pandas-dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Dataframes\n",
    "This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import openmc\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Input Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to define materials that will be used in the problem. We will create three materials for the fuel, water, and cladding of the fuel pin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.6 enriched fuel\n",
    "fuel = openmc.Material(name='1.6% Fuel')\n",
    "fuel.set_density('g/cm3', 10.31341)\n",
    "fuel.add_nuclide('U235', 3.7503e-4)\n",
    "fuel.add_nuclide('U238', 2.2625e-2)\n",
    "fuel.add_nuclide('O16', 4.6007e-2)\n",
    "\n",
    "# borated water\n",
    "water = openmc.Material(name='Borated Water')\n",
    "water.set_density('g/cm3', 0.740582)\n",
    "water.add_nuclide('H1', 4.9457e-2)\n",
    "water.add_nuclide('O16', 2.4732e-2)\n",
    "water.add_nuclide('B10', 8.0042e-6)\n",
    "\n",
    "# zircaloy\n",
    "zircaloy = openmc.Material(name='Zircaloy')\n",
    "zircaloy.set_density('g/cm3', 6.55)\n",
    "zircaloy.add_nuclide('Zr90', 7.2758e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our three materials, we can now create a materials file object that can be exported to an actual XML file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a Materials collection\n",
    "materials = openmc.Materials([fuel, water, zircaloy])\n",
    "\n",
    "# Export to \"materials.xml\"\n",
    "materials.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's move on to the geometry. This problem will be a square array of fuel pins for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create cylinders for the fuel and clad\n",
    "fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
    "clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
    "\n",
    "# Create boundary planes to surround the geometry\n",
    "# Use both reflective and vacuum boundaries to make life interesting\n",
    "min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
    "max_x = openmc.XPlane(x0=+10.71, boundary_type='vacuum')\n",
    "min_y = openmc.YPlane(y0=-10.71, boundary_type='vacuum')\n",
    "max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
    "min_z = openmc.ZPlane(z0=-10.71, boundary_type='reflective')\n",
    "max_z = openmc.ZPlane(z0=+10.71, boundary_type='reflective')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create fuel Cell\n",
    "fuel_cell = openmc.Cell(name='1.6% Fuel', fill=fuel,\n",
    "                        region=-fuel_outer_radius)\n",
    "\n",
    "# Create a clad Cell\n",
    "clad_cell = openmc.Cell(name='1.6% Clad', fill=zircaloy)\n",
    "clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
    "\n",
    "# Create a moderator Cell\n",
    "moderator_cell = openmc.Cell(name='1.6% Moderator', fill=water,\n",
    "                             region=+clad_outer_radius)\n",
    "\n",
    "# Create a Universe to encapsulate a fuel pin\n",
    "pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin', cells=[\n",
    "    fuel_cell, clad_cell, moderator_cell\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create fuel assembly Lattice\n",
    "assembly = openmc.RectLattice(name='1.6% Fuel - 0BA')\n",
    "assembly.pitch = (1.26, 1.26)\n",
    "assembly.lower_left = [-1.26 * 17. / 2.0] * 2\n",
    "assembly.universes = [[pin_cell_universe] * 17] * 17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create root Cell\n",
    "root_cell = openmc.Cell(name='root cell', fill=assembly)\n",
    "\n",
    "# Add boundary planes\n",
    "root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
    "\n",
    "# Create root Universe\n",
    "root_universe = openmc.Universe(name='root universe')\n",
    "root_universe.add_cell(root_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now must create a geometry that is assigned a root universe and export it to XML."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Geometry and export to \"geometry.xml\"\n",
    "geometry = openmc.Geometry(root_universe)\n",
    "geometry.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 minimum active batches each with 2500 particles. We also tell OpenMC to turn tally triggers on, which means it will keep running until some criterion on the uncertainty of tallies is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# OpenMC simulation parameters\n",
    "min_batches = 20\n",
    "max_batches = 200\n",
    "inactive = 5\n",
    "particles = 2500\n",
    "\n",
    "# Instantiate a Settings object\n",
    "settings = openmc.Settings()\n",
    "settings.batches = min_batches\n",
    "settings.inactive = inactive\n",
    "settings.particles = particles\n",
    "settings.output = {'tallies': False}\n",
    "settings.trigger_active = True\n",
    "settings.trigger_max_batches = max_batches\n",
    "\n",
    "# Create an initial uniform spatial source distribution over fissionable zones\n",
    "bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
    "uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
    "settings.source = openmc.IndependentSource(space=uniform_dist)\n",
    "\n",
    "# Export to \"settings.xml\"\n",
    "settings.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate a Plot\n",
    "plot = openmc.Plot(plot_id=1)\n",
    "plot.filename = 'materials-xy'\n",
    "plot.origin = [0, 0, 0]\n",
    "plot.width = [21.5, 21.5]\n",
    "plot.pixels = [250, 250]\n",
    "plot.color_by = 'material'\n",
    "\n",
    "# Show plot\n",
    "openmc.plot_inline(plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate an empty Tallies object\n",
    "tallies = openmc.Tallies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a fission rate mesh Tally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate a tally Mesh\n",
    "mesh = openmc.RegularMesh(mesh_id=1)\n",
    "mesh.dimension = [17, 17]\n",
    "mesh.lower_left = [-10.71, -10.71]\n",
    "mesh.width = [1.26, 1.26]\n",
    "\n",
    "# Instantiate tally Filter\n",
    "mesh_filter = openmc.MeshFilter(mesh)\n",
    "\n",
    "# Instantiate energy Filter\n",
    "energy_filter = openmc.EnergyFilter([0, 0.625, 20.0e6])\n",
    "\n",
    "# Instantiate the Tally\n",
    "tally = openmc.Tally(name='mesh tally')\n",
    "tally.filters = [mesh_filter, energy_filter]\n",
    "tally.scores = ['fission', 'nu-fission']\n",
    "\n",
    "# Add mesh and Tally to Tallies\n",
    "tallies.append(tally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a cell Tally with nuclides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate tally Filter\n",
    "cell_filter = openmc.CellFilter(fuel_cell)\n",
    "\n",
    "# Instantiate the tally\n",
    "tally = openmc.Tally(name='cell tally')\n",
    "tally.filters = [cell_filter]\n",
    "tally.scores = ['scatter']\n",
    "tally.nuclides = ['U235', 'U238']\n",
    "\n",
    "# Add mesh and tally to Tallies\n",
    "tallies.append(tally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a \"distribcell\" Tally. The distribcell filter allows us to tally multiple repeated instances of the same cell throughout the geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate tally Filter\n",
    "distribcell_filter = openmc.DistribcellFilter(moderator_cell)\n",
    "\n",
    "# Instantiate tally Trigger for kicks\n",
    "trigger = openmc.Trigger(trigger_type='std_dev', threshold=5e-5)\n",
    "trigger.scores = ['absorption']\n",
    "\n",
    "# Instantiate the Tally\n",
    "tally = openmc.Tally(name='distribcell tally')\n",
    "tally.filters = [distribcell_filter]\n",
    "tally.scores = ['absorption', 'scatter']\n",
    "tally.triggers = [trigger]\n",
    "\n",
    "# Add mesh and tally to Tallies\n",
    "tallies.append(tally)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export to \"tallies.xml\"\n",
    "tallies.export_to_xml()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we a have a complete set of inputs, so we can go ahead and run our simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: no matches found: statepoint.*\n",
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      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.1\n",
      "        Git SHA1 | 33bc948f4b855c037975f16d16091fe4ecd12de3\n",
      "       Date/Time | 2022-10-04 13:34:46\n",
      "  OpenMP Threads | 2\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading U235 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U235.h5\n",
      " Reading U238 from /home/pshriwise/data/xs/openmc/nndc_hdf5/U238.h5\n",
      " Reading O16 from /home/pshriwise/data/xs/openmc/nndc_hdf5/O16.h5\n",
      " Reading H1 from /home/pshriwise/data/xs/openmc/nndc_hdf5/H1.h5\n",
      " Reading B10 from /home/pshriwise/data/xs/openmc/nndc_hdf5/B10.h5\n",
      " Reading Zr90 from /home/pshriwise/data/xs/openmc/nndc_hdf5/Zr90.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Reading tallies XML file...\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for U235\n",
      " Initializing source particles...\n",
      "\n",
      " ====================>     K EIGENVALUE SIMULATION     <====================\n",
      "\n",
      "  Bat./Gen.      k            Average k\n",
      "  =========   ========   ====================\n",
      "        1/1    0.56788\n",
      "        2/1    0.66344\n",
      "        3/1    0.67801\n",
      "        4/1    0.64662\n",
      "        5/1    0.69912\n",
      "        6/1    0.72720\n",
      "        7/1    0.71337    0.72029 +/- 0.00691\n",
      "        8/1    0.70564    0.71540 +/- 0.00630\n",
      "        9/1    0.69707    0.71082 +/- 0.00639\n",
      "       10/1    0.69745    0.70815 +/- 0.00563\n",
      "       11/1    0.68982    0.70509 +/- 0.00552\n",
      "       12/1    0.69541    0.70371 +/- 0.00486\n",
      "       13/1    0.69724    0.70290 +/- 0.00429\n",
      "       14/1    0.71009    0.70370 +/- 0.00387\n",
      "       15/1    0.69614    0.70294 +/- 0.00354\n",
      "       16/1    0.68883    0.70166 +/- 0.00345\n",
      "       17/1    0.68635    0.70038 +/- 0.00340\n",
      "       18/1    0.70665    0.70087 +/- 0.00316\n",
      "       19/1    0.65747    0.69777 +/- 0.00426\n",
      "       20/1    0.69228    0.69740 +/- 0.00399\n",
      " Triggers unsatisfied, max unc./thresh. is 93.89673349355503 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 132254 --- greater than max batches\n",
      " Creating state point statepoint.020.h5...\n",
      "       21/1    0.70753    0.69803 +/- 0.00378\n",
      " Triggers unsatisfied, max unc./thresh. is 88.01427279111518 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 123950 --- greater than max batches\n",
      "       22/1    0.71245    0.69888 +/- 0.00365\n",
      " Triggers unsatisfied, max unc./thresh. is 84.70931054977731 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 121992 --- greater than max batches\n",
      "       23/1    0.65293    0.69633 +/- 0.00429\n",
      " Triggers unsatisfied, max unc./thresh. is 86.59494174561316 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 134982 --- greater than max batches\n",
      "       24/1    0.66415    0.69464 +/- 0.00439\n",
      " Triggers unsatisfied, max unc./thresh. is 83.04784122809853 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 131047 --- greater than max batches\n",
      "       25/1    0.71052    0.69543 +/- 0.00424\n",
      " Triggers unsatisfied, max unc./thresh. is 81.86075666632895 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 134029 --- greater than max batches\n",
      "       26/1    0.64576    0.69307 +/- 0.00468\n",
      " Triggers unsatisfied, max unc./thresh. is 77.87299751572496 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 127354 --- greater than max batches\n",
      "       27/1    0.66691    0.69188 +/- 0.00462\n",
      " Triggers unsatisfied, max unc./thresh. is 74.42216649773451 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 121856 --- greater than max batches\n",
      "       28/1    0.65694    0.69036 +/- 0.00467\n",
      " Triggers unsatisfied, max unc./thresh. is 71.15138613524442 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 116443 --- greater than max batches\n",
      "       29/1    0.69674    0.69062 +/- 0.00447\n",
      " Triggers unsatisfied, max unc./thresh. is 69.5666585341282 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 116154 --- greater than max batches\n",
      "       30/1    0.63556    0.68842 +/- 0.00482\n",
      " Triggers unsatisfied, max unc./thresh. is 66.88106610912472 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 111832 --- greater than max batches\n",
      "       31/1    0.69809    0.68879 +/- 0.00465\n",
      " Triggers unsatisfied, max unc./thresh. is 64.26124103687252 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 107373 --- greater than max batches\n",
      "       32/1    0.64298    0.68710 +/- 0.00479\n",
      " Triggers unsatisfied, max unc./thresh. is 62.02092253926227 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 103864 --- greater than max batches\n",
      "       33/1    0.68559    0.68704 +/- 0.00461\n",
      " Triggers unsatisfied, max unc./thresh. is 60.428539392862184 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 102251 --- greater than max batches\n",
      "       34/1    0.65886    0.68607 +/- 0.00455\n",
      " Triggers unsatisfied, max unc./thresh. is 58.33270366377693 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 98684 --- greater than max batches\n",
      "       35/1    0.68743    0.68611 +/- 0.00440\n",
      " Triggers unsatisfied, max unc./thresh. is 61.227018115820606 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 112468 --- greater than max batches\n",
      "       36/1    0.69354    0.68635 +/- 0.00426\n",
      " Triggers unsatisfied, max unc./thresh. is 59.98985062152096 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 111568 --- greater than max batches\n",
      "       37/1    0.65095    0.68525 +/- 0.00427\n",
      " Triggers unsatisfied, max unc./thresh. is 58.08611480082295 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 107973 --- greater than max batches\n",
      "       38/1    0.74212    0.68697 +/- 0.00449\n",
      " Triggers unsatisfied, max unc./thresh. is 56.428639900078956 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 105084 --- greater than max batches\n",
      "       39/1    0.70258    0.68743 +/- 0.00438\n",
      " Triggers unsatisfied, max unc./thresh. is 56.00920365851815 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 106665 --- greater than max batches\n",
      "       40/1    0.73458    0.68878 +/- 0.00446\n",
      " Triggers unsatisfied, max unc./thresh. is 54.62032757647575 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 104424 --- greater than max batches\n",
      "       41/1    0.66256    0.68805 +/- 0.00439\n",
      " Triggers unsatisfied, max unc./thresh. is 53.09826256813744 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 101505 --- greater than max batches\n",
      "       42/1    0.68262    0.68790 +/- 0.00427\n",
      " Triggers unsatisfied, max unc./thresh. is 51.6433641977662 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 98686 --- greater than max batches\n",
      "       43/1    0.70218    0.68828 +/- 0.00418\n",
      " Triggers unsatisfied, max unc./thresh. is 50.670570340521 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 97571 --- greater than max batches\n",
      "       44/1    0.67643    0.68797 +/- 0.00408\n",
      " Triggers unsatisfied, max unc./thresh. is 49.69199356348444 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 96308 --- greater than max batches\n",
      "       45/1    0.67103    0.68755 +/- 0.00400\n",
      " Triggers unsatisfied, max unc./thresh. is 48.44632012703269 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 93887 --- greater than max batches\n",
      "       46/1    0.65599    0.68678 +/- 0.00398\n",
      " Triggers unsatisfied, max unc./thresh. is 47.34469866921707 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 91908 --- greater than max batches\n",
      "       47/1    0.64796    0.68586 +/- 0.00399\n",
      " Triggers unsatisfied, max unc./thresh. is 46.49104551846509 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 90785 --- greater than max batches\n",
      "       48/1    0.69638    0.68610 +/- 0.00390\n",
      " Triggers unsatisfied, max unc./thresh. is 45.70905849558008 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 89846 --- greater than max batches\n",
      "       49/1    0.69484    0.68630 +/- 0.00382\n",
      " Triggers unsatisfied, max unc./thresh. is 44.85300785363907 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 88524 --- greater than max batches\n",
      "       50/1    0.69678    0.68653 +/- 0.00374\n",
      " Triggers unsatisfied, max unc./thresh. is 43.84946296158621 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 86530 --- greater than max batches\n",
      "       51/1    0.72852    0.68745 +/- 0.00377\n",
      " Triggers unsatisfied, max unc./thresh. is 43.78709466281985 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 88202 --- greater than max batches\n",
      "       52/1    0.69916    0.68769 +/- 0.00370\n",
      " Triggers unsatisfied, max unc./thresh. is 43.14074745406192 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 87478 --- greater than max batches\n",
      "       53/1    0.70809    0.68812 +/- 0.00364\n",
      " Triggers unsatisfied, max unc./thresh. is 42.78319111440113 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 87865 --- greater than max batches\n",
      "       54/1    0.67049    0.68776 +/- 0.00359\n",
      " Triggers unsatisfied, max unc./thresh. is 41.901523213902486 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 86037 --- greater than max batches\n",
      "       55/1    0.67612    0.68753 +/- 0.00352\n",
      " Triggers unsatisfied, max unc./thresh. is 41.247941809899636 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 85075 --- greater than max batches\n",
      "       56/1    0.68035    0.68739 +/- 0.00346\n",
      " Triggers unsatisfied, max unc./thresh. is 40.465158917081396 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 83514 --- greater than max batches\n",
      "       57/1    0.70520    0.68773 +/- 0.00341\n",
      " Triggers unsatisfied, max unc./thresh. is 40.05474461353258 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 83433 --- greater than max batches\n",
