
      "[info] Exposure: 2.600E+01 MWd/kg\n",
      "[info] Time    : 7.171E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.91448\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.91448\n",
      "[info] Buckling: -0.00157\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.89668\n",
      "[info] Buckling: -0.00191\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 21\n",
      "[info] Exposure: 2.800E+01 MWd/kg\n",
      "[info] Time    : 7.722E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.89810\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.89810\n",
      "[info] Buckling: -0.00188\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.88085\n",
      "[info] Buckling: -0.00221\n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 22\n",
      "[info] Exposure: 3.000E+01 MWd/kg\n",
      "[info] Time    : 8.274E+02 days\n",
      "[info] \n",
      "[info] Predictor:\n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.88218\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.88218\n",
      "[info] Buckling: -0.00218\n",
      "[info] \n",
      "[info] Corrector:\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.86551\n",
      "[info] Buckling: -0.00250\n",
      "[info] \n",
      "[info] \n",
      "[info] ------------------------------------------------------------\n",
      "[info] Running Time Step 23\n",
      "[info] Exposure: 3.200E+01 MWd/kg\n",
      "[info] Time    : 8.825E+02 days\n",
      "[info] \n",
      "[info] Computing Dancoff corrections for the fuel.\n",
      "[info] Computing Dancoff corrections for the cladding.\n",
      "[info] \n",
      "[info] Kinf: 0.86675\n",
      "[info] \n",
      "[info] Performing P1 criticality spectrum calculation\n",
      "[info] Kinf    : 0.86675\n",
      "[info] Buckling: -0.00248\n",
      "[info] \n"
     ]
    }
   ],
   "source": [
    "asmbly.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9e6d35-7138-42e9-b2bd-fb860210332b",
   "metadata": {},
   "source": [
    "Now comes the fun part: making plots ! Let's first take a look at $k_\\text{eff}$ as a function of the burn-up of the assembly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b0b146b-229c-4427-80fa-4f6d2617debc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1LN112uxoIiIibk3FTgpM90YVWDK8LY1DSpGakcPTn29n7KJdZGTnmh1NRETELanYSYEKKVOMrwZHMLh9DQA+2xRPr+k/cyjpotnRRERE3I6KnRQ4T5uVF7uG8sljLShXwot9CRfpMe1n5m89rteRiYiIOJGKndw27WoHsGREW9rULMfl7FxGf/0rI7+MJS0zx+xoIiIibkHFTm6r8iV9+OSxFjwfVQeb1cLi2FPc8+56DiTq0qyIiMitUrGT285qtTCkY03mP9WSCv4+HDmTTs93f2Zx7Emzo4mIiBRqKnZimqZVyvD9sDaOS7Mjvojl1cW7yczRU7MiIiL5oWInpipbwps5j7Vg2J01Afhk4zF6z9zEqQuXzY4mIiJS6KjYielsVgvPdq7DrAHN8Pf1JPb4Bbq/s461B/SuWRERkZuhYicu487QQL4f1oYGFf04fymb/h9v4Z1VB7HbNSWKiIjIjVCxE5cSUqYYXw9uRZ8WIRgGTFp5gMfm/ML59Cyzo4mIiLg8FTtxOT6eNibc14i3/9EIbw8rMfvPcPe09fx64oLZ0URERFyaip24rAeahbDgmVZUKVuMkxcu84/3NvL55mN6W4WIiMh1qNiJS6sf7M+3Q9sQWTeQrFw7Ly/czbNf7eRylqZEERER+V8qduLy/H09+eDRprzQJRSrBRZsP8m9M34mLjnd7GgiIiIuRcVOCgWr1cLTHWrw+aCWlCvhxb6Ei9wzbT3Lf0swO5qIiIjLULGTQiWiRll+GN6WZlVKczEzh6c+3caEJXvJybWbHU1ERMR0KnZS6AT6+TDvyZYMalMNgJlrj9D3o80kXcwwO5qIiIipVOykUPK0WRl7dz1mPHwHJbw92BJ3ju7vrGdL3Dmzo4mIiJhGxU4KtW4NK7B4aGtqB5bgzMVM+ny4iQ/XHtGUKCIiUiSp2EmhVyOgBIuGtKZX42By7QZvLtnL059tJzUj2+xoIiIit5WKnbiFYl4eTO7dmH/2aoCnzcKy3xLo+e7P7EtINTuaiIjIbaNiJ27DYrHwaMsqzH8qgmB/H+KS0+k1/WcWbD9hdjQREZHbQsVO3E6TyqX5fnhb2tYqR0a2nej5O3l54S4yc/S2ChERcW8qduKWyhT3YvbAFgzvVAuLBT7fHM+D72/kxPlLZkcTEREpMCp24rZsVgvRd9Vm1oDmlCrmyc4TKdw9bT0x+5PMjiYiIlIgVOzE7XWsU57vhrahUSV/LlzKZuDsX5i88gC5dk2JIiIi7kXFToqEkDLF+GpwBA+HV8YwYOqqgwz4eAvn0rPMjiYiIuI0KnZSZHh72Hjz3oZMfCAMH08r6w4m0/2ddWw7prdViIiIe3CpYrd27Vp69OhBcHAwFouFRYsW/eX406dP07dvX2rXro3VamXkyJHXHPfVV18RGhqKj48PDRs2ZMmSJQW0B1IY3N+0EouGtKZ6ueKcTsmg98xNfLROb6sQEZHCz6WKXXp6OmFhYUyfPv2GxmdmZhIQEMDYsWMJCwu75pgNGzbQp08fHn/8cXbs2EGvXr3o1asXu3fvdnJ6KUxCg/xYPLQ1dzeqQI7d4I0f9vLkp9tIuaS3VYiISOFlMVz0NIXFYmHhwoX