      "       58/1    0.70938    0.68814 +/- 0.00337\n",
      " Triggers unsatisfied, max unc./thresh. is 39.36707588200204 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 82143 --- greater than max batches\n",
      "       59/1    0.71202    0.68858 +/- 0.00333\n",
      " Triggers unsatisfied, max unc./thresh. is 38.66137944696039 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 80719 --- greater than max batches\n",
      "       60/1    0.66559    0.68816 +/- 0.00330\n",
      " Triggers unsatisfied, max unc./thresh. is 37.98682899227783 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 79370 --- greater than max batches\n",
      "       61/1    0.65921    0.68765 +/- 0.00328\n",
      " Triggers unsatisfied, max unc./thresh. is 38.02946096759635 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 80995 --- greater than max batches\n",
      "       62/1    0.66584    0.68726 +/- 0.00324\n",
      " Triggers unsatisfied, max unc./thresh. is 37.38147456631768 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 79656 --- greater than max batches\n",
      "       63/1    0.69512    0.68740 +/- 0.00319\n",
      " Triggers unsatisfied, max unc./thresh. is 36.764088830319764 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 78398 --- greater than max batches\n",
      "       64/1    0.72240    0.68799 +/- 0.00319\n",
      " Triggers unsatisfied, max unc./thresh. is 36.18011705136985 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 77237 --- greater than max batches\n",
      "       65/1    0.68739    0.68798 +/- 0.00314\n",
      " Triggers unsatisfied, max unc./thresh. is 35.60153732817237 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 76054 --- greater than max batches\n",
      "       66/1    0.63734    0.68715 +/- 0.00320\n",
      " Triggers unsatisfied, max unc./thresh. is 35.031719804043895 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74866 --- greater than max batches\n",
      "       67/1    0.67068    0.68689 +/- 0.00315\n",
      " Triggers unsatisfied, max unc./thresh. is 34.51557177787714 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73868 --- greater than max batches\n",
      "       68/1    0.67713    0.68673 +/- 0.00311\n",
      " Triggers unsatisfied, max unc./thresh. is 33.969950490099805 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72705 --- greater than max batches\n",
      "       69/1    0.71729    0.68721 +/- 0.00310\n",
      " Triggers unsatisfied, max unc./thresh. is 33.74180433615716 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72870 --- greater than max batches\n",
      "       70/1    0.69537    0.68733 +/- 0.00305\n",
      " Triggers unsatisfied, max unc./thresh. is 33.22569238074973 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71762 --- greater than max batches\n",
      "       71/1    0.67200    0.68710 +/- 0.00301\n",
      " Triggers unsatisfied, max unc./thresh. is 33.22256499836698 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72852 --- greater than max batches\n",
      "       72/1    0.71865    0.68757 +/- 0.00300\n",
      " Triggers unsatisfied, max unc./thresh. is 32.73550028508711 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71804 --- greater than max batches\n",
      "       73/1    0.69590    0.68769 +/- 0.00296\n",
      " Triggers unsatisfied, max unc./thresh. is 32.26730455836069 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70806 --- greater than max batches\n",
      "       74/1    0.68222    0.68762 +/- 0.00292\n",
      " Triggers unsatisfied, max unc./thresh. is 32.10780833971512 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71138 --- greater than max batches\n",
      "       75/1    0.70643    0.68788 +/- 0.00289\n",
      " Triggers unsatisfied, max unc./thresh. is 31.81893605358122 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70877 --- greater than max batches\n",
      "       76/1    0.67658    0.68772 +/- 0.00285\n",
      " Triggers unsatisfied, max unc./thresh. is 31.799932820986882 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71803 --- greater than max batches\n",
      "       77/1    0.67857    0.68760 +/- 0.00282\n",
      " Triggers unsatisfied, max unc./thresh. is 32.2671133722935 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74969 --- greater than max batches\n",
      "       78/1    0.69108    0.68765 +/- 0.00278\n",
      " Triggers unsatisfied, max unc./thresh. is 31.827343187502493 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73953 --- greater than max batches\n",
      "       79/1    0.68226    0.68757 +/- 0.00274\n",
      " Triggers unsatisfied, max unc./thresh. is 31.40561277543132 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72993 --- greater than max batches\n",
      "       80/1    0.67648    0.68742 +/- 0.00271\n",
      " Triggers unsatisfied, max unc./thresh. is 31.18206568121962 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72930 --- greater than max batches\n",
      "       81/1    0.65907    0.68705 +/- 0.00270\n",
      " Triggers unsatisfied, max unc./thresh. is 30.770354748328618 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71963 --- greater than max batches\n",
      "       82/1    0.63285    0.68635 +/- 0.00276\n",
      " Triggers unsatisfied, max unc./thresh. is 30.494055493974386 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71607 --- greater than max batches\n",
      "       83/1    0.65128    0.68590 +/- 0.00276\n",
      " Triggers unsatisfied, max unc./thresh. is 30.31368198832349 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71681 --- greater than max batches\n",
      "       84/1    0.65957    0.68556 +/- 0.00274\n",
      " Triggers unsatisfied, max unc./thresh. is 29.927509740510708 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70762 --- greater than max batches\n",
      "       85/1    0.69936    0.68574 +/- 0.00271\n",
      " Triggers unsatisfied, max unc./thresh. is 29.684480624880837 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70499 --- greater than max batches\n",
      "       86/1    0.71538    0.68610 +/- 0.00270\n",
      " Triggers unsatisfied, max unc./thresh. is 30.428213734574186 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75001 --- greater than max batches\n",
      "       87/1    0.67737    0.68600 +/- 0.00267\n",
      " Triggers unsatisfied, max unc./thresh. is 30.08396896887296 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74219 --- greater than max batches\n",
      "       88/1    0.67516    0.68587 +/- 0.00264\n",
      " Triggers unsatisfied, max unc./thresh. is 29.820034395214684 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73812 --- greater than max batches\n",
      "       89/1    0.71831    0.68625 +/- 0.00264\n",
      " Triggers unsatisfied, max unc./thresh. is 29.52222913175473 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73217 --- greater than max batches\n",
      "       90/1    0.69057    0.68630 +/- 0.00261\n",
      " Triggers unsatisfied, max unc./thresh. is 29.192694331349163 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72444 --- greater than max batches\n",
      "       91/1    0.72527    0.68676 +/- 0.00262\n",
      " Triggers unsatisfied, max unc./thresh. is 28.8828546327987 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71748 --- greater than max batches\n",
      "       92/1    0.68240    0.68671 +/- 0.00259\n",
      " Triggers unsatisfied, max unc./thresh. is 28.746406074791647 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71898 --- greater than max batches\n",
      "       93/1    0.68284    0.68666 +/- 0.00256\n",
      " Triggers unsatisfied, max unc./thresh. is 28.55969474438906 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71783 --- greater than max batches\n",
      "       94/1    0.67323    0.68651 +/- 0.00254\n",
      " Triggers unsatisfied, max unc./thresh. is 28.56264444554437 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72614 --- greater than max batches\n",
      "       95/1    0.67226    0.68635 +/- 0.00251\n",
      " Triggers unsatisfied, max unc./thresh. is 28.266619502256543 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71916 --- greater than max batches\n",
      "       96/1    0.69225    0.68642 +/- 0.00249\n",
      " Triggers unsatisfied, max unc./thresh. is 28.090316060577223 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71810 --- greater than max batches\n",
      "       97/1    0.67708    0.68632 +/- 0.00246\n",
      " Triggers unsatisfied, max unc./thresh. is 27.82156180416623 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71217 --- greater than max batches\n",
      "       98/1    0.68583    0.68631 +/- 0.00243\n",
      " Triggers unsatisfied, max unc./thresh. is 27.55014670870297 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70593 --- greater than max batches\n",
      "       99/1    0.64118    0.68583 +/- 0.00246\n",
      " Triggers unsatisfied, max unc./thresh. is 27.31334480980206 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70131 --- greater than max batches\n",
      "      100/1    0.67711    0.68574 +/- 0.00243\n",
      " Triggers unsatisfied, max unc./thresh. is 27.045284630831645 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69493 --- greater than max batches\n",
      "      101/1    0.68084    0.68569 +/- 0.00241\n",
      " Triggers unsatisfied, max unc./thresh. is 26.78334317556062 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 68871 --- greater than max batches\n",
      "      102/1    0.69024    0.68573 +/- 0.00238\n",
      " Triggers unsatisfied, max unc./thresh. is 26.507109183929384 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 68160 --- greater than max batches\n",
      "      103/1    0.66758    0.68555 +/- 0.00236\n",
      " Triggers unsatisfied, max unc./thresh. is 26.757066176393757 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70168 --- greater than max batches\n",
      "      104/1    0.70685    0.68576 +/- 0.00235\n",
      " Triggers unsatisfied, max unc./thresh. is 26.504222816390143 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69550 --- greater than max batches\n",
      "      105/1    0.65411    0.68545 +/- 0.00235\n",
      " Triggers unsatisfied, max unc./thresh. is 26.700673023873687 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71298 --- greater than max batches\n",
      "      106/1    0.66632    0.68526 +/- 0.00233\n",
      " Triggers unsatisfied, max unc./thresh. is 26.660322217781694 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71794 --- greater than max batches\n",
      "      107/1    0.72095    0.68561 +/- 0.00234\n",
      " Triggers unsatisfied, max unc./thresh. is 26.493921620269276 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71602 --- greater than max batches\n",
      "      108/1    0.68486    0.68560 +/- 0.00231\n",
      " Triggers unsatisfied, max unc./thresh. is 26.261958164864215 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71044 --- greater than max batches\n",
      "      109/1    0.70300    0.68577 +/- 0.00230\n",
      " Triggers unsatisfied, max unc./thresh. is 26.026316755257078 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70452 --- greater than max batches\n",
      "      110/1    0.63861    0.68532 +/- 0.00232\n",
      " Triggers unsatisfied, max unc./thresh. is 25.92602895583822 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70582 --- greater than max batches\n",
      "      111/1    0.68795    0.68534 +/- 0.00230\n",
      " Triggers unsatisfied, max unc./thresh. is 25.71712442074191 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70111 --- greater than max batches\n",
      "      112/1    0.64471    0.68496 +/- 0.00231\n",
      " Triggers unsatisfied, max unc./thresh. is 25.505784104673843 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69614 --- greater than max batches\n",
      "      113/1    0.68637    0.68498 +/- 0.00229\n",
      " Triggers unsatisfied, max unc./thresh. is 25.844123724835992 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72141 --- greater than max batches\n",
      "      114/1    0.70597    0.68517 +/- 0.00227\n",
      " Triggers unsatisfied, max unc./thresh. is 26.4137374000716 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 76053 --- greater than max batches\n",
      "      115/1    0.67258    0.68506 +/- 0.00225\n",
      " Triggers unsatisfied, max unc./thresh. is 26.356728326257265 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 76420 --- greater than max batches\n",
      "      116/1    0.68671    0.68507 +/- 0.00223\n",
      " Triggers unsatisfied, max unc./thresh. is 26.12385981970673 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75758 --- greater than max batches\n",
      "      117/1    0.65467    0.68480 +/- 0.00223\n",
      " Triggers unsatisfied, max unc./thresh. is 25.891227947363426 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75085 --- greater than max batches\n",
      "      118/1    0.68299    0.68478 +/- 0.00221\n",
      " Triggers unsatisfied, max unc./thresh. is 25.66357949026415 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74429 --- greater than max batches\n",
      "      119/1    0.71640    0.68506 +/- 0.00221\n",
      " Triggers unsatisfied, max unc./thresh. is 25.798931705980632 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75882 --- greater than max batches\n",
      "      120/1    0.67682    0.68499 +/- 0.00219\n",
      " Triggers unsatisfied, max unc./thresh. is 25.600502535564395 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75375 --- greater than max batches\n",
      "      121/1    0.64612    0.68465 +/- 0.00220\n",
      " Triggers unsatisfied, max unc./thresh. is 25.442401128263437 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75094 --- greater than max batches\n",
      "      122/1    0.65743    0.68442 +/- 0.00219\n",
      " Triggers unsatisfied, max unc./thresh. is 25.314147177380775 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74980 --- greater than max batches\n",
      "      123/1    0.67460    0.68434 +/- 0.00217\n",
      " Triggers unsatisfied, max unc./thresh. is 25.120655147896837 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74469 --- greater than max batches\n",
      "      124/1    0.63622    0.68393 +/- 0.00219\n",
      " Triggers unsatisfied, max unc./thresh. is 25.17374854397212 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 75418 --- greater than max batches\n",
      "      125/1    0.74014    0.68440 +/- 0.00222\n",
      " Triggers unsatisfied, max unc./thresh. is 24.96990847535278 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74825 --- greater than max batches\n",
      "      126/1    0.66532    0.68424 +/- 0.00221\n",
      " Triggers unsatisfied, max unc./thresh. is 24.763458905086484 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74206 --- greater than max batches\n",
      "      127/1    0.66150    0.68406 +/- 0.00220\n",
      " Triggers unsatisfied, max unc./thresh. is 24.715390376099315 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74529 --- greater than max batches\n",
      "      128/1    0.66861    0.68393 +/- 0.00219\n",
      " Triggers unsatisfied, max unc./thresh. is 24.53214499997655 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 74030 --- greater than max batches\n",
      "      129/1    0.69363    0.68401 +/- 0.00217\n",
      " Triggers unsatisfied, max unc./thresh. is 24.3508538828241 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73533 --- greater than max batches\n",
      "      130/1    0.70857    0.68421 +/- 0.00216\n",
      " Triggers unsatisfied, max unc./thresh. is 24.174442566114134 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73056 --- greater than max batches\n",
      "      131/1    0.67714    0.68415 +/- 0.00215\n",
      " Triggers unsatisfied, max unc./thresh. is 23.983232671129187 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72480 --- greater than max batches\n",
      "      132/1    0.67410    0.68407 +/- 0.00213\n",
      " Triggers unsatisfied, max unc./thresh. is 23.867449217879173 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72352 --- greater than max batches\n",
      "      133/1    0.69079    0.68412 +/- 0.00211\n",
      " Triggers unsatisfied, max unc./thresh. is 23.6857708644691 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71816 --- greater than max batches\n",
      "      134/1    0.67606    0.68406 +/- 0.00210\n",
      " Triggers unsatisfied, max unc./thresh. is 23.736182299674752 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72685 --- greater than max batches\n",
      "      135/1    0.69637    0.68416 +/- 0.00208\n",
      " Triggers unsatisfied, max unc./thresh. is 23.696430912734872 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73003 --- greater than max batches\n",
      "      136/1    0.67044    0.68405 +/- 0.00207\n",
      " Triggers unsatisfied, max unc./thresh. is 23.571101620170353 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72789 --- greater than max batches\n",
      "      137/1    0.69621    0.68414 +/- 0.00206\n",
      " Triggers unsatisfied, max unc./thresh. is 23.48120895070458 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72786 --- greater than max batches\n",
      "      138/1    0.71568    0.68438 +/- 0.00206\n",
      " Triggers unsatisfied, max unc./thresh. is 23.30415910951134 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72236 --- greater than max batches\n",
      "      139/1    0.69041    0.68443 +/- 0.00204\n",
      " Triggers unsatisfied, max unc./thresh. is 23.136930058514952 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71738 --- greater than max batches\n",
      "      140/1    0.68872    0.68446 +/- 0.00203\n",
      " Triggers unsatisfied, max unc./thresh. is 22.974363808251983 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71261 --- greater than max batches\n",
      "      141/1    0.68717    0.68448 +/- 0.00201\n",
      " Triggers unsatisfied, max unc./thresh. is 22.91818719747599 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71439 --- greater than max batches\n",
      "      142/1    0.70148    0.68460 +/- 0.00200\n",
      " Triggers unsatisfied, max unc./thresh. is 22.906648117571184 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71891 --- greater than max batches\n",
      "      143/1    0.70002    0.68471 +/- 0.00199\n",
      " Triggers unsatisfied, max unc./thresh. is 22.80072669054916 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71748 --- greater than max batches\n",
      "      144/1    0.68586    0.68472 +/- 0.00197\n",
      " Triggers unsatisfied, max unc./thresh. is 22.666610635130866 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71420 --- greater than max batches\n",
      "      145/1    0.64403    0.68443 +/- 0.00198\n",
      " Triggers unsatisfied, max unc./thresh. is 22.85078652693249 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73108 --- greater than max batches\n",
      "      146/1    0.64824    0.68417 +/- 0.00198\n",
      " Triggers unsatisfied, max unc./thresh. is 22.780126847767363 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73175 --- greater than max batches\n",
      "      147/1    0.66962    0.68407 +/- 0.00197\n",
      " Triggers unsatisfied, max unc./thresh. is 22.713317620827834 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73263 --- greater than max batches\n",
      "      148/1    0.67953    0.68404 +/- 0.00196\n",
      " Triggers unsatisfied, max unc./thresh. is 22.555984411927444 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72760 --- greater than max batches\n",
      "      149/1    0.70028    0.68415 +/- 0.00195\n",
      " Triggers unsatisfied, max unc./thresh. is 22.467339125811773 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72694 --- greater than max batches\n",
      "      150/1    0.66858    0.68405 +/- 0.00194\n",
      " Triggers unsatisfied, max unc./thresh. is 22.317895054338617 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72228 --- greater than max batches\n",
      "      151/1    0.64729    0.68379 +/- 0.00194\n",
      " Triggers unsatisfied, max unc./thresh. is 22.256511911132613 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72327 --- greater than max batches\n",
      "      152/1    0.68669    0.68381 +/- 0.00193\n",
      " Triggers unsatisfied, max unc./thresh. is 22.104617489185 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71832 --- greater than max batches\n",
      "      153/1    0.65299    0.68361 +/- 0.00193\n",
      " Triggers unsatisfied, max unc./thresh. is 21.973197923947662 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71463 --- greater than max batches\n",
      "      154/1    0.68069    0.68359 +/- 0.00191\n",
      " Triggers unsatisfied, max unc./thresh. is 21.83662123023189 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71054 --- greater than max batches\n",
      "      155/1    0.69270    0.68365 +/- 0.00190\n",
      " Triggers unsatisfied, max unc./thresh. is 21.693707709175182 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70598 --- greater than max batches\n",
      "      156/1    0.67507    0.68359 +/- 0.00189\n",
      " Triggers unsatisfied, max unc./thresh. is 21.59386795740036 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70416 --- greater than max batches\n",
      "      157/1    0.68589    0.68360 +/- 0.00188\n",
      " Triggers unsatisfied, max unc./thresh. is 21.661356097401395 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71326 --- greater than max batches\n",
      "      158/1    0.69068    0.68365 +/- 0.00187\n",
      " Triggers unsatisfied, max unc./thresh. is 21.52579051330482 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70900 --- greater than max batches\n",
      "      159/1    0.67616    0.68360 +/- 0.00185\n",
      " Triggers unsatisfied, max unc./thresh. is 21.482995971825506 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71079 --- greater than max batches\n",
      "      160/1    0.67492    0.68355 +/- 0.00184\n",
      " Triggers unsatisfied, max unc./thresh. is 21.35292717227001 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70677 --- greater than max batches\n",
      "      161/1    0.69218    0.68360 +/- 0.00183\n",
      " Triggers unsatisfied, max unc./thresh. is 21.31450245992177 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70878 --- greater than max batches\n",
      "      162/1    0.66037    0.68345 +/- 0.00183\n",
      " Triggers unsatisfied, max unc./thresh. is 21.182040871953692 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70448 --- greater than max batches\n",
      "      163/1    0.65640    0.68328 +/- 0.00182\n",
      " Triggers unsatisfied, max unc./thresh. is 21.086966342350518 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70262 --- greater than max batches\n",
      "      164/1    0.65113    0.68308 +/- 0.00182\n",
      " Triggers unsatisfied, max unc./thresh. is 20.95392746879447 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69817 --- greater than max batches\n",
      "      165/1    0.70691    0.68323 +/- 0.00182\n",
      " Triggers unsatisfied, max unc./thresh. is 20.837836858585195 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69480 --- greater than max batches\n",
      "      166/1    0.67295    0.68317 +/- 0.00181\n",
      " Triggers unsatisfied, max unc./thresh. is 20.76965344402802 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69457 --- greater than max batches\n",
      "      167/1    0.66620    0.68306 +/- 0.00180\n",
      " Triggers unsatisfied, max unc./thresh. is 20.676559815652364 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69264 --- greater than max batches\n",
      "      168/1    0.66855    0.68297 +/- 0.00179\n",
      " Triggers unsatisfied, max unc./thresh. is 20.729564534345286 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70049 --- greater than max batches\n",
      "      169/1    0.69800    0.68306 +/- 0.00178\n",
      " Triggers unsatisfied, max unc./thresh. is 20.880997532545944 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71512 --- greater than max batches\n",
      "      170/1    0.69435    0.68313 +/- 0.00177\n",
      " Triggers unsatisfied, max unc./thresh. is 20.817259379833427 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71510 --- greater than max batches\n",
      "      171/1    0.65628    0.68297 +/- 0.00177\n",
      " Triggers unsatisfied, max unc./thresh. is 20.751648716409104 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71490 --- greater than max batches\n",
      "      172/1    0.68216    0.68297 +/- 0.00176\n",
      " Triggers unsatisfied, max unc./thresh. is 20.63392061753699 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71107 --- greater than max batches\n",
      "      173/1    0.67774    0.68293 +/- 0.00175\n",
      " Triggers unsatisfied, max unc./thresh. is 20.517289218805363 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70727 --- greater than max batches\n",
      "      174/1    0.71232    0.68311 +/- 0.00175\n",
      " Triggers unsatisfied, max unc./thresh. is 20.397375101025485 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70318 --- greater than max batches\n",
      "      175/1    0.65712    0.68295 +/- 0.00174\n",
      " Triggers unsatisfied, max unc./thresh. is 20.340809150523164 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70343 --- greater than max batches\n",
      "      176/1    0.67973    0.68294 +/- 0.00173\n",
      " Triggers unsatisfied, max unc./thresh. is 20.225760844958906 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 69958 --- greater than max batches\n",
      "      177/1    0.70262    0.68305 +/- 0.00173\n",
      " Triggers unsatisfied, max unc./thresh. is 20.188734566509453 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70110 --- greater than max batches\n",
      "      178/1    0.66234    0.68293 +/- 0.00172\n",
      " Triggers unsatisfied, max unc./thresh. is 20.365846675766896 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71760 --- greater than max batches\n",
      "      179/1    0.67778    0.68290 +/- 0.00171\n",
      " Triggers unsatisfied, max unc./thresh. is 20.266332469411914 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71472 --- greater than max batches\n",
      "      180/1    0.63949    0.68265 +/- 0.00172\n",
      " Triggers unsatisfied, max unc./thresh. is 20.153693580077913 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71085 --- greater than max batches\n",
      "      181/1    0.66641    0.68256 +/- 0.00171\n",
      " Triggers unsatisfied, max unc./thresh. is 20.06531024570685 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70866 --- greater than max batches\n",
      "      182/1    0.70248    0.68267 +/- 0.00171\n",
      " Triggers unsatisfied, max unc./thresh. is 19.9948494014597 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70769 --- greater than max batches\n",
      "      183/1    0.68906    0.68271 +/- 0.00170\n",
      " Triggers unsatisfied, max unc./thresh. is 20.09202626796155 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71862 --- greater than max batches\n",
      "      184/1    0.64815    0.68252 +/- 0.00170\n",
      " Triggers unsatisfied, max unc./thresh. is 20.010212893652305 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71679 --- greater than max batches\n",
      "      185/1    0.66689    0.68243 +/- 0.00169\n",
      " Triggers unsatisfied, max unc./thresh. is 19.898829712842296 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 71279 --- greater than max batches\n",
      "      186/1    0.68573    0.68245 +/- 0.00168\n",