06tXrhsZ36NCBxo0bM2XKlDzre/fuTXp6Ot9//71jXcuWLWncuDHvv//+DW07NTUVf39/UlJS8PPzu8k9EVdmGAafbTrGP7/fS1aunUqlfZnx8B00qlTK7GgiIiJwkz3Epc7YFYSNGzcSGRmZZ11UVBQbN2687ncyMzNJTU3Ns4h7slgsPBpRlW+ebkVIGV9OnL/MP97byCcbj+rSrIiIFDpuX+wSEhIIDAzMsy4wMJCEhITrfmfChAn4+/s7lpCQkNuQVMzUsJI/3w9rS+d6gWTl2nl18W8MnbuDixm6NCsiIoWH2xe7/BgzZgwpKSmO5fjx42ZHktvA39eTmY825ZW76+FhtfDDrtP0mLaePad0xlZERAoHty92QUFBJCYm5lmXmJhIUFDQdb/j7e2Nn59fnkWKBovFwuNtqjF/cATB/j4cPXuJXjN+Zt6WeF2aFRERl+f2xS4iIoJVq1blWbdy5UoiIiJMyySu747KpflheFs61gkgK8fOmAW7iJ6/k/TMHLOjiYiIXJdLFbu0tDRiY2OJjY0FIC4ujtjYWOLj4+HKJdJ+/frl+c4f49PS0jhz5gyxsbHs2bPH8fmIESNYtmwZEydOZN++fYwfP56tW7cydOjQ27x3UtiULu7Ff/o354UuodisFhbuOEnP6T9zIPGi2dFERESuyaWmO4mJiaFjx45Xre/fvz+zZ89mwIABHD16lJiYGMdnFovlqvFVqlTh6NGjjr+/+uorxo4dy9GjR6lVqxZvvfUW3bp1u+Fcmu5ENh85y7B5O0i6mImvp403ejXg/qaVzI4lIiJFwM30EJcqdq5KxU4AktMyGflFLOsPJQPQu1kIr/Wsj4+nzexoIiLixjSPnUgBKFfCmzmPtWBUZG0sFvhy63F6Tf+ZI2fSzI4mIiICKnYiN8dmtTAishafPR5OuRJe7Eu4SI9p6/lu5ymzo4mIiKjYieRH65rlWDK8LeHVypCelcuweTt4ZdFuMnNyzY4mIiJFmIqdSD6V9/Ph80HhDOlYA4BPNx3j/vc2EH/2ktnRRESkiFKxE7kFHjYrz0eF8vHA5pQq5snuk6l0n7aOZbuv/8o6ERGRgqJiJ+IEHeuUZ8nwttxRuRQXM3IY/Nk2/vn9HrJy7GZHExGRIkTFTsRJgkv58uVTETzRthoA/1kfR+8PNnLywmWzo4mISBGhYifiRJ42Ky93r8cHjzbFz8eDHfEX6P7OOn7al3gD3xYREbk1KnYiBaBz/SB+GN6WRpX8uXApm8dmb+Vfy/aRk6tLsyIiUnBU7EQKSEiZYnw1OIIBraoC8F7MYfp+uJnE1Ayzo4mIiJtSsRMpQN4eNsbfU5/pfe+ghLcHW46eo9vUdaw7eMbsaCIi4oZU7ERug+6NKvDdsDbUreDH2fQs+s3awsQV+3VpVkREnErFTuQ2qVauOAufaUWfFiEYBkz76RAPfbCJE+c1obGIiDiHip3IbeTjaWPCfY2Y+lBjSnh7sPXYebpNXcfSXafNjiYiIm5AxU7EBD0bV2TJ8LaEhZQiNSOHpz/fzksLd5GRrXfNiohI/qnYiZikctlifD04gsHtf3/X7NzN8dzz7nr2J1w0O5qIiBRSKnYiJvK0WXmxayifPt6CciW8OZCYxj3vruezTccwDMPseCIiUsio2Im4gLa1Alg6oi3tageQmWNn7KLdPPP5dlIuZZsdTUREChEVOxEXEVDSm9kDmvNyt7p42iws3Z1A16lr+eXoObOjiYhIIaFiJ+JCrFYLT7SrzjdPt6Jq2WKcSsmg98yNvLPqILl2XZoVEZG/pmIn4oIaVSrF98Pbcm+TitgNmLTyAA9/tImEFL2OTERErk/FTsRFlfD2YHLvxkx6MIxiXjY2HTlHl6lrWbkn0exoIiLiolTsRFzcfXdU4ofhbWlQ0Y8Ll7J54pOtjP/2N815JyIiV1GxEykEqpUrzjdPt2JQm2oAzN5wlHtnbOBQUprZ0URExIWo2IkUEt4eNsbeXY+PBzSnbHEv9p5Opce09cz/5bjmvBMREVCxEyl8OoaWZ+mItrSuWZbL2bmM/uZXhn8RS2qG5rwTESnqVOxECqHyfj58+lg4o7vUwWa18N3OU3R/Zx074s+bHU1EREykYidSSFmtFp7pUJOvBkdQqbQvx89d5oH3N/JezGHsmvNORKRIUrETKeTuqFyaH4a3pXujCuTYDf61bB/9Zm0h6aLmvBMRKWpU7ETcgL+vJ+/2acK/7m+Ij6eV9YeS6TplHTH7k8yOJiIit5GKnYibsFgs9G5eme+HtSE0qCRn07MY8PEvvPnDHrJy7GbHExGR20DFTsTN1CxfkkVDWtM/ogoAH66L4/73NnA0Od3saCIiUsBU7ETckI+njdd6NuCDR5tSqpgnu06m0P2ddSzcccLsaCIiUoBU7ETcWOf6QSwd0ZYW1cqQnpXLqC93Ej0/lrTMHLOjiYhIAVCxE3FzFfx9mfdES0ZF1sZqgQXbT9L9nXVsO6Y570RE3I2KnUgRYLNaGBFZiy+fiqBiKV+Onb3EA+9vYNLKA2Tn6sEKERF3oWInUoQ0r1qGJSPa0qtxMHYD3ll1kH+8t4EjZ9LMjiYiIk6gYidSxPj7ejLloSa806cJfj4e7DyRQvd31jN3czyGoTdWiIgUZip2IkXUPWHBLBvZjlY1ynI5O5eXFu5i0JytJKdlmh1NRETyScVOpAgLLuXLZ4+HM7Z7XbxsVlbtS6LLlLWs2ptodjQREckHFTuRIs5qtTCobXUWD21NaFBJktOyeHzOVl5auItLWZoWRUSkMFGxExEA6lbwY9GQ1gxqUw2AuZvj6f7OemKPXzA7moiI3CCXKnZr166lR48eBAcHY7FYWLRo0d9+JyYmhjvuuANvb29q1qzJ7Nmz83w+fvx4LBZLniU0NLQA90Kk8PLxtDH27np8PiicID8f4pLTuf+9DUz98SA5mhZFRMTluVSxS09PJywsjOnTp9/Q+Li4OLp3707Hjh2JjY1l5MiRDBo0iOXLl+cZV79+fU6fPu1Y1q9fX0B7IOIeWtcsx/KR7bi7UQVy7QaTfzzAAzM3cuys3jcrIuLKPMwO8Gddu3ala9euNzz+/fffp1q1akycOBGAunXrsn79eiZPnkxUVJRjnIeHB0FBQQWSWcRd+RfzZFqfJkTWDeSVRbvZEX+BblPXMa5HfR5oVgmLxWJ2RBER+R8udcbuZm3cuJHIyMg866Kioti4cWOedQcPHiQ4OJjq1avz8MMPEx8ff5uTihROFouFXk0qsnRkW8KvvG929De/8tSn2ziXnmV2PBER+R+FutglJCQQGBiYZ11gYCCpqalcvnwZgPDwcGbPns2yZct47733iIuLo23btly8ePG6283MzCQ1NTXPIlKUVSpdjLlPtOTFrqF42iys2JNI1JS1rN6fZHY0ERH5k0Jd7G5E165deeCBB2jUqBFRUVEsWbKECxcuMH/+/Ot+Z8KECfj7+zuWkJCQ25pZxBXZrBYGt6/BoiGtqVW+BGcuZjLw4194dfFuLmflmh1PREQKe7ELCgoiMTHvRKqJiYn4+fnh6+t7ze+UKlWK2rVrc+jQoetud8yYMaSkpDiW48ePOz27SGFVP9if74a1YWDrqgB8svEYd09bx+6TKWZHExEp8gp1sYuIiGDVqlV51q1cuZKIiIjrfictLY3Dhw9ToUKF647x9vbGz88vzyIi/+XjaWNcj/p8+ngLAv28OXwmnV7Tf2b66kPk2vW+WRERs9xSsZswYQIAy5Ytc0qYtLQ0YmNjiY2NhSvTmcTGxjoedhgzZgz9+vVzjB88eDBHjhxh9OjR7Nu3jxkzZjB//nxGjRrlGPPcc8+xZs0ajh49yoYNG7j33nux2Wz06dPHKZlFirK2tQJYNqId3RoGkWM3eHv5fh76YCPHz10yO5qISJF0S8Vu7ty5ALz00ktOCbN161aaNGlCkyZNAIiOjqZJkya8+uqrAJw+fTrPE63VqlXjhx9+YOXKlYSFhTFx4kQ++uijPFOdnDhxgj59+lCnTh0efPBBypYty6ZNmwgICHBKZpGirnRxL6b3vYOJD4RRwtuDX46ep+vUdXy97QSGobN3IiK3k8W4hf/LO3r0aGbPns2FCxcoVaqUY71hGFgsFpKS3OOJudTUVPz9/UlJSdFlWZG/cPzcJUZ9GcvWY+cB6NYwiDd7NaR0cS+zo4mIFFo300Nu6Yxd6dKlSUpKokSJEiQlJTmWM2fOuE2pE5EbF1KmGF8+FcHzUXXwsFpYsiuBqClrWXvgjNnRRESKBKdciq1ataqz8ohIIWezWhjSsSYLn2lNjYDiJF3MpN+sLbz23W9kZGtaFBGRgnRLxa5Lly6UL1+e3bt3U758eQICAvL8p4gUXQ0r+fP9sLb0i6gCwMc/H6XHtPWaFkVEpADd0j12f7jvvvtYsGCBcxK5IN1jJ3JrVu9PYvTXv3LmYiYeVgvDO9Xi6Q418LQV6hmXRERui9t2j90fFixYwNKlS5k2bRpcmSR47969zti0iLiBjnXKs3xkO7rU/31alEkrD3DfjA0cSLz+q/1EROTmOaXYPffcc3zxxRdMnz79941arQwYMMAZmxYRN1GmuBfvPXIHUx9qjL+vJ7tOpnD3O+uZueawJjUWEXESpxS7VatWMWfOHMdrvAICAsjIyHDGpkXEjVgsFno2rsiKUe24M7Q8Wbl2Jizdx4MzNxKXnG52PBGRQs8pxc7T0xO73Y7FYgHg3LlzWK26d0ZEri3Qz4f/9G/GW/9oRAlvD7YdO0/XqWv5+Oc47Dp7JyKSb05pX8OHD6d3794kJyfzz3/+k3bt2jF69GhnbFpE3JTFYuHBZiEsH9WONjXLkZFt57Xv9tD3o016JZmISD7l66nYpKSkq6Yz2bdvH6tWrcJut9OpUyfq1avnzJym0lOxIgXLMAw+2xzP//2wl8vZuRT3svFy93r0aRHiuBIgIlJU3UwPyVexa9euHatXr8Zms131WU5ODh4eHje7SZemYidyexw7m87zX/3KlqPnAGhXO4B/3d+QCv6+ZkcTETFNgU93UqpUKYYPH37V+nPnzhEZGZmfTYqIUKVsceY92ZKx3evi5WFl7YEzdJ68lm+2ncAJU26KiLi9fBW7Tz75hJUrVzJr1izHur1799K8eXOKFy/uzHwiUsTYrBYGta3OkuFtCQspxcWMHJ79aidPfrqNpIt62l5E5K/k+80Tu3btokOHDixdupTz58/Tu3dvHn/8cd5++223eyJWl2JFzJGTa2fm2iNM+fEA2bkGpYt58s9eDbi7UbDZ0UREbpsCucfuvvvuo3Hjxo6lcuXKzJs3j2HDhpGRkcG0adMYOHCgs/bBpajYiZhr7+lUnp2/kz2nUwHo3qgC/+zZgDLFvcyOJiJS4ArkHrsaNWqwbt06Bg0aRNWqVSlbtiwffvghhmHQt29f7rjjDrKzs52RX0Qkj7oV/Fg0pDXDO9XCZrXww6+n6Tx5LSv3JJodTUTEpeTrUuzJkyeJjY3Nsxw5cgQPDw9CQ0PZuXNnwaQ1ic7YibiOX09c4Nn5OzmYlAbAfXdUZFyP+vj7epodTUSkQBT4dCfXkpaWRmxsLDt37mTIkCHO2KTLULETcS0Z2blM/vEAH6w9gmFAkJ8P//pHI9rXDjA7moiI05lS7NyZip2Ia9p27BzPzt/J0bO/v6miT4vKvNy9LiW83WsuTREp2gp8HjsREVfQtEoZlo5ox4BWVQGYtyWeLlPWsvHwWbOjiYiYQsVORAo1Xy8b4++pz9wnwqlYypcT5y/T58NNjP/2Ny5n5ZodT0TktlKxExG30KpGOZaPakefFpUBmL3hKN3eWce2Y+fMjiYictuo2ImI2yjh7cGE+xoye2Bzgvx8iEtO54H3NzJh6V4ysnX2TkTcn4qdiLidDnXKs3xUO+67oyJ2A2auOUKPaev59cQFs6OJiBQoFTsRcUv+vp5MerAxHzzalHIlvDiYlMa9Mzbwr2X7dPZORNyWip2IuLXO9YNYMao9PcKCybUbvBdzWPfeiYjbUrETEbdXprgX0/o0YeajTQko6c2RM+n84/2NvP7dHi5l5ZgdT0TEaVTsRKTIiKofxI+j2vOPppUwDJj1cxxdpqxjw+Fks6OJiDiFip2IFCn+xTz59wNhzB7YnGB/H+LPXaLvh5t5eeEuLmZkmx1PROSWqNiJSJH0x5OzD4f/Pu/d55vjiZq8lpj9SWZHExHJNxU7ESmySvp48ua9DZk7KJyQMr6cSslgwMe/8NxXO0m5pLN3IlL4qNiJSJHXqmY5lo9sx8DWVbFY4OttJ4icvIYVvyWYHU1E5Kao2ImIAMW8PBjXoz5fPRVB9XLFOXMxkyc/3caweTs4m5ZpdjwRkRuiYici8ifNqpZhyYi2DG5fA6sFvtt5is6T1/L9r6cwDMPseCIif0nFTkTkf/h42nixaygLn2lNncCSnE3PYujcHQz+bBtJFzPMjicicl0qdiIi1xEWUorvhrVhRKdaeFgtLP8tkbsmreWbbSd09k5EXJKKnYjIX/DysDLqrtp8O7QNDSr6kXI5m2e/2snA2b9w6sJls+OJiOShYicicgPqBfux6JnWjO5SBy+blZj9Z+g8eS1zN8fr7J2IuAwVOxGRG+Rhs/JMh5osGdGGJpVLkZaZw0sLd/HwR5uJP3vJ7HgiIip2IiI3q2b5knw9uBWv3F0PH08rGw6fJWrKWmb/HIfdrrN3ImIeFTsRkXywWS083qYay0a0I7xaGS5n5zL+uz08OHMjR86kmR1PRIooFTsRkVtQtVxx5j3Rkn/2akBxLxtbj52n69R1zFxzmJxcu9nxRKSIUbETEblFVquFR1tWYfmodrStVY7MHDsTlu7j/vc2sD/hotnxRKQIcalit3btWnr06EFwcDAWi4VFixb97XdiYmK444478Pb2pmbNmsyePfuqMdOnT6dq1ar4+PgQHh7Oli1bCmgPRKQoq1S6GJ881oK3/tGIkj4e7DyRwt3T1vHOqoNk6+ydiNwGLlXs0tPTCQsLY/r06Tc0Pi4uju7du9OxY0diY2MZOXIkgwYNYvny5Y4xX375JdHR0YwbN47t27cTFhZGVFQUSUlJBbgnIlJUWSwWHmwWwo/R7YmsW57sXINJKw/QY9p6dh6/YHY8EXFzFsNFJ2CyWCwsXLiQXr16XXfMCy+8wA8//MDu3bsd6x566CEuXLjAsmXLAAgPD6d58+a8++67ANjtdkJCQhg2bBgvvvjiDWVJTU3F39+flJQU/Pz8bnnfRKRoMAyDb3eeYvy3v3H+UjZWCwxsXY1nO9emmJeH2fFEpJC4mR7iUmfsbtbGjRuJjIzMsy4qKoqNGzcCkJWVxbZt2/KMsVqtREZGOsaIiBQUi8VCz8YV+TG6Pb0aB2M34D/r4+g8eS1rDpwxO56IuKFCXewSEhIIDAzMsy4wMJDU1FQuX75McnIyubm51xyTkJBw3e1mZmaSmpqaZxERya+yJbyZ8lATPh7YnIqlfDlx/jL9Z20h+stYzqVnmR1PRNxIoS52BWXChAn4+/s7lpCQELMjiYgb6FinPCtGtWNg66pYLLBgx0kiJ61h0Y6Tei2ZiDhFoS52QUFBJCYm5lmXmJiIn58fvr6+lCtXDpvNds0xQUFB193umDFjSElJcSzHjx8vsH0QkaKluLcH43rUZ8HTragTWJJz6VmM/DKWAR//wvFzei2ZiNyaQl3sIiIiWLVqVZ51K1euJCIiAgAvLy+aNm2aZ4zdbmfVqlWOMdfi7e2Nn59fnkVExJmaVC7Nd8Pa8Fzn2nh5WFlz4AydJ6/lP+vjyNVryUQkn1yq2KWlpREbG0tsbCxcmc4kNjaW+Ph4uHImrV+/fo7xgwcP5siRI4wePZp9+/YxY8YM5s+fz6hRoxxjoqOj+fDDD5kzZw579+7l6aefJj09nYEDB5qwhyIi/+XlYWXonbVYOqItLa68luyf3+/hvhk/s/e07u0VkZvnUtOdxMTE0LFjx6vW9+/fn9mzZzNgwACOHj1KTExMnu+MGjWKPXv2UKlSJV555RUGDBiQ5/vvvvsub7/9NgkJCTRu3Jh33nmH8PDwG86l6U5EpKDZ7QZf/HKcCUv2cjEzBw+rhcHtazD0zpr4eNrMjiciJrqZHuJSxc5VqdiJyO2SmJrBq4t3s/y33+8Nrl6uOP93X0NaVi9rdjQRMUmRmcdORMTdBPr5MPPRZrz/yB2UL+nNkeR0HvpgE2MW7CLlcrbZ8UTExanYiYi4oC4NKrAyuj19WlQGYN6WeO6atIZlu68/B6eIiIqdiIiL8vf1ZMJ9DfniyZZUK1ecpIuZDP5sG4M/3UZiaobZ8UTEBanYiYi4uJbVy7J0RFuGdKyBh9XCst8SiJy0hrmb47FrahQR+RMVOxGRQsDH08bzUaF8N6wNYZX8uZiRw0sLd/HQh5s4fCbN7Hgi4iJU7ERECpG6FfxY8ExrXrm7Hr6eNrbEnaPr1HW8+9NBsnLsZscTEZOp2ImIFDI2q4XH21Rjxah2tKsdQFaOnX+vOMA9764n9vgFs+OJiIlU7ERECqmQMsWYM7A5U3o3pnQxT/YlXOTeGT/z+nd7SM/MMTueiJhAxU5EpBCzWCz0alKRH6Pbc2+TihgGzPo5js6T1xKzP8nseCJym6nYiYi4gbIlvJncuzFzHmtBxVK+nLxwmQEf/8LIL3ZwNi3T7Hgicpuo2ImIuJH2tQNYMaodj7ephtUCi2JP0WnSGuZvPY7eICni/lTsRETcTHFvD165ux4LnmlNaFBJLlzKZvTXv/LQB5s4lKSpUUTcmYqdiIibahxSiu+GtWFM11B8PK1sjjtHt6nrmLTyABnZuWbHE5ECoGInIuLGPG1Wnmpfg5Wj2tOxTgBZuXbeWXWQrlPXseFQstnxRMTJVOxERIqAkDLFmDWgOTMevoPyJb2JS06n70ebiZ4fq4crRNyIip2ISBFhsVjo1rACPz7bnkdbVsFigQXbT+rhChE3omInIlLE+Pl48s9eDVjwdCs9XCHiZlTsRESKqCaVS1/1cEXXqWv1cIVIIaZiJyJShP3vwxXZuYYerhApxFTsRERED1eIuAkVOxERAT1cIeIWVOxERCSP6z1c0VsPV4i4PBU7ERG5pj8ernipWyi+nja26OEKEZenYiciItflabPyZLsarBjVTg9XiBQCKnYiIvK39HCFSOGgYiciIjfkzw9X9IvQwxUirkjFTkREboqfjyev97zewxUXzY4nUqSp2ImISL5c++GKdUxasV8PV4iYRMVORETy7ZoPV/x0iK5T1/GzHq4Que1U7ERE5JZd6+GKhz/azKgvYzlzUQ9XiNwuKnYiIuIU13q4YuGOk3SaGMOnm46Ra9fDFSIFzWLoMaa/lZqair+/PykpKfj5+ZkdR0SkUIg9foGxi3ax+2QqAGGV/HmjV0MaVvI3O5pIoXIzPUTF7gao2ImI5E+u3eCzTcf49/L9XMzMwWKBR1tW4dnOdfD39TQ7nkihcDM9RJdiRUSkwNisFvq3qsqqZ9vTs3EwhgGfbDxGp4lrWLTjpOa+E3EyFTsRESlw5f18mPpQEz4fFE71gOIkp2Uy8stY+n64WXPfiTiRip2IiNw2rWuWY+mItjzXuTbeHlY2HjlL16nreGvZPi5nae47kVulYiciIreVt4eNoXfW4sfo9twZWp7sXIMZMYeJnLSGH/ckmh1PpFBTsRMREVOElCnGf/o3Y+ajTQn29+HkhcsM+mQrg+Zs5cT5S2bHEymUVOxERMQ0FouFqPpB/Phse55qXx0Pq4Uf9yYSOWkNM2IOkZVjNzuiSKGiYiciIqYr5uXBmK51WTKiLS2qlSEj285by/bT7Z11bDx81ux4IoWGip2IiLiM2oEl+fLJlkx8IIyyxb04lJRGnw836dVkIjdIxU5ERFyKxWLh/qaV+OnZDjwcXtnxarI7J8bw6cajejWZyF/QmydugN48ISJinv99NVmjSv680asBjSqVMjuayG1RqN88MX36dKpWrYqPjw/h4eFs2bLlumOzs7N5/fXXqVGjBj4+PoSFhbFs2bI8Y8aPH4/FYsmzhIaG3oY9ERERZ2gcUorFQ9rw2j31Kentwa8nUug5/WdeWbSblMvZZscTcSkuVey+/PJLoqOjGTduHNu3bycsLIyoqCiSkpKuOX7s2LHMnDmTadOmsWfPHgYPHsy9997Ljh078oyrX78+p0+fdizr16+/TXskIiLO4Hg12XP/fTXZp5uO0WliDAt3nNCryUSucKlLseHh4TRv3px3330XALvdTkhICMOGDePFF1+8anxwcDAvv/wyQ4YMcay7//778fX15bPPPoMrZ+wWLVpEbGxsvnPpUqyIiGvZcCiZsYt3c+RMOgAtq5fhjV4NqFm+pNnRRJyuUF6KzcrKYtu2bURGRjrWWa1WIiMj2bhx4zW/k5mZiY+PT551vr6+V52RO3jwIMHBwVSvXp2HH36Y+Pj4AtoLERG5HVpdeTXZ81F18PawsunIObpOXce/9GoyKeJcptglJyeTm5tLYGBgnvWBgYEkJCRc8ztRUVFMmjSJgwcPYrfbWblyJQsWLOD06dOOMeHh4cyePZtly5bx3nvvERcXR9u2bbl48fovnc7MzCQ1NTXPIiIirsXbw8aQjjX5Mbo9na68muy9K68mW6lXk0kR5TLFLj+mTp1KrVq1CA0NxcvLi6FDhzJw4ECs1v/uVteuXXnggQdo1KgRUVFRLFmyhAsXLjB//vzrbnfChAn4+/s7lpCQkNu0RyIicrNCyhTjo/7N+ODRplQs5cvJC5d54pOtDJrzC/Fn9WoyKVpcptiVK1cOm81GYmLe/5WVmJhIUFDQNb8TEBDAokWLSE9P59ixY+zbt48SJUpQvXr16/57SpUqRe3atTl06NB1x4wZM4aUlBTHcvz48VvYMxERKWgWi4XO9YNYGd2Owe1rXHk1WRKRk9cweeUBMrJ1eVaKBpcpdl5eXjRt2pRVq1Y51tntdlatWkVERMRfftfHx4eKFSuSk5PDN998Q8+ePa87Ni0tjcOHD1OhQoXrjvH29sbPzy/PIiIirq+Ylwcvdg1l6Yi2tKpRlqwcO1NXHeSuyb9fnnWh5wVFCoTLFDuA6OhoPvzwQ+bMmcPevXt5+umnSU9PZ+DAgQD069ePMWPGOMZv3ryZBQsWcOTIEdatW0eXLl2w2+2MHj3aMea5555jzZo1HD16lA0bNnDvvfdis9no06ePKfsoIiIFr1ZgST4fFM67fZsQ5OfD8XO/X559bPYvHE1ONzueSIHxMDvAn/Xu3ZszZ87w6quvkpCQQOPGjVm2bJnjgYr4+Pg8989lZGQwduxYjhw5QokSJejWrRuffvoppUr9dzbyEydO0KdPH86ePUtAQABt2rRh06ZNBAQEmLKPIiJye1gsFu5uFEzHOuWZ9tMh/rP+CKv3n+HnQ2t5qn11nulQE18vm9kxRZzKpeaxc1Wax05EpPA7fCaN8d/+xrqDyQBULOXLK3fXI6p+IBaLxex4Itd1Mz1Exe4GqNiJiLgHwzBY/lsCr3+3h1MpGQC0rVWO1+6pT/WAEmbHE7kmFTsnU7ETEXEvl7JymLH6MB+sPUJWrh1Pm4VBbasz7M6aFPNyqbuURFTsnE3FTkTEPcUlp/Pad78Rs/8MABX8fRjbvR7dGgbp8qy4DBU7J1OxExFxX4ZhsHJPIq9/v4cT5y8D0LpmWV67p77ePSsuQcXOyVTsRETcX0Z2LjNiDvP+msNk5djxsFp4vE01hnWqRQlvXZ4V89xMD3GpeexERETM4uNpI/qu2qwc1Y5OoeXJsRvMXHuEThNjWBx7UpMbS6GgM3Y3QGfsRESKnlV7E3ntuz3En/v9fbMtq5fh9Z4NqB2oy7Nye+lSrJOp2ImIFE0Z2bnMXHOEGTGHyMyxY7NaGNCqKiMja1HSx9PseFJE6FKsiIiIE/h42hgRWYsfo9vTuV4guXaD/6yP486Ja1i444Quz4rL0Rm7G6AzdiIiAhCzP4nx3/7G0bO/X55tUbUMr/WsT90K+v8NUnB0KdbJVOxEROQPmTm5fLQujmk/HSQj+/fLs4+2rMKou2rj76vLs+J8uhQrIiJSQLw9bAzpWJNVz3aga4Mgcu0GszccpdPEGL7edgK7XedLxDw6Y3cDdMZORESuZ+2BM4z/7jeOnEkHoGmV0rx2T30aVPQ3O5q4CV2KdTIVOxER+StZOXb+s/73y7OXsnKxWKBPi8o817kOZYp7mR1PCjkVOydTsRMRkRtxOuUyb/6wl+9/PQ2An48H0XfV5pGWVfCw6e4nyR8VOydTsRMRkZux6chZXvtuD3tPpwJQO7AE43rUp3XNcmZHk0JIxc7JVOxERORm5doN5m2J598r9nPhUjYAXeoH8XL3uoSUKWZ2PClEVOycTMVORETy68KlLCavPMBnm+PJtRt4eVh5ql11nu5Qg2JeHmbHk0JAxc7JVOxERORW7U+4yGvf/caGw2cBqODvw5hudenRqAIWi8XseOLCVOycTMVOREScwTAMlu1O4I0f9nLywmW48vaKcffUo36wpkeRa1OxczIVOxERcaaM7Fw+WHuEGTGHyMi2Y7XAQ5oeRa5Dxc7JVOxERKQgnLxwmQlLND2K/DUVOydTsRMRkYK0+chZxmt6FLkOFTsnU7ETEZGC9sf0KBNX7Oe8pkeRP1GxczIVOxERuV2uNT3K4HbVGazpUYosFTsnU7ETEZHbTdOjyB9U7JxMxU5ERMyg6VEEFTvnU7ETEREzaXqUok3FzslU7ERExBVoepSiScXOyVTsRETElWh6lKJFxc7JVOxERMTVaHqUokPFzslU7ERExFVduJTFlB8P8ummY47pUQa1qcYzHWtSwlvTo7gDFTsnU7ETERFX97/To5Qr4c3zUbX5R9MQbFZNj1KYqdg5mYqdiIgUBoZh8OPeJP5vyV7iktMBqFfBj7F316VVDd1/V1ip2DmZip2IiBQmWTl2Ptl4lKmrDnIxIweAzvUCealbXaqWK252PLlJKnZOpmInIiKF0bn0LKb8eIDPr7yezNNmYUCrqgy9sxb+vp5mx5MbpGLnZCp2IiJSmB1MvMgbP+xlzYEzAJQp7sWou2rTp3mI5r8rBFTsnEzFTkRE3MHq/Um8+cNeDiWlwZX578Z2r0e72gFmR5O/oGLnZCp2IiLiLrJz7czdHM/kHw9w4cr8d3eGluelbnWpWb6E2fHkGlTsnEzFTkRE3E3KpWze+ekgczYcJcdu4GG18EjLKozoVIvSev+sS1GxczIVOxERcVdHzqTxf0v28uPeJAD8fT0Z0akWj0ZUwVP337kEFTsnU7ETERF39/OhZP75/R72JVwEoHpAcV7uVpc7Q8tjsWiCYzOp2DmZip2IiBQFuXaDL385zsQV+zmbngVA21rlGNu9HnWCSpodr8i6mR7icudYp0+fTtWqVfHx8SE8PJwtW7Zcd2x2djavv/46NWrUwMfHh7CwMJYtW3ZL2xQRESmqbFYLfcMrs/r5DjzVvjpeNivrDibTdepaXl64i7NpmWZHlL/hUsXuyy+/JDo6mnHjxrF9+3bCwsKIiooiKSnpmuPHjh3LzJkzmTZtGnv27GHw4MHce++97NixI9/bFBERKer8fDwZ07UuP0a3p2uDIOwGfL45ng5vxzBzzWEyc3LNjijX4VKXYsPDw2nevDnvvvsuAHa7nZCQEIYNG8aLL7541fjg4GBefvllhgwZ4lh3//334+vry2effZavbV6LLsWKiEhRtunIWf75/R5+O5UKQOUyxXipW12i6gfq/rvboFBeis3KymLbtm1ERkY61lmtViIjI9m4ceM1v5OZmYmPj0+edb6+vqxfvz7f2/xju6mpqXkWERGRoqpl9bJ8N7QNb/+jEQElvYk/d4nBn23joQ82sftkitnx5E9cptglJyeTm5tLYGBgnvWBgYEkJCRc8ztRUVFMmjSJgwcPYrfbWblyJQsWLOD06dP53ibAhAkT8Pf3dywhISFO2UcREZHCymq18ECzEGKe68DQjjXx9rCyOe4cPd5dz+ivd5KUmmF2RHGlYpcfU6dOpVatWoSGhuLl5cXQoUMZOHAgVuut7daYMWNISUlxLMePH3daZhERkcKsuLcHz0XVYdWz7ekRFoxhwPytJ+jw7xje/ekgl7N0/52ZXKbYlStXDpvNRmJiYp71iYmJBAUFXfM7AQEBLFq0iPT0dI4dO8a+ffsoUaIE1atXz/c2Aby9vfHz88uziIiIyH9VKl2MaX2a8M3TEYSFlOJSVi7/XnGAOyfG8PW2E+TaXeYW/iLFZYqdl5cXTZs2ZdWqVY51drudVatWERER8Zff9fHxoWLFiuTk5PDNN9/Qs2fPW96miIiI/L2mVcqw8OlWTOndmIqlfDmdksFzX+3k7mnrWXfwjNnxihwPswP8WXR0NP3796dZs2a0aNGCKVOmkJ6ezsCBAwHo168fFStWZMKECQBs3ryZkydP0rhxY06ePMn48eOx2+2MHj36hrcpIiIit8ZqtdCrSUW6NAhi9oajTF99iL2nU3n0P1toXzuAMd1CCQ3S1a/bwaWKXe/evTlz5gyvvvoqCQkJNG7cmGXLljkefoiPj89z/1xGRgZjx47lyJEjlChRgm7duvHpp59SqlSpG96miIiIOIePp43B7WvwYLMQ3ll1kM82HWPNgTOsO3iGB5qGEN25NoF+PjewJckvl5rHzlVpHjsREZGbdzQ5nbeW72PJrt9novD1tPFE22o82b4GJbxd6tySS9O7Yp1MxU5ERCT/th07x5s/7GV7/AUAypXwZtRdtejdLAQPm8vc7u+yVOycTMVORETk1hiGwbLdCfy/Zfs4dvYSADXLl2BM11DuDC2vN1j8BRU7J1OxExERcY6sHDufbz7GO6sOcv5SNgAtq5fh5W71aFjJ3+x4LknFzslU7ERERJwr5XI2M2IO8fHPR8nKsQPQq3Ewz0XVoVLpYmbHcykqdk6mYiciIlIwTpy/xMQVB1i44yQAXh5WBrauyjMdauLv62l2PJegYudkKnYiIiIFa9eJFP5vyV42HjkLQOlingzvVIuHw6vg5VG0H7BQsXMyFTsREZGCZxgGq/cn8X9L9nEoKQ2AqmWLMbpLKF0bBBXZByxU7JxMxU5EROT2ycm1M3/rCSatPEByWiYAd1Quxcvd69G0Smmz4912KnZOpmInIiJy+6Vn5jBz7RE+XHuEy9m5AHRrGMToqFCqlitudrzbRsXOyVTsREREzJOYmsHklQeYv/U4dgM8bRYeaVmF4XfWonRxL7PjFTgVOydTsRMRETHf/oSLTFi6l5j9ZwAo6ePBkI41GdCqKj6eNrPjFRgVOydTsRMREXEdPx9K5s0f9rLndCoAFUv58lxUbXqGVcRqdb8HLFTsnEzFTkRExLXY7QYLd5zk3yv2czolA4C6FfwY3aUOHWoHuNUTtCp2TqZiJyIi4poysnP5z/o43l9zmIsZOQCEVyvDC11DuaOyezxBq2LnZCp2IiIiru18ehbvrTnM7A3/fUVZVP1Ano8KpWb5EmbHuyUqdk6mYiciIlI4nLpwmSk/HuDrbSewG2C1wANNQxh5Vy0q+PuaHS9fVOycTMVORESkcDmYeJG3l+9nxZ5EALw9rAxoVZWnO9SgVLHCNUWKip2TqdiJiIgUTtuOnedfy/axJe4cAH4+HgzuUIOBrarh61U4pkhRsXMyFTsREZHCyzAMYvaf4V/L9rEv4SIAgX7ejOhUmwebVcLDZjU74l9SsXMyFTsREZHCz243WLzzJBNXHODE+csAVC9XnOei6tC1QZDLTpGiYudkKnYiIiLuIzMnl7mb43n3p0OcTc8CIKySPy90CaVVzXJmx7uKip2TqdiJiIi4n7TMHD5ce4SP1h0hPSsXgLa1yvFCl1AaVPQ3O56Dip2TqdiJiIi4r+S0TN796RCfbz5Gdu7vtahHWDDPda5NlbLFzY6nYudsKnYiIiLuL/7sJSat3M/inacwDPCwWujTojLDOtWkfEkf03Kp2DmZip2IiEjRsedUKm8t30fM/jMA+HraGNS2Gk+2q05JH8/bnkfFzslU7ERERIqeTUfO8v+W7iP2+AUAShfzZEjHmjzSsgo+nrdvDjwVOydTsRMRESmaDMNg+W+JvL18H4fPpANQsZQvo+6qzb1NKmKzFvwUKSp2TqZiJyIiUrTl5Nr5ZvsJJq88SEJqBgB1AkvyfFQdOtUtX6Bz4KnYOZmKnYiIiABkZOcyZ8NRZsQcJuVyNgDNqpTmtZ71qR9cMFOk3EwPce13aIiIiIi4EB9PG0+1r8Ha0R15ukMNfDytbD12npxc1zhP5mF2ABEREZHCxt/Xkxe6hDKgVVVW7U0iLKSU2ZFAZ+xERERE8i/Qz4e+4ZXNjuGgYiciIiLiJlTsRERERNyEip2IiIiIm1CxExEREXETKnYiIiIibkLFTkRERMRNqNiJiIiIuAkVOxERERE3oWInIiIi4iZU7ERERETchIqdiIiIiJtwuWI3ffp0qlatio+PD+Hh4WzZsuUvx0+ZMoU6derg6+tLSEgIo0aNIiMjw/H5+PHjsVgseZbQ0NDbsCciIiIit5eH2QH+7MsvvyQ6Opr333+f8PBwpkyZQlRUFPv376d8+fJXjZ87dy4vvvgis2bNolWrVhw4cIABAwZgsViYNGmSY1z9+vX58ccfHX97eLjUbouIiIg4hUudsZs0aRJPPPEEAwcOpF69erz//vsUK1aMWbNmXXP8hg0baN26NX379qVq1ap07tyZPn36XHWWz8PDg6CgIMdSrly527RHIiIiIrePyxS7rKwstm3bRmRkpGOd1WolMjKSjRs3XvM7rVq1Ytu2bY4id+TIEZYsWUK3bt3yjDt48CDBwcFUr16dhx9+mPj4+L/MkpmZSWpqap5FRERExNW5zDXJ5ORkcnNzCQwMzLM+MDCQffv2XfM7ffv2JTk5mTZt2mAYBjk5OQwePJiXXnrJMSY8PJzZs2dTp04dTp8+zWuvvUbbtm3ZvXs3JUuWvOZ2J0yYwGuvvXbVehU8ERERud3+6B+GYfz9YMNFnDx50gCMDRs25Fn//PPPGy1atLjmd1avXm0EBgYaH374ofHrr78aCxYsMEJCQozXX3/9uv+e8+fPG35+fsZHH3103TEZGRlGSkqKY9mzZ48BaNGiRYsWLVq0mLYcP378b/uUy5yxK1euHDabjcTExDzrExMTCQoKuuZ3XnnlFR599FEGDRoEQMOGDUlPT+fJJ5/k5Zdfxmq9+kpzqVKlqF27NocOHbpuFm9vb7y9vR1/lyhRguPHj1OyZEksFsst7OW1paamEhISwvHjx/Hz83P69t2Vjlv+6LjdPB2z/NFxu3k6Zvnj7sfNMAwuXrxIcHDw3451mWLn5eVF06ZNWbVqFb169QLAbrezatUqhg4des3vXLp06aryZrPZgOufrkxLS+Pw4cM8+uijN5zNarVSqVKlm9ib/PHz83PLH2RB03HLHx23m6djlj86bjdPxyx/3Pm4+fv739A4lyl2ANHR0fTv359mzZrRokULpkyZQnp6OgMHDgSgX79+VKxYkQkTJgDQo0cPJk2aRJMmTQgPD+fQoUO88sor9OjRw1HwnnvuOXr06EGVKlU4deoU48aNw2az0adPH1P3VURERMTZXKrY9e7dmzNnzvDqq6+SkJBA48aNWbZsmeOBivj4+Dxn6MaOHYvFYmHs2LGcPHmSgIAAevTowZtvvukYc+LECfr06cPZs2cJCAigTZs2bNq0iYCAAFP2UURERKSguFSxAxg6dOh1L73GxMTk+dvDw4Nx48Yxbty4627viy++cHpGZ/P29mbcuHF57uuTv6fjlj86bjdPxyx/dNxuno5Z/ui4/ZfFuKFnZ0VERETE1bnMBMUiIiIicmtU7ERERETchIqdiIiIiJtQsXMB06dPp2rVqvj4+BAeHu54961c2/jx47FYLHmW0NBQs2O5lLVr19KjRw+Cg4OxWCwsWrQoz+eGYfDqq69SoUIFfH19iYyM5ODBg6bldRV/d9wGDBhw1W+vS5cupuV1BRMmTKB58+aULFmS8uXL06tXL/bv359nTEZGBkOGDKFs2bKUKFGC+++//6rJ6IuaGzluHTp0uOr3NnjwYNMym+29996jUaNGjrnqIiIiWLp0qeNz/c5+p2Jnsi+//JLo6GjGjRvH9u3bCQsLIyoqiqSkJLOjubT69etz+vRpx7J+/XqzI7mU9PR0wsLCmD59+jU/f+utt3jnnXd4//332bx5M8WLFycqKoqMjIzbntWV/N1xA+jSpUue3968efNua0ZXs2bNGoYMGcKmTZtYuXIl2dnZdO7cmfT0dMeYUaNG8d133/HVV1+xZs0aTp06xX333WdqbrPdyHEDeOKJJ/L83t566y3TMputUqVK/L//9//Ytm0bW7du5c4776Rnz5789ttvoN/Zf/3tS8ekQLVo0cIYMmSI4+/c3FwjODjYmDBhgqm5XNm4ceOMsLAws2MUGoCxcOFCx992u90ICgoy3n77bce6CxcuGN7e3sa8efNMSul6/ve4GYZh9O/f3+jZs6dpmQqDpKQkAzDWrFljGFd+W56ensZXX33lGLN3714DMDZu3GhiUtfyv8fNMAyjffv2xogRI0zN5epKly5tfPTRR/qd/YnO2JkoKyuLbdu2ERkZ6VhntVqJjIxk48aNpmZzdQcPHiQ4OJjq1avz8MMPEx8fb3akQiMuLo6EhIQ8vzt/f3/Cw8P1u7sBMTExlC9fnjp16vD0009z9uxZsyO5lJSUFADKlCkDwLZt28jOzs7zewsNDaVy5cr6vf3J/x63P3z++eeUK1eOBg0aMGbMGC5dumRSQteSm5vLF198QXp6OhEREfqd/YnLTVBclCQnJ5Obm+t4s8YfAgMD2bdvn2m5XF14eDizZ8+mTp06nD59mtdee422bduye/duSpYsaXY8l5eQkABXfmd/FhgY6PhMrq1Lly7cd999VKtWjcOHD/PSSy/RtWtXNm7c6HiNYVFmt9sZOXIkrVu3pkGDBnDl9+bl5UWpUqXyjNXv7b+uddwA+vbtS5UqVQgODubXX3/lhRdeYP/+/SxYsMDUvGbatWsXERERZGRkUKJECRYuXEi9evWIjY3V7+wKFTspdLp27er450aNGhEeHk6VKlWYP38+jz/+uKnZxL099NBDjn9u2LAhjRo1okaNGsTExNCpUydTs7mCIUOGsHv3bt3zepOud9yefPJJxz83bNiQChUq0KlTJw4fPkyNGjVMSGq+OnXqEBsbS0pKCl9//TX9+/dnzZo1ZsdyKboUa6Jy5cphs9muemonMTGRoKAg03IVNqVKlaJ27docOnTI7CiFwh+/Lf3ubl316tUpV66cfntXXgf5/fffs3r1aipVquRYHxQURFZWFhcuXMgzXr+3313vuF1LeHg4QJH+vXl5eVGzZk2aNm3KhAkTCAsLY+rUqfqd/YmKnYm8vLxo2rQpq1atcqyz2+2sWrWKiIgIU7MVJmlpaRw+fJgKFSqYHaVQqFatGkFBQXl+d6mpqWzevFm/u5t04sQJzp49W6R/e4ZhMHToUBYuXMhPP/1EtWrV8nzetGlTPD098/ze9u/fT3x8fJH+vf3dcbuW2NhYgCL9e/tfdrudzMxM/c7+RJdiTRYdHU3//v1p1qwZLVq0YMqUKaSnpzNw4ECzo7ms5557jh49elClShVOnTrFuHHjsNls9OnTx+xoLiMtLS3P/6qPi4sjNjaWMmXKULlyZUaOHMkbb7xBrVq1qFatGq+88grBwcH06tXL1Nxm+6vjVqZMGV577TXuv/9+goKCOHz4MKNHj6ZmzZpERUWZmttMQ4YMYe7cuSxevJiSJUs67mfy9/fH19cXf39/Hn/8caKjoylTpgx+fn4MGzaMiIgIWrZsaXZ80/zdcTt8+DBz586lW7dulC1bll9//ZVRo0bRrl07GjVqZHZ8U4wZM4auXbtSuXJlLl68yNy5c4mJiWH58uX6nf2Z2Y/limFMmzbNqFy5suHl5WW0aNHC2LRpk9mRXFrv3r2NChUqGF5eXkbFihWN3r17G4cOHTI7lktZvXq1AVy19O/f3zCuTHnyyiuvGIGBgYa3t7fRqVMnY//+/WbHNt1fHbdLly4ZnTt3NgICAgxPT0+jSpUqxhNPPGEkJCSYHdtU1zpegPHxxx87xly+fNl45plnjNKlSxvFihUz7r33XuP06dOm5jbb3x23+Ph4o127dkaZMmUMb29vo2bNmsbzzz9vpKSkmB3dNI899phRpUoVw8vLywgICDA6depkrFixwvG5fme/sxi//8BEREREpJDTPXYiIiIibkLFTkRERMRNqNiJiIiIuAkVOxERERE3oWInIiIi4iZU7ERERETchIqdiIiIiJtQsRMRERFxEyp2IiK3ydGjR7FYLFgsFho3bmx2HACqVq3KlClTrvv57NmzKVWq1C39O2JiYhz7XdRfWydS0FTsRKTADBgwwPH/0P+8dOnSxexopvrxxx/zvKx8/Pjx1z0ub7/9NhaLhQ4dOgCwbNkyLBaL492if6hQoQJVq1bNs+6PIvnnf9eNqlatGj/++ONNf+9aWrVqxenTp3nwwQedsj0RuT4VOxEpUF26dOH06dN5lnnz5pkd65ZkZWXd0vfLli1L2bJl86yrUKECq1ev5sSJE3nWz5o1i8qVKzv+btOmDR4eHsTExDjW7d27l8uXL3P+/HmOHj3qWL969Wq8vb1p3br1TeX79ddfOX/+PO3bt8/H3l3Ny8uLoKAgfH19nbI9Ebk+FTsRKVDe3t4EBQXlWUqXLg1XLtF5eXmxbt06x/i33nqL8uXLk5iYCECHDh0YOnQoQ4cOxd/fn3LlyvHKK6/w59dcnz9/nn79+lG6dGmKFStG165dOXjwoOPzY8eO0aNHD0qXLk3x4sWpX78+S5Ysgetcaly0aBEWi8Xx9/jx42ncuDEfffQR1apVw8fHB4ALFy4waNAgAgIC8PPz484772Tnzp35Ok7ly5enc+fOzJkzx7Fuw4YNJCcn0717d8e6EiVK0Lx58zzFLiYmhjZt2tC6deur1rds2dKRNykpiR49euDr60u1atX4/PPPr5ll8eLFdOnSBU9Pz6s+O3PmDM2aNePee+8lMzMTgG+//ZZatWrh4+NDx44dmTNnDhaLhQsXLuTrWIhI/qnYiYhpOnTowMiRI3n00UdJSUlhx44dvPLKK3z00UcEBgY6xs2ZMwcPDw+2bNnC1KlTmTRpEh999JHj8wEDBrB161a+/fZbNm7ciGEYdOvWjezsbACGDBlCZmYma9euZdeuXfzrX/+iRIkSN5X10KFDfPPNNyxYsIDY2FgAHnjgAZKSkli6dCnbtm3jjjvuoFOnTpw7dy5fx+Oxxx5j9uzZjr9nzZrFww8/jJeXV55xHTt2ZPXq1Y6/V69eTYcOHWjfvn2e9TExMXTs2NHx94ABAzh+/DirV6/m66+/ZsaMGSQlJV2V49tvv6Vnz55XrT9+/Dht27alQYMGfP3113h7exMXF8c//vEPevXqxc6dO3nqqad4+eWX87X/IuIEhohIAenfv79hs9mM4sWL51nefPNNx5jMzEyjcePGxoMPPmjUq1fPeOKJJ/Jso3379kbdunUNu93uWPfCCy8YdevWNQzDMA4cOGAAxs8//+z4PDk52fD19TXmz59vGIZhNGzY0Bg/fvw1M3788ceGv79/nnULFy40/vx/HseNG2d4enoaSUlJjnXr1q0z/Pz8jIyMjDzfrVGjhjFz5sxr/rvi4uIMwNixY0ee9ePGjTPCwsKMrKwso3z58saaNWuMtLQ0o2TJksbOnTuNESNGGO3bt3eMX7lypQEYp06dMgzDMMqXL29s2bLF2LBhg1GlShXDMAzj8OHDBmCsWbPGMAzD2L9/vwEYW7ZscWxn7969BmBMnjzZse7EiROGl5eXcf78+TzHZ9++fUZISIgxfPjwq/67aNCgQZ79efnllw3AsY0/9O/f3+jZs+c1j42IOIeH2cVSRNxbx44dee+99/KsK1OmjOOfvby8+Pzzz2nUqBFVqlRh8uTJV22jZcuWeS6NRkREMHHiRHJzc9m7dy8eHh6Eh4c7Pi9btix16tRh7969AAwfPpynn36aFStWEBkZyf3330+jRo1uaj+qVKlCQECA4++dO3eSlpZ21b1yly9f5vDhwze17T94enryyCOP8PHHH3PkyBFq1659zZytWrXCy8uLmJgYwsLCuHz5MnfccQd2u50zZ84QFxdHTEwMvr6+tGzZEq7ch+fh4UHTpk0d2wkNDb3qMvS3335LmzZt8qy/fPkybdu2pW/fvlc9Qbt//36aN2+eZ12LFi3ytf8icutU7ESkQBUvXpyaNWv+5ZgNGzYAcO7cOc6dO0fx4sWdmmHQoEFERUXxww8/sGLFCiZMmMDEiRMZNmwYVqs1z/16gOMS7v/ux5+lpaVRoUKFPPe0/eFWpgd57LHHCA8PZ/fu3Tz22GPXHFOsWDFatGjB6tWrOXfuHG3atMFms2Gz2WjVqhWrV69m9erVtG7d+qrLuH/n22+/5Z577smzztvbm8jISL7//nuef/55KlasmO/9E5GCpXvsRMRUhw8fZtSoUXz44YeEh4fTv39/7HZ7njGbN2/O8/emTZuoVasWNpuNunXrkpOTk2fM2bNn2b9/P/Xq1XOsCwkJYfDgwSxYsIBnn32WDz/8EICAgAAuXrxIenq6Y+wf99D9lTvuuIOEhAQ8PDyoWbNmnqVcuXL5Ph7169enfv367N69m759+153XMeOHYmJiSEmJsYxFQpAu3btiImJYc2aNXnurwsNDSUnJ4dt27Y51u3fvz/PAw5paWmsXr36qvvrrFYrn376KU2bNqVjx46cOnXK8VmdOnXYunVrnvG//PJLvvdfRG6Nip2IFKjMzEwSEhLyLMnJyQDk5ubyyCOPEBUVxcCBA/n444/59ddfmThxYp5txMfHEx0dzf79+5k3bx7Tpk1jxIgRANSqVYuePXvyxBNPsH79enbu3MkjjzxCxYoVHQVl5MiRLF++nLi4OLZv387q1aupW7cuAOHh4RQrVoyXXnqJw4cPM3fu3DwPMFxPZGQkERER9OrVixUrVnD06FE2bNjAyy+/fFXRuVk//fQTp0+f/sszfx07duTgwYMsX748z7Qk7du3Z9GiRRw/fjxPsatTpw5dunThqaeeYvPmzWzbto1BgwblmYJk2bJl1K5d+6r58ABsNhuff/45YWFh3HnnnY559J566in27dvHCy+8wIEDB5g/f77j+