      " Triggers unsatisfied, max unc./thresh. is 19.796151037696795 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70937 --- greater than max batches\n",
      "      187/1    0.66673    0.68236 +/- 0.00167\n",
      " Triggers unsatisfied, max unc./thresh. is 19.7015255155044 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 70649 --- greater than max batches\n",
      "      188/1    0.65563    0.68222 +/- 0.00167\n",
      " Triggers unsatisfied, max unc./thresh. is 19.864637065809237 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72218 --- greater than max batches\n",
      "      189/1    0.69960    0.68231 +/- 0.00167\n",
      " Triggers unsatisfied, max unc./thresh. is 19.85591184981352 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72549 --- greater than max batches\n",
      "      190/1    0.68178    0.68231 +/- 0.00166\n",
      " Triggers unsatisfied, max unc./thresh. is 19.762972685185634 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72262 --- greater than max batches\n",
      "      191/1    0.69760    0.68239 +/- 0.00165\n",
      " Triggers unsatisfied, max unc./thresh. is 19.7609343058741 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 72637 --- greater than max batches\n",
      "      192/1    0.66122    0.68228 +/- 0.00164\n",
      " Triggers unsatisfied, max unc./thresh. is 19.80418051094423 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73348 --- greater than max batches\n",
      "      193/1    0.69346    0.68234 +/- 0.00164\n",
      " Triggers unsatisfied, max unc./thresh. is 19.801360007084376 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73719 --- greater than max batches\n",
      "      194/1    0.67831    0.68231 +/- 0.00163\n",
      " Triggers unsatisfied, max unc./thresh. is 19.73891559058344 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73645 --- greater than max batches\n",
      "      195/1    0.65285    0.68216 +/- 0.00163\n",
      " Triggers unsatisfied, max unc./thresh. is 19.65303479951205 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73391 --- greater than max batches\n",
      "      196/1    0.66672    0.68208 +/- 0.00162\n",
      " Triggers unsatisfied, max unc./thresh. is 19.625879969536705 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73574 --- greater than max batches\n",
      "      197/1    0.67536    0.68204 +/- 0.00161\n",
      " Triggers unsatisfied, max unc./thresh. is 19.528488790397677 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73227 --- greater than max batches\n",
      "      198/1    0.66675    0.68196 +/- 0.00161\n",
      " Triggers unsatisfied, max unc./thresh. is 19.511910293025363 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73483 --- greater than max batches\n",
      "      199/1    0.64492    0.68177 +/- 0.00161\n",
      " Triggers unsatisfied, max unc./thresh. is 19.511121679344882 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73858 --- greater than max batches\n",
      "      200/1    0.69441    0.68184 +/- 0.00160\n",
      " Triggers unsatisfied, max unc./thresh. is 19.41826281743187 for absorption in\n",
      " tally 3\n",
      " WARNING: The estimated number of batches is 73534 --- greater than max batches\n",
      " Creating state point statepoint.200.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 6.7469e-01 seconds\n",
      "   Reading cross sections          = 6.6495e-01 seconds\n",
      " Total time in simulation          = 1.3884e+02 seconds\n",
      "   Time in transport only          = 1.3872e+02 seconds\n",
      "   Time in inactive batches        = 1.6291e+00 seconds\n",
      "   Time in active batches          = 1.3721e+02 seconds\n",
      "   Time synchronizing fission bank = 4.2426e-02 seconds\n",
      "     Sampling source sites         = 3.9046e-02 seconds\n",
      "     SEND/RECV source sites        = 3.2353e-03 seconds\n",
      "   Time accumulating tallies       = 2.5633e-02 seconds\n",
      "   Time writing statepoints        = 4.3956e-03 seconds\n",
      " Total time for finalization       = 1.8500e-06 seconds\n",
      " Total time elapsed                = 1.3954e+02 seconds\n",
      " Calculation Rate (inactive)       = 7672.85 particles/second\n",
      " Calculation Rate (active)         = 3552.9 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " k-effective (Collision)     = 0.68290 +/- 0.00158\n",
      " k-effective (Track-length)  = 0.68184 +/- 0.00160\n",
      " k-effective (Absorption)    = 0.68167 +/- 0.00151\n",
      " Combined k-effective        = 0.68200 +/- 0.00134\n",
      " Leakage Fraction            = 0.33996 +/- 0.00079\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Remove old HDF5 (summary, statepoint) files\n",
    "!rm statepoint.*\n",
    "\n",
    "# Run OpenMC!\n",
    "openmc.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tally Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We do not know how many batches were needed to satisfy the \n",
    "# tally trigger(s), so find the statepoint file(s)\n",
    "statepoints = glob.glob('statepoint.*.h5')\n",
    "\n",
    "# Load the last statepoint file\n",
    "sp = openmc.StatePoint(statepoints[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze the mesh fission rate tally**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tally\n",
      "\tID             =\t1\n",
      "\tName           =\tmesh tally\n",
      "\tFilters        =\tMeshFilter, EnergyFilter\n",
      "\tNuclides       =\ttotal\n",
      "\tScores         =\t['fission', 'nu-fission']\n",
      "\tEstimator      =\ttracklength\n"
     ]
    }
   ],
   "source": [
    "# Find the mesh tally with the StatePoint API\n",
    "tally = sp.get_tally(name='mesh tally')\n",
    "\n",
    "# Print a little info about the mesh tally to the screen\n",
    "print(tally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the new Tally data retrieval API with pure NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.04969994]]\n",
      "\n",
      " [[0.02200773]]\n",
      "\n",
      " [[0.12289506]]\n",
      "\n",
      " [[0.05303359]]]\n"
     ]
    }
   ],
   "source": [
    "# Get the relative error for the thermal fission reaction \n",
    "# rates in the four corner pins \n",
    "data = tally.get_values(scores=['fission'],\n",
    "                        filters=[openmc.MeshFilter, openmc.EnergyFilter], \\\n",
    "                        filter_bins=[((1,1),(1,17), (17,1), (17,17)), \\\n",
    "                                    ((0., 0.625),)], value='rel_err')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>energy low [eV]</th>\n",
       "      <th>energy high [eV]</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>fission</td>\n",
       "      <td>2.10e-04</td>\n",
       "      <td>1.04e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>5.11e-04</td>\n",
       "      <td>2.54e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>fission</td>\n",
       "      <td>7.19e-05</td>\n",
       "      <td>1.83e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>1.89e-04</td>\n",
       "      <td>4.75e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>fission</td>\n",
       "      <td>2.19e-04</td>\n",
       "      <td>9.36e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>5.33e-04</td>\n",
       "      <td>2.28e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>fission</td>\n",
       "      <td>6.98e-05</td>\n",
       "      <td>1.63e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>1.85e-04</td>\n",
       "      <td>4.24e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>fission</td>\n",
       "      <td>2.23e-04</td>\n",
       "      <td>1.02e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>5.44e-04</td>\n",
       "      <td>2.48e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>fission</td>\n",
       "      <td>6.69e-05</td>\n",
       "      <td>1.65e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>1.76e-04</td>\n",
       "      <td>4.27e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>fission</td>\n",
       "      <td>2.09e-04</td>\n",
       "      <td>9.24e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>5.09e-04</td>\n",
       "      <td>2.25e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>fission</td>\n",
       "      <td>6.72e-05</td>\n",
       "      <td>1.65e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>1.77e-04</td>\n",
       "      <td>4.32e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>fission</td>\n",
       "      <td>2.11e-04</td>\n",
       "      <td>8.34e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00e+00</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>5.15e-04</td>\n",
       "      <td>2.03e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>fission</td>\n",
       "      <td>6.47e-05</td>\n",
       "      <td>1.40e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.25e-01</td>\n",
       "      <td>2.00e+07</td>\n",
       "      <td>nu-fission</td>\n",
       "      <td>1.70e-04</td>\n",
       "      <td>3.66e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mesh 1       energy low [eV] energy high [eV]       score     mean  \\\n",
       "        x  y  z                                                         \n",