P358rmI3B4qdiJSoJYtW0aFChXyLG3atAHgzTff5NixY8ycOROuzOX2wQcfMHbs2DzThvTr14/Lly/TokULhgwZwogRI3jyyScdn3/88cc0bdqUu+++m4iICAzDYMmSJY7pOnJzcxkyZAh169alS5cu1K5dmxkzZsCV+/0+++wzlixZQsOGDZk3bx7jx4//2/2yWCwsWbKEdu3aMXDgQGrXrs1DDz3EsWPH8jzRmx/Fixf/28u5EREReHt7YxhGnvvmwsPDyc7OdkyL8mcff/wxwcHBtG/fnvvuu48nn3yS8uXLOz5fvHjxVZdh/8zDw4N58+ZRv3597rzzTpKSkqhWrRpff/01CxYsoFGjRrz33nuOp2K9vb1v4SiISH5YjP+9uURExIV06NCBxo0b/+VrrwqLo0ePUq1aNXbs2OEyrxT7Q05ODoGBgSxduvSWH3548803ef/99zl+/Hie9QMGDODChQssWrToFtOKyPXojJ2IyG3WqlUrWrVqZXaMPM6dO8eoUaOuOst3I2bMmMEvv/zCkSNH+PTTT3n77bfp37+/4/N169ZRokSJ606ILCLOo6diRURuk0qVKjneiOFqlynLly/P2LFj8/XdgwcP8sYbb3Du3DkqV67Ms88+y5gxYxyfN2vWzPFAys1ODC0iN0eXYkVERETchC7FioiIiLgJFTsRERERN6FiJyIiIuImVOxERERE3ISKnYiIiIibULETERERcRMqdiIiIiJuQsVORERExE2o2ImIiIi4if8PgaTUpQ7O+xEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(asmbly.exposures, asmbly.keff)\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(r\"$k_\\text{eff}$\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae83ccb-f878-4123-a369-f352d100b96f",
   "metadata": {},
   "source": [
    "At the beginning, there is a sharp decrease in reactivity due to the Xe-135 build up, but then the reactivity decreases in almost a linear fashion with burn-up (which is where the linear reactivity model for fuel management comes from). In addition to the reactivity, we can also look at our fuel composition. Helper methods are provided to obtain the average atom density of a desired nuclide in all the fuel across the assembly. Let's look at just a few important ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d291126b-3c58-4c29-9dff-97b252e97d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U235 = np.zeros(asmbly.exposures.size)\n",
    "U238 = np.zeros(asmbly.exposures.size)\n",
    "Pu239 = np.zeros(asmbly.exposures.size)\n",
    "Xe135 = np.zeros(asmbly.exposures.size)\n",
    "for t in range(asmbly.exposures.size):\n",
    "    U235[t] = asmbly.get_average_fuel_nuclide_density(t, \"U235\")\n",
    "    U238[t] = asmbly.get_average_fuel_nuclide_density(t, \"U238\")\n",
    "    Pu239[t] = asmbly.get_average_fuel_nuclide_density(t, \"Pu239\")\n",
    "    Xe135[t] = asmbly.get_average_fuel_nuclide_density(t, \"Xe135\")\n",
    "\n",
    "plt.plot(asmbly.exposures, Xe135, label=\"Xe135\")\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(\"Average Atom Density in Fuel [atoms / (barn-cm)]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d6dac48-8f30-489d-bd4f-2e10766e19a7",
   "metadata": {},
   "source": [
    "Here, we can see the rapid build up of Xe-135. Let's take a look at U-235 and Pu-239 next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fa0e5cf-f003-4007-ba3d-5dd8612e2e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(asmbly.exposures, U235, label=\"U235\")\n",
    "plt.plot(asmbly.exposures, Pu239, label=\"Pu239\")\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(\"Average Atom Density in Fuel [atoms / (barn-cm)]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daeaadb4-2ed9-41d2-86d7-428487577451",
   "metadata": {},
   "source": [
    "At a burn-up of 30 MWd/kg, we can see that there is now a comparable amount of Pu-239 and U-235. Lastly, we can look at how the quantity of U-238 has decreased:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48d29047-005f-4b38-8c23-8593fa9ff94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(asmbly.exposures, U238, label=\"U238\")\n",
    "plt.xlabel(\"Exposure [MWd/kg]\")\n",
    "plt.ylabel(\"Average Atom Density in Fuel [atoms / (barn-cm)]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72612f3a-6d99-4d1d-94d3-e12b39610c0f",
   "metadata": {},
   "source": [
    "There are many other things which can be done with Scarabée, but this should be sufficient for you to get started with lattice physics calculations !"
   ]
  }
 ],
 "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": 5
}