       "0       1  1  1        0.00e+00         6.25e-01     fission 2.10e-04   \n",
       "1       1  1  1        0.00e+00         6.25e-01  nu-fission 5.11e-04   \n",
       "2       1  1  1        6.25e-01         2.00e+07     fission 7.19e-05   \n",
       "3       1  1  1        6.25e-01         2.00e+07  nu-fission 1.89e-04   \n",
       "4       2  1  1        0.00e+00         6.25e-01     fission 2.19e-04   \n",
       "5       2  1  1        0.00e+00         6.25e-01  nu-fission 5.33e-04   \n",
       "6       2  1  1        6.25e-01         2.00e+07     fission 6.98e-05   \n",
       "7       2  1  1        6.25e-01         2.00e+07  nu-fission 1.85e-04   \n",
       "8       3  1  1        0.00e+00         6.25e-01     fission 2.23e-04   \n",
       "9       3  1  1        0.00e+00         6.25e-01  nu-fission 5.44e-04   \n",
       "10      3  1  1        6.25e-01         2.00e+07     fission 6.69e-05   \n",
       "11      3  1  1        6.25e-01         2.00e+07  nu-fission 1.76e-04   \n",
       "12      4  1  1        0.00e+00         6.25e-01     fission 2.09e-04   \n",
       "13      4  1  1        0.00e+00         6.25e-01  nu-fission 5.09e-04   \n",
       "14      4  1  1        6.25e-01         2.00e+07     fission 6.72e-05   \n",
       "15      4  1  1        6.25e-01         2.00e+07  nu-fission 1.77e-04   \n",
       "16      5  1  1        0.00e+00         6.25e-01     fission 2.11e-04   \n",
       "17      5  1  1        0.00e+00         6.25e-01  nu-fission 5.15e-04   \n",
       "18      5  1  1        6.25e-01         2.00e+07     fission 6.47e-05   \n",
       "19      5  1  1        6.25e-01         2.00e+07  nu-fission 1.70e-04   \n",
       "\n",
       "   std. dev.  \n",
       "              \n",
       "0   1.04e-05  \n",
       "1   2.54e-05  \n",
       "2   1.83e-06  \n",
       "3   4.75e-06  \n",
       "4   9.36e-06  \n",
       "5   2.28e-05  \n",
       "6   1.63e-06  \n",
       "7   4.24e-06  \n",
       "8   1.02e-05  \n",
       "9   2.48e-05  \n",
       "10  1.65e-06  \n",
       "11  4.27e-06  \n",
       "12  9.24e-06  \n",
       "13  2.25e-05  \n",
       "14  1.65e-06  \n",
       "15  4.32e-06  \n",
       "16  8.34e-06  \n",
       "17  2.03e-05  \n",
       "18  1.40e-06  \n",
       "19  3.66e-06  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a pandas dataframe for the mesh tally data\n",
    "df = tally.get_pandas_dataframe(nuclides=False)\n",
    "\n",
    "# Set the Pandas float display settings\n",
    "pd.options.display.float_format = '{:.2e}'.format\n",
    "\n",
    "# Print the first twenty rows in the dataframe\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a boxplot to view the distribution of\n",
    "# fission and nu-fission rates in the pins\n",
    "bp = df.boxplot(column='mean', by='score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fa602978940>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract thermal nu-fission rates from pandas\n",
    "fiss = df[df['score'] == 'nu-fission']\n",
    "fiss = fiss[fiss['energy low [eV]'] == 0.0]\n",
    "\n",
    "# Extract mean and reshape as 2D NumPy arrays\n",
    "mean = fiss['mean'].values.reshape((17,17))\n",
    "\n",
    "plt.imshow(mean, interpolation='nearest')\n",
    "plt.title('fission rate')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze the cell+nuclides scatter-y2 rate tally**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tally\n",
      "\tID             =\t2\n",
      "\tName           =\tcell tally\n",
      "\tFilters        =\tCellFilter\n",
      "\tNuclides       =\tU235 U238\n",
      "\tScores         =\t['scatter']\n",
      "\tEstimator      =\ttracklength\n"
     ]
    }
   ],
   "source": [
    "# Find the cell Tally with the StatePoint API\n",
    "tally = sp.get_tally(name='cell tally')\n",
    "\n",
    "# Print a little info about the cell tally to the screen\n",
    "print(tally)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>U235</td>\n",
       "      <td>scatter</td>\n",
       "      <td>3.82e-02</td>\n",
       "      <td>4.48e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>U238</td>\n",
       "      <td>scatter</td>\n",
       "      <td>2.34e+00</td>\n",
       "      <td>2.52e-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cell nuclide    score     mean  std. dev.\n",
       "0     1    U235  scatter 3.82e-02   4.48e-05\n",
       "1     1    U238  scatter 2.34e+00   2.52e-03"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a pandas dataframe for the cell tally data\n",
    "df = tally.get_pandas_dataframe()\n",
    "\n",
    "# Print the first twenty rows in the dataframe\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the new Tally data retrieval API with pure NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[2.52057168e-03]\n",
      "  [4.48210729e-05]]]\n"
     ]
    }
   ],
   "source": [
    "# Get the standard deviations the total scattering rate\n",
    "data = tally.get_values(scores=['scatter'], \n",
    "                        nuclides=['U238', 'U235'], value='std_dev')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze the distribcell tally**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tally\n",
      "\tID             =\t3\n",
      "\tName           =\tdistribcell tally\n",
      "\tFilters        =\tDistribcellFilter\n",
      "\tNuclides       =\ttotal\n",
      "\tScores         =\t['absorption', 'scatter']\n",
      "\tEstimator      =\ttracklength\n"
     ]
    }
   ],
   "source": [
    "# Find the distribcell Tally with the StatePoint API\n",
    "tally = sp.get_tally(name='distribcell tally')\n",
    "\n",
    "# Print a little info about the distribcell tally to the screen\n",
    "print(tally)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the new Tally data retrieval API with pure NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.01301476]]\n",
      "\n",
      " [[0.0136507 ]]\n",
      "\n",
      " [[0.01260538]]\n",
      "\n",
      " [[0.01293374]]\n",
      "\n",
      " [[0.01219012]]\n",
      "\n",
      " [[0.01191931]]\n",
      "\n",
      " [[0.0127884 ]]\n",
      "\n",
      " [[0.01339325]]\n",
      "\n",
      " [[0.01309524]]\n",
      "\n",
      " [[0.0133674 ]]]\n"
     ]
    }
   ],
   "source": [
    "# Get the relative error for the scattering reaction rates in\n",
    "# the first 10 distribcell instances \n",
    "data = tally.get_values(scores=['scatter'], filters=[openmc.DistribcellFilter],\n",
    "                        filter_bins=[tuple(range(10))], value='rel_err')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we're done getting data from the statepoint file, we'll close it to free the file handle for subsequent OpenMC runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Close the statepoint file now that we're done gathering info from it\n",
    "sp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the distribcell tally dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">level 1</th>\n",
       "      <th colspan=\"3\" halign=\"left\">level 2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">level 3</th>\n",
       "      <th>distribcell</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>univ</th>\n",
       "      <th>cell</th>\n",
       "      <th colspan=\"3\" halign=\"left\">lat</th>\n",
       "      <th>univ</th>\n",
       "      <th>cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>id</th>\n",
       "      <th>id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>id</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>279</td>\n",
       "      <td>absorption</td>\n",
       "      <td>7.23e-04</td>\n",
       "      <td>1.05e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>279</td>\n",
       "      <td>scatter</td>\n",
       "      <td>9.08e-02</td>\n",
       "      <td>6.39e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>280</td>\n",
       "      <td>absorption</td>\n",
       "      <td>6.75e-04</td>\n",
       "      <td>1.09e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>280</td>\n",
       "      <td>scatter</td>\n",
       "      <td>8.49e-02</td>\n",
       "      <td>6.75e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>281</td>\n",
       "      <td>absorption</td>\n",
       "      <td>6.22e-04</td>\n",
       "      <td>1.06e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>281</td>\n",
       "      <td>scatter</td>\n",
       "      <td>7.85e-02</td>\n",
       "      <td>6.36e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>282</td>\n",
       "      <td>absorption</td>\n",
       "      <td>5.66e-04</td>\n",
       "      <td>1.01e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>282</td>\n",
       "      <td>scatter</td>\n",
       "      <td>7.17e-02</td>\n",
       "      <td>5.80e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>283</td>\n",
       "      <td>absorption</td>\n",
       "      <td>4.94e-04</td>\n",
       "      <td>9.18e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>283</td>\n",
       "      <td>scatter</td>\n",
       "      <td>6.35e-02</td>\n",
       "      <td>5.45e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>284</td>\n",
       "      <td>absorption</td>\n",
       "      <td>4.34e-04</td>\n",
       "      <td>8.41e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>284</td>\n",
       "      <td>scatter</td>\n",
       "      <td>5.54e-02</td>\n",
       "      <td>5.15e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>285</td>\n",
       "      <td>absorption</td>\n",
       "      <td>3.58e-04</td>\n",
       "      <td>7.60e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>285</td>\n",
       "      <td>scatter</td>\n",
       "      <td>4.70e-02</td>\n",
       "      <td>4.81e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>286</td>\n",
       "      <td>absorption</td>\n",
       "      <td>2.83e-04</td>\n",
       "      <td>7.08e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>286</td>\n",
       "      <td>scatter</td>\n",
       "      <td>3.84e-02</td>\n",
       "      <td>4.47e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>287</td>\n",
       "      <td>absorption</td>\n",
       "      <td>2.19e-04</td>\n",
       "      <td>6.36e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>287</td>\n",
       "      <td>scatter</td>\n",
       "      <td>2.99e-02</td>\n",
       "      <td>3.89e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>288</td>\n",
       "      <td>absorption</td>\n",
       "      <td>1.18e-04</td>\n",
       "      <td>4.29e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>288</td>\n",
       "      <td>scatter</td>\n",
       "      <td>1.85e-02</td>\n",
       "      <td>2.75e-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    level 1      level 2         level 3      distribcell       score  \\\n",
       "       univ cell     lat            univ cell                           \n",
       "         id   id      id   x   y      id   id                           \n",
       "558       3    4       2   7  16       1    3         279  absorption   \n",
       "559       3    4       2   7  16       1    3         279     scatter   \n",
       "560       3    4       2   8  16       1    3         280  absorption   \n",
       "561       3    4       2   8  16       1    3         280     scatter   \n",
       "562       3    4       2   9  16       1    3         281  absorption   \n",
       "563       3    4       2   9  16       1    3         281     scatter   \n",
       "564       3    4       2  10  16       1    3         282  absorption   \n",
       "565       3    4       2  10  16       1    3         282     scatter   \n",
       "566       3    4       2  11  16       1    3         283  absorption   \n",
       "567       3    4       2  11  16       1    3         283     scatter   \n",
       "568       3    4       2  12  16       1    3         284  absorption   \n",
       "569       3    4       2  12  16       1    3         284     scatter   \n",
       "570       3    4       2  13  16       1    3         285  absorption   \n",
       "571       3    4       2  13  16       1    3         285     scatter   \n",
       "572       3    4       2  14  16       1    3         286  absorption   \n",
       "573       3    4       2  14  16       1    3         286     scatter   \n",
       "574       3    4       2  15  16       1    3         287  absorption   \n",
       "575       3    4       2  15  16       1    3         287     scatter   \n",
       "576       3    4       2  16  16       1    3         288  absorption   \n",
       "577       3    4       2  16  16       1    3         288     scatter   \n",
       "\n",
       "        mean std. dev.  \n",
       "                        \n",
       "                        \n",
       "558 7.23e-04  1.05e-05  \n",
       "559 9.08e-02  6.39e-04  \n",
       "560 6.75e-04  1.09e-05  \n",
       "561 8.49e-02  6.75e-04  \n",
       "562 6.22e-04  1.06e-05  \n",
       "563 7.85e-02  6.36e-04  \n",
       "564 5.66e-04  1.01e-05  \n",
       "565 7.17e-02  5.80e-04  \n",
       "566 4.94e-04  9.18e-06  \n",
       "567 6.35e-02  5.45e-04  \n",
       "568 4.34e-04  8.41e-06  \n",
       "569 5.54e-02  5.15e-04  \n",
       "570 3.58e-04  7.60e-06  \n",
       "571 4.70e-02  4.81e-04  \n",
       "572 2.83e-04  7.08e-06  \n",
       "573 3.84e-02  4.47e-04  \n",
       "574 2.19e-04  6.36e-06  \n",
       "575 2.99e-02  3.89e-04  \n",
       "576 1.18e-04  4.29e-06  \n",
       "577 1.85e-02  2.75e-04  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a pandas dataframe for the distribcell tally data\n",
    "df = tally.get_pandas_dataframe(nuclides=False)\n",
    "\n",
    "# Print the last twenty rows in the dataframe\n",
    "df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.89e+02</td>\n",
       "      <td>2.89e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.19e-04</td>\n",
       "      <td>6.85e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.43e-04</td>\n",
       "      <td>2.55e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.65e-05</td>\n",
       "      <td>1.25e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.08e-04</td>\n",
       "      <td>4.81e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.95e-04</td>\n",
       "      <td>6.87e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.19e-04</td>\n",
       "      <td>8.77e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.04e-04</td>\n",
       "      <td>1.51e-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean std. dev.\n",
       "                        \n",
       "                        \n",
       "count 2.89e+02  2.89e+02\n",
       "mean  4.19e-04  6.85e-06\n",
       "std   2.43e-04  2.55e-06\n",
       "min   1.65e-05  1.25e-06\n",
       "25%   2.08e-04  4.81e-06\n",
       "50%   3.95e-04  6.87e-06\n",
       "75%   6.19e-04  8.77e-06\n",
       "max   9.04e-04  1.51e-05"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show summary statistics for absorption distribcell tally data\n",
    "absorption = df[df['score'] == 'absorption']\n",
    "absorption[['mean', 'std. dev.']].dropna().describe()\n",
    "\n",
    "# Note that the maximum standard deviation does indeed\n",
    "# meet the 5e-5 threshold set by the tally trigger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a statistical test comparing the tally sample distributions for two categories of fuel pins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney Test p-value: 0.9772478044850605\n"
     ]
    }
   ],
   "source": [
    "# Extract tally data from pins in the pins divided along y=-x diagonal \n",
    "multi_index = ('level 2', 'lat',)\n",
    "lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n",
    "upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n",
    "lower = lower[lower['score'] == 'absorption']\n",
    "upper = upper[upper['score'] == 'absorption']\n",
    "\n",
    "# Perform non-parametric Mann-Whitney U Test to see if the \n",
    "# absorption rates (may) come from same sampling distribution\n",
    "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
    "print('Mann-Whitney Test p-value: {0}'.format(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the symmetry implied by the y=-x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **not reject** the null hypothesis that the two sampling distributions are identical.\n",
    "\n",
    "Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney Test p-value: 6.571087274814189e-42\n"
     ]
    }
   ],
   "source": [
    "# Extract tally data from pins in the pins divided along y=x diagonal\n",
    "multi_index = ('level 2', 'lat',)\n",
    "lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n",
    "upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n",
    "lower = lower[lower['score'] == 'absorption']\n",
    "upper = upper[upper['score'] == 'absorption']\n",
    "\n",
    "# Perform non-parametric Mann-Whitney U Test to see if the \n",
    "# absorption rates (may) come from same sampling distribution\n",
    "u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
    "print('Mann-Whitney Test p-value: {0}'.format(p))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the asymmetry implied by the y=x diagonal ensures that the two sampling distributions are *not* identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **reject** the null hypothesis that the two sampling distributions are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_458910/1422565182.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Scattering Rates'}, xlabel='mean', ylabel='rel. err.'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the scatter tally data from pandas\n",
    "scatter = df[df['score'] == 'scatter']\n",
    "\n",
    "scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n",
    "\n",
    "# Show a scatter plot of the mean vs. the std. dev.\n",
    "scatter.plot(kind='scatter', x='mean', y='rel. err.', title='Scattering Rates')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa601d820a0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram and kernel density estimate for the scattering rates\n",
    "scatter['mean'].plot(kind='hist', bins=25)\n",
    "scatter['mean'].plot(kind='kde')\n",
    "plt.title('Scattering Rates')\n",
    "plt.xlabel('Mean')\n",
    "plt.legend(['KDE', 'Histogram'])"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}